diff --git a/Classification Models/Market Trend Classification Model/Market Trend Classification Model.ipynb b/Classification Models/Market Trend Classification Model/Market Trend Classification Model.ipynb new file mode 100644 index 000000000..9c0bb8883 --- /dev/null +++ b/Classification Models/Market Trend Classification Model/Market Trend Classification Model.ipynb @@ -0,0 +1,471 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "141dc98c-e1f3-40fd-98b5-83299444940d", + "metadata": {}, + "source": [ + "# Market Trend Classification Model\n", + "\n", + "## Overview\n", + "The Market Trend Classification Model aims to identify different market conditions (regimes) in historical stock price data using clustering techniques. By classifying these regimes, the project provides insights into periods of market behavior, such as bull, bear, or neutral phases, helping investors or financial analysts understand market trends and develop effective strategies.\n" + ] + }, + { + "cell_type": "markdown", + "id": "49439176-3972-44de-9788-4a389d912e40", + "metadata": {}, + "source": [ + "# Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cbbda166-cb81-431e-acc7-f8866f9aae7e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in c:\\users\\asus\\anaconda3\\lib\\site-packages (2.1.4)\n", + "Requirement already satisfied: numpy in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.26.4)\n", + "Requirement already satisfied: matplotlib in c:\\users\\asus\\anaconda3\\lib\\site-packages (3.8.0)\n", + "Requirement already satisfied: scikit-learn in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.2.2)\n", + "Requirement already satisfied: yfinance in c:\\users\\asus\\anaconda3\\lib\\site-packages (0.2.44)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n", + "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n", + "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (10.2.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n", + "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn) (1.11.4)\n", + "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n", + "Requirement already satisfied: requests>=2.31 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from yfinance) (2.31.0)\n", + "Requirement already satisfied: multitasking>=0.0.7 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from yfinance) (0.0.11)\n", + "Requirement already satisfied: lxml>=4.9.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from yfinance) (4.9.3)\n", + "Requirement already satisfied: platformdirs>=2.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from yfinance) (3.10.0)\n", + "Requirement already satisfied: frozendict>=2.3.4 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from yfinance) (2.4.4)\n", + "Requirement already satisfied: peewee>=3.16.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from yfinance) (3.17.6)\n", + "Requirement already satisfied: beautifulsoup4>=4.11.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from yfinance) (4.12.2)\n", + "Requirement already satisfied: html5lib>=1.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from yfinance) (1.1)\n", + "Requirement already satisfied: soupsieve>1.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5)\n", + "Requirement already satisfied: six>=1.9 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n", + "Requirement already satisfied: webencodings in c:\\users\\asus\\anaconda3\\lib\\site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2.0.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (3.4)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2024.2.2)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install pandas numpy matplotlib scikit-learn yfinance\n" + ] + }, + { + "cell_type": "markdown", + "id": "76abe973-63cc-4a65-ab3e-b3a1d2e32fe8", + "metadata": {}, + "source": [ + "## Steps Involved\n", + "### 1: **Import Libraries**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f07cd4fc-d510-4ce2-ad19-78c369a7aad4", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import yfinance as yf\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n" + ] + }, + { + "cell_type": "markdown", + "id": "4d2a2991-9ffe-4cdb-88e2-fb74eb3e66c9", + "metadata": {}, + "source": [ + "### 2. **Data Collection**\n", + "Historical stock price data is collected using Yahoo Finance (via the `yfinance` library). In this example, the data of the S&P 500 index is fetched to observe trends and detect market regimes over time.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cbf3670a-ca22-41a2-91c0-c9f4a2f92869", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[*********************100%***********************] 1 of 1 completed\n" + ] + } + ], + "source": [ + "# Download historical stock data for a selected stock (e.g., S&P 500: ^GSPC)\n", + "stock_data = yf.download('^GSPC', start='2010-01-01', end='2024-01-01')" + ] + }, + { + "cell_type": "markdown", + "id": "3b2ea549-dab7-4d37-b9f4-6ff74e7746e3", + "metadata": {}, + "source": [ + "### 3. **Data Preprocessing**\n", + "- **Daily Returns**: Calculate the daily percentage change in stock prices to observe the day-to-day performance.\n", + "- **Feature Engineering**: Compute additional features such as moving averages (50-day and 200-day) and volatility (standard deviation of returns) to capture market behavior over time.\n", + "- **Normalization**: Standardize these features using the `StandardScaler` for uniform scaling before applying clustering algorithms.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8b09ddac-6d12-4adc-857d-86a17e6a0d6d", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate daily returns\n", + "stock_data['Return'] = stock_data['Adj Close'].pct_change()\n", + "\n", + "# Calculate moving averages and volatility as features\n", + "stock_data['MA_50'] = stock_data['Adj Close'].rolling(window=50).mean()\n", + "stock_data['MA_200'] = stock_data['Adj Close'].rolling(window=200).mean()\n", + "stock_data['Volatility'] = stock_data['Return'].rolling(window=50).std()\n", + "\n", + "# Drop NaN values created by rolling calculations\n", + "stock_data.dropna(inplace=True)\n", + "\n", + "# Selecting relevant features for clustering\n", + "features = stock_data[['Return', 'MA_50', 'MA_200', 'Volatility']]\n", + "\n", + "# Normalize the features\n", + "scaler = StandardScaler()\n", + "features_scaled = scaler.fit_transform(features)" + ] + }, + { + "cell_type": "markdown", + "id": "a731b0f3-4cac-4c85-afcd-a1e952d95f21", + "metadata": {}, + "source": [ + "### 4. **Clustering with K-Means**\n", + "The K-means clustering algorithm is applied to classify the market data into distinct regimes:\n", + "- **Optimal Number of Clusters**: The Elbow Method is used to determine the ideal number of clusters (`k`). By plotting the Sum of Squared Errors (SSE) against the number of clusters, the elbow point helps identify the best value of `k`.\n", + "- **Clustering**: The market data is then clustered based on the selected features, and each data point is assigned a regime label.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1ea3aeb3-5d68-40aa-a29f-eef45197f9c7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Determine the optimal number of clusters using the elbow method\n", + "sse = []\n", + "for k in range(2, 10):\n", + " kmeans = KMeans(n_clusters=k, random_state=42)\n", + " kmeans.fit(features_scaled)\n", + " sse.append(kmeans.inertia_)\n", + "\n", + "# Plot the elbow curve\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(range(2, 10), sse, marker='o')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('SSE (Sum of Squared Errors)')\n", + "plt.title('Elbow Method for Optimal K')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3f25d290-7b74-4028-a51e-41fa9e14a0fb", + "metadata": {}, + "source": [ + "### 5 **Applying K-Means and Analyzing Clusters**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2bd5ec18-a000-4401-81f2-5414c8f604db", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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5hgsuuKDNcZqmYbPZUo1pIfkv+//4xz9267o33ngjmZmZXHnllQSDQe666y4g+UXYZrOxefNmzjzzzHbP0bTCoGkj3s5wOp08/PDDTJ8+ndtvv53HH3887TmMHj2afv368c9//jPV7Bpg27ZtfPrpp6nqnO5y2mmnsWjRIhKJBFOmTEnrGMMwmDJlCmPGjGHhwoUsX758t0Ok+fPn8+9//5tzzjkn1Wz9tNNO409/+hOlpaUtlgvtyul0tqhgazxHOvd10kknYRgGjz76KEcddVS3Xadxt66FCxdy+OGHpx7/5z//iWmabR63q5dffpk///nPqbAmEAiwePFijj322GZ/rvLz8/npT3/KI488QiwW4/TTT0+9vu258soreeqpp/j1r3/NBx980GKHNqUUr776Kj/5yU9aPT4vLw+Xy8Xq1aubPf6vf/2r3esOGjSI3/72t7z33nt88sknqcf39HXvLlOmTMHpdPL88883263u888/Z+vWrRIiCSGEACREEkIIIdrUdClXW0499VT+8pe/cM455zB37lyqq6u5995706osacvvfvc7vF4vc+fOpaGhgQcffJAhQ4bwxz/+kRtuuIHCwkJmzpxJr1692LFjB19++SUejye1xfeECRMAuPvuu/nhD3+IYRhMnDgxtRwsXdOmTeOUU05h/vz5/P73v2fo0KFpzUHXdW699VYuueQSzjrrLH7xi19QV1fHrbfeSn5+Prrevavpf/azn7Fw4UJOOeUUfve73/H9738fu91OSUkJH3zwAT/60Y/4yU9+wmOPPcb777/PqaeeyqBBg4hEIjz11FMAnHDCCR1eJxwO8/nnn6d+XVhYyKuvvsrrr7/OtGnTeOyxx1Jjjz76aObOncucOXNYtmwZxx13HB6Ph7KyMj7++GMmTJjApZdeCiTfv5dffplHH32Uww8/PFUllu59DRkyhOuvv57bbruNcDjM//t//w+fz8e6deuoqqpq9jnZk+uMHTuWn//85zzwwAPY7XZOOOEE1q5dy7333ktWVlba75dhGJx44olcddVVWJbF3XffTX19fWqeTf3ud79LBSzz589P6/xDhw5l0aJFzJ49m0mTJvHb3/6WyZMnA7Bu3TqeeuoplFJthkiNvayeeuophg8fzqGHHsqXX37Js88+22yc3+9nxowZnHPOOYwZM4bMzEyWLl3KkiVLmoUye/q6d5ecnByuuuoq7rrrLnr16sVPfvITSkpK9tqfWyGEEPuJnu3rLYQQQuwbmu7O1p7Wdmd76qmn1OjRo5XT6VTDhg1Td911l3ryyScVoLZs2ZIaN3jwYHXqqae2el527s7W1HPPPadsNpuaM2eOSiQSSimlXn31VTVjxgyVlZWlnE6nGjx4sDrrrLPUu+++mzouGo2qiy66SPXp00dpmtZiHrtq3J2tNWvWrFG6rqs5c+akHktnDkop9be//U2NGDFCORwONWrUKPXUU0+pH/3oR2ry5Mkt7r213dl2fS9uvvlmBajKysoO5x+Px9W9996rDj30UOVyuZTX61VjxoxRl1xyifr222+VUsldp37yk5+owYMHK6fTqXJzc9W0adPUa6+91uZr1WjatGkKSP14PB41bNgwddZZZ6kXXngh9X7t6qmnnlJTpkxRHo9Hud1uNXz4cHX++eerZcuWpcbU1NSos846S2VnZ6fev87cV6O///3v6nvf+15q3OTJk5vtMNYV14lGo+rqq69Wffv2Te2C9tlnn7XYIaw1jbue3X333erWW29VAwYMUA6HQ02ePFm99dZbbR43ZMgQNXbs2HbP3ZrNmzerX//612rEiBHK6XQqt9utxo0bp6666qpmfz523Z1NKaX8fr+66KKLVF5envJ4POr0009XRUVFzT67kUhE/epXv1ITJ05UWVlZyu12q9GjR6ubb75ZBYPB1Lm64nVv7++StnZne+GFF5qNa23XOcuy1O233556LyZOnKhef/11deihh6qf/OQnab7SQgghDmSaUrtsHyKEEEII0Q3q6uoYNWoUP/7xj/nb3/7W09MR+6HVq1dz6KGH8vDDD/PrX/+6p6dzUNiyZQtjxozh5ptv5vrrr+/p6QghhOhhEiIJIYQQosuVl5dzxx13MGPGDHJzc9m6dSv3338/33zzDcuWLeOQQw7p6SmK/cjmzZvZunUr119/Pdu2bWPTpk1kZGT09LQOOKtWreK5555j6tSpZGVlsWHDBu655x7q6+tZu3at7NImhBBCeiIJIYQQous5nU6Kior49a9/TU1NDRkZGRx55JE89thjEiCJTrvtttv4xz/+wdixY3nhhRckQOomHo+HZcuW8eSTT1JXV4fP52P69OnccccdEiAJIYQApBJJCCGEEEIIIYQQQqRBtlkQQgghhBBCCCGEEB2SEEkIIYQQQgghhBBCdEhCJCGEEEIIIYQQQgjRIWmsnSbLsti+fTuZmZlomtbT0xFCCCGEEEIIIYToEkopAoEA/fv3R9fbrjeSEClN27dvZ+DAgT09DSGEEEIIIYQQQohuUVxczIABA9p8XkKkNGVmZgLJFzQrK6uHZyOEEEIIIYQQQgjRNerr6xk4cGAq+2iLhEhpalzClpWVJSGSEEIIIYQQQgghDjgdte+RxtpCCCGEEEIIIYQQokMSIgkhhBBCCCGEEEKIDkmIJIQQQgghhBBCCCE6JD2RupBSCtM0SSQSPT0VsR8yDAObzdbhGlQhhBBCCCGEEKInSIjURWKxGGVlZYRCoZ6eitiPZWRkkJ+fj8Ph6OmpCCGEEEIIIYQQzUiI1AUsy2LLli0YhkH//v1xOBxSTSI6RSlFLBajsrKSLVu2MHLkSHRdVpsKIYQQQgghhNh3SIjUBWKxGJZlMXDgQDIyMnp6OmI/5Xa7sdvtbN26lVgshsvl6ukpCSGEEEIIIYQQKVLq0IWkckTsKfkMCSGEEEIIIYTYV8k3ViGEEEIIIYQQQgjRIQmRhBBCCCGEEEIIIUSHJEQSohMWLFhAdnZ2T09DCCGEEEIIIYTY6yREOohVVFRwySWXMGjQIJxOJ/369ePkk0/ms88+azbumWeeYcyYMbhcLoYMGcJtt93W4lxFRUVompb66dWrF8cddxwfffRRm9ff9ZjGnyVLljQb99FHH3H44YfjcrkYNmwYjz32WItzvfTSS4wbNw6n08m4ceN45ZVX2r33Dz/8sNk1+/Tpww9/+ENWrVrV7nGzZ89m48aN7Y4RQgghhBBCCCEORBIiHcTOPPNMVq1axdNPP83GjRt57bXXmD59OjU1NakxRUVFnH/++fz4xz9m/fr1/POf/2To0KFtnvPdd9+lrKyMjz76iKysLE455RS2bNnS7jwaj2n8+cEPfpB6bsuWLZxyyikce+yxrFixguuvv57LL7+cl156KTXms88+Y/bs2Zx33nmsWrWK8847j7PPPpsvvviiw9dgw4YNlJWV8cYbb1BbW8vMmTPx+/2tjo3H47jdbvr27dvheYUQQgghhBBCiAONppRSPT2J/UF9fT0+nw+/309WVlaz5yKRCFu2bGHo0KG4XC6UUoTjiR6Zp9tuoGlah+Pq6uro1asXH374IdOmTWtz3NatWxk2bBjr169n1KhRbY4rKipi6NChrFixgkmTJgFQWlrKgAEDeOyxx7jkkkvSOmZX1113Ha+99hrr169PPfarX/2KVatWpSqmZs+eTX19Pf/+979TY2bOnEmvXr147rnnWj3vhx9+yIwZM6itrU0tT/vkk0845phjWLJkCaNHj2bo0KE8//zzPPLII3z++ec8+uijaJrGFVdcQV1dXepcr732Gn/84x9Zu3YtXq+X4447jpdffhmAWCzGjTfeyMKFC6mrq2P8+PHcfffdTJ8+vdV57fpZEkIIIYQQQgghult7mUdTtr04p4NGOJ5g3E1v9ci11/3xZDIcHb+tXq8Xr9fLq6++ypFHHonT6Wx1XEFBAUcccQS//e1vee211zoVbGRkZADJCp72nHHGGUQiEUaOHMmVV17JWWedlXrus88+46STTmo2/uSTT+bJJ58kHo9jt9v57LPPuPLKK1uMeeCBB9KeK4Db7W4x3+uuu4777ruP+fPn43Q6efvtt5sd88YbbzBr1ixuuOEG/vGPfxCLxXjjjTdSz8+ZM4eioiIWLVpE//79eeWVV5g5cyZr1qxh5MiRnZqfEEIIIYQQQgjRk2Q520HKZrOxYMECnn76abKzszn66KO5/vrrWb16dbNxF198MUophg0bxsyZM6mvr089d9ppp3HZZZe1ev5gMMgf/vAHDMNos9LJ6/Xyl7/8hRdffJE333yT448/ntmzZ/PMM8+kxpSXl5OXl9fsuLy8PEzTpKqqqt0x5eXlab8e1dXV3HrrrWRmZvL9738/9fgVV1zBrFmzGDp0KP37929x3B133MHPfvYzbr31VsaOHcuhhx7K9ddfD8DmzZt57rnneOGFFzj22GMZPnw411xzDccccwzz589Pe25CCCGEEEIIIcS+QCqRuoHbbrDujyf32LXTdeaZZ3Lqqafy3//+l88++4wlS5Zwzz338MQTT3DhhReybt06FixYwNdff83YsWOZM2cO06dPZ8mSJfTt25evv/6a8847r9k5p06diq7rhEIh8vPzWbBgARMmTGj1+r17925WQXTEEUdQW1vLPffcw89//vPU47suz2tcgdn08dbGpLOsb8CAAUAy9Bo5ciQvvPACffv2paioKDWn9qxcuZKLL7641eeWL1+OUqrFMsBoNEpubm6HcxNCCCGEEEJ0LctSFFUHCURMMl02huR60PWOvzcIIZIkROoGmqaltaRsX+ByuTjxxBM58cQTuemmm7jooou4+eabufDCC1m9ejUOh4Nx48YB8OSTTzJ79myOPvpo/ud//odAIMAZZ5zR7HzPP/8848aNIzs7e7eCkiOPPJInnngi9ft+/fq1qCiqqKjAZrOlzt/WmF2rk1rz3//+l6ysLPr06dPquk+Px9Pu8Y1L4FpjWRaGYfDVV19hGM3DPa/X2+HchBBCCCGEEF1n1bZaHvrgW77dEcRC0cfrYsKALM46fCDjC3w9PT0h9guynE00M27cOILBIJDshxSLxVK7nBmGwbPPPsuIESO45JJLuOGGG1qEKAMHDmT48OG7XWmzYsUK8vPzU78/6qijeOedd5qNefvttzniiCOw2+3tjpk6dWqH1xs6dCjDhw9vt3FYeyZOnMh7773X6nOTJ08mkUhQUVHBiBEjmv3069dvt64nhBBCCCGE6LyHP/iWn837gnfWV1JUE2JbTZiVJbW8vLyU219fx9rS1ndoFqI9pmnx0YYKXlxWzEcbKjBNq6en1O32j3IZ0eWqq6v56U9/yi9+8QsmTpxIZmYmy5Yt45577uFHP/oRAMcccwxTp05l9uzZPPDAA0yYMIE1a9ZQWFiIx+Ph2Wef5ZJLLkk10O6sp59+GrvdzuTJk9F1ncWLF/Pggw9y9913p8b86le/4qGHHuKqq67i4osv5rPPPuPJJ59stuva7373O4477jjuvvtufvSjH/Gvf/2Ld999l48//njPXqQ03HzzzRx//PEMHz6cn/3sZ5imyb///W+uvfZaRo0axbnnnsv555/Pfffdx+TJk6mqquL9999nwoQJnHLKKd0+PyGEEEIIIQ52r6wo4YF3N7LrBtoJCwIRk9Wlfub9p5D7Z0+SpW0ibf9aWcrjH22mpDaMaSlsusaAXm4umTacH00q6OnpdRsJkQ5SXq+XKVOmcP/997N582bi8TgDBw7k4osvTjWG1jSNJUuWcMstt3DVVVdRWlrKiBEjuPTSSzn77LOZMmUK5557Li+99NJuz+P2229n69atGIbBqFGjeOqpp5r1Qxo6dChvvvkmV155JQ8//DD9+/fnwQcf5Mwzz0yNmTp1KosWLeLGG2/kf//3fxk+fDjPP/88U6ZM2f0XKE3Tp0/nhRde4LbbbuNPf/oTWVlZHHfccann58+fz+23387VV19NaWkpubm5HHXUURIgCSGEEEIIsReYpsW9b33TIkBqpIBIPMHSohoKqxoY0Tdzr85P7J9eXVHCTf/6mmDURNNAA+LAxvIAt7z2NcABGyRpqrFLsWhXfX09Pp8Pv9/fYulTJBJhy5YtDB06FJfL1UMzFAcC+SwJIYQQQgjRdT7csINfLFiG1cG33gyHwb0/PZRTJuS3P1Ac9FYW13L+k19SHzFTj2mAppFMJYEhvT28fcVx2Gz7Tweh9jKPpvafOxJCCCGEEEIIITrh6+31HQZIAAlLkUoAhGjD2lI/t/7r62YBEiQ/OZYCtOSvt9WG+HhzZU9MsdtJiCSEEEIIIYQQ4oAUisbTGmc3NEblyVI20TbLUry0vITy+kjbY1SyIslMKL7efmA2a5cQSQghhBBCCCHEASnH40xr3JDcDIb19nbzbMT+rKg6yKaKBgyj/RilsWFQOHZg7tQmIZIQQgghhBBCiANSns+NLY0d106d2F92ZhPtCkRMIrEE9aFou+MUoGswut+BWdkmIZIQQgghhBBCiAPSmH6Z9MqwY7STD2U6DY4f23fvTUrslzJdNgKROPXRjiuMvE4bY/Pbbk69P5MQSQghhBBCCCHEAcfa2VF7QK8MNK31FEkD7IbBs59vY23pgdnDRnSNQb0yqAnF0ho7up/3gF0eaevpCQghhBBCCCGEEHvKshRF1UECEZNyf5jPC2tYUVzLxh0NO3dfa0nXwFKKN9eW882OADeeOo7xBb69PHOxP3jvmwr8oY4btesanDC23wG7PFJCJCGEEEIIIYQQ+yXLUhRWNfDOuh188M0OKgMxQrEEwWgCTVOYliKeUGgke9XsymZoZLntBKNxvikLMO8/hdw/e9IBGwCI3WNZisWrSoknWg8jm8py2g7o5ZESIgkhhBBCCCGE2O+sLfXzt/9s5j8bK6kLm7t1jqipqAnGyHbbCcZMlm6tobCqgRF9D8ymyGL3FFUHWVcWIJ391sbkZx2wS9lAeiKJveDDDz9E0zTq6up6eipCCCGEEEKIA8DaUj+3v76O99ZX7HaA1CgYNakKRnEYOg0Rk407Al00S3Gg8Ifj1IaipFOfdtTw3AO6kk1CpIPYhRdeiKZpaJqGzWZj0KBBXHrppdTW1nbpdaZOnUpZWRk+395fWxyNRrnsssvo3bs3Ho+HM844g5KSkr0+DyGEEEIIIUTXsCzFi18Vs7mygUgs0SXnjJkWgUhjv5sDNwAQu6c+HMe0QO8gQXEYGttqQqmm7gciCZH2IZalKKxsYFVxHYWVDXvlgzdz5kzKysooKiriiSeeYPHixfz617/u0ms4HA769evX5o4I3emKK67glVdeYdGiRXz88cc0NDRw2mmnkUh0zf/YCCGEEEIIIfauouoga0rrqY+adMX/q1eAUhA2LVx2nVF5B+5SJLF7stx23DYdQ9fajRh7e51U1Ecpqg7utbntbRIi7SPWlvq57Y113Pza19zxxnpufu1rbntjXbdvM+l0OunXrx8DBgzgpJNOYvbs2bz99tvNxsyfP5+xY8ficrkYM2YMjzzySLPnP/30UyZNmoTL5eKII47g1VdfRdM0Vq5cCbRczrZgwQKys7N5/fXXGT16NBkZGZx11lkEg0GefvpphgwZQq9evbjsssuahT2xWIxrr72WgoICPB4PU6ZM4cMPP2zz3vx+P08++ST33XcfJ5xwApMnT+aZZ55hzZo1vPvuu13y+gkhhBBCCCH2rkDEpDoQIRpPp0NNehqDpME5ngO6n43YPT63nYJeGbhsRqvPaySrkIbmeoiaFoHIni2x3JdJY+19wNpSPw++9y01wRj5Pjdun0E4lmBNiZ/S2jCXHz9yr2wzWVhYyJIlS7Db7anH5s2bx80338xDDz3E5MmTWbFiBRdffDEej4cLLriAQCDA6aefzimnnMKzzz7L1q1bueKKKzq8VigU4sEHH2TRokUEAgFmzZrFrFmzyM7O5s0336SwsJAzzzyTY445htmzZwMwZ84cioqKWLRoEf379+eVV15h5syZrFmzhpEjR7a4xldffUU8Huekk05KPda/f3/Gjx/Pp59+ysknn7znL5oQQgghhBBir/I4DWrDHW+1vjsOKcg8oPvZiN0zJNfDpEHZ1EfixEyLiJkMMDVA18Bm6PTLcpHjcVAXiZPpOnCjlgP3zvYTlqV4aXkJNcEYI/p6U0u+vC4bI5xeNlU08PLyUsblZ3XLX2avv/46Xq+XRCJBJBIB4C9/+Uvq+dtuu4377ruPWbNmATB06FDWrVvH448/zgUXXMDChQvRNI158+bhcrkYN24cpaWlXHzxxe1eNx6P8+ijjzJ8+HAAzjrrLP7xj3+wY8cOvF4v48aNY8aMGXzwwQfMnj2bzZs389xzz1FSUkL//v0BuOaaa1iyZAnz58/nzjvvbHGN8vJyHA4HvXr1avZ4Xl4e5eXlu/+iCSGEEEIIIXqUmei6KqSmviqqxbKUBEmiGV3XOPOwAZTUhAiE40QaYgBkumwYukaGw2BMv0zK6iNMHJDNkFxPD8+4+0iI1MOKqoNsqmgg3+du0TNI0zTyfW6+rQhQVB1kWJ+uL6ucMWMGjz76KKFQiCeeeIKNGzdy2WWXAVBZWUlxcTG//OUvm4VCpmmmmmRv2LCBiRMn4nK5Us9///vf7/C6GRkZqQAJksHOkCFD8Hq9zR6rqKgAYPny5SilGDVqVLPzRKNRcnNzO3XPSqke6c8khBBCCCGE2HOBiNlt/39+U0WQTZUBRuVldcv5xf5rfIGP0w7tz6bKBtgZIoViJh6njbwsF9XBGDkeB7MOKzigQ0gJkXpYIGISjVu4fa2vrXQ7DHbUd9+aSo/Hw4gRIwB48MEHmTFjBrfeeiu33XYblpVM9+fNm8eUKVOaHWcYyfm2Fsgo1XFD8KZL5iAZmLX2WOMcLMvCMAy++uqr1LUbNQ2emurXrx+xWIza2tpm1UgVFRVMnTq1wzkKIYQQQggh9j314Tjd8R1dA2IJi082VbcbIlmWoqg6SCBikumyMSTXc0CHBiJpbamfN1aXkZPhYAshADxOOwnLYmt1iGNG9Obi44btlVY0PUlCpB6W6bLhtOuEYwm8raybDMcSOO36XltTefPNN/PDH/6QSy+9lP79+1NQUEBhYSHnnntuq+PHjBnDwoULiUajOJ1OAJYtW9bl85o8eTKJRIKKigqOPfbYtI45/PDDsdvtvPPOO5x99tkAlJWVsXbtWu65554un6MQQgghhBCi+2W57V2+Q5Sukdp1Kxxv+x/w15b6eWl5CZsqGojGLZx2nRF9vZx52IADPjw4mDVtQzO8j4evttUBML4giwy7QZk/Qo7Hwbj8A7+CTXZn62FDcj2M6OulzB9uUcGjlKLMH2Zk38y9tqZy+vTpHHLIIakeQ7fccgt33XUX//d//8fGjRtZs2YN8+fPT/VNOuecc7Asi7lz57J+/Xreeust7r33XoAuLTEdNWoU5557Lueffz4vv/wyW7ZsYenSpdx99928+eabrR7j8/n45S9/ydVXX817773HihUr+PnPf86ECRM44YQTumxuQgghhBBCiL2noj5KLNHx6od0JZsja2gaGIbeZhDQuCHSmhI/2W4HQ3p7yHY7WFOSfLy7d9YWPaexDU2Gw2B5cV3q8W93NLC1JkSOx8GmygaKqoM9N8m9REKkHtbYoCvH42BTRQMNEZOEpWiImGyqaOiRNZVXXXUV8+bNo7i4mIsuuognnniCBQsWMGHCBKZNm8aCBQsYOnQoAFlZWSxevJiVK1cyadIkbrjhBm666SaAZn2SusL8+fM5//zzufrqqxk9ejRnnHEGX3zxBQMHDmzzmPvvv58f//jHnH322Rx99NFkZGSwePHiFkvihBBCCCGEEPs+y1J8VlhFV3470gANhVIaA7LdHDO8T6vXbbohkndnQ2Wvy8aIvl5qgjFeXl6KZXVduCX2HYGISU0wxuaKBmqCsdTjTpve7PHuakOzL9FUOg1sBPX19fh8Pvx+P1lZzZPpSCTCli1bGDp06G4HJ62VRY7sm8mswwr2u7LIhQsXMmfOHPx+P263u6ens1/pis+SEEIIIYQQB6rCygau/ucqNu0IEIgluuScugaGlgyEbjnjEH40qaDV69782tdkux14XTbK6sJ8WVTDuPws8rJcaIA/EufWMw7plg2RRM/aVBHg/Ke+JBxL4LLrlPmjaMCwPskVQzXBGG6Hwd9/8X1G9M3s2cnupvYyj6akJ9I+YnyBj3H5Wftlg7a///3vDBs2jIKCAlatWsV1113H2WefLQGSEEIIIYQQokv5w3G2+8Og7V4thNOmg1JEmyyH0zSNkXleLpk2vNUACZpviFQTjPHhxkoAlm2tpV+Wi0yXDYehHxSVKAermGkRjJoEIt99dqoaonidti6tjNvXSYi0D9F1bb9MrcvLy7npppsoLy8nPz+fn/70p9xxxx09PS0hhBBCCCHEAaY+HE8FOrtDKQWahk1TZGc4qArGGJTj5tVLj6bEH2bFtlrqw3Gy3HZ8bnvqH/YbN0Qq90fYXNnQ7JwOm051QxRN0yj3hzl0YHYX3KnojO7eMe/NNeX4w3HMhKIxQlJAKJYgHEvgc9vp5XYQjHZNddy+TEIksceuvfZarr322p6ehhBCCCGEEOIAVxGIEIknsFSywW9noyRLgU2HvGw3HoeNqmCM+rDJH15ZQ2FVkO11YaIJC6dNp7/PzaRB2Zx52ADG5WcxvI+HN9eUY+3SESZhWVhK4dA1Pi+s4cRx/faLFSUHijUldcz/eAvrygOYCQuf28GEAVmcdfjALmkNs7qkjue+3ErCUmhA03c/nlDYDQ27oZPjcey1XdV70oF/h0IIIYQQQggh9nuWpXh73Q4g2cdo97r7KnIznPTxOimuCwFQHYzx76/LUUrhtBlkOAyiZoKyujBRM0FpbZjLjx/JUcN6s3hVWYsQaUd9FEPX8DhtrCyuo6g6uF+uMNkf/WtlKXe9uZ7qYAylkk3SDSPMlqoGvikLcONp4/YoSLIsxYJPi/CH4mhA0zqjxhAzkVD4w3H6Zjn32q7qPUl2ZxNCCCGEEEIIsc8rqg5SUR/B0DWUal4Rki6loLfXQWFVENVkJzUNRcy0CMdMNA2yMxyYSmEmVGrntb5ZTrIz7MQTzeufNA28LhtR02JLVZBVTbaAF91nVXEtt72+jopAlERCgaYw9GTw0xA1Wbu9nnn/KdyjHfOKqoNsrmjAtCCxy2kskmGmBcQTFlOH5x4UFWgSIgkhhBBCCCGE2OcFIibBWAKl1G4FSJAMAr7ZEcC0FKPzvttFK7EzJIgmFKV1EWpDMTIcNup39tj5tiLAmpI6aoOxliESEIklMDSNhFJ8vKlqj4IL0bHVJXX8+pmvqGqIYalkkGMmIGIqrJ0BYzxhsbSohsKqho5O16ZAxKQmGCOWaH3hZOPbbOg6vb3O3b7O/kRCJCGEEEIIIYQQ+zyP06AuGKON7/NpS1jJXdostNSuWrsuUasJxrHpGglLYWgakViCL7fUgPZdcNAollCYlkV9JE6ux0FFfZSi6uCeTVK0aW2pn2v+uZJSf7TV501LkbAUSinqIyYbd+x+iPR5YRXFteEOxzlsOllu+25fZ38iIZIQQgghhBBCiH2epVSyEqmDce0tKNI18DoNQFHuj6DtHJxopXIoFEtg6MnqIgsor48yOMfTai+meCIZXBT43ERNi0DETPOuRGdYluLxjzazuaL9kM5SyfdEKcXuLXyEhz/4lj8v2ZDWWF3joGiqDdJYWwghhBBCCCHEPsA0LT7ZXEVlIEqfTCdHD++NzfZd3cMH6yuJxPesDMlh07EZOg5DJxiNJ0Mk1bLfDUBNMEa/LCeBiEl/n5sd9RHiympRtdRIKQjFTbwu+0ETKOxthVUNfF5Y1azBdVsU4LIbjGqybDFdK4treeSDTZhp5k/mQbR8UT7Zott9+OGHzJgxg9raWrKzs3t6OkIIIYQQQoh9zKsrSvjr+5so80ewlMJh6Azo5eaSacP50aQCLEvx1rqytGpK2hvjc9rwZTioCEQIxxJY7WRSSikaYiZjPA5Om5jPox9uYmN5fYvlbJCsfFFKsa0mzBmH9joodunqCRt3BAhE0omQksb1z2JY787tlGdZioc/2EQwln5gqRQHTfWZLGfbl1gWVG2C0q+S/23vb7QucOGFF6JpGpqmYbPZGDRoEJdeeim1tbVdep2pU6dSVlaGz7f7Wyvurr/97W9Mnz6drKwsNE2jrq5ur89BCCGEEEII0baHP/iW615aQ2FlkFAsQSRuUR8x2VAe4OZ/reVfK0sprGrg2zR72xiatsvvk198NcCw6eR6HITiFrEm5UetfTH2OG247AZnHlbA8WP6EoiaRNspTVFAJJ7gyGE5B8UuXT1Da7MSbFd2Hc75/kCKqpM75hVWNqTV8LyoOkhRVed6Wuka1IfjnTpmfyWVSPuKslWw8jmo3ABmBGwu6DMaJv0/yD+02y47c+ZM5s+fj2marFu3jl/84hfU1dXx3HPPddk1HA4H/fr167LzdUYoFGLmzJnMnDmTP/zhDz0yByGEEEIIIUTrVhbX8vD7m4iaLf8BPaGgLmxyz5JvuO7kMYRi6VWg+Nw2akLffaFXgKaDy2aQSCjK6iMYGtidBqFoIjVmVy67TrbLTj+fm221IeJmgo7+mT9qWjRED46KlJ4woq+HdHscGbrOG6vLePLjIkLxBBl2gwkFPs48fADjC9oucAhETOLprmMjGSC5HYY01hZ7Udkq+OhuKFsBGb0gd3jyv2Urdj6+qtsu7XQ66devHwMGDOCkk05i9uzZvP32283GzJ8/n7Fjx+JyuRgzZgyPPPJIs+c//fRTJk2ahMvl4ogjjuDVV19F0zRWrlwJJJezNa0CWrBgAdnZ2bz++uuMHj2ajIwMzjrrLILBIE8//TRDhgyhV69eXHbZZSQS3/0PRSwW49prr6WgoACPx8OUKVP48MMP272/K664gt///vcceeSRe/xaCSGEEEIIIbqOZSkeen8ToQ76HJXWRXjz6+2tLiPblQYtQhylktVJ2W47kbhFZSCKz2Vn8sBsjHYqhqoCUUpqQ5T7w6wsrqOyIdbudSHZ/Putr3ekVfEiOk/XNGx6ejFG1LR46+sdbCgPUFobYlNFgH+tKuX3L63mrbXlbb5HmS4bWW4j7Tl5HAb9fW58B0mIJJVIPc2ykhVIoWroPZrU9gDOzOTvqzbAqkWQNwHS/MOyuwoLC1myZAl2+3cf/nnz5nHzzTfz0EMPMXnyZFasWMHFF1+Mx+PhggsuIBAIcPrpp3PKKafw7LPPsnXrVq644ooOrxUKhXjwwQdZtGgRgUCAWbNmMWvWLLKzs3nzzTcpLCzkzDPP5JhjjmH27NkAzJkzh6KiIhYtWkT//v155ZVXmDlzJmvWrGHkyJHd9bIIIYQQQgghukFRdZC1pf60xq4qTq/lhtG4bq2JxpyoIWpiKYXbYTBxQDa9PN9972ktTjAVNMQSLF61nWA8QbSdsKvxeF2DivoIRdVBhvXpXC8e0bFVxXWtVq21RgFxS2FGTQw9+dVbAXWhODe+uobPC6tbrUoakushzUtgaDCgVwaTBx08fbD2mUqku+66C03TmgUQTXv2NP7sWlESjUa57LLL6N27Nx6PhzPOOIOSkpJmY2praznvvPPw+Xz4fD7OO++8fac3Tk1hcglbVsF3AVIjTUs+XvFNclw3eP311/F6vbjdboYPH866deu47rrrUs/fdttt3HfffcyaNYuhQ4cya9YsrrzySh5//HEAFi5ciKZpzJs3j3HjxvHDH/6Q//mf/+nwuvF4nEcffZTJkydz3HHHcdZZZ/Hxxx/z5JNPMm7cOE477TRmzJjBBx98AMDmzZt57rnneOGFFzj22GMZPnw411xzDccccwzz58/vltdGCCGEEEII0X384Ti1obare5qqaYgnA6IOuOwGWa7vwiGHTWdwTgYZDgObrnHUsFzG9MvCaTdoiCZafAXblVLw3jcVrNxai5FOcYqCYDRx0DRZ3pssS/HCV8Wt7qTXHgWYFlg7f20pqAvFWFpUw4PvfdsiyFxdWsfmikBa5x6Um8HAnAxmHVZw0PTB2icqkZYuXcrf/vY3Jk6c2OK5xp49jRwOR7Pnr7jiChYvXsyiRYvIzc3l6quv5rTTTuOrr77C2Pmn/JxzzqGkpIQlS5YAMHfuXM477zwWL17cjXeVpqg/2QPJntH683Y3mGXJcd1gxowZPProo4RCIZ544gk2btzIZZddBkBlZSXFxcX88pe/5OKLL04dY5pmqkn2hg0bmDhxIi6XK/X897///Q6vm5GRwfDhw1O/z8vLY8iQIXi93maPVVRUALB8+XKUUowaNarZeaLRKLm5ubtx50IIIYQQQoietLq4rt1G1U2ZVjIQSiiLtvoqGzqM6OulMvBdMGXTNUyVXAalaRqzDivgy6Ja1pT46ZXR/vIjm568bjRuEU8ocjIcVDXE2u2LZAF14RgeZ/rLoUR6OtNcvSNxC6JmgppgjJeXlzIuPwtd17AsxZ/+/Q3RNNpv9c10MGN0HrMOK2i3x9KBpsdDpIaGBs4991zmzZvH7bff3uL5xp49rfH7/Tz55JP84x//4IQTTgDgmWeeYeDAgbz77rucfPLJrF+/niVLlvD5558zZcoUILlE66ijjmLDhg2MHj26+24uHU5fsol2PJRcwrareDj5vLN7PpQej4cRI0YA8OCDDzJjxgxuvfVWbrvtNqydu8PNmzcv9do1agzolFJou8T3Ko1u+U2XzAFomtbqY41zsCwLwzCahYONmgZPQgghhBBCiH3f6pI6HvtoU9rjEyr5PcMAWqvx8dh10DTyfW7ystwopahqiOGyGcRMi1yvE4eh0z87gzMP81BaG2ZzZQOJdnoXNS5psgCUwtA1slw26jqoMgpETL6taGBE31a+34ndtnFHA5G4hd3QMBMqzfbabdtaE2bKUBffVgRSyw8LqxpYX1YPgE3b+blr5VibBnOmDuWSacMPmgqkRj2+nO03v/kNp556aioE2tWHH35I3759GTVqFBdffHGqMgXgq6++Ih6Pc9JJJ6Ue69+/P+PHj+fTTz8F4LPPPsPn8zULQY488kh8Pl9qTGui0Sj19fXNfrpFzrDkLmz1pbSI1JVKPt53THLcXnDzzTdz7733sn37dvLy8igoKKCwsJARI0Y0+xk6dCgAY8aMYfXq1USj0dQ5li1b1uXzmjx5MolEgoqKihZz6amd34QQQgghhBCdt7bUz5/eXE9FIL2lbI1iCdVqvyO7DjZDx+e2Y9M0cjwOjhvVhxPH5XHEkF4cPrgXo/MyyfE6yHTZGF/g49SJ+cTMRFrNuhs1xBLYbW1/hdb47ivdv1eXSXPtLqfQtWR1WVdIJCy21YSIxq3U8sONOwLETCv5XjYZq2vJSjdj56UddoNx/bMOugAJejhEWrRoEcuXL+euu+5q9fkf/vCHLFy4kPfff5/77ruPpUuX8oMf/CAVWJSXl+NwOOjVq1ez4/Ly8igvL0+N6du3b4tz9+3bNzWmNXfddVeqh5LP52PgwIG7e5vt03WY9P8gIzfZRDsaAMtM/rdqA2T0hkN/1u1NtRtNnz6dQw45hDvvvBOAW265hbvuuov/+7//Y+PGjaxZs4b58+fzl7/8BUguFbQsi7lz57J+/Xreeust7r33XoAWFUp7YtSoUZx77rmcf/75vPzyy2zZsoWlS5dy99138+abb7Z5XHl5OStXrmTTpuS/cqxZs4aVK1dSU1PTZXMTQgghhBBCpMeyFC8tL6GoOtjp3jaWSv40fs2w6RqGlgxulFKM7ZdJWX0ktVoiy20n1+sk02WjvD7CyL6ZDMn1YFmKlcV15GW5yfWkt6OWpSAUM6luZ4c2XUvOLaEsimuDFFUHO3eDol2j8jJx2pOVZV0RzymgpiGGpRSZrsZFWsnPlE1PvueN19G05JLIRjkZDo4e3rsLZrH/6bEQqbi4mN/97nc888wzzfrpNDV79mxOPfVUxo8fz+mnn86///1vNm7cyBtvvNHuuXddYtVamNHaMqym/vCHP+D3+1M/xcXFad7Zbsg/FKZdB/mTIVQL1YXJ//Y/DKZdm3x+L7rqqquYN28excXFXHTRRTzxxBMsWLCACRMmMG3aNBYsWJCqRMrKymLx4sWsXLmSSZMmccMNN3DTTTcBtPm+7q758+dz/vnnc/XVVzN69GjOOOMMvvjii3YDvscee4zJkyenejodd9xxTJ48mddee61L5yaEEEIIIYToWFF1kJXb6qgLxXfreNWkGEkjWYFkMzRsNoNTJuaT43GwqaKBhohJwlI