diff --git a/Spaghettification.ipynb b/Spaghettification.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9nUUNAQW1R6m"
+   },
+   "source": [
+    "# Spaghettification of the Magic School Bus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "58Az-fqTXLbO"
+   },
+   "source": [
+    "**Prepared by:** Logan Hennes (lhennes@nd.edu) and Joseph Emery (jemery@nd.edu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Edited by:** Kristin Swartz-Schult (kswartzs@nd.edu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QZh4kSflXqo3"
+   },
+   "source": [
+    "**Reference:** This is an original problem created by the authors with inspiration from Dr. Brian Olson's  class on problem solving."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aLj4IXguX5zf"
+   },
+   "source": [
+    "**Intended Audience:** This problem is intended for sophomores or juniors from the University of Notre Dame who are taking an introductory physics course. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Pkxa4qn9YTt7"
+   },
+   "source": [
+    "## Learning Objectives\n",
+    "\n",
+    "After studying this notebook, completing the activities, and asking questions in class, you should be able to:\n",
+    "\n",
+    "\n",
+    "\n",
+    "*   Perform a degrees of freedom analysis\n",
+    "*   Solve a nonlinear system of equations using Newton's Method and Python tools\n",
+    "*   Properly visualize data using Matplotlib\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FNoltRl9aH62"
+   },
+   "source": [
+    "## Resources\n",
+    "\n",
+    "Relevant modules from the class website:\n",
+    "\n",
+    "1.5. [Functions and Scope](https://ndcbe.github.io/data-and-computing/notebooks/01/Functions-and-Scope.html)\n",
+    "\n",
+    "6.1. [Modeling Systems of Nonlinear Equations: Flash Calculation Example](https://ndcbe.github.io/data-and-computing/notebooks/06/Modeling-Systems-of-Nonlinear-Equations.html)\n",
+    "\n",
+    "6.2. [Newton-Raphson Method in One Dimension](https://ndcbe.github.io/data-and-computing/notebooks/06/Newton-Raphson-Method-in-One-Dimension.html#)\n",
+    "\n",
+    "6.3. [More Newton-Type Methods](https://ndcbe.github.io/data-and-computing/notebooks/06/More-Newton-Type-Methods.html)\n",
+    "\n",
+    "6.6. [Newton's Method in Scipy](https://ndcbe.github.io/data-and-computing/notebooks/06/Newton-Methods-in-Scipy.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yAS2pJ3QcYTX"
+   },
+   "source": [
+    "## Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "OLKOoqj5ZpEm"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy import optimize\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import ticker, cm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KeO3vNqWcjBA"
+   },
+   "source": [
+    "## Problem Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oJ-yNo_Xc5R5"
+   },
+   "source": [
+    "Ms. Frizzle and her students have boarded the Magic School Bus for an adventure into outer space. However, while exploring the Milky Way, an asteroid collides with the bus, hurtling it toward a black hole at the center of the galaxy known as Sagittarius A*. "
+   ]
+  },
+  {
+   "attachments": {
+    "black_hole.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3CWQjedfGKJJ"
+   },
+   "source": [
+    "![black_hole.png](attachment:black_hole.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are going to write a Python script to find at what distance away the Magic School Bus needs to be to experience a certain amount of stretching by a black hole. You will need to use Newton’s law of universal gravitation and Young's modulus."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cj5zRYWDGIDZ"
+   },
+   "source": [
+    "$$F = G\\frac{m_1m_2}{r^2}$$\n",
+    "\n",
+    "$$∆L = \\frac{1}{Y}\\frac{F}{A}\\ L_0 $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Nj2qV2_WVDvB"
+   },
+   "source": [
+    "$F$ = the force exerted on an object by gravity\n",
+    "\n",
+    "$G$ = the gravitational constant\n",
+    "\n",
+    "$m_1$ = the mass of the black hole\n",
+    "\n",
+    "$m_2$ = the mass of the magic school bus\n",
+    "\n",
+    "$r_1$ = the distance between the black hole and the front of the bus\n",
+    "\n",
+    "$r_2$ = the distance between the black hole and the back of the bus\n",
+    "\n",
+    "$\\Delta L$ = the change in length of the bus\n",
+    "\n",
+    "$Y$ = a measure of a solid's resistance to elastic deformation under a load (Young's modulus)\n",
+    "\n",
+    "$F$ = the force exerted on the object being stretched\n",
+    "\n",
+    "$A$ = the cross-sectional area of the bus\n",
+    "\n",
+    "$L_0$ = the length of the bus before stretching"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "thYmW7wlcUht"
+   },
+   "source": [
+    "## 1. Degree of freedom analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uIE6CkUAdO1N"
+   },
+   "source": [
+    "### 1.1. Setup\n",
+    "\n",
+    "Define the constants used in this problem. Use scholarly sources such as NASA to determine the mass of Sagittarius A*. Approximate the bus as a cylinder, using the height of the bus as the diameter of the front face."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7hZMqDHYA1qT"
+   },
+   "outputs": [],
+   "source": [
+    "# Given constants\n",
+    "Y = 69e9 # Young's modulus for aluminum, N/m^2\n",
+    "m_2 = 11000 # mass of school bus, kg\n",
+    "L_0 = 10 # length of school bus, meters\n",
+    "h = 3.5 # height of bus, meters\n",
+    "\n",
+    "#Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "id": "uVtvwV5QCUpQ"
+   },
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Given constants\n",
+    "Y = 69e9 # Young's modulus for aluminum, N/m^2\n",
+    "m_2 = 11000 # mass of school bus, kg\n",
+    "L_0 = 10 # length of school bus, meters\n",
+    "h = 3.5 # height of bus, meters\n",
+    "\n",
+    "# Add your solution here\n",
+    "G = 6.67e-11  # gravitational constant, N m^2/kg^2\n",
+    "Solar_Mass = 1.989e30  # value of one solar mass, kg\n",
+    "m_1 = Solar_Mass * 4.297e6  # mass of Sagittarius A*, kg\n",
+    "A = np.pi*(h/2)**2 # are at front of bus, m^2\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uBftpcsWdwYF"
+   },
+   "source": [
+    "### 1.2. Analysis\n",
+    "\n",
+    "Perform a degree of freedom analysis using the equations provided and the unknown variables. What does this analysis tell you? How should you approach the problem moving forward?\n",
+    "\n",
+    "**Discuss** in 2-4 sentences\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "There are two equations and three unknowns -- the tensile force due to the black hole's gravity,\n",
+    "the change in the length of the bus, and the distance from the black hole. There is more than one\n",
+    "solution to this system. In order to provide a unique solution, one of the variables must be fixed,\n",
+    "whether it be the distance from the black hole to the front of the bus, the change in the length of the bus,\n",
+    "or the differential force experienced by the bus.\n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rAoDFU9Fd-5U"
+   },
+   "source": [
+    "Submit your written work via **Gradescope**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ojiZf6Hccv40"
+   },
+   "source": [
+    "## 2. Nonlinear equation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "R4ufgGbOfNaG"
+   },
+   "source": [
+    "### 2.1. Define a nonlinear equation\n",
+    "\n",
+    "Combining Newton’s law of universal gravitation and Young's modulus, write a single, nonlinear equation in canonical form, $c(r_1,Δ𝐿) = 0$. \n",
+    "\n",
+    "*Hint*: Just like a tidal force, the tensile force on the bus will be generated by the difference in gravitational force between the front of the bus (nearer to the black hole) and the back of the bus (further from the black hole)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hHm34P-tfvO3"
+   },
+   "source": [
+    "Submit your written work via **Gradescope**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "0 = ((L_0 * G * m_1 * m_2) / (Y * A)) * ((1 / r^2) - (1 / (r + L_0 + ΔL)^2)) - ΔL\n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CqEWYdt0f76r"
+   },
+   "source": [
+    "### 2.2. Define a function that returns the residuals\n",
+    "\n",
+    "Using the equation you derived in **2.1**, write a Python function that returns its residual given some value of $r_1$ and some value of $\\Delta L$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 130
+    },
+    "id": "Iq6ayqYhD9jZ",
+    "outputId": "bd3cf883-a0ef-414c-c852-c852276836e2"
+   },
+   "outputs": [],
+   "source": [
+    "# function for nonlinear equation residual\n",
+    "def residual_function(r_1,ΔL):\n",
+    "    ''' Returns the residual of the nonlinear equation given in canonical form\n",
+    "    \n",
+    "    Args:\n",
+    "        r_1, ΔL\n",
+    "        \n",
+    "    Returns:\n",
+    "        residual    \n",
+    "    '''\n",
+    "  # Add your solution here\n",
+    "    residual = \n",
+    "    return residual"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "bDXosTblE8zc"
+   },
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# function for nonlinear equation residual\n",
+    "def residual_function(r_1,ΔL):\n",
+    "    ''' Returns the residual of the nonlinear equation given in canonical form\n",
+    "  \n",
+    "  Args:\n",
+    "      r_1, ΔL\n",
+    "      \n",
+    "  Returns:\n",
+    "      residual    \n",
+    "  '''\n",
+    "  # Add your solution here\n",
+    "    residual =  ((L_0 * G * m_1 * m_2) / (Y * A)) * ((1 / r_1**2) - (1 / (r_1 + L_0 + ΔL)**2)) - ΔL\n",
+    "    return residual\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3. Plot the residuals\n",
+    "\n",
+    "Next, define an array of $r_1$ values and an array of $\\Delta L$ values. Use these arrays to calculate the residual for each pair of $r_1$ and $\\Delta L$ values. Use the 3-D plot function in Matplotlib to analyze the results.\n",
+    "\n",
+    "*Hint*: Try $\\Delta L$ values in the tens to hundreds of meters and $r_1$ values on the order of $10$ to $10^{10}$ meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 362
+    },
+    "id": "A5WPN5V5Faon",
+    "outputId": "e21d27b8-ad24-4608-cd3f-5654cb5abaf9"
+   },
+   "outputs": [],
+   "source": [
+    "# Create array\n",
+    "# Add your solution here\n",
+    "\n",
+    "# Initialize matrix\n",
+    "# Add your solution here\n",
+    "\n",
+    "for i in range(len(r_1)):\n",
+    "    for j in range(len(ΔL)):\n",
+    "        r_grid[i,j] = r_1[i]\n",
+    "        # Add your solution here \n",
+    "\n",
+    "#Plot figure\n",
+    "fig, ax = plt.subplots(figsize = (6.4,4), dpi=300)\n",
+    "plt.xscale('log')\n",
+    "\n",
+    "cs = ax.contourf(r_grid, ΔL_grid, f_grid, locator=ticker.LogLocator(), cmap=cm.coolwarm, levels=100)\n",
+    "\n",
+    "cbar = fig.colorbar(cs)\n",
+    "cbar.ax.set_ylabel('Residual', fontsize=16, fontweight='bold')\n",
+    "cbar.ax.tick_params(labelsize=16)\n",
+    "\n",
+    "cs2 = plt.contour(cs, levels=cs.levels[::15], colors='k', alpha=0.7, linestyles='dashed', linewidths=3)\n",
+    "\n",
+    "# plot heatmap label\n",
+    "plt.clabel(cs2, fmt='%2.2f', colors='k', fontsize=16)\n",
+    "\n",
+    "# define tick size\n",
+    "plt.xticks(fontsize=15)\n",
+    "plt.yticks(fontsize=15)\n",
+    "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+    "\n",
+    "# plot titile and x,y label\n",
+    "plt.xlabel('Distance (m)', fontsize=16, fontweight='bold')\n",
+    "plt.ylabel('ΔL (m)', fontsize=16, fontweight='bold')\n",
+    "plt.title('Contour of Residuals', fontsize=16, fontweight='bold')\n",
+    "plt.show()\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 362
+    },
+    "id": "HgAE37rCH35D",
+    "outputId": "7bf1c6bb-be21-4dd0-dc68-4cdcd554c209"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/tk/4cs7tv315mq209yzyj47_wd80000gn/T/ipykernel_76301/1539189335.py:24: UserWarning: Log scale: values of z <= 0 have been masked\n",
+      "  cs = ax.contourf(r_grid, ΔL_grid, f_grid, locator=ticker.LogLocator(), cmap=cm.coolwarm, levels=100)\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1920x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Create array\n",
+    "r_1 = np.linspace(1e4, 1e8, 1000)\n",
+    "ΔL = np.linspace(0, 100, 1000)\n",
+    "\n",
+    "# Initialize matrices\n",
+    "r_grid = np.zeros((len(r_1), len(ΔL)))\n",
+    "ΔL_grid = np.zeros((len(r_1), len(ΔL)))\n",
+    "f_grid = np.zeros((len(r_1), len(ΔL)))\n",
+    "\n",
+    "for i in range(len(r_1)):\n",
+    "    for j in range(len(ΔL)):\n",
+    "        r_grid[i, j] = r_1[i]\n",
+    "        # Add your solution here\n",
+    "        ΔL_grid[i, j] = ΔL[j]\n",
+    "\n",
+    "#fill f_grid\n",
+    "f_grid = residual_function(r_grid, ΔL_grid)\n",
+    "\n",
+    "#Plot figure\n",
+    "fig, ax = plt.subplots(figsize = (6.4,4), dpi=300)\n",
+    "plt.xscale('log')\n",
+    "\n",
+    "cs = ax.contourf(r_grid, ΔL_grid, f_grid, locator=ticker.LogLocator(), cmap=cm.coolwarm, levels=100)\n",
+    "\n",
+    "cbar = fig.colorbar(cs)\n",
+    "cbar.ax.set_ylabel('Residual', fontsize=16, fontweight='bold')\n",
+    "cbar.ax.tick_params(labelsize=16)\n",
+    "\n",
+    "cs2 = plt.contour(cs, levels=cs.levels[::15], colors='k', alpha=0.7, linestyles='dashed', linewidths=3)\n",
+    "\n",
+    "# plot heatmap label\n",
+    "plt.clabel(cs2, fmt='%2.2f', colors='k', fontsize=16)\n",
+    "\n",
+    "# define tick size\n",
+    "plt.xticks(fontsize=15)\n",
+    "plt.yticks(fontsize=15)\n",
+    "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+    "\n",
+    "# plot titile and x,y label\n",
+    "plt.xlabel('Distance (m)', fontsize=16, fontweight='bold')\n",
+    "plt.ylabel('ΔL (m)', fontsize=16, fontweight='bold')\n",
+    "plt.title('Contour of Residuals', fontsize=16, fontweight='bold')\n",
+    "plt.show()\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9tDNzCiqh6jk"
+   },
+   "source": [
+    "#### 2.3.1 Discussion\n",
+    "\n",
+    "What does the 3-D plot tell you about the solution? Are there multiple solutions?\n",
+    "\n",
+    "**Discuss** in 1-3 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "There are multiple solutions. The amount the bus stretches will change with its distance from the black hole. \n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eX4pivRVdW3s"
+   },
+   "source": [
+    "## 3. Inexact Newton's Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "I_x85LE1iqOr"
+   },
+   "source": [
+    "Typically, a body of aluminum can be subjected to 10% to 25% elongation before deformation becomes permanent (plastic deformation). For simplicity, we will define our maximum tolerance for stretching to be one meter. Solve for the distance when the bus experiences this stretching using the inexact Newton's Method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "QqdQo3sR4DwU"
+   },
+   "outputs": [],
+   "source": [
+    "def inexact_newton(f,x0,delta = 1.0e-7, epsilon=1.0e-6, LOUD=False,max_iter=50):\n",
+    "    \"\"\"Find the root of the function f via Newton-Raphson method\n",
+    "    Args:\n",
+    "        f: function to find root of [function]\n",
+    "        x0: initial guess [float]\n",
+    "        delta: finite difference parameter [float]\n",
+    "        epsilon: tolerance [float]\n",
+    "        LOUD: toggle on/off print statements [boolean]\n",
+    "        max_iter: maximum number of iterations [int]\n",
+    "        \n",
+    "    Returns:\n",
+    "        estimate of root [float]\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    assert callable(f), \"Warning: 'f' should be a Python function\"\n",
+    "    assert type(x0) is float or type(x0) is int, \"Warning: 'x0' should be a float or integer\"\n",
+    "    assert type(delta) is float, \"Warning: 'delta' should be a float\"\n",
+    "    assert type(epsilon) is float, \"Warning: 'eps' should be a float\"\n",
+    "    assert type(max_iter) is int, \"Warning: 'max_iter' should be an integer\"\n",
+    "    assert max_iter >= 0, \"Warning: 'max_iter' should be non-negative\"\n",
+    "    \n",
+    "    x = x0\n",
+    "\n",
+    "    # print intial guess\n",
+    "    #if (LOUD):\n",
+    "        #print(\"x0 =\",x0)\n",
+    "    \n",
+    "    iterations = 0\n",
+    "    converged = False\n",
+    "    \n",
+    "    # Check if the residual is close enough to zero\n",
+    "    while (not converged and iterations < max_iter):\n",
+    "        \n",
+    "        print(f'Guess {iterations}: {x}')\n",
+    "        \n",
+    "        # evaluate function 'f' at new 'x'\n",
+    "        fx = f(x)\n",
+    "        print(f'Residual {iterations}: {fx}\\n')\n",
+    "        \n",
+    "        # calculate 'slope'\n",
+    "        slope = (f(x+delta)-f(x))/delta\n",
+    "        \n",
+    "        # print every iteration\n",
+    "        #if (LOUD):\n",
+    "            #print(\"x_\",iterations+1,\"=\",x,\"-\",fx,\"/\",slope,\"=\",x - fx/slope)\n",
+    "        x = x - fx/slope\n",
+    "        \n",
+    "        iterations += 1\n",
+    "        \n",
+    "        # check if converged\n",
+    "        if np.fabs(f(x)) < epsilon:\n",
+    "            converged = True\n",
+    "            \n",
+    "    if (LOUD):\n",
+    "      print(\"It took\",iterations,\"iterations\")\n",
+    "    \n",
+    "    if not converged:\n",
+    "        print(\"Warning: Not a solution. Maximum number of iterations exceeded.\")\n",
+    "    return x #return estimate of root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Implementing inexact Newton's\n",
+    "You will need to write a new function for your nonlinear equation so that the only variable is $r_1$. This can be achieved by fixing the value of $\\Delta L$ to be one meter. Use this function to implement inexact Newton's.\n",
+    "\n",
+    "For more information on how to use the inexact Newton's Method, click [here](https://ndcbe.github.io/data-and-computing/notebooks/06/More-Newton-Type-Methods.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "cB-IP69ZfE0Z"
+   },
+   "outputs": [],
+   "source": [
+    "# Set value of ΔL\n",
+    "ΔL = 1              #length the bus is streteched, meters\n",
+    "guess = 100_000     #distance from black hole, meters\n",
+    "\n",
+    "# Define f(r_1) for the given ΔL; use canonical form\n",
+    "# Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "id": "K9heOP7lfsoH"
+   },
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Set value of ΔL\n",
+    "ΔL = 1\n",
+    "guess = 100_000\n",
+    "\n",
+    "# Define f(r_1) for the given ΔL; use canonical form\n",
+    "# Add your solution here\n",
+    "canonical_form = lambda r_1: ((L_0 * G * m_1 * m_2) / (Y * A)) * ((1 / r_1**2) - (1 / (r_1 + L_0 + ΔL)**2)) - ΔL\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ON_oco6kvKic"
+   },
+   "outputs": [],
+   "source": [
+    "# Use inexact_newton to find a solution\n",
+    "# Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vgGNcVwvvQOU",
+    "outputId": "9ca22c1f-25e8-4042-b4d4-e142b95ab5c9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 100000\n",
+      "Residual 0: 2077755.3674266443\n",
+      "\n",
+      "Guess 1: 128364.93230407775\n",
+      "Residual 1: 982360.3477070075\n",
+      "\n",
+      "Guess 2: 208830.37649762759\n",
+      "Residual 2: 228165.01024245314\n",
+      "\n",
+      "Guess 3: 246208.5116013881\n",
+      "Residual 3: 139227.4485254872\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[36], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### BEGIN SOLUTION\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# Use inexact_newton to find a solution\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m newton_sln \u001b[38;5;241m=\u001b[39m inexact_newton(canonical_form, guess, epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-2\u001b[39m, LOUD\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe result of the inexact newton function is\u001b[39m\u001b[38;5;124m'\u001b[39m, newton_sln)\n",
+      "Cell \u001b[0;32mIn[34], line 46\u001b[0m, in \u001b[0;36minexact_newton\u001b[0;34m(f, x0, delta, epsilon, LOUD, max_iter)\u001b[0m\n\u001b[1;32m     41\u001b[0m slope \u001b[38;5;241m=\u001b[39m (f(x\u001b[38;5;241m+\u001b[39mdelta)\u001b[38;5;241m-\u001b[39mf(x))\u001b[38;5;241m/\u001b[39mdelta\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# print every iteration\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;66;03m#if (LOUD):\u001b[39;00m\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;66;03m#print(\"x_\",iterations+1,\"=\",x,\"-\",fx,\"/\",slope,\"=\",x - fx/slope)\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m fx\u001b[38;5;241m/\u001b[39mslope\n\u001b[1;32m     48\u001b[0m iterations \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;66;03m# check if converged\u001b[39;00m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Use inexact_newton to find a solution\n",
+    "newton_sln = inexact_newton(canonical_form, guess, epsilon=1e-2, LOUD=True)\n",
+    "print('The result of the inexact newton function is', newton_sln)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.1. Discussion\n",
+    "\n",
+    "Did the inexact Newton's Method work? Try a few different initial guesses for $r_1$.\n",
+    "\n",
+    "**Discuss** in 1 or 2 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "The inexact Newton's Method keeps encountering division by zero. This is because subsequent guesses are\n",
+    "generating nearly equal answers and leading the slope to be calculated as zero.\n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Troubleshooting inexact Newton's\n",
+    "\n",
+    "Consider the order of magnitude of our guesses and our correspodnig intial residuals. Do you think the default step size, ```delta = 1.0e-7```, is appropriate for our problem? Try running inexact Newton's with progressively larger step sizes until you find one that works for an initial guess of $r_1 = 100,000$ meters. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use inexact_newton to find solution\n",
+    "# Add your solution ehre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 100000\n",
+      "Residual 0: 2077755.3674266443\n",
+      "\n",
+      "Guess 1: 133335.81739941452\n",
+      "Residual 1: 876539.6319734784\n",
+      "\n",
+      "Guess 2: 177783.53889143027\n",
+      "Residual 2: 369785.7367675986\n",
+      "\n",
+      "Guess 3: 237047.05907945527\n",
+      "Residual 3: 156001.7780356129\n",
+      "\n",
+      "Guess 4: 316064.7386210726\n",
+      "Residual 4: 65812.57209957817\n",
+      "\n",
+      "Guess 5: 421420.53716633125\n",
+      "Residual 5: 27764.288028924195\n",
+      "\n",
+      "Guess 6: 561891.4661279671\n",
+      "Residual 6: 11712.757107201945\n",
+      "\n",
+      "Guess 7: 749175.040867439\n",
+      "Residual 7: 4941.0463061199025\n",
+      "\n",
+      "Guess 8: 998851.8586936642\n",
+      "Residual 8: 2084.2390800205067\n",
+      "\n",
+      "Guess 9: 1331645.8383812758\n",
+      "Residual 9: 879.0243440693794\n",
+      "\n",
+      "Guess 10: 1775025.7183716786\n",
+      "Residual 10: 370.576542279553\n",
+      "\n",
+      "Guess 11: 2365110.3665443407\n",
+      "Residual 11: 156.0757026458498\n",
+      "\n",
+      "Guess 12: 3148474.4408281716\n",
+      "Residual 12: 65.58301910899844\n",
+      "\n",
+      "Guess 13: 4182249.3172298237\n",
+      "Residual 13: 27.407617109269168\n",
+      "\n",
+      "Guess 14: 5527224.847222105\n",
+      "Residual 14: 11.306755808452309\n",
+      "\n",
+      "Guess 15: 7219972.172748446\n",
+      "Residual 15: 4.521520796924896\n",
+      "\n",
+      "Guess 16: 9190590.90150826\n",
+      "Residual 16: 1.6769187615314816\n",
+      "\n",
+      "Guess 17: 11110108.88462495\n",
+      "Residual 17: 0.5153420858280657\n",
+      "\n",
+      "Guess 18: 12369385.253475262\n",
+      "Residual 18: 0.09804805785069015\n",
+      "\n",
+      "It took 19 iterations\n",
+      "The result of the inexact newton function is 12737445.506581789\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Use inexact_newton to find solution\n",
+    "delta = 1.0\n",
+    "newton_sln = inexact_newton(canonical_form, guess, delta = delta, epsilon=1e-2, LOUD=True)\n",
+    "print('The result of the inexact newton function is', newton_sln)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2.1. Discussion\n",
+    "\n",
+    "Why was the smaller delta value leading to division by zero?\n",
+    "\n",
+    "**Discuss** in 1-3 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "'''\n",
+    "Because of the order of magnitude of our inputs and outputs, altering inputs by such a small step size was\n",
+    "bumping up against the limits of floating point precision, causing the difference between to equation outputs\n",
+    "to compute as zero.\n",
+    "'''\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5gkAvogwoJgI"
+   },
+   "source": [
+    "## 4. Exact Newton's Method using Scipy\n",
+    "\n",
+    "Now that we've estimated a solution at $\\Delta L = 1$ using inexact Newton's, let's try using SciPy to solve this problem.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JTSYn9QfrrMv"
+   },
+   "source": [
+    "### 4.1. Setup equations\n",
+    "\n",
+    "When using Newton's Method in Scipy, the derivative of $f(r_1)$ is required to determine the solution. Calculate the derivative of $f(r_1)$ and define it as a function in Python.\n",
+    "\n",
+    "Submit your written work via **Gradescope**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_v_Ca_9EuuBg"
+   },
+   "outputs": [],
+   "source": [
+    "# Set value of ΔL\n",
+    "ΔL = 1\n",
+    "guess = 100_000\n",
+    "\n",
+    "def derivative_form(r_1):\n",
+    "    ''' Find max distance using canonical derivative\n",
+    "    \n",
+    "    Args:\n",
+    "        r_1\n",
+    "        \n",
+    "    Returns:\n",
+    "        derivative    \n",
+    "    '''\n",
+    "    # Add your solution here\n",
+    "    derivative = \n",
+    "    return derivative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Set value of ΔL\n",
+    "ΔL = 1\n",
+    "guess = 100_000\n",
+    "\n",
+    "def derivative_form(r_1):\n",
+    "    ''' Find max distance using canonical derivative\n",
+    "    \n",
+    "    Args:\n",
+    "        r_1\n",
+    "        \n",
+    "    Returns:\n",
+    "        derivative    \n",
+    "    '''\n",
+    "\n",
+    "    \n",
+    "    derivative = (L_0) / (Y * A) * G * m_1 * m_2 * (2/(L_0 + ΔL + r_1)**3 - 2/r_1**3)\n",
+    "    return derivative\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Nh_KMPubua1v"
+   },
+   "source": [
+    "### 4.2. Use Scipy to determine $r_1$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use SciPy's Newton method\n",
+    "# Add your solution here\n",
+    "scipy_sln = \n",
+    "print('The result of the scipy optimize newton function is \\n',scipy_sln)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rOWGNX6vf6T5",
+    "outputId": "c28dfbc0-52dc-4498-addf-c0d1c8dea91b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The result of the scipy optimize newton function is \n",
+      " (12761113.50181692,       converged: True\n",
+      "           flag: converged\n",
+      " function_calls: 44\n",
+      "     iterations: 22\n",
+      "           root: 12761113.50181692)\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Use SciPy's Newton method\n",
+    "scipy_sln = optimize.newton(func=canonical_form, x0 = guess, fprime=derivative_form, tol=1e-2, full_output=True)\n",
+    "print('The result of the scipy optimize newton function is \\n',scipy_sln)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zZ9TDztFve7l"
+   },
+   "source": [
+    "## 5. Analysis and Comparison of Each Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.1. Testing the inexact Newton's Method\n",
+    "\n",
+    "Test the inexact Newton's Method with guesses of different orders of magnitude. Find a guess that gives you a solution and a guess that does not. You should continue adjusting your initial guess until you get an error.\n",
+    "\n",
+    "*Hint:* Remember to pass a reasonable value for delta to ```inexact_newton```."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Intial guess\n",
+    "# add your solution here\n",
+    "guess1 =\n",
+    "newton_sln1 = \n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 100.0\n",
+      "Residual 0: 1779396688341057.0\n",
+      "\n",
+      "Guess 1: 135.70871381673226\n",
+      "Residual 1: 740287617039185.4\n",
+      "\n",
+      "Guess 2: 183.3516956970089\n",
+      "Residual 2: 309058458886371.4\n",
+      "\n",
+      "Guess 3: 246.89911947369208\n",
+      "Residual 3: 129364558441638.86\n",
+      "\n",
+      "Guess 4: 331.6465520952954\n",
+      "Residual 4: 54255190565019.32\n",
+      "\n",
+      "Guess 5: 444.65624650991634\n",
+      "Residual 5: 22787994444815.258\n",
+      "\n",
+      "Guess 6: 595.3456569597986\n",
+      "Residual 6: 9581870128968.305\n",
+      "\n",
+      "Guess 7: 796.2722222050768\n",
+      "Residual 7: 4032312039270.9365\n",
+      "\n",
+      "Guess 8: 1064.1798155792612\n",
+      "Residual 8: 1697961491797.7612\n",
+      "\n",
+      "Guess 9: 1421.3940657445287\n",
+      "Residual 9: 715325958679.188\n",
+      "\n",
+      "Guess 10: 1897.682824515651\n",
+      "Residual 10: 301461600693.3122\n",
+      "\n",
+      "Guess 11: 2532.7368204306767\n",
+      "Residual 11: 127079042732.76134\n",
+      "\n",
+      "Guess 12: 3379.477219071339\n",
+      "Residual 12: 53579828398.53758\n",
+      "\n",
+      "Guess 13: 4508.4657198282475\n",
+      "Residual 13: 22593982890.42541\n",
+      "\n",
+      "Guess 14: 6013.784697524499\n",
+      "Residual 14: 9528671312.327644\n",
+      "\n",
+      "Guess 15: 8020.877400027863\n",
+      "Residual 15: 4018906981.5285544\n",
+      "\n",
+      "Guess 16: 10697.001551751415\n",
+      "Residual 16: 1695159651.2643793\n",
+      "\n",
+      "Guess 17: 14265.167498030089\n",
+      "Residual 17: 715045276.8437557\n",
+      "\n",
+      "Guess 18: 19022.722397448808\n",
+      "Residual 18: 301628025.3185566\n",
+      "\n",
+      "Guess 19: 25366.12914993318\n",
+      "Residual 19: 127239293.46102208\n",
+      "\n",
+      "Guess 20: 33824.00493986472\n",
+      "Residual 20: 53675903.524093725\n",
+      "\n",
+      "Guess 21: 45101.172662368204\n",
+      "Residual 21: 22643517.558834456\n",
+      "\n",
+      "Guess 22: 60137.39593775892\n",
+      "Residual 22: 9552416.05380719\n",
+      "\n",
+      "Guess 23: 80185.69234148588\n",
+      "Residual 23: 4029824.746011326\n",
+      "\n",
+      "Guess 24: 106916.74883437951\n",
+      "Residual 24: 1700050.2929604116\n",
+      "\n",
+      "Guess 25: 142558.14445037514\n",
+      "Residual 25: 717198.3878427617\n",
+      "\n",
+      "Guess 26: 190079.96408570683\n",
+      "Residual 26: 302564.60320213286\n",
+      "\n",
+      "Guess 27: 253442.24494545473\n",
+      "Residual 27: 127643.16950270759\n",
+      "\n",
+      "Guess 28: 337924.825747605\n",
+      "Residual 28: 53848.884391301646\n",
+      "\n",
+      "Guess 29: 450566.8790540061\n",
+      "Residual 29: 22717.13001460965\n",
+      "\n",
+      "Guess 30: 600751.7878325778\n",
+      "Residual 30: 9583.49272491798\n",
+      "\n",
+      "Guess 31: 800984.1706324774\n",
+      "Residual 31: 4042.7614641464456\n",
+      "\n",
+      "Guess 32: 1067915.3647484349\n",
+      "Residual 32: 1705.2751301611981\n",
+      "\n",
+      "Guess 33: 1423680.8194451618\n",
+      "Residual 33: 719.1506225678659\n",
+      "\n",
+      "Guess 34: 1897584.654710977\n",
+      "Residual 34: 303.1298309916237\n",
+      "\n",
+      "Guess 35: 2528039.0459216526\n",
+      "Residual 35: 127.62106733184729\n",
+      "\n",
+      "Guess 36: 3364155.8826782126\n",
+      "Residual 36: 53.58028665026278\n",
+      "\n",
+      "Guess 37: 4464999.087625733\n",
+      "Residual 37: 22.345358502300154\n",
+      "\n",
+      "Guess 38: 5889566.303128481\n",
+      "Residual 38: 9.172205219005237\n",
+      "\n",
+      "Guess 39: 7659756.901196487\n",
+      "Residual 39: 3.624027206162788\n",
+      "\n",
+      "Guess 40: 9660531.628038287\n",
+      "Residual 40: 1.3049550160245462\n",
+      "\n",
+      "Guess 41: 11484004.727697873\n",
+      "Residual 41: 0.37209936727929804\n",
+      "\n",
+      "Guess 42: 12522177.958796358\n",
+      "Residual 42: 0.05834214113622749\n",
+      "\n",
+      "It took 43 iterations\n",
+      "The root using the inexact newton function for guess2 is found at 12752256.643475775\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Initial Guess\n",
+    "guess1 = 1e2\n",
+    "newton_sln1 = inexact_newton(canonical_form, guess1, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln1)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Higher guess\n",
+    "# add your solution here\n",
+    "guess2 =\n",
+    "newton_sln2 = \n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 1000000.0\n",
+      "Residual 0: 2077.064915367613\n",
+      "\n",
+      "Guess 1: 1333175.7597753783\n",
+      "Residual 1: 875.9981457928803\n",
+      "\n",
+      "Guess 2: 1777064.3732635635\n",
+      "Residual 2: 369.29919074037207\n",
+      "\n",
+      "Guess 3: 2367820.155411785\n",
+      "Residual 3: 155.5370374879682\n",
+      "\n",
+      "Guess 4: 3152047.145116894\n",
+      "Residual 4: 65.35686941627573\n",
+      "\n",
+      "Guess 5: 4186924.9157805927\n",
+      "Residual 5: 27.31255390260715\n",
+      "\n",
+      "Guess 6: 5533300.869885832\n",
+      "Residual 6: 11.266258838866499\n",
+      "\n",
+      "Guess 7: 7227393.696748207\n",
+      "Residual 7: 4.50452877772687\n",
+      "\n",
+      "Guess 8: 9198544.641646804\n",
+      "Residual 8: 1.6699807839471856\n",
+      "\n",
+      "Guess 9: 11116665.185947483\n",
+      "Residual 9: 0.5126625476845439\n",
+      "\n",
+      "Guess 10: 12372595.476381885\n",
+      "Residual 10: 0.09719357366585712\n",
+      "\n",
+      "It took 11 iterations\n",
+      "The root using the inexact newton function for guess2 is found at 12737754.529301692\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Higher guess\n",
+    "guess2 = 1e6\n",
+    "newton_sln2 = inexact_newton(canonical_form, guess2, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln2)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Even higher guess\n",
+    "# add your solution here\n",
+    "guess3 =\n",
+    "newton_sln3 = \n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 1000000000000.0\n",
+      "Residual 0: -0.9999999999999979\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[43], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### BEGIN SOLUTION\u001b[39;00m\n\u001b[1;32m      2\u001b[0m guess3 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e12\u001b[39m\n\u001b[0;32m----> 3\u001b[0m newton_sln3 \u001b[38;5;241m=\u001b[39m inexact_newton(canonical_form, guess3, delta \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m, epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0e-2\u001b[39m, LOUD\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe root using the inexact newton function for guess3 is found at\u001b[39m\u001b[38;5;124m\"\u001b[39m, newton_sln3)\n",
+      "Cell \u001b[0;32mIn[34], line 46\u001b[0m, in \u001b[0;36minexact_newton\u001b[0;34m(f, x0, delta, epsilon, LOUD, max_iter)\u001b[0m\n\u001b[1;32m     41\u001b[0m slope \u001b[38;5;241m=\u001b[39m (f(x\u001b[38;5;241m+\u001b[39mdelta)\u001b[38;5;241m-\u001b[39mf(x))\u001b[38;5;241m/\u001b[39mdelta\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# print every iteration\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;66;03m#if (LOUD):\u001b[39;00m\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;66;03m#print(\"x_\",iterations+1,\"=\",x,\"-\",fx,\"/\",slope,\"=\",x - fx/slope)\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m fx\u001b[38;5;241m/\u001b[39mslope\n\u001b[1;32m     48\u001b[0m iterations \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;66;03m# check if converged\u001b[39;00m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "guess3 = 1e12\n",
+    "newton_sln3 = inexact_newton(canonical_form, guess3, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "print(\"The root using the inexact newton function for guess3 is found at\", newton_sln3)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The result of guess1 is 12752256.643475775 \n",
+      "\n",
+      "The result of guess2 is 12737754.529301692 \n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'newton_sln3' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[45], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess1 is\u001b[39m\u001b[38;5;124m\"\u001b[39m, newton_sln1, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess2 is\u001b[39m\u001b[38;5;124m\"\u001b[39m, newton_sln2, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess3 is\u001b[39m\u001b[38;5;124m\"\u001b[39m, newton_sln3)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'newton_sln3' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The result of guess1 is\", newton_sln1, \"\\n\")\n",
+    "print(\"The result of guess2 is\", newton_sln2, \"\\n\")\n",
+    "print(\"The result of guess3 is\", newton_sln3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.1.1. Discussion\n",
+    "\n",
+    "How do the number of iterations required to converge change with the initial guess?\n",
+    "\n",
+    "**Discuss** in 1 - 2 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "The better the initial guess, the more quickly the inexact Newton's method converges. For a guess that is far\n",
+    "from the true value, the method might not converge at all.\n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2. Finding the limits of the inexact Newton's Method\n",
+    "\n",
+    "Determine the upper and lower limit for an intial guess where the inexact Newton's Method gives a usable solution. Continue making your guess larger until you get an error. The error is your upper limit. Then do the same for the lower limit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the upper limit of inexact Newton's\n",
+    "# Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 20255000.0\n",
+      "Residual 0: -0.7499256322111911\n",
+      "\n",
+      "Guess 1: -15054.494520939887\n",
+      "Residual 1: -609738952.5990518\n",
+      "\n",
+      "Guess 2: -20070.15849125805\n",
+      "Residual 2: -257259265.92451903\n",
+      "\n",
+      "Guess 3: -26757.71068325505\n",
+      "Residual 3: -108539368.04275693\n",
+      "\n",
+      "Guess 4: -35674.447191027415\n",
+      "Residual 4: -45792613.81668643\n",
+      "\n",
+      "Guess 5: -47563.42945585742\n",
+      "Residual 5: -19319571.733188815\n",
+      "\n",
+      "Guess 6: -63415.40636500947\n",
+      "Residual 6: -8150701.718564938\n",
+      "\n",
+      "Guess 7: -84551.3774541377\n",
+      "Residual 7: -3438658.9051913465\n",
+      "\n",
+      "Guess 8: -112732.67870982541\n",
+      "Residual 8: -1450710.1978667194\n",
+      "\n",
+      "Guess 9: -150307.76417317457\n",
+      "Residual 9: -612026.7691303235\n",
+      "\n",
+      "Guess 10: -200407.93198444886\n",
+      "Residual 10: -258201.63959173448\n",
+      "\n",
+      "Guess 11: -267208.34454244113\n",
+      "Residual 11: -108929.88163112903\n",
+      "\n",
+      "Guess 12: -356276.079257975\n",
+      "Residual 12: -45955.32553686122\n",
+      "\n",
+      "Guess 13: -475034.85393074446\n",
+      "Residual 13: -19387.74662652107\n",
+      "\n",
+      "Guess 14: -633385.3905444695\n",
+      "Residual 14: -8179.49634671949\n",
+      "\n",
+      "Guess 15: -844537.1289579207\n",
+      "Residual 15: -3450.995418887658\n",
+      "\n",
+      "Guess 16: -1126127.8574480342\n",
+      "Residual 16: -1456.1559300287538\n",
+      "\n",
+      "Guess 17: -1501759.1081598364\n",
+      "Residual 17: -614.5786276195389\n",
+      "\n",
+      "Guess 18: -2003161.6501154355\n",
+      "Residual 18: -259.5364979161046\n",
+      "\n",
+      "Guess 19: -2673465.781321577\n",
+      "Residual 19: -109.7539568159819\n",
+      "\n",
+      "Guess 20: -3572809.8975080587\n",
+      "Residual 20: -46.56575336410984\n",
+      "\n",
+      "Guess 21: -4789890.5865597455\n",
+      "Residual 21: -19.909976650008613\n",
+      "\n",
+      "Guess 22: -6471032.760618892\n",
+      "Residual 22: -8.669138620106366\n",
+      "\n",
+      "Guess 23: -8909020.312278708\n",
+      "Residual 23: -3.9388487330527977\n",
+      "\n",
+      "Guess 24: -12888825.354763772\n",
+      "Residual 24: -1.970569891813332\n",
+      "\n",
+      "Guess 25: -21605126.720753416\n",
+      "Residual 25: -1.2060611663813787\n",
+      "\n",
+      "Guess 26: -63754787.11928131\n",
+      "Residual 26: -1.0080191435627595\n",
+      "\n",
+      "Guess 27: -2735897909.586219\n",
+      "Residual 27: -1.000000101476764\n",
+      "\n",
+      "Guess 28: -450362744336030.9\n",
+      "Residual 28: -1.0\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[46], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### BEGIN SOLUTION\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# Find the upper limit using scipy method\u001b[39;00m\n\u001b[1;32m      3\u001b[0m upper_limit_inexact \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m20.255e6\u001b[39m\n\u001b[0;32m----> 4\u001b[0m inexact_newton(canonical_form, upper_limit_inexact, delta \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m, epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0e-2\u001b[39m, LOUD\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "Cell \u001b[0;32mIn[34], line 46\u001b[0m, in \u001b[0;36minexact_newton\u001b[0;34m(f, x0, delta, epsilon, LOUD, max_iter)\u001b[0m\n\u001b[1;32m     41\u001b[0m slope \u001b[38;5;241m=\u001b[39m (f(x\u001b[38;5;241m+\u001b[39mdelta)\u001b[38;5;241m-\u001b[39mf(x))\u001b[38;5;241m/\u001b[39mdelta\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# print every iteration\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;66;03m#if (LOUD):\u001b[39;00m\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;66;03m#print(\"x_\",iterations+1,\"=\",x,\"-\",fx,\"/\",slope,\"=\",x - fx/slope)\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m fx\u001b[38;5;241m/\u001b[39mslope\n\u001b[1;32m     48\u001b[0m iterations \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;66;03m# check if converged\u001b[39;00m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Find the upper limit using scipy method\n",
+    "upper_limit_inexact = 20.255e6\n",
+    "inexact_newton(canonical_form, upper_limit_inexact, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the lower limit of inexact Newton's for delta = 1.0\n",
+    "# Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 10\n",
+      "Residual 0: 7.30397679238864e+17\n",
+      "\n",
+      "Guess 1: 15.040434888834593\n",
+      "Residual 1: 2.782650462545619e+17\n",
+      "\n",
+      "Guess 2: 21.940392145763077\n",
+      "Residual 2: 1.0917175408693384e+17\n",
+      "\n",
+      "Guess 3: 31.287034743375926\n",
+      "Residual 3: 4.367347854330747e+16\n",
+      "\n",
+      "Guess 4: 43.86414759288348\n",
+      "Residual 4: 1.771255686338696e+16\n",
+      "\n",
+      "Guess 5: 60.72150012198905\n",
+      "Residual 5: 7255737740891424.0\n",
+      "\n",
+      "Guess 6: 83.26430420031042\n",
+      "Residual 6: 2994279811880757.0\n",
+      "\n",
+      "Guess 7: 113.37114611165549\n",
+      "Residual 7: 1242505668600765.2\n",
+      "\n",
+      "Guess 8: 153.550845437472\n",
+      "Residual 8: 517723165558624.8\n",
+      "\n",
+      "Guess 9: 207.1516237973737\n",
+      "Residual 9: 216392096900096.78\n",
+      "\n",
+      "Guess 10: 278.64014763243847\n",
+      "Residual 10: 90655461732963.78\n",
+      "\n",
+      "Guess 11: 373.9737505483072\n",
+      "Residual 11: 38045521335762.21\n",
+      "\n",
+      "Guess 12: 501.0968722450732\n",
+      "Residual 12: 15987521793935.463\n",
+      "\n",
+      "Guess 13: 670.6030897239657\n",
+      "Residual 13: 6724887067719.164\n",
+      "\n",
+      "Guess 14: 896.6179113548496\n",
+      "Residual 14: 2830796469093.3423\n",
+      "\n",
+      "Guess 15: 1197.975899916288\n",
+      "Residual 15: 1192263432076.8613\n",
+      "\n",
+      "Guess 16: 1599.7902178372299\n",
+      "Residual 16: 502360858914.0219\n",
+      "\n",
+      "Guess 17: 2135.5453897240104\n",
+      "Residual 17: 211735844118.94693\n",
+      "\n",
+      "Guess 18: 2849.8876788226007\n",
+      "Residual 18: 89263570616.61972\n",
+      "\n",
+      "Guess 19: 3802.345608615149\n",
+      "Residual 19: 37638308185.15326\n",
+      "\n",
+      "Guess 20: 5072.290672894123\n",
+      "Residual 20: 15872411502.719048\n",
+      "\n",
+      "Guess 21: 6765.551626754832\n",
+      "Residual 21: 6694196763.649567\n",
+      "\n",
+      "Guess 22: 9023.233549437558\n",
+      "Residual 22: 2823488923.481483\n",
+      "\n",
+      "Guess 23: 12033.47660101067\n",
+      "Residual 23: 1190961564.1606927\n",
+      "\n",
+      "Guess 24: 16047.134365680335\n",
+      "Residual 24: 502374324.959376\n",
+      "\n",
+      "Guess 25: 21398.678320575953\n",
+      "Residual 25: 211919367.8419206\n",
+      "\n",
+      "Guess 26: 28534.070441130738\n",
+      "Residual 26: 89397218.61775942\n",
+      "\n",
+      "Guess 27: 38047.9266872557\n",
+      "Residual 27: 37712469.33863661\n",
+      "\n",
+      "Guess 28: 50733.06825526741\n",
+      "Residual 28: 15909320.629091263\n",
+      "\n",
+      "Guess 29: 67646.58973415378\n",
+      "Residual 29: 6711545.954610961\n",
+      "\n",
+      "Guess 30: 90197.94955854154\n",
+      "Residual 30: 2831370.398807547\n",
+      "\n",
+      "Guess 31: 120266.42310119365\n",
+      "Residual 31: 1194464.2293482446\n",
+      "\n",
+      "Guess 32: 160357.70015824796\n",
+      "Residual 32: 503908.02193665155\n",
+      "\n",
+      "Guess 33: 213812.66137551583\n",
+      "Residual 33: 212583.94461804072\n",
+      "\n",
+      "Guess 34: 285085.71777252416\n",
+      "Residual 34: 89682.95641939779\n",
+      "\n",
+      "Guess 35: 380115.7565568809\n",
+      "Residual 35: 37834.52873223905\n",
+      "\n",
+      "Guess 36: 506820.17719574063\n",
+      "Residual 36: 15961.115463940445\n",
+      "\n",
+      "Guess 37: 675752.0585857017\n",
+      "Residual 37: 6733.316748332508\n",
+      "\n",
+      "Guess 38: 900971.6769844894\n",
+      "Residual 38: 2840.3511295504227\n",
+      "\n",
+      "Guess 39: 1201191.68694343\n",
+      "Residual 39: 1198.0115220610107\n",
+      "\n",
+      "Guess 40: 1601257.3635572637\n",
+      "Residual 40: 505.14899755645723\n",
+      "\n",
+      "Guess 41: 2133955.058827273\n",
+      "Residual 41: 212.84894453561233\n",
+      "\n",
+      "Guess 42: 2841963.0883751614\n",
+      "Residual 42: 89.53332179115505\n",
+      "\n",
+      "Guess 43: 3778821.1831136704\n",
+      "Residual 43: 37.51193641416687\n",
+      "\n",
+      "Guess 44: 5005703.109634448\n",
+      "Residual 44: 15.56798073287592\n",
+      "\n",
+      "Guess 45: 6573542.184327551\n",
+      "Residual 45: 6.315884206363025\n",
+      "\n",
+      "Guess 46: 8465333.998628428\n",
+      "Residual 46: 2.4255722827515442\n",
+      "\n",
+      "Guess 47: 10463335.412506893\n",
+      "Residual 47: 0.8140746278650042\n",
+      "\n",
+      "Guess 48: 12028517.135861859\n",
+      "Residual 48: 0.1940689532415114\n",
+      "\n",
+      "Guess 49: 12679949.370644793\n",
+      "Residual 49: 0.019326118336442155\n",
+      "\n",
+      "It took 50 iterations\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "12760122.035803474"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Find the lower limit of inexact Newton's for delta = 1.0\n",
+    "lower_limit_inexact = 10\n",
+    "inexact_newton(canonical_form, lower_limit_inexact, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The range of guesses you can use for the inexact Newton's method is 10 to 20255000.0 meters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'The range of guesses you can use for the inexact Newton\\'s method is {lower_limit_inexact} to {upper_limit_inexact} meters.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HwZYjV0RB9o7"
+   },
+   "source": [
+    "### 5.3. Testing Newton's Method with Scipy\n",
+    "\n",
+    "Using SciPy, test Newton's Method for guesses of different orders of magnitude. Find a guess that gives you a solution and a guess that doesn't give you a solution. You should continue adjusting your initial guess until you get an error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first order of magnitude\n",
+    "# Add your solution here\n",
+    "guess1 =\n",
+    "\n",
+    "scipy_sln1 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess1, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# first order of magnitude\n",
+    "guess1 = 100\n",
+    "\n",
+    "scipy_sln1 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess1, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# next order of magnitude\n",
+    "# Add your solution here\n",
+    "guess2 =\n",
+    "\n",
+    "scipy_sln2 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess2, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# next order of magnitude\n",
+    "guess2 = 1e6\n",
+    "\n",
+    "scipy_sln2 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess2, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# very large magnitude\n",
+    "# Add your solution here\n",
+    "guess3 = \n",
+    "\n",
+    "scipy_sln3 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess3, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Derivative was zero. Failed to converge after 2 iterations, value is -1.60403199328293e+26.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[51], line 6\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### BEGIN SOLUTION\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# very large magnitude\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Add your solution here\u001b[39;00m\n\u001b[1;32m      4\u001b[0m guess3 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e12\u001b[39m\n\u001b[0;32m----> 6\u001b[0m scipy_sln3 \u001b[38;5;241m=\u001b[39m optimize\u001b[38;5;241m.\u001b[39mnewton(\n\u001b[1;32m      7\u001b[0m     func\u001b[38;5;241m=\u001b[39mcanonical_form, x0\u001b[38;5;241m=\u001b[39mguess3, fprime\u001b[38;5;241m=\u001b[39mderivative_form, tol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-2\u001b[39m, full_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m      8\u001b[0m )\n",
+      "File \u001b[0;32m~/miniconda3/envs/CBE/lib/python3.11/site-packages/scipy/optimize/_zeros_py.py:314\u001b[0m, in \u001b[0;36mnewton\u001b[0;34m(func, x0, fprime, args, tol, maxiter, fprime2, x1, rtol, full_output, disp)\u001b[0m\n\u001b[1;32m    310\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m disp:\n\u001b[1;32m    311\u001b[0m     msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    312\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Failed to converge after \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m iterations, value is \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    313\u001b[0m         \u001b[38;5;241m%\u001b[39m (itr \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, p0))\n\u001b[0;32m--> 314\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(msg)\n\u001b[1;32m    315\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(msg, \u001b[38;5;167;01mRuntimeWarning\u001b[39;00m)\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _results_select(\n\u001b[1;32m    317\u001b[0m     full_output, (p0, funcalls, itr \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, _ECONVERR))\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Derivative was zero. Failed to converge after 2 iterations, value is -1.60403199328293e+26."
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# very large magnitude\n",
+    "# Add your solution here\n",
+    "guess3 = 1e12\n",
+    "\n",
+    "scipy_sln3 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess3, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The result of guess1 is \n",
+      " (12761113.501660362,       converged: True\n",
+      "           flag: converged\n",
+      " function_calls: 92\n",
+      "     iterations: 46\n",
+      "           root: 12761113.501660362) \n",
+      "\n",
+      "The result of guess2 is \n",
+      " (12761113.50174952,       converged: True\n",
+      "           flag: converged\n",
+      " function_calls: 28\n",
+      "     iterations: 14\n",
+      "           root: 12761113.50174952) \n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'scipy_sln3' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[52], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess1 is \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, scipy_sln1, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess2 is \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, scipy_sln2, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess3 is \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, scipy_sln3)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'scipy_sln3' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The result of guess1 is \\n\", scipy_sln1, \"\\n\")\n",
+    "print(\"The result of guess2 is \\n\", scipy_sln2, \"\\n\")\n",
+    "print(\"The result of guess3 is \\n\", scipy_sln3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "S-1298pcOt0v"
+   },
+   "source": [
+    "#### 5.3.1 Discussion\n",
+    "\n",
+    "How do different initial guesses change the number of iterations required to converge? What types of errors does SciPy's method encounter and how does this compare with the error encountered with the inexact Newton's Method? Do you notice any other notable differences between the two methods?\n",
+    "\n",
+    "**Discuss** in 4-6 sentences\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "'''\n",
+    "As with the inexact Newton's Method, the quality of the initial guess changes the number of iterations\n",
+    "required to converge. Also, it appears that even SciPy's implementation of Newton's method struggles with\n",
+    "division by zero for some guesses. Notably, SciPy's final estimates are more consistent than our implementation\n",
+    "of inexact Newton's.\n",
+    "'''\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFdcM25JELJe"
+   },
+   "source": [
+    "### 5.4. Finding the limits of Newton's Method with Scipy\n",
+    "\n",
+    "Determine the upper and lower limit for an intial guess for which SciPy's ```optimize.newton``` gives a usable solution. Continue making your guess larger until you get an error; this error is your upper limit. Then do the same for the lower limit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the upper limit using scipy method\n",
+    "upper_limit_scipy = 1e7\n",
+    "\n",
+    "while True:\n",
+    "  try:\n",
+    "    # Add your solution here\n",
+    "    \n",
+    "  except:\n",
+    "    print(upper_limit_scipy)\n",
+    "    break   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "id": "8tGe8PNKl0XA"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20257000.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Find the upper limit using scipy method\n",
+    "upper_limit_scipy = 1e7\n",
+    "\n",
+    "while True:\n",
+    "  try:\n",
+    "    upper_limit_scipy += 100\n",
+    "    optimize.newton(func = canonical_form, x0 = upper_limit_scipy, fprime = derivative_form, tol=1e-2)\n",
+    "  except:\n",
+    "    print(upper_limit_scipy)\n",
+    "    break  \n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the lower limit using scipy method\n",
+    "lower_limit_scipy = 1e2\n",
+    "\n",
+    "while True:\n",
+    "  try:\n",
+    "    # Add your solution here\n",
+    "    \n",
+    "  except:\n",
+    "    print(lower_limit_scipy)\n",
+    "    break  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "id": "n7usWlAzl1VF"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### BEING SOLUTION\n",
+    "# Find the lower limit using scipy method\n",
+    "lower_limit_scipy = 1e2\n",
+    "\n",
+    "while True:\n",
+    "  try:\n",
+    "    lower_limit_scipy -= 1\n",
+    "    optimize.newton(func = canonical_form, x0 = lower_limit_scipy, fprime = derivative_form, tol=1e-2)\n",
+    "  except:\n",
+    "    print(lower_limit_scipy)\n",
+    "    break  \n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QEAmwkB-MD3V",
+    "outputId": "4f6f3640-8c29-422f-a725-76474a9a8f5b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For this problem, SciPy's optimize.newton works from 25.0 meters to 20257000.0 meters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('For this problem, SciPy\\'s optimize.newton works from', lower_limit_scipy, 'meters to', upper_limit_scipy, 'meters.')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Taylor Series Approximation\n",
+    "\n",
+    "Apart from Newton's Method, we have another tool up our sleeve to help us more easily express the relationship between $r_1$ and $\\Delta L$: a Taylor Series expansion!\n",
+    "\n",
+    "The difference in gravitational force between the front and back of the bus is given by\n",
+    "\n",
+    "$$\n",
+    "\\Delta F = G m_1 m_2 \\left( \\frac{1}{r_1^2} - \\frac{1}{\\left( r_1 + L_0 + \\Delta L \\right)^2} \\right).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For ease of manipulation, let's temporarily express $L_0 + \\Delta L$ as, simply $L$. Further, let's say $r_1$, rather the reaching to the front of the bus, extends from the center of the black hole to the center of the bus; we'll just call this slight alteration $r$. Now, the difference in gravitational force between the front and back of the bus becomes\n",
+    "\n",
+    "$$\n",
+    "\\Delta F = G m_1 m_2 \\left( \\frac{1}{\\left( r - \\frac{L}{2} \\right)^2} - \\frac{1}{\\left( r + \\frac{L}{2} \\right)^2} \\right).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Immediately, this doesn't appear too helpful. But let's focus on the terms in parentheses, which are now symmetric and can together be expressed by\n",
+    "\n",
+    "$$\n",
+    "\\frac{1}{\\left( r \\pm \\frac{L}{2} \\right)^2}.\n",
+    "$$\n",
+    "\n",
+    "Factoring out $r$, we have\n",
+    "\n",
+    "$$\n",
+    "\\frac{1}{\\left(r \\left( 1 \\pm \\frac{L}{2r} \\right) \\right)^2} = \\frac{1}{r^2 \\left( 1 \\pm \\frac{L}{2r} \\right)^2}.\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, by recognizing that $\\frac{L}{2r} << 1$, we determine that a first-order Taylor Series expansion about zero will be a decent approximation of the term $\\left( 1 \\pm \\frac{L}{2r}\\right)^{-2}$.\n",
+    "\n",
+    "Given the funcion\n",
+    "\n",
+    "$$\n",
+    "f\\left(\\frac{L}{2r}\\right) = \\left( 1 \\pm \\frac{L}{2r}\\right)^{-2},\n",
+    "$$\n",
+    "then\n",
+    "$$\n",
+    "f\\left(\\frac{L}{2r}\\right) \\approx f(0) + \\frac{L}{2r} f'(0) \\approx 1 \\mp \\frac{L}{r}.\n",
+    "$$\n",
+    "\n",
+    "As such, \n",
+    "\n",
+    "$$\n",
+    "\\left( \\frac{1}{\\left( r - \\frac{L}{2} \\right)^2} - \\frac{1}{\\left( r + \\frac{L}{2} \\right)^2} \\right) = \\frac{1}{r^2} \\left( 1 + \\frac{L}{r}\\right) - \\frac{1}{r^2} \\left( 1 - \\frac{L}{r}\\right) = \\frac{1}{r^2} + \\frac{L}{r^3} - \\frac{1}{r^2} + \\frac{L}{r^3}\n",
+    "$$\n",
+    "which reduces to\n",
+    "$$\n",
+    "\\frac{2L}{r^3}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From here, it follows that\n",
+    "\n",
+    "$$\n",
+    "\\Delta F \\approx G m_1 m_2 \\frac{2L}{r^3} = \\frac{2 G m_1 m_2 \\left(L_0 + \\Delta L \\right)}{r^3}.\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Equating this to Young's Modulus, it becomes a simple matter to solve for $r$ in terms of $\\Delta L$:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\Delta L Y A}{L_0} \\approx \\frac{2 G m_1 m_2 \\left(L_0 + \\Delta L \\right)}{r^3}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\therefore r \\approx \\left( \\frac{2 G m_1 m_2 L_0 \\left(L_0 + \\Delta L \\right)}{\\Delta L Y A} \\right) ^{1/3}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.1. Analyzing the approximation of $r(\\Delta L)$\n",
+    "First, write a python function that returns $r$ for a given $\\Delta L$ using the constants we have established for this problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write a function for r(ΔL)\n",
+    "# Add your solution here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Write a function for r(ΔL)\n",
+    "def r(ΔL):\n",
+    "    return np.power((2 * G * m_1 * m_2 * L_0 * (L_0 + ΔL)) / (ΔL * Y * A), 1/3)\n",
+    "### END SOluTOIN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's see if evaluating $r$ at $\\Delta L = 1$ gives us a similar approximation as Netwon's methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The bus will stretch 1 meter(s) at distance of approximately 12761119.001831042 meters from Sagittarius A*.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Evalute r at ΔL = 1\n",
+    "ΔL = 1\n",
+    "print(f'The bus will stretch {ΔL} meter(s) at distance of approximately {r(1)} meters from Sagittarius A*.')\n",
+    "### END SOLUTOIN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.1.1. Discussion\n",
+    "\n",
+    "How does this value of $r_1$ agree with the value returned by the inexact Newton's Method? What about Scipy's Newton's Method? Does it appear to be an accurate approximation?\n",
+    "\n",
+    "**Discuss** in 1 - 3 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "'''\n",
+    "Our Taylor Serie's expansion of r_1 agrees with theh first three terms to the results of the inexact Newton's \n",
+    "Method. It agrees with the first seven terms to the results of Scipy's Newton's Method. As such, it seems \n",
+    "to be a very accurate estimate, at least for calculations that satisfy our assumption that L_0 + ΔL << r_1.\n",
+    "'''\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.2. Graphing $r(\\Delta L)$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, make a plot $r(\\Delta L)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a plot of r(ΔL)\n",
+    "# Add you solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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FURACAABwR1O1veh1q1atytr+8uLFi9HZ2TmhNcvo4YcfTs4qCKsr5/lPpJwHAKB8FIQAAADc5uTJk3Hw4MHkfENDQ7zyyisTWnP69Onx8ssvZ11jm9F8TzzxRHL2l19+KXASKsl5/jmfKwAAKAgBAAC4zY4dO2J0dDQ539bWFvPnz5/wurnbjHZ3d8fg4OCE1y2TefPmxbx585KyZ8+ejbNnzxY8EXcyPDwcx48fT8o2NjYqCAEAyKIgBAAA4Dbt7e1Z+c2bN0/Kuk8//XQ89NBDyfnh4eHYuXPnpKxdJq2trcnZo0ePFjgJY/n111/j8uXLSdlly5bFrFmzCp4IAIB6oiAEAADgJj09PXHixImsa958881oaGiY8FdjY2P89ttvWWvbZjRfW1tbcra3t7fASRhLznPP+TwBACBCQQgAAMAtaq1w6+vri2PHjhVy75UrV05K8XnrV2dnZyHzpnr++eeTsz/99FOBkzCWH3/8MTm7bt264gYBAKAuKQgBAAC44dq1a7Fr165qj5Gt1krNalu6dGm0tLQkZX/++ecYGRkpdiBuk1oQNjc3xzPPPFPoLAAA1B8FIQAAADd0dXXFuXPnqj1Gto6ODiVWpvXr1yflLly4EH19fcUOw03OnDkT/f39SdlVq1Y5fxAAgGwKQgAAAG6o1TfxBgYG4sCBA9Ueo6Zs2bIlOfvdd98VOAm3+vbbb5OzOZ8jAABcpyAEAAAgIiKGhoZi//791R5j3Gq13KyWtra2WLRoUVJ23759BU/D/0t93vfee29s3Lix4GkAAKhHCkIAAAAiImL37t1x5cqVao8xbnv27IlLly5Ve4yasnXr1qTcoUOH4tSpUwVPQ0TEX3/9FT/88ENSdtOmTdHc3FzwRAAA1CMFIQAAABFR+2/gXbx4MTo7O6s9Rk159dVXY/r06UlZbxFOja6uruSi/rXXXit4GgAA6pWCEAAAgDh58mQcPHiw2mNMWK2XnFNtwYIFyVtUfvnllwVPQ0T6c25tbY2nnnqq4GkAAKhXCkIAAACivb09RkdHqz3GhHV3d8fg4GC1x6gp77zzTlKut7c3jhw5UvA05XbmzJn4/vvvk7Jvv/12wdMAAFDPGkbr4TdAAAAAYNza2tqS3iB966234vPPP5+Cicpp+/bt8f7771fMtbS0RH9/f/L2sAAAcCtvEAIAAEDJbdu2LSnX0dERly5dKniachoZGYkvvvgiKfvee+8pBwEAmBAFIQAAAJTcunXrks6z+/PPP51FWJBvvvkm+vv7K+YWLlwYr7/++hRMBABAPVMQAgAAALF9+/ak3GeffVYX51X+23z66adJuQ8++CCampoKngYAgHrnDEIAAAAgIiLWrl0b3d3dFXNfffVVvPjii1MwUTkcOnQonnzyyYq5pUuXxtGjR2PaNP/vDQDAxPiJEgAAAIiIiE8++STpbLsPP/xwCqYpj48++igp9/HHHysHAQCYFH6qBAAAACIi4vHHH4833nijYq6npye6urqmYKL6d+TIkdi7d2/F3AsvvBDr16+fgokAACgDW4wCAAAAN5w/fz4WL14cf/zxx11zK1asiMOHD0/RVPVrw4YN8fXXX981M3PmzDh+/HgsWrRoiqYCAKDeeYMQAAAAuOGBBx5I2kK0t7c36c03xnb48OGK5WBExLvvvqscBABgUnmDEAAAAAAAAErEG4QAAAAAAABQIgpCAAAAAAAAKBEFIQAAAAAAAJSIghAAAAAAAABKREEIAAAAAAAAJaIgBAAAAAAAgBJREAIAAAAAAECJKAgBAAAAAACgRBSEAAAAAAAAUCIKQgAAAAAAACgRBSEAAAAAAACUiIIQAAAAAAAASkRBCAAAAAAAACWiIAQAAAAAAIASURACAAAAAABAiSgIAQAAAAAAoEQUhAAAAAAAAFAiCkIAAAAAAAAoEQUhAAAAAAAAlIiCEAAAAAAAAEpEQQgAAAAAAAAloiAEAAAAAACAElEQAgAAAAAAQIkoCAEAAAAAAKBEFIQAAAAAAABQIgpCAAAAAAAAKBEFIQAAAAAAAJSIghAAAAAAAABKREEIAAAAAAAAJaIgBAAAAAAAgBJREAIAAAAAAECJKAgBAAAAAACgRBSEAAAAAAAAUCL/AbqVWd2oprDNAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1920x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Make a plot of r(ΔL)\n",
+    "dL = np.arange(1,1000,1)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 300)\n",
+    "ax.plot(dL, r(dL))\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xscale(\"log\")\n",
+    "\n",
+    "\n",
+    "# define tick size\n",
+    "plt.xticks(fontsize=15)\n",
+    "plt.yticks(fontsize=15)\n",
+    "plt.tick_params(direction=\"in\", top=True, right=True)\n",
+    "\n",
+    "# plot titile and x,y label\n",
+    "plt.xlabel(\"ΔL (m)\", fontsize=16, fontweight=\"bold\")\n",
+    "plt.ylabel(\"Distance (m)\", fontsize=16, fontweight=\"bold\")\n",
+    "plt.title(\"Distance vs. Change in Bus Length\", fontsize=16, fontweight=\"bold\")\n",
+    "plt.show()\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.2.1. Discussion\n",
+    "\n",
+    "Does the plot make sense given what you know about the relationship between the distance from the black hole and the subsequent tensile force on the bus?\n",
+    "\n",
+    "**Discuss** in 1-3 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "'''\n",
+    "Yes, the plot make sense. As the bus gets closer to the black hole (distance decreases), ΔL increases due to the\n",
+    "increase in the strength of the tensile force.\n",
+    "'''\n",
+    "### END SOLUTION"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python [conda env:CBE]",
+   "language": "python",
+   "name": "conda-env-CBE-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/contrib-dev/Spaghettification.ipynb b/notebooks/contrib-dev/Spaghettification.ipynb
index 4354fa15..b8f1323d 100644
--- a/notebooks/contrib-dev/Spaghettification.ipynb
+++ b/notebooks/contrib-dev/Spaghettification.ipynb
@@ -1,1308 +1,2375 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9nUUNAQW1R6m"
-      },
-      "source": [
-        "# Spaghettification of the Magic School Bus"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "58Az-fqTXLbO"
-      },
-      "source": [
-        "**Prepared by:** Logan Hennes (lhennes@nd.edu) and Joseph Emery (jemery@nd.edu)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QZh4kSflXqo3"
-      },
-      "source": [
-        "**Reference:** This is an original problem created by the authors. With inspiration from Dr. Brian Olson's Problem Solving class."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aLj4IXguX5zf"
-      },
-      "source": [
-        "**Intended Audience:** This problem is intended for sophomores or juniors from the University of Notre Dame who are taking an introductory physics course. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Pkxa4qn9YTt7"
-      },
-      "source": [
-        "## Learning Objectives\n",
-        "\n",
-        "After studying this notebook, completing the activities, and asking questions in class, you should be able to:\n",
-        "\n",
-        "\n",
-        "\n",
-        "*   Perform a degrees of freedom analysis\n",
-        "*   Solve a nonlinear system of equations using Newton's Method and Python tools\n",
-        "*   Properly visualize data using matplotlib\n",
-        "\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FNoltRl9aH62"
-      },
-      "source": [
-        "## Resources\n",
-        "\n",
-        "Relevant Modules in Class Website:\n",
-        "\n",
-        "1.5. [Functions and Scope](https://ndcbe.github.io/data-and-computing/notebooks/01/Functions-and-Scope.html)\n",
-        "\n",
-        "6.1. [Modeling Systems of Nonlinear Equations: Flash Calculation Example](https://ndcbe.github.io/data-and-computing/notebooks/06/Modeling-Systems-of-Nonlinear-Equations.html)\n",
-        "\n",
-        "6.2. [Newton-Raphson Method in One Dimension](https://ndcbe.github.io/data-and-computing/notebooks/06/Newton-Raphson-Method-in-One-Dimension.html#)\n",
-        "\n",
-        "6.3. [More Newton-Type Methods](https://ndcbe.github.io/data-and-computing/notebooks/06/More-Newton-Type-Methods.html)\n",
-        "\n",
-        "6.6. [Newton's Method in Scipy](https://ndcbe.github.io/data-and-computing/notebooks/06/Newton-Methods-in-Scipy.html)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yAS2pJ3QcYTX"
-      },
-      "source": [
-        "## Import Libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "OLKOoqj5ZpEm"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "from scipy import optimize\n",
-        "import matplotlib.pyplot as plt\n",
-        "from matplotlib import ticker, cm"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KeO3vNqWcjBA"
-      },
-      "source": [
-        "## Problem Statement"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kBPTEtoB0mE5"
-      },
-      "source": [
-        "**Homework Problem**\n",
-        "\n",
-        "Complete the following problem outside of class to practice the concepts discussed.\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "oJ-yNo_Xc5R5"
-      },
-      "source": [
-        "Ms. Frizzle and her students have boarded the Magic School Bus for an adventure in space. However, while exploring the Milky Way galaxy, an asteroid collided with the bus, hurtling the bus toward a black hole. Write a python script to find at what distance away the Magic School Bus needs to be to experience a certain amount of stretching by a black hole."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3CWQjedfGKJJ"
-      },
-      "source": [
-        "![](../../media/black_hole.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cj5zRYWDGIDZ"
-      },
-      "source": [
-        "You will need to use Newton’s Law of Universal Gravitation and Youngs modulus.\n",
-        "\n",
-        "\n",
-        "$$F = G\\frac{m_1m_2}{r^2}$$\n",
-        "\n",
-        "$$∆L = \\frac{1}{Y}\\frac{F}{A}\\ L_0 $$"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Nj2qV2_WVDvB"
-      },
-      "source": [
-        "$F$ = the force exerted on an object by gravity\n",
-        "\n",
-        "$G$ = the gravitational constant\n",
-        "\n",
-        "$m_1$ = the mass of the black hole\n",
-        "\n",
-        "$m_2$ = the mass of the magic school bus\n",
-        "\n",
-        "$r_1$ = the distance between the black hole and the front of the bus\n",
-        "\n",
-        "$r_2$ = the distance between the black hole and the back of the bus\n",
-        "\n",
-        "$\\Delta L$ = the change in length of the bus\n",
-        "\n",
-        "$Y$ = a measure of a solid's resistance to elastic deformation under a load (Young's Modulus)\n",
-        "\n",
-        "$F$ = the force exerted on the object causing stretching\n",
-        "\n",
-        "$A$ = cross-sectional area of the bus\n",
-        "\n",
-        "$L_0$ = the length of the bus before stretching"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "thYmW7wlcUht"
-      },
-      "source": [
-        "## 1. Degree of Freedom Analysis"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uIE6CkUAdO1N"
-      },
-      "source": [
-        "### 1.1. Setup\n",
-        "\n",
-        "Define the constants used in this problem. Use scholarly sources such as NASA to determine the mass of the sun. Use the height of the bus as the diameter of your cylinder."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "7hZMqDHYA1qT"
-      },
-      "outputs": [],
-      "source": [
-        "# Constants\n",
-        "Y = 69e9  # Youngs modulus for aluminum\n",
-        "m_2 = 15000  # Mass of school bus in kg\n",
-        "L_0 = 0.01  # length of school bus in km\n",
-        "# Add your solution here"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "uVtvwV5QCUpQ"
-      },
-      "outputs": [],
-      "source": [
-        "### BEGIN SOLUTION\n",
-        "# Constants\n",
-        "Y = 69e9  # Youngs modulus for aluminum\n",
-        "m_2 = 15000  # Mass of school bus in kg\n",
-        "L_0 = 0.01  # length of school bus in km\n",
-        "# Add your solution here\n",
-        "G_standard = 6.67e-11  # Units N m^2/kg^2\n",
-        "G = G_standard / 1000**2  # Units N m^2/kg^2\n",
-        "Solar_Mass = 1.989e30  # value of one solar mass in kg\n",
-        "m_1 = Solar_Mass * 4.154  # Mass of Milkyway Blackhole in kg\n",
-        "\n",
-        "A = np.pi * (1.2192 / 1000) ** 2\n",
-        "### END SOLUTION"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uBftpcsWdwYF"
-      },
-      "source": [
-        "### 1.2. Analysis\n",
-        "\n",
-        "Perform a degree of freedom analysis using the equations provided and the unknown variables. What does this analysis tell you? How should you approach the problem moving forward?\n",
-        "\n",
-        "**Discuss** in 2-4 sentences\n",
-        "\n",
-        "**Answer:**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rAoDFU9Fd-5U"
-      },
-      "source": [
-        "Submit your written work via **Gradescope**."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ojiZf6Hccv40"
-      },
-      "source": [
-        "## 2. Nonlinear Equation"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "R4ufgGbOfNaG"
-      },
-      "source": [
-        "### 2.1. Define a nonlinear equation\n",
-        "\n",
-        "Using substitution, define one nonlinear equation $f(r_1, \\Delta L)$. \n",
-        "\n",
-        "*Hint:* The definition of the function above suggests what the variables are."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hHm34P-tfvO3"
-      },
-      "source": [
-        "Submit your answer and written work via **Gradescope**."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CqEWYdt0f76r"
-      },
-      "source": [
-        "### 2.2. Create a plot and analyze\n",
-        "\n",
-        "Using the nonlinear equation you derived in part a, write a function that calculates the residual of that function given some value of $r_1$ and some value of $\\Delta L$. Then define an array of $r_1$ values labeled as r_1 and an array of $\\Delta L$ values labeled Delta_L. Then use these arrays to calculate the residual for each pair of values.\n",
-        "\n",
-        "Use the 3-D plot function in matplotlib to analyze the results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 130
-        },
-        "id": "Iq6ayqYhD9jZ",
-        "outputId": "bd3cf883-a0ef-414c-c852-c852276836e2"
-      },
-      "outputs": [
-        {
-          "ename": "SyntaxError",
-          "evalue": "ignored",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-0e08dbf484a5>\"\u001b[0;36m, line \u001b[0;32m12\u001b[0m\n\u001b[0;31m    Residual =\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Function for Nonlinear Equation Residual\n",
-        "def Residual_Function(r_1,ΔL):\n",
-        "    ''' Find residual using one Nonlinear equation\n",
-        "    \n",
-        "    Args:\n",
-        "        r_1, ΔL\n",
-        "        \n",
-        "    Returns:\n",
-        "        Residual    \n",
-        "    '''\n",
-        "  # Add your solution here\n",
-        "    Residual = \n",
-        "    return Residual"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bDXosTblE8zc"
-      },
-      "outputs": [],
-      "source": [
-        "### BEGIN SOLUTION\n",
-        "# Function for Nonlinear Equation Residual\n",
-        "def Residual_Function(r_1, ΔL):\n",
-        "    \"\"\"Find residual using one Nonlinear equation\n",
-        "\n",
-        "    Args:\n",
-        "        r_1, ΔL\n",
-        "\n",
-        "    Returns:\n",
-        "        Residual\n",
-        "    \"\"\"\n",
-        "    # Add your solution here\n",
-        "    Residual = (L_0 + ΔL) / (Y * A) * G * m_1 * m_2 * (\n",
-        "        1 / r_1**2 - 1 / (L_0 + ΔL + r_1) ** 2\n",
-        "    ) - ΔL\n",
-        "    return Residual\n",
-        "\n",
-        "\n",
-        "### END SOLUTION"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sgHwoMMyFX_Q"
-      },
-      "source": [
-        "Plot Residual:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 362
-        },
-        "id": "A5WPN5V5Faon",
-        "outputId": "e21d27b8-ad24-4608-cd3f-5654cb5abaf9"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "<ipython-input-13-a0fea10ee60f>:16: UserWarning: Log scale: values of z <= 0 have been masked\n",
-            "  cs = ax.contourf(np.exp(r_grid), ΔL_grid, f_grid, locator=ticker.LogLocator(), cmap=cm.coolwarm, levels=100)\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 432x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Create array\n",
-        "# Add your solution here\n",
-        "\n",
-        "# Initialize Matrix\n",
-        "# Add your solution here\n",
-        "\n",
-        "for i in range(len(r_1)):\n",
-        "    for j in range(len(ΔL)):\n",
-        "        r_grid[i, j] = r_1[i]\n",
-        "        # Add your solution here\n",
-        "\n",
-        "# Plot Figure\n",
-        "fig, ax = plt.subplots()\n",
-        "plt.xscale(\"log\")\n",
-        "\n",
-        "cs = ax.contourf(\n",
-        "    np.exp(r_grid),\n",
-        "    ΔL_grid,\n",
-        "    f_grid,\n",
-        "    locator=ticker.LogLocator(),\n",
-        "    cmap=cm.coolwarm,\n",
-        "    levels=100,\n",
-        ")\n",
-        "\n",
-        "cbar = fig.colorbar(cs)\n",
-        "cbar.ax.set_ylabel(\"Residual\", fontsize=16, fontweight=\"bold\")\n",
-        "cbar.ax.tick_params(labelsize=16)\n",
-        "\n",
-        "cs2 = plt.contour(\n",
-        "    cs, levels=cs.levels[::15], colors=\"k\", alpha=0.7, linestyles=\"dashed\", linewidths=3\n",
-        ")\n",
-        "\n",
-        "# plot heatmap label\n",
-        "plt.clabel(cs2, fmt=\"%2.2f\", colors=\"k\", fontsize=16)\n",
-        "\n",
-        "# define tick size\n",
-        "plt.xticks(fontsize=16)\n",
-        "plt.yticks(fontsize=16)\n",
-        "plt.tick_params(direction=\"in\", top=True, right=True)\n",
-        "\n",
-        "# plot titile and x,y label\n",
-        "plt.xlabel(\"Distance (km)\", fontsize=16, fontweight=\"bold\")\n",
-        "plt.ylabel(\"ΔL (km)\", fontsize=16, fontweight=\"bold\")\n",
-        "plt.title(\"Contour of Residuals\", fontsize=16, fontweight=\"bold\")\n",
-        "plt.show()\n",
-        "### END SOLUTION"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 362
-        },
-        "id": "HgAE37rCH35D",
-        "outputId": "7bf1c6bb-be21-4dd0-dc68-4cdcd554c209"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "<ipython-input-14-08db34fb24a9>:22: UserWarning: Log scale: values of z <= 0 have been masked\n",
-            "  cs = ax.contourf(np.exp(r_grid), ΔL_grid, f_grid, locator=ticker.LogLocator(), cmap=cm.coolwarm, levels=100)\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 432x288 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "### BEGIN SOLUTION\n",
-        "# Create array\n",
-        "r_1 = np.linspace(np.log(1e3), np.log(1e5), 1000)\n",
-        "ΔL = np.linspace(0, 2, 1000)\n",
-        "\n",
-        "# Initialize Matrix\n",
-        "r_grid = np.zeros((len(r_1), len(ΔL)))\n",
-        "ΔL_grid = np.zeros((len(r_1), len(ΔL)))\n",
-        "f_grid = np.zeros((len(r_1), len(ΔL)))\n",
-        "\n",
-        "for i in range(len(r_1)):\n",
-        "    for j in range(len(ΔL)):\n",
-        "        # Add your solution here\n",
-        "        r_grid[i, j] = r_1[i]\n",
-        "        ΔL_grid[i, j] = ΔL[j]\n",
-        "        f_grid[i, j] = Residual_Function(np.exp(r_1[i]), np.exp(ΔL[j]))\n",
-        "\n",
-        "# Plot Figure\n",
-        "fig, ax = plt.subplots()\n",
-        "plt.xscale(\"log\")\n",
-        "\n",
-        "cs = ax.contourf(\n",
-        "    np.exp(r_grid),\n",
-        "    ΔL_grid,\n",
-        "    f_grid,\n",
-        "    locator=ticker.LogLocator(),\n",
-        "    cmap=cm.coolwarm,\n",
-        "    levels=100,\n",
-        ")\n",
-        "\n",
-        "cbar = fig.colorbar(cs)\n",
-        "cbar.ax.set_ylabel(\"Residual\", fontsize=16, fontweight=\"bold\")\n",
-        "cbar.ax.tick_params(labelsize=16)\n",
-        "\n",
-        "cs2 = plt.contour(\n",
-        "    cs, levels=cs.levels[::15], colors=\"k\", alpha=0.7, linestyles=\"dashed\", linewidths=3\n",
-        ")\n",
-        "\n",
-        "# plot heatmap label\n",
-        "plt.clabel(cs2, fmt=\"%2.2f\", colors=\"k\", fontsize=16)\n",
-        "\n",
-        "# define tick size\n",
-        "plt.xticks(fontsize=16)\n",
-        "plt.yticks(fontsize=16)\n",
-        "plt.tick_params(direction=\"in\", top=True, right=True)\n",
-        "\n",
-        "# plot titile and x,y label\n",
-        "plt.xlabel(\"Distance (km)\", fontsize=16, fontweight=\"bold\")\n",
-        "plt.ylabel(\"ΔL (km)\", fontsize=16, fontweight=\"bold\")\n",
-        "plt.title(\"Contour of Residuals\", fontsize=16, fontweight=\"bold\")\n",
-        "plt.show()\n",
-        "### END SOLUTION"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9tDNzCiqh6jk"
-      },
-      "source": [
-        "### 2.3. Discussion\n",
-        "\n",
-        "What does the 3-D plot tell you about the solution? Are there multiple solutions?\n",
-        "\n",
-        "**Discuss** in 1-3 sentences"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eX4pivRVdW3s"
-      },
-      "source": [
-        "## 3. Inexact Newton's Method"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "I_x85LE1iqOr"
-      },
-      "source": [
-        "Typically, a body of aluminum can be subjected to 10% to 25% elongation before deformation becomes permanent (plastic deformation). For simplicity, we will define our maximum tolerance for stretching to be one meter. Solve for the distance when the bus experiences this stretching using the inexact Newton's Method.\n",
-        "\n",
-        "You will need to write a new function for your nonlinear equation, $f(r_1)$, so that the only variable is $r_1$.\n",
-        "\n",
-        "For more information on how to use the inexact Newton's Method, click [here](https://ndcbe.github.io/data-and-computing/notebooks/06/More-Newton-Type-Methods.html)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "QqdQo3sR4DwU"
-      },
-      "outputs": [],
-      "source": [
-        "def inexact_newton(f, x0, delta=1.0e-7, epsilon=1.0e-6, LOUD=False, max_iter=50):\n",
-        "    \"\"\"Find the root of the function f via Newton-Raphson method\n",
-        "    Args:\n",
-        "        f: function to find root of [function]\n",
-        "        x0: initial guess [float]\n",
-        "        delta: finite difference parameter [float]\n",
-        "        epsilon: tolerance [float]\n",
-        "        LOUD: toggle on/off print statements [boolean]\n",
-        "        max_iter: maximum number of iterations [int]\n",
-        "\n",
-        "    Returns:\n",
-        "        estimate of root [float]\n",
-        "    \"\"\"\n",
-        "\n",
-        "    assert callable(f), \"Warning: 'f' should be a Python function\"\n",
-        "    assert (\n",
-        "        type(x0) is float or type(x0) is int\n",
-        "    ), \"Warning: 'x0' should be a float or integer\"\n",
-        "    assert type(delta) is float, \"Warning: 'delta' should be a float\"\n",
-        "    assert type(epsilon) is float, \"Warning: 'eps' should be a float\"\n",
-        "    assert type(max_iter) is int, \"Warning: 'max_iter' should be an integer\"\n",
-        "    assert max_iter >= 0, \"Warning: 'max_iter' should be non-negative\"\n",
-        "\n",
-        "    x = x0\n",
-        "\n",
-        "    # print intial guess\n",
-        "    # if (LOUD):\n",
-        "    # print(\"x0 =\",x0)\n",
-        "\n",
-        "    iterations = 0\n",
-        "    converged = False\n",
-        "\n",
-        "    # Check if the residual is close enough to zero\n",
-        "    while not converged and iterations < max_iter:\n",
-        "        # evaluate function 'f' at new 'x'\n",
-        "        fx = f(x)\n",
-        "\n",
-        "        # calculate 'slope'\n",
-        "        slope = (f(x + delta) - f(x)) / delta\n",
-        "\n",
-        "        # print every iteration\n",
-        "        # if (LOUD):\n",
-        "        # print(\"x_\",iterations+1,\"=\",x,\"-\",fx,\"/\",slope,\"=\",x - fx/slope)\n",
-        "        x = x - fx / slope\n",
-        "\n",
-        "        iterations += 1\n",
-        "\n",
-        "        # check if converged\n",
-        "        if np.fabs(f(x)) < epsilon:\n",
-        "            converged = True\n",
-        "\n",
-        "    if LOUD:\n",
-        "        print(\"It took\", iterations, \"iterations\")\n",
-        "\n",
-        "    if not converged:\n",
-        "        print(\"Warning: Not a solution. Maximum number of iterations exceeded.\")\n",
-        "    return x  # return estimate of root"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "cB-IP69ZfE0Z"
-      },
-      "outputs": [],
-      "source": [
-        "# Set Value of ΔL\n",
-        "ΔL = 1\n",
-        "guess = 1000\n",
-        "\n",
-        "# Add your solution here"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ON_oco6kvKic"
-      },
-      "outputs": [],
-      "source": [
-        "# Use inexact newton method\n",
-        "# Add your solution here"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "K9heOP7lfsoH"
-      },
-      "outputs": [],
-      "source": [
-        "### BEGIN SOLUTION\n",
-        "# Set Value of ΔL\n",
-        "ΔL = 1\n",
-        "guess = 1000\n",
-        "\n",
-        "# Add your solution here\n",
-        "canonical_form = (\n",
-        "    lambda r_1: (L_0 + ΔL)\n",
-        "    / (Y * A)\n",
-        "    * G\n",
-        "    * m_1\n",
-        "    * m_2\n",
-        "    * (1 / r_1**2 - 1 / (L_0 + ΔL + r_1) ** 2)\n",
-        "    - ΔL\n",
-        ")\n",
-        "### END SOLUTION"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "vgGNcVwvvQOU",
-        "outputId": "9ca22c1f-25e8-4042-b4d4-e142b95ab5c9"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "It took 15 iterations\n",
-            "The result of the inexact newton function is 37407.4751508785\n"
-          ]
-        }
-      ],
-      "source": [
-        "### BEGIN SOLUTION\n",
-        "# use inexact newton method\n",
-        "\n",
-        "newton_sln = inexact_newton(canonical_form, guess, epsilon=1e-2, LOUD=True)\n",
-        "print(\"The result of the inexact newton function is\", newton_sln)\n",
-        "### END SOLUTION"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5gkAvogwoJgI"
-      },
-      "source": [
-        "## 4. Newton's Method using Scipy\n",
-        "\n",
-        "Now we have a working estimate for the correct result. Let's use one of the functions in scipy to solve for the distance at which the bus experiences one meter of stretching.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JTSYn9QfrrMv"
-      },
-      "source": [
-        "### 4.1. Setup equations\n",
-        "\n",
-        "When using Newton's Method in Scipy, the derivative of $f(r_1)$ is required to determine the solution. Calculate the derivative of $f(r_1)$ and define it as a function.\n",
-        "\n",
-        "Submit your written work via **Gradescope**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_v_Ca_9EuuBg"
-      },
-      "outputs": [],
-      "source": [
-        "# Set Value of ΔL\n",
-        "ΔL = 1\n",
-        "guess = 1000\n",
-        "\n",
-        "\n",
-        "def derivative_form(r_1):\n",
-        "    \"\"\"Find max distance using canonical derivative\n",
-        "\n",
-        "    Args:\n",
-        "        r_1\n",
-        "\n",
-        "    Returns:\n",
-        "        derivative\n",
-        "    \"\"\"\n",
-        "\n",
-        "    ### BEGIN SOLUTION\n",
-        "    derivative = (\n",
-        "        (L_0 + ΔL)\n",
-        "        / (Y * A)\n",
-        "        * G\n",
-        "        * m_1\n",
-        "        * m_2\n",
-        "        * (2 / (L_0 + ΔL + r_1) ** 3 - 2 / r_1**3)\n",
-        "    )\n",
-        "    ### END SOLUTION\n",
-        "    return derivative"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Nh_KMPubua1v"
-      },
-      "source": [
-        "### 4.2. Use Scipy to determine $r_1$"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "rOWGNX6vf6T5",
-        "outputId": "c28dfbc0-52dc-4498-addf-c0d1c8dea91b"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "The result of the scipy optimize newton function is \n",
-            " (37406.02418060243,       converged: True\n",
-            "           flag: 'converged'\n",
-            " function_calls: 36\n",
-            "     iterations: 18\n",
-            "           root: 37406.02418060243)\n"
-          ]
-        }
-      ],
-      "source": [
-        "# use scipy method\n",
-        "### BEGIN SOLUTION\n",
-        "\n",
-        "scipy_sln = optimize.newton(\n",
-        "    func=canonical_form, x0=guess, fprime=derivative_form, tol=1e-2, full_output=True\n",
-        ")\n",
-        "print(\"The result of the scipy optimize newton function is \\n\", scipy_sln)\n",
-        "### END SOLUTION"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zZ9TDztFve7l"
-      },
-      "source": [
-        "## 5. Analysis and Comparison of Each Method"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UwmLDY2cdjUc"
-      },
-      "source": [
-        "### 5.1. Test the inexact Newton's Method \n",
-        "\n",
-        "Test the inexact Newton's Method function with guesses of different orders of magnitude. Find a guess that gives you a solution and a guess that doesn't give you a solution. You should continue adjusting your initial guess until you get an error."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Va1f7nUau0dj"
-      },
-      "outputs": [],
-      "source": [
-        "# Initial Guess\n",
-        "\n",
-        "### BEGIN SOLUTION\n",
-        "guess1 = 1e2\n",
-        "### END SOLUTION\n",
-        "\n",
-        "newton_sln1 = inexact_newton(canonical_form, guess1, epsilon=1.0e-2, LOUD=True)\n",
-        "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "u7nAggzAukXM"
-      },
-      "source": [
-        "Try Newtons inexact with a very large guess to see if the function fails."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "uuWJ8c_2vEGX"
-      },
-      "outputs": [],
-      "source": [
-        "# Higher Guess\n",
-        "\n",
-        "### BEGIN SOLUTION\n",
-        "guess2 = 1e3\n",
-        "### END SOLUTION\n",
-        "\n",
-        "newton_sln2 = inexact_newton(canonical_form, guess2, epsilon=1.0e-2, LOUD=True)\n",
-        "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln2)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "kHXu4gpJuLBG",
-        "outputId": "6a5fbafb-b7cb-4331-ce7f-95e847ff9296"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "It took 23 iterations\n",
-            "The root using the inexact newton function for guess1 is found at 37459.136814048645\n",
-            "It took 15 iterations\n",
-            "The root using the inexact newton function for guess2 is found at 37407.4751508785\n",
-            "It took 7 iterations\n",
-            "The root using the inexact newton function for guess3 is found at 37424.27645628167\n"
-          ]
-        }
-      ],
-      "source": [
-        "### BEGIN SOLUTION\n",
-        "guess3 = 1e4\n",
-        "### END SOLUTION\n",
-        "\n",
-        "newton_sln3 = inexact_newton(canonical_form, guess3, epsilon=1.0e-2, LOUD=True)\n",
-        "print(\"The root using the inexact newton function for guess3 is found at\", newton_sln3)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bhmwhxKfMjZe"
-      },
-      "source": [
-        "### 5.1.1 Discussion\n",
-        "\n",
-        "How do different orders of magnitude change result from guess? How do the number of iterations change?\n",
-        "\n",
-        "**Discuss** in 2-4 sentences\n",
-        "\n",
-        "**Answer:**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7l6N6kJADNpm"
-      },
-      "source": [
-        "### 5.2. Finding the limits of inexact Newton's Method\n",
-        "\n",
-        "Determine the upper and lower limit for your guess where the inexact Newton's Method function gives you a usable solution. Continue making your guess larger until you get an error. The error is your upper limit. Then do the same for the lower limit.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "s6LKn4bXwuU1"
-      },
-      "outputs": [],
-      "source": [
-        "# Find the upper limit using inexact newton method\n",
-        "\n",
-        "### BEGIN SOLUTION\n",
-        "upper_limit_inexact = 5.9326e4\n",
-        "### END SOLUTION\n",
-        "\n",
-        "inexact_newton(canonical_form, upper_limit_inexact, epsilon=1.0e-2)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "TWpP6LQql3Wv"
-      },
-      "outputs": [],
-      "source": [
-        "# Find the lower limit using inexact newton method\n",
-        "\n",
-        "### BEGIN SOLUTION\n",
-        "lower_limit_inexact = 7.7240e-3\n",
-        "### END SOLTUON\n",
-        "\n",
-        "inexact_newton(canonical_form, lower_limit_inexact, epsilon=1.0e-2)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "RScEtUcT_J0e",
-        "outputId": "48f8c601-6fc5-44f2-eae6-dab04ce46177"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "The range of guesses that you can use for the inexact newton method is 0.007724 km < guess < 59326.0 km\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(\n",
-        "    \"The range of guesses that you can use for the inexact newton method is\",\n",
-        "    lower_limit_inexact,\n",
-        "    \"km < guess <\",\n",
-        "    upper_limit_inexact,\n",
-        "    \"km\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HwZYjV0RB9o7"
-      },
-      "source": [
-        "### 5.3. Test Newton's Method with Scipy\n",
-        "\n",
-        "Now do similar tests using the same numbers for the optimize.newton function in scipy. Find a guess that gives you a solution and a guess that doesn't give you a solution. For the incorrect guess, you should get an error."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "yq9yEHgvHuYL"
-      },
-      "outputs": [],
-      "source": [
-        "# Initial Guess\n",
-        "### BEGIN SOLUTION\n",
-        "guess1 = 1e2\n",
-        "### END SOLUTION\n",
-        "\n",
-        "scipy_sln1 = optimize.newton(\n",
-        "    func=canonical_form, x0=guess1, fprime=derivative_form, tol=1e-2, full_output=True\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UHcqGO2VuhQF"
-      },
-      "source": [
-        "Try scipy with a very large guess to see if the function fails."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "J9Kt-k9vHt4V"
-      },
-      "outputs": [],
-      "source": [
-        "# higher Guess\n",
-        "### BEGIN SOLUTION\n",
-        "guess2 = 1e3\n",
-        "### END SOLTUION\n",
-        "scipy_sln2 = optimize.newton(\n",
-        "    func=canonical_form, x0=guess2, fprime=derivative_form, tol=1e-2, full_output=True\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "5KFUb9_TF3_M",
-        "outputId": "e45d820e-1c8e-490d-e8dd-bd17d2f176fc"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "The result of guess1 is \n",
-            " (37406.024180626926,       converged: True\n",
-            "           flag: 'converged'\n",
-            " function_calls: 52\n",
-            "     iterations: 26\n",
-            "           root: 37406.024180626926) \n",
-            "\n",
-            "The result of guess2 is \n",
-            " (37406.02418060243,       converged: True\n",
-            "           flag: 'converged'\n",
-            " function_calls: 36\n",
-            "     iterations: 18\n",
-            "           root: 37406.02418060243) \n",
-            "\n",
-            "The result of guess3 is \n",
-            " (37406.02418060992,       converged: True\n",
-            "           flag: 'converged'\n",
-            " function_calls: 20\n",
-            "     iterations: 10\n",
-            "           root: 37406.02418060992)\n"
-          ]
-        }
-      ],
-      "source": [
-        "# even larger guess\n",
-        "### BEGIN SOLUTION\n",
-        "guess3 = 1e5\n",
-        "### END SOLUTION\n",
-        "scipy_sln3 = optimize.newton(\n",
-        "    func=canonical_form, x0=guess3, fprime=derivative_form, tol=1e-2, full_output=True\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(\"The result of guess1 is \\n\", scipy_sln1, \"\\n\")\n",
-        "print(\"The result of guess2 is \\n\", scipy_sln2, \"\\n\")\n",
-        "print(\"The result of guess3 is \\n\", scipy_sln3)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 402
-        },
-        "id": "u5nAg7J_ufr3",
-        "outputId": "26d62bc5-5fd4-4423-8daa-98450f34aab9"
-      },
-      "outputs": [
-        {
-          "ename": "RuntimeError",
-          "evalue": "ignored",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-140-7238afa42cc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Use Scipy method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Add your solution here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mscipy_sln\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimize\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcanonical_form\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mguess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfprime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mderivative_form\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The root is found at\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscipy_sln\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m### END SOLUTION\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/scipy/optimize/zeros.py\u001b[0m in \u001b[0;36mnewton\u001b[0;34m(func, x0, fprime, args, tol, maxiter, fprime2, x1, rtol, full_output, disp)\u001b[0m\n\u001b[1;32m    292\u001b[0m                         \u001b[0;34m\" Failed to converge after %d iterations, value is %s.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m                         % (itr + 1, p0))\n\u001b[0;32m--> 294\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    295\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRuntimeWarning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m                 return _results_select(\n",
