211104.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d562e49",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example of learning with logistic regression\n",
    "#The label or target is 0 or 1 for logistic regression\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import linear_model\n",
    "\n",
    "#import scipy to plot the logistic sigmoid function\n",
    "\n",
    "from scipy.special import expit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b383892f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a dataset\n",
    "#Let's use a straight line with some Gaussian noise:\n",
    "\n",
    "n_samples = 500\n",
    "np.random.seed(0)\n",
    "X = np.random.normal(size=n_samples)\n",
    "#ndarray has a single dimension, there is no difference bewteen \n",
    "#row vector and a column vector\n",
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b4efcfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.76405235,  0.40015721,  0.97873798,  2.2408932 ,  1.86755799,\n",
       "       -0.97727788,  0.95008842, -0.15135721, -0.10321885,  0.4105985 ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00fe653e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 0., 1., 0., 0., 1.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = (X > 0).astype(float)\n",
    "y[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "de94ad6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.52810469,  0.80031442,  1.95747597,  4.4817864 ,  3.73511598,\n",
       "       -0.97727788,  1.90017684, -0.15135721, -0.10321885,  0.821197  ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's scale X by 10 if it is a positive number\n",
    "X[X > 0] *= 2\n",
    "X[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da3b65e6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x274a7b56490>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "730ba503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x274a7c4f670>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit the classifier\n",
    "#first let's create a model object\n",
    "lrm = linear_model.LogisticRegression()\n",
    "\n",
    "#Recall that scikit-learn datasets have to be \n",
    "#a matrix so we add a dimension\n",
    "\n",
    "lrm.fit(X[:,np.newaxis], y)\n",
    "\n",
    "# and plot the result\n",
    "plt.figure(1, figsize=(4, 3))\n",
    "\n",
    "#zorder is the drawing order\n",
    "#using zorder - Line2D / LineCollection is 2\n",
    "plt.scatter(X, y, color=\"black\", zorder=3)\n",
    "\n",
    "X_test = np.linspace(-5, 10, 300)\n",
    "\n",
    "#The expit function, also known as the logistic sigmoid function, \n",
    "#is defined as expit(x) = 1/(1+exp(-x)).\n",
    "\n",
    "logsig = expit(X_test * lrm.coef_ + lrm.intercept_).ravel()\n",
    "\n",
    "plt.plot(X_test, logsig, color=\"red\", linewidth=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04c0625f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logsig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ac99461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(logsig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "726c1da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's look at a real world example\n",
    "#dataset was taken from a study performed in Australia\n",
    "#on how age,gender,weight,height,medication, and bone_density\n",
    "#is a predictor for hip fracture\n",
    "#with some minor modifications\n",
    "df = pd.read_csv('Fracture Dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b9990a2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>fracture</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>medication</th>\n",
       "      <th>bone_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>469</td>\n",
       "      <td>57.052768</td>\n",
       "      <td>F</td>\n",
       "      <td>no fracture</td>\n",
       "      <td>64.0</td>\n",
       "      <td>155.5</td>\n",
       "      <td>Anticonvulsant</td>\n",
       "      <td>0.8793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8724</td>\n",
       "      <td>75.741225</td>\n",
       "      <td>F</td>\n",
       "      <td>no fracture</td>\n",
       "      <td>78.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>No medication</td>\n",
       "      <td>0.7946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6736</td>\n",
       "      <td>70.778900</td>\n",
       "      <td>M</td>\n",
       "      <td>no fracture</td>\n",
       "      <td>73.0</td>\n",
       "      <td>170.5</td>\n",
       "      <td>No medication</td>\n",
       "      <td>0.9067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24180</td>\n",
       "      <td>78.247175</td>\n",
       "      <td>F</td>\n",
       "      <td>no fracture</td>\n",
       "      <td>60.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>No medication</td>\n",
       "      <td>0.7112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17072</td>\n",
       "      <td>54.191877</td>\n",
       "      <td>M</td>\n",
       "      <td>no fracture</td>\n",
       "      <td>55.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>No medication</td>\n",
       "      <td>0.7909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id        age sex     fracture  weight_kg  height_cm      medication  \\\n",
       "0    469  57.052768   F  no fracture       64.0      155.5  Anticonvulsant   \n",
       "1   8724  75.741225   F  no fracture       78.0      162.0   No medication   \n",
       "2   6736  70.778900   M  no fracture       73.0      170.5   No medication   \n",
       "3  24180  78.247175   F  no fracture       60.0      148.0   No medication   \n",
       "4  17072  54.191877   M  no fracture       55.0      161.0   No medication   \n",
       "\n",
       "   bone_density  \n",
       "0        0.8793  \n",
       "1        0.7946  \n",
       "2        0.9067  \n",
       "3        0.7112  \n",
       "4        0.7909  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#partial data understanding\n",
    "#look at a sample of data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "84e0fde3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'age', 'sex', 'fracture', 'weight_kg', 'height_cm', 'medication',\n",
       "       'bone_density'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fab6ce1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 169 entries, 0 to 168\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            169 non-null    int64  \n",
      " 1   age           169 non-null    float64\n",
      " 2   sex           169 non-null    object \n",
      " 3   fracture      169 non-null    object \n",
      " 4   weight_kg     169 non-null    float64\n",
      " 5   height_cm     169 non-null    float64\n",
      " 6   medication    169 non-null    object \n",
      " 7   bone_density  169 non-null    float64\n",
      "dtypes: float64(4), int64(1), object(3)\n",
      "memory usage: 10.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "82fbec0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>id</th>\n",
       "      <th>age</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>bone_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9102.556213</td>\n",
       "      <td>63.631531</td>\n",
       "      <td>64.665680</td>\n",
       "      <td>160.254438</td>\n",
       "      <td>0.783104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8744.623598</td>\n",
       "      <td>12.356936</td>\n",
       "      <td>11.537171</td>\n",
       "      <td>7.928272</td>\n",
       "      <td>0.166529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.814058</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>0.407600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>54.424211</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>0.670800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6702.000000</td>\n",
       "      <td>63.487837</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>160.500000</td>\n",
       "      <td>0.786100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17100.000000</td>\n",
       "      <td>72.080558</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>0.888800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24208.000000</td>\n",
       "      <td>88.753795</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>1.362400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         age   weight_kg   height_cm  bone_density\n",
       "count    169.000000  169.000000  169.000000  169.000000    169.000000\n",
       "mean    9102.556213   63.631531   64.665680  160.254438      0.783104\n",
       "std     8744.623598   12.356936   11.537171    7.928272      0.166529\n",
       "min       35.000000   35.814058   36.000000  142.000000      0.407600\n",
       "25%     2018.000000   54.424211   56.000000  154.000000      0.670800\n",
       "50%     6702.000000   63.487837   64.500000  160.500000      0.786100\n",
       "75%    17100.000000   72.080558   73.000000  166.000000      0.888800\n",
       "max    24208.000000   88.753795   96.000000  177.000000      1.362400"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's get a summary of the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e5130ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "#How are we going to handle the string columns?\n",
    "#Pandas has a map function that you can use. \n",
    "#for example you have this dictionary that maps the strings to codes:\n",
    "#codes = {'x':0, 'b':1, 'k':2}\n",
    "#You can use the map function to map the column in the pandas dataframe:\n",
    "#df[col] = df[col].map(codes)\n",
    "#What are the unique values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "66421a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'M'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "adc1afa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no fracture', 'fracture'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fracture'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26e6b693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Anticonvulsant', 'No medication', 'Glucocorticoids'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['medication'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9a08bf80",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_code = {'F':1,'M':0}\n",
    "df['sex'] = df['sex'].map(gender_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d7ad9ac4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 169 entries, 0 to 168\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            169 non-null    int64  \n",
      " 1   age           169 non-null    float64\n",
      " 2   sex           169 non-null    int64  \n",
      " 3   fracture      169 non-null    object \n",
      " 4   weight_kg     169 non-null    float64\n",
      " 5   height_cm     169 non-null    float64\n",
      " 6   medication    169 non-null    object \n",
      " 7   bone_density  169 non-null    float64\n",
      "dtypes: float64(4), int64(2), object(2)\n",
      "memory usage: 10.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a0e0dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "fracture_code = {'no fracture':0, 'fracture':1}\n",
    "df['fracture'] = df['fracture'].map(fracture_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "79ee4d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "medication_code = {'No medication':0, 'Anticonvulsant':1, 'Glucocorticoids':2}\n",
    "df['medication'] = df['medication'].map(medication_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e58f354f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 169 entries, 0 to 168\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            169 non-null    int64  \n",
      " 1   age           169 non-null    float64\n",
      " 2   sex           169 non-null    int64  \n",
      " 3   fracture      169 non-null    int64  \n",
      " 4   weight_kg     169 non-null    float64\n",
      " 5   height_cm     169 non-null    float64\n",
      " 6   medication    169 non-null    int64  \n",
      " 7   bone_density  169 non-null    float64\n",
      "dtypes: float64(4), int64(4)\n",
      "memory usage: 10.7 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "16cb1403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>fracture</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>medication</th>\n",
       "      <th>bone_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9102.556213</td>\n",
       "      <td>63.631531</td>\n",
       "      <td>0.491124</td>\n",
       "      <td>0.295858</td>\n",
       "      <td>64.665680</td>\n",
       "      <td>160.254438</td>\n",
       "      <td>0.337278</td>\n",
       "      <td>0.783104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8744.623598</td>\n",
       "      <td>12.356936</td>\n",
       "      <td>0.501407</td>\n",
       "      <td>0.457784</td>\n",
       "      <td>11.537171</td>\n",
       "      <td>7.928272</td>\n",
       "      <td>0.714539</td>\n",
       "      <td>0.166529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.814058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.407600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>54.424211</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.670800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6702.000000</td>\n",
       "      <td>63.487837</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>160.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.786100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17100.000000</td>\n",
       "      <td>72.080558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.888800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24208.000000</td>\n",
       "      <td>88.753795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.362400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         age         sex    fracture   weight_kg  \\\n",
       "count    169.000000  169.000000  169.000000  169.000000  169.000000   \n",
       "mean    9102.556213   63.631531    0.491124    0.295858   64.665680   \n",
       "std     8744.623598   12.356936    0.501407    0.457784   11.537171   \n",
       "min       35.000000   35.814058    0.000000    0.000000   36.000000   \n",
       "25%     2018.000000   54.424211    0.000000    0.000000   56.000000   \n",
       "50%     6702.000000   63.487837    0.000000    0.000000   64.500000   \n",
       "75%    17100.000000   72.080558    1.000000    1.000000   73.000000   \n",
       "max    24208.000000   88.753795    1.000000    1.000000   96.000000   \n",
       "\n",
       "        height_cm  medication  bone_density  \n",
       "count  169.000000  169.000000    169.000000  \n",
       "mean   160.254438    0.337278      0.783104  \n",
       "std      7.928272    0.714539      0.166529  \n",
       "min    142.000000    0.000000      0.407600  \n",
       "25%    154.000000    0.000000      0.670800  \n",
       "50%    160.500000    0.000000      0.786100  \n",
       "75%    166.000000    0.000000      0.888800  \n",
       "max    177.000000    2.000000      1.362400  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's get a summary of the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7248164c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's drop id column\n",
    "df = df.drop(labels = ['id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "02cd2657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x274a74379d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RJkF9G0OQ//HBFBEIa46PotXwzvzO91vehasWgiEUbA2iTR9EU9blXPXK2o7D/vjZDnQaFZeNSz76PUxKudZeh0aPnD4b6bOD0sRyP4OLXg+cTYei1gidLwWFQxl1NWz7EOzNQgNGpRFabZU7xEtSifkCQF0hvHOh0OkC2P01zPgdzHjomDdU+hqfbinlH1/vAqCs0cb1b65n4e2TlRLavZD+3RNPMX7Kq2ZkUihq1bEtGNKjLFwzKYWb3lpPbUsvyGVX6BW43R7kTW97leVF9uDc8UXgjOppLFFw7tPi4X6ArHmKjkGgCB8oUhoOZtYfOp0mXaWpDJY94d1mrYeKbZ3vIwfBDUvgsvdg/svCOXMcUQ15lS0dzpcDvPtLERVNxy4o6XC52VnWyI+7Ksktb8Lh6gWCmacy5ig49/+8x4chF0Ds8ICZdEy4XbD6Be82jwv2LsV2ww9Yz3uJtnP/i/3aJbye77sZ8+bqQlrthwhT2xoV4cs+gMvtQs5Z7N3oceMuXB0YgxQUjoX4kXDTd3DhyxA7Emb/2fvz0/4oIlZBpIQecL4cYNWzIqK2ixTVtfFzXhXrC+tosHYhBbMXUNti581VhV5tsgybi+oDY5DCEVEiYPoRP+ZWkn2cZaUnp0dSUm/lzvc28d7NE5SdWgXUEoCv/oXGT1u/Im0W3PqzSCswhokQVyUCJjCoNTDqGuEIaSgRpXVjhoDWeGzXkWX/1T4OOBfLt8Ga56F0g8gpH3nVsac4tWPSqn3aQoxadJpjG1Odbg+LNpXy+0+245FBrZL450XDuWBUwjE72RW6kbRZcOsykZbYF8cH2Xf8drtd3PVNA0t3iejZ0cmNzM7y1VKIDtKjVbf3vcYSoR2z5V2IyIRp9ytpmr0YCQnZ4zsGuj0eZSGg0DeIHiJeAKWb4dx/C+HwiDRIGAdag/jMn+6a7PHf7odtJQ1c9/o66ttE5O052bE8et5QYoIN3fBD9Bw6tYoIi47yRu/NnmCjks7XG1FW2f0Ep9vDusI6shOPzwEDcPHoRGxOtyLKqyBQqWDEld5tkoRq8DmBsedk4WyD5nIR1lpf0JmSohAYdCZIHCfyuJMn+K+QcDQcrTDxkAothhAR2VRfCO/Oh20fiUX18n/Bd38U5xwHQxOCGRhl8Wr7w7lZRAcd2+StoLqVP36aw4EidW6PzCOfbKew5vjsUugmVGrRb4ZeAGkzusf5Ur4VfvkfrHkRyrZ0eaEACOHo8m2iOlhj6ZGPVWtg0l3ebZKKmuQzWbqrqqNpU1EDqZEmQg6auGtUEnfMHIhOoxaRNGteEILlNXsg70t4e57QXVDolag1Ghh9tXejpEKdMTswBh3KAS2lghVQvVsUAVDoH3g8Ypzo+NseIZLT1gR7f4Sf/wlbP4L6/b7HVO2EN8+GL38FPz0OC28S6cPu9ui86CEiWvFgJt4lxHqPQpvDxVPf5XU4XwC+2l7BtpLGLvyggSXIqOU3Z2R6ZU3HBOsZk6ykH/VGFMd3P2FbSSOxwQaCDcfv6VSpJG6fkc4jn2zn9KwYRiaFdp+BCn0Ot9sN+T/BGX8T4mcqDaRMwV2wAk3WWYE2r+fY9jF8dX/n+8hBoiJDpFISs09SkQNvniOqssz4HexfLaq0pM0CczSUrIfWGu9zdn0B9Y+IaJtjJDHMxGvXj2XT/nqqmu2MSAplxHE4xmta7bg83gtxu8tDTYud9GjLYc5S6HOUbhL909km3mv0cN1XkDTu6OdaG0Ro/apnxAI2KFZU94ofdfhzUqfC1Ytg/esikmzsjfxnoxnwdux9sK6IBbdNYmNRPVaHmzEpYQw7EGHbVALrX/G+rqNVOGCis7r8oyucPJwOO5r8H4UIb8HPQoQ3dQpS0RoYfGagzYO8r2HRzeJ7oNHDvOdFNKJaWab0aTwe8Tz95DbhLNYYRLWirHn+9Vi2fgDf/LbzfdwouOKQilhVuZ3j5QHWvQzjb4WwFBERc81nQjOmbBMMvxwyzhDO86PQbHOxtdjX2VJS3+bn6N7H5PRIFt4+ic1FDQQbtYxJCSM9Spkv9EaUka2fsDq/hsGxJy6gG2rSccX4ZB5atI2v7p2mhLqfwqhVKpz2JtTf/1FM6D0e+O4PMPb2QJvWc9QVwo+PebfV7BYPccUB0zdoqRITNJcNorIgf6kQF839HPZ8J/qyywYbXhViof6qbai1J1SFIyXCTEqE+ajHVTfb2VXeRKvDxcBoCwOjO6N74kKMmHRq2hydO8HBBg1xIb07DFrhGNm+0Hsx4bILgeiuOGDKt8DKpzvfN1cgL3kE6ZI3RfReW61wNkZmdi52tEYYOFu82hlaVQTrvaNnThscw6DYIAbF+ok4U2lBawZ3g3e7Wnd0mxUCglalQm6rgR/eb3+eu+Db3yON7wXP89p9sPjWzu+Byw6f3SW0laIHB9Y2hROjLr/T+QLi2fvp7SJ181Att/r9sPSQ+Vf5ZlG1beAc0Lc/U/2NM1qTtzZX7FCI/esxmZpf1cLuymZ+fcYgdpQ18dH64o7P+ooTQ6dRMSYlnDEpiph1b0dxwPQT1uTXMim9eyoxTB0YyU951SzeVNLlktYK/RBJQjX6Gtj1udilBdDo0Qw5N7B29SSOFqGyfwgee7OSr9kXqC+CT26FojXifcwwUX7yAC6b+MzRAiFJ0FAsdsYSx0PJus7jpvwKwo5RA8beLBYSKrVY9B6lBG95g5VfL9jK6vxaAAxaFe/cNIFxqWLilBph4oUrR/PAx1uob3MSYdbx78tHktwFx45CH6K50k9buYhoOUoFLld9sc8kTtKZRBrdgQgVtU5E8B0h1WTGoCguGp3Aok3CCXN2dixzhhyiAdNUDk2lYAyF8HQ4/VH46oHOz8PT+44Y8amIBGSeDXu/h9KNok1rREo4QrTUyaK1UozJB+N2iO+B4oDp27RUdDpfDuC0QnOFrwPG7RROuNBkGHaxcBrbGqF2j9gIm3SXKEEdO1w8n+sLOs+d/ScISThuMzcX1XP1q2tpbd/wyE4I5tpJKXywroi7Zw1kRGLocV9bQcEfigOmH+Bye9hS3MB1k1O75XqSJHHp2ET+/cMe5o9KUAR5T2HUjhY45/+gYrso8xebjdrRFGizeow2QwzGYZcgbfugs1FjwB02UHHA9AWKVnc6XwAqc2DCbb7HDToT1r4MU38thFQvelVoaFTlQsokSJpwbKHvdYXwzYMiwgaEdtJpf4SQ+MOesrWkocP5AmBzenjym128eeM4LHotkiQxZWAEn989hbpWJ1FBeuJDj1F8WCEgeDwyDrcHgx9BZh9GXAY7Fnq3jb3x8M4Xt0tEL2gNtBriODS5zZM+B9W3B5Vbdzvgi3vglp9EipIf4kON/O2CbG6ZloZHlkmNNGPSHdT/i9fDgmtFNTGtSTwTsi6A0BQoXC4WQwOmQVgXSlQrBARZUiMHJSKd3f48V2sgJhvZEkPA45wtMULb6+DND7UOguIOf87Jxu0SAtYafaAt6VtYYsWYcXCUX8oUCEn0PTY0CSbdLTYvVj0nzgmOh7n/EFEzA2cLQf6wFLh6odCUqS8Q6cWJ44/bRKvDzdPf7e5wvgBsL23iukmpXHPfNFIjzGgPWQe53B48siw0sRQUjgPFAdMP2FXRTKRFf0L6L4cyODaYMJOWr7aXc/7I4/cqK/RhPG4o2QCrnwVThFCRX/cyzPo99FMh3hq3Cd3gq4nVm2H3EghJxjnuFra5BzAm0MYpHJ3qPN+23M/hsvfh5yfB0QxD50NrLZz9L/j57+CwwqQ7Ycg8GHXV8d13x+JO5wvA1vfFgnTklYc9parZt3Tv7spmWmwunC6ZZbureHvNfuKCDdw4dYCSetRH2FnWyLtri9hW0sCFoxM5c2jskR1nKZPg4jdF/5RdMO1BSJ3he5wsi1D8NS8KDZZxt1BjGYVz1D1EbnlefB4cj01twif2qqlM7CQfxgEDYNSpGRznJ425pVpElTW1l3V1tsFnd8Ity0RUTW8RcVU4Ii6XA+3+FUIvyBwpnu/Wl2DO3yB9VmCNC0+D+S/D4puFlpDGAOe/4BshEQjcLij+BVY/L1L6JrY7AgzHX/DilCJioNB8+eQ28Tubcp9wnCy6CcbdIiJQze3R+xo9DD4b3jirU4i8qQx++LOIiGmp8r5uRPekhbfYXeyq9I18rm5xkBHtnYLpdHvYUFjPqyv20WxzccOUVKYOjCRIqTSkcIwoDph+wKaiejJ6QJTxjKGxvLayQHHAnMoU/yL+bevcqadsc2BsOQmEm3UsKjNyQfwUgkKScGktbG2NxG06cjqJQi8hyc8uWHga7P0BZj4kSkvbmsRO6/sXd07yilbDZe9C1nnHfk+XA3K/8G3P/+mIDhh/Y/Y5w+OIsOhZsKGYRz7JAcAWY+LLtTsJ1g1jUHzosduncNIorGnlqlfXdlTQyCndSUF1C4+eOxStvzLkbXVQsFyIVE66W+zuHk74uXwrvHWeiGgBKN1I4plPcUf1mVw2dQYmuY2ctnBmqmWyJMm7klL8aDAepyZASyXU7fNuk2VoKIL4kcd3TYWTjlalEuK74CU6LpVtCpBFh5B5Fty2Qiy4LTFice1PpPVkU7YJ3r0QUqcJB+YXv4JznobhlwTasr6BJInnavQQaCyG9y/1GsM452kYd1Pn8Q3FvlXg6gsg+2IR+dIDhJt1zBsRz2srC7zasxN8nWxbixu46tVfOqoTrius44UrR3HO8MNHuyoo+KMXjG4KJ8r6groeEYgakxxGWYOVvApfz7DCqYAkUjEOJaH/xoI025xcLP1EyGc3oPrhT+i++TXjVtxAslQRaNMUukLSBFHp6ICAbtos0Flg4+vw4RVCGyN2GOxd6jvJW/OicKYcKxqdCIH2Z8sRGJ4YyuPzh2HRi32Q2VnR3Do9jWabkxeX5aNRSbx8ppkPEhfx54q7SV7/mP8IH4Vew+7KZq/ypQDvryumpMHq/4RtH8GC6yBnEXx+lyitWrPH/7ElGzoXLu3of3mOX02L48GVHq75QcOqKi3m5OEimuDADn1UFoy+FhbeKJyCbqefix8BU4T/yJnelB6icFRckgZP8mSfdk9veZ5LEkSki8jBqEG9w/kCoiz8aX8UEWQ1u2HCrSISzY9WnMJhkCRRxKAu32cMY+UznQ7BqlyhM3UoQbHCARY5qEfMU6skrp2UwlnDYpEkMOnU/Pm8IYxM8nXA/JRXxSHFCXllxT6sTqVsusKxoUTA9AM2FzcwLSPq6AceIyqVxNSBUSzYWMwfzjn2cqwKfRxJgpTJULgKIgaIKkiNZf1aaDHYUYl57dPeja01BDXsAoYFxCaFY8AUDtN/C9mXiJ3U1c/Biv/r/DxnkRDY1flJCdGZhc7R0XC0iTB5U0TnImHklZD3DdS0O0iSJnpVmfGHWa/hqgkpzBgUhc3pJj7UiEmnobHNgV6r4s5xQZy27X40dWJBbqh/DcrXwzWfiBQChV6Hv6qBGpWE32KCTWUi7ehgrPVQvs1/6oXGT+UPjYHhSeF8c+902pwu4oONmA0aiLpUOADLt4nomm9+KxY++1fCjUv8Owdba0SFJN0hIs/BcXD+i/DRVUI8U5Jg5u+VctN9Ddkt+lXEQKjdK9riR+ExhNNrVCxcTlGxzhDce7RWzJGw6MZOh331LuGQkXrNb63v4K96kcbQ+bss3yrKkU99QKTKybIYk05/VIw9Rysj3VYvnsnHkR6WEmHm6UtH8usGK3qNisQwI5IfHS5/ul5GrUaJZlA4ZgLmgJEk6X7gZkAGtgM3ACbgIyAVKAQulWW5PkAm9gnqWx3UtTpI6CFxxsnpEfzf93k8clYWKqUk9amFLEPhGhg2X5RFldQw9iYoWQ+Dzgi0dT2CWafxjYwA9Gql7/cZ1Bqx0GiuEKlH/hh8Dqx5XpQ7BbGonHz30YV3i9fBsiegaqdw8oy9CcJTISoTrv1c7JCq1GIH19w1p3himHd6W4hJxwOzBxFSvaHD+dJBxTaxi6g4YHolg2ODSY0wUVjbKTh516x0ksL8pDDKCF0tvx/4IWEsGELFAvUAs34PpjAS/GVIGkPhx794R9TIHihY6e2AaSiGLe/BpreFqO5pf4DkSd4RCOmnwa3LRdqROVL0d60iCN2X0EgSntwvRLTesIvEmFdfiFS+BTh8quRJo2oXrPkP7PkeUqeKRXhsL9j0qMzxnRPkLITxfsTdFY5M4jjfMew0MYYBYnxqqW538j7cGS2z9DEISRZaRf4cc9YG2PUVrHgKVFqRbpxxBuiPLTPAqFMz8ChyDjMHRfHCT3uxOTvH7ttnpqHviuC6gsJBBMQBI0lSAnAvMESWZaskSR8DlwNDgKWyLD8hSdJDwEPA745wqVOerSUNDIy29JhzJCnchF6jZnNxA2NSwnrkHgq9FEkSVVyWHPQVXPI7mPefwNnU04Qk4pnyK1TL/t7ZZgxDl9B/o376LZGZogx1ZU5n2+BzhQ6MzgI3fAO7vhYRLUPOEwvcI1G1C965QES/gIiuaakU3weNHoJjxesEqG2xU9viYFRyKAbDYRw46l6yM6zgQ0KYkdevH8dPedXkVTQxKzOa8Wnh/p/PIfFCcPf7P3S2GUJEn/VHzBC4/kvIWyLK82adC4lHSHNTacDg55mtD8LhclNSb0WvgvgN/0Na87z4rKkU3jkfbl4KcSM6z5Ek4VSM6pkUAIWTgFojBGTfu7izTVKhvv7rwNl0gNYaEWVSuUO8z1kkKm/d9K2oghNITBG+bcZw8f1SODais8QYtvtbEQE4+FxvZ3DcSEASkYAbXvc+N2704aOO9v0shMEPsPAGuGohZMzp7p+A7MRQPr5tEt/vrKTJ6uSs7DhGJYVS1WSjweokOkhPqMlPpI+CwiEEcgTRAEZJkpyIyJcy4GFgZvvnbwHLUBwwR2RrcQOpEeajH3gCjEkO5fudFYoD5lRD9iDnfeVTolLe9zPS6GsDYlKPI0moxt4oxN62vCfSrUZc3juqMSgcG0HRcMlbsPNTyP8RsuZB5tmi3CkILaNj0T+o3tXpfDnA9gUw/XcQmX7C5m4orOM3C7dSWNNGcriR1y7PJHzohaLC0gFGXiVy6RV6LWlRFtK6qsk28gqhb7D5HbE4GXklRA8+/PGx2eLVFXRmmPk7seA+sINvCMWaMInHv9jJB+uL+fP0EK7Z8Kr3eW6n0GI42AGj0C+ojRyLa977hO94B7faQNOwa9CFD+c45Zm7j7qCTufLARqLoDY/8A6YATOEY9TWKN5LEkx/0H8aq8LROdIYFjMELnkdavYKfawDz1uVGibf5T9C1eOG9a/6tm9f0CMOGBD6bcMTQwGQZZmVe2p4cOE2KppsDI4N4p8XD+/4XEHhcATEASPLcqkkSU8BRYAV+E6W5e8kSYqRZbm8/ZhySZKi/Z0vSdKtwK0AycnJJ8vsXsmW4oYe/6KPSg7jrTWFPHSWkvN9LPT5fqpS4zLHcWhxPYcpln69B2+JEk6XEZcH2pIep8/30aMRORCm/0a8ThStnzwPnQU0J15+srTeyq3vbKSuVYRcF9VZueiNHJbf/mdCs84VWh7xI4W2jK77Bdd7M/26j5ojRTWVnqqokjodrv8a9i0HYzAMmMGiAjPvrhWL3WqrLFKVmg8RGVfSi46ZvtBPv8pt4LEvIDvhblwemR3vN/H8lY2cO7xnN/GOitYgHBuHpvr0hn4YO0x8hwqWiwp6aTP6bCGCvtBHiR8lXlGDxO/caRO/8/jR/o9XqUU04aFYTiwatavkV7dw89sbsLtEStKuimbufn8Ti++YQmRQv54pK5wgAdENkiQpDDgfGADEA2ZJkq7u6vmyLL8sy/JYWZbHRkV1