ML 2  (DBSCAN clustering).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5ce9f9f0",
   "metadata": {},
   "source": [
    "# DBSCAN (density based spatial clustering of application with noise)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "82486a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a52afff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dataset\n",
    "data = pd.read_csv(r'C:\\Users\\ASUS\\Downloads\\penguins.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f990cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        y  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g\n",
       "0  Adelie            39.1           18.7              181.0       3750.0\n",
       "1  Adelie            39.5           17.4              186.0       3800.0\n",
       "2  Adelie            40.3           18.0              195.0       3250.0\n",
       "3  Adelie            36.7           19.3              193.0       3450.0\n",
       "4  Adelie            39.3           20.6              190.0       3650.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "077e04f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1=data.drop(columns=['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09870cb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>55.8</td>\n",
       "      <td>19.8</td>\n",
       "      <td>207.0</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>43.5</td>\n",
       "      <td>18.1</td>\n",
       "      <td>202.0</td>\n",
       "      <td>3400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>49.6</td>\n",
       "      <td>18.2</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>50.8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>4100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>50.2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>198.0</td>\n",
       "      <td>3775.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g\n",
       "0              39.1           18.7              181.0       3750.0\n",
       "1              39.5           17.4              186.0       3800.0\n",
       "2              40.3           18.0              195.0       3250.0\n",
       "3              36.7           19.3              193.0       3450.0\n",
       "4              39.3           20.6              190.0       3650.0\n",
       "..              ...            ...                ...          ...\n",
       "337            55.8           19.8              207.0       4000.0\n",
       "338            43.5           18.1              202.0       3400.0\n",
       "339            49.6           18.2              193.0       3775.0\n",
       "340            50.8           19.0              210.0       4100.0\n",
       "341            50.2           18.7              198.0       3775.0\n",
       "\n",
       "[342 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b166c509",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NORMALIZING DATA USING STANDARDSCALER\n",
    "dataScaled= StandardScaler().fit_transform(data_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d97a90c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.88449874,  0.78544923, -1.41834665, -0.56414208],\n",
       "       [-0.81112573,  0.1261879 , -1.06225022, -0.50170305],\n",
       "       [-0.66437972,  0.43046236, -0.42127665, -1.18853234],\n",
       "       ...,\n",
       "       [ 1.04154272,  0.53188718, -0.56371522, -0.53292256],\n",
       "       [ 1.26166175,  0.93758646,  0.64701263, -0.1270689 ],\n",
       "       [ 1.15160224,  0.78544923, -0.20761879, -0.53292256]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataScaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "82e756f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataScaled_df= pd.DataFrame(dataScaled, columns= data_1.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5980cb72",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.884499</td>\n",
       "      <td>0.785449</td>\n",
       "      <td>-1.418347</td>\n",
       "      <td>-0.564142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.811126</td>\n",
       "      <td>0.126188</td>\n",
       "      <td>-1.062250</td>\n",
       "      <td>-0.501703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.664380</td>\n",
       "      <td>0.430462</td>\n",
       "      <td>-0.421277</td>\n",
       "      <td>-1.188532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.324737</td>\n",
       "      <td>1.089724</td>\n",
       "      <td>-0.563715</td>\n",
       "      <td>-0.938776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.847812</td>\n",
       "      <td>1.748985</td>\n",
       "      <td>-0.777373</td>\n",
       "      <td>-0.689020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2.178824</td>\n",
       "      <td>1.343286</td>\n",
       "      <td>0.433355</td>\n",
       "      <td>-0.251947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>-0.077396</td>\n",
       "      <td>0.481175</td>\n",
       "      <td>0.077258</td>\n",
       "      <td>-1.001215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>1.041543</td>\n",
       "      <td>0.531887</td>\n",
       "      <td>-0.563715</td>\n",
       "      <td>-0.532923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>1.261662</td>\n",
       "      <td>0.937586</td>\n",
       "      <td>0.647013</td>\n",
       "      <td>-0.127069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>1.151602</td>\n",
       "      <td>0.785449</td>\n",
       "      <td>-0.207619</td>\n",
       "      <td>-0.532923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g\n",
       "0         -0.884499       0.785449          -1.418347    -0.564142\n",
       "1         -0.811126       0.126188          -1.062250    -0.501703\n",
       "2         -0.664380       0.430462          -0.421277    -1.188532\n",
       "3         -1.324737       1.089724          -0.563715    -0.938776\n",
       "4         -0.847812       1.748985          -0.777373    -0.689020\n",
       "..              ...            ...                ...          ...\n",
       "337        2.178824       1.343286           0.433355    -0.251947\n",
       "338       -0.077396       0.481175           0.077258    -1.001215\n",
       "339        1.041543       0.531887          -0.563715    -0.532923\n",
       "340        1.261662       0.937586           0.647013    -0.127069\n",
       "341        1.151602       0.785449          -0.207619    -0.532923\n",
       "\n",
       "[342 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataScaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "25b7dde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#finding the Epsilon values when min points are k=7  WITH THE HELP OF KNN algorithm\n",
    "\n",
    "knn = NearestNeighbors(n_neighbors= 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "566e3873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(n_neighbors=7)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(dataScaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "91b8d8bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dist,ind = knn.kneighbors(dataScaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a00d9fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sort the distance\n",
    "dist = np.sort(dist,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0f343a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dist= dist[:,1]#finding all the min distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d2cdff4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x22fca2d9af0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the min dist\n",
    "plt.plot(dist)\n",
    "plt.grid(axis='y')\n",
    "plt.axhline(y=0.6, color='r',ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a3e7d4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from the above k distance graph we got eps value = 0.6 when the min points are 7\n",
    "\n",
    "#train the DBSCAN\n",
    "dbscan=DBSCAN(eps=0.6,min_samples=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "61027fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=0.6, min_samples=7)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan.fit(dataScaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8d724e7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bill_length_mm', ylabel='bill_depth_mm'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JEydOMGLECPr27cuzzz5LdHTDVn01NC2m+qk+EKqfBC6FMouDwAp+NWUWO4GqluPKbLQ5KLM4EeFtCRGgEBZqBRqOEmsJFqcFsUhMqDIUmaR+vZ5qUv3kcDk4UXqiUlAD8PdxEy+vKkAEfoLhxqp+EqjMZVX9JCDQlKQWGnnpl8MUnM2x55RZePbHQ2SV1mwZuynxeDycLjDy2Lf7Gfn6Bka8voEnvttPaqGxqacm0AqxOCyk5KfwwJ8PMPb7sfzrx3/xQcoH5JnymmQ+YlHVt7lhnQJ49ZYORAX63zyjApVCQNOCEb6qCQhchEKjjfHzk8k32Cg02nj9ll7c/fkuDmXrOZ5nYOndgwltxi0nMkos3PTxVr9mnb8eyGHrqUJWzRwuGDMK1CtHio8wbc00PGfXP8xOM4sPLWZX3i7ev/J9wlSVW5E0FFKxlBBlCAXmgirHb+wTx639OjeJo7BAwyCs1AgIXASFVMyj13QGYMOxAgbO/ZND2XokYhFPj+uCphmncZwuNyt2ZFTZfbzE7OCHPVm4XO4mmJlAa6TEWsKrO171BTQVOVB4gHR9w/Y3Oh+RSESwIhi1rHLgHhUQhUwsQyIWMaRDKP/q05YhHUKFgKaFIwQ1AgJVUGax+37WKmWM6RLOE2M7++3z/oS+JCWEopDVzBeiKdBbHfx5tPpl/3VH8jBYnY04I4HWjNlh5kjxkWrHt2ZvbcTZeJFJZLTTtCMuMI5QVSgR6ggSgxIJUgQhETff965A3RCCGgGB88gsNvPsj4fILfPqZfL1VlKLzPyyP9tvv+92V70C0pyQScQXbMYZqJIikwrfTAXqB7FYjEJSfSo2RFn/1UQ1QSaRESALICoginB1OAqpQghoWilCUCMgUIEio40pn+1g1b5sHli6hwKDlfRiM8/+dJDDOQYkYhHX9fQKCDccK+DJ7/f7xMPNEa1SxowRCdWOzxiRQICi8TqQC7RuQhQh3JR4U5VjIkQMazuskWckcLkhBDUCAhVQySU8Na4LIhHsSS9l4Nw/2ZdZSs+2QUjEIhZNHcCrN/fk9Vt6ATCqczgKafN+G/WLDeKWfm0rbZ8wMIYebSt7ZQgI1BWFVMH0ntPpFNTJb7sIES8Pf5lwdXgTzUzgckHwqRFolhQbbZgdLiQiEWFaBTJJ4wUOFruTX/bn8Ph3+33bvrtvCDqVjPYhahQyCQargzy9t1riQumd5kKxyU52qYU1B3MQiURc2yOKNoEqggNajs+OQMuhwFzAqbJTbMnaQrgqnFHtRhEREIFaWn+VdpfapVug+VEfPjXNt2xD4LLEbHNyILuM//1ymINZerQKKXcMbc+UpLhKfhINhd7qZOWeTL9tL68+wvwp/X2iYK1ShlbZ/IOZckIC5IQEyIWVGYFGIVwdTrg6nKTopKaeisBlRvNeNxe47NibUcqEBckczNIDYLA5+WjDKWYv30Ohwdbg1y8wWHl4eQrbThcDcF3PaF8q6t4vd5Ovb776GQEBAYHLHSGoEWg2FBptPLfqEFUlRHeklpBRYm7wOcglYkZ09Ob935/Ql1dv7sm8yf0RiWBoh7BGTYMJCAi0XnJzc3nooYdITExEqVQSGRnJ8OHDmTdvHmZz/X3WXXHFFTz88MP1dr7mjpB+Emg2mGxOTuRXb92ffLqIvrHBDTqHQLWcSYNjubpbJG2CVAQopIzoGMbvD48kTKsgWC1oUAQEBC6N06dPM2zYMIKCgnj55Zfp2bMnTqeT48eP89lnn9GmTRtuuOGGpp5mi0T42inQbJCIRcgk1XumhDSSqDVQLadjpNbX8FEtl9IxUisENAICrRG3C1I3w4HvvP+7XQ1+yQceeACpVMquXbu47bbb6Nq1Kz179uTmm2/m119/5frrrwegrKyMe+65h4iICHQ6HVdeeSX79u3znef555+nT58+fPnll8TFxREYGMiECRMwGAwATJs2jU2bNvHee+8hEokQiUSkpaUBsGnTJgYNGoRCoSA6OpqnnnoKp/OcEafNZmP27NlERESgVCoZPnw4O3fubPDn5lIRghqBZkOoRs6/+lQuPQZvwJOUENrIMxIQEGjVHF4F7/aAz/8J30/3/v9uD+/2BqKoqIjff/+dBx98kICAgCr3EYlEeDwerrvuOnJzc1m9ejW7d++mX79+XHXVVRQXF/v2PXXqFD/++CO//PILv/zyC5s2beLVV18F4L333mPIkCHMmDGDnJwccnJyiImJISsri3/84x8MHDiQffv28cknn7Bo0SJeeukl33mfeOIJvv/+ez7//HP27NlDYmIiY8eO9bt2c0QIagSaDSqZlEeu7kSHcP83uljk1bdE6ISyTQEBgXri8Cr45g7Q+zuFo8/xbm+gwObkyZN4PB46d/ZvuxIWFoZGo0Gj0fDkk0+yYcMGDhw4wLfffsuAAQPo2LEjb775JkFBQXz33Xe+49xuN0uWLKFHjx6MGDGCKVOm8OeffwIQGBiIXC5HrVYTFRVFVFQUEomEjz/+mJiYGD788EO6dOnCjTfeyAsvvMBbb72F2+3GZDLxySef8MYbbzBu3Di6devGwoULUalULFq0qEGel/pC0NQINCvaBqn4ekYSx3INbD5RQFSgkqu6RBKpU6Bqxj2WGgKny02h0YbT7UElkzTrTuCNTanZjsnmRCwSEapRIG/mBogCzQy3C9Y8CVU03vRuE8Gap6DLddBA7RREIv9U+44dO3C73UyaNAmbzcbu3bsxGo2EhvqvUFssFk6dOuX7PS4uDq1W6/s9Ojqa/Pz8C177yJEjDBkyxG8Ow4YNw2g0kpmZSWlpKQ6Hg2HDzjlAy2QyBg0axJEj1ff2ag4IQY1AsyNSpyRSp2Rkp8vXfTRPb2XZjnQ++zsVvcVJ12gtz1zXjd7tAtG0IH+c+sbmcHEsz8D/fjnMzrQSlDIx4wfEcO+oDrQJUjX19C5bCi2FlFpLAQhSBhGmCmvaCV2MM1srr9D44QF9lne/+BH1eunExEREIhFHjx71256Q4G1nolJ5/47dbjfR0dFs3Lix0jmCgoJ8P8tk/p8HIpEIt9t9wTl4PJ5KQVW5D2956qv854sd19wQvt4ICDQzio12nvhuP+/+cQK9xSvcO5JjYNKn29me2rzz2Q3NiXwj//54KzvTSgCwOtx8vu0MUxfv8DUgFWh8Sq2l3LTqJm5adZMvuGnWGKvvXF+n/WpBaGgoV199NR9++CEmk6na/fr160dubi5SqZTExES/f2FhNQ8a5XI5Lpe/+Llbt25s3bqVig0Ftm7dilarpW3btiQmJiKXy9myZYtv3OFwsGvXLrp27VqLR9v4CEGNgEAzI6fMwqbjBVWOvfDz4cvWAFBvcfDKb0dwuiunDE7kGTmSY2iCWV3eFFoKOVlykjzzuZt/njmPkyUnOVlykkJLYRPO7gJoIut3v1ry8ccf43Q6GTBgACtWrODIkSMcO3aMr776iqNHjyKRSBgzZgxDhgzhxhtvZO3ataSlpbF161aeeeYZdu3aVeNrxcXFsX37dtLS0igsLMTtdvPAAw+QkZHBrFmzOHr0KD/99BPPPfccc+bMQSwWExAQwP3338/jjz/OmjVrOHz4MDNmzMBsNjN9+vQGeU7qCyH9JCDQzNiTUVLtWHqxGaPNSUQVYyUmO2Z7uc5EjlzaujRIJpuTbaeKqh1feyiX0V2qemYEGoryFZqK3PfHfb6ff7jhh+aZimo/FHRtvKLgKnU1Iu94+6ENcvkOHTqQkpLCyy+/zNNPP01mZiYKhYJu3brx2GOP8cADDyASiVi9ejX/+c9/uOuuuygoKCAqKoqRI0cSGVnzYOuxxx5j6tSpdOvWDYvFQmpqKnFxcaxevZrHH3+c3r17ExISwvTp03nmmWd8x7366qu43W6mTJmCwWBgwIABrF27luDghvUKu1SEhpYCAs2MX/ZnM/PrlCrHJGIRGx67gtiQc40BrQ4XR3P0vPjrYfacKUUtl3D7oBjuHp5AdCvSmeSWWbj2vc2Umh1Vjs+6MpFHr+lc5ZhAw3Cy5GSloKYiP9zwA4nBiQ1y7UtuaFle/QT4BzZnNSO3fQHdBAO8xqQ+GloK6ScBgWZGr3ZByKtpxzC2eyQhan9h4LFcAzfP28aeM6UAmO0uFm1JY/rnO8lrRamqMI2CO4bEVTt+fa82jTcZAcArCv7hhh+YN2aeb9u8MfP44YYf+OGGHwhSBjXd5C5Gtxu8gYsu2n+7ro0Q0LRghPSTgEAzI0qn4JPJ/bjny924KuhHYkPU/N8/uvpVP5Wa7bz4y2G//co5nGPgRL6RyFbi7yOViJk4KJbNJwpISS/1G3vhhu5EB7WOx9mSCFOFVUovRaojG2x1pt7pdoO3bPvMVq8oWBPpTTk1UBm3QMMjBDUCAvWI0+XGZHMSWKGlQpnZ7vf7xZBLJQxLDGP9o6PYeKyAzBIzQzuE0TVaS1SgfzrJZHex+0z1Gpw/j+QxPLEZahrqSFSgkvlT+pNaaOLPI/kEq+Vc083rY3Q5l7oLXAJiSb2XbQs0HUJQIyBQTzhdbg5m61l3OJe7hycQHCAntdDIxxtO8fi1nYnQ1nwlQSmT0D40gKlDq7ZRL0cMBMglmOxV96sJb4WGfRFaJRFaJYPjhbYZzYXyNFT5zwICTYUQ1AgI1BPZpVZuX5CMxeHC7nRzx5A4xs9PJt9gw+Jw8b9/9SC4nptyhmnlTEpqz4K/Tlc5PrZ7VL1eT0CgKqpKQwkINAWCUFhAoJ7QKKVMHBQLwMLNqYx4fQP5BhsahZT7r+hAoKr+0yMyiYS7hsXRs23laoBXb+5JVKCgMxEQELh8EFZqBFocpWY7AXIpsrP9fkw2J3ggQCmtpF+pSs/idnvQWx0EXYLupSpCAuTMuiqRXIOFX/fn+ravuDeJrlE6xOLq7cVLzXb/+VjsBKpqNp+oQBWLpg7kdIGJP4/mEapRcHW3SKJ0SgIUwltcoOVgsBsw2A2IRWKCFEEopUJQLlA7hJUagRZFicnOF9vS2JdZisPpFeWuP5rP5pMFpBaaeP7nQ74y5twyC/9ddYiMYrPveLfbw5FcPR9uOEmR0QZAepH/cZc0P7Odnan+wt0fU7Ios1TtrQJwMt/Aa2uOUmjwzierxMyzPx4ipxa2/xE6JUkdQvnPdd24b1QHOoRrhIBGwI9y99/m6PTrdDk5XnycORvnMPb7sVy38jpe3fEq2cYL9WcSEKiM8Kkn0GKwOJz8uDeLt9ed4KMNp1h692ByyqzMXp7CDw8MY8qi7WSWWMgts/L+7f14cOkedqeXsi+jlO/vH0qoRkGu3sr4+ckYbU7MdhczRycy8VPvcaVmB2/d1puQgLqJa9OLTD4NjUYhZVBcMOuPFbBwcypikYj7RnWopKkpMFi5bX4yxSY7ZRYHz13fnamLd3Iy30h6sYlFUwcK3bkF6oWK7r/Nzen3jOEMt/96O3a3HQC72873J75ne852Fl+7mKgAQRsmUDOElRqBFoNKJmVM10jiQtXYnG5umbeNWctS8Hjgr+P5PD62MyIRbDtdzMC5f7A7vRSRCJ76R1dUcq/vhFou4c5hcQB8vT2doa+uJ7PEgkom4eExndBdQlmwUiaha7QOjULKinuTeHt8H6YPi0ciFjG0Q6hvDhVRSCXMutLr6bH6QC6DX/6Tk/lGZBIRT43rilYpfO8QuDSae38mk8PEx3s/9gU0Fck0ZrInb08TzKrpEYlE/Pjjj9WOb9y4EZFIRGlpaaPNqSUgBDUCLYqYEDVfz0hCV+FmnxAWwO2DYrm6WyQvXN/db//Xbu7FqI5hqOXe/YPUcu4ekcD4ATF++y29ezDd2+iQVuPkWxMidEreuKUXPz44lK5ROoLUch68MpE1D41gcEIoSlnloEanknFL/3Y8cEUHv+0L7hhAv9igVte/SaDxKV+hqdiT6b4/7ms2XbWNdiPJOcnVjq89sxan21lpu8vtwuV2YXfZqxxv7uTm5jJr1iwSEhJQKBTExMRw/fXX8+eff9bo+KFDh5KTk0NgYGC9zOf555+nT58+9XKupkT4GijQojDZnOw+U4LBdu5DLKvUQkaxmehAFT/tzfLbf+WeTEZ1CkclP/enXma2s+Wk/7fT73Zn0j5UfcmpngidkogKDr4hAXJCLlLGrbc4+P1wnt+2b3dl0LNNIGFaIagR8FJoKfQFIEHKoCZJHzXEHMQiMVq5Fr1dX+V4qCIUicj/fVBoKeS3E7/RztEOj96DyqEiUh2JWqpG0gLcgNPS0hg2bBhBQUG8/vrr9OrVC4fDwdq1a3nwwQc5evToRc8hl8uJimr8tJzD4UAma75Gl8JKjUCLweJw8dfxAmYv96ac+sQEERviTUWVmh08+PUeX8rpn72iEYkg+XQxDy1PocDgFQFnlpi5feF2skq9Kaeru3q73X69I5231x2nyGRr1MeUW2bxaWhkEhHXnvWVWX0gl/+uOkihsXHnI9B8KV9xqe3qSn32Z6rrHC5EmCqMyV0nVzt+S+dbEInOVQ6W2kp5Ofllvjz8JW6PGwCb00a6Ph2jw0htezS73C525u5k9enV7MzdictdtZFlfVLehXvHjh3ccsstdOrUie7duzNnzhySk8+tWhUWFnLTTTehVqvp2LEjq1at8o2dn35asmQJQUFBrF27lq5du6LRaLj22mvJycnxO2bQoEEEBAQQFBTEsGHDOHPmDEuWLOGFF15g3759iEQiRCIRS5YsAbxpsHnz5vGvf/2LgIAAXnrpJVwuF9OnTyc+Ph6VSkXnzp157733/B7jtGnTuPHGG3nhhReIiIhAp9Nx7733YrdXTjPWJ8JKjUCLQSkVkxAegFYppVu0jvcm9MXudDNl0XbOFJtJSgglJaOUeZP7M7RDKGO7RzF7eQojOob7GkQqpBJ6tg2k2GRn+T1JxIUFsGjLaT5Yf5KRncJRVZEiakhkEjED2gdzpsjEl9MH0y1ax+CEEF74+TBXdI5AIRW+d1zOVFwZOV8TU87FVkwutT/TxeZwqSs2IpGIsXFj2ZS5qVIaalafWbTTtPOfj7mQdenriJaf14gSyDXlopaqkUlqtpLwx5k/eHXHq36PK1IdyVODnmJM+zF1eDQXp7i4mDVr1jB37lwCAio7hgcFBfl+fuGFF3j99dd54403+OCDD5g0aRJnzpwhJCSkynObzWbefPNNvvzyS8RiMZMnT+axxx5j6dKlOJ1ObrzxRmbMmMGyZcuw2+3s2LEDkUjE+PHjOXjwIGvWrOGPP/4A8EtrPffcc7zyyiu88847SCQS3G437dq145tvviEsLIytW7dyzz33EB0dzW233eY77s8//0SpVLJhwwbS0tK48847CQsLY+7cufX0bFZGCGoEWgwut4dInZIf7h+GRiklUqekzGzny+mDAdAopNzUty1tg1Wo5VKu7BLB7w+PJEKr8HnQhGsV/O/GHpRZ7MSFBiCViLlrWDzX92rjO64xCdUoeHxsZ6YPj6d9qBq5VMLN/doxomMYkTolWqGf0WVNxYqlilTUxzR0JdPF5lAf1w9Xh/PqiFfJMGSwPn09GpmGK9tfSaQ6Eq1c67fvkeIj1Z7H6Xbi8riQcfH3zR9n/mDOxjl48F/ZyTfnM2fjHN6+4u06BTZuj9un8ZGIJJXSYSdPnsTj8dClS5eLnmvatGncfvvtALz88st88MEH7Nixg2uvvbbK/R0OB/PmzaNDB69Gb+bMmbz44osA6PV6ysrK+Oc//+kb79q1q+9YjUaDVCqtMqU1ceJE7rrrLr9tL7zwgu/n+Ph4tm7dyjfffOMX1Mjlcj777DPUajXdu3fnxRdf5PHHH+d///sfYnHDfGETghqBFoHT5eZwtp41h3K5e0QCIQFyUgtNfLjhJE+O7ezTsVQsmQ5QSOkYqa10rnCtgnDtOe1MkFruZ3zX2IRqFH5aHp1Khq4B3IcFLm+ae38mDx50ch3jO48nQB5AkCKoyv0CFRcWxlZMVVWHy+3i1R2vVgpoyuchQsRrO15jdMzoWml07C47+eZ89HY9Ho8HjVxDpDoShUThm1d5eqwm8+zVq5fv54CAALRaLfn5+dXur1arfQELQHR0tG//kJAQpk2bxtixY7n66qsZM2YMt912G9HRlVe8zmfAgAGVts2bN49PP/2UM2fOYLFYsNvtlYTGvXv3Rq1W+34fMmQIRqORjIwM2rdvf9Hr1gUhqBFoEWSXWRl/tq+SzeFm2rA4JizYRp7ehsXu5KUbe15UkCsg0NKoGIjkmfN8qyPzxswjUh3p26cm1LU/08XmUF8B0vk+OtUFNYlBiaikqirHNHINUtHFb2t78vf4pZzOx4OHXHMue/L3MDBq4MUnjzegSS1L9avEMtqNmB1mEoISUEi8X1w6duyISCTiyJEj3HjjjRc85/mCXJFIhNvtrtX+FTVGixcvZvbs2axZs4YVK1bwzDPPsG7dOpKSki44j/PTZN988w2PPPIIb731FkOGDEGr1fLGG2+wffv2C56n4rwaCiFhL9Ai0CikTE7y9lVa9Le3r1Ke3mty9+AViQQJKxsCrZAwVRiJwYkkBif6ghg4p4lJDE5s8Cqoi83hUq5f0eW4Kh+dqjx0wlXhvDf6vUorKDKxjOiA6BqtrBSYC2o0v5ru5/F4MNgNVZaWuz1uiixFPlFzSEgIY8eO5aOPPsJkMlXav6F9Z/r27cvTTz/N1q1b6dGjB19//TXgTRW5XDUTSW/evJmhQ4fywAMP0LdvXxITEzl16lSl/fbt24fFcs4ZPTk5GY1GQ7t27SrtW18IQY1AiyAkQM6DoxO5oXcbv+3L70mia/SF+yoJNB52l6tSS4hSc8NWOwi0XCpWU1Xlo1NVhZVMIqN/ZH8+HvMxgYpAghRBxOpiiQ+MRy6p2WptuDq8Xvdze9wY7IZqx412o19V1ccff4zL5WLQoEF8//33nDhxgiNHjvD+++8zZMiQGl2ztqSmpvL000+zbds2zpw5w++//87x48d9upq4uDhSU1PZu3cvhYWF2GzVV14mJiaya9cu1q5dy/Hjx3n22WfZuXNnpf3sdjvTp0/n8OHD/Pbbbzz33HPMnDmzwfQ0IKSfBFoQJWYH21OL/Lb9sCeLNleqhNRTM8DucrE3vZTdaSXcPjiWILWcE3kGlvydxiNXdyJMK7R7uBQaUhNTU/+Z5qLLkUvkRAdEY5VZCVeHo5TXrvFlv4h+RKojyTfnV6mrESEiUh1Jv4h+NTqfSCS64AqRRCzxS7nEx8ezZ88e5s6dy6OPPkpOTg7h4eH079+fTz75pFaPpaao1WqOHj3K559/TlFREdHR0cycOZN7770XgJtvvpmVK1cyevRoSktLWbx4MdOmTavyXPfddx979+5l/PjxiEQibr/9dh544AF+++03v/2uuuoqOnbsyMiRI7HZbEyYMIHnn3++QR5fOSJPbYv6WzB6vZ7AwEDKysrQ6XRNPR2BWpBeZObW+Vt9KadB8SGsP+oVwM0YEc/9VyQKgU0TcyLfwHXvbcHucvPo1Z24rlc0N3+ylRKzg0mDYnn82s5NKsgWqJ6TJSf99Cw1Lfe+VM4vF69Kr1NdgGW1WklNTSU+Ph6lsvbdvMurnwC/wEaEN/iobfWTyWEirSytyrG2mrbNUpzdkEybNo3S0tILtno4nwu9pjW9fwsrNQItAqVcTNdoHSZbCSvuSaJtsIqPNpzks7/TGNohDHUVfZUEGpcQtZwb+7bhm12ZvLXuOG+tOw5AkFrG1GFxBAq6p2ZFfXjgXCrViZdr46NTV8a0H8PbV7xdpU/Nk4OerHU5t0KiIFQVSpHFfzVZJ9cRIK/sRyPQMAhBjUCLIEKr5PVbeqG3OEkIC0AsFnH/FYncNiCGmBB1lX2V6kKZ2U6AQurrAWWwOpCIRY3uX9MSCdUoeHpcV/L0VjYdPyfw/P6+oSSEBzRoxUNro8BcgMPtQCqWEq4Kb5Dnrjl44DQ1Y9qPYXTMaPbk76HAXEC4Opx+Ef3q1GpBKpYSpgojSBHkK+nWyrXIJXKkYuHzo7EQnmmBFkOEVklEBduZmvRVqg3FJjufbj7N2O5RdG+jw+pwseZgLjqVjBEVmmIKVE+h0cb+zDK/bb8eyOGOIe2F1FMNKLWWsiN3B+/ueZcMQwZhqjBm9JzB2LixhKpC6/VaLk/DtwOoDU2l15GIJTUu274YUrEUqViKUlr7dFhro7zNQmMjfEoLCOBtlPn19jN8vPEUi/9OY8W9SRzNNfDEd/sRieDbe4cwIK5qa3IBL6cLjNw2fxslZgdBahm92gby14lC3l53HIlYxOSk9kIK6gI4XU5+S/2Nl3e87NtWaCnklR2vkFqWykP9HkIj19Tb9VRSFW+OetP384N/PgjUzQOnPqirj46AQEWEoEZAAK/78PW927BsRwZZpRZu+PBv39ioTuHEhqgvcLQAQIBcSmK4hhMFRr65dwjhGgWv/naEH1KyGdA+GJVMcJC4EPmWfN5Lea/KsRXHVjCl25RLDmrO19E8tukxAJb+Y6lvn8bQswgINBRCUCNQr7jdHvRWh1+qocxs9/Veas60Dw1gxb1JjHl7E1aH1yirW7SON2/tTZimeZcjl5rtBKpkiEQijDYnVocTnVKGXCrB7nRhtrsaPP0TGajkw4n9MNmdJIR7b75PXNuFu0ck+PpaCVRPma0Mk6OyGRt4q3MyjZnE6mIv6RrV6WhyTDlV7C0g0PIQvjoJ1Btut4cjuXo+WH+SIqPXuCm92Mxzqw6Rp7c28ewujtHqYOupIl9AA5BaaCKrxILTVb01eVNTaLDx5tpjnMg3YrQ6WHc4l43HCjieZ8Rid7Ivs4wvt52hpBFM8CIDlb6ABrzi4Y6RWiGgqQEXM44LkNWugqbcrbcqV97zsblsvDnqTVZct6JeUk4VnYJrcn0BgfpCWKkRqDdy9VYmzE/GYHNitjuZdWVHJi5MJrPEQqnZwdvjexMS0DxXPEw2J78fzuOJ7/YDMLB9MFmlFrLLrExYkMy39w2hR9sLN9JrCsosdt5ff4KvtqeTp7cyKak9j6zYh0gEz1/fHYPVybTFO7A53ehUMsYPbIdSJrztmyPBymC6hHThaPHRSmMhyhC/FgU1oXxVpmIFU3V9nEKVoRf1hanLtaH1V1AJNC+ETzeBekMtl3DX8Hje+/MEy3ZksGxHBgBKmZiHx3REp2y+IlGVTEKnSC0ahZQBccG8cUtvzHYnExduRyWXEKxunnMPVMmZktSeX/bnsPlkITf3j6F/+2B2nynhuVWHfPvFhaq5qmuEENA0Zzwwd9hcZqybQbG12LdZJVXx9hVvY3VaKbQUXjBAqMp75nzfmar0MvWho7mY701De94ICIAQ1AhQWfNSUZ9RG4LUcu4aHk++weoLaACW3j2YHm0Dfd4v1WGyOcEDAUrvn6XT5cZoczZKKbBYLKJbtI4fHxyGTiklXKsAFCy7ZzASkYi2wc1PKFz+unWM1PL9fUPYeLyAl1cf5qUbe/LcqkOkFnr1GTqVlK9nJNEmqOrOxi2Rcv2JWCQmRBlS454/zZliazGPbHyEZ5OeJd+cz8nSk7TTtKNDUAfe2f0O+wr2XXTVoyrNTGP5zlzM90ZYsRFoDARNzWVObpmV/646REaxGYAio413/zjB0VwDdemgUWZxsPmEfw79u92ZlZocno/J5mT90Xz+OJqHyebE6XKzL7OMJX+nNYoWBLyBTWKEhgjdOY+J2JCAZhnQZBSb+e+qQ+SWWSkz2zFYnaSkl/LO+D6Y7E6yS891xjVYnexJL8Fsq9xBuKVhc9k4VHiI2etnM/b7sVz/w/W8uetNck25TT21eiHdkM4jGx/hqyNfkWHI4OfTPzNz/Uz2Feyr92sFKYP49aZfWXHdClwel6B9aUJWrlzJ2LFjCQsLQyQSsXfv3qaeUotFWKm5jCky2nj0m738faqIlPRSvrk3iTd/P853uzNZuSeT3x8ZRVRgzU2kskrMPg2NUiZmZMdwfj+cx7IdGYgQ8eg1nQitoorI4/GwN6OU2ctT8HjgvfF9iAlVM3FhMjanG61KysRB7VEJrRAA7+s2edF2zhSZySqx8O74Prz4y2FsTjclZgcPL9+LzekmNkRNSICcvRmlzFqWwieT+jGqcwSqenJfbgpOl55m8urJOD3eAM3qsrLs6DJ25OxgwTULiFBHNPEM6051epfa+MZUdY7qjg9ThXHSepLxv44HLn0l5WLzv9x6H9UGk8nEsGHDuPXWW5kxY0ZTT6dFIwQ1lzGBKhlzrunMroXJpBebSXplvW/snpEJKGvpKyKXSujZNpBCo43lM5JICNfw2ZZU3lt/ghGdwqoNSkQi7wrJkIRQtp4q4qEVe31jsSFqrukWJQQ0FVDJJTw9rgv3L93DrjMlDH99AwDhWgVtg1RE6BRIRCJeu7kXbYNUPPrtPo7mGogP06CUttzFWYPdwFu73vIFNBU5VXaKY8XHWnRQUx99kKo6R1XHl+tf6rPnU1P2cboUXC4XKSkpFBYWEhYWRt++fZFIGvfzZsqUKQCkpaU16nVbI0JQcxkjlYjp3S6QL6cP4rb5yb7tU5Lac8eQOHS1dH8N1yp48V89KDPbiQsLQCoRM21YHNf1iqZtsAqH041T4vZpa4xWByKRiACFlEidkvcm9GXCgmROFRgB0CqkLJvhbV5ZX7qf1oBaLmVU53Be/Xcvnvx+v2/7rNGJtA9R8/XdSTjdbgLkEiJ0Kt6b0Bez3Ul8WMvuv2RymNiZt7Pa8T/T/2REuxGNOKOWS1Nqb5oT69ev58033yQ/P9+3LSIigscee4wrr7yyCWcmUFda7tc2gXqhzOLgm12Zfts2HS+4qAamOsK1ChIjtb7AJeiskNXqcLNg82kOZeu9AmCrg7WH8th8ogCz3auhSS82k1li9p3LYHOy60wxOaUWnv3pnO6n0GjjnT+Ocyyvbrqf1oDe4uSHFP/XbdW+bPRWJzEhauLDNETovMLgqLPeMS05oAEQIUIr11Y73ppuwuWpnB9u+KHOaZvyczRF2qc+5t/QrF+/nieeeMIvoAHIz8/niSeeYP369dUcKdCcEYKay5gio41X1xzlu93em+PYbpEopGLSi81M+nS7n9j0UjDbnKzYmc5HG04xYUEyh7L1rDmUy6Pf7uP+pXs4lW/gYLbep6FpF6yiX2wQAA8t38um4wXYXW4mL9pObpmFV387wudbz3Db/G3k6W31MseWRIHBykPLU0g+XYxIBNf1jEYkgl1nSpj59R7yDc3f6LAuhKpCub3L7dWOXxt3bSPOpmEJU4WRGJxIYnBinYO18nNUdXx50DFvzDzftnlj5tVbIFIf829IXC4Xb7755gX3eeutt3C56r/p59KlS9FoNL5/mzdvrvdrXM4I6afLGKVMwqhO4Xy3O5PHru7MlCHtOZlv5PaFyfRuF4i8nvQXaoWU63q1Yen2dDJLLPzro3N9lUZ2DCcqUIXN6SYqUInH4y0BV0jFPLxiLwezy+jWRseX29I4U+Sv+7lvZIda635aAzKJmOGJYWxPLeb9CX25onM41/eO5v6lexiaGIrsIqXzLRWpWMqtnW5lW9Y29hX6VwP93+D/Iyogqolm1vKoqfamtZKSklJpheZ88vLySElJYcCAAfV67RtuuIHBgwf7fm/btm29nv9yRwhqLmMCFFJGd47g94dHEq5VoFPJ6NUukF9nDydILb9gv6NSs92/v5PFgUouRl6NwC42RM3ye5K4+u2/sDi83366RGl569behGm911l692A8Hog52zzy3fF9MFidxIaoePaf3Zmw8JzuZ+rQ9kxOal+l7sdsd+Jye9CeNftzutyYbM4W0X+qJgSp5UxOas/Y7lG0CVIRoJAyslO473W8VF+fMrMdo82JWCQiVKOot+C2PohQR/Dule+SVpbGpsxNBCmCuDL2SsLV4Whk9dfBWqB1U1hYs/L1mu5XG7RaLVpt9WlUgUtDCGoucwIUUjpGnnuDSSViEiMu/IYrMNh4e90x7hoWT8dILaVmO0u3pzMwLpg+sUFVBjZGq4Pk00W+gAbgTJGZzFILQWoZUomYduf5wUTolETovBqab3dn+I1tPFbA3cMTKgU1ZruTzScKKbM4GNcjCpVMwuEcPb8dyGXGyARCAlpPYFMxeFHL/V/HumB3ujiWZ+SlXw6xPbUEpUzMrQNiuG9UB9o2I+O+8lWGAVH1+w36cqRiGXZNUk4VXYNbskNwWFjN5l3T/S6V4uJi0tPTyc7OBuDYsWMAREVFERUlrEDWhubzFUygRVBqtvPW78dYtiOD8QuSOV1g5POtabyx9hhTFu0gvaiyDsdsc/LHkTwe+9ZbqTOgfTBtg1RYHC5uX5DM0VxDtdcrMtp49bcjfL8nCzin+zlTZGbyosq6n8PZeu77ajdPfLef1QdyOZyjZ/z8ZD7ZdIqvt5/xuhYLVMmJfCM3ffQ321NLALA63Hy57Qx3fLad3LLWqdO53Kmt9qW8auqmVTf5gpuWSN++fYmIuHD5f2RkJH379m2U+axatYq+ffty3XXXATBhwgT69u3LvHnzLnKkwPm0qKDmr7/+4vrrr6dNmzaIRCJ+/PHHpp7SZUegSsadw+IJVMkoNtm58q1NvPPHCQBu6tu2yh5JSpmEjmf7Ko3qFM4nk/ux7J4k2gWraBOkJPgCqydKmYSRHb0fPk+M7cwbt/bm6xlJKKRi+sYEVUqNxIaouaJTOABPfr+fGz78G4vDRZtAJdf3bkOAQlicrAq9xcGrvx3F6a5cTXYq38Sh7LImmJVAc6Bix+3zfW1aahduiUTCY489dsF9Hn300Ubzq5k2bRoej6fSv+eff75Rrt+aEHlaUE3sb7/9xt9//02/fv24+eab+eGHH7jxxhtrfLxerycwMJCysjJ0Ol3DTbSV4/F4OFVgYszbm3zbRncO5+3b+lQboLjdHlILTWiVUl8bgvRiMxKx6KKpDZPNSYnJjlYpJVAtx+F0k11mQauUVtn1u9Bo445FOzicowe8DTX/mDOqUnrrUjjfN6fMbCdAIb1of6sLYbA6kIpFqOTewMvudGFxuAhU1TxlVmZxEFghJVdmsdfo+JxSCyNe31BlUANw64B2vHFL7xrPQ6D1cLLkZJU9ncr54YYfmkRgbLVaSU1NJT4+HqWy5s7nFanKpyYyMpJHH31U8KlpAi70mtb0/t2ivraOGzeOcePGNfU0LnvKLA5+3Z/tt21fZhmFRlu1QY1YLKJDhL+QMzakZkGGwerg9bXHeHxsZwLVcsqsDj77O5XbB8USrJb7+a84XW6ySy2+Zo7gTaP8fbKIf/SU+cTDl0K+3spra48xc3Qi8WEBFJvsLNp8mmu6R9G9ja5OgY3B6uDX/TmEahQMTwxFIhaxK62Eg9l6bhvQrkbi39MFRj7ZdIrHx3YmQqsku9TCq78d5clxXS4aOIpEInRnV9+qIvwConGB1sX5upnWzJVXXsmoUaOa3FFYoP5oUUGNQNNTarbzVfIZX8ppZMcw9meVUWyyM35BMt/dN4SE8PqrQiky2nh4xV6STxezL7OUFfck8ebvx/hudxY/pGTx+8P+/amO5hoYPz/Zl3JqG6xiZ1oJT36/H6lYxLU9oi4pBVVssvPCz4f59UAOm48X8P39Q/l8axqfbknls7/TWPvIyBoHa+W4XG62nSriqZUHEItg/pQBBMglTF28A4fLQ5Baxo192l6wCqnQaGP8gmQKDDaKjXZevbknd3++k8M5Bk7mG/hy+uAq+26VE66RM3VIe9/rej7/6iOUnV4uVHQbLvesac09nSQSSb2XbQs0Ha06qLHZbNhs58zZ9Hp9E86mdaCWSxgQF4JMIuLmfu148touFBht3DZ/G4kRAfWuWdEpZTx2TWcmfbq9Rj41wWoZbYNVWOwuvp4xGLVcyhPf7WNHajGdo7SX3MwxSCXjgSs6sOl4AfkGGyPO9l0CmJQUi6YOj18iEdOzXSB9YoLYm1HKjC92+cY6RmoYlhh20bJqhVTMo1d34qmVB/jzaD4D5/7pPbdYxFPjul70dZFIxEwYFMvmE4XsOlPiN/bc9d1oE1S35X2BlkNN+kFV5HLytRFoObQoTU1FRCLRRTU1zz//PC+88EKl7S1FU1NmdhCorr0+oqGxO12cKTITEiAnVKPA4/HqZcp7ONU3Dpeb3WkllXxqHr26c5U+NVklZlxuD7GhAYA3XaS3OkkIC8BgdVTqIaVTyhCLa95CwO32cCRHz3UfbPFtu75XNP+7sccF00TnX0tvcSCXilGeDbRyyizcOm8bmSXeiq7QADmrZg2vcTm10epgydY03vz9uG/bvMn9GN05AkUNg7kCg43UQiN/HM4nSC3jmu5RROoUuD2eZvG3J9Bw1EQ7A/it4jRlUFMfmhqB5kV9aGpaVPVTbXn66acpKyvz/cvIyLj4Qc2EUwVGXl59hIKzlvfZpRae/fFQvbUuuBTkUm81U3k6QyQSkRCuaZCABrwanqp8aqrrT9U2WO0LaMDrd5MYoaHAaOP5nw9xpsirtyky2vhow0mO5OhxVyOQrYpSi4MfUrL8tm1PLabEXH2/rCKjjQ/Wn+RorgG324PeYufHlCy2ny7G5nBRarZzJEdPnv5c6XSJ2c6eMyUUGWvWCkJvdbL6QI7ftm93ZVJmrXkfr3CtgiidkmC1jAmDYkiM0GC0OXn2x0N+fbkEmpaKFUn1WX00d/hc3hz1JnOHz61yvDwV9etNv+LyuC547Yaao4DAhWjVQY1CoUCn0/n9awkUGmyMn7+NFbsy+L+VB8nXW7lryU5W7ctmxhe7anyTaw3U1qemOopNNp76fj8/pGQzceF2skotvL3uOAs3pzJ+QTI5+pr5sBSb7MzbeIpPt6QCcGWXCDQKKflnX7P0oso3foPVwadbUlm0JZXx87dxLM/AypQs/rvqEHd9vpPjeQZO5Ru598vdOFweOoQH0C1ai9sDDy1PYUdq8UX9dfL0Vp+GRiIW8Y+eXsOuP4/m8/T3Bygw1OxvpuisNue1tceYvWwvBQYb93+1h1X7spn62Y7L6m+vOdMQfjFByiASAxN5bNNjKCTeLyzn94Mq97Wxu+zc8vMtF7x2a/G0EWhZtChNjdFo5OTJk77fU1NT2bt3LyEhIcTGxjbhzOoXhVTMI1d34j8/HGTdkTzWHfHmtaViEU+P61In3UZLpdyn5rvdWTwxtjOTk9pzIt/IxIXJVfrUVIdOKePhMZ1IPl1MVqmFYa+e0+ZMGxaHuobpGbVcwtDEUBb9ncqdw+KYOTqRrBIL4xck0zVaV2UvKq1Sxm0D2vHd7kwKDDbGvXeugd013SKJClShtzgI0yjQKKT857quOFwePlh/gqwSCwnhmotqYmQSEYPjQzmWZ2TxtIH0jQ1iZKdwnvr+AKM6h6Oo4fOkkEl48touPLxiL1tOFjJw7h8AiEXw9LiuqOVCVUhTUhPdS21dfitWO5XYvHoqm8vGm6PeJFgR7Dtn+cpLxWtWde2GmKOAQE1pUZqajRs3Mnr06Erbp06dypIlSy56fEvyqTFaHSzakupXjbJwSn9GdgqvsT6iLpSa7agVEl+rA7PNiRuaLJDSWxy4PR4KDDbCtQqUMgk2h4t8o43gi/SnOh+ny82BrDJu+nirb9tt/dvxn+u61rgvlMnqxOF2U2CwEapRoFVIMdmcFJltaOQyIitUYpWa7QSqZDhcbqxOF6UmB1e+tcnnBdM3NoiPJ/bD7fH2qSoy2cgqtfLg0j04XG4+nNiXmGB1jdsfFJtsFBrttA9Ro5BJMFgd5OmtRGiVVWqPqn2MNicrdmbw4i+Hfdvevq23t+2EvPkF1Ba7kxKzAzEglVkJCwj2jeltenSK5v1erw0N4RlT03PW936XiqCpaX1cdpqaK664okrXxZoENC0NvdXJ6oP++ogVuzLQ10IfUVvKy7X3pZdhd7kw25xsOJbPusO5GJugvYDe4uDHlCz2ZZQSG6JGKZOwI7WYH/ZmEaVT1iqgAa8257vdmX7b/j5VRGk12pzzMdmc/HE0j80nCmkTpEKrkLIvs5QvktNQSaXMXX3EpzspNNp4e91xjuUaOJhVxu60Uv48mu9nbncyz8iRXD0zvtjNg1/vwWB18ulfpyizODDbXcz6OoWcMisWe82e+5AABZ0itb6gV6uUkRihrVVAA6C3Oli1z9+H6PvdWRisza/FREaxmf/+dIjpS3ZwvPQk76d8QHppntevyJjNS9tfIs+Ud/ETCQgItApa1ErNpdJSVmry9Fbu+Gw7x3KNSMUirukeyeoDuQBc3S2Cl2/qRbi2fs3QrA4n3+zK5L8/HUIhFfPV9MHkGazMWpaCxwOf3zWIEYlhtaoSuhQcLjdrD+Uy8+sUJGIRi6YOQCwSceeSnbjcHt6b0Id/9IxCVkOTrGKTnXfWHefL5DMAXN01ki0nC7E4XLQNUvHNvUNoG1x9lZHb7WHrqUImL9qBSATvT+hLdKCSSZ9ux+Z088S1nckoNrPtVBHL7xnC62uOsjIli0CVjO/uG8Lvh/N4Y623Sd3g+BBOF5ooMNjQKqR8NKkfz/50kNwyKwvvGMBra47i8Xg4nGNALIJPpw5geGIYcmnDp37yDVbu+WI3ezNKEYtgXI9ofj0rPh7WIYx3J/Sp97+9upJdauGWT7ZSZLKzclYP7t1wO2W2Mq5sN5anBj/OPevuJk2fRt/wvrx75buEKEOaesqXTMXUzvl+MXDp6afy81Z1ztruV59zrIqWvlLz8ccf88Ybb5CTk0P37t159913GTFiRFNPq0m57ByFLxekYhGD40I5lW9iyZ0D6R0TxPDEMP7vh4Nc0SkCZQ31EbVBKZNyZZcIFv+dRmqhiVvnb/ONDYoPpnOkttECGgCZREz/2GB6tNFxMFvPtMU7fWPdorUMjAupcUAD3lYJwxJD+Wr7GWaOTuTuEQmkFZqYsCCZHm10F9XmiMUiOkZqSUoIIfl0MbOWpfjG2oequbprJHd8toOcMitJr/zpG7tvVALpRWY6RmjOziGMh6/qRInZzqPf7iNMI8fp9lBktGNzulm2I513xvdGLZcy8+sUMorNxIYENEpAAyATixjSIZT9maUsuGMAg+NDuLpbJA+v2MuITmHIJY33N3AxtpwsJPtso82fUwq5LXEaCw+9x/rMtazPXAuATCzjof4PoZVdWgfz5kJ5h/KKXKpfTE3PWd/7Xc6sWLGChx9+mI8//phhw4Yxf/58xo0bx+HDh1uVPrQpaFHpp8uFUI2Ch8Z05LeHRjAwLgStUsb1vdrwx5yR3NC7DdpaphNqSrtgNUvvHoxOdS7WjQ8L4KOJ/f1ce+tKmdmO0+X2/W60Oi5Y1RMdpGLRtIFEVSgVj9Aq+OzOQbSpoXdLOWq5lJGdwvn94ZHcdbYhZ2yIit8eGsFLN/UkXKvAbHdiuEB6TymT8MHt/egQfq5cXKeUsmxGEnFhAbx1m39vpLuGxTFpcHv6tg9iy4kCVtwzhPtGdeCD9cf5vx8O8NatvXl8bBceWpbiS+9tO12ESialXbCaTyb349v7hpAYUX8OzRcjOEDBPSPiWfvwSIZ1CEWrlDGmWyTrHhnJ7YNiaqw9amhsTpdf+fq8jdlgGMwtiZP99vvwqg/pFdYLmaRh3jMCLRu73c6uXbsoT1h4PB527dqF3V51u5D64u2332b69OncfffddO3alXfffZeYmBg++eSTBr3u5YAQ1DRTQjUKOlbUR6i8+oiGCmjAKwpOSS/x005kl1pIKzRhd7ku6dzFJjsLNp/mULYep8uN0epg7aE8Np8owFyNZsTqcHEs10BBhTLiIpOdozl6rI7az0ctl9IxUktwgJxSs51vdmWSU2YhUCXFbHey5UQhvx3MrTKwSS008tU27ypWuTkegMHmZPeZEopNdr7d6a/XWX80nzKLg2KTg25tA/n9UC5yiQitUkZmiYVHv9nHrK/3YKgQ2JULiwGiA1X12nKipgQHeP/2ykXBGoX3eWtO5nsSsYiQCgFWsFpG7/ZyduVv8dvvx5M/ore3Pifxcr+Y8lLri1FTz5jy8wYpgy54TMX9ajrH8vM1F88au93Oo48+yn333cfbb7+N2+3mrbfe4r777uPRRx9tsMDGbreze/durrnmGr/t11xzDVu3bq3mKIGaIgQ1AoA3gNh8soCZZzU0vdsF0j5Ujc3pZvKi7RzILKuVQV1FzDYnK3am89GGU0xYkMyhbD1rDuXy6Lf7uH/pHg5mlVU6xuF0s/tMiU9D0zlSQ5coDS63h+mf72JnWnGdAy2706vXeeW3o9zx2Q52nylhy4lC7vtqN098t5/k08W4KqwoFRptPPvjQTpFaZmyyKuhiQlR0bNtIB4PzF6ewl/HCzA7vMHJtd29XjppRWamLNqO0+XmWK6BjzaeYsYXu5kwyLu8XGC0YbL7P4ab+7Xj+VWHSKvQkFOgMlKxmClD2gNeC4QP7+