-            "\u001b[0;31mRuntimeError\u001b[0m: Derivative was zero. Failed to converge after 3 iterations, value is -1.0466994161548342e+25."
-          ]
-        }
-      ],
-      "source": [
-        "# Initial Guess\n",
-        "guess = 1e6\n",
-        "\n",
-        "scipy_sln = optimize.newton(\n",
-        "    func=canonical_form, x0=guess, fprime=derivative_form, tol=1e-2\n",
-        ")\n",
-        "print(\"The root is found at\", scipy_sln)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wPcge79MN97Z"
-      },
-      "source": [
-        "### 5.3.1 Discussion\n",
-        "\n",
-        "How do different orders of magnitude change the result from guess? How do the number of iterations change?\n",
-        "\n",
-        "**Discuss** in 2-4 sentences\n",
-        "\n",
-        "**Answer:**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zFdcM25JELJe"
-      },
-      "source": [
-        "### 5.4. Finding the limits of Newton's Method with Scipy\n",
-        "\n",
-        "Determine the upper and lower limit for your guess where the optimize.newton function in scipy gives you a useable solution."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8tGe8PNKl0XA"
-      },
-      "outputs": [],
-      "source": [
-        "# Find the upper limit using scipy method\n",
-        "upper_limit_scipy = 2.9e5\n",
-        "\n",
-        "while True:\n",
-        "    try:\n",
-        "        upper_limit_scipy += 1\n",
-        "        optimize.newton(\n",
-        "            func=canonical_form, x0=upper_limit_scipy, fprime=derivative_form, tol=1e-2\n",
-        "        )\n",
-        "    except:\n",
-        "        print(upper_limit_scipy)\n",
-        "        break"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "n7usWlAzl1VF"
-      },
-      "outputs": [],
-      "source": [
-        "# Find the lower limit using scipy method\n",
-        "lower_limit_scipy = 1e3\n",
-        "\n",
-        "while True:\n",
-        "    try:\n",
-        "        lower_limit_scipy -= 1\n",
-        "        optimize.newton(\n",
-        "            func=canonical_form, x0=lower_limit_scipy, fprime=derivative_form, tol=1e-2\n",
-        "        )\n",
-        "    except:\n",
-        "        print(lower_limit_scipy)\n",
-        "        break"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "QEAmwkB-MD3V",
-        "outputId": "4f6f3640-8c29-422f-a725-76474a9a8f5b"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "optimize.newton function from scipy works over 0.0 km < guess < 290001.0 km\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(\n",
-        "    \"optimize.newton function from scipy works over\",\n",
-        "    lower_limit_scipy,\n",
-        "    \"km < guess <\",\n",
-        "    upper_limit_scipy,\n",
-        "    \"km\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "S-1298pcOt0v"
-      },
-      "source": [
-        "### 5.5. Discussion\n",
-        "\n",
-        "Over what range of guesses will the inexact newton function give you a result? Over what range of guesses will the optimize.newton function from scipy gives you a result? Which of the ranges is bigger? Which method uses fewer iterations?\n",
-        "\n",
-        "**Discuss** in 2-4 sentences\n",
-        "\n",
-        "**Answer:**"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9nUUNAQW1R6m"
+   },
+   "source": [
+    "# Spaghettification of the Magic School Bus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "58Az-fqTXLbO"
+   },
+   "source": [
+    "**Prepared by:** Logan Hennes (lhennes@nd.edu) and Joseph Emery (jemery@nd.edu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Edited by:** Kristin Swartz-Schult (kswartzs@nd.edu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QZh4kSflXqo3"
+   },
+   "source": [
+    "**Reference:** This is an original problem created by the authors with inspiration from Dr. Brian Olson's  class on problem solving."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aLj4IXguX5zf"
+   },
+   "source": [
+    "**Intended Audience:** This problem is intended for sophomores or juniors from the University of Notre Dame who are taking an introductory physics course. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Pkxa4qn9YTt7"
+   },
+   "source": [
+    "## Learning Objectives\n",
+    "\n",
+    "After studying this notebook, completing the activities, and asking questions in class, you should be able to:\n",
+    "\n",
+    "\n",
+    "\n",
+    "*   Perform a degrees of freedom analysis\n",
+    "*   Solve a nonlinear system of equations using Newton's Method and Python tools\n",
+    "*   Properly visualize data using Matplotlib\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FNoltRl9aH62"
+   },
+   "source": [
+    "## Resources\n",
+    "\n",
+    "Relevant modules from the class website:\n",
+    "\n",
+    "1.5. [Functions and Scope](https://ndcbe.github.io/data-and-computing/notebooks/01/Functions-and-Scope.html)\n",
+    "\n",
+    "6.1. [Modeling Systems of Nonlinear Equations: Flash Calculation Example](https://ndcbe.github.io/data-and-computing/notebooks/06/Modeling-Systems-of-Nonlinear-Equations.html)\n",
+    "\n",
+    "6.2. [Newton-Raphson Method in One Dimension](https://ndcbe.github.io/data-and-computing/notebooks/06/Newton-Raphson-Method-in-One-Dimension.html#)\n",
+    "\n",
+    "6.3. [More Newton-Type Methods](https://ndcbe.github.io/data-and-computing/notebooks/06/More-Newton-Type-Methods.html)\n",
+    "\n",
+    "6.6. [Newton's Method in Scipy](https://ndcbe.github.io/data-and-computing/notebooks/06/Newton-Methods-in-Scipy.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yAS2pJ3QcYTX"
+   },
+   "source": [
+    "## Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "OLKOoqj5ZpEm"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy import optimize\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import ticker, cm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KeO3vNqWcjBA"
+   },
+   "source": [
+    "## Problem Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oJ-yNo_Xc5R5"
+   },
+   "source": [
+    "Ms. Frizzle and her students have boarded the Magic School Bus for an adventure into outer space. However, while exploring the Milky Way, an asteroid collides with the bus, hurtling it toward a black hole at the center of the galaxy known as Sagittarius A*. "
+   ]
+  },
+  {
+   "attachments": {
+    "black_hole.png": {
+     "image/png": 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QqO/Fvco2IeAFhiUoPzs358dclfpxaZteA/h0CCCAAAIIIIAAAggggAACCCCAAAIIINAdAgTqu2M/sBYIrLvAwsJC0+Zt5ufnjbZbT4cAAggggAACCCCAAAIIIIAAAggggAAC3SFAoL479gNrgcC6C0xOTjbUnNcAvQbq6RBAAAEEEEAAAQQQQAABBBBAAAEEEECgewQI1HfPvmBNEFhXAQ3KLy4uunfIulZvzNTUlBkdHV3XcsgMAQQQQAABBBBAAAEEEEAAAQQQQAABBO6cAIH6O+fH0gh0pUDa7E3Fv0pWY/UHaPamK/cXK4UAAggggAACCCCAAAIIIIAAAggg0N8CBOr7e/+z9T0qoLXm63vrxmT5Bg4ODprZ2dl8AkMIIIAAAggggAACCCCAAAIIIIAAAggg0BUCBOq7YjewEgisr0Bo9sa4hm9s5jR7s77G5IYAAggggAACCCCAAAIIIIAAAggggMB6CRCoXy9J8kGgSwS01vzw8HBcG988vcmypHp9nMsAAggggAACCCCAAAIIIIAAAggggAACCHRagEB9p/cA5SOwzgJj0uzNtG32xofopTeyQ6ZNT61zSWSHAAIIIIAAAggggAACCCCAAAIIIIAAAushQKB+PRTJA4EuEqhUNECfdzq2Z2rKaLv1dAgggAACCCCAAAIIIIAAAggggAACCCDQfQIE6rtvn7BGCKxZQNuh37lzZ7K8q1VPszcJCYMIIIAAAggggAACCCCAAAIIIIAAAgh0mQCB+i7bIawOAndGQGvN1+t7JYu8PfrxiQkzMT5+Z7JlWQQQQAABBBBAAAEEEEAAAQQQQAABBBDYQAEC9RuIS9YItFtgizR7oyF6V4/elT4zM2OGhobavSqUhwACCCCAAAIIIIAAAggggAACCCCAAAIrFCBQv0IokiHQ7QKTk5NmQmrPhy4E62n2JojQRwABBBBAAAEEEEAAAQQQQAABBBBAoDsFCNR3535hrRBYtcDw8LCZnZ2V5UKIvmLGx3cVgverzpQFEEAAAQQQQAABBBBAAAEEEEAAAQQQQGDDBQjUbzgxBSCw8QIaoNdAvesqEqrPbBM4Bw4cMLVabeNXgBIQQAABBBBAAAEEEEAAAQQQQAABBBBAYM0CBOrXTMeCCHSPgLZBv39uvw/Pu/UaqA6YhfmF7llJ1gQBBBBAAAEEEEAAAQQQQAABBBBAAAEEmgoQqG/KwkQENo/AwsJCrDVvG73xLd/wEtnNsw9ZUwQQQAABBBBAAAEEEEAAAQQQQACB/hYgUN/f+5+t7wGB0dFRU6/XY8v0ukkDA1KbXgL4dAgggAACCCCAAAIIIIAAAggggAACCCDQ/QIE6rt/H7GGCLQUWFxcNLVq1bZHnyaampoyGsCnQwABBBBAAAEEEEAAAQQQQAABBBBAAIHuFyBQ3/37iDVEoKWAq02/V+ZnhTRZVhwvzGQEAQQQQAABBBBAAAEEEEAAAQQQQAABBLpKgEB9V+0OVgaB1QlUKpVCkzdGxqam9lCbfnWMpEYAAQQQQAABBBBAAAEEEEAAAQQQQKCjAgTqO8pP4QisXWBiYsJMTk7aDCReb0IlemrTr92UJRFAAAEEEEAAAQQQQAABBBBAAAEEEOiEAIH6TqhTJgLrILBlSyUG50N2GrwfHx8Po/QRQAABBBBAAAEEEEAAAQQQQAABBBBAYBMIEKjfBDuJVUSgLKAB+d27JwuBeqlUbw7Mz5uqvFyWDgEEEEAAAQQQQAABBBBAAAEEEEAAAQQ2jwCB+s2zr1hTBKKABuMXFxfjuA5oTXoN4NMhgAACCCCAAAIIIIAAAggggAACCCCAwOYSIFC/ufYXa4uAmZqeMjvHdup7Y00lk7bpvcnCwoIZGBhACAEEEEAAAQQQQAABBBBAAAEEEEAAAQQ2mQCB+k22w1hdBGq1mllYkNr0Nkpf0Xi9uXzH5aZer4ODAAIIIIAAAggggAACCCCAAAIIIIAAAptQgED9JtxprHL/CkxPT0tt+rFYiz5IzM7OmsHBwTBKHwEEEEAAAQQQQAABBBBAAAEEEEAAAQQ2kQCB+k20s1hVBIaGhszc3FwOIdXpR3aMGA3g0yGAAAIIIIAAAggggAACCCCAAAIIIIDA5hQgUL859xtr3YcC+qLYyclJv+USoff16vft22eGh4f7UIRNRgABBBBAAAEEEEAAAQQQQAABBBBAoDcECNT3xn5kK/pAoFLR4Lx9h2xs+kabu9Fmb+gQQAABBBBAAAEEEEAAAQQQQAABBBBAYPMKEKjfvPuONe8jgdDkjb46NothemOD9LRN30cHApuKAAIIIIAAAggggAACCCCAAAIIINCTAgTqe3K3slG9JDA7M2uGtzc2bTM+Pm60ORw6BBBAAAEEEEAAAQQQQAABBBBAAAEEENjcAgTqN/f+Y+37QCDUpo+bKi3gVDJj7sjkgw4BBBBAAAEEEEAAAQQQQAABBBBAAAEENr0AgfpNvwvZgF4WmJ6eNmNjYw2bODU1ZUZHRxumMwEBBBBAAAEEEEAAAQQQQAABBBBAAAEENp8AgfrNt89Y4z4S0BfI6itkbd15P8ALZPvoAGBTEUAAAQQQQAABBBBAAAEEEEAAAQT6QoBAfV/sZjZyMwqMjo6Z+t5pidK7CH0I2M/MzBhtDocOAQQQQAABBBBAAAEEEEAAAQQQQAABBHpDgEB9b+xHtqLHBBYWFkytVstr0/vtGxkZMdocDh0CCCCAAAIIIIAAAggggAACCCCAAAII9I4Agfre2ZdsSQ8JFF8g6+vSS+/A1w7YAH4PbSqbggACCCCAAAIIIIAAAggggAACCCCAQN8LEKjv+0MAgG4TaHyBrAvU8wLZbttTrA8CCCCAAAIIIIAAAggggAACCCCAAALrI0Cgfn0cyQWBdROoVqtm8eZF/wZZl+3AwIDR5nDoEEAAAQQQQAABBBBAAAEEEEAAAQQQQKD3BAjU994+ZYs2scDo6Kip1+t+C1xNem2ofmYfL5DdxLuVVUcAAQQQQAABBBBAAAEEEEAAAQQQQGBJAQL1S/IwE4H2CczOzprh4WFTkcB8lpn4IlleINu+fUBJCCCAAAIIIIAAAggggAACCCCAAAIIdEKAQH0n1CkTgSYCtWrVLCwuyhyJ1BuJ1Pu+NnmjTd/QIYAAAggggAACCCCAAAIIIIAAAggggEBvChCo7839ylZtMgGtST87N2vj8zZMrx/STe2ZMtocDh0CCCCAAAIIIIAAAggggAACCCCAAAII9K4Agfre3bds2SYRmJqaNjt37pS11Vr0eTc4OGi0ORw6BBBAAAEEEEAAAQQQQAABBBBAAAEEEOhtAQL1vb1/2bouF5ibmzNDQ0NxLUOjNzph/sABU63V4jwGEEAAAQQQQAABBBBAAAEEEEAAAQQQQKA3BQjU9+Z+Zas2iYAG6TVYH5ul9wMzMzOFAP4m2RxWEwEEEEAAAQQQQAABBBBAAAEEEEAAAQTWIECgfg1oLILAeggMSbv0c75pm4oE6DPf9M2OHTtMvV5fjyLIAwEEEEAAAQQQQAABBBBAAAEEekDgF7/4hfnUpz5lfvazn5l73vOe5vzzz++BrVp6E/pxm5cWYW6vCxCo7/U9zPZ1pYC2Pa8vkHVdRWrUS/v08j/t0nfl7mKlEEAAAQQQQAABBBBAAAEEOijw4x//2Bx11FGmUpH75z7t9u7da0ZGRuzWq8Itt95qTj755J7WsNs8Ktss8RLtbu2DbXZbyme/ChCo79c9z3Z3VKCyRb5W/ReNroh+yeooTd6oBh0CCCCAAAIIIIAAAggg0H8CN954o3n/+99vfvnLX9qA9IknnmguueQSc+SRR64Z48tf/rK5ft/15uf/+3Ob57HHHWee9MQnmkMOOWTNebZzwfe+973maU97mvmf//kfc8IJJ5gbbrjBnHrqqe1chYaybrnlFvPOd77TfPWrXzU33XST+dGPfmSOP/54c7/73c9s377dPPKRjzQHHXRQw3J3dsL09LTZOTYWQwmLi4tm69atdzbbrl5+amrK7Ny5069jxSwuLqx5mz/72c/acyG7IwnG+JwPO+ww+yCoVquZc8891xx99NFd7cLK9a4Agfre3bdsWZcKxHbpk/XTQP0+2qVPRBhEAAEEEEAAAQQQQAABBPpHIMsyM7B1wNaSDr+41q2/733vaz7zmc+sKVj/H//xH+a3fuuh5n8lSJ92r3nNa8xTnvKUdFLXDj/2sY81GqwPndawvvzyy8NoW/s/+MEPzO7du81rX/ta83//93+FstPmbE855RTzyle+0lx66aWFNHd2xAWtr5BsXKC5HwL1e/bsMVdcodvsurVu8x133GGOk4dUug9DRUl5ciWUzlKnaRVKHTvs8EPNlU++0jznOc+xD4fsLD4QaJMAgfo2QVMMAiowMTFhdk9Oxifg9ld78k2wbXDQaHM4dAgggAACCCCAAAIIIIAAAv0n8POf/9wccZcj7Ia7llFdOFE/X33dq81Tn/pnq0Z59KMfbf7tX/+14f7zuc97nnnhC1+46vw6scDjL3u8eee73hHiqUaD1aOjo21flV/96lfmoosuMrNSwc6FdnUV3D6yKxMGbb9iTjzxBPONb3xjXddTt/0KqV0eyl9YWDQDA71do356Sn5FcIX8isBv9M0337ymX1To+XUXPb8kn+BX2Dm633wXzr+jjjrSfPrTnzZnnnlmmEUfgQ0XIFC/4cQUgIATiO3SyxeAb5JeZlTsF+vCwgJMCCCAAAIIIIAAAggggAACfSqgNbQPP/xwu/Uu1ms/bVTxnHPOMdpsx2q6AwcOmNNPP93ckd1RuP/UDJ+3iQL12sTM5ZfvkBrsP7dNkrzvfe8zWmO93d3VV19t/uZv/sYWqxXuKpUt5klPepL5zd/8TXPEEUeYr33ta7bm/5e+9CWbRtvT13b117PTpm/GpOmbUBF8rbXL13OdNjqv9foVgTu/9EGYC9OfffbZ8muT35Lxirn99l/YppVuvPEmacroRvf4xdeqPOfsc2xzS4ceeuhGbyr5I2AFCNRzICDQJoHqwIBZvOXm8L0QS53Zt88MxRfLxskMIIAAAggggAACCCCAAAII9InAL37xC2ly47CG+8Ww+Z/4xCfMgx/84DC6bP+v/vIvzctefo3N71Bpj/4Xt98el9Gg8wte8II43u0D2myJ+oQHGZ1Y33vd6ySpIf9NW/TBBx9srr/+erNt27aGVfnoRz9qnv/855vbpN36L/qgfUOiNU5wQevQXrsxa61dvsbiO7JYaPrmzj6cSB+E6YY885nPNNdee23DNr3rXe+yv9j4yU9+Eue9/e1vN5dddlkcZwCBjRQgUL+RuuSNgBdo1i69zhofH7fN4QCFAAIIIIAAAggggAACCCDQvwIhkOhq0xvzmEseY2to+3r1ZmR0xGgzICvptJmPk08+2Xz/+9+3yXfs2GG0bffQbaYa9WGdO9nXF9meeuopsfkVfbnpG9/4xravUghaa8F6XCz00ctk1zNQr+8TeMYzn9E0UK+2L3/5y81f/MVfylBmnZ+3yR5s6TbQbV4BAvWbd9+x5ptEYGho0Mzt3y/X+OT1MvKtuu1hMn1udpNsBauJAAIIIIAAAggggAACCCCwUQIhUO/a3TBm165d5sMf+rC0kf0p21iHNq/y9a9/3dz97ndfdhVcEyk7JavMLvvJT36yUBt/JYF6rcWuL6OdlyZ0vv2d70iQOrNtg5911lnmtNNOk+ZXNFS8fKfL3XLLLeaLX/yiufXWW6Sd8CNtPve5z31sHmk+d7vb3Rpqzevy8/Pz5mc/+5k59thjzUknnbRsobfeeqv5whe+YJc75phjzBlnnGEe8IAHrHidywV88IMfNL/zO78TJ+u+mZR3z61Hp+v6+c9/3mhzuLp96tJqXdMa9RpdmF+Yl6Z0B+xq/PCHPzT7Je6g+eny559/vm2SZy3reLv8+uLGG280//Vf/2V+fNtt5gx5ofH9739/c/zxx68lO6Pt+//3f/+3+dznPmebA9L9sdL8pqVd/jF5MBK6tTb3Y98BIeeQ7eTQfeYzmteo1/m67fb49CfjY+SFxu9+97vdssmnbtdNN90kTefcbm1OOOGEZG7j4Nflgc/35WW2ei7/2q/9WmOCZMp35JxTsxu/+lX7a5gTTzzRPOhBD7IP4JJkDPaigFz06BBAYIMEBgeHtAG0/J/8pSTfCTJeyeSPjQ0qlWwRQAABBBBAAAEEEEAAAQQ2k8D//u//untFja7L/aI0n5K9/vWv1yq97n5S+q961atWtEkXXHBBXOaCBz4wk2Zj4rjen0qgvmU+EpzNRkdGMwmaF5bR9XD3sia74IJfzyT43zKPMEOCm1mtWpV89C1tsh1+W8I9cZxmp1ey02q1sGjsv/nNb47LbtmyJZMXtMZ56YA8WMiuu+41mQS7XXpfXihLfmGQSVMnmQRV08VWNPyZz3zGrnvY/oc//KIVLdcqkVvX67LjjjvOmfjtt/nL8Cknn2LX9Ze//GUhC6lRH7dN00rTN5m0iZ9Jk0jZQWITnWXeQQcdlD3+8Y/PpJ38Qh5LjXzsYx/LJBicSRNDsZywz7R/z3veM/vbv/3brLxerfL8+Mc/nv2G5HfYYT4/u53hOKpkEtjO/vZFS+f3xj1vdOsiy+o2S6C+VXFLTpdAfeH4k6ZvWqa/9db/KWz/4x//B03TSs37mOchBx+c6TncqtP4T2WLOw90O97xjnc0TSqB/+zSSy8tlB/OE11OWmvI5KFX02WZ2BsC+lSUDgEENkBAmrWJXyj2wioX1dDft2/fBpRIlggggAACCCCAAAIIIIAAAptRwAYS9Z7RBiQrmdTazm677bZMXkoq95ES4JPp8gLMZTft05/+dLzv1PtPDfZLbf1YYUyntQrUS018G+CV2toxDw0OhvK1H+ZJTfjsbW97W8v1efKTnxzz0DJjPpqfD9imgWVNc9e73rUhP6lFXih/caExUKvB+4df9HBXXsw7v/9Oy5cXwDaUsdwE3TcHH3KwXY+w/a997WuXW6zpfLuuD/frmjjrOtp/fv/r8BOf+MRCHmoRytf5L33pS+X4uGtcLixv+z6/s84+K/v2t79dyKc8ooF3+YWA3ffh+At5xP1mXd3+f8hDHpLJy4rL2cTxQn5+PWI+Oh73kTvOfuuhv9Uyv5Xs/1jwEgPu/PLHtZT/rGc9q2Xq97znPc5U1lXX+7nPfW7TtPrQIh7Dkqe0a980nU788pe/XEj7pje9qSGtpjnyyCML+zi6Jf5rOYYbCmNC1woQqO/aXcOKbWaBmZmZ5MvHfRG5C6zJdB4dAggggAACCCCAAAIIIIAAAkEgBup9YFNr1Gsn7aHHoKEGT+WlsmGRpn1pj97ei+r959FH3z376U9/amvUh/tRzaNVoP6xj31soazT73Of7A/+4A8yrX38hCc8IZOmTwrzTz31VJt/eUW0Jn0sTwKMR0kA/koJ3L/4xS/O/uRP/iQ7/IgjXD42+Giye9zjHvYhRDkwrfmmtch13ZvVqL7kkksa7r/PuM8Z2aMf/ejs3PPOlSD7IXF9Dj300PLqrmhcazKH4LX29UGFBnulyZkVLR8SuXXNA8aalzSz4tb13HOzQ2RdQzmHHnZYWMz2yxYhnfbvccw9Mnm5rdTGPznufztfjK/2x1Ihs2TkKU95Siwz5ClNBmUXXXRRJk3+FH+lIGVpGmm6pmXNes0v3f8amD82ye+4cBz5/b9UfuVtbrb/k01pOVg4v/y+a5ZYg+26bc5OH0yYTJrsaZbU/rogeOk2Lh+od3bSBlPWLFD/QPn1S8xPyv3t3/7t7EUvelH2whe+MHvc4x4Xf+mgNe7peleAQH3v7lu2rEMCNkjvv7zil5MfJ0jfoZ1CsQgggAACCCCAAAIIIIBAFwvYQKIE8DRQp/eRIVCvgfkYvJNg4MjISMutkHatpZmRw2L6pz/taTZtbPrG35e2CtRfrkF+SfN7v/d7mTZbUu7k5bS26Y30Pvftb397OVl2zjnnxO04/vh7Zl/5ylcKaaTt7eyu8kuBkM/vSxCyVbdnKm/uRYOh5UDt9ddfH7dX89MAszbhknbanMhll11m0931bo219tO0rYZ1nQ8/wjXhEtZbrfThhQZTf/SjH7VaNE7XX9bHfdliXb/2ta/ZdZVXADT8wiAErdPyNb+rrrqq8MDkJS95SbZFjiWXrmJN9IFNs05rcccHGbLMwdKES7MmlvThS3n7dX3Knc1P8tD10vIPPviQpvm9613vioFn90sNk+2RXwyUu+X2fzl9q/FCoF7W60/lgZGeL/pPf3Hw7//+79l0vZ7d+973tuut/roN+tChVedq1Pvgu6RdcaBe0talrLSTFxbLA5bG8z9No+v57Gc/O/vHf/zHdDLDPSZAoL7Hdiib01kBG6T3F/T8C9hdbHeM7OjsylE6AggggAACCCCAAAIIIIBAVwrENuoliKf3kiFQryt75pm+hq9M19roP/jBD5pugwaM06Y45CWlNp02fRPuTzV42ipQ/73vfi/TQPFSnbZhH8uQvDQonHYaENVgb2jeRJtnadZpUythnQ499BBb679Zutj0iZSl6eWlq4Vk5513XsxH599ww2cK89ORd77zndl1r351OmlVw7r8EXe5S2H7w3be7ei72eaKlgrYn3fuefHhhK6rtn3fqrPret11hdnOQh1cjEHzUMdmnT5s0fnhnwaim3Vpe+h6bOzdu7dZMjvtox/9qM/PlX/Kqac07LdLL/19l8bHRVaWn1vPU045xTbTlK7Acvs/TbvUcAjUu4cXuUvw0X553tOf/vSWvxrQsmygPon/LPU+ANf0TV5GuUb9+9///kL5//mf/7nU5jCvhwUI1PfwzmXT2i+QXuTzL89KNjg42P6VoUQEEEAAAQQQQAABBBBAAIFNIRACiSFgmAbqr7nmmhhw1fnNajz/6le/yqrVqqRzNakf9rCHxe0u1qivZFdffXWct5YBbUc73PteeeWVhSy++tWvFgKOn/rUpwrzw4jWCg7bqv1WDwj27PEvE5U0mk5foBo6bXYmrIdutzZPs9GdPvx4wAMekJQbgr4ueH3sscdl//zP/9ywGvm6unRrWddQoz5s88TEREM5YYLWgLfpfCD5X//1X8Os2P/ud79b2FdnnHFGpi+6XarT9ulD+doPD4N0me9973syL9TkN9lK8tMX4ab5fcE/XArrsNT+D2lW0s/PL/+Qw7uUg/O6LmHaiSeeJA+i/q5l9sUa9ZUV1qh35ZcfYNxwww3RQctv9QCm5cowo2cECNT3zK5kQzotoMF4e1FPnqjqBZYgfaf3DOUjgAACCCCAAAIIIIAAAt0tkAcSXeBXXyYbOm3yQttWDwHNs846K8yK/fe9730xwKjp/umf/inOi4F6f6/aqkZ9XGCZAW0eJKzLH/3RHxVSa6De1qj3tb5bNf8aAvUun0rLXwmUg9NpjXqtkR6aKNF89rzxjYV12agRDWarrwai0/JDgFfXRdsVTzsbiE1iBc2ajUnTNxvOa9S7Y6TcDFC6zGc/+1m/jyQwLE2q/P3f/0M62w7nLx6W4Lqs28tf/vKGNOUJ+hLdsO+1/973vjcmyfPT9atk+oBpue51r3udTRsc0/x02aX2/3J5p/P1Fyvh1w+63vqw6cQTT8xOPOnE7KSTTsqO8M0apdsWXtxbPsZDvhqoD+utyy3V9I02nZSWX65Rr+uXnuOa31V/flWmD1Po+kuAQH1/7W+2doMEQpA+XtT1C1j+bds2uEElki0CCCCAAAIIIIAAAggggECvCPyfNBmT1kZOa9TrNj7ussdJIN7XBpYgXvmlsvriyXA/etxxx2Ua+AtdDNT75VcSqP/6179ug6QveMELbDvdj3nsY7LffuRvZ4985CNjOVre5ZdfHoqxfX3gcNBBB8U0T33qUwvzw8iFF14Y02zdujVMbujH4LTcX+v2p8Hpt7zlLS4Pf/+tTbO0s9NfMbznPe/JznmAtMmv6yAe4Z8G7bX9/NC99a1vlXn5/kvnhTTL9ctB69SivOw3vvGNuC66Ttdee205SfbmN785ptH1/bd/+7eGNOUJs7OzcRnNNw3uFx++rC4/95BDgvsvKwb3l9r/5XVbatw1/5T76wuSy50+ELv+o9fbl+iG/Rj6uv/KXaxR74+/pZq+0fc0pPu/XKNe89aHc+nDHi37KHmXwxVXXLFkM0nl9WJ8cwsQqN/c+4+17wKB8tvfw4Vc+3QIIIAAAggggAACCCCAAAIILCdQrlFfDtR/8IMfdIE+HxDeIS9+Dd2NN96YbdmyJQZQ/+qv/irMsv08UO8qlC0VqNea2Nu3b8/zk/Ly4KHWzs6D0XrP26y28SMe8Yi4Lhq013bsb7/9drsuWuv4afKSW1025PuHT3pSYX3TEQ1Oh3Qa6EybvtGHCOn9t9Za7kT3y1/+MtOa5umLfHW97n//+8fVievq/dayrjZQn/gvFajX9xhYY5/+Fa94RVyXMLB79+6C3+c+97kwq2X/i1/8YmGZ5z73uTFt+t4BLTttFicmKg184QtfaJmfJl1q/5eyWnI0nl/eo1mgPs3gGc94hlsvSa/H333ve990th2OgXqZr9u7VI360EZ9OH+aBer1HHnylU8ueGj6cPxr7Em96HpbgEhib+9ftm6DBbYNbssvov6t6vYiKhfTVj/x2+BVInsEEEAAAQQQQAABBBBAAIFNJhADiT7oVw7Ua+3tU6XmuQYE9Z++1DS8VPaqq66K0ytyX3rTTTcVtt4G6mW6LqdNdbQK1Gtg9Zhjjol5hbI0QK7NctxD5h177LFuvr//vbzU9I0WrC9CdcuGGswVeQnu4ZnWnD/4YK1t76fLuhx33PGZ1v5u1bla5CGfYhv1z3nOc1wQU/LR8r70pS+1yqYt07V2vf01gV+fijw80bbptdN1Df66/WtZ1zdK0z5xn4j/8oH64FbJmgXqNcge94Wskz6kWa7TY8Sug9//Vz/veXERzS/EQzTNSvLThwNum9y6poF/zXip/R8LXsFAOL9cUzWVbLlAvabXpnHCuul5ddtttxVKsk3fyHaG4PtKAvWh/GaB+pD5Rz7yUfu+BVe2HtthP8rLpA8/vOk7EMKy9De/AIH6zb8P2YIOCQzZNunlgum/hNOLJ0H6Du0UikUAAQQQQAABBBBAAAEENqGAbUNbA+ka+JN/5UC9btL4+ETh/lNfKvvTn/40O+Ye9/ABRZM98hGPbNh6rambB/2aB+o1zb3uda9Yvt7nXnzxxdlHPvKR7Pvf/34hT22bPdz/lmvUaxMj5557bp5Psk3pOuiwBu737dtXyLs84gK1waWSLSwuxCTl9tI//OEPx3mdGnjoQx5qrcN+/PjHP25X5TWveU1hH6xlXffsmSrs/6UC9brPgreuS9pETbD5h3/4h8J+Wsk6ffBD+suOPHg8PT0dspN28P9e8nNBZS3zQx/6UJzXakDThPXU/vTUVCHpUvu/kHCZkXB+hbK0xvxyXdqclG7Pf//3lwuLlGvUf+tb3yrMT0dsG/XWzZ3f9Xo9nd10WGvP669PjjjiCL+fXPzpHnK+L1VW08yYuGkECNRvml3FinaTgG2TPgbo5UIrw+GLmCB9N+0p1gUBBBBAAAEEEEAAAQQQ6H4BW+PX3mO6QGezQL2+SNU2cePvP88+++zsDW94gwt0+vvTd7/73Q0bqzXqw/2qBiqb1ajXF7O6Smiu/Ct27mzIJ0w4Q5oBCfmVA/Vp4FVr3+vDBL1/PklqJ+u6a3Bem8Z50YtfnP3sZz8LWbbs20CtbpvfvjQ4rWWF9dD5GijudHfllVcWAs+f/OQn7Sq5potckFb3wd9LkHy1XQhah/hD2gxQOa/Q9E0ITF/bpOkbbdPfzdd9Xsle9rKXlbNpGNeAfyhfl/3Yxz4W0+T5ue186UtfGue1GrD52eC+C0Kn+ekyS+3/Vnk2mx5q1AePZz7rWc2SFaa59yi49dJtLjcNdN111xWOv6Wa+olN3/hg/d43valQ1lIjt956a3b++eflx5Wsy9ve9ralFmHeJhYgUL+Jdx6r3hkB/SMjfbN3qEmgF26C9J3ZJ5SKAAIIIIAAAggggAACCGxmAQ0kxqCzBPOufv7zm25OrOUr958adDzINiXjhk8++ZTYFny6sGv6Jg8SNwvUv/KVryqU///dcEOaRWHY1qj35ZcD9fpCTBsMlfnDw8OF5dYyooHa9P47DU67F3Tm2/Ubv/GbayliXZfR9v3D9uuDiR/96Ec2//K6Pvg3V7+uLlDvHqRo/CF9aFHeCA3Uu+PJBZpfcW1jG/UHDhzIg79yLN3vfvcrZ1MY1+aX7nOf+8Tt0+Zg9AWsoSvkJ+t35plnhllN+5rf6aefHtdhSyk/XWip/d800xYTy4H6P18mUK/pTzrpJLtu4fgrvyz2Xe96V1x33efvfe97m5Z+xx13ZFc+pfgA502rCNRrpl/96ld9WW7/v1gedNH1pgCB+t7cr2zVBgkMbttW+OPFfgHLBVm/JPXt53QIIIAAAggggAACCCCAAAIIrFYgDyS6QJwGvJt173jHO2Lt8ng/qvek8m98fLzZIlmhRr3cuzYL1P/d3/1dIRD46U9/umleWmvdtTPvyiwH6v/iqr+IwUsNvL7whS+0bZVrW+26HqvtpqQplHQ70+C05nfKKacU5rdqvkVf+Kqml1xySaaB09V0IyMj2e///u8v+3LUf/mXf8m2VPKX+t5XmggKnTYtZNdV/MP2tFpXTTsu+/KSi4vrGi18HqlFKCf080C9K+/aa68Ns2JfA+X6wtuwPtpPm7KJCf2ANpVj0/ryn/jEJxaS2PzOzPPTBwVL5afHksZSQvlPfMITCvnpSNxmf4wvtc0NCycTtEmmdN2Xa6P+qU99qk0fHp6dd955SW5u8BOf+ERcd837UY96VEMaPdb+9E//NI8j+e0tt1Gv7d+HhzoNmciE7373u3lZkscrX/nKZsmY1gMCBOp7YCeyCe0R0Jr07kukIhdZ98dTeBEINenbsw8oBQEEEEAAAQQQQAABBBDoRYG8DW13r9ms6Rvdbg04HnfccT5o59LqfepBBx2c3XLLLU1pbI16fw+rvwi/+uqrG9LlTda4PLV5mrRteg0e//Vf/7WU68u0gdNKVn6Z7Ac++IFCmlAbOSx36CGHZne/+92z06Vm9u/+7u9mExMT2Xe+852G9QkTbI3q5P67HKh93WtfG+/TNVh6hLy0du/eN8WHAhoo1YD4+eefb80OO/SwTB+KrKY7+eST7bL6olgN2L///e8vBFW/973vZbt3786OOuqowrq84AUvKBTj2tQPfvKCXXkxqAZs1VY7XVd9J8B5vpmTww47LNPjInSFGvWyrWWLkE77oemb4P+KJoF6Tae1wEOgXPv6K4BrrrkmS2uP63sQ9MFBiH9oOl03rUFf7t77z5pf2EZ9eXHF5/eTmFSbPNL8NJ8QCNeXFc/Pz8c0YWC5/R/SLdf/+c99oN6vmwbPNfit/3T/ffvb37EPYvRB2IMf8mBnYoPqLv7z1re+taEIdTlOX67s0+n2XP38qzN9YKHdN7/5zUwfZuj03MTZlAP1T3rSk7IjjzzSPkTT5dJOz3l9WJT6f8K/+yBNx3BvCBCo7439yFZsoIAG4W2Q3l5c86e97mJLczcbSE/WCCCAAAIIIIAAAggggEBfCOSBenfP2SyYHiCuuuqqUvDPZI95zGPC7Ia+DdTbYGLrvDUof8wxx/h8XbpDDjnEtid/ySUXZ0cffXRhXgg+Xn755Q3l6UMGIwFaF5z0Qduk/HAvHfoaoNxb39uQj05wwen8PrwcnNYgd7VajesWAr8aBL+vtKV/1JESPNd7+aT8NPjdtNDSRPfy3HwdwnprG/z3vOc9Y9k6PZSvDzpCwDZkp/shXdeQj13XM+5rA7VhWuin7fgvZxHK0b4L1Id9IC+TfcXL09mF4Yc+1L0AN5SpfQ3Yn3XWWfbfwQcfXNhGnf/sZz+7kEc68pCHPKRh/+tDjpb5yb756//nr9Ms4vBqtjku1GSgfH6l25oeG4XpetzIvyc/+clNcnST7LHu02la3f93vetdM31/hO7XEKDXBxZp3uVA/aN+51Fx/qFy3j3sYQ/LRkdHsz/8wz/MTpaXPKfLahNFGryn600BAvW9uV/ZqnUS0OZs0qej6QVcL8A0d7NO0GSDAAIIIIAAAggggAACCPSxQLFpjkrWqka9En3hC1+IAeEQwPvABz7QUs/VqM8Dzc2avtGF3/e+9y15/6tlnXDCidmjpCZ8CEiXm77R2uqhnfaQRtsqv4+0RX7qqadmGtw+SILAYb1DX2tUhxevphsSArUhr3KgXtN+6UtfskHgGBSV9dR8wzJhuk5brsmTtOww/PrXvz47JAarXcA1zzt3DeUMDm5r+SsBXdezJQAetjvvpw803PCzSu2ou2Zg8vL15cKtumLTN5XsFU1eJhuW1V806C8F7LrYBxrpuhQtjzjiLtlrX/e6sGjTvuZ36aWXNvXPt9fle5e7aH6vbZqPTlzJ/m+5cDJDzwF9WFAuPx9Pt9kN6wMkbbqp/MAlydbWxv/1X/91n2++bzTfcIwcdtjhWb1eL7TH/yb51Ufa/fmf//mK/I8++u6ZvpiWrncFCNT37r5ly+6kgNakDxftcIF143LxlS8vnU+HAAIIIIAAAggggAACCCCAwJ0V0JrhtukUH+C75mXXLJnlRRddFIODZ97/zCXbXdf22e92t7vF+9uXvOQlLfPW+9wHxcCjBlPd/a/WqH/c4x5nm/N4wxveEPN6ylOeUsgrr+3vArGvvu7Vhfk6orX33/3ud2cXX3xxfFGs3nP/2Z/9WUPad77znf7hgcm0hn/68tI0sdY813bFTzjxxLhu4X5ea4Q//OEPz7R5n7V2+jLPpz3tadnWrVtL+bvgrJahAdsPfPCDyxYR1/WEEwrbr+t78MGHZA+/8MLsIx/+SEM+9v0EkkbT6YONb33rWw1pwgRtlsXV6HbpNeC9XKcPAs4++5zsMMk72IX9fy+p1a37Sx80rLTT/M46+yy7rnl+7pgK+X3xi19cMruV7v8lM/EzL7/8j+wvBcIDFV0n15yMC6rr+VernZY9Qs6ta19xbXbrrbeuJNtM9+cf//Ef25r07pckzlzPmUsec0kWtvHccx/gXSvZe97znkLemoc+FNDym8WftKmhpz/96dnXv/71wnKM9J5ARTdJDk46BBBIBObm5szQ0LBMaX56yB8vMn8oWYJBBBBAAAEEEEAAAQQQQAABBNYuILXqjQSxjQTljDRDs2RGGsqRoLWRds2NNL9ipLbwkumlRrGR9rht3lKrfcm0OlPayTY33nij7UtTG0ZqxRtpDiUuJ0Firfhpyw7TJThsxyXoKLFQY0ZHRo0Ea+My5QFd92qtZm65+WaZVTEPetCvG3mJbTmZNZGmS6zJEUcc0TC/POEb3/iG+dznPmd+8pOfmBNOOMFIkytG2sUvJ1vzuLSlbm6Wdf7ud75jDjv8cHPve9/bnHbaaUaC56vO85vf/Jas62fNbT/+sTlB9uM555xjJMDbMh89PtRCt0dqfLdMpzPk1w1G2l83arbc8ZRmJA927L6XmttGHvDYdTr++OPTJKsaTvOTYLg599xzzWryC9us27CS/b+qlVvHxHo86znz+c9/3gwMDJgHPvCBhXNGz0E9Z+VXBEvuD/mlhLnpppvsca/+NTlHpMkke+6u4+qSVZcKEKjv0h3DanVOYFaC8MMXbrcroK8NyUKwXv7Q0Gfl+wjSd27nUDICCCCAAAIIIIAAAggggEBXCvzHf/6nueD888MdtJGmdMyjH/3oJdf19NNPNzd97Sab5qKHP8J8+MMfWjI9MxFAAIFeFiBQ38t7l21btcD09LQZGxtrupy8UNZIm/RN5zERAQQQQAABBBBAAAEEEEAAgX4W0JrEWiPcVqeXcL00g2OuueaaliRve9vbzBOe8IQ4f9euXWZycjKOM4AAAgj0mwCB+n7b42xvSwFtyma/NHnT0NiN1KQfGhwy2twNHQIIIIAAAggggAACCCCAAAIINApoEyfarM5tt90WZ2og/vLLLzdnn322ba5FXjRqtGkPrST3lre8xegy2mlzIDfccINtYicuzAACCCDQZwIE6vtsh7O5jQIagB/bOWYWFxZlpkTlNVTve5qamvSqQIcAAggggAACCCCAAAIIIIDA0gKvetWrzDOf+cxiIrm/Ljcr62rI5fffb3/b281ll11WXI4xBBBAoM8ECNT32Q5nc4sC2pTN8PCwxOWTtuh9Ev2TYRvN3RTBGEMAAQQQQAABBBBAAAEEEEBgCYHXvOY15sUvfrG55ZZbJFUejA8/X0/vvy+44ALz/Oc/31x88cVL5MgsBBBAoD8ECNT3x35mK5sIaFM3c9LUjf3DQd8SW2rzZseOHaZerzdZkkkIIIAAAggggAACCCCAAAIIINBK4Fe/+pX5wAc+YD7+8Y+br3zlK+aHP/yhuf32280JJ5xgqrWqqQ5UzQMf+EDz4Ac/uFUWTEcAAQT6ToBAfd/tcjZY28MbHRs1c7MapHddRXoapw99bQ5HA/l0CCCAAAIIIIAAAggggAACCCCAAAIIIIDARgsQqN9oYfLvKoHQ1E15pUKAXoPze/bsMbVarZyEcQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYEMECNRvCCuZdqPAxMSEmZycjLXmdR0rPkKvtek1SK816ekQQAABBBBAAAEEEEAAAQQQQAABBBBAAIF2ChCob6c2ZXVMYGhY2qMPTd1IcD5vkt5F6mnqpmO7hoIRQAABBBBAAAEEEEAAAQQQQAABBBDoewEC9X1/CPQ2wMzsrNk+PGxr0euWZr4GfdjqgYEBMz01ZYYkDR0CCCCAAAIIIIAAAggggAACCCCAAAIIINAJAQL1nVCnzLYIjI2NmenpaSmrFJ33pQ8ODZrZmVk/Rg8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEOiMAIH6zrhT6gYKaDM227dvjyVoO/TaBr1Wp6/IkA5re/Xj4+MxDQMIIICIbbN/AABAAElEQVQAAggggAACCCCAAAIIIIAAAggggECnBAjUd0qectddYG5uzgbgZ+dmbWTe1aMv1aaX0Zl9M/bFseu+AmSIAAIIIIAAAggggAACCCCAAAIIIIAAAgisQYBA/RrQWKT7BLR2/O7du/2K+eB8iNH7/uCgNHUjbdbTIYAAAggggAACCCCAAAIIIIAAAggggAAC3SRAoL6b9gbrsmoBDbyPjo6axcXFxmVDoF7mTMkLYzUdHQIIIIAAAggggAACCCCAAAIIIIAAAggg0G0CBOq7bY+wPisW0MB7fW89aeamcVFq0TeaMAUBBBBAAAEEEEAAAQQQQAABBBBAAAEEukuAQH137Q/WZgUC2hb90NCwpLSviPVLJNXnZXBg64CZnp6mLfoVeJIEAQQQQAABBBBAAAEEEEAAAQQQQAABBDorQKC+s/6UvgqB+YV5MzY6ZvZLoD4N0Zez0PbqJyYmypMZRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEulKAQH1X7hZWKhVYWFiwgfd6vW4q8l/WIkyvzdxoLfpqtZouzjACCCCAAAIIIIAAAggggEAPC+g9o94Lzsk7zGalYpfrwq+upW8HpbpXmNRgIXeaFbnTLNUIW+r+M2RRkTzDcsXsw5j07SDl4y9HTekYC8cqxx/nX7iOxGuLnDCt4l8xjVxbwnLhihOOKXewdeb6U63WzODgNrsq2mz10NBQWGX6ywgQqF8GiNmdE1iUP7bGpWb8XgnQt/guM5XMXbh4WWzn9hMlI4AAAggggAACCCCAAAKdEpicnDQTkxOu+KY3jumaJaGsZFBTuNElQvMa7/L3n2mOdlgX1o7ynUPLzwQ9GdTkbhT/lqFZAeL4a6GjB492nH/OoeVnctIlg5rcjW7M+TcwMGCGh4eNxu3olhcgUL+8ESnaLGBr0EvzNfW9e5uXLFcQqexgr8G8LLY5EVMRQAABBBBAAAEEEEAAgV4X0Fqa+g6zpbq0xrtNlwSoksHWWST3nzacFW5GWy9RmEP5cu+eBlAT9GSwYFYYwT/GPzj+CgdD4TBpNcL51z3nnwbsZ+VXT9VqtdXuYroIEKjnMOgaARugl5oQ9WkN0Os3eeuvbT3B9aeN/Hyma3YfK4IAAggggAACCCCAAAIItE1A7wX1/WXaSUV3dwspPTsYx8OA9O1gJk3caODKT7c9P7zE/WfTe1PJSAIqWjzlB0L8Of4K50M4MKRvBzn/+v36U60OmJmZWVOtVuVIoWsmQKC+mQrT2iqQtkGvBYdLeViJOO4HeFlskKGPAAIIIIAAAggggAACCPSfgKtJv182PK2q7RxC4w3xPlImp8NlrfK8OB4HQr5amkyMNepLCXzGlO+aJ0l10mH8iwJlmzgeBzj+VMBxyCfnn7/slQ4Qf1hthuuPVrzVOCBdcwEC9c1dmNoGgXKA3ldt8CXrRUc6exF2FyAC9I6ETwQQQAABBBBAAAEEEECgXwVskH7/XIzXxXCVDIzv2mUmJiZtEGhGmljYOTYWmWI6mTIyMmJ/oe1+x51+htC/huRlCa0xr1Vgm3ZuueKsfJodko9M7mltXpown20Xc6PpJ+Wrhirgz/HH+Sdnwya9/sxLIH5Oas6P7RyzZ7ReRd25bUfNiLxgdpo26x1G6ZNAfQmE0Y0XiAH6vfXCmVo+cXVN9EmbviF6Ql4qS4cAAggggAACCCCAAAIIINC/Atq+8fDwdgHQAJbrBY2ZfTNmaHjIT9SZxixKsGhwcNgs3rJgx9NI0fTUHgkWjeXT7SIaSpKBtBeGQ5EyW+uT2fLd0vl9bZpHOs9ODxNCP8kkDvqBtBeGpa/ZaKs9lC8QqakaaWeneTA3Je7LMJr3k3Rx0A+kvTAsffw5/jj/Vnf9WZhfNEPbB83i4mJ+nfSn6h4J1Gu8j64oQKC+6MHYBgpom/L6h1W9Xrel2C85HYoDvnAZH9hKgN5r0EMAAQQQQAABBBBAAAEEEBCBSqhdWrqHnJmRIL20WR86WxHeBvMrtnb9oMy7RQJFGnMN9596zzkzN2tq1apdzMdj7XDhw85wc9MK9snkQnIdScvPx2VI1zt0koEN+vlxV0KYmfSTgihfjwFnk7AkWH6ezNQGeEJitz9kHv65lfBw/OWHhD2ecp18yM5wczn/4inlnmW2QCtf/+bnF8x2uQYv3LzowHU56bRirr4MXPt0uQCB+tyCoQ0Q0Nrz0/VpMzmxW3J3Z6N+seqJm5+h+m2pEypygm6lBr3S0CGAAAIIIIAAAggggAACCEQBrXm5V36VHV8Ea+dU5MWE10uQfjimKw64+8zp6SkzNnaFzHLjoT8yskOaX5h2t6Z2wUxSaKMrki5EMcMicdkkG79MkoGdkn+EhUPfzUkDfmm2ul6Ujz/HH+dfr11/pqf3yjV4VC6AefxPr3ehGbL8mskQgXqOgQ0RCDXntRZ9OA3t02z9+8R2MhBnGFOtVu0JShM3wYc+AggggAACCCCAAAIIIIBAEIi16cME6Q8ODsmvtmdC3N3PyYN8rmanTs7MqLRXH37d7RPaX3LrvWu1NiCT9Aa12ElOcWo6nAfUffrCTBnxQf60fFe7O03o02kWDW3ZuHxLqeO6UH54oIG/FUgPFDnWOf7kXJb/Of/C9UuOCftTlPRA8ceJHkBtuv6Mjo7INXivP2ldj1r1BQ47QqC+0YQpaxTQP3D0J4e7d2vt+dDpHztaJ0A/3UVC+2GIAL3F4AMBBBBAAAEEEEAAAQQQQKCFgK1NL02opveSeo95h0Tiwn1mWDSMu37+qe3V12q1kCz2tUbnlNS4r9jgeikAHFPJQDHjOCdMDhPCeF6yrR+dBNk1ZUjllrKlUr4NGLpfFATNpB/IQt/PKo1GWTc9/UzyiqncNPxFneOP408C9ht5/mmLG82uwTvkl0316Xp6gvb1MIH6vt79d37j9UTTWglT8hII+3IIm2UIy/v89cmdPspMOn1qNiY1GsbHx5OpDCKAAAIIIIAAAggggAACCCBQFKhs0XvK4rTxXeNmYnLCT/QzfU32Ysp8TH/xrfehEpGTf26ZanXATO2ZkhfRDttpLlAl8yQvH7t0yW02Ol0X9+uj2djO5RVqMoepzfq2lq9dPARHJVWYKJlTft70Df5yqNnjLRxJHH+cf3IsbOLrz5Q0NbZz50696NmDWjdlq7wvRK/N6XtGwhHfj30C9f241+/kNmtAXgPzeiIt6ssgQifnmf6dEr5H3GkXZkpfZo6OjNombjgBExcGEUAAAQQQQAABBBBAAAEEmgpMTk4abSLV3mvqh++yO/SOMwS27aC/IdUbU5/Q3pzaD7tUrNEpszUAqnN0oZEdl8u71ep2zKVO8g1l5NnYdO4jSRfmx6C7pLDTwgy/WBgNfZ2cLOMmJ/lSvghI8D718pSr3f92sZBP6OPP8cf5F6+ZG339WZAXy9Z+rVa4/up5PCLvIJmWOCOdfjdJBwQCywlorfn5+XmzW/5ICgeM/unTbDjkFeZXq7Q/H0zoI4AAAggggAACCCCAAAIIrFxA7ye1sli4v9QlJ+SX2bskeK+dTned3p26sTAU+iGF9rXC2U6pVa+1tfWGVntbpVb9vASQ7NJJ0E7Tp0F0ewPsI8aat3auRB3KSwtDoa9zi50LxOu0EICOaSlfUHJV/OXICB72IHFHin5ql0u56TotDIW+Tit2HH/68Ec7zj9FyI+ZwvmmQOn1yB5Q7qjST+3WcvzVpamxUa1VL5no8notHpBa9foglU6PSQL1HAdNBPQE0TbnNUCv/eU7/XmanmfupNUhbetPa85re4J0CCCAAAIIIIAAAggggAACCKxGQIPqrqma4lLzBxbcC2Dz2880YmQDQDECF+5Rfdp5udc9rXaaZKgT8m5m3z7f/I1fXGeF/PNkbihML/dDOju9NNOPhqkNeScBsZZpCvnLSEgY+oX5YaLvF3v5snEZSeADwmHJmH9IE/ohQblfmF+a6UfD1Ia82X78Of7sGdTyHCmcXzISEoZ+YX6Y6PvFXr5sXEYStMl/cWHR1GrVUHLsa+xxcHAwjvfrAIH6ft3zpe0OgXn9Q2hubq4410Xg7R8++rTRPXkMLefp2Z53elJpYJ7gfG7CEAIIIIAAAggggAACCCCAwOoFhqXi1+zcflkwv//MXzzo70VtQ+bFvHVOqClr55QmDMl969z+/b6ymVtW72VnJFBkb38lfajEXMxZ18Sti4t0ydw1lB9rqdr1cjklt91xdrlsHad8/EM1SXt8cPzZWFV6rpROd3+C+ZPNnkT+BOf8s3ZBJvY9T2oahtfr+qPvBNkvsUctM3R6DV5ZReGwRG/2CdT35n5dcqv0Z4MzszNG24bSk8AG5sNfBeFPlTjusrKjhWl+RHqD2wZtzXltN5AOAQQQQAABBBBAAAEEEEAAgfUQqMRoeX7/OXu91LocHrTZh8DSkmVJokyi9i646VLOzs2a4aFhGcnz1YjRzMyMGRocygN/aQGhxncyLRlsvQqSqFx+IXE5k3S8MCwj6pFMSwYLWRZGJBHlF/d/2cceBmFiiloYlhH8Of6SYyIZDEdPY18Scf41nn8aixwe3i5eel65nuLZa7A8oO3njkB9j+99G4yXP0JCjXlXW774x4gS6B8trtmaHMSlSs4Yl9CeR/qkS5u1ITifezGEAAIIIIAAAggggAACCCCwPgJ6rzm5e7IQxxkYkHaMpcKZrUQcAuehuBA1s3390EXlTtdPdz03Xe+ANSA/t38uLC1TjNkxIi80lPaTbTBc5+jE0Pl8dN56lB/ziflLxuHBhJalHeU7B/3E3x0PHH+cf3JdkGBufr0onR/uorf09a8brj+Dw0O2Vr2uvl7q9Ho9Kk1oT+k1uI87AvWbfOdrAF47DcDry1610ydTOn1x8WYZk5NXunDQF8ftLDfXtWkTJhT7srC+2EED8/qPZm2KPIwhgAACCCCAAAIIIIAAAgisr4Dee8ZmWd0NrRmXl8iurrKY3g/rwtKFQd9v1v69PgiYX5iXJbQimywZlrEZrOUjySAMhn6SXTopNC1hp6UzkvQrH0wyCIOhn2SSTqJ8V/vXmqQwidfKB5MMwmDoJ5mkk/DHv1+uP02vwfJib30Y288dgfou2Psh2J6uik7Tn/mF4Hs6vijz9EIeasc31oUPgfk0x9Kw/0MnTA2jaX9Q/jAKNef1jyQ6BBBAAAEEEEAAAQQQQAABBNohUKlskWI0aBeqmxnjXiK7VaboVOnkxtg10Z2HOkNF03yKW94v4QLwsqjeY59Wq8VxTa9dQ/M3dqrPzfaSnGVwreXbbNO88wnFFU7TUH7x6Qn+HH9yYvvHau5MkWOi1EJRPMc1gT2FbMrw0TilMZFPY3tJehnk/F+7v8Y2q3INTq/xulf6vfkbAvXh3Gxzf3JyslATIB6Y9oqiJ74e7O7Tji1V473hsLYLytRSCD8W4uannwNbt9o33NdqNbNt2zZpK2o4nc0wAggggAACCCCAAAIIIIAAAm0RmJiUZm8mJgtludruC/butzBDRkJwvhiGi1E0TSH/3A1xJpG10MLMoL7QUH6Rrp3O1c/nS639yYnxuIQOuGBcnotNmnystfywVklWcVviPMrHXw5Od/SG4zQ9YuS4lGPEHdPxqJEEyYETj2apr58c/2nqkGPIK85LsonTQmLfD8sUz5BkQcoXqcbrTzPPYBnnJYxxWg/56wvDtRJyuP7qNo5P7DIT4xOlreyfUQL1bd7X+tRem6aZmpo2+6U9PHsx1ROv2Xq4I9XOcXF6d2LbyfKhJ7B2bqobLoyFGbYfRlyKU21TNoOmdlrNvkRHa87TIYAAAggggAACCCCAAAIIINBpAW1utV7fK6vhatTrTe+uXdLsjQZvwq2t9m2nN8ZxpDApzpGBEGxPE0zLffnYzjE3yWcxIm0kT++Z9tNiDuli7gY+FllKoyn9pDhHBpqV79LFVMUFNZ9y2zshaehrmlCYHfYffn5MJgOUnx4lKVBUyi11knb4K4KlsB+BKvQLE/Nk4ZCMyWSA4y+V9DK2F5UEMJmunH1w/E3Xp8zY2M4Ux4zskGvw9LQK9GVHoH6Dd3sIzC8sLhj9I2BxcdEegC7wroXrRU9PRte5S2CpJrzMsqkKUf3ScjIaAvc2J01bmGDMscceay644ALz7Gc/29CUjQenhwACCCCAAAIIIIAAAggg0FUCW+R+Nivd/87PHzDVatWHspLgVhi0t9VSY1iW07vlvAsJ8ilhSJteqNVOkzztwnZy4YW1PmGo5aqjLrckzzBos1hd+T57e+tuNzdOkHJkI8J2UL5YeAz89bgIB108IN2BqUfyKo7/eLhJdsHXTpNxjj/Ov3ZcfxYXFqX5m2o4FG3fXoPl2tyvHYH6Ddrz+vRH21Xau1drASQnuB7pek1dqvNpbG+J9HohLcXik1yLC+qYdnqx1fKr8pIcbYNeayoQtLc0fCCAAAIIIIAAAggggAACCHRYQH+B3qwp1qzlza/eYIf733DnK5MaJvsJ0suk5lwI51e2yDIyzebgsym3kayB/JC+kaehIJekYbKfIL20fJs4jcQ3FiClUz7+ybFdOEYaDjSOPxVoYOH8s1e5Lrz+6Ps50+uv7r7yNVin9UtHoH4d97T+QaH/JndPui96OdLc3xL2kFuyJD0s9cu30PnFbC//cAewJCwH6mMpYTnpr6T8arVqXxpL0L6gzwgCCCCAAAIIIIAAAggggECbBSYmpH16e0/tbmz1c4c2R6NNIeSxNntfHMZj36+rS+YTp9PSSXY4kwps0k69tJEcbrRDedpcrd5zF/L2y8dswkDop2UVFvTZpOnssEQBpDadLUeLkht4G7KScmPsvmGZZJXCvNCnfCvgOIoodiyd5Cfgz/HH+ecuHJ26/gwPSzv10jS4XjLtJVc++rn5GwL1/ovszvQ0OK9B7psXb5bjSq/2xU5PejtVv4BDCnv0uXRbpXa7phmSduKrVddmvMsns+OaqlodkM+K0Z+FaG6a38L8gnxKf1H6MmFuds4cWDwgf2Tst9PDR2WZ8m06vz76Utnpep1a9gGPPgIIIIAAAggggAACCCCAQNsEarWa0SZktbNBa7nZ3TM1ZUZHRu00mehvsKXna8br/bFtDkTuue18l9J+2uCT3hQ3dG6p+nTdjI6N5nMlqd4XL9h773yyvQnX0XUuPy/BrY8bT4d9Cp2kHeWv6/53qPqZmqfDPgX+DoLjj+PPnwvrcf3Vg0ofwo6NjRWOr2q1aubn5920PvskUH8ndngI0Nt25yWfeL3yA64Xp7qSZLQqL3IdGR2zgfkheXJU/MMhvfrLIjpqG7SXPzs0Kx11Pf81YhMUpuiI/mGj7e3Nyx8X0/JHjX06pTNkYdc+vuYiyxZ6+Yi2CTUlL9DRJ1t0CCCAAAIIIIAAAggggAACCLRDwDVF4+9NtUAZnD8wbwaqVRlsfv9bWC9NYu91NY+k84uW77/13rl22mmyiK9YZ4uumDuyO+zCIRe3+PqX79bQl2Kzlw9Zl9Xc/ydbaTd9NdtP+SqAvz0OOP7kUOD8a/f1Z0He5Vmrnmav7/a65z5srNRdn/rrk0D9GvZ3XWqcj8vb5hdvXvBf5vo1qFe00OlFXsZ9b0Bqww8N5u3Bu5RJG3M6IbkY6KDt/HeFH0t6kkAP3GR+YRE/Ep5u6YL6x4c+WND+jPT1p31u9dxnWNe40r40Ddjr0y3asU/4GUQAAQQQQAABBBBAAAEEEFh3Ab1nHd4+LLfHGpJ3N7bVga1Ss3JxVfe/6Yq5XJa+/9b73WLltorZN7PP3wdLDqu8/15t+TZ9cn+fLm9jC5S/ofsffxHg+CuednGM878d1z+txDwncUo9DsP1v1/bqSdQH0++5Qf0jwZtL0+D3O7LfulltB29MWkSR4P0MWiuC9pIvF4F5U8P+cIt/grPJrDzYu76payd9MJcN0HGZUIl5FeaGUdloFy+Nm9Tl5r2s6GZHNs+jmYmOeuCpW5QmuXRbSdgX4JhFAEEEEAAAQQQQAABBBBAYF0E9J5zcnIyz0vuUyd27TLjMl27td7/hhvdVvffQ0ODEiQqNiE7IvfzU1Jpzd+Nb2j5hZvwDbj/X277KT8JguBvj/X1jD9x/LlAW6vrD+dfZmONza7BWnG43zoC9Svc4/oHw255oY3+YVDofGDb9+xLWTUwPzE5EZPpIvHLPSxfmKC1BSRNIWFc3AfnW8zMk7lzO6xIzD+fbJOWyl9YmJea9nP2D5C8lr2mDD/7k4xkozU7/WXAoGxbP54o1o4PBBBAAAEEEEAAAQQQQACBDRMYlXaK6yE47m5FzZS2Ty8V4Bq6cIsc+j5BYbR0/xvujm2aJOG0tFO/U9qpz3wFNr3/HZTg/czMbEOxcUJYPvT9jMKojminGdrOzbWfhYRhfmEN84nNhsLyoe/TFEZ1RDvKdw5pdKUA5WdLz01uMTNPFhLm/UIJyYgO4p+AdC7+FXaW3bstdjH7Xw/XFjh+L9peSBL6hT2cjOjgCo5/ve6P7twpu8jFH3WZbdsGJV456zPrnx6B+mX29ax8MeuLZUI79HlyPdL0iHSdBrAnJsbtU6D0OI3DcrDpl348PnWxODMZlmn6ALeYTibaPxiazZNlC4mbZ6sH+3Llz87JLwakSZ/4kz+fta5m2lWrVWn6Z7z5H0tpQoYRQAABBBBAAAEEEEAAAQQQWKHAFrnvLd9/6gsF9R7Udjpzne5/bT6Sn95/6/vdarVamGSLGpB3y80vLhSL26DyC5sk9+7tuP8PG9up+APly2Hmjz/2vz3l3AfHf1+e/9pM92m1WuH6r01x6/R+6wjUL7HHtZkX20aSpNELp34na5cOF5qECQlcMvdZuOKGGZpQZjQLnsckITNJ1yQPn0NI7fphkXRqk2XdlrQuf06eWGkgfm7//piTZhOyD8P6U0Bq10ciBhBAAAEEEEAAAQQQQAABBNYooDUnh4eH7dLhnlNH9OWvG3n/awuUMipbtthB/QjlhzaS21G+K1xK1sJLHeU3YQkBitSqid1y8Q+7uAaHbYc/x58/FJIe5197zr9wDdbTOJyRM/vkXSH+eyHZJT09SKC+ye7VPxC0Xbw5qWEer9c+nW8QxjZxo7XP9YUHjZ07jcPJHPoxXWFCMiKD5afZYa7r+7HixJhtPlBI3fhHTVjeLpCMyGBavm7/LtlGbRIndGH7w7jWbNgnJ06tVguT6COAAAIIIIAAAggggAACCCCwKoGG9ullaW1WVoPlMXKuEZyGzt3Thjvb0I/JChOSERlM73+H5d5em4VNu+mpaaMV1NpRvpYb1s71/VhxYrp6friQOuYRE4bl7YRkRAbT7dfZYa7r+7HixJhtPlBIHfOI88PydkIyIoOUXwyABh3X92PFiZE1Hyikxl9gCpeJ4MfxJwIJhgx22/k3KJWly/HHPVN7+q41DwL1+dXNDjU+xS+Hpo0Er8fNpDRzY0//5DgPWTVOSqbYwXxcHwTIr/vy8yXM8v0wGvLWvpvWOkHjMskUO5iPL1f+/MKi2T87I83/jKWr4IZlvfVtzFsHtkqzPxN9d/I0gjAFAQQQQAABBBBAAAEEEEBgLQJj0g79dH2vBNlcDXrNQ3/prS+SjW0m57eysYjGSckUO5iPL3X/W69LG8ljOwvl5y+U9XnkWa17+RpdbJJ9W+//Kd9GeeK+1QFn4mWaADVOSqbYwXx8qeOP/R+sC/z4C0c7rn/dcPxNybtCrhgbtfs8HAWj8r2g7ynpp45AfbK39QDYW68XDopktq1Fr0/Uq7WqnVy85oaLb+hLkmTQLpBOKi7sZhem6Yh28m2tGYVHXTaNneGyj+NhIPTdYnZxl9x+xrl2II41yVDnaefKX5hftG3126aAZJK++NalyEd27Nhh6uJHhwACCCCAAAIIIIAAAggggMBqBPTX2uV3w03tkRfJSuBGO3v3aj/iWJiqE3wCNxg+i8njmJtdyDCzzbqOSaA+7Qb1ZYbyS3PtCsndWJhq58dJbsx+xhKLCyfpQwrtayf315pRh+//KV/jHG5XhJhKcRem+00TFtO6CckhWVzYzS5M0xHt2P8c/3Is9On5ry/1HitVEtbmxrVCdT91BOr93h4aGrZN3bjrYnpFdhfMPfIER5/wFzt7ZZWTyPf99Tmk0bb0Kr66vL63OM5OzrvSt31YtKHvSyhNb3/52ia9O3GCUbJKsq1D27aZmT47iRIBBhFAAAEEEEAAAQQQQAABBNYgUKlskaX0HjfvCi+SDZHwDbr/XpBfk9dq1bxwGSq+zLD999+Fldng7U/L6pb4Q7pO7tiQOMQG7f+0LLbfPTJITfDn/LdHxQaef/ri2FqtVjjsitfgwqyeHSFQL7t2aCh/aazu6TQErU9vJuWndtpWUqHTc1Q7/Z5wPf99IWPy9MvG520C+QhpdVwzL3Vh+ZjOpolTS6n9aMizA+XryaMetywuyirLCsTq9brimf3lQb898Wq+k5iKAAIIIIAAAggggAACCCCwEgFXyS25H9d73Tv8jW+b7n91HdxdrayxH7AvlJW28m2n6xRm2QH56PH7f7vdbfJ3yE0+Kd+hcPxx/smRYC9NPXr92SLXYHu6++uvHvgaX9TYbL90fR+oLwTpNbpunw7p7q+YbXIgzEn77O47wdeJ1/mFKHx6qGhKPZrSLp9mh+Qjk8C2+/qXdPlsu5AbTT9dEv20y3RJ+Rqsn5icMHX5aYrtZLNDvF4F9KnXrLyEVvt0CCCAAAIIIIAAAggggAACCLQS0GD49u0X+ptKvaPM5EWyg/5FsjrerCvdTNsk+TQ7JB+ruf8eHpZKfLP7JSdbJc3ers/MzErlvsEmK5CXlc/Mp9kh+VhN+W7LNbfuuv/Pty8dyrc1n5pPY/vD4bz54z/5/k2H8n2dT82nsf/Z/zZGuMr456C0drJfmhvLr4UV+R7YZytY58dZbw/1baBeA83a1t1c8lb3/EAwJm8Lz19o0l4Ylr5dRj70ALQj4XjRce00gf16twM60jDqJpZmxPz8QNoLw9LvdPl1bQpn5063UXZl8q3ZunWrNCc0Z6rVaj6RIQQQQAABBBBAAAEEEEAAAQQSgQn5Ffvk5KS7v/XT44tk23j/OzkxLhXSdidrZoy+UFabgLV15mSObT7ar1NMqOPa2dv+0szSqEuon8mMOOgH0l4Yln6n7/8pn/3P8S/noT8n47ms49px/gtCCac0ap3sRzIjDmZmYmLSfhfk6YzptxfK9m2gXmvS75cgsh4Phb8GZHRGatEPyU/b4rGSHiE6bGe4uWkF92RyeQn/pS4pfG388CXvTmSfXGbbi14+WpgdM00K6oby5+Rp1/D2YecSV9IN2Jr18jMVgvUlGEYRQAABBBBAAAEEEEAAAQSsgAZi6vW617DhYDMl74nT6e28/7YV0caKFdFGd4yYqfqUrJs0yZDf0qer5dc777n7/TxxL93/+5AG269HhI3N5Ps9DLH/7dnSk/Evjn93lNtDfwOOf/dC2VE9gOy1X3s7/MNSV3Lvf/ZloF6D9FrTO+75cARI3/6kQtuf06PBdpl8AWmjM3IEhih6PBjjgP/jIV8mySBM9P2wTOi7yekXvvvGC4ttjvJn5eeANlhv4WTb7EllP+QnKvqTxdmwQfQRQAABBBBAAAEEEEAAAQQQiAK1Ws3or95d5+4jZ/ZJBTppiia/P07uoZPBJIFfPu2FhKHv5rW6/56VSnvDwxdKIk0vnazKwKlVs7A4LyNJHslgYbpdKP0ICUPfzWtVvuZF/IH4C/EnOV+Iv/m4mlwz9JLYJ9efGWl6bLte97WzXwUVaVJ7a/L94Gb18mffBepdkH6/7FM58d33f9y/Lkg/7MZbPBpNv17T4fwL1WdXmCkj/iLjnqxqGplmH8WlCX06nb0Jy5+RZoTGRsfM4uKCbkHslFnb+9cXQNAhgAACCCCAAAIIIIAAAgggkApUtshdo94aJ938/LwZqFZdjEqmp3fOOpa0/F2a6e+rNUsZ1PtRm2AF99/6sKBWq9klwof+Slynt6P8UGbop2Wmw7o9G7H9odzQT8tMhykff44/faDku8LJISN9EP/bqP2/1DU4cPd6v68C9Rqkj83d2FNKzybXuZcTDIdR97VnT67SBTimkIFwMoa+n1caLSVzc8tp8sxcJrbUTVi+nlSD4nzz4qLX0EuXnsL993MVD0APAQQQQAABBBBAAAEEEEBgCYGKDaK7e0d3q67NzNxRXCLcRIe+n1savdP335UtW1ygTXPyq6QPDaoD1Thup29Q+YUAWCoQNjT0Kd8KlDju9P7HPwlAc/zlAuFAC33Ovw07/7ZU5BqcXH+1IHsNrlZ1sOe7vgnUj42N2RfANNuj++QndcPy04o8OC6p4m/RQqBezkYJnPvYeXgsrwnt/7Z2fOGE1RGdLd/s+uW+RBeK6pXyZ6VmvXrGTv/o0o2UTl8IpC8KokMAAQQQQAABBBBAAAEEEEBAf3k9PDwcYuIWZGhwmzSfOteR++/BoWHz/4b32dm1kSZyZ/fZ99hx/0/8wz0J0BiHHBwx1tO/8R93BWP7rUMfxv82Yv9vl+azZ+f2u6z1JJMWR2xTaFIpuB+6vgjUhy/+8M2v11J/GZEvf/fi2DAvXmhD9Nyn1TbC8ierfrhwYQ6HS5IuzE/ychfzMMMvE0ZDXycny7jJSb5hXdL0ofgwz+YhH3ZjJaGtoaD56rTSgmE09O2yMuKXcZNXV/5++WNrSP7YKnSyLloLQYP1o6OjhVmMIIAAAggggAACCCCAAAII9J/AxOSkmbSVufI79ZGRHWZquu5vz9t7/62/ENdf4qfdHnmx7djIqLu/Tmd0yf23yrl7fe3n9/Ltuv+nfPw5/uQYsJcwzr8QS1zr9WdsdEReLr5XQPMuvlw8n9SzQ30RqN8iAWcNNhc7/+LY+ETGfflrmhDHdgFqmZB+0WmCdNwmcilDGfZLStPFK1U+FPO089OP3iu/Pj1lRsd2umtVsqlVaeNvRgL51Wo1mcogAggggAACCCCAAAIIIIBAvwlMSEWuyRfstptd0V+xy3+FX2K3+f57WoPyO3faoJvGBvQefmRkxP1C397Qu7t6/dSO+3/nkEc98iEnFean/d6Lf+RbnQ+x/en5wf7PBTj+XQPZzeOv0/W6XIPHbGw2XGfjNThH7Nmhng/Uu5fHzskO1K/PsIuNsU9j/BPxePGMA35/J38QxFlxoHRMhOnlfkhmp5dm+tEw1a5e/i0vqytz5CGDdi3TFPJPEsYFfAI7Hib6frGXFBKWkQR3snxt5mZSakiU/QcHh+TlsjO+IHoIIIAAAggggAACCCCAAAL9KKC/tq5LYCbtbA12/RW2v2dN59nhML3cDwnt9NJMPxqmNuTt77+np6eNNp2bdg1BopBJuR8WstNLM/1omNqqfM2iZZpC/knCuIBPYMfDRN8v9pJCwjKS4E7e/4fVcxtA+XkcROI6+FuOcFQEj/yY4fjj/JPzRLopuQbvXO4aHA+c3hvo6UC9Bohte+i6r/Vq4LvBQWnvaGZWLhLxEiHz/UXBTwpzYt/PDnmkffcsLCmkSbtUNp+QmS5cntDD5Y+OyR9e8rPFcleoJVGeyTgCCCCAAAIIIIAAAggggEDPC9SqVbOwuJhvp9xa77vevUcun5gPbfT999yctJkv7dRrXT9Xo75iBga2moWFBbsSG12+FBvDE/lW50OUHxol1qCKdMRf7LHqMNxnOdzUT/Ene0jIRxruY/tLIF0ef5yReO32C+W9JXIgu5r3mW1Ge35h3h3gPf7Zs4F62y79dvly9dfudD9md8hXm+zxin7zNuvsVS2ZkY4XhmVEnzgn05LBJIPSoCTqp/L1Dxr9ZcNi6Y+vga0D9ueDOo8OAQQQQAABBBBAAAEEEECg/wSq1apZvHmxcO++MD9vBmR6eq8do9fJTXcy2BpulfffC/ML5rTTajb+G+IJA/KutYX5A9z/E/8oHJMbcfwVj3k5rCXkZLtScFWnUX7O45CafK7y/C+gpsD4t+36t7i4YKpyDQ7XX93L6cPSJnu5pyb1bKBea83v37/fXtP03LKdXOD27ZMn89Lsis5w51yY6yYUHsaGE1EXTpLZvMI0WUzn2eXS9KX57nGeJpXHA67gvip/dk7ct2/3jh5NeoPb5NcN0l49HQIIIIAAAggggAACCCCAQP8JVDT4K527S3SfWrkuTnRD7l7Szd7w+++wTiGgoMXeoevUpvKJP/idrt6h83EU4i/En4i/yaWox+OPlS1y8vvrre/Js1r/vRCuCT3a78lAfd6mXNidbv/uGp+QpnB2ya7U6dIlF/owyc3IZ7lkrva9TR6WCQlX3U8yCIOhn+SVTgo/bbPT0hlJ+pUPJhmEwdBPMkknrVf5ExOT0l79hC0l3zP+fQHa/iAdAggggAACCCCAAAIIIIBA3wjor69rtVphe7dWt5rF+UV3u57emBZSrXQkySAMhn6SRTpJ739r1Vqxlr/cwM5Ipb/V/xo8yTkMhv4S5euv/22yJmmTxVYwmGQQBkM/WTqdtF73/y77JOcwGPqUHwVSEvyJv3H+Sw36WtXcvLDozhG5/uoFcWZmLdfgeJptmoGeDNTr02+/H+OOGBgYMPPyR4BO1654IYyhezezMLfVJJ+D7SW5yaCrlZ9PCw+68in9Wb76j0lAfm5uzoEriHT6k0b9aSMdAggggAACCCCAAAIIIIBA/wjYJmuHhwv37/k75VIHfzdte35YZ8vgRtx/66/wZ+UX+lpAiC3YIJH9df7Gl98YkvBl2h7lxwiOUGzE/sdfDv0QPNPzLIBw/IkL5188ODbw/BuS74W5udl4/dWjUL8v9Puh17ueC9RrIHjavzHePYXSkyg8eZEd6q7iMkWnu6/cTKb5X9vFqbpM6BoC7bJoyCbkEtKGflgmL0fnJAvGkuR5aR+VPzszJ03gDAem2N+xY4ep+/0WJzKAAAIIIIAAAggggAACCCDQswJ6Dzjqf13t4oIVc/nIiKlPT8U75vQ2+v9n713A8zqqs9Et506SJuRKUmJ9MtcSKBACPIRgSQ6lgQAh/FAOB2JJDlDgeSC0PW0h/WtJDn85hVKghRZCY0kO5c5fKKQt8GNJLqW09EChQKEFfZK5lEu5F9qSxPusd82s2Wv2t2VLtnX79I6tPTNr1syaeeeyv7X27NmrpX+P7BqWOkxluE9MTBRDUtdQzywpHZ9P/d/3EO0fyXCURvPmsv+w/bJa6IJh84L9v1T7J9bafTUbIdZgu1/kK3B3xbrKUI+PlbZafdJDmATiMCEkODQ8VEzundS4TQ9/swdruqEqQ+LK6WDzT88QN1bzQUtEjYRLTE9sEjBjfydD4qrKAgmuC+Tv8g9TYh9tlTce8CNtMzwdCx3JKxEgAkSACBABIkAEiAARIAJEYHMjMDY2JsejjgcQoBuKGx0dLcZ2j2l4rfTfcRybu2eP1CHsqIdtYfduqZfQzc4QDHCoptffERcXSSlFAptV/9duZfvZ/zIQ4hSvTZA0S3I65lEX2L84/kO3Lnf906Oz94yFMSMgYjO03hvkntHtrqsM9TgvTo9VQa9hizx6UtycHKvS12ppOFz8QuDILljtiI9EyeIHlk8PpbkyLQhf7tCl1EVq45wxOFIt6MvXJMnSLfLnF9rFttY2/e0SlmoFqhiSnRP4vgAdESACRIAIEAEiQASIABEgAkSACHQ/AjDU7xnfA605NVaNMWIQX0v9d2pishi+YZfUCYo4qtYj+upO1VcRjSfIIyGkQ+FXPur/tH/Q/kP7l64M8RJWDE+ph7vZ/lctkPVWV/Gm9k/smyp2jQyn9RcB7KbHrvpud11jqO/8CE24U2avRtj8MF/vpMIn8VKe1GXLacdIyYcCfkhk/FmyCTA/JlrU/E0sf3JqshgZGclQQ2SzfByio+EkEAEiQASIABEgAkSACBABIkAENhkCMLxMiUEGqrFq8HKZ2Nt5vMFq69/YQFbXV4flTf0JMeB3OlPwzY8cFjV/E+v/2rlsvwwMGeAyHmh/qtnTaH+ToYEVsNmt9vpXr8VayK+vwYBn587Nsbm3awz1uMHj/CLcA8MdvihaLfmAbHtex1i4N6Y7ZEXzJA3LEJRH9zZHSlkw1CCP9VTSld6RR9daFWv3nuSrJEv3GSPNkzS8OeTjwQrOnDqAD8uKA8aY/NxVHwcMPSJABIgAESACRIAIEAEiQASIQJcjoIZ6O4cYdirRiSfFUD8kOynXUv8OG8vCjvpYrUIN9Q1H6qohwOv1oRmq4QbjROjEqO5XpE2k/wMBtl+63o8T9r8CQvsb7Y9N9teJCdlRv2u4Wl5lIb7++s3xbcuuMNTbbnoz9obboDyJn9hbDA+NVDfCmKDGdxsJxqy+XzVB8HEfjplAgrM7twTtyWjglqt/X0+ZhQTjP+Xra4Npl0LEsHfrVv0YMI4xoiMCRIAIEAEiQASIABEgAkSACBCB7kVgoL+/OHDgr0XzNuXa3rLul0ZDSXTOWFZB/56ZmS4GB3dIDYIwiO7f3l/MzMxQ/18F/HNbTBwDq9j/lA+w127+EX/iPzMzW+yQNRhjwaY+vmmpa3BcErrV6wpDvT2FT/eLGIBBXJ16cqkbx0EX3txwHvPYoqQ8chGDO2zucNHTwZKdTRcpgctdtUi5UL4DpSjm5W2HgR0DxcL8gmKKdxcwCfXjv42vFGbZGSECRIAIEAEiQASIABEgAkSACBCBDYxAX19fMb8wH1qAN9tFH5xrzxWt3r411b/n2weLvm2tUK+gphZb5Y39Bfn+Xd0iEJjclfq/gEH7B+0/ZjmLcwPzQki0v/mNu7pYBGAAk2IkF9ofi7bcF7bJ/QFjJuBSFL1bewts1O521xWG+i09W6TfbICjy3qK0d27i9HxUQnVFgdJDZwwC8e02mTQ15FCMbg2uDBxLDsYTLqWGCO2u75eQEimfOA/OTklZ/8NV5NPaL29WzfF5KuPC8aJABEgAkSACBABIkAEiAARIAKbCYHe3lZx8OBC1uS2GMNbrZajrb7+jc1krb5QB+j40N57W6KnuqN1UUGkmTGA+r+zcQCX6Gj/ABAOGwACo1M0xtL+FAeKTqYYzrzVn/+ZeExw6asw2UMKuhCO819AiGAc7/WvOjklzB7MmZbcL3B/6Ha34Q31+Er8+Ph4Rz/hKR3Gi02cNGiUaCnCIxMu3+hepaVCMSnhxLPUQJC4EHp0Ze1MTLwSoPx4Y1JQDJlSJtlCsW1bH0CMYAqykpx9BNjApk8EiAARIAJEgAgQASJABIgAESACXYNAz5aoR7sWlYegL4rrVLFXVf/WuomeKkYTs0UVh6Ru1P+jPm9qfeitylYidNo/mu0ftD/R/kb7Y1ww1LNFBH50zv7as2VLtv6CI52cYvxd6G94Q/3A4EAxK2cXyT083TxHd48WY+Njqbus65Vg/Y8MiRAPsMkYLT2Umx9xU6VlIctvfkzMoojAUX7AQXot7KofSbAAot5WS3YrtCMPPSJABIgAESACRIAIEAEiQASIABHoNgS2iNUqqMhiwJNQKfFDhw5p2CnNzc1GRujV5keuLBoKd0WFVL1mjJUI4+jrk2N5ZGe9d9luf8tv/nGW7yrtq1CFTa75lK8IZHAgAodxoi6k6jVjtHQbToskVmzGWPkxLcuJCBzlBxziZFWMMqBisniBvEhixWaMlZ9JcBEEib8DZOPYP1utVrGwsBDrHvqxPdcuQO9mt6EN9fYqROggzDxM5qKYEwNvX+y4NL3xFFxu+uBKLiUKxcLi4wFOzidEyQuezjTJmzFXRUGOFYtH/5TfjP+CnDHV6usDXJnbv3+/fMBnMKMxQgSIABEgAkSACBABIkAEiAARIAIbHwEYYFqtVtaQVm9vONpgHejfOGZhIR3LA6W/LKb3TxfYLOhd0vmVI5oHqP/T/iFjGKMmuaaB0mhjEuI6GP+0f9H+t9b2z94+ORpNDfVh/cVc2gx2wg1tqG869qZXbux2bpwuiNnKqBS5xBWy6eaZWMADJwU0lBFLCCx2tSwWh9+Ql/I78R8YkDcjDswqNIAMHENDQ7LbflJCdESACBABIkAEiAARIAJEgAgQASLQTQjMzMzoxizT/9C2/v7+AvS6Wwv9W3XU2VlV6U3Vn54WQ73oruqM6CtL/d+jEcOx92h/6Xx4YWgJNsHJAGoYQxFB4w6+ZfHUhry0P3H86aDagPMPa+0BWYP9UMf9AfeJbnYb2lCPp+/ZaxDSU+Fs8yEJxdc5JBSnZdWPGcFFJFh/YmSpwY+xnFiVm0IZN+ULLtn9wvBTvEIEBvmRkZGEIHK05GM97fixHpfAIBEgAkSACBABIkAEiAARIAJEgAhscARmxQCTjN6i/+Hs98ds75ejbWeiDh0VR9Mfze9od0iwZPMTW0ZwEQkeTv8fHOgXI9FfS12QJ7jMUG/EvLYxlhJrBomly08WDctivis6BEOCJZuf2DKCi0jwcO2n/IiVQWZ+AtYCIcGSzbfUfEC4VAkS/9xWZOgEP8ZyYoK1CmTcOdxgsvyawUUkSPzXN/6DAzuKmdkZ7Tm7cEe9IbEOfT32Rj5C2iMzy984cWYczjfPDMN+Zuq8rCYnHlziraLEYknRt6iHINAWZ+jM4ygarOKUH/DHWxB99+oL/RDBRrfs9zsWfCcwTASIABEgAkSACBABIkAEiAARIAIbFoH6Zi3of9cPDRdTkxNZm4L2HHXoSpVOPJ0kR9FgFV+O/j08LHWZmkpyYGSYvHWyGBoZqmgSqko3sqNosIovR35eWiyjKsqSKV+QwNipnANJg1Wc+NP+RfufzBSbEjXfotVcMtaY0sDQSXIUDVbx5c6/bA2WSQ77796JvQXo3ew27I768fHxAkff6JIsT94x0MJrctOBhl6rxgNi6hJJAynmEo0GHw5LvoTtUZslB6oMlMiShCUC5TsoBCV1iaSBFNO0gX55rUWOvwHV3OiofBhY+9ko9IkAESACRIAIEAEiQASIABEgAkRgoyMAPW+P6PWd+t/outC/x8bHCtgdYBKIJodidPdYMTYu9aP+LxhEW4mZydGRIDmXSBpIscCR0RCBi2XS/pKgMEwzuDj+qrGSAaSDKF3SiMvBC+kZDRE4jj9d29bJ/BsblTX45vG0/qJ/xsZGC9gJu9ltWEN99mQl9tCodCI6zVwpj2t64uMyCdn0zcZdWN90hlq2Rr+ZI1LxWAil129KlL8s/HVHxa6R8KMs3niGhnYWoNMRASJABIgAESACRIAIEAEiQASIQPcgAEP9+J7xTP8bHd0tOv1YMkPmrV1d/XsqO54Vyn4ZvqM2MSFhiYPkHO0PtL/Q/hTmCe1vtD+m5VGWbbP7L9f+Wr11FcYVllvYgnHkeTe7DWuo7+vrKxbm56WfpcPi4+25ufmi1bdVR4Ha563ncD83l0aLEXC7jfdY41OeRK0YfcjxGmew10tMRiHlO7AMK5AOg78ef4PjjITNsrRaLTmnvu0KY5AIEAEiQASIABEgAkSACBABIkAENjoCQ0NDxb59+zL9L3xzbrizaaYgirKIoOqMGpDLCunflZEoVAcyd8rRPBNyNM9qyM9AWIP2U75DgPgHMFZx/jn0KwMR5a/a+rce8PdrsK65svLuHLpejkebyqrXbZENaajXr8PvGAx94RfMQ4eEhu7zDgyBpiG5lGLYT8+3qmTNFKL+amtCzANrfGaFb5ZVUSsBGpIL5S+Of3gCDfRiP0pfTe+fdh8ZqpBdy9BPf/rT4uMf/9viJz/5z+KCCy4oLrvssrWszhFlh/p+XOr7kw1R3yM26DgxZLhcKP340PXdj8ep2SyGCBABIkAEiAARIAJEgAisOQLDIyNicJmM9Qj638TkXtm1Piy0tde/94kxCHVU3RSbA8UN7RwqYDwKrtL1I0G8iqYhuVD/X1z/D2j5a0BwPfS/2oxo/6H9i/a/annLQmHeZqTjvP5Nys75kV03yBiELHHi4QFvtQYHcrddN6ShHq/C7ZFX5HQHu/ZIT9G/fXv+NeDYj8HeWxtAtWjVqS4hBWPAexYWHz8n9DWOSEtlIQ6nvzdqibVoYMTVJaRgDHjPwuJ3m/yBgcFidnY2QYL27VyHE/G2224rdu7cGevZU3z1q18pfvZnfzbVe70F8BGk4ZHh+PLJ+q/vauGHHTxY6OEw1r7y1a+u637UivJCBIgAESACRIAIEAEiQAS6AIFdYgSfSEbv0KBJ+VDg0PBIUo1V9V0j/XdyarIYUUO96AqiLMD+AN0BdVa7ERQIc6gjnNJihQMltcWile/4UjAGvGdh8VE87Q+CQ8QkYYk4HPEXEGrg1KKKk15cQgrGgPcsLD7HH+ffaq4/WGtxn7D1F8OWR99UM3hdhXCzrD9BwStyQ3JWUceCHWuuD0KxYMWnYSEuibqQG1OcdFU0S47kuO6F1UrLiWUoJZATqwUoH1AfGf8J+TG0a9dIwDiCdzyemH3qU58qpqen5ccVOig47N5H/OSTTy7OPPPMotVqFQ996EOLs846y1gW9THedu3aldLn5Rim3t7eFF9vgb17J4obbtgVbqxSuYWFhWLrVjkmagXc61//ej2u6KSTTiqe9rSnFZdffvmSpODhx6c//enihBNOKK6++upicHBwSfmOhUn7UXDR3zJS0Ericiz1ZF4iQASIABEgAkSACBABItBtCEDPw8aZpKRIAxuPvlEdWy9qLBc1Tp0jd0BzPPRv2Bxge4A4yILbKTvqp0RnhVtp+SqkJkjbtUrtp/yIgOto4p9Mahz/MjxofwQIaaVIgeOx/qIwrMEw1Nv6C9rxsA+inPXsNuSOehhUDy4clM6SV6hkUJTySKc9Nyfn07ci1rqSLoK7pZkf2PyCG1Ycy65SZOwJvz46EnrKmgKOhnyObsUk39LMDwmUHxZ8GLy39fUpggYZ+vtYzqk/JEcinXfe+cX3vvddLTKMGQRl8KS+Cv1xyiknF89//vOLm266SY+I0QwNFzPU2/hbWJhfMcN3g/hlkyYm4gMQyYk6z8+vjKH+v//7v4tTTz01QttTPPe5zyluueWWJdX3Hve4qPjmN7+hvXLNE59YvP/9719SvmNhCv0or1LFcbDe+/FY2sq8RIAIEAEiQASIABEgAkRgPSEwMDCgb1ObVoa6TU/vL/oHBteF/j0zPVsMXjUoqoIegiIaQylHsvZLHWdMfYhw5rp9JNbSch7q/0EvVZAyaGh/CQcFCSi0P+Umm2S7kVFTGzOBMZ95IWaM5keqRGEXUZclcfytp/E3MzsjGzh3RMtdsP9u395fzMzMxM7rTm9DGuqrc8yrTinLQzJXww1UqbXJZotceLIDDpuZnlHCWAzhFnk0VuPWARMy2IQOsY6FIy6ylG9LaDP+8+2Fok8+KBtnYgRTugXAHaWrjMfSt6kYyJcCUzwvHDvs//Zv/7a49NJL84QYM0O9Ja73ndha3xvEIB3bu1IG6YR1BOa5z33uMgz19xBD/Tc15xNX1VBfvRmx3vvRxht9IkAEiAARIAJEgAgQASKw0REwQ722I+pmeAsadHNQX5AE58OIrbT+D2NQ/S3f/n4zEq28/HqbV7v9lJ+POeJfzcXVmH8cfxx/07IG76idtFCtwRgh3ek2nKEe55fbjbuys+IIE3xINrh8Aa0Gd6D7q+WAn+fS264a12s34KYsedZaSVXJXnItC+U71Fqtlh5Bkizp0tHH8kFZGI9Pk13ehvkDH/Sg4sorr9SevPPOO+V8+a8WX/rXfy3+9Utf8r1bPPjBDy7+/u//Xo/GyRIkAsP3DXL0DcpEPefn2+v66JsJOetxl3yEw+bMShmk/+u//qs47bTTFBVclmOov+iii4pvfOMbmvcJ1zyhuP0DiW6IbgAAQABJREFUt6dyVipgr7Na+SuFi5VPnwgQASJABIgAESACRIAIEIGAwKDsnMeOycr1yG71j8Qd9RU1hUyhMz8m1KJJ7wt0f00lSSDP1aT/q6F+x2BglRzQpbbDUI8d9YhEl5dUlewl13kqrlBIk3wrP/lWiPmUrwjU4Eg9G+j+mpCUQJ6L+MtjL9q/ZF7T/pc9AG2aMvnUqc2kamb5mVfLsuT5pzvq5T4BhyUX5fT3DxQzM9Mgda3bcIZ6PGHfseMqnUDaS9I14UOys9WI0O5CF4qLO9lDpPlqr51VizPySX59F8YmqsSlrLh2hVGixYEuAfCqbzIQEUf5DqsASf1qUBv+gzsw8WbTRAR/fWdFvYzDxeu7vF9844uL1732dR1Z3vOe9+iHKX784/8I3S8c73jnO4unP/3pHbwbbUf9ahmkA9Yw1IfxvxxD/T3uEXbUYyo94QnXFB/4wAc6cD/ehL1798rZ/Tj6Jiz7NNQfb4RZHhEgAkSACBABIkAEiAARaEYAG/CwEU9VaVEf8It8f+3om7XUv/Xomx2DUiupGd64l//9/dujoV5oUHlQaXWIiKP+7zAJkNSvdf1fMTSigBoMhcCb9pe1HP/En+NvrcfftDzI3SFH39j6i7UE9w3YB7vZbThD/djYWDE+Pq59gnsiBs7o6GgxNjoW+8kWdiTKnzJJQI3uRrOElCXyRX6Q043CinHl2s2jVkwozfFZuisr1MkSQo6qnhJHfeFcnsDtyu1y+cPDI/EDPdp5Cof2sfT90bhkqI/FveTGG4vXvPa1jUW95jWvKX71V39N0gLqv/VbNxUvf/nLO3jTjvrYXwsrdOZ7h+CjJKhB+jnh6BtUeX6FPiZrWFvPPe95zyve9KY3LanWF4mh/ht69E1P8cQnXrOKZ9Tv0nmH394LBw8Wl1xyyZLqSyYiQASIABEgAkSACBABIkAEjh6BQTG4zIih3ivj4Yz6AVGL117/nZbNY/7YBeg42+WMet1Rn5rt6mlqvtPlg1ppCTGTRc0H2eUJZFeuYeH5KT+BmXYAGz4OS+IvMEHRTYYmCVrUfCDpMAtkjr80rjj/BAE5ZtyPF4wZdW6cWLobS8c6/2bd8WNqY5JLP8+oN/DXj4+vrmN3MJx2lPg41gPGXYwLONCDs5HSvBYZl/Fi8MHZAEy5/UADg48rU+DEFY7yAw4V6lUoYWosyQ8THNF98uHT4RtGNAWvXyFleHhYj5tJ7MsI/PSnPy1OOeWUlONwhvp/lSNw7nff++oDIGS47ilPLf73/35PymuBvXL0zXPS0Tdi4D2C4fuuu+4qPvWpTxVz8tHjb3/72zKEyuKel9yz+PkH/XzR19cnz5GqUWMyDud/5StfKT772c+q3Lvf/e7FfaXOD3nIQxYtZ6lvAMDQjg8z3yn1Pf/884sLL7zwcNXoSDNDPRLQoucs54x6Ofrmm3L0DfI9QQz1H3j/0nbU4/iiL37xi8VnPvOZ4gc/+EFx//vfX78tgPofydmbBpCJh37z7fklHWEEOZ/+9KeLz3/+88U555yj2N/73vcutmzZciSRTCcCRIAIEAEiQASIABEgAkRAEGiJHrQwP6+///FbHG5+Dr/Ht4oyEQkgrpH+Pb8wX2xr9WV2hq1be2XTU1sqBT01uKqmlbZrIfMjq/Mq/Zf2B4FFQExY+f4GYj6uTIETV7j1ij/q/S9iX7hD7BH3vOclxdlnnxUqLNdvf+ubxYf37y9+9MMfFdt6W8XDH/mI4qyzzw5tce29S3Td/cL3pS9/uTj7rLOKyy9/eHHv+9xLpscWjr+I5nrtf9QrjenU8xbg/F+K/RX3B9wn4AzP3l5Zg4Xe1U4MhhvKDQ0NYaxnf2KElCPqYzPqvrVO6bXEGDVqKiPlSSlVUkUyruAb3fmHJCwGxPKf/umfyu999/vCVyXKRzPLt/7pW8s33fLG8q8++MHye9/7XpVsJUsBd9xxR/mhD32ofMMf/VH5p3/6p+W/fPFfykMouO6MVPeNT+m1xBg1aqpeypNSqqSKZFzBN3rdNy6l1xJj1KgmBP1Z7+Pt27dbScv25dx0KU9//mi5N95446JlyHn1mexnPOMZjbyyQz3jE0N9I98//uM/lhizZ511dsZv7ZPfn+XDHnZ5+bGPfawxvyei39/whjeU5557bkNZPaXc/MvXvva1pRiufTYN1zFtqu+Xv/zlcuvWralseXhQfuc73+ko63CEHOueUo6+ORx7liYPBZLsa665Jktrinz0ox8tH/7wh5fyEEby4ZGO9XHwL7jggvJ3fud3GvGw8ipcQp4mXIwXvhzHU97vfvcre7aYLFuLesozz/yZ8tnPfnYpRnyfhWEiQASIABEgAkSACBABIkAEGhAQg0v6/W/6UbvdVs66jtiR3RjqvjEqvZYYo0Y1/dOyiKJdBSWEuli9zEed1Rlr3Q+pUbevJcaoUY8kP5Nj5ZpvhdT9LL2WGKNGpXwDK/q1/ldqAqvOa3lqvrFJvre85bY0fi6//HIdEx/80F+VD3noQ0sxtKc0jK0zzjijHN29O9l6IPa2224rL77o4owPvOedd155+wf+Ikiy+pnv5FcGppiYe1VyylMVkkIpYEzRN3rdNzal1xJj1KgcfwaWYZqQqaCpSDmz0eu+cSm9lhijRq2ErF/57bnDrMHW1i70sbN3Q7mB/n5ZqIKRzAxzco5dbAOGnPzBqzkleXqdYIty5DHW5FugVi6ih5JAMMlf5H3LW94SFlUYY+PCDKP8Q3Vhzg19Z5x+einHu5TlXSEzrliYL7pYFubwsF59eYokC/P55e233y4cwS0m39Lha6mxXkqvE1ag/UcrX86bym9G0n45h8oXt6xwMB6bQbUoD2eo/7M/+7NM9k033dQoC4Z6G3+4WTYZeKempkrZYV31n/DZD7y6D6P4O97xjkZZIP7bv/1b+djHPhYbTaSMfPzXy3rmM//vjnKO9GABD47ufZ97x/JDPR/1qEelHwodBS5CMEN9qGdRPuc5z1mEs5N84T0uTPIPZ6jHgwg5/qo88YQTF8FTP4OT0h595ZXl3Nxcp0ChVIb60OamfkRGeVNAx81S8L/Xve5dfvKTn2yURyIRIAJEgAgQASJABIgAESACAYHeVm/6/W86zVy7rYmmnjZhtVr6b1vqUv/9D0P9aslvajtolK/GDEVCLR0WdYApydPrBBtgkcdYk28BV6YFl4r/3lurzX0PfOADy5tv3iMG+twOZHqzjf8Xy6bCO++6s5QTI5L9p0n/P+GEE8q9e28NGDTUVUmeXiesQvtloFaO8kNfGSLEPyARx4gNleTHwHwbhvrc/oX7Rre7DWeob7Va0QAXFzgx4srrcamfrGMToSkgTNXi2sBQL8THs3CMOJoLlnsn6gvzzdXCLPX2hl4zxt9440vKO++4sxzZJQuzM+zqAi55jHaCGCn33oqFWZwT6oIhrekqTKvR/ibRSjuC/On9Yqh3bUWb++UBzdG6uqH+JS95SWNR//Ef/1FeKjdQxTjKx9sQTe5Wu+nGfmwy8F731OtSf6HM+9znPiV26P/Kr/xK+cxnPrM8//wLqnQpB7vZf/zjHzeJK6+99toaJj2lHHdTPvnJTy7lyJvypBNPTOPp5JNP7iijwyB9sHoDADvAL3vYZbEuYV497GEPC295dJR0eEKGtbRpOTvq5WOyUocg/3CG+he84AWJD+ME8+jcc84pH/e4x5WPf/zjdYeB70OE8cOo6U2D9ABDygFfUz+ixSoz8oDvjNPPKK+77rpSvplRPv/5zy/vc2885IhrkqTjBzyM+3REgAgQASJABIgAESACRIAINCMQdPv4Gzr+1m6325l+63XdtOPdKb0u2CwE1CPon5mMyA+vPQ8jkengoZ5qJHJCXRBZmt1RytfCvICacQ/pPrlZeGBaz/p/RyN8o7JwjDiaCy7afJS/Fu1PuibGUNRbbTxd/LMXl1dccYW+la20OP5hyH/YZQ8T3TLkQdq97rWtfMQjHlmeeqq8Se7KOV82cP7kP/9z8XZbyhq138SvFf6UHxHYwP3fnrM1uLpPwNbS7W7DGerRKVis/JPHthjqZezJ6qvXqs8sGhLBEPkCS0jGNTKKF0ORwcUQdFFlsLj4GqzJxxNUX8/K+NxTXiw75R/1aCzMZ8T22MCThVmMpFUbe2RhvpcszI+QhfnUZKxFueede175n1iYF5Gf6hsqpw1IwdQcUJRalaONAznSEXdslmzZ4Cun57c8yQ8FBL5QgoZ9wUIArd0OkzFgF3CBEftoXTIex5uffOC0/Na3vlXKWfHqY/eznFWuOOtNEjdF+YNxdjGX3XTFQNtk4L3++uu1/5/4xCeWf/M3f9NR1He/+119UyD0dWjnO97Zuav+Ix/5iNZH6yZtkDPRSxz74t2XvvSl8ulPf7rKO/NnzvRJGtb6xvbDGC5neikdxuSrrrpK8sXxJ+2+7LLLStTtaJxineQU5eDgoLzy95aOP7wt8hY5yglpONIpvX0SsV/MUP+FL3yhPFEfSoQ+wk6CP/iDP+io6nve857ytNNOy+YfMKg760drv+Hi+SATcgz/n/u5nytB8+4nP/mJHnGkYza2/w//8A89C8NEgAgQASJABIgAESACRIAIOARMt9ff2aIH4Ld0ux034UExxJ93Fhdfg8dJ/0wifHkioD0nemn8bR9007AhB8JXQ34QkmpX4UH5GwL/vRO3Rj3ebD1Bhx0fHyvv+Okd2p/f+/73y0c/+tHK5+0fmBPQe9/85jent9wPLhzUY1htcxt40uZNDBMdlOYjsrr2L8pHJ4gLi4MGUtDImhg7SrwYQi6JuBiCLhrS9ap0TfL8SDL+kKiEFEzJoERG8WJIaCC7GIIuGtL1qnRN8vxIMv6QqIQUTMmgREbxYkhoILsYgvLXbnvbYJg/NNQrWuvrkm7mzhiIzlu6Q49HZ0HzjS6+J9nTV6X5BMffFNy7dyIZWP2ii2M75IMiKuT7WJivuDL9ALAfKX5hvqu8S4tf+IoszPe9XzIYgudW21XfVIFGmmuABc13/J50tO13xbmgK9mC5kcum4wJC+nrY5mMMB57/FGu/dAKMuTG6cYTaC960YtK+Qitq3cerO9QP3jwYM4gMZzvPjc310H3BPkgqfRnJf93f/d3fbKGH/LQh2R9/olPfKKDxwjvfve79Rx7i5sf6lsZ4/FgQT5wq8Z9L/9od9KbnPRQRDD0PyCWi/9ihvqnPvWpGRb79u0z0R3+hz/84cAb+xtn+Nd3uQdDfYV/0wOXa58ibzOgPTJGTjrppFI+ONwhCwSU7c/47+1tNfKRSASIABEgAkSACBABIkAEiECZfqsHnSwYYYDLauufQWbVI15+qpvTFyvOpYScsmtB8112T/LyM8OE41960JVsQfNdIZ5E+QENvXpgHF5LCXbag4ryVa96VW5skoJgM+jBkbmqQwe9E0b6d777XR288mHZxAcd+4YbbjhCVVwDLGi+y+lJ7P+Ahl49MA6vpQddARY03xXiScQ/oKFXuZgtJs0PGffd7jZcC9E5dSNrtnpJR7pu1f47FAjZPS6SQnpHL/vUmNhB8oW6RAmG2CE5M6w6+sYGlS7MUqTLUX55bq48IS7M1jYcZfKud8nCbC5mwMJsPCjzhl04A9yVJsEQq2hr1f5U9ViVqkZWx8Dh6aC023KjkrYZZvBbx/B6C9468GVp2P3QQtzkmX+RHMNifWXt8H7Vt+E89CYDr+c/XPh0+XCMyX2BHKPiHR7kVPXrOeqz+ifcQyOUN78wX77whS9MuEA+jPRHu5Pe6myGemuPYi1ld/hHwL/JUI+6VVgU5f3vf3956FofPVaT4OOcfS+7fpSRPXCx+tb7Ee3BdwZCes8Rz9x/xStekdURxynREQEiQASIABEgAkSACBABItCJgP9tb7/HE1fHz/xIUM8lSjDEKpqpCBXFeELpnr4YxVRsX8egV/RIFleCBEOsoh1P+QkPk6liKlkgh1hFo/zOXq3QMbwqZJsoBnfFFUtQz5UmwRCraIb/rd4eJPrvzTffrMVVnFb6oXL7Yx6T6a1vf+vbLDH6UYrs4bz4IhwZG3TsX/zFX1xUvpezWDgU7lMzca4OkUc9xy/BEKto1v6KYjyLSfOclK8IdEASCeq5RAmGWEXrNvzt3mDrL+Ld7jZcC21B8n5TJ9ngtGEbeNIolqgNZHleZUFH9WVaemJzxSSaz4ByJKFuHL355XsqsTX5aWGOHxd529uqhdnLh2Hyoosuqhbmq6+uSQ5Ry+MEolYuajVfufbHmrj6LV0++hcTMBxJEnY8u4KWFTTjse2aP/1ud1MMcSY6/nCkUJr87uMuoF3/7Gc3yqoM9bhBNh9905ixgXhvPd883GhxXI53//AP/6DlW92X/wZFKE3r64zjePLu59DDjuG4G1/fhHXsuzPPPLPctm1b9ncvieM4p4ze1xeOl4n447iguvu7v/u7rM6/9+rfq7N0xN/4xjdWeaT9733vezMe60fr//qbEZ///Ocz/A+3gx8F4+Ga4Yqx+5nPfCaTxwgRIAJEgAgQASJABIgAESACAQH93VzTv5BiuqxprF6NTbQaiJbHKbwoyUUt5/L0X/tt7/2aaI2ulPxQeNUMa0W9DpRviHiEjr3/jwV/fOzVj5uwKax5/A0PDyf7x0knn2JiQ6NcM9C6Rz3qilTugx/84MCbmp0CQncZEQ6caX4hatSQFq42llKaKybRfAaUkxJSAFQnwOjN7ffFWVmWwxeTaD4D5Xc1/n4OhTAelna325CGejXgOqOjdlE2Y7NI6MFISikSSGFfgBJ9SgzD60gzmvNdWeljslrXYMxNJUsghSXP8PBQWmxPPvkkLSVcIhc8DR4qr5Bdwj2x/bYwZ4XlkY5ilCBlxZJDusWijEgUL3LB60gzmvMlmPJoOF5cMUqReCQ1MBwKWKCN8Q99frQuGI+rY19ulC+p1x3OrMfbCo997GNTP5jst7/97XX2csJ9KBh1q+/Ermf4+te/Lnkmyj179ujZ99de+xT9+OnV8qDFjMRYdJ5dezCABzaoR+DpKXFe/dE4M0hDhpeHcXTG6acf8056q1P29oKUje8BLNWFj8mGBxZNO+pxlr3VH/W+/fbbj1j07OxsPNYo9P+rX/3qLA9w8XjU+/F973tfhj9eQbxEjtC55JJ7lve8Z/i75JJLNAwfR+OEm0cYu0upY1YhRogAESACRIAIEAEiQASIwCZBIP1uFh1Fw/IbP2mJUBZVYcy1xqREZuQsEtCLpJQigRRWDs/gUxxdgl7/sPr67JmwEMlIqWQJpLAvQIk+JYbhdaQZzfm+LA3HiyvGWCKpgcGnuIwa9GmS1aLmW+Gx1OTF9MQmgRT2eZToU2IYXkea0Zzvy9JwvLhijCWSGhh8isuoQZ8mWS1qvhUeS02epJsObvrmvBw/W7lYgHqHyt/4jd9Ito+TTzk5yIlpVR4JCe1JT3pS0jkvvfSBgZgxVaQoRfOlsPLGmHo+xdE70qpyEw6+LA3HiyvGWLyUBGSHDJexI01KskLMt8Kj2OS5Yowly2IFKdGnuIwdaVKSsZpvhSfBMeCKMZYsixWkRJ/iMnakSUnGar4VHsUmzxVjLFkWK0iJPsVl7EiTkozVfCs8CY4BV4yxZFmsICX6FJcxpmH+mP3T1uO6uG6LH731c42QaLVacVGqDK9z7bbUxnWuBeHL4ziLVlXupFRpIWRP8RJdsvhcPj3QXWoM4mOydjOHXxkBHW8UoAtzNKKeIk9QffnKIlksV1iYQ/svvfTSSLdU4bYgfCnIolGUY6go9dDh5IPXp4fynRQLwj8K+fPyASGPG8LHeka9L+/Gl3Qa6n37X/ziF2fy8fHQurMjU6zcBTlKpsnhDPodO3ZUHyOVtlSGd/wgrcYxyqob6m+++eVaF725y26T+kdMm2Q20dJDo0Xk/8mf/ElTtmXT0o76uDPmuc997pLLuPDCC9Vojp3oTYZ6fNtBH56gDUvcrf7Zz34268ubbropq48/NxD4V3M0sMGwD7rh7+Vb32u6KBU+XsT2/+Vf/mUmjxEiQASIABEgAkSACBABIkAEAgL2Ozu8RR1+TydsRJc0tRK0ldQ/6+VrHaL8pt//oV6udhaEfxT67+Hka11CsRbsOv2/W9tvhnrT/+u6ZupQCbz0pS9L+uTJJwdDvQ2rOj5PvvbaZLiEPShNFI4/zr8uXH+CnSW3m/m5043hDWeo7926NS1g9lSl3W7X+saWNPNjskXNtxVN4h3mbP9LoFY6oh38GU8Q4F91gqHv4Hx8gtog/6Uve2lqV3iCmpiykhG5VhZmMw7qwtzBYXnNjwwWNX+F21/dMZYnvz3fDu2DQTYaQI/VUK9P4WJ5TTvqPYTYFY7d3cE4i6d3PeWPfvQjz6JPx238oS+abro49uTcc89NbbHywH+K3HzPOecc/QsLT/hhWjfUv+xl1Q0bfJ/73Oeyeiw1knaORzx/+7d/O403lHvaaaeVn/zkJ5da3KJ8+KBqaE9YSJdrqNe8UscmQz2M7KHsgBUeghzJfVr6AHms/3/rf/7PLEv68SQ84Kv34ytf+cpMJo4puuyyh+l5/pdf/rDycjnXH2f729/ll1+ews94xjNKjCU6IkAEiAARIAJEgAgQASJABDoRgI5nv//tNzt0wSa3FP37aPVPldeg/8POoPWLugL0v2a91BRs82MLLGr+OtW/F2u/74e1wH+jy5+4VTZuRv076Jpxc5+MhzqeL33pS5P9QQ31vvES9vxmD0KZwR5kA8z8svz+975X/t4rX1U+ZvtjygsvuLA866yzyoc//PISmxL//d//PStPRTWMf18FL9/TQ9jkmh85LGo+x3+CqwNP4t85rITSbldrsNnfmtfgxuwblrjhDPX9A/1hAZNFyTpqenrapnx1b06LQeibEM2JGvOkSPBzRD9WGXkSvSNPJTZVRHiq41GC0bK+6zqK0wre5AyyJ+EJanRN8p/y5E5DfaqSBaL/I/mY5a/92v9T3l92hT/ykY+wYtX38j0htVOITfJTG5EpyjGxFk++FmxsiUupi8nfv3+64wcb+v1oXWU8DsZY3JyO5K6++vHppgoj7z//8z9nWdTwnd10/WtsZXnnnXeWF198cfxhF/r/yU9+cvnhD39Yb4y+MHwU1XaR1A31f/zHf5xu2NhFjvxH4+oG6fn5+XJkZCT98IR8nBn/PbmhH4vTHfURF8zPozLUC97XXNN5Rv0tt9yi9dUHHsLzwQ9+8IhVBY9/a2FycjLLY7gY/nVD/bvf/e4M/z//8z/P8jNCBIgAESACRIAIEAEiQASIwNEhkAz18tteDeKiP8zNzZnyWO2i92pkDCeSBcyPVQnRnKgxT4qExfTfubn5TP/whvpUjAXMP47yU7182TGcSBYwn/IVgQBHDorGPCkSEs6S83jaP/zGTYxvPfpmEfm6QS/q0djUB5fqVctjhnroxWnjpuP5zD99tjz3vPPCnLK55fzzJO3LX/pynGdiMnZ5j2f700TW1iRxMYYGIkj5xN8NCYARxyOC7Xa7w/7bkge83e42nKF+oL8y1OvNXBYcGOq1M7VPQ6+mawhk/aiLT0axSJ3Zx3048oNk5Bi2J2OI2sKsDxRk0V1YOKgZm+S/7KZq5zSOvgnOCkesCj8l7qivnqDGZGHx8j/4wb8qW72t8EBD5G/ZcoIW2yRfE5yMznglP6TJFSQjx7CXr4mWnjIJFTOu0QX6zPRMflORum/fvr0xx1KIem663vR6dJIfaUc9yrzqqquqOkje+u5tM/DaGKwbeMNHYMMPTvT/rl27Fq0qDPX2pP3Zz3pWxqeGZq17KOtNb3pTlr7USKhvaD/qjI+m/vjHPy4f9KAHhXZGGU+Sj7gu3j9HlpadUS9yjsZQD6N50456nM+PNDPUv+pVrzpihX7/938/jX8Y7D/60Y9meWyOGv54gOHdpz71qdQ3wO0Nb3iDT2aYCBABIkAEiAARIAJEgAgQgaNEwAz1tmkGv7dhmAmuQWcEycgxfDz0zyjQFQ7KIa2L1z9Qv7SbcxXkV/WKoVVuP+XXEFgm/nv3TiTdFfpm3WZQDeay9G/S6wkLaaC7OkT5laG+p3zApQ9QBiRpeRL4kze/WXXY8847v/xNOfv+rW99a/n6179exy7GMP6ufvzVgV/z2SWUEmI+HNNBMnIMr+T8s1oln/I3Hf44FtvsPzZ20xqcBkb3BTacob5fDPVmVIOPTlNDvfaNrRq1jtIJ3ZAWSblhUpmrApRHLuF/WpfAoEkNFMs8IR+q9Lt5OxbmUIAaRsOO+rDz+uRT3MdklScIRxB/1z4l7KhH+x8YF+YgU5nL73//++X1118fB3QoE4N6y5YtVjXPXjPMmpTIqkXKJfxvaK0yeOZcBmJgOeT5Iksk1fGfnt4f+zgaZaWdA/0DMdPyvXRuOm5KUtZLGj4m60vFDvyLLr5I+04XBcnTefTNhJZli0a9b1/3utdl+H/iE5/wIrLwfe9738SLfvPui1/8YsICffjIRz7SJy85nB4sSFtQZ6svyj/jjDOSfMh4xf/7iiWXW2fUtxdEBsqBnOc+93l1lkXjOKMe+dBH1zzxmg6+ubm5lA6+pm8H+Ex33XVXeZ/73DfkQX3kCKNvf/vbnqX2gZ8efYDhGX7wgx9k+F955ZU+mWEiQASIABEgAkSACBABIkAEjhIBGFzs9z90AOjO83NtKU0UxfBfVUkrPqiPUYlUog8bV8i+HP1ThVl2LVIu8n+u3a7qF/WUVu9W44yCXNSCkvd4yNdirEwUqeFwDWQfrjEuQ/+2kqvipdzwP8r00rxMH6b8hABgEfxVB4/6N8b2QjwKuW7/ANhh42awf6g96DD4V4Z6t6M+CS/LT/5/nyxHd+8uf/jDHzpqWX7tq18rz/yZM3VMY9f+HXfciYrGv8iqXRqE11LiWFAGzxzDztOMni9nb2p/yq3Z5BL+R5khNZQYrp6S8loALBz/hkblR+g2Ev5zbVmDo30JPmxMNNRXXbpuQkNDQ5VRMd7Mx0bHdAKnKasTE3M7UbL6h2SXpkG5hP/qL5I1lmOMVbEoAn/qYuTWiVuzG7sZR0Ny4taML7vJzqjvKU8+SV51cslWrPlPufbJ+iMGAzV85Tuv8uv/8A0q98STT9Svh//6b/y6xs1Q3yRfS5AEFRsYTFyDb4xVUpYlRo4W/9HdowE3/BiKP4hGR0crYcsMZYZ6Ke9IR9+84AUvyOQ/9KEP7ZBoN13ccLHLor4TW882dwvKxz/+8Y4yQHjTm24pTzrppLT4POtZz874fvrTn5b3vOc9VYZiIfVf7PibO+64o9wtN+Rrr32K3JfQCZVLhvqIqa/vO9/5ziQf7TnhhBNKHD90NC5gHR8OSfuXs6Me3wWAfLTzibKzv+5geH/AAx6Qzf+pqak6W4rj7QMbP/Cf+cxnpjQL7JVdDp5n3r4jYQzi49x5v8tnMfxdFgaJABEgAkSACBABIkAEiAAROAICrVYr/f7X3+Tym73dbjfkEt0mV280mkgIKEuiZGWEZJemQbmE/6Fsl2yZURfUK2zOEj1F9JteeWMdDuwpS4wcrf6bF6bF1y4iIAkLSVFkFqH8GkgRxYCVS9OgXML/WmfWoNeoMVZpKAJ/6mKkjn9dBzd7UMgYytQy5PIy/zHZU8LRN1Z8VdFAeYro+zZf0sdktTgtrcoWQ6Ba3XDErOm/eHCgOQJDR76KIAzKmFEqUsxvMiquEArJrgANyiX8D2W75Hr+irFKAXvKEiOUnxCpgIo4Zdgom1zC/3WNP9Zgv/7CXtS7tTdrXzdGNtyO+okJ9/qQ3DSxyAwN7Qx9IwPNuxSVQBqYSrQUoVowZTQC/PhnwUhJrIgru14Cu0u8VXfUV8bmgwtyjrnnR61i1pfZhzLl5h8+HoKE+GfBSPFH3zzgAZdGauW9973vLZ/61KeW//yFLwixLMfHxvVHxQnYUa/y9KIZTL4y6qVKW0x+xSscvjzLGhlSVALLwR8PY+zGY/4xG+oF1zDBi/J5v/w8PSceH1HBH3ZZ48Ov73rXu8pHPepRHbLf8Y53+CZr+NZb40OYOAZxlIx39SNrHve4x5Xf/e53EwuM6i/9zd+sZMlub7S1fkY9MrzxjW/M+E499dQSBmoY8eFglIfxGA8UUAbGT/0jpviRYO3HD8z0I0FLKMsXvehFQYakoQzsbv/a174WU5fuZQ9FpKxlG+qj/Kajb1ALjO3UDqkndsm/+tWvLv9DvsVgDkf6YLygnTZ+gMnc3JyxJD/9eIr413EB44EDB1I5hfABf7w62OSAGY7bueKKK8o3/BGPyWnCiDQiQASIABEgAkSACBABIgAEBgYGst/s+O0+LW9XmxoMnqRTIiJuJfTPWDJKD3/Rm5YjdvH73+sf27f3R77Kk5C6VFcJLEf/rXLn8kFPZQamVW0/5R8b/uGEBdFJoZfK38GF+dh/sTPVCz38spfZxs1oD4od39T/1+rGzaDrpjPqpazEK4HFxt/jn/CEqPf3xB33lgt+/LOgL1PrKhzKrhcnMCRGqtIXkx/yx8LUs1zw458FIyXjVna9UH6EwfBJUQl0C/77Z2ay9Rf3CJyy0u1uQxrqzfimvix4qaNsZJofey+LItJBiKSMXnV9IC+SWLFV5UbWzDgqA0qNgEjLigqRl8aPyeJHQDDUVwUHjiqTveqE9l/6QBjqozMW8yN5bFwM9cKrO+qRlqWHiF4zuhVq7IskVmzGWPkxLcuJSAchkiId/Rl2quOGFgzYeEBztM4bj8OPrFBmNo4EH8SrH2HhB9nzfvmXG8VWBt6Qr27gxUdZzz33XFee9KvsnP9FMdij/37mZ34m3CCjXKtLk6EeRv2+Viucte74Tz3lFPlI8P3L008/PZODsn7yk59k9bb6Wvvr9cWRNY94+MNdOT3lY658jLwOd0dWzpEiHmvUYzmG+gsvuCDJv6ZhR73JvuLRV1TY4UePyDlR3gLAefsPfNAD9Y0Aw9P68zfloUiTw7gyXvh1XCzPdddd14H/OeecU175mCv14cqTnvQk/ZCPL+uXFxk7ViZ9IkAEiAARIAJEgAgQASKwmRHAd8js97rpf9NimAlqYaY0NsNkLOZHriyKSAch1z/rhQf2Q+XMjBjqRUfwf8n2gExWrvmxoCyKSAdhafJjcYt7Vq75lK8IZHAg0kFYHfwre1CwPwRdM1RGr65eOKNe54LYP06STWa1SseeDR7sCTZv0o56x+GKDcVEAnT1s84KdohesS/kuFQFBPaslCrRh4zF/JiWRRHpIERSRq8KDuRFEiu2qtwaaxZFpINA+QpJhksFbCAvklixVbjWWLMoIh2EpeOvD0v9Guztv74uXRbecIb6drsdb5SVsbV+RpEfByksj+5S2DrREywsvgWNLT32bExLXClg+c04asd5zB+UHfXeGaPQ/KtOYWF2jPbYMcoPhvrQ/kt1R73jlaArVsPj43sUs54Tms+o19yWKcrISqzJz9OyWFYUIlYsMExhy+IJFhZ/q51VKBPSbkDtdttyLdvHznM8pPA7rP2PLZPhaaeffrfy5pffXN55552N8qq+bTbUI9P73ve+fKxGo7KXc48L71FeY0+1pb31M+pN+Oc///nygQ98oPuRWI3/UF4Vf8lLXmLZkq/1dfL90TfGhLcC8HDB1w9H6SzHqaHeyXnOc5+z5Ozh6JuA5xMbzqi3gr7zne/IGyP/I6unzTGtu5N/6mmnyhsJb7KsHX714ynIrb8ZYRnwNgT6psKmwruOv42z5WJnsugTASJABIgAESACRIAIEIHNgIDuqPdGGAnPyC72o9W/gZmplcvRP1MeAz3qv9P7vaE+/P7v1x31xhh8nz+Fl6H/pjxWbJSPxnSmGVPle54UpvzDY2dAiW/BhOhxwv/WCXyzsHrQY2fUqxwTGuXbx2Rhm8AGP+Mxtqpu8s1CMdSb/gtDvedJ4Yb+v+VNt2h9IOOml910GBmxFPFSeVaBDkLOk5Ib5GeFGaP4FjQR62X+o2KddUu1TAHPk8Js/+GxM6DEt2AFaKSItx9vNekcquwv2/u3J9ZuDWxIQ32TYVU7CP1Z/2vsOTCJa5o8IUXTdIGIrEY2v5Fck7331ok0qFDnarduzO3k68IcDYz4sEeQLXyR1eTC90ff+FedOtoe88JQD/l2Rn0q1Mn35WtY0o61/U11D3JixRaRv2j/dlRy6QT9uG7cnW+G1Oqm2VOeeeaZZV9fX/kLj31s+ZrXvOaIx77gmBzLjzcgvvnNbzZWBk8AH/7wR6QHDppH+vmss88qn/a0p5Xf+MY3yje/+U9CWUJ/wfOf31gOiDjS5YUvfKEeS2OyzT/xxBPLx151VfmhD32oMf9S6/sXf/EX5WlytIuV+/SnP72xvMWI2IEPLEP+Hv1GwmK8dfr97nc/zYf+f9aznlVP7ohjNzx20Z8ibxZYfc2/+OKL9QfM5z73uY58nvDud79b8oZFH98KQH8czt1+++3lZZddpsffmCzv41icX/iFXyhRNzua6HDlMY0IEAEiQASIABEgAkSACGxWBMKb1NXmLPyuhnG8yTWoxUGtRYL/a8psiuki+qdmadB/bTen1087dtR72Y2VROkxYZnyfVMai67LbmSi/LXCP+ngoudD1/ym6JqLddHv/K//lXTa888/33e9hn0+v4EMR/cuZfzjyN9z5Y1wzLEL5E12bH7LXMP49+lefqKDWP9LiT4Qc3P8L9r/h7P/AcnNij/WYL/+YvziAW+3ux40UBq7oZx0TDE7O5vVWTqwAD04NClYvaVT9REm/ORCcoy6iAQxCjyvpQY/xnJiKrYKBIZ3vftdxTOe/ksqXxbm4qtf/VpxwQXn1yoUeF/xilcUN910kxYhC3PxrW99S8O5qBDbef3O4ra33KbpsjAXH/vYxyrRLpflHd+zpxgbHS22bDmhuOuuOxvlW7bj2X6Tb36qZEZwEQlOz84UOwYHhVU+3yn2U4xOeWOikB3gKft6Ccgu60J2kBdnnXVWIcfPHLZaYgAuvvSlLxVf//rXC/kgqv7Jg5OUB/2NqYi+9/TEUAugvE9/+tOFnM0uY+qCQgzWxdlnn13jyqNyHE8hR+IUd7/73Yu73e1ueaKLoU3glfPfteyl1MdlL8RAXcgPgUIeYBTnnXeeTzpsWD4Wq+NePmarOED+Upw8HFBsv/CFLxTykKB48IMfrPmXkhc86Ec507+Qo2yK0047bUnZ5LsAOiYhE32AdsobAcV973vfQh6aLKkMMhEBIkAEiAARIAJEgAgQgc2MwKDofTMzMxGCoP99ZP90MSh6fdASo64YvU7F3tDLuDvZLL+yu4gED6f/zs5Mi256lZSHPMGJod7V2aihTCvZfEvNK+RSJXg4+cmiYVnMTwVbICRYsvmWSvmCRFItHToSXGn8vwObwU/+qzj73LsXp6uuubj8f//3bxf//dM7CnnLvZDNaJVFy7JEH7qotx+ceOIJ2sAaW+r+O++4q7j6CVcXH/k//0dxeMfb31H80i/9kjR+5duvlbCK1SOUv+Ljrw65drpNhg2A/+zMgWJgcECbYRfZUV/MzsxatCv9DWmoTzd0WWxxkjhunPLxyGJsbEw7KVsH6hRNrDhgCFZ7oJFqvkV97wdaTGlg8KRgBIRx9JxgHNXEisPL//Z3vl3c8d+yMJ8nC/PJp+giWnFWNbhLFuZvf+uber+94NwLihNOwsJcuXqePXvGBZ8xNQDfdeddAE2Yw53Ky1eSJUXfolXpNrUXZ+jM4ygarOJ1+WNSz3GpLxxqiPNq+rdvL2bEgE9HBIgAESACRIAIEAEiQASIABEgAt2BwPDwcDE1NZUaA/3v1onJYmR4KNEQCNpj1CErVTLxdJIcRYNVvK5/xsJNSIqi8MlJqcvISJIDBXX4+uFiYmqiokmoKt3IjqLBKr4c+XlpsYyqKEumfEECY6dyDiQNVvHNiP8vP/95xS23vFnh+bVf+/Xi937vldWgNWiib9EKS2NdnKEzj6NosIpvRvzXs/0tLR6xi6qeqkZAoC3O0JnHUTRYxZfb/9kaLJMc9t+dQzt1ba5q2H2hDWmoR2ftkhsmuluXZDE8D+1EZ01JvBoEPqisPrU2YDQ9o4XSzVycHnXVi09xC5jvhZl0Vztlc7xgyWiIwOGWI2F71OuyZOypsXWGotgjO+rxIAM7o++UXct6E8szq6SVkh8KdyIQXET+yMhQMTm1z2fJHsJkCYwQASJABIgAESACRIAIEAEiQASIwIZEAIb6fWKoh2poTo6QLIZhqD8G/dfKgq9qZwqkWGDRqNHgw1X69+RUNNQLCXvdwDG0c1j01QkJIRZ5g4ZdkSTFnHEF9hQLyRo1Gny4Sr4Fs+KNnfIrrDKAFMR0yeFKsZCuUaPBh+su/HG6wvjYqJ5U8Ixn/FLxtre9Xd+c16ZugvZb73L+YWgnNLT7c0zW5/ifxIPbG0bS+ov5ifsD7hPd7DakoR7HoPT1bZN+scHUfDwKjhIJx2dgz728Smc9Kdnsvp8PTmPo9CEp5U/JkYrHQkitMawX+TfLDvXd8rbBlp4tcvSN7Khfx+1v9bWKhfmDgqchXhZzc3PS330JdQaIABEgAkSACBABIkAEiAARIAJEYGMjoDvq900F5TzqfxMTe8UQM5y0wbyFq6t/T2U76qHsywbBIdlYpkYiiYPk3HrR/1HPzWz/YPtD/7/uta8tfuVXflVGaFkM7riq+Mu/vL045SSc3GC2Fjd4a8FmjtWdf7Xppe3QSbfO7W8cf8dv/al21If1F8MU9wca6msTdr1EYYAPXSXX+Hh7//4ZOb9ou8wLSfOzGuuJOU+PtLQIGZ/yJKrlzH3Ha5xhvZDYOpN/8/jNYqjfXZwghvo7D4mhvuas/rJ+B7dG7Z+ZnpEbyGDs16qS+MFDRwSIABEgAkSACBABIkAEiAARIALdgwCM3vv27cv0v7CjfrizkaYSiq5q+utK69+VkShUB2ryziExEk1OhDprReSyzvT/pNej2qrbh/rb1fBLfGuk/1N+7JEVwH/v3r3Fc5/znOKQgHzFFVcUH/7QB4u7nX6GCmT/x2kBIOBWAP9QsJWdYhog/g5yIHIY/P0aDDY8gNw5dH0xpaepKJxdedmQO+rREwPygZnZA7PhLg2C9NqwHH8z0dFhaRrofUBt+nIJZn7JVyWjlBgNREuCH4aECMGvgewpgCbGi+VopmmqXMpVlr9nz816fEyPHH1zSM6oX235ceYdEf+x8XF5LWssgodpiH7uL/ChYDoiQASIABEgAkSACBABIkAEiAAR6B4EmvS/Udlghu+rrQf9e8/YeIE6qj4LQ4K40d3Vt/FQR9N1NVEvFU1Dclmv+jeqG2rrr4GGq9pMNqD9w/pks+J/++1/UTz5yU8u8NHZ8887v3j/7e8vzj7rbJjMikMCipmz7n7O3Yvzz79A6Ox/jJmAAsc/cFgv839MjvAeF3tmeAtEq7UpjsbesIZ6fHQGrzwEhyVHXkOTs4pwhpG6MLriGm1TLiSlGRijlef4UjAGvGdh8VWyXPS+jYg58MApLWYIlDDzPa/RfcVSlhjwnoXFRzF6jE+kWVE/+uGPQmmyCr/85XuKV77yVUXPlp7ih9//gTxrkFxbyuLMM8409ui7QlIwBrxnYfEXk6/CUaq2M2YwabWokQfFKD8ze8Ci6i+6oyLjYoQIEAEiQASIABEgAkSACBABIkAENhIC42IEH0sbtULNh+VDgbr5znROIZuurTZjiTfpv0ejf2aKedJRK8Fj42MF6ggH4ybk49tvo1Jnq5Mm4oJscMvQf48kH2VpbaxK4qP41Wo/5W9M/J/61OuKP/uz9+pgwTjFIxdv6NRxKpcH//yDin/89GdCNI6x2OLc4/hTLFd7/eH8k/VW1to9sgbb+ovBinsG1uFudhvWUB/Oqe+r+kbvWEUxvX9ajr8ZqOgxFCaVrDDx8aFNMr3TGbck603P8oiPYjucLlR6yTbYO3JnFknUZ3SrIP+973tvcd1116U6RGg07sNvf+vbimc88/9KfPgVsJbtD98TkOq4SvJ8+qp7GCICRIAIEAEiQASIABEgAkSACHQLAtiUtWvXrkz/0zPg5Wz4zDlFW/X4qKQ7csaOyPHQ/7ExEBsEnXpa7N07UYyMDKu8lZavQnBxglaz/ZQfEdhg+L/wBS8o/viNb9TK+7GbwjFw+cMuL/7+Hz6RbF7azNTpLrDB2o+aH4/5nxBg+2WNDqNjtdcfrMHNHxwfTt3TjYENa6hHZwzI8TcH5Pgb3SGudy/5CrucGTc5uVdSsfo0OVt+zA88fsCFG6HltQ8hCL9ZsVPWFIg3zyrPWsr/wAfeXzxJXnXS+qIxuhCnZTk9jXrXu95dPO1p/0MrvdbtD0/K9khd9HmvdkFvb2+BBzJ0RIAIEAEiQASIABEgAkSACBABItBdCNj5w5WmCn0+7KjXTW5rrH8PD40UU7dNqV4d6liKrWFCbQ51m8Fa6v81Y0RtkJjNwvyQvNb6P+XLiMGggsu6ZuPZn2oNCG1KV2uc+SGB/c/+X8r4H5aHolNT9h0TsRXKnMHD0up0lTTQuiqwoQ31+fE3oV96e1ti3G3X1gpZFOJNXhcEZRWajgy/YEQ+pHe8yxbKr3G7xwG2oAa++mJL+TKj5P9i+LdarWJhYSGCB/zlNZfflvP/5HVDOiJABIgAESACRIAIEAEiQASIABHoLgRmZmaKwcHB0CjR/6BD9/fLcahCN7eW+jc2Bs7OzlpV1Mf300BHZeMp7iHdVxQNof3hsPp/Ag04wdH+AhAUCn/xw8qHOf44/zbD+tMva+2BRddgP1O6K7yhDfXV8Tfhrh6uRbFfbp6DevMMnWULWvD91XemcVkeGfZ6c60tAE1Z8qy4LWdLrMWD769NhW0++VU/ejwKeeAyX2BXPR0RIAJEgAgQASJABIgAESACRIAIdBcCnXpgj+h/lxTt+YVMn06tzhXrDrIRcraj1791M9nBBX2AgLJhb5hrt4uWbA70FTR54IGzuJdstMDhuQJFrQ60PwiutL9kBthqwNQHVkqpjy2Lc/xhmnoUEmQSMJQCjfNvfdo/W32yoVfuB3BYf9FrbazBrZaEutdtaEM9uqXpKXd1/I0w6M3u8B1or91UkxP5ZAj0hKEQFkqJS1nx3hlGiRYLugTAq77JQkQc5TusAiT16+iofCBizx6ApUmAcvv2fCdFPQ/jRIAIEAEiQASIABEgAkSACBABIrBxETBDvarSogpC+97auzUa6oWwxvp3q9UX3/qWmmHHt/xvt+eioZ76P+0fMmAxaNXJ4ICj/cdhEiCpX83URvubGccxbmT8YCGUSUX7Y094wCJzqbevVRxcOJjWX4ylzbChd8Mb6vX4m5Fh9JfeODG0L5Fd2As42xxrpY51G/SBR2+y1Yrq+CK/eNVEsWSbMJoYJo+VD1Jyjs/S06QTJqVZQsxkUfNBdnkC2ZUrhejk9fyxKAgIExtlyN8GaL9+RFbqGX/7aKXD2X9DqVUMEAEiQASIABEgAkSACBABIkAEiEB3IaC6YFBapWGqvBaHRBfGTtik11rY9NsMAsdn6U6XDjqxJcSMFjUfZJcnkEV6z5ZMEmqHuuVu5eSvdfspHwZDuNjHfrykQcD+T+PE8HFz6VjmXyqX+AsCMhYN3zT23NgMwQ1h/wtjQirs2+PGTCCHebVFH16EBuvdQS7lIXB0t9vwhnqca95qteItveqs8LX4CSHY0mq3faNUvCEUBgLCNgHSuHGDRnl9XJkCJ65wQSJCqYQUqihI925zyh8bGy/G5Rx6YIaHzwAKnr5S2GpJiI4IEAEiQASIABEgAkSACBABIkAEuhEBGOqTLigNxPGzc3NfLlp9fVVz10j/7tkSjGNezz8EI1E0GHh6qCwoaE1lCagoSnaXzan/VwCw/TC+wtH+BBCqOeMfmilAazT/VTYulC/9E8ZqMGyGVW011j8Y6r0chEv0R5e7DW+oR/+MjY2JsXePLnNY7jHLe7duLeYX5pEcnJJDWloCYtSoMavlyCbkojzGbQx1P0uvJcaoUTej/L5t+PjvgqCEiQ8k5COyo/IRWelTOiJABIgAESACRIAIEAEiQASIABHoXgSw6Q6b77zDGcS9QoeG2KEjG6Mp0XU/S68lxqhRO8p2Brm2vKG/ra/PSlMf30/DsQvqrJC6H1JjvWuJMWrUw8lflCcrXyLGaH6WbsTo516VN+URhmiQs5ypfOMx3xjqfpZeS4xRo3aU7fBflCcrXyLGaH6WbsTo516VN+URBrZf0TDkEr6GkfnGUPez9FpijBq1o2z2P8efm384JaWvtgZvFTtv/X5hQ66b/K4w1ONmmXegPPmWWb/bGXx1MUgrgnRhnWCLQuQx1uRLII6Zjv4Pz4LxMwLc8CSsvypCVElysSelStWCrXShbEL5Y+Oj+oBF8YgXwMbd9B4RhokAESACRIAIEAEiQASIABEgAt2JQNM356Znpov+7QNrqn9PT88UO64aVB0+7Hwui4H+gQJ1o/5vh5LQ/qGzkvYf2r9o/8tuUHVz59HYX2dnZQ0eHJSsADesOdv7txczMzOZrG6MdIWhHh0zPDxc7JvapzdN66jeljzxbs9btPJl1JRiNYc5v9E5+7mm+3gWlgis947mgo1FW3mUXxbbWtuqtx7C3CvCh4AnFseOKUSACBABIkAEiAARIAJEgAgQASLQFQgMiCFmFoaXqA+iUdP7p4uBwQEEM127vrmtnqz8TRdR0perf8NINLBjMMjXMnuK/v7HiJFoNqsT9f8OG21nDxwF/slU4wGubW6EIJ/cKThSKH/Z45/4V2OnwkIGEu1/2aRbyfk3I2vw4ICswerCDQIPdqenpyOte72uMdTbrnrtvtCH2mtjsqt+dHRMJhSGEMaUmOfjaApeoOvtRYLZw1C7EYSMmj9NUqNFWZrP89fSKT/gbPiPybn043vGwyQXrMI7EKWcRzhX9PX1Bax5JQJEgAgQASJABIgAESACRIAIEIGuRWBoeEg33KGBQbXuKSYm9hbDQ8OhzSCaCwq86pArrX9PTkwWI7tGrFJat507h4rJqclVka8WaLR7jdpP+XHQEf8IhHirOP84/jj+JidlDb5B1mAZd5iGGH7hW6STEZzu9brGUI8u6nxtTs6q792qr0a0Wq3Yi7a6SNSC5kcOeJ5kr7YpzSc4/qUHXQEWNN8V4kndKN9/FEKnnTw9GcIPH5mMdESACBABIkAEiAARIAJEgAgQASLQ/QiE782NZw3dPba7GB8dDzq5V4wzrqVGXAEWNN8V4UnQv8fHxrONZbAUje4+mm+puZItaP5h5IeNbKIpN/C6bEsIugIsaL7L7UndaH/QplojzWf7EwIeEvZ/OH1DMfHAJLSWE3AFWNB8V4wnEf+A/6hs7t0j67A6WX9xQ9gs37PsKkO97apHR8Z+1D7Fk+8pefKdD/7AAwZP1wwNlE5SzKWeK0GCYVd+RbON9hUll+npm0G+PlA5MBtACA3W/tovr7AgjY4IEAEiQASIABEgAkSACBABIkAEuh8B3TU5MpLp77prUna0KzFBELVm9ZwGLcGV0L/H5a38sT17RDqMRkF1VSNRelsf1JCwEvJpfxBsI8SKswGySv1v4oJsXOOYo3zpl4hFhIXjH0O1woT2P8FD5m6FyOLhML88Z6Ag89i4PCwVY72tv0jBg12sw93uuspQbx03Lh0KZ8epIIxzjAYG+mWESDejp9OwkedVQsNA8tQQizQZJNlAk7gVI8FQnM8gYZuclRwwuIybVP7M9GwxuGOwhlZR7Ny5Ux6mTHXQSSACRIAIEAEiQASIABEgAkSACBCB7t+g1IUAACwfSURBVEQAOiC+NwcXVPKe4voh2Wg3OZE0Zq9Gr5b+PbJrWOqQ66cTExPFkNQ11FOrnC7U/4PNhPYPP0Jp/0mGszSbaX+j/XFp9lestftqNkKswXa/SItvFwa6zlDflo/H7pAPz8wvLIQ7fVwney9pCa2dlgd/sw/9GhnVi2FNcHTE/dNDxI3VfNASUSPh4orRG7vEzdjfyeALcxnBuMHl98uO+b+enVWEtDkAQ5p4CL9s6IgAESACRIAIEAEiQASIABEgAkRg0yAwIx+SHZQPyqpTRbmQj7b2FzP7ZyLN68ZCsqj5ypVFQr5ISikSWI7+Pdg/UMwcOKACtVpy2btXjEQ4Ox+EVDDEZREQEimlLFO+FolyNrj+z/aH4YL+XM74Y/9j8Ivj+AcICoVebEKZnxErNluSEtsGHH8D8iHZ2QMzofUCAUyGNNS7Pt5oQX19bteu0JOu8tV5Rmm4utQ8WD0Rj/TawPbpoTRXpgXhywwpZTu+m1pKyyZbFOE9X77SpSy/sPv0IM6EuuLXmXw8+dJd8wIG1lutniCjHwuSNDoiQASIABEgAkSACBABIkAEiAAR2DwI4PjabX3boDWnRvf29hbzsgFvLfXfvl7Z6PeVg1InU1x7inZ7rmi1WlpTf9RFJEQFl/o/7R+0/9D+lZazsIbULII+FWFv39M0LDuwm0VGn46VcjOsP73b+oqDC/NxXQUSZYH7Be4P3e66bke9dZiegy47t83ZAN8rr9AN7xyOIz4Mcb33iuU4W079TLBCnI8fEhm/SwsjKQykamoJQxSXfAsIvexy+bNxpwQWG212xEt3S0gaHREgAkSACBABIkAEiAARIAJEgAhsLgT0O3NikIGOqKpi1BdL6OPOrbb+3YOzb2uuXqcquUPRD0kd5EgQr9v1fwWA7Y+DWtDQ4cT+VyA4/jn/l2D/rK/BWJIPHcIc6n7XtYZ6fTIvN/xwf493e+lPPH3B63XpSbjdPNDXGpafAGJNtvsybsZqkJciku2+I0/MmsqwskAILmTxGZO4uGgLXxfLr08ywyV8O2DAovSJABEgAkSACBABIkAEiAARIAJEYBMh0NfXpzsl1ZgpOjG097l2W3T3VtDLvRodw4lkAfMjbiGaEzXmSZFQ1/8Pzi8UrXv1iX4e7AjhKlEYBGL+VIwFzD8O8ml/EMTlP+0vAoMNPvhwHH9hbAgUConNO/MVJIMpJ2rMkyKhPv85/wRZ+b+W829edtL3yZtW6En0s3aVBGiojwN8I3tj8oXg8bFxXdzQsbjR4hWRx2zfXsy63fZVG/2s1QxywbCAq6dFEpJs5IAUnwwFbrni5m5FgFecGv/tSUAgxWtdho/7sGNHcJ3LH9wh5/sB74g/WoI67711bzEyMoIYHREgAkSACBABIkAEiAARIAJEgAhsQgTqb8NDV5zev7/AGcWZU0VSKJKu6jm8FdC//bn5arQTYdtxbv70jEgUt8Lyg5CNq/8rPqERet1s9g+233W+BNn/YmzeRPa/4zH+Z2am5dslO6SosNhiNdxMp3F07Y56WxqGh0fkXPRJi+pNVe7lxe7RsWJsdFTj+cKBIQCHASEOUWQQIzNs7nDRi6Z7ZQgJkRIjladFyqU+OWPWbpY/Jg9KxuWBSXICHuDcjo/zyOSjIwJEgAgQASJABIgAESACRIAIEIHNi8DA4EDYSGcbu0RnnLh1shgaGVJQVlv/npyakA1lN4hsKOzipAL92/uLaXkzPzsbmvp/AEdBcheFTS6b0P6hKLD9AgP7n+PfVu64NmBeCGkp9s/JiX3FyK7hkBHFSF4a6gMcXXHFETh4Qr+wsNDRHhjqR8fGwv3XGeMRVIcB0eiEAVZ7l55liRF7ul8vIiS7M+5B6EL50wemix0DOwJOBlAEA5OTjggQASJABIgAESACRIAIEAEiQAQ2NwJjopOPj49XIIiePbZ7THT10UgT3XEV9W/UZ8/4HrENBZ0Vav9uqc/YuNUnmBBQOTUJBDapotPxY83hhWSXBkIX6v9sv+tj9n9CgOMfULixwfl/xPUPJ6PghBQ4rLFAb3Rst2y2HgOp613X76hHD+qrazsGw70QBL2bFkVr61bZWT8uT8uHQY1OZ42Ew3KiRLeV3lITtxB61LIvhdYSU1QC6aalREvJz8MPZVZpJkN/lCDSKSKeGxXzWNaYMUUlsNryDfMEo9Rdf4tI3XgufewgekSACBABIkAEiAARIAJEgAgQgU2OwMTERLFr164MhaGdQ8WkvBmfdNqYGs5NjtRaYopK4Fj03/BW/lSQKLvCxWhS7JU6Dg8Pr1v9G5U9Xu3fiPYHtp/9z/EfTZ3HuP6th/mPtXZqn6zBcf1F3+I+AfpmcJvCUI+OtKf0Yi+WG1i4gt7bu1XSxosh6XCzt4PuXZjwadr7pDxsLObH1CyKCByqoC6k6jVjtHRbcBdJrNiMsfJjWpYTEbgVlI+z+wZ3DCYxJhKE9BaDpvJCBIgAESACRIAIEAEiQASIABEgApsZAd3kpecRy0Y2HJcihvGW6Olt+ajrYZ0puuZH5ixqyugy9N/eVqv4Ct7Ilzz2InjjZjMTZP5xkh+KqxXaBISxmE/5ikAGByJwy+j/kD8rJZRRvxqL+TE9iyICR/kBh/g4STHKgIrJ4gXyIokVmzFWfibBRRAk/g6QeIDXIhCvB/y3tXqLhYMH0/qLyjeuwbFV3eZtGkM9Om5oeKjYN7Wvow9bvb3S6TNFq6+lk7z2Vp3QZKjqDwYJygRPcxwlNQxuT0phKQM/OhbNa4zid8oQ4gaR37GTHhhpq/EBHvmI78ysUnghAkSACBABIkAEiAARIAJEgAgQASKA42r7+voUCOjLUI17RUefb8836MaSmCnVuUpuajV0+KPVv2EfgJHIu7bUpSXGI+r/K48/7S8bx/6DOZLmnA8fw/xj/7P/W3I/yI8v7yna7TlZg1sYcl3vNpWhHj8AsLNeX6HAapJcT9xZP1a9SgHjvDr5FVD7IQCyX4wCXySmSAw05E25mxYvy78B5fud9KEZaHxAql+M9DDi0xEBIkAEiAARIAJEgAgQASJABIgAEfAI9GBjmnOIzbXbyTCzmvp3z5awwc5ZBIq7RD/PaygVNAZX704mJMbad5n+z/b7jo/hjkHC/uf45/xXo+oy1r8tcj+w5RVTCuHN9J3LTWWoxxIJYz3ONZo9cAA9HYzwcQT0bu0tRuWDBSPu3CMk2cBIX3jPiSi25gLDomyWoLlcRIL13fSWGvwYy4k12Yhm3DHm2Cy/klxEgkcrP7yuOKgl4jcWoIUDdtv7+2mkVzR4IQJEgAgQASJABIgAESACRIAIEIE6AoODg8Xs7EzSI5E+Pb2/GBgQHdNUVvPrmY+j/jstm8t2SF3MwWS/fbtsOpO6pXpYYvJDxax65teSY9SlSvBo9e9UtgZCmVay+YknI7iIBCk/2CwMK0Mn+DGWE43V+Rl35zCx/JrDRSRI/Ik/bGbmbHQEP8ZyorE6P+Pe8ONvVtZg3A/gsP7Ku1FF/yazKW46Qz06G8b6gYGB+CoFpgUGtjgJ9l7Sq18XHh4a1rjNCU2Pl2waNDB0khxFg1VcnxVYFRr8irOqQaDFlAaGTpKjaLCKHw/5+NFiEynVEm0Rt/0x/fqDK8R4JQJEgAgQASJABIgAESACRIAIEAEikCMA/fzA7Kxo5maaqT4eGLTXqMNWqmwqoJPkKBqs4kfSf0fl+3U3y+Y95DC3c2iomJqckKgouVVRltxAckwarOJHkl9xpuJj+TGlgaGT5CgarOKUL73YYHexTqyQIv6GQMAkItMAUCfJUTRYxTn+OP6ONP/2yCkoY+PjNvzUH5I1eHJyMqN1c2RTGurRoZmx3hZqoSO4Vc6kG5bz7HFMTqDIwmKPOqs1JqzlKW4B8yWrC0pMXSJpIMVcotHgw8XKrVP5UxNTxcgNw6GtqK5z+LGFLzO3Wi1HZZAIEAEiQASIABEgAkSACBABIkAEiECFAPTGXbt2VQQJqXFmYjKoxBLPVWivN0NnNoYQtKtx1TKH5KxARAqxA4wUU1NTIT1eR0dH1TaQsSdlP0nYFPp/T2quBcwXsFzQAEwkDaRYSM5oiMCtb/sH2x+7KHW261MX1K6USyJpIMVCckZDBI79r6itU/vfaox/2GKn/LdFZUiMjY4VWIc3i9u0hnp0MIz1OAbnwAF5co+JgGVEvRAekl31k/rk3C0w2cjQlUUSo49szuEMpXDWHl7WCOfcabKw27wLK1fM7/LWg80ckbpG8gcGsFv+QFVVeTQmA0rjPO6mgoUhIkAEiAARIAJEgAgQASJABIgAEVgcAeyWHBmBoT7ok+DsPO5g5fVfbDLLP2JY7exPVoE10r8pf+X730ZolGTR6FO+Gsw4/mU8iPFvk9n/Vmv9OfwaXJuSXRrd1IZ69CmM9dg575+aY75hCYbD1+an5EdDv+wOz5wxCLMt4mG9kphY4cVmXTnjBcXTI4flT0KVJ1GrcnzIylwD+TiPfkgecHxlYUGqLBXAYzWtDyq++c6P8t3CMBEgAkSACBABIkAEiAARIAJEgAgsDwHo5X19fZopaJWii7d6i/n2fCholfRfGIkOqp4rYmNF2viobW8r1ENoqIomaUAuXa7/a8NXCf8AcsOV8gMoHH+cfzISunn96Wu1inlZg239xcDH/QG22c3iNr2h3jpdjfX79slN1t0BkChG6K1bW2LM3y0fmR0J6ZkV3g8V5MWU8a6iaUgupZSZ9tdXyZopRP1V+GOK5kH91lD+zPR0MbjjKsUlVRgBaTbs9XigMS08dESACBABIkAEiAARIAJEgAgQASJABJaKQE/PlqBU4vVz0YJx3b9fPijrPu6alwVN+fjq31u2SHlOPiTgTflmd/zlh5ZDWrQZrLH+r/aRNbQ/UL6MMeLfPP2Syd4nV3NSQ3LZyPa3zTj+9T4Q13/0Idb4sjzkO7nrwzTUxy7GExrsqofB3rvqRhlevRuV9MH+Af09oPdMYcZ9HEbq7DdCGFGRVkusRSt5LiEFY8B7FhZf67cK8rGTYXhkWI66mdXq4l5R/V7RWoQzBDfRBx6qfmOICBABIkAEiAARIAJEgAgQASJABI4FgYGBQfmg7Iya36yc/dgotkr694zI3iEPBaBum8ORrrPTM6p4byb932yganpYA/sD5csIhM0FHvHn/Iv2N7W8ycXGhK1TadECg00eS4zjx6KV7xJS0AabcEEmPCOJv9LyZ2em5cHsjqqKEuo8Ai1L7soIDfW1bsWxLoM7BsOITGk6HHVU9m7tLUbk7PrR8VFJxZMduSJZnI5fvYS4v4abesVsN3kd6cYoyWr0j/FFisoErYb8sbHxYnx8LM7K5Fmt1cdOh8FFdzpkrIwQASJABIgAESACRIAIEAEiQASIABHIEMD348KRtJX+PbRzqJjUD8oG7Xgl9V9sytszPi51quSP7pYPycqHDM1atZLy14v+n3UKIlqxlcef7Q8jj/jXEOD42zTrz7iswePje8KiE5dhbKbeTB+Sxeinob62BiCK3fUjIyMFjPbBxRGCiAZ75Iy63mJicm+Bp/7qsrtKFgnp6Wpp5sfsEjWDf/4AzD5EKwxmxU9ZUyDePE2Ioxsp+ZZm/uLy0X58cXlh4SvCBH5xsf0BkXAePT7802q1NJkXIkAEiAARIAJEgAgQASJABIgAESACy0UgfFB2RLIFbRP5h4bMUI+Y02FdMKODLXPGaH5I9AZ3K7bjQYGwTkxMFMOyUU+rZIwoIisuiyDVOUszPyQ1yY8pUjQOvRX+Vdb/KR8IrJ39hfgT/80+/mB/nJq6Teehzge5FUzslTVYHuJuJkdD/SK9jS+9Y0CM7xlfhCOQ8RoGnvAMDPTHG2pkz+7D1U1Wb8jKIjS1zHvGyId0e78kFmdejTv8XtBEu6FETs/obvJLkQ8D/die0WJ25oCJbfQHBngefSMwJBIBIkAEiAARIAJEgAgQASJABIjAshDAR1u3bduW5dkqG+QWZCMdXKbiJoOqJtUSo14tRp6l6L9agujfrVafbFJbiAUGb07q1NdqaWSl5cenAZl8L9OHK4NeZM8SJRKN/MtpP+XLgKk5D6sPE//jZ39SyNfQ/kX5gsA6wR8bgOtrsH7MO67BtenZtVEa6o/QtTiTHU/xFxYOCqcszVi7sUKLq4I9cm7S9gKvaeBjqvkCHtiNV5+MS846T8UVytZlT2+utQUwJBtTVQkIiK5etsWD76+WA34pbxDM6kMHO4c+Fa6Vjy0QD8f/YLcDDPV0RIAIEAEiQASIABEgAkSACBABIkAEjgcCPdjMVtM/p/fLucXQPXPFNokzshEs7jVfoxlPVVigTMvZyDsGr5KIcDr52YdkrRDzY2G1aK2aIbXOU5e/Fvp/aGhoBOXLewyrbH8h/phoHH9AgPOvlG+BHJBjyAey9RdrcXkIq+fmcjTUL6G/cRQODNPjel4dMshowROnhvHSK0/8Jyen5IfE9lByfJJ9ODH6lBsDUP6Fm4NwGzFQRSLkyTWK1h8PWmisB37QoD7iBRcrtwT5E3LmH9q2sDCvWbUoyY6iYikpvHPnznhuYJDCKxEgAkSACBABIkAEiAARIAJEgAgQgeOBAAzy2DjmddGJvZPF8MhQLF40VCipx6D/mqrt9e+x0dFiz549mf6LD8lOy9vm+WbTlZG/mvp/U/spX/oVY0pGgD96aCXsL8Tfpu/a2L+I//rEHzbJsWhztfV/+3b5mLd85HuzORrql9HjMNjjbCTbca6DBxdzWNsljN8NOMO+X75OPzwyXAzIDT4dQI/E/E4ff2jEjFaerR6JbDcMIdjNQ8tC3DvHZ+muLK2cyMfueTx8CB/rkfwit3r2YK0wP3xpGfytVssLY5gIEAEiQASIABEgAkSACBABIkAEiMBxQQBnwu/atSsra2hop3wfbqpDjQ5MS9N/VeG1UpOeLASovOKG5S36qX23SQiJwe0W4z3emj8e+ncShKIb5FeGckt27ToO+j/lx44m/jbAKl8xkUGpDyqMzPEXHtiEAaNhm7cgJedwsvQG+xvn35Hn3/DILrFPTiZkEcBHZHHU+GZzNNQfRY/DYD0hf38tT/oxF+Ew7CysBEeD0X677AwYESM/jsZxQ9RYNTcmP5zZ8W2e+5u2Miwy8U1+VX4qoWjLQ4ZZ2Q0AP33JXi3zoeZ6DcHQllhI/3bUe6gYkrrTEQEiQASIABEgAkSACBABIkAEiAARWCkEoGuPjIyE4kUnhcq6devWYt7OjlcVN+i5uMJF1VVCgQ6ahcwHLXfBwAYaZLS2hbOR8YY7UuAmJuOHZI9C/za55muB2SWXj0YkXi8PeXxcmQInrnDHo/2UT/w5/uJc8vMNE8zHOf/CgikLxvFef3r7WsVX5g+m9Rdr0qSc/oGjyDebo6H+GHpcDfbyxP/AgQNSCr7MjsXdljfvQ0hIxdE44BoYGNSvx/fJD4KtvS1NTVnBDucWBCutgydwokgTof58e76YkQcJ8/Nt2T0/I28BoI5gyh0emkJMlTmk4yO5eHsAf3REgAgQASJABIgAESACRIAIEAEiQARWGgF8SLDVanWI6figYE3/Tfqw5dT0GlOMGtXn0bPxLW/0Vabo6kFZryVaIXXf2JReS4xRo3r5mu0Y9P/OsqREPIGIdgj1Kd/DUNlQUp8JQDCQiDPkqoAxRd8Y6r6xKb2WGKNG7Sib/U/8N/H4W3QNbrgf2DTrVp+G+uPQszDY48+OxMHqXz2HFwFY67EaL+JgvMeu+1ZfX9EnPwT6BwfSmt0rgxJztXdr8FHE/PyCXMtiQXw8GGiLMR5h0IJR/q+XJR9lmoOBHjv/uYPeEKFPBIgAESACRIAIEAEiQASIABEgAquFwODgoOq10KNha8ab59PT+2Wz24BWIexFl0RJCQQwhqBdkZLs1CDWCWYUFfrY+Jj7Hh0yFsX2xyx+NvLxlg95Wj2rJnwhRJudxHJH+XYoCVATpwfJh6BdFU8DVXnk4geEARx5jDX5EiD+hmbuc/xx/Nk2ZR0Zx2H+jcrxNuHkD8xT+ZP5B9sk7Jub0dFQfxx7HWfYYyD5V/Xst0OHGBl8/rU6G4xpVMbBafk0mtFiRDwrp/Z4IBVlZWS+y4dXCfFjCB9vCDv+M05GiAARIAJEgAgQASJABIgAESACRIAIrAoC2AS3S46/gf3H9OmhoWE5BmGv6LhCNGtqTAbbYZ3wl2KkDcalTs5hKXvqtqkkCxz4uCyMR15Wsp4fZ/mZDAh35edhSViB9mcyKJ/4c/wFW1rHXOD8W6n1Z3iXrMHyHRJzWNPxjZDNeD49MKCh3kbCcfRhsJ+entYPtR6Q42ewzqnDaNNIDKQ41oFsD75jd0ygdmatiBZyWVww5bUAds3jQ7c83gbA0hEBIkAEiAARIAJEgAgQASJABIjAWiOAzW/YSKYuKrR4A70tx7uaPqybOG1XtFXYDIzqBy1czfORHrxA14IkiHL6WvF8eikH/LhO758OO/gRNRfLgU5/POUnEb49rpomXm0JoXqULziIMUsuCT10WxofkqiwLaX/Uwm+POIfhl0CR6IOX45/jr/jOf9arVZxUI49s/UXgw82VXuLKgzGzXOloX6F+xpn7N34khuLP3/v+7JBh/XO1n69m+BGUhHyWhmz+Uj14cjdSaooCOEs/IGBfv3Rsxk/yBBhokcEiAARIAJEgAgQASJABIgAESAC6xQBbHzr6+vLdV5RaJPxfFn1NuuiZLKg+ULKHgpYuSKrPIQDPqQKjteSl+e7AixovivIk+xoEaX5BMe/9KArwILmu0I8ifLVvM/+l/HB8Q8Q3ERZdtDNLAua78rypM02/zrWYOAtgJR4cLZJHQ31q9Dxo/LKxp49e3JJcfAZ0aJ1P08PqcYT0pr24ue/acCHJ1Q44wkOO+g365MpBYAXIkAEiAARIAJEgAgQASJABIgAEVi3CAzKefQzswekftUeS2w2m5yYzK2HMOiIgoz3082iZhujK4rafZK9zdPH5XibMTkC1uvYejby9IwVJ+XCxVzquRIkeCzys7JDZBES5WuHEH+BgeMvTU7Ovw2//oyNib10fI+usLYE4uSP6U16Pj0woKHeRsIK+jhXCee/Z3f/mrz0wwCvbsUnR6AhE5ZhXYw1gEjdpdxZQocJv8aG8+hhsMcfwun1wqwURogAESACRIAIEAEiQASIABEgAkSACKweAjinPn37LYqFztqW3fZBT87rYsb5ZFDX5GTFkxiU6aAQl2JZh9oN19vXVxyUMuECqae4de+EyB5KORAIxviqFM3gLkcr32rliuo8Cp/yib8MzjB6bZz6ESPjUsZIGNN+RLmBk0az7Nd2499zW4lWVkpzxSSaMUff8uQzxGWkfEEq9CDxr9ZfjKe+VnX0GDACbUK+R7KZj+imoT4uLKvl4TU+OHuNo0dWUxyPg7j5oLXbbfkR0lYaRmo46z5MbFwxx7EYxqAO5o5YYI/rgUVseYjckYyiWq2w6x7ysVsBBnw6IkAEiAARIAJEgAgQASJABIgAESACq4lAOP5mm4gMO+pN/9XjbwYHcnugasNQbJ2Dgiuk6GnAjO2Bqyzm5xeKvr6+KlMsArp4a2sr0P3uZVD+f/bOAK9tHYnDDnuOBbMHKfD2HAukB3lA9x4N9CCFcI9t4d1jy85/pJElx1Botw0hn35tJI1kyf48EtZkIkeDEXutJpOOy6JSYolx/8MJllpDBxIp0L8gOAr/CFQRN8KhWtynUs0S8K9JZjIeFUoGsJILJ/onCCKRQqCK2KVNpqlXSiyB/tUkE5k7e+/I/j/yHFxh/vrF5uC+Tyy38BND/Ybc9B0znj/Y/zt7aJBRf6mX1OZYDxKaUJf+08B8QVa3WPLH12hFmm81NNJQSanISaZ9sZLH/ZF5E5yWbXP8ED4gAAEIQAACEIAABCAAAQhAAAK/kMDh4VG3vL3JC1dZcR7coWxh3vZasaa1rIurZa2tZG0trNpDqOoOwk6/fP+gX77n9a+KfNsbbblgh9SGNTnJaYmtkFqr2oykYit9bv+qHaFu32XWFv2XO+ymDfgnbUnqFkpnskgqRv8Yf8+c/5I2pc91zT/aeuzCt71x5fWTkcOwXiS7zQFD/YbcfXm5K4Qn/mOnLa/8UOrFYmEPEzM36seDiv7Y+0T+WAOS5zoeVfV7+6mhHlxObI97tsl5CiBlEIAABCAAAQhAAAIQgAAEIPAzBMbb32hJvLu7Z57wd6Nma0ulFrA5rIizwKIH81zb7/fTL9jzmlfRx8tFd3pyGi2UWI5sY/N/KWwspc/vv2lvxVI2tK4U/cO/0ZdGPVYUPZWuiFv9b9pD/4Zv4hq2gRL9a/SlYbSiaAGt2BbTN6er+tf3tu3NX/duo9TMKXvlwrYe2+ZtbwQPQ31SoVf/OdsxjTWtfXj49uJzjS119DZl/ZcH/u3tbdOOBp3++DfBR0oeWzFqrI6Su9rf3n5yeHJ8gtG+gUYGAhCAAAQgAAEIQAACEIAABH6WQNr+Zt/Xn27i0ZLYEnJMOzCvS61LfQnra1VLuyD1mkxC6TNJctUsurdtb/r9PopSbMd/+c+XTgZ8tVVsl3UzOV1EkYg4t5iyrdBztSgLmj2rrVM3iNE//E0/ZqYHRcelW1l/ihpFImL0zwkkHC0Uz9WiLGD82YwjPbMgx+DfNf80296o86zrvvVY30uytQFD/Ybc+ud61D/3cmS8//z5sw/Iq6tP3c3yJk361oAGZmO0jz8OuUwVNacpxAtp9Y0Xe9onJnxCAAIQgAAEIAABCEAAAhCAwM8TODKDvG/7WjWl96ldLi5dIs/4tH7VOtZWqfV+MfkYNz6FJSrLTuen3dXlledi/Xtw8M4c25ZxlMVaCFchFsHV+vhH+0+tuqVw1EH0OS6zavSfWMG/6AL692Pjn/EnAuM5ps7X6UTr/z3/nJ6edLJFxvyrXtj2JrHGUJ917rVHs9mOnaIZyPW1/i8K8ljQQ9DCfu6nWA85brL3P4TVX8NIWjzsdd91fd/7wOJFtL/oBtEsBCAAAQhAAAIQgAAEIACBLSLg29+8nxebktaf+nW3fine93tGQovTUfAls33MRmWSm0hr6r/t7NjqWuWql45ffLy0bW9OLG8y/Zo9y3Pkdir/MiAOyJJR7+lcvd04Mteo+g9HvFzZKuS6Xof+4Y/+Mf7SvBGzSJo+8iTiRXU6zzGKJN6A+SdsnDH1acwvFh+3ftsb3T0M9aKwAWHHDPXyY/+Vhvoaw539FPDm5tr3/9ND0K29qDb86DVR+ENNfoBJeZ8NchMzf2jSfvZ42tdUSUMAAhCAAAQgAAEIQAACEIDAcwnImUzvR9P6NAzoWn/+6+S4kxFfaVucegjv4pQbPlNx8ryV9PzMXmD474t8nFrINR51irNyWQ29s9Ru7vKH+vfuqi8DlPRQtZ8lOaJ/+KN/jL9hVmimjJx57vz3Guaf8w9n3cX5B7sgTXpxNZZ6dA4ern0bUhjqN+Quxzfu61JcPSDJYK+HoVvzth+G0hMA85jzl9DaTxYx2j/BiiIIQAACEIAABCAAAQhAAAIQWCGgNeh8Pm/k2oJVexyPjXdagmqxWoxWWri6JTwtTrUf9Y7e/yZvexmFkrg7O/uzOz8/19E6OMWqYqmcc5kfEu2NCkvWEk/13zr6x1GKc6D/BAL+6J9pAuMvJoY8Zb2B+Wdmv2iq519d4dnZ2TAHD5e8lSkM9Rty23f019z+fftW/QFf07nXRvvl7dKfXjR5Tp3ZWC6j/eXVVSdvewIEIAABCEAAAhCAAAQgAAEIQOApAlp/7u/vpyp5gano+Nj2qr+6TAtRCXLQurRkY5GaBefn5sl5IU/OFCRWla9m9E9b6URzTSu59iiKKhHn4iarjEJzQnkDnaZiqqbPJH6kcKgWFYc4lzVHKqNA/4lDTbcBlYstSuJHCodqUXGImx6qjJLwr4Cg/65dj6jYW9e/8/MLm4MvXB80LHS9GiBfv36xObj33LZ/YKjfEA1Yt0f9Y5jipbR62Lm/v0vVhtGWD8uCSr67u+uDU172BAhAAAIQgAAEIAABCEAAAhCAwGMETk/n9uLBy6p41u3t7fqvvvfMuKOlpkKxfZnr+4M5u4U8CmfmTV8tS118bPvSX5nXfgnJbd4bG+14U3VQag99muh7/ZcTiooWr/ZhQjt3NbZaZp00F1X1Sf/wD/V4RP+L7qB/aRxNjjHG36+cf+SELPWrg78gvJ6D68ItTGOo35CbHsq8rq1vvoep8bK3rXFSiEeg/FLaaCTElu/7vntn3vXNg1HUI4YABCAAAQhAAAIQgAAEIACBrSfQeNVXNE7kVV8beLTWXAnJKnlo687l7a2V5gVpju6+fjWj/14+yoQTbaQWRg2PrU0qnji2mNKnjKfRpL4c8ED/UwzhP6FaoTKhQ4rRv5pGTmftYfytfnkZtH7T/HNg7xvR+y99TpSu2q1R9MXmYNkGCYkAhvoN0YTX6lE/ha822rf72eeRqKHoyTwqLdKDkV4SpH2pGKBTVJFBAAIQgAAEIAABCEAAAhDYXgLaQz62TKgpXF5+7E5O5sWbOpvlhiomuDFnsqOjw7wOHYr+tPXnRexNb+I4NsU51wqHg0uqqV3aGBXnbDRmWUuOPeajtGmxFZZmh0RTm/4NjMwNJQQ/F1QZS8K/ZRV0UpxzrbBgHRJNbfTPwKB/g3aEQuidl0d/HKVJtipmb/oKRk5iqF9l8iolm2SorwF+tW/GlvZQJC8HxatBU5gmdguW3Nvd6w4PefFsAsInBCAAAQhAAAIQgAAEIAABCIiAHMKODo+6u9hyNWPZNaev5fKm6/f6LAnLomUtebO8NiP9H7msXX8+/NcqhMji6shcP2S5ZKLCqqiSeHLIy3F1VvVXOsxVhpql+1zl8Qqrx1QSTw55+oc/+pfmhXrcxzgcRgrjLwgkJpnMBKBVUSXx5INtUaYvSo+syZj8huRr3TUkrn8dMYb6dVD/gT431VBfX6q+QZMHhB6iyi9rVGF1rJrBfrc7tIGsPexluCdAAAIQgAAEIAABCEAAAhCAwHYTkAPY/P08Wc8rFPqF9s31Tdfv95W0665tDfrPP44m15/Xn69tzXlg9asFaaTD1XrV5pSqh2WvxNZMVTdOoog8UXKpuJEpo5DPhf4LCkdiZBpcBXbFtEo6yjhGmfbgVNzIlFGAv8NC/4oqoH9pZDTD5QXj79bmYNn2SqimW9kID2xLMkJLAEN9y+PV5uQ9oND3vceb/KFrOZ3Pk4e9Wez1je6D/hBosHuU0/mP5PHxcTe3+hjsN/muc+4QgAAEIAABCEAAAhCAAAR+noDWhjLYayFpBg1vUMvJ3b/v+rpxfnrSfbOypRnu5/P3Vu4mprzWTOvPd+8Ozcvz+tGTyUeMyrPU+7QOtWytgjxDk4OdvcjWCkuxHRZ2z3Qq061XTcUZ1yJL079Dh7/pAvo3DLA0TBh/r2v+uTe7nzzp5/PTQV99jrSZzCbEg4N3Vn6Tbh6fDQEM9Q0OMr+TgLbFubr6ZF725023eqDRI0iEyOubNu1LiME+yBBDAAIQgAAEIAABCEAAAhDYLgJaR2obhfv7v+zC65Vj4iATeTKVD6WxppwiNS4r+ZKIdtWeCWfWp3c7qpAbp3/4o38+UsronB4p9bgaRmapWxJ1PRMy/jZ+/pFtDyP9oPPjFIb6MRHyv52APOw1SOfvzdvBvx3XKWhWVqSHoDRD61P7D8pQ7x4UXoEPCEAAAhCAAAQgAAEIQAACENgmAlpDal14f3/vK8cn7OYFS1pVlqytNU3ynfWntxvGeR2aBL5cDXthabckqj6q5Eox/cMf/csjRKPDwsj+w/hzKImLkm9g/sFIrxv5dMBQ/zQfSn8jAT1kXV9fu9e80gorDzP5fPq+7/R2aO1hT4AABCAAAQhAAAIQgAAEIACB7SJQG+uHK69WkJZMxvScUKUwdFmyqlkOn5KpsLGpx5GTlSuhJelfyDMIgYS/KHioNCVEoVklHwn0z1Sn6E4mNwWwJpjVDv3LIKRMheF65r+Dd3jSx5h+KsZQ/xQdytZCIAz28ppfLpd2DnkGbiM/NxnsF4uFe1Os5WTpFAIQgAAEIAABCEAAAhCAAATWQkDGeq0bLy4uSv952ej5yXQtVK1xPgRZvlI8eYx35x91/cl0LZxsK1doo6GDyWOG4rr5yXQtnGyr7Xhc3XuaFKZzqIsm07WQ/tG/sT4w/mxUmEU9c1nBMzlm0tgbF9XHlnRJ5GPG+VHHK8XjToauPVXXr9PaxlrOtoTvE8BQ/31G1FgjgaurK3/wKvtX+UhPL+bxfd8srxdRnJwcuye+DPcECEAAAhCAAAQgAAEIQAACENgeAtq3Xk5eMtovb5e2SPSFowMYvJFD1saRG2hVEk+urj8H19RcN9cbDGxDG/SvNbvoBpM2jhz8g0BFxJPoXyJivvGWkP2H8Reu8ZmFRxNpjToTr2P87e3tukPtp09X3r9e9kt4HgEM9c/jtPZa+vZJil17Cqz9pH7TCZQ97OfzJ3vs+94M9idusH+yIoUQgAAEIAABCEAAAhCAAAQg8CYJ7JhlSiYhDENv8vZyURCAwAYRmM127GzNzRZD/bPvGob6Z6Nab0WUu+tksNcXFfKSUMjfF5ZU5PfshbOqo5cLESAAAQhAAAIQgAAEIAABCEBgewjMzFCvteE3DEPbc9O5UghA4FUS0Hws693Dw7dXeX6v8aQw1L/GuzJxTngFDFCWN8vu7Pws718/yIdUMtmfnJp3/dl51/f9UEQKAhCAAAQgAAEIQAACEIAABN4sgdmOeXCakR4Pzjd7i7kwCEBgQwgkQz2/cHrJ7cJQ/xJaa6yLcrfwYzscedjf3d+lQvt9YzLRD7GM9GyH07IjBwEIQAACEIAABCAAAQhA4K0SYO38Vu8s1wUBCGwaAZyOX37HMNS/nNlajvCHDbNCP3zjBQz1Dbi/v+8Wi0X3wQz2YzK10f7dwYFvh4N3fU2PNAQgAAEIQAACEIAABCAAgbdFAMPQ27qfXA0EILC5BNIXp2x985I7+D8AAAD//9Ep3J4AAEAASURBVOzdCbglV10o+rVP5kAGhgSuQJ/TQS/4FHLRwAUe2Kcjl+sTNF4+UD8vpIcMGBSSMD2GJH06ERF9kOTqpybRdHeeyL3Cpwiilyl9Gpnh43qBBwjvpc9JlOkKJExCIGe/tap27eGMVX2Gqr33r5I+u4ZVtVb9Vu3aVf+99qpWOw7B0HiBVquVlVF1LV9Vc3NzYc+ePWF2djYmSFbpsC5e42gcn5zcFvbv3x927dqVZhgIECBAgAABAgQIECBAYMQE8nvnVmi3F0Zsz+wOAQIEhktALLN6fbUE6quj1bFGdnDHuHN7wfcqq/kfPHgwzMzsD3fOz2Wh+ixtf7w+zkiB+pTOQIAAAQIECBAgQIAAAQKjJdCaSDfO8X9tEkerYu0NAQJDJyBQX73KBOqrm9WyxkRsUd8WqC9ln1rXp0D8/mv3Zxdoy600OTUZDh44GKanp5dbbB4BAgQIECBAgAABAgQIDKGAwNAQVpoiEyAwkgIaHVevVoH66ma1rOFiozp76gZn9+7dYX5+Pl85ftGRN7PPRyanpsLu2Lp+Zmam+satQYAAAQIECBAgQIAAAQKNE3Dv3LgqUSACBMZUoNWaiHve9gunCvUvUF8Bq86kvoU6Nv0UpN83sy/cdvBQPDV0I/V9Y7rCOTZZaxEgQIAAAQIECBAgQKB5AuneOd35Lej6pnmVo0QECIyVgPNx9eoWqK9uVssaWdc38XLDA3GOjf9QDNTv3rO7t3IvZp/Nm5qcDAdidzm6wukRGSNAgAABAgQIECBAgMCwCWhRP2w1prwECIyqgEbH1WtWoL66WS1reCDO+tlT3/WpK5wjR44s2ViK22+Lwfo9e/aEffv2LVluBgECBAgQIECAAAECBAg0X0Cgvvl1pIQECIyHgPNx9XoWqK9uVssaWtRvDHv3QbP792cbjL+KzPqtb/dtPrWqP3DgQJiamuqba5QAAQIECBAgQIAAAQIEmi6QN3Lza/Sm15PyESAw+gJ5LDOG3XRFVrqyBepLU9WbMDu4Y1S5vbBQb0FGJPfUqj7v5qYTqe/sV5oKsYuhbZPbwkFd4XRUvBAgQIAAAQIECBAgQGA4BLTgHI56UkoCBEZfIH1x2ootYz0zpHxdC9SXt6o1pYuNjedfsSucTuw+tahP3eCk7nIMBAgQIECAAAECBAgQINB8gXSflwa/kM4Y/CFAgEBtAs7H1ekF6qub1bKGn+9tDns6aRw6dCjsn9kf2vG/gSEG7Ce3TWaB+pmZmYFFJggQIECAAAECBAgQIECAAAECBAgQILBRAgL1GyW5ydvRon5zgWdnZ7OA/Pz8fOz4JnYx1B+0jwH7a66+Juzv9Gu/uSWxdQIECBAgQIAAAQIECBAgQIAAAQIExk1AoH5IanzP7j1Z8Dj1m27YHIHUun7Pnj1h9shsnkG7E7LPusJphV27Lsz6rd+c3G2VAAECBAgQIECAAAECBAgQIECAAIFxFRCoH9eat9/LChw9Ohdbzs9k3eF0E8RAfSsG7VMr+x07doTU+t5AgAABAgQIECBAgAABAgQIECBAgACBjRIQqN8oSdsZGYGjR4/m/dYv19VNqxX7rd8WDh8+HLZv3z4y+2xHCBAgQIAAAQIECBAgQIAAAQIECBCoT0Cgvj57OTdcYH98gOzMtdeG0G7HXuvjEP/E0WyYnJzMWtZPTU3lM/wlQIAAAQIECBAgQIAAgdoFZuJ9XBqK12zCHwIECBDYcoH0rMd2DKQ5H5enF6gvbyXlGAqkZwKkfuuXDrFl/eS27AGzu3btWrrYHAIECBAgQIAAAQIECBDYcoFWayLmGTsuLVpZbXkJZEiAAAECSaAVe6VIg/NxxlDqj0B9KSaJxlngSOyTfufOnfFSLx/SaaYYTy3r0zeEgvXjfITYdwIECBAgQIAAAQIEmiKQAvXpCWMLAvVNqRLlIEBgTAVaEzGCFgNoAvXlDwCB+vJWUo6xwNzcXBasn5+bj+eYIkzfA9m3b5+f8vQ4jBEgQIAAAQIECBAgQKAWAS04a2GXKQECBJYIZL9wSrH6hYUly8xYXkCgfnmXxs11sVF/laRg/Y4dO8Kdd96ZFyaebFrt1FYjD9ynPrdSwN5AgAABAgQIECBAgAABAvUIuHeux12uBAgQWCzgfLxYZO1pgfq1jRqRwsHdiGoIKVifAvK3HTrUCc9n0fpuXzha1jejnpSCAAECBAgQIECAAIHxFHDvPJ71bq8JEGiewETs+ib1Qqbrm/J1I1Bf3qrWlC42auUfyLwI1h+KwfqBIT4kYyo+YHbfvpmwe/fugUUmCBAgQIAAAQIECBAgQGDzBSbifVn84XPsamFpl6Wbn7scCBAgQKAQyGOZKViv65vCZK1Xgfq1hBqyPDu4XWw0pDbyYuyPLetn9l+b+r/ptKiPFRTb2acHzKZW94L1jaouhSFAgAABAgQIECBAYAwEJuLDZFP3pFpwjkFl20UCBBotkGKZKVLm4d7lq0mgvrxVrSkd3LXyr5h5CsZnLevjmaeI16eTUBas378/7Nq1a8V1LSBAgAABAgQIECBAgACBjRXIW3DqamFjVW2NAAEC1QWcj4/BLH7L7Pdg1d22fA0H95aTl8owdYOzPwbkDx48GCP1cZW+d9O2bduyIP709HSpbUlEgAABAgQIECBAgAABAusTcO+8Pj9rEyBAYKMEWvEXTqlVq67IyotqUV/eqtaU2cVGDAQ7uGuthmUzT8H6fTP74gNmb1uyPLWsn52dDVNTU0uWmUGAAAECBAgQIECAAAECGyuQ9VEfN6lN4sa62hoBAgSqCvjitKpY+l7Dp1d1tRrWcHDXgF4hy6xlfeyX/mAWrI/N6rPW9XkT+6mpyXD48JEYrJ+ssEVJCRAgQIAAAQIECBAgQKCqQGsitXDz8MKqbtITIEBgowUm4vk49eMi9FxeVqC+vFWtKQXqa+Uvlfn8/HzYsWNHSK/9QwrXb9Oyvp/EOAECBAgQIECAAAECBDZFQIv6TWG1UQIECFQWyLq+8XDvSm4C9ZW46kvs4K7PvkrOR48eDTt37uwL1uet6otgfWp5byBAgAABAgQIECBAgACBzRFIjdzS/deCx/FtDrCtEiBAoKRA3ujYL5xKcmXJBOqraNWYVquAGvErZp2C8all/Z133pmvGS8Us9/6xKk0P/VZbyBAgAABAgQIECBAgACBjRdoTcSHF8Ygva4WNt7WFgkQIFBFQO8gVbTytAL11c1qWcPBXQv7MWeagvXT09N9LevjpvLG9WE6BusPC9Yfs60VCRAgQIAAAQIECBAgsJKAe+eVZMwnQIDA1gpodFzdW6C+ulkta2QXG6lh9kJ8CoNhKARSy/nUDU4nPj9Q5n379oWZ+PBZAwECBAgQIECAAAECBAhsnIDA0MZZ2hIBAgTWI5B/carrmyqGAvVVtGpMq1VAjfjryLoI1neb0/dt68CBA2H37t19c4wSIECAAAECBAgQIECAwHoE0j1Wun9O91sGAgQIEKhPII95tcLBg87HZWtBoL6sVM3ptKivuQLWkX0vWD+4kcnJyXDkyJGQXg0ECBAgQIAAAQIECBAgQIAAAQIECIyvgED9kNR99vM9Xd8MSW0tLeb+/fsHu7qJddlqt8K2yW0h9WdvIECAAAECBAgQIECAAAECBAgQIEBgfAUE6oek7nV9MyQVtUox009+Dh06FFN0eq3vvKT5fpa5CpxFBAgQIECAAAECBAgQIECAAAECBEZcQKB+SCq4aHU9NTU1JCVWzMUCqQ6np6fD/Px8EarvJEn9J94a8r67Fq9lmgABAgQIECBAgAABAgQIECBAgACBURcQqB/1GrZ/jRJIwfrt27d3G9UXhZuK/dQfOHgwC+QX87wSIECAAAECBAgQIECAAAECBAgQIDAeAgL141HP9rJBAgdjQH7Pnj0DJUq94GyLwfrilxMDC00QIECAAAECBAgQIECAAAECBAgQIDDSAgL1I129dq6pAvv2zYRrr90fWilC387+z4q6Y8eOMDs729RiKxcBAgQIECBAgAABAgQaLdCaiDdZ7VZotxcaXU6FI0CAwKgLTMSgVwx5xfNx+msoIyBQX0ZJGgIbLJD6qd+1a1c4cuR9ccu9E1aK21+zb1+YmZnZ4BxtjgABAgQIECBAgAABAqMv0MpaQwkMjX5N20MCBJoukL44bcWQ14JAfemqEqgvTSUhgY0V6PZXv2iz2ya3hf0z+z1cdpGLSQIECBAgQIAAAQIECKwlkAL1qQGUwNBaUpYTIEBgcwV8cVrdV6C+ulkta6R+zdOwe/fu7NWf0RBI3dzs3LkzXkimnwP1WtZPTk7FLnAOh6mpqdHYUXtBgAABAgQIECBAgACBLRBotSZiLvHuSgvOLdCWBQECBFYWEKhf2WalJQL1K8k0bL6Du2EVsoHF2b9/f5jZP9PtAacI2uuvfgORbYoAAQIECBAgQIAAgbEQmIhdLaQmUO2FXkOosdhxO0mAAIGGCWSxzPTYEOfj0jUjUF+aqt6EWgXU67+ZuR89ejSkYP2hQ7fFbOLFZPqdZhriA5AOHLjVryhyDX8JECBAgAABAgQIECCwpoBGbmsSSUCAAIEtEcgfJuvh3lWwBeqraNWYdiL+fC91jeLnezVWwiZmnfqrT13gzM3Nx1x6LT8mJydjFzizusDZRHubJkCAAAECBAgQIEBgdATSwwvTLZV759GpU3tCgMBwCvjitHq9CdRXN6tlDQd3LexbmumRI0fC9M7p/jh9ln96LsGBAwe2tCwyI0CAAAECBAgQIECAwDAK6GphGGtNmQkQGEWB7IvTuGO6vilfuwL15a1qTZl+LpK6RFnQr1Ot9bDZmaeg/KFDh2I2nZ7qU73HhyAdPnw4TE9Pb3b2tk+AAAECBAgQIECAAIGhFtDIbairT+EJEBghAd14V69MgfrqZrWs4WKjFvYtz3R+fj6kh8im1zTEMH3WEU7qAid1j2MgQIAAAQIECBAgQIAAgZUFBIZWtrGEAAECWykgllldW6C+ulkta+QPYEiNq3v9l9dSEJluusDBgwfDnj17BvJJAfvn7bowHDp4aGC+CQIECBAgQIAAAQIECBDoCaRAfXx0YVhw79xDGfOxP/iDPwhHjx4NJ554YnjWs54VHv/4x4+5SG/3P/OZz4Q///M/D9/5znfCcccdF37iJ/63GI/Y20tQYiz5poaFxx9/fHj2s58dzjvvvBJrSTIOAp4ZUr2WBeqrm9Wyhm+hamGvLdP0YNkjR2ZTrzfdYWpqKuurXhc4XRIjBAgQIECAAAECBAgQGBBw7zzAMfYT3//+98PJp5ySdSmbfrJ+ycWXhJtvvnnsXQqAFF84cuR9odVqd+MPH/nIR8ITnvCEIsmqr5nvySdnaVIDw4sv4bsq2JgtzH7hFA+M9sLCmO35se+uQP2x223pmi42tpS79szSt9HnbN8e24F0+qrvlOhnfuZn4ofokdrLpwAECBAgQIAAAQIECBBoooB75ybWSn1l6g8kp85lL7nkYoH6TnXceeedITUITD03pCB7EX944YteFG688cZSlXbvvfeGk04+KU8bGxpeeuml4aabbiq1rkSjL+B8XL2OBeqrm9WyhoO7FvZaM12uC5xUoH379oWZmZlayyZzAgQIECBAgAABAgQINFHAvXMTa6W+Mg0G6mOLei2+u5Xxute9LrziFa/oThcjZ511VvjiF7+YdWVTzFvpdbGvQP1KUuM5f2IidkQWv8DRjXf5+heoL29Va0oXG7Xy15J5alW/e/fuJS3o04NlZ2dns2++aymYTAkQIECAAAECBAgQINBQgez5bllXC339iDa0rIq1+QJ5IDl2fZM1GW/HFt+XxBbfur5J8j/5mJ8M/8+nPxPH0nslb1Of5qfhHe94R/j5n//5fGKVv5nvKbHrm87bzRchq2CN4aI8lpmC9bq+KVv9AvVlpWpOlx3cLjZqroWtzz51c7Mz9hmXfeb1fW6mAP6BAwe2vkByJECAAAECBAgQIECAQIMFJuLDZGNv21pwNriOtrJoKZB8SuxDvRNHbnSL+m9/+9vhF37hF8KXvvTlMDm5LQuWpwe0bsbwP/7H/wg/9VM/1Q3PX3nFFeH6G27oTv/ar/1aeOMb37hm1kta1F8Su765Wdc3a8KNSYIUy0yhLA/3Ll/hAvXlrWpN6eCulb/WzFNQ/tChQ90PzOyb7vigl9tvvz0G8XfWWjaZEyBAgAABAgQIECBAoEkCeQtOXS00qU7qLEsRSC7avTW5xXf6Vf327edErrzP+LvvuSecfvrpm8L3spe/PPxfv/d72bZPiQ/b/epXv5oF7r/whf83y/9+p94vfPkrXw73v//9V82/8C0CFk32XXVHLNwUAefj6qwC9dXNalnDwV0LeyMyTR/W6Uns8/Pz3fLELyXDtm2TIS0zECBAgAABAgQIECBAgEAukO6RUn/I27dvR0IgZIHk1DVLGmKz+iYHko8ePRrOOScF6vPhnk0K1C8sLITJbdvCP/3zP2cZPfvZzw5vfvObw9VXXx1+67d+q8g+3HbbbeF5z3ted3q5kW6gPlvoYb3LGY3zvCJmlR5abCgnIFBfzqn2VFmgPgZn2wvFD7ZqL5ICbKFA9mDZvXuyC4t4GHR/tqcLnC2sBFkRIECAAAECBAgQIECAwFAJFIHk4j66yYH6O+64IzzykT8afWPcJxb47m/cHc4444wN937ve98bnva0p+Xbjfn8xX/7i/Cc5zwn9lf/6dhv/WO7+T/9aU8P73zXO1fNv/AtEl2q65uCwiuBYxIQqD8mtq1fSYv6rTdvWo6pVf2R9x2Jn9l5H1+p38X0YNniG8qmlVd5CBAgQIAAAQIECBAgQIBAnQLf+973QurapXhY6iUxkHxzQ/tQT/f2Rdc3yWyzWtTv3bs3PvPuYMyhHe5/v/uF//Uv/xJOjv34p+Hcc88Nn/zkJ7Px1D/+XXfdFR760Idm08v9yQL12cNk869CPKx3OSXzCJQXEKgvb1VryhSkTT/fSw8XNYynwOKfwRUKu3btCqnFvYEAAQIECBAgQIAAAQIECBDoCXRbfOdx5LHv+ua73/1u+JEf+ZHsS4Ck9Ku/+qvhTW96Uxfsta99bXjVq17VnX7DG94Qrrzyyu704pF77703nHTSSd3ZTf7FQreQRgg0WECgvsGVo2gEFgvs2bOnG5TvXGfEVvVTsVX90cVJTRMgQIAAAQIECBAgQIAAgbEWyAP1qUV93o1wkwPJWYv6c7Znv6JP5f3mN78ZTjvttA2tv7/4i78Iv/Irv9Ld5l+99a3hly64oDu9uPudn/6pnw4f//jHu8sXj3S/CMkW6KN+sY9pAlUFBOqriklPoEaBvFX9I+Ov9uJFRn6dkZVm3759YWZmpsaSyZoAAQIECBAgQIAAAQIECDRLoNf1TV6uSy+9NNx00021FvLd7353+NSnPrWkDPPz8+G//Jf/krqnz27304NdTz3l1Djeu/lP3SKfd9554alPfeqS9cvMuCAG5d/2trdnOZx++unhq1/96kCL+LSNJz3xieHDH/lIZ3Ot8NnPfiY8+tGP7kwPvgwG6kNogu9gCU0RGC4Bgfrhqi+lJRCKVvXZh3f6E4fJbfqqzyX8JUCAAAECBAgQIEBgnAVSt6Cp29h032QgsDiQ3IQW9VNTU+HOGJTPw+/ZnX2sqOJ1+TrLlqY/cVirlXueaunfr33ta+Hf/Jt/E37wwx9kbf+e+7znhdtuu21JwhtvvDFcccUV3fmvfvWrQ/rSYLkh+Z4S+7cvvkpogu9y5TSvHoGim+bdu3fXU4AhzFWgfggrTZHHWyB/wMz2gY/x9Hm9L7aoTy3rDQQIECBAgAABAgQIEBhXgYnY4jgFDVOw3kAgD9THrm+yX6U3o2uWhz3sYeGLX/pi1my+PwDfaqdjt3PcxgX9P6TP0nWq84lP/PfhQx/6cOXK/eM//uNw2WWX5bGEuMG3/fXbwi/8wi8s2c6Xv/Sl8LCHPzy0Fxay0kxOTYWjd9wRUmv+xUPxRUhRPi3qFwuN93R+zMTjur0w3hAV9l6gvgKWpASaIpC+jTx06FBfcVphKn14Hr2jb55RAgQIECBAgAABAgQIjJeAwNB41fdae1sEklO6FGe+6KJLwi233LzWapu6PHVv8+EPLw20pwe9/vVf/3U372c/+9nhhBNO6E6nL5/S8f20pz0t7N27tzu/7MhTnvKU8IEPfCAmb4UzzzwjfOUrXwknnnjisquff/754fDhw91lf//3fx/S+ouHnm8eqteifrHQeE8XX+744rT8cSBQX95KSgKNEUit6s/Zfk7v2/ZOyfRV35gqUhACBAgQIECAAAECBGoQaE3EgGFslCwwVAN+A7PsBZLzwh1rIDk9hPVd73pX+NCHPxS+FFucP+7fPS484QlPCC984QvDj/zIj2zInmfPpHvkOdnxm5q933P3PSH1I78RQ/6Q2Pi8u86wZ++ecOuf3lpMLnm95ZZbwvNjf/7xq4G4rB0u+/VfD3/4R3+0JF3y7e/6Rov6JURjPUOgvnr1C9RXN6tlje1TUyH+CirMHZ2rJX+ZNk8ga1V/26H4c7j853Gt+AG6bXJbSEF8AwECBAgQIECAAAECBMZRQGBoHGt95X2+9957w0knn9S9b7744otji/pbVl5hmSWvfOWrwu/8zmtTQ/TudtL9d+qm5pxzzgnvfe97w1SM2ax3yAL1cXsxmyz+8817vhlOO+209W42W/+6664L11xzTb6tmME7//s7w9Of/vQVt/31r389688++aUdf+ADHxC+/OUvD7TwTyt3vwjJCh3CsX4RsmJBLBhqgex8HI+N9oKuyMpWpEB9Wama07nYqLkCGph90Vd9drUQLxDSkD4br5nZF2b2zaRJAwECBAgQIECAAAECBMZKwL3zWFX3mju7OJBctcV3erDqlfHBqumOO3UHkwLRj3zkI8Nf/uVfhte97nVZsP4nf+Inw6c+9ak1y7JWgiJQX6S7556Na1H/4z/+4+Fzn/tctukHP+jB4Utf/lI4/vjji6yWfX3mM58Z3vGOd+RfHMQUb33rW8MFF1wwkLbrG+emeMTF0efmm+vtWmiggCZqFWi1JmL+8SstzwwpXQ8C9aWp6k3oW6h6/Zuae39f9Z0vsMPk5KRW9U2tMOUiQIAAAQIECBAgQGBTBdK9c7o3WhAY2lTnYdl4ahF+8kkndZq2VW/x/ahHPTp8/gv/GFIw/iMf+Ug49dRTu7v+kpe8JLzhDW/Ipv/hH/4hnHvuud1lxzKyWV3ffPzjHw+Pf/zjO0WKD9S99OJw801rB9P/7M/+LDzvec/rtg1Mfea/+c1vHti1XqA+j0hccmkM1JfY9sBGTIysgPNx9aoVqK9uVssa+ZPrPSm5FvwGZzo/Px+2T8VukRaVUV/1i0BMEiBAgAABAgQIECAwFgJZI7e4p1pwjkV1r7mTWSD5lJNTo95suCT2u37zTTetuV5KkB74+uQnPSlb9U1velP41V/91YH1svvx+Oy42K9H+D9f8Yrw2tfG7nHWMRQt6vOQdyvcc8/dG9JH/ZUvvjLccP0N3ZK95z3vCT/7sz/bnV5p5Fvf+lZ46EMeEr77r/+aJTnllFOy7m/6+81Pvv191B9L10Ir5W/+8Atk5+N4QOv6pnxdCtSXt6o1pQfi1Mrf6Mz7W9UXBdWqvpDwSoAAAQIECBAgQIDAOAkI1I9Tba+9r70W33naX/qlXwqvetWrQjpO0r+FhYUwMTEx8MXOwx/+8PDQhz409kv/O+GVr3xltuIXv/jFrM/2xTlOTW0P8/Nz4RnPeGb4m795++LFlabzQH184GsrfqsQ/9+Irm/SF1bbtm0L//RP/5SVJfV5/5nPfCbr9mal/e8v9HOe85zw/ve/vzvrjW98Y/i1X/u17nThW/TZn3xf/epXd5enkZRP/xdnD3vYw5a1HFjJxEgIpLpPQ3/9j8SObeJOCNRvIu5GblqL+o3UHK1tpW/xp2Kr+nzIv3tP4/tmZmJf9fs6870QIECAAAECBAgQIEBg9AXyRm5+jT76NV1uD4tAckqdxQzzGPjSlfN4Yjb/CY9/QtbNzYtf/OLYEv368OCzHhy++tX/tXSdOOfpT/8P4d3vfk94Ymx5/6EPfnDZNGVnfu1rXwuPecxjwle/8pXw8Ec8IvzjP/5jOCl227Oe4a677soC9WkbZfc//54gjy1kLPFP0ZPUFbG//uujSTF0ffPkMZO4pF1MdCY7iYv8H/+E3LfYhtfRFchjmQL1VWpYoL6KVo1ps4M7ntXa8dteA4HFArt37wqHDt3Wmx2Plcn4rfnc3FxvnjECBAgQIECAAAECBAiMuIAWnCNewRV37wc/+EFIXbbcd999cc1eADltJk2lv/FRl9lY8edxj3tc+MQnPhGe+9znhtSC/Ed/9EfDF77whWLxwOsv//Ivhze/5S3hx2Kaz3/+8wPLmjBxxx13hB+ND7/N97Dc/mepiqh6tmJvvcsuuyz84R/+YXfXku/J0Xch8+3O7o3EVTs/EIjz8u0Uvr1ExkZVIH1xmurfM0PK17BAfXmrWlO62KiVv/GZp4D89u3b+8qZfwDefvhw2Dk93TffKAECBAgQIECAAAECBEZXIN07p7shgaHRreOqe3bhhReG9GDUZbvfyG+d8xByZ/wZz3hG7Mbmb8J//I//MbzrXe/KWrl/8pOfXDbb1A3Mm970X2NXLg8NqXucpg1pn5/47/99+OjHPrZ80ZbZ/+USprj9afc/Pbzpv74p/PzP//xAksW+aZNp6GtYn8/o/H3mM58Z3v729XUTNLBBE40VEMusXjUC9dXNalnDz/dqYR+qTKdjQP7IkSPFl9TZz9ouvHBXOHjw4FDth8ISIECAAAECBAgQIEDgWAVarYm4amwjXfTVcawbst7YC/ynZ/2n8Nd/9dYwFR8Ye8cd/9+yHs+78Hnhz/7vPws/+mOx1f3nl291v+yKZhIYAwGB+uqVLFBf3ayWNRzctbAPVaYpIL9nz56BMqeHyh6OreoHW9sPJDFBgAABAgQIECBAgACBkRGYiF0tpN462gt5Zx8js2N2ZMsFLrn44vCnf/qn4QEPfGBI/ccvN/ziL14QW4e/LTzpyU8KH/zA+vqoX2775hEYZoEslhl/YuF8XL4WBerLW9WaUquAWvmHIvP0UNnUqn4uPnE+XZmmn5u1499duy7Uqn4oalAhCRAgQIAAAQIECBBYr4BGbusVtH4h8IpXvCK87ndfF4477vjwnW9/e9kHu577784Nn/yfnwwXXHBBeOtb31qs6pUAgSiQP0zWw72rHAwC9VW0akw7EX++lx5w4ud7NVbCEGSdt6rfG0vajdSH1KreQ2WHoPIUkQABAgQIECBAgACBdQvk3cbGOyJd36zbctw38Ed/9EfhN17wgqwB3Dvf+d/D05/+9AGSb3zjG+Hssx8SfvjDH4SrrroqXHfddQPLTRAYdwFfnFY/AgTqq5vVsoaDuxb2oct04KGyeZP6bB9S9zeptb2BAAECBAgQIECAAAECoyygq4VRrt2t3bcvfelL4eEPf3hYWFgIF110UfiTP/mTgQK8/vWvDy996UuzeZ/73OfCox71qIHlJgiMu0D2xWlE0PVN+SNBoL68Va0p089FUl8mC/rZq7UehiHzFJDPHiobC1vE6nft8lDZYag7ZSRAgAABAgQIECBAYH0CRQOl2dnZ9W3I2gSiwNOe9rTw3ve+N7O4/vrrw/Of//xw8sknh7/8y78Mu3bvil3ifCc86Umxf/oP6p/eAUNgscDOnTuzXzc5Hy+WWXlaoH5lm0Yt0aK+UdXR6MIcOnQo7N69u6+Mrdj9zTbd3/SJGCVAgAABAgQIECBAgAABAmsJfPrTn47B+v8QvvKVL2dJjz/hhHDiCSeG7373O9n0OeecE97znveE7du3r7UpywkQILCmgED9mkTNSJA/gEE/e82ojWaXInV/k1qRzM/fGQsa+6qP7erjozvC7bq/aXbFKR0BAgQIECBAgAABAgQINE7g85//fLj00kvDxz72sRig/25WvrMe/ODw5P/9fw9/8Ad/kHWP07hCKxABAkMpIFA/JNWmRf2QVFRDipk/VHZPt+ubVKzUyv7AgQMNKaFiECBAgAABAgQIECBAgACB4RFIfdWnFvannHJK+LEf+7HhKbiSEiAwNAIC9UNSVanP8fTU+qK/vSEptmLWJJA9VPac+NO71KC+M2zbti22sp8vJr0SIECAAAECBAgQIECAAAECBAgQINAQAYH6hlSEYhDYaIH00I7FD+zQqn6jlW2PAAECBAgQIECAAAECBAgQIECAwPoFBOrXb2gLBBopMND9TSsWMbaun5ycig+VPdrI8ioUAQIECBAgQIAAAQIECBAgQIAAgXEVEKgf15q33yMvkHV/E588nz9Ktt3tr/6wh8qOfN3bQQIECBAgQIAAAQLjKrB9KnYBGhsqHT2qgdK4HgP2mwCBZghMTU2F9MxN5+Py9SFQX95KSgJDJ5CeaZCeb9A/7Nq1K6TW9gYCBAgQIECAAAECBAiMmkAKCqUhPeOtynD33XeHG2+8MZxxxhnhiiuuqLKqtAQIEBhpgRtuuD7cffc92bnxzDPPLL2vx3o+Lp3BCCYUqB/BSrVLBAqBovubbDper7biteqFu3bHQP2BIolXAgQIECBAgAABAgQIjIxA1cBQCtDfcMMNWZD+njh+zb59YWZmZmQ87AgBAgTWK5DOifv37w8pSH/55ZeXDti3WhNZIKq9UO2L0/WWd5jXF6gfktpLAdd0wZFaQxsIlBVI3d+cE7u/KU6JqW3JtsnJ2E/9XNlNSEeAAAECBAgQIECAAIGhEZiI983p/metFvXf+MY3suB8akWfgvXFsE+gvqDwSoAAgUygCNRnEzGwdOYZ5QL2Vb84xZ2+11jr04tSIwQc3I2ohqEsxM6d02F29n2x7Hm4Pv0S9PbbD4fULY6BAAECBAgQIECAAAECoyTQmog3PO0YrG8vLLtbRRc318dW9KkFfWrMVDRsSiukgFQK1hsIECBAIBdI58Vrr90fz6v9Z8xWDNifHl50xeXhyiuuzFrbL/aaiOfj1AuZ0PNimZWnBepXtmnUEoH6RlXHUBWm2099itB3rkB37bowdn9zcKj2Q2EJECBAgAABAgQIECCwlsBKLeq/cXdsQX/DjeH6668P3/zmNwc30xd70qJ+kMYUAQIEUrc3KVg/OPROnCt1iZN1fRMDUQL1g3KrTQnUr6bToGUO7gZVxpAVJQXk9+7Zk8Xoi9PopO5vhqwWFZcAAQIECBAgQIAAgTICqZFbuu9Z6DxMttsHfWxBf/c996yyic7dUvbSP56v0pkTJ/Kx3nT/JgeX9dL0xvpTD4530mQv/eN5qt4W8rHedP9WBpf10vTG+lMPjnfSZC/94/JPAj3BfKw33a84uKyXpjfWn3pwvJMme+kfz1P1tpCP9ab7tzK4rJemN9afenC8kyZ76R+XfxLoCeZjvel+xcFlvTS9sf7Ug+OdNNlL/3ieqreFfKw33b+VwWW9NL2x/tSD45002Uv/eLX884dxvyj2YZ+3sM8bHa/8C6fBMphKAgL1Q3IcrNQqYEiKr5g1CszNz4ftU1NLSjA7Oxt27NixZL4ZBAgQIECAAAECBAgQGFaB1kR8eGEM0qc+6NNDYq+/Ibagv2dRC/r+sFuMSaWYfic0le12/3i2pJX6blhJZDB1fLhcvsGVki/KoUjev5X+cflHDf6OP++/Fc4og2eLppx/8oD9FdkDaFPBtahfofqWmS1QvwxKE2fp+qaJtTI8ZdqxYzq87++PZB/uxWk8PZhY9zfDU4dKSoAAAQIECBAgQIDA2gLFvXP6FfF8bLRU3P8MrBln9mK/RYridSBldyJbmv4UQ19wf9k1OzNXWib/IvZcCBWvBfDga7Y0/SkG/t1je1m5zsyVljn+HH/5dx/FEVK8Fm+wwddsafpTDBXffwL1BdzarwL1axs1IkV2sRHfFO2F/K3UiEIpxNAIpID8ntj9TW9ohZ/Z8dRwZPZIb5YxAgQIECBAgAABAgQIDLlA8Wv0okV96pc+6/Imi0ymSFM7D3Dmo73xuKSVHkJbNJ2Py3vBzDierZnj9I/nc3rLl1uWLZV/pM91sr8dqO44f8ef99/InH8GW9Tr+qb4nCjzKlBfRqkBaYpWAb6FakBlDGERUkuSqampgZLrp36AwwQBAgQIECBAgAABAiMgkN879wJDeR/1N8ZucNJDZO/JurnphdWPdYfzfvDzoH4n4rxmKD/mGpPmXecX68j/2AT450dQashZHEvFaxLtH+8JO/68/zb7/JMC9FdeeWW4/PLLQ3rArFhm7/1XdkygvqxUzem0qK+5AkYg+xSoTwH7/kE/9f0axgkQIECAAAECBAgQGHaB1kQMUsb45eJGbilgf+ONN4brr78+3NN5qOxy4cyZmZmwb9++YWdQfgIECGyYQDov7t+/f5ntpS/N2uGMGJR/0YtelAXpU4C+GATqC4nyrwL15a1qTZn9fC9db+j6ptZ6GObMp6enw5EjR/JdiMdS+lnnhbsu1E/9MFeqshMgQIAAAQIECBAgMCCwVmAodYmTAvbp39133xPXjVH9eH+UXtKQgvQpKGUgQIAAgVwgC9Rfu7/bPVMeno8B+tiC/oorrsj+9QfoCzeNjguJ8q8C9eWtak251sVGrYWT+VAI9Pqp71yFxpddF3qg7FBUnkISIECAAAECBAgQIFBKoOy9c2phf33sDueG62+IXeJ8sxOrb8VA/TUC9aWkJSJAYFwEskB91qI+jyetFaAvXFqtiTgaOwnL+9wpZntdRUCgfhWcJi3yLVSTamM4yzI3Nxe2b9+eFb4Tqg/6qR/OulRqAgQIECBAgAABAgSWF0j3zul+Z6FkYCh1g5O6w0n/UsB+30xsUb9vZvmNm0uAAIExFCgC9Yv7oF+Lour5eK3tjcNygfohqeX8yfW9B+IMSbEVs0EC/YH6/p92Hj58OKRucQwECBAgQIAAAQIECBAYdoGyLeoX72cRsE/dN6SuHAwECBAgkAvccMMN2bM9iofElnXR6LisVC+dQH3PotFjKz0Qp9GFVrjGCQz0U98p3YEDB8Lu3bsbV1YFIkCAAAECBAgQIECAQFWBYw3UV81HegIECBBYXcD5eHWf5ZYK1C+n0sB5WtQ3sFKGsEipn/q9e/YUz0kK8VehYfeu3eHWGKw3ECBAgAABAgQIECBAYNgFZmdns13wq+Fhr0nlJ0Bg2AXS+Tj1T79z585h35UtK79A/ZZRry+jLFAfo6rthYX1bcjaYy2QPVB27574pO70OI80tGI/9dtC6hbHQIAAAQIECBAgQIAAAQIECBAgQIBAPQIC9fW4V87Vz0Uqk1lhGYG5+fhA2antA0smt8VA/fz8wDwTBAgQIECAAAECBAgQIECAAAECBAhsnYBA/dZZryun6Z3TWRPo4md869qYlcdaYHJyMtx5550DBh4oO8BhggABAgQIECBAgAABAgQIECBAgMCWCgjUbym3zAjUL5A9UPZ9R4q+b7ICCdTXXy9KQIAAAQIECBAgQIAAAQIECBAgML4CAvXjW/f2fEwF9s/MhJn918a9j73Ut/KXXbt2hdR/vYEAAQIECBAgQIAAAQIECBAgQIAAga0XEKjfenM5EqhV4MCBA2Hv3r2dGH0eqReor7VKZE6AAAECBAgQIECAwAYJFA2Qdu/evUFbtBkCBAgQOBaBFH9Kw549e45