0REw2VTSQ43Gkmh8XVQfZVNFAvyxXu8vZdpXY2Zy5zed3hlzxBKwvC/DG6u27dY+idYFInEgs0enwsS2WgoZoggyHkdpZbVSel6wMBw6b0azpurKSAWhCJcPC86cOxtZOVdqBTFPpNLDpBq+++io/+clPmvW3SSQSaJqGrutEo9EWvW8ARo4cyUUXXcR1113H+++/z/HHH09NTU2zaqRDDz2UH//4x9x666089dRTXHXVVS12Y8vOzub+++9nzpw5ac23vr4en8+H3+8nKyur2XORSIQtW7YwdOjQPQtOLCu5C1vUn+yBlDNsr1UgdaWFCxcyZ84c/H4/bre7p6ezX+myz5IQQgghhBD7oBXbavnl08uoCXZuKVtr+mW5sBs6ZiJB2LT4v9mTyPU6eWl5CZsqGojGLZx2nZF9M5s1P15VXMcdb6wn1+vgow2VhOIdd1HedXlTR3QNst12bj7jEH40qWD3blCk/GtlKX/693p2+KMoOvdedOT7Q7JZNHdqqrH2Fc+v4D8bqzAti4YmHba1nT+TBmbz4qVTD7ilbO1lHk31WGPt448/njVr1jR7bM6cOYwZM4brrruu1QCpurqa4uJi8vPzATj88MOx2+288847nH322QCUlZWxdu1a7rnnHgCOOuoo/H4/X375ZWrXsC+++AK/38/UqVO78xY7T9eh94ienkWn/f3vf2fYsGEUFBSwatUqrrvuOs4++2wJkIQQQgghhBDN+MMxApHdq0Jqym5oOGw6Nh3CcYXT0Mly2xlf4GNcfhZF1UECEZNMl40huZ5mX/gzXTacdp2GiEm67ZkbR+kaafVRcto0YqbF3z4q5NTx+Qdt1UpXWF1Sx31vb6CmIdb5NC8NX28P8M66ck4en4+ua8w9bjgV9VEKq4KEYsm+WS67jtOmM7qvl5vOGH/ABUid0WMhUmZmJuPHj2/2mMfjITc3l/Hjx9PQ0MAtt9zCmWeeSX5+PkVFRVx//fX07t2bn/zkJwD4fD5++ctfcvXVV5Obm0tOTg7XXHMNEyZMSDXqHjt2LDNnzuTiiy/m8ccfB2Du3LmcdtppPb8z2wGivLycm266ifLycvLz8/npT3/KHXfc0dPTEkIIIYQQQuxjPvymgngXrEcyE4od9RE0DTwOG/17ufG5k/2NdF1jWJ+2d3AekuthRF8vS7fUdLpJc7rreBQaLodBqT/MJ5urmDa6ZZ9e0THTtHjovW+pqE9WIHVHr/JgLMHCL7Zx4rh+6LrG+AIfN542jhe/KubZL4qJJSyG5Ho4alguZx4+IFXRdrDqsRCpI4ZhsGbNGv7+979TV1dHfn4+M2bM4Pnnnycz87utEu+//35sNhtnn3024XCY448/ngULFjSrZFq4cCGXX355ahe3M844g4ceemiv39OB6tprr+Xaa6/t6WkIIYQQQggh9mGrimt5bmnX9JpV7OyNpBRR02JQTkaqr01HdF3jzMMGUFITorCyof2xgKYn+yFpJBssdxQkOW0aKIjGEyg0KgPR9g8QrVpb6uepT7bwyeYqol3UTLstX2/3U1jVwIi+yayhsaJt8eoyqhtiXHnCyFTIdLDbp0KkDz/8MPVrt9vNW2+91eExLpeLv/71r/z1r39tc0xOTg7PPPNMV0xRCCGEEEIIIUQnrS6p46IFS4maXRMFaECWy0ZCKRLtdbtuw/gCH6cd2p8vttRAvO0T6DsDJEguoWusojJ0rc2m3A5DR6ERNRO47AZ9Mp2dn+BBbm2pnwff+5bimhAaOu23NN8zGhCJW2zc8V2IZFmKwqoGYmbyuhIefWefCpGEEEIIIYQQQhxY1pb6ufqfK6kM7nkvpKYSCnp7neRluakJxSiqDra7jK0py1K8t76iw+oWS4HD0LCUIp74roOS1c66KtNSGFryv729zoN2K/jdZVmKl5aXUBOMMbR3BtvrQt16PZuu7dy5Pfmeri3187f/bGbZ1loCEROA619Zw1HDyph73DBZztbTExBCCCGEEEIIcWCyLMXjH22msCLYped12nQmDPDRL8uFpaCoKpj6wp+Od9bt4JNNVS3CIEOj2RbyQ3IzsOsa5fVR6qNmqqlze+FTJG6haclqpf/3/YHSVLuTiqqDbKpoIMNh49uKBkKxjpeyOQyIJ3az57amyHLZGJWXydpSP7e/vo5vygPo2nd9vKNxi/9srKQiEOHGU8cd1EGSfJqFEEIIIYQQQnSLwqoGvthSTaLjoZ2iSAZJmqYRjiVw2nUyXenVSFiWYvHq7YTjZptL0hpVBqKU1UcxLcXEgra3Pd91biiYOiyXXx4zLK1jxHcCEZOahhibKgPUhuLE0+imbSbAYdMwOrnqTAMspTGybyZDcjy8+FUxhVVBbIaGz21PhVLZGXYMHbZUBnnpq5J2K9EOdBIiCSGEEEIIIYToFt+UB/CH0q8QSpeZsFixrY6ahihl/nAyBEizsXZRdZDNFQ2YCYVpNe+1s+vGcQlLEYyaRHaWuTRmFG67js2gTRYwoq9XeunsBo/ToDYcIxJL4EyzissCEgnVqd3bDD3Z58ptN8jOsFNUE2RNaT2WUmQ4DKqDsdRYDfA47SSUYk2pn6Lqrq2s259IiCSEEEIIIYQQosutLfXzwrJiop3ofN2ZL6i1wRhLt9aQ43Ew67CCtAMbfzhOdTC6c7lS+8eE4hYJlQyX1pTWpypTlFIdHvvPr0r418rStOYkWopbipomQU5HLJVshJ6ubLedwbkZjM7z8k25n8c/3MQOf5iYaVEXjNEQ/S78rA3FklVrCkLxRKeWTh5oJEQS3e7DDz9E0zTq6up6eipCCCGEEEKIvaBxd63NFYFOHdfba8exc01SuxGNpqV61fzksIJO9aipD8eJJRSZLjtaJyqFmkZhcVOR2LVsaRehaILHP9qMaXbfzmIHomA0QbbbjpmwCLezc15Thrbz/elEJdLofllE4xZrt/vZsCPIi8u3U1IXoT5iUh9NNKtqCsYSVDUklzVm2I20l04eiCRE2odYyqLIX8TaqrUU+YuwVPf+ZXPhhReiaclO9DabjUGDBnHppZdSW1vbpdeZOnUqZWVl+Hx7t/lYTU0Nl112GaNHjyYjI4NBgwZx+eWX4/f79+o8hBBCCCGEOJg07q61druf7XXRtI/TgITSQNOwaWA32v66atchJ8OGroHqZHuaLLcdp6GjgJwMe+cO3ilBx5vO6xqU1Ib5ZHPVbl3jYJXpsqEUxDoRvtkNbefnJ/3rbCgPsN0fJmYqNGi3n1LCgqhpEY4nGF/gS3vp5IHo4I3P9jHrq9fz2ubX2OzfTMyM4bA5GO4bzhnDz2Bs7thuu+7MmTOZP38+pmmybt06fvGLX1BXV8dzzz3XZddwOBz069evy86Xru3bt7N9+3buvfdexo0bx9atW/nVr37F9u3befHFF/f6fIQQQgghhDgYFFUHeW9dOdvrIp06TtN2VpRYCl3XSLTzj+oRUxEPxncGTZ1LkXxuO/2z3Wz3RzAtC4/DIBjr2tbfjQVOpqWoDKQfpAkY1CsDfzjWqUDI0DQ0VKc+Cf5QDLPJAelcL2EppgzLOah7XUkl0j5gffV6Hlv1GF9Xf43P4WNQ1iB8Dh9fV3/NY6seY331+m67ttPppF+/fgwYMICTTjqJ2bNn8/bbbzcbM3/+fMaOHYvL5WLMmDE88sgjzZ7/9NNPmTRpEi6XiyOOOIJXX30VTdNYuXIl0HI524IFC8jOzub1119PVQmdddZZBINBnn76aYYMGUKvXr247LLLSCS++8s8Fotx7bXXUlBQgMfjYcqUKXz44Ydt3tv48eN56aWXOP300xk+fDg/+MEPuOOOO1i8eDGmefCuYRVCCCGEEKI7vbaylG21nQuQAGy6RixhJStKLEVHrZQSKlkdUlIb7tR1huR6mDQom1yPHZumEeqCAEnb5deGpqFpyXvqk+nc4/MfTIpqgp1+Tyyl2m103hpzl9AovQBKdbij34FOKpF6mKUsXtv8GrXRWob5hqFpyb9+vA4vHruHQn8hizcvZnTOaHStezO/wsJClixZgt3+XUnnvHnzuPnmm3nooYeYPHkyK1as4OKLL8bj8XDBBRcQCAQ4/fTTOeWUU3j22WfZunUrV1xxRYfXCoVCPPjggyxatIhAIMCsWbOYNWsW2dnZvPnmmxQWFnLmmWdyzDHHMHv2bADmzJlDUVERixYton///rzyyivMnDmTNWvWMHLkyLTu0e/3k5WVhc0mH30hhBBCCCG62uqSOp7+rKjTx+ka6LqG3dBx2xX10fRCBAU892Uxv5g6FFuaO3npusaZhw1g2ZYaqjrRuHlXdh0aW/ZoTZbVJb/SKZTSGNDLzdHDe+/2NQ5GG3cEiJvJMDHduMaX4cAfiqNhtXuMTU8uTdudGKhxPpWBzgekBxL5Jt3DttVvY7N/M3kZeakAqZGmaeRl5LHJv4lt9dsY4hvS5dd//fXX8Xq9JBIJIpHkH4a//OUvqedvu+027rvvPmbNmgXA0KFDWbduHY8//jgXXHABCxcuRNM05s2bh8vlYty4cZSWlnLxxRe3e914PM6jjz7K8OHDATjrrLP4xz/+wY4dO/B6vYwbN44ZM2bwwQcfMHv2bDZv3sxzzz1HSUkJ/fv3B+Caa65hyZIlzJ8/nzvvvLPDe62urua2227jkksu2a3XSgghhBBCCNE2y1Is+LSIhk7uXGXTwWU3GNgrgzH9MvlwQ0Wnji+tDfHx5kqmj85L+5gxeZk0xMxk1ZAOne19rWswoq+XzZUNxBK02FreUhpZbhuXTBuedrglGiX7YulaekvM7DocNSyXt77egd2mY5pWm/2qcjIcVAVjne6jBckASVPQJ9PV+YMPIBIi9bCGeAMxM4Y7w93q826bm8pQJQ3xhm65/owZM3j00UcJhUI88cQTbNy4kcsuuwyAyspKiouL+eUvf9ksFDJNM9Uke8OGDUycOBGX67s/SN///vc7vG5GRkYqQALIy8tjyJAheL3eZo9VVCT/B2T58uUopRg1alSz80SjUXJzczu8Xn19Paeeeirjxo3j5ptv7nC8EEIIIYQQonOKqoNsrujc9xYNOGJwDmd/byCTBmazrqyef68t79Q5TEuxrizQqRDpk81V1Ibi9Ml0EoolqAvHgWS/l3TyJLuhMzDHQ0GvDD7fXE0sYRHbmXjomsagnAx+d8JIfjSpoFP3ImBUnheHTU+rT5VOMsw749D+vL+hItnx3NCwlGoW7DU23a6Pmi0Cv87QdY1Red6OBx7AJETqYV67F4fNQdgM43W0/DCGzTAOmwOvvXs+qB6PhxEjRgDw4IMPMmPGDG699VZuu+02LCv51+e8efOYMmVKs+MMI7ngVCnVooJKpRHrNl0yB8mqq9Yea5yDZVkYhsFXX32VunajpsFTawKBADNnzsTr9fLKK6+0uI4QQgghhBBizwUiJuFYAivNqh4N6OWx88cfH8KovCwAvimvR9uNnsVue+eqfSoDUcyEAjvEmzRfSrcgyWXXcdh0NDQmDcpmztFDuPm1dZTUhrl0+jB+94NRUoG0m4bkePC5bdSG4h2O1XWNWYcN4NiRfSjIdrOlMojN0EhYGnbtuwVx4Z3rDuOd6dbdCq/Thr47H9ADiHyqe9igrEEM9w1nR2hHi/BFKcWO0A5G+EYwKGvQXpnPzTffzL333sv27dvJy8ujoKCAwsJCRowY0exn6NChAIwZM4bVq1cTjX6348CyZcu6fF6TJ08mkUhQUVHRYi7t7fxWX1/PSSedhMPh4LXXXmtWMSWEEEIIIYToOsmt2RXptkRWwIT+Pkb0yUw9NiovE6+zcx2SHTado0d0ru9Qn0wnaFATjDULkdKNB3RNw2M3KPOHGZWXxYzReeT7kt81xuX7ujxAsixFYWUDq4rrKKxswDqAmztvqw3RK8NJRgfBoAZkOAyOGJKDzaZzybTheF02lAWGntyrLWEpovFkfyWd5O5/u8tl08nPdhNMs1/XgUoqkXqYrumcMfwMtjdsp9BfSF5GHm6bm7AZZkdoB72cvTh9+Ond3lS70fTp0znkkEO48847eeihh7jlllu4/PLLycrK4oc//CHRaJRly5ZRW1vLVVddxTnnnMMNN9zA3Llz+f3vf8+2bdu49957AVpUKO2JUaNGce6553L++edz3333MXnyZKqqqnj//feZMGECp5xySotjAoEAJ510EqFQiGeeeYb6+nrq6+sB6NOnT4uKJiGEEEIIIcTuG9Qrg0Qnms3ogG2Xb/XDens5clhv/r22LO0+RRMLmgdR6ThqaC4OXSOUsHDZ9dRStMbilY7uQin4urye/j43sw4rQNe1VIWKmUgGPoGISabLxpBczx5tCb+21M9LX5WwptRPKJ4gw24wocDHmYcPYHyBb7fPu68KREzshs7ovExWlPhbHWPTIctlw0KjfudSxMalg49/tJnSujBxE2w2jbxMO7XhOJG4hcOmE0ukHwI11jIZGgzu7SHH4yDTdXDHKAf33e8jxuaO5VeH/orXNr/GZv9mKkOVOGwOxueO5/ThpzM2d+xenc9VV13FnDlzuO6667jooovIyMjgz3/+M9deey0ej4cJEyakdmDLyspi8eLFXHrppUyaNIkJEyZw0003cc4553R51c/8+fO5/fbbufrqqyktLSU3N5ejjjqq1QAJ4KuvvuKLL74ASC3Za7RlyxaGDBnSpfMTQgghhBDiYLatNoTD0NPqK+S2adhtBtXBOEXVQYb1Sbao0HWNS6YNp6g6yNfb6zvsX5PrcfC/p43rdEhT4g/TJ8tJQ9QkGm8y2zQCJEgugRuW62HutOGpIMfYOYenPtmCsTNUctp1RvT1cuZhuxf4rC31c/sb6yisDGKpnZPTYFtNiPXl9dx46rgDLkjKdNmIJyzK6pvvgtYY6LhsOpqWbGbutBtkub9rV/KjSQWcOj6fTzZXURmI0ifTSSAc57qX1yTfn04WcGkaeBwGhgZ1wRhTh+UyJNez5ze5H9NUOg1sBPX19fh8vtQW8U1FIhG2bNnC0KFD9yg4sZTFtvptNMQb8Nq9DMoatNcqkLrSwoULmTNnDn6/H7e79YbhonVd9VkSQgghhBBib1tVXMevF35FaV37W6AbQO9MB6CRl+Xith+P59CB2c3GrC6p41f/+Irt/rbP5bZp3HXmRH48ecBuzfWON9aj6/BNeYCGiIllKRQd5wy6Br29Dp65aEqql9PaUj9z5n9JZUMMh6GR4bDhy7AxKCeDcMwix+Pg8uNHdirwsSzFFc+v5D8bKzF0yHDYUCQDrGg8gVIwfXRf7p89aY8qnfY1q4pr+eWCZdQEYy3CSI1k9ZpN1/C57ORnu7nv7ENTIWRr3lyzneteXI0CovEE8U7sxGc3NPp4HUTiFpqmcedPxnPy+Pzdua19XnuZR1NSibQP0TWdIb4hPT2NTvv73//OsGHDKCgoYNWqVVx33XWcffbZEiAJIYQQQghxENleF6aivv0ACcBmSy4fy3bb8bntrS4PynAkW0/4XDYaYgkSTUqSnIaGhcLjtO12FU6my4bTrpPtdjBknIfy+gi1oRjbakLoKKpDZvv3YOip5WtrS/3837sbqdvZCNpSilDMJBgzqW6IMaHAR00wxsvLSxmXn5V24FNY1cCyrTVoGth0jfL6CAlLYejJEMVSik83V1FY1cCIvp1bzrevWl1Sx68XLqcqGGv1eQUkLEV+lguvy8bkQb06rAwalZdJpttOfTje6Z3ZlIKoqcj1OnEYOv188h13/ytzEfuc8vJyfv7znzN27FiuvPJKfvrTn/K3v/2tp6clhBBCCCGE2EssS/HW12VpV3lk2A3cDp2ReZmthgAbdzTQEDXJctvJz/quQl8D+ma5yct0EUsoNu5o2K35Dsn1MKKvlzJ/GDTIz3aTl+XCpuvoevtfk5UCl80gGE1gWYqXlpdQWBXE3JlQWArilkIpiMQt1pTW43bofFsRoKg6mPYcN+4I4A/FCUZNdgRixBPJbevjCUXMtLAsqA3FeW/9jt16DfY1a0v93PjKGrbvrGRrK2ozdI0st40BvTJS/ajaM6y3lyMG56Ch0ZnN2XQNfG47Ewb4GJ2XSY5X+iGBhEiiC1x77bUUFRWllmLdf//9ZGRk9PS0hBBCCCGEEHtJUXWQlcWtN0Helcdh4MuwdxACNKk8shvkZbnQNY1+PhcOm95kE5/d686i6xpnHjaAHI+DTRUNNERMDF3DUhZ14Vi7u7QpoDoQZXtdiKLqICu21lLmj6RmYqlk0GRaCqdNJ2om2FYTIhq3CETar3BqaltNmHAskWr63VRi5/ktpfiisHa/363NshQvflVMUXUw9Tq2dkcayUqvvpmutJcH6rrG3OOG0dtr73BsU0qB12mjX6aT8voII/u2HngebCREEkIIIYQQQgixR/zhOJWBaFpjszMcTBma224IMCovE6/LRkPUBBRep40huRlkOGyAoiFq4nXZGJW3+8u4xhf4uPz4kUwY4KMuHKMqEE01X3bZ2q9uCURN7lmygddXbWdzVZBYvPUdvxSgaxr+kImlVNqVLJal+KbM326D8p09tqkLxzpV4bQvKqoOsqa0HrODUiEF2A2dWZ3cmW5cfhZ2o3PxR2P/qU2VQXI8jrSqng4GUovVhaRHudhT8hkSQgghhBD7o5XFtQRj6W2d7nboqA4qiBqXIP1nYyV1oRgepx2brhFPWASjcZSC7w3OYVjvthsqp2N8gY9x+VkUVQcJREw+31zFo/8pxDQtoO370XbukDb/kyLippUMF1oJQOKmhaFDLGHR3+dKu5KlqDpIcU0Ym6G1G6wYO1+TzlQ47YsCEZNQ1CSWSG895Ki8zr3vhVUN7EijXxd8twucoUEonmBwbgZzjh56wO2Ct7ukEqkL2O3JsrhQKNTDMxH7u8bPUONnSgghhBBCiH3dv1aWcv/bG9IePyzXy9rSeh5871vWlra+BK5xCdKY/EwMXScUM6kPxwnFTGy6zpj8TC4+bliXVIbousawPl4OHZjNiYf0Y2iuh0x3+/UWLruBUlAfiWPoWpuRWHLJGbjtBqdOzE97voGISThu4bYbrS6t03b+2A0dQ9f3+149mS4buq6Rzr+pO5s0NU/Xxh2BVpcFtkfXNbKcNs6ZMkgCpCb270/aPsIwDLKzs6moqAAgIyOjyRpdITqmlCIUClFRUUF2djaGYfT0lIQQQgghhGjGslSqYifTZWNIroe12/38cfHX1EfT3zfd67YzwmVjU0VDuzuWjS/wceOp43jpqxLWlPoJxRNk2A0mFPg4s5PLmdI1JNfDpEHZLN1iUeZvfXmeBmiahs3QsFSyYsWmayR2Bka7ctkNjhnRmxPH9Ut7HpkuG26HgcPQcNt1Qrt0LDd0LRUkDe/j2e979QzJ9TAkN4NvyurbHacBOV4nwWh6VW9NjzQ0MGivviyp8S00E4oyf4RH3t/ElSeNliBpJwmRuki/fsm/EBqDJCF2R3Z2duqzJIQQQgghxL5ibamfl5aXsKmigWjcwmnXGd7HwxeF1VQH450+n6Zp5PvcqR3LhvVpfXnSrsvNGsOr7upN09hwu6QmxNrtrQcaugZmwsJu6MRMC6VpjOzjYXNVsMWSPpddZ3xBVqerpobkephQkEVxTQiHTRExrVRApWugkdz9Lcfr4MKpQ/b7Xj26rnHGoQV8tKGSYCzRZlNtj9NGrqfzu6SNyvOSleEgloiS6ERFkq7Dmu31/N973/K7NBt5H+gkROoimqaRn59P3759icc7/5eoEHa7XSqQhBBCCCHEPmdtqZ8H3/uWmmCMfJ8bt88gHEvwn42VFFal39Ijy2Vj6vDc1O/dDoMd9R3382lcbra3jC/wcdqh/XlnfesFAopkJZDXacOv4ihLMTA3g/xsN2+t29Fs7LDeXv7ww7GdDh90XeOswwfyTVmAb8oDzZ6zVPInw64z+3sDmTAgu1Pn3ledOC6PwwZn8/Gm6hbL2pw2DadNx72zEq2zlVfJHlu9eHddBbFEelVMmga9MpwopSjzh9utmjuYSIjUxQzDkCBACCGEEEIIcUCwLMVLX5Ww3R+mf5aLhLIwNAOP06C6Ib3d2BoN6+Oll8eZ+n04lsBp3/f6+awt9fPG6rI2n9c1jV4ZDsJxkwy7gddlY0d9lHyfu8XYu86csNshz/gCHzeeNo57lnzDf76tSj2uAX0yHeR6nawq9rO21H9AVMisK6snZioyHAbBaCLV3FrTkp9DS2kM7ePhzMMHdDrISfbYGs6WygbWbA90fACgFFgqWfHVy23vsGruYCGNtYUQQgghhBBCtOqddTt4a1055f4Iy7bW8u815SxeXcaGsnrqI53rS+NxfveP7Y3VHSP7Zu5T/XwsS/HS8hJqgrE2xyilCMZMEhaM6pfJ1SeNZsIAH3XhlsdM3MMqoW8rAqzZpfm4pkFepouhvT1UN0R5eXkpVmvNmPYjja+7aSkOHeDDZiRDIkVy1Y+mafTNdHLDKZ2v6mo0vsDHr2eMxG3XW21WvisF1ARjWErhddmJxvf/XfC6wr4V+QohhBBCCCGE2CesLfXz1CdbqA+beBwG9VETBTRETVaUtL6rWntM0yJhKcKxBGX+MDkeB7MOK9inlgcVVQfZVNFAvs+NodeRaCWcSSgIxRIcOSyX/zk52XD59In9KaoO8oP7PuqyufxrZSl/XLyO+nA8te08JJeyrd1ez7baMDkZdlZsq93vK2QKqxr4bHM12+vChOMJ4jv7Fmka+Fx2Budm4HYYeJx7FmH0z3YzrLeXMn+YunC81UbojTSSu+vFExaGpu2TVXM9QSqRhBBCCCGEEEI0Y5oW8z/eQmUgCigqAlFiifR3YNuVTYeoaVFUFaQuHGPigGwu3wcbFQciJtG4hdthcPjgXhhaclevXRkaROLfVaXoutZqRZVKZ8/6VpimxeMfbSYSS2A3Wn5tV0AoZlIfMdlSFWRVcd1uXWdfsLbUz2+eWc435QHqI2YqQILkkrL6iMm2mhC1ofgeVwJlumzkeB2ML8imwOdqNxBpnEXCsiiqCe5zVXM9RWI0IYQQQgghhBApjRVI72+oIBSJE9v97ChlUE4GV544iuwMR7fvsLYnMl02nHadUNSkNhjFbtOwLJrt6GXo4LDpFFWFeOmrEsblZ7GurJ6Xlpe0ON/qEj+HDswGkku20t1l7pPNVWz3R8h02fCH4y12K7PpGglLoQMxpfh4UxU/mrR3qro6cx8dWVvq5/cvrmJDRUPr11JgKYv6SBy7oTdbErk7huR6GNHXy5oSPxMG+KiPmoSiCRQKs43PeTiefGJfq5rrKRIiCSGEEEIIIYQAvtuJrbgmRDhqdkmA5LBpHDeqL8eO7LPPfwlvDBmWbqmhNhgDlQxsYk1CJB2NTJedcDzBmlI/76wr56Xlpa32UfrTv9fzh1PGomsaLy0vYVNFA9G4hdOuM6KvlzMPG9BqNVZlIIqZUGS7bDREE7DLjmKNDZ8jpkVvr5OK+uheWdK2ttTfqftoi2UpCqsaePSDb9lY0X6ja9NKVnSZXdD3Sdc1zjxsAKW1YXbUR8l02miImCTaObWuQV6mk3H5WXt8/QOBhEhCCCGEEEIIIVLNjUtqQ1Q3RIi29826E0bnZXLWbuyo1RMaQ4Z12+sJxpI9nGK7hBempagJxrDrGsGYyeuryyipDRFvZbnfquI6frdoJXmZTkxL0S/LRcKpaIiYLN1SQ0lNiN+dMKpFANMn04nNSIZXLrtOKL5riJT8b2OI4w/v+VKvjjQGjDXBWNr30dZ5XlpewuriOr7eXk8sjf7slgKboRGMdq6Ze2vGF/i4/PiRvLS8hOVFNZTWRdocqwNuu0HUVPt936muIj2RhBBCCCGEEEJQVB1k5bY6qgNRKhriXXLOsf283DVr4j7X+6g94wt8/OLoobjsOnFLsWs0pIBQPEFDLEHMtNhc2UBNQ2xn/6jm4qZFcU2Qr8vqMRMWq0vrWFpUwzfl9VQ1RFlV4mfefwpb7K529PDe9Pe5kju+tdFXyWFoHD6oFy670e1Nn5vuWpfrcbBhRz3Lt9byTXmg3fvY1apttdz0r7W8tbacLVVBYm2tIWuFmVB7vJyt0fgCH/976jiuOmk0eVlOtDbyTV1PNveu3wsh3f5CQiQhhBBCCCGEEPjDcbb7wzREu+bL8oBebgb0yuiSc+1tx4/pS16mAw1abAevkcx1EkqRsCyqG2LEEolmDaEbxVVyOVY4ZrKqxE9xTZi6UJyomayoMRMWH2+q4p115c2O+2ZHgD5eJ3FTURNu/f2YNDCbXh4HZf5wtzd9bty1LsNhY02pn82VQerCcbxOA03TiMRMPtpYyVtfl7V5jpeXF3P+/C9Zsa2O7f4INaF4i4CuLTZdw9bFlWy6rpGd4aBPpotMp61FA3VDA13TCccsomZCdmbbSUIkIYQQQgghhBDUh+NE4gniXdB7xtDgmOG51IbivLy8tMMKlX3NttoQmqa3Glwokl+kbRokLAjHEyg0wvG2l1qZVvI4RXI5XMxUBCImLrtB1LR4Y3VZ6jVqXDZW2RDF57a1CLEaOW06myoayPE4ur3pcyBiEoklKPOHCcZMLJXcba86GKMuFCeWUAQice5ZsoE1JXUtjn/4g2+57qU1+MNmiybh6cjOsNPL4+iS5WxNZbpsZLmS1U2apjV7rR02HZu+MyxUikH7aSDa1SREEkIIIYQQQghBltuOTdcwu6AXUo7Hiabr5PvcfFsRoKg62AUz3HsCERNN0/A4bWQ4mn9tNjTIcNjwOG0YuoahQTBq0pmcTN+5u1oySNLZ7o9QVB1s1peqMhClPtp26LKqxE8oZnLqxPxuXy6Y6bJhAbWhGC7bd69HIGKSsCyUUhh6siH4n5Z8w9pSf2rMyuJaHn5/U6uVWumw6RoDczLI8Ti6vBpoSK6HPJ+bhKWw7VKKpBTJnlQ2g0ynnW21oS699v5KQiQhhBBCCCGEEPjcdjxOW9pLjFpjaNDb42DqiFwA3A6DaNza7/rJZLpsuB0GNkMj1+tkYC8XfbwO+mU5KejlJsfrwGboeJ02crzOTldahWIJNA0iZgKP00DXNAIRk6LqIN/uCFAXilMbjBEz2z6vrid3aXtjdVmz0KY7DMn1kO9zEYqZBMLf9ctSJIOWaEJhWoAGNQ2xVPWZZSkeem8Tofjuf6p0DUpqw4zo4+3yJXu6rnHMiN47Qz2rWWAXNS3shsaEAT7shr7ffYa7i4RIQgghhBBCCCEY1CsDq40mzunyOG0cPrgXGY5kxUg4luj2ps/dYUiuhwkFWeiaRiiWwG4YZLkdeJx27IZOMBrH0DSOGNyLsf2y2mzM3J5w3MKyFDkZjtRrFIiYlPnDlNdHaCc/AsAfMtlWE2JTRUO3LxnUdY1DB/iIJxShNiZmqe/Cscbqs3fW7eDTzVW7fV2N5JLBmGlx5LCcblmy53HY0LWWy9kMHbxOA6dN3y8/w91FQiQhhBBCCCGEELz3TQWV/ra3O++IRrKPjH3nciel1F5p+twddF3jrMMHMqy3BzOhqAvFiJkJYmaCmmCMhAVD+3g464iBnDoxH213UiQgoZJLwBpfo3J/hB3+aNpL46Jxi+3+MB9/W8l/v63stiDJshSldWGcu3af3pWC0rowkXiCVcV1PPlxIeE9qEJSgE2HXl4H/Xzu3T5PWyxL8VlhFU6bQYa9eUhk0zXqIyZrt/u7pQpqfyUhkhBCCCGEEEIc5CxL8drKEqJ7spYN8DptZNgNGiLmXmv63F3GF/i48bRxHDeqNy6HgT8cxx+O43YYTBvVhxtPHcf4Ah8j87xkOHZ/6/kd9VFyvXYAPiusSrvxdOO4SNyisCrI/e9s5LY31nXL0rai6iCriv0dhluGkazcCkVNPv62ijJ/ZLcaaTdlt2nkZnR9PyRI3tfmyiADst1E4olmc9V1nYSV7PvUP9u1X36Gu4PUYwkhhBBCCCHEQa6oOsiGioY9OodGsgdSUXUIp11n4oBsZh1W0O1Nn7vT+AIfD8yeTGFVAxt3NACKUXmZDOvtTYUKwWiCXI+T+kjnmmunaPDm6nJOGtuPTRUNZNh1grH0diFTycNRSuG0Gawp8VNaG+by40d26evuD8cprQsRNdtPGRMJBRq47DY2VwXxh2J7fO1oXNE/29UtlUCNu84FoskG55Em96cUZDgM4gnFmhI/lqUkSEJCJCGEEEIIIYQ46AUi5h7tymZoyS3nzz5iIIcN6kWmy8aQXM8B8aVb1zVG9M1kRN/MVp/PdNnIdCcrsMLxBJaiU9U3mpZcAvbxpkr8YRNN17DrkO4qMA2wGzq+DDs5HkeqR9K4/Kwue/3rw3FCsQQdfUQswKZpHDbYx0vLSwlE0wvD2mO36TTvVtR1mu46l+V24I+YNLYF6+11AopwPJHaPW9YH2+3zGN/IsvZhBBCCCGEEOIgl+myoe9GXx8dcBjJbe51XScvy8mhA7MZ1sd7QARI6RiS60ndbx+vA4/T1qlG22ZCEUtYROIWhg5mItmgPB2W2hncGBoOm46maeT73KnG1h0ebykKKxtYVVxHYWVDmz2Vstx2OmqH1CjTaePIobkEIvE9XsqmAePyM6kJxdK6n85q3HUuErew6ZCT4QAgy2XDYdMIxxP0ynCkds8TUokkhBBCCCGEEAe9Qb0ySFidqxpx2nQchgZohGImbrvOqLzWq3UOZLquceHUISwrqqGmIUYvj4OYaRFLdFxKZOjJ3ccAxuRn8m1lA9/uaMC0OtecqiGaIHNn8OR2GOyotzoMPdaW+nlh2TaWba0jFE2Q4TQ4YnA2Pz1iUIulcD63nV4ZTuojoQ6DIV2HjRUNnSvHaoPXaZDtdlAfMbslxNF1jdMn9uezzdXUhuJ4nTZcdje6llzC57IZ5PtcKJDd2XaSSiQhhBBCCCGEOMi9900FFfWd619j0zUUEDUTaJrGxAHZDOt9cC73mTggm6tPGk2O10FtKJYKgWztVO8YGqmgJSfDwbEj+nD+UYNRShE1O05gdGh1kVc4luhwS/q1pX7+8NJq/rmshI07AhTXhti4I8A/l5Xwh5dWt2jOPSTXw9A+nrRyofqIyXvrduxWZVtTLpuOx2knoVSH97MnThyXx9EjemM39GT4Z1rEE4pcj5ND+mcRiiX2yx0Gu4uESEIIIYQQQghxELMsxTOfFzVrKtwWo8k3yFAsQTiW7AHU2+vkihNGHjRL2Frzo0kFPHzOYUwdlpsKULR2ghRLQUKBrsH5Uwdjs+ns8EfRtTQ7AGnNi32+2lZLTUOUMn+43dDDshR/XvIN68sDxBMKu6HjsuvYDZ14QrG+PMCfl2xotrRtXVk9O/zhdGaFUrC5KohrD3asc9g0XA6DLJeNQMTs1hBH1zXmHjeMQwdm0yfLyZh+XiYPymZUXy/Vwdh+vcNgd5AQSQghhBBCCCEOYoVVDXxdVo8iWR3T9Kty0y+Mu36FNnZWItl0OO+oQUwYkN3dU93nTRiQzaUzRpC1s2om3s52bYrk6ztpQDYXHTMMy1IsXr0dNI3+PidNM4vGX+q7nqCJykCUL7bUYDO0dkOPTZUBlm6tBcBp01KVNzZdw2VPXmHZ1ho2VQaAZOj0+Eeb2VzVcU+ixp3iwjGTflnODse3dQ6HoeMydGyGtldCnPEFPn53/Ei+NyQHNI3qhhh1kTgTB2R3+U53+ztZ1CeEEEIIIYQQB7GNOxqIxBLoOytbmmYThqFBQmGR/HLvddrwh5O9aXxuO5kuG267TnVDXLZA38nntiercELxdse5bBoTC3zcdMZ4dF2jsLKBMn8El13HMAzshk7UtMjJsKPrGvUREzNhpd6g7Aw7NU2u4bTpWAryMl2My89q87qfbKomaiZwGDoKDdOyMC2F3dDQNQ27oRExE3yyqZpReVm8s24H//22Kq3d+xTJ6iqVUMQTCgPo7P5syWBSJz/bzeRBvZh1WMFeCXHGF/gYl59FUXWQQMQ8oHYY7EoSIgkhhBBCCCHEQU2haRqGrjVbwqRryaqSxkdcDoO8LBf+cAMARw7LJdNlIxhNpHYDky3Qk/2DcjwOttdFWjxn06Fx1WD/bDf/e/ohqYAkEDHRgV4Zyb5KjVvNO2w64biVrEZqkuO4HbZUUGXoGocP7oWGltrJrK33Ihw3QbWsLEtYCt3QUtcJx81UdVTUtJKfj4RKqy+SpWBbTcdNuJtqbBHldRgcMyKXK04cxbDee3eXP13X5DPcAVnOJoQQQgghhBAHsVF5mWS5begazZZQoZpXJvXJdNDf50o9neW2o2kabodBNN7xbmAHC13X2uzf47AZqfAmO8OOx/ldXUemy4bLYZDvc+O0GSR2pkimpYjGEyilMAyNxjZLMbN5jY+maWQ4bR2+F+PyszAMnVjCSlY27dT4PscthWHoqaqcxuqozkQ5Nh1AQ9cg3f7a2s6llEN7Z+CPmOiaJlVA+yAJkYQQQgghhBDiIDast5fvDcnFbhg0beFjN7TUDmAOQ2dQTgb9fG6OHp7LKRPyU+PS2Q3sYJPrcbT6eJbThs1IBiOBSAJ/+LvlaENyPYzo6yUUMxnfPzMV6IWiCRJKoWmQ7bKnxtc1XS6nFHHTSuu9yM5wYNc1TAtiTZaoReMWwahJIqEo8Lk4ZnifVHVUrseBlkago8HOMDK5LM5KTi2tYzSSVVd9s9wSSu7DJEQSQgghhBBCiIOYrmscP7YvXqfRbPlRzFIoNPpmOjhqeA7hWLJqZVCuB587GWYopTrcDexgFIm3vtNd3LJI7EzqyuojPPPZVtaW+oHk+3DmYQOwGRoriv2pcQqFUuCyGdC08XmTTMdSEIyaHb4Xa0v9/OnNb7BaWc7W2M9I02FQbgbf7AikqqOyXHYsq/2lbI09tSyVrJ6K7bLbn8ehN6t0sxvgtid3hrMZOg5Dx+u0YSkloeQ+TEIkIYQQQgghhDiIrS3188bqMnI8jlQFja6By9Dp43Xw+1PG8j8njyHH42BTRQMNEZOEpWiImGyqaJAt0HexttTP6pK6Vp9rWj2U5bSxtSbEg+99mwqSgBa7rjkMDZsOoViCeELh3bkEztxl57eNFYF23wvLUrz4VTGFVUE8ToNeGfZWA4GcDAfBaIIH3/uWhqhJrwwH68r8zaqWOmIpWgRVHqeNTJct9ZhSjT24NNw2HafDINfjIBAxJZTch0mIJIQQQgghhBAHKctSvLS8hJpgjIkDsumf7QYgL8vFjLF96Z/tZk1JPePys7j8+JFMGOCjLhyjqCpIXTgmW6DvovH1rI+0vjOb4rvlXYNyMhjR10tNMMbLy0sxTYuXlpdgWoojBmdj7AyCJg7I5vDBvVAkm107kg2Hmi0TU0AwmuB7Q3q1+V4UVQdZU1pPLGERjScIREya1gppgF2HWMLC4zSobojyyvJSiquDRMzOtMj+bk5Nc6dg1MRh6Nj0ZCiW6bThctjIdBnYDA2XoWMzNAkl93H7TIh01113oWkaV1xxReoxpRS33HIL/fv3x+12M336dL7++utmx0WjUS677DJ69+6Nx+PhjDPOoKSkpNmY2tpazjvvPHw+Hz6fj/POO4+6urq9cFdCCCGEEEIIse8qqg6yqaKBfJ8bTdMIx5PNmrMzHPjcDvpnZ6R2Xhtf4ON/Tx3HrWccwg2njuXWMw7hxlPHSoDURFF1kE83VVEZiLb6fNPgx+uyoWka+T4331YE+GRzFSu31VHVEGX5Nj/mzgRmW20YS2lkOAx0TSMST7Q4r21n4PLu+ormVU1NBCImtcEYwYhJOJ5oUcmkgLgF/rDJyuI6aoIx3l9fzqqS1s+3K6uDnClqWvjDcTzO5PK4PllOXDadWEJhMwzys90cOay3hJL7uH1ikeHSpUv529/+xsSJE5s9fs899/CXv/yFBQsWMGrUKG6//XZOPPFENmzYQGZmJgBXXHEFixcvZtGiReTm5nL11Vdz2mmn8dVXX2EYBgDnnHMOJSUlLFmyBIC5c+dy3nnnsXjx4r17o0IIIYQQQgixDwlETKJxC7cv+d0purOPjcuerDdwOwx21H/X5Fi2QG/f8m21FFYFibex9EvXvgtbGgOlxtd4ZXEdW6qDGBp4nPbUnvf14TiRWAKboWNoYBg6DdGWQZLXaRCMmby8vJRx+VktKnm214UpqQsR7yjtARoiJjFTEYkn2u2D1BmGriV3kHPYGNbbw8XHDSPLbac+HCfLbcfntjMk1yMVSPu4Hg+RGhoaOPfcc5k3bx6333576nGlFA888AA33HADs2bNAuDpp58mLy+PZ599lksuuQS/38+TTz7JP/7xD0444QQAnnnmGQYOHMi7777LySefzPr161myZAmff/45U6ZMAWDevHkcddRRbNiwgdGjR+/9mxZCCCGEEEKIfYDHaWApxfa6EIaupcKiuGmBUrLzWickew6VtBkgQTJAUk1+Dcnd7Rw2ja9L/ViWIivDnlzKtvP5TJeNYNTETChiCQurlUok01JEE4peGfZU5VjTsG9tqZ9HP9xELM1laQlFqiqtq2S67LhsOv6ISZ7PxbEj+0hgtB/q8eVsv/nNbzj11FNTIVCjLVu2UF5ezkknnZR6zOl0Mm3aND799FMAvvrqK+LxeLMx/fv3Z/z48akxn332GT6fLxUgARx55JH4fL7UmNZEo1Hq6+ub/QghhBBCCCHEgWJtqZ9nPttKYWUDn26u5j8bq6gJxgD4ZkeAjzdVsbkyIE2O01RY1cC3OwJotNz5rFHTCEcpldrdLi/LTSiWINfjoCFiUlQVTI01LUWGw0bMTO7s1lYQlLAUhRVBaoKxVBgI3zXULq4NY2htz60poxuyHX8oRlVDDBQcM6K3BEj7qR4NkRYtWsTy5cu56667WjxXXl4OQF5eXrPH8/LyUs+Vl5fjcDjo1atXu2P69u3b4vx9+/ZNjWnNXXfdleqh5PP5GDhwYOduTgghhBBCCCH2UWtL/dz+xjr+taoUfyjerEIGwEwoSmrDlNSGOXSgT77wp2HjjgCRuIXd0NIKaoprQ6nd7Y4ekUssoRiRl0nMtJq9F1WBCBWBCAlL4XUYbZ5PB0LxBLWhGB7nd+MaG2oD6JqW1vK0TmzElra4lVwuqZQio537EPu2HguRiouL+d3vfsczzzyDy+Vqc5ymNf/jp5Rq8diudh3T2viOzvOHP/wBv9+f+ikuLm73mkIIIYQQQgixP7Asxd/+U8iaEj91IZO2Fi0pIBQ1eW/9jp1bsYv2aegaOGx6WiFSSW2Y8QU+Lj9+JJMGZuO06xRXBwnGmr8jsUQy1LOUanepXDieaPV9CkRMwrEElmW1aKbd9p10DwVEExZvrC6Tz9R+qsdCpK+++oqKigoOP/xwbDYbNpuNjz76iAcffBCbzZaqQNq1WqiioiL1XL9+/YjFYtTW1rY7ZseOHS2uX1lZ2aLKqSmn00lWVlazHyGEEEIIIYTY3xVWNfDZ5iqiHTRN1gBTwcffVlFY1bC3prffGpXnJdNtR9c0DFvbMUzjTmqaBtNG9WZ8gY8huR40BRt2NLT6niRU8idiJtr8Eh9LKOyGRi+3g2CTxtuZLhsuu04wmn6T7O6Od5ZtrZXP1H6qx0Kk448/njVr1rBy5crUzxFHHMG5557LypUrGTZsGP369eOdd95JHROLxfjoo4+YOnUqAIcffjh2u73ZmLKyMtauXZsac9RRR+H3+/nyyy9TY7744gv8fn9qjBBCCCGEEEIcyCxLUVjZwKriOj7aWEltKE5HPZYbF27UR0y+KQ90/yT3c8N6ezlicC90TaOtWiSHoaVCJBRUNyR7UFmWorC69QCp6ZlMq/2AR6lkJVTTRuhDcj1kOo0O3++9QdeSP/URk407JETaH/VYi/3MzEzGjx/f7DGPx0Nubm7q8SuuuII777yTkSNHMnLkSO68804yMjI455xzAPD5fPzyl7/k6quvJjc3l5ycHK655homTJiQatQ9duxYZs6cycUXX8zjjz8OwNy5cznttNNkZzYhhBBCCCHEAW9tqZ8XvypmdYkffzhOTTDaqWVNSikqA9HuneQBQNc15h43nC2VQb4pb3tjpvjO117XNfpkOgH4ZHMVNQ1xNC0ZBDU7r/ZdVRiAoSfDpNaE4gmqgzGCUbPZ49U7G6Z3lkbXViVZKrk0z6arLj6z2Fv26X0ar732WsLhML/+9a+pra1lypQpvP3222RmZqbG3H///dhsNs4++2zC4TDHH388CxYswDC+a9S1cOFCLr/88tQubmeccQYPPfTQXr8fIYQQQgghhNib1pb6uf31dWzYESAcTxA3rbSbJltqZ+WIrtEn09G9Ez2Q7Ky2aY2ukepr5HEYHD28NwAriuuImAlQrQc3mg6Nzavae/ssS2HTNV5ZsZ1D+icbohdVBwmE47t1Kw6bRrSNEiabllxi19koKKHA47QxKi+z48Fin7NPhUgffvhhs99rmsYtt9zCLbfc0uYxLpeLv/71r/z1r39tc0xOTg7PPPNMF81SCCGEEEIIIfZ9yQbam1m73U8kbmFZijYKWNqkaxrZbjtj+kmP2I40vt7FNWG8LjvRYMvgJtIkkOmV4aCoJkggYrK0sBpovcoooSCxM0Cy6Rp2A8JtVJI5bAahqMmKbbUUVQcZ1sdLIGKyu62y1a5lUTsZGug6KCv5GbGU6tSObuP7ZzGst3e35iR61j4VIgkhhBBCCCGE6BqFVQ18tLGShmhb+691zGHTmTq8t3zhT0NhVQPLttaia2C0VYrEd5VGOwIRfv/SGqKmxdbqBgxdw+wgiXHZdMLx1t9PDfA4DeIJi+3+CP6d1UeZLhu+DDtabbjTVUOxVi6lAX0yndgMjYr6GEopbIaOMq20QkpdgynDctDbeY3EvqvHGmsLIYQQQgghhOg+Cz/fhj9sdjywHcP6eLj4uGHyhT8NG3c00BAxyXAYBFtLX3ZqbFgeiiVw2jS8ThuQbIbUXsjjsmnELSt1/K4UEIjEcRg6UTNB/c4QaVCvDPplubAZnX8PNVrWMDltOplOG5MKsvG5bWQ4DJRKL0DSALfdYGCOp9NzEfsGqUQSQgghhBBCiAOMaVq8urJkj8/z/cE5jC/wdcGMDgbJCMi0VLsVRY0r0cyEYk1pPWbCIhRvO4Kx6Roeh8HAHDfflAVoryd6MGYRS8TIdNrIcttZW+rnpeUlVASinVrQ1lgt1RhYNV3VZilFaV2EPlku+ma6yHAafFsewEyYHQZJhgY+t51ReVLZtr+SSiQhhBBCCCGEOMD8Z1MFtaE9q0ICePebHZhtbQUmmhmVl4nXZSMYNTET6b1mgYjZboAEkO2208/nJi/TBVqyb1J7zIQiZlosK6rhwfe+ZU2Jn4E5HiYP6oWjnWqkps80ZkaWomVopUEsYbGhrB6HTef0ifnYbHqbFVJNKWBsfqYsj9yPSYgkhBBCCCGEEAeYTzfXdMl5KuqjfLK5qkvOdaAb1tvLEYNzsKz0m0x3NEzXwGnXGdrbQ0MsAWj43DaMDo4zLYuXV5RSE4wxoq8Xt8NgcK6H0f0yU+e1G1oqOOpMlVLcVOgahM0EMdNifH8f+T43bofR4XlcdoPTJvaX5ZH7MQmRhBBCCCGEEOIAUxWIdsl54pZiR32kS851oNN1jbnHDWNAjjut8R1V7jQ+ne9zkZ1hxx+K4bDpmBbo7VQUOWw6dsOgtDZMpsuGpmm8t34HLy8vIb4z3fK57Qzt7WFUngenTUu2ZEpr1slxCaXIdNpw2HQCUZMsl4FS7Z/DpsHIvEwOHZid5pXEvkhCJCGEEEIIIYQ4gFiWIhBpub387tBo3g9HtG98gY8fTuiPTddarcpptmSsg9dVT2Y7mAnFpooGcr1OBua4MTQNy1JtVv14HDZ0LdmbydA0SmpDVAdjmJaisLIBgKhpYdd1Zh02kFyPE6/TRtNcqqM6IZuuM6JPJnZDJ8ttJ8/nRilFW9mWTjLcGt7Hw5Bcaaq9P5MQSQghhBBCCCEOIJsrGthc0bDH59E1cBg6I6QJcqdMHpiN12kjO8OO12nDYWjoNC4h09P+Ep7Y2Y8oEDWZOCCb6344hqnDe5PtcWDorVf9aEAoFsdSyV+vLqnjyy3fLW1srESKmRbl9RFQioJsNzkeBx5H8323dp2nRrIxtl3XcDsMdD251M7ntnPMiN4YRut9kXQNMpw2nDaDY0b0lqVs+znZnU0IIYQQQgghDhD/WlnKn/69njL/ni1naww8Bua46ZXh6KLZHRyOHt6b/tkutlWH6JvpJG4paoIx4pbCoWvEE8lxmtZxNZLN0Oib6eTHk/szcUA2uqZRXB2ktDZEW4vHIqbCtEycNp3aUKxZY+zGXyYshVKKDTsasBT4w3ESymo2btezG7qG3dBQKhkm1YbjfH9ILkNyPXy7oyFZOaVprRwJGXadgl4ZspTtACCVSEIIIYQQQghxAPjXylJu/tfaPQ6QADIcBv2zXRw9oo8sP+okm03nkmnDcTsMKgJREpYiy21HB0LxRKpax2nr+Ou412HgD8d5dcV2LEsxvsDHT48YRKbL3urSse9Cov/P3n2Hx1Feix//TtmqlVbNsmRZtpEbtuWCTTMQeuiQBG5I4gvpcBMSfAkQkkAaN5Qk/EgC6Q2SAIEECAmB4IRiTCgmGHfjbtzVy0q72jYz7++PkVZaFVsykm3Z5/M8hp2Zd2bekfSonD3nvG4TazStzybfCjeIta0+io6iLWGR3s8qfJajSKQdHKUwdJ0x4QCXzy0H4I1tDRha7xI+v+E2W2qOp6koDMjX0hFAgkhCCCGEEEIIMcJZlsMvXt5Ca9x6T9fJ8ejk+U3GFwaZXJLL5XPLpfzoAHxgTjlfv2Q644qCtKdt2hIWpqmTH/BQmONmdgW9JsY+PraGBg4QTzlsrm1je2MMgNKwnwlFOZTk+fH06L2kaxAwO8rKVFdfpZ403HK5vS1xHK1re39Ux9knVxay8JzJVJWH2d4YY0td1A1a9WAaOmZH9lIs+d6+NsXhQcrZhBBCCCGEEGKEe3VrPe82xNh3Lsm+dQYSwn6TE48p4op5Y6kqDw/VFI86H5hTzsVVZby2tYH6tiSjcn3MP6aIH764iZ8v2cacinze2NZIe8ru83xbQXvSxtBS5PpN2hJuECbXb+IAaduhLN8PQHUkQdpWFOZ4MXSNxmiKlO1gO/2vmGZoELccEimbklwvNQPIYPMZGpWjclh4zmQmleQC0JawiMQtbKUozPHREE1iddTQ2YpMcKkxlmZ7Y4zKUdJjaySTIJIQQgghhBBCjGBr90T49SvbSFjvbRk1XYNw0MM3L57OeTNKJQNpCJimzhlTS7L2jckPANDcniLeTwCpk+UoWhMWCcsm1+/++T6hKIeysJ+NNW3k+U00TcfUddK2ja5ppCwHr6ljOwqnn6ZLCmiNpwl4TRQaE4pDAyqDtJXCNDRiya555/pNDB3SliI/aFKeH6CmNYGpaxTm+ABFynI6Vg2UbKSRTsrZhBBCCCGEEGKEWrsnwv0vbmZ3c/w9X8tjaOQHPEwpzZUA0jDymW5mzobq1n6zhDopwFFuMGhcQRAAXde4dNaYjsbZadK2kylZa09ZBD0G+QGTlO30uVpaJ0u5DbZB0dCa2O9cACwHokkrE9ACN6hVOSqE5ThYjoNp6IwtCFIaDuA1NeJpm6DXJBzwZJ0nRiYJIgkhhBBCCCHECOQ4iieX76YplmJ07ntfQc3UdVra00Ti6SGYneiPp6Mjdjw9sOJDQ9cI+TzsbG7P7Hv/9NGcOqkYj6GTTNuZ8rGg16SqPEzAa/bZ6Lo7pcDQwXEctnX0WxqItNUV0AI3qPXJUyaQH/TQFE2RsmyUUqRth0g8jc/QCXh1Jo/OlcbaRwAJIgkhhBBCCCHECNTZ0NhxFCt2t76na2mApRRJy6FVgkjDqqXj4zvQ4sNcn4nX0LNKwXRd49rTK5ldkc+oPB/hgJvhU5zjZU9LHL/HID/Hi76vVCQANNoSFvtZmC2LxyQroAUwa2w+N503lcKQl5Z4mpb2NMm0Ta7PQzjoYWxBUJq0HyEkl0wIIYQQQgghRqC2hEVjNMmWuijpgSyttQ8KSFsOIZ9JXsAzNBMUfaptTQxqfLqjt1HPUrCq8jD/e85knly+m3+sqQZge1M7IZ9Jrt+gKZoi7fT/daFr7r+WuEXHYm77Zeoa7Umnz2y1D8wpp7I4h9+9tp2tDTEcRxEOeJg82l3lT5q0HxkkiCSEEEIIIYQQI1COz6CuNUHqPQaQOmkaFOV4CUsQadis3RPhpfW1gzonkbIpC/v7LAWrKg+jlOL5dTUApCybtKnTFOsqceuPrml4TZ1E2g0IGZq7mlp/NCA/6CFp95+tNnNsPvd8eDbbG2O0JdzeSROKciQD6QgiQSQhhBBCCCGEGKFS9iDqkPZBA0xDp6o8LH1rhsnaPRHue2ET1ZGBZyJpgAOcMKGgz0BMZ2P1ppgb1NE1jfakRXIAgUVNg6SlCHh0QGUadPd3Zq7fRCmFzzT2ma2m6xqVo0L7vb8YmaQnkhBCCCGEEEKMQLGkjb9jpa/3ojNwkOsz+eQpEyRrZBh0NkGvjiQyjbUHyqNrzOijFKz7NVVH6MfuyD4ayB0sR6EB44tCzCrPQ9tH/yQNSKQdQGNM2C/ZakcxCSIJIYQQQgghxAiU6zcpzvW95+so3N44pXn+fQYSxIHrbIJeEPSi1MA+xhpg6uAxdKLdmmr3vGZ+wJPVEytpqwH1N9KUG0gaX5TDNy6dwbGjQ30GnzTAa+pomkKhmFORL9lqRzEJIgkhhBBCCCHECDShKIfK4pwBZZ1AdnZK99dBj8GJEwrweQz+snwPzn566YjBa0tYJNMOIb85oACPjhvY83kMgv00O++8ZixlZzKQBvOp85oanUlRM8aEuf6cKZSG/eR4jczXhwYUh7wU5XjJ8ZpoaJxcWSjZakcx6YkkhBBCCCGEECPQO9WtNERTmDqkB9AaKddvEk1aOMrtnWMrN+LwvinFFOb4iCYsNte1sb0xJj1thliu38Tn0WmLp4gm+m5K3UkDvB4d21H4PEa/5WO5fpO07bC9MTagwFRPKVuR69fZ2dTO9sYYpWE/YwuCzK7w0tCaYEtDDFPXAA1HQVHIh9fQKQ0HDuBu4kghQSQhhBBCCCGEGGE6++G0JtIEvQaRhL3fcxJp2w1QGBoleX52N8fRgMIctyQu4DWobXVo66N0Srw3E4pyKAh6eWl9LdZ+Ij4KsGxFyGcwOtfHceMK+iwfG1cQJGnZJNM2pq5hOQMrY+tk6hptSYt3G2Ks2tXC7Ip8fB4dU9eZVJrHxNG5tCUs0raDx9DRgEgiTa5fwghHMylnE0IIIYQQQogRZntjjBU7mtne2D6gAJIGmSBD0GsyqzzMCRMKuGTWmMyYeMrG59ElSDBsFOn91Jt1FokFPDpl+QHGFgS5fG55n+VjO5vb8ZkGuT6TA2ll5TUNlKOwbIdXtzQwriDIpJIQ1ZE4Sik0TSMv4KEo5CPXb1LTmmBySa70QzrKSRBJCCGEEEIIIUaYFTubeaemlfbU/gNInXyGhq6BoWvk+U0mleQS6ggYKaWojsQlSDBMtjfG2NUUxxzAymwaUJzrY35lMQvPmUxVHyuzgdsTyWPozK4oIO8AAn9KKWwFeQEPda1Jdja3c8XcsRTmeNlSFyWasLAdRTRhsaUuSmGOt9+Aljh6SIhZCCGEEEIIIUaQ1btb+MHzG0ntry6qG5+pEfSZpCxFWdjPlvoYZeEAAa9BPGVTHYlLkGAYReJp9rbE8egahg52tx5WWsc/BfhNnaDP4FuXTuf0ySX7/Fx09lnyeQxG5/ppjO2711JPScvB0DWOKcohablljLMr8ll4zmSeXL6bLXVRalsdfB6dWWPzuXxueb8BLXH0kCCSEEIIIYQQQowQa3a38OXHV7KnJTmo8yxbkbYVY/L9XH/2JJa+2yRBgoOoNZ4maTsEPAZpWxHrlkGmae4/xwFd1zht0qj9BpDA7bM0qSTEm9sa2dMSd68FA+6L5CjFqKCPwhwvLd16HVWVh5lelsf2xhhtCYtcv8mEohwJLgpAgkhCCCGEEEIIMSKs2tXMFx9Zzq6WxKDPHZXno7U9TTztMCY/wDcuni5BgoMoL+DBZ+qkbIeCoCcriKSU+0/X4JjiHK45vXJAnwtd1/jQceW8uL6WeHrgZY3g3svUNTyGRnVrgllj87PKGHVdkxX6RJ+kJ5IQQgghhBBCHOb+tnIP//PQsgMKIAGkbUV+0EvIZ/DUir0AVI4KMbsin8pRIQkgDbNwwMOYcACPrpOwHEK+bvkcHQGdoqCX68+eNKhssJDPJOA1MDo+fwPNQvKZOnkBD83xND6PLmWMYsAkE0kIIYQQQgghDmOrd7dw77820hhNHfA1inJ8HFOcg8fQ2VzXxvbGmGSaHEQTinKYMy6fpGWTth2qI13liMU5XvICHk6fPIr3Ty8d1HVX7mqhtjWJ19CJp539n4Bb8ubzuIGnoMfgv+aOlTJGMWASRBJCCCGEEEKIw5TjKH73+nYaoyn2szp8L50LgVWV5zG9LAyahu0oalvdJsri4NF1jSvmjmVPc5ymWMpd9Szpfg5y/R4qi3O4Yt7YQWUDOY7i1S0NOI4i6DWI7OdzauqAAtPUmVGWh9c0SNs2syvy38OTiaONlLMJIYQQQgghxGFqe2OMrXVRbEcNKoika262SV7AQ2lewO3cDMRTNj6PnmmiLA6eqvIwC8+ZzJh8Pw3dsspa4mn8XmPQ19veGKOuNUFRjpdYcv9BQUe5/1AQDpi0JdJMGZ2X1QtJiP2RIJIQQgghhBBCHKbaEhbxlFsCNZhEJF3TyPWZ+EwDj+n+2aeUojoSZ3JJrgQODqG61iRWVkRQ8ea2Ju549h3W7okM+DptCYuUpZhYEupxvb45ChzcVdnebWinMMcrvZDEoEkQSQghhBBCCCEOU7l+k/Z0GnuQpWwBr04saRHwGAQ9BtGExZa6qAQODiHHUfzqlW1sqGlDqa5PaI7XxHYcNlS38etXtuEMMOUs12/i8+jYDgQ8A8tk0gBbKSoKgyw8Z7L0QhKDJkEkIYQQQgghhDhMjQ0HaGgbfEPt/ICXwpCX/KCH7Y3ttMRTzBqbL4GDQ2hbQ5RlO5rQNPCaXX+Ke02D/KAXTYO3djSxrSE6oOtNKMphUkmImkh8wFlqCvDoGtecfox8HYgDIoWwQgghhBBCCHGYev3dBpLW4NKQAh6dc6aN5vK55YR8Jm0Ji1y/yYSiHMlAOoQ21bYRTViEAx5a2tM9jmqEfCaReJpNtW1MKsnd7/U6m3Uv39FMJN7zen2M10ApMHSd9qR9gE8hjnYSRBJCCCGEEEKIw9TiDXWD6oUU9Bp86dzJfOa0SgkYHXa6Ph/5QS/xtE1+0LPPcfszvSwPHQbUdN3Q3dX5fKbbcF2IAyFBJCGEEEIIIYQ4DP1t5R4eX7ZrwOMNDW44dzLXnD5xGGclDtSU0SFCfpNY0iI/6GV8t+bmSiliSYuQ32TK6NCAr7mlvo2NdVEMjX32zdIBFJi6RmnYR1iCSOIASU8kIYQQQgghhDjMrN7dwl3PvkN7emB5SAYwKuTl7GNLhndi4oBVFoc4fnwBjoLWRJq07eAoRdp2aE2kcRScML6QyuKBB5Fe29JI0rLxGNo+85c6V2UL+UxOOqZIVucTB0wykYQQQgghhBDiMOI4igdfe5fG2MAbaheGvBTl+olJr5vDlq5rXHv6ROpak2xriNGesrqOaRrHloa45vTBlSHG0xad9Y5aR8+jvmiApmmMKwxyxbyxUuooDpgEkYQQQgghhBDiMLK9Mcb66lZsZ2Dji3K8TCvLQ+Eu+y4OX1XlYb5+yXSeeHsXa/a0Ek/ZBLwGs8rDXDFv7KBXTJtelodh6KQdh301z9Jw+2V94pQJsiqbeE/kO4wQQgghhBBCHEbaEhbRhDWghto6MGNMLu0pm1lj86VMaQSoKg8zvSyP7Y2x97xy3mkTR1Ge72d7Q/s+v15yvDo5PoOZYyWAJN4b6YkkhBBCCCGEEMPEcRTb6qOs2tXCtvoozgCW0crxGSStgZWlmYZGfVuKwhwvl88tlzKlEULXNSpHhZhdkU/lqNABf95MU+eGc6cQ8hn9jtGApO1g7avzthADJJlIQgghhBBCCDEM1u6J8OTy3Wypi5JMO/g8OpNKQlwxd/9lS/31tulOA7ymwfQxeXzq1GOkTOko9YE55by1vYlH39zZ5wptCkjbEEvZrNkdYVJJ7kGfozhySCaSEEIIIYQQQgyxtXsi3P/iZtbsjpAf8DKhOIf8gJc1u939a/dE+j139e4I0QE0yC4OeTl/Rinfu3yWBJCOYmv3RHi3PkbQ2382Ehok0jbPrN47oGw4IfojQSQhhBBCCCGEGEKOo3hy+W6aYikmlYQI+U0MXSPkN5lUEqIpluIvy/f0+ce84yj+vmovSWvfXbU1YEx+gE+dOgHTlD/rjladX2uJtE1h0NvvuM7MttW7I2xriB6k2YkjkXy3EUIIIYQQQoghtL0xxpa6KGXhAJqmsbclzts7mrAdhaZplIUDbK5rY3tjrNe52xqirN3TMqCm2kUhL9PL8ob+AcSI0fm1NiY/SDin/yCSoYGhaUTiaTbUtB3EGYojjQSRhBBCCCGEEGIItSUskmmHQEd50ZJN9WyqjbK13s0ACXgNkmmHtoTV69xNtW20xnvv70tTNNVnIEocPbp/rRn7aM7tMXVMQ8NWUN+aOIgzFEcaCSIJIYQQQgghxBDK9Zv4PDrxVHZfo/aO7XjKxufRyfX3XudoZ1Oc5ABW0VJAe9ruMxAljh7dv9YCpo6uuaWORsf/dQ0MHUxdw3YUhqYxKs9/qKctRjAJIgkhhBBCCCHEEJpQlMOkkhDVkTiq2zJrugZKKaojcSaX5DKhKAfHUWyrj7JqVwtb6tp4c2vDgO7hBgi0PgNR4ujR/WstHPTgNXTQ3B5InZlJOhppy0EpCAdMji2V1dnEgZPvOEIIIYQQQggxhHRd44q5Y9nTHGdLXVcT41jSYktdlMIcL5fPLeed6laeXL6bLXVRkmkHy3FYX92KBvvtiaRpMCbsZ0JRzrA+izi8df9aq2tNUpjjpa4tiYPC7mjcrumg6Ro+XeeUicVUFocO8azFSCaZSEIIIYQQQggxxKrKw1w8q4z2VFe5WU1rgvaUxcWzygC4/8XNrNkdIez3UBTy0p60SKSdATXV9ho6Hz6hAn0ffXDE0aGqPMzCcyYzqyKf0Xl+/B63P5LX1MnxmeT4TPL8HqrG5HHN6ZXyNSPeE8lEEkIIIYQQQoghtnZPhGdWV6N1+3u9PBwg4DF4ZtVeAl6TpliKohwv66ojNERTKGdgASSAWWPDnD+9dFjmLkaeqvIw08vy2N4YY9WuFv69qZ4dTXHiaZuA12BWeZgr5o2lqjx8qKcqRjgJIgkhhBBCCCHEEHIcxa9e2caqXS2gnMz+pniKiSU57G1J0NSeYlJxiLV7W6mJxLEV5HiNAZWyBT06HztxnGSUiCy6rlE5KkTlqBAfmFPO9sYYbQmLXL/JhKIc+XoRQ+KQlrP9/Oc/Z9asWeTl5ZGXl8f8+fN57rnnMsc/+clPomla1r+TTz456xrJZJLrr7+e4uJicnJyuOyyy9i9e3fWmObmZq6++mrC4TDhcJirr76alpaWg/GIQgghhBBCiKPM8+/U8tqWBuJpm/ZUVxAp0p7m9a2NpG2HtniaHc3tJC2bzsXY4mm7nyt28Xs0ppbmMbsif5hmL44EnQGl2RX5VI4KSQBJDJlDGkQaO3Ys3/3ud1m2bBnLli3j7LPP5gMf+ADr1q3LjLnggguorq7O/PvHP/6RdY0bbriBp556iscee4xXX32VaDTKJZdcgm13fQNesGABK1euZNGiRSxatIiVK1dy9dVXH7TnFEIIIYQQQhwdHEfx99V7iactLNshZXcFkTy6RiLtsLOpHctRRNrTBL1dxSGO2n8WUsA0mFORLw21hRCHxCEtZ7v00kuztu+8805+/vOfs3TpUmbMmAGAz+ejtLTvWt9IJMJvf/tbHnroIc4991wAHn74YSoqKnjhhRc4//zzWb9+PYsWLWLp0qWcdNJJAPz6179m/vz5bNy4kalTpw7jEwohhBBCCCGOJtsbY1S3xLEdSNsOTreokOUovKZOynIwdY2U7dAaT+33mhrQmUii0Di5slAyS4QQh8Rhszqbbds89thjxGIx5s+fn9n/8ssvU1JSwpQpU7jmmmuoq6vLHHv77bdJp9Ocd955mX1jxoyhqqqK119/HYA33niDcDicCSABnHzyyYTD4cyYviSTSVpbW7P+CSGEEEIIIcS+tCUsWuIpUlZ2AAnAVpDsWH3Na+qkbYeWuNXndTrpGnhM3f1n6BSEvJSGA8P3AEIIsQ+HvLH2mjVrmD9/PolEglAoxFNPPcX06dMBuPDCC/nwhz/M+PHjeffdd/nGN77B2Wefzdtvv43P56Ompgav10tBQUHWNUePHk1NTQ0ANTU1lJSU9LpvSUlJZkxf7r77bm6//fYhfFIhhBBCCCHEkS7HZ9AYTfVbluYAuoKQR6Mtuf+12PKDXgIeA0cpUpZNUdBLrv+Q/xknhDhKHfLvPlOnTmXlypW0tLTw5JNP8olPfIIlS5Ywffp0PvKRj2TGVVVVcfzxxzN+/HieffZZLr/88n6vqZRC67aWZvfX/Y3p6Wtf+xo33nhjZru1tZWKiorBPp4QQgghhBDiKOIoRcJy+yDpuEGjnhTQmtp/AAkgmbYpCHpoaU9h6jozy8PSD0kIccgc8iCS1+tl0qRJABx//PG89dZb3Hffffzyl7/sNbasrIzx48ezefNmAEpLS0mlUjQ3N2dlI9XV1XHKKadkxtTW1va6Vn19PaNHj+53Xj6fD5/P956eTQghhBBCCHF02VIXRQcMjcyqaz0pwO5ouK2x72ba8ZRNXVsSQ9OYPDqHK+aNlX5IQohD5rDpidRJKUUymezzWGNjI7t27aKsrAyAefPm4fF4eP755zNjqqurWbt2bSaINH/+fCKRCP/5z38yY958800ikUhmjBBCCCGEEEIMDQ3T0Mnx9f1+fWf4x2Pq+w0ggZvJ5DiK06cU8/WLp1NVHh7CuQohxOAc0kykW2+9lQsvvJCKigra2tp47LHHePnll1m0aBHRaJRvf/vbXHHFFZSVlbF9+3ZuvfVWiouL+dCHPgRAOBzmM5/5DDfddBNFRUUUFhZy8803M3PmzMxqbdOmTeOCCy7gmmuuyWQ3XXvttVxyySWyMpsQQgghhBBiSE0ZHSLHZ9AQ7X/VNUMDHbXfABKAoUPQo3NhVakEkIQQh9wBBZG2bt3Kgw8+yNatW7nvvvsoKSlh0aJFVFRUMGPGjAFfp7a2lquvvprq6mrC4TCzZs1i0aJFvP/97ycej7NmzRr+8Ic/0NLSQllZGWeddRZ/+tOfyM3NzVzjhz/8IaZpcuWVVxKPxznnnHP43e9+h2EYmTGPPPIICxcuzKzidtlll/GTn/zkQB5dCCGEEEIIIfpVWRyiIOilOtJ3dYUCwn6DhL3vkrdOGhBLO/zu9R1UFOZIIEkIcUhpSqmBdXTrsGTJEi688EJOPfVUXnnlFdavX09lZSXf//73+c9//sMTTzwxXHM9pFpbWwmHw0QiEfLy8g71dIQQQgghhBCHodW7W/jUg/+hMZbud0zQo+Mo8BgabUl7n9fTgIKghzEFAU6cUMTXL54mPZGEEENuoDGPQfdE+upXv8odd9zB888/j9frzew/66yzeOONNw5stkIIIYQQQggxwjmO4nevbyeRdvAaGn2FegwNkpZDynZgAO/nKyDoNTimMIfNdW1sb4wN+byFEGKgBh1EWrNmTaYnUXejRo2isbFxSCYlhBBCCCGEECPN9sYY2+qjeA0dn6n32fPIUW4Jm6OgLeUM6LqFIR8Bn0ky7dCWsIZ20kIIMQiDDiLl5+dTXV3da/+KFSsoLy8fkkkJIYQQQgghxEjTlrCwHfB63CBSXwbVS6SDqWvEUzY+j06u/5CujSSEOMoNOoi0YMECvvKVr1BTU4OmaTiOw2uvvcbNN9/Mxz/+8eGYoxBCCCGEEEL0yXEU2+qjrNrVwrb6KI5zIGGaoZHrNwkHTAxdIzKEGUOW7VAdiTO5JJcJRTlDdl0hhBisQYex77zzTj75yU9SXl6OUorp06dj2zYLFizg61//+nDMUQghhBBCCCF6WbWzmZ++vIXtDe2gQUmun8mjQ1wxd+whWcVsQlEOuq5RG0nsd9W1wdjTkmDe+AIun1suTbWFEIfUoINIHo+HRx55hO985zssX74cx3E47rjjmDx58nDMTwghhBBCCCF6+enizfz85W3Ekm7Gjwbsampnd3M7e5rjLDxn8kEPJP1t1R7e2NowpAEkcMtHrphbfkgCY0II0d0BF9RWVlZSWVk5lHMRQgghhBBCiP16asVu7n9xM0mrK1qjgHjaYXtjO7GkxV+W72F6Wd5By9xZvbuFrz+1hrQ9tNfVgbZkmmhSGmoLIQ69QfdE+q//+i+++93v9tp/zz338OEPf3hIJiWEEEIIIYQQfbEsh3v/tTErgNRTfTTFq5vr2d4YOyhzchzF/z29jtgAV1sbDI+pkXbgkTd3snZPZMivL4QQgzHoINKSJUu4+OKLe+2/4IILeOWVV4ZkUkIIIYQQQgjRl1e31rO3ObHfcdubYjS3pw7CjGBLfRurdrcM09U1PB2rs/1l+Z5D2jhcCCEGHUSKRqN4vd5e+z0eD62trUMyKSGEEEIIIYToyxPL9jCQfB/LVmypjQ77fABe29JIeuiTkADQNcjxmZSG/Wyuazto2VVCCNGXQQeRqqqq+NOf/tRr/2OPPcb06dOHZFJCCCGEEEII0dPq3S28vLF2wOO1g7SQ2bLtTcN2bZ/HoDjkY1TIRzLt0JaQ3khCiENn0I21v/GNb3DFFVewdetWzj77bABefPFFHn30UR5//PEhn6AQQgghhBDiKOc4OI1beeH5ZZSkY8QYjdrP++FeQ2d0nn/Yp2ZZDst2DE8QSdMg5DU5pjiHeNrB59HJ9R/w2khCCPGeDfo70GWXXcZf//pX7rrrLp544gkCgQCzZs3ihRde4IwzzhiOOQohhBBCCCGOVtWrYOUfadn8Juc3NPM+08sadQyPWWeziYp+TzMNnfyAZ9in99rWBtri6WG5tqFpVJWHyQ962FIXZdbYfCYU5QzLvYQQYiAOKIx98cUX99lcWwghhBBCCCGGTPUq+OetRPduwEqmKcbNzqnQ6vgD5/V7mqFBSa6Pv62qZubYfHR9+Ora6tuS2MPU69rQNZKWxZa6JIU5Xi6fWz6szyKEEPsz6J5IQgghhBBCCDHsHAdeu5/knjXEkylqnAKed+bRpvzoOGynrN9TZ44NM2NM+KA0oi7O9WIP04ppScvhta1NNMVSXDyrjKry8LDcRwghBmpAmUiFhYVs2rSJ4uJiCgoK0PbRoa6pafiaygkhhBBCCCGOEo1bUDuX0p52iKhcfmlfyrPOfAC+Z/6y39M8OpTnBwh4DWpbh78R9Zj8AIamYTH0gSRD1/CbOo3RJI/+ZycTR4UkkCSEOKQGFET64Q9/SG5uLgA/+tGPhnM+QgghhBBCCAF167HirbSqABqw2JmTOfT/rI9gYGNjMIZ69jIqcyzgNbFsRTxlH5RG1Gv3tA5p+EiDzPVMXWNUro+W9hTv1sd48u3dTC/Lk5I2IcQhM6DvqJ/4xCcAsCw3in/++edTWlo6fLMSQgghhBBCHOUUjoIc4oS0OBVaPRvVOACKtQi6cqilkPFaLXuVG0TSNQh6TUxDozoSH/ZG1I6jeGZ19ZCVs2m4q8olbSdrb47PQ3vKYs2eCNsbY1SOCg3J/YQQYrAG1RPJNE0+//nPk0wmh2s+QgghhBBCCAEl09F0nUKtDS8WLaorcBKinXTH++G7VFcWklLg9+jUth6cRtTbGqKs3xtB1zXMbrfRcANa3bcHSut1nsLUNVDQnraHvTxPCCH2ZdCNtU866SRWrFgxHHMRQgghhBBCCFfBBDxO0g2kKGgmN3MoRoAEXgD2UpDZr4Cg12DW2HwWnjN52PsHvfhOHfXRFJatsLolI3W+7IwHTR2dg3cANSC6BqluWUhp26G+LUl7ygYNgh5j2MvzhBBiXwb9Hei6667jpptuYvfu3cybN4+cnOz00FmzZg3Z5IQQQgghhBBHqbceQLPiKOAtNZUUnsyhd9SEzOsTtE28qaoAtxTsi2dP5oIZpcPeN2jtngiPL9+F1VHK1r2XEbhZUT5TQ9c0rjt7Mr9YvIV3aqL9Xs+rQ8rJ3ucoiCUtYkmLcNDDzPLwsJbnCSHE/gw6iPSRj3wEgIULF2b2aZqGUgpN07Bte+hmJ4QQQgghhDj67F0BS76X2fxs+sv9Dp2m7cwEkTQNji3NHfYAkuMofrlkKzsbY5l9PbsiKdwgUGGOlzy/ScDnIddnEk9ZWVlLXlMn12sQSab7vJfdMVZDG/byPCGE2J9BB5Hefffd4ZiHEEIIIYQQQkD1Knj+m5BoAWCrM4Y2ggDM19bxhpqRNTytjMxrR6mDkqnz/Du1LN5YR2o/759bjmJaWS7hgBcdKA37aW5P0RBNZcaMzvXhOA6N7e5qbEqpTOAIwNBB1zR0DQJeo/dNhBDiIBpUEKmtrY1NmzaRTqc58cQTKS4uHq55CSGEEEIIIY42jgMrH4XIXsCt7fqdfX7m8Pc8v+L01H1Zp7yhpmdeK8VByUJ6+M0dxJL7r8DQgYtmlhEOePB7Dcq8JpF4Oqv0rbY1gaPcrXDQgwaZIFNBjoccr4lSitaExabaKJNKcvu6lRBCHBQDDiKtXr2aCy+8kJqaGpRS5OXl8cQTT3DuuecO5/yEEEIIIYQQR4umbfDuEmjZBcCfrTN42H5/5vBY6nud8i7lmdeWo9hc28rEUUNT0uY4iu2NMdoSFrl+kwlFOWxriLKxprVXD6S+mIZOccjHhKIcJpWEeHNbI2nLzjovbSsUbk8lU4Mcn4ek5WDqOgVBt3l4yuoMWO3vjkIIMbwGHET66le/yrhx43j88cfx+/3cfvvtfPGLX2TDhg3DOT8hhBBCCCHE0WLdX6B+Eyh3GftbrP/JHPqY/iJaH3Eh1WPB6a88uYbZFflcMXfse1qdbe2eCE8u382WuijJtIPPozOpJERprp9owsLZ/yVQKEJ+E13X+NBx5Ty3pprWRHYGk2lo2B2BpOb2NCGfh5Jcf9ZVokmLkN9kymjJQhJCHFoDDiItW7aMf/zjHxx//PEAPPDAA5SUlBCNRgmFQsM2QSGEEEIIIcRRwLbgzV9kAkhP2u/LOmxoAwnbgNfQWLM7wp7mOAvPmXxAgaS1eyLc/+JmmmIpysIBAmGDeMpmze4Ir8brSVgDm4utoC3hNswOeg1sR2EY4FhdOUVBr0nIa1AXTZK2FY2xJHkBL6auYTmKWDKNUnDC+EIqi+XvLiHEoaXvf4iroaGBcePGZbaLiooIBoPU1/dOKRVCCCGEEEKIQfn7QmhvBGCjM5ab0p/POpzqeP/7TvM3mX0GvfsS5fg8TCoJ0RRL8Zfle3CcwZWAOY7iyeW7aYqlGF8UZGt9lOqWOI5SVBYHaYgmGfAlFUQ7eidtqm0jaTmMzvVjdCu1Kw558XtNCoNedM3t69SesmiNp2lPWZi6zrFluVxzeqWszCaEOOQGnImkaRptbW34/W5qpVIqs6+1tTUzLi8vb+hnKYQQQgghhDhyWSlY+2Rm8+/2/F5DrjeeAmCB8RK3WZ8FwEbH0Nysns7Ajt/U0TSNsnCAzXVtbG+MUTlq4Bk82xtjbKmLEvSavLShjub2NLoGJbl+/B6d5ACzkMAtZ+vSFQAqyfVT15agOOTL7PeaOn6PwXHjCogmLdrTNkGPwczyMFfMe2+leUIIMVQGHERSSjFlypRe+4477rjMa03TsO39r1IghBBCCCGEEBmv/D+wEgBscsr5if2hrMPbfAvcUEtHN+sgCdrxo6HhKNB1MvVhdsf/A16D2laHtoQ1qKm0JSyaoima21NE4m4pmqNA0xRN0QTx1MCDSLqmkeM1AJgyOkTIbxJLWuQHvYwvysmMU0oRS1qEgx5uvfhYTF3PauYtGUhCiMPFgINIixcvHs55CCGEEEIIIY5GjgNbXshsvuQcl3X4VvOR7IbaGjzkvZOPpm8nrXQ0wGcaJNI2jgJHuVGkeMrG59HJ9Q/4Tx4AcnwGdW0JWuLprLK1+rYkhq4Nan20oNcgv2OFtcriEMePL+CVTQ20JtIEvSaGrmE7ivaUhdPR92jSEK0sJ4QQw2HA31HPOOOM4ZyHEEIIIYQQ4mjUtA1atmc2a1UBAJ8znuarnseA7IXtHWCJfgp5AR9JW6FpbjPttO3gdKxyppSiOhJn1th8JnTL+BmIzbVRIvE0aTs7XOQ4YA2iv5IOlOb5CQc87rauce3pE6lrTbKtIUZ7qitDStc0ji0NSd8jIcRhb3BheSGEEEIIIYQYSu2N0N6U2Vyv3MV8RmvNmX0dVWw4aGwwprKydAEnBnzEEhar90SwHTB1nbRtE0taNMdSFOZ4uXxu+aCCMo6jeGb1XhzlBqdUt5iR1jmJAdBwy+mqysNZQayq8jBfv2Q6T7y9izV7WomnbAJeg1nS90gIMUJIEEkIIYQQQghx6Lz7bzqjM3UqzFJnBgClWlPWMA2T9tHzeLXwc6ioQWM0hc+jc8aUUSjghfW1ALQl0pwwoYjL55YPOiizvTHG9sZ2NE3DoylS3dq92oOoY/OaGkUhH588ZUKvIFZVeZjpZXlsb4xJ3yMhxIgjQSQhhBBCCCHEoeE4sP3fmc3XnKrM63n65uyxY2aRe+k9XDN6Fu/vEYABOPeHS9hWH+Pj8ydwxdyxBxSUaUtYOI7C0DWcHlGjgSYiaUBhjo+bzpvCzLH5fY7RdW1QK8YJIcThQoJIQgghhBBCiIOvehUsfwR2vJbZtcKZDMAnjUWUaC3dBusw95NQNhsd+gzABDtWQRuV6zvgrJ5cv0nQZ+L3GMSVontn7YEmIpUXBPjpguOYXVFwQHMQQojDmX6gJ27ZsoV//vOfxONxwG1eJ4QQQgghhBD7Vb0KlnwPtr0MThoAR2n8wT4PgNn61uzx3hDM+dg+L2no7p829iCaX/c0oSiHmeV5eA0dn2kc0DUumTlaAkhCiCPWoINIjY2NnHvuuUyZMoWLLrqI6upqAD772c9y0003DfkEhRBCCCGEEEcQx4GVj0L9xqxV2SJ0NaA+RV+Xfc7EM8H07uOSCst2AFi1q4UVO5vZVh/FGWRASdc1/mteBZXFg1vRrbvdLYlB31cIIUaKQQeRvvSlL2GaJjt37iQYDGb2f+QjH2HRokVDOjkhhBBCCCHEEaZxC2z+FzRuI2nZnJf8HgtSt9Km3L8tAiQY3b2UzfC6pWz9WLsnwneefYcdjTEAfvPqu3zu4be56c+r+M6z77B2T2RQ0+tcQe2ECflZ+3WcAZ3/bkM72zvmIoQQR5pB90T617/+xT//+U/Gjh2btX/y5Mns2LFjyCYmhBBCCCGEOMJUr4KnvwRNbrnaZjWOTaqCTaqC01M/AiCXePY5hg+OeV+fl1u7J8L9L25md3M7KcsN8hgaJNM2e1vaSVo2e5rjLDxn8qBWajP8exlV8RpsmJrZZxa9hBWdhpMs7/Oczi5MybRNW8Ia8L2EEGIkGXQmUiwWy8pA6tTQ0IDP5xuSSQkhhBBCCCGOMNWr4OmFUL08s2uPKu41rI6e/YQUNG/vNc5xFE8u301jNEk0aWF1lJAl0g5JyyGSsIgmLJpiKf6yfM+AS8zWN67n+2/+mMXb1mTt13w1eIqWoJmN/Z6ra2AaOrl+Wb9ICHFkGnQQ6fTTT+cPf/hDZlvTNBzH4Z577uGss84a0skJIYQQQgghjgCO467E1rCJ7uucrXMm9Bo6XqvJ3qEcqFvfa9z2xhhb6qJomkZta5LOdX7SjiJlKxxHUduWBGBzXduASswc5fDAqsfZUF9DWzQ7mGW1ziG5dwHK6rtptgJ0TWP6mDwmFB14TyUhhDicDTpEfs8993DmmWeybNkyUqkUt9xyC+vWraOpqYnXXntt/xcQQgghhBBCHF2atsHOpWClANjijOHr1qdY6szoNfRezy+yd2g63QNPndoSFomUTW1rAttR6Dp09NbGdhQ+j07KVtS1JigNBwZUYrY9soPl1RtxknlYiexgkR2t6njV//vweQEPnzplArqu9TtGCCFGskFnIk2fPp3Vq1dz4okn8v73v59YLMbll1/OihUrmDhx4nDMUQghhBBCCDGSJSMQbwTlBnIets/tM4B0hb6E4/VN3fZo4M2Bkum9xub6TRwgmrQwdY2eYZtE2kGhaI6ncZQaUInZ5vp6Yqk4pu4nFZk9iAcEj67xuTMqmTk2f1DnCSHESHJAxbqlpaXcfvvtQz0XIYQQQgghxJGotRraqjObzSq3z2ET9b29d46ugqJJvXZPKMqhLOxnQ3Urhq6R7JFopDr+0560CXqNAZWYKccPygN6Es3ThEqP2u85nU6uLOQzp1UOeLwQQoxEg85EWrRoEa+++mpm+6c//Slz5sxhwYIFNDc3D+nkhBBCCCGEECOc48ArPwBlZ3a10BXQMemK/oRIZJ/rCcLxnwa9958tuq5x6awx+EydlG33KnjTAEPT0DTwGAMrL5tUMAGPlktc34yZs2n/J2TuA++fPlrK2IQQR7xBB5G+/OUv09raCsCaNWu48cYbueiii9i2bRs33njjkE9QCCGEEEIIMYK98ROofhsApeBDydtZ4swBYJa2lae838oM9ZLOPnfCaTD1on4v/f7po5kzrgANjZ7xG9PQMAyNklwfKUsNqLF2Ut+Nx9uGrdIMtGhDA8IBj5SxCSGOCoMuZ3v33XeZPt2tSX7yySe59NJLueuuu1i+fDkXXdT/N3ghhBBCCCHEUca24PUfZzb3UMwKNRkAHYc/e/8Pv5bmv40XeN2ZwSXG0q5zzSBUXd5nFlInXde4ZFYZy3c2k0jZJCwnc8zvMcj1mUwfEyYST++3sbajHJ7Z9ncKc0zS1rG0O/4BPaKuQWnYTzjgGdB4IYQYyQYdRPJ6vbS3twPwwgsv8PGPfxyAwsLCTIaSEEIIIYQQQrBtMcQaMpv/lezKOvqp5z78mpt5dKfnAZQCTQPQIKcE8sdB+fH7vcWcinyOKcphe2MsK4hUlONjamkuHkMnYdn7bay9s3UnWyNbKcoJEbOqQe2/aEMDDENj0qjQgHouCSHESDfoINJpp53GjTfeyKmnnsp//vMf/vSnPwGwadMmxo4dO+QTFEIIIYQQQoxQNWsBN7CzwymhhiIArjQWc6HxVtZQTcPNPhp7AtgJKJ8HhftvVD2hKIc54/JpiCZpbu8qhzu2NEQ46GFLXZRZY/P3G+SJpqO0JFqob6+nLRVFqf1nFinAUTBzbFj6IQkhjgqD7on0k5/8BNM0eeKJJ/j5z39OeXk5AM899xwXXHDBkE9QCCGEEEIIMUK1N2VeLu7ogwRwu/n73mN1L4w7GZwk5IyC2R/dZylb5jRd44q5Y8nxZb8/Hks5bKmLUpjj5fK55fsN8gQ9Qerb64kkI1jKQtkDK2cLeAz2tiRwnJ6tvYUQ4sgz6EykcePG8cwzz/Ta/8Mf/nBIJiSEEEIIIYQ4DDgONG2DZAR8YTcraABBnazzt76Y2Yx0rMj2MeMlAlqqx2AdSo51X46Z6waQymYP+FZV5WHOmVbChpq2zL66tgTzK4u4Yt5YqsrD+5+ucmhNtZJWbjaTsgP7PcfUIeQz2VIXZXtjjMpRoQHPWQghRqJBB5EAbNvmr3/9K+vXr0fTNKZNm8YHPvABDMMY6vkJIYQQQgghDhbbgm1LYPebsGMptDeAkwZ/AYw5Do5bMPDgTv0GqFuf2fyh9WEAHHpmBGlw7u1wzKkHFqzKXCX7umqQiUFPbHyClOMGt+zkKJzUaMAG+v4bx2vqFAa92I4aUONuIYQ4Egw6iLRlyxYuuugi9uzZw9SpU1FKsWnTJioqKnj22WeZOHHicMxTCCGEEEIIMZzWPA6v3udmH6Vj3Q7oYHqgcTPUrYXz7+oKJDkONG6BmtVuwMgThLJZUHkWLP0Znf2QmlVXhs4/7JP4nufXXZefciGctvA9TX3tngiLN9Rl7SvM8bJmT4Q9LXEWnjN5n9lIlmPx0q6XANDQSdWfC4CRswk7Ns39KPiqcZJlmXMKAh6CXoOW9jS6ru23cbcQQhwJBv2dbuHChUycOJGlS5dSWFgIQGNjI1dddRULFy7k2WefHfJJCiGEEEIIIYbRmsfhn7dBvAXsrlKzVhUgT4uDlQQrDXuWw+s/hg/9CmrXwGv3w6Z/QaoNt820BroH8isg2VVa9ox9cub1Z80efy+Ex7ynqTuO4snlu4mlsjOBHKWYNCqHLfUx/rJ8D9PL8vrti/Rm9Zu0JlvR0VGA1eYGyVS6KDNGM1uhWxCp835px2HiqBxZnU0IcVQYdJ7okiVL+P73v58JIAEUFRXx3e9+lyVLlgzqWj//+c+ZNWsWeXl55OXlMX/+fJ577rnMcaUU3/72txkzZgyBQIAzzzyTdevWZV0jmUxy/fXXU1xcTE5ODpdddhm7d+/OGtPc3MzVV19NOBwmHA5z9dVX09LSMthHF0IIIYQQ4shjW/DydyHWAHYSNxgEf7TOZlbyt3wt/dmO0jAHrIRb7rbhWXh6Iax9ClKtmXNAgZOCpq0Q68oMes2pAkDD4Trj6W4312D0rPc0/e2NMbbURfGZ2X/abKxpY/muFoJeg811bWxvjPVzBWiINwDgN/3dngWM4LZuU80OUiUth4ZoihyvySdPmSCrswkhjgqDDiL5fD7a2tp67Y9Go3i93kFda+zYsXz3u99l2bJlLFu2jLPPPpsPfOADmUDR97//fX7wgx/wk5/8hLfeeovS0lLe//73Z93/hhtu4KmnnuKxxx7j1VdfJRqNcskll2DbdmbMggULWLlyJYsWLWLRokWsXLmSq6++erCPLoQQQgghxJHnpe9A41ZQNkll8lPrA9yTvpJbrc8C8Kh9NqtVpTtWKXfFtdfvh+o1uD2D9m+ZMxWAJ7y349G6nePJgTkfe0/Tb0tYNMVS1LUms/abhkZTLMXWuihNsdQ+exYVB4oxdROP7kFHp7MMz1PwWmaM1iOIhAJDh6mjc5kxZv+Nu4UQ4kgw6CDSJZdcwrXXXsubb76JUgqlFEuXLuVzn/scl1122aCudemll3LRRRcxZcoUpkyZwp133kkoFGLp0qUopfjRj37EbbfdxuWXX05VVRW///3vaW9v549//CMAkUiE3/72t9x7772ce+65HHfccTz88MOsWbOGF154AYD169ezaNEifvOb3zB//nzmz5/Pr3/9a5555hk2btw42McXQgghhBDiyLF3Bfzn13Rm3/zLOZ57rI/wU/uDWcPWOhM6XilQNtS+Q2cA6U/WmUxMPMSHk99kqXNsr1u85UylATfIMl3bkX1w2qVgDu6N6J5yfAbN7SlStpO139A1wgEP7Wmb5vYUOb7+FwE6qewkCvwFRNNRt0G35l5L07uvIqdlvSovCHDSMYU4qH1mOQkhxJFk0EGk+++/n4kTJzJ//nz8fj9+v59TTz2VSZMmcd999x3wRGzb5rHHHiMWizF//nzeffddampqOO+88zJjfD4fZ5xxBq+//joAb7/9Nul0OmvMmDFjqKqqyox54403CIfDnHTSSZkxJ598MuFwODOmL8lkktbW1qx/QgghhBBCHDEcB166K9NEu135WO+M73NohB79ftLtmZf3WB/BxuAtdSxfTn8u+zwV5MOpb2W2A1oq6ziT3/8eHqDbdCyHpJUdRGppTxFPWb3WguuLrumUh8rR0NzQmOpoHdt9iTfHg6cjDpUXMJkxJo+y/CDJtCMrswkhjhqDbqydn5/P3/72NzZv3syGDRtQSjF9+nQmTZp0QBNYs2YN8+fPJ5FIEAqFeOqpp5g+fXomwDN69Ois8aNHj2bHDvcdjJqaGrxeLwUFBb3G1NTUZMaUlJT0um9JSUlmTF/uvvtubr/99gN6JiGEEEIIIQ57jVtg9zLAXT3tfckfESXY59A21X2/onvfoBi+zOtdqoT1TgXT9F0AzE7+Zh8T0CBWf6Czz1i9O0LccnC6B3yApKWoiSTJC5gUBLzEkv2X3u1s3Ulbqo18Xz6N7dGuGXqSePKXko7MI1i4jvzkODRrFO0pG8tWxFM2Po8uK7MJIY4aB/zdbvLkyUyePPk9T2Dq1KmsXLmSlpYWnnzyST7xiU9kNejWtOz3DpRSvfb11HNMX+P3d52vfe1r3HjjjZnt1tZWKioq9vs8QgghhBBCjAi16yDpZttvUmP7DSABtO3jmNOjuOHC1PfY7l/Qa9wDnu9n79AMCPV+s3cwHEfx6pYGlKPQNS0rkKQBSoNE2sFr7jvQs6ZhDTtad4CCoDYK96PiYGoGWsnL+PO2YHjiYCVQllsqZxoa1ZE4s8bmy8psQoijxoCCSN2DKfvzgx/8YFAT8Hq9mSym448/nrfeeov77ruPr3zlK4CbSVRW1rWUZl1dXSY7qbS0lFQqRXNzc1Y2Ul1dHaecckpmTG1tba/71tfX98py6s7n8+Hz+fo9LoQQQgghxGHJcaBpGyQj4AtDYSXoPbpYVK+CN34Oyi3Diqh9B0GiKtDn/tvSnyaJ29Pow8bLPG6fCcBX0tdwt9mVhXSitp6zjZXZJ/tyYHTVgB+rL9sbY9RG4gBYTnYmkqaBR9dI2Q5tyTTjCvoOhDnKYdH2RcTSMRzlkI65fyPo3kbQHEwjje2txbYKsC0viWSaPL+X2tYkhTleLp9bLiuzCSGOGgMKIq1YsWJAF9tfhtBAKKVIJpMcc8wxlJaW8vzzz3PccccBkEqlWLJkCd/73vcAmDdvHh6Ph+eff54rr7wSgOrqatauXcv3v+++0zF//nwikQj/+c9/OPHEEwF48803iUQimUCTEEIIIYQQR4Q9K2Hpz2DvcrCSEMiHcfPhuP+GstnumOpVsOhrUL08c1qLCvV5ubP15bzkzO0zE6lJ5fKIfS4Ak7XdzNU28zhnAvAn+yymaLsyY3/j/X+9L15xMhQdWEuMTm0Ji9rWZK+m2uC2M0o7Ch0wNI2dze1Ujup6Tkc57GzdyeJdi3m7+m0sZeFYIdp3fRoA3VeDrRKgGegeC491DK1tYQwd8oMeZo3N5/K55VSVy8psQoijx4CCSIsXLx6Wm996661ceOGFVFRU0NbWxmOPPcbLL7/MokWL0DSNG264gbvuuitTOnfXXXcRDAZZsMBNjw2Hw3zmM5/hpptuoqioiMLCQm6++WZmzpzJuee6P9CmTZvGBRdcwDXXXMMvf/lLAK699louueQSpk6dOizPJYQQQgghjgADyeg5nKz6Mzx3CyRayPQsiuyC+g2wcyl84Mcweia8dj/UrAGnq0fQdlUKQIh2HvTeQx4x6lU+EXLcIFJnJpLuBcdtjt09e+nv3tt40ZmbNZ3vWR/NvM4lnj1XfwGceet7/njm+Axa4mmUAp+hkbS7ZSN1vL9t6BpeU89qfr2+cT1Pb32aLS1b2NC4gZjtNhdPNb4vM0b31XbUxNk4QIlnBtOmj+HUSUXMqXBL2CQDSQhxtBlwTyTbtlm3bh2TJ08mEMhOZ21vb2fLli1UVVWhD+IHQW1tLVdffTXV1dWEw2FmzZrFokWLeP/73VUabrnlFuLxONdddx3Nzc2cdNJJ/Otf/yI3NzdzjR/+8IeYpsmVV15JPB7nnHPO4Xe/+x2G0bWE5yOPPMLChQszq7hddtll/OQnPxnwPIUQQgghxFGmehWsfBTqNrhBGQfwmDBqOow9AWZ/5D0vTT+kVv8J/r4QrESPAwrsFNSshn/cDJf+2A0oKRtw+LddxWo1kY3K7f15s/lnTtA3AjCV3bxizwSgtTMTyelaXa29o6H2KJrxa2nO19/KunOqo8ytSnuX7IIFHeZfB+VzhuLJ6Yzj6JpG94bfpq6hAVpHEKmzJ9L6xvX8YtUvaE42E/KEsFVXMM1JjMm8NnwdLTE0MND45Ekz+a/psyRwJIQ4qg04iPTQQw/xk5/8hDfffLPXMZ/Px6c//WluuOEGrrrqqgHf/Le//e0+j2uaxre//W2+/e1v9zvG7/fz4x//mB//+Mf9jiksLOThhx8e8LyEEEIIIcRRrHoVLPketOyEVDu07gWrI5Nm91uw8iF44VtwyhfgfTcd2rkC7F0B//pWJoBkK437rCtoI8Bt5iOYmgMo2P02rHjIbaZtu1k5V6dvzbpUpVadtR3S3OeO0rsnUrwjiJSjJQEwNYe1vk9TlXwga9ztnt91begeKJ0FMy4/4MftLpa0KQ75iCVtEuns1deUAtNwAz7l4QATinJwlMPTW5+mOdlMZbiSna07aU+3Z86x27sWDjJ8tRiaga1sNA3CIUcCSEKIo96A04Z++9vfcvPNN2dl+HQyDINbbrmFX/3qV0M6OSGEEEIIIQ4ax4H6TfDafdCwGaJ10LS9K4DUSTmQaIaXvwv/vveQTDXDcWDpL6G93p2ago+lvs799uU8aF/IrdZnusYqGza/AI4FdrLPy1Xq2UGkPNwAy25Vwp+sM+m2+Bkx5QcgQFf2U0jrmQnVdQ0ADC9UnOiWBg6BXL9JUcjH1NG5+D0GhpapYkPXwGPoBL0mF88qQ9c1drbuZGtkK6ODo2lJtvDO3hZiez+Ik+6jr5G3PpOl5DE86AP/00kIIY5YA/5OuHHjRk4++eR+j59wwgmsX79+SCYlhBBCCCHEQVW9Cp77Kjx2Nax/Fhq3QbSWLc5ofmVd3NUTqJNy3GDMGz8DK9X3NQ+Gpm1uzyPHzSz6vvUR/qOmZQ7/2T4re3yyLTMWwIOVdXgMjVnbnZlIAF+xruUfzkmZ7c5MpCB9B6Q65WrdgkgomHDakPWWmlCUw6SSELqucdqkIsYVBvGZ7rU9ho7fY3DapGLeP93t+RRNR0lZKfyGn42NG6nZsoB05ATat19Hqil70R1NcyNmGho5Zg4T8ycOyZyFEGIkG3A5WywWo7W1td/jbW1ttLe393tcCCGEEEKIw1L1Knj6eqjf6K5o1q2vzvesj/K8czx/sd/HRjWWK4x/8/887mItKN3tl7TqTzDv6kMydXa/BbVrM5s/tz/Qa0hCefBr6a4djruSWVKZpLv9OeAlja6prHNzyf79/gvp/+ULabhIfzMTUApq+w4i9cpECpXu+5kGQdc1rpg7lj3NcRpjKWaMCYOm8W5DDL/HYPbYMNecXpkpQwt5QnhNLztad1DbXguOGxxUVphk7WV93kNDI+QJMS5v3JDNWwghRqoBvwUwefJkXn/99X6Pv/rqq0yePLnf40IIIYQQQhx2HAeevRmqV4OVYK0znnqVlzm81HGzejaocSh0nrDP6JaVZLkrnLXuPAQTB9Y8Dv/4MjjpfQ5L4una0DQ6/wTYrUZljRtFS69zgyTRcHrt756R5GPf9w9kMpU09/6JyD7HD1ZVeZiF50xm5tgwkUSaZEdvpNF5fv733ClUlXeVqo3LG0dlXiXvtr6Lo3o/V5eu/kohb4g8fx6723YP6byFEGIkGnAQacGCBXz9619n9erVvY6tWrWKb37zmyxYsGBIJyeEEEIIIcSwWv807F4GKDY4FVySuouzk26fI1tpvZemB1pUqNuWgtyxB2eu3e1dAc/dBqk2AN5xxnF3+qN9Ds0OIumg3PK7nkGk+cY7vc7VNAjRu89Rd+udfWfoZK3MpoBAXn9DD1hVeZhvXDyd2y+bwelT3OeqKAwQ9Bo4Tld2la7pnFB6AspR/V0KgOB4N9vMp/s4tvBYPJqHaDo65PMWQoiRZsDlbF/60pd47rnnmDdvHueeey7HHnssmqaxfv16XnjhBU499VS+9KUvDedchRBCCCHESOI4bs+eZAR8YbeZ8hD1whkSnU2pOzJtXnNmANBGDrucYi5L3Ukzub1OayKXCuq7dlSceDBm28VxYNHXoN1dgl4puCp1K010BWf+5v06H0jdAUBSebq6TbfuzYxp61hxzUuaM/WV3Gg+nn0fzQBlEyJOG8F+p1OgDSS4onX9z9dHE+shoOsa7SmbzXVuYO3tHc186+l1TCoJccXcsZmMpFHBUYS8oX0GhXRfPR7Nw9jcsRT4C2hNtRLyhPodL4QQR4sBB5E8Hg//+te/+OEPf8gf//hHXnnlFZRSTJkyhTvvvJMbbrgBj8ez/wsJIYQQQogjX/UqWPloR5+hBJh+GDUV5nwMymYf6tm5mrZBZFdmc6canXn9vtT9WUPP0lew2DkOgGbVI5iw/mkYPY2DZsMzsHMpAGllcGP681kBpP8x/s4sbRthokQIkcTbda7qaqT9qjMTgNP11fzK+8Psexg+yB8HjZvJ0eLd20T18v88vxjYvDXDve4wWbsnwv0vbqY24pbP5fhM8gNe1uyOsKc5zsJzJmP49/LL1b+kNl6L3a1krRc9ScAMMSFvAnXtdVQVVUlPJCGEYBBBJHADSbfccgu33HLLcM1HCCGEEEKMdNWrYMn3oL0R8srBE4R0O1SvgMhOOOMrhz6Q5DhQswbizZld+yrLetB7D1elvsarzkxecOZxptGtxcPKR+F9N4ExqF+tD4zjwOs/oTOq87RzCn93ulYV+6nnPi423gTAT4oIPcrZOtyVXsBj9tkA5PRVrlZ2HMTq+z/e4fvmLzlW39XvcZcOOGB6IVSSKcEbSo6jeHL5bppiKUbl+tjdEsd2FCG/ySRfiC11UX77n1ex8v7JOw3v0Eebpyya5pByUuyO7qY8VM6lEy9F1w6jLDohhDhE5DuhEEIIIYQYOo7jBlXaG6FoCijHDdQox91ub4RVj2VWCDskqlfBX66Bvy2EjpImS+msUccAUEJz1vAvGk8BUKo1AfCw/X4aMs23dfeZti05OHNv3AI1XQGsLc6YrMOdASSAOG7Wz5Wpb7LN6VoRrU0F+JV9SWb7dKNHz1Pd65botTeAZhLD3+90QlrvnlGfN54GoJQmXvR9ufOikD8BgsXDUs62vTHGlrooZeEARsdKbLub48SSFpqmUZrnY2XLC+xsrXYzkDQDpbR9XjNhJ2iIN3D+hPOZVnQQM82EEOIwJkEkIYQQQggxdJq2uSVsnhwSu1Zw1erZ/HRTGHa9CXveAk8O1G1wxx0Ke1bCX/4H1j4F6dbM7i2qnDh+QrRzUbdAzI8993Ozx+0VdIP5ZGb/RqeiozzLA9gQqzk489/wDFhdgZs1qjLz+mL9jayhreQAECPA2akfEFVuMOh5Z15mzEeNl7jC+Hf2PXJHQ7AQHAt0nVpV0O908unRV8jw8mXfX3kxcCtvBG9iotkAngD4C9xMpJJj3d5YQ6wtYZFMOwS8Bnq3Tt5b6935KU8DcbWXgJ5DLBknsvXzxLbetN/rJq0kaxrW7GclNyGEOHpIEEkIIYQQQgydZMTNYKnfyL8b83g1PYV72i8m7RgQa4SGje7x5NAu8z4ge1fAE5+G+vXQox/OX+1TAajQ6pivu6uUTdBqOFdfnhkzVmtghvYuAGndC7rpBpEML+SUMuyqV8GKhzKbdSo/09foo8ZLfNfzm6zh53SbO8CX0tehFGzoKNt7n76a75gP9r7P+FOhtMp9PqUw9lH7lae1Z+8onIyeO4qJ/ja0nCIIjXb7YZket7Rx9keHpbl6rt/E59GJp+zs1eA6RFNRHC1KbXw3ybSBkyxFpYv7vpjW1Tcqbsd5q+YtdrbuHPI5CyHESCRBJCGEEEIIMXS8udC8HVr3YFqxzO61rV5IRCDZBu1N7riDqXoVPHMjNG/N7HrEOpuzk/+PN+xp/MK+DID1agLn6ct42HMXf/feRkBLZV3G7Ag+WZoXdMMt0wtXQOUZwzt/x4HX7ofWWmLKx6PWWTxvz80cvtP8Lbk9Ssv+z5MdIHreOZ51ajyRjgylk/T1eLQezaX9hXDS56HyLMgpBie9zyDSDG17xysDdA/oGhRPgVCx28Q70QyaBse8D84cvl5YE4pymFQSojoS7xVEUkpRE6vBNlqIW1G0/f0JpKWzzq2L19Gaat3HCUIIcfQ4CN3/hBBCCCHEUePtByHp/sEd77Yq2HJnMsfZW8FOupkpB1NnAKZ2A0rBcjWZqdoufmx9iBqK+Fj6G5mh1xjPoGlwmrG2z0t5NAdURxAJwJcLpy4c/qbaG/8BW14Aq51vWdfyhN0VtLpMfw1D6718WgktvfbtVqOIKDeIFCaWfdD0w4Xfg/I57scsfzw0be03iFSlvdsVsDE9UFAJpTOhrdYNRvkKIL8cZlwOUy8algykTrquccXcsexpjrOlrqvELmU5bK5rxQ5uIejxYCkHx/Hu40qgm12Nv3VNJ22naRuGZuBCCDESDein3Y033jjgC/7gBz844MkIIYQQQogRzErB8oczm5vU2MzrpkwjaiDR6v47WBq3wLuvgN3OP5yT+EL6fwmQIN6jYfSH9H9zk/l47/M7l6b352E2u8GalO6DokluAGnGFdCwxS3R84Xdnj9DGTBxHHj1R5BoAeBp+5SswxO0Hv2YdC84qd5ZRrgrtXVmIuVp3YNIBpx1G8y+0t1s2gapdvDkUJhso66Pvkhn6Ss7Xmmg6TBmDlz2YzcTbbg+FvtQVR5m4TmT+cHzm9jW4D5bwrKZU5ZmjxEh5J3CyvqVtO/89L4vZHRldOmaTtAMknuwM+eEEOIwNaAg0ooVK7K23377bWzbZurUqQBs2rQJwzCYN29eX6