v/hsXyKntIm0yJ59eKZHW6hpcVB2uEoOCn7p6/3U7ZHZl3q5d86tzkxB7NzAGXWSkGWZ6mY7rXZXoE3pUfp6Hz1ZNLQ5sEYMgbhR3h+c/icIPfGJbElDW4fz5QBNVhc7W8xCr2HOX2DofCGIeorRH/toXauDxrbjqLh1rGh0Qkh91kMw8U4cEYP5eENJx8dvbmulcMzvvc8JT29PBVA4Fnp7P3W43HyyqRSPDFtLGtlR1gTA0tyqAFuGEAY+VFMl+5Ieq3pzzMQOg0l3iu9RyiT/gth9gN7eR72IGwGT74EZD0LS+CPrs4292WeeyrALcbk9VDfbsDp6rkJRUV1bh/Ols81KqbJeUjgKgUpBmg0UyLJcDSBJ0mJgMlApSVJce/RLHNALngy9l6pmGzaXm6ge9rKqJInshBBW7qnh0nFJPXovhd6DWiWxpD4BzzmfkqqqAEnFblc0G1pjOUKQfJ+nuK6NH3dVYdaraGpzk50UzNiUcL+K+Aq9nxabkyabizCTDqPu2ITymq1Ovs+t5LmlQsj09fNfIMWag7qxBBJGQ+JRdGOOhssBdfkMlZu4eUwIb2xuwt1e41IlQZiSS96vaGhz8E1OOS/8lI9eo+L+2YOYNTgas97/VKymxY7LLRMTrO+W8UerVjEyKZRtJSKdosnq4sGcRF64cBHRTdvBEgPJk0U1sOL1YI6A8LQTvq9C4NGqVYxICmVUjJobsmTcsop/b3aTFRcUaNNEpMu030DaTJGKFJXZLtgaHGjLFHoZda127E4P0cGGzqpziWPhxu+g6BcREZM8kf3aAbz2xU6+zilnaHwID8wZxIikUN8LuhyiKpjLCmEDRCXFYyDc7PuMNunUhBhPPDJWoX8TKAdMETBRkiQTIgXpdGAD0ApcBzzR/u9nAbKvT7CjtIn0KMtJWRgOiQ9m2e4qxQFzinFNloTxx39j3PctAAMHzSdp5qMBtqrncLo82OuKuUK7Bu3OL3BHZmBznc+eyqEMilUmg70eWYaSDbD1A2itxjrsCv6dF8XbG2uYmhHBb88czOBj+DuuLajlgY+3cuZAI3cOrMectxxbbAbm4ZdC6AmOhW31sPa/sOIpLB43j0QPY9Lcx7npG5H3/tszB/d4dKPCyeXn3dU8vLhTFPruDzbzwS0TmGQqhf2rhd5FYyly/lKsCZNZI4/n98vbuHnaAC4bl0xMsOGE7i9JEleMS+arbeXUtkdc1dhUtMSNJ3p4e9n0fT8LEcu2WqGXdP4LQizzaCVgFXo1kiRx/ygVoT//E2nhjwA8m30p1Wm9RGbREgWZZ4mXwqlJ5U6o2we1e6BghaiGlTUPIgficLlZvruGv361k6omO1eMT+KGqQNEhTlJEim68SMBaLW5ePT9Tfy8uxoQ4+7mono+v3sqqQc/U9vqYPXzsPrfQksmdjhc+MqRdbgOISM6iDtnpvPisvyOtsfOH0pKhL/SdAoKnQRKA2atJEkLgU2AC9gMvAxYgI8lSboJ4aTpocTo/sH20kaSw09OjuzQuGAWbixBlmUlEuAUIrzoW2h3vgCYd3+COXMWxPfPXdG6FisD9i9AveJfAGjyf8Cy/WPiL10IHCYHWaH3UL4F3jqno7y0Mfdzrpj1X95VhfHjrmqK6tr4+NZJhFu6FjW4YEMJiWEG/pS0hbhlf+n8IH40XP5+R0pQQ5uDNoeIRtSqvTN761odaFQSwYfuiJVthp+f7HirqsphZsSbfHDj4+gNJjJjgpTSlv0Ih9vNO2v2+7RHt+yEDy9tL/2shj3fIwGmvT8wN3o4O4c9ztPf7yHEqOO6yaknbEdWfDCL75zM7spm1CqJzNggEkLbFwsNxZ3OFwB7Myy6CW5biTsyk7pWBxa9piOSzOn2UN1sx6xTE6JEa/VqnG4PofmfIuX/2NEmbf+YyKQJkKgIex+NJqsTl8dDuFnR9egRKnfAD4+BWi1KSgPkLxViutd8Qk6dnlve2dAhE/T6qkLUKomHzsrqjIRpp7ihrcP5ApAeZWFmZhSVTTZvB0zZJlj5f53vK7bBymfaKxt2bTwz6zXcMSOdWYOjqWqykRxuYlBMkNc6qdnmxOHyENHFeYfCqUHAqiDJsvwn4E+HNNsR0TAKXWB7acMx7eaeCFFBejQqifzqFgZG94KQVYWex+OG3C98m/d8j2rMdQEwqOcJbi1E/csLYI6CoRdA9R4oWIahdgekKg6YHqWhBGp2g1oH0Znib3CsFCzvcL4cIHXnfzkz4x98urORsgYbdS1WETbcBUdyXKiRqVFtxG18yvuDsk1QuQOPJZY1+2r565c7Kaxt5YKRCdw+I53USDN1rXa+yangfz/nY9SqeWBOJjMGRWLUtT926/b63E+9bymTzvo7BHdPxbmyBiuNVicxwXpl4RAo3C5QqVFLKmJDvCNYksNNxBd9CU6rSLk4yCEHoKvaxh2TaokwRPLxhiIuHpOAWX/ioe0pEWZS/FVPbK7odL502O/EVrufJ9e6+XJbOTMHRXLF+BQkCdYX1PH0D7tJiTDzx3OymJwe6b/ktkLAkZxWpD3f+7YXLofxNwfAor6B3elmxd4avthcgkf2MC4tirOz44hUFtPdy76fIH5E5xgYnSXmACUboCqXXTVpPhrNK/fU0DDD4ePY0KtV6DUq7C4P101Opc3h4sN1Rfyws5I/nDuEGYMi0WnUULXL146934nImOCui/gGGbWMS/VNXXK43GzaX8/20iacbg/xoQYmD4wkOujEIhkV+gcBEeFV6B52lDb1eAnqA0iSRFZcMOsK6k/K/RR6ASo19uRpPs32hIkBMObkoFEBs34P034NNXsgIh0uew9Vb6jG0J+pzIHXz4B354sIlo+vhXrfaIGjcugMDZBkGUmSuXa4ma9nlJD+9eXwxX0iAuUoXDgqgRCdDM423w+dVnIrmrju9XXsqmjG5vTw4fpinv5+N3aXm592VfP7T3IorrOyu7KF29/dyKaiBnFufZH/qkqJ40TJ0xPE7ZH5IbeSec+v5KxnV3DpS2vYVtJwwtdVOAasTbDzc3j3Qlh0M+qStdw6LRXdQRFS0UF6dLaaI14muHEXN+bdxhNTNd7RVU67qG604AZYeDMUrhR6BieCObKzTPsBJBU7m028saqQjGgLISYdV776C/NfXM1Pu6u5dXoaeRXNXP/GenIrmk7s/go9hkqtQfZXvSd2+Mk3pg+xo7SeqLrN/INnecbxGJMcv5Czr+ToJyocG2114l9jGJzxN4gaDG4HTL4bXFYGhHQ6di16DW+fbeTD1C8J//RK2L4IWjsdxykRZh6YM4jUCBM2p5sFG0podbjZX9fGre9sYHtp+zgVPsDXjsQJ3fIMBsgpa+KLbeX869s8/vVdHl9uK2fz/oZuubZC30dxwPRRGq1O6tucPjtqPUlGtIW1BbVHP1ChX+B0edgXezaOiM7y4/bYMewLmxpAq3oWbVA8cms1LHkI9i2DDa/BF/f6L3Oo0D143LDuFWgq7WzbvxoKlx/7tQZMFxE0B1E49Ha2Vbq5M+wXUlf8GqloDWx6C946D6pyj3i54UmhjBg6DGfWhd4f6IMgejB7q1pwebydPl9uK6OqycYbqwt8rrc0txJszbDkd5D/I2Se3fmhORJO/6Oo4HCC7K1q4Y53N1LT4mh/38q9H26mtsV+lDMVuo0938LH10DBz5CzEN46l2ypgEV3TuIv84by+PxhPD5/GOoD5e5LN8DAQwKAo7OgqQxVcylZxR+ikw7qa8W/wNvnwY7FkLMA3joXStadmM3hA+D8Fzu/Q5IK59wneXSV6DdTBkby6ooCbE5R9WNNfi2FNW1kxgTh8sjsrWw5sfsr9BgulUboq0QclG4UP1o4fRUOS1TzTkYsvQrT7s/Q7F9Oxk+3kd70C81WZ6BN61+knwZFa2D2X2DZP2DHJ0JU9+d/QtlmRprrGBxrAeAf03RMX30DIVteQtr7Ayy6EbZ91HEplUriivFJ/Ovi4Xy3o8LrNrIMO8uECDkJY2DYxZ0fWqJh1sOg654Nt13lzby3tgiH24Msww+5VazZV0vDyaiAp9DrCVgKksKJsbOsidRIM6qTqMeSGRvENz9UHP1AhX6BSiXx6Co7ExP+xeQhtchI/FQXRsFGJ68OC7R1PUTTfqR1L3u3tdWiqtktyrkqdD+ONjHxOpTy7TDKt/mIxI+C67+Gze9ASxWuUdfRosvm+dgWYhe/4H2svRkqcsQi9wikxEbA7EchPFnko8cMg+kPQmQGQTWVPseHm3XoNGpigw3klHpHBERa9FCXD3lfi4aMOTDrEfC4IH22sL8dp8tDRZMNvUZF9DGKrxbXteJ0ezuGCmvaKG+0KXnoJwNbE6x82rvN7UQqXEH21DFkJ4R2todNhkveFAuNrHmQOh32LhVpeIYQWCE0CjTFa0Sqkro9QmXD697Xl2XY8r4QrjwGGq1OGtschJl0BBm1MPgcuH2FSAkMiqVKk8Ter1Zj0qn9OvBW59cwZ0gMeZXNBBmVKWVvRXLakdY8L5zUwy4SKZh1BbB9IaTNCLR5vZaIyjVifD6IhJz/4Rp9HqBUuuk2EsbCpLuhsQQcrd6frX8V44CZvHrdOHJKG5lsXQa2Bu9jfn4Shs7v0GULNuoYFBtMXKiR+jZvZ1noAb2qoFg452kcY2/BY29FE5WBJjy5234kf1Gnq/NruOc0RXNJQXHA9Fl2ljeRHHZy0yLiQ400Wp3UtNiV/NdTALVKIjsxlP+sLOA/HRONZu6f3fXc2D6Hyv+ESpYkFGWDHkJvEQvP6n95tx+Pw0uSIGmceCEecMMBmspB7edxp+piEGh4Kpz+JzFB1FlAKxwiQ+NDGJcaxvrCztTMP503lJhgA7dOT2NZXnVHhEywUcOswdEgHxQlsOd78QJIm9XRXFzfxv+W5fPR+mJCjFr+cE4WZw6L7dSPOQr+nCzBBo1SGvNkIUmg8tff/LTpLWLhkH46IAunS/wYWHwjtFR1HjfkfHHs0e57DGwprufRz3LYVtLEqKRQHjt/KNmJoSL8P0pUAonzyDwwJ5Mnvsn123/SoywU11kZmxLG0PjuCd1X6H40Wp0oonCI404ee5PybDsCWq1vn5fUwsmu0I1oDTBoLmz5wPczlQYMISSGmUgMM8F2P88xP2NfiFHLQ2cO5oY31+Nufw4PjLIwIjEUEBotS/Pb+PPnLVQ32zl/ZC2/Oj2ClG6qPuivxHt2QojyHFYAFAdMnyWntIGkk1zmTCVJDIqxsLmogTlDYk7qvRUCw2VjE/lmWznlTTYAUiNMnDei/6bj2MMGoZtwB9LByvimCJyRQ1FqfPQQkgQjroTyrbDnO5BUMP52SJnUffcIjoOZj8DXv+lsM0VA7Ihjs9Mc6dUUE2zguctHsb20kfpWBwNjghgWL4TRx6aE8/ndUyhrsKFSQUq4ifToILClQ+Y5kPdV54Xix0BEBgCyLPPB2iLeW1sEQG2rg/s/3kpcqJGJaRFdMjUjxrs0pkqCx+dnkxSulMY8KeiDYMbv4KOrOtu0xiNHpxgOEtSPzoRhl4gy5bIHBs6BkVd6LzLG3gg7P+18f+B71EVK663c9OaGjnLUm4sbuOXtjXx612RiQzo3d1QqicvHJTI4Nogmq4MJA8JZWyD0Gix6DbdMS8MtywxPCOlymWyrw01Vsw2LXqNEZJ0kJLUGx9hb0O37qbNRpcaROQ9FEvTwaDNOgxVPgsvW2TjtN92SKqrgh4TRQgfGepDe5JT7OxzCAMRmgyHUOwpmxu86ol8OZnJ6BIvvmExeRTMWg4bshJCO5+COsibufG9Th3TcJ5vL0KlV/O2CbLSarit0tNpc1LTaCTFqO6NrgJmZ0QyJK2ZneTMAEWYdN0wZgEatqH8oKA6YPsuOsiaumZh60u87INLMpv31igPmFCHcrOP352axt6oFlSSREWMhrB+XG61ocbDLOJc55yYj5X4OkYNoG3guyxriOKf7IlMVDiUiDS56HeoLRaRKeBpojnNh1lgqRH1dNojKgqhBoj37EgiKh52LIWIQDD4XIk88FDgu1EhcqG80YlWzjc+3lvHGqkJ0ahW/mZtJVJCeYGMQnPWEiHjZ/TWkzRS2WETVp5oWOws2+oo8bi9p6LIDxqLXcOfMdE7PiqG6WZTGVKrXnWTSZ8E1nwiBSHOkiGCJH9m1cy3RMPtPMPoaUUUpPNVXHDd5Elz7BWx+GyS1ODZp/OGvaW0QpV5bKiE0hTpXYofz5QAVTTaK6qxeDhgQ4fzTB4n+OTEtkl0VzVidLtKjLaRFHiUq5xD2VrXwxDe5/JBbRVK4kb+dn83UjEifUrIK3YvD7eaTxgzmX/oh2u0fgNaAY9jlLK5J4sqMQFvXi4kfCTd8DTs+FUKx2RcjJU0ItFV9F6dVjEMNRWCJFSm9xoOcz1GZcN2XsPMzqNsHWeeJsfTgCNaoTLjuC5ESXJ0HIy4Xz1E/aNQqRiSFMiIp1OezvVUtPrr9n2wu497TM0gI69pmRW55E499sYM1++rIiLHwtwuGMWGAeE6nRJh544bx5FU043R7yIi2kHySCqco9H4UB0wfxOHyUFjbRlL4ya/Mkh5l4efd1Sf9vgqB4ae8an67cBsZ0RY8skx+dSsvXjmas4f77jT0B8w6DRmqClTf/R6iB0PeV8hVBQya+vdAm9b/MQRBXPaJXaOuAD66WjhgQCxar/1MiO0ZQyHrHPE6CYgS1PsAsLs8/OnzHSSGGTk9KwZCk2HCLeJ1CCathtQIM9XN3nobx6oDYzFoGZPSPeWsFY4DnVkIS6afdnzna/RH1ifS6CBtungdDXuz0Ej45cWOpsxznychNI7Shs5+plZJBBuOPC2MDNIzNej4nKOtdhePfbGD5XtE5afiOis3vbWeL+6ZSlZc8FHOVjgRtCoVk0Lq0X15F4QkgtuJZv8vTD7rrUCb1vtJGCNeCieGxyPEcr+4r7Nt6v0w7UHQH+SYiB0mXkcibrh4nQD+UoGSwo2YupjqW9dq594PNrOnSqQV76ls4YY31vPlPVNJixKO6ZhgQ5cjAxVOLZQ4qD7I3qoWYoL16AOQg5oeZWFHWROeQyp/KPQ/3B6Z1fnV/Ovi4dw4NZWbpw7gyYuyWbPvyGVT+zKRUhMD1v4RHC1QsgEairAULCHVlR9o0xS6wv5Vnc4XEAvPFc+A6/iq/zjdHtYV1PHw4m08tGgba/fV4nB5jnpei93Jgg2+USzL8qr8HA12l5uC6hZK6tswGzT8Zu4gr3LFWbFBjEpWnCkKx0nVLi/nC4Duu4d48axwr7b7Ts8gLcpMZZON3LImckobKalvo7rZxqKNJdz69gZe+Gkv+VXHV+2ootHW4Xw5gMsjk1+tVE/qaZxuN5F5H0BbrUj3rNqJunE/YSU/Btq0Xo8syxTXtVFQ04rd5Q60OX2Xun2iwuTBrHwGavIOe0pxXRvbSxqpau5MAattsfPF1jJuf2cDz/6wm7yKpsOefySyE0IYmxLa8V6tknj03KGEmf1HeVc328mvaqGxvYpRab21w/lygDaHm/21bcdlj8KphRIB0wfJLW8iJUC5/MFGLWa9msLa1g4Pr0L/RK2SOG9EAku2V/DpllJUksQlYxKZndWP08+crUgN+32a1da6ABijcMzU+ZZ+pjJHVFo6jpSmzUX1XP7yLxzwN3+0oZgPbpl4xFSgVruLH3dVkRBmZGe598TQ35hZXNfGs0v3sHhTCSadhgfnDmL+qAQ+vWsye6paMGnVDIkP7nJItIKCD221vm2OFoaEwwe3TKC80UZCqJEhccFsKW5gTX4t20sbWbqrCotOwx0z01m2u5p1BXV8t7OShRtLeO/mCcT7Sb07Eia9mnCzjrpDUp/CjP03rbW3oMWNrmabT7upYVcArOk7NFodLNxQwv99vxu7y8PFYxK457QMIQarcGzYGkUK0qG0+c6v3B6ZH3Ir+e3CbTRancSFGPjPFaMYkxLGB+uKeOq73QAs2VHJe+uKWHDbJFKOMb0nLtTI81eOYUdZI802J+lRQQyJ943Ek2WZ1fm1/G7RNkrqrWQnBPP3+cOx6DXoNSrsh2zKKCK7Cl1BiYDpg+wsbwroZDw9ysL20saA3V/h5CDLMjtKG1mwsYRIi55Qk5Z31xaRX9N/dyutukjsaXO9GyWJFktqQOxROEaSJ/q2Db8MTCJ6pNnqZFd5E0W1rciHJn/7YcGGEg4O9pNl+GBd0RHP2VnWxL0fbGHCgHAs+s49jsRQA9MzoryOlWWZD9cXsXCjuE+L3cWfPt/JluJGhsSHcP7IBOYMjVWcL6c49a0OcsubKK33s3jpCuFpoDkkDD4iA214MpPSI7lwdCIT0iJotrt48ptdFNW18UNuFbIMzXYX//w2j1mZUaRGmLDoNRTUtLK7svmYzYgLMfLYvKFeWsJnDIkh00+1EIXuxS1pqU6/yKe9PnF2AKzpO2za38Bfv8qlzeHG7ZH5aH0Jn2wuDbRZfZOQJAhN9W7TmiAs1efQ/KoW7n5/E41WUUK6vNHG3e9vZk9lCy/85B2RXNVkZ1e573jUanORV9HE/prWw0btx4YYOD0rhgtGJZKdGOJXi2pfdSs3vbWekvbxd3tpE3d/sAmLQcPvz/FOE71qQjIZMcrmtMLRUSJg+iA5pY3MGBR19AN7iORwE9tKGjl/ZELAbFDoedwemT3ljSw8R0Vq7VI8koZ9YVNZWNZ/nW/VNgn3uIdI8NjRFS4DcyT1M/9OrjuZ4yiKrHCySRoPZ/4DfnwcnG2iKsxIURlmT2Uzj3ySw/rCOpLDDCyYZySq4mdUzjbIPAsSxgpdjYNwun3TjVx+2g5mf50IP37uxz3cPG0AsiwK1JwxJIb0aO+JWX2bk083l/lcY1NRfYfo6aG02JzoNCqlDGo/orn9b+ovrTintJFfL9hKXkUzYSYtf78wmzlZMcdWSSMyA674ED6/BxqLIW4UzHvWp6pXZZONjJggluZ6p8pdNczERcaNXJmygsaQTFbK2Xi64MD0x5whMXxyx2QKaloJN+sYEh9MpFIJqcdxuj1s1k/gtDE3o9n8Jqg0OCbczUayOCvQxvVi1hXUolZJTE6PwKhVszq/lsWbSrl2UgohSuTWsREUDZe8CV/cAxXbITQVz7zncKgtGHIWQcEKiBsB6bMoaTDidHuPMRVNNlodLr9jjwfvtn3VLfz5c6E3ZdSq+c3cQVw2NgmL4dijU4rq2rA5vZ/7+2vbKGuwcfGYRIbEBVNU10ZMsIEhccEEHcc9FE49FAdMH0OWZfIqmrl2UmrAbBgQaeb7nZUBu7/CyUGjVvFQdhMJn10FHpH3HK1+kaQLFgfYsp7DoFWzoDyIqIwnCE+vxSYZaHJHkaxSHqh9AkMITLhDlHl2uyA0ETR6bE4X//f9btYXilDnJyZ5iFk0v7O06Jr/wDWfQdoMr8tdOi6JT7d4O0iunJByRBOi2wVKm6wu/v3DHgCy4oK4fnKqz7FmnZrMGAulDd6RDf7KRVc321iSU8nbawpJCDVy56yBjEsNQ5KU6jF9lcpGK19uL+eDdcWkRpi4c2Y6o1M6dVnqWx0dzhcQDru739/Ml8cqWitJopLIzUtF6VZLjBClPoQwk47aFjvJESaqW4RuUlaMifuDfyTym+cACAEujhqKbfSHx/Uz67VqRiaHMVLRNDqpGHUaxoU1odm+HqbcBx43uj1fMf700wNtWq8mKz6Ih88azJKcCqqb7dw4NRWVJGHUKg7w4yJhlKje1lpFmcPA4h3NXNvyJIZtr3cekzSRwWe96nNqkF5DlEXHrdPT+M+Pezvaw0xaBsd2jodOt4eXlu/r0JuyOt389ctcBscGM2VgpM91j0aoyXf+Z9CqCDZqMOk0jE0NZ2xquJ8zFRQOj+KA6WNUNdvxyDJhfgaEk8WASDO5FUKIV6WUjuy/yDKxee90OF8AcDuJKfoSsrtQeaMPYne5abPbiVWVkuAuoVUVxI/VbcSGZgbaNIWuIkkQ1u4kaauHojWoG8q4ITEYa7OJXfUeMuqXdTpfQOQWrXkeUqZ4lbscmxLGezdP4J01hXhkuGZSCmOPUlloaEIwl4xNZMGGEmKC9Zw5LJYLRyUQ6qd8u16r5p7TM1hbUEerQ3zPhsYFM87PZG7RplKe+EboNeypamFVfg2L75xCdkLIMf6CFHoDHo/Mu2uLOhYSe6taWLGnhk/vmtLhXKlosnU4XyQJHppkYaqljKSSr0AaJkq4HosDLihGvA5DSoSJc0fEAzAqKZS8ymbOT7ISufa/Xsfpqnega9oD0cnH8iMrBBCn04l5+zuQeaZoUGth0JkY8r+FTCW+83CEm/Xc9+HWjnLFuyqa+fv8YUoE4olgCmNvi5b5L6/i79M0BG9/0/vz4l+ItRXwuzMzeXJJHrHBeh6fLDHeXEVQ1TJuHzWUpLDhLNpUwtD4YC4ek8iAyE79l7oWB0tyKnxuu7uy+bgcMBkxQdwybQCvrOjUmPvzeUNJ7YLmjCzLVDXbMWhUhPiZAyicuigOmD7GzvImUiPNAd31DDJoMek0FNW1kRqp1LTvt8gyKodvXq3K0X81YBwuDxcF5ZK29FaQRcjpwMTp7JX/BfRj8eH+iL1FlN5d+1+0wAQgeeKj/E47CY3LT5UCW3PH3/wAOo2aKQMjmTIwElmWuzTuRpj1/OGcIVw+Lolf9tXx5qpCluZW8eDcTGZnxWDWez92RyWH8dndU9hT2YJBqyYrLojYEG9x06omG6+u2OfV5nTL5JQ2Kg6YPkpFk43XVnqLRttdHnZVNHU4YEKM2g7R2j9NNXNlwUPoanaKg9U6uOZTSJ3SbTZJkkRmbBDP/bCH5XtqGJ4YwvSBYbDG5Xuw29lt91XoeTQqIOM0+PYRsLeLgwfFYjz9sYDa1dvZUFjPoRkvb63ez7wR8ceVzqIg2FPZTLPdhRaVz3MXQCW7uH5yKlMGRjKgZQtBCy7uGHPMsdlcetl7XDJ2ot9nssWgISs2iF8KvMV940KOrxy0Ra/hntMGMntIDFVNdpLDTWTGBh11PlDWYOWDdUW888t+oix6Hj57MFMHRqHTKPKrCooIb58jt7yJpF4gyJgWaWZH2fGVflPoI6hUNA+/QWhozPideA27iJbMCwNtWY8RpWoibd2jXhMCU8lykh1KGeo+R00eHLJzH7f+Cean2Nkfc7pv5MDkO300YA5GkiRw2qB8G+z7GeoKD3tsiFHLnsoW/vVtHtUtdkrqrdz34RY27q/3e/zA6CDOyo5j1uBoH+cLiHRAk853v8SoVR7hfRWNWsKk891F16s72+JDjfxjfjY6jYppxv2dzhcAtwOWPiYcjd1EXaud+z/awtc5FbTYXazOr+XPy1twD7vU+0BzFERn+b9Id+DxQM0eyF8GlTsVZ083IKk0yEW/wJB54lk+8yFIngT1firHKXRg0fuOu8FGDWqVMvaeCAecEF8U62lLneP9YUgyRGZi1GkYHqUmaOXj3mOAPhgqdyAVrICGYp9rm/UafnvWYK+/3fSMSEYkhR63vcFGHRMGRHDeiHhGJIViOEoKmizLfLBORDg2tDnZU9XCTW9tIEcpYKLQjhIB08fIKW0kJTzwUSdJ4SZyShs5Z3hcoE1R6CHcHplCdyTZFTuQtrwPgCdpEvmOUEYF2LaeIljlENVCTvsDuOwiTLs2H7Pn2Ct+KAQYa4Nvm9vB1EQd5cYhuK5cjGbNc6JE9eS7YMBMP8e7RKqS3gL2ZljzIvz8D5GyZAiB+S9BVBaEp3qd1uZw8c4vvuXMf9xVdVhx3SMRbtbxuzMzuev9zR1tkRYdwxNDj/laCgGmZg9U7iBapeajSwZw1tvFONqFneNDDQxN8NZ2OT0rms/vnkJ83hu+16rfB45W0T+7gaI6K7mHVBP5elcDj9z2AInRWZCzUIhVT7pDOGF6it3fwMIbxXdPpYFznoERlx/RQapwZJxOO9q4bNj4Jmx+VzQmTcCTMk3ZiT0CUwZG8pcZwYzRl6CVnewhiYiUQRj9OE9PeZw2qMyBugIh8B07HIxhQhBfZ/ba9MiKC2ZglIWv8pqZPOMeZkdkE1PyLXLyFKSxN0BIe5EPR6sY5w4w+BzQGuHDK8R7SzRc+THEe89KRyeH8fndU8ivbhVaa7FBRJxEse/qZjvvrfWumCjLsKOskdFHSWNWODVQHDB9jNzyZp9SpoEgJcLEukPC+xT6F2qVxMDqpUgVWzvaVMVrGJixDhgbOMN6kqA45BkPIzUeeHBKEDYAbdSggJqlcByEpwknia3Rqy0qKYMoSzQQAwOmimgnrZ/Q5PJtsPYlKNsE2ZcK8cBlf+/83NYowvkHzoFxN4GkgsodoFKjjx7GgAjfKMEDIdB2p5s9VS2UNViJCTYwKMaC0U+Ey8HMGhzN+zdPYMWeaqKDDUwdGElalFLusk9RthXenieEcIG04ERW3vQBr+3SkhBm5PR0Cwmt26G0CIJiIXY4GmOoEJi0+RlzR14lFiDdhEmnRq2ScB9SsrVKFUPitPth3M1QuR2+/ws0l8L4W2DQWb7VlBpt5FU24/bIDIy2+BWVPix1hfDpHZ0aTR4XfPUrSBgNscNO7Ac8hVFr9Hiaa1ANPhey5onFsNOK5HYE2rReTZaumsF770eqFYLqmToznkGfA8euJdLv2fEJfHq7+L9KA+e/CKUboHAFDD4XRlwBEemAiO579bqxrMmvJb+mhb1Zd2KZ9SvMpiA4OLrIHC2qGa4WIuDEj4Yf/wrBCTD2BhEJuH+1cPQcUs46LcrSI8/ImmY7uyubsTrdpEdZ/Eox6LUqooNE+ujBKJWzFA6gOGD6EDanm7IGKwmhviHqJ5vUCDNvrS4MtBkKPYnbjal4mU+zpXQlcOfJtubkYG9GaquBnx7vTENKmQypUwNrl8KxEz4ArlwAXz0gduVSpsCZT3gtWGW1lj1VLRRU1xNi1JIZG0yYWSd28N45H9rancxL/wxz/uZ7j7p9YAiC6l3w+b0dC2t1cAKPXfgh3+eqsLtEPwo365iZGYXHI/P51jJ+u2hbh7bAI2cP5rpJqegPCWs+4KipaLQSF2pkbGoYk49DRFChF+DxwIbXO/oIgNRUQnT5Tzx89n0i2mr9K7Dkoc5zJt2Fe8bD5DdCkyOV7PP+h/7HP4jortHXwpgbjk2E9xCsDhd5FS2UN1pJCDOSFmnm7lkDeXbpno5jLhqdwMAD5dNr9wgH0oF0gM/uhrOfEo6YdgpqWrjj3Y3sqhCpUbEhet68fjyDu1q1qbXK22kKQgi+uUJxwJwAKo8DQuPhh7909sHgeFQzHwmoXQewO93srmympMFKXLCBQbFBftMuTzqFKzqcLwA4WlGtfhYuehU0Svn0DuoL4Zvfdr4fc73YsKgvFO+rcqF4HVz6DhiFbllqpPnoOpIqlXD82pth8zvtkck6mHw3fP9o51i04xO4+A0ITeqSuXWtDnZXCCdKWpSZlC4I6oLQdfnNgq2szq8FRDraOzdNYMQh0aghRh0PnZXFjW+u54A/e0CkmRFJ4mdXipgoBGR0kyQpE/jooKY04FHg7fb2VKAQuFSWZf9J86cguyubSQg1olEHPmA0wqzD7vJQ3WwnKkh5CPVLJAk5aSJS8TqvZjl+FP32sWGtg/WvwtgbxY6KJEHul3gaS5Uw7b5I8gS47kux4DBHgj7I6+NV+bXc9Ob6DifJvBHxPHreECKrcjudLwdw232vH5UJLifkfum1sKapFH3VVn66KgO300G1PpHgsCgGRgexr7qFP3ya4yXs+MQ3u5gyMJKh8Z2Cug6Xh483FvPoZzuQZdEVn5ifzcVjk1D7m7jVFwqbg+IgWEkN7XV4XFC5zbe9Klf8W7dPLCgOZs0L1Keexzlv1+N0y4SZInhh3ueMTTRRI0Wyt9pGSEsDA6MtPuLOR8PhcvPe2iL+9lVuR9tfzx/KdZNTGJsaRkFNKyOitQzWVaGv3Qrh6WI3+1A9llXPwtD5HVEwP+dVdzhfACoa7Xy4vpg/nTeka8UDLLFi7LUeNPVTaztTEhSOCxkVFK9FSpsBUYPFgFK8vnOBHEDcHpmFm0r4/Sc5HW0PnTWYGyb7OqVPOnV+NHKqdoDTqjhgDsbe0inuDGAK9+1bBT+LdCLjMSaxh6XAWf+Cqfdjq9iFIfNskUZ38FhUsh7KNnfJAVPeYOXhxdtZtrsaEGWmX712LG0ONxaDhoxoC0EHCyzXFQgHsCWGnZWmDucLQJPVxbM/7OaFK8f4pKVNGRjJojsmk1veRJBBy/DEEDQqiTdWFfDVtnImpkVwwch4BsZ4z0sUTg0CsqaQZTlPluWRsiyPBMYAbcAnwEPAUlmWM4Cl7e8V2tlZ1kRyROAFeEEIUg6INJNbrgjx9lfcMnjC0iB5Ymdj+ml4LP13cWd1AeNvht3fwvJ/wYqnYcB0ZM3xqecr9AJMYSIa5hDnS12rnT98ur3D+QLw+dYydpY14Vb5qW6x9wc4429iMQhgiRERCNW5UHeISPPIKzEWLiX+o7kkLT6P4ctvI10lymLWtzm97gngkUXpzIMpqGnlL5/v7HDUyDI8+vkOrzHX7ZHZVdZA8+ZP4KXp8MoseGUm7F91DL8ghZOCRgcjr/Ftjx4inDD2JhFOfwjFZWU43aIT1Lc5ufLDQlbXmJn73GqufX0d57+wime+301j27EJ1e6rbuXvX+d6tT325U7qWx0khRmZEesia8vf0L82E149Hd6/FHdIqs91ZK1JpBu0s92PyOSG/XU4DunzhyUsGS56DQyh4r3WCBf8DyIyuviTKfjD5XIixQwTC8mfn4RlT4BK3bNaPl3kwFh3MP9csov86tYAWXQQqdN820ZeDcbQk25KryYkEeJGdr6X/DjOJElErxwPGi1V6hh220LwZMyFhiLfY9pqfdv8sKmoocP5AtDQ5uSZH3bz4rK9XPjiav72ZS61Le0bLru/Fc/WN86El6Yxxrqa+BDvn2F7aRMtdt9KcVq1isGxQWQnhhJu1iHL8J+le/jLFzvZsL+e53/ay81vb6C80dr130MP4HC52VJUz+JNJfy4q5KqJltA7TlV6A2buqcD+bIs7wfOB95qb38LuCBQRvVGckobe0UFpAMkhZkUB0w/RpIkWoLSxELzQNUEjYHmoLRAm9ZjONRG5G0fQ2O7sr7HBWv/h6SIP/Y7WmwuCmtEOWqLXsM944N5+nQTZrmVKmM6jgjvKi+1MZNpGHo93Pg9nP8CDLsIVvwfpE4Xue0HUOsgJBHVzk86mjSl6/Bseg9kmfgQA2mRJq8KOEatmoQw79RSl8fNnbPSuXNmOmntYdp2l4dleVXkVQih1JV7a8jP3UTQl7d0pm00VwgB06ay7vpVKXQXmWfBxDtFH9GZxf+LVsOyJyEo3kfDAH0wu+wRPpfZXtZI80ET/ldXFrCjvFHsytfshYZiSuvb2FnWSH2rf42PujYHHhmCDRqGxgdj0WsIMWrZWd7E8j3VOAtWodv+fucJJeuQ9y2D2BFe1yke9QB1ns55ycxM3wX9+SMSji2SYeDpcNvPcMMSuG2l+K6pe0E6Sh/GrdIht9VB8drOxj3fAfJhzzlZNLQ5OoSoD+CRhZM80FQEZ1M57XGhJ6bS0Jh9AxUp5wXarN6HMVRovhxwWLXV+qZuj75eaLP5w9oA1buhpdr/54Dd4SBp7/uo9n4vdIwOJXrwUc10uj1IEtx7+kAmp3eOrbsrWjrSkD7aUCwcybX7xLP0QGSPo4WwJXfy6zHeY9E52XGEm33niI1WB8/9uJfz/rOSq15dy/wXV5EZF4xW3RkJWFjbxp7K7qtkdzz8kFvF/P+u5oGPt3Ljmxt4cMFWqpsD/93r7/SGJ9rlwAft/4+RZbkcQJblckmSuk9drh+QU9bEOdm9J/ogKdzod7dLoX8gSfBheQznD70ZU+EPoNLQMvxullTEcGM/1aQ1OuqRyjb7fqAsZvsdkUF6ZmZGkVfeyJszWslc9wA0luDZP566mU/wyaAnmCTlENy0m4rISbxZHMsNVghNGAVxI4STbsIdIuS5uRKay2HN8xA+EE9jmc/uhnrvElzTHqCs0cX5IxOwOT0YdWoWbyrhsfOHMaDdybK9tJH9Na28u3Y/v+yrQ69RceWEZBLCjGwraaTJ5uKVFft46MxMHvtiB08Or/JNC2mugKZyCI4/Ob9Mha4RHAcJY0TqgscFOz+Hhv3CGTP3cbj0bfjqQShZC1FZuM5+mgXfeC9MTToVeo3v3lmCpxw+eQpyPxPRIxP+yGM7BlDv1PDMpSMYclB6G0BCqJHbp6fhlmFPVTNTB0YyOC6IJTkVON0y55q2+NxDk/89OVOfJ6JsGXpbNSVR03ktP4K/HJRRMDEtglumpfHGqgLcsswFI+M5Kzv22H9XYam+DimF40bttiLtW+b7QdFamHjHSbfnYOJDjURadNQcFAVo1qlJ7AUbjksLbPxnbRY3D38Xixbez/MQ3dLAC1e50WuUSkhexA6Fyz8QOk76YOEQLlwuUoOSJ0PqFBHRdiilm+GLe6FiG4QNgHn/gQG+kUdRWiuG4u/FfGzar4Wwb+FKkbJ42h+8I3D8UNNs57VV+3hleQEuj8zMzChunjaAV1cUMCk9gi1FDR3HWp1uqsv3E+U4xDnitDIh0o5eI/TdZmZGcd3kFL9pwTvLmvjvss7o2Po2Jx9vKOaMIbF8tb28o111AjpeJ0plk40/tac5H+DnPTXsLGtihh9nukL3EVAHjCRJOmAe8PAxnncrcCtAcnJyD1jW+/B4ZHZXNnPXzIGBNqWDlAgz3+2oDLQZvZa+3k9dbg9BJiOT3m0mOmi62JFa0cg/Lux7P0tXkQ0hyNFDkap2eH9g7p/Cp329j/rDUZaDvH+1EO1LmYw+eWxn2tBBmHQaHjk7i5p9W8j84cqO9A9VyToivruXneFP8NjOgURYhlK6yUpqhMSDlvZdLpVK5KUDzVYnBnM02lm/h9HX0Wjz0JL3Ewm863U/Z8oMcqqcPPDRNvbXicibYKOGt28Yz8hkUZZyV3kTf/xkO8kRZn7ZJzRo7C4Pb6wq5OGzBjMzM4r//LgXk1ZNky2Nknorjepw4S09eAalDwaTb+REX6Tf9VG9BVY+490WPwoMwULn5OqF0FoNxjA0pnD+PK+Bez7YTEWjjbfOCyHbvQPJup25l47mvpUqtpRZyY4zkbDzFdj5qbietZ6EZQ/w61nvcck3Eg8v3s7bN00gxCi+B1VNNorq2ogJNrC5uIFledUsy6vmtMxoJqWH8+bq/TSMHUr4Iaa7UmfwyHIbxQ2jMOs1lKyz8u/LUgk1de7+RgUZ+O3cTC4fl4RHlkkKN2LQ9oa9vp6lt/dTWWtCTp6AVLbJ+4P4Y9Tj6AHiQ4387+oxPPDxForqrMSFGHjqkhFHF2g9CZQ0WKlosvG3lZ1pGWkOsDs9fc4Bc1L6qCFIvA4QdjWMuvrwxzdXwoJrO1OK6gvgg8tFBFyE93rHYA7FHT8OddNnsOV9mP4biBmKrDUiRWZ0OHdsTjd2p4eyRivVzXbiQgykRVlYv7+O/y7rLGm9LK+apDATc4fGkBZl5vOtYqNtUlo4yFApBxOlNQpH0gHUOuITU/jmviQcbg9JoSbMBv/jW3mjbypPbnkzp2fFdLwfHGthUEzgqhm2OdxUt/hGuzTalOpoPU2gn4pnAZtkWT6wiq+UJCmuPfolDqjyd5Isyy8DLwOMHTs28PGTJ4GiujYseg2Ww3zRA0FCqJGSBis2pxtDoIXSeiF9vZ9q1Cp+3FWFLENlU+cAvXF/PZeO7ZrSfF/D5vSgn/UwfH5Ppwjk6GuRzTFHPrGP0tf76KFYS3Iwvndu599OUmG/fAH6zNl+jx8UE0RabaOP9obUUMhdZ4WzptzD7soWYoPFgiDC0im6WFzXxiebS/lsSxkjk0K4ceoAhsanEAI4PW4cRbPR7fsBAFd4Bj+YzuJXr6zjjpnpfLG1jPzqVpqsLjbur+9wwGwqqmdwXDA/5Po6thutThZtLKGhzcnZ4+OIDzVy0ehEXsipZsiEPxC39m/CCaPSwPnPQ3jqif9CewF9qY/WttjRa1VY9H40hA4QPwqyL4HtC8R7YxjMfqxTo8gQLF7tDE8M5ZM7JiPV7iFswXxoEdOiFOCN899h+qd6Hp8Tg+azxT63inPsR60awNaSRqqabIQYteytElWK9lSJnd2RSaHcNHUAr60s4Me8KiYPjGBaRiRfN6u4IXUO5sLvAZAjMlCPv5nfZkby9fYyqlvs3D9nEONSD3XTgFajIj06cIuKQNDb+6nHZUfKmAv5P4mqbQBJ4yF+ZEDtOsDY1HAW3zGFmhY74WYd0cG9Q3dt4oBwrygGgEvHJRFsPMJ3vJfSK/toY7GvnoujRZSjP8QBg0aPevqvoWSNqIK05GFwO0RRiJ//iee6r1htTeKTzaUkhpl4/qe9uD0yOrWK564YyY6SRmZnRRNm0rFiTw0VTTbWFtTyx3Oy+MsXQg9rREIwMzKjueeDzcQE6fhg+lMkL7tfzBHUWpj3PKqIDNK6kBLpr2Lt6ORQRiWFMndoDGNSwpmdFR3Qvh4bbOCMITF8t7NzzqFWSaRHnlrjdyAI9Gr+CjrTjwA+B64Dnmj/97NAGNUb2VHWRGovEeA9gFatIiHUyJ7KFrITQ45+gkKfQpaFyOehePy09Re0OOHb38Ooa0SagForxFcb9gOTAm2ewlGQC5Z7V0+RPahX/Itcw1AGJcX6DRPWWA6JFBl+GYQkEfXTb/gmcwQt8y/DFp5FTLCBikYb20sbqWmxIwHf76wkv7qF/OoWfsqr5tM7J5McYSYyYSBc8iq28l1sK6pmVX0Yz3/fjNsj858f93LvaQN55gdR2rTB2pk+5PZAcX0bGdFB1LR4CwrqtWqqW+xkxQVzw5RUDFoNt81I4/kfPdyzdzT3n7aIoUFWgmMHoIrK7LbfqcLRKW+0snhTCe+sKSIu1MCvz8hkUlqE/2pVlhhRunnczWKhEZ4uRKIPR2Mp4XuWiApXLd57UiFrnuS7OxcRHWQUIrWHRDfY9BHcP3sQbo/MjrImtGoVn24u6XC+AGwpbmBiWjgWvYYWu4sBkWaqW+zsbQzlr9p7uWDWTaSH6whNymJns4nr31hNbIiBYIOWBxds5dHzhnL95FRARE3uKGtid2UzFr2G7MSQXpFGogBqSYW84TWkxHEw5HwRNVe9G3nPd6IyUi8gMkhPZC+rqhls1HLPaQN595f9tDnczBsZT2YAIxb6HYYQ0BjAdUi0iOkQx67TBkVrYNPbcOaTsOtL740TZxvunEU8vO10Lh6TxLM/7O4o/+xwe3j2+938as4gft5Tw46yJuYOi8XmdNNic/HEkl2MTA7lnOFxjEsN4+rXROXP0kY7V6+O5dEZi0jTNWEITyA+PbvLelRD44N5cO4g3lhVyLWTUgGIDtKTEGbkpWvGHs9vq9sx6tT87qzBaNQS3+RUkBhm5K/nD2NwXPDRT1Y4IQLmgJEkyQTMAW47qPkJ4GNJkm4CioBLAmFbbySntJGk8N43kUkOF0K8igOmf3LR6ER+yqsmLsSAR5aparZzelb/lWZyo4bmMlj9nPcHo64LjEGnKPtrW9la0khjm4Mh8SFkJwSj60K4t6et3qdNY6vjndX5XDbVyIikUN+ToofgGn0Dmk1viLLSGgOseAoJUBetJiTnY0Ju+p7q5kR+/fEWVh1UgvL+2RlUNNqobrFT1+pgT1ULye1CfhjD2KXN4tIldYB35SKHu9OJOS2jM71tVHIoj3+9k9/OHcyOMqH3AjBhQDgZ0RYeOTuLvIom6tqFVVMizDw+fxiVTXaMWjWhvWzxciogyzIfrCvmuaXCoVbRZOO619ex6I7JjPTX30AIVrZXl3O4PGzMr+HdX4pwezxcNTGF8anhQrTWZYflT8G2D2DcLT6XUdnqCdLIqE0hokLXu/PFOYA1fiKtEcN49v3dHVWU4kMM3DjV19lTUNNKXIiBCIuOsSlhZMQEUd9iQ6tRE2nRE9W+Q7tlZyEuj0xJvRUQYfkv/5zPvOFxhFv0rNpbyw1vrutY+GTGWHj1urEkhQc+leRUxyXLaFprIPcLUbHG44LmCqSRR0gPUWD57mo+WFfEvJEJGLUqfsitYltxI+NSw7EY+l4UTK8jPA3m/h2+eqCjyTbhXlbWhBLurmdYfAg6jQqKf4F3LhAHNJd7VV47gKepCrNOg9Pt4dB9wvNGJnDn+5s7NhXfWl3IPacNZHxqGL9eUM6OsmZumZbW8cw9QFG9nZu/tnP/7EGcmxbv5XzJr2rh862l/LKvjrOGxTJnSAwJBzmcLQYtV4xPJjMmiHs+2ILV6QYgzKTl/VsmktVLnBzpURaeuXQkD51lx6JTE25R5hEng4A5YGRZbgMiDmmrRVRFUjiE7aWNjB/gG+obaBLDjOSUNXIp/TMl5VRGkiBED79cbcFY+D2ypMGaOpvd6sAJhvU0HkkNQ+fDto87GzUG3KZolCS7k0NxXRs3vrGe/BpRglSS4JVrxjJ7yNHTwJzJU+GXf3npoRQPvoGvVrUxIr3JrwOmzKbjNfslnDnzNAZGGAj77JAFSVstVO4gVxPs5XwBeGN1IReOSuD1VYWAiAo8mEiLjlCTloaDSgSrVRJhJi0jk0K5b3YGIxI7bRoaH8wHt0xk4cZi7p8ziGCDFoteTZJjH+H7X2OivZ6S2Bks2uxieGIoRp0anUbdK53zpwpVzXbeWl3o1eb2yOSWNx3eAXMQm4rqufLVtR1ddsmOSt65aTzTMqKgfj9sehNkj9D0UanB4+44t374zZhC2h3iKZPhlmVQk4dLa6HRnMGTX5d3OF8AyhptuDwyBq0Km7NT3HdYQghjksOYOzSWEJOOEJOOZD99yqD1Ff81GzSo1RKNbQ4e/3qn18Inr7KFoso6kho3Qt4SsaudMUeIWCucVFSSCoZfAZlnixQktRbC0/BYEpRn2xEw6zXUtDi8vuPjUsNQq32/CwrHgUoNI6+EuBE4agrIbTHz310GlvyciyTBa9eN5bTBMbDhzc5zSjeKypyFK7wu1TzoAvI2N3PmsFi0aqlj7FOrJKwOd4fzZXCUgX+Mt5Fa+zo6gkiZN4mt7gGclR3nt/pPQqiR07OivdIqKxtt3PrOho5S6WsL6thU3MCTFw7HeFCFw5L6NhZuKu1wvoAQ4/12R0WvccCAiLD1N+Yr9ByBTkFS6CI7y5t6pe5GcrjJK3dQof/gkSHVupPYLy7tmPSHbnoex7xFwHFUtegDuNweMEWL9IA930FIEmSdh9VuRylEfXLYVtLQ4XwB4Uv5+ze5jEkJI8xPqUcv4sfQMP99LL88hcZWT0nWzfyvIotGaxsmvf/H3e7KZl7b2MBrqPnbDANXS74Ta6fGwCBPPt/OrqJFHcbC0lA+yGmloc2Juf26wxKCyYwN8jovMczEvy8byV3vbaLV4UarlvjbBdmcMSSaK8Ynd5xrd7kprGmlyeqkqtnOxWOSSI+0EGzSsi9nLWnfXt5RCjOcVwk+4zVsTu+JnkJg0GtUhJu1NFq9K1GZdV2bXi3aVOKlnwzw7pr9TB0YiSRJIKmEA2bTWzDnr5CzCNrqqBxyPQyZT9iByDBJgpghEDMEDaButlHaUOBzvyark9MHR/PV9goA5o9K4KJRCcQfJVWoyepkUEwQN05NZeGGEppsLgxaFa/PiySk8DucHplbhoXy+xpVR0lhvUbFEPsW+Oggp+aqf8MN30Bsdpd+Pwrdg1uWQW+Gr34l+hOINNv5rwTUrt7O5PTIjvQ8EF+zu2cNxKjoHnYfWiMkjuXb2nju+XIzB6LrZBme+GYX41LCCTo44sXtEM6XuX8X46JKCzN+R2vYWJ46PY/h2o1cNV/PD7WR/Gl5Kxa9moRwoceikuCZia1k/XBtx0bNWI2B9Ms+58d8HYNiLPzxnCz+/s0u3B6ZYKOG/7tkBMMSQr1M3lPV3OF8OcDnW8q4fXoawUYtFr2GUJMOu9NDVZOvGG9RbVv3/f4U+iSKA6YPUNVkw+X2EGnpfUvAlAgzeRXNyLIsJosK/QdZJmH32147rridJBZ/ASN6R854d+MOiqNVF4Z56xuQMkWU813xNPZLFDmqk0XzISHAIMpH2lxuP0d7ExZiwZZ1BluN2Xy6YT9frGmjoa2NpDDjYdMkDy4B+foOF3NH3EXUhqc62uToodia6oj96lJi2ydsQ7MuZuTZ9/L9PgejkkJ57vKRjEgKJcaPmN6MQVF8de80yhutRFr0DIg002h1CmcfIuLn6e/z+HRLGSatmqsnpvD+uiKyE0K47/QM4hs3dThfDpC87T+oxswFxS0YcEJNOh4+K4tb39nY0ZYYZmREF9Ny1e39LzHMyLnD49CqVQTpNeJ5GpqKa9wtaNb+F+r2wfePIg+/gsZx96IKTiYq6PDijXq1inOy43jxEAHRzFgLt89I57YZ6agliQFRZkxHcRblljfx+0+2s6mogZhgPX+eN5SyBitXprYQvuh8aK1GC1wcFE/Q6S9y+3dicTF3kIWQtY96X8zeDIWrFAfMSUYtqYTws9wZ+YTLjqp4LQw9P3CG9XKGxAfz8W0TWb67hiabk5mZUV2KbFM4dpptTp+26mY7bU43QWOvhx0LO6NbC5bDzIfbKyxJYAgmpSKHlM03QGsNAJcHxTP3lg9pC8nA5ZZJDDWQGq4nffdT3lUDXTaMhT/w1IaJVDbZePemCXx171TqWx0khJkI0mvIKW3EoteQEmFCkiS/6x1Jgq3FDfzhsx2kR5n5y7yhxIcamDowkk0HlbgGODO7f25iKnQdxQHTB8gpayQtytIrHRwhRi06jYrSBqsittfPUEsgH7LwA6E70F8JMWgpHnAJWm0c0fu/pjl8DNbxj9Kkiaf/Kt/0LgbHBaGS8EpluGpiMjFHWGwejEGrYVhqPG6NmeSEesLMesakhJIa4V+HIiPaQnqkmfyaVvbVWHm2cSr3zk0jquhb7BFZlMXMJG3J1V4TNkPuQoYkXEDo2HHc9u5G3rlpAimHub4kSaRGmkmNNFPTbOftNft5efk+LAYNfzw7i3WFdXyyWZS/bHW4eWn5Ph6cm8lT3+Uxb0Q8g2XfkGiVq9XbMaoQUGYMimLBbZPYUtze35JDSeliCd0LRyeyo6yRmZnRvLaygDaHm8wYC9MGRZEYZuRTzXymTB1IfMVS6sOGs8EwicGaODKO8n0INukYmRTK1ROS+WxLGUEGDVdNSCHSrMfmcjP8oNS3I9HQ5uDBBVvJKRPPgsomO79duI1vfzWN8C3/FCWz25Gay5jiWEVqxBRk4MIRcahWtvpe9OCyrgonBa0KsLf4fmBrPOm29DWGxIcwJF7ROexpsuKCfZ79V09MITpID+YJcP1XsPUjQIIRl0HCGJFKd4At73c4X0CMR+H7vyN8hkh5fOfmiZTV1KPxMyZZWxu5dlIKTy7J45FPt7P4jskMjg0mt7yJ297ZQG55M0atmkfPHcL80fFkRFvIig0it6K54xpnD4vjky1luD0yuytbuP6N9Xx+91RmZUbhcHv4cH0xWrWKe08byMQBET42KJxaKA6YPsD2kkZSenFuXmqEmdzyZsUB089weTxoMs+CfT95tcup0+l9rsDuYX+dlUeWlLKtJJyMmNupKbJTuaqS/16VQHYXFywKJ8aw+BDeuH4cTyzJo6LRypUTkrlqfAoqfxVlDoNeq2b8gPAu6WbFhRp5+dqxfLujgo376xmRGYucMRFp0lX8/uMtTJPrSDtoUncAta2e537cw5whMXyyqYSJaUefUC3ZUcFjX+4EwGhV02B18umWMp/jyhqshJl0NFpdqFKn+mh/MOVXQshVoVeg16oZNyCccQPCKahuYeXeGt5YVcjUjEjGDwgn1HT4SKXRyaH8+oxMrn9jfUdbXmULf/58B3+9YCh/XFqDQZvAwOg7qSi0UdPSysuxrWTEBB32miB2k5/5fjetDjeXjE2kxe7mpeX5XD4+mf21bVw5MaVLP1t5o63D+XIAl0empsVO+iFVlwAsdTt49vK7eXtNIb/9spAFk24lpfrXnQeo1JA6tUv3Vug+7E4nxqxzYf9Kr3Y5eVK/fZ4r9C2GJYTw+vXj+MfXuVQ127l6YgpXjE8Wm89qrYhKTpni/2SPx6cKHAAV2zr+OyDSzIBIM81ttxNUsq7zGEmiMGIGuvb047J6G1aHmxa1k8e+2EFuuXCyWJ1uHv5kO5lxQYxODuOFq0bz3c5KftlXy8S0CPbXtvLV9vKOy9pdHvbVtHDWsDgyooO4ZEwiJp2GOD/lqRVOPRQHTB9ga0kjw3qx9z0pzMjOskbmdEEkU6HvIKnUNKnDCJ79F8j7SqjOZ55NoyaMsEAb10M43B427BcRPjsOWnRU+RFmU+gZNGoVMzKjGZUchs3pJipI3+PRf+nRFu6MHujTHhmk5+dyDWfHT0BXtrbzA5WaUnU8O8qamDU4mtoW7/7RYnNi0KrRHCTU2Gxzegk53jJ9AF9sKyMlwkRpg3dEQJhZh0GjIiXCBMGj4NrPYfV/RBniibfDwDO65wdX6FbKGqzc8vZG9laLSIO3f9nPb+dmcvuM9MM6EDVqlZdI8wHWFtThcMkd+hM5pZ3jUbDh8FO3VrsLrVpCQoTEF9W1dYhEgxAIfn9dEecMjyPkCI6hAwQZNIQYfTVu1CoJhl8Ohd4LemnYfAwaFZ9vLcPplnlmfzr3zfg3KXveRjJHIU2+FxJGH/W+Ct2LR9JSrwol7PQ/Qd7XoNZB5lnUyCFKdKdCr0CrVjEzM5pRyaHYnZ5je/arVDDiClGq+mCy5vkcmmceQ9isF0nd/QZujYX8QTfyyFojF4wRoTcXjk4gOthAWb2VNfvqfM7fX9vK6OQw0qIsXDBSzdLcShrbnHy1rdzn2OD2SlkWoxaLUamapdCJIuPdB8gpbSQtqveWcUyOMJFTpoSx9jfUKoma8NFYW+pBHwJaM21trdSG9N/c/WCDlnQ/qQPRwUpZvpNNsFFLdLAhoKmX54+IZ1mhjVWDf48rZVq7YfHsPe0V/rZeIiHUSG2znfmjEgExMfv3D7uZ/+Jq/vBpDrnlnYtmnVrVoRFzYC2+dFcVs7NiMB8kppsVG0SQXsP/rhlDfKixM2Lg0nfghq9h+GVg6q8u0L7NroqmDufLAZ77cQ8l9UcWXIzyUz48LdJEXIiBP5yT5dV+1rBYH7FngOpmG++v3c/F/1vNXe9tYndlM3efluF1jF6jItKiw6zXoNV0bfqXGGbibxcM4+Cv4RXjk8mICYaMM2DqA0LMVWOAGQ9B2kwyYoJ484bxDIw28/nuNp6rHEnlhYuRLnsXBrRHdCmcVHQaNZvJoq6uRkTPaY1UNTSzU/J1PCsoBJIQo+74nv0Zc2HK/cK5qDEIjZi0mV6HNFud7G/R8GLlEG5X/5mbnL/m3K802D0SVU02Lh+XxB0z09GqVRh0agZE+kb2R1s60z91ahVlDTYWbSrhhikDvI6bkxXDYD9jtYICKBEwvZ7qZjtWp1vkQPZSUiLMLN5UGmgzFHqAnS0m7sqdyXVDTsPtkXhti4tH4k301ymbSiVxz+kDeXhxTkfZwAtHJ2BSKh6ckgyJD2HB7ZPILW9m14z/4Wos56dCG2/80Ibd5eBvFwxjQKSZEUmhtDpcPP5lLt/liqpwe6pa+CmvisV3TCYhzIReq+bu0wbyy75aZMDtEbIyzy7dw03TxMRNr1FxWmY0ieEmggyH7JZpFMHd3s7BJZ8PbnN5fNsPZkhcMFeOT+b9dUWAKPf8+PxsIix6zh8ZT3qUhcLaViKD9AyLDybM7DsfWLSplCe+2QVAbnkzy/fUsPiOybx09WgWbCzBotcyJD6YV1fu49+Xjjyq8O7BzB0aw+d3TRU2WHRkxQUTYtQCMXDaH2D0tSLcJiQZVCpUwJSBkSy4bTKtDhdRFj16ZQwNKJJKQhUSz6+3ncfcJAdOj8SyCj3XDVS0KBT6CcGxYjwacx0gQagYjw7GLcs8/+NebpsxgCZbMKv21nLrtChGJYcyND6Y6CBDh3N6X1Uz10xM5Z/f7sLmFOLVF49OZEh8Z/nocIueP5ybxR3vbuLHXVU8ODcTl9vD4NhgRiWHEmHpvWs3hcCiOGB6OTmljaRFmnulAO8B4oIN1LXaabI5O8LtFPo+Ho/Mgg3F5Fa28dBBlcaX5JQzu5+mm8WFGNlf18avZmdg1mtQSfDz7ioRiaBwSjIwOojoIAMyMur4GDzhzQwf7CQ53MTA6E5x9Pyqlg7nywEqm+zsqWohoV0fa1xqOIvumMzWkgbiQgzo1CoarU6eW7oXgD+ck0VWD6Wb7q9tZX9tGyFGLQOjLR0lsBW6j0ExQT7pOpeOTSIx7MjjR5hZx8NnD+aiMQk0tDlJjTCTHm0BwKjTdOjLHI6qZhuvLN/n1WZ3edhR1sSl45IYEh/CjrJGKpvsvHz12MNWBPNHs82J2yOTnRji/zyVGsIH+La3/1xHKx3vcHnYV91CZZON2BAj6VFmr9Q9he7j5eX7yE4MxRNqQg3E2Zv4ZnsF0wcpSUhHor7Vwd7qFhwuD2lRZuJClPlAr0WtOex4BKJq3c3TBvDwJzlMz4hkeGIoa/JrOHNYbMdz+gCLt5Tx064qbpmWhoyIdmmzuwgxanG6PeyrbqWi0UpKuIlP7pzM5uIGIsxC/PxwovwKCgdQZmC9nK3FDaR2sZpCoFCpJJLDzewqb+6S6KVC30CSIMjPIq0/L9zUKomzhsayeHMpb64uJCXCxF0zBzKwfTGk0H+pbLLRaHUSE6Tv0MZosbn4cVclzy7dC8jcc1oGpw+OJsioxeFy43R70GnEzr5aLaFWSbgPiXbQHrSYVKskRiSFMiIpFFmWef+WCby8fB8l9VaumZTCnKyecWyuL6jjxjfX02wXJb5vmTaAu08bSIhRiarpTgZEmnnv5gm8ubqQ7SWNXDg6gXOHx3X0kSMRZNAyJqXz+enxyBTXt+H2yCSEGdEf4RoaScKoU8MhxT00auEcTAo3kXSMQv42p5uVe2t4+rs8WuxubpuexlnZsYT7ib45XlxuD59tKeWhxdtxe2Q0KomnLhnBvBHxxyS6rXB0JEliTEooIPHy8n1o1SouGJWAoYupaKcqpfVWnv0hj3CLHo1axTtrCvnV7EEMjgs++skKAcXjkWmyOalpcaDXqDrGwLOzY4kNMfD6ygI+3lDM2dlxBBt957VBeg01LQ7+8+PejrZzs+OQZZkvt5Xx4IJtuDwyapXEExdmc+3EFMV5rNBlum0lJUmSWZZlP/UGFU6ETUX1XpOy3kpyhIkdZY2KA6YfIUkSc4fFIjlbOS/JhkeWWLxfz8T0/huy7HR7eHPNft5fK1IByhttbC7awOI7JjM0ofcKYSscP0W1rWwraeSxL3dS1WxnWHwwT140nKEJIazdV8u9H27pOPZXH23hpatHE2HR89LP+ZwzQGJqtJ3QiGhSw9K4aeoAXj4oEmF0ciiDDlOtRpIkxqaGMzwxBJdHPqaUkGOhrtXBw59s63C+ALyyooDTBkczKT2yR+55KjMsIYQnLszG7vIct7O6vs3BB2uLeHbpHpxuDxeNTuS+2RmHrTQYbtHz4NxM7juor0ZadNS1Onht5T7OGhbX5Sg+t0dmX3ULBTWt3PrOxo7233+ag1aj4tKxScf1M/lgb8ZalkdwYwNTU0z8XNCKyyPzu0XbyE4MIT1KcXp3J+r2jbIVOwp4ZLQTjyzx4Z5Srpg8KNCm9Wp2lTcREWTgjVUF2F0ezhoWy7aSRtKjLV7OdYVegssBtXtpaW1hnyuKp1dVsSyvBrNOzQNzBjE8MQSTXsN9H26hpf2Z+MaqQioabTx96UjhyG7nvBHxvPtLEQ63SD9SqySunZxCYW0bDy3a3pFa6vbIPPLJdkYmhR61Op2CwgFOeMYnSdJk4FXAAiRLkjQCuE2W5TtP9NqnOrIss720sfsmPD1IcriJ7aWKEG9/wu2RydTXc5b2RTQrvgHg9CEXs0fzayAusMb1EMV1bXy8vtirze7ysL20UXHA9EM2FNaxdl8t/166p0O/I6esiQcWbGXBbZNYsLHE55wP1hWTFmXmL2OsxH93GzSVgdYE5/wfd0w9j1FJofxSUMuQuBAmp0d4CaxWNtmwOd3Ehhg6Ihp0GjU9GYfSZHWyt8p3b6SiSans1VNo1KoT2gndUFjHP7/N63i/YGMJqZFm7pp1ePWtOVkxvHvTBJbuqkStkggyaHnquzxsTg8F1a386byhHdoGbo9MWYMVlQTxoUavFOdvd1Twj29y/ZZVf2NVAVMGRpAQemzRND7UFcI3DxK05zvmAhMHX8YrY6/k+Q2t2F0eaprtigOmm3G6PQRZS3nS82+MK5YCMHnwpaxuvoP++jzvDhptTv67LL/j/dfbK0gON2F1uNAqEYS9i7ZaUS1w9XNYPG6GxI7i8sy/sCwPWh1u/vpVLr+dm4kMHc6XA3yTU8EDZ7SREd3pQBmZFMrC2yfxU14VLo/MrMxoRiSFsmF/HXaXx+t8p1umusXeJQdMeYMVp1smLtSgOPFOYbrjL/8MMBeoBZBleSswvRuue8pTUm9FkiTCj5JD3RtIjTCz46AymQp9H7VKIqHiBzR7vulo0+5cSFLdmiOc1bexOtwE+Snx2oslmBSOk8Y2B3/4NAery+MjnppX0UxVk40YP9Wvgo1aYjUtxP5wr3C+ADjb4LM7CWvdx1nZcfxl3jAuG5fUEfJsd7r5clsZ5z63kllPLeO3C7dRWHPkgNH6VgdbihvYVdGE3eU+7p8z3KxjdHKoT3vSUXRJFALHmvxan7ZPNpfSbPMtV30Ak17D1IxIZmZG8/GGYp75fneHcOQH64spaS91Xtlk5V/f5jH76Z8545nlvLx8H/VtDgAKa1v57cJtNFldWPxE74QadXy1reyE+iMAOxbDnu863obs+ogLQ/di0KoI0muIDTEc4WSF40GWZSZYf8a4f2lHW/Cujxnn2RZAq3o/20oafNqW5FTgdHt8D1YILCUbYeUz4BHjk6ZiM5Mq32dEfKeMQ12bA7fH929n1KrRH+IMkSSJ4Umh3Dd7EL8+I5PRKaL6oEGj4r7ZGVw9IRmDVtVx/tG0gVpsLj5YV8SZz67gtP9bxmNf7DhqhTyF/ku3uN5kWS4+pOkEn84KAFuKG8g4SOSxN5McbqKwthWHS3ko9Rfcbjfm/K992g37fwyANScHvVblU0owLdJMbLCyIOhvNFqd7KpoRudnByrYqEGvVXPh6MSOCRaIKkXZiSHEa5pQNRR4nyTL0FDk9145ZU3c/f5mqlvseGT4bEsZ//15L87DjJd7Kpu55rW1XPDCKs5+dgXPfL+butbji1gJNmp57PxhJIcbO36GR88dQpaiYdBr8Rf9kZ0QjLELlYRcbg9NVu/dXY1Kol0OhiU5lfzv53zsLg+tDjf/+GYXq/bWAMLp12J30Wh1EmHWe+kiqFUSs4dE888leRTXWY//h3PZIfdzn+bo6jUMjQ/mP1eOUgQsewDJ4ySscIlPe0jZygBY03fwt6geGG1B1Qfm5accVTt9mkKKlzItsXPcDDJoKam3kZ3g/fx7YM6go+pk2Zwu3l+7n0tf+oVnf9jDj7uq+N2Zgwk3a3n+ylGkRhz5/M3F9Ty8eDuNVicuj8w7vxSx0E+UrcKpQXcknRe3pyHJkiTpgHuB3G647inPluIGBvRyAd4D6DQqYkMM7K5sZpiSqtE/kFTUx04mrHitV3NT1Dj6qwqMSachKczI787MpLzRRohRS7hJh0c+chlZhb5HuEXH+NRw1hXUMW9EPJ9vFdEskgR/uyC7Q7h00e2TWb+/HqvDhSzDSz/nc0O2Hiwx0OJd9QhLrN977a1q9mn7dHMZ950+yEebw+ny8NLyfeSUiYhCjwz/+3kfE9IimJXpv1pJi81FXmUzlU02ksJNDIq2eJX9HZYQwqI7JlNSbyXYoCU10oxaETkNGLUtdnZVNNNic5IeHeQj8j11YCSZMRbyKlsACDFquWlqWpfSmgbHBZEWaWbfQRFWd582kMQwEw6Xm8WbfCf83++s5Nzh8cQEGwg3C+2Y//2czy3T07A6XOg1agxaFW+sKkRG4oR6jkYPA2ZA2WavZm3qRJ7NHEXiMYoFK3QNDxoa46cQUrbJq70xanS/fZ53B0Pjg8mItrCnSnwXgw0a5o9KUATMeyPhaT5NbTFj2VwlNjpGJIZQ3Wzn4w3FPHv5SCRgf10bg2ODMGhUrNhTw+DYIKIPs+G2q7yZP362o+N9WaONjzcUs+j2yQzoQsrkxv31Pm0LN5Zw7aTUPpHpoNC9dIcD5nbgWSABKAG+A+7qhuue8mzcX8852X0nNzc1wsyOskbFAdOP2B9/NuaIb9DV7gLAHjOKosjp/XbCFhdioNnq4LR4F+GR1bh0QXxTbiEupL/+xKcuFr2Wv5w/hC07dpJtquC+YRHkuWJJiAglK74zj3toQghDE0IormvjkpdWU9Pi4KXNHubM+SeDlt0OTqvw2sz8PURndZxXVNtKm8NNfKiRMJPv5ColwoRZ5xvRUG91sCyvyqd9d0WzXwdMm8PFS8vzOyo1SBL86+IRXDQ6wSt6MirIQFSQEskVaCoabfxu4VZ+3iOiTgxaFe/cOMGrzHRKpJk3bxjPropmnG4PGTFBXd6MSQg18ep1Y1m+u5q8ymbOyY4jxKRlf10bsUE6UiJMbC0Rem0JIXoeGKNmdHg11O4lPjyd568cxT3vb6a21cGry/dx/5xBfLi+iN3tzqDrJ6WSFH6C6Wsjr4RdX0HtHvE+eRL6zDmK86UHkSQojD+PYeFLUNeJ37szfizFYZP67fO8O4gJNnDd5BRqWxy4ZZkQg5aEMKNSpas3kjgOsuZ1RthZommd9GvmNcQwZ7ibHaVNvPvLfianR5AWaSY7MZSc0kauePmXDpH60cmhPHe5f0dwcX1n5N/IpFCmZQgR+65uz/kTQk+PsmDyMw9Q6P+csANGluUa4KpusEXhIBwuD7sqmrjv9IxAm9JlksNNbCtp5LJxgbZEoTtQqyTKNEmsHvAMo4dWIcsS61ujyNLFB9q0HqO+1cG5UVWEf3EdNFeApOLyyQ9S4rgWUFI2+htZnnyytl7f8bdOm/EQUtYd4Kfkb1K4ibdvnMBX28rJKW0kPyie5BuXYWgpAXMkRGWC1khjm4Mvt5Xz969zaXW4GZMcxl/mDWFsahgbCsUOmEYl8ei5QzrKXR9MiEHLmOQwvt3pHV1zuAX4nqoWrzKZsgyPfpbD2JQwUvtIBOWpxLbSBn7eU4MkwfSMKAbFBPHltjIGxwURZNB2HBcXasSoU+Nyy0QGHVvp57QoC2lRFvbXtPL417l8t7MSvUbF3acNZN6IBJbtriZYr+adSRUMWPkb4UTUmeHCl5mceQ5f3DOVyiYb4WYdHlk4+TYWNXD2sFimD4rsUlntIxKVCdd/CdW7QaWBqEHiO6TQY+i0GmLCzMjDLwPZCZKELOmJCFacXkdic3EDizaWMn1QFBISG/bXs7WkkacuCT7x74FC9+JshdAUmPUIeFxgiiYqKpa5CTFsK2lAo5KYnB5BVnwQg2NDsDndPPP9bgxaNeePjAdJ4tsdFWwsqu9wwMiyTHWzHaNW3ZGKftm4JKwONy/8tBdJkmhoc3L3rHRijqIBM35AuFc0lVGr5r7TMzB0IbVUof/RHVWQnvPT3AhskGX5sxO9/qnKjrJG4kOMfeqLOSDSzCebSwNthkI3Mjg2CIcrhRpVKgCJYZ7DltXtD3hsjYT++JBYkAPIHkyrniQqYRKQGFDbFLoZayN8/aDX31pa9ndInQKpU/2eMigmiEFzDu3/gzv+t6eymQ376/n9pzkdbRuL6vnnt3nMHRLD2cPisOjVZCeGEGbUsb20kQizzmtnTK9Vc+/sDDYXN1DVLHRfzhsex4ikUL821bU4fNraHG4arIcXbFUIHDXNdgxaFb+dO5hvcsp5e00ho5JDya9qYWSyEHm0Olz8lFfNU9/l0Wp3cdv0dOaNjCfS0nVHjCzLfLi+mO/aHXl2l4f/+243r1wzhntmZTA1rI4Bn10pNFkAHK2w+Fa4bQXxEeleffLu08RGUKvdRXFdG01WFykRphNbgAbFipfCSSNo72doVv2z470OCJsbCgMyA2ZTbye/qoXNxQ1sLm7oaEuNMGF1uBUHTG9j+0JY8x/vNo2O0DHXMX2Qb/Roq91FsFHDJWMT+XhDMbIMF49JRK+W2FvVQtP/s3fW4XFcVx9+Z5nFzAyWmTHgMDNzGobmKyVt2jRt0zZpQ23TNtAwM5PDppiZQZLFzFrene+PkVder2zLtqyV5Ps+jx57787snpVmZ+b+7jm/43Czrb6Tv3+5jeRIA784uYD7ziiisdPNf5f3dMaSZV5esovRKRFcNHn/HWszok38+/Lx7GzoxuuXKUiwkp84cu+nBftnIEqQDCh3oG/3PD4f2AhcL0nScbIs3zUA73HUsaqijZz44dWGMTPGzPaGTrw+/2G14BQMHXx+ma831/PJulpUknJx6qujykhB62pDU7c6ZFzdIYzSRhyOFqheETrefmh/63aHm1+9s46ZeaEr+Qt2NDE6NZJ/f7eD9GgTfzm3hGufX0FdT5bBIxeN5Zi8WMqb7TR0OkmwGnjv1hmUN3dj0mrIjbdgM2r7eFclM0evUQW1xUyJNJIsOskMSbLjLFw6JZ2n5u+kvqcV+JLSFu54YzXv3jyDeJuB1RVt3Ppqr1fHHz/ZhFGn5tIp6YGxmjYHlS12bEYt2bHmIM8fgDa7h4/X1YS8/+LSZq6ZnklkfWmv+LIbdzd01UFMTsh+u5q7eeCTzXy1WWlzfd3MLG46JvugRCFB+PC4HJjLvg4Z11UuhunXhSGi4cGUrGieml8aNHb2uJQ+sxcFYcTrhm1fho6XL4CJV/e5S5RJx4T0qCBfl/Lmbnx+mdtfX4PXLzMtO5pLpqTz7+92cN0LK/j8zlnc/3Go2e9n62sDAky320tpQzdOr4/MGBNxVgNNnS7eWVnFE9/tQKWCu+bmM6uPewXB0cNACDC5wPGyLHsBJEn6L4oPzInA+gF4/aOSZWXN5A0zAcaoUxNr0bO9oUt02BghzNukiC+gmIG+taKKUckRZMYOr2Ozv3SrrJgSx6GtWxM07raM3LKroxZjNCRPgL1MKbGlHNLLVbU6WF3ZxnGFoSttOXEWsmLN/PykfLQqiZ++sYbmbiVzpaXbzS2vrOR/V07ihpdX4vD4MGhVPHLhWE4tSTqg10BOnJlnrprEL99ZS32Hi9x4M49cOHafRoJ70+3ysqOhi5ZuN+nRJrLjzMOi895wZUxKBLVtDp5fVB40XtniYFeLnXibgR+2NYbs9+Lickan2PDLoJLg2udX0NjlQiXBXSfkc93MTCw9JUwdTg9VrXZGJdmoag3uWGTWa/h8Qy03j0pTDHH3FGF0ZsVcei/8fpm3V1by1WYlm8bnl3lmQSkTMiI5tWT4+NQdzfgkDa6MuVj2Ot85U6YhpIR9MzEjin9eMo7yZiVrISnCwKzcuHCHJdgbjQ7yTgy9nmfM3OcuKpXE/D3OtSpJMax/ZN62wNiS0hbSok1kxpgob7azvaGLSZlRLN7ZHPRaEzIiAWjscPLIV9t4Y7nSHDgnzsyTV0xgXVUHD36xJbD9nz7dREGCBZVKhV+WyY23kCC6bR5VDESaQgqwZ6G5GUiWZdkH7LNvpiRJkZIkvSNJ0hZJkjZLkjRdkqRoSZK+kiRpe8+/UQMQ37BDlmVW7WoblqUe2bFm1vcY/AmGNx6fn2+3hJqBLuxpWToScaotdB7/IJh7brAkifYp/0ejKT+8gQkGHmME3ScE/627p/0MR0zxIb2cSadBr1Gxvb6TYwt6b9D1GhWXT03n3vfX88i8bUSYdAHxZTdOj59lu1pweHyBx3/9bAs7G7rocu6/lEiSJObkx/HR7bP48q45vHXTdMam9e/S2enw8O/vdnD2vxdx7QvLOf1fCwItiQVHBpNeQ2586LVdksCiV9bE4vrwfImx6PjTJ5s564lFPP71joDQ55fh0a+2sbGna1a73cMjX27jzCcWMSU7mkhTb+ZUSYqNLqeXV5dV0G7KgLP/o4gwAFojnPMkRIdmv6yqaOXLDfUh40tKm0PGDgWX10djp2ufbdkFh4/PL7PMcjzuhPGBse6MuSxXjQ1jVEOf+g4nzy4s49GvtvPPb3bw8JfbqO9whjssQV+MvghSJvY+zjsJcubud5c9uxfFWPQ091HSu6S0mfPGp6CSlOvtmWOSSYvqLdHMiDFx2mhlkW51ZVtAfAHY2djNV5vqeWtFZdBr3jwnhz9/tplLn1nC5f9bymXPLGFHjzeM4OhgIDJg/gaskSTpe0AC5gB/kSTJDITmO/byD+ALWZYv6GlfbQJ+A3wjy/KDkiTdA9wD3D0AMQ4rqlod+GSZ+IM03hsKZMSYWVPZdsBaSMHQR6tWMTrFFtI6ryhp+AmDB8MdP8DPTn6fWE8NXl0E75QbGNOtoejAuwqGER0ODzd9LXNC8XOMs7TTgYn/rFfx83yYdggJfBnRJn51SgF/+mQzJ49K4P9OyCMpwkBdh4v/fLczUCJU2WLHoFXh9PRONlUSqPfIOjl/QgoRRi1XP7+MWIueX5xcwPScGLRqFX6/THlzN42dLhIjDGTEKOsfCTbDQa+gbanv5D/f7ww8dnr8/OqddXxw+0ziRcekI0ZalIFHLxpLWVM3rd1u3lpRxfWzswJGy7PzYgPtoAG0aonjC+N54NPNAHy9uZ475+YGlZ7VtCuZLpvrOnjxx3IAHp23jaumZ2LSqYk261hT1cYLi8uZnReLTquFUedB4hilnbo1URFf9sp+qmt38PC8reQnWAPmkbsZlXT4HQ+31Hbwnx928uOOZmbnx3LzMdnkJ4gM2oFGo5L4tsHKwug/cmx+B35UfFJjIcMRyf6nqEc3S0qbWVvVTrxVj1GnZleznecWllGSEjGsPBqPCmJz4bK3oHkHqNQQkw/G/Z+jzh6XzBvLKuhwepGQg4SV3RQmWKluc/DAOSWMTY1Ap1Hxj0vHU9fuwKBVU5RoI6nHM2tzbUfI/t9vayQ/wcrSshYAIk1a3D4/m2o7A9vsbOzmwzXV/Pwk4cd0tDAQXZCelSTpc+BKYAtK+VGVLMvdwC/72keSJBuKUHNNz2u4AbckSWcDx/Zs9iLwPUehALOsrIXCROuwTAPPibPw1srKA28oGPL4/DLZcRYuHx/D7JhOZCTmN1tHdJpkt9vHop0tLNrZ0jOirPD+KTYybDEJjgxtdjd6rQqHMZnnqiwk2PTMKtRS1+448M676aiDjmowRqGKzuLiSemMSrZR1mQnwWbArFfzq3eDK3E/WlvD704v5ncfbsAvK/Pde04t5JO1Sqlfgk1PhFHLcz0lKjXtTq55fhnv3DKDMSkRfLa+ll+9uw6nx49Fr+Ffl47vs+ypPzT0sZJb0+6kze4RAswRotXu5skfSnl6QSmyDGlRRl68bjJFSbbAhK4g0cabN05jbVUbDrcPr1/m6fnK9rspb7ITZ9UHSoySezpwNHX1Jh53u3389wdFYLv9+FzeXlGFXqPizrl5GHe3Po3Lxx+Tx9qqNr5YvhW/X+bkUYmMS4tEo1bRavewpLSFe04tZHl5S8AYemxaBNNzeltnB7C3QGs5aE2Kl4y6b+8igPp2Jze8vILKFuUzvLeqmvXV7bz+k2kH3flJsH8klYr8RCvbK7txyHp8SGh0emKEl8l+qWlzcM8phVS02Ol2e7lsSjrLyppp63aT2EdbYUGYMccGd1TzeqBlB3idEJWFW2tjbWUbn2+oxaBVc9KoRN6/dQbrqzuwGTXsarYzPi0yYLoca9ExJTuGP3+6mQ/X1vDq9VP5xTtrKWuyI0lw3cwsxu1hkF/Yh6luepSJiyal8dGaGjqcXlIijZQ2dodst2hHM3edIKMWLc6PCgaiC9JPgJ+itAhZA0wDfgSO389u2UAj8LwkSWOBlT2vkSDLci2ALMu1kiT1eVcpSdKNwI0A6enpfW0yrFlSOvz8X3aTGWuitLELl9eH/ih3iB8Jx2mRsZVLfP9Ct/ATAI4rvph1+jvCHNWRI8ai5YwxiRQmRuDy+tCqVZQ1dZM0Qg1NR8IxeqhEmnSMSrLx8LytgbEEm55/XTp+P3tBQ6eTbqeXdMdm1O9eo5j26sxw+qNYRp3HtOxYpmUr29Z3OEm0GajbQ+ho7HQxLTuaT++cTVlTN2adGo/fz89Pyue3H6xnWk4sn62vC3pPvwxrKtuw6jX8/O21eHzKTLzL5eWnb6zm0ztnkxZ98O1k+9qnKMnaZwlMuBhpx+j6qnaeml/K9OwYZubG4vD42NnYTcFeJcd5CVbyEqz4/TJ//mwzte3BYll2nJkvN9ahkuDOuXkU9/iuZUSbkCSCxJoxKRFMz4om56KxFCRaQzzaVlW0csnTS/D6lZ2eXVjG6zdOY2pWDAlWPdmxJh77ahtXTs/ApFOjkiROLEogLXqvNucNW5DfvxGpdq0ivBxzD0y5AQx9r0KXN3cHxJfdbK/vory5e9gJMEP9OJUkidGmNi7zPIZm4RcgSZww5nLWmW4Jd2hDmilZMdz+2upAeeiHa2p46PzRw+74hKF/jA449hZY8h9Y+Cj4fZA6mY4THuOipysC58f/LSjjzZumcc54xfvts/U1XDMjk6tkGZfXT7vDwyM99whOj5911e2UNdkB5Rz77MIyZufFcmyBMl0dnxHFhRNTeXulYuafFWvihjk5FCRaee/WmWyq7UCrkmixu/lua3CJ/+mjE4X4chQxEB4wPwUmA7tkWT4OGI8iruwPDTAB+K8sy+OBbpRyo34hy/LTsixPkmV5UlzcyDPDWlbWMmxNbPUaNckRRjbvkVp3tDLcj1OVBEVt89Ft+yQwZtj0JoX2PjrHjBC8fji+MIHHvt7Gv77dwaNfbaOh0zVixcThfoweDG12N02de2QHuLy89OOuoG3qO1w0dvZtXeb1+fl6Uz1n/HMhb/ywGumDG3s7Jrm74YOboXFz0D4JNgP/uXwCadHKSmmMWcd/Lp9AVqwFm0HL/xaUcvXzy/nJiyu54aWV/PncMVw+JZ1oc2jWgM2goa7dGRBfdtPh9AayEvpDZYudjdXttHS5KEiw8tD5YzD2ZF6kRRt56PwxRA2hVfGRdoxWtNgZnRJBTpyZh+dt5d/f7eC3H2zg9WUVdNjdNHQ48ft7/8YqlcSlU9KCVlZPGZXIaSWJvHjdZD65Yza3HJuDtadLVkGilccvHofNoAk8fvD80czMi+PcCakUJ0eEZNd+sLo6IL6AIvi92vPdiLboeeSiccSYdfxvQRn/W1BGerSJvIS9Fom8LnzfP6iILwA+D3z7J3xVe5li7sG+Sjh8fhlXz4R3uDDUj1Of309R6zdotn+hDMgyurWvUORcE9a49sTl9VHf4cQ5hP72W+s6A+LLbt5YXonbN/z8iob6MbovvD4/DR1O7G7vwe1YvQLm/10RXwCqlmNc/l/GJPUKx26fny83KgsenQ4PC7c389M31/B/b63lnvfW0+XyEmMOFttunJMd9LisqTebJd5q4PdnjeKj22fy1k3TefOm6RT0nLtz4y2cNTaZU0cncXxBPBdMTA1UfJ5SkshJJYkhH6GiuZtNNe20dod60wiGNwPhAeOUZdkpSRKSJOllWd4iSdKBitiqUMqUlvY8fgdFgKmXJCmpJ/slCQh1AB3hNHQ4abG7SYs6+NXMoUJOvJk1Fa1BaXmC4Yfs92PZ+WnIuGnX1zDj2jBEdORRSRIPf7kV3x6TkUU7mrhsivA0Gq443F6+29rIw/O20u3yctOcHM4al4xPlvu8id69Mra9vpPP1teyuqKN08ckkRNv4caXV+CXYXSEjGpDaeiObRV0RRXT3OXGatQQbdYzISOK926ZQUOniyiTjuSetPW1VW2sqmgL7O72+Xnkq628cv1UfnVKIde+sDwQS4JNz4T0KDw+GY1KCposW/UaYi0HFkw8Pj/zNtbz6/fX0eHwkhNn5vGLx3HRpFSmZEXR4fCSHGkgTpQeHVHSooycUBTP499sDxp/9OvtmHQa/vXdDs4bn8JV0zNI7/H3yY238upPprKuqo3mbjdVPR2TpmXFBISX3eg0as4el8KE9Cg6nR6SIoxEmZXjo6XbTafDQ4xVh0Xfu5+9jwlvt9uHLMtIksT49Cg+uG0mte1OIo1a0mNMISKOr6sJ9Y6vQl7H27gDde5xff4ucuLMXDAxlXdW9rZ+P2tsMg/P20pmjJm7TsgnpQ9PBsHBo5d8SHsspuxGW/49TLli8APai611nTzx3Xbmb2tiamY0d52YR3Hy4XsMHS57nmt34/Io3w3BkaessYv/LSzjs/W1FCZa+eUphUxI72d/lvrQdtHm8nnMybmEtTW9Y3aXj+oWO5Vtdl5bVhG0/bMLy7hqegZP/lBKfoKF0sZuXF5/oCsSKCa8e2LRaxiTGrnf0JIijfzp7FH8ZFYWflkmI8aMWd87JXd5fHyyrpbff7SRLpeXoiQrj1w0LpDpKBj+DIQAUyVJUiTwAfCVJEmtQM3+dpBluU6SpEpJkgpkWd4KzAU29fxcDTzY8++HAxDfsGJpj//LgVqPDmWyYiys3NXKNTOzwh2K4DCQJRXOtFkYKpcGjXtSpg3IiWMo4vX5qet0cnKelZPS/dQ51DyzxkGH8yBXXgQHR3s1eB1gTQbdwInPla121la2cftrqwNjf/xkEyadmosmpXHD7Cye+K7XhNaq11CQaKW61cG1LywP+Gt8v62R+84oZve9eIVDD5Z46ApeI+jWxXLDSyv4sbSFnDgzD5xTwvScWOKsocJGc5eLKJOWM8YkYzVoWLijibKmbrpdXmbkxPL2TdNZXdFKhEnLxIxosuMseHx+Hjp/DL9+bz1unx+DVsWjF48LGPHuj+31ndzx+qrAZ9jZ2M3/vbWGt26aQdYIbSs/FBmdGkFVq4O9528+v0yL3U1Lt5v/LSxDAi7I9eNwudFEpYNay22vrcbu7hVLHrtoLOdOSO3zffYuL1uys5l7P1jPzsZupmVF8/uzRgUybc8fn8J7q6qDtr9yWkaQyBJvM+y3tXm7bMQcPw591cKg8W5DIvsq1rAYtPzqlAJOKk5gXXU7MSYtOo2aXS12vtxYx7db6rlyeuY+31PQf1x+Na7E6UTs1aa3M248MWGKaTfNXS7ueH0VWbFmrpyeQXlTNze+tJK3bp4eEKzDxfTsGNQqKWhR5vpZ2Zj1+/Y2EhwCrk7orAO9DawJgJKl+sdPNvHdVqWo4sfSFq7831I+vmMW2XH9uGZFZ4cMeVMms7ax928pSVCYZGXm377jd2eEtlqwu33kxFm4c24u3S4fzy0qI96q55j8eMqb7VwxNZ0xKZH9/pgNnU6cbj8JEXqMOg2F+xBUttR18vO31wYeb67t5HcfbOCFaydjNYhjbyQwECa85/b8935Jkr4DIoAv+rHrHcCrPR2QSoFrUUqi3pIk6XqgArjwcOMbbiza0UTBMO8AkBtv4fMNteEOQzAA1KSdQUbMx6ibtwHgTRhHRexsRqpPu9cn8+Z5MZRs+BvGRd+AOY7Tjv0jNbaRKjmFGY8TtnwKn/8S7M1QcDqc+Eelm8Fh0tLt5qHPtwTKa/bkxR/LOXNsMldOzyQxwsibyyspSLRy1fQM8hKsfLulISC+7Kbb1SvCPbnawQknPET+d7co5n6A/5hf85eVEj/2tObd2djNtS8s55M7ZpPbh6fXqCQb183K4sXF5bTaPZxUnMDV0zOJtejRqFVMyoxmUmawyalWreLsccmMSY2gsctFos0Q6JxzICpa7Oy9mLujoZv6DifR5qFTcjTSiTbrmZkbS5RJS6u9t8V4nFVPZ4/Qe1a+ies1n5H43qPgddFefDnlhTcGiS8Aj3+zneMK44k8QMnYzsYurn1heaCUYklZC3e9uYbXb5hGtFnHpMxoXrpuCs8sKMXn9/OTWdlMye7DYHc/aA1WSsf9kqLmjeBQOue1551LvaWQ/b1SvNXASaMS2VHfRWWrgzeWV+KXZS6cmEZduzOQhSM4PGRZxlN0NnL550gtSvaenDQed+qMMEcGFa12Lpmczvurq/lyYz0FCVaun51FZas97AKMx+/nN6cV8sPWRrpcXo4tiEdm+JUfDWkaNsNnv4Ly+WBNgjMfh5wTqGlzBMSX3XS7fexs6OqfAJM6GQrPgC09mV/mODjmbq5oS8QtlaHXqDmxOJ7nFpVjM2pIijCGdCjMjbOwuqKVt1ZUBbKhcuIsXDYljSunpZMdb8GsO/D9ocvj4+vNDdz/8Uaau1ycPS6Zn87NJ3Mf1+9dzaEmvSt3tdLY6RICzAhhQGcVsiz/cBDbrgEm9fHUUd0R78fSZm6akxPuMA6LlCgjLd3KSp64sR/efNsYQe6M58iVqgGJTd5kGtqtI1aA0eFi3NZ/oC3/RhnobiTru1uxXPwZkBzW2EYkdevg3et6H2/9VMmAOfvfoDk8k8OdDV3M397IhRNDy8eSI4xo1BIJNgNXTMvggompaNWqPQzwZKZmRTMzN5bkSAMSEm6fj9+cVshfPttCh8PLNfOtvHPJPAxdlbj00TQZsli2elvQ+zg9fnY1d/cpwHj8Mo/M693+8w11xFv1ATPAfaFRqwIGrQdDrCX09xlj1hFhFDdzg01mrJn/XT2Ze99fz5a6ToqTbFw4KZW/f7kVlQQ3ZzeS+P2fAttHbHiRnKg04izjadyjy5Esw+KdTdS2O5maFUNJSmjJRkOHk211ndx8TDZrKtsDxo9b6zqpbXNQ1Wqnrt1JcqSRpy6fgKSSKG+28+POZuKsevITLBi0B75VtBq1dMWM4bWxL5GnrcclmVhpT+DCuL4zdEL31/C3PQyxX16yi9+eXiTElwFCrZIwNKxDyjkeRl8IkoTUVoGpYyeKdWP40KlVfLSmimML4pmTH4dGJfHuyirGpZWENS6ABdubaOp0MTs/DmSli9lzC8s5sTiRCKO4vz1snB3wyc+gYrHyuLMW3rgMbvwBnTYbo1Yd4sFj0oeej9xeH6WN3TR1u0mJNJAVa8FnSaTh2IfRjrkRjc+B3ZqFXZPMCUUWji2IY1eznZMfn49fhgsnpfKf73bwq5MLeWFxORUtdsamRnDTMTn8+9sdAfHFoFVxy7E5jE2LPKhz04aadm57rTf77P3VNRi1Gv5w9ii06lA71r5MnlOjjNiE+DJiEMu6Q4i6diet3e6QesLhhkqSyEuwsrqilblFCeEOR3AYGHQa7v2qkeo25QKYHdvMT2YP7wyt/RErt6Hd8XnwoCxj7d4FhH+lcMTRtD10bOP7MPf3EHl4vjsen58Oh5cIo5Zos46WHhM7rVri5mNygoyV9zYDzY41kxFjpsvl5eUfd7G2qh2ACemRvHHjVBo6XFgMGl7a1sKT8yWgFUlq5adz83h3VVVQZxdbj8Dh8vgoa+6m2+UjPdrItvpQo/L311Rzy3E5JNoGftW3MNHKzcdk8+QPyuq3RiXx4Pljwr7CfLQyMSOK12+cRlu3G5Ukcdeba7C7faRGGUls/jZke8uWdzghZyavr+0VYM6dkMI9762nw+HFoFXx5o3TGbuH99rWug5uenllwKvg2IK4QIeOkmQbS8qa+dMninG0JMFfzhlNjEXHLa+uCpRc3HtaEVdNz6C5201Nm4NIk46sWHOf3TomZUZhNYxjR2MXVr2GS5Ns+y1b2pMF25tCxr7eXM/1s7KECDMAqGUvlk2vQ/XKoHGzzw8TLwhTVApOt4+TS5J4/OvtuLx+tGqJ24/LxeEKvxnvxPRIHv9me6CrTYxZx29OK9qngbTgIOms7RVfduP3QfNO0otL+NUpBfzh414vl1m5sRTutfjg8Hh5Y1klD3y6GZ9fxqxT89SVE/HLMte/uDpgXH/ueAet3Zu5dlYWx+THodOoMGjV2N0+9GoVte1O/v7lVm6ck01ihIHvtjRw5xuruWRSGqePTcKoVdPu8PDLt9fx5BUTGNdfLxpgR0NXyNh7q6u4/fhckiONASE82qwjM8ZMcZKNK6am88pSxZNGr1Hx1/OGZ/ctQd8IAWYI8WNpE8XJEahGwM1GTpyZlbuEADOcUaskttV3Ut3WO5ksbeoOaYc6knBIRkwRadAWbMTm1EYgrEmPAKY+ihOis5W2zodJTpyFrFgz//1+JzfMycIvK6LDnLy4AxqEN3W5+WpTHedNSA2ILwCrKtpYU9nOqSWJfLimhqcX9BrxyjL89/ud/GR2NsvKmpmVG0u0WUeMWUe7Q1k1/de32/HLkBlr4qfH54W8b0a0CZ1axebaDpweH+nRJmL6yFw5FCwGLbcfl8dJxYk0d7tIjzaRG39wWTSCgSXKpAt0nHrs4rGs3NVGm8ONQVUA64O39cSN4trZRagMVVS3OjiuMJ6vN9fT4VDKlpwePx+uqQ4IMB6fn+cWlgXEF4DvtzZy1wl5aFQSPz0hj5tf6V2RlWX4/UcbufW4nCC/i8e+3kZhopX/e2sNTV1u9BoV9585ivMmpKDfaxIqSRKFSbZ9+hrsj4JEK/M21QeNFSfZhPgyQKg0OtzxY9DtJcC4YwrDPhHwI/Ovb3fg8iqlHx6fzBPf7eDF66aEOTLlWrChuiPwuLnbzQ/bGjhrrMiIHRB0FjDHQvdeAqwpGkmSuHBiKvkJVrbWdZIUYWBsWiQxe4kQ2+u7gkSabreP/3trLeeNTwnqGvj+6mp+dmI+d7+zjo9un0l6tIn7zijmnvfW89Xmes4Zn8KzC8v4xzfbuXRKGtlxZjqdUeg0KjxeP4/O2xbIhHlrRVW/BZgup6dPwS4tyoRZp+bHnU3c+uoqWu0eDFoVfzl3NGeMSebuUws5Z3wKrXY3mTHmPjNpBcOXcJ93BXuwYFsTRUkj44Y4L97KN1vqD7yhYMji88tsrOkIGd9SN3JbjNd4zLRM/gN53/wk0LqwK/NEtknZhP9WcASSNA6yj4fSnhV/tRZO/VvfwsxBkhBh4KkrJ/LGsgrmbazn1JIEzh6Xss+a6z1p6HSRGWNmS13o8T9/WyOnjU7C7fOHeKq4vH4mZUTS4fTw2NdKdk+USctD54/hH3t0vSlvstPS7WZ0SgTrqxWBR6uW+NUphTz5/U6eWViGLCtZK/+6dPxBlxvtC4tBw4SM/q/aCQaHDoebt1dU8d8fdqLTqIg+ppCzY/JR9XhvobchTb+N/KQo/nyu8ve7/oXlIVkje7Yj73R6WbSzOeS9upxe3rtlBh1OT5DQAkonrr27vpw5Npl7P1hPU5eSQeby+vnNB+spSY1gdB8lT4fK6WOSeH1ZReB9Ik1azp/Yv/IlwYHx+GUqsy8mZ8sHAY8eny2N6rg5hErBg0uX0xvib+TxybTv4ZEULvZsMbybdVXt2N1eIjSiBOmwiUiB0x+Dt6/qbUE4+iKIHwUoCwczc2OZmRu7z5eo62NRsLHThaaP0h63z09dh5M2h4d4m4FzxieTn2hlV1M3KVFGcuLMvLq0gna7h4smpXH3KYXc8+46nltUHvQ6DZ39W4isbnXwwKebiDHrKEmxBcQ8jUrivjOLsbt93PnGmoAfmNPj55fvrKMoyUZRki3EB04wchACzBBBlmUW7mjinlMKwx3KgJCXYOEf327H4/P3Wd8oGB5Mz45h5a7WoLEJ6ZHhCWYQiDJr+eP2ZC6a/RZp/mq6VVY+ro9hmhQZ7tBGJrYkOO8pqNsAzjaIzYf44gF7+fwEK789vRin14epH0Z5u0mPNtFid3FsQSqLdgRPYucWJRBr0ZEUYQipT48xK+19X/5xV2Cs1e7pU8j525db+eiOWVQ22+l2e8mLt9DS7eLpBWWBbbbUdfLUDzv5y3mj0WlEyvtIZVNtJ//+XunG5fT4+cW3XfhO+BfnJLWg8ntQJRSjiQ2eJl88OY1vtgR34Tpvj45IZp2KqVkxVLVWBW1TlGQlLcZEu8ODRa+haw9z6UiTlr2v1gk2PRUtwYbUsgxVLfYBFWAKE228c/MMNtd24JdlipJs/TPaFPSbXy+WuXD8i4zSVOJHzQpHMqvWS/xzVHjjSokyYdVr6NzjWNRrVKQOgRbkkzKjeGp+8NgZY5IDpaWCASD/FLjhe2jeqWTDJJSAuf+9uZIjjUgSQZ3lkiIM7F0lKUmQF2fht6cXEWVS/n4GrYYJ6VGB1tZTsmI4d0IqWpUUEHBOGpXImyuCz6OXTE7vV2w/bGvg8w11AFw6JU3xDjJomJQZTVGSjU017TTuIZyDsvhZ0+YIdKkTjEzEzHiIsLOxGxlIjBgZhQ4mnYZEm4FNfWRQCIYP6dFGTh6ViCSBSoKzxyUTN4JrUP1+uHJmDr9bInHyV7Fc+JUBc1QiNoOY/B4xLPGQezyUnAeJJaAa2MuSSiUdlPgCkBNr4qdz83F5/UzP7r0RPCY/lpOKEzDpNMzJjeWBc0qI6ykRSo4w8N8rJlDdbg95vb7KKCakR5IaYeSEYiUzJ86qp83uDena9P22Rtoc4V8JFhw59l7B9fllfjGvmaq4Y1CNOhtiQ3MUZuTE8I9LxpGfYKEw0cq/L5vA1Kze1VK7209RkpWCPbKnzhiTxA/bmrj06SXYXV6evGJi4HyeaDPw5OUTOaYgjsQe35Yok5YZObGBx3uSdJDeQY2dTtrt7v1ukxlr5tTRSZw+JlmILwOMz+dnbGokv/y2i9PmRXHGPBv3L+hidPLAiWiHSn6Clb9fOAZrj7mqUavmwfNHUzwEYpuYEcUdx+eiVSvn8BOLErhwUqoojRtINDpIHgejz4fsYw5KfAHIi7fw13NHo9co9w5RJi1/OGsUcVY92T0Zrzajhj+eNYpnFpTywKebufuddZQ2BvuydLk8NHQ40alVQdkzU7NieOKy8RQkWshPsPCvS8czLbt/Mf6wrbeL0+vLKnnsq218sq6W4iQbapVEtFkfEIN2I0mQ0E/vLMHwRWTADBEWbm9kTErEiDqp5ydYWF7eEmQKKBg+qFUSSRFGLiuUuL/Ih4zEVj/oRvCFwazX8MLCMk4oSiDGokMG5m2sY3KWKNs4mihvtvPLd9bh88ucWJzAXSfkYdFrOGVUIo1dLrY3dJIRbeaccclMSI+ky+UlwWYg3mZgSWlwxkxRnJ5TYho472w7pU4LT2/Rs7nZx69PK8Js0FDf4eS9VVU8t1BphfnTE/L4cE01m2uVUr8pmdFEiM4HI5qUPsSMcWmRRO2ni6DFoOXscSkcVxCPJBHUmrTL5aWsqRuNSuKWY7ORkahssbNoRxNLy1oAuPW1Vbx38ww+vn0mzd1uYi36wE3/h7fPpKHTRbRJR0qUkX9dOobuyvVEu2voUkfRacsjyabnk3U1fL6+jvwEC6eUJFKQGLpi29Dh5L3V1Ty7sAybUcOvTi7k2Py4EP8YwZHFqNcyKSOSYlM02XIlfknFTimdiJjD99saCE4pSaIg0UpNm4NEm4HsOMuQuB+ONuu5c24e54xPwePzkxZt6lfbYcHgodequXBSGhMzomi1u0mONNLU5eL211ZzxtgkzpuQwuiUCH7z/oaAp+G3WxuJsezk/jNHUdXWhbWjFH1HOfVuI/PlNCYXZpHR892wGDScMSaZY/LiAKXjW3+Zlh3DlxuD7RiOyY9D1ZOekxJl5JGLxnLrq6twevyoVRK/OCk/RJQRjDzEWWSI8N3WRsamRoY7jAElL97Kj6XN/GR2drhDERwieaoq4hdciapDSb9MiM6j/vTngbjwBnaE6HZ5abF7mJRloLHThdWgrAA3dLgOvLNgSOLzy9hdXiwGzQFv6DfXdrC6opVOl5efn5jP84vK+WpTPV/1mINGm3X87K21gNKO8n9XT2bWXrXpo5Js/OKkfJ74bgc3zsrm+sgVRHxwG8gyKcDkWfdQe/ZPyEqOBODjtTU89IXSfrexy8WDn2/hnlMK2Vy7hUSbgduPzxOT1RFOUbKNX59WyN+/2IrXL5McYeCPZ4/qV4vwvUshOhwenvhuB0/PVwyi9RoV9581ike/Cm6R3m73UNvuZFRKBIkRwQJQgs1Ags1AQ4eTleUtFLYvwvr9dQFfLP/025knXc3tbym+Rp+uh/XV7fz29OIQj6XP1tfy4OdbAMWX4aaXV/LmjdOY2s8VZMHA4Pb6iHeWMW7VT5A6awAYE1vI6ulPAInhDa6HrFgLWbFDL/NJq1aRIzKyhjRqlRTklRZl0nH3KQW8sLiMvHgL66rbOXtcMnqtmmfml9Ll8vLZ+lpOH5NIUtOPJH97A/jcxADpRZfxsfsmEmeNDlx7u5xedjR20e7wkB5t6neG3vGF8Xy6roa1Ve1cOS2DeJuBrFgzNW2OQAfC0ck2fnZiPh1OLxqVxPurq6lsdfD7M4uDujUKRhZCgBkCuLw+Vuxq4fKp/aspHC4UJlp5ZckuZFkeEisZgoPD6/Nj2/pOQHwBULdsx1b+JeSMDmNkRw6NSmJuUTwPfbEVk06Ny+snK9bMsfkjU3Aa6Wyt6+DFxbtYWtbMicUJXDwpjaw9bpyau10s3tHMh2uqOWd8Cne/s47uHjNIvUbFL08u4IFPlTa9MWYd6/boiOT0+Lnn3XW8f+vMoLI8q1HLdTMzKUqyYa/dTsQ3vwwqTjcsfJBKzUSa3JPIi7fw0h5+Mb2v7eOtm6ZRaO7C1vA1bFwDyeMhfTpYh8ZkSTBwWPQarpuZxdyCeNocHtKiTYecgr6lrjMgvoBimtuxRwlbepSev0/3kdW5DOv2jbS7ZmPJnBjSVrqi2c4byyvIN7YzcenPAuILgOrHJ4g6biagfC9uOy6X77c2cMNLK7h0Sjqnj04kIcJIu93Ni30c30tKm4UAM9j4fYyq+zAgvgCom7Ywyr4UmBy+uASCvmjaAbsWKh0pM2dB6mTQ99+M3uH2EWvR8+vTivjpG2sC3ZBsRg03H5PDw/O2khtvoa2xhmOX/xZ8veWREZtfozD+ZGracsmKs9Du8PDPb7bz7ELFn82kU/Pc1ZOZlnPgc1hGjJmnrprEjoYufv7WWqpalQycrFgzz1w1kdx4K1vru/nLZ1uC9tvR0MU1MzLJHyADfsHQQwgwQ4CV5a2kRZmCUohHAjEWPUadmu0NXeIkMgxRIWOqWx4ybmpYHYZoBoc2h4e1ZbW8e5qfpO5N2PVxLHBkBXUXEQwSfh/UrIbqlUpb6tQpEJff793r2h385MUVVPbc8Oz8oZTVFW08fdUkIoxaZFnm7eVVPPjFFpIjDESb9QHxBZSJ6/LyVoqTbGyp6+DXpxXy5x4xZjdVrQ46HJ4QXySVJPHZ+lrOjO0Ed2gXDZO3lT99solnr55Egs1ARUuwb0y8Tc+UJC18fD9sfK/3iXGXw2l/U1p3CkYOdRvQVi0nV/YrEw3bmEN+qb66c3yzpYGrpmfw8pJdPDHLzZhvrggIKkatiY5LP8SW3dPnzdUFbjvrKpWW0xp3B9hDuylZvC1AFFfNyORvX27B6VFaCP/xk0202N387IR8dBo1CTZ9SCeZ2AFqrS7oPz6fB2Nt6PVcV7eqj60FQTRuhcpl4LFDyiTFr0QlMhMGFK8LHG1giID6DfDu9dBarjy34BE46wmYcGW/X+6DNdV8vqEOk04d1Iq6w+GlsctFUoSB24/Ppat6C7RXhuwfKbcFjO8313YExBcAu9vHb95fx9s3zyCmH+eyGLOeF7aXB8QXULprfbmhjtzjrfhlOWQfGaU5i2DkIgSYIcC3WxsoSR6ZbtdFSTaWljYLAWYYIsvgzz4OVeXS4CfSp4UnoEFAAn6XvYP0b/8vMJYeO4rt2c8AoiXqoFK+CF45p3fl3RwLV38K8f3rFLezsTsgvuxmaVkLFc3djE6NpLbdyT+/VUoorAYt7Y5Qg9A2u5vfnl5EpEmLzycHWkXuZnSKjVhrqE+HT4Y0g4MJMR6wpUBHde+TGj3VxLOlrhOXT+auE/K46rllgZbACTY9U7NioGlrsPgCsOZVmHKjMgEQjAxq1sALp/UKdVojXP0JpE46pJdLizKFjHU6vEQZtfztvFEUbb83KJsFjx3d9s/5rDOVGfodRCx+EKllB8cVXURK2gV8tNnIKbFFaJr2EB9VagzxuUAzfr8cEF928+yCMi6bkk5ypJGfzs1jRfmyQHvrOKueqdmitepg45fV+LOPR1W9l+CSKrJf9kvDZnjh9F4RUqWGKz+ErNnhjWskUb8R5j8M5fPhxAegvaJXfNnN1/dB7glK58QD0Njp5On5pYxKjqCxK1SQ9vr8vHjdZBZub2ZLg5bTkqeirwm+zzUn5pHU04WrviP0NUqb7LQ7PP0SYACWl7eEjK2saAOUrrGZMSbKm3sXYs4Zm0x69NDwZxIcGUQXpCHAd1saRqxRbUGCNaSNq2B4IEkyksYAo84FSaXceIy9RPn/CCVD10H68geCxnRNG8lw7wxTREcpbjvM/3vwRLG7CcoX9PsldOrQ41SSlHp+UFaXdi8wbW/o7NOD64ppGczIjaU4OYIYi55/XzaeO4/PJS3aSFasib+eN4YIY6gAY9ZruCZ2K7ZPboIZd0BUlvKEJZ7txz3Jg8u8nDRKaWc9LTuGd2+ZzgPnlPDoRWN5/YZpJEUa8LgdIa8LgE9kY40oNrwTnCXlccDKF/rc1O31sa2+k7VVbbTto6NQYaKVv18wBpNOWb3NjDVx/9nFPL2glI/XVCM5QicCro4mrI5dRL5zAVLFIuiqx7z8XxRtf4bSDomV4/+CN6ZH+DRF4z//eSIzSvjbBWMw60MzAUw6NZqekqYpWTG8e8uMwPH95o3TyI3ve0Gmze7GsUcWmmDg0GkkJJ0Fis5SToQqDYy7HFEcfgDK5gdngPl9sOBh5XsqOHw66+GNy5TFBlsKbPkY/N7Q7dzdfY/3gUatwmLQsKysmeMK4kOeP7UkiRizng/XVDGlMJ1Vo3+LJ2Gs8qQhAscZ/yUic3xg+/RoRdTWqVWcUBTPmWOSmJUbs89MvqpWO6srWqncI7P1jDHJIdudUqKUEydFGHnmqkncdlwOEzOi+P2Zxfzi5AKMOpFlNZIRGTBhprrNQVOXm+whaDx22Li7GBXh4o1lTfh9flR9TIgEQxeVJCFrDcrq/eyfAzJs/xopbXq4QztimNR+cLaHjOvl0BUQwRHE74Gu2tDx7qZ+v0RugoU5ebHM3967zxVT08nqMQlNjjRy63E5PDJvG35Zaff8y5MLeHdlFV6/zJ1zcxmfHsmu5m66XV5ueHEF1e1O9BoV955exMmjEkiw7aMVr8dO5PrnlZT1b/4AJRdAyfm4I7L4zfJEUqPhp3PzAwZ749KiGJcWhdfnZ0lZC796Zy2nZWm5JiYfVfMe5qmJYyA6p9+/A8EwoL06ZEhur0KSZWWi3EOr3c3/FpTy5A+l+PwyY1JtPHLROPL2EjP0WjUXTExlcmY0HU4PKZFGIoxaHr5wLPd9uJHy2VeQW7k4aJ+KxBNJ6N6mlAHsgWHj69x70c38cWE349Me5+SZflKTkohMyiYGuGiShbKmbl5YXE5NW+858p5TC4nv8bBRqyTGpkXud5Gpps3BB6ureX15BenRJu6am8+kzCjhHTeAqFUSqHXQ3QCzfwHIsG0eUvrIvZ4PCN2NoWOdteDzKNlqgsOjZWdvtoshUhG7VBrld7unyDXlJrCFihh9EWXS8YuTCrj11VVsq+/kN6cW4PDKqCUoSbExMSMSo07DVdMzKWuys9UbS+eUZ0iWmtjYLLO81MJfxvWKK0VJVv5x8Tgau1y8v7oah9vHNTMyA1mre7JgeyN3vr6aVrsHm0HDIxeNY25hPHML49k4JZ03l1cgSRKXT03nmLxeA/+8BCu/PLkQn18O8eMSjEyEABNmdme/qEbEF06Gug2w42uoXgUeOzE6M0bnJWz76wwKCwqV9PmMGeEOVNAPfKiQ7e1o8k6GTR8oF8VR5+J2djJSK/jthng0oy9Dt/al3kG1ju6IXCLDFtVRiCFCueH67BfB41lz+v0SUSYdfz1vDEvLmtlQ3cHEjCgmZ0UFuhpIksSlU9JJiTTy7qoqihJtzC2M54qp6fhlmY01nVzx7FLKm+zMyInhyumK14XL6+e+DzcyMT2Klm4Pu5q7ibXoyU+w9nakkbRgTYbaNcpN5OqXAfDM/h3HFEylKMlGalTozfv66nauenYpfhnWVEqknvR35rS9j6FiPuSeCJOvV0qxBCOHMReFlJrV5V2Ko6k7qNPG2so2/v1dbybeuqoOnplfyp/PGY1WE7y4IUlSSDei08ckU5ISgdfRjtP2NLql/wKNntKiW7h/lZn7S4J9iAAwROCSNZw3IZXsWAv5SVZ0Pd8fWZbZ3tBFeVM3fzl3NHa3l9UVbczOi2NCelS/P77fL/Pij+U89YNiHFzZ4mB52VI+uG0GxckR/X4dwf7xoULlaEedMxc2faiIMaMvwOPsYt/NzgVkH6tkY+7JlJvAMDJtAwadPUWsqmUw8y5Y9jTMvQ+2fan4s4y5GMZdASo1lS12tjd0opIk8hOsgU5Ce3NcQRyv3TCVimY75c2KSKxRqfi/E/MYlxaFUQcnjkrk4S+38tKPu3hmj32Lkvx4fDK7u41LSJgNGn765prANvd9tBGTXsMFE3tL0ytb7Nz22io6HEqmTofTyx2vr+KzO2eTHWfh/rOKuW5WJhJKVo2ujw5HQnw5ehACTJj5elM9Y1NHwE1G42ZY+rSSPZA6CSZdC6YYkCSK17lYFPcbCk0LFWOtxLFw1j/BEpoaKBg6SEBL0iziProSMqYrqbeL/knr2a8NkaaVA0+z3c+W+CuZMM5KzI63cduy2F7yfzS7U+j/1F8wIBSfpawy/viEsjJ2/G8hZeJBvURKlJHzolI5b0Lfz8da9Jw3IZXzJgT7+2yt6+S6F5bj9ineFot3NuP0+JhblMBXm+qJNuvYUtfJr95dF1gFu35WFnfNzcNq1IJGCzPvhJ1fKZ8BwBDJj6oJPPylktHy78vGc/peaclrKtvYvajm9cvc+EU3x+RczL+v+i0WW7QwfhyJZMzEddZT6Bc+BLKfypLb+dvmeDw7t/L4xeMw9Age2+o7Q3b9ZnMDvzjJHcg2OeBbxZgBM6ReDKNOwydLvPrVLlbWlLMgI4mCuBJ0jRsC21dPvpdr362iqcuNWiXx6k+mMq2ne9GiHc1B35Fzxyfz2zOKiTEfnDxf1+HkxcXlQWNun58tdZ1CgBlAvH5wps4h6v0rIHOmkmW46J90nPsGQtLdDykT4ZLX4NsHlPvbGXdA0ZnhjmrkEJMHE66BVS8oixU1q2DabbDkKWUuMfOung5IFrbWdXD188uoa1cy9XJizTxz9aQ+W0IbdRpm5MRS0VzBkz/s7grn50+fbCY1ysTJoxKxGbScOioppBPhldMyMOt7p8fLylr4cmNdyHs8t7CMU0sSA9vWdzgD4stunB4/NW0OsuMs6DXqkIxFwdGLEGDCiNPjY1l5C5dPywh3KIeOz6PUq5d+B3knQ/LYEI+Q4hg18+t1XH/amZB/Cqx7A56aDZe9DUmH3u1BcGSRJPhfaTTTpz5DrnszsqRmS9KNbKqK4qf980Eddri8fm78pJHM6BM4I+cUyjrg80+6eOBsUYI06FgSYPqtMPpC0OiUrJgBZHNtB8vLW3B7/UzOjGZ0SkQgE7G0qSswsdzNqoo27jg+l6821XP2uGQe+HRTUArysz03Y5Myo6lpc9CpKyLzqi9Q1SzHIetY6MjiV9/3mvj+5bMtTMuOCTLx2/OmbzeNdi9+faQQX0YqBhtfq+ewPD4FlQreXeig3WFHkuzUtzvJ6MlkyYwJNWScmBHVm3V1sOitqIHrZmahUal4akUFKXMe5kTbLozOBuotRdy9SE1Tl1IG4PPL/Pf7HUxIj6TL6eXeD9YHfUfeX13DeeNTmZ0fd1BhaNUSVoMWpye4/MmoFcf7wCLzr60RHDP9f+S4NiFLGjYl3sy2ikjuyAt3bEMYrRFP3qm0Rk9C9nmwRidi6uM8LThE9BY4/l4oOEXpNhVXACmTYcLVylzC1JtN987KqoD4ArCzqZtvNjf0KcAAeHx+3loR2uHo8/W15MZZWLijCY/Pz3PXTOIf32yjutXJjXNyOKm4d4mxze7m0a+3Ma6PEsoYi47GDieVPj9p0SaizTqMWjUOT6+PlUYlEWsdqTnjgsNBnEXCyOKdTWTHmrEM15O5ow2+/aNSmjLjTtCFdl8AGBWr5tn1bjw+Ga1aC+OvVEwpXz4HrvxAiDBDFJ9fZkVFG0/tcqHX5ALg8jo5qTh0JXakEGHSkmQzMDpeQ67Vi02nYWmVjkizSNIOG5aDm9D1h4017Vzy1BI6XcpqlVatrO5PyVJW9yP7mNTajBrsPQah41IjeX5Recg2TV0uvtlcz0NfbOHiPIldVsjNPZfVdR5+/tG6oG3bHR7c3mCRZ2J6FLEWHU1dvQarvzi54NAn2YJhgdmg4YU1HUFjcRZ9kAljcoSBYwvi+H5rY+D5K6dnBDJkDpW0aBO/PrWQ62dnYtRqMBkVT5AfftzGBXnVnJxp4an1XqpanTR0uvD4ZDqdXnY1h5YsNXUfvEF0nNXAb04r5P/eXBsYS482MipFZL8MKDKsrGhnLRquKByDV5Z4Zo2fvISRez0fCFq6XTy3sIwnfyjF65c5vrCG3585qiebTDAgWOKh4FTlZx94fX5W7moNGV9b2Ra6cWsFeB1obCkUJNpYVRG8TWasmWueXxbokqhTq3j5+ilkxZpDsgndXj9VLQ7mFiZgM2jocCr3DGqVxAUTUjnp8fm4fTKnlCRy72lFPHj+aH7+1lq8fhmVBH86p4ScfQhEgqObYTrzHxl8tal++HY/6m6EL+6B+CKlNdx+OuPY9BJJZhWr6n1MTe455DJnKf++egHc8C1EiBa/Qw2NWsVJxQms3NWKa4+J4rEFAz8hHiq4vH5ePy+KxAW/Rb9oERijOPO4B6g2lIQ7NMEA8vWm+oD4AuDxyTy3sJwJ6VFo1CoKE62cMSaJT9b1GgHfe1oR6VEmzh6XTIJNT0Gila11vZMXtUoi1qLn75+u5X/jyklf9gdwtOLaOYeouX9Fq5bw+HozZq6ankHCXjd7OfEWXr9hGsvKWmi1u5maFc2YProzCUYWxUm2IMNoSYI/nV0SNBlYU9mGRiXxfyfk4ZNlul0+HvlyKyXJNqIOsuxnb1QqicQ9DaVbSrlg159Qb/kIdBbmTvoV95WP4aQJWYEsrdm5sSzYEWyK3VeWTn84eVQir/3EwPLyFhIjDEzJigl0HhEMDDqtht/NiSBv2zNELH4FJDXHjr2VXTmXhTu0Ic2K8lae2MN76dstjRQlVvKLkwuESfQgolGrOGtccoiYckJxQu8Dt13x0/ryN+BsR8o9kbuP+SOfrusVTuJtenLiLAHxBZSSx9eXVfDYxeNC3jfeZuDqGRk89cNObjwmm06nF7fXz5z8WP7w8SbcPdf0LzbUUZxk49ZjcyhMtFLX7iTeZiAnzhzovCgQ7IkQYMKELMt8s7mBu08ZhrUcjhb4/G5ImQRZs/q1y6hYFfOrvL0CDCgiTFc9vHklXPelUmYgGFKcNCqB7Q1dvLeqCpUkcdmUdObkjVwBxu+yk7DkL+gj4mHK09DVQOL8e5HPyoIR63xz9NHUFbpSX9/pxN/TlzrKrOf+s0bx05lxaL3dWExGIk06NBG9bgmPXDiWn7+9lq11nUSZtDx43mjcXj93lThJ/+6uwHb6ivlofnyIj256hPs+20l1q53LpqZzYnECayrbiLHoglZT8xKs5CWIOvGjiXibgYcvHMvG2g5au93kxFkoSgo2+WxzePh6cwNfb24IjCXaDHQ4PYckwPjaqml1+Kjz20iOMBK9uxTO74OlzyjiC4C7i+TF9/HgeW+jylF828x6Db89o5i7313Lmsp2bAYNvz9zFMVJNvD7oasONAYwRfcrFpNOw4zcWGbkCjeSI4VflilqX4C58lulGYLPQ9ymV9GnjAIKwh2egtuh3F8aI0E3NDJMVu5qZUJ6FHPyY5Fl2NnYxWfra7lxTjYRJnHPOpicWJTI5toO3l6h3I9eOzOTGbkxvRvUroEPb+t9vOMrIk0xfHr7Q2ytd5Jh6CLGZuQfi1sCm0gSnJRr5bR0L35nJ2pj8HnX4/UHWlm/sbyS9Cgjd8zN58edTSFZgJ+sq+H6WZkUJNooSBQmzYL9EzYBRpKkcqAT8AFeWZYnSZIUDbwJZALlwEWyLIfmnI0ANtZ0oFWr9ungPWRxd8OXv4Wkcf0WXwDGxKl5d5uHX07Z64lR50HDJvj+QTjhvgENVXD4ZMVa+PO5JVw/MxNJksiJs4R03BhJxKvaMUy8DLZ8Ah/eDpFpcPrDRNNx4J0Fw4aTRyXy8pKKoLGrp2f2diWQZWKbVhDz+a+QGrdAzvFKByatSTlnmaIoSYngjRumUdfhJMKoJTnSyIbqdszeqpD3U2/9mKKTH+CFayfj9PjY2djFxU8voc3uwaLX8PCFYzixOFF0QDiKkYCSJBtx+zDULUy0oZJgz86n505IJt56kPcQXQ34V72CetFjxKq1OCf8gn+3TeCiWcXKpKG7CTa+E7JbbOdmMJ8UeFyQaOXF66ZS2+bArNeQFm2CtgpY/j/FF84cDyf/GbKPE4srQwCt7EXrqIXC02H1q6DWwsRrMHeWhTs0hfqN8O2fFT/BtGlwwu8heVy4o2JyVjS17U7+8c12ZBnGpEZw63G5mHRi/XqwSYky8sezS7hhdjYqSSIt2hScXdK8I3SnTR+QNufnJLd9iXrx46DWc8/s31CZncyiChfPn2pkwtaHMXy9ELZOU85ZyeMBaHe4eX5ROf/6dgcxZh3nTkjh/Akp5CfYKG/uDnmrSRnRGLTiuBD0j3DPpI6TZXmcLMuTeh7fA3wjy3Ie8E3P4xHJlxvrmJAeGe4wDg7ZBz88CLYkyD7moHbNj1axq8NPkyPY8wBJgum3w8rnoXZt3zsLwopeo6YoOYLCJNuIFl8AVHoT8sb3YcO74HND8054/ya0WjGBGKo4PT6Wljbzj6+38fKScnY0dB1wn4kZUTx15USKk6xkxpj42/mjg0vrGrfCK+ch1W8Avxe2z4Mtn8K2L6Dsh8BmUWYdRUm2gJCeG2/BHJUU8n5yTB7oLJj1Gmrbndzw0kra7Iohb5fLy0/fWENZ04HjFow8OuwevtpUx1PzS/nvDzv5YkMtrd2hpt+jkq385dzRjEmNIDXKyA2zszhlVFKQT8yBcHt9uDZ/jurbP4CrA+zNpC78NRfEVfDY19tweryKKWZccejOEWmhQ0YthUk2RXzx+2H5s7DoH0q3mObt8PrFUCeu60MCtRbZHAtLnwz87VnwCCpb8oH3PdJ0NSiZ0Fs/BY8dSr+F1y6CtlAD1cHG7vLy0doaepIjWVfVzqbaDiGWhwm9Rk1uvJXsOEtoaY85NINOTh6Pq3Qx6q9+q4jLHdUYP72Nx6a7+N0x0Uxb/lMMFT8omX+7FsGrFwaOu/VV7Tz+9XZ8fpmGThdP/VDKw/OU8+TMnNigDrYJNsWTa/dxUdvm4JvN9Xy4uppNNe3IshwSm+DoZqhJdWcDx/b8/0Xge+DucAVzJPlyYx2XTkkPdxgHx6pXwNkBE69RhJODQKOSGB2rZn6ll/Py95rMGqNg/BXw0Z1ww3egGtmT/OGG3y9T0+5AkiSSIwwju+65qwFpyyfBY34ftA6RVUJBCAu2N3LDSysDj+Mset64adp+je+MOg0nj0pkRnYMflkOSSV31m3B4N1rElzxI8z5Jax+GUadE/Kafr9MU5cLc8pYfPmnod72mfKEWod02sNgjmF1RSvfb22g3eEJ2tfl9VPT5iRXtKg86lhZ0crtr60O+Gy9sqSC/14xgblFwZkwSZEmThqVSEGiFY/XT1KkURE+9sDt9VHX7kKvVYX4CwGsLW9k8poXQ8ZT676hujWJ1m4PSZFmpeX7y8uVjFdQMhJSJ+//g3TVKZkveyLLULfxwPsKjjx+D9Lmj0KG5fKFSBOuDENAe9BaDi07g8e66pXrbmSo8DeYbKwJzX79dksDd83NEyVIQ42k8ZBzAuz8Wnms0eM4/s+YvvpFyKa28i+5ZPTFqPY20+9uhBbluKtoCTUaX7CtiZZuD+kxJv539SS21Xfh8fnJjbeQGqWcj6tb7dzy6irWVbUDisn/S9dNYXqOKLEU9BJOAUYG5kmSJANPybL8NJAgy3ItgCzLtZIkxYcxviNGZYudhg4X+cPpZrtqOez4CqbdesjtUEfHqfl6Vx8CDChGvju+VlpUjxOmcEOFxk4nry6t4MkfdqKWJO6cm8eFk1KJPkzTx6GKR2VEZ4mHzrqgcb/OFvZ0QUEo7XY3D32xNWisscvFmoq2fnUesO6ju5BDZSZk+qq3gdcJ0bkh29d3OHl5yS6emV+KVq3i6fN+z5TJN6DxdEJMHsQX4fL6+O/3O8mJs2DSqQMdlUAx8I23DY3vlMfnp7nLhcWgwaIX3ZeONF9srA0yOXf7/Ly3qprjC+NDxO5os47ofXRk29Xczb++3cH7q6uJNum478xiTixOCHRJqm138Ng3pTwVkYOVlUH7dpvTSY029XbbSpsMN3yvZILpTJBQAtYE9odD1mGwJiI524KfGOD28YJDw+UHKSofXdWKoHGnLYuw2x1rzUojB3mvDOkh4AOztxcTwPTsmIAZtWAIYUuCc/+rlLO5OvFG57KpK4ZiSwYmVgVt6rWloTXY+j7uDMrcrC+LiPHpkejVEnXtDmIsemb24Vu1tqo9IL6AYvL/18+28MpPpoqOhoIA4ZxTzJRleQJwKnCbJElz+rujJEk3SpK0QpKkFY2NjUcuwiPEvE31TMiIRDVcUhgdLbDwMRh9oZKefIiMS1CxoMob1AkkgKSCidfCN38AjyP0+WHIcD9OAb7Z3MDjX2/H6fHT7fbx18+3sGhHc7jDOmJUkYDvuPuCMrzklIm0RRSFMaojx3A/Rj0+mY69skmAIHHjgNhboXYdNJeyO8+8xZJPV+aJwdtNuxm2fgHjLg15iW821/PEtztwef10ubxc9vpOvnEXQ/HZkFAMkoTL42dnYxfvr67m9uNy0fWkT6tVEn86exTZseFvVVna2MVvP9jASY/N55rnl7OivOXAOx1hhvsxeiA6HN4+xtx9bLlvvD4/zy0s452VVfj8Mo1dLu54fTUbqnsmAT4Plo5SrsixU59/WfDE1hzLFttMbjkmJ3hSGZcPxWdC7tz9ii8+v8zC7U1c/upWSsffHdwRMbYg4Kcw0hnqx6nfr6Kh8IqQv31jytzwBbWb2ByYeVfw2MRrleMnzEzJiub4wt614NQoI9fOzEIzDDvbDPVjdEAwxSpZU1FZtKpiuP6VNWxOv0wxBQ9sE40z6wSIyVWyWvdk6i0Qkw9ASUoEF03qzcCKNuu4/bhcbnl1FSc9Pp/7P9pIeVOoF0xzHyb/5S3d2N2h53rB0UvYJFxZlmt6/m2QJOl9YApQL0lSUk/2SxLQsI99nwaeBpg0adKwK6z7bH1twFV7yCP7Yf7DSgpxdNZhvVS0QUWCWcWKeh/Tk/s49OKLlNXlZc/AzDsP672GAsP9OPX5Zd5bFWoo+um6Gs4cOwTqxo8AqdEmajtHkXLBC0jtVaCz4InMooYYRmLy6HA/RmOteq6fncVfP9sSGFOrJEan9rMDQf1GeP9mqFunGOye8lcYfRGRsYl8lnE3o7MuJl3ThsoSix8V5ovOVQSVPXD7fLy1IvR78tm6WiRk4m1GipNs2IxaLpyUxoOfb+G1ZRXccmwOPllmalY0U7Ni0IXZX6nb7eVPn2ziu63KjfmK8laufHYZH98xM6ylUcP9GD0Q54xL5vMNwRl3F01OP6hSz6YuN++tqg4Z31rXyaR4GZY8iXXRY5zm8+DIP4uqM99A3VaGXqfDGVtCXnQOKVGHlgexqaaDq59fhs8vc7M9gvuPeZ1idSWRUbFIKRMgKuOQXne4MdSPU7VGYl2rjsSTHkTTWaWIwrZM1reoCftfSGOAGXdC1uye8o90Rbg7jAW/gSI50sijF41lR0MXLq+f7DgzSRHDrHlGD0P9GD1sXJ1KifDXfwCvk5iUyTx83O+46Ts3D858lTx/KT5JA0njSEosAa1ayerPmKkYiEekQNJY0CsiZaxFz+/OKOKSKWnYXV4iTVqufm4Zzd3Kos+rSyto7HTxj0vGB7y4HG4vKX1kzpw/IZU4a98G64Kjk7AIMJIkmQGVLMudPf8/Cfgj8BFwNfBgz78fhiO+I0lzl4sttR3ceXxeuEPpH1s+BUcbjL5oQF5ufLyaeWWevgUYgLGXwte/h8nXD4n006Od7DgLy8qDG5Flxo7cv4sZF8bFv0Mqnx9ITdUBJdfNA0am6DTcOXd8CnqNmhcXl5Ng0/PTuXmMTok88I6uLvjyXkV8AcX88eOfQlwRMelTOWZiCeur0lnT5SLbZqYkOQL6SDvXqlQUJlpZU9kWNB5p1vHr9zbQYnfzzJWTOKE4gTPHJNPY6eSlH3fx/OIy7j65kHGpkWEXXwBqWh0B8WU3Do+PnQ3dwpvmCDIrL45/XjKeJ+fvxO+TuWFONsftaQjdD4w6NekxphC/imiLDiqXwfy/9W677SOMhiTuaDqHG4/JZXZW3GEZim6r78TX05ppe5OTy78AgyaVr39+TMATQTAEkGVmuBag+foPgSE9MOOUfwMTwhZWAFOU0m0uJ9yBhBJp0jEps38t1QVhpGYNfPHrwENV9XJmGJ8mP+ZGfvJlF5KUglal4u2bs8jebV5ujNxvUxGrQcuE9CgAPl9fGxBfdjNvUz01bQ5y4hWx8ON1tbz0Yzk/OzGfl34sp9Xu4dxxyVw7I1MYNwuCCFcGTALwfs8KjwZ4TZblLyRJWg68JUnS9UAFcGGY4jtifLWpnrFpQ+OG+4B0VMPqV2DKjQNmjDshQc0Tq1zcN0Pue4UvKgMSRindFEZAFsxwRq2SmJoVzecb6gKmoXEWPcV91ESPGLobUZXPV/6/R12wqrUc0qeGJybBfom3GrhmRibnjk9Gp1b3vytMd6PS8nRvWssgfSoJNiMJxQde6ZQkiSumZfDZ+lo6nN6emPREm3U0dyulJL//aCPj0yNJiTLy61OLuGZGJhqViqQ+VsrChV6jwqxT071X+ZbZcGieX4L+YdZrOGtcMnOL4pBlsBgO3iMgwqjl3tOLuPq5ZYES37GpEYxLjYBVq0K2j6n4nEunXklmrPmwJwWWPkTJKLMOg0YcN0MJtd9LZOnHIePWyu9h2hWDHo9AMOC0lIYMmcu/4oQJN7N4l1JhfM+pBZT0Z4GmD/ry/bHqNei1yvyoqtXOnz7eRKfLS3WrgzPGJGMzajmhKI70mJG7cCk4NMIiwMiyXAqM7WO8GRgCBalHjk/X1zIpIyrcYRwY2QcLHoWc48BycKtx+yPDJuGTYUuLn6KYfdyglZwP3z2gCD9akbIXLnx+mQ/WVHPV9AxUkoQkgdvr55st9Zw1LiXc4R0Z9DbFNLV5e/C4ZZiUDB7FRBh1tDs8NDY7iTLrsB5oImuIgNh8aNoWPG7Zv9loX5SkRPDerTPYWtdJl8tLWVM3//luR+D5ug4nDo8ibGjUKtKih97NWFq0ibtPLeS+DzcGxmbnxlKYMIIF1zDS0OHE5fWTYNOj06gxH6bh8bSsGD64bSY7Grqw6DUUJ9kUgS82P2TbrthxvLK6hd+lH34nxpIUG0WJNjbX9Wbf3HdmMbHWoWEqLVDwqTR0x0/BVh0syHXGjEXkdhyYunYnXr+fpAijyGQYQnQ6PbR2u4kw6Yjo49otJ4xm1qhsHkuUSYs2UZRkO+S/X2GilWnZ0Swp7fVG+81pRYFMP7vbR6dLWYRptXt4eckuAMakiGuoIBRh4z2ItDs8rNzVyrUzDs9LZVDY+AH4PJA+bUBfVpIkJiao+bzUs28BJjobIjNh3Zsw8eoBfX9B/1GrJM4al8LP31obNP7kFRPDFNEgYI7Bdeqj6N+8KGAG7RlzBar4EsR67tBmdUUr93+0kbVV7UzKjOL+M0dRkrKfDiymaDj9MXjtgl7j73FXQOLoQ3r/3HgrufFW1lW18ev31uPfo8L+3PEpxA/xCakkSZw3IZW8eAvb6rtIjDAwJjVCTKQHGJfHxzdbGrj/o400d7u5YGIKtx6bS8ZhrpCqVBKjkiMYlbzXMZ8+DX/GLFS7FiqPTTGsz7yWrlUqUqMOPwMrJcrE01dNZF1VGy3dboqTbPv/3gnCgk6joaHwYsw7P0XdUQmAO3YU9ozjhACzH7qcXj5dV8ODX2yh2+Xj6hmZXDszs88OOYLBZX1VO3/4eCMrdrUyNjWCf59TSOqoc2Hj+8oGeivSKX8hPy2F/AEwOoq3GXjsonGsrWqnodNJfoKVMXuc65IjDEzLimZJWa9Ao9eoyO5HR0bB0YcQYAaRbzbXU5Ic0f8U+XDRXgnr3oJptwR3NBggJiepeWWjh59N3s9GxefAosdh/JUDVv4kOHiOyY/jmYsLULWVASrk6CwmZY/c2zWXx8d9ayOYPuV1MqR67CoLb1VY+Em7ltHCBmPIUtVq5/oXV9DSU/KzoryVG19awfu3zSTBtp8suqxZcON8HA3bafIY+ajGRtSGbo4rPHSjxVGWbpZeHcHSWnhomYtp2bFK16NhUJJh0WuYnhPL9JyRaDk9NNhQ086tr/ZmIby5vAqjRsNvzyg6rM4qbXY3i3c28/aKKjJiTJw/IZXRqREQmYbqohforFhPdWMLK7vjWVBm4tGL8ok09d3S+mBJi9CS5u0AdxdEmhVzS8GQQ5NYRMN572DtLgdJTZslC3Ns2gH3O5pZVdHK3e+tDzx+ZkEpsRYdNx0zBM1qjiLqO5zc+PIKatudgNL6+eznHXx1y4NET7kRnB1Kl6PYXLbWdfLR2mrWVbVz1rhkjsmLI37P+wK/D5p3gqsDItLBuu+M56RI4z5Lhy0GLQ+cW8LD87Yxb2MdeQlW7j+zmNx4IcAIQhECzCDy8doaJmUO8fIjv0/pepR7grJCfATIi1LR6pLZ0eojN2ofN2qJo0Glhh1fQ/5JRyQOwYGJ9dQxd/NvUW37HABfyUWoM38PpIY3sCNEXYeTt1ZU8aYMsFtx6WbWqE5Gp0aGLzDBftnVbA+IL7upaXdS2WLfvwAD1GjTuOyzasqb7YADqOfSKencf1Yx+j5EE6/Pj9vnx6Tr4/JZ8SPqt64irquBM3RmTjntMeTiWWh1wVkkDreXLXWdVLc5SLAZKEq0HpL3h2D4sa2+M2TsnVVV3HRs9mF1V/l0XS33frAh8PjtFZW8d+sMChJtYI7DWnQ8SZlujnF6Oc+sH7iFIGcHrHwevv2TkjUbkwcXvkCLNY8ttZ202N1kxpgpSLSiHYate0cSvpYyor+/F+OubwGQCy6iY8Y9RGUIMWFf/FjaHDL25vJKLp2Sjs0oztnhoqLFHhBfdtPc7WZLu4YZOTN6t2vu5srnltLQobSGXrC9iVuOzebnJxYogrerG1a/BF/dBz43RGXCRS8p3ZD6oKHDyabaDhxuHznxFvITglfmcuOtPH7xWJq6irDoNQMmcgtGHuJqOEh0OD0sK2th4lD3f1n3lpJxkjbliL2FSpKYkqjmk52efW8kSVB4Jiz+5xGLQ3Bg3Bs+CIgvAOoNb+He+nUYIzqyeH1+EvpoFajRiJrvoUxfN8JqlYTVcOA1hu31nT3iSy9vLq+gYq8xgA3V7dzz3nrO/+9inplfSk2bo/fJzjp453roalAeu7vRfHgz2pZgjxmfX+btlVWc+5/F3P7aai588keeWVCGw+3txycVDHf6uiFPizZiOgxBpKnTxT++Cfat6nb72FAd3BUpwqgjNco0sFm4det6Ji891/Pm7fjn/ZYPl23jsv8t5fbXVnPWEwv5dkvDwL2n4KDx+vwYt30cEF8ArFvfQl+5IIxRDX1S+8h2yI4zB4xXBeHBatCEeLlIEtj2WsjYWt8ZEF928+yCcqp6rt2+uvXwxT2K+ALQWo786S8VYXkvqlsd3PLqSq55fjm3vLqKs55YyIrylpDtDFoNqVEmIb4I9os4gwwSX22sZ1RKRN+rpkOFxq2w+SMoPk85kx1BpiWr+XCHB1mW971R5mxo2AQNW45oLIJ94Peh2/ZpyLC046swBDM4GLVqrp2ZGXT4T0iPItYifDCGMjlxZm6ckx009n8n5JEdexipv3udAksbu7jsf0t4Z2UVm2s7+fNnm/nv9zvweHu6ZXXWK53j9kT2Q1tl0FBZUzcPfLI5aOyf325nZ2P3occqGDaMSYkI8g3QqCTuPa2ICOPwvFl3N5WFjKnKvkfvbgs89stw7/vrqWt3hGwrGBwkn5voXV+EjFtrhACzP6bnxJAR09tO3aBVceuxuX1mRwoGj+xYM3fNzQsau3lONjlxwV5a+5pi7L68uxpDOydJVUuVLol7saaylZW72gKPnR4/f/9yK90usXgiOHiGsBowsvhwTTVTMoewd4a7C354CIrOAuORN9DLjVJh98DmFj/F+zLjVWsh72RY9hSc8dgRj0kQjFeW6EycQVTl0qDx9vjJjFSHiKRIZSX65ycW4PL60KhVtHS5Ai73gqGJSafhtmNzObYgjto2B6lRJkYl29BqDrzGkJdgJTPGFJQFc8mUdNKjg//m2+o76XAE32i9tqyS62ZlkxVrVko2TdFg32tFzJoY9LDd4cHt8weNyTK02oNLqAQjk5QoE09eOZGNNe10Ob3kJVgpSjq8LhmxVj13nZDHb97vLUGy6DWUDEL3jS5dXIiJqzdxAqv3mr90ubx0uXwIwoMXDe602RhrgrsgeZKnIJYX9k12nIVXrp/Kxpp2XF4/BYlWChNFV5two9OouWZGJpOzoqlutZMUYWRUig3jXovchUk2Emx66vfIgvnJ7KzAPV2DHEXmXq/tiy1EMkSFZCjU9byGJIFZp6Hb7WV7QxfdLm+fLaoFgv0hjphBoLXbrXQ/mjlEux/JfqXldEwOJI4alLdUSRLTk9W8v81N8fT91L3nnwwf3QEn/AEM4qI3mKhVEs1ZZ2Er/xJ14yYAvMkTaUk5bsQKMJIkcdroJBbtaOb91dUUJVm5ZEq6MsEWDGkiTFpmHIJ5bHKkkWevnsxnG2pZUd7CaaOTOCY/PmSFsy+DVJ1a1ZsGHZmG76z/on7nKvC6QJLwzf0j6viioH1Sogwk2gzUdfTWr1v0GtKEyHfUkBxpHPAuKqePTibKrOPdlVVkxpg5Z3yy4v9yhKnU5yMVX0nUppeVAWMUzbP/wIevKl436dEmLp2SRpfLy4pdLYBMbrxwNB9sNBo11dnnkrrjC1TNWwHwpU6lIX4mwiJ0/6RFm0iLFufnoYbVqGVadgwQs89t0qNNvHTdFD5dV8u6qnbOHJfMnLzYwHV7pTsN6+gbiFn/jLKD3kb1zL+QZgq1iyhKsnLa6EQKE220dLuJNuuIMmmJERnSgkNACDCDwOcb6hiTFolhqHYGWPs6dNXDpOsH9W1npmr421IX90yVQ2o5A5hiIHmcEuPUmwY1vqMdSZKo06bxWcrfmFzYjIzE4vYYZuhSyA93cEeQGIues8Ylc9a45HCHIhgkcuIt3HF83n63KUq0kR1rprSpt1Tozrm5pPW08vX6/LzclEfU1DdJlhrpUEWxpDGGW91qovcoS0+0GXnyygnc/e56ttZ1kh5t5G/njyWzHyJfeVM3pY1dmPUa8hMsRJnFjZ9AIcKk5dSSJE4tSRrc941J4K+eSzh9zukY/d1s98QT6cvh35er+N0HG7l6RgZ/+WwLvp6+7LEWHW/cOG2/Iozb62dnYxfVrXbibQbyEywYtOJ29XBQqyTKpVQ+TH+UycWN+FGzqD2G2epEsg+8u0AwbClItO1TjM5OS+HuTWdyxZwTMPm7KPfFkRM1mvQ+bBgKE6xY9Boe/arX1+2SyWk4vT7Me2XeVLXa2dHQhU6tIj/BSqxVXKsFwYgr2iDw3qoq5uTHhTuMvtnxNWz9AqbdDOrBPRxSrSoiDBKLqn3MSdvPe+efAsuehik3HnFvGkEw76+u5t1VHcDuGWQ7Ln3DIWUaCATDmZQoI/+7ehILdzSxvb6T2XlxTM6KRuo5J1W02Pnr59t6yovMgBuo5YQxGUzLCV6hG5cWxRs3TqO5y02kSdsvj6HVFa1c/dwyOpxKGdRJxQn86ZySA3Z5EgiOJJmxZm48cSw/bG2kosXOcYXxTEyPwmrUMirZxl1vrgmILwBNXW6WlLbsU4CRZZnP1tfys7fWsHu335xWyFXTM4fuItYwwO+XeWtFJZ+sawd2+w21gbmJaeJ6LjhKGZcWyR2njOGbLQ14fDJzC+MZmxbZ57Y17UqXzD15Y3kll01NZ8weXTI31bRz9XPLaexSSpYmZUTx2CXjRJarIAghwBxhatocbKvv5M65+19dDQvlC2DFs0rmiz48KcGzUtS8scW9fwEmYTT4vbBrEWTOGrzgjnJ8fpnqNgenlCRSkGBFkmBtZTvVrcJIUTC86HZ7KWvsxuPzkxlrJuoQuxNkx1nIjus7Yd/p9YV4uwA4PH37XkSZdIE42u1udrXY0WtUZMaaQ8qfup1eHvp8S0B8AZi3qZ4LJ6VyYnGwx4xA0F9kWWZXs53GTifxNgMZMcFZWC6Pj7Im5XuTEWPeZ9vd3Hhrn4KKWaehsTPU22jvlvF7Ut7cza/fW88emg1//XwLM3JiKUk58v50IxWv309du5PTRieSF69cz1dXtAV3chP0icurfA/cXuV7ECHaT48YZBksei1z8uJIjDDs1+9vX9fyPcc9Pj/PLSoPiC8AK3a1smRnM840H3a3j7RoE9Hm4Wm6Lhg4hABzhPlwTTVTsqLR9uEfED5k2PyJUtYz4RqwxIctkpkpGv7vWwdtTplIwz6yWyRJyYJZ+pQQYAaZq2dk8uyCMr7YUAfA7LxYzhmfFuaoBIL+U9/h4O9fbuWdlUqHonFpkTx60dh9CimHSlqUiWlZ0Swp6zXhjTBqyY7bf2nRzoYufvXOOlZWtKKS4PpZWdx8TE5QXXmny8vGmtC2mDVtzpAxgaA/+P0y8zbV87O31mB3+7DoNTx+yThOKEoAoKHTyb++2c4rSyuQZZieE81fzh1zUH5YESYdV8/I5P6PNgaNK74NfdNm94RMdGQZmrtc+9hD0F+un5XFMwtK+Wy9cj0/Nj9u0EvWhhtNnS6e+G4HL/5YjizDlMxoHjp/NFkDfP0QDD5ur5+P1tZw7/vrcXn9RJq0PHn5xJCM1d1kxpjIijVR1tRr2J8ebSRrD+Ha4faxaldryL5rK9v45zfbqWx1UJxk4/FLxpGfILywjmaGkiow4pBlmbdXVA2tcg1nO3z/kNJuesqNYAvvxdeikxifoOa97QfoAJJ9HJR+p7R6FQwKapXEltoOVuxxMVmwvYldLfb97DX88fllNla38/HaahZsa6SpU9z4D2eWlbUExBeANZVtvLmiEv8eS+xtdjeLdzbx0Zpq1la24drHStf+sBq0/OW80Vw2JZ0Ys47jCuJ4+fopIVkFe+Lzy7y4uJyVFcp3zC/DMwvKWFXRFrRdtFnLCcWhQnluvJgEjCQcbi+rK1r5aE01S0ub6XAcuc5Y5c3d/PSN1djdyrHe5fJy1xtrKO/xOFpe1srLSyoCbVx/3NnCOysqkffV13UfnD46kd+dUURShIHCRAv/u2oSY1P3ncmSYDMQt5dfgl6jIkWk7x8WOo2aDdXtQeeW77c1UiNag++XFbtaeGFxeeB7sKy8hdeWVQRdPwTDkx0NnfzqnbW4vErmapvdw51vrKZuH98Jt1fmgXNGc1pJIjFmHWeOTeLpKycRv0cZsNWg4bTRofOqeJuBmnZlwWRTbQf/+mY7Lq/oCnc0IwSYI8iG6g7sbh8FiUNA5eyqg1Uvwfs3KRklU29SWqYOAY5N0/DKRs/+b+x0ZsiYBStfHLzAjnLcXh8LtjeHjC8rbelj65HD/G2NnPXvRdzx+hqufG4Zd7+7jkYhwgxbVvSxGvXN5ga63Uo5T7vDw9++2MplzyzlzjfWcPa/F/HJutpDeq/sOAt/OHsUn981m/9cPjGoLrwv2h1uvt4cKipvqG4LeqzTqLntuDwmZSqdGfQaFb8+tZAxoiRjxODzy7y9sopz/7OYO99Yw8VPL+Hf3+3E7vIeeOdDoLbdGZh47KbL5aW+U5kkrCgPPc9/sbGeroOMJ85q4PpZ2XxyxyzeumkGJxQnoN+Pl0typJH/XDaB5AhlUhNt1vHfKyaQLTrRHRZur48FO5pCxlftahv8YIYRq/cSw0Ep/+x0egY/GMGAUtPmZG8draHT1WfZZFljF1c9u5TL/7eU2nYnZ45J4tZjcylMCjb3lSSJCyelckKRsmCiUUlcNzOTTbUdQV5Y329t3G8ppmDkI0qQjiBvrahkVm4MqiNtHOtsh85acLSBuxt8LvA4lPGuemgpBWcHJI6BKTeBed/pv+GgKEaFxy+ztNbHtOT9HJIFp8L3f4XZPxt0w+CjEZ1GzeTMKFZVBE9gx6WP3Elfc5eL336wIehC+c2WBjbVtHNMQfhK9QSHzrjUSF5kV9DY7NzYQNeCbfWdvLasIuj5+z/ayJSs6ENqPapVq4i39s8Y16LXMCkrmo/W1ASN5/WRmpwbb+G5qydT1WrHoFWTEWPed/c4wbCjvLmbP3+6OWjsqfmlnD4m6YBC3qEQb9WjU6uCfIuMWjVxPaVvo1JCu4ZMz4nGpDu0a+/BtGqdnBXNB7fNpKHTRZRZS0qkyH45XLRqFRMzolhX1R40Xpx05FuVD2eKkkLPxdOyozHpxT3ocCcxwoAkwZ5rvzFmHTGWUH+WRTub2dmTHbi6so3VlW00d7t59KJxaDXBuQwZMWb+ccl4KlrsaNUqGjocXPa/ZUHbjE+PFF5CRzkiA+YI4fL6+HhtDbPzjkD3I9kPNath4WPw1lXw7vWw6B+w8T0o+x6qV0LzDnB1gTUZSi6AY38NxWcOOfEFFMV4boaGFzYcQA2OzlaydrZ/OTiBCThtTBLFe9yATEiP5NgRLER0u7x9pmSLlYrhy9TsmMBqFEBOnIXLpqaj6hEv2uyhK5mdLu+grHDqNGpuPiabxIjeyekJRfFMzIjqc3ubUUtxcgTZcRYhvowwOh2ekIwU6Pv4HAiy4yw8dMFotGrlONJrVPz9wl6Pl2nZMczK7b1fSIsycuW0jEE77uJtBkpSIoT4MkB4fH7So03kJ/SWLU7MiMJqEELC/piSFcOxe3QxTYk0cO3MrCHm6yg4FPLiLdx/5qjAOc2oVfPIRWNJjjSGbFva2B0ytqWuE+c+yojMeg1FSTZy4y3kJVg5Z1xy4Llos45fnFxwyGK2YGQg/vpHiHkb68mIMQXVBh4+MuxarJQSIUPSOJhwNZhjh3175tmpihlvTZefZMt+Lmx5p8CSJ6Hw9MEL7ihmTEoEr5yfiKqxFSQVvtgEYpJHbgZMnNXAiUUJzNvUWxYiSQy4YatggOisg/qN4LZDfCHEhnabS4408vBFYylt6Mbt85Mdaw46L2fGmEIyAYqTbH3ehO0LWZZZX93OivJWtGqJSZnRFPVzZbk4KYL3bplJaWMXBq2a3HgLkYfYpUkwfEmJMpIaZaRqjy5zZp2atOj+H4f7pKMWd816HPYuanWZNOrSKEmN4MwxyZQkR1Df4SQhwkB2rCXQVj01ysQ/L53AjoYuPD4/2XFmkiIGIBZBWNBp1Bi1av44x0KWvxlZUrPRa8AjurHsl+RII49dMo4d9V24fD6yYy0HdW0QhInO+p57gy6IzVfuD/ZCr1Vz6ZQ0pmZF09TlIiXKRGZM34LvzNwYnltUFjR2wcRUrIZ9Z7HIssyG6nZW7GrlxOIEzpuQgiRJZMWa99ttSXB0IASYI8RrSysGNvulu1HJeOluhLyTlYnGMBdd9sSklZiVquGF9W5+M30/olXmLFj5PDRug7j8wQvwKKV911oi374YVbfSNcEXkUHb+a8RmV4S5siODEadmrtPKcTr9/PtlkbiLHr+eM6ofk+mBYNIWwW8cz1U9aT26ixw1YeQOilk00ijjgkZfU80cuMtPHv1JH79/nqqWh1MyoziT2eXHJQIsnJXK5c+swSPT8lltug1vHHTNEr6KVYmRxrFTf1RTpzVwH8vn8C9H6xnXVUH2bEm/nreGLJiD1P8bd2F/PY16GpWoQMi9Da65rzAi7vSuO34XPISrH2WvIGyUjsla2h4xQkOn1Pjmol452LlPhKIj8qm49xXANEJaX9EmXRMFt+D4UNHDbx/i1IRAKA1whXvQcaMkE11GnWIj0tfTMyI4nenF/HoV9twef1cNjWdM8cm73efVRWtXPr00sDijlmn5s2bpgvxRQAIAeaIsKu5m821HdxybM7AvGDlMlj0GKRNh9EXgWpkpj6enKXhvoVO7pyox6Lbh7ik1kLeibDsKTj9kcEN8ChEs/6NgPgCoG7fhWrrZzBCBRiAnHgLT1w2gbp2J2a9hoQBzWITDBgVS3vFF1BWur5/EC56GXT9FzMkSWJ2fhzv3zqTTqeHOKt+v6tae+Pzyzy/qDwgvoBiZvrlhrp+CzACAcDo1Ehevn4qzV1uIozag/JN2Se7FiPVrOp97OqgaOcz/NN5G7uak0UnraMEn8+PZu3LAfEFQNVaimrHPEgfFcbIBIIBpmZVr/gCiifmV/cpIozh0BbTIk06rpuVxcklifj8MskRxhDvlz3x+2VeWFwelFnb7fbxxYY6SoR5vgAhwBwRXllSwey82AGoEZVh3Vuw+WMYexlEZQxIfEOVeJOKUbFqXtvs5sax+7nxzD8VPr4T5t4HBnEiO1L4fT4sjatDxg1N68IQzeBi0mlE2dFQp60idKx+gyLEHIQAs5s4qz6k/W1/8MsyDT2dY/ZEdM4SHAoRRh0RxgEsC2kpCxkyt2wkJ5EDtkFt7nKxvacEKSdOlF4MZ2SfG3Mf13N908YwRCMQHEE6G0LHGreAq/OQBRhQFmv6m73il2UaOkLvC+o7nWyr76Cq1UFqlIkc4ed21BLWVApJktSSJK2WJOmTnsfRkiR9JUnS9p5/+3YiHMI4PT7eWVnJ8YUJh/dCsg8W/wt2fqt0Lhrh4stuzsjR8sxaN07vflpSm2MhZSKsennwAjsKUanVdBecHzLuzj0tDNEIBHuRPD50bPQFYBpco3GtWsXFk9NCxk8oPsxrgEAwEKRNDhlqzDqbZp+J9P1MJqpa7Nz22ioueXoJVz67jAueXMyWuo4jGangCKLRGeguvDBk3JN7chiiEQiOIHEFoWPF54J58BpIaNQqrpiWGTI+PTuGU/6xgOteWMHp/1zAvE11oTsLjgrCXcvyU2DPvov3AN/IspwHfNPzeFjx0doasmMtJEYcRtmCz6Ok0reUweTrwXj0ZHlkRahIt6l4a8sBus4UnQlL/gM+7+AEdpSyI2omjgk3gEoDah32KXdSag312BAIBp3USXDKg6AzK35Yo86FSdeHpUTT4fZxx/G5ZMaYyIu38MuTC+gahC5KAsEBSZsCJz0AWhNIEt3551Kadh53nVSAdT9tUH8sa2ZJaUvgcU2bk5cW78Ln38/iiGBIszP6GJzjrgWVGjR67NN+zk7TuHCHJRAMLEnj4Mx/gL4n2yX/FJj1U9AMbtvnOXlx/P2CMcp9QYKFRy8ayytLKvD7lVuWOIue+z/aSGWLfVDjEgwNwlaCJElSKnA68GfgZz3DZwPH9vz/ReB74O7Bju1QkWWZZxeUcc74/Rsz7RefG759ALwuGH+F4nlylHFOnoYnVrm5uEiHXr2P1LzYfGWle9MHyqq3YMDx+WX+s8LBqcW3k5V+CbIkscMdzdJ1bfw91FBeIBhcDDaYejMUnKqcNyPSFLO9Qcbt9fH2yirKm7s5riAer0/miW93cPKoBM4Zn9rv16nvcLC0tJU1lW2MSY1gana06DojOHwMETD9dig8E5/XhceYzESTGc0BSqQ3VYdmuywta8Hu9h6UR9LetNs9rNjVwo+lzWTHWpiRE0NmT+trwZHD7fXxzxV2ziy5i8zMK5AliW2uGNZt7mSsuJ4LRhJ6M0y8BnKOV/xfIlKVhZpBJsKk5cJJaZxUnIAkSaza1cKqilZm5cYyLTuGsqYuYi16GjqdpEULY96jjXB6wDwO/ArY034/QZblWgBZlmslSeozX0ySpBuBGwHS09OPcJj958fSZuxuL2NSIw/tBXwu+PoPgARjL1FWKY5CcqPUpFlVvLrJzXWj9+PJUHw2LHgESs4fkh2hhupx2l/UKomzx6dw5+ur8faseuo1DTxxWR+lH4JhyXA/RpEkiMoMawg6jZpTS5J46IstfLimJjA+J7//XfC6XF4e+nwr762uDoydPiaJv547Gtt+shSOBob9MToUkCSIzkQNRPZzl0lZ0Ty/uDxo7LSSRCz6