l9bFcR6B+LKvdTo+zwNxcfKDsOdtDKzaoj/9nw+TkVJibOzrOLPadAAECBAgQIECAAIEREGi1YmOk+H97objbGYGdsgsECBAYQoHWRGoc6nxcpeoE6qtoSUtgBATm5+fD1NTUkj254447wvbt25fMN4MAAQIECBAgQIAAAQLDIpAF6mNh222B+mGpM+UkQGA0BVqtibhjbefjCtUrUF8BS1ICoyKwY8eO8L73vS/bnfT9ZrqEnZ2dDWm+gQABAgQIECBAgAABAsMqIDA0rDWn3AQIjJqAL06r16hAfXWzWtZILaDjd1Bhfm6+lvxlOloC0zunw5HZI52dykP1hw8fDtPT06O1o/aGAAECBAgQIECAAIGxEkiB+la8d17Qon6s6t3OEiDQPIGs65vYMtQvnMrXjUB9eataU/oWqlb+kcs8PVjp0KFDWd+NWXP6uIczMzNh3759I7evdogAAQIECBAgQIAAgfERcO88PnVtTwkQaLZA9gun2Da0vbDQ7II2qHQC9Q2qjNWK4mJjNR3LqgocPHiw89TtvDV9Wn/Xrl0hzTcQIECAAAECBAgQIEBgWAXcOw9rzSk3AQKjJuB8XL1GBeqrm9WyhoO7FvaRzbQXqI+72InVp/7pUz/1BgIECBAgQIAAAQIECAyrgHvnYa055SZAYNQEJiZiR2S6vqlUrQL1lbjqS+xioz77Ucx5bm4ubD/nnNRRWGilnyG1W2FycltI8w0ECBAgQIAAAQIECBAYVoGJeIMTb29iVwsxOmQgQIAAgdoE8lhmCtbr+qZsJQjUl5WqOV12cLvYqLkWRif7+fn5MDU1NbBD27ZtC2m+gQABAgQIECBAgAABAsMqMBEfJttO/3mY7LBWoXITIDAiAimWGUOZHu5doT4F6itg1ZnUwV2n/mjmvT0G6uf6A/PxBHr0jjuWBPBHc+/tFQECBAgQIECAAAECoyiQt+BMvxrWon4U69c+ESAwPALOx9XrSqC+ulktazi4a2Ef6Uynd06HI7NHuvuYvuW8/fDhMD093Z1nhAABAgQIECBAgAABAsMk4N55mGpLWQkQGGWBVvyFU2jFXzjpiqx0NQvUl6aqN2F2sREjqQ7ueuthlHLfuXNn/vDYFKHvNDY5LFA/SlVsXwgQIECAAAECBAiMnUDWR33cay3qx67q7TABAg0T8MVp9QoRqK9uVssaDu5a2Ec60z179oSDBw/GfSwi9a1w4MCtYffu3SO933aOAAECBAgQIECAAIHRFWhNpBZuHl44ujVszwgQGBaBiXg+Tr2Q+eK0fI0J1Je3qjWlQH2t/COZ+a7du8Jth24b2LcDBw4I1A+ImCBAgAABAgQIECBAYJgEtKgfptpSVgIERlkg6/rGw70rVbFAfSWu+hI7uOuzH9WcZ2Zmwv79+wd2b9++fSHNNxAgQIAAAQIECBAgQGAYBVIjt/Sb4QUPkx3G6lNmAgRGSCBvdOwXTlWqVKC+ilaNabUKqBF/RLM+dOhQX+v5vPubXbt2dbrDGdGdtlsECBAgQIAAAQIECIy0QGsiPrwwBul1tTDS1WznCBAYAgG9g1SvJIH66ma1rOHgroV9pDNN/dOnfuqzIcbp44O4w67YP33q/sZAgAABAgQIECBAgACBYRSYnp7O+kQ+cmR2GIuvzAQIEBgZgZ3pfBz3ZnZ2dmT2abN3RKB+s4U3aPtZoD4GU9sL6RA3EFi/wOHDh8P55/9s3FA7f5xsPL5+5qk/E44cObL+jdsCAQIECBAgQIAAAQIECBAgQIAAAQKlBQTqS1PVm1CL+nr9RzH39I3m+efvzFqbxPb0cRfbYceOHb7pHMXKtk8ECBAgQIAAAQIECBAgQIAAAQKNFhCob3T19AqnRX3PwtjGCMzNzYXt27cPbGxycjKk+QYCBAgQIECAAAECBAgQIECAAAECBLZOQKB+66zXlVPRn1Pqb89AYCMEBgL1eYP6MDk1FeaOHt2IzdsGAQIECBAgQIAAAQIECBAgQIAAAQIlBQTqS0JJRmDUBPoD9a3Y9U07/rdtcluYn5sftV21PwQIECBAgAABAgQIECBAgAABAgQaLSBQ3+jqUTgCmyfQC9TH5vSt+JDi+L+ubzbP25YJECBAgAABAgQIECBAgAABAgQIrCQgUL+SjPkERlygF6gvHiUbwlTso/6oPupHvObtHgECBAgQIECAAIHRFUiNjyYmJsJRXXqObiXbMwIEhkJgKnavHDtwiF0szw1FeZtQSIH6JtSCMhCoQWDZQP32qXD0Dn3U11AdsiRAgAABAgQIECBAYAMEWq0YFYpDux1/MmwgQIAAgdoEnI+r0wvUVzezBoGREZiYiBexea833X1yQdulMEKAAAECBAgQIECAwJAJtLJ7nPgMrvbCkJVccQkQIDBaAhPxi9P0lak4U/l6Fagvb1VrytnZ2Sz/6enpWssh89ESmJnZH3dosKXJzMzMaO2kvSFAgAABAgQIECBAYGwEtOAcm6q2owQINFwgfXGaHom44BdOpWtKoL40Vb0JXWzU6y93AgQIECBAgAABAgQIEGi+QLp3Tp3fCAw1v66UkACB0RYQy6xevwL11c1qWcPP92phlykBAgQIECBAgAABAgQIDJFAqzURS9vW1cIQ1ZmiEiAwmgIC9dXrVaC+ulktazi4a2GXKQECBAgQIECAAAECBAgMkUB6DlfWJ/LCYBefQ7QLikqAAIGREMhimenRiM7HpetToL40Vb0JtQqo11/uBAgQIECAAAECBAgQINB8AY3cml9HSkiAwHgI5A+T9XDvKrUtUF9Fq8a0E/Hne/HHe36+V2MdyJoAAQIECBAgQIAAAQIEmi2QdxsbW3B6eGGzK0rpCBAYeQFfnFavYoH66ma1rOHgroVdpgQIECBAgAABAgQIECAwRAK6WhiiylJUAgRGWiD74jTuoa5vylezQH15q1pTpp+LpEfXL+jXqdZ6kDkBAgQIECBAgAABAgQINFdAI7fm1o2SESAwXgK68a5e3wL11c1qWcPFRi3sMiVAgAABAgQIECBAgACBIRIQGBqiylJUAgRGWkAss3r1CtRXN6tljfwBDPrZqwVfpgQIECBAgAABAgQIECAwFAIpUB8fXRgW9FE/FPWlkAQIjK6AZ4ZUr1uB+upmtazhW6ha2GVKgAABAgQIECBAgAABAkMk4N55iCpLUQkQGGmB7BdOsRvv9sLCSO/nRu6cQP1Gam7itlxsbCKuTRMgQIAAAQIECBAgQIDASAi4dx6JarQTBAiMgIDzcfVKFKivblbLGg7uWthlSoAAAQIEVhRox5/Uf+ELXwjf//73wyMe8Yhw5plnZmnn5ubC4cOHw3e+851s/vnnnx9OO+20FbdjAQECBAgQILBxAu6dN87SlggQILAegYmJ2BFZWzfeVQwF6qto1ZjWxUaN+LImQIAAAQLLCPz5n/95+M//+T+H0GqFx5/30+EjH/louPrqq8Pv/M7vhPvuuy/2jxsvSuOfndM7w+23377MFswiQIAAAQIENloge75b1tVCjA4ZCBAgQKA2gTyWmYL1ur4pWwkC9WWlak6XHdwuNmquBdkTIECAAIGewK0Hbg0X7b0ozmiFxzz2J8P5O88PN954YzdBK3uUXTs8+UlPCh/44Ae7840QIECAAAECmycwO3skfYceduzYsXmZ2DIBAgQIrCkwOzsbz8ct5+M1pXoJBOp7Fo0eSwd2vNbw5PpG15LCESBAgMA4CRw4cCDsvWhvaKWfc8YdT5/TWdu9OJI+tY8//vjwgx/8IJz3+PPCxz76sXGisa8ECBAgQIAAAQIECBAgUFFAoL4iWF3J85+L6NepLn/5EiBAgACBxQIHDx4Me/bs6YvQ5yn27t0bXv/614dTTz01vO997wsnn3xyeMpTnrJ4ddMECBAgQIAAAQIECBAgQKArIFDfpWj2yPT0dPZzkfRwOgMBAgQIECBQv0DRoj5vRp+X5+JLLg633HxL/YVTAgIECBAgQIAAAQIECBAYKgGB+qGqLoUlQIAAAQIEmiKQBepj6/l8aIVzH/uY8JGPfjScdNJJTSmichAgQIAAAQIECBAgQIDAkAgI1A9JRSkmAQIECBAg0CyBxYH6D33og+GJT3xiswqpNAQIECBAgAABAgQIECAwFAIC9UNRTQpJgAABAgQINE2gCNQXD5Gdn58P27Zta1oxlYcAAQIECIyVwOzsbHq4W5jeuXOs9tvOEiBAoGkC2fk4Fip1520oJyBQX85JKgIECBAgQIDAgED3YbKduQL1AzwmCBAgQIBALQKtiYksUN+OwXoDAQIECNQn0GqlJk3plOx8XLYWBOrLSklHgAABAgQIEOgT6H+YbLoEnavYov7IkSPhqquuCl//2tfCK1/1qvDc5z63b+tGCRAgQIAAgWMREBg6FjXrECBAYOMFJmKgPoXoBerL2wrUl7eSkgABAgQIECDQFUiB+oviw2SL9iFlW9Tffffd4ZWvfGW46aab8hZ/Mcq/f2Z/uOaaa7rbNkKAAAECBAgcm4DA0LG5WYsAAQIbLZB/cRqD9e2Fjd70yG5PoH5IqnbHjh0hHeBF/05DUmzFJECAAAECIyuQ91F/UUit6dvxvzKB+r/5m78Jz3/+88OXvvTF1H1uHPK1r7322nD11VePrJUdI0CAAAECWyUgMLRV0vIhQIDA6gJ+4bS6z3JLBeqXU2ngvOzgjvfy7YWi3V4DC6lIBAgQIEBgjASKh8kWu1wmUH/WWWeFf/mXfwnnnntu+P3f//2QvohPPwUVqC8UvRIgQIAAgfUJtCbSjXP60Zp75/VJWpsAAQLrExCor+4nUF/drJY1sp/vCdTXYi9TAgQIECCwnEARqG/FVvFlW9RfcMEF4WlPe1p4wQteEI477rgwcdxE9iX8tddeF1vUX7VcNuYRIECAAAECFQQEhipgSUqAAIFNFNDouDquQH11s1rWcLFRC7tMCRAgQIDAigJFoD4lSB3YVH2YbFrvuInjwkLss3H/tbGP+qv1UZ9MDAQIECBAYD0C7p3Xo2ddAgQIbJxAqzURNxabNPmFU2lUgfrSVPUm9C1Uvf5yJ0CAAAECiwWKQH0K0qcf15fp+mbxNnot6vVRv9jGNAECBAgQOBaBdO+cPpsXBIaOhc86BAgQ2DAB5+PqlAL11c1qWSN/cr0nJdeCL1MCBAgQILCMQBGoLxYdS6D+uNj1zUJ8/ow+6gtFrwQIECBAYH0CWtSvz8/aBAgQ2CgBjY6rSwrUVzerZQ0PxKmFXaYECBAgQGBFgbe85S3hOc95Trb8xBNPCHfd9U/h7LPPXjH9cgsmJmIf9bHF33XXXReuukof9csZmUeAAAECBKoICNRX0ZKWAAECmyfgfFzdVqC+ulkta2hRXwu7TAkQIECAwKoCX//618P3vve9cMYZZ4T73e9+q6ZdbmERqNeifjkd8wgQIECAQHWBvJGbX6NXl7MGAQIENlYgj2XGbkJ1RVYaVqC+NFW9CbODO/a1115YqLcgcidAgAABAgQ2TECgfsMobYgAAQIECGQCWnA6EAgQINAMgfTFaSs+zMszQ8rXh0B9eataU7rYqJVf5gQIECBAYFMEjjvuuNhH/YI+6jdF10YJECBAYBwF0r2zh8mOY83bZwIEmiYgllm9RgTqq5vVsoaf79XCLlMCBAgQILCpAlrUbyqvjRMgQIDAGAq0WhNxr9u6WhjDurfLBAg0S0Cgvnp9CNRXN6tlDQd3LewyJUCAAAECmyrQDdTv3x+uvuaaTc3LxgkQIECAwDgITMSuFmJPC7Hb2PTXQIAAAQJ1CWSxzPgTJ+fj8jUgUF/eqtaUWgXUyi9zAgQIECCwIQLf//73w7333pttKz1U6cz4ENp2/IH+VVe9Orz85S/P5p9wwgnh5JNP3pD8bIQAAQIECIybgEZu41bj9pcAgaYK5A+T9XDvKvUjUF9Fq8a0E/Hne/HHe36+V2MdyJoAAQIECKxX4Nxzzw2f+uQns+B8+ll+1olu1uAv9aabt/x79KMeHT77uc+uNyvrEyBAgACBsRSYnp7O9nt2dnYs999OEyBAoCkCO3bsyIpy5MiRphSp8eUQqG98FeU4PEpZAAAsxElEQVQF1CpgSCpKMQkQIECAwCoC5z3+8eETH/94JyQfE8YH3sVv4fsC9iH8xE/8RPj0pz+9ylYsIkCAAAECBAgQIECAAIFRExCoH5IaTT8XSa3uFvSzNyQ1ppgECBAgQIAAAQIECBAgQIAAAQIECBAoJyBQX86p9lRa1NdeBQpAgAABAgQIECBAgAABAgQIECBAgACBTREQqN8U1o3f6MGDB+PP5Nthz+49G79xWyRAgAABAgQIECBAgAABAgQIECBAgACB2gQE6mujlzEBAgQIECBAgAABAgQIECBAgAABAgQIEIi9nrfjAIIAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoR0Cgvh53uRIgQIAAAQIECBAgQIAAAQIbLDA9vTNusR1mZ2c3eMs2R4AAAQJVBHbu3BlS+3Dn4/JqAvXlraQkQIAAAQIECBAgQIAAAQIEGiww0WqFdiuG6hd0HtDgalI0AgTGQKAVz8exM5cYrF8Yg73dmF0UqN8YR1shQIAAAQIECBAgQIAAAQIEahaYaE3E9vTxP7381lwTsidAYNwFUqA+heoXnI9LHwoC9aWp6k145MiR7EJjenq63oLInQABAgQIECBAgAABAgQINFQgb8EZW9QLDDW0hhSLAIFxEXA+rl7TAvXVzWpZw8FdC7tMCRAgQIAAAQIECBAgQGCIBNw7D1FlKSoBAiMt0Iq/cAqt+AsnXZGVrmeB+tJU9SbMLjb0s1dvJcidAAECBAgQIECAAAECBBotkPVRH0uoRX2jq0nhCBAYAwFfnFavZIH66ma1rOHgroVdpgQIECBAgAABAgQIECBQg8Dv//7vhx/ed1/49ec/P5xyyimlS9CaSC3cPLywNJiEBAgQ2CSBiXg+Tr2Q+eK0PLBAfXmrWlMK1NfKL3MCBAgQIECAAAECBAgQ2EKBV7/61eG3f/u3w0Me8pDwspe9LLzgBS8oFbDXon4LK0lWBAgQWEUg6/rGw71XEVq6SKB+qUkj5zi4G1ktCkWAAAECBAgQIECAAAECmyBw1VVXhdf89mtiU8x84w85++zw0hiwv+yyy8L97ne/FXNMjdxim/qw4GGyKxpZQIAAga0QyBsd+4VTFWuB+ipaNabVKqBGfFkTIECAAAECBAgQIECAwJYKXHP1NeG637ou5hnD7vFhhEXA/qyzzgoveclLwm/+5m8uG7BvTcSHF8Ygva4WtrS6ZEaAAIElAnoHWUKy5gyB+jWJmpHAwd2MelAKAgQIECBAgAABAgQIENh8gWuuiYH661KgPh9SK/nY9Xx3eNADHxReGgP2vxED9qeddlp3vnvnLoURAgQI1Cqg0XF1foH66ma1rJFdbMSLkvZC53d/tZRCpgQIECBAgAABAuMiULRGTa/p38LCQnfXi3lpRprfnzZNp2vXIn2RtthGWlakT2mK9Yv5RfrUfLbouaJIVxSgl2Zp/v3bTumLtOk1bafIJy3rbjdl1ClXtn6cTlfd/dtKaYuh2GaaTvOLdOk1dbeRYolF+iJttmxx/u24//fF7ZbJP6VJ/+JQbDONL84/LSvmp9cibZZ/zC91CtJNE9MuxId1Zttda/8X5Z/WSTktzn/V/d/M/DP03H/x/qeyptuohfZ9y+9/WqFzr1XYFG7FtrJuVJJBHLI0i/d/Pfmn7aYH7sVC5lmk1/zfivl33h9d/07+98X1stHO8VqUddX978s/VWo71lPa02zd+JryyOo1lak7Py9rL/+YayddUeb0up78v/Wtb4Xvf+97WZ5pW2m/Uv5p6B9/4AMfGF78kheHF/7mC8Ppp58eKaNlTFOUP1vBHwIECBDYcoF0zZXO2OlzxVBOQKC+nFPtqfKD28VG7RWhAAQIEIgC6cYv3Zimc3P/a3FDmF6LfwmsmF/czPZPF8tXSl9sv1hevPbPT9sotr3W9or1i3XWSt+fT7Fueu2fX2wrzV9re8U28nWy2+iuVf/6q21zffmn+sgvFBeyL797dbVc/v37WZR9K/PvL9Pm5J97FNsu6m9F/xj6SIGkfpcV0/YdD700i/03Iv/0Pryv+z4r9qXfbsPy77y3B/f/2PLPy7n6/vfn079f/fPTzc/K+9+7MUrrpCrJjv8UT7uvHX543w9jfa7nxinvBzoeFZG4CJsVr0m9fzxN50O6Z8sPj+WXF+nWfpV/Lsi/d6z1H1P9472jyfE3uu+/BzzgAeHKK68MqSV+OiaKz/te7RsjQIAAga0UEMusri1QX92sljWygzteaza9RX1xE1kg3Zda6MSh/2a5SLPSvGJ+uqEshv51Fo8X2++fX6y3nvzTjWu67UlDse10V5lakxTT6bV/eTG/uC1oRP59x01RvuJ1ubKnfUx7tVyaY9r/eDdUBAGKbRavx5p/cWyk7SzdRnZZnu1D5l82/7hG9v4qtf8ZUDwW4jHa8S32KRWpFfvQTK9NyL8bSI4FTeXtlXM5uzhvzf3vvJ9jujL7Xzr/wj+jTXWx0vtvi/JPDqkS4/DDH/4wq+e8a9T83Z21RsyOsixJ359FS4qTQV+KlUaXTVrMjK/yj3XfCfotUu4jXbSk8OtLsdLoskmLmfwdf+n06PiLb5+kED/X439Lh0VLivfP0oRL5iybtJjp/ef95/3n/HOM59/iWm7JSccMAgQIENgSAYH66swC9dXNalkj+/levFGZmpzKWrEVrQBTYbILkCyoFFtHdQJx2fx0G5X9fDK/mSoCdMU6aRuplUG2fudmKAtCpwB5MZ2CZSlYlbbfPy+t27lJK7a76Pasu42U35IhbqsVN1hso8ivu1Inr2K9bHJgXmcivhTbkX+fZ4Ib8CokO699btmcbtrOSHc6T59NDszrpeOfuzv+HH/d81l62wy8X/L3UfdvXFa8b7J53bS991Xn9Npb3E2TZvXSFdtx/Dn+HH99geOB90v2Nur9icuK9002s5u2977y/hvg6l7/5XN7ToWj84/zj/OP80/3rNE9p3bn9EbisuK8kc3spu2dV9Z7/j39jNPD5S+6vNuvfXYf2yuBMQIECBDYYoFhaXS8xSyrZidQvypPcxYW30L1xWf6rmO6Vzn54t5kkTzfke78pRdDS26y4hp5qu5KA9sYnNubysZ6k/KPat1L965LZ6Q7nawX3eTxd/xlx07fQZLegUsPnd7MYqxvlb7R7rrdkb6Fjj/vv4EgS3EsFQdcnM4Gx192Qu9760SW3lQ21pvsW9KfrJNgIJ3jz/HXvVLoe6v1HSRp7tJDpzezGOtbpW+0u253pG+h87/3n/ef9186xRRDfnroO0mkBZ3Jwbm9qWysN1kkzzfZnd8Z6U6nzW7c++/+p5+W9U//0pe+NKTub4p7Z4H6oma9EiBAoB6BVmsiZhyvNjq/Vq+nFMOVq0D9kNRX8S1UN+rbd5FT7MLSWb05+dji6b418z4VihmDr8VqxWta2j/eSb10Vm9OPrZ4usgmzpd/X0S/cFkE2+Pj32+xiKmn10uUjy2eLlLG+Y4/x9/gfXpxcPTea73Dpzevl2qZU2JvhXxs8XSxcpzv+HP8Of6KN8Tga/G2KV7T0v7xTuqls3pz8rHF00U2cb7336rvv4kskBaZYjdy6UZrIj7sMg1FEGxiIt185dPFwxu76WLSibhOui9LfYL3py3Wb8XtZf/FBGl5cROXxotlxfbTOv3bSPP7p+vOv7tPhdMW7//K+cfuihbZddNuoH93m0v2f6vz7x0nxTG14v5Hl7SsWL6e46+3//Iv3pfr8u/Uyxve8Ibw2te+Nr3d44kk/lv0eXn/+98/vPCFLwwpQJ8eKFsMc3NzWb1OTk4Ws7wSIECAQA0C2edszDd7IHsN+Q9jlgL1Q1Jr+c1HfnUyeI2Sbm8Wt4VZ9jpmcE8HN9K97ilmF6/FSvl0/9/eEvnzX3TN3D2eiqNkyeuiA6yYXPxarNd/5BVp8mWOf++/et5/6eYzuynvRKCKgFIKUKU6iQvj/3nrgeOOOy4LPmUXKWl+JzCS3cDGAzkLc8X56TyfrRfntRYFv9L8NHTTpPSdoFa23W6wpT//fJ2tzT/Ps9jXVObF+adyp2Gl/S+Wpde0nWKfs/kdo7RuWpaCKoP7v3b+i/3TGkvyWcN/M/LP9iPtXxwKo2Jees3md/Y/K29//cf5RdqUrn/8uIl4/MX/inkr7X+xPMunb//75xfj6TWrv8L/WPNfYf2V9n8Y81+yL/3v/y3Y/yX5d+qqqL8TTjghOzZSvRsIECDQNIHXvOY14aqrrlp0b9EK97/fqeE3fvM3w8te9rLwoAc9qGnFVh4CBAgQ6AgcPHgw3pyEsHvXbiYlBQTqS0LVnSzdWKfYz/JDuoFfunBJCHP5ZNkmu4viDVzW9CnOTfPS32zLq7U467t0ym8I862lVlRpUXFjnY3HP6k1Vh7AypflucTxGBiYiAGt+FVbJ5CVlne20dn/tK00ZPnE0YlWTN8XgMgDOikIlIISKZDTl3+cmwUX0k1qXxAizs72YHH+aX53H5bJP9/2Rucfy54eEZDyO5b8s/WK/e8Fbgq3oi76b9yzjFJeyT/+S4Gv/uXZOn37X2xj2f3fgvwzl1R/Wf2nfc3rNZWrP6DXK2fuMJBuUf2nZWX3v0i7XP5Fnv15LTev3zelTcOq+SfsOKRtpaFYv3gt8iheU5pivHjtn9e/XpqfBvmvfPynQLOBAAECBAgQIEBgawWKQH2R66mnxgD9b/xGFqA/66yzitleCRAgQIDAyAgI1A9JVaYAZAw9h7m5o0sCcCkQl4YUaCuCrKn1XBZ4y1p09gJ8i4N2KU3/+v3bKcYXr1NMF68pXbGNNG4gQIAAAQIECBAgQIAAAQLrESgC9fc79X7hshf8egzQvzycffbZ69mkdQkQIECAQKMFBOobXT29wk1NTaVoeJg7erQ30xgBAgQIECBAgAABAgQIEBhBgRtuuCHcdddd4eUvf3l4yEMeMoJ7aJcIECBAgMCggED9oIcpAgQIECBAgAABAgQIECBAgAABAgQIECCwpQIC9VvKLTMCBAgQIECAAAECBAgQIECAAAECBAgQIDAoIFA/6GGKAAECBAgQIECAAAECBAgQIECAAAECBNYhMDs7mz3TcseOHevYynitKlA/XvVtbwkQIECAAAECBAgQIECAAAECBAgQILCpAhPxWZvtEP+1FzY1n1HauED9KNWmfSFAgAABAgQIECBAgAABAgQIECBAgEDNAq0YqE9Du92uuSTDk71A/fDUlZISIECAAAECBAgQIECAAAECBAgQIECg8QKtiU6gfkGgvmxlCdSXlao53fT0dPyxSAiHY/9OBgIECBAgQIAAAQIECBAgQIAAAQIECDRVoNWaiEVra1FfoYIE6itg1ZnUz0Xq1Jc3AQIECBAgQIAAAQIECBAgQIAAAQJlBcQyy0r10gnU9ywaPZY/gEG/To2uJIUjQIAAAQIECBAgQIAAAQIECBAgQCBkXd/EXm/0UV/+YBCoL29Va0rfQtXKL3MCBAgQIECAAAECBAgQIECAAAECBEoKZF3fxH682wsLJdeQTKB+SI4BgfohqSjFJECAAAECBAgQIECAAAECBAgQIDDmAmKZ1Q8AgfrqZrWs4eCuhV2mBAgQIECAAAECBAgQIECAAAECBAhUFJiYaMVub3R9U4VNoL6KVo1pBeprxJc1AQIECBAgQIAAAQIECBAgQIAAAQKlBfJYZgrW6/qmLJpAfVmpmtNlB3fWr1P8KspAgAABAgQIECBAgAABAgQIECBAgACBhgqkWGYMZYaF1KzeUEpAoL4UU/2JHNz114ESECBAgAABAgQIECBAgAABAgQIECCwtkDeol7XN2tL9VII1PcsGj3m4G509SgcAQIECBAgQIAAAQIECBAgQIAAAQIdgVZrIoRWO7QXtKgve1AI1JeVqjldFqjX9U3NtSB7AgQIECBAgAABAgQIECBAgAABAgTWEtDoeC2hpcsF6peaNHKOg7uR1aJQBAgQIECAAAECBAgQIECAAAECBAgsEpiYSA+S1fXNIpZVJwXqV+VpzkKB+ubUhZIQIECAAAECBAgQIECAAAECBAgQILCyQNb1TYhd33iY7MpIi5YI1C8Caeqkg7upNaNcBAgQIECAAAECBAgQIECAAAECBAj0C0xNTWWTc3Nz/bONryIgUL8KTpMWTbTiz0VigXwL1aRaURYCBAgQIECAAAECBAgQIECAAAECBAisX0Cgfv2GW7IFXd9sCbNMCBAgQIAAAQIECBAgQIAAAQIECBAgsOUCAvVbTn5sGWaB+lZsUb+Q2tUbCBAgQIAAAQIECBAgQIAAAQIECBAgQGBUBATqh6QmDxw4EFKwfvfu3UNSYsUkQIAAAQIECBAgQIAAAQIECBAgQIAAgTICAvVllKQhQIAAAQIECBAgQIAAAQIECBAgQIAAAQKbJCBQv0mwNkuAAAECBAgQIECAAAECBAgQIECAAAECBMoICNSXUZKGAAECBAgQIECAAAECBAgQIECAAAECBEoJTE9Ph3a7HY4cOVIqvUQhCNQ7CggQIECAAAECBAgQIECAAAECBAgQIEBgwwTSszZbcWsLMVhvKCcgUF/OSSoCBAgQIECAAAECBAgQIECAAAECBAgQKCGQAvUpUt9eEKgvwZUlEagvK1VzutnZ2awE6WcjBgIECBAgQIAAAQIECBAgQIAAAQIECDRVIAvUx8Kl7m8M5QQE6ss51Z5qIn4L1Y5fQ7XbC7WXRQEIECBAgAABAgQIECBAgAABAgQIECCwkkAeyxSoX8lnufkC9cupNHBednCnYP2CQH0Dq0eRCBAgQIAAAQIECBAgQIAAAQIECBDoCLQmYh/1sTG9PurLHxIC9eWtak3p5yK18sucAAECBAgQIECAAAECBAgQIECAAIGSAmKZJaH6kgnU92E0eTR9CxXaur5pch0pGwECBAgQIECAAAECBAgQIECAAAEC8Tmy6WGycdBHffmjQaC+vFWtKR3ctfLLnAABAgQIECBAgAABAgQIECBAgACBkgJZLDO1O17wMNmSZLGrIF9rlLWqNV2rNRHzb/sWqtZakDkBAgQIECBAgAABAgQIECBAgAABAmsJ5A+T1TvIWk79ywXq+zUaPD4RA/UxTC9Q3+A6UjQCBAgQIECAAAECBAgQIECAAAECBHR9cyzHgED9sajVsI6ub2pAlyUBAgQIECBAgAABAgQIECBAgAABApUFsudtxrV0fVOeTqC+vFWtKdPPRUL8f0G/TrXWg8wJECBAgAABAgQIECBAgAABAgQIEFhdQDfeq/sst1SgfjmVBs7Tor6BlaJIBAgQIECAAAECBAgQIECAAAECBAgsERDLXEKy5gyB+jWJmpEgfwBD/LlI25OSm1EjSkGAAAECBAgQIECAAAECBAgQIECAwHICWdc3MYwplrmczvLzBOqXd2ncXN9CNa5KFIgAAQIECBAgQIAAAQIECBAgQIAAgWUEsq5vYjfe7YWFZZaatZyAQP1yKg2cJ1DfwEpRJAIECBAgQIAAAQIECBAgQIAAAQIElgiIZS4hWXOGQP2aRM1I4OBuRj0oBQECBAgQIECAAAECBAgQIECAAAECqwscOnQwdnsTwu7du1dPaGlXQKC+S9HsEYH6ZteP0hEgQIAAAQIECBAgQIAAAQIECBAgQOBYBQTqj1Vui9fLAvVZv04eJrvF9LIjQIAAAQIECBAgQIAAAQIECBAgQIDApgoI1G8q78ZtfPv27ekxyeHo3NzGbdSWCBAgQIAAAQIECBAgQIAAAQIECBAgQKB2AYH62qtAAQgQIECAAAECBAgQIECAAAECBAgQIEBgnAUE6se59u07AQIECBAgQIAAAQIECBAgQIAAAQIECNQuIFBfexUoAAECBAgQIECAAAECBAgQIECAAAECBEZHYHb2cNyZVpienh6dndrkPRGo32RgmydAgAABAgQIECBAgAABAgQIECBAgMA4CbRaE3F32/GRm+1x2u117atA/br4rEyAAAECBAgQIECAAAECBAgQIECAAAEC/QKtVitOtmKgfqF/tvFVBATqV8GxiAABAgQIECBAgAABAgQIECBAgAABAgSqCeSB+timXov60nAC9aWp6k24e/furAAHDx6styByJ0CAAAECBAgQIECAAAECBAgQIECAwCoCE7FFfer0RqB+FaRFiwTqF4E0dTL7Fir+YqS9oF+nptaRchEgQIAAAQIECBAgQIAAAQIECBAgEDu90fVN5cNAoL4yWT0r+LlIPe5yJUCAAAECBAgQIECAAAECBAgQIECgmoBYZjWvlFqgvrpZLWtoUV8Lu0wJECBAgAABAgQIECBAgAABAgQIEKgoIFBfESwmF6ivblbLGlm/Trq+qcVepgQIECBAgAABAgQIECBAgAABAgQIlBfQ6Li8VZFSoL6QaPirb6EaXkGKR4AAAQIECBDYJIH0AK4vfOEL4d577w0Pf/jDw5lnnpnldPTo0TA7Oxu+/e1vh22T28LO6Z3h9NNP36RS2CwBAgQIECBAgACB8gKt1kRM3PYw2fJkWtRXsKo1qW+hauWXOQECBAgQIECgNoE3vvGN4bnPfW78LWwI5/30eeGjH/1ouPrqq8PrXve6cN8Pfxhvf/Jh586d4fbbb6+tnDImQIAAAQIECBAgUAikWGa8fA0LsdGJoZyAFvXlnGpPlXV9Ew/vdnuh9rIoAAECBAgQIECAwNYJHDhwIFy0d28WkH/sYx8bzj///HDjDTd0A/RFSZ785CeHD3zgA8WkVwIECBAgQIAAAQK1CWh0XJ1eoL66WS1rtCZSB/Xxf99C1eIvUwIECBAgQIBAXQIpUL83BuoHh9Q+qR3SDdAJJxwfu8X5QTjvvPPCxz72scFkpggQIECAAAECBAjUIJAF6mO+Ypnl8QXqy1vVmlKL+lr5ZU6AAAECBAgQqE2gaFGffjtctNlIYfq9F10UXv/614eTTz45vP/97w8nnnRieOpTnlpbOWVMgAABAgQIECBAoBDIY5kC9YVHmVeB+jJKDUiTHdyxxVR7Qdc3DagORSBAgAABAgQIbJnAwYMHw549e2J+eSv6lPHFF18cbrnlli0rg4wIECBAgAABAgQIVBFIvYO0Yu8g+qgvryZQX96q1pR+LlIrv8wJECBAgAABArUJ3HrrreGi2Ho+DbHdRnjsY84NH/3YR8OJJ55YW5lkTIAAAQIECBAgQGA1AbHM1XSWXyZQv7xL4+bmfdR7mGzjKkaBCBAgQIAAAQKbLFB0fRMbJGWB+ve//wMhPTjWQIAAAQIECBAgQKCpAgL11WtGoL66WS1rOLhrYZcpAQIECBAgQKB2gf5AfSrM/Px82LZtW+3lUgACBAgQIECAAAECKwlMTU3FRiatcPTo0ZWSmL9IQKB+EUhTJ1utiVi0ticlN7WClIsAAQIECBAgsEkCKVC/96K96VIw66Z+fk6gfpOobZYAAQIECBAgQIBAbQIC9bXRV8t4IgbqY5heoL4am9QECBAgQIAAgaEX6LWojx3Uxydy3Tl/Z3jEIx6x6n7dfffd4U/+5E/C2972tvD5z38+/Ov3/jX82x/7t1mXOddcc0140IMetOr6FhIgQIAAAQIECBAgsLUCAvVb633Muen65pjprEiAAAECBAgQGGqBXqA+3421ur759Kc/Hc7fuTP8r6/9S4rrZw3xQ4zxpyFNP+jBDw4f/vCHwyMf+ch8pr8ECBAgQIAAAQIECNQuIFBfexWUK8DMzEyWsHgtt5ZUBAgQIECAAAECwy6QdX2zN3Z90xnWCtSnlvSXXHJJeHAMyO+N6z3ucY8LX//618Pv/u7vZv3bp8383M/9XPi7v/u7YpNeCRAgQIAAAQIECBCoWUCgvuYKkD0BAgQIECBAgACB1QR6gfrULD52fXPn6l3ffOITn4hd3rw9vOQlLw6nnXZad9P//M//HH78x388fOtb3wonnnBC+M53vxuOP/747nIjBAgQIECAAAECBAjUJyBQX5+9nAkQIECAAAECBAisKVC165vVNvjMZz4zvOMd74hJWjHgP79mX/erbcsyAgQIECBAgAABAgQ2TkCgfuMsbYkAAQIECBAgQIDAhgv0WtTnm16rRf1qBXjGM54R/vbv/jbruP6b3/zmQIv71dazjAABAgQIECBAgEAVgd27d2fJDx48WGW1sU4rUD/W1W/nCRAgQIAAAQIEmi5w6623hosuuigvZuz9Zn5uPmzbtq1ysb///e+Hhz70oeHuu+8JU1OT4ejRo5W3YQUCBAgQIECAAAECZQRardRtY2wf0m6XSS5NFBCodxgQIECAAAECBAgQaLBA1qL+or3xwj1rCJ89EPZYAvW33HJLuPTSS7M9fdWrXhVe85rXNHivFY0AAQIECBAgQGCYBSYmWjFIL1BfpQ4F6qto1Zh2bm4uy31qaqrGUsiaAAECBAgQIEBgqwXe8pa3hOc85zkx21Y44YTjw1133RUe8pCHVCrG1772tfCoRz86pNezzzorfPaznw0PfOADK21DYgIECBAgQIAAAQJlBfIW9SlYv1B2lbFPJ1A/JIdAdnDHX4y0F/xcZEiqTDEJECBAgAABAhsm8PWvfz1873vfC2eeeWY49dRTK233hz/8Yfi5/+Pnwu3veW9IV5L/7b/9RfjlX06BfwMBAgQIECBAgACBzRFIsczU+c2Crm9KAwvUl6aqN6GDu15/uRMgQIAAAQIEhlXg+c9/frj55puz4r/spS8Nv/t7vzesu6LcBAgQIECAAAECQyKQt6jX9U2V6hKor6JVY1oHd434siZAgAABAgQIDKnAtdddG2au2Rdb0rfCr/zKL4c3velNobiuHNJdUmwCBAgQIECAAIEhEGi1JtLTUfUOUqGuBOorYNWZNLuh0vVNnVUgbwIECBAgQIDAUAlcf/0N4cUvvjL7yfHO888Pf/u3fxtOOumkodoHhSVAgAABAgQIEBhOgaJxSFvXN6UrUKC+NFW9CR3c9frLnQABAgQIECAwTAK3Hrg1XHLxJWFhYSE8+clPDu985zvD/e9//2HaBWUlQIAAAQIECBAYYoGJifQgWV3fVKlCgfoqWjWmFaivEV/WBAgQIECAAIEhEnjHO94RLvjFXwz3xSD92WefHd7+9reHM844Y2APUsumBz3oQeGss84amG+CAAECBAgQIECAwEYIZF3fxA4YtagvrylQX96q1pQO7lr5ZU6AAAECBAgQGBqBZz3rWeGv/uqvsi5vYiOm7mv/WNqZcx/72PAP//N/Ds1+KSgBAgQIECBAgMDwCOSNjlOr+oXhKXTNJRWor7kCymY/0YoHdkzsW6iyYtIRIECAAAECBMZT4LLLLgt/fNNN6cJxVYDzznt8+NjHPrpqGgsJECBAgAABAgQIHIuA3kGqqwnUVzerZQ0Hdy3sMiVAgAABAgQIECBAgAABAgQIECBAoKKARscVwWJygfrqZrWskQXqW7Fh1MLqLaNqKZxMCRAgQIAAAQIECBAgQIAAAQIECBAg0BHQ9U31Q0GgvrpZLWtoUV8Lu0wJECBAgAABAgQIECBAgAABAgQIEKgoIJZZESwmF6ivblbLGlrU18IuUwIECBAgQIAAAQIECBAgQIAAAQIEKgoI1FcEi8kF6qub1bJG1q+Trm9qsZcpAQIECBAgQIAAAQIECBAgQIAAAQLlBTQ6Lm9VpBSoLyQa/upbqIZXkOIRIECAAAECBAgQIECAAAECBAgQIJAJzMzMhHa7Hfbv30+kpIBAfUmoupP5FqruGpA/AQIECBAgQIAAAQIECBAgQIAAAQIENkdAoH5zXDd8q1nXN6EVv4la2PBt2yABAgQIECBAgAABAgQIECBAgAABAgQWC9x4443hnnvuCZdffnk444wzFi82vYECAvUbiLmZm2pNpA7q4//xJyMGAgQIECBAgAABAgQIECBAgAABAgQIbLbAvpl94dr914YzzzwzXHHFFdk/AfvNUReo3xxXWyVAgAABAgQIECBAgAABAgQIECBAgMBQC6S+5lM/87EJcWpDHB7wgAdkretT0F7AfmOrVqB+Yz1tjQABAgQIECBAgAABAgQIECBAgAABAiMhUATq0860Uocfnc4+shb2V8YW9pcL2G9URQvUb5Sk7RAgQIAAAQIECBAgQIAAAQIECBAgQGCEBPoD9Ut3qxVb1Z8errzyyiVd4szNzWXJp6amlq5mzrICAvXLsjRzZvHGKH5q0v8tVipxMb8Y6S3vLllxx1rpQbXZD1j6knRWy156f4rND3yLJn/+3aOsOG7ia/4ta3dJ38E1OOr48/5z/ln0/JHifRTfKu1sPJ9RvJt65/f8vVTML07QveXdJYNvur4p7z/vP+8/77++U0L3giY7e/T+FKcX13/RpGhFlty6Z9nOiPNv4dOVGTi8+id8/vj88fnj86f/nFCcULOzR+9PMdvnTzTx+dM7YrqfMp0Rn7/F8dGV6WEtGtvMz9/FXeK0UsXEwfM2F1XCKpMC9avgNG1R6g9q/8z+WKwllzRZUZd9s8Ul2QkrpYjRnu7l4OL3bt90X6psu91PxrStzsSiS4os3ZL18rXlH20zL/6Ov+K92/d+67x5OgdJ/h4beIf3pfX+yzGcfzon174X59/up1ufis8/n/8+f11/xFOC6y/XX66/8nuRvmvK7MOyb3rJdcSiZels6vpr4BKjQ+j6Y7kjw/WH6w/XH64/HnDmA2Mf9i8K7373u8MJJ5wQDh8+vPQkas6yAgL1y7I0c2bRor4TK08RvdCKZ8Dioql7PdUZyV+6c/OdipP5OgOJOpvsm5dSFzc22ezOsjS/GI2v8ufv+Etvit7bonh/5G+T4s2Sp+m9ZzvzB176JlJy77/IFS/9M5aOTXIpRuOr84/zj/NPelP03hbF+yN/mxRvljyN809xzui4DLz0TSQu51/nX58/Pn+z00Ln3JDOC8VofHX94frD9Ud6U/TeFsX7I3+bFG+WPI3rj+Kc0XEZeOmbSFyuP1x/jOD1x9S2yXB4djZMTU11Tgpe1hIQqF9LqEHLU4v6FKzPT+fxb3aVuFwb274TfrqK6Ewu3pXudlKov5umO9JLHme14odG8W15dz35dz5M08VqcbmW2DqGBWXx2hPNxrqO/LtkfSM9rQjl+PP+K95j3feN84/zT3Yz4/xbvDfyk2bnA6fzUnwc9U6o/akWpV0ucUzi/Ov8Wxxj+RGTHRTOP84/nTOG69/e+XXRObUz2Vvu/JsEuucR9z8FRlclP0I6f7NTrc8fnz/5Obb7vnH/4/pjiK4/zjjjjKy/+ssvvzyk7nAM5QUE6stb1Z4ya1F/7bXxprn4yFqjSP0XiNmHfQpoxCHNTy9xont5nf0+LU71r5MnG/wb08mff/e4GTw6Bqf6jyXHX+/95v2XHSfOP86/3fOIz594MPj8df0RT43dN8Xgx2l+0nT95frT9edqb5Huu8b1Z+9c4vrb9Xd802TvG/cf+Udp4ZGmXH+6/nT9vSnxvzNjgP7yK67IgvRnnnlm9+PZSHkBgfryVlISIECAAAECBAgQIECAAAECBAgQIEBgbATyhsP7s2//+r8TLwBSUD61nr8iBukF6AuVY3sVqD82N2sRIECAAAECBAgQIECAAAECBAgQIEBgpAWyQH3sjjsfeqF6AfqNr3aB+o03tUUCBAgQIECAAAECBAgQIECAAAECBAgMvcBgoD4EXdxsXpUK1G+erS0TIECAAAECBAgQIECAAAECBAgQIEBgaAWKQP3pZ5werrziSl3cbGJNCtRvIq5NEyBAgAABAgQIECBAgAABAgQIECBAYFgFbrjhhnD33XcL0G9BBQrUbwGyLAgQIECAAAECBAgQIECAAAECBAgQIECAwEoCAvUryZhPgAABAgQIECBAgAABAgQIECBAgAABAgS2QECgfguQZUGAAAECBAgQIECAAAECBAgQIECAAAECBFYSEKhfScZ8AgQIECBAgAABAgQIECBAgAABAgQIECCwBQIC9VuALAsCBAgQIECAAAECBAgQIECAAAECBAgQILCSgED9SjLmEyBAgMD/344d2gAAACAM+/9rLsBO1WJIKkeAAAECBAgQIECAAAECBAgQIEAgEBDqA2QXBAgQIECAAAECBAgQIECAAAECBAgQIEDgCQj1T8ZOgAABAgQIECBAgAABAgQIECBAgAABAgQCAaE+QHZBgAABAgQIECBAgAABAgQIECBAgAABAgSegFD/ZOwECBAgQIAAAQIECBAgQIAAAQIECBAgQCAQEOoDZBcECBAgQIAAAQIECBAgQIAAAQIECBAgQOAJCPVPxk6AAAECBAgQIECAAAECBAgQIECAAAECBAIBoT5AdkGAAAECBAgQIECAAAECBAgQIECAAAECBJ6AUP9k7AQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ6gNkFwQIECBAgAABAgQIECBAgAABAgQIECBA4AkI9U/GToAAAQIECBAgQIAAAQIECBAgQIAAAQIEAgGhPkB2QYAAAQIECBAgQIAAAQIECBAgQIAAAQIEnoBQ/2TsBAgQIECAAAECBAgQIECAAAECBAgQIEAgEBDqA2QXBAgQIECAAAECBAgQIECAAAECBAgQIEDgCQj1T8ZOgAABAgQIECBAgAABAgQIECBAgAABAgQCAaE+QHZBgAABAgQIECBAgAABAgQIECBAgAABAgSegFD/ZOwECBAgQIAAAQIECBAgQIAAAQIECBAgQCAQEOoDZBcECBAgQIAAAQIECBAgQIAAAQIECBAgQOAJCPVPxk6AAAECBAgQIECAAAECBAgQIECAAAECBAIBoT5AdkGAAAECBAgQIECAAAECBAgQIECAAAECBJ6AUP9k7AQIECBAgAABAgQIECBAgAABAgQIECBAIBAQ6gNkFwQIECBAgAABAgQIECBAgAABAgQIECBA4AkI9U/GToAAAQIECBAgQIAAAQIECBAgQIAAAQIEAgGhPkB2QYAAAQIECBAgQIAAAQIECBAgQIAAAQIEnoBQ/2TsBAgQIECAAAECBAgQIECAAAECBAgQIEAgEBDqA2QXBAgQIECAAAECBAgQIECAAAECBAgQIEDgCQj1T8ZOgAABAgQIECBAgAABAgQIECBAgAABAgQCAaE+QHZBgAABAgQIECBAgAABAgQIECBAgAABAgSegFD/ZOwECBAgQIAAAQIECBAgQIAAAQIECBAgQCAQEOoDZBcECBAgQIAAAQIECBAgQIAAAQIECBAgQOAJCPVPxk6AAAECBAgQIECAAAECBAgQIECAAAECBAKBAURlY5aAGIOjAAAAAElFTkSuQmCC"
     }
-  ],
-  "metadata": {
+   },
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3CWQjedfGKJJ"
+   },
+   "source": [
+    "![black_hole.png](attachment:black_hole.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are going to write a Python script to find at what distance away the Magic School Bus needs to be to experience a certain amount of stretching by a black hole. You will need to use Newton’s law of universal gravitation and Young's modulus."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cj5zRYWDGIDZ"
+   },
+   "source": [
+    "$$F = G\\frac{m_1m_2}{r^2}$$\n",
+    "\n",
+    "$$∆L = \\frac{1}{Y}\\frac{F}{A}\\ L_0 $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Nj2qV2_WVDvB"
+   },
+   "source": [
+    "$F$ = the force exerted on an object by gravity\n",
+    "\n",
+    "$G$ = the gravitational constant\n",
+    "\n",
+    "$m_1$ = the mass of the black hole\n",
+    "\n",
+    "$m_2$ = the mass of the magic school bus\n",
+    "\n",
+    "$r_1$ = the distance between the black hole and the front of the bus\n",
+    "\n",
+    "$r_2$ = the distance between the black hole and the back of the bus\n",
+    "\n",
+    "$\\Delta L$ = the change in length of the bus\n",
+    "\n",
+    "$Y$ = a measure of a solid's resistance to elastic deformation under a load (Young's modulus)\n",
+    "\n",
+    "$F$ = the force exerted on the object being stretched\n",
+    "\n",
+    "$A$ = the cross-sectional area of the bus\n",
+    "\n",
+    "$L_0$ = the length of the bus before stretching"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "thYmW7wlcUht"
+   },
+   "source": [
+    "## 1. Degree of freedom analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uIE6CkUAdO1N"
+   },
+   "source": [
+    "### 1.1. Setup\n",
+    "\n",
+    "Define the constants used in this problem. Use scholarly sources such as NASA to determine the mass of Sagittarius A*. Approximate the bus as a cylinder, using the height of the bus as the diameter of the front face."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7hZMqDHYA1qT"
+   },
+   "outputs": [],
+   "source": [
+    "# Given constants\n",
+    "Y = 69e9 # Young's modulus for aluminum, N/m^2\n",
+    "m_2 = 11000 # mass of school bus, kg\n",
+    "L_0 = 10 # length of school bus, meters\n",
+    "h = 3.5 # height of bus, meters\n",
+    "\n",
+    "#Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "id": "uVtvwV5QCUpQ"
+   },
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Given constants\n",
+    "Y = 69e9 # Young's modulus for aluminum, N/m^2\n",
+    "m_2 = 11000 # mass of school bus, kg\n",
+    "L_0 = 10 # length of school bus, meters\n",
+    "h = 3.5 # height of bus, meters\n",
+    "\n",
+    "# Add your solution here\n",
+    "G = 6.67e-11  # gravitational constant, N m^2/kg^2\n",
+    "Solar_Mass = 1.989e30  # value of one solar mass, kg\n",
+    "m_1 = Solar_Mass * 4.297e6  # mass of Sagittarius A*, kg\n",
+    "A = np.pi*(h/2)**2 # are at front of bus, m^2\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uBftpcsWdwYF"
+   },
+   "source": [
+    "### 1.2. Analysis\n",
+    "\n",
+    "Perform a degree of freedom analysis using the equations provided and the unknown variables. What does this analysis tell you? How should you approach the problem moving forward?\n",
+    "\n",
+    "**Discuss** in 2-4 sentences\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "There are two equations and three unknowns -- the tensile force due to the black hole's gravity,\n",
+    "the change in the length of the bus, and the distance from the black hole. There is more than one\n",
+    "solution to this system. In order to provide a unique solution, one of the variables must be fixed,\n",
+    "whether it be the distance from the black hole to the front of the bus, the change in the length of the bus,\n",
+    "or the differential force experienced by the bus.\n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rAoDFU9Fd-5U"
+   },
+   "source": [
+    "Submit your written work via **Gradescope**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ojiZf6Hccv40"
+   },
+   "source": [
+    "## 2. Nonlinear equation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "R4ufgGbOfNaG"
+   },
+   "source": [
+    "### 2.1. Define a nonlinear equation\n",
+    "\n",
+    "Combining Newton’s law of universal gravitation and Young's modulus, write a single, nonlinear equation in canonical form, $c(r_1,Δ𝐿) = 0$. \n",
+    "\n",
+    "*Hint*: Just like a tidal force, the tensile force on the bus will be generated by the difference in gravitational force between the front of the bus (nearer to the black hole) and the back of the bus (further from the black hole)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hHm34P-tfvO3"
+   },
+   "source": [
+    "Submit your written work via **Gradescope**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "0 = ((L_0 * G * m_1 * m_2) / (Y * A)) * ((1 / r^2) - (1 / (r + L_0 + ΔL)^2)) - ΔL\n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CqEWYdt0f76r"
+   },
+   "source": [
+    "### 2.2. Define a function that returns the residuals\n",
+    "\n",
+    "Using the equation you derived in **2.1**, write a Python function that returns its residual given some value of $r_1$ and some value of $\\Delta L$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
+     "base_uri": "https://localhost:8080/",
+     "height": 130
+    },
+    "id": "Iq6ayqYhD9jZ",
+    "outputId": "bd3cf883-a0ef-414c-c852-c852276836e2"
+   },
+   "outputs": [],
+   "source": [
+    "# function for nonlinear equation residual\n",
+    "def residual_function(r_1,ΔL):\n",
+    "    ''' Returns the residual of the nonlinear equation given in canonical form\n",
+    "    \n",
+    "    Args:\n",
+    "        r_1, ΔL\n",
+    "        \n",
+    "    Returns:\n",
+    "        residual    \n",
+    "    '''\n",
+    "  # Add your solution here\n",
+    "    residual = \n",
+    "    return residual"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "bDXosTblE8zc"
+   },
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# function for nonlinear equation residual\n",
+    "def residual_function(r_1,ΔL):\n",
+    "    ''' Returns the residual of the nonlinear equation given in canonical form\n",
+    "  \n",
+    "  Args:\n",
+    "      r_1, ΔL\n",
+    "      \n",
+    "  Returns:\n",
+    "      residual    \n",
+    "  '''\n",
+    "  # Add your solution here\n",
+    "    residual =  ((L_0 * G * m_1 * m_2) / (Y * A)) * ((1 / r_1**2) - (1 / (r_1 + L_0 + ΔL)**2)) - ΔL\n",
+    "    return residual\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3. Plot the residuals\n",
+    "\n",
+    "Next, define an array of $r_1$ values and an array of $\\Delta L$ values. Use these arrays to calculate the residual for each pair of $r_1$ and $\\Delta L$ values. Use the 3-D plot function in Matplotlib to analyze the results.\n",
+    "\n",
+    "*Hint*: Try $\\Delta L$ values in the tens to hundreds of meters and $r_1$ values on the order of $10$ to $10^{10}$ meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 362
+    },
+    "id": "A5WPN5V5Faon",
+    "outputId": "e21d27b8-ad24-4608-cd3f-5654cb5abaf9"
+   },
+   "outputs": [],
+   "source": [
+    "# Create array\n",
+    "# Add your solution here\n",
+    "\n",
+    "# Initialize matrix\n",
+    "# Add your solution here\n",
+    "\n",
+    "for i in range(len(r_1)):\n",
+    "    for j in range(len(ΔL)):\n",
+    "        r_grid[i,j] = r_1[i]\n",
+    "        # Add your solution here \n",
+    "\n",
+    "#Plot figure\n",
+    "fig, ax = plt.subplots(figsize = (6.4,4), dpi=300)\n",
+    "plt.xscale('log')\n",
+    "\n",
+    "cs = ax.contourf(r_grid, ΔL_grid, f_grid, locator=ticker.LogLocator(), cmap=cm.coolwarm, levels=100)\n",
+    "\n",
+    "cbar = fig.colorbar(cs)\n",
+    "cbar.ax.set_ylabel('Residual', fontsize=16, fontweight='bold')\n",
+    "cbar.ax.tick_params(labelsize=16)\n",
+    "\n",
+    "cs2 = plt.contour(cs, levels=cs.levels[::15], colors='k', alpha=0.7, linestyles='dashed', linewidths=3)\n",
+    "\n",
+    "# plot heatmap label\n",
+    "plt.clabel(cs2, fmt='%2.2f', colors='k', fontsize=16)\n",
+    "\n",
+    "# define tick size\n",
+    "plt.xticks(fontsize=15)\n",
+    "plt.yticks(fontsize=15)\n",
+    "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+    "\n",
+    "# plot titile and x,y label\n",
+    "plt.xlabel('Distance (m)', fontsize=16, fontweight='bold')\n",
+    "plt.ylabel('ΔL (m)', fontsize=16, fontweight='bold')\n",
+    "plt.title('Contour of Residuals', fontsize=16, fontweight='bold')\n",
+    "plt.show()\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 362
+    },
+    "id": "HgAE37rCH35D",
+    "outputId": "7bf1c6bb-be21-4dd0-dc68-4cdcd554c209"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/tk/4cs7tv315mq209yzyj47_wd80000gn/T/ipykernel_76301/1539189335.py:24: UserWarning: Log scale: values of z <= 0 have been masked\n",
+      "  cs = ax.contourf(r_grid, ΔL_grid, f_grid, locator=ticker.LogLocator(), cmap=cm.coolwarm, levels=100)\n"
+     ]
+    },
+    {
+     "data": {
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KFTVq1MipY3/88ccC//AAAAAAgKKDgBMAAKAImzZtmipWrOjyeV9++aUHunHNkiVL9MQTT7j9zX6YLy4uTt26ddPevXsLbc0xY8bo2WefNS2kGzNmjJYtW1agGpeftOvVq5fpTxWbYerUqbrzzjuVkJDg0XUOHz6sTp06eeTJPrMcOnRI+/fvd+rYDh06WPL+4ZycHPXr18+U38ft27frnnvuuXLdTE5OVvfu3fX7778XuHZ2draGDh2q7du3F7jW1Tp37uzUcampqfrll19MXRsAAACA7yLgBAAAKMLKlCmjUaNGuXzeokWL3H7Sxwypqal68sknLVsfjp0+fVr33HOPMjIyPL7W+PHjTX3i8rJHH31U58+fd+vc3NxcDRo0SJMmTTK5K3NMnjxZzz33nHJzcwtlvYyMDPXt21c///xzoaznKldCw06dOnmwE8deeOEF/fDDD6bV++WXXzRlyhTl5uaqX79+pj51nZWVpSFDhpj6JKWzAafk2p8nAAAAgKKNgBMAAKCIe/TRRxUcHOzSOadOnXL6qSdP+Pjjj3X8+HHL1kf+du/e7ZHg8Vpz5szxSN0TJ05o+vTpbp07bNgw07fpNMu8efM0ZsyYQl83KytLffv21cGDBwt97etZu3at08datT3twoULTa/5r3/9S88995xL79t01pYtW0x9yt+V33ee4AQAAABwGQEnAABAEVe2bFl16dLF5fPWr1/vgW6c8/XXX1u2Npzz5ptvKjk52eo23PbBBx+4/JTjrFmz9P7773uoo4I5cOCAHn/8ccvWT0lJ0YMPPuh1W0pv3LjR6WNbtGjhwU4KV0pKiv7zn/94rL6Z/zto0KCBQkNDnTp227ZtysrKMm1tAAAAAL4r0OoGAAAA4HmdO3d2+b2Du3fv9lA3+cvKytKvv/7q9PF169bV0KFD1aFDB0VGRqp8+fIKDg5WWlqazp8/r/Pnz+vQoUPatWuXdu/erc2bN2vPnj0e/AqsFRUVpRtvvFGNGzdW48aNVa9ePZUuXfrKL5vNpgsXLujMmTOKj49XbGysVqxYoZ9//tmlbYnT09P1ySef6Omnn/bgV2OvZMmS6tWrl3r06KEaNWooKytLO3fu1Jw5c3T48GGXasXHx2vVqlXq1q2bU8efOHFCzz33nDttS5IqVKigLl266JZbblHlypVVrlw5ZWRkKCkpSUeOHNEvv/yi33//XZmZmW7V/8c//qG0tDSXzgkNDdWgQYN05513qlWrVipfvryysrJ05MgR/fTTT3r77bcVFxfndL2NGzfqvffe85rtpU+fPq0jR444dWzlypXdel+xJ7Rs2VL9+/dXw4YNFRISosOHD+vTTz815T2a4eHhuv/++9WhQwdVqVJFZ8+e1YoVK/Tpp5+69J7btWvX6tChQ4qMjCxwT/7+/mratKlTYfTFixe1fft2tWnTpsDrAgAAAPBtBJwAAADFQOvWrV0+Jz4+3vxGnJCQkOD0U2B33nmnvvrqK5UsWTLPXHh4uMLDw1WzZk01a9ZMd99995W5P//8UytXrtRPP/2kxYsX69y5c/muU6VKFcOtHpctW+bSVq1Tp07VDTfc4PTxUVFRTh3XoUMH3X///erVq5fq1q173eODgoJUpkwZ1atXT127dtWoUaO0c+dODRw4UFu3bnW6v2+++abQAs7OnTvro48+Up06dezG+/btq9GjR+vZZ5/VzJkzXar566+/Oh1wjh492uUAUZJq1KihV199VQ8//LBKlCiR77GZmZn64osvNHPmTJfCrJ9++knLly93qa/u3bvr448/VrVq1ezGg4OD1axZMzVr1kzDhw/Xs88+qxkzZjhdd9y4cRo6dKjh/yYL2+bNm50+tnHjxh7sxDlBQUGaNm2ahg0blmfuySef1PDhw13+b/xqXbp00WeffaaqVavajffv31+DBw9Wz549XQrYf/31V1MCTklq0qSJ00/bbt68mYATAAAAAAEnAABAcXBtKOSMEydOmN+IExITE50+durUqW4FKVWrVtVDDz2khx56SFlZWfrhhx80b948h9sklixZUp07d84z7moIfMMNNxjWcUdAQIAGDhyoESNGmPLuwGbNmmnVqlVq3bq100/t/fbbb8rIyHB6e0l3xcTEaOnSpQ4DwqCgIL377ruKj4/XDz/84HTd2NhYp46Li4vT/Pnzna57WefOnfX111+rfPnyTh1fsmRJDR48WIMHD9aKFSucDo///e9/u9RXr169tHDh/2/vXoOqvM42jl+4QQRFAUUQj4iCBzymaKBOjCZqPBBtnKitHW0Uo+0k7aSRqdG2ZlrHQzJ2aNLYUWoalTH1MEmAUaPRMWqwHgC1qGkMVgG1VEVBxQMi9ENe86Yq8izYD5vN/v9m/BLutdbNw4YJXHut9XGNgauPj4/ee+893b17VykpKZbmvnTpklJTU5WYmGjUkx1MdvV27NjRxk6sSUlJ0bRp0x75MS8vL/3hD3/Qpk2bdPnyZeO5n3jiCW3ZskV+fn6P/PjQoUP12muvacmSJZbnzMrK0vTp0417eZQOHTpYrjXdrQ0AAACgceIOTgAAAA8QGhpqPKasrMyGTpzL5I/i1WnatKnGjx+vDRs2aMaMGU7oqn4EBARo7dq1Tgk37wsMDFRSUpLl+oqKCv3jH/9w2vqPEhoaqk2bNtUYxnl5eWn+/PlGc1vdrbp69WrjuyXj4uK0Y8cOy+Hmg0aOHKnc3FxNmDDhsXUXLlzQjh07LM/btm1brV27tsbn+V3JyclGP0PWrl1rudZOJsfrtm/f3sZOajZhwoRqw837mjVrptGjRxvP3aRJE61Zs6bacPO+WbNmGc1rstu7JibP31WnCwAAAABoWAg4AQAAPEBtdti5KuBs06aN5dotW7bY2IlnGjx4sFG93feZLlq0SC1btrRU++STT6pp06aW57548aKluo0bN1qeU5JatWqljRs3GoWIj+Lj41Pj7utPPvnE6O7UX/7ylwoODjbqw9/fXzNnzrRcn5mZqUuXLhmtYQeTIMwZb5aoi4ULF1qqq80bGl544QX17t27xrqIiAij52D1+8cKk3VNgmsAAAAAjRcBJwAAgAcwCUDu8/LysqGTmoWFhcnb29pNCj/5yU+UnJxc4x2asM40/LLzKOOAgABNnTrVcr23t7e6detmub68vLzGOwcLCgr09ddfW55T+ua+xPoKzD7//HOj+pp2CVbH5GjlyspKZWZm1modZzIJ4IKCgmzs5PGioqIs381r5Y7dB7344ouWa02+f0pLS417qY7J828I4TkAAAAA1+MOTgAAAA9w8+ZN4zF236tYHT8/P8XFxWnfvn011t64cUOvvfaa5s6dq759+6pv377q1q2bunbtqoiICEVGRqpt27b10HXDUVVVpRMnTigrK0u5ubk6c+aMCgsLVVxcrKtXr+r27du6e/eu8ZGr1anNfYBWjR07tsZjNR8UEhJiVF9SUqKwsLBqP37gwAGj+by8vPTqq68ajamLQ4cOWa6NjIxUu3btarWO6RGuWVlZNR6vazeTn3umrzNneu655yzXmr6+pW+OO7bK5EjlkpIS416qY/L83eH4dAAAAAD2I+AEAADwAEVFRcZjWrRoYUMn1jz//POWAs777t27pyNHjujIkSMPfaxly5bq1auX+vTpo9jYWMXHx1s6rtHd7N27V2vXrlV6enq97nC6deuWbXN/73vfMx5j9Tjb+2rawWl6BG/v3r1rHSKaKi8vV0FBgeX606dP19vO7Ly8vHpZ53FMgjBXBpwxMTGWa01f3+3bt1dgYKDlepM3tty5c8eol8ch4AQAAABgiiNqAQAAPEBt7ixz5Z10c+bMUWhoqFPmunbtmg4cOKCUlBS9/PLLiomJUbt27ZSYmKhdu3bV6vjehuSLL77QoEGDNHToUK1evbrej28sLy+3bW6T4Oc+X19fp/Zgco+jJH3/+9936vqPU1hY2GBfv4WFha5uocbw+rtM7m51tu7du1uubdasmW1zS1KTJq75E4HJ52XydQUAAADQeBFwAgAAeICcnBzjMV26dHF+Ixa1aNFC77zzjm27zYqKirR69Wo9++yz6tGjhz788ENb1rFTVVWV5s+fr6eeekqHDx92aR92adOmjfEYZwdVV65cMaoPDw936vqP48wjQp3t2rVrrm7BKOy2M6ivSatWrSzXWr2fuDZzu5JJaOnsNzEAAAAAcE8EnAAAAB5g165dxmN69uxpQyfWTZo0SX/6059sP1Lz1KlT+tGPfqQJEya41c6g2bNna8mSJQ12B58zBAQEGI9x9g400/trg4ODnbr+49h5PHBd1ebeX2dr3ry55VpX9mtyHLjpz0OTZ+BKJq9ld/mcAAAAANiLgBMAAKCRu3TpktF9lvfFxcXZ0I2Zn/3sZ9q+fbu6detm+1ppaWmaMmWK7t27Z/tadbVq1SqlpKS4ug3b1WY3psPhsKGThslVx4la0RC+j0zuk3RlWOzj4+OWczuTScBMwAkAAABAIuAEAABo9FatWqW7d+8ajWnbtq2ioqJs6sjMiBEj9OWXXyo1NVVPPfWUrTs609LStHLlStvmd4aSkhLNmzfP1W14DJOQTDI/0rYu/Pz86m0tdxQSEmK59urVqzZ2gpqYPP/aHF0NAAAAoPEh4AQAAGjEiouLtXz5cuNxEyZMcH4zdeDt7a2pU6dqz549On/+vN5//31Nnz5dPXr0cPqOvd///vcN+ujPDz74wDiM8ff3V2JiojZv3qyTJ0+qtLRUFRUVqqqqeujfmTNnbOrcPZkeOXvhwgWbOnkYQc/jde7c2XLt+fPnbewENTl37pzlWlfeDw0AAACg4fB2dQMAAACwzyuvvFKrnUmTJk2yoRvnaNeunV566SW99NJLkr45WvLUqVM6ffq0zpw5o/z8fJ0+fVpfffWV/vWvfxnfUVlUVKTdu3drzJgxdrRfZ5s2bTKqj4+P1+bNm9WuXTtL9Xfu3KlNW42WaZiyf/9+exp5hPDwcHl7e6uiosJS/XPPPadt27bZ3FXDYfK1MwnY4HwEnAAAAABMEXACAAA0UsuWLdPf/vY343ExMTEaPny4DR3Zw8/PT/369VO/fv0e+lhJSYk+++wzJScnGwVPO3fubJAB5927d3X48GHL9UFBQUpPT1fr1q0tjykqKqpNa41Wjx49jOqPHz+uoqIihYWF2dTR/3M4HIqKitLJkyct1R89etTehhqYrl27Wq4l4HQtk+cfERFhYycAAAAA3AVH1AIAADQyFRUVeuONN2p9T2NSUpKt91zWp8DAQL344ovau3evRo0aZXmc1cCoSROz/5023U36oDNnzhjdpzp+/HijcFOS9u7da9pWoxYXF2dUX1VVpXfffdembh42ePBgy7VFRUX1usPU1QYMGGC51ur3POxh8vxNvq4AAAAAGi8CTgAAgEbk7Nmzevrpp7V06dJajX/iiSf04x//2MlduZ7D4dCsWbMs1xcXF1uqa9GihVEfpaWlRvUPunLlilG91WNp76uoqNC6deuMxjR2nTp1Uvfu3Y3GrFixot7udHz22WeN6pOTk+1p5P/k5+frxo0btq5hVXh4uMLDwy3VXrx4URcvXrS5IzzKvXv3dOLECUu13t7eBJwAAAAAJBFwAgAANApZWVmaOnWqoqKilJmZWas5HA6HVqxYYbwr0Q779u3T9evXnTqnj4+P5dqSkhJLda1atTLq4ciRI0b1DzLdAVpQUGBUv3z5cn399ddGYzyB6Z20JSUlmjx5stFu20cpLy/X6dOnH1szbtw4+fr6Wp5z06ZNSktLq1Nfj3L8+HHNmDFD3bt31+XLl50+f20NGjTIcm1ubq6NnaA6p06d0u3bty3V9unTR82aNbO5IwAAAADuwPV/vQIAAIAl5eXlunjxor766iv9/e9/18qVKzV9+nR1795dsbGxWr9+fZ0ClUWLFhmFAXZatmyZwsPD9fLLLysrK8spc27evNlyrZ+fn6W6oKAgox7+8pe/1GmXWEhIiFF9RkaG/vOf/1iq3bhxo+bPn1+bthq9mTNnyuFwGI3JzMzU6NGjdfXq1VqtuX//fg0YMEAZGRmPrWvZsqUmT55sNPcPf/hDbdmypVZ9fVdZWZlSU1M1bNgw9enTR3/961/rHOo625AhQyzX5uTk2NgJqmPy3E2+ngAAAAAaN29XNwAAAICHrVmzRmvWrKm39SZOnKhf/epX9baeFTdu3FBKSopSUlLUqVMnjRo1SqNGjdIzzzyjwMBAy/Pcvn1bixYtMjp6tUOHDpbqevToIYfDoXv37lmqv3DhgmJiYjR16lQNHDhQISEh1e5G6tmzp0JDQ//nv4WFhVla575r165p3Lhx+uijj9SxY8dH1ly/fl2LFi3S22+/Xec7QhuriIgITZ48WevXrzcat2vXLvXv31+//e1vNX36dHl7P/7Xr4qKCmVkZOjdd9/V7t27La+TlJSk1NRUVVZWWqq/deuWEhISNG3aNC1YsMDoCN7CwkLt3r1bGRkZ2rZtm8rKyiyPdYXRo0dr7ty5lmr37t2rpKQkmzvCgz7//HPLtWPGjLGvEQAAAABuhYATAADAw40cOVLr16+Xl5eXq1upVkFBwbdhp/RNANm7d2/16tVLnTt3VsuWLb/95+XlpRs3bqigoEDZ2dnasmWL5Ts17xs4cKClOn9/f/Xs2VPHjx+3PPelS5cs3YO4bt26h+5DbdGihfr376+jR49aXi8rK0s9evTQxIkTNXToULVv316SVFRUpH379umTTz4xvtvTEy1btkxpaWnGgV5BQYESExP1xhtvaPjw4YqPj1doaKiCg4N18+ZNFRcX69y5c8rMzNT+/ftrdX9lTEyMEhMTtWrVKstjqqqqvn0jRWxsrOLj49WnTx+1bt1aAQEBunfvnsrKylRUVKSCggKdPHlSR48eNT722NV69eqlLl266OzZszXW7tu3T5WVlQ3imG5PYjXg9Pf319NPP21rLwAAAADcBwEnAACAB5s4caLWrVunpk2buroVI+fOndO5c+e0fft2W+ZPSEiwXDts2DCjgLOuEhISjAJOSbp586bWrVtntIsV/6tDhw5avny55syZU6vxly5d0oYNG7RhwwYnd/aNpUuXavv27crPzzcee/jwYR0+fNiGrhqGsWPH6r333quxrrS0VEePHrX8BgfU3YULF5SXl2epdvjw4dy/CQAAAOBbvDUVAADAQ82bN0+bNm2yfN+kpxg0aJAGDx5suX727Nk2dvOwWbNm2fpHfpPP3dPMnj1bM2fOdHUbjxQUFMT3czWmTJliufbTTz+1sRM8aNu2bZZrTb6OAAAAABo/Ak4AAAAPExERoZ07d2rJkiUN+lhaV3A4HPrjH/9oNKZ37971ei9cx44d9Ytf/MKWuTt16qQ///nPtszdWKxcubLBBi2xsbFKS0tjl9sDhgwZosjISEu1H3/8sc3d4LusPu+AgAD94Ac/sLkbAAAAAO6EgBMAAMBDBAYG6je/+Y1yc3P1zDPPuLqdBik5OVlPPvmk8bj3339fYWFhNnT0aAsXLlRcXJxT5wwMDNSWLVsUFBTk1HkbG4fDodTUVCUlJbm6lUcaMWKEdu3apdDQUFe30qBMmzbNUl1WVpYKCwtt7gaSdP36de3cudNS7aRJk+Tv729zRwAAAADcCQEnAABAIxcdHa0lS5bo7Nmz+t3vfqfmzZu7uqUGx9fXV6tWrdIrr7xSq/GhoaHavn27oqOjndzZo/n5+SkjI0O9evVyynxt2rTRp59+qpiYGKfM19g5HA699dZbSk9PV/v27V3dzkPi4+OVk5NjdJess0RGRjbInzHTp0+Xw+GwVMsuzvqxdetW3blzx1LtjBkzbO4GAAAAgLsh4AQAAGhk/P39NWTIEP36179WTk6O/vnPf2revHlq1aqVq1uzbMiQIQoPD6+XtYYNG6asrCzNmjWrTvP07dtX2dnZWrBgQb3snmvdurUOHDhQ5+NSY2NjdfDgQe7erIWEhAR9+eWXWrhwYYP7/goPD1d6errS09M1YMAAW9cKDQ3Vz3/+cx04cEB5eXkKCQmxdb3a6Ny5s+UjTtesWWNzN5CsP+dBgwYpPj7e5m4AAAAAuBtvVzcAAACA6nl5ecnhcMjHx0e+vr5q1qyZAgIC1LJlSwUHByskJERhYWHq2LGjIiIiFB0dre7du1veqdRQzZs3T/PmzdPJkye1c+dO7dmzR9nZ2crPz3fK/MHBwZowYYJmzZpVqyNpq9O8eXMtWrRICxcu1I4dO3Tw4EHl5OQoLy9PpaWlunbtmm7evOm09QICAvThhx9qypQpWrx4sQ4dOmR5bOfOnZWUlKQ5c+b8z+vl/mvOKnd/rdVVQECA3nzzTb3++uvauHGjUlNT9cUXX6iioqLWc4aGhmrMmDF64YUXNGLEiDr1l5CQoISEBO3Zs0cffPCBMjIyVFxcXKc5/fz8FBcXp+HDh2v48OEaNGiQW7wO5s6dq82bN9dYl5OTo2PHjqlfv3710JVnunDhgnbs2GGp9vXXX7e5GwAAAADuyKuqqqrK1U0AAAAAVly5ckU5OTk6ceKE8vPzVVBQoIKCAl2+fFllZWUqKyvTrVu35OXlpaZNmyogIEBt2rRR+/bt1bVrV/Xt21eDBw/WgAED1KRJ4zvM5NChQ98GwmfOnFFxcbFKS0vVrFkzBQUFKSoqSgMGDNDo0aM1dOhQeXvzfkc7XLt2TXv37tWRI0eUm5ur/Px8/fvf/1ZJSYlu376tyspK+fv7q3nz5mrbtq0iIiIUGRmp/v37Ky4uTt26dbOtt8rKSmVnZ+vgwYM6duyY8vLydP78eV2+fFm3bt1SeXm5/Pz8FBAQ8O2bKbp06aLo6GhFR0erZ8+e6tu3r3x9fW3r0U5DhgxRZmZmjXWvvvqq3nnnnXroyDMtXrxYCxYsqLGuS5cuysvLc4sAHQAAAED9IuAEAAAAAHiErVu3auzYsTXWBQcHq7CwUP7+/vXQlWeprKxUdHS08vLyaqxdsWKFfvrTn9ZDVwAAAADcTeN72zoAAAAAAI8wZswYS/c5Xrlyhbs4bZKRkWEp3IyIiFBiYmI9dAQAAADAHRFwAgAAAAA8xuLFiy3VJScniwOPnG/58uWW6t588035+PjY3A0AAAAAd8URtQAAAAAAjzJy5Eh99tlnNdalpaXp+eefr4eOPENWVpZiY2NrrOvVq5dyc3Mb5V3JAAAAAJyD3xYAAAAAAB7l7bfflsPhqLFuyZIl9dCN51i6dKmlurfeeotwEwAAAMBj8RsDAAAAAMCj9OvXT3PmzKmx7sCBA9q6dWs9dNT4HTt2TB999FGNdePGjdPYsWProSMAAAAA7owjagEAAAAAHufq1auKiorS5cuXH1s3cOBAZWdn11NXjdf48eOVnp7+2BpfX1+dOHFCkZGR9dQVAAAAAHfFDk4AAAAAgMcJCgqydARtTk6OpZ2HqF52dnaN4aYkJSUlEW4CAAAAsIQdnAAAAAAAAAAAAADcBjs4AQAAAAAAAAAAALgNAk4AAAAAAAAAAAAAboOAEwAAAAAAAAAAAIDbIOAEAAAAAAAAAAAA4DYIOAEAAAAAAAAAAAC4DQJOAAAAAAAAAAAAAG6DgBMAAAAAAAAAAACA2yDgBAAAAAAAAAAAAOA2CDgBAAAAAAAAAAAAuA0CTgAAAAAAAAAAAABug4ATAAAAAAAAAAAAgNsg4AQAAAAAAAAAAADgNgg4AQAAAAAAAAAAALgNAk4AAAAAAAAAAAAAboOAEwAAAAAAAAAAAIDbIOAEAAAAAAAAAAAA4DYIOAEAAAAAAAAAAAC4DQJOAAAAAAAAAAAAAG6DgBMAAAAAAAAAAACA2yDgBAAAAAAAAAAAAOA2CDgBAAAAAAAAAAAAuA0CTgAAAAAAAAAAAABug4ATAAAAAAAAAAAAgNsg4AQAAAAAAAAAAADgNgg4AQAAAAAAAAAAALgNAk4AAAAAAAAAAAAAboOAEwAAAAAAAAAAAIDbIOAEAAAAAAAAAAAA4DYIOAEAAAAAAAAAAAC4DQJOAAAAAAAAAAAAAG6DgBMAAAAAAAAAAACA2yDgBAAAAAAAAAAAAOA2CDgBAAAAAAAAAAAAuI3/AuL9GQm1xcHdAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1920x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Create array\n",
+    "r_1 = np.linspace(1e4, 1e8, 1000)\n",
+    "ΔL = np.linspace(0, 100, 1000)\n",
+    "\n",
+    "# Initialize matrices\n",
+    "r_grid = np.zeros((len(r_1), len(ΔL)))\n",
+    "ΔL_grid = np.zeros((len(r_1), len(ΔL)))\n",
+    "f_grid = np.zeros((len(r_1), len(ΔL)))\n",
+    "\n",
+    "for i in range(len(r_1)):\n",
+    "    for j in range(len(ΔL)):\n",
+    "        r_grid[i, j] = r_1[i]\n",
+    "        # Add your solution here\n",
+    "        ΔL_grid[i, j] = ΔL[j]\n",
+    "\n",
+    "#fill f_grid\n",
+    "f_grid = residual_function(r_grid, ΔL_grid)\n",
+    "\n",
+    "#Plot figure\n",
+    "fig, ax = plt.subplots(figsize = (6.4,4), dpi=300)\n",
+    "plt.xscale('log')\n",
+    "\n",
+    "cs = ax.contourf(r_grid, ΔL_grid, f_grid, locator=ticker.LogLocator(), cmap=cm.coolwarm, levels=100)\n",
+    "\n",
+    "cbar = fig.colorbar(cs)\n",
+    "cbar.ax.set_ylabel('Residual', fontsize=16, fontweight='bold')\n",
+    "cbar.ax.tick_params(labelsize=16)\n",
+    "\n",
+    "cs2 = plt.contour(cs, levels=cs.levels[::15], colors='k', alpha=0.7, linestyles='dashed', linewidths=3)\n",
+    "\n",
+    "# plot heatmap label\n",
+    "plt.clabel(cs2, fmt='%2.2f', colors='k', fontsize=16)\n",
+    "\n",
+    "# define tick size\n",
+    "plt.xticks(fontsize=15)\n",
+    "plt.yticks(fontsize=15)\n",
+    "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+    "\n",
+    "# plot titile and x,y label\n",
+    "plt.xlabel('Distance (m)', fontsize=16, fontweight='bold')\n",
+    "plt.ylabel('ΔL (m)', fontsize=16, fontweight='bold')\n",
+    "plt.title('Contour of Residuals', fontsize=16, fontweight='bold')\n",
+    "plt.show()\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9tDNzCiqh6jk"
+   },
+   "source": [
+    "#### 2.3.1 Discussion\n",
+    "\n",
+    "What does the 3-D plot tell you about the solution? Are there multiple solutions?\n",
+    "\n",
+    "**Discuss** in 1-3 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "There are multiple solutions. The amount the bus stretches will change with its distance from the black hole. \n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eX4pivRVdW3s"
+   },
+   "source": [
+    "## 3. Inexact Newton's Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "I_x85LE1iqOr"
+   },
+   "source": [
+    "Typically, a body of aluminum can be subjected to 10% to 25% elongation before deformation becomes permanent (plastic deformation). For simplicity, we will define our maximum tolerance for stretching to be one meter. Solve for the distance when the bus experiences this stretching using the inexact Newton's Method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "QqdQo3sR4DwU"
+   },
+   "outputs": [],
+   "source": [
+    "def inexact_newton(f,x0,delta = 1.0e-7, epsilon=1.0e-6, LOUD=False,max_iter=50):\n",
+    "    \"\"\"Find the root of the function f via Newton-Raphson method\n",
+    "    Args:\n",
+    "        f: function to find root of [function]\n",
+    "        x0: initial guess [float]\n",
+    "        delta: finite difference parameter [float]\n",
+    "        epsilon: tolerance [float]\n",
+    "        LOUD: toggle on/off print statements [boolean]\n",
+    "        max_iter: maximum number of iterations [int]\n",
+    "        \n",
+    "    Returns:\n",
+    "        estimate of root [float]\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    assert callable(f), \"Warning: 'f' should be a Python function\"\n",
+    "    assert type(x0) is float or type(x0) is int, \"Warning: 'x0' should be a float or integer\"\n",
+    "    assert type(delta) is float, \"Warning: 'delta' should be a float\"\n",
+    "    assert type(epsilon) is float, \"Warning: 'eps' should be a float\"\n",
+    "    assert type(max_iter) is int, \"Warning: 'max_iter' should be an integer\"\n",
+    "    assert max_iter >= 0, \"Warning: 'max_iter' should be non-negative\"\n",
+    "    \n",
+    "    x = x0\n",
+    "\n",
+    "    # print intial guess\n",
+    "    #if (LOUD):\n",
+    "        #print(\"x0 =\",x0)\n",
+    "    \n",
+    "    iterations = 0\n",
+    "    converged = False\n",
+    "    \n",
+    "    # Check if the residual is close enough to zero\n",
+    "    while (not converged and iterations < max_iter):\n",
+    "        \n",
+    "        print(f'Guess {iterations}: {x}')\n",
+    "        \n",
+    "        # evaluate function 'f' at new 'x'\n",
+    "        fx = f(x)\n",
+    "        print(f'Residual {iterations}: {fx}\\n')\n",
+    "        \n",
+    "        # calculate 'slope'\n",
+    "        slope = (f(x+delta)-f(x))/delta\n",
+    "        \n",
+    "        # print every iteration\n",
+    "        #if (LOUD):\n",
+    "            #print(\"x_\",iterations+1,\"=\",x,\"-\",fx,\"/\",slope,\"=\",x - fx/slope)\n",
+    "        x = x - fx/slope\n",
+    "        \n",
+    "        iterations += 1\n",
+    "        \n",
+    "        # check if converged\n",
+    "        if np.fabs(f(x)) < epsilon:\n",
+    "            converged = True\n",
+    "            \n",
+    "    if (LOUD):\n",
+    "      print(\"It took\",iterations,\"iterations\")\n",
+    "    \n",
+    "    if not converged:\n",
+    "        print(\"Warning: Not a solution. Maximum number of iterations exceeded.\")\n",
+    "    return x #return estimate of root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Implementing inexact Newton's\n",
+    "You will need to write a new function for your nonlinear equation so that the only variable is $r_1$. This can be achieved by fixing the value of $\\Delta L$ to be one meter. Use this function to implement inexact Newton's.\n",
+    "\n",
+    "For more information on how to use the inexact Newton's Method, click [here](https://ndcbe.github.io/data-and-computing/notebooks/06/More-Newton-Type-Methods.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "cB-IP69ZfE0Z"
+   },
+   "outputs": [],
+   "source": [
+    "# Set value of ΔL\n",
+    "ΔL = 1              #length the bus is streteched, meters\n",
+    "guess = 100_000     #distance from black hole, meters\n",
+    "\n",
+    "# Define f(r_1) for the given ΔL; use canonical form\n",
+    "# Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "id": "K9heOP7lfsoH"
+   },
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Set value of ΔL\n",
+    "ΔL = 1\n",
+    "guess = 100_000\n",
+    "\n",
+    "# Define f(r_1) for the given ΔL; use canonical form\n",
+    "# Add your solution here\n",
+    "canonical_form = lambda r_1: ((L_0 * G * m_1 * m_2) / (Y * A)) * ((1 / r_1**2) - (1 / (r_1 + L_0 + ΔL)**2)) - ΔL\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ON_oco6kvKic"
+   },
+   "outputs": [],
+   "source": [
+    "# Use inexact_newton to find a solution\n",
+    "# Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vgGNcVwvvQOU",
+    "outputId": "9ca22c1f-25e8-4042-b4d4-e142b95ab5c9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 100000\n",
+      "Residual 0: 2077755.3674266443\n",
+      "\n",
+      "Guess 1: 128364.93230407775\n",
+      "Residual 1: 982360.3477070075\n",
+      "\n",
+      "Guess 2: 208830.37649762759\n",
+      "Residual 2: 228165.01024245314\n",
+      "\n",
+      "Guess 3: 246208.5116013881\n",
+      "Residual 3: 139227.4485254872\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[36], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### BEGIN SOLUTION\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# Use inexact_newton to find a solution\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m newton_sln \u001b[38;5;241m=\u001b[39m inexact_newton(canonical_form, guess, epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-2\u001b[39m, LOUD\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe result of the inexact newton function is\u001b[39m\u001b[38;5;124m'\u001b[39m, newton_sln)\n",
+      "Cell \u001b[0;32mIn[34], line 46\u001b[0m, in \u001b[0;36minexact_newton\u001b[0;34m(f, x0, delta, epsilon, LOUD, max_iter)\u001b[0m\n\u001b[1;32m     41\u001b[0m slope \u001b[38;5;241m=\u001b[39m (f(x\u001b[38;5;241m+\u001b[39mdelta)\u001b[38;5;241m-\u001b[39mf(x))\u001b[38;5;241m/\u001b[39mdelta\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# print every iteration\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;66;03m#if (LOUD):\u001b[39;00m\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;66;03m#print(\"x_\",iterations+1,\"=\",x,\"-\",fx,\"/\",slope,\"=\",x - fx/slope)\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m fx\u001b[38;5;241m/\u001b[39mslope\n\u001b[1;32m     48\u001b[0m iterations \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;66;03m# check if converged\u001b[39;00m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Use inexact_newton to find a solution\n",
+    "newton_sln = inexact_newton(canonical_form, guess, epsilon=1e-2, LOUD=True)\n",
+    "print('The result of the inexact newton function is', newton_sln)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1.1. Discussion\n",
+    "\n",
+    "Did the inexact Newton's Method work? Try a few different initial guesses for $r_1$.\n",
+    "\n",
+    "**Discuss** in 1 or 2 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "The inexact Newton's Method keeps encountering division by zero. This is because subsequent guesses are\n",
+    "generating nearly equal answers and leading the slope to be calculated as zero.\n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Troubleshooting inexact Newton's\n",
+    "\n",
+    "Consider the order of magnitude of our guesses and our correspodnig intial residuals. Do you think the default step size, ```delta = 1.0e-7```, is appropriate for our problem? Try running inexact Newton's with progressively larger step sizes until you find one that works for an initial guess of $r_1 = 100,000$ meters. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use inexact_newton to find solution\n",
+    "# Add your solution ehre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 100000\n",
+      "Residual 0: 2077755.3674266443\n",
+      "\n",
+      "Guess 1: 133335.81739941452\n",
+      "Residual 1: 876539.6319734784\n",
+      "\n",
+      "Guess 2: 177783.53889143027\n",
+      "Residual 2: 369785.7367675986\n",
+      "\n",
+      "Guess 3: 237047.05907945527\n",
+      "Residual 3: 156001.7780356129\n",
+      "\n",
+      "Guess 4: 316064.7386210726\n",
+      "Residual 4: 65812.57209957817\n",
+      "\n",
+      "Guess 5: 421420.53716633125\n",
+      "Residual 5: 27764.288028924195\n",
+      "\n",
+      "Guess 6: 561891.4661279671\n",
+      "Residual 6: 11712.757107201945\n",
+      "\n",
+      "Guess 7: 749175.040867439\n",
+      "Residual 7: 4941.0463061199025\n",
+      "\n",
+      "Guess 8: 998851.8586936642\n",
+      "Residual 8: 2084.2390800205067\n",
+      "\n",
+      "Guess 9: 1331645.8383812758\n",
+      "Residual 9: 879.0243440693794\n",
+      "\n",
+      "Guess 10: 1775025.7183716786\n",
+      "Residual 10: 370.576542279553\n",
+      "\n",
+      "Guess 11: 2365110.3665443407\n",
+      "Residual 11: 156.0757026458498\n",
+      "\n",
+      "Guess 12: 3148474.4408281716\n",
+      "Residual 12: 65.58301910899844\n",
+      "\n",
+      "Guess 13: 4182249.3172298237\n",
+      "Residual 13: 27.407617109269168\n",
+      "\n",
+      "Guess 14: 5527224.847222105\n",
+      "Residual 14: 11.306755808452309\n",
+      "\n",
+      "Guess 15: 7219972.172748446\n",
+      "Residual 15: 4.521520796924896\n",
+      "\n",
+      "Guess 16: 9190590.90150826\n",
+      "Residual 16: 1.6769187615314816\n",
+      "\n",
+      "Guess 17: 11110108.88462495\n",
+      "Residual 17: 0.5153420858280657\n",
+      "\n",
+      "Guess 18: 12369385.253475262\n",
+      "Residual 18: 0.09804805785069015\n",
+      "\n",
+      "It took 19 iterations\n",
+      "The result of the inexact newton function is 12737445.506581789\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Use inexact_newton to find solution\n",
+    "delta = 1.0\n",
+    "newton_sln = inexact_newton(canonical_form, guess, delta = delta, epsilon=1e-2, LOUD=True)\n",
+    "print('The result of the inexact newton function is', newton_sln)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2.1. Discussion\n",
+    "\n",
+    "Why was the smaller delta value leading to division by zero?\n",
+    "\n",
+    "**Discuss** in 1-3 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "'''\n",
+    "Because of the order of magnitude of our inputs and outputs, altering inputs by such a small step size was\n",
+    "bumping up against the limits of floating point precision, causing the difference between to equation outputs\n",
+    "to compute as zero.\n",
+    "'''\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5gkAvogwoJgI"
+   },
+   "source": [
+    "## 4. Exact Newton's Method using Scipy\n",
+    "\n",
+    "Now that we've estimated a solution at $\\Delta L = 1$ using inexact Newton's, let's try using SciPy to solve this problem.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JTSYn9QfrrMv"
+   },
+   "source": [
+    "### 4.1. Setup equations\n",
+    "\n",
+    "When using Newton's Method in Scipy, the derivative of $f(r_1)$ is required to determine the solution. Calculate the derivative of $f(r_1)$ and define it as a function in Python.\n",
+    "\n",
+    "Submit your written work via **Gradescope**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_v_Ca_9EuuBg"
+   },
+   "outputs": [],
+   "source": [
+    "# Set value of ΔL\n",
+    "ΔL = 1\n",
+    "guess = 100_000\n",
+    "\n",
+    "def derivative_form(r_1):\n",
+    "    ''' Find max distance using canonical derivative\n",
+    "    \n",
+    "    Args:\n",
+    "        r_1\n",
+    "        \n",
+    "    Returns:\n",
+    "        derivative    \n",
+    "    '''\n",
+    "    # Add your solution here\n",
+    "    derivative = \n",
+    "    return derivative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Set value of ΔL\n",
+    "ΔL = 1\n",
+    "guess = 100_000\n",
+    "\n",
+    "def derivative_form(r_1):\n",
+    "    ''' Find max distance using canonical derivative\n",
+    "    \n",
+    "    Args:\n",
+    "        r_1\n",
+    "        \n",
+    "    Returns:\n",
+    "        derivative    \n",
+    "    '''\n",
+    "\n",
+    "    \n",
+    "    derivative = (L_0) / (Y * A) * G * m_1 * m_2 * (2/(L_0 + ΔL + r_1)**3 - 2/r_1**3)\n",
+    "    return derivative\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Nh_KMPubua1v"
+   },
+   "source": [
+    "### 4.2. Use Scipy to determine $r_1$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use SciPy's Newton method\n",
+    "# Add your solution here\n",
+    "scipy_sln = \n",
+    "print('The result of the scipy optimize newton function is \\n',scipy_sln)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rOWGNX6vf6T5",
+    "outputId": "c28dfbc0-52dc-4498-addf-c0d1c8dea91b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The result of the scipy optimize newton function is \n",
+      " (12761113.50181692,       converged: True\n",
+      "           flag: converged\n",
+      " function_calls: 44\n",
+      "     iterations: 22\n",
+      "           root: 12761113.50181692)\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Use SciPy's Newton method\n",
+    "scipy_sln = optimize.newton(func=canonical_form, x0 = guess, fprime=derivative_form, tol=1e-2, full_output=True)\n",
+    "print('The result of the scipy optimize newton function is \\n',scipy_sln)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zZ9TDztFve7l"
+   },
+   "source": [
+    "## 5. Analysis and Comparison of Each Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.1. Testing the inexact Newton's Method\n",
+    "\n",
+    "Test the inexact Newton's Method with guesses of different orders of magnitude. Find a guess that gives you a solution and a guess that does not. You should continue adjusting your initial guess until you get an error.\n",
+    "\n",
+    "*Hint:* Remember to pass a reasonable value for delta to ```inexact_newton```."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Intial guess\n",
+    "# add your solution here\n",
+    "guess1 =\n",
+    "newton_sln1 = \n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 100.0\n",
+      "Residual 0: 1779396688341057.0\n",
+      "\n",
+      "Guess 1: 135.70871381673226\n",
+      "Residual 1: 740287617039185.4\n",
+      "\n",
+      "Guess 2: 183.3516956970089\n",
+      "Residual 2: 309058458886371.4\n",
+      "\n",
+      "Guess 3: 246.89911947369208\n",
+      "Residual 3: 129364558441638.86\n",
+      "\n",
+      "Guess 4: 331.6465520952954\n",
+      "Residual 4: 54255190565019.32\n",
+      "\n",
+      "Guess 5: 444.65624650991634\n",
+      "Residual 5: 22787994444815.258\n",
+      "\n",
+      "Guess 6: 595.3456569597986\n",
+      "Residual 6: 9581870128968.305\n",
+      "\n",
+      "Guess 7: 796.2722222050768\n",
+      "Residual 7: 4032312039270.9365\n",
+      "\n",
+      "Guess 8: 1064.1798155792612\n",
+      "Residual 8: 1697961491797.7612\n",
+      "\n",
+      "Guess 9: 1421.3940657445287\n",
+      "Residual 9: 715325958679.188\n",
+      "\n",
+      "Guess 10: 1897.682824515651\n",
+      "Residual 10: 301461600693.3122\n",
+      "\n",
+      "Guess 11: 2532.7368204306767\n",
+      "Residual 11: 127079042732.76134\n",
+      "\n",
+      "Guess 12: 3379.477219071339\n",
+      "Residual 12: 53579828398.53758\n",
+      "\n",
+      "Guess 13: 4508.4657198282475\n",
+      "Residual 13: 22593982890.42541\n",
+      "\n",
+      "Guess 14: 6013.784697524499\n",
+      "Residual 14: 9528671312.327644\n",
+      "\n",
+      "Guess 15: 8020.877400027863\n",
+      "Residual 15: 4018906981.5285544\n",
+      "\n",
+      "Guess 16: 10697.001551751415\n",
+      "Residual 16: 1695159651.2643793\n",
+      "\n",
+      "Guess 17: 14265.167498030089\n",
+      "Residual 17: 715045276.8437557\n",
+      "\n",
+      "Guess 18: 19022.722397448808\n",
+      "Residual 18: 301628025.3185566\n",
+      "\n",
+      "Guess 19: 25366.12914993318\n",
+      "Residual 19: 127239293.46102208\n",
+      "\n",
+      "Guess 20: 33824.00493986472\n",
+      "Residual 20: 53675903.524093725\n",
+      "\n",
+      "Guess 21: 45101.172662368204\n",
+      "Residual 21: 22643517.558834456\n",
+      "\n",
+      "Guess 22: 60137.39593775892\n",
+      "Residual 22: 9552416.05380719\n",
+      "\n",
+      "Guess 23: 80185.69234148588\n",
+      "Residual 23: 4029824.746011326\n",
+      "\n",
+      "Guess 24: 106916.74883437951\n",
+      "Residual 24: 1700050.2929604116\n",
+      "\n",
+      "Guess 25: 142558.14445037514\n",
+      "Residual 25: 717198.3878427617\n",
+      "\n",
+      "Guess 26: 190079.96408570683\n",
+      "Residual 26: 302564.60320213286\n",
+      "\n",
+      "Guess 27: 253442.24494545473\n",
+      "Residual 27: 127643.16950270759\n",
+      "\n",
+      "Guess 28: 337924.825747605\n",
+      "Residual 28: 53848.884391301646\n",
+      "\n",
+      "Guess 29: 450566.8790540061\n",
+      "Residual 29: 22717.13001460965\n",
+      "\n",
+      "Guess 30: 600751.7878325778\n",
+      "Residual 30: 9583.49272491798\n",
+      "\n",
+      "Guess 31: 800984.1706324774\n",
+      "Residual 31: 4042.7614641464456\n",
+      "\n",
+      "Guess 32: 1067915.3647484349\n",
+      "Residual 32: 1705.2751301611981\n",
+      "\n",
+      "Guess 33: 1423680.8194451618\n",
+      "Residual 33: 719.1506225678659\n",
+      "\n",
+      "Guess 34: 1897584.654710977\n",
+      "Residual 34: 303.1298309916237\n",
+      "\n",
+      "Guess 35: 2528039.0459216526\n",
+      "Residual 35: 127.62106733184729\n",
+      "\n",
+      "Guess 36: 3364155.8826782126\n",
+      "Residual 36: 53.58028665026278\n",
+      "\n",
+      "Guess 37: 4464999.087625733\n",
+      "Residual 37: 22.345358502300154\n",
+      "\n",
+      "Guess 38: 5889566.303128481\n",
+      "Residual 38: 9.172205219005237\n",
+      "\n",
+      "Guess 39: 7659756.901196487\n",
+      "Residual 39: 3.624027206162788\n",
+      "\n",
+      "Guess 40: 9660531.628038287\n",
+      "Residual 40: 1.3049550160245462\n",
+      "\n",
+      "Guess 41: 11484004.727697873\n",
+      "Residual 41: 0.37209936727929804\n",
+      "\n",
+      "Guess 42: 12522177.958796358\n",
+      "Residual 42: 0.05834214113622749\n",
+      "\n",
+      "It took 43 iterations\n",
+      "The root using the inexact newton function for guess2 is found at 12752256.643475775\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Initial Guess\n",
+    "guess1 = 1e2\n",
+    "newton_sln1 = inexact_newton(canonical_form, guess1, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln1)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Higher guess\n",
+    "# add your solution here\n",
+    "guess2 =\n",
+    "newton_sln2 = \n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 1000000.0\n",
+      "Residual 0: 2077.064915367613\n",
+      "\n",
+      "Guess 1: 1333175.7597753783\n",
+      "Residual 1: 875.9981457928803\n",
+      "\n",
+      "Guess 2: 1777064.3732635635\n",
+      "Residual 2: 369.29919074037207\n",
+      "\n",
+      "Guess 3: 2367820.155411785\n",
+      "Residual 3: 155.5370374879682\n",
+      "\n",
+      "Guess 4: 3152047.145116894\n",
+      "Residual 4: 65.35686941627573\n",
+      "\n",
+      "Guess 5: 4186924.9157805927\n",
+      "Residual 5: 27.31255390260715\n",
+      "\n",
+      "Guess 6: 5533300.869885832\n",
+      "Residual 6: 11.266258838866499\n",
+      "\n",
+      "Guess 7: 7227393.696748207\n",
+      "Residual 7: 4.50452877772687\n",
+      "\n",
+      "Guess 8: 9198544.641646804\n",
+      "Residual 8: 1.6699807839471856\n",
+      "\n",
+      "Guess 9: 11116665.185947483\n",
+      "Residual 9: 0.5126625476845439\n",
+      "\n",
+      "Guess 10: 12372595.476381885\n",
+      "Residual 10: 0.09719357366585712\n",
+      "\n",
+      "It took 11 iterations\n",
+      "The root using the inexact newton function for guess2 is found at 12737754.529301692\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Higher guess\n",
+    "guess2 = 1e6\n",
+    "newton_sln2 = inexact_newton(canonical_form, guess2, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln2)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Even higher guess\n",
+    "# add your solution here\n",
+    "guess3 =\n",
+    "newton_sln3 = \n",
+    "print(\"The root using the inexact newton function for guess2 is found at\", newton_sln3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 1000000000000.0\n",
+      "Residual 0: -0.9999999999999979\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[43], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### BEGIN SOLUTION\u001b[39;00m\n\u001b[1;32m      2\u001b[0m guess3 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e12\u001b[39m\n\u001b[0;32m----> 3\u001b[0m newton_sln3 \u001b[38;5;241m=\u001b[39m inexact_newton(canonical_form, guess3, delta \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m, epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0e-2\u001b[39m, LOUD\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe root using the inexact newton function for guess3 is found at\u001b[39m\u001b[38;5;124m\"\u001b[39m, newton_sln3)\n",
+      "Cell \u001b[0;32mIn[34], line 46\u001b[0m, in \u001b[0;36minexact_newton\u001b[0;34m(f, x0, delta, epsilon, LOUD, max_iter)\u001b[0m\n\u001b[1;32m     41\u001b[0m slope \u001b[38;5;241m=\u001b[39m (f(x\u001b[38;5;241m+\u001b[39mdelta)\u001b[38;5;241m-\u001b[39mf(x))\u001b[38;5;241m/\u001b[39mdelta\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# print every iteration\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;66;03m#if (LOUD):\u001b[39;00m\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;66;03m#print(\"x_\",iterations+1,\"=\",x,\"-\",fx,\"/\",slope,\"=\",x - fx/slope)\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m fx\u001b[38;5;241m/\u001b[39mslope\n\u001b[1;32m     48\u001b[0m iterations \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;66;03m# check if converged\u001b[39;00m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "guess3 = 1e12\n",
+    "newton_sln3 = inexact_newton(canonical_form, guess3, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "print(\"The root using the inexact newton function for guess3 is found at\", newton_sln3)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The result of guess1 is 12752256.643475775 \n",
+      "\n",
+      "The result of guess2 is 12737754.529301692 \n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'newton_sln3' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[45], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess1 is\u001b[39m\u001b[38;5;124m\"\u001b[39m, newton_sln1, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess2 is\u001b[39m\u001b[38;5;124m\"\u001b[39m, newton_sln2, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess3 is\u001b[39m\u001b[38;5;124m\"\u001b[39m, newton_sln3)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'newton_sln3' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The result of guess1 is\", newton_sln1, \"\\n\")\n",
+    "print(\"The result of guess2 is\", newton_sln2, \"\\n\")\n",
+    "print(\"The result of guess3 is\", newton_sln3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.1.1. Discussion\n",
+    "\n",
+    "How do the number of iterations required to converge change with the initial guess?\n",
+    "\n",
+    "**Discuss** in 1 - 2 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### BEGIN SOLUTION\n",
+    "\"\"\"\n",
+    "The better the initial guess, the more quickly the inexact Newton's method converges. For a guess that is far\n",
+    "from the true value, the method might not converge at all.\n",
+    "\"\"\"\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2. Finding the limits of the inexact Newton's Method\n",
+    "\n",
+    "Determine the upper and lower limit for an intial guess where the inexact Newton's Method gives a usable solution. Continue making your guess larger until you get an error. The error is your upper limit. Then do the same for the lower limit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the upper limit of inexact Newton's\n",
+    "# Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 20255000.0\n",
+      "Residual 0: -0.7499256322111911\n",
+      "\n",
+      "Guess 1: -15054.494520939887\n",
+      "Residual 1: -609738952.5990518\n",
+      "\n",
+      "Guess 2: -20070.15849125805\n",
+      "Residual 2: -257259265.92451903\n",
+      "\n",
+      "Guess 3: -26757.71068325505\n",
+      "Residual 3: -108539368.04275693\n",
+      "\n",
+      "Guess 4: -35674.447191027415\n",
+      "Residual 4: -45792613.81668643\n",
+      "\n",
+      "Guess 5: -47563.42945585742\n",
+      "Residual 5: -19319571.733188815\n",
+      "\n",
+      "Guess 6: -63415.40636500947\n",
+      "Residual 6: -8150701.718564938\n",
+      "\n",
+      "Guess 7: -84551.3774541377\n",
+      "Residual 7: -3438658.9051913465\n",
+      "\n",
+      "Guess 8: -112732.67870982541\n",
+      "Residual 8: -1450710.1978667194\n",
+      "\n",
+      "Guess 9: -150307.76417317457\n",
+      "Residual 9: -612026.7691303235\n",
+      "\n",
+      "Guess 10: -200407.93198444886\n",
+      "Residual 10: -258201.63959173448\n",
+      "\n",
+      "Guess 11: -267208.34454244113\n",
+      "Residual 11: -108929.88163112903\n",
+      "\n",
+      "Guess 12: -356276.079257975\n",
+      "Residual 12: -45955.32553686122\n",
+      "\n",
+      "Guess 13: -475034.85393074446\n",
+      "Residual 13: -19387.74662652107\n",
+      "\n",
+      "Guess 14: -633385.3905444695\n",
+      "Residual 14: -8179.49634671949\n",
+      "\n",
+      "Guess 15: -844537.1289579207\n",
+      "Residual 15: -3450.995418887658\n",
+      "\n",
+      "Guess 16: -1126127.8574480342\n",
+      "Residual 16: -1456.1559300287538\n",
+      "\n",
+      "Guess 17: -1501759.1081598364\n",
+      "Residual 17: -614.5786276195389\n",
+      "\n",
+      "Guess 18: -2003161.6501154355\n",
+      "Residual 18: -259.5364979161046\n",
+      "\n",
+      "Guess 19: -2673465.781321577\n",
+      "Residual 19: -109.7539568159819\n",
+      "\n",
+      "Guess 20: -3572809.8975080587\n",
+      "Residual 20: -46.56575336410984\n",
+      "\n",
+      "Guess 21: -4789890.5865597455\n",
+      "Residual 21: -19.909976650008613\n",
+      "\n",
+      "Guess 22: -6471032.760618892\n",
+      "Residual 22: -8.669138620106366\n",
+      "\n",
+      "Guess 23: -8909020.312278708\n",
+      "Residual 23: -3.9388487330527977\n",
+      "\n",
+      "Guess 24: -12888825.354763772\n",
+      "Residual 24: -1.970569891813332\n",
+      "\n",
+      "Guess 25: -21605126.720753416\n",
+      "Residual 25: -1.2060611663813787\n",
+      "\n",
+      "Guess 26: -63754787.11928131\n",
+      "Residual 26: -1.0080191435627595\n",
+      "\n",
+      "Guess 27: -2735897909.586219\n",
+      "Residual 27: -1.000000101476764\n",
+      "\n",
+      "Guess 28: -450362744336030.9\n",
+      "Residual 28: -1.0\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[46], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### BEGIN SOLUTION\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# Find the upper limit using scipy method\u001b[39;00m\n\u001b[1;32m      3\u001b[0m upper_limit_inexact \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m20.255e6\u001b[39m\n\u001b[0;32m----> 4\u001b[0m inexact_newton(canonical_form, upper_limit_inexact, delta \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m, epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0e-2\u001b[39m, LOUD\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "Cell \u001b[0;32mIn[34], line 46\u001b[0m, in \u001b[0;36minexact_newton\u001b[0;34m(f, x0, delta, epsilon, LOUD, max_iter)\u001b[0m\n\u001b[1;32m     41\u001b[0m slope \u001b[38;5;241m=\u001b[39m (f(x\u001b[38;5;241m+\u001b[39mdelta)\u001b[38;5;241m-\u001b[39mf(x))\u001b[38;5;241m/\u001b[39mdelta\n\u001b[1;32m     43\u001b[0m \u001b[38;5;66;03m# print every iteration\u001b[39;00m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;66;03m#if (LOUD):\u001b[39;00m\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;66;03m#print(\"x_\",iterations+1,\"=\",x,\"-\",fx,\"/\",slope,\"=\",x - fx/slope)\u001b[39;00m\n\u001b[0;32m---> 46\u001b[0m x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m fx\u001b[38;5;241m/\u001b[39mslope\n\u001b[1;32m     48\u001b[0m iterations \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;66;03m# check if converged\u001b[39;00m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Find the upper limit using scipy method\n",
+    "upper_limit_inexact = 20.255e6\n",
+    "inexact_newton(canonical_form, upper_limit_inexact, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the lower limit of inexact Newton's for delta = 1.0\n",
+    "# Add your solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess 0: 10\n",
+      "Residual 0: 7.30397679238864e+17\n",
+      "\n",
+      "Guess 1: 15.040434888834593\n",
+      "Residual 1: 2.782650462545619e+17\n",
+      "\n",
+      "Guess 2: 21.940392145763077\n",
+      "Residual 2: 1.0917175408693384e+17\n",
+      "\n",
+      "Guess 3: 31.287034743375926\n",
+      "Residual 3: 4.367347854330747e+16\n",
+      "\n",
+      "Guess 4: 43.86414759288348\n",
+      "Residual 4: 1.771255686338696e+16\n",
+      "\n",
+      "Guess 5: 60.72150012198905\n",
+      "Residual 5: 7255737740891424.0\n",
+      "\n",
+      "Guess 6: 83.26430420031042\n",
+      "Residual 6: 2994279811880757.0\n",
+      "\n",
+      "Guess 7: 113.37114611165549\n",
+      "Residual 7: 1242505668600765.2\n",
+      "\n",
+      "Guess 8: 153.550845437472\n",
+      "Residual 8: 517723165558624.8\n",
+      "\n",
+      "Guess 9: 207.1516237973737\n",
+      "Residual 9: 216392096900096.78\n",
+      "\n",
+      "Guess 10: 278.64014763243847\n",
+      "Residual 10: 90655461732963.78\n",
+      "\n",
+      "Guess 11: 373.9737505483072\n",
+      "Residual 11: 38045521335762.21\n",
+      "\n",
+      "Guess 12: 501.0968722450732\n",
+      "Residual 12: 15987521793935.463\n",
+      "\n",
+      "Guess 13: 670.6030897239657\n",
+      "Residual 13: 6724887067719.164\n",
+      "\n",
+      "Guess 14: 896.6179113548496\n",
+      "Residual 14: 2830796469093.3423\n",
+      "\n",
+      "Guess 15: 1197.975899916288\n",
+      "Residual 15: 1192263432076.8613\n",
+      "\n",
+      "Guess 16: 1599.7902178372299\n",
+      "Residual 16: 502360858914.0219\n",
+      "\n",
+      "Guess 17: 2135.5453897240104\n",
+      "Residual 17: 211735844118.94693\n",
+      "\n",
+      "Guess 18: 2849.8876788226007\n",
+      "Residual 18: 89263570616.61972\n",
+      "\n",
+      "Guess 19: 3802.345608615149\n",
+      "Residual 19: 37638308185.15326\n",
+      "\n",
+      "Guess 20: 5072.290672894123\n",
+      "Residual 20: 15872411502.719048\n",
+      "\n",
+      "Guess 21: 6765.551626754832\n",
+      "Residual 21: 6694196763.649567\n",
+      "\n",
+      "Guess 22: 9023.233549437558\n",
+      "Residual 22: 2823488923.481483\n",
+      "\n",
+      "Guess 23: 12033.47660101067\n",
+      "Residual 23: 1190961564.1606927\n",
+      "\n",
+      "Guess 24: 16047.134365680335\n",
+      "Residual 24: 502374324.959376\n",
+      "\n",
+      "Guess 25: 21398.678320575953\n",
+      "Residual 25: 211919367.8419206\n",
+      "\n",
+      "Guess 26: 28534.070441130738\n",
+      "Residual 26: 89397218.61775942\n",
+      "\n",
+      "Guess 27: 38047.9266872557\n",
+      "Residual 27: 37712469.33863661\n",
+      "\n",
+      "Guess 28: 50733.06825526741\n",
+      "Residual 28: 15909320.629091263\n",
+      "\n",
+      "Guess 29: 67646.58973415378\n",
+      "Residual 29: 6711545.954610961\n",
+      "\n",
+      "Guess 30: 90197.94955854154\n",
+      "Residual 30: 2831370.398807547\n",
+      "\n",
+      "Guess 31: 120266.42310119365\n",
+      "Residual 31: 1194464.2293482446\n",
+      "\n",
+      "Guess 32: 160357.70015824796\n",
+      "Residual 32: 503908.02193665155\n",
+      "\n",
+      "Guess 33: 213812.66137551583\n",
+      "Residual 33: 212583.94461804072\n",
+      "\n",
+      "Guess 34: 285085.71777252416\n",
+      "Residual 34: 89682.95641939779\n",
+      "\n",
+      "Guess 35: 380115.7565568809\n",
+      "Residual 35: 37834.52873223905\n",
+      "\n",
+      "Guess 36: 506820.17719574063\n",
+      "Residual 36: 15961.115463940445\n",
+      "\n",
+      "Guess 37: 675752.0585857017\n",
+      "Residual 37: 6733.316748332508\n",
+      "\n",
+      "Guess 38: 900971.6769844894\n",
+      "Residual 38: 2840.3511295504227\n",
+      "\n",
+      "Guess 39: 1201191.68694343\n",
+      "Residual 39: 1198.0115220610107\n",
+      "\n",
+      "Guess 40: 1601257.3635572637\n",
+      "Residual 40: 505.14899755645723\n",
+      "\n",
+      "Guess 41: 2133955.058827273\n",
+      "Residual 41: 212.84894453561233\n",
+      "\n",
+      "Guess 42: 2841963.0883751614\n",
+      "Residual 42: 89.53332179115505\n",
+      "\n",
+      "Guess 43: 3778821.1831136704\n",
+      "Residual 43: 37.51193641416687\n",
+      "\n",
+      "Guess 44: 5005703.109634448\n",
+      "Residual 44: 15.56798073287592\n",
+      "\n",
+      "Guess 45: 6573542.184327551\n",
+      "Residual 45: 6.315884206363025\n",
+      "\n",
+      "Guess 46: 8465333.998628428\n",
+      "Residual 46: 2.4255722827515442\n",
+      "\n",
+      "Guess 47: 10463335.412506893\n",
+      "Residual 47: 0.8140746278650042\n",
+      "\n",
+      "Guess 48: 12028517.135861859\n",
+      "Residual 48: 0.1940689532415114\n",
+      "\n",
+      "Guess 49: 12679949.370644793\n",
+      "Residual 49: 0.019326118336442155\n",
+      "\n",
+      "It took 50 iterations\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "12760122.035803474"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Find the lower limit of inexact Newton's for delta = 1.0\n",
+    "lower_limit_inexact = 10\n",
+    "inexact_newton(canonical_form, lower_limit_inexact, delta = 1.0, epsilon=1.0e-2, LOUD=True)\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The range of guesses you can use for the inexact Newton's method is 10 to 20255000.0 meters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'The range of guesses you can use for the inexact Newton\\'s method is {lower_limit_inexact} to {upper_limit_inexact} meters.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HwZYjV0RB9o7"
+   },
+   "source": [
+    "### 5.3. Testing Newton's Method with Scipy\n",
+    "\n",
+    "Using SciPy, test Newton's Method for guesses of different orders of magnitude. Find a guess that gives you a solution and a guess that doesn't give you a solution. You should continue adjusting your initial guess until you get an error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first order of magnitude\n",
+    "# Add your solution here\n",
+    "guess1 =\n",
+    "\n",
+    "scipy_sln1 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess1, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# first order of magnitude\n",
+    "guess1 = 100\n",
+    "\n",
+    "scipy_sln1 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess1, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# next order of magnitude\n",
+    "# Add your solution here\n",
+    "guess2 =\n",
+    "\n",
+    "scipy_sln2 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess2, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# next order of magnitude\n",
+    "guess2 = 1e6\n",
+    "\n",
+    "scipy_sln2 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess2, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# very large magnitude\n",
+    "# Add your solution here\n",
+    "guess3 = \n",
+    "\n",
+    "scipy_sln3 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess3, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Derivative was zero. Failed to converge after 2 iterations, value is -1.60403199328293e+26.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[51], line 6\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### BEGIN SOLUTION\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# very large magnitude\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Add your solution here\u001b[39;00m\n\u001b[1;32m      4\u001b[0m guess3 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e12\u001b[39m\n\u001b[0;32m----> 6\u001b[0m scipy_sln3 \u001b[38;5;241m=\u001b[39m optimize\u001b[38;5;241m.\u001b[39mnewton(\n\u001b[1;32m      7\u001b[0m     func\u001b[38;5;241m=\u001b[39mcanonical_form, x0\u001b[38;5;241m=\u001b[39mguess3, fprime\u001b[38;5;241m=\u001b[39mderivative_form, tol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-2\u001b[39m, full_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m      8\u001b[0m )\n",
+      "File \u001b[0;32m~/miniconda3/envs/CBE/lib/python3.11/site-packages/scipy/optimize/_zeros_py.py:314\u001b[0m, in \u001b[0;36mnewton\u001b[0;34m(func, x0, fprime, args, tol, maxiter, fprime2, x1, rtol, full_output, disp)\u001b[0m\n\u001b[1;32m    310\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m disp:\n\u001b[1;32m    311\u001b[0m     msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    312\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Failed to converge after \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m iterations, value is \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    313\u001b[0m         \u001b[38;5;241m%\u001b[39m (itr \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, p0))\n\u001b[0;32m--> 314\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(msg)\n\u001b[1;32m    315\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(msg, \u001b[38;5;167;01mRuntimeWarning\u001b[39;00m)\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _results_select(\n\u001b[1;32m    317\u001b[0m     full_output, (p0, funcalls, itr \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, _ECONVERR))\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Derivative was zero. Failed to converge after 2 iterations, value is -1.60403199328293e+26."
+     ]
     }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# very large magnitude\n",
+    "# Add your solution here\n",
+    "guess3 = 1e12\n",
+    "\n",
+    "scipy_sln3 = optimize.newton(\n",
+    "    func=canonical_form, x0=guess3, fprime=derivative_form, tol=1e-2, full_output=True\n",
+    ")\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The result of guess1 is \n",
+      " (12761113.501660362,       converged: True\n",
+      "           flag: converged\n",
+      " function_calls: 92\n",
+      "     iterations: 46\n",
+      "           root: 12761113.501660362) \n",
+      "\n",
+      "The result of guess2 is \n",
+      " (12761113.50174952,       converged: True\n",
+      "           flag: converged\n",
+      " function_calls: 28\n",
+      "     iterations: 14\n",
+      "           root: 12761113.50174952) \n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'scipy_sln3' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[52], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess1 is \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, scipy_sln1, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess2 is \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, scipy_sln2, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result of guess3 is \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, scipy_sln3)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'scipy_sln3' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The result of guess1 is \\n\", scipy_sln1, \"\\n\")\n",
+    "print(\"The result of guess2 is \\n\", scipy_sln2, \"\\n\")\n",
+    "print(\"The result of guess3 is \\n\", scipy_sln3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "S-1298pcOt0v"
+   },
+   "source": [
+    "#### 5.3.1 Discussion\n",
+    "\n",
+    "How do different initial guesses change the number of iterations required to converge? What types of errors does SciPy's method encounter and how does this compare with the error encountered with the inexact Newton's Method? Do you notice any other notable differences between the two methods?\n",
+    "\n",
+    "**Discuss** in 4-6 sentences\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "'''\n",
+    "As with the inexact Newton's Method, the quality of the initial guess changes the number of iterations\n",
+    "required to converge. Also, it appears that even SciPy's implementation of Newton's method struggles with\n",
+    "division by zero for some guesses. Notably, SciPy's final estimates are more consistent than our implementation\n",
+    "of inexact Newton's.\n",
+    "'''\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFdcM25JELJe"
+   },
+   "source": [
+    "### 5.4. Finding the limits of Newton's Method with Scipy\n",
+    "\n",
+    "Determine the upper and lower limit for an intial guess for which SciPy's ```optimize.newton``` gives a usable solution. Continue making your guess larger until you get an error; this error is your upper limit. Then do the same for the lower limit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the upper limit using scipy method\n",
+    "upper_limit_scipy = 1e7\n",
+    "\n",
+    "while True:\n",
+    "  try:\n",
+    "    # Add your solution here\n",
+    "    \n",
+    "  except:\n",
+    "    print(upper_limit_scipy)\n",
+    "    break   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "id": "8tGe8PNKl0XA"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20257000.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Find the upper limit using scipy method\n",
+    "upper_limit_scipy = 1e7\n",
+    "\n",
+    "while True:\n",
+    "  try:\n",
+    "    upper_limit_scipy += 100\n",
+    "    optimize.newton(func = canonical_form, x0 = upper_limit_scipy, fprime = derivative_form, tol=1e-2)\n",
+    "  except:\n",
+    "    print(upper_limit_scipy)\n",
+    "    break  \n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the lower limit using scipy method\n",
+    "lower_limit_scipy = 1e2\n",
+    "\n",
+    "while True:\n",
+    "  try:\n",
+    "    # Add your solution here\n",
+    "    \n",
+    "  except:\n",
+    "    print(lower_limit_scipy)\n",
+    "    break  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "id": "n7usWlAzl1VF"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### BEING SOLUTION\n",
+    "# Find the lower limit using scipy method\n",
+    "lower_limit_scipy = 1e2\n",
+    "\n",
+    "while True:\n",
+    "  try:\n",
+    "    lower_limit_scipy -= 1\n",
+    "    optimize.newton(func = canonical_form, x0 = lower_limit_scipy, fprime = derivative_form, tol=1e-2)\n",
+    "  except:\n",
+    "    print(lower_limit_scipy)\n",
+    "    break  \n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QEAmwkB-MD3V",
+    "outputId": "4f6f3640-8c29-422f-a725-76474a9a8f5b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For this problem, SciPy's optimize.newton works from 25.0 meters to 20257000.0 meters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('For this problem, SciPy\\'s optimize.newton works from', lower_limit_scipy, 'meters to', upper_limit_scipy, 'meters.')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Taylor Series Approximation\n",
+    "\n",
+    "Apart from Newton's Method, we have another tool up our sleeve to help us more easily express the relationship between $r_1$ and $\\Delta L$: a Taylor Series expansion!\n",
+    "\n",
+    "The difference in gravitational force between the front and back of the bus is given by\n",
+    "\n",
+    "$$\n",
+    "\\Delta F = G m_1 m_2 \\left( \\frac{1}{r_1^2} - \\frac{1}{\\left( r_1 + L_0 + \\Delta L \\right)^2} \\right).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For ease of manipulation, let's temporarily express $L_0 + \\Delta L$ as, simply $L$. Further, let's say $r_1$, rather the reaching to the front of the bus, extends from the center of the black hole to the center of the bus; we'll just call this slight alteration $r$. Now, the difference in gravitational force between the front and back of the bus becomes\n",
+    "\n",
+    "$$\n",
+    "\\Delta F = G m_1 m_2 \\left( \\frac{1}{\\left( r - \\frac{L}{2} \\right)^2} - \\frac{1}{\\left( r + \\frac{L}{2} \\right)^2} \\right).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Immediately, this doesn't appear too helpful. But let's focus on the terms in parentheses, which are now symmetric and can together be expressed by\n",
+    "\n",
+    "$$\n",
+    "\\frac{1}{\\left( r \\pm \\frac{L}{2} \\right)^2}.\n",
+    "$$\n",
+    "\n",
+    "Factoring out $r$, we have\n",
+    "\n",
+    "$$\n",
+    "\\frac{1}{\\left(r \\left( 1 \\pm \\frac{L}{2r} \\right) \\right)^2} = \\frac{1}{r^2 \\left( 1 \\pm \\frac{L}{2r} \\right)^2}.\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, by recognizing that $\\frac{L}{2r} << 1$, we determine that a first-order Taylor Series expansion about zero will be a decent approximation of the term $\\left( 1 \\pm \\frac{L}{2r}\\right)^{-2}$.\n",
+    "\n",
+    "Given the funcion\n",
+    "\n",
+    "$$\n",
+    "f\\left(\\frac{L}{2r}\\right) = \\left( 1 \\pm \\frac{L}{2r}\\right)^{-2},\n",
+    "$$\n",
+    "then\n",
+    "$$\n",
+    "f\\left(\\frac{L}{2r}\\right) \\approx f(0) + \\frac{L}{2r} f'(0) \\approx 1 \\mp \\frac{L}{r}.\n",
+    "$$\n",
+    "\n",
+    "As such, \n",
+    "\n",
+    "$$\n",
+    "\\left( \\frac{1}{\\left( r - \\frac{L}{2} \\right)^2} - \\frac{1}{\\left( r + \\frac{L}{2} \\right)^2} \\right) = \\frac{1}{r^2} \\left( 1 + \\frac{L}{r}\\right) - \\frac{1}{r^2} \\left( 1 - \\frac{L}{r}\\right) = \\frac{1}{r^2} + \\frac{L}{r^3} - \\frac{1}{r^2} + \\frac{L}{r^3}\n",
+    "$$\n",
+    "which reduces to\n",
+    "$$\n",
+    "\\frac{2L}{r^3}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From here, it follows that\n",
+    "\n",
+    "$$\n",
+    "\\Delta F \\approx G m_1 m_2 \\frac{2L}{r^3} = \\frac{2 G m_1 m_2 \\left(L_0 + \\Delta L \\right)}{r^3}.\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Equating this to Young's Modulus, it becomes a simple matter to solve for $r$ in terms of $\\Delta L$:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\Delta L Y A}{L_0} \\approx \\frac{2 G m_1 m_2 \\left(L_0 + \\Delta L \\right)}{r^3}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\therefore r \\approx \\left( \\frac{2 G m_1 m_2 L_0 \\left(L_0 + \\Delta L \\right)}{\\Delta L Y A} \\right) ^{1/3}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.1. Analyzing the approximation of $r(\\Delta L)$\n",
+    "First, write a python function that returns $r$ for a given $\\Delta L$ using the constants we have established for this problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write a function for r(ΔL)\n",
+    "# Add your solution here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Write a function for r(ΔL)\n",
+    "def r(ΔL):\n",
+    "    return np.power((2 * G * m_1 * m_2 * L_0 * (L_0 + ΔL)) / (ΔL * Y * A), 1/3)\n",
+    "### END SOluTOIN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's see if evaluating $r$ at $\\Delta L = 1$ gives us a similar approximation as Netwon's methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The bus will stretch 1 meter(s) at distance of approximately 12761119.001831042 meters from Sagittarius A*.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Evalute r at ΔL = 1\n",
+    "ΔL = 1\n",
+    "print(f'The bus will stretch {ΔL} meter(s) at distance of approximately {r(1)} meters from Sagittarius A*.')\n",
+    "### END SOLUTOIN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.1.1. Discussion\n",
+    "\n",
+    "How does this value of $r_1$ agree with the value returned by the inexact Newton's Method? What about Scipy's Newton's Method? Does it appear to be an accurate approximation?\n",
+    "\n",
+    "**Discuss** in 1 - 3 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "'''\n",
+    "Our Taylor Serie's expansion of r_1 agrees with theh first three terms to the results of the inexact Newton's \n",
+    "Method. It agrees with the first seven terms to the results of Scipy's Newton's Method. As such, it seems \n",
+    "to be a very accurate estimate, at least for calculations that satisfy our assumption that L_0 + ΔL << r_1.\n",
+    "'''\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.2. Graphing $r(\\Delta L)$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, make a plot $r(\\Delta L)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a plot of r(ΔL)\n",
+    "# Add you solution here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1920x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "# Make a plot of r(ΔL)\n",
+    "dL = np.arange(1,1000,1)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 300)\n",
+    "ax.plot(dL, r(dL))\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xscale(\"log\")\n",
+    "\n",
+    "\n",
+    "# define tick size\n",
+    "plt.xticks(fontsize=15)\n",
+    "plt.yticks(fontsize=15)\n",
+    "plt.tick_params(direction=\"in\", top=True, right=True)\n",
+    "\n",
+    "# plot titile and x,y label\n",
+    "plt.xlabel(\"ΔL (m)\", fontsize=16, fontweight=\"bold\")\n",
+    "plt.ylabel(\"Distance (m)\", fontsize=16, fontweight=\"bold\")\n",
+    "plt.title(\"Distance vs. Change in Bus Length\", fontsize=16, fontweight=\"bold\")\n",
+    "plt.show()\n",
+    "### END SOLUTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.2.1. Discussion\n",
+    "\n",
+    "Does the plot make sense given what you know about the relationship between the distance from the black hole and the subsequent tensile force on the bus?\n",
+    "\n",
+    "**Discuss** in 1-3 sentences.\n",
+    "\n",
+    "**Answer:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### BEGIN SOLUTION\n",
+    "'''\n",
+    "Yes, the plot make sense. As the bus gets closer to the black hole (distance decreases), ΔL increases due to the\n",
+    "increase in the strength of the tensile force.\n",
+    "'''\n",
+    "### END SOLUTION"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python [conda env:CBE]",
+   "language": "python",
+   "name": "conda-env-CBE-py"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
 }
diff --git a/notebooks/contrib-dev/Stokes_Settling_Velocity.ipynb b/notebooks/contrib-dev/Stokes_Settling_Velocity.ipynb
new file mode 100644
index 00000000..e0eeeed2
--- /dev/null
+++ b/notebooks/contrib-dev/Stokes_Settling_Velocity.ipynb
@@ -0,0 +1,994 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9nUUNAQW1R6m"
+      },
+      "source": [
+        "# Using a New Dataset to Evaluate Stoke's Settling Velocity"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "58Az-fqTXLbO"
+      },
+      "source": [
+        "**Prepared by:** Kristin Swartz (kswarts3@nd.edu) and Alexis Laudenslager (alaudens@nd.edu)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QZh4kSflXqo3"
+      },
+      "source": [
+        "**Reference:** This is an original problem created by the authors.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aLj4IXguX5zf"
+      },
+      "source": [
+        "**Intended Audience:** The intended audience of this notebook is students learning Python coding and probability theory, for example, in CBE 60258."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pkxa4qn9YTt7"
+      },
+      "source": [
+        "## Learning Objectives\n",
+        "\n",
+        "After studying this notebook and asking questions in class, you should be able to:\n",
+        "\n",
+        "\n",
+        "*   Fit linear and nonlinear regression models to real data\n",
+        "*   Plot a histogram of the **residuals** and verify the distribution\n",
+        "*   Create a confidence interval for the model\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FNoltRl9aH62"
+      },
+      "source": [
+        "## Resources\n",
+        "\n",
+        "Relevant modules on the class website:\n",
+        "\n",
+        "1. [Linear Regression with Transformations](https://ndcbe.github.io/data-and-computing/notebooks/15/Transformations-and-Linear-Regression.html?highlight=transformation)\n",
+        "\n",
+        "\n",
+        "2. [Nonlinear Regression](https://ndcbe.github.io/data-and-computing/notebooks/15/advanced_regression.html?highlight=nonlinear)\n",
+        "\n",
+        "\n",
+        "3. [Plotting a Histogram of the Residuals](https://ndcbe.github.io/data-and-computing/notebooks/09/Visualizing-Data.html?highlight=histogram#histogram)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yAS2pJ3QcYTX"
+      },
+      "source": [
+        "## Import Libraries"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Background"
+      ],
+      "metadata": {
+        "id": "eldAvMvW1EKL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KeO3vNqWcjBA"
+      },
+      "source": [
+        "\n",
+        "### 1.1 Problem Statement\n",
+        "In this notebook, we'll be analyzing a small portion of data from experiments designed to determine the settling rate of particles in a turbulent environment. Understanding how long solid particles remain suspended in a turbulent environment is important for predicting the formation and characteristics of clouds, which require such particles for their existence. Clouds have a significant impact on the climate, so this area of research is relevant for short term and long term climate modeling.\n",
+        "\n",
+        "We have a model for particle settling rates in a nonturbulent environment known as Stoke's settling velocity, or Stoke's law. In a turbulent environment, however, things are much more complicated. In this research, we're attemping to determine if Stoke's law is predictive over some range of particle size and density in a turbulent environment.\n",
+        "\n",
+        "For the experiments, particles from 1 - 30 microns were injected into a turbulence-generating chamber. After the chamber achieved a certain concentration of particles, the injection was stopped. At that point, particle size and concentration measurements were taken at regular intervals in order to observe the decay of particles in the chamber. From these measurement, we can determine the settling velocity and compare it to Stoke's law.\n",
+        "\n",
+        "**We'll be focusing on the first part of the analysis process $-$ determining the settling rate from the collected data.** To do this, we'll use linear and nonlinear regression to determine the decay rate of 4 micron particles that were injected into the chamber. We'll then plot confidence intervals and the distribution of the residuals to assess the reliability of our model.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1.2 Derivation of Stoke's Law (Optional)"
+      ],
+      "metadata": {
+        "id": "CNCKYU5Q6laD"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "For this notebook, we want to look at Stoke's settling velocity, which requires setting the net force on the particle equal to zero and then determining the velocity at which this occurs. The forces acting on the particle are friction, the buoyant force, and gravity."
+      ],
+      "metadata": {
+        "id": "PaEcGgfKk3e4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### 1.2.1. Drag Force\n",
+        "\n",
+        "$$ F_{D} = 6\\pi\\eta r v...(Eq. 1)$$\n",
+        "\n",
+        "$F_{D}$ = drag (friction) force\n",
+        "\n",
+        "$\\eta$ = viscosity of fluid\n",
+        "\n",
+        "$r$ = radius of particle\n",
+        "\n",
+        "$v$ = velocity of the particle\n",
+        "\n",
+        "Equation 1 shows the friction force on a particle of radius $r$ falling vertically through a fluid of viscosity $\\eta$."
