Lab09/Regularization1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e946f79d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53e241fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "396f6c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"E:/Datasets_for_ML/diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7279eb21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "092c2622",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(['Outcome'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ceeaab3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e7feefc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc19cb93",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e25b8e9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(32, input_dim=8, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4ac7dde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7b8dfe3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "20/20 [==============================] - 1s 7ms/step - loss: 1.4459 - accuracy: 0.5489 - val_loss: 0.9833 - val_accuracy: 0.4805\n",
      "Epoch 2/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.8755 - accuracy: 0.5847 - val_loss: 0.7797 - val_accuracy: 0.5714\n",
      "Epoch 3/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.7395 - accuracy: 0.5912 - val_loss: 0.6608 - val_accuracy: 0.6623\n",
      "Epoch 4/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6841 - accuracy: 0.6059 - val_loss: 0.6651 - val_accuracy: 0.6753\n",
      "Epoch 5/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6501 - accuracy: 0.6564 - val_loss: 0.6487 - val_accuracy: 0.6883\n",
      "Epoch 6/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6314 - accuracy: 0.6645 - val_loss: 0.6474 - val_accuracy: 0.6883\n",
      "Epoch 7/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6162 - accuracy: 0.6531 - val_loss: 0.6351 - val_accuracy: 0.6494\n",
      "Epoch 8/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6031 - accuracy: 0.6710 - val_loss: 0.6578 - val_accuracy: 0.6948\n",
      "Epoch 9/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6159 - accuracy: 0.6873 - val_loss: 0.6580 - val_accuracy: 0.6623\n",
      "Epoch 10/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5848 - accuracy: 0.6743 - val_loss: 0.6470 - val_accuracy: 0.6883\n",
      "Epoch 11/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5963 - accuracy: 0.6857 - val_loss: 0.6928 - val_accuracy: 0.6494\n",
      "Epoch 12/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6149 - accuracy: 0.6710 - val_loss: 0.7099 - val_accuracy: 0.6494\n",
      "Epoch 13/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6034 - accuracy: 0.6743 - val_loss: 0.6267 - val_accuracy: 0.7078\n",
      "Epoch 14/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5654 - accuracy: 0.7329 - val_loss: 0.6494 - val_accuracy: 0.6688\n",
      "Epoch 15/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6134 - accuracy: 0.6987 - val_loss: 0.6452 - val_accuracy: 0.6688\n",
      "Epoch 16/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5926 - accuracy: 0.6775 - val_loss: 0.6251 - val_accuracy: 0.7013\n",
      "Epoch 17/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5592 - accuracy: 0.7215 - val_loss: 0.6210 - val_accuracy: 0.7273\n",
      "Epoch 18/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5748 - accuracy: 0.6922 - val_loss: 0.6390 - val_accuracy: 0.7078\n",
      "Epoch 19/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5744 - accuracy: 0.7036 - val_loss: 0.6705 - val_accuracy: 0.6623\n",
      "Epoch 20/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5811 - accuracy: 0.7166 - val_loss: 0.6419 - val_accuracy: 0.6818\n",
      "Epoch 21/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5468 - accuracy: 0.7215 - val_loss: 0.6229 - val_accuracy: 0.6494\n",
      "Epoch 22/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5652 - accuracy: 0.7134 - val_loss: 0.7009 - val_accuracy: 0.6299\n",
      "Epoch 23/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5973 - accuracy: 0.7020 - val_loss: 0.6166 - val_accuracy: 0.6883\n",
      "Epoch 24/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5492 - accuracy: 0.7150 - val_loss: 0.6129 - val_accuracy: 0.7143\n",
      "Epoch 25/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5423 - accuracy: 0.7345 - val_loss: 0.6251 - val_accuracy: 0.7078\n",
      "Epoch 26/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5514 - accuracy: 0.7231 - val_loss: 0.6717 - val_accuracy: 0.6948\n",
      "Epoch 27/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5440 - accuracy: 0.7166 - val_loss: 0.6129 - val_accuracy: 0.6883\n",
      "Epoch 28/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5551 - accuracy: 0.6987 - val_loss: 0.6318 - val_accuracy: 0.7078\n",
      "Epoch 29/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5618 - accuracy: 0.6938 - val_loss: 0.6903 - val_accuracy: 0.6494\n",
      "Epoch 30/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5863 - accuracy: 0.7134 - val_loss: 0.6508 - val_accuracy: 0.7143\n"
     ]
    }
   ],
   "source": [
    "\n",
    "hist=model.fit(X_train, y_train, epochs=30, batch_size=32, verbose=1, validation_data=(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "73992a91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 1ms/step - loss: 0.6508 - accuracy: 0.7143\n",
      "Accuracy: 0.7142857313156128\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c0a85361",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "70a5f0e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 940us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, np.round(y_pred))\n",
    "\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "02a81ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iTUd1Z5LfBsaXc/1uYIzjOL2Bx4FXq/l4LuPv7UGwn6c2FBERERFpAjyqc2PHcRYYY6LKuX7JCZ8uAyKr83iuFhnspzZwIiIiIk1AXdYk3wx8c7orjTG3GmNWGmNWHjlypA6HVXERQb4qtxARERFpAuokJBtjzsKG5D+d7hjHcV51HGeg4zgDw8PD62JYlRYZ7MuB5CwcR72SRURERBqzWg/JxpjewOvAxY7jJNX249WmiGBfsvMKScrIdfVQRERERKQW1WpINsa0Az4HrnccZ1ttPlZdKGkDp7pkERERkUatWgv3jDEfAWOBMGNMHPAI4AngOM7LwMNAKPCSMQYg33GcgdV5TFeKDC5uA5dFn7ZBrh2MiIiIiNSa6na3mHKG628BbqnOY9QnEcHqlSwiIiLSFGjHvUoI9PEk0MdDu+6JiIiINHIKyZUUoV7JIiIiIo2eQnIlRQb7auGeiIiISCOnkFxJxRuKqFeyiIiISOOlkFxJkcG+ZOQWkJKV5+qhiIiIiEgtUUiupOJeyapLFhEREWm8FJIrKVJt4EREREQaPYXkSjpxQxERERERaZwUkiupua8n/l7uCskiIiIijZhCciUZY4gM9tOGIiIiIiKNmEJyFUQG+2omWURERKQRU0iugohgXy3cExEREWnEFJKrIDLYl7TsfPVKFhEREWmkFJKrICLI9krW9tQiIiIijZNCchUUt4HT4j0RERGRxkkhuQoitKGIiIiISKOmkFwFof5e+Hi6qdxCREREpJFSSK6C4l7JagMnIiIi0jgpJFdRRJAvcckqtxARERFpjBSSqygy2FflFiIiIiKNlEJyFUUE+3IsM4+MnHxXD0VEREREaphCchVFBhf1SlYbOBEREZFGRyG5iiKC1AZOREREpLFSSK6itsUbiqguWURERKTRUUiuorBm3nh5uKkNnIiIiEgjpJBcRW5uxraBU0gWERERaXQUkqshMtiXOC3cExEREWl0FJKrISLIlwNauCciIiLS6CgkV0NksC+J6blk5xW4eigiIiIiUoMUkquhuFey6pJFREREGheF5GqICFavZBEREZHGSCG5GiKLeyVr8Z6IiIhIo6KQXA0tAnzwcDMqtxARERFpZBSSq8HdzdAmyFe77omIiIg0MgrJ1WQ3FFFNsoiIiEhjopBcTZHBvqpJFhEREWlkFJKrKTLYj8OpOeTkq1eyiIiISGOhkFxNxW3g4pOzXTwSEREREakpCsnVVNIGTov3RERERBoNheRqigjShiIiIiIijY1CcjW1bu6Du5vR4j0RERGRRkQhuZo83N1oFeijDUVEREREGhGF5BoQEaxeySIiIiKNiUJyDYgM1q57IiIiIo2JQnINiAzy5VBqNnkFha4eioiIiIjUAIXkGhAZ7EehA4dS1CtZREREpDFQSK4BxRuK7FddsoiIiEijoJBcA7ShiIiIiEjjopBcA1o398UY1AZOREREpJFQSK4BXh5utAxQr2QRERGRxkIhuYZEBvtyIFk1ySIiIiKNQbVCsjHmTWNMgjEm9jTXG2PM88aYHcaY9caY/tV5vPrMbiiimWQRERGRxqC6M8lvA+PLuf4CoFPR263A/6r5ePVWZLAvh1KyyVevZBEREZEGr1oh2XGcBcDRcg65GHjXsZYBQcaY1tV5zPoqIsiP/EKHw2k5rh6KiIiIiFRTbdckRwD7T/g8ruiyUxhjbjXGrDTGrDxy5EgtD6vmqQ2ciIiISONR2yHZlHGZU9aBjuO86jjOQMdxBoaHh9fysGpecUiO04YiIiIiIg1ebYfkOKDtCZ9HAvG1/Jgu0SaoOCRrJllERESkoavtkDwbuKGoy8VQIMVxnIO1/Jgu4ePpTniAt8otRERERBoBj+rc2BjzETAWCDPGxAGPAJ4AjuO8DMwFJgA7gEzgpuo8Xq2aez8U5MFFz1b5LiKCfIlTr2QRERGRBq9aIdlxnClnuN4B7qzOY9SZ/BzY8Bmc/w/w8qvSXUQG+xJ7IKWGByYiIiIidU077hXrORnyMmD7d1W+i4hgXw4kZ1FYWObaRBERERFpIBSSi0WNhGYt7WxyFUUG+5FX4JCgXskiIiIiDZpCcjE3d+hxKWz/AbKrVjJR0itZdckiIiIiDZpC8ol6Xg4FObDl6yrdPFJt4EREREQaBYXkE0UOhKB2EDuzSjePCFZIFhEREWkMFJJPZIxdwLfzZ8hIrPTN/bw8CPH3UkgWERERaeAUkkvrORmcAtj0ZZVuHlnU4UJEREREGi6F5NJa9oSwLlUuuYgM9iXumBbuiYiIiDRkCsmlFZdc7F0CKQcqffOIIF8OHMvC7qMiIiIiIg2RQnJZek4GHNj4RaVvGhnsR05+IYnpuTU/LhERERGpEwrJZQmLgdZ9qlRyEVHSBk4lFyIiIiINlULy6fS8HOJXQ9LOSt0sMqR4QxEt3hMRERFpqBSST6fHpfb9xs8rdbMIbSgiIiIi0uApJJ9OUFtoNww2VK7kIsDHk+a+niq3EBEREWnAFJLL03MyHNkMhzdV6maRwbbDhYiIiIg0TArJ5el+CRg3iP2sUjeLCPJVuYWIiIhIA6aQXJ5m4RA9xna5qETf48hgPw4kq1eyiIiISEOlkHwmvS6HY3vgwOoK3yQi2JfM3AKOZebV3rhEREREpNYoJJ9J14ng7lWpnsmRwUVt4FRyISIiItIgKSSfiW8QxJxrW8EVFlToJtpQRERERKRhU0iuiJ6XQdpB2Le0Qoe3DfYD1CtZREREpKFSSK6ILheApx9sqFiXi0BfDwK8PbTrnoiIiEgDpZBcEV7+Nihv+hIKzrwYzxhDRLCvyi1EREREGiiF5IrqeTlkHYVd8yt0eGSweiWLiIiINFQKyRUVcw74NK9wl4uIIO26JyIiItJQKSRXlIc3dLsINs+BvDOH38hgP9Jy8knJUq9kERERkYZGIbkyek6G3DTY/sMZDy3ulay6ZBEREZGGRyG5MqJGg384xJ65y0VESUhWyYWIiIhIQ6OQXBnuHtD9Etj2HeSklXtoZFGvZNUli4iIiDQ8CsmV1etyyM+GLXPLPSzYzxNfT3fNJIuIiIg0QArJlRU5GAIjz9jlwhhT1AZONckiIiIiDY1CcmW5udltqnfOg8yj5R7ap20QC7cnciQtp44GJyIiIiI1QSG5KnpOhsJ82Dy73MPuGNuR3IJCXpq/o44GJiIiIiI1QSG5Klr3gdAY2FB+l4sO4c2Y3D+CD5btIz5ZtckiIiIiDYVCclUYY2eT9yyC1IPlHvrbczrh4PDfnzSbLCIiItJQKCRXVc/JgAObZpV7WGSwH1MGt+PTlfvZm5RRJ0MTERERkepRSK6q8C7QstcZSy4A7jorBnc3w3M/bq+DgYmIiIhIdSkkV0evyXBgJRzbU+5hLQJ9uHF4FF+sPcD2w+VvQiIiIiIirqeQXB09LrPvz9AzGeD2MR3x83TnmR+31fKgRERERKS6FJKrI7i93Vwk9vMzHhri78XNI6OZu+EQsQdS6mBwIiIiIlJVCsnV1XMyHI6FhC1nPPTmUR0I9PHgmR80mywiIiJSnykkV1ePS8G4VajkormvJ7eN6ci8LQms3nesDgYnIiIiIlWhkFxdAS0haiTEfgaOc8bDpw6PIqyZF099v7UOBiciIiIiVaGQXBN6Xg5Hd8HBtWc81N/bg9+MjWHxjiSW7Eys/bGJiIiISKUpJNeEbheBm2eFeiYDXDukHa0CfXjq+204FZh9FhEREZG6pZBcE/xCIOYc2PgFFBae8XAfT3fuPieGVXuPMX/rkToYoIiIiIhUhkJyTek5GVIPwP5lFTr8igFtaRviy5Pfb9VssoiIiEg9o5BcU7pMAA/fCnW5APDycOPeczqzMT6V7zYequXBiYiIiEhlKCTXFO9m0Pl82DgLCvIrdJNL+kXQMdyfp77fRkGhZpNFRERE6guF5JrU63LITITdv1TocHc3w+/P7cL2hHS+Whdfy4MTERERkYqqVkg2xow3xmw1xuwwxjxQxvXNjTFfGWPWGWM2GmNuqs7j1Xsx54J3YIVLLgAu6NmKbq0DeebHbeQVnHnRn4iIiIjUviqHZGOMO/AicAHQHZhijOle6rA7gU2O4/QBxgJPGWO8qvqY9Z6nD3SdCJu/gvycCt3Ezc1w37md2ZuUycxVcbU8QBERERGpiOrMJA8GdjiOs8txnFxgBnBxqWMcIMAYY4BmwFGgYgW7DVXPyZCTCtt/qPBNzunWgr5tg3h+3nZy8gtqcXAiIiIiUhHVCckRwP4TPo8ruuxELwDdgHhgA3CP4ziNu6agwxjwC61UyYUxhj+c14X4lGw+Wr6vFgcnIiIiIhVRnZBsyrisdIuG84G1QBugL/CCMSawzDsz5lZjzEpjzMojRxrwBhvuntD9Ytj6DeSkV/hmI2JCGdohhBd+3klmbuOebBcRERGp76oTkuOAtid8HomdMT7RTcDnjrUD2A10LevOHMd51XGcgY7jDAwPD6/GsOqBnpdDfhZs+7bCNymeTU5Mz+HdpXtrcXAiIiIicibVCckrgE7GmOiixXhXA7NLHbMPOAfAGNMS6ALsqsZjNgzthkFAG9jwWaVuNjAqhLFdwnn5l52kZefV0uBERERE5EyqHJIdx8kH7gK+AzYDnziOs9EYc7sx5vaiwx4HhhtjNgDzgD85jpNY3UHXe25u0PMy2PEjZB2r1E3vO7cLyZl5vLFody0NTkRERETOpFp9kh3Hmes4TmfHcTo6jvP3ostedhzn5aKP4x3HOc9xnF6O4/R0HOf9mhh0g9DzMijMs+3gKqFXZHPG92jFGwt3cywjt5YGJyIiIiLl0Y57taVNfwiOhlXvgFO5Lad/d25n0nPzeWVB469MEREREamPFJJrizEw8l44sBI2fFqpm3ZpFcCkPm14e8luEtKya2d8IiIiInJaCsm1qd/10KYffP9/kJNWqZveO64zeQUOL/28s5YGJyIiIiKno5Bcm9zcYcKTkH4Ifvl3pW4aHebP5f0j+XD5PuKTs2ppgCIiIiJSFoXk2hY5EPpeB8v+B4nbK3XT347rBMB/f6rc7URERESkehSS68K4R8DTF775Y6UW8UUE+TJlcFs+WRnHnsSMWhygiIiIiJxIIbkuNGsBYx+EnT/Blq8rddM7z4rB093w/DzNJouIiIjUFYXkujJ4OoR3g+8ehLyK1xi3CPThxmFRfLH2ANsPV27xn4iIiIhUjUJyXXH3hAn/huR9sPi5St309jEd8ffy4Jkft9XS4ERERETkRArJdSl6NPS4FBY9A8f2Vvhmwf5eTBsZzdwNh4g9kFKLAxQRERERUEiue+f9DYwbfPfnSt3sllHRNPf15OkfNJssIiIiUtsUkuta80gYdR9smWMX8lVQoI8nt43pwE9bEli191gtDlBEREREFJJdYfjdEBwN3/wJ8nMrfLOpw6MIa+bFHz9bx261hBMRERGpNQrJruDhDeOfgMRtsPzlCt/Mz8uD56f0Iykjl0kvLGLe5sO1OEgRERGRpksh2VW6jIdO58Mv/4K0QxW+2fCOYXx110jahfhx8zsrefqHbRQWVnyDEhERERE5M4VkVxr/TyjIhR8ertTN2ob4MfM3w7l8QCTPz9vOtHdWkJxZ8bINERERESmfQrIrhXa09cnrP4a9Syt1Ux9Pd/5zeW/+dklPFu9I5KIXFrEpPrWWBioiIiLStCgku9qo+yAwAr65HwoLKnVTYwzXDW3Px7cNIze/kMv+t5gv1sTV0kBFREREmg6FZFfz8re9kw9tgFVvVeku+rcLZs7do+gTGcTvPl7Ho7M3kptfWMMDFREREWk6FJLrgx6XQtQo+OlvkHm0SncRHuDN+7cM4ZaR0by9ZA/XvLaMhNTsGh6oiIiISNOgkFwfGAMX/BuyU2HeY1W+G093Nx6a2J3np/RjY3wqF/53ESv3VC10i9QLjgOJOyBb27GLiEjdUkiuL1p2h8G3wqq3IX5tte5qUp82zLpzBP5e7lz96jLeXrwbx1GbOGkACgtt6dHyV+Dj6+E/MfDCAHhzvH0RKSIiUkdMfQxPAwcOdFauXOnqYdS9rGT47wAI6QDTvgO36r2GScnK475P1vLj5gQu7RfBPy7tha+Xe82MVaQmFOTDoXWwdwnsWQz7lhyfNW7eDtoPh9AYmP9PiDkHrv4I3D1cO2YREWk0jDGrHMcZWNZ1+m9Tn/gGwbl/hS/vtG3h+k6p1t019/Xk1esH8uLPO3j6x21sOZTGK9cNoF2oX82MV6Sy8nMgfg3sWWSD8f7lkJturwvpCN0vhvYjbDgOanf8dv5hMOde+P4huOAJlwxdpMFyHFg3A1r3hpY9XD0akQZDM8n1TWEhvHEuJO+Du1eBT2CN3O3PWxO4d8ZaHMfhuSn9OKtLixq5X5Fy5WbCgZV2lnjvYohbAflFC0rDu0FUUSBuPwICWpV/X9/+GZa9CBc+BYNuqf2xizQWi56BHx8FvzCY/hMEt3f1iETqjfJmkhWS66MDq+C1c2DYnXD+32vsbvclZXL7+6vYfCiV343rzF1nxeDmZmrs/kXIzYB9S4tC8RL7s1yYB8YNWvU6Pkvcbjj4h1buvgsLYMY1sP0HuPZTW34hIuVb+yHM+g10Oh/2L7N9+W/+HrwDXD0ykXpBIbkhmn23/eN2+2Jo0bXG7jYrt4C/fLGBz9ccYFy3Fjx1ZV+a+3rW2P1LE+U4sPFzmPtHyEwENw9o069olngktBsCPs2r/zg5aXYRX/I+uPmHGv3dEGl0tv8AH14FUSPtC8u9i+H9y6HTuXD1h+CmNSpSBXsW2x2Dz3T2r4FQSG6IMhLhv/2hdV+44UvbJq6GOI7Du0v38vicTUQG+/Ly9QPo2qpmyjqkCUo9CF//HrbOtcH47Ieg3TC7UU5tSN4Pr50Nnr721LF/WO08jkhDFrcS3rnILnyd+vXx0r1fX4O5f4Dhv4XzHnftGKVhKciHHx62ZW+efjDiXhh+V+39ra8j5YVktYCrr/zD4KyHYPcvsHl2jd61MYYbh0cx49ahZOYWcOmLS/hg+V7yCrRLn1SC48Cqd+DFIbDzJ7tz5M0/Qsy42v2jGdQWpsyA9MMw41q7GFBEjkvcDh9cAc1awHUzT17bMni6relf8jys+cB1Y5SGJTsFPrrKBuSBN0On82D+P2xHrrUf2vVUjZBmkuuzgnx4ZTTkpMKdv4JXzXelSEjN5u6P1rB891Eigny5fWxHrhgQiY+nTsNJOY7uhq9+C7sX2HKKSc/b0291aeMX8OlU6H0VXPpKjZ5tEWmwUg/CG+dBXqatPS7r97IgDz643J42v/EraD+s7scpDUfSTvjoaji6yy6cHjDVXr5vGXz3Z7v2pHUfOO/vED3KpUOtCs0kN1TuHjDhP5Cy365OrgUtAn2YcetQ3rppEC0Dvfm/WbGM/vfPvL5wF5m5+bXymNKAFRbA0hfhpWFwYA1MfNb+k63rgAx2O/ezH7LtEhc+WfePL1LfZKfY8JuZZGuQT/d76e4JV7xtu1x8fC0c21OXo5SGZPcCeP0cWwJ6w5fHAzJAu6H27OHkNyDzKLwzET66xu6S2khoJrkh+Oxm2PwV3LkcQqJr7WEcx2HpriRe+GkHS3YmEeLvxc0jo7l+WHsCfbS4r8lL2Axf3mVbunU6HyY+A80jXDsmx4EvbrNB+fK3oOdlrh2PiKvkZcP7k20Hi2s+qVj3l8Qd8PrZENDGzjrXUMtRaSRWvglz77d17VNmlJ8/8rJg2f9g4dOQn2VLesb8CfxC6m68VaSFew1dajz8dyB0GANTPqqTh1y19xgv/ryDn7YkEODjwdThUdw0IpoQf686eXypR/Jz7ZmMBf+x/0Qv+Df0nFx/yhvyc+CdSXBwLUydC5EDXD0iqY/ysmDzHNt6MHpstXc0rVcKC+Czm2DTl3DZa9D7yorfdtd8eO8yu5ZgykfqeFERhzfBD/8HHc6yrVrry9/CmlKQb8sofn3F1h5PfqPiL6DSE+Dnf8Dqd2ybwTF/gkHTwaP+ZgeF5MaguBn8tZ/Z9j11JPZACi/+vINvYg/h5+XOdUPbc8uoaFoE+NTZGMSFDqyCL++GhI3Q83K44F/1s5tERqLteJGXZTteBLV19Yikvkg/AitegxWv2zIEgOBoGHgT9L22fv48V4bj2Nm+Fa/ZmtDhd1X+Pla8YTvUDLurRnvzNzoFebD4Wfjl30Wf59qfoYnPgIe3S4dWY7KOwac3wa6f7c/DuY9V7YVTwma7Q+qOH+3v27mPQbeL6uULCoXkxiA/x9aBAtyxtOxfSMexizWyU4reUk/4OPn4xzknXl705hti64vaDYO2Q07Z6GHb4TRe+nkHs9fF4+HuxtWD2nLbmI5EBPnW/tcudS83065cXvoiNGsFE5+GLhe4elTlS9hid6sMagfTvtVmCU3dka2w9AVY9zEU5EDnC2Do7TY0r3wT9i0Bdy+7FfrAm+3fv3r4D/yMFvwHfvobDL/bdpipqrn3w6+vwqT/Qv8bam58jcWhWPjyDji4DnpcZs+orXwD5v/T/t+86v2G/4IrcYftYHFsrw3+/a+v/n3u+BG+ewiObLabSJ3/N4ioX2f7FJIbi+0/wgeTocNY8A4sO/QWnmGxnYePva1P8xPeAu2K6PjV9pUxQFiX46G53VAIjgJj2JOYwcu/7GTm6jgcByb3j+Q3YzsSFdaw+yTKCXYvtJ0rju6yizTOfaxmNgKpCzvm2dZXOnXcNDmOXWi09AXY/r39e9dnij0lHtbp5GMTNsPKt2DdR/ZvaHg3GDgN+lzVcH7eV79rN57qfRVc8nL1SkgK8uHDK+zv/w1f2i3jxc4eL3zavhjxDYILn4buk45fHzsTZt1h2+1N+RhadnfZUKtl58/w6Y12I6irPqjZjicF+bDmPfj575BxxP68nvMwNI+suceoBoXkxuTLO21Y9g0qI+yWCr4+zcEn6Phl3oHgWU6ZRF42xK+x2wrvW2YXgGSn2OuatTwpNB/wieHVhXv5aMV+8gsKmdSnDXecFUPnlk149q4g385ObfkaUuLsZSW/X071PgY72x/W2f6zD+tsF1PUZFvA7BT44RFY9ZY9PTbpeYgeXXP3X1dWvA5f3wdD74Tx/3D1aKQu5OfaHR+XvgCHNoB/OAy+1YbeM83u5WZA7Od2VjB+jd0koedke9uI/nUz/qrY+o3dpr3DWBvOaqLmMysZXh9ny1Km/1SrC8UbhIPr7ezxoQ3Q6wo7e1zWQrQDq2xXh9x0uPxN6Hx+3Y+1On59Db75E4R3sQv0gtvXzuNkp9pylSUv2LM2w+6Ckfe6/KyfQrJUTWEhHNlyPDTvWwYp++x1nv7QdhAZLQcy+2g7ntrcnMRcT8b3aMVdZ8fQM6KBzMRUV36u3fBl05d2x7nMJDt7FdIBMCecvjVQcia3+PKiCyryMUBGgj0Nxgm/s83bnhyawzrbt4BWlTt1vO07+OpeSD8EQ++As/5SK32568w3f4LlL9tThgOnuXo0UluyjsGqt2H5K5B2EMK72lnjXleWPyFwOgdW2xeJGz6zpWut+8Kgm21ork+7iu3/1S5WbdEVbpwD3s1q7r6Tdtr6/oBWduv3ptjxIj/XtpVc+BT4hdq/I10vLP82KQdgxhQbrM/7W8NY0FeQZ/9WrnzDliNNfq1uAmvyPpj3GGz4FPxbwNl/gX7Xu+zMn0Ky1JyUA3aGed8yG54PxQIOjnHnsF8nfsjowJLcTnhHD+PiUQMY0iEEPy8PV4+6ZuVmws55sGk2bPvWnqr1CrCzB90usgsra+sfal42HN0Jidts/VjitqK37ZCXcfw4rwAIizk+8xxaFKRDOpwcHjIS4dsH7B+rFt1h0guNoztEYYFtfr9jnt1xrONZtf+YGUn2Z6H4rI17I/u5r0+O7rbtpta8b3/uo8fYmtyYcTUTTLJTYP0ndkHbkc32+exzNQy4yfWn049shTfPB99gmPY9NAuv+cfYvQDeu9R2b7jm46ZVthS/1p6xPRwLva+G8f+seBuz3Az44na7S26/621pRn3t6pB51JZX7F5gt5c+5+G6f57jVtkuGvuXQYsecMETLjl7qZAstSc7BeJWlMw0O3ErMflZAGQ63uTjDm4euHt44unlhaenF8bNwzazd/M4/lbm5+7g5nny5/4t7D+pFj3szGldBZHsVDvbunm2XYiQl2n/SXW50NanRY+p2sxVTXEcO5NWHJhL3m+H1Ljjxxk3CGpvg3NwlK2ny06FUffZt/r6B70qslNtmEg5ALf8COGda/4xkvfbHuabv7IvGk+c5ff0t7NwJWVRxR8HHg/SJ5ZCnXR5oH2h05jalNWE/b/Ckv/Cljlg3KHX5fbMR+vetfN4jgP7l9uwvGmWXbPRbpg9O9H94rrvaJBywO6mV5Br+xrXZjnEyjdhzu+aTtlSfo6tO174tC3XuejZqi1WLiy0i54X/Afaj4Ar3ztlIbzLHdlqJxFS4uCi56HvFNeNxXHs/9UfHrYvdAfdUudDUEiWupOfC4fWk7dnCYfi9nDoWBqHkzNIzcjEg0J8PQpp6e9By2buhPm54+fhYArz7WmfwgIozLOLDwvy7fuTPs+zM59OgX0sdy+7wLBldzsL2rKHfR/YpmZmkzKSbAnF5tm2l2hBru300G2inTFuP7JhzBbmpEPSDvt24sxz0g5o1Qsues5+7xqj5H321LGXP9zyU838s0rcYX8mNs+2NawALXvan4mgdjacn7iYtuTj1JM/Lsg5wwMZG5hbdIWYc6HTOGjVp+kF58IC+yJk6Qv2BblPcxtSB99qf9frSkYSrP3AlmMc3WVPw/e91i5urYsdJ7OOwZsX2GBz09d2G+DaVly2dNHzMODG2n88Vzmw2s4eJ2yyz+n5f7eTINWx/lN7n4Gtbc14i641M9bq2v6j7ant4W0X6LUb4uoRWfk59oWvC/6nKiSLyyWl57B0VxKLtieycHsiB5LtbHPr5j6MiAljZEwYI2LCCA84w8xMXrYNeQmb4PDGovebIC3++DE+ze1M80nhuVvFVqynHrSzVJtnw57FNpAHtYNuk+xb5KDGE1Icp/7XzNWE/Svg7Qtt26EbZlV+9s9x7M/a5tk2rCVsspdHDCj6ubioaiEpL7soNKdCzgktG4svK27dGLfieBj3D7clBTHjoOPZDWI3qyrLSbflFMteguS99szH0Duh7zU1W4NbWYWFdh3CyjftIl2nwP4sdDjLlvVEDq75MzJ5Wbb8IW6lLR/qMKZm7/90CvLhwyvt13v9LIgeVTePW1fysuGXf8Hi5+zi9EnP1+w+BHEr4aMpkJ9tF/TV4R4Hp3Ac+4Lnuz/b/49TPrT/20QhWeoXx3HYdzSTRTsSWbwjkcU7kkjJygOga6uAktA8ODoEf+8KvqrMPGpbOp0YnhM228BRLDDy1FnnsM42YBefMt+/3B4b1vl4AGrdp2mEycZsw2cw82bbDuyS/535+SwstC0Ri4Px0V22VKXdcPsz0W1i3bYvSj9i6+C3/2DfZx2z44kYYGeZY8ZBm34N+wVcfq7d8nz3Qtiz0JZWFOTYvu3D7rILp+pbbWzqQTu7vP17G4icAltmEzXSBuYOZ9mOAdX5+1FYAJ/cYAP55W/YRYR1KTsFXj/XLhye/lPRouRGIG6lbd2WuNXWD5//99pp/Ze83wblhI1w/j9gyO11//8kPxfm3mdbBnadCJe+4toXmvWMQrLUawWFDhvjU0pC84o9x8jNL8TT3dCvXXDJLHOfyOZ4uFciBDgOpOy3M80JG4veb7Iz0cX9pI378fKNVr1tMO4+yf5jk8Zl/hO28f85D9v669IKC2xd8abZ9mxC6gFbDx89xv5MdLmwdhZJVVZhgZ1Z3v4D7PjBnirGsaf/O55jZ6s6nlP/6iBLK8i3W4nv/sUG4/3Lba0/xpYBRY+G7pdA20EuHmgFZafAnkWw8yfbc/boTnt5QBvbpq3j2fZ9ZX6GHMfWBa96C8b/y26G4gpHd9myJf8WcMsPDaePdFnysm3N8JL/QkBrO3scM652HzMnHb64zf5dGTAVJjxp19nUhYwk+yJr7yIY9Qfbuaghv5iuBQrJ0qBk5xWwcs+xktAcG5+C40CAtwcDooLp2zaIfu2C6RsZRHO/Kvyhyc+FpO3HZ559g+2r66beE7SxcxyYeQvEfgZXvAM9Lilq4bfAzhhv+RoyE20Lv5hxdsa48/nVr02sbRmJNpTt+MF288hMBIzt8Rszzs40R/R3/SxsYYHtN7tnof2e710KuWn2uhbdIWqUPZ3ffkTjKCNJ3mefl50/2RcCWcfs5S17QcexNjS3Gwae5exaWvzCbuTvYNyjdTHq09u9EN67xL5ovOaThrEeo7T9v9o64cRtRRslPV53Le4KC+Gnx2HR0/Zn/cp3a+/nPD0Bdv1it5be/r0t37r4Reh9Re08XgOnkCwN2tGMXJbuTGLRjkRW7T3K9oT0kn02OoT507ddEP3aBtG3bTBdWwfgWZnZZmla8rLhnYtsWOs6wS5iyUkBr2ZFLfwm2WDZUE9FFhbCwTX269rxoy1fcArtRjQdzy4KzefY3cHqYixHNttwtXuBnckq3pwoNMbOFEeNsm/1YYa+NhUW2O2Md/1sg/O+ZXYhsru33dmsw1n2+WnZ8/gsX3F3ib7X2oBTH0q+Vr1jd+Mc8hvbrquhyMuyW3cvfdGWSU163n6/XWHdDLtLYmCEfbFRE113cjNh7xL787Vrvm1fB3YzsQ5jYMQ99W4r6PpEIVkalbTsPNbHpbB2fzJr9iWzdv8xEtPtdtreHm70imhO37ZBNjy3C6ZNcx9MffgHI/VDegK8ca4NbF0utDPGHca6toVfbck8amcydxSF5owj9nL/8OOt5krel2pTV971ZX2vHMd2Tdmz4HhdcWaSvS6ovZ0ljh5j63XrsitFfZSbYUNN8Uzzkc32cr8w+7MY2tG2EIsZB1d/WHen5ivi2wftYsqJz8LAm1w9mlPlZds644TNRetTNtuynowjtivKuY+5fIc39i23uyUW5MEVb9kXrpVRWGC/pp1FoXj/ctt9yd3L7ozbYax94dW6j+vPIDUACsnSqDmOQ9yxrJNCc2x8Krn5hQCEB3jb0Nw2iH7tgugdGUSzii4IlMYpP9cufGuIp4yrqrAQDq23C/+S95/QWaPU++ISiPK4e5Xq6dzMBuT0Q/b6wIjj5RNRo2pvm9vGIvWgDTs7f7LvMxIgYiDcOLt+7fQHtpb8o6vtrOX1X7hu6/rCAlsrXdzhqHix9tGd9uwJ2J/T8C62nKffda4ba1mS98GHV9tdbcc/AYOnl3+24