cLIYQQQoijwao/ZS3f/qh1duZ11gpfVhzeegAqjj8481r2W4jVAfCUfRpArwASwMfNf+HvVsoEuMGj4qlw2kKY/iE8P30e9jpYE8+HYwPQ3gLPfQWqV0M65jYOHzMHjlswNKVbjgNv/Rr2dPU3SuHJGjJBr+22pcPsj3X0TuqdQdSu/D0ykXS3r5M3BOO6BaeSEfd5DC8/8v+K6xOfY3NHULCceq4w/s0XzafIBJB00y3pM0wonvTen/sAVZWH+e+TxvHSBvfzfcaUUVx5CvzgbYccbw4ew4NKF+3zGlq3j5vX8FIaKiXPm7ePM4QQ4ugxoCDS4sWLM69/8IMfkJuby+9//3sKCtx3JJqbm/nUpz7F+973vuGZpRBCCCGEOPzteQtwM3XaVIBaCjOHtqrspejZ9AxYP3JX7BpOe1dkZUeFe64m1k0xPbKjAkVw1tdg3qcy5Woe0oCBtWUxvLsErKQ71ht0y8E0oGU71K2F8+96b4Gk6lWw4pGO+btZRSudib2GjdXquza8QQiV4k6kt6XO9K4gUmcmkicHUG7Pqk6+sLtf1zhW383z/q/RRB7/sudymfY6QS3hnoPm9kLKHw/lBykouB9esyvzqTDoJc/nw2t6iaaipOzUPs7s0K2xtqmbzCqexbi8ccMxVSGEGHEGnVt67733cvfdd2cCSAAFBQXccccd3HvvvUM6OSGEEEIIMUI4DtR09RFa4xyTdfjfzixSyujakYy5mUvDPacnr4G0GzhqVQG2q/5XUcsu7wKO/zSceE1Xv6M1j2M2rAMgrQcADZQNykIlojjo4Am6zbZr1sLiu2DXMrfUzRnkEvHVq2DRre7HqFuD8kfsc3oNLaZb8CedhN1L+w3O/c05ldbOcjbaAcfNHtN0CHTLtimsdDOqNA+dfzIUEuGj5isEDct9dnQ3gOQPw4TT3HMOA2aP8rlxeeOYGJ5IXbwOy7H6PMc3+q9dG5r7uQoYATRN4/jRx6Nrsh6REELAAQSRWltbqa2t7bW/rq6OtjZpOCeEEEIIcVRq3OL2wumwQrklTbO0rtXQGgm7LzQTUBDZAfWbYN1fYd3f3NeDDbZ0Z6Xg7Ydg8Z3u/1/9ITRuzhxekPo6b6upWacsNP6SeZ1L99XNDCgc37VpW/DqfXgcN5Pll+1nsC5V4q48ppksTF/HKU1fp8XyuT2U0nHYuhieuxn+8WX4561uYGggHAde/D/YtRSSLVmHHrfP7DW8SOv2O7iyIFoPOUX0l43URhDolomEcvsW+cJdg3TdLckbNdltmo0GSrlBM8dyz9F08Phg1BSY87GD1vtof7xm9nPrms7M4pk0xZuwVO8gkr/sz3gLl2LmukFQb9G/MTSD4kAxpYFSRueMPijzFkKIkWDQy0h86EMf4lOf+hT33nsvJ598MgBLly7ly1/+MpdffvmQT1AIIYQQQowAtesyq7IB/Nj6EACXGEvZYpXTjp+0MjuCLpobKFn/d1j+kNsjCa2jL8/JbrPqwZaBvXwPvPajTNZRT3tVIWvVMb32f958mvcZawiQRO9c4Uw33YyiULcSvLcfhIZNmOpMAHY6xVycvIPtgatBg787bj+h37dU8b/mUx0nae7qbcECqF4BkZ1wxgCWud/wDGx7uSNYA+86pfzKvpgPGq/1OTwva5U1zf14Fk6Etlou11/hL87pfNv8HQ/aF7CjWyZWZnU2w+824+6pbLZbkvfa/W5j8ngTOHbXxyhYCMecDqdcPzT9n4ZIz0yk9Y3rWfTuItJ2uu8TdDcw6C9/BGXlkuNPUxIcy9TCqbSl2wh5QsM9ZSGEGDEGHUT6xS9+wc0338xVV11FOu1+IzZNk8985jPcc889Qz5BIYQQQggxAuxZDk4a0IgqHwl8AIzSWvCSph0/KUzA6BgHNGwGNDB94PFDKuouYx+tGVw/occ/Ceue6vdwUpmckvxJr/2TtN0EtBQnaBu7dmoet9F0wQS3UTS4GUTL/wBOGrMz0NRJ2aC6Ml+anI5VvJQClLvCWWGu25y7YSOsegxGz+w/a8dx4PlvZQJIW50yzkm5LSMe7aOUDdyYXNaGJwjBIgiN5rv2r/mMeo5p2k42qHHssN0gkp9k1+ptynIDQn0tY182Gy7/tZtpVrsW2moADXJLYfQMd4W6wyQDqZNpdH1AFIqntz5NTXtNf4lZaLr79ahpCs3TSsBTyKT8SdTH66kqqpJ+SEII0c2gg0jBYJCf/exn3HPPPWzduhWlFJMmTSInJ2c45ieEEEIIIQ53jgN16zs2FK85VZlDp+lr8OIGRJJ4oOM1mgG6QYsdZEliBueml5NjdjRqrl0PKx/dd7Cl08pHsgJIL9uzKNWaOVbfldn3besTWac87b2NDc445uvv9L6e6XGzh05d6PZCchx3LnYKNAOPSvYxia7AUhKP+2zKZrE9i+9sv5p7jDXMK05DXjnUbYCmbf2vYPbGT6B5m3tVBR9M/V+vIbO1LeRoCV53qnjM2+O4JwilM93m3gUT8LbVMEPtAKBMa8wM89EtK8exwBPILmfrTtfdkrVRU/o+fpjxGF1fM63JVqojWwl7wyTtJOnWql7jNU9T1nahr5CmZBMFvgIunXip9EMSQohuBh1E6lRdXU11dTWnn346gUAApRSa1k94XwghhBBCHLmatkHbXgCiys//pG/MHBqlteLpCBylO3/1NP2stsaxOVXGOmc8D9gXMDrdxBv+G9B1zS2L2/7qvoMt4PYp+uc3Mpt/ss7kK9a1ALzmu57yjqBJ9wyed30L0DSYpb/b+3q6CYWT4LSFMPPDXc9WvxFCZVC/uWN1ti6O0tB6BpE6egZ9Kn0LAF/eUsVLiV+52U1WApIR+mRb8OavMpt/ts+kjd5v1E7W9/D/PL8krYyubKJOZXPgpM/Dv78Pkd1ullfa/fhvcLoyarxYmWAXygF//mHTGPu96h5ESjkpUlYKr+HFUQ6JPVf1Gq97u4JrOjoew0NVURWXTryUaUXTDsqchRBipBh0EKmxsZErr7ySxYsXo2kamzdvprKyks9+9rPk5+fLCm1CCCGEEEebeAtE9gBQowp7HfZpabeyC9NdNt5KcFni2wCMohmAWgqpVmHKnWbcptu7ING87/tuWwxxNwAQUcFMAAngkuRdLPf9Dz3f4+z7PU8NwhVu9tG8T3WtxgZuwCcdh/ZGwMEkO2iTxJO1r1153fl3q53SNQXtDe7HKVzef8bPtiUQqwNgs1Oe9TzdjcF95l4BJE8OvP//oHyO23tpxR+hZRek3d5HVxpLeM45CQAvaTeAhOY2yB5/6mFXlnagulWzEU9qeANe6trrcOivaXvX/pJgCXe/726OCR8jGUhCCNGHQX9n/NKXvoTH42Hnzp0Eg8HM/o985CMsWrRoSCcnhBBCCCFGgOoVmabaTeRmdv+v8SRAt0wkL2gmCafrV9B6Crpeq3zcP+gVJNtgz8p937dmTebln3usWtZMLk85p5FWxr6vYXhhxuXw0YfhxGuyA0jgBnyUgtY9gMLp8evzdjW6K8MKiHasfOZ0a500XqsB3ed+jKykm5HUlz1vge2Wy73kHJfZPUvbyueNv2W2u5eldT2HD06/CcbO7Rg0Gy64G878qpthBczVN/VxUwVmAKZe2PecRpi1eyL8+KWuFfmWbo2zqx52RHZ07LF7ndMZWDQ1k6umXcXE/IkSQBJCiH4M+rvjv/71L773ve8xduzYrP2TJ09mx44d/ZwlhBBCCCGOSI7jNovuyOZoUnkAzNM28iWPG0Tq7ImUUiakIuxQfS+Z3qC6ZegoBzY+516/5/3qN8Hav8A7XYGVNhWkpxvT17FNlWW2V/quyR5g5rgNvK/4Tf9NvAsrIW+Mm40ELHOy+wL91T61o2G4q7VjHjXdgmNlqg7a9rjBHMMHzdt738dxYO2Tmc2oCgBwqf46T3i/zWitKyurVxDJmwuX3g/vuyl7v67D8Z+BXPdjEKSrn5Pd/c8A5UCy71XtRpK1eyLc8cw7LN5Qn9kXaU+xpzWCpWw09KzStZ4m5U/i6hlXH4ypCiHEiDXoIFIsFsvKQOrU0NCAz+cbkkkJIYQQQogRomET1K7LbP7Ndpe6t+jKAPLjLqGe6Ai2bO+2zHx3d1kLsnfUrXNXBetUvQr+cg389nx44lNQvTJzKI67RP21xjNUansz+3epEsDN5snXYl3X0kyYer4bZNlXGZeuw4RT6CxP6wyIdXrGnk8KT2a7BXc5+GpV1HUrcLOZrKSbadRXT6T1T7sfyw4/tj8EQEiL49VsSrSWzLEx3YNIugeu+gvM+Wjf82/eDqHRYPrxaF0BuXRnA3DD56bivPXr3gG7EcRxFL96ZSsbatpwuvWoMswkSo+C40fDi9pHZpqu6Wxs2tjvcSGEEAcQRDr99NP5wx/+kNnWNA3Hcbjnnns466yzhnRyQgghhBDiMLftZbdZNLDDKcn03OnebNqvuUGkOO4bjv1lIm1TY7ptKUjFulZ9q14FTy+EdX+FRFOvczsDU/laG58yulosVHf0aApnAkgdPYAM0y1jG0gfoLLjwOs2uO4eHAPYSxFvOsdmtpuUW873b2dmZp+F6a6aZlsQrXMzh7pzHHjxzq4n71YK13mdEPGu6WjNHc9hgsfvlv71JxkBxwZvyP3XoZE8dxW6UInbfLthk9tEfITa1hBl2Y5mdA1yfV1BPU23MXQFyoOTDqIp95iZtzIzRkcn1+N+Tv6+9e84auQG04QQYrgNurH2Pffcw5lnnsmyZctIpVLccsstrFu3jqamJl577bXhmKMQQgghhDhcNW2lc4n7x+yuNxTv8DyYeR3EDTK1Kx8x5csqMeupVQXI0zoCJlbS7UW09i+w9BdQvYbuPW1iysdj9lkcr2/iRcftBXSWvpKp2m6+YX0agPXKXZEsTGcQSbnZO748GDV1YM/ozwOvH1JtWD3eg1XoXJ9e2DUnAvwwfQX32Vdk9lmdTbY15ZaO9bThGWjeCsBaZzyfSH01c+hu8zcATNS7sqtyTQv0APgLINUGsZr+5+4LuxlHALmjuV9/jP9tuZKPBpa5ASQ7DbrXbbLd36pxI8Cm2ijRhEU44MHstjqbZsQAE035UFoMpdxjpq5ncsp0TacwUEhFXgVbIlvY2bqTCeEJB/0ZhBBiJBh0EGn69OmsXr2an//85xiGQSwW4/LLL+cLX/gCZWX9/0IghBBCCCGOMI4DNV2lbJ0rs33FfJQqfXtmf6CjnK2JPM5I/ogG+lmdDNirisnTdnVcPwUvfxccK7PCGMAKZyJfS3+WDWp81rknauuZprvnHqvtYIMazx/tcwEo1Lpn6+iQW+oGYAZK94IniJXs+vU5SIJ2/L2G3mdfQTGRzHNaynADYpoOwYLs+zoOLH+oY6U0uC39WRo7ztNxeJ+xFoByrZHHvbeTb6TcrCZ/nnuubkJO3+WB7oNXQvFkaNoMjsVloQ2cGPghxXrHxzMdcwNq/vz+V40bEbrSt9LGLvw5MRKxsaTNbXiNFhQpd0hHEMkf3kSiZRaaEcVn+JiUP4mgGaShvYFoeuT3hxJCiOEy6CDSzp07qaio4Pbbb+/z2Lhx44ZkYkIIIYQQ4jDXsAkaulbCqutoJt29CTRAQHMbOm9zyvoNIFVodexSJexQozmWXV0Hkm3gD2cFkb6YWsgeRvW6RmfZHIDdo+xsjNbQtaFs8AQGHjRJtUGwEByLj8SXcIf135ygbWCDquj3lFFac6ZRuIUBynIbeXtzs+/btK0jm8u1RxVnXncvYQM4Qd8M4XFglrg1b+3VbpCo8oz+567rcPL/wK43INYAwWJKjY4St3TM7YnkCUDJNPdaI9SU0bmE/CZtznaM4Is4MTczTXNC6FYpjrkDNAvVmUnm2U148g/weVLMKJ5Bgb+AaCqK1/QS8oT2cSchhDi6Dbon0jHHHEN9fX2v/Y2NjRxzzDFDMikhhBBCCHGYq14F//w6xLuaPNepfABKaMkaGuhYFayRvH4v19Cxqtvn0jf0OOKQSCSy9tRQ2Oc1XnG6VljbrLJXEu5s7u1e0nIDUwMNmvjCECyGUdP4lPdFHvV8hwe938fTx3LxnXZ3NPSGzoCW1lGetzd7JbR4C7S5v1srRVaQrbMMMEM33aBQqh3aqt0+TacudPs77cuY4+Dsr0NOMcSb3X/phJuBFMiH/PEw+6MD6w91mKosDjFvXD52zjLSqg1NuRlidjqEkwpDohKPEYCOxtpBr4+KggCnVMxjXN44lFLUttcyKTyJcXnyprgQQvRn0D8plFJomtZrfzQaxe/vnc4rhBBCCCGOMNWrYNHXYMermTIs6BZE6pGJ1Lm0fGegqKf/NZ7kVN0ti1M9fj1dZB/PrMQv+K11AQAJ5emVZdSXOzt6CXWar7+TPWD8aQMPmhRWuv2TEk0Y/hDzPVsIaQmaugXFTtTW4+sWqGqjazXjNIZbyqbrblbT0p90rYSWaIZ0OwCbVXnWbYMdGVwZyoZorTu+sBLOvxNmfnhgzzDzw/Bfv4djL4KCYyCvFMIVMOF9cMYtUDZ7/9c4jOm6xmXHe8kJNaLZ+aSTblZcMjYOXdMo8BdwfMk86PjaGR0s5tiiY8nz5hFNRdkW2UaBr4BLJ16Kro3cYJoQQgy3AZez3XjjjYC7Gts3vvENgsGuH4y2bfPmm28yZ86cIZ+gEEIIIYQ4jDgOvHA77FyaFUBKKA+RjuXtuy9HDxDQ0gCsVxP6vOQUfTfnact4ITWvV/bN59Lu76A/sD7MZ8xFtBHIHLtA/w/jtVp+aV8KwBX6ksyxY/WukrhHPd9hqr6766KeIEy9cIAPjBv8mfMxqFkNdtJd1SyevUJcUEsyhkbe7aNpeJPKdRtq+4vdsrYdb0DjFhg1BRKtgBtQet6Zl3Xex41/ZV8oVAJzr4axJ7slbPvLQOqpfA584GduCV0y4mZYFVaO6Ayk7soKNSqKPCRi+azZ2ZXRVVEYpHJUDrl+I9MTaVZJFdXJTTS0N+A1vVQVVXHpxEuZVjTtUE1fCCFGhAH/5FmxYgXgZiKtWbMGr9ebOeb1epk9ezY333zz0M9QCCGEEEIcPjY8A9uWgLJxlMYv7Es4VusK2HhJdVsJzeUn2fMqWSZruzNZN3Y/ifKxjuBRq8oBIJcYv/D+iNfsGZkg0i2eP2XGT9d2EKKdUVqE+cb67ItVnAhFkwbwsN2UzYaTr4PnvgKpGJCdmR8kyU8993FR6ru9Tl2uJtOsQhRYcQgUQKIF6t4BKw5v/orOIFKzcpeZv1hfym2ehxmjdQtUaQbklMDMj0DxIOfena6/t/MPYyFPiIJgkLx8H2tWd+2fO74ATYOW9q6G5iePOZEpxR8ibsUJeUKMyxsnGUhCCDEAAw4iLV68GIBPfepT3HfffeTl9V/TLoQQQgghjkCOA6/92M2mAZ52TuH71seyhhynbSG784FGQE9njTlLX8Hp+mputz4BwASthhbcAEp6P7+edpaJ5eGWgE3Xd2SOFdAVJAhoKd70fQFPZiH3DqYfTvjsgWXfTL0Itv8bti52A0DdBEgwXd/J182HuMO6OuuYQieighRYzWBbgAbNO2D1n6C+K8DVGSibqu/KDiABGB4YP39EN78ebuPyxjExPJF1jevQUKiOQJ+mwc7ITlbXvQOcBcB3ln6bMXmFfLrq01xUedEhnLUQQowsg16d7cEHH8zabm1t5aWXXuLYY4/l2GOPHbKJCSGEEEKIw8z6Z2Hv25nNxfacXkNO1ntk/aBjKpW1J0iCdnyZba9m41VuoMlBx1I6puZm53iwsgJLzcotmcvR3LK3Ai3Kn73uqsFef65bbmYlAYecrJ5CmlvGduxFbjDoQOg6zFkAO98AOzsw1plJFegn6yqFCXbKzULyh91MpMYtYMVJKxMTi5jyZT4+2TQYdax77yOk9Gw46JrOZRMvY290L2hOpon2pqZNvNP4Do7tyYwNeHzsju7mnrfuAZBAkhBCDNCgg0hXXnklp59+Ol/84heJx+Mcf/zxbN++HaUUjz32GFdcccVwzFMIIYQQQhxKe1fA89/M6oP0ijOr17BTjbU99jhoqnfA5cPGK/zKuoSLjaUAeOgKNKUxMVSKNEZWAGlC4o8YHSui5dO1wtmJ+ka3cbVZDMECaG8AIwB55e4y9poOugcKxsEp17/3QIyVdHscdX+mjsCPT0v3dYYbRAJ3Pr5caNgM6XZijpezU9+jUtvLu04pACGys5womQ6X3T/im18fDNOKpvG52Z/j2Re3YHV8qW5p2YKGRrF/NJGOcUGPj6BnFPWJeh5c+yDnTTgPUx/0n0ZCCHHUGfR3yldeeYXbbrsNgKeeegqlFC0tLfz+97/njjvukCCSEEIIIcSRpnoVPHsTtGzL7Pq7fTLNHSVoz3hvpV6FqVf5bkAni0LXsjORckhQorXwtu9zHRlHGt5uGTxvOsfyXetjbFDje02lc2W2U4x12QfMgLvqmAaEx0JoNLQ3gRVwS9hKjnWXsX8vgRjHgRV/hPZmMLxZhzozkXxkB5GKaaGBfFJ0ZcEQb4JkGxheVjoTqVUF1KqCXtcCoGASfPh3bhNuMSDTiqbhM3Zg2W4USUMjz5eHnSjqGqQ5aJpGrieX6lg1b1a/yanlpx6iGQshxMgx6CBSJBKhsLAQgEWLFnHFFVcQDAa5+OKL+fKXvzzkExRCCCGEEIeQ48Br90PtO5ldltL5SvrazHaYGFXG9t7naiYou0cLaijU3N5FnSVroDDpynD6ZPqr+53W/xp/6bFHuc1vyue6waLRM4d+FbKmbbB3JTipnn21CdI7iFRMC3laOw0qnz/a53CstssNENkpcGxIJ/CqnqVrbpCta6Nw8E3ABYbe9QmK23FSdopkc25mX2ffLq/hJZqO0hBvONhTFEKIEWnQQaSKigreeOMNCgsLWbRoEY899hgAzc3N+P3+IZ+gEEIIIYQ4hBq3uEvSdzSS/nb64/zOviBrSL4W7X1ezmg32JKI9urxM0Gr6TVc6xlp6sbEwurxa2v2eB3Gnwbn3+kGXDqDRUO9ClkyAqkopBOAxmc9/+I36fP6HV6ktWZiTU/YZxAizrc9DwEqUxbYrny9zsvRupWzRWs6SuekF9KgaN3KDRVYWDh6S8exJNFUlJA3RMpOYWgGxYHiQzJNIYQYaQb90+iGG27gv//7vxk7dixjxozhzDPPBNwyt5kzZw71/IQQQgghxKFU9w7E6jOb3QNIHiwe9txFrtajh0/RZCie7AaS/HmcY6zOOnyMVkOvVJ5e210+bLyy7zkaHjjza27J13A2nvaFQTPcAJCmc1vwqcyhNuWurJbqFuxqx4/T7blec6p6XbJ7g/FOerf+ULQ3wbYlQzH7o4ajHNJOt5JA1fE5UO7nxgxupznRTCKdoC3dRllOGSeVnXQIZiqEECPPoH/KXnfddbzxxhs88MADvPrqq+gdP6grKyu54447hnyCQgghhBDiEHIcN6MIcFR2oGe+vo7TejbS9oZg0rlw8uchtwy8QTy6zfv0rkDSeL0eMoESA3JGsa9fSy/Wl+57jqES8OcN8IHeg8JKKJoISoFjo6G4O/Aws/R3WWC+BMC5+vLMcA8WbSqY2S7VmnpdMkZ2Jn8xEaZrO7p22GmI9c7cEv3b2boTR1mZ7ZA2kfje/yIdneru0GwsZVEfrydgBPhU1aekqbYQQgzQAX23PP744zn++OOz9l188cVDMiEhhBBCCHGYcBzY/lpmc5nKbu58hr6qxwkanPMtOOGz7ua7/4aa1aDp7FGjMqPytPaOGJIGgTDklGZlO3VnYjFfX8cC4wXecGbwrirjBG1DtxE6BPLdUrPhpusw8wrY9hKkYmAn+ZjnFT6mvwi2m/ni19LcZf6GO6yruMN8gHVqAndaVwFQRCuQ3WS8RYUyr79q/pFrjGcxujci1w334yMGLJqOougqZ6vd8lGsdFcwD80NMJm6ySdnfJKLKi862FMUQogRa9BBpE9/+tP7PP7AAw8c8GSEEEIIIcRhYvcyePbLUL0is6v7CmJ3mr/hw0aPMquy2W4AqbOk7Jj3wdonwBNgt+rWc0bT3RXTRk2DthqI7Ox3GjoKQ1Pc5XkAR2k855zICXq3IJIvBJ4ct9TsYJh6kZtptW0xpOIdTbKdrCELzJf4iLEYQ1Ocwjv4SfEN69Mku6/Q1qFZuc2eP2ks4nPmM73vFx4LlWcMy6McqUKeEJre9TnJCiABHkMjz5NHeaiceaXzDvb0hBBiRBt0EKm5uTlrO51Os3btWlpaWjj77LOHbGJCCCGEEOIQcBx48hpY90SvQ60dpVnv15fx3x3lWxm6By7+YXZPorwyyCsHK0GqpVsAJX88lEzryCBqg1hdv9PpvmqbrikuNt7MvqcnCGOOc0vNDgZdh1MXQrodInvB4wPdC42b3efoaIJtdGvs3LliW19BpM2qHIAxWmMf9/LC+74MhpRaDca4vHF49WUk+znukESh8BgeQp5QP6OEEEL0ZdA/kZ566qle+xzH4brrrqOy8iD98BZCCCGEEENvz0p47L+hbXevQ285U7nNcsvUcmnvfe7xn4Wxc7P3+cJgeiHdY/U20+sur5aOg2N1BF76dkWfTbU1t8G1x+828J7zseFtqN1T2Ww486uw8lGo3whWAsIVboaVlXSfKRWDjr483o7yqSTerMvElI8XHfdjNkXblX0PzYSzvgqzrxz+5znC6JpO2J9HW3u6z+Oa7mApi+ZEM7F07CDPTgghRrYheVtD13W+9KUvceaZZ3LLLbcMxSWFEEIIIcTBtOrP8PRCsLtWWvuzdQa3WP/Dd81f8YTdVVI1R9+afa43DLM/3PuaqTa311GsIXt/eyMko+APuwEXcLOK+nCb+UjvnboJwUI45nQ45Xo3qHOwlc2G0TOhaZvbj8kXdp9p9aOwdyW07nFL9TSTgKZBGmJadtbLjenPY2MA9FjhToMrfgNVHzp4z3OECXp8QN9BJNOAfG8+Od4cnt32LNOKpqFrBzEIKYQQI9iQ5cZu3boVy7L2P1AIIYQQQhxeVj4Gf70OOkrH0spgsTOHW6z/AeCr1rWZoZ82/sHVxvPZ5xdU9O5J5Diw6k/gywXHhtZuxzxBN7CklBtIMjxuBk8P5+rL8GvdAgG6F0pnwvwvQmkVFE06uBlIPek6FE/K3lc2yw0s1a6FpT8DX5iixBjYAk1aftbQfzonZl5nZXf5C2DapcM48aNbjsfHjOIZmLrJlsgWdrbuZEJ4wqGelhBCjAiDDiLdeOONWdtKKaqrq3n22Wf5xCc+MWQTE0IIIYQQB8GKR+Fvn6dz1bAl9izutT7MajWxz+EfMxajad126B43I6dnT6KmbW6pV/EUt/l0rbvbS9rdzi11zzU8ECiCtj18x3yAR+2zeUdNAGC01tJ1PU2HUAlc/AMonzMUTz48OgNLhZWwcylUr6Ao5H5sGu1A5rfvNhXIOi1f61ZW5cuB5u29A1RiSJSGisn3p7Edm/r2eqI9yy2FEEL0a9BBpBUrVmRt67rOqFGjuPfee/e7cpsQQgghhDiMvPL/4KXvZDZXOhP5RPqrWUMqtDp2qRIARtHMJG1P9jX8+XDS53pnBCUjbq8gTxB8uTxe9SZ3bJvE7eXLIO8kd3/tO+4S9nljIFbH1eaLXG2+wBJ7Fn+2z+BG83H3WprhlrDN+8ThHUDqTtfdXk21ayja/nfgdGLKR1x5CWgpdqlRmaH/Zz5ISfeAmTfkfvzEsDAMN2Aat+J4Ta801xZCiEEYdBBp8eLFwzEPIYQQQghxMK3+E7x0R9au5c7krO0z9JX82nMvU5IPAfAT74+zs5A0A067oe/Aji8Mpt9dxcyXywnhCH877m1AA8Luqmz+fMgrhbp33LI2Kwl2kjNYyxnGGvc6hs/NQMoZDTMuH6KHP4iSUXJTDXhJk8JDI3mMpYFmlQu4DbU/bnYrD9QM8AZ7lweKIVPTvodx8Vaakk1UFVUxLm/coZ6SEEKMGNJBTgghhBDiaGNb8NxtgEIpWOocyweTt/N/1sezhv3ccx9ezeYsfQUTtBpmaz0aak84HU6+ru97FFbCqKlug2mlso8p5e4fPQ1O/BzklbuZRoYXQqUQGg3+PMgpgfHz3UylcSf2Lpk7nDkOrPgjtNWgBcIU6W65WqPKA6CNIAB5PVe603QomjKynvUw1PNLrrtYuoW3at7C1EwunXipNNUWQohBGFAm0ty5c3nxxRcpKCjguOOOQ8t6CypbKBRixowZ3HrrrVRUVAzZRIUQQgghxBB56TsQrwfgDWc6C9JfzxwaRTNtBPmg8RpBzW12/YDnHhw0DK3bX+aeHDjxs/03tu4s54rshIaNbqDIE4B03A0gBYth9kfdVc7O/Cq8dj9sexnize643DI3eJRuh5xR7thD2UR7sJq2uau04YA3SJHRTrWTzzpnArP1bURUDgB53Xshabpb3nfsxSPrWQ9DSbt3o/ZOlkqQdtL4DB9TC6cexFkJIcTIN6Ag0gc+8AF8Ph8AH/zgB/c5NplM8uKLL3LVVVexZMmS9zxBIYQQQggxhGwL3v59ZnO5yi5he8F3C2EtlpXJoWlgdDTeRjPAF4LJ58HUi/Z9r7LZcMZXYOWjbpNtq9otcRsztyuA1Dnu8l/Dxn/Aur9AZI8bUFH0HjtSJCOQjrnPoJtYyg0KLXOmsIDFrO1oHj5Oq8Mt8VPuxzZcAeExh2rWR4T1jetpjDcCeX0eD3g8+Awf65rWsXjnYs4Zf87BnaAQQoxgAwoifetb3+rzdX+2bt3KjBkzDnxWQgghhBBieLzwbUg0Zzb3dGvw/KL3JsIdmTG9E88NGD0d0CC/Ak65fmDZMmWz3dXbmra5gRVf2C3V6nmursO0S9zA1P7GjgS+sJutpQGOxWzPLjZYpRiaAyhec6oAmK+/A2hucM3wQbBI+iG9B45yeHrr09iqvN8xNinCvjANiQb+ueOfnDXuLClpE0KIARp0Y+2BmDhxIrW1tcNxaSGEEEIIcaAWfxfe+HFmM6Z8/MM+EYCHPHcxUa/u+zzdA4UT3LKykmMHnxnUuez9UI89nBVWwpg50LId4hGmONuAE0goL0llsk252Ubz9M1uCRuAk4ZwufRDeg92tu5ka2Qrpj6+3zE2CZJ2Er/hpzpazc7WnUwITzh4kxRCiBFsQCH34447jrlz5w7oX6dweP/voNx9992ccMIJ5ObmUlJSwgc/+EE2btyYNeaTn/wkmqZl/Tv55JOzxiSTSa6//nqKi4vJycnhsssuY/fu3VljmpubufrqqwmHw4TDYa6++mpaWloG8vhCCCGEECPfnz8BS+7O2vWEfQYRQozXajhFX9fHSTqUzIRzvwUf+iVcdA+cd+fIKy07FHQdjlsAuaWQiuK3owAk8JLu9j5uiDgoB+wUKBumf2hkZl4dJqLpKCkrhaEZ/Y5xVIr2dDv5vnx0TSeajh7EGQohxMg2oEyk7n2QEokEP/vZz5g+fTrz588HYOnSpaxbt47rrutndY5+LFmyhC984QuccMIJWJbFbbfdxnnnncc777xDTk5OZtwFF1zAgw8+mNn2er1Z17nhhhv4+9//zmOPPUZRURE33XQTl1xyCW+//TaG4f4AWbBgAbt372bRokUAXHvttVx99dX8/e9/H9SchRBCCCEOa47Tuxzs3/fCO3/NDIkpH4/aZ/OUfRoAHzUWZzfNBvAXwtm3wrxPgTEsyetHvtEzoWgStOwkkE4BbhBpjXNMZoiJ5S4lpungy3NXtBMHLOQJ4TW9OPtYns3GJq3S5Hhz8Jk+Qp7QQZyhEEKMbIPuifTZz36WhQsX8p3vfKfXmF27dg3q5p0BnU4PPvggJSUlvP3225x++umZ/T6fj9LS0j6vEYlE+O1vf8tDDz3EueeeC8DDDz9MRUUFL7zwAueffz7r169n0aJFLF26lJNOOgmAX//618yfP5+NGzcydar8sBZCCCHECGWlYPlDsGkRNG+H9mZwUmB6IVQK5SfBqocywxtUHmcmf0C0Y4l5gDnaluxrenLh6qegfM7BeYYjVdM2aG+Coin4Y2kAksrDx9LfyAwxcDpe6RAsgFTbIZjokWNc3jjCxlgSVrrfMTomXt3LjsgOTis/jXF54w7iDIUQYmQb9NtKjz/+OMuWLeu1/6qrruL444/ngQceOODJRCIRAAoLC7P2v/zyy5SUlJCfn88ZZ5zBnXfeSUlJCQBvv/026XSa8847LzN+zJgxVFVV8frrr3P++efzxhtvEA6HMwEkgJNPPplwOMzrr7/eZxApmUySTHYtDdra2nrAzyWEEEIIMSz+fS8suQeseO9jSSBWD7VrMrscpXF88he9hp6gZ7cTYPplEkAaCskIpOPQthe/KgKgloKsIZkG5roGnpA01X6P3tnbxt5dM3Cc5D5G6SSsBB7Dg6L/jCUhhBC9DbrgOhAI8Oqrr/ba/+qrr+L3+w94IkopbrzxRk477TSqqqoy+y+88EIeeeQRXnrpJe69917eeustzj777EyAp6amBq/XS0FB9g/k0aNHU1NTkxnTGXTqrqSkJDOmp7vvvjvTPykcDlNRUXHAzyaEEEIIMeT+fS+8eEffAaR+bFeje+27RH8DU3OydxZPfK+zE+AGhJSC9ib8uL+77lB9Z9fj2JBbJk213wPHUTy5fDfJ9jK8erDfccrRUCi8upfdbbvZ2brzIM5SCCFGtkFnIt1www18/vOf5+233840uF66dCkPPPAA3/zmNw94Il/84hdZvXp1rwDVRz7ykczrqqoqjj/+eMaPH8+zzz7L5Zdf3u/1lFJo3dam1XqvU9trTHdf+9rXuPHGGzPbra2tEkgSQgghxKHTvdeRGYCX7wG6gj+v2lW8q0p52ZnDf5yp3O75PZcbXb9XrXIqecdxV6wqpoVl/utIKQMPdvZ9NBNGzzoYT3TkK6wEbxDSUXxa/+VVAGgGTLtYmmq/B9sbY2ypi1Ka52NNTQrw9DnOm/sufsN987smVkNrSioOhBBioAYdRPrqV79KZWUl9913H3/84x8BmDZtGr/73e+48sorD2gS119/PU8//TSvvPIKY8eO3efYsrIyxo8fz+bNmwEoLS0llUrR3NyclY1UV1fHKaeckhlTW1vb61r19fWMHt37HTlw+zD5fL4Deh4hhBBCiCHjOLDxH7DuL9Cy213Fq2Un2G4GUr3K44Lk92gkuwzqK+lrmadtYpxWxwo1ictT/5c5dpaxEgCv1iOABFBwDEw8a9ge56iju79u+0nte1yoBMaeeBAmdORqS1gk0w4qt4FwydvU7Tq9z3Ee0yalHLyal6SdpE36UAkhxIAd0FIbV155ZZ8Bo5UrVzJnzpwBX0cpxfXXX89TTz3Fyy+/zDHHHLPfcxobG9m1axdlZWUAzJs3D4/Hw/PPP5+ZU3V1NWvXruX73/8+APPnzycSifCf//yHE090fzi/+eabRCKRTKBJCCGEEOKw4jjwzt9gyXeh6V2wk4AGPXq4vGTP7RVAAkhjckbqR/zI81PWO9nZ1FO1fhZD8YXhrK/IamxDpWkbxCOge/E7+wkioSApS82/F7l+E59HJ5qKEsjbBvQdRAKFchRpJ43P8JHrzT2Y0xRCiBHtPefLRiIRfvaznzF37lzmzZs3qHO/8IUv8PDDD/PHP/6R3NxcampqqKmpIR5331mLRqPcfPPNvPHGG2zfvp2XX36ZSy+9lOLiYj70oQ8BEA6H+cxnPsNNN93Eiy++yIoVK7jqqquYOXNmZrW2adOmccEFF3DNNdewdOlSli5dyjXXXMMll1wiK7MJIYQQ4vBTvQp+cRo88Umo3wB2EkdprHYmkFJGZtg2p5SvWNdmtq8ynuda45msS/3KuphWcrL2TdT29r5n4SS45F6Y+eEhfZSjWjICVjt4cwgY+xinGeAJwJo/ucFDcUAmFOUwqSREc1THwep13MxbSbDyB6TsJLayMXWT0tD/Z+++46uo0j+Of2ZuSw8kIZBQpBcFgxWwAYqIiuWna8NF3bUX1FXXurYt9rZ2197b2rAsdhEVRJAg0pQmLSGUkISQ3Dbz++Mkk9wkVGmB7/v1yit3zpw5c+bmiuHhOc9pQ0YwYzvMVkSkedrsINIXX3zB6aefTl5eHg899BBHHXVUk7u2rc9jjz1GWVkZgwYNIi8vz/t6/fXXAfD5fEybNo3jjjuO7t27c+aZZ9K9e3fGjx9Penrdvxjcf//9HH/88Zx88skceOCBpKSk8P777+Pz1f3f+uWXX6ZPnz4MHTqUoUOHsueee/Liiy82mpOIiIjIdlU0FV47HUqmJzS/GD+cYyP/4rbY6V7bt07dZiSvBf/OPwPPcn3glYTrZrgdeTV+WELbIfZPiffseQxc/L0CSFtaKBMCqeAPkJS0jjIJlg1JmZDVBUpmmewl2Sy2bXHi3u3ITc5jbbTx7mzB7K8JhFbg4GBjk52cTUFOAR0yOmyH2YqINE+blKu8ePFinnvuOZ555hkqKys5+eSTiUajvPXWW+y+++6bfHPXXf+WmsnJyXz88ccbHCcpKYmHHnqIhx56aJ19srKyeOmllzZ5jiIiIiLbjOPA1/dDmVluNt9pwzJakM8qbo6dBcBz8WEUuVncHniaGa4plH2Wbwz97VkbHP4/gXsZYv+IbdX7HcyyYe8ztIRta8jqDPl9YfUCkpx17KLnC0F6G0htVVc4XTZb77aZnDIglV8mJrGywbmAL45t2fjw4bN9ZCVlcUyXY7AtFTMXEdlYG/3bwlFHHcU333zD8OHDeeihhxg2bBg+n4/HH398a85PREREZNcx6wOY9b53eFb06ia3hP/Y2Z+Pw3VFmPffiABSd2sRhzUMIAFktIUuh27+nGXdbBv2GgGLJpBUPCPh1GX+txnin2qykLK7QKwK/Ekme0l+l7wsi07Z6Sxs0G7ZZglbsj+ZgB3g2C7H0iu713aZo4hIc7XRQaRPPvmESy+9lAsvvJBu3bptzTmJiIiINE+OU5dNEso0mSgb2rLdcUzdo/GPwM9vg2tquVS7gSYDSE1pbZUmHP878BCXRUcltI0JXts4gBRIgSE3KwtpawulYYfSCFZFidRsO3+6/3NyA2FotS8kZ8GK2ZC/t/nMyO+SFkgj5jSuidQyqQWdWnYiKZBE1InSJ6fPdpidiEjzttG/MYwbN45nnnmGfffdl549ezJy5EhOOeWUrTk3ERERkeajaCoUvgrLZ0Os2mSVtOoBfU+DvILG/R0HZr4PH98A5Ym7pTmuxZdO30aX3OV/gqtj5zdqb2utSDg+zjeezlYxx0T+BcC9gccaB5DsABz9gOogbU2OYz4TTgx6Dcc3AW9zvVBqKn+zcwAAge9JREFUNrhlUL4UYhFIzYGCUzccdJQNqoxWUlxZ3Ki9LLya+eWryUnJYb/W+6kWkojIZtjo/0sNGDCAJ598kqKiIs4//3xee+012rZti+M4fPrpp1RUVGzNeYqIiIjsuIqmwtg7YclkiIcBF6JVMOdzeO8SmPgkxGOJ/Z85At48o1EACeCZ+DAujP4FgPZWCQuSRrAgaQQn+8c26nuS7yvaNMhEAuhjz+fZwJ28Fvw7J/rGNThrwTEPQl/9g+BWtWqeCSpmtAXbxmdb3qmgHQVcWLvKZB8NvLrpYKNsEsd1eGHGC6yNrm10znXjlIZLKa0u5ejOR6sWkojIZtjkPzlTUlL485//zDfffMO0adO48sorueOOO8jNzeXYY4/dGnMUERER2XHVZpssnw0rf4VFE2HhBFg0Hlb+AsU/wf+ugUf6wU+vmwDSe6Ng8cSEYUrcTKY5nXg2dgRf1ctC6mPNT+j3U+hsHgn82zs+3/fBOqc22De16YLbg64ztXpk6wqXmay0QAoASXbcOxVsvze02xda7Ab7/VkBpC1kQfkCJhZNJOJEGp+0XHyWj7gTJzmQvO0nJyKyE/hdC+B79OjBXXfdxe23387777/PM888s6XmJSIiItI8rJoH88ZC6UJworwdP5AiN4uL/aPr+rhxWDUH3r4AcneHkukJQzwWO4Y7Y6c1Gvov/jf5s29MQluGVcXRvu9J4m5Wuhl0tZfWO2tDWhtYU4S3bqqhfc+GQdds5sPKJgllmmWN0bUQSifFF4eoOeVLzoSwDcktIanl9p3nTmTswrGsDq/Gwmp0zrZsMoOZVDvVzCmdQ+dM1Z8SEdlUW6SKos/n4/jjj+f444/fEsOJiIiINB+LJpiiyK7JMrkiehEAB9vTaGutwMalpbWmprMDJT97l/7g9OCkyM1NDpvJGi70jSZoxZs8f5hvSmKDHYDsbnDy8zD5OSh8CarXAC5YFoQy4IBL4JCrfs/TyqbI6mzqYhVNgZwepNTLRMJ1oXyJimlvQY7r8MOyH3BwSPIlNTrv4lAVj2BZVpNBJhER2TBtxSEiIiKyuYqmwrj7vQBSzK2rFPB47Fi+d3oSw8eE0CUkW42X19wZPXWdQx/h+2EdASSLhCyj2uBRKM0sj8ruCsNugyG3wNTXoXwhZHSAglPAH9zMB5XNYtumsHrZQlgxmxRr37pzRYWQ0U7FtLegheULKY+UE7SDZnc2Kw6uzzvvsyyqY9W0TGpJlxZdtuNMRUSaL/0fS0RERGRzOA5MeQVKf/Oa1hLyXn/k9GMlmZSRxh1NLFUDWOjmeq+fDdzJfwL3esdD7B8b9LZg/wuh0yHQoiPk9obOh0KvYyA5A9JyEwMS/iDsMxIG32C+K4C0feQVwMBrIKMdKZGVde1rV4Lq8mxRa6Jr8OEjJzkHF5cWnR9POB9zY7i4dGvZjY6ZHbfPJEVEmjllIomIiIhsjlXzYOb74JoiN4ucVhwc+XeTXV+MH85itxVxbJ4K3IPfcoi7FuWYgstfBK+gs13M1/E+3jXtrZLEQdofYDKMlk2rK+Qdq4aq1WZJVMGpKs68I4uuJTlgeTWRaLc/lC82u/oNvEY/uy0gLZBGKBCig78D4ViYCms52GFwTHDXwiU5kMLJ3U/WzmwiIptJQSQRERGRzfHzW1CxxDv8a+y8dXZ1sPnc2RuAruGXuNz/Xw62p1FNiBARdrOW1fSrq9PSxiqtN4INe//RZBnlFUDrPiaIFS4zxZuzOmtJ1I6qdve+qlWkpKRC7c7zSRkQSjf1tKa+Zn6m+hn+Lh0yOtAlswvTV05nz1Z7sqB8ASuoW/yZHkpjQNt9GNxh8PacpohIs6YgkoiIiMimisdg8rPe4afxvZng7OEdX+AbzRw3nw5WCc/Ej2p0+QOxP/AAfwAgTBCfZf6am2Gt9frUFeO2zbKntNZ1A9g25HTdgg8kW82qeSZrLKMtKWVO4jnLgoy2UDLL9NPP9HexLZtjuxzL0jVLWRVeRdcWXZlu20Rr3vbe2Xtw5h5nKgtJROR3UBBJREREZFPN+xLW1C03uzR6CQB9rHm8H/pbQtemgkjrspc1h3/4n6GbXQS+EPiCYNkQSDF1j6T5CZeZZYeBFI7KKebVZe3IDtQrsh5IhliR6Se/W6/sXlxQcAGj545mbtlc3HpF6M/vex69snttx9mJiDR/CiKJiIiIbKqin7wd2RzXogqznfjF/vcadc2hjBVkAvA3/4u0slZzWXSUd/7fgYe815YFI/1fQGoO+HIgFgGf3+zildRyaz6RbC2hTPAnQXQtB7dYxX/7TKRTcmXd+WiVOR/K3H5z3Mn0yu5Fj6weLChbwHffzmRNzLT3yOq+fScmIrITUC6niIiIyKZwHCia6h0WkQVAgBhD7MmNun8eupLb/E9xoe89TvSNoxWJGSdDG15jWTUBKhfSckxAqf1+pu6RND9ZnaFVDyhfAq7LvhmryQ7UVNd2XdOe21M/3y1s9qrZvPnLm4TjYa/tvsn3MnPlzO04KxGR5k+ZSCIiIiIbq2gqTH4JZn7kNX3vmOUxna2l+C2n0SWZ1lpG+L/wjnvaiwgRIZkwP4YuwLbcxAtScyG/AFwHqssgtZXZeU1Fl5sn24a+p0HZQlNEO6OtWcIWrTIBpJQc/Xy3sJkrZ/L41McpDZdiW3XZRzNWTqe4cikXFFygZW0iIptJQSQRERGRjVE0FUZfCsumU7dPO7wVPwSAo33fb9QwWVYFX4SuJJlI4wCSLwSZbaG63CxxaruPCTBo+/fmLa8ABl5jdmlbPtvUQPInQf7e+vluYY7rMHruaErDpXTO7MwPdRse0qVFZ+aVzeP9ue/TI6uHCmyLiGwGBZFERERE1sdxYPkseOtsWDEH6hXqdV34yekEwOH2pMTrMtrD2uWmqHIDba2Vje/jT4bhD0C7fU2R5VCmWeKkDJWdQ14BtO5jdmHTz3erWVi+kLllc2md0hrLsrDqBZEsy6J1SmvmlM1hYflCOmZ23G7zFBFprhREEhEREanlOOYv+dWlsHa1yTr6+Q1YNgswhbRdF+6OnUKatZYStyUVpALQySpOHGu/s6HTQPj6blg2DaLVEA+DHQDbD9G14ETNgCk5cPjNsOcp2/Z5Zduybcjpur1nsVNbE11DJBYhOSUZAKtBtl+yP5nla5ezJrpme0xPRKTZUxBJREREBGBJIUx8HJZMgdIFEG+cQQSwyM3l0fhxCW1tWEmSFU3smJwF7faGU1+uyz4pL4IF46BkNlSvBssHrbpDv/Mgf6+t8lgiu5K0QBpBf5CqWBVpwTQC/njC+apYFUF/kLRA2naaoYhI86YgkoiIiMi0N+GLf0LZUnAi6+26jJaN2kb6P23QYoOvZplS/eyTtkCPo7SkSWQr6ZDRgS6ZXZi+cjqpgVRCgbogkuu6LFu7jN7ZvemQ0WE7zlJEpPlSEElERER2bUunmADS6kXgxql2A1SSRBYVXj2Vq6Pn8kZ8MEGinOgbl3D5kfb3XOwfnTimPwRp+U3fT0uaRLYa27I5tsuxLF2zlHll87B9nYB0AOaVzaNlqCXHdDlGRbVFRDaTgkgiIiKy63IcmPAElC0B12QsHBW5nXluXQConVXCYjcXgAgBXo0f6p3b2/qFq/2vNx43Mx86D9y6cxeRJvXK7sUFBRcweu5ofrGrvPYOaR04rddp9MrutR1nJyLSvCmIJCIiIruuVfNMJpJj6hlNczolBJAAL4DU0Mm+L7kr8GTjE5YN+/wZfPo1S2R76ZXdC8d1+GzyeGpL3pdUlfDBvA+wLVuBJBGRzaQ8ThEREdl1Va2GskXe4Y3Rs9bZ9RB7Kq8H/+4dn+f7sOmOnQZD/4u20ARFZHPMXDmT//z0H6rdUq8tM5TJ9JXTeXzq48xcOXM7zk5EpPlSEElERER2XdWlEK3bha3YzfJeF1hzsHG84wt9o+lnz2JB0ggWJI2gq7208XiZu8HhN6tQtsh25LgOo+eOpjRcSuu0dK89LZhG58zOlIZLeX/u+zius55RRESkKfoNR0RERHZd1eXey1I3zdt57T+Be3k3eBNp1NVT2d+etf6xUlvDyS9AXsFWmaqIbJyF5QuZWzaX1imtCdbbnQ3Asixap7RmTtkcFpYv3E4zFBFpvhREEhERkV1T0VQofAUwf8ksdlviYpPDaob6JmNZ8EDgEQDO943GZ7lNj2P7oc1ecPrr0Lbvtpm7iKzTmugaIrEIyf5kcrPWNDqf7E8mEouwJtr4nIiIrJ8qPoqIiMiup2gqjL7MfK+xhhQA0q267KNDfYWMsy4l31rZYAALAsmQnAW7DYADRikDSWQHkRZII+gPUhWromWGjyMOmE1KUtQ7XxWrIugPkhZI246zFBFpnhREEhERkeYhHoNfP4WZo6FqFeTuDnucAGuKobIEUttA54Eb3hXNceDzv0NRIeBS5qbwSXxf0q21AKRSndC9vb0CsGqObEhtBfkFUDACWu8B2V1VA0lkB9IhowNdMrswfeV0UgOp5Leq8M65rsuytcvond2bDhkdtuMsRUSaJwWRREREZMdX+Cp8dBVE6i0/+WUMfHMfYIEvaL7S2kDBySa4lNW56eDOil9g7leAWZ52Q/RsPnAGeKfTaoJJiWyTeZTeGnJ7wcCrlXkksoOyLZtjuxzL0jVLmVc2j9YprUn2J1MVq2LZ2mW0DLXkmC7HYFsK/oqIbCoFkURERGTH4zgm2DPnM5jwOJQvWk9nF+Jh87WqAr66A6a+Bnl9ofcJ0OOoxGDSuPvAjQGmDlL9ABJAJ6s4cXh/EqTnQcuOJoBUcKoCSCI7uF7Zvbig4AJGzx3N3LK5LF+7nKA/SO/s3hzT5Rh6Zffa3lMUEWmWFEQSERGRHcviH+H9y2DZNGqzhWpFXR/jnD7sZ89OqF2UwI3DqrlQ+pvJVmrdGw64BHoOh+KpMOM9r+vH8f0aXT7InprYkNEODrvJLF1bV3aTiOxwemX3okdWDxaWL2RNdA1pgTQ6ZHRQBpKIyO+gIJKIiIjsOMbdC1/8ywSC6lnrhng1Pph/xM7w2vJZwVuhW/hn9I8sdHN5KngPra3VdRe5MYjGYPH38N8p0OlgqFgGcVPzqMxN4ebYWQC0t0pY5OYCMNiekjin9DbQ6xgFj0SaIduy6ZjZcXtPQ0Rkp6EgkoiIiOwYfnodPv8n4ADguBafO3vRx57Pc7EjeDx+bEL3peQwIPywd/xe/ED2t2fxdvxgrvS/QWb92kZOBOZ+nnD9aZG/ea+fDNzL904v9rZ/JWjVD2BZ0HWIAkgiIiIiKIgkIiIiO4J4DL64k9oAEkDn8MubNMQKN5PjI/8A4IX4UM7zfcD1gVea7FvmpjDD7egd97AW0dPfRN2lpBbQ8+hNmoeIiIjIzkr/rCYiIiLb37yxUP4bAK4Lx4dvbbJbCyp4Pfh37g880ujcDHe3hOP/xIfTsfoVro6ei5tYWonro2d7r+8LPIplNRzNgmAadBsC2V03+XFEREREdkbKRBIREdkcjgMlM2DiU7B6EbTsAPudY3bvsm1zftU8CJdBKNMUZHZiMPV1KF8IGR2g4BTwB7f3k+wYKpaa9wdYTiaFbrcmuz0VvJd97V8ASCZMgDhfO3vyfPwIvnH6NHnNG/HBXOV/A9e1SCJCprWWD+vtyHaQ/XPji1JbQasecMAoLWUTERERqaEgkoiI7LqaCvSsK2BQ27e6FJYUwvhHYPX8xD6Tn4HsbnDIVTD3S1j2M8SjkNzCpNesmA3hNea1ZcEnf4MDR5n+u5qG7/2SQu/Uh/H+3uvpoT9h43J45C7yWekFkACG+SYBMMdtu8HbvR8fwD9iZ9DDWsgHwRuwcHCxGRe8lNz6xbgB0vJgj+Oh72mQV/B7nlJERERkp6IgkoiI7HpiEfjqTpj5ngnqJLcwO3C16tl04GBJIUx4FBZ8B2uXe7t7NWnlr/DO+YBV7wsgcbcxXCC8Gr74B5QvheH3bf7zbEowbEew+Ef4+h5YPhNwITkHVvzqnV7utgCgwJpLqhUG4KvgFfjq1Uuqz8Jtsr2+2l3dZrsd+MHpgYuNnxhtrZWJHZOz4Yx3Iaf7jv0eioiIiGwHCiKJiMiuZdy9MPZuiFXVta0pguW/mK9F38Pux0OPI00wZvwjMPZOiFY2GiruWjwTP5L2VomXFVPHrflKFHb9VJBCjlVe1zjpaahcCb1PMMvhsrtufACjaCpMeRkWTjBzDKRCh/6w1+nQus+2DS45DqycY5b5YTX9LGPvgXF3Q6xeIK60LqPLdWGyY5ayDfNN9Nr9VtMBJIBit2WjtkcDD3CUbyIdqxsX1n7PORCAHMqwrQY/oz4nQ27P9T2liIiIyC5LQSQREdl1jL0HvvwXNJnREoeKJeareBqMf9gsRasubXIo14ULo5fzibMfACOcz7jF/7y3Pfxspx2vxg/lYHsah/mmADDHyee0yA0spyVvBG9lf3t23YAz34VfPoKkltDxIDjosg0vpSqaCqMvheWzIRateS4Lls+CuV+Y69euMgEbf5Kp8bO1lmgVTYVvH4QF30C4wrQFUyF/Lxh8nfn+1d3w1W00/f4bP7ud+N7dnSARjrQnNt3JDkBKrsnkilYSJrGu1BfBK+hsFwNwvP0N7zoHJZx/PT4YgNYNl7H5k2G3/oiIiIhI0xREEhGR5m9jMmAW/wBf/pPa7KBVbjqvxQdR6HRlpO9TDvbVK67sRKGyJOEWc508romexwB7Bu2tEl6MH840t7N3/pX4EOa7eTwRuI8StyVHRO4C4DNnby+I9E78IJZjsma+ivdNDCIBxCNmudysD2DVXDj2wXUHfBwHPrsVin8GN9ZgnLhZVrdyHqS3hvR8E9BZ+iOULYSB12zZQFJtMKtkpgm81QaJopXw6ydQ9BP0PAomPUPt+z/HyefMyDWc7/+AM/yfUuy2JIcylruZAPS0FtHRXpZ4n0Cayaba7QBou5dZklg6jwt87/NyfAgAJ/u+9AJIAPcHHiUetfnA6c+lvnf4d/xE71wrq36A0IaUbMjdfcu9LyIiIiI7GQWRRESkeVtSCF/9CxZ+X7NEzYakzMRsnqKp8PJJ1AYwStxM9g8/5g3xibMfC3wjWOJm80l8X07wfUOmZZavuS5Mcbtye3QEk9weTIr3WOdUxjt7MCR8D2Wkem2L3Vwc18K2XF6MH+61vxgfggtc6B9NOlV1W8y7jgkmlcw0mT0nPNn0ErQVv8D8r70A0rOxI6gkiUv879XrFDe7nlUsBSxIawPRtVD4qsm6iVT8/mVujgPf/LsmgBRuooNrlgtOerquxYU7YqexhFbcFPsTu9u/8YfILQy0CznRNw6AFKtB3alAKhz3MLTpY+a7ah4E08H2057lLEgawVI3i1xWJ1xmWfBQ8GHudp8gyYqyyG3F284hAIkFtS2gdW8TfBQRERGRJimIJCIizUdtxlHxTyZosXKuyXRpWK+oshpmvGeyeY55AMb9G6pM1onjWlwVvaDR0GHXzzmRK5npduSh2P8xKXQhVQQ5OnI7C9w265zSAfbPfOf09o5LaFyfZ4a7G7vzG+X1gksVpPJY/Dgeix+HjUO+tYJh9g9c73/F1OmJR8zSsJVzoFX3xjced5/JmALK3FRujZ0JwGH2j/SyFwFQ5Qa5MnohQ3yTOcH3jQnmrF0BZUtNVpLl+/3L3Fb8Ar9+7AWQStxM/hX9IyP9nybspFZrlZvO1dFz+czZx2t7OnYkAGOdvox1+gIQIpp4YXo+9DoGfDW/umR1NrWfVv1aU37KJd9atc5pJllmvN72gqaDSNiwx3Eqpi0iIiKyHvpNSUREmoclhfDKyfD4QfDW2TDuHpjxTkIAaYWbQditCTK4MVPbaMwNMOdTr8/fYyP52mkcLHk1figz3Y4ArCKDElrwSvyw9QaQAPawFjDAnr7ePl84ezF/PeM42Cx2c3kqfjRT3NpMGBeqy2qW6DUQj8HM0d7hN/WCWM/ETUDGdeE/8aP5yOnHFdGLcGvrRztRqFppglNggjJLfzTFw4umrvc5GllSCK+PhMgar+nCyOW85xzIHyK38I/oH+vuW+P0yHV85uyb0LaiZglbfSsbtgVCULqg7ti2Ye/TzS5qlmUyiewgWAESf72xEoZpZy33XremQb2rlFZNP6eIiIiIAAoiiYhIczDtTXj+aBMManLJFNwUPYt9w49zSfTSukY3XrNrmQlyuK6pS1TrreDN3uuGwaJLI5dQ6HTZ4NRO8o3llcC/mB76E0kkzi0dE+Ba6mYzvSZAVWDNXe9485y8uoN4hEZRGIDP/+7tLlfstuTi6GXeqa/iBbwbP5BO4Ve4P3aS1/5o/NjEMdauhEUTYNFEqC6H1Qth6msm22tjTHvTBPVW1mUbhV0/k9265X5Px49ikZvLN/HerHTTWezmeIG6+n5wG++G1t1aVO/INrWWwmWJnfIKTN2oToeYjCoc8+UPQHY3OPRvsOfJpgh3jbbWCu+1yUSqCTK5DoTLEREREZF103I2ERHZsS2dAh9cmZDtAlDmphAiSpIV5f14f16IDwXgc2dvrwaREfeuqSCZMtIAmBU6kyQryon2WN5yBvJcfFjC+BPdXrUllDx7WnP5ya0LLI0O3kB3ewkAqYQZZE9ljLO/d36g/RMfOAN4LX4or8UPBaC3PZ+p8XUHp1bQIAMnlJZ4HI/B1Fe9w8lO4lK35bTk8ujFjca9O3YqF/tN9lKJm8lr8UM51fcludWrTQAlmAqLfjC1hnI2UBdo0ST48K+Ndq47MnJHo66HRB4AoA0r+UfgWa99fmgE18TO5Y2andIaujZQ94xYFvgCpn5TQ3kFcPp/zbK6eV9BtAra9IbOg02WleOYHfm+uQ9cl052XcH0fN9qsGwTqLMsqFzeeHwRERER8SiIJCIiOy7Hgc/+6WWgxF2LoZG7mOu29brMDp3BnbFT6y7BppQ0sqloNNwqNwOAFKq9GjlN1TBqKJUqQkR5LngXb8UP5l+xP9KCCrpbixP6ne77zAsiXeAbzf72LD5wBiT06WX9hp8YsXX8L3i1Wy9o5AtCUoP5zRtrMoeApW5WQhbShpwcvpGBvp/4Ot6H793dGRvfk7dCt0K4wtRHKlvcKDDUyE+vw+i/QMxkWZW7yVwSvZQj7B9Y7JrlYIfbk/i0wZK1YrIpddMBGGQXYllwqD2lySDSxNBF5Fr1so4sG1rXFNRuim1Dbk/z1dS51r3B9oPjkmLHeTl0BwucXFM7yvKB44JtQVrr9T+7iIiIyC5OQSQREdlxzf4I5n/pHf4tdnZCAAngyfjRLHZzyWE1q0kjhp/34wM4yP6Z3axlPBc/ggPtn+loLeP86F8AyKCujtIh9k+Mc/Zc5xQyqGRy6ALi2CRZUc71f8QJNTuI1Qaiah3s+5m3rJtpY62irbWSmU77RuPlWas4wv6BD2uCS08E7qOaALOdDjwaP44wgbrOme0guUXiAJXF4JjsqtlOh4RT1/pf4Y7YCO/4+9BF9As/6h1PdHsxMdbLO65beuaa5XEWULWeJV3T3oT36wJIAPfH/sDXTkFCnamb/C/yaWTfRpdfHTsfgBaYrLJD7GlN3qZFwwBgUgvod8HmF71uvYd5H9eWgmVzYHAuB1pzwQ2ZJY+WA8ktTT8RERERWSfVRBIRkR2T48AHV5m/5GN2Gnu1ZklYfffETgFggD2DvJrduW6JncWQyD28ER/Iv2J/5KjIHVwavZjZrgm6OPX+93e67/P1TmNi6CICVjwhYJRtVZBtNc50AtjH/pW21koA2liNs3rSrbX8I/Ac/awZ/M3/Ikf4JnGcbzwhKwJAxPv3Hcss1WqYfRNeA66ZSwXJXvP1/pfZ3fotoWttsGajxKrNsq6kJpaMgVlG9+XtCYXMXReerSnkXV+2Vc7f/c82avfmZZl5pVhhPghel1CYPEiUoFW3BBFfEA66HNr23fhnaTShrtDxEAgk1ezkFjeBODdujgMhU1cpewPL+ERERER2cQoiiYjIjunpoVBZ5B3eVi/DpilZVgUtG2Sw/OLWZQLV3xHsCN8P3usUK7EY9sW+d73X5/g+bJRt1KSkLFOzp4EWrKGztTShLYO1ZFkVvB76J+f4/+e1B4kBEHZrxgmkwJpiWFYvW6doKhTW1Qpa45og0kH2NM71fUiGtTbhXrVjbjTLhqSMps/N+9IU3wZirs3FkUvpFW4cKAoSIZkwp/s+ow0rmxwqs14mWG/7N/7uf847TqG6Xk8bOh4M/S/atOdoyLbhwEtNUC65BQRSwZ9svie3gLy+cMCozc90EhEREdlF6LclERHZ9uIx+PVzKHzZfI83CHaMvhyW1AV6vovvzos1hbMBrva/Ss8GWTdJRLjK/0ZCWzJN7+SWba17ydYV/jd5KPAghaFz+Vvg5cYd7KDZCcyfDCk50PtEOPNdOPZRaNEB8HldLQveCd7Ef4O3eG359XYHqy+ICVZFCEBqLnQZDE6sbsc0x4Epr5gC0jXW1GQi5bIay4J0EoNIlmXqOa1LFvXfB8tk/axL4SvgmDn+7HbiQ6c/1YQadYsQNHWwLZerAm80Og+JO6QB+OsVP0/1gki2qWOUvOGaVRslrwCOuA32OB5yukGL9ub7Hv8HR/zLnBcRERGR9VJNJBER2focB1bOgZIZ8NsEmPs5rF1ulhTZfshoCwddBn1OMjuP/ViX4fJxfF8uiF7uHT8buJPBvqkcYM/g+Mg/vPYQEfKtxMyX8c7uTU7nDN+nCcf7WrOZ5PbgRHssPsvlGN+Epp8jtzec+GRNIMeC3F5mCZRtmyBE7xNgzufwxd9h2QzAIdNay77WL1zjf5V8ayUZVm1Qxw/EMP+e4xKqWcIVSc2DHkeaCJA/BCWzzI5pALPe95aT/eq05bbY6QCk1YyZXi8TqTaj6tvQpdwY/RPvOwc0epxVZLDaTaWFVQm4Jpsq0sQyvaVT4NfPvMPf3HUXoN7fmum9/oNvHIPtQmY77RkR/ZvXfoxvfMI1/noZUylW2GRh2f66Yt8bs2PcxsgrMAW6V80zxdpDmWa5oDKQRERERDaKgkgiIrJ1LZ4EH/3VBENi9TNlbAgkAxasmgtjrjeZN1/c7vWocJMZFb0EtyZx9uHAvxlkTwXqMndqJVkR2tTURKo11W0ceBgfuoSsBvWMng/exWvxgRzr+27dz5HZAf7vUWi9u/lqis8PPY6AjDbw/uUm+IILwIX+9+ue2x8yz+r6zOvUXIJ2Z1gKEX+aCSCByXiqKoXF38PSqVC+xLvVnTW1oABK3BaAWSpXq71ltrJvYVUyzPdDoyBSW5azhFb87HTiIN/PgAUVy+C38dB2n7qOjgPjH4dIXX2ln52OCWMdbU/gL/7/Ms/NY397VsK5bKuCA3wzmGKfx4vxwznBN47kmvpPtbKCcahpitsBk33kTzK7xkXXervzbRG2vWUCUiIiIiK7oO36T2+33347++23H+np6eTm5nL88ccze/bshD6u63LLLbeQn59PcnIygwYNYvr06Ql9wuEwo0aNIicnh9TUVI499lgWL07cdrm0tJSRI0eSmZlJZmYmI0eOZPXq1Vv7EUVEdg2OAyvmwJLJ5rvjmPb3r4SnDoelPzYIIAE4JqsmXGYCBZUr4LO/mzpAmLo7l0cvJoJZYvVLaCTDfd978ZUcKzGwkESUNKuaDalfjwcAy0dqwOXspK9oZTVRiNoOQOdD4dSXNn7JU14BDL8PsrqYbBrLNuME0yG9DQTTTACt3f7Qpg+03ZtQmlm2FY5EzXtYMsMEdFbNg6/vgx+e8YZf7OYk1Hha6mYDEKoXWGtvLfdeN7Wsr689F4B3nQNrWnyAC1NfT1xeuGoeLJ4ImJ/p87GhPBkfDsClvrf4KXQ2jwQfpKu9lKG+yTVZTY21tNZwqf8d2jVczmf5SA3V1ZNaHU8xgbPK5WYegRSTMSQiIiIi2912DSKNHTuWiy++mAkTJvDpp58Si8UYOnQolZV1v4Dedddd3HfffTz88MP88MMPtGnThsMPP5yKirp/Rb788st55513eO211/jmm29Ys2YNw4cPJx6vq7EwYsQICgsLGTNmDGPGjKGwsJCRI0du0+cVEdlp1A8azXgf3rkAXj0V3jwL/vtnGHMtPH8cTH6K2uADQMT1Uey2xHXhvuiJPB4bjut6g0LFUoibQMg4pw+fO3t71ybs2AXkNgoiJWa3rEujgIrtMwEey6opju0Dy28CFx0OhPPHwR/f2vSaOfl7wYlPm6LNSS1NwepAMrgxc8+8AjjydsjfGyqWEqo0BbgjVWtg4QRYNBFK50GsygRUanZkW+pmcVD4wYRbneAbB5hHuMz3Fv9nf8MAe4Z3vn7NoVq1Qbj/xgeaBsuFUBqUL4Z5Y+s6LpkEZYu8w0Kni/c6z1pVb3neZvAnmfe+erXXtIp0E3SLVJogY2a7xjvUiYiIiMh2sV2Xs40ZMybh+NlnnyU3N5fJkydzyCGH4LouDzzwADfccAMnnHACAM8//zytW7fmlVde4fzzz6esrIynn36aF198kSFDhgDw0ksv0b59ez777DOOOOIIZs6cyZgxY5gwYQL9+vUD4Mknn2TAgAHMnj2bHj16bNsHFxFpzoqmwpSXYcF3ULYQwuXULtkyFkLx1IRLqt0A10fP4W3nYAD+6n+NB+MnArCfPZt9rF/r9TZBp+U1S7Rq+zdlP2sWP7g9AbOcrSl7WPOZ7nbyjmszmQBIb2eCJ9UV4AuB3wehVAhlQMuOMPBqaN1rnW/FBrXtC8fcbwpiLy00QZFAKrTdC/qeZgJJlg0LxxNcMQMYZgpru/Xez3jUBLVqjHf2SLjFPYHHON7+1jv+S+Atk/HkJJkAFNDdXkyQqBkb6Gv9ylG+73k+fgQAYddPyLYgFgYsqDTZYDgOTHvLK6i9ws3gnZqfIcD/+b5p+rltvwmO9ToGJj9fU9OpLpiIPwnS8yEjH5bPhqrEZYjEIybQ5g81+IGJiIiIyPa0Q9VEKisz/yqalZUFwPz58ykuLmbo0LodeUKhEAMHDuS7777j/PPPZ/LkyUSj0YQ++fn59O7dm++++44jjjiC8ePHk5mZ6QWQAPr3709mZibfffddk0GkcDhMOFz3r9Xl5eveyUdEZJdRNBVGXwYl081f9DdC2PXTM/x8QtvdsVO91ydGbmVG6E+moHI9ZaQCsLu1gPN8HzY5drq11otf1WYi9bQWMsvt4PXJtCq9PmkJu5dZ0Okg2P98+OE/sOJXcOOQ1MIUzC44dcvs2LWhYs65e0BkDcGa5w27fqiXOTTN6chP1Z0Y4fsCy4LlbuLSriPtifitegGa5CzoMMAEhBaMg3iYNtZqvgldzq3RP/Kxsx+3BZ6mh1WXXVRhpxMKxCFabYI2KbnmxMo5JliIySI7IPyQd839gUdIshLrUpGaC92HQf+LoFUP84wDLoGZ78PEJ6CiyASG0tqY93i3A+HbB6AiibzqVRS5WWaHOV/NDnVZnaFy5ZYrrC0iIiIiv8sOE0RyXZcrrriCgw46iN69ewNQXGz+JbR168RdYFq3bs1vv/3m9QkGg7Rs2bJRn9rri4uLyc3NbXTP3Nxcr09Dt99+O7feeuvveygRkZ1JPAYfXQ1FUwBT9Hq1m0Z729TemePk8078IM70f0Kutdq7bN/wYxsc+mNnX/7P921CW7Fr/kFhP3s2AavhciwLcDnYnsYXNUveaoNIzwfv4P34AP4ZM0uWe1iL+A7z/5U/+L6uu96yIXd3aLc35D+6dXfsWl8x53ljYU0JQZ9ZsjXbbc8VkQu4KfAiLaxKjon8C4A4Ps7wf8rXzp4Jlyc3XMbXoj2ktITIWvAnm2Cf5SPXKuehwMOsIclbgpbGWtaQQoWdSQ6rTBDNn2zGAJj9EcRMnahZbgcvkwkgpeGywOwecNF3prh4w2ff4ziTldTwPS6aYpazdRrI81k/8M+Fu3N5q8mQdTgkZZr5rJy3ZQtri4iIiMhm22H2tL3kkkv46aefePXVVxudsxqksruu26itoYZ9muq/vnGuu+46ysrKvK9FixY12U9EZKcXj8H3T8JD+8KiCV7zOZErGRi5n//F9+OPkesYErmHR+LHc0bkWm81VqUboqImw2YPaz6TQhc0eYu34wc3ans6fhQAKTRRLLumWPUw/2SvyVezXKq1tZpz/P/jvsCjDLCnc4n/Xb4K/oXr/S9zrf9Vc20gxdQnatnRXFwb5Gm7j/m+Lbd8X/IDRNcSitdlSb3tHMJTsaMSuo13dsd14Tunt9d2b+AxbKve0jc7YJa+rZwHVauh2xBIyTbZRZaNbVkJNYxSa97bSjfJFDe3A5CRZ5bdOQ788onXd2q9WkgA7eoV7gag8yGNA0j1NfUehzLN0rZYFd2zLF7oO5O926ZAcgsz52iVOa/C2iIiIiI7hB0iE2nUqFGMHj2ar7/+mnbt2nntbdq0AUwmUV5entdeUlLiZSe1adOGSCRCaWlpQjZSSUkJBxxwgNdn2bJlje67fPnyRllOtUKhEKFQ6Pc/nIhIczbtTfjyDlg1l/p1j8Kun+9ds839hdG/JFwyy+3Al05fDvUVMq1eLaL3gjfitxxeCfyTEdG/0YpSlmP+3B7n7MkvTlu6WEvx1Q+KAD3thYlzqs2u8QVp7dRtslBJUkK3E3zfcEJNzZ5sq5LzAp8CFqS0MbWCgilmSdX25DhQ9BO4LkESl4a9Ez+I8/0feMdBYt4SP4Dr/K9wYk1BbcAEj9rsCYNvgJQWddk+09+Cz/8Ja1eYWkvxmrpHToyQFQUXwnEgOd0Ed4Lp5tpV80yh8xr1f5YAe9i/JT5Lmz6b/vxZnc2yt6IpkNMjsf6R60L5ElNbSYW1RURERHYI2zUTyXVdLrnkEt5++22++OILOnVK/AW1U6dOtGnThk8//dRri0QijB071gsQ7bPPPgQCgYQ+RUVF/Pzzz16fAQMGUFZWxsSJE70+33//PWVlZV4fERFpYNqb8OFfYdUcagNIcddiktOdt+KHrPfSe2MnATDZ6Q7AsHp1ew7wzWBB0gh+SLo44ZqhkbvpG36SuGsxy2nvtR9qFyYOnt3NFG6OR7HrZb60t1dilrnVsANmyZoVMAEjyzZZLbW1nDoMgOztXGdn1TwIV0IgmZCbmHG1hFb0CT/tHb/nHMivbt0/tJzlS9ycgtQc6NAPugxOzPbpcxKc9LxZTtaqp9kpzheCpBYkWTEAwv50SG5p5hJMMbvDTX4eKur+AabMNQGs1qziucAdiff2BSG356Y/v22bAuMp2bBiNoQrwImZ7ytmQ0qOqU21LTPDRERERGSdtmsm0sUXX8wrr7zCe++9R3p6ulefKDMzk+TkZCzL4vLLL+e2226jW7dudOvWjdtuu42UlBRGjBjh9T377LO58soryc7OJisri6uuuoo+ffp4u7X16tWLYcOGce655/LEE08AcN555zF8+HDtzCYi0pR4DD67FapLE5r/HTvB21WtKd2tRfzitidUk1XzRnwQAAfY05vsv481m8lu3Z/DFaSw0G3NtdFzvLY06m0hb9mw1+nw4/NQugDiUd5PvoXfnDb0SVoObprJtLF9kNMdyotNPZ3aXccC/ppzPeGAUds/OBEuAycCWV1IK/ptg91fiB0OwF7Wr4RqAkCACaq16rnugEvbvnBcTd2n6W/D949DZK33cwrHXCidD9R8nzfWLCVz6+4RJgjAVf43GOT7qd7gFmR1heTsTX16I68ABl4Dha+andpiRSbYl7/3lituLiIiIiJbxHYNIj32mCm2OmjQoIT2Z599lrPOOguAq6++mqqqKi666CJKS0vp168fn3zyCenp6V7/+++/H7/fz8knn0xVVRWHHXYYzz33HD6fz+vz8ssvc+mll3q7uB177LE8/PDDW/cBRUSaq0lPQ5mpBVftBngsdizz3DzedxKzN/eyfmWK2807vtj/HpdFL6GKIL86bfnNNcuSD7F/oinPBO9hv/AjRGoCFAAOFqvIACCHssQd3pOyoN0+JoBk+SBSSZ/wKvqwEnxpphizEzU1j5KzzJIoX8Bkt4TSTcZL/l4m+2VHCE7U1gRyHLMr2Qb8UpOJ1MEqSTyR0RaG3Lr+Z7Jtsyxs7SqTdRQLk1RTHDvs2OCrWUYYj5j3KlK3VHB8vBdfOX3NlBvuyJbRDroM+n1Lzja0g52IiIiI7BC2axDJdd0N9rEsi1tuuYVbbrllnX2SkpJ46KGHeOihh9bZJysri5deemlzpikismtxHPjuQe/w/fgA/t1E9tH+1kzeCP0DgGlOJ6oIeovJ1pDCj05dcKmj3bguHUCmVclR9kTedQ7y2taQzELX1Kt7IXh74gXprc1yrL6nQdlCs/17TjeToeQ6UF1mlnUdcpWp7RMuM9/BBEV2tOBEVmeTMTXtTZIa1ERqSm0QKc9aWdcYSIVB15psow1ZNQ9KZpnXluVlIlVSVwNwsdOS8JoAXWp+mCVuJqdFb/TOJyXsBueDnC5bZsnZ+nawExEREZEdwg5RWFtERHYg4x+GssXe4Ry3baMu//A/wym+L73jPvZ8AH52OgKwyM3lmth5APzBN7bxPQKpZgcw4Az/J7wbqQsivRg/3HudbZXXu8gyu6nVBoESlkBVm4yets1sCZRtQ6eDYeqrWJaFhYPbRLnCM30f83z8CO9chlVZdzK5JbTbf+PuFy6D6tVQuQLCa0iq2Z3tyuiFdLGWkm+tYHD4PqL4eSjwIMf4JvBALDGAGKof7PL5YM9Tms/7LSIiIiK/i4JIIiJSJxaB7+qW+roufObsndDlTN/HjPR/1uTliVkqRierKLHB9kP/C+Gb+8GNs7c9x9uxDeC/8YFe15bULanCsqDn0XUZLzvLEqiMPEjPg2gVSUSoarDL3CuBf1JFiOfjR3ht6fXrRFWVQosOG3evUCZgQXU54NKiXjDqtugIrgm8RrTmV4MZzm4cZX/PK/EhCUNE6//qYAcgteldTkVERERk59PMftMWEZHN5jiwYg4smWy+O07i+aKp8OaZUFm39Oy62DnMrZeJ1Nuaz83+F9Z5i9ZWaaO2M32fJDa06GiyiDLqxj3AN8N7nUzdLmVBK17zygZ/MqS1SRyrdglU/d3ImptQpnkuX7DJJW0H+GZwoP1zQlu6VS+I5MRg/riNu1dWZ0hKB8zPvgVrvFMT3V6E3YB3XEWIj5x+CZfnsTJxLr6AqUMlIiIiIrsEZSKJiOwKiqZC4SuwZIpZzmQHoE1v6H+Byeb54WmY8CisXuRdstzN4LX4oQAMtAt5InA/Phxsq2E9OxtwAZd0q4qLfO/xaPw4AI62J5BmJW5dT7/zwB+E/c6Gz272mofYk/nM2Yccq5xFbhIdrNpglmUKZSel75wBi6zOJhtpUZRQE5lcAElWlM7WUua5+QB0tpbWnbRtqCzeuHvZNnQYAPPNEsO97DkQrzsdrlfg/Ln4MJJrCm/XGhe6DL9VL/iYkg3JLTbu3iIiIiLS7CmIJCKyM4rHYO6XUDIN1pbCgm+g9DeIrgUnDriwYjbM/MCsWYtVNhrixuifvdfPB+9q+j52AAbfAN88AOHVAFwdeN0LIjUKiiS1hH3PNq97HAXj7oWwqXuUWpOBtMI1O7Mdak+pucgy881ou3MGLGwbsjuDEyfJipp4XI13gnUFrbMpZx4miNTWWmEaLdvUgkptkKG1Pu32N1ldsSqOsr9POPWn6NUJxzHqdjn9Onh5YgDJss1Yv2dXNhERERFpVhREEhHZ2Ux9A774B5QtISHNBEw9IstvdjJzIuAkLp+qcoM8GjuO7vYixjimWPPR9oSm7xNIhUOuhAMvg3lfwfyvvFMPBR7k+dgRXBl4M/GafheCr+Z/PZEKaNkJyhZB1Soya+rz1NYESqV+BpNjdjHbGQMWjgMr54E/lLCc7bPgVXS16zKO0qwqL8CUUpsh5LqQkgOdB7LROg+Elp1h+XQsa/1dV7gm8+sE+2s62CWJJ5NaQL8LmucSQhERERHZLAoiiYjsTMbdC1/dAfGml0VNiHXljdhg/hp4nTxrVaPz/4qdzkvxw73YU4E1h0eCDzYeqGVn+MNTphYRwP7nwuKJJtMJOMY3gWN8DYJP6fnQp95OX6FMsxyqugyqVtVbvlZzb3tu3YHrQMdDds6Axap5UFEMKVmEKiNeoKirXQT4qP1h1A+qJRQwT2lpsoI2ls8Pe50Gn9xEbW2kdSkmy9y74ZJEgAMvh7Z9N/6+IiIiItLs7YS/jYuI7KKWTIav7kwIILkNyhf9NXoBbzsH88/oHwGIuxbznDY8EDuBjtWvmABSPf3tGTQSagEXf18XQAKzNK3HUaZmkeVL7O9PNkGnPY5PzCTK6gzZ3aDCZNt0rreLm494vXs74AtCWu7GvAvNT7gMqlZCPJJYWNuyqJ9JlmTV/Vwt2zbvSTAVypfCyjmbds8O/SGYBqwn0wyY4nQFEn82AGS0g04Hbdo9RURERKTZUyaSiMiOznHqtrEPpMLq36BkOvhToPMgs8wL4P0rIG6WOc1z2nBS5GaqCXK9/2U6WsvY157NItcEYr5xelPlBrk3dhJPxY9e563b1dbeqe+Qq0xh7PpsGw681GQilS0BXBNMCqaY3cNSW0HBqYmZRLYNrbqZ+k1AJ6uuOLSfOBn1dyDDguSMjX3HmpdgOqxdBU6MkN/CSzKyA+a9c+OAxSGpS/hvec259DwTnHOiJpOrZCa06r7x96wq917eEniOD8P9m+xWTQiAPe15NS0+c5/0PJNJJiIiIiK7FAWRRER2ZIsnwef/gJVzTYHsWCXeeifLBl8S7DYAOg2E4kLvsmfjw1iJ+Uv+DbFzGg1bRhpDwnezhFbrvX2WVZHY0KITDLi46c55BTDoWih8FZbPhlg1+EKmveBU872hQJL3sp213Htdf5cww90lghZJdr3lZWmtIVYFlcvBshmWPJO/WR9zaNIvJpgINavRXBKqcW/UjTKpLYjUyipnuD2eD5wB3ukh9iQ+c/YFwMZhd+s3c8LyQVWpCV7ujPWpRERERGS9FEQSEdlRfXAl/PicyUZpiutAbC3M/dx81ahyg7wYH7rB4TcUQDrW/pYj7B8SGwdevf66RHkF0LpPXeZUKNMEG9Z1Tb2YScCKN90HTDHwnVWkwtSGqlrF4f4pfEIv0q0qsH3mZ19T7yhIhHPSx9dd57oQWWMymXJ337R7JrcwgaRwGQDXB17mg7AJIr0c+BeFbhcviNTVWkJy7VI6N2qynzoetHPWpxIRERGR9dqJfysXEWnGPrwSJj3V5KmI6yO4noDLA7ETNulWQSLsbv3G3YH/MM/N43B7MrbVRGZLRjtov/+GB7RtyOm6cTfP7QG+QM0yPIu7/Y9zdew8rvD/N7FfarYJtuyMaguMp2ZzYnAe6fZz9PUtgFgY0tqY92ftKoisNYEl22+CS5G1JpC42wDI3sj3u1ZWZ8jpAWULzSF1721vewFVTsg7bsGaehe6ZhllRt7veGARERERaa4URBIR2dEs/gF+eBYAx7V4LH4sd8dO4d+BhyhxW/Kv2B/JpZRR/ncY6f8s4dIlbjZPxI/dpNuNCV5LZ9vUI+rGkqY7BVKh1zFbfglTchZktjOZS7ic5B/HcN/3JFvhuj7+FLON/c66nC2rM7TqAUVTsNvty7DcNRDvXlM4Ow2KpphMLNcxNadcwMJkKLXuDQeM2vSsINuGfc6ABWNNQW8rytOBuwHItCopsOsKdU90eyVe68Z33p+FiIiIiKyXgkgiIjuC2uLZVSvhzT9TuyvX0/EjuTt2CgCXRUd53UtoyY2xP3OMbzwtrEpWuemcHbmKKW63JodvZ5VwtD2BeW4+v7jt+M1t453Lblj3KIFlAkht94K+p235JUxZnaHr4fDTqxBeA268ZumUZTJufCEIpUH+XjtvDR7bNu9t2UJY+QtktDXLzaJV5rjFbnDgZbBoEiwthGhl4s+kqVpTG6PncOh4MMz/GpwYh/mmeKdaWeUNOlsk1OJq2XHz7ikiIiIizZqCSCIi29viSfDZ32HZdFO0uN627q/HB6330mVuS1pYlXzp9E0IIB1pf8/l/rdY5rbk1tgZ3B14gr1rsksWOK0ZFLkfgBARMqhsYmQLAskmU2i3ASbbZXODFetj27DXCCj5GYqnm2Va/qAp4OzEARdyum2dANaOJK8ABl5Tryh5EfiTIH/vuqLke5y48bWmNoZtw5CbYcx15p7htRBfu4GLLEhvDaULNn7JooiIiIjsNBREEhHZXhwH3joXpr9FU7trTXU6M8dtt94hVriZ9GAxE52eCe2PBv6NZUEPFvO5768J59paK7zXWVTUbtJVwzZBi25DTcZJbi9Tb2drBnDyCuCI2+DbB2HhhJraR3GTgdRhKwawdjQbKkq+KbWmNuWew26HKa+YLKcVv0D1KgAOtH/mW6c3B9o/430+c7qZ7LCagtwiIiIismtREElEZHsomgqvnwmr56+zy6MxU9voBHscl/jf4ebYWYxz9kzoU0kSAK/HBwOQw2qeCt7bIDCUqP4uaL3tBvdPbgkH/QVadd+Up/n98grghCdh5RwomQFY2yaAtaPZGoGiDakfvFr2M3zyN6hYxkOBh3g7fhD/5/vG1GfK3QOyO8PaUtVEEhEREdlFKYgkIrKtFU2FN/7kBZBirs1nzj6MdQo40J7Gt04fwm6Ajx2zE9qp/i/obBfzYvAOnoodybdOb8rdFCa7PagihOPWRYxeCN7B7vbCDU7hAPtnvnN6c5F/tFk65jrmRF7fTd/pa0uxbRO82tYBLKkLXmV1NtlgS38kCx/nxJZCYE/I7gKWBStmmyV2O2t9KhERERFZLwWRRES2pXgMPvsHlM4FYJbTnvtjJ3oBo1fjhza6pI9Vly10jv9/nMP/OCdyJbhQ5QZZ7Lbyznexlja+Z1ILyGwPFUvNtvHRKh4NPMhKMuliF4Fbs1QpmAr7/XnXyvyRRPWLfK9daQKKgWRTzLt8idklr+BUfUZEREREdlEKIomIbCtLCk0R40XfASZ2c0bkWkpouc5LOlrFNbuVJUrCtF0bO89ry6KckBVL7NiqBxx0hckemfQ0/PYtVK+hxZpiWjgl4NrgD5hAQdfDoMdRv/85pXnbmCLfIiIiIrJLUhBJRGRbmPoGfPgXiKzxmha7rRICSFf43+S+2EkJly11s5scLs2qatR2jv/DxIZAOhx6E/Qabo77joCyRVC50hRIjlVDPGK2ks9sZwpYK8NEYMNFvkVERERkl6TfBkVEtrafXof3LkoIIAEscFsnHPe15hAkmtAWIdDkkEfYPzRq62fPqndkQc+jEzOLajNM8vcCJ2a+AinQ8UAYdI0yTCRRbZ2ktvuY7wogiYiIiOzylIkkIrI1LZ0CH/wVHBMcmuvkcW70Sua5+QndjrO/5QB7OmNDf+H66Nl86ewFwK3+55ocdpA9tVFbJpV1B7bfZCA1/Iu/MkxERERERGQzKYgkIrK1OA58dC1EyrymP0aup4jEJWpD7R/4d/ARAPJYxQOBR7kv9gf62zM40tc44wjMRllvBW/mxMitXlsLq16mkx2A9Lym57U9tpEXEREREZFmT0EkEZEtxXESM3yKp8Piid7pR2LHNQogAdwSeD7hONOq5NYGbXUswOymto/9KzYOTs3K5IRMJDcO1aW/63FERERERETqUxBJRGRLKJoKU16BpYVmO3R/CqyeDzgAVLoh7o6d0uSl+daqjbiBBcE0yOkJS+uyk5x6pe0CVrymqw24EK7YvGcRERERERFpgoJIIiIbo2GWUf06QkVT4ePrYcUckwHkxCEehUhdEOf22Ajv9Qn2ONaQxCfOfoz0fbKOG1o13yyTeBRIgoP/Ajk94K1zIbZ23de5gOUHy/d7n1pERERERMSjIJKIyIYsKYTvH4Pin8GJQFJLs8PZXiNMkepvH4Rl003wJhYGNwrxeMIQPzsdvdd3Bv5DFD/jnd050P65rpMdgH3PgXmfQ3kRuDFTIDs9Hw65EvY8BZZMhsz2UDoXnBj3Bx7h6uj5PBh42ASNLNsEvJLSILfXNnl7RERERERk16AgkojIujgOTHgUvrkfqlaD6wKuyUBa9rOpd3TgZbBwgsk8ilbxbqw/n8b34V+Bp2lh1dUoWurmAPB+8AYCVpwAcQ7zTam7VyAVeh4Fw24D14F5Y6GyGFLbQOeB4Kv54zqUCRn5ZsncmmX8nzWRo4OFBG2AkJmHbUH+3pCt4tkiIiIiIrLlKIgkItKUoqkw7gGYOdpkBNXnOODEYOmP8MFfIFoFsWrA5fLoxQAkRcPcG3zCdHctVpEOQCtrdeN7ZbaHNnvCAaNqlsjZ0O2wpueV1Rlye0LVCnCiEC4n6MZN6aWaFXCk5cLA6+qW24mIiIiIiGwBCiKJiDS0pBDeOQ9WzPaaStwW3BM7if72TE7wfVPXt6rpoti/uu2815Pc7sRq/rjNojyxY0Y76HUMFJwKeQUbnpttQ9/ToGyhOQ6mQbgc4hFTiyktDw67Adr23ZgnFRERERER2WgKIomI1Ld0Crx0QqPg0KOxY3kjPpg34oPpaBWztz2n0aWuW/f6J7cLi90c8lnJyZGbvfagVb9WkgUHXAL7n79pWUN5BTDwGih8FUpmQfVqUw+pVXfod56p1yQiIiIiIrKFKYgkIruGeAzmfgkl08CfAp0HQU73xOBN0VR4/QwvgOS68Ex8GO/GD2Ka29nrdkLk7zwRuI8jfJMSbvGr2zbh+KDwgwy0C9c9J9sPWV03b9lZXoEp6r2uHeNERERERES2MAWRRGTnN/UN+PIfZsczJ2Z2MPMFIb8vDLvdZO44jqmBVLtMDPjI6cc/Ymc0OeT50SuYZ5+ObZn0o1VuOkMjdzfqN9bp672+0f9C4slAMiS33Pznsm3IUfFsERERERHZNvRP1iKycxt3L7x/CaxeaApR44Ibh1gVLBwPzx0P3z4IM9+DuV94l30c35eLo5etd+hl1AWAXosP8l6/FLitUd/+9nTO9o9JbMxoC8ktNuOhREREREREtj0FkURk57VkMoy9G2JhAGKuzfh4L8rclLo+kdXw6U3w4VVmWViNO2Oneq8Ptn/iBHscN/ufZ17odHJYDUCpm+b1WeVmeK8PtH9mdPCGhKmsrtcXADtgMqCyOiMiIiIiItIcaDmbiOycHAc+uclkHAHlbjJ7hp8GYLg9nnsDjxGyYjWdXVi7IuHyJW4OYAJITwfuTiiInWlVssJtwVOxoxnq+4FhvknMcjsA8Hf/s1gW7GnNZ1zwMg6O/BtoIojkC8J+56qGkYiIiIiINBv624uI7JxWzoGiQu/wwdgJ3usPnAH0CL/AMreF1+a4lve62g0QJgjAo4F/N9hRDTKpBOBt52AuiF7Bp/G9+cbpA8AB9nSvX3t7ufe6djxPehtIykBERERERKS5UBBJRHZOs/8HkTXe4VPxoxt1+VPkagBeiw1iz/CTfO/0BKDQ7QKAjzhpVDW6bg97QcLxudGrAGjLcrpYSxPODarZne0ge1pdoy9kAkj1ls+JiIiIiIjs6LScTUR2Po4DPz6/wW4z3I7sV/0Iy2sKZF8XPYcvQlfxi9MegCBRLKvxdSN8n/NifGij9lP9Xzbqf6P/Rfo7MzjN9yVYfvAHTT0kywehzE1/NhERERERke1EmUgisvNZPhtWzfMOX4od5r1+NnCnlx0EeAEkAAsXgHE1S9P+6PusyeF72YvIY2Wj9gPtnxu1dfGVcEHoEzKDLoTSIKkmcJTTXUW1RURERESkWVEQSUR2PhMehZqAUKUb4m+xs71Te9tzONqe0ORlXaylLHJa8amzLzYOJ/vGrvMW34VGcbHvXe94T2sufa25iZ0snwkapeRAag4ktYBwBaRkwf7nq6i2iIiIiIg0K/objIjsHOIx+OVTGHcvzPrAa55YU+cI4Hr/y2RalRzpm9jkEBYu37umfx9rPt3sJYkd/Cl1fS240D+aztZS9rDm82rwn9iWm9g/NdcEjHAhshaqSyG1FRx6A7Tt+7seV0REREREZFtTTSQRaf6mvQlf3gFliyAeoTYLyXEt/hn7I2CKXp/n/xCANKuaP/s+4pn4UQnDfOzsz8fO/gDsbv+WeA9fyAR/Jj0Hq371xvkidFXTc2rdBzoeCCWzoXq1yUpq1R36nQf5e22RxxYREREREdmWFEQSkeZt2pvw0dVQXYZJrqzLBlpOJnPdtgC8ELwj4bJ8q3FNo/pO9n2Z2JDVGfpfZAJDL5wA1avWfXEwE459GPL2NLWZwmWmiHZWZy1hExERERGRZktBJBFpvuIxGPcAVJeDZROPO0xzu7CnNQ/bcnkjPsjr2sUuSrj0j77PmOfmcZD9M6vc9IS6SX/1v8ZedoP6Rr2ONQGg/L1g5Dvw5pmw+jfqB60AU/fo6HvqlqvldN1STysiIiIiIrJdKYgkIs1PPAZzv4RJT0HJdMAFF56MD+eO2Aj+6n+Nc30fcm/s5HUOkWRFuS3wDABfxfdMOHe273+Jnf1JcEi9ZWtt+8IpL8Dkl2DeWAiXgj8ZOvSHARdquZqIiIiIiOyUFEQSkeal8FX49EaoXN7o1B2xEQDcHTuVl2NDNjCQDTgAtLNWJJxJsqKJXfc8FfzBxLa8Ajiqj5ariYiIiIjILkNBJBFpPt44E2a826g57PpZ6uYktC2l7niU753ECywfpORA5TIAutpL13NTn1nK1hTb1nI1ERERERHZZeifzEWkefjgioQA0iynPeVuMu/H+9Mj/AKDI/c1edm3oVFc4X8zsbFlR+g8COyQ11TbZ39rZmLftFaQnLEFHkBERERERKR5UyaSiOzYHMcEjyY97TWNie/LBdErNnhpd2sRbRvuwuZPgm5DYeg/ISMfvnsQ3DiX+N6li1XE/na9IFJSC8hsD0ktt8yziIiIiIiINGMKIonIjmvxJPjs7/Dbt17TXCdvowJIAIPtKY0bc3pA39PA54fDboJl02DBN9ixCEf7vjd9bD+k5kJqNrTfz9Q6EhERERER2cUpiCQiOxbHMcWqP7sVZn8Ibjzh9HPxIzZ6qLP8nyQ2+JJg4NWmKDaYmkaH3QRf3QEr55ngkT8JUlpCuBxSW0HBqSqWLSIiIiIigoJIIrIjKZoKha/AlFchUuY1uy5YFqxxk/hv/BAAbvC/xL9ifwTgneCN9LXm4mAx381jSOQeAHIpTRy/fX/ocVRiW14BDLrW7Pq2fDbEqsGJQ9t9TACpNuAkIiIiIiKyi1MQSUR2DEVTYcz18NsEIOY1x1ybM6PXUuEmc2vgOapIIpM1nO37H+f4PiKGj4BlspV8uHS1lnKz/3myrXJ8lls3vuWDglOazirKK4DWfUwGVLgMQplmCZsykERERERERDzb9W9IX3/9Nccccwz5+flYlsW7776bcP6ss87CsqyEr/79+yf0CYfDjBo1ipycHFJTUzn22GNZvHhxQp/S0lJGjhxJZmYmmZmZjBw5ktWrV2/lpxORjeY4Zvnab99QP4D0s7MbXcMv8a3Tm5/cLoyOHwBAB6sE23KxLLwAUn1/8n/Msb7x9VpsaNEB2u+/7jnYNuR0NRlIOV0VQBIREREREWlgu/4tqbKykoKCAh5++OF19hk2bBhFRUXe10cffZRw/vLLL+edd97htdde45tvvmHNmjUMHz6ceLzuL5YjRoygsLCQMWPGMGbMGAoLCxk5cuRWey4R2UTjH4a5n3uHs5z2HBx+gOGR2xO6PRs/EoBhvokbObAFgVTI6gQ9jlSBbBERERERkd9huy5nO/LIIznyyCPX2ycUCtGmTZsmz5WVlfH000/z4osvMmTIEABeeukl2rdvz2effcYRRxzBzJkzGTNmDBMmTKBfv34APPnkkwwYMIDZs2fTo0ePLftQIrJplk6Bz//pHX7v9OSUyE3rveQ834eNG5NaQijNFMcO1nwPpIATgzQVyBYREREREfm9dvi/UX311Vfk5ubSvXt3zj33XEpKSrxzkydPJhqNMnToUK8tPz+f3r1789133wEwfvx4MjMzvQASQP/+/cnMzPT6NCUcDlNeXp7wJSJbmOPAh9eAEwagzE1tFEC62v9qwvEwe2LjJWyt94SrfoFTX4buw8yuaoFk8Ieg3T6JO7KJiIiIiIjIZtmhC2sfeeSRnHTSSey2227Mnz+fG2+8kUMPPZTJkycTCoUoLi4mGAzSsmXLhOtat25NcXExAMXFxeTm5jYaOzc31+vTlNtvv51bb711yz6QSHMUi8CPL8KcT8xxlyGmvtDaEnCA3B6QnLXphajjMXh9JCz53mt6Lj60UbeRvs/oZ8/ixMg6/nu0/XDM/eAPqkC2iIiIiIjIVrRDB5FOOeUU73Xv3r3Zd9992W233fjwww854YQT1nmd67pYluUd13+9rj4NXXfddVxxxRXecXl5Oe3bt9/URxBp3sbdC2PvhlhVXdsvY2pe1Pz3Y/shLRc6Hgz9L4D8vTY87rQ34YO/QrjUa3ovPoD7YycBcJrvCyrcZPa3Z5FuVbGP9avXr6PVIPjbcSC027fuuLZAtoiIiIiIiGxRO3QQqaG8vDx22203fv3V/IWyTZs2RCIRSktLE7KRSkpKOOCAA7w+y5YtazTW8uXLad269TrvFQqFCIVCW/gJRJqRcffW1Cpy1tHBNd+cKJQvMYGh+V/D0L9Dn5OavsRxYMKjZtx4XWCq2G3J7dERAJzlG8MtgRcaXfpU4B7eix/ARf736hrtIOxzxmY8nIiIiIiIiGyqZrXGY+XKlSxatIi8vDwA9tlnHwKBAJ9++qnXp6ioiJ9//tkLIg0YMICysjImTqzbzen777+nrKzM6yMiDcQiMO5BwMF14ct4ASVui/Vf48ZhTTF8chMsKWx8vmgq/O/ahABS1PXxSOw4+ocfoZhsAIb7JjQ5/BDfjzwUfJgMqwrwQyDNZEDl7r7ZjykiIiIiIiIbb7tmIq1Zs4Y5c+Z4x/Pnz6ewsJCsrCyysrK45ZZbOPHEE8nLy2PBggVcf/315OTk8H//938AZGZmcvbZZ3PllVeSnZ1NVlYWV111FX369PF2a+vVqxfDhg3j3HPP5YknngDgvPPOY/jw4dqZTWRdvr4HIqsBGOvsyZ+i1wBwru8DLvKP5tboGWRYaznN9zmPxI7nCv+bdLaLwXVgzXL4/nE4/tG6WkRFU2HsnVD0c0IG0rDIHcx123rHQ+xJ7FVv6VoiC+yAKZbtCwAu7DYAsrV0TUREREREZFvYrkGkSZMmMXjwYO+4tgbRmWeeyWOPPca0adN44YUXWL16NXl5eQwePJjXX3+d9PR075r7778fv9/PySefTFVVFYcddhjPPfccPp/P6/Pyyy9z6aWXeru4HXvssTz88MPb6ClFmpmiqTD1Fe/wB6en9/rJ+HCeiw8jWvNHxws1hbBnRHfji9BVppMbhbmfm68uh5m2wldh6U9QvijhVvUDSF2sJTwVvK/xfCw/+PzgC5ogku0Dy4acbnDAKBXNFhERERER2UYs13Xd7T2J5qC8vJzMzEzKysrIyMjY3tORnZ3jwMo5UDIDsCC3l8m42ZoBk3gM5nwOX/wdls2gthbS9dE/80p8yAYvX5A0IrEhqSV0HmyCPd8/bnZLA+6NnsRD8f/j29AoDgw/BEAaa3kzeCu97MQgE1YATnwKFn4HS6dCtBICqdB2L+h7mtmNTURERERERH6XjY15NKvC2iI7PceB2R/BxKdg6ZSaXdEsSMqEjgfBQZdtncDJtDfhyztg1TzqF9L+Lr67F0C6wv8m98XWUTC7KdWlMOPtRs0Pxc1y1NoAEsDXob+QZVU0HmPQtdD7eNj9WDO3cBmEMiGrszKQREREREREtjEFkUR2FIt/hE9uhEUTwI0lnqushunvwopfYa8RULbYtHcaCF0ONcu9Nlfhq/DBFRBb2+jUudErvde7WY13Oazvtdgg8q2VFLtZDPVNooVVmXDedeEzZ+9G19k4tGBN4wH3PQcG1iyRs23IUe0jERERERGR7UlBJJEdwbh74au7E4pOA5S5KfhwSLOqgTgs+wnG/FTXYfwjkNEWDr8F+mxCllCtsXfDl//0DmOuTSVJZFomoFRJsnfuEPsnzvZ9xJdOX94J3kRB+KmEoa6Nnee9/sDpzwvBOxPOf+X05dzoVY2mkEUFttVgVe1hN8PBV2z684iIiIiIiMhWoyCSyPb20+vwxW0J2UcznA6Mio5irtuWjlYxnwWvwm85TVzsQvlieG+UOdyUQNJXd8NXdQGktW6IoyO3Md/NYx9rNs8G7/LOPR64n5bWGm4MvMSNvATAd6FLeC42jBSrmgdif0gY+munbsnd+/H+PBw7ntluhyan0dZantjQoiN0HrjxzyEiIiIiIiLbhIqKiGxP8Rh8fKMXQKp2A5wTuYKjInd4O5ctcNswq14AJubafBkvYJbTvm6cWBV8dqsZb0McB8bdlxBAqnYDXBW9gPluHgCT3R7sGX7aO3+4PanRMPnWKq4PvEJnq6jRuWSqAbgueg6jopc2CiCd4fvEe51rrU68OJBk6h6JiIiIiIjIDkWZSCLb06+fQmVdraFbY2fymbNvo26/uO3ozQLirsU+4ccpIw2AGaE/kWKFTaeyxTD3c+h+ROP7OI4pTL1kEkx6DhaNrzvlWpwbvZJxzp7rnKav4XKzevrbMxq1dbWWUuam8mr80IT2U3xf0sEq4Rzfh7wQHwpAvrUy8eK0PFM4W0RERERERHYoCiLJjqE2yLGr7L7lOPDtg/Dlv7ymcje5UdCl1pj4fnSwSpjqdPYCSACz3Pbsbc2pOXJh/teNg0hLCmHCo7DwOygvBjeacPpDp58XQEoiTDtrOXPcdt75M30fr/dRcq0yJoYuJESMX9x2nBS5mQpS+KXeGAD3Bh7jOPtbb1neab4v+DDejwv879d1smzY8+Sd+2cvIiIiIiLSTCmIJNvf4h9NgedlP4ETh1AG5O4Oe/4Behy18wUUiqbCG2dB6byE5nlufsLx7tYCbvC/zOnRG/jE2Y9PIvs1GmqR24q9mVPXsOKXutfxGHz+T5j0JEQa735W5qZyefQivnT2AsyStSeD9+G6sJYQXzh70c+eSa5VlnihHYT8fWHxd15TbZ8M1+zIVuqm8XG8LqPq7eBN7G3PSRjmNv9T/N3/LAErXjswtNgN2u/faK4iIiIiIiKy/SmIJNvXuHvhq7sgXl3XtqYIVs6GX/9ngkgHXQ55BU1f39wymBZPghdOhMhqrynq+ljqZlPupgDQ0Srm3sBjdLWWkkElbVhJMdlNDrfaTU9sWDjRBI9mvANf3A6lc71TxW5LMqkk2YrwdbwPL8WHeAEkgGv8rwFgWZBKmGN8Exrf0PJBq55w1nvw3sUw7Y2E0+k1u7qVkcZT8aMBuMv/RKMAUu19AsTNmIFUSGtlsqi0lE1ERERERGSHpCCSbDnxGMwbC5XFkNrG7LDlW89HrIldyRLEqmH627C0EAZeDe32qwsSOQ7M/gim/RdKZpolWr4UaNEe8gug7X4bvv+21mA3NDAFrXuGn09oy6WUfexfveMOVgnFbtNBpDtip3KG7xMsq6YhvBpeGwFLp0Bl3a5n8502DI3cxV7WrzwWfIAzotc1GqtVwwLXDVk2JLWAgy4FfxBOfBKSM2Hi04BZopZFRaPLjvRNbHq8UCa03A1C6eDEILUVFJy6YwcBRUREREREdmE70N+wpVmb+gZ8fTdULDVL0nwBszTpoMsSt52Px0wx6RnvwfR3vADSXCeP66Nnc6BvOqN879QFRcAs+xp9CeT1NYGkdvvA7DHwyxiIVAL1ij6XTINfPgJfEmS2h0F/hT1P2RbvwPp9dBVMfNI7jLk2T8aP5s7YaY26drGXJhyHCTbqk0w1VSRRRRI/uZ0psOotjfv1E8ACXOKuhYPNvbGTiOJnotuLl+NDmpxiOlVNz92ywQ5Aiw4w6JrEn+dR90Dnw0zgCockK8rHwas5InKXuRSHdKvBuP5k6HiQCRzFwuALmUyzglPXnXEmIiIiIiIi253luu66t10ST3l5OZmZmZSVlZGRkbG9p7NjGXsPfH0nxCMNTtgQSIZB18KAS0zW0JhroWployFGRS7hfecA73iE7zNuCzzTYLgApLcxgYdwBcSqiLsW7zgHsb81mw52SYNRLfAFTeDj4Cu3zLNujoUT4ZnDvcPlbga3R0fwtnNIk93nhP7oFZ8G+GPkOr5x+gDwV//r/OK045rAqxwQftjrsyBpRKNxqt0Ah4XvwWc5LHRbr3eKA+1Cng/eldg44HLIaGMywtr0hs6Dm87sisfg33tB+UKvqWP1KwBkU8bkpAvrdbZh6D+g/0XNaxmiiIiIiIjITmxjYx7KRJLfZ+qr8NVt4JriyI5rEcFPkhUFHIhWwqc3wff/gfIl1C57aug7Z4+E41fiQzjX9xHtrRKWuDkspwX78guULTGZMTUZTO86B3FV9EKSqWZG6M+Md3anq72kptCzC/EwfH0vdBoM7fbeim/EOiydAq/UZUJ9E+/NH6PXJ3Q5z/cB/4kP947rB5AAbvE/zxXRC7nE/y5DfZO99rN8Y3guPgyASjdEqhVOuG6GuxtLaJWQqNWUv/pf4yLf6HotFqTlwt6nQ6vuG35Gnx96HgkTn/Ca8lhJEdmc4f8ksW9mW9htgAkY5XTd8NgiIiIiIiKyw9A//cvmWzoF3r/CCyABXBG9kD3DTzLHqb/TmAvli6gfQKpyg0RcHwDfOz1ZSWaj4QdH7uOm2FkMjDzAHyK3sNTNMmPUBJAWOa24MmqyXKpI4iOnHyOif+OEyK1UufWWgEXXwrh7TB2lbaloKrx6KlSvAqDEzWwUQDrBHsf1gVe84xxWNxqmq72U0aEbEwJIANf5664b6zReBnZV9IKEY3sdAbxUqhOXD/qToNMhkL0JQZ7dDjRZXzVeD/6Dy/3/5U++MQkzICnTZB6JiIiIiIhIs6Mgkmwex4H3RkFsrdf0bvxA3nUOIkKQj5x+fBDvx3fx3RMui7sW50cup1f4OY6L/IMStwWnRG5a521eqVe/Z5Gbm3Du6fiRCccfxfsBsNjN5fX4oHpnXFg+0yyf2lYcB944CyqKvaaG8wcY5X8HgGcDd9LT+o1ng3c36GGZ3cuaELJinOv7AIAvnL1Y6aZzXfQcpjqdibk2C9w2iVNax3/uWVaDYtiZbeGAUZu2vCy3F6S0AjsIlk0HezmX+98lw46Y4JIVMBlLrfto9zUREREREZFmSsvZZNM5jlm6tGya1/R8bCg3x87yju+L1RVf/sa+lHbWCgAuj17Mx87+AMx0O3JR5DKv39m+j/jW6Q24zHJ3a3TbajexwHSxm5Vw/KHT33s9xenGWdRbSuW6pv7OtvLN/aYgeI2/Rf/ES3FTF6nAmst7oRsTug/2TWWwb2pdgy/JfE9KB2wTrItU1WR91a1PO8CewZPx4fzodGN4+DaKyOZ/8f1wsZoMGr0dvIkTI7dwo/8l8q0VzHB240i7we5pvf+w6QWus7uaZWq/