+      ],
+      "metadata": {
+        "id": "6_YKGpPZjaZ5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### 1.2.2. Gravity Force\n",
+        "\n",
+        "$$ F_{G} = \\frac{4}{3}\\pi r^{3} \\rho g...(Eq. 2)$$\n",
+        "\n",
+        "$F_{G}$ = gravitational force\n",
+        "\n",
+        "$\\rho$ = density of the particle\n",
+        "\n",
+        "$g$ = acceleration due to gravity\n",
+        "\n",
+        "Equation 2 shows the force of gravity on a particle of radius $r$ and density $\\rho$."
+      ],
+      "metadata": {
+        "id": "04NqytOUmVwP"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### 1.2.3. Bouyant Force\n",
+        "$$F_{B} = \\frac{4}{3}\\pi r^{3} \\sigma g...(Eq. 3)$$\n",
+        "\n",
+        "$F_{B}$ = buoyancy force\n",
+        "\n",
+        "$\\sigma$ = the density of the fluid\n",
+        "\n",
+        "Equation 3 shows the bouyant on a particle of radius $r$ in a fluid of density $\\sigma$."
+      ],
+      "metadata": {
+        "id": "klnSz2G1nTno"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### 1.2.4. Sum of Forces\n",
+        "Since we are looking for the velocity of the particle as it is falling through a moving fluid, we know that the friction force and buoyancy forces will be acting upwards and the gravitational force will be acting downwards. Thus, we can set the net force of the system equal to zero, which leads to Equation 4."
+      ],
+      "metadata": {
+        "id": "roqGGu4-npSQ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "$$F_{D} + F_{B} = F_{G}...(Eq. 4)$$"
+      ],
+      "metadata": {
+        "id": "ofXURQapn74z"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can now solve for Stoke's settling velocity using some simple alegebra. The result is shown below in Equation 5. Please write out the derivation of Equation 5 by hand and **upload to Gradescope**.\n",
+        "\n",
+        "*Hint: Plug Equations 1, 2, and 3 into Equation 4, then rearrange to solve for v.*\n"
+      ],
+      "metadata": {
+        "id": "_vtXje7M-iH1"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "$$v = \\frac{2 r^{2} g (\\rho - \\sigma)}{9 \\eta}...(Eq. 5)$$"
+      ],
+      "metadata": {
+        "id": "bLVGhJCosw4s"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "**Answer:**\n",
+        "$$6 \\pi \\eta r v + \\frac{4}{3} \\pi r^3 \\sigma g = \\frac{4}{3} \\pi r^{3} \\rho g$$\n",
+        "\n",
+        "$$6 \\eta v + \\frac{4}{3} \\pi r^{2} \\sigma g = \\frac{4}{3} \\pi r^{2} \\rho g$$\n",
+        "\n",
+        "$$6 \\eta v = \\frac{4}{3} r^{2} g (\\rho - \\sigma)$$\n",
+        "\n",
+        "$$v = \\frac{\\frac{4}{3} r^{2} g (\\rho - \\sigma)}{6 \\eta}$$\n",
+        "\n",
+        "$$v = \\frac{2 r^{2} g (\\rho - \\sigma)}{9 \\eta}$$\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "V7KK6VmfrbAz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1.3 Derivation of the Model\n",
+        "\n",
+        "After we inject particles into the chamber, their only means of exiting the turbulent circulation is to settle along the bottom of the chamber. As such, we can use conservation principles to build a mathematical model for this settling behavior. Using Reynold's transport theorem, we express\n",
+        "\n",
+        "$$\n",
+        "\\frac{\\partial}{\\partial t} \\oint_V c \\ dV + \\oint_S c \\left( \\vec v_s \\cdot \\hat n \\right) dA = 0\n",
+        "$$\n",
+        "\n",
+        "where $c$ is the concentration of particles in the chamber and $\\vec v$ is the velocity at which the particles leave the circulation through settling. Evaluating this integral gives\n",
+        "\n",
+        "$$\n",
+        "\\frac{\\partial N}{\\partial t} = - c A v...(Eq. 6)\n",
+        "$$\n",
+        "\n",
+        "which says that the change over time in the number of particles in the chamber, $N$, is equal to minus the concentration multiplied by the area over which the particles settle and their settling vecocity. If we express the settling velocity as\n",
+        "\n",
+        "$$\n",
+        "v = \\frac{h}{t'},\n",
+        "$$\n",
+        "\n",
+        "where $h$ is the height of the chamber and $t'$ is the time the particles spend in the circulation before settling out, then our differential equation becomes\n",
+        "\n",
+        "$$\n",
+        "\\frac{\\partial N}{\\partial t} = - \\frac{c A h}{t'}.\n",
+        "$$\n",
+        "\n",
+        "Given that\n",
+        "\n",
+        "$$\n",
+        "Ah = V,\n",
+        "$$\n",
+        "\n",
+        "where $V$ is the volume of the chamber, then\n",
+        "\n",
+        "$$\n",
+        "cAh = cV = N,\n",
+        "$$\n",
+        "\n",
+        "and we can write\n",
+        "\n",
+        "$$\n",
+        "\\frac{\\partial N}{\\partial t} = - \\frac{N}{t'}.\n",
+        "$$\n",
+        "\n",
+        "The solution to this differential equation is\n",
+        "\n",
+        "$$\n",
+        "N = N_0 e^{-\\lambda t}\n",
+        "$$\n",
+        "\n",
+        "where $N_{0}$ is the initial number of particles in the chamber and $\\lambda = -\\frac{1}{t'}$. We can divide this through by the volume of the chamber to get an equation more in line with the collected data (concentration over time):\n",
+        "\n",
+        "$$\n",
+        "c = c_0 e^{-\\lambda t}...(Eq. 7)\n",
+        "$$\n",
+        "\n",
+        "\n",
+        "We now have a mathematical model from which we can determine the settling rate. By filling the chamber with an initial concentration of particles, we can measure the decrease in concentration over time as particles fall out of the circulation and settle at the bottom of the chamber. By fitting an exponential function to this decay, we can determine the value of $\\lambda$, which will be proportional to the settling velocity.\n"
+      ],
+      "metadata": {
+        "id": "bdDIDVeZcpf2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Analysis: Linear Regression\n",
+        "\n",
+        "Below is the recorded data for particles with a diameter of 4 microns in one experimental trial. The array `concentration` is the series of measurements of the number of 4 micron particles per cubic centimeter in the chamber. The array `time` is the time in seconds at which each measurement was taken realative to the time of the first measurement."
+      ],
+      "metadata": {
+        "id": "Y-jBLi1pY6eL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OLKOoqj5ZpEm"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import scipy\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import scipy.optimize as optimize\n",
+        "from scipy.optimize import curve_fit\n",
+        "\n",
+        "from scipy.stats import f, norm\n",
+        "import scipy.stats as stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# loading the dataset\n",
+        "\n",
+        "concentration = np.array([137.48, 223.66, 122.06 , 131.4  , 106.48 ,  97.   ,  74.16 ,\n",
+        "        72.2  ,  53.94 ,  70.13 ,  50.72 ,  46.43 ,  80.1  ,  45.86 ,\n",
+        "        53.87 ,  40.14 ,  54.99 ,  24.815,  25.997,  22.662,  43.249,\n",
+        "        19.87 ,  17.472,  10.329,  30.588,  12.854,  15.806,  10.899,\n",
+        "        10.516,  10.376,   6.161,   5.558,  24.208,   4.49 ,   6.519,\n",
+        "        5.238,  14.103,   3.952,   4.726,   9.496,   4.172,   5.48 ,\n",
+        "        2.564,   3.088,   3.23 ,   3.385]) #particles per cm^3\n",
+        "time = np.array([0,  64, 111,  158,  206,  253,  316,  367,  424,  469,  521,\n",
+        "       571,  637,  687,  735,  783,  832,  895,  943,  998, 1048, 1096,\n",
+        "       1145, 1275, 1323, 1376, 1426, 1482, 1552, 1598, 1650, 1701, 1750,\n",
+        "       1803, 1856, 1911, 1962, 2009, 2094, 2149, 2199, 2258, 2313, 2361,\n",
+        "       2417, 2469]) #seconds"
+      ],
+      "metadata": {
+        "id": "dweDcOjV7UYd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In order to do linear regression on an exponential equation, we must use a transformation. We will begin by applying a log function to both sides of Equation 7.\n",
+        "\n",
+        "$$\n",
+        "ln(c) = ln(c_{0}) - \\lambda t...(Eq. 8)\n",
+        "$$\n",
+        "\n",
+        "From Equation 8, we can define a linear relationship of the form $Y = \\beta_{0} + \\beta_{1}X$. Write out which term in Equation 8 corresponds to $Y$, $X$, $\\beta_{0}$, and $\\beta_{1}$. *Upload your answers to Gradescope.*\n",
+        "\n",
+        "\n",
+        "**Answer**: $Y = ln(c)$, $X = t$, $\\beta_{0} = ln(c_0)$, $\\beta_{1} = -\\lambda$"
+      ],
+      "metadata": {
+        "id": "kxJV7rhUmj-0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will use SciPy's `curve_fit` to fit a linear function to our transformed data. To do this, we define a function that returns the result of Equation 8, then provide that function as an arguement to `curvefit` along with the time and log-transformed concentration arrays."
+      ],
+      "metadata": {
+        "id": "764uugeIcpgU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#linear regression\n",
+        "\n",
+        "#linear regression function\n",
+        "def linear(X, beta_0, beta_1, ):\n",
+        "    model = beta_0 + beta_1 * X\n",
+        "    return model\n",
+        "\n",
+        "#transform the concentration for our linear modeldata\n",
+        "log_concentration = np.log(concentration)\n",
+        "\n",
+        "#use scipy to determine best linear fit\n",
+        "parameters, covariance = curve_fit(linear, time, log_concentration)\n",
+        "\n",
+        "#extract linear model parameters\n",
+        "beta_0 = parameters[0]\n",
+        "beta_1 = parameters[1]\n"
+      ],
+      "metadata": {
+        "id": "q_HobbhoiMKz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.1. Plot Experimental Data and Model Prediction\n",
+        "\n",
+        "We want to plot our fitted model together with the experimental data in order to visually confirm the goodness of fit. But to do that, we need to transform our $\\beta_0$ and $\\beta_1$ back to $c_0$ and $\\lambda$. Do this in the code block below, saving $\\lambda$ as `decay_rate`."
+      ],
+      "metadata": {
+        "id": "tM-EIMqWcJAB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#transform model parameters to nonlinear function\n",
+        "### BEGIN SOLUTION\n",
+        "c_0 = np.exp(beta_0)\n",
+        "decay_rate = -beta_1\n",
+        "###END SOLUTION\n",
+        "\n",
+        "#calculate modeled data values\n",
+        "model_concentration = c_0 * np.exp(-decay_rate * time)\n",
+        "\n",
+        "print(f'Initial Concentration: {round(c_0)}')\n",
+        "print(f'Decay Rate: {round(decay_rate,4)}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e8qljjuJgKUw",
+        "outputId": "c5e9838f-fe91-45ad-e524-65b4d4e569b7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initial Concentration: 141\n",
+            "Decay Rate: 0.0016\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#plot original data and fitted model\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "ax.scatter(time, concentration, label = 'Experimental Data')\n",
+        "ax.plot(time, model_concentration, c = 'orange', label = 'Model', linewidth = 3)\n",
+        "\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Time (s)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Concentration ($ \\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_title(\"Concentration of 4 $\\mu m$ Particles over Time\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "#add legend\n",
+        "plt.legend()\n",
+        "\n",
+        "#show plot\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 627
+        },
+        "id": "j1S2i6u6boB-",
+        "outputId": "180413a2-1179-423d-9054-5d5fca7b92b8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.2. Plot the Resiudals\n",
+        "Visually, the model looks like a decent fit of the data. We can further assess our model by observing the distribution of the residuals. If our model is good, we expect the residuals to have a normal distribution. Let's check by plotting our residuals in a histogram."
+      ],
+      "metadata": {
+        "id": "75_SM4D6UpBL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# calculating the residuals\n",
+        "r = concentration - model_concentration\n",
+        "\n",
+        "## histogram of the residuals\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "plt.hist(r, bins=15, density=True)\n",
+        "\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Residual ($\\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Frequency\", fontsize = 16)\n",
+        "ax.set_title(\"Distribution of Residuals\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 630
+        },
+        "id": "zeo3inTAUqz9",
+        "outputId": "e09d9534-8f2a-440d-9bc6-27aa44fa705d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Discussion:** What are some features we notice about the residuals? Does it follow a normal distribution?\n",
+        "\n",
+        "**Answer:** The distribution is roughly normal, but not perfectly so. It's skewed a bit to the right and there is at least one outlier. This is probably due to the fact that we do not have many data points"
+      ],
+      "metadata": {
+        "id": "gTriTWnMVa7B"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.3. Plot the Confidence Intervals"
+      ],
+      "metadata": {
+        "id": "ASNGnoAYTelL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To plot confidence intervals, we will use the Working-Hotelling method. It is the same as the standard method for calculating confidence intervals, but it uses the F statistic rather than the t statisic. We'll use a 95% confidence interval."
+      ],
+      "metadata": {
+        "id": "j0AuwK-VYvao"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# calculating confidence interval using Working-Hotelling method, plotting\n",
+        "\n",
+        "# Compute the standard error\n",
+        "se = np.sqrt(np.sum((concentration - model_concentration)**2) / (len(time) - 2)) * np.sqrt(1 / len(time) + (time - np.mean(time))**2 / np.sum((time - np.mean(time))**2))\n",
+        "\n",
+        "# Compute W for the confidence bands Working-Hoteling method\n",
+        "W = np.sqrt(2 * f.ppf(1 - 0.05, 2, len(time) - 2))\n",
+        "\n",
+        "# Build the upper and lower confidence interval\n",
+        "wh_upper = model_concentration + W * se\n",
+        "wh_lower = model_concentration - W * se\n",
+        "\n",
+        "#plot original data and fitted model\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "ax.scatter(time, concentration, label = 'Experimental Data')\n",
+        "ax.plot(time, model_concentration, c = 'orange', label = 'Model', linewidth = 3)\n",
+        "\n",
+        "ax.plot(time, wh_upper, color='forestgreen', linestyle='dashed', label=\"WH Upper\")\n",
+        "ax.plot(time, wh_lower, color='forestgreen', linestyle='dashed', label=\"WH Lower\")\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Time (s)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Concentration ($ \\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_title(\"Concentration of 4 $\\mu m$ Particles over Time\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "#add legend\n",
+        "plt.legend()\n",
+        "\n",
+        "#show plot\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 627
+        },
+        "id": "SyxN9goZTZij",
+        "outputId": "437d64e5-d559-4b20-f643-eb32b1dcf18c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Discussion:** What do the green dashed lines tell us?\n",
+        "\n",
+        "**Answer:** The dashed lines show the range within which we are 95% confident that the concentration of 4 micron particles will be given some initial concentration."
+      ],
+      "metadata": {
+        "id": "q4odLO4ZOpI4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Analysis: Nonlinear Regression\n",
+        "\n",
+        "Though we can easily transform an exponential function into a linear one, linearizing can cause model parameters to be weighted differently and muddies the interpretation of our error analysis. Let's compare regression results by doing a least-squares nonlinear regression. For this, we'll use SciPy's `least_squares` function. (You'll need to complete the `expoential` function before you can run the cell below.)"
+      ],
+      "metadata": {
+        "id": "crFNLf6Co58Z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#non linear regression\n",
+        "\n",
+        "#nonlinear regression function\n",
+        "def exponential(theta, x):\n",
+        "    c_0 = theta[0]\n",
+        "    decay_rate = theta[1]\n",
+        "    ### BEGIN SOLUTION ###\n",
+        "    model = c_0 * np.exp(-decay_rate * x)\n",
+        "    ### END SOLUTION\n",
+        "    return model\n",
+        "\n",
+        "#regression residual function to use with scipy optimize\n",
+        "def regression(theta, x, y):\n",
+        "    residual = y - exponential(theta, x)\n",
+        "    return residual\n",
+        "\n",
+        "#initial guess of scale factor and decay rate\n",
+        "theta0 = np.array([100, -0.002])\n",
+        "\n",
+        "#use scipy to determine best nonlinear fit\n",
+        "results = optimize.least_squares(regression, theta0, args = (time, concentration))\n",
+        "\n",
+        "#extra nonlinear model parameters\n",
+        "nl_c_0, nl_decay_rate  = results.x\n",
+        "\n",
+        "print(f'Initial Concentration: {round(nl_c_0)}')\n",
+        "print(f'Decay Rate: {round(nl_decay_rate,4)}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F_4uTBQgjS7W",
+        "outputId": "8d6dd0f5-6caa-4ead-824e-0a70b7bff2e9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initial Concentration: 167\n",
+            "Decay Rate: 0.0018\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3.1. Plot Experimental Data and Model Prediction\n",
+        "\n",
+        "Again, we'd like to plot the model along with the experimental data."
+      ],
+      "metadata": {
+        "id": "P3Dx5AlMrxiD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#calculate modeled data values\n",
+        "nl_model_concentration = nl_c_0 * np.exp(-nl_decay_rate * time)\n",
+        "\n",
+        "#plot original data and fitted model\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "ax.scatter(time, concentration, label = 'Experimental Data')\n",
+        "ax.plot(time, nl_model_concentration, c = 'orange', label = 'Model', linewidth = 3)\n",
+        "\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Time (s)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Concentration ($ \\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_title(\"Concentration of 4 $\\mu m$ Particles over Time\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "#add legend\n",
+        "plt.legend()\n",
+        "\n",
+        "#show plot\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 627
+        },
+        "id": "1BIYX468rw0h",
+        "outputId": "3c43cd56-e2a4-4350-86e6-1b1f1edfa7a2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3.2. Plot the Residuals\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "jXpSSO4-Ueks"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Using the previous residual plotting code, fill in the missing pieces to plot the distribution of the residuals.\n",
+        "\n",
+        "*Hint: Be sure to use the variables from the nonlinear model.*"
+      ],
+      "metadata": {
+        "id": "94zdgdF4Oq5W"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# calculating the residuals\n",
+        "### BEGIN SOLUTION\n",
+        "r = concentration - nl_model_concentration\n",
+        "### END SOLUTION\n",
+        "\n",
+        "## histogram of the residuals\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "plt.hist(r, bins=15, density=True)\n",
+        "\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Residual ($\\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Frequency\", fontsize = 16)\n",
+        "ax.set_title(\"Distribution of Residuals\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 630
+        },
+        "id": "VlYEUGHfLYYz",
+        "outputId": "4b76cbf1-feae-49b8-8dc4-762bb388acf5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Discussion:** How does this residual plot compare to that for the linear regression model?\n",
+        "\n",
+        "**Answer**: This residual plot is perhaps a bit closwer to being normally distributed, though it has a bit of an extended tail to the left and maintains a couple outliers."
+      ],
+      "metadata": {
+        "id": "CqurN7gcu19r"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3.3. Plot the Confidence Intervals"
+      ],
+      "metadata": {
+        "id": "IAZsjLQ9m3F-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Using the previous confidence interval plotting code above, fill in the missing parts to plot the confidence bands.\n",
+        "\n",
+        "*Hint: Be sure to use the variable names from the nonlinear model.*"
+      ],
+      "metadata": {
+        "id": "aMvcQs0wPVt9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# calculating confidence interval using Working-Hotelling method, plotting\n",
+        "\n",
+        "# Compute the standard error\n",
+        "### BEGIN SOLUTION\n",
+        "se = np.sqrt(np.sum((concentration - nl_model_concentration)**2) / (len(time) - 2)) * np.sqrt(1 / len(time) + (time - np.mean(time))**2 / np.sum((time - np.mean(time))**2))\n",
+        "### END SOLUTION\n",
+        "\n",
+        "# Compute W for the confidence bands Working-Hoteling method\n",
+        "### BEGING SOLUTION\n",
+        "W = np.sqrt(2 * f.ppf(1 - 0.05, 2, len(time) - 2))\n",
+        "### END SOLUTION\n",
+        "\n",
+        "# Build the upper and lower confidence interval\n",
+        "wh_upper = nl_model_concentration + W * se\n",
+        "wh_lower = nl_model_concentration - W * se\n",
+        "\n",
+        "#plot original data and fitted model\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "ax.scatter(time, concentration, label = 'Experimental Data')\n",
+        "ax.plot(time, nl_model_concentration, c = 'orange', label = 'Model', linewidth = 3)\n",
+        "\n",
+        "ax.plot(time, wh_upper, color='forestgreen', linestyle='dashed', label=\"WH Upper\")\n",
+        "ax.plot(time, wh_lower, color='forestgreen', linestyle='dashed', label=\"WH Lower\")\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Time (s)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Concentration ($ \\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_title(\"Concentration of 4 $\\mu m$ Particles over Time\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "#add legend\n",
+        "plt.legend()\n",
+        "\n",
+        "#show plot\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 627
+        },
+        "id": "5-IL-0EJm6wX",
+        "outputId": "5d850e4c-ef96-4ab9-c602-09b1ea3dd26c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Conclusion\n",
+        "**Discussion:** How do the results of the linear and nonlinear regression compare? Which do you think is better?\n",
+        "\n",
+        "**Answer:** The nonlinear regression estimates a greater initial concentration and a greater rate of decay. Based on its residual distribution, it appears to be a modestly more reliable model than that given by the linear regression. It also visually appears to better capture the early data points."
+      ],
+      "metadata": {
+        "id": "zt6LqMH1N_dk"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## References\n",
+        "\n",
+        "1. **Deriving Stoke's Settling Velocity from Stoke's Law**:\n",
+        "\n",
+        "Cadence CFD Solutions, Cadence CFD Solutions, et al. “Deriving Stoke’s Law for Settling Velocity.” Cadence, 13 Dec. 2022, https://resources.system-analysis.cadence.com/blog/msa2022-deriving-stokes-law-for-settling-velocity"
+      ],
+      "metadata": {
+        "id": "d-wEieOjZAeJ"
+      }
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/notebooks/contrib/Stokes_Settling_Velocity.ipynb b/notebooks/contrib/Stokes_Settling_Velocity.ipynb
new file mode 100644
index 00000000..e0eeeed2
--- /dev/null
+++ b/notebooks/contrib/Stokes_Settling_Velocity.ipynb
@@ -0,0 +1,994 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9nUUNAQW1R6m"
+      },
+      "source": [
+        "# Using a New Dataset to Evaluate Stoke's Settling Velocity"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "58Az-fqTXLbO"
+      },
+      "source": [
+        "**Prepared by:** Kristin Swartz (kswarts3@nd.edu) and Alexis Laudenslager (alaudens@nd.edu)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QZh4kSflXqo3"
+      },
+      "source": [
+        "**Reference:** This is an original problem created by the authors.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aLj4IXguX5zf"
+      },
+      "source": [
+        "**Intended Audience:** The intended audience of this notebook is students learning Python coding and probability theory, for example, in CBE 60258."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pkxa4qn9YTt7"
+      },
+      "source": [
+        "## Learning Objectives\n",
+        "\n",
+        "After studying this notebook and asking questions in class, you should be able to:\n",
+        "\n",
+        "\n",
+        "*   Fit linear and nonlinear regression models to real data\n",
+        "*   Plot a histogram of the **residuals** and verify the distribution\n",
+        "*   Create a confidence interval for the model\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FNoltRl9aH62"
+      },
+      "source": [
+        "## Resources\n",
+        "\n",
+        "Relevant modules on the class website:\n",
+        "\n",
+        "1. [Linear Regression with Transformations](https://ndcbe.github.io/data-and-computing/notebooks/15/Transformations-and-Linear-Regression.html?highlight=transformation)\n",
+        "\n",
+        "\n",
+        "2. [Nonlinear Regression](https://ndcbe.github.io/data-and-computing/notebooks/15/advanced_regression.html?highlight=nonlinear)\n",
+        "\n",
+        "\n",
+        "3. [Plotting a Histogram of the Residuals](https://ndcbe.github.io/data-and-computing/notebooks/09/Visualizing-Data.html?highlight=histogram#histogram)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yAS2pJ3QcYTX"
+      },
+      "source": [
+        "## Import Libraries"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Background"
+      ],
+      "metadata": {
+        "id": "eldAvMvW1EKL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KeO3vNqWcjBA"
+      },
+      "source": [
+        "\n",
+        "### 1.1 Problem Statement\n",
+        "In this notebook, we'll be analyzing a small portion of data from experiments designed to determine the settling rate of particles in a turbulent environment. Understanding how long solid particles remain suspended in a turbulent environment is important for predicting the formation and characteristics of clouds, which require such particles for their existence. Clouds have a significant impact on the climate, so this area of research is relevant for short term and long term climate modeling.\n",
+        "\n",
+        "We have a model for particle settling rates in a nonturbulent environment known as Stoke's settling velocity, or Stoke's law. In a turbulent environment, however, things are much more complicated. In this research, we're attemping to determine if Stoke's law is predictive over some range of particle size and density in a turbulent environment.\n",
+        "\n",
+        "For the experiments, particles from 1 - 30 microns were injected into a turbulence-generating chamber. After the chamber achieved a certain concentration of particles, the injection was stopped. At that point, particle size and concentration measurements were taken at regular intervals in order to observe the decay of particles in the chamber. From these measurement, we can determine the settling velocity and compare it to Stoke's law.\n",
+        "\n",
+        "**We'll be focusing on the first part of the analysis process $-$ determining the settling rate from the collected data.** To do this, we'll use linear and nonlinear regression to determine the decay rate of 4 micron particles that were injected into the chamber. We'll then plot confidence intervals and the distribution of the residuals to assess the reliability of our model.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1.2 Derivation of Stoke's Law (Optional)"
+      ],
+      "metadata": {
+        "id": "CNCKYU5Q6laD"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "For this notebook, we want to look at Stoke's settling velocity, which requires setting the net force on the particle equal to zero and then determining the velocity at which this occurs. The forces acting on the particle are friction, the buoyant force, and gravity."
+      ],
+      "metadata": {
+        "id": "PaEcGgfKk3e4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### 1.2.1. Drag Force\n",
+        "\n",
+        "$$ F_{D} = 6\\pi\\eta r v...(Eq. 1)$$\n",
+        "\n",
+        "$F_{D}$ = drag (friction) force\n",
+        "\n",
+        "$\\eta$ = viscosity of fluid\n",
+        "\n",
+        "$r$ = radius of particle\n",
+        "\n",
+        "$v$ = velocity of the particle\n",
+        "\n",
+        "Equation 1 shows the friction force on a particle of radius $r$ falling vertically through a fluid of viscosity $\\eta$."
+      ],
+      "metadata": {
+        "id": "6_YKGpPZjaZ5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### 1.2.2. Gravity Force\n",
+        "\n",
+        "$$ F_{G} = \\frac{4}{3}\\pi r^{3} \\rho g...(Eq. 2)$$\n",
+        "\n",
+        "$F_{G}$ = gravitational force\n",
+        "\n",
+        "$\\rho$ = density of the particle\n",
+        "\n",
+        "$g$ = acceleration due to gravity\n",
+        "\n",
+        "Equation 2 shows the force of gravity on a particle of radius $r$ and density $\\rho$."
+      ],
+      "metadata": {
+        "id": "04NqytOUmVwP"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### 1.2.3. Bouyant Force\n",
+        "$$F_{B} = \\frac{4}{3}\\pi r^{3} \\sigma g...(Eq. 3)$$\n",
+        "\n",
+        "$F_{B}$ = buoyancy force\n",
+        "\n",
+        "$\\sigma$ = the density of the fluid\n",
+        "\n",
+        "Equation 3 shows the bouyant on a particle of radius $r$ in a fluid of density $\\sigma$."
+      ],
+      "metadata": {
+        "id": "klnSz2G1nTno"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### 1.2.4. Sum of Forces\n",
+        "Since we are looking for the velocity of the particle as it is falling through a moving fluid, we know that the friction force and buoyancy forces will be acting upwards and the gravitational force will be acting downwards. Thus, we can set the net force of the system equal to zero, which leads to Equation 4."
+      ],
+      "metadata": {
+        "id": "roqGGu4-npSQ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "$$F_{D} + F_{B} = F_{G}...(Eq. 4)$$"
+      ],
+      "metadata": {
+        "id": "ofXURQapn74z"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can now solve for Stoke's settling velocity using some simple alegebra. The result is shown below in Equation 5. Please write out the derivation of Equation 5 by hand and **upload to Gradescope**.\n",
+        "\n",
+        "*Hint: Plug Equations 1, 2, and 3 into Equation 4, then rearrange to solve for v.*\n"
+      ],
+      "metadata": {
+        "id": "_vtXje7M-iH1"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "$$v = \\frac{2 r^{2} g (\\rho - \\sigma)}{9 \\eta}...(Eq. 5)$$"
+      ],
+      "metadata": {
+        "id": "bLVGhJCosw4s"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "**Answer:**\n",
+        "$$6 \\pi \\eta r v + \\frac{4}{3} \\pi r^3 \\sigma g = \\frac{4}{3} \\pi r^{3} \\rho g$$\n",
+        "\n",
+        "$$6 \\eta v + \\frac{4}{3} \\pi r^{2} \\sigma g = \\frac{4}{3} \\pi r^{2} \\rho g$$\n",
+        "\n",
+        "$$6 \\eta v = \\frac{4}{3} r^{2} g (\\rho - \\sigma)$$\n",
+        "\n",
+        "$$v = \\frac{\\frac{4}{3} r^{2} g (\\rho - \\sigma)}{6 \\eta}$$\n",
+        "\n",
+        "$$v = \\frac{2 r^{2} g (\\rho - \\sigma)}{9 \\eta}$$\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "V7KK6VmfrbAz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1.3 Derivation of the Model\n",
+        "\n",
+        "After we inject particles into the chamber, their only means of exiting the turbulent circulation is to settle along the bottom of the chamber. As such, we can use conservation principles to build a mathematical model for this settling behavior. Using Reynold's transport theorem, we express\n",
+        "\n",
+        "$$\n",
+        "\\frac{\\partial}{\\partial t} \\oint_V c \\ dV + \\oint_S c \\left( \\vec v_s \\cdot \\hat n \\right) dA = 0\n",
+        "$$\n",
+        "\n",
+        "where $c$ is the concentration of particles in the chamber and $\\vec v$ is the velocity at which the particles leave the circulation through settling. Evaluating this integral gives\n",
+        "\n",
+        "$$\n",
+        "\\frac{\\partial N}{\\partial t} = - c A v...(Eq. 6)\n",
+        "$$\n",
+        "\n",
+        "which says that the change over time in the number of particles in the chamber, $N$, is equal to minus the concentration multiplied by the area over which the particles settle and their settling vecocity. If we express the settling velocity as\n",
+        "\n",
+        "$$\n",
+        "v = \\frac{h}{t'},\n",
+        "$$\n",
+        "\n",
+        "where $h$ is the height of the chamber and $t'$ is the time the particles spend in the circulation before settling out, then our differential equation becomes\n",
+        "\n",
+        "$$\n",
+        "\\frac{\\partial N}{\\partial t} = - \\frac{c A h}{t'}.\n",
+        "$$\n",
+        "\n",
+        "Given that\n",
+        "\n",
+        "$$\n",
+        "Ah = V,\n",
+        "$$\n",
+        "\n",
+        "where $V$ is the volume of the chamber, then\n",
+        "\n",
+        "$$\n",
+        "cAh = cV = N,\n",
+        "$$\n",
+        "\n",
+        "and we can write\n",
+        "\n",
+        "$$\n",
+        "\\frac{\\partial N}{\\partial t} = - \\frac{N}{t'}.\n",
+        "$$\n",
+        "\n",
+        "The solution to this differential equation is\n",
+        "\n",
+        "$$\n",
+        "N = N_0 e^{-\\lambda t}\n",
+        "$$\n",
+        "\n",
+        "where $N_{0}$ is the initial number of particles in the chamber and $\\lambda = -\\frac{1}{t'}$. We can divide this through by the volume of the chamber to get an equation more in line with the collected data (concentration over time):\n",
+        "\n",
+        "$$\n",
+        "c = c_0 e^{-\\lambda t}...(Eq. 7)\n",
+        "$$\n",
+        "\n",
+        "\n",
+        "We now have a mathematical model from which we can determine the settling rate. By filling the chamber with an initial concentration of particles, we can measure the decrease in concentration over time as particles fall out of the circulation and settle at the bottom of the chamber. By fitting an exponential function to this decay, we can determine the value of $\\lambda$, which will be proportional to the settling velocity.\n"
+      ],
+      "metadata": {
+        "id": "bdDIDVeZcpf2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Analysis: Linear Regression\n",
+        "\n",
+        "Below is the recorded data for particles with a diameter of 4 microns in one experimental trial. The array `concentration` is the series of measurements of the number of 4 micron particles per cubic centimeter in the chamber. The array `time` is the time in seconds at which each measurement was taken realative to the time of the first measurement."
+      ],
+      "metadata": {
+        "id": "Y-jBLi1pY6eL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OLKOoqj5ZpEm"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import scipy\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import scipy.optimize as optimize\n",
+        "from scipy.optimize import curve_fit\n",
+        "\n",
+        "from scipy.stats import f, norm\n",
+        "import scipy.stats as stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# loading the dataset\n",
+        "\n",
+        "concentration = np.array([137.48, 223.66, 122.06 , 131.4  , 106.48 ,  97.   ,  74.16 ,\n",
+        "        72.2  ,  53.94 ,  70.13 ,  50.72 ,  46.43 ,  80.1  ,  45.86 ,\n",
+        "        53.87 ,  40.14 ,  54.99 ,  24.815,  25.997,  22.662,  43.249,\n",
+        "        19.87 ,  17.472,  10.329,  30.588,  12.854,  15.806,  10.899,\n",
+        "        10.516,  10.376,   6.161,   5.558,  24.208,   4.49 ,   6.519,\n",
+        "        5.238,  14.103,   3.952,   4.726,   9.496,   4.172,   5.48 ,\n",
+        "        2.564,   3.088,   3.23 ,   3.385]) #particles per cm^3\n",
+        "time = np.array([0,  64, 111,  158,  206,  253,  316,  367,  424,  469,  521,\n",
+        "       571,  637,  687,  735,  783,  832,  895,  943,  998, 1048, 1096,\n",
+        "       1145, 1275, 1323, 1376, 1426, 1482, 1552, 1598, 1650, 1701, 1750,\n",
+        "       1803, 1856, 1911, 1962, 2009, 2094, 2149, 2199, 2258, 2313, 2361,\n",
+        "       2417, 2469]) #seconds"
+      ],
+      "metadata": {
+        "id": "dweDcOjV7UYd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In order to do linear regression on an exponential equation, we must use a transformation. We will begin by applying a log function to both sides of Equation 7.\n",
+        "\n",
+        "$$\n",
+        "ln(c) = ln(c_{0}) - \\lambda t...(Eq. 8)\n",
+        "$$\n",
+        "\n",
+        "From Equation 8, we can define a linear relationship of the form $Y = \\beta_{0} + \\beta_{1}X$. Write out which term in Equation 8 corresponds to $Y$, $X$, $\\beta_{0}$, and $\\beta_{1}$. *Upload your answers to Gradescope.*\n",
+        "\n",
+        "\n",
+        "**Answer**: $Y = ln(c)$, $X = t$, $\\beta_{0} = ln(c_0)$, $\\beta_{1} = -\\lambda$"
+      ],
+      "metadata": {
+        "id": "kxJV7rhUmj-0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will use SciPy's `curve_fit` to fit a linear function to our transformed data. To do this, we define a function that returns the result of Equation 8, then provide that function as an arguement to `curvefit` along with the time and log-transformed concentration arrays."
+      ],
+      "metadata": {
+        "id": "764uugeIcpgU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#linear regression\n",
+        "\n",
+        "#linear regression function\n",
+        "def linear(X, beta_0, beta_1, ):\n",
+        "    model = beta_0 + beta_1 * X\n",
+        "    return model\n",
+        "\n",
+        "#transform the concentration for our linear modeldata\n",
+        "log_concentration = np.log(concentration)\n",
+        "\n",
+        "#use scipy to determine best linear fit\n",
+        "parameters, covariance = curve_fit(linear, time, log_concentration)\n",
+        "\n",
+        "#extract linear model parameters\n",
+        "beta_0 = parameters[0]\n",
+        "beta_1 = parameters[1]\n"
+      ],
+      "metadata": {
+        "id": "q_HobbhoiMKz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.1. Plot Experimental Data and Model Prediction\n",
+        "\n",
+        "We want to plot our fitted model together with the experimental data in order to visually confirm the goodness of fit. But to do that, we need to transform our $\\beta_0$ and $\\beta_1$ back to $c_0$ and $\\lambda$. Do this in the code block below, saving $\\lambda$ as `decay_rate`."
+      ],
+      "metadata": {
+        "id": "tM-EIMqWcJAB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#transform model parameters to nonlinear function\n",
+        "### BEGIN SOLUTION\n",
+        "c_0 = np.exp(beta_0)\n",
+        "decay_rate = -beta_1\n",
+        "###END SOLUTION\n",
+        "\n",
+        "#calculate modeled data values\n",
+        "model_concentration = c_0 * np.exp(-decay_rate * time)\n",
+        "\n",
+        "print(f'Initial Concentration: {round(c_0)}')\n",
+        "print(f'Decay Rate: {round(decay_rate,4)}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e8qljjuJgKUw",
+        "outputId": "c5e9838f-fe91-45ad-e524-65b4d4e569b7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initial Concentration: 141\n",
+            "Decay Rate: 0.0016\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#plot original data and fitted model\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "ax.scatter(time, concentration, label = 'Experimental Data')\n",
+        "ax.plot(time, model_concentration, c = 'orange', label = 'Model', linewidth = 3)\n",
+        "\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Time (s)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Concentration ($ \\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_title(\"Concentration of 4 $\\mu m$ Particles over Time\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "#add legend\n",
+        "plt.legend()\n",
+        "\n",
+        "#show plot\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 627
+        },
+        "id": "j1S2i6u6boB-",
+        "outputId": "180413a2-1179-423d-9054-5d5fca7b92b8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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dGFpZWYlXr17VWLZv374q5evUqaPxi7T8/HyxWbNmUtnt27eXKGOs/6vKhokhGUVKSorKH+zSpUsNWv+cOXOkutu2bVvqCWj79u0qF9JPnz4tUaZ4vO+8847W+lJTU6XEVxAEMSUlpUSZb7/9VuViszzf3C1dulSq691339Vpn2nTpkn7/PHHHyW2G/p4RbH8ieHo0aN12kdfzz//fKmxi6JxEsPK8F7VhzGORxSNmxgW7wU7duyY1rYNnRi+8847Ut269EDs3btXKq9pnNNff/2l8h5YtWqV1jqLj70SBEE8d+6c1vLFE8ng4OBSY9ZG18Rw165d0kVZ0XulvGMcdf27L8tnkLESQ0OeN0pry9DnlXv37ql8NsjB0J9HKSkpKuMjb968qbGuNWvWlJpEGuPcLYrynDefJXdiOHPmTK11Fv/STZfrzM8++0wqO2vWrBLbjfV/VdlwjCEZRdHYiyK2trYGrb/4+MWiGf+0eeGFF1C7dm0kJycjJycH586dw8CBAzWWHzVqlNb67Ozs4OnpievXr0MURdy5cwetW7dWKfP3339Lj1977TVYWlpqrVObAwcOSI/HjRun0z69e/fGqlWrAACnT5/GiBEjNJY1xPEawpgxY8q0X1xcHEJCQnDjxg08ffoUWVlZEEVR2l40vkAURURERKB79+4GiVcXleG9qg9jH4+hHT58GBs3bgQATJo0CQEBAbK1DaiOBdQ0XrC44uML27ZtW2qdzZs3x2uvvaa1zuIz940cObLU8Zo5OTnS49q1a2stq48DBw6UmCH36dOnCAkJQXR0tMrrixcvhqurq9b6jPF3X9bPIEMx5HmjNIY+rzg7O8PKygrZ2dm4evUqzpw5Y/C5BZ5l6M8je3t7BAYGYseOHQCAzZs34z//+Y/auorPcjl+/Hi1ZYx97gZM/541ltLG2D97HiutfPE1sYs+G4qT4/+qMmBiSEbx7ED89PR0g9UtiqLKhVHx6c01sbCwgL+/v3TSDQ8P13pxqsuFs5OTk/Q4NTW1xPbg4GDpcXkvRs+dOyc9Xr16NTZs2FDqPnfv3pUex8fHay1riOM1hPbt2+tV/ty5c/joo49w6tQplQtCbYy5dMOzKst7VVdyHI8hZWRkSBNhODk54fvvv5el3SJFCQlQuDajpkSvOH0nnpkyZUqpF8PFk67iy4Kok5ubi7i4OACFE564u7uXFrLOQkNDERoaqrWMnZ0dli1bpjVOY/7d6/sZZGiGPG+UxtDnlRo1amD48OHYtm0b8vPz0bt3b7z00ksYOXIkevToAUdHR4PFDhjv82j8+PGlJoZ3797FiRMnpDpfeuklte0Z+9wNmP49ayzFEzl1atWqJT12cHAodbKn4l9yqTsPyvF/VRkwMSSjsLe3h7m5ufRN9dOnTw1Wd0pKispi2LpeuHh4eEiPS7tI0GXmxuKzfRWPByj80MnKypKeN2nSRKcY1UlPT1fpgf3ll1/0rqP4TH/qlPd4DaW0GeCKW7t2LaZOnarzhWGRZ3uzjakyvFf1IcfxGNInn3wizdq4aNEiODs7y9Y2ULgOX9EFSNOmTWFjY1PqPvr2GD47e+uzcnNzERUVBQAwNzdHjx49tJb/999/oVQqARTOcmvsWQ1tbW3h5OSENm3aoG/fvpg4caLWBMLYf/f6fAYZmiHPG6Ux1nllyZIlCAsLQ3R0NHJzc7Fx40Zs3LgRZmZmaNWqFbp3745+/frh+eefL3dvqLE+jwYOHAhnZ2ckJibixo0bCA0NhZ+fn0qZLVu2SO/BovLPkuPcDZj2PWtMpZ3bzM3/l8Loch4sXv7Z86Bc/1eVQRWZW5UqouIf0kVTCxvCs72PulxsPVuutIuE8k5jb8hbaVNSUsoVCwC1i8AWJ8e0/bqwtrbWqdy1a9cwbdo06cTcqlUrLFu2DCEhIXj06JF0S1nRz6RJk6R9CwoKjBK7OpXhvaoPOY7HUMLDw6Xp/gMCAlTeA3LR9zbSJ0+eSAunF90C/Kz09HRpOYYaNWqgTZs2WuuMjIyUlgUoWvDZkDHrY968eSp/l6IoIi0tDbGxsdi7dy9mzJihNSmU4+9e188gYzD2EIzijHVeqVevHi5cuIBPP/0UdevWlV4vKCjAlStXsGLFCrzwwguoX78+vv32W+lLiLIw1ufRsz2AmzZtKlGm+GsTJkxQ244c527AtO9ZY9Ln3Fbe86Bc/1eVAXsMyWi6desmXcAUvz2mvJ49WWZkZOh0QsjIyJAeq1tzypDU3Upb1pP8s8eWnJyscgtFdbR06VLpQ3jAgAHYu3cvatSoobG8nL2ExVWG96o+KtPxXL58WUoG4uLitI6rS0hIkB4/ePBApexnn32GwYMHlymG4r1/zz33nF7lfX191V7sRERESMfl4+NTao+eLremFmfMxLC8KsvffVkZ8rxRGmOeV+zt7fHFF19g/vz5uHDhAk6dOoUzZ87g9OnTUg/dkydPMHfuXJw/fx67du0q04W9MT+Pxo8fj59++gkAsH37dixevBgKhQIAcOXKFVy5cgVAYU9VYGCg2jp47q48+H/1P+wxJKPp3bu39PjOnTs4e/asQep1cHBQuRgqGg9TmqJbygAY/ZYye3t7lW/x1A101pWjo6PKLTcPHz4sV2xVQdFC6ADw5Zdfar04BCD1wsitMrxX9VFZj+fWrVsIDg7W+HP79m2pbG5ursq24kmjvoonWb6+vqWWP3XqlPRYl/GF+k5mo29yWtESw8ryd19WhjxvlEaO84pCoUDHjh0xe/Zs7Nq1C48ePcKpU6cwdOhQqcyePXvwxx9/lKl+Y34ederUCV5eXgCAR48e4ciRI9K24r2FI0eOhJWVldo6eO6uPPh/9T9MDMloRo0apfKhu3jxYoPUKwiCytgbXRLO/Px8lUkPdLlAKq+OHTtKj4vPnFYW/v7+0uMzZ86Uqy5jkuu2xvv370uPS5t8JSUlBZcvX9apXkPHX1neq7qqasdjbMWTuMaNG5dafu/evdJjXcYX6psY6lK+aLIcbTGYirH+7svCWJ91hjxvlEbu84qZmRm6deuG3bt3o1+/ftLrxd/3+jD259HLL78sPS6agVQURWzdulV6XdNspEUqy7mb+H9VhIkhGY21tTVmzJghPf/jjz/K9M1gRkZGiQ/84r2RGzZsKHUigt27dyMpKQkAYGVlhc6dO+sdh76ef/556fGaNWtUpoDX15AhQ6THK1eu1HviBbkU/+bUWBPUAIUXGEUyMzO1lv3ll190jsUY8VeG96o+KsvxTJ48ucR4Nk0/69atk/Zzd3dX2TZ58uQytZ+YmIh79+5Jz4vG+WkSFBSE8PBw6bkhEsOCggIp0RMEodTysbGx0kRh7u7uBl2qwhCM9XdfFsb6rDPkeaM0pjqvCIKgcvvlo0ePylyXMT+Piid9u3fvRmZmJk6cOCHNPunq6oqePXtqba+ynLuJ/1dFmBiSUX344Ycq38hNmDAB+/bt03n/q1evolOnTjh8+LDK66+99pp0kRAeHo7Vq1drrOPp06f48MMPpedjx47VaQar8nrttdekMRB37tzBu+++W+a6pk2bJk3KEB4ejgULFui8b2JiYrkG+Ouj+LIIxS+KDa34bH3avm2Ojo7W63dljPgrw3tVH1XteIyleE8doHqb6LMSExNV1iI0NzdHq1atSpTLz8/H1atXARQmSaXdnhodHS1N0OHl5VXq+M6KPL4QMN7ffVkY67POkOeN0hj6vJKWllbqFyBFik/tX6dOHZ3bfZYxP4+8vLyk8cbp6enYvXu3ytqFL7/8cqk9x5Xl3E38vyrCxJCMytLSEr///rv0wZ+VlYXhw4dj4sSJ+Pfff9XuI4oiQkNDMWnSJPj6+koXQsV5enpK65MBwNtvv42ffvqpxMxzN2/eRP/+/aWxGvb29hoXqzW0WrVqYeHChdLzn3/+GS+99JLKujfFRUZGYubMmSWSYKBwLMWSJUuk5wsWLMCkSZM0jqkQRRFnzpzBm2++CTc3N5Up0I2p+LpDv//+u9HaKf5t8/vvv49Dhw6VKHP06FH06tULaWlpOs9WZ4z4K8N7VR9V7XiMpXiSBQBz587F8ePHS5Q7duwYOnfuLE3UBRT2ZNSoUQPZ2dkqZaOioqTXvL29S31fV6WJZwDj/d2XhbE+6wx53iiNoc8rYWFh8PDwwPz58zXORK5UKrF9+3ZpxmBAtZdUX8b+PCrea/jrr79i586dardpUlnO3cT/qyKclZSMrkmTJggODkZgYCCuXr2KgoICaW0jDw8PtGnTBs7OzlAqlXj48CEuXbpU4tYSdd90f//997hw4QJCQ0ORn5+Pt99+G99++y26desGW1tb3Lp1CydPnpS+xTE3N8evv/6qsoaRsb355pu4evUqVq5cCQDYsWMH/vjjD/j5+cHb2xtWVlZISEjAxYsXpQHxmhY1njx5Mm7fvo0vvvgCAPDbb79h8+bNaNu2LZo3bw5bW1ukp6fj7t27uHTpkkGmX9bXiy++iFWrVgEAVqxYgbCwMDz33HOoWbOmVOaNN95QOw2/Pt5991388ssvSEhIQHJyMgYOHIjnnnsOLVu2hCAICA8PR2RkJIDC2Qvr1KmDjRs3miz+yvBe1UdVOx5jKJ5kubu7486dOwgICEDLli3h6ekJpVKJK1euSD0n7dq1kxK59PR0abHu4osuG3vimYqeGBrr774sjPlZZ8jzRmkMfV558OABFixYgAULFqBevXpo27Yt6tWrB3Nzczx69AhhYWEqY0W7d++OMWPGlCn2Isb8PHrppZfw3nvvIS8vT2XMZ7t27dT26qtTGc7dVIj/VwBEIpmkpaWJn3/+uejo6CgC0OnH19dX3LVrl9Y6R48eXWo99evXFw8cOKA1vuLlddGzZ0+pfFBQkNayS5cuFe3t7UuNUxAE8dChQ1rr2r59u9igQQOdf4f+/v5idna2bMc7duxYrfE8u6++cRQ5e/as6OzsrLWt4cOHi0+fPhUnTZokvbZu3Tqt9eobv7u7u7QtJiZGY72V5b2qK0MejyiK4rp166R9Jk2aZJAYdVW8bXd3d4PU2aJFC6nO48ePi/Xr19f4O5o+fbp45cqVEq8PGTJEpc5Zs2ZJ27777rtSY+jXr59UvrTPFVFUfS/Hx8eX+diLK/7emzdvXrnrM8bffVk/g/T9rBBF3T8vRLH85w192jLEeeX8+fOiubm5znWMHDlSTE1NLe3XrBNDfx4VN2TIkBL1LFq0SO8YDXXuFsWyv2fLY968eVKbPXv21GtfXd+L+hxXTEyMXp/bQUFBOsdvyP+ryoaJIcnu6dOn4pYtW8QpU6aIbdq0EevVqyfWqFFDtLW1Fd3c3MT+/fuLn332mRgWFqZznWfPnhXfeOMNsUWLFqKDg4NYo0YNsUGDBmL//v3FH374QUxPTy+1Dn0/aPW92E5MTBS///57sV+/fmLDhg1FS0tL0dLSUmzYsKHYt29f8YsvvhBv3LihU9vZ2dni+vXrxbFjx4peXl6ig4ODqFAoRHt7e7FFixbiiBEjxCVLlojXr1+X/XgLCgrEzZs3i0OGDBEbNWokWllZab1YKs8J7tGjR+LcuXNFHx8fsWbNmmLNmjVFT09PcfTo0eLevXulcvokhvrGr8/FlyhWjveqPgxxPKJYtRLDzMxMUaFQiABEBwcHURRFMT4+Xpw+fbro5uYm1qhRQ6xfv744dOhQ8a+//pL2+/rrr0UnJyfR2dlZfOGFF8SQkBCVevv06SPFeeTIkVLjKJ5AJSQkaC375MkTqayzs7P+B62BoRNDUTT8331ZP4P0/awQRf0/L8pz3tC3LUOcV5KTk8UdO3aIM2bMELt37y42aNBAtLS0FM3NzcXatWuLfn5+4jvvvCMGBweXGk9ZGOrzqLjt27er/L8qFArx/v37ZYrPEL9jUWRiKIrGTQxF0XD/V5WNIIrVdNodIiIiIwgJCZGWHejWrZvWiWeIiIgqCk4+Q0REZEDFx/bpsrA9ERFRRcDEkIiIyICKT+LCxJCIiCoLJoZEREQGVDwxbNOmjekCISIi0gPHGBIRERlIQUEB7OzskJmZCTMzM6SlpaksYUBERFRRsceQiIjIQG7cuIHMzEwAgJeXF5NCIiKqNJgYEhERGQhvIyUiosqKt5ISERERERFVc+wxJCIiIiIiquaYGBIREREREVVzTAyJiIiIiIiqOXNTB0CmVa9ePWRkZMDNzc3UoRARERERVXtxcXGwsbHBw4cPZW2XPYbVXEZGBvLy8kwdBm7duoVbt26ZOgyqwPgeodLwPUKl4XuESsP3CJVGjvdIXl4eMjIyjNqGOuwxrOaKegojIyNNGkerVq0qRBxUcfE9QqXhe4RKw/cIlYbvESqNHO+Rojbkxh5DIiIiIiKiao6JIRERERERUTXHxJCIiIiIiKiaY2JIRERERERUzTExJCIiIiIiquYEURRFUwdBpsPZt4iIiIiIKg5TXZ+zx5CIiIiIiKia4zqGREREROUgiiJ4AxYRPUsQBAiCYOowdMbEkIiIiEhPSqUSSUlJSEtLQ25urqnDIaIKSqFQoGbNmrC3t4ednV2FThSZGBIRERHpQalUIi4uDtnZ2aYOhYgqOKVSibS0NKSlpcHR0RF169aFmVnFHM3HxJCIiIhID0lJScjOzoZCoUDdunVhY2NTYS/0iMh0RFFETk4O0tLSkJycjKdPn8LKygq1atUydWhqMTEkIiIi0kNaWhoAoG7dunBwcDBxNERUkdWsWRM1a9aEubk5Hj9+jCdPnjAxJDKWhLQcbA+NQ3BMMtJz8mFraY5OTZwwuoMrXOwsTR0eERFVIaIoSmMKbWxsTBwNEVUW9vb2ePz4MXJyciCKYoUca8jEkCqt7DwlFuyLxM6wu8hTqs4Gdyo6EUv/uYGR7V0xL7AlrCwUJoqSiIiqkuKzj/L2USLSlULxv2tRJoZEBpSdp8SktSEIjknWWCZPKWJrSBxuJ6Rjwyv+TA6JiIiIiDTgV11UKS3YF6k1KSwuOCYZC/ZdM3JERERERESVFxNDqnQep2VjZ9hdvfbZGRaPhLQcI0VERERERFS5MTGkSmdHaHyJMYWlyVOK2HEh3kgRERERkSAIpf5MnjzZ1GEaTK9evSAIAmJjY00dSoWl7+/o+PHjJd4z1tbWqFevHjp37oyZM2fi/Pnzxg26GuMYQ6p0dL2F9FnnbyfhrQAvA0dDRERExU2aNEnjtm7duskYCemjV69eOHHiBGJiYuDh4WHSWOrWrYuBAwcCAPLz85GcnIyIiAicP38eP/zwA/r3748NGzagXr165W5r8uTJ2LBhA4KCgtCrV69y11eZMTGkSic9J1/W/YiIiEh369evN3UIsvjtt9+QmZmJhg0bmjqUKqd58+Zq30enTp3CjBkzcPjwYQQEBCA4OBj29vbyB1hF8VZSqnRsLcv2fUZZ9yMiIiJ6lpubG5o3bw4LCwtTh1JtdO/eHWfOnEHr1q0RFRWF+fPnmzqkKoWJIVU6HRvXLtN+nZo4GTgSIiIi40pIy8GPx6Ix4ddgvLDiDCb8Goyfgm5WiQnV5syZA0EQMHr06BLbEhMT0aBBAygUCpw+fVp6ffLkyRAEAcePH8fBgwfRrVs32NraolatWhgxYgSioqI0thccHIxRo0ahfv36qFGjBho1aoSpU6ciLi6uRNn58+dDEASsX78eISEhGDJkCJycnCAIAi5dugRA8/g5QRDg4eGB/Px8fPHFF/Dy8oK1tTVatGiBdevWSeWOHTuGgIAA2Nvbo1atWpg4cSKSkpLUxp6fn4+VK1eic+fOsLe3h7W1Ndq2bYulS5ciP7/kHVEeHh7SOnm//PIL2rRpI43VmzZtGp4+fSqVjY2NhSAIOHHiBACgcePGKmP8ijx48ADfffcdevbsiYYNG6JGjRqoV68eRowYgdDQUI2/d0OrWbMmlixZAgBYvXo1srOzpW3Z2dn49ddfMWzYMDRp0gTW1tZwdHREjx49sG3bthJ1CYKADRs2AAACAgJUjrvo//Xp06dYvnw5BgwYAHd3d1haWsLJyQkDBw7EkSNHjH/AMmIXClU6o/1csexotF4T0FgoBIzu4GrEqIiIiAwnO0+JBfsisTPsbonz3anoRCz95wZGtnfFvMCWlXad3i+++AJHjhzB77//jg0bNqiMTXzttdfw4MEDfPLJJ2rHJf7+++9YuXIlOnTogMDAQFy+fBm7du3CsWPHcOLECfj6+qqUX7FiBd555x0AgJ+fH7p3747r16/j119/xd69e3HixAm0aNGiRDsnT57E66+/Dm9vb/Tv3x/379+HmZlu/SqjR4+Wkj9PT0+cOHECr7zyCgDAzs4OY8eORadOnTBgwACcO3cOGzduRExMDE6ePKmSkGVlZWHw4MEICgpC7dq10alTJ1hZWSE4OBjvvfcegoKCsGvXLrVxffjhh1i2bBl69eoFLy8vnDlzBqtXr8a///6LEydOQBAE2NraYtKkSfj777/x6NEjvPjii7C1tS1R1549ezBnzhw0a9YMbdq0gb29PaKjo7Fr1y7s378f+/fvR//+/XX63ZRXnz594OLigoSEBISGhqJ79+4ACpPcqVOnokGDBmjWrBn8/f3x8OFDnD17FqdOnSrRyzhp0iScPn0at27dwoABA1TGLBb9Ds6fP48ZM2bAw8MDzZo1Q+fOnREXF4fDhw/j8OHD+OWXX6T/10pPpGqtZcuWYsuWLU0dht4++iNCdJ+zX+efj/64bOqQiYioClAqleK1a9fEa9euiUql0ihtZOXmi6N/PqvT+W30z2fFrNx8o8ShLwCivpeW165dE62trUU7Ozvx9u3boiiK4urVq0UAop+fn5iXl6dSftKkSVI7q1evll4vKCgQ58yZIwIQ27Ztq7LPuXPnRIVCITZs2FC8cOGCyrZffvlFBCB27NhR5fV58+ZJ7SxcuFBt7D179hQBiDExMWp/Dz4+PuLjx4+l148dOyYCEOvXry86OTmJ+/fvl7alpKSIrVq1EgGIx44dU6nvzTffFAGIL730kvj06VPp9dTUVHHQoEEiAHHlypUq+7i7u4sAxHr16olRUVHS6wkJCaKXl5cIQDx69KhOx1Pk8uXL4tWrV0u8/vfff4s1atQQPT09xYKCAr3qfFZQUJAIQOzZs2epZfv27SsCEFetWiW9lpiYKB45cqREHLdv3xY9PDxEMzOzErEUvaeCgoLUtnP79m3x3LlzJV4PDw8XHR0dRXt7ezEtLa3UePX57DDV9TlvJaVKaV5gK51vKe3YuDbmBbY0ckRERESGsWBfpM4zcAfHJGPBvmtGjkg/2par2L17t0rZFi1a4Pvvv0daWhpefvll/Pvvv3jvvfdQs2ZNbNq0Cebm6m9u69KlC1577TWVNr/44gs0atQIly5dUrn99Ntvv4VSqcTPP/+M9u3bq9Tz6quvYujQoQgODsbFixdLtNO6dWt88MEHZfo9LF26FC4uLtLzgIAAtGvXDg8ePMDzzz+PwYMHS9vs7e3x+uuvA4B0SycAPH78GGvWrIGrqyvWrVsHBwcHaZudnR1+/fVX1KhRAytXrlQbwxdffIFmzZpJz52dnTF9+nQAhb2h+mjdujVatWpV4vUBAwZg1KhRuHXrFq5evapXneXh7OwMAHjy5In0mpOTE/r27avS4woU3h77ySefoKCgAPv27dOrncaNG6NTp04lXm/Xrh3eeustpKamIigoqAxHUPHwVlKqlKwsFNjwij8W7LuGnWHq1zW0UAiV/jYbIiKqXh6nZWNn2F299tkZFo/3+3nDxc7SSFHpR9tyFW5ubiVee/PNN3HgwAH89ddf6NSpEzIyMrBq1Sp4e3trrGfMmDElXrOwsMDIkSOxdOlSnDp1Ct26dUNBQQGOHj2KmjVrYsCAAWrr6t69O/bu3YuQkBC0a9dOZduQIUNKJBm6sLCwULv0QZMmTXDx4kW1t1w2adIEQOFYviLHjx9HXl4eBg4cCGtr6xL71KtXD02bNsWVK1eQlZVVooy6dop+r8Xb0VVOTg7+/vtvhISEICEhAbm5uQCAK1euAACio6PRunVrvestC1EsvPZT9/9z+vRpHD9+HPfu3UN2djZEUZSONzo6Wu+2lEoljh49irNnz+LBgwfIyclRqassdVZETAyp0rKyUOCbEa3xfj9v7LgQj/O3k5Cekw9bS3N0auKE0R1cK8xJkoiISBc7QtV/2alNnlLEjgvxFWat3rIsV/Hrr7/Cw8MDqampeP7556XeM03c3d3Vvl60/t79+/cBFE5ik56eDgCoUaOG1joTExNLvKYukdVFvXr1oFCU/FK6aNyauiUuirYVJR0ApAlQ1qxZgzVr1mhtMzk5uUS9jRo1KlHOzs6uRDu6uHLlCoYOHap1sfq0tDS96iyPov+v2rX/dwdZSkoKRowYgWPHjmncT98Y7969iyFDhiAiIsJgdVZUTAyp0nOxs8RbAV4V5oRIRERUVrreQvqs87eTKvV5cO/evdLsktevX0d6erraCVD0VVBQAKAw6XrxxRe1llV3m6SVlVWZ2i1tghpdJ7Apir9t27YlJtR5lqVlyS/DdW2nNKIoYvTo0YiNjcX06dMxffp0NGnSBLa2thAEAR9//DG++eYbqRfP2ERRlBK1li3/N1xozpw5OHbsGHr27IkFCxbAx8cHjo6OUCgUOHz4MAYMGKB3jFOnTkVERARefPFFfPjhh2jWrBns7OxgZmaG1atXY9q0abIdt7ExMSQiIiKqINJzSi49YMz9KoLo6GhpXOHAgQPx559/YsaMGVi7dq3Gfe7cuaP19QYNGgAoHIdmZWUFMzMzrFu3rky3hZpSUY9ft27dsHz5cpPFERUVhaioKHTo0EHteMbbt2/LGs/Ro0eRmJgIOzs7lXGju3btgkKhwN69e0ssfF+WGDMyMnDkyBHUrVsX27dvL9ELLPdxGxsnnyEiIiKqIGwty/adfVn3M7X8/HyMHz8eGRkZWLx4MTZt2oTmzZtj3bp1+OOPPzTut2PHDrV1Fe1TtMSFubk5evXqhdTUVBw9etQ4B2FEAQEBUCgU2L9/P/Ly8ozaVtGtturWRSya4EXdralPnjyRdT2/zMxMvP/++wCA6dOnq/SUPnnyBPb29iWSQkD9ewbQftwpKSkoKChA/fr1SySFeXl52LVrV5mPoyJiYkhERERUQeg64/azOjVxMnAk8vj8888REhKCwMBATJs2DdbW1ti0aRMsLCzw+uuvS2MFn3X69OkSPYrz5s1DXFwc2rRpI61rBwCffPIJzMzMMGXKFBw/frxEXenp6Vi7di2ysrIMemyG0LBhQ7zyyiuIjY3F2LFj8ejRoxJlbt68qTWJ1lVRL+v169dLbPPy8oKZmRmOHTumMtFKdnY2pk+fjuTkst0Cra/Tp0+ja9euuHLlClq1aoXPPvtMZbu3tzeePHmC7du3q7y+ZMkSjTOHajvuOnXqwMHBAVevXsWZM2ek15VKJebMmYMbN26U95AqlMr59RIRERFRFTTazxXLjkbrNQGNhULA6A6uRoxKP5MnT9a4zc3NDZ9//jkA4Ny5c/j6669Rt25d/Prrr1KZ9u3bY/78+fjkk08wefJkHDp0qMQtoG+88QamTp2KVatWwdPTE5cvX0ZkZCTs7e1LTH7TrVs3/PTTT3j77bcREBAAHx8feHt7w8LCArGxsbh06RJycnIwYsQItTN/mtqyZcsQGxuLP/74A3///Tfatm0LNzc3ZGRk4Nq1a7h58yaGDRtW6hjK0gwdOhQbNmzAuHHj0L9/f2lpjF9++QV16tTBq6++ijVr1sDX1xe9e/eGtbU1Tp06BaVSicmTJ5dp0iFNoqKipPdRfn4+njx5goiICNy7dw8AMHDgQKxfv16aSKfI3LlzMX78eIwZMwY//fQTGjVqhIiICERFReG9997DkiVLSrQVGBiIzz//HLNnz8aRI0ekZTAWLlwIJycnfPjhh/jkk0/Qs2dP9O7dG7Vr10ZwcDAePXqEt956Cz/99JPBjtvUmBgSERERVRB17Kwwsn0jbA2J13mfke0r1izcGzZs0LjN19cXn3/+OdLT0zF+/HgolUqsXbtWZb0/APjoo49w8OBBHDlyBEuXLsV7772nsn306NEYNGgQvv76a+zZswcWFhYYNmwYvv76a5XJSIpMnz4dnTp1wtKlS3H8+HHs378fNWvWRMOGDfHyyy9jxIgRKmsEViTW1tY4ePAgNm/ejA0bNuDSpUsICQmBi4sL3N3dMWHCBLXLd+hrxIgRWLJkCdasWYN9+/ZJs5b+8ssvAICVK1eiefPm+PXXX3H06FE4ODigb9+++Oqrr7Bu3bpyt1/co0ePpPeRpaUlHBwc0KRJE7z44osYO3as2nUFAeDll19GrVq18MUXX+DSpUu4cuUKOnTogBUrVkAURbWJYfv27bFp0yYsWrQIhw8flnqOP/30Uzg5OeHjjz9Go0aNsHTpUpw5cwbW1tbo1q0bPv/8c4SHhxv0uE1NEKvKNDpUJkUzcEVGRpo4EiIiooqvoKBAuuWsWbNmBpv1sbjsPCUmrQ3RaYbSjo1rY8Mr/tVmvd7Jkydjw4YNCAoKUrtOIFFFpc9nh6muzznGkIiIiKgCsbJQYMMr/hjr7wYLhfpZNC0UAsb6u1WrpJCIjIu3khIRERFVMFYWCnwzojXe7+eNHRficf52EtJz8mFraY5OTZwwukPFun2UiCo/JoZEREREFZSLnSXeCvCq1IvXE1HlwFtJiYiIiKhSWL9+PURR5PhCIiNgYkhERERERFTNMTEkIiIiIiKq5pgYEhERERERVXMmnXxGFEUkJiYiIyMDAGBjYwNnZ2cIgvqpmYmIiIiIiMjwZE0Mw8LCcOTIEYSEhCAiIgJ3795Ffn6+akDm5mjUqBF8fX3h7++Pfv36oX379nKGSUREREREVK0YPTG8du0a1q1bhy1btuDhw4cq20RRLFE+Ly8PsbGxiI2NxZ49e/DJJ5+gXr16GDduHCZPnoxWrVoZO2QiIiIiIqJqxWhjDI8dO4YBAwagdevWWLx4MR4+fAhRFFV+NHm23MOHD7F48WK0adMGAwYMwLFjx4wVNhERERERUbVj8B7D8PBwfPDBBzh+/DiA//UKCoIAHx8fdO/eHW3btkWLFi3QsGFDODs7o2bNmhBFEVlZWUhISMC9e/fw77//IiIiAqdOnUJkZKRUzz///IN//vkHvXr1wn//+18899xzhj4EIiIiIiKiasWgieG0adOwdu1aFBQUQBRFCIKAPn36YPTo0Rg+fDhcXFy07m9nZwc7Ozs0adIE3bt3l15PSEjA7t278fvvv+Po0aMQRRHHjx9Hx44d8eqrr+Lnn3825GEQERERERFVK4Ko7Z5OPZmZFd6Zam9vj+nTp2PatGlo3LixoaoHAMTGxmLlypVYtWoVUlNTIQgClEqlQduoTorGbEZGRpo4EiIiooqvoKAA169fBwA0a9ZMuvYhItJGn88OU12fG/TTzM7ODgsWLEBcXBy+/fZbgyeFAODh4YGFCxciLi4O8+fPh62trcHbICIiIiL9CIIg/Zw7d05juR07dkjlPDw8jB6Xh4eHQZZCO378OARBwOTJk8sfFFEFZNDE8NatW/jss89gb29vyGrVsre3x3/+8x/cunXL6G0RERERke42b96scdumTZtkjISIdGXQxNDZ2dmQ1VXYNomIiIioJIVCgdatW2P79u0l1qoGgKSkJPz999+cPJCoAuKN8URERERkMC+//DISExNx6NChEtu2b9+OvLw8jB8/3gSREZE2TAyJiIiIDKEgH0iPrbw/BSV7+Mpi3LhxEARB7S2jmzZtgq2tLYYNG6a1jgMHDqBfv36oVasWrKys0KxZM3z00Ud4+vSp2vJZWVn45JNP0LhxY1hZWcHT0xPz5s1Dbm6u1nb+/fdfTJ48Ga6urrC0tETdunUxZswYTspH1ZLB1zEkIiIiqpYy7wJ7DT/xnmyGxgC2HuWuxtXVFT169MDevXuRnp4uTRR4+/ZtnDt3DhMmTEDNmjU17v/NN9/g448/hrm5OXr27AlnZ2ecOXMGCxcuxK5du3Dy5EnUrVtXKp+bm4sBAwbg1KlTqFWrFgYPHoycnBz897//xcWLF6FpAv7du3djzJgxyMnJQdu2bdGpUyfEx8djx44d2LdvHw4ePIgePXqU+/dBVFmwxxBARkYG9uzZg/fffx89e/ZE/fr1YWlpCRsbG7i7u2PYsGFYs2YN0tPT9ap39+7dGDVqFBo3bgxra2s4Ozujffv2mD9/Pu7fv69XXUqlEhs3bsTgwYNVvtXq2rUr/vvf/2r8Bo2IiIhIbuPHj0dmZib+/PNP6bWiCWm03UYaGhqKTz/9FLa2tjh9+jT++ecfbNu2DTdv3sSoUaNw48YNvPXWWyr7LFmyBKdOnUK7du0QHR2NP/74A/v378fly5cRFhaGuLi4Eu3ExsZi/PjxsLCwwJEjR3Dx4kX8/vvvOH/+PA4cOCDd7lpajyNRVWKQxPDevXtYvXo1lixZgtOnT6tsi4+Px/Tp09GhQwe0a9cOr776KiIiIgzRrEEsXrwYzs7OGD58OJYsWYKTJ0/i4cOHyM3NRWZmJuLi4rB37168/vrr8Pb2xl9//VVqnU+ePMHAgQPxwgsvYOfOnYiNjUV2djaSkpIQHh6OBQsWoGXLltixY4dOMd65cwedO3fGxIkTceDAAdy9exe5ubl4/Pgxzp49iw8//BCtWrVCUFBQeX8dREREROU2cuRIWFpaqsxOunnzZtSvXx99+vTRuN+PP/6IgoICvPPOO+jYsaP0uqWlJX788UdYW1tj165diI+Pl7atWLECALBo0SI4OTlJr3t5eeGzzz5T287SpUuRkZGBb775Bn379lXZNnDgQLzxxhuIj4/X6bqPqKood2K4bds2eHt744033sDs2bPRs2dPTJgwAQAQFRWFNm3aYM2aNQgPD0dERATWrVsHPz8/7Ny5s9zBG8KNGzeQnZ0NAKhbty7GjRuHJUuWYOvWrdi0aRM+/PBD1K9fHwDw4MEDDBs2DLt379ZYX3Z2NoYMGSINuHZxccHcuXOxefNmrFy5Ev379wcApKSkYNy4cdi3b5/W+BITE9G/f3+EhoYCANzc3PD5559j69atWLZsmfShef/+fQQGBkrliIiIiEzF0dERgwcPxtGjR/Hw4UOEhobi+vXrGDNmDBQKhcb9Tp06BaBwAptn1alTB/3790dBQQHOnDkDAIiLi0NcXBzq1KmDgICAEvuMHTtWbTuHDx8GAIwYMULt9u7duwMAQkJCtBwlUdVSrjGG169fx6RJk5CXlwdHR0c0adIEUVFR2LJlC7p3746tW7ciJSUF/fv3R2BgIPLz87Fz506cOXMGr7zyCrp06YIGDRoY6ljKRBAE9O7dG7NmzUL//v1hbq76K3n55Zfx6aefYty4cdi/fz+USiVee+01BAQEwMHBoUR9//3vf3H27FkAgLe3N4KCglSOcfr06Vi0aBFmz54NpVKJqVOnIjo6WuPaj3PmzMGNGzcAAF27dsWBAwdUyr7zzjuYOXMmli9fjoyMDEyZMgURERFaP3SJiIiIjG38+PH4888/sW3bNsTExEivaVM01EbTwvdFr9+7d0+lvLu7u9ryDg4OcHR0LDHkJjY2FgDQsGFDrfEkJiZq3U5UlZQrMVy2bBny8vIwYsQIbN68GZaWlkhMTMSAAQOwcOFC3LlzB6+++irWrFkj7TNz5ky89NJL2LlzJ1avXo358+eX9xjK5auvvkLt2rW1lrGzs8O2bdvQtGlTPHjwAImJidizZw8mTpyoUi41NRULFy6Unm/cuFFt4jtr1iwcPXoUBw8exOPHj7FkyRLMmzevRLno6GisX78eQOEtFFu2bCmRQAqCgMWLF+P48eO4cuUKIiMjsWnTJkyaNEnXXwERERGRwQ0aNAiOjo747bffcP/+fbRo0aLc6xcKgmCQ2AoKCgCg1Oul4rezElV15UoMg4KCYGFhgeXLl8PS0hJA4YLzX375JQYPHgxzc3N8+eWXJfb77rvvsHPnThw5csTkiWFpSWERGxsbDB06FKtWrQIAteMk9+zZg4yMDABAt27d4O/vr7G+WbNm4eDBgwCALVu2qE0Mt23bJn1wjRo1Cm5ubmrrMjc3x8yZMzF16lQAhffwMzEkIiKSWc1GhTN7VlY1Gxm0OktLS4waNUrqIJgxY0ap+zRo0AAxMTG4c+cOWrZsWWL7sz19RcN97ty5o7a+1NRUtRP0NWrUCLdu3SoxLpGoOitXYhgfHw93d3fpj7KIn58fgMI/uuLTCRdxd3eHh4eHdItkZVG8ty4zM7PE9gMHDkiPBw8erLWunj17wsbGBhkZGbhx4waio6PRtGnTMtc3aNAg6XFQUBCysrJgbW2tdR8iIiIyIDNzgyz3UJVMmDABf/75JwRBUDtu8Fndu3dHTEwMtm7dii+++EJlW0JCAg4dOgRBENC1a1cAhdeUrq6uiI+Px4kTJ9CzZ0+VfbZt26a2nX79+uHWrVvYtWuX9MU6UXVXrslnlEolatWqVeJ1Z2dnANA6frBOnTpITU0tT/Oyu3z5svRY3b3vxbdr6y0ECnv52rVrp3ZfABBFEVevXtW5vvr166NRo8Jv+vLz83Ht2jWt5YmIiIiMrXv37khMTERCQoLGcYDFvfXWWzAzM8MPP/yACxcuSK/n5ubinXfeQVZWFkaMGAFXV1dp2xtvvAGg8G6s5ORk6fXbt2/j888/V9vOrFmzYG1tjdmzZ6ssqVEkJycHO3fuxN27d3U+VqLKrlyJobOzMx49eqS5cjPN1efm5sLGxqY8zcvq+vXrOHLkiPQ8MDBQZbsoioiOjpaeN25c+gK3xctERUWpbLt37560bqJCoVD5ACxLfUREREQVnb+/P7744gukpqaic+fO6NevH8aOHQsvLy9s374dTZs2xU8//aSyz6xZs9C1a1eEhYXBy8sLI0eORGBgIHx8fNCuXTu1Q3G8vLywdetW5OXl4cUXX0TTpk0xdOhQjB07Fj169ICTkxNGjRrFyWeoWinXraSurq4ICwtDTk6ONMawyK5du7Tesx0XF6f2NtOKqGj20KLxfi+++GKJ+97T09ORl5cnPS/qNdWmeJln739/8uSJ9NjBwQEWFhblqk+bW7duoVWrVmq3RUZG6lwPERERUXl9/PHH8PX1xZIlSxAaGoqsrCy4ubnhww8/xEcffVTibrUaNWrg8OHD+PLLL7F582bs27cPDRo0wHvvvYd58+bB29tbbTvDhg3D5cuXsXjxYhw5cgRHjhyBhYUFGjRogMDAQIwYMULtOEciQ9F0/X3r1i14enrKHE05E8P27dsjODgYp0+fLrFY6bBhwzTud+PGDSQlJaldb6YimjFjBk6fPg2g8BbYH374oUSZtLQ0lee6jO8rXubZ22qL16frWEFt9REREREZkyiKOpetV6+e1vKDBw8udX6F4mrWrImvv/4aX3/9dYltRRPWqOPp6VmiB1KTXr166XWMRJVNuRLD559/HlFRUXj48KFe+23cuBFA4R9YRfftt99ixYoVAAq/kdq+fbvJ1140NE9PT/YMEhERERHJSNP1t6aeRGMrV2I4aNAgldkwdTV8+HD07dsXvr6+5Wne6BYvXoy5c+cCACwsLLBjxw6NyaydnZ3K86ysrBKvPSsrK0t6/Oz6hMX3LV6urPURERERERFpUq7EsKzat29vimb18v333+ODDz4AUNhTuHPnzhITzhRna2sLc3Nz5OfnAwASExNLTQyLD2h2dHRU2Vb8eUpKCvLz82Furv2/S1t9REREREREmpRrVtKq6ptvvpGSQktLS+zevVtrUggAgiCoDG6OiSl9gdviZZo3b66yrVGjRrC1tQVQOPlNXFxcueojIiIiIiLShInhMxYsWICPP/4YQOFkLnv37sXzzz+v075t2rSRHoeEhGgtm5+fj4sXL6rdFyhMNH18fHSu78GDB9JaOwqFgrNoERERERGRzpgYFvPJJ59g/vz5AApnt9q/fz/69++v8/7Fx1seOHBAa9kTJ04gIyMDANC0aVM0bdq0XPUV3x4QEKDzTKZEREREREQmSQxzcnJw9epVXLhwAbdv34ZSqTRFGCrmzJkjTXFsa2uLgwcPonfv3nrVMXToUNjY2AAATp06pbWXb9GiRdLjcePGqS3z0ksvwcys8L9ox44diI+PV1suPz8fy5Ytk56//PLLesVNRERERETVm6yJ4b179/DSSy/BwcEBvr6+6NixI5o2bQpra2u0bt0aEydOxJIlS3D8+HG9Fmgvr1mzZuG7774DUDib56FDh9CjRw+963FwcJDGJgLAxIkTcf/+/RLlFi1ahIMHDwIoXJT+vffeU1uft7c3Jk6cCKAwmR43blyJ9QlFUcSsWbNw5coVAECLFi0wfvx4vWMnIiKi0gmCID0uKCgwYSREVJkU7wgr/jlSkQiiTCt1Pnz4EH5+frh//77axUHV/YLc3d3Rrl07lR9DryH42Wef4csvv5Sez507F/7+/qXu5+zsjG7dupV4PSsrC71798b58+cBAC4uLnjttdfg4+OD1NRU7Nq1C4cOHQJQOBZw586dGD58uMZ2EhIS0KVLF9y8eRNA4e9k6tSp8PLyQkJCArZs2SK1VbNmTRw7dgwdO3bU+fiL1knhOoZERES6uXXrFnJzc9GgQQM4ODiYOhwiqgSSkpLw+PFjWFpaokmTJlrLmur6XLbE8K233sLKlSshCAJEUUSNGjXQpEkT5OTkIC4uTu3tpOqSRRcXFzx8+NBgcfXq1QsnTpzQe7+ePXvi+PHjarclJydjzJgxOHLkiMb97e3t8fPPP2Ps2LGlthUTE4NRo0YhLCxMY5l69eph06ZN6NOnT6n1FcfEkIiISD+PHz9GUlISFAoF6tatCxsbG2noBxFREVEUkZOTg7S0NCQnJwMovGavVauW1v1MdX0u2zqGxSdHmTBhAn788Udpnb+8vDz8+++/uHTpEi5duoSLFy8iIiJC7e2kCQkJcoVcZrVr18bhw4exa9cubN68GaGhoXj06BFsbGzg7u6OIUOGYNq0aWjYsKFO9TVu3BjBwcHYtGkTtm/fjsuXLyMhIQEODg7w9PTE8OHD8frrr5f6JiMiIqLyc3JyQkZGBrKzs9UOGSEiUsfR0bFC32UgW4+hpaUl8vPz0bhxY0RFRZW6WDsA3LlzR0oWi3409S5S2bDHkIiISH9KpRJJSUlIS0tDbm6uqcMhogpKoVDAxsYGdnZ2sLOz02l8YZXvMbSxsUFKSgo6d+6sU1IIFI6nc3d3x7Bhw6TXUlJSjBUiERERkU4UCgXq1KmDOnXqQBRFtfMnEFH1JghChZ1oRh3ZEsPWrVvj9OnT5b4HvyJ3vxIREVH1U9ku/oiI1JFtpPSIESMgiiKCg4PlapKIiIiIiIh0IFtiOHXqVNSvXx/R0dH4+++/5WqWiIiIiIiISiFbYmhjY4NNmzZBEAS89tprePDggVxNExERERERkRayLroTEBCAn3/+GQ8fPkSXLl0QGhoqZ/NERERERESkhuyrsb7yyiuYPXs27ty5g86dO2Ps2LE4dOgQsrKy5A6FiIiIiIiIIOOspABw9OhRTJkyBffu3YMgCCgoKMCOHTuwY8cOmJubo0WLFmjXrh2ee+45tGvXDu3atYONjY2cIRIREREREVU7si1wf+XKFfj7+yMnJ6ewYUEosebPs1M9C4IALy8vlWTxueeeQ+3ateUIuVrgAvdERERERBVHlV/g/quvvkJOTo6UEDZr1gy+vr7Iz89HdHQ0oqKikJubq7KPKIqIjo5GdHQ0duzYAaAwWczPz5crbCIiIiIioipPtsTw+PHjUo/gN998gzlz5qhsz8vLQ2RkJC5evCj9REREID09Xa4QiYiIiIiIqiXZbiW1srJCXl4e2rZti7CwMJ32KeoxLJ4sXrx4EY8fPzZytNUHbyUlIiIiIqo4qvytpPXr10dcXBzatm2r8z6CIMDb2xve3t546aWXjBccERERERFRNSbbchW+vr4AgIyMDLmaJCIiIiIiIh3IlhiOHz8eoiji5MmTJWYjJSIiIiIiItORLTEcMWIEOnTogEePHmHNmjVyNUtERERERESlkC0xNDMzw2+//QZHR0e8//77CAkJkatpIiIiIiIi0kK2xHDChAnYs2cPZs+eDaVSiT59+uDXX3+Vq3kiIiIiIiLSQLblKszMzKR1DIHCpSgEQUDz5s0xZcoU9OnTB23atIFCoZAjHPp/XK6CiIiIiKjiqPLLVQCQJp0RBAGCIEAURURFRUmL3deoUQM+Pj5o166d9OPr6wtra2s5wyQiIiIiIqpWZEsMt27diosXL+LSpUuIiIjAo0ePAEBlhtKcnByEh4cjPDxces3MzAze3t4qyWLv3r3lCpuIiIiIiKjKk+1W0mc9fPhQShSLfm7duoWCgoISZYvfgioIAvLz8+UMtUrjraRERERERBVHtbiVtLh69erh+eefx/PPPy+9lpGRgYiICJVk8erVq8jOzjZVmERERERERFWeyRJDdWxsbNClSxd06dJFek2pVCIqKkolWSQiIiIiIiLDMdmtpFQx8FZSIiIiIqKKw1TX57KtY0hEREREREQVk2yJYXp6Ojp16oSpU6di1apVcjVLREREREREpZAtMdy5cydCQkKwbt06nDt3Tq5miYiIiIiIqBSyJYZ///239LhoQXtdpaenGzocIiIiIiIi+n+yJYZFi9Y3bdoULVq00GvfIUOGwNvbG2PHjuUahkRERERERAYmW2J47949CIKAdu3a6b3vtGnTcPPmTezYsQMHDx40QnRERERERETVl2yJYUFBAQDA0dFR732HDh0KS0tLAKq3pBIREREREVH5yZYYOjs7AwCSk5P13tfGxga+vr4QRREXLlwwdGhERERERETVmmyJoYeHB0RRlMYa6svb2xsAEBMTY8iwiIiIiIiIqj3ZEsOAgAAAwO3bt8uUHNra2gIAUlJSDBoXERERERFRdSdbYjhixAjp8UcffaT3/kW3oJqbmxssJiIiIiIiIpIxMWzbti369+8PURRx9OhRvPnmmxBFUef9Q0JCAPxvrCIREREREREZhmyJIQD89NNPcHBwAACsWrUKgwYNwsOHD0vd788//0RsbGyZl7sgIiIiIiIizWRNDD09PbFlyxZp6YnDhw/Dy8sLH330ESIjI9Xu88cff+DVV1+VngcGBsoSKxERERERUXUhiPrcz2kgZ8+exbBhw5CUlFQYhCAAABo1agQvLy/UqVMHmZmZuHjxIu7duyfdcuru7o6oqCgpsaTya9WqFQBoTMyJiIiIiEg+pro+N8lMLl26dMGFCxfw3nvvYffu3VLid/fuXdy9e1cqJ4qilDTa29tj+/btTAqJiIiIiIgMTNZbSYtzd3fHn3/+iRMnTmDYsGGwtLSEKIoqP0Bhcti3b1+cO3cO/v7+pgqXiIiIiIioyjL52g/du3dH9+7dkZOTg7CwMERHRyM5ORkKhQJ169ZFly5d4OrqauowiYiIiIiIqiyDJ4YPHz5EvXr19N7P0tISXbp0QZcuXQwdEhEREREREWlh8FtJGzVqhE6dOuGrr77C5cuXDV09ERERERERGZjBZyU1MzOTJowBADc3NwwZMgRDhw5Fr169YGFhYcjmqJw4KykRERERUcVhqutzg/cYDhs2DDVr1pQmkLlz5w5WrFiBgQMHwtnZGaNHj8amTZuQnJxs6KaJiIiIiIioDIyyjmFubi6OHj2Kffv2Yd++fbh3797/Gvz/3kSFQoHOnTsjMDAQQ4cOhbe3t6HDIB2wx5CIiIiIqOIw1fW5LAvcX7x4Efv27cPevXsRHh7+v8aL3XLq5eWFoUOHYujQoejatSvMzEy2kka1wsSQiIiIiKjiqNKJYXH379+XehKPHTuG7OzswkCKJYm1atXCoEGDEBgYiIEDB8LOzk7OEKsVJoZERERERBVHtUkMi8vMzMSRI0ewb98+/PXXX3j06NH/Avv/RNHCwgI9evSQehPd3NxMFW6VxMSQiIiIiKjiqJaJYXGiKCIkJAR79+7Fvn37cPXqVWlb8d5EHx8fDB06FIGBgfD39zdFqFUKE0MiIiIiooqj2ieGz4qNjZVuOT1x4gTy8vIAqCaJ9erVU5nYhvTHxJCIiIiIqOJgYqhFWloa/v77b+zduxcHDx6UlroQBAFKpdLE0VVuTAyJiIiIiCoOU12fm8vaWhnZ2dlh1KhRGDVqFAoKCnD69Gns3bsX+/fvN3VoRERERERElV6l6DEk42GPIRERERFRxWGq63MuFkhERERERFTNyXor6T///IOzZ88CAP7zn//I2TQRERERERFpIGtieOjQISxatAiCIDAxJCIiIiIiqiAq1OQzmzdvxtGjRyEIAn799VdTh0NERERERFQtGHyMYUFBQZn3PX/+PNavX4/169cbLiAiIiIiIiLSyuA9hvb29vD390fnzp3RpUsXdOnSBbVq1TJ0M0RERERERGQgBk8MMzMzceLECZw4cUJ6zdvbG126dMHt27cN3RwRERERERGVk8ETw/Hjx+Ps2bMqSeD169dx48YNlXLjxo1Dr1690LNnTzRr1szQYRAREREREZGOjLbA/aNHj3DmzBnp5+LFi8jLy4MgCBBFEYIgSGXr1q2LHj16IC4uDufPn4cgCFAqlcYIi57BBe6JiIiIiCoOU12fGy0xfFZ2djamT5+O3377DYIgwNraGpmZmf8L5P8TxaKkccSIEejRowd69OgBX19fOUKslpgYEhERERFVHKa6PpdtuQorKyu4uLhIz58+fYrQ0FBpPOLZs2eRlpYm9Sj++eef+PPPPwEADg4O6NKli5QodurUSa6wiYiIiIiIqjyTrWNobm6Ozp07o3Pnzvjoo49QUFCAKVOmYOPGjRAEAfb29khJSQFQmEQePHgQBw8ehCAIyM/PN1XYREREREREVY7B1zF8++23sWXLFty5c0e/QMzMYG9vLz1PTk7GxYsX8cMPP2DUqFGoW7cuRFGETHe+EhERERERVRsG7zFcsWIFVq5cCQCoX7++tJZh165dkZeXp3M9giDA19cXvr6+ePvttwEAt27dwqlTpwwdMhERERERUbVm8MSwaIwgANy/fx9//PEH/vjjjxLl/vnnH3Tt2hXW1tY61+3p6QlPT0+DxUpERERERERGSAyfPHmC4OBgnD17FufOnUNwcLA0VhD43+yjAwYMgIWFBfz8/BAQEICAgABkZWUZOhwiIiIiIiIqhdGXqxBFEdeuXcO5c+ewatUqhIWFqfQqFl/PsKi8IAg4cuQIOnfurFePYnnjvH37NsLDw3Hx4kXp59GjRypldNGrVy+cOHFC57b37duHIUOGlFpOqVRiy5Yt2LZtGy5fvozHjx/D0dERXl5eGD58OF577TU4Ojrq3C7A5SqIiIiIiCqSKrtchSAIaNWqFVq1aoXr168jLCwMALBu3ToEBQXhxIkTKhPVFCWK/fr1g7m5Odq1a4fu3buje/fu6NatG2rXrm2UOGfPno3FixcbpW5DuHPnDkaNGoXQ0FCV1x8/fozHjx/j7NmzWLp0KTZt2oSAgAATRUlERERERJWRyZarmDRpEiZNmgQAiI2NxfHjx7Fs2TJERERIPYp5eXkICQlBaGiolLS1bNkSV65cMXg8SqVS5bmlpSV8fHykRLasVq1ahTp16mgt4+fnp3V7YmIi+vfvjxs3bgAA3NzcMHXqVDRt2hSPHz/Gli1bEBwcjPv37yMwMBBBQUGl1klERERERFTEZIlhcR4eHpg8eTLCwsIQEREBANi2bRtOnDiBkydP4tq1a9JtnNeuXTNKDC1btsTMmTPRrl07tGvXDi1btoS5uXmJW1311b9/f3h4eJSrjjlz5khJYdeuXXHgwAGVpT3eeecdzJw5E8uXL0dGRgamTJmCiIgIKBSKcrVLRERERETVQ4VIDNUZPXo0Ro8eDaBwTcOTJ0/ixIkTRluu4vXXXzdKveUVHR2N9evXAyjsxdyyZYtKUggU3n67ePFiHD9+HFeuXEFkZCQ2bdok9cgSERERERFpY/AF7o2hdu3aGD58OJYsWYILFy6YOhxZbdu2DQUFBQCAUaNGwc3NTW05c3NzzJw5U3q+efNmWeIjIiIiIqLKT9Yew/79+8PW1lbOJiu9AwcOSI8HDx6steygQYOkx0FBQcjKypJtVlciIiIiIqq8ZE0M+/Xrh379+snZpMm9/vrruH79Oh49eoQaNWqgTp068PPzw/DhwzFy5Eit4wBFUcTVq1el5/7+/lrbql+/Pho1aoS7d+8iPz8f165dQ/v27Q12LEREREREVDVVqDGGEyZMQIcOHUwdhkEdOXJEepyTk4O0tDTcunUL27Ztg7e3N7Zs2aIxebt37x7S09MBAAqFAq6urqW217hxY9y9excAEBUVxcSQiIiIiIhKVaESQ39//1J7xSoLR0dH9O3bF35+fmjUqBHMzc3x4MEDnDx5Env37kV+fj5u3LiBbt264ejRo+jSpUuJOp48eSI9dnBwgIWFRantOjs7S4+fPn2qU6y3bt2SFtJ8Fhe+JyIiIiIyPE3X37du3YKnp6fM0VSwxLCq+Prrr9G+fXtYWlqW2DZz5kxcv34dI0eOxNWrV5GdnY0XX3wRN2/ehI2NjUrZtLQ06bGuYwWLl0tNTS3jERARERERUXXCxNAI1PX+FdesWTMcOXIErVu3RmJiIh4+fIiff/4Zs2bNkilCVZ6enuwZJCIiIiKSkabrb009icZWKZarqIrq1aunsrzE3r17S5Sxs7OTHmdlZelUb/Fyz653SEREREREpI5BE8P79+8bsjqdPHjwQPY2DaV3797S43///bfEdkdHR+lxSkoK8vPzS60zMTFR7f5ERERERESaGDQx9Pb2xqeffqoyaYqxPHnyBB9//DG8vb2N3paxuLi4SI/VTRTTqFEjad1HpVKJuLi4UuuMiYmRHjdv3rz8QRIRERERUZVn0MQwMzMT33zzDdzd3TF79mzcunXLkNUDKJyl5/3334e7uzsWLlyIzMxMg7chl+K9e7Vq1SqxXRAE+Pj4SM9DQkK01vfgwQNpqQqFQoGWLVsaKFIiIiIiIqrKDJoYzpgxAwqFAunp6ViyZAmaNWuG3r17Y9WqVXj06FGZ63306BFWrVqF3r17w9vbG8uWLUN6ejoUCoXKOL3KJigoSHrcrFkztWUGDRokPT5w4IDW+opvDwgI0HkmUyIiIiIiqt4EURRFQ1Z49epVfPjhh/j7778LGxAEaVvz5s3RvXt3tGnTBs2bN0ejRo3g5OSEmjVrQhRFZGVlITExEXfv3sX169cRERGB06dPIyoqSqqjKNznn38e3333ndFn7SkevyF/VY8fP4aPjw8SEhIAAN999x0++OCDEuVu3LiBFi1aoKCgAJaWloiOjla70H1+fj6ee+45XLlyBQCwbt06TJ48udQ4in5/nJWUiIiIiMj0THV9bvDEsMiZM2fw7bff4sCBA1JCVTzJ0kfx/QcPHoy5c+eic+fOBotVG30Tw2XLlqFDhw7o2rWrxjI3b97EyJEjERERAQCoW7cuoqOjVWYhLW7KlClYv349AKBbt27466+/VGYcFUUR7777Ln744QcAQIsWLXD58mWYm5e+GgkTQyIiIiKiisNU1+dGW8ewa9eu2LdvH27evIm1a9diy5YtOk2eoo6bmxtefvllTJkyBV5eXgaOtNDTp0/x/fffay3z6aeflnjtyy+/VHkeFBSEd999F40bN0bfvn3h4+MDFxcXmJub4+HDhzh58iT27NmDvLw8AIUL0v/+++8ak0KgsDfx9OnTuHnzJk6fPo02bdpg6tSp8PLyQkJCArZs2YLz588DAGrWrIl169bplBQSEREREREBRuwxVOfKlSs4cuQIgoODcfnyZdy5cwfZ2dkqZaysrODh4YE2bdrA398f/fr1Q+vWrY0eW2xsLBo3bqz3fs/++oYPH449e/botG+rVq2wYcMGtG/fvtSyMTExGDVqFMLCwjSWqVevHjZt2oQ+ffro1H5RDAB7DImIiIiIKoIqdyuprlJSUpCRkQEAsLGxgYODg0niMFRieOvWLZw8eRLBwcG4dOkSHj9+jKSkJGRmZsLe3h4NGzaEv78/XnjhBTz//PMwM9N9/h+lUolNmzZh+/btuHz5MhISEuDg4ABPT08MHz4cr7/+utrZTbVhYkhEREREVHFU28SQTKvCJIa5T4HIb4DW8wDzmqaNhYiIiIjIRKrcGEMind3/GwieCmTdA5TZQIdlpo6IiIiIiKhaMeg6hkR6yUsFgl8Hjj9fmBQCwI0fgEcnTBsXEREREVE1w8SQTOPJZeCv1sCtNSW3nZ8C5KXLHxMRERERUTXFxJBMw8YNEJXqt2XEAJc+lDceIiIiIqJqjIkhmUYNR6DjL5q3R68EHv4jWzhERERERNUZE0Myiew8Jeaeb4DtTwZoLPPk2ERkZz6RMSoiIiIiouqJiSHJLjtPiUlrQ7A1JB5f3HsVd3Nd1JarhQc4s2MSsvM03HJKREREREQGwcSQZLdgXySCY5IBAOkFNTHn7kyNZfvU2Ietu1fLFRoRERERUbXExJBk9TgtGzvD7qq8dia9LTYmDtK4z/OZnyEx+ZGxQyMiIiIiqraYGJKsdoTGI08plnj9m4dTEJdTV+0+9SySkHDiDWOHRkRERERUbTExJFkV3UL6rMwCa3xw912N+7XI2gXc3WekqIiIiIiIqjcmhiSr9Jx8jduCM1pjXWKg5p1DXgdykowQFRERERFR9cbEkGRla2mudfvCB5MQk1Nf/cbsh8CFGUaIioiIiIioemNiSLLq2Li21u3ZohVmx7+HAlFQX+DOFiD+TyNERkRERERUfWnvvjGy9PR03LlzB6mpqcjLy9N5vx49ehgxKjKm0X6uWHY0Wu0ENEXCMlvil8TheN1ll/oCIdMBl+6Alfr1D4mIiIiISD+yJ4ZpaWlYtmwZtm7diuvXr0MUNScI6giCgPx8zePUqGKrY2eFke0bYWtIvNZyix6OR2+7UHhZ3S25MScBuPAW0G2HkaIkIiIiIqpeZE0Mw8PDMXToUDx48AAA9E4KqWqYF9gKtxMyNM5QCgA5oiVmx7+HP70+gJlQULJA3O/AnR2A+2gjRqoqIS0H20PjEByTjPScfNhamqNTEyeM7uAKFztL2eIgIiIiIjI02RLDpKQkDBgwAElJ/5tV0tzcHJ6ennBycoKFhYVcoZCJWVkosOEVfyzYdw07w9Sva2ihENCidT8UeMyGWdR36iu68CZQpydgrX79Q0PJzlNiwb5I7Ay7WyLWU9GJWPrPDYxs74p5gS1hZaEwaixERERERMYgW2K4aNEiJCUlQRAE2NnZ4euvv8bEiRNha2srVwhUgVhZKPDNiNZ4v583dlyIx/nbSep74ZSfAw/+AlIiS1aSkwSETgO67wIEDZPVlFN2nhKT1oZo7d3MU4rYGhKH2wnp2PCKP5NDIiIiIqp0BFGm+znbtm2Ly5cvw8zMDMePH0e3bt3kaJZK0apVKwBAZKSaxKuiSA4DDnUERKX67Z03AY1fNkrTc/+8XOp4yOLG+rvhmxGtjRILEREREVV9pro+l225ipiYGAiCgK5duzIpJP3Ubg+0+ljz9gtvA5n3Dd7s47Rs7AxTM/mNFjvD4pGQlmPwWIiIiIiIjEm2xLCgoHACkebNm8vVJFUlrT4FHH3Vb8t7CoS8Dhi483tHqPrxj9rkKUXsuKB7DyMRERERUUUgW2Lo6uoKAMjKypKrSapKFDWAzhsAQcOw2Pt/ATEbDNqktnGF2py/nVR6ISIiIiKiCkS2xLB///4QRREXLlyQq0mqamr5Aj7/0bw9bCaQYbjeuvScsq2XWdb9iIiIiIhMRbbE8M0334SlpSWuX7+OI0eOyNUsVTWtPiocc6hOXioQPNVgt5TaWpZt0t6y7kdEREREZCqyJYbe3t5YsmQJRFHEhAkTKvYsmFRxmVkAndYDZjXUb394GLj1i0Ga6ti4dpn269TEySDtExERERHJRbbEEACmT5+OdevWISUlBX5+fnjvvfdw5swZpKamyhkGVXaOPkDrBZq3h78PpMeWu5nRfq6wUOi3PqKFQsDoDq7lbpuIiIiISE6yrWOoUKgu+i2KIoQyLEouCALy8zmGy1AqxTqG6hTkA0e6AUnB6rfXDQB6/wMI5fvug+sYEhEREZGcqvw6hkX5Z9G/xZNCURT1+iGCmfn/31JqqX77oyDg30XlbmZeYCudbynt2Lg25gW2LHebRERERERyk/VW0uLJIRM9KjeH5oDvV5q3R3wMJJ4vVxNWFgpseMUfY/3dNN5WaqEQMNbfDRte8YeVhUJtGSIiIiKiiky2W0mpYqq0t5IWKVACR3sCCWfUb7fxAJ6/CNRwLHdTCWk52HEhHudvJyE9Jx+2lubo1MQJozu4wsVOQ88lEREREZEeTHV9zsSwmqv0iSEApN0EDrYD8tPVb3cbBXTdDpRhTCsRERERkZyq/BhDIqOx8wL8VmreHvc7cHO1fPEQEREREVUyXImbqobG44GH/wAxG9RvD38XcOkCOBbOGJqQloPtoXEIjknmbaFEREREVO2Z9FbStLQ0nDt3DuHh4UhMTERaWhrs7Ozg7OyM5557Dp07d4adnZ2pwqsWqsStpEXy0oFDHYDU6+q327dAdp/zWHAwFjvD7iJPWfKtb6EQMLK9K+YFtuREMkREREQkO1Ndn5ukxzAuLg6ff/45tmzZgpycHI3lrKysMG7cOHz22Wdwc3OTMUKqlCxsga7bgEOdgAI176vUf3F2+3hsjZqmsYo8pYitIXG4nZDOWUaJiIiIqNqQfYzhn3/+CV9fX6xbtw7Z2dla1yvMysrC2rVr4evri127dskdKlVGtdoCz2lev7B3jX0Y6nii1GqCY5KxYN81AwZGRERERFRxyXor6d9//42hQ4dCqVRK6xfWrl0b/v7+8PDwgI2NDTIyMhAbG4vQ0FAkJSVBEASIoggLCwvs27cP/fv3lyvcaqFK3UpaRBSBUy8Cd9V/mZCmtMbg6B8Ql1tfazUWCgFnP+rDMYdEREREJJsqv1xFZmYmmjZtigcPHgAAPDw8sHDhQrzwwgswNy95R6tSqcSuXbswZ84cxMTEAAAaNmyIGzduwNraWo6Qq4UqmRgCQO4T4EBbIDNO7eaIzKYYees75IkWWqv5YEAzvBXgZYQAiYiIiIhKqvLLVaxbtw4PHjyAIAjo0KEDwsPDMWrUKLVJIQAoFAqMHDkS4eHh6NChAwDg/v37WLdunVwhU2VWoxbQdSsgqB8j6FszGh/W0zCDaTHnbycZOjIiIiIiogpHtsRw//79AAoTvq1bt8LR0VGn/RwcHLBlyxYoFIUX+Pv27TNWiFTVuHQB2nyhcfNrLrsRYBeqtYr0nHxDR0VEREREVOHIlhhevXoVgiCgW7du8PT01GtfLy8v9OjRA6Io4urVq0aKkKqklnOAen01bv7edQnqmidq3G5ryaU+iYiIiKjqky0xTEwsvPj28irbeK2iZLKoHiKdCGZA543INHNSu9nJPBVL3RbBDEq12zs1Ub8fEREREVFVIltiWDRhTHp6epn2L9qPE8+Q3qzrIddvvcbNnW2v4O06O0q8bqEQMLqDqxEDIyIiIiKqGGRLDBs0aABRFHHq1Cm99xVFEadPn4YgCGjQoIERoqOqztFzCI4rpmjcPrPuVvjbqN6mPLK9K5eqICIiIqJqQbbEMCAgAEDhzKI//vijXvuuXLkSd+/eBQD06tXL0KFRNdFp+ArcULZSu00hFOAHt+/gbP4EANCxcW3MC2wpZ3hERERERCYjW2I4fvx46fH777+Pn3/+Waf91qxZg/fee096PmHCBIPHRtWDlaUV3AP3IAt2arfXs0jGSvdv8bJffWx4xR9WFuqXuiAiIiIiqmpkSww7duyI0aNHQxRF5Ofn46233kL79u2xfPlyhIWFITExEVlZWUhMTER4eDh+/PFH+Pn5Yfr06cjLy4MgCBg9ejQ6duwoV8hUBVk6esK621qN2/1sIvGV+wYmhURERERUrQiiKIpyNZaZmYmAgACEhoZCEASd9ikKz9/fH8eOHUPNmjWNGWK106pV4a2VkZGRJo5EZiFvADe19Fp33gg0Hq95OxERERGREZjq+ly2HkMAqFmzJo4fP47p06dDEASIoljqj5mZGd544w0EBQUxKSTDab8EqN1e8/aQ14Dki/LFQ0RERERkQrImhkDhchMrVqzAjRs3MGfOHPj5+cHCwkKljIWFBfz8/DBnzhzcuHEDP/30E5epIMNSWAHd/wQsndVvV2YDp0YAOUnyxkVEREREZAKy3kqqTUpKCtLT02FrawsHBwdTh1NtVNtbSYs8PAYE9QPEAvXb6/UDeh0EzDjmkIiIiIiMr1rcSqqNg4MDGjZsyKSQ5FWvN9D2O83bHx4BLn8iXzxERERERCZQYRJDIpNp/j7gPkbz9msLgbid8sVDRERERCQzJoZEggB0/AVwbK25zPnJwNNqerstEREREVV55oasLC4uTuW5m5ubxm3lUbxeIoMwtwG67wL+7gDkPS25PT8DOPUCMCAUqMHbnYmIiIioajFoYujh4SGtTygIAvLz89VuK49n6yUyGDtPoOsW4PhgAGrmZEqLBs5NAHrsBgR2thMRERFR1WGUq9uiNQi1bSvPD5HRNHgeaPO55u339gFXv5QvHiIiIiIiGRi0x9DNzU1jr6C2bUQVSquPgeQLwN096rdfmQ/Ubg80HCxrWERERERExmLQxDA2NrZM24gqFMEM6PwbcMgfSL2upoAInH25cLyhfVPZwyMiIiIiMjQOlCJSx8K+cDIac1v12/NSCiejyUuXNy4iIiIiIiNgYkikiUOLwp5DTVIiC5exEAtkC4mIiIiIyBhkSwxPnjyJkydP4tatW2XaPyYmRqqDSDauLxSOOdQk/g8g4hP54iEiIiIiMgKDjjHUplevXhAEAW+99RZ++OEHvfdfsWIFFi9ezOUqSH6tPweSw4AHh9Rvv/YtYNcU8HylxKaEtBxsD41DcEwy0nPyYWtpjk5NnDC6gytc7CyNHDgRERERkW5kSwwNgUtVkEmYKYAuW4BDfkD6bfVlQqYBNu5AvT4AgOw8JRbsi8TOsLvIU6q+b09FJ2LpPzcwsr0r5gW2hJWFwthHQERERESkFccYEunCsrb2yWjEfODUi0DKv8jOU2LS2hBsDYkvkRQWyVOK2BoSh0lrQ5CdpzRi4EREREREpas0iWFmZiYAwMrKysSRULVVqw3QdXvhchbq5KUAxwfj+70nERyTrFOVwTHJWLDvmgGDJCIiIiLSX6VJDMPDwwEAzs7OJo6EqrWGg4DnlmnenhGDQU/ehKWQq3OVO8PikZCWY4DgiIiIiIjKxihjDOPi4jRuS0tL07q9uLy8PNy7dw+///47goODIQgC2rZta6Aoicqo2dtAWjRwQ/0kSs/VjML3rkswI+4DiDp895KnFLHjQjzeCvAydKRERERERDoxSmLo4eEBQRBKvC6KIn777Tf89puWteFKMXbs2PKERmQYzy0unIjm/n61mwMdTyEmpwEWP5qgU3XnbycxMSQiIiIikzHqraSiKEo/6l7T5wcoTArHjBljzJCJdGOmALpuBWq11VhkRt3teLHWUZ2qS8/hEixEREREZDpG6TF0c3Mr0WN4584dCIIAW1tb1K5du9Q6BEGAlZUVnJyc4OPjgxdffBF9+/Y1RrhEZWNhC/TcBxzqCGTdV1vkm4bLcS/XBecz2mitytayUq0cQ0RERERVjFGuRmNjY0u8ZmZW2Dk5adKkMi1wb2yiKOL27dsIDw/HxYsXpZ9Hjx6plNHX7t27sXnzZly4cAEPHz6EjY0N3N3dERgYiNdffx0NGjTQuS6lUoktW7Zg27ZtuHz5Mh4/fgxHR0d4eXlh+PDheO211+Do6Kh3jFQONRsBPfcDR7oByswSm2uY5eNn968x4tb3uJ3TSGM1nZo4GTNKIiIiIiKtBFGmVeOLEsO33367QiaGs2bNwuLFi7WW0edX9eTJE4wdOxaHDh3SWMbBwQGrV6/G6NGjS63vzp07GDVqFEJDQzWWadCgATZt2oSAgACd42zVqhUAIDIyUud9SI27e4GTwwGof4/E5tTHCze/xxOlQ4ltFgoBZz/qAxc7S+PGSEREREQVnqmuz2W7fy0mJgYAYG9vL1eTelEqVRcZt7S0hI+PD8LCwvSuKzs7G0OGDMHZs2cBAC4uLpg6dSp8fHyQmpqKXbt24fDhw0hJScG4ceNgbW2NwMBAjfUlJiaif//+uHHjBoDCW3WnTp2Kpk2b4vHjx9iyZQuCg4Nx//59BAYGIigoCH5+fnrHTeXQaGjhhDTh76nd7GH5AKs8vsL4218hV7RQ2TayvSuTQiIiIiIyKdl6DCu61atX49q1a2jXrh3atWuHli1bwtzcXGWspK6/qi+++AL/+c9/AADe3t4ICgoqccvookWLMHv2bABAnTp1EB0drTFpfvXVV7F27VoAQNeuXXHgwAGVsqIoYubMmVi+fDmAwm8ZIiIioFAoSo2VPYYGJIrAhbeB6BUai+x+0hPvxs8GUPi+6ti4Nja84g8ri9L/r4iIiIio6jPV9TkTw1LomximpqaiQYMGyMjIAAAEBwfD399fbdlBgwbh4MGDAID58+dj3rx5JcpER0ejefPmKCgogKWlJW7cuAE3N7cS5fLz8/Hcc8/hypUrAID169dj0qRJpcbLxNDACvKBE0OBBwc1Fln2aCx+TByPke1dMS+wJZNCIiIiIpKY6vrcqMtVVEd79uyRksJu3bppTAqBwnGNRbZs2aK2zLZt21BQUAAAGDVqlNqkEADMzc0xc+ZM6fnmzZv1jp0MwMwc6LYNcGytscjMulsRPuYmvhnRmkkhEREREVUIJp0jPz09HREREUhMTERaWpqUAJVm4sSJRo6s7A4cOCA9Hjx4sNayPXv2hI2NDTIyMnDjxg1ER0ejadOmZa5v0KBB0uOgoCBkZWXB2tpan/DJECzsC2cqPdQRyH6otojdlXcBuzqAB9flJCIiIiLTM0liuGXLFixfvhyhoaF6LwEhCEKFTgwvX74sPdbWWwgU9vK1a9cOp0+flvYtnhiKooirV6/qXF/9+vXRqFEj3L17F/n5+bh27Rrat29flsOg8rJxK1zj8J8egDJLTQERODcBqOEINBgod3RERERERCpkvZU0KysLgYGBmDBhAkJCQlBQUABRFNX+ANC6rSISRRHR0dHS88aNG5e6T/EyUVFRKtvu3buH9PR0AIBCoYCrq2u56iOZOXUAumxB0UQzJYj5wKkXgYRzsoZFRERERPQsWXsMX331Vfz1118AACsrKwQEBCAmJgZRUVFST2BaWhru3LmDy5cvIy8vD4IgwMbGBiNGjFCZCKYiSk9PR15envTc2dm51H2Kl3n69KnKtidPnkiPHRwcYGGhusyBvvVpcuvWLWmQ67M4KU05uQ4H/FYAoW+o367MBE4MBvqeBBx9AAAJaTnYHhqH4JhkpOfkw9bSHJ2aOGF0By5rQURERFRVaLr+vnXrFjw9PWWORsbEMDg4GNu2bYMgCPDy8sLhw4fh7u6Od955R+rZWrdunVQ+NTUVa9asweeff4709HQ8fvwY27dvh52dnVwh6y0tLU3luS7j+4qXSU1N1VifrmMFtdVHJtJ0OpCTBFz+VP323CdAUH/kBJzE/GOZ2Bl2F3lK1Z7xU9GJWPrPDc5kSkRERERGIVtiuGHDBunx2rVr4e7urrW8vb09Zs2ahaFDhyIgIACHDh3ClClTsHPnTmOHWu14enqyZ9DYWn0M5CQC15eq3571AMn7A3Ak6hvkKWupLZKnFLE1JA63E9K59iERERFRJafp+ltTT6KxyTbGsGiCFU9PT3Tt2lXn/Zo2bYoNGzZAFEXs2rVLuhW1Inq2NzMrS92kI9BY5tkF7ovXp0tdpdVHJiQIwHOLAI8JGovUN7uLDY3nwc4sQ2tVwTHJWLDvmqEjJCIiIqJqTLbE8P79+xAEAe3atVN5vfi4wdzcXLX79unTR8qcN23aZLwgy8nW1hbm5v/rhE1MTCx1n+JlHB0dVbYVf56SkoL8/Pxy1UcmJpgBnX4FGgzRWKSV9W384vE5LIUcrVXtDItHQpr2MkREREREupItMSwa7+bk5KTyuq5j4p577jmIooiwsDDjBGgAgiDA29tbeh4TE1PqPsXLNG/eXGVbo0aNYGtrCwBQKpWIi4srV31UAZhZAN12AC7dNRbpaBuJH90XwhyavwjIU4rYcSHeGBESERERUTUkW2JYs2ZNAFCZtRNQ7dW6c+eOxv2Llql48OCB4YMzoDZt2kiPQ0JCtJbNz8/HxYsX1e4LFCaaPj4+Otf34MED3L17F0Dh8hYtW7bUOW6Skbl14RqHjr4ai/SzD8FC12UQUKCxzPnbScaIjoiIiIiqIdkSw6I1+JKSVC9mi/ewnTuneT23a9cqx5iqQYMGSY8PHDigteyJEyeQkVE4nqxp06Yqi9uXpb7i2wMCAnSeyZRMoIYDEHAIsPXSWOTFWkH4tP6vANSv3ZmeU/qtxUREREREupAtMWzdujVEUcT169dVXvf395fGGa5evVrtOLrDhw8jPDwcgiCgSZMmssRbVkOHDoWNjQ0A4NSpU1p7+RYtWiQ9HjdunNoyL730EszMCv+bduzYgfh49bcP5ufnY9myZdLzl19+We/YSWbWdYHeh/FE1Lze5asue/Cmy+9qt9layroMKRERERFVYbIlhj169AAAXL9+HcnJydLrrq6u6NatG0RRRGRkJIYNG4aLFy8iLy8PKSkp2Lhxo0qSExgYKFfIZeLg4IAPPvhAej5x4kTcv3+/RLlFixbh4MGDAAoXpX/vvffU1uft7Y2JEycCAHJycjBu3LgSYzFFUcSsWbNw5coVAECLFi0wfvx4gxwPGZltYxx0Xo+UfBuNRT6s/xvGO5WcjbdTEyc1pYmIiIiI9CeIRYP3jCwuLg6NGzcGULiQfVGyAxTeQtqtWzet+4uiCBcXF0RGRsLZWXMPS1k9ffoU33//fYnXv/rqK+nxJ598UmL7l19+WeK1rKws9O7dG+fPnwcAuLi44LXXXoOPjw9SU1Oxa9cuHDp0CEDhWMCdO3di+PDhGmNLSEhAly5dcPPmTQCAu7s7pk6dCi8vLyQkJGDLli1SWzVr1sSxY8fQsWNHnY67aLZXrmNoOo/TsvHWsp+wweMT1DTTPNPop3ffxKbkwluLLRQCzn7UBy52lnKFSUREREQyMNX1uWyJIQB88MEHuHv3Ljp06IBZs2apbFu7di2mT5+ucUkGFxcX7NmzB506dTJKbLGxsVLiqg9Nv77k5GSMGTMGR44c0bivvb09fv75Z4wdO7bUdmJiYjBq1Cits7LWq1cPmzZtQp8+fUoP/P8xMawY5v55Gfev7cYvjT+HhaDUWO6ze9OxMWkIxvq74ZsRrWWMkIiIiIjkUC0Sw9L8+++/WLJkCY4dO4b79+/DzMwMTZo0QWBgIN599124uLgYrW1DJ4ZFdu3ahc2bNyM0NBSPHj2CjY0N3N3dMWTIEEybNg0NGzbUuS2lUolNmzZh+/btuHz5MhISEuDg4ABPT08MHz4cr7/+OmrVqqVX/EwMK4bsPCUmrQ1BneRdWOb2PcwEze+rtdnvY9yE72BloZAxQiIiIiKSAxNDMgkmhhVHdp4SC/ZdQ43bK7GgwUrthdsvA5rNkCcwIiIiIpKNqa7POa0hUQVhZaHANyNaIyFtCU4fr4VuaV9rLhw2ExALgObvyhYfEREREVVdsiWGI0aMAABYWFhg06ZNsLCwkKtpokrFxc4SLoFfAVF1gPB3NRcMfw+ACDRXP6MtEREREZGuZEsMd+/eDUEQ0KdPHyaFRLpoPhMQzIAwLbeMhr9f2HPYYpbmMkREREREpZBtHcOiSVE8PDzkapKo8mv2DtB+ufYyF2cD/5ZcaoWIiIiISFeyJYYNGjQAAGRmZsrVJFHV0OxtoMOP2stc/AC49p088RARERFRlSNbYtinTx+IooiQkBC5miSqOrzfAvxWaC9zaQ4Q+a088RARERFRlSJbYvjKK69AoVDg1q1b2Ldvn1zNElUdTd8A/H7WXiZiLhD5jTzxEBEREVGVIVti2KZNG3zyyScQRRGTJk3CmTNn5GqaqOpoOg3wX6W9TMTHwNWv5ImHiIiIiKoE2RJDAJg/fz6+++47pKeno1evXhg3bhz27NmDu3fvIicnR85QiCovr9cB/zXay1z+FLjyhTzxEBEREVGlJ4iiKMrRkEKhUHkuiiIEQdC7HkEQkJ+fb6iwqr1WrVoBACIjI00cCent1logeCoALX/CrRcArf8jW0hEREREVD6muj6XbR3DZxPB4o9lyk2JqhbPVwAIQPCr0JgcXplXuM5hm/kyBkZERERElY1siSHABJDI4DynAIIAnH8FGpPDqwsKt7WeX1iWiIiIiOgZsiWGBQUFcjVFVL00mQzADDg/GZqTw88BMR9o8yWTQyIiIiIqQdbJZ4jISJpMBDpvAKAl6Yv8GgidDhQoZQuLiIiIiCoHJoZEVUXjCUDn3wBBy5/1zdXA6VGAMlu+uIiIiIiowpPtVtKTJ08CABo2bAhPT0+994+JiUF8fDwAoEePHgaNjajKaDy+MDE8N6Fw0hl17u4CggYAPfYANRxlDY+IiIiIKibZegx79eqFgIAALFu2rEz7r1ixAgEBAejdu7eBIyOqYjzGAZ03ae85fHwS+KcHkHlfvriIiIiIqMKqVLeSiqLImU2JdOExFui2EzCz1Fzm6RXgSFcg9YZ8cRERERFRhSTrchVEJCPXF4CAQ8DJoUBeqvoyGbGFyWGvA4CTn6zhVWYJaTnYHhqH4JhkpOfkw9bSHJ2aOGF0B1e42GlJxomIiIgqqEqTGGZmZgIArKysTBwJUSVStyfQ9yQQNBDIfqi+TE4icDQA6P4nUL+/vPFVMtl5SizYF4mdYXeRp1S9e+FUdCKW/nMDI9u7Yl5gS1hZKEwUJREREZH+Ks2tpOHh4QAAZ2dnE0dCVMnU8gX6nwXsmmouk58BHB8MxG6RL65KJjtPiUlrQ7A1JL5EUlgkTylia0gcJq0NQXYelwUhIiKiysMoPYZxcXEat6WlpWndXlxeXh7u3buH33//HcHBwRAEAW3btjVQlETViG1joN8Z4PggIPmC+jJiPnD2ZSD7MdD8XVnDqwwW7ItEcEyyTmWDY5KxYN81fDOitZGjIiIiIjIMQTTCbC5mZmYQBNWFtouaefZ1XYmiCEEQsHnzZowZM6bcMVKhVq1aAQAiIyNNHAnJIi8NOPUi8PCI9nIt5wC+3wBl/Hutah6nZaPrt8c09hSqY6EQcPajPhxzSERERHox1fW5UW8lLZpFtHjuWfw1fX4AYOzYsUwKicrDwg7ouR9wH6u93LWFQPArQEG+PHFVcDtCNd8+qkmeUsSOC/FGioiIiIjIsIxyK6mbm1uJnsE7d+5AEATY2tqidu3apdYhCAKsrKzg5OQEHx8fvPjii+jbt68xwiWqXhQ1gC6bAKs6wHUt64reXg9kJwLdtgPmNWULryLS9RbSZ52/nYS3ArwMHA0RERGR4RklMYyNjS3xmplZYefkpEmT8MMPPxijWSLSlWAGPLcEsKoHRMzVXO7+fuBob6DHbsC6nmzhVTTpOWXrOS3rfkRERERyk3VWUi5OT1SBCALQ6iOg41pA0LK0QlIwcMgPSL4oX2wVjK1l2b5DK+t+RERERHKT7aolJiYGAGBvby9Xk0SkC88pgKUzcGY0oMxWXybzLnCkG9BlI+A6Qt74KoCOjWvjVHSi3vt1auJkhGiIiIiIDE+2HkN3d3e4u7ujVq1acjVJRLpqFAj0/geooeXvU5lZOKPp1S+Batb7P9rPFRYK/WZotVAIGN3B1UgRERERERkW73MiqoIS0nKwPTQOwTHJSM/Jh62lOTo1ccLoDq6al09w6Qr0PQUEDQCy7mmu/PJnQMo1oOOvgLm1cQ6ggqljZ4WR7Rtha4jus4yObK/ld01ERERUwRhlHUNdpaen486dO0hNTUVeXp7O+/Xo0cOIUVUvXMewasnOU2LBvkjsDLurdnkFC4WAke1dMS+wJawsNIwrzLwPnBwOJIdqb6y2H9BzD2Bdv/yBVwLZeUpMWhui0wylHRvXxoZX/DX/jomIiIg0MNX1ueyJYVpaGpYtW4atW7fi+vXrek9IIwgC8vM505+hMDGsOgyauORnAeenAHHbtVdk3RDouReo/VwZIq58ChPva9gZpn5dQ50SbyIiIiItqkViGB4ejqFDh+LBgwcAyjZLqSAIUCqVhg6t2mJiWHXM/fOyXrc6jvV3wzcjWmsuIIrA1S+AK/O0V6SwBjr/BriN1Lntyi4hLQc7LsTj/O0k3W/VJSIiItJBlU8Mk5KS0Lx5cyQlJUmvmZubw9PTE05OTrCwsNC5rqCgIGOEWC0xMawaHqdlo+u3x9T2YmlioRBw9qM+KomMurGJL9cPwYDUORAKsrRX2HoB4PNZ4TIYRERERFQmpro+l23ymUWLFiEpKQmCIMDOzg5ff/01Jk6cCFtbW7lCIKqydoSqv7VRmzyliB0X4vFWgJfWsYmnopugrc032OD5NRzwWHOFV+YBqf8WrotYTSalISIiIqoqZFuu4sCBAwAKbwXdv38/3nzzTSaFRAaiy7hCdc7fTpLGJm4N0ZxcXsrwQr9r/8VNZQvtFd7ZBvzTs3ACGyIiIiKqNGRLDGNiYiAIArp27Ypu3brJ1SxRtZCeU7YJmdJz8rFgX6ROieXjfCcMvvYlIswGai+YHAoc8gOSLpQpJiIiIiKSn2y3khYUFAAAmjdvLleTRNWGrWXZ/pRrKMywM+yuzuVzREuMvPI2Lo3wh831zzUXzLoP/NMD6LQecB+tsViZ1lskIiIiIoOTLTF0dXXF9evXkZVVygQWRKS3jo1r41R0ot77WSjMyjA2EVifOgFvdWsDnJsAKDX8TSuzgDMvAU/CgTZfAmb/+7jRPqYxEUv/ucFlH4iIiIhkJNutpP3794coirhwgbeXERnaaD9XWCj0mw3UQiEgT1lQpvbO304C3F4E+p0uXMtQm2sLgWN9gayHAKDTmMY8pYitIXGYtDYE2XlcnoaIiIjI2GRLDN98801YWlri+vXrOHLkiFzNElULdeysMLJ9I732GdneFbllTAylMY21nwMGhgJO/tp3eHwCONgOeHxS5zGNQOGkOgv2XStTjERERESkO9kSQ29vbyxZsgSiKGLChAlcN4/IwOYFtkLHxrV1KtuxcW3MC2xZ5rGJKvtZ1wf6HAfcx2jfKfshxKO9USt2KQDdb1/dGRaPhLScMkRJRERERLqSLTEEgOnTp2PdunVISUmBn58f3nvvPZw5cwapqalyhkFUJVlZKLDhFX+M9XfTeFuphULAWH83bHjFH1YWCp0TyWd1auKk+oK5NdBlS+FYQi0EUYkP663DGvcvYW+WrlNbRestEhEREZHxCKIo6jfzRBkpFKoTSIiiCEHQb0wUULgOYn5+2abmp5JatWoFAOzBrWIS0nKw40I8zt9O0jrb5+O0bHT99pheE9BYKASc/aiP5llD7/1VOClN7hOt9dzJqYc378xFZLZnqW12b+qMja921DlGIiIiosrKVNfnss1KWpQIFv1bPCmUKTclqjZc7CzxVoAX3grw0lquaGzi1hDde+RGti9lKYmGg4GB4cDpUUCy5smm3C0f4k+v2fjP/enYntwfgOYvisq6TiMRERER6UbWW0mLEkBRFFV+iMh0yjI2sVS2HoUzlnpN11rM0iwPCxstx38bLYOVkK25ujKOhSQiIiIi3ciWGBYUFBjkR6nk1PVEhlSWsYk6UVgC/iuBzhsBRU2tRUfV/ge7vGbDo8Y9tdtLjGkkIiIiIoOSbYwhVUwcY0jF6To2UW9PI4HTLwKp17UWS1NaY+7dd7A/pYf0WqljGomIiIiqEFNdnzMxrOaYGJJs8tKA4NeAuO2lFv0juTfm3Z+O9IKaGOvvhm9GtJYhQCIiIiLTM9X1uaxjDImoGrOwA7puRV67ZcgXtY8ZfLH2MRxo+g7Ge97TbUwjEREREZWLyWZ0yM7Oxt9//43Tp08jPj4eT548gVKpxNGjR1XKiaKIrKwsAICFhQUsLCxMES4RGYIgwKLFDOTUao/0YyPhiIcai7pZPsIXVm9CiHoAtPoEMOMENERERETGYpIrre+//x7fffcdkpKSpNc0rWuYnJwMNzc3ZGdno2PHjjh79qycoRKREVjW6wrLEVeQe2ocaiQc0VhOEJXAlfnAg0NAl02AbRP5giQiIiKqRmS9lTQvLw+DBw/GnDlzkJSUpNOSFU5OTpg0aRJEUURwcDBu3rwpY8REZDRWzqjR92+gzReAUMpMp4nngANtgdu/ARwWTURERGRwsiaGb7zxBg4ePAhRFGFpaYlp06Zh+/btGDZsmNb9xo8fLz0+cOCAscMkIrkIZoDPp4VrHpbWG5ifBpyfBJwZC+Q+kSc+IiIiompCtsQwLCwM69atgyAIaNSoEcLDw7Fy5UqMGjUKjRo10rpvly5d4ODgAAA4deqUHOESkZycOwHPXwKaTC69bNx24IAv8OiEsaMiIiIiqjZkSwzXrVsn3TK6ceNGNG/eXK/927ZtC1EU8e+//xojPCIyNQs7oNM6oNsOwMJRe9nMeOBoAHDpY0CZK0t4RERERFWZbIlhUFAQAMDHxwc9e/bUe/+iXsV79+4ZNC4iqmDcRgGDLgN1epVSUASufQMc6QKkXpcjMiIiIqIqS7bE8P79+xAEAe3atSvT/ra2tgCAjIwMQ4ZFRBWRjSvQ+x+g7ULArJQlapLDgIPPAdE/A2KBPPERERERVTGyJYbZ2dkAACsrqzLtn56eDuB/CSIRVXFmCqDlh0D/c4B9M+1llZlA6BvAsb5A2i154iMiIiKqQmRLDF1cXAAADx9qXtBam6ioKJV6iKiaqN0eGBgGeE0vveyjIOBAayBqKVCgNHpoRERERFWFbIlh8+bNIYoizp07B6VSvwu2+Ph4XLp0CYIgwM/Pz0gRElGFZW4D+K8EeuwBLJ21l1VmAeHvAf90B1I4WRURERGRLmRLDAcOHAgASExMxG+//abXvp999pmUTA4YMMDgsRFRJdFoaOHENPV1+BxIPAccbAtEfgMU5Bk9NCIiIqLKTLbEcPLkydJahO+//z4uXLig036ff/45fvvtNwiCgAYNGmDMmDHGDJOIKjrr+kCvA8BzSwFFKWOWC3KBiI+BQ52AJxGyhEdERERUGcmWGNauXRtffvklRFFEamoqunfvjtmzZyMsLAw5OTlSudTUVFy/fh1r166Fn58fFixYIG1bsmQJLCxKmaGQiKo+wQxoPhN4/jLg0r308k/Cgb87AJf/AyhzSi9PREREVM0IYtGq8zJ599138cMPP0AQBJXXi8LQ9Pp//vMfzJ8/X5YYq5NWrVoBACIjI00cCVEZiQWFS1Vc+hDI12E5G4dWQMe1gLO/8WMjIiIi0pOprs9l6zEssnTpUvzyyy9wcHCAKIoqCaEgCNJrRT+Ojo5Yt24dk0IiUk8wA7zfBAZdBer1K718SiRwpDNw8QMgP8v48RERERFVArL3GBZJT0/H2rVrceDAAZw7dw5paWnSNktLS/j7+2PIkCGYNm0a7O3tTRFitcAeQ6pSRBG4vb5wVtK8lNLL2zUF/FYA9foaPbTKICEtB9tD4xAck4z0nHzYWpqjUxMnjO7gChc7S1OHR0REVC2Y6vrcZInhszIyMpCSkgIbGxtpkhoyPiaGVCVl3i9c8P7eXt3Ku44EnlsM2LgaN64KKjtPiQX7IrEz7C7ylCVPCRYKASPbu2JeYEtYWShMECEREVH1Ue0TQzINJoZkSBWqx0kUgTvbgbC3gZyk0ssragI+nwLN3wcUFad3zNi/0+w8JSatDUFwTHKpZTs2ro0Nr/gzOSQiIjIiJoZkEkwMyRAqdI9T9mPgwgwgbrtu5e2aAu2XAw1Mu2aqXL/TuX9extaQeJ3Lj/V3wzcjWpe5Pfq/9u47vql6/x/462Q06aali9GWAmWWJdAiVoUqS9kgV1QEERwo6IUf3qteBdTrBCcqXhwgCgJlKIgMBRkCLUPgy2qhlLaUAt07aZqc3x+hIaUjs2navJ6Px3nkpPl8PueT5HDI+3wWERFR/Vxm8hkial6qWpzWJGbUGsAAgEYrYk1iOqZ+mwiVRuvYCiqDgNifgLs3AcoQ0+mLLwB/Dgf2jQdK0xq+frVw1Gd6o1iF+GNXLMoTfywD2cVc8oOIiKi5cWhguHPnTvzyyy/4/fffLcr3+++/W5WPiBreoi1nzOqGCAAJqXlYtOVsA9eoDqFjgZFngfZPmJf+yiZga1fg9FuAVtWgVbudoz7TdUfqDjzrotGKWHfU/BZGIiIiahocFhiePXsWw4cPx7hx4/Drr79alHfr1q0YN24chg8fjpSUlAaqof1MmzbNsPyGOdvSpUvNKnfz5s146KGHEBERAXd3dwQEBKBv375YuHAhrl692sDviqimJtfi5OYHDPgWGHIAaNHLdHptOXDqNeDXKCDTsuuWtRz5mZobfN7u8CUzxmwSERFRk+KwwDA+Pt6wP3PmTIvyzpw507Cu4dq1Zo4Takby8/MNQXV8fDwuX74MlUqF3NxcHD9+HIsWLUK3bt2wbt26xq4quZgm2+IUeBcw/Kh+LKHcjFmQS1KAvSOBvaOBkksNWjVHfqYl6kqL89iSj4iIiJyXzFEH2rdvHwCgXbt26Natm0V5u3fvjnbt2iEtLQ179+7FK6+80hBVbBBvvvkmoqKi6k3Tq1fdLRcqlQojR47EwYMHAQCBgYGYMWMGoqKiUFRUhE2bNmHnzp0oLCzEI488And3d4waNcqu74GoLra0OD03uKNNx7Z5tk6JDOj8PBA+CTjxMnDpW9N5MrcAWTuBbv8Guv0LkLnb9B5q48jP1Eth3X8B1uYjIiIi5+Ww/93PnTsHQRDQu3dvq/L36dMHly9fxrlz5+xbsQYWGxuLQYMGWZ3/gw8+MASFnTp1wp49e9C6dWvD68888wyWLFmC//f//h+0Wi1mzJiBCxcuwMfHx9aqE5nUGC1O9c3Wuf9CDj7+Pdmy2TqVQcCAb4COM4EjzwH5x+tPr1MDpxcBqd8DfT8G2owCBMHq93M7R36mMRH+2H8hx+J8A9q3tDgPEREROTeHdSXNzdWPSQkMDLQqf1W+nBzLf8Q0VUVFRXjvvfcMz1etWlUtKKwyb948jBgxAgBw48YNfPTRRw6rI7k2R7c4NehsnQEDgGGJQP8v9WMRTSlNBfaNAf58ECi+aP5xTHDkZzqpfyjkUsuCWrlUwKR+oRYfi4iIiJybwwJDuVwOQN810hrW5mvKfv75Z5SWlgLQtzxGR0fXmXbevHmG/dWrVzd43YgAfYuTNaxtcWrw2TolUiDyGWBkMtBhJgAzgqas34BfuwPH/x+gtq4bqDFHfqZB3kpM7NvWojwT+5rZVZeIiIiaFIcFhgEBAQCACxcuWJU/OTm5WjmuYNu2bYb9Bx98sN609957Lzw9PQHoPytrP2ciSziyxcmhM6AqA4CY/wHDEgD//qbT6yqA80uAXzoAZz+waXkLR7fiLRjV3exgNCbCHwtGWTZGnIiIiJoGhwWGPXr0gCiKOHLkCDIzMy3Km5mZiSNHjkAQBIsnrmlsCxYsQGRkJDw8PODp6Ynw8HCMHz8eX3/9tclW0FOnThn262stBACZTIY+ffrUmpeooTiyxalRZkBt2R8YdhiI/h+gMKNFTlMAnHgJ2NIZuPQ9oLN84XlHt+Ip5VKsnB6NydFhdQakcqmAydFhWDk92rxxm0RERNTkOCwwHDp0KABAq9Vi/vz5FuWdN28etFr9D6xhw4bZvW4Nad++fbh48SLKy8tRVlaG9PR0bNq0CTNnzkT79u2xa9euWvOJolit1S8iIsLksYzTnD9/3vbKE5nBUS1OjbbmniDRT0wzMhno+AzM6l5alg4cngps7wtc3QGIlgW0jm7FU8qleGd8Dxz8932YP6wz7o4MQJ+wFrg7MgDzh3XGwX/fh3fG92BQSERE1Iw5bFbSqVOnYsGCBSgoKMDatWvh7++Pjz76yDD2sDYajQYvvviiYX0+Hx8fTJs2zUE1to2Hhwfi4uIQHR2NiIgIKBQKZGdn49ChQ9iwYQPKy8uRlZWF4cOHY926dZgwYUK1/CUlJdBoNIbn5nShNU5TUFBgdl1TUlLQvXv3Wl87c+aM2eWQa6pqcVq05Szij9XeqieXCpbNFFqLRl9zT+EPRH8JdJyhn700N8F0noKTwJ/DgeD7gD7vA/53mHUoR32mtwv0VuC5wR1tXkqEiIiITKvr93dKSgo6dOjg4No4MDD09vbGf//7X8yaNQuCIODLL7/Etm3bMHPmTAwePBjt27eHl5cXSkpKkJqait27d2P58uVIS0sDAAiCgDfeeAN+fmbMFtjInn/+eSxduhReXl41Xps1axbef/99TJ48GXv37oVOp8OUKVMQExODtm1vdR8rLi6uls/d3fR6acZpioqKbHgHRJapanGaO6QT1h3NwOFLudatLVgPe8zWafPahwDg3xcYehC4tBI4+QqgumY6z/U/9K2H4Y8Avd4CvEz3AHDEZ0pERERURRBFC/s42WjOnDlYunQpBEGAKIoQTKz/VVW9WbNmYenSpY6ookOUlpaib9++SEpKAqAPJj/77DPD61evXkWbNm0MzzUaDWSy+n8Yv/rqq3j77bcBAE899RS++uork/WoulPBlkFydkt3X8DinckW55s/rDOejI2oc+1DwIbWt8pS4PxHwNn3gcpi0+kBaCHHzsoJWFM6BaJbSwZ5REREVE1j/T532BjDKp9++im++OILQ2uaKIr1bl5eXvj888+bVVAIAJ6envjPf/5jeP7LL79Ue93b27va8/LycpNlGqfhAvfU3Fg7W+foXq0bbu1DmScQ9R9g9EWg0/OAYLpVUwoNRsh+wlKvSehR8AU+33UCA9/9Ay9v/D/Ljk1ERERkRw4PDAHgmWeeQVpaGt59910MGjSoRjdJd3d3DBo0CO+99x7S09Px7LPPNkY1G1xcXJxhPz09HWVlZYbnXl5e1VoIc3JyTJZnnKZFixb2qSSRk7B2ts4v/rzYsGsfAoAyCOj3GTDyHBA2yawsPtJSvNTqe+zv8iSm+6/H5iPJlgemRERERHbSKIEhoA9cXnrpJezevRulpaUoLCzElStXUFhYiNLSUuzevRvz589v1gFOYGBgtefGE8YIgoBOnToZnqempposzzhNly5dbK8gkZOxdLbOWYM6OG7tQwDw7gjErgWGJgBB95qVpaWsCC+3WoH9XZ9Ez+Kv8fYvR607NhEREZENGi0wvJ23tzdat25dowtlc3Z7K+DtE+v07NnTsJ+YmFhvWZWVlfj7779rzUvUXFi65t7PJzIdv/YhAAREA/ftQUH0RiSrws3LIivEq62/xZzS4Sg58T5QWWY6ExEREZGdOGxWUqppz549hv3Q0NAaXWofeOAB/PTTTwCAbdu24d///nedZe3duxelpaUAgMjISERGRjZAjYkanyWzddqy9qHNSzYIAn5Ij8KHyZ9igt8fmBv8I1q5mV5TMUBWAJz9F3DpQ6Dbv4GOTwMy07MSk2XsMkMtERFRM8LAsJGUlZXhrbfeMjwfNWpUjTSjR4+Gp6cnSktLsX//fiQmJiI6OrrW8pYsWWLYf+SRR+xfYSInY86ae4299mFCah50kGJ9/lBsKbgH0wN+wTNB8fCRmtEaqLoOHP8ncPa9mwHiUwwQ7UCl0dY5Q+3+Czn4+Pdku68PSURE1BQ0amBYUlKCtLQ0FBUVVVvM3ZR77rmnAWtlm5UrVyIoKAjDhg2DRFJ7T93r16/jkUcewblz5wAASqUS//rXv2qk8/X1xfz587Fw4UIAwOOPP47du3ejdevW1dItWbIEv/32GwD9Ivf//Oc/7fiOiJoue6x9aAvjAFMlKvFF9iSszhuOmYGbMK3lFnhKVaYLUV0Djr8InHsP6PYy0HEmIFXapX6uRqXRYuq3ifW2JFfNUHspuwQrp0czOCQiIpfh8MCwuLgYn3zyCdasWYOkpCRYuoyiIAiorLTP3fyG8Pfff+OTTz5BSEgIhg4dip49eyIkJAQKhQI5OTk4dOgQ4uPjDTOQSiQSrFy5EmFhYbWW99JLL2H79u04fPgwkpKS0Lt3b8ycORNRUVEoKirCpk2bsGPHDgCAVCrF8uXL4evr67D3S+TMYiL8sf+C6Rl9bzegfUu7HL+2ALNA64MPrk3F19lj8VTgJjzecqt5AWJ5FnBsDnD2XaDLP/VdTOWuMybbHhZtOWPxDLXvjO/RwLUiIiJyDg5d4P748eMYPXo0srKyAMDioBDQB4ZarfNO5/7iiy/ik08+MSttaGgovvnmGwwZMqTedHl5eXj44Yexa9euOtP4+Phg2bJlmDx5skX15QL31JzdKFbhrnd3WzQBjVwq4OC/77PLOLOluy9g8c7ketP4SwvxVOAGPB7wKzwkFsyGKm8BdJoFdJoDuAfbVlEX0NjnAhERkbka6/e5wwLD3NxcdOnSBbm5tyZfkMlk6NChA1q2bAm5XG52WcaTtjibq1ev4s8//0RCQgKOHz+Oa9euITc3F8XFxfDy8kJISAj69euHkSNHYvz48Ra9702bNuHHH3/EkSNHcP36dXh6eiI8PBwjR47E008/jTZt2lhcXwaG1Ny9vPEU1iSaP8vo5Ogwu7USWRKMtJQW4OmgDZjSchvcLQkQpUqg/RNA1/8HeLW3obbNmzlBem3mD+ts+0REREREFmj2geErr7yCd999F4IgwNvbG2+//TYef/xxeHl5OeLwVAcGhtTcmTOurEpMhL/dx5VZGpjO7O+JVyO2ABe+ALRmdDGtIkiAsElAt38hW9aVM27eZso3CVZ1K747MgCrnoxpgBoRERHVrrF+nztsjOG2bdsA6LuCbt26FbGxsY46NBG5sKq1DxdtOYv4Yxm1tt7JpUKDzUS5YFR3XMouNTswnTc6GpAPArrO189IenGZeQGiqAPSfgLSfsL5kjvw1/WJOFTaA4B+vUdXn3GzsWeoJSIicnYOazH09fVFSUkJYmNjsXfvXkcckszAFkNyJdnFapNrHzYE/RIJVgam5VnAmXeBlP9Z1oII4ERZJL688RB2FcVAh1vlNkTLqLNjiyERETUVzb4rqbe3N8rKyjBjxgx89dVXjjgkmYGBIZHj2BSYqm4ASZ8BFz4HKvItOm6Kug1W5IzChvz7UKbTr4Voz7GUTQHHGBIRUVPR7APDbt26ISkpCY8++ii+//57RxySzMDAkKiJ0RQDF5cD5z8EyjMtylqk9cC6vCFYmTsK17StXGrGTc5KSkRETUVj/T6vfQX2BjB06FCIooijR4866pBERM2P3BvoOhcYfQkY8B3g08XsrD7SMswI/Bl7O8/EF23fwF9/rQMct2JRowryVmJi37YW5ZnY13Un6yEiItfjsMBw1qxZUCgUSEpKqnc9PiIiMoPUDWg/DXjwDD5Sv4/jpZ3NzioRRAzxTcDYvMeBbT31LZCVZQ1XVyexYFR3xET4m5U2JsIfC0Z1a+AaEREROQ+HBYadOnXCRx99BFEUMWXKFHZdJCKyB0GCfeUDMT5lMSalvIvdRf0sy194Gkh8CtgcCpz4N1Ca3jD1dAJVM9ROjg6DXCrUmkYuFTA5OszlJuchIiJy2BjDKitXrsQzzzwDQRDw9NNPY+LEiejRowd8fHwcWQ26iWMMiZq+22fc7KJMxdOBGzCqxT7IBJ1lhQlSoO04oPMcIDAWEGoPoJq6xpqhloiIyJRmP/mMVFr9zqsoihCs+MEhCAIqK7mulL0wMCRq+uqacbOVPBuPtdyGyf474C8rsrxg3yig40yg3WOAwrwumERERGSbZj/5TFX8WfVoHBSKomjRRkREt0zqH1pr18gsTSA+uDYVd577Dv+6Mhvny8MtK7jwNHDsBWBTa+DgY8D1vS4zWQ0REZGrcVhgCFQPDhnoERHZh6kZN9WiAmvzhmH4haWYnPI2dhQOgA4W9NjQqYHLPwJ/DAK2dgHOfqBfV5GIiIiaDYcFhjqdzi6bVqt1VJWJiJoM82bcFHCotCe+lXwIzQPJQJd5gNzXsgMVJwMnXgI2twUOTAKydgGiheMYiYiIyOk4tMWQiIgahqUzbipadATuWAyMvQL0+xzwMX+5CwCATgOkrwf2DAV+6Qic/i9QdtUO74SIiIgag8NnJSXnwslniJofq2bcFHX61r/kT4GrvwGw4r8GQQq0flA/YU2r4YBEZtP7ICIickXNflZSck4MDImohtI0IOVb4NK3QNkV68pwbwN0mA60fwLwirBv/YiIiJoxlwwMi4uLcejQIRw/fhw5OTkoLi6Gt7c3AgICcMcdd+DOO++Et7d3Y1XPJTAwJKI66bRA1nYgZTmQuRUQrRzjHRgLREwBwh4C3PzsW0ciIqJmxqUCw/T0dLzxxhtYvXo11Gp1nemUSiUeeeQRvPbaawgLC3NgDV0HA0MiMkvZVSB1BXDxa6A01boyJG5Am5H6dRFbPwBIuZA8ERHR7VwmMNy4cSOefPJJFBUVmbVUhSAI8PHxwbfffotx48Y5oIauhYEhEVlE1AHX/tC3Il7ZrJ+ExhpufkDYJH1LYsBAQLBg+QxyqOxiNdYeSUdCap75Y1aJiMhqLhEYbt++HaNHj4ZWqzUEhf7+/oiOjka7du3g6emJ0tJSXL58GUeOHEFubi4EQYAoipDL5diyZQuGDh3qqOq6BAaGRGQ11Q0g9Xvg4nL9MhbW8owAIh7TtyT6dLJf/cgmKo0Wi7acQfyxK9Boa/5UkEsFTOwbigWjukEplzZCDYmImqdmHxiWlZUhMjISWVlZAIB27drhvffew7hx4yCT1Zy5TqvVYtOmTfjXv/6F1FR9t6U2bdogOTkZ7u7ujqiyS2BgSEQ2E0Uge78+QExfD+jqHiJgUstoIPwRIGwi4NHGfnUki6g0Wkz9NhEJqXkm08ZE+GPl9GgGh0REdtJYv88dto7hd999h6ysLAiCgH79+uH48eN46KGHag0KAUAqlWLixIk4fvw4+vXrBwC4evUqvvvuO0dVmYiIzCEIQNA9wMBVwPgsoO+ngF8f68rKTQSOvwhsbgvsigXOfwKUZdq1umTaoi1nzAoKASAhNQ+Ltpxt4BoREVFDc1hguHXrVgD6gG/NmjVo0aKFWfl8fX2xevVqSKX6O5FbtmxpqCoSEZGt3PyAzrOBEceBB04D3f4NeIRaV1b2XwwSG8GNYhXij1m2TEn8sQxkF9vQUkxERI3OYYHh6dOnIQgCYmNj0aFDB4vyduzYEffccw9EUcTp06cbqIZERGRXLboDvd8BxlwG7tsDdHgSkPtYVxaDRIdZdySj1jGF9dFoRaw7mtFANSIiIkdwWGCYk5MDQB/kWaMqmKwqh4iImghBAgQPAmK+BsZdA2LXAW1GAULtQwlMYpDYoMztQnq7w5dy7VwTIiJyJCv/V7acu7s7KioqUFJSYlX+qnyceIaIqAmTuesXug97CFDlAOlrgdQfgNzD1pWX/detQDFgIBA6AWg7BvC2rGcK3VKirnRoPiIicg4OazFs3bo1RFHE/v37Lc4riiIOHDgAQRDQunXrBqgdERE5nDIA6PQcMOwQMDIZ6LEI8I2yvrycg8Df84AtHYFfuwMnXgFyDuvXXiSzeSmsu2dsbT4iInIODgsMBw8eDEA/s+jSpUstyvvll1/iyhX9QPhBgwbZu2pERNSIsovVWHoUmJI4DOPSluFfqjVI8JqDSu/u1hdaeBY4+w6w805gU2sgYSZwZQtQWW6/ijdTMRH+VuUb0L6lnWtCRESO5LB1DBMSEnDnnXdCEARIpVJ8+umneOaZZ0zmW758OZ5//nloNBoIgoCDBw8iJibGATV2DVzHkIgaizkLqD/bR4PZkX9DfmUDUGiHycek7kCroUCbMUCbBwFlkO1lNjM3ilW4693dFk1AI5cKOPjv+xDorWjAmhERuYZmv8A9ADz88MNYt26d/sCCgN69e2PatGkYOHAgwsPD4enpidLSUqSnp+PgwYNYuXIljh8/DlEUIQgCJk2ahDVr1jiqui6BgSER2VN2sRprj6QjITUPJepKeClkGNC+JSb1C60WNFi1gHrZBSB9PZC+zj5BIgQg4E79mMTWDwC+3fVrMhJe3ngKaxLNn2V0cnQY3hnfowFrRETkOlwiMCwrK8PgwYNx5MgRCGb+51tVvejoaOzevRseHh4NWUWXw8CQiOzBnNa/iX1DsWBUNyjlUtsDj8Lzdg4SAXi0BVoNB1qPAILvA9x87VNuE2RV4C6XOqBmZA/m3sAhosbhEoEhAJSXl2PevHn43//+B53O9IQAEokETz/9NBYvXswZSRsAA0MispWlQcTih3ohbsmf9uuqWHgeyIgHrvwC5B2xpOp1E2RA4MBbgWKLXi7XmqgP9s8i/ljt6xreHuyT87P0Bg4RNQ6XCQyrXLp0CcuXL8fu3btx4sQJaDQaw2tyuRy9e/dGXFwcZs6cifbt2zdGFV0CA0MispWlrX892/ri1JVCi48zf1hnPDfYxFq4ZZlA5lYg8xfg2h+ATm3xcWqlDAFaD9cHiiFDAIV1E7Q0RdnFaqw7moHDl3LZutSEsRWYqOlwucDwdoWFhSgpKYGXlxd8fV23+46jMTAkIltYM1GJAMCa/3jujgzAqierTz5Wb5c4pQa4tlPfknh1K6C20wLsggRoGaMPEEPuA1oOAKRu9imbqIFw3ChR09FYv8+dZtEhX19fBoRERE3MuiO1dzOsj7V3I40XUK+vS9z+Czn4+Pfkm13ixkAZOh7QVQI5h4ArP+u3kotW1gL6dRFzDum3028AUg8g6B59kBh8H+DXSx88EjmJG8UqxB+7YlGe+GMZmDukE1uFiVyI0wSGRETU9JjTLc1eqhZQN6dLnEYrYk1iOi5ll9zsEicDgu7Wb30+AIqSgMyf9d1Ocw4Botb6imnLgKzt+g0AFC2BoMFAyP36YNGrg8uNTyTnYs0NHI1WxLqjGaa7bxNRs9FggeHixYtRVlYGABgyZAjuvPNOi8s4dOgQdu3aBQDw9vbGP//5T7vWkYiIbGPcitfQqhZQX7TljNkBaUJqHhZtOVu9S5wgAL5d9Fu3fwEVBcC13/WB3dXfgPKrtlVUnaufDCcjXv/cI+xWa2LwYMCjtW3lE1nI2hs4hy/lMjAkciENEhj+9NNPeOmllyAIAqKiojB//nyryunVqxeefvppQ//ajh07YtSoUfasKhER2aCqFc9Slo4zlEsFTOoX2jBd4txaAGET9Zso6pe/uPqbPlDMPgDoNLXnM1dZOnDpO/0GAJ7tgICB+llPA+8CfKMACTvwUMOx9gaOI2/8EFHja5BBEAsXLtQXLpFgzZo1Vi8z4eHhgdWrV0MQBIiiaCiXiIicQ0yEdbNz9mhr2ZjyiX31M2Da0iXOLIIAtOgBdHsJuG83MCEXuGcz0PFpfcufPZReBtJWA0efB37rA8T7AX/cD5xaAFzdAVRYPmMrUX2svYFjbT4iaprsHhgePnwYycnJEAQBjz76KLp162ZTeVFRUXj00UcBACdOnMCJEyfsUEsiIrKHSf1DIZdaNn5OLhXw+SN3mB1UxkT4Y8Eo/f8ltnSJs4rcG2g7Bohehuy4JPwYsA0/VryAo5qBKBc9rCvzdpUlwPU/9BPZ/DlcHyhu6wkkPgNc+h4oTtG3ZBJZydobOFXdt4nINdj9VtAvv/xi2J89e7Zdypw9ezZWrVoFANi8eTN69+5tl3KJiMg2Qd5KTOzb1qJp8Cf2DUWovwdWTo+2eAH1xugSV3MG1CEAhkCGSvT0uIB7fE5idPB5ROAkBNHGbqcAABEo+D/9dvEr/Z+UQfrupwE3u5/63wFIlXY4FrmCSf1D8ckfFyxqba/qvk1ErsPugeGRI0cAAMHBwejbt69dyuzXrx+Cg4Nx48YNJCQk2KVMIiKyjwWjuuNSdqnZC2dXtf4p5VK8M74H5g7pZPYC6o7uElffDKiVkOF4WVccL+uKj68B90S44+sHVHDL+RO49geQ/zesX5zj9orcAK5s1m8AIHED/Pvqg8SqgNE92D7HombH2hs4XKqCyLXYPTCs6kbaq1cvu5bbu3dv7NixA+fPn7druUREZBulXGpV61+VQG8Fnhvc0azZD2Mi/LH/Qo7FdbS2S5wlM6DuSy3HgiNheGf8+/o/qHOB63/qu4le+x0ovmBVHWqlq7i1lmIVrw63JrUJGAj4dgck0rrLIJdi7Q0cInIddg8M8/PzAQAhISF2LbeqvLw8x62ZRURE5rGm9c8ajuwSZ/MMqIqWQNgEZPuNxNridJy7cQGhuhPo4XYWd3icQ4j2NARdhcX1qlNJin67rB96AbkP0HLArdlPW8box0ySS7L1Bg4RNX92DwwrKvT/yUml9r2gVJVXVT4RETkfS1r/rOHILnG2Lgpec2wiAPS6uQGesko837sIM7pkQZ53GMj5S99l1F40RcC1nfoNAAQJ0KKn0VjFgfqlMwTLJg+ipstRN3CIqGmye2AYEBCArKwsZGdn27XcnBx91yF/f+tm1iIioubBUV3ibJkB9cnYiDrHJlYprZThvaP++DO3I1ZOnwelTAKUXAJyDgLZB/WBYsFp2G2coqgD8k/otwtf6P+mCAD87tBPZuPfV//oGcFgsZlr6Bs4RNQ02T0wDAwMxNWrV3Hq1Cm7lnvq1CkIgoCgoCC7lktERE2Lo7rE2TIDqiVjExNS87Boy1m8M74H4N1Bv0VM0b9YUQjkJtwKFHMO65e3sBd1TvVWRQCQt7gVKPrdfPTuoG9xdJDsYjXWHklHQmoeW7SIiBzE7oFhv379cPLkSaSnp+PcuXPo2rWrzWWeP38ely9fhiAIdpvplIiImi5HdImzdiZTN6nEtrGJ1QrzBVoN1W8AoNMChaeB7L9utSyWplpVzzppCoDru/VbFbkP4NdHHyj69Qb8egE+XQGpm10PXXv3W739F3Lw8e/JHANHRNRA7B4YDhkyBN988w0A4J133sH3339vc5nvvPNOtfKJiIiAhu0SZ+0MqHKpxKaxifWSSPVBmV8voNMs/d/Ks262KN4MFPOPATp7rKdoXMEi4MZe/VZFkAG+XYEWN+vToqd+38plM+pbGsRQDa2INYnpuJRdgpXToxkcEhHZkSCKop0GL+gVFxejXbt2yM/Ph0QiwaZNmzBq1Ciry9u6dSvGjh0LnU4HPz8/XL58Gd7enFXNXrp37w4AOHPmTCPXhIjIudwoVuGud3dbPAPqHWF+Vo1PvDsyAKuejLE4Xw1aFZB7VB8o5hzUty6qLQ9wraYMqhks+nQx2br48sZTFk0qNDk6TN/9lqgO7JJMTVVj/T63e4uht7c35s2bh//85z/Q6XR4+OGH8f3332PChAkWl7Vp0yZMmTIFOp0OgiBg7ty5DAqJiMghrJ0B9fy1IquOZ+2YxhqkSiAoVr8BgCgCxRerB4qFDfhjQ3UDuLZLv1WRyPXBoW/36ptXB0AitX1pECIj7JJMZB27txgCQHl5OQYMGIDTp09DFEUIgoAJEybgn//8J+68806T+Q8fPowPP/wQGzZsMOSPiorCoUOH4OHhYe/qujS2GBIR1c2c7o1VYiL8sXJ6NGZ+f9SqLqh2azE0R0UBkP83kHccyDsG5B8HipJhtxlQzSVRAD5dkKQKw8+pfrigDkOyKgwZFcHQwfQP9vnDOnNmTarGmn+zDA7J2TTW7/MGCQwBID09HTExMbhx44YhuAOAsLAwxMTEoHv37mjRogW8vLxQUlKCgoICnD17FgkJCUhLSwMAVFUtODgYCQkJCAsLa4iqujQGhkRE9dO3Ppg/A+rS3ReweGeyxcdp9CBHU6xfyiLvmD5gzD8GFJ3XL3PhYCqdG1LUbZGsCsMFVRguqdsgRd0WaRWtUSHKDekcGkxTk8AuydQcNLvAEAAuX76Mhx56CMeOHYMgCIZAT6hnfSTjNKIo4o477sD69esRERHRUNV0aQwMiYjMk12sNmsGVGvHJh78933O1y2yshTIP3kzWDwGFJwECs8CuopGqY5WlCCjItgQKGo8IzFr9EjAu7N+bCPXX3RpzerfHrm0ZhkYAoBGo8F7772Hzz77rNqi97UFh8ZVCQgIwJw5c/DSSy/Bzc2+02HTLQwMiYjsr1m3Wug0QFGSPmAsOKUPFgtO6WdHbUzyFoBPZ/1YRp/O+s27M+DVHpC5N27dyCGabGs90W2abWBYRaVSIT4+Hrt378b+/fuRlpaGyspbA+1lMhnCw8MRGxuLuLg4PPTQQ1AqlY6omktjYEhEZH8uOc5JdQMoOIVf922H6sbf6Oqeio6KDLhJ7DSpji082uonuvHuCHh1vPnYAfDuoF+jkZqFKd8kOP/4XiIzNJtZSeuiVCrx2GOP4bHHHjP8rbi4GMXFxfD29uZso0RE1Gwo5VKsnB5t0djEJk8ZhBuesXjxZAU02sEAABkq0UF5BV2UqeikTEcnRToilekIc7sGieDAiW7Krug343UYjeoNrw63BYz6/Wy1J9YezeByB02EtTP72m1GYKImzmGBYW0YEBIRUXOllEvxzvgemDukk1ljEx2hodd1W3ekehBcCRmSVO2QpGpXLZ1SUKGDIhORynR0UqZhRGgefCqS4afLdGzACOhbOlU3gJxDNV5SaD1xj7oVwitaIU3dChklwTiYFYyNe0NwZ48++M/oXjWCeq6d13i8FNb9rLU2H1Fz47CupOSc2JWUiKj5q29dN8B+rZe2dOVb/ng/PPXtXuRd+z90UqShkzIdHZXpaK/IRJjbNcgEx8+OWh+tKEG+GAj/oE6QeEeg0j0cm5Ll2HzBDZdVQcjStKy25EazayF2Qs19jCFvOriOZj/GkJwTA0MioubNkeMdx33xF/5OL7A4X5+wFtg06646lwaRCxqEuV1DJ/dMjG5fgiFt8yErSdYvp1GRb1VdG5pGlOKaJgBXKwKRqQlEZkUQrmoC4e3fHvPG3Q+FTwQg92rsajYrzXVWUkfd2CHn0ezHGBIREZHjLdpyxqygEAASUvOwaMtZq2dItbUrX/3db6NqtoyIIrJzr2DOV2sQJr+C9opMdFBkOEUro1zQItTtOkLdrtd8ccfNRzc/wCMM8Ay7+Rh+cz9UP2GOeytAIq+Zn2oV5K3ExL5tLZoReGJf525tM+fGjkYrYk1iOi5llzSPiayo0TAwJCIiaqZuFKsQf+yKRXnij2Vg7pBOVv1Yjonwt6or6YD2Las9D/RW4LnBHU137xMErD2lwqHi7jiE7tVekgsatJHfQDtFFsLcstDOLQvhiiyEu2Uh1O0aFM4wW2pFvn4rOFlHAgFQBuuDxKrNvU3N51yOw2DBqO64lF1qdgv5glHdHFAr6znyxg4RA0MiIqJm6vbJYMyh0YpYdzTDqjFXk/qH4pM/LljclW9Sv1CLj1Wlrh/NGlGOyxVtcLmiTY3XJNBidCcdPn7AGyhJAYovAsUXkZ7+fwhABjwkaqvrY18ioLqm3/KO1p3Mzf9WkOjeCnBvDXi0BpQ3991bAe4hLtH62JxmBHb0jR0iBoZERETNlLktDbc7fCnXqsCwMbryWbPUgA5SpKlaAiF3AbjP8PcXvvgLf6fnI1CWj3ZuVxGuuIZwtyy0U1xFqNt1tJHfQKC8wOq6NpiKPP1WcKqeRAKgDLwtWDQKGpUhNx+DAZmnw6reEJxxRmBrOPrGDhEDQyIiomaqMdZ1c3RXPnsuUaD/m4DsSn9kV/rjSFlUjTTuggpt3G4g1O062t4cQ9hWfvPR7Qb8ZMVW1afhibeW5qiz6+pNMi99gFgVMCqDbwaOwbeeVwWRUqVZR2+MGTXN7pLspBx9Y4eIgSEREVEz1Rjrujm6K5+9xjWaW1a5qMRFdRguqsNqfd1bUmoIHFvJs9Fano02btloI7+B1m7ZCJLlO36tRktVlgAlJfputqbIvPUtkYogQBlktK9/rJC3xLLDRVj7f2pcV/ug0uin5/4LOfj49+Qm0a2zMTTGjR1ybQwMiYiImil7Bk2WcGRXPnuOa7SmLIkA6IySF+s8cV4VgfOqiNqPLWgQLMvFvLuUGNdZB03xZRw/dxLqglRDIOkpVZl9/EZXWQyUFAMll2p92Q3AHABzOuufF1Z6IkfbAvmVPsiv9EGe1gf56T6IXxWCf8T2gdwjCFAE6DdlACD3RXaJxiXX72uMGzu349qJroWBIRERUTPVGJPBGHNEVz57jmu0pqzRvdrg1/+7avZnrBHluK5rhdi77oNKKbu5FMEdRilE+EhKESzPRSt5DkJuPvbwL8HgsApIVZlA2RWnXb/RFF9ZKXxlpYAis+aLtcyvo4UUQqU3hlb6oL/WG/mCD/LLvFF40hsrTnijfZtwjOofBTePAP0kPIqW+kcnnanVkkCrsW7sAPWvnciW3uaLgSEREVEz1RzXdauNPcc1WlrWuxN6wN1NYtVn/PLGU7UcR0CRzgtFai9cUIff+vMNYLJPGN4Z3wMqjRYzv92HjMwLaCXPNQSRQfI8BMnyECzXb0GyPCgkGrPr5Yyk0CJAVoAAWUHtCSoBHKoto7J6oOjmr1830uTWokFmb7Um0LLHjR1rWvy4dqLrEkRRdPKO7tSQunfXr/t05syZRq4JERE1BHN+5FWJifBvsj/y9D+87TOu0dKyrPmMi1Qa3PXubot/9B/89334cFeSmYGoCF9pCab1UeCfd3kB5VlA+dWb28191TX9vrbc7Ho0dxWCJ4q0XigRvaCCNxQefmgVEAylh78+cJT76rdq+76AvIX+UeoBCIKhPFv+Db688ZRFNx0mR9+6eVBXIArU/+/B2mOS/TTW73MGhi6OgSERUfNnz6DJ2WUXq+02rtGSsiz9jJfuvoDFO5Mtfn/PDuqAr/dfsiqgrPP9i6J+whnVdaD85rqJRvvasmtIvZICpTYHgbJ8KCSc3KRegrRa4JhSKEVKvgxFWk8U6zz0j1oPlOg8UGJ4dDc8H9Y7Eq+NGwBI5FYFlQCsDkRvFKusvmFhr54GtoxrtDavs42lZGBIjYKBIRGR67Bn0ES1M/cznvJNglXjx8JbeiAtt8zifPOHdbZ6rGf1FiQRPtJSBMry0VJWiJayArSUFukfZYXwlxWim58K8soceIh58JMWO/8srM5KogDkPtDJfHCtTI4rJTIUad1RqvVAqc4dpTolSnXuUInu6Ny2DR68IxJuSl8sP3QdW86W3EzjjjKtPp0Wtd/0MW7xs/aGRW3nl6XBli2tnNbmteWYDYmBITUKBoZERESON+6Lv/B3eoHF+byVMhSrLG+xuzsyAKuejLE4n7UtSHeE+SEhNQ9SaOEnK4K/tBAtZYUIkBXAX1YEP1kR/KRF+n2jxwB5EeRC0x4X6axUOjeU6pQo1ylvPWqVUIlK3NW1HZRKH2xPKsGFXBFlOqVhK9W5o0ynQLnR31Q6xc2/KaASFbg7MtBwflkTbNnS3dbavM7czb6xfp9z8pkmZs+ePVi5ciUOHDiArKwsuLm5oW3bthg+fDieeuopREZGNnYViYiIyAR7LilgDmvXtlt3pPausfXRaEXIpRIA+llFcyr9kFPpB6hN550/tBMOnL+MjKyMmwGlPoi8FUAWwldaghbSYrSQ3XyUFsNLyjGSpiglFVBKKgAU1XwxMxEAMFwGDA+2vGyVqAQ2eEOUeiC7WIKH1TKMCdcHjuU3g8pyUQGVzk2/n6rA2lV+eGRgZ8gVXog/mg33nCIM8LwVbJbr9OlVogJqnRxq0Q2AgITUPCzactbQyrloyxmzgjsA1fJam685Y2DYRKjVasyYMQM//PBDtb+XlZWhoKAAp0+fxtKlS/Huu+/ihRdeaKRaEhERkTmsXYrA39PNqhZDawNRc384306j1UEuFSyfUbN/GA6n5uGKJhhXNOZHKHJBg6Ed3fD5xHaAOg+oyAPUufrHirybf8uvvmnyoVMXQAKtFe+QjCkFFaBWQQAQKgVCPczMeFz/8BiAx2pf+rMalc5NvxUroN3sC1Hqjsn5FRjb3g1q0Q0qnQIq8WYanQJqUQ61zk3/ePPvlSkKZP4dhZILqYjzlkMlut1M42YIQNU6OVSiAmU6JTSifpba+GMZmDukU7Puds/AsAkQRRGPPvooNmzYAADw8vLC9OnT0b9/f6jVauzYsQPx8fFQqVR48cUXIZfLMWvWrEauNREREdXF2qUIHohqhS/3plh8PGvXtrO2pbFCq7N6qRRrgmaNKEe39p0AX8vGUU795jD+TsmAr7QEPtIS+N7cfKQl8JGWwkdaBh9pCbwlZfCWlsJbWqYfY6lQIdhdBVQUAiIn43GUW62eJUBZLgCgpzVLVp4DPjNjudZFmTPxXe4YAPqW8HVHMxp0XdbGxsCwCfjhhx8MQWFgYCD27t2Lrl27Gl5/8sknsX79evzjH/+AKIqYO3cuHnjgAbRr166RakxERET1sXaNySdi2+HrA5bPSmq8tp0lrG1p9FLIrF5f0h7r95mrRK3Vzwaq80CmJsjsfH3CWmDTY3fpZ3TVlgMVBYCmUL9VFAIa/fO/zqXg+MU0fXAp1QeXPhJ9sOkpKYentBzekjK4caZXp6TvvnrL4Uu5DAyp8YiiiNdee83wfOnSpdWCwioPPfQQ9uzZgy+//BJqtRqLFi3Cd99958iqEhERkQWsCZyUcqnVLXHWsLbL64D2LaGUS7FyerTFS6VYGzRb8x5tCXwB6NcrlHnoN7SukS4yWIVph01P3uMmaOApKYOXtBx+8jJ8P6UbWshVgKYIqCzWP2qKAM3N/coS/X5lyc3X9Y8V6mK4iZbPWku1uz0wtLYFvalgYOjkDhw4gLS0NABAeHg4Jk6cWGfaefPm4csvvwQAbNiwAcuWLYNC0Xz7QRMRETVl1gZO1rbEWcPW1julXIp3xvfA3CGdLFoqxVHv0ZbA1xzmBrkVohwVWl/ka30R2zsMLdpZN8lJQbEKd7+7CzJRpW+RlKjgKS2/uV8OD4kK7hIVPCUqeNzcvGRq/KOPH9wFFaApgVZTgtRr16HTlMJDUg4PiRqeknIoJK43W6xaJ6/23NGTRjla8353zcC2bdsM+8OHD4dEIqkzbYcOHdCpUyckJyejuLgY+/btw5AhQxxRTSIiIrKCNYGTtQGlNezVehforcBzgzua3Q3PUe/REd1WHRnIB3krMb5vONYkZqBUZ94MMJOjw+AeeysQlQJoq9HW+Oyl0EIpUcNDooaPTI1RUS3wbGwIFFADlaVY+vtJpF3PhvvNNO4SNdxvBp/uEhWUgn58oLtEDXdBDaVEv3nKNHATVXCXqJ0u+Ly9xdDasbpNBQNDJ3fq1CnDfnR0tMn00dHRSE5ONuRlYEhEROT8rAmcrGmJs4YjAxtjjniPjui26shAHrDP92XVZx/WBevPJ1tc32cHdcDX+/XjZiXQQiFobgaV+sBRIWhuPlbcnHxGDU+pBgseiAC0KizbfRpyqKEUKqCQVEApqKGUVEAhVEAh0VR7rJq8ppWXgJKykpvpKyARar8xoNLdCgxtGavbVDAwdHJJSUmG/YgI0/P4Gqc5f/58g9SJiIiInIOlAaU1HB3Y3K6h36MjAl9HBvL2/L4s+eytbX2dflcECsoqsCYxAzpIUS5KUa5Vor5VRCZHh8E7St/KmZdyyqLAfnJ0GN4Z3wPvb6zKJ0IuVEIhVAWRFfp9SQWuVNxaMsWWsbpNBQNDJ5efn2/YDwgIMJneOE1BQYFZx0hJSUH37t1rfe3MmTNmlUFERETNlyMDG0dzZODriEAeaJzvy5bWV1uCc2vzGufTiHJoRDlK4FFrQGrPlnBjdf3+TklJQYcOHex+PFMEURTND+vJ4dzc3KDR6PtbX7hwAR071n8hWb58OZ566ikAwNChQ7Fjx45603fv3r3ek4+BIREREbmK7GJ1swt8HUml0WLqt4lmB2krp0cbAm1VLeMajdUXnFub15Zj2oOpwNDRv8MZGDo5RwSGAANAIiIiIrKdrcGWLcG5tXmd7YZAY/0+Z1dSJ+ft7Y28PP1dl/LycpPpjdP4+Pg0WL2IiIiIiG5nazdWW7rbWpvXUV18nR0DQyfXokULQ2CYk2N6nR3jNC1atGioahERERER1YnBVtNT96J45BS6dOli2E9NTTWZ3jiNcV4iIiIiIqK6MDB0cj179jTsJyYmmkxvnMY4LxERERERUV0YGDq5Bx54wLC/fft26HS6OtOmpKQYFrf39vbG3Xff3eD1IyIiIiKipo+BoZO76667EBYWBgBIS0tDfHx8nWmXLFli2B8/fjyUSmWD14+IiIiIiJo+BoZOTiKR4I033jA8nz17Ns6fP18jXXx8PJYtWwYAUCgUeP311x1WRyIiIiIiato4K2kT8Pjjj2Pz5s3YvHkzbty4gejoaEyfPh39+/eHWq3Gjh07sH79elQtSfnBBx+gffv2jVxry3A9RTKF5wiZwnOETOE5QqbwHCFTmvM5wsCwCRAEAWvWrMH06dOxZs0aFBcX45NPPqmRTqFQ4O2338bs2bMboZZERERERNRUMTBsIpRKJVavXo0ZM2ZgxYoV+Ouvv5CVlQU3Nze0bdsWw4YNw9NPP41OnTo1dlWJiIiIiKiJYWDYxMTFxSEuLq6xq0FERERERM0IJ58hIiIiIiJycQwMiYiIiIiIXJwgVk1lSS7J29sbGo0GHTp0aNR6pKSkAECj14OcF88RMoXnCJnCc4RM4TlCpjjiHElJSYFcLkdxcXGDHaM2DAxdXEhICEpLSxEWFtbYVSEiIiIicnnp6enw9PTEtWvXHHpcBoZEREREREQujmMMiYiIiIiIXBwDQyIiIiIiIhfHwJCIiIiIiMjFMTAkIiIiIiJycQwMiYiIiIiIXBwDQyIiIiIiIhfHwJCIiIiIiMjFMTCkRrNnzx5MmzYNHTt2hKenJ/z8/NCjRw/Mnz8fFy5caOzqkZmmTZsGQRDM3pYuXWpWuZs3b8ZDDz2EiIgIuLu7IyAgAH379sXChQtx9epVi+qo1WqxatUqPPjggwgNDYVCoUBwcDDuuusufPDBBygoKLDinZMoikhJScH69evxyiuvYMSIEQgJCan2fVvDmb97lUqFL774AnFxcWjdujUUCgVat26NuLg4fPHFF1CpVBaV19zZ8xwZNGiQRdearVu3mlUuz5HGVVpaip9//hlz587Fvffei1atWkGhUMDT0xPh4eEYM2YMli9fjpKSEovK5XWk+bDnOcLriAkikYOpVCrxscceEwHUuSmVSvHjjz9u7KqSGaZOnVrvd3n79tlnn9VbXl5enjhs2LB6y/D19RXXrl1rVv0uX74s9u/fv97yWrduLe7evdseH4dLmTt3rsnv2xLO/t3//fffYmRkZL3lde7cWTx58qRF77s5s+c5cu+991p0rdmyZYvJMnmONK4lS5aISqXSrO+zVatW4tatW02WyetI82Lvc4TXkfrJQORAoiji0UcfxYYNGwAAXl5emD59Ovr37w+1Wo0dO3YgPj4eKpUKL774IuRyOWbNmtXItSZzvfnmm4iKiqo3Ta9evep8TaVSYeTIkTh48CAAIDAwEDNmzEBUVBSKioqwadMm7Ny5E4WFhXjkkUfg7u6OUaNG1VleTk4Ohg4diuTkZABAWFgYZsyYgcjISNy4cQOrV69GQkICrl69ilGjRmHPnj3o37+/Fe/cNWm12mrPFQoFoqKicOzYMYvLcvbv/uLFixg6dCiys7MBAN26dcO0adMQGhqKjIwMrFixAmfPnkVSUhKGDh2KQ4cOISIiwuLPobmx5zli7KuvvkJQUFC9aUz9W+Y50viSk5MNLR/BwcG477770L9/f4SEhECr1eLUqVNYtWoVsrKykJWVhTFjxiA+Ph5jx46ttTxeR5ofe58jxngdqYXdQ02ienz//feGux2BgYHi2bNna6RZt26dKAiCCEBUKBRiamqq4ytKZjNuMdyzZ49NZb3xxhuGsjp16iRmZmbWSLN48WJDmqCgILGwsLDO8qZPn25Ie9ddd9VIq9PpxNmzZxvSdO/eXaysrLTpPbiSr776SnzhhRfEFStWiCdPnhQ1Go0oiqJVrUHO/t3HxcUZ0k6YMEFUq9XVXler1eL48eMNaYYNG2b2e2/O7HmOGN/pt8f/CzxHGt8zzzwjxsXFib/++qvh3LhdUVGROHLkSMPnFhAQIBYUFNSalteR5sfe5wivI/VjYEgOo9PpxPDwcMPJXF83jmeffdaQbtq0aQ6sJVnKXoFhYWGh6OnpaSgrISGhzrQjRowwpFu4cGGtaZKTk0WJRGK4wZCWllZrOo1GI/bo0cNQ3ooVK6x+D6Rn6Y9+Z//u//jjD0Oa4ODgOn9IFhYWisHBwYa0f/75p4l37roaOzDkOeIccnNzzUpXUlIitmrVyvC5rVy5skYaXkeaJ3ueI6LI64gpnHyGHObAgQNIS0sDAISHh2PixIl1pp03b55hf8OGDVCr1Q1eP2pcP//8M0pLSwEAsbGxiI6OrjOt8fmxevXqWtP89NNP0Ol0AICHHnoIYWFhtaaTyWR44YUXDM9//PFHi+tOtnH279747zNnzoSPj0+t6Xx8fDBz5kyT5VHj4zniHPz9/c1K5+npidGjRxuenzx5skYaXkeaJ3ueI/bWHM8RBobkMNu2bTPsDx8+HBJJ3adfhw4d0KlTJwBAcXEx9u3b1+D1o8ZlfH48+OCD9aa999574enpCUA//qC2WWwtKe+BBx4w7O/Zswfl5eVm1Znsw9m/e2vL+/XXX+tNS42H50jTY/wjuaysrMbrvI6QqXPE3prjOcLAkBzm1KlThv367uTVlsY4LzmvBQsWIDIyEh4eHoZppMePH4+vv/7a5NTKlpwfMpkMffr0qTUvAIiiiNOnT5tdXqtWrdC2bVsAQGVlJc6ePVtverIvZ/7uc3JycO3aNQCAVCpF37596y2vb9++hpteV69eRW5ubr3pyTpPPfUUwsPDoVQq4ePjg44dO2Ly5MlYu3ZtjQlvbsdzpGky/rferl27el/ndcQ1mTpHbsfrSE0MDMlhkpKSDPvmzKBknOb8+fMNUieyr3379uHixYsoLy9HWVkZ0tPTsWnTJsycORPt27fHrl27as0nimK1O7a2nh+ZmZmG9YykUilCQ0NtKo8ajrN/98bP27RpA7lcXm9Zbm5uaNOmTZ3lkX3s2rUL6enpUKvVKC4uRkpKCn766Sc8/PDD6NatW72znvIcaXqSkpKq/f9x+0yivI6QqXOkNryO1MTlKshh8vPzDfsBAQEm0xun4QLkzs3DwwNxcXGIjo5GREQEFAoFsrOzcejQIWzYsAHl5eXIysrC8OHDsW7dOkyYMKFa/pKSEmg0GsNzW88P43PN19fX5AXWVHnUcJz9u7f0ulWVLiMjo9byyDYtWrTA/fffj/79+6Nt27aQyWTIysrCvn378Msvv6CyshLJycmIjY3FH3/8gYEDB9Yog+dI06LVajFjxgzDWK4JEyagW7du1dLwOuLazDlHjPE6UjcGhuQwxcXFhn13d3eT6Y3TFBUVNUidyHbPP/88li5dCi8vrxqvzZo1C++//z4mT56MvXv3QqfTYcqUKYiJiTF0qQCqnxuA7eeHpeeaqfKo4Tj7d89zyXm8/fbb6Nu3LxQKRY3XXnjhBSQlJWHixIk4ffo0VCoVJkyYgIsXLxrGklXhOdK0zJkzBwcOHAAABAUF4dNPP62RhtcR12bOOVKF15H6sSspEdmkX79+tQaFVVq1aoVff/0VnTt3BgCUl5fjvffec1T1iKiZGDhwYK0/5qp07twZu3btMtxtv3btGpYtW+ao6lEDePfdd/HFF18A0HedW7t2LVq3bt3ItSJnYuk5wutI/RgYksN4e3sb9s2Z9dE4TV1T9lLT4Onpif/85z+G57/88ku1143PDcD288PSc81UedRwnP2757nUtISEhFSbFv72aw3Ac6Sp+PDDD/Hyyy8DAORyOdatW4dBgwbVmpbXEddkyTliCVe+jjAwJIdp0aKFYT8nJ8dkeuM0xnmpaYqLizPsp6enV5tK2svLCzLZrZ7ttp4fxs8LCwtRWVlpU3nUcJz9u7f0umWqPGp4xteac+fO1Xid54jzW7x4sWGtQTc3N2zYsAFjxoypMz2vI67H0nPEUq56HWFgSA7TpUsXw35qaqrJ9MZpjPNS0xQYGFjtufFAaUEQDOtWArafH23btjV0b9VqtUhPT7epPGo4zv7dGz/PzMysNsFFbTQaDTIzM+ssjxqe8bWmtgkZeI44t3feeQfz588HACgUCmzevNnkDJO8jrgWa84RS7nqdYSBITlMz549DfuJiYkm0xunMc5LTdPtd8D8/PyqPbfk/KisrMTff/9da15A/yMhKirK7PKysrJw5coVAPppp+ubzYzsz5m/+8DAQISEhADQ/+df3/TlAHD06FHDzHitW7dGy5Yt601P9md8rbn9OgPwHHFmixYtwiuvvAJAP7HGL7/8ghEjRpiVl9cR12DLOWIJV72OMDAkh3nggQcM+9u3bzec0LVJSUlBcnIyAH2/67vvvrvB60cNa8+ePYb90NDQGrNuGZ8f27Ztq7esvXv3orS0FAAQGRmJyMjIGmksKc/49cGDB5s9IxjZh7N/99aWZ5yPHMf4WlM16dXteI44n1dffRULFy4EoF8CaevWrRg6dKjZ+Xkdaf5sPUcs4bLXEZHIQbRarRgWFiYCEAGIa9eurTPts88+a0g3depUx1WSGkRpaanYtWtXw3c6a9asGmkKCgpET09PQ5qEhIQ6yxsxYoQh3YIFC2pNk5SUJEokEhGAqFAoxPT09FrTaTQasUePHobyvvvuO2veIhmp+izN/S/G2b/733//3ZAmODhYLCoqqjVdYWGhGBwcbEi7Z8+e+t62S7P0HDHX9evXxcDAQEPZ77//fq3peI44l5deesnwmXh5eYl79+61uAxeR5o3e5wj5nLl6wgDQ3KoFStWGE7koKAg8dy5czXSrF+/XhQEwfAPLSUlpRFqSuZYsWKFuG3bNlGr1daZ5tq1a2JcXJzhe1cqlWJaWlqtaRcuXGhI17lzZzEzM7NGmsWLFxvSBAQEiAUFBXUee9q0aYa0sbGxYmFhYbXXdTqdOGfOHEOarl27ihqNxsx3T3Wx5ke/s3/3gwcPNqSdMGGCqFarq72uVqvFCRMmGNLcf//9Zr93V2TpOfLxxx+LBw4cqDfNhQsXxF69epn1w0oUeY44i7lz5xo+Ex8fH/Gvv/6yuixeR5one50jvI6YJoiiKILIQURRxPjx47F582YA+m6i06dPR//+/aFWq7Fjxw6sX78eVaflp59+itmzZzdijak+L774Ij755BOEhIRg6NCh6NmzJ0JCQqBQKJCTk4NDhw4hPj7eMAOpRCLBmjVrMGnSpFrLKy8vR1xcHA4fPgxA3+d+5syZiIqKQlFRETZt2oQdO3YA0PfRj4+Px9ixY+usX3Z2NgYOHIiLFy8CAMLDwzFjxgx07NgR2dnZWL16teFYHh4e2L17N2JiYuz18TR7BQUFWLx4cY2///e//zXsv/rqqzVef+utt2r8zdm/+wsXLmDgwIGGcSfdunXDE088gdDQUGRkZOC7777D2bNnAegXWD548CA6dOhQZ3muwl7nyNixY/Hzzz8jIiIC999/P6KiohAYGAiZTIZr165h3759+Pnnnw0TNri7u2PHjh31DkPgOdL4XnvttWrf9csvv4zo6GiT+QICAhAbG1vj77yOND/2PEd4HTGDXcNMIjOUl5eLkydPrnbH+PZNoVCIS5YsaeyqkgkvvPBCvd+j8RYaGiru3LnTZJm5ubnikCFD6i3Lx8dHXL16tVl1vHTpkti3b996ywsJCRF///13Wz8Ol5Oammr292+81cXZv/tjx46JHTp0qLe8yMhI8e+//zarPFdgr3NkzJgxZuft3r27ePToUbPqx3Okcd17771WnR/33ntvnWXyOtK82PMc4XXENAaG1Gj++OMPccqUKWL79u1Fd3d30dfXV+zevbs4d+5cMSkpqbGrR2bIzMwUf/zxR3HOnDlibGys2LFjR9HPz0+UyWRiixYtxC5duoiPPfaY+NNPP4kVFRUWlb1x40ZxwoQJYlhYmKhQKER/f3+xT58+4muvvSZeuXLForIqKyvFFStWiCNGjBDbtGkjurm5iYGBgeKAAQPEd999V8zLy7OoPNKzd2BYxZm/+7KyMvGzzz4TBw0aJIaEhIhyuVwMCQkRBw0aJH722WdiWVmZReU1d/Y6Ry5evCh+++234tNPPy3GxMSIERERoo+PjyiTyUR/f3+xR48e4pNPPilu3bq13q7tteE50ngaIjCswutI82DPc4TXEdPYlZSIiIiIiMjFcbkKIiIiIiIiF8fAkIiIiIiIyMUxMCQiIiIiInJxDAyJiIiIiIhcHANDIiIiIiIiF8fAkIiIiIiIyMUxMCQiIiIiInJxDAyJiIiIiIhcHANDIiIiIiIiF8fAkIiIiIiIyMUxMCQiIiIiInJxDAyJiIiIiIhcHANDIiIiIiIiF8fAkIiI6DYLFy6EIAgQBAELFy5s7Oo0CceOHYNUKoUgCPjwww8b7Djbt283fDc//vhjgx2HiMjVMDAkIqIm6/Lly4YgwV4bA0HLiaKI559/HjqdDuHh4Xjuueca7FjDhw/HoEGDAAAvvfQSSkpKGuxYRESuhIEhERER2WT9+vU4fPgwAODll1+GQqFo0OO9/vrrAICrV69iyZIlDXosIiJXIWvsChAREVnLx8fHZOtUYmIijhw5AgBo3bo1xo0bV2/66OhoJCYm2q2OzZ1Wq8WCBQsAAMHBwZg2bVqDH3Pw4MGG7+nDDz/E7Nmz4e/v3+DHJSJqzgRRFMXGrgQREVFDWbhwIRYtWgQAuPfee/Hnn382boWamQ0bNmDixIkAgFdffRVvvfWWQ467YsUKPPHEEwCAt956C6+++qpDjktE1FyxKykRERFZ7eOPPzbsP/nkkw477qRJk+Dt7Q0A+Pzzz6HRaBx2bCKi5oiBIREREVklOTkZBw4cAKDvghsREeGwY3t4eGDkyJEAgKysLGzfvt1hxyYiao4YGBIREd3GnOUqVqxYYUhTNa5Op9Nh9erVGDFiBEJDQ6FQKBAcHIwJEybg0KFDNcqoqKjAqlWrcN999yE0NBRKpRJhYWGYOnUqzp07Z1GdNRoNVq1ahUmTJqF9+/bw9vaGp6cnIiIiMHnyZGzatAn2Hj1ivFzE2LFjza7nDz/8gPHjx6N9+/bw8vKCTCaDt7c3OnbsiGHDhuH11183a5yn8XjRH374weL6ExHRLZx8hoiIyA5ycnLwj3/8A7t376729xs3bmDjxo3YtGkTvvnmG8O4uIsXL2L06NE1AsCMjAx8//33+Omnn7B27VqzAq4///wTM2bMQEpKSo3XLl++jMuXL+Onn37CgAEDEB8fjzZt2lj/Ro1s2bLFsB8XF2cyfXJyMsaOHVtr0FtSUoKSkhKkpKRg586dePPNN3HhwgV07NixzvIGDx4MQRAgiiJ27NiByspKyGT8aUNEZA1ePYmIiGxUWVmJ8ePHY//+/VAqlbj33nsRFhaGvLw8/PHHHygoKIAoipgxYwYiIyPRqVMnxMXFISMjAz4+PrjnnnvQqlUrXL9+Hb///jvKyspQUVGBRx55BGfOnKm3i+b69evx6KOPGsbYubu7Y8CAAWjXrh0kEgmSk5Nx6NAhVFZW4vDhw7jzzjtx5MgRBAcH2/Sec3JycOLECcMx+/btW2/64uJi3H///cjIyAAASCQS9OnTB127doWXlxfKysqQmZmJkydPIicnx6w6BAQEoEuXLjh37hwKCwuRmJiIgQMH2vS+iIhcFQNDIiIiG8XHx0OtVmPMmDH43//+h6CgIMNr+fn5GDNmDPbv3w+dTofXX38dvr6+yMjIwDPPPIP333/fMIkKAFy5cgVDhw7FuXPnUF5ejjfffBPffvttrcc9c+YMpk6dCo1GA0EQMG/ePLz66qto0aJFtXSXLl3C1KlTceDAAWRkZOCJJ57Atm3bbHrPiYmJhq6p3bp1M9lS9+233xqCwm7dumHjxo3o3LlzjXSiKOLo0aP47rvvzFoPsXfv3oYWSAaGRETW4xhDIiIiG6nVagwaNAgbNmyoFhQCgJ+fH1atWgWpVAoA2LNnDzZv3oypU6fiyy+/rBYUAkDbtm2xfPlyw/P4+HhUVlbWetw5c+agvLwcALBkyRJ88MEHNYJCAGjfvj22b9+Obt26AQB+++03JCQkWP1+AeDUqVOG/S5duphMv3//fsP+J598UmtQCACCIKB///744osvEBoaarLcrl27GvZPnjxpMj0REdWOgSEREZEdfPTRR4bg73bh4eHVWrIUCgXef//9Osu66667DEFRcXExzp8/XyPNyZMnDeMZ+/TpgxdffLHe+nl6euK1114zPDeeOMYaqamphv22bduaTF9UVGTYDwwMtOnYxozHS16+fNlu5RIRuRoGhkRERDbq0KEDevfuXW+aHj16GPbvvvvuGi2Lt4uKijLsGwdhVYy7gk6ePBmCIJisp/EEMVXLTFjr+vXrhv2WLVuaTG/c+rds2TKbjm0sICDAsH/t2jW7lUtE5Go4xpCIiMhGxkFcXfz8/Az73bt3N5ne39/fsG/c2lbFePmLPXv2IC0tzWSZxstVVI33s1Zpaalh38PDw2T6SZMmGcZKLlu2DMeOHcPUqVMxbNiwemceNcX42MZ1IiIiyzAwJCIispGvr6/JNMaTs1iavmrGUWNXr1417P/2228my7tdfn6+xXnqYs76iMOGDcPs2bPx2WefAQCOHDmCI0eOAACCg4MRGxuLQYMGYezYsWZ1TbXk2EREZBq7khIREdnInG6ctqSvTWFhoU35tVqtTfk9PT0N+1UT4Jjy6aefYuPGjYiOjq729+vXr2PDhg2YPXs2wsLCMHHiRKSnp5tVpvGxjetERESWYWBIRETUBBkHQRs3boQoihZvtggJCTHsm7vuIACMGzcOCQkJSEtLw8qVK/H0008bZksF9C2AGzZswB133IHk5GST5WVnZ9daJyIisgwDQyIioibIeIH6xph0JSIiwrB/5coVi/OHhYXh8ccfx7Jly3DmzBmkp6dj0aJFhjGDubm5mDt3rslyMjMzDfvt2rWzuB5ERKTHwJCIiKgJiomJMez/9ddfDj9+z549DftJSUk2lxcaGorXX38d//vf/wx/27lzJ9Rqdb35qha3B4BevXrZXA8iIlfFwJCIiKgJGjlypGF/48aN1ZaPcIT+/fsbxkqePXsWlZWVdil39OjRhn2NRoO8vLx60xsvan/72EUiIjIfA0MiIqImKDo6GoMGDQKgn4BlypQpqKioMCtvRUWFzbOSBgQEGNZuLC8vx7Fjx+pNb+44RONlNCQSSb1rJObk5OD8+fMA9DO9MjAkIrIeA0MiIqIm6rPPPoOXlxcAYNeuXbjnnnuQkJBQZ/rk5GS8+eabaNeunV26n44aNcqwv3v37nrT3nnnnXjkkUfw22+/1RnAJicnY+rUqYbn9913H9zc3Oosc8+ePYZJdIYNG1ZtiQ8iIrIMr6BERERNVFRUFNasWYN//OMfKCsrQ0JCAgYMGIAOHTrgjjvugL+/P1QqFW7cuIFTp05Vm6jFHh599FG88cYbAIDNmzfj5ZdfrjOtRqPBmjVrsGbNGri7u6Nnz55o3749fHx8kJ+fj0uXLuHo0aOG9O7u7li8eHG9x9+0aVO1uhARkfUYGBIRETVhI0eOxMGDB/Hkk08aunOmpKQgJSWlzjzt2rWzaBH5unTq1AmxsbE4cOAAjhw5gtTU1GqzlRrz9vY27JeXlyMhIaHO1s2IiAj88MMP1Sa4uV15eTl+/fVXAPplKkaMGGHDOyEiIgaGRERETVyvXr1w9OhR7Ny5E5s3b8Zff/2Fq1evoqCgAAqFAoGBgejcuTNiYmIwbNgw3HnnnYaJY2z14osv4sCBAxBFEV9//TX++9//1pruxIkTOHz4MPbs2YPExEQkJSXh6tWrKCsrg4eHB0JCQtC7d2+MHj0akyZNgkKhqPe469atQ1FREQDgueeeg1wut8v7ISJyVYJo6wq3RERE5LJ0Oh26d++O8+fPIzg4GJcvX4ZSqWzw48bExCAxMRHe3t5ITU2td5IaIiIyjZPPEBERkdUkEgkWLVoEALh+/TpWrFjR4Mf8888/kZiYCACYO3cug0IiIjtgiyERERHZRBRFDBw4EIcPH0Z4eDiSkpJMdgW1RVxcHPbs2YNWrVohKSmp2vhFIiKyDlsMiYiIyCaCIGDp0qWQSCRIS0vD559/3mDH2rFjB/bs2QMA+OCDDxgUEhHZCVsMiYiIiIiIXBxbDImIiIiIiFwcA0MiIiIiIiIXx8CQiIiIiIjIxTEwJCIiIiIicnEMDImIiIiIiFwcA0MiIiIiIiIXx8CQiIiIiIjIxTEwJCIiIiIicnEMDImIiIiIiFwcA0MiIiIiIiIXx8CQiIiIiIjIxTEwJCIiIiIicnEMDImIiIiIiFwcA0MiIiIiIiIXx8CQiIiIiIjIxTEwJCIiIiIicnEMDImIiIiIiFzc/wctdSL7Tu2MTAAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.2. Plot the Resiudals\n",
+        "Visually, the model looks like a decent fit of the data. We can further assess our model by observing the distribution of the residuals. If our model is good, we expect the residuals to have a normal distribution. Let's check by plotting our residuals in a histogram."
+      ],
+      "metadata": {
+        "id": "75_SM4D6UpBL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# calculating the residuals\n",
+        "r = concentration - model_concentration\n",
+        "\n",
+        "## histogram of the residuals\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "plt.hist(r, bins=15, density=True)\n",
+        "\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Residual ($\\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Frequency\", fontsize = 16)\n",
+        "ax.set_title(\"Distribution of Residuals\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 630
+        },
+        "id": "zeo3inTAUqz9",
+        "outputId": "e09d9534-8f2a-440d-9bc6-27aa44fa705d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Discussion:** What are some features we notice about the residuals? Does it follow a normal distribution?\n",
+        "\n",
+        "**Answer:** The distribution is roughly normal, but not perfectly so. It's skewed a bit to the right and there is at least one outlier. This is probably due to the fact that we do not have many data points"
+      ],
+      "metadata": {
+        "id": "gTriTWnMVa7B"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2.3. Plot the Confidence Intervals"
+      ],
+      "metadata": {
+        "id": "ASNGnoAYTelL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To plot confidence intervals, we will use the Working-Hotelling method. It is the same as the standard method for calculating confidence intervals, but it uses the F statistic rather than the t statisic. We'll use a 95% confidence interval."
+      ],
+      "metadata": {
+        "id": "j0AuwK-VYvao"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# calculating confidence interval using Working-Hotelling method, plotting\n",
+        "\n",
+        "# Compute the standard error\n",
+        "se = np.sqrt(np.sum((concentration - model_concentration)**2) / (len(time) - 2)) * np.sqrt(1 / len(time) + (time - np.mean(time))**2 / np.sum((time - np.mean(time))**2))\n",
+        "\n",
+        "# Compute W for the confidence bands Working-Hoteling method\n",
+        "W = np.sqrt(2 * f.ppf(1 - 0.05, 2, len(time) - 2))\n",
+        "\n",
+        "# Build the upper and lower confidence interval\n",
+        "wh_upper = model_concentration + W * se\n",
+        "wh_lower = model_concentration - W * se\n",
+        "\n",
+        "#plot original data and fitted model\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "ax.scatter(time, concentration, label = 'Experimental Data')\n",
+        "ax.plot(time, model_concentration, c = 'orange', label = 'Model', linewidth = 3)\n",
+        "\n",
+        "ax.plot(time, wh_upper, color='forestgreen', linestyle='dashed', label=\"WH Upper\")\n",
+        "ax.plot(time, wh_lower, color='forestgreen', linestyle='dashed', label=\"WH Lower\")\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Time (s)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Concentration ($ \\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_title(\"Concentration of 4 $\\mu m$ Particles over Time\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "#add legend\n",
+        "plt.legend()\n",
+        "\n",
+        "#show plot\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 627
+        },
+        "id": "SyxN9goZTZij",
+        "outputId": "437d64e5-d559-4b20-f643-eb32b1dcf18c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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5ojJtu3btFNJXrFhR5Q9pWVlZYs2aNaW0mzdvzpfGUP9XJQ0DQzKI169fK/zBzp8/X6/lT5kyRSq7QYMGGi9AmzdvVriRfvXqVb40ees7ceJEteUlJCRIga8gCOLr16/zpfnpp58UbjYL88vd/PnzpbI++ugjrfKMHTtWyrNt27Z8+/V9vqJY+MCwf//+WuXRVefOnTXWXRQNExiWhM+qLgxxPqJo2MAwbyvY0aNH1R5b34HhxIkTpbK1aYHYvXu3lF7VOKd//vlH4TOwdOlStWXmHXslCIJ4+vRptenzBpJhYWEa66yOtoHhjh07pJuy3M9KYcc4avt3X5DvIEMFhvq8bmg6lr6vK48ePVL4bjAGfX8fvX79WmF85O3bt1WWtXz5co1BpCGu3aJonOvmm4wdGE6ePFltmXl/dNPmPnPatGlS2k8//TTffkP9X5U0HGNIBpE79iKXjY2NXsvPO34xd8Y/dd555x2UK1cO8fHxSE9Px+nTp9GpUyeV6fv166e2PFtbW3h4eODGjRsQRRH3799HvXr1FNIcOHBAev7+++/DwsJCbZnq7Nu3T3o+aNAgrfK0adMGS5cuBQCEhoaid+/eKtPq43z1YcCAAQXKFxMTg/DwcNy8eROvXr1CamoqRFGU9ueOLxBFEZGRkfD399dLfbVREj6rujD0+ejboUOHsHbtWgDA8OHDERQUZLRjA4pjAVWNF8wr7/jCBg0aaCyzVq1aeP/999WWmXfmvr59+2ocr5meni49L1eunNq0uti3b1++GXJfvXqF8PBw3Lp1S2H73LlzUbVqVbXlGeLvvqDfQfqiz+uGJvq+rpQvXx6WlpZIS0vDlStXcPLkSb3PLfAmfX8f2dnZoXv37tiyZQsAYP369fjmm2+UlpV3lsshQ4YoTWPoazdQ9J9ZQ9E0xv7N65im9HnXxM79bsjLGP9XJQEDQzKINwfiJyUl6a1sURQVbozyTm+uipmZGZo2bSpddM+fP6/25lSbG2dHR0fpeUJCQr79YWFh0vPC3oyePn1aer5s2TKsWbNGY56HDx9Kzx88eKA2rT7OVx8aN26sU/rTp09j6tSpCAkJUbghVMeQSze8qaR8VrVljPPRp+TkZGkiDEdHR/zyyy9GOW6u3IAEyFmbUVWgl5euE8+MHDlS481w3qAr77IgymRkZCAmJgZAzoQnbm5umqqstYiICERERKhNY2triwULFqitpyH/7nX9DtI3fV43NNH3dcXc3By9evXCpk2bkJWVhTZt2uDdd99F37590bp1azg4OOit7oDhvo+GDBmiMTB8+PAhjh8/LpX57rvvKj2eoa/dQNF/Zg0lbyCnTNmyZaXn9vb2Gid7yvsjl7LroDH+r0oCBoZkEHZ2djA1NZV+qX716pXeyn79+rXCYtja3ri4u7tLzzXdJGgzc2Pe2b7y1gfI+dJJTU2VXlevXl2rOiqTlJSk0AL7559/6lxG3pn+lCns+eqLphng8lq5ciVGjx6t9Y1hrjdbsw2pJHxWdWGM89Gnr7/+Wpq18ddff0X58uWNdmwgZx2+3BuQGjVqwNraWmMeXVsM35y99U0ZGRmIiooCAJiamqJ169Zq01+/fh3Z2dkAcma5NfSshjY2NnB0dET9+vXRrl07DBs2TG0AYei/e12+g/RNn9cNTQx1XZk3bx7OnTuHW7duISMjA2vXrsXatWthYmICb29v+Pv7o3379ujcuXOhW0MN9X3UqVMnlC9fHnFxcbh58yYiIiLg6+urkGbDhg3SZzA3/ZuMce0GivYza0iarm2mpv+FMNpcB/Omf/M6aKz/q5LgLZlblYqjvF/SuVML68ObrY/a3Gy9mU7TTUJhp7HXZ1fa169fF6ouAJQuApuXMabt14aVlZVW6a5du4axY8dKF2Zvb28sWLAA4eHhePbsmdSlLPcxfPhwKa9cLjdI3ZUpCZ9VXRjjfPTl/Pnz0nT/QUFBCp8BY9G1G+nLly+lhdNzuwC/KSkpSVqOwdzcHPXr11db5tWrV6VlAXIXfNZnnXUxffp0hb9LURSRmJiI6Oho7N69G5MmTVIbFBrj717b7yBDMPQQjLwMdV1xcnLC2bNn8b///Q+VKlWStsvlcly+fBmLFy/GO++8g8qVK+Onn36SfoQoCEN9H73ZArhu3bp8afJuGzp0qNLjGOPaDRTtZ9aQdLm2FfY6aKz/q5KALYZkMH5+ftINTN7uMYX15sUyOTlZqwtCcnKy9FzZmlP6pKwrbUEv8m+eW3x8vEIXitJo/vz50pdwx44dsXv3bpibm6tMb8xWwrxKwmdVFyXpfC5duiQFAzExMWrH1cXGxkrPnzx5opB22rRp6Nq1a4HqkLf1r1GjRjql9/HxUXqzExkZKZ1X3bp1NbboadM1NS9DBoaFVVL+7gtKn9cNTQx5XbGzs8OsWbMwY8YMnD17FiEhITh58iRCQ0OlFrqXL1/iyy+/xJkzZ7Bjx44C3dgb8vtoyJAhWLRoEQBg8+bNmDt3LmQyGQDg8uXLuHz5MoCclqru3bsrLYPX7pKD/1f/YYshGUybNm2k5/fv38epU6f0Uq69vb3CzVDueBhNcruUATB4lzI7OzuFX/GUDXTWloODg0KXm6dPnxaqbm+D3IXQAeC7775Te3MIQGqFMbaS8FnVRUk9nzt37iAsLEzl4+7du1LajIwMhX15g0Zd5Q2yfHx8NKYPCQmRnmszvlDXyWx0DU6LW2BYUv7uC0qf1w1NjHFdkclkaNasGT777DPs2LEDz549Q0hICHr06CGl2bVrF7Zt21ag8g35fdS8eXN4enoCAJ49e4bDhw9L+/K2Fvbt2xeWlpZKy+C1u+Tg/9V/GBiSwfTr10/hS3fu3Ll6KVcQBIWxN9oEnFlZWQqTHmhzg1RYzZo1k57nnTmtIJo2bSo9P3nyZKHKMiRjdWt8/Pix9FzT5CuvX7/GpUuXtCpX3/UvKZ9Vbb1t52NoeYO4atWqaUy/e/du6bk24wt1DQy1SZ87WY66OhQVQ/3dF4Shvuv0ed3QxNjXFRMTE/j5+WHnzp1o3769tD3v514Xhv4+Gjx4sPQ8dwZSURSxceNGabuq2UhzlZRrN/H/KhcDQzIYKysrTJo0SXq9bdu2Av0ymJycnO8LP29r5Jo1azRORLBz5068ePECAGBpaYkWLVroXA9dde7cWXq+fPlyhSngddWtWzfp+ZIlS3SeeMFY8v5yaqgJaoCcG4xcKSkpatP++eefWtfFEPUvCZ9VXZSU8xkxYkS+8WyqHqtWrZLyubm5KewbMWJEgY4fFxeHR48eSa9zx/mpcuzYMZw/f156rY/AUC6XS4GeIAga00dHR0sThbm5uel1qQp9MNTffUEY6rtOn9cNTYrquiIIgkL3y2fPnhW4LEN+H+UN+nbu3ImUlBQcP35cmn2yatWqCAgIUHu8knLtJv5f5WJgSAb1xRdfKPwiN3ToUOzZs0fr/FeuXEHz5s1x6NAhhe3vv/++dJNw/vx5LFu2TGUZr169whdffCG9HjhwoFYzWBXW+++/L42BuH//Pj766KMClzV27FhpUobz589j5syZWueNi4sr1AB/XeRdFiHvTbG+5Z2tT92vzbdu3dLpvTJE/UvCZ1UXb9v5GEreljpAsZvom+Li4hTWIjQ1NYW3t3e+dFlZWbhy5QqAnCBJU/fUW7duSRN0eHp6ahzfWZzHFwKG+7svCEN91+nzuqGJvq8riYmJGn8AyZV3av+KFStqfdw3GfL7yNPTUxpvnJSUhJ07dyqsXTh48GCNLccl5dpN/L/KxcCQDMrCwgJ///239MWfmpqKXr16YdiwYbh+/brSPKIoIiIiAsOHD4ePj490I5SXh4eHtD4ZAHz44YdYtGhRvpnnbt++jQ4dOkhjNezs7FQuVqtvZcuWxezZs6XXf/zxB959912FdW/yunr1KiZPnpwvCAZyxlLMmzdPej1z5kwMHz5c5ZgKURRx8uRJfPDBB3B1dVWYAt2Q8q479PfffxvsOHl/bf7kk09w8ODBfGmOHDmCwMBAJCYmaj1bnSHqXxI+q7p4287HUPIGWQDw5ZdfIjg4OF+6o0ePokWLFtJEXUBOS4a5uTnS0tIU0kZFRUnbvLy8NH6u36aJZwDD/d0XhKG+6/R53dBE39eVc+fOwd3dHTNmzFA5E3l2djY2b94szRgMKLaS6srQ30d5Ww1XrFiBrVu3Kt2nSkm5dhP/r3JxVlIyuOrVqyMsLAzdu3fHlStXIJfLpbWN3N3dUb9+fZQvXx7Z2dl4+vQpLl68mK9ribJfun/55RecPXsWERERyMrKwocffoiffvoJfn5+sLGxwZ07d3DixAnpVxxTU1OsWLFCYQ0jQ/vggw9w5coVLFmyBACwZcsWbNu2Db6+vvDy8oKlpSViY2Nx4cIFaUC8qkWNR4wYgbt372LWrFkAgL/++gvr169HgwYNUKtWLdjY2CApKQkPHz7ExYsX9TL9sq769OmDpUuXAgAWL16Mc+fOoVGjRihTpoyUZvz48Uqn4dfFRx99hD///BOxsbGIj49Hp06d0KhRI9SpUweCIOD8+fO4evUqgJzZCytWrIi1a9cWWf1LwmdVF2/b+RhC3iDLzc0N9+/fR1BQEOrUqQMPDw9kZ2fj8uXLUstJw4YNpUAuKSlJWqw776LLhp54prgHhob6uy8IQ37X6fO6oYm+rytPnjzBzJkzMXPmTDg5OaFBgwZwcnKCqakpnj17hnPnzimMFfX398eAAQMKVPdchvw+evfdd/Hxxx8jMzNTYcxnw4YNlbbqK1MSrt2Ug/9XAEQiI0lMTBS//fZb0cHBQQSg1cPHx0fcsWOH2jL79++vsZzKlSuL+/btU1u/vOm1ERAQIKU/duyY2rTz588X7ezsNNZTEATx4MGDasvavHmz6OzsrPV72LRpUzEtLc1o5ztw4EC19Xkzr671yHXq1CmxfPnyao/Vq1cv8dWrV+Lw4cOlbatWrVJbrq71d3Nzk/bdu3dPZbkl5bOqLX2ejyiK4qpVq6Q8w4cP10sdtZX32G5ubnops3bt2lKZwcHBYuXKlVW+R+PGjRMvX76cb3u3bt0Uyvz000+lfXPmzNFYh/bt20vpNX2viKLiZ/nBgwcFPve88n72pk+fXujyDPF3X9DvIF2/K0RR++8LUSz8dUOXY+njunLmzBnR1NRU6zL69u0rJiQkaHqbtaLv76O8unXrlq+cX3/9Vec66uvaLYoF/8wWxvTp06VjBgQE6JRX28+iLud17949nb63jx07pnX99fl/VdIwMCSje/XqlbhhwwZx5MiRYv369UUnJyfR3NxctLGxEV1dXcUOHTqI06ZNE8+dO6d1madOnRLHjx8v1q5dW7S3txfNzc1FZ2dnsUOHDuJvv/0mJiUlaSxD1y9aXW+24+LixF9++UVs3769WKVKFdHCwkK0sLAQq1SpIrZr106cNWuWePPmTa2OnZaWJq5evVocOHCg6OnpKdrb24symUy0s7MTa9euLfbu3VucN2+eeOPGDaOfr1wuF9evXy9269ZNdHFxES0tLdXeLBXmAvfs2TPxyy+/FOvWrSuWKVNGLFOmjOjh4SH2799f3L17t5ROl8BQ1/rrcvMliiXjs6oLfZyPKL5dgWFKSoook8lEAKK9vb0oiqL44MEDcdy4caKrq6tobm4uVq5cWezRo4f4zz//SPl++OEH0dHRUSxfvrz4zjvviOHh4Qrltm3bVqrn4cOHNdYjbwAVGxurNu3Lly+ltOXLl9f9pFXQd2Aoivr/uy/od5Cu3xWiqPv3RWGuG7oeSx/Xlfj4eHHLli3ipEmTRH9/f9HZ2Vm0sLAQTU1NxXLlyom+vr7ixIkTxbCwMI31KQh9fR/ltXnzZoX/V5lMJj5+/LhA9dPHeyyKDAxF0bCBoSjq7/+qpBFEsZROu0NERGQA4eHh0rIDfn5+aieeISIiKi44+QwREZEe5R3bp83C9kRERMUBA0MiIiI9yjuJCwNDIiIqKRgYEhER6VHewLB+/fpFVxEiIiIdcIwhERGRnsjlctja2iIlJQUmJiZITExUWMKAiIiouGKLIRERkZ7cvHkTKSkpAABPT08GhUREVGIwMCQiItITdiMlIqKSil1JiYiIiIiISjm2GBIREREREZVyDAyJiIiIiIhKOQaGREREREREpZxpUVeAipaTkxOSk5Ph6upa1FUhIiIiIir1YmJiYG1tjadPnxr1uGwxLOWSk5ORmZlZ1NXAnTt3cOfOnaKuBhVj/IyQJvyMkCb8jJAm/IyQJsb4jGRmZiI5Odmgx1CGLYalXG5L4dWrV4u0Ht7e3sWiHlR88TNCmvAzQprwM0Ka8DNCmhjjM5J7DGNjiyEREREREVEpx8CQiIiIiIiolGNgSEREREREVMoxMCQiIiIiIirlGBgSERERERGVcoIoimJRV4KKDmffIiIiIiIqPorq/pwthkRERERERKUc1zEkIiIiKgRRFMEOWET0JkEQIAhCUVdDawwMiYiIiHSUnZ2NFy9eIDExERkZGUVdHSIqpmQyGcqUKQM7OzvY2toW60CRgSERERGRDrKzsxETE4O0tLSirgoRFXPZ2dlITExEYmIiHBwcUKlSJZiYFM/RfAwMiYiIiHTw4sULpKWlQSaToVKlSrC2ti62N3pEVHREUUR6ejoSExMRHx+PV69ewdLSEmXLli3qqinFwJCIiIhIB4mJiQCASpUqwd7evohrQ0TFWZkyZVCmTBmYmpri+fPnePnyJQNDIkOJTUzH5ogYhN2LR1J6FmwsTNG8uiP6N6mKCrYWRV09IiJ6i4iiKI0ptLa2LuLaEFFJYWdnh+fPnyM9PR2iKBbLsYYMDKnESsvMxsw9V7H13ENkZivOBhdyKw7z/72Jvo2rYnr3OrA0kxVRLYmI6G2Sd/ZRdh8lIm3JZP/dizIwJNKjtMxsDF8ZjrB78SrTZGaL2Bgeg7uxSVjzXlMGh0REREREKvCnLiqRZu65qjYozCvsXjxm7rlm4BoREREREZVcDAypxHmemIat5x7qlGfruQeITUw3UI2IiIiIiEo2BoZU4myJeJBvTKEmmdkitpx9YKAaERERkSAIGh8jRowo6mrqTWBgIARBQHR0dFFXpdjS9T0KDg7O95mxsrKCk5MTWrRogcmTJ+PMmTOGrXQpxjGGVOJo24X0TWfuvsCEIE8914aIiIjyGj58uMp9fn5+RqwJ6SIwMBDHjx/HvXv34O7uXqR1qVSpEjp16gQAyMrKQnx8PCIjI3HmzBn89ttv6NChA9asWQMnJ6dCH2vEiBFYs2YNjh07hsDAwEKXV5IxMKQSJyk9y6j5iIiISHurV68u6ioYxV9//YWUlBRUqVKlqKvy1qlVq5bSz1FISAgmTZqEQ4cOISgoCGFhYbCzszN+Bd9S7EpKJY6NRcF+zyhoPiIiIqI3ubq6olatWjAzMyvqqpQa/v7+OHnyJOrVq4eoqCjMmDGjqKv0VmFgSCVOs2rlCpSveXVHPdeEiIjIsGIT0/H70VsYuiIM7yw+iaErwrDo2O23YkK1KVOmQBAE9O/fP9++uLg4ODs7QyaTITQ0VNo+YsQICIKA4OBg7N+/H35+frCxsUHZsmXRu3dvREVFqTxeWFgY+vXrh8qVK8Pc3BwuLi4YPXo0YmJi8qWdMWMGBEHA6tWrER4ejm7dusHR0RGCIODixYsAVI+fEwQB7u7uyMrKwqxZs+Dp6QkrKyvUrl0bq1atktIdPXoUQUFBsLOzQ9myZTFs2DC8ePFCad2zsrKwZMkStGjRAnZ2drCyskKDBg0wf/58ZGXl7xHl7u4urZP3559/on79+tJYvbFjx+LVq1dS2ujoaAiCgOPHjwMAqlWrpjDGL9eTJ08wZ84cBAQEoEqVKjA3N4eTkxN69+6NiIgIle+7vpUpUwbz5s0DACxbtgxpaWnSvrS0NKxYsQI9e/ZE9erVYWVlBQcHB7Ru3RqbNm3KV5YgCFizZg0AICgoSOG8c/9fX716hYULF6Jjx45wc3ODhYUFHB0d0alTJxw+fNjwJ2xEbEKhEqe/b1UsOHJLpwlozGQC+jepasBaERER6U9aZjZm7rmKrece5rvehdyKw/x/b6Jv46qY3r1OiV2nd9asWTh8+DD+/vtvrFmzRmFs4vvvv48nT57g66+/Vjou8e+//8aSJUvQpEkTdO/eHZcuXcKOHTtw9OhRHD9+HD4+PgrpFy9ejIkTJwIAfH194e/vjxs3bmDFihXYvXs3jh8/jtq1a+c7zokTJzBmzBh4eXmhQ4cOePz4MUxMtGtX6d+/vxT8eXh44Pjx43jvvfcAALa2thg4cCCaN2+Ojh074vTp01i7di3u3buHEydOKARkqamp6Nq1K44dO4Zy5cqhefPmsLS0RFhYGD7++GMcO3YMO3bsUFqvL774AgsWLEBgYCA8PT1x8uRJLFu2DNevX8fx48chCAJsbGwwfPhwHDhwAM+ePUOfPn1gY2OTr6xdu3ZhypQpqFmzJurXrw87OzvcunULO3bswN69e7F371506NBBq/emsNq2bYsKFSogNjYWERER8Pf3B5AT5I4ePRrOzs6oWbMmmjZtiqdPn+LUqVMICQnJ18o4fPhwhIaG4s6dO+jYsaPCmMXc9+DMmTOYNGkS3N3dUbNmTbRo0QIxMTE4dOgQDh06hD///FP6fy3xRCrV6tSpI9apU6eoq6GzqdsiRbcpe7V+TN12qairTEREb4Hs7Gzx2rVr4rVr18Ts7GyDHCM1I0vs/8cpra5v/f84JaZmZBmkHroCIOp6a3nt2jXRyspKtLW1Fe/evSuKoiguW7ZMBCD6+vqKmZmZCumHDx8uHWfZsmXSdrlcLk6ZMkUEIDZo0EAhz+nTp0WZTCZWqVJFPHv2rMK+P//8UwQgNmvWTGH79OnTpePMnj1bad0DAgJEAOK9e/eUvg9169YVnz9/Lm0/evSoCECsXLmy6OjoKO7du1fa9/r1a9Hb21sEIB49elShvA8++EAEIL777rviq1evpO0JCQlily5dRADikiVLFPK4ubmJAEQnJycxKipK2h4bGyt6enqKAMQjR45odT65Ll26JF65ciXf9gMHDojm5uaih4eHKJfLdSrzTceOHRMBiAEBARrTtmvXTgQgLl26VNoWFxcnHj58OF897t69K7q7u4smJib56pL7mTp27JjS49y9e1c8ffp0vu3nz58XHRwcRDs7OzExMVFjfXX57iiq+3N2JaUSaXp3b627lDarVg7Tu9cxcI2IiIj0Y+aeq1rPwB12Lx4z91wzcI10o265ip07dyqkrV27Nn755RckJiZi8ODBuH79Oj7++GOUKVMG69atg6mp8s5tLVu2xPvvv69wzFmzZsHFxQUXL15U6H76008/ITs7G3/88QcaN26sUM6oUaPQo0cPhIWF4cKFC/mOU69ePXz++ecFeh/mz5+PChUqSK+DgoLQsGFDPHnyBJ07d0bXrl2lfXZ2dhgzZgwASF06AeD58+dYvnw5qlatilWrVsHe3l7aZ2trixUrVsDc3BxLlixRWodZs2ahZs2a0uvy5ctj3LhxAHJaQ3VRr149eHt759vesWNH9OvXD3fu3MGVK1d0KrMwypcvDwB4+fKltM3R0RHt2rVTaHEFcrrHfv3115DL5dizZ49Ox6lWrRqaN2+eb3vDhg0xYcIEJCQk4NixYwU4g+KHXUmpRLI0k2HNe00xc881bD2nfF1DM5lQ4rvZEBFR6fI8MQ1bzz3UKc/Wcw/wSXsvVLC1MFCtdKNuuQpXV9d82z744APs27cP//zzD5o3b47k5GQsXboUXl5eKssZMGBAvm1mZmbo27cv5s+fj5CQEPj5+UEul+PIkSMoU6YMOnbsqLQsf39/7N69G+Hh4WjYsKHCvm7duuULMrRhZmamdOmD6tWr48KFC0q7XFavXh1Azli+XMHBwcjMzESnTp1gZWWVL4+TkxNq1KiBy5cvIzU1NV8aZcfJfV/zHkdb6enpOHDgAMLDwxEbG4uMjAwAwOXLlwEAt27dQr169XQutyBEMefeT9n/T2hoKIKDg/Ho0SOkpaVBFEXpfG/duqXzsbKzs3HkyBGcOnUKT548QXp6ukJZBSmzOGJgSCWWpZkMP/auh0/ae2HL2Qc4c/cFktKzYGNhiubVHdG/SdVic5EkIiLSxpYI5T92qpOZLWLL2QfFZq3egixXsWLFCri7uyMhIQGdO3eWWs9UcXNzU7o9d/29x48fA8iZxCYpKQkAYG5urrbMuLi4fNuUBbLacHJygkyW/0fp3HFrypa4yN2XG3QAkCZAWb58OZYvX672mPHx8fnKdXFxyZfO1tY233G0cfnyZfTo0UPtYvWJiYk6lVkYuf9f5cr914Ps9evX6N27N44ePaoyn651fPjwIbp164bIyEi9lVlcMTCkEq+CrQUmBHkWmwsiERFRQWnbhfRNZ+6+KNHXwd27d0uzS964cQNJSUlKJ0DRlVwuB5ATdPXp00dtWmXdJC0tLQt0XE0T1Gg7gU1u/Rs0aJBvQp03WVjk/zFc2+NoIooi+vfvj+joaIwbNw7jxo1D9erVYWNjA0EQ8NVXX+HHH3+UWvEMTRRFKVCrU+e/4UJTpkzB0aNHERAQgJkzZ6Ju3bpwcHCATCbDoUOH0LFjR53rOHr0aERGRqJPnz744osvULNmTdja2sLExATLli3D2LFjjXbehsbAkIiIiKiYSErPv/SAIfMVB7du3ZLGFXbq1Anbt2/HpEmTsHLlSpV57t+/r3a7s7MzgJxxaJaWljAxMcGqVasK1C20KOW2+Pn5+WHhwoVFVo+oqChERUWhSZMmSscz3r1716j1OXLkCOLi4mBra6swbnTHjh2QyWTYvXt3voXvC1LH5ORkHD58GJUqVcLmzZvztQIb+7wNjZPPEBERERUTNhYF+82+oPmKWlZWFoYMGYLk5GTMnTsX69atQ61atbBq1Sps27ZNZb4tW7YoLSs3T+4SF6ampggMDERCQgKOHDlimJMwoKCgIMhkMuzduxeZmZkGPVZuV1tl6yLmTvCirGvqy5cvjbqeX0pKCj755BMAwLhx4xRaSl++fAk7O7t8QSGg/DMDqD/v169fQy6Xo3LlyvmCwszMTOzYsaPA51EcMTAkIiIiKia0nXH7Tc2rO+q5Jsbx7bffIjw8HN27d8fYsWNhZWWFdevWwczMDGPGjJHGCr4pNDQ0X4vi9OnTERMTg/r160vr2gHA119/DRMTE4wcORLBwcH5ykpKSsLKlSuRmpqq13PThypVquC9995DdHQ0Bg4ciGfPnuVLc/v2bbVBtLZyW1lv3LiRb5+npydMTExw9OhRhYlW0tLSMG7cOMTHF6wLtK5CQ0PRqlUrXL58Gd7e3pg2bZrCfi8vL7x8+RKbN29W2D5v3jyVM4eqO++KFSvC3t4eV65cwcmTJ6Xt2dnZmDJlCm7evFnYUypWSubPS0RERERvof6+VbHgyC2dJqAxkwno36SqAWulmxEjRqjc5+rqim+//RYAcPr0afzwww+oVKkSVqxYIaVp3LgxZsyYga+//hojRozAwYMH83UBHT9+PEaPHo2lS5fCw8MDly5dwtWrV2FnZ5dv8hs/Pz8sWrQIH374IYKCglC3bl14eXnBzMwM0dHRuHjxItLT09G7d2+lM38WtQULFiA6Ohrbtm3DgQMH0KBBA7i6uiI5ORnXrl3D7du30bNnT41jKDXp0aMH1qxZg0GDBqFDhw7S0hh//vknKlasiFGjRmH58uXw8fFBmzZtYGVlhZCQEGRnZ2PEiBEFmnRIlaioKOlzlJWVhZcvXyIyMhKPHj0CAHTq1AmrV6+WJtLJ9eWXX2LIkCEYMGAAFi1aBBcXF0RGRiIqKgoff/wx5s2bl+9Y3bt3x7fffovPPvsMhw8flpbBmD17NhwdHfHFF1/g66+/RkBAANq0aYNy5cohLCwMz549w4QJE7Bo0SK9nXdRY2BIREREVExUtLVE38Yu2Bj+QOs8fRsXr1m416xZo3Kfj48Pvv32WyQlJWHIkCHIzs7GypUrFdb7A4CpU6di//79OHz4MObPn4+PP/5YYX///v3RpUsX/PDDD9i1axfMzMzQs2dP/PDDDwqTkeQaN24cmjdvjvnz5yM4OBh79+5FmTJlUKVKFQwePBi9e/dWWCOwOLGyssL+/fuxfv16rFmzBhcvXkR4eDgqVKgANzc3DB06VOnyHbrq3bs35s2bh+XLl2PPnj3SrKV//vknAGDJkiWoVasWVqxYgSNHjsDe3h7t2rXD999/j1WrVhX6+Hk9e/ZM+hxZWFjA3t4e1atXR58+fTBw4ECl6woCwODBg1G2bFnMmjULFy9exOXLl9GkSRMsXrwYoigqDQwbN26MdevW4ddff8WhQ4ekluP//e9/cHR0xFdffQUXFxfMnz8fJ0+ehJWVFfz8/PDtt9/i/Pnzej3voiaIb8s0OlQguTNwXb16tYhrQkREVPzJ5XKpy1nNmjX1NutjXmmZ2Ri+MlyrGUqbVSuHNe81LTXr9Y4YMQJr1qzBsWPHlK4TSFRc6fLdUVT35xxjSERERFSMWJrJsOa9phjY1BVmMuWzaJrJBAxs6lqqgkIiMix2JSUiIiIqZizNZPixdz180t4LW84+wJm7L5CUngUbC1M0r+6I/k2KV/dRIir5GBgSERERFVMVbC0wIcizRC9eT0QlA7uSEhEREVGJsHr1aoiiyPGFRAbAwJCIiIiIiKiUY2BIRERERERUyjEwJCIiIiIiKuWKdPIZURQRFxeH5ORkAIC1tTXKly8PQVA+NTMRERERERHpn1EDw3PnzuHw4cMIDw9HZGQkHj58iKysLMUKmZrCxcUFPj4+aNq0Kdq3b4/GjRsbs5pERERERESlisEDw2vXrmHVqlXYsGEDnj59qrBPFMV86TMzMxEdHY3o6Gjs2rULX3/9NZycnDBo0CCMGDEC3t7ehq4yERERERFRqWKwMYZHjx5Fx44dUa9ePcydOxdPnz6FKIoKD1XeTPf06VPMnTsX9evXR8eOHXH06FFDVZuIiIiIiKjU0XuL4fnz5/H5558jODgYwH+tgoIgoG7duvD390eDBg1Qu3ZtVKlSBeXLl0eZMmUgiiJSU1MRGxuLR48e4fr164iMjERISAiuXr0qlfPvv//i33//RWBgIH7++Wc0atRI36dARERERERUqug1MBw7dixWrlwJuVwOURQhCALatm2L/v37o1evXqhQoYLa/La2trC1tUX16tXh7+8vbY+NjcXOnTvx999/48iRIxBFEcHBwWjWrBlGjRqFP/74Q5+nQUREREREVKoIoro+nToyMcnpmWpnZ4dx48Zh7NixqFatmr6KBwBER0djyZIlWLp0KRISEiAIArKzs/V6jNIkd8zm1atXi7gmRERExZ9cLseNGzcAADVr1pTufYiI1NHlu6Oo7s/1+m1ma2uLmTNnIiYmBj/99JPeg0IAcHd3x+zZsxETE4MZM2bAxsZG78cgIiIiIt0IgiA9Tp8+rTLdli1bpHTu7u4Gr5e7u7telkILDg6GIAgYMWJE4StFVAzpNTC8c+cOpk2bBjs7O30Wq5SdnR2++eYb3Llzx+DHIiIiIiLtrV+/XuW+devWGbEmRKQtvQaG5cuX12dxxfaYRERERJSfTCZDvXr1sHnz5nxrVQPAixcvcODAAU4eSFQMsWM8EREREenN4MGDERcXh4MHD+bbt3nzZmRmZmLIkCFFUDMiUoeBIYDk5GTs2rULn3zyCQICAlC5cmVYWFjA2toabm5u6NmzJ5YvX46kpCSdyt25cyf69euHatWqwcrKCuXLl0fjxo0xY8YMPH78WKeysrOzsXbtWnTt2hVVq1aFhYUFKlWqhFatWuHnn3/Gq1evdCqPiIiI9EyeBSRFl9yHPH8LX0EMGjQIgiAo7TK6bt062NjYoGfPnmrL2LdvH9q3b4+yZcvC0tISNWvWxNSpU1Xe76SmpuLrr79GtWrVYGlpCQ8PD0yfPh0ZGRlqj3P9+nWMGDFC4d5qwIABnJSPSiW9zkpaEs2dOxdff/010tLSNKatXLkyli9fjq5du6pN9/LlSwwcOFDpL2W57O3tsWzZMvTv31/jce/fv49+/fohIiJCZRpnZ2esW7cOQUFBGsvLi7OSEhERaU/tzIJJ0cBu/U+8ZzQ97gE27gXOLggCZDIZsrKyEBgYiIiICDx79kyaKPDu3bvw8PDA0KFDMWfOHFSuXBlubm6Ijo5WKOfHH3/EV199BVNTUwQEBKB8+fI4efIkHj58CC8vL5w4cQKVKlWS0mdkZKBdu3YICQlB2bJlERQUhPT0dBw9ehTt2rVDZGQkYmJi8OYt786dOzFgwACkp6ejQYMG8PT0xIMHDxAeHg4rKyvs378frVu3ltIHBwcjKCgIw4cPx+rVqwv8PlHpVBJmJdX7Avclzc2bN6WgsFKlSmjbti18fX3h5OSE7OxsXLp0CWvXrsWTJ0/w5MkT9OzZE1u3bkWvXr2UlpeWloZu3brh1KlTAIAKFSpg9OjRqFu3LhISErBjxw4cOnQIr1+/xqBBg2BlZYXu3burrF9cXBw6dOiAmzdvAgBcXV0xevRo1KhRA8+fP8eGDRsQFhaGx48fo3v37jh27Bh8fX31+yYRERER6WDIkCE4fvw4tm/fjmHDhgH4b0Iadd1IIyIi8L///Q82Njb4999/0axZMwBAeno6hg4dir///hsTJkzA1q1bpTzz5s1DSEgIGjZsiMOHD8PR0REAcPv2bQQEBCjtpRUdHY0hQ4bAzMwMe/fuRbt27aR9Bw4cQI8ePTBkyBDcvn0b5ubmhX9DiEoAvXQlffToEZYtW4Z58+YhNDRUYd+DBw8wbtw4NGnSBA0bNsSoUaMQGRmpj8PqhSAIaNOmDf755x88fPgQ69evx0cffYQBAwZg8ODBmD17Nm7cuIFu3boByOnS+f777+P169dKy/v555+loNDLywsXL17EDz/8gEGDBmHcuHE4ePAgfvnlF6ms0aNHIyEhQWX9pkyZIgWFrVq1wuXLlzFt2jQMGDAAkyZNwunTpzFx4kQAOV1iR44cyXUdiYiIqEj17dsXFhYWCrOTrl+/HpUrV0bbtm1V5vv9998hl8sxceJEKSgEAAsLC/z++++wsrLCjh078ODBA2nf4sWLAQC//vqrFBQCgKenJ6ZNm6b0OPPnz0dycjJ+/PFHhaAQADp16oTx48fjwYMH+Oeff3Q7caISrNCB4aZNm+Dl5YXx48fjs88+Q0BAAIYOHQoAiIqKQv369bF8+XKcP38ekZGRWLVqFXx9fRV+6SlK33//PY4cOYIuXbrA1FR5A6qtrS02bdqEypUrA8hpxdu1a1e+dAkJCZg9e7b0eu3atXB2ds6X7tNPP0Xnzp0BAM+fP8e8efOUHvfWrVtSVwULCwts2LAh31IggiBg7ty5qFevHoCcJmdOA01ERERFycHBAV27dsWRI0fw9OlTRERE4MaNGxgwYABkMpnKfCEhIQByJrB5U8WKFdGhQwfI5XKcPHkSABATE4OYmBhUrFhR6XCagQMHKj3OoUOHAAC9e/dWut/f3x8AEB4eruYsid4uhQoMb9y4geHDhyM1NRX29vZo2LAhrKyssGHDBixbtgzjx4/H69ev0b59eyxcuBDz5s1Dq1atkJWVhffee0/nCVgMoVy5clqls7a2Ro8ePaTXylo9d+3aheTkZACAn58fmjZtqrK8Tz/9VHq+YcMGpWk2bdoEuVwOAOjXrx9cXV2VpjM1NcXkyZOl1+rWDiIiIiIyhiFDhiA7OxubNm2SfrTWNBtp7r2hqoXvc7c/evRIIb2bm5vS9Pb29nBwcMi3PXdcY5UqVSAIQr5Hv379AOQ0BhCVFoUaY7hgwQJkZmaid+/eWL9+PSwsLBAXF4eOHTti9uzZuH//PkaNGoXly5dLeSZPnox3330XW7duxbJlyzBjxozCnoPR5G2tS0lJybd/37590nNNE9QEBATA2toaycnJuHnzJm7duoUaNWoUuLwuXbpIz48dO4bU1FRYWVmpzUNERERkKF26dIGDgwP++usvPH78GLVr1y70+oWCIOilbrk/vA8fPlxturzdWYnedoUKDI8dOwYzMzMsXLgQFhYWAHIWnP/uu+/QtWtXmJqa4rvvvsuXb86cOdi6dSsOHz5cogLDS5cuSc+V/ZKVd7+61kIgp5WvYcOG0pjMS5cuKQSGoijiypUrWpdXuXJluLi44OHDh8jKysK1a9fQuHFjtXmIiIhIj8q45MzsWVKVcdFrcRYWFujXr5/UQDBp0iSNeZydnXHv3j3cv38fderUybc/b0sfAGmYz/3795WWl5CQoHSJCxcXF9y5cyffuESi0qxQgeGDBw/g5uYm/VHmyp0V08XFRWE64Vxubm5wd3eXJlUpCW7cuIHDhw9Lr9+cSVQURdy6dUt6Xa2a5umqq1WrJgWGUVFRCvsePXokrZsok8lQtWpVrcp7+PChVB4DQyIiIiMyMS3Ucg9vo6FDh2L79u0QBEHpuME3+fv74969e9i4cSNmzZqlsC82NhYHDx6EIAho1aoVgJx7yqpVq+LBgwc4fvw4AgICFPJs2rRJ6XHat2+PO3fuYMeOHRg9enQBz47o7VKoMYbZ2dkoW7Zsvu3ly5cHAKUTr+SqWLGi2tk4i5Pc2UNzux306dMn369YSUlJyMzMlF7nvgfq5E3z5q9ZL1++lJ7b29vDzMysUOWpc+fOHXh7eyt9EBERERWUv78/4uLiEBsbq3IcYF4TJkyAiYkJfvvtN5w9e1banpGRgYkTJyI1NRW9e/dW+MF8/PjxAHLmb4iPj5e23717F99++63S43z66aewsrLCZ599hu3bt+fbn56ejq1bt0o/uBMZgqr77zt37hRJfQrVYli+fHk8e/ZM5X51CzdmZGTA2tq6MIc3mkmTJkktexUrVsRvv/2WL01iYqLCa23G9+VN82aQnLc8bccKqiuPiIiIqLhr2rQpZs2aha+//hotWrRAYGCgtMD9gwcPUKNGDSxatEghz6effop//vkHJ0+ehKenJ9q0aYP09HQcOXIEbdu2hUwmQ0xMjEIeT09PbNy4EYMGDUKfPn3g6emJ2rVrw9raGo8ePcL58+eRnJyMCxcuwMVFv11siYqrQgWGVatWxblz55Ceni6NMcy1Y8cOtX22Y2JilHYzLW5++uknaX0cc3NzbN68WW1LaEnk4eGBq1evFnU1iIiIiPDVV1/Bx8cH8+bNQ0REBFJTU+Hq6oovvvgCU6dOzddbzdzcHIcOHcJ3332H9evXY8+ePXB2dsbHH3+M6dOnw8vLS+lxevbsiUuXLmHu3Lk4fPgwDh8+DDMzMzg7O6N79+7o3bu30nGORPqi6v67qHrtFSowbNy4McLCwhAaGppvsdKePXuqzHfz5k28ePFC6XozxcncuXPx5ZdfAgDMzMywZcsWBAYGKk1ra2ur8Do1NTXftjelpqZKz99cnzBv3rzpCloeERERkSGJoqh1WicnJ7Xpu3btqnFG9rzKlCmDH374AT/88EO+fbkT1ijj4eGRrwVSlcDAQJ3OkaikKVRg2LlzZ0RFReHp06c65Vu7di0AqAyyioNffvkFn3/+OYCcX6K2bt2ab8KZvGxsbGBqaoqsrCwAOeveaAoM866N8+YaO3lfv379GllZWTA1Vf/fpa48IiIiIiIiVQoVGHbp0kVh/Txt9erVC+3atYOPj09hDm8wP/74I7766isAOVMt79ixA507d1abRxAEeHl54dq1awCAe/fuaZyZ9N69/6a0rlWrlsI+FxcX2NjYICkpCdnZ2YiJiUH16tULXB4REREREZEqhZqVtKAaN26MgICAYtmqNXPmTCkotLKywu7duzUGhbnq168vPQ8PD1ebNisrCxcuXFCaF8gJNOvWrat1eU+ePJFmzpLJZOwTT0REREREWiuSwLC4+vrrrzFjxgwAOX3V9+7diw4dOmidP2/r6b59+9SmPX78OJKTkwEANWrUUFjcviDl5d0fFBSk9UymREREREREDAz/35QpU6QByzY2Nti/fz/atGmjUxk9evSQluAICQlR28r366+/Ss8HDRqkNM27774rLfmxZcsWPHjwQGm6rKwsLFiwQHqtzQKyREREREREuRgYImf9mzlz5gDImc3z4MGDaN26tc7l2NvbSxPWAMCwYcPw+PHjfOl+/fVX7N+/H0DOWpAff/yx0vK8vLwwbNgwADkLrQ4aNCjf+oSiKOLTTz/F5cuXAQC1a9fGkCFDdK47ERERERGVXoWafKag0tPTcevWLaSlpaFcuXJwc3ODTCYriqpg2rRpmDt3rvR6woQJeP78OXbu3Kk2X/ny5eHn55dv+xdffIEDBw7gzJkzuHHjBho0aID3338fdevWRUJCAnbs2IGDBw8CyBkLuHz5ctjb26s8zpw5cxAaGorbt28jNDQU9evXx+jRo+Hp6YnY2Fhs2LABZ86cAZDT/XXVqlUaZy8lIiIiIiLKSxCNuCDLo0eP8Mknn2DXrl3IzMyUtstkMtSsWRMNGzaUHg0aNDDK5DSBgYE4fvy4zvkCAgIQHBysdF98fDwGDBiAw4cPq8xvZ2eHP/74AwMHDtR4rHv37qFfv344d+6cyjROTk5Yt25dvvUkNcldQJML3BMREWkml8tx48YNAEDNmjWlIR9EROro8t1RVPfnRmtaevr0KZo3b47Hjx/nWxw0KysL165dw7Vr17B+/Xppu5ubm0Kw2LBhQzg7OxurygVWrlw5HDp0CDt27MD69esRERGBZ8+ewdraGm5ubujWrRvGjh2LKlWqaFVetWrVEBYWhnXr1mHz5s24dOkSYmNjYW9vDw8PD/Tq1QtjxoxB2bJlDXxmRERERET0NjJai+GECROwZMkSCIIAURRhbm6O6tWrIz09HTExMcjOzs5fOUHIt61ChQp4+vSpMapcKrDFkIiISHtsMSSigmCLYR55l1MYOnQofv/9d9ja2gIAMjMzcf36dVy8eBEXL17EhQsXEBkZiVevXuUrJzY21lhVJiIiIiIiKhWMFhg+fvwYgiCgWrVqWLFihcIEKWZmZqhfvz7q168vzcIJAPfv35eCxdxHTEyMsapMRERERERUKhgtMLS2tsbr16/RokULrWfNdHNzg5ubG3r27Clte/36taGqSEREREREVCoZrWN8vXr1cg5YyL746pZ2ICIiIiLjunDhAgRBUDmpnlwuh4ODAwRBwJgxY5SmOXHiBARBkMZWAcDq1ashCAJGjBih9viBgYEQBEHlbPFvCg4OhiAIcHd316rc1atXa1UuUUlntMCwd+/eEEURYWFhxjokERERERmYj48P7Ozs8PjxY9y9ezff/suXL0s9vkJDQ5WWERISAgDw9/c3XEWJSC2jBYajR49G5cqVcevWLRw4cMBYhyUiIiIiAzIxMUHLli0BKA/8coM+Hx8fREVF4cWLFyrTMDAkKjpGCwytra2xbt06CIKA999/H0+ePDHWoYmIiIjIgHIDOmWBYWhoKMzMzPDRRx9BFEWcPHlSYb9cLsfp06cVyiEi4zPq4jtBQUH4448/8PTpU7Rs2RIRERHGPDwRERERGUBuQJfb8pdXSEgIGjVqhPbt2ytNExkZiYSEBLi6usLV1dXwlS0kd3d3aV3uBQsWoE6dOrC0tESVKlUwadIkpcutjRgxQhoHuX//fvj5+cHGxgZly5ZF7969ERUVpfJ4YWFh6NevHypXrgxzc3O4uLhg9OjRSmfqnzFjhjQuMjw8HN26dYOjoyMEQcDFixf1+C7Q28joq7K+9957+Oyzz3D//n20aNECAwcOxMGDB5GammrsqhARERGRHjRt2hQWFha4ceMG4uLipO13797F48eP4efnhypVqsDNzS1fq2JJ7UY6ceJEfP7553BxcUHPnj2RnZ2NhQsXIiAgAAkJCUrz/P333+jatSsyMjLQvXt3ODs7Y8eOHWjevDkiIyPzpV+8eDFatmyJ7du3w83NDb169YKjoyNWrFiBJk2a4Pr160qPc+LECfj5+SE6OhodOnRA69atCz0BJL39jPoJOXLkCNzd3TFnzhwIggC5XI4tW7agS5cucHBwQIMGDTBy5EgsXLgQoaGhSE5ONmb1iIiIiKgALCws4Ovrm6+raG4Q6OfnBwBo1aoVzp8/r9AgkJumpAWGa9euxenTp3Ho0CFs3rwZt2/fRps2bXDp0iV88803SvMsXrwYS5cuRXh4ODZu3IgrV65gypQpeP36db7ZV8+cOYNJkyahcuXKCA8Px5kzZ7BlyxZERkbizz//RGxsLEaOHKn0OKtWrcJ3332HK1euYOPGjTh+/Djq16+v77eA3jJGCwwvX76Mbt264eHDhxBFUdouiiJEUURmZiYuX76Mv/76Cx999BECAgJgb2+PWrVqYeDAgfj555/x77//Ij4+3lhVJiIiIiqw9Kx0JKQlaP3Ie3+UKzE9Uev8qZn5e19lZGeozZOela6381XWnTT3eatWraR/MzIyFGap1xQYrlmzBoIgqHwcP35cb+egiw8//BCNGzeWXtvY2GDhwoUQBAErVqxAWlpavjwtW7bE+++/L70WBAGzZs2Ci4sLLl68qNCa+tNPPyE7Oxt//PGHwnEAYNSoUejRowfCwsJw4cKFfMepV68ePv/8c32cJpUiRlvg/vvvv0d6errUJ7tmzZrw8fFBVlYWbt26haioKGRkZCjkEUURt27dwq1bt7BlyxYAOX9AWVlZxqo2ERERUYH8EfYHfjv9m9bpL3x4AXaWdgrb/Jf5IzE9Uav8vb174+fOPyts2319N6YcmKIyz6QWkzC51WSt66iOv78/fvzxR4XgJjQ0FF5eXqhQoQKA/wLE0NBQBAYG4s6dO3jy5AkcHR1Ru3ZtpeV6eHhILY7KHDhwAM+ePdPLOehiwIAB+bbVqVMHPj4+uHjxIi5cuIAWLVpozGNmZoa+ffti/vz5CAkJgZ+fH+RyOY4cOYIyZcqgY8eOSo/v7++P3bt3Izw8HA0bNlTY161bNwiCUIizo9LIaIFh7mKiAPDjjz9iyhTFL6nMzExcvXoVFy5ckB6RkZFISkoyVhWJiIiIqIBatmwJExMTqatoUlISoqKi8N5770lp6tWrBzs7Oyl4zG1R9PPzUxnI+Pn5qV1kPjAwUKfAUNuAKbcFV1V6Nzc3pdvd3d1x8eJFPH78WKc8AKQ8cXFx0j2wubm52nrmHdOZqyRM4kPFj9ECw9wZmho0aJAvKARyfi1p0KCBNM4Q+K/FMG+wqKy5nIiIiIiKlr29PerXr4+LFy/izJkz0r1f3tY+ExMTNG/eHKdPn0Z2dnaRTDxTpkwZAEBKSoradLn7ra2tDV6nN8nlcgA53VP79OmjNq23t3e+bZaWlgapF73djBYYVq5cGTExMWjQoIHWeQRBgJeXF7y8vPDuu+8arnJEREREejau2TiMbKx8chBlbC1s820LGROidOyhMmYys3zbetTugQ6eHVTmsTC10Lp+2vD395fGyikLDIGc7qSHDh3CpUuXimTiGRcXFwA5LW2JiYmwtc3/vgM5M6rmTf+m+/fvo169ekq3A4Czs7PKfZrylC9fHpaWljAxMcGqVavYLZSMwmiTz/j4+AAAZxolIiKiUsHC1AJ2lnZaP5Td/Nta2Gqd38rMKl9+c5m52jyGCAyBnDGEISEhqFSpEmrUqKGQJnec4fbt23Hz5k1YW1ujUaNGeq2HOpUrV4anpydEUcS+ffuUpjl58iTi4+NhY2OjslEjd/6LvKKionDx4kWV+ZTlycrKwrZt2wD8F0SbmpoiMDAQCQkJOHLkiJZnRlQ4RgsMhwwZAlEUceLECa1/+SIiIiKikiM3MDx16hQuXLggBYF5NWvWDDKZDIsWLQIANG/eHKamRuvEBgCYPDlnwp0vvvgCN2/eVNj35MkTfPDBBwCAcePGqeyWuXDhQoUhTikpKZg4cSJEUcTIkSNhZZU/UA8NDcXKlSsVtk2fPh0xMTGoX7++Qsvp119/DRMTE4wcORLBwcH5ykpKSsLKlSu5FjjpjdH+Cnv37o0mTZrg3LlzWL58OcaMGWOsQxMRERGRETg5OcHT0xO3b98GkL8bKZAzbs7Hxwfnz58HUDTrF06YMAEnT57Epk2bULduXbRq1QpVqlRBbGwsQkJCkJqaioCAAHz77bcqyxgyZAiaNWuGNm3awN7eHidOnMDTp0/h7e2NWbNmKc0zfvx4jB49GkuXLoWHhwcuXbqEq1evws7OLt8EO35+fli0aBE+/PBDBAUFoW7duvDy8oKZmRmio6Nx8eJFpKeno3fv3kqDUCJdGa3F0MTEBH/99RccHBzwySefIDw83FiHJiIiIiIjyRvoqVpmIm9LYlEEhoIgYMOGDdi4cSOCgoJw5coVbN68GREREWjSpAmWLFmCw4cPqw24fvvtN/z444+4f/8+du3aBUEQMGHCBISEhMDe3l5pnv79+2P37t2QyWTYtWsXHj58iJ49e+L06dP5lpwAclosz549i+HDhyMxMRF79+7FwYMHkZSUhMGDB2Pv3r0qj0WkK0E0Ur/OoUOHom7dupDL5fj2229hamqK+fPnY9SoUcY4PKmQO5PV1atXi7gmRERExZ9cLseNGzcAADVr1oSJidF+Y6diwt3dHffv39dpaNSIESOwZs0aHDt2DIGBgYarHBVbunx3FNX9udG6kq5fv15hUHV6ejrGjBmDuXPnYuTIkWjbti3q168PmUxmrCoRERERERERjBgYAooLhQqCAFEUERUVJa1raG5ujrp166Jhw4bSw8fHh/2miYiIiIiIDMhogeHGjRtx4cIFXLx4EZGRkXj27BkAKDTDp6en4/z589JgZCBnbKKXl5dCsNimTRtjVZuIiIiIiOitZ7Qxhm96+vSpFCjmPu7cuQO5XJ4vbd4uqIIgICsry5hVfatxjCEREZH2OMaQiAqCYwzVcHJyQufOndG5c2dpW3JyMiIjIxWCxStXriAtLa2oqklERERERPTWK7LAUBlra2u0bNkSLVu2lLZlZ2cjKipKIVgkIiIiIiIi/SlWgaEyMpkM3t7e8Pb2xuDBg4u6OkRERERERG8ddownIiIiIiIq5YwWGCYlJaF58+YYPXo0li5daqzDEhERERERkQZGCwy3bt2K8PBwrFq1CqdPnzbWYYmIiIiIiEgDowWGBw4ckJ7nLmivraSkJH1Xh4iIiIiIiP6f0QLD3EXra9Sogdq1a+uUt1u3bvDy8sLAgQO5hiEREREREZGeGS0wfPToEQRBQMOGDXXOO3bsWNy+fRtbtmzB/v37DVA7IiIiIiKi0stogaFcLgcAODg46Jy3R48esLCwAKDYJZWIiIiIiIgKz2iBYfny5QEA8fHxOue1traGj48PRFHE2bNn9V01IiIiIiqgCxcuQBAEVKlSRel+uVwOBwcHCIKAMWPGKE1z4sQJCIIAb29vadvq1ashCAJGjBih9viBgYEQBAHBwcFa1Tc6OhqCIMDd3V2r9ESlhdECQ3d3d4iiKI011JWXlxcA4N69e/qsFhEREREVgo+PD+zs7PD48WPcvXs33/7Lly/j9evXAIDQ0FClZYSEhAAA/P39DVdRIlLLaIFhUFAQAODu3bsFCg5tbGwAQPpiISIiIqKiZ2JigpYtWwJQHvjlBn0+Pj6IiorCixcvVKZhYEhUdIwWGPbu3Vt6PnXqVJ3z53ZBNTU11VudiIiIiKjwcgM6ZYFhaGgozMzM8NFHH0EURZw8eVJhv1wul9a4ZmBIVHSMFhg2aNAAHTp0gCiKOHLkCD744AOIoqh1/vDwcAD/jVUkIiIiouIhN6DLbfnLKyQkBI0aNUL79u2VpomMjERCQgJcXV3h6upq+MoWwLVr1zB48GBUrlwZ5ubmqFKlCoYNG4YbN24opMsdvxgYGJivjAYNGkAQBAwZMkRhuyiKqFChAhwcHJCdna2w78GDB/jwww/h4eEBS0tLlCtXDt26dcOpU6fylR8cHCyNyXz69ClGjx4NFxcXmJqaYv78+YV+D+jtZ7TAEAAWLVoEe3t7AMDSpUvRpUsXPH36VGO+7du3S39oBVnugoiIiIgMp2nTprCwsMCNGzcQFxcnbb979y4eP34MPz8/VKlSBW5ubvlaFYt7N9IjR46gSZMm2LBhAypXrow+ffqgYsWKWLt2LZo0aaIQ6Lq7u8PNzQ1nzpxBWlqatD0+Ph6XLl0CABw/flyh/CtXriAuLg5+fn6QyWTS9tOnT8PHxweLFi2CmZkZunbtirp16+LgwYNo3bo1Nm/erLS+sbGx8PX1xT///IMWLVqgc+fOKFOmjD7fEnpLGTUw9PDwwIYNG6SlJw4dOgRPT09MnToVV69eVZpn27ZtGDVqlPS6e/fuRqkrERERUWGkZ6UjIS1B64eynlSJ6Yla50/NTM2XPyM7Q22e9Kx0vZyrhYUFfH1983UVzQ0C/fz8AACtWrXC+fPnkZqami9NcQwMk5OTMXjwYKSmpuL333/H+fPnsXHjRly4cAFz585FUlISBg0apBAEBgQEID09HWfOnJG2HT9+HKIowtvbGw8fPsTt27elfbmzqeZtZUxISECfPn2QkJCAdevWISoqCtu2bcOJEydw+vRp2NnZYfTo0YiNjc1X53379sHX1xf37t3D33//jT179qicDZYoL6MP2OvcuTP+/fdf9OzZEy9evEBKSgp+/vln/Pzzz3BxcYGnpycqVqyIlJQUXLhwAY8ePZK+KF1dXfM1vxMREREVR3+E/YHfTv+mdfoLH16AnaWdwjb/Zf5ITE/UKn9v7974ufPPCtt2X9+NKQemqMwzqcUkTG41Wes6quPv74/Q0FCEhISgZ8+eAP5rDWzVqpX074YNGxAWFiYFQpoCwzVr1mDNmjV6qaOutmzZgmfPnqFFixaYMGGCwr6PP/4Y69evx7lz57Bt2zYMHjwYQE6A99dffyE4OFg6x9zg75tvvsG7776L4OBgeHp6KuwLCAiQyl65ciWePHmCTz/9VCo3V5MmTTBt2jR88sknWLduHT7++GOF/RYWFli4cCEsLS319TZQKVEkM7m0bNkSZ8+exccff4ydO3dKgd/Dhw/x8OFDKZ0oihAEAQBgZ2eHzZs3S62NRERERFR8+Pv748cff1ToKhoaGgovLy9UqFABwH8BYmhoKAIDA3Hnzh08efIEjo6OqF27ttJyPTw8pBZHZQ4cOIBnz57p8Uz+kxvYvhmc5RoyZAjOnTuHkJAQKU1ugJd3XcXg4GDUrl0bPXr0gIWFBYKDgzF69GiIoogTJ07Azs4OjRo1ktIfOnQIgOLkjXnlBtG5c3Dk1ahRI5VrShKpU2RTfLq5uWH79u0ICQnB3LlzceDAAaSn5+/OIIoi2rVrhwULFqj8wiAiIiKiotWyZUuYmJhIXUWTkpIQFRWF9957T0pTr1492NnZScFjbuDl5+cnNQa8yc/PD6tXr1Z53MDAQIMFho8fPwaQM3ZQmdztjx49krZVr14drq6u0jjDlJQUXL58GePHj4elpSWaN28ujTPMHV/YpUsXhfGF0dHRAP4LpFXJO54zV3GdwIeKvyJf+8Hf3x/+/v5IT0/HuXPncOvWLcTHx0Mmk6FSpUpo2bIlqlatWtTVJCIiItLJuGbjMLLxSK3T21rY5tsWMiZE61nczWRm+bb1qN0DHTw7qMxjYaq/nlj29vaoX78+Ll68iDNnzuDVq1cAoNDaZ2JigubNm+P06dPIzs4u9hPPaKIqmA0ICMDatWtx5swZvHz5EqIoSt1KAwMDcfz4cdy+fVtpN1IgZwkPAOjbty+sra1VHr9WrVr5trELKRWU3gPDp0+fwsnJSed8FhYWaNmypbRAKhEREVFJZmFqUejAS1mwqAtzmTnMZeaFKkMX/v7+uHjxIkJDQ5UGhkBOK9ihQ4dw6dKlYj3xDAA4OzsDAO7fv690f27L3ptdN3MDw+DgYLx8+RIAFALDmTNnIjg4WOnEMwDg4uKCGzduYOrUqWjcuLF+ToZIA73PSuri4oLmzZvj+++/l6blJSIiIqK3X96F7kNCQlCpUiXUqFFDIU1u98jt27fj5s2bsLa2VhhfV5zkns/GjRuV7l+3bp1Culx5J50JDg6Gt7e3NM6yefPmsLCwwLFjx3DixAnY2trmC/5y13zcsWOH3s6FSBO9B4ZyuRwRERH45ptv0LBhQ1SrVg0TJ07E4cOHkZmZqe/DEREREVExkRsgnTp1ChcuXFA6Rq5Zs2aQyWRYtGgRgJxAydS0yEc3KdW/f39UqlQJoaGhWLZsmcK+3377DWfPnkWVKlXQp08fhX0eHh5wcXHB6dOncfnyZYUWwdxxhtu3b0dcXBxatWqlML4QAMaOHYuKFStizpw5WLZsmdS1NFdWVhYOHjyIK1eu6PeEqVTTe2DYs2dPlClTBqIoQhRF3L9/H4sXL0anTp1Qvnx59O/fH+vWrUN8fLy+D01ERERERcjJyQmenp5ISkpCVlaW0tlEbWxs4OPjI3WxLKpupE+ePEHz5s1VPv755x9YW1tj/fr1sLKywtixY9GkSRMMGjQIjRo1wuTJk2FjY4ONGzcqHdcXEBCAjIwMhfGFuQIDA6W1D9/cBwAODg7YtWsX7O3tMXbsWLi7u6NLly4YPHgw2rZtiwoVKqBTp04K6yESFZbeA8MdO3bgxYsX+OeffzBu3DhUqVJFChITExOxbds2DB8+HE5OTggICMAvv/yCmzdv6rsaRERERFQE8gZ6qpaZyNuSWFSBYUZGBsLCwlQ+chePb9u2LSIiIjBw4EA8fPgQW7duxdOnTzFkyBCcPXtWZf1zAz5BEPJNLpM3GFQWGAI5LamXL1/GF198ATs7Oxw/fhw7d+7E/fv3ERAQgNWrV6Ndu3aFfh+IcgmitlNdFcKFCxewZ88e7N69G+fPn//v4HlmcvL09ESPHj3Qo0cPtGrVCiYmeo9ZSQlvb28AwNWrV4u4JkRERMWfXC7HjRs3AAA1a9bk/QoRaUWX746iuj83SmCY1+PHj7Fnzx7s2bMHR48elZrR8waJZcuWRZcuXdC9e3d06tQJtraFm5GLVGNgSEREpD0GhkRUEAwMNUhJScHhw4exZ88e/PPPPwqLk+YGimZmZmjdurXUmshFO/WLgSEREZH2GBgSUUEwMNSBKIoIDw/H7t27sWfPHoVZlvK2JtatWxc9evRA9+7d0bRp06Ko6luFgSEREZH2GBgSUUEwMCyE6Ohoqcvp8ePHpaUu8gaJTk5OePToUVFV8a3AwJCIiEh7DAyJqCBKQmBYbL/N3N3dMXHiRBw6dAhxcXHYvHkzBg8ejLJly0qznD59+rSoq0lERERERFTiFc/VRN9ga2uLfv36oV+/fpDL5QgNDcXu3buxd+/eoq4aERERERFRiVdsWwxVMTExQevWrfHLL78gKiqqqKtDRERERERU4pW4wJCIiIioqOSd60AulxdhTYioJMnOzpae5/0eKU6M2pX033//xalTpwAA33zzjTEPTURERFRogiDA3NwcGRkZSE5Ohr29fVFXiYhKgISEBACAhYUFA0MAOHjwIH799VcIgsDAkIiIiEokW1tbvHjxQlp/2dramrOTElE+oigiPT0diYmJiI+PBwCULVu2iGulWrGafGb9+vU4cuQIBEHAihUriro6RERERPk4OjoiOTkZaWlpePz4cVFXh4hKCAcHh2Ldy0DvgaFcLi/wr2ZnzpzB6tWrGRgSERFRsSWTyeDq6ooXL14gMTERGRkZRV0lIiqmZDIZrK2tYWtrC1tb22LbjRQwQGBoZ2eHpk2bokWLFmjZsiVatmxZrJtMiYiIiHQlk8lQsWJFVKxYUVpfmYgoL0EQinUg+Ca9B4YpKSk4fvw4jh8/Lm3z8vJCy5YtcffuXX0fjoiIiKhIlbSbPyIiZfQeGA4ZMgSnTp1SCAJv3LiBmzdvKqQbNGgQAgMDERAQgJo1a+q7GkRERERERKQlQTRQ34dnz57h5MmT0uPChQvIzMyEIAgQRVHhl7VKlSqhdevWiImJwZkzZyAIgsJaH2Q43t7eAICrV68WcU2IiIiIiKio7s8NFhi+KS0tDePGjcNff/0FQRBgZWWFlJSU/yry/4FibtDYu3dvtG7dGq1bt4aPj4/B6yeKIu7evYvz58/jwoUL0iN3KurcNNoIDAxU6EqryZ49e9CtWzeN6bKzs7FhwwZs2rQJly5dwvPnz+Hg4ABPT0/06tUL77//PhwcHLQ+LsDAkIiIiIioOCmq+3OjLVdhaWmJChUqSK9fvXqFiIgIaTziqVOnkJiYKLUobt++Hdu3bwcA2Nvbo2XLllKg2Lx5c73X77PPPsPcuXP1Xq6+3L9/H/369UNERITC9ufPn+P58+c4deoU5s+fj3Xr1iEoKKiIaklERERERCVRka1jaGpqihYtWqBFixaYOnUq5HI5Ro4cibVr10IQBNjZ2eH169cAcoLI/fv3Y//+/RAEAVlZWXqvz5tdVy0sLFC3bl2cO3euUOUuXboUFStWVJvG19dX7f64uDh06NBBGqfp6uqK0aNHo0aNGnj+/Dk2bNiAsLAwPH78GN27d8exY8c0llnciKKIPVF7IBNk6Fqra1FXh4iIiIioVNF7YPjhhx+iZcuWaNWqFdzc3LTOZ2JiAjs7O+l1fHw8Ll26hJCQEOnx9OlTfVdXUqdOHUyePBkNGzZEw4YNUadOHZiamhZ6lrEOHTrA3d29UGVMmTJFCgpbtWqFffv2KbxXEydOxOTJk7Fw4UIkJydj5MiRiIyMhEwmK9RxjeVl6kt88+832HdjH2wtbNHQuSGc7ZyLulpERERERKWG3gPDxYsXY8mSJQCAypUrS2sZtmrVCpmZmVqXIwgCfHx84OPjgw8//BAAcOfOHYSEhOi7ygCAMWPGGKTcwrp16xZWr14NIKcVc8OGDQpBIZDzXs2dOxfBwcG4fPkyrl69inXr1mH48OFFUGPdpWam4sS9EwCAxPRETD04Fav7roaJYFLENSMiIiIiKh30fuedO0ZQFEU8fvwY27Ztw6efformzZtj4cKFUrp///0XqampOpXt4eGBESNG6LnGxdumTZsgl8sBAP369YOrq6vSdKamppg8ebL0ev369Uapnz442znjmzbfSK9P3j+J9RdLTv2JiIiIiEo6vbcYvnz5EmFhYTh16hROnz6NsLAwaawg8N/sox07doSZmRl8fX0RFBSEoKAgnQPF0mDfvn3S865d1Y+969Kli/T82LFjSE1NhZWVlcHqpk+9vXvj8O3DOHz7MADgp+M/wc/ND9XKVSvimhERERERvf0MvlyFKIq4du0aTp8+jaVLl+LcuXNSqyKAfGP4cperOHz4MFq0aFHkgU3e+hVkuYr27dvjxo0bePbsGczNzVGxYkX4+vqiV69e6Nu3r9pxgKIows7ODklJSQByutJWr15d7bGrVq2Khw8fAgDOnj2Lxo0bq01fnJariEuOQ+fVnRGfGg8AaFC5ATYP3AxTkyKbI4mIiIiIyKiK6v7c4IO4BEGAt7c3Ro8ejcDAQGn7qlWrMGzYMLi6ukpdT/MufN++fXs4ODigefPm+Pzzz7F7927Ex8cburp6d/jwYcTExCA9PR2JiYm4c+cONm3ahAEDBqBOnTpqZz199OiRFBTKZDJUrVpV4/GqVfuvhS0qKqrwJ2BE5a3L44eOP0ivLz65iKXhS4uwRkREREREpUORNcUMHz5cmhwlOjoawcHBWLBgASIjI6UWxczMTISHhyMiIkJaY7BOnTq4fPlyUVVbaw4ODmjXrh18fX3h4uICU1NTPHnyBCdOnMDu3buRlZWFmzdvws/PD0eOHEHLli3zlfHy5Uvpub29PczMzDQet3z58tLzV69eaVXXO3fuSL9MvMnYv1S092yP3t69sf1qzhqWv536DYHVAuFdSXn9iIiIiIhKIlX333fu3IGHh4eRa1OEgWFe7u7uGDFiBM6dO4fIyEgAOZOuHD9+HCdOnMC1a9ekbpzXrl0ryqpq5YcffkDjxo1hYWGRb9/kyZNx48YN9O3bF1euXEFaWhr69OmD27dvw9raWiFtYmKi9FzbLrV50yUkJBTwDIrWN22+wemY03iS+ARZ8ix8uu9T7Bq6Cxam+d9PIiIiIiIqvGIRGCrTv39/9O/fH0DOmoYnTpzA8ePHDbZchT4pa/3Lq2bNmjh8+DDq1auHuLg4PH36FH/88Qc+/fRTI9VQkYeHR7EYY5jL1sIWczrPwdAtQwEAt17cwqpzqzCu2bgirhkRERERkX6ouv9W1ZJoaCViobhy5cqhV69emDdvHs6ePVvU1dELJycnheUldu/enS+Nra2t9FzbGVvzpntzvcOSpKVrS4xoNAIAMLLxSOk5ERERERHpn1FbDDt06AAbGxtjHrJYa9OmDaZNmwYAuH79er79Dg4O0vPXr18jKysLpqbq/8vi4uKU5i+JPvf/HJ28OsHXxbeoq0JERERE9FYzamDYvn17tG/f3piHLNYqVKggPVc2UYyLiwtsbGyQlJSE7OxsxMTEaFyu4t69e9LzWrVq6a2uRcHSzJJBIRERERGRERSrMYZDhw5FkyZNiroaRpO3da9s2bL59guCgLp16+LMmTMAgPDwcLWB4ZMnT6Q1DGUyGerUqaPnGhcP6VnpnIiGiIiIiEiPitUYw6ZNmyosY/G2O3bsmPS8Zs2aStN06dJFer5v3z615eXdHxQUpPVMpiWFXJRj5dmV6LCyA+JTSt6alkRERERExVWxCgxLk+fPn2P+/PnS6+7duytN9+6778LEJOe/acuWLXjw4IHSdFlZWViwYIH0evDgwfqrbDEgiiLe3/4+vg/+Hg8THmLa4WnSEiZERERERFQ4DAz1bMGCBTh58qTaNLdv30aHDh0QGxsLAKhUqRLGjVO+FIOXlxeGDRsGAEhPT8egQYPyrU8oiiI+/fRTXL58GQBQu3ZtDBkypLCnUqwIgoBWbq2k1wduHcDu6/lnciUiIiIiIt0VqzGGRenVq1f45Zdf1Kb53//+l2/bd999p/D62LFj+Oijj1CtWjW0a9cOdevWRYUKFWBqaoqnT5/ixIkT2LVrFzIzMwHkLEj/999/KyxN8aY5c+YgNDQUt2/fRmhoKOrXr4/Ro0fD09MTsbGx2LBhgzQOsUyZMli1apXG2UtLohGNR+Df2/8i7GEYAGDGkRloWrUpKttWLuKaERERERGVbIKox/54jx8/hrOzs76K08qTJ09QuXLhA4Po6GhUq1ZN53xvvn29evXCrl27tMrr7e2NNWvWoHHjxhrT3rt3D/369cO5c+dUpnFycsK6devQtm1brY6fWwdA9QKbRiWKgCCoTfLw9UN0XdMVSRlJAAA/Nz+s7rsagoZ8REREREQlQVHdn+u1K6mXlxf+97//4eXLl/osVqmXL1/iq6++gpeXl8GPpYtff/0VK1euxNixY9GsWTNUq1YNdnZ2MDU1Rbly5VCvXj2MGjUKe/fuxaVLl7QKCgGgWrVqCAsLw+rVq9G5c2dUqVIF5ubmqFChApo3b46ffvoJ165d0ykoLFaeHAYONAbSnqtN5mLvgmlB06TXofdDsf7iekPXjoiIiIjorabXFkMTExMIggBra2uMGTMG48ePh4eHh76KBwDcuXMHixYtwp9//onk5GQAQHZ2tl6PUZoUeYthdjoQ+RUQNTfntXM3IGC32pZDURQxbuc4/HvnXwCApakl9g7fi2pldW/xJSIiIiIqTt6KFsNJkyZBJpMhKSkJ8+bNQ82aNdGmTRssXboUz549K3C5z549w9KlS9GmTRt4eXlhwYIFSEpKgkwmw+TJk/V4BmRUr68BB5v9FxQCwOO9wO0/1GYTBAHfd/ge5azKAQDSstLw+b7PkSXPMmRtiYiIiIjeWnptMQSAK1eu4IsvvsCBAwdyDpCn5adWrVrw9/dH/fr1UatWLbi4uMDR0RFlypSBKIpITU1FXFwcHj58iBs3biAyMhKhoaGIioqSysitbufOnTFnzhwpoqaCKbIWw9iTwNF2QHZa/n0yS6DTecC+ttoiDt46iA92fSC9/tTvU3zQ/AM1OYiIiIiIireiuj/Xe2CY6+TJk/jpp5+wb98+KZgr6AQhefN37doVX375JVq0aKG3upZmRRYYZqfntBa+ilS6O9POB2adwwCZhdpiPtv3GXZc2wEAaObSDGv7r4XMRKb36hIRERERGcNb0ZU0r1atWmHPnj24ceMGpk6dCldXV4iiWKCHq6srvvzyS9y4cQO7d+9mUPgWSJObYl7KdKTJzZXuN0uIxImtY5GWqX786DdtvoGrgys+9/+cQSERERERUQEZrMVQmcuXL+Pw4cMICwvDpUuXcP/+faSlKXYltLS0hLu7O+rXr4+mTZuiffv2qFevnrGqWOoUxS8SaZnZGL4yHGH34jHE8R98V2WJyrTfpSzAZ8MnwNJMdcCXnpUOC1P1LYtERERERCVBUbUYGnUV9Hr16uUL8l6/fi3NLmptbQ17e3tjVomKwMw9VxF2Lx4AsO5FFwTZnkVbuwilaUebzcTPuxtiWh9/leUxKCQiIiIiKhyDdSXVlr29PZydneHs7MygsBR4npiGrece5tki4IsHkxGb6aA0vZNZPJrFTkFsgpJJatQIjQ7F08SnBa8oEREREVEpUuSBIZUuWyIeIDNbsffyi2wHfP7wI5V5OtidxuWQeVqVnyXPwq+hv2LE1hH46J+PuIQFEREREZEWGBiSUeV2IX1TcGITrIrrrjJfq1ffAgk3NZYffDcYi88shggREQ8jMP/k/IJWlYiIiIio1GBgSEaVlK66Be+nJyNwI81V6T4LIQ04NRiQZ6otv61HW/Sq00t6vSRsCU7cO1GguhIRERERlRYMDMmobCxUz3eULlpgcsznSJerSBN/Frg8Q235giDg23bfonq56tK2T/d9yvGGRERERERqMDAko2pWrZza/VFp1TD76UjVCa7+CDxX3wJobW6N37v/Ls1WGp8aj4//+ZjjDYmIiIiIVGBgSEbV37cqzGSC2jSr4rrjRGJDFXtF4NQQIOOV2jJqVqiJGW1nSK/DH4ZjwakFulWWiIiIiKiUYGBIRlXR1hJ9G7uoTSPCBJ8++BjxWXbKE6Q8AMLHAaKofP//61e3H3rW7im9XnKmcOMNYxPT8fvRWxi6IgzvLD6JoSvCsOjYbcQmphe4TCIiIiKi4kAQRQ131/RW8/b2BgBcvXrVaMdMy8zG8JXhKmcozdXe7gyWu3+nOkGLv4BqQ9WWkZyRjF7reuFu/F0AQDmrctgzbA+cbJ10qu/MPVex9dzDfEttAICZTEDfxlUxvXsdWJrJtC6XiIiIiOhNRXF/DrDFkIqApZkMa95rioFNXVV2KzWTCShfqz+yqo9RXVDEBCDprtpjWZtbY2H3hQrjDWefmK11XXOD2I3h+ddfzJWZLWJjeAyGrwxHWma21mUTERERERUXbDEs5YrqF4lcsYnp2HL2Ac7cfYGk9CzYWJiieXVH9G9SFRVsLYCsZOBAYyDhhvICyrcA2p0ATFTPdgoAmy5twteHvkY7z3aY02kO7C3ttarfl9svYWP4A63PZ2BTV/zYu57W6YmIiIiI8iqq+3MGhqVcUQeGWok/BxxsDogqZhWtNwOoN11tEaIoIvheMAKrBUIQ1E9+k+t5Yhpa/XRUZUuhMmYyAaemts0JaomIiIiIdMSupESqlGsM+KgZa3hlFhB7Wm0RgiAgqHqQ1kEhAGyJUN19VJXMbBFbzmrfwkhEREREVBwwMKSSodZnQMVA5fvEbODUYCAzQediHyc8Vrm+oabJcVQ5c/dFgfIRERERERUVBoZUMpjIcmYhNXNQvj/5HnB2kk5FHrx1EF3WdMHCUwuV7k9KV9F1VYOC5iMiIiIiKirqZ+wwsKSkJNy/fx8JCQnIzMzUOl/r1q0NWCsqtqyrAs2WAaH9le+/twZw7gy4vauxqF3XduGTfZ8AABadWQRfF1/4ufsppLGxKNifR0HzEREREREVFaPfwSYmJmLBggXYuHEjbty4AV3nvhEEAVlZbJEptVz7AdVHAHdXK98fPi5nplJrV7XFtPVsC/ey7oh+GQ0RIj765yPsHLITLvYuUppm1coh5FaczlVsXt1R5zxEREREREXJqF1Jz58/j9q1a2P69OmIioqCXC6HKIo6P+jtlJieiOdJzzUnbPwbYFNd+b7MV8DpYYBc/XqCNuY2+L3779L6hi9TX2LcznFIzUyV0vT3rapynUVVzGQC+jepqlMeIiIiIqKiZrQWwxcvXqBjx4548eK/iTlMTU3h4eEBR0dHmJmZGasqVAxFPonEpD2T4GTrhPXvroepunUJzWyBluuBw345E8+86flx4PrPgPdUtcesXbE2vm//PT7b/xkA4HrsdUw9OBXzu86HIAioaGuJvo1ddFrHsG/jqlyqgoiIiIhKHKMFhr/++itevHgBQRBga2uLH374AcOGDYONjY2xqkDF1K24W+i/sT+y5Fl4mPAQv536DZ/4faI+U/nmQN3pwOVvlO+/NA2o6A9UaKW2mHe838G159ew8txKAMDeqL3wruiNMU3HAACmd/fG3dhkrWYobVatHKZ3r6MxHRERERFRcWO0rqT79u0DkDNGcO/evfjggw8YFBIAoEb5GuhWq5v0evGZxTgVc0pzRu8vVQd+YlbOJDWpzzQWMyVgClq4tpBe/xzyM07cOwEAsDSTYc17TTGwqavKbqVmMgEDm7pizXtNYWkm01xvIiIiIqJiRhCNNGjP3t4eSUlJ8PPzw/Hjx41xSNKCt7c3AODq1atFWo+kjCT0XNsT0S+jAQAVrCtg77C9KG9dXkPGaGC/j+o1DCsGAm0OA+q6pgKIT4nHO+vewcOEhwAAOws77BiyA+5l3aU0sYnp2HL2Ac7cfYGk9CzYWJiieXVH9G/C7qNEREREpB9FdX9utBZDuVwOAKhVq5axDkkliI25DX7r9hvMZeYAgNjkWHy+/3PIRbmGjO5Ak8Wq9z8PBiK/1nj8cmXK4Y9ef8DS1BIAkJCegM/3f64w2VEFWwtMCPLE2lHNsOODVlg7qhkmBHkyKCQiIiKiEs9ogWHVqjkzNaampmpISaWVdyVvTA34b8KYE9En8GfEn5ozVhsMeLyvev/1OcCDHRqLqV2xNmZ3mp1TZNlq+KnjTxAE3WYlJSIiIiIqiYw2+UyHDh0QFRWFs2fPGuuQVAINazgMp2JO4d/b/wIAfg39FU2rNkWDyg3UZ2zyG/DyPBB/Tvn+08MBe2/AzgtATrfQzRExCLsX/0a30Pb4pfMvaOfZDrYWtno8MyIiIiKi4stoYwxv3rwJHx8fZGRk4MCBA2jfvr0xDksaFJcxhnm9Sn2Fbn91w5PEJwAAFzsX7Bm2B3aWduozJkUDBxoDGSpmELWvi7Q2JzFzfzS2nnuIzOz8H30zmYC+jatievc6nEiGiIiIiIzurR9j6OXlhXnz5kEURQwdOrRYBSJUvDhYOWB+1/mQCTmB2cOEh/j6kOZxgrBxz1nfECq6f76+grNb+mFjeIzSoBAAMrNFbAyPwfCV4UjL/G+NxMT0RMS8itHxTIiIiIiISgajBYYAMG7cOKxatQqvX7+Gr68vPv74Y5w8eRIJCSpmlKRSq4lLE0xuNRkAUM6qHPrU7aNdRudOQL3pKnf7mR3CUMd/NBYTdi8eM/dcAwDceXEHvdf3xqjto5CYnqhdPYiIiIiIShCjdSWVyRS75YmiWKCJPQRBQFZWlr6qVeoVx66kubLl2fg19FcMbzQclWwqaZ9RlAPB3YAn+5XuzpCb4t27P+FCivoZcs1kAv6Z3BD9N3eWAsKg6kFY9s4ymAhG/U2FiIiIiEqJt74raW78mftv3qBQFEWdHlQ6yExk+KL1F7oFhQAgmAAt1wHW7kp3m5tkYbHrj3CUvVJbTGa2iMNXkzGy0Uhp27G7xzD/5Hzd6kNEREREVMwZtdkjb3DIQI8KSqvPjEU5wH8rYKJ8jcHK5i/wm+scyJCtdH+uM3dfYGLLiWjn0U7atujMIhy4eUCnOhMRERERFWdGXeBeH4/sbPU38vR2i0uOw8htI7H7+m7Nics1Bpr8rnJ3K9tL+MRpndoiktKzYCKY4Jcuv8CznKe0/fP9n+NG7A2t601EREREVJxxoBSVGBceX0C3v7ohJDoE0w5PQ/TLaM2ZPEcD1d9TuXtCxb/R3u6Myv02FjlLfdpa2OKPXn9IaxumZKZg9PbReJ70XKdzICIiIiIqjhgYUolRvkx5pGWlAQCSMpIwee9kpGela87Y5Hc8N62jcvevVefCzfyx0n3NqztKz6uVq4Z5XedB+P/lMB4nPsboHaORnJGsw1kQERERERU/DAypxKjqUBU/dPhBen3l2RX8EvKL5oymVjAJ2IZXWTZKd9vJUvCH2w+wFNIUtpvJBPRvUlVhW1D1IPyvzf+k11efXcVHez9CtpxdnImIiIio5DItyoMnJibi9OnTOH/+POLi4pCYmAhbW1uUL18ejRo1QosWLWBra1uUVaRipkvNLjgVcwobIzcCAFaeW4kWri3QxqON2nzlK9XCaqufMCxjIkyE/JPX1LaKxg8ui/DJg0+A/28R7Nu4KirY5p+8ZkSjEXjw6gFWn18NIGem0oiHEWju2rxwJ0dEREREVESMto5hXjExMfj222+xYcMGpKer7gpoaWmJQYMGYdq0aXB1dTViDUuP4ryOoSppmWl4Z/07uBl3EwBQ1qos9gzbg8q2lTXky8bedWPQ12KlyjQ/PhmBpbF90axaOax5rykszWRK02XLs/HB7g9wMvok5nebj3ae7ZSmIyIiIiLSRVHdnxs9MNy+fTtGjRqFhIQErZYdEAQBdnZ2WLlyJd555x0j1LB0KYmBIQDciruFXut6SWMOm7o0xdr+a2Fqor4RPC0jAzHbg+AlP6V0v1wUsM58Pvr3mqAyKMyVkpGC+6/uo3bF2gU7CSIiIiKiN5SKwPDAgQPo0aMHsrOzpaCwXLlyaNq0Kdzd3WFtbY3k5GRER0cjIiICL168gCAIEEURZmZm2LNnDzp06GCs6pYKJTUwBIC/L/+NqQenSq8ntZiEya0ma86Y/gLZ+xpClvpA+X5Ta6B9KFC2gX4qSkRERESkpbc+MExJSUGNGjXw5MkTAIC7uztmz56Nd955B6am+Vt5srOzsWPHDkyZMgX37t0DAFSpUgU3b96ElZWVMapcKpTkwFAURXz8z8fYE7UHAGAimGBtv7XajfV7cRb41x/ITlO+v0xVoGM4YOWkU52y5dn47dRv6Fu3L6o6VNWcgYiIiIgoj6K6PzfarKSrVq3CkydPIAgCmjRpgvPnz6Nfv35Kg0IAkMlk6Nu3L86fP48mTZoAAB4/foxVq1YZq8pUzAmCgFntZ8HV4b/xp7njDjVybAI0U/NZSnkAnOilOnBUIjUzFRN2T8DvZ37HqO2j8DrttdZ5iYiIiIiKktECw7179wLICfg2btwIBwcHrfLZ29tjw4YNkMlyxnvt2bPHUFWkEsjWwhYLui2Aq4MrNry7AcMaDdM+s/sAoO501ftfhAFn3gO0bFQ/ef8kDt8+DAC4E38H43eN126dRSIiIiKiIma0wPDKlSsQBAF+fn7w8PDQKa+npydat24NURRx5coVA9WQSqr6TvVx+L3D8HXx1T1zvW8A1/6q99/fCFz9Xqui2nm2w+SW/41xDHsQhq8OfaXVJEtEREREREXJaIFhXFwcgJwgryByg8nccojy0jQbqUqCCdB8FVCuieo0l6YBMX9rVdzEFhPxTp3/Zs/deW0nFpxaULC6EREREREZidECw9wJY5KSkgqUPzcfJ54hbWRkZ+C7Y9/h+vPrmhOblgFa7wKsqqhOc3p4zoQ1GgiCgB86/oDmVf+bAGfh6YXYemWrNtUmIiIiIioSRgsMnZ2dIYoiQkJCdM4riiJCQ0MhCAKcnZ0NUDt6m8Qlx2HY38Ow6twqjNs5Di9TX2rOVMYZCNgNyFT88JCdCpzoCaQ80liUucwci3suhme5/1rHvz70NU7eP6ntKRARERERGZXRAsOgoCAAOTOL/v777zrlXbJkCR4+fAgACAwM1HfV6C0TFRuFsw9zWvceJjzExD0TkSXP0pyxXCOgxVrV+1Mf5wSHWSkai7K3tMeKPivgWMYRAJAlz8IHuz7AjdgbWp0DEREREZExGS0wHDJkiPT8k08+wR9//KFVvuXLl+Pjjz+WXg8dOlTvdaO3i5+7Hz73/1x6fTrmNGYfn61dZtc+QP3vVO+PP5fTrVSUayzKxd4Ff77zJyxNLQEASRlJeG/be3ia+FS7uhARERERGYnRAsNmzZqhf//+EEURWVlZmDBhAho3boyFCxfi3LlziIuLQ2pqKuLi4nD+/Hn8/vvv8PX1xbhx45CZmQlBENC/f380a9bMWFWmEmxM0zHoWrOr9HrluZXYfnW7dpm9vwLcB6ve/2ArcHmGVkXVr1wf87vNhwABANDEpQnKlSmnXT2IiIiIiIxEEI04l35KSgqCgoIQEREBQRC0ypNbvaZNm+Lo0aMoU6aMIatY6nh7ewMArl69WsQ10b+UjBT039gf12NzJqAxl5lj88DNqO9UX3Pm7DTg3yDgxRnVaVquB9wHaVWXjZEbcf35dUxvOx0yE5lWeYiIiIio9Cmq+3OjtRgCQJkyZRAcHIxx48ZBEASIoqjxYWJigvHjx+PYsWMMCkknZczL4I9ef6CsVVkAOTOVjt81HnHJWix5IrMEWu8EylRVnebMe0CcmsAxj4E+A/Ft+28ZFBIRERFRsWTUwBDIWW5i8eLFuHnzJqZMmQJfX1+YmZkppDEzM4Ovry+mTJmCmzdvYtGiRVymggrExd4Fv3X/DTIhJyB7mvgUE3ZPQEZ2hubMVpWAgD2AqbXy/fJ04Hh3IOFmget36ckl7epCRERERGRARu1Kqs7r16+RlJQEGxsb2NvbF3V1So23uStpXqvOrcJ3x/6bVGawz2B82/5b7TI/3A2c6AVAxZ+KtTvQ4RRgVTnfrtjEdGyOiEHYvXgkpWfBxsIUzas7on+Tqjj/JBgf/fMR2nm0w/xu89maSERERERFdn9uatSjqWFvb8+AkAxmRKMRuPb8mjQBTUpmCrLl2doFYy49gAazgYtfKN+fHA0c6wy0Ow6Y53yG0zKzMXPPVWw99xCZ2YoBZcitOMwL3gPYLoYIOfbd3Afbf23xffvvtR57S0RERESkT8UmMCQyJEEQ8F3773Dv5T10rdkVIxqN0C0Iq/0ZkHANuLta+f5XkTmtikEHkCY3xfCV4Qi7F6+yuKz06jCxqAnBPGdinM2XNsPewh5TAqZoXyciIiIiIj1hYEilhoWpBTYP2FywLpuCAPguBVIeAk//VZ7meTBweii+ffal2qAwhynkCaNgYr8YgtldAMCyiGWwt7THuGbjdK8fEREREVEh6DUwjImJUXjt6uqqcl9h5C2XSBeFGscnMwf8twNHgnIWulcm5m/UfpEKYCwATS2S5pAnjIHMfiFg+ggA8HPIz7CztMMgH+2WwSAiIiIi0ge9Bobu7u5S9zxBEJCVlaV0X2G8WS5RYW27sg3nHp/TboyfmS0QuA841BJIuqM0yVDHvXiaWRaLnr+r+eBiGWS/Hg9H5yV4lZ4THH5z+BvYWtiie63uup4KEREREVGBGGS5itw1CNXtK8yDSB+y5FmYdXQWvjjwBTZf2ozfTv2mXUbLikDQwZx/VfjcaS36lz2kXXmiHapbfAEnW6eclxDx2b7PcOzuMe3yExEREREVkl5bDF1dXVW2uKjbR1QU5KIc159fl17/dvo3VLGvgr51+2rObOsBBO4H/g0AspKUJvnR5Xe8yLLHkcRmGovLyiqLv979CwM2DUB8ajyy5FmYsHsC1vRdA18XX63PiYiIiIioIPQaGEZHRxdoX3EgiiLu3r2L8+fP48KFC9Lj2bNnCml0tXPnTqxfvx5nz57F06dPYW1tDTc3N3Tv3h1jxoyBs7Oz1mVlZ2djw4YN2LRpEy5duoTnz5/DwcEBnp6e6NWrF95//304ODjoXMfSylxmjiW9lqDfhn64E5/TLfTrQ1+jsm1ltHJrpbmAco2A1juA4C6APDPfbpkgxyK32Rh093ucT6mttigbC1N4OHpgZZ+VGLJlCJIykmBrbgtbC9sCnRsRERERkS6KzQL3Re3TTz/F3Llz1abR5a16+fIlBg4ciIMHD6pMY29vj2XLlqF///4ay7t//z769euHiIgIlWmcnZ2xbt06BAUFaV3P0rLAvToPXj1A3w19EZcSBwCwMbfBloFbULNCTe0KiN4EnBqocverLBv0vTMHt9NVT5r0eceamBDkCQAIexCGaYenYdk7y+Be1l3r8yAiIiKikq+o7s8NMsawJMrOzlZ4bWFhgcaNGxeorLS0NHTr1k0KCitUqIAvv/wS69evx5IlS9ChQwcAwOvXrzFo0CDs2bNHbXlxcXHo0KGDFBS6urri22+/xcaNG7FgwQI0a5bTVfHx48fo3r272uCR8qvqUBXLey+HlakVACApIwnvbX8PTxOfaleA+wCg8QKVux1Mk/BXtW/gZBandL+ZTED/JlWl182qNsO+EfsYFBIRERGR0RitxfDEiRMAgCpVqsDDw0Pn/Pfu3cODBw8AAK1bt9Zr3QBg2bJluHbtGho2bIiGDRuiTp06MDU1VRgXqe1bNWvWLHzzzTcAAC8vLxw7dixfl9Fff/0Vn332GQCgYsWKuHXrFuzs7JSWN2rUKKxcuRIA0KpVK+zbt08hrSiKmDx5MhYuXAgg51eGyMhIyGSal2Zgi+F/jtw5gnE7x0EuygEAtSvUxqaBm2BjbqNdARe/BK79pHL3jTRX9LszBwnZiuUNbOqKH3vX01h8fEo8HiY8RH2n+trVh4iIiIhKnKK6PzdaYGhiYgJBEDBhwgT89puWsz/m8fnnn2Pu3LlGX65C18AwISEBzs7OSE5OBgCEhYWhadOmStN26dIF+/fvBwDMmDED06dPz5fm1q1bqFWrFuRyOSwsLHDz5k2l6zhmZWWhUaNGuHz5MgBg9erVGD58uMb6MjBUtPbCWsw4MkN63dq9NZa9swxmMjPNmUURCHsPuLtaZZKI5DoYcncW0kULAECzauWw5r2msDRTH8S/TH2JIVuG4OHrh1jRZwWaVGmizekQERERUQnDrqRaKAnLVezatUsKCv38/FQGhUDOuMZcGzZsUJpm06ZNkMtzWrD69eunNCgEAFNTU0yePFl6vX79ep3rTsDQhkMxqsko6fWJ6BNYcEp1N1EFggA0XQY4d1GZxNf6Gha5/YQyplkY2NRVq6BQFEW8v/19RMVGISkjCSO3jkT4g3Dt6kREREREpIUSFRiWBPv27ZOed+3aVW3agIAAWFtbAwBu3ryJW7duFaq8Ll3+C0iOHTuG1NRUrepMiqYGTEVnr84AgLqV6mJ4I80trxITM8BvC+CoeomKdnYRuNB2DX7sVVtjUAjktFpPbDkR5jJzAEBKZgre2/YezsSc0b5eRERERERqlJjAMCUlBQBgaWlZxDVR79KlS9Jzda2FQE4rX8OGDZXmBXJaiq5cuaJ1eZUrV4aLiwuAnK6l165d07re9B8TwQS/dP4F45uNx8Z3N6KCdQXdCjC1BgL2AnaqZzW1eLIDOD0UkGerTJNXQLUALHtnGSxMc7qgpmalYtT2UTh5/6RudSMiIiIiUqLEBIbnz58HAJQvX76Ia6KaKIoKrX7VqlXTmCdvmqioKIV9jx49QlJSzuLpMpkMVatWhSbqyiPtWZpZ4jP/z1DGvEwBCygPBB0ErNSsU3l/ExA2Cvj/yW408Xf3x/J3lsPSNOfHkbSsNLy/432ERIcUrI5ERERERP9Prwvc54qJiVG5LzExUe3+vDIzM/Ho0SP8/fffCAsLgyAIaNCggZ5qqX9JSUnIzPxvoXNtgti8aV69eqWw7+XLl9Jze3t7mJlpngBFXXlUeI9eP0J6djqql6uuObG1GxB0ADjcGsh8pTzNvTWAiTnQ9A9AyPmdJjYxHZsjYhB2Lx5J6VmwsTBF8+qO6N+kKlq5tcKK3iswevtopGalIj0rHWN2jMEfvf5AQLUA/Z0oEREREZUqBgkM3d3dFWbzzCWKIv766y/89ddfBS574EDVC4kXtcTERIXXVlZWGvPkTZOQkKCyPG3K0lSeKnfu3JFmP3oTZyv9z5VnVzB6+2hYyCywbfA2lLfWovXaoR7Q5hBwtB2QqeL/485ywMQcaT7zMXPvNWw99xCZ2YqTLIXcisP8f2+ib+OqmN7dFyv7rMSo7aOQkpmCjOwMjNs5Dot7LEaQR5AezpSIiIiIDE3V/fedO3cKtLxfYRm0K2nuLKJ5ZxLNu02XB5ATFA4YMMCQVSZS6kXKCwzePBixybF4mPAQo7ePRkpGinaZHX2BwP2AqZr1EG8twr8bhmBjeEy+oDBXZraIjeExGL4yHPWdGmNV31WwNsuZvCgjOwMf7P4A91/d1/XUiIiIiIgM02Lo6uqar8Xw/v37EAQBNjY2KFeunMYyBEGApaUlHB0dUbduXfT5P/buO77Jan/g+OfJ6t6DttBCC2XvUUSRKagoDgQUVEQU53Xr1avXn6Je9brXVdwiCsoQFBcgoILsjexR2gKF7t2kafL8/ggNhHQk6aDj+/aVV5LznOc8J2kt+eac8z3XXccll1xSH92tMwEBAQ7PS0tLncrOdXbm0HM3uD/7XFczjFbXXlXat28vI4M1CPMN475B9/HSHy8BsOvULu794V4+vPZDe7bQakVcCMN+glWXg6XygPJKw7ekRll45eQtgPOIe4UNyTnMWLKHl8b154sJX3DrglspKivigQsfoG1wW09enhBCCCGEaGBVff6uaiSxvtVLYHj06FGnMo3GNjh5yy23eLTBfVPg7++PTqejvLwcgKysrBoDw6ysLPvj4OBgh2NnP8/Pz6e8vBydrvofWXXtidq5rf9tHMs/xuztswHbHoeP/PQIb135FlpNzdtOEDkEhv4Af1wJFmOlVe6JXECZquetUzdW29SCLWk8PKojfWP68uWEL9l4bCPTB0x3+zUJIYQQQggBDZyVtLFvTl9biqLQsWNH+/Pk5OQazzm7TufOnR2OtWnTBn9/2/RDi8XiUtKe6toTtaMoCk+PeNq+xyHAzwd+5qllT2F1MbMoUSPh4kW2hDNVeLDVXO6JmFdtM2aLyrzNaQD0iu4lQaEQQgghhKiVBgsMk5OTSU5OZsaMGQ11yfOiZ8+e9scbN26stm55eTnbtm2r9FywBSLdu3d3ub309HSOHTsG2La36Nq1q8v9Fq7RarS8ccUbDGk3xF42/+/5/GfVf1z/4iPmMhi8AJSqR3//Gf0lt4d/V20z649kV3t85oaZ/LD3B9f6JIQQQgghWrQGCwzbtm1L27ZtCQkJaahLnhdjxoyxP/7555+rrfvHH39QXFwMQGJiIomJibVq7+zjw4cPdzmTqXCPQWvg/avfZ0CbAfayL7Z+wdtr33a9kTZjYfC3oFQ9BfXfMZ9xS9iSKo8XmcqrPPbhxg95dfWrPPLzIyzavcj1fgkhhBBCiBapyWxw31RcddVV+PnZMkWuXr262lG+119/3f548uTJlda5/vrr7esz582bR1paWqX1ysvLefvtM4HJjTdWv0ZN1I6P3oePr/2Y7q3OjOi+u+5dPt38qeuNxI6DQV9hVav+33BG6w+ZHPpLpcf8vSofccwtzeWTTZ8AYFWtPPbLY8zfNd/1fgkhhBBCiBZHAsM6FhQUxGOPPWZ/PmXKFE6cOOFU7/XXX+eXX2wf+MPDw3nooYcqba9jx45MmTIFAJPJxOTJk532J1RVlUceeYRdu3YB0KVLF2666aY6eT2iagFeAXx+3eckhtlGeg1ag/tZQdvdwIrgl7CqVWchfbHN/7g+dKlT+QUJYZXWD/EJ4evrvybM13ZcReWJpU/w4YYPm/06XyGEEEII4RlFPY+fFIuKitixYwdZWVkUFhZitbqWwKMiUKpLeXl5vPbaa07l//nPf+yPn3rqKafjL7zwglNZaWkpI0aMYP369QBEREQwffp0unfvTkFBAYsWLWLpUtsHfa1Wy4IFC7jmmmuq7FtmZiYXXnghhw4dAmzTcm+//XY6dOhAZmYmc+bMsV/L19eXlStXMnDgQJded0U6XNmuwnOnik4xbeE0nhz2JBe1vcjt8zMKjbw183FebF19tt7nT9zOp1nXAKDXKqx9YiQRAV5V1j+UfYib5t1EZnGmvWxq36k8NfwpNIp8JySEEEII0Ridr8/n5yUwnDNnDu+++y6bNm1yewRDURT7dhB16ejRo8THx7t9XlX9z8nJ4YYbbmD58uVVnhsYGMjMmTOZNGlSjddJTk5mwoQJbNmypco6UVFRfPXVV4wcObLmjp8mgWHdsKrWWgVb//puJ9rDM3mh9QfV1nvr1CTeOjWZSUlteWlcjxrbTc1L5ZYFt5Cadyaj7RWdruDVy1/FS1d1UCmEEEIIIc6PFhEYlpaWMnHiRHuSlOourShKpccVRcFisdR53+o6MKywaNEivv76azZt2sSpU6fw8/Ojbdu2XHnlldx55520bt3a5WtZLBa++uorvv32W3bu3ElmZiZBQUG0b9+ea665hjvuuMPt5D4SGNYfi9XCkZwjJIY7JxU6l9Fs4ZbPNtKt8HP+L+bjauv+VHY9Iyd/hbfBtW1Is4qzuO272/j71N/2sgvjLuT9q98nwKv6fTaFEEIIIUTDahGB4eTJk/nmm28A8Pb2Zvjw4SQnJ7Nv3z4URWHKlCkUFhaSkpLCzp07MZvNKIqCn58f48aNQ1Fs67A+//zzhupysyeBYf0os5TxyE+P8EfyH8yeOJte0b1qPMdotjBjyR5CU97gsVZfVF85YRokfQSaqrOanq2orIh7v7+XNSlr7GXdIrvx2XWfEe4X7lIbQgghhBCi/p2vz+cNttBow4YNfPPNNyiKQmJiInv37uWnn35ymPb4+eefs2DBAjZt2kRmZiavvvoq/v7+FBcXk5GRwTvvvCNBoWgSHv7pYX4+8DPF5mJuXXgr+zP313iOt17LS+N6MPX2mawJ+Ff1lY98BmsngaXMpf74G/z5eNzHjO081l6m1+rx1fu6dL4QQgghhGjeGiwwnDVrlv3xZ599Rtu21WdvDAwM5JFHHmHz5s1ER0ezdOlSbr311vruphB1YkKPCeg1egDyjflMmT+F5Nxkl86NCPBi8NgXIeljoOpspaTOhz+vgfISl9o1aA28ccUbTOs3jYTQBD6+9mN8DRIYCiGEEEKIBgwM16yxTWFr3749F13keubGxMREZs2ahaqqLFq0iJ9++qm+uihEnRkaP5S3rnzLnpAmqySLKfOmcKLAeeuSKnW4HS6aC0o1awnTf4FVl4G5oOo6Z9EoGp4c9iTf3fgdob6hrvdFCCGEEEI0aw0WGJ44cQJFUejTp49DecW6QYCyssqnxY0cOdI+1/arr76qv04KUYcu63gZL136kv35icIT3DjvRveCw7bXw5DvQetddZ3M1bBiBBizXGpSUZRKk87sPLmTJfuWuN43IYQQQgjRbDRYYFixKXtYmOOm3D4+Pk51KtO3b19UVa12uwYhGpvx3cfz9Iin7c9T81KZ/O1kjuUfc72R1mNg2K+gqyaDaM4WWDEUSo571M/k3GRuW3gbD/74IJ9vkXW8QgghhBAtTYMFhr6+trVMZrPZoTw4ONj+OCUlpcrzK5Knpqen133nhKhHU/tO5fEhj9ufp+WnMembSQ57C9ao1VAYuRIM1Uz/zN8Dyy+GoiNu9U9VVR79+VFySnMAeGHVC/z3j/+6vceoEEIIIYRouhosMIyNjQUgOzvbobxjx472x+vWravy/D179tRPx4RoAHck3cFTw56yPz9ReIJJ307iZOFJ1xsJ6w+X/Ak+0VXXKU6G5YMhz/X0xoqi8Nrlr9EmsI297KNNH/HYL49htpirOVMIIYQQQjQXDRYY9ujRA1VV2b/fMW1/UlKSfZ3hRx99RHl5udO5y5YtY+vWrSiKQkJCQoP0V4i6Nq3/NJ4Z8Yz9+cA2A4nwi3CvkeBuMGoN+MVXXac03TatNHuzy83Gh8Yzf/J8Okd0tpct2rOI27+7nQKja4lthBBCCCFE09VggeGQIUMA2L9/Pzk5Ofby2NhYBg8ejKqq7N69m6uvvppt27ZhNpvJz89n9uzZ3Hjjjfb6Y8eOdWpbiKZiSt8pPH/J81zZ+UpeufwVtC5uUO/APwFGrYbALlXXMWXbEtJk/Olys5H+kXxzwzcMjB1oL1uTsobr5lzn8lYbQgghhBCiaVLUBlpIlJqaSny8bZTj888/Z8qUKfZj69atY/DgwdWer6oqERER7N69m/Dw8Hrta0tSke11927Xpx6K2lNV1SEjr0eMWfD7ZbbEM1XResPghbYENi4ylZt49JdH+Xn/z/ayIO8g3rvqPS6Mu7A2PRZCCCGEEDU4X5/PG2zEMC4ujocffpiJEyeSmZnpcGzQoEF8/PHHaLVaVFWt9BYREcH3338vQaFoFioLCn879Bt7M/a63oh3OIxYAREXV13HYoQ/r4aUeS4366Xz4u0r3+b+Qffby/KN+UydP5Xvdn/nev+EEEIIIUST0WAjhq7Yu3cvb775JitXruTEiRNoNBoSEhIYO3YsDz74IBERbq7HEjWSEcPGYeXhldzz/T34GfyYNWEW3Vt1d/3k8hJYfR2k/1p1HUUDA2ZCh+lu9eunfT/x2K+PYSo34a3z5psbvqFHVA+32hBCCCGEEK47X5/PG1VgKBqeBIbnX3ZJNkM/GkppeSkAgV6BzBo/i57RPV1vxFIG626C1PnV1+v6L+j1gi1QdNHOkzu5e/HdPDnsSa7ofIXrfRJCCCGEEG5r9lNJhRCVC/MN47Uxr6HT6AAoMBVw8/yb2Xpiq+uNaA1w4Vxof1v19fa8BH9NgtNBqCt6RvVk+bTllQaF8r2SEEIIIUTz0GCB4bhx4xg3bhzXX3+90yb3QrR0l3W8jPeueg+9Rg9AUVkRU+dPZfMx17ecQKOFpI+h00PV10udBytHgjGz+npn8TX4OpWVmku5ZcEtrD662vU+CiGEEEKIRqnBAsPFixfz/fffk5ubi16vb6jLCtFkjOowivevfh+D1gBAsbmYWxfeytrUta43oijQ93Xo8Wz19bLWwbILoGB/9fWqoKoq//z1n/yV8he3LbyNWVtnyeihEEIIIUQT1mCBYUhICADt2rVrqEsK0eSMaD+CmdfMtAeHJeYSblt4Gz/t+8n1RhQFejwD/d6tfi1h0RFYNghO/e52Pw9lH2LV4VUAWFQLz618jqd/exqzRWYDCCGEEEI0RQ0WGMbExABQUlLSUJcUokkaGj+UT8Z9grfOG4AySxkP/PgAn2/53L2GOv0DhnwPOr+q65TlwqrRcORLt5pODE/k20nfEhUQZS+bu2Mutyy4hdzSXPf62QRlFpp4b+VBbv50A9e+/xc3f7qB/606RGah6Xx3TQghhBDCIw0WGI4cORJVVdm4cWNDXVKIJuuithfx1cSvCPYOBkBF5bPNn1FoKnSvodZXwiWrwSem6jpWM6y/BXY+A25MB+3WqhuLb1pM7+je9rINaRsY99U4DmYddK+fTYTRbOFf3+3kwpdX8NqyA6w+mMW21DxWH8zi1aX7ufDlFfzru10YzZbz3VUhhBBCCLc0WGA4bdo0tFothw8fZsmSJQ11WSGarD4xfZg3aR6tA1sT7B3M5+M/J8ArwP2GQvvApRsguFf19f5+DtbeBBbXR70i/CKYc/0cru5ytb0sNT+V8XPG8/uR393vayNmNFu45bONzN2YhtlSeQBttqjM3ZjKLZ9tlOBQCCGEEE1KgwWGPXv25KmnnkJVVW655Rb++uuvhrq0EE1W+7D2zJ88n8+v+5wOYR08b8i3DYxaDTFjqq+XMgdWXgLGLJeb9tJ58fqY13ns4sdQUABbVtXpi6bz/vr3sapWz/vdiMxYspsNyTku1d2QnMOMJXvquUdCCCGEEHWnQfcxfPbZZ3nllVcoKipi2LBhTJ48me+//55jx45hMsnaHCEq08q/VaWb3acXppNV7HoAhz7AtuYw8Z7q62WusSWlKXB9OqiiKNw18C4+uOYDfPW2rS2sqpXX17zOluNbXO9jI5VRaGTBlmNunbNgS5qsORRCCCFEk9FggaFWq0Wr1fL4449jsViwWCx8++23jBs3jrZt2+Lr62uvU91Np9M1VJeFaLTyjfncuuBWJsyZQHJususnanTQ/z3o+yacHt2rVNEh23YWGe7tUTiqwyjmT55Pm8A2ANzW7zYGtBngVhuN0bxNVU8frYrZojJvc1o99UgIIYQQom41WGB47h5niqI4HHPnJkRLZraYuXPxnRzMPkhqfioT50xkZ/pO1xtQFOj8IAxZBFrnjevtynJs00rdzFjaOaIzP0z5gbsH3s1jQx5z69zGytUppOdafyS7jnsihBBCCFE/GnQqqQR6QtSeTqNjZPuR9uc5pTlM/nay+8le2lwNl/wB3lFV17GW2TKWbr4fLGUuNx3kHcSjFz+KXqt3KFdVlVlbZ1FqLnWvr+dZkam8Qc8TQgghhGhoDRYYWq3WOrlZLJLpT7RsiqIwfcB03hjzBjqNbWp1aXkpdyy6gwV/L3CvsbD+toylQd2rr3fgXVg5AkrTPey1zaebP+W5lc8x7utxJOe4MQX2PPP38mwKu6fnCSGEEEI0tAYdMRRC1J2ru17Np+M+xU9v28Deolp4/NfHeX/9++6NxPvFwei/IGp09fUy/4Jf+truPbDr5C5e+fMVAA5kHeCar67hl/2/eNRWQxsYH+rReRckhNVxT4QQQggh6oeiylzOFq1bt24A7N69+zz3RHjq71N/M23hNLJLzqxnu67rJNp43cSmo/kUmcrx99JxQUIYE/vHEhHgVXlDVjNs/gcc+qj6Cyo66PeWLbupUk0Cm3OYLWZeX/M6H2/62KH81n638s8h/8SgNbjcVkPLKDRy0csr3UpAo9cqrH1iZNXvtxBCCCFEJc7X5/MGCwz//PNPAFq3bk379u3dPj85OZm0NFuGvyFDhtRp31oyCQybh9S8VKYumEpKXoq9TDX1wlo4BTizzk+vVRjfL5ZnxnbFW691bkhVYf9bsO0xUGuYth0/BQbMBJ2PW31ddnAZj/3yGEVlRfayPjF9eHfsu0QHRLvVVkP613c7mbvR9Syjk5LieGlcj3rskRBCCCGao/P1+bzBppIOGzaM4cOH8/bbb3t0/vvvv8/w4cMZMWJEHfdMiKYvLjiOryZ8i68mwV6meO0A/QGHemaLytyNqdzy2UaM5koCP0WBzg/BiOXgFVH9RZO/hOUXQpF7awVHJ47m+5u/p0tEF3vZthPbuOrLq/gz+U+32mpIz4zt5vKU0oHxoTwztms990gIIYQQou40qTWGksVUiKq9uyKdwoy7UctsAYm1eCyYu1Vad0NyDjOW7Km6sVbD4bItEJZU/UVzt8Ov/eDEUrf62i6kHQsmL2BC9wn2spzSHG5deCv/WfUfTOWNb2N4b72WWdOSmJQUh15b+RRavVZhUlIcs6YlVT4iK4QQQgjRSEnKPCGagYxCIwu2HAO8sBZMRzFsRy3rW+05C7ak8fCojg5r4DILTXy7KZUNyTkUmcoJ9nqNR0P/R7fSb6tuqCwXfr8cer0AXZ8AxbXvm7z13rx82cv0a92PZ1Y8Yw8GP9vyGe3D2nNDzxtcaqcheeu1vDSuBw+P6si8zWmsP5Lt+hpOIYQQQohGrMkEhiUlJQB4e3uf554I0fjM25R2VmIULWpZv0pqWW1TS82dAdu00nmb07h3eAeMZgszluxmwZZjTglWVnEzk8Na8VzrD9BhrqIHKux4CrI3waBZoA90ue8TekygZ1RPHvjxAQ5mH+SC2AuY2GOiy+efDxEBXtw7vAP3Du9wvrsihBBCCFEnmsxU0q1btwIQHh5+nnsiROOzITmnxjqK749og95H8VsE2NYXrj+SjdFs4ZbPNjJ3Y1qVWTfnZI/muoMvk2WNrP4ixxbDrwMgv5ppqpXoFNGJxTctZvqA6bw25jU0Lo46CiGEEEKIulEvI4apqalVHissLKz2+NnMZjPHjx9n/vz5bNiwAUVR6N27dx31Uojmo8hUXu1xxbAVje9vAGh8VqFq07EWTqXIVM6MJbtdCix3lHbi0r1vMK/H27S3bqq6YuEBWJoEF3wBceNdfg3eem+eGPpEpcdmbphJ54jODEsY5nJ7QgghhBDCdfWyXYVGo0E5Z3+zisucW+4qVVVRFIWvv/6aG25ofGuPmirZrqJ5uPnTDaw+mFV1BU02msCPUXQn7EWqJYLuAY+y97ivW/vzeWutbL1qFb6H36y5cpfHoNeLoKn8O6hz1zRWtlZvbepapsybgorKLX1v4fEhj+Olk3V8QgghhGiemtU+hhWBYV03PXnyZL766qs6bbOlk8CweXhv5UFeW3aghlomNAFfo3htt5do8aEs/2Ywd3freo9d2ol7E7bChmlQXlx95fAL4aI54NfWXlTdmkY4s9/iU1d0ZOzsy0jNOzPLoGN4R9664i06RXRyq89CCCGEEE1BswoM27Vr5zQymJKSgqIo+Pv7Expa815giqLg7e1NWFgY3bt357rrruOSSy6p6662eBIYNg8ZhUYuenmlCyN/KorPUjR+P58pURXUkitQS0cBro3oX5wYzuzbBkLeblh9LRQerP4EfRAM/BjiJtjXNLoyfXVgfCj/viqYx5c+wv6s/fZyg9bAE0OfYEqfKR7PQhBCCCGEaIyaVWBYmYpRxHvvvZd33nmnIS4pXCCBYfPxr+92MndjmmuVDTvxCvqKctVoL7Ka+qIWTgYMNZ7eJy6YRfdcZHtSlg/rpsDxH2q+bvvp/N+xaXy5Kdu1fgKTkuJ49qqOvPLnK3yx9QuHY0PaDeHVy18l3E+SUgkhhBCieThfn88bNPWfbE4vRP15Zmw3BsbXPBoPMLD1MBZMXoCX5kyWUY3XVjTBb4FSw9RQwN/rrDWDhiAYsgh6vkCNI46HP+aWggl08T7iUj/Btt9iQSk8PeJpPrvuM8J9zwSBfx79k8u/uJyVh1e63J4QQgghhHDWYIFhcnIyycnJzJgxo6EuKUSL4q3XMmtaEpOS4tBrKw/Q9FqFSUlxzJqWRI/oLtza/T3Uso5nKlhCQfWp8VoXJIQ5Figa6P4UDPsZDCHVntve6xiLOzzClLAlQM1fFlXstwgwNH4oP93yEyMSRtiP55TmMH3RdB75+RHMlqr2WRRCCCGEENVpsKmkonGSqaTNU2ahiXmb01h/JLvKbJ9gW5t44cvLsXgvQtEfwJr3MFB9xk+9VmHtEyMd2nFQnAbrboKMP2vs5/KCJP6Z9gC5lqBq69nXNJ6mqipfb/+aF/94EVO5CYCrulzFm1e4kClVCCGEEKIRa/ZrDEXjJIGhOLM20UTlQaEF0NqfTUqK46VxPapv1GqB3S/C38+Caq226klzKA+lPsq64p5V1nFY03iWg1kHefzXxzlWcIylty4lxKf60UohhBBCiMbufH0+r5cN7l1VVFRESkoKBQUFmM2uTwEbMmRIPfZKiJblmbHdOJJZXGmWUMVrE4rPSqwFt4I1koHxoTwztmvNjWq00ONpaDUc1k6GkqqT4kTpc/g64Sn+lzGRt09NorySP0sOaxrPkhieyPzJ80nJS3EKClVVZeOxjSS1SZLMpUIIIYQQNWjwwLCwsJC3336buXPnsn//frcT0iiKQnl5eT31ToiWp2Jt4owle1iwJe3MlhfaUyj+36IoZWhDXmVgq3v4bNJleOu11Td4tsjBMGYHbJgOaQurrKZRVO5r9S0X+u/gwbRHSSuLcjjutKbxLFqNloTQBKfyJfuW8NBPDzE6cTTPXfIcEX4RrvdbCCGEEKKFadCppFu3buWqq64iPT0d8CxLqaIoWCyWuu5aiyVTScXZzl6bmFy0hHTzXIfjE3tM5JkRz+Ct93avYVWFw5+gbn4AxVpabdViizcvpk/j65zLAaXmNY2VyCrO4rIvLiO3NBeAIO8gnh7+NNd0vUZGD4UQQgjRqDX7NYbZ2dl07tyZ7Owz+5fpdDrat29PWFgYer3e5bZWrVpVH11skSQwFNVZenApT/z6BAWmAntZx/COvDv2XTqEdXC/wfw9pP98DdHqwRqrri7szRPH7mdIn/41r2k8x4mCEzz2y2OsT1vvUD48YTgvjHqBqICoKs4UQgghhDi/mn1g+OSTT/Lyyy+jKAoBAQG8+OKLTJkyBX9//4a4vKiCBIaiJsfyj3H/kvvZcXKHvcxH58Nzo55jXLdxbrdnNBbx+zdTucxQ9dTSCiWqL/r+b6LvOB3cHOmzqla+2fENL//xMsXmM3sz+hv8eWr4U0zoPkFGD4UQQgjR6DT7wLB3797s3LkTjUbD77//zuDBgxvisqIGEhgKV5RZynht9Wt8uvlTh/Lrul3HsyOfxdfg61Z7RrOFeYv/x9jSfxOiK6z5hOhLYeAn4NvGreuAbfTwyWVPsvroaofywW0H88KoF4gNjnW7TSGEEEKI+tLsA8OgoCCKiooYPHgwf/zxR0NcUrhAAkPhjhWHV/DPX/5JnjHPXpYYlsin4z6ldVBrt9vLzjhM6Z830qZsQ82V9UHQ722In+L26KGqqiz4ewH/+f0/FJrOBKJeOi8eGPQAdw68092uCyGEEELUi/P1+VzTUBeyWm17mXXu3LmhLimEqGMj249kyZQl9I3pay/TarSE+VadNbQ6YZHtaXPdWuj7Fmh9qq9szof1U+GPq6A03a3rKIrChB4T+HXqr4xIGGEvN5WbHKaZCiGEEEK0VA0WGMbG2qZrlZZWn5FQCNG4xQTGMOf6Odwx4A78Df68O/Zd97OUnk3RQOcH4PLtEH5hzfVP/Ag/dYPkr23ZTt0QFRDFR9d+xJtXvEmEXwRtAttw98C7Peu3EEIIIUQz0mCB4ejRo1FVlc2bNzfUJYUQ9USv1fP40MdZcduKSvcQPHu6pssCO8Ilf0KfV0FTw9YUZbmw7iZYPQ5KT7l1GUVRuKrLVSyftpyZ18zER+84UllmKePLrV9iKje5+wqEEEIIIZqsBgsM77nnHry8vNi/fz/Lly9vqMsKIepRuF+4U9mejD0M+WgIc3bMcX+vUo0WujwKl2+DsKSa6x9bDD93g5R57l0HCPAKoEtkF6fyz7d8zoyVMxgza4xTwhohhBBCiOaqwQLDjh078uabb6KqKjfffLMkOxGiGSoqK+K+JfdRYCrg6eVPc9f3d5FVnOV+Q0FdYNRf0Osl0Biqr2vKhr+uhz/HQckxzzp+2omCE7y37j0AjuYeZeqCqdz7/b2cKDhRq3aFEEIIIRq7BgsMAe666y4+//xz8vPzGTBgAA899BB//fUXBQUFNZ8shGj0UnJTKDCe+f/5t0O/cfkXl7P04FL3G9PooNsTcNkWCOlbc/1ji+DHLrDvLbCWu389bKOIE3pMQKOc+dP468FfGf3ZaD7c8CFlljKP2hVCCCGEaOwabLsKrVbr8FxVVY82l1YUhfJyzz70CWeyXYWoa6eKTvHYL4/xV8pfDuXXdL2GZ0Y8Q6B3oPuNWs2w+2X4+zlQXfj/P6QPJH0IYQPcvxawN2Mv//fb/7H1xFaH8oTQBJ4d+SwXtb3Io3aFEEIIIWrS7Pcx1Gg0KIpSaUDoThcURcFisdR191osCQxFfbCqVr7e/jUv//EyxnKjvTwqIIpXLnvF88Aqdzusmwp5O1yorEDHe6HnC2AIcvtSVtXKot2LePmPl8kpzXE4NqbTGJ4c9iTRAdFutyuEEEIIUZ0WERjWBQkM65YEhqI+Jeck89gvj7EtfZtD+c19bubxIY87ZQR1iaUMdr8Au18E1YW/BT7Rtn0S4yaAB7MU8o35vLnmTb7e8TVW1Wovj/KPYtX0VRi0NayBFEIIIYRwQ4vY4L4ubhIUCtF0xIfG882kb3j04kfRa/T28tnbZvP8quc9a1RrgJ7PwaUbIKR3zfVL023JaX4fA0VH3L5ckHcQz17yLItuWkSf6D728juT7pSgUAghhBDNRoMmnxFCtDw6jY67B97NopsW0Sm8EwCBXoHcP+j+2jUc2g8u3QR93wCdX83103+Fn7rZRho9SCLTvVV35k2ex8uXvszgtoOZ3HuyUx2P9m8UQgghhGgEGmwqqWicZCqpaEimchPvrH2HThGduKrLVXXXcHEabLnftq+hK4K6woCZEHlxnXWhwFjAyE9HMjxhOA8NfkjWHwohhBDCI81+jaFonCQwFI3Fn8l/suvULqYPmO75FM1jP8Dm+6Ak1bX6CdOg90vgHenZ9c7yyp+v8OHGDwHw1nlza79buTPpTgK8AmrddkPJLDTx7aZUNiTnUGQqx99LxwUJYUzsH0tEgNf57p4QQgjRIrS4wNBoNPLrr7+yZs0a0tLSyM3NxWKxsGLFCod6qqpSWloKgF6vR6/XV9ac8JAEhqIxyCvNY8ysMZwqOkXH8I68OPpF+sT0qfnEypiL4O8ZsO9N15LT6AOh21PQ6X7Qent0SYvVwrVfX8vuU47/H4X6hHLfoPuY1GsSem3j/dtlNFuYsWQ3C7Ycw2xx/idBr1UY3y+WZ8Z2xVuvraQFIYQQQtSVFhUYvvbaa7zyyitkZ2fbyyq2sTg3uUx2djZxcXEYjUYGDhzI2rVrG7q7zZoEhqIueTri9OxvzzJ7+2z7cwWFm3rfxCMXP+L5iFvuDth4F2Svd62+Xzz0+S/Ejvcoe2m5tZz5u+bz1l9vkVWS5XCsXUg7/nnxPxmdONrt/VvrexTPaLZwy2cb2ZCcU2PdgfGhzJqWJMGhEEIIUY9aRGBoNpu55ppr+PXXXwHn/Qur2orinnvuYebMmSiKwv79++nQoUOD9LclkMBQ1IXajjgVGAt4dfWrzNkxx6E8yj+KZ0Y+w+jE0Z51TLXCoY9h+xNgznPtnIjBtoQ2YQM8umRxWTGfbPqEjzd9TGl5qcOxvjF9eWLoE/Rr3a/GdhpqFO9f3+1k7sY0l+tPSorjpXE9PL6eEEIIIarX7LerALj77rv55ZdfUFUVLy8v7rzzTr799luuvvrqas+76aab7I9//vnn+u6mEMINFSNOczemVRrAAJgtKnM3pnLLZxsxmp2//An0DuT5Uc/z7aRv6RB65oufk0Unufv7u7n7+7s5WXjS/c4pGki8E67cB22ds4hWKnMNLE2CtVOg5Jjbl/Qz+PHARQ+w8vaV3NDzBjTKmT+zW09sZeLciSw9sLTaNuriPXVFRqGRBVvce40LtqSRWWjy6HpCCCGEaLwaLDDcsmULn3/+OYqi0KZNG7Zu3coHH3zAhAkTaNOmTbXnXnjhhQQFBQGwevXqhuhurUydOhVFUVy+vffeey61u3jxYiZMmEB8fDw+Pj6Eh4fTr18/nn32WU6cOFHPr0qIys1YstulaYgAG5JzmLFkT5XH+7fuzw9TfuDBix50SECz7OAyLv38Ur7a9pXDJvMu82kFF30NI5aDv4szDo7OhiUdYeczUF7s9iUj/SP5z+j/8PMtPzMiYYS9PDogmqHxQ6s9ty7f0+rM21R14FkVs0Vl3mbXRxiFEEII0TQ0WGD4+eef26eOzp49m86dO7t1fu/evVFVlb1799ZH9xq13NxcLrvsMq699loWLFjA0aNHMRqNZGdns3XrVmbMmEHXrl2ZN2/e+e6qaGHqY8TJS+fFfYPu46dbfiKpTZK9vKisiGdWPMPnWz73uL9EXQJX7ILe/wWdC2sXLaXw93O2APHILNvUVDclhify8biP+Xri13Rv1Z2HBz+Mt94xyU2hqdA+ItqQo3iuBp/nWn8ku+ZKQgghhGhSdA11oVWrVgHQvXt3hg6t/tvyylSMKh4/frxO+1Xfnn/+ebp3715tnV69elV5zGg0cuWVV9qT7kRERHD77bfTvXt3CgoKWLRoEcuWLSM/P5/Jkyfj4+PD2LFj6/Q1CFGV2ow43Tu8+pG7hNAEvr7+axb8vYCXfn+JAlMB0QHRXN/z+tp02ZZ5tOs/IWGqbTTw8Ec1B3ylJ2D9VDjwrm39YeQQty97QdwFLLppUaXHPt38KR9u/JAbe9+IvmxUvb2n5yoylbtVv7bnCSGEEKLxarDA8MSJEyiKQp8+nqWg9/f3B6C42P0pXefT4MGDGTZsmMfnv/rqq/agsGPHjqxatYqYmBj78bvuuovXX3+dRx99FIvFwu23387BgwcJDAysbdeFqFFtRpxcCWI0ioaJPSYyImEEz696nqu6XIW/wfa3oNbZOr0jIekD6HgvbH0ETi6r+ZycLfDbUIgdB71fgYD2NZ9zzus5V15pHp9v+ZwySxmfb/kcDV+j+A5GLR0JqusZWV19T8/m7+XZPwGenieEEEKIxqvBppIajUYAvL092yesqKgIOBMgtgQFBQX897//tT+fPXu2Q1BY4ZFHHuHyyy8HICMjgzfffLPB+ihatoYacQr3C+ftK99mZPuRGM0W/vXdTi58eQWvLTvAmrTv2J7zHqsPHebVpfu58OUV/Ou7Xa4nZAnuDsN/haE/QaCLU9zTvoOfusK2x6As363Xcq4juUfw1fvan1spQ+O7Ek3oDBTfJaC49mWYJz+LgfGhbp8DcEFCmEfnCSGEEKLxarDAMCIiAoCTJz3ILAjs27fPoZ2W4Pvvv7ePkA4ePJikpKQq6z7yyCP2x3PmzKmynhB1qaFHnJyydWpyUXyXoPHeiCbkBRSf3zBbytzP1qko0HoMjNkJ/d4FgwsBk7UM9r4GSzrAwQ/AavboNfWN6cuq21fx1LCnCPM9E3ApShka3+VoQp5F8f0ZlJJq2/HkPZ04IBa91r19FfVahYn9Y92+lhBCCCEatwYLDDt37oyqqqxbt67SvQqrk5aWxvbt21EUhQEDPNtbrCk6e2uOK664otq6Q4cOxc/PD4ADBw5w8ODBeu2bENDwI07nZutUvP9A0diSrigaExq/H9CEvAyGXWxIznY/W6dGD53+AVcdgs4P257XxJQFm+6BH7tA8ldgdX/rCG+9N9P6T+P323/n4ta3oVr97McUjQmN769oQmag+PwKlFXahifvaWSAN+P7VZ8V+lzj+7k4VVcIIYQQTUqDBYaXXXYZAFlZWXz55Zdunfv000/bg8lLL720zvtWn5555hkSExPx9fXFz8+Ptm3bMm7cOD755BP79Nqq7Ny50/64utFCAJ1O57B+8+xzhagvDTniVFm2TrVkLNaiCajWM1MxFW0m2sCP0QTOZP62TZ7tuWcIgb6vw5jd0OYa184pOgzrboafe0DqfI8ymPoafHnlyofR5D+LtfhKx9elKUXxXgc4b2Zfm1G8Z8Z2cznAHxgfyjNju3p0HSGEEEI0bg0WGE6dOtW+F+HDDz/M5s2bXTrvueee48svv0RRFGJiYrjhhhvqs5t17s8//+TQoUOUlpZSUlJCamoqixYtYvr06SQkJLB8+fJKz1NV1WHULz4+vsZrnV2nYuqtEPWpIUecKs+AqkU1Xow192mspUNQ1TN/0hTDXqyBL3Hvon9TaCp0+3oABCbCkEUwciWE9HbtnIK9sGYi/NoPjv8IqnsZRiMDvJnQrwNq6Wisuc9gLb4c1Wpbm62WjsA5MFRrNYrnrdcya1oSk5Liqgzy9VqFSUlxzJqWhLfeOTAVQgghRNPXYKnlQkNDeeGFF7jvvvsoKCjg4osv5t5772XSpEmYTGe+0S8oKCA9PZ2//vqLDz74gK1bt9qPvfnmm+j1LkztagR8fX0ZMWIESUlJxMfH4+XlRWZmJuvWrWPhwoWUlpaSnp7OZZddxrx587juuusczi8qKsJsPrNmKTw8vMZrnl0nLy+vzl6LENV5Zmw3jmQWu5ShtDYjTtW2r/qhFo9HNV6Ixu87FMMBABTFypaM7xjxye88PeJprupylUfXptVwuHQzJH8JO54EowtrpXO3wx9jIWwg9Hzetoei4tro6tnvqVp6OapxKIr3n6jGQU5128T8Tonewv7Mu+gU0cnNF2bjrdfy0rgePDyqI/M2p7H+SLZnmV6FEEII0WQpqurm19m19OCDD/LOO++gnPMBqaIbVZX/3//9H88++2yD9LG2Nm/eTOfOnavMoJqens6kSZP4448/APDx8eHAgQP2vRrBtr1H69at7c/NZjM6XfVx/FNPPcWLL74IwB133MGHH35YY1+7devG4cOHad++8rT7u3fvrrENIYxmCzOW7GHBlsr3NdRrFcb3i+WZsV09HnG69v2/2Jaa50JNFQw70PgtRtGeCSafGfEMlydeX7stLgDMRbDnv7DvNbBUPx3cQeQQ6PkCRF7sUnVX3tOr+4Tx24k7KSyzjYiObD+SuwbeRd+Yvq73SwghhBDnRbdu3Sotr/hs3tCfwxt8M6q33nqLnj178uijj9pHtRRFsQeE58apwcHBvPnmm9xyyy0N3VWP9e/fv9rj0dHR/PTTT/Tr14/9+/dTWlrKf//7X959990G6qEQdashRpxcz7qpQFlvrGVdUXxWofP7jYSwOHYn9+D5hSucgqzVB7N467cDrgeuen/o9Tx0mA5/PwdHvgDVhYQzGX/Cb0NIMVzMpwW3ssvYsdr3x5X39NdD81h09Mw02RWHV7Di8AqS2iRx18C7GNJuiNOXbUIIIYQQlWnwEcMKRUVFfPbZZ/z888+sW7eOwsIzH268vLxISkriyiuv5M4772y2m7V/9dVX3HzzzQDExcWRkpJiP1ZYWOjwugsKCggIqH6z64cffti+h+Gjjz7Kq6++WmMfKr6pkJFB0di9t/Igry074PZ5tw0JZuPRNHalnvP/j1KE4r0a1TgMVB/ANtXV7XV0BQfh7xlwdA7g+p/T5fkDeePUjew1Jng8olpuLeeXA78wc8NM9mU6ryvuGtmVO5Pu5PKOl6PVyNpAIYQQoik4X5/Pz1tgeK7i4mLy8/Px8/OzJ6lp7s6dLlpcXIyvry0LoaqqGAwGysttm1YfOXKkxgQ0U6ZMYfbs2QC88MILPPXUUzX2QQJD0VRkFBq56OWVlU6rrIpeq3BFjxgWbz/udEzxXYzGdyWq1Q+1ZBSq8WJAz6SkOF4a18P9Dubthl3PQNpCt077rWAA72dMZGtJF88CU2x/L35P/p2ZG2ay+bhzYq82gW2Y0ncKN/W+CS+drBUUQgghGrPz9fm8wbKS1sTPz4+YmJgWExQCREREODw/O2GMoih07NjR/jw5ObnG9s6u07lz59p3UIhGxJMMqFf0iOGnXSecD2hyUXz+BEDRFKPxX4wm5HkUr3XM33LUsy0ugrvBxQvgsi0QU/2+o2e7JHAT33V4jLkJ/0KfuYIZP7j/j4CiKAxPGM63k77l2xu+ZVj8MIfjxwqOMXvbbHSaBl89IIQQQogmotEEhi1RVlaWw/OQkBCH5z179rQ/3rhxY7VtlZeXs23btkrPFaK5cHfPvXZhvpWPMKreqKWXoKoGe5GizUMTMBdrwIu8sHyO03pnl4X2hWE/wqi1lIUPd/m0Qf67+CrhaW7InUD+Ac/2QQTo36Y/n173KT9O+ZErO1+JVrGNPk7pM8VpOqmp3OT56xRCCCFEsyKB4Xm0atUq++PY2Fh8fHwcjo8ZM8b++Oeff662rT/++IPi4mIAEhMTSUxMrMOeCtE4uLvn3pbU3MobUn1QS8ZgzXkGa+lQVPVMwKToMvjxyAtc89U1rD662vPAKWIQH+k+ZNLhF9lc3MXl03r5HiBo80T4uQckfwXWco8u3yWyC29f+Ta/T/+duwfezYQeE5zqvPnXm1wx6wrm75qPqdyDUdImLLPQxHsrD3Lzpxu49v2/uPnTDfxv1SHPRouFEEKIZqBB1xguW7YMo9GIr68vl1xyicvn/fbbb5SUlLh9XmNWUlJC//792bt3LwD33HMP//vf/xzq5Ofn07p1a3vAt2HDBpKSkiptb8yYMfzyyy8APPPMMy5v7SFrDEVTlVloqjEDqstbXGhyUHx/QfHaiKI4/km8IPYC3rjiDVr5t3K7jzd/uoHVB7MAlWEBW3ik1Wx6+B52rxG/eOj6T0iYClpvt/tQleKyYgZ/OJgCUwEAoT6hTOo1iRt73+jRa20qbNuA7GbBlmP1trWKEEIIURvNPvnMnj176N69O4qicP/999uzZ7riwQcf5N1330VRFPbv31/lnnuNwaxZs4iMjOTSSy9Fo6l8QPbUqVNMnjyZlStXAuDt7c3+/fuJi4tzqjtjxgx7kNepUydWrlxJTEyMQ53XX3+dRx99FLBtcn/o0CGX12pKYCiaszOBmYu06Wh8f0Lx2mkvig2KZdm0ZRi0hmpOrJxzYKoyOnA990V+436A6B0FXR6BDneCvvoMxa5Yfmg5dy2+y6lcr9EzptMYbul7Cz2jejar7S6MZgu3fLaRDck5Ndb1NBGQEEIIUVvNPjB87rnnePbZZ1EUhV27dtG1a1eXz929ezc9evRAURSef/55nnzyyXrsae08+OCDvP3220RFRTF69Gh69uxJVFQUXl5eZGVlsW7dOhYsWEBJSQkAGo2GuXPnMnHixErbKy0tZcSIEaxfvx6wJayZPn063bt3p6CggEWLFrF06VIAtFotCxYs4JprrnG5vxIYiubM0y0ubhqsklw8l/Vp63l9zOtc0/Uaj65fdWCqMsR/K/dGzmeg/9/uNaoPhg53QMd/gF+sR/2qcCj7ELO2zuK73d9hLDc6He8a2ZUbet7AVV2uIsCr9sHo+fav73Yyd2Oay/U9zlArhBBC1EKzDwwvueQSVq5cSXx8PIcPu/lNOZCQkEBKSgqXXHKJPRBqjCoCQ1fExsby6aefMmrUqGrr5eTkcMMNN7B8+fIq6wQGBjJz5kwmTZrkVn8lMBTNmadbXKx9YiTh/gY2HttI/9b9nZK2vLvuXXJLc7ln4D2E+4VX2ZYrgWl/393cEzmfEYHO20xUS9FC3ATo9BCEVz7F3FV5pXl8u+tbvtz2JScLTzod99H5sPDGhXSK6FSr65xPtfldqJiaLIQQQjSEZr9dxd69e1EUhd69e3t0fp8+fVBV1b4mr7H65z//yddff83999/P4MGD6dChAyEhIeh0OoKDg+ncuTM33XQT33zzDYcPH64xKAQIDQ1l2bJlfPfdd1x33XXExcXh5eVFaGgoffr04emnn2bPnj1uB4VCNHeebHExvp9tjaKiKAyMHegUFGYVZ/HRxo+YtXUWwz8Zzhtr3qDAWFBpWxMHxFaZJKfC5pJuTDv6LGMOvMOPeRdjVV2cuqlaIOUbWDYQll0EqQs8TlQT7BPMnUl38vvtv/POle/Qv3V/h+OhvqF0COvgUduNxbxNaW4FhQBmi8q8za6PMAohhBBNWYONGHp7e2M2m5k+fTozZ850+/y77rqLjz76CG9vb/s0TFF7MmIomru6Xlf22urX+GDDBw5l/gZ/bu5zM7f2u5Uw3zCHY+5OX7yvv5VHYr+H5C/Banb5PAD82kGn+8mKvIlvtuexITmnysQ8NTmYdZBvd33Ld7u/Y1q/afxj0D8cjh/NPcoHGz7ghp430Du6d6Nfi+j2etPTLk4MZ/ZtA+uhR0IIIUTlmv2IoV6vB8BodF7H4gpPzxNCtGzubnFRU7KR+wbdx1PDniLU58x+ikVlRXyw4QOGfjSUF1a94DAd0929F++9egwM/ATGHoZOD4DWp+YTKxQfha0P4/1TW3x2PUby0b/ZlprH6oNZvLp0Pxe+vIJ/fbcLo9lSY1OJ4Yn8e/i/WXfXOqb2m+p0fN6ueSz4ewHj54znyi+vZPa22VWOnDYGRSbPRlM9PU8IIYRoahpsxDA+Pp7U1FQuuOAC/vrrL7fPv/DCC1m/fj1t2rQhNTW1HnrYMsmIoWhJXNniwlWFpkI+2/wZX2z9wr7lQwWD1sB13a/jzgF3Ehsce3qLhD0s2FL5dMZqt0gwZsL+t+DA+2DOc6uPFlXDsoIL+DTzajaXdAVsgXFtM26WWcoY/OFgskuyHcq9dd5c0ekKbuh1A32i+zSqUUQZMRRCCNFUNPvkM1dddRU//vgjOp2O5ORkWrdu7fK5x48fp127dlitVkaNGsWvv/5ajz1tWSQwFKJ2Ck2FfL39az7d/Ck5pY7TVduHtmfprUvtAVKtAlNzESTPgn1vQdEht/u5t7QdX+dczuLc4RRZfWuVcdNsMfPLgV/4Zsc3bDi2odI6HcM7cn3P67mq81WE+ro2YlqfPM1Q+9ilnbh3eNNeXymEEKJpafaB4Xvvvcf999+Poihcf/31zJkzx+Vzb7jhBubNm4eiKLz22ms89NBD9djTlkUCQyHqRqm5lG92fsMnmz7hZJFtKulLl77ExB6Vb0XjMdUKx3+C/W/CqVVun15k8eH7vKF8k3sFnz00vdYZN4/kHOGbnd/w3e7vyC3NdTqu0+h4+KKHuXPgnbW6Tm1JVlIhhBBNRbMPDAsLC2nXrh15eXkA3H333bz55pv2tYeVMZvNPPjgg3zwgS3RQ1BQEEeOHCEkJKQhutwiSGAoRN0ylZtYtGcRP+z5gS8mfIFBa3A4vmDXAuJD4+nXul/tL5a7Hfa9hSV5DlrcTFQDnNT3JqrfQ7ZtL3RurGWshKncxLJDy5i7Yy4b0hxHEWdeM5NRHWrOwFzfZB9DIYQQTUGzDwwBZs6cyT333GOfVtW2bVumT5/O8OHDSUhIwN/fn6KiIpKTk1m5ciUff/wxKSkpqKqKoii89dZb3HfffQ3V3RZBAkMhGk5OSQ5DPhpCaXkpA2MHMr3/dIYmDEWj1C4P2D8+/ZnEgtncFPYzYToPEsAYQiFhKnS4CwITa9UXgOScZL7d9S3f7/kes9XM2rvWOgXIj//6OK38W3Ft12uJD42v9TVdUdcZaoUQQoj60CICQ4D777+f9957D0VR7AFfdSq6d8899/Dee+81RBdbFAkMhWg4b6x5g/+t/59DWUJoArf2u5Vru16Lj96zUbtr3/+Lbal5eCkmrg1ZxbTwH+jo7WGSrlYjIfFuaHMVaKqe0eEKi9VCSl4KCaEJDuXphelc/OHFqNj+vveO7s01Xa/hik5X1Pt6xFolAhJCCCEaQIsJDME2cvj4449TWFhYY11/f3/++9//cvfddzdAz1oeCQyFaDh/pfzFO2vfYfPxzU7HQnxCmNRrEjf3vplI/0i32nXOuKkyxH8rU8OXMCxgCxrFgz/zPtEQf4vtFtTZ/fOr8eGGD3ll9StO5TqNjmHxw7ii8xWMaD8Cf4N/nV73bHWZoVYIIYSoSy0qMATIy8vjo48+4tdff2XDhg2Ulpbaj/n4+DBw4EAuv/xypk+fTnBw8PnoYosggaEQDW/TsU18uvlTfjv0m33UrIJeo2dsl7FM6zeNLpFdXGqvuoybbfSnmBT2KxNDlhOhz/Osw2EX2Kaatr0eDMGetXGWU0Wn+H7P9yzes5j9WfsrrWPQGhgaP5QxncYwqsMoj0dThRBCiKamxQWG5yosLKSwsJCAgAACAgLOd3daDAkMhTh/juYeZdbWWSz4ewEl5hKHY4lhifwy9ReX9gJ0JeOmXjFzaeA6bgr7mQv8//aswxovaHONLUiMGgWa2k+13Jux15asZ+8PZBZnOh1XUFh39zoi/CJqfS0hhBCiKWjxgaE4PyQwFOL8yzfm883Ob/hy65f2rS7+M/o/3NDzBpfbcCfjZgevVGZ0XcNFmp/AnO9Rn/GJgfibT081dW1kszoWq4W1qWv5ad9PLDu0jHyjrV9JbZKYe8Nch7pGs5Hlh5czImEEfga/Wl9bCCGEaEwkMBTnhQSGQjQeFRvHf7f7O2ZePRNvvbfD8TfWvIHZYmZSr0nEBcc5HPMo46ZihJRv4OAHkLPF846HJZ2eanoDGGq/nZDZYmZt6lp+2f8L/dv0Z3z38Q7Hlx1cxt3f342Xzoth8cO4vNPlEiQKIYRoNiQwFOeFBIZCNA15pXkM/nAwpeWlKChc3O5ibupzE8Pih6E9PaWzVhk3szfbAsSUuWApdTrXJRqDbapp/C0QPRo0Os/aqcGDPz7Ikn1LHMoqgsRRHUYxLGEYIT6y360QQoimqUUGhkVFRaSkpFBQUIDZ7PrmzEOGDKnHXrUsEhgK0TRUlckzJiCGG3rdwPU9rifcLxyoZcbNsjxInQdHvoCsdZ532DvKNtW03U0Q3ANcWCvpqudWPsfiPYvt003PpVE09G/dn5HtRzKyw0jiQxpmn0QhhBCiLrSYwLCwsJC3336buXPnsn//fty9vKIolJeX11PvWh4JDIVoGgqMBSzcvZA5O+ZwJOeI03G9Rs/oxNHc2PtGktokuZS0puaL7ocjs+DobCg55nk7gV1s00zbXg+BnWrfL6DMUsbalLX8cuAXlh1cRoGpoNJ68SHx/Hbbb3VyTSGEEKIhtIjAcOvWrVx11VWkp6cDuB0Ugi0wtFgsdd21FksCQyGaFlVVWZe2jjnb57D80HLKrc5flCWGJfLVxK/sI4i1ZrXAqZW2UcRj34HF6HlbIb1tQWLc9eDfrk66VxEkLj+0nBWHVzhkN50+YDpPDH3Cof7h7MMczjnM4LaD8TX41kkfhBBCiLrS7APD7OxsOnfuTHZ2tr1Mp9PRvn17wsLC0Ov1Lre1atWq+uhiiySBoRBN16miU8zbOY9vdn5jz2YK7m114bayfNtU0+RZkPlX7doKG3g6SJwAvq3rpHtW1cquk7v47fBvrDi0ghmXzGBAmwEOdV7+42U+3vQxBq2BC+MuZGT7kQyJH0KboDZ10ofmJrPQxLebUtmQnOP+1GQhhBBua/aB4ZNPPsnLL7+MoigEBATw4osvMmXKFPz9/Rvi8qIKEhgK0fSVW8tZcXgFc7bPYU3KGp4Z8QxT+k5xqLM3Yy/rUtdxdderCfMNq5sLFxy0BYjJX0KJa1tlVE6ByIttQWLsdeAdWTf9q8Koz0ZVOh03ITSBIe2GcHG7ixkYOxAfvU+99qOxsyUz2s2CLcfcT2YkhBDCY80+MOzduzc7d+5Eo9Hw+++/M3jw4Ia4rKiBBIZCNC/JuclE+EXgb3D80u3fy//N3B1z0Wl0jGg/gvHdxzM0fii6usgcqlrh1CrbVNO0hZ5nNQVQtBA5FNpcC7HXgG/djuIZzUaeWPoEvyf/TqGpsMp6Bq2BpDZJXNn5Sib0mFCnfWgKPNr+RIJDIYSoE80+MAwKCqKoqIjBgwfzxx9/NMQlhQskMBSi+Ss1l3LBBxdQVFbkUB7hF8G1Xa9lfPfxtA9rXzcXMxdA6nw4+jWc+h2o5T8xoQMg9lpboBjUuS56CNj2Stx0fBMrDq3gz6N/VjqCCDC++3j+e9l/6+y6TcW/vtvJ3I2ujwJPSorjpXE96rFHQgjRcjT7wDAgIICSkhJuv/12Pvzww4a4pHCBBIZCNH8FxgI+2vQR3+3+jlNFpyqt0yemD+O7j+eKTlcQ4BVQNxcuTbcFiSnfQtba2rcX2MkWILa5FsL6g6KpfZunHcs/xp9H/+TP5D9Zl7rOHkS/c+U7XNH5Coe6H238iOWHljMobhCD4gbRN6YvXrrms9Yuo9DIRS+vrHT6aFX0WoW1T4yUNYdCCFEHmn1g2LVrV/bv38+NN97Il19+2RCXFC6QwFCIlqPcWs7qo6tZ+PdCfjv0G2ar8/6x3jpvnhr+FJN7Ta7bixenQMo8SPkGcrfWvj2f1tDmattoYuRQ0LiewKwmZouZbSe28efRP7m9/+0E+wQ7HJ/0zSQ2Httof27QGugb05dBcYO4IO4Cekb1xKA11Fl/Gtp7Kw/y2rIDbp/32KWduHd4h3rokRBCtCzn6/N5HSwucc3o0aPZt28fmzdvbqhLCiGEOItOo2N4wnCGJwwnpySHH/b9wIJdC9ibuddex1hupF1Iu7q/uF9b6PqY7VZwEFK/tQWJ+R7+o1d6HA6+b7vpg6H1lbYgMfpS0PnVqqt6rZ6k2CSSYpOcjhWaCtl6wjGwLbOUsT5tPevT1sNf4Kv3pV/rfgyKG8TF7S6ma2TXWvWnobmyrrAy649kS2AohBBNWIONGB44cIBevXpRVlbGr7/+yqhRoxrisqIGMmIohNh9ajfz/57PD3t/IMAQwKrpq9CcM03z7u/vJjEskau6XEWHsDr88J/3t22qaco3UHSo9u1pDBA5DFpfATFjIKBuAxWramX3qd2sTV3LutR1bD62mdLyqpPtXJZ4Gf+7+n912of6du37f7EtNc/t8/rEBbPonovqvkNCCNHCNPuppAAzZ87knnvuITIykhUrVthftDh/JDAUQlQwlZtIzUslMTzRofxg1kEu++Iy+/MuEV0Y22UsYzuPJSYwptbXzSw08e3GFE6mrKcHvzPI8CdxmsO1bheAgERbgBhzBUQOAW3droErs5Sx8+RO1qeuZ13qOrae2EqZpcx+/NmRz3Jzn5sdzvl8y+f8tP8n+kT3oVd0L3pH96Z1YOv62XfSAzd/uoHVB7PcPu/ixHBm3zawHnokhBAtS4sIDAFmzZrFXXfdhaIo3HnnnYwfP54ePXoQGBjYkN0Qp0lgKISoyRtr3uB/6ysf9erXuh9Xdb6Kyztd7vb+iNXtk9fWcILLg9dzQ/Q22qrbUWqb3RRsU0xbjTwdKI4Bv9jat3kOU7mJrSe2si51HevT1vPi6BedRljvWnwXyw8tdygL9w2nd3Rvesf0pld0L3pG9XTacqShyBpDIYQ4v5p9YKjVOu5vpKqqR9+OKopCeXl5XXWrxZPAUAhRk/TCdH7c9yNL9i1h96nK/1ZoFS0Xtr2Q4fFjyMntwtaUEopM5fh76bggIYyJ/WMdMla6s0/e6ASV/w09hv7E93BqBVSSNMcjwT3OBInhg+o0gU1VVFVl0MxBZBZnVltPQSExPJGL213Mk8OerPd+nU2ykgohxPnV7ANDjUaDoiiVBoTudEFRFCwWS113r8WSwFAI4Y4jOUdYsncJS/YtITk32bmCqsWS8x9QfR2K9VqF8f1ieWZsV7z1Ws/3yTMXwPGf4dgiOPEzlBfVfLIrdP62tYnRoyDqEgjsAvUwtVNVVXae3Mn29O32W2peapX1h7QbwufjP3coO1V0ip0nd9I7ujcRfhF13keQfQybu8xCE99uSmVDck61X+AIIc6PFhEY1gUJDOuWBIZCCE+oqsrfp/5myb4l/LjvR/v+iKqpO9bCOxwrK8Uohj2oZd0Z2K41r03oxYjXf6/9iJTFCCdX2oLE4z+AMaMuXpqNT2tbgFhx84mqu7bPkV2Szc70nWxL38aO9B3sOLmDQlMhAPcPup8HLnrAof63O7/lyWW2UcTWga3pFd3Lvl6xe6vudbKnojsjugPjQ5k1LQlvvbbGuuL8qm76Njh/gSOEOD+afWAoGicJDIUQtfXEwu18s+13FK+tqOauUOY4cqR4bUAT8DWqqgVzJ1r7DiTtVAdQA9y6TrVr2FQr5GyxjSKe+BmyN0FdrEusENwDWl1iG1GMHFLrLTGqY1WtHMk5wvb07fRo1YNOEZ0cjv9r6b+Yt2tepefqNXo6R3Smd0xv25rF6N60DW7r0dINWxCxhwVb0iSIaAYk2Bei6ZDAUJwXEhgKIWrDlfVomsAPUQyOf2NUVYHy9qimXqhlPcEaUuO1Kst6WdWUuOt7eBFesMoWJKYvBXOeR6+v8hekh/ALodUIaDUMwpJA61137dfgpd9fYsm+JfZR2uroNDp23LcDb73n/cssNDFvcxrrj2TLtMMmTKYHC9F0SGAozgsJDIUQteFKBkvFbz6K11YUTXGVdVRzHGpZL1RTL7BGVlrn7H3y3JoSp1Uhaz2c+MkWKObtdOMVukDjZUte02oYRA6F8AsaJFBML0xnR/oOtp3Yxo6TO9h1chfGcqNDne6tuvP9zd87lGUWZ3LjtzfSM6onXSK70DmiM50jOrudVVY0HZJQSIim5Xx9Ptc16NWEEEI0K65MS1OLJ6AWjwP9ERTDDttNm+9QR9GnouhTUQ07seY/Umk7/l62f7JcmRJntqjM3ZjKkcwi25S4yMEQORh6vwQlx+DEL7ZA8eSK2iewsZog43fbDU4HigNtyWxaDYOwC0DnU7trVCI6IJrogGgu62jbY9JsMXMg64A9qc2O9B30ienjdN729O0czjnM4ZzDLNqzyF4e4RdB54jOdArvZA8YE0ITMGgNdd530bDmbap8OnB1zBaVeZvTZAsSIVqQ8xoYFhYWsm7dOrZu3UpWVhaFhYUEBAQQHh5O3759GTRoEAEB7q1BEUII0XCKTK5uH6QFcyKqOdEWJOrSbAGi1w4U7ZmtG9Sy7s6n6lJRtCfpFXcVADOW7HYpIAVb4DpjyR7HKXG+baDDdNvNUgbZG+Dkb3ByOWRvBLWWCc6sJsj403b7+znQGCBsoG00MXIIhA0AQ3DtrlEJvVZPt1bd6NaqGzf2vtHWFdXqVG9H+o5Kz88sziSzOJPVR1efaVOjZ+4NcysNMEXT4er/L+dafyRbAkMhWpDzEhimpqby3HPPMWfOHEwmU5X1vL29mTx5Mk8//TRxcXEN2EMhhBCuqBjFc48Gpbwtanlb1JKxoE23B4lqWVen2orXWjQ+a/lw5xz+OtWLXUfagLYbWKKBmpOqLNiSxsOjOlY+JU5rgMiLbbeeM6As3zbyl77cFigWur/RuxNrGWSutt0qZgUFdrYFi+EDbSOKwT1AU/f/JGsU54zgk3tNpkNYB/ac2sO+rH3szdhLTmnlgYPZaiYu2Pnf36u+vAqdRkf7sPZ0COtAYlgiHcI60DqwNVqNJCxpbFz/AqduzhNCNE0Nvsbwu+++47bbbqOgoMCl/QsVRSEwMJDPPvuMa6+9tgF62LLIGkMhRG24ssawMj3bBLHzWH7NFVHRhPyf09RTANUSilrWDbWsG5gTgao3qK82o2l1ilPto4nW9N/QlGW534YrtD4Q2s8xWPRtUy97KZ5LVVWySrLYl7nP4XY4+zAhPiGsu3udQ32j2Uj3t7ujVpL11UvnRftQW7DYPrQ9iWGJ9IjqQUxgTL2/DlG1mz/dwOqD7v/uVpbwSQhR/1rEGsNff/2VG264AYvFYg8KQ0NDSUpKol27dvj5+VFcXMzRo0fZtGkT2dnZAOTn53PDDTewZMkSRo8e3ZBdFkIIUY2JA2J5e8VBt5Na/G9yXx6dv8OFKW5lRHj3RTHsJrPYcZ9CRZuD4rMafFajqgYo64jVNADKnKc9ejwlzi8OY9wtzNjRn4XbbqSDPpmL/Lcz0H8XSX67CdSWuN9mZSylkLnGdqvgE20LFCuCxdD+oK/75RWKohDhF0GEXwQXt7vYXl5mKSOjyHlvyCO5RyoNCgFM5Sb2ZOxhT8Yee9ldSXfx2JDHHOrtz9xPdmk27YLbERUQVenIpqg7A+NDPQoML0iQhERCtCQNFhiWlJRw2223UV5um5bQrl07/vvf/3Lttdei0zl3w2KxsGjRIh5//HGSk5Mxm83cdtttHDhwAB+ful/EL4QQwn2RAd6M79fGrTT44/vFEhvqy6xpSS7sk5fIM2M/xEunYXfGbu74ZhYnSzaj6FMd6ipKGXj9jWINR60kMPR0SpxjohuFPZYE9hgT+DhrHBosdPVJZqDfLi7w38WFAXvwU2qZyOZspelwbLHtBqBoIKjbWcHiBRDYBepp6qZBa6BNUBun8oSQBBZMXsCh7EMOt2MFxyptp31Ye6eyOTvm8NX2rwDw1nkTFxxHfEg87ULa2W7B7YgPjSfcN9yjPRiFI0+/wJnYP7YeeyWEaGwabCrp//73P+677z4URaFfv34sW7aM4ODgGs/Lz89n1KhRbN68GUVRePfdd7nnnnvqv8MthEwlFULUVm03znZnnzz7lDilAMWw27Y/omGfLTAELPl3gdlxnaLi+xPBgSncNvByhrQbQrdW3VxeB+fO3m8aLDzY38j93Y/Dqd9tyWfqcv/Eyuj8bclszh5Z9Imu32tWoaSshCO5RxyCxcM5h3ljzBv0iHLcD++W+bewJmVNFS2d4af3o2urrnxzwzcO5RarBVO5CV+Db52+huZM9jEUoulo9vsYXn755SxduhSdTsfevXtp3975G8SqHDp0iC5dumC1Whk9ejS//PJLPfa0ZZHAUAhRF2z7CtY0+nd6X0G95yNcla9pNIP+EIphL2rxlYDj9gqa4P+i6I7bn4f4hHBh3IVc3O5iBrcbTHRA5YFUrfd+s1ogfxdFqSs4dehXwo1bCFJyXW7LY75xp9cpnr6F9quX7TJq44EfH2BD2gYyizNrrNslogs/3vKjQ9mRnCOM+mwUoT6hxAbF0iaojdN9TGCMbLVxltp+gSOEaDjNPjCMjY3lxIkTDB06lJUrV7p9/siRI1m1ahWtW7cmLc31b7xE9SQwFELUJXdG/zzhdrCmFKAN+3e1VTqEdmBQ3CAGxQ1iYOxAgn2CAc8T61QkurEFy7tZsOXY6f6qtNFn0Md3H719D9DHbz89fI+gp8zta7hF0UFwT8dgMbCjbWrqeVZUVkRKbgpHc4+SnJvM0dyjHM07Skpuij1T6qgOo5h5zUyH8/5I/oNpC6dV27aCQqR/JNEB0Twx9AkGtBngcLzAWICvwRddPWSDbawa6gscIUTtNPvkM1lZtkXPHTp4th9O+/btWbVqlb0dIYQQjU9EgBf3Du9Qb3ufub2mUfVnZMwr9IxPZ/XR1Ww7sQ3LOfsUHso5xKGcQ8zePpvrul3HK5e/AtRu77fbBsdXMjqjcMzcimP5rViSPxQAvWJmQkI2MwYXo8/dZNtTsfCgR9etkloOuVttt4Mf2Mr0wbYpqKH9IKS37ebfod7WK1bF3+Bv33vxXPnGfFJyU9BrnbPNHsuvfD3j2VRUThWd4lTRqUqzoN+35D7Wpq4lwi+C6IBoovyjiA603UcFRBEdEE10QDQRfhGV9qEp8tZreWlcDx4e1bFev8ARQjRNDRYY+vj4UFZWRlGRZwvzK86TxDNCCNGyPTO2G0cyi12cEhfOOxNsU+L+MegfFJoKWZe6jtVHV7P66GrS8h0DzAvbXmh/XJGwRvHaCNoMVHMimOM5d6rquYpM5cxYstul/plVPXMOR6GGxfHSuPtthaZsyN4IWRtsgWL2Biir4ymo5jzbPo0nl58p0/ra9lOsCBRDetue6/zq9touyCw08e2mDDYkl1JkKsTfa4ND4DKp1yRGtB/BsfxjpOWnOd2fLDzpkDk1KiDK6RrphelYVas9eKyKRtFwUduL+GL8Fw7lWcVZbEvfZsvo6htBuF84XrqmEVTV9xc4QoimqcECw5iYGPLy8li9erXb56qqypo1a1AUhZgY2QtJCCFaMm+91sWMps5T4gK8AhidOJrRibatj47mHmVd6jrWp61nXeo6Loi9wF7X38v2T6TitRHFcABYhqpqwRyPak5ELe8A5racGygatBoWbKl5ROtsC7ak8fCojrbRGq8wiLncdgNQVSg8BNnrzwSLudttI4F1yVJyJhC1UyAg0TlY9GldL3ssOk+/PWP1wSze+u2A/edaMaJ37hRRsG21caLgBOmF6aQXplcaGJ4sPOlSn6yqtdKAb+fJndy1+C6HsiDvICL9Ign3CyfSL5Iw3zDCfMMI9Q1lRMIIwv3CXbqmEEKcDw0WGA4fPpw9e/Zw4sQJ3nvvPf7xj3+4fO4HH3zAsWPHUBSFYcOG1V8nhRBCNAl1NSWuYnuESb0moaqqw9YItr3f0kGfbC9TFAsYDqEYDgHYAsXyOFRze1RzeyhPQK/VuJWwBsBsUZm3Oa3yERxFgcBE2y3+ZluZxQg52xyDxeKjbl3TNSoUHrDdUuedKdYHQ3B3COrueO/l+b53riRHMVtU5m5M5UhmUbXJUQxag/1nW5WN92zkZNFJ0gvTOVl40va4IJ2TRSc5WWgrr1jnWFmCosoS5+Qb88k35nMw23k68MIbFzoFhvd+fy/HCo7Zgkef0DOBpE8oQT5BhHiHEOQdRIhPCIHegZJMRwhRrxos+cyGDRsYNGgQiqKg1Wp55513uOuuu2o87+OPP+Yf//gHZrMZRVFYu3YtAwcObIAetwySfEYIISqXUWjkwpd/wWJYjaI/CLrDKBpT9SeV9aZ/2KMerU+8ODGc2bfV4t+30lO2KagVwWLOJjAXeN6eJ3yibUFiRaAY3AOCuro0HbUxbqdgNBs5WXQSg9ZATKDjjKXFexYzc8NMMoszyTPm1djWqttXERcc51A28tORHM096lJfLoi9gK+v/9qh7GDWQb7a/tWZ4NErkEDvQAK9AgnyDiLQK5AArwD8DH5oGkGyoYZmm5KcyobkHFlLKZqUZp98ZuDAgUycOJF58+ZRXl7Ovffey8cff8zUqVO58MILadu2LX5+fhQXF5OamsratWuZNWsWW7dutX+LO3HiRAkKhRBCNIjIAG8m9GvP3I0G1NKRgAV0qSj6Ayj6w6A/Yt8/sULv6H6UlVodyhTDFjAcBHMCqjkerOGA8zTMijWNHvNpBW3G2m4AqhUK9p0eUTwdLObvspXXl9J02+3stYsA/gkQ1A0Cu0BQFwjsbHtsCAJsQXitpt/WE2+9d5Wjjtd0vYZrul4DgKncRHZJNhnFGWQWZ5JZlElmcSbZJdnklOaQU5JDmK/zaGpOietfIIT4hDiVHck5wlfbv6rxXI2iIcArgI+u+Yj+bfo7HPt6+9dkFmfiZ/DD3+CPv5c//vrT9wZ/e7mfwa/JrKF0Z0qyZF8V4owGzdH82WefkZyczKZNmwDYvn07Dz74YLXnVAxoDhgwgE8//bS+uyiEEELYOSa60UJ5PGp5PGop2ALF4yi6Qyj6w+i8kvn36LG88WuJQxuK1w4Ur+3gvRYA1eoH5W1RzW1Ry9tCeVtQ/exrGuuMorGN1gV1hfa32srKiyFniy1IzN0OedttwWN9BosARUdst+NLHMt9oiGwMycLo5kUHMBhUyyHjG04VR5GZcHz2aqdftvAvHRexATGOI0q1uR/V//PFjyW5NiDyOySbPJK88gz5tnvyyxl9m1UzubKSCXY1knmG/MrDezm75rPrlO7XGqnX+t+zJs0z6HsUPYhXl/zOr56X3z1vvjoffDT++Gj97GVGRzLO4R1cHot507jro26nJIsREvToIGhr68vv//+O4888ggfffQRVmvN/xBpNBruvPNOXnvtNclIKoQQokFVn+jGtr5Qp7ZlfM+pPH1lZ3z0OgbGH2L1wYqtlVTQH3ZoU9EUg2EPimGPvUy1RJCr9GTBrjTG9xhffy9I5weRQ2y3CuWlkP835O44Eyzm7oByz7KIu+X0CGNPoGfrM8WFFh8Om9pw2BjLYVMbjpbFcNQUQ7IphlLV215v/ZHsRhEYeurCuAtrrKOqKqXmUqdtVgASwxOZ1m8auaW55BnzKDAVUGAsoNBUSIGpgBKz45cUgd6BTm0UmFyfbuyt83YqO1V0imUHl7ncxsxrZjKqwyiHsjsX38nqo6vx0nnhrfN2vOlt9146L3x0PrQObM1jQx5zOD+vNI+Fuxdi0Br4cUcGG08UoBh0qOhA1QM6UHWgagEtWINB9WNDcg4zluzhpXE9MJXbponrtfoWOe22uSkuK8ZYbqTcWk65pZxy9fS99czNbDVTbinHoDPQN6bv+e5yo9Dgu7r6+Pjw/vvv8+ijj/Lxxx+zcuVKtm/fjtlsttfR6/X07t2bESNGMH36dBISEhq6m0IIIQTgfqKbiQNieXvFwdNBpBW1+CpU/WEU3REUXUal11C0mezNWcGP+8ucAsOKhCaxQbF1NqriQOdj29Mw7KzsnqrVNsKXu/1MwJi7DUqP1/31KxGgLaW370F6+zoncTlpDiXZ1JpkUwxm2sOxk7bMqf7tQds0pjq6Q1EUfA2+lR7rG9O32g+0ZovZHiQWmAqI8nfOzjqm0xhOFp6k2FxMkamIorIiisuKHe4r+Bmc14oWlxW79Xp89c6vpdRcSpmljDJLGYWmwmrP7xDawSkwzCjO4MXfX7Q/1wRU3wdr4fWopouAM1OS31w3g293fguAVtGi0+rQa/RoNVq0itbh3s/gxy9Tf3Fo02g2ctO8mxzrn/NYp+jQaDToNDpGdRjFZR0vc2hj1eFVrDqyCkVRUBQFjaJB4cy9vUxRUFC4b9B9eOsdg/X31r2HyXJmLbRyzsi7imqfjdctshuXd7rc4fjO9J38uP9HKnZ7USv+U233VtWKqtruraqVfwz6h1Nyplf+fIXjBcftdS2qxVbfarU/tlht910ju/LU8Kcczt92YhtPLnsSi9WCRbVgsVoot5afuT+rzKpaef/q9xmeMNyhjfuX3M/vyb9X+vM/V9vgtqy8faVLdZu7Bg8MKyQkJPDSSy/Zn+fn51NUVIS/vz9BQUHnq1tCCCFEpVzd+y0ywJvx/dqcTqSiRTUNBNNA2+cspQR0KSi6FBR9CuiO2kYQT+sV1cupveUHl/P40scJ9QmlV3QvekX3ond0b3pF9ap0BKhOKBoI6GC7xZ0VqJblQt5u2whj3q4z93W9z2I1ovQ5ROlzGOR/evrjn++f6bNvnC1IDOh4+j7Rtr7Rvx1onUe7mju9Vk+obyihvqFV1nn04kerbcOqWikuK6a4rLjSkbTE8ESeGPoEJWUllJhLKDWXUmIusd9KzaWUmkspNhdTai4lwOActRnLjS6/JoPOOTNrmaWskprVOTN1tGJKstlyZoDColqwlFswUXmyKX+Dv1OZ2WpmW/o2l3vQJrCNU2C48+ROvt7xdRVnOLsz6U6nwPCTzZ/UGFxXuK7bdU6B4YGsA3y62fWlWzf1vskpMPwj+Q/2Ze5z6fyz9xutUFpeyoGsAy73wWJ1Hk3XalyfHlzZ+S3VeQsMzxUUFCQBoRBCiGbBcW3iWVRfMHdBNXc5vU5RpXdbCzdfDHsydjIkfohTW9tPbgcgpzSHVUdsIwoVEkIT6BV1OlCM7kXniM7otfr6e2GGEIgcbLvZX5MKxpOQ9zfk/83mHX+gK9xDR+9UfGvK4lqXVKtty47io87JbwCLdwzagITTgeLpm1+87d4nyhZYCicViWsCvCofhosPiWf6gOm1usZ7V71HoakQY7kRY7kRU7nJ9th8zvNyY6VrLf0MfoxOHM3G5AxyS0pAKQfKbfeK+cxjLLab6vj/yPoj2YRHuZ78Sadx/vhcbnUveVRlbVjrYK2vO5sNVBaUuauyPrszFbey/moV99Z8uhIYKijotXp0Gp3TLdI/0q3rNWcNtl2FaJxkuwohhKgftsyIla1NtNFrFZcyI961+C6WH3IOdCrjp/dj8z82V7nfXX2n788oNHLRyysxW1QUrLQxZNDJO4WOXil08E6jvdcx2nsdw19bWutr1Smt95kg0T/eHjjmKG2Yv1fLmhSTy++XbJFw/lz7/l9sS81z+7w+ccF8eVtPSs2lmC1mzFYzZou50mmL5dZyNIqGgbGOWfJN5SaWHlxqm/547hRI1bHMoloY2GYgSbFJDm2sPrqav1L+sgVbKlix2qdjnj2VsyIYe3r4004jhs+ueNa+XvLcj/gqqn1KKkC/mH5OU9e3ndjG4j2LbVNQT89CrThHQUGraO1TWjWKhpv73ExUgOMU5Xm75pFdko0GjUNdjUZjm1qraNFobGURfhFO00BzS3PZcnyLfUpvxTTcs6fn6jSny08HdueO4paUlaCiotPomuS60fP1+bzeAsPXXnuNkhLboudRo0YxaNAgt9tYt24dy5fb/jEMCAjgoYceqtM+CgkMhRCivmUWmlxam1idvNI8dpzcwY70HWxP386O9B2VZqTsGtmVJVMcM39mFWfx+K//Iisvit2pIZSXxYHqmMzN1SC1Ju+tPMhry2qaAqYSrc+ivdcxOnil0cE7jWHROQSVHSKAbI+vXZ+yyoM4VtaKY2WRHDdHcrI8knZxXblh2GC8ghLAEFTtFglQd++xqNrNn244K/GT62q9h6gQdaxZBYbffPMNkydPRlEUunfvzvr16z3KKFpSUsIFF1xgf1MWL17M2LFj67q7LZoEhkII0fSoqkpKXopDoLgnYw/je4znhVEvONT9Zf9y/rHkrrPOVcASeXqrjFjU8jZgaQ2qNwPjQ2uVvr82H8w/ntKfez77jbxTu2jvlUYH72MkeB0j3nCCOMNJDJpa7vNYj1R9EKnGCA4Wh3D8dPB4vCySE+YI0s3hZJhDsJ5e11bb91hUzbUvJpw9dmmnJpHdVkajW45mFRh27tyZAwcOoNVq2bFjB127dvW4rb///pvevXtjtVrp06cPW7ZsqcOeCgkMhRCieTCVmygxlzhthH7lJ4+zN29BteeqqgLWcNTyWMZ2uJN3rr/Eoz7UZirfonsuqnL6rRYLMYZMEn2Oc037Usa0K0JXfBAKD6IWHUWhnvdhrKVyVUOGOZR0czjp5nCCwhK4uFdv8I0F3za2e+8ocCNhhnB29lRmV+m1CmufGNmoAysZjW55ztfn8zpPPrN+/XoOHDiAoijceOONtQoKAbp3786NN97I7Nmz2b59O9u3b6d3795101khhBCimfDSeTltYJ5RaGRfalusustQdEdBl4qiKXE6V1FU0GaiaDP5eVcWT48xOXxQ/nLrl0QHRtMtshvRAdFVbpvh7+XZx4qK86rfGqRrpSMjH6zYzcI/V9POcIJ4r+PEe504/fgEUfpsNMr5T6WgU6zEGLKIMZweTbWsga1fOlZStOATczpQbGN77NP6dFnMmed654yYwsYxI7Brxvdr3KNtRrOFWz7b6JzI6ixmi8rcjakcySyS0WhRK3UeGP7www/2x/fdd1+dtHnfffcxe/ZswDadVAJDIYQQombzNqVRXtYWytqezj+ogiYTRX8UdGkoumOgPYZyOnuoagnCUh7AvM1p9ql1RrORF1a9YN9gPcg7iM7hnekU0YkukV3oHNGZjmEd8dbbpqJ6MpX0goQwh+eubg0CsO5oEYdNsRw2xcI5WfoNipnW+gziDCeJ9TpJrOEUcYaTxBlOkuB9Eh/FOUg+b1QLlKTZbtXRBTgGij4xjsGjd5Qty6rOec/BlqDKjMCVGBgfyjNjazeAUd9mLNnt0msB2JCcw4wle3hpXI967pVoruo8MNy0aRMArVq1ol+/fnXSZv/+/WnVqhUZGRls2LChTtoUQgghmjvnD5QKWCNRTZFgSjodLFpBk42iSzud2t+Wvr8iKDuQdcAeFALkG/PZcGwDG46d+fdYo2iID4mnXXAiOq/2lJs6udxHvVZhYv9YD18hFJmqXntYpupJLmtNcllrKHI81icuiEW3dYGiI1CUDEVHWLVlPfrSo8QZThJjyESnNMIpquWFULDfdquOLsAWIFYEit7Rjs99om2PvSKa1RRWb72WWdOS6iQj8PmWUWhkwZZjbp2zYEsaD4/q2KhHQUXjVeeBYcU00l69nDfprY3evXuzdOlS9u1zbcNMIYQQoqWrLmg6QwPWCNSyiErP02g0jOowit2ndnOi8ESlLVhVK4dzDnM45zBJCfexbu85FQw7UZRiVEsMlEcBZz601nYqn+fTV/XgFWa7hQ0A4J0/zqyR1GIhWp9JnJdthLG1PpPWhgxa6zNobcggSp/dOAPHCuWFUFgIhQerr6dowCscvCLBOxK8W52+P/343HKdr0fdacjEKdVPSW46yVrmbao8sK2O2aI6jPgL4Y46Dwxzc3MBiIqKqqGmeyray8lxbThdCCGEaOlqu+YPoHur7sy8ZiZg2zZjX+Y+9mXtY1/GPvZm7uVg9kH7vmkA/xw1kpeNRQ6jlRrv1SiGMyNcqiUMyqOJDoinX+Iw9mXqiQ+Jd1oj6Yq6mr4Kjq/bgpZj5iiOmaNYW8n5Wiy00mefDhTPDhoz7fc+GlMlZzYyqhWMGbZbvgv1dX6OAaNXOHhHnA4uz7r3tt0bVR9m/Lin0sQpqw9m8dZvB+pt9M6dKcmNkatTSM919oi/EO6o88CwrKwMAK22bv/nrmivon0hhBBCVK8ugyaAYJ9gLoi7gAviLrCXlVvLOZp71BYwZu6ja2RHZk3TnTWVzwq64w7tKNps0GZzsuxvHl9q23dRq2iJD4lnePvhPDH0CZf7OnFALG+vOOh2JsrKpq+6835Z0HLCHMkJcySbKl2qqBKkLSJan+Vwi9FnEqXPoltwAcGcAkupy/1uFMqLT0+/PeJSdY1q4H5zADclBJFTHkhOeRA5lkDyygPItQSQZwnk2J4Ans3ezIwJF+PlF2mbBntWgqOWuk2DayP+dXdeZVrqe99S1XlgGB4eTnp6OpmZmXXablaW7Q91aGhonbYrhBBCNFd1GTRVRafR0SGsAx3COnBl5yvt5RVT+eZsPMw3u/tRWJ6GyXocK5WPollUC4dyDtE5srPTsc3HNzN722w6hHUgMSyR9qHtaRvSFoPWUKeZKD15v3QaAIVy67nnKORbAsi3BLDPGO9wRK9VWDt5JEYvLfd+/hsn0g86BI+t9Dm00mcTqcshSp9NsO6cBZJNiEEpI9qQTTTZNVf++fS9Rg+GUKyGMFIKvTmY70V4eQAXlfuTZwkkr8ifvzP8Wb8ukAEd23PnJf3w8otwCigbI3cCrboY8fdUdVtk1PdIrzh/6jwwjIiI4MSJE+zcubNO2925cyeKohAZGVmn7QohhBDN1flO3x8R4MUDI7vywMjPANtaxGP5xziQdcB+25+1nyM5Ryi32kY5OoZ3dGpn+4nt/LjvR4cynUZHu+B2dAjrQNvwBDq0gUPpAWCJAAxV9qm6TJSevF8T+scBqkfv8b++28mKZAuQwF5jQpX1vRQTkfpcbuqp5c4BPpiLjvHLpq2oxcdtAaTeFkD6NoWpq66wmsF4Co3xFPFAfGA1dcs4E1AqWjAEgyEUDCGnb2c/DrEd1wefrnfWY30QaOr8Y7GdJ4FWXYz4ezLiJ1tktFx1/n9A//792bFjB6mpqezdu5cuXbrUus19+/Zx9OhRFEWps0ynQgghREvQmNL3axQNccFxxAXHcUmHS+zlZZYyjuYe5UDWATqFO2c0PZR9yKms3FrOoZxDHMo5c0wbAqgKlpznQXWMJnS6Uib07VTjCIen75e757iTcdKkepFWFsVr2xTGXTqSN7bsZ+6+c0dWVfw0pUToconQ5RKpt91H6HMZGG2mf6QRjCeh9CSYMmxrC5sb1QKmbNvNEzp/LLog8sr9yDL5UGj1o0zxJyAwnPYxMfj6hdgCSH1g1fda5y8lPA20ajPiX5sRP9kio+VSVFWt051f582bxw033GDf4P7LL7+s+aQa3HLLLcyePRtFUZgzZw7XX399HfRUAHTr1g2A3bt3n+eeCCGEqC+2D4lNN33/mqNr2HBsA4ezD3Mo+xBHc486bKFxNj+DP3d0X+AwQjKgXSDvbb+aAK8AEkITbLeQBOJD42kf2p7Y4FgMZ32g9+T9cvec91Ye5LVlB9x+L+4e1p5PVh9xO1hY+8TIMyNEVguYMqE0/XTimVO2YLHisTHDsdxqdrufLZbGCwxBoAu03esD2Z0J+7IUCq2+FFr8KLL62O+LLT4UW8/ciiw+XN6nIzPGJYGi4V/f7XRrNHpSUhzPjO1aYyBaYWB8qMOIX0ahkYteXlm7369aqs26Rk/PbWxrKc/X5/M6DwwLCwtp164dubm5aDQaFi1axNixYz1u78cff+Saa67BarUSEhLC0aNHCQgIqMMet2wSGAohRMuRWWhq0un7K1SMMB7KPsTh7MMczD7IoexDJOcm0zWyKwtvXOhQ/3D2YUZ/PrrK9rSKltjgWNqHtic+JJ7LO11O7+jeHr1frp5z86cbPJom2DbMl5TsSrPdVOuxSzt5lqlSVZnx3V/8sXM3Ydo8wnT5hOvziNDlEaItIFRXQKgun1BtPqG6AkJ0BeiVyoN24SadP6ougPQSPVlGAyVW79PBpO/pQNKbEqsPJVYviq0+RIeGM31ET77anMWPe4sosXpTbPWhxOJNsdUbo+oFOK/BnJQUZx/x8/QLi8p+v9wNtqob5YTqv8Dy9NzaXLM+NZvAEODFF1/k3//+NwA+Pj58+eWXXHfddW63s2jRIm6++WZKSkpQFIXnnnuOp556qq6726SsWrWKWbNmsWbNGtLT0zEYDLRp04bLLruMO+64g8TERLfak8BQCCFEc1FuLSevNI9wv3CH8hWHV3DHojtcbufF0S9yfU/H2Uk/7P2BtPw02gW3o12I7eZn8PO4r9e+f2bPRHcEeOsoNLqfdfLixHBm3zbQ7fPcH0FSCdWXcHEspJ1MtQWL2nzC7AGkLXgM0RYQrCskRFtIkLYIjVLnH0fFOayqQqnVixKrN6VWL0pVL0qt3pRavenbvg1eXgH8caSE5DyVUqs3Jfa63mfOqbiptufG08/7JsTwybQhoGg8CrZcmW5b4dxRTk/Prc0161uzCgxLS0u54IIL+Pvvv1FVFUVRuO6663jooYcYNGhQjeevX7+eN954g4ULF9rP7969O+vWrcPX17ONVZs6k8nE7bffzldffVVlHW9vb15++WUeeOABl9uVwFAIIURzp6oq2SXZHMk5wpHcIxzJOUJyTjKHcw5zLP+Y07TUb274hgFtBjiUTVs4jT+S/3Aoi/CLID4k3hYonhUwtg1ui7feu9o+eTpi6Glg2CcumEX3XOT2eZ6OIA3uEM6aQ669PgUrgdpiQrQF/OOiEPalHCUnJ51gXRHB2gJCtIWE6AoJ1hYQrCsiSFtEkLaQQK37I6eifqkab4osBgrNBoyqwR44llq97M9NVgOlqhfBAYFc1jMencGPn/bksi6lFJPVgNFqoFT1xnj6sUl1vr+6bwLPjevv8XTbl8b18Pi8hnC+Pp/XS/olHx8flixZwsCBA8nIyEBVVRYuXMjChQuJi4tj4MCBdOvWjeDgYPz9/SkqKiIvL489e/awYcMGUlJSANsfcoDIyEiWLFnSYoNCVVW58cYbWbjQNjXG39+fadOmMWDAAEwmE0uXLmXBggUYjUYefPBB9Ho999xzz3nutRBCCNE4KIpCuF844X7hJMUmORwrs5SRmpfK4ZzDJOckcyTnCO1D2zu1cTT3qFNZZnEmmcWZbDy20aE8MSyRX2/91aGsqKyI1LxU4oLj8Df4e5xxMtTP4FFg6OkWBp5usm62WNFrFZdGGlU05FsCKCGQoReO5Ptj21mdV/N7o8VCgLaY4NOjjhfFafnn8Egoy4WynNP3Zz/OB3MelOWBOd+j1yWqp1iNBChGAqpODOxon+3uCuCK1m5cyATMBVVj4F9mPQ930WNUvTBZz7q3GihT9ZhUAyb7Yz3m4way1iQQnXqKuyNsx8ustmO2uqcfWw0cMbXmVLltBsKCLWk8PKpjk5p27656y8sbFxfHunXrmDBhAlu2bAFsAU5KSgqpqanMnz+/0vMqgkHl9D40ffv2Zf78+cTFxdVXVxu9r776yh4URkRE8Mcffzhke73tttuYP38+119/Paqq8vDDDzNmzBjatWt3nnoshBBCNA0GrcG+D2N1ruh8hT3xTUpeCqbyqreGaBvS1qlsR/oOpsyfAkCoTygxgXFoA/RYy8PBGo5qCQdLOKj+VLYODGxT8MZ0j+aDPw67/gJPO3sLA3d4ull6mcXq8VYprgbNFrS2fQ0ttgy0o9t1gnYurqO0WqC8kIe+WsX+1GMEaosI1BafdSsiUFOMv7YEf00JAdrTN00J/toSgnUleClGl1+bqB+KtYxAbRm4O8MzFe53YQe8Z47fyaxsW64Us0Vl3uY0z9bqNhH1t2EL0K5dO9auXct///tf3n33Xfum9xXTQ8919qzWsLAw7r//fv75z39iMLj6tUPzo6oqTz/9tP35e++9V+kWIBMmTGDVqlV88MEHmEwmZsyYweeff96QXRVCCCGarUcGP2J/bFWtnCw8SXJuMim5KRzNPcrRvKMczT1qX4d4rpTcFPvjnNIcckpzwMuWxPJsqtUbrOFYC28CS4zDsev6tubWwe34ZI37WUkn9o91uf7ZarPJuqdbf9RmmwaXaWx7Hh41RrLH6P7nzD5xwSy6ayCYC07f8s+5tz3efPAou1PS8NeWEHg6qAw4HWz6aUrx15bi01z2n2yGTKre4fn6I9kSGNaGXq/n3//+N48++igLFixg5cqVrF69mpSUFMrLz3wLpdPpaNu2LYMHD2bEiBFMmDABb+/q5+e3BGvWrLFPrW3bti3jx4+vsu4jjzzCBx98AMDChQuZOXMmXl7Nd7hbCCGEOB80ioaYwBhiAmO4qK3juj2L1VLpaGJRWRFeOq9qRxoBFI0RNMdAdfz3e2B8KDsK/sV1cwoJiw7lVJ4/WMLAGoZqCat2tLFiJM4Ttdlk3VuvZda0JLe3/ogM8PZ4tNFdtQl80ejAK9R2q0JcrJFJNSTv0WLBT1OKr9aIn6aUYH0pn07uTLDeBOWFYC6E8qLT9xXPi21lZ9/MRZiMBWit3ZQZZgAAK39JREFUxeiUZrhP5XlQZnUMDD0dQW8q6j0wrODt7c1NN93ETTfdZC8rLCyksLCQgIAA2YKiCj///LP98WWXXYZGo6mybvv27enYsSMHDhygsLCQP//8k1GjRjVEN4UQQggBaDVafA3OORHuSLqD2wfcTkZRBql5qaTkpZCSl0JyzlE2pBwg13gCNKUAqKoGrMHAmcDp/67sQr/3j54OLE+gqeS7c1U1nA4WQ1FN3VFNFzmMxHmitqN33notL43rwcOjOrq19Yeno43uqk3g6wpXglwLWgqs/hRY/QGY1CuO4ATPkpzkFxq56OUVaKxl+GiM+GmM+GpKbffaUnw1Jnw1Rnw0RnwU22N/nYmpSZH4aoxQXozFXMTu1HTMpkJ8NCZ8NEZ8T997K2UYNM07ODqbSXUcTfb0i4Sm4ry+OgkIa7Zz507746SkpGpqnqlz4MAB+7kSGAohhBCNg0bREBUQRVRAlFMSnIwCI19u2MOaI3spMGXTOrGVQ+CUUZRR82ijUga6dCAdxRrOxF5xDiNxi/cs5pNNn9A6qDWxQbG0DmxNm6A2tAlsQ+vA1gR6Bzq1WVejdxEBXtw7vIPL0/A8HW10V0NMW22oIBcqfl6xzN2YhslisK+/rM6kpDh8B50JRLVAR7OlyvdeRzkBejPje4fx6Ig2eCkmKC/hP0s2c/BEhi2YVEz4aEx4a0yng0sT3koZ3mffa8rwVkwEGcpRrLag08teXoaPxoT2PI98nhsYerpWt6lo3mFvM7B//3774/j4+Brrn11n37599dInIYQQQtStyEBvHh3Vl0fpW+nxMN8wVty2grT8NNLy0kjNTyUtL40juSkczUmhzOq4dcODwwdx30WOo06Hsg+xN3MvezP3VnoNf4M/rQNb225BrXn04kfxN/g3aGBzNk9HG93RENNWGyrIrVAXPy9P3vugtiH8vt/9rU3uHtaeT1ZXvm5WRzlepwNFL00Z3poyvJQy/HVmPr6xO1hKeXLBJnSY8FLOHPfWlGFQzHhpzHgptjKDxoyXYsZHU8bAtv7sOZaBQTldTzHjdfrcivO8NWWUWc+ESrVZq9tUSGDYyOXm5tofh4eHV1PTuU5eXl59dEkIIYQQDUyr0dr3STyXqqrkG/NJy0/jeMFx0vLTGNx2sFO9tPzqg5+isiL2Z+1nf9Z+NIqGp4Y9BZwJbJ79YTfzD/4D1eqPag0FSyhYQ1CtIegIZVzv7jx3Va863wTc3dFGdzVE4NsQQe7Z16qrQNSd997T0ddpF8WTV1JWaXBejo5yq45ifOGs7UYnJcURHG/74iOoY7zb+xGOvLwHi2rcx9DxddRmrW5TUS8b3Iu6YzAYMJvNABw8eJAOHar/H/Pjjz/mjjvuAGD06NEsXbq02vrdunXj8OHDtG/vvGcTyMb3QgghRHNxIOsAB7IOcDz/OGkFaRzLP8bxguMcLzjuNE01OiCaNXeucSjLLskm6f3ql7VE+EUQE2BLzHN116sZ1aFpLGkxVjN1Eup2RK8hZRaa6j0QPZunm8YbzRZu+Wyjy8H5rGlJ9p+Dp+fW5pp1pWIj+3NVfDZvFhvcCyGEEEKIxqVjeEc6hnd0KldVleySbHuQeLzgOFrF+QPw8YLjNV4jsziTzOJMdpzcQd8Y52mx76x9h+/3fk+UfxTRAdFEBUQRExBjfxwdEE2Qd1Cl25rVp4Yc0WtI9T3aei5PR19rM8rp6bkNPcW3KZARw0YuLCyMnBzb/1w7d+6kR4/qs1S98847PPDAAwCMHz+e+fPnV1u/4psKGRkUQgghRHUKjAVsOr7JPtKYXphOekE6JwpPkFGUgXrO1Lv/XfU/Lut4mUPZwz89zPd7v6/2Oj46H6IComgT1IbPr/vcIUismDZ7PoJH4Zrajr7WZpTT03MbemS1Jufr87mMGDZywcHB9sAwK6vmdMpn1wkODq6vbgkhhBCihQn0DmRk+5GVHiuzlHGq8BQnCk9wouAEJwpP0DXSeS3eicITNV6ntLyU5NxkjOVGp+AvqySLCz64AC+dF1H+UbZbQBSt/FvZMr6e9TzCLwKtpvmP8jQ2tR19rc0op6fnNvTIamMlgWEj17lzZ44cOQJAcnIyw4cPr7Z+cnKyw7lCCCGEEPXNoDUQGxxLbHD1WRv/M+o/pOalkl6UTnphOicLT9pGHk/fzl7rGB0Q7XT+ycKTAJjKTfa9IKuiVbQ8P+p5ru95vUP5rpO7KDAVEOUfRaR/JAFesnVafZBgq+mRwLCR69mzp32T+40bNzJt2rRq62/cuNHhXCGEEEKIxqJ9WHvah1We8E5VVfKMefaAUa/VO9U5VXTK5WtZVAtB3kFO5Z9s/oQf9/1of+6n9yPSP5JW/q3st0j/SNs6yMBoekf3dvmaQjRlEhg2cmPGjOHll18G4Ndff8VqtaLRaCqte/jwYfvm9gEBAVx88cUN1k8hhBBCiNpQFIUQnxBCfEIqnYYKMLL9SDbfu5mThSc5WXSSU0WnOFlou08vTOdU0SlOFZ0i35gPQFRAlFMb5waXxeZiknOTSc5NdqobGxTL79N/dyjLKs7ipT9eIsIvwj5lNdIvkkj/SCL9IvE1+Hr4Dghxfklg2MhddNFFxMXFkZqaSkpKCgsWLGDixImV1n399dftj8eNG4e3t3dDdVMIIYQQot6dHTx2iexSZb1Scykni04SExDjdCzC17alRkZxBuXW8mqvF+EX4VR2vOA4i/csrvIcf4O/LVg8HSje3Odm+rXu51CnzFKGXqOXBDqiUZHAsJHTaDQ899xzTJ06FYD77ruPnj17Oq0fXLBgATNnzgTAy8uL//u//2vorgohhBBCNAo+eh/iQ+IrPfbuVe8CYFWt5JTk2EcZz71lFGXQIcx5fVxGcUa11y4qK6KorMg+Ajmm0xinOo/89Airjqwiwi+CcL9wIv0i7Y8j/CIcysN8wyqdVitEXZPAsAmYMmUKixcvZvHixWRkZJCUlMS0adMYMGAAJpOJpUuXMn/+fCp2Hnn11VdJSEg4z70WQgghhGi8NIqGcL9wwv3C6daq8o3GK9MuuB33DbqPU0WnyCzOJKM4g4yiDLJLsrGqVqf6kf6RTmUZxRmUlpeSmp9Kan5q9dcLaceK21Y4lOWU5LBw90LCfcPtQWS4bzghPiGSiVV4TALDJkBRFObOncu0adOYO3cuhYWFvP322071vLy8ePHFF7nvvvvOQy+FEEIIIZq/xPBEHgx/0Km83FpuH4GsCBgzizKJC4pzqptRVP2o49kqm856NO8oL//xslO5RtEQ6hNqH3kM8w0j3Deccd3G0Smik0NdVVVlKqtwIIFhE+Ht7c2cOXO4/fbb+eKLL/jrr79IT0/HYDDQpk0bLr30Uu688046dux4vrsqhBBCCNHi6DQ627rCSkYIzzV/8nyySrLIKMogsziTrOIsMksyySx2vBWaConwdQ4MM4szK23XqlrJKskiqySLfZn77OUDYwc6BYb3/HAPm49tJtwv3B5AhvmGOTw/u8xL1/AbvYuGJYFhEzNixAhGjBhxvrshhBBCCCE8VDGFtXNE9XtOG81GTBaTU3mIdwiXdLiErOIse1B59h6QTtfzDXcqyyzKJKc0h5zSnBr72yG0A0unLXUoyyjK4KvtXxHqG0qYb5jt5mO7lymtTZMEhkIIIYQQQjRC3npvvPXOWeaTYpNIik2yP1dVlaKyIrJKssguzraNQpacCRpjAp2zs2aXZLvcjzC/MKeylLwU/rf+f5XWV7Blj7UHjL5h3N7/dnpGO+6xnVmciU6jI8g7CI1S+XZsouFIYCiEEEIIIUQTpigKAV4BBHgFVJmN9VxfTviSjGJb0pyskiyyS7LJLj7zuOI+35hf6YhjdYGlimofjTyYfRCA67pf51TvX0v/xaojq9AqWoJ9ggn1sY0+hvqGEuZjuw/1CSXUN5SogCj6xvR18R0RnpDAUAghhBBCiBYmNjiW2ODYGuuVWcoqnaYa6R/Jdd2uswWUp29ZJVmUWcoqbSfUJ9SpLKfENo3VolrsbVQEkufqHNGZn275yaEsOTeZx3993B48hvqcCSRDfELsz0N8QvA1+Nb4Wls6CQyFEEIIIYQQlTJoDRi0BqfyvjF9nUbwVFWl2FzsECxW3NoEtXFqo8BU4HI/KgssTxWeYsvxLS6d763z5o0r3uDSxEsdypcdXEZmcSYhPiGE+ITQJaILwT7BLverOZHAUAghhBBCCFFriqLgb/DH3+BP2+C2NdZfPm05haZCskttwWNOSY7tvtR2n1uaS06JbUpq+9D2Tue7kjingrHciJ/ez6l87o65/Hn0T/vzj679iJHtR7rcbnMigaEQQgghhBCiwSmKQqB3IIHegS6vjTxbz6ievHLZK7b1jKcDyIr73NJccktzHUYlQ3xCnNrILc11eF5ZnZZCAkMhhBBCCCFEk9MmqE2lU1TPZraYyTPmkVOSU+koZv82/QnyDrKNTpbmEObrnIG1pZDAUAghhBBCCNEs6bV6IvwiiPCLqPT4v4f/u4F71HjJhiFCCCGEEEII0cJJYCiEEEIIIYQQLZwEhkIIIYQQQgjRwklgKIQQQgghhBAtnASGQgghhBBCCNHCSWAohBBCCCGEEC2cBIZCCCGEEEII0cJJYCiEEEIIIYQQLZwEhkIIIYQQQgjRwklgKIQQQgghhBAtnASGQgghhBBCCNHCSWAoGoVu3brRrVu3890N0YjJ74ioifyOiJrI74ioifyOiJo0598RCQyFEEIIIYQQooWTwFAIIYQQQgghWjgJDIUQQgghhBCihZPAUAghhBBCCCFaOAkMhRBCCCGEEKKFU1RVVc93J8T5ExAQgNlspn379ue1H4cPHwY47/0QjZf8joiayO+IqIn8joiayO+IqElD/I4cPnwYvV5PYWFhvV2jMhIYtnBRUVEUFxcTFxd3vrsihBBCCCFEi5eamoqfnx8nT55s0OtKYCiEEEIIIYQQLZysMRRCCCGEEEKIFk4CQyGEEEIIIYRo4SQwFEIIIYQQQogWTgJDIYQQQgghhGjhJDAUQgghhBBCiBZOAkMhhBBCCCGEaOEkMBRCCCGEEEKIFk4CQ3HerFq1iqlTp9KhQwf8/PwICQmhR48ePPbYYxw8ePB8d0+4aOrUqSiK4vLtvffec6ndxYsXM2HCBOLj4/Hx8SE8PJx+/frx7LPPcuLECbf6aLFYmD17NldccQWxsbF4eXnRqlUrLrroIl599VXy8vI8eOVCVVUOHz7M/PnzefLJJ7n88suJiopy+Hl7ojH/7I1GI++//z4jRowgJiYGLy8vYmJiGDFiBO+//z5Go9Gt9pq7uvwdGTZsmFt/a3788UeX2pXfkfOruLiY77//nocffpihQ4cSHR2Nl5cXfn5+tG3blquvvpqPP/6YoqIit9qVvyPNR13+jsjfkRqoQjQwo9Go3nTTTSpQ5c3b21t96623zndXhQtuueWWan+W597efffdatvLyclRL7300mrbCAoKUr/99luX+nf06FF1wIAB1bYXExOjrly5si7ejhbl4YcfrvHn7Y7G/rPftm2bmpiYWG17nTp1Unfs2OHW627O6vJ3ZOjQoW79rVmyZEmNbcrvyPn1+uuvq97e3i79PKOjo9Uff/yxxjbl70jzUte/I/J3pHo6hGhAqqpy4403snDhQgD8/f2ZNm0aAwYMwGQysXTpUhYsWIDRaOTBBx9Er9dzzz33nOdeC1c9//zzdO/evdo6vXr1qvKY0WjkyiuvZO3atQBERERw++230717dwoKCli0aBHLli0jPz+fyZMn4+Pjw9ixY6tsLysri9GjR3PgwAEA4uLiuP3220lMTCQjI4M5c+awYcMGTpw4wdixY1m1ahUDBgzw4JW3TBaLxeG5l5cX3bt3Z8uWLW631dh/9ocOHWL06NFkZmYC0LVrV6ZOnUpsbCxpaWl88cUX7Nmzh/379zN69GjWrVtHfHy82+9Dc1OXvyNn+/DDD4mMjKy2Tk3/L8vvyPl34MAB+8hHq1atGDlyJAMGDCAqKgqLxcLOnTuZPXs26enppKenc/XVV7NgwQKuueaaStuTvyPNT13/jpxN/o5Uos5DTSGq8eWXX9q/7YiIiFD37NnjVGfevHmqoigqoHp5eanJyckN31HhsrNHDFetWlWrtp577jl7Wx07dlSPHz/uVOe1116z14mMjFTz8/OrbG/atGn2uhdddJFTXavVqt533332Ot26dVPLy8tr9Rpakg8//FB94IEH1C+++ELdsWOHajabVVVVPRoNauw/+xEjRtjrXnfddarJZHI4bjKZ1HHjxtnrXHrppS6/9uasLn9Hzv6mvy7+XZDfkfPvrrvuUkeMGKH+9NNP9t+NcxUUFKhXXnml/X0LDw9X8/LyKq0rf0ean7r+HZG/I9WTwFA0GKvVqrZt29b+y1zdNI67777bXm/q1KkN2EvhrroKDPPz81U/Pz97Wxs2bKiy7uWXX26v9+yzz1Za58CBA6pGo7F/wZCSklJpPbPZrPbo0cPe3hdffOHxaxA27n7ob+w/+xUrVtjrtGrVqsoPkvn5+WqrVq3sdX///fcaXnnLdb4DQ/kdaRyys7NdqldUVKRGR0fb37dZs2Y51ZG/I81TXf6OqKr8HamJJJ8RDWbNmjWkpKQA0LZtW8aPH19l3UceecT+eOHChZhMpnrvnzi/vv/+e4qLiwEYPHgwSUlJVdY9+/djzpw5ldb55ptvsFqtAEyYMIG4uLhK6+l0Oh544AH786+//trtvovaaew/+7PL/7+9Ow+q6jz/AP69iF52lYCgsi+iiFuooJQEJFYktUrEISWpJVZt2iQax04zY6mK1bSpUZtokjppqrQaV7bYuOCGC0Y2J9GJIgiCIAgRlUW5YX1/fzCc32W7C/ey3u9nhpn3cp/znvdyHl95OOe8Z+XKlbCysuoyzsrKCitXrlTbH/U/5sjAYG1trVGcubk5Fi5cKL2+fv16pxjOI0OTPnNE34ZijrAwpD5z4sQJqT1//nwYGXWffu7u7pgwYQIAoLa2FpcuXer18VH/Us6Pn//85ypjg4KCYG5uDqD1/oOuVrHVpr+XX35ZaqempkKhUGg0ZtKPgX7se9rf8ePHVcZS/2GODD7KvyTX1dV1ep/zCKnLEX0bijnCwpD6zI0bN6S2qr/kdRWjvC0NXBs3boSnpyfMzMykZaQXL16ML774Qu3Sytrkh7GxMWbMmNHltgAghMD333+vcX9jx46Fg4MDAKCpqQm3bt1SGU/6NZCPfWVlJcrLywEAw4YNg6+vr8r+fH19pT96lZWV4dGjRyrjqWd++9vfwtnZGSYmJrCysoKHhweioqJw+PDhTgvedMQcGZyU/627uLiofJ/ziGFSlyMdcR7pjIUh9Znc3FyprckKSsoxt2/f7pUxkX5dunQJ+fn5UCgUqKurQ3FxMZKSkrBy5Uq4ubnhzJkzXW4nhGj3F1td86O0tFR6ntGwYcPg6OioU3/Uewb6sVd+PX78eAwfPlxlXyNGjMD48eO77Y/048yZMyguLkZ9fT1qa2tRUFCAQ4cO4Ze//CW8vb1VrnrKHBl8cnNz2/3/0XElUc4jpC5HusJ5pDM+roL6zJMnT6S2jY2N2njlGD6AfGAzMzNDSEgI/Pz84OrqCrlcjocPH+Lq1atISEiAQqHAgwcPMH/+fBw5cgQRERHttn/69CkaGxul17rmh3KujRw5Uu0Eq64/6j0D/dhrO2+1xZWUlHTZH+lm1KhRmDt3LmbOnAkHBwcYGxvjwYMHuHTpEo4dO4ampibk5eUhMDAQ586dQ0BAQKc+mCODS3NzM1asWCHdyxUREQFvb+92MZxHDJsmOaKM80j3WBhSn6mtrZXapqamauOVY2pqanplTKS7d955B5988gksLCw6vffWW29h69atiIqKwsWLF9HS0oKlS5fC399fuqQCaJ8bgO75oW2uqeuPes9AP/bMpYHjr3/9K3x9fSGXyzu99+677yI3NxdLlizB999/jx9//BERERHIz8+X7iVrwxwZXFavXo20tDQAwJgxY7Bz585OMZxHDJsmOdKG84hqvJSUiHTyk5/8pMuisM3YsWNx/PhxeHl5AQAUCgX+/ve/99XwiGiICAgI6PKXuTZeXl44c+aM9Nf28vJy7N69u6+GR73ggw8+wGeffQag9dK5w4cPY9y4cf08KhpItM0RziOqsTCkPmNpaSm1NVn1UTmmuyV7aXAwNzfHn//8Z+n1sWPH2r2vnBuA7vmhba6p6496z0A/9sylwcXe3r7dsvAd5xqAOTJY7NixA+vWrQMADB8+HEeOHEFwcHCXsZxHDJM2OaINQ55HWBhSnxk1apTUrqysVBuvHKO8LQ1OISEhUru4uLjdUtIWFhYwNv7/K9t1zQ/l19XV1WhqatKpP+o9A/3YaztvqeuPep/yXJOTk9PpfebIwLdt2zbpWYMjRoxAQkICFi1a1G085xHDo22OaMtQ5xEWhtRnJk6cKLULCwvVxivHKG9Lg5OtrW2718o3SstkMum5lYDu+eHg4CBd3trc3Izi4mKd+qPeM9CPvfLr0tLSdgtcdKWxsRGlpaXd9ke9T3mu6WpBBubIwPa3v/0Nf/zjHwEAcrkcycnJaleY5DxiWHqSI9oy1HmEhSH1malTp0rtzMxMtfHKMcrb0uDU8S9go0ePbvdam/xoamrCt99+2+W2QOsvCT4+Phr39+DBA9y/fx9A67LTqlYzI/0byMfe1tYW9vb2AFr/81e1fDkAZGdnSyvjjRs3Ds8995zKeNI/5bmm4zwDMEcGsk2bNuFPf/oTgNaFNY4dO4awsDCNtuU8Yhh0yRFtGOo8wsKQ+szLL78stU+dOiUldFcKCgqQl5cHoPW66xdeeKHXx0e9KzU1VWo7Ojp2WnVLOT9OnDihsq+LFy/i2bNnAABPT094enp2itGmP+X358yZo/GKYKQfA/3Y97Q/5e2o7yjPNW2LXnXEHBl4YmJiEBsbC6D1EUhff/015s2bp/H2nEeGPl1zRBsGO48Ioj7S3NwsnJycBAABQBw+fLjb2N///vdSXHR0dN8NknrFs2fPxKRJk6Rj+tZbb3WKqaqqEubm5lJMRkZGt/2FhYVJcRs3buwyJjc3VxgZGQkAQi6Xi+Li4i7jGhsbxZQpU6T+9u7d25OPSErafpaa/hcz0I/92bNnpRg7OztRU1PTZVx1dbWws7OTYlNTU1V9bIOmbY5oqqKiQtja2kp9b926tcs45sjA8t5770k/EwsLC3Hx4kWt++A8MrTpI0c0ZcjzCAtD6lNxcXFSIo8ZM0bk5OR0ijl69KiQyWTSP7SCgoJ+GClpIi4uTpw4cUI0Nzd3G1NeXi5CQkKk425iYiLu3bvXZWxsbKwU5+XlJUpLSzvFbNu2TYqxsbERVVVV3e77jTfekGIDAwNFdXV1u/dbWlrE6tWrpZhJkyaJxsZGDT89dacnv/QP9GM/Z84cKTYiIkLU19e3e7++vl5ERERIMXPnztX4sxsibXPko48+EmlpaSpj7ty5I6ZNm6bRL1ZCMEcGirVr10o/EysrK3HlypUe98V5ZGjSV45wHlFPJoQQIOojQggsXrwYycnJAFovE/3Nb36DmTNnor6+HikpKTh69Cja0nLnzp1YtWpVP46YVFmzZg0+/vhj2NvbY968eZg6dSrs7e0hl8tRWVmJq1evIj4+XlqB1MjICAcPHkRkZGSX/SkUCoSEhCA9PR1A6zX3K1euhI+PD2pqapCUlISUlBQArdfox8fHIzw8vNvxPXz4EAEBAcjPzwcAODs7Y8WKFfDw8MDDhw9x4MABaV9mZmY4f/48/P399fXjGfKqqqqwbdu2Tt9///33pXZMTEyn97ds2dLpewP92N+5cwcBAQHSfSfe3t5YtmwZHB0dUVJSgr179+LWrVsAWh+w/M0338Dd3b3b/gyFvnIkPDwcX331FVxdXTF37lz4+PjA1tYWxsbGKC8vx6VLl/DVV19JCzaYmpoiJSVF5W0IzJH+t379+nbHet26dfDz81O7nY2NDQIDAzt9n/PI0KPPHOE8ogG9lplEGlAoFCIqKqrdX4w7fsnlcrF9+/b+Hiqp8e6776o8jspfjo6O4vTp02r7fPTokfjZz36msi8rKytx4MABjcZ49+5d4evrq7I/e3t7cfbsWV1/HAansLBQ4+Ov/NWdgX7sr127Jtzd3VX25+npKb799luN+jME+sqRRYsWabzt5MmTRXZ2tkbjY470r6CgoB7lR1BQULd9ch4ZWvSZI5xH1GNhSP3m3LlzYunSpcLNzU2YmpqKkSNHismTJ4u1a9eK3Nzc/h4eaaC0tFR8+eWXYvXq1SIwMFB4eHiI0aNHC2NjYzFq1CgxceJE8atf/UocOnRINDQ0aNV3YmKiiIiIEE5OTkIulwtra2sxY8YMsX79enH//n2t+mpqahJxcXEiLCxMjB8/XowYMULY2tqKWbNmiQ8++EA8fvxYq/6olb4LwzYD+djX1dWJXbt2ieDgYGFvby+GDx8u7O3tRXBwsNi1a5eoq6vTqr+hTl85kp+fL/bs2SPefPNN4e/vL1xdXYWVlZUwNjYW1tbWYsqUKWL58uXi66+/Vnlpe1eYI/2nNwrDNpxHhgZ95gjnEfV4KSkREREREZGB4+MqiIiIiIiIDBwLQyIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiog5iY2Mhk8kgk8kQGxvb38MZFK5du4Zhw4ZBJpNhx44dvbafU6dOScfmyy+/7LX9EBEZGhaGREQ0aBUVFUlFgr6+WAhqTwiBd955By0tLXB2dsbbb7/da/uaP38+goODAQDvvfcenj592mv7IiIyJCwMiYiISCdHjx5Feno6AGDdunWQy+W9ur8NGzYAAMrKyrB9+/Ze3RcRkaEw7u8BEBER9ZSVlZXas1OZmZnIysoCAIwbNw6vvPKKyng/Pz9kZmbqbYxDXXNzMzZu3AgAsLOzwxtvvNHr+5wzZ450nHbs2IFVq1bB2tq61/dLRDSUyYQQor8HQURE1FtiY2OxadMmAEBQUBAuXLjQvwMaYhISErBkyRIAQExMDLZs2dIn+42Li8OyZcsAAFu2bEFMTEyf7JeIaKjipaRERETUYx999JHUXr58eZ/tNzIyEpaWlgCATz/9FI2NjX22byKioYiFIREREfVIXl4e0tLSALReguvq6tpn+zYzM8OCBQsAAA8ePMCpU6f6bN9EREMRC0MiIqIONHlcRVxcnBTTdl9dS0sLDhw4gLCwMDg6OkIul8POzg4RERG4evVqpz4aGhqwb98+vPTSS3B0dISJiQmcnJwQHR2NnJwcrcbc2NiIffv2ITIyEm5ubrC0tIS5uTlcXV0RFRWFpKQk6PvuEeXHRYSHh2s8zv3792Px4sVwc3ODhYUFjI2NYWlpCQ8PD4SGhmLDhg0a3eepfL/o/v37tR4/ERH9Py4+Q0REpAeVlZV49dVXcf78+Xbf/+GHH5CYmIikpCT8+9//lu6Ly8/Px8KFCzsVgCUlJfjvf/+LQ4cO4fDhwxoVXBcuXMCKFStQUFDQ6b2ioiIUFRXh0KFDmDVrFuLj4zF+/Pief1Al//vf/6R2SEiI2vi8vDyEh4d3WfQ+ffoUT58+RUFBAU6fPo3Nmzfjzp078PDw6La/OXPmQCaTQQiBlJQUNDU1wdiYv9oQEfUEZ08iIiIdNTU1YfHixbh8+TJMTEwQFBQEJycnPH78GOfOnUNVVRWEEFixYgU8PT0xYcIEhISEoKSkBFZWVnjxxRcxduxYVFRU4OzZs6irq0NDQwNee+013Lx5U+UlmkePHsXrr78u3WNnamqKWbNmwcXFBUZGRsjLy8PVq1fR1NSE9PR0zJ49G1lZWbCzs9PpM1dWVuK7776T9unr66syvra2FnPnzkVJSQkAwMjICDNmzMCkSZNgYWGBuro6lJaW4vr166isrNRoDDY2Npg4cSJycnJQXV2NzMxMBAQE6PS5iIgMFQtDIiIiHcXHx6O+vh6LFi3C559/jjFjxkjvPXnyBIsWLcLly5fR0tKCDRs2YOTIkSgpKcHvfvc7bN26VVpEBQDu37+PefPmIScnBwqFAps3b8aePXu63O/NmzcRHR2NxsZGyGQy/OEPf0BMTAxGjRrVLu7u3buIjo5GWloaSkpKsGzZMpw4cUKnz5yZmSldmurt7a32TN2ePXukotDb2xuJiYnw8vLqFCeEQHZ2Nvbu3avR8xCnT58unYFkYUhE1HO8x5CIiEhH9fX1CA4ORkJCQruiEABGjx6Nffv2YdiwYQCA1NRUJCcnIzo6Gv/85z/bFYUA4ODggH/961/S6/j4eDQ1NXW539WrV0OhUAAAtm/fjg8//LBTUQgAbm5uOHXqFLy9vQEAJ0+eREZGRo8/LwDcuHFDak+cOFFt/OXLl6X2xx9/3GVRCAAymQwzZ87EZ599BkdHR7X9Tpo0SWpfv35dbTwREXWNhSEREZEe/OMf/5CKv46cnZ3bncmSy+XYunVrt3399Kc/lYqi2tpa3L59u1PM9evXpfsZZ8yYgTVr1qgcn7m5OdavXy+9Vl44picKCwultoODg9r4mpoaqW1ra6vTvpUp3y9ZVFSkt36JiAwNC0MiIiIdubu7Y/r06SpjpkyZIrVfeOGFTmcWO/Lx8ZHaykVYG+VLQaOioiCTydSOU3mBmLbHTPRURUWF1H7uuefUxiuf/du9e7dO+1ZmY2MjtcvLy/XWLxGRoeE9hkRERDpSLuK6M3r0aKk9efJktfHW1tZSW/lsWxvlx1+kpqbi3r17avtUflxF2/1+PfXs2TOpbWZmpjY+MjJSuldy9+7duHbtGqKjoxEaGqpy5VF1lPetPCYiItIOC0MiIiIdjRw5Um2M8uIs2sa3rTiqrKysTGqfPHlSbX8dPXnyROttuqPJ8xFDQ0OxatUq7Nq1CwCQlZWFrKwsAICdnR0CAwMRHByM8PBwjS5N1WbfRESkHi8lJSIi0pEml3HqEt+V6upqnbZvbm7WaXtzc3Op3bYAjjo7d+5EYmIi/Pz82n2/oqICCQkJWLVqFZycnLBkyRIUFxdr1KfyvpXHRERE2mFhSERENAgpF0GJiYkQQmj9pQt7e3uprelzBwHglVdeQUZGBu7du4f//Oc/ePPNN6XVUoHWM4AJCQl4/vnnkZeXp7a/hw8fdjkmIiLSDgtDIiKiQUj5AfX9seiKq6ur1L5//77W2zs5OeHXv/41du/ejZs3b6K4uBibNm2S7hl89OgR1q5dq7af0tJSqe3i4qL1OIiIqBULQyIiokHI399fal+5cqXP9z916lSpnZubq3N/jo6O2LBhAz7//HPpe6dPn0Z9fb3K7doebg8A06ZN03kcRESGioUhERHRILRgwQKpnZiY2O7xEX1h5syZ0r2St27dQlNTk176XbhwodRubGzE48ePVcYrP9S+472LRESkORaGREREg5Cfnx+Cg4MBtC7AsnTpUjQ0NGi0bUNDg86rktrY2EjPblQoFLh27ZrKeE3vQ1R+jIaRkZHKZyRWVlbi9u3bAFpXemVhSETUcywMiYiIBqldu3bBwsICAHDmzBm8+OKLyMjI6DY+Ly8PmzdvhouLi14uP/3FL34htc+fP68ydvbs2Xjttddw8uTJbgvYvLw8REdHS69feukljBgxots+U1NTpUV0QkND2z3ig4iItMMZlIiIaJDy8fHBwYMH8eqrr6Kurg4ZGRmYNWsW3N3d8fzzz8Pa2ho//vgjfvjhB9y4caPdQi368Prrr+Mvf/kLACA5ORnr1q3rNraxsREHDx7EwYMHYWpqiqlTp8LNzQ1WVlZ48uQJ7t69i+zsbCne1NQU27ZtU7n/pKSkdmMhIqKeY2FIREQ0iC1YsADffPMNli9fLl3OWVBQgIKCgm63cXFx0eoh8t2ZMGECAgMDkZaWhqysLBQWFrZbrVSZpaWl1FYoFMjIyOj27Karqyv279/fboGbjhQKBY4fPw6g9TEVYWFhOnwSIiJiYUhERDTITZs2DdnZ2Th9+jSSk5Nx5coVlJWVoaqqCnK5HLa2tvDy8oK/vz9CQ0Mxe/ZsaeEYXa1ZswZpaWkQQuCLL77A+++/32Xcd999h/T0dKSmpiIzMxO5ubkoKytDXV0dzMzMYG9vj+nTp2PhwoWIjIyEXC5Xud8jR46gpqYGAPD2229j+PDhevk8RESGSiZ0fcItERERGayWlhZMnjwZt2/fhp2dHYqKimBiYtLr+/X390dmZiYsLS1RWFiocpEaIiJSj4vPEBERUY8ZGRlh06ZNAICKigrExcX1+j4vXLiAzMxMAMDatWtZFBIR6QHPGBIREZFOhBAICAhAeno6nJ2dkZubq/ZSUF2EhIQgNTUVY8eORW5ubrv7F4mIqGd4xpCIiIh0IpPJ8Mknn8DIyAj37t3Dp59+2mv7SklJQWpqKgDgww8/ZFFIRKQnPGNIRERERERk4HjGkIiIiIiIyMCxMCQiIiIiIjJwLAyJiIiIiIgMHAtDIiIiIiIiA8fCkIiIiIiIyMCxMCQiIiIiIjJwLAyJiIiIiIgMHAtDIiIiIiIiA8fCkIiIiIiIyMCxMCQiIiIiIjJwLAyJiIiIiIgMHAtDIiIiIiIiA8fCkIiIiIiIyMCxMCQiIiIiIjJwLAyJiIiIiIgMHAtDIiIiIiIiA8fCkIiIiIiIyMD9Hx7BbHiEtUYiAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Discussion:** What do the green dashed lines tell us?\n",
+        "\n",
+        "**Answer:** The dashed lines show the range within which we are 95% confident that the concentration of 4 micron particles will be given some initial concentration."
+      ],
+      "metadata": {
+        "id": "q4odLO4ZOpI4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Analysis: Nonlinear Regression\n",
+        "\n",
+        "Though we can easily transform an exponential function into a linear one, linearizing can cause model parameters to be weighted differently and muddies the interpretation of our error analysis. Let's compare regression results by doing a least-squares nonlinear regression. For this, we'll use SciPy's `least_squares` function. (You'll need to complete the `expoential` function before you can run the cell below.)"
+      ],
+      "metadata": {
+        "id": "crFNLf6Co58Z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#non linear regression\n",
+        "\n",
+        "#nonlinear regression function\n",
+        "def exponential(theta, x):\n",
+        "    c_0 = theta[0]\n",
+        "    decay_rate = theta[1]\n",
+        "    ### BEGIN SOLUTION ###\n",
+        "    model = c_0 * np.exp(-decay_rate * x)\n",
+        "    ### END SOLUTION\n",
+        "    return model\n",
+        "\n",
+        "#regression residual function to use with scipy optimize\n",
+        "def regression(theta, x, y):\n",
+        "    residual = y - exponential(theta, x)\n",
+        "    return residual\n",
+        "\n",
+        "#initial guess of scale factor and decay rate\n",
+        "theta0 = np.array([100, -0.002])\n",
+        "\n",
+        "#use scipy to determine best nonlinear fit\n",
+        "results = optimize.least_squares(regression, theta0, args = (time, concentration))\n",
+        "\n",
+        "#extra nonlinear model parameters\n",
+        "nl_c_0, nl_decay_rate  = results.x\n",
+        "\n",
+        "print(f'Initial Concentration: {round(nl_c_0)}')\n",
+        "print(f'Decay Rate: {round(nl_decay_rate,4)}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F_4uTBQgjS7W",
+        "outputId": "8d6dd0f5-6caa-4ead-824e-0a70b7bff2e9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initial Concentration: 167\n",
+            "Decay Rate: 0.0018\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3.1. Plot Experimental Data and Model Prediction\n",
+        "\n",
+        "Again, we'd like to plot the model along with the experimental data."
+      ],
+      "metadata": {
+        "id": "P3Dx5AlMrxiD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#calculate modeled data values\n",
+        "nl_model_concentration = nl_c_0 * np.exp(-nl_decay_rate * time)\n",
+        "\n",
+        "#plot original data and fitted model\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "ax.scatter(time, concentration, label = 'Experimental Data')\n",
+        "ax.plot(time, nl_model_concentration, c = 'orange', label = 'Model', linewidth = 3)\n",
+        "\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Time (s)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Concentration ($ \\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_title(\"Concentration of 4 $\\mu m$ Particles over Time\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "#add legend\n",
+        "plt.legend()\n",
+        "\n",
+        "#show plot\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 627
+        },
+        "id": "1BIYX468rw0h",
+        "outputId": "3c43cd56-e2a4-4350-86e6-1b1f1edfa7a2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3.2. Plot the Residuals\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "jXpSSO4-Ueks"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Using the previous residual plotting code, fill in the missing pieces to plot the distribution of the residuals.\n",
+        "\n",
+        "*Hint: Be sure to use the variables from the nonlinear model.*"
+      ],
+      "metadata": {
+        "id": "94zdgdF4Oq5W"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# calculating the residuals\n",
+        "### BEGIN SOLUTION\n",
+        "r = concentration - nl_model_concentration\n",
+        "### END SOLUTION\n",
+        "\n",
+        "## histogram of the residuals\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "plt.hist(r, bins=15, density=True)\n",
+        "\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Residual ($\\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Frequency\", fontsize = 16)\n",
+        "ax.set_title(\"Distribution of Residuals\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 630
+        },
+        "id": "VlYEUGHfLYYz",
+        "outputId": "4b76cbf1-feae-49b8-8dc4-762bb388acf5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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tOO5WhmGk+AL7yCOPZLhO4cKF1bhxY61du1aStGfPnhSh60716tXLsM3kk7kkP5qSFcnfD3u2R5KaNWumn376SVLi9tzNXn31Vb355puWR8OvXr1q3kKjSJEiKW5xk56DBw+ay3fehqR379765ptvJCWGxj/++EMDBw5Uhw4dsjWpy50Mw9Bff/1lPrbn97FmzZoqWbKkrl27lmPjyIxr165p3Lhx+vbbb+3+g1xWJwu6mz6HgXsRoRFAgZf8FMKko0SZNX36dP3xxx86duyYbt26pQULFmjBggVycnJSnTp11KJFC7Vr106PP/64XFxccmroqTjy9LjKlStnuu7y5cuOGo7DhYeHp7hpvbe3t13rJb/HXkZfgJOfGm0l6fRQSSnGkxXJ3w9HbI8j1apVS48++qj5OCYmRmfOnNHvv/9uBpJx48bp6NGjmj9/fprB8fz58+byrVu3Uswga687T2Hv0KGDRo4cac7CvGvXLnMG5DJlyqh58+Zq3bq1unfvrooVK2a6vyTh4eEpZvzMzO9jXoTG0NBQtWzZUqdPn87Uelk52yPJ3fI5DNyLOD0VQIEWHR2tf/75x3yc0el6VsqWLavdu3frrbfeUpkyZcznExIS9Pfff+vzzz/Xk08+qXLlymny5MnmNVU5zc3NzSHtSlLRokXtqnN3dzeXs/MFMK8l3S4gSfLtSk9mtj+3r7tKvk2O2B5HevjhhzVz5kzz39dff62goCCdOXNGL730klm3cOFCTZ06Nc02MrrG1B5xcXGpnvvkk0/0ww8/pLpFzsWLF7VixQqNHDlSlStXVs+ePTMdopLcuT9m5fcxN/Xv39/c1mLFiumll17S2rVrdeLECUVFRSk+Pl5G4oSMKU67TbqGOCvuls9h4F5EaARQoO3evTvFF4f07oGYEU9PT7377rs6e/astm/frqlTp6p79+4qVaqUWXP9+nW9/vrreuqpp8wJLfKLGzdu2FUXHR1tLhcrVizb/WbnS2R2JL/WVUq5XenJ6e3PScm36V7YHinxaO1HH30kf39/87mkI453Sh6gPD09zdCSmX/z5s1LcxxPPvmkduzYodDQUM2fP19Dhw5V7dq1zdcNw9CKFSv0r3/9K82xZeTO/TErv485JaPfyW3btmnbtm2SEse9fft2ffTRR+rQoYOqVq0qd3f3FEeCc/KPEff65zCQVwiNAAq0ZcuWmctOTk5q3rx5ttssVKiQHn74YY0ZM0YBAQG6ePGifvvtN3Xr1s2sWblyZYobi+cH9h4hSX7NV/IvakmSn26Z1lGbO+XE0aGs8PLySjFWe7c/+WQ1aW1/Xkp++vK9sD3JTZ8+XRUqVJCUeOrpm2++maom+dGniIgIu4NXZlSuXFkDBgzQrFmzdODAAZ0+fVoTJ040jwxevXpVL7/8cqbb9fLyUpEiRczHWfl9tJLTv5O//vqruTxw4MAU4TktoaGhGfaZWffq5zCQVwiNAAqsq1evav78+ebjjh072nWNWWYlhdHAwEC1a9fOfP7HH39MVXs3TxO/fft2u+qSzxj6r3/9K9XryScbunr1aobt/f333xnWOOLnZrPZ1KBBA/Nx0pGT9MTFxZnXs0lpb39eSj4Tqz3bc2fd3bY9yXl4eGjChAnm4+XLl6eaiKlcuXIpZju192eQHZUqVdK4ceM0e/Zs87l169YpNjY2U+3YbDY99NBD5mN7fh+PHTtm1+9YTv9Onjt3zly2Z7KnzZs3Z1iTXZn5HAaQGqERQIFkGIYGDhyY4jqht956y6F92mw2de3a1Xx88eLFVDVJU8ZL2Z/0JKdt3brVvMeflQMHDqSYYbN169apapJPrGLP9PpLly7NsMZRP7fkt1qYP39+hqeyBQYGml+6XV1d7Z5xNrck357Vq1fr0qVL6dafO3dOa9asSXP9u5Gfn1+K/evdd99NVdOlSxdz+fPPP8+NYUlSiiNct2/fztLkNG3atDGXFy5cmGH9t99+a1e7mfmd3L17d4afA8lPPc3oaO65c+e0cuXKDMeYU+z5HAaQGqERQIETFRWlvn37atWqVeZzzz77bJa/4EdGRqaY1TA9yU8Vu//++1O9nvz2CmfPns3SeBzFMAy9+OKLlsEpPj5e//3vf83HzZs3V61atVLV+fr6mkcGd+zYoUOHDln2+fnnn+vAgQMZjq148eLmF9XLly/nWHAcMmSI2e6ePXtSHC26U1hYmF555RXzcb9+/Rxy5Do72rdvr6pVq0pKvI9kejd/NwxDI0eONH+WPj4+euyxx3JjmFnm7OysN954w3wcGBio/fv3p6gZPXq0eQ/HgIAAy2sU03LhwoVUz9k7o2zy330nJ6cUv+v2eu6558zl7du3pxscjx8/runTp9vV7sMPP2wuJz/74k5xcXF68cUXM2yvWrVq5nJ6R/Li4+P1/PPP2/35mZ6c/BwGkBqhEUCBceHCBX344YeqXbt2iqNXjzzyiL766qsst/vHH3+oSpUqmjBhQor7uSUXHx+v77//3pyWX5Ief/zxVHV169Y1l9etW5dn1/OlpUiRIvrpp5/k5+eXauKK69evq1+/fubN4202m95///002ylbtqx5xMowDPXr1y/FDLZS4pfTadOm6b///a9dU+O7uLioRo0akhKP4gQGBmZ289Lk4+OjoUOHmo9feOEF80byyR0/flzt27c3j8B4enpq3LhxOTKGnOTk5KTJkyebj5csWaIhQ4akmpkzMjJSgwYN0g8//GA+98EHH1je//Bu4ufnZ96OwjAM/e9//0vxuo+PT4qzCgYPHqwxY8ZYhr+4uDitW7dOzz77bIrTe5M0bdpU/fv315o1ayxDy9GjRzVw4EDz8aOPPpri+kR71axZU35+fuZjf3//NEPe7t271a5dO0VHR9vVT58+fcz39vfff9drr72WambRf/75R126dNG2bdsy/J3s3Lmz+YehjRs3asyYMbp582aKmgsXLuipp57SqlWrcmSG15z8HAaQGvdpBHDPWL16dYovfgkJCYqIiFBYWJgOHjyY5ilVQ4YM0fTp07N9z67z589r4sSJmjhxosqWLasGDRqobNmycnZ21sWLF/XHH3+kuM6nRYsW6tu3b6p2GjdurEqVKunMmTM6f/68atWqpfbt26tUqVLmlzBfX1/16dMnW+PNitdff10zZszQt99+q4CAALVt21b333+/Lly4oA0bNqSYpfH1119Pd1Kh9957T8HBwUpISNBff/2lmjVrqm3btqpQoYKuXbumzZs369KlS/Lw8ND777+vkSNHZji+p556SpMmTZIkPf3005o3b56qV6+eYpKPDz/8MNPb/eGHH2r37t3atWuX4uLi9MILL2jy5Mlq3ry5PDw8FBISos2bN5tfsp2dnTVnzpwUp/zdTXr37q3Nmzeb9yj8+uuv9f3336tNmzYqU6aMLl26pF9//TVFkBw1apR69OiRV0POlMKFC+v111/X8OHDJSVOdjVhwoQUR73Hjx+vU6dOmaccT5s2TZ9++qn+/e9/y8fHR0WLFlVERIROnTqlffv2mft2WkcHb9++rSVLlmjJkiVyc3NT/fr1Va1aNXl6eur69es6ceKEdu/ebda7ubllaT9M8tFHH+n333/XkSNHFBsbKz8/P73zzjtq2rSpXFxcdODAAe3cuVOGYahHjx66evWqNm3alG6b3t7eGjZsmHm67pQpU7RkyRK1bNlSrq6uCgkJ0datW3Xr1i099thjKlu2bLpHOWvVqqVnn33WPD122rRpWrx4sXx9fXX//ffr1KlT2rx5s27duqVixYpp6tSpGjZsWJZ/Jkly6nMYQBoMAMjHWrVqZUjK1L9ChQoZXbp0MX755Re7+jh58qS5rre3d6rXt2/fbjg7O9vdf8+ePY2IiAjL/n766SejSJEilusPHDgwRf3AgQPN1+bOnZuln11wcHCaNXe2vXXrVqNcuXLp/mzffPNNu/qfM2eOUahQIcu2ypUrZ2zevNkIDg42n2vVqpVle2FhYUatWrXS/dknl9H7mlxkZKTRu3fvDN/bcuXKGatXr063rfHjx5v148ePz/DnZO/2Z9a7775ruLi4pLs9rq6uxqRJkzJsK6v7YGbavXO/T09sbKxRsWJFc91nnnkmzbpPPvnEKFGihF2/tzabzejWrVuqNurWrWv3737VqlWNrVu3pjmWzOyPZ8+eNf7973+n21e3bt2MiIgIu37PDcMwbt68aXTq1CndNrt06WJcv37drvc7OjraaN++fbrtVaxY0diyZYtd+3hufw4DSIkjjQDuWUWKFJGnp6e8vLxUtmxZNWzYUI0aNdJjjz2mihUr5lg/Dz/8sC5duqRffvlFW7Zs0d69exUSEqKrV68qPj5enp6e8vHxUZMmTfTMM8+kugH4nbp06aLdu3frs88+05YtW3T69GlFRUXdFfcTe+SRR/TXX39p9uzZCggI0KlTpxQVFaXy5curbdu2GjFihN0zbA4ePFhNmjTRRx99pA0bNuj8+fNydXVV1apV9dRTT2no0KEqVaqUNm7caFd7Xl5e2rVrlz7//HOtWrVKhw4dUlhYWI5c3+jh4aHvv/9eo0aN0oIFC7Rx40adO3dON2/eVKlSpVS3bl116dJFgwcPzrObqWfWW2+9pWeffVZff/21goKCdPLkSYWFhal48eKqVq2aOnToIH9/f/NUz/ykSJEieu211/TCCy9ISjwNd/z48apevXqKupEjR8rPz08LFizQ+vXr9ddff+ny5cuKiYlRsWLFVLFiRdWpU0etW7dWp06dUsy8muTPP//U9u3bFRwcrJ07d+rIkSM6d+6cbty4oaJFi5pHvLp166bevXtn+6wGSSpfvry2b9+ub7/9VosWLdK+ffsUHh6uMmXK6KGHHtLAgQP11FNPZWpWYVdXV/38889asmSJ5s+frz179ig8PFz333+/HnroIfn5+alnz552t1m0aFGtWbNGixcv1vz587V3715FRESoVKlSqlatmp566in5+fmpRIkSdv+OpyenP4cBpGQz7oZvIQAAAACAu9Ldf0U7AAAAACDPEBoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJac83oAuLuULVtW0dHRqly5cl4PBQAAACjwTp8+LXd3d124cCHPxsCRRqQQHR2t27dv5/UwCqSQkBCFhITk9TCQh9gHILEfgH0A7ANIuQ/cvn1b0dHReToejjQihaQjjAcOHMjjkRQ8derUkcTPviBjH4DEfgD2AbAPIOU+kLSclzjSCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEs2wzCMvB4E7h7M1gUAAADcPe6G7+ccaQQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwJJzXg8AAAqKKq+tyushZNqpyZ3zeggAACCPcaQRAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgpsaAwODpafn5+qV68ud3d3lShRQvXq1dPYsWN17Ngxh/UbGBioXr16qWrVqnJzc1OpUqXUqFEjTZgwQefOnbOrjSpVqshms9n9b//+/Q7bHgAAAAD3Nue8HkBui42Nlb+/vxYuXJji+Rs3bigsLEz79+/XzJkzNXnyZL344os51u/169fVr18/BQUFpXg+JiZGV69e1Z49e/Txxx9r9uzZ6t27d471CwAAAADZUaBCo2EYevrpp7VixQpJkoeHhwYPHixfX1/FxsYqKChIy5cvV0xMjEaNGqXChQtrxIgR2e43JiZGXbp00bZt2yRJpUuXlr+/v+rWrauIiAgFBARo3bp1Cg8PV//+/eXm5qauXbva1XZAQECGNd7e3tkaPwAAAICCy2YYhpHXg8gtCxYs0IABAyQlBrdNmzbpwQcfTFGzbNky9enTR4ZhyMXFRYcPH1aVKlWy1e+7776rcePGSZJq1qyp4OBglS9fPkXNtGnTNGbMGEnS/fffr2PHjsnT0zPN9qpUqaLQ0FBJiUE4J9WpU0eSdODAgRxtF4BU5bVVeT2ETDs1uXNeDwEAgALtbvh+XmCuaTQMQ2+//bb5eObMmakCoyT16tVLw4YNk5R4KuvEiROz1W9ERISmTJliPl6wYEGqwChJo0eP1uOPPy5JunTpkqZPn56tfgEAAAAgJxSY0Lhlyxbz6Jy3t7d69uxpWTt69GhzecWKFYqNjc1yvytXrlR0dLQkqXnz5mrcuLFd/S5evDjLfQIAAABATikwoXH16tXmcseOHeXkZL3pPj4+qlmzpiQpMjJSmzdvzpF+O3dO/zSvVq1ayd3dXZJ09OhRh87iCgAAAAD2KDChcd++feZyekf70qpJvq4j+3V2dlbDhg0z1W+XLl1UoUIFFSlSRMWLF9eDDz6oQYMGac2aNVkeMwAAAAAkKTCzpx45csRcrlq1aob1yWsOHz6cpT4Nw0hxtNDefrds2WJ3v6tW/d/EGuHh4QoPD9fhw4c1b948NW7cWIsXL5aPj0+mxh0SEmJecHsnJsgBAAAAcp7V9++QkJBMf5/PaQUmNF6/ft1cLlWqVIb1yWvCwsKy1GdUVJRu377tkH7LlCmjdu3a6V//+pfKlSsnSfrnn3+0fv16rV+/XoZhaOfOnWrSpIm2bdumGjVqZGkbAAAAABRsBSY0RkZGmstubm4Z1ieviYiIyHafOdnvt99+q2bNmqlQoUKpXhszZox27NihXr166cyZM7py5Yp69eqlPXv2pHsdZ3I+Pj4cUQQAAABykdX3b6sjkLmpwFzTeC9p2bJlmoExycMPP6y1a9eqSJEikqS//vpLAQEBuTU8AAAAAPeQAhMaixUrZi7fvHkzw/rkNZ6entnuMzf7laTatWvr2WefNR//+OOPWW4LAAAAQMFVYEJj8eLFzeUrV65kWJ+8Jvm6meHh4SFn5/87Azi3+k3Stm1bc/nQoUPZagsAAABAwVRgQmOtWrXM5ZMnT2ZYn7wm+bqZYbPZzPs95ma/SUqXLm0uZ3UyHwAAAAAFW4EJjfXr1zeXd+7cmWF98prk6zqy37i4OO3duzdH+pVSHrUsUaJEttoCAAAAUDAVmNDYqVMnc3nt2rVKSEiwrA0JCdHRo0clJV6X2KJFixzpd/Xq1enWbtq0SdHR0ZKkGjVqZPs2GcHBwebyAw88kK22AAAAABRMBSY0NmvWTJUrV5YkhYaGavny5Za106ZNM5d79OghV1fXLPfbrVs3ubu7S5J+++23dI82Ju+3f//+We5Tkg4fPqxvv/3WfNy1a9dstQcAAACgYCowodHJyUnvvPOO+XjkyJE6fPhwqrrly5dr1qxZkiQXFxeNGzfOss3WrVvLZrPJZrNpwoQJadZ4eXlp7Nix5uMBAwbo3LlzqeqmTZumNWvWSJJKlSqll156Kc323n33Xe3bt89yTJK0e/dudejQQbGxsZKkevXqqUePHumuAwAAAABpcc645N4xYMAABQYGKjAwUJcuXVLjxo01ePBg+fr6KjY2VkFBQVq2bJkMw5AkTZ06VdWqVct2v6+88orWrl2r7du368iRI2rQoIGGDBmiunXrKiIiQgEBAQoKCpIkFSpUSF999ZW8vLzSbGvFihUaN26cateurTZt2qh27doqWbKkbDab/vnnH/3yyy8KCgoyt6FUqVJaunRpuvd1BAAAAAArBSo02mw2LVmyRIMHD9aSJUsUGRmpGTNmpKpzcXHRpEmTNHLkyBzp183NTatWrVLfvn21fv16Xb58WZMmTUpV5+npqVmzZql79+4Ztnnw4EEdPHgw3ZpmzZpp3rx5ql69elaHDgAAAKCAK1ChUZJcXV21ePFi+fv7a968edq6davOnz+vIkWKqGLFiurQoYOGDh2a4lYZOaFkyZJat26dAgICtGjRIu3atUsXL16Uu7u7vL291aVLFw0dOlQVKlRIt52FCxdqy5Yt2rFjh/7++29duXJFV69eVWxsrLy8vOTt7a0mTZqoT58+2ZrABwAAAAAkyWYknccISKpTp44k6cCBA3k8EuDeU+W1VXk9hEw7NblzXg8BAIAC7W74fl5gJsIBAAAAAGQeoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEsFNjQGBwfLz89P1atXl7u7u0qUKKF69epp7NixOnbsmMP6DQwMVK9evVS1alW5ubmpVKlSatSokSZMmKBz585lu/033nhDNpvN/Ne6devsDxoAAABAgeWc1wPIbbGxsfL399fChQtTPH/jxg2FhYVp//79mjlzpiZPnqwXX3wxx/q9fv26+vXrp6CgoBTPx8TE6OrVq9qzZ48+/vhjzZ49W717985SHzt37tQHH3yQE8MFAAAAAEkFLDQahqGnn35aK1askCR5eHho8ODB8vX1VWxsrIKCgrR8+XLFxMRo1KhRKly4sEaMGJHtfmNiYtSlSxdt27ZNklS6dGn5+/urbt26ioiIUEBAgNatW6fw8HD1799fbm5u6tq1a6b78PPzU3x8vNzd3RUdHZ3tcQMAAABAgQqNCxcuNANj6dKltWnTJj344IPm688995yWLVumPn36yDAMvfzyy+rUqZOqVKmSrX6nTp1qBsaaNWsqODhY5cuXN18fNmyYpk2bpjFjxig+Pl7+/v46duyYPD097e7jrbfe0qFDh+Tp6amxY8fq7bffztaYAQAAAEAqQNc0GoaRIkjNnDkzRWBM0qtXLw0bNkxS4qmsEydOzFa/ERERmjJlivl4wYIFKQJjktGjR+vxxx+XJF26dEnTp0+3u49t27aZ9R9++KEqVqyYrTEDAAAAQJICExq3bNmi0NBQSZK3t7d69uxpWTt69GhzecWKFYqNjc1yvytXrjRPFW3evLkaN25sV7+LFy+2q/0bN27Iz89PCQkJevTRRzVkyJAsjxUAAAAA7lRgQuPq1avN5Y4dO8rJyXrTfXx8VLNmTUlSZGSkNm/enCP9du7cOd3aVq1ayd3dXZJ09OhRu2Zxff3113Xs2DG5u7vr66+/zvI4AQAAACAtBSY07tu3z1xO72hfWjXJ13Vkv87OzmrYsKHd/W7evFmffvqpJOn999/P9rWXAAAAAHCnAhMajxw5Yi5XrVo1w/rkNYcPH85Sn4ZhpDhamJP9RkdHa9CgQTIMQ82bN9cLL7yQpTECAAAAQHoKzOyp169fN5dLlSqVYX3ymrCwsCz1GRUVpdu3bzuk37Fjx+rEiRNydXXVnDlzZLPZsjTGtISEhKhOnTppvnbgwIEc6wcAAABAIqvv3yEhIfLx8cnl0aRUYI40RkZGmstubm4Z1ieviYiIyHafOdnvr7/+qlmzZkmSJk6caF5/CQAAAAA5rcAcabxXREZGavDgwTIMQ76+vilmXM0pPj4+HFEEAAAAcpHV92+rI5C5qcAcaSxWrJi5fPPmzQzrk9d4enpmu8+c6vfll1/W6dOnVaRIEX3zzTcqVKhQlsYGAAAAAPZweGj8+eefZRiGo7vJUPHixc3lK1euZFifvCb5upnh4eEhZ+f/O5ib3X6DgoLM22q8+eabqlu3bpbGBQAAAAD2cvjpqd26dVP58uU1aNAgDR482K4ZRB2hVq1aOnHihCTp5MmTatOmTbr1J0+eTLFuVthsNtWsWVMHDx4028xo+9PrN+k6xsKFCys+Pl7/+9//0mxj79695nJoaKhZ5+rqqjFjxmR+QwAAAAAUWLlyTeP58+c1adIkvf/++2rdurWGDBmiJ598UkWKFMmN7iVJ9evX1+rVqyVJO3fu1ODBg9Ot37lzZ4p1s9NvUmjcuXOn2rZta1kbFxeXIvDd2W/SEdvbt2/rnXfesav/U6dO6e2335YkeXl5ERoBAAAAZIrDT0/19vaWYRgyDEMJCQkKDg5W//79Vb58eb300kvav3+/o4cgSerUqZO5vHbtWiUkJFjWhoSE6OjRo5ISr0ts0aJFjvSbFFqtbNq0SdHR0ZKkGjVqqEaNGlnuFwAAAABygsND48mTJ7V+/Xr16dNHLi4uZoC8du2aPvnkEz300ENq0qSJ5syZYwYmR2jWrJkqV64sKfGUzeXLl1vWTps2zVzu0aOHXF1ds9xvt27d5O7uLkn67bffUhzBTK/f/v37p3o9MDDQ/Pml92/u3LnmOq1atTKfz+r9JgEAAAAUXLkye+qjjz6qJUuW6Ny5c5oxY4YeeughSTLDzK5du/T888+rXLly8vf31++//57jY3ByckpxSufIkSN1+PDhVHXLly83rx10cXHRuHHjLNts3bq1bDabbDabJkyYkGaNl5eXxo4daz4eMGCAzp07l6pu2rRpWrNmjSSpVKlSeumll+zaLgAAAABwpFy95UaJEiU0cuRI7d27V7t379awYcPk5eVlhseoqCjNnTtXzZs3V926dfXxxx/r6tWrOdb/gAED1L17d0nSpUuX1LhxY40aNUqLFi3SN998oz59+qh3797mtYNTp05VtWrVst3vK6+8oiZNmkiSjhw5ogYNGujNN9/UkiVL9OWXX6pjx47mtYaFChXSV199JS8vr2z3CwAAAADZlWf3afzXv/6lzz//XOfPn9e3335rHrVLCpAHDx7U6NGjVbFiRfXt21fr16/Pdp82m01LlixRv379JEmRkZGaMWOGnnnmGT333HNaunSpDMOQi4uLpk2bppEjR2a7T0lyc3PTqlWr1K5dO0nS5cuXNWnSJPXv31/Dhg1TUFCQpMT7Mi5YsMAMtgAAAACQ1/IsNCZxdXXVM888ow0bNujYsWN6/fXXVb58eUmJp6/GxsZq2bJl6tixo6pVq6b33ntPFy5cyFZ/ixcv1q+//qpnn31W1apVk5ubm7y8vFSnTh29/PLL2rdvn15++eWc2kRJUsmSJbVu3Tr98MMPeuqpp1S5cmW5uLioZMmSatiwod5++20dPHjQDLQAAAAAcDewGUnnYt5FQkJC9Mwzz2jHjh2y2WySEgNk0rKzs7P69Omj8ePHy8fHJy+Hes+pU6eOJOnAgQN5PBLg3lPltVV5PYRMOzW5c14PAQCAAu1u+H6e50cak9y6dUvff/+92rdvrwceeEA7d+5Mcbpq8plXb9++rUWLFqlevXr68ssv83roAAAAAHDPyvPQuG/fPr344osqX768+vfvr19//VUJCQnmkcVOnTopMDBQkZGR2rhxo5555hm5urrKMAzFxMRoxIgR5qyjAAAAAICclSehMSIiQrNmzZKvr68aNmyomTNn6tq1a+aRxEqVKmn8+PE6deqUfv75Z3Xr1k3Ozs5q2bKlvv32W508eVK9evWSlHja6ocffpgXmwEAAAAA9zzn3Oxs06ZNmjNnjlasWKGYmBhJMm9vUahQIXXq1EnPP/+8Hn/8cTk5WefZMmXKaMmSJfrrr7909OhR/fHHH7kyfgAAAAAoaBweGs+fP6958+bpm2++0YkTJyT9X1CUJG9vbz333HMaPHiwOWuqPZycnNSiRQsdPXpUkZGROT5uAAAAAEAuhMZKlSqZITHpv87OzurSpYuef/55dejQwZwVNbM8PDxybJwAAAAAgNQcHhoTEhLM5apVq8rf31+DBg1S2bJls91248aNNXDgwGy3AwAAAABIm8NDY+HChdWtWzc9//zzateuXY623a9fP/Xr1y9H2wQAAAAA/B+Hh8Z//vlHpUuXdnQ3AAAAAAAHcPgtNwiMAAAAAJB/5cl9GgEAAAAA+YPDT0+Nj4/Xs88+q5iYGNWoUUNTpkyxe91XX31Vx44dU7FixTR//nwHjhIAAAAAkBaHH2lcvXq1vvvuO61cuVLVq1fP1LrVq1dXYGCgFi5cqKCgIAeNEAAAAABgxeGhcdWqVZISZ1Ht3bt3ptbt3bu3ChcuLEn66aefcnxsAAAAAID0OTw07tq1S5L00EMPycvLK1Prenl5qUGDBjIMQzt37nTE8AAAAAAA6XB4aDxx4oRsNpseeOCBLK1fs2ZNsx0AAAAAQO5yeGiMjo6WJLm7u2dpfQ8PD0lSREREjo0JAAAAAGAfh4dGT09PSdK1a9eytH7SelkNnQAAAACArHN4aCxXrly2rklMWq9MmTI5OSwAAAAAgB0cHhqbNWsmSTp9+rTWr1+fqXXXrVun0NBQ2Ww2NW3a1BHDAwAAAACkw+GhsXv37uby8OHDdeXKFbvWu3TpkoYPH55mOwAAAACA3OHw0NixY0c1atRIknTy5Ek1btw4wyOO69evV5MmTXTy5EnZbDY99NBDeuKJJxw9VAAAAADAHZxzo5N58+bpkUceUVRUlE6dOqWOHTuqRo0aat26tapVqyYPDw9FRUXp5MmTCg4O1rFjx8x1PTw8NH/+/NwYJgAAAADgDrkSGuvUqaOVK1eqd+/eunLligzD0LFjx1KEw+QMw5AklSxZUkuXLlW9evVyY5gAAAAAgDs4/PTUJK1bt9aePXvUv39/OTs7yzAMy3/Ozs56+umn9eeff6pt27a5NUQAAAAAwB1y5UhjkooVK2rhwoX64IMP9Msvv2j79u26ePGiIiMjVaxYMZUpU0ZNmjRRu3btVK5cudwcGgAAAAAgDbkaGpOUL19eAwYM0IABA/KiewAAAACAnXLt9FQAAAAAQP5DaAQAAAAAWCI0AgAAAAAs5fo1jRcvXtTu3bt16tQpRURE6Pbt23avO27cOAeODAAAAABwp1wLjTt37tQbb7yhjRs3mvdhzCxCIwAAAADkrlwJjV9//bWGDx+uhISELAdGm82Ww6MCkF9VeW1VXg8BAACgwHB4aDxw4ICGDx+u+Ph4SYnhz9fXVw0bNtR9992nwoULO3oIAAAAAIAscnhonD59uuLj42Wz2VSnTh0tWbJEderUcXS3AAAAAIAc4PDQGBwcLElyc3PTmjVrVKFCBUd3CQAAAADIIQ6/5cb58+dls9nUtm1bAiMAAAAA5DMOD43u7u6SRGAEAAAAgHzI4aGxatWqkqSrV686uisAAAAAQA5zeGh86qmnZBiGfvvtNyUkJDi6OwAAAABADnJ4aHz++edVvnx5Xbp0SZ9++qmjuwMAAAAA5CCHh8YSJUro+++/l7u7u8aOHas5c+Y4uksAAAAAQA5x+C03Nm/eLEl6//33NWbMGD3//PP69NNP1atXL9WrV09eXl6y2Wx2tdWyZUtHDhUAAAAAcAeHh8bWrVunCIWGYejvv//W33//nal2bDab4uLicnp4AAAAAIB0ODw0SolBMb3HAAAAAIC7k8NDY8uWLe0+/RQAAAAAcHdxeGjcuHGjo7sAAAAAADiIw2dPBQAAAADkX4RGAAAAAIAlQiMAAAAAwFKuzJ56p+vXr2vr1q06c+aMrl+/rri4OI0bNy4vhgIAAAAASEeuhsYdO3bonXfeUVBQUKrbbtwZGi9evKgnnnhCCQkJatq0qWbMmJGbQwUAAAAAKBdPT508ebJatGihtWvXKiEhQYZhmP/SUqZMGZUtW1a7d+/Wl19+qatXr+bWUAEAAAAA/1+uhMbPPvtMb7zxhuLi4mQYhmrVqqXhw4fr3//+d7rrDRo0SJJ0+/ZtrV69OjeGCgAAAABIxuGh8fz583rllVckSa6urpo7d64OHjyozz77TA8//HC663bs2FEuLi6SpODgYEcPFQAAAABwB4eHxlmzZunmzZuy2WyaPn26Bg4caPe6Li4uqlu3rgzD0N9//+3AUQIAAAAA0uLw0BgUFCRJKl++vJ5//vlMr+/j4yNJCg0NzdFxBQcHy8/PT9WrV5e7u7tKlCihevXqaezYsTp27FiO9pVcYGCgevXqpapVq8rNzU2lSpVSo0aNNGHCBJ07dy7D9W/duqUNGzbogw8+UJ8+fdSwYUNVrlxZRYsWlaurq8qWLavWrVtr/PjxOnHihMO2AwAAAEDB4PDZU0+cOCGbzaZmzZrJZrNlev3ixYtLkiIiInJkPLGxsfL399fChQtTPH/jxg2FhYVp//79mjlzpiZPnqwXX3wxR/qUEm8z0q9fPzNEJ4mJidHVq1e1Z88effzxx5o9e7Z69+5t2c7Bgwf16KOPWr5+8eJFXbx4UZs2bdL777+v0aNH67333pOTE7fkBAAAAJB5Dg+N4eHhkqSSJUtmaf3Y2FhJUuHChbM9FsMw9PTTT2vFihWSJA8PDw0ePFi+vr6KjY1VUFCQli9frpiYGI0aNUqFCxfWiBEjst1vTEyMunTpom3btkmSSpcuLX9/f9WtW1cREREKCAjQunXrFB4erv79+8vNzU1du3ZNt82qVavK19dXtWvXlre3t4oVK6aYmBidOHFCP/30k3bt2qXbt29r8uTJun79umbNmpXt7QAAAABQ8Dg8NJYoUUKXL1/W9evXs7T+6dOnJUmlSpXK9lgWLlxoBsbSpUtr06ZNevDBB83Xn3vuOS1btkx9+vSRYRh6+eWX1alTJ1WpUiVb/U6dOtUMjDVr1lRwcLDKly9vvj5s2DBNmzZNY8aMUXx8vPz9/XXs2DF5enqmasvHx0cnTpxQ1apVLft7++23NWfOHPn7+0uSvvzySw0ZMkSNGjXK1nYAAAAAKHgcfs6it7e3DMPQH3/8kel1o6OjtWPHDtlsNtWuXTtb4zAMQ2+//bb5eObMmSkCY5JevXpp2LBhkhKPck6cODFb/UZERGjKlCnm4wULFqQIjElGjx6txx9/XJJ06dIlTZ8+Pc32ihUrlm5gTPLcc8+pS5cu5uMff/wxs0MHAAAAAMeHxscee0ySFBISot9++y1T63766ae6ceNGinayasuWLeZkOt7e3urZs6dl7ejRo83lFStWmKfIZsXKlSsVHR0tSWrevLkaN25sV7+LFy/Ocp9J6tatay5fuHAh2+0BAAAAKHgcHhqfffZZFSpUSJI0dOhQXbt2za71fv31V02YMEGSVLRoUQ0YMCBb41i9erW53LFjx3QnhvHx8VHNmjUlSZGRkdq8eXOO9Nu5c+d0a1u1aiV3d3dJ0tGjR7M9i2vy9cuVK5ettgAAAAAUTA4PjbVq1dJzzz0nwzB05MgRNWnSRD///LMMw0iz/sSJExozZow6deqkW7duyWazafTo0brvvvuyNY59+/aZy+kd7UurJvm6juzX2dlZDRs2zJF+f/jhBwUEBEiSnJyc9NRTT2W5LQAAAAAFl8MnwpGkTz75RPv379e2bdsUEhKiJ554Qp6enilmRG3YsKEuXLigS5cuSZIZKtu3b6/x48dnewxHjhwxl+25JjB5zeHDh7PUp2EYKY722dvvli1b7O73559/VlxcnKTEWVpPnz6toKAgbdiwQZJks9k0ZcoU1atXz+5xh4SEqE6dOmm+duDAAbvbAQAAAGAfq+/fISEh5r3r80quhMYiRYpo3bp1Gj58uBYsWCAp8VYcNpvNvHfjvn37Uh19HDhwoL788sss3d/xTslnb7VnJtbkNWFhYVnqMyoqSrdv33Zov3379jWvmUwu6d6Yb731ljp06GDfgAEAAADgDrkSGqXE6xLnz58vf39/ffTRR/rll1/SDDsuLi5q27atXn31VbVs2TLH+o+MjDSX3dzcMqxPXhMREZHtPnOzXynxGsZ27dqpVq1amV7Xx8eHI4oAAABALrL6/m11BDI35VpoTNKiRQu1aNFC8fHx2rdvn86dO6fw8HC5u7urTJkyatCggVxdXXN7WPlWVFRUiuWjR48qMDBQ06dP1/jx4/Xhhx9q3rx56tGjRx6OEgAAAEB+leuhMUmhQoXUsGHDFBO/OFKxYsXMmVtv3ryZYX3yGk9Pzyz3eWebdz6Xk/16eHjoX//6l/71r3/p2WefVcuWLXXhwgX17NlT69ev16OPPpqp9gAAAADA4bOn3i2KFy9uLl+5ciXD+uQ1ydfNDA8PDzk7/18uz61+JalGjRqaPHmypMQJed5+++0stwUAAACg4CowoTH5tX0nT57MsD55TVauC5QSJ6NJut9jbvabpFOnTubyjh07dOPGjWy1BwAAAKDgKTChsX79+ubyzp07M6xPXpN8XUf2GxcXp7179+ZIv1LK02MTEhIUHh6erfYAAAAAFDwOv6axbdu2OdKOzWbTr7/+muX1O3XqZJ6uuXbtWiUkJMjJKe3MHBISoqNHj0pKDF4tWrTIVr/fffedJGn16tV67bXXLGs3bdpkzihbo0YN1ahRI8v9Skpxj0ibzWbXLT8AAAAAIDmHh8aNGzdm+z6LhmFku41mzZqpcuXKOn36tEJDQ7V8+XL17t07zdpp06aZyz169MjWbK7dunWTu7u7oqOj9dtvv2nnzp1q3Lhxhv32798/y30m+fzzz83lhx9+WIULF852mwAAAAAKllw5PdUwjEz/S75eTnByctI777xjPh45cqQOHz6cqm758uWaNWuWpMR7Ro4bN86yzdatW8tms8lms2nChAlp1nh5eWns2LHm4wEDBujcuXOp6qZNm6Y1a9ZIkkqVKqWXXnopzfbefPNNHTp0yHJMknT79m2988475nZI0osvvpjuOgAAAACQFocfaQwODrarLumau7///ltLly7VgQMH5Orqqg8//DDHbmg5YMAABQYGKjAwUJcuXVLjxo01ePBg+fr6KjY2VkFBQVq2bJkZVKdOnapq1aplu99XXnlFa9eu1fbt23XkyBE1aNBAQ4YMUd26dRUREaGAgAAFBQVJSrwVyVdffSUvL68021q0aJEmTZqkevXqqWXLlqpdu7ZKliypQoUK6dq1a/r777+1cuVK/fPPP+Y6zz33nPr27Zvt7QAAAABQ8NiMnDqUl8M+//xzvfjiiypatKjWrFmjRx55JEfajYmJ0eDBg7VkyRLLGhcXF02aNEkvv/xyum21bt1amzZtkiSNHz/e8mijJF27dk19+/bV+vXrLWs8PT01a9Ys9evXz7KmSpUqCg0NTXdcSYoVK6YJEybopZdesvv03qSAfuDAAbvqgbxQ5bVVeT2EAuPU5M55PQQAAAq0u+H7ucOPNGbViBEjdPPmTY0dO1Z9+vTRX3/9pZIlS2a7XVdXVy1evFj+/v6aN2+etm7dqvPnz6tIkSKqWLGiOnTooKFDh6a4VUZOKFmypNatW6eAgAAtWrRIu3bt0sWLF+Xu7i5vb2916dJFQ4cOVYUKFdJtZ+vWrVq/fr1+//137du3T6dOndL169cVHx8vDw8PlS1bVvXr19djjz2mnj17qkSJEjm6HQAAAAAKlrv2SKMkxcfHq0KFCrp8+bLee++9dGceRc64G/6SAWSEI425hyONAADkrbvh+/ldfZ/GQoUKqWXLljIMQ8uXL8/r4QAAAABAgXNXh0ZJ5impJ0+ezOORAAAAAEDBc9eHxrNnz0pKnMAGAAAAAJC77urQePbsWW3YsEE2m03lypXL6+EAAAAAQIFz14bGI0eOqGvXrrp586YkqW3btnk8IgAAAAAoeBx+y4133nnH7tq4uDhdvXpVf/75p3bs2KGkiV0LFSqU4T0TAQAAAAA5z+GhccKECXbfWD65pMDo5OSkL7/8UrVq1crpoQEAAAAAMpArp6cahpHpfzabTY8++qi2bdumQYMG5cYwAQAAAAB3cPiRxvHjx9tdW7hwYXl6eqpKlSry9fVVmTJlHDgyAAAAAEBG7qrQCAAAAAC4u9y1s6cCAAAAAPIeoREAAAAAYInQCAAAAACwRGgEAAAAAFhy+EQ4gwcPdnQXstlsmjNnjsP7AQAAAICCxuGhcd68ebLZbI7uhtAIAAAAAA7g8NAoSYZhpHhss9lSPZeZ19OqBwAAAADkPIeHxrlz50qSTp8+rffee0+3bt2SJDVt2lRNmzZV5cqV5e7urujoaJ05c0a///67fv/9d0mSi4uL3njjDVWuXNnRwwQAAAAApMHhoXHgwIHasWOHXn75Zd2+fVvt27fXp59+qho1aliuc/z4cf33v//V2rVrNWPGDK1atUoPP/ywo4cKAAAAALiDw2dPvX79unr16qWwsDD17dtXa9asSTcwSlL16tW1atUq9evXT9euXVOvXr107do1Rw8VAAAAAHAHh4fGr7/+Wv/884/c3d01a9Ysu68/tNlsmjVrljw8PHT27Fl99dVXDh4pAAAAAOBODg+Ny5cvl81mU9u2bVWsWLFMrVusWDG1bdtWhmFoxYoVDhohAAAAAMCKw0PjyZMnJUlly5bN0vplypSRJJ06dSqnhgQAAAAAsJPDQ2NUVJQk6fz581la/8KFCynaAQAAAADkHoeHxnLlyskwDG3YsEHh4eGZWjc8PFwbNmyQzWZTuXLlHDRCAAAAAIAVh4fGRx99VJJ048YNDR06VIZh2L3usGHDFB0dLUlq27atQ8YHAAAAALDm8NA4fPhwFSpUSJK0bNkydezYUUeOHEl3naNHj+rxxx/X0qVLEwfp5KQRI0Y4eqgAAAAAgDs4O7qDhg0b6tVXX9WkSZNks9n0yy+/qHbt2mrUqJGaNm2qypUrq2jRorpx44ZOnz6t7du3a/fu3ZJkHpV85ZVX1LBhQ0cPFQAAAABwB4eHRkn63//+p4SEBE2ZMsUMgn/88Yf++OOPNOuTamw2m8aMGaP33nsvN4YJAAAAALiDw09PTTJp0iQFBweradOmkhKDodU/SXrkkUe0YcMGTZkyJbeGCAAAAAC4Q64caUzSsmVLbd26VYcPH1ZwcLD27t2ry5cvKyoqSh4eHipdurQaNmyoNm3aqFatWrk5NAAAAABAGnI1NCapVasWoRAAAAAA8oFcOz0VAAAAAJD/EBoBAAAAAJby5PTUP//8U1u2bNGZM2d0/fp1xcfHa86cOXkxFAAAAABAOnI1NC5fvlwTJkzQoUOHzOcMw5DNZksVGi9evKiGDRsqLi5OzZs31w8//JCbQwUAAAAAKBdPTx02bJj69OmjQ4cOpbq9RlrKlCmjRx99VFeuXNGPP/6os2fP5tZQAQAAAAD/X66ExrfeekuzZ882g2KHDh00ZcoUtWnTJt31BgwYICnxaOTq1atzY6gAAAAAgGQcHhqPHTumDz74QJJUvHhxbdiwQWvWrNHYsWNVu3btdNdt27at3N3dJUkbN2509FABAAAAAHdweGicPXu24uLiZLPZ9NVXX6l169Z2r1uoUCHVr19fhmHowIEDjhskAAAAACBNDg+Nv/76qyTJx8dHTz31VKbXr1KliiTpn3/+yclhAQAAAADs4PDQGBoaKpvNpocffjhL63t6ekqSIiMjc3JYAAAAAAA7ODw0RkdHS5I8PDyytP6NGzckSa6urjk2JgAAAACAfRweGu+77z5J0uXLl7O0fkhIiCSpdOnSOTYmAAAAAIB9HB4afXx8ZBiGdu7cmel1r169qt27d8tms+mhhx5ywOgAAAAAAOlxeGhs3769JOns2bNauXJlptadPHmybt26JUlq165djo8NAAAAAJA+h4dGPz8/83rEESNG6NSpU3atN3/+fH300Uey2WwqUaKEnn32WQeOEgAAAACQFoeHxooVK2rMmDEyDEMXLlyQr6+vZs6cqatXr6aqjYmJ0YYNG9SzZ08NHjxYhmFIkiZOnCh3d3dHDxUAAAAAcAebkZTMHMgwDPXq1Us//PCDbDab+XyRIkUUGxsrm80mT09PRUREpFhHSjxS+c033zh6iPj/6tSpI0k6cOBAHo8EsFbltVV5PYQC49Tkznk9BAAACrS74fu5w480SpLNZtPSpUv11ltvycnJSYZhyDAM3bp1ywyR4eHh5vOGYahQoUIaP348gREAAAAA8lCuhEZJcnJy0jvvvKPjx4/rxRdfVM2aNVOExCSVKlXS8OHDdeTIEY0fPz63hgcAAAAASINzbnfo7e2t6dOna/r06bp27ZrOnz+v8PBwubu7q0yZMipbtmxuDwkAAAAAYMHhofHll1+WlHik8f3331fhwoXN10qWLKmSJUs6eggAAAAAgCxyeGj8+OOPZbPZ1KxZsxSBEQAAAABw93P4NY2enp6SpJo1azq6KwAAAABADnN4aCxXrpwk6fbt247uKlOCg4Pl5+en6tWry93dXSVKlFC9evU0duxYHTt2zGH9BgYGqlevXqpatarc3NxUqlQpNWrUSBMmTNC5c+fsauP8+fOaP3++nnvuOf373//Wfffdp8KFC8vLy0sPPvigBgwYoJ9//lkJCQkO2w4AAAAABYPD79M4ZMgQzZkzR/Xr19eff/7pyK7sEhsbK39/fy1cuNCyxtXVVZMnT9aLL76YY/1ev35d/fr1U1BQkGWNl5eXZs+erd69e1vW9OrVSytWrJA9b5uvr68WLFigBx54wO5x3g33gQEywn0acw/3aQQAIG/dDd/PHX5No5+fn+bMmaO///5b27Zt0yOPPOLoLi0ZhqGnn35aK1askCR5eHho8ODB8vX1VWxsrIKCgrR8+XLFxMRo1KhRKly4sEaMGJHtfmNiYtSlSxdt27ZNklS6dGn5+/urbt26ioiIUEBAgNatW6fw8HD1799fbm5u6tq1a5pt/f3332ZgrFWrltq0aaP69evrvvvuU2RkpLZu3aolS5bo5s2b2rVrl1q2bKlt27bJx8cn29sBAAAAoOBx+JFGSRo2bJhmz54tb29v/fLLL3kWYBYsWKABAwZISgxumzZt0oMPPpiiZtmyZerTp48Mw5CLi4sOHz6sKlWqZKvfd999V+PGjZOUeG1ncHCwypcvn6Jm2rRpGjNmjCTp/vvv17Fjx8zrQZOrX7++fH19NWLECDVq1CjN/kJCQtShQweFhIRIktq1a6d169bZNda74S8ZQEY40ph7ONIIAEDeuhu+nzv8mkZJ+vTTTzVixAiFhoaqQYMGeuONN/TXX3/l6jV3hmHo7bffNh/PnDkzVWCUEk//HDZsmKTEU1knTpyYrX4jIiI0ZcoU8/GCBQtSBUZJGj16tB5//HFJ0qVLlzR9+vQ029u4caPmzJljGRglycfHR0uXLjUfr1+/XqGhoVndBAAAAAAFmMOPNFarVs1cPnPmjOLj42Wz2SRJhQsXVokSJeTm5pZhOzabzTxylhW//fabWrZsKUny9vbWiRMn5OSUdmYOCQlR9erVJUnFihXT5cuX5eLikqV+kx/dbN68uX777TfL2l9//VWPPfaYpMQjkkeOHMlSn0lq1apltvHjjz9anvKa3N3wlwwgIxxpzD0caQQAIG/dDd/PHX5N46lTp8yQKMlcNgxDt27d0qVLlzJswzCMFG1kxerVq83ljh07WgZGKfFIXc2aNXX06FFFRkZq8+bNateuXbb77dw5/S9frVq1kru7u6Kjo3X06FEdO3ZMNWrUyFK/klKc3nrjxo0stwMAAACg4MqV01MNw0j1L73XrGqzY9++feZy48aNM6xPXpN8XUf26+zsrIYNG+ZIv7GxsSmOVGb3ukwAAAAABZPDjzSePHnS0V3YJXmAqlq1aob1yWsOHz6cpT4Nw0hxz0d7+92yZUu2+pUST4uNiIiQJJUtW1a+vr5ZbgsAAABAwZVjoXHz5s2SpAoVKqSYHdXb2zunusiW69evm8ulSpXKsD55TVhYWJb6jIqK0u3bt3O93/Pnz+vVV181H7/55pvpno57p5CQEPPc6TtxrSMAAACQ86y+f4eEhOT57fNy7PTU1q1bq02bNpoxY0a6defPn9e+ffuydeplVkRGRprL9ky8k7wm6YhddvrMrX5v3Lihbt266dq1a5ISr5McPnx4ptsBAAAAACkXTk+906RJk/T555/LZrMpLi4ut7u/p92+fVt9+/bV7t27JUkVK1bUd999p0KFCmWqHR8fH44oAgAAALnI6vu31RHI3JQrE+HcKScnuLFXsWLFzOWbN29mWJ+8JvkspFnt09H9xsXFqW/fvvrpp58kJZ4mHBwcrLJly9rdBgAAAADcKU9CY14oXry4uXzlypUM65PXJF83Mzw8POTs/H8Hcx3V7+3bt9W7d2/98MMPkqRKlSpp48aN5r0mAQAAACCrCkxorFWrlrlsz4yuyWuSr5sZNptNNWvWdGi/t27d0lNPPaWAgABJiRMPbdq0icAIAAAAIEcUmNBYv359c3nnzp0Z1ievSb6uI/uNi4vT3r177e43JiZGTzzxhHlKarVq1bRp0ya7bu0BAAAAAPYoMKGxU6dO5vLatWuVkJBgWRsSEqKjR49KSrwusUWLFjnS7+rVq9Ot3bRpk6KjoyVJNWrUUI0aNSxrb968qa5du2rt2rVm/aZNm+6aW5wAAAAAuDcUmNDYrFkzVa5cWZIUGhqq5cuXW9ZOmzbNXO7Ro4dcXV2z3G+3bt3k7u4uSfrtt9/SPdqYvN/+/ftb1kVHR6tTp0765ZdfJCWexrpp0yZVrFgxy+MEAAAAgLQUmNDo5OSkd955x3w8cuRIHT58OFXd8uXLNWvWLEmSi4uLxo0bZ9lm69atZbPZZLPZNGHChDRrvLy8NHbsWPPxgAEDdO7cuVR106ZN05o1ayRJpUqV0ksvvZRme1FRUerUqZM2btwoKXEK3o0bN6pcuXKW4wQAAACArMr1+zTmpQEDBigwMFCBgYG6dOmSGjdurMGDB8vX11exsbEKCgrSsmXLzNuBTJ06VdWqVct2v6+88orWrl2r7du368iRI2rQoIGGDBmiunXrKiIiQgEBAQoKCpIkFSpUSF999ZW8vLzSbOvxxx/Xli1bJElFixbVqFGj9Pvvv2c4hlq1amV5Qh8AAAAABVeOh8adO3emOKKX1utJ0qtLS3pH/exhs9m0ZMkSDR48WEuWLFFkZKRmzJiRqs7FxUWTJk3SyJEjs9VfEjc3N61atUp9+/bV+vXrdfnyZU2aNClVnaenp2bNmqXu3btbtpUUGCXpxo0bGjJkiF1jGD9+vOXRUAAAAACwkuOhcdeuXdq1a1e6NTabTZI0ceLETLWd3dAoSa6urlq8eLH8/f01b948bd26VefPn1eRIkVUsWJFdejQQUOHDk1xq4ycULJkSa1bt04BAQFatGiRdu3apYsXL8rd3V3e3t7q0qWLhg4dqgoVKuRovwAAAACQHTYj6VzMbHJycpLNZlMONZeKzWZTfHy8Q9rG/6lTp44k6cCBA3k8EsBalddW5fUQCoxTkzvn9RAAACjQ7obv5zl2pLFly5bmEUQAAAAAwL0hx0Jj0myeAAAAAIB7R4G55QYAAAAAIPMIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwJJzXg8AQN6q8tqqvB4C7mLsH7nj1OTOeT0EAAAscaQRAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCpwIbG4OBg+fn5qXr16nJ3d1eJEiVUr149jR07VseOHXNYv4GBgerVq5eqVq0qNzc3lSpVSo0aNdKECRN07tw5u9q4ffu2/vzzT82dO1f//e9/1aJFCxUrVkw2m002m02tW7d22PgBAAAAFCzOeT2A3BYbGyt/f38tXLgwxfM3btxQWFiY9u/fr5kzZ2ry5Ml68cUXc6zf69evq1+/fgoKCkrxfExMjK5evao9e/bo448/1uzZs9W7d+9022rSpIn27NmTY2MDAAAAACsFKjQahqGnn35aK1askCR5eHho8ODB8vX1VWxsrIKCgrR8+XLFxMRo1KhRKly4sEaMGJHtfmNiYtSlSxdt27ZNklS6dGn5+/urbt26ioiIUEBAgNatW6fw8HD1799fbm5u6tq1q2V78fHxKR57eXmpYsWKOnDgQLbHCgAAAADJFajQuHDhQjMwli5dWps2bdKDDz5ovv7cc89p2bJl6tOnjwzD0Msvv6xOnTqpSpUq2ep36tSpZmCsWbOmgoODVb58efP1YcOGadq0aRozZozi4+Pl7++vY8eOydPTM8322rRpoy5duqhhw4Zq2LChqlWrpo0bN6pNmzbZGicAAAAA3KnAXNNoGIbefvtt8/HMmTNTBMYkvXr10rBhwyQlnso6ceLEbPUbERGhKVOmmI8XLFiQIjAmGT16tB5//HFJ0qVLlzR9+nTLNqdPn67//e9/euqpp1StWrVsjQ8AAAAA0lNgQuOWLVsUGhoqSfL29lbPnj0ta0ePHm0ur1ixQrGxsVnud+XKlYqOjpYkNW/eXI0bN7ar38WLF2e5TwAAAADIKQUmNK5evdpc7tixo5ycrDfdx8dHNWvWlCRFRkZq8+bNOdJv586d061t1aqV3N3dJUlHjx516CyuAAAAAGCPAhMa9+3bZy6nd7QvrZrk6zqyX2dnZzVs2DBH+gUAAACAnFBgJsI5cuSIuVy1atUM65PXHD58OEt9GoaR4mihvf1u2bIlW/1mV0hIiOrUqZPma8zQCgAAAOQ8q+/fISEh8vHxyeXRpFRgjjRev37dXC5VqlSG9clrwsLCstRnVFSUbt++nev9AgAAAEBOKTBHGiMjI81lNze3DOuT10RERGS7z9zsN7t8fHw4oggAAADkIqvv31ZHIHNTgTnSCAAAAADIvAITGosVK2Yu37x5M8P65DWenp7Z7jM3+wUAAACAnFJgQmPx4sXN5StXrmRYn7wm+bqZ4eHhIWfn/zsDOLf6BQAAAICcUmBCY61atczlkydPZlifvCb5uplhs9nM+z3mZr8AAAAAkFMKTGisX7++ubxz584M65PXJF/Xkf3GxcVp7969OdIvAAAAAOSEAhMaO3XqZC6vXbtWCQkJlrUhISE6evSopMTrElu0aJEj/a5evTrd2k2bNik6OlqSVKNGDdWoUSPL/QIAAABATigwobFZs2aqXLmyJCk0NFTLly+3rJ02bZq53KNHD7m6uma5327dusnd3V2S9Ntvv6V7tDF5v/37989ynwAAAACQUwpMaHRyctI777xjPh45cqQOHz6cqm758uWaNWuWJMnFxUXjxo2zbLN169ay2Wyy2WyaMGFCmjVeXl4aO3as+XjAgAE6d+5cqrpp06ZpzZo1kqRSpUrppZdesmu7AAAAAMCRnDMuuXcMGDBAgYGBCgwM1KVLl9S4cWMNHjxYvr6+io2NVVBQkJYtWybDMCRJU6dOVbVq1bLd7yuvvKK1a9dq+/btOnLkiBo0aKAhQ4aobt26ioiIUEBAgIKCgiRJhQoV0ldffSUvLy/L9vbu3asVK1akeC40NNRcPnnypN56660Ur1etWlXPPfdctrcFAAAAQMFSoEKjzWbTkiVLNHjwYC1ZskSRkZGaMWNGqjoXFxdNmjRJI0eOzJF+3dzctGrVKvXt21fr16/X5cuXNWnSpFR1np6emjVrlrp3755ue3/99Zfee+89y9dPnz6d6vVWrVoRGgEAAABkWoEKjZLk6uqqxYsXy9/fX/PmzdPWrVt1/vx5FSlSRBUrVlSHDh00dOjQFLfKyAklS5bUunXrFBAQoEWLFmnXrl26ePGi3N3d5e3trS5dumjo0KGqUKFCjvYLAAAAANlhM5LOxQQk1alTR5J04MCBPB4JckuV11bl9RCAAu/U5M55PQQAwF3qbvh+XmAmwgEAAAAAZB6hEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAl57weAGClymur8noImXZqcue8HgIAAACQozjSCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMCSc14PALiXVHltVV4PAQByRX78vDs1uXNeDwEo0PLj54bEZ4fEkUYAAAAAQDoIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBUYENjcHCw/Pz8VL16dbm7u6tEiRKqV6+exo4dq2PHjjms38DAQPXq1UtVq1aVm5ubSpUqpUaNGmnChAk6d+5cptqKj4/XggUL1LlzZ1WqVEkuLi4qU6aMmjVrpqlTpyosLMwxGwEAAACgwHDO6wHkttjYWPn7+2vhwoUpnr9x44bCwsK0f/9+zZw5U5MnT9aLL76YY/1ev35d/fr1U1BQUIrnY2JidPXqVe3Zs0cff/yxZs+erd69e2fYXmhoqHr16qVdu3aleP7SpUu6dOmStm3bpo8//lgLFy5UmzZtcmw7AAAAABQsBSo0Goahp59+WitWrJAkeXh4aPDgwfL19VVsbKyCgoK0fPlyxcTEaNSoUSpcuLBGjBiR7X5jYmLUpUsXbdu2TZJUunRp+fv7q27duoqIiFBAQIDWrVun8PBw9e/fX25uburatatle1euXFH79u119OhRSVLlypXl7++vGjVq6NKlS1q8eLF27Nihc+fOqWvXrgoODpavr2+2twMAAABAwVOgQuPChQvNwFi6dGlt2rRJDz74oPn6c889p2XLlqlPnz4yDEMvv/yyOnXqpCpVqmSr36lTp5qBsWbNmgoODlb58uXN14cNG6Zp06ZpzJgxio+Pl7+/v44dOyZPT88023v11VfNwNisWTOtXr06Re3IkSP14osv6tNPP1V0dLQGDRqkv/76S4UKFcrWdsCxzn2d+AeK8v6f5/FIkFfYByBJderUkSQdOHAgj0eCvMI+APYB3G37QIG5ptEwDL399tvm45kzZ6YIjEl69eqlYcOGSUo8lXXixInZ6jciIkJTpkwxHy9YsCBFYEwyevRoPf7445ISTzGdPn16mu0dO3ZM8+bNkyS5uLho8eLFqcKlzWbTRx99pHr16klK3NnuPB0XAAAAAOxRYELjli1bFBoaKkny9vZWz549LWtHjx5tLq9YsUKxsbFZ7nflypWKjo6WJDVv3lyNGze2q9/FixenWfPdd98pISFBUmLArVy5cpp1zs7OKa7JXLRoUabHDgAAAAAFJjSuXr3aXO7YsaOcnKw33cfHRzVr1pQkRUZGavPmzTnSb+fOndOtbdWqldzd3SVJR48eTXMW18y016lTJ3M5ODhYN2/etGvMAAAAAJCkwITGffv2mcvpHe1Lqyb5uo7s19nZWQ0bNrTs1zAM7d+/3+72ypUrp4oVK0qS4uLidPDgQbvHDQAAAABSAQqNR44cMZerVq2aYX3ymsOHD2epT8MwUhwtzG6/Z8+eVVRUlCSpUKFCqlSpUrbaAwAAAICMFJjZU69fv24ulypVKsP65DVhYWFZ6jMqKkq3b9/OsX6Tb4OXl5cKFy6crfbScvr0aUVHR8vV1TXN1318fDJsI6ecuxiVa33dDW6HnZf0fzNoouBhHyi46vzkYS6HhIQkPvf/Z867W+XHz+jkP+e7WX7ZB+A49+o+kB8/N6Tc++xIet8l6datW5IkV1dX3bp1y7yELa8UmNAYGRlpLru5uWVYn7wmIiIi233mRL+Z3YaM2kuLu7u7bty4YVfbjlajTP74n3uOKVMjr0eAvMY+AOXuH+eyo8B9Ruei/LIPwHHu1X2Azw37FSlSxFy22WyERtxdLly4kNdDAAAAAHAXKTDXNBYrVsxctmcW0eQ1d94HMSt95kS/md2GjNoDAAAAgIwUmNBYvHhxc/nKlSsZ1ievSb5uZnh4eMjZ+f8O5ma33+SPw8PDFRcXl632AAAAACAjBSY01qpVy1w+efJkhvXJa5Kvmxk2m82832NO9FuxYkV5eCSeCx4fH6/Tp09nqz0AAAAAyEiBCY3169c3l3fu3JlhffKa5Os6st+4uDjt3bvXsl+bzaa6deva3d758+f1zz//SEq8RUft2rXtHjcAAAAASAUoNHbq1MlcXrt2rRISEixrQ0JCdPToUUmJ1xG2aNEiR/pdvXp1urWbNm1SdHS0JKlGjRqqUSP1TIqZaS/5623atLF7xlUAAAAASFJgQmOzZs1UuXJlSVJoaKiWL19uWTtt2jRzuUePHpb3LLRHt27dzClyf/vtt3SPDibvt3///mnW9OnTR05OiW/b0qVLdebMmTTr4uLiNGPGDPPx008/nemxAwAAAECBCY1OTk565513zMcjR47U4cOHU9UtX75cs2bNkiS5uLho3Lhxlm22bt1aNptNNptNEyZMSLPGy8tLY8eONR8PGDBA586dS1U3bdo0rVmzRpJUqlQpvfTSS2m2V7NmTQ0YMECSFBsbq/79+6e6/6JhGBo9erT+/vtvSdKDDz6oZ555xnI7AAAAAMBKgQmNUmJg6969uyTp0qVLaty4sUaNGqVFixbpm2++UZ8+fdS7d28ZhiFJmjp1qqpVq5btfl955RU1adJEknTkyBE1aNBAb775ppYsWaIvv/xSHTt21JgxYyQlXnv41VdfycvLy7K9Dz74QNWrV5ckbdmyRfXr19f//vc/fffdd/r000/1yCOP6JNPPpEkFS1aVHPnzk0xiyvuDrGxsQoKCtLrr7+uxx57TBUrVpSrq6vc3NxUoUIFdezYUdOnT9fVq1cz3XZwcLD8/PxUvXp1ubu7q0SJEqpXr57Gjh2rY8eOOWBrkFW8V/ee6OhorVy5Ui+//LJatWqlcuXKycXFRe7u7vL29tYTTzyhr776SlFRUZlqNzAwUL169VLVqlXl5uamUqVKqVGjRpowYUKaf4zE3SkoKMj8g3PSP3vxeZF/HT16VBMmTFDTpk1Vvnx5ubi4qHTp0qpbt6769++v2bNn69KlSxm2w+dA/nPu3DlNmjRJbdu2VdmyZeXi4iI3NzeVL19ejz32mKZMmZKp+6Tn2T5gFDA3b940+vXrZ0iy/Ofi4mJMmzYtw7ZatWplrjN+/Ph0a69evWq0a9cu3X49PT2NxYsX27UdJ06cMBo1apRue2XLljV++eUXu9pD7vruu+8MT0/PdN+/pH9eXl7GN998Y1e7MTExxjPPPJNue66ursbHH3/s4C1ERniv7k3Tpk0zXF1d7frdLleunPHzzz9n2Oa1a9eMDh06ZPg58f333+fCFiI7wsLCjIoVK6Z6/zLC50X+dfPmTWPUqFGGs7Nzhp8Jc+fOtWyHz4H86fPPPzeKFi2a4Xvv4eFhzJ49O9228nofKHCHn1xdXbV48WL5+/tr3rx52rp1q86fP68iRYqoYsWK6tChg4YOHZriVhk5oWTJklq3bp0CAgK0aNEi7dq1SxcvXjT/8tylSxcNHTpUFSpUsKu9qlWraseOHVq4cKG+//577du3T5cvX5aXl5d8fHzUvXt3Pf/88ypRokSObgdyxsmTJ83TiosXL662bduqSZMmKl++vAoVKqTDhw9r0aJFOn78uMLDwzV48GBFRUVp5MiRlm0ahqGnn35aK1askJR4n9DBgwfL19fXPKq5fPlyxcTEaNSoUSpcuLBGjBiRK9uLlHiv7l1Hjx5VTEyMJKlMmTJ69NFH5evrq7Jlyyo+Pl779u3TggULdP78eZ0/f15PPPGEli9fbp4Fc6eYmBh16dJF27ZtkySVLl1a/v7+qlu3riIiIhQQEKB169YpPDxc/fv3l5ubm7p27Zpbm4tMGjVqlP755x+5u7ubE99lhM+L/CsqKkpdu3bVxo0bJSX+/nbv3l2NGjVSyZIlFRsbq1OnTmnbtm0KDg62bIfPgfxp9uzZKX4XH3jgAfXr109VqlSRlPhdMOm7XlRUlJ5//nkVKVJEAwcOTNXWXbEPOCSKAkjX+++/b/z73/82vvvuO+PmzZtp1ty6dcsYMmRIiiPgISEhlm1+++23Zm3p0qWNgwcPpqpZunSpYbPZzPZOnjyZU5uETOC9uncNGzbMaNu2rbFq1Srj9u3badZEREQYXbp0MfeBUqVKGWFhYWnWvvPOO2ZdzZo1jbNnz6aq+fDDD82a+++/3wgPD8/RbULO+Omnn8z36ZNPPrH7SCOfF/lXz549zfdu+PDhRmRkpGVtdHS0cfny5TRf43Mg/7l586bh5eVlvievvfaaER8fn6ouLi7OGD16tFlXpkwZIy4uLlXd3bAPEBqBPHD16lW76uLj440GDRqYHwITJ05Msy4hIcHw9vY269I7NWH48OFmnZ+fX5bGj6zjvbq32fu7HRUVZZQrV858f+fPn5+qJjw83HB3dzdrduzYYdne448/btZNmDAhy+OHY1y7ds18v/v372+cPHnSrtDI50X+9d1335nvx6BBg7LcDp8D+dMvv/ySYRBMEhcXZ5QuXdqs379/f4rX75Z9gNAI3OXef/998wOgR48eadZs3rzZrPH29k7zr1lJjh8/btYWK1bMiImJcdTQkQbeKyQZOnSo+f6+/PLLqV5PfoSpefPm6baV/AtKzZo1HTVkZNHTTz9t/vX/ypUrdodGPi/yr/r16xuSjKJFixrXr1/Pcjt8DuRPixYtMt+LRx55JMP6Jk2amPW///57itfuln2gQM2eCuRHnp6e5vKNGzfSrFm9erW53LFjR/Nenmnx8fExr9mNjIzU5s2bc2iksAfvFZJk9LudfF/p3Llzum21atXKvCfw0aNHmUnzLhIYGKhFixZJkmbOnKn77rvP7nX5vMifduzYoX379kmSnnjiCRUvXjzLbfE5kD+VKVPGXA4JCVF8fLxlbVxcnEJCQiRJzs7OeuCBB1K8frfsA4RG4C6X9D8eSebF0+nVNG7cOMM2k9ckXxeOx3uFJBn9bmdmX3F2dlbDhg3TXBd55+rVqxo2bJgkqUePHurVq1em1ufzIn9KmvhGknnLtdWrV6tHjx6qWLGiebuN5s2b67333tO1a9cs2+JzIH9q1qyZ7r//fknSxYsX9dZbbykhISFVXUJCgl599VVdvnxZkjR48OBUk1jeLftAgZs9FchPrl69qu+++858bDUT1pEjR8zlqlWrZthu8prDhw9nY4TILN4rSIn7wfr1683Hd/5uG4aR4i/E9u4rW7ZskcS+crcYMWKELl68qJIlS+rzzz/P9Pp8XuRPO3fuNJfLli2rfv36pfh/uSRduXJFV65c0datWzV16lR9++236tatW4oaPgfyL1dXV3399dfq1auXYmNjNXnyZAUEBKh///7y9vaWJJ06dUoLFy7U8ePHJUkDBw7UjBkzUrRzN+0DhEbgLjZy5EiFh4dLknx9ffX444+nWXf9+nVzuVSpUhm2m7wmLCwse4NEpvBeIT4+Xv7+/uZfnZ966inVrl07RU1UVJRu375tPmZfyX+WLl2qpUuXSpI+/vjjFKer2YvPi/zp/Pnz5vK4ceN05MgROTs7q2/fvmrTpo3c3d119OhRzZs3TydOnFB4eLiefPJJrV69Wh06dDDX5XMgf+vatas2bdqk//znP/rjjz905MgRjR8/PlVdx44d9dprr6lVq1apXrub9gFCI3CXmjp1qpYsWSJJcnNz09dffy2bzZZmbWRkpLns5uaWYdvJa5LuF4ncwXuF//73v+Zfge+//3598sknqWqS7ycS+0p+c+nSJf3nP/+RJHXq1EnPPvtsltrh8yJ/Sh72jxw5Ik9PTwUFBZmnqiZ55ZVX1K9fPwUEBCghIUF+fn46efKkXF1dJfE5cC94+OGH9emnn+qNN95IcdpycuvWrZOUeN/uhx56KMVrd9M+wDWNQBq6d+8um82WI/+srkNMz+LFi/Xqq69Kkmw2m7766ivVr18/h7cSQG6bPHmyeZpikSJF9P3336t8+fJ5PCrktGHDhunKlSvy9PTUl19+mdfDQS6789q1KVOmpAqMkuTi4qJvv/1W5cqVkyRduHBBixcvzpUxwvHCwsLUuXNnPfLII/r999/11ltvaf/+/bp586aio6O1e/du849La9euVfPmzbV27do8HrU1QiNwl1m6dKkGDBggwzAkJc629/TTT6e7TrFixczlmzdvZthH8prkMzjC8XivCq6PPvpIr7/+uiSpcOHCWrp0qVq3bp1mbfL9RGJfyU8WLVqkgIAASYlnjFSsWDHLbfF5kT8lf99cXV3l5+dnWevh4ZHiSHTSUac725HYB/KTmzdvqmXLllq9erUKFy6s9evX691331WdOnXk6uqqokWLqlGjRpo5c6Y5u3JUVJT69u1rTooj3V37AKenAmno2rVrlo4QpqVkyZJ213733Xd65plnFB8fL5vNps8//9yceS89xYsXN2dfu3LlSob1yWuyMxU4Mo/3qmD68MMPNXbsWEmJRxiXL19uObGVlPhF0tnZWXFxcZIS94M7vzzciX0l750/f17//e9/JUlt27bV888/n632+LzIn5LPflm3bl3zdFMrvr6+5nLSpCgSnwP52axZs/T3339LSpzgpkWLFpa1ffv21ddff61ff/1V4eHhmjt3rl555RVJd9c+QGgE0vDcc8/lep8LFy6Un5+f4uPj5eTkpNmzZ9s9jlq1aunEiROSpJMnT6pNmzbp1p88eTLFusg9vFcFz/vvv6833nhDUuLpaAEBAZaTWiWx2WyqWbOmDh48KClxP8ho1jz2lby3dOlSM+RVrVpV//vf/9Ksu3NyiuR1Q4YMMSfN4fMif6pVq5Z++eUXSZKXl1eG9cm/2Ce/Bo3Pgfxr5cqV5nL79u0zrO/QoYN+/fVXSYn3+UxyN+0DhEbgLjB37lxzNkUnJyd98803GjhwoN3r169f37z5686dOzV48OB065NPB861krmL96pgmThxoiZMmCApcXKCwMBAu75ASInvd9IXhZ07d6pt27aWtXFxcdq7d2+KdZH7ki4rkKQ5c+bYvd7bb79tLnfp0sUMjXxe5E8NGjQwl5NmQE9P8j8i3HlkiM+B/OncuXPmsj1H+5L/cSEqKirFa3fLPsA1jUAe+/LLL/Xcc88pISFBhQoV0rfffpupwCglzs6XZO3atWneQDZJSEiIjh49KinxXPn0TplAzuO9KjjefPNNMzAWLVpUP//8s92BUUq5ryQFByubNm1SdHS0JKlGjRqqUaNG5geMuw6fF/lT586d5eSU+BX7wIEDiomJSbd+9+7d5vIDDzyQ4jU+B/Kn5KeQnj59OsP60NBQc/nO22rcNfuAASDPzJw507DZbIYkw9nZ2fj++++z1E58fLxRuXJlQ5IhKd12hg8fbtYNHDgwiyNHVvFeFQyvvPKK+d55eHgYmzZtynQbYWFhhru7u9nOjh07LGsff/xxs278+PHZGDlyw8mTJ833K72vYnxe5F/t27c3348vvvjCsi4yMtIoV66cWbtkyZIUr/M5kD/5+fmZ70WbNm3Srb1165bh4+Nj1s+YMSPF63fLPkBoBPLIxx9/bP5iFy5c2Pjhhx+y1d68efPM9u6//37j0KFDqWqWLVtmhlQXFxcjJCQkW30ia3iv7m0vv/yy+f56enoaW7duzXJbEyZMMNt64IEHjLNnz6aq+fDDD82aUqVKGWFhYdkZPnKBvaHRMPi8yK/++OMPw8nJyfwc+P3331PVxMTEGE8++aT5/taoUcO4fft2qjo+B/KfX375JcXv+JtvvmkkJCSkqouNjTWeeeYZs87d3d04f/58qrq7YR+wGUayE/AB5Iqvv/5aQ4YMMR/7+/urc+fOGa5XtGhRy9PbDMNQjx49FBgYKCnx1IjBgwfL19dXsbGxCgoK0rJly8xrbj755BONHDky+xuDTOO9une9/fbbKSY1ef3119W4ceMM1ytVqpSaN2+e6vmbN2+qbdu22r59uySpdOnSGjJkiOrWrauIiAgFBAQoKChIklSoUCEtX75c3bt3z5mNgcOcOnUqxWQW6X0V4/Mi/3rvvff01ltvSZKcnZ3Vr18/tWnTRkWLFtWxY8c0b948hYSESEq85vm3335To0aNUrXD50D+9Oyzz2rhwoXm4/r166tPnz7y8fFRQkKCDh48qEWLFqWYuObzzz/X8OHDU7V1V+wDORpBAdhl4MCBKf4CZe8/b2/vdNu9efOm0a9fv3TbcHFxMaZNm5Y7GwpLvFf3platWmXpd7tVq1aWbV69etVo165duut7enoaixcvzr0NRbZk5kijYfB5kZ9NmTLFcHFxSfe9q1SpkrF9+/Z02+FzIP+5ffu2MXLkSPMsgPT+ubu7G19++WW67eX1PsCRRiAP+Pn5af78+Zlez9vbW6dOncqwbsOGDZo3b562bt2q8+fPq0iRIqpYsaI6dOigoUOHqmbNmlkYNRyB9+re0rp1a23atCnT67Vq1UobN25MtyYgIECLFi3Srl27dPHiRbm7u8vb21tdunTR0KFDVaFChSyOGrktM0cak+PzIn86fvy4vv76a61du1anT59WVFSU7rvvPj300EN64oknNGjQoAzv5ZiEz4H859ChQ5o/f762bNmio0ePKjw8XDabTSVLllSdOnX02GOPyc/Pz5w1OSN5tQ8QGgEAAAAAlrjlBgAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgiNAAAA+ciqVav0wgsvqGnTpqpYsaJcXV3l7u6uWrVqafjw4Tp8+HBeDxHAPcZmGIaR14MAAACAfR577DH9+uuvcnZ2Vrly5VSmTBldv35doaGhiouLU+HChTVv3jz1798/r4cK4B5BaAQAAMhHFixYoLJly6p58+Zyc3Mznz979qxGjhypgIAAubq66ujRo6pUqVIejhTAvYLQCAAAcI+IiYlRuXLlFBYWpi+++ELDhg3L6yEBuAdwTSMAIF+oUqWKbDabbDabTp06lSNttm7d2mxz48aNOdJmXnHEzye5UaNGyWazqWjRovrnn39yvH3kDFdXV1WrVk2SFB0dnWbN5cuX5enpKZvNpiFDhuTm8ADkU4RGAMiHkoedtP4VK1ZMlStXVseOHTVp0iSdPXs2r4eMfGz//v367LPPJEkvvviiKlasmKV2wsPDVahQIdlsNo0YMSLV6wMGDDCDaVxcXJb6ePLJJ2Wz2eTk5KRz585lqY387MqVK+ZEOL6+vmnWlC5dWqNHj5YkffPNN9q9e3eujQ9A/kRoBIB7UFRUlM6cOaOgoCC9+eabqlq1qiZOnCiuSEBWvPLKK4qLi5O7u7vGjBmT5XZ27NihhIQESdIjjzyS6vWtW7dKSgw7zs7OmW4/NjZW69evlyQ1atRI5cuXz/JY85vLly9rzZo1at++vW7cuKF+/fqpZcuWlvWjRo2Sp6enEhISNHbs2FwcKYD8KPOfyACAu4qvr68aN26c4rnw8HD99ddf+vvvvyVJt2/f1oQJExQWFqbp06fnxTCRT23dulVr1qyRJA0ZMkT33Xdfltvatm2budysWbMUr124cEEnTpyQlHagtMevv/5qnpLZrVu3LI4y/wgMDNSTTz6Z4rkqVaroiy++0NChQ9Nd18vLS8OGDdMHH3ygjRs36pdfftFjjz3myOECyMcIjQCQz3Xq1EkTJkxI87Vt27apX79+On36tCTp448/1tNPP61///vfuTjCnOGI6/SQscmTJ0uS5SmlmfH7779LksqVK6eqVaumeC3pKKOU9dD4008/mctdu3bNUhv5yX333admzZopISFB586d0z///KPQ0FAtWbJErVq10oMPPpju+kOHDtXUqVNlGIamTJlCaARgidNTAeAe9sgjj2jlypWy2Wzmc7Nnz87DESE/OXbsmFatWiVJatmypWrUqJHlthISErRjxw5J6Z+aavW6PZJCY+XKldWgQYMstZGftGjRQlu2bNG2bdt06tQpnTlzRoMGDdLmzZvVpEkThYaGprt+tWrV1LZtW0nSL7/8ov379+fGsAHkQ4RGALjHNWjQQK1btzYfb968Oe8Gg3xl7ty55nWwffr0yVZbBw8eVHh4uKT0Q2PNmjWzdArsnj17zAmfunTpko2R5l/lypXTnDlz1L59e0VEROi9997LcJ3evXuby3PnznXk8ADkY4RGACgAkh91ycyMklevXtW0adPUrl07VapUSa6uripevLhq166t//znP3bPunjmzBlNnDhRLVu2VJkyZeTi4qIiRYrovvvu00MPPaT+/fvriy++0IULFyzbyMwtJRISEjR//ny1a9dOZcuWlaurq6pUqaInnnhCgYGBdm//qVOnzD6rVKli1zr2jjM0NFRffPGF+vXrp7p168rLy0uFCxfWfffdp3r16mn48OHavn273WN1hEWLFpnL3bt3z1ZbSaemSqmvZ7x586b27t0rKWdOTbX3esa9e/fq7bffVvPmzVWxYkW5urqqaNGiqlGjhvr06aO5c+cqMjIy1Xrfffed+R4//fTTkqT4+HgtX75cTzzxhKpUqSIXFxeVKVNGffr00b59+1K1ERUVpRkzZqhly5YqUaKEihQpoipVqmjEiBE6f/58ln4GSZJOzbXn97N79+7mmQiLFy9msiwAaeKaRgAoANzc3MzlmJgYu9b57LPP9Oabb5pHh5LExsYqPDxchw4d0hdffKFBgwbpiy++UJEiRdJsZ/bs2Ro1apRu3ryZ6rVr167p2rVr2rdvn5YsWaJFixZpy5Ytmdiy1C5cuKDu3bubp0ImCQ0NVWhoqH788Uc9+eSTmj9/frb6yY6xY8dq2rRpaX5BT/qZ7N+/X7NmzVLfvn01Z84cFS1aNFfHuG/fPvNa2Fq1aqlcuXJ2rbdu3TqtW7cu1fPJ74O5ePFiLVu2zHx8+fJl3b59W5J04sSJVDO0tm/fXu3bt0+33x9//FGS5OHhkeLIelqOHz+uF154QUFBQZavHz9+XEuXLtUXX3yhnTt3png9KeBKUt26dXXo0CENHDhQu3btSlF36dIlLV26VCtXrtRPP/2kdu3aSZKCg4P1zDPPpPoDTtIfElauXKnt27erUqVK6W6HlaTblcTHx2dYe//996tOnTrav3+/Lly4oN27d1veqgNAwUVoBIACIPmX0zJlymRYP2rUKM2YMcN8XKpUKTVt2lRly5ZVTEyM9u7dq/3798swDH3zzTc6d+6cVq1aJSenlCewBAYGppjF0dPTU02bNlXFihXl7Oys8PBwHT16VPv379etW7eyvZ1hYWFq27atDh06ZD5XtWpVNW3aVC4uLjpw4IB27typgICAVGPNTWfOnJFhGLLZbHrggQf0wAMP6L777lPhwoV19epV7d27VyEhIZISj2pFRETo559/TnFtqqMl3bpCSrx2zl7btm3TtGnT0q355JNPLF/bvHlzqlOoPTw80g2NZ8+e1Z49eyRJHTp0kIuLi2XtmjVr1K9fvxR/DKlWrZr+9a9/qWTJkgoLC9PevXt17NgxSYmh8E5//vmnuWwYhpo1a6br16+rTJkyatmypdzd3fXnn3+adbGxsRo0aJBOnTqlefPmaejQoUpISFCNGjX08MMPKyEhQb///rtOnjwpKfH39eWXX04RrO1lGIaWL18uSWrYsKFd67Ro0cK8nnH9+vWERgCpGQCAfKdVq1aGJEOSMX78+HRrb9++bVSqVMms79mzZ7r1c+bMMWs9PT2Nr776yrh161aqug0bNhgVKlQwa/9fe3ceVFX5xgH8e+GKigoibiECKimkKZmAOCqmMi5ZOYS4ZVCR5laGKZk2yU+TccllTExFoLRUzIGcStMxRhEEFQvcQBpwEFcU2RUSzu8P5r5zLtxzFxBI+X5mnDmX85z3vPcenLkP7/s+79q1a+vEuLm5ifMLFiyQysrKdN6zpKREiomJkUJCQhT75ejoKNrKycnRGfP++++LGAsLC2n37t11YlJSUkRbFhYWIj4+Pr5ObE5Ojjjv6Oio2DdT+7lu3TopKipKys/PV2zn1KlTkrOzs2hrz549Db6vKaZMmSLa27p1q9HXffXVV+K6p/XP0O/49u3bRez333+vGJeYmCi1adNGxI4ePVo6e/aszthLly5Js2bNksLDw+uc69atm2ijdevWUtu2baXw8HDpyZMnWnFhYWFa7yMkJEQyNzeXXnjhBenXX3/Viq2oqJBmzZolYlu1aiWVl5fXufe5c+ek5cuXSxkZGXXOXb9+XfL395cASObm5tKFCxcUPwu5HTt2iPu+9dZbRl1DRC0Lk0YiomeQKUnjqlWrtL64Hjt2TDG2uLhY6tixo0iokpOT9bZ95coV8SXc1tZWKyksKSkR9+zZs6dUXV1t0nuszVBSlJmZKalUKhETHR2t2FZmZqZkaWmp9bk0ZdJorJycHPH5enh4NNl9JUmS+vXrJ9o7fvx4g9qKiooSbR09elTrXGVlpXgWU6dOrVf7EydOFImSUiKek5Mj2drain7Mnj1bqqqqMth27Zhbt25p/d5YWlpKSUlJOq+trq6WXFxctOJ79uwp3bhxQ2d8UVGR1h8y0tLS6sTEx8eL87a2ttIrr7wieXp6Sr169RK//+3atZP2799v8L1pnDp1SrTp5ORk9HVE1HKwEA4R0XOouLgYCQkJmDFjBr788kvx808//VSsq9IlMjIShYWFAIB58+bB09NT731cXV0REBAAoKZoztGjR7X6oGFra9voUyt3794t1gh6eHiIfunSt29fLFq0qFH78zQ4OTnhtddeAwCcO3dO6zNtTJIkaW3XYG9v36D24uPjAQCtWrXC8OHDtc6dO3cO5eXlAGBwLaIuZWVl+PPPPwEAXl5e6Ny5s8644OBgPHjwAADg4+OD8PBwo6Yo146Rr2cEgE2bNsHLy0vntSqVCoMGDdJ6HRMTo/h5WllZoXfv3uK1rvXHgwYNwtatW/H222/D1tYW2dnZSE1NRVFRETw9PbFixQpkZGSYVO22R48e4jgvL8+otZBE1LJwTSMR0TMuNDQUoaGhemNsbW2xZMkShISE6I37/fffxfGMGTOMuv/o0aOxY8cOAMDp06fh6+sLoGYdZJs2bfD48WNcunQJiYmJdapmPk2axAQAZs2aZTA+ICAAa9asabT+GCs3Nxdnz57FtWvXUFhYiEePHmkVyNGsc5MkCWlpaSatL6yvoqIirYSlPltgyJ08eRIA4O7ujnbt2uk8BwDe3t4mt338+HHRV03V0NoSEhIQGxsLoKYoVHR0NMzNzU2+F6C9ntHFxQUffvih3nhNURoA8PPzw9ChQ/XGV1RUiONOnTrVOW9jY4MFCxZgwYIFRvbYMHmi/eTJE9y/f9+otc9E1HIwaSQies6Zm5tj7dq1+OCDDwzGyrdF2Llzp1EVRvPy8sTxjRs3xLGFhQUmT56M/fv348mTJxg9ejSmTp0KPz8/jBw5Eh07djTtjeihSag0lEZ+5Pr27YtOnTqhoKDgqfXDFGfOnMHnn3+OhIQEo7c5uH//fiP3qkZZWZnW64ZUbs3JyRGjlppRUzlN0titWze4urqa3L4xW21s3LhRHM+fPx92dnYm30dDnjS+9957BkfQNQV1NPH6VFZWioq15ubmcHR0rHc/TVH7+dZ+/kRETBqJiJ5x7u7u8PDwEK9LS0uRm5uLpKQkVFRUoKqqCkFBQcjOzta72XdpaanWnnQREREm9+Xhw4darzdt2oTU1FRkZWWhsrISe/bswZ49e2BmZob+/ftjxIgR8PHxwYQJE/RWvDSkqKhIq/qqg4ODUdc5ODg0S9IYGRmJoKAgk/fE07VnYFMwtZ9y8q02aieNVVVVSEpKAgCMHDnS5Larq6vx22+/AQCcnZ3h4uJSJ+bx48c4cuSIeP3uu++afB85edI4ZswYvbGVlZXIyMgAAKjVaoPv8erVq2JqaL9+/dCqVasG9dVYDXm+RNQyMGkkInrGTZw4EStXrqzz8zt37iA4OBj79u0DAKxZswaDBg2Cv7+/znZq78dYH/KpeADQvXt3nD9/HuvXr8euXbtw9+5dADVf9i9evIiLFy8iPDwcNjY2WLp0KZYsWVKvaYOlpaVar40dGas9VbIpXLlyBXPmzBFf1Pv374/Zs2fDy8sLjo6OsLKyQps2bUR8YGCgGPGtrq5ukj7W/lwePXqE9u3b670mJSUFe/bsqfNz+b6bP/30k5gmCtSse9Ukwrm5uXWmXA4dOhTvvPOO4j3Pnj0rfqeURhlTU1PFlE97e3u8/PLLet+HPqWlpWIrFAsLCwwcOFBv/OXLl8UfM1xdXQ3+vskTUmO3y3gaau+h2hz/L4jov41JIxHRc6p79+7Yu3cvCgoKxCbmc+fOhY+PD2xsbOrE1/6iWFBQoDPOVFZWVli1ahVWrlyJ8+fPIyEhAYmJiTh9+rSYbvnw4UMsW7YMycnJiI2NNbloTu2Epry83Kgvvo0xDc9QYrd582aRXI8bNw6HDx+GhYWFYnxzjC5aW1uL9ahAzbTYLl266L3m6tWr2LZtm96YyMhIxXMpKSlISUnR+llpaanepPHw4cPiWClplE8PdXZ21ts/Q9LS0sTzHTBggMGRQHnRHGOSwOZKGvPz88WxWq1WLCZERC0Xq6cSET3HzMzMEBERIRKogoICxeIvHTt21JoieufOnafaF3Nzc3h6euKzzz5DbGws7t69i4SEBK0v+7/88gsOHTpkctvW1tZaiZdmXZgh8jWYusiTgtqjqEoMjdieOHFCHK9evVpvwghAq4ppU1GpVHBychKv5etW/0s06xltbGwUiyzJpx937dq1QfczNamTJ42DBw82Kb4pk8abN2+KY3t7+3oXCSKi5xeTRiKi55y9vb3W9hLffvutYkIoXxuZmJjYqP0yMzPD8OHDERcXp7UNiHz0yFi1tzZITk42eE1WVpbYgkGJlZWVOH748KHBtV+5ubkGt8W4deuWODY0VbKoqAjp6el6YxqLfOplZmamwfjAwEBINfs/i3/79+8X548dO6Z1rqqqCtbW1gAAX1/fOtdKkoTo6GjF++Xk5ODSpUsAaqZoq9W6J0/JEyDN1h711ZCk0Zh4eTEnNzc3k/rWEFevXhXH8v9HREQaTBqJiFqAxYsXo0OHDgBqCoOsW7dOZ9ykSZPE8fbt25ukQIZKpdLaKkGzRs1U8iIre/fuNRj/ww8/GIzp0KGD2PagvLwc165d0xsfExNjsE35vn+GkpiIiAj8+++/BttsDPI/IMiTGVNoKqOq1WoMGzZM61x6eroYla1PERx51VSlrTYAaG0d8ddffzXod9qUpLG6ulp8biqVymD89evXxR6pjo6OOrfbaCzy5yt/7kREGkwaiYhaABsbGyxcuFC83rFjh9Y6Jo05c+aIrTAuXLhgcP9Hufv372ttCl5SUqJV0VQf+TTR+k4hlG8pkpycrDdx/Oeff7Bp0yaj2vX09BTH+ka+8vLyEBYWZrA9+ebt+kZVs7KyTPr8nzb56K+8mI0pTp06BaAmwaq9xjQhIUEcNyRpbNWqFcaPH68YJ09Wb968iZ9//tnkewE105M1I5tmZmYGR+SysrJEgSZnZ2fxRxslzbWeEdB+FvLnTkSkwaSRiKiFCA4OFgVjysvL8c0339SJsba21kqmQkNDERAQoLhGUJIkJCYmYt68eXBwcNCqwpiamgonJyesXLkSV65c0Xl9VVUVDhw4gK1bt4qfTZgwoV7vr2/fvggMDBSvg4KCdO4zef78efj4+KCsrMzgekIAmDFjhjjeuHGjzjWXycnJ8Pb2xsOHDw22KR8VCw4OFkWK5E6cOIFRo0ahpKSk2SpZDhw4UGxdkpGRgdu3b5t0/YMHD8RzHzFiRJ3zmkTF2tra5CmRxcXFYhRz5MiRYpqrLg4ODhg7dqx4HRQUhEOHDukccSwtLcW+ffvg7e1dZ5pxRkaGKAzUt29fg8/lWSmCc+/ePVy+fBlATfGsV199tcnuTUTPDlZPJSJqIWxtbTF//nysXbsWALBt2zYsXbq0zjS4wMBAZGdnY9WqVQBqpnH++OOPcHNzg4uLC9q3b4/S0lLk5eXh77//1lv45fbt2wgNDUVoaCi6d+8ONzc3dO/eHWq1Gnfv3kVqaqrWGr8RI0Zg2rRp9X6PGzduxJkzZ5CZmYmKigoEBgbif//7H7y8vNC6dWtcvnwZZ8+ehSRJ8PX1xYMHD0TyoWTatGnYsGED0tLSUFlZCT8/PwwePBhubm6oqqpCenq6SBBWrlyJqKgovcVrFi1ahIiICOTn56OgoADjx4/H4MGD8dJLL0GlUuHChQviS/y4cePQtWtXnVtZNIWZM2eK0dO4uDjMnTvX6GtPnTolEjN9SeOwYcO0puwa4+jRo2LarlLVVLktW7bAy8sLxcXFKC4uhp+fH5ycnODh4YFOnTqhtLQU165dQ1paGioqKtCnTx+t9axA4xfBaa6kMS4uTjyn6dOnm/wsiKiFkIiI6Jnj7e0tAZAASF999ZXR1927d09q166duHbFihWKsQcOHJDs7OxErKF/Hh4e0uPHj8X1ycnJklqtNvp6Pz8/qbi4WLE/jo6OIjYnJ0cx7ubNm9KQIUP03uvNN9+UiouLtT7H+Ph4xTazs7Ol3r17K7anUqmk5cuXS9XV1Ub1MykpSercubPePk6ePFkqLCyUAgICxM+ioqIa/PmY4tq1a5JKpZIASN7e3iZdu2jRItGf/Px8rXNZWVniXFhYmMn9mjlzpsnv9cyZM5KDg4NRv4tTpkypc/3ixYvF+XXr1hm8n4+Pj4j/448/DMbLn9+NGzeMek9Pw9ixY8V909PTm+y+RPRs4Z+TiIhakC5dumiNFm3dulUU36jN398f2dnZiI6OxvTp0+Hs7Axra2uYm5vDysoKrq6u8PX1xaZNm5CZmYmUlBStLTs8PT1x7949xMTE4OOPP8aIESNgZ2eH1q1bQ61Wo1OnTnB3d8fChQuRkpKCgwcPGlz3ZQw7OzskJycjMjISY8aMQZcuXWBhYYGePXti0qRJOHjwIOLi4ky6V69evZCeno6wsDC4u7uLfQx79+6NgIAAnDlzBqtXrzZ6f0kvLy9cvnwZy5Ytw4ABA2BpaQlLS0v06dMH/v7+OHz4MGJjY/VOu2wKL774Il5//XUANUVt5HseGqJZz+jq6lpn37+GrGesqqrCkSNHANTslSjfGkSfoUOHIiMjAzt37sQbb7wBe3t7tGnTBmq1Gra2thgyZAg++ugjxMXF6VwP25gjjYWFhWJ0unPnzrC3tzfqPTVUTk6O2AJmzJgxBqv5ElHLpZKkJiiNR0RERM+kpKQksQfiJ598gs2bNzdrf06ePIlRo0YBAL744gt8/fXXzdqfZ1lISIiopHzs2DEWwSEiRRxpJCIiIkXDhg0TxYkiIiIM7m3Z2IzdaoP0KyoqwnfffQcA8Pb2ZsJIRHoxaSQiIiK91q1bB7VajbKyMmzYsKFZ+6JJGrt166a1HQqZZsuWLSguLoaZmRnWr1/f3N0hov84Tk8lIiIigxYtWoQtW7agbdu2yMrKQo8ePZq7S1RP+fn56NOnD0pKShAUFIRdu3Y1d5eI6D+OSSMREREREREp4vRUIiIiIiIiUsSkkYiIiIiIiBQxaSQiIiIiIiJFTBqJiIiIiIhIEZNGIiIiIiIiUsSkkYiIiIiIiBQxaSQiIiIiIiJFTBqJiIiIiIhIEZNGIiIiIiIiUsSkkYiIiIiIiBQxaSQiIiIiIiJFTBqJiIiIiIhIEZNGIiIiIiIiUsSkkYiIiIiIiBQxaSQiIiIiIiJFTBqJiIiIiIhIEZNGIiIiIiIiUvR//TtCL+M7x98AAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Discussion:** How does this residual plot compare to that for the linear regression model?\n",
+        "\n",
+        "**Answer**: This residual plot is perhaps a bit closwer to being normally distributed, though it has a bit of an extended tail to the left and maintains a couple outliers."
+      ],
+      "metadata": {
+        "id": "CqurN7gcu19r"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3.3. Plot the Confidence Intervals"
+      ],
+      "metadata": {
+        "id": "IAZsjLQ9m3F-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Using the previous confidence interval plotting code above, fill in the missing parts to plot the confidence bands.\n",
+        "\n",
+        "*Hint: Be sure to use the variable names from the nonlinear model.*"
+      ],
+      "metadata": {
+        "id": "aMvcQs0wPVt9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# calculating confidence interval using Working-Hotelling method, plotting\n",
+        "\n",
+        "# Compute the standard error\n",
+        "### BEGIN SOLUTION\n",
+        "se = np.sqrt(np.sum((concentration - nl_model_concentration)**2) / (len(time) - 2)) * np.sqrt(1 / len(time) + (time - np.mean(time))**2 / np.sum((time - np.mean(time))**2))\n",
+        "### END SOLUTION\n",
+        "\n",
+        "# Compute W for the confidence bands Working-Hoteling method\n",
+        "### BEGING SOLUTION\n",
+        "W = np.sqrt(2 * f.ppf(1 - 0.05, 2, len(time) - 2))\n",
+        "### END SOLUTION\n",
+        "\n",
+        "# Build the upper and lower confidence interval\n",
+        "wh_upper = nl_model_concentration + W * se\n",
+        "wh_lower = nl_model_concentration - W * se\n",
+        "\n",
+        "#plot original data and fitted model\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize = (6.4,4), dpi = 150)\n",
+        "ax.scatter(time, concentration, label = 'Experimental Data')\n",
+        "ax.plot(time, nl_model_concentration, c = 'orange', label = 'Model', linewidth = 3)\n",
+        "\n",
+        "ax.plot(time, wh_upper, color='forestgreen', linestyle='dashed', label=\"WH Upper\")\n",
+        "ax.plot(time, wh_lower, color='forestgreen', linestyle='dashed', label=\"WH Lower\")\n",
+        "#add plot labels\n",
+        "ax.set_xlabel(\"Time (s)\", fontsize = 16)\n",
+        "ax.set_ylabel(\"Concentration ($ \\#/cm^3$)\", fontsize = 16)\n",
+        "ax.set_title(\"Concentration of 4 $\\mu m$ Particles over Time\", fontsize = 16)\n",
+        "\n",
+        "#set tick parameters\n",
+        "plt.xticks(fontsize=15)\n",
+        "plt.yticks(fontsize=15)\n",
+        "plt.tick_params(direction=\"in\",top=True, right=True)\n",
+        "\n",
+        "#add legend\n",
+        "plt.legend()\n",
+        "\n",
+        "#show plot\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 627
+        },
+        "id": "5-IL-0EJm6wX",
+        "outputId": "5d850e4c-ef96-4ab9-c602-09b1ea3dd26c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 960x600 with 1 Axes>"
+            ],
+            "image/png": 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OoRNDc3Nz8cqVKxrLdujQQal8pUqVNP6QlpWVJdauXVsqu23bNpUy+vq/Km2YGJJevHjxQukPduHChTqtf9q0aVLdDRs2zPcCtG3bNqUv0s+fP1cpkzveiRMn5llfUlKSlPgKgiC+ePFCpcy8efOUvmwW5Ze7hQsXSnV9/PHHWu0zduxYaZ+dO3eqbNf1+Ypi0RPDAQMGaLVPQXXt2jXf2EVRP4lhaXivFoQ+zkcU9ZsY5m4FO378eJ7H1nViOHHiRKlubVog9u7dK5XXdJ/TP//8o/QeWLFiRZ515r73ShAE8ezZs3mWz51IhoSE5BtzXrRNDHfv3i19Kct5rxT1Hkdt/+4L8xmkr8RQl9eN/I6l6+vKgwcPlD4bDEHXn0cvXrxQuj/y5s2bGutatWpVvkmkPq7domiY6+abDJ0YTp48Oc86c//ops33zK+//loq++mnn6ps19f/VWnDewxJL3LuvchhbW2t0/pz37+YM+JfXv73v/+hfPnySExMREZGBs6ePYsuXbpoLN+/f/8867OxsUHNmjURHR0NURRx9+5dNGjQQKnMoUOHpOdjxoyBmZlZnnXm5cCBA9LzIUOGaLVPu3btsGLFCgBAcHAw+vbtq7GsLs5XFwYNGlSo/WJjYxEaGorr16/j+fPnSEtLgyiK0vac+wtEUURkZCT8/Px0Eq82SsN7tSD0fT66duTIEWzYsAEAMGLECLRt29ZgxwaU7wXUdL9gbrnvL2zYsGG+ddapUwdjxozJs87cI/f169cv3/s1MzIypOfly5fPs2xBHDhwQGWE3OfPnyM0NBQ3btxQWj9//nxUq1Ytz/r08Xdf2M8gXdHldSM/ur6uVKhQAebm5khPT8eVK1dw+vRpnY8t8CZdfx7Z2tqiZ8+e2L59OwBg06ZN+Oabb9TWlXuUy2HDhqkto+9rN1D871l9ye8e+zevY/mVzz0nds5nQ26G+L8qDZgYkl68eSN+SkqKzuoWRVHpi1Hu4c01MTExQbNmzaSLbnh4eJ5fTrX54uzg4CA9T0pKUtkeEhIiPS/ql9GzZ89Kz1euXIn169fnu8/9+/el5/fu3cuzrC7OVxeaNGlSoPJnz57F9OnTERQUpPSFMC/6nLrhTaXlvaotQ5yPLqWmpkoDYTg4OOCXX34xyHFz5CQkQPbcjJoSvdwKOvDMqFGj8v0ynDvpyj0tiDqvXr1CbGwsgOwBT1xdXfMLWWthYWEICwvLs4yNjQ0WLVqUZ5z6/Lsv6GeQrunyupEfXV9XTE1N0adPH2zduhVZWVlo164dBg4ciH79+qFNmzawt7fXWeyA/j6Phg0blm9ieP/+fZw8eVKqc+DAgWqPp+9rN1D871l9yZ3IqVOuXDnpuZ2dXb6DPeX+kUvdddAQ/1elARND0gtbW1sYGxtLv1Q/f/5cZ3W/ePFCaTJsbb+4uLm5Sc/z+5KgzciNuUf7yh0PkP2hk5aWJi3XqFFDqxjVSUlJUWqB/eOPPwpcR+6R/tQp6vnqSn4jwOW2Zs0ajB49WusvhjnebM3Wp9LwXi0IQ5yPLs2YMUMatfHXX39FhQoVDHZsIHsevpwvILVq1YKVlVW++xS0xfDN0Vvf9OrVK0RFRQEAjI2N0aZNmzzL//fff5DL5QCyR7nV96iG1tbWcHBwgJeXFzp06IB33303zwRC33/3BfkM0jVdXjfyo6/ryoIFC3DhwgXcuHEDr169woYNG7BhwwYYGRnB09MTfn5+6NixI7p27Vrk1lB9fR516dIFFSpUQEJCAq5fv46wsDD4+Pgoldm8ebP0Hswp/yZDXLuB4n3P6lN+1zZj49cpjDbXwdzl37wOGur/qjR4S8ZWpZIo94d0ztDCuvBm66M2X7beLJffl4SiDmOvy660L168KFIsANROApubIYbt14aFhYVW5a5du4axY8dKF2ZPT08sWrQIoaGhePz4sdSlLOcxYsQIaV+FQqGX2NUpDe/VgjDE+ehKeHi4NNx/27Ztld4DhlLQbqTPnj2TJk7P6QL8ppSUFGk6BlNTU3h5eeVZ59WrV6VpAXImfNZlzAUxc+ZMpb9LURSRnJyMmJgY7N27F5MmTcozKTTE3722n0H6oO9bMHLT13XF0dER58+fx1dffYXKlStL6xUKBS5fvoxly5bhf//7H6pUqYJ58+ZJP0IUhr4+j95sAdy4caNKmdzrhg8frvY4hrh2A8X7ntWnglzbinodNNT/VWnAFkPSG19fX+kLTO7uMUX15sUyNTVVqwtCamqq9FzdnFO6pK4rbWEv8m+eW2JiolIXirJo4cKF0odw586dsXfvXpiammosb8hWwtxKw3u1IErT+Vy6dElKBmJjY/O8ry4+Pl56/vDhQ6WyX3/9Nbp3716oGHK3/jVu3LhA5b29vdV+2YmMjJTOq379+vm26GnTNTU3fSaGRVVa/u4LS5fXjfzo87pia2uLuXPnYtasWTh//jyCgoJw+vRpBAcHSy10z549wxdffIFz585h9+7dhfpir8/Po2HDhmHp0qUAgG3btmH+/PmQyWQAgMuXL+Py5csAsluqevbsqbYOXrtLD/5fvcYWQ9Kbdu3aSc/v3r2LM2fO6KReOzs7pS9DOffD5CenSxkAvXcps7W1VfoVT92Nztqyt7dX6nLz6NGjIsX2NsiZCB0Avv322zy/HAKQWmEMrTS8VwuitJ7PrVu3EBISovFx+/ZtqeyrV6+UtuVOGgsqd5Ll7e2db/mgoCDpuTb3FxZ0MJuCJqclLTEsLX/3haXL60Z+DHFdkclkaN68OT777DPs3r0bjx8/RlBQEHr16iWV2bNnD3bu3Fmo+vX5edSiRQu4u7sDAB4/foyjR49K23K3Fvbr1w/m5uZq6+C1u/Tg/9VrTAxJb/r376/0oTt//nyd1CsIgtK9N9oknFlZWUqDHmjzBamomjdvLj3PPXJaYTRr1kx6fvr06SLVpU+G6tYYFxcnPc9v8JUXL17g0qVLWtWr6/hLy3tVW2/b+ehb7iSuevXq+Zbfu3ev9Fyb+wsLmhhqUz5nsJy8Yigu+vq7Lwx9fdbp8rqRH0NfV4yMjODr64u///4bHTt2lNbnft8XhL4/j4YOHSo9zxmBVBRFbNmyRVqvaTTSHKXl2k38v8rBxJD0xsLCApMmTZKWd+7cWahfBlNTU1U+8HO3Rq5fvz7fgQj+/vtvPH36FABgbm6Oli1bFjiOguratav0fNWqVUpDwBdUjx49pOfLly8v8MALhpL7l1N9DVADZH/ByPHy5cs8y/7xxx9ax6KP+EvDe7UgSsv5jBw5UuV+Nk2PtWvXSvu5uroqbRs5cmShjp+QkIAHDx5Iyzn3+Wly4sQJhIeHS8u6SAwVCoWU6AmCkG/5mJgYaaAwV1dXnU5VoQv6+rsvDH191unyupGf4rquCIKg1P3y8ePHha5Ln59HuZO+v//+Gy9fvsTJkyel0SerVasGf3//PI9XWq7dxP+rHEwMSa+mTp2q9Ivc8OHDsW/fPq33v3LlClq0aIEjR44orR8zZoz0JSE8PBwrV67UWMfz588xdepUaXnw4MFajWBVVGPGjJHugbh79y4+/vjjQtc1duxYaVCG8PBwzJ49W+t9ExISinSDf0HknhYh95diXcs9Wl9evzbfuHGjQK+VPuIvDe/VgnjbzkdfcrfUAcrdRN+UkJCgNBehsbExPD09VcplZWXhypUrALKTpPy6p964cUMaoMPd3T3f+ztL8v2FgP7+7gtDX591urxu5EfX15Xk5OR8fwDJkXto/0qVKml93Dfp8/PI3d1dut84JSUFf//9t9LchUOHDs235bi0XLuJ/1c5mBiSXpmZmeGvv/6SPvjT0tLQp08fvPvuu/jvv//U7iOKIsLCwjBixAh4e3tLX4Ryq1mzpjQ/GQB89NFHWLp0qcrIczdv3kSnTp2kezVsbW01Tlara+XKlcOPP/4oLf/+++8YOHCg0rw3uV29ehWTJ09WSYKB7HspFixYIC3Pnj0bI0aM0HhPhSiKOH36ND788EO4uLgoDYGuT7nnHfrrr7/0dpzcvzZ/8sknOHz4sEqZY8eOISAgAMnJyVqPVqeP+EvDe7Ug3rbz0ZfcSRYAfPHFFwgMDFQpd/z4cbRs2VIaqAvIbskwNTVFenq6UtmoqChpnYeHR77v67dp4BlAf3/3haGvzzpdXjfyo+vryoULF+Dm5oZZs2ZpHIlcLpdj27Zt0ojBgHIraUHp+/Mod6vh6tWrsWPHDrXbNCkt127i/1UOjkpKelejRg2EhISgZ8+euHLlChQKhTS3kZubG7y8vFChQgXI5XI8evQIERERKl1L1P3S/csvv+D8+fMICwtDVlYWPvroI8ybNw++vr6wtrbGrVu3cOrUKelXHGNjY6xevVppDiN9+/DDD3HlyhUsX74cALB9+3bs3LkTPj4+8PDwgLm5OeLj43Hx4kXphnhNkxqPHDkSt2/fxty5cwEAf/75JzZt2oSGDRuiTp06sLa2RkpKCu7fv4+IiAidDL9cUO+88w5WrFgBAFi2bBkuXLiAxo0bw9LSUiozfvx4tcPwF8THH3+MP/74A/Hx8UhMTESXLl3QuHFj1KtXD4IgIDw8HFevXgWQPXphpUqVsGHDhmKLvzS8VwvibTsffcidZLm6uuLu3bto27Yt6tWrh5o1a0Iul+Py5ctSy0mjRo2kRC4lJUWarDv3pMv6HnimpCeG+vq7Lwx9ftbp8rqRH11fVx4+fIjZs2dj9uzZcHR0RMOGDeHo6AhjY2M8fvwYFy5cULpX1M/PD4MGDSpU7Dn0+Xk0cOBATJkyBZmZmUr3fDZq1Ehtq746peHaTdn4fwVAJDKQ5ORkcc6cOaK9vb0IQKuHt7e3uHv37jzrHDBgQL71VKlSRTxw4ECe8eUurw1/f3+p/IkTJ/Isu3DhQtHW1jbfOAVBEA8fPpxnXdu2bROdnJy0fg2bNWsmpqenG+x8Bw8enGc8b+5b0DhynDlzRqxQoUKex+rTp4/4/PlzccSIEdK6tWvX5llvQeN3dXWVtt25c0djvaXlvaotXZ6PKIri2rVrpX1GjBihkxi1lfvYrq6uOqmzbt26Up2BgYFilSpVNL5G48aNEy9fvqyyvkePHkp1fvrpp9K2n376Kd8YOnbsKJXP73NFFJXfy/fu3Sv0ueeW+703c+bMItenj7/7wn4GFfSzQhS1/7wQxaJfNwpyLF1cV86dOycaGxtrXUe/fv3EpKSk/F5mrej68yi3Hj16qNTz66+/FjhGXV27RbHw79mimDlzpnRMf3//Au2r7XuxIOd1586dAn1unzhxQuv4dfl/VdowMSSDe/78ubh582Zx1KhRopeXl+jo6CiampqK1tbWoouLi9ipUyfx66+/Fi9cuKB1nWfOnBHHjx8v1q1bV7SzsxNNTU1FJycnsVOnTuJvv/0mpqSk5FtHQT9oC/plOyEhQfzll1/Ejh07ilWrVhXNzMxEMzMzsWrVqmKHDh3EuXPnitevX9fq2Onp6eK6devEwYMHi+7u7qKdnZ0ok8lEW1tbsW7dumLfvn3FBQsWiNHR0QY/X4VCIW7atEns0aOH6OzsLJqbm+f5ZakoF7jHjx+LX3zxhVi/fn3R0tJStLS0FGvWrCkOGDBA3Lt3r1SuIIlhQeMvyJcvUSwd79WC0MX5iOLblRi+fPlSlMlkIgDRzs5OFEVRvHfvnjhu3DjRxcVFNDU1FatUqSL26tVL/Oeff6T9vv/+e9HBwUGsUKGC+L///U8MDQ1Vqrd9+/ZSnEePHs03jtwJVHx8fJ5lnz17JpWtUKFCwU9aA10nhqKo+7/7wn4GFfSzQhQL/nlRlOtGQY+li+tKYmKiuH37dnHSpEmin5+f6OTkJJqZmYnGxsZi+fLlRR8fH3HixIliSEhIvvEUhq4+j3Lbtm2b0v+rTCYT4+LiChWfLl5jUWRiKIr6TQxFUXf/V6WNIIpldNgdIiIiPQgNDZWmHfD19c1z4BkiIqKSgoPPEBER6VDue/u0mdieiIioJGBiSEREpEO5B3FhYkhERKUFE0MiIiIdyp0Yenl5FV8gREREBcB7DImIiHREoVDAxsYGL1++hJGREZKTk5WmMCAiIiqp2GJIRESkI9evX8fLly8BAO7u7kwKiYio1GBiSEREpCPsRkpERKUVu5ISERERERGVcWwxJCIiIiIiKuOYGBIREREREZVxTAyJiIiIiIjKOOPiDoCKl6OjI1JTU+Hi4lLcoRARERERlXmxsbGwsrLCo0ePDHpcthiWcampqcjMzCzuMHDr1i3cunWruMOgEozvEcoP3yOUH75HKD98j1B+DPEeyczMRGpqql6PoQ5bDMu4nJbCq1evFmscnp6eJSIOKrn4HqH88D1C+eF7hPLD9wjlxxDvkZxjGBpbDImIiIiIiMo4JoZERERERERlHBNDIiIiIiKiMo6JIRERERERURnHxJCIiIiIiKiME0RRFIs7CCo+HH2LiIiIiKjkKK7v52wxJCIiIiIiKuM4jyERERFREYiiCHbAIqI3CYIAQRCKOwytMTEkIiIiKiC5XI6nT58iOTkZr169Ku5wiKiEkslksLS0hK2tLWxsbEp0osjEkIiIiKgA5HI5YmNjkZ6eXtyhEFEJJ5fLkZycjOTkZNjb26Ny5cowMiqZd/MxMSQiIiIqgKdPnyI9PR0ymQyVK1eGlZVVif2iR0TFRxRFZGRkIDk5GYmJiXj+/DnMzc1Rrly54g5NLSaGRERERAWQnJwMAKhcuTLs7OyKORoiKsksLS1haWkJY2NjPHnyBM+ePWNiSKQv8ckZ2BYWi5A7iUjJyIK1mTFa1HDAgKbVUNHGrLjDIyKit4goitI9hVZWVsUcDRGVFra2tnjy5AkyMjIgimKJvNeQiSGVWumZcszedxU7LtxHplx5NLigGwlY+O919GtSDTN71oO5iayYoiQiordJ7tFH2X2UiLQlk73+LsrEkEiH0jPlGLEmFCF3EjWWyZSL2BIai9vxKVj/XjMmh0REREREGvCnLiqVZu+7mmdSmFvInUTM3ndNzxEREREREZVeTAyp1HmSnI4dF+4XaJ8dF+4hPjlDTxEREREREZVuTAyp1Nkedk/lnsL8ZMpFbD9/T08RERERkSAI+T5GjhxZ3GHqTEBAAARBQExMTHGHUmIV9DUKDAxUec9YWFjA0dERLVu2xOTJk3Hu3Dn9Bl2G8R5DKnW07UL6pnO3n2JCW3cdR0NERES5jRgxQuM2X19fA0ZCBREQEICTJ0/izp07cHNzK9ZYKleujC5dugAAsrKykJiYiMjISJw7dw6//fYbOnXqhPXr18PR0bHIxxo5ciTWr1+PEydOICAgoMj1lWZMDKnUScnIMuh+REREpL1169YVdwgG8eeff+Lly5eoWrVqcYfy1qlTp47a91FQUBAmTZqEI0eOoG3btggJCYGtra3hA3xLsSsplTrWZoX7PaOw+xERERG9ycXFBXXq1IGJiUlxh1Jm+Pn54fTp02jQoAGioqIwa9as4g7prcLEkEqd5tXLF2q/FjUcdBwJERGRfsUnZ2DJ8RsYvjoE/1t2GsNXh2DpiZtvxYBq06ZNgyAIGDBggMq2hIQEODk5QSaTITg4WFo/cuRICIKAwMBAHDx4EL6+vrC2tka5cuXQt29fREVFaTxeSEgI+vfvjypVqsDU1BTOzs4YPXo0YmNjVcrOmjULgiBg3bp1CA0NRY8ePeDg4ABBEBAREQFA8/1zgiDAzc0NWVlZmDt3Ltzd3WFhYYG6deti7dq1Urnjx4+jbdu2sLW1Rbly5fDuu+/i6dOnamPPysrC8uXL0bJlS9ja2sLCwgINGzbEwoULkZWl2iPKzc1Nmifvjz/+gJeXl3Sv3tixY/H8+XOpbExMDARBwMmTJwEA1atXV7rHL8fDhw/x008/wd/fH1WrVoWpqSkcHR3Rt29fhIWFaXzddc3S0hILFiwAAKxcuRLp6enStvT0dKxevRq9e/dGjRo1YGFhAXt7e7Rp0wZbt25VqUsQBKxfvx4A0LZtW6Xzzvl/ff78ORYvXozOnTvD1dUVZmZmcHBwQJcuXXD06FH9n7ABsQmFSp0BPtWw6NiNAg1AYyITMKBpNT1GRUREpDvpmXLM3ncVOy7cV7neBd1IwMJ/r6Nfk2qY2bNeqZ2nd+7cuTh69Cj++usvrF+/XunexDFjxuDhw4eYMWOG2vsS//rrLyxfvhxNmzZFz549cenSJezevRvHjx/HyZMn4e3trVR+2bJlmDhxIgDAx8cHfn5+iI6OxurVq7F3716cPHkSdevWVTnOqVOn8MEHH8DDwwOdOnVCXFwcjIy0a1cZMGCAlPzVrFkTJ0+exHvvvQcAsLGxweDBg9GiRQt07twZZ8+exYYNG3Dnzh2cOnVKKSFLS0tD9+7dceLECZQvXx4tWrSAubk5QkJCMGXKFJw4cQK7d+9WG9fUqVOxaNEiBAQEwN3dHadPn8bKlSvx33//4eTJkxAEAdbW1hgxYgQOHTqEx48f45133oG1tbVKXXv27MG0adNQu3ZteHl5wdbWFjdu3MDu3buxf/9+7N+/H506ddLqtSmq9u3bo2LFioiPj0dYWBj8/PwAZCe5o0ePhpOTE2rXro1mzZrh0aNHOHPmDIKCglRaGUeMGIHg4GDcunULnTt3VrpnMec1OHfuHCZNmgQ3NzfUrl0bLVu2RGxsLI4cOYIjR47gjz/+kP5fSz2RyrR69eqJ9erVK+4wCmz6zkjRddp+rR/Td14q7pCJiOgtIJfLxWvXronXrl0T5XK5Xo6R9ipLHPD7Ga2ubwN+PyOmvcrSSxwFBUAs6FfLa9euiRYWFqKNjY14+/ZtURRFceXKlSIA0cfHR8zMzFQqP2LECOk4K1eulNYrFApx2rRpIgCxYcOGSvucPXtWlMlkYtWqVcXz588rbfvjjz9EAGLz5s2V1s+cOVM6zo8//qg2dn9/fxGAeOfOHbWvQ/369cUnT55I648fPy4CEKtUqSI6ODiI+/fvl7a9ePFC9PT0FAGIx48fV6rvww8/FAGIAwcOFJ8/fy6tT0pKErt16yYCEJcvX660j6urqwhAdHR0FKOioqT18fHxoru7uwhAPHbsmFbnk+PSpUvilStXVNYfOnRINDU1FWvWrCkqFIoC1fmmEydOiABEf3//fMt26NBBBCCuWLFCWpeQkCAePXpUJY7bt2+Lbm5uopGRkUosOe+pEydOqD3O7du3xbNnz6qsDw8PF+3t7UVbW1sxOTk533gL8tlRXN/P2ZWUSqWZPT217lLavHp5zOxZT88RERER6cbsfVe1HoE75E4iZu+7pueICiav6Sr+/vtvpbJ169bFL7/8guTkZAwdOhT//fcfpkyZAktLS2zcuBHGxuo7t7Vq1QpjxoxROubcuXPh7OyMiIgIpe6n8+bNg1wux++//44mTZoo1fP++++jV69eCAkJwcWLF1WO06BBA3z++eeFeh0WLlyIihUrSstt27ZFo0aN8PDhQ3Tt2hXdu3eXttna2uKDDz4AAKlLJwA8efIEq1atQrVq1bB27VrY2dlJ22xsbLB69WqYmppi+fLlamOYO3cuateuLS1XqFAB48aNA5DdGloQDRo0gKenp8r6zp07o3///rh16xauXLlSoDqLokKFCgCAZ8+eSescHBzQoUMHpRZXILt77IwZM6BQKLBv374CHad69epo0aKFyvpGjRphwoQJSEpKwokTJwpxBiUPu5JSqWRuIsP695ph9r5r2HFB/byGJjKh1HezISKisuVJcjp2XLhfoH12XLiHTzp6oKKNmZ6iKpi8pqtwcXFRWffhhx/iwIED+Oeff9CiRQukpqZixYoV8PDw0FjPoEGDVNaZmJigX79+WLhwIYKCguDr6wuFQoFjx47B0tISnTt3VluXn58f9u7di9DQUDRq1EhpW48ePVSSDG2YmJionfqgRo0auHjxotoulzVq1ACQfS9fjsDAQGRmZqJLly6wsLBQ2cfR0RG1atXC5cuXkZaWplJG3XFyXtfcx9FWRkYGDh06hNDQUMTHx+PVq1cAgMuXLwMAbty4gQYNGhS43sIQxezvfur+f4KDgxEYGIgHDx4gPT0doihK53vjxo0CH0sul+PYsWM4c+YMHj58iIyMDKW6ClNnScTEkEotcxMZfujbAJ909MD28/dw7vZTpGRkwdrMGC1qOGBA02ol5iJJRESkje1h6n/szEumXMT28/dKzFy9hZmuYvXq1XBzc0NSUhK6du0qtZ5p4urqqnZ9zvx7cXFxALIHsUlJSQEAmJqa5llnQkKCyjp1iaw2HB0dIZOp/iidc9+auikucrblJB0ApAFQVq1ahVWrVuV5zMTERJV6nZ2dVcrZ2NioHEcbly9fRq9evfKcrD45OblAdRZFzv9X+fKve5C9ePECffv2xfHjxzXuV9AY79+/jx49eiAyMlJndZZUTAyp1KtoY4YJbd1LzAWRiIiosLTtQvqmc7eflurr4N69e6XRJaOjo5GSkqJ2AJSCUigUALKTrnfeeSfPsuq6SZqbmxfquPkNUKPtADY58Tds2FBlQJ03mZmp/hiu7XHyI4oiBgwYgJiYGIwbNw7jxo1DjRo1YG1tDUEQ8OWXX+KHH36QWvH0TRRFKVGrV+/17ULTpk3D8ePH4e/vj9mzZ6N+/fqwt7eHTCbDkSNH0Llz5wLHOHr0aERGRuKdd97B1KlTUbt2bdjY2MDIyAgrV67E2LFjDXbe+sbEkIiIiKiESMlQnXpAn/uVBDdu3JDuK+zSpQt27dqFSZMmYc2aNRr3uXv3bp7rnZycAGTfh2Zubg4jIyOsXbu2UN1Ci1NOi5+vry8WL15cbHFERUUhKioKTZs2VXs/4+3btw0az7Fjx5CQkAAbGxul+0Z3794NmUyGvXv3qkx8X5gYU1NTcfToUVSuXBnbtm1TaQU29HnrGwefISIiIiohrM0K95t9YfcrbllZWRg2bBhSU1Mxf/58bNy4EXXq1MHatWuxc+dOjftt375dbV05++RMcWFsbIyAgAAkJSXh2LFj+jkJPWrbti1kMhn279+PzMxMvR4rp6utunkRcwZ4Udc19dmzZwadz+/ly5f45JNPAADjxo1Tail99uwZbG1tVZJCQP17Bsj7vF+8eAGFQoEqVaqoJIWZmZnYvXt3oc+jJGJiSERERFRCaDvi9pta1HDQcSSGMWfOHISGhqJnz54YO3YsLCwssHHjRpiYmOCDDz6Q7hV8U3BwsEqL4syZMxEbGwsvLy9pXjsAmDFjBoyMjDBq1CgEBgaq1JWSkoI1a9YgLS1Np+emC1WrVsV7772HmJgYDB48GI8fP1Ypc/PmzTyTaG3ltLJGR0erbHN3d4eRkRGOHz+uNNBKeno6xo0bh8TEwnWBLqjg4GC0bt0aly9fhqenJ77++mul7R4eHnj27Bm2bdumtH7BggUaRw7N67wrVaoEOzs7XLlyBadPn5bWy+VyTJs2DdevXy/qKZUopfPnJSIiIqK30ACfalh07EaBBqAxkQkY0LSaHqMqmJEjR2rc5uLigjlz5gAAzp49i++//x6VK1fG6tWrpTJNmjTBrFmzMGPGDIwcORKHDx9W6QI6fvx4jB49GitWrEDNmjVx6dIlXL16Fba2tiqD3/j6+mLp0qX46KOP0LZtW9SvXx8eHh4wMTFBTEwMIiIikJGRgb59+6od+bO4LVq0CDExMdi5cycOHTqEhg0bwsXFBampqbh27Rpu3ryJ3r1753sPZX569eqF9evXY8iQIejUqZM0NcYff/yBSpUq4f3338eqVavg7e2Ndu3awcLCAkFBQZDL5Rg5cmShBh3SJCoqSnofZWVl4dmzZ4iMjMSDBw8AAF26dMG6deukgXRyfPHFFxg2bBgGDRqEpUuXwtnZGZGRkYiKisKUKVOwYMEClWP17NkTc+bMwWeffYajR49K02D8+OOPcHBwwNSpUzFjxgz4+/ujXbt2KF++PEJCQvD48WNMmDABS5cu1dl5FzcmhkREREQlRCUbc/Rr4owtofe03qdfk5I1Cvf69es1bvP29sacOXOQkpKCYcOGQS6XY82aNUrz/QHA9OnTcfDgQRw9ehQLFy7ElClTlLYPGDAA3bp1w/fff489e/bAxMQEvXv3xvfff680GEmOcePGoUWLFli4cCECAwOxf/9+WFpaomrVqhg6dCj69u2rNEdgSWJhYYGDBw9i06ZNWL9+PSIiIhAaGoqKFSvC1dUVw4cPVzt9R0H17dsXCxYswKpVq7Bv3z5p1NI//vgDALB8+XLUqVMHq1evxrFjx2BnZ4cOHTrgu+++w9q1a4t8/NweP34svY/MzMxgZ2eHGjVq4J133sHgwYPVzisIAEOHDkW5cuUwd+5cRERE4PLly2jatCmWLVsGURTVJoZNmjTBxo0b8euvv+LIkSNSy/FXX30FBwcHfPnll3B2dsbChQtx+vRpWFhYwNfXF3PmzEF4eLhOz7u4CeLbMowOFUrOCFxXr14t5kiIiIhKPoVCIXU5q127ts5GfcwtPVOOEWtCtRqhtHn18lj/XrMyM1/vyJEjsX79epw4cULtPIFEJVVBPjuK6/s57zEkIiIiKkHMTWRY/14zDG7mAhOZ+lE0TWQCBjdzKVNJIRHpF7uSEhEREZUw5iYy/NC3AT7p6IHt5+/h3O2nSMnIgrWZMVrUcMCApiWr+ygRlX5MDImIiIhKqIo2ZpjQ1r1UT15PRKUDu5ISERERUamwbt06iKLI+wuJ9ICJIRERERERURnHxJCIiIiIiKiMY2JIRERERERUxhXr4DOiKCIhIQGpqakAACsrK1SoUAGCoH5oZiIiIiIiItI9gyaGFy5cwNGjRxEaGorIyEjcv38fWVlZygEZG8PZ2Rne3t5o1qwZOnbsiCZNmhgyTCIiIiIiojJF74nhtWvXsHbtWmzevBmPHj1S2iaKokr5zMxMxMTEICYmBnv27MGMGTPg6OiIIUOGYOTIkfD09NR3yERERERERGWK3u4xPH78ODp37owGDRpg/vz5ePToEURRVHpo8ma5R48eYf78+fDy8kLnzp1x/PhxfYVNRERERERU5ui8xTA8PByff/45AgMDAbxuFRQEAfXr14efnx8aNmyIunXromrVqqhQoQIsLS0hiiLS0tIQHx+PBw8e4L///kNkZCSCgoJw9epVqZ5///0X//77LwICAvDzzz+jcePGuj4FIiIiIiKiMkWnieHYsWOxZs0aKBQKiKIIQRDQvn17DBgwAH369EHFihXz3N/GxgY2NjaoUaMG/Pz8pPXx8fH4+++/8ddff+HYsWMQRRGBgYFo3rw53n//ffz++++6PA0iIiIiIqIyRRDz6tNZQEZG2T1TbW1tMW7cOIwdOxbVq1fXVfUAgJiYGCxfvhwrVqxAUlISBEGAXC7X6THKkpx7Nq9evVrMkRAREZV8CoUC0dHRAIDatWtL332IiPJSkM+O4vp+rtNPMxsbG8yePRuxsbGYN2+ezpNCAHBzc8OPP/6I2NhYzJo1C9bW1jo/BhEREREVjCAI0uPs2bMay23fvl0q5+bmpve43NzcdDIVWmBgIARBwMiRI4seFFEJpNPE8NatW/j6669ha2ury2rVsrW1xTfffINbt27p/VhEREREpL1NmzZp3LZx40YDRkJE2tJpYlihQgVdVldij0lEREREqmQyGRo0aIBt27apzFUNAE+fPsWhQ4c4eCBRCcSO8URERESkM0OHDkVCQgIOHz6ssm3btm3IzMzEsGHDiiEyIsoLE0MAqamp2LNnDz755BP4+/ujSpUqMDMzg5WVFVxdXdG7d2+sWrUKKSkpBar377//Rv/+/VG9enVYWFigQoUKaNKkCWbNmoW4uLgC1SWXy7FhwwZ0794d1apVg5mZGSpXrozWrVvj559/xvPnzwtUHxEREemYIgtIiSm9D4VqC19hDBkyBIIgqO0yunHjRlhbW6N379551nHgwAF07NgR5cqVg7m5OWrXro3p06dr/L6TlpaGGTNmoHr16jA3N0fNmjUxc+ZMvHr1Ks/j/Pfffxg5cqTSd6tBgwZxUD4qk3Q6KmlpNH/+fMyYMQPp6en5lq1SpQpWrVqF7t2751nu2bNnGDx4sNpfynLY2dlh5cqVGDBgQL7HvXv3Lvr374+wsDCNZZycnLBx40a0bds23/py46ikRERE2stzZMGUGGCv7gfeM5hedwBrt0LvLggCZDIZsrKyEBAQgLCwMDx+/FgaKPD27duoWbMmhg8fjp9++glVqlSBq6srYmJilOr54Ycf8OWXX8LY2Bj+/v6oUKECTp8+jfv378PDwwOnTp1C5cqVpfKvXr1Chw4dEBQUhHLlyqFt27bIyMjA8ePH0aFDB0RGRiI2NhZvfuX9+++/MWjQIGRkZKBhw4Zwd3fHvXv3EBoaCgsLCxw8eBBt2rSRygcGBqJt27YYMWIE1q1bV+jXicqm0jAqqc4nuC9trl+/LiWFlStXRvv27eHj4wNHR0fI5XJcunQJGzZswMOHD/Hw4UP07t0bO3bsQJ8+fdTWl56ejh49euDMmTMAgIoVK2L06NGoX78+kpKSsHv3bhw5cgQvXrzAkCFDYGFhgZ49e2qMLyEhAZ06dcL169cBAC4uLhg9ejRq1aqFJ0+eYPPmzQgJCUFcXBx69uyJEydOwMfHR7cvEhEREVEBDBs2DCdPnsSuXbvw7rvvAng9IE1e3UjDwsLw1VdfwdraGv/++y+aN28OAMjIyMDw4cPx119/YcKECdixY4e0z4IFCxAUFIRGjRrh6NGjcHBwAADcvHkT/v7+antpxcTEYNiwYTAxMcH+/fvRoUMHaduhQ4fQq1cvDBs2DDdv3oSpqWnRXxCiUkAnXUkfPHiAlStXYsGCBQgODlbadu/ePYwbNw5NmzZFo0aN8P777yMyMlIXh9UJQRDQrl07/PPPP7h//z42bdqEjz/+GIMGDcLQoUPx448/Ijo6Gj169ACQ3aVzzJgxePHihdr6fv75Zykp9PDwQEREBL7//nsMGTIE48aNw+HDh/HLL79IdY0ePRpJSUka45s2bZqUFLZu3RqXL1/G119/jUGDBmHSpEk4e/YsJk6cCCC7S+yoUaM4ryMREREVq379+sHMzExpdNJNmzahSpUqaN++vcb9lixZAoVCgYkTJ0pJIQCYmZlhyZIlsLCwwO7du3Hv3j1p27JlywAAv/76q5QUAoC7uzu+/vprtcdZuHAhUlNT8cMPPyglhQDQpUsXjB8/Hvfu3cM///xTsBMnKsWKnBhu3boVHh4eGD9+PD777DP4+/tj+PDhAICoqCh4eXlh1apVCA8PR2RkJNauXQsfHx+lX3qK03fffYdjx46hW7duMDZW34BqY2ODrVu3okqVKgCyW/H27NmjUi4pKQk//vijtLxhwwY4OTmplPv000/RtWtXAMCTJ0+wYMECtce9ceOG1FXBzMwMmzdvVpkKRBAEzJ8/Hw0aNACQ3eTMYaCJiIioONnb26N79+44duwYHj16hLCwMERHR2PQoEGQyWQa9wsKCgKQPYDNmypVqoROnTpBoVDg9OnTAIDY2FjExsaiUqVKam+nGTx4sNrjHDlyBADQt29ftdv9/PwAAKGhoXmcJdHbpUiJYXR0NEaMGIG0tDTY2dmhUaNGsLCwwObNm7Fy5UqMHz8eL168QMeOHbF48WIsWLAArVu3RlZWFt57770CD8CiD+XLl9eqnJWVFXr16iUtq2v13LNnD1JTUwEAvr6+aNasmcb6Pv30U+n55s2b1ZbZunUrFAoFAKB///5wcXFRW87Y2BiTJ0+WlvOaO4iIiIjIEIYNGwa5XI6tW7dKP1rnNxppzndDTRPf56x/8OCBUnlXV1e15e3s7GBvb6+yPue+xqpVq0IQBJVH//79AWQ3BhCVFUW6x3DRokXIzMxE3759sWnTJpiZmSEhIQGdO3fGjz/+iLt37+L999/HqlWrpH0mT56MgQMHYseOHVi5ciVmzZpV1HMwmNytdS9fvlTZfuDAAel5fgPU+Pv7w8rKCqmpqbh+/Tpu3LiBWrVqFbq+bt26Sc9PnDiBtLQ0WFhY5LkPERERkb5069YN9vb2+PPPPxEXF4e6desWef5CQRB0ElvOD+8jRozIs1zu7qxEb7siJYYnTpyAiYkJFi9eDDMzMwDZE85/++236N69O4yNjfHtt9+q7PfTTz9hx44dOHr0aKlKDC9duiQ9V/dLVu7tebUWAtmtfI0aNZLuybx06ZJSYiiKIq5cuaJ1fVWqVIGzszPu37+PrKwsXLt2DU2aNMlzHyIiItIhS+fskT1LK0tnnVZnZmaG/v37Sw0EkyZNyncfJycn3LlzB3fv3kW9evVUtudu6QMg3eZz9+5dtfUlJSWpneLC2dkZt27dUrkvkagsK1JieO/ePbi6ukp/lDlyRsV0dnZWGk44h6urK9zc3KRBVUqD6OhoHD16VFp+cyRRURRx48YNabl69fyHq65evbqUGEZFRSlte/DggTRvokwmQ7Vq1bSq7/79+1J9TAyJiIgMyMi4SNM9vI2GDx+OXbt2QRAEtfcNvsnPzw937tzBli1bMHfuXKVt8fHxOHz4MARBQOvWrQFkf6esVq0a7t27h5MnT8Lf319pn61bt6o9TseOHXHr1i3s3r0bo0ePLuTZEb1dinSPoVwuR7ly5VTWV6hQAQDUDrySo1KlSnmOxlmS5IwemtPt4J133lH5FSslJQWZmZnScs5rkJfcZd78NevZs2fSczs7O5iYmBSpvrzcunULnp6eah9EREREheXn54eEhATEx8drvA8wtwkTJsDIyAi//fYbzp8/L61/9eoVJk6ciLS0NPTt21fpB/Px48cDyB6/ITExUVp/+/ZtzJkzR+1xPv30U1hYWOCzzz7Drl27VLZnZGRgx44d0g/uRPqg6fv3rVu3iiWeIrUYVqhQAY8fP9a4Pa+JG1+9egUrK6uiHN5gJk2aJLXsVapUCb/99ptKmeTkZKVlbe7vy13mzSQ5d33a3iuYV31EREREJV2zZs0wd+5czJgxAy1btkRAQIA0wf29e/dQq1YtLF26VGmfTz/9FP/88w9Onz4Nd3d3tGvXDhkZGTh27Bjat28PmUyG2NhYpX3c3d2xZcsWDBkyBO+88w7c3d1Rt25dWFlZ4cGDBwgPD0dqaiouXrwIZ2fddrElKqmKlBhWq1YNFy5cQEZGhnSPYY7du3fn2Wc7NjZWbTfTkmbevHnS/DimpqbYtm1bni2hpVHNmjVx9erV4g6DiIiICF9++SW8vb2xYMEChIWFIS0tDS4uLpg6dSqmT5+u0lvN1NQUR44cwbfffotNmzZh3759cHJywpQpUzBz5kx4eHioPU7v3r1x6dIlzJ8/H0ePHsXRo0dhYmICJycn9OzZE3379lV7nyORrmj6/l1cvfaKlBg2adIEISEhCA4OVpmstHfv3hr3u379Op4+fap2vpmSZP78+fjiiy8AACYmJti+fTsCAgLUlrWxsVFaTktLU1n3prS0NOn5m/MT5t43d7nC1kdERESkT6Ioal3W0dExz/Ldu3fPd0T23CwtLfH999/j+++/V9mWM2CNOjVr1lRpgdQkICCgQOdIVNoUKTHs2rUroqKi8OjRowLtt2HDBgDQmGSVBL/88gs+//xzANm/RO3YsUNlwJncrK2tYWxsjKysLADZ897klxjmnhvnzTl2ci+/ePECWVlZMDbO+78rr/qIiIiIiIg0KVJi2K1bN6X587TVp08fdOjQAd7e3kU5vN788MMP+PLLLwFkD7W8e/dudO3aNc99BEGAh4cHrl27BgC4c+dOviOT3rnzekjrOnXqKG1zdnaGtbU1UlJSIJfLERsbixo1ahS6PiIiIiIiIk2KNCppYTVp0gT+/v4lslVr9uzZUlJoYWGBvXv35psU5vDy8pKeh4aG5lk2KysLFy9eVLsvkJ1o1q9fX+v6Hj58KI2cJZPJ2CeeiIiIiIi0ViyJYUk1Y8YMzJo1C0B2X/X9+/ejU6dOWu+fu/X0wIEDeZY9efIkUlNTAQC1atVSmty+MPXl3t62bVutRzIlIiIiIiJiYvj/pk2bJt2wbG1tjYMHD6Jdu3YFqqNXr17SFBxBQUF5tvL9+uuv0vMhQ4aoLTNw4EBpyo/t27fj3r17astlZWVh0aJF0rI2E8gSERERERHlYGKI7PlvfvrpJwDZo3kePnwYbdq0KXA9dnZ20oA1APDuu+8iLi5Opdyvv/6KgwcPAsieC3LKlClq6/Pw8MC7774LIHui1SFDhqjMTyiKIj799FNcvnwZAFC3bl0MGzaswLETEREREVHZVaTBZworIyMDN27cQHp6OsqXLw9XV1fIZLLiCAVff/015s+fLy1PmDABT548wd9//53nfhUqVICvr6/K+qlTp+LQoUM4d+4coqOj0bBhQ4wZMwb169dHUlISdu/ejcOHDwPIvhdw1apVsLOz03icn376CcHBwbh58yaCg4Ph5eWF0aNHw93dHfHx8di8eTPOnTsHILv769q1a/MdvZSIiIiIiCg3QTTghCwPHjzAJ598gj179iAzM1NaL5PJULt2bTRq1Eh6NGzY0CCD0wQEBODkyZMF3s/f3x+BgYFqtyUmJmLQoEE4evSoxv1tbW3x+++/Y/Dgwfke686dO+jfvz8uXLigsYyjoyM2btyoMp9kfnIm0OQE90RERPlTKBSIjo4GANSuXVu65YOIKC8F+eworu/nBmtaevToEVq0aIG4uDiVyUGzsrJw7do1XLt2DZs2bZLWu7q6KiWLjRo1gpOTk6FCLrTy5cvjyJEj2L17NzZt2oSwsDA8fvwYVlZWcHV1RY8ePTB27FhUrVpVq/qqV6+OkJAQbNy4Edu2bcOlS5cQHx8POzs71KxZE3369MEHH3yAcuXK6fnMiIiIiIjobWSwFsMJEyZg+fLlEAQBoijC1NQUNWrUQEZGBmJjYyGXy1WDEwSVdRUrVsSjR48MEXKZwBZDIiIi7bHFkIgKgy2GueSeTmH48OFYsmQJbGxsAACZmZn477//EBERgYiICFy8eBGRkZF4/vy5Sj3x8fGGCpmIiIiIiKhMMFhiGBcXB0EQUL16daxevVppgBQTExN4eXnBy8tLGoUTAO7evSslizmP2NhYQ4VMRERERERUJhgsMbSyssKLFy/QsmVLrUfNdHV1haurK3r37i2te/Hihb5CJCIiIiIiKpMM1jG+QYMG2QcsYl/8vKZ2ICIiIiLDunjxIgRB0DionkKhgL29PQRBwAcffKC2zKlTpyAIgnRvFQCsW7cOgiBg5MiReR4/ICAAgiBoHC3+TYGBgRAEAW5ublrVu27dOq3qJSrtDJYY9u3bF6IoIiQkxFCHJCIiIiI98/b2hq2tLeLi4nD79m2V7ZcvX5Z6fAUHB6utIygoCADg5+env0CJKE8GSwxHjx6NKlWq4MaNGzh06JChDktEREREemRkZIRWrVoBUJ/45SR93t7eiIqKwtOnTzWWYWJIVHwMlhhaWVlh48aNEAQBY8aMwcOHDw11aCIiIiLSo5yETl1iGBwcDBMTE3z88ccQRRGnT59W2q5QKHD27FmleojI8Aw6+U7btm3x+++/49GjR2jVqhXCwsIMeXgiIiIi0oOchC6n5S+3oKAgNG7cGB07dlRbJjIyEklJSXBxcYGLi4v+gy0iNzc3aV7uRYsWoV69ejA3N0fVqlUxadIktdOtjRw5UroP8uDBg/D19YW1tTXKlSuHvn37IioqSuPxQkJC0L9/f1SpUgWmpqZwdnbG6NGj1Y7UP2vWLOm+yNDQUPTo0QMODg4QBAERERE6fBXobWTwWVnfe+89fPbZZ7h79y5atmyJwYMH4/Dhw0hLSzN0KERERESkA82aNYOZmRmio6ORkJAgrb99+zbi4uLg6+uLqlWrwtXVVaVVsbR2I504cSI+//xzODs7o3fv3pDL5Vi8eDH8/f2RlJSkdp+//voL3bt3x6tXr9CzZ084OTlh9+7daNGiBSIjI1XKL1u2DK1atcKuXbvg6uqKPn36wMHBAatXr0bTpk3x33//qT3OqVOn4Ovri5iYGHTq1Alt2rQp8gCQ9PYz6Dvk2LFjcHNzw08//QRBEKBQKLB9+3Z069YN9vb2aNiwIUaNGoXFixcjODgYqamphgyPiIiIiArBzMwMPj4+Kl1Fc5JAX19fAEDr1q0RHh6u1CCQU6a0JYYbNmzA2bNnceTIEWzbtg03b95Eu3btcOnSJXzzzTdq91m2bBlWrFiB0NBQbNmyBVeuXMG0adPw4sULldFXz507h0mTJqFKlSoIDQ3FuXPnsH37dkRGRuKPP/5AfHw8Ro0apfY4a9euxbfffosrV65gy5YtOHnyJLy8vHT9EtBbxmCJ4eXLl9GjRw/cv38foihK60VRhCiKyMzMxOXLl/Hnn3/i448/hr+/P+zs7FCnTh0MHjwYP//8M/79918kJiYaKmQiIiKiQsvIykBSepLWj9zfj3IkZyRrvX9apmrvq1fyV3nuk5GVobPzVdedNOd569atpX9fvXqlNEp9fonh+vXrIQiCxsfJkyd1dg4F8dFHH6FJkybSsrW1NRYvXgxBELB69Wqkp6er7NOqVSuMGTNGWhYEAXPnzoWzszMiIiKUWlPnzZsHuVyO33//Xek4APD++++jV69eCAkJwcWLF1WO06BBA3z++ee6OE0qQww2wf13332HjIwMqU927dq14e3tjaysLNy4cQNRUVF49eqV0j6iKOLGjRu4ceMGtm/fDiD7DygrK8tQYRMREREVyu8hv+O3s79pXf7iRxdha26rtM5vpR+SM5K12r+vZ1/83PVnpXV7/9uLaYemadxnUstJmNx6stYx5sXPzw8//PCDUnITHBwMDw8PVKxYEcDrBDE4OBgBAQG4desWHj58CAcHB9StW1dtvTVr1pRaHNU5dOgQHj9+rJNzKIhBgwaprKtXrx68vb0RERGBixcvomXLlvnuY2Jign79+mHhwoUICgqCr68vFAoFjh07BktLS3Tu3Fnt8f38/LB3716EhoaiUaNGStt69OgBQRCKcHZUFhksMcyZTBQAfvjhB0ybpvwhlZmZiatXr+LixYvSIzIyEikpKYYKkYiIiIgKqVWrVjAyMpK6iqakpCAqKgrvvfeeVKZBgwawtbWVksecFkVfX1+NiYyvr2+ek8wHBAQUKDHUNmHKacHVVN7V1VXtejc3N0RERCAuLq5A+wCQ9klISJC+A5uamuYZZ+57OnOUhkF8qOQxWGKYM0JTw4YNVZJCIPvXkoYNG0r3GQKvWwxzJ4vqmsuJiIiIqHjZ2dnBy8sLEREROHfunPTdL3drn5GREVq0aIGzZ89CLpcXy8AzlpaWAICXL1/mWS5nu5WVld5jepNCoQCQ3T31nXfeybOsp6enyjpzc3O9xEVvN4MlhlWqVEFsbCwaNmyo9T6CIMDDwwMeHh4YOHCg/oIjIiIi0rFxzcdhVBP1g4OoY2Nmo7Iu6IMgtfceqmMiM1FZ16tuL3Ry76RxHzNjM63j04afn590r5y6xBDI7k565MgRXLp0qVgGnnF2dgaQ3dKWnJwMGxvV1x3IHlE1d/k33b17Fw0aNFC7HgCcnJw0bstvnwoVKsDc3BxGRkZYu3Ytu4WSQRhs8Blvb28A4EijREREVCaYGZvB1txW64e6L/82ZjZa729hYqGyv6nMNM999JEYAtn3EAYFBaFy5cqoVauWUpmc+wx37dqF69evw8rKCo0bN9ZpHHmpUqUK3N3dIYoiDhw4oLbM6dOnkZiYCGtra42NGjnjX+QWFRWFiIgIjfup2ycrKws7d+4E8DqJNjY2RkBAAJKSknDs2DEtz4yoaAyWGA4bNgyiKOLUqVNa//JFRERERKVHTmJ45swZXLx4UUoCc2vevDlkMhmWLl0KAGjRogWMjQ3WiQ0AMHly9oA7U6dOxfXr15W2PXz4EB9++CEAYNy4cRq7ZS5evFjpFqeXL19i4sSJEEURo0aNgoWFaqIeHByMNWvWKK2bOXMmYmNj4eXlpdRyOmPGDBgZGWHUqFEIDAxUqSslJQVr1qzhXOCkMwb7K+zbty+aNm2KCxcuYNWqVfjggw8MdWgiIiIiMgBHR0e4u7vj5s2bAFS7kQLZ9815e3sjPDwcQPHMXzhhwgScPn0aW7duRf369dG6dWtUrVoV8fHxCAoKQlpaGvz9/TFnzhyNdQwbNgzNmzdHu3btYGdnh1OnTuHRo0fw9PTE3Llz1e4zfvx4jB49GitWrEDNmjVx6dIlXL16Fba2tioD7Pj6+mLp0qX46KOP0LZtW9SvXx8eHh4wMTFBTEwMIiIikJGRgb59+6pNQokKymAthkZGRvjzzz9hb2+PTz75BKGhoYY6NBEREREZSO5ET9M0E7lbEosjMRQEAZs3b8aWLVvQtm1bXLlyBdu2bUNYWBiaNm2K5cuX4+jRo3kmXL/99ht++OEH3L17F3v27IEgCJgwYQKCgoJgZ2endp8BAwZg7969kMlk2LNnD+7fv4/evXvj7NmzKlNOANktlufPn8eIESOQnJyM/fv34/Dhw0hJScHQoUOxf/9+jcciKihBNFC/zuHDh6N+/fpQKBSYM2cOjI2NsXDhQrz//vuGODxpkDOS1dWrV4s5EiIiopJPoVAgOjoaAFC7dm0YGRnsN3YqIdzc3HD37t0C3Ro1cuRIrF+/HidOnEBAQID+gqMSqyCfHcX1/dxgXUk3bdqkdFN1RkYGPvjgA8yfPx+jRo1C+/bt4eXlBZlMZqiQiIiIiIiICAZMDAHliUIFQYAoioiKipLmNTQ1NUX9+vXRqFEj6eHt7c1+00RERERERHpksMRwy5YtuHjxIiIiIhAZGYnHjx8DgFIzfEZGBsLDw6WbkYHsexM9PDyUksV27doZKmwiIiIiIqK3nsHuMXzTo0ePpEQx53Hr1i0oFAqVsrm7oAqCgKysLEOG+lbjPYZERETa4z2GRFQYvMcwD46OjujatSu6du0qrUtNTUVkZKRSsnjlyhWkp6cXV5hERERERERvvWJLDNWxsrJCq1at0KpVK2mdXC5HVFSUUrJIb6fo+GhkyDPg5ehV3KEQEREREZUpJSoxVEcmk8HT0xOenp4YOnRocYdDehD7PBbzg+djf9R+eFb2xN/D/lbqPkxERERERPrFjvFU7J6nPce+qH0QIeLK4ys4evNocYdERERERFSmGCwxTElJQYsWLTB69GisWLHCUIelUsCrihc6uHeQlhecXgCFqDoIERERERER6YfBEsMdO3YgNDQUa9euxdmzZw11WColprSeIj2/nnAdB6IPFGM0RERERERli8ESw0OHDknPcya011ZKSoquw6ESpk7FOuhWu5u0vOjMImQpOC0JEREREZEhGCwxzJm0vlatWqhbt26B9u3Rowc8PDwwePBgzmH4FpvcajKMhOy35O3E29hzbU8xR0REREREVDYYLDF88OABBEFAo0aNCrzv2LFjcfPmTWzfvh0HDx7UQ3RUErg7uKN33d7S8uKzi5EpzyzGiIiIiIiIygaDJYYKRfZgIvb29gXet1evXjAzMwOg3CWV3j4TW02ETJABAO69uIcdV3YUc0RERERERG8/gyWGFSpUAAAkJiYWeF8rKyt4e3tDFEWcP39e16FRCeJq74p+DfpJy0vOLUFGVkYxRkRERER5uXjxIgRBQNWqVdVuVygUsLe3hyAI+OCDD9SWOXXqFARBgKenp7Ru3bp1EAQBI0eOzPP4AQEBEAQBgYGBWsUbExMDQRDg5uamVXmissJgiaGbmxtEUZTuNSwoDw8PAMCdO3d0GRaVQB+1+AimMlMAwKPkR5zXkIiIqATz9vaGra0t4uLicPv2bZXtly9fxosXLwAAwcHBausICgoCAPj5+ekvUCLKk8ESw7Zt2wIAbt++Xajk0NraGgCkDxZ6eznZOmGg10A0rNIQf/b/E91rdy/ukIiIiEgDIyMjtGrVCoD6xC8n6fP29kZUVBSePn2qsQwTQ6LiY7DEsG/fvtLz6dOnF3j/nC6oxsbGOouJSq4v/L/AjiE70Nq1NQRBKO5wiIiIKA85CZ26xDA4OBgmJib4+OOPIYoiTp8+rbRdoVBIc1wzMSQqPgZLDBs2bIhOnTpBFEUcO3YMH374IURR1Hr/0NBQAK/vVaS3m5mxGRNCIiKiUiInoctp+cstKCgIjRs3RseOHdWWiYyMRFJSElxcXODi4qL/YAvh2rVrGDp0KKpUqQJTU1NUrVoV7777LqKjo5XK5dy/GBAQoFJHw4YNIQgChg0bprReFEVUrFgR9vb2kMvlStvu3buHjz76CDVr1oS5uTnKly+PHj164MyZMyr1BwYGSvdkPnr0CKNHj4azszOMjY2xcOHCIr8G9PYzWGIIAEuXLoWdnR0AYMWKFejWrRsePXqU7367du2S/tAKM90FEREREelPs2bNYGZmhujoaCQkJEjrb9++jbi4OPj6+qJq1apwdXVVaVUs6d1Ijx07hqZNm2Lz5s2oUqUK3nnnHVSqVAkbNmxA06ZNlRJdNzc3uLq64ty5c0hPT5fWJyYm4tKlSwCAkydPKtV/5coVJCQkwNfXFzKZTFp/9uxZeHt7Y+nSpTAxMUH37t1Rv359HD58GG3atMG2bdvUxhsfHw8fHx/8888/aNmyJbp27QpLS0tdviT0ljJoYlizZk1s3rxZmnriyJEjcHd3x/Tp03H16lW1++zcuRPvv/++tNyzZ0+DxEoly+3E25h6cCpepPMeUyIiKh0ysjKQlJ6k9UNdT6rkjGSt90/LTFPZ/5X8VZ776GrkbzMzM/j4+Kh0Fc1JAn19fQEArVu3Rnh4ONLS0lTKlMTEMDU1FUOHDkVaWhqWLFmC8PBwbNmyBRcvXsT8+fORkpKCIUOGKCWB/v7+yMjIwLlz56R1J0+ehCiK8PT0xP3793Hz5k1pW85oqrlbGZOSkvDOO+8gKSkJGzduRFRUFHbu3IlTp07h7NmzsLW1xejRoxEfH68S84EDB+Dj44M7d+7gr7/+wr59+zSOBkuUm8Fv2OvatSv+/fdf9O7dG0+fPsXLly/x888/4+eff4azszPc3d1RqVIlvHz5EhcvXsSDBw+kD0oXFxeV5nd6+809Phd/XvwTClGByjaV8anvp8UdEhERUb5+D/kdv539TevyFz+6CFtzW6V1fiv9kJyRrNX+fT374ueuPyut2/vfXkw7NE3jPpNaTsLk1pO1jjEvfn5+CA4ORlBQEHr37g3gdWtg69atpX83b96MkJAQKRHKLzFcv3491q9fr5MYC2r79u14/PgxWrZsiQkTJihtmzJlCjZt2oQLFy5g586dGDp0KIDsBO/PP/9EYGCgdI45yd8333yDgQMHIjAwEO7u7krb/P39pbrXrFmDhw8f4tNPP5XqzdG0aVN8/fXX+OSTT7Bx40ZMmTJFabuZmRkWL14Mc3NzXb0MVEYUy0gurVq1wvnz5zFlyhT8/fffUuJ3//593L9/XyoniqJ0n5mtrS22bdsmtTbSW0ZUALF/ARZVgEptlDaVsygHhagAAKy7sA4jG4+Eg6VDcURJREREGvj5+eGHH35Q6ioaHBwMDw8PVKxYEcDrBDE4OBgBAQG4desWHj58CAcHB9StW1dtvTVr1pRaHNU5dOgQHj9+rMMzeS0nsX0zOcsxbNgwXLhwAUFBQVKZnAQv97yKgYGBqFu3Lnr16gUzMzMEBgZi9OjREEURp06dgq2tLRo3biyVP3LkCADlwRtzy0mic8bgyK1x48Ya55QkykuxDfHp6uqKXbt2ISgoCPPnz8ehQ4eQkaHanUEURXTo0AGLFi3S+IFBpZgoAg8PAZEzgGcXgXKNgS5hgPC6l/PIJiOxLnwdnqU9w8vMl1gZuhJfBHxRjEETERHRm1q1agUjIyOpq2hKSgqioqLw3nvvSWUaNGgAW1tbKXnMSbx8fX01Djrn6+uLdevWaTxuQECA3hLDuLg4ANn3DqqTs/7BgwfSuho1asDFxUW6z/Dly5e4fPkyxo8fD3Nzc7Ro0UK6zzDn/sJu3bop3V8YExMD4HUirUnu+zlzlNQBfKjkK/a5H/z8/ODn54eMjAxcuHABN27cQGJiImQyGSpXroxWrVqhWrVqxR0m6UP8aSDiCyA+1+hkz8KBe7sAl37SKmtTa4xtNhbzTs4DAGyI2ID3m76PStaVDB0xERGR1sY1H4dRTUZpXd7GzEZlXdAHQVqP4m4iM1FZ16tuL3Ry76RxHzNj3fXEsrOzg5eXFyIiInDu3Dk8f/4cAJRa+4yMjNCiRQucPXsWcrm8xA88kx9Nyay/vz82bNiAc+fO4dmzZxBFUepWGhAQgJMnT+LmzZtqu5EC2VN4AEC/fv1gZWWl8fh16tRRWccupFRYOk8MHz16BEdHxwLvZ2ZmhlatWkkTpNJbLikaOKqhW8ilrwDnPoDR67fnsIbDsPr8asSnxiMjKwPLQpZhVvtZBgmViIioMMyMzYqceKlLFgvCVGYKU5lpkeooCD8/P0RERCA4OFhtYghkt4IdOXIEly5dKtEDzwCAk5MTAODu3btqt+e07L3ZdTMnMQwMDMSzZ88AQCkxnD17NgIDA9UOPAMAzs7OiI6OxvTp09GkSRPdnAxRPnQ+KqmzszNatGiB7777ThqWl0iFbe3s5E+dpGjgzp9KqyxMLDC++XhpedulbYhLitNjgERERFRQuSe6DwoKQuXKlVGrVi2lMjndI3ft2oXr16/DyspK6f66kiTnfLZs2aJ2+8aNG5XK5cg96ExgYCA8PT2l+yxbtGgBMzMznDhxAqdOnYKNjY1K8pcz5+Pu3bt1di5E+dF5YqhQKBAWFoZvvvkGjRo1QvXq1TFx4kQcPXoUmZmZuj4clWZe3wLQMIn95VmAPF1p1SCvQahiUwVA9vDbS84t0W98REREVCA5CdKZM2dw8eJFtffINW/eHDKZDEuXLgWQnSgZGxf73U1qDRgwAJUrV0ZwcDBWrlyptO23337D+fPnUbVqVbzzzjtK22rWrAlnZ2ecPXsWly9fVmoRzLnPcNeuXUhISEDr1q2V7i8EgLFjx6JSpUr46aefsHLlSqlraY6srCwcPnwYV65c0e0JU5mm88Swd+/esLS0hCiKEEURd+/exbJly9ClSxdUqFABAwYMwMaNG5GYmKjrQ1NpY+8JVB+uftvLe8CN35VWmRmbYUKL10NF77i8AzHPYvQYIBERERWEo6Mj3N3dkZKSgqysLLWjiVpbW8Pb21vqYllc3UgfPnyIFi1aaHz8888/sLKywqZNm2BhYYGxY8eiadOmGDJkCBo3bozJkyfD2toaW7ZsUXtfn7+/P169eqV0f2GOgIAAae7DN7cBgL29Pfbs2QM7OzuMHTsWbm5u6NatG4YOHYr27dujYsWK6NKli9J8iERFpfPEcPfu3Xj69Cn++ecfjBs3DlWrVpWSxOTkZOzcuRMjRoyAo6Mj/P398csvv+D69eu6DoNKiwazASPVm+UBAFe/BzKV527qV78fXOyyR9uSi3IsPrtY3xESERFRAeRO9DRNM5G7JbG4EsNXr14hJCRE4yNn8vj27dsjLCwMgwcPxv3797Fjxw48evQIw4YNw/nz5zXGn5PwCYKgMrhM7mRQXWIIZLekXr58GVOnToWtrS1OnjyJv//+G3fv3oW/vz/WrVuHDh06FPl1IMohiNoOdVUEFy9exL59+7B3716Eh4e/PniukZzc3d3Rq1cv9OrVC61bt4aRkc5zVlLD09MTAHD16tXiC+L8ROC6hm6hDeYADb5WWrXr6i58fvBzAECTqk2wacAmtSOxERER6ZpCoUB0dDQAoHbt2vy+QkRaKchnR3F9PzdIYphbXFwc9u3bh3379uH48eNSM3ruJLFcuXLo1q0bevbsiS5dusDGpmgjcpFmJSIxTHsE7K0JyF+qbjOxBXrdBsxeT2gvV8jx8T8fo69nXwRUD9A4VDQREZGuMTEkosJgYpiPly9f4ujRo9i3bx/++ecfpclJc77sm5iYoE2bNlJrIift1K0SkRgCQMSXwLUf1G+r+xnQ6GfDxkNERKQGE0MiKgwmhgUgiiJCQ0Oxd+9e7Nu3T2mUpdwtQvXr10evXr3Qs2dPNGvWrDhCfauUmMTw1TNgTw0g87nqNpk50PMmYFlVdRsREZEBMTEkosIoDYlhifk0EwQBzZs3l+Y/vH37NhYtWoQOHTrA2NhYGsDmypUr+P7779GyZUuVyUSpFDMtB9Sbpn6bPB24MjffKuQKuY6DIiIiIiIqG0pMYvgmNzc3TJw4EUeOHEFCQgK2bduGoUOHoly5clKS+OjRo+IOk3Sp9kTA3FH9tlurgWT1QzLffX4Xnx34DB/s/kCPwRERERERvb1KbGKYm42NDfr3748NGzbgyZMnCAwMxCeffIJatWoVd2ikS8ZWQP2v1G8Ts4BL36isvpFwA53WdMLua7sReCcQYffD9BwkEREREdHbp1QkhrkZGRmhTZs2+OWXXxAVFVXc4ZCu1RwDWFVXv+3uFuBZpNIqdwd3eFfxlpYXBC9ACbltloiIiIio1Ch1iSG95WSmgNdszdsjZygtCoKAT1p/Ii2H3A/Bmdgz+oqOiIjKuNwD4ikUimKMhIhKE7n89VgYJXWqNYMmhv/++y/mzJmDOXPmGPKwVNq4DgHsPNVvi/sHiD+ttKqFSwu0cmklLf8a9CtbDYmISC8EQYCpqSkAIDU1tZijIaLSIikpCQBgZmZWYhNDY0Me7PDhw/j1118hCAK++Ub1fjEiAICRDPD+DjjVR/32yC+B9oFArj+qKb5TcGZzdkth5KNI7Ivah151e+k9VCIiKntsbGzw9OlTaf5lKysrTltBRCpEUURGRgaSk5ORmJgIAChXrlwxR6WZQRPD/GzatAnHjh2DIAhYvXp1cYdDxalqL8ChOfA0RHXbk1PAw8OAUxdpVWOnxuhUqxOO3DgCAPjp1E/o5N4J5ibmhoqYiIjKCAcHB6SmpiI9PR1xcXHFHQ4RlRL29vaws7Mr7jA00vnPW0Xpb3/u3DmsW7cO69at011AVDoJAuD9vebtkV8CovJ7bbr/dJgYmQAAHiY/xB/n/9BnhEREVEbJZDK4uLjAwcFB6lZKRKSOTCaDra0tqlatCkdHxxLdu0DnLYa2trZo1qwZWrZsiVatWqFVq1YlusmUSjDHdoBjB+DRv6rbnl0EYncArgOkVa72rhjReISUEK4IXYH+DfqjsnVlQ0VMRERlhEwmQ6VKlVCpUiVpfmUiotwEQSix9xOqo/PE8OXLlzh58iROnjwprfPw8ECrVq1w+/ZtXR+O3nbe36tPDAHg0tdAtb6A0eu38UctP8Kuq7uQmJaIl5kv8WvQr/ip608GCpaIiMqi0vblj4hIHZ23ZQ4bNgzVq1eXfj0TRRHR0dFYt24dTp06JZUbMmQIVq5ciejoaF2HQG8TB5/s5E+d5OvAnfVKq2zMbDCl9RQAQIPKDTCgwQB1exIRERERUS6CqKe+D48fP8bp06elx8WLF5GZmQlBECCKotIva5UrV0abNm0QGxuLc+fOQRAEpbk+SH88PbOnhbh69WqxxpGRlYGNERtha2aL/g36K298cQ040EDlnkIAgKUz0PMGIHs9yEyWIgtHbxxFZ4/OMBJKbj9uIiIiIqI3Fdf3c70lhm9KT0/HuHHj8Oeff0IQBFhYWODly5evA/n/RDEnaezbty/atGmDNm3awNvbW+/xiaKI27dvIzw8HBcvXpQeOUNR55TRRkBAgFJX2vzs27cPPXr0yLecXC7H5s2bsXXrVly6dAlPnjyBvb093N3d0adPH4wZMwb29vZaHxcoGYnhxbiLmLRvEuKS41DeojyOjz4OGzMb5UJnR6q0DkoazwfqTNF7nERERERE+lZc388NNl2Fubk5KlasKC0/f/4cYWFh0v2IZ86cQXJystSiuGvXLuzatQsAYGdnh1atWkmJYosWLXQe32effYb58+frvF5duXv3Lvr374+wsDCl9U+ePMGTJ09w5swZLFy4EBs3bkTbtm2LKcrCcbZzxrO0ZwCAxLRErD6/Gh+3/li5UINZwN3NgCJTtYKr3wM1RwMmNqrbiIiIiIgoX8U2j6GxsTFatmyJli1bYvr06VAoFBg1ahQ2bNgAQRBga2uLFy9eAMhOIg8ePIiDBw9CEARkZWXpPJ43u66amZmhfv36uHDhQpHqXbFiBSpVqpRnGR8fnzy3JyQkoFOnTrh+/ToAwMXFBaNHj0atWrXw5MkTbN68GSEhIYiLi0PPnj1x4sSJfOssSSpaVcT7Td/HknNLAACrz6/G0IZDUdHq9Q8JsHYD3McB1xerVpCRAEQtABp8o/EY0fHRuJV4C91qd9Nx9EREREREpZ/OE8OPPvoIrVq1QuvWreHq6qr1fkZGRrC1tZWWExMTcenSJQQFBUmPR48e6TpcSb169TB58mQ0atQIjRo1Qr169WBsbFzkUcY6deoENze3ItUxbdo0KSls3bo1Dhw4oPRaTZw4EZMnT8bixYuRmpqKUaNGITIyEjKZrEjHNaTRPqOxOXKzNJrokrNLMLvDbOVCnjOA22uArFTVCv77Baj1IWBeQWn1i/QX+CXoF2y9tBWWJpbwcfZRTjiJiIiIiEj3o5IuW7YMw4cPR40aNeDs7IwBAwZg4cKFCAsLQ2ammm6AGgiCAG9vb3z00UfYtm0b4uLicOPGDaxevVrXIQMAPvjgAyxcuBAjRoyAl5cXjI2LrTFVyY0bN7Bu3ToA2a2YmzdvVkoKgezXav78+WjQoAGA7P7IGzduNHSoRWJjZoMJLSdIy1svbcWdZ3eUC1lUBmp/rL6CrGTg2jyV1aIo4kD0AShEBVJepWB+cMntLkxEREREVFx0nhjm3CMoiiLi4uKwc+dOfPrpp2jRogUWL37dDfDff/9FWlpagequWbMmRo4cqeOIS7atW7dCocgejbN///5wcXFRW87Y2BiTJ0+Wljdt2mSQ+HRpsNdgVLOrBiB7ZFG1SVzdzwDTcuoruL4EeHlfaZW9hT0mt3r9uvx1+S9ce3JNZzETEREREb0NdJ4YPnv2DIcPH8bMmTPRqVMn2NraKs1pmNM1s3PnzihXrhz8/PzwzTff4MSJEwVOFMuCAwcOSM+7d++eZ9lu3V7fP1caX08zYzN84vuJtHwg+gAuPbykXMjUHqg3TX0Figzg8hyV1YO9B6Nm+ZoAABEivj3xrdYjzBIRERERlQV6n65CFEVcu3YNZ8+exYoVK3DhwgWpVRGAyj18Ocnj0aNH0bJlS1hYWOgzvHzljq8w01V07NgR0dHRePz4MUxNTVGpUiX4+PigT58+6NevX573AYqiCFtbW6SkpAAAbt26hRo1auR57GrVquH+/exWs/Pnz6NJkyZ5li8J01XkphAV6LOhD64+yY6nebXm2DRgk/L7JOslsM8dSHuoWoEgA7r/B9jWUlodeDsQ7+96X1pe3ns5OtXqpJdzICIiIiIqrOL6fq732b8FQYCnpydGjx6NgIAAaf3atWvx7rvvwsXFRW2LYseOHWFvb48WLVrg888/x969e5GYmKjvcHXu6NGjiI2NRUZGBpKTk3Hr1i1s3boVgwYNQr169fIc9fTBgwdSUiiTyVCtWrV8j1e9enXpeVRUVNFPwMCMBCNMbTNVWg65F4JTMaeUCxlbAvW/Vl+BKAcuq45OGlAjAH5uftLyDyd/QEZWhk5iJiIiIiIq7YpthJURI0ZgxIgRAICYmBgEBgZi0aJFiIyMlFoUMzMzERoairCwMGmOwXr16uHy5cvFFbbW7O3t0aFDB/j4+MDZ2RnGxsZ4+PAhTp06hb179yIrKwvXr1+Hr68vjh07hlatWqnU8ezZM+m5nZ0dTExM8j1uhQqvR+V8/vy5VrHeunVL+mXiTcXRkujr5ovWrq1x+u5p2Jnb4Xnac9VCNd7PHok05bbqtrtbs7ublmuotPrLgC/RY30PyEU5Yp/HYsPFDRjtM1ov50BERERElBdN379v3bqFmjVrGjiaYkwMc3Nzc8PIkSNx4cIFREZGAsgedOXkyZM4deoUrl27JnXjvHat5A8c8v3336NJkyYwMzNT2TZ58mRER0ejX79+uHLlCtLT0/HOO+/g5s2bsLKyUiqbnJwsPde2S23ucklJSYU8g+L3ud/n8KzkiXHNx8HO3E61gMwUaDAbODtcfQWRM4CAf5RWeVTwwGDvwdgYkT1i6+Kzi/E/z//BwdJB1+ETEREREZUqJSIxVGfAgAEYMGAAgOw5DU+dOoWTJ08iKCiomCPLn7rWv9xq166No0ePokGDBkhISMCjR4/w+++/49NPPzVQhMpq1qxZYu4xzNHAsQEaODbIu5DrYODaj8CLK6rb4g4AjwOBygFKqye3mow9/+1BckYyUl6lYNHpRZjTUXXAGiIiIiIifdL0/VtTS6K+6f0eQ10oX748+vTpgwULFuD8+fPFHY5OODo6Kk0vsXfvXpUyNjY20nNtRxjNXe7N+Q7fOkYywPs7zdsvTAYUWUqryluWx8SWE6Xl2BexkCvk+oqQiIiIiKhUMGiLYadOnWBtbW3IQ5Zo7dq1w9dfZw+i8t9//6lst7e3l56/ePECWVlZMDbO+78sISFB7f5vg5evXkKECCvTXF1uq/YEKrQEEs6q7vD8EnBrFVBrvNLq4Y2G48zdMxjScAja1WinMjIuEREREVFZY9DEsGPHjujYsaMhD1miVaxYUXqubqAYZ2dnWFtbIyUlBXK5HLGxsflOV3Hnzh3peZ06dXQWa3HKlGfiryt/4bczv6F/g/741DdXl1tBABr+CPzbRv3OkV8BLgMBs/LSKlOZKVa/s1rPURMRERERlR4lqivp8OHDsXbtWqxZs6a4QzGI3K175cqVU9kuCALq168vLYeGhuZZ38OHD6U5DGUyGerVq6ejSIvXhosb8PXRrxGfGo8159fgScoT5QKV/ACXAep3fpUIXFKdvoKIiIiIiF4rUYlhs2bNlKaxeNudOHFCel67dm21Zbp16yY9P3DgQJ715d7etm1brUcyLekGeA1AeYvsFr/0rHT8dvY31UKNfgZkGs735nLgef5TnMQ+jy1KmEREREREpVaJSgzLkidPnmDhwoXScs+ePdWWGzhwIIyMsv+btm/fjnv37qktl5WVhUWLFknLQ4cO1V2wxcza1FppwJjtl7bjduIb8xdauQD1pquvQFRkD0Tz/1OevOlR8iN8tPcjdF7bGXcS76gtQ0RERET0NmNiqGOLFi3C6dOn8yxz8+ZNdOrUCfHx8QCAypUrY9y4cWrLenh44N133wUAZGRkYMiQISrzE4qiiE8//RSXL2e3itWtWxfDhg0r6qmUKIO8B8HF3gUAIBfl+DXoV9VCdT8HrFzVV/D4BHBvp8pqURQxcsdIHLx+EK/kr/D1v19Lc2YSEREREZUVgshvwQCyB3/55ZdfVNZ/993r6RBmzJihsv3bb79VWu7Tpw/27NmD6tWro0OHDqhfvz4qVqwIY2NjPHr0CKdOncKePXuQmZkJIHtC+sOHD8PPz09jbPHx8WjVqhVu3rwJAHB1dcXo0aPh7u6O+Ph4bN68GefOnQMAWFpa4vjx42jevLlW550zT0pJm8dQnf1R+zF5/+spPnYM2YFGTo2UC8XuAIL7q6/AyhXo/h9grNzl9PD1w/hw74fS8vxu89G7Xm+dxU1EREREpK3i+n6u08QwLi4OTk5OuqpOKw8fPkSVKlWKXE9MTAyqV69e4P3efPlyEkNteHp6Yv369WjSpEm+Ze/cuYP+/fvjwoULGss4Ojpi48aNaN++vVbHz4kBKB2JoUJU4H8b/4crj7MntG/m3AybB25Wnm5CFIHj7bNbCNVpMBtooDwYjSiK+GD3Bzh++zgAwMHSAUffOwo7czu9nAcRERERkSbF9f1cp11JPTw88NVXX+HZs2e6rFatZ8+e4csvv4SHh4fej1UQv/76K9asWYOxY8eiefPmqF69OmxtbWFsbIzy5cujQYMGeP/997F//35cunRJq6QQAKpXr46QkBCsW7cOXbt2RdWqVWFqaoqKFSuiRYsWmDdvHq5du1agpLC0MRKMMLXNVGk59H4oAu8EKhcSBKDJIkDQ8Na+Ng9IjX1jFwEz28+EubE5AODpy6f4OehnXYZORERERFSi6bTF0MjICIIgwMrKCh988AHGjx+PmjVr6qp6AMCtW7ewdOlS/PHHH0hNTQUAyOVynR6jLClNLYY5Ru4YiaCYIABA7Qq1se/dfZAZyZQLhX0E3FiqvgKXAYDvNpXVK0JW4Kegn6Tlv4b8hcZOjXUWNxERERFRft6KFsNJkyZBJpMhJSUFCxYsQO3atdGuXTusWLECjx8/LnS9jx8/xooVK9CuXTt4eHhg0aJFSElJgUwmw+TJk/OvgN4qn/t9Lj2PTojGvqh9qoW85gCm5VXXA0DsduDxSZXV7zV9D7UcaknLXx35CpnyzCLHS0RERERU0ul88JkrV65g6tSpOHToUPYBct3/VadOHfj5+cHLywt16tSBs7MzHBwcYGlpCVEUkZaWhoSEBNy/fx/R0dGIjIxEcHAwoqKipDpywu3atSt++uknKaOmwimNLYYAMOWfKfj35r8Y4zMG7zV9D9am1qqFbiwHwj5UXQ8A9l5AlwuAkbHS6vP3z2Pg1oHS8nT/6RjjM0aXoRMRERERafRWDD6T2+nTpzFv3jwcOHBASuaUBgkpgNz7d+/eHV988QVatmyps1jLsuJODOOTM7AtLBYhdxKRkpEFazNjtKjhgAFNq6GijZnG/R6nPIZMkKGCVQXNlSvkwKHGwPNL6rf7LANqjVdZ/cXhL7D98nYAgIWxBY68dwROtoYdVImIiIiIyqa3LjHMcfPmTaxZswabN29GbGxs/juo4eLigqFDh2LUqFFwd3fXcYRlW3G98dIz5Zi97yp2XLiPTLnqW9BEJqBfk2qY2bMezE1kamrQ0uOTwLEA9dtMywM9bwBmyl1On6U9Q6c1nZCYlggAmNthLoY0HFL4GIiIiIiItPTWJoa5Xb58GUePHkVISAguXbqEu3fvIj09XamMubk53Nzc4OXlhWbNmqFjx45o0KCBoUIsc4rjjZeeKceINaEIuZOYb9nm1ctj/XvNipYcBg/Mvq9QnVoTAJ8lKqt3XtmJ1edXY27HuWhSVbuRY4mIiIiIiqpMJIbqvHjxQhpd1MrKCnZ2nDvOkIrjjffFrkvYEnpP6/KDm7ngh755/ziQkZWBTRGb0K12NzjaOCpvTI0F9tcB5GmqOwpGQNcIwF65flEUkaXIgonMROs4iYiIiIiK6q0YlbQw7Ozs4OTkBCcnJyaFZcCT5HTsuHC/QPvsuHAP8ckZGrcfvnEYHdd0xHeB3+G3s7+pFrByAepNU7+zqAAuTAbe+H1EEAQmhURERERUZhR7Ykhly/awe2rvKcxLplzE9vOaWxifpj7Fg6QHAIC/Lv+FW09vqRaq+zlg6aK+gscngHs7841DISrwLO2ZVjETEREREZUmTAzJoLS5r1Cdc7efatzWv0F/uJVzA5CdvOWepF5ibAk0/lXzAS5+BmSp6Wr6/64nXMeQbUMwZtcYKESFtmETEREREZUKTAzJoFIysnS+n4nMBJ/5fiYt/3vzXwTFBKkWrPYOUClAfSWpd4H/fla76e7zu+j1Zy+E3Q/DxYcXsfXS1oKETkRERERU4jExJIOyNjPOv1Ah9uvi0QWNnRpLy7OPzUZG1hv3JQoC0GRR9oAz6lybB6TcVlntau+KTrU6Scs/n/oZCakJ2gdPRERERFTCMTEkg2pevXz+hdRoUcMhz+2CIGB2h9kw+v+k786zO1h7Ya1qwXJegLvqpPYAskctDR2vMhANAHzV9itYm1oDAJIykvB94PcFOwEiIiIiohKMiSEZ1ACfajCRCQXax0QmYEDTavmWq1epHoZ6D5WWl5xdgrikONWCXnOyJ7dX59ERIGaTyupK1pXwmd/r7qp7/tuD03dP5x88EREREVEpwMSQDKqSjTn6NXEu0D79mlRDRRszrcpO8Z2C8hbZSV9aVpr6lj2z8oD3d5orCZ8CpKt2Fe1Y4x1UtvSQlsfs/BQL/r2c51QaRERERESlARNDMriZPT217lLavHp5zOxZT+u67cztMK3N6zkLD14/qL5lz/0DoEJL9ZVkJADhn0iL6ZlyfLHrEvx+CkTcg94Qxew/mwxFPH47+wtazTuGL3ZdRnqmXOs4iYiIiIhKEiaGZHDmJjKsf68ZBjdz0dit1EQmYHAzF6x/rxnMTWQFqr9v/b5oVKURAKC8RXmkZKSoFhKMgGarACMNk9jHbAAeHkV6phwj1oRiS+j/z78orwYxraNUzMgiGJlGUdgSGosRa0KZHBIRERFRqSSIopqRNqjM8PT0BABcvXq1WI4fn5yB7efv4dztp0jJyIK1mTFa1HDAgKbadx9V58rjK9h+aTs+8f0E9hb2mgte+ga4Mlf9Nusa+CZzM/4Me7NbaRaM7H+FYPwAACDKy0HxfDogWmBwMxf80LdBoeMmIiIiorKtuL6fMzEs44o7MSx28nTgYEMgKVrt5lUJ7+C7uFGqG2QPYGT/MwRBAVE0gSJpDJBZByYyAWemty9SUktEREREZVdxfT9nV1Iq22TmQLOVGjePctgNT/NbqhvkVSG+7AIx0w2KZ9OAzDoAgEy5iO3n7+krWiIiIiIivWBiSGXG87TnuJ5wXXVDpTZAzTFq9zEWFPjBeTFkUL13UEzrCMWLjwFFJaX1524/1UW4REREREQGw8SQ3noKUYHtl7ej45qOmLh3Il7JX6kWavQTYO6odn8vy5sYWWGvmi0yqPsTSsnIKlrAREREREQGZlycB09JScHdu3eRlJSEzMxMrfdr06aNHqOit82NhBv48vCXECEiMS0R68PXY4zPGy2EpvZA08VAcH+1dXzquBGHX7TE/Uz1yeNrryDIHugkbiIiIiIiQzF4YpicnIxFixZhy5YtiI6ORkHHvhEEAVlZbJEh7dWuWBuDvQdjc+RmAMDiM4vRs05PONq8keRVeweo2gt4oNo6aGmUge+cl2HEndkA1E+xAePbMLLZhBtpWXiW1hHlLMrp+EyIiIiIiPTDoF1Jw8PDUbduXcycORNRUVFQKBQQRbHAD6KC+tT3UylRS81MxQ8nf1AtJAiAz1LA2FptHf424ehlf1L9AYRUGNktgyCLx8usZ5h1bJaOIiciIiIi0j+DtRg+ffoUnTt3xtOnrwfmMDY2Rs2aNeHg4AATEw0TjRPpgL2FPT7z+wwzjswAAOyP2o9BXoPQ0qWlckFLZ8D7B+DCRLX1fOO0CqeSG+O53FZ5g2gFMbUbBOu/pfq71OqCrrW76vpUiIiIiIh0zmCJ4a+//oqnT59CEATY2Njg+++/x7vvvgtra/WtM0S6NqDBAGy7tA2XHl0CAMw+Nhv73t0HE9kbP0rUGg/EbAKenlOpo4LxC3xVZTU+uz9FZZuPY18obGJx8WE4AOCbf7+Bj7MPKlhV0P3JEBERERHpkMG6kh44cABA9j2C+/fvx4cffsikkAzKSDDC7PazIfz/PYI3nt7Anxf/VFNQBjRfBRipb8XuV/4YWltHSMsmMgGDm7ngz/db4JduP8Pc2BwAkJiWiJn/zmT3ZyIiIiIq8QyWGN65cweCIKB169bw9fU11GGJlHhV8cJAr4HS8m9nfsOTlCeqBe3rA3WnaaznV9dlaF/LGp93ro0z09vjh74NYG4ig1s5N0xtM1Uqd+jGIeyL2qfTcyAiIiIi0jWDJYYKhQIAUKdOHUMdkkitz/w+g725PQAg5VUK5p2cp75g/RmAjYfaTY6yOKxuchAT2rqjoo2Z0rbhjYajebXm0vKsY7PUJ59ERERERCWEwRLDatWqAQDS0tIMdUgitcpZlMOnfp9Ky+Fx4UjOSFYtKDMHmq3UXNF/vwDPIlRWGwlG+LHzj7AysQIAvEh/ga+OfsUupURERERUYhksMezUqRNEUcT58+cNdUgijdq59YGTlSeczfvC7tVMfLjxGpaeuIn45AzlgpX9gZqj1VciyoGQ0YAiU2VTNftqmB4wXVo+dusYdl/brctTICIiIiLSGYMlhh9++CHMzMwQHR2No0ePGuqwRErSM+X4Ytcl+P0UiHt3x+Du/QBE3nuJoBsJ+PlwNFrNO4Yvdl1Geqb89U6NfgLMK6uvMPECcOU7tZsGew2Gr2v2/bT25vZSCyIRERERUUljsMTQw8MDCxYsgCiKGD58OK5evWqoQxMByE4KR6wJxZbQe8iUi1D39s+Ui9gSGosRa0JfJ4em5YCmizVXfPVbICFUZbUgCPih8w/oVbcXDo06hM4enXV0JkREREREumWwxBAAxo0bh7Vr1+LFixfw8fHBlClTcPr0aSQlJRkyDCqjZu+7ipA7iXkXMnoCQI6QO4mYve/a6/XV+gFVe6rfR5QDZ4cBWakqm5xsnbCg+wJUtKpY+MCJiIiIiPRMEA00IoZMJlNaFkURgiAUuB5BEJCVlaWrsMo8T09PAHjrW3CfJKej9bzj/99SqI4cgsVxCJYHIb7sDDGtM0xkAs5Mb/961NGXccCBBsArDcllrQ8Bn6V6iZ+IiIiIyobi+n5usBbDnPwz59/cSaEoigV6EBXU9rB7eSSFgGBxFEZW+yAIWRAsDwGyh8iUi9h+/t7rQpZOQLPfNR/kxjIg7lC+sSSkJmD6oelISmdLORERERGVDAbtSpo7OWSiR4aUXxdSMd0fotwOACAIchhZbwGgwLnbT5ULuvQH3IblcaD3gIynGjefvnsaPf7sgb+u/IUZR2bw/U9EREREJYJBJ7jXxUMul+d/MKI3pGTk0/1YtIAiZaC0KJjEQDA/qX6/posBS2f19aQ9BMLGAxoSvoiHEYhPjQcAHLh+ANsub9MqfiIiIiIifTJoiyFRcbE2M86/UGZ9KNKbSIuC1X4YG6tp/TO1B1qs11xP7F9AzGa1m8Y1G4eWLi2l5TnH5+B6wvX8YyMiIiIi0iMmhlQmNK9eXqtyYuo7EBXWAABByES8uE59d0/HdkDtjzVXdH4CkBqrslpmJMOv3X5FeYvseDKyMjBp3ySkZaZpFR8RERERkT4wMaQyYYBPNZjItBgFV7SGmNJPWryXHImtl7aqL+v9PWBXT/22zBfAuZGAqFDZVNm6Mn7u+rO0fOPpDXx74tv8YyMiIiIi0hMt+tfpT3JyMs6ePYvw8HAkJCQgOTkZNjY2qFChAho3boyWLVvCxsamOEOkt0QlG3P0a+KMLaH38i0rvmoEMSMcgtklAMC8k/PgX90fTrZOygWNLYCWG4EjzQFFpmpFj08A0b8BdT5W2RRQIwDvN30fq8+vBgBsvbQVrV1bo1vtbgU+NyIiIiKiojLYPIa5xcbGYs6cOdi8eTMyMjI0ljM3N8eQIUPw9ddfw8XFxYARlh1lZR5DAEjPlGPEmtD8J7kH0MhVQEzWDCRlZE8pEVA9AH/0/UP93JtXvwciZ6ivyMgM6HIBsPdU2fRK/goDNg/A5ceXAQA2ZjbYN3wfqtlX0/qciIiIiOjt8tbPY5hj165d8Pb2xtq1a5Genp7nfIVpaWlYs2YNvL29sXv3bkOHSm8ZcxMZ1r/XDIObuWjsVmoiEzC4mQu2jO6MGW1fJ3vJr5KR8ipFfcV1pwIVWqnfpsgAzg4H5K9UNpnKTLGo5yJYm2bf05ickYyP//kYmXI1rY9ERERERHpk0BbDQ4cOoVevXpDL5dKAHuXLl0ezZs3g5uYGKysrpKamIiYmBmFhYXj69CkEQYAoijAxMcG+ffvQqVMnQ4VbJpSlFsPc4pMzsP38PZy7/RQpGVmwNjNGixoOGNC0GiramAHInm9zwt4JaF6tOYY3Gg4jIY/fUZJvAQe9gaxU9ds9vwS8v1O7aV/UPny8/2MAgJFghHX91qG1a+uinB4RERERlVLF9f3cYInhy5cvUatWLTx8+BAA4Obmhh9//BH/+9//YGysequjXC7H7t27MW3aNNy5cwcAULVqVVy/fh0WFhaGCLlMKKuJobZEUVTffVSdm6uA0A/UbxOMgA5BQEX1LYtfHP4Cp2JOYWH3hfBx9ilktERERERU2r31XUnXrl2Lhw8fQhAENG3aFOHh4ejfv7/apBAAZDIZ+vXrh/DwcDRt2hQAEBcXh7Vr1xoqZCLtk0IAqDkacOqhfpuoyO5Smqm+O+rXbb/GP+/+w6SQiIiIiIqFwRLD/fv3A8hO+LZs2QJ7e3ut9rOzs8PmzZshk8kAAPv27dNXiERaufDgAp6lPVPdIAhA81WAWQX1O6bcBsI/UbvJ0tQS9hb2uguSiIiIiKgADJYYXrlyBYIgwNfXFzVr1izQvu7u7mjTpg1EUcSVK1f0FCFR3lJfpWLO8TkYuGUgvjuh/n5BWDgCzVZqruTWKuDBfq2Ol6XIQui90EJESkRERERUMAZLDBMSEgBkJ3mFkZNM5tRDZGi7r+7G+vD1ECFi97XdOHH7hPqC1f4H1BipuaKQ94G0h3keKy4pDkO2DcGw7cMQHhde+KCJiIiIiLRgsMQwZ8CYlBQNQ/7nI2c/DjxDxWWQ9yB4OXpJy18c/gJPXz5VX7jJIsDKVf229CfA6cGAIkvtZlEUMfbvsbjw4ALkohwf7f0IT1KeFDV8IiIiIiKNDJYYOjk5QRRFBAUFFXhfURQRHBwMQRDg5OSkh+iI8mdsZIx5nefBVGYKAIhPjcf0Q9OhdmBfE1ug5Z8ANAxe8+QkcHmW2k2CIGBW+1kwNsoemOlxymN8uPdDZGRl6OAsiIiIiIhUGSwxbNu2LYDskUWXLFlSoH2XL1+O+/fvAwACAgJ0HRqR1mpXrI2pbaZKy8dvH8emiE3qC1dqA9T9THNlV78D4g6q3dSkahN81fYrafli3EXMOjZLfRJKRERERFREBksMhw0bJj3/5JNP8Pvvv2u136pVqzBlyhRpefjw4TqPjaggRjYeiTZubaTl709+j+sJ19UX9voWcGimubKzw4HUe2o3DWs4DAMaDJCWt1/ejo0RGwsVMxERERFRXgyWGDZv3hwDBgyAKIrIysrChAkT0KRJEyxevBgXLlxAQkIC0tLSkJCQgPDwcCxZsgQ+Pj4YN24cMjMzIQgCBgwYgObNmxsqZCK1BEHAz11/RnmL8gCAjKwMTPlnivqunjJToPU2wMRefWUZT4HTgwBFptrjzGo/C42cGknrvj3xLULuhejiNIiIiIiIJIJowL5pL1++RNu2bREWFqb1xOE54TVr1gzHjx+HpaWlPkMsczw9PQEAV69eLeZISp8Tt09g9K7R0vKoJqOUun8qub8XONVbc2V1PwMa/ax205OUJ+izsQ8epzwGAJS3KI+/h/2NqnZVCx07EREREZVMxfX93GAthgBgaWmJwMBAjBs3DoIgQBTFfB9GRkYYP348Tpw4waSQSpS2Ndri3UbvSstrL6xFcEyw+sLOvYC6n2uu7L9fspNHNSpZV8Ly3sulQW8S0xIxfs94pGWmFTp2IiIiIqLcDJoYAtnTTSxbtgzXr1/HtGnT4OPjAxMTE6UyJiYm8PHxwbRp03D9+nUsXbqU01RQiTTdfzo8KngAAHrU6aE0nYUK7++Aiq01bz87AkiJUb9rFW982/Fbafnqk6vYH7W/MCETEREREakwaFfSvLx48QIpKSmwtraGnZ1dcYdTZrAradFFx0fj6pOr+F+9/6l0kY5PzsC2sFiE3ElESkYWqpkl4kerEbBQPFNfWXkfoGMQIDNTu3nu8bnYGLERM9vNxJCGQ3R9KkRERERUzIrr+3mJSQypeDAx1I/0TDlm77uKHRfuI1Ou/Cfmb30Ba6vPgpGg4U/PYyLQ9De1m7IUWYiKj0L9yvV1HTIRERERlQBl4h5DorIgPVOOEWtCsSU0ViUpBICTKU2w5MkANXv+v+uLgdi/1G4yNjJmUkhEREREOsfEkEjHZu+7itC4IzCy+w2A6jQUALDw8RCcScnjfsRz7wPJN7U+5p5re5CckVzASImIiIiIshnrsrLY2FilZRcXF43biiJ3vUQlyaOkl9j+3/cwsgkDAAhWeyGmvqNSTgEZJsd+jgO1JqKiyXPVirKSgeD+QMczgLHmgZdeyV9hzvE52BK5Be1rtsfvfX6HkcDfe4iIiIioYHSaGLq5uUmDbwiCgKysLLXbiuLNeolKkh3nH0ChsJSa4o0sTkL+qi6QWU+lbHxWOUyKnYqNNb6CTFCoVvYsAgj/GGi2QuPxdl7ZiS2RWwAAx24dw6LTizDFd0rRT4SIiIiIyhS9NC3kzEGY17aiPIhKqpA7iRBTe0HMqiKtM7LZBAjqu3meTfXCgsd5jC56cyVwZ5PGzQO9BqJdjXbS8pJzS3D4+uGCB05EREREZZpOWwxdXFw0tgrmtY3obZGSkQXABIrkETCy/wWCkAXBKBlGNpugSBoLQPVvYOmTAWjncB2NTULVVxo2FijfGLCrq7LJSDDC/O7z0XdTX9xOvA0A+OzgZ3Ar54baFWvr8MyIiIiI6G3G6SrKOE5XoVvDV4cg6EYCAEAwPwkj653SNkVKP4jpbdTu162WDMvKjwbSHqiv2LbO/7F33/FNVvsDxz9P0jTp3lBGW8reslEZMmQ4QEGmCiqouLdex3Xg/V33BreggMhliQMVUNmrbBGQVUoLFOhu05WmyfP7IzQQ0qTppLTf9+vmleR5zjnPeXIB880553tg6FbwLn2Pz2MZxxg9f7Q9AU1UUBTLbl9GiE9IJe5GCCGEEELUNNmu4hJTVZX4+HgWL17M888/z3XXXUdkZCSKotgfFfHDDz8wduxYYmNj8fHxITw8nO7du/PKK6+QnJxcrrYsFgvz5s3jhhtuICoqCr1eT8OGDenTpw9vv/02WVlZFeqjqDq9Y0Ptr9XC/qhF59cWKn4/gPZ0qfU6NG8Jff4Hirb0hnMOwqbxYC19fW3z0Oa8f8P7KOdGJE9kn+CRnx+h2EV5IYQQQgghLiQjhuc8+eSTvPfee27LlOejyszMZOLEiaxc6Xq9V1BQEF988QXjxrnZ0+6cxMRExo4dy/bt212Wady4Md9++y0DBw70uJ8yYli1UoyF9Hlj9fn9CxUjmpA3UDS2kTy1OBJr1pOA3l5Hp1XY/OxgIgL0cOBt2POM6wu0fhh6fOTy9Gdxn/H2hrft76d0n8ILA1+o1D0JIYQQQoiaU+dHDNevX8/69euJj4+vUP2EhAR7G9XBYrE4vNfr9XTv3r1CbRUWFnLjjTfag8KIiAiee+455s+fz6effsrQoUMByM7O5tZbb+Xnn392215aWhpDhw61B4XR0dG8+uqrLFiwgA8//JDevXsDkJyczIgRI9wGj6J6NQgwMKZ70/MH1ACsxtvsbxWvMygBC4DzPzKM6R5lCwoB2j0JTUa4vsDhGXDkU5enp/Waxg1tbrC/n71zNt/v/77c9yGEEEIIIeqXGhsx1Gg0KIrCgw8+yEcfuR7xcOXpp5/mvffeq7btKr744gsOHDhA165d6dq1K+3bt8fLy8thCqmnH9V//vMfXnrpJQBat27NmjVraNy4sUOZd999l6eeegqABg0acOTIEQIDA0ttb+rUqcyePRuAPn368OuvvzqUVVWVRx99lBkzZgC2Xxn++usvtFoX0xIvICOGVa/QbOGO2duIS8iwH1N8f0Lj+wcAqjkKa85DoPrQOzaUOVN6YdBd8P9VUSasugpyDpV+AUULA1dA5LWlns4vymfcgnH8k/oPAL2b9mb++PmS/EkIIYQQ4jJQ50cMq0J1bldx77338sEHH3DHHXfQuXNnvLwqlrA1JyeHN9980/5+3rx5TkEh2KauXnfddQCkpKTw/vvvl9rekSNH+OabbwDbKOZ3333nFEAqisJ7771Hp06dANsfom+//bZC/ReVZ9BpmTOlFxN7RaPT2oIxNf8G1KLWWAuvxJr9GDqNLxN7RTsHhQDeIdD/Z9tzaVQLbBgLOYdLPe3r7ctnN39GqE8oYzqO4esxX0tQKIQQQggh3LqsAsPLwY8//kheXh4Affv2pVevXi7LPvnkk/bX3333Xall/ve//2G12jY/Hzt2LNHR0aWW8/Ly4tFHH7W/nz/f9d53ovoZdFpeH92Jzc8O5ulhbejXqiGdgp6hb6NHeHpYRzY/O5jXR3dyDgpLBLaCfktBcfEDhTkL1t0IpoxSTzcNaspPk3/ijWFvoPfSl1pGCCGEEEKIElW6j2F1ys/PB8BgMFzinrj366+/2l/fcMMNbkrCNddcg5+fH3l5eRw+fJgjR47QqlWrCrd3/fXX21+vWbOGgoICfHx8ytN9UcUiAvQ8OLAlDw5sWf7KDQdCj5mw/b7SzxuPwMaxtmmlGp3T6UYBjcp/TSGEEEIIUS9dNiOGu3btAiA8PPwS98S9vXv32l+7Gy0E2yhf165dS60Ltqmz+/bt87i9Ro0a0bSpLfFJcXExBw4c8Ljf4tLYe2YvH2/92HWBVtOgzaOuz59dDTseBg+nWG9O2swTvzwh21gIIYQQQggH1TJimJSU5PKc0Wh0e/5CZrOZU6dOsXjxYuLi4lAUhS5dulRRL6ueqqocOXLE/j42NrbMOrGxsWzcuBGAgwcPOpw7deoUubm5AGi1WqKiojxq7+TJk/b2KppZVVS/hXsX8sqfr1BkKSIqKIqR7UYCkGo0sXB7EnEJGeSaignUj+XV4D3EmNaV3tDRzyGoPbR5xO31lh9czlO/PoXZakbvpee1oa/J2kMhhBBCCAFUU2DYrFmzUr9wqqrK3LlzmTt3boXbnjhxYmW6Vq1yc3Mxm832956Mbl5Y5uIN6jMzM+2vg4KC0OmcpwuWpz1X4uPj7dmPLibZSqtHkaWIb/d8S5GlCIDnVz5Ps+CWLNhSzJKdJ8/vg3jOjZoH+L5lAq0MLn5U2fU4BLSGxsNLPa2qKr8e+hWz1fbnc9Hfi2jo35DH+jxWZfckhBBCCCE85+r7d3x8PC1atKjh3lTzVNKSLKIXZhK98Fh5HmALCidMmFCdXa4Uo9Ho8N6T9X0XlsnJyXHZnqdrBd21J2oPb603H4/8mCBDEAAFxQVM+O5uFmw/7BQUAhitfkw5/hLpxaVvaYJqhU3jIbv06cOKovDe9e/Rvcn5EeQZW2bw3V+lJz0SQgghhBD1S7WMGEZHRzuNGCYmJqIoCv7+/oSGhpbZhqIoGAwGwsLC6NixI7fccgvXXlv6vm2iclq0aCEjg5dAdHA0713/Hnd/fzcqKib1LBr/eViNd1PabzYniiK57/jzzG/+b7w1pawRNOfAuhEwNA4MzqPVBp2BL27+gvELxnM04ygAL//xMuG+4QxtNbSqb08IIYQQQrjh6vu3q5HE6lYtgeHx48edjmk0ti+6d9xxR4U2uL8cBAQEOLwvKChwOnaxgoIC++uL9ye8sO6F5Sranqh9BjQfwNTuD/DVTlsCGkW/D6X4D9SC0gO17fkdef7UQ7wT9UHpDeYeg423wMDfQevtdDrYJ5ivx3zNmO/GcDb3LFbVymO/PMbcsXPp0aRHVd2WEEIIIYS4zNRoVtLq2py+tvD398fL63ysnZaWVmadC8sEBwc7nLvwfXZ2NsXFZWeSdNeeqJ38rTegFrW3v1d8fwHdPy7LL8m8ls9SRrtuMGW9bYsLF3/fGgc25utbviZAb/vhwVRs4t5l93I0/WjFbkAIIYQQQlz2aiwwTEhIICEhgenTp9fUJWucoii0bt3a/j4hIaHMOheWadu2rcO5pk2b4u/vD4DFYvEom6u79kTttO14FlbjJFRLGACKoqIJmAua0jevB3jrzB3stPRz3eixr+Hgey5Pt4low+c3f473uVHF7MJs7lxyJ2eMZyp2E0IIIYQQ4rJWY4FhTEwMMTExhISE1NQlL4nOnTvbX2/bts1t2eLiYnbv3l1qXbAFmh07dvS4vdOnT9u3qtBqtbRv395teVE75JqKQfXDmjMVVbVlnlU0eWgCZgPmUutY0fKO8UUI7lzqeQB2Pw0nf3Z5undUb967/j0UbOuBTxtP88jyR+r8yL4QQgghhHB22Wxwf7m4/vrr7a9//fVXt2XXrVtHXl4eAK1ataJVq1aVau/C8wMHDvQ4k6m4tPz156YfW5qi5o4/f0KbDtpUl/W89IFwzU9gaOCihAqbb4XMvS7buK7Ndbw46EUAwn3DeXnQy7K3oRBCCCFEPSSBYRUbOXIkfn5+AGzYsMHtKN+7775rf33rrbeWWmb8+PH2xD2LFi3ixIkTpZYrLi7mww8/tL+/7bbbyt13cWn0jj2fpVc19cJa0BfVHI0162mwNHZZ78rmYeAXA/1+AI2+9ELFubZMpQVnXbZzR7c7eGHACyy5dQkdGl6aLFhCCCGEEOLSUtRLOG8sNzeXv/76i7S0NIxGI1ar1aN6kydPruaenXfh6ImnH9X06dN55ZVXAGjTpg2rV6+mcWPHL/jvvvsuTz31FGDblP7o0aMEBQWV2t5dd93FN998A0Dfvn355ZdfHDKOqqrKY489Zs/22q5dO/bu3euQCMeVknS4sl3FpZNiLKTPG6sv2L+wZPqozmUdnVZh87ODiQg4FxAmzIctt7u+SPjVMPhP0BqqpM9CCCGEEKJ6XKrv55ckMPzuu++YMWMG27dvL/d6JkVRPMrOWV5ZWVm88847Tsf/+9//2l+/8MILTuf/7//+z+lYQUEBgwYNYuvWrQBERERwzz330LFjR3Jycli2bBkrV64EbGsBlyxZws033+yyb6mpqVx99dUcPWrLGhkTE8Pdd99Ny5YtSU1N5bvvvrNfy9fXl9WrV9O7d2+P7lsCw9rhue/3smBb6aPBpZnYK5rXR3dyPPjXv2H/f0uvANDsNrhqLiieTRTILcplzbE1jGg7wuN+CSGEEEKIyqkXgWFBQQHjxo2zr4Vzd2lFUUo9rygKFoulyvt2/PhxYmNjy13P1T1kZGQwYcIEfv/9d5d1AwMD+eyzz5g4cWKZ10lISGDs2LHs3LnTZZnIyEi+/fZbBg8eXHbHz5HAsHYoNFu4Y/Y24hJcZCLVZKPxW4g1bzy9Y2KZM6UXBp3WsYxqhY1j4cT3ri/U9gno+g6UsY4wLS+Nqd9PZd/ZfTw/4Hmm9phazjsSQgghhBAVcam+n1fLBveuTJ06lV9++QUAg8HAwIEDSUhI4ODBgyiKwuTJkzEajSQmJrJ3717MZjOKouDn58fo0aMvq6QYoaGhrFq1imXLljF//ny2b9/O2bNn8fPzIyYmhhtvvJFp06bRpEkTj9qLjY0lLi6Ob7/9loULF7J3715SU1MJCgqiRYsW3Hzzzdx77711PutrXWXQaZkzpRfTfz7Akp0nLphWCmiT0QR+jqLNJCwgn09uX+ocFIJtJPCquZB7HDJ3lX6hg++BPgw6PO+2P0//9jT7zu4D4LW1r6FRNNzV/a4K3p0QQgghhKjtamzEMC4ujquuugpFUWjZsiWrVq0iJiaGhx9+mI8//thpJDAnJ4cvv/ySV199ldzcXIYNG8bChQsJCAioie7WGzJiWPukGk0s2nGCrcfSyTUVk8tajuXPtp8f2Hwgn938GV4aF7/r5J+Elb2g4LTri/T8BFrd7/L0aeNpbv3frSRln9878+VBLzO5W82t7xVCCCGEqI8u1ffzGstKOmfOHPvr2bNnExMT47Z8YGAgTz75JDt27KBRo0asXLmSu+6SEQtR90UE6HlwYEvmTe3Nsgf68PsDLzC1+/mpnGuOreHVP191PRXbtyn0/xG0brYr2f4gHF/g8nSjgEbMHz+fpoFN7cemr57O/D3zy30/QgghhBCi9quxwHDjxo0AtGjRgj59+nhcr1WrVsyZMwdVVVm2bJl9KqoQ9cmzA55lWKth9vfz/5rPrB2zXFcI6wl9l4Diara4ClsmwynXe2M2DmzM/PHzaRxwPqPuS3+8xIK/XAeUQgghhBDi8lRjgWFycjKKotC1a1eH4xeuGywqKiq17uDBg+1Dqt9++231dVKIWkqjaHj3+nfp0qiL/djr617nt0O/ua7U5HrbmkNcrM1Vi2HjLZCywWUTTYOaMn/8fBoFNLIf+/fv/2bR34vKeQdCCCGEEKI2q7HAMCcnB4CwsDCH4z4+Pk5lStOtWzdUVXWblVOIusxH58PnN39OVFCU/diTvz3JrmQXiWYAmk2Enh+7Pm8phHU3QsZul0Wig6OZP34+kf6R9mPPr3yeJfuWlKv/QgghhBCi9qqxwNDX1xcAs9nscDw4ONj+OjEx0WX9kvVUp0+7SaghRB0X7hfOrNGzCDIEAWAqNjFt2TQSs1z/3aHV/XCFm/0NzTmwZhjkHHZZJCY4hm/Hf0sDvwYAqKg8u+JZDqYerNB9CCGEEEKI2qXGAsOoKNsoR3p6usPx1q1b219v2bLFZf0DBw5UT8eEuMy0CGvBZzd9hrfWG4CMggymLp1KobnQdaX2z0HbJ12fN6XC6iG2jKYuxIbEMn/8fCL8IgB4vO/jtI1oW6F7EEIIIYQQtUuNBYadOnVCVVUOHTrkcLxXr172dYZffPEFxcXFTnVXrVrFrl27UBSF5s2b10h/hajNekX14o1hbwCgVbTc0/MeDDqD6wqKAl3fhuZTXJfJT7IFh4VpLos0D23Ot+O+5cVBL/LglQ9WtPtCCCGEEKKWqbHAsH///gAcOnSIjIwM+/GoqCj69u2Lqqrs37+fm266id27d2M2m8nOzmbevHncdttt9vIjRoyoqS4LUavd1P4mnr3mWb4a/RXjO48vu4KiQK/PIWq06zI5B2HtdbbppS60DGvJnd3uLH+HhRBCCCFErVVjG9wnJSURGxsLwNdff83kyec3yt6yZQt9+/Z1W19VVSIiIti/fz/h4eHV2tf6RDa4r4csJlh7A5z903WZBgNg4G+gdTMKeZHZO2bTOLAxw1sPr3wfhRBCCCHqqTq/wX10dDRPPPEE48aNIzU11eHcVVddxZdffolWq0VV1VIfERER/PjjjxIUClEGVVXdJ4XR6qH/DxDW23WZlLWwcTxYnad2l+bzbZ/z37X/5dHlj7LqyKpy9VcIIYQQQlx6NTZi6Il//vmH999/n9WrV5OcnIxGo6F58+aMGDGCxx57jIiIiEvdxTpHRgzrlmJrMS//8TKL/17MrFtm0a9ZP9eFTenwxzWQ7eb/+9jJcOXXoLj+DSktL42hXw8luzAbAJ1Gx8c3fczgFoMrehtCCCGEEPXWpfp+XqsCQ1HzJDCsW5757RmW7l8KgL+3PwsnLnSfOTQ/GX7vA3nHXZdp/TB0/9C2RtGFv8/8zeTFk8kx2dYmemu9+eSmTxjYfGBFbkMIIYQQot6q81NJhRDVb3K3yfjqbHuG5hblMvX7qZzNPeu6gm9jGPQHGCJdlzk8A3Y8DKrVZZFOkZ2YM2YOAfoAAIosRTzw4wP8Ge9mHaMQQgghhKg1aiwwHD16NKNHj2b8+PFOm9wLIapGx4Yd+fDGD9Gcm/p5xniGe76/h7yiPNeVAlrAwJWgC3Zd5sjHsG2a2+Cwc6POfDPmG/y9/QFbcHj/D/ezbP+yityKEEIIIYSoQTUWGP7www/8+OOPZGZmotPpauqyQtQ7g1oM4qVBL9nf70/Zz4M/PYip2OS6UkhnGPALaH1dl4n/CrZOAavFZZEujbrw9Ziv7cGhRbXw1G9P8fXOr8t9H0IIIYQQoubUWGAYEhICQLNmzWrqkkLUW5O6TmJq96n29xuOb+DR5Y9itrgZrY+4Gvp9Dxo3P9wkzIEtk9xmK+3WuBvfjf+OMN8w+7H/W/N/vL/xfWRJsxBCCCFE7VRjgWHjxo0ByM/Pr6lLClGvPTvgWa5vc739/e9Hf+fJX5/E4mbEj8bDzgWH3q7LJC6ATRPB6jrI7NCwAwsnLqRJYBP7sfXH17sftbyMpBpNzFx9hEmz4hj1ySYmzYrj4zVHSTXWjfsTQgghRP3jVVMXGjx4MPv372fbtm01dUkh6jWNouG969/DVGyyJ4H55dAveGu9eeu6t+zrEJ00uRH6/wQbbgZLYellTiyBDUXQd5FtX8RSxIbEsmjiIu5ccieqqjJr9CwMOkMV3NmlU2i2MP3n/SzZeRKzxXH0c8ORND744zBjukfx8oj2GHTaS9RLIYQQQojyq7ERwylTpqDVaomPj+fnn3+uqcsKUa/ptDpmjJjhsJ9hdmG2+ymlYBs5vGY5aH1clzn1E6wfBcUFLotEBkSyYMICvhn7DaG+oeXtfq1SaLZwx+xtLNh2wikoLGG2qCzYlsQds7dRaHYzMiuEEEIIUcvUWGDYuXNnXnjhBVRV5Y477mDTpk01dWkh6jW9l55Pb/qU3lG9Gd5qOB/f9DF6r9JH+RxEDoaBK8DL33WZ07/B+pFQ7HqKeIhPCI0CGjkdP5x2mOzCbE9uoVaY/vN+4hIyPCobl5DB9J8PVHOPhBBCCCGqTo3uY/jKK6/w1ltvkZuby4ABA7j11lv58ccfOXnyJCaTrM0Rorr46Hz4atRXfDjiQ7y1btYPXqxB/3NbWQS6LnPmD1h7A5hzPW42ITOB2xfdzq0LbyUlN8Xz/lwiKcZCluw8Wa46S3aekDWHQgghhLhsKGoNpQnUah3X26iqiqIo5W5HURSKi11nRBTl06FDBwD2799/iXsiLpXknGQaBzZ2Xyh9O6weCuYs12Ui+sCAX90HkUBmQSY3zbuJUzmnAIgOimbO2DlEB0eXs+c1Z+bqI7yz6nC56z09rA0PDmxZDT0SQgghRF11qb6f19iI4cXx54VBoaqq5XoIIarGpsRNDJ09lM+3fe6+YFhPGLwa9GGuy6RugtVDoCjTbVPBhmBubHuj/X1SdhLjFozjUOqh8nS9Rnk6hfRiW4+lV3FPhBBCCCGqR41OJZVAT4jaY1PiJu7+/m4Kigt4a/1bfLPrG/cVQrvC4DVgaOC6TPo2+PNaMLkOiBRF4Zn+z/Cv/v+yH0vNS2XC/yaw49SOct5Fzcg1VWyWQkXrCSGEEELUtBoLDK1Wa5U8LBbJ9CdEVWgZ1pLIgEj7+/+s/g8L/lrgvlJwJxi8FgyRrstk7oI/B0Gh+7WD9/a6lzeGvWHfNiPHlMOkRZP46Z+fPLyDmuOvr9jOPhWtJ4QQQghR02p0xFAIUXs09G/I/HHzHTahf/H3F1m2f5n7ikHt4Np14NPEdZmsvfDnQCg47bapsZ3GMnPkTHtCnCJLEY//8jgfbf6oVs0m6B1bsa02rmzuZuqtEEIIIUQtIoGhEHVQqtHEzNVHmDQrjlGfbGLSrDg+XnPUKUtm48DGzBs3j0h/2wigisozK57hl4O/uL9AYGsYsh78YlyXyT4AfwyAvBNumxrWahhzx84lxCfEfuzDzR/y5K9PYiquHVk9x/WMQqctX7IsnVZhXI+oauqREEIIIUTVqrGspOvXrwegSZMmtGjRotz1ExISOHHC9gWzf//+Vdq3+kyyktYthWYL03/ez5KdJ0vdhF2nVRjTPYqXR7THoDufKfhYxjEm/m8iaflpAHhpvJg5ciZDWg5xf8G8RNu00dxjrsv4NIGBv9mmobpxPPM4d39/NwmZCbZqXj4svW0pbSLauO9DDXnu+70s2OY+yL3QxF7RvD7a/T0LIYQQQlyszmclHTBgAAMHDuTDDz+sUP1PPvmEgQMHMmjQoCrumRB1Q6HZwh2zt7Fg24lSg0IAs0VlwbYk7pi9jULz+fW6zUObO4zaFVuLeeTnR1h7bK37i/rFwLXrIaC16zIFp+D3fnB2jdummoU0Y+ltS7kq+ioUFN674b1aExQCvDyig8dTSnvHhvLyiPbV3CMhhBBCiKpzWU0llSymQrg2/ef9Hm+rEJeQwfSfDzgcaxPRhm/GfEOAPgCwrfe774f7+OPoH+4b820C166FIDeBkDkb1gyH4/9z21SQIYivb/ma2bfMZmiroZ7cSo0x6LTMmdKLib2iXU4r1WkVJvaKZs6UXg4jskIIIYQQtd1lFRgKIUqXYixkyc6T5aqzZOcJpzWHDX1bcUOz/6DBAICq6tl0GKdyTnwa2baycDdd1FoEmyfCP++Cmx94dFod/WOdp4vnF+Wz/OBy9/2oZgadltdHd2Lzs4N5elgb+rUKp2t0MP1ahfP0sDZsfnYwr4/uJEGhEEIIIS47l00u9fz8fAAMBsMl7okQtc+i7a6nj7pitqgs2nGCBwe2vGhtoga8HkAT8DUm4xS+Xm/i201/lro20YGhgS04XHsjpG91feHdT0H+Cej6Lmg8C6CsqpUnfn2C34/+zp7Te3jumufQeli3OkQE6HlwYEseHNjykvVBCCGEEKIqXTYjhrt27QIgPDz8EvdEiNrH0ymkF9t6LL30tYnFzbBmvgjFtqyjrtYmOtGHweA/oelN7i986EPYNB4shR7189O4T/n96O8AfL3za+774T5yi3I9qiuEEEIIIcpWLSOGSUlJLs8ZjUa35y9kNps5deoUixcvJi4uDkVR6NKlSxX1Uoi6I9dUXOF6rtcmOv/zEHf8JBPn/cHSu16wb0zvXM0X+i6FnQ/DkU9dX/zEUlidAv1/AL37pC63dLiFlYdXsj/Flp1r9bHVTFgwgS9GfUHjwMZu6wohhBBCiLJVy3YVGo0GRXFMzlBymYuPe0pVVRRFYf78+UyYMKHSfRQ2sl1F3TBpVhwbjqSVu17v2FB2JWV6OA3VhCboExRdAte3vpn3b3wTL42b35ZUFQ68AX8977bVDG0LXs17h8TCCPz1XlzZPIxxPaKICNA7lMsvyufxXx93SIYT4RfBFzd/QedGnT3ovxBCCCFE7Vcnt6soySJ6Yex54bHyPAAmTpwoQaEQpfB0G4WL6bQaj9cmKn6/ouhsewz+evgHHvrpIfcb0CsKdHgOrpoLiusAMtQSz3PaqRSm7GLDkTTeXnmIq9/4k+e+/9th2qqvty+fjPyEu3vcbT+WmpfKxIUT+e3Qbx7dgxBCCCGEKF21jBg2a9bMaWQwMTERRVHw9/cnNLTsL7GKomAwGAgLC6Njx47ccsstXHvttVXd1XpPRgzrhhRjIX3eWF2uBDQ6rUK36BDP1ycqBWgCvkTxPmo/dHX01Xx282f4efu5r3v6d9hwCxQbXRYxWny4L/EFNuV2sR/rHRta6tYPC/cu5KU/XqLYen4K7b097+XJfk+6H8UUQgghhKjlLtX382oJDEtTMr30wQcf5KOPPqqJSwoPSGBYdzz3/V4WbDvhcfmJvaI5eCaH3UlZ5bhKEZqAb1D0++xHujbqylejvyLYJ9h91cw9sOY6KDzjsohZ1fLsyUdYmjnYoZ+vj3beBmNT4iYe/OlBjKbzweaVUVcyZ+wcCQ6FEEIIcdmqk1NJLyab0wtRfV4e0cHjKaW9Y0N5eUR7/PXlDaC8sRqnEu59tf3I7tO7mbhwIim5Ke6rhnQhrc9a4k1NXRbRKRbejXqffzf6Ei22aaSl7bcI0CemD8tuW0arsFb2Yz2b9pSgUAghhBCiAmosMExISCAhIYHp06fX1CWFqFcMOi1zpvRiYq9odNrSkzzptAoTe0Xbp2dWbG2ilkkdX+T2LrfbjxxOO8z4/43nRJb7Ecv/7dcw+ujbbMtr77bc3RE/8k3sywRpjfb9FksTGxrL97d9z8h2I+nXrB8PX/Vw+W9HCCGEEELU3FRSUTvJVNK6KdVoYtGOE2w9lk6uqdhlts+Krk3c/Oxgwv29eXfju3wad35Liob+DZkzZg6twluVWrcke6peKeL9qHe4Pniz22slmiK55/i/aRjdg3lTe7ssp6oqpmITBp3B4bjFaqGwuLDsNZBCCCGEELVEvZhKKoSoGREBeh4c2JJ5U3uz7IE+zJvamwcHtnTaAqJBgIEx3V1P7SzNmO624FJRFJ7q9xT/6v8v+7mzuWdZl7DOZd2S/RZNqjcPJf2Lr9NGuL1WjP4My1o+RQfrn27LKYriFBQCzNwyk5u/vZkjaUfc1hdCCCGEqO8u6WKc3NxcEhMTycnJwWw2e1yvf//+1dgrIeqXl0d04FhqnkfZSUvWJl7o3l73EmgI5N+r/s3UHlOZ2mOqy/oXrmm0omV68r2cKIrkhUaz0CrWUuv4aQt51u8F+NsMHV8ExbPfs9YeW8uMLTNQURk9fzSvDXuNEW3dB6JCCCGEEPVVjQeGRqORDz/8kAULFnDo0KFyJ6RRFIXi4uKyCwohPFKyNnH6zwdYsvNEqdNKdVqFMd2jeHlEe6etIwAmdJ5Au4h2dI7s7LRVzYV6x4ay4UjaBUcUZqfdxOHCaGZGv0mwV67rjv79CmT+BVfNAV1Amfd1KPUQKrZ7yTfn89jyx9idvJtnr3kWb613mfWFEEIIIeqTGl1juGvXLkaOHMnp06eBimUpVRQFi8VSdkHhEVljKC7k6dpET6mqysbEjfSN6YuiKG7XNEZ7n+bLZv+hjSHJfaNBHeGaH8G/eZnXXx2/mid/fZIcU479WLfG3ZgxYgaRAZHlvh8hhBBCiOpW5/cxTE9Pp23btqSnp9uPeXl50aJFC8LCwtDpdB63tWbNmuroYr0kgaGoTl/v/Jr/W/N/jO04lleHvIq31tvtfot+mnzei3qPYUFb3TfsHQp9F0LktWX2ISkriQd/epADKQfsx0J9QvlwxIdcHX21m5pCCCGEEDWvzgeGzz//PG+88QaKohAQEMBrr73G5MmT8ff3r4nLCxckMBTVZX3CeqYsnWKfztk7qjefjPwEg1cAd8ze5nJNo4KVRxsu4LGGC9xfQNFA13ehzaPgZvoqQKG5kFf+fIXF+xbbj2kUDY9c9Qj3X3m/7H0ohBBCiFqjzmcl/fXXXwHbVNDly5fzwAMPSFAoRB3WsWFHujXpZn8fdyKOMd+N4XRuktv9FlU0fJx2O/N076Nq3WwzoVph1+Ow9S6wFLrti0Fn4I3hb/D6sNft6wutqpUPNn/A+AXjMZqMFbtJIYQQQog6osZGDIOCgsjNzaVv376sW+c6nb2oWTJiKKqTqdjE86ue54cDP9iPBRuC+fSmT+kV1avsNY1Zf8P6myH3mPsLhfWCft+Db5My+7Tv7D4e+ukhTmTbprNe1/o6ZoyY4TZpjhBCCCFETanzU0kDAgLIz8/n7rvv5vPPP6+JSwoPSGAoqpuqqszcOpMPNn1gP6bT6Pjv0P9yS8dbym7AlAGbxsOZP9yXMzSAq76FRkPKbNJoMvLq6lfZlLiJX+74hRCfkLL7IYQQQghRA+r8VNKoqCgACgoKauqSQohaQFEUHr7qYT648QP7NE6z1cwzK57hnQ3vYFVL37/QTh8KA36DNo+5L1eYAmuGwZ7nwep+X9QAfQBvX/c2P0/+udSg8O8zf7u/lhBCCCFEHVNjgeHQoUNRVZUdO3bU1CWFELXIiLYjmD9+PqE+ofZjn8Z9yiM/P0Kh2f0aQTRe0P19uPIb0LjbNkOFA6/DHwMgr4xtL4Aw3zCnY78c/IWbv72Z51Y+R15RXpltCCGEEELUBTUWGD7wwAPo9XoOHTrE77//XlOXFULUIt0ad+P7276nVVgr+7HUvFTP1/c1vwOuXQc+jdyXS9sMv3WBkz+Wq39njGd48Y8XAVj09yJunHsju5J3lasNIYQQQojLUY0Fhq1bt+b9999HVVUmTZoka9qEqKeigqNYfOti+jXrR3RQNJ/e9Cl6L3ejgBcJ7w3DdkDYle7LFWXaEtfseAQsJo+aNllMRAdF298nZSUxfsF43tv4HmaL++mpQgghhBCXsxpLPlNizpw53HfffSiKwrRp0xgzZgydOnUiMDCwJrshzpHkM+JSKbYWk5qXSqOAMkb/XLGYYM+zcOiDssuGdIM+/4PAVmUWNVvMzNgyg0/jPnVY/9ipYSfeu+E9moc2r1h/hRBCCCE8UOezkmq1Wof3qqpWKD28oigUFxdXVbfqPQkMRW2z4fgG/jj6B88PeN6zkcSTP8PWO6Eow305L3/o9Tk0u9WjfuxK3sWTvz5JUtb5tYoGLwPPXvMst3e5Xba3EEIIIUS1qPNZSUviz5LnC79UqaparocQom5Kzknm8V8e59s93zLxfxNJzkkuu1LTEXDdHojo675ccS5svg3i7obi/DKb7da4G8snL2dcp3H2Y4XFhbzy5ytMWTqFlNyUsvsmhBBCCHGZqLHAEByDQwn0hBAXm7l1JpkFmQD8deYvRs4byYbjG8qu6BcFg9dAh38DZYzkxc+ClT0ha1/ZzXr78fqw1/n85s8dsqmuP76er3Z8VXa/hBBCCCEuEzUWGFqt1ip5WCyWmuqyEKKGvTzoZSZ0nmB/n1mQyV1L7mLmlpll73eo8YIr/gODfgdDQ/dlsw/YgsOjX4IHP05d2/JafrvzNwY1HwRAbEgsj/d5vMx6QgghhBCXixpPPiNqF1ljKGqjJfuW8NIfL2EqPp9NdEDsAN69/l2CfYLLbqDgLGyZBGc82Bqn6c3Q81PwiSyzqKqqLPx7Ie0j2tO5UWeHc1bVikap0UkYQgghhKiD6vwaQyGE8NSYjmNYeutSooPPbx2xNmEtN827iX1ny54Cik9DGLgCrngdFK37sid/gF/aQ8L8MkcPFUVhQucJTkEhwOfbPmfaD9M8WxcphBBCCFHLyIhhPScjhqI2yynM4enfnuaP+D/sx7y13ky/drpDUhi3UjfDpomQn1R22SYjoddn4FO+LTQSsxK57pvrMBWb8NP58US/J5jUZRJaTRlBaS2TajSxcHsScQkZ5JqK8dd7cWXzMMb1iCIioBx7TQohhBCiwur8dhUXKywsZMWKFWzcuJETJ06QmZmJxWLhzz//dCinqioFBQUA6HQ6dDrdpehunSWBoajtrKqVL7Z9wbsb37WvM2wS2ITf7vwNP28/zxoxZUDcVNvoYFm8Q6D7R9DsNvBwS4q31r/F59s+dzjWObIz/x36X9o3aO9ZHy+hQrOF6T/vZ8nOk5gtzv9J0GkVxnSP4uUR7THoLq9gVwghhLjc1KvA8J133uGtt94iPT3dfqxkX8OLk8ukp6cTHR1NYWEhvXv3ZvPmzTXd3TpNAkNRlapzxGlT4iYeW/4YuUW5LJy4kM6RztM53VJVODwTdj8F1qKyyzcZAT0/A9/GHjStsnTfUl5b9xrZhdn241pFy13d7+LRqx/F19u3fP09p7pH8QrNFu6YvY24hDL2gQR6x4YyZ0ovCQ6FEEKIalQvAkOz2czNN9/MihUrAJy2qigtMAR44IEH+Oyzz1AUhUOHDtGyZcsa6W99IIGhqAo1NeJ02niafWf3MaTlkIp3NvMv2HonZO4pu6wuGLp/CLGTPBo9TMtL4/V1r/PDgR8cjjcJbMKr177KgOYDPO5mTX2mz32/lwXbTnhcfmKvaF4f3anC1xNCCCGEe/Ui+cz999/Pb7/9hqqq6PV6pk2bxsKFC7npppvc1rv99tvtr3/99dfq7qYQohxKRpwWbDtRagADYLaoLNiWxB2zt1ForviWM40CGpUaFB5NP8p/Vv+HAnNB2Y2EXAHDtkGn6aB4uS9rzoKtd8C6kZBfdlKZcL9w3r3+XeaMmeOQOOdUzimmfj+VR35+hNS81DLbqanPNMVYyJKdJ8tVZ8nOE6QaTWUXFEIIIcRlpcYCw507d/L111+jKApNmzZl165dfPrpp4wdO5amTZu6rXv11VcTFBQEwIYNHmx2LYSoMdN/3u/RNESAuIQMpv98oEqvX2Qp4olfnuCbXd94nrVUo4NOL8HwHRDStezyycvhlw5wbI5H+x72bdaX3+74jft734+X5nzw+cuhX/j9SNlbaNTUZ7pou+vA0xWzRWXRDs9HGIUQQghxeaixwPDrr7+2Tx2dN28ebdu2LVf9Ll26oKoq//zzT3V0r0rdeeedKIri8WPmzJketfvDDz8wduxYYmNj8fHxITw8nO7du/PKK6+QnCwp8kXNqw0jTt/u/pb9KbapFvEZ8YyZP4bP4z7HYvVgFC3kChgWB51etQWL7pizbFNQ190I+afKbNqgM/BUv6f4adJPdG1sCz67NurKhCsmuK1Xk5+pp8HnxbYeSy+7kBBCCCEuKzUWGK5ZswaAjh07cs0115S7fsmo4qlTZX8hq2syMzMZPnw4o0aNYsmSJRw/fpzCwkLS09PZtWsX06dPp3379ixatOhSd1XUM7VhxOm2LrdxT897ULCtATRbzby14S1uX3Q7p7I9+PdCo4NOL8KwHRDSrezyyb/aRg/jv/Zo9LBNRBsWTVzEq9e+yn+H/heN4vjPblZBFml5afb3NfmZ5pqKy12nMvWEEEIIUXuVscCm6iQnJ6MoCl27ejBtqxT+/v4A5OXlVWW3qt1//vMfOnbs6LbMFVdc4fJcYWEhN954oz0ba0REBHfffTcdO3YkJyeHZcuWsWrVKrKzs7n11lvx8fFhxIgRVXoPQrhSmRGnBwdWLomUY7bOfrT3b0Ci6XNyzbYga9vJbVw/53pevfZVRrYbiVJW8piQzjBsKxx4C/ZNB6vZdVlzNsRNgaRF0PtL8HU/HV6jaLity22lnntn4zv8/M/PPHzVw0zuNrlGP1N/fcX+E1DRekIIIYSovWrsv+6FhYUAGAyGCtXPzc0FzgeIl4u+ffsyYMCACtd/++237UFh69atWbNmDY0bn0+ff9999/Huu+/y1FNPYbFYuPvuuzly5AiBgYGV7boQZboUI06us3U2BOUptAGLwHuX7TpFuTzx6xOsPraaV699lSBDkPvGNTro+AI0vck2bTRjp/vyp1fYRg+7vQ/N7/J438MSB1MPsnDvQqyqldfXvc6CvQsgfxTQDChfWxX5THvHhrLhSFrZBS9yZfOwctcRQgghRO1WY1NJIyIiADhz5kyF6h88eNChnfogJyeHN9980/5+3rx5DkFhiSeffJLrrrsOgJSUFN5///0a66Oo32p6xKnMbJ2qL5acO7EaJ6PFx354+cHlXD/nenYn7/bsQsEdYehWuOI10Hi7L2vOgbip8OdAyPIg8c0FErMS8fc+/2PX8czjHDe9jybwU9CeLldbFflMx/WMQqctXwCq0yqM6xFV7msJIYQQonarscCwbdu2qKrKli1bSt2r0J0TJ06wZ88eFEWhZ8+e1dTD2ufHH3+0T53t27cvvXr1cln2ySeftL/+7rvvqr1vQoBtxKkiKjri5Gm2TtXUg6KMfxFh6GA/llOYQ4hPiOcX03hBh+dg+E4I7VF2+ZR18FsX2Pk4FGWXWRxgWKth/Dn1T2674jaHtYeK90E0wW+i+C0FxbPp8xX5TBsEGBjT3f002IuN6R5FRIC+3NcSQgghRO1WY4Hh8OHDAUhLS2Pu3Lnlqvviiy/ag8lhw4ZVed9qqwv3bLzhhhvclr3mmmvw8/MD4PDhwxw5cqRa+yYE1OyIU7mzdVpDSTt9Lw/0fgKdRseLg16kWUizcl/XNnq4Ba54vezRQ9UChz6A5W0gYZ5HyWlCfUN5dcirLJ+8nKuir7IfVxQrGp91aEL+g2JYD7j+Qa0yo3gvj+jgcYDfOzaUl0e0r9B1hBBCCFG71VhgeOedd9r3InziiSfYsWOHR/VeffVV5s6di6IoNG7cmAkT3Kd6r21efvllWrVqha+vL35+fsTExDB69Gi++uor+7pLV/bu3Wt/7W60EMDLy8shsc+FdYWoLjU54lSRbJ3FFgVD8TBW3LWCsR3HOp1PyEig2OrB2jyNF3R4Fq7bDWHu/y4CUHgWtkyGP/pD5l8e9bVNRBvmjZ3Hpzd9ip9XQ/txRZOPxn8JmoCvXdatzCieQadlzpReTOwV7TLI12kVJvaKZs6UXhh02gpdRwghhBC1W40FhqGhofzf//0fqqqSk5NDv379eOqpp9i5cycm0/n9t3Jycjh06BCzZ8+mZ8+eTJ8+3X7u/fffR6crY6+xWmb9+vUcPXqUgoIC8vPzSUpKYtmyZdxzzz00b96c338vfbNrVVUdRv1iY2PLvNaFZUrWZApR3WpqxKky2TqbhTRzykpqNBm5fdHtjPp2FHtPe/hDSlB7GLIJurwJGg8CsdSNsKIb7HgEirLKLK4oCkNbDWXjtD9p4j0e1Xr+GtbCK0utUxWjeAadltdHd2Lzs4N5elgb+rUKp2t0MP1ahfP0sDZsfnYwr4/uJEGhEEIIUYfVaM7xBx98kCNHjvDRRx9RVFTE+++/75AoRVVVQkIc1wCp56Zivfjii4wZM6Ymu1spvr6+DBo0iF69ehEbG4teryc1NZUtW7awdOlSCgoKOH36NMOHD2fRokXccsstDvVzc3Mxm8+nyw8PDy/zmheWycrK8riv8fHxdOjQodRz+/fv97gdUT+VjDhN//kAS3aWPqqn0yqM6R7FyyPaVzi4qIoMqBducXHI+DXpxWc4k3uGW767hcldJ/N438cdksGUSuMF7Z+BJiNhx4NwdrX78qoVDs+ApIW2gDJ2Mijuf5ML9PFh1f3/4dllw/n5yOeoSg6YHf+O6rQKI7oE8drNVTeKFxGg58GBLSu9lYgQQgghyubq+3d8fDwtWrSo4d7UcGAI8MEHH9C5c2eeeuope/CiKIr913z1ojU5wcHBvP/++9xxxx013dUKe+ihh5g5c2apW2s88MADvPXWW0ycOJF169ZhtVqZNGkSvXv3pmnT81PyjEajQz0fH5+Lm3JyYZmcnJxK3IEQ5VMy4vTEkNYs2nGCrcfSyTUV46/34srmYYzrUfmEJZXJgOq8xYWK4p+H5tzuOVbVyje7vmHF4ZW8OmQ6g1sMLrvhoLYw6A9IWgy7noCCU+7LF6bA1rs4vfMjPjY+yv7Clm4/H4NOywfj+vOCsTf/236cbQlZDp/pla0sTFoyCv3qm3j4qodpHOicsVgIIYQQwlOKenEkVkNyc3OZPXs2v/76K1u2bHEIhPR6Pb169eLGG29k2rRpdXJPvry8PLp3786hQ4cAWzA5Y8YM+/nk5GSaNGlif282m/Hycv/F+IUXXuC1114D4N577+Xzzz8vsx8lv1TIyKCo7WauPsI7qw6Xu95j17ZiS3x66VNRvY6g8V+I4pXicPi61tfx0qCXaODfwLOLmHNh///BwffAai6zuEXVMD/9Ot49ezvZloAKjag+uvxRlh9cDoC31ptJXSZxX+/7CPWtWKZYIYQQQtQOl+r7eY2tMbyYv78/jzzyCCtWrCA7Oxuj0cjJkyfJzMykoKCAdevW8fTTT9fJoBDAz8+Pf//73/b3P/30k8P5gIAAh/cFBQVltnlhmbr6uYn6q6IZUI+n5bten1jcCmvWv7DmD0dVzwdkvx3+jSGzh/Ddnu+wqlYPLuQPXd6A6/+GyCFlFtcqViaH/8KaNtO4K+xHsBaxYFsSd8zeRqG57O188ovy2Xf2/J6JRZYiZu2cxcCvBvLR5o/ILcotu89CCCGEEBe4ZIHhxfz8/GjcuLE9c2l9MGjQIPvrpKQk8vPz7e/9/f0dRgjT0tLKbO/CMsHBwVXTSSFqiYpkQL2hU2N++Tu5jFI61PzrsWY9g2pubj+aW5TLi3+8yPgF4zmVXcY00RKBbWDgSui3FHzL3j4i1CuHl5t8yR+t7+fGoPVsS0hj+s8Hyqzn6+3LijtX8J8h/6GB3/lRzdyiXD7c/CEDvxzI7B2zMRWb3LQihBBCCHFerQkM66OIiAiH9xcmjFEUhdatW9vfJyQklNnehWXatm1b+Q4KUcuUNwNqszBfz7e4sDTCmv0IVuME9Fo/++HknGQCDeUYgVcUiBoNN/5DXqt/UWQte21kjP4MM2Pe4oeWT5L0zy+kGssO6HRaHbdecSur717NM/2fIchw/ke1jIIM/rv2vwyeNZhFfy/ybEsOIYQQQtRrEhheQhePAl6ckbVz587219u2bXPbVnFxMbt37y61rhB1RXn33NuZlFnOK2hQTVfTyf8trm9zPQAvDX6JAH1AGfVK4eXH18apDD38MWtyuntU5QrfI8yPfY68VcMha1/ZFQAfnQ/Tek1j7d1reeDKB/DV+drPnTae5rmVz7HtpPt/P+qjVKOJmauPMGlWHKM+2cSkWXF8vOaoR0G5EEIIURfVaPKZVatWUVhYiK+vL9dee63H9f744w/y8/PLXa+2++6777jtttsAiIqKIikpyeH8vHnzmDx5MgD9+vVj/fr1Ltv6888/7Z9Nq1atOHzYsyQdknxGXK5SjaYyM6CO+mQTu5Oyyt121+hglj3Qh79O/8UVja5wOr85aTPtItoR4hNSSu3zJs2KY8ORNEBlSGAcLzX+kijvs551QtFA7J3Q+VXwbVJm8RJpeWl8vPVjFvy1ALPVzBWRV7D0tqVO+zjWV84Zah1VxdYqQgghRGVcqu/nNbZdxYEDBxg+fDiKovDII4+UK8Bbvnw5M2bMQFEUDh06dEn29ahq+fn5/N///Z/9/YgRI5zKjBw5Ej8/P/Ly8tiwYQPbtm2jV69epbb37rvv2l/feuutVd9hIWoZT/bcq8wWF0CpQeHZ3LPc/8P9eGu9eWHgC9zU7iaXQdf5PRQVfs+5kvXGrtwXsZRpDZbiqyljZEq1wrHZkLgA2j4O7Z4B77LXYIf7hfPy4JeZ2mMqH23+iOGthzv1Lz49nj/i/+C2LreVvW9jHVJotnDH7G2ukxEBZovKgm1JHEvNZc6UqtsjUgghhKjtamwq6ZIlS+yv77nnnnLVveeee1BVFVVVWbhwYVV3rUrNmTOH3377DavVdSbDs2fPMmLECP755x8ADAYD//rXv5zKBQUF8fTTT9vfT548meRk50Qa7777Lr/99htg2+T+8ccfr+xtCFEneLoe8WJXNg9zee7VP18ltyiXjIIMnvz1Se5ccicJmaWvAb44MDWpej5MuZVrDn7J/PThFKse/BNsKYD9r8HPLeDQR2Ap8ugemgY15a3r3mJQi0FO5z6N+5S31r9F/y/689Hmj8guzPaozcvd9J/3uw0KLxSXkOFRIiAhhBCirqixqaTXXnstq1evJjY2lvj4+HLXb968OYmJiVx77bWsXLmyGnpYNR577DE+/PBDIiMjGTp0KJ07dyYyMhK9Xk9aWhpbtmxhyZIl9gykGo2GBQsWMG7cuFLbKygoYNCgQWzduhWwJay555576NixIzk5OSxbtsz+eWi1WpYsWcLNN9/scX9lKqmoy1KMhfR5Y7XnCWiwTSXc/Oxgpw3nAVRVZfbO2by/8X0Kis9vD6PT6JjcdTIPXfWQQ6KasvZebKE/wTORcxgWtNXj/uHfHDq+CM1uB035R0STspK4dta1WNTz22L4e/tze5fbuav7XYT7hZe7zctBVf9ZEEIIIarLpfp+XmOBYZMmTThz5gw333wzS5cuLXf9W265hWXLltG0aVOntXi1SUlg6ImoqChmzZrFkCHu9z3LyMhgwoQJ/P777y7LBAYG8tlnnzFx4sRy9VcCQ1HXPff9XhZsO+Fx+Ym9onl9dCe3ZU5mn+SlP15iXcI6h+MhPiE8evWjTLxiIl4aL4+DkR6++3m+0Wy6+R3yuJ/4t4CO/y53gJhZkMlX27/i2z3fOu13aPAyMKHzBO7ueTeNAhp53pfLQFlBuitPD2vjdrqyEEIIUdXqfGBoMBgwm83cc889fPbZZ+Wuf9999/HFF19gMBgc9vurbZKTk1m7di1xcXHs2rWLM2fOkJ6ejtFoxN/fn8jISHr06MGNN97I6NGj0el0Hre9bNky5s+fz/bt2zl79ix+fn7ExMRw4403Mm3aNJo08TxBRQkJDEVd58m6shK9Y0M9Xlemqiq/HvqVN9a9QbLRcYp3y9CWPDvgWQbEDuD5ZX97GJiqDA/czP/Ffke4muhB+XNKCRBTjSYWbk8iLiHDZWKe7MJs5u6ay9e7vnaaSqrT6BjVYRR3db+L1uGtnS55OTqfCKh8+rUKZ97U3tXQIyGEEKJ0dT4wDAgIID8/n0mTJvHNN9+Uu/6dd97J3Llza31geLmRwFDUB7ZMlAdYsvNElWeiLDQXMnvnbD6L+4w8c57DuS9GfUGf6AHlC0zv7IohcTb8/QqYUj3viH8LzO2e55W9XVi064zH95lblMv8PfOZtWMW6fnpTnV+veNX2kS08bwftVRlM9QKIYQQNeVSfT+vseQz4eG2dStHjhypUP2S7RdK2hFCCE8ZdFpeH92Jzc8O5ulhbejXKpyu0cH0axXO08PasPnZwbw+ulOFMlAadAYeuPIB/rz7T8Z1GoeCLQNom/A2DIgdUO69Fw16A7R+AEbG29YSan1LreMkNx7d9qnck3MjNwX+jhaLU5GSjJt3zN5Godl23t/bn2m9prH+nvW8NOglIgMi7eU7R3auMyOGlc1QK4QQQtR1NTZiOHLkSJYvX46XlxcJCQnlmvZ46tQpmjVrhtVqZciQIaxYsaIae1q/yIihEFXrn5R/+O/a/3J/7/vpE+M40pSYkcEPu8+w47jR5RRPJwWnbaOH8bNAdQ72XDluasTMlPEsyxyIBeeA19VayiJLET/98xNzds3h7h53c1P7mxzOH0g5wA/7f2BS10lEBUd53J9LTdYYCiGEuFzU+RHDoUOHAmCxWBy2YPDEk08+icVi+0I0bNiwKu+bEEJUlXYN2jFv7DynoBBg3l+fsOzYA9zaN4Pv77+aeVN78+DAlu6zXvo0gl6fw4jD0HwKKJ6NajbTn+adqA9Y3WYaY0NWoVPMDueX7DxBqtF5L0VvrTdjOo7hp0k/MaKd8/6qs3fMZtbOWQyaNYj7f7yfbSe2UUO/L1bKuJ5RLkdsXdFpFcb1uHyCXyGEEKIyaiwwvOOOOwgJCQFg4cKFPPTQQ5jNZrd1zGYzDz74IIsWLQJsmTfvvPPO6u6qEEJUSmkb3idkJDBv9zxOZJ/goZ8fYsL/JrDj1A7PG/VvDlfOKneAGKM/w9tRH7G+zd3cG7GUAI1tHaTZorJoh+ukOIqioFEc/xORmpfK8oPLAbCqVlYdWcXEhRMZOW8kS/ctxVTsHGjWFg0CDIzp3rRcdcZ0dzOSK4QQQtQxNRYYBgQE8N///tf+y/Knn35KmzZteP3119m6dSspKSnk5+eTkpJCXFwcr7/+Om3atLFnMFUUhVdffdUeXAohxOVkx6kdDiNrO07tYPyC8dy15C72ntnreUMVDBAbeafzfKOv2dTuLp5rNJtIXRpbjzknm3HHT+fH8wOeJyY4xuH4gZQDPLPiGfp90Y8PN31Ial45kubUoJdHdKB3bKhHZXvHhvLyiPbV3CMhhBCi9qixNYYlHnnkEWbOnImiKKiqWuov6xcq6d4DDzzAzJkza6KL9YqsMRSi5sSnx/PGujdYfWy107mhrYby2NWPlT8DaO4x/lj8CAO8f8NLsXpcrcjqxcbiIQy6+S0I7liuS1pVK2uPreWbXd+wKXGT03mdRscNbW/gwSsfpHlo83K1Xd2qM0OtEEIIURXq/HYVF/rss8/417/+hdFoLLOsv78/b775Jvfff38N9Kz+kcBQiJq3JWkL7258l93Jux2OKyjc2PZGHr36UWJDYz1ub9KsOI4n7uOhBgu5JeTPcgWIADS6Dto/DQ0GQBk/1l3sUOoh5uyeww8HfnCaSrpg/AJ6RfUqX19qSKrRxKIdJ9h6LN3zREBCCCFEDahXgSFAVlYWX3zxBStWrCAuLo6CggL7OR8fH3r37s11113HPffcQ3Bw8KXoYr0ggaEQl4aqqqxLWMd7m95j/1nHv39tI9qyfPLyMmdUlLgw42aU95mKB4ih3aHd0xB1C2jKt01DRn4GC/9eyLzd8zibe5YODTrw46QfHe5BVVX2nd1Hx4YdPb43IYQQor6pd4HhxYxGI0ajkYCAAAICAi51d+oNCQyFuLRUVWXVkVW8v+l9jqTb9nn9eOTHDG893OM2UoyF9HljtcPUyCjvM9wXsYQxIX+i17hP9OXELxbaPg7N7wKdf7mqmi1mVhxZQYB3AAOaD3A4t/f0XkbNH0WrsFaM6zSOm9vfTKivZ2v+hBBCiPqi3geG4tKQwFCI2sFitfDLoV9YdWQVH434yCkj6Kojq+jWuBvhfuGl1n/u+70s2OacZTRMm8Xk8OVMDvuFEK+yp+870AVC7GRodT8EVT4RywurXuB/e/9nf++t9WZIyyGM7zyeq6KvcrpnIYQQoj6SwFBcEhIYClH7JeckM3jWYLw0Xky8YiJTe0yloX9DhzKFZgt3zN5GXEJGqW34KIWMDf2du8N/IFp/tvydaDDAFiA2vRm03uWurqoq9/94P78f/b3U89FB0YztNJZbOt7idG9CCCFEfSKBobgkJDAUovZ7ftXzLNy70P7eW+vNqA6juLfnvTQLaWY/7knGzXHdGvFy10N4H34XMsqxj2IJQyS0uBta3gt+5d/8/XjmcZbsW8KSfUtK3dZCo2joE9OHcZ3GcX2b68vfPyGEEOIyVy8Dw9zcXBITE8nJySlzs/sL9e/fvxp7Vb9IYChE7ff1zq/5ZOsnZBQ4jgZqFA3DWw/n/t73077B+ameHmXcVFVIWQf/vA3Jv5a/U4oGmoy0jSJGXmt7Xw5mi5m1CWtZuHch6xLWYVUdE+Xc0uEW3rrurfL3SwghhLjM1ZvA0Gg08uGHH7JgwQIOHTpEeS+vKArFxcXV1Lv6RwJDIS4PeUV5LNy7kFk7ZnEm94zT+f7N+nN/7/vp2bRn+TN+Zu2Hg+/A8flgLWeiGgD/lrYAsfmdoC9/MpnTxtMs3beUxX8v5mTOSQDmjp1Ln5g+DuW2JG1Bp9HRrUk3WY8ohBCizqoXgeGuXbsYOXIkp0+fBih3UAi2wNBisVR11+otCQyFuLyYik38+M+PfL7tc45nHnc6P6HzBP479L8Vazz/FByeAfFfgSm9/PW1BoiZAK0egLCe5a5uVa3sOLmD34/+zrPXPItW47jB/KhvR7H3zF6aBDZhZLuRjGw3ktbhrcvfTyGEEKIWq/OBYXp6Om3btiU9/fyXDS8vL1q0aEFYWBg6nc7jttasWVMdXayXJDAU4vJksVpYeWQln8V9xv6U839/Z4yYUfm1eZZCSFoMRz6FtC0VayO0uy1AjJkAXr6V6w+QkJnAtbOudTrevkF7RrYbyYi2I4gMiKz0dYSzVKOJhduTiEvIcD01WQghRJWp84Hh888/zxtvvIGiKAQEBPDaa68xefJk/P3Lt0eWqFoSGApxeVNVlY2JG/k07lNSclNYeddKp5G2NfFr6BnVE3/vCvx7m7HbFiAenw+W/PLX9wqAmHEQewdE9IUKbmy//+x+3lz/JluStjitRwRQUOgd1Zvr21zP0FZDifCLqNB1xHm2ZEb7WbLzpMtkRmO6R/HyiPYYdNpSWhBCCFERdT4w7NKlC3v37kWj0bB27Vr69u1bE5cVZZDAUIi6I7swmyBDkMOxU9mnGPDVAHx1vozpOIbJ3SYTExxT/saLsiFhLhz5BHIOVqyD/i1s+yLGTgb/ZhVq4mzuWZYfXM6P//zI/rOl/7uloPD5qM8Z3GJwxfopytz+5EK9Y0OZM6WXBIdCCFFF6nxgGBQURG5uLn379mXdunU1cUnhAQkMhajb3lz3Jl9s/8L+XkFhYPOB3Nn9Tq6Ovrr8iWpKspke+QROLAO1gsnAGgyA5ndA1BjQVWzmSHx6PD/+8yM//fMTJ7JP2I9rFS1xD8QR4hPiUP6M8YxMN/XQc9/vZcG2E2UXPGdir2heH92pGnskhBD1x6X6fl5jad2sVtvUn7Zt29bUJYUQot7z9fYlQB9gf6+isvrYaiYvnsx131zHd399R35ROaaIKgo0HAB9F8FNidBpOvg0KX/HUtbC1rtgWSRsuQPOroFSpoi60yKsBU/0fYI1d69h8a2Lmdp9Kk0Cm3Bl9JVOQWFuUS4DvxrI0NlDeXfju+w7u69CCdDqgxRjIUt2nixXnSU7T5BqNFVTj4QQQtSEGhsxbN++PYcOHeK2225j7ty5NXFJ4QEZMRSi7ssryuOHAz8wZ9cc4jPinc4HGYIY12kck7pMoklQBYI8azGc+tk2injmj4p31C8Gmk2C2EkQWLFso6qqkmPKcZpS+8vBX3hk+SMOx5oGNmVYq2EMbTWULo274KXxqnDX65KZq4/wzqrD5a739LA2PDiwZTX0SAgh6pc6P2I4dOhQVFVlx44dNXVJIYQQgJ+3H7d1uY0Vd63gmzHfMCB2gMP57MJsvtz+JU/+9mTFLqDxgqhRMOh3uPEQtHkc9GHlbycvEfb/HyxvA791hf1vQO6xcjWhKIpTUAiw7+w+p2Mnc04ya+csxv9vPL0+6cVjyx/jxwM/kpFf9rq6usyTdYWl2XqsAlucCCGEqDVqbMTw8OHDXHHFFRQVFbFixQqGDBlSE5cVZZARQyHqp4TMBObumsvSfUvJM+cBpW91oapq+dchAliKIPkXSJgDp36p+FpEgNCetsym0ePAL7rCzZzIOsGqo6tYcXgFu5J3uSynUTRsnLaRhv4NK3yty9moTzaxOymr3PW6Rgez7IE+Vd8hIYSoZ+p88hmAzz77jAceeIAGDRrw559/2m9aXDoSGApRvxlNRpbuX8qqI6uYM2YOOq3jnrIP//QwWo2WcZ3HcWXUlWiUCkw0KUyB4wsg4RvI3FO5DodfBdHjIXos+DaucDNnc8/yx9E/WHlkJdtObMNsNdvPtQhtwaopqxzKmy1m1hxbw9UxV1ds24/LyKRZcWw4klbuev1ahTNvau9q6JEQQtQv9SIwBJgzZw733XcfiqIwbdo0xowZQ6dOnQgMDKzJbohzJDAUQrhyNvcs/T7vh0W1ABAdFM3YTmO5peMtFR9Ny/wLjs2BxPm2gLHCFGjQzzaKGDUGfCo+umc0GdmcuJm1CWtZe2wtI9qN4PkBzzuU2Zq0ldsW3YZOo6NXVC8GxA6gT0wfWoe3rtiIai0mawyFEOLSqvOBoVbruL9RRacnKYpCcXElpiQJBxIYCiFc+XL7l7yx7g2n4xpFw4DYAYzrPI4BsQMcRhlTjSYWbk8iLiGDXFMx/novrmwexrgeUUQE6O3lCk2FfDhvBp2KfuDawG14ayrx77qisW1/ETMemo4CQ8U3t7eqVkzFJnx0Pg7H31j3Bl9u/9KpfLhvOFfHXE2f6D5cHXM1jQMrPopZW6QYC+nzxupSN7V3RadV2PzsYIf/j4UQQlRMnQ8MNRoNiqKUGhCWpwuKomCxWKq6e/WWBIZCCFeKLEWsiV/Dwr8XsuH4BqylbCcR4RfBLR1uYWTb0XyzMY8lO0+WGlDotApjukfx8oj2GHRah33ygrU5jAhez+jgNXT1O1S5TisaCL8amt4ETW6CwFaVa++ct9e/zYK9C8guzHZbrllIM66OvprnrnkOX2/fKrn2pSD7GNZtnv6AI4S4NOpFYFgVJDCsWhIYCiE8kZyTzNL9S1n892JO5ZwqtYwl42Wwus9G2js2lHfGXsGgd9eWGkA21Z3l+uCN3Bi0gc6+Ryvf8cB20HSkLUgM720LHCuo2FrMnuQ9rDm2hk2Jm2x7IeJ8DxF+EWy5b4vDj6BW1YrZYkbvdXl86S40W7hj9jaPMpT2jg1lzpReGHTaMsuKS6vQbGH6z/s9/gFHCHFp1PnAUNROEhgKIcrDqlrZnLiZRX8v4vejv1NkKQJANcdizX78otIl/3lxnCXSuWkQe0+6H3kDiPFO5oagjdwYvIH2PgmV77yhITQZYRtNbDgYvHzKruNGVkEWW09sZVPiJjYlbiIxKxGAm9rdxHs3vOdQ9kDKAW6ZfwudIzvTpVEXujXuRpfGXWp15lNbEHGAJTtPSBBRB0iwL8TlQwJDcUlIYCiEqKiM/Ay+3b2EDzfOxZo/ENV0pWMB3X40fj+gmnqgmrqDNRywhYnl/Q9PC/0JHmi+i1vCN0H2gcp3XusLjYbagsTGN4IhvNJNnso+xeakzUQHR9M7yjE7p6v1mo0CGtG1UVe6Nu5Kl8Zd6NCgQ60bVUw1mli04wRbj6XLtMPLmEwPFuLyIYGhuCQkMBRCVIYtg+UhbKGe4zRNxX8OGsNO+3vV3OxckNgV1IByX8u+T17WfooTFpB58Fsi1MRK3gG26aVhvaHRcNsjtDtoqnakZNqyafwR/0eZ5XQaHe0btOeLUV8Q7lf5YFUIkIRCQlxuLtX3c68avZoQQog6xTYtTeHi6aJgRtH/7XBE0R1H0R1H9fsezG1QTd1RizqDavDoWv5623+yCv3acse2G4hLuJL2hgRuCN7ADUEbaaY/XbGbUK2QtsX2+Ptl0IdB5NBzgeJQ8ImsWLsXmDFyBvvO7mNP8h52n97NnuQ9JBuTncqZrWbiM+IJ9Q11OG5VrczeMZtOkZ3o1LDTZZ3YRtS8RdtLnw7sjtmismjHCdmCRIh65JIGhkajkS1btrBr1y7S0tIwGo0EBAQQHh5Ot27duOqqqwgIKP+vykIIIWpGrsnVNhM6rJnPouh3ouh3oHidtZ9RFCt4/4Pi/Q+qqkMt6oRaeBWY27i91pXNbYltpv+83x6QHihszoEzzXn7zGRa6ZMYEhjHkMC4ymU3NaVD4gLbAyCkqy1IbDwcwq8Cjc59/VJ4a73p1rgb3Rp3sx87m3v2fKB4eg9/n/mbwuJCrmh0BZqLkuQczzzO6+teB0CraGkb0ZYujbrQpXEXujbqSrOQZnVuP0VRdTxZV1iarcfSJTAUoh65JIFhUlISr776Kt999x0mk8llOYPBwK233sqLL75IdHR0DfZQCCGEJ0pG8UplDUctGIZaMBS0p2wBon4nivZ84hlFMaPod2G1BqK6CQx1WoVxPaJIMRayZOfJUkooHDHFcCQ1hk9SxxHhlcG1gdsYEriVPv5/odeYK36TmbttjwOvg1cARF5rCxIjh4B/bIWbbejfkGGthzGs9TAAzBYzB1MPYlGdM2/vTt5tf21RLexP2c/+lP3M/2s+AMGGYIdAsWvjrvh5+1W4b6Jucf0DTvXUE0Jcnmo8MPz++++ZOnUqOTk5Ze5fWFBQwOzZs1myZAmzZ89m1KhRNdRLIYQQnugdG8qGI2lllFLA0hQ1vylq/kjwiic8bC8Z5m0omgIAW3Kai2t570JFB0VtGdO9BREBemauPuLRlLjU4lAWZAxnQcZwfDUFvNv3LNeFbINTy6GoYqMnABQb4eQy2wPI0TZF13gQPk2HQMOB4Nukwk3rtDo6RZae7CPIEMRV0Vex9/Re8sx5TuezCrNYm7CWtQlrAfhu/HdOCXBE/eX2B5xqqCeEuDzV6N/4FStWMGHCBCwWiz0oDA0NpVevXjRr1gw/Pz/y8vI4fvw427dvJz09HYDs7GwmTJjAzz//zNChQ2uyy0IIIdwY1zOKD//0LFiz0aBTW7Psjnt5YtEOtidvRtEdguKLZ4WoKH7L0WjT0GDA6DWIXw4OZ/Mx/3L3Md/qw3ene3Dd9Q+CtRhSN8HJH+HUj5B7rNztXSjQchJOzLU9AALbQMNBtkeDAVWS7RTg2pbXcm3La7FYLRxNP8ru07vZnWxbq3g0w3G/R42ioVND5wDz5nk3E2gIpEujLnRt3JUrIq9wWsso6ibPfsBxVjJ9WwhRP9RYVtL8/HxatWrF6dO25ADNmjXjzTffZNSoUXh5OcenFouFZcuW8a9//YuEBNv+VU2aNOHw4cP4+FRu7ylxnmQlFUJUVkXT4LvdJ097Cm3Im051FXRYTW1Qi7qgFnUE1bMkLPaMphdSVcg5CKdXwukVcHYtWF0vb6iQ4CtsQWLkIGjQH3SBVds+kFOYw19n/mJ3si1YLLIUMX/8fIcymQWZ9Pi4h1PdSP9I2kS0oW1EW9pGtKVNeBuahzZHpy3/OkpRe0lWUiEuL3V+u4qPP/6Yhx9+GEVR6N69O6tWrSI4OLjMetnZ2QwZMoQdO3agKAozZszggQceqP4O1xMSGAohKquyG2eXtk9ei0Y5nDb9yJak9aVOnQRQVQ2YW9mCRFNnt1tg9GsVzrypZUytLM6HlPW2IPH0CsipRAKb0ihaWyKb8Csh7Erbs39zqIGkMWvi13D3srs9KqvT6Pj3wH9ze9fbq7lXoibJPoZCXD7qfGB43XXXsXLlSry8vPjnn39o0aKFx3WPHj1Ku3btsFqtDB06lN9++60ae1q/SGAohKgKbkf/sI0+jOkexcsj2jsEhWUxFZvYmLiRFYdX8Gf8n2QXZpdazpIzBYq6uGzn6WFtyp1dMe3sQT6aN5O+fju52v8v/LUF5arvEX34+SAx/EoI61kto4oF5gL+Ov2XfauMXcm7yChwHch/ctMnDGs1zOHYG+veYP/Z/bQMa0nLsJa0CG1By7CWhPmGSUbUy0Blf8ARQtScOh8YRkVFkZyczDXXXMPq1avLXX/w4MGsWbOGJk2acOKE5794CfckMBRCVKXSRv+ubB7GuB5RlZ6SZraYWXl4A49+/w2qbi+KxgiAquqwpr8GOLav+PyBWtwEL2srtjw7vNzXn7n6CO+sOgyATjHT3fcfrgnYRf+AXXTwqdzaRNcUCOpg2xajJFgMbAsXbV9RWaqqcjb3LAdTD3Io9RAH0w5yMPUgxzKOUWwtZs3da4gOdlz3OerbUew9s9eprSBDkD1IbBHaghZhLWgd1pomQRVPxCOqR3X9gCOEqFp1PjD08fGhqKiIqVOn8sUXX5S7/r333stXX32FXq+noKAafrWtpyQwFEJcbmxT4hLBKwFF/xdgQc0b61hIyUEb9m8AtIqegc37MbDFQAbEDiAywLMN6yfNinOZsCNEm01vv31c7b+Xq/3/oqWhtC00qoguEMJ6XzAFtTfoqycpiKnYRHxGPG0j2jrspWixWuj8UWcKiws9aqdHkx4snLjQ4VhmQSanck4RExxDgF72KL6UqvMHHCFE5V2q7+c1lpW0JDDMzc2tUP2SepJ4Rggh6reXR3TgWGoecQka1OLSlyUo3v/YX1tUE3/E/8Ef8X8A0L5BewY2H8jA5gPpHNkZrab0kRF3e7hlWoJYkdOHFTm2hDYNvNK5yn8vV/vvZUDQPhpqT1f09pyZc+DM77ZHiYBWjlNQgzuBpvIJY/Reeto3aO90XEXli1FfcDjtMEfTjxKfEU98erzL6agtwpz/f9lwfAOP//I4AGG+YcQEx9geITE0C2lGTHAMzYKbEWgIJNVoYuH2JOISMiRwqQYRAXoeHNhSNq8XQjiosRHDjh07cuDAgQpNBVVVlZiYGE6dOkW7du3Yt29fNfWy/pERQyHE5aisKXFe+sM0bridDPPfFJjzXbYT6hNK/9j+PN7ncZoGNXU4527E0J3esaGknP6HHj5/nRtR3EtDXSX2TvSE1gdCe1ywVvFK8G1cvdcEMvIziM+ItweLR9OPEp8ez5QeU7ir+10OZWdsnsEHmz8os01vTQBFRWFYcu4Ca4jDOS+tytjuMTLVUQhRp9X5EcOBAwdy4MABkpOTmTlzJg899JDHdT/99FNOnjyJoigMGDCg+jophBDismDQaXl9dCeeGNLaxZS4a4kI0GMqNrHt5DbWHFvDmvg1JGUnObSTUZDBDwd+4LlrnnO6Ro9mgRUKDHVaDQmFDUkoHMrizKGASgv9Sbr7/kNX34N08ztIK/0JNEoV/i5rKYDUDbZHCd+oC6afXgWhXUFrqLprAqG+oYT6htKzaU+H46X95lxYXIjBy1DmdNQiqxG8jGB13orEGvRvFh7R8evMBgxu3Y6Y4CiaBDahSVATmgY2JTIgUrbaEEKICqqxEcO4uDiuuuoqFEVBq9Xy0Ucfcd9995VZ78svv+Shhx7CbDajKAqbN2+md+8yUo4Lj8mIoRCivlBVlWMZx1hzbA2rj61m56mdFFuLad+gPT9P/tmhbH5RPld+dhV5eY2xFrVBLWoLliaA+yQwOq1Ct+iQMjM/Bmjy6Ox7hK6+B+nqe4iuvocI9cqp7C26p9HZtsuwT0HtDX6xNbJdRomSpDeJWYm2R2YixzOPk5iVyJG0BIpVW9CoWgOxZvyfY2WlEG3YM27b1ygaGvo3pGlQU27pcAtjOzmuPTUVm/DSeLmcPiyEELVBnU8+AzBhwgQWLVpku7Ci0KVLF+68806uvvpqYmJi8PPzIy8vj6SkJDZv3sycOXPYtWsXqqqiKArjxo1jwYIFNdXdekECQyFEfWU0GdlwfAMaRcPw1sMdzq09tpap3091OKZa/VHNraGoLaq5jdM0R7Dt/XbwTA67k7LK2RuV65rl8ekwM6RthfStkPkXqK7XOVYJXTCEXAHBV0BIF9vroA6grdl1fCnGQq5+40+KrTmgTQWlEMwdHAtpk9GGvOFxm0/2fZIHrnTc9/iDTR/wydZPiPCLoKF/QxoGNKShf0Mi/SNp4N/A/rphQEP8vf2r4tbEJSRrVcXlqs5PJQWYPXs2CQkJbN++HYA9e/bw2GOPua1TErf27NmTWbNmVXcXhRBC1BMB+gCub3N9qee2JG1xOqZoclH0u0C/CwDVEo5qbgnmVqhFHendrAkvj2jPPXN3VKA3Crm6ZhDbG2LPbSxfnA8Zu2xBYtpWSNsCBckVaNsNcxakrLM97F3xsm2RURIohnSxBY6GiKq99gUWbT9BsQUgEIpd7ONoaYgl8wXQZKBoM0CTQbsmZnx9sknOSeZs7lmH4k0CnbfLOJt7Fotq4UzuGc7knoEzrvvk7+3Pjgd3OExNVVWVnw/+TJhvGOG+4YT7hRPiE+KQwVVcerY1yPtZsvOk0xrkDUfS+OCPw7IthxClqNHA0NfXl7Vr1/Lkk0/yxRdfYLVay6yj0WiYNm0a77zzjmQkFUIIUSOe6vcUQ1oOYWPiRtYnbGDvmb2oOP43S9GmoWjTwLCV66M+5e3Rtg3Be8eGVmht4pXNL9qCwssXGvS1PUrknzwXJJ4bVUzfAVZTRW7RNbUYsvfZHse/PX/cp7EtQAzuZBtVDO4Age1s/XTB0xEbTzZdBy1YGoKlIarZdiSMcObdalteYio2kWxM5lT2KU7lnKJ7k+5OLZwxuokEL+Kt9XZar5hdmG3PrGrvlaIl1DeUcN9wW8DoF24PHK+KvopOkZ08vqaovEKzhTtmb3P7Z8psUVmwLYljqbnMmdJLgkMhzqnRqaQXOnbsGF9++SWrV69mz549mM1m+zmdTkeXLl0YNGgQ99xzD82bN78UXawXZCqpEEKULacwh5WHN/DdrpUczthBofX86FQDv0g237cR5dxaPdu0yN+xBryNWhwN5pao5lZgDXbZvk6rsPnZweWf3mYpgqy/HIPF3GMVucUKUsC/+blAsaPtOagDhT6tmP7r0VJHbMB5I/VRn2yqwPRb6BodzLIH+nhcPq8oj7O5ZzmTe4azxrOczXV8nMk9Q2puKhbVQruIdiy/Y7lD/aPpRxn29TCPr/fCgBeY0mOKw7G31r/F4r8XE+oTSohvCCE+IQQbgm3vfc699wkmSB9EoCGQVmGt7H+2RNls+5x6nv1+Yq9oXh8twbuoXerFVNILNW/enNdff93+Pjs7m9zcXPz9/QkKCrpU3RJCCCGcBBoCGdv5BsZ2vgGA5Jxk4k7EEXcijhCfEIcv7g0CDAzqYOLP5FMoXqfAYJuWqloiUM3NobgZqrkZWCIB20jFmO4VXPOk9YawnrZHm4dtxwpTIC3u/BTU9G1QXLE9hMumQm687XHqJ/tRnarlblMj+jeN5nBhDIcLozlcGE1iUWOKVJ3TiI2/vmJfR8pbz8/bj+ahzWke6voHZ4vVQnp+OvmlbHNisVroHNmZtLw00vLTKLIUub1eiI/zOtS0vDQyCjJse0CWMVCq99Jz4LEDDseKLEXc+r9bCdAHEGQIIshgCyCD9EEE6AMI0Afg7+2Pv94ff29/Gvo3JMhQP75XpRgLWbLzZLnqLNl5gieGtJY1h0JwCQPDiwUFBUlAKIQQ4rLQOLAxozqMYlSHUaWe7xybyp8XLQdUtKko2lQgDgBV9YbiaBr5tWVq/8edG6koQwNoOsL2ALBaIOeA46hi9gH3bVSSVrHQwnCSFoaTXBe02X7como4UdSQY6YmHDM1IT47irk/HmRAdFs2HFGB8o2MOU2/pfIJR7QaLQ38G5R6rk1EG5bdvgywrTfMLcq1B4lpeWmk56fbXp973yykmVMbmQWZHt9foN55vWVOYQ67T+/2uI3H+jzGw1c97HBs3u55/HDgB1sA6e2Pr7cvfjo/fHQ+Dq/9vP3w1fkyoPkAvDSOXxlNxSa8td61ajRz0fbS9zV1x2xRWbTjBA8ObFlNvRLi8lFrAkMhhBCirhjbaTQRfhF8sXkVx7P3gDbdqYyiFIHuKGeKjlKs3u90Pj49nqZBTdF7VXIkQ6O1rQsM7kRqw8ks3J7E3jNJhBcfoq0+nh5BJ2jlHY/OuL/q1yteRKtYaaY/TTP9aQZxLklPEZAKYzv42YLFwqbEm2yPY6YmJBU1wqR6O7Wl0yqM6xFlf18dCUfKCjJLRuhiQ2Od6iSmZ/DqD/n46+Mc6ky/djr3595PZkEmmfmZZBZm2l4XZJJRkEFmQSY5hTlkF2YT4eec8Ce7MNujvpcoLbtqYlYie07v8biNQ08ccnivqiqdPuyEVbXio/PB4GWwPXQGfLx80HvpHY4PajGIm9vf7NDG/rP72ZW8C2+tN3ovPd5ab4fXDse0ehoFNnIKTi/m2VpVZ1uPpUtgWMtZrBaKrcX2h06jw9fbcX1zWl4ap42n7WXMVjPFFtuz2WI+/2wxU2wtZlT7UU5tzNgyg1s63ELjwMY1eXu1RrUFhu+88w75+bZpGEOGDOGqq64qdxtbtmzh999/ByAgIIDHH6/CX1SFEEKIatLQvyHjO49hfOcxpBpNfLVpB+uObSWj6DCF6jEKLIlYsQDgq/OlVVgrh/qqqjLhfxMwmoy0a9COLo260LVxV7o06kJUUFS5R2lKD5pizj3OBVndGvHSNVr0ufsgc49tu4ysPbapqTUgUJtHF9/DdPE97HDcqiqcNoeTWNSI46ZGJJ17bte6GxEGM6Cv8oQjFQkyy1OnMl86w/3Ceff6d8kuzCbHlGMPIrNN2eSacsktOvcw5WIsMhLgHeDURq7J86nF3lpvp4CsyFKERbX9+c0355c67fZCjQIaOQWGGxM38tb6tzzux8ZpG2kU0MjhWL/P+5FZkImX1gudRkdOoYomRAG0oGodntWitqgF1zk26nUMxWc9B3IMPP3bEvsel16K7VmraFEUBUVR0Cgabu9yu9P/d4v/XkxqXioaRWMvV/JQFAUN59+H+4UztNVQh/ppeWmsS1hHaUr7e967aW+aBDlm3N2UuIlTOadQVRUV1f5s+5/qcNxX58uYTmMc6ucU5jB391xUVcWqWm0PrPb3FqvF9hrb62GthnFl9JUObXy//3vWHluLxWrBolrs9SyqxfH53OuSkfcSFquF/l/0p9hajEW1BYElwaDZYrbdzwWmdJ/CCwNfcDj2w4EfeH3d63hqYPOBToHh//b+jz4xfSQwrEr/+9//eOaZZ1AUhY4dO/L0009XqJ0rrriCadOm2RdetmzZkhEjRlRlV4UQQohqFRGg57nhfXiO80lSCs2F7EvZx57kPeQV5TltuJ6UnWRbgwbsPbOXvWf2Mnf3XABCfULp0qgLXRp3oUujLnSO7EyA3vnL//lreRY0zd+ezNG0UOZMGY+h2a3nTxacOR8kZp3LVpr9T7WPLpbQKCpNvFNp4p3K1f57z58oAhYDhoacMTVirDmMXg0iSSxqzHFTIxKLGpFtcf5c4hIymP7zAZcJRyoSZAI1lgkzyBDkFGS5U1qOwdu63MbVMVfbg8cCcwH5RfnkmfPsgV5+ke354j+bQJmB4MV8dM5Z5ctan3kxb63zqHFBcQEFxQVwwXafiouPVbWEOh1TtOlo9LvIssD3HuT4GN5quFPA8O2eb9l3dl/ZlYFujbs5BYYJmQk8s+IZj+oD3Bj7bzKy2juMYO9Mn8u64394VL9RQCOnwDC3KJf3N73vcR+igqKcAsMDKQf45dAvHrdhVa0O27xoFI1tCxkPma1mp2Ne2vKFNaW14a3xpthSzfvH1mLVEhi+8sorgG2riQULFlR4mwlfX1++++47unTpgtVq5ZVXXpHAUAghxGXPoDPQo0kPejTpUer5xMxEDF4GCosLnc5lFGSw+thqVh9bDYCCwvRrp3Nbl9tKbWv6z/s9nmJXatDkE2l7NL4gG6fVYst+mr3/XKC4H7L3Y878B51Sw1+qCs/SjLM0c/7eT3axHyfNDTlZ1ICTReeezQ3Zv/8YaQMaEB7a0KlORT4vUCv3GVej0kadOkV2qtQ2GkGGIDZN20RhceH5h7mQguICTMUmCosLKTAX2M+V9uc8JjiGa2KvwVRsoshSRJGlyOXrIktRqYGh2eL8xd610iLGsrdNu1Bpn2V5kvuXFmSX149/JUPR+TWwG46koQ1MBeePp1Sl9be8MxCKrc5/x8u7l6fFakGjPV9HURS8NF6ltu2q/sV8db4EGYLw0thGkLUarX3Eu2T7GS+NFzqtDp3G9rjYqA6jSp3CXV9UeWC4detWDh8+jKIo3HbbbbRv375S7XXs2JHbbruNefPmsWfPHvbs2UOXLl2qprNCCCFELdQ/tj97Ht7DobRD/HX6L3Yn72bP6T0kZCY4lVVRS01y8uX2LzmTk8HivQoo0aC6HlW8kEdZGjVaCGxle0TdbD98z6xNnEj6m9aGRFrrk2hlSKKNIZFm+mR0ivMXueoW5JVHkNcxOviUsoXHikdAFwR+zcC/Gfg1w+jVhKzDObQ3NOCkuQE5Fj/KSoizeEdSmWUudrlnwtQoGiIDIivVxsh2IxnZbqRHZV0FXz9O+pEiS5F9umFKbh73f7vtXHBhAcWCcu65tBFDtTgKJX8E91wTi8EL+3THC6czomKfWhnm65zsqH9sf5qHNseqnp96acXq+P7co3V4a6f6AfoAejXt5dy3c1MnrVaVQ2eNGAvPBUyqn1NZa3EjFPIJ9NHRoXEQWo0GBcUe8JW8VlAI9XX+HHx1vtzU7qbSp8IqGvt02JLgrbQfFQY0H0CoTygaRWObhntuKq5W0aLRaNAq2vNTdTVepQaS88fNR6PR4KXxspU9V6dkqnDJcZ1WV+oPBeM6jWNcp3FOx8vjkasfqVT9y12V72P4/PPP88Ybb6AoCtu2baN7d+cNZstrx44d9OrVC0VRePHFF+0jkqLyZB9DIYS4fGQWZPLX6b/Yc3qP/ZFrymX3w7udppMOnT2U+Ix4+3vVEo5qjrFtl1HcDIqb4Or34aeHtalQMo6Zq4/wzqrDTsd1iplo7zM015+iuf4kLfQnaa4/RQv9SUK8jOW+Tk3JsfhyqmS00dyA0+ZwzpjDSS4K57Q5grPmUIor+Bt7RT9j4V5d28ewrt2P8Eyd2cdw+/btADRs2LBKgkKAHj160LBhQ1JSUoiLi6uSNoUQQojLTYhPCAOaD2BA8wGAbSTjRPYJp6AwuzDbISgEULRpKNo0YCcAqqoFSyPU4qaoRZ2g6PyXyYpmaRzXM4oP/zzilHzFrOqIN0URb4pyqtPAO4eVUxoSYkmAnIOQc8j2nHsM1JofZbxQoDafQJ/jtPM5Xup5q6qQWhzCaXMYp80RnDGHkVx07tkcwRlzOKnFIRSpzlPWJBNm9Xh5RAeOpeZ5NLW3d2woL4+o3My26iT7MoqaVuWBYck00iuuuKJK2+3SpQsrV67k4MGDVdquEEIIcbnSKBpigmNKPfdM/2f4bNOfZJuPoGhynM4rigW8TqJ4ncRq9bMFh+fkmoo5lHqIlLwU2jdoX+oUutI0CDAwpnvTco1wDO7SkZBmpYxwWIogLwGM8ZB7FIznHrnx54LGS58gQqOoNNRl0FCXQReOuCyXXhxIijmUs+YwzhaHcsYchl5tCifPgk9j28PQ0DZFt46q7P6SnjLotMyZ0ovpPx9gyc7S9zXUaZVyb11yKci+jKKmVXlgmJlp27g1MrJyc88vVtJeRkbF9qgRQggh6osgQxDTek1j499d2HAkFTSZKF7HQXfc9ux1whYYlihu6lDfX+/For8X8c2ubwCI9I+kXYN2tG/QnrYRbWkb0ZaY4JhSk2lU2YiN1hsC29geF7MWQ/4JMB5lzc4tHInfQzPv08Tok4nxPoNBU76Ml9UtzCuHMK8c55HH9e/YX6poyNOEk1IcRpollDwlHP/gprSNbU1AUBNbAiDDuURAXs7rzGqr6thfsiwGnZbXR3fiiSGtWbTjBFuPpVdrMFpdZF9GUdOqPDAsKrL9Y6zVVu0vMCXtlbQvhBBCCPd6x4ay4UgaWENRi0KhqNu5lBZm0J5B8ToJXidtaw4vcGXzMDaePWB/fyb3DGdyz7Dm2Br7Mb2XnlZhrWgb0ZaODTsyqeskoIZGbDRe4B8L/rF0uKYf925Zbb+OgpUGXhk005+miS6Fpt4pNPU+a3vWnaWRd9olSYRTFgUr/tYU/DUpNC/Jy5EL/F1KYS9/2wijTyQmrwYcyvblcLYfKeZgirRhRDVuxoArOhIW1hS8AqCcWSerSlXtL1nR0caIAD0PDmx52QZJuaaKjYpXtF5pamqkV9QOVR4YhoeHc/r0aVJTU6u03bS0NABCQ0vJRy2EEEIIJ67W/IEOLFGolii4aDtCnVZhXI8oDq4JJ9Qn1L6f4sVMxSb2nd3HvrP7+PvM3/bAEGzB4b+ui6JN9AGOnQ0k/rQ/+UWaavlSefH0VRUNZ4vDOVscXmp5DRbu7enDs/0MkHccco/bnvOOU2w8jjU3CW/NpZ+m6lZxLuTmQm48eqAz0FkHlCxlTAdsu5mgarxRDA1A3wAMEeeezz30Eeeew889wmyZWqsokKzsVimXYrSxupUn0PLXV+xrekXrXagufvaibFUeGEZERJCcnMzevXvLLlwOe/fuRVEUGjRoUHZhIYQQQlRozd+Y7rYvqDNGzkBVVVLyUjhw9gD7U/bzT8o/HEo7RGJWIlb1/B5wbSPaOrXz15m/+M/a5wHQKlpiQmJoFNYKk64lW0+1olVYK2JDYtF7VT5ALM/01Z6xETw2shfotEA/h3NewPNLd7N6z16a6s46jDY21qUSqUunkS6VAG1BpftcUxRrEeSftD08quBlCxAvDBbtr8PJsQaxLtHCjtNaUor8sepC6Bwbw7ieMQ6BTWUTp1TVaGNtUZFAyz7iX05XNj+/JrgiI3517bMXnqvy7SruueceZs2ahaIo7Nu3j3bt2lW6zYMHD9K+fXsUReGuu+7iq6++qoKeCpDtKoQQoq7z5Eteid6xoR59ySswF3Ak/QiHUw9zMO0gXRp14ca2NzqU+Xzb57y1/i237ZQEjC3DWvLcNc8RHRxd9g25YPviXfnpq558XgGaPCJ1aQyMMvF0Pz+UghOs37MHL9MpGunSaKRLw/8yCh4ry6oqZFv8sehCCA1piEYfxqFMHVtOqmRbAsgq9ifb4k+WJYAsSwDZFn9yLLZjF2dsLdnG41Jt01AdUycr+ncwxVhInzdWlysBjU6rsPnZwQQYvFwGoiXlXP19kC0yLr1L9f28ygPDRYsWMWHCBPsG93Pnzq10m3fccQfz5s1DURS+++47xo8fXwU9FSCBoRBC1AdVFTSVx4K/FrDgrwUcST9CkaXs/ACb79tMQ/+GDsem/zkdP28/moc2p0VoC5qHNnfamuNiqUZTpROOVOTzcqxjJUCTTwNdBg116UR6pdNIn0GfxmZ6RxaiNZ0mJyMRQ3FK7Z+2Ws0Krd7kWPzIsfiRbfHHyxBCm+golv6dS1ax37kg0o8c+7MfRosfRqsvRosfJlUHKPaAqDLBW0UDqbJUJtCqSN2XR7Sv8I9BlQlGq2p6eGWC84rWrW1rKetMYGg0GmnWrBmZmZloNBqWLVvGiBEjKtze8uXLufnmm7FarYSEhHD8+HECAtz/R0F4TgJDIYSoP6oiaCqvYmsxCRkJHE0/ypH0I7ZH2hGOZx7HbDUDEKAPYPdDu1EuWNtmtpjp+GFHiq2OgVOEX4RDoFjyunFgYzSKhqpUkc/L0zqTZsWx8UgKIVojDXXp57a9SKehVwYRXplE6DIJ98oiwiuTBrpMfDSmUq9X3xVZvexBoq9fKA1CI8A7yLZWUhcEukDbs3eQLRGPLhB0557PvS/Elzvm/EVcQmaZ1/N0VL1EZQOtiow2Tv95f4UD0Zmrj/DOqsMe1y1RMtJ7ofIGW5UJzitatzp/EKiMOhMYArz22mv8+9//BsDHx4e5c+dyyy23lLudZcuWMWnSJPLz81EUhVdffZUXXnihqrtbr0lgKIQQ4lIwW8wkZiVyNP0oOaYcxnUa53D+SNoRhn8z3OP27ul5D89e86zDsYz8DHRaXZmjjJfCqE82sTspy8PSKn6aAsK9sojxzcFPTbMFj15ZhJ8LIiO8sgjzyiLcK7vWbddxOSiyepFn9SHX6kuuxcf22uJLntVAvtXn3LOBPKsPbZs2YkT31rbssF7+tu1DSl7r/M+/1nhVSaBVnhHsnEJzpQLRSbPiKrSusV+rcOZN7X1Bf8sXbFVmyntF61bHNPuqcqm+n1d58hmAxx9/nIULF7Jv3z4KYdyCWgAAMjtJREFUCgoYN24ct9xyC48//jhXXXVVmfW3bt3Ke++9x9KlS1FVFUVR6NixI48//nh1dPeysmbNGubMmcPGjRs5ffo03t7eNG3alOHDh3PvvffSqlWrS91FIYQQokw6rY6WYS1pGVb6VgIB+gAe6/MYCRkJxGfEcyzjGPnmfJftxYbEOh37JO4Tvt75NaE+oTQLaXb+EWx7jgmJwd/bv8ruqTzKlzlSIc/qS16RLxmaaIyF7qafqvhqCgnzyibcK4tQbTZhXtl0CjcxqYsPmFKhMMX2KHmt1u/prADemmK8NUZCMJZduBiI86BRjZ47LAZubqu3BZYWH3ugmW/Vk281UGA1UHDudaGqJ9+qp8Cqx3x8P5zuAl5+GLS+vH6tH0/1bcOyv7PYdDyfLJOCv17nNAL31YZj5QoKwZZIZtGOEzw4sGWlt8ioaOKaymSwrWjdymbNrYuqZcQQICkpid69e5OSkmIP7gCio6Pp3bs3HTp0IDg4GH9/f3Jzc8nKyuLAgQPExcWRmJgIQEnXGjZsSFxcHNHRFV+UfrkzmUzcfffdfPvtty7LGAwG3njjDR599FGP25URQyGEEJcDVVU5m3vWHiRe+HzGeIaFExbSo2kPhzp3f3+3w96LpQn3DadZSDPahLfh1SGvVuctOKjoSFJMmC+J6a4DZFcuHNFxoKpQbARTGpjSzz2n2d//+dcBTLlnCfXKIVhrJNQrhxCvnFq5F2S9omhA6wNaX9uzl+35UJqFlHwNhVY9hao3BVY9hVY9BaqeQqs3Jqs3haoek1VHYckx1ZtWjcL51w3deGn5UbYk5juUMVm97Ws5XSn581WRNZGPD2lV4VFOFbVCdX96qC8jZ268pGsp3alTI4ZgCwC3bNnC2LFj2blzJ2D7Rz0xMZGkpCQWL15car2SYLAkkOzWrRuLFy+u10GhqqrcdtttLF26FAB/f3+mTJlCz549MZlMrFy5kiVLllBYWMhjjz2GTqfjgQceuMS9FkIIIaqOoihEBkQSGRBJn5g+DufyivLw1no71TljPFNmu2n5aaTlp5FVkFVq/cd/edw2uhgcQ3RwNFFBUUQHRxNkCKrwvYC7PSZd02kVru/YiE/XxZf7ehduYeBAUc6tuwsE/+ZOp2duKm3Kq0qAJp8gbS5BXkaCtbkEa40Ea40EaXMJ9jLSItBEpE8BuTkpBNvL5KCv58l2qoxqheI82+MCbbTQpqIzp/+AVw1Am9JP2wJLHSbVmyJVZw8YTVZvGgQFYfo9kMEpRvpEl5SzlTVZdbbyqjdF516XPAqO6NiobUof33TbsYvOF51rx6x6YVa9KCp5bYFFO06gqmqFRkj/+8s/lRpZrauqbcSwhNls5s0332TGjBkOm94rpWyeemFXwsPDeeSRR3jmmWfw9nb+x74+mTdvHpMnTwZs+0SuW7fOaRuQxYsXM378eFRVRa/Xc/DgQZo1a1Zm2zJiKIQQoq4qGWU8nnmcxKxEjmce53jWcft7U/H5hC6DWwzmi1FfONTfnLSZSYsmldp2kCHIFiQGRdsCxuAorom9hkYBjTzuX02PrlRkpKMya86+nNzjommFKgbFRLBXLgGaPIK0uQRq8wjU5tG5gcqk7oHoLDkU5KWzfv8RAjTnzmvOl9Mo1fq1VVwmTFYvrIo3xXiRX2wLGk2qDrPVMYAseS5WtZhVHUXnAkxF602eWWMPOM2qF0VW57I789sRb4qyX9flyHsVq3MjhiV0Oh3//ve/eeqpp1iyZAmrV69mw4YNJCYmUlx8/lcjLy8vYmJi6Nu3L4MGDWLs2LEYDIbq7l6tp6oqL774ov39zJkzS90bcuzYsaxZs4ZPP/0Uk8nE9OnT+frrr2uyq0IIIUStcuEo45XRVzqcs6pWzhjPcDzrOImZiTTwb+BU/3jmcZdtZxdmk12Yzb6z++zHvhnzjVNgOG/3PHKLcmkS2ISooCiaBjUl3DccRVF4eUQHjqXmeZz8oiRZx5juTcsVUI7pXvGss5XZZN2g057LklmSOAUKVQNnzAbOEA6cT0Ry64j26M4l9vAB1qY6B80KVvw1Bfhr8wnQ5BGgzbc9NHkMamFgVEd/MOeAORuKsm3PJe/N2WA22qbNWgor9FmI2sM28myLIwJ07stWxgsnH3AIDCu6BvNyUe0jhu4YjUaMRiMBAQGyBYULGzZsoH///gDExMRw7NgxNJrS03HHx8fTsqVteDsgIIDU1FT0evf/IZARQyGEEKJ0CRkJbEnawvHM4yRlJ5GUlcSJ7BMuk+D8OfVPmoU0czh2/TfXcyjtkMMxg5eBpoFNaRLUhEYBTThwUsffSVqKzQ3B4hhYVnUGx/Kqqn3tyrv1R7Xeo9VsCxLNOSzYtI8lcf/YA04/TQEB5579tfn4agrx1RTipylweG7sbyHQqxCKcyXQrMOePvEIizOH2t/LiGE1koCwbL/++qv99fDhw10GhQAtWrSgdevWHD58GKPRyPr16xkyZEhNdFMIIYSoc2JDY4kNdcx2qqoq6fnp5wPFrBMkZdsCxiaBTZzKnsw+6dRuYXEhRzOOcjTj6PmD/tAysBeNtI85BE69WpjZeXodq44m0CSwCU0CmxDhF3HRSFz17b/WIMBQJSOUEQF6HhzY0uP1Wc6jjVV4jxod6ENBH8rgqyN5ab2mQoEvJfdotYAlD8y5tkCx5HHuvdmUw7cb95OamYGf9sIA04RBU4ivxoSPYsJHU0iw3kxDXytKcZ6tTdXq+X2JKmdWHYcjXa7VrSMuaWAoyrZ371776169epVZvlevXhw+fNheVwJDIYQQouooikK4XzjhfuF0a9zNbVmz1cyEzhM4kXOCU9mnOJF9ghxTjsvyfZq3Zvq1jqMRi/9ezNsb3nY4ptPoiAyIpHFgY4b3bkS60Ze0bD+slhAa+LSlT4smLkfiKqIiU16rgkGn5fXRnXhiSOtyjTaWR5UEvhotaM4l8CmFDpgYa9uL8EsPglylJMhVVbAWgSX/XKKZ/PMJZywFtkdx/rnX+fb3xeY8Vu1NIMuYg4/GhEFThI9iwqAxoVfMtmdNEXrFTICXmQCdGcVaIEFoKczq+VBJp1UY1yPKTenLnwSGtdyhQ+enn8TGOu/RdLELyxw8eLBa+iSEEEKIsnlrvXl+4PMOx3IKcziZc5IT2eeDxVM5tucWoS2c2jiVc8rpmNlq5kT2CU5kOwczX93uPJ11xpYZ6DQ6GgU0olFAIxoHNqahf0N0Ws8WZ1Xr6J0HyjvaWF41EfhWKMhVFNDqbQ/vEI+v5QUM6mTx+P8veyBqNZ8LMgvPBZkFYC20PZcEolYTWEy2MtbCc2ULwWIirzCXxXFH0WHCWyk+F3zaAlC9pghvxWx7aM49K8XoNWZCDSqW4kK0alGtSy5UrJ7/s1yZtbqXCwkMa7nMzEz76/Dw8DLLX1gmKyvLo2vEx8fb5zJfTNYeCiGEEFUn0BBIe0N72jfwLLhoHtqcIS2HkGxMJjknmcyCTLflS8uK+tX2r8gtynU4pqDQwL8BjQIaEekfScOAhkT6R9K1cVd6Nu3p1EZNjN5dKjUZ+FZ3kFuiQv9/aXS2h4uRz7L4AYdOlj/T7uujO1FktnD77Dh2JqTaA0e9UoROKcZbU4y3Yra9Vsxc0diHZ4c1x1spBmsRRUUFzFp3kJPpWefKm8+Vt6A7V89LseCtmGkcqKV/iyC0mLEUm/grKZW8ggK8leKLytreG62+QNWOhF/I1ffv+Ph4WrRw/qGouklgWMsZjUb7ax8fnzLLX1gmJ8f1dBUhhBBC1H4j241kZLuR9vd5RXkk5yRzKucUyTnJJBttr0/lnKKwuBC9l+OXfaPJ6BQUAqjYtvI4m3vW4fikrpOcAsOVR1ay5O8l9gyvkRGR3Nc8kkj/SBr4NyBAf/nni6irgW9NBaIlKjr6agvOe9uD83w3wfkzI9rjfUFw7g3cFWMbIZ1fRmB/y4j2aM/V1QLtzWWPrE7sVT0j4bXRJc1KKsrm7e2N2WwG4MiRI/aso658+eWX3HvvvQAMHTqUlStXui0vWUmFEEKIuiurIIsZW2aQbEzmdM5pThtPk5bvevuJZ/o9w7Te0xyOfbDpA2ZsmeGyjp/Ojwb+DWjg34BI/0imXzvdKVg0W8weT10Vl7dCD4Itd6Ov5c1gWxV1K3PN6lAvs5KKsgUEBJCRYfvVpaCgoMzyF5YJDKzYVAAhhBBC1A3BPsG8OOhFh2OmYhNncs9wOuc0ycZk28ih8Sync0/TtkFbpzbOGM+4vUaeOY+EzAQSMhMAeGP4Gw7nVVWl28xuGLwMNPRvaAsi/RoQ4R9BAz/H1xF+EU6jnuLyUtnR18qMcla0bk2PrNZWEhjWcsHBwfbAMC2t7A1mLywTHBxcXd0SQgghxGVK76UnJjiGmOAYj8qP6jCKlmEtOZN7hjPGM7ag0nia1NxULKrFoWyoTyjeWm+HY0aTkXxzPvnmfDIKMvgn9R+317u/9/081e8ph2P/pPzDsYxjNPRvSLhfOBF+Efh5+3nUf3FpSLB1+ZHAsJZr27Ytx44dAyAhIYGBAwe6LZ+QkOBQVwghhBCiMnpH9aZ3lPOm3lbVSkZ+Bmdyz5CSm8LZ3LNYrBanchevYyxLaWsWfzn0C5/GfepwzFfnaw8SI3wjiPCLsL8f1WGUU4AqhHBPAsNarnPnzvZN7rdt28aUKVPclt+2bZtDXSGEEEKI6qBRNPY9HWnoulxsaCzr7llnT3ZzNvcsqXmppOSm2J7zUkjJTSGrMAuAhv7OjaXmpTodyzfnk5SVRFJWklO/xnQc43DMqlq5cc6NhPiE2PrsG37+2TecML8w+zEJKEV9JYFhLXf99dfzxhu2uforVqzAarWi0WhKLRsfH2/f3D4gIIB+/frVWD+FEEIIIUrjpfGiaVBTmgY1dVvOVGwiLS+NQINzjoRI/0jaN2hPSm4KGQUZWN1sxh7qE4pW45jUJLswm0Nph1zUcBSoD+T+3vdzb697HY7/k/IPSVlJhPqGEuYbRphvGIH6QBRF8ahdIWo7CQxruT59+hAdHU1SUhKJiYksWbKEcePGlVr23Xfftb8ePXo0BoOhprophBBCCFEpei89TYKalHru8b6P83jfxwGwWC1kFmSSmpdqf6TlpZGabxuF9NX5OtVPyys7T0OJHFMOGsX5R/if/vmJL7Z/4XBMp9E5BIphvmGE+diep/SYIplYxWVFAsNaTqPR8Oqrr3LnnXcC8PDDD9O5c2en9YNLlizhs88+A0Cv1/PSSy/VdFeFEEIIIaqdVqO1T2FtRzuP6kQGRPLZzZ+RlpdGWn4a6XnppOXbXpccM5rO7x0d7hfu1EZ6frrTMbPVXOp+kF4aL6cRR4vVQreZ3Qj1CSXUN9TpOcQnhDCfMPuxCL8IDDr5kV/UHAkMLwOTJ0/mhx9+4IcffiAlJYVevXoxZcoUevbsiclkYuXKlSxevJiSLSnffvttmjdvfol7LYQQQghROwToAxjScojbMiVTWdPy00qd9towoCHtItqRnp9Oen66U0bWC4X6hDpNMc0syCS3KJfcolySspNc1DyvtOysf8b/yebEzfZAMtTH9lzyOtgnGC+NfL0XFSN/ci4DiqKwYMECpkyZwoIFCzAajXz44YdO5fR6Pa+99hoPP/zwJeilEEIIIcTlq2Qqq6vprE/2fZIn+z4J2JLZ5BTm2IPEix8GL+eRvtJGHN0J8w1zOhaXFMc3u75xWy/IEGQPFuePm++0L+Tq+NUE6gMJ8Qkh2CeYYEOw05pMUT9JYHiZMBgMfPfdd9x999188803bNq0idOnT+Pt7U3Tpk0ZNmwY06ZNo3Xr1pe6q0IIIYQQdZpG0diCKp9gWoS18KhOs5BmLJ+8nPR82zTWzIJM0vPTySjIICM/g4yCDDILMsnIzyCrMItQn1CnNjIKMsq8TnZhNtmF2STnJDtlWC2yFHHPsnuc6gQZggg2BNsDypLX/Zr1o39sf4eyheZCFEVxCjjF5U8Cw8vMoEGDGDRo0KXuhhBCCCGEKAe9l552DTxbE1lsLbYvEbpQ/9j++Hv724PIkkdGQQZFliKHsqVNZ80uzC71eiXBZGJWosNxP28/p8Dwyx1f8sGmD/DV+dpHJ4MNtpHHkmA5xBBiP9e3WV/ZAuQyIYGhEEIIIYQQtYirdYIj241kZLuRTsdVVSXfnG8PEjMLMrFYnddAmopNtA5vTWZBJlkFWZitZrf9CPEJcTpWElzmm/PJN+dz2njabRv7H90PF81UHf71cPReekJ8QuyjlSWBpf29TzBB+iAi/CJK3cJEVD0JDIUQQgghhLiMKYqCn7cfft5+bveLbBrUlN/u/A2wBZN55jwy8zPJLMy0B4sZBRlkFWSRVZhFhwYdnNrILMj0uF8GL4NTZlVTsYkj6Uc8buPObnfy4qAXHY6tOrKKHw/8SJCPLYgM0gfZXwcaAh2O+en8ZK9JD0lgKIQQQggh/r+9e4+Kqtz7AP4dBIc7XkBABQRBFPAWBzWlQDKRMjUxO1Ye1PR09bLsrbXMN8OsczqmHVPz2OUkZXkFb3m/4QUvoK7SVSooqSCoicpFLuMMPO8fLPc7OMNcmAEG9vez1qyemfntZz/D/vXIj9n72SQzCoUCrm1d4drWFX7wM3m7T57+BP/zxP+gpKpEKibvVd2r87y4qvZhr9AtNeo7nbU+Ho4eOq9l387G7ku7Tdre3s4ex147pnMLkkVHF8FOYQcPRw+piPRQeiDUK1TvPuWAhSEREREREZnE0cERvg6+8HXzbdD2Ho4e+OGFH3CvsraYLK4qlopJ7ef3Ku+hVFWqt0grrio2eX+aGg3clG46r686swpVmiqd178d+y2GBg016zO1FiwMiYiIiIioSSjtlRgSMMSkWCEEakSNzutPBz+NTq6dpEKytKpUKixLqkpQXFmMcnW5tL9HV1BVaVR6i0JA/zeUcsHCkIiIiIiIbI5CoUAbhe49Fgf5D8Ig/0EGt1VXq1GqKkWZqkznveqaakyJnCIVkiWq2v+WVpWivaPugjtywcKQiIiIiIhaFYc2Dujo3BEdnTvqvOfc1hlzh85thlHZNrvmHgARERERERE1LxaGREREREREMsfCkIiIiIiISOZYGBIREREREckcC0MiIiIiIiKZY2FIREREREQkcywMiYiIiIiIZI6FIRERERERkcyxMCQiIiIiIpI5FoZEREREREQyx8KQiIiIiIhI5lgYkk0IDw9HeHh4cw+DbBhzhIxhjpAxzBEyhjlCxrTmHGFhSEREREREJHMsDImIiIiIiGSOhSEREREREZHMsTAkIiIiIiKSORaGREREREREMqcQQojmHgQ1Hzc3N6jVanTv3r1Zx5GbmwsAzT4Osl3METKGOULGMEfIGOYIGdMUOZKbmwsHBweUlZU12j70YWEocz4+PigvL4e/v39zD4WIiIiISPby8vLg4uKCmzdvNul+WRgSERERERHJHK8xJCIiIiIikjkWhkRERERERDLHwpCIiIiIiEjmWBgSERERERHJHAtDIiIiIiIimWNhSEREREREJHMsDImIiIiIiGSOhSE1m/T0dEyaNAnBwcFwcXFB+/bt0bt3b7z77ru4dOlScw+PTDRp0iQoFAqTH8uXLzep3y1btuCFF15AYGAgnJyc4OnpicjISCQnJ6OwsNCsMVZXV2P16tV49tln4efnB6VSCW9vbwwZMgSfffYZiouLG/DJSQiB3NxcbNy4Ee+//z4SEhLg4+NT53g3hC0f+6qqKqxYsQJxcXHo3LkzlEolOnfujLi4OKxYsQJVVVVm9dfaWTNHYmNjzZprtm/fblK/zJHmVV5ejq1bt2L27NmIiYmBr68vlEolXFxcEBAQgNGjR+Obb77B/fv3zeqX80jrYc0c4TxihCBqYlVVVeKVV14RAOp9ODo6iiVLljT3UMkESUlJBo/lo49ly5YZ7O/u3bsiPj7eYB8eHh5i/fr1Jo3v6tWrIioqymB/nTt3FgcPHrTGj0NWZs+ebfR4m8PWj/0vv/wiQkJCDPYXGhoqzp49a9bnbs2smSMxMTFmzTU///yz0T6ZI81r8eLFwtHR0aTj6evrK7Zv3260T84jrYu1c4TziGH2IGpCQgi8/PLLSEtLAwC4urpiypQpiIqKgkqlwp49e5CamoqqqirMmjULDg4OePPNN5t51GSqBQsWICIiwmBM3759632vqqoKI0eOxPHjxwEAXl5emDp1KiIiIlBaWorNmzdj7969KCkpwUsvvQQnJyc899xz9fZXVFSE4cOHIycnBwDg7++PqVOnIiQkBH/++SfWrFmDzMxMFBYW4rnnnkN6ejqioqIa8Mnlqbq6us5zpVKJiIgInDlzxuy+bP3YX758GcOHD8ft27cBAGFhYZg0aRL8/PyQn5+PlJQUnD9/HtnZ2Rg+fDhOnDiBwMBAs38OrY01c0TbV199hU6dOhmMMfb/MnOk+eXk5EjffHh7e+Opp55CVFQUfHx8UF1djXPnzmH16tW4ceMGbty4gdGjRyM1NRVjxozR2x/nkdbH2jmijfOIHlYvNYkM+OGHH6S/dnh5eYnz58/rxGzYsEEoFAoBQCiVSnHlypWmHyiZTPsbw/T0dIv6+uijj6S+evToIQoKCnRiFi1aJMV06tRJlJSU1NvflClTpNghQ4boxNbU1Ijp06dLMeHh4UKj0Vj0GeTkq6++EjNnzhQpKSni7NmzQq1WCyFEg74NsvVjHxcXJ8UmJiYKlUpV532VSiXGjh0rxcTHx5v82Vsza+aI9l/6rfHvAnOk+b3++usiLi5O7NixQ8qNR5WWloqRI0dKPzdPT09RXFysN5bzSOtj7RzhPGIYC0NqMjU1NSIgIEBKZkOncbzxxhtS3KRJk5pwlGQuaxWGJSUlwsXFReorMzOz3tiEhAQpLjk5WW9MTk6OsLOzk/7AcO3aNb1xarVa9O7dW+ovJSWlwZ+Bapn7S7+tH/sDBw5IMd7e3vX+IllSUiK8vb2l2EOHDhn55PLV3IUhc8Q23Llzx6S4+/fvC19fX+nn9v333+vEcB5pnayZI0JwHjGGi89Qk8nIyMC1a9cAAAEBARg3bly9se+8847UTktLg0qlavTxUfPaunUrysvLAQDR0dEYMGBAvbHa+bFmzRq9MevWrUNNTQ0A4IUXXoC/v7/eOHt7e8ycOVN6/tNPP5k9drKMrR977denTZsGd3d3vXHu7u6YNm2a0f6o+TFHbEOHDh1MinNxccGoUaOk52fPntWJ4TzSOlkzR6ytNeYIC0NqMjt37pTaI0aMgJ1d/enXvXt39OjRAwBQVlaGI0eONPr4qHlp58ezzz5rMDYmJgYuLi4Aaq8/0LeKrTn9PfPMM1I7PT0dlZWVJo2ZrMPWj31D+9uxY4fBWGo+zJGWR/uX5IqKCp33OY+QsRyxttaYIywMqcmcO3dOahv6S56+GO1tyXZ9+OGHCAkJgbOzs7SM9NixY/Htt98aXVrZnPywt7dH//799W4LAEII/Pbbbyb35+vri65duwIANBoNzp8/bzCerMuWj31RURFu3rwJAGjTpg0iIyMN9hcZGSn90auwsBB37twxGE8N8/e//x0BAQFwdHSEu7s7goODMWHCBKxfv15nwZtHMUdaJu3/17t162bwfc4j8mQsRx7FeUQXC0NqMtnZ2VLblBWUtGMuXrzYKGMi6zpy5AguX76MyspKVFRUIC8vD5s3b8a0adMQFBSEffv26d1OCFHnL7aW5kdBQYF0P6M2bdrAz8/Pov6o8dj6sdd+3qVLFzg4OBjsq23btujSpUu9/ZF17Nu3D3l5eVCpVCgrK0Nubi7WrVuHv/71rwgLCzO46ilzpOXJzs6u8+/HoyuJch4hYzmiD+cRXbxdBTWZe/fuSW1PT0+j8doxvAG5bXN2dkZcXBwGDBiAwMBAKJVK3L59GydOnEBaWhoqKytx48YNjBgxAhs2bEBiYmKd7e/fvw+1Wi09tzQ/tHPNw8PD6ARrrD9qPLZ+7M2dtx7G5efn6+2PLNOuXTsMGzYMUVFR6Nq1K+zt7XHjxg0cOXIE27Ztg0ajQU5ODqKjo3HgwAEMHjxYpw/mSMtSXV2NqVOnStdyJSYmIiwsrE4M5xF5MyVHtHEeqR8LQ2oyZWVlUtvJyclovHZMaWlpo4yJLPf2229j+fLlcHV11XnvzTffxMKFCzFhwgQcPnwYNTU1mDhxIgYOHCidUgHUzQ3A8vwwN9eM9UeNx9aPPXPJdvzjH/9AZGQklEqlznszZ85EdnY2xo0bh99++w1VVVVITEzE5cuXpWvJHmKOtCwzZsxARkYGAKBTp05YunSpTgznEXkzJUce4jxiGE8lJSKL/OUvf9FbFD7k6+uLHTt2IDQ0FABQWVmJf/3rX001PCJqJQYPHqz3l7mHQkNDsW/fPumv7Tdv3sTKlSubanjUCD799FOsWLECQO2pc+vXr0fnzp2beVRkS8zNEc4jhrEwpCbj5uYmtU1Z9VE7pr4le6llcHFxwf/+7/9Kz7dt21bnfe3cACzPD3NzzVh/1Hhs/dgzl1oWHx+fOsvCPzrXAMyRluLzzz/HnDlzAAAODg7YsGEDYmNj9cZyHpEnc3LEHHKeR1gYUpNp166d1C4qKjIarx2jvS21THFxcVI7Ly+vzlLSrq6usLf//zPbLc0P7eclJSXQaDQW9UeNx9aPvbnzlrH+qPFpzzUXLlzQeZ85YvsWLVok3Wuwbdu2SEtLw+jRo+uN5zwiP+bmiLnkOo+wMKQm07NnT6l95coVo/HaMdrbUsvk5eVV57n2hdIKhUK6byVgeX507dpVOr21uroaeXl5FvVHjcfWj73284KCgjoLXOijVqtRUFBQb3/U+LTnGn0LMjBHbNs///lPvPvuuwAApVKJLVu2GF1hkvOIvDQkR8wl13mEhSE1mT59+kjtrKwso/HaMdrbUsv06F/A2rdvX+e5Ofmh0Wjwyy+/6N0WqP0lISIiwuT+bty4gevXrwOoXXba0GpmZH22fOy9vLzg4+MDoPYff0PLlwPA6dOnpZXxOnfujI4dOxqMJ+vTnmsenWcA5ogtmz9/Pt5//30AtQtrbNu2DQkJCSZty3lEHizJEXPIdR5hYUhN5plnnpHau3fvlhJan9zcXOTk5ACoPe/6iSeeaPTxUeNKT0+X2n5+fjqrbmnnx86dOw32dfjwYZSXlwMAQkJCEBISohNjTn/a7w8dOtTkFcHIOmz92De0P+3tqOlozzUPF716FHPE9sydOxfJyckAam+BtH37dgwfPtzk7TmPtH6W5og5ZDuPCKImUl1dLfz9/QUAAUCsX7++3tg33nhDiktKSmq6QVKjKC8vF7169ZKO6ZtvvqkTU1xcLFxcXKSYzMzMevtLSEiQ4j788EO9MdnZ2cLOzk4AEEqlUuTl5emNU6vVonfv3lJ/q1atashHJC0Pf5am/hNj68d+//79Uoy3t7coLS3VG1dSUiK8vb2l2PT0dEMfW9bMzRFT3bp1S3h5eUl9L1y4UG8cc8S2vPfee9LPxNXVVRw+fNjsPjiPtG7WyBFTyXkeYWFITSolJUVK5E6dOokLFy7oxGzcuFEoFArpf7Tc3NxmGCmZIiUlRezcuVNUV1fXG3Pz5k0RFxcnHXdHR0dx7do1vbHJyclSXGhoqCgoKNCJWbRokRTj6ekpiouL6933pEmTpNjo6GhRUlJS5/2amhoxY8YMKaZXr15CrVab+OmpPg35pd/Wj/3QoUOl2MTERKFSqeq8r1KpRGJiohQzbNgwkz+7HJmbI0uWLBEZGRkGYy5duiT69u1r0i9WQjBHbMXs2bOln4m7u7s4duxYg/viPNI6WStHOI8YpxBCCBA1ESEExo4diy1btgCoPU10ypQpiIqKgkqlwp49e7Bx40Y8TMulS5di+vTpzThiMmTWrFn44osv4OPjg+HDh6NPnz7w8fGBUqlEUVERTpw4gdTUVGkFUjs7O6xduxbjx4/X219lZSXi4uJw8uRJALXn3E+bNg0REREoLS3F5s2bsWfPHgC15+inpqZizJgx9Y7v9u3bGDx4MC5fvgwACAgIwNSpUxEcHIzbt29jzZo10r6cnZ1x8OBBDBw40Fo/nlavuLgYixYt0nn9k08+kdpz587Vef/jjz/Wec3Wj/2lS5cwePBg6bqTsLAwTJ48GX5+fsjPz8eqVatw/vx5ALU3WD5+/Di6d+9eb39yYa0cGTNmDLZu3YrAwEAMGzYMERER8PLygr29PW7evIkjR45g69at0oINTk5O2LNnj8HLEJgjze+DDz6oc6znzJmDAQMGGN3O09MT0dHROq9zHml9rJkjnEdMYNUyk8gElZWVYsKECXX+YvzoQ6lUisWLFzf3UMmImTNnGjyO2g8/Pz+xd+9eo33euXNHPP300wb7cnd3F2vWrDFpjH/88YeIjIw02J+Pj4/Yv3+/pT8O2bly5YrJx1/7UR9bP/ZnzpwR3bt3N9hfSEiI+OWXX0zqTw6slSOjR482edvw8HBx+vRpk8bHHGleMTExDcqPmJiYevvkPNK6WDNHOI8Yx8KQms2BAwfExIkTRVBQkHBychIeHh4iPDxczJ49W2RnZzf38MgEBQUF4qeffhIzZswQ0dHRIjg4WLRv317Y29uLdu3aiZ49e4pXXnlFrFu3Tjx48MCsvjdt2iQSExOFv7+/UCqVokOHDqJ///7igw8+ENevXzerL41GI1JSUkRCQoLo0qWLaNu2rfDy8hKDBg0Sn376qbh7965Z/VEtaxeGD9nysa+oqBDLli0TsbGxwsfHRzg4OAgfHx8RGxsrli1bJioqKszqr7WzVo5cvnxZfPfdd+K1114TAwcOFIGBgcLd3V3Y29uLDh06iN69e4tXX31VbN++3eCp7fowR5pPYxSGD3EeaR2smSOcR4zjqaREREREREQyx9tVEBERERERyRwLQyIiIiIiIpljYUhERERERCRzLAyJiIiIiIhkjoUhERERERGRzLEwJCIiIiIikjkWhkRERERERDLHwpCIiIiIiEjmWBgSERERERHJHAtDIiIiIiIimWNhSEREREREJHMsDImIiIiIiGSOhSEREREREZHMsTAkIiJ6RHJyMhQKBRQKBZKTk5t7OC3CmTNn0KZNGygUCnz++eeNtp/du3dLx+ann35qtP0QEckNC0MiImqxrl69KhUJ1nqwEDSfEAJvv/02ampqEBAQgLfeeqvR9jVixAjExsYCAN577z3cv3+/0fZFRCQnLAyJiIjIIhs3bsTJkycBAHPmzIFSqWzU/c2bNw8AUFhYiMWLFzfqvoiI5MK+uQdARETUUO7u7ka/ncrKysKpU6cAAJ07d8bzzz9vMH7AgAHIysqy2hhbu+rqanz44YcAAG9vb0yaNKnR9zl06FDpOH3++eeYPn06OnTo0Oj7JSJqzRRCCNHcgyAiImosycnJmD9/PgAgJiYGhw4dat4BtTJpaWkYN24cAGDu3Ln4+OOPm2S/KSkpmDx5MgDg448/xty5c5tkv0RErRVPJSUiIqIGW7JkidR+9dVXm2y/48ePh5ubGwDgyy+/hFqtbrJ9ExG1RiwMiYiIqEFycnKQkZEBoPYU3MDAwCbbt7OzM0aOHAkAuHHjBnbv3t1k+yYiao1YGBIRET3ClNtVpKSkSDEPr6urqanBmjVrkJCQAD8/PyiVSnh7eyMxMREnTpzQ6ePBgwdYvXo1nnrqKfj5+cHR0RH+/v5ISkrChQsXzBqzWq3G6tWrMX78eAQFBcHNzQ0uLi4IDAzEhAkTsHnzZlj76hHt20WMGTPG5HH++OOPGDt2LIKCguDq6gp7e3u4ubkhODgY8fHxmDdvnknXeWpfL/rjjz+aPX4iIvp/XHyGiIjICoqKivDiiy/i4MGDdV7/888/sWnTJmzevBn//e9/peviLl++jFGjRukUgPn5+fjhhx+wbt06rF+/3qSC69ChQ5g6dSpyc3N13rt69SquXr2KdevWYdCgQUhNTUWXLl0a/kG1/Pzzz1I7Li7OaHxOTg7GjBmjt+i9f/8+7t+/j9zcXOzduxcLFizApUuXEBwcXG9/Q4cOhUKhgBACe/bsgUajgb09f7UhImoIzp5EREQW0mg0GDt2LI4ePQpHR0fExMTA398fd+/exYEDB1BcXAwhBKZOnYqQkBD06NEDcXFxyM/Ph7u7O5588kn4+vri1q1b2L9/PyoqKvDgwQO89NJL+P333w2eorlx40a8/PLL0jV2Tk5OGDRoELp16wY7Ozvk5OTgxIkT0Gg0OHnyJB5//HGcOnUK3t7eFn3moqIi/Prrr9I+IyMjDcaXlZVh2LBhyM/PBwDY2dmhf//+6NWrF1xdXVFRUYGCggKcPXsWRUVFJo3B09MTPXv2xIULF1BSUoKsrCwMHjzYos9FRCRXLAyJiIgslJqaCpVKhdGjR+Prr79Gp06dpPfu3buH0aNH4+jRo6ipqcG8efPg4eGB/Px8vP7661i4cKG0iAoAXL9+HcOHD8eFCxdQWVmJBQsW4LvvvtO7399//x1JSUlQq9VQKBR45513MHfuXLRr165O3B9//IGkpCRkZGQgPz8fkydPxs6dOy36zFlZWdKpqWFhYUa/qfvuu++kojAsLAybNm1CaGioTpwQAqdPn8aqVatMuh9iv379pG8gWRgSETUcrzEkIiKykEqlQmxsLNLS0uoUhQDQvn17rF69Gm3atAEApKenY8uWLUhKSsJ//vOfOkUhAHTt2hXffPON9Dw1NRUajUbvfmfMmIHKykoAwOLFi/HZZ5/pFIUAEBQUhN27dyMsLAwAsGvXLmRmZjb48wLAuXPnpHbPnj2Nxh89elRqf/HFF3qLQgBQKBSIiorCihUr4OfnZ7TfXr16Se2zZ88ajSciIv1YGBIREVnBv//9b6n4e1RAQECdb7KUSiUWLlxYb19DhgyRiqKysjJcvHhRJ+bs2bPS9Yz9+/fHrFmzDI7PxcUFH3zwgfRce+GYhrhy5YrU7tq1q9H40tJSqe3l5WXRvrVpXy959epVq/VLRCQ3LAyJiIgs1L17d/Tr189gTO/evaX2E088ofPN4qMiIiKktnYR9pD2qaATJkyAQqEwOk7tBWIe3maioW7duiW1O3bsaDRe+9u/lStXWrRvbZ6enlL75s2bVuuXiEhueI0hERGRhbSLuPq0b99eaoeHhxuN79Chg9TW/rbtIe3bX6Snp+PatWtG+9S+XcXD6/0aqry8XGo7OzsbjR8/frx0reTKlStx5swZJCUlIT4+3uDKo8Zo71t7TEREZB4WhkRERBby8PAwGqO9OIu58Q9XHNVWWFgotXft2mW0v0fdu3fP7G3qY8r9EePj4zF9+nQsW7YMAHDq1CmcOnUKAODt7Y3o6GjExsZizJgxJp2aas6+iYjIOJ5KSkREZCFTTuO0JF6fkpISi7avrq62aHsXFxep/XABHGOWLl2KTZs2YcCAAXVev3XrFtLS0jB9+nT4+/tj3LhxyMvLM6lP7X1rj4mIiMzDwpCIiKgF0i6CNm3aBCGE2Q9L+Pj4SG1T7zsIAM8//zwyMzNx7do1fP/993jttdek1VKB2m8A09LS8NhjjyEnJ8dof7dv39Y7JiIiMg8LQyIiohZI+wb1zbHoSmBgoNS+fv262dv7+/vjb3/7G1auXInff/8deXl5mD9/vnTN4J07dzB79myj/RQUFEjtbt26mT0OIiKqxcKQiIioBRo4cKDUPnbsWJPvv0+fPlI7Ozvb4v78/Pwwb948fP3119Jre/fuhUqlMrjdw5vbA0Dfvn0tHgcRkVyxMCQiImqBRo4cKbU3bdpU5/YRTSEqKkq6VvL8+fPQaDRW6XfUqFFSW61W4+7duwbjtW9q/+i1i0REZDoWhkRERC3QgAEDEBsbC6B2AZaJEyfiwYMHJm374MEDi1cl9fT0lO7dWFlZiTNnzhiMN/U6RO3baNjZ2Rm8R2JRUREuXrwIoHalVxaGREQNx8KQiIiohVq2bBlcXV0BAPv27cOTTz6JzMzMeuNzcnKwYMECdOvWzSqnnz733HNS++DBgwZjH3/8cbz00kvYtWtXvQVsTk4OkpKSpOdPPfUU2rZtW2+f6enp0iI68fHxdW7xQURE5uEMSkRE1EJFRERg7dq1ePHFF1FRUYHMzEwMGjQI3bt3x2OPPYYOHTqgqqoKf/75J86dO1dnoRZrePnll/HRRx8BALZs2YI5c+bUG6tWq7F27VqsXbsWTk5O6NOnD4KCguDu7o579+7hjz/+wOnTp6V4JycnLFq0yOD+N2/eXGcsRETUcCwMiYiIWrCRI0fi+PHjePXVV6XTOXNzc5Gbm1vvNt26dTPrJvL16dGjB6Kjo5GRkYFTp07hypUrdVYr1ebm5ia1KysrkZmZWe+3m4GBgfjxxx/rLHDzqMrKSuzYsQNA7W0qEhISLPgkRETEwpCIiKiF69u3L06fPo29e/diy5YtOHbsGAoLC1FcXAylUgkvLy+EhoZi4MCBiI+Px+OPPy4tHGOpWbNmISMjA0IIfPvtt/jkk0/0xv366684efIk0tPTkZWVhezsbBQWFqKiogLOzs7w8fFBv379MGrUKIwfPx5KpdLgfjds2IDS0lIAwFtvvQUHBwerfB4iIrlSCEvvcEtERESyVVNTg/DwcFy8eBHe3t64evUqHB0dG32/AwcORFZWFtzc3HDlyhWDi9QQEZFxXHyGiIiIGszOzg7z588HANy6dQspKSmNvs9Dhw4hKysLADB79mwWhUREVsBvDImIiMgiQggMHjwYJ0+eREBAALKzs42eCmqJuLg4pKenw9fXF9nZ2XWuXyQioobhN4ZERERkEYVCgeXLl8POzg7Xrl3Dl19+2Wj72rNnD9LT0wEAn332GYtCIiIr4TeGREREREREMsdvDImIiIiIiGSOhSEREREREZHMsTAkIiIiIiKSORaGREREREREMsfCkIiIiIiISOZYGBIREREREckcC0MiIiIiIiKZY2FIREREREQkcywMiYiIiIiIZI6FIRERERERkcyxMCQiIiIiIpI5FoZEREREREQyx8KQiIiIiIhI5lgYEhERERERyRwLQyIiIiIiIpljYUhERERERCRzLAyJiIiIiIhk7v8AwmizjwVd7LgAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Conclusion\n",
+        "**Discussion:** How do the results of the linear and nonlinear regression compare? Which do you think is better?\n",
+        "\n",
+        "**Answer:** The nonlinear regression estimates a greater initial concentration and a greater rate of decay. Based on its residual distribution, it appears to be a modestly more reliable model than that given by the linear regression. It also visually appears to better capture the early data points."
+      ],
+      "metadata": {
+        "id": "zt6LqMH1N_dk"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## References\n",
+        "\n",
+        "1. **Deriving Stoke's Settling Velocity from Stoke's Law**:\n",
+        "\n",
+        "Cadence CFD Solutions, Cadence CFD Solutions, et al. “Deriving Stoke’s Law for Settling Velocity.” Cadence, 13 Dec. 2022, https://resources.system-analysis.cadence.com/blog/msa2022-deriving-stokes-law-for-settling-velocity"
+      ],
+      "metadata": {
+        "id": "d-wEieOjZAeJ"
+      }
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file