Oiuop+Rn+2Zmexke3lx+U6HsXZhcUPeKdVFFJKlycnNL2TLodSi0GzfdifaHencDHRqEUC/djY494psTqcWAXh51M8yDcdxOJSaTYCPp8K91L7CgtMH6OI+z2Vd3jyyKBiPth0QdPamahzH1sw2j6x/AblYdord1CTtkO3Y4BtkZ2e9mtkXTt4BtmTJ06/6PweOYxfRlnQvKgrER7ae0Gvc2J+54u5FLbrZNmchHer3C+GcNJg5HbZ9AwNvhgv+dfysQeZRW8teHIyT99rLAyOOtxuMHl03pVONnEKyCHAsI5e1ccmsPSE4F7ee83Q3dAxvRvc2gXRvbd+6tQ4k2L9ud6DLzM1n2+F0thxMZfPBVDYfSmPLwVRSs/Np5u3BFQMjuWl4NO1CG/dsQWZuPr/uPsriHYks3ZWE40CnFs3o1DKAmBbN6NSiGe1C/CrX7aSBcRyHhdsTyc0vZERMGL5eOm0q9cjR3TYoZySc/hjjZneOLA7NJUE6oChMl74swL4wSI0/IRBvtusGigW0sSH4xEAc1qXhzqAWFsC8v9pezdFj7CLbXfPt9tg49vsUNaqoQ8pZtp5fL0BrlEKySBkcx2F3Ygax8TaQbip6n5B2fCe01s196HZCaO7eJpD2IX64uVXvj1RxiciWQ2lsPpjKlkOpbDmYxu6kjJJFif5e7nRtHUjXVgF0aRXAmn3JfLUunkLH4dzuLbl5ZAcGRQU3inrrvIJC1scls2h7Eot3JrJm3zHyChy83N3o3z4ILw93dhxOIz4lu+Q2Xu5udAj3LwrNAXRqacNz+1D/entWoKKS0nN4aFYs38Ta8gVvDzeGdwzl7G4tObtrCyKCyumIIFJXcjMh7aCdEc1Jg9z0oo9Tbduzky5LK+O49KJe9mXkkOJNoU4MxOFdG0fnk7Ks+QC+ugdw7KZVHc6ywThiQP2eDW8Eai0kG2PGA88B7sDrjuOcstzVGDMWeBbwBBIdxznjVkEKyeJKiek5J4XmzQfT2HEknYJC+7vi5+VO11YBJaG5W1GQ9fMq+w9ZRk4+Ww+nseXgyYE4LSe/5JioUD+6tgqka2t7v91aBRIZ7HtKGD+cms27S/fwwfJ9JGfm0SuiOTePjGZCr9YNKhg6jsO2w+ks2pHIkh2JLNuVREZuAcZAjzaBjIgJY0THMAZFhZw0g5qWncfOIxnsSEhne0IaOw6nsz0hnf3HMkteXHi4GdqH+pUE5+IQ3SHcHx/P+j8b+8Omwzz4+XpSs/L53bmd6RkRyE9bEpi3OYF9RzMBu+nO2V1bcE63FvRtG4x7NV+0ibiM49iFjCeG6YBWtodxI5gAqJSMJLtbo6sXFjYxtRKSjTHuwDbgXCAOWAFMcRxn0wnHBAFLgPGO4+wzxrRwHKecczOWQrLUN9l5BexISGdTfCqbDtq3zQdTScu2QdcYiA71p1tRuUZ+gVMSiPcePR7gArw96No64KRA3KVlQMV3FiySlVvA52vieGPRbnYdyaBloDc3DIvi2iHtCPKr2xKRijqQnMXi7Yks3ml3WUxMtzP2UaF+NhTHhDGsQ2iVSlyycgvYeSSdHQnpJQF6e0I6e5MyS17cuBloF+JHTFF4HtetBQPa159ZqdTsPB77ahOfrYqjW+tAnrmqD11bHe/h6jgOO49k8POWBOZtOcyKPccoKHQI9vPkrC4tOLtbC0Z1Cqe5bz3qhCAiUs/VVkgeBjzqOM75RZ8/COA4zj9POOYOoI3jOA9V5r4VkqUhKC6Z2HxCaN50MJX9R7MwBqJC/elWHIiLZp4jg31rtDyisNDhl21HeGPRbhbtSMTH043J/SOZNjKajuGu7fWbnHm8v/WSnUklCyfDmnmVzBQPjwklMrj2aglz8gvYk5hpQ/Ph4yF6V2I6eQUO47q15E/ju9CppWtnbhbvSOT+T9dxKDWbO8bG8NtzOp3xzEBKVh4Lth3hpy0J/Lw1geTMPDzcDIOiQji7qw3NHcL8G0U5johIbamtkHw5dob4lqLPrweGOI5z1wnHPIsts+gBBADPOY7z7mnu71bgVoB27doN2Lt3b5XGJeJqadl5uLuZ05Zf1JYth1J5c9FuZq2NJze/kLO6hHPzyA6MiAmt9aBUWGhfMGw7nMaKvUdZsiOpZKdEfy93hnQILZotDqVLywCXB7es3ALeXLybl+fvJCM3n8sHRPK7czvTunnd1vpm5RbwxDebeWfpXjqE+/PUFX3o167yO/wVFDqs2XeMn7Yk8NOWBLYcsm3cokL9OLtrS87p1oJBUSENqiRH6rc9iRl8tiqOnhHNGd+zlauHI1JltRWSrwDOLxWSBzuOc/cJx7wADATOAXyBpcCFjuNsK+++NZMsUnWJ6Tm8v2wv7y/bS2J6Ll1aBjBtZBQX942odk1ufkEh+45msr24rOGwLWvYeSSd7Dzbm9TT3dCvbXBJKO7TNqje7oJ4LCOXF37ewXtL92IM3DQimt+M6Vi17c4radXeY/zh03XsTszgphFR/PH8rjXWwSLuWGZRWUYCS3YmkZtfSDNvD0Z1CuPsri04r3urOvkapfFZs+8Yry7YxbcbD5WUkU3q04bHLu5Rb0u9RMrjynKLBwAfx3EeLfr8DeBbx3E+Le++FZJFqi87r4DZ6+J5c9FuthxKI9Tfi2uHtuf6oe0JD/Au97a5+YXsScpg++GiBXLFZQpHMsgtKCw5rk1zH2JaBtj2bC2a0allM7q2Cqx0jbWr7T+ayTM/bOOLtQcI9PHkzrM6csOwqFpZ6JeTX8BzP27n5V920rq5L/+5ojfDO4bV+OMUy8zNZ/GOpKJZ5sMcTs3B38ud64a25+aR0bQIbIQ7DUqNKix0mLclgdcW7OLXPUcJ9PHguqHtuW5oez5bFcfz87YT2syLJyb35qwu6tsrDUtthWQP7MK9c4AD2IV71ziOs/GEY7oBLwDnA17Ar8DVjuPElnffCskiNcdxHJbsTOKNRbv5aUsCXu5uTOrbhptHRhMd5s+uIxklQbg4FO85YcGbMdA22I9OLZoR07IZMeG2X3HHcH8CfBrXbOSm+FT+9e0Wftl2hDbNffj9eV24tF9EjXWP2BSfyu8/WcuWQ2lcOTCS/5vYvU6/h47jsD4uhTcW7WbO+ng83N24YkAkt43u2Oh7b0vlZecVMGvNAV5buIudRzKICPLl5pHRXDWo7UkvhGMPpPD7T9ay7XA6Uwa346ELuzW4F8riOtl5Bfy0JaGkF35dq80WcBOw7d3cgTcdx/m7MeZ2AMdxXi465n7gJqAQ2ybu2TPdr0KySO3YeSSdtxbv5rNVcWTnFeJmoCgL41a02DCmaEa4Uwu7cUfH8GZNbiOLJTsSeeLbLayPS6FrqwD+NL4rY7uEV7mWOr+gkFcW7OLZH7fR3NeLJy7rxbjuLWt41JWzJzGDVxbsYuaqOAoch4t6t+Y3Y2Po0krtp5q6lMw83l++l7cW7yExPYcebQK5dXQHLuzV+rQb+GTnFfDMD9t4deEuIoN9eeqKvgyOrj/dY6R+ySsoZPGORGavi+f7jYdJz8nnttEdeHBCtzofizYTEZGTJGfm8unKONJy8kvKJKLD/PH2aFphuDyO4/D1hoP857ut7E3KZEh0CA9c0LXSC+t2HUnnvk/XsWZfMhf2bs3fLu5Z5zs5ludwajavL9zFB8v3kZlbwLhuLbnjrI70r8ICwsYov6CQpIxcjqTllLwlpGXbj9NzSEzPpU9kc6aP6tDgS1fijmXyxqLdfLxiP5m5BYzuHM5tozswvGPFF/+u2HOU+z5Zx/5jmdwyMpr7zuvSIPqTS+0rLHRYufcYs9cdYO6GQxzNyCXAx4MLerZiUp8IhnYIcckuqgrJIiJVlFdQyIxf9/HcvO0kpucyoVcr/nBeFzqcocVeYaHDu0v38MS3W/D2cOfxS3oyqU+bOhp15R3LyOWdpXt4a/EeUrLyGNYhlDvO6sjImDCXdyOpaY7jkJaTf0LozSkzBCem55CUkUtZ/yYDfTwID/Cmua8n6+JScHczXD2oLbeN6djgdkSMPZDCqwt28fWGgxjsQrzpozvQrXXgGW9bloycfP4xdzMfLN9HpxbNePrKvvSKbF6zg5YGwXEcNsanMntdPHPWxROfko2PpxvndGvJpD5tGNsl3OWTMwrJIiLVlJGTz2sLd/Hagl1k5xdy9aC23HNOpzJnD+OOZfLHz9azZGcSY7uE86/JvWnZQGYZM3Ly+ejXfby6YBcJaTn0jmzOHWNjOK97y2pvx16evIJCthxMY/W+Y6zZd4y1+5NJzc7HPqTBGFsSZDD2vbGXmbIuA9xKPi4+zmCAtJw8jqTllHRjOZGXuxvhAd6EBXgT3syb8ABvWgTY98VvLQK8CWvmfdLs6N6kDP43fyczV8cBMLl/JHeMjanXdd6O47BgeyKvLtjJ4h1JNPP2YMrgttw0Ipo2NRTyf9l2hD9+to6k9FzuOjuGO8+KqbedbqRm7TySzuy18Xy1Pp5dRzLwcDOM7hzOpD5tGNe9Jc3qUc26QrKISA05kpbDCz9t54Pl+/B0d2P6qGimj+5AgI8njuPw6ao4HvtqE47j8H8Tu3PVoLYNciY2J7+Az1cf4OVfdrI3KZOYFs34zZiOTOrbpkaCzpG0HFbvO1YUipNZH5dcElxbBHjTr10Q4QHeOA442FDnOFBY8h4c7MeO4+BQdFnR9Q4OhYXHj7G19w4BPp428JYRgpv7elbruTqQnMUrv+xkxor9FBQ6XNynDXecFUNMC9du7HOi3PxCvloXz2sLd7HlUBotA72ZNiKaKUPaEVgLi0hTMvN4ZHYss9bG0yuiOU9f2cflm/dI7YhPzmLO+nhmr4sn9kAqxsCQ6BAm9Ynggp6t6lWZ2YkUkkVEatiexAye/H4rc9YfJMTfizvGdmTZrqP8uPkwg6NDeOqKPrQNqb8ziRWVX1DI3NhDvPTzDrYcSiMiyJfbxnTgyoFtK1xrmldQyOaDqazee4zV+5JZve8YcceyANtXu3ub5vRvF0T/dsH0axdERFDN7kxZ1xJSs3l1ga3zzs4vYEKv1tx9dsxJ24zXtbTsPD76dR9vLtrDodRsurQMYProDkzq06ZONpn5ZsNB/vzFBjJyC/jj+V2YNiK6Vs9MSN1ISs9hbuwhvlobz697jgLQO7I5k/q0YWLvNrRqXv/PoCkki4jUkvVxyTzxzRaW7EzCy8Ot0QYAx3H4eWsCL/68k1V7jxHWzItpI6O5bmj7U2YgE1KzWb0vmTVFM8Xr41LIybezxC0DvenfLti+tQ+iR5vmjXZhV1J6Dm8s2s27S/eSnpPPud1b8tuzO9VJfW5+QSHr4lJYsiORxTsTWb03mdyCQoZ1COXWMR0Y27nq3Vqq6khaDg9+vqHRvZBsalKz8/hx02G+XBvPoh2JFBQ6dAz35+K+EVzUpw3RYf6uHmKlKCSLiNQix3H4dfdRWgT6NLh/EJVV/LW+OH8nC7YdIcDbg+uH2Q1qVu9LZvXeYxxItrPEXu5u9IgIpF9bG4j7twuusXrXhiQ5M5e3l+zhzUW7Sc3OZ2yXcO4+O4YB7WuuRVphocPWw2ks2ZnEkh2JLN99lPScfIyB7q0DGd4xlIv6tKF3ZFCNPWZVOI7DZ0UlSYWOw0MTu3N1Ay1JakwKCx2OZuaespC1eBFrQloOiUWXpeXkAxAR5MvEPq2Z1KcN3VsHNtjnUCFZRERqXOyBFF6av4NvYu0Wxa2b+5SUTPRrF0yPNoGNdpa4KtKy83hv2V5eX7iboxm5DO8Yyl1nxzCsQ8VbrJ1oX1Imi3cmsnhHIkt3JpGUkQtAdJg/wzuGMiImjGEdQutlLeiB5Czu/3Rdg1zc2pBk5xWQkJrDkfTsovc59n1RC8PjnVxySzaQOpG/lzstAn1sDX/g8Vr+IdEh9G8X3CjOmCkki4hIrYlPzsIYaN286c0SV0Vmbj4fLt/HKwt2cSQth4Htg7n7nE6M7lR+u72EtGyW7kxiyY4kFu9MLKnrbhHgzYiYsJJg3FBm6xtam8T67FhGLjuOpLMjIZ2dCeklHx9IzjqlhaGbgdBmxxetlixebeZtA/EJXVyaws6JCskiIiL1THZeAZ+s3M/L83cSn5JNn8jm3HV2J8Z1a4ExhtTsPJbvOsriHYks2ZnItsPpgO3RPKwoEA/vGEbHcP8Ge6ob7IY7v/9kHWv32w13bhwWRYsAb1oEeuPn5bqQlldQSHxyFvuPZrH/WCZxxzJLPj5wLAtvTzdaBfrQMtCHVoE+tGpe9HFz+3mLQO8a7QFcWOhwIDmLncVh+EhGSSA+WnQWAcDbw40O4c3oGO5Px/BmRAT7ntTFJdTfG/dGMANcUxSSRURE6qnc/EI+Xx3HS/N3su9oJl1bBeDj6c6GAykUFDr4eLoxKCqE4R3DGBETSo82zRtdyDlx6/a8guO5JMDbg/BAO7PZIsCnJDy3LJnxtGE0wNuj0i8UCgsdDqdl2+B7NLMoCNuP445lcTAlixMrEDzcDG2CfGkb4ktEkC85+YUcSsnmcGo2h1Kzy+y9HeLvVRSivY+H6EAfWhYF6VaBPgT5ndx6MCe/gD2JmSVh2AbidHYdySArr6DkuCA/T2LCmxHTwr51LPq4TZBvo/v5qE0KySIiIvVcfkEhs9fF8+bi3Xh7uDOiYyjDY8Lo1y7I5buS1ZX45Cx2JKRzODW7ZAFZQpqtp00o+risMOrj6VYSoksCdKAN0eEB3qRm5bG/aCY4rigMHziWRW7B8fsyBloG+NA2xJe2wX5EhvjRNtiXyGA/2ob40irQ57TbJjuOQ2pWPoeKAvPhlOxTPj6cmk1ieu4pt/XycKNl0ViT0nPYdzTzpHAeEeR7Ugi2H/sT2sy7+t9wUUgWERGRhq94S/GE1BwSioJ06RCdkJbDkdTjXRhOFOLvZYNviB9ti8JvZLANwxHBvrX+YiQ3v5Aj6TnHZ6BPmIlOSM0h2N/ODncsCsUdw5vh69U0XiC5SnkhufFXZIuIiEijYIwh0MeTQB/PM+5kmJmbX9LRIcDHg8hgP5dvh+zl4UZEkC3XkPpPIVlEREQaHT8vD6LCPIhq5L3LpfbU/l6UIiIiIiINjEKyiIiIiEgpCskiIiIiIqUoJIuIiIiIlKKQLCIiIiJSikKyiIiIiEgpCskiIiIiIqUoJIuIiIiIlKKQLCIiIiJSikKyiIiIiEgpCskiIiIiIqUYx3FcPYZTGGOOAHtd8NBhQKILHlcqTs9R/afnqP7Tc1T/6Tmq//Qc1X8VeY7aO44TXtYV9TIku4oxZqXjOANdPQ45PT1H9Z+eo/pPz1H9p+eo/tNzVP9V9zlSuYWIiIiISCkKySIiIiIipSgkn+xVVw9AzkjPUf2n56j+03NU/+k5qv/0HNV/1XqOVJMsIiIiIlKKZpJFREREREpRSBYRERERKUUhGTDGjDfGbDXG7DDGPODq8UjZjDF7jDEbjDFrjTErXT0eAWPMm8aYBGNM7AmXhRhjfjDGbC96H+zKMTZ1p3mOHjXGHCj6XVprjJngyjE2ZcaYtsaYn40xm40xG40x9xRdrt+jeqKc50i/R/WEMcbHGPOrMWZd0XP016LLq/V71ORrko0x7sA24FwgDlgBTHEcZ5NLByanMMbsAQY6jqPm7fWEMWY0kA686zhOz6LL/g0cdRzniaIXncGO4/zJleNsyk7zHD0KpDuO86QrxyZgjGkNtHYcZ7UxJgBYBVwCTEW/R/VCOc/Rlej3qF4wxhjA33GcdGOMJ7AIuAe4jGr8HmkmGQYDOxzH2eU4Ti4wA7jYxWMSaRAcx1kAHC118cXAO0Ufv4P9ZyIucprnSOoJx3EOOo6zuujjNGAzEIF+j+qNcp4jqSccK73oU8+iN4dq/h4pJNsf9P0nfB6HfvjrKwf43hizyhhzq6sHI6fV0nGcg2D/uQAtXDweKdtdxpj1ReUYOpVfDxhjooB+wHL0e1QvlXqOQL9H9YYxxt0YsxZIAH5wHKfav0cKyWDKuKxp16DUXyMcx+kPXADcWXQaWUQq739AR6AvcBB4yqWjEYwxzYCZwL2O46S6ejxyqjKeI/0e1SOO4xQ4jtMXiAQGG2N6Vvc+FZLtzHHbEz6PBOJdNBYph+M48UXvE4AvsKUyUv8cLqrhK67lS3DxeKQUx3EOF/1DKQReQ79LLlVUQzkT+MBxnM+LLtbvUT1S1nOk36P6yXGcZGA+MJ5q/h4pJNuFep2MMdHGGC/gamC2i8ckpRhj/IsWTGCM8QfOA2LLv5W4yGzgxqKPbwS+dOFYpAzF/zSKXIp+l1ymaMHRG8Bmx3GePuEq/R7VE6d7jvR7VH8YY8KNMUFFH/sC44AtVPP3qMl3twAoatvyLOAOvOk4zt9dOyIpzRjTATt7DOABfKjnyfWMMR8BY4Ew4DDwCDAL+ARoB+wDrnAcRwvHXOQ0z9FY7CliB9gD3FZctyd1yxgzElgIbAAKiy7+M7bmVb9H9UA5z9EU9HtULxhjemMX5rljJ4A/cRznMWNMKNX4PVJIFhEREREpReUWIiIiIiKlKCSLiIiIiJSikCwiIiIiUopCsoiIiIhIKQrJIiIiIiKlKCSLiIiIiJSikCwiIiIiUopCsoiIiIhIKQrJIiIiIiKlKCSLiIiIiJSikCwiIiIiUopCsoiIiIhIKQrJIiIiIiKlKCSLiIiIiJSikCwiIiIiUopCsoiIiIhIKQrJIiIiIiKlKCSLiIiIiJSikCwiIiIiUopCsoiIiIhIKQrJIiIiIiKlKCSLiIiIiJSikCwiIiIiUopCsoiIiIhIKQrJIiIiIiKlKCSLiIiIiJSikCwiIiIiUopCsoiIiIhIKQrJIiJ1wBgz3xhzzBjj7eqxiIjImSkki4jUMmNMFDAKcIBJdfi4HnX1WCIijY1CsohI7bsBWAa8DdxYfKExpq0x5nNjzBFjTJIx5oUTrptujNlsjEkzxmwyxvQvutwxxsSccNzbxpi/FX081hgTZ4z5kzHmEPCWMSbYGDOn6DGOFX0cecLtQ4wxbxlj4ouun1V0eawx5qITjvM0xiQaY/rW0vdIRKReUUgWEal9NwAfFL2db4xpaYxxB+YAe4EoIAKYAWCMuQJ4tOh2gdjZ56QKPlYrIARoD9yK/Tv/VtHn7YAs4IUTjn8P8AN6AC2AZ4oufxe47oTjJgAHHcdZW8FxiIg0aMZxHFePQUSk0TLGjAR+Blo7jpNojNkCvIKdWZ5ddHl+qdt8B8x1HOe5Mu7PATo5jrOj6PO3gTjHcR4yxowFvgcCHcfJPs14+gI/O44TbIxpDRwAQh3HOVbquDbAViDCcZxUY8xnwK+O4/y7it8KEZEGRTPJIiK160bge8dxEos+/7DosrbA3tIBuUhbYGcVH+/IiQHZGONnjHnFGLPXGJMKLACCimay2wJHSwdkAMdx4oHFwGRjTBBwAXYmXESkSdCiDhGRWmKM8QWuBNyLaoQBvIEg4DDQzhjjUUZQ3g90PM3dZmLLI4q1AuJO+Lz06cH7gC7AEMdxDhXNJK8BTNHjhBhjghzHSS7jsd4BbsH+r1jqOM6B04xJRKTR0UyyiEjtuQQoALoDfYveugELi647CDxhjPE3xvgYY0YU3e514A/GmAHGijHGtC+6bi1wjTHG3RgzHhhzhjEEYOuQk40xIcAjxVc4jnMQ+AZ4qWiBn6cxZvQJt50F9AfuwdYoi4g0GQrJIiK150bgLcdx9jmOc6j4DbtwbgpwERAD7MPOBl8F4DjOp8DfsaUZadiwGlJ0n/cU3S4ZuLbouvI8C/gCidg66G9LXX89kAdsARKAe4uvcBwnC5gJRAOfV/zLFhFp+LRwT0RETssY8zDQ2XGc6854sIhII6KaZBERKVNRecbN2NlmEZEmReUWIiJyCmPMdOzCvm8cx1ng6vGIiNQ1lVuIiIiIiJSimWQRERERkVLqZU1yWFiYExUV5ephiIiIiEgjtmrVqkTHccLLuq5ehuSoqChWrlzp6mGIiIiISCNmjNl7uutUbiEiIiIiUopCsoiIiIhIKQrJIiIiIiKl1Mua5LLk5eURFxdHdna2q4fSKPj4+BAZGYmnp6erhyIiIiJS7zSYkBwXF0dAQABRUVEYY1w9nAbNcRySkpKIi4sjOjra1cMRERERqXcaTLlFdnY2oaGhCsg1wBhDaGioZuVFRERETqPBhGRAAbkG6XspIiIicnoNKiSLiIiIiNQFheQKSk5O5qWXXqr07SZMmEBycnLND0hEREREao1CcgWdLiQXFBSUe7u5c+cSFBRUS6MSERERkdrQYLpbuNoDDzzAzp076du3L56enjRr1ozWrVuzdu1aNm3axCWXXML+/fvJzs7mnnvu4dZbbwWOb7Gdnp7OBRdcwMiRI1myZAkRERF8+eWX+Pr6uvgrExEREZHSGmRI/utXG9kUn1qj99m9TSCPXNTjtNc/8cQTxMbGsnbtWubPn8+FF15IbGxsSQu1N998k5CQELKyshg0aBCTJ08mNDT0pPvYvn07H330Ea+99hpXXnklM2fO5LrrrqvRr0NERESsvUkZ3PzOSqLD/PnLhG5Ehfm7ekjSgKjcoooGDx58Uo/h559/nj59+jB06FD279/P9u3bT7lNdHQ0ffv2BWDAgAHs2bOnjkYrIrXtcGo2adl5rh6GiBTZk5jB1a8uIyE1myU7Ejn3mV/45zeb9XsqFdYgZ5LLm/GtK/7+x1+Nzp8/nx9//JGlS5fi5+fH2LFjy+xB7O3tXfKxu7s7WVlZdTJWEaldBYUOl720hIhgXz6+dahaLIq42K4j6Ux5bRl5BQ4zbh1GWDMv/v3dVl75ZRczVx3gj+d34fIBkbi56XdVTk8zyRUUEBBAWlpamdelpKQQHByMn58fW7ZsYdmyZXU8OhFxpZV7jnIgOYtfdx/l6w0HXT0ckSZtR0I6V7+6jPwCh4+mD6V7m0BaBPrw5BV9+PLOEbQL8eWPM9cz6cVFrNhz1NXDlXpMIbmCQkNDGTFiBD179uT+++8/6brx48eTn59P7969+b//+z+GDh3qolGKiCt8veEgPp5udGkZwD/nbiErt/yuNyJSO3YkpHH1q8sodBw+unUoXVoFnHR9n7ZBzPzNcJ67ui9J6blc8fJS7v5oDQeSdWZXTmUcx3H1GE4xcOBAZ+XKlSddtnnzZrp16+aiETVO+p6KVF9BocOQf8xjcHQwNwyL4upXl/G7cZ25Z1wnVw9NpEnZdjiNa15bhjGGj6YPIaZFQLnHZ+bm8/Ivu3jll50YA7eN7sjtYzri6+VeRyOW+sAYs8pxnIFlXaeZZBGRali+O4nE9Bwm9m7D0A6hXNirNf/7ZQfxmpkSqTNbDqUy5dVluBnDjFuHnjEgA/h5efD7czsz774xjOvWkufmbefsp+bz5doD1McJRKl7CskiItXw9fqD+Hq6c1aXFgA8cEFXHAee+GaLi0dWh5L3QXaKq0chTdSm+FSueW05Hu42IHcMb1ap20cG+/HCNf355LZhhDbz4p4Za7ni5aWsj0uunQFLg6GQLCJSRfkFhXwbe4hzurUoOUXbNsSP20Z3YPa6+KaxKOjIVnhxKLw6FlIOuHo00sTEHkjhmteX4e3hxse3DqNDJQPyiQZHh/DlnSP59+Te7EnK4OIXF3P/p+tISDu1W5U0DQrJIiJVtGzXUZIycpnYu81Jl98+tiOtAn3461cbKSxsxKdtczPgkxvA0wcyEuHtCZC839WjkiZiQ1wK176+HD9Pd2bcOrRGNgpxdzNcOagtP/9hLLeO6sCstQc46z/z+d/8neTka0FuU6OQLCJSRXPWx+Pv5c7YLuEnXe7n5cGDE7oSeyCVz1bFuWh0tcxxYM7v7Uzy5Dfg+i8g85gNysf2unp00sit25/Mta8vo5m3Bx/fNoz2oTW7k16AjycPTujG978bw7COYfzr2y2c+/QCvtt4SPXKTYhCsohIFeQVFPLtxkOM694SH89TV8NP6tOGAe2D+fd3WxrnDl+r34H1M2DsA9DxLIgcCDfMsrXJb18IR3e7eoTSSK3Zd4zr3lhOoK8nM24dStsQv1p7rOgwf16/cSDv3TwYbw83bntvFde+vpwth1Jr7TGl/lBIrqCxY8fy3XffnXTZs88+yx133HHa44vb2E2YMIHk5ORTjnn00Ud58skny33cWbNmsWnTppLPH374YX788cdKjl5EatqSnUkkZ+adUmpRzBjDIxd1JzE9lxd+2lHHo6tlB9fB3D9Ch7Ng9Al94yP6ww2zITfdBuWkna4bozRKq/Ye44Y3fiXYz4uPbxtWqwH5RKM6hfPNPaP466QebIxPZcJzC5n+7kpmrTlAamN8ESyAQnKFTZkyhRkzZpx02YwZM5gyZcoZbzt37lyCgoKq9LilQ/Jjjz3GuHHjqnRfIlJz5qyLJ8Dbg9Gdw057TO/IIK4YEMmbi3ezOzGjDkdXi7JT4JMbwS8UJr8Obu5k5RYcPwXdpi/c+BXkZdmgnNjIXiDUgaT0HHLzC109jHpn1d6j3Pjmr4Q282LGrUOJCPKt08f3cHfjxuFR/HL/WKaP6sCGuBTu/XgtAx//kZvfXsHMVXGkZCkwNyYKyRV0+eWXM2fOHHJycgDYs2cP8fHxfPjhhwwcOJAePXrwyCOPlHnbqKgoEhMTAfj73/9Oly5dGDduHFu3bi055rXXXmPQoEH06dOHyZMnk5mZyZIlS5g9ezb3338/ffv2ZefOnUydOpXPPvsMgHnz5tGvXz969erFtGnTSsYWFRXFI488Qv/+/enVqxdbtjShVlQidSA3v5DvNh7i3B4t8fYof+OB+8d3wcvdjb9/vanc4xoEx4FZd9iWb1e8Bf5h5OYXMv65Bdz+/qrjQblVL5g6BwrybI3ykW2uHXc9F5+cxRdr4nhg5nrG/udnBvztR4b9cx7/+nYL+49munp49cKKPUe54Y1faRHgzYxbh9GmjgPyiYL8vHhwQjeWPHA2M38znBuGtWfLoTTu+3QdA//2A1Pf+pVPVuwnOTPXZWOUmuHh6gFUyTcPwKENNXufrXrBBU+c9urQ0FAGDx7Mt99+y8UXX8yMGTO46qqrePDBBwkJCaGgoIBzzjmH9evX07t37zLvY9WqVcyYMYM1a9aQn59P//79GTBgAACXXXYZ06dPB+Chhx7ijTfe4O6772bSpElMnDiRyy+//KT7ys7OZurUqcybN4/OnTtzww038L///Y97770XgLCwMFavXs1LL73Ek08+yeuvv14D3yQRAVi8I5HU7Hwm9m59xmNbBPhw9zmdeOKbLfyy7QhjOoef8Tb11rKXYMscOO9v0G4oALPXxbM3KZO9SZl8uiqOKwe2tce27AFTv4Z3LrIzyjd+BS26unDw9cf+o5ks332UZbuSWL47if1H7cYzgT4eDI4O4cpBbVm7L5lXftnJy7/s5KwuLbhuaDvGdG6Bu5tx8ejr3vJdSdz09gpaNfdhxvShtAj0cfWQAHBzMwxoH8yA9sH85cJurItL4ZsNB/l6w0H+OHM9f/7CMKxjKBN6teb8Hq0I8fdy9ZClkhpmSHaR4pKL4pD85ptv8sknn/Dqq6+Sn5/PwYMH2bRp02lD8sKFC7n00kvx87M1VJMmTSq5LjY2loceeojk5GTS09M5//zzyx3L1q1biY6OpnPnzgDceOONvPjiiyUh+bLLLgNgwIABfP7559X90kXkBF+tjyfQx4ORMRULvDeNiOKjX/fx+JxNDL9nFJ7uDfAk3r7l8MPD0HUiDLsLAMdxeH3hLrq0DKC5nyePf7WJkTFhx2f5WnQ9NSi37O7CL6LuOY7DvqOZLN9VHIqPcqBoN8YgP08GR4Vw0/BohnQIoWurwJNCcHxyFjN+3cdHK/Yz7e2VRAb7cs2Qdlw5sC1hzbxd9SXVqaU7k5j29goign35cPoQWgTUj4BcmjGGvm2D6Ns2iAcusJ1t5sYeZO6Ggzz4+QYemhXL0A4hXNDTBubwgKbx/DV0DTMklzPjW5suueQSfv/737N69WqysrIIDg7mySefZMWKFQQHBzN16lSys8tvOm5M2bMAU6dOZdasWfTp04e3336b+fPnl3s/Z2pB4+1tfwHd3d3Jz88v91gRqbic/AJ+2HiY8T1b4eVRsbDr7eHOQxd2Z/q7K3l/2V5uGhFdy6OsYRlJ8NlN0DwSLn4Riv6OLdyeyJZDafzn8t4Mjg5h/LML+dPM9bw7bfDxv3XhnYuC8kT7dsOX9sxdI+U4DrsTM1i26yjLdyexfNdRDqXa/wuh/l4M6RDCraM7MKRDCJ1bBOBWzsxwmyBffn9eF+4+pxPfbzzM+8v28u9vt/LMD9uY0Ks11w1tz8D2waf9v9LQLd6RyM3vrKBdiB8f3DK0wQRLYwy9IpvTK7I5fzy/C5sOpvLNhkPM3XCQh2bF8vCXsQyODmFCr9aM79Gq3syMy6kaZkh2kWbNmjF27FimTZvGlClTSE1Nxd/fn+bNm3P48GG++eYbxo4de9rbjx49mqlTp/LAAw+Qn5/PV199xW233QZAWloarVu3Ji8vjw8++ICIiAgAAgICSEtLO+W+unbtyp49e9ixYwcxMTG89957jBkzpla+bhE5bsG2RNJy8rmwAqUWJxrXrQWjOoXxzA/buLhvRMM59VpYCJ9Pt5uF3Pw9+AaVXPXawl2EB3gzqW8bvD3c+fOErvzflxv58Nd9XDuk/fH7CIs5PqP8zkU2KLfuU/dfSy1wHIcdCeks232U5UUzxUfS7PqQ8ABvhkSHMKRDKEOjQ4hp0axKgdbT3Y0Le7fmwt6t2ZGQxvvL9jFzdRxfro2nS8sArhvWnkv7RdDMu/H8S1+w7QjT311JdJg/798ypMHOnBtj6NGmOT3aNOe+8zqz7XA6X284yDcbDvLwlxt5ZPZGBrUP4YJerRjfsxWtm7uu1lpO1Xh+o+rIlClTuOyyy5gxYwZdu3alX79+9OjRgw4dOjBixIhyb9u/f3+uuuoq+vbtS/v27Rk1alTJdY8//jhDhgyhffv29OrVqyQYX3311UyfPp3nn3++ZMEegI+PD2+99RZXXHEF+fn5DBo0iNtvv712vmgRKfH1+niC/DwZEXP6rhZlMcbw8MTujH9uIU//sJW/XdJAZlMXPgk758HEZ2zniiJbDqWycHsi95/fpWTx4rVD2vPtxkP8/evNjO4UfnJ7rtCOJwfl62fZlnEN2NKdSfz5iw0lnUtaBfowvGMoQ6JDGdIhhA5h/jU+yxvTIoBHJ/Xgj+O7MHttPO8t28v/zYrlibmbubR/BNcNbU/XVoE1+ph1bf7WBG59bxUdwvz5cPrQhvOC8gyMMXRpFUCXVgH8/tzObD+cxtwNh/gm9iB//WoTf/1qE9NGRPPQhd3KPcMgdcfUx51jBg4c6BT3GC62efNmunXr5qIRNU76nopUTnZeAQMe/4GL+rThicllrz04k0dnb+TdpXv4+rej6Na6noeZXfPh3Uug1xVw2aslZRYAf/h0HV+vP8jSB88myO94iIk7lsn4ZxfSMyKQD28Zeuo/+2N7bdlFVordpS9yQN18LTUoPSefJ77ZzPvL9tE+1I/fjOnI0A6htA/1q/PSB8dxWLs/mfeX7WPO+nhy8gsZFBXMdUPbM75nqzN2X6lvft6SwG3vrSKmRTM+uGUIwY0kIJ/JziPpvLFoNx8u38fE3q156so+De65a6iMMascxxlY1nUNcPWIiIhrzN96hIzcgkqXWpzod+M609zXk8e+2lS/t7dNPQgzb4GwznYW+YTwl5CazZdrD3DFwMiTAjJAZLAfD13YjWW7jvLu0j2n3m9we5g6F/yC4b1LYP+vtft11LAF245w/jML+GD5Pm4ZGc2394zm6sHtiKqFWeOKMMbQr10wT13Zh2UPnsNfJnTjSFoO98xYy/B//tRg2shl5xXwj7mb+eK957ghZAMfTu3dZAIyQMfwZvzj0l78eUJX5qw/yLS3V5Ce04TWE/30N9i3zNWjOIVCsohIBX294SAh/l4M6xBa5fto7ufJ78/rwtJdSXy38VANjq4GFeTDzJshNwOufBe8m5109TtL95Bf6DDtNAsQrxrUlrFdwnni2y1lb6IS1NYGZf8weO9S2Lu0Nr6KGpWSlcefPlvPDW/+io+nG5/dPpyHJnbH16v+zPYF+3sxfXQHfrpvLO/dPJgB7YN55ZedjP7Pz9z01q+s2HPU1UMs04o9R7nguYXEL/qA5z1f4KG0vxP0Qjf49CbYOMv+HDYRt47uyFNX9GHZrqNc/erSkvr2xqwwaTcs+A+JWxa7eiinaFAhuV7PujQw+l6KVE5WbgHzNtuuFh7VbOE2ZVBburYK4G9fbyY7r6CGRliDfnoc9i6Gi547pbdxZm4+7y/bx3ndWxIV5l/mzY0xPHFZb7zc3bj/03UUFJbx96Z5hK1RDmgF70+GPfXvH2Sxn7Yc5vxnFvDpqv38ZmxHvv7tKAa0D3b1sE7Lzc0wqlM4r94wkEV/Opu7z4ohNj6VK15eyp0fribuWP2YWc7MzefR2Ru58pWltMrbz7N+b0LboXDdTOh9Bez+BT69Ef7dET6+HmJnQs6pC9kbm8kDInn9xoHsTMjg8peXsDep8b5IKCx0+HrmWwB8k9fXtYMpQ4MJyT4+PiQlJSnc1QDHcUhKSsLHR21nRCrq560JZOYWMLFX1Ustinm4u/HwxO7EHcvijUW7a2B0NWjrt7D4WRhwE/S+8pSrP11pt969dXSHcu+mVXMfHp3Ug5V7j/Hm6b7GwDY2KDePgA8uh90LauALqDnJmbn8/uO1THt7Jc19PZl15wj+NL4rPp71Z/b4TIrbyC24/yzuOacT8zYf5pynfuHp77eSmeu60/lLdiZy/rMLeHvJHqYNbskHgS/i4eUDl78JMePsC7T7ttne2v2utafiP5sG/4mBGdfC+k/sFumN1FldWvDB9CGkZOUx+X9LiT3Q+L7WwkKHP3+xgeD9P5LoG8V1E8529ZBO0WAW7uXl5REXF3fGPsRSMT4+PkRGRuLp6enqoYg0CHd+sNr2vf3zuBrb9ez291axYPsRfrpvLK2a14MXrcf2wiujbd3wtO/B8+QxFRQ6nPXkfEKbefHFHeV38wH7gnz6u/ZrnPvbkcS0CCj7wPQE2/Hi2F6Y8hF0PKsmvppq+Tb2EA/NiiU5M5c7zorhzrM6NoqFVPHJWTzxzRZmr4unVaAPD1zQlYv7tqmzeur0nHz+OXczHyy3ix7/fVkvhqz/P1j3EVz/OXQ8TVAqLID9y2HTl7BpNqTFg7uXPb77xdDlAvCtv7P7VbUjIZ0b3lhOanY+r14/gOGV7KpTXxUWOjzw+Xq+XbmVNb634zb8bsy5f3XJWMpbuNdgQrKIiKtk5ubT//EfuGJAWx6/pGeN3e++pEzGPfMLE3u15umr+tbY/VZJfg68eT4k7YLbfoGQU+uNv409yO3vr+ala/szoYIz6glp2Zz3zALah/oz8/Zhpy9VST8C714MR3fC1R/Y2UQXSErP4ZHZG5mz/iA92gTy78t706NNc5eMpTat2HOUx77axIYDKfRvF8QjF/WgT9ugWn3MX7Yd4c+fbyA+JYubR0Rz33ld8I39AGbfDWMegLMerNgdFRbCgZVFgflLSNkPbh7QYWxRYL4Q/Ku+bqC+OZiSxY1v/sqexEyevqoPE3u3cfWQqqWg0OGPn61n5uo4Xuy9mwu3/cW+KG83xCXjUXcLEZFqmLc5gey8wmp1tShLu1A/po+K5vM1B1i971iN3nelffcXiF8Dl7xUZkAGeG3hbtqG+HJ+j1YVvtsWAT48fnFP1u1P5pUFu05/YLNwe2o9tBN8dA1s+76yX0G1OI7DV+viOfeZBXy38RB/OK8zs+4c0SgDMsCgqBC+vHME/768N/uOZnHxi4u575N1JKTW/NnalKw8/vjZOm4svejx6CaYe78Nt2P+WPE7dHODtoPh/L/DvRtg+k8w7E5I3G4D95Od7AuulW/aF18NXOvmvnx623D6tG3O3R+tKbtrTANRUOjwh0/XMXN1HL8b15kLvdaAXxhElplRXU4hWUSannUzKlX/+vX6g7QI8GZQVEiND+WOsTG0CPDmr19torCsBW51IXYmrHgNht0F3SaWecjqfcdYtfcY00ZEV7rcZGLv1kzo1Ypnf9zGlkOppz/QPxRunG0XC358LeyYV6nHqaqEtGxuf38Vd3+0hrbBvnz921HcdXYnPKu5QLO+c3MzXDmwLT//YQy3j+nIV+viOevJ+bz4844aW1A6b/NhznvmFz5bFXfyosfsFPjkBlsicdnr4FbFUhZjIGIAnPsY3LMOblsAI++F5P0w53fwVGd4e2Kdv+iqac39PHnv5iGc07UlD3+5kae+33rmNVoF+bDkv/bFQz2QX1DI7z5eyxdrDvCH8zpzz1lRsP0H6Dy+6s9/LWvcfwFERErLToXZv4XPb4O8M8+apefk8/PWBCb0al1jtcgn8vf24IELurJufzJfrDlQ4/d/Ronb7fej7RAY9+hpD3t94S4CfTy4cmDbSj+EMYbHL+5JoI8n932yjryCwtMf7Bdit60Oag/fPwS1WBLoOA6fr47j3KcX8PPWIzx4QVdm/mY4nVuepna6kQrw8eSBC7ry/e9GMzwmjP98t5Vzn/mFb2MPVnmx/LGMXH738VpufmclQb5eJy96dBz48i5bg375W/YsQk0wxm53fs7DcPcq+M0SGH0/pMTBh1fA57dCZv1sg1cRPp7uvHxdf64e1Jb//rSDBz/fQP7pfpcK8mwbx+8fgnmP1e1Ay5BfUMi9H69l9rp4/ji+C3ed3Qn2LoGcFFtPXk8pJItI07L1GyjIsQt/Vr9zxsPnbT5MTn7Nl1qc6JK+EfRtG8S/vt1StxsI5Gba2TwPbxtW3MteyLsvKZNvYw9x7dD2+Ht7VOmhQpt58/dLe7ExPpUXf95R/sG+wXY2MGGTbQNWCw6lZHPzOyv5/SfriGnRjG/uGcVtYzpWu71fQxYV5s9rNwzkg1uG4Ofpwe3vr+aa15az+WA5s/9l+Db2IOc+s4Cv1sXz23M68dXdI+kdGXT8gOUvw+bZ9kVZ+2E1+jWUMAZa9oCz/gx3Locxf7JnTF4cYhf+NVAe7m7887Je3H12DDNW7Oc3H6w+ddY/Pxc+nQqbZtnype3fu7R1Xl5BIb+dsYY56w/y4AVduWNsjL1i6zfg7l0vFuqeTtP9ayAiTdPGLyCgDbQfCQufgryscg+fs/4grQJ9GNCu9lbOu7kZHrmoOwlpObx0pgBZUxwHvr4PEjbDZa/ZNmyn8ebi3bi7GaYOj6rWQ47v2YpL+rbhhZ92nLmlVc/Lba3isper9ZilOY7Dxyv2ce7Tv7BkZyIPT+zOJ7cNo2N4szPfuIkYERPG178dyeMX92DzoVQufH4hf/liA0np5W9skZiew50frub291fTMtCbL+8awe/P7YyXxwlRY/8KO7vZ5UIYfnctfyVFPLxtWJ7+s+3L/cn18MmNDbZe2RjDfed14a+TevDj5sNc/8ZyUjLz7JX5Oba39JY5MP4J20ovPxu2feeSsebmF3L3h2uYu+EQD13YjdvGdLRXOA5snWvr0b3K7rdeHygki0jTkZUMO36EHpfaf5rph+3intNIzc7jl61HmNCrNW61UGpxon7tgrmsfwSvL9zNvqQ62OxhzXuw7kO7YCrmnNMelpKZxycr93NRnza0DKx+m7pHJ/UgxN+L+z5ZR05+OXWvnj4wcBps+xaSdlb7cQHijmVyw5u/8qeZG+jeJpDv7h3NtJGVr7FuCjzc3bh+WBTz/zCWG4ZFMWPFfsY+OZ83Fu0+pVzGcRxmr4vnvGcW8MPGw6df9Jh51M5wBkbYBaJ1vY136952kd/Z/2cD2ouDYf2ntVrSU5tuHB7Ff6f0Y93+FK58ZSmHkpLtpitb58KEJ2Hob6DdMAhoDbGf1/n4cvMLufPD1Xy78RAPT+zOLaNO6K2esBmS99brUgtQSBaRpmTrXCjMg56XQdQIO4ux6JnTbnv746bD5BbUbqnFif40vise7oa/z91Uuw90aMMJXQX+VO6hH/y6l8zcAm4ZWf7mIRUV5OfFE5N7sfVwGs/PO8OCokE329Zev75arcfMzivg+XnbOffpBazee4zHL+nJR9OH0j60/s5g1RdBfl48OqkH394zir5tg3h8zibGP7uAn7cmAJCQms2t763it0WLHuf8dmTZix4LC21NcEYCXPkO+AbV/RcDtqRo9B/gtoUQ0gE+vwU+mgKp8a4ZTzVN7N2Gt28axJHkFHa/eAls/w4ufBoGT7cHuLlB90tgxw91uvlKTn4Bd3ywih82Heavk3owbWSpjjlb59r3ncfX2ZiqokIh2Rgz3hiz1RizwxjzQBnX32+MWVv0FmuMKTDGhFTktiIidSb2c2jezq6GBxj7Z8g4AiteL/Pwr9cfJCLIl/7tgupkeC0DfbjzrBi+23iYxTsSa+dBKtFVIDe/kHeW7GFkTBjd2wTW2BDO7tqSKwZE8r/5O1m7P/n0Bwa0si9o1nxgF1xWkuM4zFkfb3eY+2EbZ3UN57vfjeb6oe1r/cxAY9OpZQDvThvMGzcOpNCBm95awTWvLePcZxbwy7YKLHpc9JQNauOfgDb96nbwZWnRFW7+Hs77O+z6GV4cCqvfa5CzysPb+7Mg8hWGFK7lMXM7a1pedvIBPS6FglxbA1wHsvMK+M37q/lxcwKPX9KTG8sq09r6DbTpD4F1MwFRVWcMycYYd+BF4AKgOzDFGNP9xGMcx/mP4zh9HcfpCzwI/OI4ztGK3FZEpE5kHrX/DHtccvw0b7shdtOKRc+esrAlJTOPBduPMKFXqzrbjQzg5pHRtA3x5a9fbTz9yvXq+OqeCncV+GpdPIdTc7hlVNl9k6vj/y7qTstAH+77ZG357caG3A65abDm/Urdf+yBFK56ZRl3fbiGQF9PZtw6lJeuHUBksF81R950GWM4p1tLvrt3NH+Z0I0NcSl0qsiix90L4Od/QK8rbAlNfeHmDsPvsl0wWvWE2XfB+5dB8j5Xj6zicjPhwytpdmARR895ih99x3PNa8tLZvoBiBwEgZF1UnKRnVfAbe+t4qctCfzj0l5cP7T9qQelHbabwXSZUOvjqa6KzCQPBnY4jrPLcZxcYAZwcTnHTwE+quJtRURqx5Y5UJhvZyZPNPbPkHX0lFP63286RF6BU+e7W/l4uvOXCd3ZdjidD3+t4X/WW7+xCxfPevCMXQUcx+G1hbvo0jKAMZ1rqEXXCQJ9PPnX5N7sPJLB0z9sO/2BEf2h7VD49RW7NfEZHEnL4YGZ67nohUXsPJLOPy7txZy7RzK0Q+PZgc3VvDzcmD66Ayv/bxyf3n6GRY9ph+Czm22XhYnP1n0dckWEdoQb59g63n3L4aVh9uxSYS28SK1JuRnw4ZWwZxFc+jJho25m5m+G0yHcn+nvrGTmqjh7nJubnRzY+RNk1d6mRdl5BUx/dyULth/hict6cc2QdmUfuO1b+76e1yNDxUJyBLD/hM/jii47hTHGDxgPzKzCbW81xqw0xqw8cqRhrjgVkXos9nMIjoLWfU++PHKArYtb/PxJp/TnrD9IZLAvvSPrfse183u0ZERMKE99v41jGbk1c6e5mfDNHyG8Kwy/54yHL9qRyJZDadw8KrrWZtJHdw7nmiHteG3hLlbuKad/7dDb4die4/9cy5CbX8irC3Zy1pPz+WxVHLeMjObn+8dyzZB2WphXS7w93Mv/2SjIh8+mQW46XPkueNfjDiJubraO946ldve3r++Ddy6qsUWjNS4nDd6/HPYuhktfhT5XAxAe4M2MW4cypEMI9326jlcXFI2/x2V2PcaWr2tlOFm5BdzyzkoW7UjkX5N7c/Xg0wRksC/Wm7ezLfrquYqE5LJ+A05XtHMRsNhxnOK/dhW+reM4rzqOM9BxnIHh4TU/ayEiTVhGoj3l2+Oysmeyxj4I2cm2fyt2I4TFOxK5sHfrOi21KGaM4eGJPUjLzuOR2RtrZie+hU/Z08gXPgUeXmc8/LWFuwkP8ObivrU7k/7nCd2ICPLlD5+uIzP3ND2iu15kTxcv+98pVzmOw4+b7K5u/5i7hcHRIXz3u9H85cLuBPqU3fdZ6sjPf7MhbuIztga4IQhuD9fPgkn/hUPr4X8jYOmLFTqLUWeyU21A3r8cJr8Ova846eoAH0/enDqIC3u35h9zt3Dh8wt5fI0Pmf6R5K2feZo7rbrM3Hymvb2CxTsT+c/lfcrfcCg305a9dbmgfp5VKKUiITkOOPErjgROtwz0ao6XWlT2tiIitWPzbHAKTi21KNamL3SdCEtegKxkvt90iPxCh4vquNTiRF1aBXDfeV2YvS6ex+ZsqvLOZ4DdVW/xc9D7aogaecbDtx5KY8G2I9w4rD3eHrW7XWwzbw/+c3kf9iRl8u9vt5Z9kLuHneXbsxAOxZZcvO1wGje8+Su3vLsSdzfD2zcN4s2pg9TzuD7Y+q3tHNP/xpJZzgbDGOh/g92EJHo0fPdnePN8OHKan8+6lJ1i66YPrIQr3oKek8s8zNvDnf9e3Y+HLuxGgI8H7y3fxzsp/TC75nPlM1/z6OyNfBt7qNpnqjJy8rnprRUs353E01f24fIBkeXfYNd827e5AZRaAFRk66QVQCdjTDRwABuEryl9kDGmOTAGuK6ytxURqVWxn0NoDLTsefpjxj5g65aXvcScXefQPtSPHjXY0aEq7hjbkaMZubyxaDfNfT353bmdK38njgNz/wCefnDe4xW6yesLd+Hj6ca1Q8pYdFMLhnUMZerwKN5esofze7RiWMcy6of73wDzn4Dl/yP53Gd45odtvL98H/5e7jxyUXeuG9r+1LZj4hrH9sIXt0GrXnDBv109mqoLbAPXfAwbPrWlSi+PtH8nht9jX7jVtaxkeO9S28Lxineg28RyD3dzM9wyqgO3jOpAdl4B29f64fH1V5xnlvPkCjfeXrIHgC4tAxjSIYShHUIZHB1CWDPvCg0nPSefaW+tYOXeozxzVV8u7nv6DYlKbJ0L3oHQfkSFHsPVzvgsO46Tb4y5C/gOcAfedBxnozHm9qLri7dDuhT43nGcjDPdtqa/CBGR00o7bE/5jvpD+af3WvWC7hdTuPRFNqbHcPXo3i4ptTiRMYaHLuxGalYez83bTnNfz1P7jZ7Jxs/t7M2EJ6FZizMenpCWzZdr47lqUFuC/c9cllFT/ji+C/O3JnD/Z+v49t7RNCu9/bVfCIV9rsZZ8wGXrh3L3mw/rhnSjt+f24WQOhynnEF+jt0wxCm0dcie1d+AxqWMgd5X2p7iX98H8x6z21pf/KLtiFFXMo/agJywCa56r9IzsT6e7vQaOBqWduCW5mu54a5HWR+XzPLdR1m2K4lPV8bx7tK9AMS0aMaQaBuah3QIoUXAqc9hWnYeN721gjX7k3l+Sr+KLXAuLLTrCmLGVajkqz6o0Eshx3HmAnNLXfZyqc/fBt6uyG1FGrUfHrGN8kf+ztUjESgqtSg8fanFicY8gNk0m2luczi7d/1ocm+M4Z+X9SItO5/H5mwiwMeDK8qr+TtRdip8+2e7WLGCrbfeXbKXvMJCbq5sGK8mPy8PnryiD1e8spR/zN3MPy7tddL1i7Yn8tbWgbxR+Ba/CVhI79v+RtdWrp3plzJ8/xDEr4Yr37ObdTQWzVrYcLpxlj0z8+oYu3V6j0ugw1m1+2Ig8yi8OwmObIOrPoDO51Xtfoyx6zIWPY1XdhIDo8IZGBXCnWfFkFdQyIYDKSzfZUPzrDUH+GC57a7TIcyfIR1CGBJtQ7O/twdT3/yV9XEp/HdKPyb0qmCv4wOrbF/6BtD6rZgLzheINGJHd9naT2Og49nQuo+rRySxn9uODi26nfnYlt1Z6juGm7O+w6d5DXWVqAEe7m48N6Uvt7yzkj/NXE+Ajyfje7Y68w3n/9NuvT3lw3I3DSmWmZvPe8v2cl73lkSF1f1udAOjQpg+qgOvLtjF+B6tGN05nD2JGfzt6838uPkw7UIiSGw5kisyvsOEPVnn45MziJ1pWykOvRO6T3L1aGpHj0sgapRdlBj7OayfAV4B0GU8dL/YzpJ6+tbc42UkwrsX23UFUz60918dPS6FhU/C5i9h0C0lF3u6u9G/XTD92wXzm7EdyS8oZGN8Kst2JbF891HmrDvIR7/aZmX+Xu7k5BfywjX9K/Z3qNjWuWDcoVM1v4Y6ZKq1GKSWDBw40Fm5cqWrhyFSed88ACteA5/mEBwNN/9gWwuJa6TGw9PdbfeKseVvvwy2x+6Uf77D915/wm3E3XDuY3UwyIrLyMnnujeWs/FAKm9OHcTITmGnP/jgejvbNeAmmPh0he7/3aV7ePjLjXx2+zAGRoXU0KgrJzuvgAufX0hmbgEX9WnDW4t34+Xuxp1nxzBtRDQ+e36CDy63uwWWWtUvLpS4HV4dCy26w01z7fbPjV1Bnu2as+lLu54hMwk8/e1Mb/eLodN54FWNF5vpCfDOJDi2G6bMgI5nVX/MjgMvDgb/FnBTxdvBFRQ6bD5oQ/OGAylc0i+Cs7qcuXzrJC8OBf8wmDqnkoOuXcaYVY7jDCzrOv33Fqkp2al2V7Ael8L5/7Crj9e86+pRNW2bvgQc+5xUwLcbD7GjMIK0TpfAr6/Zf1L1iL+3B29PHUyHcH9ufW8lq/edZmOAwkJbP+kbAuf8X4Xuu6DQ4Y1Fu+nbNogB7YNrcNSV4+PpzlNX9uVwajavLtjFJX0j+PkPY7ljbAw+nu7Q8Ry7CHPZSw1yC+FGKTfTbnXu7mU7LjSFgAz264w5ByY9D/dtgxu+hD5X2c09Pp0K/+4IH18HGz6r/LbqaYfh7Ym2P/g1n9RMQIbjJRd7F9uNXirI3c3QM6I5t4zqwHNX96t8QD66G45sblClFqCQLFJz1n5gt88d8hvofZVdvfvjo5CR5OqRNV2xn9uOFuEV6woxZ108MS2aEXj+X+wCpMXP1fIAK6+5nyfv3jyYFgHeTH3zV7YcKuOf79r3Ie5XOO9v4FuxwPvDpsPsTcpk+qgOLl+w2LdtEO9OG8Kcu0fynyv60CLwhHpPNze7VXX8aohb4bpBynFz74eEzTD5NWh+hhZgjZW7h13cN/EZuG8rTP0a+l8P+1fAzJvhPzHw0RRYN8N2qShP6kF4+0JIiYPrPoMOY2p2rD0uBZyiSYQ6UrLLXv1Y61FRCskiNaGwAJa/ApGD7Q5uxthNG3LS4MdHXD26pil5vw2KFZxFTkjN5tc9R5nYuzUmLMb2dl3xeqVmW+pKiwAf3rt5CH5eHlz/xq/sScw4fmVGEvzwMLQbXqn+tK8v3EVksC/n92hZCyOuvJGdwugZcZrdDvtMAe/mdjZZXGvN+/ZF2ej7q18v21i4udt+5BP+A7/fDNO+g0E3w8F1tjXef2Lggyvs9y6z1E6TKQdsQE47aANyBfqaV1qLrrYsJvbzmr/v09k6F8K7NbjFnArJIjVh23e2bmzob45f1qIbDLsT1rwH+5a7bmxN1aZZ9n0FQ/LcDQdxHLiweKX26D/YmsNFz9TO+KqpbYgf798ymPyCQq57YzmHUrLtFfMetS/OLnyqwjtardl3jJV7jzFtRDQeDaHXsHczGHCDbcWVEufq0TRdh2JtWU/0GNs/WE7l5gbthsL4f8K9sXDLPBhyGyRsgS/vhCc72dZuq962IfrtC22Z13WfQ/vhtTeuHpfB/mU2lNe2rGOwZ3GD2UDkROpuITUvcTt8/XuIX1fz993naphQD5vTL/8fBEZAt4tOvnz0H2HDTPv9uPUX1zSgL0tBHnw2zQb5s/7s6tHUjtjPbXeR0I4VOvzrDQfp0jKATi0D7AUhHaDvNbDyLRj+W2hegUb5dSymRQDvTBvMNa8t5/o3ljNzogeBq9+F4XdDy+4Vvp/XF+4m0MeDKwdVsLVcfTD4Vrtd8IrXYdyjrh5N0+M4tozAJ8hujVyB7ilNnpsbRA60b+f9DeLX2JKHTV/CV/fYY7wD4YZZ9pja1ONS26Fj0yw7mVObdsyzO542sHpkUEiWmlSQD0tfgJ//YVvg9L4S3GrwRyxhk20vNHCaPV1UXxyKtSucz3nk1AUr3s3ggifs4o1fX4Vhd1T67lOz8/jz5xvw83LnzxO6EeRXA03Y5/3V9g/e8rV94dHAToGd0bE9tmZ13F8rdPihlGxW7DnGfaV3tBt9P6z7CBY9bWdm66HekUG8dsNApr21lMSPH6JZQGvcxlR8Vm//0Uy+iT3IraM7nrqBR30W1M5uJb7yLfti1MvP1SNqWuJWwJEtcMn/KrRJjZRiDET0t2/jHrW76G37DjqfD6171/7jh8XYDZQ2flH7IXnrXPAPh4gBtfs4taAB/UWUeu3wRnvqKH6N/cd14dMQUMO1jRlJ8Fxv+OUJuOLtmr3v6lj+Mnj4woCpZV/fdaJtBfTz322PzcAK7ExUZHdiBje/s4J9SZkA/LTlCH+7pAfje1aweXtZtnwNS/4LPSfbjxc8CZfUTG1nZm4+nu5urt8eeOMX9n0FSy2+3nAQgAm9S31fg9tDv+th1Tsw4l4Iqp8zrcM6hjJr4CY6rN3N0x4PcYebLxXd2uCNRbtxM4apw6Nqc4i1Y+hv7Iu99R/DwJtcPZqmJfZzcPe2f9+keoyxwbguwvGJelxmJ0yS99kXnbUhPxe2/2j7ZjfAdqgNb8RSv+Tnwvx/wStj7EKpK96Gq96v+YAM4B9qV7Vv/MKG8vogIxHWf2Lb/vidpq+sMXDBv6EwH777S4XveuH2I1z8wiKSM/P44JYhzL5rJC0Dvbn9/dXc+eFqktJzKj/eo7vhi9/YHdgu+Z9tJr/uI0jaWfn7OkFeQSFvLtrN0H/M4/xnF7D5YCXbHdW02M/trEVw+wod/vX6eLq1DqRjeLNTrxx1n30OF9bjzStSD9Jl0/McbjGS/x7qxl0friavoPCMN0vJzOOTlfuZ1KcNrZo3wO2D2w2DVr3tC1W1g6s7hYX2NH3MOPDRrocNVvEkQvGkQm3YtwRyUhpkqQUoJEt1xK+B186C+f+wM6R3FnUSqM32UcPutDVb8/9Ze49RGavegoIc2/atPCHRNmxt/Bx2/lTuoY7j8Nbi3Ux9awVtgnz58s4RDOkQSvc2gcy6cwT3n9+FHzYe5txnFjB7XTwV3hAoLxs+vREMcOU74OENI+4BDx/45V8Vu48yzN+awPhnF/DYnE30jGhOWnY+F7+4mA+W/3979x0eZZX+f/x90oDQe0d67x2lqigiig0Fe8WG3bV+f1vcdXXV1V372nbtYAFFmlhAsAAJnQChQyYJvYaacn5/nEQxpEySyTwzyed1XVyTzDzlhMlk7jnPfe57q/9jC6Q9G2H7CjdL4ofk/UdZsm0/o3LPIueo0RR6XudWou/bErhxBtLsxyHzBPWveJEnLuzMt2t28tBnK8jKKvj//6NF2zhyIpObB4Vpuo0x0P8Od9l/0xyvR1N+JC1w1Rf8afUuoatWC2jUo3SD5MSZ7j2m5dDSO0cpUpAsRZd+DL79C7x5lptJHfuxW7hRuXbpnzu2lntTXPOVWwnspYwTEPc2tBzmX4706XdDrVYw/UFXgzcPJzKyeOTzlfzlq9Wc1b4en99+Ok1r/ZZrGR0ZwZ3DWjPt7oE0rRXL3R8v5db3F7Pz4LHCz//1o+7/7KLXoWZzd1+VetD3Flj5KexK9OOH/s3GXWnc8N9FXP/fODKzLG9d25sPb+7HzHsG0a9FLR6fsooJHy/l4LH0Ih23xBKyyxp1usivzWescKkW+QbJAIPud+1U5z1bwsGVgo1zXDvgQfdD7VZcM6A5fzi3HVOWJvPnrxLy/aByIiOL//28mTNauw9gYavzJa572ILXvR5J+bFqsgt82oZXzVvJQ6dL3ITX3k2BP7a1Lh+55dCwXTOgIFmKJmkR/GeQW8jUfRzcuRDaB/kySv/bXdvnuU8H97y5rf7Szab0L2QWOUd0RVc3c+9G+OnFUx7enXacq95awKT4JO46szWvX92LyvkspGpbvyqf3zaAx0a254d1uxj+wjw+X+zLf+Z2xacQ/44L1HM/X6ffA9Gxfs8mHziazt+mrebcF+YRt2Ufj41sz9f3DebsjvUxxlCnSgXevaEvD41ox6xV2xn14o+s8O3369gBkfAFNO3nd1ODaStT6dK4OqfVLqB9bLVGbsHospKnpgRUxnGY8aBrgX7Gvb/efcfQVowf3JL3ftnK89+sy3PXaStS2HHwePjOIueIquBq0K7/GnZvCP75y1uaR1am+9vX5hy3MFnCW85kQmnMJu9c7fKdw7D0Ww4FyeKfE0dg1mPw9jmQftTVcBz9ClSqEfyxVKoBA+5yn1CTlwT//DkWvuZmhlsP93+f1me5lJT5z7n84GyrUw4y+uWfWJl8gJfG9eCBc9oREVFw2kpUZATjB7di5j2DaFOvCg98upwb/xdH6oGjv99wV6IrL9RsAJz1x1MPVLm2q9u5ajLsWJ3v+TKzLB8t3Maw5+by9k+buaxXE+Y8OJTxg1tRIer35Z8iIgx3DG3NJ7f2JyMzi0tf+5m3f9xc+ukXu9bBjlV+p1ok7T3C8qT9nF/QLHKOgfe5trvzQig3+ecXYc8GGPmc+xCWzRjDo+e1Z2yfprz0/QbenPf7WSJrLW/O30ybelUY2rZusEcdeL1vdM/Nov8E97y7N8CL3WH2/wX3vF7a+hMc3qlUi7KiRjNo0qd0guTEGe42jK84KEiWwm35EV47HRa84t6M7vjFBXte6nera7frVW5yUhwkL3YLCYu6Yvfcv7vSeDMfAmuZtSqVS1/7mcwsy6e3ns4F3fyvfgHQsm4VPrl1AH+6oCMLNu3lnOfnMXHRNheQnjgMn1zrSvJd9s6pJepyDJgAMVVc5ZA8/LJxD6Ne+pHHpqykdd0qfDVhIE9f2pW6VSsUOLZep9Vixj2DGNK2Hn+dtppb3lvMvsMnivTzFUnCZMBAx9F+bZ5T1eLXBiIFqVrfzViumOjNjGVu+7a4gL3jaGhzaqczYwxPXtyF87s05MkZa5gUt+3Xx37asIc1qQdDogV1QFSp56q1LP2w8Ja/gbJrnWv8sG8r/Pyy+3tQHqya7K48tTnX65FIoHS6xJWgC/TftcSZbgF11QaBPW4QKUiW/B0/BNPud28EWLhuGox6HipU9XpkbkX16XfD+tkuYA22Ba+6trjdryz6vtUauQYe62fz1SdvcdsHS2jXoCpTJ5xBlyb5tOEtRESE4YYzWjDr3kF0alyNRyav5Jq3FnL487vcTPKlbxVcei62lqvhvPpL98cyW9LeI9z+wWLGvbmAg0fTefnKHky6tX/+7YLzUCM2hjev7cWfLujID+t2MvLF+cRv2Vv4jsWRMMV1qarmX4m86StS6da0xu/yvgt0xr0lXugYENbCjIdcnvS5+X9QjIwwvHBFdwa3rcujk1cyI/tDwZvzN1GnSgVG9yjaB7KQ1u82SD/sFliWtp1r3d9Fmwk3zYYq9d3fyqzM0j+3lzIzXMm9tiPCNsdU8pAzqZCzniMQDm13HxzDONUCFCRLfjZ8B68OcHms/e+E23+GFoO8HtXv9R0PsbVddY1gOpDsgsme1xQ7J+9Ij5vwxbSk5+qnGNu9FhPH96detZKX4DqtdmU+urk/f7uoM62TPqNy4ucsa3UbWS2GFr5z/ztc4D/3aQ4fz+DZr9dy1vM/MDdxF/cPb8t3DwxhVNdGxZp5NMYF8Z/ffjrRkRFc8cYCXpmzodDqC0WyY7WrcuBnbeQtuw+zMvkAo/yZRc5Rpa77vSvGQseASpzhcnCHPVpoJ8CYqAhev7onPZvV5J6JS3lr/iZ+WLeL6wacdkqaTFhr1B2ane5SLkozWN2x2gXIxsD106FpXzj3SUhd5v5elmVb5sGRPUq1KGuqN3bpeIFMuVg3y92Gaem3HAqS5feO7ndNQT64xF2iv2k2jPg7xBSwqMkrFaq4mb2N38PWX4J33rg3AeuqQhRD8v6jjHkjjnvTrqGx2cNTtWdRMTpwwUpEhOHqZvv4U/R7rKjYi4sTzmDcmwvYsvtwwTtWqkHWgDth7TRuf/ZtXpmzkZGdG/D9g0O4+6w2ARlj1yY1mHb3QM7r3IBnv07kuv8uYtehYtR7zkvCZDARRU61OKWBSGFOv9u9HrxaOHriMMx8GOp1dLOnfoiNieLt6/vQpl5V/jZ9DRWjI7i6v381pMNK/9vdQqGcXMhA274S3h3l0paunw5127n7O18KLYbAd3+FtJ2lc+5QkDDFpWW1PjW9R8Jcp0vcQrudawNzvMSZLt+5XsfAHM8jCpLlN2tnwCv93Ar+gffDrfPdLEko63OzK/8UrNnkE0dg8f/cp+OcMmpFsHjrXka//CPb9hzhzmuvhh5XY355OXB/mMB90Pn0OkxsbbpMmMQ/Lu3O6tSDjPj3PN6av4nMfGZvl2zbx5Ure7LfVmaC+YzPbz+df43tQcPqlQI3NqBaxWheGteDpy7pwqLNexn54nx+2rC7ZAe11r2BNx/od4vc6StS6dmsBo1rFPHn87qpzbxn4UCS62qZX455HqpXiubdG/vSpXF1xg9qSc3KAWhvHmranw/Vm5VOObjU5fDuBS7d5vrpUKfNb48Z49qWpx+B2f8v8OcOBZnprvRmu5FuAkXKlo4XAiYwKRcnDsOmue53JczXPChIFtfu+fObYeI4qFwHbvkOzv7T71bLh6yYWFd1YPM82Dy/9M+38hM4us//sm8n+TQ+iXFvLKRKhSim3Hk6w9rXg7OfcDne0x8ITCkpa92VgAM+GPM/TJW6XN6nKd/cN4QzWtXhb9PXMOb1n9mwM+3XXbYfOMZ9k5Zxyas/s+mgwdfhFvqmL6JXZCnUzcxmjGFc32Z8OeEMqlWM4uq3F/L87EQy/OgSl6ftK12VBz+rWmzalcbq1IOc37WYObkD7nTPW7Bnk3eudS3Fu18Fpw0o8u51q1bgq7sGcv857UphcCEgItJd4dn6I6SuCNxxU5bCuxe6WdTrp0PtVqduU6eNa86zYqJb7FzWbJrr/vYp1aJsqtrATTIkTCn5e9GmuZBxLOzzkUFBsiRMgVf6utqyQx+DW+a4DjzhpPcNUKWBq3RRmiXGrHUzVA26wGln+L1bZpblb9NW84fPVtCnRU2+uPMMWtfLXvxYuTac/Wf3pr7ik5KP8ZdXYO00OPsv0Kzfr3c3qF6Rt67rzQtXdGPjrsOMfHE+r83dyMvfr2fYc3OZvjKVO4e1Ys6DQ+l88YNBy/Vu36AaX901kMt6NuHF7zdw5VsL2X7Aj8YouSVMcYvYOlzo1+bTsxuIjOxSzFXXvza1mRrYYKwg1rqayDFVYPgTwTlnOOp5DURXdq2qA8G3GN4d7RYLXz/ddSnLz6AH3CXm6Q+4mdeyJGGKW7PQ6kyvRyKlpdPFsHtdya+QJc5wvytFeJ8MVQqSy6tDO2DSNfDp9a7t7q0/wNCHISoML8FGV3JvTlt/gs0/lN55Ns2FXWtcC2o/LyEdOJrOjf+L460fN3P96c353w19qRGb6/+4x7XQuLdrLVyS8lXbFsK3f4L2o9xMZy7GGC7u0YRv7h/Mme3q8Y9Za3lu9jqGtK3Lt/cN4Q/ntnfNSypUdTNiG751xyxlsTFRPDumG89f3o1VyQc479/zmLO2CHmd1rpLhC2HnNL1MT0ziw0705i1KpWXvlvP3R8vZcS/5vHi9+vpfVrNkqWSBLupzcpPYct8d5Wncp3gnDMcVarpGh2t/BTSdpXsWElx8P5FEFvTBcg1C8njjomF8551C0gXvFqyc4eSjOOwZppLZ4kquOyjhLEOF7p1HSVZwJeVCYmzXFnKIqSDhSpT6sX9i6F37942Pj7e62GUTda6GctZD7v82mGPuRq5kXl3dgsb6cfgpZ6uy9qNX5dOHtSHl7uSNvcl+JWKsmlXGje/F8+2PUf460WdGde3Wf4bpyyDN4dB75vg/GI0qzi8G14f5D7kjP+h0CYv1lrmJO6kckwU/Vrm0U78xGH4dzeo3wmu/bLo4ymmjbvSmPDRUtakHmT84JY8eE47YqIK+SyfvATeHMb2oc+xuNYo1u88xPqdaazfcYjNuw+Tnvnb37gmNSvRpl4V2tavysU9G9O+QQnbMf/wLMz5G4yfW7pXYI7uh5f7uA+0N33j0gokf7vXw8u93dWxoQ8X7xjbFsAHl7kPJNdP87uDIwAfXwmb5sCEuKLtF6oSZ8LHY+Gqz6BNEZonSfh5b7Rb/HrXkuK9jyYtgreHw6VvQ5fLAj++UmCMWWyt7Z3XY2EeGUmRHEiGafe50lFN+7mOeScvPgln0RXdbPL0+121i0A3O9mz0f2/DXnYrwB53rpdTPhoCVGREXx4c7+8A9GTNeoOfW6BRW9Aj6uKFnBlZcLkW1xppptm+9UF0RjDme3r579BTGVXOWT247DlJ2genMtmrepWYcodp/Pk9DW8MW8Tizbv5aVxPX6tY3wsPZPNuw+zfmcaG3YcYt2ONIZte4lLbCTnzqrGAZZgDDSrFUubelU4q0N92tSrQpt6VWlVrzKxMQH+k9fvVtdkZ85TcFUA0mXyM+dJOLLbnUMBcuHqtHGdMOPfhoH3Fn32c8tP8OEYl6d5/bSCa4zn5byn4eW+MOsRuCIIdZtLW8IUqFjDVfCQsq3TJfDV3W6haqPuRd8/cYZrluV1w7EAUZBcHlgLS95zrVMz02HE067Wa1l7s+1xDfz4Asz5u8ubC+Rs8sL/QES0m+ktxP9+2swT01bTtn5V3ry2t/+NKs583L0ZTbsfbv7W/+dn3nPug8GoF4r3Ry0/vW90bY/nPuUChSCpGB3JXy/qzIBWtXn4sxWMfHE+/VrUZuOuNLbuOUxOcY4IA81rxfI3+zNJNfvzxNCBtK5XhVZ1qwS0pF7Bg60Gp98F3z0BvnhokudkRMmkLIW4t1wll3BbL+Cl/re7UpYJU6DbWP/32zwfPrrczQBf91XxuoXVaAZDHoLv/gLrZkPbc4p+jFCRfsxVPup0UXim40nRdLjATTYlTClmkDzTNXSqVDPgQ/OCcpLLun1b3OWTr+6Ght3gjp/dm0dZC5DB/QEf/AdIjof13wTuuMcOwLIPXS3UqvnPvlpreXrmWv781WrO6lCfz28/3f8AGVx+67l/h5QlrsycPzbOcUFsl8uh1w3+n8sfMbGuFOCW+a56SJCN7NKQGfcMomuT6mzenUb7BlWZcGYbXhrXg1n3DmLNX0fw/dgq1MnYQcuh1zC6e2M6NaoevAA5R05TmzmlsNAxK9N9aIqtA8MeD/zxy7JWZ0KddrDgNf8X9G6a62aQazRzHUZL0k53wASo0xZm/gHSjxb/OF7b8A2cOKSqFuVFbC1oOdSt8yhqOu6ejS4fP8wbiJxMM8llVVaWm3369s8uEX/UC9Dzeogo45+Lul8J8//pLk+3GR6Y2eSlH8CJNOiff+OG9MwsHvl8JZ8v8XFVv2Y8MbozkRHFOHeXy2DJu25mssOFrsNbfg6muNJ9ddq657c08rB7XQ8//csFgM0HBb3mZdNasXx4c//8N0iYApEx0N7DP8o5Cx2/+aO7YlO9aeCOve0X96Hpkjf9SqORkxjj0mGm3w9JC6FZAb9H4LqMTrwSarWEa6cW/NrzR1SMq5387gXuCtewx0p2PK8kTHEfApsP9nokEiydLnalRJOXQJNe/u+X02Wv7YjSGZcHFCSXRbs3wNQJ7g221Vlwwb/dgp/yIDLaXeb88k532aekwVNWpisl1bR/vpe6j57I5M6PlvD92p3cd3Zb7j6rdbFaNwO/NSV47QxXqeKifFbIZ6bDZze65gWXTy92e+xC5eR6z3jQLUQKpfJPWVnuDbz12W4W3kt9bnbl96beFfhjtxgMXcYE/rjlQbex7gPnglcLDpLXfwMTr3IfOK/98pQqKcXWYrC7yvPjC9D1irzrK4eyE0dcpYKul4f/4m7xX/vz4at73WxyUYLkxJmuw15BZRLDjH7ry5KsTPdGPedJt1Dloteg27iw73hTZF3HujzdOX93n2hLMnueONOt9B3+1zwf3nf4BDe+G8fypP08eXFnruoXgFa/ddu5PNcfn4ceV7v8rty+e8J9CLrkTajXvuTnLEjPa+HHf7n/z5bDQuf3KWkhHEqBTiFQMzimsqtwsW9rYI9rjPtwFir/5+EmpjL0us41YNm/zaVR5JY4Cz65Buq2dwFybK3AjuGcv7kZthkPwtWTw+u5XP81pB9WqkV5U6mmW3iX8IV77/PnPfTIXtj6s1soW4YoSC4rdq7JvjyyGNqdD6OeL1k+XTiLjIKhj8CUW11jjY7+NZjI08LX3eXz9qNOeSh5/1GufXshSfuO8upVPRnRuWEJBp3L4D/Ays9cU4Jb5/2+3uTaGW5BXa8b3AxPaYuqAIMfhGn3utrJoVICKmGKaxHcLkQu7VVrVPQqCFL6+twCP78Mi96Ec3J92F07HT65Dhp0hmumlM5io6r14cz/g5kPweov3KXscJEwBSrXKxNNIaSIOl3sPtz54n7XmCpfG74Fm1mm8pFBC/fCX2Y6zHsW/jPYLdK77B0Y+2H5DZBzdL4Mardxi9qyitnqOHWFW7TW95ZTLjWu23GIS1/9mZ0Hj/PejX0DGyBDdlOCf8DO1b/vHLZvC3xxm1uEOSKILZG7X+Vm4eY8WbpdDf2VlekCjjbDXU6wSH5qNHUr9pe86+p/51jzFXxyLTTsCtd8Ubqr8XvfBA26wqxH4fih0jtPIB1Pc5U5Oo4umwu9pWDtRkJkBf8biyTOcB+oGvUs3XEFmYLkcJa6wjWg+P5vbqbzzkWuAkM4Xc7z07Y9R/jn7ETemr+JY+mZhe+QM5u8c7ULpopj4X8gOtalG5wkfsteLnvtZzKt5ZPbBtC/sBrIxdV+JLQ9z9XgPZDsul59ej1YYMy7ftVrDpioGBj8kCtHlrM4w0vbfoG0Ha6mp0hh+t/uqtQsn+i+T5jiZpAb9cyeQa5RuuePjHKLaw9tD16HxpJaNwsyjirVoryqWM1NQqz+ovCJpowTsP5bd1WvjBUHKFs/TXmRcdwFxm8Og7Sdrlj9mP+WuVa1mVmWb1fv4Pr/LmLIc3N4Zc4G/jZ9DcNf+IFZq1IptFtkp4tdnuHcp93MY1Gk7YKVn7ic7pNmmL5ZvYOr3lpI7SoVmHz76XRoWMKObYU572mwWfD1o/D1Yy5IvehVbxZGdBsLNVu43GSvZ5NXTXYfYNqe6+04JDw07edyuxe+7tKYPrsJmvaFayYHb9Fnk94uP3rBa7B9VXDOWRIJU6BqQ7doWcqnThfDoVQ3KVGQrT+6MoFlLNUCFCSHn9QVLrVi3rNu1fQdC9ylxDJk16HjvDJnA4OfmcPN78WzOuUgd5/Zhp8fOYsPbupHpehIbvtgCVe+uZA1qQfzP1BEpJtN3p3ogqqiWPxfyDwB/X4r+zYpbhu3vh9P+wZV+ey2AUWrgVxcNZu7fODVX7qSfgMmQIdT86ODIjLadRzcvsLlcnolM8P9f7Q91y3MEimMMdDvdti9Dj6/yVW6uOqz4KfqnPUnN2s9/f7ip4EFw7GDruJHx4vK3MygFEHbERBVqfCUi8SZbrsy2JFRv/3hZupdbhXpVZ/Bxa8FfiW2R6y1LNy0hwkfLeH0p7/j2a8TaV4nlteu6slPj5zJfcPb0qB6RQa2qcOMuwfx19GdWLP9IOe/OJ/Hp6xkT9rxvA/cYTTU6wQ/PO2CK39knHABaauzoG5brLW8MmcDD3++koFt6vLRLf2pXaWIbW5L4vS7oH4XOG0gnP3n4J03L13GQO3WJcv1LqmtP7oWzUq1kKLodLG7EtJiCFz1aemVTSxIbC0Y/oSrzLL8o+Cf31+JMyHzuFItyrsKVVy3yNVf5n811lr3+9JqmFtLU8aoukU4OXEYtq+EgfeFToWBEjp0LJ0pS5P5YMFW1u1Io1rFKK7p35yr+jejVd2838SiIiO4ZkBzLujWiH99u573F2xl6vIU7j27LdcOOI3oyJM++0VEwLBHYdLVsPJT6D6u8EElTHH5rqNfJSvL8sS01fzv5y1c1L0Rz1zWjZioIH+2jKoAt3wPEVHez+pERsGQR2DyzbBmqmtVG2yrJkNMlTLzGpAgiYpxV96iKni7bqPblbDkfZj9/9zl6VCc6EiYDNWaQONSaLMu4aXTxS5I3vqTq/ud245VcCDJ9ScogzSTHE5SlrkSK036eD2SEktIOcCjk1fS7+/f8ccvE6gYHckzl3Zl4WNn88cLOuYbIJ+sRmwMf76wE7PuGUT3pjX467TVjPjXPOYk7vz9hu1HuZXlP/zDVQMpiLWw8DWo3YbjzYdw18Sl/O/nLdw8sAXPX949+AFyjqgY7wPkHJ0vce1+5z5V9FzvkspMd8F5u/MgulJwzy3hL7qi9wubIyJcw6BjB+C7v3g7lrwc3ee6D3a6KHT+5oh32pwL0ZXzT1lMnAmYMtVl72R6BYST5Hh32yQ8P90fS89k8hIfl7z6E+e/+COTl/g4v0tDvrzzDKZOGMjlfZpSKabopYba1K/Kezf25e3repNl4Yb/xnHDfxexcVea28AY1xJ23+bfVrfnJ2khpCzlWK9buPHdxUxfkcpjI9vzf6M6ElGcNtNlUU6u9661/pcHCpTNP7g3caVaSDhr0NlV3Fj8LiTFeT2a31s7A7LSlWohTkysq1qxZmreKYuJM1xMUqVe8McWBAqSw4kvzi3kCrMqFlv3HOapGWsY8NR33P/JcvYfSef/zu/AwsfO4tkx3ejWtEaJz2GM4awO9fn63sE8PrID8Vv2ce4L8/jrtNUcOJruPuU26gHznnE5x/lZ8BpZFapzZVxLFmzayz/HdGP84DBrJRsMHS9y7UeLUzmkJFZNgQrVXDcokXA29BFXz376/f6vlwiGhMlQ47QyV+9WSqDTxXBkD2yZ9/v7D6a4ikvtzvNmXEGgIDmc+OLDJtUiM8vyzeodXPfOIoY8O5e3ftxMvxa1+fDmfnz3wBBuHtSSGrExAT9vTFQEtwxuyZw/DGVM7ya889Nmhj03lw8XbSNzyGOuNe2yD/PeeX8Sds1XTMoaxprdmbx1bW8u7dUk4GMsEyIiYOijsGe9K6kVDBknYO1X0P58l1cqEs4qVIURT7lqMfFvez0a58he2DTXBUVep6VI6Gg9HGKqnppykVMzvwyWfsuhIDlcHEh29QrDJEi+48PF3PJePGu3H+Ses9rw08Nn8vo1vTijdR1MEP741qlSgacu6cpXEwbSul4VHp+yivNnVOBQnR4w7zlXazqXXd+/TJa1vJdxDh/e0o9h7cvm5aOAaT8KGnQpWuWQktg0x+VxKtVCyoqOF0GrM13d+0PbvR6N60KYlRFerbOl9EVXdM2t1nz1+yuxiTPd1e267T0bWmlTkBwufNl5a2GQj7xl92G+TtjBTQNb8OPDv5Vv80LnxtWZNL4/r17Vk0PHM7k9ZQQc9LHvx9/P3CxYu42Y5e8zL6IvL90+mp7NSrFFbVkREQFDH4O9m2DFpNI/36rJULEGtBxa+ucSCQZjYORzkHEMZv+f16NxqRa1Wrq29yIn63QxHNvv1oWAq7a16Qc3i1yGrzooSA4XvjjXR71+F69HUqhJ8UlERhjGD275+3JsHjHGMLJLQ757YAj9zryEeNueE3Oe4fmZyzl8PINpK1KY8eELVDeH6XLZo7Su50H91HDV7jxo2N2/yiElkX7MLRDpMMpV+hApK2q3cmU9V34Km+cVvn1pObzbnb/TJWU66JFianUmVKj+W8rFxjmulnYZzkcGBcnhwxfvPt2HeICQnpnFZ4t9DGtXj/rVvJk9zk/F6EjuOrstLcc8SX2zj4M/vsXgZ+Zw18dLGB/zDZn1u1KnQ9nrGFSqjIFhj8P+rbCsFJsjbPwOjh9UqoWUTQPvc5etpz9Q8MLi0rT6S7BZSrWQvEVVcJMUa6e7dMXEma6le7MBXo+sVPkVJBtjRhhjEo0xG4wxj+SzzVBjzDJjTIIx5oeT7t9ijFmZ/Vh8oAZermSmQ+qysMhHnrN2J7sOHWdsn6ZeDyVftTqfDc0H8Xi1mXSoE8UDLZNpkplE5IA7NINSHG2Gu6YD854tvTf4VZOhUq28i9mLhLvoSi7tYvc6+OVlb8aQMAXqtIX6nbw5v4S+ThfD8QOuZfm6WdDmHIiM9npUparQINkYEwm8ApwHdATGGWM65tqmBvAqcKG1thMwJtdhhllru1trQz+hNhTtWOVy1pr08nokhZoYl0T9ahUY2q6u10Mp2NBHiT66iw+6JTCh0jdQuZ7qghZXTh3qA0mw9P3AHz/9qJu16Hhhmf+DLOVYm+HQ4QL44RlXhSeYDu2ALT8q1UIK1nIoVKrpmuAc2V3mUy3Av5nkvsAGa+0ma+0JYCIwOtc2VwKTrbXbAKy1uVqeSYn4cpqIhPZMcuqBo8xN3MmYXk2JCoFc5AI1P8O94Oc9Axu+gT43qaxYSbQ6E5r2h/n/dPnDgbR+NqQfVqqFlH0jngYTATPzvGBbelZ/CVilWkjBIqPdB7nd6yAiClqf7fWISl2UH9s0BpJO+t4H9Mu1TVsg2hgzF6gK/Nta+172YxaYbYyxwH+stW/kdRJjzHhgPECzZs38/gHKBV88VKkP1UM3hQHgs3gfWRYu7x3a4/zV0MfgnXMgMgZ63+j1aMJbzmzyexfCJ9dC9caBO/a2hVC5Lpx2RuCOKRKKqjeBoQ/DN3+E1VPd1ZNgSJjsmgPVK7ulvCRAOl0MS96D5gNdTnIZ50+QnNe1F5vHcXoBZwGVgF+MMQusteuAM6y1KcaYesA3xpi11tpTlvBmB89vAPTu3Tv38cs3X5zL+Qzhy2BZWZZJ8Umc0bo2zWrHej0c/zTrBz2vdakWZbSlZlC1GAydL3Ur5FOWBPbY/W+HSH/+XImEuf53wKrPYfItriVwac/WHUyBbb/AsBAoQSehr/lgaDEEet/k9UiCwp93HR9w8tRgEyAlj212W2sPA4eNMfOAbsA6a20KuBQMY8wUXPqGh3VuwsyRvbB3I/S42uuRFOinjbvx7TvKwyPCbCbiwpe8HkHZYQxc9o7XoxAJb5HRcPUUeH80fHwlXPEBtD2n9M6X8IW7VaqF+CMyCq6b6vUogsafxNE4oI0xpoUxJgYYC+T+H/oSGGSMiTLGxOLSMdYYYyobY6oCGGMqA+cAqwI3/HIgebG7DfF85IlxSdSIjeacTvW9HoqISHirXBuunerSHyZd5RaulpaEya5zZp3WpXcOkTBVaJBsrc0AJgBfA2uAT6y1CcaY24wxt2VvswaYBawAFgFvWWtXAfWBH40xy7Pvn26tnVU6P0oZ5YtzCzka9fB6JPnae/gEsxO2c0mPJlSIivR6OCIi4S+2Flz7JdTvDJOugTXTAn+O/dvce4wWxYrkya8kP2vtDGBGrvtez/X9s8Czue7bhEu7kOLyxbkFFRVCtwvc5CU+0jMtY/uGyYI9EZFwUKkmXDMFPrgUPr3OpTN1zF1cqgSUaiFSoBCv01XOZWWBbzE0Cd3y0tZaJsYl0bNZDdrWr+r1cEREypZKNVyg3LgXfHqDa/oRKAmT3VXKWi0Cd0yRMkRBcijbs8F1twnhfOQl2/axYWcaY/uobJ+ISKmoWA2u/hya9oXPboKVn5X8mHs3Q8pSpVqIFEBBcijzxbnbxqE7k/zxoiSqVIji/K4NvR6KiEjZVaEqXPUZNBvgysMtn1Sy4+XMSHe6qMRDEymrFCSHMl8cVKgGddp6PZI8HTyWzvQVqVzQrRGVK6iGrYhIqapQBa76xDVymHIrLPuo+MdKmOKuUtbQVUCR/ChIDmXJ8S4PLSI0n6avlqdwND2TsX20YE9EJChiKsO4SdByKHxxh+t+VlR7NsL2FUq1EClEaEZfAicOw46EkF60N3FREu0bVKVrk7LfmlJEJGTExMK4j6H1WTD1Loj/b9H2XzXZ3QayUoZIGaQgOVSlLAWbFbKL9lYlH2Bl8gHG9W2GCeF22SIiZVJ0JbjiQ2hzLky7Fxa96f++CVNcbnP1xqU2PJGyQEFyqPLFu9sQXbT3SXwSMVERXNRdf2RFRDwRXRGueB/ajYQZD8KC1wvfZ1ci7ExQqoWIHxQkhypfHNRq6dqThphj6ZlMWZrMyM4NqB4b7fVwRETKr6gKMOZdaD8KZj0Mv7xS8ParJgMGOl4YlOGJhDMFyaHIWjeTHKKzyDNWpnLoWAZj+2pVtIiI56JiYMz/XI7x14/BT//OeztrXapF84FQtUFQhygSjhQkh6KDyZC2PWTzkSfGJdG8diz9WtTyeigiIgIQGQ2XvuPSKL75I8z/56nb7FwNuxNVG1nETypuG4pymoiEYGWLTbvSWLR5Lw+PaK8FeyIioSQyCi55EyIi4bsnICsThjz02+OrJoOJgA6qaiHiDwXJocgXD5EVoH5nr0dyiklxSURFGC7tpQV7IiIhJzIKLv4PmEiY8yRkZcDQR91jCVOgxWCoUtfbMYqECQXJocgXB426uzyzEHIiI4vPl/g4q0M96lWt6PVwREQkLxGRcNGrEBEFP/zDzSh3vBD2boQz7vZ6dCJhQ0FyqMk4AanLoc/NXo/kFN+v3cHutBOM7aMFeyIiIS0iEi58yXVsnf8crPrcBc0dVNVCxF8KkkPNjlWQcSwk85E/XpREw+oVGdxWl+pEREJeRASM+rcLjuPfgdZnQ6wWXIv4S0FyqAnRJiLJ+48yb/0u7jqzDZERWrAnIhIWIiLg/OehQVfXZU9E/KYgOdQkx0OVBlC9idcj+Z1P45MAGNMrtMYlIiKFMAZ63+D1KETCjuokhxpfnEu1CKHyaplZlk/ikhjYug5Na8V6PRwRERGRUqcgOZQc3gN7N4VcPvL89btIOXCMceqwJyIiIuWEguRQkrzY3YZYp71JcUnUqhzD2R3qez0UERERkaBQkBxKfHGuG1KjHl6P5Fe7047zzeodXNqzMTFR+nURERGR8kFRTyjxxUG9ThBT2euR/OrzxT4ysixX9Gnq9VBEREREgkZBcqjIyoLkJSGVj2ytZVJcEn2a16R1vapeD0dEREQkaBQkh4o96+H4gZDKR47bso9Nuw9zhTrsiYiISDmjIDlU+OLcbQgFyRMXbaNqhShGdmng9VBEREREgkpBcqjwxUGF6lC7tdcjAeDA0XSmr0xldI9GxMao54yIiIiULwqSQ4VvMTTp5VqIhoCpy5I5npHFWKVaiIiISDkUGhFZeXc8DXYmhEyqhbWWjxcl0alRNTo3ru71cERERESCTkFyKEhZCjYLGodGZYtVyQdZnXqQseqwJyIiIuWUguRQkBzvbkOk/NvEuG1UjI7gwm6NvB6KiIiIiCcUJIcCXzzUagWxtbweCUdOZPDlshRGdmlI9UrRXg9HRERExBMKkr1mratsESKzyNNXpJJ2PEML9kRERKRcU5DstQM+SNsRMov2JsUl0bJuZfo0r+n1UEREREQ8oyDZa782EfF+JnnDzkPEb93H2D5NMcZ4PRwRERERzyhI9povHqIqQv3OXo+EiYuSiI40XNKziddDEREREfGUgmSv+eKgYXeI9HaR3PGMTCYvTWZ4x/rUqVLB07GIiIiIeE1BspcyTkDq8pBItfh29U72Hj7BFVqwJyIiIqIg2VM7VkLm8ZBYtDcxbhuNa1RiYOs6Xg9FRERExHMKkr3kC40mIkl7jzB//W4u792UyAgt2BMRERFRkOwlXzxUbQjVGns6jI8XbcMYGNNbC/ZEREREAKK8HkC5ltNExKNya+mZWfxz9jpe/2Ej53aqT6MalTwZh4iIiEioUZDslcO7Yd9m6HW9J6f37TvC3R8vZcm2/VzZrxl/HNXRk3GIiIiIhCIFyV5JXuxuPVi093XCdv7w6XKyLLw0rgcXdGsU9DGIiIiIhDK/cpKNMSOMMYnGmA3GmEfy2WaoMWaZMSbBGPNDUfYtl3xxYCKhUfegnfJ4RiZ/nprAre8v5rTalZl+90AFyCIiIiJ5KHQm2RgTCbwCDAd8QJwxZqq1dvVJ29QAXgVGWGu3GWPq+btvueWLg/qdIKZyUE63ZfdhJny8hFXJB7nxjBY8fF47KkRFBuXcIiIiIuHGn3SLvsAGa+0mAGPMRGA0cHKgeyUw2Vq7DcBau7MI+5Y/WVmQvAS6XBaU001dnsJjk1cSGWF489reDO9YPyjnFREREQlX/gTJjYGkk773Af1ybdMWiDbGzAWqAv+21r7n577lz+51cPxgqecjHz2RyRPTEvh4URK9TqvJi+N60FgVLEREREQK5U+QnFd9MpvHcXoBZwGVgF+MMQv83NedxJjxwHiAZs3KeGtkX5y7LcUgef2OQ9z50RLW7UjjjqGtuG94W6IjVRZbRERExB/+BMk+oOlJ3zcBUvLYZre19jBw2BgzD+jm574AWGvfAN4A6N27d56BdJnhi4OK1aFWq4Af2lrLp4t9/PHLVVSOieLdG/sypG3dgJ9HREREpCzzZ2oxDmhjjGlhjIkBxgJTc23zJTDIGBNljInFpVSs8XPf8id5MTTuDRGBndlNO57BfZOW8dBnK+jZrCYz7xmkAFlERESkGAqdSbbWZhhjJgBfA5HAO9baBGPMbdmPv26tXWOMmQWsALKAt6y1qwDy2reUfpbwcPwQ7FwN7UcF9LAJKQeY8NFStu45zP3D23LnsNZERnjTyU9EREQk3PnVTMRaOwOYkeu+13N9/yzwrD/7lmspS8FmBSwf2VrLBwu28tfpa6gZG81Ht/Snf8vaATm2iIiISHmljnvB5ot3t417lvhQB46m88jnK5i5ajtD29Xln2O6UbtKhRIfV0RERKS8U5AcbL54qN0aYmuV6DBLt+3jro+Xsv3AMR4b2Z6bB7YkQukVIiIiIgGhIDmYrHWVLVqfVexDZGVZ3v5xM/+YtZb61Sry6W0D6NGsZgAHKSIiIiIKkoPpQBIc3gmNexV515T9R5m5ajtTlyWz3HeAEZ0a8I9Lu1I9NroUBioiIiJSvilIDqYiNhFJ2nuEWau2M31lKsuS9gPQoWE1nrqkC2P7NMUYpVeIiIiIlAYFycHki4eoSlC/U76bbN1zmBkrtzNzVSorfAcA6Ny4Gg+NaMd5nRvSok7lYI1WREREpNxSkBxMvnho1B0if58isWlXGjNXbWfGylQSUg4C0K1JdR49rz3ndW5Is9qxHgxWREREpPxSkBwsGcchdTn0Gw/Ahp2HmLHSBcZrtx8CoGezGvzf+R0Y0bkBTWoqMBYRERHxioLkILHbV2Iyj/PVnia89MIPrNuRhjHQ+7Sa/HFUR0Z0bkCjGpW8HqaIiIiIoCC5VFlrWZN6iJmrUqmw+GMmAE+uqMxpzWP4y4WdGNG5AfWrVfR6mCIiIiKSi4LkUnTPxGVMXZ5ChIH3a2zkcIV6TH1gDPWqKjAWERERCWURXg+grPppw26mLk/h+tObs+jxszmj4mYqt+yvAFlEREQkDChILgVZWZa/z1hD4xqVeOS89tQxh2DfFr/rI4uIiIiItxQkl4IvliWTkHKQh0a0o2J0pCv9BtCkt7cDExERERG/KEgOsGPpmTz3dSJdGlfngq6N3J2+ODCR0LC7p2MTEREREf8oSA6wd37aTMqBYzw2sgMREdlto31x0KAzxKj2sYiIiEg4UJAcQHvSjvPanI2c3aEeA1rVdndmZULyEmisVAsRERGRcKEgOYBe+n4DR9IzeeS89r/duXsdnDikRXsiIiIiYURBcoBs2pXGBwu2ckWfprSuV/W3B3xx7lZBsoiIiEjYUJAcIM/MSqRCVAT3nt3m9w/44qBiDajdypNxiYiIiEjRKUgOgPgte5mVsJ1bh7Q6tVmIb7Er/WaMN4MTERERkSJTkFxC1lqenLGGelUrcPOgFr9/8Pgh2LlaqRYiIiIiYUZBcgnNWLmdpdv288A5bYmNifr9g8lLAKsmIiIiIiJhRkFyCZzIyOKZr9fSrn5VLuvV9NQNkrM77TXuFdyBiYiIiEiJKEgugQ8WbGXrniM8OrI9kRF55Bz74qF2G6hUM/iDExEREZFiU5BcTAeOpvPi9+sZ2LoOQ9rWPXUDa11lC6VaiIiIiIQdBcnF9OqcDRw4ms6jI9tj8qpcsX8bHN6lIFlEREQkDClILoakvUf4789buKRHEzo1qp73RmoiIiIiIhK2FCQXw3OzEzHAg+e2zX8jXzxEVYJ6nYI2LhEREREJDAXJRbTCt58vl6Vw86AWNKxeKf8NfXHQuCdERuW/jYiIiIiEJAXJRWCt5e8z1lC7cgy3DSmgzXTGcdi+QqXfRERERMKUguQi+G7NThZs2ss9Z7ehasXo/DfcvhIyTygfWURERCRMKUj2U0ZmFk/NXEPLOpUZ17dZwRtr0Z6IiIhIWFOQ7KdJ8Uls3HWYh89rT3RkIf9tvnio1hiqNQzO4EREREQkoBQk+yHteAYvfLOOPs1rck7H+oXvoCYiIiIiImFNQbIf3vhhI7vTTvDYyA55Nw45Wdou2L9VqRYiIiIiYUxBciF2HDzGm/M3M6prQ3o0q1n4Dsnx7raxZpJFREREwpWC5EI8P3sdGVlZPHRue/928MVBRBQ07Fa6AxMRERGRUqMguQBrtx/kk8VJXDugOc1qx/q3ky8O6neGGD+3FxEREZGQoyC5AE/NWEvVClHcdWZr/3bIyoTkJVq0JyIiIhLmFCTnY/76Xfywbhd3ndmGGrEx/u20KxFOpGnRnoiIiEiYU5Cch8wsy99nrKVJzUpce/pp/u+oJiIiIiIiZYKC5DxMWZrMmtSDPDSiPRWiIv3f0RcHlWpCrZalNzgRERERKXUKknM5eiKTf85OpFuT6lzQtYgd85IXu9JvhdVSFhEREZGQ5leQbIwZYYxJNMZsMMY8ksfjQ40xB4wxy7L//fGkx7YYY1Zm3x8fyMGXhnd+2kzqgWP+NQ452bGDsHONUi1EREREyoCowjYwxkQCrwDDAR8QZ4yZaq1dnWvT+dbaUfkcZpi1dnfJhlr6dqcd57W5GxnesT79WtYu2s4pSwCryhYiIiIiZYA/M8l9gQ3W2k3W2hPARGB06Q7LGy9+t56j6Zk8cp6fjUNOlrNor3GvwA5KRERERILOnyC5MZB00ve+7PtyG2CMWW6MmWmM6XTS/RaYbYxZbIwZX4KxlqqNu9L4cOE2xvVtSqu6VYp+AN9iqNMWKtUI+NhEREREJLgKTbcA8krMtbm+XwKcZq1NM8aMBL4A2mQ/doa1NsUYUw/4xhiz1lo775STuAB6PECzZs38HX/A/GPmWipFR3Lv2W2LvrO1bia57bmBH5iIiIiIBJ0/M8k+oOlJ3zcBUk7ewFp70Fqblv31DCDaGFMn+/uU7NudwBRc+sYprLVvWGt7W2t7161bt8g/SElYaxnesT4Pn9eeOlUqFP0A+7bAkd1KtRAREREpI/yZSY4D2hhjWgDJwFjgypM3MMY0AHZYa60xpi8u+N5jjKkMRFhrD2V/fQ7wREB/ggAwxjCmd9PCN8xP8mJ3q8oWIiIiImVCoUGytTbDGDMB+BqIBN6x1iYYY27Lfvx14DLgdmNMBnAUGJsdMNcHpmSXUosCPrLWziqln8U7vjiIjoV6Hb0eiYiIiIgEgD8zyTkpFDNy3ff6SV+/DLycx36bgG4lHGPo88VBo54Q6dd/p4iIiIiEOHXcK6n0Y5C6ApooH1lERESkrFCQXFLbV0JWuvKRRURERMoQBckl9WsTEXXaExERESkrFCSXlC8OqjWBag29HomIiIiIBIiC5JJKjocmmkUWERERKUsUJJfEoR2wf5vykUVERETKGAXJJZEc7241kywiIiJSpihILglfPEREQcOyXwpaREREpDxRkFwSvjho0AWiK3k9EhEREREJIAXJxZWVCclLVPpNREREpAxSkFxcO9dA+mEt2hMREREpgxQkF5cW7YmIiIiUWQqSi8sXB5VqQa2WXo9ERERERAJMQXJx+bKbiBjj9UhEREREJMAUJBfHsQOwK1H5yCIiIiJllILk4kheAljlI4uIiIiUUQqSi8OXvWivUU9vxyEiIiIipUJBcnH44qBOO6hUw+uRiIiIiEgpUJBcVNa68m/KRxYREREpsxQkF9W+zXBkDzTp5fVIRERERKSUKEguqpx8ZM0ki4iIiJRZCpKLyhcP0ZWhbgevRyIiIiIipURBclH54qBxT4iM8nokIiIiIlJKFCQXRfpR2L4CGisfWURERKQsU5BcFKkrICtD+cgiIiIiZZyC5KJIzlm0p057IiIiImWZguSi8MVB9aZQtYHXIxERERGRUqQguSh88ZpFFhERESkHFCT769B2OJCkfGQRERGRckBBsr9ymog01kyyiIiISFmnINlfvjiIiIaGXb0eiYiIiIiUMgXJ/kpeDA26QHQlr0ciIiIiIqVMQbI/MjMgeYkW7YmIiIiUEwqS/bFrDaQf1qI9ERERkXJCQbI/fHHuVjPJIiIiIuWCgmR/+BZDbG2o2cLrkYiIiIhIEChI9ocvzpV+M8brkYiIiIhIEChILszR/bA7UfnIIiIiIuWIguTCpCxxt8pHFhERESk3FCQXxhcPGGjc0+uRiIiIiEiQKEgujC8O6raDitW9HomIiIiIBImC5IJY62aSlWohIiIiUq4oSC7I3k1wdK+rbCEiIiIi5YaC5IL44t2tKluIiIiIlCt+BcnGmBHGmERjzAZjzCN5PD7UGHPAGLMs+98f/d03pPniILoy1Ovg9UhEREREJIiiCtvAGBMJvAIMB3xAnDFmqrV2da5N51trRxVz39CUHO+qWkREej0SEREREQkif2aS+wIbrLWbrLUngInAaD+PX5J9vZV+FLav1KI9ERERkXLInyC5MZB00ve+7PtyG2CMWW6MmWmM6VTEfUNP6nLIylA+soiIiEg5VGi6BWDyuM/m+n4JcJq1Ns0YMxL4Amjj577uJMaMB8YDNGvWzI9hlTJfnLtVZQsRERGRcsefmWQf0PSk75sAKSdvYK09aK1Ny/56BhBtjKnjz74nHeMNa21va23vunXrFuFHKCW+eKjeDKrW93okIiIiIhJk/gTJcUAbY0wLY0wMMBaYevIGxpgGxhiT/XXf7OPu8WffkKUmIiIiIiLlVqHpFtbaDGPMBOBrIBJ4x1qbYIy5Lfvx14HLgNuNMRnAUWCstdYCee5bSj9L4BxMhYM+aHKn1yMREREREQ/4k5Ock0IxI9d9r5/09cvAy/7uG/KSc5qIaCZZREREpDxSx728+OIgIhoadPV6JCIiIiLiAQXJefHFQ8OuEF3R65GIiIiIiAcUJOeWmQEpS1X6TURERKQcU5Cc287VkH5ETUREREREyjEFybnlNBHRoj0RERGRcktBcm6+eIitAzWbez0SEREREfGIguTckrObiJi8OmqLiIiISHmgIPlkR/fB7nVKtRAREREp5xQknyx5sbvVoj0RERGRck1B8sl8iwEDjXp6PRIRERER8ZCC5JP54qBue6hYzeuRiIiIiIiHFCTnsNYFycpHFhERESn3FCTn2LMRju1XkCwiIiIiCpJ/lRzvbrVoT0RERKTcU5CcwxcHMVVcTrKIiIiIlGsKknOkroDGPSEi0uuRiIiIiIjHorweQMi4YQYc2ev1KEREREQkBGgmOUdkNFSt7/UoRERERCQEKEgWEREREclFQbKIiIiISC4KkkVEREREclGQLCIiIiKSi4JkEREREZFcFCSLiIiIiOSiIFlEREREJBcFySIiIiIiuShIFhERERHJRUGyiIiIiEguCpJFRERERHIx1lqvx3AKY8wuYKsHp64D7PbgvOI/PUehT89R6NNzFPr0HIU+PUehz5/n6DRrbd28HgjJINkrxph4a21vr8ch+dNzFPr0HIU+PUehT89R6NNzFPpK+hwp3UJEREREJBcFySIiIiIiuShI/r03vB6AFErPUejTcxT69ByFPj1HoU/PUegr0XOknGQRERERkVw0kywiIiIikouCZBERERGRXBQkA8aYEcaYRGPMBmPMI16PR/JmjNlijFlpjFlmjIn3ejwCxph3jDE7jTGrTrqvljHmG2PM+uzbml6OsbzL5zn6szEmOfu1tMwYM9LLMZZnxpimxpg5xpg1xpgEY8w92ffrdRQiCniO9DoKEcaYisaYRcaY5dnP0V+y7y/R66jc5yQbYyKBdcBwwAfEAeOstas9HZicwhizBehtrVXx9hBhjBkMpAHvWWs7Z9/3DLDXWvt09ofOmtbah70cZ3mWz3P0ZyDNWvucl2MTMMY0BBpaa5cYY6oCi4GLgOvR6ygkFPAcXY5eRyHBGGOAytbaNGNMNPAjcA9wCSV4HWkmGfoCG6y1m6y1J4CJwGiPxyQSFqy184C9ue4eDbyb/fW7uDcT8Ug+z5GECGttqrV2SfbXh4A1QGP0OgoZBTxHEiKsk5b9bXT2P0sJX0cKkt0vetJJ3/vQL3+ossBsY8xiY8x4rwcj+apvrU0F9+YC1PN4PJK3CcaYFdnpGLqUHwKMMc2BHsBC9DoKSbmeI9DrKGQYYyKNMcuAncA31toSv44UJIPJ477ynYMSus6w1vYEzgPuzL6MLCJF9xrQCugOpAL/9HQ0gjGmCvA5cK+19qDX45FT5fEc6XUUQqy1mdba7kAToK8xpnNJj6kg2c0cNz3p+yZAikdjkQJYa1Oyb3cCU3CpMhJ6dmTn8OXk8u30eDySi7V2R/YbShbwJnoteSo7h/Jz4ENr7eTsu/U6CiF5PUd6HYUma+1+YC4wghK+jhQku4V6bYwxLYwxMcBYYKrHY5JcjDGVsxdMYIypDJwDrCp4L/HIVOC67K+vA770cCySh5w3jWwXo9eSZ7IXHL0NrLHWPn/SQ3odhYj8niO9jkKHMaauMaZG9teVgLOBtZTwdVTuq1sAZJdt+RcQCbxjrX3S2xFJbsaYlrjZY4Ao4CM9T94zxnwMDAXqADuAPwFfAJ8AzYBtwBhrrRaOeSSf52go7hKxBbYAt+bk7UlwGWMGAvOBlUBW9t2P4XJe9ToKAQU8R+PQ6ygkGGO64hbmReImgD+x1j5hjKlNCV5HCpJFRERERHJRuoWIiIiISC4KkkVEREREclGQLCIiIiKSi4JkEREREZFcFCSLiIiIiOSiIFlEREREJBcFySIiIiIiufx/72AKe73nTh0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "pyplot.figure(figsize=(12,12))\n",
    "# plot loss during training\n",
    "pyplot.subplot(211)\n",
    "pyplot.title('Loss')\n",
    "pyplot.plot(hist.history['loss'], label='train')\n",
    "pyplot.plot(hist.history['val_loss'], label='Validation')\n",
    "pyplot.legend()\n",
    "# plot accuracy during training\n",
    "pyplot.subplot(212)\n",
    "pyplot.title('Accuracy')\n",
    "pyplot.plot(hist.history['accuracy'], label='train')\n",
    "pyplot.plot(hist.history['val_accuracy'], label='Validation')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d4623516",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras import regularizers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8268fcf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9b4dd010",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e01b0583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nmodel = Sequential()\\nmodel.add(Dense(32, input_dim=8, activation='relu', kernel_regularizer=regularizers.l1_l2(l1=0.01, l2=0.01)))\\nmodel.add(Dropout(0.2))\\n\\nmodel.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l1_l2(l1=0.01, l2=0.01)))\\nmodel.add(Dropout(0.2)) \\n\\nmodel.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l1_l2(l1=0.01, l2=0.01)))\\nmodel.add(Dropout(0.2)) \\n\\nmodel.add(Dense(1, activation='sigmoid'))\\n\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#earlystopping\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(32, input_dim=8, activation='relu'))\n",
    "\n",
    "model.add(Dense(32, activation='relu'))\n",
    "\n",
    "model.add(Dense(32, activation='relu'))\n",
    "\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "'''\n",
    "model = Sequential()\n",
    "model.add(Dense(32, input_dim=8, activation='relu', kernel_regularizer=regularizers.l1_l2(l1=0.01, l2=0.01)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l1_l2(l1=0.01, l2=0.01)))\n",
    "model.add(Dropout(0.2)) \n",
    "\n",
    "model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l1_l2(l1=0.01, l2=0.01)))\n",
    "model.add(Dropout(0.2)) \n",
    "\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "abb6f7dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "304bce60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "20/20 [==============================] - 1s 7ms/step - loss: 0.6712 - accuracy: 0.6042 - val_loss: 0.6326 - val_accuracy: 0.7532\n",
      "Epoch 2/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6098 - accuracy: 0.7264 - val_loss: 0.5855 - val_accuracy: 0.7532\n",
      "Epoch 3/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5573 - accuracy: 0.7459 - val_loss: 0.5413 - val_accuracy: 0.7792\n",
      "Epoch 4/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5105 - accuracy: 0.7524 - val_loss: 0.5152 - val_accuracy: 0.7597\n",
      "Epoch 5/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4804 - accuracy: 0.7590 - val_loss: 0.5124 - val_accuracy: 0.7532\n",
      "Epoch 6/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4653 - accuracy: 0.7573 - val_loss: 0.5148 - val_accuracy: 0.7468\n",
      "Epoch 7/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4520 - accuracy: 0.7736 - val_loss: 0.5131 - val_accuracy: 0.7727\n",
      "Epoch 8/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4470 - accuracy: 0.7736 - val_loss: 0.5198 - val_accuracy: 0.7597\n",
      "Epoch 9/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4393 - accuracy: 0.7785 - val_loss: 0.5218 - val_accuracy: 0.7597\n",
      "Epoch 10/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4335 - accuracy: 0.7834 - val_loss: 0.5267 - val_accuracy: 0.7468\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5)\n",
    "\n",
    "hist=model.fit(X_train, y_train, epochs=30, batch_size=32, verbose=1, validation_data=(X_test, y_test),callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1748af59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 1ms/step - loss: 0.5267 - accuracy: 0.7468\n",
      "Accuracy: 0.7467532753944397\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "567e76e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, np.round(y_pred))\n",
    "\n",
    "sns.heatmap(cm, annot=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0917d83e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "pyplot.figure(figsize=(12,12))\n",
    "# plot loss during training\n",
    "pyplot.subplot(211)\n",
    "pyplot.title('Loss')\n",
    "pyplot.plot(hist.history['loss'], label='train')\n",
    "pyplot.plot(hist.history['val_loss'], label='Validation')\n",
    "pyplot.legend()\n",
    "# plot accuracy during training\n",
    "pyplot.subplot(212)\n",
    "pyplot.title('Accuracy')\n",
    "pyplot.plot(hist.history['accuracy'], label='train')\n",
    "pyplot.plot(hist.history['val_accuracy'], label='Validation')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "23286990",
   "metadata": {},
   "outputs": [],
   "source": [
    "#droput\n",
    "model1 = Sequential()\n",
    "model1.add(Dense(32, input_dim=8, activation='relu'))\n",
    "model1.add(Dropout(0.2))\n",
    "\n",
    "model1.add(Dense(32, activation='relu'))\n",
    "model1.add(Dropout(0.2)) \n",
    "\n",
    "model1.add(Dense(32, activation='relu'))\n",
    "model1.add(Dropout(0.2)) \n",
    "\n",
    "model1.add(Dense(1, activation='sigmoid'))\n",
    "model1.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "77cd5c1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "20/20 [==============================] - 1s 8ms/step - loss: 0.7465 - accuracy: 0.4528 - val_loss: 0.7011 - val_accuracy: 0.5260\n",
      "Epoch 2/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6809 - accuracy: 0.6075 - val_loss: 0.6462 - val_accuracy: 0.6558\n",
      "Epoch 3/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6354 - accuracy: 0.6743 - val_loss: 0.5993 - val_accuracy: 0.6623\n",
      "Epoch 4/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5914 - accuracy: 0.6759 - val_loss: 0.5537 - val_accuracy: 0.7338\n",
      "Epoch 5/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5515 - accuracy: 0.7052 - val_loss: 0.5187 - val_accuracy: 0.7403\n",
      "Epoch 6/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5398 - accuracy: 0.7362 - val_loss: 0.4959 - val_accuracy: 0.7792\n",
      "Epoch 7/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5171 - accuracy: 0.7296 - val_loss: 0.4931 - val_accuracy: 0.7597\n",
      "Epoch 8/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5071 - accuracy: 0.7443 - val_loss: 0.4914 - val_accuracy: 0.7792\n",
      "Epoch 9/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4817 - accuracy: 0.7573 - val_loss: 0.4919 - val_accuracy: 0.7857\n",
      "Epoch 10/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4882 - accuracy: 0.7524 - val_loss: 0.4915 - val_accuracy: 0.7857\n",
      "Epoch 11/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4882 - accuracy: 0.7427 - val_loss: 0.4922 - val_accuracy: 0.7792\n",
      "Epoch 12/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4947 - accuracy: 0.7687 - val_loss: 0.4935 - val_accuracy: 0.7597\n",
      "Epoch 13/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4653 - accuracy: 0.7736 - val_loss: 0.4980 - val_accuracy: 0.7532\n",
      "Epoch 14/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4594 - accuracy: 0.7769 - val_loss: 0.5013 - val_accuracy: 0.7532\n",
      "Epoch 15/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4670 - accuracy: 0.7671 - val_loss: 0.5058 - val_accuracy: 0.7468\n",
      "Epoch 16/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4726 - accuracy: 0.7606 - val_loss: 0.5034 - val_accuracy: 0.7403\n",
      "Epoch 17/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4587 - accuracy: 0.7801 - val_loss: 0.5094 - val_accuracy: 0.7403\n",
      "Epoch 18/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4600 - accuracy: 0.7704 - val_loss: 0.5130 - val_accuracy: 0.7468\n",
      "Epoch 19/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4577 - accuracy: 0.7752 - val_loss: 0.5139 - val_accuracy: 0.7468\n",
      "Epoch 20/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4595 - accuracy: 0.7736 - val_loss: 0.5142 - val_accuracy: 0.7532\n",