fgxRTF1vnw9cqyZzzDW7r/W7QLWPREREREREmikFkWTTLP4Rvr4Hfvmf1xRz7YQAUkMHhR+kMHQuqVQnFM8GmOT2AExh5xsDL3ntj8aO4a4GO5dVkpRwvNw1y6KSCFNNKOFcOSmJkwikbrtlVEsmwxe3eYf/i+/nBZAAOljL1n1tcrZZTla92tQlGnS9GW9pIUTWmKykyBoonQtAgW2+z3Prlg+uJr3JoXtYC9nbnsPc0EhsywSihvkm1XWwfBBIgXb7b+IDYwJDB14Ka4ph2UyzhDBeE/CyamosHf53aNt308cWERERERGRHYKCSLLxxt0L4+6HSOLyp/q1dx4P3McF0SsaXTrHbcun8X2845cD/+L06A3eccPt5S/yv0+1G+LB+Ale2wxnN47y1WXNlNASgL3sOYxvUJh7jZucOIHqMmjZcQMP2EA8BvPGQmUxpLaBzgOb3p2svqKp8NofAVO36RenLRdG/5LQZYA9o4kLbUipyayKrjFBl0NvgD4nQZ8/JO5ktnIOvGqKdbekoomxmnaB/31zJ6uJStuWH/xByOpknnNz5BXAEbdB4SuwZIoJhNkBaNMH+p8P+Xtt3rgiIiIiIiKyQ1AQSTbOlJdNdk1NUetar8cG8a5zEADX+l8xmS3Rxpc/GxvGJ86+3vGe9jxu8r/A32NnAOAn3uiai/zv0cFexmSnO6/GD2NuvWwb163LRLre/wrHRP6VcG0paYmDrSmG2f+D3Y/ZuOed9iZ882+zo1w8anaES82Bff8E/S9uekmW48CHV0HFUq9pgpNYE6oVpRxk/5x4nS8ErXuD65hsoFbdod95dUGXhjuZxSPey4SC2E34s+8j2lormOZ05ij7+yZ62CYwZvshlG6yiTYUKFufvAJT96h+0Curs5awiYiIiIiI7AQURJING3s3fPlP73BcvDevxQdT5Gbzo1u3BfwZvk8BeCFwO6Oio/DhsIoMILFe0UuB20i3qvizfwwhIjwVP5rL/W81um2SFeUPvnFkUsmr8cP41W0LmN3cbo6e6S1h624t4oPgdZwYuZX+9gzGOn2Z47ajzE0l06o0gzkx+PEF6Hn0hgMa096Ej2+ASKVZ3uXEIVplAiOf3QKzPoRhtydm1jgOfP8ELK7LlHo3fiA3xf4EwJ98/+PmwIu4bhOBnyG3QrfDNz7osuIXsEPghBOak6nGxqUSk4V1vm801/pfW3egybJrMpCSTJbWgZeazKffq2HQS0RERERERHYKCiLJujkOjLkeJj7mNUVcHyMbbFMPpqZRimWCGof4pjHVdx4AF0dG8aEzIKHvAHu69/p0/xec7v9ivdPYz56NjcMctx0dq19JOJdDGSErRm/rN2YnnQXAQeEHWOzm8lJ8CBf738NsQuhA6XwTCFpfgCMeMxlIkUriSVl8XL4bPZhPF9bUvCcxWDgBXj0dDr0OHGDJD1A0DYp+9IapdENcHr3YO+5sFQFNBJCyusH+525i9o8FwWSI2RCr4nTfZ7wcH8Kt/ue5J3ayF0Q6yfd10wEkKwB5e8I+Z5rMp/S8jVuqJyIiIiIiIrs0/a1RmlY0FT6+CRZ85TWVuancHzuxye751qom2+8NPM434T6U1Swvu9n/PL6mavLUl9YGQhmw8hcAWliV5FsrWOzmNuqaZ61s1Ha673PujJ3G6/FBNUEkx5ywrA3v0DZvrFnCFsrgrfIeXF39J3azivkgeAPVBGhllQMuVCyB9y5pcohKN8Se4ae84yH2JE7yjW3cMaklDL5m04M3ub0gmAa2D/xJ3OS+wGm+L9jDWsC/Y3U1pDKstYnXWT6zZK11bxh226bvwCYiIiIiIiK7NBUqkcaKpsJbZycEkOKuRUH4SZ6LD2vykv/zjWuyPcmK0s1a4h2f6vty3fe1/ZCeD0f8E059Gfx1xbH/4X+uyUs61WT41DfENhlBFW79Hdos8AU3vENbZTHEwrB2BfOi2QD85rahT/hp9gs/Tpmbsv7rgf/EhhPHB8Bh9o88FbyPJKtBoai0PDj67s1bPpbdFTr0r6mhBCEbetsLsCzItsq9bq1YnXhdRj4UnKYAkoiIiIiIiGwWZSJJIseBt86HFb8mNH/m7JNwfLP/eQ61p/BGfBA+4uxvz17nkMtqdlEDSLYiiScDqZDcEpIyoc2edbt4OQ5kd4NlPwEw2FfYZMHuI+vt1lYrxaoGoLThVvex6g3v0BZZa+of4ZBaM059H8T7Jyy/W+mae2RbFbwXH8Bd0VNZQisALBweCDzS+B7ZPeHCcWY3tM1h26Z+0ZpiWDbTBJNq3BZ4mtHxAzjMNwXLFwAsU9PJF4ABl8D+56nItYiIiIiIiGwWBZF2VY7T9A5aL58EK2Z63d6OH8RV0Qtw6iWtne8bzWm+L0iyovzVfqPx2IF0CKZA5TIAwm5gHZOw4YjbIa9344LStg2HXAlvnun1HhO8hmGROwHYx5pND3sRQ+1JjUZNpS74M8fJp6tdZJZyhSvMM7fq3uga7z2Z8QG1y99+cdo16nJD7Bx62QvZ257DIieHgyMPAjA1dC5PxoZ7ASSAKaHzSbeqEgfwJcEJT2x+AKlWXgEccRuMfxRmvA8xs3Stt38JvZNGm6VupEA8DoQhuQV0OVQBJBEREREREdlsCiLtioqmwpSX4bfxEF5ttpjP3xtadoK5nyV0vTl6ZkIA6d3gjfS1565jYBtSWppgRefB8P5l4MQ4w/8p98ROZpBdmNg3OQva7A5t92l6uF7HQnZ3rzZST3sRs0JnUko6eeuowYQdJMWpS1n6zN2XrqEvTQ2hyFqY+wVEKpreBW3WB7DoOwDWuEm87xzQ5C1GRq6jMHQef6xXYPzC6GUscFsn9GtRuzNcfcc8AG37Nj33TZVXAMfVZDrN/h9E19YEjyxwXfMVrzYBtI4HmWVwIiIiIiIiIptJQaRdTdFUGH0pLJ9llnfVWpm4fO3DeD8ujl7mHd/lf4K97Dl0s5fQpKSWkNbKLEE7YJRZppbTDVbN43z3AwqsuexlzzUBDdtvCjy37GSuWxfbhhMeh2eGQdwsg0uyouTRRADJn2x2TsvII4gFxaa5Y2rc1B+qrjBBlh+egsKaektZnaDLYGi7r1nm9t0j3n2+c/ZY57TaW8sZ7RzAb24br+07p3dCn7v8TzS+sOsw6Hvaup93c/j8MOAiKF9sfrbxKDjRmiCSY97v3N3N8jdlIYmIiIiIiMjvoCDSrsRx4LNbofhncGPr7LbUzUoIIF3gG83J/iZ2FwOwAyYYk9HW7BpWcKrJkHEc6DQQLB+BSCUHVy0GNwD+NEjJMjV62u9nsoHWp+0+MOhaGHunKXjdlLQ2kNPdBMYiayA5iwGh+YwPdyJiBU0AqbIE3DiUF5ngihODJZNh5nummHd6HhRN8YYc5/RZ55SyrXJmOLs1eS6PlTwcfJC9rcSgHEnZcNrL63/WzVW7tO3bB2uyy2p2oAumQseDTQBJhbRFRERERETkd1IQaVey4hcTZHBjOK7Fc/EjyLNWcqTvB69LlRvk+ug53nEma/ir//Wmx2vRyeyklpHXdE2jvqdB2UKoXAE5XcGyTQCnugxSW5mA08Zkxxx8JWS0h6/vNuPFImBhluFldYLOg2DPk+G7h2Hu51C1mqBjAk6RyjKw6+3gFl2TOHY0Bqvmmq8aPzg9eDE+dJ3TKXXTSLGaDmgV2HPZx24QQMIHR95hsoa2lrwCOOFJWDkHSmYAlgnqZXdVBpKIiIiIiIhsEQoi7UrmfeUVYB7j7MffY2cAcJYzhlsCLwBwe2wEXzl9ATjT9zFX+P+Lz3Ibj7XHH+DEJ9cfoMgrgIHXQOGrsHy2WT7nTzLZRbUZSxur4GTofQLMGwtrlpra17k9TF2l2uDVgZeaIMryWQTiph5RxIF6JZ14MTaEFCvMib5xlLvJlLgt6Wov9c6vcZM4KXLzeqey2G1FGFMs/AT7a952DvHOpdBEcGnQdWb+W5ttm6Lh6yocLiIiIiIiIvI7KIi0q3AcWFK3k9lsp733+rn4MG7yv4htufzkdAIgSJS/+P9LZlPFoc/6H3Rsuuh0I3kF0LpP0zvBbSqfH7odtuF+jkMIU1w7Wu8jPstpz42xPwNwtD2By6KXMNYp4KXAbRzgmwHAcrdFwlD97elMcPYgi3LeCd7EwMgDlJPKb24uAPnWyoT+UXyJc+l1Agz666Y8pYiIiIiIiMgOSetcdgVFU+GlE2Ham15TGakJXea5eTiuxWrSAXgpeFvTu4sNvnHjA0i1bNssZ2u7j/m+NZZXOQ5MeQUqiiGURsCKA7DczeSiyGV8Ee/L587eXvciN5svnb1wsLk+Vrd87534gQnD3u1/grv9j/NB6Ho6WCWkYIqRf+3sCUDIinKFv+59ne52TJzXbv225FOKiIiIiIiIbDfKRNrZ1e7GVlSY0FzmJgaRhkTuSTjOoqLxWAOvg4FXbekZbhmr5sHSQsABO0AQs8vaw/H/A+Ajpx8n2F973QdH7vNeL3DbUOqm0dJaw0vxwxOGbW+voH29607zfcHT8aOoIgmAIDHO9H3MT04nPnP25XL/W3UX2wFIa72FH1RERERERERk+1AQaWfmOPDjy1A0FdeFZbRkhZuBBUxzO6330hZWgwLUXQ6Hwdduvbn+XuEyiFaCC4QryKRxFlWh23Wdl5e5qbS01uAj7rW9Gby1Ub+97V95uq4LaawlyYryZOA+VpJBNhWYqt9ASktovcfmPpGIiIiIiIjIDkVBpJ3Zqnkw9wtMZAX6hx9p1OX14N85JXJTo/YWNAgi7TVia8xwywllQiAVYmGIrWVf+xeejCd2mefmr/PytYSIuD5WkAnAN6FLaWetaNSvwJ6bcNzPngWAZUEO5TWtFgRSoNNAszuaiIiIiIiIyE5ANZF2ZtWlUGG2t7esxqcPtn+iwJrb+ATgt5x6Bylg+Zrst8PI6myKeMeqAEhn7SZdflTkDj5wBuBiEyRCWxoHkAByKEs43s1altjBDkBKFrTdGw4YtXXqP4mIiIiIiIhsB8pE2plVlYMbb9Tc1VrMi8E7yLNWAfBX/+v84PTgcHsSqVaY3azius6W3wRFcnttq1lvHtuGzofAlBcAyLA2LYgEcEX0IgBaUdZE0M0CXJKsqNfiJ+YV8DZdbMjfyxQQ73uaCWqJiIiIiIiI7CQURNqZJWWaZVU12TntrBIWu7mc6/vICyABXOx/b91jBJJhtwHNY1lWRh6k50Hpgk3ORKpvCa0SG9LywPZB+eKE5mxv+VqN9HwYeC10GawMJBEREREREdnp6G+6O7PkFpDVCXxBAN4K3sLjgfs5yTd2465PyoT8vs1nWVYoEzI7QCidNCu8zm5/87/kvQ4Q28CgPghlQDDFfMfHUPsHAI73fWu6WH4IppuMrZQWzeO9EhEREREREdlEykTamWV1hnb7QfVqWL2I1vHVDPP90HRfy1+z9M0FLMhsCz2PaV7LsrI6Q+teUL6I9Orfmuxyf+ARDrcn81b8IPrbM/nR6c5Ut0tCnxvqBZnMKrYY+JIhkAQpWTwUeZPvqn9gP/+vEGpr3rtYlcn6CmVuxQcUERERERER2X4URNqZ2bYJApUtBH8yVJVCxUpww4ADVk0R6HgY4lFT0yc1BwpOg94nmqBMc8qqqX3eeV8StJrOMGpFGWlWNf8LXQ/AvtWPNuozyJ5ad2DZJiMrpzvM+woiawklZTI4uQRqdnKjqtQUHs/fy7xnIiIiIiIiIjshBZF2dnkFMPAaKHwVSmZBaisT8GjVHfqdB637wLyxUFkMqW2g80DwNeOPhetAZdM7qwEkN1jmtoIWjfpkWmvqjedCTg/od4GpibRsOlStgmCaOR9ZY/q07mkCWM0p6CYiIiIiIiKyCZpxtEA2Wl6BCRatmgfhMrPkqn6WUbfDtu/8thTHgQlPQKwa7ECTXZJJDCJd73+Z22KnJ7RlUll3YNvQ82ho2xeOuA2+fRAWTjBLBLEglAYdBpi6Uc1l2Z+IiIiIiIjIZlAQaVdh25DTDHZY+z1WzYMVv4IvBE7icrYgEdpby+lsFSW0n+v7kMPsHzkzeg2L3VwAQvWXwiVnQ2a+eZ1XACc8CSvnQMkMwILcXmbnOmUgiYiIiIiIyE5OQSTZeYTLTHFwfxBweDd4I7dFR/Bw8BGyKMdHHMtyazpbgItlQReriCwqWExuvcEss+zP9pmd12rZtlkK2Kr7tnsuERERERERkR2Agkiy8whlQlILiFRALEpf/0LesP9Rc9Kq6+dlKllm5zWghbXGbEwHZrc1XxD8TS+JExEREREREdkVaQ2O7DyyOkNuTwgkQzAZ/ElmdzUsajOPvOyipExou5fZtQ6b830fAjDI95O5PpAEoXSze12kYjs+lIiIiIiIiMiOQZlIsvOwbbNDWtlCc+wPmZ3awhVmmRuWCQ5ldYEDL4U2BTB6FDgxDlyzjM+r/0lbeyX40yG5BaTnmZ3XQpnb86lEREREREREdggKIsnOJa8ABl4Dha9CySxTz8i1ICUD2veHdvtD54Hg85vd3PL3gqIp0HkwXSIVEI+CL2CuW/kL5O9tMpxEREREREREdnEKIsnOJ68AWvcxu7WFy0wmUVbnxjuo1c9cWvkLZLQ1GUjRKnOckgMFp2rnNREREREREREURJKdlW1DTtcN96ufubR8NsSKTC2l/L1NACmvYOvPVURERERERKQZUBBJZGMzl0RERERERER2YQoiicDGZy6JiIiIiIiI7KKUaiEiIiIiIiIiIhukIJKIiIiIiIiIiGzQdg0iff311xxzzDHk5+djWRbvvvtuwnnXdbnlllvIz88nOTmZQYMGMX369IQ+4XCYUaNGkZOTQ2pqKsceeyyLFy9O6FNaWsrIkSPJzMwkMzOTkSNHsnr16q38dCIiIiIiIiIiO4/tGkSqrKykoKCAhx9+uMnzd911F/fddx8PP/wwP/zwA23atOHwww+noqLC63P55Zfzzjvv8Nprr/HNN9+wZs0ahg8fTjwe9/qMGDGCwsJCxowZw5gxYygsLGTkyJFb/flERERERERERHYWluu67vaeBIBlWbzzzjscf/zxgMlCys/P5/LLL+eaa64BTNZR69atufPOOzn//PMpKyujVatWvPjii5xyyikALF26lPbt2/PRRx9xxBFHMHPmTHbffXcmTJhAv379AJgwYQIDBgxg1qxZ9OjRY6PmV15eTmZmJmVlZWRkZGz5N0BEREREREREZDvY2JjHDlsTaf78+RQXFzN06FCvLRQKMXDgQL777jsAJk+eTDQaTeiTn59P7969vT7jx48nMzPTCyAB9O/fn8zMTK9PU8LhMOXl5QlfIiIiIiIiIiK7qh02iFRcXAxA69atE9pbt27tnSsuLiYYDNKyZcv19snNzW00fm5urtenKbfffrtXQykzM5P27dv/rucREREREREREWnOdtggUi3LshKOXddt1NZQwz5N9d/QONdddx1lZWXe16JFizZx5iIiIiIiIiIiO48dNojUpk0bgEbZQiUlJV52Ups2bYhEIpSWlq63z7JlyxqNv3z58kZZTvWFQiEyMjISvkREREREREREdlU7bBCpU6dOtGnThk8//dRri0QijB07lgMOOACAffbZh0AgkNCnqKiIn3/+2eszYMAAysrKmDhxotfn+++/p6yszOsjIiIiIiIiIiLr59+eN1+zZg1z5szxjufPn09hYSFZWVl06NCByy+/nNtuu41u3brRrVs3brvtNlJSUhgxYgQAmZmZnH322Vx55ZVkZ2eTlZXFVVddRZ8+fRgyZAgAvXr1YtiwYZx77rk88cQTAJx33nkMHz58o3dmExERERERERHZ1W3XINKkSZMYPHiwd3zFFVcAcOaZZ/Lcc89x9dVXU1VVxUUXXURpaSn9+vXjk08+IT093bvm/vvvx+/3c/LJJ1NVVcVhhx3Gc889h8/n8/q8/PLLXHrppd4ubsceeywPP/zwNnpKEREREREREZHmz3Jd193ek2gOysvLyczMpKysTPWRRERERERERGSnsbExjx22JpKIiIiIiIiIiOw4FEQSEREREREREZENUhBJREREREREREQ2SEEkERERERERERHZoO26O1tzUlt/vLy8fDvPRERERERERERky6mNdWxo7zUFkTZSRUUFAO3bt9/OMxERERERERER2fIqKirIzMxc53nL3VCYSQBwHIelS5eSnp6OZVnbezqyAysvL6d9+/YsWrRovVsjimwKfa5kS9NnSrYGfa5kS9NnSrYGfa5kS9sZPlOu61JRUUF+fj62ve7KR8pE2ki2bdOuXbvtPQ1pRjIyMprtHyCy49LnSrY0faZka9DnSrY0faZka9DnSra05v6ZWl8GUi0V1hYRERERERERkQ1SEElERERERERERDZIQSSRLSwUCnHzzTcTCoW291RkJ6LPlWxp+kzJ1qDPlWxp+kzJ1qDPlWxpu9JnSoW1RURERERERERkg5SJJCIiIiIiIiIiG6QgkoiIiIiIiIiIbJCCSCIiIiIiIiIiskEKIomIiIiIiIiIyAYpiCTSwO23385+++1Heno6ubm5HH/88cyePTuhj+u63HLLLeTn55OcnMygQYOYPn16Qp///Oc/DBo0iIyMDCzLYvXq1QnnFyxYwNlnn02nTp1ITk6mS5cu3HzzzUQika39iLIdbKvPVX3hcJi+fftiWRaFhYVb4alke9rWn6kPP/yQfv36kZycTE5ODieccMLWejTZjrbl5+qXX37huOOOIycnh4yMDA488EC+/PLLrfl4sh1sic/UqlWrGDVqFD169CAlJYUOHTpw6aWXUlZWljBOaWkpI0eOJDMzk8zMTEaOHLne/09K87WtPlf6fX3XsS3/rKrVXH9XVxBJpIGxY8dy8cUXM2HCBD799FNisRhDhw6lsrLS63PXXXdx33338fDDD/PDDz/Qpk0bDj/8cCoqKrw+a9euZdiwYVx//fVN3mfWrFk4jsMTTzzB9OnTuf/++3n88cfX2V+at231uarv6quvJj8/f6s8j2x/2/Iz9dZbbzFy5Ej+9Kc/MXXqVL799ltGjBixVZ9Pto9t+bk6+uijicVifPHFF0yePJm+ffsyfPhwiouLt+ozyra1JT5TS5cuZenSpdxzzz1MmzaN5557jjFjxnD22Wcn3GvEiBEUFhYyZswYxowZQ2FhISNHjtymzyvbxrb6XOn39V3Htvyzqlaz/V3dFZH1KikpcQF37Nixruu6ruM4bps2bdw77rjD61NdXe1mZma6jz/+eKPrv/zySxdwS0tLN3ivu+66y+3UqdMWm7vsuLb25+qjjz5ye/bs6U6fPt0F3P9v7/5jqqr/OI6/LghXxSsG6wKhS5rZcpKBLpR+mM7Yzag/aq4aG+Gshc5mpXPrx4abS6tNa7C1friu1tpMN0rnlGZdqDQ0Swgc2cqwJl4QMbmpS7jw+f7RvOtKev3GPYfL5fnY+INzPnzO5729uHx479x7GhoarCgDMcSqTPX29prs7GyzadMmS9eP2GRVrjo7O40k89VXX4WOBQIBI8l8/vnn1hSDmDDYTF2ybds2k5ycbHp7e40xxrS0tBhJ5sCBA6Ex9fX1RpI5evSoRdUgVliVq3/Dfn1ksDpTw3mvzp1IQASXbj9MS0uTJLW2tqq9vV1FRUWhMU6nU3PnztU333wz6Gtdug7im5W56ujo0FNPPaUPP/xQY8eOjd6iEdOsytThw4fV1tamhIQE5eXlKSsrS/fff/+Aty8hPlmVq/T0dN1666364IMPdP78eQWDQb3zzjvKyMjQzJkzo1sEYkq0MtXd3a3x48dr1KhRkqT6+nqlpqaqoKAgNGb27NlKTU0d9P4Msc+qXF1pDPv1+Gdlpob7Xp0mEnAVxhg9//zzuuuuuzR9+nRJCt1mn5GRETY2IyNjULfgHzt2TFVVVSovL//vC8awYGWujDEqKytTeXm5Zs2aFb1FI6ZZmalff/1VkrRmzRq9/PLL2rVrl6677jrNnTtXZ86ciVIFiEVW5srhcGjv3r1qaGiQy+XS6NGj9cYbb6impkYTJkyIWg2ILdHKVFdXl9auXaunn346dKy9vV1ut3vAWLfbzVsk45yVuboc+/WRwcpMxcNe/cotVgBavny5mpqatG/fvgHnHA5H2PfGmAHHrtXJkyfl8Xi0aNEiPfnkk/9pDgwfVuaqqqpKgUBAL7zwwqDXieHDykz19/dLkl566SU98sgjkiSv16uJEydq+/btV91sY3izMlfGGC1btkxut1tff/21xowZo02bNqm4uFiHDh1SVlbWoNeP2BONTAUCAT3wwAOaNm2aKioqrjrH1eZB/LA6V5ewXx85rMxUPOzVuRMJuIJnnnlGO3fuVG1trSZOnBg6npmZKUkDOs6nTp0a0Jm+FidPntS8efM0Z84cvfvuu4NbNGKe1bny+Xw6cOCAnE6nRo0apSlTpkiSZs2apSeeeCIKFSDWWJ2pS//MT5s2LXTM6XTqpptu0u+//z6YpSOG2fFatWvXLm3dulV33nmn8vPz9dZbb2nMmDHasmVLdIpATIlGpv788095PB6NGzdOn3zyiZKSksLm6ejoGHDdzs7O/7Q/w/Bgda4uYb8+clidqXjYq9NEAi5jjNHy5ctVXV0tn8+nnJycsPM5OTnKzMzU3r17Q8d6enr05ZdfqrCw8P+6Vltbm+69917l5+fL6/UqIYFfyXhlV64qKyv1ww8/qLGxUY2Njdq9e7ck6eOPP9Yrr7wSnWIQE+zK1MyZM+V0OsMec9vb26vjx4/rxhtvHHwhiCl25erChQuSNODvXkJCQujuN8SHaGUqEAioqKhIycnJ2rlzp0aPHh02z5w5c9Td3a1vv/02dOzgwYPq7u7+v/dniH125Upivz5S2JWpuNir2/YR3sAwsXTpUpOammrq6uqM3+8PfV24cCE05tVXXzW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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Choosing the optimal K (e.g., based on the elbow method)\n", + "optimal_k = 3\n", + "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n", + "stock_data['Regime'] = kmeans.fit_predict(features_scaled)\n", + "\n", + "# Visualizing the regimes\n", + "plt.figure(figsize=(14, 7))\n", + "plt.plot(stock_data['Adj Close'], label='S&P 500 Price')\n", + "for i in range(optimal_k):\n", + " plt.scatter(stock_data[stock_data['Regime'] == i].index, \n", + " stock_data[stock_data['Regime'] == i]['Adj Close'], label=f'Regime {i}', alpha=0.6)\n", + "\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Adjusted Close Price')\n", + "plt.title('Market Regimes Detected by Clustering')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cf4d659c-1ccc-4fed-bcea-acdcd8bb219e", + "metadata": {}, + "source": [ + "### 6. **Analyzing and Labeling Clusters**\n", + "- **Average Metrics**: For each cluster, the average returns and volatility are calculated to understand the characteristics of each regime.\n", + "- **Manual Labeling**: Based on the analysis, regimes are labeled manually (e.g., high return and low volatility might indicate a Bull Market, while high volatility and low returns could indicate a Bear Market).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5cbdacce-1a40-4da8-bb69-d8154f40292a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Regime 0: Avg Return = 0.0002, Avg Volatility = 0.0109\n", + "Regime 1: Avg Return = 0.0005, Avg Volatility = 0.0083\n", + "Regime 2: Avg Return = 0.0046, Avg Volatility = 0.0389\n" + ] + } + ], + "source": [ + "# Calculate average returns and volatility for each regime\n", + "for i in range(optimal_k):\n", + " regime_data = stock_data[stock_data['Regime'] == i]\n", + " avg_return = regime_data['Return'].mean()\n", + " avg_volatility = regime_data['Volatility'].mean()\n", + " print(f'Regime {i}: Avg Return = {avg_return:.4f}, Avg Volatility = {avg_volatility:.4f}')\n", + "\n", + "# Manually label the regimes based on characteristics (e.g., high return, low volatility = Bull Market)\n", + "regime_labels = {0: 'Bull', 1: 'Bear', 2: 'Neutral'} # Example labels; adjust based on analysis\n", + "stock_data['Regime_Label'] = stock_data['Regime'].map(regime_labels)" + ] + }, + { + "cell_type": "markdown", + "id": "59dd5e82-308e-42f8-a30e-5d043691dc72", + "metadata": {}, + "source": [ + "### 7. **Model Validation**\n", + "- **Cumulative Returns**: The cumulative returns of each regime are calculated and plotted to validate the accuracy and behavior of the detected regimes over time.\n", + "- **Backtesting**: By examining the performance of each regime, the reliability of the clustering model can be assessed.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aae5a8bc-fdb1-438b-8035-ad4b13b98b51", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate cumulative returns for each regime\n", + "stock_data['Cumulative_Return'] = (1 + stock_data['Return']).cumprod()\n", + "\n", + "# Plot cumulative returns per regime\n", + "plt.figure(figsize=(14, 7))\n", + "for label in stock_data['Regime_Label'].unique():\n", + " regime_returns = stock_data[stock_data['Regime_Label'] == label]['Cumulative_Return']\n", + " plt.plot(regime_returns, label=f'Regime: {label}')\n", + "\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Cumulative Return')\n", + "plt.title('Cumulative Returns by Market Regime')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "46f80bdf-f871-4b34-b8e0-877418d0578e", + "metadata": {}, + "source": [ + "### 8. **Predicting Market Regimes**\n", + "To extend the project, a supervised learning model (e.g., Logistic Regression) can be implemented to predict future market regimes based on current market features:\n", + "- **Model Training**: The Logistic Regression model is trained using historical features and the identified regimes.\n", + "- **Evaluation**: Model performance is evaluated using metrics like precision, recall, and F1-score, helping to assess its effectiveness in predicting future market behavior." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e3f54c17-5411-43d2-bb56-4f6edd091eb1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 276\n", + " 1 1.00 1.00 1.00 701\n", + " 2 1.00 0.95 0.97 20\n", + "\n", + " accuracy 1.00 997\n", + " macro avg 1.00 0.98 0.99 997\n", + "weighted avg 1.00 1.00 1.00 997\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import classification_report\n", + "\n", + "# Prepare the dataset for supervised learning\n", + "X = features_scaled\n", + "y = stock_data['Regime']\n", + "\n", + "# Split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# Train the Logistic Regression model\n", + "model = LogisticRegression(random_state=42)\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "id": "1abd5342-d02a-4aff-8809-34adf5d3bb05", + "metadata": {}, + "source": [ + "## Conclusion\n", + "This project implements a basic Market Trend Classification Model using clustering techniques and evaluates it with historical stock data. The approach can be expanded by using different clustering algorithms, incorporating additional market features, or even integrating reinforcement learning for predictive trading strategies based on identified regimes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b04c95f2-526a-4e0c-a2d6-6f82df4e598c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05a95de9-4f40-4aee-954b-6f7c1b2ae6a7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Classification Models/Market Trend Classification Model/MarketTrend Analytics - Classification Model.png b/Classification Models/Market Trend Classification Model/MarketTrend Analytics - Classification Model.png new file mode 100644 index 000000000..f1cdb4328 Binary files /dev/null and b/Classification Models/Market Trend Classification Model/MarketTrend Analytics - Classification Model.png differ diff --git a/Classification Models/Market Trend Classification Model/README.md b/Classification Models/Market Trend Classification Model/README.md new file mode 100644 index 000000000..b5e867208 --- /dev/null +++ b/Classification Models/Market Trend Classification Model/README.md @@ -0,0 +1,69 @@ +# πŸ“ˆ Market Trend Classification Model + +

+ Market Trend Classification Model +

+ +## πŸ“– Overview +The Market Trend Classification Model aims to identify different market conditions by analyzing historical stock price data. This project classifies market regimes by utilizing advanced data analysis techniques to aid in informed trading decisions and strategy development. + +## πŸ“š Table of Contents +- [πŸš€ Problem Statement](#-problem-statement) +- [πŸ’‘ Proposed Solution](#-proposed-solution) + - [Key Components](#key-components) +- [πŸ“¦ Installation & Usage](#-installation--usage) +- [βš™οΈ Alternatives Considered](#-alternatives-considered) +- [πŸ“Š Results](#-results) +- [πŸ” Conclusion](#-conclusion) +- [🀝 Acknowledgments](#-acknowledgments) +- [πŸ“§ Contact](#-contact) + +## πŸš€ Problem Statement +Accurate Market Trend Classification Model is crucial for investors and traders. Identifying whether the market is in a bull, bear, or neutral phase can significantly influence trading strategies and risk management. + +## πŸ’‘ Proposed Solution +This project employs clustering algorithms to categorize market regimes based on features derived from stock price movements. + +### Key Components +| Component | Description | +|-------------------------|--------------------------------------------------------------| +| **Data Collection** | Historical stock data is sourced from Yahoo Finance. | +| **Data Preprocessing** | Calculating daily returns, moving averages, and volatility. | +| **Feature Engineering** | Normalizing data for effective clustering. | +| **Clustering** | K-Means clustering is used to classify market regimes. | +| **Model Validation** | Evaluating the effectiveness of detected regimes. | + +## πŸ“¦ Installation & Usage +To get started, ensure you have Python and the following libraries installed: + +| Library | Installation Command | +|------------------|------------------------------------------| +| **Pandas** | `pip install pandas` | +| **NumPy** | `pip install numpy` | +| **Matplotlib** | `pip install matplotlib` | +| **Scikit-learn** | `pip install scikit-learn` | +| **yfinance** | `pip install yfinance` | + +## βš™οΈ Alternatives Considered +Several alternative approaches were evaluated, including: + +| Alternative Approach | Description | +|----------------------------|--------------------------------------------------| +| **Traditional Machine Learning** | Algorithms like SVM and k-NN were considered; effective for smaller datasets but struggled with complexity. | + +## πŸ“Š Results +The model aims to classify market regimes accurately, providing valuable insights for trading strategies. + +## πŸ” Conclusion +This project demonstrates the significance of time series analysis and clustering techniques in financial market analysis. The identified regimes can enhance traders' and investors' decision-making processes. + +## 🀝 Acknowledgments +- **Dataset:** Yahoo Finance +- **Frameworks:** Pandas, NumPy, Matplotlib, Scikit-learn, yfinance + +## πŸ“§ Contact +For any inquiries or contributions, feel free to reach out: + +| Name | Email | GitHub | +|--------------------|-----------------------------|---------------------| +| Alolika Bhowmik | alolikabhowmik72@gmail.com | [alo7lika](https://github.com/alo7lika) | diff --git a/Prediction Models/ClusterLogic Model/ClusterLogic Model.ipynb b/Prediction Models/ClusterLogic Model/ClusterLogic Model.ipynb new file mode 100644 index 000000000..b55d4bffb --- /dev/null +++ b/Prediction Models/ClusterLogic Model/ClusterLogic Model.ipynb @@ -0,0 +1,571 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "21941ceb-179f-47d8-abdb-944260365c5c", + "metadata": {}, + "source": [ + "# ClusterLogic Model" + ] + }, + { + "cell_type": "markdown", + "id": "81f2d0ac-24b7-4818-ba0a-79f327ffbbd3", + "metadata": {}, + "source": [ + "## Aim\n", + "\n", + "The primary aim of the ClusterLogic Model project is to develop a robust customer segmentation tool that leverages data analytics and machine learning techniques to categorize customers into distinct groups based on their purchasing behavior, preferences, and demographic characteristics. By achieving this aim, the project seeks to:\n", + "\n", + "- **Enhance Marketing Strategies**: Enable businesses to tailor their marketing efforts to specific customer segments, resulting in more effective and personalized campaigns.\n", + "- **Improve Customer Understanding**: Provide insights into customer behavior and preferences, allowing businesses to better understand their target audience.\n", + "- **Optimize Resource Allocation**: Help businesses allocate resources more efficiently by focusing on high-value customer segments.\n", + "- **Drive Business Growth**: Facilitate data-driven decision-making to enhance customer satisfaction, loyalty, and ultimately increase sales and profitability.\n", + "- **Foster Data-Driven Culture**: Encourage organizations to adopt data-driven approaches for their marketing and customer relationship management efforts, leading to improved overall performance." + ] + }, + { + "cell_type": "markdown", + "id": "95bd9466-b14e-443a-804e-6198c5fd6d51", + "metadata": {}, + "source": [ + "## Brief Explanation\n", + "\n", + "The ClusterLogic Model project aims to develop a customer segmentation tool that leverages data analytics and machine learning to categorize customers based on their purchasing behavior and demographic characteristics. The primary objective is to enhance marketing strategies by allowing businesses to tailor their campaigns for specific customer segments, leading to more effective and personalized outreach. Additionally, the project seeks to improve customer understanding by providing valuable insights into preferences and behaviors, which can help organizations better connect with their target audience. \n", + "\n", + "By optimizing resource allocation and focusing on high-value segments, businesses can drive growth through data-driven decision-making that enhances customer satisfaction and loyalty. Ultimately, Customer Clust aspires to foster a data-driven culture within organizations, empowering them with actionable insights that significantly impact their success in the marketplace." + ] + }, + { + "cell_type": "markdown", + "id": "b925ca53-87b0-4f14-90ab-26342ee81955", + "metadata": {}, + "source": [ + "## 1. **Install Required Libraries**: " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e20fc9d1-8485-4ce5-be30-f54f64fd1a76", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in c:\\users\\asus\\anaconda3\\lib\\site-packages (2.1.4)\n", + "Requirement already satisfied: numpy in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.26.4)\n", + "Requirement already satisfied: matplotlib in c:\\users\\asus\\anaconda3\\lib\\site-packages (3.8.0)\n", + "Requirement already satisfied: seaborn in c:\\users\\asus\\anaconda3\\lib\\site-packages (0.12.2)\n", + "Requirement already satisfied: scikit-learn in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.2.2)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2023.3.post1)\n", + "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n", + "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (10.2.