jAm/seI02fhkws46qYMQD8lvobr+98nWJLcVNOt94p94tJDE6skT6lpp4x5ecNU4Vd8JiK+9V0juXnaw6eNeUBTXKy16V82bJlTJo0ieXLlwOQnJzcYIFNYWEhLpeLyMhIv+2RkZHk5ubW+/UuNwRNjQDgvTHEh2rQKWV0jtLwwe39cLjcTFm0A6fbTbhWWWdNjVoh5bpebVi6PZ3MEgv/+uhv39jIjuHEVXD/LUcmFdMmSElogJwwjZxPpw5EBMz4Yhd5Bhttg1S+svPaIpeKGZ4YTsdIDSfyjNy+cLtvrG9sED3b6pBU6LStkIq5pX8MCqmESJ0SkQhevKE7VqebRVtSOZqrJzFCw4K/TvPUtV2YODiW43kGJn26nT4xQeTqrVzVJYJf9ueQb7Cx7nAed4+I59PNqX7zGtExjA7hAWw7VYhK8IO5KPFhAcy6MpEPN5wkvchGp6AeZBmzeGP4x/SK6ELfyD68uetNhrUdhlzafFaZGovzhb3llP9clWD3YsdUd1xt5tAcPGv279/Ptm3b/LadOHHOPsPj8bBt2zb279/fYM0uRSL/z1OPx1Npm0DtEaqfBHyU93BSy6U+DU1miRm3h1p3nq6KzBIzV7/9F5azqaMuUVq+mj6YsAtU06QVmpBLxbQJUuF0ucnVW3G4PMSHeQOhMrO9zjqP3DILt81PZvrweN5edxytUsrK+4cil4oJUntTVOW9nIxWJ9/uzqBDuAaPx8Ocb/bx0o096BMbhNnuIlqnIKvUSrhWQZBajsPpJq3IRHCAHIfLjcPpXfkp96mZNjSOq7pGsOdMCSa7i2u7RxETosZgdTRYD63WSJnFTl6ZjTWHchjUQUGw1k6ooi1hGjV6m54CSwGRAZFoZI2fxmtq6uIXc7FjKh5XaCnE7rIjFUsJV4VXeUNuSM+aS6l+8ng8vPXWW76Vmaq4/fbbmTNnTr0HGna7HbVazbfffstNN517bh566CH27t3Lpk2b6vV6LQmh+kmgXinv4VSRdsGXHsyAVxScfLrIF9AAnCkyk1lqIUgtQyqpOhMadzZ4cbrcHM7Ws/5oPlOHxQGQWmjiww0neXJsZyJqGQSY7U4OZ+t5/oburNyTybvj+/DUyv2UWRx89ncqdwyJ49f92UwdGk+4VoHB6uDbXRlkl1nxeLyeN9/syqB/+2A6nH3OOkae0zvJpGI/0zy9xc7KlCyfT82SrWn8tDeLlQ8Mo32I2rcK1lzKpZsjFb/1l3/DD1TJCVTJ6RRVubpJp9C1KuO9S2Hu8Lm+1gc2l43/bPlPnc8lFUtZm7qWd1PeJdOQSbgqnHt63cPV7a8mVBVaX1NuUEQiEXPmzGH37t1+KzTldOzYkUceeaRBVk7kcjn9+/dn3bp1fkHNunXr+Ne//lXv17vcEFZqBBocs83J74dzeXjFPgAGtA8mp8xKVqkFlUzCt/cNoUfbwAueI73YzNh3vKs804fFM21YHLfM20qe3sY/ekbx0o09CQmo2YqNzeHi75OFIBLx7h/H2Z9ZRmKEhg8n9uX1NcdYfzSfkAA5X04fxFfbzjD7qo5Mr9BX6druUT7vltGdw3n9ll6Ea6sPqgxWBz/tzeaZHw8CMCQhhJMF53xqvrt/KJ2ruCkL+FPxW39Td4huCVQMAs1OM5NWTwJg6T+Wopaqa5R+Ot9nRiFWsClrE6/tfK3S9SZ2mcisvrPQyDUXPF99eda01JUa8JZ0T5kyhXnz5jFkyBAWLFjAwoULOXToEO3bt6/367UUhJUagRaBUubt6q1RSOnfPpg3b+2FxeFm4sJkFFIxwTUIRjQKKZOTYlm4OZVFf3v/lW9/8IpEgmpRFaaQSYgNVfPW78e5b1QHHlqewsl8I9e+e64n07ShcfyyP5tpw+KRS8UkJXj7Kn02dQD92gczqnM4T3y3v0Z9lVQyCT3aBqKUiRndOYKXbuyB3urgtvnJhGvk6JTC27A66lvjcblSaiv1+1ktrXoFtiqPGTjnM5NtzOaDlA+qPHb5seVM6jrJL6hprp41u3fvvmBAA17x8KhRoxpEUzN+/HiKiop48cUXycnJoUePHqxevfqyDmjqC+HTVKDBEYtFdI3S8dODw9AqpYRrlVgdLr6+ezASiZi2QSrcbg8Gq6NafUxIgJwHRyeSp7f5LPzH9Yji8bGdAcgoMdM2UIn0rEmd3uJALhWjrKZPVmKElsfHdiZAIWH+lP7ctWQXALOuTESrlGKwOrmpbzuidEqkEhEzr0zk9kGxxJ7tqzSuRxT9YoNq1FdJKhHTo42OVTOHE6yWE6pREKpRsPyeJJRSMdFnPXcMVgdSschXSm13uig1OwhSy3zme2a7E5fb2yvqcqC8YuZ8ylcQwLtqIwQ1lanuuXvwzweBuj1vZbYyzM7KnegB3B43OaYcYnXN3zyuV69eDBkyhOTkZJ9HTceOHX2pKJFIRFJSEr169WqwOTzwwAM88MADDXb+yxWhpFugURCLRXSI0BChU2JzuNieWsyGYwVoFVLcbg9Hcw28++cJiip40pxPidnB9tQiAD69YwBjukZy55KdXPnWJm6Zt40vktPJKTWjt3jTPcmniy7oZ5MQrkEsEvPtLq+/zDPXdUVvddIhXMOIjmGkFhhZcyiXzScKfatNde2rVGZxsPZgLgUGG263B73Fzu60ElILzNgcLgxWB7/uz2HLySIsdid2p4sdqSV8syuDkwVG7E4XZruTv44XsOZgLsYG7AHWmmluXin1RUM8rqq8cC5mYljdClDF89XEV6ehkcvlvPXWWyQlJQHeVNPSpUuZMGECAElJSbz11lvI5Zdf1VxLR1ipEWh0TuQbueus/4zH42FwQijj52/DYHOikIp5cHRipZWI9CIzExZsI09vY86YjpwuNPLy6qO+8QKDjRd/OczpQiNXd43i2Z8OIhGLWPnAUHq3C6pyHoVGG8+tOshvB3OZPjwevcXB51vT+GZnBp9M7keRyc7//XAQkQi+vXcIA+Lq1jvIYHXw6ZZUPtl4igV/neab+4awPbWI51cdRiIW8fsjIzmVb+SplQcQi2D+lAGo5RLuXLIDh8uDWi5FhFdYff/SPXg83pWrKzpHIGnE1hVNQfmNEKrWeJTvU1PKVy9a2+pOVY/rYs/dxZ63qlJHIYoQOgV34njJ8Ur7hypDCVeH1+p8TUl5YLN//3769++PSCTi0Ucf5YorrqBXr15CQNNCEYIagUYnUqdg7Nkmnc//fNi3PVyr4LYBMVWmVpRyMV2jdZhsJVzXuw3/+vDvSvsAfL09nau6eG92V3eNpE1g9e0UFFIxozpFsPpALjFBSgYmhPL9niyySi1+vaZGdQq/pJJ2rVLGbQPa8d3uTAoMNsa9d067c023SHRKGT3bBdInJoi9GaXM+GKXbzwxQkN8WAATFmyn7Gw38f6xQXRvE9jqAxq4uMajJjRXr5RLpSaPq6rn6FI0LSGqEF4f+Tp3rrmTEluJb7tKquL9K98nQh1Rp/M2FXK53E8zIxKJGsyXRqBxEIIagUuizOwgUH0uCCmz2C9qpy+Xivnfv3qQq7dyKEvPk9d2YfHfqSy9ezCx5xnxlWtjIrRKXr+lF2abC6vD5euVdD5uDxSZbAyOD2buTT0I1VRfIq1VyvhHzyj6tw9CLZficLlZfk8SV7+zCavD6yvTLVrHm7f2JuwC56mKUrOdALkU2VkRcbhGycr7hjD6rE8NeI3+Xrrx3BznT+nPzZ9s9bVhCA2Qs3jaQD7ecII5V3fi7XXHub5XNA+OTkQpqz5zbLQ5EeM1PQTOpq5cPs+dinPUKKS+cnqT1QkiCFBIK/n/1OR1ba5UpS1pDZqcpnpcHYI6sOKfK9hfsJ/9hftJDEpkYNRAogKiEIsERYNA0yL8BQrUmZP5Rl757QgFBq8OJrvUwrM/HiK71FLtMRnFZp798RB6q5MCvY13xvdhb0Ypc2/qSfLpIvSWc1oRvcXBjylZbD+rjdEpZaQXm/n7VCF3j4iv9hpquZRjuUZyy6wXbe1Qro1pE6QiNEDOttNFvoAGvF44WSUWv0acF6PEbGfJ1jT2Z5XhcLox2Zz8eTSP5NRibujTxrffyTwjeXqvxsbudHEq30ie3uobnzQ4ltUHc7hzWAI704pZNHUAfWODKDTaeOW3I5zMN1a6ttHm5I/Deaw/lo/Z5sTucrEvs4wvt52hxHzO8r3EZOezv9PYn1mG0+Wd47ojeWw9VUhqoZEXfj5M/tm55JR5X9fMkqoFoo1FbfsdCZyjps9dTbU50ZpoxsaP5fGBj3NTx5top22HVCx8RxZoeoS/QoE6UWCwMX7+NopMdkpMdv53Yw/uXLKDY7lGUguNLLlzUKVVkiKjjamf7eB0oYm0IhOfTRvIMz8eZHtqMbvSivlkcn9+2pvFjX3bopRJ+OtEAf9ddQiJWMSiqQMQi0S+XlBzb+rBuB5R/HbQv1dKaIAcj8dDqcXB+AXJfHffELpEX9yTyGRz8vvhPJ74bj8AA9sHk1VqIbvMyoQFyTXy0gGw2F1eM78/TvDJxlN8PSOJzGIzD63YC8CHE/sSqJTyy4Fc33O4auZw8g1Wpi72amg6RAQwrnsUWpWMjhEa5ny7l4NZeo7nGvhgYj9e/e0oG47l88fhfNY8PNJn2Od2e9hzpoSHV+xFJIIPbu9LpFbJ5LP9qnQqGeMHtsPjEfHdnkze//ME8zedYtk9SaQXmXl4xV5WzRzGxIXbySmzkm/wBp33f7WHvRmlHMou45t7h1xw9ashqasmoyptSV01Oc2J2jyumj53rVVzJHD5IAQ1AnVCIRPzyJhOPPPTQdYezmPtYW8uXyoW8dS4rmir8F5RyyU8/Y+u3PvlLvZnlnH1O3/5xh4YnciyHWeYMDAWlUyCVCKmf2wwPdroOJit99O4dIvWMrRDKAs2nfY7v0rmLc+O1CmI0CoI1chrXJ2kkknodNZLZ0BcMG/c0huz3cnEhdtRySUEq2t4HrmEa7pF8cW2M5wpMnPzJ+e67g6MCyY0QM7MKztyx9A4n0+NWi4mUqckTKNAq5Sy5M5BSMQiUguMrEzJYsaIBB77dh/H842Mfffcc/bQmI4oKqShxGIRnSK1DI4PYXtqMTO/TvGNxYWquaprBEqZ93W5tnsUX247Q3qxmX9/fG6Om08U8vjYzsz5Zh9bThYycO4f3nOL4OlxXVG3wJ5UzdUr5VKpr8fVWjVHApcngqNwK8TmcPm+mYP3G7ze6qikqbhUDFYHn24+zXt/nvRt+3TqAEZ2DPP5qoC347bZ7iRQLcdid7LpeAHpxRZeX3MUp9vDc9d347qe0ZRZHMSHBfi1TMjTW/nXh3+TezYVEqFVsGrWcKJ0StIKTexJL2FfZimdI7UkJYTSNkiJQib16xlVU9xuD6cLTeiUUl/bhfRiExKRiLZn20VU1J2c/9gqklVq4dp3/sJwVvvTIVzD53cNRCGVEK5VYLI5KTTakEu8PjVOl5uMYjMKmcQ351y9BbvTwxtrjjCyUwSPn11FAq+fzr0jE9BUIaouMFi5bX4yqWc7fetUUtY8NLLSc5FVamHsO3/59EmJERqWzRiMWi5lxc4MXvzlnIj77dt6M65HlM9Dp6VS7krc2hyJL+VxNWR/pobkUhyFBZon9eEoLGhqWhnlHjDf78mkzOLA7fZwLM/AexfxgKkLBquzUvrn250Zviod8N7092WWsnhrGiVmO2abV7B6NFfPG7f2RioW8dPebFweDx0jtX4BjdXh4liugYIK8y4y2Tmao8fqcBEXFsC/+7XjhRt6MHFwexLCNSjOrkTEhQXUKqAB70pH4lkvnXJiQwLOBTQ2J38czWP90XxMNqfvsX2+LY0S0zm9isnmZHdaiS+gAW8zz9wyK0EqGWa7k80nCtiZVoxWKcXpcnMoW8/qgzmoKpgF6pQyjmSXMbqLt1KsImsP5aK3VhZL210u0orMfromg9XJnvQSzBXmY7I52Zla7Ce4zig2k1liQW9x+AwOy/l+dxaGKq7X0mhOXin1SV0eV7l+xu6yX3xnAYEWQp2+dlmtVj744AM2bNhAfn4+bre/iHLPnj31MjmB2nPyrAeM0+3B44GhiV4PGL3ViUwiZuboxBqnZC5Ent7KtMU7OJ5nRCoWcc3ZEu3yNNTcf/ckTKMgo8TMpE+9mg6H083YHlHM/HoPhUY7breH/1zXlRd+Psz9X+1hwZT+voDC4XSz+0yJT0PTOVKDSARHc41M/3wXS+4cyOCEEOSSxkmHuN0eUtJLeGi5V6/y/oS+RAcqfY9Nq5QxYVAMeGDT8QJmL/emfvrGBFFospFRbGHSp9tZfk8SMrHI5zfj8UDnKC0TFiRjcbgQi0TcMSQOiVjE1pNFGGxOft2fy4Zj+QCM7R7J+qP5HM8zMm3xDr6YPpios8+Z2+3hYJaeyWfnFBuiJiRAzt6MUmYtS+GTSf0Y1TkCj8fD+qP5PHxW59MvNoh8g43MEgtlFgfPrzrEvswyxCIY1yOaXw/k8PepQh5ZsY93J/Rp0U03m5tXSn1Rl8dVrp+ZO3wuK65bgVwib1WaI4HLkzoFNXfddRfr1q3jlltuYdCgQQ3S8EugbkTolFzbI4pf9uf4pQ/CNQomDIypl4AGvNqZgXEhnC4w8fldg+jVLpChHcJ45seDfv2QAlUybh3Qjq+S0/lo4yk+2ngK8KZE7hmZQKnZgVgEQzuE+sqfwdvluk2QktAAOWEaOZ9OHYgImPHFLvIMNtoGqRotoAHvKk7HSC1JCSEkny5m1rJzepX2oWrGdI1EJZPi8XiIDwtAp5LSo00g747vg83pZvKi7QCEaRQoZWJGdgxn0/ECv5RSu2AV1/WM9qW22oeq2b6riKSEEDYcy+eFG7oTrJbxz15tmPPNXgbEhSCr4FUjFosI1yiI0CmQiER8dfdgZBIxs5elcDTXQHyYBqVUjMfjTTXplFJ6tg3infG9sTndTPp0OxnFZoYkhHIgq4wFdwxgcHwIV3eL5OEVexnRKQy5RHivt2Sq0s/8Z8t/fEFMeWDUGjRHzZm//vqLN954g927d5OTk8MPP/zAjTfe2NTTahXUSVMTGBjI6tWrGTZsWEPMqcG4XDQ1xUYb93y5m11nvOZYErGI9Y+Oov15HjCXSpHRRrHJTvtQNXKpBL3VQb7eSqRO6WegV2Ky88R3+1l35Jz4cM1DI+gcpcXqcJFZYiFMqyC4Cs3P+dqYnFILZoeL2GC1Lwgy2ZzggYBGaAxZYLAxYcE2ThWc1asopax52F+v4vF4tTkahZTIs6soGcXecuiYsyZ+hUYbkz/dztFcA+AVKq+bM5J2wf4mf6cLjKhkEow2J5GBSgwWB+DBaHMTppFXWYmUUWxGJMJ3rtwyK2a7k/iwAEQiEU6XG4PVSbHJjvasfqjM4kBvcSASQYBcQqHRTrtgFSq5FKPNSU6phQidosV61Qh4uZh+ZsV1Kxj/6/hmq6OpSEvW1Pz222/8/fff9OvXj5tvvlkIas7SZJqatm3botVq63KoQAPjdnvIM9g4nmfwbXO5Pfx5JN9P61IfhGoUdIzU+kTBurOeL+c7Aheb7KRklPht+3l/NqUWByq5lI6R2ioDGqisjVHKJGw4mse+zFKfB8z6o/msO5LnDW4aEIfTzZkik88cD8Bgc7L7TInftUUiER3CNb6ABrzBTHlA43S5ySqxcKbonO+LxeFi26kiDOf1dEoI1+ABlu/IwGh10jZYjVwqZdXeLErMVb+eMSFqv+AoKlBJQrjGF9AcyCrjs79TCQmQE6FTcrrAyEu/HkYhE9MuWE1wgPd1LRcFaxTe10gIaPwp16S0pl5SGrmmVWqOmhvjxo3jpZde4t///ndTT6XVUaevtm+99RZPPvkk8+bNE1qlNzNO5ht9GppwjYKOkRq2nirixV+8fYZu6tcWXSN2eE4rNDF+wTYKjXZ0Kin9YoLZeLyAjzacQiIScdfw+BpXZVkcTn7cm8VLvx5FIRWz9O7B5JRZmb08BY8Hvpo+iKEdwhA3QPsAt9vDwewyn4YmJkRFaICCvRmlzF6ewkcT+zG6S4Sf0Lc6juQafBqaNoFK2gap2HmmhMe/249UImJstyifG3CR0ca9X+7iQJaezScL+Gr6YP770yHWHMpl+c4MP5+ampBVauH2hclYHW7sTjeTkmIZvyCZAoMNu9PN8zd0rzbAFPCnoqNvS/F1uVg/KLVM3SK6bNcX6enpmM2VTSXVajWxsZfP89CaqFNQM2DAAKxWKwkJCajVamSy876ZFxfXy+QEao9WJSU2RE2e3saKe5MIVMl4btUh/jiSR8+2gahrcNOtT1RyCR0jNNhder65dwhROiVv/n6M5TsyGJwQWivfE5VMypiukXy+NY20IjO3zNvmG0tKCKFjpLZBAhrw6lXCNAoidUpEIvhq+mDkUjEPLU/hcI6eDuEBKKU1W/gMCZDTJkiJzenm6xlJqGRiHvt2P7vPlNApUouywmukVUp5elxXpp4VZQ96+U/f2Pk+NTVBq5Ry+8BYFm9NY/5fp5n/l9frR6uQcu/IDgQ2YsDbEqlKk3L+z83Z16U+emm1FtLT0y+4UrJy5UohsGmB1Cmouf3228nKyuLll18mMjJSEAo3I6IDVSycOgCrw018mFdD89z13Xnoqo6VPGAag0idkncn9MVoc5JwVtPxyJhOTB0S59Pi1IaYEDVfz0ji2nf/8pU0J4QF8MHtfQnXNmxePSZEzdK7B/t+BnjvvMdWE9oGqfhi+mBcbo+vUeYbt/TCZHMSoVP4AjO704XF4WJAXDDzJvdn+ufnGl3OujKRf/dtW6VPzYUICVDw0JiO5OqtfuX439w3hM4NGBQ2NmaHmTJ7GXhAp9ARIKsfPVlV/ZagYXsuldnKMDlMiEVighXBKKQtt/qsOVHVCk1txgWaJ3UKarZu3cq2bdvo3bt3fc9HoB6IPq8zdbhW0aRluJE6JZEVfg/VKOpstW86q2Gp6AGTVerVpwSp5H4VVA1BzHndus9/bDWl7XkeOiq5hPVH8wnVKBieGIpELGJXWgmHcvRc1zOab3dl+u2/9lAutw+KrXVQA16NU7mIvJwfU7K4b1QHggNafurpjP4MH6R8wB9n/sCDh9Exo3m438O017VvUV/A7C47p0pP8cauN9iZuxOFRMG/OvyLu3veTbQmGvBfOartClHFVFRNNTSXcj0BgcagTkFNly5dsFiqb1ooINAQWBwu/jrrAePxQJ+YIIpNdtKLzT4PmN7tglrcaoPL5WbbqSKeWnkAsQjmTxlAgFzC1MU7eOXfPXlu1UHWHfb61IzrEcUfR/Kq9KmpCWeKTD4NjVYhZWBcMOuPFTD/r9OIRSLuGZnQogObLEMWU1ZPocR2Lmj7M/1PdubuZMU/V9BO2+6Szn8xTUr5PvVBmj6Niasn4nR7A3iby8Y3x79he+52Fl2ziMiAyEvS9VyKt01dricg0BjUKah59dVXefTRR5k7dy49e/aspKlpzeXSAk2HUiomITwArVJKt2gd703oi93pZsqi7bg9Xg+YlhbQAEgkYnq2C6RPTBB7M0qZ8cW5NFNmiZlRnSJYdzif//2rBzf2acOBrDKmLt5RyaemJihlEm8pvd3FN/cNITpQyXt/nOCL5DMM6RCKqgX2dirH5Xbx8+mf/QKacvR2Pd8d/46ZfWYildS99L+xNClGu5H397zvC2gqck+ve8g15WKwGxpF13MxHZGwYlN7jEYjJ0+eay+TmprK3r17CQkJEXQ8l0id3t3XXnstAFdddZXfdo/Hg0gkwuVyXfrMBGqE0+XGaHP6VRCVmu0EqeWUmu0EqmS+JXe9xYFCKkbRyGLhmnK+38z5j00k8jZsXPXgcNQKCRFnNTRfTj+ncykz2/36MJ3/+6VQZnEQWMG8sMxir7cy5+hAFR9P6ssH60+ybEcGAON6RHJrf69hYlJCCBFn/X8GxAWzevYIQgKq9qm5EJE6JW/d2huD1asDEotFzLoqkYmDY4kJUfuJlM/H7XFTYCnA5XahkCgIVYVe0mOub4wOIxszNlY7vilzE3d0u4MQVUijzamumBwmtudsr3JMIVEw+bfJlbY3lK7nYjoiYcWm9uzatYvRo0f7fp8zZw4AU6dOZcmSJU00q9ZBnYKaDRs21Pc8BOqA0+Vmf2YZG48XcOewOILVck4VGFmw6RQPjenIgr9SGT8whi5RWvRWJ9/tzqBzpJaBcSHNLrAp95txezyM6RqJQipmX2YZm48XMPXsYwMoNTtYtS+LoR3CCFbJsbvc7M0oxeX2eki+ve4YT43rSqROSW6ZhVd+O8pj13SupIWpLamFRj7ecIonru1MuFZJTpmFV1Yf5clxXSrpY+pCqdnO+iP59IkJotTswGhzcm2PaH7am834ATEkRpzzhZJLJXSMrLtPVKROSWSFxdSQAAUhARcOjgothfxy6hcWH1pMsbWYeF08cwbMoV9EP3SK5rEyKxPLCFQEVjuuk+uQiuvPoLEumpSaIhKJ0Mq1WC3Wej1vY9Lc9Tdq9YU/Ey42filcccUVXEa9pBuVOr3DR40aVd/zEKgDmSVezxGb043T5Wb8wBgmzE9mclIs8/86zedbz7ByTybf3jeUv04UMPfXI0jFIlbNHE63Ns3jRgTeFb5yvxePB94b34eYUDUTzz42rUrKxEHtQeThx71ZvL3uBB9tOFXJp+ajiX3JLbPx8PIU3r+9Hw8u3cPu9FL2ZZTy/f1D6yxOLjTaGD8/mXyDjUKjjddv6cXdn+/iULae43kGlt49uM7nBrCfNd575qdDiEXw2s29UMslPLxiLw6XhyC1jBv7tPV51zQ2epuet3a9xS+nf/FtS9WnMmv9LOYOn8s/E/6JWNT0vXHVMjV3dLuDrdlbqxyf1n1avQZgDdlHKlQZyqSuk3h3z7uVxmwuG8uvW45ComhwXU/5uS6kI6ruWs1dfxMbG8vKlSsFn5pWRp0/Ja1WK/v376+yoeUNN9xwyRMTuDhapZTxA2P4YtsZPt54io/P9lVaeyiXt2/rw6/7cyg02hn77l++Y/7RM5qIZtaQUCTydscekhDK1lNFPHS20SJAbIiaa7pF+bQe1fnUDE4IoUOEhpSMEqwONwPn/nH23PDUP7peklZEIRXz6DWdefL7/Ww4VsDAuV6vGIlYxNPjuqC5xGBDLpPQrY2OXu0C2Z9Z5tcPKjFCw5AOYU0W0AAUWgv9ApqKvLXrLQZFDSIqIKqRZ1U1XUO6ckunW/ju+Hd+269PuJ5e4b2aaFa1RyKWcEOHG9ictZndebv9xootxYyOGY1W7r9a11BeM7XVEZWv0LQEHx8hcGl91OmTcs2aNdxxxx0UFla2Bhc0NY1HqEbBI1d3IrfMyu+Hz31ovD2+D50jtXx3/1CufHMjZzMzDIoL5vkbuhNSx+qWuuhVztedlOt9zidSp+S9CX2ZsCCZUwVGwGsIt2xGEm2Dz6V3qvOp+fD2vgQopPzfuK78d9Uh3/6v3dyLUR3DfJb/dUGrlHFdz2iKjDZeX3vMt/39CX1JSgitMpVXarYTIJf6SsyNVqe3r1I1wUn70AA+mdSP8QuSfW0YQgPkLJ420Jc6s7tcmG2uKvVTDcnp0tPVjhVbizHYDX5Bjd3poshox+3xoFZIG9WhOEQVwkN9H2J85/H8ceYP3B43Y9qPITogmmBlcKPNoz4IV4fz5qg3yTBksD59PTq5jqtiryJcHV4poGlOVKXBaUgfHwGBitRpzXjmzJnceuut5OTk4Ha7/f4JAU3jUmyyk5Je6rdt1d5s8g1W1h3O8wU0AEdzDeTprXXK5aYXm3n+50Pk6b05/twyK/9ddcjXqLEqckotPPvjIbJKvPsUGmy8vuYYJyr0pSrH6XKTXmwms+Tc+Qw2J7vOFPv1VbqQT43R6uCnvVl+5125J9MX/FwKBquDX/Zn+237bndGlf20Ssx2lmxNY39WmV9/qj+P5Ffbn8rudHGmyOx7fsvPczTXgMXuxO5ysTe9lK+Sz1BqtgNwMt/Aa2uOUmCwXfLjuxAXu4HKxOfE0zllFt5Ye4yr3t7EsNc2cNeSnaSkl2BxNN7nQpAyiC4hXZjZdyaz+82mW2i3FhfQlBOmCqNvRF8eHfAoM3rNICEowe/1KE8NNVa/psa+noBAbalTUJOfn8+cOXOIjKyL7ZhAfZFWZGLC/GQKjDZ0SimjO4cD3iqnH/ZkMffXIwAM6xBKmEaO3upk/PxtnMw31uo6RUYbExcm80NKNrOXpVBgsDHz6z38tDebyYu2U2SsfFMtMtp44Os9rNqXzR2Ld5Cnt/LcqoN8vSOd2+Zvo8Bw7ubt8Xg4mK33aWjaBavoFxsEwEPL97LxWAEWu7NKn5rYEDU2p5tJn27nYLYem8uNSAT/7BWNSATJp4t5aHmK3/VqS77eyvTPd3I4x4BELOK6nl7jsw3HCnjy+/1+57bYXazck8m7f5xg4sJk9meV8ceRPGYvT2H28hRSMkorBZVOl5uU9FKmLt6Bw+WhQ0QA3dvocHvg3i93se10ETklVqYs2sGbvx9nyd9ppBaauG1+Mst2ZPDW78d8gU5DEKONQSPTVDnWP7K/L2DI11uZ8fkuFm5OxWz3BjEp6aXc/MlWjuboG2x+lzNhqjASgxNJDE5slNWPmlyvPPCZN2aeb9u8MfOEYEigUahTUHPLLbewcePGep6KQG1RyyR0itSiU0pZce8Q3h7fhzuGtOd4noHeMUEopGJu6N2G92/vy4p7hhCmkdM+VI1WWbtUjFIm4alxXRCJYHtqMQPn/sGuMyWIRPD0uC5V6lW0SilPXtsFmUTEqXwTg1/+k18PeG35Z13VEUWF9ggikYgwjZyoQCWxIWqWzUhi3uT+DO0Qik4l9fZVkkn8fGqSEkKYP6U/S+8eTFyomkidktAABXl6K/Mm9+eVf/fk/Ql9EYlgRMdw5JfQHkIqEZGU4HX5/WzqAF69uSev3+LVZ4zqHI6igouxSi7hmm5RtA/1Bls3f7KVh5bvBWBoQigdIzSVXG2lEjGROiVhGgWdIjV8eddgFk0dSJ+YIILVcmJDAtCpZPy7X1sA3v3zBKPf3EixyVuyf9eweL9S8/omQh3BB1d+gFwsr7T9haEv+CqOThUYOZhdOXhxe+DFXw5TYmq4wEug+VAe+JSLluGc/qaxgi+ByxeRpw65CLPZzK233kp4eHiV5nuzZ8+utwnWJ3q9nsDAQMrKylqNQWC+3ur1HAn39h4qMtooMtlpF6Qiu8xCkEpO2FlhcGqhEZVMQtTZNgoFeisKmQTd2Rui0erAbHcRUYVDrdnmZGVKFs/8eNC37dV/9+SabpGEVKj8qaihsTtdbD1VxLTFO33j94/qwAOjO6Ctwt4/s8SMx3OuFcH5jw28qzqnC0xolFIiz86zPAWmVUopMNhoG6xCLZdisjkpMFgJkEsJr/CY6uIvU2yyUWS0E65VIJOIcXs85OmthJ997Odri7JKLVz7zl8YbE5GdAxjYPtgbh/cnnCtAo/HQ5nFUUkLc6bIhEwips3ZEvGcMgsWu4uEcO8qSYnJzpxv9rLhWIHvmD/mjKRDeOVAqb5xupzkmnPZlr2NNH0a/SP70y20m5+W5pXVR3wNMqtiy5OjaRfccGWyAs2LkyUn/aqf6lvEbLVaSU1NJT4+HqWyYfu+CTQOF3pNa3r/rpN68uuvv2bt2rWoVCo2btzo94EqEomabVDTGonQKYmo8PpW7KtU0dsEID7sXAqhwGBl1f5sYoLVJCWEIhbBjrRijucZ+XfftpUCG73VyY8p5+tVsugQocHl8faXyi618OpvR3lqXBfaBKnQW5x8uyvD75jfj+QyOSm2yqDm/Bve+Y8NvH9fHSL8UyEVPWgqBgoBCik5ZR7e/uM4c67uTLhWQVaJhdfWnJtjTQkJUKCQSvjjSB5ikYgru0TQPiSAfZmlbD1VyJSkOF97AZPNye40r+5nVKdw/t2vLSfzjRSbbAQppZwuMrN0+xlmX9WRsAoBYftQ/6aL5/fwKjLZ2JdZ5rft53053DksrsHFwlKJlHbadtza+dZq9wnVVD8HtVyCuJn0XaronxIgC8DkMAHNqyqnvqiLV0x9+cs0pI+PgEB11CmoeeaZZ3jxxRd56qmnEIub3p9CoHZYHd4VlP/9cgSJWMRHE/shk4i498vdON0eQtQyru/dxlcxlK+3MvPrPb6U0z96RLP6YA470op59bejPDG2Mx6PhzuX7OBYrpHUQiOf3jGQF3855Es5Xds9ij+P5nEq38Qdn+1g6d1JRAU27LerAoPXX6bIZKfU7OD5G7ozbckOTuQZOVNk4rNpA2vsL+N2e0hJL+Gh5XsRibyVT9GBSiZ9ut3rpaOUMWFQDHhg01ndT4fwAB64ogOTF23H4fLgcnu4vncbJixIpsziQCoW8dCYTjVKHZVraMpTTn1igth0vID3/jyBVCxiypD2DR7YXIwxXSN55bejVLX2e/ugWMIuEPQ0JhWrc1Zct4Lxv44HWmdVTl28YurLX6YhfXwEBKqjTkGN3W5n/PjxQkDTQlHKJPSNDaZ7Gx2HsvXc99U5H4zOkVqSEkL9SqBlUjHDEsPYnV7CJ5P6MSwxjGt7RDF7eQr9YoP5+1Qh4xckAyAVi3hqXFcUMjFXdI7g1wO5/Pf6btzSvx2Hs/VMWbSdAe2DkUka/lu7Qipm9lUdeW7VIX47mMtvB70Blkwi4slxXWqlLRKLRXSM1JKUEELy6WJmLUvxjbUPVTOmayQqmRSPx0N8WAA6lZSoQCXtgtVc1zOaH/dm+3kJhQbImTi4fY21MCqZhMSIAI7levj23iGEaxW8vvYo3+3OZGB8COpm0LMpUqfk9Zt78cT3+/0Cm+5tdNw9Ih65tOnmeH7/ornD56KQKMi35Pv2yTHl+H5uyas2F+vVBFU/vpbkLyMgUB110tQ88sgjhIeH83//938NMacGozVqai6FrFILt3yylZwyb/VOuFbBjw8MpW0VuocSs53C8/Qq+QYrVoeL6z/4G6fbg04l5dt7hxAfFoBcKsFodZCrtxIaIEcsEqGSS0grMhMaIEcqFhOorvqGfr73SqnZjkYhRVoHsa/B6mD+ptN8uOFc87jPpg1geGIYFocLlUziu9ma7U5cbk+l1FiZ2Y7ubA+tAoONCQu2carAm7LQKaWseXikXyrL4/FwutCERuHV/RSbbDzw1R6SU4sBryHg+kevID7MP910MXLLrJjtTuLDzumnik122oeqmzRgqIjJ5iRP77UTKDTaGN0lgsRwTZU6rQvhdDsxOUx+bQ/0Nn2dHYEr6jsA3hz1Jo9teqza/RtCA9JYnP9Yq6Kqx3ex45rbc9JSNTWvvPIKK1eu5OjRo6hUKoYOHcprr71G586dG+R6bo8bl9uFBw8SkQSJuHl8VlRFk2lqXC4Xr7/+OmvXrqVXr16VhMJvv/12XU4r0IgYrQ6O5er9PE6KjDYOZOnRKmU+8XA5wWq5n4lagEKKo9TDp5tTeePW3rz0y2HevLU3H6w/yZPXdiEmRI1GKaO9VMK+zFK2nCxk6tA4OkVqOZVvZOHm0zx6TSfCtf5/uPl6K2+sPcYDozsQH6ahxGRn0ZbTjOkWRY82uloHNnqLk7WHc/22fbsrk65ROn7el03PdoH0bx+M0+1hy4lCSi0OxvWI8gU2hUYb7/1xgslJ7YkPU5NaaPKZ44HXS2f3mRICVTKfsZ5IJKLDWXGvx+OhwGDncO65qiCPB9YezGXCoJhapYzOT9dV1E81FwIUUhLCNdw7quoS8JrgdDs5VHiILdlbmNRlEkHKINLK0vjs4GfM7jf7klYLyldo2uva1/kcAq2P9PR0zGZzo7RH2LRpEw8++CADBw7E6XTyn//8h2uuuYbDhw8TEFC7LzoXw+6ye1fgbF4riQBZAJEBkSgkimbR2qQhqFNQc+DAAfr27QvAwYMH/cYaugpD4NKxObw34nu+8GpoEiM0SEQijuUZeGDpbhbcMYChCSGoFdWnRvL0Vm5f4NWrFJnsfHffEO79ajfH84ykFZlYNHUgkTolGSVmn+7E6fJwy4B2jF+wjUKjHYfLwzP/7OoLlopNdv73y2F+3p/DxuMFfH//EL5KPsOCv1L5dEsqvz88ktjQmr/pc8ssPg2NTCJiTNdIXxrK5fZwdbdI7vhsB1/cNQiD1cl9X+3G7fEGcFd2Dsdoc/HB+pN8mXyG43l6Hh/bhSmLvI8lJkRFaIDC17Pqo4n9GN0lAtV57sKnCozcOn8reouT0AA5XaO1bDlZxKtrjiKViLhtQEylAPJyJ8uYxfTfp2Nz2XC6nfy747+ZtmYaRdYiHC4HTw56stbC03LRqt1lZ/yv4/ni2i/4ctyXpBvS+c+W/wDw8VUfEx0Q7du/pXKxXk3l+1R3XGP0k2pOpKen8+9//9v3+8qVKxs0sFmzZo3f74sXLyYiIoLdu3czcuTIeruOw+XgjP4Mdtc5KwWTw0RqWSoJgQkopS1ndas2NGiX7szMTNq0aSNob5oZCpmUNkEqQjXe1ZcFU/qDSMT9X+0mX28jJlh1wYAGAA/cOyqBl1cfZeOxAjYe2wR4NTUPj+nk824JVMm4dUA7vkpO58MNJ31pIJ1KyowR8QRVuKEHqWTcd0UHNh4roMBgY+TrG31jEwfFoqmiYupCyCRiBrYPIa3QxOd3DaJnm0CSEkJ5btUhrugcTmaJGYfLw+0Lt/uO6RsbRM+2OiQSMYFqMVOSYvllXzanCkyY7S4idUpEInj7tj5E6RQ8+u0+DufovV460sp/5wEKKQlhGjKKzay4dwghAXL+9/NhVh/MoU9MUKUgSMDbTfvmjjfz9dGv+fTAp3x64FMAtDIt03rUvillRY1JkbUIgFJbKUGKIGK1scwdPpf/bPkPocrQVqEbqW2vpgsd11D9pJoT5ze0rKrBZUNSVuataAwJCanX85qdZr+AphyPx0O+OZ+2mrbNOhVVVxq0S163bt3Yu3cvCQkJDXkZgTrQMVLL0ruTkEtEvtWPTyb3x+Zw0THy4n1lIgOV3NSnLXqL00+v8tGkfgxsH+zzbQnVKHj06s7kldlYd+Sc6PDbe4fSKdLfX0UsFtE1SseKe5P4x/tbfNv/0TOK2Vd1rHV1T6hGwaPXdOLOYXE+3clNfdsyLDGUSJ0Sk83Jl8npFJ81hWsXrGL+5P5++o/ECG8PrTs+205GsZl3x/dBKhERG6xGJIZ3x/fBaHNW6xUTHajik8n9/Pxm/vPPrjwwugNxoQG+3lAC5whWBnN/n/vJM+XxZ8afvu1Lrl1CYnBirZfNq+pFNHvDOduJN0e9CUCGMQO5RN7igxqBmlGeckpNTfXbXv57Y6SiPB4Pc+bMYfjw4fTo0aNez6u3V+/ibXKYcHvcSBCCmlpRlx5DAo1H4nl+L7EhtTNGc7g9/H6eXmXl7kxfi4Nyik12UjJK/Lat2pfF3SMSKjU7LLM4+PG8/k0700ooqWPjxvN1JzqVVy9ktjvZn1nm114gT2/lVIGRILXMJ7wtNdvZerKQuTf2JK3QREaxGZvTRZBKxvd7shieGMqBrDJCAxQ+n5rzOd9vJkyj8POnEahMqbWUvQV7/batTl3NNPW0ek+DRKojeXPUm9hcDdtDqymoi1fM5eAvc37KqSLPPvus7+eGTkXNnDmT/fv3s2XLlovvXAtEIhFSUfW3d4mo9QUz5TRoUCPQesnTW5m2eAfH84xIxSKu6R7J6gO5rD3bLXzuv3sSplGQVmjyaWh0Kin9YoLZeLyAjzacQiIScdfweF+wUmK2M/+vUyz4y/tN6crO4exMK6HAYOO2+cl8f9+QWmlqqsPmcJF8qsinoenRVofF7uJUwTkPnX6xQZjtLr7blUlSh1A+WH+C63u1YeayFEQi+N8N3ekSrWXyoh3YnG5EIhETBsWgkglvqUslQ5/h09BoZVr6RPRhc9ZmFh1chFgkZmq3qQQqAy9+orNUvEnnmHJ44M8H+OiqjwhSBJFjyiHdkE6oMpQOgR1a3U28Ll4xl4O/TE1TTA2Zipo1axarVq3ir7/+ol27dvV+/iBlEMXW4irHQlQhSMWt87OqdT4qgQZHKhYxMC6E0wVevUqvdoEM7RDGMz8e9OuHpJJL6Bihwe7S8829Q4jSKXnz92Ms35HB4IRQP38VtUzCkIQwFm5OZeqQ9sy+qiNZpRYmzE+ma7QWZT3pTxQyCbGhaoLU3l5Yn0zqh8sN05bswGR1EqlTIJWIUco89I4N4ptdGQxNDEMiETGgfTC7zpTwzE+HfOer6FMjcOkopUoSgxKxF9lZfO1iogKi+Hjvx3xz7BsGRA1AKaudwLGqm3SbgDYAvrLu5lauLNCwqNU1W5Wu6X61wePxMGvWLH744Qc2btxIfHx8vV8DQC6WE6GOIN+c77c9QBZAoDyw1Rb11MmnpqZotVr27dvXbDQ1gk9N/XK+T4re6iBfbyVSp/TzesnTWzHanCTUwF/F5nCRXmwmVKMgJECO2+3hdKERnVJ2Qa+TUrMdnVKGWOx9o+otDuRScZWBkNvtQW91UGyyo5Z7e2GVme0YbS5cbrffapDd5eJMoZlAlYwfUrLoGq3jhZ8PXdCnpq4YrF6X4XLjQ7vThcXhqnWfqrqQr7did7m9zTW1iib/wCswF2B0GGmva49YJKbEWkKxtZh22nYoJHVP3ZV7sZSv3DRkbyKBhuVSfWoqamoqppz+97//ER8f32CamgceeICvv/6an376yc+bJjAwEJXq0j9HKuJyu3C4HRjsBlweFzq5DrlE3mxXaZrMp6amNPUHo0DDUkmvopShq6JCKVKnJPICx1VEIZP4CZXFYlGlHlbnU2S08cnGU9zUty1do3UYbU5+2ptNTIiKpIRQv8DG7fZwJFfPDylZ3D+qA6EaBelFJt754zhPjetK22D/9JZc4p1PTqmFk/kGYkLUF/WpqQsGq4Nf9+cQqlEwPNHbEXxXWgkHs/XcNqBdg7VAKDHb2Xy8kDd+P0pGsYVInYJZV3ZkXI+oJvXACVeHE0647/dgZTDByuBLPm95Kqo8zdTatSMC1VNdwBIfH0+XLl0a7LqffPIJAFdccYXf9sWLFzNt2rR6vZZE7DXba63l21UhCIUFWjQGq4PP/vb62CzfmcE39yax60wJ//3pEBKxiJUPDKV3uyDf/rl6K+PnJ2O0OTHbXcwcncjET7eTWWKh1Ozgrdt6ExLgfzPP11uZ++th/tW3LTO/TvH51ASp5BzIKrugT01NcLncbDtVxFMrDyAWwfwpAwiQS5i6eAcOl4cgtYwb+7RFXs+VUnani+93Z/LSr0d82/L0Np758SCphUYeGdOp1mX0zZ3zU1GtXTsicHHOTzE1RMqpIsJ9sWFp0KDm8OHDtGnTpiEvIXCZo1XKuKV/O77dlUm+weZXCn5110janFd5pJZLuHNYHB+sP8nX29P5ens64O2t9PCYTlWuNEklIuLDAlBIJT6fmk8m9SOjxMKizac5mmeo1qemJkgkYnq2C6RPTBB7M0qZ8cUu31jHSA3DEsPqPaAByDfYeHvd8SrHFv+dxh1D4lpdUCMgcD6xsbGsXLmy0RyFBRqWOgU1JpOJV199lT///JP8/Hzcbrff+OnTpwGIiYm59BkKNBnlmg6Pxxs8iEXekmuxWIRMIm42xnHxYRq+u38oV721EYfL+y2oT0wgc2/qUSmFEqSWc/eIBPL1NlbsyvBtX3r3YLqfbcNwfu+pkAAFdw6Pp9Ts4Ku7ByMCgtUyFFIx707oi8Xh8umF6kp0oIqPJ/Xlg/UnWbbDO69xPSJ55rputK0HvU5VlJgdmO2uKsfcHsgps9K+HqrNBASaO0Ig03qoU1Bz9913s2nTJqZMmUJ0dLSgnWmF2J0u9qSXIhbB74fzuLFPWwIUElLSSzHbXbQJUjGkQ2izCGz0Fgcbj+X7AhqAk/kmcsusBKvlPvFwOWVmO1tOFvpt+3Z3Bu1D1bg98Pa6Y0wfHu+n5QkJUFRKS9XnKkap2c76I/n0iQmi1OzAaHNybY9oftqbzfgBMYRq61/fIr9IH63m0Pn7UqnoJtyc3IKb67wEBFo6dQpqfvvtN3799VeGDRtW3/MRaCakFZrZfLwAm8vNp5tTWbY9nY8n9yO71Mqbvx9DLIIfHhhG75igJp2nwergl/05/PdsiXVSgrfMPN9gY/yCZL67bwhdos8p5TNLzNy+cDtZpRZUMglDOoSy/mg+y3ZkIBaJGNohlGU7Mlh7KI+1D48kvAGCifOxO1xsO1XEMz8dQiyC127uhVou4eEVe/00NepLECJXRahGTqdIDcfzjJXGwrWKRnnsDU1FN+Efbvih2QQPzXVeAgItnTp9SgYHB9d7nwqB5kWIRo7d5WJkxwh+3pdNnt7G1M92+sbHdI2kTVDTK+pVMgnd2+hQySSM6hTOSzf1wGB1MH5+MqEaeaVmkQqphJ5tAyk22Vl2TxKBKintQ9Us2ZpG9zaB/HbQ65A8+6pEn9dOQyOXSejWRkevdoHszyzj8e/2+8YSIzQM6RBW7wENeJ2NP5zYj/Hzt1Fidvi2q+USFt4xgKgLlNA3ZyquguSZz7XmqPhzU6yONNd5CQi0JurkU/PVV1/x008/8fnnnze4Urw+EXxqakexyc4LPx9kSlIcExdux+7yaqd6tQvks2kDm43Vv9PlJq3IRKBK7ltdSCs0IZeKq/SPKTDYKLPYiQsNwO3xcDzPiNnu4ottafyyP4cHRydy36gEn9dOmdmOTiXzpVnLLHZUMkklj51LJavEzPgFyb6S8dAAOT8+OIyYWravqP11LaRklLA3vZTOUVqSEkJpE6RCIm6ZaeVyL5oL0RS+NM11Xi2Vck+TuLi4evd3EWgaLBYLaWlpjeNT07dvXz/tzMmTJ4mMjCQuLg6ZzP/b8J49e2p6WoFmisfjodBoIypQRfLpYl9AA3Aq3+jTqzSHG59UIq7kZRMXVr3AtWJqpcBg46MNJzmSoyetyGuJ/vvhXCYOikWrlFFotPHeHyeYnNSeTpEayiwOvtn1/+3dd3hUZfbA8e/0mfRGEkqoQYqACKhEUUBdxLq6K4IFy4oCKorlt+iua1tZXMXFTlEUd9eCZVHsjWKBIFV6TwgQ0sskM8lMpvz+GDLJJJOQMpMpOZ/nmedJ3nvn3jM3QA7vPfe8xxjSPYZRveJ9lthYbXaOFJvJN1a7x0rNVvbmVZAUpXU35POH7vEGuscbuGKYPKkoQkft7x2z2SxJTZioXZaiYU7RGi2eqXnyySdbfNDHH3+8zQH5k8zUtNyhwkqWrcuie2wEz3y9F4BRveI5Wmom32ghUqvio5nnMqhr6F7HokoLj326ky93uG45XTokle/35FNjd9I/JYplt57N0p+zePOXLGINGj6ekcF3ewr459d70agUfD5rDANS2//5bXYHm4+UctPSDdTYnfRLjkSvVrEr14hSAW/cMoox6Uk+nxkKVw1v88z4fgYAiy5eREqEqw1kMNx+Cpa4QtmJEycoKysjOTmZiIgIeWglRDmdTsxmMwUFBcTFxdG1a9dG+/h8piZYExXhH5FaFRarnfSUKAwaFWP6JzLt/L5Yahz8+aPtxEVoiDWEdg8TnVrJ2NOS+XJHHk9cNZg/ntmDnbnl3Pzmr4zqFY9eo+SGc9L4dNtxik1WLl7wo/u9Vwzt5rOOu2qVkpQYPUlROqL1apbddjZKhYIZ/93M0RIzPRMiJaFphaYWZEyJSAnorZ1gjSuUpaamAlBQUHCKPUUoiIuLc/9M26pNNTV9+/Zl48aNJCYmeoyXlZUxYsQId5+aYCMzNa2TV16F1ebAancSa1CjUCioqKpBoVB4rVcpN1uJrdffpcxsJVqv8fktqjKzlUitGs3JQl6TxQZOiNS3/hZNRXUN+cZqkqP1xBg07ttACZFad9KSVWTiwufXUPs3JaNfIq/eMIKESN8uXXCk2IRGVXddT5RXUWW107dLVJuP6XQ6MVqNKFCgU+vQqXQYLUZ0Kh1Wh5VobfNLUPhMTTWYi8DpBF00GOI65LT161iCqV4lWOMKVXa7nZqamlPvKIKWRqNBpWr6P29+XfspOzsbu71x0y6LxcKxY8fackgRhFJjG9+nbqo4ON9YzTNf7WX2xf3plRhJcaWFxT8e5qozujGoa4zPEptSk5X/ZGZzbr8kzugRh9XuYNXeAuwOJ78bnNLq9Zei9RqPxTe1as+1p8rMVr7ZmUf91H93rpHCCgvxERqfTnc3bHTX1cv1bw2n08mB0gMcLj9MYVUh/eL60Se6D19nf03fuL7kmfO4rM9l/k9syo7Cz/+Cbe+CrRp6nw8T/wFJA0HdvsTwVP1eatd6qv3aF8f0hbbEdSqdufeNSqVq9hei6Dxa9Rtg5cqV7q+/+eYbYmNj3d/b7XZ++OEHvy2jLoJXicnKX1fs4Ps9BWw4XMyHM89l8ZpD/DvzCO9kHuHb+y+ge3z7n+CpqrHxybbj/Ou7A7y6+hDvTDuHE+XV3Pv+VpxO+O/tZ3Nuv6RGzfbaylhVw4ebjrlrisakJ7HnhJFik5XrFq/n45nnkp7c9lkUfyswF/D2rrcZ2mUoz258FrVCzdNjnibeEM99q+/D7rQTp4vjorSLUKv8VIhcfhz+fRWU1Ju9zf4JXr8Ipq+F5MHtOvyp+r00dcunPcf0hbbEdSrtirsiH6pKGo8bEiA6pfG4EEGqVf+SXX311YBr9e1bbrnFY5tGo6F37948//zzPgtOhIZYg4Z7L+rPukPF5JZXc94zq9zbbjm3t896rBg0ai4elMLb67LJLjZz7aL17m2j+ybQPyXaZwkNgF6jZHjPOHRqJZcP7cqjVwymxGRh8uJM0hIiiNIF9/8M9Wo9p8WfRreobpwWfxr7S/fz8E8Pu7cPThzMGV3O8F9CA3Bso2dCU8tuhVVPwzWLXbejWsFf/V5qjxtqPWR8EndVCbw2uvH4XZmS1IiQ0qaamj59+rBx40aSkoLvL3hzOmtNTcNal/IqK7EG39aD2B1OckpMXL9kA3knH0t+5fozOf+0JJ+fK7esiokv/Iix2gZA36RIlk8fTZfoppvFVVTXoFIqiNCqcTiclFdZUSoV7tgaXqNaVrudI0VmEqO0qJRKYg0aDhdWEqlVodeqWvXZysxWYvQad+JlrKpBq1ai9+NSE0aLkSXblzAubRz/9+P/UVTlWh4iJSKFdy9/l+SIZL+dG4cDPv4T7Frhfbs2Eu7ZCDHdW3VYf/V7OdVxg7X2xSdxF+xpOqlJHtTOCIVov5b+/m5Ty9SsrKyQS2g6q+wiE099sYeCk4lGXnkVf/tkF8dKzT49T155FS98f4Bnrx1G11g9j185mF+zS8gqMmF3tDpvbpLJYmPzkVIqLDb32PGyKo4Um6mxOby+p6K6hq925vHzgSJMFhuHiyrZfKSMdzfkUG62klNs4onPdnn0iKmlVblqbMqranj6890UVlTTt0sUDuBvn+zieFlVi+IurrTw8qqD7M2rwOFwYqyy8snW42w4XIKlxvuikr5QYa2gsqaSY5XHKK0urYunqpiDpQex2C1+OzdKJUQ28798fRwognu2SwgRWto87/zDDz+wYMEC9uzZg0KhYODAgcyePZuLL77Yl/E18tprr/Hcc89x4sQJTj/9dF544QXOP/98v54zVBVXWpiyJJM8YzWFFdU8P+kM7vj3JnYcN7L7hJHld472yWPJ+eVVPPrJTlbvK2RjVgnLbjuLJT8e5uMtx/l48zG+mn0BPX3QFbeqxs6P+wvdNTTD0+IoMVnJKTFz4xsbeP/O0ZzRI87jFpTd7mDD4RL+/NF2FAp47YYR2BxOHvjg5LpKBi0HCipYsTWXMnMNz193RqOFK4sqXbecCiosFFVaePbaYUx7exO7co3sz6/gnWnnNHsdK6preOPnLJb+nMUHG4/ywYwMNmQV88TK3aiUCj65+zyGdo9t8v1tdcJ0gpe3vsyY7mN49JdHsTvt9I/rj91p53D5Ye7+4W4W/W4RI5NH+u8W1Jk3wa+LvG87ZwZEtn6mqH6RbXP9Xtp6XF8esyOEatxC+EOb/iV75ZVXuP/++7n22mu57777AMjMzOSyyy7jX//6F/fcc49Pg6y1fPlyZs+ezWuvvcZ5553H4sWLufTSS9m9e7csHe+FTq3k/y4ZwIMf/saP+4s4a+4PACgV8JdLB/qs1iVKr+GucelsyCoht7yaS174yb3turPS8NUSSnq1kr5dIonWqxncNYYXp5yJ1eZg6tINOJyuJ7Ma1tSoVEqGdI/hzJ5xbM0pY+Y7dd2u+3WJomucnn9+sxeDRsXsi08jxsvK2zq1kgcnDGDOx9tZva/QfR1VSgWPXDqQqFNcx2i9hutG9eCjzccorLBw6Yt112fC4BS/rbGkVWqJ1cWSYEggThdHSmQKj41+jOLqYl7c8iJlljJSI1P9W1MT3xMufgq+f8xzvPf5MOw612xOK/mr34u344ZCD5lQjVsIf2hTTU337t155JFHGiUvr776KnPnziU3N9dnAdZ3zjnnMGLECBYuXOgeGzRoEFdffTXz5s075fvDraamYW1MmdlKXIO6EJPFxrsbcpj75R732IuTh3PJkFSf1HKUm2uIjdBgstg4UFDBb0fLef7bfRirbfxhRHfmTBxASoyBKqsNm8Pp8fh0WzidTg4WVBKt15Aa60oGcopNOGn8SHR9BcZq/rhoHUdLXLeLEiK1LLppJA98sI07zu/L8LQ4Tu8Wg1ql9HodK6tt/Ht9Ns9+s8899uoNI7h4UDK6etfRZndgstg8a5hO1uvkFJu48Pm12E7ejjuzZxxv3DzKZ038vCmuKsZUY8KJE51Kh0FtoKS6BJ1Kh81ho2dMB/xnoLocjCdg7+dQbYSBl0FCX4hqfz2PP/q9hGoPmXbFLU8/iSDn15oao9HIxIkTG41PmDABo9HYlkOektVqZfPmzUyYMKHROdetW+eXcwaz2tqY4ydrYworLDz3zT7251d47FdRXcPK3457jH205RjGqvY3qjpYUMm8r/ZQWGHBYrPjdMLG7BJemHImMXo1mYeKqbI6qLLa+OVQMV9sP0FFdfvOW2au4acDhRwvc9XQmCw2dp8wsvO40dWEzwurzc7hIhN55Z7rKh0pNrFg8nA2ZpdgdzoxVtdQWGHhn1/v5WBB4+v4+XbPZP2jzUcpr3cdbXYHO3ONLPnpMKUmKwBZRZU8/cUecsuqWLWvwJ3QABzMryTfaMHhw5qjhhINifSM6UmvmF6kRqYSq4ulT2wfukV165iEBkAfC8kD4YKHYMJT0HO0TxIaqLv1suKqFURqIjlYepCDpQfdBdHtPWYo3bppV9zRKa6C4IYvSWhEiGlTUnPVVVexYkXjJxo+/fRTrrzyynYH5U1RURF2u52UFM+/ZCkpKeTl5Xl9j8ViwWg0erzCQXGlhbve2cLK33KZuvRX8o3VPPnZLt7ZkMPkxevdRcEFxmp3DY1SAZcPda2n8dOBIh788DcKK9peJFpYYWHy4vW8v/Eof12xA2OVjTkfb+fz7Sf413f7ePWGEeSWV3PD665Vpx/8YBsP/28H6w4VY7d7L+g9ldo+NU99vocbXt/Ab8fKWLW3gJnvbOHud7ewNae0UYJgszvYdrScqSfXVeqbFMngrjE4nZAYpePpL3bz+fYT/Pmj7ZRX2Xjsk5289+tRrluc6b4+BcZqbn97I7tPVKBSKtzXcfW+QuZ8vJ3CCtf1zi2r5volmby6+hCvrTnI0RIzkxdnolIq+Hx7Lk+s3A1ARt8EukTrqLDYmLx4PQcKKtv6Y+j0kgxJpMenkx6fjqnGxDUrr+Galde4H/lu7zGD8RHupgRt3BX5rqerGr4q8k/9XiFaqU030wcNGsTcuXNZs2YNGRkZgKum5pdffuHBBx/kpZdecu977733+ibSkxp2cHU6nU12dZ03b16rFuIMFdF6NXMmDmTq0l85XGTinH/84N529/h09+0QtUpJRr9EduUaeeOWszirdzwXDkzmwQ9/Y+xpXdC2o9hFp1Fy/8Wn8einO/l2dz7f7nb9A6VWKphzyUAMWhURWteTQ7tyjZgsdoanxTGsRywqVdvO25Y+Na51lXQkR+sxaFW8ectZnCiv4h9f7uHDTUd5aMIA/rRsIwcLKhk/f437ffdelI7u5PVRqxSM7pvIvvxK3rxlFCN6xTN2QBf+/NF2xg7o4t4vSq/mhrN7svSXLF7/yfUCyCkxc+3IHug1SsYPSObpq4dgrK7husWZdInSEtOG5R1EnVDtL9NpSA8c0YHa3KemRQdXKHy2DpTVaiUiIoIPP/yQa66p68lw3333sW3bNtauXdvoPRaLBYulbjbCaDSSlpYWFjU1VrudzEMl3Pzmr+6x6Rf05Z4L0z3qVkpNVooqLfRIiMCgUVFZXcOJ8mqSY/StXpCyYS+Xoopq/pN5hBd/OOgee33qSMYO6EKJqYbsYtctn4c+/I3UWD0fzshoc+v/+ufOLavii+25fLj5GPvzK+mXFMl/7ziHlGh9kz1gjhabUCqVdI83nLxdZsNmd9ItzsBPB4q4/e1N7nPdPT6dGWP7elzHEpOF4korPRMi0GlUjdaMqlVqsvLoJzv4Ykfd7OHns8YwICWKI8Vm4iO1qJUKYiO0ZBWZMKiVGLQqrz1yRMuEan+ZTqOz9sAxFUGNGZQqV2sDfxbkBwOHHSrzwWEDtd5nt5hr+b1PTUtevlzYUqvVMnLkSL777juP8e+++45zzz3X63t0Oh0xMTEer3BRbrbxwaajHmPf7c5vVCsTH6mlf0o0hpO/3KP0GvqnRLc6ockpMXv0cskvrya7xMxXOzxv/X20+RhFlVY2ZZfwfx/+xkMf/obN4STfWM2eXCNlZmtrP6qrfujTXRwtMWO22Cg1WTlYaOKRSwcxICWK28b04fUfD7P7hPFkD5gaPt2WS+bhYqpr7FRU17A+q4RdueWYrTZiDWqOlVbz7a58iiutfLTZc72yb3fnYazyrM9JiNTRPyXaPQsWrdeQnhztkdDY7A4KKixszC71eO/yjUcprLSQnhJNpcXGk5/tpsBYTZ+kSFDg/myiZYqqity1MycqT2C1W5k/dj5zx8wNdGhCgKUSsn6C/1wDLwyF1zJg7T+hwnuZRFioyId1r8CiMa7PvOxy2P+16yGBDtauh22tViv79u3DZvNeoOlrDzzwAG+88QZvvvkme/bs4f777ycnJ4cZM2Z0yPmDRVGlhac+28Xn208AMPH0VLQqJYeLTExd+isnylvWEK6liist3PB6Jiu25nLve1sprLBwtNTMIx/vYH9BJWqlgsuGupaL/2Z3Po9/uotqm52jpVWclhJFvy6R1NidTP/vZnYcK8da0/I/L8WVFmYv38bK33K5aekGiiotvLbmEMs3HuW+5VtZPHUkWUUm3vwlmylLMtmbZ+STbcf526c7uf3tTRwrNbv71Ez/72Z+OlDE5iOlTF26gbhILU98tpuvdrr+sbl0SCoalYID+ZXcuuxX8lp5HXNKzExduoGCCgtROjXjB3QB4D+ZR1j8YxYFRlfNzf+2Hmf28m0UVliYebI26uY3f6W40o+N8MJI7RpH16y8htLqUiZ/MZmH1j5E75je7n0WXbwoJIt9w0ptLY2tk/25ProB3r4C8ra7vq8uhx+fhf9Nh8rCwMbmD+YS+OYvrrYN5mLXWNF+eHcy7P/W1Vm8A7VpPsxsNjNr1izefvttAPbv30/fvn2599576datGw8//PApjtA2kydPpri4mKeeeooTJ04wZMgQvvzyS3r16uWX8wUrvVrJ2AFd+Gz7CR69fBDXnZXGnlwjU5f+yohe8WjbWLPS5Pk0Kh6+dCCz3tvKhqwSzpr7PU/9/nSGdI8lq8jEstvO4oy0OM7tl8Sjn+zkgtOSOJBfydDusdx7UX/USgX/+HIPlRYb3eIMaDUt/2MXo9fw0IQB3PjGBo4Um7nguTXubdPP74dWreKigcl89lsuBRUWLnvpZ/f23w1KIdagZUh3tbtPzfT/bHZvP1ZqZtyALnyzK48nrhrMH8/swc7ccm5+81dG9YpH08rrqNeoGJASjdlq58Upwykz15Aco+ejzcc4v38SGpWCOZcO5L73t7HuUDFnzf0ecPUNeuTSgURopbtuU+qv9wQwf+x8AAqqCtxjZZYy93hadFrHPd0lvKutpZm0LNCRdJzKfPjq/7xvy1oDxmMQ1aVDQ/K7ygLY+ZH3bd/+BXqdC7GtWwqlPdpUU3Pffffxyy+/8MILLzBx4kS2b99O3759WblyJY8//jhbt271R6ztFk59ahrWxlhtdo4Um0mI1Pql74nZYuPjLcf426e7AFAoYMXMc4nQqemdGIFWrcJYXUOBsZqUGD35xmqOllRx97tb0KiUvHrDmXSLM9C3i+eq1mVmK7EGjbvYu7zKikGjQquu+wVfY3ewObuUKa9nusduzujFQxMGEGPQkFtWhcXmYMKCtdTYXX+ch6fFsvSWs9zXwlufmi/uHUOUTu1RG9Pe65hbVsWJ8ioe+3QXu3KN/PXyQYxJT6J3QgQGnRqTxcaHm4+6n4QCmD9pGJcP7YpBG+b33Nuhft3M/LHzeWjtQ83uv/zy5QxOat8K4KKdamtprlnkqrGoFd8H1Lrw7IFTfBBeHtn09kvmQcZdHRdPR9j5P/jotqa3z1wPKe3/u+jXmppPPvmEV155hTFjxng8eTR48GAOHTrUlkOKVmpYG6NVu5408lcjtyKTlf9tqet343TC3C/3EKVTuxOQmJN1Jmqlgtyyau55dwtmq53yqhpmvbeV4koLVlvdOkdFlRb+9d1+9udX4HQ6KTdb+WDTMTZml3rsV15Vw4ebPeuH1u4vdPeIidKp+elAoTuhAThY4CpSdjicTfap2XGsHJVS4VEb097raLHZeXLlbnblutoHzP1iD6v3FlB9cl2qimpXvU99/9ty3L04p/ANlVJmvYLGihnw4a11L7UufHvgKDWgbOY/J5GJHRdLRznV7V21/5qLetOmpKawsJDk5MaVzSaTqcnHq0Xoyi2r4r73trL1aBkKBVw2tCsKBWzMLmXWe1vJrbeoo9VmJ/NwCbe+9Ssmq53Tu8XQLzmSUnMNN7yxga05ZdjsDsqranht9UH+vf4Ikxav52BBJe9vPMrcL/Zwy5u/crjQBLhqap75ag8fn0yoLhmcgk6t5EixmZuWbuB4qZnPt5/gsZMzSKP7JpAcraPSYmPykkyyi02efWq6RDKku6tPzfT/bmb9oWKfLSiZU2xi8uJMth8vJ0qn5sKTNTXPfrOPRWsPkVtWxcx3trA1p8yjb9C6Q8Xcv3ybu9+NaKx+Y7l+sf2YP3Y+88fO5+ULX3bvU7+OJtEQhr88RPCLSILBV3vfptJA2jkdGk6HSEoHbZT3bX0ugIiO/bvYpqTmrLPO4osvvnB/X5vIvP766+6+NSJ8aFQKzu2XiEIBT/9+COf2S+TJq05HoYDz0hPRqOoSWa1aRVpCBPERWoanxbH0lrP4z5/O4bSUKJKidKTE6FGrlMQaNNxwTi8SI7UYq2z8bsGPzPtqLwBXDOtKUrQru9drVFzQ35VA//mSATw36QzevWM0OrWSM9Pi0GuVnN4tBoNGxcTTU3nlhhEsnz6a5GgdaQkGIrQqd5+a9OQo/nP7Obxx8yjO7BlHfISWXokRHssctIdeo2JQ1xiidGqWTx/NvyYP5/bz+qBSuq6fVqUgo28iSgUsnjqKZ/44lBenDAfg/P5dWl3D05nUbywH8NDah3ho7UMkG+r+c1W73lHQNZ8TnYcuEi5+HBL7eY4rVXDtMohKDUhYfhXdDa5/D1QN2lLEdIMrXwRDXIeG06aamnXr1jFx4kRuvPFGli1bxvTp09m1axfr169n7dq1jBzZzD3FAAqnmpqOVlhRTb7RwuK1h/hs+wmuHt6dOy7oQ3K0ji7RjRdkzCqqxKBRu9dnyi2rosbuaLQ+U3aRiQufX0NtI+Bz0xN54brhJNdb5LGwohqTxU5chIa4CC01NgfZJSbiI7QkRemw2R1kF5uINWjpcjIZyi4yoVUr6Rbn6ouTU2xCpVLS/eT3eeVVmK32RjU+7VVgrMZYXUPfpCiUSgUlJivFlRbSEiLQa1SUmq0UVVjoEW/AoFVTVGGh1GylS7SOuAgt5WYrEVo1Gl+tAhqG6tfXLL98OZO/mAxIP5qg05nXkzKegPydcHitq0i2/wTXL3lN2/p0BT27FcqPw6HVrrqi3udB1+E+LRBu6e/vNiU1ADt27GD+/Pls3rwZh8PBiBEjmDNnDkOHDm1z0P4mSU3b5ZVXu5cUqPX7M7rxl8sHkdLGVabLzVbe33jUPUOjUSl4ccpwVu0p5O4L+9EnKYpSk5WlPx/m4sGpDDm54GS4KKyo5vlv93PXuH70TIyk1GTlP5nZnNsviTN6xEli04T6T0JFaiIx1bhuVXZk5+DaGKRbsRAdw+9JTSiSpKZtCozV3PXOFjYdKXXV1Azpypc7T+B0wjl9EnjlhjO9ztY0x3iy+Pfvn7tWDx+Tnsjvh3fnh70FfL0zjy7ROj6emcF/M4+w5Mcs9Bol386+gJ7NrMQdSsrMVp7+Yg8fbT5GYqSWj2eey8dbjvHyqoPo1Eq+uu98n88iCd+pnS0KmtmhzjwrIjqFlv7+bvEzpK1ZDFIShvCiUSs5Lz2JzTmlLLxxBOelJ3Hp0FRmvbeVMelJbaoFMWhUnNEjDp1ayaVDuvLYlYOoqLaRFKXjlwNFFFZYuODZNe79bzi7J1H61nVBDmaxBg13nN+Hb3fnUWyyMq7eulPXjuzR6o7Pwv/qzxDVri0VNGtMyfpKQgCtmKlRKpUtfrLJbvfN0yS+JjM1bVdbC9I93kCE1tVvJbesyl0L0hY1NlctTHykqzYG4GiJifIqG1e8XNdE77KhqfzjmqFtPk97lZutxJyil05bOJ1ODhRUMmHBj+6xiwcl89y1ZxAf6dvParQY0al16FSu62yxWbA6rERro316nnAW1GtMddb1lUSn4fOZmtWrV7u/zs7O5uGHH+bWW291P+20fv163n77bebNm9eOsEWwio/QEl8vqYjUqemf0r5fiBq1stExonQa/pN5xGNsY3YppWZrQJKaokoLL35/gJtG9+K0lCjKq2r4YNMxhnSPYVSv+HYlNmXmGlb+5tmzZmtOGcUmq0+TGqPVyMpDK+kb15eRKSPBCb/m/UquKZfL+lwmiY0QImy0qabmoosuYtq0aVx//fUe4++++y5LlixhzZo1vorPp2SmJriVmq0sXnuIRWtdC6FeOKALG7NLqbDYXDU2MzI6tKam3FzDv77fz9vrsok1aPh4Rgbf7Sngn1/vRaNSuFbfTm3bn6Mys5W3fsnmxR8OADDutC5sOVqKscrmrrHpndT+z1pjr+Gb7G945OdHUCvUvHrRq9iddmatmoXdaWf+2PlclHYR6nBfQdgHGt5+mvH9DBZdvIiUCNftnYDefpKZGhHmfD5TU9/69etZtGhRo/FRo0Yxbdq0thxSCCI0KjL6JvH6T1ncktGLey/qz/GyKqYszmRQ12j0Puon01KxERqmju7J57/lUmyycnG920RXDO3Wru7NEVoVZ/dJQK1UMPmsNP7vkgHkG6uZtHg9p6VEYfDROlAalYZRqaM4Lf409pfuZ/r3093bBicO5owuZ0hC00JJhqRGSUttbxwhRHBo00zNgAEDuOKKK3j++ec9xh988EE+//xz9u3b57MAfUlmanynrMHtoPKqGgxaJVqV5y/j8iorsQZtk+9ryFJjJ6fETGKUjoRILQ6Hk8NFlcToNR69azpS1sleOrV/UzL6JfLqDSNIaOctIqvNTn5lGVHaKOIjdDidTnLKijGoI0iOjvBB5HXyTflM+WIKRVVFgOuX8buXv0tyROPO4OCqwYnRxTT5fTiy2CyUWkpxOB1EaaKa/bzy9JMQHcuvaz8tWLCA1157jSFDhjBt2jSmTZvGkCFDeO2111iwYEGbgxahobDCwj+/3suB/ArAlaj8N/MI23LKsNYrEj9RVsXfPtnF8VIzAEUVFp79ep/7fd7oNK61l2oTBuXJtZkCldCUma18szOP+qn/7lwjhRUW2tsNwVhTytLdL1NW46qrKbeU81XORxyv2ofN7ru1oCw2C/tL91NaXeoeK64q5mDpQSx2S6P9c4w5PPPrMxSaCwFXQjR3w1xyK3Mb7etvRVVFHCw9yMHSg+6EzB9yK3N5ZuMzXLHiCi75+BIeWPMA+0r2UWOv8bp/7bINcada96ajRKe4bjM1fElCIzqZNvepOXbsGAsXLmTPnj04nU4GDx7MjBkzSEtL83WMPiMzNe1XZrbyzFd7eX/jURIitXw0I4PPfstlwfcH0KmVfHHv+aQnR1FcaWHavzexNaeMfsmRvDttNE99tosvduQRH6Hh2/svaHVvm45mrKph+cajzP2ytpdOEntOGCk2uVYW/3jmuaQnt62XTLmlnPmb5vPJwU9I0Cfwn0v/w8pDK1m8fTFapZaPr/qY3rG92/0Zauw1bMzbyF0/3IXdaee0+NOwO+wcKj+EWqFm0e8WMTJ5pPsWVElVCdd9fh355nxGdx3NvPPnce+qe9lRtIM+MX1YdukyEvQJ7Y6rpeo/ceSvWZE8Ux63fXMbxyqOeYxrlVqWX7mc9LggmIkRopPza00NQI8ePZg7d26z+9x111089dRTJCVJx81wEWvQcNt5ffhqZx4lJisXPr/Wve2aM7sTH+HqrxKtVzNn4kCmLt3AoQIT5/zjB/d+sy7qj66dj0N3BL1GyfCerl46lw/tyqNXDKbEZGHy4kzSEiKI0rX9M8RoY7h58M2syllFSXUJl6+43L3t6v5X++xWj0aloVtUN+J0caREpvDi+BcBuPuHuymzlJEamepRU6NT67h/5P08/NPDZJ7IZPwH4wFQKpQ8MOoBDCr/t3n31g+m/te+LsjdVrCtUUIDYHVYeXXrqzw95mkiNeHR9FGIcOfXjsIxMTFs27aNvn37+usUrSIzNb7hdDo5VGji4n/VJTTjB3ThX9cN93gU2Wqzs+5QMbe+tdE9NnNsP+4a34/ok430ysyuWY/29oApM1uJ0WtQKl3HMVbVoFUr211cbLXbOVJk9uilc7iwkgititTY9v2CdzqdHCo/xDWf1vU+GdtjLE+f97TPb2scMR5Bp9KRGulaUC/PlIfVbqVnTM9G+5przHx84GOe3fise2zumLlM6DUBvdr/s2sd2Q/G4XDw4NoH+T7ne6/bIzWRfPr7T0mJlNs4QgSSX2tqWqoTrcDQqZRX1fDFds/6it+OlVNU6VmfYayy8eGmox5j3+7Jw1jlqlMoqrTwr+/2sz+/AqfTSbnZygebjrExuxSrreUNHIsrLbyy6iB7ThhxOJwYq2r4dFsumYeLqa5pXyNIrcpV45NU70mnvl2i2p3QwMkamsNfeYztKNpBicVLwWc79Yrp5U5oAFIjU70mNAAV1gq+yvKMa+XBlVRYm66FClVKpZIkQxJzx8xl/tj5zB87n7lj6magY7QxKBWyBpcQoUL+topWqS0KXvC9q7/KBf2TiIvQUGKyMnlJJocLKwFXUfDjK3fyxY48ACaenopGpeBQgYmb3/yVE2VVvLb6IP9ef4RJi9dzsKCS9zceZe4Xe7jlzV85XGhqUTwV1TW8+UsWb/ycxeQlmezNM/LJtuP87dOd3P72JvY1U5QcSOWWct7d+y5LdiwBYEy3McRoYyipLuG2r28jx5gTkLgKzYXcv+Z+dhTtQKlQckmvSwDYkLeBR356xK/FurVqi3BXXLWCRRfXtY5YdPEivxTn/qH/H9CpdDy09iEeWvuQu+sywNRBU0k0JPr0fCGjIt/V/6bhqyL/1O8VIkCkQYVolQitilG9E9CoFPxxRA/mTBxIYaWF6xavJz05kkid64+UTqNk3IBkvtiRx2NXDubakT3YnWtk6tINjOoVj16j5IZzevHpNlcPmN/V7wEzrCtJ0S3rAROt13DtyB58uOkYBRUWLnupbnmF3w1KoZsPZlT8waA2MDJlJGqFmj+c9gdmnTmLQnMht359K/3i+nXIbR5vNEoNZ6eeza7iXbw4/kVGpYxifM/xPPzTw5zX/Ty0Sv93dfbWDwZ83xOmtnZHpVChUtTdprTYLcwfOx+DykB6fHrnnamR9aRECPJrTU10dDS//fab1NSEGavNzpFiMwmRWhKjXP1VsopMROrUpNR79LqyuoY8YzXJ0XpiDBqsNjvZxWYST74PIPtkDxjHyT+F5/RNYOGNI0iIbF1ju5wSMxc9v4Yau+tAw9NiWXrLWe1qkOdvVruVoxVHSdAnEK+Px+l0km3MJlIT2WT/mI5QVl1GcXUx3SK7YdAYMNWYyDPl0cXQpWN71dhrOFh2kGs+vw7w/dNPp6rdae85y6vLMdlMKBVKEvQJaFWBWbuszaRLsQgifn/6SXReWrXKY80mhUJB3y6NH22O0mtIr7eytlat4rR67ys3W/lmV547oQHYc8LVAyY+QtviBVSNVTWs2VfgTmgADhaYyCuvJj5C6y4eDjZalZZ+cf3c3ysUCvrE9glgRC5x+jiPWzyRmkiPODuEMRc2LCGu21BWZMwDtZ44J2A1gTa4n0Sy2C0cKD3AcxufY0vBFgxqA39I/wO3DrnVo65JCOF7fk1qbrrpJpkREV4Zq2r4aMsx5n21F4Ax6YnsOVFBscnKpMXr+XjGuS1aMLOiuobPt5/gsU93ATC6bwKHC00UVFiYvCSTj2ZkMLCr/BkMKZUF8OFtcDSTJMB9I0qhgFs+h95jfHKa2todqFvLCWi0nlNrHSo7xNQvp2JzuhooVtmqeGfvO/ya9yuLfrcooLNwQoS7Fic127dvb/FBhw0bBsDChQtbH5HoFAwaFWf0cPWAuXRIVx67chAlphomL17v6gGjb9kfTYNGxendYjBoVIw9rQtPXzOEiuoaJi/OJDFKS4xBc+qDiOBSkgVHMxuPO53w1RyY+glEdWn3afxRu2O0GJm/ab47oanvQNkB9pful6RGCD9qcVIzfPhwFApFk49p125TKBTY7e17jFaEP41ayRk94vh81hjiI7UkROpIiNTx0cwM9BoVXVtY4KtWKTm9WwyfzTqPWIOrl0xSlI4PpmegVSvpFhechcKiGVk/Nr0tfydYK4H2JzX+YLaZ2ZS3qcntq3JWMaa7b2aa/M6Q4Kqf8TYuRJBqcVKTlZXlzzhEJ6RRKxvdYuqT1PplB9QqJenJnsfpnRTcdReiGZHNPEKt1oHS93fN69+Kas8j4woURGujMVqNXrf7shOy30WnyFNOIuS0+F+HXr16+TMOIYRw6TMOFEpwOhpvG3Y9RPo+MWjqVlRrJeoTmTJgirv/UEOXpF3oeqqoPl+vpO2rFbubOw7IquAiKLU4qVm5cmWLD3rVVVe1KRghhCA6Ff7wBvzvdjyWR+8yEMb+H2iC95aiWqVm8sDJZJ7IZHuRZx3io2f/hZRfXoEt//F8k6/7vviqv0xzxwHpYSOCUouTmquvvrpF+0lNTfiw2uxU1diJNbj6azidTsqraoiLCLF+GyK0aCNgwES4ZzPs/xqMJyD9QldvlOiugY7ulJIjknlh/AscMR5h7bG1xOniuLDnhSTbHUQunxHo8IQIay1OahwOL1PBImxZbXa25JTx29EyppyVRoxBw4GCSv69LpvZF5/W4o6/QrSJNhIS+0HG3YGOpE26RHShS0QXRqWOqhtseNtJCOFz0nxPeJVdZGbq0g3U2J3YHE4mnp7KdYvXU2auQalU8OCE09wzOEKIdlDrvCc8TdWn+KpmRogw1OKk5qWXXuLOO+9Er9fz0ksvNbvvvffe2+7ARGAlRGn5/fBufLT5OM99s4/nvtkHQHyEhqmje0lCI4SvVBthydjG403Vp8iaTEI0qcVrP/Xp04dNmzaRmJhInz5Nt3JXKBQcPnzYZwH6UrCu/VReVUNldQ0KhYKESC16jerUb+oAJSYr9763hZ8PFrvHVj041uuSCOBa9iC2Xr1NmdlKtF6DKkiXKRCiQzU1w+KwwSIvvWuaWmPpVGsyydNPIgz5fO2n+n1q6n9dmxO1dJ0eUcdqs3OwwMTfP9/F+sMl6NRK/jiiB3ePT6d7fGCf8HA6nRRVWtiZ69lv46udedx4Ts9GxcL5xmqe+Wovsy/uT6/ESIorLSz+8TBXndGNQV1jJLERoqm+L76utfFVf5lTHUeSFxGE2lxTs3TpUhYsWMCBAwcA6N+/P7Nnz2batGk+Cy7cZRWZufrVX7DaXUXYFpuDd3/N4ZdDRbx/x2i6BrAb7uEik7uGJj5Cw5Dusfx0oIjnvtmHWqlgytlp7ltQJSYrf12xg+/3FLDhcDEfzjyXxWsO8e/MI7yTeYRv77+A7vERAfssQjTL24xEMMw42CyeCU9tTGodTFrWeH+NoXW1OUKEoTYlNX/7299YsGABs2bNIiMjA4D169dz//33k52dzdNPP+3TIMNRpaWG57/d505o6jtSbGZLTimXBzCpidSq6JcUyeEiEx9MzyAxSsc/vtzDym25nNkzDoO27hZZrEHDvRf1Z92hYnLLqznvmVXubbec25sIndSjiyDmrUYlGOpTSrPgw1vrvq+NqdroOV7rjjXw+rjG48HwWYToIG36bbNw4UJef/11rr/+evfYVVddxbBhw5g1a5YkNS1QUW3jl4NFTW7/cscJLh3SFWWAbtukxhp49cYRmK12dw3Nw5cOZPoFfemVFIFWVZfUqJQKTu8Wy7vTzuHq19a5x68d0YPpY/v6rKjYZndgstg86nYa1vEIEXIarrFks7gSGlu19/2VTdTcyR1e/6nIB7vFtURHVCoolYGOSDShTUmN3W5n1KhRjcZHjhyJzdZ4dVrRmEqhIMagwWT13qgwKUoXsISmVmqDRSVrF4v0psxs5X9bjnuMrTtURJm5xidJjc3uYHeuka935THt/L4kRGrJKjLxyuqDzLlkAMkx+nafQ4iAaFi7UrDH+0xMrSbXvpKsxufMpZD9E3z/OJQchqhkGPMADPmj62sRdNqUbt50000sXLiw0fiSJUu48cYb2x1UZ5AUpeO283o3uf26s9I6Lph2KjFZeOmHA/w78wgAFw9KJkKrIre8muuXZHKs1Nzuc+SWVzN5SSavrTnEq6sOcrTEzJQl6/l48zGe+GwXJSZru88hRIepyHclLw1fFfltP2bLHmQVLWW3we5P4YOproQGoLIAvn4YVs2F6vLAxie8avFMzQMPPOD+WqFQ8MYbb/Dtt98yerTrXnRmZiZHjx7l5ptv9n2UYUipVPD74d1ZvbeA9Yc9ixTnTBxAjwA//dQaeo2K89KT+E/mEWaO68edF/TjSLGJKUsyOb1bLDp1+6dqo3Rqbhrdk9d/ymLpL65X7fjd49KJM2jafQ4hOsyp1lVqC4ck9j5VmeeaofFm69tw7izQx3ZsTOKUWtynZvz48S07oELBqlWrTr1jAARjn5rCCgtHik18tzufaL2aS05PJTVGT3QH/5K2O5xUVHuu69SaehWz1cbx0iqSonXER2ixO5xkFVUSa9DQJdo3t4bKzFYe+3QXK3/LdY99PmsMg7vGBPxWnQhhgXj6qbleM4aE5nvAVORDxQlX3U19CX1dszVqnff3idbJ2+G9f1CtGz6A0y7puHg6OZ/3qVm9erVPAhOeukTr6BKtY1TvhIDFYHc42ZVbzlc78rjjgj4kROrILjLx0qoDPDxxYIvqVSK0avqnRLu/VykVpCdHN/OO1is117Ahq9hjbMWW43S70EBCpBQLizbyVV8XX2lJf5iqEu91N0017BOtpzrFvyn64PiPsfAkz9oKcsuqmLIkE7PVTnWNndvP78OUJZnkGasxW+3Mu2Yo8QFOGnKKXTU0+UYLUTo1Z/dJYNXeApb+koVSCTPHpUtiIzqvaxaBWu96cipvBzhOPoCgj3GNQd2MTdlRMBc3PkZEIsSFTi2f30UkQfcRcHxL422GeIiVaxWMJKkRROrUTB3di8U/Huatddm8tS7bNa5Vcc/4dGKCoF5Fr1UyqGsMJkspy+8cTfd4A6+uPsibv2Rzbr8kIrTBsbSEEAGh1nufuZm0rG68tl+Nudj7WlN3rpWkpr7IRLhmMSy73FUgXEtjgOvfh+iugYtNNEmSGkFCpJa7xvUjz1jNp9vq6lXevzMjaJY4SI7W8+y1wzBW2eibFIlSqWDmuHSuG5VGWkJE0KyXJUSLNOxNU3+8Le+vnY0RvpV0GkxbDblbICcTkgdCnwsgpkfT/YJEQElSI4CT9SoNnsL635ZjdI9PJyHSe2+ajpYcrad+mU5CpFZuOYnQ1N46Hm+9bYR/xPVwvQZfFehIRAtIUiNO9nxx1dBEalWM7pvID3sLeGtdNkqlgrvHS72K6GAdsdJ0MBUHt8SpVs3uLMLpZyp8TpIagV6jZHC3GCqqa1g+PYO0BAOvrT7E6z8d5tx+iR7rPAnRIZrr49KaX1y+Ok4w8Edvm1AUTj9T4XOS1Ai6ROv55x+HUl5VQ5+kKFRKBdPH9uOPI3uQlhCBQepVhPCfygLXOk9KNUSltK1Wo36NjcPm+fRT7XjtjE5EoqsouKGIxNafN9Q4nVCRB44a1yPb0amBjkj4mCQ1AnAlNvWb5Em9ihB+Vl0OR3+Fbx+Fwr0QkQAZ98Dwm1o/49CaGp24tM75lJOpEPZ+AWv/CcZciOsFFz4K/S5yPekkwoIkNUKIjiG1EHWcTji0yvMxbHMJ/PAU5G6DK190JTm1KvJ984RTU92TIbx/NpZKWPcK/PJC3VjZEfjfHXDxk3DOTNAExwMRon0kqRFCdAyphahTcQK+fsT7tj0rYfwjnklNVUnjZRHawtvPoPb2VDj/bEyFsP5l79vWPgOn/wHie3ZsTMIvJKkRQgSf9vZx8fVxfM1idCU2TTmxHZIHe47Zql3N9GrF93Gt8xToz9LR2vIzrcyvqzNqqKYKzEWS1IQJSWqEEMHHV+sxBdu6TrVUWlAoXLehvDHENx5bMcPz+866zlNbfqYaQ/PbGy4CKkKWJDVCiPAXqHqepmpYIhJdBaoHv2/8Hk1E+CcrHf3ziOwCMd1cBcINJfWHiC6+P6cICElqhBDhL1D1PE3VsEQPgsvmw9tXQvnRum0qDUx5F6LC/FHjjv55RHd1Xde3rwRLRd24IR4mvQ3Ryb4/pwgISWrayel0UmC0UONwoFUrSa73WLQQop5grW8JlIQ+8KdvIG87ZP8MCX2h34UQ0x3UDdopeLt2bbluzR0nnH82CgWkngEz10H2L66VzLuPgLRzILZHoKMTPiRJTTsUV1r4ZlceL/5wgHyjhR7xBh6aMIALTusiPV6EaChY61sCKba76zXg0ub364gao3D/2SiVENcThvcErg90NMJPJKlpI5PFxuIfD7Pkx8PusWOlVcxevo2/XDqQW8/rjVYtnXiFCFnSV0eIkCNJTRsVVVpY+rP3vhELvj/AZcO60iM+ooOjEkL4jPTVESLkSFLTRvnGauwO749jVtXYKTVZJakRIlgEqp7HV7Uw4Ubqq4SfSFLTRgZt85dObj0JEUQCVc8jdUTeyXURfiJJTRslRWlJidGRb2y8Hku/LlEkSqGwEK0TbL1khBAhR5KaNkqN0fP6zaO4fkkmJmtd++24CA2v3TiCpGjpUClEqwRbLxkhRMiRpKaNFAoFp3eL5evZF7Ahq4S9J4yckRbHiJ5xdJdaGiFCn9R9CBFyJKlpB5VSQVpCBGkJksQIEXak7kOIkCNJjRCic6qtpbE1rovzul9Dwdqvprl4wXv9UDB+DiHaQJIaIUTnVFtLM2lZy/ZrKFj71TQXLzSxFlUQfg4h2kCSGiFEcAhUDYut2jOxie8TXHUzVaVgqXStXxSZBGpZX06IpoRMUjN37ly++OILtm3bhlarpaysLNAhCSF8KVA1LCtmeH4fLDMXNisU7oVvHnEteKnWw/AbYcz9EJcW6OiECEohk9RYrVYmTZpERkYGS5cuDXQ4QnQuoVZXEg6KD8AbF4K9xvW9rRo2LYWsNXDL5xDTzfv7HLamx5VquGaR52yPzQIFe+RnKcJCyCQ1Tz75JADLli0LbCBCdEahVlcS6qor4Ien6hKa+ooPwfEtzSQ19qbHlWpXQvPhrY23y89ShIGQSWqEEMKnWlrDE4haH4sRstY2vX3XChh0hfdt+hjvxc/6GNBEumqGhAhTYZ3UWCwWLJa6xzWNRmMAoxFCBJWW1vAEotZHoQR9LNRUed8e1Uw8NkvTMzEJfb3fRhQiTCgDefInnngChULR7GvTpk1tPv68efOIjY11v9LSpLhOiLBXke+qEWn4qsgPvhia2k+hgHNmeD82wPAb/Bu/ECEqoDM199xzD1OmTGl2n969e7f5+I888ggPPPCA+3uj0SiJjRDhLhjqf1oaQ3P7nXE9HPgOjvziue13T0Os/DsmhDcBTWqSkpJISkry2/F1Oh06nSwsKUS7yTpIHS861VUbU3IY9n4B+jhXHU10V1d9TFNO9bOSn6UIYyFTU5OTk0NJSQk5OTnY7Xa2bdsGQHp6OlFRUYENTohwJ+sgBUZUsuvV08tsTlNO9bOSn6UIYyGT1Dz22GO8/fbb7u/PPPNMAFavXs24ceMCFJUQosO0p1fONYvq+rG09r1NxeCwnXx82gnaaLBWusb1MU2vJ+WwecZgs7hmY2zVnk0A1brGsdaPtyXXwlf7CBFCQiapWbZsmfSoEaIza0+tjFoPS8a27b0tiWHSsronjppbS8phh0VjvL+/vmpj8/G25Fr4ah8hQkjIJDVCCNEi3mpGTrUSt6/ZquHOta4Zl/oafl8rvo9nzE11BRZCNEuSGiFEePFWM+LtVo4/rZjhSlKSB7UsDrXOc9+OjleIMCFJjRAiPNWvF+nomRohREBIUiOECE/160Waq3MRQoQNhdPpdAY6iI5iNBqJjY2lvLycmJhm+jwIIYJPa5/UKdhTl9TUX5k6vk9dbYs/n37yduyWfoZT7SdPP4lOpqW/vyWpEUKEp/pJTX3eal2EEEGtpb+/5faTECL4+GsGwVf9avxNZlCEaBNJaoQQwcdf/VN81a/G36R/jBBtIkmNECI8BUO/GiFEh5KkRggRnoKhX40QokNJUiNEqAnVeotQjVsIETIkqREi1IRqvUWoxi2ECBmS1Aghgo+3epja8WA8rq+FSpxCBBlJaoQQwcdbPUwwH9fXQiVOIYKMJDVCiODnrR6ndtbCF3U6vujg295zCCHaTZIaIUTw81aPU3t7xhd1Oqeq9/FFPZDUFAnhd5LUCBFqQrXeIlTjFkKEDElqhAg1oVpvEapxCyFChiQ1QojGmqph6YikpOwomIs9x5xOmLUZ8nbUjdV2B75mEayY0fg43j6DWgdWEygb/NPnsLUt1obrSPnqGkn9jRBtIkmNEKKxpmpYOuIXqrnY+/pMd6yGD29tPD5pmffjePsMk5Z5P8ada1sXY63SLM/j+eoaSf2NEG0iSY0QIkQovA/H92lcq2NI8D7T0RR9TPP1Pg3rgWwWV0Jjq275OaSmSAi/k6RGCBHa1DpIHtR4vDVJjc3i/Ri1GtYDFezxPuPTHKkpEsLvJKkRQjTvmkWg1vuvfqQhbZT3W0oKZeuOo9Y1Pk58b5jxMxQfbLyvv0mdjBB+J0mNEKJ5ar33WQl/1XdYKryf7441rTtOtbHpGpyG422tqWkNqZMRwu8kqRFCNFa//qP2KaOOolR5Hz9V3UtLj9PefWvP6c/6GKm/EaJNJKkRQjRWv/6j/i2njqDSeB+3W5uve2mo4WPbvtoX/F8fI/U3QrSJJDVCiI4RrDUl1yzqmH4zHT3j1fD89QX6mgvhJ5LUCCE6RrDWlKj1jfvi+KPfTFP9dPwpWK+5EH4iSY0QonkdXd/hq/N5O47DBtrIxj1nOoKt2pXYxPfxfNpK6mSE8BlJaoQQzevo+g5fna+lx+momqHapRzuymxdbZAQosUkqRGis2pNvUWw1ma0Ja6G72nNTE2wXgd/66yfW4QcSWqE6KxaU28RrLUZbYmr4XtaU+sSrNfB3zrr5xYhR5IaIUTHCNbeK95qXfzZb6YjP2+wXnMh/ESSGiFExwjW3iv+rHUJ9GcO9PmF6GCS1AghwlNTdSAdsc5TUxrG5LCBw+7qlly/tkdqVYRoE0lqhBDhqak6kI5Y56kpTcXUcD0qqVURok0kqRGis2pNvUWw1mY0F5e3WRpwrfPU1s8SrNfB3zrr5xYhR5IaITqr1tRbBGttRnNxNZnUqNteOxOs18HfOuvnFiFHkhohhHfSm6QxuSZCBDVJaoQQ3klvksbkmggR1CSpEUKEp2CsA2kYU/2nn+qPS62KEG0iSY0QIjwFYx1IMMYkRBiRpEYI0To2i2sRSLUOqo2uMaXKVYAL/qsvaVjPUnv+jji3ECIkSFIjhGid0ixXT5WGvVVq+au+xNuaTQ3PL7UtQnRqktQIIbzzVpNis0DhnsDEEwyCsU5HCOEmSY0Qwjtv9R8Fe+rWSuqMpCZGiKAmSY0QIrC89X5x2EAbCVaT6+kgAKcTZvwMxQddK2sHMj6Q+h0hgpAkNUKIwGrpekj1xyYt83tYbtKbRoiQIUmNEKLl6teUqHV1i0M2fALJ3+L7uHq73Lm2488thAhaktQIIVouWGpK1DpI6BvoKIQQQUaSGiECqaPrNSryocZU11+mllIFkcnBkbC0hN3qKloG//fFsVk8b3fZqjt3sbQQQUySGiECqaPrNapKoHBvx/aX8QdbNSy+wPV1R/XFqdWR9TxCiFaRpEYIEVjeer/UPv00/UfPJ500Ea6kQhPRoSF6iO/jilfqd4QIOpLUCCECq7k6nYI9dTMy9QVytkStg+RBgTu/EKJJktQIIXxD+rkIIQJMkhohhG9IPxchRIBJUiNEIHX0WkKGBOh6Rl1/mVpKVXDWiNS/Pg5bXXdhfUzduD+vlazzJERIkaRGiEDq6L4voTZjEsi+OMHSk0cI0WKS1AgRDqSepWXkOgkR1iSpESIcSD1Ly8h1EiKsSVIjhPANqUERQgSYJDVCCN+QGhQhRIBJUiOE8E7qT4QQIUaSGiGEd1J/IoQIMZLUCBEOpJ6lZeQ6CRHWJKkRIhxIPUvLyHUSIqxJUiOECCyp3RFC+IgkNUKIwJLaHSGEj4REUpOdnc3f//53Vq1aRV5eHt26deOmm27ir3/9K1qtNtDhCRGepP5ECBFiQiKp2bt3Lw6Hg8WLF5Oens7OnTu54447MJlMzJ8/P9DhCRGepP5ECBFiQiKpmThxIhMnTnR/37dvX/bt28fChQslqRFCeJIaHSE6rZBIarwpLy8nIaH5aXCLxYLFYnF/bzQa/R2WECLQpEZHiE4rJJOaQ4cO8fLLL/P88883u9+8efN48sknOygqIUSbSO2OEMJHlIE8+RNPPIFCoWj2tWnTJo/35ObmMnHiRCZNmsS0adOaPf4jjzxCeXm5+3X06FF/fhwhRFtEp0DyoMYvmVURQrRSQGdq7rnnHqZMmdLsPr1793Z/nZuby/jx48nIyGDJkiWnPL5Op0On07U3TCHCm7caFKk/EUKEoIAmNUlJSSQlJbVo3+PHjzN+/HhGjhzJW2+9hVIZ0EkmIcKHtxoUqT8RQoSgkKipyc3NZdy4cfTs2ZP58+dTWFjo3paamhrAyIQQQUdqdITotEIiqfn22285ePAgBw8epEePHh7bnE5ngKISQgQl6a8jRKcVEvdwbr31VpxOp9eXEEIIIQSESFIjhBBCCHEqIXH7SQjhR95qUKT+RAgRgiSpEaKzkxoUIUSYkNtPQgghhAgLktQIIYQQIixIUiOEEEKIsCBJjRBCCCHCgiQ1QgghhAgLktQIIYQQIixIUiOEEEKIsCBJjRBCCCHCgiQ1QgghhAgLktQIIYQQIixIUiOEEEKIsNCp1n5yOp0AGI3GAEcihBBCiJaq/b1d+3u8KZ0qqamoqAAgLS0twJEIIYQQorUqKiqIjY1tcrvCeaq0J4w4HA5yc3OJjo5GoVAEOhw3o9FIWloaR48eJSYmJtDhhCW5xv4n17hjyHX2P7nG/tfaa+x0OqmoqKBbt24olU1XznSqmRqlUkmPHj0CHUaTYmJi5C+Qn8k19j+5xh1DrrP/yTX2v9Zc4+ZmaGpJobAQQgghwoIkNUIIIYQIC5LUBAGdTsfjjz+OTqcLdChhS66x/8k17hhynf1PrrH/+esad6pCYSGEEEKEL5mpEUIIIURYkKRGCCGEEGFBkhohhBBChAVJaoJIdnY2t99+O3369MFgMNCvXz8ef/xxrFZroEMLK3PnzuXcc88lIiKCuLi4QIcTNl577TX69OmDXq9n5MiR/PTTT4EOKaz8+OOPXHnllXTr1g2FQsEnn3wS6JDCzrx58zjrrLOIjo4mOTmZq6++mn379gU6rLCycOFChg0b5u5Pk5GRwVdffeWz40tSE0T27t2Lw+Fg8eLF7Nq1iwULFrBo0SL+8pe/BDq0sGK1Wpk0aRIzZ84MdChhY/ny5cyePZu//vWvbN26lfPPP59LL72UnJycQIcWNkwmE2eccQavvPJKoEMJW2vXruXuu+8mMzOT7777DpvNxoQJEzCZTIEOLWz06NGDZ555hk2bNrFp0yYuvPBCfv/737Nr1y6fHF+efgpyzz33HAsXLuTw4cOBDiXsLFu2jNmzZ1NWVhboUELeOeecw4gRI1i4cKF7bNCgQVx99dXMmzcvgJGFJ4VCwYoVK7j66qsDHUpYKywsJDk5mbVr13LBBRcEOpywlZCQwHPPPcftt9/e7mPJTE2QKy8vJyEhIdBhCNEkq9XK5s2bmTBhgsf4hAkTWLduXYCiEqL9ysvLAeTfYD+x2+28//77mEwmMjIyfHLMTrX2U6g5dOgQL7/8Ms8//3ygQxGiSUVFRdjtdlJSUjzGU1JSyMvLC1BUQrSP0+nkgQceYMyYMQwZMiTQ4YSVHTt2kJGRQXV1NVFRUaxYsYLBgwf75NgyU9MBnnjiCRQKRbOvTZs2ebwnNzeXiRMnMmnSJKZNmxagyENHW66x8C2FQuHxvdPpbDQmRKi455572L59O++9916gQwk7AwYMYNu2bWRmZjJz5kxuueUWdu/e7ZNjy0xNB7jnnnuYMmVKs/v07t3b/XVubi7jx48nIyODJUuW+Dm68NDaayx8JykpCZVK1WhWpqCgoNHsjRChYNasWaxcuZIff/yRHj16BDqcsKPVaklPTwdg1KhRbNy4kRdffJHFixe3+9iS1HSApKQkkpKSWrTv8ePHGT9+PCNHjuStt95CqZTJtJZozTUWvqXVahk5ciTfffcd11xzjXv8u+++4/e//30AIxOidZxOJ7NmzWLFihWsWbOGPn36BDqkTsHpdGKxWHxyLElqgkhubi7jxo2jZ8+ezJ8/n8LCQve21NTUAEYWXnJycigpKSEnJwe73c62bdsASE9PJyoqKrDBhagHHniAqVOnMmrUKPcMY05ODjNmzAh0aGGjsrKSgwcPur/Pyspi27ZtJCQk0LNnzwBGFj7uvvtu3n33XT799FOio6Pds4+xsbEYDIYARxce/vKXv3DppZeSlpZGRUUF77//PmvWrOHrr7/2zQmcImi89dZbTsDrS/jOLbfc4vUar169OtChhbRXX33V2atXL6dWq3WOGDHCuXbt2kCHFFZWr17t9c/tLbfcEujQwkZT//6+9dZbgQ4tbPzpT39y/zvRpUsX50UXXeT89ttvfXZ86VMjhBBCiLAgBRtCCCGECAuS1AghhBAiLEhSI4QQQoiwIEmNEEIIIcKCJDVCCCGECAuS1AghhBAiLEhSI4QQQoiwIEmNEEIIIcKCJDVCdGLjxo1j9uzZTW7v3bs3L7zwgvt7hULBJ598AkB2djYKhcK9zERz1qxZg0KhoKysrF3x+sqpPrcQIjTJ2k9CiCZt3LiRyMjIQIfRZmvWrGH8+PGUlpYSFxcX6HCEEH4mSY0QokldunQJdAhCCNFicvtJiE7OZrNxzz33EBcXR2JiIo8++ii1S8I1vP3kS+vWreOCCy7AYDCQlpbGvffei8lkcm/v3bs3//jHP/jTn/5EdHQ0PXv2ZMmSJY2OMXz4cPR6PaNGjeKTTz5x3xLLzs5m/PjxAMTHx6NQKLj11lvd73U4HPz5z38mISGB1NRUnnjiiRbHrlAoWLx4MVdccQUREREMGjSI9evXc/DgQcaNG0dkZCQZGRkcOnTI/Z4nnniC4cOH8+abb9KzZ0+ioqKYOXMmdrudZ599ltTUVJKTk5k7d27bLqgQQpIaITq7t99+G7VazYYNG3jppZdYsGABb7zxhl/PuWPHDi655BL+8Ic/sH37dpYvX87PP//MPffc47Hf888/z6hRo9i6dSt33XUXM2fOZO/evQBUVFRw5ZVXMnToULZs2cLf//535syZ435vWloaH3/8MQD79u3jxIkTvPjiix6fOzIykg0bNvDss8/y1FNP8d1337X4M/z973/n5ptvZtu2bQwcOJAbbriB6dOn88gjj7Bp0yaARp/n0KFDfPXVV3z99de89957vPnmm1x++eUcO3aMtWvX8s9//pNHH32UzMzM1l1QIYSLz9b7FkKEnLFjxzoHDRrkdDgc7rE5c+Y4Bw0a5HQ6nc5evXo5FyxY4N4GOFesWOF0Op3OrKwsJ+DcunXrKc+zevVqJ+AsLS11Op1O59SpU5133nmnxz4//fSTU6lUOquqqtznvummm9zbHQ6HMzk52blw4UKn0+l0Lly40JmYmOje3+l0Ol9//XWPmBqet/7nHjNmjMfYWWed5ZwzZ84pP4vT6boOjz76qPv79evXOwHn0qVL3WPvvfeeU6/Xu79//PHHnREREU6j0egeu+SSS5y9e/d22u1299iAAQOc8+bNa1EcQghPMlMjRCc3evRoFAqF+/uMjAwOHDiA3W732zk3b97MsmXLiIqKcr8uueQSHA4HWVlZ7v2GDRvm/lqhUJCamkpBQQHgmn0ZNmwYer3evc/ZZ5/d4hjqHxuga9eu7mO39v0pKSkADB061GOsuroao9HoHuvduzfR0dEe+wwePBilUukx1po4hBB1pFBYCNHhHA4H06dP59577220rWfPnu6vNRqNxzaFQoHD4QDA6XR6JGO1Yy3V3LFb+/7aOLyN1T+mt3O2Nw4hRB1JaoTo5BrWb2RmZtK/f39UKpXfzjlixAh27dpFenp6m48xcOBA3nnnHSwWCzqdDsBdy1JLq9UC+HXWSQgRPOT2kxCd3NGjR3nggQfYt28f7733Hi+//DL33XefX885Z84c1q9fz9133822bds4cOAAK1euZNasWS0+xg033IDD4eDOO+9kz549fPPNN8yfPx+omyXp1asXCoWCzz//nMLCQiorK/3yeYQQwUGSGiE6uZtvvpmqqirOPvts7r77bmbNmsWdd97p13MOGzaMtWvXcuDAAc4//3zOPPNM/va3v9G1a9cWHyMmJobPPvuMbdu2MXz4cP7617/y2GOPAbjrbLp3786TTz7Jww8/TEpKSqOnkYQQ4UXhbM1NaCGECGLvvPMOt912G+Xl5RgMhkCHI4ToYFJTI4QIWf/+97/p27cv3bt357fffmPOnDlcd911ktAI0UnJ7SchRLvNmDHD4/Hs+q8ZM2b47bx5eXncdNNNDBo0iPvvv59JkyY16jrcWu+8806Tn+X000/3UeRCCH+Q209CiHYrKCjw6MdSX0xMDMnJyR0cUdtVVFSQn5/vdZtGo6FXr14dHJEQoqUkqRFCCCFEWJDbT0IIIYQIC5LUCCGEECIsSFIjhBBCiLAgSY0QQgghwoIkNUIIIYQIC5LUCCGEECIsSFIjhBBCiLAgSY0QQgghwsL/AyoB7OYimVdHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the cluster\n",
    "sns.scatterplot(dataScaled_df['bill_length_mm'],dataScaled_df['bill_depth_mm'],hue=y,style=dbscan.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "335d1aee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}