      "Epoch 21/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4510 - accuracy: 0.7720 - val_loss: 0.5156 - val_accuracy: 0.7403\n",
      "Epoch 22/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4751 - accuracy: 0.7736 - val_loss: 0.5183 - val_accuracy: 0.7338\n",
      "Epoch 23/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4554 - accuracy: 0.7573 - val_loss: 0.5184 - val_accuracy: 0.7532\n",
      "Epoch 24/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4426 - accuracy: 0.7932 - val_loss: 0.5218 - val_accuracy: 0.7403\n",
      "Epoch 25/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4399 - accuracy: 0.7883 - val_loss: 0.5276 - val_accuracy: 0.7403\n",
      "Epoch 26/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4261 - accuracy: 0.7964 - val_loss: 0.5304 - val_accuracy: 0.7403\n",
      "Epoch 27/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4364 - accuracy: 0.7964 - val_loss: 0.5360 - val_accuracy: 0.7338\n",
      "Epoch 28/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4422 - accuracy: 0.7752 - val_loss: 0.5377 - val_accuracy: 0.7403\n",
      "Epoch 29/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4405 - accuracy: 0.8013 - val_loss: 0.5390 - val_accuracy: 0.7338\n",
      "Epoch 30/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4505 - accuracy: 0.7899 - val_loss: 0.5348 - val_accuracy: 0.7338\n"
     ]
    }
   ],
   "source": [
    "\n",
    "hist=model1.fit(X_train, y_train, epochs=30, batch_size=32, verbose=1, validation_data=(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5a48e73b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 998us/step - loss: 0.5348 - accuracy: 0.7338\n",
      "Accuracy: 0.7337662577629089\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model1.evaluate(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4eaa8acc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 756us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAD4CAYAAACt8i4nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAR/klEQVR4nO3de5BcZZnH8e+TCyoJlAmQK3gBA94WcGVZWVZA7okXYrkooBjcaFYEBZUVsFxQWZXCOyuKQQ0B5ZLFBeIFNA4EwmqQIAHBEBGEkCImCCI3IZnuZ/+YNjuQOD1D5u3uOfl+rFPd53TP6ceq1K8enn7P6chMJEnlDGt3AZJUdQatJBVm0EpSYQatJBVm0EpSYSNKf8C6P97jsgZtYNJOU9tdgjrQg39eHpt6joFkzshtd9zkz+sPO1pJKqx4RytJLVWvtbuCDRi0kqql1t3uCjZg0EqqlMx6u0vYgEErqVrqBq0klWVHK0mF+WWYJBVmRytJZaWrDiSpML8Mk6TCHB1IUmF+GSZJhdnRSlJhfhkmSYX5ZZgklZXpjFaSynJGK0mFOTqQpMLsaCWpsNq6dlewAYNWUrU4OpCkwjpwdOCv4Eqqlnq9/1sfImKXiFjaa3s0Ik6MiLERsSAi7mo8jmlWkkErqVoGKWgzc3lm7p6ZuwOvBZ4ELgdOAboycwrQ1djvk6MDSZWSZb4MOwC4OzPvi4jDgP0ax+cCC4GT+/pjg1ZStZSZ0R4BXNx4Pj4zVwFk5qqIGNfsjx0dSKqWAYwOImJWRCzptc169ukiYgvgLcB/P9eS7GglVcsAOtrMnA3MbvK2qcCvMnN1Y391RExsdLMTgTXNPseOVlK1DNKXYb0cyf+PDQDmAzMaz2cAVzY7gR2tpGoZxBltRGwJHAT8W6/DZwLzImImsAI4vNl5DFpJ1dI9eDf+zswngW2edewhelYh9JtBK6laOvDKMINWUrV4rwNJKsyOVpIKs6OVpMLsaCWpsEFcdTBYDFpJ1ZLZ7go2YNBKqhZntJJUmEErSYX5ZZgkFVartbuCDRi0kqrF0YEkFWbQSlJhzmglqaysu45WkspydCBJhbnqQJIKs6OVpMIM2s3H7+9byUmnfW79/soHVnH8e49m6e13cu+KlQA89vjjbDV6NN+fe067ylQLTZo8gXPOPYtx47elXq9z4fnzmH3uBZx+xsc45NA3sHbtOu79/Qo+dNypPPrnx9pd7tDVgTeViSxc1Lo/3tN5/69brFarsf/0o7n4vC8zacL49cc//1/nMXrUlhz7r+9sY3XtMWmnqe0uoeXGj9+O8RO247Zbf8Oo0aPouu77vPuo45g0eQKLrltMrVbjPz51EgBnnP6FNlfbHg/+eXls6jme/NL7+p05W37kvE3+vP5o2tFGxMuBw4DJQAIPAPMzc1nh2ipj8ZKl7DB54jNCNjO5+prr+c7ZZ7axMrXS6tUPsnr1gwA88fgT/Hb5PUycNJ6F1/zv+vfcfNNS3nzYoe0qsRo6cHnXsL5ejIiTgUuAAH4J3NR4fnFEnFK+vGq4qus6ph247zOO3Xzr7WwzZgwv3mFym6pSO+3wosn83a6v4OYltz7j+FHvehtdC65vU1UVUav1f2uRPoMWmAn8Q2aemZnfbWxnAns2XtuoiJgVEUsiYsm3Lrh4MOsdctatW8fCG27k4P1f/4zjP16wkGkH7fs3/kpVNmrUlsy58Gw+cepnefyxJ9Yf//BJ76e7u8Zl8+a3sbqhL+v1fm+t0mx0UAcmAfc96/jExmsblZmzgdngjHbR4iW8Yued2HbsmPXHurtr/Oy6nzPvO2e3sTK1w4gRI5hz4dlcNu8H/OgHC9Yff8eR0znokP1421uOaV9xVdGBo4NmQXsi0BURdwH3N469CHgZcHzBuiqjp3Pd7xnHFi+5hR1fvD0Txm3XnqLUNl/52mf47fJ7OPec89cf2/+A1/PBE9/HYdPexV/+8lT7iquKoXavg8y8OiJ2pmdUMJme+exK4KbM7LzLLzrMX556il/cdAunf+xDzzh+1c+uY+qB+7WnKLXNP77utbzjyOnccftyrl10BQCf+fSX+OxZn2CLLbbgsivmALBkya38+4dPb2OlQ1wHdrQu71JbbI7Lu9TcYCzveuK0I/qdOaM+fUlnLO+SpCGlA0cHzVYdSNLQUs/+b01ExAsj4rKIuDMilkXEXhExNiIWRMRdjccxzc5j0EqqlEFe3vVV4OrMfDmwG7AMOAXoyswpQFdjv08GraRqGaSONiK2BvYBvg2QmWsz8xF6rpSd23jbXGB6s5IMWknVMoCg7X1xVWOb1etMOwIPAnMi4paI+FZEjALGZ+YqgMbjuGYl+WWYpGoZwKW1vS+u2ogRwN8DH8zMGyPiq/RjTLAxdrSSKiXr2e+tiZXAysy8sbF/GT3BuzoiJgI0Htc0O5FBK6laBmlGm5l/AO6PiF0ahw4AfgPMB2Y0js0ArmxWkqMDSdUyuDeL+SDwvYjYArgHeA89Deq8iJgJrAAOb3YSg1ZStQziJbiZuRTYYyMvHTCQ8xi0kqqlA+91YNBKqpSsdd4luAatpGqxo5WksvqxbKvlDFpJ1WLQSlJhnTeiNWglVUt2d17SGrSSqqXzctaglVQtfhkmSaXZ0UpSWXa0klSaHa0klZXd7a5gQwatpErpwF8bN2glVYxBK0ll2dFKUmEGrSQVlrVodwkbMGglVYodrSQVlnU7Wkkqyo5WkgrLtKOVpKLsaCWpsLqrDiSpLL8Mk6TCDFpJKiw773a0Bq2karGjlaTCBnN5V0TcCzwG1IDuzNwjIsYClwIvAe4F3p6Zf+rrPMMGrSJJ6gC1WvR766c3ZObumblHY/8UoCszpwBdjf0+GbSSKiUz+r09R4cBcxvP5wLTm/2BQSupUrIe/d76czrgpxFxc0TMahwbn5mrABqP45qdxBmtpEoZyKqDRnjO6nVodmbO7rW/d2Y+EBHjgAURcedzqcmglVQpA1l10AjV2X28/kDjcU1EXA7sCayOiImZuSoiJgJrmn2OowNJlVKrD+v31peIGBURW/31OXAwcDswH5jReNsM4MpmNdnRSqqUQbxgYTxweURAT1ZelJlXR8RNwLyImAmsAA5vdiKDVlKl1AdpHW1m3gPstpHjDwEHDORcBq2kSvF+tJJU2GZ5r4MXTHp96Y/QEHTipH3aXYIqarBGB4PJjlZSpTRbTdAOBq2kSunAyYFBK6laHB1IUmGuOpCkwjrwR3ANWknVktjRSlJR3Y4OJKksO1pJKswZrSQVZkcrSYXZ0UpSYTU7WkkqawC/ZNMyBq2kSqnb0UpSWd5URpIK88swSSqsHo4OJKmoWrsL2AiDVlKluOpAkgpz1YEkFeaqA0kqzNGBJBXm8i5JKqxmRytJZdnRSlJhnRi0w9pdgCQNpoz+b/0REcMj4paI+GFjf2xELIiIuxqPY5qdw6CVVCn1AWz9dAKwrNf+KUBXZk4Buhr7fTJoJVVKbQBbMxGxPfBG4Fu9Dh8GzG08nwtMb3Yeg1ZSpdSj/1tEzIqIJb22Wc863VeAj/HMBnh8Zq4CaDyOa1aTX4ZJqpSBfBmWmbOB2Rt7LSLeBKzJzJsjYr9NqcmglVQpg7jqYG/gLRExDXg+sHVEfBdYHRETM3NVREwE1jQ7kaMDSZWSA9j6PE/mqZm5fWa+BDgCuCYz3wXMB2Y03jYDuLJZTXa0kiqlBfc6OBOYFxEzgRXA4c3+wKCVVCklbvydmQuBhY3nDwEHDOTvDVpJlVLvwBslGrSSKqUTL8E1aCVVSuf1swatpIqxo5Wkwrqj83pag1ZSpXRezBq0kirG0YEkFebyLkkqrPNi1qCVVDGODiSpsFoH9rQGraRKsaOVpMLSjlaSyrKj3YycN/uLvHHagax58I/s/pqeO6rtuusr+frXzmTU6C25776VHP3u43nsscfbXKlaZcTzRvKBS09jxPNGMmz4cG676kZ++uXLANh7xiHs/e6DqdfqLLvmFn505kVtrnbocnnXZuSCC+bx9a/PYc6cr64/9s1zP8/JJ5/B9YsWc8yMd3DSR4/l9E9+vo1VqpW6n17HuUf9J2uffJphI4Zz/GWf5M6FSxn5/C141UGv5YtTT6a2tpvR22zd7lKHtM6LWX/KpphFN9zIw3965BnHdtl5J65ftBiAn3Ut4q1vndaGytROa598GoDhI4YzbMRwyOSf3nkQ135jPrW13QA8/tCj7SxxyOsm+721ikHbQnfcsZw3v/lgAP7lbW9ih+0ntbkitVoMCz7848/xyZu/yV03/JoVS+9m2x0n8NI9X86HrjiDYy89jR123bHdZQ5pOYD/tcpzDtqIeE8fr63/rfR6/Ynn+hGV895ZH+ED7z+GGxdfxVZbjWLt2nXtLkktlvXky9NO5Yy9jmOH3XZiws7bM3z4cF6w9SjOnv4f/PCz3+Poc05od5lDWn0AW6tsyoz2U8Ccjb3Q+7fSR2wxuRNHJm2xfPndTH3jUQBMmbIj06YO6GeHVCFPPfokdy9exi777sYjf3iY23/ySwDuv/Vu6vVk1NiteOLhx9pc5dA05JZ3RcRtf+slYPzgl1Nt2223DQ8++BARwcdPPYFvzr6w3SWphUaN3Ypad42nHn2SEc8byZS9X821585n7ZNP87K9XsXdi5ex7UsnMGLkCEN2EwzF5V3jgUOAPz3reAA/L1JRRXz3wnPYd5+92Hbbsdx7zxI+9ekvMHr0KI499hgArrjix5w/99L2FqmW2nrcGI744rHEsGEMGxbc+qPFLLvmFoaPHM7bz3o/J/3kLLrXdXPJR7/R7lKHtFp2Xkcb2UdREfFtYE5m3rCR1y7KzKOafYCjA23MiZP2aXcJ6kBfuPfi2NRzHPXit/Y7cy667/JN/rz+6LOjzcyZfbzWNGQlqdWG3IxWkoaaoTijlaQhxUtwJakwRweSVFgnrjrwElxJlVIn+731JSKeHxG/jIhbI+KOiPhU4/jYiFgQEXc1Hsc0q8mglVQpg3gJ7tPA/pm5G7A7cGhEvA44BejKzClAV2O/TwatpEoZrJvKZI+/3jB6ZGNL4DBgbuP4XGB6s5oMWkmVMlijA4CIGB4RS4E1wILMvBEYn5mrABqP45qdx6CVVCmZ2e+t950GG9usZ52rlpm7A9sDe0bEq59LTa46kFQpA/m58d53GmzyvkciYiFwKLA6IiZm5qqImEhPt9snO1pJlTKIqw62i4gXNp6/ADgQuBOYD8xovG0GcGWzmuxoJVVKXzfKGqCJwNyIGE5PUzovM38YEb8A5kXETGAFcHizExm0kiplsC7BzczbgNds5PhDwIDu2m/QSqoUL8GVpMI68RJcg1ZSpXj3LkkqzKCVpMIGcdXBoDFoJVWKHa0kFeaqA0kqrJad96thBq2kSnFGK0mFOaOVpMKc0UpSYXVHB5JUlh2tJBXmqgNJKszRgSQV5uhAkgqzo5WkwuxoJamwWtbaXcIGDFpJleIluJJUmJfgSlJhdrSSVJirDiSpMFcdSFJhXoIrSYU5o5WkwpzRSlJhdrSSVFgnrqMd1u4CJGkwZWa/t75ExA4RcW1ELIuIOyLihMbxsRGxICLuajyOaVaTQSupUmpZ7/fWRDfw0cx8BfA64LiIeCVwCtCVmVOArsZ+nwxaSZVSz+z31pfMXJWZv2o8fwxYBkwGDgPmNt42F5jerCaDVlKlDGR0EBGzImJJr23Wxs4ZES8BXgPcCIzPzFWNz1oFjGtWk1+GSaqUgVwZlpmzgdl9vSciRgPfB07MzEcjYsA1GbSSKmUwl3dFxEh6QvZ7mfk/jcOrI2JiZq6KiInAmmbncXQgqVIGa0YbPa3rt4FlmfmlXi/NB2Y0ns8ArmxWU3Ti4t6qiohZjf9Ukdbz30Vnioh/BhYBvwb+ukTh4/TMaecBLwJWAIdn5sN9nsugbZ2IWJKZe7S7DnUW/11Un6MDSSrMoJWkwgza1nIOp43x30XFOaOVpMLsaCWpMINWkgozaFskIg6NiOUR8buIaHq3H1VfRHwnItZExO3trkVlGbQtEBHDgXOAqcArgSMbt1vT5u184NB2F6HyDNrW2BP4XWbek5lrgUvoudWaNmOZeT3Q5xVFqgaDtjUmA/f32l/ZOCZpM2DQtsbG7qvmujppM2HQtsZKYIde+9sDD7SpFkktZtC2xk3AlIh4aURsARxBz63WJG0GDNoWyMxu4HjgJ/T87tC8zLyjvVWp3SLiYuAXwC4RsTIiZra7JpXhJbiSVJgdrSQVZtBKUmEGrSQVZtBKUmEGrSQVZtBKUmEGrSQV9n8p0Zt+Ba8cNAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model1.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, np.round(y_pred))\n",
    "\n",
    "sns.heatmap(cm, annot=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bba5a736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "pyplot.figure(figsize=(12,12))\n",
    "# plot loss during training\n",
    "pyplot.subplot(211)\n",
    "pyplot.title('Loss')\n",
    "pyplot.plot(hist.history['loss'], label='train')\n",
    "pyplot.plot(hist.history['val_loss'], label='Validation')\n",
    "pyplot.legend()\n",
    "# plot accuracy during training\n",
    "pyplot.subplot(212)\n",
    "pyplot.title('Accuracy')\n",
    "pyplot.plot(hist.history['accuracy'], label='train')\n",
    "pyplot.plot(hist.history['val_accuracy'], label='Validation')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b703a263",
   "metadata": {},
   "outputs": [],
   "source": [
    "#l1 regularisation\n",
    "model2 = Sequential()\n",
    "model2.add(Dense(32, input_dim=8, activation='relu', kernel_regularizer=regularizers.l1(0.01)))\n",
    "\n",
    "model2.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l1(0.01)))\n",
    "\n",
    "model2.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l1(0.01)))\n",
    "\n",
    "model2.add(Dense(1, activation='sigmoid'))\n",
    "model2.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "109b8d96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "20/20 [==============================] - 1s 16ms/step - loss: 4.1615 - accuracy: 0.6547 - val_loss: 3.9567 - val_accuracy: 0.6494\n",
      "Epoch 2/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 3.7498 - accuracy: 0.6612 - val_loss: 3.5669 - val_accuracy: 0.6558\n",
      "Epoch 3/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 3.3715 - accuracy: 0.6678 - val_loss: 3.2027 - val_accuracy: 0.6753\n",
      "Epoch 4/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 3.0205 - accuracy: 0.6873 - val_loss: 2.8646 - val_accuracy: 0.6883\n",
      "Epoch 5/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.6955 - accuracy: 0.7085 - val_loss: 2.5539 - val_accuracy: 0.7208\n",
      "Epoch 6/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.3942 - accuracy: 0.7427 - val_loss: 2.2640 - val_accuracy: 0.7338\n",
      "Epoch 7/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.1160 - accuracy: 0.7524 - val_loss: 1.9989 - val_accuracy: 0.7532\n",
      "Epoch 8/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8620 - accuracy: 0.7476 - val_loss: 1.7575 - val_accuracy: 0.7662\n",
      "Epoch 9/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.6318 - accuracy: 0.7671 - val_loss: 1.5418 - val_accuracy: 0.7857\n",
      "Epoch 10/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.4284 - accuracy: 0.7638 - val_loss: 1.3536 - val_accuracy: 0.7857\n",
      "Epoch 11/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.2492 - accuracy: 0.7638 - val_loss: 1.1905 - val_accuracy: 0.7922\n",
      "Epoch 12/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.0977 - accuracy: 0.7704 - val_loss: 1.0559 - val_accuracy: 0.7857\n",
      "Epoch 13/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.9728 - accuracy: 0.7671 - val_loss: 0.9459 - val_accuracy: 0.7922\n",
      "Epoch 14/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.8735 - accuracy: 0.7736 - val_loss: 0.8631 - val_accuracy: 0.7792\n",
      "Epoch 15/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.8011 - accuracy: 0.7622 - val_loss: 0.8055 - val_accuracy: 0.7727\n",
      "Epoch 16/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.7521 - accuracy: 0.7655 - val_loss: 0.7695 - val_accuracy: 0.7662\n",
      "Epoch 17/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.7190 - accuracy: 0.7638 - val_loss: 0.7432 - val_accuracy: 0.7662\n",
      "Epoch 18/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6940 - accuracy: 0.7622 - val_loss: 0.7233 - val_accuracy: 0.7532\n",
      "Epoch 19/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6757 - accuracy: 0.7590 - val_loss: 0.7083 - val_accuracy: 0.7403\n",
      "Epoch 20/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6603 - accuracy: 0.7557 - val_loss: 0.6948 - val_accuracy: 0.7403\n",
      "Epoch 21/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6474 - accuracy: 0.7573 - val_loss: 0.6844 - val_accuracy: 0.7468\n",
      "Epoch 22/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6363 - accuracy: 0.7655 - val_loss: 0.6749 - val_accuracy: 0.7597\n",
      "Epoch 23/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6268 - accuracy: 0.7622 - val_loss: 0.6673 - val_accuracy: 0.7403\n",
      "Epoch 24/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6187 - accuracy: 0.7590 - val_loss: 0.6609 - val_accuracy: 0.7338\n",
      "Epoch 25/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6127 - accuracy: 0.7638 - val_loss: 0.6545 - val_accuracy: 0.7338\n",
      "Epoch 26/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6053 - accuracy: 0.7655 - val_loss: 0.6486 - val_accuracy: 0.7338\n",
      "Epoch 27/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6003 - accuracy: 0.7704 - val_loss: 0.6441 - val_accuracy: 0.7273\n",
      "Epoch 28/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5953 - accuracy: 0.7687 - val_loss: 0.6398 - val_accuracy: 0.7403\n",
      "Epoch 29/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5896 - accuracy: 0.7736 - val_loss: 0.6347 - val_accuracy: 0.7403\n",
      "Epoch 30/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5849 - accuracy: 0.7720 - val_loss: 0.6300 - val_accuracy: 0.7403\n"
     ]
    }
   ],
   "source": [
    "\n",
    "hist=model2.fit(X_train, y_train, epochs=30, batch_size=32, verbose=1, validation_data=(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7010047b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 1ms/step - loss: 0.6300 - accuracy: 0.7403\n",
      "Accuracy: 0.7402597665786743\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model2.evaluate(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "53035453",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 749us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dwOR6NRm0kiqlUVMHABExLCIWA6uBeZm5EBiTmZ0AtdfR9cYxaCVVymAuWOh7u4DaNq3vWJnZnZnjgX2AwyLioBdTk6sOJFXKYJZ3ZeYsYNYAvvfHiFgAHA+sioj2zOyMiHZ6u91+2dFKqpQGrjrYMyJeWnv/EuAY4EFgLjCl9rUpwHX1arKjlVQp/V3tOkjtQEdEDKO3KZ2dmddHxG3A7IiYCjwK1L17v0ErqVIa9bjxzLwPeMMWjj8OTBzMWAatpErxmWGSVFgDpw4axqCVVCl2tJJUmHfvkqTCWvHG3watpEpx6kCSCjNoJakwVx1IUmF2tJJUmKsOJKmw7my9p4YZtJIqxTlaSSrMOVpJKsw5WkkqrMepA0kqy45Wkgpz1YEkFebUgSQV5tSBJBVmRytJhdnRSlJh3dnd7BI2Y9BKqhQvwZWkwrwEV5IKs6OVpMJcdSBJhbnqQJIKa8VLcNuaXYAkNVJmDnjrT0TsGxE3R8TSiFgSEZ+oHR8VEfMiYlntdWS9mgxaSZXSkzngrY4u4MzMPBA4HPhYRLwWmA7Mz8xxwPzafr8MWkmV0qiONjM7M/Pu2vungKXAWGAS0FH7WgcwuV5NBq2kSukhB7xFxLSIWNRnm7alMSNif+ANwEJgTGZ2Qm8YA6Pr1eTJMEmVMph1tJk5C5jV33ciYlfgWuCTmbk2IgZdk0ErqVIaueogInagN2SvyMwf1A6vioj2zOyMiHZgdb1xnDqQVCmNOhkWva3rJcDSzJzZ56O5wJTa+ynAdfVqsqOVVCkNvAT3TcCpwP0Rsbh27CxgBjA7IqYCjwIn1xvIoJVUKY26MiwzbwVeaEJ24mDGMmglVYo3lZGkwlrxpjLRiulfVRExrbacRNrIfxfV56qDobXFxdDa7vnvouIMWkkqzKCVpMIM2qHlPJy2xH8XFefJMEkqzI5WkgozaCWpMIN2iETE8RHxq4h4KCLq3pFd1RcR34uI1RHxQLNrUVkG7RCIiGHAt4ETgNcC76k9EkPbt/8Ajm92ESrPoB0ahwEPZebyzHwOuJrex2FoO5aZtwBPNLsOlWfQDo2xwGN99lfUjknaDhi0Q2NLt1pzXZ20nTBoh8YKYN8++/sAK5tUi6QhZtAOjTuBcRHx8ojYETiF3sdhSNoOGLRDIDO7gNOAH9P7bPjZmbmkuVWp2SLiKuA24NURsaL2aBRVkJfgSlJhdrSSVJhBK0mFGbSSVJhBK0mFGbSSVJhBK0mFGbSSVNj/AW2eiU+OYmRfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model2.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, np.round(y_pred))\n",
    "\n",
    "sns.heatmap(cm, annot=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "44940bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "pyplot.figure(figsize=(12,12))\n",
    "# plot loss during training\n",
    "pyplot.subplot(211)\n",
    "pyplot.title('Loss')\n",
    "pyplot.plot(hist.history['loss'], label='train')\n",
    "pyplot.plot(hist.history['val_loss'], label='Validation')\n",
    "pyplot.legend()\n",
    "# plot accuracy during training\n",
    "pyplot.subplot(212)\n",
    "pyplot.title('Accuracy')\n",
    "pyplot.plot(hist.history['accuracy'], label='train')\n",
    "pyplot.plot(hist.history['val_accuracy'], label='Validation')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "79512986",
   "metadata": {},
   "outputs": [],
   "source": [
    "#l2\n",
    "\n",
    "model3 = Sequential()\n",
    "model3.add(Dense(32, input_dim=8, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "\n",
    "model3.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "\n",
    "model3.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "\n",
    "model3.add(Dense(1, activation='sigmoid'))\n",
    "model3.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9fc15c20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "20/20 [==============================] - 1s 8ms/step - loss: 1.3614 - accuracy: 0.7020 - val_loss: 1.2841 - val_accuracy: 0.7208\n",
      "Epoch 2/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.2030 - accuracy: 0.7394 - val_loss: 1.1679 - val_accuracy: 0.7597\n",
      "Epoch 3/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.0890 - accuracy: 0.7557 - val_loss: 1.0721 - val_accuracy: 0.7532\n",
      "Epoch 4/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.0034 - accuracy: 0.7655 - val_loss: 1.0000 - val_accuracy: 0.7662\n",
      "Epoch 5/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.9325 - accuracy: 0.7720 - val_loss: 0.9390 - val_accuracy: 0.7597\n",
      "Epoch 6/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.8758 - accuracy: 0.7866 - val_loss: 0.8942 - val_accuracy: 0.7662\n",
      "Epoch 7/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.8274 - accuracy: 0.7850 - val_loss: 0.8525 - val_accuracy: 0.7727\n",
      "Epoch 8/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.7855 - accuracy: 0.7818 - val_loss: 0.8183 - val_accuracy: 0.7727\n",
      "Epoch 9/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.7521 - accuracy: 0.7785 - val_loss: 0.7906 - val_accuracy: 0.7727\n",
      "Epoch 10/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.7202 - accuracy: 0.7801 - val_loss: 0.7631 - val_accuracy: 0.7662\n",
      "Epoch 11/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6929 - accuracy: 0.7850 - val_loss: 0.7413 - val_accuracy: 0.7597\n",
      "Epoch 12/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6711 - accuracy: 0.7915 - val_loss: 0.7200 - val_accuracy: 0.7727\n",
      "Epoch 13/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6519 - accuracy: 0.7915 - val_loss: 0.7028 - val_accuracy: 0.7727\n",
      "Epoch 14/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6329 - accuracy: 0.7899 - val_loss: 0.6900 - val_accuracy: 0.7662\n",
      "Epoch 15/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6202 - accuracy: 0.7948 - val_loss: 0.6808 - val_accuracy: 0.7662\n",