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n", + "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn) (1.11.4)\n", + "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install pandas numpy matplotlib seaborn scikit-learn" + ] + }, + { + "cell_type": "markdown", + "id": "418a026e-efa3-42bd-800d-81b52304be5f", + "metadata": {}, + "source": [ + "## 2.**Data Collection**: \n", + " - Collect data from various sources, such as sales records, customer feedback, and demographic information. A sample dataset can be found on platforms like Kaggle.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b18800ed-b32d-4469-8457-fecafd7a88c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " CustomerID Genre Age Annual Income (k$) Spending Score (1-100)\n", + "0 1 Male 19 15 39\n", + "1 2 Male 21 15 81\n", + "2 3 Female 20 16 6\n", + "3 4 Female 23 16 77\n", + "4 5 Female 31 17 40\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Load Data\n", + "data = pd.read_csv(r'C:\\Users\\ASUS\\Downloads\\Mall_Customers.csv')\n", + "\n", + "# Display the first few rows of the dataset\n", + "print(data.head())" + ] + }, + { + "cell_type": "markdown", + "id": "07306a92-3734-4596-b4f5-9feea059b992", + "metadata": {}, + "source": [ + "## 3.**Data Preprocessing**: \n", + " - Clean and preprocess the data to handle missing values, outliers, and categorical variables.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2808e997-f9b0-49a9-9150-aa080f4ceb86", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CustomerID 0\n", + "Genre 0\n", + "Age 0\n", + "Annual Income (k$) 0\n", + "Spending Score (1-100) 0\n", + "dtype: int64\n", + " Age Annual Income (k$) Spending Score (1-100)\n", + "count 200.000000 200.000000 200.000000\n", + "mean 38.850000 60.560000 50.200000\n", + "std 13.969007 26.264721 25.823522\n", + "min 18.000000 15.000000 1.000000\n", + "25% 28.750000 41.500000 34.750000\n", + "50% 36.000000 61.500000 50.000000\n", + "75% 49.000000 78.000000 73.000000\n", + "max 70.000000 137.000000 99.000000\n" + ] + } + ], + "source": [ + "# Check for missing values\n", + "print(data.isnull().sum())\n", + "\n", + "# Select relevant features for segmentation\n", + "features = ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']\n", + "X = data[features]\n", + "\n", + "# Display the selected features\n", + "print(X.describe())" + ] + }, + { + "cell_type": "markdown", + "id": "24c2c98e-b02a-46e1-bd54-485fb99a88c1", + "metadata": {}, + "source": [ + "## 4. **Data Normalization**\n", + "Next, we’ll normalize the data to ensure all features are on the same scale, which is crucial for distance-based algorithms like K-means..\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ae12ddfd-d7a2-44fe-b567-131f5956491a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-1.42456879 -1.73899919 -0.43480148]\n", + " [-1.28103541 -1.73899919 1.19570407]\n", + " [-1.3528021 -1.70082976 -1.71591298]\n", + " [-1.13750203 -1.70082976 1.04041783]\n", + " [-0.56336851 -1.66266033 -0.39597992]]\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# Normalize data\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "# Display the scaled features\n", + "print(X_scaled[:5]) # Show the first five rows of scaled data" + ] + }, + { + "cell_type": "markdown", + "id": "b545995d-82fb-4f76-969d-7209c9f4977d", + "metadata": {}, + "source": [ + "## 5. **Determine the Optimal Number of Clusters**\n", + "We will use the Elbow Method to find the optimal number of clusters for our K-means algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b05aec3b-d9cd-42d1-aa99-dfa95bf4aae6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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qKChIQUFBRa43P35+frrttts0adIkVa9eXXXr1lV8fLzmzp2rypUrX/PxfX19tXLlSoWFhdlnbuzSpcu1Fw4AV2LtXBcAUPpdadY+ScZ7771nb5vfrH0ffvih8dRTTxk1atQwPDw8jE6dOhk//PBDrs/67LPPjHbt2hmenp6Gj4+P0a1bN+Obb77J1S45OdlwcXExfHx8jPT0dPv2jz76yJBkhIWFFejcLp2170rymnnvzJkzxosvvmg0atTIqFChglGpUiWjWbNmxqhRo4ykpCR7u61btxodO3Y0vL29c8yed+msbJfKa1a3zMxMY+LEiUbDhg0Nd3d3o3r16sbAgQONgwcP5nhvVlaWMWHCBCM4ONioUKGC0bx5c+Pzzz83QkJCijxrn2EYxvHjx40nnnjCCAwMNNzc3Iw6deoY48aNM86fP5+jnSRj+PDhV/2cS6WnpxuzZs0y2rVrZ1SsWNHw8PAwGjVqZIwdO9b4+++/c7WvU6eOceeddxpLly41mjRpYlSoUMGoW7euMXXq1Fxt//Of/xg33nij4e7ubkgyXnnlFcMw8p+1784778x1jLzOae/evYYkY9KkSfZthw4dMvr162dUqVLF8PX1NW6//XZj+/bt+f43UZhZ+7KlpaUZ/fr1Mzw9PY0vv/zyiu8HgGtlM4xLxogAAIBSrW7dumratKn9QbkAgJLBPVIAAAAAUEgEKQAAAAAoJIb2AQAAAEAh0SMFAAAAAIVEkAIAAACAQiJIAQAAAEAh8UBeSVlZWTpy5Ih8fX1ls9msLgcAAACARQzD0OnTpxUUFCQXl/z7nQhSko4cOaLg4GCrywAAAADgJA4ePKjatWvnu58gJcnX11eS+cvy8/OzuBoURUZGhmJjY9WjRw+5u7tbXQ7KAa45OBrXHByJ6w2O5kzXXEpKioKDg+0ZIT8EKck+nM/Pz48gVUplZGTI29tbfn5+lv/Hh/KBaw6OxjUHR+J6g6M54zV3tVt+mGwCAAAAAAqJIAUAAAAAhUSQAgAAAIBCIkgBAAAAQCERpAAAAACgkAhSAAAAAFBIBCkAAAAAKCSCFAAAAAAUEkEKAAAAAAqJIAUAAAAAhUSQAgAAAIBCIkgBAAAAQCERpAAAAACgkNysLgAXZWZKGzZIiYlSYKDUqZPk6mp1VQAAAAAuZ3mP1OHDhzVw4EBVq1ZN3t7eatmypbZs2WLfbxiGIiMjFRQUJC8vL3Xu3Fk7duzIcYy0tDSNHDlS1atXl4+Pj/r06aNDhw45+lSuybJlUt26Upcu0oAB5s+6dc3tAAAAAJyLpUEqOTlZHTt2lLu7u1asWKGdO3dqypQpqly5sr1NdHS0pk6dqpkzZ2rz5s0KCAhQaGioTp8+bW8THh6umJgYLVmyRAkJCTpz5ox69+6tzMxMC86q8JYtk+69V7o8+x0+bG4nTAEAAADOxdKhfRMnTlRwcLDmz59v31a3bl37umEYmj59ul544QWFhYVJkhYuXCh/f38tXrxYQ4cO1alTpzR37lx9+OGH6t69uyRp0aJFCg4O1qpVq9SzZ0+HnlNhZWZKTz8tGUbufYYh2WxSeLh0990M8wMAAACchaVBavny5erZs6fuu+8+xcfHq1atWho2bJgee+wxSdLevXuVlJSkHj162N/j4eGhkJAQbdy4UUOHDtWWLVuUkZGRo01QUJCaNm2qjRs35hmk0tLSlJaWZn+dkpIiScrIyFBGRkZJnW6e4uNtOnQo/6/BMKSDB6W1ay8oJCSPtAVJsn9vjv7+UH5xzcHRuObgSFxvcDRnuuYKWoOlQerPP//U7NmzFRERoeeff17ff/+9nnrqKXl4eOjhhx9WUlKSJMnf3z/H+/z9/bV//35JUlJSkipUqKAqVarkapP9/stNmDBB48ePz7U9NjZW3t7exXFqBbZ+fS1Jba/absWKrTp79nDJF1TKxcXFWV0CyhmuOTga1xwciesNjuYM19y5c+cK1M7SIJWVlaW2bdsqKipKktSqVSvt2LFDs2fP1sMPP2xvZ7PZcrzPMIxc2y53pTbjxo1TRESE/XVKSoqCg4PVo0cP+fn5FfV0isTHx6apU6/erlevlgoJaVHyBZVSGRkZiouLU2hoqNzd3a0uB+UA1xwcjWsOjsT1Bkdzpmsue7Ta1VgapAIDA3XTTTfl2Na4cWN9+umnkqSAgABJZq9TYGCgvc2xY8fsvVQBAQFKT09XcnJyjl6pY8eOqUOHDnl+roeHhzw8PHJtd3d3d/gX16WLVLu2ObFEXvdJ2Wzm/i5d3LhHqgCs+A5RvnHNwdG45uBIXG9wNGe45gr6+ZbO2texY0ft3r07x7bffvtNderUkSTVq1dPAQEBObr40tPTFR8fbw9Jbdq0kbu7e442iYmJ2r59e75Bypm4ukpvvWWu59fJNn06E00AAAAAzsTSIDVq1Cht2rRJUVFR+v3337V48WLNmTNHw4cPl2QO6QsPD1dUVJRiYmK0fft2DRo0SN7e3howYIAkqVKlSnr00Uc1evRorV69Wj/99JMGDhyoZs2a2Wfxc3ZhYdLSpVKtWrn3DRhg7gcAAADgPCwd2nfzzTcrJiZG48aN06uvvqp69epp+vTpevDBB+1txo4dq9TUVA0bNkzJyclq166dYmNj5evra28zbdo0ubm5qX///kpNTVW3bt20YMECuZaibpywMHOK8w0bpMRE6eefpYkTpdWrpbQ0KY+RiAAAAAAsYmmQkqTevXurd+/e+e632WyKjIxUZGRkvm08PT01Y8YMzZgxowQqdBxXV6lzZ3O9Xz9p0SLz3qlFi6RHH7W0NAAAAACXsHRoH/JXoYL5IF5JmjxZysqytBwAAAAAlyBIObHHH5f8/KRff5W+/NLqagAAAABkI0g5MT8/6YknzPXoaGtrAQAAAHARQcrJPf205O4uJSRI335rdTUAAAAAJIKU0wsKkgYONNcnTbK2FgAAAAAmglQpMGaM+fOzz6TffrO0FAAAAAAiSJUKN90k9e4tGYY0darV1QAAAAAgSJUSzzxj/lywQDp61NJSAAAAgHKPIFVKdOok/eMfUlqaNHOm1dUAAAAA5RtBqpSw2aSxY831WbOkM2esrQcAAAAozwhSpUjfvtINN0jJydK8eVZXAwAAAJRfBKlSxNVViogw16dOlS5csLYeAAAAoLwiSJUygwZJNWpI+/dLn3xidTUAAABA+USQKmW8vKQRI8z1SZPMKdEBAAAAOBZBqhQaPlzy9pZ++klas8bqagAAAIDyhyBVClWrJg0ebK5HR1tbCwAAAFAeEaRKqYgIycVFio2Vfv7Z6moAAACA8oUgVUrVqyfdd5+5PmmStbUAAAAA5Q1BqhR75hnz55Il0oED1tYCAAAAlCcEqVKsTRupa1cpM1OaPt3qagAAAIDygyBVymX3Ss2ZIyUnW1sLAAAAUF4QpEq5nj2lZs2ks2eld9+1uhoAAACgfCBIlXI228Veqbfeks6ft7YeAAAAoDwgSJUB998v1a4tHT0qLVpkdTUAAABA2UeQKgPc3aXwcHN98mQpK8vScgAAAIAyjyBVRjz2mFSpkrR7t/T551ZXAwAAAJRtBKkyws9PeuIJc50H9AIAAAAliyBVhjz1lFShgvTNN9LGjVZXAwAAAJRdBKkyJChIGjjQXKdXCgAAACg5BKkyZswY8+f//mfeLwUAAACg+BGkypjGjaW77pIMQ5oyxepqAAAAgLKJIFUGZT+g94MPzGdLAQAAACheBKky6NZbpVtukdLSpBkzrK4GAAAAKHsIUmWQzXaxV+qdd6QzZ6ytBwAAAChrCFJl1N13SzfcICUnS3PnWl0NAAAAULYQpMooV9eLM/hNnSplZFhbDwAAAFCWEKTKsIcflmrUkA4ckD75xOpqAAAAgLKDIFWGeXlJTz1lrk+aZE6JDgAAAODaEaTKuCeflLy9pa1bpVWrrK4GAAAAKBsIUmVctWrSo4+a65MmWVsLAAAAUFYQpMqBiAhz8om4OLNnCgAAAMC1IUiVA3XrSvfdZ67TKwUAAABcO4JUOZH9gN7//lfav9/aWgAAAIDSjiBVTrRuLXXrJmVmStOmWV0NAAAAULoRpMqR7F6p99+XkpOtrQUAAAAozQhS5UiPHlLz5tLZs9Ls2VZXAwAAAJReBKlyxGa72Cv19tvS+fPW1gMAAACUVgSpcuaf/5SCg6WjR6UPP7S6GgAAAKB0IkiVM+7u0qhR5vrkyVJWlrX1AAAAAKURQaocGjJEqlRJ+u03aflyq6sBAAAASh+CVDnk6ysNG2au84BeAAAAoPAIUuXUyJFShQrSxo3SN99YXQ0AAABQuhCkyqnAQOmhh8x1eqUAAACAwiFIlWNjxpg/ly+Xfv3V2loAAACA0oQgVY7deKPUp49kGNKUKVZXAwAAAJQeBKlyLvsBvR98ICUlWVsLAAAAUFoQpMq5jh2l9u2l9HTp7betrgYAAAAoHQhS5ZzNdrFXavZs6fRpa+sBAAAASgOCFNSnj9SwoXTypDR3rtXVAAAAAM6PIAW5ukqjR5vrU6dKGRnW1gMAAAA4O4IUJEkPPyzVrCkdPCh9/LHV1QAAAADOjSAFSZKnp/TUU+b6pEnmlOgAAAAA8kaQgt2TT0o+PtLPP0txcVZXAwAAADgvghTsqlaVhgwx1ydNsrYWAAAAwJkRpJDDqFHm5BOrVkk//mh1NQAAAIBzIkghhzp1pP79zfXJk62tBQAAAHBWBCnkkv2A3o8/lvbts7QUAAAAwCkRpJBLq1ZS9+5SZqY0bZrV1QAAAADOhyCFPGX3Sr3/vnT8uLW1AAAAAM6GIIU8hYZKLVpI585Js2dbXQ0AAADgXCwNUpGRkbLZbDmWgIAA+37DMBQZGamgoCB5eXmpc+fO2rFjR45jpKWlaeTIkapevbp8fHzUp08fHTp0yNGnUubYbBd7pWbMkM6ft7YeAAAAwJlY3iPVpEkTJSYm2pdt27bZ90VHR2vq1KmaOXOmNm/erICAAIWGhur06dP2NuHh4YqJidGSJUuUkJCgM2fOqHfv3srMzLTidMqU/v2l666Tjh2TPvjA6moAAAAA52F5kHJzc1NAQIB9qVGjhiSzN2r69Ol64YUXFBYWpqZNm2rhwoU6d+6cFi9eLEk6deqU5s6dqylTpqh79+5q1aqVFi1apG3btmnVqlVWnlaZ4O5uPldKMqdCJ5sCAAAAJjerC9izZ4+CgoLk4eGhdu3aKSoqSvXr19fevXuVlJSkHj162Nt6eHgoJCREGzdu1NChQ7VlyxZlZGTkaBMUFKSmTZtq48aN6tmzZ56fmZaWprS0NPvrlJQUSVJGRoYyMjJK6ExLp0cekcaPd9OePTYtW3ZBffsaVpeUp+zvje8PjsI1B0fjmoMjcb3B0ZzpmitoDZYGqXbt2umDDz5Qw4YNdfToUb3++uvq0KGDduzYoaSkJEmSv79/jvf4+/tr//79kqSkpCRVqFBBVapUydUm+/15mTBhgsaPH59re2xsrLy9va/1tMqc7t0ba+nShnrppRS5u2+QzWZ1RfmLi4uzugSUM1xzcDSuOTgS1xsczRmuuXPnzhWonaVBqlevXvb1Zs2aqX379rr++uu1cOFC3XLLLZIk22V/azcMI9e2y12tzbhx4xQREWF/nZKSouDgYPXo0UN+fn5FOZUyrXVraflyQ7t3V1XlyneqY0fn65XKyMhQXFycQkND5e7ubnU5KAe45uBoXHNwJK43OJozXXPZo9WuxvKhfZfy8fFRs2bNtGfPHvXt21eS2esUGBhob3Ps2DF7L1VAQIDS09OVnJyco1fq2LFj6tChQ76f4+HhIQ8Pj1zb3d3dLf/inFFwsDnE7733pGnT3NS5s9UV5Y/vEI7GNQdH45qDI3G9wdGc4Zor6OdbPtnEpdLS0rRr1y4FBgaqXr16CggIyNG9l56ervj4eHtIatOmjdzd3XO0SUxM1Pbt268YpFB4o0ebU6IvXy7t2mV1NQAAAIC1LA1SY8aMUXx8vPbu3avvvvtO9957r1JSUvTII4/IZrMpPDxcUVFRiomJ0fbt2zVo0CB5e3trwIABkqRKlSrp0Ucf1ejRo7V69Wr99NNPGjhwoJo1a6bu3btbeWplTqNGUp8+5vqUKdbWAgAAAFjN0qF9hw4d0gMPPKC///5bNWrU0C233KJNmzapTp06kqSxY8cqNTVVw4YNU3Jystq1a6fY2Fj5+vrajzFt2jS5ubmpf//+Sk1NVbdu3bRgwQK5urpadVpl1tix0v/+J334ofTaa9IlIy4BAACAcsXSILVkyZIr7rfZbIqMjFRkZGS+bTw9PTVjxgzNmDGjmKvD5Tp0MJeNG6W335YmTLC6IgAAAMAaTnWPFJzf2LHmz9mzpdOnra0FAAAAsApBCoVy113m/VKnTpmz+AEAAADlEUEKheLiYs7gJ0nTp0tO8PBpAAAAwOEIUii0hx6S/P2lgwel//7X6moAAAAAxyNIodA8PaWnnjLXo6Mlw7C2HgAAAMDRCFIokieflHx8pG3bpNhYq6sBAAAAHIsghSKpUkV67DFzfdIka2sBAAAAHI0ghSILD5dcXaXVq6Uff7S6GgAAAMBxCFIosjp1pPvvN9fplQIAAEB5QpDCNXnmGfPnxx9Le/daWwsAAADgKAQpXJMWLaTQUCkrS5o2zepqAAAAAMcgSOGajR1r/pw7Vzp+3NpaAAAAAEcgSOGadesmtWwpnTsnvfOO1dUAAAAAJY8ghWtms13slZoxQ0pNtbYeAAAAoKQRpFAs7rvPnMXvr7+khQutrgYAAAAoWQQpFAs3N2nUKHN9yhQpM9PaegAAAICSRJBCsXn0UalKFen336X//c/qagAAAICSQ5BCsalYURo2zFyPjpYMw9p6AAAAgJJCkEKxGjlS8vCQvvtOSkiwuhoAAACgZBCkUKz8/aVHHjHXJ02ythYAAACgpBCkUOxGjzanRP/8c2nnTqurAQAAAIofQQrFrmFDqW9fc33KFEtLAQAAAEoEQQol4plnzJ8ffigdOWJtLQAAAEBxI0ihRLRvL3XsKGVkSG+/bXU1AAAAQPEiSKHEjB1r/nz3XSklxdpaAAAAgOJEkEKJ6d1buvFG6dQp6b33rK4GAAAAKD4EKZQYFxdpzBhzffp0KT3d0nIAAACAYkOQQokaOFAKCJAOHZKWLLG6GgAAAKB4EKRQojw8pKeeMtcnT5YMw9p6AAAAgOJAkEKJe+IJqWJFads26euvra4GAAAAuHYEKZS4KlWkxx4z16Ojra0FAAAAKA4EKThEeLjk6iqtXStt2WJ1NQAAAMC1IUjBIa67TnrgAXN90iRrawEAAACuFUEKDpM9Ffonn0h//mltLQAAAMC1IEjBYVq0kHr2lLKypGnTrK4GAAAAKDqCFBzqmWfMn3PnSn//bW0tAAAAQFERpOBQXbtKrVpJqanSO+9YXQ0AAABQNAQpOJTNJo0da67PmGEGKgAAAKC0IUjB4e69V6pb1xzat2CB1dUAAAAAhUeQgsO5uUkREeb6lClSZqa19QAAAACFRZCCJQYPlqpWlf74Q4qJsboaAAAAoHAIUrCEj480bJi5PmmSZBjW1gMAAAAUBkEKlhk5UvLwkL7/XtqwwepqAAAAgIIjSMEyNWtKgwaZ69HRlpYCAAAAFApBCpYaPdqcEv3LL6WdO62uBgAAACgYghQs1aCBdM895vrkydbWAgAAABQUQQqWe+YZ8+eiRdLhw9bWAgAAABQEQQqWu+UWqVMnKSNDevttq6sBAAAAro4gBaeQ3Sv17rtSSoq1tQAAAABXQ5CCU7jzTunGG80QNWeO1dUAAAAAV0aQglNwcbnYKzV9upSebmk5AAAAwBURpOA0HnxQCgw0J5z4z3+srgYAAADIH0EKTsPDQ3r6aXN90iTJMKytBwAAAMgPQQpOZehQqWJFaccOacUKq6sBAAAA8kaQglOpXFl6/HFzfdIkS0sBAAAA8kWQgtMJD5fc3KR166TNm62uBgAAAMiNIAWnExwsPfCAuU6vFAAAAJwRQQpOKXsq9E8/lf7809paAAAAgMsRpOCUmjWTbr9dysqSpk61uhoAAAAgJ4IUnFZ2r9S8edLff1tbCwAAAHApghScVpcuUps2UmqqNGuW1dUAAAAAFxGk4LRstou9UjNmSOfOWVsPAAAAkI0gBafWr59Ut650/Li0YIHV1QAAAAAmghScmpubNHq0uT5lipSZaW09AAAAgESQQinwr39JVaua06AvW2Z1NQAAAABBCqWAj480YoS5PmmSZBjW1gMAAAAQpFAqjBgheXpKmzdL8fFWVwMAAIDyjiCFUqFGDWnQIHN90iRLSwEAAAAIUig9Ro82p0T/6itp+3arqwEAAEB5RpBCqXHDDVJYmLk+ebK1tQAAAKB8c5ogNWHCBNlsNoWHh9u3GYahyMhIBQUFycvLS507d9aOHTtyvC8tLU0jR45U9erV5ePjoz59+ujQoUMOrh6Okv2A3sWLpcOHra0FAAAA5ZdTBKnNmzdrzpw5at68eY7t0dHRmjp1qmbOnKnNmzcrICBAoaGhOn36tL1NeHi4YmJitGTJEiUkJOjMmTPq3bu3MnngUJnUrp10221SRob01ltWVwMAAIDyyvIgdebMGT344IN67733VKVKFft2wzA0ffp0vfDCCwoLC1PTpk21cOFCnTt3TosXL5YknTp1SnPnztWUKVPUvXt3tWrVSosWLdK2bdu0atUqq04JJSy7V+rdd6VTp6ytBQAAAOWTm9UFDB8+XHfeeae6d++u119/3b597969SkpKUo8ePezbPDw8FBISoo0bN2ro0KHasmWLMjIycrQJCgpS06ZNtXHjRvXs2TPPz0xLS1NaWpr9dUpKiiQpIyNDGRkZxX2KKGahoVLjxm7atcum2bMzNXp0lv174/uDo3DNwdG45uBIXG9wNGe65gpag6VBasmSJfrxxx+1efPmXPuSkpIkSf7+/jm2+/v7a//+/fY2FSpUyNGTld0m+/15mTBhgsaPH59re2xsrLy9vQt9HnC87t2v065drTRpUrpuuCFO7u7mU3rj4uIsrgzlDdccHI1rDo7E9QZHc4Zr7ty5cwVqZ1mQOnjwoJ5++mnFxsbK09Mz33Y2my3Ha8Mwcm273NXajBs3ThEREfbXKSkpCg4OVo8ePeTn51fAM4CVunWTli41lJjopVOn7tADD6QrLi5OoaGhcnd3t7o8lAMZGRlcc3Aorjk4EtcbHM2Zrrns0WpXY1mQ2rJli44dO6Y2bdrYt2VmZmr9+vWaOXOmdu/eLcnsdQoMDLS3OXbsmL2XKiAgQOnp6UpOTs7RK3Xs2DF16NAh38/28PCQh4dHru3u7u6Wf3EoGHd3KTxcevZZado0Nw0caPz/dr5DOBbXHByNaw6OxPUGR3OGa66gn2/ZZBPdunXTtm3btHXrVvvStm1bPfjgg9q6davq16+vgICAHN176enpio+Pt4ekNm3ayN3dPUebxMREbd++/YpBCmXD0KGSr6+0Y4c0aZKL1q+vpfh4m5iwEQAAACXNsh4pX19fNW3aNMc2Hx8fVatWzb49PDxcUVFRatCggRo0aKCoqCh5e3trwIABkqRKlSrp0Ucf1ejRo1WtWjVVrVpVY8aMUbNmzdS9e3eHnxMcq1IlqUsXafly6aWXXCW11dSpUu3a5tTo2Q/vBQAAAIqb5bP2XcnYsWOVmpqqYcOGKTk5We3atVNsbKx8fX3tbaZNmyY3Nzf1799fqamp6tatmxYsWCBXV1cLK4cjLFsmff557u2HD0v33istXUqYAgAAQMlwqiC1bt26HK9tNpsiIyMVGRmZ73s8PT01Y8YMzZgxo2SLg1PJzJSefloyjNz7DEOy2cx7qO6+WyJTAwAAoLhZ/kBeoCg2bJAOHcp/v2FIBw+a7QAAAIDiRpBCqZSYWLztAAAAgMIgSKFUumRG/GJpBwAAABQGQQqlUqdO5ux8V3o2c2Cg2Q4AAAAobgQplEquruYU51L+YSo1Vdqzx3E1AQAAoPwgSKHUCgszpzivVSvn9qAgKThYOnlSCgmRtm+3pDwAAACUYQQplGphYdK+fVJc3AVFRPyguLgLOnBA+vFHqWVL6dgxqXNn6aefLC4UAAAAZQpBCqWeq6sUEmLottsOKyTEkKurVL26tGaNdPPN0vHjUteu0ubNVlcKAACAsoIghTKrShUpLk7q0MEc5te9u7Rxo9VVAQAAoCwgSKFMq1RJ+vpr816plBSpRw8pPt7qqgAAAFDaEaRQ5lWsKH31ldkjdfas1KuXtGqV1VUBAACgNCNIoVzw9pY+/9wMUampUu/e0ooVVlcFAACA0ooghXLD01OKiZHuvltKS5P69pWWL7e6KgAAAJRGBCmUKx4e0iefSPfdJ6WnS/36mc+iAgAAAAqDIIVyx91dWrxYevBB6cIF6f77zdcAAABAQRGkUC65uUkLF0qDBkmZmdLAgdKCBVZXBQAAgNKCIIVyy9VVmjtXGjpUMgzpX/+S5syxuioAAACUBgQplGsuLtLs2dJTT5mvhw6VZs60tiYAAAA4P4IUyj2bTZo+XXrmGfP1yJHSlCmWlgQAAAAnR5ACZIapiROlF180X48ZI73xhrU1AQAAwHkRpID/Z7NJr70mvfqq+frFF6VXXjHvnwIAAAAuRZACLvPSS2bvlGSGqnHjCFMAAADIiSAF5GHsWPO+KckMVRERhCkAAABc5FbUN27evFmffPKJDhw4oPT09Bz7li1bds2FAVZ7+mnJw0N68kkzVKWlmTP6ufDPDwAAAOVekf5KuGTJEnXs2FE7d+5UTEyMMjIytHPnTq1Zs0aVKlUq7hoByzzxhPmsKZvNnCb98cfNB/gCAACgfCtSkIqKitK0adP0xRdfqEKFCnrrrbe0a9cu9e/fX9ddd11x1whYavBg6YMPzJ6ouXPNB/deuGB1VQAAALBSkYLUH3/8oTvvvFOS5OHhobNnz8pms2nUqFGaM2dOsRYIOIOBA6XFiyVXV+nDD83XGRlWVwUAAACrFClIVa1aVadPn5Yk1apVS9u3b5cknTx5UufOnSu+6gAn8s9/Sp98Irm7S//9r/n6stsDAQAAUE4UKUh16tRJcXFxkqT+/fvr6aef1mOPPaYHHnhA3bp1K9YCAWdyzz1STIw5CUVMjBQWJp0/b3VVAAAAcLQizdo3c+ZMnf//vz2OGzdO7u7uSkhIUFhYmF566aViLRBwNnfeKS1fLt19t/Tll+bPmBjJ29vqygAAAOAoRQpSVatWta+7uLho7NixGjt2bLEVBTi7Hj2kr76SeveWYmPNn59/Lvn4WF0ZAAAAHKHAQ/tSUlJyrF9pAcqDLl2kr7+WfH2ltWul22+XuPwBAADKhwL3SFWpUkWJiYmqWbOmKleuLJvNlquNYRiy2WzK5EE7KCduvVWKi5N69pQSEsyeqpUrpcqVra4MAAAAJanAQWrNmjX2IX1r164tsYKA0qZdO2nNGik0VPruO6lbN3O4X7VqVlcGAACAklLgIBUSEmJfr1evnoKDg3P1ShmGoYMHDxZfdUAp0bq1Obyve3fpxx+lrl3NnqqaNa2uDAAAACWhSNOf16tXT3/99Veu7SdOnFC9evWuuSigNGreXFq3TgoIkH75xbyHKjHR6qoAAABQEooUpLLvhbrcmTNn5Onpec1FAaXVTTdJ8fFSrVrSzp1S587S4cNWVwUAAIDiVqjpzyMiIiRJNptNL730krwveXBOZmamvvvuO7Vs2bJYCwRKm4YNpfXrzeF9v/0m3XabeQ9VnTpWVwYAAIDiUqgg9dNPP0kye6S2bdumChUq2PdVqFBBLVq00JgxY4q3QqAUql/f7Jnq2lX688+LYer6662uDAAAAMWhUEEqe7a+QYMGacaMGfL19S2RooCyoE6dnD1TISHS6tVSo0ZWVwYAAIBrVeh7pC5cuKBFixZp//79JVEPUKbUqmX2TN10k3mvVEiIee8UAAAASrdCByk3NzfVqVOHh+4CBRQQYM7m16KFdPSoOQHFL79YXRUAAACuRZFm7XvxxRc1btw4nThxorjrAcqkGjXMe6TatJH++sucGn3LFqurAgAAQFEV6h6pbG+//bZ+//13BQUFqU6dOvLx8cmx/8cffyyW4oCypGpVadUqqVcvadMmqVs3aeVK6ZZbrK4MAAAAhVWkINW3b99iLgMoHypXlmJjpTvukBISpNBQacUK6dZbra4MAAAAhVGkIPXKK68Udx1AueHra/ZE3XWXtHat1LOn9MUX5nA/AAAAlA5FukdKkk6ePKn3338/x71SP/74ow4fPlxsxQFllY+P9OWXZog6d87soYqNtboqAAAAFFSRgtQvv/yihg0bauLEiZo8ebJOnjwpSYqJidG4ceOKsz6gzPLykj77TOrdWzp/3uyh+uILq6sCAABAQRQpSEVERGjQoEHas2ePPD097dt79eql9evXF1txQFnn6Sl9+qkUFialp5s/Y2KsrgoAAABXU6QgtXnzZg0dOjTX9lq1aikpKemaiwLKkwoVpCVLpPvvlzIypPvuk/77X6urAgAAwJUUKUh5enoqJSUl1/bdu3erRo0a11wUUN64u0uLFkkPPSRlZkoDBkgffmh1VQAAAMhPkYLU3XffrVdffVUZGRmSJJvNpgMHDui5555Tv379irVAoLxwdZXmz5eGDJGysqRHHpHmzbO6KgAAAOSlSEFq8uTJ+uuvv1SzZk2lpqYqJCREN9xwg3x9ffXGG28Ud41AueHqKv3739KwYZJhSI8+Ks2ebXVVAAAAuFyRniPl5+enhIQErVmzRj/++KOysrLUunVrde/evbjrA8odFxdp5kzJw0OaNs0MVWlpUni41ZUBAAAgW5GCVLauXbuqa9euxVULgP9ns0lTpphh6s03pVGjzDD17LNWVwYAAADpGoLU6tWrtXr1ah07dkxZWVk59s3jxg7gmtlsUlSUGabGj5eee86cIv2ll6yuDAAAAEUKUuPHj9err76qtm3bKjAwUDabrbjrAiAzTEVGmlOkv/CC9PLLZs/Ua6+Z+wAAAGCNIgWpd999VwsWLNBDDz1U3PUAyMPzz5sP7x09WnrjDen8eWnSJMIUAACAVYo0a196ero6dOhQ3LUAuIKICGnGDHN9yhTp6afNmf0AAADgeEUKUkOGDNHixYuLuxYAVzFihDk9us1mhqonnjCfOQUAAADHKtLQvvPnz2vOnDlatWqVmjdvLnd39xz7p06dWizFAcjt8cfNCSgGD5bmzDEnoHj/ffMZVAAAAHCMIgWpX375RS1btpQkbd++vTjrAVAAjzxiTkDx0EPSggVmmFq4UHK7pgcaAAAAoKCK9NeutWvXFncdAArpgQfMMHX//dLixWaYWrxYuqyDGAAAACWgUEEqLCzsqm1sNps+/fTTIhcEoOD69ZM+/VS67z5p6VIzTH38sTn0DwAAACWnUEGqUqVKJVUHgCLq00f63/+kvn2l5cule+4xw5WXl9WVAQAAlF2FClLz588vqToAXIPbb5e+/FK66y5pxYqL4crb2+rKAAAAyqYiTX8OwPl06yatXClVrCitWiX16iWdPm11VQAAAGUTQQooQ267TYqNlfz8pPXrpZ49pVOnrK4KAACg7CFIAWVM+/Zmj1TlytK330qhoVJystVVAQAAlC0EKaAMuvlmae1aqVo1afNmqWtX6e+/ra4KAACg7LA0SM2ePVvNmzeXn5+f/Pz81L59e61YscK+3zAMRUZGKigoSF5eXurcubN27NiR4xhpaWkaOXKkqlevLh8fH/Xp00eHDh1y9KkATqdlS2ndOqlmTWnrVqlLF+noUYuLAgAAKCMsDVK1a9fWm2++qR9++EE//PCDunbtqrvvvtselqKjozV16lTNnDlTmzdvVkBAgEJDQ3X6kjvow8PDFRMToyVLlighIUFnzpxR7969lZmZadVpAU6jaVMpPl4KDJS2b5c6d5aOHLG6KgAAgNLP0iB111136Y477lDDhg3VsGFDvfHGG6pYsaI2bdokwzA0ffp0vfDCCwoLC1PTpk21cOFCnTt3TosXL5YknTp1SnPnztWUKVPUvXt3tWrVSosWLdK2bdu0atUqK08NcBo33mhOPBEcLP36qxQSIh08aHVVAAAApVuhniNVkjIzM/XJJ5/o7Nmzat++vfbu3aukpCT16NHD3sbDw0MhISHauHGjhg4dqi1btigjIyNHm6CgIDVt2lQbN25Uz5498/ystLQ0paWl2V+npKRIkjIyMpSRkVFCZ4iSlP298f3lrU4dafVqqUcPN/3+u0233Wbo668v6LrrpIQEmxITzV6rW2815OpqdbWlA9ccHI1rDo7E9QZHc6ZrrqA1WB6ktm3bpvbt2+v8+fOqWLGiYmJidNNNN2njxo2SJH9//xzt/f39tX//fklSUlKSKlSooCpVquRqk5SUlO9nTpgwQePHj8+1PTY2Vt48wbRUi4uLs7oEp/bCC556+eWO2revotq2zZKrq6GTJz3t+6tVS9WQIdvUvn2ihVWWLlxzcDSuOTgS1xsczRmuuXPnzhWoneVBqlGjRtq6datOnjypTz/9VI888oji4+Pt+202W472hmHk2na5q7UZN26cIiIi7K9TUlIUHBysHj16yM/Pr4hnAitlZGQoLi5OoaGhcnd3t7ocpxYaKnXoYOjIEQ9JRo59J054Kjr6Zi1Zkql77jHyPgAkcc3B8bjm4Ehcb3A0Z7rmskerXY3lQapChQq64YYbJElt27bV5s2b9dZbb+nZZ5+VZPY6BQYG2tsfO3bM3ksVEBCg9PR0JScn5+iVOnbsmDp06JDvZ3p4eMjDwyPXdnd3d8u/OFwbvsOrq11bMuwZ6fJ/qLDJZpPGjHFTv35imF8BcM3B0bjm4Ehcb3A0Z7jmCvr5TvccKcMwlJaWpnr16ikgICBH9156erri4+PtIalNmzZyd3fP0SYxMVHbt2+/YpACyrMNG6TEK4zcMwxzMooNGxxXEwAAQGljaY/U888/r169eik4OFinT5/WkiVLtG7dOq1cuVI2m03h4eGKiopSgwYN1KBBA0VFRcnb21sDBgyQJFWqVEmPPvqoRo8erWrVqqlq1aoaM2aMmjVrpu7du1t5aoDTulKIKko7AACA8sjSIHX06FE99NBDSkxMVKVKldS8eXOtXLlSoaGhkqSxY8cqNTVVw4YNU3Jystq1a6fY2Fj5+vrajzFt2jS5ubmpf//+Sk1NVbdu3bRgwQK5MiYJyNMlI2WLpR0AAEB5ZGmQmjt37hX322w2RUZGKjIyMt82np6emjFjhmbMmFHM1QFlU6dO5n1Shw9feq9UTlWrmu0AAACQN6e7RwpAyXJ1ld56y1zPb3LLEyekd95xXE0AAAClDUEKKIfCwqSlS6VatXJuDw6Wevc21596Snr11fx7rQAAAMozghRQToWFSfv2SWvXSosXmz/37pWWL5eyn1f9yitSRISUlWVpqQAAAE7H8udIAbCOq6vUuXPu7S+/LFWuLD39tDR9unTypPTee5Ibf2IAAABIokcKQD6eekpauNAMWwsWSP/8p5SWZnVVAAAAzoEgBSBfDz9s3ktVoYK0bJl0113SmTNWVwUAAGA9ghSAK+rbV/rqK8nHR4qLk0JDpeRkq6sCAACwFkEKwFV16yatXi1VqSJt2iSFhEhJSVZXBQAAYB2CFIACaddOWr9eCgyUtm2Tbr3VnPUPAACgPCJIASiwpk2lDRukevWkP/6QOnaUdu60uioAAADHI0gBKJTrr5cSEqSbbpKOHJFuu0364QerqwIAAHAsghSAQgsKMof53XyzdPy41LWrtG6d1VUBAAA4DkEKQJFUq2ZOQNGli3T6tHT77dLnn1tdFQAAgGMQpAAUma+vOTX63XebD+u95x7po4+srgoAAKDkEaQAXBNPT/OhvQ89JGVmmj/fecfqqgAAAEoWQQrANXNzkxYskEaMkAxDGj5ciooy1wEAAMoighSAYuHiIr39tvTSS+brF16Qxo4lTAEAgLKJIAWg2Nhs0quvSlOnmq8nT5Yef9wc8gcAAFCWEKQAFLtRo6S5c81eqvfflx54QEpPt7oqAACA4kOQAlAiBg+WPv5YcneXPvlE6tNHOnvW6qoAAACKB0EKQInp10/64gvJ21v6+mupZ0/p5EmrqwIAALh2BCkAJapHDykuTqpcWfrmG/MBvkePWl0VAADAtSFIAShxHTpI8fGSv7+0davUqZN04IDVVQEAABQdQQqAQzRvLm3YINWpI+3ZI3XsKP36q9VVAQAAFA1BCoDDNGggJSRIN94oHTpk9kz9+KPVVQEAABQeQQqAQ9WuLa1fL7VpI/39t3nP1IYNVlcFAABQOAQpAA5Xo4a0Zo10221SSoo5IcVXX1ldFQAAQMERpABYws9PWrlSuvNO6fx56e67pf/+1+qqAAAACoYgBcAyXl5STIw0YIB04YL0wAPSnDlWVwUAAHB1BCkAlnJ3lz78UHrySckwpKFDpYkTra4KAADgyghSACzn4iLNmiWNG2e+fu45c90wrK0LAAAgPwQpAE7BZpOioqToaPP1m29Kw4ZJmZnW1gUAAJAXghQAp/LMM+Z9Ujab9O670sCBUkaG1VUBAADkRJAC4HQee0xassS8f2rJEqlvX+ncOaurAgAAuIggBcAp9e8vLV9uzuz31VfS7bdLp05ZXRUAAICJIAXAad1+uxQbaz5zasMGqWtX6a+/rK4KAACAIAXAyd16q7RunVSjhvTjj9Jtt0kHD1pdFQAAKO8IUgCcXqtWZo9UcLD0669muNqzx+qqAABAeUaQAlAqNGokJSRIDRtKBw6YYernn62uCgAAlFcEKQClxnXXmT1TLVtKx45JISHSN99YXRUAACiPCFIASpWaNaW1a80eqVOnpNBQ6euvra4KAACUNwQpAKVO5cpmeLr9dik1VbrrLmnpUqurAgAA5QlBCkCp5O0t/e9/5vOmMjKkf/5TmjvX6qoAAEB5QZACUGpVqCAtXiw99piUlSUNGSJNmWJ1VQAAoDwgSAEo1VxdpX//Wxo71nw9Zoz04ouSYVhbFwAAKNsIUgBKPZtNmjhRmjDBfP3GG9LIkWYvFQAAQEkgSAEoM557TnrnHTNYzZolPfKIef8UAABAcSNIAShTnnxS+ugjyc1NWrRI6tdPOn/e6qoAAEBZQ5ACUOY88ID02WeSp6f0+edSr17S6dNWVwUAAMoSghSAMunOO6WVKyVfX2ndOqlrV+nvv62uCgAAlBUEKQBlVkiItHatVK2a9MMP5uvDh62uCgAAlAUEKQBlWps20oYNUq1a0s6d0q23Sn/8YXVVAACgtCNIASjzGjeWEhKkG26Q9u0zw9S2bVZXBQAASjOCFIByoW5ds2eqeXMpKckc5rdpk9VVAQCA0oogBaDcCAgwJ55o315KTpa6d5dWrbK6KgAAUBoRpACUK1WqSHFxUo8e0tmz5ux+MTFWVwUAAEobghSAcsfHR1q+3HxYb3q6dO+90oIFVlcFAABKE4IUgHLJw0NaskQaPFjKypL+9S/prbesrgoAAJQWBCkA5Zabm/T++1JEhPk6PFyKjJQMw8qqAABAaUCQAlCu2WzS5MnSa6+Zr8ePNwNVVpalZQEAACdHkAJQ7tls0osvSjNmmK/fftsc8nfhgrV1AQAA50WQAoD/N2KE9MEHkqurtHChdN990vnzVlcFAACcEUEKAC7x0EPSp5+ak1F89pnUu7d05ozVVQEAAGdDkAKAy9x9t7RihVSxorR6tfng3hMnrK4KAAA4E4IUAOShSxczRFWtKn33nRQSIiUmWl0VAABwFgQpAMjHP/4hrV8vBQZK27dLt94q7d0rZWZK8fE2rV9fS/HxNmVmWl0pAABwNIIUAFxBkyZSQoJUv770559S69ZSrVpSaKibpk5tq9BQN9WtKy1bZnWlAADAkQhSAHAV9eubYSo4WDp5Ujp6NOf+w4ele+8lTAEAUJ4QpACgAGrWVL5D+AzD/Bkenn8bAABQthCkAKAANmyQjhzJf79hSAcPmu0AAEDZZ2mQmjBhgm6++Wb5+vqqZs2a6tu3r3bv3p2jjWEYioyMVFBQkLy8vNS5c2ft2LEjR5u0tDSNHDlS1atXl4+Pj/r06aNDhw458lQAlHEFnbFvxQp6pQAAKA8sDVLx8fEaPny4Nm3apLi4OF24cEE9evTQ2bNn7W2io6M1depUzZw5U5s3b1ZAQIBCQ0N1+vRpe5vw8HDFxMRoyZIlSkhI0JkzZ9S7d29l8rcZAMUkMLBg7aKjpTp1pBdekH7/vWRrAgAA1rE0SK1cuVKDBg1SkyZN1KJFC82fP18HDhzQli1bJJm9UdOnT9cLL7ygsLAwNW3aVAsXLtS5c+e0ePFiSdKpU6c0d+5cTZkyRd27d1erVq20aNEibdu2TatWrbLy9ACUIZ06SbVrSzZb3vttNvMBvlWrmpNPREVJDRqYz59auFC65N+HAABAGeBmdQGXOnXqlCSpatWqkqS9e/cqKSlJPXr0sLfx8PBQSEiINm7cqKFDh2rLli3KyMjI0SYoKEhNmzbVxo0b1bNnz1yfk5aWprS0NPvrlJQUSVJGRoYyMjJK5NxQsrK/N74/lKQpU2y6/35X2WySYVxMVDabOdvE3LmZuuMOQ19+adOCBS6KjbVp/Xqb1q+XRoww1L+/oUGDstSunZFvIAPyw59zcCSuNziaM11zBa3BaYKUYRiKiIjQrbfeqqZNm0qSkpKSJEn+/v452vr7+2v//v32NhUqVFCVKlVytcl+/+UmTJig8ePH59oeGxsrb2/vaz4XWCcuLs7qElCGeXhIY8cG6v33m+n4cS/79mrVUvXoo9vl4ZGo1aslT0/piSeke+/11Nq1wVq9+jolJVXUvHk2zZvnotq1T6tbtwPq3PmgqlRJu8InArnx5xwciesNjuYM19y5c+cK1M5pgtSIESP0yy+/KCEhIdc+22X/dGsYRq5tl7tSm3HjxikiIsL+OiUlRcHBwerRo4f8/PyKUD2slpGRobi4OIWGhsrd3d3qclCG3XGHFBkprVt3XnFx2xUa2lSdO7vL1bWVpFa52j/8sDmjX0LCBS1Y4KJPP7Xp0CFfLVzYRIsW3aRevcxeql69DHHp4kr4cw6OxPUGR3Omay57tNrVOEWQGjlypJYvX67169erdu3a9u0BAQGSzF6nwEvu9D527Ji9lyogIEDp6elKTk7O0St17NgxdejQIc/P8/DwkIeHR67t7u7uln9xuDZ8h3AEd3epWzcpLe2wunVrUaBrrmtXc5k5U/r4Y2nePOnbb2364gubvvjCRTVrmqFr8GCpcWMHnARKLf6cgyNxvcHRnOGaK+jnWzrZhGEYGjFihJYtW6Y1a9aoXr16OfbXq1dPAQEBObr40tPTFR8fbw9Jbdq0kbu7e442iYmJ2r59e75BCgCs4ucnDRkibdwo7dwpPfOM5O8vHTsmTZ4s3XST1L699N57UgH/QQwAAFjA0iA1fPhwLVq0SIsXL5avr6+SkpKUlJSk1NRUSeaQvvDwcEVFRSkmJkbbt2/XoEGD5O3trQEDBkiSKlWqpEcffVSjR4/W6tWr9dNPP2ngwIFq1qyZunfvbuXpAcAVNW5sTpd+8KD0v/9Jd98tubpKmzZJjz8uBQRIjzwixcebwwMBAIDzsHRo3+zZsyVJnTt3zrF9/vz5GjRokCRp7NixSk1N1bBhw5ScnKx27dopNjZWvr6+9vbTpk2Tm5ub+vfvr9TUVHXr1k0LFiyQq6uro04FAIrM3V3q08dckpKkRYvMoX+7dkkffGAu118v/etfZrC6ZAQ0AACwiOVD+/JaskOUZPZKRUZGKjExUefPn1d8fLx9Vr9snp6emjFjho4fP65z587p888/V3BwsIPPBgCuXUCANGaMtGOH9O230mOPSb6+0h9/SC++aD7st1cv6ZNPpDQm/AMAwDKWBikAQN5sNumWW6Q5c6TERPOhviEhUlaWtHKl1L+/FBQkPf209PPPVlcLAED5Q5ACACfn42PO6LdunbRnj/TCC1KtWtKJE9Lbb0stW0pt2kizZknJyVZXCwBA+UCQAoBS5IYbpNdfl/bvl1askO67z7zH6scfpREjpMBA6YEHpLg4s/cKAACUDIIUAJRCrq7S7bebz6Q6ckR66y2peXPzvqklS6QePaR69aRXXpH27rW6WgAAyh6CFACUctWrS089JW3dKm3ZIg0fLlWuLB04IL36qlS/vvkA4Y8+kv7/6RIAAOAaEaQAoIyw2aTWraWZM80JKv7zHyk01Ny+Zo00cKA59O/JJ6XNm3k2FQAA14IgBQBlkKendP/9UmysObRv/Hipbl3p1Cnp3Xelf/zDHAo4bZr0119WVwsAQOlDkAKAMq5OHenll81nUa1eLT34oBm0tm+XIiLMGQD79ZO+/FK6cMHqagEAKB0IUgBQTri4SF27SosWmUP/3nlHattWysiQli2Tevc2Q9fzz5vTrAMAgPwRpACgHKpc+eK9Uj//LIWHS9WqmTMATpggNWwodeokzZ8vnTljdbUAADgfghQAlHPZ90odOSItXSrdcYfZe5WQIA0eLAUESI8+Km3cyAQVAABkI0gBACRJFSpcvFfqwAEpKsp8APDZs9K8eVLHjlLjxlJ0tDk0EACA8owgBQDIpVYtadw46bffpPXrpUGDJG9vafdu6dlnpeBgqU8f6bPPzHusAAAobwhSAIB82WwX75VKSpLef1/q0EHKzJQ+/1y65x4zdI0ZI+3YYXW1AAA4DkEKAFAgvr7mvVLffCPt2iWNHSv5+5vPoZoyRWraVLrlFmnOHPN5VZfLzJTWrTMfFLxunfkaAIDSiiAFACi0G2+UJk6UDh6Uli+X+vaV3Nyk776Thg6VAgOlhx82A1NWljm9et26Upcu0oAB5s+6dc3tAACURgQpAECRubtLd90lxcRIhw5JkyebE1KkpkoffmgGpqAgcxKLQ4dyvvfwYeneewlTAIDSiSAFACgW/v7S6NHmvVKbNkmPPy5VrCgdPZp3++yp1MPDGeYHACh9CFIAgGJls0nt2kn//rf08cdXbmsY5vDADRscUxsAAMWFIAUAKDEnTxasXXS0tGULD/wFAJQeBCkAQIkJDCxYuxUrpLZtzfurXntN+uOPkq0LAIBrRZACAJSYTp2k2rXN4X55sdmk6tXNSSc8Pc0H/r78snTDDVL79tLMmeb06gAAOBuCFACgxLi6Sm+9Za5fHqayX//739Inn5iTUixYIIWGSi4u5oQVI0eavVp33CF99JF09qxDywcAIF8EKQBAiQoLk5YulWrVyrm9dm1ze1iY+drPT3rkESk21pwqfdo0c7hfZqY59G/gQKlmTenBB6WvvpIyMhx/LgAAZCNIAQBKXFiYtG+ftHattHix+XPv3osh6nKBgea06Js3S7/+ag73u/566dw58/133mkGs5EjzZ4rJqkAADgaQQoA4BCurlLnztIDD5g/XV0L9r5GjaTx46U9e8zQNGKEVKOGee/UzJnmvVQNGphha/fukjwDAAAuIkgBAEqF7OdTzZghHT5sDu978EHJx8ec5e+116QbbzSHA06bJiUmWl0xAKAsI0gBAEodd3epVy9p0SJzkoqPPjInpHB1NZ9HFRFh3oMVGmpOYJGSYnXFAICyhiAFACjVfHykAQOkL780e6Gyh/tlZUmrVkn/+pfk7y/17y8tXy6lp1tdMQCgLCBIAQDKjBo1pOHDpY0bcw73O3/enGL97rvNiSyeeELasMEMWwAAFAVBCgBQJtWvL734orRzpzncb9QoM0SdOGE+u+q226R69aRx46Tt262uFgBQ2hCkAABlms0mtW4tTZ0qHTwoxcVJgwZJvr7SgQPSm29KzZpJLVpI0dFmGwAAroYgBQAoN1xdpe7dpfnzzUkqPv7YHO7n7i798ov07LNSnTrm9OzvvSclJ1tdMQDAWRGkAADlkpeXdN990mefSUlJF4f7GYYUHy89/rgUEGA+NPjTT837rAAAyEaQAgCUe1WrmsEpPl7av//icL/0dCkmRrr3XjNUPfqotGaNlJlpdcUAAKsRpAAAuMR115lD/H75Rfr5Z2nsWCk4WDp1Spo3T+rWzWwzZoz0009mDxYAoPwhSAEAkI/mzaWJE6V9+6R166THHpMqV5aOHJGmTDEnsWjSRHrjDWnvXouLBQA4FEEKAICrcHGRQkKkOXPM+6myh/t5eEi7dpnTrNevL3XsKL3zjvT331ZXDAAoaQQpAAAKwcND6tvXfMDv0aMXh/vZbOaDgIcPN59Xdddd0pIl0rlzVlcMACgJBCkAAIqoUiXpX/+SVq2SDh26ONzvwgXpiy+kBx6Q/P2lhx+Wvv7a3A4AKBsIUgAAFIOgICkiQtqyRdq5U3rhBalePenMGenDD6Xbb5dq15aeflr6/nsmqQCA0o4gBQBAMWvcWHr9demPP6RvvpGGDZOqVTOHAr79ttSundSokRQZKe3ZY3W1AICiIEgBAFBCbDapQwdp1iwpMfHicD8vLzNAjR8vNWwo/eMfZsA6ejTv42RmSvHxNq1fX0vx8TaeYwUAToAgBQCAA7i7S3feKS1eLB07dnG4n6urtHmzOeSvVi1z24cfSqdPm+9btkyqW1cKDXXT1KltFRrqprp1ze0AAOsQpAAAcLCKFaWBA6UVK6TDhy8O98vMNCelePhhc5KKW2+V+vUzJ7K41OHD5vTrhCkAsA5BCgAAC/n7SyNHSps2Sb/9Zt431aCBlJpq3l+Vl+yJKsLDxTA/ALAIQQoAACfRoIH0yivS7t3S7NlXbmsY0sGD0mOPmQ8I3rOHUAUAjuRmdQEAACAnm818RlVBzJ9vLpI5icVNN0lNm0rNmpk/mzY1p2a32UquXgAojwhSAAA4ocDAgrXr2VP66y/z2VWpqeZzrLZsydmmSpWLoerSgFWlSvHXDQDlBUEKAAAn1KmT+QDfw4fzfnivzWbu//JLc+a/zEzpzz+lbduk7dvNZds2c8hfcrK0YYO5XKpWrYuhKjtkNW4seXs75hwBoDQjSAEA4IRcXaW33jJn57PZcoap7GF606eb7bLbN2hgLmFhF9ueP2/ec3V5wDpwwAxphw+bMwVeeuzrr8/Zc9WsmXlcN/7WAAB2/JEIAICTCguTli41nzF16RTotWubIerSwJQfT0+pRQtzudSpU+ZwwMsD1t9/S7//bi4xMRfbV6gg3Xhj7oB13XXcfwWgfCJIAQDgxMLCpLvvltauvaAVK7aqV6+W6tLFzd4TVVSVKknt25tLNsMwHxZ8abDKXj97VvrlF3O5lK+v1KRJ7oBVo8a11QcAzo4gBQCAk3N1lUJCDJ09e1ghIS2uOUTlx2Yzn2vl7y9163Zxe1aWtH9/7oD166/S6dPmM7A2bcp5rJo1c09u0aSJGbwAoCwgSAEAgCtycZHq1TOXu+66uD0jw3yI8OUB688/zZ6tNWvM5VJ16+aeQbBRI8nDw6GnBADXjCAFAACKxN3d7GVq0kT65z8vbj971rz/6vKAlZgo7dtnLl98cbG9m5vUsGHugFWvnq6p9y0z05ypMDHRnE6+U6drOx4AXIogBQAAipWPj3TzzeZyqePHpR07ck9wkT3xxc6d0scfX2zv5WWGtMsDVmDg1Se4WLYs70k63nqrYJN0AMDVEKQAAIBDVKsm3XabuWQzDHMK9ssnt8h+wPAPP5jLpapUyT25RZMmFx8wvGyZOW385c/fOnzY3L50KWEKwLUjSAEAAMtkP1i4dm3p9tsvbs/MlP74I3fA+u038wHD69eby6WyHzD8zTd5P8TYMMzPCw83Z0JkmB+Aa0GQAgAATsfV1bxvqmHD3A8Y/vXX3PdfXfqA4SsxDOngQWn+fPO+LmYRBFBUBCkAAFBqeHpKLVuay6VOnTLvv5o3T5o79+rHeewxc6lRQ7r+eumGG8yfly41a/KwYQD5I0gBAIBSr1IlqUMHKT29YEHKz09KSZH++stcLn8OlmROmnF5uMperrvOnG0QQPnFHwEAAKDM6NTJvN/q8OG875PKvidr717pzBnzPqy8loMHzWncf/nFXC7n5ibVqZN3yKpf3wxhAMo2ghQAACgzXF3NKc7vvdcMTZeGqexhetOnm+0qVZJatzaXy6Wlmc+7yitk/fmnuT/7dV4CA/PvzapWjSGDQFlAkAIAAGVKWJg5xXlez5GaPr1gU597eEiNGpnL5bKyzB6v/HqzTp40HwKcmCglJOR+v59f/iGrdm1mEwRKC4IUAAAoc8LCzCnON2wwA01goDnsrzhCiouLFBxsLp07595/4kT+IevwYfPerJ9+MpfLVagg1auXd8iqV8+cbAOAcyBIAQCAMsnVNe+gU9KqVjWXm2/OvS811bw/648/pN9/zxmy9u0zJ8vYvdtcLmezmc/Kyq83K/uBxNcqM1OKj7dp/fpa8vGxqUsXesmAvBCkAAAAHMTLS7rpJnO5XGamOclFfr1Zp0+bQxUPHZLi43O/v0qVnMHq0indAwPNnrSrWbYse0ikm6S2mjrVHG741lsFGxIJlCcEKQAAACfg6irVrWsu3brl3GcY0t9/XwxVl/dmHT0qJSdLP/xgLpfz9DRnE8yrJ6tuXXNI4bJl5iQdl892ePiwuX3pUsIUcCmCFAAAgJOz2cyHB9eoId1yS+79Z86Yswnm1ZO1f790/ry0c6e5XM7Fxex1Ono07ynjDcP8/PBw874zhvkBJkuD1Pr16zVp0iRt2bJFiYmJiomJUd++fe37DcPQ+PHjNWfOHCUnJ6tdu3aaNWuWmjRpYm+TlpamMWPG6D//+Y9SU1PVrVs3vfPOO6pdu7YFZwQAAOB4FStKzZuby+UyMqQDB/LuzfrzT+ncOXP/lRiGOeywWzdzuvhatczwVauWuQQFmTMdAuWJpUHq7NmzatGihf71r3+pX79+ufZHR0dr6tSpWrBggRo2bKjXX39doaGh2r17t3x9fSVJ4eHh+vzzz7VkyRJVq1ZNo0ePVu/evbVlyxa58k8mAACgnHN3vziM73KGISUlSf/+tzR+/NWPFR+f9/1ZktlbdmnAujRoZa/7+fEMLZQdlgapXr16qVevXnnuMwxD06dP1wsvvKCw/x+Qu3DhQvn7+2vx4sUaOnSoTp06pblz5+rDDz9U9+7dJUmLFi1ScHCwVq1apZ49ezrsXAAAAEobm82ciKJz54IFqeHDzQkzDh0y7506fNhcT0+X/vrLXLZuzf/9Pj5XDlq1akk1azJ8EKWD094jtXfvXiUlJalHjx72bR4eHgoJCdHGjRs1dOhQbdmyRRkZGTnaBAUFqWnTptq4cWO+QSotLU1paWn21ykpKZKkjIwMZWRklNAZoSRlf298f3AUrjk4GtccStItt0i1arnpyBHJMHJ3GdlshmrVkiZPvpAr5BiGdPx4drCy6cgR6dAhm44cuXRdOnnSprNn85/ePZubm6HAQKlWLUNBQeZPc/ig8f9hy9zOM7XKFmf6M66gNThtkEpKSpIk+fv759ju7++v/fv329tUqFBBVS57cIK/v7/9/XmZMGGCxufxzy6xsbHy9va+1tJhobi4OKtLQDnDNQdH45pDSRk4MFATJ94syZB0aZgyZBjSgw9u1tdfJ171OEFB5nK58+dddfy4p06c8NLff5s/jx/31PHjXjpxwlPHj3vq5ElPXbhg08GD0sGDVx4D6OeXpqpVz6tatVRVq2b+rFr1vKpXT7Vv9/G54LChhJmZ0s6d1ZSc7KkqVc7rppuO07NWBM7wZ9y5c+cK1M5pg1Q222VXv2EYubZd7mptxo0bp4iICPvrlJQUBQcHq0ePHvLz87u2gmGJjIwMxcXFKTQ0VO7u7laXg3KAaw6OxjWHknbHHVLr1pmKiHDV4cMXt9euLU2Zkql77mklqVWJ1nDhwgUlJZk9W4cPS0eO2HTokOy9W9nbz5+3KSXFQykpHtq3r1K+x/P2NnuvatfO3btVu7b509//2ocSxsTY/v/3dvHvn7VqGZo6NVP33JPHVIjIxZn+jMserXY1ThukAgICJJm9ToGBgfbtx44ds/dSBQQEKD09XcnJyTl6pY4dO6YOHTrke2wPDw955DG1jLu7u+VfHK4N3yEcjWsOjsY1h5LUv7/Ur5+0du0FrVixVb16tVSXLm5ydXXMXxnd3aV69cwlP4YhnTiR8x6ty9cPHTKfq3XunE2//y79/nv+/8Du6qr/H0qY/31btWqZ94blZdky6f77c08df+SITfff78bztwrJGf6MK+jnO22QqlevngICAhQXF6dWrcx//UhPT1d8fLwmTpwoSWrTpo3c3d0VFxen/v37S5ISExO1fft2RUdHW1Y7AABAaeXqKoWEGDp79rBCQlo43fA0m02qVs1c8pruPdu5c/r/e7TyDlqHD0uJieaQvEOHzOW77/I/XtWquYNWYKD0wgs8f6u8sjRInTlzRr///rv99d69e7V161ZVrVpV1113ncLDwxUVFaUGDRqoQYMGioqKkre3twYMGCBJqlSpkh599FGNHj1a1apVU9WqVTVmzBg1a9bMPosfAAAAyh9vb+mGG8wlPxcumA8ivlrvVmqq2Qt24oT0yy8FryH7+VszZkh9+5rhy81puzFQWJZ+lT/88IO6dOlif51939IjjzyiBQsWaOzYsUpNTdWwYcPsD+SNjY21P0NKkqZNmyY3Nzf179/f/kDeBQsW8AwpAAAAXJGb28UepvwYhnTyZN5Ba/PmK0/3nm3UKHNxcTE/q04dqW5d8+ely3XX5T+EEM7H0iDVuXNnGXn1hf4/m82myMhIRUZG5tvG09NTM2bM0IwZM0qgQgAAAJRnNptUpYq5NG2ac9+6ddIlfQL5CgqS/v7bfN6WOSOhlJCQd1t//5zh6vLAxbxozoPORQAAAKAIOnUyh+sdPpz3fVI2m7l/715z/ehRad8+af/+nEv2trNnzTZHj0rff5/3Z1aunHdvVnboqlZNDpvyvbwjSAEAAABF4OoqvfWWdO+9Zni5NExlh5np0y9ONBEYaC7t2+c+VvZshHkFrOzlxAlzmOHWrfkPKfT2vnKPVmCgOcQQ144gBQAAABRRWJi0dKn09NPmfVPZatc2Q1RBpz6/dDbC1q3zbnP6dO7erEsDV1KSOVvhrl3mkhd3d/NerPx6tGrXNtvg6ghSAAAAwDUICzOnON+wwZxSPTDQHPZX3HOf+fqa92ldfq9WtvPnzfuv8ho+uH+/GfQyMqQ//jCXvLi4mPd05dejVadO8U+IkZkpxcfbtH59Lfn42NSlS+mYLp4gBQAAAFwjV1epc2dra/D0lBo0MJe8XLhg3s+V3/DBAwektLSLz9X65pu8j1OjRt4BK3tbpUoFr3nZsuzePDdJbTV1qtkr9tZbzv8gY4IUAAAAUA64uV0MPXnJypKOHbvyhBhnzkh//WUumzfnfZxKla7co1WjhjmUcdky8/6yyyfqOHzY3L50qXOHKYIUAAAAALm4SAEB5nLLLbn3G4aUnJz/ZBj790vHj0unTpkPLs7v4cVeXuZ9Wvv25T3boWGYQSs83Bwy6azD/AhSAAAAAK7KZpOqVjWXVq3ybnPmzJUnxEhMlFJTpd27r/xZhmHe77Vhg/VDJvNDkAIAAABQLCpWlJo0MZe8pKWZAWn+fCkq6urHS0ws3vqKE7PIAwAAAHAIDw/phhuk0NCCtQ8MLNl6rgVBCgAAAIBDdepkzs6X/eDiy9lsUnCw2c5ZEaQAAAAAOJSrqznFuZQ7TGW/nj7deSeakAhSAAAAACwQFmZOcV6rVs7ttWs7/9TnEpNNAAAAALBIWJg5xfnatRe0YsVW9erVUl26uDl1T1Q2ghQAAAAAy7i6SiEhhs6ePayQkBalIkRJDO0DAAAAgEIjSAEAAABAIRGkAAAAAKCQCFIAAAAAUEgEKQAAAAAoJIIUAAAAABQSQQoAAAAACokgBQAAAACFRJACAAAAgEIiSAEAAABAIRGkAAAAAKCQCFIAAAAAUEgEKQAAAAAoJDerC3AGhmFIklJSUiyuBEWVkZGhc+fOKSUlRe7u7laXg3KAaw6OxjUHR+J6g6M50zWXnQmyM0J+CFKSTp8+LUkKDg62uBIAAAAAzuD06dOqVKlSvvttxtWiVjmQlZWlI0eOyNfXVzabzepyUAQpKSkKDg7WwYMH5efnZ3U5KAe45uBoXHNwJK43OJozXXOGYej06dMKCgqSi0v+d0LRIyXJxcVFtWvXtroMFAM/Pz/L/+ND+cI1B0fjmoMjcb3B0ZzlmrtST1Q2JpsAAAAAgEIiSAEAAABAIRGkUCZ4eHjolVdekYeHh9WloJzgmoOjcc3Bkbje4Gil8ZpjsgkAAAAAKCR6pAAAAACgkAhSAAAAAFBIBCkAAAAAKCSCFAAAAAAUEkEKpdqECRN08803y9fXVzVr1lTfvn21e/duq8tCOTFhwgTZbDaFh4dbXQrKsMOHD2vgwIGqVq2avL291bJlS23ZssXqslBGXbhwQS+++KLq1asnLy8v1a9fX6+++qqysrKsLg1lwPr163XXXXcpKChINptNn332WY79hmEoMjJSQUFB8vLyUufOnbVjxw5rii0AghRKtfj4eA0fPlybNm1SXFycLly4oB49eujs2bNWl4YybvPmzZozZ46aN29udSkow5KTk9WxY0e5u7trxYoV2rlzp6ZMmaLKlStbXRrKqIkTJ+rdd9/VzJkztWvXLkVHR2vSpEmaMWOG1aWhDDh79qxatGihmTNn5rk/OjpaU6dO1cyZM7V582YFBAQoNDRUp0+fdnClBcP05yhT/vrrL9WsWVPx8fG67bbbrC4HZdSZM2fUunVrvfPOO3r99dfVsmVLTZ8+3eqyUAY999xz+uabb7RhwwarS0E50bt3b/n7+2vu3Ln2bf369ZO3t7c+/PBDCytDWWOz2RQTE6O+fftKMnujgoKCFB4ermeffVaSlJaWJn9/f02cOFFDhw61sNq80SOFMuXUqVOSpKpVq1pcCcqy4cOH684771T37t2tLgVl3PLly9W2bVvdd999qlmzplq1aqX33nvP6rJQht16661avXq1fvvtN0nSzz//rISEBN1xxx0WV4aybu/evUpKSlKPHj3s2zw8PBQSEqKNGzdaWFn+3KwuACguhmEoIiJCt956q5o2bWp1OSijlixZoh9//FGbN2+2uhSUA3/++admz56tiIgIPf/88/r+++/11FNPycPDQw8//LDV5aEMevbZZ3Xq1CndeOONcnV1VWZmpt544w098MADVpeGMi4pKUmS5O/vn2O7v7+/9u/fb0VJV0WQQpkxYsQI/fLLL0pISLC6FJRRBw8e1NNPP63Y2Fh5enpaXQ7KgaysLLVt21ZRUVGSpFatWmnHjh2aPXs2QQol4r///a8WLVqkxYsXq0mTJtq6davCw8MVFBSkRx55xOryUA7YbLYcrw3DyLXNWRCkUCaMHDlSy5cv1/r161W7dm2ry0EZtWXLFh07dkxt2rSxb8vMzNT69es1c+ZMpaWlydXV1cIKUdYEBgbqpptuyrGtcePG+vTTTy2qCGXdM888o+eee07333+/JKlZs2bav3+/JkyYQJBCiQoICJBk9kwFBgbatx87dixXL5Wz4B4plGqGYWjEiBFatmyZ1qxZo3r16lldEsqwbt26adu2bdq6dat9adu2rR588EFt3bqVEIVi17Fjx1yPdPjtt99Up04diypCWXfu3Dm5uOT866GrqyvTn6PE1atXTwEBAYqLi7NvS09PV3x8vDp06GBhZfmjRwql2vDhw7V48WL973//k6+vr318baVKleTl5WVxdShrfH19c91/5+Pjo2rVqnFfHkrEqFGj1KFDB0VFRal///76/vvvNWfOHM2ZM8fq0lBG3XXXXXrjjTd03XXXqUmTJvrpp580depUDR482OrSUAacOXNGv//+u/313r17tXXrVlWtWlXXXXedwsPDFRUVpQYNGqhBgwaKioqSt7e3BgwYYGHV+WP6c5Rq+Y2ZnT9/vgYNGuTYYlAude7cmenPUaK++OILjRs3Tnv27FG9evUUERGhxx57zOqyUEadPn1aL730kmJiYnTs2DEFBQXpgQce0Msvv6wKFSpYXR5KuXXr1qlLly65tj/yyCNasGCBDMPQ+PHj9e9//1vJyclq166dZs2a5bT/WEmQAgAAAIBC4h4pAAAAACgkghQAAAAAFBJBCgAAAAAKiSAFAAAAAIVEkAIAAACAQiJIAQAAAEAhEaQAAAAAoJAIUgAAAABQSAQpAIDl9u3bJ5vNpq1bt1pdit2vv/6qW265RZ6enmrZsuU1Hctms+mzzz4rlroAAM6BIAUA0KBBg2Sz2fTmm2/m2P7ZZ5/JZrNZVJW1XnnlFfn4+Gj37t1avXp1vu2SkpI0cuRI1a9fXx4eHgoODtZdd911xfdci3Xr1slms+nkyZMlcnwAQMEQpAAAkiRPT09NnDhRycnJVpdSbNLT04v83j/++EO33nqr6tSpo2rVquXZZt++fWrTpo3WrFmj6Ohobdu2TStXrlSXLl00fPjwIn+2IxiGoQsXLlhdBgCUWgQpAIAkqXv37goICNCECRPybRMZGZlrmNv06dNVt25d++tBgwapb9++ioqKkr+/vypXrqzx48frwoULeuaZZ1S1alXVrl1b8+bNy3X8X3/9VR06dJCnp6eaNGmidevW5di/c+dO3XHHHapYsaL8/f310EMP6e+//7bv79y5s0aMGKGIiAhVr15doaGheZ5HVlaWXn31VdWuXVseHh5q2bKlVq5cad9vs9m0ZcsWvfrqq7LZbIqMjMzzOMOGDZPNZtP333+ve++9Vw0bNlSTJk0UERGhTZs25fmevHqUtm7dKpvNpn379kmS9u/fr7vuuktVqlSRj4+PmjRpoq+++kr79u1Tly5dJElVqlSRzWbToEGDJJnBKDo6WvXr15eXl5datGihpUuX5vrcr7/+Wm3btpWHh4c2bNign3/+WV26dJGvr6/8/PzUpk0b/fDDD3nWDgC4iCAFAJAkubq6KioqSjNmzNChQ4eu6Vhr1qzRkSNHtH79ek2dOlWRkZHq3bu3qlSpou+++05PPPGEnnjiCR08eDDH+5555hmNHj1aP/30kzp06KA+ffro+PHjkqTExESFhISoZcuW+uGHH7Ry5UodPXpU/fv3z3GMhQsXys3NTd98843+/e9/51nfW2+9pSlTpmjy5Mn65Zdf1LNnT/Xp00d79uyxf1aTJk00evRoJSYmasyYMbmOceLECa1cuVLDhw+Xj49Prv2VK1cuyq9OkjR8+HClpaVp/fr12rZtmyZOnKiKFSsqODhYn376qSRp9+7dSkxM1FtvvSVJevHFFzV//nzNnj1bO3bs0KhRozRw4EDFx8fnOPbYsWM1YcIE7dq1S82bN9eDDz6o2rVra/PmzdqyZYuee+45ubu7F7l2ACgv3KwuAADgPO655x61bNlSr7zyiubOnVvk41StWlVvv/22XFxc1KhRI0VHR+vcuXN6/vnnJUnjxo3Tm2++qW+++Ub333+//X0jRoxQv379JEmzZ8/WypUrNXfuXI0dO1azZ89W69atFRUVZW8/b948BQcH67ffflPDhg0lSTfccIOio6OvWN/kyZP17LPP2j974sSJWrt2raZPn65Zs2YpICBAbm5uqlixogICAvI8xu+//y7DMHTjjTcW+feUnwMHDqhfv35q1qyZJKl+/fr2fVWrVpUk1axZ0x7Wzp49q6lTp2rNmjVq3769/T0JCQn697//rZCQEPv7X3311Rw9dQcOHNAzzzxjP48GDRoU+/kAQFlEkAIA5DBx4kR17dpVo0ePLvIxmjRpIheXi4Me/P391bRpU/trV1dXVatWTceOHcvxvuwQIElubm5q27atdu3aJUnasmWL1q5dq4oVK+b6vD/++MMepNq2bXvF2lJSUnTkyBF17Ngxx/aOHTvq559/LuAZmkPpJJXIZBxPPfWUnnzyScXGxqp79+7q16+fmjdvnm/7nTt36vz587mGMqanp6tVq1Y5tl3++4mIiNCQIUP04Ycfqnv37rrvvvt0/fXXF9/JAEAZxdA+AEAOt912m3r27GnvPbqUi4uLPUBky8jIyNXu8qFhNpstz21ZWVlXrSc7qGRlZemuu+7S1q1bcyx79uzRbbfdZm+f1zC7Kx03m2EYhQpFDRo0kM1mswe9gsoOmJf+Hi//HQ4ZMkR//vmnHnroIW3btk1t27bVjBkz8j1m9u/xyy+/zPG72blzZ477pKTcv5/IyEjt2LFDd955p9asWaObbrpJMTExhTonACiPCFIAgFzefPNNff7559q4cWOO7TVq1FBSUlKOEFCcz366dIKGCxcuaMuWLfYhZ61bt9aOHTtUt25d3XDDDTmWgoYnSfLz81NQUJASEhJybN+4caMaN25c4ONUrVpVPXv21KxZs3T27Nlc+/ObnrxGjRqSzPuwsuX1OwwODtYTTzyhZcuWafTo0XrvvfckSRUqVJAkZWZm2tvedNNN8vDw0IEDB3L9boKDg696Lg0bNtSoUaMUGxursLAwzZ8//6rvAYDyjiAFAMilWbNmevDBB3P1gnTu3Fl//fWXoqOj9ccff2jWrFlasWJFsX3urFmzFBMTo19//VXDhw9XcnKyBg8eLMmcgOHEiRN64IEH9P333+vPP/9UbGysBg8enCNUFMQzzzyjiRMn6r///a92796t5557Tlu3btXTTz9dqOO88847yszM1D/+8Q99+umn2rNnj3bt2qW33347xzDFS2WHm8jISP3222/68ssvNWXKlBxtwsPD9fXXX2vv3r368ccftWbNGnvIq1Onjmw2m7744gv99ddfOnPmjHx9fTVmzBiNGjVKCxcu1B9//KGffvpJs2bN0sKFC/OtPzU1VSNGjNC6deu0f/9+ffPNN9q8eXOhAiUAlFcEKQBAnl577bVcw/gaN26sd955R7NmzVKLFi30/fff5zmjXVG9+eabmjhxolq0aKENGzbof//7n6pXry5JCgoK0jfffKPMzEz17NlTTZs21dNPP61KlSrluB+rIJ566imNHj1ao0ePVrNmzbRy5UotX7680BMt1KtXTz/++KO6dOmi0aNHq2nTpgoNDdXq1as1e/bsPN/j7u6u//znP/r111/VokULTZw4Ua+//nqONpmZmRo+fLgaN26s22+/XY0aNdI777wjSapVq5bGjx+v5557Tv7+/hoxYoQk8/t6+eWXNWHCBDVu3Fg9e/bU559/rnr16uVbv6urq44fP66HH35YDRs2VP/+/dWrVy+NHz++UL8HACiPbMbl/5cEAAAAAFwRPVIAAAAAUEgEKQAAAAAoJIIUAAAAABQSQQoAAAAACokgBQAAAACFRJACAAAAgEIiSAEAAABAIRGkAAAAAKCQCFIAAAAAUEgEKQAAAAAoJIIUAAAAABTS/wFcpVKauODORQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from sklearn.cluster import KMeans\n", + "\n", + "# Step 1: Determine the optimal number of clusters (Elbow Method)\n", + "inertia = []\n", + "K = range(1, 11)\n", + "for k in K:\n", + " kmeans = KMeans(n_clusters=k, random_state=0)\n", + " kmeans.fit(X_scaled)\n", + " inertia.append(kmeans.inertia_)\n", + "\n", + "# Plotting the elbow graph\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(K, inertia, 'bo-')\n", + "plt.xlabel('Number of Clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.title('Elbow Method for Optimal k')\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "22d10a29-f7db-4064-b1b1-5d19fdf31706", + "metadata": {}, + "source": [ + "## 6. **Fit K-means Model**\n", + "Now we will fit the K-means model using the optimal number of clusters determined in the previous step." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9cbcbff1-2632-4990-8fd3-6d3a80bb793a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " CustomerID Genre Age Annual Income (k$) Spending Score (1-100) \\\n", + "0 1 Male 19 15 39 \n", + "1 2 Male 21 15 81 \n", + "2 3 Female 20 16 6 \n", + "3 4 Female 23 16 77 \n", + "4 5 Female 31 17 40 \n", + "\n", + " Cluster \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 \n" + ] + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "\n", + "# Replace with the optimal k found from the elbow method (let’s assume it's 4 for this example)\n", + "optimal_k = 4 \n", + "kmeans = KMeans(n_clusters=optimal_k, random_state=0)\n", + "clusters = kmeans.fit_predict(X_scaled)\n", + "\n", + "# Add cluster labels to original data\n", + "data['Cluster'] = clusters\n", + "\n", + "# Display the updated data with cluster labels\n", + "print(data.head())" + ] + }, + { + "cell_type": "markdown", + "id": "91cae6ec-f759-4076-8e15-33ae728b232a", + "metadata": {}, + "source": [ + "## 7. **Visualization**: \n", + " - Visualize the segments using plots and graphs for better understanding." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b1b5be95-c9cf-47b4-9021-ee4f6e817b10", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "import seaborn as sns\n", + "\n", + "# Reduce dimensions for visualization\n", + "pca = PCA(n_components=2)\n", + "X_pca = pca.fit_transform(X_scaled)\n", + "\n", + "# Plot the clusters\n", + "plt.figure(figsize=(10, 6))\n", + "sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=data['Cluster'], palette='viridis', s=100)\n", + "plt.title('Customer Segmentation using K-means Clustering')\n", + "plt.xlabel('Principal Component 1')\n", + "plt.ylabel('Principal Component 2')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7ead1683-9bc1-4b15-b584-dd401b5750f4", + "metadata": {}, + "source": [ + "## 8. **Deployment**: \n", + " - Deploy the tool using a web framework like Flask or Django for easy access and use.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "87457cf4-3c8e-487e-86f5-1858e94f8419", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Cluster 0 characteristics:\n", + " CustomerID Age Annual Income (k$) Spending Score (1-100) \\\n", + "count 40.00000 40.000000 40.000000 40.000000 \n", + "mean 161.02500 32.875000 86.100000 81.525000 \n", + "std 23.33863 3.857643 16.339036 9.999968 \n", + "min 123.00000 27.000000 69.000000 58.000000 \n", + "25% 141.50000 30.000000 74.750000 74.000000 \n", + "50% 161.00000 32.000000 78.500000 83.000000 \n", + "75% 180.50000 36.000000 94.000000 90.000000 \n", + "max 200.00000 40.000000 137.000000 97.000000 \n", + "\n", + " Cluster \n", + "count 40.0 \n", + "mean 0.0 \n", + "std 0.0 \n", + "min 0.0 \n", + "25% 0.0 \n", + "50% 0.0 \n", + "75% 0.0 \n", + "max 0.0 \n", + "\n", + "Cluster 1 characteristics:\n", + " CustomerID Age Annual Income (k$) Spending Score (1-100) \\\n", + "count 57.000000 57.000000 57.000000 57.000000 \n", + "mean 53.438596 25.438596 40.000000 60.298246 \n", + "std 36.936730 5.707193 17.031483 18.434212 \n", + "min 1.000000 18.000000 15.000000 6.000000 \n", + "25% 21.000000 21.000000 24.000000 48.000000 \n", + "50% 48.000000 24.000000 40.000000 56.000000 \n", + "75% 88.000000 31.000000 57.000000 73.000000 \n", + "max 121.000000 38.000000 67.000000 99.000000 \n", + "\n", + " Cluster \n", + "count 57.0 \n", + "mean 1.0 \n", + "std 0.0 \n", + "min 1.0 \n", + "25% 1.0 \n", + "50% 1.0 \n", + "75% 1.0 \n", + "max 1.0 \n", + "\n", + "Cluster 2 characteristics:\n", + " CustomerID Age Annual Income (k$) Spending Score (1-100) \\\n", + "count 38.000000 38.000000 38.000000 38.000000 \n", + "mean 160.552632 39.368421 86.500000 19.578947 \n", + "std 23.885648 10.617225 16.761845 11.684204 \n", + "min 113.000000 19.000000 64.000000 1.000000 \n", + "25% 141.500000 34.000000 75.250000 10.500000 \n", + "50% 161.000000 40.500000 79.500000 17.000000 \n", + "75% 180.500000 46.750000 96.000000 27.750000 \n", + "max 199.000000 59.000000 137.000000 42.000000 \n", + "\n", + " Cluster \n", + "count 38.0 \n", + "mean 2.0 \n", + "std 0.0 \n", + "min 2.0 \n", + "25% 2.0 \n", + "50% 2.0 \n", + "75% 2.0 \n", + "max 2.0 \n", + "\n", + "Cluster 3 characteristics:\n", + " CustomerID Age Annual Income (k$) Spending Score (1-100) \\\n", + "count 65.000000 65.000000 65.000000 65.000000 \n", + "mean 69.415385 53.984615 47.707692 39.969231 \n", + "std 34.295541 9.418221 14.648723 16.405953 \n", + "min 7.000000 35.000000 18.000000 3.000000 \n", + "25% 43.000000 48.000000 39.000000 32.000000 \n", + "50% 71.000000 52.000000 49.000000 46.000000 \n", + "75% 94.000000 63.000000 60.000000 51.000000 \n", + "max 161.000000 70.000000 79.000000 60.000000 \n", + "\n", + " Cluster \n", + "count 65.0 \n", + "mean 3.0 \n", + "std 0.0 \n", + "min 3.0 \n", + "25% 3.0 \n", + "50% 3.0 \n", + "75% 3.0 \n", + "max 3.0 \n" + ] + } + ], + "source": [ + "# Analyze Cluster Characteristics\n", + "for i in range(optimal_k):\n", + " print(f\"\\nCluster {i} characteristics:\")\n", + " print(data[data['Cluster'] == i].describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1e089fc9-c630-49cd-9a5e-4fd65da4804e", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the resulting data with clusters\n", + "data.to_csv('customer_segments.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "2030cf5e-c98a-445b-a2dd-9eab8610145b", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "By following the steps outlined in this project,we created a customer segmentation tool using K-means clustering in Python. The tool will help businesses better understand their customers and optimize their marketing strategies based on the segments identified. we can further explore the dataset, experiment with different clustering algorithms, or apply this approach to other datasets to enhance the analytical capabilities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64dcc0af-9afd-4ccf-9957-6e6b5b01716a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Prediction Models/ClusterLogic Model/ClusterLogic Model.png b/Prediction Models/ClusterLogic Model/ClusterLogic Model.png new file mode 100644 index 000000000..aba9ad6e8 Binary files /dev/null and b/Prediction Models/ClusterLogic Model/ClusterLogic Model.png differ diff --git a/Prediction Models/ClusterLogic Model/Mall_Customers.csv b/Prediction Models/ClusterLogic Model/Mall_Customers.csv new file mode 100644 index 000000000..b324941f4 --- /dev/null +++ b/Prediction Models/ClusterLogic Model/Mall_Customers.csv @@ -0,0 +1,201 @@ +CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100) +0001,Male,19,15,39 +0002,Male,21,15,81 +0003,Female,20,16,6 +0004,Female,23,16,77 +0005,Female,31,17,40 +0006,Female,22,17,76 +0007,Female,35,18,6 +0008,Female,23,18,94 +0009,Male,64,19,3 +0010,Female,30,19,72 +0011,Male,67,19,14 +0012,Female,35,19,99 +0013,Female,58,20,15 +0014,Female,24,20,77 +0015,Male,37,20,13 +0016,Male,22,20,79 +0017,Female,35,21,35 +0018,Male,20,21,66 +0019,Male,52,23,29 +0020,Female,35,23,98 +0021,Male,35,24,35 +0022,Male,25,24,73 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b/Prediction Models/ClusterLogic Model/README.md @@ -0,0 +1,108 @@ +# πŸ›οΈ ClusterLogic Model + +

+ Customer Clust Segmentation Tool +

+ + +## πŸ“š Table of Contents +1. [Overview](#-overview) +2. [Features](#-features) +3. [How It Works](#-how-it-works) +4. [Tech Stack](#-tech-stack) +5. [Installation](#-installation) +6. [Usage](#-usage) +7. [Visualizations](#-visualizations) +8. [Machine Learning Models](#-machine-learning-models) +9. [Goals](#-goals) +10. [License](#-license) +11. [Contact](#-contact) + + +## πŸ“‹ Overview +ClusterLogic Model is a powerful customer segmentation tool designed to categorize customers based on their purchasing behavior, preferences, and demographic characteristics. By leveraging advanced data analytics and machine learning techniques, this tool helps businesses: + +- πŸ“ˆ Enhance marketing strategies +- 🧠 Improve customer understanding +- βš™οΈ Optimize resource allocation +- πŸš€ Drive business growth +- πŸ’‘ Foster a data-driven culture + +## πŸ” Features +- **Segmentation**: Classifies customers into distinct groups for targeted marketing. +- **Behavioral Insights**: Provides valuable insights into customer preferences and purchasing habits. +- **Visualization**: Interactive charts and graphs for easy interpretation of customer segments. +- **Advanced Metrics**: Incorporates KPIs to measure the impact of different segments on business growth. + +## πŸ§‘β€πŸ’» How It Works +1. **Data Collection**: Input customer purchase history, preferences, and demographic data. +2. **Data Preprocessing**: Clean and preprocess the data for machine learning models. +3. **Modeling**: Apply clustering algorithms like K-Means or Hierarchical Clustering to identify customer groups. +4. **Evaluation**: Analyze the results using metrics like silhouette score or within-cluster sum of squares (WCSS). +5. **Visualization**: Visualize the segmentation results using intuitive dashboards. + +## πŸ› οΈ Tech Stack +- **Languages**: Python 🐍 +- **Libraries**: + - pandas πŸ“Š + - numpy πŸ”’ + - scikit-learn πŸ“š + - matplotlib πŸ“‰ + - seaborn πŸ“ˆ + +## πŸš€ Getting Started + +### Prerequisites +- Python 3.8+ +- Jupyter Notebook +- Required libraries in `requirements.txt` + +### Installation +Clone this repository: +```bash +git clone https://github.com/yourusername/ClusterLogic.git +cd ClusterLogic +``` +Install the necessary dependencies: + +```bash +pip install -r requirements.txt +``` +### Usage +Run the Jupyter notebook to explore the data and generate customer segments: + +```bash +jupyter notebook notebooks/ClusterLogic_Model.ipynb +``` +To run the segmentation pipeline as a script: + +```bash +python scripts/segment_customers.py +``` + +## πŸ“Š Visualizations +The tool provides insightful visualizations to help you understand customer clusters and trends, such as: + +- πŸ“‰ **Purchase trends over time** +- 🧩 **Segmented customer behavior** +- πŸ—ΊοΈ **Demographic distribution maps** +- 🎯 **Targeted marketing groupings** + +## 🧠 Machine Learning Models +ClusterLogic Model uses unsupervised learning techniques, primarily focusing on: + +- **K-Means Clustering**: For grouping customers into meaningful clusters. +- **Hierarchical Clustering**: To provide more granular segmentation if needed. + +## πŸ† Goals +- Improve customer retention and acquisition. +- Maximize marketing campaign efficiency. +- Tailor product recommendations to specific customer segments. + +## πŸ›‘οΈ License +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. + +## πŸ’¬ Contact +For more information or queries, feel free to contact the project maintainers at: [alolikabhowmik72@gmail.com] + +