slides_clustering_interpret.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playground for Interpretting Clusters\n",
    "\n",
    "- Stephen W. Thomas\n",
    "- Used for MMA 869, MMAI 869, and GMMA 869"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-09 08:00:37.375056\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Default plot settings\n",
    "SMALL_SIZE = 12\n",
    "MEDIUM_SIZE = 14\n",
    "BIGGER_SIZE = 16\n",
    "\n",
    "\n",
    "plt.rc('figure', figsize=[8.0, 5.0])     # controls the size of the figure\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "plt.rc('axes', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CustomerID', 'Male', 'Age', 'AnnualIncome', 'SpendingScore']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 200 entries, 0 to 199\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   CustomerID     200 non-null    int64\n",
      " 1   Male           200 non-null    bool \n",
      " 2   Age            200 non-null    int64\n",
      " 3   AnnualIncome   200 non-null    int64\n",
      " 4   SpendingScore  200 non-null    int64\n",
      "dtypes: bool(1), int64(4)\n",
      "memory usage: 8.0+ KB\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "      <th>max</th>\n",
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       "      <th>CustomerID</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.50</td>\n",
       "      <td>57.879185</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.75</td>\n",
       "      <td>100.5</td>\n",
       "      <td>150.25</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>200.0</td>\n",
       "      <td>38.85</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>18.0</td>\n",
       "      <td>28.75</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnnualIncome</th>\n",
       "      <td>200.0</td>\n",
       "      <td>60.56</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>15.0</td>\n",
       "      <td>41.50</td>\n",
       "      <td>61.5</td>\n",
       "      <td>78.00</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>200.0</td>\n",
       "      <td>50.20</td>\n",
       "      <td>25.823522</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.75</td>\n",
       "      <td>50.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count    mean        std   min    25%    50%     75%    max\n",
       "CustomerID     200.0  100.50  57.879185   1.0  50.75  100.5  150.25  200.0\n",
       "Age            200.0   38.85  13.969007  18.0  28.75   36.0   49.00   70.0\n",
       "AnnualIncome   200.0   60.56  26.264721  15.0  41.50   61.5   78.00  137.0\n",
       "SpendingScore  200.0   50.20  25.823522   1.0  34.75   50.0   73.00   99.0"
      ]
     },
     "execution_count": 4,
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     "output_type": "execute_result"
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       "      <th>AnnualIncome</th>\n",
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       "      <td>6</td>\n",
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       "      <td>8</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    CustomerID   Male  Age  AnnualIncome  SpendingScore\n",
       "0            1   True   19            15             39\n",
       "1            2   True   21            15             81\n",
       "2            3  False   20            16              6\n",
       "3            4  False   23            16             77\n",
       "4            5  False   31            17             40\n",
       "5            6  False   22            17             76\n",
       "6            7  False   35            18              6\n",
       "7            8  False   23            18             94\n",
       "8            9   True   64            19              3\n",
       "9           10  False   30            19             72\n",
       "10          11   True   67            19             14\n",
       "11          12  False   35            19             99\n",
       "12          13  False   58            20             15\n",
       "13          14  False   24            20             77\n",
       "14          15   True   37            20             13\n",
       "15          16   True   22            20             79\n",
       "16          17  False   35            21             35\n",
       "17          18   True   20            21             66\n",
       "18          19   True   52            23             29\n",
       "19          20  False   35            23             98"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Male</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
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       "      <td>18</td>\n",
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       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>True</td>\n",
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       "      <td>83</td>\n",
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      "text/plain": [
       "     CustomerID   Male  Age  AnnualIncome  SpendingScore\n",
       "195         196  False   35           120             79\n",
       "196         197  False   45           126             28\n",
       "197         198   True   32           126             74\n",
       "198         199   True   32           137             18\n",
       "199         200   True   30           137             83"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/stepthom/sandbox/master/data/Mall_Customers.csv\")\n",
    "\n",
    "df = df.rename(index=str, columns={\"Genre\": \"Male\"})\n",
    "df['Male'] = df['Male'].replace(['Male', 'Female'], [True, False])\n",
    "\n",
    "list(df)\n",
    "df.shape\n",
    "df.info()\n",
    "df.describe().transpose()\n",
    "df.head(n=20)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting Clusters Case Study\n",
    "We will use all 4 features, with Hierarchcial Clustering, wiht Euclidean distance and Ward Linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    Male  Age  AnnualIncome  SpendingScore\n",
       "0   True   19            15             39\n",
       "1   True   21            15             81\n",
       "2  False   20            16              6\n",
       "3  False   23            16             77\n",
       "4  False   31            17             40"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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       "       [-0.88640526, -1.13750203, -1.62449091,  1.70038436],\n",
       "       [ 1.12815215,  1.80493225, -1.58632148, -1.83237767],\n",
       "       [-0.88640526, -0.6351352 , -1.58632148,  0.84631002]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.copy()\n",
    "X = X.drop(['CustomerID'], axis=1)\n",
    "col_names = X.columns\n",
    "X.head()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "X[1:10,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster\n",
    "\n",
    "aggl = scipy.cluster.hierarchy.linkage(X, method='ward', metric='euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the dendogram 0 this time with better labels\n",
    "plt.figure(figsize=(16, 8));\n",
    "plt.grid(False)\n",
    "plt.title(\"Mall Dendogram\");  \n",
    "dend = scipy.cluster.hierarchy.dendrogram(aggl); \n",
    "plt.savefig('out/mall_dendro_1.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6LOqWtC5p160pLDzd65PAxIh4Obf18SG2regkUtJ8pV20JG0DrAFcCYwrHG+ly08GLhpqo9wxMTMzM7Mlr1sHqP/J78cAV0s6BFgWeNeCR2k74ExgA9LUr3n5+BXAAa0Mj4oG25ZHYZ7N7eo2VeobpE7C8m3HNwL2kbQ38ARwaETcX6MtQXrnAL4dEVMBJO1BmjJ2W3vSfJm8o9jXgA8DO7Wd7pQu34hzTMzMzMxsJGmS/L4vcHZErAvsBpyX/+AmIn4VEW8gJb8fJWnpfHy3mp2Sbm0rv1jaHXg8Im4pOT0aeCGvDTmd1Hmq420RsTWwK3CQpHdIWgb4PFBp0X72KeCKiCh2BnulyzfiERMzMzMzG0l6Jr/nUY9iEvnHgV0AIuLG3PlYFRicHhURd+d1EVtSsrC9ZtsezTkhvRaAvw3YQ9JupKlkK0j6bkR8KNdq7Wp1KXBWnYYU0tofl3QpaQrc08CGpLU0kL6j30jaNiL+2KHUW4HtJX0KWA54taS5wO9YkC4/Clhd0sxCkGNtHjExMzMzs5GkSfL7I+RpSJI2J3UCnsg1RuXjGwCvBx4eQttmsCAI8QPATyMiOqW1R8RREbFuTmifkK//UD79A9KaGUhJ7ffldpYmtBdJWlbS8q3PwM7A7Ii4IyJWLyTNPwpsHRF/7FQ3IvaLiPXz9YcD50bEkT3S5RvxiImZmZmZLVkDA41vbZj8/lng9JyvEcCk3GF4O3CkpJeAl4FPtXaxarjG5DukaWIPkEZKJuTjTdLaTwDOz22eCxyQj3dKaC9aA7g0j4qMAqZFxJU97qlSd7Fyx8TMzMzMRpSIuIK05W/78QdJU5baj99FmjbVfvw84LwOz9itQbteIOWOtHsLaYvjbvfOBGYWfn+GtFtXu+0q1HoQ+NfurYU82lGn7tnA2SXHHyZNgRsST+UyMzMzsxFF0i6S7pX0gKQjC8c3lPQrSfdLujBP9ULSSZIG8s99kp4p3HOlpGckXVbx8WsAN+ZtisvadlRu172S3gMprT0iZvR4p2/mtRut30vbHBFHRMTtFdtaVncDSddKul3SzLxdcaW6kmZIml34/URJ9+Ral0paKR+fBvw3NdPuPWJiZmZmZkve3LmNMlCaBCxGxGGF+w8B3lQoeSKwDPCfFZtwITA3Ir5a0rYtSNO33kBKP/+JpE0jYn6PdxpHSqUf1KPNlZTVBb5KWidyjqQdgS+RtgLuVWuRIEXgGuCoPL3uy8BRwOciYqKk/waqdvYAj5iYmZmZ2ZK2xhqw3HJN7x4MWIyIvwOtgEWRFotfnK87B9ir5P59gQtav0TEtcBfmzamzZ7A9Ih4MSIeAh6gZGpZUe5onUjKMulkoTZX0aXuFkBrIf51uc29arWCFI8vHo+Iqwu5MTeRdvlqzCMmZmZmZrZkrbVW+llyAYu5rDYgbZf7015NlHQsMKvXFKyStt3U1rZ1OlzbcjBpV7HHygIP67S5Yt3bgPcD/wPsDSwvaZWI+HOXWlWCFD9GGk1qzB0TMzMzMxuagYF607IGBlLyezNNAhZbJgAX95paBRARdUIIe7Wt/GJpbdJi+fFdalZuc8W6hwOnSJoE/AyYQ5cdwwpBiodJGtPhms/nGudXbWMZd0zMzMzMrLmJE5f0E5sELLZMAA4ahrZ18iZgY+CBPKqxjKQHImLjwjVN2tyxbt7++H0wOEXr/RHxbJdab6VLkKKk/YHdgZ1ylkxj7piYmZmZWXOTJ6efOhosei8YDFgk/df+CcDEnEvSCliczsIBi0h6PbAycONQHt7DDGCapK+TFr9vAtycn38t8JGImNO6OCIuB9YstHFusVPSqc2S7omIzTo1oltdSasCT0XEy6TF6md2qxsRpwKn5vNjgMsKnZJdSNkxO0REt2lelXjxu5mZmZmNGHk0pBWweDdwUVvA4mdywOEqLAhYhLSAfHr7f9WX9HPge8BOkh5tbfEr6VhJe9Rs253ARcBdwJXAQRExX9K/kEYwnqr3tou2OXcsui7C6WE8cK+k+0hbH08ZQt1TgOWBa/K2xqcNoV0eMTEzMzOzkaVuwGI+d0yH49t3ON5kjQkRMYX8x37BFsAlEdE1WT0ilmv7/ZiSy3qGNXarGxEXs2Dnslp124MU26acDZk7JmZmZmZm1c0FJktaoWrnJSJmUzNssEutWtkgw1FX0vbAt0jrfirzVC4zMzMzG1H6nPz+ZUmz888+FR6/HLBsl7Ytkvxe8Z0qJbTXqHe2pIcK7z227fybJc2X9IEaNSslv2dLAavWabNHTMzMzMxsyau7xXDWz+R3Se8FtgbGAqOB6yX9OCL+0qMZJy3u5HcaJrS3OSJP3Wp/3lKk7+qqqoVqJr//XNJuOPndzMzMzF7RJk4cSo5JP5PftwCuj4h5EfE3UvjgLk0bRn+T32sntNdwCHAJ8HiVi538bmZmZma9TZ0K06b1p9bQgg+r67XF8JJLfr8N+GLe3ncZ4J2kHbVeCcnvTRLa202RdDSpg3NkRLwoaZ1cb0fgzRXrLJHkd4+YmJmZmY1k06alDsU/j74lv0fE1aTdvW4gjaLcSE5Bj4ija3ZKurWt/OIFCe3fLDl9OLCDpFuBHeiR0F7iKGAzUufjtaStlAG+QZpuVSlJvpD8fmmXa5z8bmZmZmakUY6ZM4deZ2jBh0tKX5Pfi9v7SpoG3L8Y2tZJPxPaFxIRj+WPL0o6i9TRARgHTM/PWxXYTdK8iPhBh1JLLPndIyZmZmZmNpIMJr/nXbcmkKZCBWktRmuXqZ7J75KWkrRK/rwVsBVw9RDaNgOYIGl0TqZfKPk9T6MaFBGXR8SaETEmIsYAzxUT2nMwI5QktPdqiKS18r8irbWZnZ+5YeF5FwOfanVKyupGxKkRsXa+/u3AfSXJ73v0I/ndIyZmZmZmNmLkHaBaye9LAWe2Jb9Pl3Q8cCu9k99fBfw8jx78BfhQa41KkzUmEXGnpFby+zyGlvw+HviSpAB+Rh7pqZHQfr6k1fK1A8CB3S4eQvL7aFLyO8BNEdH1Od24Y2JmZmZmI0q/kt8j4gXS7ldl1w9r8vtQEtrz/TtWuGZSnbqLO/ndU7nMzMzMbERpELA4SdIThbDBAwr3rC/pakl3S7pL0pgej/830kjG9R3atkjAYkTMjojS5PdOQYiSNpN0o6QXJbXWhxARl0XEydW+KZB0uKTIIyLF4wsFLHarqxTweG+hjavn46Pz9/xA/t7H5OMHAfeQdkCrzB0TMzMzMxsxCgGLu5JGIvbNwYawIGBxE+BpUsBiy4URMTb/nFE4fi5wYkRsThpt6ZXtcQMpVHCHkrYVAxZ3Ab6V29vLEYW2tbZYewo4lBS02Iik9UhBlI+0Ha8dsAjsV2hj6zv6OPB0Hjk5KdckIv6XtCPYQ3Xa66lcZmZmZrbkNc9fGQxYBJDUCli8m5TNMTFfdw5wDHBqp0K5IzEqIq4BiIj2ZPO6BgMWgYcktQIWb+x+26LyH/+PK6XTN3USKbjxh23HWwGLVXNMOtmT9B1DmnZ2iiQ13Z3LIyZmZmZmtuQ1z18pC1hch94Bi++XdLuki/NIAsCmwDOSvi/pVkkntkY4JB0raY8+ta2XKbltJ0kaXfOZpXLb50TEbW3HWwGLp9UseVaexvV/805fUHjf/L0/S/rfoRGPmJiZmZnZ8OiUv9I9+b1JwOKPgAty8vmBpNGUHUl/C29PyhN5hJRcPgn4TsPF77UCFrOjgD8CrwamknYWO7bBsxc0QloG+Dywc8npwYBFdf+ei/aLiDmSlieNtHyYNAWuyft25BETMzMzMxtJegYsth0nIv6cp1cBnA5sU6h1a0Q8mP+L/w+ArRdD2zqKiMcieRE4iw67itW0EWnh+W05GHFd4DeS1mRBwOLDpMyXb0naq0cb5+R//wpMK7Rx8H3z974i9bZEXog7JmZmZma2eE2dmlLliz/NpnFBg4DFVthgtgdwd6HWyjnvA9Ioyl1NG0bNgMVi29qDELvpVKslIu6IiNULQYqPAltHxB/rBixKGtXa0UvSq0gp7602ziB9z5C+958OJf3dU7nMzMzMbPFqrScZO3bIpRoGLB6a11zMI/0X/Um51vy8Fe+1uWNwC2lEZUkGLJYGIebRjVnACsDLkv4PaReyuV1qNdYlYHE0cFXulCwF/IT8HZG+3/PyIv+nSJ3ExtwxMTMzM7PFr309yfjxjUvVDViMiKNIaznKal0DbFVyfIkELHYKQoyIP5KmYC1E0padanVp05gOxycVfi0NWIyIv7Fg6lv7uReAD1ZtRy/umJiZmZmZVTcXmCxphaqdl4iYDZQGLNbVz1ptdS/rVy1J2wPfIq37qcxrTMzMzMxsROln8ns+v4KkOZJOqfD4LYFXUbKoXcnJuV23S+q5kL5uqnpdkmZIml34/bjctgGlxPu1K9ZZ5Dvq1HbSAvsVqbBepsgjJmZmZmY2PAYGak/pKiS/v5u0qPvXkmZExF0sSH6fLuk0UjJ5K2Dxwog4uEPZ44DrazTjiIi4uOT4rqQF75sA2+Vnb1eh3n4RMavt2GCquqQJpHfbp0YbkfQ+0ghP0YkR8X/z+UOBo8nrWnro9B0t0vaIOEnS06QOSmUeMTEzMzOzJW/ixKaL4QeT3yPi70Ar+V2kXbVaHYZzSLtcdSVpG2AN4OomjWmzJ3Bu3v73JtL2xWv1uqlLrXPy54uBnQrBhj1JWo405ev44vGI+Evh12WpkDvS5++oI4+YmJmZmdmSN3ly+inT/e/vsnT17aiW/P4O4D7gsIj4fd4t62ukwMCdFm6CzgBOKxnJqNu2dYDHetx3lqT5pPDC4/OWuwulqktqpapXXbdxHOndnms/IWkK8BFSUvs7uxXp9h11aXsjHjExMzMzs5GkafL7mIjYirTdbWsk4lPAFRHx+0VujDigZqekW9u62S8i3khKoN+e1AFoWivdKI0FNo6IS8vOR8TnI2I94Hyg0/S2lo7fUZe2N+KOiZmZmZmNJP1Mfn8rcHBOQf8q8BFJJyyGtnW0mFLV3wpsk9/rF8CmkmaWXDcNeH+FWqXfUZe2N+KpXGZmZma2QIMF6ZVq9iFcMRtMfgfmkEL9JkZESGolv0+nLfk9IlrTqQaT3yNiv1ZRSZOAcRExuMtXAzNIf8RPJ00ve7b1XEn3RMRmxYtzh2OliHiykKr+k0Kt/YEbKaSq58T3cyOibFoV+b1OJS/6z7t5XRYR4/Pvm0TE/fnSPYB78vFtgYMj4iNttUq/ox5tb8QdEzMzMzNLJk4c7hb01M/k924arjG5AtgNeIC0tuOjuVY/U9XXyu/R1AmSXg+8DPyOBTtyrQ9UDm3s0fZGVGd9yrhx42LWrLpT7czMzMxssWmNbhRT1V9parZR0i0RUWur2SVF0tmkEYiy7YI73bM78LqIOLkPzz8YeCQiZgy1VlvdE4HzIuL2PtWbRBpd6bWGZZDXmJiZmZnZiNLPgEVJ++fr75e0f4XHbwtMk3R+SbtKAxYj4rJenZKSIMR/lXSjpDsk/UjSCrnWKXU6JSV1T5R0T27fpZJWynWP6NQpkfRwbseApFm9akn6JvBtauauuGNiZmZmZiNGIWBxV2ALYF9JW+TTrYDFTYCnSSGFLRdGxNj8c0au9Vrgi6T1INsCX5S0co8m3Exa07JfybliwOJkFoQ79nqnsiDEM4Aj865XlwJHVKlVoe41wJZ5h7L7gKMqlntn/u6KI1mltSLiEOD1wJ/qtNcdEzMzMzMbHlOnpmle7T/d9TNg8T3ANRHxVEQ8TfpDe5cmr5LVDljsFIRI+sP+Z/nzNfTePatS3Yi4upD1chNp57BG+lkL3DExMzMzs+EybVrasaueTiGGVQIWb5d0saTWlr6daiHpDEl117l0rNdFpyDE2aRdswA+yMLbEFfRMWCx4GPAjyvUCuBqSbdI6pCKWblWR96Vy8zMzMyGz9ixiy6K75783jRg8YKIeFHSgaTRlB273RMRB5Sc66VWKGIhCPGwvK1v0ceAkyUdTdo6+O+VG9G9buuaz5N291pkrUyJt0XEHyStDlyTtz5ujebUrdWRR0zMzMzMbCTpZ8Bi7UDEhm3rpGMQYkTcExE7R8Q2wAXAb2u0o2vAYl7kvzstZMc9AAAgAElEQVQpub3nFr0R0foeHyetdxkMUqxbqxt3TMzMzMxs8WsFNxZ/6k/jgkLAYt51awIwI/9R3ApYhLaAxcL9gwGLpCyUnSWtnBe975yPNTWDlIwuSW+hLWCx/eKIODUi1o6IMcDbgfsKQYir53//BfgCcFr+fR1J13ZrRI+6u5DyXvaIiMFpXp3qSlpW0vKtz6TvaHa3Wk15KpeZmZmZLV59DG7sZ8BiRDwl6ThSZwfg2Ih4CpZYwGI3+0o6KH/+PnBW/jzUgMVTSOGI16T9ArgpIg7sUncN4NJ87ShgWkRc2aNWIw5YNDMzMxvJRkLAYicd2u6Axa61FlfAYl/r5rUtl0XEllXv8YiJmZmZmVl1zwLHSVo1Ik6rckNEXNavh0fEKf2qtbjqStoe+BZp3U9lXmNiZmZmZiNK3eT3wvkPSIriNsCStsoJ63fmdPOlezx+RaD0mk7J7z3eZWZ+l1YqfWttyej8Dg/kdxrTq1aH+u3J76+VdE3+jq6pECiJpPmF9s0oHD9b0kOFc2PzqbWB17BouGNXHjExMzMzs+HTWhRfUSH5/d2kXbB+LWlGRNzFguT36ZJOIyW/n5rvWx44FPhVodYo4LvAhyPiNkmrAC9VaMYRHaZyFZPft8vP3q5Cvf1K1rJ8HHg6IjaWNCG/2z4Vag1SefL7kcC1EXFC7tQdSVqb083zETG2w7lFvouIuFDSn4DD67TXIyZmZmZmNjwmTkw5JvU0TX4/DvgK8ELh2M7A7RFxGwxuKzy//osMqp383qPWOfnzxcBO+R0rUedE+WLd9u9oWHnExMzMzP6xTJ2aEsX/WQwMNPnj/pVh8uT00677399l6erb0SX5XdKbgPUi4jJJxf+KvykQkq4CVgOmR8RX8j1NduXqlPz+WI/7zpI0H7gEOD5vfTxYK+9E9mx+x6rrNjolv6/R2sI4Ih5rTR3rYWlJs0i7dp0QET8onJuiFAJ5LXBkIS+mNo+YmJmZ2T+WadOa5mPYyFAr+T3ngJwEfLbk/ChSzsd++d+9Je0EKfm9ZqekW9u62S8i3ghsn38+PIRa6cYFye+XVrm+gvXzLmkTgW9I2igfPwrYDHgz8Fp6TwnryiMmZmZm9o9n7NiRuX1uEzXWZ/yD6Jn8nkdNWseXB7YEZuaZUGsCM3KuyaPA9RHxJICkK4CtSf/1v59t6ygi5uR//yppGmmq2rmFWo/mtTArkjJYqigmv48CVpc0M4cs/knSWnm0ZC3g8V7FCsnvDyolyL8J+G1r5AV4UdJZ1FxT0s4jJmZmZmY2ktRKfo+IZyNi1YgYk5PQbyIllc8ihTRuJWmZ/Mf/DsBdQ2hbreR3SaNy+CKSXgXsTk5Vz7X2z58/APw0IkJDTH5vq7s/8MP8/G0lnVvSxpUljc6fVwXeRv6OWutn8tqXvQptb8QjJmZmZmY2YjRMfu9U62lJXyd1dgK4IiIuhyWW/D4auCp3SpYCfgKcns99BzhP0gOkkZIJ+fhQk99PAC6S9HHgEeCD+fj6wPMl128OfFvSy6RBjRPyDmgA50taLb/bANA49R3cMTEzMzOzESYiriB1AtqPP0iaCtXt3vFtv3+XtGVw+3UHNGhXAAeVnHoLaYvj9uv/BmzTodYLLOg09KzVpU0Pk6aytX7/M7BTyaXbdWjjDcAbO9TesWo7qvBULjMzMzMbUboELB6cj0VrilQ+3jH4UNKVkp6RVDWdfTPgAkmXd2jbUfk590p6D6Tk94g4ucc7fVPS3MLvG0i6Nrd3pqR1c61TImJG50oLvfMUSfdJulvSofn4frnm7ZJukPSvue4REXF7zTauL+k6Sbfmervl418kjWi9vVc7i9wxMTMzM7MRoxCwuCuwBbCvpC3y6V8C7wJ+13ZbMfhwMjl0MTuRBTthVXElcFREvLekbVuQply9AdgF+FZub693Gges1Hb4q6RMlK2AY4Ev1WgjwCTS4vnNImJzUt4LwEPADrnuccDUKsU6tPELwEUR8SbSe38LICL+H/B60gL+yjyVy8zMzMyGR7PMmcGARQBJ00mhgXdFxK35WPs9g8GHwE2SVmrtTBUR10oaP5TXaHvO9Jzl8VBeH7ItcGOnG3LH5UTSVrx7F05tARyWP18H/IB6PglMjIiXASLi8fzvDYVrbiLtHNZVlzYGsEL+vCI9diDrxSMmZmZmZjY8mmXOdAox7Os9ko7NWwov7rYdTNpVrD2E8Tbg/fnz3sDyklap0ZaNgH0kzZL0Y0mblFzzceDHFWp1auMxwIckPUpa83NIjfYtwiMmZmZmZjZ8yjJnuie/NwkerH1PRBzdo+aQnyNpbdIC9/Elpw8HTpE0CfgZMId6u3GNBl6IiHGS3gecSQpwbD37naSOSdd1ID3auC9wdkR8TdJbSbuIbdkapanLHRMzMzMzG0lqhxg2vKeJus95E7Ax8ECefraMpAciYuMcavg+AEnLAe+PiGdrtuWS/PlS4KzWCUlbAWcAu+Zdurrp2EZSx2YXgIi4UdLSwKpUCG0s46lcZmZmZrZkTZ2aEuvrT+OCDgGLPe7pGHzYZzOACZJGS9qQtNj+ZoC8w9ZC07oi4vKIWLMQ/vhc/oMfSatKav2tfhRpxIN8bpGwxhI/AFrb+e4A3JfvXR/4PvDhiLiveEPdNpJyUHbK924OLA08UaFtpdwxMTMzM7Mlq9naEiAFLJLWPFwF3E3aFepOAEmH5vUO6wK355BESOsfHiQFH54OfKpVT9LPge8BO0l6tLXFb5M1JrkdF5GS0a8EDoqI+bmDsTEpKLGq8cC9ku4D1gCm5HZ1CmtsdwLwfkl3kHb0auWyHA2sQtoxbEDSrFy3SRs/C3xC0m3ABcCkvMFAI57KZWZmZmZL3tixjW/tErB4MrBIXkiX4EMiYvsOx5usMSEippA7EQVbAJdERFmyevHe5QqfLwYuLrmsUsBiRDwDLLKlcQ6OLAuPbNLGu4C39WpLVe6YmJmZmZlVNxeYLGmFqp2XiJgNfKYfD4+IqkGQdev2rY2StidlmjxZ5z5P5TIzMzOzEaVL8vuGkn4l6X5JF+Y1KB1T1PO59SVdndPR75I0psfjlwOW7dK2RZLfK75Te6r6SXmq1UBOb3+maq18f6fkd0k6Obfxdklb16g5Q9Lswu8XFtr4sKTi/LylSAvhK/OIiZmZmZkNn4GBtBC+okLy+7tJO0/9WtKMPK3oy8BJETFd0mmkXaNOZUGK+jmSdiStuWilvZ8LTImIa/LuV1W2uj0pIr5a0rZi8vvawE8kbRoR83u80yKp6hFxWOH8IaTdseqYxILk95clrZ6P70palL8JsB3p+9muV7G85fDc4rGI2Kdw/mvAs/n4zyXtBtQa3fGIiZmZmZkNj4kTm6w1GUx+j4i/A9OBPZX2st2RBesyzgH2yp+3AK7Nn68jJbS3OhKjIuIagIiYGxHPNX0dCsnvEfEQabH9tt1u0IJU9f/qctm+pMXldXwSOLY9+T238dxIbgJWkrRWjzYuR5rmdXyH8wL+o0EbF+IREzMzMzNbPKZOTTtwtRsYSB2SyZPTT7vuAYtl6erbkXaaeibv2tU63tr6tpWi/j8snKK+KfCMpO8DGwI/AY7MO2kdC8yKiF5bEbe37aa2tlVOflfJe0vaILftpzXaAQuS3/cmbeF7aETcT+d0+m7bJx8HfA3o1GnbHvhTrt+YR0zMzMzMbPEYwrbAXXRKV++Wun44sIOkW0mZHq0U9VGkP6oPB94MvI40BYqIOLpmp6Rb28ovXpCq/s0uNScAF/eaDlZiMPmdtEVyKwelbhvHAhtHxKVdntVkRGcRHjExMzMzs8Vn7FiYOXPhYzXWlJTolK7+JGla0qg8ajKYut4pRT1nntwaEQ/mcz8gbcf7nT63rZNuqeotE+iw1XGFtpQlv9dt41uBbSQ9TOo7rC5pZkSMB5A0ivTdbtOgjQvxiImZmZmZjSSlye85q+Q64AP5uv2BH0LXFPVfAytLWi3/viMpHLGpviW/53teD6wM3Fi8byjJ77mNH8m7c70FeDYiHutUNyJOjYi1c/veDtzX6pRk7wLuiYhHK7SpK3dMzMzMzGzE6Jb8DnwO+IykB0hrTlojH+MpSVHP06MOB67NCekiTXt6JSS/Q5oiNb2Ypt6H5PcrgAdJC/NPBz5Vs267CfRhGhd4KpeZmZmZjTBdkt8fpGQXrC4p6uQdubYqOT6sye/592NKLhtq8ntQPjWsZ92IeBjYsu3YpF5tqcodEzMzMzOz6v5Rk9/7VtfJ72ZmZmb2T6FB8vv6kq6TdGtOO98tH3+1pLMk3SHpNknjKzx+S+BVlCwYb5KqrpREf28hQX31fHx0focH8juNqfLdVKhb+l1UqLdUvueywjGpJF2eFC75GtoCGXvxiImZmZmZLXk1E99bGia/f4G0FuXUHKp4BTAG+ARARLwx/+H+Y0lvboUSdnFEnh7WrlGqOrBfRMxqO/Zx4OmI2FjShPxu+yx6a+26nb6LXj5NWtOzQuHYJErS5SPiQkl/Iq3fqcwdEzMzMzOrplNgYietIMV2EycOpRWDye8AklrJ73eTdqFqFT8HOIbUOQgW/EG9IgtGOwYT4SPicUnPAOPIO2k1MJiqDtwkaSVJa7V2vWpQ65j8+WLgFEkqLoRvqNN30ZGkdUnrVaaw8JS0TwITS9LlG3HHxMzMzMyqaQUmlnU26uiU+N7S/+T3Y4CrJR0CLEva4hZSIvyeuXOzHimLYz3gZklnAKeVjDjUbVuvVHWAsyTNJ+WOHJ87H4O1ImKepGfzO9ZZt1FW9xjKv4tuvgH8F7B82/FO6fKNuGNiZmZmZtWVBSZ2MrQgxU6aJL/vC5wdEV+T9FbgPElbkvJMNgdmAb8DbiAlwhMRB5TUa9q2bvaLiDmSlid1ID4MnNuwVpW6pd9Fp+lrknYHHo+IW0rW4Aymy0t6H+n73L5GGxfixe9mZmZmNpL0TH5vOw5pvcZFABFxI7A0sGpEzIuIwyJibETsCawENP4v/l3a1lFEzMn//hWYxoLtjgdr5XdakRo5KF3qln4XXUq9DdgjJ79PB3aU9N1CG4vp8otsu1yHOyZmZmZmNpLUTn4HHgF2ApC0OemP8SckLSNp2Xz83cC8vIi+qVqp6pJG5WBDJL0K2B2YXai1f/78AeCnERGS1pF0bbdG9Kjb6bsorRsRR0XEujn5fUJux4fy6U7p8o14KpeZmZmZjRh5vUUr+X0p4My25Pfpko4HbmVB8vtngdMlHUaaDjUp/5G/OnCVpJeBOaTpTgA0XGNyBbAbKVX9OeCjuVanVPXR+fmvyu/yE3LyfG77eUop9k+ROgUAa5Gnm3XRrW6n76JK3XYnAOfnWnNZkC7fiDsmZmZmZjaiNEh+v4s0Jan9+MPA6zs8o/Yf2XVT1SPib6QF92W1XgA+WLVWjbql30XFujOBmYXfS9Plm/JULjMzMzMbUdQ5YPHgfCxaU5ny8c0k3SjpRUmHt9U6TNKdkmZLukDS0j0evxlwgaTLO7TtqNyGeyW9B1KqekSc3OH6syU9VAhCHNutzRFxSkTM6PUd5RqH5HbcKekr+dh+hWcNSHpZ0tgqdSUdXvxuJe2pFNI4IGmWpLfn44cCVwLvqdLOFo+YmJmZmdni0zBIsZMeAYu/BC6j8F/1s6eAQ4G92mqtk49vERHPS7qINGXq7C5NuBK4OCK+WtK2LfL9byCln/9E0qYRMb/Ha5UFNpa2uSpJ7yRloWwVES8Wwg/PB87P17wR+GFEDFSotx7pO3+kcPha8voeSVuRFtVvljthJ0uqlfzuERMzMzMzWzwmThx65smiBgMWI+LvpJ2i9gSIiFvz9KyFRMTjEfFr4KWSeqOA1+Sdr5ahQuBgF3sC0yPixYh4iLTWZJGpZVX0aHMVnwROiIgXW/VKrtkXuKBivZNIWSaDWxZHxNxC4OOy1NvOeBEeMTEzMzOzxaNXkGInzQIWa8s5H18ljQI8D1wdEVenJuhYYFbVaVOFtt3U1rZ1OlxbNEXS0aQRiCNbnYkh2hTYXtIU4AXg8NzRKdqH3KnrRtIewJyIuE1t/9vkcMUvAaszxPUm7piYmZmZjXR9ni7V9Tn9HwGpa6jBgwsKSSuT/jDfEHgG+J6kD0XEdyPi6CXUtqOAPwKvBqaSdhY7tsGz240CViYtan8zcJGk17VGOCRtBzwXEbO71EDSMsDngZ3LzkfEpcClkt4BHEe1JPlSnsplZmZmNpItnulSr2S1Qwy7eBfwUEQ8EREvAd8H/m1Jti0iHovkReAsGk796tCW7+faNwMvs3CQ4gSqTePaiNRxuy2HLK4L/EbSmsWLIuJnwEbFTQfq8oiJmZmZ2UjWdLpUE0tiVKa3wYBFUvbIBGBiw1qPAG/JowLPk4IH6+SWtJsBTJP0ddLi902AmwFyeOFHWonsLZLWiojHlOZI7cWCIMSOOtVq0wo/nClpU9KIzJP5/n8hbUX8jra65wKn5I4MABFxB2maVuuah4FxEfGkpI2B3+bF71vnZ/y5V/s7ccfEzMzMzEaMbgGLeZva/wLWBG6XdEVEHJD/6/4sYAXgZUn/h7QT168kXQz8hhQueCtpOlWjNSYRcWfe2euuXO+giJifOwIbk3baane+pNVI08AGgAPz80vbTAoy7FSr6EzgTEmzgb8D+xcWqr8DeDTnvhRtBTxW9X2B95OS7l8idez2KTyjNtW5d9y4cTFr1lA6kWZmZmaLWeu/6s+cOZyt+Me0hL5bSbdExLjF+pCGJB0DzC3bLrjLPVsCH4uIz/Th+X2r1VZ3BeA7EVEW6ti05tyIWK7q9V5jYmZmZmZW3Vxgch5RqSQiZverI9HPWm11/9KvTomkjSQNAH+qc587JmZmZmY2ovQ5+f3TOfX9zjxdqpcnSbtdrd2hbftLuj//7F/hXY6RNKeQxL5b4dwiKfJ11El+b9LGLinyvwWeBtaQVHnky2tMzMzMzGz4TJ0K06ZVvrzPye9bAp8g7YT1d+BKSZdHxP09mnFhRBxc0rbXAl8ExpG2Cb4lt+3pHvVOap8aNoQU+db9fU1+L2tjt1oR8U5JM6u0tcUjJmZmZmY2fKZNS/ko1fUz+X1z4KaIeC4i5gHXA3s3eIuW9wDXRMRTuTNyDbBLw1pDTZHvd/J7L0Ou5Y6JmZmZmQ2vsWPTgvrWT3dlye9V0tXLzAbeIWmVvGXwbuQcEkkHSjqwZr2mbTtY0u2Szsyhj0Op1dJKfv+VpOslvbnkmn2o3pkoa2PTWqXcMTEzMzOzkaRvye8RcTfwZdLIxpXAbaRtfomI0yLitCXQtlNJIYZjSVv1fm0ItYqKye9HkJLfB2tWTX7v0cYmtbo22MzMzMysmoGB/gYtDgzUTa7vZ/I7EfEd4DsAkv6/XL+pR4HxbW2b2eP5gztXSTqdtEamVWso7zmY/A7cLKmV/P5EPl81+b1bG1sq1+rGIyZmZmZmVs3EiXU7EYvDYPK7pFeT/iiuHILYrrUoXNL6wPsY2h/YVwE7S1o5T3faOR9D0rmSFlkjImmtwq97syD5fQYwQdJopZT7hVLkJfWa1tVKfkedk9+nt7Wlbhs71mrCIyZmZmZmVs3kyemnn2qOvvQ5+f0vwCWSViEtjD+otYNWa31JnelcEfGUpONInSeAYyOildDeKVX9K3m73gAeBv4z12qSIl/Uz+T30jb2qFWbOyZmZmZmNqJExBXAFSXHTwZOLjn+R9JUqLJa23c4Xnd9Seu+M0mdgkE5Vf3+iPh9yfUf7lJrCjCl7fAWwCUR8XyPdvwd+FCHczNJa0/60cZFajXlqVxmZmZmNqL0K2BR0uvbAgL/UiFkcVvgk5Lu6dC2RQIWq6SqSzq82O48HezSvBPWzTlzpVbye4eAxTGSni+882m92tglYPHVks6SdIek2ySNL9zzNGk0ZZMqbQWPmJiZmZnZcKuxoL6fAYsRcS9pp6lW3TnApT2acDPwcj8DFiWtl9/nkcLh/wYGImJvSZvld96pR9uKNUsDFrPfRkTdxUKLBCySwimJiDfm+j+W9OaIeDkiVs4Bi73CKgd5xMTMzMzMhk/9BfX9DFgs2on0B/vv6jSmTdOAxZNIa2OK2wFvAVwLEBH3AGMkrVGjLVUCFoeq2MbHgWdInbJGPGJiZmb/nKZOTYnT9o+n/vazNpzKFtSrLMJjUFnw4HZ9aMlCW942WfzeoW1dd8+StAcwJyJu08LvfRtpl7Bf5J2yNiCtk/nTolVKtQIWpwAvAIfnzhnAhpJuBf4CfCEifl6h3sGSPkLaROCzueN1G7CnpOmkrY23yf/eXLGNC/GIiZmZ/XOaNi39AWtmI03fAhYHC6Zth/cAvjdYcAkELCqlzX8eOLrk9AnAypIGgEOAW8nhjxV1Clh8DFg/It4EfAaYlhe+d9MpYPFMUudrFvAN4IaabVykwWZmZv+cxo6FmTOHuxXWb/0M/7NXor4GLGa7Ar8pBgk2VDdgcSNgQ6A1WrIu8BtJ2+adxD4KkDsUD+WfOm1ZJGAxIp4AWtO7bpH0W9LoyqxOhToFLEbEPOCwwrkbqLGmpJ1HTMzMzMxsJOlrwGK2L31ILqdmwGJE3BERq0fEmIgYQ+pMbB0Rf5S0Un4/gAOAn+XclSEFLEpaLS/0R9LrSLtmPdipjfl4acCipGUkLZs/vxuYlzchaMQjJmZmZmY2YvQ7YDFPp3o3C4cGLsmAxU42B86VNJ8Usvjx3K4hBSxKegdwrKR5wHzgwKYhkMDqwFV5NGYO0DHvpAp3TMzMzMxsROlzwOJzwColx5dIwGLbvWMKn2+kPANkSAGLEXEJcEn78SYBi3kHtNd3a0cdnsplZmZmZlbd88Cuks6oekOVgMUatSoHLNas27c2Aki6Dngd3bdoXog7JmZmZmY2onRJft9Q0q9y6vqFhTUaSPoPSXflFPRp+dg7tXDy+wuS9ip7ZsHmwNJ0WHAv6ajcrnslvafCu0jSFEn3Sbo7T0dD0oqSfpQT1e+U9NEKX01Z/W9Kmlv4fX1J10m6VSlVfreKdZbK91xWOLaTpN/k7+4XkjbOxw8jLeyfERG3VW2rp3KZmZmZ2fBokCek7snvXyYllE+XdBppXcapkjYBjgLeFhFPt1LQI+I6FiS/vxZ4ALi6QjPKUtCRtAVpMf4bgLWBn0jaNCLmd6k1ibTL2GYR8bIWJLQfBNwVEf8uaTXgXknn5ylalUgaB6zUdvgLwEURcWpu7xXAmArlPg3cTVqn03IqsGdE3C3pU7n2pIg4SdLT1Axb9IiJmZmZmQ2PZnlCpcnveUvdHYGL83XnAK3Rj08A/5tDATuloH8A+HFec9LUnsD0iHgxIh4idXQW2eWqzSdJi+RfbmtbAMvn91qOtNi9ckZI7sCdSNoMoChY0LlYkQpbLUtaF3gv0D59rXatbjxiYmZmZmbDpyxPqFny+yrAMzlbo3W8taXupqmsfknayeuYiLiyre4E4OsLmqBjgVkRUWcr4nWAm9ra1mtb342AfSTtDTwBHBoR9wOnkLZB/gOwPLBPq/NS0cGkqVSPaeHv8xjgakmHAMsC76pQ6xukDs7ybccPAK6Q9DwpRf4tNdq3CI+YmJmZmdlI0ildvVvq+ijSDlfjSZklZ0ganOKUczreSM4cAYiIo2t2Srq1rZvRwAsRMQ44nQU7er0HGCBNCRsLnKLeCe2pEdLawAeBb5ac3hc4OyLWBXYDzstbEHeqtTvweETcUnL6MGC3XOssCh27JjxiYmZmZma9NVgP0tPAQBoxqadT8vuTwEqSRuVRk2Ii/KPATRHxEvCQpHtJHZVW3sh/AJfm80PRJJX+URZs33sp6Q98SKnvJ+Tk9gckPQRsBtxcoR1vImWdPJBHS5aR9EBEbExad7MLpC2JJS0NrAqUTW8DeBuwR14kvzSwgqTvkjol/xoRv8rXXQi0j0LV4hETMzMzM+ut2XqQxaE0+T3/AX8daa0IwP7AD/PnHwDvBJC0Kmlq14OFmv1Kfp8BTJA0WtKGpM7Pzfm5ndLaBxPagR2A+/LnR4Cd8r1rkPJCWgnt93RrRERcHhFrFhLln8udkva6rR3GnpC0jqRrS2odFRHr5joTgJ9GxIeAp4EVlVLlIW1GcHe3dvXiERMzMzMzq6ZsPchQjB9f+5Zuye/A54Dpko4HbgW+k49fBews6S5S2vkREfFnAEljSKMc1xef02SNSUTcKekiUlL7POCgiJjfI639BOD8vMXuXNK6DYDjgLMl3UGaIva5iHgyd6y6LsLp4bPA6fl5QdpFK/J0tsqL6/P/Dp8ALsnJ708DHxtCu9wxMTMzM7ORpUvy+4OU7IKVR1M+k3/azz1MyQL1iDi6YdumAFPaDndMa4+IZ0g7XrUf/wOwc8kj3kLaLrlOm5YrfL6LND2rdt2ImAnMLPx+KWn6WV94KpeZmZmZjSh9DFgcK+nGfOx2SftUePy/AV+SdH3ZybKAxW5p7V0CFo/QguDH2ZLmS3ptRFwWESfX+K4OlxR5pKVb3VM6jQ5JOlvSQ4X7Wtkv++Xv7XZJN0j613z8v4DTgE+2nluFR0zMzMzMbPgMDNSa0tXPgEXgOeAjEXF/3snqFklX5VGMTm4Arl7cAYsRcSIphwRJ/w4cFhFlU8E6krQe6Xt6pHVsCHWPiIiL2449BOyQv9NdganAdhHxFeArkh6u016PmJiZmZnZ8Jg4scmuXH0LWIyI+3JmSGvq1OPAakN4o34GLBY1XZx/Eil/pNOWxUNa9B8RN7S+U1J+y7pNa4FHTMzMzMxsuEyenH7aDUPAoqRtgVcDv82/D3fAYqtdy5C29z24RjuQtAcwJyJuU8n32aDuFElHA9cCR0bEi23nPw78uE4b27ljYmZmZvbPpkkmSbPMkcVhqAGL6wI/l7Rla8pW3pHqPGD/wshFk8XvQwpYlPQ+UsDi9oXz/2MN7C8AACAASURBVA78ss40rtzp+Dzli+eb1D0K+COp4zaVtPvZsYXnvZPUMXl71TaW8VQuMzMzs382r5xMkiZ6Biy2HW/d88OIeClPsWoFLKKUpn458IWIKI529LNtve4pBixu1XZ+AvWnW20EbAjcltd5rAv8RtKaTepGxGORvEgKgBycniZpK+AMYM/WFsxNecTEzMzM7J9R3UySBpkji8lgwCIwh/QH9sScxdEKWJzOogGL+5JyQQYDFvOuXZcC50bE9/rQthnANElfJy1+XyhgkbTQfk7bPa2AxTNZOGARSSvmYx8q3tClFgARcQeweuH6h4FxEfFkk7qS1oqIx/I6nr2A2fn4+sD3gQ9HxH0MkTsmZmZmZjZi9DNgUdKHgHcAq0ialK+dFBEDr4CARYC9STuA/a11oEetqurWPV/SaqSpagPAgfn40aS1Pd/K61jmRcS4po1yx8TMzMzMRpR+BSxGxHeB73Z4xrAGLOZzZwNnV63VpU1jhlI3InbsUPcAFu5IDYnXmJiZmZmZVTcXmJxHVCrpFrBYVz9rLa66kl4jaQB4FfBy1fv+f/buPf7zuc7//+1ewyjHigqlEcmWtZOkdkvNUkhFYqPpQGWtlrRE5VfbinyzOqh+ii9yKtOQ0k5ySDJb+qEl4yxUVtQ6H3YqFPffH8/ne+Y173mfP+/PjI/u18vlc5nP+3V4vh7v16Uul8/T83BPxyQiIiIippRRkt/r+V1qCvrmjWNLJbX38WbKTluXd6hren3uLbWOGQN8lzUknSnpxpr8/rf1+CGS7mikrW8/QG3Nds+TdJVKqv2xNZiylXZ/aW3z8rpNcq92XiDpinr9dZL2bpybX99bq8bWupa9gWcCZw2zm1imckVERETEYIZMaZ8MoyS/1/tWBfYDLmu0NUpSO8A7bS/VManPu9/2hpJ2q/Xs2qetLwHn2d6ldqSe3jh3VKeE+QG93fZDdcH6mcA/UDYFOBL4lO1za2fnSMo2yt38Dvg7249IWgW4tr7v1m5jS70L20dJuh8Yar1JRkwiIiIior/RUtonwyjJ7wCHUf4If7hxbJSk9l52rM+l1rG1OqUbVnWr4tdSF+nbfrSVrTJRth+qv06j5I+08lQMrFZ/X50+2xnXmlphitOZxP5DRkwiIiIior9uKe2TYczJ75JeBjzf9tmSDmxrq2NSu6RzgD0bIwODWFRb3T3swVrXPV2ufyEl7f0kSX8DXAF8qLFb1r6S3kOZNvZh2/cPUQuSzqd0tM5lcYftX4DzJX2O0sn4uwHaeT4l62VDyo5mzXdykqTHKFksn64bDYwkIyYRERERMZUMlfxet8E9CvjwEG1he/shOyU92+tiGrAZcIztlwG/B1prZo6hBCXOpEyn+vyQtWB7W2BtykhHa2etDwD7234+sD+Lt1Tu1c5vbG9K6ZjsLuk59dQ7bf81Jal+S+Ddw9bYlI5JREREREwlwya/rwpsAsyvQYOvAubVBfCjJLUPVFutY3V6543cDtxuu7Xu5UxKRwXbd9p+zPbjwPGMOMXM9sOU4Mcd66HdKaGIAN8apt3aUbuO0gmhFcRo+3+BOaPW2JKOSURERERMJYuS3+ti8d2AeXUKUSv5HWryu+0Hba9pe0bN87gU2KEu2J4H7FZ301qfRlL7iObV51Lr+FFNpF+3pqovwfb/AL+R9OJ6aGtKOCOS1m5cuhOL09Y7ttUkaZXW/bWDtD1wYz39W0rqO5RRlJvrdVtIOrVDW8+T9LT6+zOAVwO/kDRN0pr1+AqU3cqu7VVXP1ljEhERERFTxojJ793a6pjUDiOvMfka8HVJt1BGSnarx9eu7XfyQUqy+orAr4D31uNHSppJmQp2K/BPA7TVsjJlVGg65R39CDi2nvtH4Eu1w/Iw0Fo4tB7QKbTxr4DPS2pNl/uc7WskrUxZq7JCfcYPKSM7I0vHJCIiIuLJ4LjjYM6cwa5dsOCJssPWSIZNfm+7Zlbb505J7dgeKjek3vMwZVvedq+ibHHc6Z4FdNhW13a39Rpd22rceyfwii7nLgZe3uHUKzu1a/sCYNMOx3/fpZ2RZSpXRERExJPBnDmlw/EXoEfA4r71mFvTjOpxSfpyPXe1pM0a59aT9IMabnj9AKGIzwEukTS3S21LBTbaPtr2vC7XS9Lhkm6qNezXODerEWz4n/3a6tD2B2sd10k6sh7bohGIeJWknWq7B9m+uks7J0v6deO+mY3al3qvdcTqWBaPxgwkIyYRERERTxYzZ8L8+f2vW14hicOM6nSh3gGLPwXOBua33fZGyvqRF1FGBo6p/wKcChxu+4IaIPh4nxJOBxZ2Cj7UaIGNe1AWzG9s+3HV9HRJawBfBbazfZsWp6oPRNLfUxa8b1rDEVv3XwtsXqfErQ1cJel7jW2WuznI9pltxzq+V9ufAD4haeEwNWfEJCIiIiKWjfGM6nQMWASwfaXtWzvcsyNwqotLKbt3rV07EtPqdCVsL7T9hwnUNkpg4weAQ+vuW9i+qx6fDXzH9m1txwf1AeCIVjhi637bf2h0Qlai93bG/XR8r6M2lhGTiIiIiFh2BhnVGS1gcdh71qVsD/yApO8A61MWcH/M9mOSDgUuH3TaVOM5HQMbe9gA2LVOqbob2M/2zcBGwAqS5lO2PP6S7aV2zephI2BLSYdTFrkfaPu/ACS9EjgReAHw7gFGSwAOl/RJ4ELKO3qE7u/1d0PUuUhGTCIiIiJiKhk2xLDXPdMomRwHUhaLv5AytQrbnxyyUzJqbdOBh21vTtnV6sR6fBplcfmbgG2Bf5W00RC1TAOeQVksfxBwhlR6fLYvs/1Sync+WNJKfdo6GNi4Xv9Myu5nMNr37Sodk4iIiIiYSkYJRex2z+3AlXVa2J+B71IDDpdxbd+uv5/F4h2wbgfOs/172/cAPwb+ZshavlOnWf2MsnZmzeYFtm+gpM1v0qsh27+r7TwCnMTi6WljDahMxyQiIiIiJu6448qi+l4/49k1rGPAYp975gHvqbtIvQp40PbvalvPkLRWvW4rasDhiLoGNkq6UFKnaV3frc+FEnx4U/39PyhTsaZJejplutoNfdrq2G4daVkRuKe+t2n1+AuAF1NyUrq2q8VhjQLeyuIgxW7vdSRZYxIRERERE9da2D7J+Si9AhbrVrsfAZ4LXC3pHNt7UjJPtqcsRv8DNcSwriU5ELiw/tF9BTUkcJQ1Jt0CGyU9BdiQErrY7ghKwOL+wEJgz9rWDZLOA66mjHacYPvaPm01nQicKOla4FFg95pC/xrgY5L+VNv9Z9v39Gn3tNp5E7AA2Lse7/heR5WOSURERESMR7+F7WPaprhHwOKXgS93OG5gny5tdQsQ/OSItXUKbHwJ8G3bSyWr236Aso6kU1ufBT47aFtt9z4KvKvD8a8DX+9wS68at+pwfc/3OopM5YqIiIiIGNxCYK86ojIQ29faPmAcDx9nW5PVrqQNJC0A7hzmvnRMIiIiImJKUffk9/UlXSbpZkmn1zUoSHpBXT9xtaT5kp7XuGf3ev3NknYf4PGbACvQYZF3tyT0Pt9lRUnHqSS/3yhp53p8PUkXSbqytrX9ALU1251f31Errb0V3Nj1XYzQ1h6S7m4c37Pt1ucMU3OmckVERETEsrNgwYSmdKl38vu/A0fZnivpWOD9lDTyz1GCAE+RtBXwGeDdkp4J/BuwOWWb2ytqW/f3KaNTCjr0Tpjv5uPAXbY3qus8nlmPfwI4w/YxKkGQ5wAz+rTV7p22L2871vFdjNgWwOm2920esP1LYKaS/B4RERERT0izZ49jcXzH5Pe6eH0roNVhOIWygxSU9RMX1t8voibFU/JBLrB9X+2MXABsN4HaRklCfx+lc4Dtx+vWwFA6SqvV31dnAtvwtun2Lpa7jJhERERE/CWa4MhFx/b6dTr22qv89DNa8vuzgAcaKebN1PWrgJ2BLwE7AatKelaXttYtJegE4NguowTD1NY1CV3SGvXXwyTNAn4J7Gv7TuAQ4AeSPgisDLx+iDpaTpL0GCUn5dN1sXrHd2H73hHaAthZ0msp2xzvb/s3XVvoIyMmEREREX9pxjNysbx0SxvvlUJ+IPA6SVdSskLuoGzn2/Ue23sO2SnpVVs30yihhD+1vRlwCWWqFcA7gJNtP4+yJe/X61SvQb3T9l9Tku23ZPF0rW7vYpS2vgfMsL0p8EPKKNXIMmISERER8Zdm0JGLYYxz9KW3bmnj91CmTk2royaLUsht/xZ4G4CkVYCdbT8o6XZgVltb8yehtm7upeR/nFU/f4uyLob673a1/kskrURJbr9rkEJs31H//V9JcyhT4E7t9i5GbKs5ynI8ZY3PyDJiEhERERFTScfk9zq16CJgl3rd7pT0dCSt2RhtOJgSPgglpHEbSc+Q9Axgm3psVF2T0CXd2H5xrfl7LO4cbc3i5Pnb6mck/RWwEnC3pHUlXUgPKmnxa9bfVwDeTE1r7/EuOtbYp63m+pkdqMn0o8qISURERERMGb2S34GPAnMlfRq4EvhaPT4L+IwkAz+mhgLavk/SYZTODsChtu+DkdeYdExCr3/Yd1s481HKNK0vAnezOD39w8DxKonwBvaoye1r03/q1XTg/NqReCplmtXx9dwsOryLHjX2ams/STvUeu4D9uhTV0/pmERERETElNIj+f1XlGlG7cfPZPFuXe3nTqQxatA43p7JMUhd3ZLQX0XZ4rjTPf8NvLbD8euBVw/TVuPe3wMv73Ku27vo2G6ftg6mjLqMRaZyRURERMSUou4Bi/vWY25NP6rHN5Z0iaRHJB3Y1tYaks6s4YY3SPrbPo9fhzIqc3GX2pYKbLR9tu0v9/g+H6zf5zpJR9ZjWzSCC6+StFNt62jb8/q9ox7tzpD0x0bbxw5SY733wOa7lbSjSlDjAkmXS3pNPf73kv4IPH2QOlsyYhIRERER4zHuLYg76BOw+FPgbJZewH4fsB+Lc02avgScZ3uXumal3x/Tc4Cb2kMFa21DBzZK+ntKlsimth9RTVWnrOPYvE5dWxu4StL3Gtsh99SjXYBf2h5qWzZJz6e889sahy+kru+RtClwBrCx7YuApw0bsJiOSURERDz5LIM/kJ9wBskRmUyzZy+rJy0KWASQNJfyB/j1tq+sx5a4wfZdwF2S3tQ8Lmk1yjSqPep1jwKPTqC2RYGNtf1WYOM3e9zzAeAI2480asX2HxrXrETvbYcHbncCjgI+Qt1QoLbZ7HisPEKNS0jHJCIiIp5clt0fyNE0zi2IRwtYHMULKQvOT5L0N8AVwIds/17S3gC2jx2iva6BjT1sBGwp6XDgYeBA2/8FIOmVlPUvLwDePehoSb92gfVrjslDwCds/6RXQ3WB+x22r2rv9NUpZp8Bng28qcPtA0vHJCIiIp5cJiOjYyr4yxkhGjbEsJdpwGbAB21fJulLwMeAfx2yQzKR2qYBz6AsPn8FcIakF7q4DHhp3S74FEnn2n54wFo6tktJoV/P9r2SXg58V9JLbT/U8QtJTwc+TtlKeekvZ58FnKWS/n4YoyXUA1n8HhERERFTy7Ahhv3aur12AKDsVrXZMq7tduA7tSPyM+BxSpDiIrZvAH4PbDJkLUu1a/uRVjCi7SuAX1JGV7rZAFifssbl1vqdfi7puW01/hjYoLnpwLDSMYmIiIiIqaRjwOIoDdn+H+A3kl5cDzUDDkfRNbBR0qmSltrKGPgusFW9ZiNgReCe+v2m1eMvAF4M3Fo/Xyip3xSxbu2uVTcQoI6gvAhorddZqkbb19h+tu0ZtmdQOjyb2f4fSRuqzu2StFl9RjMNfiiZyhURERERU0avgEVJ+1EWaD8XuFrSObb3rP91/3JgNeBxSf8CvKROX/ogcFrt5PyKxaGIQ68x6RXYCGxKmUbV7kTgREnXUhbe7153uXoN8DFJf6KMdvyz7XtUUts3pOw01ku3dl8LHCrpz8BjwN4D1NjNzpSk+z8BfwR2rVkuI9Ew926++ea+/PJhwi8jIiKeoFrz8efPX55VRIzPk+h/05KusL358q6jE0l7ULbxXWq74B73rAZ8zfY/jOH5mwDvs33ARNtqa3dsNTbaXGh7lUGvz1SuiIiIiIjB/RF4o6QTBr3B9kPj+oPf9rXj7pTUdsdWo6QNJC0A7hzmvnRMIiIiImJKUffk9/UlXaaSun56nZ6FpPUkXSTpyppUvn3jnk1VUuGvk3SNpJX6PP6NlL+hl5pGpOLLta6r67qLft9lfv0urST2Z9fje0i6u3F8zwFfT6vdkyX9unH/zHr8oMaxayU9phIMOUpbq0v6nkoy/XWS3tt263OGqTlrTCIiIiJi8hx3HMyZM7bm1Dv5/d+Bo2zPlXQs8H7gGOATwBm2j5H0EuAcYEZdXP4NSkbIVZKeBfxpgDIOsn1mh+NvpCwmfxElW+UYBstYeaftTuslTh9mylgHS9Vp+7PAZwEkvQXYv7HGZKi2gH0owZZvkbQW8AtJp9n+JTBTQya/Z8QkIiIiIibPnDkllX58FiW/16T2ucCOdXeorShb/gKcAry1/m7KwneA1Vm8he82wNW2rwKwfa/txyZQ247AqXWL3kuBNSStPYH2Jts76J1K34+BVeu7X4WyIH+YEMglZMQkIiIiIibXzJnDLcofLfn9WcADjXT0Zur6IcAPJH0QWJnFIYAbAZZ0PrAWMNf2kaUEnQAc22UkY5ja1qX/TlcnSXoM+Dbw6cbOVjvXXbRuooxs/KZrC50dLumTwIXAx2w/0jqhEpy4HTDoiEynto6mbNX8W2BVyq5cjw9Z4yIZMYmIiIiIqaRbunqv1PV3ACfbfh6wPfD1uu3uNOA1wDvrvztJ2hrA9p5Ddkp61dbLO23/NbBl/Xl3Pf49YIbtTYEfUkaAhnEwsDEl9f2ZwEfbzr8F+OmA07i6tbUtsABYB5gJHF139xpJRkwiIiIiYjSDrB9ZsKCMmIxPt3T1eyhTp6bVUZNm6vr7KaMD2L6kLnBfs7b1n7bvAZB0DiX5/cIx19aV7Tvqv/8raQ5lqtqprXT26njK+pmB2W6N0jwi6STgwLZLdmPAaVw92novcEQd4blF0q8pHZifDVNrS0ZMIiIiImI0418/MoiOye/1j+OLgF3qdbsD/1F/v42S6o6kvwJWAu6mhDRuKunpdSH865hY8vs8SuCgJL0KeLD1R72kG9svljRN0pr19xWANwPX1s/NtSk7ADc07luqrQ5tr13/FWWtzbWNc6tTvut/tN3TMVG+R1vN9/ocSjr9r/rV1k1GTCIiIiJidP3Wj7SCH8ekV/I7ZYrRXEmfBq4EvlaPfxg4XtL+lKlVe9SOzP2SvkDp7Bg4x/b3YeQ1JudQpordAvyBxSnya9J5mtd04PzaKXkqZcrW8fXcfpJ2oCwmvw/Yo09b7U6rO2WJMt1q78a5nYAf2P5964B6J8p3a+sw4GRJ19RzH22NPo0iHZOIiIiImFJsn0PpBLQf/xVlKlT78euBV3dp6xuULYPbjw+VG1LvMWUL3Xavomxx3H7974GXd2nrYMrajoHa6nD/Vj3OnQyc3Hb4JcC3bf9x0LZs/5ays9lYZCpXREREREwpGjJgsZ57u6TraxDgnHrsBZKuqKGB10nau9Pz2mwBzJF0Woe6pA4Bi7bPtv3lLt+lY8Bi4/wukixp835tdWl/nqTmNK7TG8+6VSWhvWeifL3umnrP5Y3jf6MSTnlNDVpcrR7fVdLDwAqD1gkZMYmIiIiIybZgwdimdI0SsCjpRZTRh1fbvr/xx//vgL+z/YikVYBra1u9Fqz/DPjksghYlLQqsB9w2QBtLEXS24AlQg5t79o4/3ngwQGb+/sO07ROAA60/Z+S3gccBPyr7dMlXQacPUy9GTGJiIiIiMkze/a4d+UaJWDxH4Gv2L4fwPZd9d9HG9ke05n438bjDlg8DDgSeHjYG2tH6wDg013OC3g7EwtYfDHw4/r7BcDOE2grIyYRERERMYn22qv8DGP8AYsblWb1U8oi80Nsn1ePPR/4PmXh90Gt0ZIRF7+PLWBR0suA59s+W1L7Vr+DOAz4PGURfidbAnfavnmAtkwJqDTwf20fV49fS9kx7D+Af2DJrZKHlhGTiIiIiJhKRglYnEaZXjWLErZ4gqQ1AGz/poYYbgjsXre9Xa4Bi3WHrKMou4kNTdJMYEPbZ/W47B0MPlryatubUaaq7aOSRg/wvvr5Ckry+6Oj1NuSjklERERETCV9Axbbjrfu+Q/bf7L9a+AXlI7KInWk5DpK52DctXXVDFgEWgGLqwKbAPMl3UrZiWteawH8AP4WeHm992JgI0nzWyfrO3obcPogjbVGkeoUuLNqjdi+0fY2tl9O6eT8csD6OspUroiIiIgnizEuMh/4eeNdPzKIRQGLwB2UgMXZdfpTK2BxLksGLH6XMkJwcs0B2Qj4laTnAffa/qOkZ1C2FP7CBGqbB+wraS5letkSAYu2N25eXDsIa9i+pxGw+EPbD1KS6VvXzacsMr+8BiCeanvrbkXYPoay8B5JM4Czbc9qXPJ64Ebbtzee0bFdSSsDT6nJ9CtTtgc+tJ57tu276gjPJ4BjB3xPHaVjEhEREfFkMHv28q5gmRgxYPF8YBtJ1wOPUdaS3CvpDcDn69oJAZ+zfQ08IQIWu1mbEro4Ebux9DSubu0+BzirrJVnGjCntT4HeIekVm7Ld4CTJlKUSg7MYDbffHNffvmwU+0iIiKegFr/VblXYnVE9DZJ/z+SdIXtQactLVOSTqaMQHTaLrjbPW8GXjhM/kiPtvYFbrM9b6JtTWa7jZGaTQa9JyMmERERERGDexA4TNKatgeaumR7qDyPPm0dPa62JqtdSVsCX6Ws+xlYFr9HRERExJQybPK7pKMaaec3SXqgHh8l+X11YKUudXVMfu/zXVaUdFyt60ZJO9fj0+t3uKV+pxkD1NZst2Oi/Cjtqkvyez33wfqc6yQd2Tj1VBrrZAaREZOIiIiIWHaOOw7mzBn5do2Q/G57/8b9HwReVj+OkvwOZY3KuJLfPw7cZXujuoj8mfX4+4H7bW8oabf63Xbt1kgXnRLlR213qeR3SX9PCZXctL7DZwPY/omk7Unye0REREQ8Yc2ZU3bzGt0oye9Ni/I7niDJ7+8DPlPrebzxx/+O9TtA+U5b1+84UeNs9wPAEa13WLcTHllGTCIiIiJi2Zo5s/eC+fEnv9dm9QJgfeBHjWPLLfm9FfJIWbMyi5IDsq/tO5tt1Z3IHqzfcZh1G0slyo/Ybrfk942ALSUdDjxM2dL4v4aobwkZMYmIiIiIqWSU5PeW3YAzbT+26ILlm/w+jRLC+NOarH4J8LkR22q3VKL8BNrtlvw+DXgGJQDyIOCMiYzqpGMSEREREVPJKMnvLZ3yO4Dllvx+LyXv5Kz6+VtAa8H8orbqd1oduG/QQrokyo/Ubrfk99rWd+rUtZ8BjzPkgvemTOWKiIiIiNENmzY/8bT4UZLfkfRiyn/dv6RxbLkmv9eavwfMokwv2xq4vtHW7rXeXYAf1ev7Jr+rS6L8KO32Sn4HvktZ1zNf0kbAigy5RXBTOiYRERERMZrlkDY/YvI7lEXvc71kuvhfsXyT31s1f13SF4G7W/fU2r8u6RbKiMZu9fggye+9EuWHbbdX8vuJwImSrgUeBXZve79DSfJ7REwdE9xiMmIJrf9qm+T3iGVrgLT4JL/3bOtJm/yeNSYRMXVMfIvJiIh4EhhjwOJMSZfUcMCrJQ2S57Ex8E1J3+9S28G1rl9I2hZK8nu3TknJZNThta4bJO3XOL5UWKPtowfpPEg6WdKvG9975kTblTSvjo60Pv9NfX/XSPqepNXq8X2AGyk7oA0sU7kiYmrJf+GOcRlmTnxEjNew61IaNN6AxT8A77F9s6R1gCsknW/7gR4lnEfZ2etz7SckvYQyNeqlwDrADyVt1NwFrIM9KIvRN7b9eCukkNHCGtt1CoIcqV1JbwMWth0+gbJF8H9Keh9lZ65/tf2V2nFLwGJEREREPEHNnj3Rxe/jDFi8yfbN9fffAncBa02gth0p61gesf1rylqTLfrc8wHgUNuP1zpaIYWjhDUOWuNQ7UpaBTgA+HTbqRcDP66/XwDsPJHCMmISEREREcvOXnuVn16WYcBi49wWlF2lflk/HwpcPuSai3WBS9tqW7fLtS0bALtK2omy+H2/2lkaKqyxi8MlfRK4EPhYTWgfpd3DgM9TRpiargV2oOx+9g8suVXy0DJiEhERERFTyVgDFgHqiMHXgfc2Ri4+OcJC8FHCC6cDD9fF/sdTdroata2mgynrYV4BPJOy+9fQ7da1KRvaPqvD6fdRAhevAFal7Mw1snRMIiIiImIqGWvAYl2w/X3gE3Vq02TU1u+eb9ffzwI2nUBbi9j+XZ2u9QhwEh0CFgds92+Bl0u6FbgY2EjS/PqMG21vY/vllPf6y0Hr6yQdk4iIiIiYShYFLNZdt3YD5tX8jFbAIgwWsLgipTNwqu1vjaG2ecBukqarBEC+CPhZfdaFNcSwXSukEOB1wE2Ntt5Td9F6FW1hjf0Kaa0bqWtv3kqZdjV0u7aPsb2O7RnAa4CbbM+q1z+7/vsU4BPAsf3q6iVrTCIiIiJiyhhzwOLbgdcCz5K0Rz22h+0Fo6wxsX2dpDMo6e1/Bvax/Vj9w31DSqBhuyOA0yTtT9n1as96fJSwxqbTJK1Vr10A7D2mdpveUbcGBvgOZWRmZAlYjIipY4BQroiB5X9PEU9YemIHLB4CLOy0XXCPezYB3mf7gDE8f2xhjZPZrkYIWMyISURERETE4BYCe0lazfYnB7nB9rWU7XYnzPZQ2SDLo11JWwJfpaz7GVjWmERERETElKLhk98PkHR9TTq/sG4b3LrnSJXk9xtqInq/6UybACvQYcF4t1T1Ab9Te6r6MyVdUL/LBZKeMURbz5d0mGvtJgAAIABJREFUUf1O10n6UONcx7T2Hm2tJOlnkq6qbX2qcW7f+l1dp4K1rAM8jaUDGXvKiElERERETK7jjoM5c8bSlEZIfqesN9nc9h8kfQA4kpId8nfAq1m8E9bFlAXo8/uU0SlRHcabqv4x4ELbR9TO18dYvOVvP38GPmz755JWpSTaX1DfUce09h5tPQJsZXuhpBWAiyWdW3cw+ykl3X1+8wbbp0u6EzhwwHqBjJhERERExGSbMwcWLBhXa0Mnv9u+yHYrHPBSyha5UPI7VqIEK06njITcOYHaxpmqvmP9Dkt8l0HUrYJ/Xn//X+AGFgc9DpXWXr9Lq9O0Qv1xPXel7VsHraufjJhERERExOSbOXPwzSYmKfm9ej9wLoDtSyRdREk9F3C07RtKCToBONb2MDs/jTNV/TmtbXxt/661Ne+w6iL0lwGX1UNDp7XXUaorKDuLfcX2ZX1uGUlGTCIiIiJiKhk5+V3Su4DNgc/WzxsCf0UZQVkX2ErSawFs7zlkp6RXbZ0v7p2qPmF1NObbwL/YfqgeHjqt3fZjtmdS3tMWdZexsUvHJCIiIiKmkpGS3yW9Hvg4sENNQwfYCbjU9sI6Xelc4FWTUFs3XVPVgTsbIYlrA3cNU0hdD/Jt4DTb32kdn0hau+0HKOtJthumlkFlKldEREREDGfYxewLFpSpXOOxKPkduIOS/D7btuu0rF0o604WJb9Lehnwf4HtbDf/wL8N+EdJn6GMdrwO+OIEapsH7CtpLmV62RKp6rY3bl5s+xjKAvlm7sesRlu7UwIYm99lC2Bf2+/pVkRdb/M14AbbX2g792zbd7Wntauk0p9qe+u269cC/mT7AUlPA15P2WRg7DJiEhERERHDGe9i9qHUNSSt5PcbgDPakt8PkHQLZc1JK/n9s8AqwLckLZDUSnM/kzJicA1wFXCV7e9BWWMiadiQx3OAX1FS1Y8H/rm2NUqq+hHAGyTdTNmB7Ih6fD3gj33ufTXwbsrUtAX1Z/t67h2SbgJupIzmtNLa16bs5tVubeAiSVdTOoUXtDJPJO0n6XbKyNDVdV3OyDJiEhERERHDG2Yx+6xZY3207XMonYD247+i7NrVfvz1Xdp5DPinLuf2HKEuA/t0OPUqyhbHve69lZKR0vp8L7B1h0tfOUBbF9OlI2T7S8CXBq3R9tWUxfOd2voyMLYE+oyYRERERMSUMuaAxfUk/aCGEV5fp1T1sjHwTUnf71LbwbWuX0jaFkqqev0jvtd3+n8lLWx83kPS3Y0Rjz1rWwfVzkJPkk6rNVwr6cS65gRJq9dgxVZg4ntru0fbntelrfMa1x9bd+lC0mcl3Vjf61mS1qjH/40yovWafnU2ZcQkIiIiIibfggVjGTkZZ8BibfJU4HDbF9RdrB7vU8J5wJm2P9ehtpdQ1ry8lJJ+/kNJG9WRmV7faXNgjQ6nTre9b596ujkNeFf9fQ6wJ+Vd7ANcb/stdf3ILySdVjNhunm77Yfq2pUzKdsMz6XkoBxs+8+S/h04GPio7U9JOoUSvjiwjJhERERExOSaPXuci9/HFrBYOxLTbF9Qr1vYuG4UOwJzbT9i+9eUtSZLTS1rqh2tzwIfmcBzl2L7nBqOaOBnLBkquWp9X6sA99F5bUmzrdZWw9MoYZStgMUfNHJjmsGVI8mISURERERMrr32Kj+DWkYBi8BGwAOSvgOsD/wQ+JjtxyQdClzebXpTj9oubautUw1N+wLzaohi+7mda67KTcD+tn+z1N191Clc7wY+VA8dTdnx67eUHJNdbfcbJULS+ZRO1rks7vw1vQ84fdj6mjJiEhERERG9HXdcmYbV+llOO3JVYwtYpPxH+i2BA4FXAC8E9gCw/ckhOyW9aut8sbQOZVrU/9vh9PeAGbY3pXSYThmylpavAj+2/ZP6eVtgAWWq2UzgaEmr9WvE9raUHbqmU0ammt/j45RRl9NGrBFIxyQiIiIi+lmO2wN3MM6AxduBK+u0sD8D3wU2m4TaunkZsCFwSw1ZfHrd6hjb9zbqPB54+bDF1EXoawEHNA6/F/hOneV1C/BryoL+vmw/TBlt2bHxjN2BNwPvrNPGRpaOSURERET019oeeP78ca4XGcWigMW669ZulKlQBloBi9A5YHGHtoDF/wKeUReBQxkJuH4Ctc0DdpM0vQZAvoiyvoO6G9gS07psf9/2c23PsD0D+IPtDev1azcu3YGS2UI9d2O/QuouXtsC72ibqnUbdRtiSc8BXkzJXunYrqRVtDiBfhqwPSUDBUnbUbJjdpjg2hwga0wiIiIiYgqpO0C1AhafCpzYFrA4V9KnKTtxdQpYBLjN9g51LcmBwIV1MfgVlNEJRlljYvs6SWdQOjd/Bvapz3gKZWTkviG+6n6Sdqjt3EedYjZEWOOxwH8Dl9Tv/B3bhwKHASdLuqa281Hb9/Rod2VgnqTplPf9o9o2lPUq04EL6jMutb33EN9xCemYRERERMSUMq6AxXruAmDTDsc/OWJthwOHtx1+CfBt2z0T222v0vj9YMr2u+36hjXW+zv+nW/7t8A2g7Zr+07K+ptObW3Yr45hpGMSERERETG4hcBeklYbtPNi+1qWXOcxMttDZYMsj3YlbUlZdH/PMPdljUlERERETCnDJr83zu8iyTXQsHl8PUkL67Suft5M2Wnr8g51Ta/PvaXWMWPA7/NUSVdKOrtxbCtJP6/J7ac0FvUP0t5Kkn7WSGv/1KjtSpop6ZLaztWSdm2c27q2tUDSxZJaIyibA6sD1w5aM2TEJCIiIiJGMaYk92GNmPyOpFWB/YDLOjR7FIuzTQbxTttLdUzq8+63vaGk3Wo9u3a4rt2HKIvbV6u1PoWyPfDWtm+q6112Z/GamX4eAbayvbDmmFws6VzKQvxh2/0D8B7bN9ftja+QdL7tByjvdkfbN0j6Z+ATwB62j5J0P6WDMrCMmERERETEcMab5D6soZPfq8OAI4GHm41JeitlV6rrmLgdWZw3ciawtTqkJrY9/3nAm4ATGoefBTxi+6b6+QJg50GLqFsBL6wfV6g/HqVd2zfZvrn+/lvgLsoWxNQ2Wxkoq9N7a+S+MmISEREREcMZNsl9WGNOfq/bBT/f9tnN6VqSVqbs5PUGSshiowSdA+xZ/xgf1KLa6u5hD9a6eq21+CLwEUoKe8s9wAqSNq8jM7uwZD5KX3Vk6QrKbmBfsX1Z7SSN3K6kLYAVgV/WQ3sC50j6I/AQZQH9yNIxiYiIiJjqjjuuhCBOlgULlnd2SdNQye91WtRR1O1223yKMvVrYfvAhu3tx1hb54ulNwN32b5C0qzGs12ngh1Vt+n9AWXb4IHZfgyYKWkN4CxJm9i+dtR2a5bJ14HdG7ko+wPb107PQcAXKJ2VkaRjEhERETHVtZLZnzidh8nUN/m9jpq0jq8KbALMr52P51JyOXagjLTsIulIYA3gcUkP2z56grXdXheVr07v7JJXAztI2h5YCVhN0jdsv8v2JcCWAJK2ATYapSDbD0iaD2wHXDtKu5JWA74PfML2pfXYWsDf2G6t2TkdOG+UGlvSMYmIiIh4Mmgls0+G5bDIvYdFye/AHZTk99l1lKGV/D6Xmvxu+0FgzdbN9Y/0A+tUpi0bxw8BFk6gUwIl+X134JJax49qXesCp9reunlxM6ukjpgcaPtd9fOzbd9VRzY+Ss1GqdOp9rX9nm5F1E7Dn2qn5GnA6ykL8Ydut+5sdlat/1uNU/cDq0vaqK5ZeQONdPpRpGMSEREREVPGiMnvQxtxjcnXgK9LuoUyUrJbPb42Q07FAg6qU72eAhxj+0f1+HpAz6DG+rxT6jqTpwBnNHJKhm337cBrgWdJ2qMe28P2Akn/CHxb0uOUjsr7hvyOS0jHJCIiIiKmlGGT39uumdXl+CFtn4deY2L7YeAfOpzqm9Zuez4wv/H5IOCgDpe+coC2rgZe1uXcUO3a/gbwjS5tnUUZTRmLbBccEREREVOKhgxYlLS3pGsaQYAvqcdXqCGD10i6QdLBAzz+pcBP66hMe10dAxZtH217XofrewUhStLhkm6qte1X2zqodjx6vZ9e7f6kvocFkn4r6bu92lXvgMWONUo6BTgO2LbPu1xCRkwiIiIiYuIme2ewSqMFLM6xfWy9fwfK7lHbUUY3ptv+a0lPB66X9E3bt/Yo4ToWr1FpN2zAYscgxLrAfA/KQvqNbT8u6dmDvJ9+7dpurqv5NvAffdrqFbDYsUbbu0s6ibYtmPvJiElERERETFxrZ7DJN3TAou2HGvevzOItfA2sXHfQehrwKCWPY1RDBSz2CEIE+ABwaGtrXtt3DVpEn3YBkLQq5X19t09bvQIWR66xk4yYRERERMR4jGtnsDEHLJYmtQ9wACUgcKt6+ExKZ+J3wNOB/W3fV69fJgGL6hCEWE9tAOwqaSfgbmC/VgdhED3abdkJuLCt09avzfaAxQnV2C4jJhERERExlQwVsLjoF/srtjeg7Nz1iXp4C+AxYB1gfeDDkl5Yr99+yE5Jr9q6sv2Y7ZmU3JUtJG1ST00HHra9OXA8cOIwhfRot+UdwDcHbU+LAxbf2whYnFCN7dIxiYiIiIippG/AYtvxdnOpU7yA2cB5tv9UpyH9FNh8HLVpsIDFReqajfmUtS+ttr5dfz8L2HSUgjq0i6RnUTpl3x+kDXUIWBxnjS3pmEREREREfwsWlKDFbj/LZn0JNAIW665buwHzbBtoBSxCDVgEkPSixv1vAlrTjW4Dtqq7S61M2db3xgnU1gpYhLaARUkXtl8saS1Ja9TfW0GIred/l8VTzl4H3FSv20LSqb2K6NMulEX/Z9ftjVv3dGxX3QMWu9Y4qqwxiYiIiIjeZs9e3hUsMmLA4r6SXg/8iRIE2Oo8fAU4CbiWMg3rpNaWuSOuMRk2YLFXEOIRwGmS9gcWAnvW4xMNWKTWdUTbPUMHLPaocSTpmEREREREb3vtVX56mTVrmZQCwwcs2v5Ql3YW0jkQcZkELPYJQnyAMrrTbkIBi/X8rEHb7ROw2K3GkWQqV0RERETE4O4DTq55KAPpFrA4ikECFpd3uzWE8auU0amBZcQkIiIiYjIti+DBBQvKVr1/ISRtB3yJMpXrBNtH1OPrUxa3PxP4OfBu249K2hvYh7ID10JgrxrIiKRNgf8LrAY8Dryiufaig4coWSjrdKhLta7tKcGEe9j++YDfaR7wQtub1M+fBd5CyVb5JWU3rAcGaavefx5lStc04CfAPrYfa5w/EPgssJbtrtsZN9p6FXCx7Tc3jv8EWLV+fDbwM9utjQVWBJ4xaL2QjklERETE5GoFD/4ldBxaC+Qn0TiT3+vOWd+gdGCuqrtV/WmAMg6yfWaH428EXlR/Xlmf/coBvtPbKB2mpguAg+uamn8HDqasoRnU220/VDtLZ1KmmM2tz3s+5f3dNmBbn6XkvPxT82C3FHnbp0u6kyGT39MxiYiIiJhs4woe7GYZru/oatktkF+U/A4gqZX8fgNlh6hWIacAhwDH9Eh+3wa42vZVALbvnWBtO1J2rzJwqaQ1JK1t+3fdbpC0CiX4cS/gjNZx2z9oXHYpi3cbG0jjO0+jjF4081SOAj5C7UgM0NaFkmZ1O99IkX/vMDW2S8ckIiIiIiZukAXyg1p2ye8bAZZ0PrAWMNf2kfX6E4BjbV8+ROWdaluXkizfzWHA5ylTv7p5H3D6EHUAUL/XFsC5lFGT1ojRHXWEaNgmuxk6Rb6TLH6PiIiIiKlknMnv04DXAO+s/+4kaet6/Z5Ddkp61db5YmkmsKHts3pc83HKVsOnDVkLtrelrDOZTslreTrwceCTw7bVx1Ap8t2kYxIRERERU8k4k99vB/7T9j22/0DZgnizSaitm78FXi7pVuBiYCNJ81snJe0OvBl4Z50eNrS6kH8eZZrZBsD6wFX1mc8Dfi7puaO0XWscKkW+l3RMIiIiImIqGWfy+/nAppKeXjs0rwOun0Bt84D31CT5VwEPttaXSFoqUd72MbbXsT2DMmJzUytjpO489lFgh9ppoh7vmCLfJGkVSWvX36dRdgm70fY1tp9te0Z95u3AZrb/Z5BE+S6WSpEfVdaYRERERMSUMc7kd9v3S/oCpbNj4Bzb34eR15icQ+kE3EJZM/Le2taadJ7m1cvRlClYF9S1IJfa3pvuKfJNKwPzJE2nvKMfAcf2uadronzdFnhjYBVJtwPvt31+Pd0pRX4k6ZhERERExJQyruT3eq5jsrntPUeoy5S8lHYdk9/b7r0V2KTxecMulw7S1p3AK3pdU6+b0fjYNVG+uS1wh3Oz+j1nUJnKFRERERFTiqTtJP1C0i2SPtY4vr6kyyTdLOn0OtULSa+V9HNJf5a0S1tb60n6gaQbJF0vaUafx28MfFNSxzUVkg6udf1C0rYAts+2/eUu139N0lWSrpZ0Zt0+GEl7SLpb0oL6s2dta6AUeUmn1RqulXSipBXq8Y0lXSLpkRqySG23a/L7sDVK2g84D9i2X51NGTGJiIiIiMkz5uT7EQMWbwP2oHPg36nA4bYvqH9wP96nhPOAM21/rkNtL6FMbXopJRn+h5I2aiaud7B/a5vdOq1sXxZPjTrd9r596unmNOBd9fc5wJ6Ud3EfsB+LNwAYxFA11k7YlyW1h0b2lBGTiIiIiJg8c+aURPjxWRSwaPtRyi5bO9aE862oeR2UgMW3QpkmVUcDluh01I7ENNsX1OsWNheaj2BHShbKI7Z/TVlrstTUsqbGH/wCnkaP7YWHYfscV8DPKDtwYfsu2//FYAn3k1pju4yYRERERMTkGjb5fhICFrvYCHhA0nco2+j+EPiY7cckHQpcPsi0qbbaLm2rrV8NSDqJsmj+euDDjVM7S3otcBNl1OI3ne7v0/YKwLuBrutsBmxn0mpsyYhJREREREwlIwUsdjEN2JIyxesVwAspU76w/ckhOyW9auvJ9nspU79uAHath78HzLC9KaXDdMqQtbR8Ffix7Z+MeP+yqBFIxyQiIiIippaJBiy2t3VlnRb2Z+C7LNuAxUXqOpTTgZ3r53ttP1JPHw+8fNhiJP0bsBZwwLD3LqsamzKVKyIiIuLJYMECmDVreVextAULylSu8VkUsAjcQVlsPtu2JbUCFufSCFjs09YzJK1l+27KGpVhckvazQPm1AXi6wAvoqzvoIYivsf2Ha2L65qNDWzfUn9/C3BjPbd2K5wR2IEyUtG670bbG/cqpO6QtS2wte1+C/pb94ytxlGkYxIREREx1c2evbwrWGZGCViU9ArgLOAZwFskfcr2S+takgOBC+sf3VdQ/ss/o6wxsX2dpDMo6zD+DOxTn/EUYEPKjlhNAk6RtFr9/SrgA/XcfpJ2qO3cR51iNkRY47HAfwOX1IDG79g+VNJzKZ2v1YDHJf0L8BJg4bhqHJXKQv3BbL755r788ol0IiMiJqD1XwKHWUAZ0U3+9xQTMcwWuK0Rg7/U/62N8P81SVfY3nxS6pkgSYcACzttF9zjnk2A99me8JQqSW8GXtgtF2UC7Y6txkabC22vMuj1WWMSERERMazxb4EbU8dCYK86ojIQ29eO6w/+XmGNE2x3bDVK2kDSAuDOYe7LVK6IiIiIUQw6CvJEXPcxxUnaDvgSZSrXCbaPqMfXp6wveSbwc+Ddth+VNJ0SpPhy4F5gV9u3NtpbjzL96pABRkLeTNlpa6lpRP2e0+H6lYAfA9Mpf5efafvf6rl9gX8BNgDWsn1Pn7ra2z6x1nqX7U0ax08HXlw/rkHZYrnvIqAabHk5cIftN9djHd83Zb3JMylrbgaWjklERERETK4xLswfMfn9/cD9tjeUtFu9btdGs0cB5w5Rxjttd1rf0O857R4BtrK9sOaNXCzpXNuXAj8FzgbmD1FX08nA0ZSO0iK2F9Uj6fPAgwO29yHK4vbVGsc6vm/bR0m6HxhqOl6mckVERETE5Jk9e9y7cg2d/E5JZG9lbJwJbF2vR9JbgV8BrQX0E9H1OZ3UYPaF9eMK9cf13JW9Rlv6sf1jll7Ivkit6+3AN/u1Jel5wJuAE9ru7/a+R5IRk4iIiIiYPHvtVX6GMf7k90X31F29HgSeJemPlJ283kAJWWyUoHOAPW0PlEPS6zmUjJWO6gjQFZQdsb5i+7IhnjcRWwJ32r55gGu/CHwEWLVxrNf7HklGTCIiIiJiKhkl+b3buU9RpiItXOqkvf2QnZJez+nK9mN1jcfzgC3q7ljLwjsYbLSktU7livZTHS4ffLvfDjJiEhERERFTSd/k9/pf8Zup6617bq/J8KtTpjm9EthF0pGUheCPS3rY9tETrK39OX3ZfkDSfGA74NoRnz+QWtvbGCyp/dXADpK2B1YCVpP0DeDddH/fI8mISURERERMJYuS3yWtSEl+n+cSztdKfoclk9/n1c/U8z+q6zu2tD3D9gzKdKX/M4FOSdfnSFq3pqovQdJaktaovz8NeD01Vb0bSVtIOrXXNQN4PXCj7dsb7Xas0fbBtp9X39FulO/0rj7veyTpmERERETElFH/63wr+f0G4Iy25PcDJN1CWQPxtXr8a5Q1JbcABwAf6/ccSedIWmfI8ro9Z21KOnq7tYGLJF1N6XBdYPvs+vz9JN1OGYm4WlJr4fl6wB8HqP+bwCXAiyXdLun9jdO7sfQ0rm419tLtfY8kU7kiIiIiYkqxfQ5wTofjv6Ls2tV+/GHgH/q0eUjb5+1HqKvbc15F2eK4/fqrgZd1aevLQKcgxVd2aqvD/e/ocW6PQWtsu28+je2Lu73vUWXEJCIiIiKmFEnbSfqFpFskfaxxfH1Jl0m6WdLpdaoXkqbXz7fU8zMa92wq6RJJ10m6poYe9rI+8P9J+mqHuiTpy/U5V0vaDMD20bY7hg1KOk/SVfX5x9ZdupB0iKQ7JC2oP9vXtg6qHZp+7+hwSb+RtLDt+HqSLpJ0Za2x1W7XGtvuX1HScZJuknSjpJ3r8f0l3SbpaEn7AwcDD/VrrykjJhEREREBxx0Hc+Ys7yr6GmfAYl0E/g1KYvlVkp4F/KlPCRcBZ9s+s8O5NwIvqj+vrM9+ZZ/23m77oZoLciZlxGVuPXfUAEn03XyPErDYvh3wJyjT346R9BLKyNOMIdr9OGWXro0kPYWS8E4zVNH2UZTQyqFkxCQiIiIiSqdkwYLlXcUgxhmwuA1wte2rAGzfa/uxCdS2I3BqXVh/KWXXqrV73WC7NaowDViRCW6522j3Utu/63SKxentqzP8TlrvAz5Tn/G47a4ZLcPKiElEREREFDNnwvz5y7uKZRawCGwEWNL5wFrAXNtHlhJ0AnCs7cuHqLxTbesCnToIi9TnbwGcy+KOFcC+kt4DXA582Pb9Q9TSzSHADyR9EFiZskPXQFo7iAGHSZoF/BLY1/adY6grIyYRERERMaWMM2BxGvAa4J31350kbQ1ge88hOyW9ntOT7W0pu2JNp4z6QJkGtgEwk9Kx+fyQtXTzDuBk288Dtge+XqdkDWIaZZewn9rejLLr16hTzZaSjklERERETCV9Axbbji9xT1vw4e3Af9q+x/YfKOstNpuE2vqqO3rNo0wHw/adNRX+ceB4xrf71fuBM+ozLqGEJq454L33An8Azqqfv8XE3tcSMpUrIiIiYrItWACzZi3vKnpbsKBM5XriWxSwCNxByeSYXYMMW4F/c+kcsHgJSwYfng98RNLTgUeB1zHCou2GeZTpV3Mp08sebK3zkHSj7Y2bF0taBVjV9u9qh2l74Cf13NqNNSI7UdPgJa1LWcey9Yg13gZsDZws6a8oHZO7B2m3vrPvAbOAH9V2rh+xjqWkYxIRERExmWbPXt4VPKnUNSKtgMWnAie2BSzOlfRp4EqWDFj8eg0CvI/SmcH2/ZK+QOnsGDjH9vdh5DUm51A6F7dQRhbeW9tak87TvFYG5kmaXr/Lj4Bj67kjJc2sdd0K/FM9PlAQoqQjgdnA02tQ4wk1q+XDwPF1S18De9QOx6ABix+lvMsvAne3vuM4pGMSERERMZn22qv8PNE90Ud0GsYZsGj7G5Qtg9uP7zlCXQb26XCqW8DincArurT17i6P6RuEWO//CPCRDsevB149gXb/G3htv+tGkY5JRERERMTgHqTsSrWm7WP7Xg3YPntcD7d99LjaGme7dQRmb+Dbo7aRxe8RERERMaWMK/ld0hskXVET36+QtFXnJy5hdcq6jE51dUx+7/E9ni7p+zVB/TpJRzTOdU2rH0S35Pd67u2Srq/PHCpVs37Hw2vy+w2S9qunfkuZjrbpMO01ZcQkIiIiIoopsEh/nMnvlJ283mL7t5I2oaxbWXfppy7loDEmv3/O9kW1E3WhpDfaPrdHzYPqmPwu6UXAwcCr6xqbZw/RJsAelJ3HNrb9eOt+26dLuhM4cMj2FsmISURERESURfpTY1eusSW/277Sdms73+uAlepC9FENlfxu+w+2L6q/Pwr8nLLFcNeaBy2kR/L7PwJfaYU12r5r0DarDwCH1m2MR7m/q4yYRERERMQTa5H+skt+v6fRzs7AlbYfKSUsu+T3+rw1gLcAXxqi5lFsVJ/3U8rUq0NsnzfE/RsAu0raibIr1362b+5zz0DSMYmIiIiIqWScye/lpPRSylSpbRadHGFXrn7P6XpTyTD5JvDlurPYyG0NYBplqtksyujMTyRtYvuBAe+fDjxse3NJbwNOBLYcQ13pmERExF+wKTCfPp6gpk4Y4ZNR3+T3OmrSKfn99rbkdyQ9j5Jk/h7bv5yk2vo5DrjZ9hc7tLVUzWOo8VLbfwJ+LekXlI7Kfw1xf2vnrbOAk8ZQE5A1JhER8Zdq6synj4glLUp+rwvGdwPm1QyRVvI7dE5+hyWT39cAvg8cbPunY6htHvCeunPVq2hLfu90Qw2DXB34lw5tdap5XUkXTqDG7wJ/X5+9JmVq16961djh/tZYB7PuAAAgAElEQVTuZa8DbppALUvIiElERPxleiLNp4+pJyNty804k9+BfYENgX+V9K/12Da271oWye91tObjwI3Az+va9qNtn9Cj5okmv58PbCPpeuAxyg5j9/ZIp293BHBazS1ZCIwy5a2jdEwiIiIiYkoZV/K77U8Dn+7yjGWR/H47XToDPdLqJ5r8buCA+jNKuw8Ab+p33SgylSsiIiIippQRAhb3riGKCyRdLOklbe2tJ2mhpEEyOF5PGTH4aoe6OoYi2j7b9pd7fJ+nSrpS0tmNY1tJ+rmkayWdUteZYPto2/P6vJ+VJP1M0lU1RPFTjXNfq8evlnSmpFV61SjpWZIuqu+nYzq8pHmSbgO+Ctwv6bOS/mfA97lIRkwiIiIiYnIcdxzMGSpYvK8RAxbn2D623r8D8AVgu0azRwHnDljCLcBbu0zxGjUU8UPADcBqtcanUDJMtrZ9k6RDKetNvta9iSU8Amxle6GkFYCLJZ1bs1X2t/1Qfc4XKNPZjujR1sPAvwKb1J8l1J25FgIP2d6kcfz3A9a6SEZMIiIiImJyzJlTdjAbr6EDFlt/iFcrs+RWwW+lLP6+jokbOhSxrjN5E3BC4/CzgEdstxaWX0DJWRlIDXhcWD+uUH9cz7U6JQKeRp8tiG3/3vbFlA5Ke+2rUKaEdZwON6yMmERERETE5Jk5E+bPH+6e8QcsImkfyh/RK1J3lZK0MmXB/BuAJaYdSToH2LORDD+IUUIRv0hZC7Jq49g9wAqSNq8jM7uw5DbEfdWRpSsoi/u/YvuyxrmTKIv0rwc+PEy7bQ4DPk9Z6D9hGTGJiIiIiKlklIBFbH/F9gaUjsgn6uFPUaZ+LVzqRnv7ITslvWrrfLH0ZuAu21e0PduUXbiOkvQz4H8ZYCeutjYesz2TkqWyhaRNGufeC6xDmT42yFSzTrXPBDa0fdYo93eSjklERERETCV9AxbbjrebS53iRRlpOVLSrZQckf+nbkU84doGDEV8NbBDff5cYCtJ3wCwfYntLW1vAfwYuHmUguouWvNZck0Nth8DTmeIKWJt/hZ4ea39YmAjSfNHbAtIxyQiIiIiJuK440quS6ef8a8vgRECFiW9qHH/m6h/5Nc//GfYnkGZUvV/bHfceWpAQ4Ui2j7Y9vPq83er17+r1vzs+u90yihPa/H+FpJO7VWEpLVUwiOR9DTKTmI3qtiwHhfwFkqGCpJ2kvSZQb+o7WNsr1Nrfw1wk+1Zg97fSdaYRERERMToWgvcZ85cJo8bMWBxX0mvB/4E3M/izkNXI64xmVAoYpuD6lSvpwDH2P5RPb4e8Mc+964NnFLXmTwFOMP22a3dviStRpl2dhXwgXrPBsBDnRqroyKrASvWzQK2qbugjVU6JhERERExMd0WuM+aNSmPGyFg8UMDtHlI2+ftR6hr5FBE2/MpU65anw8CDupw6SsHaOtq4GUdjj9OmT7WyUxg/y7tzejzvFvpsJXwsDKVKyIiIiJicPcBJ9c8lIEMEoo4RFsH1Y7HWNl+l+27x9GWpM8C7wKGyjJJxyQiIiIiphQNn/x+gKTra9r5hZJe0NbeapLu6JZs3uYhShbKOh3qkvT/s3en4XJVZfr/v7eMIpMSRhllEJGGMImKQIAWARVEUCAIBkWckBYMDfxRpAlcImjTDA00IJMSwQbBMEPzI0wSGTMxT2kMIIMydEDQkOf/Yq2d7FR2Ve1dZwhH7s91nSvnrF177VXFm1qstZ5bp+RxTZa0cY33Mj6/l4n5Z7mW67tLCkmb1hhbcc9ikq6W9LBS8vvxpWur5iT3+/MYu64MSTpX0guSpra5PjqPcVhuKsInP1t3zOCtXGZmZmY2kCZO7NctXT0mv98PbBoRb0j6NnACc5fJHQPc0mAYh0bEpRXtOwJr55/N87M3r9Hf3lVJ8pKWAA4C/jDvLV39LCJuzpOzmyTtGBHXkkol/yYizpC0HmlL3Opd+jofOA2Y59C9pFVI/y2eLtoi4hJJz9OSDdONV0zMzMzMbGCMHDkQh+J7SX6/OSKKEMAJpFLCAEjaBFgeuKEfxrYLcGFOXp9AKl+8Yh/6G0OaRM2Tut5JRLwRETfn3/8G3Mec9xykg+yQyhl3PdwfEbfSvuzxSaSAyI4J8nV4xcTMzMzMOjvrrFR9q0qnilwHHJB+mhqA5PeSrwPXpsfoPaTk8n2A7eYegs4BzqxayWg4tg8Cz3W57zxJbwOXAcfmEsMbAavkalqNVh7KctngzwMn56ajgRskfY+0Je2f+9D3zsAzETFJnf+b1eIVEzMzMzPrrCgJ/M7QU/I7gKSvAJsCJ+am7wDXRMQf57kxYv+Gk5JOY+tk74j4J2DL/LNPnjCdBPyg4fPnHkwKefw1cEquWAawF3B+RKwM7EQqb9x4TiBpMeBI4Ki+jLHMKyZmZmZm1t0glwTuoGvye141mSv5PeeYHAlsHRFv5eZPAFtK+g6wOCmnY0ZEHE5v2o2trYh4Jv/7f5LGkraq/Y5Ufnd8XolYARgnaeeGk6WzgMci4j9KbV8np8BHxJ2SFgWGAS806BdS7skaQLFasjJwn6SPRcSfGvYFeMXEzMzMzIaWXpLfNwL+C9g5ImZ/AY+IvSNi1ZzTMZp0PqTXSQmk5Pd9c3WujwOvRsRzeQwPt75Y0oJFJStJCwGfA6ZGxKsRMayUSj8hj/0etUmRr+j7WNIZku+3XHqavG1N0keARYEX6/ZbiIgpEbFcaYzTgY17nZSAJyZmZmZmNoTk1ZAi+f0hUoWpcvL7ITl5fRnmJL+fSFoR+e9ckrdrpoikc5qU6M2uAZ4EHgfOJm0VI08+qrZ5LQJcL2kyMBF4Jt/XSdcUeUkrk1aH1iOtYkyUtH++/APgG5ImkbZ5jcqTurb9Svo1cCfwYUnTJX29yxh74q1cZmZmZjak9JD83vWAd0ScTyqLW/y9f9sXt+8jgO9WXKpMfo+I14FNavQ7oltfLa+fTvVEiFxWuSr9vW2/EbFXjTGu3u013XjFxMzMzMyGlP4KWJQ0XNKdOYRwsqQ92j2zZF3g15KubjO2I/K4HpH0GYCIuCoiTunynk6VNKOl7ct53A/k8ye1U+QlXZTHMDUHJC6U20dIelVzAh2Patjv0UphlMX9O+X2LfNYp+bP8XTg5W79lXnFxMzMzMz6pp9DFDvp54DFN4B9I+IxSSsB90q6PiJe6TCE64BLI+JnFWNbj3Tm5aOkZPj/kbRORLzd5T1tCizd0rY2cASwRUS8rJZE+BouAr6Sfx8L7E/6LABui4jPNeyv7KTW9x8Rt+VJylURcQlwSdNOvWJiZmZmZr0bmBDFTvotYDEiHo2Ix/Lvz5IqUy3bh7HtAlwcEW9FxFOksybzbC0ryxOtE0khhWXfAP4zIl7O42tUNSsirslBjwHcRSlU8p3KKyZmZmZmvRjEVYL5biBCFDsZpIDFuR+pjwELA0/kv48B7qmzvallbBNaxlY1hrIDSVXFnmsJKVwnj+MOYAHg6Ii4rsFYyPcvRAqQ/JdS8yfy4fdngdGl4gF1HShpX+Ae4AfF5KmvPDExMzMza2rkyPk9gnez/ghY3LqlfUXgl8BXI2IWQET0EhzYKGAxbx/7EjCi4vKCwNr52srAbZLW77LNrMrpwK0RcVv++z5gtYiYkbdeXZGfU9cZwBjS+xoD/Bz4WsMxVfLExMzMzKypgVgleCd7Z60M9WfAIpKWBK4GfhgR5dWO/hxbOxsBawGP59WSxSQ9HhFr5b4mRMTfgackPUKaQNxddzCSfkzamvbNoi0iXiv9fo2k0yUNi4iX6vQZEc+X+j8buKrueLrxGRMzMzMzG0r6LWAx3385KVjxv/thbOOAPSUtImkN0kTirvysmyTNta0rIq6OiBVKIYVv5EkJpJWMbfK9w0hbu57Mf88T1tgq55Z8BtirWAXK7Svk8zjF9rX3AH9uN8aKflcs/bkrMLXbWOryiomZmZmZDRkRMVNSEbC4AHBuS8DixTn1/H6qAxYBno6InYEvA1sBy0galV87KiIm9nLGJCIekPQb4EFSWOF3I+JtSe8hrYz8pcFbvR7YXtKDwNvAoRHx5w5hja3OBP4XuDO/599GxDGkidu3Jc0E/grsGRHRYIwnSBpO2so1jdJqTF95YmJmZmZmQ0p/BSxGxK+AX7W51ssZEyLiOOC4lub1gMsi4q9d7l289HsAh+Sfsq4Bi/n+yu/5EXEacFrFpbpj3Kfbs3vliYmZmZmZWX0zgAMkLVl38hIRU5l3gtGTiOi3Mx0t/fZpjJK2JB20r3VWpYrPmJiZmZnZkNJD8vtWku6TNFPS7qXX95L8vjjwvg5jmyf5vct7OV/SU6Uk9eG5fSlJV0qalMe3X42xlfttl/wuSafkMU6WtHGNvo6T9EfNm0w/StKLkiYCpwInR8QISWvm9zKjusdqXjExMzMzs+7eIbktPSa/Pw2MAka3dNdL8jtUJJ/nsfWU/E46P3JpS9t3gQcj4vOSlgUekXRRDpWso13y+46kQ/lrk/Jfzsj/dnIlafvXYxXXLomIA8sNEfEEMLzpxMQrJmZmZmbW2eCnu3fSS/L7tIiYDMwqd/ROSH7vIIAl8vtanHQofWbnW0o3t09+34VUhSxyeeSlWyptVfU1ISKe6+ldNOAVEzMzMzPrbLBzWwY2+b3NI+db8jvAcZKOAm4CDs85K6eRyg8/CywB7FEu+1tXRfJ71ef3QaDXicdukrYCHgUOjog/druhHa+YmJmZmdlQ0nPye9sO5yS/71dOfm84Kek0tk6OANYFNgM+QCp5DCmDZCJpS9hw4LQcBtlUa/J7z59ThSuB1SNiA+B/SKtUPfPExMzMzMyGkq7J7y3tHc3n5Hci4rm8reot4DzmbP3aj5Q9EhHxOPAUaQJTWyn5vVxtq/EYO4z9z3ncAGcDm/TST8ETEzMzMzMbShonv7czv5Pfc/uK+V+RzsQUSepPA9vla8sDH6Yfkt/zGPfN1bk+DrxanB+p02/V2LOdgYea3N/KExMzMzMzGzLyGZIi+f0h4Dctye+HSHqcdObkFwCSNpM0HfgS8F+SitcXye+jKsr1HiNp54ZjewAokt+vo17y+0WSpgBTgGHAsbl9DPDJfO0m4LCIeKlh8vvypOT3ifkMC6RgyidJB/PPBr6T32/bfiWdkD+/xSRNl3R0vnRQLmU8CTiIVPmsZ0qTy3o23XTTuOeee/ryPDOz3hVlKsePn5+jMDOzASbp3ojYdH6Po0r+Uj6jqlxwh3vWB74WEX0OWZT0OeBDEXFKX/sa6H4lzSin2XfjFRMzMzMzG1LUPGBxtbyVarKk8ZJWzu29BCx+EviJpFsqxrWMpJslzZB0WtEeEVO7TUokjZYUeeWibcBiRFxVZ/KQt2odJ+lRSQ9JOqjUPk/AYp1+JY2TNLX094b585uSx7pkbt9D0pvAQt3GWeZywWZmZmY2eM46C8aO7fl29Raw+DPSOZILJG0L/IRUQreXgMXfAze0WTF5E/gRsH7+qfueVsnv5+lSc18DFkeRDrmvGxGzJC2X23sJWETSF4HWwMRzgNERcYukrwGHAj+KiEsk/QG4quZYAa+YmJmZmdlgGjs2pcj3rnHAIrAe6ZwGpAPyu0D/ByxGxOsRcTtpgtLEScC/MnfZ3j4FLALfBo4plT9+Ibc3DliUtDipstexLZc+DNyaf78R2K3B+ObhFRMzMzMzG1zDh3c+L9j/AYuTSF+aTwZ2JX3hXyYi/jznkf0SsNhYPmD/TERM0tzvu68Bi2sCe0jaFXgROChPwnoJWBwD/Jy0wlQ2lVSN63ekwgKr0AdeMTEzMzOzoaSXgMXRwNaS7ge2Bp6htPqg/gtYbETSYsCRwFEVl/sasLgI8GYuInA2cG7x2IrXtq2GlauUrRURl1dc/hrwXUn3kiZPdbeZVfKKiZmZmZkNJV0DFvOqyezgwLxN64swe1vSbhHxav67PwMWm1oTWAMoVktWBu7Lqzf7AcfnfJbHJRUBi3fV7Hs6cFn+/XJSeGPR3iRg8RPAJpKmkeYOy0kaHxEjIuJhYHsASesAn605tkpeMTEzMzOzwXHWWX09XwI9BCxKGpazRACOIK8eqP8DFtuSdGGecMwWEVMiYrmIWD0iVidNGjaOiD/Rx4BF4ArSmRtIq0SP5t8bBSxGxBkRsVIe36eARyNiRH79cvnf9wA/JGWn9MwTEzMzMzMbHGPHwquv9qmLXgIWgRGkqlaPkkIHj8vt/RqwmO+bBvx77nO6pPXypQ3ofI6jVV8DFo8Hdsv3/wTYP7c3DljsYK/8mT5MWnU5r8vrO/JWLjMzMzMbPEst1ecuIuIa0hfs1vYnSVW7WtsvZU61rnL7r4BftXlG1bmPOmNbvbUtbxd7LCL+OO8d1ffm7WfbV7zs46Ryyd3G8QoVW6vyytJ3e+k3IqZRKoMcESeTCgr0C09MzMzMzMzqmwEcIGnJupOXiHiNVLWqzyKiUTbI/OhX0pbA6aRzP7V5K5eZmZmZDSlqnvy+ak5kvz+nne+U21eX9NfSNq46ZyTWJyWaz3NgXNK6OQn9LUmjG76nUyXNKP1dOeYG/Y3Pn1Hx3pZrub67UtL8pjX7WyCP5apSm1SRLk+qJPZe5g1k7MgrJmZmZmY2uCZOhBEjerq1x+T3H5LOopyRz3xcA6yeu3wiIoY3HMaheXtYq78ABzEn2LHue9oUWLqludOY69o7Iu6peN4SeZx/aNDXv5DO9JRLFo+iIl0+J78/TyrTXJtXTMzMzMxs8Cy/fApY7F0vye/BnC/US9G5PG7PIuKFiLgb+Hvde/JE60RS8vtc3TFwYx4DnEDNhHpJK5POq5zTcqldunxPvGJiZmZmZv3nrLNS9a0qEyd2T32HgUh+Pxq4QdL3gPcB/1y6f40cvPgaKcvktjQEnQOcWbXi0M8OJJU7fq4l+b3TmOs6T9LbpDyTYyMiJG0ErBIRVzXYbvYfpInTEi3t7dLle+IVEzMzMzPrP2PH9kdWSSe9JL/vBZwfESsDOwG/zNkbzwGrRsRGwCHA2CJdPSL2H+hJiaSVSIfiT6243G7Mde0dEf8EbJl/9sn3nwT8oMEYPwe8EBH3Vlxuly7fE6+YmJmZmVn/arcq0uO5khaNk99JZ012AIiIOyUtCgzLW4/eyu33SnoCWAcY6FWSwkbAWqRkd4DFJD0eEWu1GzNQa7tURDyT//0/SWNJW+B+Rzq8Pz4/bwVgnKSdO0zCtgB2zofvFwWWlPSriPgK7dPle+IVEzMzMzMbShonvzN3ivpHSF+wX5S0bD7jgaQPAWuT09X7m6SbJH2w3BYRV0fECqXk9zfypKTTmD8o6aYuz1owByYiaSHgc8DUiHg1IoaVnjcB2Dki7mnXb0QcEREr59fvCfy/PCmB9unyPfGKiZmZmZkNGRExU1KR/L4AcG5L8vvFko4F7mdO8vsPgLMlHUza3jUqn7fYCjhG0kzgbeBbEfEX6O2MiaQVSKstSwKzJH0fWI9UNnctUtWuutqNeUVgZudbWQS4Pk9KFgD+h7TVqpM6/bY6Hrgoj3EGc9Lle+KJiZmZmZkNKT0kvz9I2pLU2n4Zc7YitV5r/CU7Iv5E2kI2F0nrA5dFxF+73L94tzFTL6H9dWCTGuMd0bDf8cD40t+V6fK98lYuMzMzMxtSeghYXC1vpZqcgwdXLt1zgqQHckDgKVLnkmCkic9YSRdVjKsyYDEipkbEIV3eU62AxYg4LSLG1fiMFpZ0Vg4/fFjSbrl9K0n3SZopqdj2VqtfSeMkTS39fWLue7KkyyUtndt/TFrR+lS3cZZ5xcTMzMzMBk8fwhWh54DFnwEXRsQFkrYFfkKqUvVJ0qrEBrn720lnJcZ3GMJdwFFDIGDxSFI1rXVyNa4P5PanScGITZPpv8i8Se43Akfk7XU/BY4ADouIf5N0AXBVaz+deMXEzMzMzAbHyJF9DVeE3gIW1wOKg903A7vk34N0qHxh0rmMhYDnex3YOyxg8WukCRgRMSsiXsq/T4uIycCsBmNcnFRO+di5BhhxQyk3ZgIV29ia8IqJmZmZ2btZp0DEXhQhilUOOCD9dNP/AYuTgN2Ak4FdgSUkLZPL8N5MyjMRcFpEPJSGMHQDFostVcAYSSOAJ4ADI6LXSdcY4OfAGx1e8zXgkh77B7xiYmZmZvbuNvCBiP2tl4DF0cDWSgnvWwPPADMlrQV8hPR/+j8IbJsrdQ31gMUFSe/pjojYGLiTtJ2tlzEOB9aKiMs7vOZIUkWvec7dNOEVEzMzM7N3u3aBiL3onxDFThoHLEbEs8AXYfa2pN0i4lVJBwATImJGvnYtqTrVrQP9JrKBClj8M2l1o5hM/HfurxefADaRNI00d1hO0viiopekr5JyUrbLWTI984qJmZmZmQ0ljQMWJQ0rrTYcAZybf3+atJKyYM782Bp4aCAGPZgBi/mzuBIYkZu2Ax6sMcaHK/o6IyJWyuP7FPBoaVKyAyk7ZueI6LTNqxZPTMzMzMxsyMirIUXA4kOkylXlgMVDJD1OOnNSBCyOAB6R9CiwPHBcbr+UdP5iCukcyqSIuBLSGZNcLas2SStImk46KP5DSdMlLZknRb0ELH5D0iTg1+SAReoHIR4GHC1pMrBP7g9Jm+Uxfgn4L0kP5PZhVG+H6+Q0YAngRkkTcyW0nnkrl5mZmZkNKT0ELF7KnGpd5fa3gW+2ecaQDVjM9/8vsFVF+91VY6zTb0RMA9Yv/b1W+1c354mJmZmZmVl9r5KqXQ2LiForBBExlbSK0mcRcVp/9FPRb6PMkU4kbQmcTjr3U5snJmb/qPq7/OM7QacSlGZm9q6RzzacDCwAnBMRx+f2A4HvA2sCyxbZHTnj5GRSdas3SNui7iv1tyRpW9jlEXFgl8cvRTrvUTWudYHzgI2BIyOidiUsSacC+xWrJpJWI52FWZa0BewrETG9QX8i5Y58CXgbOCMiTpG0N2mbF6TAxG9HxKQufZ1POn/zam4aFRET87URwH+QMmBeioitgXvyMz+ZJ3C1JiiemJj9oyrKP/qLvJmZDbY+prt30iX5/Q5S2vj4ltt2BNbOP5uT0uA3L10fA9zSYBiHDkLye2VafYNuR5Gql60bEbMkLZfbnwK2joiXJe0InMXcn0U787znnJdyOrBDRDxdPCNvWRueK3nV5omJ2T+y/iz/+E4w8CUozcysr0aOHOgnzE5+B5B0MSnJ/cGIuD+3td6zC+lLfgATJC0tacUcargJ6UD8dUCjw+6tIuIF4AVJn617Tyn5fSQp/LGwHnBw/v1m4IqGw/k2MDIiZpXGRkT8vvSavqa1jwR+GxFPl5/RK09MzMzMzKz/1E1376S35Pem93xQ0vOkRPN9yKV55wxhvie/t0ur/3PNftcE9pC0K/AicFBEPNbymq8D19bs7zhJRwE3AYdHxFvAOsBCksaTqnOdHBEX1uxvHp6YmJmZmf2jqnPecOht++2U8N70nu8A10TEH1tXWXqpytVUKfl9RMXl0cBpkkaRAh+foV6Z4MIiwJsRsamkL5LOq2xZevY2pInJp2r0dQTwJ2Bh0tavw4BjSHOJTUiTuvcCd0qaEBGPNhjnbJ6YmJmZmf2j+sc8b9gu+b2Xez4BbCnpO8DiwMKSZkTE4f043k7aJr+3S6tv0Pd04LL8++WkQ/nk/jYAzgF2rLMCExHP5V/fknQeadJUPOOliHgdeF3SrcCGgCcmZmZmZtai23nDoXd+b3byO2kVYU/SWYdOxgEH5vMomwOv5i/bexcvyCsTmw7UpCSnte8bEc8UbRFxNbBC6TUzimyQHHj4l3xGpJxWj6SHI2LdLo+8Atg237c1ebIgaVXgt8A+rSsbVWPM7cV5HJEO9k/Nl35HWtVZkLSasjlwUp3Po4onJmZmZmY2ZETEzFwW+HpSueBzi+R3SQcB/0r6sj9Z0jV5S9Y1pFLBj5PKBe/X7Tm9nDGRtAKpVO6SwCxJ3ycdYp9B8+T3EcBPJAVpK9d38zPqJrQfD1wk6eD8/GJr2lHAMsDpeZVmZt7u1Smd/iJJy+bnTgS+BRARD0m6DpgMzCKVbp5acX8tnpiYmZmZ2ZDSIfn9FOCUivYgf7Hv0Of5wPmlv+d38ntlWj31k99fAeapDpbfV9V7W6/dGCNi2w7POZFUVazP3tMfnZiZmZmZDRZJO0h6RNLjkg4vta8h6Q+SHpN0iaSFc/uqkm6WdL+kyZJ2yu3L5PYZkuomqn8MGCvpoopxrSvpTklvSSrOYRARUyOiMvld0i8kTcrjujSfJ2k75oi4Kk/Aun1G4/NnNDH/LNdyfXdJkTNUuo1xmqQpuZ97Su1HS3qm9Izic91O0pukamizuo214ImJmZmZmQ0ZpYDFHUn/l38vSevlyz8FToqItYGXSVWnAH4I/CYiNiKdSTk9t78J/Ig5h7nruIuUD7J3xbUiYLF24jtwcERsGBEbAE+Tygd3GnMTe0fE8PwzO2NE0hJ5nH9o0Nc2uZ/WrJeTSs+4BiAibgLWBR6JiNrb17yVy8zMzMwGR53yxd1VBixKeoh02Ls4CH8BcDQp5T1I5z4AliJX8crVpG6XtFZfB5X7axywGBGvAeSD5e9lTunjyjH3kzHACTSbkA04r5iYmZmZ2eAoyhf3TWVYIulA9ysRMbOlHdIE5SuSppPOpnyv20MknVNscxpouQTvn0irDKfm5qNpOOYK5+UtVj/KEx8kbQSsEhFXNegngBsk3SupNT3zwLzV7FxJ7+9hjLN5xcTMzMzMBk+38sXQLfm9XVhip+DFvYDzI+Lnkj4B/FLS+rkUb6XBCFgsPWu/vEXtVGAPUuZI4zG32Dsinsnbti4D9pH0K1I531ENh7hFRDybz6ncmMsV30pajRpD+pzHAD8Hvtaw79m8YmJmZmZmQ0m7sMSXgKVzpka5HdJZk98ARMSdwKLAsNIJuCwAACAASURBVEEZbU0R8TZwCbBbburTmIsskoj4P2AsaQvcEsD6wHhJ00gVvsZ1WxnKYY/FVrXLc19ExPMR8XaeLJ1dtPfKKyZmZmZm73YTJw5O0GL/pNBXBixGREi6GdgduBj4KikAENKh8u2A8yV9hPQl/8W+DqSJqvDCvL1qzYh4PP/+eeDhTmOW9EHgwojYrsOzFgSWjoiXJC0EfA74n5wcP6z0uvHA6Ii4p12/kt4HvCci/i//vj1wTL62YikVflfmBC/2xBMTMzMzs3ezkd1C099ZOgUsAocBF0s6Frgf+EVu/wFwdg4bDGBUzjYhrxwsCSws6QvA9hHx4CAFLAq4QNKS+fdJwLc7jVnSisBMOlsEuD5PShYA/oe0otFJu36XBy7PR1QWBMZGxHX52gmShufxTQO+2eUZHXliYmZmZvZudsAB6Wcw9NOqTIeAxSep2E4UEQ8CW7Tpa/U27QMesJi3QLUbV7sxdw1YzNXGNqkx3hHd+s2f6YZt7t+n2zOa8MTEzMzMzKy+V4ExkoZFxJl1boiIqUBleGFTEVE3CHK+9StpS1LuyktN7vPExMzMzGww9E+GRzP9c6bjHUfSDsDJpG1K50TE8bl9DdL5kg8A9wH7RMTfJJ0EbJNvXwxYLiKWlrQNqUpVYV1gz4i4osPjXyZt1VqpYlzLAJcCm5Eqah3Y+poO72k0cCKwbD4bovwedwLeIG3luq9Bf+cDW5MmUuT7J5aubwZMAPaIiEtr9jkO+FBErJ//Hg6cSTr/MhP4TkTcRfps3kvawlabJyZmZmZmg6HI8PgHnCg00seD9qXk90+TKnTdLWlc3vpUJL9fLOlMUmWrMyLi4NL93wM2AoiIm4Hhuf0DwOPADTWGcVJEVKW7F0ny6+efuu9plfx+ni417wisnX82J5Xm3bxun9mhVZOO/Bn+lHROp+4Yv8i8E40TgH+LiGsl7ZT/HhERl0h6noYBjp6YmJmZmQ2WOhke/WkwKm010T8H7XtJfi/bC/hxRb+7A9dGxBu9DqwPSfInAf/KnCpiALuQqmQFMEHS0i1VsPrie6Rsk83qvFjS4qStaAeQSxhn/ZpO74mJmZmZmQ2OugftOwcsViW/b07n5PfcrVYD1gD+X0W/ewL/XnrtMcA9ETGu+4B7J2ln4JmImKS533e7hPsmE5PjJB0F3AQcHhFv5bLAu5ImcbUmJswJT2ydtH2fVP3rZ6R8xE82GNs8HLBoZmZmZkNJL8nvhT2BS3OY4ZwOUwnef6K0tSkijhqEScliwJHAUVWXK9pa308nR5DOzGxGOnNzWG7/D+Cw1s+gwxiHA2tFxOUVl78NHBwRqwAHM6c8c088MTEzMzOzoaSX5PfCnsCvK/r8MnB5RPy9n8fazZqkFZxJOU9lZeC+nIfS7n3WEhHPRfIWcB5zyihvSsp6mUbavnZ6zm9p5xPAJvn1twPr5GBGSCGWv82//zd9TH73xMTMzMzMhpLZye+SFiZNNsblsxhF8jvMnfyOpA8D7wfurOhzL6onLP1G0oWS5vriHhFTImK5iFg956lMBzbOeSjjgH2VfBx4tThfIunh1v4rnrdi/lfAF8ip7BGxRul5l5IqaV3Rrt+IOCMiVsqv/xTwaCn/5FlS5S9IW8Meq/2BVPAZEzMzMzMbMnpMfoc0+bi4SHwvSFqdtDJxS0t7T2dM2iXJAxvQ7HzINaRSwY+Tznbsl/sfRvU2r1YXSVo2v3Yi8K0u467bb9k3gJPzKtWbpMPxPfPExMzMzMyGlKbJ7/na0W3ap9FySD63V537qDO21VvbJC0JPBYRf5z3jup78wTquxUv65r8nu/ftsZrRjXpN39W65f+vp0aCfN1eSuXmZmZmQ0pknaQ9IikxyUdXmo/MLdFXgFovW8zSW9L2r3UtqqkGyQ9JOnBvILSySeBn0i6pfWCpGUk3SxphqTZSeoR8VpEfKnLexpdHrekpSRdKWmSpAck7Zf7uioiTukyRiTdJmli/nlWUrFd69BS+9T8eXygU7+SfpHHMVnSpbl8cHHty/lze0DS2Nx2EHAd8Jlu4yzzxMTMzMzMhoxSwOKOwHrAXpLWy5fvAP4Z+N8291WFCl4InBgRHyGttrzQZQi/B46IiK0rrhUBi42CBdsELH4XeDAiNgRGAD/PZ2pqiYgtI2J4RAwnnav5bW4/sdR+BHBLRPylS3cHR8SGEbFBHuOBedxr5z62iIiPksoHExGnRMSiwFt1xwveymVmZmZmA+Wss1Liff+qDFgkfYm/P7dV3TdPqGCe0CwYETcCRERrsnkj/RywGMAS+fD64sBfgJkV93YkaQnSwfT9Ki7XOvQfEa/lvgS8lzlli78B/GdEvJxf121S15FXTMzMzMxsYIwdCxMn9nev7YIH2yqFCp7Zcmkd4BVJv5V0v6QT88oKko7J4YcDqhyw2HLpNOAjpMpXU4B/iYhZPTxiV+CmYnJReu5iwA6kyVqdcZ4H/ImUjXJqbl6HVD74DkkTJO3Qw/hm84qJmZmZmQ2c4cNh/Phm93ROfu8leHB2qGDLasqCwJbARqQtSpcAo4Bf9Hr4vYlSwOL2FZc/Q6qmtS0p7+RGSbe1TjBq2As4p6L988AdNbZxARAR++VJ26nAHqRslAWBtUlbzVYGbpO0fkS80nCMgFdMzMzMzGxo6SV4sF2o4HTg/oh4MiJmAlcAG/f/kNvqFLC4H/DbHJL4OPAUabWiNknLkLa+XV1xuV3YZFs5Lf4SYLfcNB34XUT8PSKeAh4hTVR64hUTMzMzs39kEyfCiBHz79nDh/d3r7MDFoFnSF+wR3a6ISLWKH6XdD5wVURckVcA3i9p2Yh4kbQ6cU9/Dzg/90LgtIi4qzSuKcBypddMAzaNiJckPQ1sR1qFWB74MFCcq3k4IupMUr5Eeq9vtoxlKVIw4lda2m8C9o2IZ0ptAtaMiMfz758HiiDGK0grMufnamLrFGPshScmZmZmZv+oRnb8vj4kdQpYzGVq/xVYAZgs6ZqI2L9DX29LGg3clL903wucnfua3wGLY0hf+KeQtq8dlicsTYIQ9wSOr2jfFbghH9Yvxv0eYC3SIfu53hJwQc5iETAJ+Ha+dj2wvaQHgbeBQyPizzXHNg9PTMzMzMz+UR1wQPqZXwZopaZDwOIpQMeMj5ZQQXJFrg0qXje/AxafpfrsSa2AxdzHiDbt5wPntzSvB1wWEX9tee0sYIs2/QRwSP7pM09MzMzMzMzqmwEcIGnJupOXfGC9Y8BiXRFxVX/0U9HvVPppgiFpTVK1r+eb3OfD72ZmZmY2pDRNfpe0d04tnyzp95I2LF1bOqeZP5zT3z/R5fEvAe8HVmoztuskvSKp0QRC0u553Jvmvz9WSmifJGnXhv1dUrp/mqSJub0ynb5LX2PyZzdR0g2SVsrtIyS9WnpOMVErihGsUv7v0I1XTMzMzMxs4PTz4ftS8vunSVWh7pY0Lp/juAO4ChjfcttTwNYR8bKkHYGzgM3ztZOB6yJi95ysvliNYVwSEQe2uXZi7uObDd7TEsBBwB9KzVNJB+FnSlqRVLnrylw9rKuI2KPU/8+BV/OfRTr9+vmnjhMj4ke5r4OAo4Bv5Wu3RcTnWp79V2B4Pm9Tm1dMzMzMzGxgjBw5EFW5Zie/R8TfgCL5nYi4PyKmtd4QEb8v0smBCaSyvMXZj62AX+TX/a3XDI7Ss24C/q/hbWOAE0iThqKfN0qTkEXpntVSKR/q/zK5NHBEvB4Rt5ef1U1Ldsr7eh1LN14xMTMzM7OB0evh+84Bi1XJ75u3eW2VrwPX5t8/BLwInJe3d91LSlh/XdK3ACKiNS2+X0naCFglIq7KFcLK1zYHzgVWA/apu1rSYkvg+Yh4rI/jPA7Yl7Tysk3p0ickTSJt3xpdVEjrhVdMzMzMzGwo6SX5Pd0obUOamByWmxYkBSqeEREbAa8Dh0OakAzCpOQ9wEnAD6quR8QfIuKjwGbAEZIW7eExe9EwSLHNWI6MiFWAi4BiG9t9wGoRsSEpEf6KvjzDExMzMzMzG0p6SX5H0gbAOcAupayN6cD0iCjOdlzK4Ca/L0E65zE+n8f4ODCuOABfiIiHSJOmumdCAJC0IPBFUlp7fxlLTn6PiNciYkb+/RpgoSaH3Vt5K5eZmZkNjLPOgrFj5/co3jkGJgX93ahx8rukVYHfkrZDPVq0R8SfJP1R0ocj4hFS0vqDAzFoST8B7oqIy0vPfxUoVw8bT9oOdU9+f3/Mh99XIyW/T8uvmyehvY1/Bh6OiOk1xzhPOn1uX7u0FWxncvK7pBVI28RC0sdIix4OWDQzM7N3mLFj/WXc+l2Pye9HAcsAp6ez4MyMiGJV4nvARbki15PAfrmvns6YSLoNWBdYXNJ04OsRcT3wT0CTFPlPAYdL+jswC/hOTn5vl9BeZU8qtnH1kE5/vKQP53H8L3Mqcu0OfFvSTOCvwJ45dLEnnpiYmZnZwBk+HMaPn9+jeGcYoBT0d6Omye95crJ/m74mAptWtPd0viQitmxzaaGIuLPLvSNKv/8S+GXFyyoT2tv0N6pN++qtbZ3S6SNitzb9nAbUykKpw2dMzMzMzGxI6RCwuIakP0h6LAcMLpzbR0l6sRQEuH/pnrdL7XVWND4LfEvSfRXjahteGBGf6fKeRpeDISUdWhrX1DzOD0TE1IiondBe0e8upbDEeyR9Ko/vtYjomE4vaZykqaW/vyTpAUmzyudiJG0n6U1SBbVZdcfqiYmZmZmZDRmlgMUdSasHe0laL1/+KXBSRKwNvEyqwFW4JCKG559zSu1/LbXvXGMIDwCHR0TVIfkivHB0xbVO72kVUmDk00VbRJxYjAs4ArglIups3+rYL3ATsGHu92ukggB1+voiMKOleSrpcP2t5cac5bIu8EiTMXsrl5mZmZkNnr4XRZgdsAgg6WJgF0kPAdsy5yD8BcDRwBl9eVgTEfE6cLuktRreehLpbMzv2lzvteTvPP0WVbSyWmGJkhYHDgEOAH5T6uuhfL2Hoc3LKyZmZmZmNniKogi9qwpY/CDpcPsrpRDCor2wW97CdGleSSgsmrc0TcgHwQGQdIykOisofZKf8UxETGpzfTFgB+Cy/upX0q6SHgauJq2adDMG+DnwRpMxNOUVEzMzMzMbXN2KInT+P/DtAhY7BS9eCfw6It7K1bYuIK2uAKwaEc9K+hDw/yRNiYgnIuKozm+i7/Kk40hg+w4v+zxwR5MtUd36zSWLL5e0FWnS8c8d+hoOrBURB0tave4YeuEVEzMzMzMbStoFLL4ELJ1DBcvtRMSfI+Kt3H42sElxc0QUr3kSGA9sNJCDb7EmsAYwKZfwXRm4L+eDFCpL/vZDv0TErcCaXUIRPwFskvu5HVgn5630O6+YmJmZmVnfNDk30vdsm8qAxRzydzMpW+Ni4KvksxWSVoyIIp9jZ6A4G/F+4I28kjIM2AI4oS+Da6cqvDAipgDLlV4zDdg0Il7Kfy8FbA18paWvjgGLnfrN51+eyJ/XxsDC5FBESQ9HxLotfZ1BPqeTV0yuKpc17k+emJiZmZlZ3wximGangEXgMOBiSccC9wO/yO0H5TMXM0nBhKNy+0eA/5I0i7ST6PgcNIikY4B7IqJJKGIv4YWd7ArckA/VF/03CVisshuwbw5u/CuwR56kDKN6O1xbknYFTgWWBa6WNLFbWeROPDExMzMzs76rG6bZD0GTHQIWnyRV7WptP4JUcre1/fekRPaqZ/R0xqRpeGGneyPifOD8lpfVDlis6jcifkoqq9zq46QyzJ36mQasX/r7cuDyuuPoxhMTMzMzM7P6ZgAHSFqy7uQlIl4DOoYX1hURU0mle/tVRFzVX31J2hI4nXTupzYffjczMzOzIaVp8nvp+u45BX3T/PenJd0raUr+d9vWZ1VYH1iIfLC+pf91Jd0p6S1JTUMWT5U0o/T3qjlF/v5c5ninhv1dJ2lSTmY/MwdTImnDPMYpkq7Mqzmd+llU0l2lvv6tdE2SjpP0qKSHJB2UL60EvJd5Axk78oqJmZmZmQ2uiRN73tJVSn7/NKlC192SxuVzHEXy+8WSziQlvxcHt5cADgL+UOruJeDzuVzw+qRzK+Xsk3YOjYhLK9r/kp/xhYprnd7TpsDSLc0/BH4TEWfkZPtrgNUbdPvliHhNKf3wUtKKzcWkpPfREXGLpK8Bh5LS6tt5C9g2ImZIWogUIHltREwgndVZBVg3ImZJWg4gIi6R9DzQaHLmFRMzMzMzGzwjR/b1kPzs5PeI+Bvpy/Yu+Qv4tqQv4ZCySsoThDGkiltvFg0RcX9RLhh4gBS2uEivA4uIFyLibuDvde/JE60TSQntc3VHOkQPsBQVKzRdxvJa/nVBUuWtItPlw8Ct+fcbSYfhO/UTpbT4hfJP0de3gWMiYlZ+7QtNxtjKKyZmZmZm73ZNyv1WaVKR64AD0k8nnQMWq5LfN6dD8rukjYBVIuKqDlusdgPuL/JOJJ0DnBkR93QebJ8dCIyLiOc09/s+GrhB0veA99EhBLEdSdeTJnLXMmfCNpVUMvl3pFWUVarvnqufBYB7SdXA/jMiilWnNYE9cnWuF4GDIuKxpuMseMXEzMzM7N2uKPc7NDRKfs/ldU8CftC2Q+mjpG1g35x9Y8T+Az0pkbQSaXJwasXlvYDzI2JlYCfgl/m91JZL964ILMKcpPuvAd+VdC+wBPC3Gv28HRHDSUGNH8vb3sj9vhkRm5KCK89tMr5WXjExMzMzs/rlfqv0QwngBromv+dVk6J9CdKB9fF5RWIFYJyknSPiHkkrk0re7hsRTwzi+4CUMr8W8Hge22KSHo+ItUjnY3YAiIg7JS0KDAMabZeKiDcljQN2AW6MiIeB7QEkrQN8tkFfr+TU9x1IKy/Tgcvy5cuB85qMrZVXTMzMzMxsKJmd/J6rbu1J2goVQJH8Djn5PSJejYhhEbF6zvOYABSTkqWBq4EjIuKOgRy0pJskzXWwPiKujogVSmN7I09KAJ4Gtsv3fgRYFHhR0gdz8nunZy0uacX8+4KkFZeH89/L5X/fQzpgf2b+u7JfScvmzwlJ7yVtKXs4X76COSsxWwOP1v08qnhiYmZmZmZDRl4NKZLfHyJVrionvx8i6XHSmZNfVPcy24GkFYsfSZqYf4ov7ucUZYXrkrSCpOmknJEfSpouaUn1ltb+A+AbkiYBvwZG5cnXiqQE+07eR1oVmgxMIq2ynJmv7SXpUdLk4lnmrHK063dF4Obc192kVZci8+R4YDdJU4CfAPs3eH/z8FYuMzMzMxtSmia/t7xmROn3Y4Fj27yu8ZfsiPgTaQvZXPKZjK5p7RGxeOn3B4EtKl5WJ6H9eWCzNtdOBk6u229ETCZtOavq6xUabAXrxismZmZmZjakqGHAoqRRkl4srYrsX7rnp5Km5p89ajx+XeDXkq6uGNcyORRxhqTTivaImBoRlWntko6W9ExpbDvl9o+V2iblyldExGkRMa7BZzVaKVRyWKltRO73AUm31O1XLSGQue3Lkh7MfY3NbQcB1wGfqTtO8IqJmZmZmQ0hvQYsApdExIEtfX0W2BgYTqowdUsOD3yN9q4DLo2In1Vce5MUVrh+/qnrpIr+pgKbRsTMfF5kkqQrS+WQu5K0CulzerrUtjRwOrBDRDxdbF2r0dc8IZCS1gaOALaIiJdLAYunAKe0TmK68cTEzMzMzPquD2nuDc0OWASQVAQsPkQ6iD0yv+4CUhbIGVWdZOsBt+Qv+zPzeY4dgN/0MrCIeJ2UjL5W1xd37+uN0p+LMifUsImTSMGNvyu1jQR+GxFP5+d0rfJVCoEcCexauvQNUq7Jy3X76sQTEzMzM7PBMnhf3ptpEpBYZeTI7q/pP40DFrPdJG1Fqhx1cET8kXQw/MeS/h1YDNgGeBBA0jHAPU22TfXBgZL2Be4BflB80Ze0OSkbZDVgn4arJTsDz0TEJM0d3LgOsFAu+7sEcHJEXNhtfFSHQK6Tn3UHsABwdERcV3eMrTwxMTMzMxsMg/vlfXDVSXNvonPye6OAxfzvlcCvI+ItSd8iraZsGxE3SNoM+D0pufxOcmWqiDiqx9E3dQYwJo91DPBzUggiOWH9o7lc8AV5m9mb3TqUtBhwJDmvpMWCwCakUsTvBe6UNCEiKkv9ak4I5Ig2fa2dr60M3CZp/XwovjFPTMzMzMwGQ39/ee9P78RVnPaaBiwSEX8uvf5s0lkU8rXjgOMA8uHtxwZ09C1yBS3y888Grqp4zUOSXiedW6mTRr8msAbpXAqkz+I+SR8jfX4v5W1nr0u6FdiQ9hkknUIgpwMTIuLvwFOSHiFNVO6uMcZ5uCqXmZmZmQ0ljQIWAYqwwWxnUv4JkhaQtEz+fQNgA+CGgRi0pAvzxKC1vTy2XUmH3osKYwvm31cDPgxMy3/PE9ZYFhFTImK5UnDjdGDjXM74d8CWkhbMKyubM+fzaBoCeQVp+xu56tc6wJNNPpcyr5iYmZmZ2ZCRq1QVAYsLAOe2BCxeLOlY4H7mBCwelM9czCSFHI7K7QuRth8BvAZ8pTjH0esZE0nTgCWBhSV9Adg+VwzbAHiu4pYTJA0nbeWaBnwzt38KOFzS34FZwHci4iX1FtY4W159uQ6YnPs9JyKm9tjv9cD2kh4E3gYObVmdasQTEzMzMzMbUpoGLEbEEaSytq3tb5Iqc1U9o6czJnlVYS6SlgQeywfuW1+/T5t+fgn8suLSetQIa+w0pog4kVRlq3G/LSGQQUq5r8xoacpbuczMzMzM6psBHJBXVGqJiNci4kv98fBOYY3vlH4lrSlpIvB81xeXeGJiZmZmZkOKmie/byXpPkkzJe3e0tcJObX8IUmnSJ1LgpEOoC9EPljf0te6ku6U9Jak0TXfy/mSntKclPfhuX1vSZPzz+8lbVinv1K/J0p6ON9/eQ5WbJso36Wvi/LnPVXSuZIWyu3vz31PlnSXpCJUsvhsVlEpcb4bb+UyMzMzs3duxkoL9Zb8/jTpXMnolr4+CWxBOv8BcDuwNTC+yzAOjYhLK9r/AhwEfKHh26rq7ylg65yoviNwFumgel03AkfkMzk/JW1lO4zeEuUvAr6Sfx8L7E/6XP8/YGJE7CppXdJ/l+3ydrDh+bxNbV4xMTMzM3u3GzmybwGLg2t28ntE/A0okt9FSn4vvuBfQJ4gRMS0iCgOe5cFKVV9YWAR0kpIo+1Hc3UW8UJE3A38vdc+Sn39vghaBCaQSv42uf+G0mRj9v0R8UapvVaifERcExlwV2ks6wE35dc8DKwuafkm4yzziomZmZnZu907LWOl826qXpPf5xERd0q6mVQtS8BpEVGUzj0HODMi6uSG9NVxko4ifck/PCLearn+deDaPvT/NeCS4g/1mCift3DtA/xLbpoEfBG4PZdCXo00aelpcucVEzMzMzMbSnpJfq/uSFoL+Ajpy/QHgW0lbQUQEfsP0qTkCGBdYDPgA6TtVuUxbkOamBw2763dSTqSVCb5oqItIv4QER/NzzxC0qI1uzsduDUibst/Hw+8Px90/x6pRHOtSU4Vr5iYmZmZ2VDSOPm9g11JyeUzACRdC3wcuLXfR91GRBTZJm9JOo/SOZgc+ngOsGMv+SCSvgp8jnTuY55JWpNEeUk/BpZlTs4KEfEasF++LtK5mKeajrPgFRMzMzMzG0oaJ7938DSwdU5BX4h08P2hgRh0u7T2Ivk9f7H/AnOS31cFfkvaavVonb5aXrMDaZVl54h4o9S+hhomykvaH/gMsFdEzCq1L11UPiMdiL81T1Z64omJmZmZmQ0ZeTWkSH5/CPhNS/L7IZIeJ505+QWApM0kTQe+BPyXpOL1lwJPAFNI5yUmRcSV+Z5zJG3aZGySVsjPOQT4oaTpkpbskqp+kaQpeQzDgGNz+1H5PZyeS/vek59RN6H9NGAJ4MZ8/5m5/VOkSlwTgcuplyh/JrA8cGfuqwif/AjwgKSHgR2Zc/akJ97KZWZmZmZDSg/J73dTUdUqIt6mtDWp5dr+PYzrT1XPyfkelanqEbFth+dXjaFuQvtabdobJ8pHROWcISLuBNbuNI4mvGJiZmZmZlbfq8AYSd+qe0N/pqoPkeT39+YVmYWYt0RzW56YmJmZmdmQovbJ7wfmtignjndKZG/XVwcvA0sCK1WMaxlJN0uaIem0Bu/ne3kMD0g6IbetLumvpZT2M7v1U9HvlyU9mPsdm9tWk3Rv7vOBOhMsSUdLeqY0lp1y+6dzX1Pyv9sC5FWXl4GlgA/VHa+3cpmZmZnZ/HHWWTB2bKNb1Dn5/Q7gKuZNbq9MZO/SVycnRcTPKtrfBH5EqnK1fs33sw2wC7BBRLwlabnS5ScioqfkS0lrk0oRb5HT44t+nwM+mZ+1ODA1v+duFcyq3vNLwOcj4tm8Xe16cnZMRGwjaXyTMXvFxMzMzMzmj7FjYeLEpndVJr8DRMT9ETGt9YYOiext++pFRLweEbeTJih1fRs4vghVjIgXen1+i28A/1mkxxf9RsTfSgGOi9CH+UD+vIsJzQPAopIW6bU/T0zMzMzMbP4ZPhzGj5/7p7Oq5PeOpXN76UvSMZJ27rHfJtYBtpT0B0m3SNqsdG0NSffn9i176HcdSXdImpDLBwMgaRVJk0nv/ac1VksADpQ0WdK5kt5fcX034P6K1PraPDExMzMzs6GkccJ7L31FxFERMa7HfptYEHg/KdjxUOA3OdPkOWDViNiIVH54rKQlG/a7NjAC2As4R9LSABHxx4jYgFQe+KuSlu/S1xnAmsDwPK6fly9K+ijwU9pUOGsyYDMzMzOz/tftDMnEiWnFpJl2ye+96M++ejUd+G0OiLxL0ixgWES8CBTbu+6V9ARpFaRjQntLvxMi4u/AU5IeIU1U7i5ekM+GPABsScp0qRQRzxe/SzqbdI6n+HtlUh7KvhHxRM2xVfKKu69jxwAAIABJREFUiZmZmZkNjN7OkHRTmfz+DuirI0kXSponYwW4Atg2v2YdYGHgJUnL5sP5SPoQaVLxZJe+WvvdJr9+GGlS86SklSW9N7e/H9gCeKRTv8rp9NmuzEmnXxq4GjgiIu7o/il05hUTMzMzMxs4xRmSKiNGNO4uImZKKpLfFwDOLZLfJR0E/CuwAjBZ0jURsb+kFUgrDUsCsyR9H1gvIl7r0NcxwD1Nt3NJmpafs7CkLwDb5ypfG5C2QbU6FzhX0lTgb8BXIyIkbQUcI2km8DbwrYgoUtnb9VV2PbC9pAfz/YdGxJ8lfRr4uaQgbWX7WURM6dLvCZKGk7a5TWPOlq0DSdvBfiTpR7lt+14P8HtiYmZmZmZDSofk91OAUyraKxPZu/R1VI9jW721LZ8NeSwi/ljx+r8BX6lovwy4rElfLfcH6WzKIS3tN5ImIE3GuE+bZxwLHNtpHE14K5eZmZmZDSntQhHVPmBRkk7J1yZL2rh07aeSpuafPWo8/pPATyTdUjGuyoDFiHgtIr7U4f3ME7CY2zdQCoZ8IIcYLtqtr5Z+awcs1ulX0ujyZytpKUlXSpqU+9ovt68p6f+ArZi3RHNbXjExMzMzs/ln4sRGW7rUW8DijqQzGmsDm5OqTG0u6bPAxqRqU4sAt0i6NiJe6zCE3wM3DHTAoqQFgV8B+0TEJEnL0OBLvvo5YFHSKqTP/OlS83eBByPi85KWBR6RdFE+BL+EpBkRManumL1iYmZmZmbzx8iRvVTlahywmK9fGMkEYOl8oHs94JaImBkRrwOTgB0q7q+lnwMWtwcmF1/sI+LPEfF2g377O2DxJNL5nXJp5iBNQAQsDvwFmNlgjHPxiomZmZmZNdOtDHChWzngAw5IP61UFS8yW1Uo4uZdRtIuSHES8GNJ/w4sRqpi9WAaQm+H33tQBCweR5rQjM4p9esAIel6YFng4og4oUM/Vf0i6Q7Swf6jI+K63LYKqZrWWqRD8d1WS3YGnskrN+VLp5GqmD0LLAHsERGzGoxxLp6YmJmZmVkzRRng5qsd/aGXgMXKeyLiBqWk9d8DLwJ3kv+Pf6+H33tQDljcjBSw+KHc/qnc9gZwk6R7I+KmBv0WAYsrA7dJWj8iXskH3DeQtBJwhaRLy1klZZIWA44kreC0+gwwkVTueE3gRkm3ddkK13HAZmZm9m5X9/+ANzH/vrjaYOhUBrjQQzngGnoJRWx7T0QcBxwHkA+IP9ZvI62nMmAxt98SES/lsV1DOg9Td2LSXwGLawJrAMVqycrAfTnvZD/SNrQAHpf0FLAucFftd1/iMyZmZmY2UEF4ZgOhl1DEccC+uTrXx4FXI+I5SQvkQ+VI2oBURveGgRi0GgYsknJINpC0WD4IvzVztpkNWsBiREyJiOUiYvVcCnk6sHEuwfw0sF2+d3ngw+QQyF54xcTMzMySOv8HvImB+b/l9i7XS8AiKadkJ+Bx0rao/XJ3C5G2OAG8BnwlImbmvuZrwCLwcj77cjdpq9o1EXF1vmewAxbbGQOcL2lK7uuwYoWnF56YmJmZmdmQ0kPAYpBK27a2v0mqzFX1jPkasJiv/YpUMrhWXy339lvAYsv9q5d+f5bqsyc98VYuMzMzM7P6ZgAH5BWVWpqEIg5mXwPVbw5YnAhUHqhvxxMTMzMzMxtS1E/J75KGl5LVJ6te8vviwPvajKsy+b3G+5kn+V3S3krp7MXPLEmNqkmoOvm98XuWNErSi6Wx7F+69lNJU/PPHgA5YPEB4P2Sdq87Xm/lMjMzM7OB0zDZvRv1Y/I76bzJvhHxWC6de6+k6yPilS7DOGmgk98j4iLgovyafwJ+FxG1K1SoffJ7r+/5kog4sOUZnyVVChtOCmu8RdK1efVlb0nn1x0veGJiZmZmZgNl5MiB6HV28juApCL5/cGIuD+3td4zO/kdmCBpaUkrRsSjxQty6dwXSGGG3b6kV8rp8bdLWqvBbe2S38v2An7dcDjtkt/78z2vRyppPBOYKWkSsAPwmx768sTEzMzMzAZIu2T3bgYv+X12BapcJndh4In89/xOfi/bgzS5atpvZfJ7ofU9d7GbpK2AR4GD8yH5ScCPc/WwxUjliR9sOM7ZPDExMzMzezfqS6jm/A3P7Lfk99kXpRWBX5JK9c6C+Z/8nld3kLQ58EZETO2h38rk99zvPO+5gyuBX+etZt8CLgC2jYgbJG0G/z97dxpuR1Wmffx/K5OMYVBAHEIzCIh0ZLZpNICCYgMyCQRBUEQUpAVBpVVEIi+DAzYdlQYExCYCgtBhUjASAWUwwAkEggyCyCDzYIAgIc/7Ya2dVHZq711VZ5+E07l/15UrOat2PbV2+Q5nsYabPwBPAjcAM2v2czZvfjczMzNbGA3fUM2+Jr/nY3IvB74eETf2sZ9VzU5+j4ibgVbye8ue1F/G1ar7vxHxakQ8QApRXAvqf+eIeLq11Aw4HdiocO24iBgVER8iDQDvbdBXwDMmZmZmZguvpqGaCzY8c3byO/AI6Rf3XptZJgCH5P0omzEn+X0x4GLS/pNfDGWnJZ0DjMuDj6JW8vuktuR3JL0B2B14f8Va7XX3IgUgFpPfO35nSccDN0fExW3tq0ZEa9nbjsC03P5GYEQObtyAlI9yVe+3Uc4DEzMzMzMbNvqc/P5x0i/9K0raL7ftFxEDr4Pkd3LfHm5t9C8YTPL7J+jwnYH3kAZx7Q6VtCNpmdYzQOu+RUlLxABeAD6RN8I34oGJmZmZmQ0rfUx+nydZvXDt9ZD8Pom096RSrbZ7OyW/d/zOwKIRcUNJraNIRw+3t88gnczVF95jYmZmZmbDSr8CFvO1k3LY4LT8ma5HggEfBY6XdHlJv9bJ4YWvSDqi1d4tVT337ThJ9+Q+HJrbl5N0qaQpuX/796pVse5O+R0MSJos6V8L/dyuR80JeWan9fPYQq2rci4KkvaQ9HfSjNSMXn1t8YyJmZmZmQ2twZwA1kZ9DFiU9C/AFqSlUQDXAx8oub/oTuDEiLiw5NozwKHAx2p8pf1IG/PXiYhZhSDEg0nZLDtIejPwJ0nn5hmWwdSdCEyIiMj7Qi4A1ulVTNIuwPS25u9ExDfy9UOBo4GDIuJ8SY+Tjj6+rGJ/PWNiZmZmZkOsvyeAzQ5YzL+ktwIWiYjbIuLBkntmByzmU6hG5ONyA1iCtOF8cdKeicebdiwinsgZJK/WuO1zwLGFY4pbAYsBLJNncJYmDXrq7N8orRsR0wt7WJai91HLSFqatCTs28X2iHih8GOlWt14xsTMzMzMhl6dE8DmU8BiRNwg6RrSRnKRTrpqnTh1BnBqREyu1unG1gD2kLQzKQvk0Ii4FxhH2oj+KLAMsEeFvJEqdcltxwNvIS1N62Us8D3SwQFzycGQ+wLPkwIWG/OMiZmZmZkNJ30LWJS0JrAuKddkNWDrnG5ORBwwHwYlkGZqZkTExqSMkDNz+3bAAPBWYBQwLm98H2xdIuLiiFiHtORsbLcikkYBa7YfIVyo9bWIeDtwLnBIjf7NwwMTMzMzMxtO+hmwuDNwY17eNB24krZTsOaDh4GL8r8vZs5+l/2ZE7x4H/AAFfaCVKg7W0RcC6xRPCigxPuAjfIxyNcDa0uaVPK58cCuNfo3Dy/lMjMzM7P6BgaqBy0ODKSlXP3Rz4DFh4DP5GBBkTa+/6BfHS2SNBHYNyIeabvUClg8Mz//ntz+ELANKSdkZeBdwJ9zrbvzjEc3pXXzLNH9efP7hqT9NU93qhsRPyYdFoCkkcBlETE6/7xWa3kYKXjx7irvohMPTMzMzMysnjG9xgFDp88BixeSfnm/g7Qc7FcRcWmuVXuPiaRVgMmkgMVZkr5IyvmYDqxJ2sDe7gTgXEmH5c8dkNvHklLb7yANmr4SEU/l2Y1eRxp3q7srsK+kV4GXSXtXokbduZ4h6V3ALOAvwEE175+LByZmZmZmVs+BB6Y/VVWdWamojwGLrwGf7fCMA8rae/Trb6RlYnORtD5wUUS8XHLPc5RsQI+IR4FtSx6zOem45F596VT3RODEJnXziWfrF34e1NKtdh6YmJmZmZlV9zwwVtJKEXFqlRsiYiptCexN1ckFWVB1Je0BfBO4pc593vxuZmZmZsNKl+T31SXdJOleSedLWiy3v0PSNZJuy0nl2+f2D0m6RdId+e+tKzx+OVL2SVm/SpPfe3yXTgntR+ZE9QFJUyW9JmmFKjXz/cdIeqRQY/u26++QNL1qP/M9/yVpeuHn90u6VdJMSbsVPjoZ+AfpcIHKPGNiZmZmZkOvzmb5Lnokv58InBwR50k6Ffg0aeP214ELIuLHktYjLQMbCTwF7BARj+blVr8mHRvcy5FDnfweEd8BvpO/8w7AYRFRtkelm5Mj4rudrpFOIatE0sbAiLbmh0j9n2twExH3A6OKg5gqPGNiZmZmZkNrzJh+nspVmvyeE9K3Jm1oB/gpcwYIQdqQDmnG41GYnRTfOmr4TmAJSYs37Vifk9+L9gJ+3rRf7SR9jHTC150VP/9G0iDpy8X2iHgwIm4nbX4fNM+YmJmZmdnQqrtZvlny+4rAcxExs9Demv04BrhK0heApYAPltTdFbgtIl5JXVjgye/kfiwJfJhm4YWHSNqXtLTqSxHxrKSlgK+QZpyqLuM6BJiQj1hu0I1qPDAxMzMzG65OOw3Gj292b3+zReanTsnv3RLh9wLOjojvSXof8DNJ67dmKSS9m7QMbPYpWE1O5WpodkK7pF1IuSNbFq7vAPy+wTKuH5OOHI789/eATwHfIi3xml5lkCHprcDuwOiaz6/NS7nMzMzMhqvx49MAY+HSKcX9KWCEpEXa2iHtNbkAICJuIG1eXwlA0ttIyej75r0R81uvhPY9abCMKyIej4jX8uDrdNISOEizSyflJPcvAv+Rc2E6eS8pg+W+fM+Sku6r258qPGNiZmZmNpyNGgWTJtW/r8/ZIvNRafJ7Dgm8BtiNtO/kk8D/5ntaKepnS1qXNDB5UtII4HLgqIj4/VB2ukHyO5KWy22fqFir+JlVI+Kx/OPOwFSAiNiy8JljgOkRMa5T3Yi4nBRY2bpnekSsWfFr1+IZEzMzMzMbNvIeklby+zTSaVutTdxfAQ7P/0V/ReAnuf1LwGckTSHNPuyXQxcPIc0GfKNwrO5bIO0xySdRVSZpFUkPkzJLvi7pYUnLSnoD3ZPfd80J78czJ6Ed0oDiqoh4sfCMbrWKTsrHIN8ObAUc1qPvVesW79kkf9/dgf+WVGkzfSeeMTEzMzOzYaVL8vufmbNkqdh+F7BFSfu3gW93eMYCTX7P184Gzm5rXq9TrbZ796nQ32Ma1F268O8/UvJ9m/KMiZmZmZkNKw0CFk8uzIjcI+m5wj2fzJ+/V9InKzx+U2C8pHNL+lUasBgRUyOiNPld0nWFvj0q6ZLcLkmn5O94u6QNe9XqUL89FPGdkibmmpPyHptefZyU33f7rNJ+kp4stB+Q27eS9DKwZNV+gmdMzMzMzGxBqnmyWJOAxYg4rHD/F0gbulFKUv8msDHp9Kpbcq1nu3ThZuDofgUstu35uIg5+2I+AqyV/2xGOmVrs6p1c72yUMTvAudExE+Vku6PB3rOrgB7dzg6+fyImGvzfERcA7zJAYtmZmZmNnzUP1msScBiUTGscDvg6oh4Jg9GriZlhjTSMGARAEnLkPp/SW7aiTSAiIi4kXTi2Ko16pWGIpKWbE3M/74mP+d1wQMTMzMzM1uwWieLtf50VxawuBrdAxaBtIwJWB34bY9ajTa/D9LOwMSIeKFX3yqaHYrY1j6FFCbZeuYyklasUO+svFzrG5o7AGXXvCzsQklv73h3BR6YmJmZmdlw0iRgsWVP4MKIeK1HLSLigPmQ+l5UnMmBat+nVCEU8b9KLh8BfEDSbaSjiB8BZpZ8rmjviHgPKfhxS+Ys/boUGBkRGwC/Ic1SNeaBiZmZmZkNJ00CFlvawwo71Zqv8ozFpqRMlZbB9K1jKGJEPBoRu0TEe4Gv5bbnuxVr5ZpExN+B8bmvRMTTEfFK/tjpwEYV+1fKm9/NzMzMrP+qbmofGEhLuaprErCIpHcBywM3FGr9Gvh/kpbPP28LHFWnM1X1CEXcHbgsImYU2iYAh0g6j7Tp/fnWsixJd0fEOp2e1S0UUdJKwDM5Ef4oUrBj63Pz1M0DvRER8ZSkRYF/I82OtIc47kjKlWnMMyZmZmZm1n/1N7VX0jBgEdJSqfNysGKr1jPAWNJg54/AsbltfgYswrwzOZByWv4M3Eeajfh8fsZKlC/zqmo08CdJ9wArA8f1qLs48Osc1DhAGgyenq8dKulOpeDKQ4H9BtEvz5iYmZmZ2RBpbWrvZvTo2mXrBizma8d0aD+TwqxBoX2+BCzm+0aXtAVwcMnHNycdl1ynX8VQxAuZc3JZz7o5db50iVZEHEUfZ5g8MDEzMzMzq+55YKyklSLi1Co3RMRU0izKoEXEZf2oM5R1Ja0BXAQ8Xuc+L+UyMzMzs2GlbvJ7vvZxSXflpUfjC+11k9+XA5bo0K/S5Pce36VW8ntd7cnvua30XXSpUZr8XqHWynX66hkTMzMzM1uwBgYqL+lqkvwuaS3SkqMtIuLZ1i/WDZPfAY4crsnvnd5FBfMkv3eqFRH3A6Oc/G5mZmZmw8eYMXVP5WqS/P4Z4IetAUdEPJHbF8bk907vool+1vKMiZmZmdlCq8ZMRaPaVQYcBx6Y/hSp66FTZYnom9E9+X3tVFa/B94IHBMRv+pQa3byO3DqfAxZrJr83p7k3sns5Pe5g9o7votezpL0GmnvyLfz5vymtUp5YGJmZma2MBozZkH3oKkmye+LkJZEjSadmnVdPimra/L7oHtaz17AGYWf+5H8Prrkcum7iIjnupTcOyIeybM6F5GS389pWKsjD0zMzMzMFkZlMxX9NFQzMRWS3/OsSTEp/WHgxoh4FXhA0p9Iv1A/zNy/vL8NmDRUHe+kkPy+c6G5X8nvkJPfc8hip3fxx07FisnveYP7pqSBSe1a3XiPiZmZmZkNJ7OT3/OpW3uSliwF0Ep+h7mT3y8BtoLZQYJrk8ILfw1sK2n5nP6+bW7rO0kTJa3W4XKn5Pd98+lcm9OW/N7tWRFxeUSsEhEjI2Ik8FIr+Z3O76K0rqRF8ucoJL9P7VWrCc+YmJmZmdmwEREzJbWS398InNmW/H6epG8DtzEn+b01ALkLeI10qtbTAJJaye/QlvxOzT0mklYBJgPLArMkfRFYD5hO7+T3E9rargC2JyW/vwTsn58x2OT30ndRIfl9UdL7/g1zkt87vtcmPDAxMzMzs2GlbvJ7nk05nJKQw4Uw+b3Tu2iS/N7xvTbhpVxmZmZmNqzUDViUtJ+kJwsBgQfk9q0KbQOSZkjqlUGyDvBzSZeX9GtFSddImi5pXKs9IqZGRMdf3iV9IX+fOyWdlNsWk3SWpDskTZE0Ote6LCJOqfGudpMUOdOk2P6O3M8jqtatGtaY3+vLwJJV+wmeMTEzMzOzoTIExxE3CVjMt54fEYcUa0XENcCoXHcF0rKpq3p04VfAhRHx3ZJrM4BvAOvnP1W+z1akzJINIuKVQuDhZ3If35PbrpS0SUTMqlI3116GFPh4U8nlk4Era9SqHNaY3+ubHLBoZmZmZgte/eDEqpoELFaxG3BlRLzUtGMR8WJEXE8aoFT1OeCEiHgl12iFFK4HTCy0PUdKqK9jLHBSe3/yrNCfgTvLbmo3n8IaPWNiZmZmZkNgMMcR9z9gEWBXSe8H7gEOi4hiDUgb0L8/pws6FpgcERMafYfq1ga2lHQcaQBxRE6Pn0IacJ1HOjZ4o/z3zVWKSnov8PaIuKy1XCu3L0U6JOBDwBGd7m/T77DGUh6YmJmZ2dAZymRxq56u/n9Lk4DFS4Gf56VSB5FmU7aeXVBaFXgPhaOCI+LovvW4u0WA5UmbzzcBLpD0T6QN+euSTvn6C/AHYGanIkWS3kBaqrVfyeVvkZa7TVf3AWCrVr/DGjvywMTMzMyGxvBNFrfXt9oBi21H2J5O2otS9HHg4hwUOL89DPwyn3B1s6RZwEoR8SRwWOtDkv4A3Fux5jKkPS6T8uBjFWCCpB1Js0u75U32I0jHGs+IiHEdavU1rLEbD0zMzMxsaAx1srgtrLNRswMWgUdIS7DGRERIagUsnkchYFHSqq1wQmBHYFpbzb1Im7iHjKRzgHER0b4U6xLS7M0kSWsDiwFPSVoSUES8KOlDwMy8wb9bLQAi4nlgpcKzJ5GWiE0Gtiy0HwNMbw1KJE0E9m0lvedal5MGNq17preFNe4FnO2ARTMzMzNbqDQMWDw0zxbMJIUc7teqJ2kkaQbmd8XnNN1jIulBUsDiYnmT+bZ5QLEB8FjJLWcCZ0qaCvwD+GQeZL2FFGw4izQA26dwT6dajeXlX91CIMs4YNHMzMzMFl4NAhaPosOMSEQ8yNyb5FvtjfaYRMTI9jZJywL3lmy4J58s9okO/XpXnVpd+jS6Q/sxhR/Xo0sIZOGeKmGNjfi4YDMzMzOz6qYDB+YZlUoi4oWI2L0fD+9nrba6XUMg65C0hqQB4PE693nGxMyGF5/wYzY0Fs7TnWyYkvRh4D9JS7nOiIgTcvvqpP0lKwC3AvtExD8kvYN0EteIfM9X86wLko4iBTG+BhwaEb9uf16bfyOd9jW5pF/vB35AWmq1Z0Rc2P6ZknsmAasCrZmKbSPiCUknA1vltiWBt0TEiJISneqeDXwAeD437RcRA4XrmwA3Anv06meXPvb6vitX7S94YGJmw4lP+DEz+7/jtNNg/PjatzVMfv86cEFE/FjSeqRlYCPzv/cE3g28FfiNpLUj4rUe3dg7byRv9xBp/0rVfJCO9SKieCLXF0inY9V1ZNmgI7/DEykcj9ykj3T4vhFxPzCqbvK7ByZmNnz4hB+zoeOZSJvfxo9vOlM3O/kdIAcQ7iRpGul0q9Z/xfopcAxpYBKkDekAy5GPEQZ2As7LqesPSLov17+hyVfK+0LIG9b7aS/gm32s9wXgIlJuSmP9/r4emJiZmZnZgjFqFEyaNG97/5PfjwGuyjMPSwEfLNS6sa3WaqkLugI4ICIeZeidJek10mDh23lTObkf7wRWB37boO5xko4GJpKWr70iaTVgZ9Igrs7ApGMf+8Wb383MzMxsOGmS/L4XcHZEvA3YHvhZPh634z0Rsf18GpTsHRHvIeWLbMncxwJDWmp2YYXlZe2OAtYhDT5WIB2lDGlPyFdq1uvVx77wjImZmZmZ9UedfSPND1yonfxO2mvyYYCIuEHSEqQAwk615ptWmGFE/F3SeNJSsnMKH9kTOLhB3VbOySuSzmLOPpCNSVkvkN7B9pJmRsQlg+hjX3jGxMzMzMz6o7VvZGjNTn6XtBjpF/cJeWlRK/kdCsnvpE3a2wBIWhdYAngSmADsKWnxfKLXWkBpmvpgSbq7pG2RnJiOpEVJJ35NLVx/F7A8bXteymqV1F41/y3gY626EbF6RIzMeSsXAp9vDUqa9LGfPGNiZmZmZv3Tad9Iu4YHLjRMfv8ScLqkw0hLtfbLA5k7JV0A3EVKhT+4tcSpyR6TfATvxaTBxA6SvhUR786/2JctG1uclO6+aP4uvwFOL1zfi7Q5v7jnpFOtdudKenP+7ABwUI++1+5jp+9boW+lPDAxMzMzs2GlQfL7XcAWHWodBxxX0r59g379kbQcrN3mpCOO2z//IrBRl3rHVK1Vcu/WFT6z32D62OX7NuKlXGZmZmY2rEj6sKQ/SbpP0lcL7atLuknSvZLOz0u9kHSypIH85x5Jz7XVW1bSI5LGVXj86sAfJP2opF/rSLpB0iuSZmd7RMRlEXFKh+9ytqQHCv0blduXl3SxpNsl3Sxp/V612uoek79Tq+72bdffIWl6q59V6kr6r2I2Saf3KmkrSS+TgiEr84yJmZmZmS0YAwO1l3Q1CVisEFY4FvhdxS5cA1zWIS39GeBQ0p6OOsqCEP8DGIiInSWtQ/rO29Sse3JEfLfTNeDKqoUkbQzMlTzf6b1GxDXAm+oGLHrGxMzMzMzmvzFjmp7KNTtgMSL+AbQCFkXK5mj9gv9TygcIewE/b/0gaSNgZeCqJp0piogn8vKmVwdbC1iPlD9CRNxNSqpfuQ91kfQx4M/Anb0+mz//RuA7wJe7fGyu99qEByZmZmZmNv8deGDaJF/2p7uygMXV6B6wCMwbVpizTL4HHNn+EEln5FmC+eG4vGTrZEmL57YpwC65L5sC76T+fo5Dct0zJS2fay1FOiTgW3XqkE4+e6zs4iBDIGfzUi4zMzMzm1edTJKW5tkkdTQJWGxpDyv8PHBFRPxVbWnzEXHAYDta0VHA34DFgNNIg4ZjgROA/5Q0ANxBOmVsZqciJX5MWqIW+e/vAZ8iDUhOjojp7d+5jKS3ArsDo7t8rGkI5Fw8MDEzMzOzebUySYZ+oFFXk4DFlvawwvcBW0r6PLA0sJik6RHxVeaTTkGIEfECsD/MziJ5IP+pWvfx1r8lnQ5cln/cDNhN0kmkPSOzJM2IiE4b/98LrAnclwcyS0q6LyLWLHymUQhkOw9MzMzMzKxc1UySlobZJDXNDlgEHiH9UjwmIkJSK2DxPOYOWCwNK4yIvQvX9wM2HqpBiaSJwL6tFPVC+6oR8Vh7EKKkEcBLeR/NAcC1ebDSsVZZ3fzjzswJWNyy8JljgOmtQUlZ3Yi4HFilcM/04qCk7L025YGJmZmZmQ0bDQMWoSSssBtJZwCnRsTkqn2TtAowGViWNBPxRdIm9umkWYdnSm7rFIS4LnCOpNdIAZCfzs94Q5daRSflo4cDeBD4bI++V63brtZ77cYDEzMzMzMbVuoGLOZrx/SoeTZwduHn2ntMIuJvlGxQzxkkF0XEyyX3lAYhRsQNwFoll9brVKvt/n0q9PeYBnWX7lJjUHwql5m7XSKIAAAgAElEQVSZmZlZdc8DYyUd1POTWURMjYjD+/HwftYaqrqS1sib9h/v+eECD0zMzMzMbFjpkvx+SG4LSSsV2iXplHztdkkb5vatCsnlA5Jm5IyPbp4lLdV6a0m/VpR0TU5Ur5IiXzuhvQ5JX8jv6c682R1JmxaeNUXSzjXqzZX8nts+Lumu/Iz2Y9xq5a54KZeZmZmZ9U+DNPc6eiS//550+tSktts+QloWtRbpVKofA5vlhPJRue4KwH1UC1rslKg+A/gGsH7+U1XfEtpbJG0F7ARsEBGvSHpLvjSVtMl/pqRVgSmSLi3kv3SqN0/yu6S1SMcdbxERz7aeERH3A6PqJr97YGJmZmZm/TFmzPx4yuzkdwBJ55F+Ab8rIm7Lbe337ASckzdo3yhpRNupVZBO87oyIl5q2rGIeBG4XtKaPT9cQSGh/cUGt38OOCEiXsl9eyL/Xfx+SzBv1ktZP1rJ72NIJ3y1fAb4YUQ8W3xGUx6YmJmZmVl/HHhg+jNY3YP/ypLfN+tRsVNafHFgsifw/Tld0LHA5IiYUKHHg3WIpH1JJ3p9Kc8+tBLaP0TONqlpbVJGy3GkmZwjIuKPAJI2A84kpcnv02u2hELye9ugb+1c7/ekE9KOiYhfNegr4IGJmZmZ2fA2VEunXp/hilAt4b3WPXlJ03tIRxCnixFHN+pdfX1JaC+xCClfZHNgE+ACSf8UyU3AuyWtC/xU0pURMaOsSI/k90VIy+NGk04ju07S+hHxXNMOm5mZmdlwNH+WTr3edEp+H8w9HwcujohX+9LDGvqY0N7uYeCXefnazZJmASsBTxaePU3Si6T9MJ3yWrolvz8M3Jjf2wOS/kQaqPyxYh/n4oGJmZmZ2XDVr6VTZeZPinsTpcnvPe6ZQFoudR7pF/7n2/aX7EXaxD1kJJ0DjIuIm9va+5LQXuISYGtgkqS1gcWAp/J7+2ve/P5O4F2kAMYmye+XkN7d2fkUtLVJe2Ia8cDEzMzMzIaNbsnvkg4Fvkz6Rfp2SVfkoMQrgO1Jp269BOzfqidpJGk25XfF5zTdYyLpQdJxwovlzevb5hPDNmDuPS0tQ5XQfiZwpqSpwD+AT0ZESPpX4KuSXgVmAZ+PiKcaJr//GthW0l3Aa8CREfF0jfvn4oGJmZmZmQ0rXZLfTwFOKWkP4OAOtR4kbYRvb2+0xyQiRra3SVoWuDci/lry+aFKaP8H8ImS9p8BPyu5pXbye36vh+c/g+aARTMzMzMbVroELK4u6SZJ90o6X9JihWsdgwAlLZtDDqvs3/gocLyky0v6tY6kGyS9UgxEjIgXImL3Dt9Fko6TdI+kaXnWB0k7KYVBDkianGc6aie0t4ci5sDGayTdlutv36uupAcl3dHqS6F99/w+Z+Wck1b7HpJmAItW7Sd4xsTMzMzM+u2002B8ewh4f/QIWDyRdJLVeZJOBT4N/LhTEGDBWNqWcnVxJ3BiRFxYcu0Z4FCgV3p80X6kpWTrRMSsQt8mko7oDUkbABcA69SoWxqKCHwduCAifixpPdLM08gK5baKiKfa2qYCuwD/XWyMiPMl3cScjfyVeMbEzMzMzPpr/Ph03PDQmB2wmJcrnQfspHRk1NZAa8DwU+YMEDoGAUraCFiZaonvXUXEEzkrpM7pXp8Djo2IWcW+RcT0vFQKYCkqBCEWFUIRv9zeTdIeGIDl6H2iWUcRMS0i/tT0/naeMTEzMzOz/hs1CiZNanZvs4DFFYHnCmGBrRBF6BAEmDd8fw/YB9hm7i7oDODUiOh0jG6/rAHsIWln0lG+h0bEvbkPOwPHA28hLSGro1Mo4jHAVZK+QBrwfLBCrcj3BPDfEXFazb5U4hkTMzMzMxtOOoUldgtRLAYB7gWcIWkE8Hngig6b0g+YD4MSgMWBGRGxMXA66TStVh8ujoh1SDM/Y6sWLIQi/lfJ5b2AsyPibaSTyn6WB2jdbBERGwIfAQ6W9P6qfanDMyZmZmZmNpx0Ckt8ChghaZE8a1IMUewUBPg+YEtJnweWJh3xOz0ivsr88zBwUf73xcBZ7R+IiGslrSFppZJ9HmW6hSJ+GvhwrnuDpCVIwYtPdCoWEY/mv5+QdDFpOd21Vb9gVR6YmJmZmVm5gYFmQYsDA2kp19AoDVjMm8SvAXYj7Tv5JPC/+Z7SIMCI2LtVVNJ+wMZDNSjpEorYCkI8E/gAcE/+/JrA/fl7bUgKSHw6X7s7z6SU6hGK+BBp2drZktYFlgCelLQacE5EtC9pWwp4Q0T8Pf97W+DYRi+hBw9MzMzMzGxeY3qFqS8Y3QIWga8A50n6NnAb8JPcXjsIsMkeE0mrAJNJm8tnSfoiKR9kOp3DC08AzpV0WP7cAbl9V2DfHIT4MrBHHqSsRPmytaq+BJyenxfAfrnuqsDMks+vDFycZ14WAcZHxK/y992ZtFzszcDlkgYiYrumHdOczf69bbzxxjF58vxYamdmg9b6L1xNNx6a2cLF/2+G9dMg/8+TpFvynovXHUlnA5d1OC640z3rA5+qkz/Spda/Af+UwyT7Jg/2HqqbdN+l3kjSe1q/6j2eMTEzMzMzq+55YGze73FqlRsiYip9SkePiFrZIDXqVgmXrETSlsCPSPt+KvOpXGZmZmY2rNRNfpe0n6Qnc3L5gKQDcvs7Jd2S2+6UdFCFxy9H2pdR1q/S5Pce3+VsSQ8U+jYqty8v6eKczn5znnWpTdIRklpLwDomyveosZik05TS6e+WtGtuXzy/5/vyex9ZuO2NpE31lXnGxMzMzMz6r+nG+R6aJL/nW8+PiEPayj0G/EtEvCJpaWBqrtUrdPDIPia/d6r3H8BAROwsaR3Sd95m3ls7k/R20nt6qNDcJFH+a8ATEbF2Plp4hdz+aeDZiFhT0p6k979HRFwnaXuc/G5mZmZmC9SYMUN5KleT5PdSEfGPiHgl/7g4g/zduGHyeyfrkQYRRMTdwEhJK9escTIp+X32pvKGifKfIgU9EhGzCkcW70R6z5De+zZS93TMbjxjYmZmZmb9deCB6U9T/U9+B9g1BwPeAxzWClXMswqXk07NOrI1WzIfk98BjpN0NGkg8tU8WJoC7AJcL2lT4J2kbJbHqxSUtCPwSERMaR8r1EmUz0GUkPbVjAbuBw6JiMcp/G+RT0t7nvS/Q629JS2eMTEzMzOz4aRJ8vulwMiI2AD4DXP+Kz8R8dfcvibwydasxHxMfj+KtJRqE9ISqa/k9hOA5SUNAF8gHX9cdpzvPCQtSVp+dXTZ9ZqJ8ouQBkS/z+nvNwDfbT2qrHyVPpbxwMTMzMzMhpOeye9t7UTE04UlW6cDG7UXzTMldwJbDlG/S0XEY5G8Qkp93zS3vxAR+0fEKGBfUlbIAxXLrgGsDkyR9CDpXdyac1aKz74WWKO1Mb6Dp4GXSKn0AL8ANsz/nv2/RX7vy1Ge1VLJsFnKddppMH78gu6F2TAy8AMmjfrigu6FmZlZv9VOfpe0akQ8lu/fEZiW298GPB0RL0taHtgC+P5QdLpT8nurb3lvxseAqbl9BPBS3kdzAHBtRLzQrVZLRNxBWqbVesaDpFT7p1QzUT5/7lJgNPBb0gb8u/LlCaT3fAPpvf+2sH+ltmEzMBk/Ph3uMHT7qMzMzMzs9a5h8vuhec/FTNJ/0d8vt68LfE9SaynYd/Mv9fMz+f1cSW/Ozx8AWkcWrwucI+k10kDg0/kZb+hSq4omifJfAX4m6QfAk8D+uf0nuf2+3J89G/YJGEYDE0iDEgfSmlU02rMlZmb2f1NEXAFcUdL+Z/JSqLb2o0h7OdrbrwY26PCMAxr062+kZVNzyRkkF0XEyyX3bN2h1g3AWiWX1utUq0u/Rhb+fSLpWN92m5OOJC67/y/A+0vaZwC7V+1HL95jYmZmZmbDSoOAxYMk3ZFDBa+XtF5u/1AOWLwj/106SGizKTBe0rkl/SoNWIyIqRFRmvzeJWCxNAixW622uj+RNCXXuDDntHR8FxFxWUScUuH7t+pPkDS18PN3JP1NKdBxS9KBA7VO5xpWMyZmZmZm9jo3xBuDGwYsjo+IU/P9O5L2kXyY9IvzDhHxaJ7V+DVzHzFc5mbg6PkQsNgkCLHosMKelO8Dh5BO+ur0LiqTtAtpedpsEXGkpBfzv68D3lOnJnjGxMzMzMz6qbUxeOjUDlhs/YKezQ4VjIjbCinvdwJLSFq8acf6GbDYMAixeH9rUCLgTcz5zqXvoqo883I48O0691XhGRMzMzMz66/BbgwegoBFSQeTfqFejDSAabcrcFvrWOHXQcBirSDEMpLOArYnbZ7/UqG917voZizwPdIRwn3lGRMzMzMzG06aBCwSET+MiDVIJ0x9fa6C0rtJy8A+W/j8gg5YrBuEOI+I2B94K+l45D0K7R3fRTd5/8uaEXFxzw834IGJmZmZmQ0ntQMW25xHYQ9IzjK5mJQLcv+Q9LiLTgGLbZ+pEoTYqf5rwPmkGaF2c72LCt4HbJRzUa4H1pY0qW6fOvFSLjMzM7OF1VBsVB/64LkmAYtrRcS9+f6PAvfm9hHA5cBREfH7oex0g4DFWkGIbTUFrBER9+V/7wDcna91eherAedExDad6kbEj0mHCSBpJHBZRIyu9SK68MDEzMzMbGE1DBOsGwYsHiLpg6RN6c+SBi2QTqpaE/iGpG/ktm0j4onXQcBikyDE2V0Bfipp2fzvKcDneryLVUkBlAuMByZmZmZmC7N+J1iPHt2/Wh00CFj89w51vk2H06VeBwGLtYMQC/fOArbocK30XVSp21bnQWD9qp+vwgMTMzMzM7PqngfGSlqplQfSS0RMJZ2CNWgRcVk/6pTUHTeY+yV9B9iZdGJXI978bmZmZmbDSr+S3/O1o3KdP0narsLj/5n0O/Q8G+slvV/SrZJmStqt4ndZTNJpku6RdLekXXP74vk73Je/08gq9Qp1f5WT3++UdGoOpkTSCpKuzu/oaknL16mba7xR0m2SioOkt5JOFXuybr0Wz5iYmZmZWX8NDAzZkq5+Jr/nAcqewLtJv1j/RtLa+SSrbvbusPfkIWA/4IgaX+lrwBMRsbakN5B+uSf3/dmIWFPSnvm77dGpSImPR8QLefP7hcDupEMBvgpMjIgT8qDuqxSOKK7o30lHEC/baoiIvSWdXbPOXDxjYmZmZmb9M2bMUG+m71vyO7ATcF5EvBIRDwD3UbJHpaqIeDAibgdm1bjtU6QQRSJiVkQ8VejbT/O/LwS2yd+xal9a33kR0olexe/cqjv7HVWVj1f+KHBGnfuq8IyJmZmZmfXPgQemP4Mx/5LfVwNubKu1Wv78FcABEVGWhdIX+bhiSHtWRgP3A4dExOMUvmc+iex50nd8qqxWh/q/Jg20rmTOgG3liHgs131M0ltqdvsHwJeBZWre15NnTMzMzMxsOOln8nvHeyJi+6EclGSLkE7x+n1EbAjcAHy3V9+qiojtSMcAL86cwVhjkv6NtOzslsHWKuOBiZmZmZkNJ/1Mfu9Ua355GniJlDwP8Atgw/zv2X3L32k5ynNQuoqIGcAE0hIugMclrZrrrgo8UaPcFsCOOfn9PGBrSf9Tt0+deCmXmZmZJUO4Ydlep4ZZuGLWt+R30i/s4yV9n7T5fS3g5qHodFlae+7zpcBo4LfANsBdhb59kjSLshvw2/z5ngntkpYGlslLtRYBtgeua6t7AnO/o01Jy8j27VQ3Io4CjsqfHw0cERGfqPwSevDAxMzMzNKGZbNhoJ/J7xFxp6QLSIOBmcDBrRO5muwxkbQJafZjeWAHSd+KiHf3SGv/CvAzST8gHbW7f27/SW6/jzRTsmdur5LQvhQwQdLipHf0W6CVuXICcIGkT5NOEds9t7+DlDC/wHhgYmZmZv3ZsGzDzzCdIetX8nu+dhxwXEn79g369UdKkt/pkqoeEX8B3l/SPoM5g4ZKtQr3Pg5s0uHa06SZmXab9arbVmcSMKnq56vwHhMzMzMzG1YaBCx2DD6U9Mn8+XslfbLC41cH/iDpRyX9WkfSDZJekTQ7yyQiLouIUzp8F0k6LgcsTpN0aG4/MgdCDkiaKuk1SStExLiImFDhHe2ewxVnSdq40L6YpLOUAien5CVZRMSR+ajjyrUK198h6VVgB2CGpDflfv8jzxZV4hkTMzMzM+uv006D8eOHpHTDgMXS4ENJKwDfBDYmnXh1S671bJcuXANcFhEXllx7BjiUetkg+5E2ua8TEbNax/dGxHeA7+R+7gAcFhF1Nr9PBXYB/rut/TO5/nvys66UtElEdMte6VSr5WTgEuCmiGilwY/Km+Qr84yJmZmZmfXX+PFpY/3QaBKw2Cn4cDvg6oh4Jg9GrgY+3LRjEfFEXs71ao3bPgcc2xoYRETZKVl7AT+v2ZdpEfGnkkvrARMLz3qONDBrUgtJHwP+DNxZdr0Oz5iYmZmZWf+NGgWTJjW7dwgCFmvUagUsngGcGhGTq3e8kTWAPSTtTNr8fmjhBDEkLUkaLB3Sp+dNIQ3kziPN1GyU/659GpmkpUib9z9E22xUEx6YmJmZmdlw0ihgsWYtIuKAmv1qanFgRkRsLGkX4Exgy8L1HUgBjLUzTDo4E1gXmAz8BfgDvU/56uRbpKVz09V9MFmJByZmZmZmNpz0DFjMsyZVwhIfJmWIFGtN6ltPq3kYuCj/+2LgrLbre1JzGVc3+d0c1vpZ0h+Yk+tS12bAbpJOAkYAsyTNiIhxTYp5YGJmZma2MBuKYM2hDW6sHbDYxa+B/ydp+fzztuQAwX6TNBHYNyIeabt0CWlvzJnAB4B7Cvcsl9vmCjHsUqtKP5YEFBEvSvoQMDMfHICkc4BxEVFpWVdEzJ7ZkXQMML3poAS8+d3MzMxs4TVmzLBLfs//xb8VsDgNuKAtYPHwHEq4IjlgUdImkh4m5YL8t6Q7c61ngLGkwc4fSZvQn8n3nFF2NG43klbJzzkc+LqkhyUtK+kNwJqkU7vanQDsKukO4HiguIRsZ+CqiHix8IxutYp92Tn35X3A5ZJ+nS+9BbhV0jTS+9qncNsGwGM1avWVInotvZtj4403jsmTh3r/T7nWQL7pHiqzhY7/L42ZmS0og/z/gyTdEhG1BgXzi6Sz6XxccKd71gc+FRGH9+H5favVVndZ4CcRURbq2LTmg8DGEfFUlc97xsTMzMzMrLrngbGSDqp6Q0RM7ddAop+12uq+0K9BSStgEViUeY9o7sgDEzMzMzMbVhokvy+ef74vXx+Z2xeV9NOcgj5NUpX9Jf9M+h16no316pIw3+W7LCbptJz8frekXduu7yYpGiwrO07SXyVNb2t/p6SJkm6XNEnS22rW/UlOjL9d0oWSls7th0l6SNK4iHg5IkZFxGp1ThPz5nczMzMz67+h2FRP4+T3TwPPRsSakvbMn9uDtOdk8ZyCviRwl6SfR8SDPbqxd4d8k9KE+R6+BjwREWvn/SMrFL7rMqQk+Ztq1Gu5FBjHvCdufRc4JyJ+Kmlr0r6Wfdpv7uKwiHgh9+/7pP0+J0TEyZKepUdYYzeeMTEzMzOz/hraTfW1k9+BnfLP5Ovb5M8HsJSkRYA3Af8AXmjasS4J8918ijQ4ICJmte3HGAucBMxo0JcbI2KejewUkt+Ba0jvpk7d1qBEpHdWfcN6D54xMTMzM7P+OvDA9Kep/ie/z74nImZKej5//kLSL+aPAUuSZgNap3JdARwQEb2yUBqTNCL/c6yk0cD9wCER8bik9wJvj4jLJA06Vb1gCrAr8J+kU7+WkbRiRDxdo99nAdsDdwFf6lfHPGNiZmZmZsNJk+T3Ttc2BV4D3gqsDnxJ0j8BRMT2QzkoyRYhhTr+PiI2BG4AvpuXdJ1MH3/pLzgC+ICk20gZKY9QM/k9IvYnvbNppCVxfeGBiZmZmZkNJz2T39va57onX1+OlAMyBvhVRLwaEU8Av2cQeyQaeBp4iZT4DvALYENgGWB9YFI+cndzYELdDfBlIuLRiNglIt5L2t9CRDzfoM5rwPmk2Ze+8MDEzMzMzIaT2cnv+dStPYEJkcL5WsnvMHfy+4T8M/n6b/PnHwK2VrIUaQBw91B0WtI8dXMfLgVG56ZtgLsi4vmIWCkiRkbESOBGYMeImCxptZz83rQfK+UZGUgp92d262PbvZK0ZuvfwA708X15YGJmZmZmw0aT5Pf894q5/XCgdcTwD4GlgamkAc9ZefM6kq6Q9NY6fVOHhHlJK1G+nKzV52Mk3U46HavX8q1VqbD0StJJuS9LKiXQH5MvjQb+JOkeYGXguAp9nF0W+KlSSv0duS/H9upLVd78bmZmZmbDSkRcAVxR0v5n0r6R9vYZpMFCe/v0svZ8bfsG/fojaQlZu81Jg6Cye/4CvL9H3dFVarXd82XgyyXtFzLn5LJKfSzcOwvYotezm/KMiZmZmZkNK10CFg/JbZFnAFrty0u6OIcC3ixp/cK1wyTdKWmqpJ9LWqLH41cGbpB0Xkm/VpR0jaTpksa12iPisog4pcN3GZv7NSDpqtYsjaS9c/vtkv4g6Z9zrXERMaFDrU7ff6fCMyZL+tfcvlVuGwC+DZwk6WNltQu1SkMkJR0GfB/YQyms8pS83IvCO+m6R8YzJmZmZmY2t9NOg/HjF3QvSql7wOLvgcuASW23/QcwEBE7S1on37+NpNVIAYbrRcTLki4g7Vk5u0sXzgemR8R3S67NAL5B2ri+fsn1Mt+JiG/k73YocDRwEPAA8IGIeFbSR4DTSMcid9Pp+08k78ORtAFwAbBORFwDjMrPXgG4D7iqxzM6hUjeRFpa15r9uZ506tekiNhKUnuf5uEZEzMzMzOb2/jxKbn99ak0YBEgIm7rkNo+O1QwIu4GRkpaOV9bBHhTPq1rSeac5FVbRLwYEddTIxCxFViYLUU+4jgi/hARz+b2GylfItZeq/T7R8T0vNF+rme02Q24MiJe6vGMTiGSASwBLAYsDiwKPN6rz0WeMTEzMzOzeY0aBZMmLZhnNwtY7GYKsAtwvaRNgXcCb4uIWyR9lzQL8DJwVURclbqgY4HJnZZN9ZOk44B9geeBrUo+8mngykE+Y2dSwvxbgI+WfGRP0lKsRiLiBknXkMIqBYyLiGl1anjGxMzMzMyGk25Bip2cACyf91J8AbgNmClpedJsy+qkwMClJH0CICKOnh+Dkvysr0XE24FzSSeOzSZpK9LA5CuDfMbFEbEO8DFgbNszVgXeQzrprJF8jPC6pJmd1UjHMHfd1N/OAxMzMzMzG046BSx2FBEvRMT+ETGKNDPxZtIejg8CD0TEkxHxKvBL4F+GptuVjKcQWJj3g5wB7BQRT/fjARFxLbBGcXM88HHg4vwOmtoZuDEvG5tOmuHZvE4BL+UyMzOzhcPreEP3687AQFrK9fo0O2AReIS0BGlMtxskjQBeyntSDgCujYgXJD0EbC5pSdJSrm2AyUPRaUnnkJY33dzWvlZE3Jt/3JEcWCjpHaSB0j4RcU/bPROBfSPikYrPXhO4P29+35C0D6Q40NmLFLZYvOd44OaIuJhqHgI+k+8TaeP7DyreC3jGxMzMzBYWr+8N3VZRt4BFSYfmUMG3AbdLOiPfti5wZ042/wjw77nWTaRMj1tJgYFvIJ1+haRjJe1Yt3+SHiTt1dgvBxuuly9tQNp/0e6EfFTx7cC2rb6RTudaEfhR65jfXP8NwJrAMyXP7vT9dwWm5qVsPwT2aG2GlzSSNAP1u7Zy7wH+VvKM0hBJ0nu8n/QepwBTIuLS8rdUzjMmZmZmtvBYkBu6h5PRoxd0D7rqErB4CjBPXkhE3ACs1aHWN4FvlrQf3bBvI9vbJC0L3BsRfy35/K7tbbn9ANLsTrv1gIsi4uWSezp9/xOBEzs850HSnpB2i+b31v750hDJiHgN+GzZM6ryjImZmZmZWXXTgQPzqV2V5D0upQnzdUXE1Ig4vB+1ejxnu37Vyqd1/RPQdQ+LByZmZmZmNqz0K/ld0ttzKvm0nP7+72XPa/MUsDzpFK+yvv1K0nOSLqv4XQ6SdEdernV9a+mXpE1bqeySpuTjfnvV6vT915F0g6RXJB1RaF8iv48p+ft/q0qfC/cvJ+nSwv375/Y1cr+nA0TEVhHxjoiY0q2el3KZmZmZ2bwGBl6XS7r6mfwOzAS+FBG3SloGuEXS1blWN+dHxCEdrn2HFNRYdVnT+Ig4NX+3HUn7Uz4MTAU2joiZ+TjfKZIuzXtsOun0/Z8hJdx/rK39FWDriJguaVFSzsuVEXFjxb4fDNwVETtIejPwJ0nnRsT9wKjWwKQqz5iYmZmZ2dzGjHk9n8rVt+T3iHgsIm7N7X8nbaYv229RWURMBP5e4/Odkt9fKgxClqB3Vku35Pcn8t6QV9vaIx/tCympfdEqzymWAJaRJGBp0gCo28CpK8+YmJmZmdncDjww/VlQ5lPyO/D4nEdqJPBe4Kb880EArdmMoSTpYOBw0jG+WxfaNwPOzP3dp8dsSdNnvxG4hXTS1w/zSWVVjQMmkHJkliGd9jWraV88Y2JmZmZmw0nfkt9nF5SWBi4CvtiawYiIU+fHoCQ/64cRsQYp3f3rhfabIuLdwCbAUZKWGIJnv5aDJ98GbNraf1PRdsAAab/NKGBcPoGsEQ9MzMzMzGw46WfyO3lvxUXAuRHxy6HpcmXnMe8+ECJiGvAiUGfQUEtEPEfam/LhGrftD/wyLwm7j/RO12naBw9MzMzMzGw4mZ38LmkxUvL7hG43SBqRPwtzJ78L+AkwLSK+P5SdlnR82clakor5Kh8F7s3tq0taJP/7ncC7gAfzzxMlDWovTK7zZkkj8r/fBHyQOcnzpf1t8xDpEAEkrZz7+Oem/fHAxMzMzMyGjX4mvwNbAPsAWxeO5t0+1zqotc+kDknXAb8AtlFKfm/lgZQmqQOH5KN2B0j7TD6Z2/+VdBLXAHAx8PmIeEoNkt8lrZLbDwe+nlt8ZnMAACAASURBVPu1LLAqcI1S6vwfgasjonXMcaf+Fo0F/kXSHaTDBb4SEU/1uKcjb343MzMzs2GlX8nvEXE95XtWGm96j4gtO1zqlKRemp0SET8DflZyqUny+98oSWsHbidt+K/c37a6jwLbdvtMHZ4xMTMzM7NhpUHA4k45XHFA0mRJ/1q4dlKesZgm6ZS8vKub/YDPSrq+pF+lQYbQOUm9S59LQyGrJr9LOr8wC/RgnnkpXn+HpOnt/Szrb5c+Kr+z+3I/N8ztW0l6mZTnUplnTMzMzMxs/jvtNBg/vvZtahawOBGYEBEhaQPgAmAdSf9CWs61Qf7c9cAHSu4vehAYFxEXllzrFGTYTd1QyEoiYo/WvyV9D3i+7SMnA1cOso8fIc1ErUU6svnHwGYRcQ3wJgcsmpmZmdnr3/jxKV2+vtoBixExPSJaRwrPDjHMfy9Byg9ZnBQw+Hj7/VV1CjLscU+tUMi6fcozQB8Hfl5o+xhpk/qdg+zjTsA5+VSuG4ERSin1jXjGxMzMzMwWjFGjYNKkedv7H7BIPmHqeOAtpNOviIgbJF0DPEbaazIuH81L3jh+akRMrvBNhkLPUMiKtgQej4jWaV9LkfJSPgSULuOqoex/i9VI77M2D0zMzMxsaDVcstN3AwPpF2Eb7poELBIRFwMXS3o/6TSpD0pak3RiV2tj+NWS3h8R10bEAX3rcTMnAP+Z94bcQVsoZA17UZgtAb4FnBwR03tvp+mp0f8WnXhgYmZmZkOrtWTHgwLrj9oBi0URca2kNfIm7p2BGyNiOoCkK4HNgWv72N9GcgL9/jB7OdYD+U9lOQdlF2CjQvNmwG6STgJGALMkzYiIcQ26Oaj/Ldp5YGJmZmZDr9OSnflp9OgF+/yFWdmsWfPB6uyAReARUsDimG435JmR+/Pm9w1Je0qeJgUEfkbS8aT/+v8B4AdNOtWLpInAvhHxSMXPjwBeyvtoZodC1qz1QeDuiHi41VA8zljSMcD01qCkbh9JwZaHSDqPNOB5PiIaLeMCb343MzMzs6HWfKP7PBoGLO4KTM3Lon4I7JE3w18I3E9aKjUFmBIRl+ZaZ0jauE7fOgUZNglFpEMoZLdaJfZk7mVc3frepI9XkDbR3wecDny+yrM68YyJmZmZmQ299lmzQcxgNQhYPBE4saT9NeCzHZ5Re49JpyDDnEFSNxSxNBSSLgGLJTX263H9mCp1u/QxgIN79aMqz5iYmZmZmVX3PDBW0kFVb6gaiji/aw1V3byHZ4CaJ4h5YGJmZmZmw0o/k9/z9WUlPSKpygbw5UjZJ2X96pj83uW7nJu/y1RJZ0paNLeXpqpXJem6QvL7o5Iuye1d30WFumdLeqBQe1Ru3yP39bKIuD8iRkXEGnVqeymXmZmZmS0YAwO1l3T1M/m9cH0s8Lsa3Tiyj8nv5wKfyP8eT9ro/mM6pKpXLdq2yf0i4H/zj73eRRXzfP+IOF/S4wwiG8UzJmZmZmY2/40Z0/RUrn4mvyNpI2Bl4KomnWl7TpPk9ytycnoANzNnj0pfUtUlLQNsDVySn9fxXSxonjExMzMzs/7oFKZZdjTwgQemP2XmU/J7Ponqe8A+wDZtn5+vye95Cdc+5NO36F+q+s7AxNZRw/lZ87yLmo6TdDRp9uWrEfFKgxrz8IyJmZmZmfVHH48F7qJx8ntErENaZjU2N38euCIi/lry+QPm16Ak+xEpq+S6/HO/UtXbk987vYuqjiIt/doEWAH4SoM+lfKMiZmZmZn1T1mYZn/DLfuZ/P4+YEtJnweWBhaTND0ivtq9Sn9J+ibwZuY+unjQqeqSViQtfdu57HrxXUTEU1VqFgIUX5F0FoPYU9LOMyZmZmZmNpzMTn6XtBgpRHBCtxskrSml9WHF5PeI2Dsi3hERI0m/YJ8zVIMSSRMlrVbSfgCwHbBXRMwqXJoA7JtP59qcQqp6Dl2sYnfgsoiYUXhe6buoWre1zyXX+BgwtWJfevKMiZmZmZkNGxExU1Ir+f2NwJnF5Hfgy8AqpITyK3JQ4q6kX/JfBV5mTvJ7R032mEhaBZgMLAvMkvRFUnDhdDqntZ8K/AW4IY8XfhkRx5ICJLcnpaq/BOyfn7ES5cu8yuwJnNDWVvouatQ9V9Kb82cHgMp5Lr14YGJmZmZmw0q/kt/bPnM2cHbh5/mV/F76+3iXVPXNScclV+nP6JK2Tu+iUt2I2LrKs5vwUi4zMzMzG1bqBizma6NzIOCdkn6X294u6RpJ03L7v7c/q8RbgfMkXd+hb7+S9Jyky1pt3VLVJb1f0q2SZkrare3aiTl4caqkPXKty/IArCtJy0m6VNKU/N3279HHqnVHS3q+ELB4dG7/BPAr4CPt774qz5iYmZmZ2dBrEKZYpknAoqQRpFOvPhwRD0l6S740E/hSRNya8z5ukXR1rtXJeOCeiDikw/XvAEsy90b2bh4C9qNtE7mkjwIbAqOAxYHfSbqyeOxvDwcDd0XEDnnp1Z8knZuzX+r2sd11EfFvxYaI+B/gfyQ92LCmZ0zMzMzMbIg1D1MsUztgERhD2rvxUP7cE/nvxyLi1vzvvwPTSFkhjUXERODvNT7/YETcDsxqu7Qe8LuImBkRLwJTgA/X6QqwTN6kvjRpf8vMJn2cXzxjYmZmZguPPv1Xe+ugLEgRuocplul/wOLawKKSJgHLAP8ZEefM/UiNBN4L3JR/PgggIk6t3vG+mgJ8U9L3SbMbWwHdZnLajSOd7PUo6Tvv0Xbq12C8T9KUXPuI1uEDg+WBiZmZmS0cxoxZ0D2w/mgSPLgIsBEp3f1NpBOwboyIewAkLQ1cBHyxtVRqAQ5IyM+/StImwB+AJ4EbyDMeFW1HOjVra2AN4GpJ19VYCtbJrcA7I2K6pO2BS4C1BlkT8MDEzMzMFhZ1/6u91Td/ZqOaBA8+DDyVl0S9KOla4J+BeyQtShqUnBsRvxyKDjcVEccBxwFIGg/cW+P2/YET8ule90l6gJTYfvMg+/RC4d9XSPpRnYDGbrzHxMzMzMyGk9oBi8D/khLeF5G0JGnp17S8/+InwLSI+P5QdlrS8ZJKE9g7fP6NObkdSRsAGwBX1aj1EGmGCEkrA+8C/jzYPkpapRDQuClpPPF072/UmwcmZmZmZjZsRMRMoBWwOA24oBiwKOlh0izK7TkkkYiYRjrK9nbSjMEZETEV2ALYB9i6cPzt9rnWQa19JnVIug74BbCNpIclbZcvvef/s3fv8ZeP9f7/H085n22HFEKOSUxFoS0ThRSyI2PKqS3bDrUVya/IJlsOEXuKZCM7QzVSTjk0mYxCDT7DmHGMPaFITt9BhNfvj+taM+9Z815rvd/vtT4zPvW8326fm8/neq/39b7WmuhzzXW9rifwp5LXb57HvAfwXUmteo1FgMmSpgPnAJ/K771jX22OB7aSdBcwETiytapRd4xtdgem5RqTM4ExvcIqq/JWLjMzMzMbUeoGLOZrp5COyS223USHtPOmNSYRsXWHS4tExM0lr/8dJaGMEfFX0slclftqu/8xYPtBjLHt3nGkwvqB84qJmZmZmVl1L5JCBM+tc1NE7ND7VfO/r0H1K2kJSUOklZ5Gp395YmJmZmZmI0qT5Pd8fXNJr5YkrC8r6VFJVVYCPkz6HXpKSf8bSrpZ0kuSDp/31tL3clF+L9MknZeL8TsmrFcl6YeFex/OkwYkfbLQPiTpNUldQ2Yk7ZHT41+TtFmh/UOSbsvbxW4CvhARq0XEU5JukDSr+PpevJXLzMzMzAZnmLNimiS/F+47iVSb0u544Fc1hnFEREwoaX8K+BzwsRp9XQR8Kn8/HjgAOCv/PE/CelURsWfre0nfBJ7N7RflZyLpHcDPImKoR3fTgH8BvtvW/iSwc0Q8Jmlj0me7Wn7OB3JuTGWemJiZmZnZYMyfrJjZye8AklrJ79Mj4o7cVnbfoaRjgTcvNkp6N/BGUnF85b/dL5MT5Z+Q9JEa98yulZH0W0rqTfqRT9D6BCnPpN1ewMW9+siHB8zzubY+7+xuYHFJi0XES03G6omJmZmZmQ3GoLJiBpz8Lmk1YDfSL+ebF9oXAr5JOplru7Z7zgXOjoh5tmwNh7yFa2/g84XmQSSsbw08HhFlGSh7kiZ1g/Bx4I6mkxLwxMTMzMzMRpYmye/fIh2X+2rb3/p/Frg6Iv5QshpwQF+jrO87wI0RMTn/PKiE9dJVEUnvBV7Ixyb3RdLbSdvkSk8Bq8oTEzMzMzMbSZokv28GXJInHysBO0l6BdiSFLz4WWBpYFFJsyLiy527GjxJXwNWBv6t1TaIhHVJC5NqQ95dcnkMFbZxVXjG6sBlwD4R8WA/fflULjMzMzMbSWonv0fE2hGxVkSsBUwAPhsRP42IT0bEW3L74cCFwzUpkTQxbylrbz8A2AHYKyJeK7R3TFjv1FeJDwL3RMQjbc9ciBToeElb+4X5WVXf0/LAVcBREfHrqvd14omJmZmZmY0YTZLfm5B0bp2jbvM9q+bnfwH4ak5VXzZPBNYlndrV7mxS8f3NbccClyas9+irXadVkfcDj7QOECjYBPhjyfvaLb+vLYGrJLVONjskj+XowvHDq1QYVylv5TIzMzOzEaVJ8nvhNft1aL8AuKDwc+0ak4j4EyWnauWjdC+NiBdL7in9fbxLwvpGnfoq6WO/Du2TgC3axrgscH9E/KHk9ZeRtmu1t38d+HqvcVTlFRMzMzMzG1HqBixKOqLwN/rTcsjiP3Xrq4sNgYslXVUyrhULwYKzJxURMS0ivtDjPR1eHLeSM/O47pT0rqp95fu3k3R7fs83SVo3t59e+Czuk/RM7ve5iNijQ18nSPqDpFlt7QdJuqvwjI1y+9aSnge2Av7Wa6wtXjExMzMzs+F3zjkwfnzf3TQJWIyIU4BT8v07A4fldPJufXVyDTAhIk4tufZX4Ghg4/xV9T2tkccws9D8YdIpXOuRjkM+ix7HIrc5C9g1Imbk4v6vAvtFxGGF5x4KvLNCX1eQVm/ajxweHxFn5752AU4DdoyIyfmkrisjYmrVAXvFxMzMzMyG3/jxKRW+f7MDFiPiZVIB966QAv8i4uEe9xePz+3YVxMR8XxE3ESaoNRxOvAl5j72eFdSMX5ExC3A8pLeVGc4wLL5++UoP7msasDiLRExT+1J8eQwYCl6H9vclVdMzMzMzGz+GDUKJk3q/boBByzO6VZLAjuSira79iXpOGBKRHQ98atfeaXh0YiY2palUja21SgpTu/gAOBqSS8CzzFvTcmawNrALxsOvdXPwaRi/0UpT5evzCsmZmZmZjaSNAlYbNkZ+HVEtE606thXRBwzHyYlSwJfAY4pu9xpbBUdBuwUEasD55O2WRWNIW1Je7VGn/MOKOLbEbEOcCRpu1hjnpiYmZmZ2UjSJGCxpf343H76GoR1SKsWUyU9nJ9/u6RV+xmbpJWBTSPi1tz0Q1IhetFAAhYLLgE+1k8HnpiYmZmZ2fA45xwYPTp9Daa+BBoELAJIWg7YBvhZv301URZeGBF3RcQqhfDHR4B35WOHLwf2yadzbQE826rzkHRPj8c9DSwnaf3884dImS+tsWwArADc3DbGXv22v6f1Cj9+hHmL42vxxMTMzMzMhsfgCt5n6yNgcTfguoh4vmJfx+X6j1ryysdpwH45YHGjfKk0vLCLq4HfAw8A3wM+m/tfifJtXrPl9/UZ4NIc0Lg3cEThJXsBl0TE7K1h3fqVdHL+XJfM7+nYfOkQSXdLGiLVmexb4/3Nw8XvZmZmZjZ8WgXvo0cPrMsmAYvtAYoV+iqr+6gytrXa27qFF3a6N08aDi552RakI457jaM0FDFfO7ZOvxHxJdKpYe3tn+81jjo8MTEzMzMzq24WcKCkZatOXvKxuqXhhXVFxJWD6Gc4+5W0NfAd4Mk693krl5mZmZmNKHWT3/O10Tmh/G5Jvyq0Ly9pgqR7JM2QtGWPxz9Jqs94c4exXSPpGUm1ftGXtHse92b55w9Jui0nq98mqdZRvJLeklPo78jJ8TsVrm0i6eb8WdwlafEefZ2SP587JV0mafnCtaPyZ36vpB1y8xTgVWCr9j+HbrxiYmZmZmbzx9BQ31u6miS/51+kv0NKJZ8paZXC5TOAayJi91wAv2SFYfwwIg7pcO2U3Me/1XhPywCfA24tND8J7BwRj0namFQHs1rVPklH9/4oIs7KdS5XA2tJWhj4AbB3zk5ZEfhbj76uB46KiFcknQQcBRyZ+x0DvJ00UfuFpPUj4kVgVK63qcwrJmZmZmY2/MaOTfUm/WuS/D4W+ElEzMyvewJm1368H/if3P5yRDzTz+AiYiLw/2redjxwMoXE+PxeWscD3w0sLmmxOkOhPPl9e+DOiJian/OXXlkmEXFdLqgHuIV0uACkz/2SiHgpIh4iFeq/p6yPKrxiYmZmZma9nXNOOmWrjqGhOZORAw9MX1UMPvl9fWARSZOAZYAzIuJC4K3An4HzJW0K3AZ8PiKel3QQQEScXW3QzUh6J7BGRFwp6fAOL/s4cEdEvFSj62OB6yQdCiwFfDC3rw+EpGuBlUkTi5Nr9PtpUi4KpD+LWwrXWun0jXjFxMzMzMx6G4ajfxtqkoi+MPBuUtbGDsDROeNjYeBdwFkR8U7geeDLkCYk82FSshBwOvDFLq95O3ASNbaGZXsBF+Tk952A/83PWxj4Z+CT+Z+7Sdqu4ni/ArwCXNRqKnlZnXT6uXjFxMzMzMyqaR39W9UAjwguaJKI/gjwZM4weV7SjcCmwGTgkUJC+gTyxGQ+WQbYGJiktEq0KnC5pF0iYoqk1UlH/u4TEQ/W7PtfgR0BIuLmXOC+Eumz+FVEPAkg6WrS5Gxit84k7Qt8FNiukH/SOJ2+jFdMzMzMzGwkaZLW/jNga0kLS1qStPVrRk5Y/0NOQgfYDpg+HIOWdKKk3YptEfFsRKxUSH6/BWhNSpYHriIVnf+6ra95UuRLzCS9HyS9DVictG3tWmATSUvmQvhtyO+5U7+SdgSOzGN7oXDpcmCMpMUkrQ2sB/y20gdSwhMTMzMzMxsxmiS/R8QM4BrgTtIvzudGxLTc5aHARZLuBEYB/5X7OqhVZ1KHpMnAj4Htckp66wjddwB/qtHVIcC6pG1nQ/mrdZpYlRT5LwKfycnvFwP7RfI0KZn+d8AQcHtEXNWj33Gk1Z3r8zjOBsif+49IE5trgIN7FdJ3461cZmZmZjaiNEx+P4V0lG97+xCwWUl7o/qSiNi6w6VFIuLmHveOLnz/deDr7a+pkSI/HXhfh2s/IB0ZXKnfiFi3y3NOAE7oNpaqvGJiZmZmZiNK3YDFHK74bGHl4ZhefXXxEeAgSbeXjGvFHGo4S9K44rWI2KH99W33Hl4ct6Rdc6DhkKQpkv459/NcRPRMkZc0ufB+H5P009y+nKQrJE3NAYv79+pX0gmS/iBpVlv76YVn3Cfpmdy+kaQXgbcAr/Uaa4tXTMzMzMxs+AwgVLGoScBiNjkiPlqjr07uBm6NiFNLrv0VOJpU0L5xjfe0Rh7DzELzRODyiAhJm5C2TG1Ytc/iyo2kS0l1NgAHA9MjYmdJKwP3SrooZ8J0cgVpO9f9bc84rPCMQ4F35vbpwBKSZkXEU1XH7ImJmZkNnya5B/b3p5hlYf9Yxo4djl5nBywCSGoFLE6PiDtyW999NRlYPvXrJkkdtz51cDrwJeZMHoiI4urEUjQ8hlcpVX5bYP9W18AySh/S0sBTpCOAO4qIW3Jf3V62F/C1JmNs8cTEzMyGTyv3wL+Umo0sZX+p0OTf5TqhikWDD1gE2DIXgj8GHJ4Ltzv2Jek4YEpE9Drxqy+SdgEejYip7b/451O8TgRWIW0ha2I3YGJEPJd/Hkc6TesxUkH7nhFRebtVGUlrAmsDv+ynH09MzMxseNXNPbC/P8OTZWHD6fX9lwpNQv1uB9aMiFmSdgJ+SjratmNfEXFMybWBykcXfwXYvux6RFwGXCbp/cDxzElvr2Mv4NzCzzuQTuPaFliHdNLW5MLEpYkxwIR+TuQCF7+bmZmZWZnWXyq0vl4/k5TaoX65sHtW/v5qYJFcZD7QgMAG1iGtNEyV9HB+/u2SVi2+KCJuBNYpFvRXIWlF0na1qwrN+wM/yUcHPwA8RI3alQ7GkI4k7osnJmZmZmY2ktQOWJS0aq6pIAcILgT8pUlfTZWFF0bEXRGxSiFg8RHgXRHxJ0nrFsb8LmDRPGYk3VPxsXsAV0bEXwttxeDFNwIbAK0am6r9Ft/XBsAKQNejkKvwxMTMzMzMRowmAYvA7sC0XGNyJjAmrxh06+u4XP9RS175OA3YLwcsbpQvVQlFLPp4HvMQ6eSwPfMJXStRvgWtTNlKxvHAVpLuIp38dWREPNmtX0kn5891yfyeji1c3gu4JCIaFecXucbEzMzMzEaUugGLETGOVPRdp69GNSZ55WMuNUIR1yp8fxJwUsnLtiBNVKqMZXRJ22OU17R07DcivkQ6Nazs2rFVxlKFJyZmZmZmZtXNAg6UtGzVyUsuLO8ZilixrysH0c9w9itpHeBS4PE693krl5mZmZmNKA2S30tT1PO1fSXdn7/2rfD4jYFFKCmSl7ShpJslvSTp8Jrv6b+LyeqdUtVr9Ncp+b3xGPP9m+b778oJ8svm9q0lTZc0LSIejIhREbFOnb69YmJmZmZm1Qw4xb2JhsnvpSnqkv6JFAq4GemY4NtyX0/3GMYRETGhpP0p4HPAx2q+p82A5YttnVLVq+qS/N5ojAXnknJgfiXp08ARwNERMTkfxdx45cUrJmZmZmbW29ixr5cjg2entUfEy0ArrZ2IuCMiHm6/ISJmFYqziynqOwDXR8RTeTJyPbBj04FFxBMR8Tvgb1XvyROtU+hQw5HtRcPjeAvJ7z9tOsY2GwA35u+vJxXpD4RXTMzMzMyst6Yp7k0MQ/J7hxT1sr5Wy68/Fzg7IqZUHnczh5BWc/7Ynvyex9Fvqnp78nu/pgG7kFZg9mDuHJi+eGJiZmZmNpKdc05Kah+k12/qOzRLfu+Uot4t+f2AfgZZhaQ3k365H93lZf2mqrcnv/fr08CZko4hZb68PKiOvZXLzMzMbCQbPz5NJP5x9JXW3paivqCT398JrAs8kPNPlpT0QNtrGqeqd0h+70tE3BMR20fEu/O4HhxU314xMTMzMxvpRo2CSZMG198CLnDvYXZaO/Ao6Rf3sd1ukLQu8GAufi+mqF8L/JekFfJLtweOGo5BS5oI7BMRj7baIuIqYNXCa2ZFxLqFn0tT1SXdExEbVnhsWfJ7rTGWvGaViHhC0kLAV4Gzq/RdhVdMzMzMzGzEaJj8XpqiHhFPkbZ1/S5/HZfbkHRuPi2rMkmr5ud/AfhqTklfNv8Svy7pRKw65klV7zf5fQBj3EvSfcA9pNWl8yuOpSevmJiZmZnZiNIg+b1TijoRcR5wXkl77RqTiPgTaVI0F0kbA5dGxIs97l+67edjS17Wb/J7v2M8AzijyvPr8oqJmZmZmY0oDQIWl8thgFMl3S1p/9w+KocF3p0DGPes8PgNgYslzVO3IWlFSTdImiVpXKs9IqZFxBd6vKfDS8Y9Ogck3i3pV7mvK/MErKsuQYiLSPp+bp8h6aheY+zyuY6W9Gwe4/3ArcCTkpbIbS8XX9+LV0zMzMzMrL7hOA2sgoYBiwcD0yNiZ0krA/dKugh4gVRTcX8+Ies2SddGRLeU9WtIp2SdWnLtr8DRpHT4jWu8pzXy+5lZaFse+A6wY0TMlLRK1f6y0iBEUt3JYhHxDklLAtMlXVyW/1LQ6XMFmBwRHy1pH5UL+ivziomZmZmZ1bfgTgOrHbBIOgJ4GaWgkKVJdRSvRMR9EXF/vvcx4Alg5aYDi4jnI+Im0gSljtNJAYvFY4/HAj+JiJm57ydq9tkpCDGApSQtDCxBOu63a8ZJl891oLxiYmZmZmbNDPo0sJbBByyOI2VuPAYsQyp+f23uR+o9pNO6Hsw/HwdMiYjLa429Jkm7AI9GxNS2gMX1gUUkTcpjPiMiLqzRdacgxAmkidwfgSWBw1oF/w1tKWkq6bM9vHUQQROemJiZmZnZSNIkYHEHYAjYFlgHuF7S5FYauqQ3Af8L7NuasETEMYMbcrm8leorpGOK2y0MvBvYjrSycbOkWyLivorddwpCfA/wKvBm0lHEkyX9IiJ+3+At3A6sGRGzJO0E/BRYr0E/gLdymZmZmdnI0iQUcX/StqiIiAeAh0hF7OSi8KuAr0bELcMw3m7WAdYGpuZ6jNWB2yWtSnqf1+TtYU+StmVtWrXjLkGIY3O/f8vbw34N1DoWufCM5yJiVv7+atIKT+Vi93ZeMTEzMzOzeQ0NdQ9aHBpKW7nmv9oBi6Si8u1IqwNvJNVf/F7SosBlwIUR8eNhHDOSLgTGRcRvW20RcRewSuE1DwObRcSTkn4GjMu1IIuStqudnl/XTxDiTGBbST8gbeXaAvhW1X7bnrEq8HgOrnwPadHjL1XuLeMVEzMzMzOb29ixC2rS0VPDgMXjga0k3QVMBI7MqxCfAN4P7JePtx2SNCr3dVyu/6glTy5Oy30+ImmjfGkTUl1H1fc5g3QC2J3Ab4FzI2LaAIIQv006AGAaaZJ3fkTc2a3fLp/r7qTgyqmk/JgxxTDIurxiYmZmZmZzO/DA9NVNt9WUYdYgYPExSuo4IuIHwA86PKNRjUlErNXelreL3R8Rf5j3js73RsQpwCltL9uIPoIQ89arPUpu6dhvl891HOlggYHwiomZmZmZWXWzgAPzqV2V5FqMsslAbVXCGhd0v62ARWAR4LVer2/xxMTMzMzMRpS6ye/52jwp6oVrb5B0h6QrKzx+aWCpDuMqTX7v8V6OlfRoYSvZToVrR+X3c6+kHar0V7h3lKRbcp9Tcg1Ie1r7UD61q1dfF+UxTJN0nqRFCtc6fq6kTJjK8w1v/QM6bwAAIABJREFU5TIzMzOzZnoVyA+DJsnvFVLUP0+qV1m24jBOH2Tye1l/uTZlDPB20tG+v5C0fkS8WrHPk4H/jIif58nOycDofK1TWnsnFwGfyt+PBw4Azur0uebtYE5+NzMzM7P5YMEVyDdJfu+Yoi5pdeAjwLkl99XSR/J7mV2BSyLipYh4CHiA9N4rD4c5E63l6H2kcueOIq7ORy0HqRB/9Xyp33T6uXjFxMzMzMzqq1Ig39Tgk9+7pah/C/hSbi8MYf4kv2eHSNoHmAJ8MSKeJr3PYq7KI7mtqv8ArpV0KmkxYqvCtUZp7XkL196kFSboP51+Lp6YmJmZmQ3COefA+PHz/7kLLk9kQWmS/F6aok76xfqJiLhN0ui5OpwPye/ZWaTjjCP/85uk1PYm77Po34HDIuJSSZ8A/gf4IP2ltX8HuDEiJuef+02nn4u3cpmZmZkNwvjxaZJgw61J8nunFPX3AbvkWohLmBM8ON9ExOMR8WpEvAZ8jznbtZq8z6J9gZ/k73/c6rdpWrukr5GK2Ysnd/WVTt/OKyZmZmZmgzJqFEyaNH+fuQDzRBaQJsnvpSnqOe39KEinS5G2NX2qYy99KEt+z+1viohW8OJupOBDgMuB8ZJOIxW/r0eq76ia0P4YsA3pIIBtgfvzvR3T2jv1K+kAYAdguzyBaumYTt+EJyZmZmZmNmJExCuSWsnvbwDOKya/k+pFViUllF8dEQdExAxJrRT118gp6t2e07TGJK++LAssKuljwPb5xLBOye8n57T5AB4G/i2/z7sl/QiYDrwCHBwRr9ZIfv8McEaeNPwVaBUE7Q78u6RXgBfJae09+j0b+D/SVi1IBe/HNflcu/HExMzMzMxGlLrJ7/laWYp68fokCscMz6/k94jYu0tfJwAntDVXTX6/iVT/0d7eKa29W/J7xzlDr8+1DteYmJmZmdmIUjdgsVuooKTDcjjgNEkXS1q8x+O3Ak4sCRPsGLDYLfld0vGS7szjuk7Sm9uuby7pVUm7574qJbRLmlx4v49J+mluX07SFZKm5ve9f81+O33Ge+b2K9Uw+d0rJmZmZmY2f/VxglmTgMVsnlBBSasBnwM2iogX89apMcAFXYbwG+C6AQYsnhIRR+fxfA44Bjgo//wG4CTStrVaImLr1veSLiXVgwAcDEyPiJ0lrQzcK+minAlTRelnHBE/lPQ4qU7nRaD2UXFeMTEzMzOz+au/E8yaBCx2szCwRK7FWJL+gghrByxGxHOFH5di7iOBDwUuBRoHF0pahlT8/tPWI4FllIpFlibVlLxSY7xNPuNKvGJiZmZmZvNftxPMBh+wCCWhghHxaA4gnEkqBL8uIq5LQ5h/AYuSTgD2AZ4FPpDbViOd0rUtsHkf3e8GTCxMgMaRTvx6jBSKuGfbSVsLjFdMzMzMzGwkaRI82AoV3BT4b/LqgaQVSKsta5OO5F1K0qcgFb/Pp9R3IuIrEbEGcBFwSG7+FnBkRLzaZ/d7ARcXft4BGCK931Gk436X7fMZA+GJiZmZmZmNJLWDB7uECn4QeCgi/hwRfyMFEm41PMOuZDzw8fz9ZsAl+fjh3YHv5OOHK5O0Imnr21WF5v1Jx/1GRDwAPARs2O/AB8FbuczMzMysf3UK2oeG0lauZmoHLHYJFZwJbCFpSdJWru2AKU0H1mMMnQIW14uI+/OPuwD3AETE2oXXXABcGRGtlZ57IqLKZGKPfF+x5mUm6X1OlvRGYAPg9zX7HRZeMTEzMzOz/vVX0F5ZRLxC2u50LTAD+FExYFHSI6RVlDslnZtv2x2YlmtMziSHCkbErcAE0lavu0i/G5+T+zpO0i51x5dXOE4D9pP0iKSN8qVOAYvfyEcV3wlsD3y+R/8rUb6drcwY5t7GBXA8sJWku4CJpO1iT1btt8tn3DevmJiZmZnZYHQraC8aPbqvx9QNWOwSKkhEfA34Wkn7/ApY/Hh7W8lr9iv8uAXpuOQqYxld0vYYaQLUrlK/3UIs++WJiZmZmZlZdbOAAyUtW3Xykk/EKg1YrCsirhxEP4PuV9KepAnebU378FYuMzMzMxtRBpz8vrykCZLukTRD0pY9Hv8ksALpVKuysV0j6RlJlX7Rl3SQpLvyuG5qbf2S9MnCeIckvSapcmGOpNML994n6ZnCtZNz6vsMSWfmTJOq/b5f0u2SXmml0WdTgJdJxxM34hUTMzMzM5v/hoYabekaZPJ7dgZwTUTsLmlRUshiLz+MiEM6XDsl9/FvFfoBGB8RZwPkmpbTgB0j4iLS8cFIegfws4ioXMQTEYe1vpd0KPDO/P1WwPtINS8ANwHbUP6ZlZkJ7Acc3va8B4FRkmZVHWM7T0zMzMzMbP4a2/UQrV5mJ78DSGolv0+PiDtyW6WOcu3H+0m/aJOT5F/uZ3ARMVHS6Bqv75b83tKeRVLXXsypowlgcWBRUrH7IsDjVTtqpb5LGngooycmZmZmZiNdw9WHgY+h6hHABx6YvjqZT8nvwFuBPwPnS9qUVB/x+Yh4XtJBAK3VjOEk6WDgC6TJwrYlL9mTNPlq0veapADJXwJExM2SbiCdECbSEcYzmvQ9aK4xMTMzMxvJxo7tJxNkJBpY8jvpL+nfBZwVEe8Enge+DGlCMj8mJflZ346IdYAjga8Wr0l6L/BCRExr2P0YYEIrQV7SusDbSMf9rgZsK+n9jQc/QF4xMTMzMxvJeq0+zC/zb8WmUfJ74furJX0nF8c/AjyS80wgZZp8uayP+eQS4Ky2trIskjrGAAcXft4NuCUiZgFI+jnpqOAb+3jGQHjFxMzMzMxGktnJ77lYfQxwebcbJK3aOnmqmPweEX8C/iBpg/zS7YDpwzFoSSdKmufEKknrFX78CHB/4dpCpGOGL2m758L8Pno9cwPSCWI3F5pnAttIWljSIqTC9xl1+h0unpiYmZmZ2YgxyOT3fO1Q4KKcvD4K+K/c10GtOpM6JE0Gfgxsl5Pfd8iX3gH8qeSWQ/LRvUOkOpN9C9feT1rR+X3bPZ1S5NvtBVxSeK+QVoUeJCXdTwWmRsQVVfuVtHn+jPcAvivp7grjqMRbuczMzGz4vR6Ks4dbneJv68uAk9+HgM1K2hvVl0TE1h0uLRIRN7c3RsTnu/Q1ibTNarZuKfIl9x9b0vYqJUcZV+03In5HmvgNnFdMzMzMbHj94xVn2zBrELB4RCFscJqkVyX9k6Q1JN2QgwbvltRxklDwHuDfJd3TYWylAYsRsUOH15cGLOZrm0i6OY/tLkmLR8RzEdEzRV7SW/J7u0PSnZJ2yu2lwY3d+pW0Rx7Da5I2a7t2VP7Mfy/pQeBxSUvkvl8u/jn04hUTMzMzG16vl+Ls4fb3viL0OtEkYDEiTiEFHyJpZ+CwiHhK0mLAFyPidknLALdJuj731clvgdeGO2BR0sLAD4C9I2KqpBWBv1XsE9LpXj+KiLPyZOdqYK2GwY3TgH8BvltszP2OAd4OvBn4BbB+XpUZJenhGuP1xMTMzMzMBmT+bNnrN2BxdlhhRPyRXFMREf9P0gzSEbqNC+AHGLC4PXBnREzNr/tL3aEAy+bvl6P85LJKwY2tnJOSz3VXUg3LS8BDkh4g/fnMs2WtCk9MzMzMzKx//aW519E0YBFJSwI7korn26+tBbwTuDX/vKADFtcHQtK1wMqkCcDJNbo9FrhO0qGkCc8HS17TOLgxWw24pfDzI7mtEU9MzMzMzKx/g9yy133Fo0nAYsvOwK8j4qm5H6elgUuB/2itYMyvcMX8rG8D35Y0lrQFa1/S7+n/DGwOvABMlHRbREys2O1ewAUR8U1JWwL/K2njiHgNBhLcCP39WczDxe9mZmZmNpLUDlgsmCesMGd5XApcFBE/GcgIm7sE+Fj+/hHgVxHxZES8QKoReVeNvv4V+BFAPg1scaBYiN5vcGNrjE3/LObhiYmZmZmZjSS1AxYBJC1HChP8WaFNwP8AMyLitGEab+tZdQMWrwU2kbRkLoTfhlz7UjEIcSYpMBJJbyNNTP6cf+4ruLHgcmCMpMUkrQ2sRzocoBFPTMzMzMxsxGgYsAiwG3BdRDxfaHsfsDewbeH43Naxugs0YDEiniad0PU7YAi4PSKuyvdUCVj8IvAZpVDJi4H9CkGLtYIbJe2WP9ctgaty3Qv5c/8RacJ0DXBwPpGrEdeYmJmZmdmIUjdgMV+7ALigre0myuskXi8Biz8gHRk8W40gxOmkiVfZtUnUCG6MiMuAyzr0dQJwQrexVOUVEzMzMzOz6l