      "Epoch 16/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6053 - accuracy: 0.7932 - val_loss: 0.6696 - val_accuracy: 0.7662\n",
      "Epoch 17/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5949 - accuracy: 0.7899 - val_loss: 0.6574 - val_accuracy: 0.7662\n",
      "Epoch 18/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5833 - accuracy: 0.7915 - val_loss: 0.6501 - val_accuracy: 0.7662\n",
      "Epoch 19/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5765 - accuracy: 0.7899 - val_loss: 0.6458 - val_accuracy: 0.7662\n",
      "Epoch 20/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5691 - accuracy: 0.7915 - val_loss: 0.6374 - val_accuracy: 0.7662\n",
      "Epoch 21/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5594 - accuracy: 0.7980 - val_loss: 0.6291 - val_accuracy: 0.7597\n",
      "Epoch 22/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5522 - accuracy: 0.7948 - val_loss: 0.6229 - val_accuracy: 0.7662\n",
      "Epoch 23/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5469 - accuracy: 0.7980 - val_loss: 0.6186 - val_accuracy: 0.7662\n",
      "Epoch 24/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5426 - accuracy: 0.7997 - val_loss: 0.6135 - val_accuracy: 0.7662\n",
      "Epoch 25/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5367 - accuracy: 0.7997 - val_loss: 0.6178 - val_accuracy: 0.7662\n",
      "Epoch 26/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5327 - accuracy: 0.8029 - val_loss: 0.6127 - val_accuracy: 0.7662\n",
      "Epoch 27/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5283 - accuracy: 0.7964 - val_loss: 0.6072 - val_accuracy: 0.7662\n",
      "Epoch 28/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5242 - accuracy: 0.7980 - val_loss: 0.6030 - val_accuracy: 0.7662\n",
      "Epoch 29/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5201 - accuracy: 0.7997 - val_loss: 0.6004 - val_accuracy: 0.7597\n",
      "Epoch 30/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5182 - accuracy: 0.8029 - val_loss: 0.6057 - val_accuracy: 0.7662\n"
     ]
    }
   ],
   "source": [
    "\n",
    "hist=model3.fit(X_train, y_train, epochs=30, batch_size=32, verbose=1, validation_data=(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "74f92238",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 1ms/step - loss: 0.6057 - accuracy: 0.7662\n",
      "Accuracy: 0.7662337422370911\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model3.evaluate(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a124704d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model3.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, np.round(y_pred))\n",
    "\n",
    "sns.heatmap(cm, annot=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "912e1858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ftnvS8CRKyip4OzXdDcWJiIiISENRSD4eXl7OlHAFO+Gn/xy2u3v7UIZ2jGTmL2lUVGgAn4iIiEhzpZB8vDoMh94Xw09PQu72w3ZfPSKJ9L2FfLch2w3FiYiIiEhDUEg+EaMfBFvhdLuo4exe7YkJ9WfmfA3gExEREWmuFJJPRJskGH4jLJ8N2xcdssvPx4uJQxL5dl0W6XsPuKlAEREREakPheQTdeqfICgavvg/qLGAyMRhHfAyhlm/bHNTcSIiIiJSH/UKycaYl40xWcaYlUfYf5UxZrnr9rMxpn99rudRAsLgjP+DbfNh9YeH7IoND2R0z7a8lZpOUWm5mwoUERERkRNV35bkV4ExR9m/BTjNWtsPeBiYXs/reZaBV0PbXs5y1aVFh+yaPDyZvftL+GxlppuKExEREZETVa+QbK39Hth7lP0/W2v3ub5dACTU53oex9sHznkEctLg1+cP2XVS5yg6RQcz/fstlGs6OBEREZFmpSn7JF8HfNaE12sanc+ArmfD949DwcFp37y8DLeO7sqazDzeWaTFRURERESakyYJycaY03FC8p1HOWaaMSbVGJOand3M5hg++y/OCnzz/nbI5nH940hJasM/vlhHflGpm4oTERERkePV6CHZGNMPeBG40Fq750jHWWunW2tTrLUpMTExjV1Ww4rpDinXwqJXIGtN1WZjDPdd0IvdBSU89e1GNxYoIiIiIsejUUOyMaYD8B4w2Vq7vjGv5Xaj7ga/UGdKuGr6JURw6eAEXv5pC1t273dTcSIiIiJyPOo7BdwbwHyguzEmwxhznTHmBmPMDa5D7gOigGeMMUuNMan1rNdzBUfBaXfApm9gw9eH7LpjTHf8vL145NM1R3iwiIiIiHgSY63nzbyQkpJiU1ObYZ4uK4anh4GPP9zwkzP7hcsz8zby2OfrmHndME7pGu3GIkVEREQEwBizyFqbUts+rbjXkHz84eyHIXstLH71kF3XntyRDpFBPPTJKsrKK9xTn4iIiIjUiUJyQ+txPiSdAnP/CoU5VZsDfL3589ierN9VwOu/arlqEREREU+mkNzQjHEWGDmwF354/JBd5/Rux4hOUfzrq/XkHChxU4EiIiIiciwKyY0hbgD0nwi/PA97N1dtrpwSLq+wlCe+3uC++kRERETkqBSSG8uZ94KXjzMlXLXBkT1jw5g4tAMzFqSxYVe+GwsUERERkSNRSG4sYXEw6i5YNweWv3nIrj+e1Y1gP28e/nQNnji7iIiIiEhrp5DcmEbcDInDYc4dkJtRtTkqxJ9bR3fj+/XZzF2X5cYCRURERKQ2CsmNycsbLn4WKsrhg5ug4uDUb1ePSKJTTDAPf7KGkjJNCSciIiLiSRSSG1tkJzjnL7DlO1j4YtVmX28v7j2/F1t27+e1+VvdV5+IiIiIHEYhuSkM/g10GQ1f3Qe7N1ZtPr17W0Z1j+E/X29gd0GxGwsUERERkeoUkpuCMTDuv86KfO9fD+VlVbvuOa8XhaXl/PPL9W4sUERERESqU0huKmFxcN4/YXsq/PTvqs1d2oZw9YhkZi/cxqoduW4sUEREREQqKSQ3pT6XQO+LYd6jkLmsavOtZ3YlItCXhz5erSnhRERERDyAQnJTMgbO+xcERcH7N0CZ0w85PMiXP53dnV+27OXzlTvdXKSIiIiIKCQ3taBIGPcUZK2GuY9Ubb5iSCI92ofyyJw1FJWWu7FAEREREVFIdoduZ8Oga+CnJyFtPgA+3l7cd34vMvYV8tKPW9xcoIiIiEjrppDsLuc8AhEd4IMboLgAgJO6RHNO73Y8PXcju/KK3FygiIiISOulkOwu/qFw8XOwLw2+vKdq8/+N7UVZueWxz9e5sTgRERGR1k0h2Z2SToKTboZFr8CGrwDoEBXEtad05N3FGSxNz3FvfSIiIiKtlEKyu51+D8T0hA9vhgN7Abj5jC7EhPrz0MerNCWciIiIiBsoJLubbwCMfx4O7IY5twMQ4u/DHed0Z/G2HD5atsPNBYqIiIi0PgrJniC2P5x2F6x817kBEwYl0Dc+nL/NWcuBkrJjnEBEREREGpJCsqc45Q8QPxg+/RPkZeLlZbj/gl7szCviue82u7s6ERERkVZFIdlTePvAxc9DaSF8dAtYS0pyJBf0j+P57zaxPafQ3RWKiIiItBoKyZ4kuiuc9RBs/AoWvQrAXef2wBj425w17q1NREREpBVRSPY0Q6ZCx5Hwxf/B3i3ERwRy/cjOfLI8k4Vb97q7OhEREZFWQSHZ03h5wYXPgJc3fHAjVJRzw2mdiQ0P4MGPV1FRoSnhRERERBqbQrInikiEcx+DbfNh/tME+nlz17k9WLk9j3cWZbi7OhEREZEWTyHZU/W/AnqcD98+DLtWM65/HIOT2vDYF+vILyp1d3UiIiIiLZpCsqcyBs5/AvzD4P1pmPJS7r+gF7sLinnymw3urk5ERESkRVNI9mQhMTDuSdi5Ar5/jH4JEVw5rAMv/riFeeuy3F2diIiISIulkOzpepwH/a+EH/4JGancd34vurcL5bY3l2ruZBEREZFGopDcHJz7KITGwfvXE2CLeXbSYMrLLTfNWkxxWbm7qxMRERFpcRSSm4OAcLjoGdizEb5+gI7Rwfzj0n4sS8/hr59qkRERERGRhqaQ3Fx0Og2G3QC/Pg+b5jKmTyxTT+3I/+an8dGyHe6uTkRERKRFUUhuTs68H6K6wrvXwe6N/L8xPRiS3Ia73l3Oxqx8d1cnIiIi0mIoJDcnfkEwcbZzf8bF+O7fxVNXDiLIz5sbZi5mf3GZe+sTERERaSEUkpub6C5w1TtQuBdmXkI73yL+c8VANmcX8Of3V2Ctlq0WERERqS+F5OYofhBcPhN2r4c3ruDkpGD+eFY3Ply6g5m/bHN3dSIiIiLNnkJyc9X5dBg/HbYtgHeu5aaRyZzePYaHP17NsvQcd1cnIiIi0qwpJDdnfcbD2H/Aujl4fXob/76sPzGh/tw0azH79pe4uzoRERGRZkshubkbOhVG/j9YMpOI+Y/yzFWDyM4v5g9vLaWiQv2TRURERE6EQnJLcPqfYfAU+PFf9M94nXsv6MW8ddk8M2+juysTERERaZYUklsCY+C8f0HPC+CLu5kUuIALB8Txr6/W89PG3e6uTkRERKTZUUhuKby8YfyLkHwq5sOb+Hu/LDrHhPD7N5awM7fI3dWJiIiINCsKyS2JbwBcMQtiehLw/hRePgsKS8u5+fXFlJZXuLs6ERERkWZDIbmlCQiHSe9CSFsS50zhqbNDSE3bx98/W+vuykRERESaDYXklii0HUx+H7x8OOPX67llcCAv/riFz1dmursyERERkWZBIbmliuwEk96Bolz+sOsuTo735o63l7Nl9353VyYiIiLi8RSSW7LY/jDxdbz2beFlv8cJ9irhxpmLKCwpd3dlIiIiIh5NIbml6zgSLnkR/8xUPo19kU279nHvhyuxVguNiIiIiByJQnJr0OtCOP9fRO2YxwcJb/LOonTeSk13d1UiIiIiHsvH3QVIE0m5Fgqy6T3vrzwVHcQfP/Smd1w4feLD3V2ZiIiIiMdRS3Jrctr/gyFTOb/gbW72/4ybZi0mt7DU3VWJiIiIeByF5NbEGDj379DrIn5f/j+G5n3B7W8vU/9kERERkRrqFZKNMS8bY7KMMSuPsN8YY540xmw0xiw3xgyqz/WkAXh5w/jp0PE0HvOdTtnaz5n+/WZ3VyUiIiLiUerbkvwqMOYo+88Furpu04Bn63k9aQg+/nDFLEz7vjzv/yRff/kxv2ze4+6qRERERDxGvUKytfZ7YO9RDrkQeM06FgARxpjY+lxTGoh/KOaqd/CJiOcl33/w2IwP2JiV7+6qRERERDxCY/dJjgeqzzWW4domniAkBq+r3ycoKIjp9kEenP4mGfsOuLsqEREREbdr7JBsatlW6ygxY8w0Y0yqMSY1Ozu7kcuSKm2S8bl2DmEhwTxddi9/n/4q2fnF7q5KRERExK0aOyRnAInVvk8AdtR2oLV2urU2xVqbEhMT08hlySGiu+L72y/xC2vHYwfu57/PP0NekaaGExERkdarsUPyR8DVrlkuhgO51trMRr6mnIiIRAKmfUVZZBfuzX+Yl599nKLScndXJSIiIuIW9Z0C7g1gPtDdGJNhjLnOGHODMeYG1yFzgM3ARuAF4KZ6VSuNKySG0Os/Jzd6IL/P+TtvPfsgpeUV7q5KREREpMkZT1xIIiUlxaamprq7jNartJCM6ZeRkP09n8b8lnNv+Ade3lp3RkRERFoWY8wia21KbfuUfORwvoEk3PAe69qey3nZL7Lg+ZuwFWpRFhERkdZDIVlq5+1Ltxtm8WvbCZyU9Qarn78GysvcXZWIiIhIk1BIliMyXt6kXP8CX0ZPofeuj9j6/GVQpunhREREpOVTSJaj8vL24owb/83sqJtIzvqGrOfGQXGBu8sSERERaVQKyXJMPt5eXHTDX3g64nYis38ld/pYOHC01chFREREmjeFZKmTAF9vrr7hLh4N+zMBu1dzYPo5kKcpr0VERKRlUkiWOgsN8OXG63/P3UH3Yfdto+SFs2DPJneXJSIiItLgFJLluESF+HP79VO5xe8hDuTnUPbSObBzpbvLEhEREWlQCsly3OIiAvm/aVdxndfD7D1QQcUrY2HbL+4uS0RERKTBKCTLCekcE8ID147nKvsQO0qDsa9dCBu/dndZIiIiIg1CIVlOWN+EcB66+lwuLbmPLcRiX78CVr7n7rJERERE6k0hWeplROcoHpx4OuMP/Jl1Pt2w71wLqa+4uywRERGRelFIlno7u3d77r1kBBfl3c6qoCHwyW3w47/dXZaIiIjICVNIlgZxyeAE7jh/IBfvvZkl4aPh6wfg09uhtNDdpYmIiIgcN4VkaTDXndKRG87owfhdU/i1/RWw8AV4/jTYvtjdpYmIiIgcF4VkaVB/PKsbk4Z35LKt4/igz1PY4nx4cTTM/SuUl7q7PBEREZE6UUiWBmWM4cFxvRk/MJ7bUiN5KPFFKvpMgO/+Di+cAbtWu7tEERERkWNSSJYG5+VlePzS/vzu9M68sjiHKbm/pfDiVyFvB0w/DX58AirK3V2miIiIyBEpJEuj8PIy3HFOD/5+SV9+2ribi+dGsXPSXOh6Nnx9P7wyFvZscneZIiIiIrVSSJZGdfmQDrz6myFs31fIha+uZ+UpT8PFz0PWGnjuFFj4Iljr7jJFREREDqGQLI3u1K4xvH3jCLyN4bLpC5jrfwbcNB86DIdP/wQzLobcDHeXKSIiIlJFIVmaRI/2Ybz/u5PpFBPMdf9byIzVpTDpPTjvX5D+CzxzEix9Q63KIiIi4hEUkqXJtAsL4M1pIzijR1vu/XAVj8xZQ8Xga+HGn6BtT/jgBnhzEhRku7tUERERaeUUkqVJBfv78PzkFKaclMwLP2zhplmLKQxJgt/MgbMegg1fwjPDYc3H7i5VREREWjGFZGly3l6GB8b15r7ze/HF6p1c8cICsveXwcm3wvXfQ3i806L83jQo3OfuckVERKQVUkgWt7n2lI48N2kw63bmcfEzP7ExK9/pdvHbb+C0u2DFO05f5Y1fu7tUERERaWUUksWtzundnjenjaCotJzxz/zM/E17wNsXTr8bfvs1+IfCzEvgkz9AcYG7yxUREZFWQiFZ3K5/YgTv33QybcMCuPrlX3hvsWs6uPhBcP13MOJmSH0FnjsZtv7k3mJFRESkVVBIFo+QGBnEuzeexJDkSP741jKe+Ho91lrwDYRzHnEG9gG8OhY+vhUKc9xar4iIiLRsCsniMcIDfXn1N0OZMDiBJ77ewJ/eXkZJWYWzM+kkuPFnp1V58Wvw9DBY/aHmVRYREZFGoZAsHsXPx4t/TOjHn87qxnuLt3P1y7+Qe6DUtTPYaVWe+i2EtIW3robZV0LudvcWLSIiIi2OQrJ4HGMMt5zZlScuH8DitBzGP/sT6XsPHDwgbiBMnevMq7xprtOq/Mt0qCh3X9EiIiLSoigki8e6aGA8r103lN0FJVz8zE8s2VZtzmRvH2de5ZvmQ0IKfHYHvHwO7FrtvoJFRESkxVBIFo82vFMU7910EkF+PlwxfQGfLs889IDIjjD5fbj4edizCZ4fCd/+BUqL3FOwiIiItAgKyeLxOseE8P5NJ9E7Lozfvb6Yez9YSVFpta4VxkD/K+DmVOhzCXz/D9d0cT+6r2gRERFp1hSSpVmICvFn9rQRTBvZiRkL0rjo6Z/YmFVjcZHgKBj/vNOyXF4Kr54HH92ipa1FRETkuCkkS7Ph5+PFn8f25JUpQ8jKL+aC//7I26npznzK1XU+A25aACf9HpbMgqeGwsr3NF2ciIiI1JlCsjQ7p/doy2e3nkr/xHDueGc5f3xrGQXFZYce5BcEZz8M0+ZCWBy88xt44wrISXdP0SIiItKsKCRLs9QuLIBZvx3OH8/qxodLt3P+kz+wcnvu4QfG9offfgNnPwJbvodnhsOC5zRdnIiIiByVQrI0W95eht+f2ZU3pg6nqLSC8c/8zCs/bTm8+4W3D5x0s9MFo8Nw+PxOeOks2LnSPYWLiIiIx1NIlmZvWKcoPrv1VEZ2i+bBj1cz9bVF7NtfcviBbZLgqnfgkpdgXxpMPw2+fhBKC5u+aBEREfFoCsnSIrQJ9uOFq1O47/xefLc+i7FP/sCvW/YefqAx0HcC3LwQ+l0OP/4L/psCi1+D8rLDjxcREZFWSSFZWgxjDNee0pH3bjwZPx8vrpg+n/9+s4HyilpmtQiKhIuegWs+gdB2zlRxTw+FFe9ARUXTFy8iIiIeRSFZWpy+CeF8csspXNA/jn9+tZ7JL/1CVt4RVuDreKozsO+KN8AnAN69Dp4/FdbO0ZRxIiIirZhCsrRIoQG+PHH5AB6b0I8l23I49z8/MG9dVu0HGwM9xsINPzr9lUsPwOyJ8OJo2DyvSesWERERz6CQLC2WMYbLUhL5+JaTiQn1Z8orC/nbnDWUlh+hO4WXl9Nf+Xe/wgVPQv5OeO1CePV8SP+1aYsXERERt1JIlhavS9tQPvjdyUwa3oHnv9/Mpc/NJ33vgSM/wNsXBl8DtyyCMX+H7LXOlHGzLoPM5U1XuIiIiLiNOWxOWQ+QkpJiU1NT3V2GtEBzVmRy57vLwcKjl/TjvH6xx35QyX745Tn46T9QlAu9L4bT/w+iuzZ+wSIiItJojDGLrLUpte5TSJbWJn3vAW55YwlL03O4clgH7ju/FwG+3sd+YGEO/PxfWPAslBVC/4lw2p3O/MsiIiLS7Cgki9RQWl7BP79cz3PfbaJTTDCPju/H0I6RdXtwQTb8+G9Y+CLYChg8BUbeDqHtG7VmERERaVgKySJH8OOG3dz13nIy9hVy1bAO3HluD8ICfOv24Nzt8P1jsGQmePnCsGlw8m3OHMwiIiLi8RSSRY7iQEkZ//pyPS//tIWYUH8eHNeHMX2Oo1V472aY9ygsfwv8Q2HE72D4TRAQ1nhFi4iISL0pJIvUwfKMHO58dwVrMvMY07s9D17Ym3ZhAXU/wa7VMPcRWPsJ+IXCgIkwZCrEdGu8okVEROSEKSSL1FFpeQUv/rCFJ75ej5+PF3ef25MrhiTi5WXqfpIdS2DBc7DqPSgvgY6nwdBp0G0MePs0XvEiIiJyXBSSRY7T1t37ufu9FczfvIehyZH87ZK+dI4JOb6TFGTD4v9B6suQtx3CEyHlWhh0NQRHN07hIiIiUmeNFpKNMWOA/wDewIvW2kdr7A8HZgIdAB/gcWvtK8c6r0KyeAJrLW+nZvCXT1dTVFrBLWd04frTOuPnc5xr8JSXwfrP4NfpsOV78PaHPuNh6FSIH9w4xYuIiMgxNUpINsZ4A+uBs4AMYCEw0Vq7utoxfwbCrbV3GmNigHVAe2ttydHOrZAsniQrv4gHP17Np8sz6d4ulEcv6cvADm1O8GRrYeELsGw2lBQ4IXnoNOh1EfgeR/9nERERqbejheT6LEs9FNhord3sCr2zgQtrHGOBUGOMAUKAvUBZPa4p0uTahgbw9JWDePHqFPKKShn/7M888NEqCopP4J9y2x5w3j/hj2vg3H9AUR68fz38uzd88xDkZjT8DyAiIiLHrT4tyROAMdba37q+nwwMs9beXO2YUOAjoAcQClxurf30COebBkwD6NChw+C0tLQTqkukMeUXlfL4F+t4bUEasWEB/OXiPpzRo92Jn9Ba2DwPfn3B6ZIB0H2s07rccSSY4xgwKCIiIselsVqSa/vrXTNxnwMsBeKAAcBTxphaJ4+11k631qZYa1NiYmLqUZZI4wkN8OXBC/vwzg0nEezvw7WvpnLLG0vYXVB8Yic0BjqfDhNfh1uXwcm3QtrP8No4eHqYE56L8xv2hxAREZFjqk9IzgASq32fAOyoccxvgPesYyOwBadVWaRZG5zUhk9/fyp/GN2NL1buZPS/vuPt1HTqNVtMRAcY/YDTFeOiZ8E3EObcDv/sCXPugOz1DVa/iIiIHF19ulv44AzcOxPYjjNw70pr7apqxzwL7LLWPmCMaQcsBvpba3cf7dwauCfNycasfO5+bwULt+7j5C5R/PXiviRFBdf/xNbC9kXOrBir3q825/JU6Hau5lwWERGpp8acAm4s8ATOFHAvW2sfMcbcAGCtfc4YEwe8CsTidM941Fo781jnVUiW5qaiwvL6r9t49LO1lFVUcNOoLlx7SkdC/BsoyFbNufwK5GVAWAKk/AYGXQMh6p4kIiJyIrSYiEgT2ZlbxIMfr+KzlTuJCPLl+pGduXpEEsENFZbLy2D95645l78Dbz9n+rihUyFhiAb6iYiIHAeFZJEmtiw9hye+Xs/cddlEBftxw2mdmTQ8iUA/74a7SPZ6WPgiLH0dSvKhfT9nVow+l4BfUMNdR0REpIVSSBZxk0Vp+3ji6/X8sGE30SH+3DSqM1cO60CAbwOG5eJ8WP4m/PoiZK+BgAgYOAmGXAeRnRruOiIiIi2MQrKIm/26ZS///mo98zfvoV2YPzef3oXLhiTi79OAYdlaSPvJmTZuzcdgK6DrWTBkKnQZDV71mcxGRESk5VFIFvEQP2/azb+/Ws/CrfuICw/g5jO6MmFwAn4+DRxg8zJh0auw6BUo2AVtkiHlOqeFOSiyYa8lIiLSTCkki3gQay0/btzNv75az5JtOSS0CeT3Z3Rl/KB4fLwbOCyXlcDaj52uGNt+Bp8A6DvBaV2OG9Cw1xIREWlmFJJFPJC1lnnrs/n3V+tZnpFLUlQQt57ZlXH94xo+LAPsXAkLX4Dlb0HpAWc2jEFXQ4/z1bosIiKtkkKyiAez1vLNmiz+9dV6Vmfm0SkmmFvP7Mr5/eLw9mqEKd0Kc2DZG87MGHs2gpePs0hJ74sUmEVEpFVRSBZpBioqLF+u3sm/v9rAul35dG0bwm2ju3Fun/Z4NUZYthYyl8KqD2D1B7BvKxhv6HSaM/dyj/MhOKrhrysiIuIhFJJFmpGKCsuclZk88fUGNmYV0KN9KLeN7sY5vdthGmuxEGshc5kTlld9APu2OIG540hXC/MFCswiItLiKCSLNEPlFZZPlu/gia83sGX3fnrFhnHzGV0Y07uRWpYrWQs7l8Oq92sE5lOdFuaeF0BwdONdX0REpIkoJIs0Y2XlFXy4dAdPz93I5t376do2hN+d3oXz+8U2zgC/6qoC8wdOK/PezU5gTj7FaWHuOU6BWUREmi2FZJEWoLzC8umKTJ76dgPrdxWQHBXETaO6cPGgeHwbOyyDKzCvONglY+8mMF6QfOrBLhkhMY1fh4iISANRSBZpQZwBfrt4au4GVm7PIz4ikBtGdebSwQkNu9z10VgLu1YebGHes9EVmE+BXhc6gTm0XdPUIiIicoIUkkVaIGst89Zl8+S3G1iyLYd2Yf5MG9mZK4d2INCvicKyUwjsWuVqYX7fCcwY6DDc6Y7R8wKISGy6ekREROpIIVmkBbPWMn/THp78dgMLNu8lKtiP357aickjkgjx92nqYiBrDaz5CFZ/BFmrnO3xg53A3GscRHZq2ppERESOQCFZpJVYuHUvT327ke/WZxMe6Mu1J3dkyknJhAf5uqegPZtg9YdOaN6xxNnWrq8TlnuOg7Y93FOXiIgICskirc6y9ByemruRr1bvIsTfh6tHJHHdKR2JCvF3X1E522DNx04Lc/ovgIXobgdbmNv3g8aaB1pERKQWCskirdTqHXk8PW8jc1ZkEuDjzVXDOjBtZCfahgW4t7D8nU5gXvMRbP0RbAW0SXb6L/e6COIGgVcTzNghIiKtmkKySCu3MSufZ+Zu4sNlO/D2MlwxJJHrT+tMfESgu0uD/bth7adOYN78HVSUQli8E5h7jnMGAHo14UBEERFpNRSSRQSAtD37eXbeJt5dnIG1MG5AHFePSGZAYoS7S3MU5sD6z50uGZu+gbIiCI6BrudAp9Og42maWk5ERBqMQrKIHGJ7TiEvfL+Zt1PT2V9STr+EcCYNT+KCfnFNO33c0RQXwIYvnW4Zm+dC4T5ne0wP6DTKCczJJ0NAuFvLFBGR5kshWURqlV9UygdLtjNjQRrrdxUQHujLpYMTuGp4Eh2jg91d3kEVFc7y2JvnwZbvIG0+lBU6S2THDXRCc6fTIGEo+Lq5v7WIiDQbCskiclTWWn7dspfXFqTxxcqdlFVYRnaLYfLwJM7o0RZvLw+bdaKsGDIWOqF583ewfRHYcvAJcPowdzzNCc6x/dWfWUREjkghWUTqLCuviNkL03n9l23szCsiPiKQK4d14PIhiUS7cwq5oynKg7SfnMC85TvIWu1sDwiH5FNdLc2jIKqLppkTEZEqCskictzKyiv4es0uZixI46eNe/D1NoztG8vk4UkMTmqD8eSwmb8LtnwPW+bB5u8hd5uzPTTu4ADADsOdaec8+ecQEZFGpZAsIvWyMauAmQvSeHdRBvnFZfSMDWPy8CQuHBBHcFMvfX28rIV9Ww52zdjyPRTudfb5h0NsP6dbRuUtqou6aIiItBIKySLSIA6UlPHh0h28Nj+NNZl5hPr7cMngBCYNT6JL2xB3l1c3FRWwa6XTjzlzmTMgcOdKKC929vsGQbs+hwbnmB7g4+feukVEpMEpJItIg7LWsnjbPmbMT2POip2UlFdwUucoJg9PYnSvdvh6N7PV8spLYfd6JzRnLj8YnksKnP3eftC2pxOY2/eD2AHQrjf4Bbm1bBERqR+FZBFpNLsLinnTNdBve04hkcF+nNO7HWP7xjKiUxQ+zS0wV6qogL2bYecyV3h23SrnazZeEN390O4aMT0hKFL9nEVEmgmFZBFpdOUVlrlrs/ho2Q6+WbOL/SXlLScwV7IWcjMOBuadrlbn/MyDx/iHQ2RHiOx0+C2krQK0iIgHUUgWkSZVVFrOvHXZzFmRWRWY2wT5MqZP+5YTmKsryHLC8u4NTutz5S1nmzN/cyXfYFeAriVEh8aBVwv6nYiINAMKySLiNq0uMFdXXgq56a7QvOXQAL1vK5SXHDzW279GeHbdj+oK4QlqgRYRaQQKySLiEY4UmM/p3Z7z+rXwwFxTRTnkbT80OO/dcjBMlxUePNY/zJlho21PaNvL9bUnBMcoPIuI1INCsoh4HAXmo7AW8nfC3k2QvQ6y1kD2Wti16uAczwBBUYeG5pie0LYHBLZxX+0iIs2IQrKIeLSjBeaxfWMZ0Tmq+U0r1xishf3ZzrLbWWtcX9c690vyDx4XGncwOFcF6B7gF+y+2kVEPJBCsog0G7UF5tAAH0Z2jeH0Hm0Z1T2G6BB/d5fpWSpn3agMztlrXV/XQVmR6yADbZJcLc+9oH0fZ87nNh01YFBEWi2FZBFplopKy/lufTbfrNnF3HXZZOcXYwz0S4jg9O4xnNGjLX3iwvHyUr/cWlWUOwMEq1qeXSF694aDs274BjsLo7Tv46w02L4ftOulVmcRaRUUkkWk2auosKzOzOPbtVl8uzaLZRk5WAvRIf6McgXmU7pGExbg6+5SPV9pkdPavHOFs0T3zhXO0tzFua4DDER1doXmyuDcB8LiNFBQRFoUhWQRaXH2FBTz3fpsvl2bxffrs8krKsPHy5CS3IYzerTljB5t6RwTglGoqxtrnenqKgPzzuVOgN639eAxgZGuFue+0L6vcz+6O/j4ua1sEZH6UEgWkRatrLyCxdty+HZtFnPXZrFulzOILTEykNO7t+X0Hm0Z0SmKAF9vN1faDBXlObNq7HIF550rnS4blX2dvXydQYFRnSAswWltDo8/eD+0PXjp9y4inkkhWURale05hcx1BeafNu2mqLSCAF8vTuoczemuVub4iEB3l9l8lZc509PtXHGwy8a+NGfe59IDhx5rvJ2gHBbvCs/xh98PaasgLSJuoZAsIq1WUWk5CzbvYe7aLL5dl0X6XmeRjm7tQji9e1tGdW9LSnIbTTHXEKyFwn2Qt8MJzHnbIXf74ferZtxw8fKB0FhXaI47GKCDYyAgAgLCITDi4H117xCRBqKQLCICWGvZlL3faWVel8XCrXspLbeE+vtwStdoV2iOoW1YgLtLbbkqg3RuhitMZ7jCc41gXV585HP4BjmBOTDCCc1V9yMOD9SV9yuP9Q3S4EMRqaKQLCJSi/yiUn7auId565zQvCvPCWa948JcfZljGJDYBm9NMde0rIUDe5xbUS4U5kBRzqH3D9uW63xfnHf0c/uHQUSSM2d01dcOB+9r6juRVkUhWUTkGKy1rMnMZ+66LOaty2JR2j4qLEQE+boWMolhZNcYorSQiWcrL3OCclWQzj00VOftcPpP56RBzrbD+1AHRTuh+ZAQXXlLBB89/yItiUKyiMhxyj1Qyg8bs5m7Npvv1mexu6AEY6B/QkRVK7MWMmnmrIX9u53AvG/rweBcFaLToaK02gOM03e6Kjh3OPR+WDx4+7jrpxGRE6CQLCJSDxUVlpU7cp0p5tZls7xqIRM/TuvmBOZTu8QQHqSFTFqUinLIzzw0OFf/mrcdqPY31Hg7gw4jqnXhiOhw8BYWp1k8RDyMQrKISAPaXVDM9+uzmbsum+/XZ5NbWIq3l6FnbCh94sLpHR9O77gwerYPI9BPoajFKitxBh5Wheht1W5pTsCuzssHwhOqBefkg/fbJEFIe/DSLCsiTUkhWUSkkZSVV7AsI4e5a7NZmp7Dyh255BxwPqL3MtClbcghwblXXJiWzm4tyoqdWTz2bT08QOdsg4Jdhx7v7XcwRFfOHx3S3vW1nTPfdEhb8AvRDB0iDUQhWUSkiVhr2Z5TyKodeazansvKHXms3J5LVv7BKc2So4LoHR/uhOe4MPrEhxMZrLl/W53SQleIrjaQsPJr/k4nRFeUHf443yAnNIe0g1DX16pA7bof2t4ZhKg+0iJHpZAsIuJmWflFB4Pz9jxW7sglY19h1f648IDDgnO7MH+MWgxbr4oKZ07pgl1QsBMKspz7+btc26rdinJrOYGB4OiDrdFBUceeRzogAvxD1VItrYZCsoiIB8o5UMLqHU5gXrk9j1U7ctm8ez+V/y1Hh/jRLyGCwUltGNShDf0TwwnyU8ug1KK00BWis1yBepdzP3/nwW2F+w5Oi8dR/vYbb1dgrkOgDop0wndQFARGgq+HLcRjrTPNn7e/WtWlVgrJIiLNxP7iMtZkOl00Vu7IY2l6DhuzCgDw9jL0ig1jUIcIBiW1YXBSG+IjAtXaLMenosI1l3TukeeTPtoiLodMi1eDb7ArNEceGqArtwXWsu1Yc09XVEDxEeo5bIGZWn6myi4rAeE1rh11sMbD6opy3gxoNpIWTyFZRKQZyzlQwpJtOSxK28fibftYmp7DgZJyANqF+TOogxOYByW1oXdcGP4++sMujcRap9W6MogW7ju4OuKBPXBgLxTurbFtnxNyj8Qv5NCwWl5SLezmulZRPEpW8fI5xvLkYc4gyuo1Vn3dDWVFRzixgcA2NcJ+jTAdHOP0/Q523deKjc2OQrKISAtSVl7B2p35LN62ryo4p+91+jf7+XjRNz68qovGoKQI2oZ62Efg0vqUlRwaqA8J0nurBdc9ziwftYbdI3T58AuuXx/qkgOHhvrDgn/NcL3HCfK18Ql0+oFXBujD7ke77kc59+tbu9Rbo4VkY8wY4D+AN/CitfbRWo4ZBTwB+AK7rbWnHeu8CskiIscnK6+oWmjOYUVGLiXlFQB0iAxiUIeIqtbmbu1C8fXWfLwiJ8RaKClwVms8sMf1dTfsz662rcb9I7VW+wS4WqOjnBAd2KbGG4Lwg28GqvcT9w9rGV1ByksPtugHRkJYbJOX0Cgh2RjjDawHzgIygIXARGvt6mrHRAA/A2OstduMMW2ttVnHOrdCsohI/RSXlbNyex6LXS3NqWn7yHZNQ+frbUiKCqZLTAhd2jq3zjEhdG4brIGBIg3NWijZ7wrSrtB8YLcTovdnHxq0C/e5uprkgq04+nn9ww4Pz7WFav8Q8A10pg485Guw89UnoGEWsbEWivOdn+PA3oNvEg5Uvlmo3irv2lZ9VpbT74HT7qh/HcfpaCG5Pv8bDgU2Wms3uy4yG7gQWF3tmCuB96y12wDqEpBFRKT+/H28Gewa3AfO/M0Z+wpZvG0fa3fmsymrgPVZ+Xy1ZhflFQcbS+IjAuncNqRGgA4mKuQYg6tEpHbGOEHVPwTaJNftMZWBszIwVw5SrBqoWMv2fVsP7ivJP74afQIPBmi/mmE6qMb9ACf0V4XgPcfuhuLt53QvCYpyuppEDHT16452+nkHR0O7vsdXcxOoT0iOB9KrfZ8BDKtxTDfA1xgzDwgF/mOtfa22kxljpgHTADp06FCPskREpCZjDImRQSRGBnFhte0lZRWk7dnPxqwC55btfP11yx6KSg+2ZLUJ8j2k1bnyflx4IF5e6lMp0qCMcQYcBoQBicf/+PIy1wwmOU6gLS10psKr/Fpy4PBtVV+r3S/Od+blrtrm2u4XdDD0RnSAuAHVQnD04QG4ma4SWZ+QXNtPW7Pvhg8wGDgTCATmG2MWWGvXH/ZAa6cD08HpblGPukREpI78fLzo2i6Uru1CD9leUWHZkVtYFZ43ucLz5yt3su/AwSnAAn296RQTTLd2ofRoH0r39qH0aB+mhVBE3Mnb5+A0fHLC6hOSMzj07U0CsKOWY3Zba/cD+40x3wP9cfoyi4iIh/LyMiS0CSKhTRCjurc9ZN+egmJXcN5f1fo8f9Me3l+yveqY8EBferSvDM5hdHcF6BB/9XkWkeahPv9bLQS6GmM6AtuBK3D6IFf3IfCUMcYH8MPpjvHvelxTRETcLCrEn6gQf4Z1ijpke86BEtbuzGfdznzW7sxn7c483lmUwX7XnM4AiZGBdG8XVq3VOZSO0cH4aLYNEfEwJxySrbVlxpibgS9wpoB72Vq7yhhzg2v/c9baNcaYz4HlQAXONHErG6JwERHxLBFBfgzvFMXwauG5osKyPafQFZ7zWOMK0XPXZVUNGPTz8aJLTEhVcFaXDRHxBFpMREREmlxRaTmbsgtYm5nPul35VSF6V15x1TH+Pl60CwugXZi/62sA7cMCaOv6vr1rW6BfC5gvVkTcorGmgBMRETkhAb7e9I4Lp3dc+CHb9+0vqQrM23MK2ZVXzK68IlZuz+XrNbsOmXGjUmiAT1VgrhmqK+/HhPprARUROS4KySIi4jHaBPsxonMUIzpHHbbPWkt+cRm7covYlVfMzrwiduUVkZVX5LpfzOZNu8nKL6as4tBPSY2BqGB/YsMDiA0PIC4i0LkfEUic62u7UH/1jRaRKgrJIiLSLBhjCAvwJSzA97Ap66qrqLDs2V/CLleIrmyN3pVXRGZuEVv37Gf+pj3kF5cd8jgvA21DA4iNCCAu/PAQHRceQHSIv+aFFmklFJJFRKRF8fIyxIT6ExPqT5/48CMel19USmZuETtyCsnMLSIzp5AduUVk5hayJjOPb9Ye3r3D19vQLswVoiMCiA0PpENkEMnRQXSKDtFgQ5EWRCFZRERapdAAX0IDfOl2hFZpay05B0rZkVtIZo4TnndUC9OLt+1jZ24mpeUHu3YE+XmTFBVMp+hgOkYHk+z62ik6mDbBfk31o4lIA1BIFhERqYUxhjbBfrQJ9jtsgGGlypUJt+4+wJbdBWxxfV21I5fPV+2smuYOnAVWKgNzZXiuvAVrkRURj6NXpYiIyAmqvjLhKV2jD9lXWl5B+t4DbNm9/5Dbgs17eK/a6oQAbUP9SY4+2AKd0CaI9q5Bhm01oFDELRSSRUREGoGvtxedYkLoFBNy2L7CknLS9u5nS/Z+Nu/ez1ZXgP5q9S727C855NjKAYXtwwOIiwigfZgzqLDq+/BA2mqKO5EGp5AsIiLSxAL9vOnRPowe7cMO25dbWEpmVT/oIna6+kLvzC1i3c585q3L5kC1pb7BmeKubag/7cMDiQ07NEDHhjsLr8SE+hPgq4VXROpKIVlERMSDhAf6Eh7oW2uABmdAYV5RGTtdM3HszC1yhWhnlo6N2QX8sCGb/TWCNDgDCyOD/Q7egpw+15E1bm2C/IgK9iM80FdT3kmrpZAsIiLSjBhjqoJ09/ZHni86v6j0kAC9u6CEvftL2Le/hL0HnPsbswrYu7/ksJbpSl4G2lQG6SBXgA52AnSbYD8iXHWEub46930I9PXWVHjS7Ckki4iItECVU9wdbeGVSkWl5ezd7wrRrgBd223z7gL2ppWw70DpITN31OTr7QT5sICDAdr56lO1Pby2gB3ghGwFbPEECskiIiKtXICvN3ERgcRFBNbp+IoKS15RKbmFB295hWUH7xdV315KzoES0vbsd+0rO2rA9vEyRIX4ERPqT3TIwZvzvR8xIf5Eh/oTE+Kv7iDSqBSSRURE5Lh4eRkigvyICDr+BVKstewvKa8K0NXDdG5hKXv2l7A7v5jdBcXsLihhbWY+e/YXH7JoS6XKQH0wRFfenJBdGagjXC3W/j5eaqWWOlNIFhERkSZjjCHE34cQfx/i69hyba0lt7CU3QXFZOU74bkySGdXC9Trduazu6D2QA1ON5CwAF9CA3wIdXXtCPV3vg8LdH2tsT8swLfaNh/NWd2KKCSLiIiIRzPmYMt1l7ZH72NdPVBn55eQXVBMbmEp+UVOl5D8olLyi8rIc33Nyiuo+v5IAxirC/LzJjTAp6ofdXigX9X9iKCD/avDq92vbMnWXNbNi0KyiIiItBiHBurje2xZeQUFxWXkFTqhuTJI5xU6XyvDdJ6r33XOgVIy9h1g9Y5ScgqPHbKD/byJCPKrGsQYURmwgw4OYgxztWo7gxt9qgY/qqtI01NIFhEREQF8vL1OuK81QElZRVV4rhq0WFhC7oFScgvLnPtVgxlL2ZRdQG6hE7BLyiqOem4/b6+q7h+hlWG6epeQGgH70O2+BPgqZB8vhWQRERGRBuDn41U1ePB4FZWWV7VQ5xaWHdKKnefqKnKwFdvpNrIjp5A81zHFxwjZlf2xjx6mXdvVkg0oJIuIiIi4XYCvNwG+3rQNCzihxxeVlh/SHSS/qMzVF/vgtsop+CqD9/GE7Oot2f6+3vj5eOHvuvl5e+Hn41W1zc/HCz9v70O2Hdx+8Fg/by/nXN5edIgKqvNAzqaikCwiIiLSzFWG7JjQ42/FhsNDds1AXb0lu6i0nOKyCkrKnD7cJa77ldtKyiuqtpWUHz18V/rjWd34/ZldT6j2xqKQLCIiItLK1TdkH0lFhXVCc/Xg7ArPxaUVlJQ7gTuxTVCDXrchKCSLiIiISKPw8jIEeDkBvLnRhH0iIiIiIjUoJIuIiIiI1KCQLCIiIiJSg0KyiIiIiEgNCskiIiIiIjUoJIuIiIiI1KCQLCIiIiJSg0KyiIiIiEgNCskiIiIiIjUoJIuIiIiI1KCQLCIiIiJSg7HWuruGwxhjsoE0N1w6GtjthutK3ek58nx6jjyfniPPp+fI8+k58nx1eY6SrLUxte3wyJDsLsaYVGttirvrkCPTc+T59Bx5Pj1Hnk/PkefTc+T56vscqbuFiIiIiEgNCskiIiIiIjUoJB9qursLkGPSc+T59Bx5Pj1Hnk/PkefTc+T56vUcqU+yiIiIiEgNakkWEREREalBIVlEREREpAaFZMAYM8YYs84Ys9EYc5e765HaGWO2GmNWGGOWGmNS3V2PgDHmZWNMljFmZbVtkcaYr4wxG1xf27izxtbuCM/RA8aY7a7X0lJjzFh31tiaGWMSjTFzjTFrjDGrjDG3urbrdeQhjvIc6XXkIYwxAcaYX40xy1zP0YOu7fV6HbX6PsnGGG9gPXAWkAEsBCZaa1e7tTA5jDFmK5BirdXk7R7CGDMSKABes9b2cW17DNhrrX3U9aazjbX2TnfW2Zod4Tl6ACiw1j7uztoEjDGxQKy1drExJhRYBFwETEGvI49wlOfoMvQ68gjGGAMEW2sLjDG+wI/ArcB46vE6UksyDAU2Wms3W2tLgNnAhW6uSaRZsNZ+D+ytsflC4H+u+//D+WMibnKE50g8hLU201q72HU/H1gDxKPXkcc4ynMkHsI6Clzf+rpulnq+jhSSnX/o6dW+z0D/+D2VBb40xiwyxkxzdzFyRO2stZng/HEB2rq5HqndzcaY5a7uGPoo3wMYY5KBgcAv6HXkkWo8R6DXkccwxngbY5YCWcBX1tp6v44UksHUsq1190HxXCdbawcB5wK/c32MLCLH71mgMzAAyAT+6dZqBGNMCPAucJu1Ns/d9cjhanmO9DryINbacmvtACABGGqM6VPfcyokOy3HidW+TwB2uKkWOQpr7Q7X1yzgfZyuMuJ5drn68FX25ctycz1Sg7V2l+sPSgXwAnotuZWrD+W7wCxr7XuuzXodeZDaniO9jjyTtTYHmAeMoZ6vI4VkZ6BeV2NMR2OMH3AF8JGba5IajDHBrgETGGOCgbOBlUd/lLjJR8A1rvvXAB+6sRapReUfDZeL0WvJbVwDjl4C1lhr/1Vtl15HHuJIz5FeR57DGBNjjIlw3Q8ERgNrqefrqNXPbgHgmrblCcAbeNla+4h7K5KajDGdcFqPAXyA1/U8uZ8x5g1gFBAN7ALuBz4A3gI6ANuAS621GjjmJkd4jkbhfERsga3A9ZX99qRpGWNOAX4AVgAVrs1/xunzqteRBzjKczQRvY48gjGmH87APG+cBuC3rLUPGWOiqMfrSCFZRERERKQGdbcQEREREalBIVlEREREpAaFZBERERGRGhSSRURERERqUEgWEREREalBIVlEREREpAaFZBERERGRGhSSRURERERqUEgWEREREalBIVlEREREpAaFZBERERGRGhSSRURERERqUEgWEREREalBIVlEREREpAaFZBERERGRGhSSRURERERqUEgWEREREalBIVlEREREpAaFZBERERGRGhSSRURERERqUEgWEREREalBIVlEREREpAaFZBERERGRGhSSRURERERqUEgWEREREalBIVlEREREpAaFZBERERGRGhSSRURERERqUEgWEWkCxph5xph9xhh/d9ciIiLHppAsItLIjDHJwKmABcY14XV9mupaIiItjUKyiEjjuxpYALwKXFO50RiTaIx5zxiTbYzZY4x5qtq+qcaYNcaYfGPMamPMINd2a4zpUu24V40xf3HdH2WMyTDG3GmM2Qm8YoxpY4z5xHWNfa77CdUeH2mMecUYs8O1/wPX9pXGmAuqHedrjNltjBnQSL8jERGPopAsItL4rgZmuW7nGGPaGWO8gU+ANCAZiAdmAxhjLgUecD0uDKf1eU8dr9UeiASSgGk4/8+/4vq+A1AIPFXt+BlAENAbaAv827X9NWBStePGApnW2qV1rENEpFkz1lp31yAi0mIZY04B5gKx1trdxpi1wPM4LcsfubaX1XjMF8Aca+1/ajmfBbpaaze6vn8VyLDW3mOMGQV8CYRZa4uOUM8AYK61to0xJhbYDkRZa/fVOC4OWAfEW2vzjDHvAL9aax87wV+FiEizopZkEZHGdQ3wpbV2t+v7113bEoG0mgHZJRHYdILXy64ekI0xQcaY540xacaYPOB7IMLVkp0I7K0ZkAGstTuAn4BLjDERwLk4LeEiIq2CBnWIiDQSY0wgcBng7eojDOAPRAC7gA7GGJ9agnI60PkIpz2A0z2iUnsgo9r3NT8e/BPQHRhmrd3pakleAhjXdSKNMRHW2pxarvU/4Lc4fyvmW2u3H6EmEZEWRy3JIiKN5yKgHOgFDHDdegI/uPZlAo8aY4KNMQHGmJNdj3sRuN0YM9g4uhhjklz7lgJXGmO8jTFjgNOOUUMoTj/kHGNMJHB/5Q5rbSbwGfCMa4CfrzFmZLXHfgAMAm7F6aMsItJqKCSLiDSea4BXrLXbrLU7K284A+cmAhcAXYBtOK3BlwNYa98GHsHpmpGPE1YjXee81fW4HOAq176jeQIIBHbj9IP+vMb+yUApsBbIAm6r3GGtLQTeBToC79X9xxYRaf40cE9ERI7IGHMf0M1aO+mYB4uItCDqkywiIrVydc+4Dqe1WUSkVVF3CxEROYwxZirOwL7PrLXfu7seEZGmpu4WIiIiIiI1qCVZRERERKQGj+yTHB0dbZOTk91dhoiIiIi0YIsWLdptrY2pbZ9HhuTk5GRSU1PdXYaIiIiItGDGmLQj7VN3CxERERGRGhSSRURERERqUEgWEREREanBI/sk16a0tJSMjAyKiorcXUqLEBAQQEJCAr6+vu4uRURERMTjNJuQnJGRQWhoKMnJyRhj3F1Os2atZc+ePWRkZNCxY0d3lyMiIiLicZpNd4uioiKioqIUkBuAMYaoqCi1youIiIgcQbMJyYACcgPS71JERETkyJpVSBYRERERaQoKyXWUk5PDM888c9yPGzt2LDk5OQ1fkIiIiIg0GoXkOjpSSC4vLz/q4+bMmUNEREQjVSUiIiIijaHZzG7hbnfddRebNm1iwIAB+Pr6EhISQmxsLEuXLmX16tVcdNFFpKenU1RUxK233sq0adOAg0tsFxQUcO6553LKKafw888/Ex8fz4cffkhgYKCbfzIRERER9ykqLcdaCPTzdncph2iWIfnBj1exekdeg56zV1wY91/Q+4j7H330UVauXMnSpUuZN28e5513HitXrqyaQu3ll18mMjKSwsJChgwZwiWXXEJUVNQh59iwYQNvvPEGL7zwApdddhnvvvsukyZNatCfQ0RERBrH9pxCXv8ljXcXbeeMnm154ILe+PnoQ/kTlb73ALN+2cZbqen87vQuXHeKZ01L2yxDsicYOnToIXMMP/nkk7z//vsApKens2HDhsNCcseOHRkwYAAAgwcPZuvWrU1VroiIiJyAigrLDxt3M2N+Gt+u3QXAgMQIXv9lGxuzCnhu0mAig/3cXGXzUV5h+X59NjMWpDF3XRYGOKtXOwYkhru7tMM0y5B8tBbfphIcHFx1f968eXz99dfMnz+foKAgRo0aVescxP7+/lX3vb29KSwsbJJaRURE5PjkHCjhnUUZzFyQxtY9B4gK9uPGUZ2ZOLQDCW2C+HDpdv7fO8sZ99SPvHTNELq3D3V3yR5t7/4S3kpNZ9YvaaTvLSQm1J9bTu/CxGEdiA33zK6nzTIku0NoaCj5+fm17svNzaVNmzYEBQWxdu1aFixY0MTViYiISENYnpHDjPlpfLRsB8VlFQxJbsMfzurGmD7t8fc52Gf2wgHxJEcFM/W1VMY/8xP/uWIgo3u1c2Plnsday5L0HGbOT+OTFZmUlFUwrGMkd47pwdm92nt8VxWF5DqKiori5JNPpk+fPgQGBtKu3cEXwpgxY3juuefo168f3bt3Z/jw4W6sVERERI5HUWk5Hy/bwcwFaSzLyCXIz5sJgxOYNDyJnrFhR3xc/8QIPrr5FKbNSGXqjFTuHNOD60d2avULdh0oKeOjpTuYsSCNVTvyCPH34YohiUwankS3ds2nxd1Ya499kDFjgP8A3sCL1tpHa+wPB2YCHXCC9+PW2lfq8tjapKSk2NTU1EO2rVmzhp49e9blZ5I60u9URERas6279zPrlzTeSs0gt7CUrm1DmDwiiYsHxhMa4Fvn8xSWlHPHO8v4ZHkm4wfG89fxfQnw9ayZGprCpuwCZi5I451FGeQXldGjfSiThidx0cB4Qvw9s13WGLPIWptS275jVmyM8QaeBs4CMoCFxpiPrLWrqx32O2C1tfYCY0wMsM4YMwsor8NjRURERJpEeYXl27VZzFiQxvfrs/HxMpzTuz2ThicxvFPkCbUCB/p589+JA+neLpR/frWezbv3M/3qwbQNDWiEn8CzlJVX8PWaXcxYkMZPG/fg6204t08sk0ckkZLUplm3qtcl1g8FNlprNwMYY2YDFwLVg64FQo3zmwgB9gJlwLA6PFZERESkUe0uKObNhem8/ss2tucU0i7Mnz+M7sYVQxNpF1b/MGuM4ZYzu9K1XQh/eHMZFz71Ey9cnUKfeM+btaEhZOUV8cav6bzx6zZ25hURFx7AHed057KURGJC/Y99gmagLiE5Hkiv9n0GTvit7ingI2AHEApcbq2tMMbU5bEiIiLSyu0vLmN1Zh4FxWUNet7i0go+W5nJnBWZlJZbTu4Sxb3n9+TMnu3w9W74gWNj+sSSGBnE1P+lMuG5n/nXZQMY2ze2wa/jDnlFpSzZlsNbC9P5YtVOyiosI7vF8PBFfTijR1u8vZpvq3Ft6hKSa/uJa3ZkPgdYCpwBdAa+Msb8UMfHOhcxZhowDaBDhw51KEtERESao+KyctZm5rM8I4dlGbksz8hhY1YBFcceJnVCQgN8mDQ8iauGJdGlbUjjXKSa3nHhfHjzKVw/I5WbZi3mD6O78fszuzSrrgdFpeWszsxjeXoOyzNyWZqRw+bs/QCEB/rym5OTuWpYEsnRwcc4U/NVl5CcASRW+z4Bp8W4ut8Aj1pnFOBGY8wWoEcdHwuAtXY6MB2cgXt1ql5EREQ8WnmFZWNWAcsyclie4QSuNZl5lJY7f+qjgv3olxDOuX1i6ZcQ3uALcxhj6NYuhCC/ph04FhPqzxvThnP3eyv499frWZ+Vz+MT+nvc0svg9Ctev6vgkDct63bmU+Z61xIT6k//hHAuGhBPv4RwhneKahUDE+vyL2Yh0NUY0xHYDlwBXFnjmG3AmcAPxph2QHdgM5BTh8eKiIhIC2CtZdveA07QcrVArtyRy4GScgBC/X3oEx/Otad0pH9CBP0SwomPCGxWLazHw9/Hm39e2p8e7UP522drSduznxeuTnHr4hkVFZate/azPCPX9cYll1U7cikqrQAgLMCHfgkRTBvZiX4JEfRPDKd9WECLfY6O5pgh2VpbZoy5GfgCZxq3l621q4wxN7j2Pwc8DLxqjFmB08XiTmvtboDaHts4P0rjGjVqFHfffTfnnHNO1bYnnniC9evX88wzz9R6/OOPP05KSgpjx47l9ddfJyIi4pBjHnjgAUJCQrj99tuPeN0PPviAbt260atXLwDuu+8+Ro4cyejRoxvmBxMRETlBu/KKWOYKw8syclixPZecA6UA+Pt40SsujMtSEumXEE6/hAg6RQfj1cL6rR6LMYZpIzvTOSaEW2cvZdxTPzF98mAGdmjT6Ne21pKZW3RIC/HyjFzyi5x+3wG+XvSJC+fKoUn0T3Seo6TIoFb3HB1JnT57sNbOAebU2PZctfs7gLPr+tjmaOLEicyePfuQkDx79mz+8Y9/HPOxc+ac+I//wQcfcP7551eF5IceeuiEzyUiIp5ty+793Dp7CeGBvkwensQZPdri0wiDy+pjT0Exb6VmMHvhNtL2HADA28vQrV0oY3q3p5+rhbh7+9BGGRjXXJ3Zsx3v3XQS1/1vIZdPX8Bjl/TjooHxDXqNvftLqoJwZTDOzi8GwMfL0CM2lAv6x9Hf9aala9sQj/v35Uk8c2ZnDzRhwgTuueceiouL8ff3Z+vWrezYsYPXX3+dP/zhDxQWFjJhwgQefPDBwx6bnJxMamoq0dHRPPLII7z22mskJiYSExPD4MGDAXjhhReYPn06JSUldOnShRkzZrB06VI++ugjvvvuO/7yl7/w7rvv8vDDD3P++eczYcIEvvnmG26//XbKysoYMmQIzz77LP7+/iQnJ3PNNdfw8ccfU1payttvv02PHj2a+lcmIiLH4aeNu7lp1mK8DGT7ejNtxiLiwgO4clgHLh/Swa3TallrWbwth5kL0vh0eSYl5RUM7xTJNSOS6Z8YTq/YcI/sa+tpurUL5cPfncKNMxdx25tLWb8rn9vP7n5CLbcFxWWs3J57SCtx+t5CAIyBTtHBnNol2mnFT4ygV2xYq+hH3JCaZ0j+7C7YuaJhz9m+L5x75MUAo6KiGDp0KJ9//jkXXnghs2fP5vLLL+fuu+8mMjKS8vJyzjzzTJYvX06/fv1qPceiRYuYPXs2S5YsoaysjEGDBlWF5PHjxzN16lQA7rnnHl566SVuueUWxo0bVxWKqysqKmLKlCl88803dOvWjauvvppnn32W2267DYDo6GgWL17MM888w+OPP86LL77YAL8kERFpDDPmb+WBj1fTOSaYl64ZQmx4AF+vyWLmgjQe/3I9//lmA2P6xDJ5eBJDkptugYYDJWV8uHQHM+ansTrTWV544tBErmpmywt7kshgP2ZcN4z7P1rJM/M2sSGrgH9fPuCoK9IVl5WzpnI2kHTXbCDZBVQumhwfEUj/xHCuGpZEv4Rw+saHH9eKgVK75hmS3aSyy0VlSH755Zd56623mD59OmVlZWRmZrJ69eojhuQffviBiy++mKCgIADGjRtXtW/lypXcc8895OTkUFBQcEi3jtqsW7eOjh070q1bNwCuueYann766aqQPH78eAAGDx7Me++9V98fXUREGkFpeQUPfryKmQu2cWaPtjxxxYCqcDOmT3vG9GnPpuwCZi3YxtuL0vl42Y4mWep3U3YBM+an8e7ig8sLP3JxHy4c4LnLCzcnfj5e/PXivnRrF8rDn6xmwrM/88LVKSRGBlFWXsHG7AKWpx8cWLd258HZQKJD/OiXEMF5/WKrBj9GhbSMxTs8TfP8l36UFt/GdNFFF/HHP/6RxYsXU1hYSJs2bXj88cdZuHAhbdq0YcqUKRQVFR31HEd69z9lyhQ++OAD+vfvz6uvvsq8efOOeh5rjz5Lnr+/84Lx9vamrKxhJ2YXEZH6yzlQwk2zFvPzpj1cf1on/t85PWpdjKFzTAj3XdCL28/pxsfLdvDa/DTu+WAlj362lvGD4pnUQK26LXl5YU9kjOE3J3ekc0wIv3t9MRc+/ROdY4JZuT2PwtKDs4H0TQjnulM6Of2IEyOIC2+dM024Q/MMyW4SEhLCqFGjuPbaa5k4cSJ5eXkEBwcTHh7Orl27+Oyzzxg1atQRHz9y5EimTJnCXXfdRVlZGR9//DHXX389APn5+cTGxlJaWsqsWbOIj3c684eGhpKfn3/YuXr06MHWrVvZuHFjVR/m0047rVF+bhERaVgbs/K57n+pZOYU8c9L+3PJ4IRjPibIz4fLh3TgspRElqbnMGNBGrMXpvPa/DSGdYxk8ogkzu7VHj+f4xuI1RqWF/ZkI7vF8MHvTubOd5ZTXmG5fEhi1UwTHaNa32wgnkQh+ThNnDiR8ePHM3v2bHr06MHAgQPp3bs3nTp14uSTTz7qYwcNGsTll1/OgAEDSEpK4tRTT63a9/DDDzNs2DCSkpLo27dvVTC+4oormDp1Kk8++STvvPNO1fEBAQG88sorXHrppVUD92644YbG+aFFRKTBzF2Xxe9fX4K/rxdvTBvO4KTjmwrMGMPADm0Y2KEN95zXi7dT05n5Sxo3v76EmFB/Jg5JZOKwDkedi9day4LNe5m5IK1VLC/s6TrHhPDOjSe5uwypwRzrY3t3SElJsampqYdsW7NmDT179nRTRS2TfqciIk3HWstLP27hr3PW0KN9GC9ck0J8RMMsKlFRYfluQzYz56fx7bosvIxhdM+2TB6ezMldoqo+ns8rKuX9xduZsSCNjVkFhAf6cllKQotfXljkSIwxi6y1KbXtU0uyiIiH2plbRElZBYmRLXdFstaiuKycez9YyVupGYzp3Z5/Xd6/QZdJ9vIynN69Lad3b0v63gPM+mUbb6Wm88WqXXSKDuaKoYls3XOAD5Zs50BJOf0TwvnHhH5c0D9O04KJHIFCsoiIhyksKee/325g+vebKauwtAnypW9CRNUCAP0TwmkbFuDuMqWOdhcUc+PMRSzcuo/fn9GF20Z3a9R+pomRQdx1bg9uG92Vz1ZmMmN+Gn+dsxZ/Hy/G9Y9j0vAk+idGNNr1RVqKZhWSrbVqTWkgntjNRkRg3ros7v1wJel7C5kwOIGBHSKqpoJ6Zt5uyiuc1277sAD6JYTTP9GZAqpffAThQZoX1dOsyczjt/9LZXdBMf+dOJAL+sc12bUDfL25eGACFw9MYMvu/bQJ8iUiyK/Jri/S3DWbkBwQEMCePXuIiopSUK4nay179uwhIEAtUSKeIiuviAc/Wc2nyzPpFBPMG1OHM6JzFABXDXOOKSwpZ9WO3KrVtZZn5PLl6l1V50iOCqpaErh/YgS948Ia9CN9OT5frNrJH95cSmiAD2/fMIJ+CRFuq6Wj+huLHLdmM3CvtLSUjIyMY85DLHUTEBBAQkICvr5qeRJxp/IKy6xf0vjH5+soLq/g5tO7cP1pnfD3qVs/0dwDpazYXrnogBOcM3Od/ye9jLMMbr+qbhoRdG8fetxThMnxsdbyzLxN/OOLdfRPCGf61Sm0U/cYEY90tIF7zSYki4i0NKt25PLn91eyLD2HU7pE8/BFfRqkxS8rv4jlrqVrK1ud9x0oBZyVvk7tEs2kEUmc1jVGc7A2sKLScu58dzkfLt3BuP5xPDahnwbGiXgwhWQREQ+yv7iMf3+1nld+3kqbIF/uPb8X4/rHNVpXMmstGfsKWZaRw+K0HD5atoPdBcV0iAziqmHO4hRtgtVXtb6y8oqYOmMRy9JzuP3sbvzu9C7qHiji4RSSRUQ8xJerdvLAR6vYkVvElcM6cOc5PZp8wF1JWQVfrNrJjAVp/LplL34+XlzQL47JI5LonxCuYHcCVmTkMvW1VHILS/n35QMY06e9u0sSkTpQSBYRcbMdOYXc/9Eqvlq9ix7tQ3nk4r7HvdJaY1i3M5+ZC9J4b3EG+0vK6RsfzuThSVzQP45AP3UTqItPlu/g9reXERnkxwvXpNA7LtzdJYlIHSkki0izZK3lg6XbWb0jr8HP3S4soElmgCgrr+DVn7fyr6/WYy3cNror157SEV9vzxo8V1BcxvtLtjNj/lbW7yogLMCHS1MSuWpYBzrFhDRZHXv3l7AsI4eVGbkE+/vQPzGc3nHhHtevd3dBMcszcpi3LpvX5qcxOKkNz00aTEyov7tLE5HjoJAsIs1OSVkF9324ktkL0wnw9cKrAbsAWAuFpeVA484AsWTbPv78/krWZOZxRo+2PDiuN4mRQfU+b2Oy1rJw6z5mLEjjsxWZlFVYTu0azaThSZzZoy0+DRjuC4rLWFFtOrtlGTlk7Cs87DhvL0P3dqH0TwyvmuKuW7vQJnujkVdUysqMQ6fe257j1GkMXDY4kYcu6l3nGUlExHMoJItIs7KnoJgbZy7m1617ueWMLvyhEVYoO9YMED1jww5Z4a5TTAjedawhr6iUf3y+jpm/pNE21J8HLujNmD7tm11f36z8It5amM6sX7aRmVtEbHgAVw7twOVDE2kbenxTmhWVlrMmM68qDC/PyGVTdgGVf4IS2gTS3xWA+yVE0DchnIKiskOmtluekUtuofMc+ft40TsuzHl+XOG5Y1Rwvf+dFJWWs2pH3iHBfXP2/qr9iZGBVf8m+iVE0Cc+nBB/zUUt0lwpJItIs7F2p7NCWXZ+MY9N6MeFA+Kb5LrVZ4BYnpHLsvQcVm7PZX+J0+Ic7OdNn/iDK9z1T4ggoU3gIcHXWssnyzN56JPV7Cko5uoRyfzp7G6EBjTv+cjLyiv4dm0WMxak8cOG3fh6G8b0iWXy8CSGJLc5LPyXlVewIavgkDcgazPzKXOtFhgd4l8VMvslhtMvPpyokGN3U7DWkrbnQNVztDwjh5Xb86o+FQj196FvtTc2/RIjiAsPOOKbk7LyCtbvOrTOdTsP1hkTWq1O19dIzQIi0qIoJItIs/DV6l3cNnsJwf4+vHB1Cv0TI9xaT3mFZXN2QVWAWpaRy5odeZSUVwAQGexH3/hw+ieE0yM2jNkL0/l+fTZ94sP468V93brCWmPZnF3ArF+28XZqOnlFZXRvF8qk4R0IC/RlmatlftWOasE1wKdaVxbna+xRguvxKiuvYGN2QdXS3cszclm7M4/S8spA7lct5IaTV1hWddyqHbkUlTrPZViAzyFhuH9iOO3DGq5OEfFMCski4tGstTz33WYe+2ItfePDmT45hfbhnrlCWUlZBet25h/SDWD9rnwqrNPa/Kezu3P1iKQG7bvriQpLyvl42Q5eW7CVldudgZUBvl70jguvamnvlxBOcgN0gThexWXlrMnMd97YuIL7xmpdOwJ8vegTF35IV43kqCAFYpFWSCFZRDxWUWk5d7+3gveXbOf8frH8Y0L/Zjf12IGSMtZk5tMhMqjVzW5grWVNZj4A3dqFeOybg4LiMlbvyCM0wIeubT23ThFpWkcLyRptICJuk5VfxLTXFrE0PYc/ndWNm89oniuUBfn5eMScx+5gjKFXXJi7yzimEH8fhnaMdHcZItKMKCSLiFus3O6sUJZzoJTnJg1iTJ9Yd5ckIiJSRSFZRJrcnBWZ/PGtpUQG+fHOjSO0QpmIiHgchWQRaTLWWp78ZiP//no9gzpE8PzklFbXh1dERJoHhWQRaRKFJeXc/s4yPl2eyfhB8fxtfF+tUCYiIh5LIVlEGl1mbiFTX0tl1Y48/jy2B1NP7dQsB+iJiEjroZAszcbPG3ezafd++sWH0yM2VK2QzcSSbfuYNmMRhSXlvHh1Cmf2bOfukkRERI5JIVmahT0FxUx9LbVqiWA/by96xIZWLVjQPzGCzjEheDfxogVydB8s2c7/e3c57cL8mfXbYXRrF+rukkREROpEIVmahafmbqSwtJzXfzuMnMJSlqXnsCwjh/eXbGfGgjQAgvy86eNaIthZAjeCxMhAfazvBhUVlse/XMcz8zYxrGMkz04aTGSwn7vLEhERqTOFZPF46XsPMHNBGpelJHJSl2gAxvZ15tStqLBs3l1QtfTssoxc/jc/jZKyLQC0CfKlb0JEteAcTtswz1zuuKUoKC7jD28u5avVu5g4NJEHx/XBz0erm4mISPOikCwe719frcfLGG4b3e2wfV5ehi5tQ+nSNpRLBicAUFJWwfpd+SzLyGF5ei7LMnJ4Zt5uyiucJdjbhwVUddHolxDOoA5tCPbXS6EhpO89wNTXUlm/K58HLujFNSclqyVfRESaJSUD8WhrMvP4YOl2rh/ZmfbhdWsB9vPxok98OH3iw7lqmLOtsKScVTtyWZbhtDgvz8jly9W7AAj282b8oAQmDU+ie3v1mT0R5RWW139J47HP14GBV38zlJHdYtxdloiIyAlTSBaP9tjnawn19+HG0zrX6zyBft6kJEeSkhxZtS3X1bf5g6XbeTM1nRkL0hjaMZLJw5M4p3d7dRGoo1U7cvnz+ytZlp7DyV2i+OvFfUmKCnZ3WSIiIvWikCwea8HmPcxdl83d5/YgPMi3wc8fHujLyG4xjOwWwz3n9eLt1HRm/pLGLW8sITrEn4lDE5k4tANxEYENfu2WYH9xGf/+aj2v/LyVNkG+PHH5AC4cEKfuFSIi0iIYa627azhMSkqKTU1NdXcZ4kbWWi5+5md25RUx9/ZRBPg2zZzIFRWW7zZkM3N+Gt+uy8IAo3u24+oRyZzUOQovTTEHwJerdvLAR6vYkVvExKEduGtM47yRERERaUzGmEXW2pTa9qklWTzSF6t2sTQ9h79f0rfJAjI4AwFP796W07u3JX3vAV7/dRtvLkzny9W76BQdzFXDk5gwKKHVBsIdOYXc/9Eqvlq9i+7tQnln4sBDurCIiIi0FGpJFo9TVl7B2U98j5cxfH7rqfh4u7dvcHFZOZ+t2MmMBWksSttHgK8XF/aPZ/KIJPrEh7u1tqZSVl7Bqz9v5V9frafCWm49sxu/PbUjvm5+bkREROpDLcnSrLyzKIPN2ft5fvJgtwdkAH8fby4aGM9FA+NZtSOXmQu28cESZ7DfgMQIJg9P4rx+sU3a4t2Ulqbn8Of3VrA6M4/Tu8fw0IV9SIwMcndZIiIijUotyeJRCkvKOf3xecRFBPDujSd57CCwvKJS3luUwYwFaWzK3k+bIF8uS0nkqmFJdIhqGQEyr6iUx79Yx4wFabQN9ef+C3pzbp/2HvuciIiIHC+1JEuz8erPW9mZV8R/rhjg0WEsLMCXKSd35JqTkpm/eQ8zF6Tx4o9bmP7DZk7rFsPk4UmM6t4W72Y40M9ay6crMnno49VkFxRzzYhk/nR2N0IDWmc/bBERaZ0UksVj5B4o5dl5GzmjR1uGdYpydzl1YozhpM7RnNQ5mp25RcxeuI3Xf9nGdf9LJaFNIFcNS+KylASiQvzdXWqdbNtzgHs/XMl367PpEx/Gi9ek0C8hwt1liYiINDl1txCP8bfP1jD9+818duup9Ggf5u5yTlhpeQVfrd7FjPlpzN+8Bz9vL87rF8uk4UkM6hDhkS3kJWUVvPDDZp78ZgM+XoY/nd2dq0ckeUSfcBERkcZS7+4WxpgxwH8Ab+BFa+2jNfbfAVxV7Zw9gRhr7V5jzB+A3wIWWAH8xlpbdEI/ibRYmbmFvPrTVi4eGN+sAzKAr7cXY/vGMrZvLBuz8pm5YBvvLsrg/SXb6RUbxuQRSVw4II4gP8/4IGfh1r383/srWL+rgDG923P/uF7EhmsBFRERad2O2ZJsjPEG1gNnARnAQmCitXb1EY6/APiDtfYMY0w88CPQy1pbaIx5C5hjrX31aNdUS3Lrc+c7y3l/yXa+vf00Etq0jIFv1e0vLuPDpTt4bf5W1u7MJ9Tfh0sGJzBpeBJd2oa4paa9+0t47PO1zF6YTnxEIA9d2Jsze7ZzSy0iIiLuUN+W5KHARmvtZtfJZgMXArWGZGAi8EaNawQaY0qBIGBHXQuX1mFjVj5vL0pnykkdW2RABgj29+HKYR2YODSRxdv2MWN+Gq//so1Xf97KSZ2jmDw8idG92jXavMNFpeWsycxjeUYuyzJyWJ6Ry6bsAryMYdrITtw2uqvHtGyLiIh4grr8VYwH0qt9nwEMq+1AY0wQMAa4GcBau90Y8ziwDSgEvrTWfnmEx04DpgF06NChrvVLC/DY5+sI8vPh5jO6uLuURmeMYXBSJIOTIrnn/GLeSk1n1oJt3DhrMe3C/Jk4tAMTh3agXVjACV+jrLyCDVkFLM/IYVlGLsszclibmU9ZhfOpUXSIP/0TwrmgXxxj+rSne/vQhvrxREREWoy6hOTaRhkdqY/GBcBP1tq9AMaYNjitzh2BHOBtY8wka+3Mw05o7XRgOjjdLepQl7QAi9L28eXqXfzprG5EBvu5u5wmFR3iz02junD9yM7MW5fFa/PTeOLrDfz3242c07sdk4YnMaJT1FEH+llr2brngBOI051AvGpHHoWl5QCEBvjQLyGcqSM70T8hnH4JEcSGB3jk4EERERFPUpeQnAEkVvs+gSN3mbiCQ7tajAa2WGuzAYwx7wEnAYeFZGl9rLX8/bO1RIf4c92pHd1djtt4exnO7NmOM3u2I23Pfmb9so23UtOZs2InXdqGMGlYB8YPTiDU34edeUVVYXi5q5U4r6gMgABfL3rHhXPF0ET6J0TQLyGc5KhgvJrhXM0iIiLuVpeBez44A/fOBLbjDNy70lq7qsZx4cAWINFau9+1bRjwMjAEp7vFq0Cqtfa/R7umBu61Dt+u3cW1r6by8EV9mDw8yd3leJSi0nI+WZ7JjAVpLEvPIcjPmxB/H7LyiwHw8TJ0bx9Kv4SIqhbibu1CNGWbiIjIcajXwD1rbZkx5mbgC5wp4F621q4yxtzg2v+c69CLcfoc76/22F+MMe8Ai4EyYAmuLhXSupVXWB77fB3JUUFcMSTx2A9oZQJ8vZkwOIEJgxNYnpHDG7+mU1xaTr+EcPolRtArNowAX293lykiItJiaTERcYv3Fmfwx7eW8dSVAzm/X5y7yxEREZFW6GgtyfpsVppccVk5//xyPX3jwxnbJ9bd5YiIiIgcRiFZmtzMBdvYnlPInWN6aFCZiIiIeCSFZGlSeUWlPPXtBk7tGs0pXaPdXY6IiIhIrRSSpUm98P1m9h0o5c4xPdxdioiIiMgRKSRLk8nKL+LFH7Zwfr9Y+sSHu7scERERkSNSSJYm899vNlJaXsHtZ3d3dykiIiIiR6WQLE1i6+79vPHrNiYO7UBydLC7yxERERE5KoVkaRKPf7kOX28vbjmzi7tLERERETkmhWRpdCsycvlkeSZTT+1I29AAd5cjIiIickwKydLo/v75WiKD/Zg6spO7SxERERGpE4VkaVQ/btjNjxt387vTuxAa4OvuckRERETqRCFZGk1FheXvn68lPiKQScM7uLscERERkTrzcXcB0nJ9uiKTFdtz+ddl/fH38XZ3ObXLWgs7V7i7irpJHAJtkt1dRdPbswm2L2748yaNgPCEhj+viIi0CArJ0ihKyyv455fr6NE+lAsHxLu7nNotmQkf3wYVpe6upG78QmHCS9DtHHdX0nTyd8H0UVCc1/DnDgiHS1+Fzmc0/LlFRKTZU0iWRjH7121s3XOAV6YMwdvLuLucQ1WUw1f3wfynoNMoOOdv4O3n7qqOrqQAPv49vH45nPUgnPR7MB72e20MX94DZUUwZQ6EtGu48xbnwoe3wMwJMOZvMHRa6/h9iohInSkkS4N7a2E6D32ymuGdIhnVPcbd5RyqKBfeuRY2fg3DboCzHwHvZvIy+M3n8OFNTsDftRou+A/4tuAp9bb8ACvegpF3QPLJDX/+676A966Hz/4f7FoFYx8HHw9/syQiIk2mmaQDaQ7KKyx/m7OGF3/cwqldo3lq4iCMJ7XO7dkEb1wBezc7AXPwFHdXdHz8gmDCK9C2N8z9C+zdBJfPgtAGbGH1FOWlMOd2iOgAp/yxca7hHwqXz3R+lz/8E/ZshMteg+DoxrmeiIg0K5rdQhpEXlEp1766kBd/3MKUk5J5ZcoQwoM8aMq3TXPhhTPgwB64+qPmF5ArGQOn3QGXzXBaP184HXYsdXdVDW/BM5C9Fs59zHlz0Fi8vODM+2D8i7B9kfP73LWq8a4nIiLNhkKy1NvW3fu5+Omf+Gnjbv56cV8eGNcbH28P+adlLfwyHWZeAmFxMPXbxvnovqn1GgfXfgHGC14eA6ved3dFDSd3O8z7O3Q7F7qf2zTX7Hcp/GYOlJXAS2fD2k+b5roiIuKxPCTJSHP188bdXPj0T+zZX8KM64Zx5TAPmg+5rAQ+uQ0+u8OZEeK6L1vWFGqx/WDqXIjtD29Pgbl/hYoKd1dVf1/cDbYczn20aa8bPximzYPobjD7KqcLhrVNW4OIiHgMhWQ5YTMWpDH55V9pG+rPR787hRGdo9xd0kH798CMi2HRq06f1stnOX1QW5qQGLjmIxgwCb77O7x9DZTsd3dVJ27jN7D6Qzj1dve8oQmLdVqU+1wC3zwE702F0sKmr0NERNxOA/fkuJWWV/DQx6uZsSCNM3q05T9XDPCsJad3rXYG6OXvdPqa9rvU3RU1Lh9/uPApaNfLmTLt5XPgijcgItHdlR2fsmKYcwdEdoaTf+++OnwD4ZIXnd/nNw85Az6veN0J0CIi0mqoJVmOS86BEq55+VdmLEjj+pGdeOHqFM8KyGvnwEtnOYHrN5+1/IBcyRgY8Tu48m3Yl+YMQNv2i7urOj4/PenM2DH2H07wdydj4NQ/OeE4e53z+9y+yL01iYhIk1JIljrbmJXPRU//ROrWfTx+aX/uHtvTcxYKsRZ++BfMvhKiu8K0uZAw2N1VNb2uo+G33zhdS/53PiyZ5e6K6mbfVvjhceh1IXQ5093VHNTjPPjtV+DtC6+MhRXvuLsiERFpIgrJUifz1mVx8dM/U1BcxhvThjFhcIK7SzqotAjemwbfPAh9xjstyGFx7q7KfWK6OUE56SRn8ZEv/s9ZZdCTfXYXGG9n9UNP0663M0AybhC8e53TBaMlDJAUEZGjUkiWo7LW8tKPW7j21YUkRAbx4c2nMDgp0t1lHZS/E14d66zMdsa9cMlLTp/S1i4oEq56F4Ze7yy//frlzmqDnmjdZ7D+Mxh1J4THu7ua2gVHw9UfwqCrnVkv3pwExfnurkpERBqRQrIcUUlZBXe9u4KHP1nN2b3a8+6NI4iP8KAAun0xTD8dstY6s1eMvN3pSyoObx8Y+xic/wRsngsvjnYGoXmSkgPOstAxPWD4Te6u5uh8/OCCJ2HM351Q/9I5Tv9vERFpkRSSpVZ7CoqZ9OIvvJmazu/P6MIzVw0iyM+DJkNZ+S68ci54+TjzH/c8390Vea6U3zitoPt3O6sObp7n7ooO+vFfkLMNxj7u9Pv1dMbA8Btg0ruQl+EM6Nv6k7urEhGRRqCQLIdZk5nHuKd+YllGDv+dOJA/nt0dL08ZoFdRAd/+Bd651ukjOm0utO/j7qo8X/Ipzu8qLA5mjIdfX3D/Qhl7NsFP/4G+l0HHU91by/HqfAb89lsIbAOvXQiL/ufuikREpIF5UNOguI21Vd0Uvly1k9veXEpogA9v3zCCfgkR7q2tuuICeP96WPuJ0zd07D+dj8ClbtokO63u706FObfDrlXOdGvuaMG11qnBJwDO/kvTX78hRHdxBki+cy18/HvIWg2jHwQvb3dXJk2tOXwKUlHhrGQprYxxut55urxMCGkHXp7VdtsMfnPSqKyF1y/HFufxatz9PPTdXvrFhzP96hTahQW4u7qDcrbBGxOdIDLm7zDsevU/PhH+oc7cv98+7HR12L0BLnsNgpt4tcTVH8Kmb53nMrRd0167IQVGwJVvwVf3woJn4Jfn3F2RuEPPcXDRs+Af4u5KardjCbx1tfP/qLQ+AybBef8EXw/6m17d1h/hzckw7AZnALcHMdbdH7nWIiUlxaamprq7jNZh2Wx4/3rK8SLLRjAz+a/cMukyAnw9qDUsbb4zm0B5KVz6MnQZ7e6KWoblb8GHN0Noe7jyTWjbs2muW1wATw1xgvnUec2jlaMu1n0Gu1a6uwppavv3wK/PQ9tezhvQNknuruhQK9+FD37nzNAy6BpQ20LrkrcDUl+GhCHOAHdPa5RIfcX5VDGyE0ycDVGdm7wEY8wia21KrfsUkluxwhzsUylsLIni1oKrmR32X0LL92Euegb6XOLu6hyLX4NP/uj84Zk421koRBpOxiJnAZaSAmcp5u7nNv41v7wXfn4SrvsKEoc2/vVEGtumb+HtKc5A4stnOnOUu1tFBcz7G3z/GHQYAZfNgJAYd1cl7rD6Q3j/BmcMxcQ3ILa/uyuC8jL44s/OG8wuo2HCyxAQ7pZSjhaSPavzhzStuY9g9+/htoLJXDvhQsJu+QETN9DpY/ntX9y7YEJ5GXx+N3x0izOo67dfKyA3hoTBzoC+6K5Od5Yf/924A/qy1jjdEgZOUkCWlqP6QM7/jXPe3LtTcQG8NdkJyAMnObPbKCC3Xr0uhGu/AIwzdeWq991bT+E+mHWJE5BH3Ox0WXNTQD4WheTWasdSKn59kRllZ3LSKWc4K+iFxMDVH8HAyfD9P5z/ZIsLmr62whx4/TInTA2/Ca582/njI40jLM5ZpbDPePj6AWdwZGlRw1/HWvj0dvALcQa4ibQklQM5O4503tx/dpfzZr+p5WyDl8+BdXOcFSzHPQU+/k1fh3iW2H6u2aD6Op96zP2bexrCstfDC2c6U2de+DSc84hHD3ZWSG6NKirY//6t7LWh/NThRu4c0+PgPh8/GPdfGPOo85/sy+c07WCP3RudRS+2fO+q428tp8+qJ/MNdFYrPOMeWP6ms4ph/s6GvcaKtyHtRxh9v9M/UqSlqRzIOfwm+OVZeP1S501/U9m2wFlgKScdrnobRtykAc5yUEhbmPIJDLgKvnsU3r4GSvY33fU3fO38fS/Oc+oYOKnprn2CFJJbobz5LxOcvZTn/K/h71edio93jX8GxsDwG53/ZHPSnf900+Y3fmGbvoUXz4DCvXDNR840b9J0jIGRdzh9KrPWOs/79sUNc+6iXPji/5y5rQdd0zDnFPFE3j7Om/txT8GWH+DFM503/41t8Qx49XznY+up32iAs9TOx99pwT37L850qi+f4/ydb0zWwvynnTeNER1g6rfQYXjjXrOBKCS3MsV52fD1g6TaHlwy5XbaBB9lnuEuo53/bAPC4X8XNF4/O2thwXMwcwKEJcDUuZ4x8KW16nmBM5+yl4+zquHKd+t/zrl/hf3ZzjREHvzRmkiDGTQZrvnYaUl+8QzY+E3jXKe8DD7/M3x0MySf7PyfrfEbcjTGwEm3OJ967EtzVg7d9kvjXKus2JlF6Ys/Q4/z4NrPnaDcTCgktzLLXv0DQRUFHBj9d3rG1aGjfHRX5z/d5FOcfnaf392w/ezKSuDjW+HzO6HbGLjuC8+bQqk1at/HebffEAM5M5fDr9Mh5VqIH9SwdYp4sqQRTj/Q8ESYNQEWPNuwA2Orxm887cwxe9W7Gr8hddf1LGdQvF8I/O98WPp6w56/INsZyLp0Jpx2J1z6mufOJX4ECsmtyOdffEzKnk9YEncFI08dVfcHBraBq96BYTc6g+lev6xh+tnt3w0zLoLF/4NT/+R8zO8fWv/zSsNoiIGcFRXw6Z8gMBLOvLdx6hTxZBEdnJkFuo+Fz+9yVmcsK6n/efdsco3f+A4u+A+c+3eN35DjF9Pd1f1hBHxwo9MtrqIBVmbcucJpoc5cBhNegdP/7HGr6dVF86tYTsjCzdkk/HwvuT6RDLr678d/Am8fOPdRuOBJZ1Ddi6Pr189u1yrnBbR9EYx/Ec68r1m+gFq8+g7kXDoLMn6Fsx5SC5e0Xv4hzjzFI+9wuq29dqHTSHCiNn3r/P9ZuNd5Izt4SoOVKq1QUCRMeheGTIX5T8EbVzjjSE7Umo/hpbOdsH2ta+akZkqppBXIzC3k25mP0sdswf+8R/EODDvxkw2+xplzs3Cv089u07fHf461nzovoLIS+M0c6Hfpidcjje9EB3Ie2Atf3w+Jw6H/xMavU8STeXk5s8dc8hLsWOy8jnYe5wqN1sIvz7vGb8Q7LYDJJzdOvdK6ePvCeY/D+f92DaI/y/m04nhYC9895qyQ27aX09UobmDj1NtEFJJbuKLScu7839fcWP46BxJOJWhgAwTS5JOdwXVhCc5/1gueq1s/O2vhh3/C7KsguhtMmwfxg+tfjzSN4x3I+c1DTrec8/6pTwlEKvWd4MxLXlHqNBas/bRuj6scv/HZ/4Nu5ziDa9skN2qp0gqlXAuTP4D9WfDCGbD5u7o9ruSAM35l7iPQ7wqY8imEtm/UUpuC/nK1YNZa/u/9lVyY/TwhXqUEXfTvhpszs02SM8iu2xhn0N3Htx69n11pIbw31QlOfS5xWpDDYhumFmk6dR3IuX0RLHoVhl3vDAIUkYPiBzkNDW17OMvCf//40Rsa9u85OH7jlD/C5bM0fkMaT8dTnX+fobEw42L49YWjH5+73ZkJadX7zkJRFz8HvgFNU2sjq1NINsaMMcasM8ZsNMbcVcv+O4wxS123lcaYcmNMpGtfhDHmHWPMWmPMGmPMiIb+IaR2//t5K9uWfM0l3j/gdfItDT8tkH+oM9ju1D85/3nPuKj2fnZ5mfDKWGcxiTPvg0tedBavkObpWAM5K8rhkz9CSDsYdbfbyhTxaGGxTmtb38vg24fh3d86jQk17VoFL4yCjFRn/Mbo+/XJjDS+yI7OpxVdz4I5t8Mnf4Dy0sOPy0h1+sfv2QgTZ8Mpt7WoBWyO+UozxngDTwPnAr2AicaYXtWPsdb+w1o7wFo7ALgb+M5au9e1+z/A59baHkB/YE0D1i9HMH/THv726Qr+HTIDG54AI29vnAt5eTnBd/yLTuvhC6c7/6lXqtyWvQ6ueN0J1C3oBdRqHW0g56JXIHOps9xoQD36v4u0dL6BMH46nHm/Mx/5K+dC3o6D+9fOqTZ+4zON35CmFRDm/N0++TZIfdlpVT6w9+D+ZW86DWC+gc5Uct3HuK3UxmLsMfqSulp+H7DWnuP6/m4Aa+3fjnD868Bca+0LxpgwYBnQyR7rQtWkpKTY1NTUuh4uNWTsO8C4p35iqs8cbix+2floruf5jX/h7YvgjSuhpADG///27j267rLO9/j7m7Tp/ULpvSnQQpFrG7QgCiOIRxd4Q+bokbr06IyCeMRh1niYg65zmeNaM2dmvBxdojIoiuOljGdQYXmqwDpcFFCxmKRQClhKS8JO29CSpE0vaZLn/PHbYWLIZSfZyd5N3q+1unb23s9v72/58Vv99Nfv8zzfhGOH4K5PZlthbrgDlpw99jVo/O18JFserrsT3vEl+L9/BUvXZhsp+BciqTDP/Dy7m1w1Owsmzz+Utactr8mez11e6go1mdX/S9ZiN3cZXL0RtvwLPPJlOOVP4H3fhVknlrrCEYuIx1NK6/t9r4CQ/F7g8pTSx/LPPwS8PqV0fT9jZwKNwGkppf0RUQPcCjxFdhf5ceCGlNKrNguPiGuBawFOOumk1+3atavw32G52vqTbDHt8z82bv88driji/fe8ihH9jVyX9WnqTjlomxXnfEKK21NWY9dLr+d8UlvhPd/D2YtHJ/vV2m8vAs2boC9W6FiKnzikWz9TUmF2/MUbHx/fpvglM3fuPJrtqepPDRuzv58P7gXSNkkvyv+MVsZ4zg2WEguZOXx/tLVQMn6XcAjvVotpgCvBT6VUvptRHwFuAl41a4CKaVbyQI169evL+KWRCX0/z4H+3fArofhPd+Aqllj+nUpJW768RaeamrjN2vupuLFzmyB+fG8mzd3WTYp757PwpTpWRP/lEG2vtbE0DOR857PwpJzDcjSSCw5C655EDZ9Ols6641/4b/GqHxUr88m9G26EU57C5z/0VJXNOYKCcmNwMpez6uB3ABjrwY29jm2MaXUsyn4v5KF5InvcEsWkJefB0/dDfufhw0bYV71mH3lt371PHfV5fjKBa0s2bIpmzS1YPWYfd+Aps7I1lrU5DJtTrbxiKSRm3UivO/2Ulch9W/eCthQ5O2ry1ghPQC/A9ZExKqIqCILwnf3HRQR84BLgLt6Xksp7QYaIqLnttJbyFovJr6m+uzxsv+WtTu8vDNbPL7hsTH5ul/9oZn/9fNtvPvsBbz7xS/BCauyZntJkiQN25AhOaXUCVwP3EO2MsWPUkpbI+K6iLiu19CrgHv76Tf+FPCDiNgC1AB/V5TKy12uNntcfh6c/jb46H1Zu8Xt74C64v4t7IV9h7j+h7WsWTyHL1Q/TOz7A7z98xNmnUJJkqTxVki7BSmlTcCmPq/d0uf57cDt/RxbB/TbED2hNdXB/JOyPdEhWzT+mvvh/3wYfvoJ2PtU1q9bUTmqr2k/2sm138tWArntPUuo+sEX4Yx3ZmsbSpIkaURckXys5GpfvWf5zAXwwR/D+dfAo1+FjVfDkbYRf0VKiRv/tZ5n9xzgqxvOo/q3n8smeVz+96MsXpIkaXIzJI+FQ/uzHuS+IRmypVLe8YVsPdnn7s82Ydj33Ii+5usPPsemJ3Zz0xVn8CZq4emfwZtuhPkrhz5YkiRJAzIkj4WeSXvLagYec/5H4UM/gfa98K23wI6HhvUVDzy9ly/c+wzvXrecay5cBj+/ERaeDm941fLVkiRJGiZD8lh4ZdJezeDjVr0p61OevSTb7vGxbxb08c+/1M5f3FHLmUvn8g//fi3xyFeyO9dv/4JrEkuSJBWBIXks5GqzJdhmnDD02AWrs5Uv1rwVNv1n+NlfQdexAYcfPNrJNf+8mamVFdz6H1/HjIO74OH/ne3MtPqSIv4mJEmSJi9D8lhoqhv6LnJv0+fC1T+Ei26Azbdld5UP7e936H/9yRM8/1I7N3/gPKrnz4BNfw2VVfC2vy1K6ZIkSTIkF1/7Pmh5of9Je4OpqIS3fg6u+ido+C18882w9+k/GvLsngP8tC7HdZes5o2nLswm6m2/D978mWw7aEmSJBWFIbnYmnptIjIS666Gj2yCjkPZyhfP3vPKW19/YDszqyr52MWroaMdfn4TLD4bLvh4EQqXJElSD0NyseXqssdl60b+GSvPh2sfhBNXww/fD498hZ3NB7m7PscHLzyZE2ZVwS8/D22N8I4vQmVBe8JIkiSpQIbkYsvVwoJTYfq80X3OvBXwZ7+As98D9/139v/gz5lZ2cnHLl4Fzc/CozfDug/AyW8oStmSJEn6N96CLLZcHZx0YXE+q2omvPc7tM09ndf++h/4+dxGFseFsOnT2Xtv/VxxvkeSJEl/xJBcTAebsxaI4axsMZQIvnT0SvYe6+Dmjlvg5vPhaGu2JvLsRcX7HkmSJL3CdotiaqrLHkc6aa8fzQeOsvGxF5h93lVUfPTebLm4Feth/Z8X7TskSZL0x7yTXEy5WiBg6dqifeS3Ht7Bsa5uPnHpabBwFnzq95C6syXjJEmSNCYMycWUq4MTT8vu9hbBy+0dfP/Xu3jn2uWsWjgre9FtpyVJksac7RbFlKstaqvFdx7dSXtHF59882lF+0xJkiQNzZBcLAf2wIFc0ULygSPHuP2R53nbWUt4zdI5RflMSZIkFcaQXCyvTNqrKcrHfe83u2g70sn1l3kXWZIkabwZkouliJP2Dnd0cduvnueS0xextnr+qD9PkiRJw2NILpZcLSx6DUybPeqP2vjYC+xr7/AusiRJUokYkoslVwfLakb9MUc7u/inXz7H61ct4PxTFoz68yRJkjR8huRiaGuCg7uLMmnvzsdfZE/bUe8iS5IklZAhuRhytdnjKENyZ1c333hoO+tWzufi0xYWoTBJkiSNhCG5GJrqICpg6bmj+pi763M07D/Mp958GhFRnNokSZI0bIbkYsjVwqIzoGrmiD+iuzvxtQe2c8bSObzlzMVFLE6SJEnDZUgerZSKstPeL7bu5rnmdq6/zLvIkiRJpWZIHq22HLQ3j2pli5QSX71/O6sXzeKKc5YVrzZJkiSNiCF5tIowae/+p/eyramN/3TpaVRWeBdZkiSp1AzJo5WrhaiEpeeM6PCeu8jVJ8zgyprlRS5OkiRJI2FIHq2mOlh8JkydMaLDH31uH3UNLVx3yalMrfR0SJIklQNT2Wi8MmmvZsQfcfP921kydxrvfV118eqSJEnSqBiSR6O1AQ7tG3E/8uad+/n1jn1c8yermT61ssjFSZIkaaQMyaORq8sel40sJN/8wHYWzKriA68/qXg1SZIkadQMyaORq4WKKbDk7GEf+uSLrTz4TDMfvXgVM6umjEFxkiRJGilD8mjkamHxWTB1+rAPvfn+7cyZPoUPveHkMShMkiRJo2FIHqmUspUtRjBp79k9B/jF1t185I2nMHf61KKXJkmSpNExJI9Uyy44/PKIJu19/YHtzKyq5M8uWjUGhUmSJGm0DMkjNcKd9nbta+fu+hwfvPBkFsyqGoPCJEmSNFqG5JHK1UHF1KwneRi+8eBzTKms4GMXexdZkiSpXBmSRypXm61qMWVa4Ye0HObO3zdy9fkrWTx3+JP9JEmSND4MySPxyqS94bVa3PrLHaQEH7/k1LGpS5IkSUVhSB6Jl5+HI63DWtmi+cBRNj72An/62hWsmD9j7GqTJEnSqBmSR2IEk/a+9fAOjnV184lLTxujoiRJklQshuSRyNVC5TRYdGZBw19u7+D7v97FO9cuZ9XCWWNcnCRJkkaroJAcEZdHxDMRsT0iburn/Rsjoi7/68mI6IqIBb3er4yI2oj4WTGLL5lcXX7SXmFLuH3n0Z20d3TxyTd7F1mSJOl4MGRIjohK4GvAFcBZwIaI+KN1z1JKn08p1aSUaoDPAA+llPb3GnIDsK1oVZdSdzc01RfcanHgyDFuf+R53nbWEl6zdM4YFydJkqRiKORO8gXA9pTSjpRSB3AHcOUg4zcAG3ueREQ18A7gW6MptGzs3wFH2woOyd/7zS7ajnRy/WXeRZYkSTpeFBKSVwANvZ435l97lYiYCVwO3Nnr5S8Dfw10j6zEMtNUlz0WsLLF4Y4ubvvV87zp9EWsrZ4/llVJkiSpiAoJydHPa2mAse8CHulptYiIdwJ7U0qPD/klEddGxOaI2Nzc3FxAWSWSq4Up02HRGUMO3fjYC+xr7+BT3kWWJEk6rhQSkhuBlb2eVwO5AcZeTa9WC+Ai4N0RsZOsTeOyiPh+fwemlG5NKa1PKa1ftGhRAWWVSK4Olp4LlVOHHPrj2kbOO2k+55+yYMixkiRJKh+FhOTfAWsiYlVEVJEF4bv7DoqIecAlwF09r6WUPpNSqk4pnZI/7v6U0geLUnkpdHdn7RbLaoYceuRYF083HeANq08c87IkSZJUXFOGGpBS6oyI64F7gErg2ymlrRFxXf79W/JDrwLuTSm1j1m1pbZvO3QcLGjS3tZcG53diXUr5499XZIkSSqqIUMyQEppE7Cpz2u39Hl+O3D7IJ/xIPDgMOsrL69M2hs6JG9pbAGgxpAsSZJ03HHHveHI1cKUGbDw9CGH1je0sHTudJbMnT4OhUmSJKmYDMnDkauFZWuhcugb8PWNraytnjcORUmSJKnYDMmF6u6Cpi0FtVq0HjrG8y+1248sSZJ0nDIkF+qlP8Cx9oJWttjyYgtgP7IkSdLxypBcqFxt9ljAneT6hhYAzrXdQpIk6bhkSC5UUx1MnQUL1ww5tK6hldWLZjF3+tAbjkiSJKn8GJIL1TNpr6Jy0GEpJeobW6ipnj8+dUmSJKnoDMmF6OoseNLe7rYjNB846qQ9SZKk45ghuRAvPQudh4fVj2xIliRJOn4ZkgvRM2mvgJUt6hpamVoZnLlsztjWJEmSpDFjSC5ErhaqZsOJpw05tL6hhTOXzWXalMF7lyVJklS+DMmFaKrL7iJXDP6fq7s78cSLraxz0p4kSdJxzZA8lK5jsPsJWF4z5NAdLx3k4NFO+5ElSZKOc4bkoTQ/DZ1HCpq0V9fQCsA6NxGRJEk6rhmSh5Kryx4LXNli9rQprF40e2xrkiRJ0pgyJA8lVwvT5sIJq4YcuqWxhXNXzKOyIsahMEmSJI0VQ/JQcrWwbN2Qk/aOdnbxVFOb/ciSJEkTgCF5MJ0dsGdrQa0W25oOcKwr2Y8sSZI0ARiSB9O8DbqOFrSyxZbGFsCd9iRJkiYCQ/JgenbaK2hlixYWzZnGsnnTx7goSZIkjTVD8mBydTB9XkGT9uobWlhXPY8IJ+1JkiQd7wzJg8nVZjvtDRF8244c47nmdnfakyRJmiAMyQPpPFrwpL0nG/ObiNiPLEmSNCEYkgey9ynoPlZYP3J+0t5aV7aQJEmaEAzJA3ll0l7NkEPrG1o45cSZzJ9ZNbY1SZIkaVwYkgeSq4UZJ8D8k4ccWt/QaquFJEnSBGJIHkiuLmu1GGLS3p62I+xuO+KkPUmSpAnEkNyfY0eynuRlNUMOrW9oAZy0J0mSNJEYkvuzZyt0dxY0aa++sYUpFcHZy+eOQ2GSJEkaD4bk/jQVvtNefUMrr1k6h+lTK8e4KEmSJI0XQ3J/crUw80SYVz3osO7uxJbGFlstJEmSJhhDcn8KnLS3c187bUc6qXHSniRJ0oRiSO7r2GHYu63gfmSAtSvdRESSJGkiMST3tftJSF0FrmzRysyqStYsnjP2dUmSJGncGJL7yg1j0l5jC+esmEdlxeBtGZIkSTq+GJL7aqqDWYth7vJBh3V0drM110aNk/YkSZImHENyX7laWF4z5KS9Z3YfoKOzm7XV9iNLkiRNNIbk3jraofnpYU3acztqSZKkiceQ3NvuJyF1F7iJSAsnzqqi+oQZ41CYJEmSxpMhubeeSXuFrGyR30QkhmjLkCRJ0vHHkNxbrhZmL4W5ywYddvBoJ3/Ye9B+ZEmSpAnKkNxbU11BrRZPvthKSrgdtSRJ0gRlSO5x9CA0P5OtbDGE+oYWwEl7kiRJE5UhucfuLUAqeGWLkxbMZMGsqrGvS5IkSePOkNwjV5c9Frgdtf3IkiRJE1dBITkiLo+IZyJie0Tc1M/7N0ZEXf7XkxHRFRELImJlRDwQEdsiYmtE3FD830KR5GphznKYs2TQYc0HjvJiy2F32pMkSZrAhgzJEVEJfA24AjgL2BARZ/Uek1L6fEqpJqVUA3wGeCiltB/oBD6dUjoTuBD4ZN9jy8aUabD60iGHbenZRMSQLEmSNGFNKWDMBcD2lNIOgIi4A7gSeGqA8RuAjQAppSagKf/zgYjYBqwY5NjSufLmgobVN7RQWRGcvXzuGBckSZKkUimk3WIF0NDreWP+tVeJiJnA5cCd/bx3CnAe8NsBjr02IjZHxObm5uYCyiqNusZW1iyezcyqQv5+IUmSpONRISG5vy3l0gBj3wU8km+1+LcPiJhNFpz/MqXU1t+BKaVbU0rrU0rrFy1aVEBZ4y+lxJbGFvuRJUmSJrhCQnIjsLLX82ogN8DYq8m3WvSIiKlkAfkHKaUfj6TIcvHC/kO0HDpmP7IkSdIEV0hI/h2wJiJWRUQVWRC+u++giJgHXALc1eu1AG4DtqWUvlSckkunzk1EJEmSJoUhQ3JKqRO4HrgH2Ab8KKW0NSKui4jreg29Crg3pdTe67WLgA8Bl/VaIu7tRax/XNU3tDJ9agWnL5ld6lIkSZI0hgqafZZS2gRs6vPaLX2e3w7c3ue1h+m/p/m4tKWxhXOWz2NKpXuwSJIkTWSmvQId6+rmyVyr/ciSJEmTgCG5QM/uOcCRY92GZEmSpEnAkFyg+oZWANZVzytxJZIkSRprhuQCbWlsYf7MqZy0YGapS5EkSdIYMyQXqK6hhXXV88lWtZMkSdJEZkguwKGOTp7dc8B+ZEmSpEnCkFyArbk2upP9yJIkSZOFIbkA9fmd9ta6054kSdKkYEguQF1DCyvmz2DRnGmlLkWSJEnjwJBcgPrGFmrsR5YkSZo0DMlD2N/eQcP+w6y1H1mSJGnSMCQPob6xBcCVLSRJkiYRQ/IQ6htaqAg4d4V3kiVJkiYLQ/IQ6htaWLN4DrOmTSl1KZIkSRonhuRBpJTY0thqP7IkSdIkY0geROPLh9nX3mE/siRJ0iRjSB5Ez6Q9l3+TJEmaXAzJg6hvaKFqSgWvWTqn1KVIkiRpHBmSB1Hf2MrZy+cytdL/TJIkSZOJ6W8AnV3dPNHYyrrq+aUuRZIkSePMkDyA7c0HOXysy35kSZKkSciQPID6hhbAnfYkSZImI0PyAOobW5k7fQqnnDiz1KVIkiRpnBmSB1Df0MK6lfOJiFKXIkmSpHFmSO7HkWNdPL37gJP2JEmSJilDcj+25tro6k5uRy1JkjRJGZL70TNpz5UtJEmSJidDcj/qG1tYNm86i+dOL3UpkiRJKgFDcj/qG1rsR5YkSZrEDMl9tBzqYOe+Q6xdaT+yJEnSZGVI7mNLYysANd5JliRJmrQMyX3UN7QQAee4soUkSdKkZUjuo76xhVMXzWbu9KmlLkWSJEklYkjuJaVEXUOr6yNLkiRNcobkXppaj/DSwaOujyxJkjTJGZJ76dlExOXfJEmSJjdDci91jS1UVVZwxrI5pS5FkiRJJWRI7mVLQytnLpvDtCmVpS5FkiRJJWRIzuvqTjzxYivr7EeWJEma9AzJeTuaD3LwaKf9yJIkSTIk96jrmbTnnWRJkqRJz5Cct6WxlTnTprB64axSlyJJkqQSMyTnPdXUxrnV86ioiFKXIkmSpBKbUuoCysUd117Iy4c6Sl2GJEmSykBBd5Ij4vKIeCYitkfETf28f2NE1OV/PRkRXRGxoJBjy8XUygoWz5le6jIkSZJUBoYMyRFRCXwNuAI4C9gQEWf1HpNS+nxKqSalVAN8BngopbS/kGMlSZKkclPIneQLgO0ppR0ppQ7gDuDKQcZvADaO8FhJkiSp5AoJySuAhl7PG/OvvUpEzAQuB+4c7rGSJElSuSgkJPe33EMaYOy7gEdSSvuHe2xEXBsRmyNic3NzcwFlSZIkSWOjkJDcCKzs9bwayA0w9mr+rdViWMemlG5NKa1PKa1ftGhRAWVJkiRJY6OQkPw7YE1ErIqIKrIgfHffQRExD7gEuGu4x0qSJEnlZMh1klNKnRFxPXAPUAl8O6W0NSKuy79/S37oVcC9KaX2oY4t9m9CkiRJKqZIaaD24tJZv3592rx5c6nLkCRJ0gQWEY+nlNb3957bUkuSJEl9GJIlSZKkPgzJkiRJUh9l2ZMcEc3ArhJ89ULgpRJ8rwrnOSp/nqPy5zkqf56j8uc5Kn+FnKOTU0r9rj1cliG5VCJi80DN2yoPnqPy5zkqf56j8uc5Kn+eo/I32nNku4UkSZLUhyFZkiRJ6sOQ/MduLXUBGpLnqPx5jsqf56j8eY7Kn+eo/I3qHNmTLEmSJPXhnWRJkiSpD0OyJEmS1IchGYiIyyPimYjYHhE3lboe9S8idkbEExFRFxGbS12PICK+HRF7I+LJXq8tiIj7IuIP+ccTSlnjZDfAOfqbiHgxfy3VRcTbS1njZBYRKyPigYjYFhFbI+KG/OteR2VikHPkdVQmImJ6RDwWEfX5c/Q/86+P6jqa9D3JEVEJPAu8FWgEfgdsSCk9VdLC9CoRsRNYn1Jy8fYyERFvAg4C/5xSOif/2j8C+1NKf5//S+cJKaX/Uso6J7MBztHfAAdTSl8oZW2CiFgGLEsp/T4i5gCPA+8BPoLXUVkY5Bz9B7yOykJEBDArpXQwIqYCDwM3AH/KKK4j7yTDBcD2lNKOlFIHcAdwZYlrko4LKaVfAvv7vHwl8N38z98l+8NEJTLAOVKZSCk1pZR+n//5ALANWIHXUdkY5BypTKTMwfzTqflfiVFeR4bk7H/0hl7PG/F//nKVgHsj4vGIuLbUxWhAS1JKTZD94QIsLnE96t/1EbEl347hP+WXgYg4BTgP+C1eR2WpzzkCr6OyERGVEVEH7AXuSymN+joyJEP089rk7kEpXxellF4LXAF8Mv/PyJKG7xvAqUAN0AR8saTViIiYDdwJ/GVKqa3U9ejV+jlHXkdlJKXUlVKqAaqBCyLinNF+piE5u3O8stfzaiBXolo0iJRSLv+4F/gJWauMys+efA9fTy/f3hLXoz5SSnvyf6B0A9/Ea6mk8j2UdwI/SCn9OP+y11EZ6e8ceR2Vp5RSC/AgcDmjvI4MydlEvTURsSoiqoCrgbtLXJP6iIhZ+QkTRMQs4G3Ak4MfpRK5G/hw/ucPA3eVsBb1o+cPjbyr8FoqmfyEo9uAbSmlL/V6y+uoTAx0jryOykdELIqI+fmfZwD/DniaUV5Hk351C4D8si1fBiqBb6eU/ra0FamviFhNdvcYYArwQ89T6UXERuBSYCGwB/gfwE+BHwEnAS8A70spOXGsRAY4R5eS/RNxAnYCH+/p29P4ioiLgV8BTwDd+Zc/S9bz6nVUBgY5RxvwOioLEbGWbGJeJdkN4B+llD4XEScyiuvIkCxJkiT1YbuFJEmS1IchWZIkSerDkCxJkiT1YUiWJEmS+jAkS5IkSX0YkiVJkqQ+DMmSJElSH/8fPdN7FkTc6ZkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "pyplot.figure(figsize=(12,12))\n",
    "# plot loss during training\n",
    "pyplot.subplot(211)\n",
    "pyplot.title('Loss')\n",
    "pyplot.plot(hist.history['loss'], label='train')\n",
    "pyplot.plot(hist.history['val_loss'], label='Validation')\n",
    "pyplot.legend()\n",
    "# plot accuracy during training\n",
    "pyplot.subplot(212)\n",
    "pyplot.title('Accuracy')\n",
    "pyplot.plot(hist.history['accuracy'], label='train')\n",
    "pyplot.plot(hist.history['val_accuracy'], label='Validation')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8d79ec44",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import BatchNormalization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a09b77a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#batchnormalization\n",
    "\n",
    "model4 = Sequential()\n",
    "model4.add(Dense(32, input_dim=8, activation='relu'))\n",
    "model4.add(BatchNormalization())\n",
    "\n",
    "model4.add(Dense(32, activation='relu'))\n",
    "model4.add(BatchNormalization())\n",
    "\n",
    "model4.add(Dense(32, activation='relu'))\n",
    "model4.add(BatchNormalization())\n",
    "\n",
    "model4.add(Dense(1, activation='sigmoid'))\n",
    "model4.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2fdd8a16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "20/20 [==============================] - 1s 9ms/step - loss: 0.8265 - accuracy: 0.4870 - val_loss: 0.6660 - val_accuracy: 0.6688\n",
      "Epoch 2/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6048 - accuracy: 0.6743 - val_loss: 0.6302 - val_accuracy: 0.7208\n",
      "Epoch 3/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5257 - accuracy: 0.7296 - val_loss: 0.6011 - val_accuracy: 0.7208\n",
      "Epoch 4/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5068 - accuracy: 0.7410 - val_loss: 0.5765 - val_accuracy: 0.7338\n",
      "Epoch 5/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4872 - accuracy: 0.7687 - val_loss: 0.5615 - val_accuracy: 0.7338\n",
      "Epoch 6/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4566 - accuracy: 0.7785 - val_loss: 0.5482 - val_accuracy: 0.7338\n",
      "Epoch 7/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4634 - accuracy: 0.7736 - val_loss: 0.5395 - val_accuracy: 0.7468\n",
      "Epoch 8/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4349 - accuracy: 0.8029 - val_loss: 0.5344 - val_accuracy: 0.7468\n",
      "Epoch 9/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4395 - accuracy: 0.7997 - val_loss: 0.5258 - val_accuracy: 0.7468\n",
      "Epoch 10/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4308 - accuracy: 0.7964 - val_loss: 0.5223 - val_accuracy: 0.7662\n",
      "Epoch 11/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4195 - accuracy: 0.8029 - val_loss: 0.5184 - val_accuracy: 0.7727\n",
      "Epoch 12/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4136 - accuracy: 0.8127 - val_loss: 0.5224 - val_accuracy: 0.7597\n",
      "Epoch 13/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.4073 - accuracy: 0.8143 - val_loss: 0.5223 - val_accuracy: 0.7532\n",
      "Epoch 14/30\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.3998 - accuracy: 0.8127 - val_loss: 0.5341 - val_accuracy: 0.7532\n",
      "Epoch 15/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3916 - accuracy: 0.8241 - val_loss: 0.5444 - val_accuracy: 0.7468\n",
      "Epoch 16/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3962 - accuracy: 0.8274 - val_loss: 0.5378 - val_accuracy: 0.7468\n",
      "Epoch 17/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3945 - accuracy: 0.8143 - val_loss: 0.5415 - val_accuracy: 0.7468\n",
      "Epoch 18/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3696 - accuracy: 0.8469 - val_loss: 0.5596 - val_accuracy: 0.7403\n",
      "Epoch 19/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3728 - accuracy: 0.8355 - val_loss: 0.5522 - val_accuracy: 0.7273\n",
      "Epoch 20/30\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.3759 - accuracy: 0.8257 - val_loss: 0.5512 - val_accuracy: 0.7468\n",
      "Epoch 21/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3571 - accuracy: 0.8420 - val_loss: 0.5648 - val_accuracy: 0.7338\n",
      "Epoch 22/30\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.3701 - accuracy: 0.8241 - val_loss: 0.5697 - val_accuracy: 0.7273\n",
      "Epoch 23/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3664 - accuracy: 0.8339 - val_loss: 0.5803 - val_accuracy: 0.7338\n",
      "Epoch 24/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3603 - accuracy: 0.8485 - val_loss: 0.5785 - val_accuracy: 0.7468\n",
      "Epoch 25/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3611 - accuracy: 0.8404 - val_loss: 0.5816 - val_accuracy: 0.7338\n",
      "Epoch 26/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3582 - accuracy: 0.8485 - val_loss: 0.5797 - val_accuracy: 0.7403\n",
      "Epoch 27/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3491 - accuracy: 0.8485 - val_loss: 0.5820 - val_accuracy: 0.7403\n",
      "Epoch 28/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3512 - accuracy: 0.8355 - val_loss: 0.5879 - val_accuracy: 0.7273\n",
      "Epoch 29/30\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.3487 - accuracy: 0.8339 - val_loss: 0.5860 - val_accuracy: 0.7403\n",
      "Epoch 30/30\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.3518 - accuracy: 0.8485 - val_loss: 0.5923 - val_accuracy: 0.7208\n"
     ]
    }
   ],
   "source": [
    "\n",
    "hist=model4.fit(X_train, y_train, epochs=30, batch_size=32, verbose=1, validation_data=(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bbb7e7a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 1ms/step - loss: 0.5923 - accuracy: 0.7208\n",
      "Accuracy: 0.7207792401313782\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model4.evaluate(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b8050965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model4.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, np.round(y_pred))\n",
    "\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0913f35e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "pyplot.figure(figsize=(12,12))\n",
    "# plot loss during training\n",
    "pyplot.subplot(211)\n",
    "pyplot.title('Loss')\n",
    "pyplot.plot(hist.history['loss'], label='train')\n",
    "pyplot.plot(hist.history['val_loss'], label='Validation')\n",
    "pyplot.legend()\n",
    "# plot accuracy during training\n",
    "pyplot.subplot(212)\n",
    "pyplot.title('Accuracy')\n",
    "pyplot.plot(hist.history['accuracy'], label='train')\n",
    "pyplot.plot(hist.history['val_accuracy'], label='Validation')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}