4EPty2GtNTp4DFuqoGLC7IflsBi8AiwGtV7/PExMzMzMxGFA0o+T1fOyxvpZom6WJJi/d4fKteY54ib0kr5rT1WZLGVXwvx0p6tGQr2aKSzs+J71PrZKPk+9eUNDGnvk+StHqh/bb8rLurbFfrlvyer78lv+fD2y6tTI35hrdymZmZmdn8d845MH587dsGnPy+GvA5YKOIeFHSj0jF9Bf0GMbpEXFqSftfgaOBjfNXVWX9fSaP/R2SVgF+Lmnz1nG/FZwKXBgR35e0LXAiqZ7mj8BWEfFSPiZ5Wv78up2mVZr8Xhw/8PPWDxHxIg2S371iYmZmZmbz3/jxKSm+vtnJ7xHxMulkqV0BIuKOiHi4x/3taecLA0vkk6+WpI/jbiPi+Vy38temfRRsBEzM/T4BPAPMs1pR5X7gBuZ8Ri/npHaAxagwH4iIGRFxb9k1SR8Dfg/cXWNspTwxMTMzM7MFY9QomDRp3q/uypLfK6WNF5LfLwWIiEdJKwszSSsJz0bEdfm1x0napepb6dMhecvVeZJWyG1TgV0lLZyP4n03c2eG9DIV+Hj+fjdgGUkrAkhaQ9KdpM/xpB6rJR1JWgo4EvjPJve388TEzMzMzEaSgSW/50nArsDawJuBpSR9CiAijomInvkoA3AWsA4wijQ5+mZuP4806ZoCfAv4DfBKjX4PB7aRdAcpA+XR1v0R8YeI2ARYF9hX0hsbjv0/SdvQZjW8fy6uMTEzMzOzkWSQye8fBB6KiFbw4E+ArWg7onc4RcTjre8lfY9UI9PKazmscO03zAlfrNLvY6S6EHItyccj4tn210i6G9gamNBg+O8Fdpd0MrA88Jqkv0ZEpcL/dp6YmJmZmVl3DQvVuxoaSlu56pud/E5aBRgDjO11UyH5/VOF5pnAFnmL14ukpPQpTQZV4fkXAuMi4rdt7W+KiFao4W6kQvPWtjNFxPOSPgS8kgv8O/bV1u9KwFO5WP4o0goM+XSuv+Ri/xVIWSenVe23qJjZIulYYFbTSQl4K5eZmZmZ9dK8UH3gBpn8HhG3klYKbgfuIv1ufE7uq1GNST6J6jRgv5z8vlG+1Cmt/eR8JPCdwAeYs0qyCnC7pBmkOo69C/dUSX4fDdwr6T7gjcwJQXwbcGtOhP8VcGpE3NWt307J74PmFRMzMzMz661VqD4oo0c3vnVQye+5/WvA10raj2k4trXa23qkqu/d3pbbHwY2qNNX2/0TKNmeFRHXkyYgdcbYMfm98Jpju12vwismZmZmZjaiDDhgcXlJEyTdI2mGpC17PH5D4BuSbu8wtmskPSPpylZbt1R1SftJ+nNhfAcUrp2cgw1nSDpTkqomtOfQwxsk3ZFP/GoFNy4i6ft5lWaGpKMqjLHT5zpa0rOFsR+T21eQ9CKwJqn2pBKvmJiZmZnZiDHIgMV8+QzgmojYXdKipCyTbq4BnoyIQzpcPyX38W813tYP2/uTtBWp/qO1unETqUZmUsU+v0ra5nZW3k52NbAWsAewWA5uXBKYLuniHvkvpZ9rNjkiPlpsiIinSdkwD5PyVyrxxMTMzMzMFoyhoSZbumYHLAJIagUsTo+IO3Jbt/tnByzm7UvvB/aDFD4IvFx3QEURMVHS6H76aHUFLA4sSjoieRHg8a53zHv/svn75ZhzclmQjkVeGFiC9H6f69pRtc+1b97KZWZmZmbz39ixTU/lGljAIvBW4M/A+XnL07k5NBBJB0k6qMkAG/h43m41QdIaABFxMymx/Y/569qImFGjz2OBT+Wi9auBQ3P7BOD53OdMUvH7U6U9VLOlpKmSfi7p7X304xUTMzMzs4FptgLw+tf8aN/ODjwwfZXp/jfzAwtYJP0u/C7g0Ii4VdIZwJeBoyPi7Ip99usK4OKIeClPhL4PbCtpXdIJWqvn110v6f0RcWPFfvcCLoiIb+a6mf+VtDFpxelVUqDkCsBkSb9orUDVdDuwZkTMyjUsPwXWa9AP4BUTMzMzs8FovgJg9QwyYPER4JF8bDCk1YR39T3CGiLiLxHxUv7xe8C78/e7AbdExKycrP5zYIsaXf8r8KP8jJtJ28JWImW+XBMRf4uIJ0j1I5s1HPtzrdT3fFLaIsXi+Lq8YmJmZmY2CN1WAEa619cq0MACFiPiT5L+IGmDiLiXFLA4fTgGLelE4Lf56N1iezFgcRdSNgukbVafyfcpj/1b+Z4qQYgzSe/nAklvI01M/pzbt5X0A1KR/haFficC+0TEoxXf06rA4xERkt5DWvT4S5V7y3jFxMzMzMxGjEEGLGaHAhflgMNRwH/lvhrVmEiaDPwY2C4HLO6QL70D+FPJLZ/LRwJPBT5HLsQnrd48SAp+nApMjYgr8rUqAYtfJE1sppJWifaLiCCdaLY0KWH+d8D5EXGnpIWAdYF56k26fK67A9PyM84ExuRnNOIVEzMzMzMbUQYcsDhEyVampjUmEbF1h0uL5C1V7a8/CjiqpP1VSo4crhGwOJ103HB7+yzSkcHtNgIujYgXS+4p/VwjYhwwrts46vCKiZmZmZlZdS8CH25bjekpInbo/apK/VQKWGzQ77SI+MIg+pK0hKQh0hHHr1W9zxMTMzMzMxtRGiS/fzIfx3unpN9I2rRw7bC8lWqapIslLd7j8a16jXkK7iWtmNPWZ0mqtJIg6VhJjxbS01sJ7YtKOj8ntE+tm40i6fRCn/dJeqZwbZ50+h59vV/S7ZJekbR727WypPsXgbtJOSnbVh2zt3KZmZmZWW+vk6OQmyS/Aw8B20TE05I+DJwDvFfSaqS6jo0i4kVJPyIV01/QYxinR8SpJe1/BY4GNs5fVZX19xmAnNC+CvBzSZtHRKUViIg4rPW9pEOBdxYu102nn0mqfTm85FppXxHxSUkXVOwf8MTE7O/b6+T/ROwf2HBkH5jZ/De256FX81Pt5PeI+E3hx1uYkw0C6ffhJST9jfQLdtWjh+eRC+tvyhkk/doImJj7fSKveGwGdDuJq5O9gK8VxlkrnT4iHgaQNM+kaIBJ956YmP3den39n4iZmY1k8/so5O4Bi2XJ7++t0fu/kjJBiIhHJZ1KWhF4kXRq13VpCDoOmBIRl9fou6lDJO0DTAG+GBFPk07i2jVPvNYg5ZusQc2JiaQ1gbWBXw52yIPniYnZ36u/5/P0beTwip2ZDV7j5HdJHyBNTP45/7wCabVlbeAZ4MeSPhURP4iIYwY03l7OAo4nvYfjgW8CnwbOI9WzTAH+D/gN8EqD/scAE/IpX69rLn43MzMzs5GkUfK7pE2Ac4FdI6IVAvhB4KGI+HNE/A34CbDVgMfbVUQ8HhGv5tqR75G2qhERr0TEYRExKiJ2BZYH7m/wiPa0+9ctT0zMzMzMbCSZnfwuaVHSL95dt1tJegtp0rF3RNxXuDQT2ELSkkqFKdsxJ3l9oCRdmNPR29vfVPhxN1LwIXlMS+XvPwS8kgv8O/ZV0vcGwArAPPkpHV5/oqTdqrx2OHhiYmZmZmYjRsPk92OAFYHv5ONzp+S+biUlrN9OSlhfiHRiF5KOk7RL3fFJehg4DdgvJ79vlC91Sms/OR8JfCfwAaB1mtYqwO2SZgBHAnsX7qmS/A6p6P2S9jT2uun0kjbPn+sewHcl3V2hr9pcY2JmZmZmI0rd5PeIOAA4oENfX6NwYlWhvVGNSUSs1d7WLa09IvZub8vtDwMb1OmrpI9jO7TXTaf/HXOfZFalr9q8YmJmZmZmI0qDgMUVJF2WAxZ/K2njXn11sRVwoqRfdRjbUbmve1urB93S2iX9Tw5QvFPSBElLt13fPb+fzSr0dZ6kJyRNa2v/J0nXS7o//3OF3L5rfm5rFen4bm88by+7StI9SqGU3yhcOyiv/AxJuknSFcA2wFslTW8fUxlPTMzMzMxsxCgELH6YlPWxV2G71K9JBe3/13bb/wcMRcQmwD7AGRX66uQ3wFERsU3J2DYi1by8HdiRtHXsDT36OywiNs1jm0naptbqbxlSAOStPfpouSA/t92XgYkRsR4pG6U1AZsIbBoRo0gngZ1bcm+7UyNiQ1Jg4/tyYCXA+Ih4R+7rZNLqy9oRcTKwU5XBeyuXmZmZmQ3GOefA+PHD/ZTaAYukSceJABFxj6S1JL0ReGunvhqObVdSTcdLwEOSHsjj7Vh8HhHP5WcLWIK5jz4+nvRLflniellfN0paq8O4Rufvvw9MAo6MiFmF1yxFj2OXI+IF4Ib8/cuSbidv8Wq9j6p9lfGKiZmZmZkNxvjxMDQ03E8pC1hcrcc9U4F/AcinWa1J+oW6Y18Ni9+bjA1J55OKzjcE/ju3vRNYIyKurDmGMm+MiD8C5H+uUnj2bpLuAa4irZpUIml5YGdyOn1uO1jSg6TJ1OfqDtIrJmZmZmY2OKNGwaRJ/fXRPfm9ScDiN4AzJA2RTt+6gxRW2LGvhsXvjcIfI2L/vOXrv4E9JX0fOB3Yr8EYaomIy4DLJL2ftELzwV73SFqYlI1yZmu1Kff1beDbksYCXwX2rTMWr5iYmZmZ2UhSO2AxF4zvn+sf9gFWBh5q0tegx1YY46vAD4GPA8sAGwOT8vHDWwCXtwrgG3i8lZeS//lEyfNvBNYpHhrQxTmkk8G+1eH6JcDH6g7SKyZmZmZm1lmdupGhobRiMrxmBywCj5KKzcd2uyFvO3ohIl4mHRt8Y0Q8J6l2Xz1cDoyXdBrwZmA94Ld5DBOBfSLi0cK4BKwTEQ/k73cG7omIZ4HiqWKTgMMjYoqk1YALI2K7muPal7RytC/ws9zvusCDERGS3gUsCvwlX7snF7nPRdLXgeVoO35Z0noR0Uqm/wgNUuq9YmJmZmZmnc2fupHKGgYsvg24O9dSfBj4fIW+ateY5Ht/RCqevwY4OCJelbQQsC7wVNstAr4v6S7SFrM3Acf1eMybSNvQ5iHpYlKh/QY57PBf86VvAB+SdD/wofwzpNWZaXmL27eBPfMkZSVKtqVJWh34Cukwgdvz0cCtCcoh+QjhIeAL1NzGBV4xMTMzM7NeqtaNjB493CMBGgUs3kxavajTV9OAxROAE9qaNwIujYgX2177GvC+Cn2OLvy4BWkSUfa6vTq0/wWYZ4UlIk4CTiq5pfQZEfEI5XU0RMTny9rr8MTEzMzMzKy6WcCBkpatOnmJiGmkVYS+RcS4QfTT4xmDOAkMAElbA98Bnuz1Wm/lMjMzM7MRpVNauzonvy8n6YqcsH63pP0L107ObTMknamSEJQ2GwOLUFLUruTMPIY7c91G1fd0eTEdXdIeeVyvVS16V5/J75L+ucIzTpD0B0mz2tpPz/0MSbpP0jP50mPAq0DP9+AVEzMzMzMbnKGhYd3SpTlp7R8inYL1O0mXR8R0UvL7laQAwaKDSQGMO0taGbhX0kWkX5bfB2ySX3cTsE3J/e2OiIgJJe0fJm0ZWw94L3BW/mev9/QvpJWYommk7JXv9rq/4AJgHHBhW3sr+f0beSL3ZeBIUgbJ5bmuZBNSfcw8Be9trsjPmKu4PSIOK7yfQ0nJ8ETEg8Co9olMGa+YmJmZmdlgjB07P07lmp38nk/ZaqW1ExF3RMTDJfcEsExeDVmaVIT+Sm5fnHQa1WKklZDH+xjbrqQTsyIibgGWbx3T24mkpUnbvL4+14AjZkTEvXUeno/8bS+wb43r+/n775OP8o2IWRHRylmplNYeEbe0whq72IuUc1KLV0zMzMzMbDAOPDB99av7bqqydPVeqxLjSEfmPkbKCNkzF57fLOkG4I+kou5xETEjDUHnAmdHxJQaI++U/N7tF/njgW8CL9R4Tl1zJb9Lmiv5HTiRlAb/kX4fJGlNYG3gl3Xv9YqJmZmZmY0kTdLVdwCGSNkio4BxkpbNOR5vIx0vvBqwbU5AJyIOqDkpqT02SaOAdXP6+gIREZflvJKPkSZJ/RoDTMiBkbV4YmJmZmZmI0mTdPX9gZ/kLVYPkFLfNwR2A27JW5pmAT8nHZU7v8a2JfDunO5+E7B+DlMctEEnv3czhgbbuMBbuczMzMz+MVVNdJ8/ae51NElrn0nK8Zgs6Y3ABsDvSVuOPiPpRNJqxzbAt/oY2+WkoMFLSNvLnm1toSpLUo+Is0gF8khaC7iyLbNkHgs6+b3H2DYAViCFPNbmFRMzMzOzf0Svs0T3qhomvx8PbJUT1icCR0bEk8AE4EFS6vpUYGpEXJH7OrfqMb0FV5MmPA8A3wM+m/sqTVLvRtJu+b1sCVwl6dp8aYElv+dnnJzHtWR+xrGFy3sBlxQK6mtRnfs222yzmDKl7la7wWidOlcldNTMzF4n/B9vs9evqv9+LoB/jyXdFhF1JwXzhaQLSCsbZccFd7rno8BbczJ9v88/BJgZEZf321eXZwxsvIU+Z0XE0t1e4xUTMzMzMxtRBhmwmK8vK+lRSVVS1d8DjM85KO3jKg1YjIgre/2SXzVgMSLGVZmUSNozj+FuSScX2teUNDFfmyRp9fZ728cr6ZrCZ3d2zpJB0qaSbpZ0V/58l83tW0uaLmmapHXyikzPY5g9MTEzMzOzEaMQsPhhYCNgL0kb5cu/Bj4I/F/bba2AxU2B0cA3JS1auH488KuKQ/gtMDYiPllyrRiweCC5fqSXHgGLN1YcV7G/FYFTgO0i4u3AGyW1alJOJdWobAIcRzoquJdP5M9uY2BlYI/cfi7w5Yh4B3AZcARLRT8cAAAgAElEQVRAREwGdsrfPxgRoyJinV4PcfG7mZmZmQ2fqkX21c0OWATIhea7kiYed+S29ns6BSwi6d3AG4FrSEnw/ZgdsAjcIml5SW/qFkhYCFg8kJS8ngY8J0+lyTjeCtwXEX/OP/+CVE8ykTSZa6W03wD8tFdnEfFc/nZhUoF8qxZkA+ZMnK4n1f0c3WTA4BUTMzMzMxtOgy+y7xRi2M04Ul7JY6RC989HxGuSFiKFGx7RfkPD4vcmYxuOgMUHgA0lrSVpYVJGSesY46mkSQqk45KXySssXeXi+yeA/0c6NADSqs4u+fs9mPuo5Nq8YmJmZmZmw2vUqHrF891XCfoJWNwWWAe4XtJkYB/g6oj4Q/vKREQcUHm8DcdWCFg8LB8XPBAR8bSkfwd+CLwG/Ia0igJwOClgcj/SasejdDjlq63PHSQtDlxE+hyvBz4NnCnpGNKRxC/3M25PTMzMzMxsJGkasPiNvMXqAUmtgMUtga0lfZa0xWvRfHrUl7v0NcixFQMWFwZWkTSpV5ZJFfnY49bRxwcCr+b2x0i1K61tZB+PiGcr9vlXSZeTtqxdHxH3ANvnvtYHPtLPmD0xMTMzM7PuhobmHBvc5N7BBjQOLGCxWMCeVxA262NSAq+jgEVJq0TEE5JWIOWpfCK3rwQ8FRGvAUcB5xXumWeMefKyTET8MW8L2wmY3PaMhYCvAmd3G1MvrjExMzMzs87Gjn1dJb8POGCxo4Y1Jgs0YLHNGZKmk04q+0ZE3JfbRwP3SrqPVPR/Qo8xLgVcLulOUn3KE8yZgOyV+7mHtDJ0fp332M4Bi2ZmNnz8H2+z16/59e9ng+fIAYvd+hqWgMVBjjH3txbpc9q46j3eymVmZmZmVt2zwPGSVoqISluXIuLKQT08IqqEQDbpd2BjlLQ18B2g66pUO2/lMjMzM7MRRQNMfpe0r6T789e+FR6/HLB4h3FJJcnvPd5Lp1T1H0oayl8P5/T0ytQ5+f30Qr/3SXqmjzGeIume/JzLJC1fuO0NwErlPZbziomZmZnZgjD44MF6Bl+U3v1ZTYvn22hO8vuHSKdg/U7S5RHRqqe4EpjUdlsr+X1nSSuTaiwuIp3E9TVSsGIAt+W+nu4xjCM6bOUqJr+/l1TY/t4efX0iIp7L4Y8TSHkgl0TEnoX3/E3SSk0lmpP8/u6I+LOk70vaLiImRsRhhdcdCryzQpelYyQdGXxURLwi6SRSMf2RETFZ0k6kP4vKvGJiZmZmtiAMPnjw9WnwxfOzk98j4mXSL8i7AkTEHRHxcMk9nZLfdyAde/tUnoxcD+zYx9hmJ79HxC3A8pLe1O2GLqnqQFqFIZ2odXGNcXRKfm+3V5V+O40xIq7LhxEA3EI6dKAxr5iYmZmZLSh1gwcHaUArGD0deGD6qqN7wGJZunqvVYlxpKN8HwOWAfbMye8dk9rziV5nR0Sdk5869ffHbjflE7feA/ycOanqLVsDj0fE/TXGMTv5PY/hY6QJRfGZawJrA7+s0mGPMUIKW/xhjTHOwysmZmZmZjaS9JP8/mZgFCn5fNlufUXEATUnJU3HRkTsQDoGeDFSqnpRpVWNtv6eBlrJ75OBh5n3iOExwISIeLVinx3HKOkruf+L6oyznScmZmZmZjaSNE1+/0neYvUA0Ep+b9LXoMcGpFR10qrOrq22HGj4LzRYiYiIKyLivRGxJXAv0L7iMob6E56yMe4LfBT4ZNTJISnhiYmZmZmZjSSzk98lLUr6BbtXpkcr+Z1i8jsppHF7SSvkhPTtc1tTlwP75NO5tqAt+b39xZKWbtWgFFLVi6/7IHBPRDxSuGc1SRN7DUTSKvmfreT3cwvXNgBWAG5uu6fWGCXtCBwJ7BIRL/QaUy+uMTEzMzOzESOfANVKfn8DcF4x+R34ErAqKfn96og4gJT8foFS8rsoJL9LOp402QE4LiKeyu1NakyuJv3i/gDwAmmlpkqq+mL5vfySOanqUL6qUSf5fdP8/XGF5HdI28MuKa5wNBzjONLWrutTjT63RMRBFcZWyhMTMzMzMxtRIuJq0iSgvf1MYJ7k8oh4jLQaUtbXecB5Je0HNBhXkI4mbrcF6Yjj9tc/Dmzepb/9qvZVcu9eXa4dO4gxRsS6vcZRhycmZmZmZmbVOfm9Bye/m5mZmdk/hLrJ7/na6Jx2frekXxXaD8tt0yRdLKk01b3gaWBZ0glfZWM7Ko/hXkk7VHgv/5NT1e+UNEHS0oVrn5A0PY+vVhqnpO0k3Z7f802S1s3ttZLfJS0p6aqc8H63pG8UrnXq6zHgVVJwZWVeMTEzMzOz4TEM6fZNkt8lLU/6G/wdI2JmoTB8NeBzwEYR8aKkH5HqOi7oMYzTI+LUkrFtlO9/O2ni8gtJ6/c4kvewVoChpNOAQ4BvSFqPlKT+voh4ujXmGs4Cdo2IGZI+C3wV2K9h8vupEXFDPmxgoqQPR8TPO/UVEQ8CoyTNqjNgr5iYmZmZ2fAYnnT7JsnvY0nHBc/Mr3uicG1hYIl84tSS9Hdc8K6kovKXIuIhUhH8e7rdUJiUCFiCObknnwG+nTNJ2sdcRZBWdgCWo/x99cxIiYgXIuKG/P3LwO2UJ7zXzltp5xUTMzMzMxs+TdLtB5/8vj6wiKRJpOT3MyLiwoh4VNKppOOEXwSui4jr0hB0HDAlInodRdw+tlvaxrZar5sknU86zWs68MXCmJH0a9JpWMdGxDU1xnIAcLWkF4HnSMXtxWfWSn7P9ywP7Ayc0W9fZbxiYmZmZmYjSZN09YWBdwMfIaXAHy1p/ZzxsSvpl+o3A0tJ+hRARBxTc1LSdGxExP75+TOAPQtjXg8YTVqNODdPDKo6DNgpIlYHzgdOa7teK/k9ryhdDJwZEb/vp69OvGJiZmZm9o9qaAhGjx7e/keNGnSvTdLVHwGejIjngecl3Qi0Mj4eiog/A0j6CbAV8IP5ODYAIuJVST8EjiBNJB4h5YL8DXhI0r2kicrvunQDgKSVgU0j4tbc9EOgfbVlDOVHG3dyDnB/RHyr5Frdvkp5xcTMzMzsH9HYscMxaZgfmiS//wzYWtLCkpYkbf2aQdrCtUU+eUqkdPgZfYztcmCMpMUkrU2aSPwWQNLEXGw/m5LWaVkibZNqpa//FPhAvrYSaWvX7/PP8yS0t3kaWE7S+vnnDxXfl2okv+f2r5PqVP6j5FppX014xcTMzMzsH9GBB6av4TQMqzFNkt/zyVTXAHcCrwHnRsS0fM8EUkH3K8AdpJWBRjUmEXF3Ptlreu7v4LwSshCwLvBU2y0Cvi9p2fz9VODf87Vrge0lTScdvXtERPxFnRPa2z+jzwCXSnqNNFH5dOEllZPfJa0OfIU0Ybo9zZ8YFxHnduqrKdXpY7PNNospU6b0+8xGWv+7rls7ZWZmC5D/420jwTAcaVtJa5vT3/O/Hw3/GyDptoiolYExv0g6FphVdlxwl3s2Bj4dEV8YwPM/Crw1p9wPzHD0K2lWRCzd+5WJt3KZmZnZP7bhOdLWhpEaBCzm65tLelXS7oW2k3Nw4AxJZ+YtVd18BDhR0lUl/Sv38YBSYOK7ACJiWqdJiaRrlAIW75Z0ds5pKV4/vPh+IuLKXpMHSRsUgg+HJD0n6T/ytVGSbsntUyS9p1u/+fU35/HdKWnPwrWL8p/DNEnnSVokt39O0kv0WNlp561cZmZmZgti5WI4i85fTwZcYK8GAYuF+04ibZFqtW0FvA/YJDfdBGxTdn/B3cBJETGh5NqHSXUl65HqWM6i91HGn4iI5/KEaAKwBymbBUlr5Pc5s0cfc4mIe4FRuY83AI8Cl+XLJwP/GRE/l7RT/nl0l+5eAPaJiPslvRm4TdK1EfEMcBHwqfy68aQjis+KiDMl3QkcXmfcXjExMzMzs+ExPAX2TQIWAQ4FLgWKQYUBLA4sCiwGLAI83sfYdgUujOQWYHlJb+p2QytgkbRgsChzHy98Oqlmpp/6je2AByPi/1qPpHfwYnF890XE/fn7x0if38r556vzew1SkX9Z8GJlXjExMzMzs+HRtMB+wAGL+TSs3YBtgc1b7RFxs6QbgD+Sth2Ni4gZ+Z5zgbMjok6BddnYVsv9dxvftaQJ1//P3p2Gy1WVaR//3xJAmUcNo0EGkRYIEhQHmhiUSQZRkBBkEkyDTKLQwGs30iAtigpiBDoyBFoiYBiMEIYYSQsyaAInhJk00MosEMAwacLzflirTnYqu4Zdp3Ligft3XeeialXttZ8qvtTKGu7rSbMmSNoNeDIiZrReXdbUSBZMZP86cKNSsOS7SMcjtyUv+1oK+N+69iWB/YCj+1KoZ0zMzMzMbCDpJMTwLOD4+gDAfFTvh0j/0r8WMELSPwPk07yqnvrUacDiDsAapFmbEflI428BJ1W8/4LFpOOUdwN+WWg+DDgmItYhhTBe0GZfawD/DRwUEW/VvXwO8LuIuKUv9XrGxMzMzMwGkk5CDIcBl+WZh9WAnSXNJe0FuSMi5gBIuh7YGvhdP9YGQES8IWkiaTnYM6Q0+tpsydqko3o/GhHPVKhnJ+CuiCguTzuA+TMbvwTOX+iqOvk44+uAf8tL1IqvfZu0tOtfKtRVyjMmZmZmZjaQVA5YjIj1ImJIRAwhLZX6WkRcQ9pUvq1S8OKSpI3vfQ1Y3D+fzrU18HJEPA3l4YWSlqvtQZE0CNgZeDAiZkbEews1PwF8JCKekfRRSZe0Wc8+LLiMC9JAadv8eATwSL5/ab/5O76atHfml3WvHQLsAOxTMotSmQcmZmZmZjZgRMRcoBaw+ABwRTFgUdITpBmGe/I+kWYmkPZLzCSFG86IiF/nvs6XVDVLZRIpnX0W8DPga7mvRqGIywIT8wlWM0gby89rcY91gddbFZKXg30WuKrupa8CP5Q0A/hPoLYJqFG/XwL+GTiwcPxw7USD84D3Abfn9j4tPfNSLjMzMzMbUCJiEmkQUN9+NtA04yMiDiw8nkeDJUgRcUgHdQVweMlLW5OOOK5//7MUNuM36XdI4enHyvoqueY1YNWS9luBLUsuKe03In4O/LzBPbo6lvDAxMzMzMysfS8Dp0paLSJazW4AKbywWzePiOO61dei6jeHMH4bmF7lOi/lMjMzM7MBpWryu6SVJV2dk8v/IOnDhddWkjRB0oNK6e8fb3H7u0kb6EuXeUk6QNIj+e+ACp9pgYR3ScMlvVxYPtVymVSTz79v/uz3SLpN0uaF1yp9/hZ9HSPpPuDfSUvTvprbL5X0oqQ9m/XtGRMzMzMz646xY2H8+EV6iw6T3/8f0BMRe0jaOF+/XX7tx8ANEbFn3ui9TBtlXB4RR5TUtgpppmAY6Zjg6bm22S0+U6OE91siYpc26qlp9PkfA7aNiNmSdgLGMj/7pernL+0rZ8UcBWwSEa9LuoJ0MMG4iNhX0rhWxXvGxMzMzMy6Y/x46OlZ1HfpJPl9E2BKfs+DwBBJ78vH4P4zOcsjIv4WES/1obYdgMkR8WIejEwGdmzjum4kvDf8/BFxW2FwdAc5ob2Tz9+or2wQ8J58wtgytHlUcvFiMzMzM7PuGDoUpk7tWx9dTn4nLSv6AnBrTi9/P+kH9TzgL8BFeUnSdODoiHhV0qEA7e4jaVLbWs0uaJHw/vF8etZTwLG108f66GBSwjzAB2jw+av2FRFPKqXJ/4l0utdNEXFTlcI8Y2JmZmZmA0kn6eqnAytL6gGOJO0TmUv6R/qPAOdGxBbAq8AJkAYkFQcllWtrkfB+F/D+iNgc+AlwTcVayu73adJg4vjc1PDzV+1L0sqkmav1gDWBZSV9uUp9njExMzMzW1x6emD48MVdRff09KQZk0Wrcrp6RLwCHASgNC3xWP5bBngiIu7Mb51Amz/Mm9Q2vK62qU3evz5tJLxHxCRJ5+STwJ7vpDBJm5FS3neKiBcK9Vb+/A36+gzwWET8Jb/nKuATNDhquIxnTMzMzMwWh1Gj+uNH/NtR5eT3fPLUUvnpIcDvIuKV/OP/z5I+mF/bDri/D7XdCGyfTwFbGdg+tyHpkryMrFeLhPfBeRBFvu5dwAv5+ZS82bwtktYlBS3uFxEPF+7f8PPnE77KNviX9kVawrW1pGVy3duRAjDb5hkTMzMzs8Vh9Oj093bSD7M/ETE3/2C+EVgCuLCY/E7aRD6YlPw+KQclfgi4RNI80g/vgwtdHglcmgcujzJ/ZqXyHpOIeFHSqaTBE8ApEfFifrwZ8HSFj7oncJikuaQ9GyMjIiS9C9gAeLH+giaf/yRS2OI5eawzNyJqxx2Xfn5gY9IpX/VK+4qIOyVNIC1Bm0taLje2wuf1wMTMzMzMBpaqye8RcTuwYYO+eijJJOlgf0ntuguBC4tt+fSrRyLiz+VX9V47pPB4DDCm5G2bAFdGxOsl1zf6/IeQZorK7ln6+YEhwDcq9vVt0nHJHfFSLjMzMzMbUNTFgMX8+hKS7pbUTkL754BDJd3VoLYTcw0PSdoB0h6XiNirxWf6iaQ5hednan644sOSXsp93RsRCw0Y8jWnSfpzsZ/c/g1J9+fPP0XS+wuvlQZCRsQu+Tjm+nvsJek+SW9JGlb32maSbs+vz5T07tz+DLA/Cx4tvBDPmJiZmZlZ9yziDf3qfsAiwNGk/RArtFHCfcCdEfGDkto2Ie15+SfSyVS/kbRRRMxr8ZmGASsV2yLimMLrRwJbtFHbr0mzLI/Utd8NDIuI1yQdBnwf2FudBULeSzp6+b/qPsMg0kb3/fLRx6sCf8+fZbCkqcCtzYr3jImZmZmZdUf/bOjvWsAigKS1SbMg53ehtt2ByyLizYh4DJiV620oD7TOIO0NaWQf4Betbh4Rd0TEQvtYIuLmiHgtPy2GIlYOhIyIByLioZKXtgfuiYgZ+X0vtBqQ1fOMiZmZmZl1R7c29PdfwOKzwFmkQcHyC5agU4BpEdH0xK+S2u6oq63V6VlHABMj4umSgEXysqv1gN9WqKOZYsBi5UDIJjYCQtKNwOqkAdr3q3TggYmZmZmZDSSdBiz+OAcsziQHLEraBXguIqZLGr5AhxFloYddrU3SmsBeLJh9Um8kMKHq7EOD+32ZtGxr21pTydtafZeNDAI+BWwFvAZMkTQ9Iqa024GXcpmZmZnZQNJRwGJEHBQRQ0mbsFcnBSx+EthN0uOkJWEjJLUdCNiF2rYgHf07K9ewjKRZde8ZSRvLuFqR9BlSyvxuEfFmh/U28wTwPxHxfF42NomUKt82D0zMzMzMbCDpZsDiiRGxdj6mdyTw24j4ch9qmwiMlLS0pPVIRxT/IdewUChiRFwXEYMLAYuvRcQGhbo/CKwM3F73eR6sUpSkLUib1XeLiOcKL1UKhGzhRmCzHLA4iDQrUyms0gMTMzMzMxswImIuaV/GjaSTtK4oBixKeoL0L//3SKptaP8QcF/+Qb8T6RSupiSdImm3irXdB1xB+kF+A3B4RMxrForYwj6kvRq9y6vyMcilm3AkfT9//mUkPSHp5PzSGcBywC/z8cMTc70vArVAyD/SRiCkpD3yPT4OXJf3lJA3z/8o99MD3BUR11X5sN5jYmZmZmYDSjcDFgvvmUrhmOEO95gQEacBp9U1NwxFrLt2ubrnJ5e8bWvSccdl1/8rJad7RcRnmtyzUiBkRFwNXN2gr5+TjgzuiAcmZmZmZmbtmwOMlrRCu4OXiLiXkhT1TkREOyGQfb3HK6RN+V0h6WbgA+Rck0a8lMvMzMzMBpQOkt+HS3q5kKR+Ul1/VZLflwOWbVLbQsnvbX6mtpLfK/R3S+H6pyRdk9tXlPRrSTNyQvtBbfRVmiifX/uSUqr8fZLG57b1833nAETEpyNi3VrGSSOeMTEzMzOz/jN2LIwf3/HlHSa/A9wSEbs06LZK8jvAmf+gye/F67cpXH8l8Kv89HDg/ojYVdLqwEOSLs1hlY2UJspL2hA4EfhkRMyW9N587/8FhpYNZJrxjImZmZmZ9Z/x46Gnpy89dJL83tDbKfm9Qf/LAyOAa3JTAMsrpTkuR9qQP7dZH40S5YGvAj/NG9+pO/GrMs+YmJnZotXTA8OHL+4qzBrr6YGhQxd3Fe8sQ4fC1KmNX+9+8jvAxyXNIOV0HFs7yYu3f/L7HsCUvG8E0szHRNL3sDywd0S81WHfG+Uafw8sAZwcETd02JcHJmZmtgiNGrW4KzCzt59O0srvAt4fEXMk7UyaPdjwHZL8vg8LzgbtQDrOdwSwPjBZ0i2FgUsVg0innQ0nHdF8i6QPR0Sl/TDFzszMzBaN0aPTn9k/Ms/odU87+0f6PkPVUfJ74fEkSefkzfG15PedgXcDK0j6eR9CFvuS/A45+b0YskgamBzeSTGSViUtJduj0HwQcHrORpkl6TFgY3IQZEVPAHdExN+BxyQ9RBqo/LGTer3HxMzMzMy6o+/7R9rRSfL74Lyngpxm/i7ghXdA8vtewLUR8Uah7U/Adrmf9wEfBB6t2G/NNcCn87WrkZZ2PVqxj16eMTEzMzOz7mm1f6SPM1QRMVdSLfl9CeDCYvI7ab/IYFLy+6SIOATYEzhM0lzgdWBkMU29TCd7TCLiPkm15Pe59HPye4mRwOl1bacC4yTNzP0cHxHPt0qUB0aRE+WB83P4443A9pLuB+YBx0XEC21/ujoemJiZmZnZgNJB8vsY0qbvZn1OZYAnv5f0N7yk7Slg+yr9NkmUD1JwZFfCI72Uy8zMzMwGlG4FLEpaR9LNkh7IAYFHt3H7jYHTJd3VoLYDJD2S/w6AlPweEU1/vEs6tr7u3L6VpHmS9sx9XZsHYE1VDVhs1K+kZSRdJ+nB/P7TC68tLeny/J3fKWlIbt9b0hvAkq3qLPKMiZmZmZn1rz4cI97lgMW5wDcj4q6c9zFd0uTcVyM3AM9HxBElta0CfBsYRjqNa3qubXaLz7RO/jx/Kvms3yMtmaqkywGLP4iIm/OenimSdoqI64GDgdkRsYGkkbnWvSPickl3kv5ftM0zJmZmZmbWf0aN6uupXF0LWIyIpyPirvz4r6T091a5I83sAEyOiBfzYGQysGMb151JWipVv+/lSOBKoOPgwr4GLEbEaxFxc378N9LRy2vnl3cHLs6PJwDb1Q4Z6IRnTMzMzMys/7RzjHj/BizmW2oI6fjeO/PzQwEi4rw2+m5WW9OBjqTdgCcjYkbxN30+wWsP0qBiqwo11OtawKKklYBdgR/npt7Pmw8leBlYFXi+k0I9MDEzMzN7J2sne6Rdfc8oaUfXAhZ7O5SWI81MfL32A77igKSj2iQtA3yL8s3oZ5FOzJrXh0kI6FLAoqRBwC+AsyOidiRwJ/8vGvJSLjMzM7N3sv7JHummjgIWI2JOfjwJWLK2yVzSkqRByaURcVU/17Y+sB4wQ9Lj+f13SRpM2qdyWW7fEzhH0uerFFMIWLyu0HwQcFUks4BawGIrY4FHIuKsQlvv580DlxWpfiRyL8+YmJmZmb3TtcoeaVcfM0ra1BuwCDxJyuoY1eyC/EP/2YiIYsBi3g9xAfBARPyoC7XdCPynpJXz8+2BE3MNlwBjIqI3YT0iZgLvLdT5ODAsIp4nDVhq7eNIQYm1k7UejIh2BhPNAhZvKQtYLOtX0ndIg45D6l6aCBxACoDckxRQ6RkTMzMzM3v7i4i5QC1g8QHgimLAYg4AXJsUsFhbwrQncG/eY3I28wMWPwnsB4woHK27c+7r0No+kwq1vUgKMPxj/jsltwFsBjzd8QfPOghY/EVd26nAJ3LA4hRaBCxKWpu03GwT0mxOj6TaAOUCYFVJs0hZJifUX1+FZ0zMzMzMbEDpVsBiRNxKgx/5He4xISIuBC4stklagbQM6s/lV/VeO6RB+4GFp/0asBgRT9D4O3qDNCvTFR6YmJmZmZm173VgJ0nnR0T90qZSeWN5V37AR0SlbJDF0a+kbYBzqHg6l5dymZmZmdmA0kHy++6S7snLkKZJ+lThtXUl3ZTT3++vpZc38SHg3TTY1C7pxFzDQ5J2qPCZfiJpTkn7nvnzDKvQ1/KFpWk9kp6XdFZ+7VBJM3P7rZI26VK/x0j6k6QxEXFLRGxaNmPTjGdMzMzMzKx7+pDq3o4Ok9+nABPz5vfNgCuYfxLVJcBpETE5HxvcTqbHmRHxg5LaNiHt6/gnYE3gN5I2ioh5LT7TMGClkvblgaPI2SrtymGRvec2S5oO1E4cG19bppYzVH5EeyGQTfuNiDMlzSadJtYRD0zMzMzM3m6qZJN0M3tkVNPDsbqlN/kdQFIt+f3+iLg7ty1wQe2o4GxZctZGHkgMiojJJe/rxO7AZRHxJvBY3hT+UdKpVaXyQOsM0slie9S9fCrwfeDYTguStCHp5K9boHdZWU3vd9HXfrvBAxMzMzOzt5taNsmiDztcUDup7u1YBMnvkvYAvkv6Mf253LwR8JKkq0jH8/4GOCGHGp4CTIuIiRUqXwu4o662psnvpBPGJkbE03XJ71sA60TEtZI6HpiQAhYvLx7jK+lw0ilaS5GCFrvSb195YGJmZmb2dtRuNkn/ZI90U0dp4xFxNXC1pH8mzUR8hvRbeBtgC1K+x+XAgcAFEXHSoq5N0pqkTfHD69rfBZyZa+mrkaQjkecXFPFT4KeSRgH/Rsoi6XO/feXN72ZmZmY2kFROfi+KiN8B6+fN8U8Ad0fEozkf5RrgI/1Y2xbABsCsHK64TF7+tTzwYWBqbt8amFhlAzyApM1JS9WmN3jLZUClNPk2++2IByZmZmZmNpD0Jr9LWor0L/dNl1tJ2iCnvCPpI6QlTC/kvlaWtHp+6wjg/j7UNhEYKWnpnEy/IfCHfN8pkhZY1hUR10XE4IgYkjNMXouIDSLi5YhYrdB+B7BbREyTtJakKW3Wsw91AYt5b0jN54BHcnuf+u0GL+UyMzMzswEjIuZKqiW/LwFcWEx+B/4VGExKfp+Us0a+COwv6e+kHJK9896IeXn/xpQ8cEnkM8IAACAASURBVJkO/Cz3VXmPSUTcJ+kK0uBmLnB43q/yLtLMyItNO2jPGrnvdnwJ2Lmu7QhJnwH+Dsxm/jKuvvbbZx6YmJmZmdmA0kHy+/eA7zXoazKwWUl7J3tMiIjTgNPqmjcBroyI11tcu1yD9uGFp1WS3z9Q0nZ0g7f3qd9u8FIuMzMzMxtQqgYsFl7fStI8SXsW2g6Q9Ej+a2cT+OeA70q6rqR/STo713BPXjZGRNwbEd9o8Fmm5s9SCy18b24/s9D2sKSXcl9j2pnFkXRL4fqnJF2T24dLernw2kkV+z1Z0pOF63fO7T8B/gvYu1UfjXjGxMzMzOydbhGHInZThwGLteu+R1oCVmtbBfg2KRQwgOm5r9lNSrgP+F5ETCh5bSfSvpINSUcYn0sbRxkD+0bEtGJDRBxTqPNI0kb5tkXENoXrrwR+VXj5lojYpUp/dRYKmIyIIyX9kPT9d8QzJmZmZmbvZKNG9X/eSd/0BixGxN9IJ0vtDhARd0fE4w2uOxK4Eniu0LYDMDkiXsyDkcm0mYLewO7AJZHcAawkaY0+9FfT8WbznB4/gnTi2D80z5iYmZmZvZN1KxSxm7ocsJhPw9qD9AN9qxZ9rZWvOR84r34mo4VG/T3d4rqLJM0jDZy+UxeG+H5S+ONvK9RRtAcwpS7x/eOSZpCOMj62dnhABUdI2h+YBnyzxQxT2zxjYmZmZmYDSScBi2cBx0fEvHb7iohDKg5KOq1t34jYlBT0uA0LhxaOBCaU1N6u+tmWu4D3R8TmwE+oPpNyLrA+MJQ04Pphh3UtxAMTMzMzMxtIOglYHAZclsMK9wTOkfT5Dvvqam0R8WT+71+B8aSlakUj6XwZ16q5v96N+hHxSkTMyY8nAUuWHRTQpN5nI2JeRLxFOlq5vt6OeWBiZmZmZgNJ5YDFiFivEFY4AfhaRFxD2gi/vaSVJa0MbE9hc3wHJpLyUiRpa+DliHgaQNKD9W+WNKg2KJC0JLALcG/h9Q8CKwO31123UF8N7AVcGxFvFK4dXAib/ChpPPBCfr5QCGRJzcU9M3sU6+0r7zExMzMzswGjw4DFRn29KOlU0mAH4JSIeDH31ckek0mk4MFZwGvAQbmv1Shf5rU0cGMelCwB/IYc8JjtA1xWt+ekUV9lRgKn17XtCRwmaS4pbHJkRESFEMjvSxpKWqL2OPAvbdbSkgcmZmZmZgPouNy29PQMtJO2KqkasFj3ngPrnl8IXFjyvoYDmiZ9B3B4yUul4YUR8SqwZZP+Tm63rwbXDy9pGwOMKXl7uyGQ9XtgusYDEzMzM3tnGzVqcVdgA8vLwKmSVouI89q5ICI6zvZYlH3V9XsvUBoC2Q5J2wDnAM932ocHJmZmZvbO9o94XG5fvZ1mf0pI2hH4MWn50/kRcXpuPwL4OunUqNUj4vncviLwc2Bd0u/fH0TERfm1dYHzSZvWA9i5SRYKwGxgBWDNBrWdCBwMzAOOioime1YkjQO2JQ14AA6MiJ7C61sBdwB7Nwh1LPZ1GrA/sHJELFdoXxq4hDQ780Lu6/G8OX4C6QjlcRFxRLP+c19nALsCfwP+FzgoIl6KiFuATSWtK2kOcHJE/EDSe0h7ZDYB1qz9PynjgYmZmZmZdWbsWBg/vl9v2WHy++HA/RGxq6TVgYckXZoDGi8BTouIyZKWA95qo4yFks9zbZuQ9nX8E2ng8htJG7Vx1O9xZYOOsrT6Fn5NWqb1SF37wcDsiNhA0sjc597AG8C/Ax/Of+2YDJyY9/p8DzgROL7w+pnA9bUneWnY0HwiWlM+lcvMzMzMOjN+fNrP0r86SX4PYPl8GtVypA3ec/NAYlBETM7Xz4mI1/pQ2+6kzepvRsRjpE3wfTlOtyytvqGIuKN2ClhJXRfnxxOA7SQpIl6NiFtJA5S2RMRNETE3P72DdCQyAPkI5keBqoGNgGdMzMzMzKwvhg6FqVO722eXk99JswgTSZkiy5OWMr0laSPgJUlXkdLVfwOcEBHzJJ0CTIuIpkcRl9R2R11tTY/fzU6TdBIwJd//zSZp9Z3o/c7yTMfLwKr0YT9I9hXgcgBJy5JmTj4LHNtJZ54xMTMzM7OBpJN09R2AHtLyqqHAGEkrkP6RfhvSD+mtgA8ABwJExEkVByWd1nYisHG+/yrMXxbVKK2+E53U1bxD6VvAXODS3PQfpCVuczrt0zMmZmZmZpZU3TOyeI4l7iSt/SDg9Hyc7yxJj5EGA08Ad0fEowCSriEdx3tBf9VWWHr1pqSLmD/bUEurB1gN2FnS3BwM2WldT0gaBKxI67yShiQdQAqD3K6QsfIxYE9J3wdWAt6S9EY+nrgtnjExMzMzs2Tx7BmpqnLyO/AnYDsASe8DPkjaC/FHYOW8IR7Ssqn7+1DbRGCkpKUlrQdsCPwh37c0Vb2WpJ73v3yenKTeJK2+SvJ7sa4D8uM9gd8WQxvLSLokJ8PXt+9ImtXZrbgfJyK2KdR7FvCfVQYl4BkTMzMzMyuqsmdkMRxL3GHy+6nAOEkzScuaji8cJXwsMCUPDKaTk9c72WMSEfdJuoI0uJkLHJ73qzRLVb80D4xEWm52aLN7NEt+z7MVo4BlJD1BOkr5ZNIM0H9LmpVrGFm45nHS8cdL5c3r2+cTzjYDyjbSjyEl1k/Oszl3RETTmtvlgYmZmZmZDShVk98j4ilg+wZ9TSb9CK9vP6nD2k4DTqtrbpiqHhEj2ujzwMLThsnvEfGvpIFZffsbwF4NrhlS35b33zwSEX8uef8GbdR7cqv3lPFSLjMzMzMbUCTtKOkhSbMknVBoPyK3RZ5ZqLUfJ6kn/90raZ6kVQqvLyHpbkntpKp/AviupP9pUNuJuYaHJO0AKVU9IkpT1SWNk/RYob6huX1fSffkv9skbZ77ujYPwFp9R0Ml3ZH7nFZbliVpRUm/ljRD0n2SDqq/NiJeiYi9Cn2dJunPOTixeI9DJc3M97g1H7+MpG0k3Z+/6/dI6gGWpEVGjGdMzMzMzKxzPT39uqSrk4DFiDgDOCNfvytwTEQUl1UdDTxAWtLUym3ATf0QsPgYsG1EzJa0EzCW1sciF30f+I+IuF7Szvn5cJqHTTbSKLhxfEScByBpN+BHwI4RcUu+57W1gMV2CvaMiZmZmZl1ZtSoxXEqVycBi0X7AL+oPZG0NvA54Pwu1Na1gMWIuC0iZuenCwQZttsF8wdaKzL/dLDSsMkWtZQGN0bEK4Wny9LHI4g9Y2JmZmZmnRk9Ov11W/cDFnO3WgbYETii0HwWaV/G8nXvXawBi3WvHwxcX6EOgK8DN0r6AWky4hO5vTRssmLfvSQdDnwDWIp0qlnHPDAxMzMzezvqZInV4sklqaovYYG7Ar+vLeOStAvwXERMlzR8gQ472/zeacDiM6Qf9mNJR/Ge0tuh9GnSwORTFWs5jLRk7UpJXyKdzPUZ5odNjgDWJ52udUvd7EfbIuKnwE8ljQL+jfnHElfmpVxmZmZmbzeLZ4lVf+kkYLFmJIVlXMAngd3ykbmXASMk/bw/a4uIpyN5E7iIwtIvSZuRlpjtHhEvVKzlAOCq/PiXhX4PAq7K95xF2suyccW+y1xGymHpmGdMzMzMzN5uOl1itRhySTrQG7AIPEkabIxqdZGkFYFtgS/X2iLiRNKMBXnG5NiI+HJpB+2ZCIyX9CPS5vcFAhaB/SPiybq61oiIp+sDFiWtSxpY7BcRD9ddU9pXnadIn3cqaXaktnG9FjZ5S13YJJIejIi2BymSNoyIWr+fY+HN8ZV4YGJmZmZmA0aHAYsAe5BO03q1nfv8AwQsngSsCpyTgwznRsSwFn0VfRX4saRBwBtAbaRaGjbZYXDjEZI+A/wdmE0flnEBqEUa/QKGDRsW06ZN68v9OlYbwLcbRGpmZmZmFf2D/OCSND0ihi3WIhqQdDIwp+y44CbXfBj4SqMsk4r371pfdf3uAnygnYyUNvsbQjou+MPtXuMZEzMzMzOz9s0BRktaod0N8hFxL+nkqj7rZl91/bYTLtkWSdsA5wDPV7nOm9/NzMzMbECpmvyeXxueE8rvK6a2N+qrieVImR2Nalso+b3FZylNfm9WczskXV7o8/Gcvo6kpSRdlBPbZ9SfRtagr1OVEuh7JN0kac1CfS8X7lMbqE0D5gGfqP//0IxnTMzMzMxsvn5Ocq+qk+R3SSuR/gV/x4j4k6T3ttFXM2cu6uT3RjW3KyL2LvT1Q+Dl/PSr+fVNc5/XS9qqRZbJGRHx77mvo0j7X2p7YW6JiF3q7v06MDSfdtY2z5iYmZmZWTIwjhnuJPl9FOmI3D/l9z3Xqq8OdS35vUnNleTTvr7E/GOSNyEFOdb6fAloup+n2wnvjXjGxMzMzMySRZXkXlX3k983ApaUNJWUdv7jiLikWV//AMnvjWquahvg2cKxvjOA3SVdRspc2TL/9w/NOpF0GrA/aebl04WXPi5pBul44mNrJ6R1wjMmZmZmZjaQdJKuPoj0A/xzpOTzf5e0UbO+IuKkioOSTms7kRRwuBWwCin5vVnNVe3DgqGSF5IGTNOAs4DbSEcbNxUR34qIdYBLgSNy813A+yNic+AnwDUd1NfLAxMzMzMzG0g6SX5/ArghIl6NiOeB3wGbd9hXV2trkvzeqOa25QyTLwCXF+43NyKOiYihEbE7sBLVghHHA1/Mfb0SEXPy40mkGZ62N7vX88DEzMzMzAaS3uR3SUuRNpu3mtn4FbCNpEGSliEt13qgw76amQiMlLR0TqZfIPld0kLLuiStkf+7QPJ7k5ob9lXiM8CDEfFE4X7LSFo2P/4sKbjx/vz8EkkL7YmRtGHh6W7Ag7l9cK6bfN27gBfaqKuU95iYmZmZ2YDRSfJ7RDwg6QbgHuAtUnL5vfmaRn0t1uT3RjW36KveSBZcxgXwXuBGSW8BTwL7FV7bDHi6pJ/TJX0w1/F/zD+Ra0/gMElzgdeBkVElvb2Ok9/NzMzM7B+KnPzeL33V9bsCcEFE7NXFPh8HhuWlaC15KZeZmZmZDShVAxYl7ZsDAu+RdJukzXP7OpJulvRADjE8uo3bb0yaQbirQW0HSHok/x0AKa291UBC0rHFupWcnT/PPZI+0m5fhetPk/Rw/nxH5faVJV2d+/xDHujU9ouUDkrUIARS0oqSfq0U1HifpINy+yaSXgfWJc2ytMVLuczMzMysT8ZOH8v4meP75V7qIGAReAzYNiJmS9oJGEvaszEX+GZE3CVpeWC6pMktAhZvAJ6PiCPqX5C0CvBtUi5I5P4mRsTsFp9pnfx5/lRo3om0R2XDXOu5tD4WuehA0kb8jSPiLc0PaPx/QE9E7CFpY9J3uV0b/S0UAgkcDtwfEbvm5WgPSbo0f3/vkTQnItpZcgZ4xsTMzMzM+mj8zPH0PNPTX7erHLAYEbcVBgd3kE7Lqp2IdVd+/FfS5vJ2NpU3sgMwOSJezPebDOzYxnVnkvbGFPdY7A5ckk/sugNYqbZRvk2HAafUEt0LAY3FgMUHgSGS3leh36IAls8b4Jcj7XtpefRwI54xMTMzM7M+Gzp4KFMPnNqVvnRQ1wMWiw4Grl/ontIQYAvgzvy8tgn9vAp9l9XWdKAjaTfgyYiYoQWDJRv1VbY5vcz6wN6S9gD+AhyVQxZnkI4QvjWfpPV+0kDt2Rb9lYVAjiGdRPYUKQRy79pAqBOeMTEzMzOzgaSTEMN0ofRp0sDk+Lr25YArga9HxCuQBiQVByWVa8vHAH8LOKmvfZVYGngjHyLwM1KwIsDpwMqSeoAjgbtpPcvRKARyB9JJYmsCQ4ExeRN9RzwwMTMzM7OBpKNQREmbAecDu0fEC4X2JUmDkksj4qp+rm19YD1gRj7Bam3gLkmDO+irrJYr8+OrSUcB1za5HxQRQ4H9gdVJe3AaahICeRBwVX5tVu5n4wo1LsBLuczMzMwM6HwTe88zPQwdPHQRVFSqNxSRlMMxEhjV7AJJ6wJXAftFxMOFdgEXAA9ExI+6UNuNwH9KWjk/354024CkS4AxEfGH2psjYiYpV6RWz+Pk43UlTQSOkHQZaanayxHxdH7fgxHRagBwDTCCNFOyLfBwvnYl4LW8P+cQ4He1WSJJU4D9I+LJYkeS1oiIp/P3VQyB/BNp4/wteZ/KB4FH2/uqFuYZEzMzMzMD+n0Te0ciYi5QC0V8ALiiGLAo6QnS7MI9ks7Pl50ErAqck4+7rQXzfZIUMDiicBTuzrmvQ2v7TCrU9iJwKmnw9EfS5vPaqVSNwgsbmUT6kT+LtBTra7mu1Shf5lXvdOCLkmYC3yUNQgA+BNwn6UHSyV9H535bhUDOBGYCqwHfye2nAp/Ir00Bjm83s6SMZ0zMzMzMrFcnm9iHjxu+SGppJCImkX6417efDZxd0n4I83+YF9tvpcGP/A72l9Suu5D5+zmA3vDCRyLiz+VX9V47pPA4SMfx1tuadMRvqzpeAj5X0n476QjiepsAV0bE6yXXjGhwj6dIs0Jd4RkTMzMzM7P2vQ7sVJiNaalZeGFVEXFtHoB1VbvBje2QtH7eXN/qpK8FeGBiZmZmZgNKk+T39STdmVPXL5e0VG5fNye8350Tz2vLtZaUdLGkmTkd/cQ2bn8Y6RSriSV1LZ3vOyvXMaTF53h3Tl+vJaf/R8l7fiJpTht11V83r7A8bWKhfTtJd+X2WyVt0EZfm0m6Pdc4U9K7c/uW+fkspZT62uzTocBgUihk27yUy8zMzMz6rOeZnn5Z0tUi+f17wJkRcZmk80hHA58L/BtpL8q5kjYhLQMbAuwFLB0Rm+aje++X9IuykMY6+0bEtJL2g4HZEbGBpJG5nr2b9PMmMCIi5uTTwW6VdH0OVETSMGCl1t9KqdfzyVv1ziWdTPaApK+RvpsDG3UiaRDwc9LBATMkrQr8vdDXaFJo5SRSmOT1EXGcpFerFuwZEzMzMzPrk1GbjurPU7lKk9/zv9aPACbk911MOkEKUv5HLV9jReYfuxvAsvnH93uAvwGv9KG23fN9yXVsV5hFWEg+Zrc2G7Jk/gvoHYCdQUqE76ZG30Uj2wP3RMSMXPMLETFPKYV+hYi4Pe+HuYT533dHPGNiZmZmZn0yesvRjN5ydNf66zD5fVXgpXxqV629lrp+MnCTpCOBZYHP5PYJpMHE08AywDG1U7QkTQIOyRu829VbW0TMlfRyrqvhSVV5ADKddCLWTyPizvzSEcDEfExvhRJ6vTufPjYXOD0irsnthwCTJL1OGoRt3aKfjYCQdCMp8+SyiPg+6bM+UXhfy5T7VjxjYmZmZmYDSaNE9GZJ6fsA4yJibWBn4L/z8bgfBeaRksvXA74p6QMAEbFzxUFJs9oaioh5ecnV2sBHJX1Y0pqkZWY/qXj/onVz6vso4CxJ6+f2Y4Cd83dxEdAqv2UQ8Clg3/zfPSRtR9+T6RfigYmZmZmZDSSNEtGfB1bKy7KK7ZD2flwBvcflvpuUxzEKuCEi/h4RzwG/B4Z1o7Zcx4qU54IsJB/vO5W0T2ML0gzKrBy6uIykWVUKqQ2qIuLR3O8WklYHNi/MylwOfKJFV08A/xMRz0fEa6S9JB/J7WsX3lc1mX4hHpiYmZmZvcONnT6W4eOG/8OHK2a9ye/51K2RpCVPAdwM7JnfdwDwq/y4llCOpA+RBiZ/ye0jlCxLWtb0YB9qm5jvS67jtxERktbKqeoLkLS6UhI7kt5DWmL2YERcFxGDI2JIzjZ5LSI2yO/bQ9J3mxUhaWVJS+fHq5GCJO8HZgMrStoov/WzpJDKZv3eCGwmaZk82NoWuD+n0P9V0tZ5H83+zP++O+I9JmZmZmbvcAMh8b0m792oJb8vAVxYS34Hjgcuk/Qd4G7ggtz+TeBnko4hLTc6MA8YfkpaznQvaWnSRRFxD3S8x+QC0jKxWaSZkpG5fQ3SXo96awAX530m7yKdHHZti3usT+sN+h8C/kvSW7nf0/OpZUj6KnBlfm028JVm/UbEbEk/Ig0IA5gUEdfllw8DxpEODrg+/3XMAxMzMzMz689TtfqsSfL7o6R9I/Xt95NmDerb55D2cpTdY+cO6nqjQX+lae15ELRFG/0uV3g6lLRPpNn7bwM2bfDa1cDVJS817Dcifk46Mri+fRrw4Wa1VOGlXGZmZmY2oKh6wOL7JU1RClecKmnt3P7pQghhj6Q3JLU68vZ9wO2SLmtQ24m5rock7QAQEWMiYqFAxvz+S/N775V0Yc4zQdJxhbruVQpMXCUivhwRf2nzezoy932fpO+3qLFhv5LGSXqsUM/Q3C6lYMVZ+bv9SG4fC5wIfKedOms8Y2JmZmZmXTV2+ljGzxy/SPpWZwGLPwAuiYiLJY0AvksKDLyZNFOApFWAWcBNLUq4HJgTET8oqW0T0vKtfyKd9PUbSRtFxLwm/V0KfDk/Hk86zvfciDiDlGOCpF0pHGXcDkmfJh2FvFlEvCnpvX2oEeC4iJhQ17YTsGH++xjpu/5YRIwGRqtiYr1nTMzMzMysqxbxnpVOAhY3AWqbz28m/WCvtycptfy1PtS2Oynn482IeIw00FloaVlRREzKQYsB/IEFT7qq2Qf4RcVaDiPtLXkz3+e5TmtsYnfSgC9yWv1KSsGLHfGMiZmZmZl13dDBQ5l64NSOrl0EAYszgC8CPwb2AJaXtGpEvFDoZySFTA9JpwDTGi3BalLbHXW1tRU6mJdw7QccXde+DOkI4SMq1AEpGHEbSacBbwDHRsQf+1DjaZJOIg3wTsgDnrL/F2uRAisr84yJmZmZmQ0knQQsHgtsK+lu0nG3T1I4JSv/K/+mpJO+0oURJ1UclDSrrR3nAL+LiFvq2ncFfl9lGVc2CFiZtPH+OOCKPKvUSY0nAhsDWwGrkE4/o8O+GvKMiZmZmZkNJC0DFvOsSW/gXz7y9wsAkpYDvhgRLxf6+BJwdUT8fRHV1pSkbwOrA/9S8vJIqi/jqtVyVW2JWD4eeLVOasyZJQBvSrqINNCr3aPy523EAxMzMzMz69XzTA/Dxw3vcx+L8Pjh3oBF0szHSGBUziWpBSxeRiFgMYcMvhgRb5H+9f/Cuj73ye19NREYn3M/1iRtCv9DrmEKsH9EPFm8QNIhwA7Adrm+4msrkmZ4vlzXXtpXnWtIe26m5kDFpUiDt05qXCMins4zLp8n5b7UPu8R+YSyjwEvFwYxlXlgYmZmZmYAjNp01OIuoaUOAxaHA9+VFMDvgMNr/UkaQvpX//8p3qeTPSYRcZ+kK0gp63OBwyNinqR3ARuQQhfrnQf8H+kIYkizHKfk1/YAboqIVwt1Neur6ELgQkn3An8DDsizJ53UeKmk1UlLt3qAQ3P7JGBn0gb614CDWtTUlFJ97Rk2bFhMmzatL/fr2PDh6b9Tpy6W25uZmZm9bdVmSDrdrN7t/iRNj4hhXSmmyySdTIPjgptc82HgKxHxjS7cv2t9Lep+Jc2pC4dsypvfzczMzMzaN4eU0XFKy3dmEXFvt37wd7OvRdWvpPUl9QDPVrnOAxMzMzMzG1DUpeT3wnUrSHpS0pg2bv9hYElKNnk3SkJv8VlukDQjp7OflwMkkXSGpAdzP1dLWqmN2or9LiVprKSHcz9fzO1L5+9mVv6uhrTZ3xKS7pZ0baGtNLUeGAYsBzxQpWbvMTEzMzOzruvGJvoy3Ux+L3R7KnV7TFooS0GHBknoLfr6UkS8kjeWTwD2Im3enwycmPfUfI+0Of/4Jv3U+xbwXERslPePrJLbDwZmR8QGkkaSvrO92+jvaNJAY4VCW6PU+sslPcv807va4hkTMzMzM+uqUZuOWpSncnU1+V3SlsD7gJu6UFvlJPSIeCU/HEQ6OSty+02FsMg7KE+Eb+YrpAEYEfFWRDxfqPHi/HgCsF3+7hrKM0yfA86vq72d1Pq2ecbEzMzMzLpq9JajGb3l6I6v76/kd2A28EPS7Ml2C9QgnQ+cFxFVTn7qKAld0o2kAdf1zB9YFX0FuLzdIgrLvk6VNBz4X+CIiHi2WGOejXmZ9N09X9ZXdhbwr8DyDe5XmlpflQcmZmZmZv1g7PSxjJ85fnGXUWoR5450W6fJ72MkHUg6LriW/P41YFJE/Ll+0iAiDulibU1FxA6S3k1aGjWCtIwrdSh9K9d6aYU6BpFmL34fEd+Q9A3Scrb9qtYoaRfSkrDpeZBTplFqfSVeymVmZmbWD8bPHE/PMz2Lu4y3g5bJ73XtRMRTEfGFiNiCtPeCnPz+cVJA4OOkH+77Szp9EdTWUkS8QQosLC4zOwDYBdg3qmR8wAukXJGr8/NfArWN+L015u9qRZpnonwS2C1/R5cBIyT9vFBjLbW+zyd6ecbEzMzMrJ8MHTy0a1kh3bQoNqkvQl1Lfo+IfWud5tmUYRFxAp1rmIQu6cGI2Lj4ZknLAcvnVPVBpLDCW/JrO5I2u28bEa8VrlmLtI9lgaVnRfm7+DUpWPK3pGVq9xdqPAC4nfRd/Ta/v7TfiDiR9J2RZ0yOjYgv5+cNU+s74RkTMzMzMxsw8h6SWvL7A8AVdcnv35A0i7Rvopj8/pCkh0kb3U9rdR9J50uqGvI4CXiUlIT+M9JSsdrAqGwJ1bLAREn3kPbBPEdKggcYQ9rTMVlSTz5lDGAN0tKuVo4HTs597wd8M7dfAKyav6NvALWBWLv9Fp1H+j5vzzWeVPH6BXjGxMzMzMwGlIiYRBoE1Lc/StpEXt8+gfJN5cX3jAPGFZ5X3mOSl1sdXvLS1qQjjuvf/yywVYO+Nmhwm9K+Sq7/P+CfS9rfIB1JXLnfiJgKTC087+pYwjMmZmZmZjagNAlYPCK3RZ6lqLXvm4MK75F0m6TNC69dKOk5Sfe2efs1gcsk3dqgtgOUAh4fyXtEiIhrI+LsBu8/NdfVI+kmSWvm9hUl/boQvnhQ7mtMRExs4zuSpNNywOIDko7K7cfle/XkYMR5klZpp19JP5E0p/C8NKwx7zu5bP+Q3gAAIABJREFUEfhUqzqLPGNiZmZmZotN1dPKWgQs/h64lsK/6mePkfZqzJa0EzCW+cGH40jLpi5ps4TxwMMRcURJbasA3yYlnwcwPdc2u0l/Z0TEv+frjwJOAg4lzbzcHxG7SlqdtBTt0pzd0o4DSZvcN46ItyS9FyAizgDOyPfbFTgmIpptfq99tmFAffp8aVhjRPyHpItJ/y/a5hkTMzMzM1tsOjitrDRgESAi7o6Ix+sviIjbCoODBcIKI+J3ND+VqoodgMkR8WK+32Rgx2YXFAIWIe05qZ2+FaS8FQHL5Rqr7AE5DDiltik9Ip4rec8+wC9adZQHg2eQskyKKoc1NuMZEzMzMzNbrOpPK+swYLFdB5OCDJuSdChARJzX6r0talurwXuL9zoN2B94Gfh0bh5DOkHrKdIm+L0rnny1PrC3pD2AvwBHRcQjhXsuQxo0LTTzU+IIYGI+PazY3klYY0OeMTEzMzOzgaSjEEMASZ8mDUyOb/XeiDiv4qCk49oi4lsRsQ4pRLE2UNgB6CHtaRlKCohcoUItSwNvRMQw0glhF9a9vispgLHpbFHe87IX8JOyl0vaquStLMADEzMzMzMbSDoKMZS0GXA+sHtEvPCPVFvBeOCL+fFBwFWRzCLtk9m44ZXltVyZH18NbFb3+kjaWMYFbAFsAMzKIYvL5KOGa/eoEtbYlAcmZmZmZtYvxk4fy/Bxwxf4q7i/BAoBi5KWIv3AbnWa1LrAVcB+EfFwR8W350Zge0krS1oZ2D63IekSSQsdZSxpw8LT3YAH8+M/kYIRkfQ+4IOkjBQkPUhr1wAj8uNtgd7PLWnF3Parulqm5KDFXhFxXUQMjoghETEEeK1wlHEtrBEKYY1t1FbKAxMzMzMz6xcdbHRfSLOARUlHSXqCNFNxj6Tz82UnkfY+nJOPyZ1W60/SL0gp6B+U9ISkg3P7obV9JhVqexE4lTR4+iNp83ltBmEz4OmSy07Px/beQxrIHJ3bTwU+IWkmMAU4PiKebxLWuFC/wBfz9d8FirksewA3RcSrtQZJ7yLNjFSZ8WgU1tgRb343MzMzs35Tv9F9+LjhlftoErB4NrBQXkgOSywNTIyIfRq0V91fUrvuQur2c+S9IY9ExJ9L3v/F+rbc/hRpoFKv3YDFl4DPNXhtHIUwyWwT4MqIeL1Fv8sVHjcKa+yIZ0zMzMzMzNr3OrBTYTampYh4JSK68gO+WVhjH/u9NyK+0Y2+JG0D/JqKp3N5YGJmZmZmA0oHye8bS7pd0puSji20ryPp5pyMfp+ko+vvVeI9QMPTscqS31t8lkrJ7+1qlPxeeH2rnPq+Z4U+J0q6t/D8DEkP5vqvllQMYFwCWG3hXhrzUi4zMzMzW6x6nulpe0lXh8nvLwJHAZ+va58LfDMi7pK0PCmpfXLuq5nLB2rye77PEqSU9hvb7AtJXwDm1DVPBk7MGSbfA04k7YW5RdLOOPndzMzMzAaKUZuOYujgoVUu6ST5/bmI+CPw97r2pyPirvz4r6TN9C0DEZsYKMnvR5KOEi5Lg1+IpOVIm9u/U1f7TfkwAoA7SIcOdMwzJmZmZmZWadaiL/eoH4SM3nI0o7ccvUDbIk5+LyVpCCmz4878/G2Z/J6PA96DdJTwVm32dSrwQ+C1Ju/5CnB5hfoW4hkTMzMzs3e4DmYtFqeupo1D74zAlcDXazMYb+Pk97NIy63mtdOJpKHABhFxdZP3fIs0m3NphfoW4hkTMzMzs3e4slmLRaFLMzJ9TVdfgKQlSYOSSyPiqi7UNrzwfG0W3u/SzHjgOtI+lYOA03Ng4SxJteT3P1SopZj8flF+PAy4LK0QYzVgZ0lzI+KaBv18HNgyp74PAt4raWpEDIe02R/YBdiuL+GK4BkTMzMzMxtYKie/N5L3b1wAPBARP+pCbf/wye8RsV4hxX0C8LXaoKSs34g4NyLWzO//FPBwYVCyI3A8sFtENFvm1RbPmJiZmZnZgJFPgKolvy8BXFhMfgf+FRhMSn6fFBGHSBoMTCMd8/uWpK+TAgU3A/YDZkqqRdL/v4iY1Mkek4h4UVIt+R3aT37/IPAW8H+kE7kg7esYl5PbRWfJ75dKOoZ0mlZpwGRNhX6LxpCWjE3OMzB3RMShzS9pzAMTMzMzMxtQOkh+f4byE6NupcGP8bdz8nvhPQdW6TefePbhwvMNWtVRhZdymZmZmdmA0iRgcT1Jd+Zww8vzUi8kvV/SlBwEOFXS2oX26Tnc8L7aLEkLBwL/IunWkrok6exc1z2SPgLtJb+XhBeuImly/iyT89KwtpPfJa0kaUIOQHxA0sf70q+kFSQ9KWlMoW1LSTPz5z07L41D0njgEVIYZds8Y2JmZmZm/aavxxK3CFj8HnBmRFwm6TzgYOBc4AfAJRFxsaQRwHdJS7ieBj4REW/mk7nuzX0120z/ODAmIiaUvLYTsGH++1i+d8ujjBuEF54ATImI0/Pg6wTSfo52/Ri4ISL2zAO0ZfrY76nA/9S1nQuMJmWYTCJltlwfEaMkPVzymZryjImZmZmZ9YsuHUtcGrCY/7V+BGlDN8DFzE963wSYkh/fzPxAxr9FxJu5fWn6/tt4d9IAKCLiDmAlSWs0u6BReGHu6+L8uPhZWspLx/6ZtLG/9jlf6rRfSVsC7wNuKrStAawQEbfn07guqVJjGc+YmJmZmVm/aPdY4g4DFlcFXiokkRfDDWcAXyTNIuxBSlRfNSJekLQO6YjeDYDjarMlks4HzouIaW1+vEa1rUX5pveaRuGF74uIpyEl1Et6b4U6PkAKVbxI0ubAdODoiHi1ar+S3pXr2498Sli2Funz1bQVJtmMZ0zMzMzMbCBpFGLYLNzwWGBbSXeTjs59khQISET8OSI2Iw1MDshH8xIRh1QclDSrrfzNbYQXdmgQ8BHg3IjYAniVtGSrE18DJpVs3O960KVnTMzMzMxsIGkUsPg8aenUoDxr0hu8mGdBvgC9S6e+GBEvFzuNiKck3Qdsw/zlYN2qrZFm4YXPSlojz2qsATxXsY4nIuLO/HwC8wcmVfv9OLCNpK8BywFLSZpDmn0qnnTWp6BL8IyJmZmZmQ0spQGLeZ/DzcCe+X0HAL+ClNGRlyQBnEg+zlfS2pLekx+vDHwSeKgPtU0E9s+nc20NvFxbNlU1vDD3dUDJZ/mopEuaFZGPR/5zzkeBtATr/k76jYh9I2LdXOOxpD00J+TP9VdJW+f9PfvX+uqUByZmZmZmNmDk2ZBawOIDwBW1gEXS6VLfkDSLtOfkgtw+HHgonxT1PuC03P4h4E5JM0gnTv0gImZC2mMiaVjF8iaR0tlnAT8jLYPqNLzwdOCzkh4hnUB2em5fF3i9jeuPJAUs3gMMBf6zS/0WHQacT/q8/wtcX/H6BXgpl5mZmZkNKE0CFh8lndpV3z6BkuVZETGZlMhedo+mSekNrgng8JKXOgkvfIEFN5vXfKxVX/n6HmChgVVf+o2IccC4wvNpxZr7ygMTMzMzM7P2vQycKmm1dtPhI+Labt08Io7rVl+Lql9JZ5BOP/thleu8lMvMzMzMBhRVTH4vvL6npKgt0ZK0lKSLcnr5DEnD27j9isC7G9RVmvze4rPckO99n6TzcoAkkvbKbW91sKSs2H9bifJt9LNA8rukZSRdp5Qsf5+k0wtvfwpYCvinKrV6xsTMzMzMFpux08cyfub4tt/fYfI7kpYHjgLuLHT3VYCI2DTneVwvaauIeKtFGcd1Mfn9SxHxSt5APgHYixQaeS/pJLH/anF9Q11OlC9Lfv9BRNycB4BTJO0UEddHxJmSZlOylKwZD0zMzMzMqP4DuaqeZ3q6kXr+tjN+5viq301v8juApFry+wOk5PdR+X0XAyeTByakH9bfJ50sVdObCB8Rz0l6ifRj+g8dfpze5HfgDkkr1Y7mbXRBRLySHw4izTJEbn8gf76OCikkyo8GrqircXh+fDEwlRYDk0Ly+w3kwUZEvEY6BY2I+Juku1jw+ODKvJTLzMzMjPk/kK3/DR08lKkHTu39a6FRunrD5HdJWwDrlOz1mEEa1AyStB6wJTmHpMNTuRrV1pSkG0l5In+l8wyVem0lygPtJr833IMiaSVgV/Igr1OeMTEzMzPLaj+QF4Xh44Yvkn7fgSolv+cf1mcCB5a8fiHpyOBpwP8BtzE/Eb7yqVxNamsqInaQ9G7gUtKsz+QO7j2/iPmJ8sdIGtKXvigkv5fN3kgaBPwCOLs2i9UpD0zMzMzMbCCpmvy+POlI26n5h/VgYKKk3fJxt8fUOpJ0G/DIIqitpYh4Q9JE0lKrPg1M6G6ifGnye0TUDh0YCzwSEWf1sWYPTMzMzMys+9rds9PB3pve5HfgSVLy+6iICEm15PfLyKnmEfEysFrtYklTgWMjYpqkZQBFxKuSPgvMzZvoOzUROCLve/kYdcnvEbFx8c15H8jyeZAwCNgZuKXZDSStRdrHUpZFAqREeeZv+h8CXFuSKH86dcnvwBERsX9dX/sW7n0gMKw2KJH0HdIpZZ3MLi3Ee0zMzMzMrOsW1Z6dDpPfG3kvcFfeOH88sF/thX5Kfl+WNHtzD2m/y3PAefmaPSQ9QZqxuC7vQwFYg7zcrENdSX6XtDbwLdIBAndJ6pHUpwGKZ0zMzMzMbJFoZ89OJ3tvqia/171neOHx48AHG7xvkSe/R8SzwFYN+roauLrdvprU9DhdSpQvJr9HxBOUD7Y65hkTMzMzMxtQuhiwuKSki3PA4gOSTmzj9p8BLpV0TkldS+f7zsp1DIGU/B4RZ5e8fx1JN+d73yfp6MJrm0u6Pdf2a0kr5L7GRMTE9r6p9gMWI+K4iLinST9tByxKuoY0Y7R1u3WCByZmZmZmtpj1PNPD8HHDe/+aKQQs7kRaRrSPpE3yy7WAxQ2B2aSAxdp1ZQGLewFLR8SmpKOC/6WNU6xmAZ+MiK+VvHYwMDsiNiCdBPa9Fn3NBb4ZER8i/Yg/vPBZzgdOyLVdTZPjehtpEbC4Iel43xMWurBco4DFjYEtgE9K2gkgIj5PCq+8o0q9HpiYmZmZ2WIzatNRVTe/9wYsRsTfSBvdd8/J6SOYnwNyMfD5wnW1gMU3Cm0BLJs3nr8H+BvwCp37/+zdebRcVZn+8e/DLDOCGAYRZBBR8CIgKg1EQCYlEQGBIBJaTKvgAIKanzYiSIsjCCgYEBElBgTByIyBtKBEJHBJQsIk0MgkMhvDYML7+2Pvc3NSOVV1qm4l8XY/n7WyUnWGfd4qFmvVzh6ekfm55Dp2VYuExIh4PCJuz6//TlozU+SevBn4XX59PbBfJ4WUAha/3qLGxu+oWVtFwOJ1pdrnRMRAwCIw6IBFrzExMzMzsyVmzDZjGLPNmAWO6fCWSxeqQgy3p2bAoqRy8vslpB/qjwMrAkdHxDP5nquAIyKi1na/jbVFxFxJz+e6nmp3Yx6p2Zr5IzozgBGkXbMOYMFtiOuoFbAoqW7A4qFUr00pByx+v8MaF+AREzMzMzPrmXFTxzH8/OGLZEeurNuAxc9XnH8nMA9YF9gI+LykNwFExN4ddkpa1db6pjS6cSnwuYgoRmz+nTS1ayopi+WV2kXMD1isWjzfqYGAxSbPcsCimZmZmf3rWVTbBJf0LGARGAVcExH/BJ6U9HtgW9KWv4Op7ZH8g3014JlWN0haltQpuTAiflUcj4i7gd3zNZsB7++gjiEZsOgREzMzMzPrqb5hfZ2uG+nEQMBi3nXrIGBi3qq3CFiEUsBiRKwVERtGxIakBdlF6vvDwC5KViItQL97ELUV4YXkOm7IwY/rSZrUeHFef/JjYFZEfK/h3Nr576WArzA/36SyrbKIOCsi1s2f99+AeysCFqEhYFHSBRVtHRIRG+S2jiWFOzYGLH6uVT11uWNiZmZmZkNGjwMWf0AaBZhB6vD8pNgyV9JVktbtsLwfA2vm5x/D/B2vmoUi7kBau7FLDijsl7R3PnewpHtJHaXHgJ+0aasuByyamZmZmfVCDwMWZ5MWllddt3fV8TZtv9SkvWYBizfTJKQwIr5P9WLy/7UBi+6YmJmZmZnV9wxwvqT/VzfoMCLO7NXDe9lWQ7sd56Q0I+lo4BOktTO1eSqXmZmZmQ0p3SS/S/qwpJk5pXx86fg3Jc3Ifw6s8fhpwBqkRfJVtY3Ndd0jaY8an+XHku6UNE3SJXmHLiQdk+udJmmSpDfWqK2q/TMkzS6930Apbf6O3HatkSFJS+d7rigdu6k0Be2xnPgOaerZ0sBWndTqERMzMzMzWySKRPdeKiW/v4+0C9afJE2MiJnMT36fIOlsUhL7WZI2BcaSEtufLS0sfz/wDqAPWB74b0lXl7bsbebUiPhORW1bkBbjv5W0BfFvJW0WEfNatHV08TxJ3yOtnzkFuAPYNiLmSPokKRyyTsepXM+2wOoNh79CWpdzVq73KmDDGs19lrSmZ9XiQETsWHrWpeSF9BFxkaS/khbL1+YREzMzMzPruS4S3evqJvn948APIuJZgIgotsjdAvjviJgbEf8A7gT2HERtI4EJEfFyRDwI3E/7NS9Fp0Sk9PnIx2+MiCIccQodpqrnDty3gS80PpL5nYvVSKMb7dpan7Rd8blNzq9C+u4vrzpfl0dMzMzMzKznqhLd6+p18juwGUDOKVkaOCEiriF1RL6aRypWBN4LzMzXngjcVncdSam2KQ21rdfk2gGSfgLsnZ9dFQT5MeDqDuqANPIyMeeVlI+fAFwn6dPASsBuNdo6jdTBWaXJ+X2BSTVGmlpyx8TMzMzMahk3dRzjp49veU3/E/2LMsMEOkx+z38vA2wKDCeNPNwk6W0RcZ2k7YA/AH8DbiFvxRsRx/ewtpYi4vA8wnEGabpWsTUwkj5CWs+yc+0i0jbHB5A+b6ODgfMj4ruS3g38LH8XrzZp6wPAkxExVVJVe0WblaMpnfBULjMzMzOrZTGkutfRNvm94Xhxz68j4p95itU9pI4KEXFyRPRFxPtIHYv7FkFtbeV1KBcB+xXHJO1GygoZEREvd1DH1sAmwP05/X3FnK0CafTl4vzMW4AVgLVatLUDMCK3M4GUufLzUo1rkqarXdlBfZU8YmJmZmZmtfUN62Py6MlNz/d6sXuFgeR34FHSYvNROWG9SH6fQCnVnLT24WDSNr9rkaZ2PZBHKVaPiKclbUXaReq6QdQ2ERifp4atS+r83AqQ09o/GhGPFhfndSUbR8T9+fU+5OR5SVsDPwL2LK2JKe67OyI2b1ZERFwJDCtdPzsiNslvHyblmJwv6S2kjsnfJK1HSnXftaGtsaSNA8gjJsdGxEdKlxwAXJEzXAbFHRMzMzMzGzIiYq6kIvl9aeC8huT3CZK+TtrVqkh+vxbYXdJMYB5wXO6MrECa1gXwAvCRYo1KN2tMIuIuSReT1orMBY6MiHmSliKNYDzTcIuAn0paNb++E/hkPvdtUir9L3N9D0fEiNyxGkyw4eeBc3LWSACjc6eu20T5g5ifHj8o7piYmZmZ2ZDSafJ7RARwTP5TPv4SaWeuqmd0s8aEiDgZOLnh8BbApRHxYsO1r5KmSlW102xRekfJ77mtlUuvZzZ5Ztt2I2IyMLnh2PBOamnFa0zMzMzMbEjpNGBR0mhJfyuFAR5RuudbOXRxlqTT1bCFVYX3A9+QtNCaCiWn57qmSXoHQETMiIhjKq5fUdKVku7ONZxSOndqqd57JT2X27oiIk7v4LtaIGAxH1sobDIizmw2OiRpcv6+i3qKHJidJN0uaa6k/UvXfwa4BmgbMFnmERMzMzMzW2zq7OzViroIWMy3XhQRRzW09R7S6EGRUH4zaferyS1KuAv4ZkRcUnFuL9K6kk1JWxiflf9u5TsRcWPuRE2StFdEXB0RR5fq/DRpQXtHqgIW1SRssoZDIuK2hmMPA6NpCFLMHafTGztE7XjExMzMzMwWmx7s7NVNwGIzQVr8vRwp+X1Z4K+DqG0kaQF5RMQU0i5h6zR9eMSciLgxv34FuJ3qIMWDgV90UkiLgMVmYZMdi4iHImIaULnVcKc8YmJmZmZmi1W7nb0WQcAiwH6SdgLuBY6OiL9ExC15J6/HSQvKz4yIWQCSzgXOrhgl6LS29XL7LUlanbQr1/cbjr8R2Ai4oYM6oHnAYrOwyXZ+ImkecCnw9bxup6c8YmJmZmZmQ0k3AYu/ATaMiK2A35JGU5C0CfAW0ijFeqSMjp0AIuKIDjslrWprfVPKXvkFcHpewF92EHBJzjmpV8T8gMUzKk6XwyYPBs7NnaJWDomILYEd859D69bSCXdMzMzMzGwo6ThgMSKeLgUUngNsk1/vC0yJiNkRMRu4mrQ7Va9ra2cccF9EnFZx7iA6nMZF64DFpmGTzRTZKxHxd2A8FTuf9YKncpmZmZlZT/U/0d80aLH/iX76hvUNpvmOAxYlrRMRxXSqEcCs/Pph4OOSvkEa7dgZqOoc1DUROErSBNL0sueL5zYLRcyZK6sBR1ScezOwBnBLw/HBBCxWhk02azd39FaPiKckLQt8gDTq1HPumJiZmZlZz4zactQibb/LgMXPSBpBChB8hrSTFKSF8rsA00lTrq6JiN9A12tMrgL2Bu4H5gCH57YqQxElrQ98mZT2fnteC3JmRJybLzkYmFBez9GDgMVmYZPN2l0euDZ3SpYmdUrOybVsB1xG6jztI+lrEfHWbgtzx8TMzMzMembMNmMYs82YpuebjaR0oouAxbGkLXIbj88D/qPJMxYawahRVwBHVpyqDC+MiEdo0cmIiBPqttWmrnLAYmXYZIsa/8H8qW+N5/5E9S5iXXHHxMzMzMysvueBkyStFRFn17khIq7o1cN72daialfSxqTduzraetkdEzMzM7PFpNXai6GgB+tDekLSnqRtdZcGzo2IU/LxjUjrS15LygQ5NOeDFPftD/wS2C4ibpN0CHBcqemtgHdERKugldVI2SdVdSnXtTdpKtfoiLi9xedYMdezMWla1W8i4kv53KnAe/OlKwJrR0S73bOqnnEGcHgxaiJpeeAC0ijI08CBEfFQjXaWBm4DHo2ID+RjNwGr5EvWBm6NiA8C2wIrM38tTy3umJiZmZktBot67cVQMpgOWrfJ75JWAT4D/LFoKyIuBC7M57ck7VZVJ/3xuKGa/E76Tp6NiE0kHUT6zg6s0dxnSR2NVYsDEbFj6VmXkjcbiIiLJP2VhkT4dtwxMTMzM1sM2q29GAp6MdrTgw7aQPI7QN4Ba6SkWaSF7MUDfgqcQO6YACcB36L5j+WO09UrDCS/A1Mkrd6wI9gCImIOMJD8LqlV8vtXOymklPw+irQtcrnGE/LrS4AzJalVYGJepP9+4GQWXptSdPp2IS/275Y7JmZmZma22NTpoPU6+V3S1sAbIuIKSc06JgeSfrST7/nfmvw+UGPe4ex50nf3VIu2TgO+wPxpW432BSZFxAsd1rgAd0zMzMzMbCjpKPld0lLAqczfInjhBqXtgTkRMWPgxi525WpRW+ubFl3y+/DB1ijpA8CTETFVUlV7kBPk69bXjJPfzczMzGwo6TT5fRXgbcDknIL+LmBiXn9R6CZdvZPa2lncye9vgIEO0WqkbJdmdgBG5HYmALtI+nlxUtKapOl1V3ZY40LcMTEzMzOzoWQg+T0vGD+INGUpSOs19s/XHUZazP58RKwVERtGxIbAFGBEMUUrj6gcQPrRPVgTgY8qeRcNye9VN5SS3z9Xca5p8nurIiLiyogYVvrMc0rJ7xNJ3w2k7+qGiAhJ60maVNHW2IhYP7dzUL7+I6VLDgCuiIiXWtVUhzsmZmZmZjZk5DUkRfL7LODihuT3Y/LowJrMT35vZSfgkcYpVJLObRhVqeMq4AFS8vs5wKdyW+2S37cgJb/3SypPIVsUye8/BtbM39ExwJfy8XWAuU3vaq5Xo01eY2JmZmZmQ0unye8N1wxveD+ZNL2r8br/rcnvL5FGOTpuN39XkxuODe+kllY8YmJmZmZmQ4qkPSXdI+l+SV8qHd9I0h8l3SfpojzVC0mjJf0tj0gMjEpI6pN0i6S7JE2TVCfPY3PgF5Iq11RIGpvrukfSHpBS1SPi9CbX/1jSnfn5l0gqghB3knS7pLk5GJJ2bTVp/wxJs0vvPyFpev4ebpa0RW73zIiY2KSNh0r3LLRLmaRjJUUezUHSacB1QEdpnB4xMTMzM7NFbtzUcYyfPn7Q7XQbsAhcFBFHNTQ3B/hoRNyXd7KaKunaiHiuRQnXkHbJ+k5FbVuQpja9FVgX+K2kzdrsqHV0sc2upO+RpqmdAjxM2kmso5DChnqqAhbHR8TZ+fwI4HvAnjWae29ELLSlsKQ3kP5bPFwci4jPSbq809o9YmJmZmZmi9z46ePpf6JOqHpbAwGLEfEKadH6SKWwjl1IoYGQAhY/2KqhiLg3Iu7Lrx8DngReN4jaRpLWhLwcEQ+S1pq0m1pWdEoEvIa8dW9EPBQR04BXuymkFLD4harnZStRYzvjNk7NzxhsOx4xMTMzM7PFo29YH5NHT257Xa8DFrP9JO0E3EsapSi3gaR3AssBf87vTwRuaza9qUVtUxpqW6/JteVn/wTYG5gJfL6D57XSLGARSUeSFr4vR+rMtRPAdZIC+FFEjMvtjAAejYg7G5/RDY+YmJmZmdlQ0lHAYv77N8CGEbEV8FvSaMr8BqV1gJ8Bh0fEqwARcXyHnZJWtbUUEYeTpn7NIiXQD0opYPGMJs/7QURsTNrF7Cs1mtwhIt4B7AUcmde/rEjaUez4wdZbcMfEzMzMzIaSTgMWiYinI+LlfPwcYJviZkmrksIBvxIR5dGOXtbWVl6HchGw3yBrgNYBi2UTaDPdLddWfI9PApeRpqdtDGwE3JmfsT5py+Nh3RbtqVxmZmZmVlv/E/0MP394V/f1Detok6ZmBgIWgUdJi81H5ZDAImBxAjlgEdKISBF0CIyaYTO8AAAgAElEQVQgjUyQd+26DLggIn7Zg9omAuPzIvZ1gU2BW/OzJpEW2j9aXJzXlWwcEffn1/sALcMT8313R8Tmzc5HxJXAsNL1s4uARUmbFutqgPcD9+Xj65G+h10bnrUSsFRE/D2/3h04MSKmA2uXrnsI2LZqgXxd7piYmZmZWS2jthy1pEsgIuZKKgIWlwbOawhYnJDT1O9gfsDiZ/J6iLnAM6TdrgA+TApYXFNScWx0RPR3s8YkIu6SdDFprchc4MiImKeULr9JfnaZgJ/mURsBdwKfBJC0HanTtAawj6SvRcRbexCweJSk3YB/As8yPwW+WcDi64HL8hqSZUi7el0ziOc35Y6JmZmZmdUyZpsxjNlmTFf3djPK0kynAYsRMRYYW3H858DPmzyjq7UTEXEycHLD4S2ASyPixYZrXwV2aNLOn0jToxoNNmDxs00uaxYC+QDw9hrP2LCTmqq4Y2JmZmZmVt9sYIykVet2XiJiBmkXrEGLiCt60U5Fu2f2qq0cVPlVYGon93nxu5mZmZkNKZ0mv5fO758TyrfN799ZSoO/U9K+NR7/AdJOW1UJ6Mvn596f69iwzedYQdKt+dl3Sfpa6dxRuZ2BRPW62rS7S06UnyHpp6XNAlq1t4Gk6yTNkjSz+FxtalyONA2tNo+YmJmZmdli0e3C+bJuk98lrQJ8BvhjqbkZpAXbc/OWwXdK+k0pC6WZQyJioY5Jft6zEbGJpINyPa22/30Z2CUiZktaFrhZ0tV5d7DfA1cAk9vUUrtd0kL8nwK7RsS9eR3NYcxfi9PMBcDJEXG9pJWZH/pYWWNEXCTprzj53czMzMz+1YzaclSvduXqNvn9JOBbwEvFgYiYU+qErMDg08tHMj8j5RJgV7VIHoxkdn67bP5TJL/fEREPdVNEi3bXBF6OiHvzuetpsz2xpC2AZSLi+tz27IiYM9gaq3jExMzMzMwWuU4Wzvc6+V3S1sAbIuIKSQv8K76k7YHzgDcChxb3S7oKOKLI8KhpoLY8CvN8rqvpFrp5BGgqadeuH0TEH5td24mqdnMnaVlJ2+YRn/1ZMHelymbAc5J+Rcot+S3wpZy70lMeMTEzMzOzoaSj5Pe8Ve+pwOerGouIP0bEW4HtgLGSVsjH9+6wU9KqtqYiYl5E9JF24HqnpLd1+Mza7UZEkHJfTpV0K/B3qrcILlsG2JE0LWs74E3M3265p9wxMTMzM7OhpNPk91WAtwGTcwjgu4CJxQL4QkTMAv6Rrx10bbmO1Vg4u6RSRDxHWqux5yCe37bdiLglInaMiHcCvyMHLLbwCHBHnjo3F7gceEcvayy4Y2JmZmZmQ8lA8nvedesgYGIeDSiS3yEnv0fE8xGxVkRsmLM2pgAjIuK23MYyAJLeCLwZeGgQtU1kfmDh/sANOZF+vZz8vgBJr5O0en79GmA32iS/553ELmhzTdN2Ja2d/16eFEh5dpt2/wSsIel1+f0upADJnnPHxMzMzMyGjPyv9kXy+yzg4obk92Mk3U9a29Fut6l/I+3E1U9KWf9URDwFaY2JpHU7LO/HpBT5+0m5JcVWxs1S1dcBbpQ0jdQBuL7IKZH0GUmPkEZ+pkk6N9+zAfBiRVu12gWOkzQLmAb8JiJuaNVuXktyLDBJ0nTSdLVz2tTYFS9+NzMzM7MhpdPk94Zrhpde/wz4WZPr9u6irpeAAypONUtVnwZs3aSt04HTK05tX9VWB+0eBxzXSbt5R66tOqixKx4xMTMzM7MhpUXAYmXgn6TNJd0i6eWKXbkekjQ9hyxWZZM0WhmYIum6JrUdphTweJ+kwyClqkfExCbXH5BDEF8tr3uRtJykn+Ta7pQ0PLd1XO54tCXpwzkQ8S5J40vH52l+sOTEdu1KGi3pb6V7jmj1eZWS368E9mhcy9OKR0zMzOz/lHFTxzF++vj2F9r/Of1P9PcqZ8M61Mn/l20CFpuFEj5DClf8INXeW0zhquFMUijjURW1vRb4KrAtaTeuqbm2Z1u0NwP4EPCjhuMfB4iILfO6kKslbRcRrzY2UEXSpsBYYIeIeLZYW5K9mHfs6sRFjZ+5xee9CLhI0uROHuAREzMz+z9l/PTx9D/Rv6TLMLOSDv+/rAxYhOaBfxHxZET8Cfhnj0puZg/Seo5ncmfketrsshURsyLinopTWwCT8jVPAs+ROgB1fZyUX/JsqY1e6/jztuIREzMz+z+nb1gfk0dPXtJl2L+Y4ecPX9Il/J9W/v+yy4DFbgVwnaQAfhQR4wAkfQIgIs7uoK2q2tbrsq47SYn2E0hbEG+T/7615v2bAUj6PbA0cEJEXJPPrZCnrc0FTomIy2u0t5+knYB7gaMj4i/09vO6Y2JmZmZmQ0rHIYZt7BARj+WpTtdLujsiftdhh2RR1HYe8BbgNuB/gD/QPgyxbBlgU2A4adesm3LI4nPABvkzvwm4QdL0iPhzi7Z+A/wiIl7OHbafkrYN7ul/C0/lMjMzM7OhpFnAYleKdPc81eky2uzqtbhqi4i5EXF0RPRFxEhgddqHITbW8uuI+GdEPAjcQ+qolD/zA6T1OJU7eJVqeToiXs5vzyGN3hTP6Nl/C3dMzMzMzGyxGjd1HMPPHz7wp8N1X5UBi93UIWklSasUr4HdSYvRu3UtsLukNSStkdu7Nrd/gaTanR5JK+aakPQ+YG5e4F+3rcuB9+br1yJN7Xog17Z86fgO5MBESd+QtG9FLeuU3o4g5ce0/Lzd8FQuMzMzM1usisXu3eyCFhFzJRUBi0sD5xUBi5I+A3wBGEYK/LsqIo6QNIw0JWpV4FVJnyMtLl8LuEwSpN/F44t1GN2sMYmIZySdROo8AZwYEc/k11sBjzfekzsCZwCvA66U1B8RewBrA9dKehV4FDi0dFtlWw2KTsNMYB5wXEQ8Lek9wI9yu0uR1pgUSe5bUt3J+4ykEaSpZM8Ao2t83o65Y2JmZmZmi115sXunGw+0CFisDPyLiCdI04wavQC8vckzulljQkScR1ofMkDSqsB9ecF44/WXkaaQNR5/CHhz4/FWbTXcH6T0+WMajv+B1AGpsmxE3FLR1ljS1sNVz1no83bLU7nMzMzMzOp7EdhL0rl1b4iIFyKiKhG+Y71sq6LtPXrVlqQbgTfRwRbN7piYmZmZ2ZDSy+T3fH5pSXdIuqLG498CrECTRd6SxuYa7pHU9od+i5pXk/SbnPp+l6TDa9RWbleSTpZ0r6RZeZobkoZLer6U4n58B22eIWl26f0Gkm7M3900SXvn4zsCrwdeiIg767bvqVxmZmZmtsT1P9Ffa0rXIkp+/yxpQfeqNcs9NSK+U1HbFqTF+G8F1gV+K2mziJjXoq1mNR8JzIyIfSS9DrhH0oU5VLKO0aQdszaPiFcbkt9viogP1GwHAEnbknYGK/sKcHFEnJU/+1XAhhFxU+6k1OnoDfCIiZmZmZktUaO2HNXJQvieJr9LWh94P1B7alYLI4EJEfFy3qL3ftpsP9ysZlIeyCpKK/NXJnWuOskx+SRpMfqr+TldJ7/nzuC3SRsLNNZYdOZWYxBbBYNHTMzMzMysS+OmjmP89PEd39e4I9eYbcYwZpsxA+8Xc/L7aaQf3KuUD0o6EbgtIjrZing9YEpDbd0moZ9J2iHrsVzbgUUno6aNgQPzrl9/Az4TEUUOyrsl3ZnbPrbY1ayFo4CJEfF43sGscAJwnaRPAysBu3VQ30I8YmJmZmZmXSm2/V3MepY2LukDwJMRMXWhBiOO77BT0tPagD2AftKUsD7gzLwjV13LAy9FxLakUMRi56zbgTdGxNtJ2xRf3qoRSesCB+RrGx0MnB8R6wN7Az+T1HX/wiMmZmZmZta18ra/dXW6PXCDXqaN7wCMyOshVgBWlfTziPjIv0Bth5MyRgK4X9KDwObArR3Ucml+fRnwE0i7ehUXRMRVkn4oaa2IeKpJO1sDm+QaAFaUdH9EbAJ8DNgzt3WLpBVI2TBdTRvziImZmZmZDSU9S36PiLERsX5EbJjbuWEQnRJyHQdJWl7SRsCm5I6EpEmSOpnW9TCwa7739aRMkwfy+7tr3H85sEt+vTNwb753WF63Qk6PXwp4ulmNEXFlRAyLiA3z9zQnd0oaayx2K/tbB59xAe6YmJmZmdmQERFzSWseriXtpHVxOfld0iOkkYppRdZI/jH+CCls8CuSHmk3LUrSiTntvJPa7gIuBmYC1wBHRsS8PL1pE9IC9sbnVNYMnAS8R9J0YBLwxYh4Km8p3HIRTnYKsF++/xvAEfn4/sCMvMbkdOCgiIhWNbbweeDjua1fAKPzCE9XPJXLzMzMzIaUHia/l6+ZTGnL3oione/R0M7JwMkNh7cALo2IFyuub1bzY8DuFY94F2m75HZ1PEfabazx+JmkhfWNmtbYcP/KpdczSdPhesIjJmZmZmY2pPQyYFHSeZKelDSj5uM3B06RdHuT2g6TdF/+cxhARMyIiGOaXH9ADlB8NWeFFMeXk/QTSdNzyOLw3NYVuTPTlqQPS5qZ2x+fj71R0tQcrniXpE/UqPEESY+WQhn3bji/gaTZxXcraVfSaNYW5f8O7XjExMzMzMwWu7qBio0WQcDi+aQRhAtqlnAN8FREHFVR22uBrwLbknbjmppre7ZFezOADwE/ajj+cYCI2DKHI14tabu6WwZL2hQYC+wQEc+WAhYfB94TES9LWpk0rWtiHqFppTJUsjgHXF28iYhJwGskPVSn1oI7JmZmZma2WI3actRgbh8IWASQVAQszoyIO/KxBW7I4YJPSqqa2vQ7SRsOpqCSPYDrI+KZXMf1pF2rftHshoiYVVUzaWrVpHzNk5KeI3V46u7K9XHgB0WnqAhYbEiOX55BzqCS9EHSovx/DKYdcMfEzMzMzBazxkDFRos5YLG6hvlTnM7u4Laq2roNWLwTGJk7Xm8Atsl/1+2YbAYg6ffA0sAJEXFNPvYG4ErSYvfjaoyWABwl6aPAbcDn8yjMSsAXSaNXx7a8uwavMTEzMzOzoaSXIYZNRcTZHXZKoLe1nUfq2NxGSqf/AzC3g/uXIW1XPJwUhHiupNUBIuIvEbEVqWNyWN6OuJWzSEnyfaSpYN/Nx79GmuI1u4O6WhZsZmZmZlZp3NRxjJ8+vvJc/xP99A3rW8wV9TTEsNceIXUECuuz8HqXWvK2yEcX7yX9Abivw1qmRMQ/gQcl3UPqqPyp9IzHJN0F7Ahc0qKWv5bqOIe0jgfSSNX+kr4FrA68KumlvPNXxzxiYmZmZmZNjZ8+nv4n+pd0GWU9C1hcBK4Fdpe0hqQ1SNv9Xgsg6YIcaFiLpBXzVCkkvQ+Ymxf4123rcuC9+fq1SFO7HpC0vqTX5ONrkLb7vadVu5LWKb3dl7Rgn4jYsRS8eBrwX912SsAjJmZmZmbWRt+wPiaPnrzQ8W521RqsiJgrqQhYXBo4rxywCHwBGEYKK7wqIo6QNIw0JWpV0r/qfw7YIiJekPQL0ijHWjno8KsR8eNu1phExDOSTmL+qMSJxUJ4YCvSNKgFSNoXOAN4HXClpP6I2ANYG7hW0qvAo8Chpdsq22pQdJJmAvNIa0mezp2c70oK0tSz70TE9DbtfktSH2la2kPAf7R5dlfcMTEzMzOzIaWXAYsRcXCT452uLynuO4+0PmSAUsr8fRHxl4rrLwMuqzj+EPDmxuOt2mq4P0hJ98c0HL+e1AGp3W5EHNp4rOKaE9pd046ncpmZmZmZ1fcisJekc+veEBEvRMQBvXh4L9taVO1Keo2kfmBZoFbuCrhjYmZmZmZDTBfJ75J0ej43TdI7SufmlRLN66xVeQuwAk0W3Esam59zj6Q9anyWC/O1M5RS6JfNx5um1dcl6dO57bvyAnUkvS8nv0/Pf+/SQXvHlr9bSYfk73OapD9IenvDLa+jg/6Gp3KZmZmZ2RLTatevKl0mv+9F2pFqU9JOUmcxP/vkxYjodGuxyhR0SVuQFuO/FVgX+K2kzSJiXou2LgQ+kl+PB47I9bVKq29L0ntJwZNb5ZT3Ivn9KWCfvCPX20hrUdpmreTsk/cBD5cOPwjsnDNN9gLGAdtHxItAX6fJ7x4xMTMzM7MlpotdvwaS33OKeZH8TkTckddmNBoJXBDJFGD1hp2memUkMCEiXo6IB4H7c71NRcRVua4ghSeun48/GRF/Av7ZZS2fBE6JiJeL9vLfd5QCFe8CVpC0fI32TiVtLDCQyxIRfyiS5YEpNFnHU5c7JmZmZma2RBW7fhV/2ugmXb3VPStIuk3SFEkDoxOSTpQ0otYHGFxtxfOWJe28dU2Hz2xmM2BHSX+U9N+Stqu4Zj/gjqLz0qK2EcCjEXFni8s+BlzdfbmeymVmZmZmQ0s36eqt7tkgT2t6E3CDpOkR8eeIOH4x1Vb4IfC7iLipi+dWWQZYA3gXsB1wsaQ35ZEZJL0V+CYpa6UpSSsCX251XZ429jHg3wZTsEdMzMzMzGwo6Sb5vek9xbSmiHiAtDZl68VcG5K+Sloofky7azus5Vd5ltitpN2xikXr65O2KP5oRPy5TTsbAxsBd+Y1I+sDt+dsGCRtBZwLjIyIpwdTsEdMzMzMzKxr/U/0Dyposf+JfvqGdbT2fCD5nRQ8eBAwqs09E4GjJE0gLXp/PiIez8nnc/Li8LVIKejf6vhDLPic8ZK+R1r8vilp3QiSJpE6Ao+Wb5B0BLAHsGtE1Npat1lbDS4HdgEmS9oMWA54StLqwJXA2Ij4fUO7FwBn5o4MADl8ce3SNQ8B20bEU5I2AH4FHBoR99apvRV3TMzMzMysK6O2bNcf6L1ukt9JYYx7kxajzwEOz829BfhRTldfirRYfGZu60Tgtoios4VwUdtdki4GZgJzgSMjYp6kpYBNSDttNTob+B/gFkmQRjlObJZWD8xu0VbZecB5kmYArwCHRUTk724T4D8l/We+dve8OL5OonzZ8cCawA9z7XMjYtsO7l+AOyZmZmZm1pUx24xhzDZjBtVGN6MtXSS/B3BkxfE/AFs2eUY3a0yIiJOBkxsObwFcmrfRbby+8vd4s7T6vMVvZVsN97/C/G2Iy8e/Dny9ot26ifIbll4fQdreuCe8xsTMzMzMhpQeByweJum+/OewGo9/P/ANSVdW1FX5nIiYERELrR+RtKKkKyXdnUMQTymd+0QOQeyXdHPOSGnaVkXby0kaJ+ne3P5++fgxkmbm+iZJemNut23yu6SJeQSmeH9ArvtVSduWju8q6SXSjmS1k989YmJmZmZmS1Qn61R6GbAo6bXAV4FtSbtnTc1tPUtzdwHfjIhLKs61CnJs5jsRcaOk5YBJkvaKiKuB8RFxdv7MI4DvAXu2aavsy8CTEbFZnkr22nz8DtIakTmSPklaU3Ngu8YkfYg0jaxsBvAh4EflgxExSdLmwBUR0W7K2QB3TMzMzMxsielincpAwCJAXtA+EpgZEXfkY433DAQsAlMkFQGLw4Hrix/Pkq4n/fj/RXefpvo5EVG5biMi5gA35tevSLqd+QGLL5QuXYn62w4X/h3YPLf1KinxnYi4sXTNFCqmezWStDJpx7AxwMWl+mfl8x2WVs0dEzMzMzNbYqrWqejwlj90q0IM241KNAs+bBqIKOlc4OyIuK1N23We03ZBed4tax/g+6VjR5I6BMuRdtiqJbcFcJKk4cCfgaMi4q8Nl9YNRTwJ+C5p44BFxh0TMzMzsyFm3NRxjJ8+frE8q4vtfBe1XgYsNm0rL+zuVFcBi5KWIY3SnF6MBOUafgD8QNIo4CtAnTUwkH7jrw/8PiKOkXQM8B1SsnzxzI+QprDt3Ka2PmCTiDha0oY1n98VL343MzMzG2LGTx9P/xP9S7qMJaWXAYtdBSL2uDaAcaQdsU5rcn4C8MEO6niaNLpxWX7/S6C84H830hqUERHxcpu23g1sk/NLbgY2kzS5g1pq84iJmZmZ2RDUN6yPyaMnL/LnDCY8cRHpZcDitcB/5aBFgN2BsYOorfI5AJLujojNG2+Q9HVgNRq23ZW0aUTcl9++H7gvH1+PtI5l12ZF5LyS35DW0NwA7ErKVkHS1qTF6nvm7JLyMxeqMSLOIi3iJ4+YXBERw9t9Ed3wiImZmZmZDRkRMRcoAhZnAReXAxYlPUIaqZiW14lAyjx5gBSweA7wqdzWM6T1E3/Kf04sLYQ/t7wFbk2Vz8lbFy80zUvS+qSRiy2A2/PWwEUH5ai8FW8/aZ1JMY1rHVJ4YztfBE6QNI00hevz+fi3gZWBX+bnTWxVYyuS9s3f97uBK3NHr2seMTEzMzOzIaVXAYv53HmklPTG4x2vMWnxnHeRtjhuvP4RmnQGIuKzTR5T2VbF/f8D7FRxfLdu242Ih4C3ld5fxvzpYoPmjomZmZmZWX3Pk3a7WqvIGWknIq7o1cMj4sxetdXQbs9qlLQj8EPyFsV1eSqXmZmZmQ0pLZLfN5L0x5ziflEOLUTSBpJulHRHTjzfOx8/JE9nKv68mnehamU1YIUmdVUmv7f4HK2S3ysT2uuSdL6kB0ufrS8fP650bIakeTlospu2hkt6vnT8+HzLbcA84D15ilgtHjExMzMzsyWim22P1Tr5/ZvAqRExQdLZpJyOs0hb7V4cEWdJ2oI0DWzDiLgQuDC3uyXw64ios93ZcYsh+b2rhPZ2dUbEt0nrTJC0D3B0zXT2Zp/5poj4QMMzXgT68k5etXnExMzMzMyWiC63PR5Ifo+IV0hb6Y5Uih/fBSh+PP+U+VvsBrBqfr0a1Vv4Hkz3ie+FgeT3iJgCFAnzlSJiTpHEnj9LOfn9xpwMDymhff1B1lalF5+5ZzxiYmZmZmZLTNW2x10mv68JPJd37SqOr5dfnwBcJ+nTwEpA1QLwA0kdi1TDv0Dye0ndhPZGJ+fpVZOAL5UzSyStCOxJ2uFsMG29W9KdpM7escUOad3wiImZmZmZDSUdp7iTRgbOj4j1gb2Bn0ka+B0saXtgTkTMGLgx4ogOOyWtamt9U5Pk93yuSGj/doe1jAU2B7YDXkvaPrhsH1IyfJ1pXM3auh14Y0S8HTgDuLzDGhfgjomZmZmZDSXN0tWfIk2dWqbhOKQRh4sBIuIW0uL18qLsg+jNlKaeJr93mNC+gIh4PE8pexn4CWkKXFntz9ysrYh4ISJm59dXAct2sti9kTsmZmZmZrZYjZs6juHnD+9mfQmUkt/zgvGDgIk5Q+RGYP983WHAr/Prh0np50h6C6lj8rf8fingANJalcGaCHw07871LhqS36tuKCW/f67heJHQPqIqob1dIcXalrz25oPAjNK51YCdmf/9FMcnKSXL12pL0rB8DEnvJPUtnm5XWzNeY2JmZmZmi1WXi96BlPwuqUh+Xxo4r7Su4YvAhPxj/w7gx/n454FzJB1Nmlo1OndkIIUQPlIxhaqbNSZXkaaK3Q/MAQ7PbbVLfr+blPwOcGZEnMuCCe0AD0fEiGZtVbhQ0uvytf3AJ0rn9gWui4h/lGpZCtgEqJra1ayt/YFPSpoLvAgcVPpeO+aOiZmZmZktdn3D2sWFNNci+f0BFp6yRN5KeIcmbU0mpZ43Hl/Sye9dJ7Tn+3dpce584PyGw1sAl+atfmu1lcMeexb46KlcZmZmZjak9CpgsXTfBpJmSzq2xuN3I40g/LCiruXzc+/PdWwIKVU9Ik6vuP4Nua5ZOWDxs6VzB+Rjr0ratjjerK2Ktifn76gIP1y74fz+kqJoOyJmRMQxTdp6SNL03M5tpeMXldp/SFJ/Pr6rpJdIO5K92q7WgjsmZmZmZjZklAIW9yL9K//BOTQR5gcsbgo8S1r0DvMDFrcmrUlp7FScSv3teO8HdoiIT1Wc+xjwbERsktv8Zpu25gKfj4i3kEZCjix9lhnAh4Df1ayryiER0Zf/DKxTkbQK8Bngjx209d7cTrmTdGDRPnAp8Kt8fBJpF697au76BXgql5mZmZktQf1P9DP8/OGd3DIQsAggqQhYnEUKWByVr/spKb/kLFoELEr6IPAAMLDeYhBG5mdCCno8U5KarbvIC+Mfz6//nj/DesDMiJiV6+tBWQs5iZQkX2eEqK28AP7DpO+/ax4xMTMzM7MlYtSWo7pZa9IsxLBdwOJHJD1CWpvyaQBJK5EWzH+t8SGSrpK0bre15Tqez3W1lad9bU1noxjt/CRPs/rP0u5ZWwNviIgrOmgnSAGVUyWNqTi/I/DXiLhvMMV6xMTMzMzMWupiVKNte33D+hizzRjGbLPw79w2ye+DCVj8rqR3kwIW30bqkJwaEbMbRyYiYm86123A4sqkqVCfi4gXunhulUMi4tE8betS4FBJPydNMRvdYVs7RMRjeZ3K9ZLujojyFLOD6UEOjDsmZmZmZtbUqC1Htb9o8WobsJhHKxoDFveEFLAoqQhY3B7YX9K3gNWBVyW9lHebGkxtjygFPa5G9fa7AyQtS+o4XBgRv+ryuQuJiEfz33+XNJ40Be7XwNuAybkjNgyYKGlEq22RI+Kx/PeTki7Lbf0u178MaS3MNoOt2R0TMzMzM2uq2ajGYAxy9GUgYBF4lLSYfVREhKQiYHEC1QGL56sUsBgROxaNSjoBmD2ITgmkgMXDgFtyHTfkutYDLoiIXcsX5+lVPwZmRcT36jygWVsN1ywDrB4RT+WOzweA30bE85QS7yVNBo6NiNta1LgSsFTu4KwE7A6cWLpkN+DuvPXxoHiNiZmZmZkNGXk0pAhYnEXabascsHiMpPtJazvKAYsfl3QnacpROWCxUpdrTH4MrJmffwxQbGW8DmkHrkY7AIcCu5S23d07P3/fvCbm3cCVkq5t01bZ8sC1kqaRAhEfBc5pc0+zdl8P3Jy/u1uBKyPimtL5g+jBNC7wiImZmZmZDTG9DFgsXXNCw/uO15hExEvAARWnmgUs3kzzgMXLgMvqttVw7z+oMbUqIobXqPEB4O0t2hjd7jl1uWNiZmZmlszOUQQAACAASURBVPV6kfeiUiwetyXiGdKUsP8XERPr3DDI6WGLrK1F1a6kHUlZMU91cp+ncpmZmZnR9da1tgR0kfx+ammq1L2Snivd862csD5L0unFtrotvACsBCw0zUvJ6bmuaZLe0eZzrCjpSkl35xpOKZ37RClt/eZS8GIt6jD5vU1bzZLfv51rnybpMkmrl25bmtJ6ljo8YmJmZmbGolnkvagMhVGddrodndL85Pf3kXbB+pOkiXm6VpH8PkHS2aTduM6KiKNL93+alBeCpPeQpnhtlU/fDOwMTG5TxnERcUnF8b2ATfOf7Unhjtu3aes7EXFj7kRNkrRXRFwNjI+Is3OdI4DvkXcW68AhVbttqfvk98YRkOuBsRExV9I3gbHAFyPiprxWppOsFI+YmJmZmdniNcjRqYHk94h4hbQD18g80rELKXEdUvL7ByvuL2duBGmHruVIC8aXBf7abWGk5PcLIplC2r54nWYXR8SciLgxv34FuJ20zTENeSYrUSMPpQNF8vtLg2kkIq4rBVpOIdfeLY+YmJmZmdli1W50qk3AYlXy+/a0Tn5P7UpvBDYCboCBTJMbgcdJi9DPjIhZ+dpzgbNb5XvUrG293H5LeRrUPsD3S8eOJO3utRyp09Wpn0iaR8pJ+Xreungg+V3SsTXbKZLfA/hRRIyruObfgYu6qHGAR0zMzMzMbCjpJvm9cBBwSUTMA5C0CfAW0r/0r0fatncngIg4osNOSavaWt+Uckd+AZyed8Ei1/CDiNiYtA3yVzqs5ZCI2BLYMf85VNJSpOT3z3fY1g4R8Q7SVLUji++oVP+XSVsNX9hhuwtwx8TMzMzMhpK2ye8Nx8saMzf2BaZExOyImA1cTdo2t9e1tTMOuC8iTmtyfgLV09KaKie/A0Xy+yrMT35/iPRZJ7ZbAF9OfidtYTywJbOkw0gBjoe0y4Zpxx0TMzMzMxtKBpLf84Lxg4CJ+UdxkfwOCya/I+nNwBqkVPbCw8DOkpbJCek7k0IbuzUR+GjenetdwPMR8Xh+/t1VN0j6OrAa8LmG45uW3r4fuC8fX0/SpFZF5M+zVn5dJL/PiIjnI2KtiNgwIjYkrQsZUSS/V7UraaW8WL5Igd8dmJHf70kazRkREXNafjM1eI2JmZmZmQ0ZeQeoIvl9aeC8huT3CfnH/h3MT36HtOh9QsO/6l9CWrsxnTTl6pqI+A10vcbkKmBv4H5gDnB4bmstKqZ5SVof+DJwN3B73qn4zIg4FzhK0m7AP4FnSR0t6Cz5fVnSd/RbBpf8flmubRnSbmFF8vuZ+VnX5/NTIuITbZ7TlDsmZmZmZjakdJr8ns+dUHFsHvAfTa4/oou6Ajiy4lSzVPVHaJ78/tkmj/mXSX6PiE3aPaMTnsplZmZmZkNKi4DFo/KxKKYy5eMjcwhgv6TbJP1bPt4n6ZYcbjhN0oE1Hr858AtJVzapbWyu4R5JewBExBURcXqT6y/M186QdF4e5UDScEnPlwISj89tnVkncV4pYLK49yFJ/fn4+yRNzYGJUyXt0q5dSSeVvr/rJK3bcH47SfMk7Z/fH0iaEveWdnWWuWNiZmZmZkNGKWBxL2AL4GDNT0X/PbAb8D8Nt00C3h4RfaRtbc/Nx+cAH42It5LCC09rSC+vcg0pVPD9FbVtQVrzUrT3w1xvKxeSOjtbAq8ByiM1N0VEX/5zYpt2FhARBxb3krYL/lU+9RSwT96x6zDgZzWa+3ZEbJXbugI4vjiRP983SVPrimdfFBGvAV7spGZP5TIzMzOzxWbc1HGMnz5+ME0MBCwCSJpACjacGRF35GML3JB33CoMhBVGxL2lax6T9CTwOuC5LmsbSVrH8jLwoKT7c723NLshT0sj130rgwwpbJSDJz9MzkEpvqPsLmAFScvnmpvV2Crs8dOkjs92g63VIyZmZmZmttiMnz6e/if6B9NEsxDDliTtm3fGupI0atJ4/p2kIMM/5/cnShqxOGrLz1sWOJQ0IlN4t6Q7JV0t6a0d1lLYEfhrRNxXcW4/4I5WnZJSfSdL+gtwCHnERNJ6pC2Xz+6ytgV4xMTMzMzMFqu+YX1MHj256fk2ye9dhRhGxGWk3aV2Ak4iTflKDUrrkKY0HRYRr+brj69sqLWuast+CPwuIm7K728H3hgRsyXtDVwObNr07uYOZsHsllRo6uh8k7T9b1sR8WXgy5LGAkcBXwVOA74YEfMaR6m64RETMzMzMxtKug0xBCAifgdsXMr5WJU0ivKViJiyJGqT9FXSFLJjSnW+UExBy9O9li0v6K8jh01+CLio4fj6pKDEj0bEnztpkxTWuF9+vS1pe+aHSPkxP5TUURBkmUdMzMzMzKwn6qwf6X+in75hfYN5zEDAIvAoabH5qFY3SNoE+HNEhKR3kKZsPZ0DGi8DLoiIXw6mqGwiMF7S94B1SSMct+YaJpE6Ao821HYEsAewazFak48PI03BijzNbCng6VZtVdgNuDtvS1y0uzqpIzY2In7fUMsFpByVWxuOb1qaCjaClLtCRGxUuuZ84IqIuLxNTU15xMTMzMzMeqIH60faioi5pKlE15K2pL24CFiU9BlJj5BGKqblkERI/8I/I2+Z+wPgwJw58mFgJ2B0aWvdvtxWx2tMch0XAzNJa0WOzNOclgI2AZ6puO1sUojhLeVtgUkjEDMk3QmcDhyUOymt2mp0EAtP4zoq3/+fpc+8dj63FfB4RTun5O2Mp5GmfjXLWBkUj5iYmZmZWc+0Wz8y/Pzhg35Gi4DF00k/4huPf5O0nqLx+M+Bnzd5RjdrTIiIk4GTGw5vAVwaEQttnxsRlb/HI+JMUrJ6o6ZtVbQxuuLY14GvNx7PU9rui4i/VNyzX+OxOs/qlEdMzMzMzMzqmw2MkVQ7VyQiZkTEMe2vXLxtNbT7QkQc0Iu2JG2cR6f+2sl97piYmZmZ2ZCizpPfD8nJ5dMk/UHS2xvaW1rSHZKuqPH4lUlZHs1qWyj5vc1naZb8XplWX5ekm0pTtR6TdHk+Lkmn5xqn5TU3rdpZUdKVku6WdJekU0rn3ihpUm5ncl5UX/b6Tmr2VC4zMzMzW6z6n+jvekqX5ie/v4+0C9afJE2MiJmk5PcrgMkNtz0I7BwRz0raCxgHbF86/1nSepVVa5ZxakR8p6K2cvL7usBvJW0WEfNatHUh8JH8ejwp+f0sUlr9xLyuZCvS2pXNa9ZHROxYqutS4Nf57V6kRfmbkr6Ds1jwu6jynYi4MW8WMEnSXhFxNfAd0sYBP5W0C/AN4NC801efpNmtGm3kERMzMzMzW2xGbTlqsLtyDSS/R8QrQJH8TkTcEREPNd4QEX+IiGfz2ymU0tXzv/K/Hzi38b4uDCS/R8SDQJH83lREXBUZaQev9fPx2fkYLJy2XpukVUip78VuWSNJnYnI2yOvnnNcmtU3JyJuzK9fIeWrFN/fFqQOFMCNue2uecTEzMzMbAgazKjDolJnK+Ax24xhzDZjWl7TJmCxKl293b/4l30MuLr0/jTgC8AqC9SQ1pDcFhETO2h7PVLHp1xbp8nvny0d25c0CrE2qfPUjX2BSRHxQqnGqnT6qt24GmtcHdgH+H4+dCdpx7Pv5+esImnNiHi6m0I9YmJmZmY2xPRg1GEo6zpdXdJ7SR2TL+b3HwCejIipCzUYcXyHnZJB1cbCye9ExGURsTnwQVJafTcak9+7qjGHNf4COD0iHsiHjwV2lnQHsDMpV2Zul3V6xMTMzMxsqKkz6rAkLKYRnG7T1bciTdfaq/Qv+jsAIyTtDawArCrp5xHxkWbtLKLaiuT3/6g6HxG/yztdrRURT9UtRtKapKlk+w62RtK6nPsi4rRSXY+RkuWRtDKwX0Q8X7e+Ru6YmJl1oE6qsf1r60HqtJktWd0kv28A/Iq0MPve4nhEjAXG5muGA8cOolMCvU1+r0yrz+fuziMp7RxASmN/qaHGoyRNIE2Bez4iHm/VrqSvA6uRFuaXj68FPJPrHgucV6OmpjyVy8ysA4sj1djMzJrrMvn9eGBN4IfF9rvtnvMvkPxemVafOwMtF+GUVCW/XwU8QFqYfw7wKRjoZCzUbt4c4Mukhe635xqLDspw4B5J9+bP0Bgs2RGPmJiZdahdqrH9a/tXWyxsZp3rIvn9CBr+tb/imsmUthn+F0h+r0yrB95F6qjUqWV4xbEAjqzbbkQ8QpOOUERcAlxSp5Y6PGJiZmZmZkNKpwGLpfPbSZonaf/8vk/SLTk4cJqkA2s8/j3ANyT9d5PaFgpYbJXW3iJgcTVJv5F0Z67v8NzWFbkD1u47qgxYzOeG5+N3FZ+jTruSzihnk0haXtJF+fP+UdKG+fiBkl4Clm1XZ5k7JmZmZmY2ZJQCFvcijUQcnIMNIQUs7gb8T5P7vkmaAlaYQ1r38VZgT+C0vCVuK38AxkbEzhXPKAcs7kmaOrZ0m/YuJAUnbgm8hvkjO0cCMyPi7aQpU9/NAYe1RMSOEdEXEX3ALaQ1NsWWvz8ERuTPfUCd9iRtCzR+Nx8Dno2ITYBTySM8EXFR/kz31a0XPJXLzMzMzHpoMeSrDAQsAuRF3CNJP+LvyMeq7vs0cCmwXXGgYSH8Y5KeJO2O9VyXtQ0ELAIPSioCFm9pdkOelkaueyBgkbSF7ypKH2Zl0vqUjrfiLQUsHp4PjQJ+FREP5+c/WaONpYFv53vLO3yNBE7Iry8BzpSkUjBkRzxiYmZmZmY9sZjyVZoFBDYlaT3SD+qzW1zzTtLOV3/O7zte/N5NbaXnFwGL1+RDZwJvIW3lOx34bHnXrg40BixuBqwhabKkqZI+WqONo4CJxe5dJQOfN29K8Dxpk4GueMTEzMzMzHqiV/kqbZLfuwkIPA34Yt4ha+EGpXWAnwGHFT/+u1z83suAxT2AftJox8bA9ZJuKnUw6jqYlN9SWAbYBtiVNHXsFklTyqNHZZLWJU33Gl51uuJYV6MlRWFmZmZmZkNFNwGB2wITcqdkLWBvSXMj4nJJqwJXAl+JiClLoLZmAYuHA6fkaVH3S3qQtG7j1rrFtAhYfCoi/gH8Q9LvgLcDlR0TYGvSVsf35+9vRUn353Ulxed9RCkZfjWqt0SuxVO5zMzMzGwoGQhYzIvBDyKFBjYVERtFxIYRsSFpLcSncqdkOeAy4IKI+GUPapsIHJR3q9qIhoDFPKVsAaWAxYMbpmo9TBrVQNLrgTeT8keQdHfNeqoCFn8N7ChpGUkrkkIWZzWrMSKujIhhpe9vTu6UFJ/3sPx6f+CGbteXgDsmZmZmZjaEdBmw2MyHgZ2A0aWtdftyW0s6YPEk4D2SpgOTSFPRnhpswGJEzMq1TSN1ms6NiBltamzmx8CaeZH/McCX2lzfkqdymZmZmdmQ0mnAYsM1o0uvfw78vMl1Szpg8TFg94pTgwpYzMe/Tdplq1aNDfeuXHr9EjW3G67DHRMzMzMzs/pmA2MkrVq38xIRM0gjCoMWEVf0op2KdntWo6QdSYv5n+rkPk/lMjMzM7MhpUXy+0Y5gfy+nEi+XD7+CUnT81Spm4tARkmHlKZw9Ut6tZjK1cLbSInmCy1qV3J6rmuapHfU+CzXlNLdzy4CGSW9VtL1+bNcL2mNDr6fFSTdWmr3axXXLJDi3qKtpt9Rs9qBEaTF/B11ojxiYmZmZmaDNm7qOMZPH7/In6P5ye/vI+0K9SdJEyNiJil5/NSImCDpbFIy+VnA+Ig4O98/AvgesGdEXEhKXkfSlsCvI6K/RhnHRcQlFcf3Ii1435S0qPys/HcrH46IF3KQ4iWkqVETSOs1JkXEKbnz9SXgizVqA3gZ2CUiZud8lJslXV3sOqbqFPdKbb6jytoj4jhJ/6hZ6wCPmJiZmZnZoI2fPp7+J+r8ph+0geT3iHiF9CN+ZP5xvAvpBzLAT4EPAjRkf6xEddbGwTQsFO/CSNIOX5E7AavnjJSmSrUtQwp4LGobSfoMUPosdeTnF6Mhy+Y/AQukuH+hbnslC3xHLWrvikdMzMzMzKwn+ob1MXn05EG30yZgsSpdfXtS4vhzedeu4vjA1reSjiStoViO1IFpdCCpM1Bcfy5wdkTc1kHpzZLfGxPTFyDpWlKH62rmd6xeXyStR8TjktbuoI6iAzKVtNPWDyLij/nUQIp7VdhkGwt8Ry1q74pHTMzMzMxsKGmWNt4yhTwifhARG5OmQ31lgQal7Un5HDNK1x/RYaekVW0tRcQewDrA8lR3mjoWEfMioo+0dfI7Jb2tlOJ+RqftVX1H+Tk9q90dEzMzMzMbSpqlqz9Fmjq1TMPxRhNYeFrUQnkfPa6trbz17kTmj0j8tZgGlv9+spuCIuI5YDKwJwumuD/E/2fv3uM3m+v9/z+e5RzKRpFi5JgcJpT2btOECu1IKUw5lNmzFRHRzjdhJzuhxE9oSLS3acihJoZoMigkh8+cjHPSyCGH2M4Nr98f7/eaWXPNWtd1retzfWbmk+f9dpubz+e91nqv13Xt/cfn3fvwzCnuXXZV+x1V1N4TL+UyMzMzs0FvXh94ZICRq3U60Kov5ia/Aw+R/mAeHREh6RpSAvkEUiL5LwAkrRcR9+TnPwoUP5ODBT9FClocrInAgZImkJaXPV0sx5J0Z0RsWL5Z0vLACnlZ1RLATsD1pb72AY5v+SzvBQ6MiL3ripC0KvD3iPibpGWB7YHvRMTlwGql+54tUtwl7Qq8NyKOqOhvge+oQ+098cDEzMzMzOZuXl9Ig4ueRcQcSUXy++uBc4rkd9IyrQmSvgXcTkomhzRY2B74O/AU6Q/9wjbA7Ii4v/yeHveYTCL9gX4v8DzwudxXXVr7G4CJkpbOn+U3pCR4SAOSCyXtBzzIvCDDNYG2IYikpVXn5X0mrwMu7CL/ZB3gmZprVd9Ru9p74oGJmZmZmQGD27w+6txRfa2lnTbJ7/eTNmK3th/cpq8ppDT11vYxPdQVwAEVlyrT2iPiUeA9NX09AWxXcWmrqr5anp1GWrbVqd7lS7+OBA6puW8KLd9Ru9p75YGJmZmZmVn3ngaOlbRKkY3SST/T2iPi8H711dLvZ/vVl6QTgV2B7zZ5zpvfzczMzGxY6SH5fen8+735+ojcvpSkH+dU+KmSRnXx+jcCy9TUtVgkv+fnz5H0mKQZLe2bSboxf+ZfSlqxQz8btCS/PyPpy/naiZLuzJ/1UklFaONE4GWqZ49qecbEzMzMzPpi4JGBIV/S1WPy+37AUxGxrqQ98n27A/8OEBGb5JyQKyS9JyJe7VDG4p78DnAucBrwk5b2s4HDIuJaSZ8HDge+UddJRNxFWuZVfPcPAZfmy1cDR+R9P98BjgD+MyKul7QT0GimyDMmZmZmZjZoozcZvbA2zjdOfmf+FPWLgO3y/RsBkwEi4jHgb8CWg6htsUh+z/1eBzxZcWkD4Lr889XAJxt0ux1wX0T8Kb/jqlKg5U2k45F75hkTMzMzMxu0sVuMZewWY/vS1xAkv899Jv+v+0/n+6eSBjUTSPkjW+T/3jzck9/bmAHsTDp++FPMn7vSSbu8l88DFwymMM+YmJmZmdlw0kvye921c0iDh1uA7wM3AHNg+Ce/t/F54ABJtwIrkPaCdJT36+wM/Kzi2tdJ39v5gynMMyZmZmZmNpx0TH7Psybl1PXimdk5DPCNwJP5eN+5R+RKuoFS+GIfa+soIl6UVKSnX01Ofs+zJT0nv1e8507gwwCS1icFTnZjR+C2fEzwXJL2Af4N2C5/nz3zjImZmZmZDSdzk9/z/4q/BzAx/1FcJL9DKS2deSnq5Ou/yUnxy0l6A4CkDwFz8ib6Xk0E9s6nc72PluT31pslLV/sQSmlp99Z6quoeb7kd0mtG9q7ViwJy2nuR5JDESWtIWlym0f3pGUZl6QdSBvyd46I53utqeCBiZmZmZkNG3k2pEh+n0VKNS8nvx8q6V7SHpIi+f1HwMq5/VDSCVcAbwZukzQrP7tX8R5JZ0tquhF+EnA/Kfn9LOCLua9Oye/TSPtdHmP+5PcPSbqHdALZ8bm9m+R3JP0UuBHYQNJspQR5gD0l3U0aAP0F+HFuX528jK2ir+VyDZe0XDqNtBzs6nyUsJPfzczMzOy1o4fk9xdJG71b2x8gnVJV9Y5hm/yen9+zpv0U4JRua8zPPE8a6LW2r9upjiY8MDEzMzN7DRh36zjGTx9fe33gkYGFddzvoOUlRKcArwfOjojjc/vapOOD/wm4DdgrIl6WtD9pwPAK8CwwNiLuyEvBfkg6IvhV4OCImNLh9e8FRkvaNSI+01KXcl07Ac8D+0bEbXXJ73km4mfAOrm2X0bE1/K1pUkZJFsATwC7R8QD3Sa/S7qSNAuyBHA9cEBEvCLpAuYNxt5EOslsZEScVtPPBsx/2tY7gKMi4vuSjiFlwfw1X/t/ETFJ0gGk1PdXuqm14IGJmZmZ2WvA+OnjF/ngo9PgqBs9BiyOj4hiL8XOwPeAHegtYPFm0h/m/QpYPCkirsmDpMmSdoyIK6gPhexWZXBjRMztQ9J3gafbddIhYBHS931SyzM/kHQ5DQMWPTAxMzMze40YudpIpuw7pfLaUCe2Q98GR3MDFgFyBskueZ/ItsDofN95wDHAGaUQQ0j7OorTo+YLWJRUBCze3GNtcwMWgZskvak4Wavq5rxE6pr888uSbmNeSOEuuX5IA4vTJKnbk6/aBDcCc2d3Pk2z44nnC1jsNw9MzMzMzGyhaTc4KgxBwCJ5edGhpD/Siz/GF4uAxVzfm4CPMW//R10o5OPdFlMT3FjYGng0Ipocj1wVsHigpL1JWTBfiYinGvQ3H5/KZWZmZmbDSS8Bi0TEDyJiHdLpW0fm5sUiYDEfFfxT4NRiJqjXvua7uX1w4wLH/3aosSpg8QzS/piRpMHXd5vU18oDEzMzMzMbTjoGLLa0t5oAfBzSTEREHJI3f+9C2gy+KAIWxwH3RMT3q/oqh0I2LSifSFYEN1Lq7xPMv6m9kwUCFiPi0Yh4Je/JOYuKE9Ga8FIuMzMzM+uLhXTy19yARdJG7D2A0TkwsQhYnMD8oYTrlZYsfZQ8+MinYikinutjwOKBeWnYVrQELEbEhq0PSPoWadDRejxxEbB4I/OHQq5B2sdSdZRw0efywAo5Nb4Ibry+dMv2wJ0RMbv0TKd+qwIWy/tndgVm1NXUDQ9MzMzMzKwvFsbJX3m/RRGw+HrgnJaAxQn5j/3bmReweKCk7YG/A08xL1H9zcCvJL1KGuTMF7BI8z0mk0iDgHtJxwV/LvdVGbAo6W3A10lhh7el/eicFhFn59r/J4dCPkkagEGbIMSSIrhxadJ39BvmBTdC9V6RbgIW/6Pl0gmSRpKWmD1Qcb0RD0zMzMzMrG8WxslfPQQsHlzTzwMs2oDF2VTvJakNhazrq+XZ2uDGfH3fJv22CVjcq+L2nnlgYmZmZmbWvaeBYyWtUmSjdFIXsNiLuiDExalfSVsDp9PgBDHw5nczMzMzG2Yk7SDpLkn3SvpaqX1tSb+XdI+kC/JJUkjaX9J0SQOSfitpo9y+sqRrJD0rqds/zN8ILFNTlySdmuuaJmnzDp9jOUmXS7pT0kxJx5eubSPpNklzJO3WZW3lvn8kaWqu46K87wRJS+fv5t78XY3o0M8G+Xsr/j0j6cv52rG5/wFJV0l6a37srcCywLNNavaMiZmZmZktNAOPDAxqSVefk99fBL4BbJz/devwhZD8/iCwL3BYg7rKDilCFiV9DzgQOJ6GifIdkt9PjIhv5GsHAUcB+0fEBZIebVq7Z0zMzMzMbKEYvcnofmyMn5v8HhEvk07g2iUnmW/LvCDB85h3LHBl8ntEPBcRvyUNUPphbvJ7RNxEOr549bqbI+L5iJib/A7MTX6PiAciYhrwai+FlAYlIs1eFBkou5C+G0jf1Xb5nm7Ml/xe9732yjMmZmZmZgYMfjaj04lcY7cYy9gtxnbsZyEmv9fXsOiT3wdN0o9Jp4TdAXyltcYeEuUXOM1L0nHA3qS9Nx8cTL2eMTEzMzOzfs1mLAz9TH6vtRgkvw9aRHyOtN9jFvOWa/VaY1XyOxHx9Yh4O3A+ablYzzxjYmZmZmZdz2a006/jgDvomPyeZ03aJb+fsZBr66Qq+b0vIuIVSRcAhwM/LtU4u2Gi/ALJ7y3GA5cDR/daq2dMzMzMzGw4mZv8nv9X/D2AiTlDpEh+h5bk99Lzc5Pfh8BEYO98Otf7aEl+r3qglPz+5W5fUtdX6bokrVv8TFoiVjxTJMpDS6K8pMltuq1Kfi9/rzuX3tETz5iYmZmZ2bDR5+R3JD0ArAgsJenjwIcj4o5Fnfwu6T2k069WAj4m6b8i4l11fbV2DZwnacX881TgC/la40T5Nsnvx0vagLRB/0/A/h3qassDEzMzMzMbVvqV/J6vjahpX9TJ738gn9DVTV8tz74KvL/mWuNE+TbJ759sV0dTXsplZmZmZsNK04DF0vXdJIWkLfPvIyS9UAoP7CbJ/b3AeEnnV9RVGbAYEZdFxKkV99cGLNbVXNdXxXNX5oDFmZLOzBkkSNpM0o1KgZO/zLMqRMRpETGxpq83KYU03ilplqR/zu3HSHqo9P3tlNuPJs1o/WunOss8Y2JmZmZmfTPYI4c76TFgEUkrAAcBv2/p8r6IaHIc2c3AUQshYLFdzd34dEQ8k/eYXESaJZkAnA0cFhHXSvo8aVP8Nzr0dQpwZUTslutcrnTt5Ig4qXxzRPyXpPOAy5oU7BkTMzMzM+uLhXTkcOOAxexY4AT6F6ZYpW8Bi4OtuRR+uAQpu6U4EngD4Lr889VA2+VYeUZlG/J+nYh4OSL+1rSebnjGxMzMzMz6oh9HDkP/AxYlvRt4e0RcJumwlv7WlnQ78AxwZERcn59ZpAGLHWruiqRfkQZyVzBvwDaDdILWL0izKG+vfnqudwB/BX4saTPgVuDgiHguXz9Q0t7ALcBXIuKpMMjfsAAAIABJREFUXmoFz5iYmZmZ2fDSKGBR0uuAk5mXfF72MLBmRLyblAo/vrTnYpEFLHaouWsR8RHSaVtLMy/t/vPAAZJuBVYAXu7QzRLA5sAZ+Xt6Dij29ZwBrAOMJH2X3x1MvR6YmJmZmdlw0jFgsaV9BWBjYEo+Gvh9wERJW0bESxHxBEBE3ArcB6w/BLV10hqwWFtz04LyKVwTScvMiIg7I+LDEbEFaTB0X4cuZgOzI6LY53IRaaBCRDwaEa/kU8DOouJEtCY8MDEzMzOz4aRRwGJEPB0Rq0TEiHw08E3AzhFxi6RVS6dVvYO0af3+QdTWl4DFDjV3CkJE0vLF3pY8UNuJHH4o6c35v68DjgTOzL9X9hsRjwB/znklANsBd+RnyvtndiUtE+uZByZmZmZmNmzkPSRFwOIs4MKWgMVDc3jgyswLWKyzDTBN0lTSTMD+EfEkpD0mPcxQTCINbO4lzSB8MffVKWBxI1LA4oCkTvkptUGIJW8gzbBMI4UrPkYegAB7SrqbNFD5C/DjLvr9EnB+7m8k8N+5/YR87PA04IPAIR3qasub383MzMxsWGkasNhyz6jSzxcDF9fct0gDFlvuG9Wpr5b7HwXeU3PtFPIG+25qzM8MAAsM0iJir3Z1NOWBiZmZmdkiNO7WcYyfPn7I3zPwyMDCOMr3teBp4FhJq0REN4GMRESjPI8OfZ3Wr76Gql9JWwOnk/b9dM1LuczMzMwWofHTxzPwyMCiLmNYUcPkd0n75yVHA5J+K2mj3N5L8vsbgWVq6pIqkt/bfI7a5HdJ20i6TdIcSbu166em7x8pJb9PU0ptXz63V34XHfo6JNc3Q9JPJS2T2w/MnzXycrXCW4FlgWeb1OwZEzMzM7NFbORqI5my75QhfcdQprF3o18zQ+ot+X18MbshaWfge8AOucumye8Ahy+E5PcHgX2BnjJMgEOKkEVJ3yPtyzme9t/FAiStQUqf3ygiXpB0IenAgXOB35HS3aeUn4mICyQ92rR2z5iYmZmZ2ZDr48xQ4+T3Ugo6pI3hHbNFetS35PeIeCAipgGv9lJIaVAi0uxFlNuzbr+LJYBl8wlfy5GPQI6I2yPigV7qq3uJmZmZmdmQ63ZmqN/J7wCSDiCFKC7FvLBBWEyT3/tB0o9JRwXfQSmssc13sYCIeEjSSaQZnBeAqyLiqn7VWOYZEzMzMzMbTholv8/9IeIHEbEO6UjhI3PzYpn83vCdtSLic6T9HrOA3UvtVd9FXW0rkWaC1s59vUHSZ/tVY5lnTMzMzMyskV72i/TxVLCOye951qQudX0Cae8HEfES8FL++VZJRfJ70wFJp9o6aU1+75uIeEXSBcDhzMssKcz9LtrYHvhjRPwVQNIlwL8A/9vvWj1jYmZmZmaNLOKTxBolvwNIWq/0/EeBe3L7Ypn83kldX6XrkrRu8TNpiViR/F73XdQlyj8IvC+fICZS8vusbmttwjMmZmZmZtZY05PE+nUqWETMkVQkv78eOKcl+X1C/mP/duYlvx8oaXvg78BTpEELpOT3b0qaA7xCS/I7zfeYTCLt6bgXeB74XO6rU/L7naTkd4DTIuJsSe8BLgVWAj4m6b8i4l11fbV2DZyXl6WJlP7+hQ7fRWXye0T8XtJFpI35c0jf67hc/0HAV4HVgGmSJvUSTFnwwMTMzMzMhpWmye8RcXBNP4tt8ntE/IF8Qlc3fbU8+yrw/pprld9Fu34j4mjg6Ir2U4FT29XShJdymZmZmdmwouYBi4dKuiOHDU6WtFbpme/k4MAZknavel+LDYGfSrq8prYjcl13SfoIpOT3/Ed81f3n53tnSDpH0pK5fZdc74CkWyT9a6e+uux3JUmX5r5vlrRx7ve0iJhY09d2SmGPRShjsUzsZM0Lp7xb0t9y+0HAlcBHOtVZ5hkTMzMzM1soBh4ZGPSSLvUWsHg7sGVEPC/pC8AJwO6SPgpsDowElgaulXRFS9ZHqyuBiyLipIraNiLteXkX6QSrX0taPyJeadPf+UBxytV4YEyueTJ574ykTYELSYOibtX1+/+AgYjYVdKGpO9yuw59nQHsEhGzJH2RdJLXvhFxSHGDpC8B74Z5MymSGiW/e8bEzMzMzIbc6E1G9+tUrl4CFq+JiOdz+03MWyK1EXBtRMyJiOdIezFqU9C7sAswISJeiog/kvaaLLC0rCwiJuVAxgBuZl7A4rO5DXoIhazrl/SZJ+d77gRGSHpLp+6AFfPPb6T6pLE9SUce98wzJmZmZmY25MZuMZaxW4zt6t6hCFgs2Q+4Iv88FTha0vdIieYfJIURIumbwC11y5va1HZTS21VNSwgL7XaCzi41LYr8G3gzaQTtBqr6Hcq8Angt5LeC6xFGrQ82qabMcAkSS+Qgijf1/KOtUg5J7/ppcaCByZmZmZmVqkur6SPmSS96ClgEUApGHBL4AMAEXFVPv3qBuCvwI3kk6ki4qg+1taN04HriuT5XMOlwKWStgGOJWWKNNXa7/HAKZIGgOmkZW4LnMbV4hBgp3xC1+HA90iDlcIepOVt7ZasdeSlXGZmZmZWaRHnldTpGLDY0g5APiL368DOOVgRgIg4LiJGRsSHSAOLe4agtrYkHQ2sSkqfX0BEXAesk48K7lpVvxHxTER8LiJGAnvn639s08eqwGYR8fvcdAEpYLFsDwa5jAs8Y2JmZmZmbVTllfQrk6RHcwMWgYdIfxSPzpvEi4DFCcwfsPhu4IfADhHxWNFR3kj/poh4Im8w3xS4ahC1TQTG56VhbyUFNt6c3zUZ2DsiHio/IGkM6fSq7fIxv0X7usB9+XNtDiwFPJGv3RkRbTfCt+n3TcDzeX/OGNJsyjNtanwKeGPexH836dCBWaX+NiBlrdzY7ZdUxwMTMzMzMxs2egxYPBFYHvhZDjF8MCJ2BpYErs9tzwCfLfao9LLHJCJmSrqQtE9lDnBARLwi6XXAusCTFY+dCfwJuDHXcUlEfBP4JClF/u/AC8DueZDSTcBiu37fCfxE0iu5zv3y562sMX/f/w5cLOlV0kDl86Vb9iRt+G+0Ob+KByZmZmZmNqz0ELBYuTcjIl4knVJVda2XPSZExHHAcS3NGwEXR8QLFfdX/j0eEd8hHX/cqmPAYod+byTN5LRqV+OlpBT6qv6O6VRLtzwwMTMzMzPr3rPAWEkrdjt4iYgZ1OwfaSoiLutHPxX99q1GSesAF9P+pK8FePO7mZmZmQ0rPSS/L51/vzdfH5HbP1NKLh+Q9KqkTseNbUxaArbApnYlp+b3TMt7Q9p9juUkXS7pTkkzJR1fura/pOmltPXKmZ02fR8n6c+tIYeS1pR0jaTbc407ddHXwUoJ8jMlfbni+mGSimVmkE4+W57SXpRueMbEzMzMzBrrR4p7L3pMft8PeCoi1pW0R75v94g4n5SQjqRNgF9ERDfHkB0eERdVtO9IWia1Hilb5Yz833ZOiohr8iBqsqQdI+IKYHxEnJlr25l0RG+T8MdfAqex4CljRwIXRsQZebAzCRhR14mkjYF/Jy2Rexm4UtLlEXFPvv520v8tHiyeiYgLJD0KHNagXs+YmJmZmVkzfUxx70Xj5HdSIvt5+eeLgO3y/WWDTi7P7/lJDly/iXR88ep1N0fE8xFxTf75ZeA25iW/P1O6tZfk95si4uGqS3ROcS97J3BTrnUOcC2wa+n6ycBXm9ZXxTMmZmZmZtZIkxT3XgxB8vvcZ/IpU0/n+x8v9bM7aWCRapDOBs6MiFsalF5V2xpA1QBhPvkY348Bp5TaDiDt+1iKNOjqh2OAqyR9iTTg6RTaOAM4TtLKpNPBdgJuyfXtDDwUEVMXHOc15xkTMzMzMxtOekl+b5vILmkrUrbHjLkXI8Y0HJR0fE/tQykU8qfAqflksaKGH0TEOqRjkI9sWEudPYFzI+JtpEHG/+SjgitFxCzS0rergSuBqcAcScuRAit7Or2sigcmZmZmZjac9JL8PveZfP2NzJ/X0Zfk8ja1dTIOuCcivl9zfQLzlqUN1n7AhTD36OBlgLaJ8hHxo4jYPCK2IX1v9wDrAGsDUyU9QPqst0lardfCPDAxMzMzs+FkbvJ73jC+BzAxB/wVye9QSn4nJbLvk3/eDfhNEQiYZws+Rfrjf7AmkkIRJel9wNPFPg9Jd1Y9kMMg3wh8uaW9nDXyUfImdklr5IT2Xj0IbJf7eidpYPLXdv1KenP+75rAJ4CfRsT0iHhzRIyIiBGkQdnmEfFIr4V5YGJmZmZmw0beQ1Ikv88inTBVTn4/VNK9pD0kRfL7j4CVc/uhwNdKXW4DzC4voYK0x0TSlg3LmwTcD9wLnAV8MfdVmdYu6W2k5VAbkWYbBiSNyZcPzMfzDuSai4HV6qRU+bYknSBpNrCcpNmSjsmXvgL8u6SppFmiffMgrV2/F0u6g3TS1wER8VSn9/fCm9/NzMzMXiOaHvE78MjAojx9q1YPye8vkmZFqvqaQkpTb20fs+DdHesK4ICKS5Vp7RExm+p9KUTEwTWv6Tb5/auk07Ja2+8A3t+k34jYuov3jeh0TyeeMTEzMzN7DVjER/z2VR8DFkdIeqEUsHhmF69/LzBe0vkVdVUGLEbEZRFxasX97QIWD5V0R+5nsqS1cl+nRcTELr6jA3Md5eBDJB1e+rwzJL0i6Z/a9Svp+tIzf5H089K1Ubl9pqRrc9tnSRvldyy/uxPPmJiZmZm9BvRyxO/CClAcd+s4xk8f39W96mPAYu7yvohoMmK7GThqIQQs3g5sGRHPS/oCcEKp5m78DrgMmFJujIgTgRMBJH0MOCQinlzg6fmfmTtjIuli8t6dfMTx6cAOEfFgsRclIv4X+N+8Kb5rnjExMzMzs0Vq/PTxDDzSTeA6MHQBi/3Qz4DFayLi+XzrTUV7tyLi9oh4oMNtjUIlJa1A+o6LGZPRwCUR8WB+52NNamzlGRMzM3vNabrO3mwoLa77OBa2kauNZMq+U4CFGrAIsLak24FngCMj4npYPAIWS/YDrmhQR0c5h2QH0kEC3doVmFxKpV8fWFLSFGAF4JSI+EmvNXlgYmZmrymjNxm9qEsws8HpZ8Diw8CaEfGEpC2An0t6V0Q808vm9w411D9UE7CYr30W2BL4QA/1tPMx4HedlnG12BM4u/T7EsAWpOOHlwVulHRTRNzdS0EemJiZ2WtKL+vszYaSZ+8a6xiwmGdNqgIWZ6sUsJhP0XoJICJulXQfaRagaeJ7p9o6qQxYlLQ96TjhD0TESz3WVKdRqKSklUnL6HYtNc8GHo+I54DnJF0HbAb0NDDxHhMzMzMzW+jG3TqOUeeOYtS5o5rsL4E+BixKWjVvpkfSO0ib1uebsWionwGL7wZ+COzcunejrq9uSXojaQbmFy3tkyWtUf0UnwIuy0cvF34BbC1pibw0bCtStkxPPDAxMzMzs4Wu4Yb3ufocsLgNMC2HDV4E7F8sbdKiD1g8EVge+Flun9iur4q+D1IKWHxb/ozlJVi7AlflmY7i/tcB6wJ1S7sWmGGJiFmkY4GnkU4rOzsiZnSqrY6XcpmZmZnZIlFseG+6nK1fAYsRcTFwcc07FnXA4vY1r+k2YPFUYIHslHztXODcluaNgIsj4oWaZ0bVtM89fniwPDAxMzMzM+ve08CxklaJiG4CGYmIy/r18n721dLvDNJs0qBJWha4EVgSeLXb57yUy8zMzMyGFfUv+f29pUTzqZJ2rX7jfDYj/Q29wKb2uve0+Rxvl3SNpFk5Of3g0rVjlVLfByRdJemtXdRW7vscSY9JWmBplaQv5e9vpqQTOvSzQek7GpD0jKQv52sXlNofkFSszdsSWIoUatn1qV8emJiZmZnZIlfkC3Va1lVKft+RtPxoT0kb5ctF8vt6wFOk/A8oJb8DJ+f7AGaQ0tVHkjI9fphP7erkMxExsaK97j115gBfiYh3kpZoHVD6LCdGxKa5tsuAo7qoq+xc0meaj6QPkoIgN42IdwEnteskIu6KiJG5ji2A54FL87XdS9cuBi7J7dcDOzWs1wMTMzMzM1u0Rm8yuknIZN+S33PyehHIuAxdZI500ChhPiIejojb8s//R9rMv0b+/ZnSrW9oWltEXEf1RvYvAMcXxw83TGvfDrgvIv5Ubsyf8dM0OH64iveYmJmZmVmtYiZjKPotBiOt+UILMfn9cUlbAecAawF7Fc9LmgSMiYhuckgWqK31PZ0ezMu+3g38vtR2HLA3aV/LBxvU0c76pCN+jwNeBA6LiD90+Wxd9snWwKMRcc9gCvOMiZmZmZlVajiTsbD0M/mdiPh9XtL0HuAIScvk9p0aDkravqftQ9LypKVQXy7PlETE1yPi7cD5pCOS+2EJYCXS0rHDgQvbzeqUalwK2Bn4WcXlPRnkbElRmJmZmZnZAlpnMvppELMwfUt+L3caEbMkPQdszOCT32vf00rSkqRByfkRcUnNbeOBy4Gje6yrtcZL8tHGN0t6FVgF+GuH53YEbouIR8uN+XN+grT/ZFA8Y2JmZmZmw0k/k9/XLja7S1oL2AB4YBC11b1nDUmTW2/OMxU/AmZFxPdarq1X+nVn4M7c/l5JPxlEjT8n7cVB0vqk07Mer6uxpG5WZHvgzpzJMigemJiZmZnZsNHn5Pd/BabmY24vBb4YEY9D2mPS9IjeNu9ZnXQCV6v3A3sB25aO3S1Oszpe0gxJ04APA8VRwmsClSGIZZJ+SsoS2UDSbEnFCWXnAO/IxwhPAPbJg7q6GpG0HPAh8qlbLer2nTTmpVxmZmZmNqz0Mfn9f4D/qXlH4+Nu695DffL7b6lPfv9kzWu2quqr4vk9a9pfBj7bbY35medJA72qa/t2qqVbnjExMzMzs2Glh4DFbSTdJmmOpN1K968l6dY8UzFT0v5dvH5t4AZJp1fUJUmn5rqmSdocICJOq8o9kbScpMsl3Znff3zFPbtJCklb5r4Oj4hpHb6f2n5VEwLZpsbGAYuSDiAtPVu7XZ2tPGNiZmZmZotEL0cRlwIWP0TayP0HSRMj4g7mBSxOkHQmKfDwDOBBYF/gsJbuHgb+JSJeyidjzch9tTuN6xrgsoi4qOLajsB6+d9W+d1bdfhIJ0XENXkQNVnSjhFxRf6sKwAHUTpCuIG6fueGQErag/Sd7V7XSUTcBYzM9bweeIhSwGJxn6Tvko41JiJ+IOlyUjBk1zxjYmZmZmYL3SCOIm4csBgRD+RZhlfLHUXEy0XQILA0g//beBfgJ5HcRDolbPW6m3PA4zVFLcBtpNPECscCJ5DyRrrWod9GIZAtHLBoZmZmZv9Y2h1FPAQBi7UkvZ10FO+6wOHFbImks4EzI6LJ0cFVta1BmpnpVMebgI8Bp+Tf3w28PSIuk9Q609O11n4ZRAgkDlg0MzMzM5url4DFWhHx54jYlDQw2UfSW3L7mIaDkna1tX8oHVn8U+DUiLhf0uuAk4GvNHx/234HWeOQByx6YGJmZmZmw0nHgMWW9q7kmZKZpP/1v9+1dTIOuCcivp9/X4EU9DhF0gOkE7MmFhvgG2jtd74a1WUIZNYpYPGChrUtwAMTMzMzMxtOeglYrCTpbZKWzT+vRMoVuWsQtU0E9s6nc70PeDoiHs7931lTw7dIg4MvF20R8XRErBIRIyJiBHATsHNE3NJFEGJtv6Uauw6BLHHAopmZmZlZoZeARUnvkTSblDHyQ0nF/e8Efi9pKnAt6SSr6fmZs3uYoZgE3A/cC5wFfDH3tQoVS6gkvQ34OrARcFs+dndMh3fUBiF22W/TEEgHLJqZmZmZVekhYPEPzH/aVdF+NbBpzTs6DRCqngnggIpLdQGLs6kJWGy5b1Snvrrtt2kIZH5moQQsemBiZmZmZta9p4FjJa0SEWd280BENMrz6NDXaf3qa6j6lbQ1cDrdnfQ1l5dymZmZmdmwoj4lv+dr35E0I/+rDRoseQpYEXhrTW1H5LrukvSRLj7LjyRNVUqKvygHPSJpX0l/LSWrN5rBkXR+rmGGpHMkLZnbd8nvGpB0i6R/7aKv3fMzMyWdUGpfU9I1km7P13cqPfZ6YJUmNXvGxMzMzMwWiXG3jmP89PGNnuln8rukjwKbk5LNlwaulXRFRDzToYyTI+Kkito2Iu25eBdp4PJrSetHxCtt+jqkeJ+k75H2zxyfr10QEQd2qKXO+cBn88/jgTGk72Iy+bAASZsCFwIb1nUiaWXgRGCLiPirpPMkbRcRk4EjSXt8zsiffRIwIiKuz4MUJ7+bmZmZ2eJv/PTxDDwy0PSxviW/kzaHXxsRcyLiOWAqsENvnwZIqeoTIuKliPgjaRP8AnteykqDEgHL0kWmSDciYlJOoA/gZvIem4h4NrcBvKGL970DuDsi/pp//zXwyeI1pNkjSCeAdX08cxXPmJiZmZnZIjNytZFM2XfKfG0LMfl9KnB0nqlYDvggcAeApG8Ct0TExK4+yLzabmqprZv0+R8DO+V3l0MVPylpG+Bu0szKn6ue79D3ksBewMGltl2BbwNvBj7aoYt7gQ0ljSB9no8DS+VrxwBXSfoSaZCzfdP6yjxjYmZmZmbDSd+S3yPiKtLyoxtIR97eSD4yNyKOajgoaVdbWxHxOdLSr1lAsc/ll6RlUZuSZinOa1hL4XTguoi4vvS+SyNiQ9Ig49gOtT0FfIEUoHg98ADzjhXeEzg3It5GGlj9T06t74lnTMzMzMwWsYFHBhh17qhFXcZCN/DIACNXG9n0sY7J73nWpKvU9Yg4DjgOQNJ44J6mBXVRW0cR8YqkC4DDgR9HxBOly2eR9s80IuloYFXgP2reeZ2kdfIJY7UnaEXEL0kDJSSNBYo9M/uRl75FxI2SliFteH+saa3gGRMzMzOzRWr0JqN7+eP8tayfye+vz5u7yRvBNwWuGkRtE4E9JC0taW1gPdL+DiRNljTfsq6cEL9u8TPwMeDO/PvqpVt3Js2mFM9Vpsi39D0G+AiwZ0S8WmpfN78LSZuTlmU90a5fSW/O/12JFBp5dr70ILBdvvZOYBngr1V9dMMzJmZmZmaL0NgtxjJ2i7GLuoxFopdZooiYI6lIfn89cE5L8vsESd8CbqeU/A5cCqwEfEzSf0XEu4Algevz3+nPAJ8t9qj0ssckImZKupC0V2QOcECeCXkdsC7wZMsjAs6TtGL+eSpp2RTAQZJ2zv08STpVrDZFvsKZwJ+AG/PnuyQivknauL63pL8DLwC75xO62vV7iqTN8s/fjIi7889fAc6SdAhpydq+pY31jXlgYmZmZmbDSh+T318kncxV9Y6jeqxt7tKwko2AiyPihZZ7XwXeX9PPEcARFZc6Jr/n5yv/zo+I71C9LKxd8vueNe13UFN/L7yUy8zMzMyGFfU3YPFKSX+T1G3mxr8A35Z0bU1tCwQsRsSMiDi05v5GAYsRcVlEnNrFd3RgrqOYDSnaV5J0aX7fzZI27tSvpG3z9zcj55gs0a4vSduRlp5tVH53J54xMXuN6yXc6rWsx42aZmZWo+nGf/UxYDE7kXRUcOUG8Qo3AFcNg4DF35ECDqe0tP8/YCAidpW0Iem73K6uk7wM7Txgu4i4Oy9x24e0TK6yrxy+uKykB5oU7BkTs9e4HsOtzMzMBq3Hjf/9DFgk/xH9fz1+hFaLU8Di7RHxQMWljUjp70TEncAISW9p09XKwEulfSVXMy9gsWlfbXnGxMwqw62s2mvxOE8zs6FSt/F/IQYs1upl8zuLYcBihanAJ4DfSnovsBZp/82jNfc/DiwpacuIuIV06llxJHLTvtryjImZmZmZDSd9C1hs5x8oYLHV8cBKkgaAL5FOL5tTd3M+ZWsP4GRJN5Nml4r7G/XViWdMzMzMzGw46WvA4kKqraOhCFisec8zwOdg7vKxP+Z/7Z65Edg6P/NhYP1e+2rHMyZmZmZmNpz0LWBxCCw2AYt1JL2pOK0MGANcV9rnskCNub0IWFyalBVzZqe+euGBiZmZmZkNG3k2pAhYnAVc2BKweKike0l7TuYGLEqaDXwK+KGk4n4kXQ/8DNhO0uziiF9J38wBh01qmwkUAYtX0n3A4nRgOrA68M187SBJMyVNBQ6iYcCipIPyZ34bME1Skdb+TmBmHtzsCByc76+rEeBwSbOAacAvI+I37frqlZdymZmZmdmw0q+AxXxt65r24R6weCqwQC5JXpa1XsUjlTXmZw4nLTHrtq+eeGBiZmZmZta9Z4GxklbsdvASETOAyoDFpiKi2yDIpv32rUZJywI3AktScURzHS/lMjMzM7NhRc2T3w+VdEdOKJ8saa3SM/vk+++RtE8Xr9+Y9Af3ApvaJW0o6UZJL0mqCnOs+iyNkt+7Jen8/B3NkHSOpCVbrr9H0iuSdqvro3TvlbnGmZLOzCGXSDpG0kOlGnfKj2wJLAU8FRFVS8MqecbEzMzMzGqNu3Uc46ePX9RlzKXekt9vB7aMiOclfQE4Adhd0j8BR5P+kA7g1tzXUx3KODwiLqpof5K0H+TjDT7SUCW/nw98Nv88nrQ5/Yz8nteTvqtfddnXpyPimbxB/yLSXp0J+drJEXFS+eaIuD4PUhrN7njGxMzMzMxqjZ8+noFHBhZ1GWW9JL9fExHP5/abmLff5CPA1RHxZB6MXA3s0GthEfFY3s/y9wbPDFXy+6TISCeDlffYfAm4GHisSY2kSY2l+lVjK8+YmJmZmVlbI1cbyZR9pyy09w1x8vt+wBVt+loDIJ9idWZOOx9SGsLk97yEay/mnb61BrAraRD3ngb9/Io0KLyCeYM/gAMl7Q3cAnyli9mmWp4xMTMzM7PhpOfkd0mfJS3bOrFDX0TEmIUxKMnvGsrk99NJ+SLX59+/D/xnRLzSsMaPkI4zXpo0qIG0NGwdYCTwMPDdHmsEPDAxMzMzs+GlY/J7SzsAkrYHvg7sHBEvdehrocsDhQuAT+bfnyjVeRawRdM+JR0NrMqfC3SnAAAgAElEQVT8p21tCUyQ9AApjPJ0SV3tiYmIF0khkrvk3x+NiFfyscdnUXFUcxNeymVmZmb2D2CoNqkPPDLAyNVG9r3fQZib/A48REp+Hx0RIalIfp9AKfld0ruBHwI7RER5X8WvgP+WtFL+/cNUZ4cMmqTJwN4R8VCpTcA6EXFvVfJ7RDycb10g+T0iNuzwvjGkPTTb5YEDABGxdumec4HLIuLndf3mU8JWiIiH86BvJ+D6ihp3BWZ0/YVU8MDEzMzM7B9AsUl9MRtE9F1EzJFUJL+/HjinJfl9gqRvkU7i+lFuPxFYHvhZ+vufByNi54h4UtKxpMEOwDeL42172WMiaTXSXosVgVclfZkUXPgs7ZPfV8w/TwW+kK8dlJPn5+Tn9s3v6Cr5HTgT+BNwY/7Ml0TEN+tubtPvG4CJkpYmfd+/yX0DnCBpJGn52wPAf3RRVy0PTMzMzMz+QQzFJvVR547qa3/90EPy+/Zt+joHOKeivVFuSH7mESoS5iVtzMJPfu/4d35E7Nup34h4lJpN8hGxV6d3NOE9JmZmZmY2rPQQsLiNpNskzakKFJS0Yg4KPK2L178XGC/p/Ip+KgMWI2JGRFSmqrcJWDy5FFx4t6S/5b4ui4hTu/iOKgMWlZyav7tpkjbv1K+k4yT9WdKzLe37S5qea/ytpI1y+wGkJWlrV/VXxzMmZmZmZtbWwCMDi83MSY8Biw+SlkLVpbEfC1zbZQk3A0cNdcBiRBxS3CDpS8C7G/QJ9QGLOwLr5X9b5batOvT1S+A04J6W9vERcWaucWfge6R9PD+QdDkOWDQzMzOzfhm9yejFbd9KLwGLD0TENODV1s4kbQG8BbhqsIUNYcDinsBPG9ZSF7C4C/CTfOkm0klmq3fo66bSJvcFas/eUFN71zxjYmZmZma1xm4xlrFbjF2o7xzigMV575FeR8re2AvYruXa4hCwiKS1SEuiftNj3/MFLFIfKrnAwKPL/g8gHUe8FPPyTXrigYmZmZkt9obqKNx/JK+FE7myngMWK3wRmBQRf84nV817sIfN772KiM/lJWr/Hylg8cely3sAFzUNRCxpDVjs5XuqFRE/AH4gaTRwJOmY5p54KZeZmZkt9oqjcM3oMWCxxj8DB+awwZOAvSUd399yu9MasFiyBw2XcRVqAhaHKlRyAs321izAMyZmZmY2LAzFUbj/SBaXzekLQeOAxToR8ZniZ0n7AltGxNfqn+hd04DFfH0DYCXgxpa+eg5YJCW3HyhpAmkJ3NPF/pFu+m15x3oRUWyI/ygLbo5vxDMmZmZmZjZs5D0kRcDiLODCloDFQyXdS9pz8iMASe+RNBv4FPBDSTMX7Hl+ks6WtGWT2iStlt9zKHCkpNn5KOLX0T5gcTowHVgdKIcg7glMyBvYi3c0CVh8CylgcUDSUbl9EnA/cC9wFmk5W9t+JZ2QP9dy+TMdky8dKGmmpIH8mXtexgWeMTEzMzOzYaaHgMU/UBF82HLPucC5pd8XacBivn5MRfOgAhbzIOeAJv1GxFeBr1a0H1xxe888MDEzMzMz697TwLGSVikyPDqJiBnMv8+jZxHRKBtkUfQraWvSpvvHmzznpVxmZmZmNqz0kPy+dP793nx9ROmZI3L7XZI+0sXrNyP9Db3AhnF1SJiv+Sx1qeprSZqc09mnSGo741PR74H5c0VeplW0j5L0tOalyh/Vrp/8TF06fV2NfwFeARothfOMiZmZmdk/iMUpoX2o9Jj8vh/wVESsK2mPfN/ukjYibZ5/F/BW4NeS1u/iaN7P1OSbdEqYr1KXqn4SKQjxPEnbAt8m5ZF063ek5PUpFdeuj4h/a9BXZTp9XY0RcR8wsnWw1YlnTMzMzMz+ASyGCe1DpXHyOynt/Lz880XAdvn+XUiby1+KiD+SNoQvsEelW+0S5ts8U5mqDmwETM4/X5NrbVLL7RHxQJNn2vRVl04/qBpbecbEzMzM7B/AokhoHypDkPw+95mImCPp6Xz/GsBNLX2tASBpEjAmIvqR8dGLqaRMk1OAXYEVJK0cEU/0oe9/ljSVtOTqsNKpZrVq0un7WqNnTMzMzMxsOOkl+b3xMxGx0yIclEBaDvYBSbcDHyBltsxp/0hXbgPWiojNSEnzP+/moYj4HGm52yxSOn3fa/TAxMzMzMyGk16S3+c+k6+/kZQpMlQp6IMWEX+JiE9ExLuBr+e2p/vQ7zMR8Wz+eRKwZHlzfIdn50un73eNHpiYmZmZ2XAyN/k9n7q1BzAx53MUye8wf/L7ROaF/+0G/CbfPxHYI5/atTawHnDzUBQt6c7Od813/yo5mBHgCOCcXvtq6Xe1vFcESe8ljQeeyL9PlrRGy/2StG7xM6V0+nY19sIDEzMzMzMbNnpJfs//XTm3Hwp8Lfc1E7iQtG/iSuCA4kQuSZMkvbVJbapJmO8xVX0UcJeku0kJ7sd16qul34Nyv28Dpkk6O1/aDZiR95icCuwREaHe0ukra+yVN7+bmZmZ2bDSQ/L7i6TBQlVfx1HxB3VE7NRDXXUJ872kql/EvBPGuuqr5flTSQOP1vbTSMcTt9qIhun0bWrsiWdMzMzMzGxYaROwWBcqKEmn5mvTJG2e29eSdGsOGpwpaf8uXr82cIOk0yvq2lDSjZJekjQ3yyQiLssDharPcn7+LDMknSNpydK1UaXaru3UV0XfX8p9z5R0Qm77UP7M0/N/t839zoiIynR6ScdIekjzQhl3yu0jJL1Qaj8zt68j6f9Is0Bdhyx6xsTMzMzMFrpxt45j/PTxjZ9T+4DFulDBHUn7R9YjHS18Rv7vw8C/RMRLSmnmM3Jf7TbAXwNclmcLWj0JHMS8/JRunA98Nv88HhgDnCHpTcDpwA4R8aCkNzfoE0kfJOWKbJo/X/H848DHIuIvkjYmLYlbo66fkpMj4qSK9vsiYr4AnRywuIKkKU1q9oyJmZmZmS1046ePZ+CRgV4erQxYhLahgruQEsojIm4ind61ekS8HBEv5XuWZpB/G0fEY3k5198bPDMp1xWkjffFUrDRwCUR8WDRd8NyvgAcX3y+4vn8HRUDr5nAMpKWbtj3kPCMiZmZmZktEiNXG8mUfacs0N5jwGLTZ9YAHpb0duBy0sbvw4s/2vNm8TMj4pYOffdFXsK1F3BwblqfdJTvFGAF4JSI+EmDLtcHtpZ0HPAiKUjxDy33fBK4vTQ4a+dASXsDtwBfiYincvvaOcfkGeDIiLi+QY3z8YyJmZmZmQ0n7YIUGz8TEX+OiE1JA5N9JL0lt49ZWIOS7HTgutIf9ksAWwAfBT4CfEPS+g36WwJYibRZ/nDgwuKYYABJ7wK+A/xHF32dAawDjCQtf/tubn8YWDPnmBwKjJe0YoMa5+OBiZmZmZkNJ72EInZ8Js+UzAS27kONjUg6GliV9Md9YTZwZUQ8FxGPA9cBmzXodjZpKVhExM3Aq8Aq+X1vAy4F9s77QdqKiEcj4pV8QtdZ5JPPIuKliHgi/3wrcB9ppqYnHpiYmZmZ2ZAbd+s4Rp07au6/HveXQE3AYodnJgJ759O53gc8HREPS3qbpGUBJK1EOhb3rl4La0cV4YW5fQxpRmTP/Id/4RekpVhLSFqOtFxtVru+Wvwc2Dbfvz6wFPB43lR/OXBERPyupZafKIUutta4eunXXYEZuX3VfBgBkt5BOlzg/g511fLAxMzMzMyG3CA2u8+nXcBim1DBSaQ/mO8l/S/+X8zt7wR+n8MGrwVOiojpua+zmxx1m59ZLb//UOBIpcDEFduEFwKcSQonvDEfuXtU/pyzSKGP00ib4s+OiBkd+io7B3iHpBmkAwL2yRvsD8zPf6N0zG9xYtempOVZrU7IxwtPAz4IHJLbtyF9z1NJeSb7R0Snump587uZmZmZLRTlze6jzh3Vcz9tAhbrQgUDOKCi/WrSH+NV7xjTQ12PUBGwmI/lXSC8MD9T+/d4RJwInNjSXBmEWPHsy8w7hrjc/i3gWxU1rgjcExF/rnhmr5p3XAxc3K6OJjxjYmZmZmbWvaeBY7sMYwTahxc21c++Wvp9JiI+1a/+JF0DvIMGRyd7YGJmZmZmw4rqk9/XlvR7SfdIuiDvQSkS3icrpb5PyZu/i2deKS1p6rRXBeApYEXgrRV1rSzpGknPSjqty89yoqQ7c22X5j0g5etr5v4Oq+ujTd8LJL/n9iPyd3eXpI900c+5kv5Y+p5G5vbP5LqnSbpB0ma5fVnSiWCrAQ91W6+XcpmZmZnZIjHwyEDjJV1qn/z+HVJC+QRJZwL7kY66PYkUsHiepG2Bb5MyQwBeaE0u70JdCvqLwDeAjfO/blxN2og+R9J3gCOA/yy/C7iiYX21ye+SNiIdGPAu0uDq15LWj4hXOnR5eEXa/R+BD0TEU5J2BMYBW+VlZiMlPdCkZs+YmJmZmdlCN3qT0Yxcrel4AKhJfs8ZHduSNmEDnAd8PP+8ETA5/3wNOSm+3/LRvr8lDVC6feaqvKEf4CZKe1QkfZy0aX9mD+VUJr+TPvuEfNTvH0kHAixwEleXtd9QClqcr/ZeeMbEzMzMzIbEuFvHMX76eCDNjpQHImO3GMvYLcZWPtdj8vvKwN9Kf+QX6e4AU0kp56eQjrtdQdLKOYNjGUm3AHNIf8j/HEDSN4FbIqKb5V398nnggvz+N5BmTj4ENF7GRX3y+xqkQUSh/D21c1w+MWwy8LWKtPj96GFmp8wzJmZmZmY2JPp1RHCLuhT3donwhwEfkHQ78AHSvodiALNmRGwJjAa+L2kdgIg4amEOSiR9Pdd0fm76L9KSsWd77LIu+b3d91TnCGBD4D3APzH/UrNi2dh+re29FGxmZmZmNiSKI4IHczxwi7oU98eBN0laIs+azE13z6nunwCQtDzwyYh4unSNiLhf0hTg3aQE84VG0j7AvwHb5aONIc0C7ZY3rb8JeFXSixHR1aZ6SsnvwM2SiuT3uu+vVkQU2SYvSfoxpRkcSZsCZwM7FinwvfKMiZmZmZkNJ5XJ7/kP8GuA3fJ9+5DS05G0Sg4mhPS//p+T21eStHRxDyn5/Y6hKLpNqvoOpJmGnSPi+aI9IraOiBERMQL4PvDfxaBEg0h+ByYCe0haWtLapLT2m9v1q5z8nmdcPs685Pc1gUuAvSLi7q6/jBqeMTEzMzOzYSOfXlUkv78eOKdIfif9gT9B0reA24Ef5fZRwLclBXAd88IW3wn8MM8mvI60x+QO6H2PST6JakVgqbx5/cO5z7pU9dOApYGr09/93BQRtRkpDZPfz8nJ7y8zL/l9pqQLSQOwOcABEfFKh37Pl7QqaRnYAFDUdxRpb8/pufY5eVlcTzwwMTMzM7NhpU3y+/1UnDCVj7ltPeqWiLgB2KTmHUf1WNuI1rYOqerrdtHnMaVfB5X8nq8dBxzX0lzbb0RsW9PPGGBMuzqa8FIuMzMzMxtWeghYPLkUDni3pL+VnmkasPgvpNmXayvqqgxYbJeq3i5gUdKmkm7MAYnTJS3TbfK7pH0l/bX02ca0XF9R0kNFne36lXRMvrfoa6eW6/OFQOYlci8Aa5H2x3TFMyZmZmZmtlD0EqjYqpeAxYg4pPT8l0gb3AtNAxZvAK4a6oBFSUsA/0vavzFV0srA3xvUCXBBRBxYc+1YYIHBVRt1oZLQEgKZs02Wzcva/lbzzAI8MDEzM7NBK+dVDIXWDAwbfkZvMrpfXc0NWASQVAQsziJt9i5edB5wDCn5vWxP4Oh+FVMWEc8Bv5XUcXlW6ZmrSr/exLzN+x8GpkXE1HzfoE68KpO0BfAW4Eqg5z0hua8iBPK5wdblgYmZmZkNWpFX4cHD8DRUA8vy/0+0C1RsNQQBi6lfaS1gbeA3pebFMmCRFJAYkn4FrEpKaz+hYX+flLQNcDdwSET8OW9y/y6wF7Bdg74OlLQ3cAvwlYh4qg8hkPPxwMTMzMz6osirGAp9zMCwCsNsYNlLwGJhD+CiiHil1LZmRPxF0juA30iaHhH39br5vVcVAYtLAP9KCjV8Hpgs6daImNxll78EfhoRL0nanzSDtC3wRWBSHqR0W94ZpKVfkf/7XdIgam4IZIO+anlgYmZmZmZDMrAcogFl44DFkj2Yd1QwsFgHLM4Gro2Ix/M9k4DNga4GJi1Lv84i7b8B+Gdga0lfBJYnHWv8bER8rbWPUl+Plmo9C7gs/zrYEMj5+FQuMzMzMxtOGgcsAkjaAFgJuLHUttgGLJJyWjaVtFzeCP+Bora6vlr6Xb30687ALICI+ExErJmPNT4M+EkxKGlTY7mvXckBi+1CIHvhGRMzMzMzGzZ6DFiEtOl9QmlGAhbjgMW8h+N7pIFYkJZfXZ6fqeur7CBJO5OWhz0J7NtF+XX9niBpZK7jAeA/uuirMQ9MzMzMzGxYaRqwmK8dU9G2WAcsRsT/ko4M7qqvlmePIB093O6ec4Fzu6hxr3b95HuO6XRPJ17KZWZmZmbWvWeBsXlGpSvtAhab6mdfQ9WvpGUlDQBLAq92+5wHJmZmZmY2rLRJfj8wt0XeM1K0fyYnq0+TdIOkzTr11cbywBtq6qpMfu/wWSqT33PNA6V/r+blVF2T9GlJd+Tk+PG57YMt/b6Yl5y166c2RT6nvl8laVZ+14iIeAGYCSxLOgmsK17KZWZmZmZDph9p72Udkt9/RzoxakrLY38EPpD3bewIjAO26tBXO3Up6H1Lfo+I88lHB0vaBPhFRAx02SeS1st9vT9/7jcDRMQ1wMh8zz8B9wJX1XY0T12K/E+A4yLiaknLk2dIIuIzks7ttl7wwMTMzMzMhkgf097LKpPfgTsi4vbcNt8DeS9J4SbSUcJt++qlsD4nv5ftCfy0YTn/DvwgIp7K73ms4p7dgCtaTgPrmqSNgCUi4ur8jmd76afggYmZmZmZDYkmae9lPSa/d2s/4IpOfS0Gye9lu5MGTE2sDyDpd6TTy46JiCtb7tkD+F6X/S2QIp/f8TdJlwBrA78GvtYSYNk1D0zMzMzMbDjpJuG9+kHpg6SByb926msxSH4v2rcCno+IGQ27XAJYDxhFmiG6XtLGEfG33O/qpBPJftVFX3Up8ksAW5NCKR8kDar2Zf5jmrvmze9mZmZmNpzUJb+3JWlT4Gxgl1Iqek999Vsp+f0zLTkrkGY1mi7jgvTZfhERf4+IPwJ3kQYqhU8Dl0bE3zt1FBFPRMRL+dezgC1K77g9Iu6PiDnAz0np9D3xwMTMzMzMhpPK5Pd2D0haE7gE2Csi7h5MX73qIfkdSa8DPgVM6KavFj8HPpjvX4W07Or+0vUF9q1I+rakXStqrEyRJ31/K0laNf++LT3uzwEv5TIzMzP7hzfu1nGMnz6+9vrAIwOMXK3RSbSLTLvkd0kHAV8FVgOmSZoUEWOAo4CVgdPzxvg5EbFlh74WafJ7vrYNMLvYnF/STfL7r4APS7oDeAU4vJgpkjSCNFN0bcszm1A9MKtMkY/4/9m703C5qjL9/99bmQkIrUKcABGQKRgMoKJAGEQcGkRBIIBEwAgOqDS28sOpQf4yqKidVqBpQGwiIhAaQSYjQWQQgpwMEBTQiAFjZDbMSZ7/i7XqZKeyq2rvOnVOcvD+XFeunLN27bVXVd7UylrruWORpOOAKUqDv5O0otIVT0zMzMzMXuImzZw0rCYfnbRJfv8+8P2S9iOBI5vbO/S1IiS/TwXeXrWvpnsDODb/ab42h3Twv9nKEXFryetbpsjnilzbtBtLVd7KZWZmZvZPYPTI0UwdP7X0z3CbsHQRsLi5pFslPZ//h7/Rvpqk2yVNzyGE/1Hh8TsC35TUvNrQMmCxXaq6pP3zsxdL2q7Qvoqk8yTNzOMb26mvkr6XCVgsXFtb0kNN43xPh/6OK/lsx+bQxbsbn4lS8vtjwGHAKlXGCl4xMTMzM7PlpNMWszJdBiw+BhwDNCecPw/sFhELJK1MyiC5OiJuazOEW4DrehiwOAv4EHBWU/vHASJiVA5HvFrS9hGxuEqnrQIWC05i2a1c7fp7A+kzf7DQtg7wA2CviHiwEOL4LPAveVtblfBGwCsmZmZmZracNLaY1dQfihgRL5AOhu8DEBF35W1KS4mI+RFxB/BiU3sUQgFXzn8qlR4uExFPR8RvSBOUqvfMjojfl1zaEpiSXzMfeALYruR1rbQMWJQ0BlifGpMG4AzS+Z3i5zMOuCwiHmx+Rje8YmJmZmZmy01ji1nRIAcsLv2stAJzJ7AJ6Yv8b3P78ghYLJoO7JPT6N9AKtH7BuD2iveXBizmSl/fBg4Fdq/SUT74/lBETM8H9IvPWFnSVGAt4HsRcUHF8S3DExMzMzMzG066Dlgsk1PKR+dtSZNzCOGsoQ5YLHEusAUwDfgzaQvZwhr3lwYsAocAv4iIvzRNMkpJWgM4AdizxTPGkCY4qwO3SrqtqSRzrQGbmZmZmQ0XgxKKGBFP5P/534t07mO5yoGFn2/8LukW4L4aXcwllR5+EfiTpEbA4juAnSR9EhhBKmu8ICK+1KKfNwFvBBqrJa8HfpdzVOYCj0TE08DTkn4NvAXwxMTMzMzMVjytDrl3WcK4PxQReIgUijium3HlYMAX86RkdWAP4NRu+qrwrAuAiRFRaStWXqlQRDwt6d2k7JV7avR1OSlE8fxiwGJEHFx4xnhgu8akpKzfiJgJrFe4Z06+5xFJ/wdMlLQSqfrW20hnUbriw+9mZmZmNqi6POReKq8kNEIRZwMXFwMWJc0l/a/+DEnn5PaRuf1Y4MuS5uY8kNcAN0iaQZrwXB8RV+Z7TsxnK2rJX9y/A4zPz9kyXyoNRZS0bx7bO4CrJF2bL61HWpmYTUqGP7RwW9WAxUdzwOINFAIW26jSb7+ImA1cA8wgnX05JyK6Xm3yiomZmZmZDbqyQ+5jzx/bVV9dBCzOI01Wms0Atm3xjKEKWJwMTC5pnwO8uU5fTfe3DFgsvOZ84Pya/W7U9PvpwOnt7qnKKyZmZmZmZtUtACbkql2V1AlFHMq+BqvfHLDYRyq/XCl3BTwxMTMzM7Nhpk3y+xsl/VbSfZJ+KmmV3L6hpCmSZkiaKun1hXtOlTQr/zmgwuNHAGu2GFdp8nuH93K6pHvz2Cbn6mCNa8fn9/h7SW1T2Vv0/Zl8792STut2jIX+lkp+l7RuHvMMSbfnql9Fr6bGfMNbuczMzMxsuemb11drS1eH5PdTgTMi4iJJZwJHAD8EvgVcEBE/krQb8E3gUEnvB94KjAZWBW7Mye9PdRjGGT1Mfr8eOD4iFko6lZTW/sV8NuVAYCvgtcAvJW2Wyxt3JGlXUvDkNhHxfCH5vZsxlia/A/8P6IuIfSVtTvp32T0nv4/O520q84qJmZmZmS0X40aN66YqV2nyu1It292AS/LrfgR8MP/cn6JOOgi+T6H9xohYmEveTieVC+5Kl8nv1+UD/QC3seQszD7ARRHxfET8Cbif9N6rOho4JSKez8+Z3+0Ys7Lk92I6/b3ARpLWr9lvP6+YmJmZmVntlYu6fZdNQCaMmcCEMROWae8y+f2VwBOFL/lz82shTTg+DHwP2BdYS9Irc/vXJH0HWAPYFWiU5F0eye+HAz/NP7+ONFFpKL6fKjYj5ZWcTJqEHBcRd3QzqDbJ79OBDwG/ybkmG5ImVn/r5jmemJiZmZn9kxs3qqsYkOWlVfJ7u0T440h5G+OBX5PyTxZGxHWStielqv8duJWcrj7Uye+STsjPvrDRVPKyOgn3KwHrAm8HtgculrRxrtZVZ1ztkt9PAb6XD7rPBO6iXjr9MgM2MzMzs39irVYueqXHKzGtkt8fAdaRtFJeNelPhI+Ih0n/s4+kEcCHI+LJfO1k4OR8bRL10tV7QtJhwAdI5zMaE4eBJtzPBS7L/d0uaTHwKtIErI6Wye+5DPPH8nsQ8Kf8pys+Y2JmZmZmw0l/8nuuunUgcEX+An4DsF9+3WHA/wFIepWkxvfe44Fzc/vL85YuJG1DChi8bjAGLemCvN2puX0vUoDi3hHxTOHSFcCBklZVSrnflBRiSK4w1mlb1+WkMzdI2oyUzP5I3TFGxMyIWC8iNsoZJnOBt0bEPEnrNCqfAUcCv65QOKAlr5iYmZmZ2bCRq1c1kt9fDpzbSH4nfcG/SNI3SNuK/ie3jwW+KSlIW7k+ldtXBm7KKwFPAYc0zqh0e8YkV6JaG1hF0geBPXPFsFap6hNJFcGuz+O4LSKOioi7JV1MOvOyEPhURCzKE6xNgMc6DOVc4FxJs4AXgMMaqzFdjLGVLYALJC3K4zyixr3L8MTEzMzMzIaVNsnvf6SkclVEXMKSal3F9udIlaXKnjFUye+btOmrf5tZwZbApbkkb7txvAAc0osxtro3Im4lreT0hLdymZmZmdmw0uOAxWskPSHpyoqP35G0+nJjybhKwwvbpapL2j8HIC6WtF2hfWVJP5I0U9JsScfnvmZFxLEVPqOvS3pIUl/+877cvoqk83K/0yWN7TTGQp/NAYubS7pV0vOSjiu8bl1Jz5KqdK3Tqr9mXjExMzOzYWEwy9m+1LUq1zvUY+jFv596GLCYuzydVCr4ExWHcAtwXQ8DFmeRDuaf1dS+P7BqRIzKlbHukfSTiJhTsV8oD4L8OEDudz3gaknbR8Tidh21CFh8DDiGJXkx5L4fB1bPW8aeqDpYr5iYmZnZCq/LID5bQfT436+XAYtExBTgH70YWJcBi7Mj4vdll4A1Ja0ErE46J9L1wfKCYijifNLEYbu2dyTLBCxGxPycjfJiD8blFRMzMzNb8Q12OduXuuW90lT332+oAhYj4tGWY1g+AYtFl5AmUH8lreh8PiI6HXhv9mlJHwWmAf+WVzKmkyZyF5HKEY/Jf9/eqpM2AYs95RUTMzMzMxtOug1Y3EXSXcAu5IDFdg+JiK8ux0kJpJWhRcBrSTki/yZp4xr3/5CUQTKaNLn5dm4/lzRpmwZ8lwF7X6kAACAASURBVLQ1reVnUQhYHPTASa+YmJmZmdlw0tOAxRXYOOCaiHgRmC/pZtKWqz9WuTki/tb4WdJ/A1fm9oXA5wvXbqF9qGSngMWe8YqJmZmZmQ0nPQtYHEqtAhbbeBDYTcmawNuBe3NfHQMWJb2m8Ou+pEP2SFoj94ekdwMLc+GA2gGLNd5LJV4xMTMzM7Nho8cBi0i6CdgcGCFpLnBERFw7VAGLkvYF/hN4NXCVpL6IeA+p8th5pAmFgPMiYkaNgMXTJI0mbWebw5KqY+sB10paTNrSdmjhnloBi5JGkraErQ0slvQ5YMtu0989MTEzMzOzYaVXAYv52k4t2ocqYHEyMLmkfQGpZHCzqgGLh7ZonwO8uc4Ym+7fqPDzPNLWrp7wVi4zMzMzs+oWABPyikolVcILa/RVKWCxi357NkZJq0vqA1YG2uajFHliYmZmZmbDSt3k93ztI5LuySnrkwrtp+W22ZK+r871cLcmfeF+uGRcpUnoHd7Lp/P76E9Uz+3rSpqslFZ/u6SqgY3N/Tentb9C0s9z6vvdkj5WoY+p+fNupMivl9s3zOddZuTXNFZPXpv/fkWdEseemJiZmZnZcnf2nWcz9vyxHTNXCsnv7yVtazpI0pb5ciP5fVPgcVLyO5I2JR16f2dEbAV8LrfvCLyTdLZia2B7UjnhTr4QEWeWtDeS0MtS4Vu5GdgD+HNT+/8D+iJiG+CjpAyWWlqktX8KuCci3kI6e/Pt4gSujYMjYnT+Mz+3fQu4II/xROCbABHxQETUTtT0xMTMzMzMlrtJMyfRN6+vyku7SX7/OPBfOWCQwhfrAFYDVgFWJa2E9JfZraubJPSIuCuf+2hWTGi/F9hI0vo1h7RMWnv+ea38eY0gTabaZrq00T9GUkW0fbrsB/DExMzMzMxWEKNHjmbq+KmdXlaW/P462ie/bwZsJulmSbdJ2gsgIm4lfaH+a/5zbUTMBpB0jqTtBv6uujadJdkrOwAbUuOgeTGtvenSRGAL0la0mcBnI6LKOZDz8jaurxS2u00HPpx/3pc04Xll1TE288TEzMzMzIaTbpLfVwI2JW1dOgg4R9I6kjYhfUl/PWkSs5uknQEi4siImNbjsddxCrBuPkT+GVL540orGx3S2t8D9JHOgYwGJuaKXO0cHBGjgJ3yn0bFr+OAXSTdRdoC91DVMZZxuWAzMzMzG05qJ7/ne27LKep/kvR7lkxUbsuleZF0NSnI8NdD8UbayVkgHwPIKxR/yn+qaJnWnvs8JQdS3i/pT6Qcl9vbjOWh/Pc/cuGAHUhnSx5myarOCODDEfFk3ffa4BUTMzMzMxtyxcPuY88fW/V8CXSR/A5cDuwKKQWetLXrj6RD4btIWknSyqT/9Z/dg7e3jCpp7U2vX6dwKP1I4NeN4MJOfXVIa38Q2D33sz4p0+SP+fd7S8axUqGi18rAB1iSIv+qHPgIqbjAuVXfXxlPTMzMzMxsyNU47L6UvBrSSH6fDVzclPx+rKT7SWdOGsnv1wKPSrqHNHn5QkQ8Sjoo/wDprMV0YHpE/By6O2MiaaRSevyxwJclzZW0dru0dknH5HteD8yQdE6+tAVwd54svBf4bH591eT3Vk4CdpQ0k3Rw/YsR8UiefJRth1uVlBQ/g7QF7CHgv/O1scDvJf0BWB84ucsxAd7KZWZmZmbLSfGwe6cywUVdJL8HabJwbFP7IuATLZ5xZOUBLbmnNAk9Z5CUprVHxPeB75e030rabtasUvJ7U18bFX5+GNiz5GVvJ5Vhbr73aWBMi34vYUkVtAHziomZmZmZDSvqbcDiokJw4BUVHr858BNJV5WM65WSbpC0QNLERnu7tHZJ++cxLS6u0EhaWdKPJM1UCn88vlNfLfqvFLAYEVfmSVJZH7UCFiXtKulZYI2q4wRPTMzMzMxsBdE3r29IAxazZwvBgXtXGOY1wPER8f6Sa88BXyFVq6pqFukAefOB+/2BVXM1rDHAJyRtVKPf5RmweENErA48U2e8npiYmZmZ2XI3btQ4Ro+sFBbey4DFnoqIpyPiN6QJStV7ZkfE78suAWtKWglYHXgBeKrmkBywaGZmZmZWx4QxE5g6fuqQBixmq0maltsbExkknagUUri8XAI8TQp+fBD4VkRUPvCuYRiw6MPvZmZmZv8EqmyTGkp98/qqrpA0G2jA4uuBmyRtHRFPABtExMOSNgZ+JWlmRDwQEWXhhENpB2ARKQhxXdKYf5kP+LelJQGLZYfcGwGLu5HyTq6XdFOjFHELB0fEQ5LWAi4lBSxeQNqyNlHSeNJWtAEFLHrFxMzMzOwlrsY2qeGgY8BiU3vjnv+LiBcj4k9AI2CxUaWqUdFrKrDtYL+BisYB1+QxzwduBqqWLy4GLM5hScDiSFLA4mWR3E8Kbdy8XWfFgEWgEbBIRDwcER+KiG1JEyEGErDoFRMzMzOzl7gJYyYwYcyE5T2MpQxg9aY/YJH0P/QHAuMiIiQ1AhYvYtmAxYOA84sBi5LWBZ6JiOdz+zuB07odWDuSLgAmRkTLhPUmDwK7SfpfUnWrtwPfzX1NAT7amDA0i4iZwHqFZ88Btst5JY2AxZvKAhYjYqlJSp7orZPvbQQs/jJfexXwWN4K5oBFMzMzM/vn0eOAxS2AaZKm5/ZTIuIe6P6MSZ4EfAcYnwMWGxXDtiGdF2l+/b45YPEdwFWSrs2X/ot0OH0WaTJ2XkTMcMCimZmZmdkKoocBi7cAo1o8o6szJsUwwwZJawP3RcRfSl4/GZhc0r6AVDK42Us2YNETEzMzMzOz6hYAEyStXXXykg+Wl00yaouIWTRNsHrU75W96kvSm0iH5P9W5z5v5TIzMzOzYaXHye/XSHpCUtUv5iOANVuMqzT5vcN7aZX8voqk83Ly+3RJYyuOr7n//XLye7HvbSTdmp87U9JqHfr4uqSHCsnv78vtG0l6ttB+JkBEPEAKuFy/+NxOvGJiZmZmZstFNyWMC8nv7yZV27pD0hX5bEgj+f2i/CX5COCHTcnvj0tar9Dl6aTD5Z+oMYwzIuJbJe2N5Pet858qGsnvZzW1fxwgIkbl8V4tafuKmSMA5PK+xwC/LbStBPwvcGhETM+5Iy9W6K7Ve34gIpYp+RYRu0qaWnWs4BUTMzMzM1sOBlDCuKfJ7xExBfhHd+9iaT1Ofu9PVc/jfYLq5YIbTiJVGSuOZ09gRiN4MSIejYhFNfsdFF4xMTMzM7Mh166EsT5WVhyqX1ny+9vonPyOpJuBlwNfj4hr2j1E0onAtIi4ov07GTTTSROui0i5LWPy35XKDUvaFnhDRFwp6bjCpc2AyNW/Xg1cFBFVSiR/WtJHgWnAvzUmecAbJd0FPAV8OSJuqjK+Mp6YmJnVtKKlJ5utCAaQ4m1WV6+T30utAMnv55LLGQN/Bm6hYqp6Lil8BjC+5PJKwLuA7YFngCmS7swrR638kLT6EvnvbwOHk8ofbxARj0oaA1wuaasOKfIteWJiZlbDuFHjlvcQzMz+2XVMfs+rJs3J77dFxIvAnyQ1kt/vGLph15Pfw+cbv0u6Bbiv4u1rkc64TE073BgJXJFzWeYCN0bEI7nfXwBvJW8bazGW/upakv4buDK3Pw88n3++U9IDpBWZaRXHuRRPTMzMalgR05PNVgReRbQh1LPk96EcdN3kd0lrAIqIpyW9G1hYCH9s21dEPAm8qtDXVOC4iJiWJw//nvt/AdiFtLrSsl9Jr4mIRjjkvqQD+0h6NSn5fZGkjUmTva4/V09MzMzMzGzYiIiFkhrJ7y8Hzm1Kfr9I0jeAu1g6+X3PnPy+iCXJ70i6CdgcGJET2I+IiGu7PWOSk9/XBlaR9EFgzzyhaJn8Dvwn6bzHVZL6IuI9wHqkxPXFpAnYoYXbSvuqIlcl+w5pghfALyLiqg79niZpdH79HJZUMNsZOFHSQtLnelREdJtI74mJmZmZmQ0vvUp+z9d2avGM5Z38Pgd4c52+2oxpbNPv/0sqGVx1jIc2t+X2S0lBij3hcsFmZmZmNqzUDViUdGwOV5whaYqkDQv31A1Y3BH4pqQbS8ZVGrAYEU9FRGnyu6TTJd2bxzZZ0jq5vVV4Ycu+mvpt+Z7z9bVzaOLECmP8aWEccyT15fYdCu3T8+oPklaX9A/SNrHSMMoyXjExMzMzs547+86zmTRzUucX1tRNwCJpW9d2EfGMpKNJ2R4H5C7rBizeAlzXw4DF64Hj8xa1U0lBkF/M10rDCytq954hVddaZnJVJiL675P0beDJ/Ous/IyFkl4DTJf084h4Flgrb2u7e5kOW/CKiZmZmZn13KSZk+ib1zcYXdcOWIyIGyLimdx+G6liF/na8g5YvK6QvbLU2AY4lpbvOZf2XR+4rk6f+TP+CPCT/IxnCmNfjSXlmbviFRMzMzMzGxSjR45m6vipte8bhIDFoiOAqzuOYfkELB4O/LTwe6/CC/vfc844+TbpMP3uNfvZCfhbRPSXLZb0NlLmyobAoYXPvzZPTMzMzMxsOOkmYDHdKB0CbEc6+9DWUAcsSjqBFKB4YW7qSXhhyXv+JKkS119yxkkdB5FXSxoi4rfAVpK2AH4k6eqIqLxiVOSJiZmZmZkNJ90ELCJpD+AEYJccDLjCkHQY8AFg91xBrCfhhS3e8zuAnSR9EhhBKmu8ICK+1Kqf3NdKwIeAMWXXI2K2pKdJZ2scsGhmZmZmQ6vVIfe+eX2MHtntue22agcsStoWOAvYKyLmD8agOmkTXrgX6bD7LoUzIW3DC6uENbZ6zxFxcOE140mH179Uod89gHsjYm7h/jcCf8mH3zcklTeeU+XzKOPD72ZmZmbWtUE85F4qr4Y0AhZnAxc3BSweK+l+0pmTRsDi6aTVgZ/l0rb950ZywOLPgN0lzZX0ntx+oqS9644vV6L6DjA+97dlvtQqvHAisBZwfbEsMCm8cIak6aQD/cXwwioBiy3fcxvt+j2Qpm1cwLtIlbj6SFksn4yIRyo8p5RXTMzMzMxsQMoOuY89f+ygPa+LgMU92vS1vAMWN2nRT2l4YdWAxXbvufCa84Hzq/QbEeNL2n4M/LjTc6ryiomZmZmZWXULgAm5alclVUMRh7qvweo3Byz2ASsDi6ve54mJmZmZmQ0rXSS/7yzpd5IWStqv8PpdC8nlfZKek/TBDo8fQYs0c7VIfu/wXkqT3/O14/N7/H1ji1lVrfrtZoz5vs/kcdwt6bRC+zaSbs3tMyWtlgMWHwdeAWxc9RneymVmZmZmg6JvXl/Pt3R1mfz+IDAeOK7YV0TcAIzO/f4LcD/VQgfPGOzk93w25UBgK+C1wC8lbRYRiwbSbzdjlLQrsA+wTUQ8L2m93L4S8L+k/JLpkl4JvAgQEbtKmlpxrIBXTMzMzMxsEIwbNW6wqnJ1k/w+JyJm0H5b0X7A1cXKWHX1OPl9H+CiiHg+Iv5EmjQtc36mbr/djBE4GjilUXK4UOVrT2BGREzP7Y/WmDgtwysmZmZmZtZzE8ZMYMKYCV3dO8jJ760cSKqmlcaw/JPfX0eaUDTUfT+t+u3GZqTsk5NJE5rjIuKO3B6SrgVeTZpIndamn7Y8MTEzMzOz4aTr5PeWHUqvAUaRShCnG5d/8nvX76dDv91YCVgXeDuwPXBxzlZZiVQyeHvgGWCKpDsjYko3D/FWLjMzMzMbTjomvze1V/ERYHJEvNizUdZQSH4/uJH8Tuv3OdB+uzEXuCyS20lb4l6V22+MiEfyFrhfAG/t9iFeMTEzMzMzoHWKezuDmPDeSu3k9woOIh0OHzR1k9+BK4BJkr5DOvy+KXB7vmcK8NGIeKjN81r1W3uMwOWk8ztTJW0GrEKaCF4L/LukNYAXgF2AM6o8q4xXTMzMzMwMGPoU9250k/wuaXtJc4H9gbMkNV6PpI1IKxM3Fp+zvJPf83u6GLgHuAb4VEQskvQyYBPgsZK+Ovbb5RjPBTaWNIs86curJ4/nfu4A+oDfRcRVHcbVkldMzMzMzKxfWYp7O4OZ8N5KF8nvd7Ck2lXztTmUHCpf3snv+drJwMlNzVsCl+askHbjaNdv3TG+ABzSoq//JZUMHjCvmJiZmZnZsNKrgMV8bVEhYLFKBa4dgW9KurH5Qqvwwnap6m2CEN8t6c4cWninpN1yX7Mi4tgKn9Gxku7J/U6RtGHh2mH5M7ovn0PpNMafFj6jOUqp7khaRdJ5eYzTJY0t3PM4sDNpC1olXjExMzMzsyHVzVmWhl4GLGbPRkSdQzK3ANcNdsAi6QzHv0bEw5K2Jm1dq1Mu+C5gu4h4RtLRwGnAATlI8mvAdqQqX3fmz+/xVh1FxAGNnyV9G3gy//rxfH1UDl28WtL2EbE4ItbNAYv3VR2wV0zMzMzMbEgN8CzLYAUsDlgvAxYj4q6IaFThuhtYTdKqNfq9oXDovRjc+B7g+oh4LE9Grgf2qtJn/ow/AvwkN20JTMnPmw88QZrwdMUrJmZmZmY25NqdZRnigMXVJE0jZX2cEhGXwwoRsFj0YeCuRvJ6F44Ars4/l31+VVdidgL+FhGNVZDppEnhRaQCAmPy381VvSrxxMTMzMzMhpNeByxukLdLbQz8StLMiHhgBQhYbLRvRdqitmeX/R5CWsXYpdFU8rKqGScHsWS1BFK1ri2AacCfSdvcFpbcV4knJmZmZmY2nHQMWMyrJpUCCRvbpSLij/lMxLbAA70edDuFIMTdi0GIkl4PTCZlltQek6Q9gBNIWSaN1Za5wNjCy14PTK3Q10rAh0irIkB/6ebPF15zCzXOlDTzxMTMzMzMBqRvXl+tssEDDGXsWcCipHWBZyLieUmvAt5JOiTec3UDFnN1rqtIB+NvrtJX02u2Bc4C9srnPxquBf6//N4hrcQcX6HfPYB7I2Ju4RlrAIqIpyW9G1iYixB0xRMTMzMzM+vauFHjhvR5uXpVI2Dx5cC5TQGLF0n6BqkqVX/AImnlYV3gXyX9R0RsRdqGdJakxaSiUKc0vlh3e8YkhxeuDawi6YPAnrnPdgGLq5KCEAFui4ijSCGSmwBfkfSV/No98ySjVV9FpwMjgJ/lfh+MiL0j4jFJJ5EmeAAnRkQjrLFdvwey9DYugPWAa/Pn9xBwaIcxteWJiZmZmZl1bcKYCUwYM6HWPQMNZexVwGJE3AKMavGM5RqwGBHfAL5Rp6+m+/doc+1c0vmQyv1GxPiStjnAm9uNow6XCzYzMzMzq24BMCGvqFTSLrywrl72NZj95m11GwMvVr3HExMzMzMzG1a6SH5fNf9+f76+UW4vTWrvYGtgZUoO1kvaXNKtkp6XVBbmWPZeLszvZZakcyWt3G1fTf3uL+luSYslbVdo30jSs4Uk9zMr9DVa0m359dMk7dBujJJWJ22bG0na4lWJt3KZmZmZvcR0m6w+wEPptZ/VzZauLpPfjwAej4hNJB2YX3cA3SW1A3whIi4paX8MOIYc7FjRhcAh+edJwJF5zN30VTSLVEXrrJJrD9RMuz8N+I+IuFrS+/LvY1uNMSKeBUbn8zaVecXEzMzM7CVmgMnqg27cqHEDmQDVTn4H9sm/k6/vLkndJLW3ExHz83mWytuXIuIXkZGCCRvJ77X7aup3dkT8vpt7y7ojHegHeAV5tWigY2zmFRMzMzOzl6B2yeqtDPRQelWdDswPQvJ7/z25qteT+fWPtByDdA5wZkRMa/tmeiRv4ToU+OwQPO6Nku4CngK+HBE3dXj950jVt75FWtjYcTAG5YmJmZmZmQ0n3SS/1047j4gja45roH4A/LrCJGGg/kpKu39U0hjgcklbRcRTbe45Gvh8RFwq6SOkMswtq351y1u5zMzMzGw46Zj83tS+1D35+itI5yNWCJK+BrwaOHawnxURz0fEo/nnO0kp95t1uO0w4LL8888oKcncC56YmJmZmdlw0p/8nqtuHQhckc9oNJLfYenk9yvy7+Trv8qvHzKSpkh6XUn7kcB7gIMiYvFA+qp476tzAQEkbQxsCvwx/35Bo+JWk4eBXfLPuwH3dfPsTryVy8zMzMyGjW6S3/PfP5Z0P2ml5MBGf62S2rs5YyJpJDAt97dY0ueALUnZJ5tQvkpzJvBn4Nac0H5ZRJzYZV/FsewL/CdpJeYqSX0R8R5gZ+BESQuBRcBRFZLfPw58L682PQdMaPd+O2wLa8kTEzMzMzMbVrpIfn8OKA0PLEtqz+21z5hExDxKEuYlbQ1cmsvoNt9T+n28m76a7p8MTC5pvxS4tKTfdun0vwHGVB1jt7yVy8zMzMyGlS4CFo+VdI+kGXkb1IaFexYVwgavqPD4zYGfSLqqZFylgY0RMSsiSs+PSDpd0r15bJMlrZPbV5F0nqSZkqZLGtupr6Z+273n03L44mxJ38+lk1smv7cJWDw49z9D0i2S3pLb15X0LLAhsE6nsTZ4xcTMzMzMBk23YY+tdBmweBewXUQ8I+loUkDgAbnLZ2uGDV4DXBIR3yq51k1g4/XA8XmL2qnA8aQtaR8HiIhRktYDrpa0fdVzKLR4z5J2BN5J2rYF8BvS+ZGpbfpqFbD4J2CXiHhc0nuBs4G3RcTjwOp5m9wTFcfrFRMzMzMzGzyDEPZYO2AxIm6IiGdy+230cPtRUTeBjRFxXSF7pTi2LYEp+TXzSV/wt6vRb6v3HMBqwCrAqsDKwN86dUd5wOIteRLS/IyueMXEzMzMzAZV3bDHQQhYLDoCuLrw+2qSpgELgVMi4nIASScC0yKiyvauXjkc+Gn+eTppwnURqdTxmPz37V302/+eI+JWSTeQDrkLmBgRszvcXyVgsflzrc0TEzMzMzMbTroJWEw3SoeQVh12KTRvEBEP59K5v5I0MyIeiIiv9mzEFUg6gTQ5ujA3nQtsQap69Wfglny9br9LvWdJm+R+G6sb10vaOSJ+3aabtgGLknYlTUzeVXd8Rd7KZWZmZmbDSTcBi0jaAzgB2Dsinm+0R0RjW9IfSecsth3MwZeRdBjwAeDgRr5KRCyMiM9HxOiI2Id0iLxWfkiL97wvcFtELIiIBaRVjrd36KplwKKkbYBzgH0awY3d8oqJmZmZmfXrm9fH2PPH9rS/0SPrnC3vqD9gEXiIlEkyLiIib1Haj3TupD9gUdK2wFnAXvm8Brl9XeCZiHhe0qtIh8JP6+VgC8+6gLRt6vam9r1Ih913KZwJQdIagCLiaUnvBhbmA/4t+2rqt/Q9Aw8CH5f0TdIq0y7Adzv02whYnEohYFHSBqQJy6ER8Yc6n0cZT0zMzMzMDIBxo8Yt7yF01GXA4unACOBnOcTwwYjYm7Sl6SxJi0k7iU4pfPnv6oxJq8BGWocXTiQdQr8+j+22iDgKWI90rmMxaQJ2aOGeVn0VtXrPl5AmFzNJW92uiYifd+i3NGAR+CrpbM8P8jMWRkTlA/rNPDExMzMzMwAmjJnAhDETOr+whl6uvjR0EbC4R3Nbbr8FGNXiWldnTMoCGzuEF27Sop85wJvr9NV0f6v3vAj4RM0xtgpYPBKoHUTZiicmZmZmZiuYgWZ/DML2KVtiATBB0tpVJy8R8RQtkufr6mVfg9WvpNWBW0mliKvmrvjwu5mZmdmKZhCyP15SepX8LmlDSXfmRPO7JR1V4fFbk75wP9x8QdLmkm6V9Lyk4yq+l//Jye4zJF0iaURu31nS7yQtlLRflb6a+r0wf0azJJ0raeXcPlbSk1qSdt9xciXppsLrH5bUKKm8Tx53IxG+UZXrtfnvV0TEY1XH7BUTMzMzsxVQ3eyPosHYPjUQvTxQ3+Pk978CO+bD7yOAWbmvZSYdTb4QEZeUtD8GHEMOdqzo83m1AknfAT4NnEI6pD4eqDTBKXEhcEj+eRJpy9UP8+83RcQHqnYUETs1fpZ0KbmoACkA8opceGAb4GJg84h4ABgtaUGdAXvFxMzMzMwGzbhR43q9raxnye8R8UKhjO6qDPC7cUTMj4g7gBdr3NOYlAhYnZy9EhFzImIGNbZCNfX7i8hIoYwDTruXtBbpM748P2NBo7wxsCZNuTF1ecXEzMzMzAZNNwfqhzL5XdIbgKuATUgrIQ/n9nOAMyNiWq3Bd0HSecD7gHuAf+tx3yuTKnp9ttD8DknTSdvRjitUNetkX2BKYzKV+98X+Capitj7BzJWr5iYmZmZ2XDSi+T30/tfEPGXiNiGNDE5TNL6uf3IoZiU5Gd9jHQuYzZpi1kv/QD4dUTclH//HbBhRLwF+E/y6kdFBwE/KTZExOSI2Jy0OnXSQAbqiYmZmZmZDSc9TX5vyCsldwM7NV8bCrmM70+BD/eqT0lfA14NHFt4zlM58b1RdnnlHC7Zqa9XkrbRXVV2PSJ+DbypSl+teGJiZmZmZsNJf/J7rrp1IPkANtBIfofy5Pe9m5LfX59L2zZS4N8J/H4wBp2rgb2uqU2SNmn8DPwrcG+Fvqq85kjgPcBBEbG40D4yPwtJO5DmA4+2GmPB/sCVEfFcoa9NCn29FVil0Vc3fMbEzMzMzIaNQUh+/7akxlawb0XETOjujImkkcA0UvL7YkmfA7YkZZ9sQqratdQtwI9yuKGA6cDRua/tgcnAusC/SvqPiNgqr0i0PYSTnQn8Gbg1v+fLIuJE0sTtaEkLgWeBA3NVrZe1GGPDgaRqYUUfBj4q6cXc1wGFw/C1eWJiZmZmZsNKD5Pfrwe2aXGtdqJ5RMyjpPqVpK2BSyPi2abXLyat0pT1dUdZX8DbSeWSO42l9Ht+REwEJpZc2rJsjIX7xpa0nUoq0dwT3splZmZmZsNK3YDFwvX9JIWk7fLvr5R0g6QFksq+rJfZAZgk6cKScZUGLEbErIg4tvn1+Z7SIMTC9e0lLWqELEbElRHx/U6DVOvgxg3ye74rX3tfhTF+No/v7rwK1Gg/XdK9uZ/JktbJ7QdIeo4URFmZV0zMzMzMrKfOvvNsJs2cNCh9dxmw2MjgOAb4baG754CvNvU7BgAAIABJREFUkNLct644hNuBr/YwYLFlEGJ+r6eStq3V1Sq48cvAxRHxQ0lbklaeNmrVSV7t+ThpQvYCcI2kqyLiPuB64Pi8ve5U4HjgixHxU0m/Ba6sM2CvmJiZmZlZT02aOYm+eX2D1X3tgMXsJFLie//h7Yh4OiJ+U2wbiC4DFtsFIX4GuBSYX3pz+35Lgxvz32vnn19BoXJZC1sAt0XEMzkj5kZSngkRcV0hN6Y/uLJbXjExMzMzs54bPXI0U8dP7ereXgcs5qpcb4iIK4tbrNqOYQgDFvPzlgpCzNWx9iVNtrbvss+y4MavA9dJ+gwprb30/E3BLODkXC742dxf2WdyOKnccde8YmJmZmZmw0mtgMVcbeoMaiaqD2XAYtYchPhd0raoRd122CK48SDg/Ih4PWmS8eP8GbXqYzZpO9n1wDWkymELi6+RdEJuW+bcTR2emJiZmZnZcFI3YHEt0vmRqZLmkKpaXdE4AL8iUEkQIimh/qI85v2AH0iqc3YFKA1uPAK4OF+7FVgNaBuKGBH/ExFvjYidSedo7iuM/TDgA8DBAykVDN7KZWZmZvaS1Devj7Hnj11uzx49cvRgdd8fsAg8RMrXGJezOBoBixeRAxYj4kkKX7wlTQWOG+LVECRNAT4aEQ81tTeCEHcvBiFGxBsLrzmfFG54ef793ojYvM2zBLwpIu4vCW58ENgdOF/SFqSJyd/z1rELImL3kv7Wi4j5kjYAPgS8I7fvRcqO2SUinqn3iSzLExMzMzPrieX5RfilZqBf7MeNGtfD0axYugxYbCmvSKwNrJJXJPaMiHuGKGARWgchtnpGlYDFlsGNpC1t/y3p86QtcOPzpO41NG3RKrg0nzF5EfhURDye2ycCqwLX57HfFhFHdRhbS56YmJmZ2YC9lL8ID0cTxkxgwpgJy+35gz1BrRuw2PSasU2/b9TidYMesJjv6fh9PCLGF37tGLDYIbjxnhbXWvYbETu1aN+k3Tjq8sTEzMzMBmx5fxF+qfHK0wrtSeAkSa+KiDOr3BARs1j6/EjXIqJWNkiNfqsGTHYkaSfSYf5H6tznw+9mZmZmNqzUTX6XdJSkmZL6JP0mBwsiaYfc1pdT0vet8Pi3kL5DL5P/IWlnSb+TtLCR1F7hvZws6S+SFjS1lya0VyXpmvye7pZ0Zg5rRNJJub8+SddJem2Fvk7Nye+zJB1QaG+VWv9aUnbKgtIOW/CKiZmZmZn13GCdOeoy+X1SY3VD0t7Ad4C9SBkd2+VzK68Bpkv6eSELpZWDW5w9eRAYD1TKSsl+TjqrcV9Te62E9hIfiYin8uH3S4D9SUUBTo+IrwBIOgb4KtDyXIik9wNvBUaTzpPcKOnqHOBYmlqfk9//Rr3PwSsmZmZmZtZb40aNG8yqXLWT3xsp6Nma5BT0Qpo5pOpUAyp3GxFzImIGsLjji5fcc1tE/LXsEvUS2pv7bbznlYBVWPKeSz+LNrYEboyIhRHxNOkg/V65r3ap9bV5xcTMzMzMemqgZ456nfwOIOlTpHMeq5AmMI32twHnAhsChzbul/QL4MiIqDUh6KGvUy+hfRmSriVN5K5myYQNSScDHyWdl9m1QzfTga9J+g6wRn79PU3PWSq1vlteMTEzMzOz4aRW8nv/DxH/FRFvIpUU/nKh/bcRsRWwPXC8pNVy+/uW46QEaia0l4mI9wCvIW3B2q3QfkJEvIG0FevTHfq4jrSN7BbgJ8CtLFtWuDm1viuemJiZmZnZcFI3+b3ZReQtXkURMRt4mpQSvyKondBeJiKeA64A9im5PIklifDt+jg5IkZHxLtJE8Bi8ntZan1XPDExMzMzs+GkP/k9V906ELgin3NoJL9DTn4HkLRp4f73k79Y5z5Wyj9vCLwZmDMYg5Z0b+dXLaWR0E5zQntOkW/3rBH5MD/5/b2PnPze9FnsXWjfQdIFJX29PIcrImkbYBvguvx7I7X+oGJqfbc8MTEzMzOzYSOfAWkkv88mVa4qJr8fK+l+0pmTRvL7p3PZ3D7S/+wfltvfRarE1QdMBj4ZEY9AOmNSpZRukaTtJc0lVcA6S9Ldub1lWruk0/I9a0iaK+nr+dK/AR+XNJ20hWp8nny1S2hvWBO4QtIM0hmR+aSEeYBTcnnfGcCeLDkXsgGwTAAksDJwk6R7gLOBQwrneM4E1iel1vdJ+mqHcbXlw+9mZmZmNqzUTX6PiNJD2RHxY+DHLa7Vyg3J99xBeWWqdqnq/w78e0l77YT2wr1/I52ZKbvWauvW28r6zVvBtmzRV0/nEl4xMTMzM7NhpYuAxfGS/l4IUzyycM81kp6QVDVRvbFCcFHJuF6ZQxEXSOpPUo+IKyPi+y3eS2ngoaR9Cu3TJL0r9zUxIq6oOFYk/acK4Y2SjpV0T+57St7CRkR8IZc6LutjFUlnS/qDpHslfTi3r5o/5/vz575Rbv8aaUXrXVXHCZ6YmJmZmdkwUghYfC/pf/IPygGEsCRgcVPgcdIB8oaf5gPcoyPinEL76aRSt1X9FDg+Ig4sufYc8BXqBQueHhHbRMRo4EpS4CHAFOAtuf1w4JxWHbQiaTtgnabmu0ihktuQSgifVqGrE4D5EbEZOdcktx8BPB4RmwBnkD5/IuI/SOd15tYZr7dymZmZmVlPnH3n2UyaOWmwH9MfsAiQVy72kTSbVBJ3XH7dj0hZID9s11lETJE0thcDywGEv5G0SY17WoU/LihrrypP4E4nfR77Fp53Q+Flt7Ekub2dw4HN8/2LSRXQIFX6+nr++RJgoiTlszC1ecXEzMzMzHpi0sxJ9M3rG+zHlAUsvo4OAYvAh/P2pUskFcsNl5J0oqS9ezXoDs86WdJfgINZsmKCpH1zNa+rSJODOj5NqlZWlirfcAQpfLHd2BorLidJ+p2kn0laP7f1/1vkz/1J0r9DV7xiYmZmZmY9M3rkaKaOnzqgPjokv3cTsPhz4CcR8byko0irKbuVvH7JjREDqjBVR0ScAJwg6XjShOJruX0yMFnSzsBJVEx/z+dU9gfGtnnNIcB2wC4duluJdKD/5og4VtKxwLdI29/ahlrW5RUTMzMzMxtOagcsRsSjEfF8bv9vYMwQjbWu0sDDiPg18KZcdriKbYFNgPslzSGVIr6/cVHSHqRzI3sXPpdWHgWeIZVTBvgZ8Nb8c/+/Rf7cXwE8VnGMy/CKiZmZmdkKqG9eH2PPH7u8h1FL37w+Ro8cPdiP6Q9YBB4iBSyOi4iQ1AhYvIilAxZfU9jStDcp/2RI5fDCiRFxe1P7phHRSFIvBh5uAjyQ39dbgVVIkwQk3RsRm7d6VkRcBYwsPGNBPqCOpG2Bs4C9ImJ+01iW6Tc//+ek1ZdfkUIf78mXryB9zreSPvdfdXu+BDwxMTMzM1vhjBs1rvOL/klFxEJJjYDFlwPnNgUsXiTpG6TqU42AxWPyeZGFpP/RH9/oT9JNpIPdI3LQ4RERca2kE4FpdUrz5v7mAGsDq0j6ILBnziTZBig773GKpDcDi4E/A0fl9g8DH5X0Iin48IA8SWgZ1ljR6cAI4GeSAB6MiL079PtF4MeSvgv8HfhYbv+f3H4/6XMtq1RWmScmZmZmZiuYCWMmMGHMhOU9jNqGaoWni4DF44HjW/S1U4v2rs6YRMRGzW2S1gbui4i/lLy+NPAwIk4ll99t0jFgsaSvEYWfW51TaRcC+Wdg55L250hnWXrCExMzMzMzs+oWABMkrV118pJLAvfkC3xEVA2CXG79StoJ+AFLygpX4sPvZmZmZjasdJH8Xpp2LmnXQhp8n6Tn8vardkaQckXKxlWa/N7hvbRKfh8r6cnC2Gqt4Eg6Pae0z5A0uVH2V9LKkn4kaaak2bkSWKe+lEsa/yHfc0xu31zSrZKel1QMlZwGLAJ2rHFg3ysmZmZmZtY7g31ov5D8/m5SVag7JF2Rz3E0kt8vknQmKafjhyxJO39G0tGktPMDctjg6NzvvwD3A9dVGMYZEfGtkvZG8vvW+U8Vp0fEV/IYjiHlmDTOmdwUER+o2E+z60kJ9QslnUrayvZF0srNqhExStIawD2SfhIRc9r0NZ5UfWvziFgsab3c/hhwDLDUZC4ingVG5/M2lQ2riUlfH4wdu7xHYfbS0jfvu4z+0ueW9zDMzOwlYIgO7ddOfq+Ydr4fcHVEPNPtwHqZ/D5QEVGcYN1Gen/k/tfM5X1XB14AnqK9o0mVzxbnvucX/p4v6f29GPOwmZiMc3EKMzMzsxVarw7tdwhYLEt+fxudk98bWqWdHwh8p38MXVbl6oakk4GPkpLTdy1ceoek6aQ8luMK1cfqOhz4af75EmAfUoWwNYDPR0Sn7JE3AQdI2pdUleuYQonjnhk2E5MJE9IfM+utsed7tcTMzIaVbpLf040t0s4lvQYYRSpBnG5c/snvvwM2jIgFkt4HXA5sWrdvSSeQyiRfmJt2IJ3/eC2wLnCTpF82VqBaWBV4LiK2k/Qh4FygtJrZQPjwu5mZmZkNJ7WT36Fj2vlHgMkR8eKgjbqa/uT3iHgqIhbkn38BrFznIDmApMOADwAHF4IPxwHXRMSLeSvWzaTJWjtzgUvzz5NJmSw954mJmZmZmQ0n/cnvuerWgcAV+Yt3I/kdlk5+b6Sd792cdp4dBPxkMAct6QJJy2SsSCqughST30cqJyDm+17GkuT3KZLKtqkV+92LdNh976ZzMw8Cu+VKW2uS8ksaz2zV7+Wk8zuQVpv+0On9dmPYbOUyMzMzM+sy+b007RxA0kakFZgbi8/p9oyJepf8vh9wtKSFpOT3A3Py+8uATUgVsdqZSNqCdX1+z7dFxFGkimbnAbNI29/Oi4gZHfo9BbhQ0udJOS5H5vc6klQaeG1gsaTPAVs2HeivzBMTMzMzMxtWukh+b5V2Ti6Tu8wqwQqQ/D6RNLlotiVwaS7J224cpZXB8vawsrDHlv1GxBPAMpW3ImIeactcT3grl5mZmZkNK20CFj+d26LsPIak7SUtkrRfoe1USbPynwMqPH5H4JuSbmy+0CpgMZ8XKU1+bxWw2GrMETErIo7tNEhJNxXCGR+WdHluf4Wkn0uaLuluSR/r1K+kA/IY75Z0WqF9VaUgy/uVgi03yu27S3qONOFb3GmsDZ6YmJmZmdmwUQhYfC/pf/kPkrRlvnwzsAdpS1TZfadSqLyV8zfeSgpZfBvwhby60c4tpODCXUquNQIWjyu51srpEbFNRIwGriQFLLYcc1URsVNEjM793gpcli99CrgnIt4CjAW+nc/qlJL0StJWuN0jYitgfUm758tHAI/n1Zkz8liJiCnA5sDvK5Qi7uetXGZmZmY2pM6+82wmzZzU7e2lAYukL9t35bay+z5Dqiy1faFtS+DGnH2yMGeG7AVc3M3ABiFgsWzMtUhai3Rw/WONRwJr5YP1I0hnSha2uB1gY+APEfH3/PsvSZXDppA+96/n9kuAiZJUqABWi1dMzMzMzGxITZo5ib55fd3eXhaw2KlC1euAfYEzmy5NB94raY289WtXciliSSdK2rvbQdYh6WRJfwEOJq+YtBlzXfsCUwoToInAFqRSyjOBzzYS3Vu4H9hc0ka5FPMHWVKuuf/fIk/uniQFXXbFKyZmZmZmNuRGjxzN1PFTS691SH7vGKRY4rvAFyNiUXE1JSKuk7Q9aXvW30lbnhbma8s7YLF0zF04CDin8Pt7gD7SKsqbSFW7bmpVSSsiHpd0NCk5fjHps9o4X+7m36Ilr5iYmZmZ2XDSKmCxne1IZYTnkMrw/iCX8iUiTs5nMd5N+qJ9X++HXFl/wCJtxlxVPh+yA3BVofljwGWR3A/8iXQepKWI+HlEvC0i3gH8niWfUf+/RV5NeQWdyxi35BUTMzMzM+upTmdI+ub1MXrk6G677w9YBB4iBSyOa3dDRLyx8bOk84ErI+LyfLh8nYh4VNI2pKyR67odWDuSLgAmRsTtTe2bRkTji35/wGKrMeff742ItpOJbP9833OFtgeB3YGbJK0PvBlonNcp7VfSehExX9K6wCeBj+RLV5CCLG8lTZ5+1e35EvDExMzMzMx6rHGGZACTj5baBSxKOgb4d2AkMEPSLyLiyDbdrUz6gg7wFHBIPiuxIgQstur/VZRvoSpzICkcsegk4HxJM3M/X4yIRzr0+z1Jb8k/nxgRjeT3/wF+LOl+0krJgRXHVcoTEzMzMzPruXZnSMaeP3ZAfbcJWPw+8P0O944v/PwcqTJX2euWa8Bi02vGF359O6lccpWxjC1pexjYs+TlLfuNiINatD9HeVhjVzwxMTMzMzOrbgEwQdLaVScv+WB5T77AR8SVvehnMPuVtBPwA+CROvf58LuZmZmZDSs9Tn6/RtITkqp+MR9ByhspG1dp8nuH91Ka/C7p4Nw+Q9Itha1UleRE9kby+xxJfbl9I0nPFq51LEcs6S2SbpU0M6fGr53bdyj0M13SvvmWacAiYMeyf4dWvGJiZmZmZkOub15fV1u6Csnv7yZVhbpD0hX5HMfNpPT0qS3uK0tRPx1YA/hEjWGcERHfKmlvJL9vnf9UcXpEfCWP8RhSjslRpGpZu+Ryve8Fzial01cSEQc0fpb0bVLGSMMDORG+qnOA4yLiRkmHA18gvc9ZwHb53M9rgOmSfh4RzwKj83mbyrxiYmZmZmZDatyocQM5GN+f/B4RLwCN5Hci4q6ImNPivkaK+vxiY0RMAf7R7WCa+no6In5DmqBUvac0+T0ibomIx3P7baSyyLXlhPePAD/p5v7szcCv88/Xk0saR8QzjWIBwGoMIMMEvGJiZmZmZlmnMr9VdarINWHMBCaMmdDyeoeAxbLk97YrCYUU9d2A7du9tnBPV1W5uiHpZOCjpFWNXUtecgRwdZfd7wT8rVCSGOCNku4iVSL7ckTc1KGPWaRSxv9HOivTnyMj6W3AucCGwKGFiUptXjExMzMzM2BJmd8V3ICS36s+JCK+OhSTkvysEyLiDcCFpOT3fpJ2JU1Mvthl9wex9GrJX4ENImJb4FhgUuPMSBuHA5+SdCewFvBCYey/jYitSBO+4yWt1uU4vWJiZmZmZku0K/Nb1UDLAXcwkOR3gFcB75O0sBFYuAKZREpp/xpADn08B3hvRDxat7Ocxv4hYEyjLSKeB57PP98p6QFgM9KB9VIRcS+5xLCkzYD3l7xmtqSnSWdrWvbVjldMzMzMzGw46U9+l7QKKdSv7cpGRLwxIjbKGSOXAJ8c6kmJpAsk7VDSvmnh1/7kd0kbAJeRtkf9oemeKXl7Wid7APdGxNzCva/OhQCQtDGwKUuS31uNcb3898uALwNn5t/fmCc/SNqQdBZlToVxlfKKiZmZmZkNGz1OfkfSTcDmwAhJc4EjIuLaFSD5/avAK4Ef5JWehRGxXZ4cbEJKWu/kQJY99L4zcKKkhaSSvkdFRKOvVmM8SNKn8s+XAefln98FfEnSi3n8n4yIWtklRZ6YmJmZmdmw0qvk9/z7Ti1et1yT3/OEqmxStSVwaS7J22ks40vaLiVVJ6szxu8B3ytp/zHw407jqMpbuczMzMxsWOlxwOKiQkhgldWRHYFvSrqxpP/SgMWIeCoiSpPfJZ0u6d4cpDhZ0jq5fWVJP8qhhrMlHZ/7mhURx3YaZJuAxYML7X2SFksa3WGMrQIWS/uStLqkfwAbkFZhKvHExMzMzMyGjULA4ntJqwcHSdoyX76ZdK7izy3uKwtYfDYiRuc/e1cYwi3A8RGxS8m1RsDicZXeTHI9sHVEbAP8ATg+t+8PrBoRo0iH1z8haaOqnUbEAY33RVohuSy3X1hoPxSYExGdSrGdA3wpj2UyKWCxZV8R8WxErEXKPnmqZa9NvJXLzMzMzHqu22T3CvoDFgEkNQIW74mIu3Jb2X2NgMVKOSbdiIingd9I2qTGPdcVfr0NaKzmBLBmPly+OqlEb+Uv+Q2FgMXdSi43lxJupTlg8VrSBKybvlryiomZmZmZ9dQAk907KQtYbFuhqhCweGbJ5dUkTZN0Wz6s3rjnRElVVlB66XCWBCleAjxNOoz+IPCtwiH1OsoCFhsOoNpkohGwCE0Bi1301ZJXTMzMzMyspzolu3fSIfl9QAGLJaspG0TEw7l07q8kzYyIB7o9/N4tSScAC0khi5BWhhYBrwXWBW6S9MvGSlENpSsZObH9mYiYVaGPw4HvS/oqqTTzC8WLNftqyRMTMzMzMxtOehqwGBEPA0TEHyVNBbYFHuj5qNuQdBjwAWD3iGhMssYB10TEi8B8STfn91F5YlIWsFhQVkq4VIWAxcp9teOtXGZmZmY2nPQsYFHSupJWBchVvN4J3DMYg24TXrgX8EVg74h4pnDpQWA3JWsCb2dJ+GLXAYv5/peRtmRdVHGMpQGL7frqhicmZmZmZjZsRMRCoBGwOBu4uBiwmEMSX08KWDynQ3dbANMkTQduAE7JYYhdnzHJAYvfAcZLmluoGNYqvHAisBZwfS652/jS/1/ACNL5jjuA8yJiRg8CFiGFLM4t2RbWLmDxD6SJ0cMsCVhs11dt3splZmZmZsNKrwIWI+IWYFSL1w1VwGJpBa+IWEBaiWg2oIDF3D6VtAJTdYylAYut+uqWJyZmZmZmL1Fn33k2k2ZOqvz6vnl9g1lN66ViATBB0tpVJy8R8RTlk4za8gHzjgGLXfTbszECSLoB2Bh4seo93splZmZm9hI1aeYk+uZ1ys4bfuomv+eE8hn5zy2S3pLb39yUXP6UpM91ePwjpCpZr20xtmskPSHpyprvab//n707j7KrqtM+/n2UeRCQGRVQ5iFYAgKtAmFQpoaIqEBUBsE0NDhgSyOvStvSvEzOLwqdRoy4KAEFZJIApgmDgHQiFRImBUFkEkEGI2FI8nv/2PtWTm7OvffcU7cqKfv5rJVl1T7n7LPvtVev2u79208e9/aFtpPz53lQ0l5d9tcq+b00nb5DX62S3zeUNKfwnubjmN8MLF11zF4xMTMzM/s71rdOH1OPmFrp3mEKRCzV7WpOQyH5/f2kE7r+R9JVuTbkV8A1wNSmxx4Bdo2I5yXtA0wEdoyIB4G+Qr9PkJLNO7kkIo5vce1sYAXgn7r4TCsDnwF+XWjbklQjshVpEvRLSZtGxLwqfUbEwYW+vgG8mH9tpNNvnf9VcT7whYi4WdInScnvjYDFh3Pye/P7d8unnFXmFRMzMzMzG3FDWM0ZTH6PiNdIp0GNA4iIuyPi0eYHIuL2iHg+/3onqTi+2R6kP7L/UGdQhXdNAf7a5WOnAmeRJg0N44CLI+LViHgEeIj02btSSH7/SR7f3yLitqZ3ddKc/H5Qt+OowismZmZmZrZYtFrN6RCwWJb8vmMXrz2KBenqRQudYCXpGICIKEuL7xlJ7wLeFhHXSPpC4dJbSJOoho4J9y20S36vqpH8fiWLJr+/XdLdwEvAlyPi1rov8cTEzMzMzEaTOsnv6UFpN9LE5H1N7cuQ/vA+ebDDYZ6Q5Pe+AfgWcETZ5ZK2Sp+zSWnye5daJb8/BawfEc9J2g74uaStciF91zwxMTMzM7PRpE7yO5K2IdVK7BMRzzVd3gf4TUT8qWejrGZlUp3H1JxKvw5wVc5PqfU5izokv1fWKvk9Il4FXs0/T5f0MLApMK3OezwxMTMzM7Nh0a7AfQhHEw8mv5OK1Q8Bxrd7QNL6wOXAJyLityW39GJVoS1JpwN3RcRgcX1EvAgUTw+bSioynyZpDtAv6Zuk4vdNgLvyfVOAwyLiiQ6vLU1+bzPGC4FzIuKupva1IuKZ5uR3SWsCf4mIeZLekcdYO2jRxe9mZmZmNiyG47jimsnvpwCrA9/Px9oO/i/6klYgnfB1efE9ko5p1Jl0Q9KtwE+BPXLye+OY3zHA0118znuBS4H7gMnAcXkCMOTk9xrp9K2S33chfc8zgJ8Bx0RElXGV8oqJmZmZmQ2bVgXuQzmauNvk94g4Gji6RV8vkyYtze21akwiYucWl5aOiDs6PDu26ffTgNOabutF8vuGzW11kt8j4jLgsk7jqMorJmZmZmZm1c0B9imsxlQSEV0FJLbpZ1ZEDEvye0Q4+d3MzMzMrKoeJr8vJ+kuSTMk3Svp3yu8fgtgOUoK0Wumqp+axzUg6QZJ6+X21SRdka/dJalqGGKx70/n7+leSWfltmUk/TCnuM+QNLZCP62S3zt9Xie/m5mZmdmSb+Dpga63dPUy+Z10otTuETFb0tLAbZKui4g7ae9bEfH1kvY6qepnR8RX8mf7DKke5hjg/wADEXGgpM3zZ96jYp+No5HHAdtExKuS1sqXPgUQEWNy23WS3h0R89t01yr5veXndfK7mZmZmY0K48eMr3sqV8+S3yOZnduXzv/qZIU03tN1qnpT5seKhfdvCUzJ9zwAbChp7S6GcyxwRj7Sl4h4pqTfZ4AXgO079FWa/F4zRb4lr5iYmZmZ2aA6qxjt+mo1+Ziw3QQmbDeh9NpIJr/nFZjppJOuvhcRv87tXwOmRcRVXfRdi6TTgMOAF4HdcvMMUgbJbZJ2ADYgTaiqZq1sCuyc+36FtOLxP7nfcZIuJuWkbJf/866WPbVPfu8Zr5iYmZmZGTCkVYyR1Ivk95MGH4yYFxF9pD/6d2jUckTEKSMxKcnv+lJEvA24iHQUMsAZwGqSBoBPA3cDc7vodilgNWAn0tarS5VSHC8gTeamAd8Gbq/Q7yeB4yRNJ4VCvtbh/lq8YmJmZmZmQPtVjDp6tfLSZDiS34mIF3JNxN6kFYLFoR+4Fvi3vMXrSIA8oXgk/6vqceDyiAjgLknzgTUi4s/ACY2bJN0O/K5dR62S33vNKyZmZmZmNpoMJr9LWoYUIth2ZaNV8rukNSWtmn9enpyUPhyDlnRh3pLV3L5J4dcDGu+XtGr+fJAyWG5p1KNImiLpLR1e+XNg93z/psAywLOSVpC0Ym5/PzA3HxzQboxr5f/AMqx3AAAgAElEQVRcKPm917xiYmZmZmajRkTMldRIfn8jcEEx+R34V2AdUiL5L3K4YjH5HdIf49sD6wI/ynUmbyClyF+T+6pVY5JT1d8ELCPpg8AH8h/+rVLVz5C0GTAf+APpRC5IxxJfKGkeKf39qNx/1eT3C4ALJM0ibb06PCIiTzKuzysoTwCfKDzTLvn9uPzz5SxIfm/3ebvmiYmZmZmZjSq9Sn6PiHuAd7V4xyk1x7Zhc1uHVPWDWvRzB7BJyaVKye/5xLKPl7Q/Sjplq5sxlia/52sbthtHN7yVy8zMzMxGlRoBi2MlvZhDDAcknVK4doGkZ/LKQhWbk1Y5ftNibJMlvSDpmkZbu1R1Scfk4MIBSbdJ2jK3lwYhVk1+l/Stwuf9raQXCtfOUgpdvF/SdyWpwxj7JN2Z+5rW2O4laRWlwMVGQGWjJmYjSX8FdqGL5HevmJiZmZmNUhOnT6R/Zn/L6+2O6x0pvTx+GGoHLALcGhH/WNI+CTgHuLDiECYDz0bE8S2unw2sAPxTxf76I+I8AEkHAN8kFeDXCUIcFBHFAvdPk1eGJL0HeC9p2xbAbcCulH9nDWcB/x4R10naN/8+FjgOuC8i9pe0JvCgpIsi4mFgZUmzI2JGlfGCV0zMzMzMRq3+mf0MPD2wuIfR0jAdP9x1wGI7EXELnes1uulvCvDXLu6vErBYNQixlUOBnzReCSxHKoZflhQq2SkbJUh1JACrsOAUtCBNQASsRPoeuznSeCFeMTEzMzMbxfrW6WPqEVNLrw3Tcb2V1T1+eJgCFv9B0gzSH9VfaBTMtxyDdAxAYzVjOOXC8s+TJgu75+Y6QYhlfW8AvB34b0i1K5JuIhW5CzgnIu7v0M3nSAXzXyctbLwnt59DOhHtSVK+ycFVV3TKeMXEzMzMzEaTOgGLvwE2iIh3Av+PdJRuWxFx3khMSvK7vhcRG5GCH7+cm+sEIZY5BPhZRMwDkLQx6cSvt5ImebtL2qVDH8cCJ+QQyBOAH+T2vYABYD2gDzgnF9HX4omJmZmZmY0mXQcs5sLu2fnnXwBLF4vjlyAXAx+EdCxyRJwQEX0RMQ5YlQ5BiC0cwoJtXAAHAndGxOz8nVxHSodv53DSMcEAPyVtp4MUAHl5JA+RAiA3rzFGwBMTMzMzMxtd6gQsrpPrIMgnSr0BWCT9fThJOl3SgSXtxSOB9yNPPuoEIZb0vRmwGnBHofkxYFdJS0lamlT4fn+Hfp/M90HaataYID0G7JGfXZt0DPHvO42rFdeYmJmZmdmoUTNg8cPAsZLmAnOAQyIi8jM/IZ0wtYakx4F/i4gf1K0xkXQradVgpdzfURFxPTCG8gnU8ZL2JB2r+zxpdQKgThBis0OBixufNfsZaXIxk7QFbnJEXN2h308B35G0FPAK0CgcOhWYJGkmaYvdSRHxbIVxlfLExMzMzMxGlRoBi+eQCrXL+jq0RXut+pKI2LnFpaVzaGLz/Z9t0c+jdBmEWNLHV0va5lFylHGHgMXbSMX3ze1PAh/oNI6qvJXLzMzMzKy6OcA+ks7v5qGI2KsXL28XhLik9JsDFgfofAzxQjwxMTMzM7NRpUby+4mFFPRZkuZJenO+9tncdq+kz1V4/fIsyPQoG9siye8dPkur5PcNJc0pjLvbLWUbSJoi6R5JUyW9tdA+Pfd5b2PLWoe+3inpjjzOq4snb0k6OX/nD0pqTL4ahxG8rZtDBryVy8zMzMxGTKe0+k7qJL9HxNmkRHYk7U86+vYvkrYm1U/sALwGTJZ0bUR0Ov3qkhFIfgd4OCLqJlR+HbgwIn4kaXfgdFKdylPAeyLiVUkrAbPy99fuZLPzSdkvN0v6JHAi8JU8iToE2Ip0ZPAvJW0aEXOAPkmPdjNgr5iYmZmZ2YjpQVr9UJPfiynoW5COzn05IuYCN5OO062th8nvQzWYHA/cxILv6LWIeDW3L0u1+cBmwC355xuBg/LP40jF9a9GxCPAQyw4SrhrXjExMzMzsxHVLq0ehi35HUkrkFYjGqsds4DTJK1Oqh3ZlxRouCQkvwO8XdLdwEvAlyPi1i66nUGaQHyHNNlaWdLqEfGcpLcB1wIbAyd2WC2B9D0dAFwJfIQFOTJvAe4s3Pd4bqvFKyZmZmZmNprUSX5v2B/4VUT8BSAi7gfOJK0CTCb9MT83X1vcye9PAetHxLtIk5b+LlPVv0DKK7mblEHyBAs+2x8jYhvSxOTwnEHSzieB4yRNB1YmbXuDof13sQivmJiZmZlZLXXqRQaeHqBvnbplE0CN5PeC5hR0IuIHwA8AJP3f3P/icjFwbh7Xq8Cr+efpkh4GNiWv6HSSV0E+BJBrSQ6KiBeb75F0L7AzKd+kVV8PkI8FlrQpKQgShvbfxSK8YmJmZmZmtfSgXqSOrpPfASStQlo5uLKpfa38n+uT/pD/yaJPD12N5Pc1c6E/kt4BbEJOVa+S/C5pDUmNv/VPBi7I7W+VtHz+eTXgvcCD7fotfEdvIK3oNFaSrgIOkbSspLfnMd7V6btoxSsmZmZmZlZbp3qRZmMnjR3S+2omv0Oqs7ghIv7W1OVlucbkdeC4iHg+97W4k993Ab6mlFY/DzimsQWNasnvY4HTJQWpcP243L4F8I3cLuDrETGzQ7+H5joYgMuBHwJExL2SLgXuI20TOy4HONbiiYmZmZmZjSrdJr/na5OASSXtpUntS0Dy+2XAZc3tVZPfI+JnlGzPiogbSROQyv1GxHdIRfRl7zkNOK3dWKryVi4zMzMzG1V6FbAoaTlJd0makcMG/73C6/cDjpH0m5JxrS7pJkmzJZ1TvNYq+V3SqTkEcUDSDZLWK1wbWwhCvDn3UymhXVKfpDvz89MaW7RafRft+pW0u6Tf5Pt/JGmp3P6xPPZ7JN0u6Z25fTVJc4ANgFU7jbXBKyZmZmZmNqIGnh6ovaVLvQ1YFLB7RMyWtDRwm6TrIqJ4BG6ze4FfR8TXS669AnwF2Dr/q+LsiPhKHttngFNIE59Vge8De0fEY406jy6cBfx7RFwnad/8+9hW30WrTnJdyY+APSLit5K+Rtpu9gPgEWDXiHhe0j7ARGDHvB1ueaWAxReqDtgrJmZmZmY2YsaPGT/UU7l6FrAYyezcvnT+V/u424j4W0TcRpqgVH2mVcDieODyiHgs3/dMt8MBGscLr0L5aVnFsMlWVgdejYjf5t8HAxYj4vZGTQ4pz+StXY5xIV4xMTMzM7MRM2G7CUzYbkLbe0YwYLGxAjOdlOnxvYj4dW7/GjAtIjqe+DVUkk4DDgNeBHbLzZsCS0uaSsoO+U5EXNhFt58Drpf0ddJixHua3rnId9HCs3kc20fENODDLHxEcMNRwHVdjG8RnpiYmZmZ/R0byrapKn0PcfWjjp4FLALkU6T68tapKyRtHRGzIuKUHoy1koj4EvAlSSeTJgr/Rvo7fTtgD2B54A5JdxZWLjo5lrRN6zJJHyVtvdqzcH2R76LF2ELSIcC3JC0L3EAOamyQtBtpYvK+imMr5a1cZmZmZn+nerBtaknU04DFhoh4gVSbsvdQBjdE/eRtUqTPOTlvD3uWdOTvO7vo63DS0b4APyVtgStq+V00i4g7ImLniNghj+N3jWuStgHOB8ZFxHNdjG8RXjExMzMz+ztVZdvUUAzXSkwHgwGLwBOkP7DHd3qoELD48ULbmsDrEfFCDh3cEzhzOAYt6ULgnIi4q6l9k4ho/KF/APBA/vlK4Jx8AtYypO1q38rPTAEOi4gn2rzySdLnnQrszsKTiUW+i3b9SlorIp7JKyYnkY8HVgqlvBz4RBcrOS15YmJmZmY2TCZOn0j/zP5h638xbaVarHocsLgu8KNcZ/IG4NKIuCb3VavGJJ9E9SZgGUkfBD6QTwxrFV54hqTNgPnAH4BGsOP9kiYD9+Rr50fErHxK1sZA2y1YwKeA7+SJzStAcYa6yHfRod8TJf0j6Ts6NyL+O7efQiqO/3464Iy5EbF9h3G15ImJmZmZ2TDpn9n/v3LyMNx6FbAYEfcA72pxf60ak4jYsLmtQ3jhQc1thWuDR/sWbAlcFhFzOozjNlKNStm1SSwaNtmy34g4ETixpP1o4Ojm9ro8MTEzMzMbRn3r9DH1iKnD0vdi2kr1v91sYIKkN1WdvOQjgTuGIlbsaxbw+V70NVz95m1xd5COX55f9TkXv5uZmZnZqFIj+X0VSVcXEt6PLFybV0hCr7JtayVS3kjZuFomv7f5LKXJ70q+mz/PPZK2rdJfod9Wae2rSboi93mXpI5BkG36Kk2RLzy6Jl3MN7xiYmZmZma1DedxxGXqJL8DxwH3RcT+ueD9QUkX5YDGORHR7V67bw138juwD7BJ/rcjcC7V81rapbX/H2AgIg6UtDnpu9yjTl8dUuT7cr1NZV4xMTMzM7NaFtNxxHWS3wNYWalCeyVSgffckvuGpMfJ7+OAC3M6/Z3AqpLWrdhty7R2Ui3JlPzuB4ANJa1ds6+iKinybXnFxMzMzMxqGa7jiIch+f0c4CrSEborAwdHRKP2YTlJ00gTlTMi4uewRCS/l33Ot1B+slezdmntM4APAbdJ2gHYgJQF86cafTXGXzVFvi2vmJiZmZnZaFIn+X0vYABYD+gj5YO8KV9bPx9xOx74tqSNIJ3KNRKTkvyuL0XE24CLWPDHfe2E+4gIUr7LtyTdBfyVBStEZwCrSRoAPg3cTZvVow59NVRKke/EExMzMzMzG03qJL8fCVyet0U9BDwCbA4QEU/m//w9qTal9PjgEdKc/F434b5lWntEvBQRR+a6msNIBeqP1OmroHKKfDuemJiZmZnZaDKY/C5pGdIfxZ1WNh4jF3jneorNgN/nE6qWze1rAO8F7huOQUu6MG+dam7fpPBrMfn9KuCwfDrXTsCLEfFUfuYBOpC0Vv7PRlr7efn3VfP3BimD5JZGnYukKZLeUrWv3NZIkb+y05g6cY2JmZmZmY0aNZPfTwUmSZpJ2iJ1UkQ8K+k9wH9Kmk/6H+zPyKd7Lfbkd1KA5L7AQ8DLpFWfxgSqbRFO1iqtfQvgQknzSJOwo3K/dZLfoSRFvi5PTMzMzMxsVOk2+T1v1/pASfvtwJgW71isye+5tuO4kks7kY747TSOVmntd5COIG7WdfJ7vjaJRVPka/FWLjMzMzMbVWoELI4rhBhOk/S+wrXJkl6QdE3F178HOF3SzSXjKg1YzHUdpcnvbQIWS4MQI+KaPAHr9B1dUgg/fDQXu7cb46yIKE1+b9PXhpLmFK41tostL+mvwPqklaJKvGJiZmZmZovdxOkT6Z/Z3/G+mgGLU4CrIiIkbQNcSi5+JwUErgD8U8Wh3k7aujTcAYtdBSE2i4iDGz9L+gbpKOJaY2zTF8DDzQGVedVlZUlTgWJOS1teMTEzMzOzxa5/Zj8DTw9UubXrgMWImJ23RsHCIYZExBTSEbhD1uOAxW6DEEvlUMmPkk/NqjPGVn31micmZmZmZrZE6Funj6lHTO10W6vgwbYkHZhPs7oW+GSF+78m6YBO9/WCpNMk/RH4GGnFBBYEIdIUhNitnYE/RUTzEb91lPX1dkl3S7pZ0s5D6dwTEzMzMzMbTWoFD0bEFRGxOfBB0ildne5f3AGLXQUhtnEovVvhaO7rKVJA5buAzwP9heDKrrnGxMzMzMxGk6EGD94iaSNJa0TEsz0f3dD0k1Z0/i1v8WocESxSCGLbIMRmkpYirbpsN9SBlfUVEa8Cr+afp0t6GNgUmFbnHZ6YmJmZmVlPVC1gLzPw9AB96/R1vrEQsAg8QQpYHN/uAUkbk4q0Q9K2wDLAc7UGWpOkC4FzIuKupvZNClujBgMWJa0KvJzraBYJQgQOi4gnOrx2T+CBiHh8KGNs1ZekNYG/RMQ8Se8gHUP8+yrvKuOJiZmZmZn1RKOAveIEo5aaAYsHkVLUXwfmAAc3iuEl3Uo6oWslSY8DR0XE9UtAwGKdIMRmh1CyjavGGFv1tQvwNUlzgXnAMRFRZVylPDExMzMzs56pWMC+iLGTxla+t0bA4pnAmS36Ki3YXgICFrsOQizp44hejLFVXxFxGXBZp3FU5eJ3MzMzM7PqZgMT8opKJe0CFrvVLghxiP32bIwAkm4C3gG8XvUZT0zMzMzMbFSpkfz+sZygfo+k2yW9s3Dts5JmSbpX0ucqvP5ZYDVgvRZj6ypJXtIxkmbm5PTbJG2Z25eW9KN87X5JJ1fpr9BvV8nvNfsqTX4veDOwdNUxeyuXmZmZmS0RBp4e6Lilq2by+yPArhHxvKR9gInAjpK2Bj5FCm18DZgs6doKmR+XRMTxLa51myTfHxHn5c92APBNYG/gI8CyETFG0grAfZJ+UhYgWWZxJr/nZ3bLye+VecXEzMzMzBa78WPGVy2ar5P8fntEPJ9/vZMFQYVbAHdGxMsRMRe4GThwKJ+j2yT5NsnvAayYj+ldnjRxeokujabkd6+YmJmZmY1iVVYZRspQTuSasN0EJmw3AQAdWZahOKgs+X3HLl51FHBd/nkWcJqk1Umnde1LzuCQdAxAYzVjOEk6jhRQuAywe27+GWnC9RRpBeaEmidejUjyO2nS9OWIuLVu556YmJmZmY1S48e0je/4e1Ur+R1A0m6kicn7ACLifklnAjeSitpnkNPVR2JC0hAR3wO+J2k88GXgcNLK0DxSLctqwK2SfhkR3eaEjETy+3OStgN+LmmrplWgyjwxMTMzMxuliqsMS4IRWrmplfwuaRvgfGCfiBgMV4yIHwA/yPf839z/4nIxcG7+eTwwOSJeB56R9Ctge7oIMBxtye+uMTEzMzOz0WQw+V3SMqTgv7YhiJLWBy4HPhERv226tlbhng8xTPUTkk6XtEj9iqRiVsl+QGOb1GPA7kpWBHZiQSr8FElvqfDarpPfJe1QtS9Ja+bDCHDyu5mZmZn9r1Iz+f0UYHXg+6l+m7kRsX3u8rJcY/I6cFyjSL5ujUmrJHlgDOUTqOMl7Znf/zxpGxekk8d+SKqDEfDDiLjHye9mZmZmZkuIGsnvRwNHt+irVfJ7rRqTVv0BS+c09+b7P9uin9mkI4Ob/d0mv3tiYmZmZqPexOkT6Z/Zv7iHsYihnFJlrUnaG/gOacXk/Ig4I7cfD3wO2AhYMyKeze3jgFOB+aTi9s9FxG25GP5bha43Bw6JiJ+3ef1+wIGS3hMR2zaNa3XSaVrvBiYVs04iYq8Wn+VU0ulb84FngCMi4klJHwNOyrfNBo6NiBkRMYt0gldbki4BNsu/rgq8EBF9eavWxMZtwFcj4opcsF6a/J5XgVbOv64F3BURH2zzvW4EDJCOP66c/O6JiZmZmY16/TP7PQlYQgz38cU1AxanAFdFROQi+EuBzSPiJqAv9/tm4CHghg5DuBf4dUR8veRa1+GFwNkR8ZU8hs+Qtp0dQ4tQyIp9tgtFnAVsn7fErQvMkHR1znFp1dfgKpCky4Ar86+tvteHgZUlzY6IGVXH7ImJmZmZ/V3oW6ePqUdMXdzDWMiSki8yUkbo+OLBgEUASY2Axfsi4u7cttADeVtUQzHEsOjDwHUR8XLdgUXE34DbJG3cxTOlAYsRcXuhvRgK2ZVCKOLuud/i51uOikct575Wzv0cmfuq8r1W5omJmZmZmfVEr44vHo6AxXwi1umkrUj7ldxyCPDNwv1fA6ZFRNsTv3pB0mnAYaRVjd1KbimGQnZrkVBESTsCFwAbkE4qa7la0uRAYEpxMlXhe63MxwWbmZmZ2WhSK2Ax11FsDnyQVBexoMO0pWkM6aSvxv2njMSkJL/rSxHxNuAi4PjitUIo5Ellz1awSMBiRPw6IrYi1cKcLGm5IfTV8nvtlicmZmZmZjaa1ApYbIiIW4CNJK1RaP4ocEUOM1yc+oGDGr8UQiHHFUMhqyqEIl5Sdj0i7gf+RoV6mFzYvwNwbYu+yr7XrnhiYmZmZmajSZ2AxY1zrQWStgWWAYp/6C+yEtBrrcILmwIWD2BBiGK7UMjaAYv5e1sq/7wB6eSuRyv0+xHgmoh4pdBXp++1K64xMTMzM7NRo2bA4kHAYZJeB+YAB0dE5Gc2JK3A3Fx8T90akxrhhWdI2ox05O4fSCdyQYtQyB4ELL4P+GL+LuYD/xwRz1bo9xDgjKa2lt9rHZ6YmJmZmdmoUiNg8UzgzBZ9PUoqqG9uP6Xm2DZsbmsXXhgRBzW35fZWoZBDCliMiB8DP+6234gYW9LW8nutw1u5zMzMzMyqmw1MyCsqlUTESxFRGl7YrYiYFREdAxYXZ7+SNpI0APypm+c8MTEzMzOzUUXS3pIelPSQpC8W2o/PbVEswpZ0oqSB/G+WpHmS3ixps0L7gKSXJH2uw+ufBVYD1msxtsmSXpB0TcXP8nlJ90m6J9d4bJDbd2sa2yt5a1glklaRdLWkGZLulXRk4dpZue1+Sd9t1Il06O/T+Tu/V9JZuW0ZST+UNDO/ZyxADlh8Hlhb0vZVx+ytXGZmZmY2YiZOn0j/zP7az9dJfo+Is4Gz8/P7AydExF9I9RR9hX6fAK6oMIxLIuL4FtfOBlYA/qniR7qblMT+sqRjgbNItRp1UumLjiOFTu4vaU3gQUkXAdsD7yXVvADcBuxK03dWlI8sHgdsExGvSlorX/oUQESMyW3XSXp3RMyPiN0kteyzjFdMzMzMzGzE9M/sZ+DpgaF0MZj8HhGvAY3kdyLi7lwz0k6rE7j2AB6OiD8MZXARMQX4axf331RIY2+V8F4nlT6AlfNqyEqkSdjc3L4c6QStZYGl6bzl6ljgjIh4NY/5mdy+JTCl0PYCaeJTi1dMzMzMzGxE9a3Tx9Qjpra8PhzJ7wCSVgD2pinEMFvoBCtJxwBExHlV+u6RVgnvC6XSV3QO6RjlJ4GVSasw84E7JN1EOiFMwDk5z6SdTYGdc0L9K8AXIuJ/gBnAOEkXk0422y7/511djhXwxMTMzMzMRpdaye/Z/sCv8jauBR2mPJQDgJMHOxzZCQmSPk5abdi1qX2RVPqK9gIGgN2BjYAbJd0KrAVswYKVmRsl7ZIDEltZilRXsxMpLf5SSe8ALsh9TSMddXw7aVWmFk9MzMzMzKwnqtSPDDw9QN86fUN5zVCS38tyPQD2AX4TEV2dItUrkvYEvgTs2tguVVA3lf5I0varAB6S9AiwOWnic2dEzM7vvo404Wg3MXkcuDz3dZek+cAaEfFn4ITC57gd+F2X4xzkGhMzMzMz64ke1I9U0XXyO6RTqkh/lF9Zcnkkkt9Pl3RgSfu7gP8EDijUbrQdW6u+mjxGqptB0tqkhPff5/ZdJS0laWnSd3J/vq80nR74OWnlBUmbkupTnpW0gqQVc/v7SSGQ93UYV0teMTEzMzOznulUPzJ20tgh9V8z+R3gQOCGiPhbsb9cd/J+mk7RqltjkrdLbQ6sJOlx4KiIuJ60HatsAnU2qTj9p/nU3sci4oDc14aUpNK36avoVGCSpJmk7W8n5YT3n5EmGTNJW+AmR8TV+ZlW6fQXABdImgW8BhweEZFP4ro+r6A8AXyiw5ja8sTEzMzMzEaVbpPf87VJwKSS9peB1Uvaa9WYRMTOLS4tHRF3lNy/Z5u+HqUklb5VX03PPgl8oKR9HiVHGXdIp38N+HiL8W3Wbhzd8FYuMzMzMxtVehWwmK9dIOmZvBpQxQ7AsZIeaDG20oDFiNirxf2lAYv5WmkQYqu+mvpdX9JNku7Ofe+b2z+mhYMb50vqa5dOL+mSwv2PKqW6I2lDSXMK184rPPM8sAuwSaexNnjFxMzMzMxG1MDTA7W3dPU4YBHSKso5wIUVh3AXMH+4AxYlvYcugxCbfBm4NCLOlbQlaYVpw4i4CLgIQNIY4MqIaFsYFBEHN36W9A3gxcLlhyNikdMMImK1HLBYuRjeExMzMzMzGzHjx4wfaheDAYsAOUNjHCnl/O7c1u75hYrJI+KWXMvRExExRdLYLu6/qfDrnSzYMlUMQhTVghAX6hp4U/55FcpPLuuq6D+v2HyUXAjfa56YmJmZmdmImbDdBCZsN6HtPYspYLH53sUasBgRdYIQi74K3CDp08CKQFkty8GkSV1VOwN/iojiKsjbJd0NvAR8OSJu7aK/hXhiYmZmZma1FbNLepBRUkXPAxbLLO6ARUkb030QYtGhwKSI+IakfwB+LGnrnP6OpB2BlyOiam1No8/iCstTwPoR8Zyk7YCfS9oqIl7qos9BLn43MzMzs9pGKLukaDgCFherQsDiAYWAxQPJQYg5DLERhFjVUcClkFZfSNvC1ihc7+q7kLQU8CHgkkZbRLwaEc/ln6cDDwObdjHGhXhiYmZmZmZD0sguGYHVEhiegMVhVyNgsU4QYlExYHEL0sTkz/n3NwAfAS5uGku7fvcEHoiIxwv3r5kPI0DSO0gncP2+w7ha8sTEzMzMzEaNiJhLqhG5nvSH+qXFgMUcavhWUsDi+YVHWwUs/gS4A9hM0uOSjsrtxzTqTLqRAxZ/CuyR+2sc7TsGeLrkkWLA4oCkxiTrZ6QViJnADGBGhSDEon8BPiVpBmll5IiIaGx52wV4vHGAQEG7fstWWHYhfc8z8niPqbJNrhXXmJiZWVeK+8nNlhQjVNtgS4geBywe2uL+xRqwWCcIsen5+0jHDZddm0rTtrBO/UbEESVtlwGXtRtHN7xiYmZmXVkM+8nNzJYkc4B9mlZjOqoSilixn5ZBiEtSv/lEsXcAr1d9xismZmbWtcZ+crMlRd2wPhudJO0NfAd4I3B+RJyR248HPgdsBKwZEc8WnhkLfJuUB/JsRDROv/os8CnSaV//FRHf7vD65VmQD1I2tsmk1YjbIuIfK3yWY4DjgHnAbGBCRNwn6f3AGaQck9eAEyPivzv1V+h3feBHwKqk7+mLEfELSR8DTizcug2wbaeQxXzs8PHAXODaiPjXXI8ysXEL8NWIuELS8sBqwDrAE1XH7Onlwt4AACAASURBVImJmZmZmfXMUFLdq6iT/C5pVeD7wN4R8ZiktXL71qRJyQ6kP/4nS7q2KaejzCU9TH7vb2wbk3QA8E1S1sqzwP4R8WQe5/WkDJeqepb8Lmk3Ut7JNhHxauP7A2aRUuvnSloXmCHp6oiYA/RJerSL8XpiYmZmZjachvsP9cWtWN/Tg1T3Kuokv48HLo+IxwAKp19tQTqS9+X83M2kIvmz6g6uRvJ7MfNjRXImS+OzZPcCy0latnCccMeu6V3y+7HAGY13N76/xveWLUf1PJlSnpiYmZmZDZMR+kN9iVEl1b2KYUh+3xRYWtJUYGXgOxFxIel/8T9N0uqk2pF9gWkwssnvko4DPk/atrV7yS0HAXd3MSmB3ia/bwrsLOk04BXgCxHxP3nsOwIXABsAn8inptXiiYmZmZnZMOnVH+pLssWwGlQn+X0pYDtSrsfywB2S7oyI+yWdCdxIqu+YQaqhGNHk94j4HvA9SeNJW7AOb1yTtBVwJvCBLrvtZfL7UqSakZ2AdwOXSnpHJL8GtspZKT+SdF1EvNLlWAGfymVmZmZmo0ud5PfHgckR8bdcEH8L8E6AiPhBRGwbEbsAfwE61ZcMp4uBDzZ+kfRW4ArgsIh4uMu+epn8/jhpK1xExF3A/Ka+iIj7gb8BW3c5zkGemJiZmZnZaFIn+f1K0lakpSStQNr61UhRbxTCrw98iOp/rHelTfL7JoVf9yNPjHLB/rXAyRHxq6ZnRjr5/efkLWaSNiVtOXs2/3ewVG7fANgMeLTDuFryVi4zMzMzGzXyCVCN5Pc3AhcUk9+BfyUdU3uPpF9ExNF5y9Zk4B7S/9p/fmEL02W5xuR14LiIeD73VavGJCe/bw6slFPoj4qI60nJ72UTqOMl7Znf/zwLtnEdD2wMfEXSV3LbB3LhedXk9/+SdAJpq9tQkt8vAC6QNIt0etnhERGS3gd8UdLrpO/1n4tHNHfLExMzMzMzG1VqJr+fTTrKt7m9NKl9BJPfP9uin/8A/qO5fXEkv0fEa8DHS9p/DPy43Ti64a1cZmZmZjaqSNpb0oOSHpL0xUL78bktJK1RaF9F0tWSZki6V9KRuX03SQOFf69I+mDZOwv2A46R9JuSca0u6SZJsyWdU7zWKvld0tmSHpB0j6Qr8hYuJC0t6UeSZkq6X9LJuZ+WCe1tPr8kfTdfu0fStoVrkyW9QMpTqZT8Lumjku7L32V/of2s3PaApD+TwiznF76T7dv16xUTMzMzMxuSkcxqUY2ARVKy+n0Rsb+kNYEHJV0UETcBfbnfNwMPATd0GMK9wK8j4usl114BvkIqAK9aBH4jqY5kbj4h7GTgJFINyLIRMSbXxdwn6ScR8Wibvlp9/n2ATfK/HYFzWXDEcleBkLkm5mTgvRHxfKFG5z2kFZpt8q235c/1F2A3paOa2/KKiZmZmZnVNn7M+MGAxREyGLCYtxg1AhaJiLtb/OEewMqSBKxEOn2rOW/jw8B1TaGBXcmnft1GmqBUfeaGQvbHnaRTxhpjXjEXly9Pqu14qaSLYl+tPv844MJ8qtadwKpKSe1ExBTgr1XHC3wK+F6jFqcQVhmkAvtlgGVJqyV/6qJfr5iYmZmZWX3DkdUyDAGL55AKz58kBSwe3MjzKDgE+ObgGKSvAdMiotOJX730SeCS/PPPSBOKp0grGifk1Yc6yr6zt9C5gL7MpgCSfkU6fOCrETE5Iu6QdFPuU8A5+QjhyjwxMTMzM7PRpE7A4l7AAOnI242AGyXdGhEvAeTVgzGkk75ShxGn9Ga41Uj6EmkV56LctAMwD1iPFG54q6RflpykVan7krZO31krS5G2hI0lre7cKmlrUq7JFixY8blR0i4RcUvVjr2Vy8zMzMxGkzoBi0eyICDwIeAR0pG+DR8FroiI13s60ookHQ78I/CxwpG+40mhkK/n7VK/AtoWj7dR5ztr19eVeVyPAA+SJioHAndGxOyImA1cR9PJX514YmJmZmZmo0mdgMVi2ODapCDA4srDoQxTsGJDq/BCSXuTit0PaKpveQzYPZ+otSLpj/wH8jNTJL2li9dfBRyW+9oJeDEi2m7jUotASFLY4m75njVIW7t+n8e7q1KI5dLAruQQy6o8MTEzMzOzUSMXijcCFu8HLi0GLOZQw7eSAhbPz4+dCrxH0kxgCnBSIwhQ0oak1YSbi++R9DVJB3Q7PkmPkmpVjpD0uKQt86VW4YXnkOpebsxHFjfyU75HKtSfRZqM/TAi7lFKbd+YVMDf/O5Wn/8XpMnDQ8B/Af9ceOZW4KfAHnm8jWONxwBPl4z3euA5SfcBNwEnRsRzpJqYh4GZwAxgRkRc3earWoRrTMzMzMxsVOk2YDEingQ+0KKvR0mF4M3ttWpMImLD5ja1Dy/cuEU/s0lHBjfbErgsIuaUPNPq8wfpyOSy93QbCBnA5/O/Yvs8Kh453IpXTMzMzMzMqpsNTMindlXSLhSxWxExKyI+3/nOIb+nNBCyjnxa1zuAtjU8npiYmZmZ2aii7pPfV8up6vdIuiufItW49llJs3Ji+ecqvP5Z0ilZ67UY22RJL0i6puJnOSanuw9Iuq2w9QtJJ+fP82Bhi1W7vlp9/nH5sw9Imibpfbl9Ny1IvR9QheR7SevnJPe7c5/75vYdCv3MaNSnSFo+f1/rAE+069tbuczMzMxsyCZOn0j/zP5hf4/qJb//H2AgIg6UtHl+fo88QfkU6Wje14DJkq6NiN91GMYlEXF8i2tdJakD/RFxXv5sB5DqU/bOE5RDgK1Ik6BfSto0b5lqpdXnnwJcFREhaRvgUmDzqJd8/2VSXc+5eYy/ADYk1cJsnxPs1wVmSLo6bznry7U3bXnFxMzMzMyGrH9mPwNPD4zEq+okv29J+uOciHgA2DCfzrUF6Yjbl3NR/c2kY29r6zZJvZGlkq3IgnyRccDFEfFqPpb3IdJnb9dX6efPR/g2+i2+o+jDUCn5PoA35Z9XIR87XPgOISXAd52T4hUTMzMzM+uJvnX6mHrE1CH3MwzJ7zOADwG35SN7NyCdXDULOE3S6sAcYF9gGqQtVgCN1YzhJOk4UjH5MqQQSEif887CbY209rrvOBA4HVgL2K/klkNIqzWdfBW4QdKnSZOcPQvv2BG4gPT9fqIwUanEKyZmZmZmNprUSTE/A1hN0gDwaeBuYG5E3A+cCdwITCZNYOZCmpCMxKQkv+t7EbERKc/ky7m5l2ntRMQVEbE58EHS8cmD8tarMaSjgDs5FJgUEW8lTeR+nI8wJiJ+HRFbAe8GTpa0XDdj9MTEzMzMzEaTrlPM86lYR0ZEH3AYsCYp/Z2I+EFEbBsRu5CyQTrVlwyni0kTB+htWvugiLgF2KhYHE93yfdHkWpUyMcJLwcU+yJP+P4GbL3I0214K5eZmZn9XRh4eoCxk8Yu7mH8rzXw9AB96/SNxKsGk99JpzwdAoxv94CkVYGXc03K0cAtjdoOSWtFxDOS1idt9/qH4Ri0pNOBuyLiiqb2TQrF9vuxYGJ0FdAv6Zuk4vdNgLvyM1OAwyKi7SlXhXdsDDyci9+3JW0Ze65wy6HAyVXGS0p43wOYJGkL0sTkz/m/jz/m4vcNgM2AR6uMr8ETEzMzMxv1xo9p+3ep/R3Jf/g2kt/fCFxQTH4H/pV0NO09kn4REUeTitwvlDQPuI/0v/o3XJZrTF4HjouI53NftWpMcpL65sBKOYX9qIi4nrRV6qqSR46XtGd+//PA4fm990q6NI93bh7bvE7J7y0+/0HAYZJeJ9XSHNwohpe0IWll5uam7lqN91+A/5J0Amlr2RF5wvM+4Iv5HfOBf46IZzt/Y4XxLyjQ72z77bePadOmddO/mS3hGv/rYi+KFe1/B//fjJmV6eX/b5A0PSK2H3JHw0DSEaRjcVsdF9zquet7EVqYjzj+5HCHLPZqvIX+HiV9by0nK64xMTMzM7NRpVcBi5I2awoYfKlCyOIOwLGSHmgxttKAxVZ/5LcKWJT0fknT87XpknbP/bRMfpf0EaWgyPmSti+0l/aVry0jaaKk30p6QNJBHcb7sabvbL6kvqZ7rpI0K/+8vKRnSKsyx5b12eCtXGZmZmbWEyNR59PLgMWIeJAFAYNvJNWsNNdUNLsLmD/cAYukhPn9I+LJPJG6ns7HBc8i1cn8Z1N7u76+BDwTEZvmbWJvbveCiLgIuCiPdwxwZUQMBthI+hAwu3D/HGAtSV8lbSNryRMTMzMzMxuyEazzGQxYBJDUCFi8LyLuzm3Nz2xJyvAgIh6QtKGktSPiT4V79iAViP9hKIOLiCmSxnZxf2nAYuOzZPcCy0laNiJebdPX/bDo5+/Q1ydJNTFExHzSJKaqQ4GfNH6RtBIpj2UC+eSubnhiYmZmZmZDNmG7CUzYbkJP+hrBgMXixOQQFv4je3EHLBYdBNzdblLShcG+8mllAKfmydTDwPFNE7Z2DiZNChtOBb4BdEqPL+UaEzMzMzMbTXoWsDjYobQMcADw08EOF3/AYmNsW5FCIKtuDWuppK+lSBO0X0XEtsAdwNcr9rUj6QjmRi1JH7BxyfHClXnFxMzMzMxGk1oBi8CRAEr7nB7J/xr2AX7TxUrBcLkYOLfxi6S3kmpeDouIh4fScYu+niOtbjQmEz9l4aOU21lohYmU/7JdPn1rKVJdydSIGFt1jF4xMTMzM7PRZDBgMa90HEJ53sYgSavme6EpYDFbqFZiOEg6XdKBJe2bFH4dDFjM26yuBU6OiF81PXNh3pJW9d2lfeUsk6uBsblpD1JuCpIOVApZLOvvDcBHSBOpRl/nRsR6EbEh8D7gt91MSsATEzMzMzMbRSJiLtAIWLwfuLQYsJhDDd9KChg8Pz+2BXBvPuJ3H+Czjf4krUA64evy4nvyMb7HdDs+pYDFnwJ7SHpcUuPY3THA0yWPHJ+P+B0g1Zkc3mgnBSl+pXA071r52jbAUyXvPjB//n8ArpV0fYW+TgK+Kuke4BOkAEWAjYDi5K1oF+DxxgEEveKtXGZmZmY2qkTEL4BflLR/F/huSfsdwCbN7fnay8DqJe216ksiYucWl5bO42i+/7NlN0fEfwD/0dwu6U3A7yLijyXPXEHJccet+srX/kCaaDTrA05o8cxUYKeya/n6o8DWra634hUTMzMzM7Pq5gD7FFZjKulVinpEvBQRH+lFXx3e8/GI+HMv+pJ0NvBx4G/t7vPExMzMzMxGFXWf/D5OKfV9QNI0Se/L7RvkJPSBvJ2qytatfUh/Q08rGZckfTeP4R5J21b4LJMlzcjvPy8HPbZMca9C0spN6ezPSvp2vvZ5Sffl8U2RtMEQ+tpF0m8kzZX04cIzG+V7ZwNExIkRsXFEnNvqPeCtXGZmZmY2wiZOn0j/zP5az6pe8vsU4KqICEnbkML/NifVabwnZ3qsBMzKfbU95Qs4MSJ+VtK+D2nL2CakbJVz6Zyx8tGIeCmfFvYzFhSVt0px7ygi/kpOtAeQNJ0FNTR3A9tHxMuSjgXOIuWR1OnrMeAI4AtNzzwM9DUmJlV5xcTMzMzMRlT/zH4Gnh6o+/hg8ntEvEb6I34cpITzXN+wkIiYnU+ggoXT1V8rhBYuy9D/Nh4HXBjJncCqktZt90DhdLClSAGLjbHdHxEPDnE8jVO/1gJuzf3elOtqAO4kHRRQt69HI+IeYP5QxwleMTEzMzOzxaBvnT6mHjG19NowJL+Tj+o9nfSH9X6F9reRjtLdmLQS8mRuPx84LyIW2bLV5djeQskJWk1ju5404bqOtGrSS4cClxQmZkVH5Xf2oq8h84qJmZmZmY0mdZLfiYgrImJz4IPAqYX2P0bENqSJyeGS1s7tR3c5KRnK2PYC1iWt2uze5Ts7aQ5CBEDSx4HtgbOH2leveGJiZmZmZqNJ18nvRRFxC7BRsTg+tz8J3Au0Ou53WMcWEa+QgiLHDeH9C5H0TmCpiJje1L4n8CXggMJWtlp99ZInJmZmZmY2rCZOn8jYSWMH/w2hvgTqJb9vnIvLySdlLQM8J+mtkpbP7asB7wWGUtdxFXBYPp1rJ+DFiHgq9/9AybhWatSgSFoK2BdY5L6mZ94iaUrF8SySaC/pXaSC+gMi4pmma+3evUhfveaJiZmZmZkNqyEWuy+kZvL7QaQTtwZIJ3odnOsktgB+LWkGcDPw9YiYmfs6v9tjekmhj78HHgL+C/jn3NcalG/zWhG4KqeuzwCeAc7Lz7RKcV8XmFtxPB9l0cnE2cBKwE/zkb5XdRhjy74kvTuP8SPAf0q6t+K4Srn43czMzMyGXbHYfeyksUPqq0by+5nAmSXtNwLbtHjH0TXGFcBxJZd2Ik2Imu//E/DuFn2Vpri36qtFH+8oaduzxe1t+23R1//QxalenXjFxMzMzMxGlRoBix/LgYL3SLo910s0rj0qaWZePahS7L4ecLGk21qM7XBJv8v/DgeIiGvypKns/lO1IPzxBknr5fbNJd0h6VVJgzkhEXFORLTdupafv6QQivhoXi1qXDs5f08PStqrwhhvLfT1pKSf5/YTC+2zJM2T9GZJW0qaA6zYXMvTjldMzMzMzGzEDTw9UGvlpGbA4iPArhHxvKR9gIksfMTwbhHxbMUh9AO/jYjjS8b2ZuDfSKddBTA9j+35Nv2dHRFfyc9/BjgFOAb4C/AZ0iliXYuIwdBESd8AXsw/b0mqy9mKNMn6paRNI2Jem74GDwSQdBlwZW4/m3yql6T9gRMi4i957MtLerSbMXvFxMzMzMxG1Pgx4+lbp6/zjeXqBCzeXpgcdBUq2KW9gBsj4i/5fTcCe7d7oBCwCAuHPz6Tt0q9PpQB5aL/Yn3IOODiiHg1Ih4h1cPsULGvlUnHGf+85PKQi+O9YmJmZmZmI2rCdhOYsN2ElteHI2CxoDlUMIAbJAXwnxExEUDSMQARcV4XfbcKWGxL0mnAYaRVjd26eF8VOwN/iojfFcZ4Z7djzA4EpjRNppC0AmkCtsgqUje8YmJmZmZmo0mtEEMASbuRJiYnFZrfGxHbAvsAx0naBdKEpMtJSe2xRcSXIuJtwEUM8Y/7Es0rGbW/v5K+GvYHfpW3cdXmiYmZmZmZjSa1QgwlbQOcD4yLiOca7TlYkZzpcQUVtzX1cmwF/aSjjXsiZ6N8CLik0Fz3+1ud9N1cW3K5J4nwnpiYmZmZ2bCZOH1izzJMsjoBi+sDlwOfiIjfFtpXzHUTSFoR+AAwawhjux74gKTVcmDjB3Ibki6UtMikR9ImhV8PoEPAYn5miqQq26/2BB6IiMcLbVcBh0haVtLbgU2Auyr0+xHgmpxQXxzLKsCu5IL4oXCNiZmZmZkNm/6Z/bz46os96y8i5kpqBCy+EbigGLAI/CuwDilg8Rc5j+QUYHXg+6kWnLkRsT2wNnBFblsK6I+IybmvrmtMIuIvkk4lTZ4AvlbY3rQN8FTJY2dI2gyYD/yBdCIXktYBpgFvAuZL+hywJTAb2Jh08lUni6xkRMS9ki4F7iMFNR4XEfMkvaFDv4cAZ5S0HwjcEBF/qzCetjwxMTMzM7Nhtcqyq/S0vxoBi0cDiwQmRsTvgXc2t+dr3daXNJ67ALig2CbpTcDvIuKPJfeXbt2KiKcpOT1M0tbAZRExp8JYjmjRfhpwWlPzlu36jYixLdonAZM6jaUKb+UyMzMzM6tuDrCPpPOrPhARL0XER3rx8oiYFRGf70Vfw9WvpOVzoOPSpJWgSjwxMTMzM7NRpUby+1hJLxZSyk8pXPtsTi2/N2+X6mR50vaqVmNbJPm9w2dplfwuSd/Nn+ceSdtWGFux31Zp7atIulrSjPyZj+zQz8qFfgYkPSvp2/naMZJm5vbbcngjpIDJZYDnuzmpy1u5zMzMzGyxmDh9Iv0z+7t6pmbyO8CtEfGPTX1tDXyKdNrUa8BkSdcWMj9auWQEkt/3IRWmb0LKaTmXLvJaWqW1A8cB90XE/pLWBB6UdFEOqyzr56/AYBqmpOmkgwQg1eScl9sPAL4J7B0Rt0ral/TfRWVeMTEzMzOzxaJ/Zn+dE7u6Tn5vYwvgzoh4OSLmAjeTirnr6lnyO+kzXRjJncCqktbtdkAlae0BrJwT4VciFbvPrdjXJsBawK0dxl6LV0zMzMzMbLHpW6ePqUdMXahtmJLf/0HSDFJmxxfySV6zgNNyRsccYF/SSVhLQvJ7q77KTvZqpzmt/RzSkcFPAisDB0dE1TqQQ0mrRYMTEEnHAZ8nbd3avcuxLcQrJmZmZmY2mtRJLv8NsEFEvBP4f+TVg4i4HziTtLIxGZhBXj1YApLfh5LQXtSc1r4XMACsR9qidU4+NayKsuOHvxcRGwEnAV+uMb5BnpiYmZmZ2WjSdXJ5PhVrdv75F8DSjeL4iPhBRGwbEbuQtjV1qi/p6diaFJPfh9pXq7T2I4HL8xaxh4BHgM0r9PVOYKmImN7ilouBD3YzvmaemJiZmZnZsBt4eoCxk8Yu9K9mInyd5Pd1ck0FOX39DcBz+fe18n+uD3yIphWBLvUy+f0q4LB8OtdOwIsR8VR+pmM6fFaW1v4YsEfuZ21gM+D3FfptXnlpHvt+DG1S5xoTMzMzMxtea6+4Nuuu3HXddqmaye8fBo6VNJdUS3JIoU7isryy8DopBf353NdiTX4nBUjuCzwEvExa6SCv9LQtwikoS2s/FZgkaWbu56SIeLZCvx/N4yk6XtKepO/ueaDj8cjteGJiZmZmZpXUOd534OmB0gJ3gLGTxtYaR43k93NIRd9lfe3con1xJ78H6WjfZjuRjkuuMpaxJW1PklZyuuo3It5R0vbZKuOoylu5zMzMzKySmsf79ly3AYuF6++WNE/Sh/PvuzWFB74iqVOdxH7AMZJ+02JsJ+cxPChpL2if/C7pBzns8B5JP5O0Um7/vKT7cvsUSRvkvq7JE7BO31GrgMUTC+2z8vfx5nb9tuqrcL35ez0YuJ90HHNlXjExMzMzs8parX600mlVpFF7UlXdgMX83Jnkmg+AiLiJHB6YwxEfAm7oMIR7gV9HxNdL3rElafvUVqRTr34padOImNemvxMaR/lK+ibpVK4zgLuB7SPiZUnHAmcBB3cY26BWAYsRcTZwdm7fP7+/bTp7m7DGVt/rJcAlkmZXHS94xcTMzMzMFpPxY8bTt05f5xsXVjdg8dPAZcAzLa5/GLguIl7udkAF44CLI+LViHiENNFZpOC9qDApEbA8+UjgiLipMJY7Sadyda0kYLFokYL2Gn11+l4r84qJmZmZmS0WE7abwITtJizS3uuARUlvIQUN7g68u8VthwDfLDzzNWBaRLQ98atkbHc2ja1KwOIPSYXl9wH/UnLLUcB1XYyjqDlgsfHOFUip9MeXPlWhr4rfa2VeMTEzMzOz0aRO8OC3SadPlW6pkrQuMIaFtyOd0uWkpO7YiIgjSVu/7qdpu5akjwPbk7df1dBqVWR/4FedtnF16Kvt99otr5iYmZmZ2WhSJ3hwe+DiHGWyBrCvpLkR0diS9FHgioh4fTGMDYCImCfpEuBE+P/s/Xnc7mO9//8/nmWMxE6i2lGGJMMqK9qpTShDxbYbDA2WsD6k+qS2XT7tjfgpReNWSgj9WkWmKOO2E+0M28q1LJaxTZLMIpEMz+8fx3Fe6+1c53xda3HV8367XTfXdbzP9/F+nadbt87DMTz5LkA9ivczwKa2Hxu2mEbA4g4dLi+Q4j5CX/0+16FkxiQiIiIippKhAxZtv8L2arZXA04BPtz25XmovRY9nAnsJGlJSa8A1gSuAKgnaz1tWVcNT1yj9TtlFuP6+vdrgW8D29m+u+2+iQQsIukFwKY0NrF3q7FXXwN8rkPJwCQiIiIipgzbT1D2RZxHWfp0cjNgUdLtlJmKqyUd068/SatRZjl+3tZ+sKTthqztWuBkyl6RcymBjU9Keg6wBtC+bErACTXscC6wCnBwvXY4sCzwo3pM75m1rmEDFjsNuHYAzrf9p/FCutfYr69Jk6VcERERETGlDBuw2PaaGW1/30qHDeq2DxixtkOBQ9ua1wFOtf1o22ufAjbp0s+WXR4xoYDF2n48cPwgNfbrq3F9xiA19ZKBSURERETE4B4GZkpabtDBi+1rgE9MxsNt/2Qy+unQ76TVKGl1yhHCdw1zX5ZyRURERMSUMmzyu6Tta4L6mKQrJb2pce0LNQH9mppY3s+ywDI9alsg+b3Pe+mW/D5D0j2NxPU9Bqit2e9Qae09+nl+o58xSfdK+mq91jGdvuHFw9ScGZOIiIiIWKiGTXfvZcTk9wuBM21b0vqUfSBrS3o78DpK+vuSwM8lndOe+dHBVxZB8jvASbaHyRkZN2xae49+/kj5fFr3zgZOq392TKe3/Wtg2rDJ7xmYRMSk/h9G/PUbu3NslKTmiFhEjp59NLPmzloofY/yv/9d1ttlsssYT34HkNRKfp9n+6ra9rQbbDe/IC/D/GyRdYCf1w31T0iaQwkdPHnE2saT34FbJLWS3y/tdkO35PfJ0khr363R3EprHyoUUdKawErAJVDS6RuXLwPeP5FaMzCJ+Bu3EP4PIyIinkGz5s56Vv0HhG7p7r1MdvI7gKQdgM9Tvli/vTbPAQ6sMxXPA95COVHr2ZL8/i5J/wjcSJlZ+W2n+/uYzLT2nSmzOJ0GTxNJpwcyMIn4mzfK/2HE37bMrkU8+01beRoXzbho0vt9lvzvf9R09dOB0+sX/UOALW2fL+n1wC+BeygzG0/U149yKtfIye91edV/UJLfvwucBfzA9mOS9gJOoAwmhrUz0Dw2eTytvX1maQA7AR9ob2yk0286Qn3jsvk9IiIiIqaSkdPVAWxfDKze2hxv+1Db02y/lTKwuOmZqK3uQzkJeFf9+75G2vt3gA2HLaaR1v7TRnMrrf1W4N3ANyX90wB9bQAsZnt2W3srnX67UdLpmzIwiYiIiIipZOjkd0lrq1FTUAAAIABJREFU1D0cSHodsARwn6Tn1i/v1E3x6wPnT6C2yUx+X6Xx0u0oYZKt+0ZOfu+V1t6n351pC1hUj3T6UWQpV0RERERMGbafkNRKfn8ucFwz+R34V2BlSvL72bb3oMxCfFDS48CjlJOjLGlx4JI6ZnkIeH/dCD/SHhPb10pqJb8/weDJ78vV3+cAe9drH1NJnn+i3jej1jVs8vthfV81WL/vpeyDaWqm0wPcZnu7AWtbQAYmERERETGlDJv8bvsLlONx29v/TDmZq9Mznunk9/2B/TtcmnDye+P6jEH7tf3KDm3d0ulHkqVcERERETGlaMiAxXptsxoQeK2knzfab5U0t167coDHrw0cJulXXWrbVdJN9WdXKKnqtjumqks6RPPDH8+X9JJeNdv+SR2A9SRpmqTLND9UcqNGnw82AhMP6NevuoQ1dutL0jqSHgWWaf/30EtmTCIiIiJikZiMjBWNELAoaXngm8DWtm+TtFJbt2+xfe+AJZwL3Nsp+FDS3wEHUjaYG5hda3ugR3+H2/73ev/HgAOAvQaouZ8vAp+1fY6kbevfm9Vrl9h+x6Ad9Qpr7NRX/XexdN1gP7DMmERERETEItHKWJmg8YBF238BWgGL2L7K9q0d7tkFOM32bfV1E96o3cVWwAW276+DkQsogY1dtaXMN8MfJ1qzgeXq7y9giJPLummENZ4x0b46yYxJRERERCwyg2SsLISAxbWAxSVdBDwf+JrtE+s1A+dLMvBt20cD1OwQbH+rT9/9ahskYPFQ4IPAg5SQx341D+LjwHmSjqBMRryxce0fVFLu7wD+pXV4wACeFtY4wb4WkIFJREREREwlo4QYLkbJAdkCWBq4VNJltm8ENrF9R10qdYGk621fPOSAZCK1YfszwGck7Q98hLIcrFfNg9ibkhZ/qqT3AscCWwK/Ala1/XBd4nUG5VjjQbSHNU6krwVkKVdERERETCWjhBjeDpxr+091L8nFwAYAtu+o/7wbOJ2yVGxR1tY0ixqw2KvmAe0KnFZ//xH1fdl+yPbD9fezKbMyfTeodwprHLWvbjJjEhEREREDG7tzjM2O32zke6etPG2iJYwHLAK/o2R17NLnnh8DR0pajBKuuDHwFUnLAM+x/cf6+9uAgydQ23nA5yStUP9+G/XIX0knAkfavqJ5g6Q1bbfS5rejBix2q7necyHwQdu/61HLHcCmlIMANqcm2ktaGbir5rhsRJmouG+AfhcIa+zV1ygyMImIiIiIgeyyXr/v/wvfKAGLtq+TdC5wNfAUcIztayS9Eji9hgMuBsyyfW7ta+g9Jrbvl3QIZfAEcLDtVqji+sDvO9x2mKRX1bp+A7Se263mbmGN7fYEvlYHNn8GZtb2dwN7S3qCEja5Ux1Y9Ou3U1hjx7761NWVhrl3+vTpvvLKQY53joiIv1at/1Lab/NqRDwzns3/Gx20NkmzbU9f+BUNT9IMYHqn44J73LMccKzt90zC89cFPtQtF+XZ1G89Lnj6oEcxZ49JRERERMTgHgW2kXRM31dWdS/GhAclta+uYY3Pln4lLS1pDFicMtszkAxMIiIiImJKGTb5vUdC+VKSrpA0p6arf3aAx78aWIoum9ol7V9ruEHSVgO8l2Pr86+WdIqkZWv7yyX9TNJV9dq2A9TW6vP5jfc6JuleSV8dpV9Jr2rr6yFJH29c/2h9r9dK+mJtnk7ZF/NAYylbX9ljEhERERGLzEQ2z8Noye9Vp7Tzx4DN63G3iwO/kHSO7cv6lPEV20d0qG0dyl6M1wAvAf5T0lq2n+zR176tXBBJX6YcF3wY8G/AybaPqv2eDazWpy4AbP8RGD9lQNJs5p/QNVS/tm9o9VU/+99RTi9D0lso4Zbr236slU5v+5I64PnJIPW2ZGASEREREYvEJG2eH09+B5DUSn6fZ/uq2jZQR3Wj9sP1z8Xrz8ibt2sdP7T9GHCLpJtrvZf2qKE1KBElr6T1/ElJbpe0JrAScMkk9LsF8Gvbv6l/7w0cVt/vKOn0T5OBSUREREQsEjM3nMnMDWf2fd1CSH6HLgnldRZgNuVEqm/Yvry2HwxcafvMAfpu1tacbRk0+f27wLbAPOCTtfkgSiL9R4FlKOGIo9gZOKlxWtZE+t0J+EHj77WAN6sk1/+Z8rn+T8c7B5A9JhERERExlYySrt5KKN8A+A9KQnm50X7S9jRKGOJG9XQqbB8w5KBk1NqwvRtl6dd1wI61eWfgeNsvowxavleP9B1W+2BipH4lLUHJWflRo3kxYAXgDcB+wMkadLqqgwxMIiIiImIqGTpdfZCEctt/oOxN2XpR1tZ4/pPAScxPft8dOLleu5Sy4X6oVHVJGwCL2Z7daB61322AX9m+q9F2O3CaiysoJ3CNnPyegUlERERETCXjye/1v+LvBPSc2ZC0cuu/5DcTyiW9SNLytX1pyrKm67v31NeZwE6SllRJpl8TuKL2f6Gkpy3rUrFG63fgnY3n30bZ04Gk1klg90h6qUpC+yB25umzJRPpt1NfZ1BS5ZG0FuUkroEySzrJHpOIiIiImDJGSX6ne9r5KsAJdZ/JcyinVf2k9jX0HhPb10o6mbJX5AlgH9tPqnuquurzl6u/z6FsKIey1+Q7kvalLAeb0aj5iQFLei9luVbT0P1Keh7lFLT/03bpOOA4SdcAfwF2nUjyewYmERERETGl1OVYZ3do/zrw9Q7tRwJHdmi/Gnhtl2ccMGJthwKHtjWvA5xq+9G21z4FbNKln3ldrr2BclzyILW8cjL6tf0I8MIO7X8B3j9ILYPIUq6IiIiImFI0SQGL9dqtkubW9isHePzawGGSftWltl0l3VR/doXeqeqSDpd0fQ07PL2xtOx9bcGGT0maZvvIQWZxJH2lce+Nkv7QuPbFGoh4naSvS1KvfiVNk3RZ6zOqy+GQtEKt+WqVoMp1a/sWlI3867Tv5eklMyYRERERsUgdPftoZs2dNdK9mtyAxZa32B50b8S5wL22P9Khtr8DDqQknxuYXWt7oEd/FwD71yVqXwD2Bz5l+/vA92u/6wE/tj02YI3Y3rdR10epM0OS3kiZMVm/Xv4FsCmdP7OWLwKftX2OSnDiF4HNgP8HjNneQdLalH8vW9i+EFha0q2D1guZMYmIiIiIRWzW3FmM3Tnwd+x24wGLdSlRK2AR21fZvnVyqhzJVsAFtu+vg5EL6HPKl+3zbbf2dlxGOcmrXaeN58No3m/KhvclgCUpoZJ3dblvvEw6hzKuA1wIYPt6YDVJLx61yMyYRERERMQiN23laVw046KO1xZlwCLlS/f5kgx82/bRAJL2ArD9rQH67lVb34DFhg9RjgxutyN18DUsSasCrwD+C8oRwZJ+BvyesuH+SNvX9enm48B5ko6gTGy8sbbPAf4Z+EVd3rUqZWDVb6DTUQYmERERETGVTCRg8eG6FOkMylG+AJvYvkPSSsAFkq63ffGQA5KJ1FZulD5DORXr+23tGwOP2L5mhHqgHKd8Ss1JoR5P/Grmz8xcIOkfbV/co4+9gX1tnyrpvcCxlKOVDwO+JmkMmAtcxeAnhi0gS7kiIiIiYiqZ1IBF23fUf94NnE5ZKrbIaoOyYR54B/C+Dsfttie3D6v9/h2Ay2w/XD+TcygncvWyK3Ba/f1H1M+ofq672Z4GfBB4EXDLqIVmxiQiIiKiYSIbs58Nxu4cY9rK057pMhbQ/FwnWON4wCLwO8oX71163SBpZeCumtfRDFhcBniO7T/W398GHDxqYZRslc9JWqH+/TbKZnYknUhZNnVFW21bA58CNq3H8javPQd4D/CPbe0d+2on6VXACsCljebbgD0lfZ4yw7Mp8NU+/d7B/A3ymwM31dcvT5nN+QuwB3Cx7Yd61dRLZkwiIiIiGia4MTu6mKzPtW4UbwUsXkcJRRwPWJR0O2Wm4mpJx9Tb3g1cU/eYfJ0asAi8mLI/Yg4lof2nts+tfe3V2mcyRG33A4dQBk//Axxc26CcgvX7DrcdCTyfsqRqTFJzCdk/Arfb/t+2e7r11W5n4IdtszCnAL+mLL2aA8yxfVaffvcEvlQ/p88BM2v7q4FrJV0PbAP83wFq6iozJhERERFtem3Mfrbb7PjNnukSump9rhOtcRIDFv8X2KDLM0bZY4Lt4yiJ6ONqsvtNtn/b4fVr9OjrItqWWfXqq8P9B3Voe5IFE9z71fgLYMMO7Zcyf6/OhGXGJCIiIiJicI8C2zRmY/qqezHeMxkPn8y+Fla/kpauG+IXB54a9L4MTCIiIiJiShkh+X37mk7eSi5/U+Pak42E9L6J6pTlS0vRZVO7pP1rDTdI2mqA9/L9+tprJB0nafHa3jFVfVCSTmq8r1vrQAFJGzXa50jaYYC+tpD0q3rPL+rJXkh6uaSfSbqq1rltvWU6JSflgcZStr6ylCsiIiIiFrmxO8dGWtI1YvL7hcCZdfP7+sDJwNr12qP1VKlhfMX2ER1qW4eyGf81wEuA/5S0Vuuo3i6+D7y//j6Lson8KLqkqg9aoO0dG3V9CXiw/nkNML0mza8CzJF0ViPksZOjgO1tXyfpw8C/ATPqP0+2fVR972cDq9m+pA5SfjJovZCBSUREREQsYrus1/MQrX7Gk98BJLWS3+fZvqq2Pe2G1lHB1TIMmC0ygu0pm80fA26RdHOt99JuN9T9MgBIuoL5+SLrAJ+vr7le0mqSXmx7qPBClQ/jvZTTtGg7+WspBvssuiW/d2sfSQYmERERETGpOh253DwieOaGM5m54cxOtwILJ/m9Lln6PLAS8PbGpaUkXUkJBjzM9hn19QcDV9oeZHlXs7bL2mobKPm9LuH6APNPtpqsVPU3U45KvqnxrI0pG/RXBT7QZ7YEyizO2ZIeBR5i/ob8g4DzJX2UMuDbcsjaniZ7TCIiIiJiUi3kI5dHSle3fbrttYF/ohzp2/Jy29MpWShflbR6ff0BQw5KRq6t+iYlB+SS+vdhwAp1b8hHGT1VfWfaAhptX277NcDrgf0lLdWnj32BbW2/DPgu8OVG38fX9m2B79XslZFkxiQiIiIiJl37kcuTeIzxSOnqLbYvlrS6pBVt39tIfv9fSRcBr6XkfCyy2iQdSElNHz/GtwYV7lavi5KoPlSquqTFKLMuCxz1W59xnaQ/AesCV3bp40XABrYvr00nAefW33cHtq59XVoHOCsCdw9TZ0tmTCIiIiJiKhlPfpe0BGWzec+ZDUlr1C/3SHod5cSo++rJV0vW9hWBTYB5E6jtTGAnSUuqJNOvSQluRNKFkhZY1iVpD2ArYGfbTzXal6/vD9pS1bv11cGWwPW2b2/0+4o6YEHSqsCrgFt79PsA8AJJa9W/30oJtoSSIr9Fvbd1Wtk9A9TVUWZMIiIiImLKqKdJtZLfnwsc10x+B/4VWJmS/H627T2AdwEflPQ4JYdkx3pC16uBb0t6ivIf7A+rp3uNtMfE9rWSTqYMbp4A9rH9ZF3etAbQ6ejcbwG/AS6tY6fTbB9MOZb4RElP1v52r3X16qvdTrQt4wLeBHy6fhZPAR+2fW+3fuvnvSdwav2cHgA+VC9/EviOpH0pS9ZmtKXMDyUDk4iIiIiYUkZIfv8C8IUO7b8E1uvyjANGrO1Q4NC25nWAU20/2uH1Hb+P90hV79pXhz5mdGj7HvC9Yfq1fTpweof2eZRZpkmRpVwRERERMaVoyIDFxvXXqwQqvrv+vaqk2TU48FpJew3w+LcDn5f00w79S9LXaw1X12Vj2L7G9ie6vJdza9DhtZK+VXNakHS4pOtrP6dLWr5fX40+n6/5IYpjku6V9NXG9fdKmlefOWuAGneWNLfWcm7rs1X3EMd9gOuBV/T9NBsyYxIRERERi8SooYpNGi1gsXXfFyhLwFp+D7zR9mOSlgWuqX312rB+LfAF26d0uLYNZZZjTcoRxkfR/yjj99p+qO6BOQV4D/BD4AJg/7qU6gvA/sCn+vQFgO0/AuOhkZJmA6fV39esfW1i+wFJK/Xqq+5H+RqwTl3y9UXgI8BB3UIcbX+jDtwSsBgRERERzy4TDFVsGjpgsfoocCrliFwAbP+lcX1JJr6aaHvgxLrP4rK6gX0V27/vdkNrQzvle/kS1OOFbZ/feNllwLtHKagORFYCWscQ7wl8w/YD9Tn9TtBS/VlG0n2UQMWb257xtBDHUWVgEhERERELXb9QxabJDlisJ03tQPni/Pq2a38P/JSy8Xu/1myJpGOAb9nueIzuELW9lDIz06u+8ygDrnMosybtPkQ5pncUOwMnNTalr1Wf+d+UwwMOsn1ut5ttPy5pb2Au8CfgJmCftpctEOI4iuwxiYiIiIipZJQQw68Cn7L95AI32r+1vT5lYLKrpBfX9j2GHJSMWhu2twJWoczaPG3WQdJnKCd8fX/IWlraT+ZajLLUbDPKoOWY1v6VTlQS6fem5Lu8BLiashSsaYEQx1FkxiQiIiIiBnL07KOZNXdW39eN3TnGtJWn9X3diEYJMZwO/LAu8VoR2FbSE7bPaL3A9h2SrqX81/9OsxYLq7bW8/8s6UzKcrALACTtCrwD2GKUY3glbQAsZnt2W42X2X4cuEXSDZSByv906WZare/Xtc+TgeaBAz1DHIeRGZOIiIiIGMisubMYu3PsmS5j6IBF26+wvZrt1SiDjg/bPkPSyyQtDSBpBcrRtzdMoLYzKXkpkvQG4MHW/hJJ17e/WNKyklapvy8GbEs5zQpJW1M2u29n+5HGPS+VdOGA9XSayTgDeEvta0XK0q7Wfp0FagR+B6yjkgAPTw9YhA4hjqPKjElEREREDGzaytO4aMZFPV8z0ZO3ehkxYLGbVwNfkmTKMqwjbM+tfY2yx+RsyuDiZuARYLfa14p0Xua1DHCmSvr8c4H/ogQuAhxJWdp1QZ3pucz2XpQlX08MWM97az1N5wFvkzQPeJKyr+a+bjXWmaTPAherhDL+BpjReEmnEMeRZGASEREREVPKsAGLba+Z0fj9AmD9Lq/rNaDp1rdZcGM4wBsoRxy3v/4u2jbjN66t0eUxHfvq0scru9T4ifozUL+2v8X8AVP7tRmD1DKIDEwiIiIiIgb3IHCIpBXrF/a+bA+V59GnryMnq6+2fietRklvBr4J3DvMfdljEhERERFTikZIfpe0meYnvP+80X6cpLslXTPg46+ibKCf3qW2XSXdVH92HeC9dEx4l7SEpO/WxPU5kjYbsL5Wv19ppLLfKOkPjWtfkHRN/dmxVz/19S+X9DNJV9U6t63tGzWeMUfSDvWWKynLxN7Y/u+hl8yYRERERMQiN+gJX+1GSX6vX/a/CWxt+7a2tPPjKfs5ThyijJNsf6RDbX8HHEgZtBiYXWt7oEdf3RLe9wSwvV6t9xxJr7f91CAF2t63UddHKcf9IuntwOsop20tCfxc0jmNoMdO/g042fZRktahLKNbDbgGmF5rXwWYI+ks248C0yTdOkitLZkxiYiIiIhFbgInfI0nv9fk9lbyO7avsn1rh3t2AU6zfVt93Xjaue2LgftHKaSDrYALbN9fByMXAFv3usH2+bZbm9kvoxwxDLAOcGGj3j/QZZZmAM3TudYBfm77Cdt/Aub0q5EyyFqu/v4C6hHIth9p1L4UA2S29JIZk4iIiIh4RnQ74Wuyk98pR+IuLuki4PnA12z3nCGRtBeMb/weVLfk90E1E97nANtL+iElG2XD+s8rhugPSasCr6Cc+NXq90BJXwaeRzk6eF6fbg4Czq8zL8tQjghu9b8xcBywKvCBxkBlaBmYRERERMRUMkq6+mKUL/ZbAEsDl0q6zPaN3W4YckAykdrKjQsmvB9HOc74SsoRvb9k8GOCm3YCTmml3ts+X9Lra3/3AJcO0O/OwPG2vyTpH4DvSVrX9lO2LwdeI+nVwAl1WdifR6gzS7kiIiIiYkoZJV39duBc23+yfS9wMbDBs6S2ZsL7+1oJ73Wp1b62p9neHlgeuGmEmhbIGbF9aO33rZTBVL9+dwdOrvdeSlm29bRN7bavA/4ErDtCjUBmTCIiIiJiIRi7c6xn0OLYnWNMW3naKF2PJ79TUsl3ouwh6eXHwJE1XX0JytKvr4zy8D7OAz5XU+QB3kbZzI6kE4EjbT9tKVYj4X3TtoT35wGy/SdJbwWeqBv8u/bVTtKrgBUosyKttucCy9dQxfUpOS7n12ufB66wfXpbV7dRZpuOrzMjSwH31H8Hv62b31cFXgXcOsgH1UkGJhERERExqXZZr984YXSjJL/bvk7SucDVwFPAMbavqff8ANgMWFHS7cCBto8dZY+J7fslHUIZPAEcbLu1sX594PcdbuuW8L4ScJ6kpygDsA807unWV7udgR+2ZmGqxYFL6rMeAt7f2BeyHnBmh34+CXxH0r6UpWkzbFvSm4BP10T4p4AP1xmpkWRgEhERERGTauaGM5m54cyer+k1m9LPKMnvtg8HDu/QvnOX14+yxwTbx1H2h4yTtBxwk+3fdnh9x4T3errYq9rbe/XVoY+DOrT9mXIyVyeL16Va7ffMAzbp0P494Hv96hhU9phERERExJQybMBiDVd8sBEGeEBtf1WjbUzSQ5I+3ufxGwF7S7q+S20LBCzafsj2e7q8/pAaWjgm6XxJL6ntkvT1+n6ulvS6fn219XtJ433dIemM2v4CSWfVQMRrJe3Wusf2Vh36eX7bZ3SvpK/Wa92CF7eQ9GfKiWQD5a5AZkwiIiIi/ur0298xkX5H3BfStb9h6xwlYLG6xPY7mg22b6AEDbb6/R3Qvr+i3RXAU5MYsHi47X+v938MOADYC9gGWLP+bAwcRf9jkZvv7c2Nuk6l7LMB2AeYZ/udkl4E3CDp+zUTplM/f6R+RrWv2cBp9c+OwYu2L5S0NvCTxlK2vjIwiYiIiPgrsjD3d0ymCdQ5HrAIUHM+tqd82b6qto3S7xbAr23/ZtTCaAQs1jpaAYs/6HZDW+L6Msw/Xnh74MS6P+QySctLWsX2IHtLxkl6PrA50JoZMfB8lQ9pWUq45EDHEEtak7L35ZJGXwsEL44qA5OIiIiIvyKD7O8Y1WTOwvSqcyEELAL8g6Q5lC/P/9LaMN/wtGN1F2XAoqRDgQ8CD1ICD3v1NdTABNgBuLAxADqSssH9DkrY5I62B11utTNwUmMz/UF0CV4cRfaYRERERMRUMkqI4a+AVW1vAPwHcMbTOpSWALYDfjTeof2tETbAjxSwaPsztv+eEq7YWiI2clhjm515+ozNVsAY8BLKEq0j64b6QbRnorSCF18GbEsJXhx5fJGBSURERERMJUOHGNYN4w/X388GFm9ujqfs5/iV7bsWdW1tZgHvmqS+kPRCytK3nzaadwNOc3EzcAuw9gB9bQAsZnt2o7lv8OIwMjCJiIiIiKlkPGCxznTsROfsjXGSVq57KpC0EeU78H2Nl7TPKozqPOBtklaoIYtvq21IOrE+u722NRt/bge0Tvs6E/hgPZ3rDcCDrf0l3U4E6+A9lA3of260tcISkfRiypHErf06vfrt9Bk1+xoPXhywtgVkj0lERERETBmjBCwC76Yc8fsE8CiwU2ufhErC+luB/9N8ziIMWDxMJaH9KeA3lBO5oJxwtS1wM/AIdfN6nekZdHf/TsBhbW2HUBLc59Z+PmX73gH6fW+tp6lj8OKAtS0gA5OIiIiImFKGDVi0fSRl03envh4BXtihfVEFLL6rva22m3K0b7s3UI5LHqSWzTq03UGZyRmqX9uv7NDWMXhxVBmYREREREQM7lFgG0nH1NmYvuqJWH1DEQfs6yeT0c/C7FfSm4FvAvcOc1/2mERERETElDJs8nu9tllNLr9W0s9r2yjJ70szP7ujU20LJL/3eS/dkt+3b7RfKelN/fpq63cLSb+q9/9C0hqNa++VNK9+FrMG6GvHWsu1kr7YaJ8h6Z7G59caqN0BPEkJmhxYZkwiIiIiYpE5evbRzJrb97twV6Mkv0tanvJf8Le2fZuklWDk5HcoWR4LO/n9QuBM25a0PuX0q76nZzUcBWxv+zpJH6aktM+om+33Bzax/UDrs+imnux1OLCh7XsknSBpC9sX1pcs8FnY/jUwTdLDQ9SbGZOIiIiIWHRmzZ3F2J1jE+liPPnd9l+AVvI7tq+yfWuHe3ahHJF7W33d3R1eM6nJ73Uw0kp+76pb8rvthxsbyZuJ8IPqlsq+J/CN1mCpy2fR9ErgRtut07b+k/lHGk+qzJhERERExCI1beVpXDTjoq7XF0Ly+1qU7JKLKGnnX7N9Yttrnm3J70jaAfg8sBLw9iHqANgDOFvSo8BDlM3tUD4LJP035VSzg2yf26Ofm4G1Ja1GeT//BCzRuP4uSf8I3Ajs22mD/6AyYxIRERERU8koieiLARtSvtxvBfy7pLXGO3x2Jr9j+3Tba1MGA4cMWcu+wLY1lf27wJdr+2LAmsBmlGySY+pSt261PQDsDZwEXALcCjxRL58FrGZ7fcpMyglD1vg0GZhERERExFQySiL67cC5tv9k+17gYmCDxvVnY/L7ONsXA6u3b+jvRtKLgA1sX16bTgLe2Kjxx7Yft30LcANloNKV7bNsb2z7H+rrb6rt99l+rL7sO5TB38iylCsiIiIiBjZ25xibHb/ZhO6ftvK0iZQwnvxO2ay+E2UPSS8/Bo6UtBhlGdLGwFca1ycz+f1zNfUdSl7I/lCS34EjbV/RvEHSmrZvqn+OJ7/XU7R+XTe/v67WfV+9dn2dSenmAeAFktayfSPloIDr6rUzKO/3+DrQWYtG8nunfiWtZPvu+r4+TAlbRNIqrTT6Wvt17fcOIwOTiIgY2kS/mEQ8m03CF+e/Wrus1+/7/8I3SvJ7PZnqXOBqSsL6Mbavqfc8W5Pf3wV8UNLjlOyUHesgpW/ye/2M9gROlfQUZaDyoXr5POBtkuZRjvTdz/Z9ffr9mqTWDNPBdbAD8DGREkt4AAAgAElEQVRJ21GWdt0PzOhVVz8aJjV++vTpvvLKKyfyvIiImOImetRnxLNda2DSa3N2jK71HzV6bn6XZtseKgNjUZE0A5je6bjgHvcsBxxre8Ihi5LeAbyyptxPmoXRr6SHbS876OszYxIREUOZueFMZm4485kuI2KhyWzgs5+krYGvUWZMjrF9WG3/CPBxYHXgRXU/SeuezYCvAosD99reVNJSlP0mS1K+F59i+8A+j387sIOkN9p+XYfa9gd2p8xGfMz2eb2S3yUdS8k9EeVkqxm2H64nXX2VMtOyk+1TYPCEdklbUPJHngM8XPu9eZR+Je0IfIbyef/U9r/W9r2Afep7fRiYaXteff0J9JnZaZeBSUREREQsUhNZDjqZAYvAY8DmdSCwOPALSefYvqxHCdcCl9s+okNt61D2vLwGeAnwn3Wfx5M9+tu3lWUi6cuUU7kOA26jLI36lx739tIxYHHYfvsELM5qLXWrS7q+TPmMT5J0OeXfxcAyMImIiIiIRWYS9qmMBywCSGoFLM6zfVVtW+CxdAhYrAGGrXTyxevPsEGGTdsDP6wnVd0i6eZa76XdbmgMSgQszfyAxVtr+1Mj1tIxYHGEfrsFLF7YLRxyVBmYRERERMQiM8hy0EUZsFhnYGYDa1AS0S+v7QcDV9o+s0/f7bU1Z1sGDVj8LrAtMA/45BDP66VbwOKwegYsStoH+ERt23wC9SbHJCIiIiKmlEkNWLT9pO1plMyRjSStW9sPGHJQMmpt2N6NsvTrOmDHIZ/ZTbeAxaH0CVjE9jdsrw58irJcbGQZmERERETEVLIwAhax/QfK3pStF3Ftrec/Sfnyv0DA4rD6BCwOrVvAYpsfUmZTRpaBSURERERMJeMBi5KWoGw27zez8WPgzZIWq7klGwPXSXpR3RiPpKWBLakBhyM6E9hJ0pI1AHJN4Ira/4WSnrasS8Uard+Bdw7yfEn9XjMesFj/bgYsDt1v67CARsDiMfXvZmL82+k8YBlY9phERERExJQxmQGLktYHTqj7TJ4DnNw6NneUPSa2r5V0MmWvyBPAPraflPQcyh6W+9tuUX3+cvX3OZRlU0h6PXA6sALwTkmftf2aiQYsjthvt4DFj0jaEni8PmPXvh9SDwlYjIiIiGgYJAAwFq5necDiQcDDnY4L7nHPusCHbH9iEp4/JQIW62b5n9hed9B7MmMSERERETG4h4GZkpazfcAgN9i+hnJy1YQNGrD4TPYr6c2U3Jh7+722KXtMIiIiImJKkbS1pBsk3Szp0432j9Q216VJrfYXSDpL0hxJ10rarbavKmm2pLHavtcAj1+XkneywKb2umfk67WGqyUtkAzf9vrnSfqppOvr8w9rXFu17ku5WtJFkl42QG3NvjeX9CtJ19RQxMV6fRZ9+tpZ0txay7mtz1bSNEmX1c/vSkkb1VteQslkebhrpx1kxiQiIiIiFqmjZx/NrLmzRrpXIyS/A/tQAhjfWU+sukHS94HfA2+0/ZikZYFral/9TtLaz/YpHdq3oWx4X5Oywf4o+mesHGH7Z3Uj/4WStrF9DnAEcKLtEyRtDnwe+ECfvgCoe1pOALawfWPdL7MrcCxdPgvbf+nS12LA14B1bN8r6YuUdPqDgC8Cn7V9jqRt69+b1eT3uxgytT4zJhERERGxSM2aO4uxO8dGvX08+b1+mW4lv2P7qlayeRsDz68nXy1L2YT+hO2/1JR2gCWZ+Hfj7SmDCdu+DFhe0irdXmz7Eds/q7//BfgV5YhhgHWAC+vvP6t9D+qFwGONTeoXMP8Y4o6fRY++VH+Wqfcsx/zZoo7p8qPKjElERERELHLTVp7W9YCBhZD8fiTlKN87KMnvO9p+CkDS3wM/pZyatV9rtkTSMcC3bA9z8lOn2l5KmZnpSeXY4ndSZiegnND1rvr3DpTBxAtt3zdAHfdSku6n1/rfzfx8la6fRSe2H5e0NzAX+BPlSOB96uWPA+dJOoIyqBs5KwUyYxIRERERU8so6epbAWOUvQ/TgCPrEb3Y/q3t9SkDk10lvbi27zHkoGTU2lrLpX4AfN32/9bmfwE2lXQVsCnwO3rPbMx/YDl2dyfgK5KuAP7YuLfrZ9GltsUpRxi/tt5zNbB/vbw3sK/tv6ckzR87SH3dZGASEREREVPJKOnquwGn1SVWNwO3AGs3X1BnSq4F3ryIawM4GrjJ9leb9dj+Z9uvBT5T2x4ctBDbl9p+s+2NKEn3rfDDvp9Fm2m1v1/XAc/JzJ8Z2RU4rf7+I8oyu5FlKVdERERELBTdNrmP3TnGtJWnjdrtePI7ZRZhJ2CXPvfcBmwBXFJnRF4F/G896eo+24+qpJpvAnx51MIoS6Q+IumHlOVlD9r+PZRUddsLDAAk/f8o+zP2aGtfEbi/LrPaHziuca1jX233r2T7bklLAp8CDq2XOn4WPfr9HbCOpBfZvoenp8jfQZnNuQjYnAkmv2fGJCIiIiIWiglucu/I9hOUU6HOo3xBPrmZ/C7pdspMxdV1nwjAIcAbJc2lbCj/lO17gVcDl0uaA/ycckLW3NrXMZKGDXk8m/Il/2bgO8CHa18dU9XrwOgzlI3uv6rH7rYGKJtRTsy6EXgxdWDRra8O9pN0HWXp1Vm2/6u2d/wsuvVbZ5I+C1ws6WrKDMrn6uU9gS/Vz+9zwMwB6uoqMyYRERERsdB02uS+2fGbTahP22dTBgHt7V8HFkgur1+u39ah/QJg/S7P2KNTe5+6zPyN4U1voBxx3P762+kyyKjHEXc6krhjXx3u3w/Yr0N7x8+iV7+2vwV8q0P7L4AN+9UyqMyYRERERMSUMlkBi/XauZL+IGnQ5PO1gR9I+mmX2vavNdwgaSsoqep10NTp9cfWuq6WdErNU0HSJyTNq+0XSlq1X19t/XYLWNyvzsyM1WtPSvq7PjVuUfsak/QLSWs0rr231nmtpFm17WPAuZSN9gPLjElERERELHJjd46NNHMymQGLNTvkcOB5wP8ZsIRzgVNsH9GhtnUoe15eQznB6j8lrWX7yR797Wv7oXr/lynL1A4DrgKm236kHtf7RWDHQQrsFbBo+3DKe0bSO+vz7+/T5VHA9ravk/Rh4N+AGZLWpOx/2cT2A5JWgvkzV5KGSn7PjElERERELFK7rLfLRDa/T1rAYr3nQspxupNhe+CHth+zfQtlr0nPk6oagxIBS9dasf0z24/Ul13G/ODFQfQKWGzamXJMcT/dghT3BL5h+4Fa891D1LiAzJhERERExCI1c8OZzNyw+z7pRRmw2LWGMstwpe0z+/TdXttlbbW9tN9Nkr4LbAvMAz7Z4SW7A+cMUUevgMXWM58HbE2ZoelnD+BsSY8CD1H2owCsVfv6b+C5wEG2zx2izqfJwCQiIiIiFtDtqN9hTPBY4G4mErC4ObA6cIGkS1qzFZ3YPmAR1Ybt3eoStf+gLNf67niH0vuB6ZRjeQdi25JaAYtLAuezYDjjO4H/HmAZF5TwxG1tXy5pP8qRyntQxhJrUk4QexnlCOJ1bf9h0FqbspQrIiIiIhawMI76nSQLJWDxGawNgLoP5SQaS64kbUk5Tng7248NU0iPgMWWnRhgGVfdk7OB7ctr00nMD1i8Hfix7cfr0rUbKAOVkWTGJCIiIiI66nTU7zAmeixwF5MWsLgQajsTmFU3sb+E8iX9CgBJFwIftP271ovrvpLVbd9cf38ncH299lrg28DW7Xs3JhiwiKQXUGZg3t92zwI1Ag8AL6ib+G/k6QGLZ1D2qRxfT0Fbiwl8rhmYRERERMSUYfsJSa2AxecCxzUDFoF/BVamBCyeXfNIDqF8eZ5LWW7VClhE0iWU2ZNlazjj7rbPG2WPie1rJZ1M2SvyBLCP7SfrKVlrUDbdNwk4QdJy9fc5wN712uGUjfo/KmMWbrO93ZABi++grJA6qhGwCLADcL7tP40X0qXG+nnvCZwq6SnKQOVD9fJ5wNskzQOeBPazfd8AtXWUgUlERERETCmTFbBYr725S/soe0ywfSiN2YlqHeBU24+2vfYpYJMu/WzZ5RETClis144Hjh+kxvr604HTO7Qb+ET9mbAMTCIiIiIiBvcwMFPScoMOXmxfwyR9ebc9aBDksP1OWo2SVgdOBe4a5r5sfo+IiIiIKWWE5PeOaef12q2S5tZrVw7w+HuBFSh7SDrVtqukm+rPrgO8l0NquvuYpPMlvaS2S9LX6/u5WtLrBqit2W/HtHZJL5f0M0lX1X63HaCvfWuy+zWSfiBpqUaNh0q6UdJ1dSkdlFPElmX+XpSBZMYkIiIiIhaaURPeu9EIye8DpJ2/pbXnZEAn2V4g/6MOdg6kfDE3MLvW9kCPvg63/e/1/o8BBwB7AdtQNs+vSclpOYr+eS1NHdPa6z9Ptn2USlL92cBq3TqR9FLgY8A6th+te2h2oiwFm0E5hWxt2081kt9PknQX8C9D1JuBSURERMTfgmFzSSYjg2SX9fodljWS8eR3AEmt5Pd5tq+qbb3uHzTtfBRbARe0Bj2SLqCEGHZ9XluWyjLMzz3ZHjix7uO4TNLyklax/fsBa+mW1t6tvZfFgKUlPQ48r3HP3sAurbDKJL9HRERERF+tXJKFEHjYVb+E924WQvJ76bdz2rmB8yUZ+Lbto+tr9wKw/a3BK+9Y2yDJ74cCHwQeBN7Sp69BBybd0toPorzfj1IGQt022QNg+3eSjqAcufwo5TSv8+vl1YEdJe0A3AN8zHZ7XsrAMjCJiIiI+BsxTC7JQsogmQwjpatXndLON7F9R12GdEHNCLl4yAHJhGqz/RngM5L2pwyaDhy1r4Zuae07A8fb/pKkfwC+V9Pan+rUiaQVKLM3rwD+QDm++P22///AksCfbU+X9M/AcUDHU84Gkc3vERERETGVjJyuToe083qUcGsZ0umUpWLPRG0As5if/D5yX+qd1r47cDKUdHhgKWDFBTqZb0vgFtv32H4cOI2nJ7+fWn8/HVh/kPq6ycAkIiIiIqaS8eR3SUtQBht9QxAbaec/brQtI+n5rd8pWSfXTKC2VuDgCnWm4W21DUknSlpg0CNpzcaf21GT3ynv6YP15Ks3AA+29pdIup7extPa69/NtPbbgC1qP6+mDEzukfRSleT3drcBb5D0PJXNO1vw9OT3zevvmwI39qmrpyzlioiIiIgpY8Tkd+iQdg68GDi9bpZfDJhl+9za19B7TGzfL+kQyuAJ4ODGsrH16bw/5DBJrwKeAn5DOZELymlZ2wI3A48Au9W6+ia/90lr/yTwHUn7UpaGzbBtSatQ0urb+7pc0inAr+r1q4CjW7UD3699PUxZKjayDEwiIiIiYkoZNvm9XjuetrTzerLXBl1eP8oeE2wfR9lrMU7ScsBNtn/b4fXvam+r7Qb26XBp0OT3bmnt8+icNt+1X9sHUva9tLf/AXh7v1oGlaVcERERETGlaPiAxRUknV4DBa+QtG7j2nGS7pY06BKujYC9uy2nUoeARdsP2X5Pl9d3C1h8X22/WtIvJW1Q+/pJHYB16qvb+5c6hDVKWlXSbEljlKN/O4ZGdnjOR+vnf62kL9a2JSR9VyWs8lbKIPCBeu1nkh6WNL1XvxmYRERERMSUofkBi9sA6wA716BAKAGLW1KWRDX9P2DM9vqUY3m/1rh2POUI4UFdARxle+0OtbUCFjemDGAOrHtNejnc9vq2p1HCIQ+o7bcAm9aaD2H+8qleur3/ZljjTEr4IpSlZW+sz94Y+HRrYNSNpLdQTula3/ZrgCPqpT0BbK9Hee/3Aa2B2VuAK/sVn6VcEREREdHRZKe2T5JRAhbXAT4PYPt6SatJerHtu2xfLGm1Sapt0gIWbf+y0X4Z5VSunnq8/0HCGpdksEmLvYHDbD9Wn9kKVVwHuLDVJukPwHTKQG4gmTGJiIiIiAXsst4uizSMcQijhBjOAf4ZoJ6MtSp9vuhL2qu1AX4h14akQyX9Fngf82dMmnYHzhmyloHqkvT3kq6u17/QOj65h7WAN0u6XNLPJb2+ts8Btpe0mKRXABvy9OOO+8qMSUREREQsYNTU9snQJ/l9lODBw4Cv1b0UcyknSy1wAtXTOnzmAxZLh2Xp1O7Am0aop29ddUP++nUJ1xmSTrF9V4++FgNWoGyWfz1wsqRXUjb8v5qyZOs3wC/p8xm3y4xJREREREwlQwcP1s3nu9W9FB8EXkTZw/GM19amGbCIpPWBY4Dtbd+3MOuqMyXX0j+5/XbgNBdXUI45XtH2E7b3tT3N9vbA8sBNwxSZgUlERERETCVDByzWPRVL1D/3AC5u29sxWSYtYFHSyykp6x+wfWPbPRdK6rtErKFjWKOkl0lauva5AuUY4Rt61UsjVFElwHEJ4F6VAMZlavtbgSfq0cQDy1KuiIiIiJgyRgxYfDVwoqQngXmUpVHUe34AbAasKOl24EDbx+qZD1g8AHgh8M26mf0J29MlPQdYA7i/vaMe779jWGP9XL4kyZTlXkfYntun3uOA4+rxyn8Bdq0BjSsB56kEOv4O+ECvz6oTlc35g5k+fbqvvLLvSV8RERERU9Zmx2/G2J1jz9aN3yNrvaeLZlz0TJfSl6TZtntmXjxTJM0Aptv+yBD3LAcc2y3LZMjnrwt8yPYnJtpXj2dMWr2NPi8C/sV218FEZkwiIiIiGnZZb5dnuoR4dnsU2EbSMXU2oq+6bGxSvuTbvgZYaIOS+oxJqxdKwCLwSuDxXq/LwCQiIiKi4Zk8jWphehbmkYxM0taUkMTnAsfYPqy2fwT4OLA68CLb99b2FShLkFYH/kyZcbhG0lLAxZQMj8WAU2wf2P68Nq8GlqLLpvZ6stbuwJPAx2yf1+e9HEvJ+xBwIzDD9sN1j8kJlE3kzwU+bfvsPn0dB7wDuNt2M93+PcBBtfaNWrMWdQ9JK7hRwEG2T+/zjM0poYpLALOB3evyurWB7wKvAz5j+4j6+qUpp3itTFni1VUGJhERERExqY6efTSz5s5aKH03kt/fSjkh6n8knVk3Wv83JT39orbbWsnvO9Qv0N8AtgAeAzavA4HFgV9IOsf2ZX3K+Erri3dbbetQNuO/BngJ8J+S1rL9ZI++9m1txJf0ZcpxwYcB/wacbPuo2u/ZwGp96joeOBI4sa39GkqOy7c7tE+vA4tVgDmSzrLd8Zjfur/lBGAL2zdKOpiS7n4sZc/Lx4B/at5j+1FgmqRb+9SeU7kiIiIiYnLNmjuLsTvHFlb348nvtv8CtJLfsX2V7Vs73NNMJb8eaCW/2/bD9TWL15/BN2AvaHvgh7Yfs30LZbN5p5OtxjUGJQKWbjzfwHL19xcwwLHDti+mw6Z429fZvqFD+yONQchS9H/vLwQea5wSdgH1eGPbd9v+H/os1+olMyYRERERMekmstG+T8BipxTzjft02Up+/0Vb8vtddQZmNuWkq2/YvhygzgZcabvnUcQdamvOtgya/P5dyqlZ84BP1uaDgPMlfRRYBthyiDoGJmljyjK3VSlHE/cKRbwXWFzS9Loc7N0Mme7eS2ZMIiIiImIqGTX5fYWa/P5RGsnvtp+swYsvAzaqp15h+4AhByWj1obt3ShLv64DdqzNOwPH234ZZdDyvbqUalLZvtz2aygp7vvXfTfdXmvKUrWvSLoC+CNDprv3koFJREREREwlCyX53fYfKHtTtl6UtTWe/yRwEvOT33cHTq7XLqUstVpxArX1e/51wJ+Adfu87lLbb7a9EeXggKHS3XvJUq6IiIiIGEm3Te4LOQdmPPmdcsrTTkDPM54lLQ88UvekjCe/S3oR8LjtP9TTo7YEvjCB2s4EZtVN7C8B1gSuqDVcCHzQ9vjJVHVfyeq2b66/v5Oa/A7cRtmgf7yk1klg99TE9xNtbzGBOlvPfwXw27r5fVXgVcCt3eqt7SvZvlvSksCngEMnWkdLZkwiIiIiYiQLeZN7R3UPRCv5/TrKyVXjye81vf1llOTzY+ptrwaulXQ9sA3wf2v7KsDPJF1NGfBcYPsnta+DJW03ZG3XUmY55gHnAvvYfrJHWruAEyTNBebWeg6u1z4J7ClpDvADyjHCrq/pdmrWD4BLgVdJul3S7rV9h/q5/APwU0mtI4zfRDmJaww4Hfiw7Xt7pcsD+0m6DrgaOMv2f9VnrFyf8Qng3+rzl+twf1eZMYmIiIiIkXXa5L6wM1NqnscCmR62vw58vUP7pZTZi/b2q4HXdnnGASPWdigLziKsA5xaj85tvvYpYJMu/czrcu0NlOOOO92zc5f20ykDj/b27wHf63BLx3rrPfsB+3Vov5MyIBxZZkwiIiIiYkqRtLWkGyTdLOnTjfaP1DZLWrHR/gJJZ0maI+laSbs1rn1B0jX1Z8f2Z3XwRuDzkn7epbb9aw03SNoKSlq77Y5p7ZKOrXVdLekUScs2rr1X0rxa86za15HdNuVLOlTSbyU93Na+pKSTal2XS1qttm8kaaz+zJG0wwD1biHpV/WeX0hao7bPkHRPo789avs6kh4FXg481fVTJQOTiIiIiJhCGgGL21D+y/7ONYAQSsDilsBv2m7bB5hnewNgM+BLkpaQ9HZKUvk0ypHD+w2w/OiXwP62N+1QWzNgcWvgm7XeXva1vYHt9Sn7Sj5S+1oT2B/YpJ6a9fE+/QCcRefclN2BB2yvAXyF+ftoWgGL02q935bUb0XVUcD76j2zKEGQLSfZnlZ/joEy82N7acoen05Lw8ZlKVdERERETLqxO8cW1pKu8YBFAEmtgMV5tq+qbe33GHh+3WC+LGXvxBOUgc3P676VJ+p+jq2pp2GNYDxgEbhFUitg8dJuN/QIWNyTkqvyQH3d3f0e3kqs7/D+t6fkogCcAhwpSbYfabxmkIBFGCH4cVCZMYmIiIiISbXLersszFO5OgUs9gsxPJKyAf4Oyibz/1v3d8wBtpH0vLr06y3U435H2fw+Ym2tgMU7gbWB/6jNawFrSfpvSZdJmsgxxuN11UHYg5QUdyRtLOlayueyV5+ARSinmp1dN7p/gJIR0/KuxpK0oYMXM2MSERER8Tdismcxuh0LPHPDmczccObI/fZJfh8lxHArYAzYHFgduEDSJbbPl/R6yvKseygzG63gxVE2v48csFiXfP0HJWDxu5Tv6WtSlp69DLhE0ro1b2XS6qpJ96+pRxKfIOkc23/u0de+wLa2L5e0H/BlymDlLOAHth+TtBdwAuXzHlhmTCIiIiL+BizkWYxFaZQQw92A01zcTAlXXBvKKVp1T8RbKV/gJxIYOJkBi7cDP7b9uO1bgBvocLLYsHXVPSQvoO0o4EECFlVyXzaogxlqvW+s999Xl7ABfAfYcNgiM2MSERER8TdgorMYnSzsY4G7GDpgkflhhZdIejElSPB/6yzF8rbvk7Q+sD5w/gRqm8yAxTOAnSkBiytSlna19tVcb3vtIevalTIj9G7gv2xbwwcsPgC8QNJatm8E3krJkkHSKrZ/X1+3Xat9GBmYRERERMSUUb9EtwIWnwsc1wxYBP4VWJkSsHi27T2AQyhf8OdSZkU+VYMEl6IMVgAeAt7f2mMh6WDgym5H83ap7VpJrYDFJxg8YHG5+vscYO967TzgbZLmAU8C+9UB1Ip0XpqFpC9SBmnPq3tAjrF9EHAs8L26Gf9+ymAOSsDipyU9TjnKt2fAYv3s9wROlfQUZaDyoXr5Y3VPzhP1vhmDfm7j9ZcAycFMnz7dV1555bDPiIiIiIi/Qq0Zk/aAxYmSNNv29EntdJJIOgh42PYRQ9yzLvChbtkgQz7/HcAra5jkQjGZ9Tb6fNj2sr1ekxmTiIiIiIjBPQzMlLTcoBvkbV8DTMqXfNs/mYx++jxj0uqVtDpwKnBXv9dm83tERERETCkaPvl9BUmn16Nsr6gzAq1rt0qaW9PKB1kadC+wAmUPSafadpV0U/3ZdYD3ckita0zS+ZJeUts3k/RgI0m97yCox/t/X33G1ZJ+KWmDxrWOn2WPZ3ylUdONkv7QuPYFSdfUnx0BbP8auBZYQdK7e/WdGZOIiIiIGNlCDFLsSPOT399KOW3qfySdaXseJfn9J8BFbbf9P2DM9g6S1q73b9G4/hbb9w5Rxkm2P9Khtr8DDgSmU47jnV1re6BHX4fb/vd6/8eAA4C96rVLbL9jiLq6vf9bgE1tPyBpG+BoYOM+n2VHtvdt/S7po8Br6+9vB14HTOP/a+/OY+0oyziOf39AhVio7CAkbQUFWSwoICqKErZAVGQxASoFg9Fompi4xCgpbUACEvnDBS0EBEoKIqVFJVZAsOwYC6VCk4KxpYoQljbQ3i5g4fGP9z303NMzZ5nbe+fcnt8nOeHemXfmvvPwzHTeM++8L2wPPJCHHl4dEZMl3diu8n5iYmZmZmalVDQE8bszv0fEW0Bt5nciYlFEPN9km4OB+3KZpcDEPDrXlnYycG9ErMqNkXtJM8kXqs38no2ls9nXi/bV9Pgj4tG6xtHjpGGMoUUsO3QOcGv++WDggYjYGBFrSS/ydzUppJ+YmJmZmVkpwzEEMbSdYLHZ7OpHt9nlYuAM4GFJHwcmkG7OXyY1BO6RFMA1EXEtQJ4kkIiY2UXVy878fhkwhTQj+3F1qz4paTFpLpTv1UYfG6ILgfkt6tsulgDk4YU/ANyfFy0Gpuehkt9LOo7CJy/NuGFiZmZmZqNJmdnVrwB+Jukp4GlgEXmGd+CYiHhR0p6kGeGXRsSDXTZIhlI3IuIi4CJJPwSmkrqDPQlMiIgBSaeS5jUpO8Fiqpx0HKlh8umh1Dc7G5iTJ4YkIu6RdBTwKPAqac6UjS2234y7cpmZmZnZaNL17Or5PYevRsThpCcTe5DeuyAiXsz/fQWYR+reNGJ1a3ALeeb3XOeB/POfgDH1L7R3K08geR1wWkSs3AL1PZtN3bjI9bwsIg6PiBNJjZ5/dlNHN0zMzMzMbDR5d+Z3STtgxuAAAAfoSURBVO8h3SC3nARR0s65LMDXgAcjYrWksZJ2ymXGAicBzwyhbrVJEXeRtEve3915/7NyN7LGutU/BfkieeZ3SXsrz/yYt9sGWJl/v09S2y5idX9jPDAXOC/P2F5TGEtJl0s6vWB/B5JGJnusbtm2knbLP08CJgH3dFpH6LIr1xNPPPGapBXdbDOMdicN12blOH7lOXblOXblOXZD4/iV59iV59iVd2DRipIzvx8EzJL0Num9hwvz7vYC5uX7/+2AWyLiz3lfXb9jEhGrJF1KuuEHuCQiarOnTwJearLZFflG/x1gBZtG5DoL+KakjcB64OyICBXPIt/q+C8GdgN+lY91Y0Qc2SqWwEcobvCdA/w2Bs/UPgZ4KO9/NfCViOiqK1dXM7/3EkkLe3VG0NHA8SvPsSvPsSvPsRsax688x648x668Xo6dpAuAI5sNF9xim3HA9RHx5S3w97f4rOwFf+fuiDh5C+7vRuCuiJhTVMZduczMzMzMOrceOEXSdZ1ukN8XGXKjJO/rmeFulOS/syUbJbOBzwIbWpXzqFxmZmZmZh2KiNuA26qux2gSEZM7KTean5hcW3UFRjnHrzzHrjzHrjzHbmgcv/Icu/Icu/Icuz40at8xMTMzMzOzrcdofmJiZmZmZmZbCTdMzMzMzMyscm6YmJmZmZlZ5XqmYSJpe0nXS1ohaY2kRZJOqVt/vKSlktZJ+qukCS32NTGXWZe3OWFkjqI6kqZKWijpzTxOdG35ZEkDdZ91kkLSEQX7WSBpQ135Z0fsICrSInYTc6zq4zetxX6cd5uWf0LSvZJWSXpV0u2S3t9iP867wet8vetCwzk6IOltSb8oKHtBXl9f/nMjXOWe0un5p+Qnklbmz5XKM6n1o3b3LQ1lnXdNSNpV0jxJa3Mczy0o59zrEz3TMCENXfwf0hjH7wOmAb/L/+juDszNy3YFFtJ6mLZbgUWkGS4vAuZI2mMY694LXgR+DPymfmFEzI6IHWsf4FvAMuDJFvuaWrdN4cyrW5Gmsauzc108Lm2xH+fdJruQRlSZCEwA1gA3tNmX8w7w9a57Dde4vUhzDNzeYpPH6reJiAUjUtHe1sn593XgS8BhpBmsPw98Y6Qq2IMK71sKyjvvNnc18BbpvJ0M/FrSIU3KOff6RM80TCJibUTMiIjnI+KdiLgLWA4cAZwBLImI2yNiAzADOEzShxv3I+kA4GPA9IhYHxF3AE8DZ47YwVQgIuZGxJ3AyjZFzwdmhYdje1cXsSvkvBscu4iYn8/X1RGxDvglcEwllexRLfLO17uhOQt4BXio6opshc4HroqIFyLiv8BVwAXVVqk6be5brA1JY0nXqmkRMRARDwN/AM5rUty51yd6pmHSSNJewAHAEuAQYHFtXUSsBf6Vlzc6BFgWEWvqli0uKNtXcneQY4FZbYpeLuk1SY/4UTMAKyS9IOmG/G12M8671o4lncutOO8SX++GppMvXz6ac+05SdMkebLhzs6/QbmJc22QhvuWZpx3gx0AvB0Rz9UtK8op516f6MmGiaQxwGzgpohYCuwIvNFQ7A1gpyabd1O230wBHoqI5S3K/ADYD9iX1BXnj5L2H4nK9aDXgKNIXZGOIOXQ7IKyzrsCkiYBFwPfb1HMebeJr3clSRpP6lZzU4tiDwKHAnuSvq09h9a52Q86Pf8a8+0NYEf39W9639LIebe5oVzrnHtbqZ5rmEjaBriZ1Odwal48AIxrKDqO1G+9UTdl+80UWv+DTUT8LSLWRMSbEXET8Ahw6ojUrsfkR8sLI2JjRLxMyseTJDXmFzjvmpL0QWA+8O2IKOxa47wbxNe78qYAD7f68iUilkXE8tz15mngElL3r77VxfnXmG/jgIF+7xpccN8yiPOuqaFc65x7W6meapjklu/1pJegzoyI/+VVS0gvPNXKjQX2p/nj0iXAfpLqW9yHFZTtG5KOAfYB5nS5aQD+RiKpXQCbxcN51yB3HfwLcGlE3Nzl5v2cd77eldf2y5cm+jnXihTFZFBu0t+5BrS8b2nHeQfPAdtJ+lDdsqKccu71iZ5qmAC/Bg4CvhAR6+uWzwMOlXSmpB1I3UL+0exxae6r+BQwXdIOkk4njeBwx/BXvzqStsux2RbYNh97ff/V84E7GvqiN+5jZ0kn17aVNJn0bsDdw1v7ahXFTtLRkg6UtI2k3YCfAwsiovHRs/Nu89jtC9wPXB0RM9vsw3k3+Jz19a4ESZ8idUVqNRoXkk7J7wKQBxSYBvx++GvYm7o8/2YB35G0r6R9gO8CN45gdXtR0X3LIM67zeX35+YCl0gam79APY309KmRc69fRERPfEj9+APYQHpkV/tMzutPAJaShoFcAEys23YmMLPu94m5zHrgWeCEqo9vBOI3I8ev/jMjr9sBeB04vsl2PwLm55/3AP5Oeoz6OvA4cGLVx1ZV7Eh9gJcDa4GXSBfGvZ13HcVuev65/lwecN61j11e5+td9/G8Bri5yfLxOf/G599/Crycz+tlpC41Y6quf4VxKzz/gM80nLcCrgRW5c+VgKo+hgpjV3jf4rzrOIa7AnfmuPwbODcvd+716Uf5f7iZmZmZmVlleq0rl5mZmZmZ9SE3TMzMzMzMrHJumJiZmZmZWeXcMDEzMzMzs8q5YWJmZmZmZpVzw8TMzMzMzCrnhomZmZmZmVXODRMzMzMzM6vc/wGzGRSl2K463QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x2520 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the dendogram 0 this time with better labels\n",
    "\n",
    "# Cool custom function to give each leaf of the dendrogram a label - here, I'm just printing out feature values for each isntance in a pretty way.\n",
    "def llf(id):\n",
    "    Xr = [int(x) for x in scaler.inverse_transform(X[id, :])]\n",
    "    return \"{:>04d}: [{:>2d}, {:>2d}, {:>3d}, {:>2d}]\".format(id, Xr[0], Xr[1], Xr[2], Xr[3])\n",
    "\n",
    "\n",
    "# Plot the dendogram 0 this time with better labels\n",
    "plt.figure(figsize=(12, 35));\n",
    "plt.grid(False)\n",
    "plt.title(\"Mall Dendogram\");  \n",
    "dend = scipy.cluster.hierarchy.dendrogram(aggl, orientation=\"left\", leaf_font_size=10, leaf_label_func=llf); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's find K=5 clusters\n",
    "K=5\n",
    "labels = scipy.cluster.hierarchy.fcluster(aggl, K, criterion=\"maxclust\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For Each Cluster, Show Feature Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 1 (n=39):\n",
      "[ 0.46 32.69 86.54 82.13]\n",
      "\n",
      "Cluster 2 (n=61):\n",
      "[ 0.38 26.15 43.77 58.97]\n",
      "\n",
      "Cluster 3 (n=33):\n",
      "[ 0.55 41.45 89.09 16.18]\n",
      "\n",
      "Cluster 4 (n=29):\n",
      "[ 1.   56.55 50.03 41.34]\n",
      "\n",
      "Cluster 5 (n=38):\n",
      "[ 0.   49.79 44.11 39.66]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.04, -0.44,  0.99,  1.24],\n",
       "       [-0.13, -0.91, -0.64,  0.34],\n",
       "       [ 0.21,  0.19,  1.09, -1.32],\n",
       "       [ 1.13,  1.27, -0.4 , -0.34],\n",
       "       [-0.89,  0.79, -0.63, -0.41]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(precision=2)\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "means = np.zeros((K, X.shape[1]))\n",
    "\n",
    "for i, label in enumerate(set(labels)):\n",
    "    means[i,:] = X[labels==label].mean(axis=0)\n",
    "    print('\\nCluster {} (n={}):'.format(label, sum(labels==label)))\n",
    "    print(scaler.inverse_transform(means[i,:]))\n",
    "    \n",
    "means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For Each Cluster, Show (Even More) Feature Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Data:\n",
      "Number of Instances: 200\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>18.00</td>\n",
       "      <td>38.85</td>\n",
       "      <td>70.00</td>\n",
       "      <td>52.85</td>\n",
       "      <td>45.57</td>\n",
       "      <td>29.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnnualIncome</th>\n",
       "      <td>15.00</td>\n",
       "      <td>60.56</td>\n",
       "      <td>137.00</td>\n",
       "      <td>86.89</td>\n",
       "      <td>68.93</td>\n",
       "      <td>57.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>1.00</td>\n",
       "      <td>50.20</td>\n",
       "      <td>99.00</td>\n",
       "      <td>76.09</td>\n",
       "      <td>48.99</td>\n",
       "      <td>28.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Min  Mean    Max  Variance  Skewness  Kurtosis\n",
       "Male           0.00  0.44   1.00      0.94      0.56     -0.52\n",
       "Age           18.00 38.85  70.00     52.85     45.57     29.31\n",
       "AnnualIncome  15.00 60.56 137.00     86.89     68.93     57.26\n",
       "SpendingScore  1.00 50.20  99.00     76.09     48.99     28.67"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 1:\n",
      "Number of Instances: 39\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.52</td>\n",
       "      <td>-0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>27.00</td>\n",
       "      <td>32.69</td>\n",
       "      <td>40.00</td>\n",
       "      <td>39.85</td>\n",
       "      <td>44.55</td>\n",
       "      <td>25.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnnualIncome</th>\n",
       "      <td>69.00</td>\n",
       "      <td>86.54</td>\n",
       "      <td>137.00</td>\n",
       "      <td>70.72</td>\n",
       "      <td>96.41</td>\n",
       "      <td>96.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>63.00</td>\n",
       "      <td>82.13</td>\n",
       "      <td>97.00</td>\n",
       "      <td>53.60</td>\n",
       "      <td>47.41</td>\n",
       "      <td>19.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Min  Mean    Max  Variance  Skewness  Kurtosis\n",
       "Male           0.00  0.46   1.00      0.95      0.52     -0.54\n",
       "Age           27.00 32.69  40.00     39.85     44.55     25.92\n",
       "AnnualIncome  69.00 86.54 137.00     70.72     96.41     96.51\n",
       "SpendingScore 63.00 82.13  97.00     53.60     47.41     19.20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 2:\n",
      "Number of Instances: 61\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.69</td>\n",
       "      <td>-0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>18.00</td>\n",
       "      <td>26.15</td>\n",
       "      <td>40.00</td>\n",
       "      <td>41.68</td>\n",
       "      <td>46.73</td>\n",
       "      <td>28.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnnualIncome</th>\n",
       "      <td>15.00</td>\n",
       "      <td>43.77</td>\n",
       "      <td>76.00</td>\n",
       "      <td>73.21</td>\n",
       "      <td>58.64</td>\n",
       "      <td>25.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>13.00</td>\n",
       "      <td>58.97</td>\n",
       "      <td>99.00</td>\n",
       "      <td>63.39</td>\n",
       "      <td>57.67</td>\n",
       "      <td>41.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Min  Mean   Max  Variance  Skewness  Kurtosis\n",
       "Male           0.00  0.38  1.00      0.92      0.69     -0.42\n",
       "Age           18.00 26.15 40.00     41.68     46.73     28.13\n",
       "AnnualIncome  15.00 43.77 76.00     73.21     58.64     25.12\n",
       "SpendingScore 13.00 58.97 99.00     63.39     57.67     41.55"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 3:\n",
      "Number of Instances: 33\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Min</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>19.00</td>\n",
       "      <td>41.45</td>\n",
       "      <td>59.00</td>\n",
       "      <td>47.81</td>\n",
       "      <td>34.55</td>\n",
       "      <td>32.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnnualIncome</th>\n",
       "      <td>71.00</td>\n",
       "      <td>89.09</td>\n",
       "      <td>137.00</td>\n",
       "      <td>70.89</td>\n",
       "      <td>93.18</td>\n",
       "      <td>86.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>1.00</td>\n",
       "      <td>16.18</td>\n",
       "      <td>39.00</td>\n",
       "      <td>53.65</td>\n",
       "      <td>63.71</td>\n",
       "      <td>44.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Min  Mean    Max  Variance  Skewness  Kurtosis\n",
       "Male           0.00  0.55   1.00      0.96      0.35     -0.54\n",
       "Age           19.00 41.45  59.00     47.81     34.55     32.56\n",
       "AnnualIncome  71.00 89.09 137.00     70.89     93.18     86.22\n",
       "SpendingScore  1.00 16.18  39.00     53.65     63.71     44.61"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 4:\n",
      "Number of Instances: 29\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>38.00</td>\n",
       "      <td>56.55</td>\n",
       "      <td>70.00</td>\n",
       "      <td>45.58</td>\n",
       "      <td>36.55</td>\n",
       "      <td>22.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnnualIncome</th>\n",
       "      <td>19.00</td>\n",
       "      <td>50.03</td>\n",
       "      <td>77.00</td>\n",
       "      <td>68.85</td>\n",
       "      <td>45.31</td>\n",
       "      <td>55.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>3.00</td>\n",
       "      <td>41.34</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.31</td>\n",
       "      <td>15.55</td>\n",
       "      <td>72.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Min  Mean   Max  Variance  Skewness  Kurtosis\n",
       "Male           1.00  1.00  1.00      0.44      0.44     -1.05\n",
       "Age           38.00 56.55 70.00     45.58     36.55     22.07\n",
       "AnnualIncome  19.00 50.03 77.00     68.85     45.31     55.02\n",
       "SpendingScore  3.00 41.34 60.00     60.31     15.55     72.17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 5:\n",
      "Number of Instances: 38\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Max</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>20.00</td>\n",
       "      <td>49.79</td>\n",
       "      <td>68.00</td>\n",
       "      <td>46.87</td>\n",
       "      <td>34.32</td>\n",
       "      <td>45.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnnualIncome</th>\n",
       "      <td>16.00</td>\n",
       "      <td>44.11</td>\n",
       "      <td>67.00</td>\n",
       "      <td>69.50</td>\n",
       "      <td>53.00</td>\n",
       "      <td>34.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>5.00</td>\n",
       "      <td>39.66</td>\n",
       "      <td>59.00</td>\n",
       "      <td>60.41</td>\n",
       "      <td>28.06</td>\n",
       "      <td>38.79</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                Min  Mean   Max  Variance  Skewness  Kurtosis\n",
       "Male           0.00  0.00  0.00      0.44     -0.06     -0.55\n",
       "Age           20.00 49.79 68.00     46.87     34.32     45.03\n",
       "AnnualIncome  16.00 44.11 67.00     69.50     53.00     34.67\n",
       "SpendingScore  5.00 39.66 59.00     60.41     28.06     38.79"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "pd.set_option(\"display.precision\", 2)\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "\n",
    "def stats_to_df(d, scaler):\n",
    "    tmp_df = pd.DataFrame(columns=col_names)\n",
    "    \n",
    "    tmp_df.loc[0] = scaler.inverse_transform(d.minmax[0])\n",
    "    tmp_df.loc[1] = scaler.inverse_transform(d.mean)\n",
    "    tmp_df.loc[2] = scaler.inverse_transform(d.minmax[1])\n",
    "    tmp_df.loc[3] = scaler.inverse_transform(d.variance)\n",
    "    tmp_df.loc[4] = scaler.inverse_transform(d.skewness)\n",
    "    tmp_df.loc[5] = scaler.inverse_transform(d.kurtosis)\n",
    "    tmp_df.index = ['Min', 'Mean', 'Max', 'Variance', 'Skewness', 'Kurtosis'] \n",
    "    return tmp_df.T\n",
    "\n",
    "print('All Data:')\n",
    "print('Number of Instances: {}'.format(X.shape[0]))\n",
    "d = stats.describe(X, axis=0)\n",
    "display(stats_to_df(d, scaler))\n",
    "\n",
    "for i, label in enumerate(set(labels)):\n",
    "    d = stats.describe(X[labels==label], axis=0)\n",
    "    print('\\nCluster {}:'.format(label))\n",
    "    print('Number of Instances: {}'.format(d.nobs))\n",
    "    display(stats_to_df(d, scaler))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>77.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Male   Age  AnnualIncome  SpendingScore  cluster\n",
       "0  1.00 19.00         15.00          39.00        2\n",
       "1  1.00 21.00         15.00          81.00        2\n",
       "2  0.00 20.00         16.00           6.00        5\n",
       "3  0.00 23.00         16.00          77.00        2\n",
       "4  0.00 31.00         17.00          40.00        5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th colspan=\"8\" halign=\"left\">Male</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Age</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AnnualIncome</th>\n",
       "      <th colspan=\"8\" halign=\"left\">SpendingScore</th>\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.00</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>32.69</td>\n",
       "      <td>...</td>\n",
       "      <td>95.00</td>\n",
       "      <td>137.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>82.13</td>\n",
       "      <td>9.36</td>\n",
       "      <td>63.00</td>\n",
       "      <td>74.50</td>\n",
       "      <td>83.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>97.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>26.15</td>\n",
       "      <td>...</td>\n",
       "      <td>60.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>58.97</td>\n",
       "      <td>18.43</td>\n",
       "      <td>13.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>73.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>41.45</td>\n",
       "      <td>...</td>\n",
       "      <td>98.00</td>\n",
       "      <td>137.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>16.18</td>\n",
       "      <td>9.42</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>39.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>56.55</td>\n",
       "      <td>...</td>\n",
       "      <td>62.00</td>\n",
       "      <td>77.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>41.34</td>\n",
       "      <td>16.13</td>\n",
       "      <td>3.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>60.00</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>38.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>49.79</td>\n",
       "      <td>...</td>\n",
       "      <td>56.25</td>\n",
       "      <td>67.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>39.66</td>\n",
       "      <td>16.22</td>\n",
       "      <td>5.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>44.50</td>\n",
       "      <td>51.50</td>\n",
       "      <td>59.00</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Male                                      Age        ...  \\\n",
       "        count mean  std  min  25%  50%  75%  max count  mean  ...   \n",
       "cluster                                                       ...   \n",
       "1       39.00 0.46 0.51 0.00 0.00 0.00 1.00 1.00 39.00 32.69  ...   \n",
       "2       61.00 0.38 0.49 0.00 0.00 0.00 1.00 1.00 61.00 26.15  ...   \n",
       "3       33.00 0.55 0.51 0.00 0.00 1.00 1.00 1.00 33.00 41.45  ...   \n",
       "4       29.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00 29.00 56.55  ...   \n",
       "5       38.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 38.00 49.79  ...   \n",
       "\n",
       "        AnnualIncome        SpendingScore                                      \\\n",
       "                 75%    max         count  mean   std   min   25%   50%   75%   \n",
       "cluster                                                                         \n",
       "1              95.00 137.00         39.00 82.13  9.36 63.00 74.50 83.00 90.00   \n",
       "2              60.00  76.00         61.00 58.97 18.43 13.00 46.00 55.00 73.00   \n",
       "3              98.00 137.00         33.00 16.18  9.42  1.00 10.00 15.00 22.00   \n",
       "4              62.00  77.00         29.00 41.34 16.13  3.00 36.00 46.00 51.00   \n",
       "5              56.25  67.00         38.00 39.66 16.22  5.00 31.25 44.50 51.50   \n",
       "\n",
       "               \n",
       "          max  \n",
       "cluster        \n",
       "1       97.00  \n",
       "2       99.00  \n",
       "3       39.00  \n",
       "4       60.00  \n",
       "5       59.00  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 12,
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5 entries, 1 to 5\n",
      "Data columns (total 32 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   (Male, count)           5 non-null      float64\n",
      " 1   (Male, mean)            5 non-null      float64\n",
      " 2   (Male, std)             5 non-null      float64\n",
      " 3   (Male, min)             5 non-null      float64\n",
      " 4   (Male, 25%)             5 non-null      float64\n",
      " 5   (Male, 50%)             5 non-null      float64\n",
      " 6   (Male, 75%)             5 non-null      float64\n",
      " 7   (Male, max)             5 non-null      float64\n",
      " 8   (Age, count)            5 non-null      float64\n",
      " 9   (Age, mean)             5 non-null      float64\n",
      " 10  (Age, std)              5 non-null      float64\n",
      " 11  (Age, min)              5 non-null      float64\n",
      " 12  (Age, 25%)              5 non-null      float64\n",
      " 13  (Age, 50%)              5 non-null      float64\n",
      " 14  (Age, 75%)              5 non-null      float64\n",
      " 15  (Age, max)              5 non-null      float64\n",
      " 16  (AnnualIncome, count)   5 non-null      float64\n",
      " 17  (AnnualIncome, mean)    5 non-null      float64\n",
      " 18  (AnnualIncome, std)     5 non-null      float64\n",
      " 19  (AnnualIncome, min)     5 non-null      float64\n",
      " 20  (AnnualIncome, 25%)     5 non-null      float64\n",
      " 21  (AnnualIncome, 50%)     5 non-null      float64\n",
      " 22  (AnnualIncome, 75%)     5 non-null      float64\n",
      " 23  (AnnualIncome, max)     5 non-null      float64\n",
      " 24  (SpendingScore, count)  5 non-null      float64\n",
      " 25  (SpendingScore, mean)   5 non-null      float64\n",
      " 26  (SpendingScore, std)    5 non-null      float64\n",
      " 27  (SpendingScore, min)    5 non-null      float64\n",
      " 28  (SpendingScore, 25%)    5 non-null      float64\n",
      " 29  (SpendingScore, 50%)    5 non-null      float64\n",
      " 30  (SpendingScore, 75%)    5 non-null      float64\n",
      " 31  (SpendingScore, max)    5 non-null      float64\n",
      "dtypes: float64(32)\n",
      "memory usage: 1.3 KB\n"
     ]
    },
    {
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       "      <th>cluster</th>\n",
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       "      <th>1</th>\n",
       "      <td>39.00</td>\n",
       "      <td>32.69</td>\n",
       "      <td>3.73</td>\n",
       "      <td>27.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>35.50</td>\n",
       "      <td>40.00</td>\n",
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       "      <th>2</th>\n",
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       "      <td>6.28</td>\n",
       "      <td>18.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>40.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.00</td>\n",
       "      <td>41.45</td>\n",
       "      <td>11.17</td>\n",
       "      <td>19.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>59.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.00</td>\n",
       "      <td>56.55</td>\n",
       "      <td>9.68</td>\n",
       "      <td>38.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>38.00</td>\n",
       "      <td>49.79</td>\n",
       "      <td>10.57</td>\n",
       "      <td>20.00</td>\n",
       "      <td>45.25</td>\n",
       "      <td>49.00</td>\n",
       "      <td>54.75</td>\n",
       "      <td>68.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count  mean   std   min   25%   50%   75%   max\n",
       "cluster                                                 \n",
       "1        39.00 32.69  3.73 27.00 30.00 32.00 35.50 40.00\n",
       "2        61.00 26.15  6.28 18.00 21.00 24.00 31.00 40.00\n",
       "3        33.00 41.45 11.17 19.00 34.00 42.00 47.00 59.00\n",
       "4        29.00 56.55  9.68 38.00 48.00 57.00 66.00 70.00\n",
       "5        38.00 49.79 10.57 20.00 45.25 49.00 54.75 68.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or, we could have used panda's group-by function\n",
    "import pandas as pd\n",
    "\n",
    "X_df = pd.DataFrame(scaler.inverse_transform(X), columns=col_names)\n",
    "X_df['cluster'] = labels\n",
    "X_df.head()\n",
    "\n",
    "cl_group = X_df.groupby(['cluster']).agg('describe')\n",
    "cl_group\n",
    "\n",
    "cl_group.info()\n",
    "cl_group['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examplars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 1:\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Male</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>176</td>\n",
       "      <td>False</td>\n",
       "      <td>30</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID   Male  Age  AnnualIncome  SpendingScore\n",
       "175         176  False   30            88             86"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 2:\n"
     ]
    },
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       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>False</td>\n",
       "      <td>27</td>\n",
       "      <td>46</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID   Male  Age  AnnualIncome  SpendingScore\n",
       "58          59  False   27            46             51"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 3:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Male</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>171</td>\n",
       "      <td>True</td>\n",
       "      <td>40</td>\n",
       "      <td>87</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Male  Age  AnnualIncome  SpendingScore\n",
       "170         171  True   40            87             13"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 4:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>CustomerID</th>\n",
       "      <th>Male</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>True</td>\n",
       "      <td>59</td>\n",
       "      <td>54</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID  Male  Age  AnnualIncome  SpendingScore\n",
       "74          75  True   59            54             47"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 5:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>CustomerID</th>\n",
       "      <th>Male</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>False</td>\n",
       "      <td>50</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID   Male  Age  AnnualIncome  SpendingScore\n",
       "54          55  False   50            43             45"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.spatial import distance\n",
    "\n",
    "for i, label in enumerate(set(labels)):\n",
    "    X_tmp= X\n",
    "    exemplar_idx = distance.cdist([means[i]], X).argmin()\n",
    "   \n",
    "    print('\\nCluster {}:'.format(label))\n",
    "    #print(\"  Examplar ID: {}\".format(exemplar_idx))\n",
    "    #print(\"  Label: {}\".format(labels[exemplar_idx]))\n",
    "    #print(\"  Features:\")\n",
    "    display(df.iloc[[exemplar_idx]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relative Importance Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\st50\\AppData\\Roaming\\Python\\Python36\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n",
      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
      "  \"found relative to the 'datapath' directory.\".format(key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x288 with 0 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relative importance of features')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19802592b00>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "all_means = X.mean(axis=0)\n",
    "\n",
    "relative_imp = means - all_means\n",
    "\n",
    "plt.figure(figsize=(8, 4));\n",
    "plt.title('Relative importance of features');\n",
    "sns.heatmap(data=relative_imp, \n",
    "            annot=scaler.inverse_transform(relative_imp), \n",
    "            fmt='.2f', \n",
    "            cmap='RdYlGn', \n",
    "            robust=True, \n",
    "            square=False,\n",
    "            xticklabels=col_names, \n",
    "            yticklabels=['Cluster {}'.format(x) for x in range(K)]);\n",
    "plt.savefig('out/mall_heatmap.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "small_sklearn_kernel",
   "language": "python",
   "name": "small_sklearn_kernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}