Q79tlGWZN1dU8pfPehOfCxIsPH/tlGHvLzPUj1OdRs2541O48401gSwmg7aBf182IcyRCQaLoX6MDjiRQ+MzRvR0VEyKNJEaaWR0SgQPz9saeH7exnpe/skU0R3pKCMsJUiSJJ0BNMiyvPJQ9pdl+WlZlifJsjwpLm4AWz0fJk9+X8opJUmoDkUM8Drgq98rosvoC49a8WU35xdoeWKVmy73ftKdUycr6vaOrwcvsINgqB6n/UWWZT5fXxsQXwBcXj+LdzSHMSrBQDLcj9Ghwhljkjipx/NFkuDSKWnMzOm/F01ZY3eQ+ALw6bpaShu7BjTO4Yg4RsPDlMworpuZyW5/yDn5sZw/KRXpMBY7KlsdPPbVtqCxrfVdI8JbZqgfpz6/zEdraoJKyJweP8vLWvazl2AkMdSP0ZFOTpyZhy4YwytLdgWNlzV3s7lm+J8DBQdHuDJgZgJnSZJ0GmAAbJIkvQLUS5KU1JP9kgT0YWU9NNlc28HGmnaun5V18Dt77Ir4ojUpPgYjtM30wZAZoWJUrIon17j4xZR9+OlIKii5AL7/K+SeMCSzYIYzsgztjlAfi3bhbSEQBJEWbeKxi8exq7kbtUoiM8aMXtt/Ed3t67sbzZ4tLAWCwSTOauDuUwu5eHI6Xr+fjGgTlsMoPQLw+vx9HtNurzjOjzR+Waatj2t3h1P46AkEg4FGrSI1yoizj+5z4lp/9BGWmb4sy7+WZTlVluVM4BLgW1mWrwA+Aq7u2exq4MNwxHcoPPHtdk4elYhuP33h+8TdCV/eC3orlAjxZU8uKtDy0kY3NV37OTFlzoLuJqVblGBAUakkrpyudN/KjjWTEaOkR547PiWcYQkEQxKzXkNxcgQFibaDEl8AsmLNlCQHt8fMS7CQJbwxBGFEr1FTkGhlVHLEYYsvAClRRi6cGOyLZDNoyE+w7mMPwUChVau4dkYmoKzEp/d4Tpw+JimMUQkERxfJkUYunxrc1dasU4tz4FFIOD1g+uJB4C1Jkq4HKoDQnnlDkB0NnSza0cyjF407uB0dLTDvd4p5ZMGpIoNjL2JNKk7M1PDAj07+c+I+aiNVasUv5+v7FcMt8TscUGZkRrDmajOaTe8hq9T4zjoPQ7rtwDsKBMMBewuUL4DNH0FsIRSdAfFFgx5GtFnP45eM57Wlu/h+ayOz82O5YmoGcdbD6KYnODqo3wibP4Hm7VB8DmTMBFNUuKPqE71Gze3H55IWZeK91VUUJdm4aU422XGWcId2VDA7K4I1VxnRbHobWW3AN+o8DOlHT5dNwQjH54GqFbDhXeVxyfmKVYF66Ex1tWoVN87JJjHCwNsrqihItHDTnBzyhABz1CHJ8vBuKThp0iR5xYoVYY3hlldWEmnSctbYg8gM6KhWxJekMZB9nBAO9oHLK3P3D04eOc7IrNR9nERlP3z2Czj2HuWEO7j06w83FI7TQ6JsAbx0plKPBIrgdc3nkD41vHEJDoaRfYweDoufgHn39j62xMN1X0J0dljC8ftlOl1eLHoNatVRdU0Qx+ih0LQDnj9ZyQLdzWkPw5QbwhdTP+lweDBq1WgPNms4vAzv47T0e3j5nN7ruVqrXM/TJoczKsHAMryP0cOhfBG8eIYyJwDFpuCaTyFjRnjj2gcdDg8GrQqd5qj0/DyqbnD6Ylhd+YYi66vaWVbWwknFif3fqX4jfP4ryJwpsjYOgF4jcVWJjl/Pd+Dw7EMslFQw4Rr46j7wOAc1vhGNLMOyp3tv1gD8Plj/dvhiEggGiI66cvjhweDBrgaoWx+WeEAp+4swao828UVwqNStCxZfAL77M3TU9L39EMJm1KLVqOhwuPlxZzPvrapiaWkzXcJj7Mjg88KP/wm+nvs8sGnYVPoLBPtn1Yu94gso/1/18iG/XGOnk++3NvD+6irWVLbh7sO75XCwGbVHq/giYOiVIA0rZFnmj59s5NzxKRj6W/O/9QtY9QKUXAhxeUc0vpHChAQ1P1areGiZk/tn7qNVZdIYpR3twsfguF8PanwjFlnG73WFqLQ+jxNxyRAMZ3Y0dLJmYw0X+PswoPQP7E2WQHDEkPvwR/N7gyfZQxinx8vT88t44rsdgbFfnJTPjXOyxcTkCOD3uUOu516PU0wEBCMDr7t/Y/2gucvFb97fwFeb6gFlnfyJSycIzyTBgCEyYA6DLzbU0dDp4tiC+ANv7HUq4sCGt2HyDUJ8OUiuKtHx8U4vi6v349g/8TpY+iQ0btv3NoL+o1LRWHhV8Jgk0Zh9TljCEQgGiu+2NPLvVU4ax9wc/IQhEhJKwhLTvqhssbOmso3qVke4QxEMNRJKFAP/PZn1M7AlD1oI3W4vm2ra2VzbjsN9cOLlzoZu/v39jqCxx77eTmlj90CGKABklZrG4muDByWJ5ozTwhOQQDDQTLy6j7GrQsf2oKHDyZrKNsqautjTkmNTbUdAfAFF077vww3UtovrsGBgEML3IdLl8nL/xxu5cU7OgdPFW3bA9w+BNRGm3gwa/eAEOYKw6iRuHKPjrm8dfHq+mThTH9qhJV4x5P3gZrhu3pAy3hqO+PwyS/yFTDjtZRI2v4BfpaWx6GpWuLI5N9zBCQSHwc7GLsqaHTxtP5YrZiaSUfEuXZFFGKddhzouP9zhAUqG5XdbG/jZW2tps3uINuv4x8XjmJ0fF+7QBEOF+EK4+mNY8QI0boEJV0HeiYNW1lzVaucvn23ms/V1AJw7PplfnlxIcuQ+MlX3osPpDUnW8fll0Rr5CODx+1kqFzP+1BdI2PwSfrWexuJrWOHKEtdzwcggfTpc8Z6yECupYOpNkLZvv8K1la3c+upqqtscGLVq/nTOKM4ck4xeq6bdEVoK2dztxn6QIrNAsC9EBswh8pdPNzMq2UZx0n46wvjcsOol+PK3Srvk0RcK8eUwGBOvZlaKmtu+cuDx7SPFuuA0kAn1dhAcNGqVxK4OmWM/0HCF/edc1nkHc96VaXMLfwrB8GZuoZK1+Mzqbk5bkMmV/vv5Nudu1CnjwhvYHpQ327n9tdW02ZUbwZZuN7e9toqKZpEdINiD5PFw1j/gms9g/OXKQsQg8dWm+oD4AvD+6hp+2NrY7/3Too1Em3VBYwk2PWlR/RNwBP1Hp1azo83PsR/qudL5Cy7tuI057/jo8ojruWCEoDVA7ly49E245HXFY1PbdyfBlm4XP397LdVtSkaLw+PjF2+vY1tDFwDZsWY0ey2uH5MfR2KE6EwoGBiEAHMIfLe1ga8213PZlIx9b1SzCj68TTHcnXabcpMkOGzOL9DilWV+t9BJnx28JBXMugtWvgDb5g12eCMKn1/mh22N+Pwyy3a1saqiHVmGpaXN4Q5NIDgsJmdF8/szi7HqNXj9MqPTYpiaHR3usIKobXeErLZ1OL3UtgujcUEfqAfXM0WWZb7YUBcy/vXm+j627pvUKBPPXj2J0SnKQtb4tEieuWoSSf3MoBH0H7fXx/dblev50vI2Vld2IMuwYldruEMTCAYWlUr52Q+NnW52NIQuZlS22AEoSLTxv6snkRplRJLgxKIE7jujGLNOZNYLBgZxJB0kVa12fv7WWm47Lhezvo9fX3sFLH8OWsqUbIyEosEPcgSjkiRuG6/ngR9dPL7Cxf9N7kONNkbBnF/B+zfBVR9A0thBj3MkoFZJzMiJYeVeN2iTs2LCFJFAMDBEmnRcOzOLU0Yl4pNlkiOMqIZY56FYsx6tWgrK9tNrVMRaRBalIPxIknJ9WFrWEjR+sELm+PQoXvnJVNrsHqJMOmxG7UCGKehBp1EzIyeGdVXtQeMTM6LCFJFAED6iTFoSbQbqOoIXNOJtyvVVrZI4tiCeD2+bSbfLS5zVgFEnjMEFA4fIgDkIOpwernthOWeMTgotPeqohgUPw6e/BHMczLxTiC9HCJNW4ldT9by73cNjy/eRCRNfpNR/vnI+1G0Y/CBHCGePSyE3zhJ4PDolIlC+IRAMd5IijaRGmYac+AKQFWfmgbNL2B2aWiXx1/NGkxVrDm9gAkEPZ4xJJj+h9/pQkmzjxOLEg36dCKOOjBizEF+OMOdPTCV7j/PHuLRIjhGeUoKjkHibgb9dMAaDtncafMfxuRQmBM/tYix60mPMQnwRDDgiA6afdDo9XP3cMrLjLJxSsscNRtNW2PAu1KyF9Gkw+2egFemzR5pIvcS90w38W8qmFgABAABJREFUbamLOrvMn2YZ0Kn3mkRlzFTadL50Fpz/LOQcF55ghzG58RZeu2Eq2xu6UEkSufEW4qxiBV4gONJo1SrOnZDCmNRI6jqcJEUYyIm3DEmxSHB0khNv4ZXrp7KjsQsJidx4M3FW4ZEwVMmLt/LajVPZ2dCFSlKRl2ARGXWCo5bZebF8esdsKlrsxFh05MZbMIkSI8EgIY60flDZYucnL64gM9bEldMykDzdUL4Itn4GjlZFeJnzc9CIG4/BJFIv8bsZep5c7eb8D7p5fK6RnMi9VOrM2WCIgHevh4nXwDF3CyPkgyTeZiDeJo5tgWCw0WnUFCXbKErej9m7QBBGxPVheJFoM5JoE4uEAoEkSeTEW8iJtxx4Y4FggBECzH7w+WXeXlHJQ19s4YziaE61liN99wrUroWYHCXDIjb/gGZPgiOHUSNx1yQd88q9nPd+N5cV67hprJ5Iwx6rxIlj4PTHYNmT8MQUOP63MOpc0aZaIBAIBAKBQCAQCASDhpiB9kFrp53Plm7kuWV1aH12fmn4gaxNmxXRJbYAck8EnSncYQp6kCSJk7O0TEpU8/42D7Nf7+TULC1n52qZlKRGr5bAFA3H/gZq1sDif8K8e5W24AWnQeokkRUjEAgEAoFAIBAIBIIjysgWYNzdsOMb8HvA7wOfB3wu8Dgpa/OwvEFFp9NDu8NLvR1KHSY2epKwYyBTquU4UxmjozxgLaDWegyoespb2h2AI6wfTdA3pyfDzBiJBfVebprnpssjEaX3UxDhJ93iJ86QQmTkXVgsLei3bCZx6W+Z7luFpDNBTC5EZoA1AYzRoLcqHZVGnQt6kaIoEAgEAoFAIBAIBIJDR+qzg8wwQpKkRmBXX8/dPFEb/d8zjFl9Pfd/7lt43z+7z9eMk1tQ4x3QX4wEkgxh/WUPhRgGOw47RqlDsu53m2/8P/HmmOz7FCPPf8u+/b3N3o59PN0ky/IpB4pjf8fpMCIWaAp3EIPMSPjMh3uMDoXfgYhhZMcwHM6jQ+F3PxCMlM8Bg/9ZDvY4Hcq/axHbwTNU44Le2IbDuXRPhvLvdCARnzOYfh2nI5lhL8D0B0mSVsiyPEnEEP4YhkocQyGG4cbR+Ds7Gj/z3gyF34GIQcQQbkbK5x4pnwOG/mcZyvGJ2A6eoRoXDO3Y9sdwjftgEZ9TsDfCPVYgEAgEAoFAIBAIBAKB4AgjBBiBQCAQCAQCgUAgEAgEgiPM0SLAPB3uABAx7MlQiGMoxDDcOBp/Z0fjZ96bofA7EDEoiBjCx0j53CPlc8DQ/yxDOT4R28EzVOOCoR3b/hiucR8s4nMKgjgqPGAEAoFAIBAIBAKBQCAQCMLJ0ZIBIxAIBAKBQCAQCAQCgUAQNoQAIxAIBAKBQCAQCAQCgUBwhBECjEAgEAgEAoFAIBAIBALBEUYIMAKBQCAQCAQCgUAgEAgERxghwAgEAoFAIBAIBAKBQCAQHGGGvQBzyimnyID4ET/h+ukX4jgVP2H86RfiGBU/YfzpF+IYFT9h/ukX4jgVP2H86RfiGBU/Yf456hn2AkxTU1O4QxAIDog4TgVDHXGMCoY64hgVDAfEcSoY6ohjVCAIL8NegBEIBAKBQCAQCAQCgUAgGOoIAUYgEAgEAoFAIBAIBAKB4AijGaw3kiTpOeAMoEGW5ZI+npeAfwCnAXbgGlmWVw1WfIOF2+tjbWU7S0qbsRm1TMuOoSDRCsDWug62NXSxsaqdMzI85DnWoWqvQJU9B6lmNVJnDaRMwqWPZq0jjgRVB4mtK8Dnpi5mChvkbMwGLXHeBpLaV2PuLMWZMJH5jizc2kjUKpkZ1kaMNT+icnfgS5/FVnUuS3Z14fD4mJ0bg9sns7y8heJoiWMi6jA56pDaKiEuDxq3gt8LMbmAhN8QQWdzLa3R44mQujHV/AgaHdrkcahqVkB3E6RNRva68JgSaYyeSKqqBWfpImjYgi91KjqdFnXFYtDoUeUeD34flC0Agw0yZ0NCcXj/YAKB4MjStAN2LYTWXRBXAEiQMApi86FmFbSUg88JzaWQPA7Sp4EtObC7y+NjTWUbNW0OdGqJU6xlqCp/BHcXpE/DL0uo3B3QVgk+F8TkILscuONK2Gi3Mdq3CW31UrAlI8cV0elWgdeOBTv/z95dhzd2nYkf/x6xZElmhvEwYwbC3FCbpCmmKaaQ4rbd7Za38Nvytt3CljEppE3SpG3SBhqmgcwkw+AZj8fMbDGd3x93xmPZHrYl2X4/zzNPoqOrq1f28b1X7z3nPQT7UYNNULAArTXKloU/biHhyMHa/jJKmQiUXcghVc0qSz3WxheIKBtUrscS7Mbcvgt0gsHyi/l9YwHZbheLS7yYg13MCe7B0b2bQO5C9pqX4LcXkZ9l5Zmabs6fk08wGmd7Yx8ep5WKHCfZTismk2LrkV7cDiul2Q7sFsWaqjxs/lZU02ZMXQdIlK1GVW7AnV+art/o9BYegqat0LgJsiuh+iJC3llsb+xny5Feso/+vo509vOGkk6yOzZjdmZD9SXE8xcQrN+Ks/k5FKDz5xN35GJVcWjcAjmzwGSGtp1QMB+dXQUDTSiHl2hHDZbKNdB1EPzdJGZdRMCah6v5OUyJGLp4KdpsJ9rwEspTQkv2GjYP5FOa46A8Wk/1wFas4V506Uq0xUmsZQeYrZiLlxBv2U40dx7hsvX4LPkcbutlMYcpVn3QU0siHmOg9GIOmOYzp8hLWa5r/J9NNAjN26D+RfAUwayLoXBBKn874iRCwQHsTS9B40YwW6HqAtTcK9IdFgCDzQfI6tiG6thFonAxwZL1eCqXpjssAOJ1z2Nq2gwRH7rqAoaK1pGdm5/usDJPoBeathjHgIL5MOtCyKmCoXZo3Gwc12ZdZJyb23cTyV9IrWMZsVicpYkaTG07UHOugPAANG9Fe0qgZAUd5lL0UCfFdIOvwzi2uMtQ4QFoegnMVhKzLuK58DwudhzB0roNhjrQleezKbGYhM1DdaSG3I6XMNsctOWupc48G184hjPcy/nWQzi6dhHMX4Kv6DwO+N0c6Q7QMRTizYusFPfvwtS+k2jRMnrzVlMfduOPJqjt9GE1mZiV78JmViwtz6a5L0htl4+G7gCz8l04rWZm5bswmRQvHuohgWZRiYeqvCwWl3owvvqKmShlCRjgTuBHwO9O8Pz1wPyj/zYAPz3632ll0+Ee3nXnVvTREkQ5Liv33nEBsUSCjYd7+NrD+/n3DR4WPPN5bF274HW/hL9/CPobhvfhuO6bnNf9MBZfOxx4EIBqkwXra+/j4ToXFzR+AUfbS8a2wEXr/pO31lzM965yUvzArRDsM3a0UZF7ze/44ZMWElpT6LbztYf3k59l428X1OEOB+CJL8G1X4P73wvxiPE6ix2u/ALm1u3kFMwje2AL6rHPQjwKl34S7n8X+LuOfmBQN/8Y+4MfoPimnxJ/8Xs4jjxtPHft1+HxLxpJHbMV7B741+cY/uE4c+FdD0sSRojpqvcI/OF1Scc3rvgcPP1VuPH/4LlvgbsE9j5w/Pmlt8CNPwBHNgDPHuziz1ubSCQ0v7osjPm+tycd48xvuBMe+TQEe402ZUJd81Xsz3+N1fOuxvTY54Z3rXJnk/3q70Dd48ZFZMee4+97/geh9ik8V30B7nvD8PHQaXGQ/cY/YLn7VkjEcF74b9C2DZ75uvFlHcgxfYdXXfMHbvi75t0bSvmU6Q/YXvk1YByjV8++ni+pD7JhyRyerulkdmEW/3nfruG3rspz8sHL5/HZB3YPtxV57LxxbSWhwW6urfki9vqnhp8LrP0QkVd9EZvdeba/GXEie+6Hhz52/HHBAlqv+z1v+dXh4abyHAd/uy5C3r1vSTqf6df9Gvef3mSc8wCsLsy3/BweeN/RpON82Pnn4f2oOVfCkpvh/vdgu+xT8OC/DZ9bTZt+iPear8IL3zESHxYHXPlfWDZ+G4DZ7jIsN93DvubDLNz67uN/E9d9A/71BSzHYrBlYb30UzgefA/W5W/lsaJ/Y4PaS5lugBe+D+FBzECBMrH4dffx2y0VfPTqBdgs4wygPvQvuPcdxx97yuBdDx29aSPSzd60GfXn25L6n7717rQnYQZ6O/C89D1Mu4y+bwLc869l4OrvkF1cldbYEnXPY773bRDqB0Bt/CHeN94FuTelNa6Mk4jD1l/B01873lZ1gfEd5pmvw467oWwN+NqN/wdswNzqq7CufD2mv38I5l9rXPc/9d8AKIDCRRRf8xWU7yA8/Q24/DMQGgSTFe5/t/G9AzBbnVz21gcwPfA+GGwxXr/5x1x44w85GCih8rG3GDECc2xZNF/8eyyuWZzf+Auy9/0eADvgXHgzd7s+wk82dfHapTlU7vgjjt1/HP5ItgWvxbfuK3zwj3uJxBPG6ywmPnHNQpr6guxuGeTBna3D21++oJBXryjlsw/sJpYwzgVOq5l/f9V8IvE4qypzJ/o3IaaIlE1B0lo/B/SeZJObgd9pw2YgRyk1rW7h+cMxvv/EoeHrMYD+QJSdzf0cbPfx1+0taA2XeDuM5AsYF/Ajv5wAPP8dLGUroGTE3YFEjKJ9d3GRt3M4+XJM/is/5AMrLXjbNx+/CAPQmuo9P+bqeR7WVefxxP4O4gnN+1baKRvaBTv+CIWLoPnl48kXgFjYyGQfeRaceai6Z4yDoMkC6OPJl2M2/hDO/xCW/X/FPNhotJWtgSPPHb8QmHMF7PkLST+cYB80vHjaP18hxBTTtmvs8W3rr2He1VD7hPHfkckXgL1/he5DAAwEIvzPYzWsrMimfTCIuXnsMY5NP4aK80a0JaDmYdTa92B67tvJ++47Av2N4ClNTr4AvHwnLLoB9j0IeXOOtxcvxfzKb44fy7IKjf0cTb4AkIgzr+ZXXDTby1pPL7btv0natfvII9xc4eMnT9fylZuX8cvnjiQ9f96sPH7+7OGkts6hMBaToiBYn5R8AXC98nNCHYcQE2ygBZ74cnJb90HirbuSmlYW28jb9r3k81neHEzbfn28nwB4Sox+HgsZfX3Xvcn7rnsK/J3GDQqdGHtu3fFHWHCd8f+xELTvHu6bJl8r1q69LI/tOf43UX4eHH46OYaI3+iv3jIcu//Iancfs2t+Bb5OCA8e304n8G77P3Q8wsH2Ee3H+Hvg8S8ltw21QuuOsduKlIuFfMYxbOTvPhqAmkfTFtMxjt6a4eTLMerQYzj7atIU0Yg4Gl8cTr4Axt/hph/j7+9IW0wZqfcIjD6fNm6C9l3DCRfmX52UYAaw1z+JafBowmLlrfDC/ybvo+sAqqfO+B4y5zLYfR8UL4Pd9w4nXwAoWozq2j+cfDlGHXyMeYd/O5x8ASDiZ8nQixRFmoaTL8Px1Pyd9Z4eAD64TCclXwCyDv6N/FDjcPIFIBxLsKdlAFBJyReAvkCEx/d1DCdfAILROHVdfp7a14mYuTKpBkw50DTicfPRtjGUUncopbYppbZ1dXWNt0lGisUT9AUiY9rDsTjheJzBkHEwsekR28THbk94CKxO0PGkZnOwG7sOj90+HsFl0VgjYy+arJE+su3gslkYChknZo9VG8mU8CDYsozhgGNiGASzxTgZhY4+b7IkHxCPCQ2CzQOBbrC5jTZbVvLFnc2d/Hj4teO89xQxVfvpePa3DfKDJw5R2+lLdyhiAqW9j8aCY9vCgyOOBycYnhsLARCNawaDUeJaU+x1nOAY0n/8uDPcNghmmzEUerR4ZMyxdfg9TRbjy6wt63i7zZ2c9FEm40vtKOZQD16bwqqjyV/Mj7LqCEOhGCalGAolH0ezbObh4/NI0XgC63jH/EQcHRvn3DEFpb2PjpSIjttnTPFQ0uMcu8IcGnW/yeZGBUe3ZUHgWN/Rxvl0vPc0WWC832doMLkvHvvbORZXIow1OiJem3v8c2p4CKzGtCK7imNORMbvw+FevHaSvnwkxTnetUI0MLZtGsqofjoOpSPHRwGOFOob25Zq0dD47bEM6DuhsecUFR7AFB3nuJvhJrWPxiPjf18Z/bsd9xh37HVq/OPFsWOLLev472P0cczmNm4Oj8MUGNvvbeE+TInxz5HWhLEfqz7BOTQ2tr8OhWLjXq647JZxv/f5wjF8kbHndDFzZFICZrwr7XHXCtda/0JrvVZrvbawsHCSw5o42S4b771kTlKbScHiUi9zCrK4YZkx4OegrgC719jAU2YMLR5p1dvQtU8Zd5xG6F92OztCJeDKS2oPzb2Wv9RCX/EFMGq+YePCd/NIbYAtR3q4anERAHfXJBhUXlj6OmOkS/UlYz9M1YXGXeJ4DI4NX42FjOGDJnPytqvfDq/8lsTSNxh31QCaX4K5Vx7fpu5pWDxqSKdSUH3x2PeeIqZqPx2tpn2IW3+xmT2tA7zxZxtp7MmAiyIxIdLeR4uWGlMaR1r+Jqh52Dg+dNcYd7tGyp09PKWhwGPnPZfMptcfpa7bj666cMwxjvNuN+76j7Twetj9F/TK25LbrU7IqTYuEh05yc/Nv8a4o7f0tdC243h702YSK996/HEsBEWLx3zU1kW383Sdj+2+XBJla5KeS2TP4hV/AW9eV8nfdjTzpnWVSc8/c7CLW9cnt1lMCrfdQqulkoQnebBotOJCbAXJ55qpKu19dCRvOZz37uQ2qwtdmPz7/lddkMFV70/ervklEqvfkdzWsdcYVQVGLaRR/QJvOTjzjSlGrvyx59alt8Chx48/rjz/+Mgts5Vo3gIaPauP/000bYH5rxr7uUpXQU8t8aJlbB3w0Fb1aqMG0ygDK9/Lga4YcwvdY/fhKYHzP5TcZrZCyfKx205DGdVPx2F25sHo4x3AolenPphRInnz0MXJ/UTnVhPJW5imiEaovnjMOUWvfgfOwvROjTobk9pHc2fBwlF9yZVnnAuPHR+7aqBibXJMnjJ0VrHxoHU7LHt98j7snqO1ti6B2ieNKZm+Lljy2uTtmrYY9abMtuT9FyykbdGo4y5QX3glPbYKosUrktoTubPZHTJ+Nk91uokXJtchiufOJeSdPWZ/62fn4g/HWFLqSWrvHAzxmhVlY7ZfXp7NJQsy7zghUkfpce7ETdqbKVUN/OMERXh/Djyjtf7T0cc1wOVa67aT7XPt2rV627ZtkxHupOjxhXl0Tzu/efEIeVk2PnrVfM6fk08ioXm5oY/NR3p4+kAX/3NhnAUHf445HkGvew9s/glqoBmWvJZ42XkcCOWSHWmnbMcPUIkorcs+wDbbOpyePKoitVTt+zmurp0EF97ME47r2NTv5ZLZXs437cez5X+xhHsJnPdBanIv47+f7GAgGOVjV81jKBznzo31fHi1jVdn12PrrTGG5JetQe/8E6BQK96EjsdQhQuIHHyC/vmvJyvYimvLD8BbAWveZsTr74QVt6I9JcSVhfbiyyilm8SLP8TaspXouvdjtrswvfRztMmKuvILxvDBzT8GZx5c9mnjoGuxnfLnmkanVUFrqvXTkd7w042sqsrhqkXFPLSzlbaBIL+9fX26wxKnL3P7qNbQtBme/R/oq4eFNxjJ57LVULoSDj52tGDfTmjYBLMvgfM/nFQXqnMoxKO729BAiSPGNbY9sOlHqPAAes07IbsKNdSG3nUPKuKDZa9HR/zEZ11KczyHypZ/Yt73N3ROFWrD+wnFNapjH3ZPPvrQ46iu/ei5V0J2JUopomYnCasb20s/AZOZofX/zmHHUpaEXsG++Qck3GUkLvwo5r7DqO2/h2iA4Hkf5DtNC2gNO3j7BbMojzVRuP8PuOofx192Prsr387LwVJWVGTz2b/u5rPXL6K+x89fXm4hP8vGTavKcNrM9Pkj3L2lkSKPnRtXltE5FOKWVRUUBWsxb/kRztaX8M+5jsR5t+MpH5sEymCZ20dH628yhtHv+KORpLj0P+nMWcFDO9v4/aZ6SrMd3LKmgt7OFt7s2U3uzp8Pn8+CBctQh5/EseWHaLMNteLNJLJKMMXD6C0/hVW3QVcN6vBT6LLVsOo2VNdB9GALqm0netVtsOOPKH8niVXvIFG4CPPTXwGlUOfdTgKFadP/EfdW0Lz839gUW4CJBOvZS9XuH2EK9cD5H0In4qhtv0ab7eg178R06DFCrhJCq99Hg6mCxsZ6LjbvIRff8N/N0HkfYrvrQvILS1halj3+z2awHfbeb9SCyK6Eyz5l3KwxZdK9vnMydfrpOOItuzG1bD76u7fB+R8kXrIGS3H6Ex3h+pew7rgTU/0L6PK1RNfegW32+ekOi4GeTrxtL8Cm/0OFh9Cr34m/+mrcmXt8TV8f7a2DV34Hex6A8jVw0ceNwvmdB2DLz4wbIVd8FlpegYOPEq84nwNz3onHYaai8UFMNf+E678D9c/BfmOqrz7vdkLuCiJtB8iOdaGH2owCvJXr0B17UNt+gzZZ4IIP87JpNWtsdZg2/giGWtHL30hT6bUcDnpYOPQiZbt/RsLqomPVR3khsYyeoGKRrZ31Xffjqn+SYMVFhM77AM/05/P3Ha3Ud/v5w2tzyd9zJ86Gp/CXX0xgzR08159Pty/CX7e3YFLw+jUVROMJLphbQHNfgCf3d/JSfS+rKnNYOyuXHKeVqNb88rk6rGYTr19TwewCF+tn55NlT2Up1owy46sPZ1IC5tXARzBWQdoA/FBrfcpveZl6ojuVgWAEq9mEy5b8x+cPx/CFY3gcFlymOER8BMweQv5BXAk/2D0Mxa1YrHai8QS51gihSJTeqB2Pw4JSJqKJBPFICK8pzCBuzCYTVouZQCSKyaSwxkPYSGBx5xCOxvGFYzgsZgq9DnyhKEOhGPFEgjyXlbC/D6tKgAarKU48YcKiNLYsL4l4jMGYmZiyYTEpslQIk1IMJhx49RDmyCBRi4tITGPxFGK3Hr17F4sQDgwStXqIaYU72o3F6jg+cifYb9w5Gzm0OnNN6QuyU9ne2McH//AK33njSswmRSSW4GP3bOcvH7iQeUXj3AUVmSjz+2g0aAxVNpmNKTz2EX0r2G+0xyNGcsZsHXcXA4EIZpMiGtdkxXoxxcIEnKWY42FUsAeTwxhVaIr7UVYPVncO/nAMndCYfC1om4tAKIY9Kw+TxYw7McRgRBMOhbHa7GgS2JQmYnbQF1aU2CLEtYmYNQuX3YzdYobQIAkUvVErpngYa8yH1eYkYnHTE4iQ67ShlDFn3G7SWKJDRMwuhqJQle8mFk8wFIrhdliwmk009fixW81YzSY8DgsWs4mBQASTSRGLa7xOK2aT8euNRcKE/QM4PHmYLVPuoi7z++hogV5j2o71+AjVgUAEm8VMXCcYDEYp8TowhQfHnM+CQ30k4hHCMU3c6sWZ8GHVYbTWDJBNLv2YsgoIBX3oeBRXTgl9vZ2YHW7MMeNvxVs8i7b+AG4VwqJjaKVwZRcSGOjGbHXgS1joD0SZleekuT+E1xLFbYoRiCZQzhwsUR8aExGTDXNkCKcnF5vNGI0WiSXo8YXINweJxeNEEoqgOYtijwPT6SRTAn3GyDbbCVZLmrqmXj8dR7zrMKAwF2bWKLlgwE9sqBOzuxBXVmZdX/i7mzElYjiLqtMdyqmkt49qbUx1s7mTR7fGY8Y0IrvXOMeH+sHmJYyJQCROrt0EvjawZBkfIdhjjOJzFeOPKRIaPPghESeqrJCIYbOYMYV6iZo9mDwFJLQmEI6THe/DFA8ScJXTPhDCYTMRS2gcMT9Om40BbSfbYcUfjuILx8l3mkiEBtB2D9pkBW2UhkhoyHFaiMfjEBrA5PAyFIMcp41AJE5caywmRTyuKfI6MJkUWmv6AhHC0ThWs8JqNpPtMm4iDwWjhGMJHFYTbsf41zEziCRgUpWAUUr9CbgcKAA6gC8BVgCt9c+OLkP9I+A6jGWob9dan/LokOknOjHtTYsLshP57AO70BpuXnW8HNOftzZS4nXw2Rsy9g6QSDat+6iYFqSPiqlA+qnIdNJHxVQw4xMwKbtNprV+yyme18CHUxSOEOIUYvEED+9u5ys3Jw9YO39OPv/35CE+c/0i1Oh6G0IIIYQQQgghxjVtJuYKISbWjqZ+Ctw2Cj3JRVJn5blIaDjYISsiCSGEEEIIIcTpkgSMEGJcTx3oZHn52GKLSilWVGTz7MHONESVWqmskSWEEEIIIYSY3iQBI4QY1wuHuk+42sWysmyePdiV4ohSp7U/yHXff47FX3yUu7c0pDscIYQQQgghxDQgCRghxBiBSIxDnT7mF4+/EsHCEg87mvqJJ6bfCBGtNf/2p+0sK8/mG7es4NuP1bC9sS/dYQkhhBBCCCGmOEnACCHG2N7YT3WBy1hadxxep5W8LBv72wZTHNnke6ami15/hFtWlVOS7eAN51XyrUcPpDssIYQQQgghxBQnCRghxBjbG/uYWzj+6JdjFhR5eGUajgz55fN1XL+sBJPJWOHp0gUFHOzwUds5lObIhBBCCCGEEFOZJGCEEGO83NjHnIKTJ2BmF2RNu6k5HYMhdjUPsGF2/nCbxWTi4nkF3LutOY2RCSGEEEIIIaY6ScAIIcbY3TzAvKKsk24zp9DNzqaBFEWUGo/tbee8WbnYLMmHxvPn5PPI7jZZFUkIIYQQQghx1iQBI4RI0jkUIhxLUOC2n3S7yjwnLf1BApFYiiKbfI/v62BVZc6Y9up8F8FonMNd/tQHJYQQQgghhJgWJAEjhEiyt3WQ2QVZKKVOup3FZKIqz8WB9ulRGyUUjbOtoY8VFWOX3lZKsaoyhxcOTd+lt4UQQgghhBCTSxIwQogke1sGqMpznda2VXku9rVOj5WQdjT1U5nrwmWzjPv8ohIvz9d2pzgqIYQQQgghxHQhCRghRJJ9rYOnnYCpzHOxu3l61IHZfLiHRSWeEz6/tMzLS0d6SSSkDowQQgghhBDizEkCRgiR5ED7EJVnkICpmSZTkDYf6WFh8YkTMDkuGx67hcNdvhRGJYQQQgghhJguJAEjhBgWjsVp7g9Slu08re0rcp0c6hqa8qsDxROa3S0DzCs++dLb84s9vNwwvZbeFkIIIYQQQqSGJGCEEMPquvwUe+1jlmE+Ea/Dis1som0gNMmRTa7aTh/ZTiteh/Wk280pzGKbJGCEEEIIIYQQZ0ESMEKIYYc6fVTknt70o2Oq8lzUdEztaUg7m/uZV3jy0S8Acwrc7Grun/yAhBBCCCGEENOOJGCEEMNqO4Yo8TrO6DWlOU4Od07tuii7mweoyss65XZVeS4aegKEovEURCWEEEIIIYSYTiQBI4QYVtMxRHnO6dV/OaY028HBjqmdgNnV3M/sglOP/LFZTFTkOtnfNj2W3hZCCCGEEEKkjiRghBDDajt9lJ1hAqY8x8mhzqk7BSme0Bzs8FFdcOoRMGCMgtknCRghhBBCCCHEGZIEjBACMBIRTb1BynLObApSeY6Tui7/JEU1+Rp6/GQ7LbhsltPavjLPxd4WScAIIYQQQgghzkxKEzBKqeuUUjVKqVql1GfGeT5bKfWQUmqnUmqvUur2VMYnxEzW0hck22XFbjGf0euynVZi8QR9/sgkRTa5DrQPnVb9l2Nm5bnY1yoJGCGEEEIIIcSZSVkCRillBn4MXA8sAd6ilFoyarMPA/u01iuBy4HvKqVsqYpRiJnscLfvjOu/ACilKMtxUtc9NUfB7G8bpCLv9D93VV4WBzuH0FpPYlRCCCGEEEKI6SaVI2DWA7Va6zqtdQT4M3DzqG004FFKKcAN9AKxFMYoxIxV1+WnxGs/q9eWZjs4MkUTMPvaBqnIOf2lt90OC06rmdaB0CRGJYQQQgghhJhuUpmAKQeaRjxuPto20o+AxUArsBv4mNY6MXpHSqk7lFLblFLburq6JiteIc7JVOunhzt9FJ/hEtTHFHsd1HVNzZWQDnX4qMg9s5E/lXkuDrZP3cLDx0y1PipmHumjYiqQfioynfRRITJHKhMwapy20WP4rwV2AGXAKuBHSinvmBdp/Qut9Vqt9drCwsKJjlOICTHV+mldt4+S7LNPwNR2Tr0ETCgap30gROkZfu7yHCcHO6Z+Amaq9VEx80gfFVOB9FOR6aSPCpE5UpmAaQYqRzyuwBjpMtLtwAPaUAscARalKD4hZrTGngAl3jOvAQNQku2goScwwRFNvrouP6XZDizmMzsUluY4ONAuhXiFEEIIIYQQpy+VCZitwHyl1OyjhXVvBR4ctU0jcBWAUqoYWAjUpTBGIWakcCxOty9CoefsasCUeB009QWmXGHaQ51DlJ3h9CMwRsDUdk7NmjdCCCGEEEKI9LCk6o201jGl1EeAxwAz8But9V6l1AeOPv8z4CvAnUqp3RhTlj6tte5OVYxCzFRNvUEKPDbMpvFmCp5alt2C1WyiyxemyHN205jS4XCnj5KzqHtTnuOkrtuH1hqjZrgQQgghhBBCnFzKEjAAWuuHgYdHtf1sxP+3AtekMiYhBDT0+M96+tExZdkO6rsDUyoBc7DTx7xC9xm/zuOwYjGZ6BoKU3SWhYuFEEIIIYQQM0sqpyAJITJUfU+AorNcgvqYIq+Dhp6pNS2nttNHWc7ZJZ4qcp1TsvCwEEIIIYQQIj0kASOEoL7bT6H73BIwBW7blCrEm0homnoDZ7wC0jGl2Q7quqdWwkkIIYQQQgiRPpKAEUJQ3+On+Byn0hR7HRyZQgmJ1oEgHocFh9V8Vq8v8jg43CUjYIQQQgghhBCnRxIwQggaegIUn+MUpOIpNgWprst/1tOPwFiKWqYgCSGEEEIIIU6XJGCEmOHiCU37QOici+cWeew09QUnKKrJd6Tbf1YrIB1Tlu2krmvqJJyEEEIIIYQQ6SUJGCFmuPbBEB6nBZvl3A4H2U4r4VicwVB0giKbXIe7fOeUdCry2OkcChGJJSYwKiGEEEIIIcR0JQkYIWa4xp7AOY0EOUYpRYnXQVPv1CjEe7jLd9YFeAEsZhMFbjtNfVPj8wohhBBCCCHSSxIwQsxwTb0BijznVv/lmKIplIBp6AlQcg4JGDBWQqqfQoWHhRBCCCGEEOkjCRghZriGXj/557gE9TGFbjuNUyABE40n6BwMn3PiqWiKrfwkhBBCCCGESB9JwAgxw9V3T9wImAK3fUokJJp6A+S7bVjM53YILPbYZSlqIYQQQgghxGmRBIwQM1xjb+CcV0A6pshrp7En80fATMT0I4CSbAf13Zn/eYUQQgghhBDpJwkYIWa45r4AhRNVA2aKLEVd3+OfkFE/xV4HDVNgypUQQgghhBAi/SQBI8QMFojE8Efi5LisE7K/Qo+d9oEQiYSekP1NliPd/gkZ9VPottMlS1ELIYQQQgghToMkYISYwZp6gxR77ZiUmpD92S1m3A4LHUOhCdnfZDnS7ad4ApbelqWohRBCCCGEEKdLEjBCzGBNE1j/5Zhij52m3syehtTQE6DYOzHTrkq8jilR90YIIYQQQgiRXpKAEWIGa+4LUJBlm9B9FnrsNGVwXZR4QtM+EJqwxFOhx05DT+av/CSEEEIIIYRIL0nACDGDNfYGyHdPzEiQY/KybDRn8JSc1v4gXqcFm2ViDn+FHjv1MgJGCCGEEEIIcQqSgBFiBmvoDUzIakAjFXocNGRwQqKxN0DpBCxBfUyx10G9jIARQgghhBBCnIIkYISYwZp7gxRMcAKmyGOnMYOnIDX0BCicwLo3xVIDRgghhBBCCHEaJAEjxAzW0h+chBEwdpr7MrcIb32Pn8IJnHZV5LHT0h/M+KW3hRBCCCGEEOmV0gSMUuo6pVSNUqpWKfWZE2xzuVJqh1Jqr1Lq2VTGJ8RMMhCMktAat90yofvNd9vo8YeJxhMTut+JUt/jn7AVkAAcVjMum5kuX3jC9imEEEIIIYSYflKWgFFKmYEfA9cDS4C3KKWWjNomB/gJcJPWeinwxlTFJ8RM09QboNjrQCk1ofu1mEzkumy0D4QmdL8TpbEnQJF3YpfeLvFmdt0bIYQQQgghRPqlcgTMeqBWa12ntY4AfwZuHrXNbcADWutGAK11ZwrjE2JGae4LUDjB04+OKcrQpai11jT1TXzh4SKvQ5aiFkIIIYQQQpxUKhMw5UDTiMfNR9tGWgDkKqWeUUq9rJR6x3g7UkrdoZTappTa1tXVNUnhCnFuMr2fNvcFyc+yTcq+M7UOzEAwCjDh064K3LYpOQIm0/uoENJHxVQg/VRkOumjQmSOVCZgxpvnMLpqpQU4D3g1cC3wBaXUgjEv0voXWuu1Wuu1hYWFEx+pEBMg0/tpQ0+AggksRjtSXpYtI1dCaugJUDIJ066Mpben3giYTO+jQkgfFVOB9FOR6aSPCpE5UpmAaQYqRzyuAFrH2eZRrbVfa90NPAesTFF8Qswojb0TPxXnmEKPIyMTMI29E1//BaDYa5+SI2CEEEIIIYQQqZPKBMxWYL5SarZSygbcCjw4apu/A5copSxKKRewAdifwhiFmDEmuwZMpiZgCiZh2lWRx0FTX+Z9XiGEEEIIIUTmmNhCCCehtY4ppT4CPAaYgd9orfcqpT5w9Pmfaa33K6UeBXYBCeBXWus9qYpRiJlCa01rf2jSEjCFHjut/ZlXA6a+2z8pI2ByXFYCkTj+cIysCa4vI4QQQgghhJgeUvpNQWv9MPDwqLafjXr8beDbqYxLiJmm1x/BYla4bJNzCMhz2egLRAjH4tgt5kl5j7NR3+Pn6sXFE75fk1KUeI1pV4tLvRO+fyGEEEIIIcTUl8opSEKIDNHcF5y0+i8AJpOiwG2ntT80ae9xNozPPfEjYACKvJm59LYQQgghhBAiM0gCRogZqKkvMGmJiGOKPJmVkIjEEnT7whR4Jmfp7QJ3Zta9EUIIIYQQQmQGScAIMQM19wXJd09OIuKYAred5r7MqQPT2h8k323HYpqcw16hR1ZCEkIIIYQQQpzYWX0TUUq9Z9Rjs1LqSxMTkhBisjX2BChwT94UJIB8t53GXv+kvseZaOgNUDyJ066KPA7qezLn8wohhBBCCCEyy9neCr5KKfWwUqpUKbUM2Ax4JjAuIcQkauwNUDjJCZhMGxHS2Dt5y24DFHtlCpIQQgghhBDixM5qCRSt9W1KqTcDu4EA8Bat9YsTGpkQYtI09QW4yVM2qe9R5LHz3MGuSX2PM9HQ45/UUT+FHjttAyESCY3JpCbtfYQQQgghhBBT09lOQZoPfAy4H6gH3q6Uck1gXEKISZJIaNoGQpM6GgSO1YDJnBEhDT1+ir2TV3jYbjHjsVvoGMqslZ+EEEIIIYQQmeFspyA9BHxRa/1+4DLgELB1wqISQkyaLl8Yl9WMw2qe1PfJcVnxR+IEIrFJfZ/T1dATmNSltwGKvQ4aM2jalRBCCCGEECJznG0CZr3W+gkAbfgu8NoJi0oIMWmaegOTOhLkGJNSFHsyYyUkrTXNfUGKJvlzF3rsNEgdGCGEEEIIIcQ4zjYB41RK/Vop9SiAUmoJcOnEhSWEmCzNfcFJn350TKEnM0aE9AWimJTCbT+rslenrcBto0FWQhJCCCGEEEKM42wTMHcCjwGlRx8fBD4+AfEIISZZY2+AfLctJe9V6LbRlAF1YBp6/JRkT/6onyKPg/ru9H9eIYQQQgghROY52wRMgdb6XiABoLWOAfEJi0oIMWnqe/wpGwFT4MmMpZkbeye//gscrQGTAZ9XCCGEEEIIkXnONgHjV0rlAxpAKXU+MDBhUQkhJk1Tb4Aiz+SPBoGjNVEyYApSQ08gJUmnYq+dJknACCGEEEIIIcZxtgUR/gN4EJirlHoRKATeMGFRCSEmTXNfMCWjQcCYktPU25aS9zqZ+m5/SpJO2U4roVicoVAUj8M66e8nhBBCCCGEmDrOdgTMXOB64EKMWjCHOPtkjhAiRaLxBN2+cMpqwBR77bT0B9Fap+T9TqS+x5+SpJNSihKvIyNG/QghhBBCCCEyy9kmYL6gtR4EcoGrgV8AP52wqIQQk6K1P0helg2L6Wz/9M+My2bBbFL0BaIpeb8TaeoNpmTpbZA6MEIIIYQQQojxne23sGMFd18N/Exr/XcgNbfUhRBnrbE3kLJExDHGiJD0Lc0cisYZCEbJz0rRyk8ZUvdGCCGEEEIIkVnONgHTopT6OfAm4GGllP0c9iWESJFUrQY0UqHHTlNfMKXvOZKRdLJjMqmUvF+Rx86R7vQlnIQQQgghhBCZ6WyTJm/CqP1ynda6H8gDPjlRQQkhJkdDT4B8d4oTMG47jWkcAdPQk9pRP8VeB/Vp/LxCCCGEEEKIzHRWhXO11gHggRGP24D0L3UihDip+h4/i4o9KX3PQm96R4Q09PhTsgT1McVehyxFLYQQQgghhBgjpdOGlFLXKaVqlFK1SqnPnGS7dUqpuFJKlrYWYgI19gQoTMFyzCMVe9K7KtCR7tSsgHRMgdtOjy9COBY/9cZCCCGEEEKIGSNlCRillBn4Mcby1UuAtyillpxgu29hTHESQkwQrTVNfUY9lFQq8tjTuirQkW4/JdmpSzqZTYoCj43mNNa9EUIIIYQQQmSeVI6AWQ/Uaq3rtNYR4M/AzeNs92/A/UBnCmMTYto7thS0235WMw/PWr7bTl8gQiianhEhqa4BA+lf+UkIIYQQQgiReVKZgCkHmkY8bj7aNkwpVQ7cAvwshXEJMSM09PgpzXaiVGpWAzrGbFIUeRw096V+FEwklqBrKJzSGjBgrPx0pFvqwAghhBBCCCGOS2UCZrxvfXrU4+8Dn9Zan/RWuVLqDqXUNqXUtq6uromKT4gJlWn9NB1LUB9T7HVQn4aERHNfgHy3DYsppeWuKPI4ONLlS+l7no1M66NCjCZ9VEwF0k9FppM+KkTmSOW3kmagcsTjCqB11DZrgT8rpeqBNwA/UUq9dvSOtNa/0Fqv1VqvLSwsnKRwhTg3mdZP67tTuxrQSIUeGw1pqANT3+OnNIX1X44pyXZQl8aVn05XpvVRIUaTPiqmAumnItNJHxUic6SyGMRWYL5SajbQAtwK3DZyA6317GP/r5S6E/iH1vpvKYxRiGmrrtuf8loox6RrREhdl5+iNHzmUm96V34SQgghhBBCZJ6UjYDRWseAj2CsbrQfuFdrvVcp9QGl1AdSFYcQM1VDd4CSNCVgSrIdHEnDiJC6Lj/FaRj1U+i10zUUJhJLpPy9hRBCCCGEEJkppcuhaK0fBh4e1TZuwV2t9btSEZMQM0VDb/pGwJR4HWmZglTX7eOyBakfamsxmShw22jqCzC30J3y9xdCCCGEEEJkntRWphRCpIUvHCMYjZPrsqbl/Ys8djoHw0TjqR0R0tAToMTrTOl7HlOa4+RIV+bXgRFCCCGEEEKkhiRghJgB6rvTswT1MRaziXy3jaYUjoIJReP0+CJpKzxc7LWnZdqVEEIIIYQQIjNJAkaIGeBId3pWAxqpNNtBfU/qEhL1PX6Ks+2YTelJOhV7HdR2Zv5S1EIIIYQQQojUkASMEDNAXZePojSNBDmm2OugLoVTco50+SnLTs/0I4DSbCeH07DykxBCCCGEECIzSQJGiBmgrstPSZpHwBR7HSlNSNR1+9OadCpL8YgfIYQQQgghRGaTBIwQM0Bdtz9txWiPKc12cDiFI2AOdgxRmpO+z5ybZcMfjjEUiqYtBiGEEEIIIUTmkASMENOc1pr6Hj+lORlQAyaFRWkPd/ooT2MCxqQUZTlOKcQrhBBCCCGEACQBI8S01+OPAOCxW85+J6EB6D0M4cGz3kW+285AMIovHDv7OE6T1pojPedSeFiDrx16j0A8fNZxSB0YIYQQQgghxDHn8I1MCDEV1HX5Kc85yyWoe4/AS7+AnkPgzIVgHxQthvV3QHblGe1qeERIl5/lFdlnHssZ6BwKYzGZ8DisZ/7ihhfh5Tsh4gerC0J9MPcqWPMOsLnPaFcl2Q5qOyQBI4QQQgghhJAEjBDTXl2X7+wK8B55Fjb9FOZfDcvfCGYLxKPQvBUe/iRc9HGoOv+Mdlma7aCu2zfpCZjaTh+VuWc4/UgnYMvPofklWHwj5M0FpSDsg8NPwoMfhWu+At7y095lWbaD/e1nP2pICCGEEEIIMX3IFCQhprlDnT5KvGeYgKl/wUhGrHs3VK43ki8AZivMutAYDbLxB0Yy5gyUZDuo7Zz8ESG1nT7Kzqj+i4bNP4XOfbDhg5A/z0i+ANjdsORm43M/+hnwdZ72XstynNR2Sg0YIYQQQgghhCRghJj2DnYMnVkx2t462PQjWP0O8JSMv012Bay8DZ7/Lgw0nfauy7KdHOwYOv1YzlJN+9CZ1X858E9o2wGr3w7WE7yucj1Ung9P/j+IR05rt6XZTlr6g0TjidOPRQghhBBCCDEtSQJGiGnu8NEaMKclFoJnvgELr4fsspNvmzsL5l0Nz3zTmJp0GipynRxKQU2UQ51DlOe6Tm/jviPwyh+MhNKJki/HVF8MjmzY+uvT2rXNYqIgy0ZDj4yCEUIIIYQQYqaTBIwQ01goGqd7KEzR6U5BeuX34C6CstWnt33FOrBlwa4/n9bmpdlOmvsmf0RI7ekuQZ2IG6N4FlwDWfmn3l4pWHyTUai3ffdpxVKR50pJ0kkIIYQQQgiR2SQBI8Q0ZtRCcWA2ncYKSL1H4PBTsOg1p/8GSsGiG2H/P2Cg5ZSb2ywmCtyTOyKk2xcmltDkuk5jBaQDD4HJAuXnnf4b2Fyw8AbY9H+QOPWS2mXZjpRMuxJCCCGEEEJkNknACDGNGVNxTmf6kYaXfgZzrzRGtJwJZzbMvhRe+vlpbV6Z5+LgJI4IOdg+RFWe69TLbocGYOefjYTTmS7RXbzU+Dkd+McpNy3PdbG/XRIwQgghhBBCzHSSgBFiGjvY7qMs+zQSME1bwd9tTCk6G1UXQH+DUcj2FMpynNRM4tLMNadbdHjHH6FkhTHl6kwpBQtugJ33QOTkyaSKXCcHJQEjhBBCCCHEjCcJGCGmsQPtg6ceAaMT8PJvYd41YDrLQ4LZYhTk3fYbQJ9004pcJ/vbJi8hsbf1ND6zrx3qnoG5V5z9G3mKoXAh7Ln/pJuV5zhp7g8SjsXP/r2EEEIIIYQQU54kYISYxmo6fFSdajWg+hcABUWLzu3NSpZDNAiNW066WWWuiwOTOCJkf9sgs/JOMY1qx93GqJ0znW412twrjCWsQwMn3MRqNlHidXC4U1ZCEkIIIYQQYiaTBIwQ05QvHKPXH6b4ZCsg6YSRjJh7xZnXQRlNmYwaMjt+z8lGwZTmOOgYDBGMTPyIkHhCc7jLR2XeSUbADLUaSaJZF577GzpzjcTT3r+edLOqPBcHJnHalRBCCCGEECLzpTQBo5S6TilVo5SqVUp9Zpzn36qU2nX030al1MpUxifEdFLTPkRlrgvTyVZAatpsJF4KFkzMmxYtgXjUqClzAhaTyaiLMgkrA9X3+MlxWnHZLCfeaOe9ULUBrKdTnPg0zL4Uah4+aS2Y8lwn+9okASOEEEIIIcRMlrIEjFLKDPwYuB5YArxFKbVk1GZHgMu01iuArwC/SFV8Qkw3Ne1DVJy0Foo2VgGqvvTcR78co5Sxv11/4mSjYCrzXOyfhITEvtZBqgtOMq3I3wWNG6FqAka/HOPMhcLFsP+hE24yK8/F3hZJwAghhBBCCDGTpXIEzHqgVmtdp7WOAH8Gbh65gdZ6o9a67+jDzUBFCuMTYlrZ0zJAZd5J6r+074KwD4oXT+wbFy+DYB+07znhJpW5Lva2TnxC4pSfee9fofw8sJ2iLs6Zqr7YSMDEw+M+PSs/iwPtg2h98gLFQgghhBBCiOkrlQmYcqBpxOPmo20n8h7gkUmNSIhpbG/rALPyTzIaZNd9RuJATfBhwGSCWRfD7ntPuEl1vovdzScuXHu2drcMUH2iAryRIah9YmJHvxzjKQZvOdQ+Oe7TuS4rCQ2dQ+MnaIQQQgghhBDTXyoTMOPNcRj3drBS6gqMBMynT/D8HUqpbUqpbV1dXRMYohATJ539NJHQHOr0MetEo0H66qH3MJSumpwAylZDTy30N4z79Kz8LGo6hognJm5EiNaavSebgrT/n8ZUIWf2hL1nkuqLjSWpdWLMU0op5hRksadl4pNO50KOpSLTSR8VU4H0U5HppI8KkTlSmYBpBipHPK4AWkdvpJRaAfwKuFlr3TPejrTWv9Bar9Vary0sLJyUYIU4V+nsp3XdfrxOK1n2ExSj3fsAVF4A5pMUqz0XZqux/933jft0lt1CrstKXdeJC9eeqea+IBaTIi/LNvbJeAQOPATVF03Y+42RWw1mGzS9NO7TsyZp1M+5kGOpyHTSR8VUIP1UZDrpo0JkjlQmYLYC85VSs5VSNuBW4MGRGyilqoAHgLdrrQ+mMDYhppU9LQPMOdFIkGAvNG6CyvWTG0TlemjaAoFx86jMKcxi5wQmJHY1DzC30D3+k3VPg7sUPCUT9n5jKGUsbb3nL+M+XZ2fxY7m/sl7fyGEEEIIIURGS1kCRmsdAz4CPAbsB+7VWu9VSn1AKfWBo5t9EcgHfqKU2qGU2paq+ISYTnY19zMr/wTTj/b/E0pWTHwh2tFsLihdfcLVgWblZ7GjsW/c587G9qY+qgvG+Uw6YUwNqp6E2i+jFS+DoXboOTTmqTmFbnY3D0ghXiGEEEIIIWaoVI6AQWv9sNZ6gdZ6rtb6a0fbfqa1/tnR/3+v1jpXa73q6L+1qYxPiOliR1M/1eMV4I2H4eDDk1OIdjyzLoSaRyAaHPPUvEI325v6J+ytXm7oY16RZ+wTLS8DJsibO2HvdUImM1RdAHv+OuapAreNeELTPhia/DiEEEIIIYQQGSelCRghxOSLxRPsbxsafzrO4afBWwHuFM3/deUZiY9Dj415anZhFoe7fISi8XN+m2g8wf62QeYWjpN02vMXmHWBMUUoFSrWQss2CHQnNSulmFfkZkdjf2riEEIIIYQQQmQUScAIMc0c7PBR4LGNU4BXG8V3Z6Vo9Msx1RfB3r9BIjnRYreYqcx1TcjKQPtaBynxOnDZRn3mnkMw0GJMuUoVq9NYXWr/P8Y8Nbswi1ckASOEEEIIIcSMJAkYIaaZ7U19449+aXkF0JA3J7UB5VSC3QsNL4x5al6Rm20N514HZmt9LwuLx5l+tPs+I+FkMp/ze5yRqguMqVex5KlXC4o8bK3vTW0sQgghhBBCiIwgCRghppmtR3qZN14CZvf9UHVR6qbijFR9Mey6F0guQLug2MPmuvFXSToTm+t6mD86ATPUCq07jSlBqZaVD3nVcOiJpOa5hW5q2oeIxBKpj0kIIYQQQgiRVpKAEWKa2dbQx4LRyYjewzDQkNqpOCMVLoB45OgonOMWlXh4paGPROLsVwZKJDRb6/tYVDLqM++5HyrXgcVx1vs+J7Muhr1/BX186pXTZqYsx8HuCZh2JYQQQgghhJhaJAEjxDTSORRiIBilPNeZ/MTu+6HqfDCPrguTIsp0dBTMn5Oac1w2vE4r+9oGz3rXB9qH8Dgs5LvtxxuDvXDk+dSt9jSenCqjHkzj5qTmhSUeXjpy7qN+hBBCCCGEEFOLJGCEmEa21PWyuMSLaeQ0I1+nsSpPxfr0BQZQstKIpWNvUvPSMi8v1naf4EWntvFwN0tKvcmNu+83CuHax5mKlSpKGaNgRk29WljsZeNhScAIIYQQQggx00gCRohpZOPh7rHTj/Y+AOXnGaMx0slkgupLYccfk5qXlmbz7MGus97tUwc6WVaWfbwh2A+1j0P1JWe9zwlTtBjCQ9C+e7hpcamHVxr7iMalDowQQgghhBAziSRghJhGNtb2sKRsxGiQ0AAcfgpmXZS+oEYqWw0DzUmjYJaWe9ne1E8gEjvj3QUjcbY39bO0fMRn3vMXKF0JzuwTvzBVTEenXu08PvXK47BS7HWwq7k/fXEJIYQQQgghUk4SMEJMEy39QQaCUWblu4437v0blCwHh/eEr0spswXmXA4v38mxaTkum4V5hW421p75tJwXaruZX+TGZTta2ybQA4f+ZYy0yRSlq6C/EbprhpuWlWXzbM3Zj/oRQgghhBBCTD2SgBFimnj+YBfLyrOP138JD0LNw5kxFWeksjVGoqTl5eGmVZU5PLa3/Yx39diedlZV5hxv2PFHY7pVJox+OcZsgdmXwva7h5uWl2fztCRghBBCCCGEmFEkASPENPHE/k5WVIxIPOz9KxQvAVde+oIaj8kE86+Bl34FCWOJ5nXVuTyxv4PYGdRFicQSPL6/g7Wzjn6+/gZo2AizL5/4mM9V+XnGUuDdBwFjJaS6Lh89vnCaAxNCCCGEEEKkiiRghJgGwrE4m+t6WFmRYzSEBuDAP43pPpmoaDFY7MYIHaDQ46DQYz+j1YFerO2mLMd4HWjY/DPj89rSXGx4PGYrzL4MXrkLAKvZxIoKGQUjhBBCCCHETCIJGCGmgY21PVTlufA6rUbDrj8byz47c9Mb2IkoBYtebUwZCvYBcMGcfO57uem0d3HvtibOn5NvPDjyPAS6oXLDZEQ7McrPMwoQH10RaVVlLo/saUtzUEIIIYQQQohUkQSMENPAw7vbOG/W0WSLrx1qn4S5l6c1plPylBhJic0/BeDCeQU8faCL/kDklC/t8YV5/lA3F80tMGrdvPQLWHwTmMyTHfXZM1tg7lWw9ZegE6yZlcumwz34wme++pMQQgghhBBi6pEEjBBTXCSW4F/7Olg/+2gtlK2/gVkXgt2T3sBOx9wroacWjjyL12Flzaxc/vRS4ylf9ofNDZw/J48suxk2/RiKl0LurBQEfI5KV0A8Akeew223sLjUyxP7OtIdlRBCCCGEECIFJAEjxBT33MEuynOcFLjt0L4TuvZD9cXpDuv0mK2w/I1G/ZaBFm5YVsKvXzhCKBo/4Ut84Rh3bqzn2qUlUPMI9NYZRX2nAmWCha+Grb+CaJAL5uTzl5eb0x2VEEIIIYQQIgUkASPEFHfP1iYumlcA8Shs/BEsuAHMtnSHdfqyy2H+1fDkl5nljjO30M1vXjhyws1/8exhlpRlUxGuhVd+DyveYiRyporcWZA3B7b/gbXVuexs7qdtIJjuqIQQQgghhBCTTBIwQkxhnYMhNtX1cP6cPNh1j1F0t3hJusM6cxXrIH8+/Ou/ePPKXH7+XB313f4xmx3qGOKuTQ3cOjsET38DVrwJ3AVpCPgcLbgODj+Fvb+WC+fk8+eXTr/4sBBCCCGEEGJqkgSMEFPYHzY3cOHcfFxD9XDgH7DoRmOFoalowbXgLaf0+c9xyyIX7/vdNvr8xwvydvvCvO9323hztZ+CTV+FZa+H/LlpDPgc2N2w8Dp4/n+5amEef9jcQDh24mlXQgghhBBCiKnPkso3U0pdB/wAMAO/0lp/c9Tz6ujzNwAB4F1a61dSGePZ6g9E8IVjWE2KnEQfdosFPEV0DIZI6AQKhZswhPsxuwtxurKo6/KhML4vu2wWdCKGPdSNx+PFnJVHx0CQQDSB1prSbCdOm7HCy2AwQttACLclQa4eBB0jEY8RsmSjHNn0BaMAmFBYTJrseC9xZWXI7CUcTZBlt1Ce66KtP4Al1ItdxYm4ignHE9jMJkhEcQS7MBOjz1SAttiJxDRxncBjTpCd6MNi0gxZ8vCoMCTitMS9mE0Ki9lEQZadUDROa3+QQmuQPGuMiKuQfn+EnEQfNpUwamFEQ8aHzyrE7+sHsxUdi2BzeLARBq2N7SIBwtpEv72EIo8DNVUTDBMsGInz+80NfP6aOfDsp2Dh9eDMTndYZ08p4zO0bOOaA1+m330br/5+kPdfNp94PMbPn63lMusBruh4Hs67Hbyl6Y743JSugq4aKmvvpjLvCv6+vZU3ratMd1QZoy8QIRSNU+RxYDad5G/e3wPxMHhKjycf/d3GlDxPCf5wnKGwcUy0qzi54RawuSG7nGjHAUxK4UvYMJkteGwK4sbxtMdeTpYK4koE6CUHgDz6CUdj6EgAlVOJ1gl0fzNhazYxRz74O7FaTCRiMSIWN0EcuGM9WExmTDpGwOSmL2ajwG0jEk/gC8coNAew6jBdGEW0Y3GNMplIaI3dosjBjyPah7Y4CScUEUchVouF3CxjmmE8oekcDOG0mclxJU899IWiDIVi5LltmJSieyiM2QSxBOS7bdgtx84pUSMWtx2NptsXwWO34HFOoal900kiYaxmZ7ZDVj7hWJweXwSPw4InPmj0d3cJmI7eQwv0QXiQuDLTGXWANYtSewQiQyRcBQz0dGAlThQTvSqPEq8d3XcEpcwE3JUM+EIUm/tROoECLFYrwXCEsNlDf8JOQsMsVxSrr4WINYeAtmKKR3BmFxIa6sFGDBREEwpndjEMNBA3O+k3FxKIJajMsRHsbUNZ7eQUlBIMx2gbDFGk+nA77HRqL33+CHaLmXhCYzJBWRZEh7qxuHJxuKfweS0DJVp3gjJhKl2e7lCSRHy9MNQB7kJsnswa2TrUVotJR7Hkz8Fut6c7nKkjPAShAXDlg9VptB07Z488hgX7jPM2JvyWbKKAN9yFSQGesuPXtkMdhEJ+FAp0AmWyYDMryCow3stkIebrAjQmh5dYyEcIFzEdJ2QrwGKxUOR10DUUwhroxGyxEY4ncOgw1niQkD0fV3YhA8EoA6EIZsAfTZBlMxOPJ4xrDK0JxTS5LisacNstDASjhKIJ8tw2vA4Lvf4IkVgCl81C91AIrSDLaiYS18QSCawmE3aLQpsUdouZbKeV9oEQZqUo9NqHz80DwSiBcIwCjx2r+fTGTCQSms6hMDaLIi/r9PpqNJ6geyiMy24m2zmFShhMYSlLwCilzMCPgVcBzcBWpdSDWut9Iza7Hph/9N8G4KdH/5uxovEEL9Z289V/7uOiUsW/F+/E/vIPwGRh6IJP8d26BbzcEefuG2w4Nn4FS/sOonOvpm7Fx3jzA/2YTYrbNlSxMquPDZ334NjzZxLZlfRe8mU2Rhfzp22t7Gru5/w5+Xzq2oWEogl+8ORB3jQ7zDXeRqyJCDRugpqHycquxHf5f/Ptrdk8d7iPb7yqgOvCj+F4+RfgyCZx4X/xxR3FHO6L87XXzGHp4EYKNn8dIj5C6z7E3YMXsLDAyquymzG99HNo34NzzuXsW/wxbvv7AHfdmM1cUz2ml38D7bvJnX0pav418Mw3yFr2Xv5puoKS8irsFhN/2drAx+e2kbP9GzDQhFr+ZvLKVmN57DOQMwsu/nd4/ruw/A3ogVayihai9zyAat+Frr4YVr8dHv00JGKw+h3YB5rIK1/P07ZLWTKnipJsR7p/9Wn3xy0NLCj2UL7vl+Ath7LV6Q5pYpSvReUv5M0NL7I08BLPPlyBUnCHp5Mls8qg/MPGks5TnVKw5GbY9BNumr+cHz4V5pY15ad9kp2uovEEzx/q4mv/3E/nYJhb11fxzgtnUZHrGrVhEA49Do9/AYL9sP4OWP02aHkZnvgShIeIrvsAG7Ou5YDfzXUF3RQduRv2/RXtKYPLPoXV3w0t28iueQSdUwVr3gntezAVLSQ/HjUuCF/+LZ7rv4M68jzUPIgtbx5q9VvRgfnw8m9RtU/gKFgAl38ONdgChx6DI88RL16GuvCjmIbajLaGjbhKVtGx7D/Z5lpMbWsvby8+gvf5L2PydWJa8Q5a57+VHQMefvR0LVon+NfrbDj23YPKnwvbfoszNEDv8vfwN9PVVFbPZWGxmzs31nPP1mZKsu3816uXcNG8AqxmE6809PH1h/ezt3WQKxcXcePyUjqHwtz3chO1nX6uW1rMR6+aT6cvzNf+sZ/aLh/XLyvhwrkF/Nff9rCgxM1/3bCEdcdWVxOpMdAM235rLFfvzCNy1X/zk8ZqXqzr5QfndePe+jVUoAfWvQ/W3g5dNfDY52GwGfPCV1NYvp6Ip4LEc1/H1HsI1t9Bbm41PP+/EBrA86qvYvK1Y9ryUzDbcF78CbIjAWwvfBuyK+CqL8O232Kvf45E2Ro6l/wni0pcWJ77Ear2CSwFC3Bd+knUQBOJWBgXGixOePk3MNiKXnoLKqsQve9vOC77HG2eZcR23k/u9l+C3Yvv0i+wz7KEqrZHce/8MZhtOC78DLXm89nfp7j/lWa+c5mV8iM/xV3/DJGSNfiv/DJZs9el+Rcz9cWbtmM6/Biml34BZhv6wo+RmHUp5rKl6Q6NWMNmrM9+C9W0CV22hthln8Uy55J0h0VnZwsFTU/gef47EB4iseadDCy6lezKxekOLfO1vAKPf9E4J8+9Ci7/DPQ3wr8+D4EeWPseWPceGGyFtp3Q9BI0bsJ19f+DzT9Gde6HOZcb10l5c0m0vIxp93041rwTdALQsPcBcOWj8+eh3GVgsWKpfQqW3oTe8wC2irVYt/0WFewltPo9tMy9lQO1URZ1PkxOy5Ow4lbc2WWoXffAwUex5c4mcsWXeN+TLuq6A7xxbQVzC7Po9UUwmRRuu4WHd7ezvamP82bl8m9XzqetP8jvNjVwoH2ISxcU8MbzKtl4uJsHtreQZbNw24Yq2gcCrKsu4AdPHqRjMMy1y4q5eG4Bu5r7eXBnG++4YBaHOnw09wd514WzWFmZTcdAmK/+cz913X5uWlnGBy6bS3VB1kl/5K39Qf70UiN3bawnN8vG565fzOULC7FbzSd8TX23n188V8ffdrQwuyCLz796MRfMyZeb3ZNMaa1T80ZKXQB8WWt97dHHnwXQWn9jxDY/B57RWv/p6OMa4HKtdduJ9rt27Vq9bdu2SY39ZHY09vG6n27EZjHx8BXtzHnu40nPH7jiF2wPlvKWne8wMrxHRYuW87W8b3DnjkFWlmVxT9UDOHbcdfyFJjObrryPt/wjNNz0ocvn8tDOVpYXWvheyaPYdRgCvcYBaMTrnrn0Hj71ouLva16hdMvXkuJ55co/8rqHFQ/eEGfFU29Pem7oyq/jcHmwPvUlY79HJQqX8PiqH3J14gXMm/7POHAeU7AQKs6DHXfTdOFXeeeuZXzq2kU4e/Zw2XNvhsSIaRXLXm8sOdy20yiaeu03oO4ZyJsNO/90NPt9bL/zoWI97Pij8fiKz8Nz38b3mp/zx8GVvP+yjJl6clpHqInup4OhKJd/+xk+PbeRqtZHYP37plbh3TOhE4CaulOrTmWgBV65i29mfZLXb1jAOy6snuh3SEsfPVvbjx5TR56a3nfJHD5z/aLkkTANL8Jvb0h+8WWfge2/My7ojvJd/t98s+tC/tvyG0w77z6+rckMr/k+PPhvI9osxkXiwUeNu3PFS6FtB8TCxrHqmMs/Zzxu3Hi8bfmbjBW5Wkb8DFe/A1pfgY49x9ucuTy04W7W5Ecpf+BmRn5Q//qPcUfzdbxY18df31TA6qffCRs+YCSURmjf8Hlu2r6Gz1y3iP+4d+fx8BU88KGLyHZauen/XmAoHBt+7ouvWcK3H6shOGKFsa/cvJSv/nM/4VhiuO2iefkoFC/UduOwmnjoIxczv3jSl7SfUn10Uj37P/B08nl765V3o7Vi/dNvSd72Db+FB96bfJ5d9Vbo3G/0O4Cr/9/x/mNzw/kfhOe+nbyfq74ET30FVrzZeF1XzfHnlr0e7etE1T9/vM3qguv/BwZbjL+ZZ74+9lzffRB8nSTWvx/TU/+d9Hax19+J5f53JbV13fQHNtxn4t1rvHym45NYuvcff9KVx9A7nsBTkvbz/pTup3rLz1GPfCq57ZZfola+KU0RGcKdh7H9+Q2o3rrjje4iIm/9G7bS9CaHYvsewnLv25LaEld8AdNl/5mmiE4pM/poXwP88ork7wuFi6F8NewYcR5++9/gld8Z/7/3AbjsU7DpxxAZUQNw9qWw+CZ4+D+NY5WOw1A7HPoX+Lvgoo/Bpp/Ajf8Hf3knvOl3cP974YrPwRNfTgorcNmXCFu85D75CWNf7kLY96Cxr2PMNrZe/Rfe+HcfAB+9ch55WTZqu/w8faCTlv7jiyaUZjt4zYpSfvn88YUjlpR6mFfk4cGdx69DfnTbaj76p+0kRlzXvH5NOXMKjZsoXUNhPnbVfH7+3GFsZhP/84YVfPRPO4jEj5+bb1hewv++aSUO64lvQP7wyUP87+MHk9rue/8FJ7yREo7G+eRfdiXFajOb+PtHLmJxqfeE7zMBpukF/elL5a3WcmBkpcnmo21nuk1GOdjhI6FhTaWXWfX3jXm+svVRVri6kpIvANbO3VxUYBxg3rDAgmP33ckvTMQpCdcnNZlNiqa+INdWxrA7nGD3wv4Hx7yuKt7I9XPtlB4ctU+gfHAH84vdlPVuHvOcZ/fvsMYCSckXAFPXPtZk9WCOBZIPpgDdNcZdM6Dy8J9YXmSlfTDMHJqSL8jAiHXulcb/x6PG6Jaah41hiSOTLwDdh4zVcY5p3Awly3HVP8FzhzqTaoPMRP/7rxpWZweoqv+LccE9XZMvYExDm67JFzD6+eKbeIvvd3zv8QP0+MLpjiitatqHGH1f4M8vNdI5FEpubBrn4nHHH4w7ZiO4d9/Fh1cqTHvvT942ETfuxiW1xSAegeZtRjHrI8/B3KuTky9g1B4amXwByKtOTr4AeEuSky8AwT7KEi24+/Yz+oNm7f4d71vjBmA2rRALwcDYAs0lB+/m+jl2Dnf6sIxISiW0Uaj6SJcvKfkCxpSu4Kjl3TuHwknJF4AXa3tYXZUDQCiaoK5rbDFsMUl8nbDtN2OaKwIHqIwcHrt9d83Y8+zue2HOZcb/u/KS+0/FOqh7eux+2nZC/jzjXD4y+QKQPzc5+QIQPXot0PCi0UfHO9fPuxqqL8a04w9j3s7ctMmYkjBC1uGHmV3g5vzcoeTkC0Cgl0T3OJ9fnLa4rxt2j71G5dCjqQ9mFFPvoeTkC4CvE9VTm56ARjDVvzC2bfc9DHY1pyGaKaT38NjvC137h78vDAv0QG718e8yZnty8gWMc/L+h4z/jwaMGyLOXOP8XbbGOE+veqtxs0SZYLDNuCb2dY4Jy9V3gNx9vweLw/heZnND7eNj3m++On7cfGhXGwkg12VNSr4AtA2EhstDHLOvbYjqguQRuy19waTkC8Aje9qJxRNcONc4Fm6q62FVZQ6DoRjtA6Gk5Mux7dsGTnx92O0Lc/eWxjHtO5r7xtn6ePwP7WpNaovEE9R2+k74GjExUpmAGe8b1OjhN6ezDUqpO5RS25RS27q6uiYkuLPlcRiZyG5/DL971pjnA1lV+HGNacdsxa+NL80dAQ1ZRWM2CZqSh5qZTQqloD9qhljUOBC5x3mdctEe0ESyysY8F7IX0OuLEHKOfV08u2r8qR0mCyGzE0zj1AMwHd8+mFVJpz+B224mqMYZJucuTk7umG3GvE3TeO85aricuwiCfcQ95WTZLNitmT9NY7L66csNvTz4cj1vGvi1MWXCmTNh+xZpUrKMWfOXc3FiG5+/ZzMpHJmYMcfSY7yOsceZ4mw7jtFDaLPyx2yHt2zMRV/MW0lXyGwcf0Y7Nid9JJPFaD9aQ4bwEDhG1aDQCbCNOsYl4sZFXdJ2etwl0kPKRdw+tq5F3FNGa8A4DUatbmOalX3sXaiIu5w2v8ZptxAbdVXndljIcow9ptrGmdo2XpvXYSEQOf6F2j3OvlIpE/vopLE6jT48SsiaQ9DsHmf7ca4t3CXH/waiQbCPGL0U7DWeHy2rAEL9xv+PPh/HwmP7OoDFZpyXx+nfwzEEetHesffQdFahcf0y8m28VQwEIwS0Y9xrAmUf5/NnkEzvpwlzVvJNrWPG+f2kmrZ5jC/Oo9kmfeTdKelxzhvaUwq2cf72MlxK+6h9nN/deNf6FodRI+bYd5nx+kE0CNlH6+OZrcY/k9m4MXfsmNZ72Eg4Hzs3x0LjHrfiykIsuwoSUeN4q7WRzBklZDr++y322jGp49/BRjOParSZTWOSLS7b2M9e5LET1wzfUC502+k9+v/OcbbPcVpxWE783cdhNVGaM7Y8Q57rxHVgHFYzua6xN3Dd9mkwzT/DpfJbbDMwssJkBdB6Ftugtf6F1nqt1nptYWHhhAd6JpZVZLOoxM3BDh/7y16X/AfvyGF39qX88bCTwQWvT3pd53n/wc92G3+0D9bG6LvsK0nPR4tX0uKYn9TmsVu4/aLZ/GxnjDbHbOPLwfo7kraJF6/k2aEynqj1cWjJR5IOeAlPBdtYRI8/wn7HGvTICzGzjSOL7qDLVAiLXpO0T9+Gf+ebr1jptxbB4huTfwDr3wcHHgaLnZq5t5PrzSLLbuHJgRIihSuOb6eUMWd971+Nx6WrjCHK699vjG5ZfFPyfs+7HWoeMf7f7oWixRAaoK3oEt590ZxxD2aZZjL6ac9QiI/89nluN/0T77q3gGecL5ViaqpczxsW2jhw+Ai/f+T5U28/ATLpWHrM8opsFhQf/7JlUvC5GxaPvUioOt+oJzW8oQUu/SR0jLh7brbSu+ZjfO7pISKXfT55JFXRYsibk7zPkuXG0Ob1d0DNo1B+nlGLY9RxlvoX4ZJPJLcNtsLF/5HcNtQBGz6Y1ORb+AaaLbOosy0iljfiGK9MBC79Ane9bNytuuuwm8SyNxhFCkd+STJbObT4wxwZ0KypzEn6SItLPCwvz2ZhsYcblid/0c5324bvtB0zv9jNxfOSi12+66Jq/nH0jtjlCwtZVJLeL0GZ2Ecnjd0DV30x6QZEIqeaLdG5bI7MJpYz+/i2JrMxoqVk5fE2peDif0cPtBiPo0Fju2NJnbadUH1RcuLRlWcUsPZ1GiNS1703KaRgKIq+7DPJcc6+zCiMWboKggPG39LIGNa9F/b+FT3YTOLCjyUlaRLeckLlFybvz5VHT8VVRGIJfrFX0bXm40lPB5beirVk0cl+cmmX6f3U6nQaUyJHJu2cuTDvVekL6ihVtJTEuuRjbGLFrSSK01+bJlp1MdqTfK0cP/8jeLOnXm2slPbRgoWwKnnqFpd8Alp2HH9sMhvHHk/x8XNsxx5j9NxIc6+EJTcao1WCfRCLQM8RWHmbMd2yfI0xGq9kxfFrgtJVxojWpGsEM0Pzb+HwvHcZN5RjIeg5BBd+NOntEpXnc3+78fOxmhXvuKCagUCUnY39vG51csLybRuq8I0abfr+y+bw5L6O4ceFHjvZTgvzi45f1ygFt22ooiDLxgu13bhsZlZW5nCww8flCwsp8drZMGra0Bdes4TSnHFuGh3ltlv5z2sWJk3Vrsx1ct6ssQmmY0qyHXzxNUuS2tZV57KkbFKnHwlSWwPGAhwErgJagK3AbVrrvSO2eTXwEYxVkDYAP9Rarz/ZfjNhrm1TX4CdTf34QjGuyO3G278Pq9WCP28ZT3XnEI4lWFcYo2BoP2ZfK+TOocW1kK1tccwmRUGWjUQszHpHE5aeGsyuXPwFK3i5L4tANE5/IMqsfBfnzcolEk3wSlMfibCfC90deBjCFA2ih9qJOQvpylnBll4Xvf4Is3IdnGdvxtW3n7jZSW/2Ep7qzMJpszCvMIuCSDPevr1YdYRI/hKeHigh2xbnAlcr9sEGCPYSzZnLPvMitncmWJoHK+wtOHzN6GAfiexZxFFoXzddWQvYE6skO8uGy2ampS9EpbmH2ZGDOGKDRHLmE8WMt3c3JrsLlTPLOHA6c0nYPSSiYVQ8jA72o9xFmJw5qL4joBQ6qxAd6MOXvYBGxwKWlGZjOtmqKKmVsvm2/oE+bvvBP5ijG3nTRYvHzdqLqa+94RD/vTubb60Pc81r3z4R068yY074GWjqDbC7ZYChYJQFJR6WlWePX5y4tw5ad0AkYNRrKV0BffVGWyxIpGApr4TL8Uc1Dh1mrbkGa89BlDPHKNrX32BciPk7Sdi9KJsbFY+gdYK4u4xwcAibr4Vg7iKIh3EP1mKyZaGtLhJZxahQH/TWobMKiectQA80YyVGwtdFMKsci7cUNdCMTcUg0MOgo4z9prloVyH+YJSFzj5y+vdhigwSLVjEAT2HuMlMc1+QcDTOVWVhSn37MJmtqGAv8XiCDvciDlDNgpJsir0O9rYOUNM+RLbTyvKK7OFixZ1DIfY0D9A+GGJWvossm4VOXxh/KIYvHGN+sZGsGQrF2NXST48vTHV+FnaziQMdQxR6HKwo91KcfeILvgk05fropEnEoW2X8UXElkW0ZBXbB7M53O3j4nw/pf79WGLH+vtKow5L88tofwcJdykDeAjaC8kP1WMPdRPzlIOyYB5sJJ5I0J+3gmxTBHPnHpTZTLhgOYPBCPlD+zFb7OjsSlR4gPhgO2FPFQdM8yh0mqgM7YfeI5BVSNxTTszXhdmVi4qFMKEh0IOOhVG5VWhfF2hNsHA5L/Tlsz6rDUvXPrTVSbx4BfvC+VREjlAwdBCr1Upv9mL2hIrRQMdgiGp3jJXmI5j7G4h7yjCVr8adlxGr3k35fqoPP2tMBTFZ0EWLMVVflO6QAAh1N2JtfwU10IT2lhEtWY2jcM6pX5gCwfpt2Lp3QzREvHAp8fJ1OJ0pOS6ejczpo74uaNtu1LnLnW3Ufwn0Qet2iPiOH8OGOqBzL4SH0EPtxAsWYgoPoAZbwV2McuVD0RII9hJvfhmVlY8ymY1ETNSPjgYx5VZDcADtLjRu7ubOhsEWtCMbgn1EolECeUvYn6jG5bBTFDiEd7AGZ04JymLDFOqHgSbirkL68lbzbJeTYCROea6LwUAYj8NKQhvTc8KxBP2BKMVeO8VeB8FInPbBEAPBKJV5LgqyrPT4o3QMhrBZTOS4rAwFY5TnOmnuCzIUilGa7SDbaaFtIEQsoanIdVHf7cdhNVGV52JxqZehcIzdzQP0+CPMK3SzvMJ70vovYKyKuLd1gP1tg2TZLCyvyGZW/skL94aicXa3DFDb6SM/y8by8uyTJnomSMZ8iUuXlCVgAJRSNwDfx1iG+jda668ppT4AoLX+2dFlqH8EXIexDPXtWuuTHiEy+UQnZoTJP9lpTecrD/Kev7ZR4oLbL6pGWWQZxOnscGsv392e4POFm3jDW++AonO6+5s5F2RCjE/6qJgKpJ+KTCd9VEwFMz4Bk9J5HFrrh4GHR7X9bMT/a+DDqYxJiIwVj6L3/5PH/vUPvtB9NZeX53DLyiJZGm4GmFuWx+eyYnxvy0W88H9384UlneRf9v7ps9S4EEIIIYQQM1DmF9IQYiYJ9hk1cQ4+ysDeJ3hH4OP0W27gQxs8LCqQP9eZpCLbwlevzOUv+zdwxZ4oHzj0Sz6Uvw2W3ALzrjLmOFum8epXQgghhBBCTDPyjU6IdIpF4JlvQOc+oyaOvwsKF0HZKhrWf5n+zVl8/VIHJhn1MiM5LIq3LXdxUVWC7219DR9asxyaX4I998NAs7F0bOFCo2jdqrekO1whhBBCCCHESaS0BsxkUEp1AQ2n2KwA6E5BOBLD6cmEOCYqhm6t9XWn2uhE/bTCoyxN/+EZXsYiGNWJ+NE/yVpdzmv1d0xZBEa/TMwwQRxkEWST6b2JY20OCybL0WLUTQOJcNX3fXtO8PJz6qNMr79XiSEzYzjXPpoKmfCznwjT5XNA6j/LmfbTTP5ZS2xnLlPjguOxTYVj6UiZ/DOdSPI5k51WP53OpnwC5nQopbZprddKDOmPIVPiyIQYppqZ+DObiZ95tEz4GUgMEkO6TZfPPV0+B2T+Z8nk+CS2M5epcUFmx3YyUzXuMyWfU4w2zrqeQgghhBBCCCGEEGIiSQJGCCGEEEIIIYQQYpLNlATML9IdABLDSJkQRybEMNXMxJ/ZTPzMo2XCz0BiMEgM6TNdPvd0+RyQ+Z8lk+OT2M5cpsYFmR3byUzVuM+UfE6RZEbUgBFCCCGEEEIIIYRIp5kyAkYIIYQQQgghhBAibSQBI4QQQgghhBBCCDHJJAEjhBBCCCGEEEIIMckkASOEEEIIIYQQQggxySQBI4QQQgghhBBCCDHJpnwC5rrrrtOA/JN/6fp3WqSfyr80/jst0kflXxr/nRbpo/Ivzf9Oi/RT+ZfGf6dF+qj8S/O/GW/KJ2C6u7vTHYIQpyT9VGQ66aMi00kfFVOB9FOR6aSPCpFeUz4BI4QQQgghhBBCCJHpJAEjhBBCCCGEEEIIMcks6Q5AnFht5xC1nX5cNjOLSj0UeRzpDknMQI09fg52DKGUYmGJh4pcV7pDEkIIkSF6fGH2tw8yFIwxpzCLBcUelFLpDkuMo+Ho+dxsMs7n5TlyPhfidPUHIhxoG6THH6G6IIuFxR4sZhnLIM6cJGAy1MsNfbz911sIROIArJ2Vyw9uXU15rjPNkYmZ5EDbIG//zUt0DYUBqMh1cuft65lX5E5zZEIIIdKtczDE5/66myf2dwJgM5u4893ruHBuQZojE6Ptbxvkbb/aQo8/AkB1votfv2sdcwvlfC7EqfQHInz94f3cu60ZAJOCn7/9PF61pCTNkYmpSNJ2GcgfifGdxw4MJ18AtjX0saOpL41RiZno/leah5MvAM19Qf61tz2NEQkhhMgUe1oHh5MvAJF4gi/9fS99gUgaoxKjaa2556Wm4eQLQH1PgKcPdJ7kVUKIY/a3DQ4nXwASGj73wB7aB4JpjEpMVZKAyUD+UIyDHb4x7a3yRy5SKJ7QbG/sH9O+u2Ug9cEIMYUlEpqXG3p5sbabcCx+6hcIMUX0+sNj2g53+fCHY2mIRpxINJ5g+zg38fa2DqYhGiGmnl7/2KRyly/MUEiOdeLMyRSkDJTvtnPjyjLu3Fif1L60LDs9AYkZyWxS3LK6nG0NfVhMxnz+WEJz7VIZbinE6erxhXnf77bR7YvgspkJRuP8Rob9i2lidkHWmLbrlpVQ6LanIRpxIjaLmdetqWBn8wAWk0Jj3GS5enFRukMTYkqozs/CpIyRL8ecV5VLiVfqc4ozJwmYDGQ2Kd51YTUdAyEe2duO227hU9ctZEV5zlntLxyNs7tlgL2tg+Rl2VhZmUNVnhReE6d29ZJisuwWajt9KAULit1cMCc/3WEJMSWEonHe8ZuXmFvo5hPXLMSkFE/XdPLWX23hkY9eQm6WLd0hCnFKvlCUnc0DHOwYojTbwcrKHEqzjXp0S8uy+e4bV/L//rGXwWCMS+cX8IlXLcRuNac5ajHatUuL8TosHOr0DRfhPX+2nM+FOB0LSjz89K3n8fm/7abbF2FNVQ5fu2UZHqf1rPZX0z7IruYB4gnNiooclpR5JzhikckkAZOhqguy+N83r+IT/UHsFhOV55Aweaamk/f/4ZXhxwuK3fzmXetkNRtxSm0DIT7zwC5C0QQAHruFP7//fApkRS4hTum7/6rB47Bw67rK4VVhrlhYREt/kC/8fQ8/um1NmiMU4uS01tz3cjP/76F9w22XzC/g+29eRb7bjsNq5vXnVXD+nHwC0Rhl2U6y7HJpmYmM8/luwjHjfO51WvjzHeeT75HRSkKcitVs4tplJayoyGYoHKPE68B7lsmXva0D3PrzzQwdnarpsJr48x0XsKoyZwIjFplMasBkMKfNzLwi9zklX3p8Yb7yz/1JbQc7fOyROh7iNNy9pWE4+QIwFI7x8K62NEYkxNRwsGOI+7Y1c/uFs8csyfuGNRVsqethe6MUVheZrbE3wP88WpPU9vyhbmrah5LaynOdzC/ySPIlQyUSmrs21g8nXwAGgzEe39uRxqiEmHpKc5wsKPacdfIF4OFdbcPJF4BQNMHdWxomIjwxRUgCZpoLxxJ0+8YWyfOFpRCkOLl4QtPYGxjT3tQ3tk0IkeybjxzgxpVl416kOaxmblpVzvceP5SGyIQ4faFogmB07PWCFNmdWmI6QVPf2IUcWvplcQchUm286+imviDxkQVmxLQmCZhprtjr4K0bqpLazCbFwmIpAClOzmxS3LZ+1pj2m1aVpyEaIaaOPS0D7Gzq5+rFxSfc5rIFhexu6ae2c+yKd0JkioocJ5fOL0xqy7KZmVsk1xBTic1s5m3nV41pv2F5aRqiEWJmu3mc6+i3rKvCbFLjbC2mI0nATHNmk+L2i2bzwcvnkOuysqzMy123r2OJrKgkTsMlCwr42i3LKM12UJHr5LtvWsmG6rx0hyVERvvRU7XcsLwUm+XEp1ir2cTlC4v4/ab61AUmxBnKclj48k1LuHVdJdlOK+fPzuP379nAHFnFa8q5bEEh/33zUkq8DirznHzvzStZO0vO50Kk2vrqPL77ppVU5DopzXbwtVuWccmCgnSHJVJIJuvOABW5Lj55zSJuv3A2Dqv5nOYtipkl12XjrRtmcd3SEpSCvCwp1ifEyTT1Bth4uJvvv3n1Kbe9fEEhX3pwL59/9ZKTJmuESKc5hW6+8tpl/PurFuC2W6TOyxSVl2XnHRdUc8OyUkwmOZ8LkS4ep5XXr6ngioWFaA35bvlbnGnkLDqN1ff4aekLkpdlY25hFkWyVr04Cz2+MHVdfkwmUEqR65Klc4U4kbs21XPpgkKctlMvw1vkNUaWPXuwi1ctOfF0JSHSzWo2UTzJ1xB9gQiHO31oDXMKs+RLySTo9oWp6/ZhNpkwKUWOnM+FSJuTJUFD0TiHu3z0B6JU5bnOaUEWkXkkATNNvVDbzQd+/zK+cAyzSfG5GxZx2/oqnDb5lYvTV9fl49/v2cHOZmPVrAvm5vOt16+gSk4EQowRjMT5y7Zm/t9NS0/7Netm5/HgzhZJwIgZraHHz6f/sovNR3oBWFWZw/fevIrZBVlpjmz6qOvy8W9/2s7e1kHAWE7867csly92QmQYXyjKr144wg+ePITWkO208ut3rmWtlACYNmTM8zTUMRDiE/fuwHd0lYJ4QvOVf+znYMf4xR5j8QRDoWgqQxRTxD92tQ0nXwA2He7hmQOdaYxIiMz14M4W5he7z2i04frqPJ6p6SIck5XpxMyUSGie2N8xnHwB2NHUz8O7WtMY1fTz1+0tw8kXMJYTf/5QVxojmjrC0TiBiKz8JY5LJDRDwSiJSVi5aF/bEN9/wki+AAwEo3z6/l30+iMT/l4iPdKWgFFKfUwptUcptVcp9fGjbXlKqceVUoeO/jc3XfFNZT3+CB2DY5eebh8IjWnb3zbI5/+2m1t+spHvPX6Qhh5/KkIUU0A8nuCZmrEXZxsPd6chGiEym9aaOzfWc+WiojN6XY7LRnmOk5dGfPkUYqY41DnEb16o47lxzjXPHOxCa1mWdSJEYnGePTj2ZyzHnZOLxhO8WNvNu+/axht/tokHXmmmPyBfgme6w50+vvHIfl77k4189Z/7qe0cmtD9tw+MXR7+cJdf+t40kpYEjFJqGfA+YD2wEniNUmo+8BngSa31fODJo4/FGSpwGxf0IykFZaPamvsCvOM3L3HP1mZqO3384MlDfP2f+yXLLwAwm01cOn9sVXYZAinEWDubB+gPRFlRkXPGr11ZmcMT+zomPighMljXUJgP//EVfvhULQtLvWOef9WSYpSSZVkngs1i5urFY6c5XjhXVl45mZ1N/bz911t4sbabva2D/Me9O3lKRgHPaL3+MB+/Zzu/fP4Ih7t8/ObFI3zk7u30+Mbe+D5bZbnOMW2LSz3kZUnNpukiXSNgFgObtdYBrXUMeBa4BbgZuOvoNncBr01PeFNbkdfB9968cvgP1WY28bXXLmNBcfKykbWdPrqGkg8Yj+3roLE3kLJYReZKJDQ5LivnzTo+EO3ieQU4rDJzUYjR7tpYzxULizCdxRfGlRU5PDPO3WkhprMj3T4OdvgYDMWIxBJcNC9/+LlL5hdw7dKSNEY3/dy0sowNs4/fQLlheSkXzZMEzMm8WNvN6BkmP3+2Dp9M25+xjnT72d0ymNR2oH2Iuq6Jm0GwuMTL529YjMVkXE8Ueex843XLpWj2NJKuiqx7gK8ppfKBIHADsA0o1lq3AWit25RS447lVkrdAdwBUFVVlZqIp5j1s/N58CMX0dofJDfLxpwCN2ZT8hcDq3nsF2mLSWExyRfsiTDV+6lS8HJDH0UeOx+/ej4Au5oHJvQkI9JrqvfRTNHnj/D4vg6++8aVZ/X6WfkuBgJRmvsCVORKQcyRpI9OXyOvQe7cWM/Vi4v4+NXzWTsrl+UVOWQ7rWmM7sxMhX5aXZDFL95+Hke6/ZhMijkFWbgdU+dnnA7jLbnudVowj3P9nOmmQh+dCsb77mS0T9xovSy7hXddVM1lCwsZCEapyHVSmj12VIyYutJyBNFa7we+BTwOPArsBE573ovW+hda67Va67WFhYWTFOXUV5HrYv3sfOYXecYkXwAWFHtYVZmT1PbeS2YzawIq4g8EjS8TwcjMLSw51fupUoq3XzCL3S0DDASj9Poj1LQPcdOqsnSHJibIVO+jmeKebU2cNysX71l+YTQpxfKKbF6slfpKo0kfzTzdQ2Fa+4PEz7H45NxCN9eMWP3rif2d1PcEWF2VO6WSLzB1+qnXaaU420Gx1yHJl9Nw4dx83COSMErBR66Yh9NqTmNUZ2eq9NFMN7fAzc2jroOvX1bCnEL3uNt3DoVoGwiecT0rq9nEgmIP66rzJPkyDaVtTWKt9a+BXwMopb4ONAMdSqnSo6NfSgGZaDmJCj12fviWVWw63Mv+tgE2zM5nXXUeVsu55eW2NfTylYf2sbd1kCsXFvGf1y1kQbFngqIWqTS7wM1b1lfx8+cOY1aKj1w5j+p8WRZUiGPiCc3vNtbzwcvnntN+FpZ4eLG2hzevkzuTIjOFonGeOtDJV/+xj95AhLefP4t3XVhN+VmO2vI6rXz5pqVcv6yEnc39rK7KZcPsvHFHHYhz1z0U5r6Xm/jJ04exWkx84poF3LiiFK9TpjWcyJKybO59//k8e7CbwWCEyxcWsboqJ91hiTTKclj47PWLuHJhEdub+lhZmcOG2fljbsD4QjEe2dPG/zxaQzAa572XzOa29VVntEqimL7SdpZTShVprTuVUlXA64ALgNnAO4FvHv3v39MV30xRlZdFVV4WUDkh+zvS5eOdv34J/9GRL//a30H7UJDfvXuDzF2cgp4/1MW3H6sZfvyVf+ynPMfFdctkbr4QAE/u78DtsDCv6NySzEtLs3lwx3601lJ4VGSkXc0DfOiPrww//uXzR3Bazfz7qxacdZ8ty3Fyy5oKbllTMVFhihN4uqaTbz169Hwehs//dQ9FHjuvWiLn85NZUpbNkrLsdIchMkhJtpObV5dz8+ryE27zcmMvn/zLruHH33/iEHkuG++4sDoFEYpMl85JjPcrpfYBDwEf1lr3YSReXqWUOgS86uhjMYUc6fYPJ1+O2dU8SEvf2CXVRGaLJzT3bG0a0/6Pna1piEaIzPSL5+u4ZgK+wBR77cQTmmY5VooMtbtlYEzbn7Y20T2Bq3+IyRGJx7l7S+OY9sf2tKchGiGmv2drxk4pvvulRvxhWWlWpDEBo7W+RGu9RGu9Umv95NG2Hq31VVrr+Uf/25uu+MTZcTvGDqqyW0y4bFNvvuxMZzYp5hWNndM6u1CmIAkBsKdlgIZuPxvmnPvS7EopFpZ42NYgpz2RmcZbArUi14lTzu8Zz6JMzCseez6vLpDzuRCToXycpaRn52dhm4IFnMXEk14gJtSCYg+vHVWc6tPXLWKW1A2Zkm5dV5VUgC7HZeWG5aVpjEiIzPGzZw9zzdKSCVs5bm6hmy11koARmWlNVQ5zR3xht5gUn7x2IW67FHPNdCaT4m0bZiXdDMvPsvGqEUWQhRAT57IFBRR77cOPHVYT77t0zjnX2RTTg1Q6ExMqx2Xjv16zhNeuLqdzKMysPBfLyrMxjbMKk8h8yyuyeeBDF7K/bRClFEvLvMw9QaV3IWaSxp4Azx3q4ntvWjVh+1xQ7OF3m+onbH9CTKRZ+Vnc+e717GkZIBCJs6DEw9JSb7rDEqdpZWUOf/3QRexvG8RsMs7nJ1q5RQhxbuYVebjnjgvY2zpAJJ5gcYmXRXK8FEdJAkZMuAK3ncsXFqU7DDFBFhR7ZBUrIUb52bOHuXJhMS7bxJ1GZ+W7aOoL4A/HZCUYkZEq81xU5p3dqkci/RaWeFhYIudzIVKhuiBLpvmJcckV3gzjC8fYeqSXf+xqpSTbyfXLSlhWforq7rEINL8Eu+8Hiw2WvQ7K14JJ5n2L6cff30Wi7jlsB/9BNG8BetFr8FQtT3dYIoN0DoZ4aFcr337Dygndr9VsYlZeFrtbBjh/Tv6E7ltkjqH67ZgOPIR5sInowhsxz74Yl/fc6wgJcVKxCDRtgT33g8Ux4lpOpkSczM6mfh7e3UZfIMKNK8tYV52Lwypfn9Ii7IemTbDnAXAXwZLXQtmqs96dPxxja30v/9jVRqHHzg3LSlhekTNR0QpxQnIEmWGeOtDBR/+0Y/jxXRvruf+DF7Cw5CTD4po2w+9uAq2Nx1t/Bbc/ApXrJzdYIdJAb/8Dnme/DIAdSOy6k6HbHsJTtiCtcYnM8bNnD3PJ/AKynRNf+2JOYRY7m/olATNNDTXtwXPPLRDsA8Cx71581/0Azn9XegMT01/jRvj9a49fy237FbzrEahcl9awMtmelgHe/ItNhKIJAO7d1syv3rmWqxdL7Zy0qH0C7nvH8ccv/QLe/RiUnN1NsucOdvHBP74y/PiujfXc/4ELWVwmU4XE5JK09wzSH4jwv/86mNTmC8d4pbH/xC9KJNBbfn78hA2QiMHuv0xOkCLj9PjCvHSkh231vfT5I+kOZ1L5Outxb/pOUpvJ1w7tu9MUkcg0Pb4w973czKuXl51647NQnW8kYMQ01bZjOPlyjHvjt/D3tKUnnhNoHwix6XA3O5r6GApF0x2OOFfxGGz6SfK1XDwK+/6evpimgBdqu4eTL8f86KlaAhFZSjjlggPw7DeT2yJ+aNh0VrsbCkb5wZOHktoCkTgv1c+8QviNvQFerO1mT8sAoaj07VSQETAzSEJrYgk9pj0+TlvS62JhRk820rEQUlZ3+qvr8vHxP+9gV8sAABtm5/HtN6ykKn+a1gDQCSPBOLpZJ8bZWMxEv3r+CBfMyR93Sd6JMKcwi4d2tU7KvkX6qfGOJfEomnjqgzmB/W2DvPeurbT0hwB4/ZpyPn39Ioo8jjRHJs5JfJwbKLFQ6uOYQsLRsX+XkVgiKY8lUkWP34cTZ5cgTqCJxscej8drm8621ffynru2MRCMohR85Ip53HHJHDyTMMJXHCcjYGaQvCw7H7liXlKb3WJiVWXOCV/T0BfkcPVtyY1KEVr8hkmIUGSah3a2DidfALYc6eWpms40RjS5XAWz8K/9cHKjIwdVvDQ9AYmMMhCM8sctDbx6EpdiL8t20uOLMBCUUQfTkS5ZAdbkBLZ/w8dx51ekKaJk4VicHz55aDj5AnD/Ky3sONlIWZH5zBY4/4PJbUoZdWDECS0tz8YyahXPN62txC5LCaeeMwcu+URym9kGVRec1e6ynTY+POo7kc1sYv3smVOPq9cf4bMP7B6+3tAa/u+pWva1DaY5sulPRsDMMNctKyHLbuEPm+spy3Hx9vNnsfQkcx27fRG+tSOH/77qt8yp+wMJk5XDc95GyLSA81IYt0i9eDzBswe7x7RvPtzNuy6sTn1AKWAym0msfQ9D2RV49v2JQN4SEqvfgadiSbpDExng95vqWV2VS5F38kYCmEyK6oIs9rcNSh2YacgzaxW+W/+Kefud2Acb8C97G6b5V6U7rGEDgSgvHRk7BL+uy5+GaMSEqr4YbrsPtvwMrE7Y8H6jCK84of1tA3z2hkVsPNyDLxTj0gWFvNLYxy2ry8h2Tc4oSHESC2+AN94FL/0SvKWw7n1Qtvqsd3fV4iJ+8tY1/G5TPUUeO++8sJrlp1qYZBrpD0Q41Okb094+KCPjJpskYGaYHJeNG1eWccOyEkwmhVInn0hU5LFT0xPj1Y84OK/yE0QTCfY94uOhf5Nl1aY7s9nENUuKeaUxuV7BJQsK0xRRangKyqHg3cTXvg2XVS6whCEUjfPbF+v59HWLJv29qvKc7GuVBMx05Z57Pnr2ehLxGJ4MO8bkuKxctrCQB15pSWqfX+xOU0RiwtiyYME1MO8qQMnqR6dhTqGHD//xFZaXZ+Oymfn+Ewd5+/mzcDtkekZaOHNg6Wth0Y1G/z3Fd5hTyXbauGF5KdcuKT6t70TTTX6WjWVlXva0Jo94Kc9xpimimUOOvtOcPxzj5YY+HtzZyssNffjDRn0Ls9l0WgeayjwXP7x1FVk2My819HOg3c9337iSuYVyMTYTXL+8hEvmFww/vm5pMZdP8wRMPKHZ1zrAI/u7efFQN92+cLpDEhngwR2tVOdnUZk3+fWPKvNc7Bkx9U9ML52DIZ6v7eHRA93UtA+hM6ighM1i5gOXzWVhiQcwvt+8+6LZrKrKSW9gYkLUd/t5bH8XT9R00dwXSHc4GW/drFxuXVfJ7pYBthzpZUmpl9s2zMJsmllf1DOO2XzOyZfk3Z3edyKAgx1D/HN3K8/WdNI5xUeKZLtsfO2W5ZQcHdVrNSs+f8NiFpfKKlCTTUbATGORWJzfb27gm48cGG77zPWLePdF1dgso8vqnthlC4v4x0cvoWMgRIHHTnW+a8ZliWeqWflZ/Pi2NRzp9mNSitkFrml/5+f5Q128965twwWrr1lSzNdvWUaBFKCcsbTW/OqFOl6/JjV1Oqrzs3h+nOl/Yupr6w/yH/fuYFOdMc3HbjHxu3evZ0MGjXZaUOzhT+/dQH1PAKfNzJyCLOzW079mEJlpX+sgb/v1FnqPrmZYmevkztvXM7dIbqidSJHXwRdvXMLbL5hFJJagOj+L3EkqwC4y39Yjvbzt11sIx4xCvWtn5fLDt6ymbAqPGFlZmcPfPnIhzb1Bsp1WqguysJplfMZkkwRMmvX4wgSjcYq9jgnv8HXdfr79WE1S27cfq+HyBYUsOsPsZlWei6pRd36DkTg9/jAeh4Vsp5yQpiuv00qRx45SatonX7p9Yf7rb3tYVp7NBXPy6RwK8fDudva2DnHZQknAzFRb6/sIROIsS9Hc8IpcJ/U9fmLxBBa5EJpWdrcMDCdfwJjas7tlgIpcJ+W5mbO6XJ7bTp7bnrb390di9PkjeB1WvLIaxznTWvOnlxpw2czcsno2sYTm4d1tPHmgQxIwp+CyWSj02InFNdnSFydUnz9CIBKjwGPHfgY3htPBF47yrUcPDCdfALY19LGjqX9MAiaR0LQPhrCY1KTWjJsoJV4nJd6pm0SaiiQBkyaRWJxnarr4fw/to2sozJvWVnDHZXPHJDnORX8gMmaJ6XhCT8jqGgc7hvifRw/w1IFOFpd6+fKNS1k3gyqHzxTdQ2H+8kozP36qFpNJ8R+vms/Nq8rJmabF5wLhGK9bU05Nu49fPl9Hea6TT1yzgEBEVqSZyX63qZ4rFxVhStHIP7vFTIHbzpFuP/OLPSl5T5EaI6c0fujyuTT0BPjWowe4a2M9X75pKZcuKJzxdx/3tw3yjYf380JtNysqcvjSjUtYXZWb7rCmtGg8QZbdwlWLi7lnaxMWs+K29VVEojNryd0z5Q/HeHRPO9985AC+cIx3X1zN28+vpiQ7879UZ7JEQrPxcDdfenAv9T0BXr2ilI9fNZ85GVzewB+Oj1uwtnMoeZp6+0CQP2xp5NfPHyHLbubT1y3i+uWluO3ylVscN7PP8mm0p2WQ9//hZVr6g0TiCf6wpZHfvHCE2ASuP1+R6yLXlZytz8uyUZ57blnOgUCET963kyf2d5LQsLd1kHf99iXqusYemMTU9tyhLr75yAEi8QTBSJwvPbiPzXVjV8iYLnJcNmrahnhsbzuxhKahJ8A3HjkgI7xmsIFAlGdqurhkXmprH1XludjfPpTS9xSTb16RkVBbVu6loSfAP3e3EY1rmvqCvO9329jbOrOX/+zxhfnI3a/w3KFuEhp2NPXzrt9upaFHVmE6FzaLmUK3nbs21hONJwiE4/zkmcOUneP14HS3vbGPT9y3k66jo9V//PRh/rGrNd1hTXkH2oe4/c6tHO7yE09oHtzRyjcePkAwEk93aCeUn2XjppWlY9oXjipQ/vDudn70VC3BaJxuX4RP/mUX2xv6xrxOzGxpS8cppf4deC+ggd3A7YALuAeoBuqBN2mtp2WvPdgxxOi6e/dta+L9l86hdILmElbkuvjlO9by6ft3cbjLz9zCLL71+hVUnGqY81A7scatxLoPM+SdR1/Ocg4NWWnpD7Kg2EOO08rO5uQCkf5InCPd/ozOXoszE09oHt3Tzm+vszMvegCtzBwwLeDx/R1ct6wk3eFNiv5AlMf3dyS1xROajileaE2cvQd3tbKyMhu3I7Wny7IcBzVtg7CyLKXvKybX8nIv337DChr7AvzsmcNJzyU01HYOsaoyJz3BYYyc3dHUz8GOIarzs1hVmXPaQ+gjsTi7mwfY1TJAjtPK6qpcqvMc0LodWl4xVuGpXA8F80+4j6beAIdHLXk9EIzS0BNgVr6svni2IvE4Lx7q4s7rbMyN1qCxsM+8gOcaenldimpbTUWbDveMabt7SyNvWlspU+POQV23j2g8+UvQ4/s7aBsITv73CK2hbQe0vAxmO1SshaLFp3yZxWziPRfPoccf4ZE97XjsFj53w2JWVOQMbzMUjHL3S41jXvvE/g4C0RjZThuLSz1yU0+kJwGjlCoHPgos0VoHlVL3ArcCS4AntdbfVEp9BvgM8Ol0xDjZsl1jD9xlOU6ctomdA7m2Oo/7PnABvf4oeVlW8rJOMac72I9+9LNY9j6ABXAA9nUf45OHr2Bnq1Ex/0dvWY3DaiI0auiqnIymF7NJ8bmVfqofeivEjARElc3N/BvvS3Nkk8dpM1HosdMxmDykdLy/VzEz/GVbE9cuTX3CsSLXxZ5WWQlpunHaLLxxbSVHun38fXsLjb3BpOe9aayzFYnF+eXzdfz46eOJoRtXlvHVm5ed1jHw2YPd3PH7bcM3l2bnu3j0tWD/0+shcfTOdlYhvOufULhw3H1k2S1YzWrMlzOvU4bvnwuLMvHFNUGq//FWiBnntyq7l4U33pvmyDLbeMVVZxdkYbfKBIJzMd5xLi/LNuHfgcbVtAXueg3Ej04td+bCO/8BJctO+dLqgiz+902r+M9rgtgt5jEzCmxWE9X5LmpHTVVyWM38xz07ueOyuexrHeCdF86WlbRmuHQeQSyAUyllwRj50grcDNx19Pm7gNemJ7Qzk0hojnT52NsywEAwclqvWVGezdKy44VwzSbFf71myZnX1vD3QNtO6D1ywk3ysuzMK3KfOvkC0F2D2vtAUpP35R9xx1LN69eU85Er59HU6+eT1yZfPN2yuoz5p1PILeyD9j3QVQMx4+AXjsapaR+ipn2IcDRzhx/OROWH/zycfAEg4qO05dH0BTTJCj0O/t9NS5NWN9wwO4+lZakpvioyS2NPgIaeAMsrUv/7r8h1clCmIE1bswvcfOnGpYy8Bl9VmTNuoeduX5g9LQM09Z7FssGhoaPn3IPHv3BEAtCxFzoPDH8ZD8fi7G4ZwGxSbBhRz+2hna3Udp24H4aiMWraB9nTMsDfdjQnjexdkG/G8vz/HE++APi7oP7FE+6vuiCL/3jVgqS2t26oYp6Mrj0niXic8kO/H/59AxAepLTtqfQFNQVcMDefK+fn8r2rXPzkajs3L/HyoSvmZnzB2Ey3uNTLpfMLktq+fOMSSrOPJjSiYWPU3JEXYKh94t44FoWN/3f8WAgQ7CNW81jSZoPBKPtaB6jr8o2ppemwmplT6B63nIPdYuaDl8/Fbjn+9bo024HNYvr/7J11mFzV+cc/d9x31t194+5OSHB3d5cWCrSlFPqjBdrSAi1Q3CUQXEIIgZAQd9nYurvOjsv9/XE3uzuZjRFZyXyeZ5/NnLnnztndm3vPec/7fr9YXV7eWV1KRbM9WFIZpG8yYERRrBIE4Z9AOWAHvhdF8XtBEKJFUazpPKZGEISo3voLgnAzcDNAUlLSiRp2r3Q4PHy0oZynvtuD0+NjdJKZpy4YfkjhxPhQHa9cNZYd1W1YHB4yow3kHanves12+PRGaNgNKgOc/g8Ycj4oj0IczNXLBM/nZWiUin9taeWTTVUY1AqeumAYH940kYoWG5FGNUPjQw4dPGouge9+D3sXgUwBk++idcQtPPFzPR9trATg4rEJ3Dsn65iVYfUl/ek6/TV43B4U7ZUB7SpLRR+M5sSRGq7nkTPzaLa6UClk5ESbMJ3g8pMTxUC/Ro83X2+rZnxqGArZid+riAnRUGdx4nB70ZzEFsCD+RqdnhXJp7dPobDeQohWyZC4kIAd922Vrdz94WZKG22YNAr+et4w5g2JQaU4jGuyqQi+fQCKfgC5Eqb8BoZdBMuegPxPQZDBuJtoH3c3T/7SwofryhGBU/OiuXJiMu+uKQM4oC5Ddaudfy3ZyyebKhGA04fFcvHYRD7aID0jwjUC8qa6wI62A1usK+UyrpqYzKjEUCpabESbNAyNN/V7B77+fp16fV5UlqqAdmVHYFuQbtJ0Dl5OWoJi9XPg8zAvcx5y45PAwDOd6E/XaKRRzT8uGs6OqnZabC7SIgzdm9JtlbDtI1j+D3DbIHoYnPUsJIw5+g8WPdAeeM13NFSyfmctM7IiqWi289Cn21lf2oxaIeM3c7O4fHzSYWf5j0kO4/Pbp7ChrIUmqxO7y8v/fpayClttbrQqOQ5PcLP5ZKdPMmAEQQhFynZJBeIAvSAIVx5uf1EUXxZFcawoimMjI0+sMOL+bK9q5S9f7+qyJdtU3sq/l+w9rEyOuFAtpw6J4YIxCQxPMB+Z3ai9Db6+Vwq+ALg64PPboH7nr/gpehCeAQb/uJcnZiTrWkxYHB4AOpwe7nh/M1qVnIvGJjIzO4qIQ9lViiJsflcKvgD4PPDLvxHLV7NwUxWiKB2yYH0ly/Y0HN3P0E/oT9fpr0GhVEDeOQHtQubcPhjNicHq9PDY1zv527e7WLqrnndWl3HTOxvYWTM4MxEG+jV6vPlyqxSA6QsUMhmxIRqKTnJx88F8jSrlMkYmmrlwTCJz82ICgi/NVie//WgLpY3Sxki7w8M9H26msP4w7kc+H2x8Qwq+gLTju/EN2Pm5FHwBEH2w7iUoXcEnGyvxdT6HF+fXoVXKCdEqiTSoSY3oPfvkh111LNwoZb34RPh6Ww0RBhW6zjKCJSVOXKNvDOyYMvWgQzdolExMD+eisYlMz4o8vOzdPqa/X6dqtRpxyPkB7WLarD4YzQCiYi2Klf+S5qyAvGCxNJfdX8RxANDfrtFok5Y5udFcOCaR0cmhqPdtNFRvhqWPScEXgLrt0mvXMcgaUWqxj7ohoLkiagY3v7ORPbUW/vdzEetLJbMJUZSyAHfVHJk4em6ciTHJZl74qYiXlhd3lVSeNjSGpg4niYfS4gwy6Omrbd1TgBJRFBsABEH4FJgM1AmCENuZ/RIL1PfR+A6b8qbAjJGlu+tpsrp6rR09ZnTUQdWGwPbmEogf3fXS4faysbSZz7dWY1QrOGtEHDkxRjaVtfi1jUw0IwgCmBPhioV4f3oCeeU67Cmn0DzyNhavcTI3L5qYEA3P/1SIw+2jvNnGiB5igVUtdn7e28CKggYmpIUzOzuKpPDOm4yzXZr47Ye8cg3RxnlUt3WXuSzaUcNlE/rfDtLJhtftQt5UANN/J+1GyOQw4lKoO8ogXz+mqcOJQa3gzlkZbKtqY3SSmegQDZXNVsYkB21QTyYqmm3UtDnIiTnCzMRjSLxZS2F9R7AEbgBRWG9hcX4dO6vbmTckmskZEYfeoDgAde1OCuv9Fx0+EcqbbeQd5JoorLdQU1PNtJ1f+r8ROxz2LAo4Xlm8lKSwq/wsVrdUtHDl+CROHxHba6q9KIp8s60moH1nTTvjUsJosbr43fxs5NFDQfDBmudBY4bZf4K47p3simYry/Y0sLKwkWmZkczIjjy0UUCQI8Zms6JrLoRp98H2j6WMqBGX4avf09dD699UrA1sy/8MJt8NWvMJH85JQVNxYFvJz2CpkTaJfyWiKLKlopVS53BmzHyCsM0v4FNqKRt2L3/bbkIU7RQ1WPl+p5S1N39oDDkxRrZVtrFsbwOhehVyAfbUdfDz3nqcbh/njopnYlp4r1mqubEm3r5hPH//bjflzTZOyY1mVJKZsclhGPt5Rl+Q409fBWDKgYmCIOiQSpDmABsAK3AN8GTn9y/6aHyHTWQv7gB5sSaMx7tkQWOGkERo268cxBjt93J1URPXvbm+6/W7a8p5+eoxXPuGf9vHt07qDqbEjkB+0Rt4bC18X+jintfzu46NMKi4fkoqLywrItLYPanscLh5/JudLNoh1Wou2lHLotQa/nflGEL1KlDqIGE8NBX6jc8VlkOT1V83Z1zKwEvtHIzIlSp8oohs/auQNR9EL6z6L+KE2xis0mEGtZyUcD3//qGgqy3SoOaZS0f04aiC9AVLdtYxJtncp0J5MSEaCupO7gyYgURli41r31hPZYskrPvN9hrump3Bvadk/arrKESrJMKgorHD/xkZaTxwmfG+McQa5IyKHImhtaz7zeYSfOmzkVVv8uvjihlF3S5/p7fxqWHcNTvzgOVvgiAwPjWMtSXNfu0TUsO5aGw8aoW8e5Ex+U4YfjHIVX6L1labi4c+3c7KQslp5rv8OubmRvH0xSODov7HGLVKjVOUo974hvQ897ph5bN4p/2hr4fWv4nsxR0nYTwog45cx4391jEARGSD5ug2wbZVtnHJS2tweX2E6zO5IPdFpmXFcMenxbQ7pHu2ViljaLyJyhY70SYNz3TOBX/cXc9nmyr5v3OH8psFW7qqHj7fUs3r145jdk6gYoYgCExMC+ft68fTbnejVMgw61Qoj6TaIcigpU+uAlEU1wILgU1IFtQy4GWkwMtcQRAKgLmdr/s1w+JNnDm82xfeoFbw8Jm5xzW66fJ42WvV0DHv36DoDoL4xt1MkSyFtcVNNFgcONxeXvx5v4CH18eyPQ1E9tiRk9r2SzZSamkQQ3n4S//dkcYOSRfj2skp5PbYGS5psnUFX/axtqSZwn3p83IlTLrdr7zJHjOOctMYYkK6J5PpEXpOHxZLkL7H5/PRmn4uotoIWz+AbR8hGqNpTpjd10M7btS2O3lvbZlfW0OHk+rWoA31ycbi/FpGJfZt1lNciJa9dYOz/G0wsrvW0hV82cfLy4upbPkV4rlIDixPXjAcpbw7eHPbjDSyD6Ixt28M6ys62JZ8Hei7hS5FYywNaRfgNXbbDnuihtEcO507Z2eQ0Jnpkhyu47yR8YfUHjpnZBwp4d3ZKhmRBuYNiSbCoAmcAxmiAjIGihusrCxsQq+Sc9O0NO6ancGwBDPlQYHKY47D7WVH5Fn4lAbY8j5s/xhPSDL52vF9PbT+TcoUSJrU/VofCZPvAEUwQHjciBsJOWd1v1bqYP4ToA8/qtP+uLsel1cKnDRZXby8oZ1nVzZ02UhfMDqez7ZU8ZtTsjh/VDwfrfff4B6RGIrD7ePWGelcOSEJRWdQ/cVlhTgOIjvh8vqoanNQ0milze4+4HFBTi76TFlSFMU/A3/er9mJlA0zYIg0anj83KFcPSmZDoeH1EgDqRHHLzLudHv5aGMFj365kwiDikcmf8TUsHbUpghe2KniuRe2ApAbY+TFK8cEWDkCeHw+P5cXAE8vx3lFEW8vda4JoVpumJKKsccOlXiAeli/5phhrJvzMYrmAjyCkh+awnhtQRUPzMsiJ9aETBDIjDIQEzLwBXgHAz4ffNcax+g5r5PgLkVEoEyeSn5LBJf09eCOE74DXPP7HtpBTg7aHW62VbZx64z0Ph1HfKiWb7cHlnkE6Z/4fL08R33iUclFzMqO4pu7p1HeZCXcoCYz2ohBfeCpW88x3LDYyR8mv8FQdR0RISYKZYm8scrCtOwXmWRsJMyo5eNyA8+8UY5SJuNPZ+YSZ9aSF2fqdiM5CBlRRj64eWKX5WpWlJHokMM3AfCJInKZwP3zsvnPj4U0d2bDNlgcxIfqpOzZIMcEH7DLG0fxiFdIEyvxCTIKxQTwRjK2rwfXnzEnwcVvS45hXidEZEFocl+PanATmQPz/gajrpDkCyKypaDMUbK/mxFI96ArJiYyKT2MXwqayK9p47GzhjIlI5wXlhV1HXfFhCSKG6zc9cFmQApS/2ZuFv9YvAfPQeaHZU1W7vt4KxtKWwAYFhfCs5eNJC3o6nbSMzitPU4wZp2K8am9R2atTg8ymYD2GLlYFDZ08Ocv8vGJUn34Hd87MWnV/OuidJ77pVsTZlethc82V3HrjDRueac73VguE5iVHcW7a8r92/ZLn2u3u3G4vVw0NpG3VpV2tZu0CjKjDH7BF4CUcD3TMiNYUdDtbjAsLoS0SCkYJYoiFocHbUQy53xUhXQflHZ2M6KMzMjq1fAqSB+iUMioaLbzh8/qkZziAWp5YP7g1aPIijRy5cRkXl3RbeseolWSE3NwV7Mgg4uVBY3kxhr73H0oNkRDZasdr0/s01KoIIdHdowxoGTo6knJvWqoHC5ymUBWtJGsXrJe2h1uNAoZqh6WuD3HYHd7+dPPFs4fncnu7Rb21pXy9MUjeOiT7Tx85hge/SQft1cSl3R5ffz12118fvuUwwq+7CM2RHvA490eH3a3t9dyona7m6QwLddOTuHTTVVdwReAd9aUc+qQGKZl9r1Q6GDBqFVT1mTjlRVNwL6/VwMPnxFxsG5BQMreMgTnqCeU0CTp6xgyOyeKF38u8gvEnJoXw70LtjIiwUyEQcX9p2ajUcl47ZcSzhsVz4frK1ArZITqVaxe271uKmuysafWQkaUgZtnpKOQCbTb3Rg1CgRBwOH24vH6+KWgsSv4ArC9uo1vttVw15zMXu/fQU4eggGY40S73c3Pext48ecitAoZd8zOZHJ670JNR0J9u5P9g7jtdg+17YElEj/tqee6Kcm8evVY3lpdilYpZ1pWJBaHmxevHM0Ha8sxaZRcPTm5S/+lptXOV9uqWbC+gismJNNidXHn7AzWFDURZ9YyJM7Ua1aNSavk8XOHsmh7Ld/vrGNGVgRnjYgjwqCmrMnKxxsq+WZ7DaMSzbx340Q+XFdGm93D1ZOTmXCA4FWQvsXl8bKux4NjHxt7aRssqFRyzh0RR4xJw3c7akmJ0HPeqHjGJAd1iU4mftxdz9D4vg80qhVyQnVKKltsJIcHNQf6O8nhet65YQILN1awpaKN80fHMycn+pjX/Fe12PlyazULN1aQEWng1pnpjEoK9RvDxxsq2FrZxviUMJqsLnbuc/EQRR49K49mmzvgWe5w+2hzHJsU+e2Vrfzv5yJ21Vg4b1Q8542OJyFUR2Wzjc+2VPHZpiryYo3cOD2NN1aWBPSvbQuWfR5LLHZXgF4PwKbywfs8DxKkJ8MTQvjgpgm8taoMi8PN1ZNSiA1RExMynE82VrCntoNRiaHUtjlYtKOO80fHc/vMdEobrdT3ssbKr27n8XPzMGhU/OGz7awvbWHekGhm50TxzA8FNHY4OWtEHPOHxvBdD4kGQYAXfipk4cZKsqKN3DIjrev+HeTkIRiAOU6sLGzsSlUDuP7N9bx/4wQmZxzdbkOsWYNCJuDpEYWJMKhIMAem/Z6aF41Zp5ZKokSR6jY7f/p8BwBXTkjilavHoFLIJfcjpPS8N1aV8vJySYG82ebi+521yASBEQlm8qvb+XF3Pd/c1buFZHK4nltnpnPTtFTknRNOq8vD41/vYskuSVW8pNHK8oIGPrt9MvFmHbLgrm6/RaWQMyktnI1l/hO0cX1ky3si8Hh9fL6lmi+2VHHWiDiKG63c+NYGPr9jCtnBLJiTAlEU+XlvAw/Mz+nroQCSE1JRQ0cwADNAyI018aczh+D1+rqeg8cSj9fH6yuLee2XUgCKGqysKGzk8zumdGXJ5Maa+MPpuby9qpRnlhbQ7pAsdNUKGW6fyIOfbufO2RlolDIc7u70eYNaQcxBBH4Pl+KGDq54dW3X5z69ZC8VLTb+fGYe//mpgAXrK6XjGq14fSIT08JZVdTkd46Eo8gaChKIUaticno42yrb/NrHBBd+QU4SFHIZ41PDGZcShk+Usgt3Vrfx4CfbukR1//rtLsy64YxLCeXTTVUY1ApGJpnJiwt0QzwlN4qUcAPnvbiS2jYnAP/7uZhN5a2EaJWsKmri6e/3cvvMdEJ1SlpsbvJiTVS12vlgnaQvU9xoZUVBA5/fMYXMg2h7BRl8BKWYjwMur5c3epTt7KNnLX9xQwff7ahh6a46qo5AoC890sDTF4/oKmky65T886IRuL0iD5+Zy12zM9Cr5ExJD+esEXEA7K2zsKKwiR1V3T72H6yvoLrN2RV8Aahps5Nf1cb9p2Zz5+wMwvUqHpyfg0wQWN0p7PvcZSNJ7qFxY3N52Fzewldbq1lf0ozF7vabdFY027qCL/to7HBRWG8NBl/6OW63l2HxJiamdQdcZudE+YkuDjZq2h28vbqMhg4Xr68sZdmeBuxub1AI9SSiqEESAI07Ai2L40m0SUNRfVCUdKBxPIIvID2n317tLxRuc3nZW+t/j1LIZUzPjiIlQrpfCwI8MC+bjzqDHws3VHLvnKwuPRmTRuH3fN9bZ+GLLVV8saWKtSWNrClu4qut1awqbKTV5u/KtD976yxdwZcQrZJ75mQSG6JlU0UrH2+o9Dv2u5113DYzneTO54pcJvDbuVlB6/VjjMXuZnSimfEp3QGXubnRxJuDga5DsbfOwuebq1iwvpwtFcGMoYGOIAjUtjn4YWcd26vauGl6GueNiuO3c7O4a3YGjR1Ofn9aDgmhWjqcHtYUNREfquXC0d3i5WOSQ7lkXCKF9R1dwZd9rCtpJi+2O2DzzfYa7p6TwW/mZnHPKRks3Oh/D7S6gnPMk5FgBsxxQIZAaC81z2adJCi3vbLVb3coLULPa9eOOyzxXqVcxtkj4hgWH0Kz1YVOJefBT7axvTO4YtIqePWaceTFmQjpHINKETgR1Chkfs4KADJBID3KwD+/73Y+uveUTF69egwKuYwoo5qkHruwHq+PjzZIgsD7uGt2BrfPTEerki4tpUwWkLFzoDEF6V/IZPDZ5iomp4Vz6bgkZALsrGnn5z0NzB86OJ2qFIKARiXDZfcXVQterycPKwsbGRpv8gtO9yUxIZoukdMgQRQyGVqVHLfd49fe2z0qI8rA29dPoLTJilohJzlMy7pSqQyltt3BaytLuHJiEmmReiamhnc93zeUNnPla2txuH1EGtTcOD2VJxft7hITvmRcAn84LY8QXe9OMOoemgb3zMnk6e/3YHV5uWV6GhqlHJur2zFEFMHr8/HxrZOoaLZjVCtIjdCjDN5zjylKucCCDZVMy4rk8glJyASBHVVtrClpYn7QefKAbK9q5d4Pt3QF5g1qBS9fPYbJ6UHtnIFKcUMH1725nrImafM7RKvkj2fk8sDCbQDIBHjxitF8ettkKlrsaJUyft7bQGWrjXtPyUQUYVh8CKkRehoszoDzywQQ6V7znD8qnq+31rCxvJUbpqaiVcpxe/e7fwetqU86gn/x44BCLuP6qal+ookapYxTh0Tj9vp4fWVJV/AFpBS0XwoaDvv8giCQFmlgbEoYa0uau4IvIOnBfLGlCq9PpLLFRlOHk4RQHZeMTfBzPvrt3KyAnY8Wm4t31vjvrP33x0I0KjljU8L8gi8glRP97Zvdfm3/+bGQwobu3dpkrZ2FlyUwNqG775hkM9nRRuwuLxXNtkPupgXpG+RyOReMSWBlYQOxQhMRYhPrS1o4JS+6r4d23Ig1a3lwXg73zc3k/Rsn8MpVY5iYFuq3mxFkcLO8oIG82P6z+x4XoqW4MRiAGUi0WF1UtthwHsSaFMDicFPRbKPDefi6K7FmLQ/M6y6PM6gV3DIjjeRwHS5P4OeZdSpGJoaSG2tCp1Zy/ZTuuUmDxcmbq0rJiTF1Pd+tLg8r8kv581QjFw81ce6oOJ7/qdDPyWnB+kr21LUHfNY+cmKM5MQYGJ8axo+767F2Bly+3VHDZeP9hTWzow1kR5uIMmoYkxyKUaug0Rq4qAlydKgUMi4el8jqwkZihWbCfU2sL2tmelb/ETq2OjxUNNuw9COr3rXFzV3BF4AOp4dXV5RgPYL/s0H6F8v2NHQFX0xaBdcM1xHta+CmyYncMSudR88aQmmTDY9PZExyKE6Pj6e+28Oa4mae+aGAZ5cWcOcHmyhtspIRbWByur+O5fmjE1i2p3tNFx+qY2N5K8bObMO/nTeMofHdc8rUCD25vZQ4BRncHHUGjCAIFmB/VdY2YANwnyiKxUf7GQORMcmhfHzrJFbsbUClkDMtM4Kh8SFYnR52VgemmhU2/LoJ9v47o5FGNcMTQrj05dUU1HcwLTOCSWnh1LQ7eP3acawtbmR8ajhjkkP9dnhbrC521bQH2GV6fGKvorsA7Q5Pr/a8bTYXeN1QtBTFt79jZHsVHwy5iJ3TbqHAE8mE1HBabC5+/+l2fthdR2aUgb+cM5QJqWH9Ztc5iGR5bvC08FrmSgzfPgsyBcMn3c8GZxgweIMweXEmXl5ezDNLC4kza3hgXg7humCy4MmA1yeyvqTZL9W4r4kN0VDaePhlqkH6Dp9PZHVxE498sYOSRivzh8Zw39xs0qMCLUe3VrTyl692sqmihXHJYTxyVt5hCz+fMyKOhFAtu6rbiQ7R8K8le3ltheTaccesDFIOkk07NiVsv7lJOEPjzV3vK+vzub3uz6grVnBh1HAaR/2VBesDz9PTtWh/Ys1a/nfVWIrqO3j0q/yu9opmO0X1HTx5/jCKGztIjTAwKS2cWLOWFquLzzZX8swPBfhEuHNWBheOSSDCqD6s30mQg+NwezF7m3kldRn6b/8DchUjJj/IRkcU/eF5nl/dxuNf72R1cTOjEs38+ew8Rib2vT5NeXPgvbek0YrF6UGv7j0DLEj/Zl+5z5h4Lf8cXkXqhsdhRzMTRlxNYeJ13Le4nL31FlYVNfGH03NoswUG2xxuHxa7h9QIA3+/cDjrSprZWdPO2ORQksJ0rC5uYnpmJNMyI9hTayEzysAFYxJ4e1Upb60q5ewRcdw4NRWby8uktHASQgdvaX+Q3jkWq4p/AdXA+4AAXArEAHuA14GZx+AzBhwKuYzRSaGM3k/gTK9WcMGYeP72rX/myNSMX7cLMSMrkvd6WKNdMSGJx77a2SUotXxvIx0OD2F6FU8u2s0HN00gTB84oVlV1Mjeug5MWgXtPVKbI43qA9YIJ4RqiTFp/ByYjGoFiaE6qN0OH1wGojQO5fYPGSGXM+LMZ7C4Ba5bsIUNneKue+s6uOb1dXx919SgCFU/Qq2Uk2dbj2HFX7vaDD89zMiz4oD+IVB6rGm1OXnhpyKW7KpDr5JT3erg3gVbePPacUzrRzuFQY4PO6vbMetUXeWi/YFQvQqL002H09Ol1xGkf7K3zsK1b6zr2rT4dnstVqeXF64cjV7V/berbrVz41sbaOiQMj3WlTZz09sb+Pz2KUQfhvaQUatkZnYUoToVl7y8GlGUNks+3liJIMBfzx12wBIeuUzodW4CQEcDys+uR2gqBEBZt5XYr67kiVkfcMci6XmtUcqQC8IhS6ZTwvUkh+nYVtnGs0sLutqX7W3guimpXLpfJszq4kb+8vWurtdPfrebKJOa8/tRMHQgo1MryelYi37lk50tVgxLH2LEOW8BWX05NOrbHdzyzkYqW+wAbK5o5fo3N/DlnVP6fGE6NiWMt1eXoZLLkMmkhfe8IdHEmILaOQOVYfEhfLi+goeG20n96c6uds2mV4kVNTg8c1DIBNaWNPHbj7byzCUjCdMrabZ2B2KSw3XEda6NEkJ1JITqOL/HZ+Ttp2F17qh4nvquu4zz081V6NVy/nzWEBTB8qOTkmMxm5sviuKEHq9fFgRhjSiKfxEE4Q/H4PyDjjOHx1HRbOeDdeWoFDLumZNJWoSeL7ZUUdfuYHiCmeEJIeg6J2w1rXY2V7RS0WwjN9bEiMQQQrTSAmF8ahgPnZbDsz8U4Pb6CNEqu4Iv+9hU3sqD87MZmxzGxxsqCdWrGJMU6rcr9+XWalYXN/GbU7J4e3UZJY1WMqOkyO6+m0xFs43NFa3UttkZFi+N8aWrxvD7T7ezs6adtAgdT54/nGRqoHJDV/Cli20LYOZDVNnNXcGXfTg9PukzgwGYfoPX68W484OAdm3BlzDmgj4Y0fGnvMmG1drOp6f7iLXmY1NHscKeQmmjNRiAOQlYU9xEbmz/ugfJBIHYEC2ljdZ+YY0d5MAUNXYEZIz+vLeB2laH3/O2rMnWFXzZR02bg/Jm22EFYPYRbdvDD9MKEQWBPcpc7l/u5dNNVdw9J/OQC9fSRiubyltotroYmWhmWHwI6tayruBLF45WJoW2c2peFKOSQmm3u4kyaRA4cLbqntp2Kprt1LY7pJ3f0fF8trkKnUrB/admMSrJHNDnyy3VAW0fb6jgvFHxwczYY4Ddbidk57sB7ZrCRTDq3BM/oB5UtNi7gi/7aLa6KGuy9XkAZnxKKC9eMZr86nacHh9Z0QZGJATvw31OcwlUrANrPcSPgbhRoDx0UKy5w8mm8lZump5KvGNxwPvhexfwxNwrWFknx+XxEaZXSgHzqWloVHI+3lCBXBC4e04m60ubiDPryIszoTxIEGVYQgj51YEVBgs3VnH7zAxig0LYJyXHIgDjEwThYmBh5+sLe7zXe+3KSU6cWcsjZ+ZxQ6dOjFImcN2bG9hV211T/a+LR3D+6ASaOpw89Mk2fi5o7HrvgfnZ3DI9HblMwKxTccv0NM4YFovXJ1LVag/4PKNaQVqkntve3cQ+LdwIg4oPbprYFfDIjDKyOL+OJxft5szhsZwxPJa4EE2XN31Nq51b39lIfk33GP9x4XAuGpvI+zdNoKnDRahOSZijAt4+G8ZcE/iDmxJAoUWnVqBXybvqwrve7kW4OEjfIZfLsYVmoStf5dfuNmcwWJPCDWoF/xhSSvyPv+lqS4zIoyzr9T4cVZATxS+FjYxMNPf1MAKICdFQEgzA9HtCNL2J7yvRquR+bUaNAkHAb0IuE8CgOYIpWeUGYj4+GzxSBmqiSs8Ls97modUadEr5QbuWNVm56rW1VPRY9L5y9RjmRhpBrpRKiHugNYZy3ZRUrnptXZegfohWyYJbJpIT469dsLO6jQ/XVbCxvIX8amm+MDLRzCtXjyU7xnjABXVGlBHy/R0Ts6P7jxj2QEehUOALz0JWucGvXRmR0jcD6oFBLUcuE/DuZ9ZgOpL/D8eJdruHP36+o6vkTi4TeO+GCYfoFeS40lIG710ETd2ZdVz0Jgw575BdNSo5FS02NpY5uGVqVMD73pBkvt3dxrtbJLFyuUzg6YtG8PSSvXh9Ik9dMIy1xc3c/t4mvKLI70/LxeJwMzXzwBt0OpWCOHNgYD0+VBPwbAhy8nAs8p6uAK4C6oG6zn9fKQiCFrjzYB1PZpQKGSkRehLDdGyvavMLvgA8/s0uatvt7K2z+AVfAJ79oYCypm5RMEEQSAzTkRKhJzfWyGlDY/yOv21WOh+tr6Tns62xw8W6kuau12cMjyVUJ2XPfLKpirdXlzI8wQxImS+7atoZmWRG1+Nm8ddvd1HTZsesU5EeZSDMoIaq9dBeBZZaKSLdNUgZnP4UGCJJCtPxyJl5fmM8c3gs2cHsl35Hx5ArQNNj0acLpyPt9L4b0HEmWmglfu3jfm2qxp3E2goO0CPIYMHrE9lU3kJOTP+7D0UZ1RT/Sp2wICeO3FgTc3L8J/WPnjWkK4t0H+mRem6ZnubXdtdsKRP2YHi8PorqOyist+Db9HZX8AUAl5XchsU8cf5w6VncA7fXR0GdhV3VbdS02lhf2uwXfAH427e7aNMmwuxH/D903I3Io3N4eXmxn5thm93N8r2B5gHf76wj3KDqCr4AbKlo5Z4Ptxz0Z9s3B9mHSaPgwrHB8qNjhSgCqTNA3eP+ZohCjMjuszHtIzVCzz1zMv3abpyaSnpkoHbSiWZlUaOf3pHXJ/KfnwpwHEJgO8hxpGaLf/AFYPEfoaP+kF11KgX3nZpFdauDb5rjcYX3KKeXK2me+BAfbuvO0Pf6RN5ZU8aUTqHdF5YVgSCVfIoifLKxkhUFDdS12dlR1UZxQwc+n4jXJ1Lc0EF+dRttdjfDO6sGuj5KJvCnM4b0q3LnICeWYxFebhVF8ayeDYIgpIqiaAd+OQbnH/TYermRt9vduNwiVa2OgPecHl9AmdE+wvRq/nLOEC4Zl0hdu4MIgxqtUsaC9RUBx7b0cB/KjTXxxnXjWF3U1HXu/Oo2qlrsPPDJNtrsbuJCNPxuXjb/XCxZSlocHlz7j8PROena8DqMuQ6yT5NKkVJnQMK4rsPOHhlHepSB0iYrEQY1Q+NCCNUHb0T9Cafby6Pr5NxzxidEWAtAkFGjzeTNLXL+2bcl48cN0esER1tAu89l7eXoIIOJ3bXthGiV/XJCFG3SUNwQvAb7O+EGNU+cP4z86naarC7SIvUM6cVBTatScOuMdKZlRlLTZifOrGVonAn1QTJX2mxu3l5TynNLCxieYOYjfWXAMSZ3HeNTw/zamq1O3lhZyivLi/nd/GzeX1vOab3YDjdZXdi9AiFjr5NS+ltLwRALcSNxy/XUtQc6E/VmwVrX7iTSGPh/yOrqZb7Qg9xYE5/cNpmdNe34fCJ5cSYpKybIMcHr9SLfvhAm3SFFYwQBvG7cBctQDDv/0Cc4jqgUcq6dnMK4lDCqWm3EhmgZEmdC1w80r3oTm65rd+L2+NAcItMsyHHC1Ysova0JvIfnqDo+JYyFt05iS0UrO2a8QrxjLz6nFV18HgvLQvD4/IM7LVYXsSGmrn+bemQ6NttcjE4O5aa3N7Ktqg21QsZTFwyjus3BM0sKcHl9jEkK5akLh/O/K8eQX91Gu91DZrQh6K55knMs7m5fCYJwmiiK7QCCIOQCHwNDj8G5Twoyo4yo5DI/R6GLxybSYnNR3BAojDsrK5LksAPXDEYaNczM9k93u3ZyCo99tdOvredEzenx8tqKEr7aVtPVds+cTB7+fEdXTXt1m4OXfi7mvNEJvLumjAtGxxO7f7163Cgp20X0wcY3pLbZj0DyZL/DtCoFY1PCGJviP1kM0n9QK+VMy4xk3nvbgX2R+1r+ffGIvhzWccWtj8E29HJ0297ublSocYUO0ohTkC7WlzT3y+wXkJyQVhc19fUwghwGUSYNUaZD67iYdSqmZEQc9nm3Vrby9Pd7Adhe2Ubx3AvJLF7qd4x85GXI99Mi2Fzeyn9+LOSMYbF8srGKogYrOqWc3FgjTreP4kYpsHfNpBSiTRppYZ4yBZjSdQ490hzigU+2+Z17Vk5gCv/8odFsKmtFq5Rj77G5dPrQmF7T8HuSFmkgrR9kPQxGtFoNbdkXEPLtrX7tzvM/pD8oUJi0SialhwPhhzz2RDIlI4L//Oivi3Tt5BSMwZL5viMqF2QK8HWvixh3IxgDA8u9IQgCDo+PR77c59CmBbTMyha5eXo44B+AOXVINAs3VpIXa2JGVgTf7qjteu+cEXF8n1/Ptipp487p8VHZ4uCf3+/pOmZjeQsvLCvkyfOHMTfPv0IhyMnLsQjA/A0pCHMGkA28jVSWFOQwyY018u6N4/nH4r2UNlm5aEwCl41PYltlK++sLuM3c7NYsrOOgnoLN09Px6hR8NuPtjIswcyE1DC+z6+lzuLksnGJVLc5+D6/lmEJZuYNiSGjU/jvzOGSRszrv5Rg1im555QsShutvPZLCbNzohmdZOaHXf7pe14x0IK6tt1BlFHNrTPSuGJCMirFfjsAcaPgioWw9C+SONb4W2D4Jcf19xfk+OD1iYQblDx29hBe+6UEhUzg5ulph9QXGMjUWLxYUq5niNqMYdcC3OZU2iY9xCpLNGf39eCCHFfWFDeT2U933GNMml7tUIOcPOzuUabs8vp4piiW3894moTtz0tBkxkPQfKUgH5bKloBSI3U8832GnJijOhUcrKijcgEgasnJeMVRU4bGuunt+Lz+VhV1MRX22qwu7ycOyqO+0/N4oN1FehUcu47NZvRvYjpjk0Ow+n28aczc/l8cxUljTbOHhnL1RNT0Cj7PqPhZMVid7HbMI68U/+Fcf1z+OQqrJN+xxYx6+S0Kj1MRnXqFz39/W7a7R5ump7G/KHBRXSfEjMcrvpcWme0lcPoa2DUVSA7/Llpb8/TZXsbeOzsIbx6zVieXryHNrubqyaloFXJuGlaGpsrWnF7RW6cmspbq8qYnhXBzOxIrntzfdc5lHIBiyPQtnpJfh3XTkqmqNHGhpJm2h1uzh+TwISUMNodHpYXNLB0Vx1jk8M4JS/6kC5zQQY+R/00FEXxG0EQlMD3gBE4VxTFoGDCESAIAuNTw3nzunHYXB4iDGoEQaCmzY7F6eH/vtnJpLRwrhifREljBx+sk8qJvsuvIzFMyym50eyusfDp5mo+2tD93gfryllw8yTiQ7VEGjXcOC2N80bF43B7ufO9zWyubAVgcX4dF46J54LRCby7tqxrXL2pehvVCuYNiSYzyohM1os4nlwJGXMgYbxUn24IOscMVOQyAa8I//5hL7Oyo/D6RJ5YtJunLxre10M7bpi1Su76sY1o0+lcPe1CitsEnvmgimcvHbxBpyAgiiLrS5sD9LP6CyFaJS6PjzabmxBdcOf1ZCQxzF+89psCG4JmFP+6bgkquRx0vdhKQ5eORlOHkwSzhgvGJPBoj2zYr7ZW89EtkwJ0alYXN3P9mxu6MnO/3FrNo2cPYUpGOFqVnBEJIb0GVPRqBacOiaHN5uKUvGgEIFyv7n2+EOSEYdSqaPLqOHNlGr+Z8i4uL/x7cR1/PS+YcXQw1Eo5c/OimZAahsfrC9BXCtIHyGSQOg2u+hTcdtBHSkHoIyCyl79jTowRk1bJKbnRjE/p/nsv3FDBo192Z/8Z1ArevXE8SWE6lDIZeXEmtlZIGTBur4i+l9K5IXEm8qstPPZ1Pg63dE/9alsNz18+itVFjby7Vlq7Lc6v49NNlbx1/fjDyqQMMnD51QEYQRD+g7/LkQkoBu4SBAFRFO8+SN9sYEGPpjTgEaTsmQVAClAKXCyKYsv+/QcrerXC7z9uToyJm6el8fKKYlYVNTEmOZSPN/jXfbda3Zyb4uG6GAslHXYaUnX8VCJFditb7Oypayc+VEtVi43dtRZkAjjcvq7gyz4+2VTFBzdN4IstVVicUlqfIPp44YrR7KxpRykTWJxfy+2zMsiOOYy6RY0RKR4XZKDi9XpZuLGCy4domGouQ0QgUm5m0Y5aThmkaZSiIHDXnAzC9WoaLE4yExT8/YIwtPtnegUZVOzbDYs09s/JtSAIxJk1lDZZGaEz9/VwgpwAWm0uCus7cHt9pEUYGJUYyh0z01EpZHhFEZfHx5nDYlEZzQF97S4PRQ1Wmq0usqIN/HZuJkqZjMfPG8rLy0v8jvX4RL7ZVoPX50OlkJMeZcCgVrBib0NX8CXCoOT341XkeDYzIjGMx9d52VMXdVD71JB+qKV0MmNzuFiwvpw/Tg8nR1kDgoz7p0eyZFcds3Oj+3p4/Zq2zv+LTq+PNK+PmJD+ULQVBLXRX1T6CBgab+LCMQks3FiJWafkj5M0zI2xo23cTktIGvn1HtrsbuLNWt5cVerXt8PpYW9dByMTpaD3n87I49o31tPRuXbSqeTMGxLN4k5XN5NGwRUTk9hS0dYVfNnH8z8Vkhjmn+2yq9ZCQX1HMAAzyDmaDJgN+73eeLgdRVHcA4wEEARBDlQBnwEPAUtFUXxSEISHOl8/eBRjHNCYtErunpPJ/GExtFhdhOtVkgJ3JwqZwNvzZIz49hywt5AEDBt2A49rzuLTXdKCwueD7ZWtXPfmeho7JIGqx84eEvBZoijtsn551xRKG22EaBU0WV3c+f5mnB4fMgEeOi2HmVnBjJaTBZ8Prs/xMn7D71Fu3wHAmNhxbBj1RB+P7Dgiipi0Su74YFOX7tLl4xM5b1TQjWMws760hewYY7+2vI0J6QzA9EOb7CDHlqoWOw9/vp2f9kguQynhOp6/YjSVrXa+2FINSM5YZ4+IC+jb4fDwyopinvuxAFGUdmv/fFYej3yRz/SsCHq7wlvsLu77eBvlzTaumZTMb+dm4e30yDaqFbw7y07Oz9eCywqCwPNjH6RGEdTFGkj4gMcma0j44WYUjbsAiI6fxNCp/+jbgfVzqlvtPPLFjq4S/aQwbaelelBAdSATblDzpzPzuGB0HJmOHUR8eQU4pTJP9/h7ebd6Bt8V2tGp5Dw4P4eyxXu6NqcBfD0c4camhPHVXVMpabRi1CjIjDJwwegEbphiwerykhqp56VlRb1aTnu8Ir0UG+ATxcDGIIOKX21DLYriWwf72necIAifHOJUc4AiURTLgHOAfX3fAs79teMbLBg0CkYnhTInN5q8OBOXjUvseu+GsWEM3/5XsHcnCYVtf40rk1sBiDKpyYox8NR3e7qCLwAF9R2kRfinM585PJbkcD2pEQZm5UQRZdLwwMJtXY5IPhGeWLSb0iYpsOPx+mizufxuQi6Pl7YezkoWhxu7K2jVN1BRKuWMsfyEsmFHV5umZj1jXOsP0mtg4/H6eHLRbj/R6/fXVdDQEehGFmTwsK6kmcyo/p2KH2VQUxJ0QjopWFPS1BV8AShrtrGprKUr+AJQb3Hy7NICnPu5KO6ubefZpVLwBaTd2meXFnDWiDjq2p2cMdxfqFImSA5E+7LACmqaaW5qYG5utKT7NVpHzpoHpeALgCgSs/5JcmVVALTZXTg9R/6cb7O5cP2KfkF+HQaNisjiz7qCLwDqqtXENq7qw1H1f9aVNPvpI5Y323lzVSke74EdvYL8CnxesLVI308QIVolOk87oUvv7wq+ABjWPcN1GdL9zuby8uKyIs4Z2R3s1ihlDO+xESKKImE6JdMyIxiXEoZZpyJUr2J8WjizcqIwqRWcOiSaSKMa1X7RlttmpaPbLzCTGqHv9/ORIEfPiVBESzvE+5cCH3T+O1oUxRoAURRrBEEIlNgHBEG4GbgZICkp6ViNs9+jUsi5Y1YGKRF64qlnTkgt8m2bA46LpZmbp0/mgtHxKGUyNpX7V3F9sK6cZy8ZyaqiJvbWWTh9WCynDolGp+q+HFqsLlps/kJSEQY1LVYXe2rbeX1lKWuKm5iTE80VE5OwOj28uKyIXTXtnDMynhEJITyxaDdmnZK7ZmcyMS0cleJXx/sGJAP9OvV4PGjKlgW0qypWAref8PGcCFrtUmqpIEC8WUubzY3F6aG+FwvWwcBAv0aPFRvLmrl+SmpfD+OgRIdoKGk8+QIwJ+M1uqOyze+1TimnvNkecNyG0hba7G6iegij17YHBosrW+zMyo6k1ebipz31PHn+MBbn16JRyRmTFMq7a8ow65S8forA0JIXUX2+h5DMi/nmmrNxWNuhKDBvRtZRwws/FfLxxkoyIg3cPiudUUm969D0pKLFxqcbK/lscxW5sSZunZE+KLK6+vt1arVa0Vcul14YYyQHGWsjmpp1wC19Orb+zK6a9oC2VUVNWJ2eAVdm12+v0frdsO5lKPpR0pAcfzNEZp+QjxbtzcibCwPazZ5GQFqCNltdzM6NorzZRrhBzdy8aEK10nqptNHKB+vK+S6/lrHJodwwNY28OCk7qrZNylj8aEMFEQY1N01L5cnzh7F0dz1tdhfzh8ZySk4041LCyIo28n1+HdkxRialhQeUKgUZfJyIAMwB86gEQVABZwO/P6ITiuLLwMsAY8eOPanytGLNWuZnmwlb+n+oyjsgcTxUrPM7xhSbwR+ycwFwuD1MzYjg+511Xe97fSIlTVaKGzt47dqxhGgDHyKRRjVRRjX1FifxIWr+NcVLWtsvmCvWskM3geV7RGraXby+soRN5ZJ7yKJOa7ZnlxYwf2gMRo2C9aUtXPPGOj66ZRLjTjLL6YF+nSoUCnzpc5BVrPV/oxenjcFChEHJrRMiOCusgtjGpVgNyWyQjcBwEK2DgcxAv0aPBW02NzVtDpLD+7frQLTp5LSiPhmv0THJobzRQ3fA6vKSHhl4fc7MjsS8nyhzQi/3qoxIAyuLGrvmAT/ubuCaScncPTuDOz/YQmmTjefnahm97MquTJcw2/8wxqThbSlGzJiLEJ4BW96Fhj0gCJR6I/j7YslqtaTRysqiRj6/YwpZ0QfWZHB6vPxnaQEfdWrZlTbZ+KWgkS/unDLg7af7+3Wq1+vxDjkP+ZDzoKlQMkwISUSm7V+2z/2N7JjA63l6ZgRGzcATQ++X12hHPXx0DTTull6vfxXKV8NVX5wQEw9BH4k7ahjK+u1+7Y3yaEDkzrF6LoooI3L3k2RnDKc5cgJnvb2JKyYk8cC8bP70+Q5WFDYCUNZkY0VBI5/ePpmEUB0LN1byz+/3AlDUYGVTeQuPnJmHQibgcPtQyASMWiVtDjcfra8kwqjip931vLe2nNnZkfznstHoNUHnuMFKX/9lTwM2iaK4LzpQJwhCbGf2SyxQf5C+g5qShg721FmQCQK5sSYS9SLU7YC2cuK1YQjOBihbCac+Dh0N0FICMgVN4x+ghBRC6ztIjzKgUSq479Qsihs7KGuycd2UVOLNWjw+H/efmtVr8AUgJkTLc5eN4o73NvHf6V5GLb1c2jEBRsmV/HfW+1zwtXTsloo2pu+nDfN9p2DvpvJWRBF+KWg8aADG7vKwu9ZCRbONaJOG3FgjpgOMLciJwe1yI1cbIWUalK6QGjNPxScoGKyStIIg4zcxW1Ev/h0AYUBCSDL1mR8Bg1N4+GRnU0ULGVEG5P3cpSVoRT24aepwsrO6nVa7i5RwPZeNS+TDDRWIIoxLCWV0splbZqTxyvJifCLkxhq5ZUY6qv0EwnNijfzfOUN4/JtdaFVybp2eTpheRW27nWsmp/DO6lK8PpHXV5Zy4ZgE/nBGLre/t5EMyrvLjPQRcPrTKL+4HeW+NkGQ5hs//o22mf/Hg8tdfp9rc3nZU2s5aACmusXOwo3+RgIWp4e9dZYBH4Dp79jsdrTmJFh4HYidu+sKDbILXu/bgfVzfD6Rs0fE8dW2akQRhsWHkBKhx+ryDMggTL+jqbA7+LKPunxoLqJNHsKuGgv17Q4Sw3TkxBrRHsrK3lIHtdvB2YYzNJOtzjjqLE5SIwxkxxgD3F1jomOonPYkqT/cAm2VIFfRMePPvLxTy7xMgVvdb2NYJilp6IDw9NO5aOhtmLVKftrTwJiUUIbGh/D6yhKcHh8hWiVF9R2UNFp5a1WZ32e5vSI1bQ6+2V7DKblRpEXqJbH1ug4KGzoo7K465cc9DZS32MiNPXKtob11FvbWWdAp5eTGmYgNikb3S05EAOZgs9rL6C4/AvgSuAZ4svP7F8dxXP2W/Oo2rnh1La2dJUAz0kN4LXczih8eBjp/oWOvlyZLSx+D4ZfCsAtpjprAE/mh1KyoYVvFbt67aQLDE8xkx5hYcPMkiho6+M2CLVS1SinKZp2S926cwJC4kF7HMTEtnEX3TCPsh990BV8A8LrJrPmS9MiLKGqQyjVk+4lXqhVy3J7uFDqT9sCXmtcn8vHGSh75Ir+r7bYZadw1OxNdL3ZuQU4MggDu8g2o1QaY8aDUULEOV2MRg3XaEeKuR/3z435tQlsZ5rZ8IKdvBhXkuLKxtIWMAbD4C9Eqcbi9tDvcmIIT/0FFg8XBHz7bwZLODBW5TODdG8Zz1aQU3F4fKeE6QnQq7pipYU5OFHaXl7RIfYB7BoBGqeDyCclMTo+gps3Oze9sxNapxZYeaeDGaWm8vLwYuUxAKZeRGW3k41smoy+xSCcQZDDrYShb1R2QARBFxD3fsWruF2yymChqKQ34bPUhyowVchkapbxrPN39BmtIv/+gEATY+mF38AXA40Ao+wVyz+i7gfVz2hxuqlvt3DMnE1GE4oYOluTXcfn45L4e2uBA0bvzoEdQ8dzSQl77pdu17W/nDePScYkHtrRvq4JPb4EyacNQLVdimPMmV34jx+Pz8dJVY5mb5+/4FW3SYMueSH34t2isVWiMoQghqdyTbCPJWYDhfX8ZU03Rt9x53q1c+FUlDR1SaXqkUc2dszN4e1UZ54yM59o313POiHgMGkXXMd2fp+YvZw/hu/xaLn5pDddMSuaUXlzIFJ335yNlQ2kzV762tquEaVh8CC9cOZrEUN0hegY50Ry1KIcgCPccoq1XFyNBEHTAXODTHs1PAnMFQSjofO/Jox1ff0cURapb7dS02sHjRGwpo7KiAo+3OzvwwlQXih8f9e+48Q3ImgceJ2x6C9euxbxTqCYnLow1xc1YnJITQl2rVDceblCzqby1K/gC0Gpz8/7ackRRxOcTqWqxUdvmXz8eZdIgOFoDxq1wtaFRSpfP+aPiKd5Pm+DyCUl8u6MGkCzYpqRHHPB3UNpo5a/f7PJre/HnYgobOg7YJ8jxR6FUYh1+LTTsBbcNHO3QUoo969y+HtpxQ+62gTtQa0HuDl6Lg5V1JU1kDADBO0EQiA3RUtYYzIIZLFjsbiqabZQ12fhxd3fCr9cn8tuPthJpVDEi0UyITkVTh5NnfijgkpfXcM0b67n2jfXsqbX4nc/t8VHZYqPZ6iQhVMsbK0v9gh1FDR0o5TLUChkXjk7oCphEGtXokkYihmdC+mwoWAyewPug6GhnVZ2ctSWt3DI9vavdoFbw8MwIJkfYwOMK6Of1iVS22FApBP5wun8gOzfGSE4vZR5Bji2CIPoJjXbRy/wuSDdjk8PYW2fhmR8KeHZpAV9vr+Gm6Wm9OtoE+RWEZ8Gwi/zbhl9KuSzOL/gC8Jev8ylrOogOWs2WruALAF43WZv/xoVDDPhE+ONn26nvRSNLAASZAgzRKMNT0Ws1jE4OI0Lbe6BHIXq4eFwi84ZEI5cJNFicVLfauXJiEq+sKEYUYcnOWi4e6++eGWlQExui4dGvdrK8QCpbemt1GUatgolp/hUCt8xIIzn8yIImVpeHp7/f46cfs72qjU1lLQfpFaSvOBbpBdcAz+7Xdu2+NlEUv++tkyiKNiB8v7YmJFekk4IGi4MP1pXz4rJinpyl57Smt1Ht+oR5xlgy5j7KrWvCKGh0YMTqn4ECIIo4QzORT7qbDmMaBdoRqNtDeH9tOd5OZ6I9tRaeWLSbYYkhXDQmgV3VgQ/f/Op2GixO3llTxisrilEr5PxuXjZnj4jtKgGyDrsK095v/fpZh1zGpMowTs2LpqjBSkyIhttnptPQ4WR6ZiTxoRoMagUmrYLJ6RHkHCSNrt3h7nJb6knrfiLAQU4sLo+XWnkMpqEXoljzXxDkeKb8lkqiOXA4bWDjQIVm2EWw5b3uRpUBnyFYfjQY8Xh97Khu56Yei8n+THSnFfWwhN6zFoMMHLZWtPDolzvZXNHK6CQzfzw9l78v3t01ea5pc2B1eok07ju+lddXlnb1L2qw8uKyQp66cDhqhZzyZhsvLivi4w0VhBtU/OvikeyttwR8rs3p4f552Wwqa6Gth9sb5iSaz34bTXsJ+sX3wqS7pKzHHnaotUNuJF0fhUNo55fCRn43Lxu308l1MYWE/HQ9rKuBYRfD9AcgXPJfqG6188bKUt5eXYpereDB+dm8f+N4VhQ0khKhZ2JaOLGDVGOrP+HweDEOu0gqXe+BmDb7oGnqJzvVrXZump5Gq82Nw+0lMUzHjqo2ZmZH9fuy1QGBxghz/w+yT5cCKHGjIHEiZdWB+QEOt8/PCjoAW6BGmqK1mPQU6e9Ub3Fi3a9/QU0LobUrCV/2R2Tt5biyz0Gc+XvUMdlYjSloYkYhr+02O/FF5vFjo4nnfypkWHwID5+Ryz8W76Gq1c7ZI+L49w8FgKTb9e32Wh6an0NjhxOzTsmopFCe/HY3rv0ctJo6XPzzohGsK2lmT62FMcmhjEkJPeIMGJvTS2EvTon7b6wH6R/86gCMIAiXAZcDqYIgfNnjLSNw8ikF/gp+2lPPv5YUMDbRwOz6t1Ht/kh6o62S9KU388iMBVyzGIpcocw0xoCltruzxsxXDdHMmvIw4QY19r31PLnA3x54SkYEX2+t4fOt1WRFGUnvZZf3/FHx/Li7nv/8KKmAO9w+Hv58B3FmLbNzOhXAI8ZSP/tlUna/AoKMstybeK84ilvmpHHla+soqJeyAzRKGVFGNbdMTycjysDopMMT3Y03a0kI1VLZ0r3jZlArSAoLpsz1JSqFnPjmNSiWP9XVpvjxUVLOSuTQ5mYDE4cqFHd4DspJd0iK/CGJkDaTDkUYJ5eE9MnB7loLEQY1hgFS6hhlUFN6sB3AIAOCqhY7N7y1gcYOKVtkU3krNW0OLhqTyLtryzCqFYxLDSXapOnqs+8525MVBY202dyEG2S8sbKED9aVo1fJaba6eGDhNs4dFc/zP/k7fMSEaHji212kRugJ0/uXsvnC0rhzaTv/yryA0M1vS5ovu78BlxXHyOvIV0zgmSV7Ke98Vq8uamL55SZCPrumO1Cz9QOpjOnMZ0Ch4rPNVbyyohgAp8fFg59s570bJ/DgabnH6tcZ5DAw6g14FVrks/8k/U3lSsg5E1Ee1No7GOtKm3ljZSlGtQK1UkZjh4vkcB3XTk4ZcC5I/RZTLAw9X/rqJNnZQaRB7VfCkxKhI+5gwdqIrICmtvSz+abYg0mjYFSSmaiQ7ntqm82FqmkXEV9d3WV/rdr9GW7RBxe+zPJKL+7Mx5gS/S3h1T9hTZzBmtCzeXSRJNayvaqN2nYHl4xLJESrZNmeekYkhrC1oq3r/V017Sy6ZxqZ0UaarU7sHv8AkFohIzlcR0Ko9HU0hOtVnDcqnpeXF/u1D40Pbtj0R46mBGkV8DSwu/P7vq/7gPlHP7TBQbPVyY+76/l0UyWfba7kPz8WsHhHDY0WBx+uk8Tozk2XYdzrX2eI6CNHUc1v52bRroyk+aw38UYNBcAbnsW26S+zvs2EoVMhe0RCKH86Ixe9So5CJnDOyDjcHl/XzWtPnYX86jaunZyCVilHJZdx0ZgExqaE8vZqf6EogJ92d7smJURFUB41k19G/p36Mb8h1FbK7/JaiVa7eebSkYxOMgMQZVTzxPnDe3Vr8MPnhaqNsOZF2PAmUY4SXrpyNMMTpCyZtAgdr10zlpSI/u1KMthxu90Yd34Q0K4t+KoPRnNi6PApqY+ehq+9FsLSQfTh1EaxzRnb10MLchzYXN5CRtTAuc9EmTQU97LDFWRgUd5s6wq+7KOmzcGMRBlfzbOxbNIWXhhdjdbRXZbUm0jthLQwQnRKGixOtpW38tD8HC4dn8QtM9I5Z2QcoxNDuHRcInKZgFGt4M5Z6fy8t4Hh8WZun5kRkGUaadTw21NzWBN2DvbIUbDkUVCbsUy8H4shlZkdX/PV7Ab+NM2ITJA2cGLd5X5ZMgBsWwAddbTaXCxYXxEw7nUlwT26E43dbke+4RVY/g9JZFmlh6WP4dv17aE7n8Ts2wi0OD1d/2dHDgLb9H5NawVpdd/zy9QtLD7LzemZOkYmmvnvZaOJMHRqxrgdULYaVv0HtrwPTUUQOwLOfwV04SAIeHLOoTT3FmbkxXPJuEQuHZ+E091dktlkdaFoKeoKvuxDufdLsFTzzbYa7l7SwYWF8/it6V98FHYrN3zT3lVlANBgcZIRaaC4wYpXhKfOH87EVGm7Li5Ew/OXj2ZTRSvvrSmjrt3Jvy8a2XX9JIVpefnqMaQfIw06mUzgyonJXDgmAZkgaW8+cd6w4PXaT/nV236iKJYBZcCkYzecwYXL4+WVFSWsLW4iOVzPZ5urut67ZVoq2TEGNpW3UNEBPmMisrZSv/5OhYl/fi1ZmL0ol/Hk6S8RRhvLK30sW+3lhctTusTrQnRKrp+ayryhMawtaeb1X0rI71FyZNapWJxfR3K4jqsnJSOXCawubCREpyQjysDOGv/ypJQelqwKuYzZ8SCu/D1CxRqpcRUw/wmGTLiNN68bT2OHE5NW2X1zPBjlq+Hts7tvetpQhlz7Le/cMIGmDhchWiXhh3OeIMcVpVKJNyILylf5tcvDB0a5xq/BrFPi2rkZmb0ZEsaAvRXZhldImTWsr4cW5DiwvrSFjMiBoz8RY1KzobS5r4cR5CgxahT7V/cwJl7HjMYPUK5+rrsx7xw46znQmhmZGMLFYxP5aIMU0EgI1XLX7EzUCjk6lY8zRsTxl693dnWNN2uZlB7OX84ZwsVjE9ha2UZVq51RSaEUNXSwtaKFscnmgLENSzCTFD6G8qZMyuKuJSrUREbraiJ/uAqAEOC65Cmce+/LGMJjURZWB/6AIQmg1KJRykmN0Ae4dx10FzvIcUGhUCCGZyFUboCCbmUAeXhqH46q/xNv1jIjK5Kf90pZDynhOqakh6MJasAcHzrq4bPboGwFaiAbeG7OY9jH3o6xpzNq4RJYcGX3a3MyXP05DL8YkqeAx4FDHcULn+1hcb60jmJFCbfNSOc3czNRKeQo5TJEdS/ZIYYYUOrJiJKyVUqa7JQ02UmKjUImQI/4C4IANreHPbXt3D1nNBlRRl69diz17U4sDjfXvrGBFpsUuNOr5DxyVh7RJg13zMqQAucVrUxOj0ApPzblbElhOv563lDumJWBWiEL3mv7McdChPd8QRAKBEFoEwShXRAEiyAIvSh9nXyUNNp4eXkx07Mi+XxLld97L60o4ewRcRjVCt7dZqV43CNS2m4n3sRJLKzulshxeX3c93UZ6pgcTh2Xxwc3TQzQVREEgYRQHdnRRip6THgiDCqGxJk4Y1gMZU02XlpezAvLirh8YjLxZh03TE1F1+NhEhuiYUZ2lP8PU5ffHXzZx4+PQ0spJq2StEiDX/DF6vSws6aNvXUWnJ4e0WW3A1Y87R9xtrdA8U+EaFWkRRqCwZd+hJA8GTQ9HlD6CISYIX03oONMhK+FuHX/B8U/wfJ/wvpXUVauJd5ReOjOQQYcG8tayIzu/wK8+4g2aSgLWlEPeNIj9dw6wz+Q/cQMHco1//U/cOcX0LAHkLJTHjkzjy/umMKHN09k4a2TuixKvT6Rd1aX+nWtarXTYHGiUsixu3089tVOXl1RwvM/FfLdjlo+WF+By7tf5konIVoVPxe3cfM3reyqbMLw8yN+78vKVhLeUYDV6aFOn4MvcWL3m4IMTpOyLDRKOXfNzvBzR0oJ1zE+NVjQecLx+SB1Gqh7zBsNUQgRGX03pgFARpSBSKOK35ySyd1zMpiWFUFGlDHo3HW8qNvpL6QLKJY/idHWI5PO2gRL/O9JtJZBdadWS0g8hKezu9HN4vw6v8NeXlFMSaeQfUKoFkd4HrbE6d0HCAKOU58CYzSnDY0hXN8d9Fld1Bhw3755WhrjkkN578aJZERJmzkGtZKUcD2v/VLSFXwBSRdmc3krWytaef6nQj7aUMGzSwspbTy2Wa1qhRT4DgZf+jfHovD978BZoijuOuSRJxlOjxevT8QnigEZuiCli316+2R217ZTpRKIu3oxuvZi0IRQrcnkmRf3+B0viqBSyBiTHHrQz00N17PglknUttmxuXzkxZlIjdDz2NlDuXhsEo0dTlIidOTFSQ/iEYlmPr9jCrtr2lHIZQyJM5Ecvl9avruXSb/LCt5Ax4PyJit/+XonP+yqRybA1ZNSuH1WOlFGDfjc/lo2+7A2HPRnCnLicbncyHcshAm3AIIU6ve68exZgnzYBX09vOOD1yW5Pe2H0hcUMRtsNHY4abO7B9QkJVSvwur0YHV60A8Q3ZoggWhVCm6ZnsbUjAhq2hzEm7Wkqkr8LYL30ePZa9AoGNFLOrnL66O5F9H6feL27l5E7t1eEZfXh93lpc3uIlSnQq3sXlQ2dZZbGBS+Xp3h7LYOLnlpDQX1Hfxu0h+5dFQLoTIbssgciOnOGBybEsbnd0xhT60FjVLGkLgQEoP6biccn88D2z+GibdJDYIgOVYVLoVhF/bt4PoxaZEG7pmTRWWzDbdPJC5EQ0b0wMmaHHD0ttZw28Hbw87Z6wRbL5mgTv9Aht3tJS/WxJjkUKpa7fy8twGvT8TqlO6VgiAQl5hC8ynP4GjOR+FqQxaRiSF5NADZMSYW3jaZndXt+EQfubEmooxqpmVGUNXqIM6sYUiciRBtoBaQTxR7Fb9ts7sxahTUdk4zPT6xVxOSIIOfYzGDqwsGX3onOUzHiMQQqlsdZEYZ/ET04s0aksJ0xIZoyey6mccB4wFQtttJCC33E6bNijaQcghbso1lzTy5aA976to5Y1gsN09LI7VTSyXCqGZGdmSv/bKijWQd7KESmQ1qIzh7uCrkni2JlO7HF1uq+WGXVLvuE+HNVaWMTQnlzOFx0jkm3AZf3eXfKf2kMb8aMKhUSjpyL8Hw5Q2Qc4aUtbT3O1wXvM+gzVEyxsHoq2HD691tCjVE5hy4T5AByaayFrKijciEgeNkIRMEYkM0lDXZugLoQQYmZp2KKRk9/ORsMogdBTXdjhsYoiH80BkKUUY1105O4dmlBV1tcpnQlSGTFqknTK+i2dq9YTI1IwKfT+TuDzaztqSJyRkR3HtKJjkxUp+Z2ZG8tLyYr8sUnJJ6KtqSHoaWahM73bFUtjYyLD6Ed/Kd/GO1gk9um8+Y+MANotxYU9dYgvQNaq0eb87ZyL+5F3LOkhaxBd/jveDNY7IQGKw43B52VLfxj8V7sNg93DgtlRCdkkij5tCdgxw5kVlS1rWjrbsta75UYrQPYyyMvxmW/727TSaHmKF+pwrVKUmP1LOhrJncGBN/OiOXxfm1fhviOpUCXXI6JPdeWp8aoe9aQ+1jUvqhfUAVchlXTUphXam/BfSoJDPf5XdvQo9NCQ0GpE9SjsYFaZ9c9QZBEBYAnwNdIUpRFD89uqENfEJ0Kp6+aASv/1LCJeMS2VLeytrSZiakhnHHrAxiQw688xpj0vLK1WN54adC1pQ0MyMzgptnpGNze/l2TSnLdjcwNTOCOTnRJHUGZQrrLVz12jpsLqm8p6quEUVxCeKyzxEMMTD8IoiXIruIoiSEu22BFEkeeTkkTZSE2ToprO9gcX4tm8tbOHtEHPMv/xTV8iehPh+GXgDjbgSV/43D5vLwzfaagJ9nVWGTFIAByDkNvE/DquekgMysP0LC2KP5VQc5Dnh9InWho9Ff8i7Cjk8RlQq49EOq1Xlk9/XgjhcKJfYJdyFTmlHv+ABPaDqe6b9HEz14y65OVjaWtRxaMLwfEm3SUNZkDQZgBhu6UDj/JVj1XyhYLD2Pp/8OzIGbHAAuj49N5S18srESuUzgojEJKOUC760tJzZEw32nZjOs0/0iKVzPW9eP5z9LC9hc0cq8IdFcMSGZG95aT3WrtEv73Y5a9tS2s+CWSUQZNYxMMvPK1WP51/d7+GnUvcwJS0W99wu80cOoHnEP65rM/P60CH7aXc+w+BDizFp89hbYvQq2LoDQFCmzInZ44ODdDqhYK4lnKrUw4lJIGCctooIcF1weL22xU4m46G2EnZ8iqkLh8gVUqnNJ6evBAZUtNpbtaWDZnnomp0cwJzcqMBO7D9hU3spt727qev3Eot1olHKumZzSd4MazIRnwJWfwbInoW4b5J0rBVtUepxuL5vKW1m4sYLTk09j2gwVqs1vgCkeZj+MGDOcjuL1yPMXorTWEpZxEbdn6YnWLkJjq6HSezZDZ4xnR3U7Y1IOvwzS7ZXutZ9urEQELhidwKikUFSKg6t4TMuM4O8XDuf5nwpRymX85pRMUsL1nJobzaaKVubmRnPD1FRCtMqDnifI4EQQe6uNOZyOgvDGQd4WRVG8/tcN6cgYO3asuGHDhhPxUb8at9dHq82NTinH5vZi0ioOu37U6fHSbvdg1iqxu73c8+EWftrT7YwwKS2cF64cTahOxbfba7j9PelBoVXK+eaUJtJ+ur37ZEotXL9YUgqv2gRvzAdPj7S+Sz+AnNMBqG61c9krayhr6k4HvGFqCr+fk4zCa5NUxnuZLPl8Io99lc9b+zkrPXHeMC6bkOR/sLUJZArQDmiLtMPaPh8I1+n+eH0i4u5vUHx8ZbdapCDDfekClNmn9u3gjhOiKPLSz8W8v66MM9IUlLYLbKx28tGtkwayK9egvUaPhvNfWMm8ITEMTzD39VCOiPfXlpEbZ+L2mYNKuyF4je7D65Z00TQhUvbdAVhd1Mjlr67tujXLBPj0tikkhmlRK2UY1IGTeofbg8XhJVSnZENZC5e+vCbgmIW3TmJsj8VJu92NxycSppVTV1fNn78rY2eDizm50byxsrTruPRIPd9MLUGz6N7uk6lNcMNiiMrz/5CiH+Gd87pfyxRw3SJIHH/QX00/YEBfp578L1F8fFV3g0wuPc+z5vbdoACLw819H23l+53deh1jU0J5+aqxhOkDyztOJE9+u4v/7Wfrmx5p4LPbJ2PqnwvnAX2NduGyg8vit9b4pbCBK19d13VIQqiWD67IIDEyFNRG2ko2EfLBmZI8AsDsh+GXf3e/Blrnv8Bm81xm5eync3kQ1pY0cenLa7qnwQJ8cONEJqaHH7zjvs+0uRAEukqVnG4v7Q4PZp0SpfyopVgHKgMn9fg48av/8qIoXneQrxMSfBkoKOUyIo1q9BoFkUb1EYl3qRVyIo1qlAoZJY1Wv+ALwOripi5bUlmPy/mMbD0pO573P5nbDqUrpX8X/eQffAHEFf/CYbOwtbKVjWXNnD401i8y++6actrbmiVRwIo1YG0MGK9MJnD5hCTizN3pmaOTzEzJ7CVlTx8+0IMvgxrB50W++W1/qw7Rh3zn4E1uq25zsDi/hrfPi+a69A4enSTnzmmxAS5hQQY2bq+PXTUWMqIGjgDvPqJMGop6lLMGGWBYaqFkOZSt7PUZilwJhqiDBl9Aeh73vDX7RPhwfTnhBjVyQWB7ZSvLC+rZXdvOysJGNpQ243T7iDSqUchlfsL7+xAEKSW/JyatUloEy+QsLffx3Z425g+NDbCXnp+qQPPLU/4ndLZD9Vb/Nq8HVu03N/F5YOeXB/15gxwdHrcT+cbX/Rt9XuR7vumbAfWgtNHmF3wB2FDaQlFD39/n9BoFlw418u6pIh/OdfLgZBMxIWpEft3m9aBGFKFhr6QrVL21V+2ow0alle6DncEXn0/kzR4BX4DKFjuvbmyXMukBKtd3B1sUGunfLn9dGPP6pxkbJW22FdV38PPeevKr2nC4/a2oe7JgXYX/NFiED9aXH/aPYtap/HRi1MrOdd3JG3wJwjHQgBEE4blemtuADaIofnG05w/SzYGylfa1x5q0jE4KZVN5C3KB3gX9RG9nH19g+FH08O22Gn77uVRHbtIo+M3cLJ5ctBunx8e/ZusI/eQiaNgtHZ88Gc55EcJS/E6THWNi4a2TKarvQCEXyIwyEmEctKohgxYfInIx8KEk9HZdDRJEUeTNU0RCPjmtqwb5/NG3UpN8Rx+PLMixZFdNO9EmdcBicyAQY9KwpaK1r4cR5NfQsAc+utr/GXrui1K5zhHi7WU+4PGJdDjcvLy8mOd+LOT3p+Xw0KrtVHeKQc7NjeIv5w4lNkRLepSBKyck8e7a7oXE9VNSSYs8sB7BPvtVAUlksicHnnMceB7if3LPAT83yDHA5+v9b+E78MLzRLH/tbSPX5mgf0w5LdFLXPmz6Jb/AMB4UxJnn/EGCllw8RxA0Y+w4Aop8CIIMOdRGH+Tn7TBr0VEur/tT8+2gLlpbxeQKGLUKFi2t4Fb392Iw+1DEOCh03K4emIy2l7mBL3da329jCVIkCPhWNxBNMBIoKDzazgQBtwgCMIzx+D8Jw0Whxub68CTkNRIPZP3S3kbnWQmLdJAm81FQpiW2TkR/HZuFpERUbSPv8f/BHIVpEwFwJ40Xdpp60Hr6Dv525IyThsaw+ycKOLMWipbbFw3JYWEUC0z7EsQGnZLJUzDLpKci4p+7HWscWYt07IimZQe4R98sbUEZN4E6Z8oFEp8o672bxQEvHnn995hEGDwWTD88ICfAJxh0/+I6Njbh6MKcqzZVNYyILNfAGJCJA2YIAOQrR90B18Aylb5PUOdHi9ttkBnwd64cqJ/Sa8gwIWjE9hda+G5Hwu5cEw82yrb6HB5OGNYLDOzIvlpTwMbOkUh9SoF956SxevXjuPJ84fx/a3DuWdmIhpl4ALE5fHSanMxLiUUjVLG4vxaLhzjr03zTbEH95T7/Tuq9BA30r9NroBJ+wW0BRkMOce/zeeT5gveQHenIEeOQq3FO/pa/0ZBhjfnzD4ZT09SI/TMyIogO0rHVSNMDIvVMSIhpF9odCVYtqAr/aHrtay9nKhdb6EfeLH740t7NXx+a3fWiyjCD3+G+t0H73eYyGUC109JBaRs/1npRq4YYeKC0fFdxygSx0iZLwAeB2hM3a878U27nxq3lvs+2orD7esa6hPf7j5gpvNVE5MlG/LZGYTqpHXTZeOTej32YHQ43FidwUBzEIljcQvJAGaLougBEAThReB7YC6w/Ricf9DTZnOzdHcd//u5CL1KwV2zM5iSEeFnCQlS/eAT5w9jcX4tP+yqZ1Z2FNOzIvhgbTkfbawgI9LATdNS2V1rYXN5C3uTJhE37zViCj7Ap49CGHMtytiRABTIMog8dyHmne+hcjXTPvQatiuGcsVED99ur0GvlnPNpGS+y6/FrFXxwqVDMSx5Eub+RUrzq1wPiRNAeZiZLa0VsPVD2Po+RGTD9Psk0b0g/RpH5HB0F70liSXKlTDiMpxRwwata4Job0XesDOgXWENFJYOMnBZV9o8YAMwYXoVFkfQinrA4XFC8c+B7eVrYez1bC5v4fmfCimo7+DiMYmcOyqO+NADZ6OMSw7j3RvH8+7qcuQyuHJiMqOTzfy4u55HzszD6vJQ3WrnygnJLM6vw6RR8NBpOX7WqBFGNbPjPND0GXz5JoSlScK/iROkiA6wo6qNF5cVsr2qnbOGx/L+jRP4aEMl4XoVfzl7CIt21JAVYyQv1sRibwSTT3+ZkF3vYTem4htxBYbIvMCdvuQpcOUnsP51SZtu7PUQ30OIv7EQNrwGe76FpMkw+U4ICqEfFTanmyrtMDIufF2ai8nVMPJytpHJmD4em0mr5IX5JuQbPkJT8gPOhKl4x9+KztD3mdPqpsD5gLL8F3B1gNZ84gfUX7E1QUd9YLulGo7RFTY+NYwPb5pAbNsW4rY9jbKxArHqOjCeBVUb0W58A3H+U4gVaxHbaymTpyKe9j7xZZ+i7KimIu0Svq7JYKShgyZrYKB7c3krNqeXkYlmjJ3yC5XNNpYXNPDFlmoSQ7X89bxhxJjUDIk7fPmEdruLH3c38L+fi1ArZNw5O4NpmRG9BruDnDwci79+PKBHKjui899xoih6BUEIpjocBsv21PPbj7rrpK9/awMf3DShV6uz5HA9N09P56ZpaXh8Io9/vbNL8LasycaqoiY+v2MKI5PMXPjiagRBx7D432Jt8nJBZCQ3p0iTqs2V7Tz6lZXc2KvQKmUUfWHlgflynl3a7Si+paKNh07L4clFuylt6uCzmbchW/IwtFVKB7SUQnMxZJ9x8AeRxw0rn4X1r0ivm4uh5Ge46UeIyj2q312Q44umag3CV3fC8Iul2v2PrkJz/usQdt6hOw9ABEMYnvgJKKrW+rX7QpIP0CPIQGRTWSun5ET39TB+FTJBICZEQ2mT9YgmgUH6GIUacs6A6k3+7WkzKaizcPkra7F36hD84/s91Fkc/OnMvAPqBKiVcqZmRDI1I9KvPcKg5ncfbyMj2sCcnCj++X139t6WylZeuarHYsjng3WvwC//kl43F0PpCrhxKcQMo7TJypWvraXVJmWhPL+siOJGK/+6eCQapQxBEJiWGc7N72xiSX4dc4fEcN86E8PiH6Sl0UP5xkY+ua0tUOhapYOMU6Sv/bG3wue3Q2XnPbilVNLMueF7CIkPPD7IYaFTK4lq24Lwza0w/BLwuuDDy8i4cCGQ1reDszah/+pWqNkCgLqlFCpXwvWLJMvhPkSI7yV4kH26JDAdpBt9NJhToLW0u00Q/O2jjxKNUs5EXRW8f5l0/QLC0sckDZiv7pFkFUpXIETmUDrlKU5baCc2RMa5I+9hRV0D2xe14/LWcZVLTVyIpqs0E6SsGpvLy98W7eKRM/OYlB6B2+vjf8uLeHeNVKZZ1mRjY1krz146MmCD/GD8UtjIvQu2dL2+6e2NvHfDeKZkRh64U5BBz7EoQfo7sEUQhDcEQXgT2Az8UxAEPfDDQXsGweH28saqkoD2JTt7iST36LOlopUvtlQRYVAzJ7dbzdvu9lJYb2F7ZRsen4jbK7KpvJU9dRZeXVFMo8VJq83FG6tKAdhVY2FTeRujk8NYsL6CjAg1b85X8v3cej6b5yRabiEhVMvWynbcMnV38GUfleulSVtvNOyB/M+gYjVs3M80y22D+l299wvSL/B43Mh2fgpT7oWQRAhPg8l3I9vzdV8P7bhRZlGQP/KPiPs0GeRK2qc/wnpHQp+OK8ixo67dgc3lISZEc+iD+ykxJo2fQ12QAcLQCyBtdvfr4ZdB2nRU7eV8MquJRae28PfZBrRKOe+vLaem1eHXvabVztJddXy9tZo9tb2ny9dbnFicHuLNWj7bXOX3nijCzhpLd4OlGta+6H8CjxPq8gEorOvoCr7sY9GOWipbbAidGTJCSynPjazg01nNDNdJ5U0bylopaujA7RWPXMC8ubg7+LKP9kpoKjiy8wTxo8Nux5D/IUy9F0ISIDwdJt+Ncm8/eJ63FHcFX7rbSqRMqL4maSKMu6krI4yECTDmOghqwPhjjILzXwZD58aGUgvnvACROYd/jtYK2P0t7PhMWj/0Rl1+V/AFkDLndnzS+Zk6mHY/5J1DsmMXz84zc+qQGF5ZUczGslZcXqnk6JNNVTx2zhAiOzOstEo598zJ5Ist1eyqsbC71oLXJ1LTZufDdf5i43a3lz21FpqtvecXWJ0eNpQ28/nmKtYWN9FudwW4wgJ8vb328H8vQQYlR50BI4ria4IgfAuMR9Jm+4MoitWdb//uaM8/2JELAmG6wDTLfXWG+yOKIl9sqebBT7Z1tZ05PJbpmREsL5AcFVRyGbpeUtNNWiVKuYBcLmDWKul5S7C5PMSaNTyQUkX6j7d2ibXlZpxBQcZtvLDejlNuJGCkgtC7W0PVJnj7bHBaYOLtoDKAo9X/GMXAXQCdDMgQIO88WPJHsDVLjcYYOOUvfTuw44haLkPtaUFImwn6SEBE0VJCRGxQh2CwsLGshewYY9cCciASZVRT0hjUgRlwhKXCxW9JQQZBLi2CW8tI+uJ8hA5pQp6rCyfylFe5Z5kbpbz7Gq1ssXHbu5vYXiUlG6sVMt69YQLjUsP8PkLbuTNrdXox9DIP6OlsiFwl7R7v71bS+WzWKAMXmWqFDMW+rJzaHaR8fo5UfgCcZ4jBMOd/3LK4OzioO4KdYumz1dK8Yn/hy+B84ahQCHJkwy+AxX+QbM4BTPFo5v5f3w4MDvy37Q9/c0M0nPo4jLlWWviHpQVLjw5E0gS4eZm0UasLg7D07sDVoWgqhPcv7Q60qo1w9RewfwbS/teE2wa6Tj2qGQ/Aqv+ArQkBmGeMZeIFC/hiiwKrq1ts2uby4vOJvHrNWJbsqsPnE/loQyVVrdJ9UKuUIxNAKZOhU8tpt/vrtigVAspeAnBur4/315bz12+7N5cfP3cIYbpAK/Uwfb+0MA9yAvnVIVxBEHI6v48GYoEKoByI6Ww7VH+zIAgLBUHYLQjCLkEQJgmCECYIwhJBEAo6v4f+2vENFJQKGTfPSEPew0Nar5IHetR31ENLGbUt7Tz2Vb7fW19vq2FsijQJGxpnIj3KQE6Mkbm5/uf43bxsQnQqDGol956ShSBAuF7FNZNTmJwezu+nhZK+9k9+Svmawm84Jaye2TlRyKNzIOs0/3GNvwVfaBpVLTZqOm9eeNyw+r9S8AWkLJhxN/j3i8iCmGFH+NsKciKRKRRQtaE7+AKSheog3olM0thIXfNHSShTFKGjHt3ez8nwHiDLK8iAY2NZC+mRA1P/ZR8xIdqgFfUAxacyUqXNokabIZXi5H/RFXwBwNbEiKZF/N/ZQ4k1a6Uml4d1Jc1dwRcAp8fHP77fEyDqmBsr6bH8UtjAmSPiyI42ctuMdK6dnEJqhJ6JaT0CNoYo2H8Bbk6WhPaBnFgjD87P5tYZaV2aSbfPyiAprFObZuuHXcEXAFlHLeOEXTwzL5R7J5rIjNIzbP/yo0MRlg7jb/Fvy5wvaccF+dWolXIoXdkdfAFor0LoD8/zsHQYe4NU2jP9fsg7F4acJ80T+wNKDcQMhfjRweDLoTDFQeJ4CM84/OALQMkK/7ml0wIrnwPPflotsSPB1CMjuWYrjLoCIrOleVuP+5FgqcFYsog/n5VHWoSWx2aY+OPUEEYmhJAbayLSqCbeLLkK7gu+nDEslrxYE4IgEGvWcv9c//tOSriOEQndGjF+P0Kjlae+8xcd/tMX+Vw6PhFFjzWeViln3pCYw//dBBmUHE0GzG+Bm4Gne3lPBGb30t6TZ4HvRFG8UBAEFaAD/gAsFUXxSUEQHgIeAh48ijEOCMYmh/LxrZNYXdiERiVjUlo4eftq+z0uKFgMix4ASy1RQy7isWlX8rulFr9zhOlVPHvJSLQqOVe/vo6qVjun5kXz+rVj2VrRxqT0cEYmmruOn5IRzie3TmZLRSv//akQi8PNKdEx0FEXML4Mg5vHzx2K3qyFM/4Jwy6U0gDjR9MeOZpXl5Xz0vJiVHIZ952axcXDQ9HV9QgSWWqgcj2+s/+L0FKKYE6GlClgTgz4rCD9CJ8XoT4/sL2xH0zYjhM+ZweMukrSafjlaTDFw4wHkQWduwYN60uaOWdkXF8P46iICdGwvrT50AcG6VfUtzt4d205Ly8vQimT8fh5Qzm7bjv7L1NC23YyN0/aQCmos/D093t6FeQtabBidfmLMceEaHnxytGsK2lGr1IwIS2M134pQa+Wc/+p2STsf56cs+DqGCj9RSpNSZ4KYalY7G4W59fxv5+Lcbi9XDwugbtmZbC6uJG1JU1MSgtHqNnsf64p9xDauJFzdz4Mughun/sEqpAjzIBRamDafZJjY9UmSXw3aRLoBv1+3HHF63agaOil7PtApR4nEpUO59BLUP3wMMLyfyLGDMd96hOotEGNq5OGpqLAtvqdUnaeokcGSXgalos/xlHwM7TX0BAxDplmFAnnvI7++/sD7qWuqu1ojY0sGv4L6nXPg0LNddP/QGF7Mrd+Ukhpo40ZWZE8f/koWm1uhsSbGBrffd2dPzqeWLOGtcXNRBrVjEoyM+IAQeXaNkeAVbYoglYhl9Z4RU0oFQKT0yOC+m1Bfn0ARhTFmzu/zzrSvoIgmIDpwLWd53ABLkEQzgFmdh72FrCMkyAAo5DLGJ0UyuikXiYYtdvgo6tApgC1EfmOBZyeJ+OzlCvYWNmB0+NDrZAxMS0Mh9vHOc+vxNt5A1icX4cowrOXjkKr8p8EqRRy7G4vf/m6W+H9uXUd/Cd1DqqSpd0HCjI0MdmYOnfiCEmQAjDDLgTgq7VlPPejVKfr9Ph49KudpEaMY9rIK5At+VP3eUqWszvlauR595AdGxQvGxDI5HTkXoKhfI1fszX9DAZ2/sCB8WnDEGu2IOxdDBqzFJBc8giKKz7r66EFOQY43F721ltI2z8DxuOQds88LqlMRN+/xfFig1bUA5Klu+p4bqkUwHbg44GF25hz7lkYdvvrcAgjL0OnVtJud/P7T7ezoayFB+cHZoCcNzqeCH1gCXByuJ7kcD3P/1TI2536Ay6bjz9+voPkcD1TM3sI/Kv1kDZT+urBxvIW/vjZjq7X76wuRyWXs2RnPd9sr+WLOyaTNuJyKXADkqC+pQZhx0LpdUcdqs+uh9DFko7GkWCIgtyzpK8gxwSFRo8392zkVRsle3DRB247vrTZHGGI7JjT0ViJ4bMboE3S2xBqt6FaeBWWq3/AGJPex6MLckJInQar/+PfNvIK2C8IZ3d5uf9HG8sLUtGrM2jscHHvKRbeWV3NZ5PPI6l8td/xNUlnkdm8HPWqzlwBjwPF9w/CrDDKmySb8+UFDchlAs9dOhKDxj+zxaBRMjcvhrl5B89Y6XC42VXTToxJQ217t3ZXiFZJjFlLUpiOUb2t8YKctBy1BowgCDqkbJgkURRvFgQhE8gWRfFgyl5pQAPwhiAII4CNwD1AtCiKNQCiKNYIghB1kHOcFHjr9yKffLdkAexoB1Mc+j2LuH9yCEuqIgkzqBgaa6LN7mJHVXtX8GUfS3bVsbWyhZwYE+b96hA3lbX4vf6+0MKaM+9jIgKqkh/AFEfR+L/gkiVDTTstjbVke/ZgbN2FMioLb/xYFqz3F6gC+GZ7DaOmnoWvthTzzrdBoaFm9H28UBTBPLMlGIAZIHh9It85h3H2hLtRbXwJBDnOCXfxvTWb8/t6cMcJl6URwdYEpzwK7dWgCQGPE2dzNYFVvEEGGjuq2kgM1aHp0qUQYedXsOU9MERK9eWtFdJicsLNYOyfmTJmrRKH20ebzU3IAfTCgvQvXB4vCzb4i9g7PT5+cucxf8YfUK76N4g+bGNvpy50MlFOyUZ6Q+dzesnOeu6ek8Fbq8qwONycPSKekYkhfL6lijHJoSSH6/3O3WJz9fp83lTWyBCKMDVsQK5QSdklMUO73m+2OtlV3c73OwOzYVcXNfLCPCMxbVuI3rkT0qZIoper/wvps2HTO4E/eP2uIw/ABDnmWOwu6sJmk35+HELNNpDJEeNGsoMsRvTx2LxNpV3Bly5szfiaSiAYgBm4tNdAxTqptCh6KCSMBX2guysg3SNOewp+/Kuk6zL2ekm0fD/q2h18v0vaXN7nGuf1iTRZ3bzblM3No+4mYvtLIMiwjr+XPao8Ttt9d8B54uqWkRJ+MVPj4NK4OsItm/DtqmCrJg9TZAKhOhVbKlrZUdVGSrie0cmhxO3biO6FmjYHz/xQwO/mZfPxxgp21VhIjzTwh9Nzuks2gwTpwbGwoX4DKYAyufN1JfAxcLAAjAIYDdwliuJaQRCeRSo3OiwEQbgZqfyJpKSkXzPmAYHPJ+LSx6D95R9+TkPinD+ztNjKi2sqCNEq+c9lo7jsxbX8Zm5gvWyEQc3PextYV9LC7TPTu8XzgBizv5iVKMLzO2RsTPgTiXF3U9Eh471lDl5KgH8s2sLzcYsJ3/zfruPl2adz5Yjf80Blm9950iINOPSx3Nd0AWeMPxe7B17Z5Ka6zcZFU0+excJAv07lMoHxoVZUu5ZKQsqiD/Xerxg3fUZfD+24IagMkjXqD492N0Zkopw3p8/GdDwZ6NfokbKxrKVLywLRJwn21e+C8TdLARgArxvK18DXv5UcwPrh4lEQBOJDtRQ3dgz6XbXBco0qZDKyow1sqWj1bzdE8pLtXBInT6fD6ebNfC8Fy3bx1AVKpmaEY1QrsDg9bCpvoaLZxgWj45mRFckzPxRw+3tSCVBOjIHXrhnnV6akVcpJjdBT3uzvlnVhdC2hH14oXecgiV1e+w3EjsDp8fK/n4t5f205l40P/F3/cYKScSuuRWapkRqWCXDVlzD6avB5ofhnqNvh30kXFnCewUh/v061ShkpijqET27v+tsLaiM5F73XxyMDQWsCmVy6hvzag2Uax5ITeo3aW+Hb38Hur7rbJt8Nsx/u3bhDEwITboXsM8DnkbLt5YHrBZ1KToRBTYOluyx837rm5U1WfoqczbVj5yKTCbQpYhgVGYLFlImx2r9cssOYSmaYnN+oPiF0WadL60ZIzTyffypuYkJuCne8391nSno4z102inBDL2MHdCoFSoXAE4t2cWpeDHNyo6lstqNT93V+WZD+yrHwUUsXRfHvgBtAFEU7BJTh7U8lUCmK4j6vwYVIAZk6QRBiATq/9+rFLIriy6IojhVFcWxkZP9OFT8aKlps2FrrA2yehbUvopdLQrltdjcrChoI0Sopa7IyLqV7Mi4IcMOUFFRyGf/9sTBgIjY+OYz0yO5dM7VCxuUTkvjPL1X8YZkNuxc+OUeLwVrGzUN8hG/1t6sU9nzL6dFtGHvUn0eb1MzJiSLKqOHySWn8fpmFx1ZYqG5zMDM7krzYI3+guj0+Cuos7Khqo90+cNxoBsN1Gl/4vqT388u/YeWzUL+T2Mrv+npYxw+vC9b+z7+tsQDB1tL78QOcwXCNHglrS5rJ3BeA2fI+NO6VBMINPX52uVJKhx51Fax6Fop/6pvBHoLYEM1J4YQ0WK5RmUzgyonJfs/LkUlm1pU04xUF7lnczB+XWShokJ7TTyzahUwm8Ndzh/LyeYksudTIy2eGIUfks81VbO4RyNld2xFg96xRyrl7TgZqRfc0b15uJJH5r3cHX0ASu9z1DSCJSL66opgOpweNUkZ8jx1fk0bBMHFPd/AFpF2bxX+QyjXD02DeX6Vy6X3EjoK4UUfxWxs49Pfr1OvxIN/0RsDfXlnQ989zbUw21sn+e7DWsbejiukfwssuj5e9dRbyq9uwOAbOHHR/Tug12rDbP/gCUqZcb1ovPTEnSmXAvQRfcNmIshXx5UVmZqV1lxG73F7OHRUPQEGDjT8us2DTxdNsc/Hm6gp2JlwiBZo78RnjqIuaxgXJDkLz3/T7CFPBp8yObKOwvoPsaCN3zc7g7jkZmHUq9tb5a2/2JD5Uy2NnDcHtFflmew3//bEQl9dLdrTxgH2CnNwciwwYlyAIWiThXQRBSAcOqlgpimKtIAgVgiBki6K4B5gD7Oz8ugZ4svP7F8dgfAMWl8dHW4eN8P3fsLdiVHQ7FbXZ3ehUcj7dVMWFYxKYmhmJQS3H7vKxcFMVpw2NweX14fT4/E6THKHnrevHk1/Vjt3tJTvGSIxJzTs3TCBTLCFy8e0IO/aCUkvSmS8E7E4AGORuPrtjMrtq2pHLZAyJNZEcIQV15uRG8dntUyhu6CBUr2JIp+r4kdBsdfLKihJeXl6M1ycyITWMJ84fFqjhEOTY4/Mh6+nO0UlvbYMF0euWSv32w+2yB1qwBxlQiKLI5vIWzh8VL2lr7V0kZXb1thsH0kRwzPWw7mVQGSBh3Ikd8CGIMqopagg6IQ0khiWY+fT2yeyutSCXCYxMMPPEol0YtYFTMYvDg9vjY3ZIJYYvOvUxVAYyTvkHD+xMDTje7vIFtI1JDuOLO6awubyVeouDSJ0M2c5e7t+d4vtOj499Vcz/+7mI66akolfJiTZpSI/Uo6j8MLCvvUkKXAOkTIebfpSEXdUGiBkBIfGH/wsKctzwiR6wNga0C7bAthONUqXGM+Em2hPGI7RVIBrjUMSPQKPr+3L1RouTl5YX8dovJfhEmJoRwf+dO5TUCP2hO5/M7G9tD1LWqccR2H44tFbA0sdg+8fEAq8OvYSCGb+hWgxjaFwIcpnA+aPiaehwkhymIyNKT1OHk99/uoOblnr529R3yBbK8SGnXp/FqnoTl8Y6Au3uAaXPSZRJzbiUUF5cVoTHJzIs/tCbx6cPiyUlQk9xo5UIvZohcSbCetHoChIEjk0A5s/Ad0CiIAjvAVPoFNc9BHcB73U6IBUD1yFl5HwkCMINSJbWFx2D8Q1YEkJ1FIRkgVzVPcEBWoZczfu7u60np2ZEsLvWQlOHi4UbK1HIBO49JYt/fr+HmdmRbChtYVpGBIlhgfWLCaG6AFeEKQlOeO9haXcYwG1Hu/dLfPHjkVWt6z7QEAXhmWSYjWREBUZ5VQo5IxPNfu5LR8qW8lZ+KWgkNkRDZYudtSXNvLO6jIfPzPOz7g5yHJDJaBtyNeaSnyUnCtEH9btozzwXc1+P7TihM4UjDr0QYVuPhYZCgzo04cCdggwISptsKGQywrUCfPcs5JzttyvWK8ZoGHE5rHga5j8JoSknZKyHQ2yIloL6A+/IBemfZEYbyeyxK3ruqHh2VLWhVsj8NkkuHptImNCO9ps7uvUxXB0YF93Ony5ezKIebqdqhYycmN43JXJiTejUCs59fiU2l4cJs68gvXKt/0FDzgEgNVzP2SNi+XJrDW6vyMvLi4kyqvnyzinEhGhBMR4EmfQs2MeE27ozyGQyycI6tq9VRYLsj1ZrQBx+KVTs97fPmt83A9oPrTEUbc7Mvh5GABvLW3hlRUnX618KG1mwvpwH5uUgC85BD0x4JhhjwNIj4Bs3BkIDg8eHxd7FsP3jrrmofMcCclImkTP2uq5Dpmf5Z/WYdWouHJPIA59s484lHkDSn3lwfhj/W76HyZekkRSZh9DQbUbiMyWx1RZObLicd9eWd7Vvr2rj001VjE0OQ6novXhErZQzKil00JcFBzk2HIsSpKuBb4C/AO8DY0VRXHaoTqIobulMhRsuiuK5oii2iKLYJIriHFEUMzu/n9Q+m1qVnNC0UdScswBX4hQwxeOe9iA1uddjdUNerJHnLx9FXZuDCL2Ke+ZkcNO0VP523lC+z6/h8vGJjE4ykx1j5C/nDMGgPkz9FWsjVPg737Dzc+xT7qd92DVgjEHMOxeu+OTYW0k3FsKKf8N7F8OG15nIdj4OfZ4vsxbz9ukajGoF3+XX0mpzHfpcQY4Kr0/kF1cGvgvfhLB0xMhcfBe/yzJbSl8P7bih87YjaM2SAJwpXtL/OOVR5Pa+3yUMcnRsKG0mO8YAOz8HQwRE5x5ex9BkyD4Nfvw/cPWfkp84s5aihv4zniC/jglp4eTGmvjzWXlMSA0jxqThzlnp3D4zHV97HfKmAv8Oooi6o4KbpqUSY9IwJSOcn29KJ6vsQ3j3Qlj9AjSX+HVJCtPx9vXjmZkdxVv1aTTPfQbC0iAqDy5+B+LHQNkqTIvv5d+q/7HyEiVTU0ycPSKW/105hoUbK7nuzfUsaonBc9nHED8WzElw6l9h+MUn7pcV5NfjcSHIlXDa36W/fWQOnPMCYj+6p/VH9jerAFi0o3ZAlyKdEMwJcMVCGHKeFIgZcx2c9+KB7eStTbDjU3j/MvjhMajdT0uqagOc+rh07UZkSeWOlesPOYxJaWH85ZwhpEboyYwy8NQFwyiq7+CBedlkpqYiXPQG3pFXgjGGjuzz2Tb9f4wdmktdW2Cmzg+76mgJrj2CHCOOlQjvVGAukrvRFkEQloui+OwxOPdJT2KYHp95Fh0poxFwoTRFkQd8mZxJU4eTy19dS127VPH1454G/nh6DmePiGd2bjQGlZwOl5dQnerIskU0IVL0uufETxRRqzXYT3sKfA8jaEIOnLr/a2mvhQVXQsMu6XXBYnS5Z4G7Fc3Wb5mufp+npr/NoroQDJpjcekGORhymcC8kApkH18LSMJOws7POeOSD4EhfTm044aoCUEs+hmZxwZpM8BSB4v/gOvST9AcunuQfsy6kmbSw1TSJG/cTUfWOW4UtJbDqudg5kMcWubs+BMboqGi2YbXJwazAQcwBrWCU4fE0G53c2peNIIgEKZXIQgCFk+olGna4S+HpwiJ5Q9jc7l1RjoGrKi/uBUKFktvFi6BXV/Bpe+CrruAeWh8CM9eMhKL04NJOwVGnStls2hDoGwVvHmGtLMMxO/4iDeu+JzasGyufG0dZU2SLs1Pu+u5YXIqD13xGUrRDfqAAukg/RWFClEmR/jpb5B5Cng98D+syGIAAQAASURBVN2DCGf/99B9T2JyYgKzJMenhKFTB+eghyRmGJz3EjjaQBvau67LPra8D0selv6991vY9CZcvwQiMqS29Dnw2U3dJUO7voILXjvkEArqO/jn4j3MzI7C4/Px+Ne7eO3asYxP3XfvykF+5jPgaMEr6slUqdGrFbTYAgNso5LMGDWHuZEdJMghOOo7iCiKPwqC8DMwDpgF3Iq0OgsGYA5Adaud4oYONEo5GVGGLnvoZquLwnoLHq9IWpSeGJNUMiSTCZhMZr9zmHUqfilo7Aq+7OM/PxZy5vA4YjvF8zSqA/+JRVGkpNFKRbONML2KjCgDWpUC9OF4zvg3ig8v6d7xHXMdCq0Jo9wLumPsDt5cLO3YiT5pQtfQ473dX8P0B6QJorOdYfISUmZegFoRVBY/7vh8KLa+C2OuBUO0pOrcWo5i56eQM6+vR3dcqHZp6Rj3GNlLr5cmBEDH0KspElL63KozyNGxoayFmyJ3Szu/hl8hQJh9uiTQXPgDZMw99gM8QjRKOWadkqoWO0nhQZvLgUxjh5PC+g4UAqhVcnZUtRFp1GB1GcmZ9yzGL64Fj/Sst068D2XcUARBkBw5qvK7gy/7KF+Ft3Yn8rRpfs1qpRz1Pgv2njvRWz/0LysSRZQbX6d+4vCu4Ms+3lxTyuUTk0iPCu9+dmvMEJl16JK+IH2Gx+1EvuNTydpXHyE9z5UaKFkOQ87t6+H1W8anhnHX7HQUMhleUcTjFTl7RBxK+bEoIDgJUKilIPLBaK2E5X/3b7M1S1pt+wIwu77012tJniKtTwqXSqK9YWkBp3V5vLy0vJh2h4cvt1Z3tS/f29gjAAMolGCIoqfKy8gkM/OGRLM4X9LHCtUpuWt2Jtur2nB7faRF6gnTqSis76Cxw0mcWUt6pCFYlhbksDnqAIwgCEsBPbAaWAGME0WxV/eiILCrpp3r3lxHbZs0mZqbG8Vfzh2K1yvyu0+2sbqoCYDkMC2vXDOOrIMoaIsEikf5ApsOyMrCRm56eyN2tySue8+cTG6alkab3cXfVmk5Zfz7zAutR2ctlx7SL8+UbOSm3y9lyRwLylbD+xeDs1P4dPglkDkXCpZIr0URevycMSY1iXFBa8IThZA1D9a82K0HFDMcYdwNfTuo44ko4pMpYdKdIHpBrkQUtbi8R/AfK0i/o9Xmor7dQZL9U5hwhNkv+5ArYeiFsP41aWfPEHNsB/kriDdrKWywBAMwA5jSRiv3fLiZmjYH101J4V9L9nLZ+CR2VrezoayFWJOK/5u+kPFmC+gjUMbkotX3ECftRUQSgMa9dJjSMUTEHXoQYqCAL6IXsZcJhbjv8/Z/dk+8HWY8CFrzoT8vSN+QfTqs6eFEEzdKEhoPckAEQWBbZRs/75XKkONDNJw98jD+TwU5fJqLe78H+a1xevw7a560BvnyTum12gRXfCyVjPegtMmKp5d7mMsTaCiyP9EmLU9dMJwbp3Zgc3mIMqn51/d7WLJL2iFODNXy57OGcNM7GxBFSYfrv5ePZm5e9CHPHSQIHBsNmG2ACxgKDAeGdroinbw42iVrx/1weby89HNRV/AFYMmueraWt7KyqKkr+AJQ1mxn7d5qfNamA06wcmNNmPZzT7hrdkZX9ss+bC4PbTYXrTYXTrcXXFbaW/6fvfMOj6O6+vA7W7W76r13ybLce8fGxmCaAVNN772EhBBI8iWQBEIanUDo1ZiO6c0GXHDv3ZZk9d7baut8f4zaaiVbsiXtSr7v8yxoz045a13NzD33nN+p5K9f7GsPvgA8tfIwB0rr2ZBTxZd7ythbo0a/9jFF++DIz8pGvzyNs2R3n/9JuqW5Gr74dccDHMCu9yB2asf7EWcp2S8AOl+0MSdHS0uvQKWCmjyoL4aRiyHjHKUsbZi2ZAbwsdcxYtMflACgIQiaqvH7+c+ky0eOvbPAa9maV0OaqRl1cIKy+nu8+EdB4mxY+2QPD4yDS2SAgcNlohPSUOaH/WXsLKzjggkxPPdjNipJws9Hy5ZW7YmSeis3ftXAP3PikaMm4tR0CbaFpCLPuBPGXAR+UYotajzqwg04i7d3bGdtAnNt+9sGs41Giw2bzYp9wtWKuGUn7JNuICLAh9gg1+eJy6fGE29scb93b/ivsmIt8Eo0Wn1rWbmklG5c8D/l3m6p87Rr7dgdTmqarNgcnr+2trHpSBW/ZFcxPyOcc8ZG0WC189q6XK/ycUhTfQS+/i1MvMrVbgiCiDEd7yff2PFz7BTY9X7He0u9cj1qezatL8Fenc/bv+SycKRrQEStkogPMVJSY6bRopQZ1TVbMVtdgzJmqwMJmJIUzNwR4ewrrm8PvgAU1JhZsaOItHBF/Nxid3LfBzspqHbNGBQIeqI/SpDuBZAkyRelk9FrQCSchF1bzfVKKvDax0Glgbn3Q8p80Cnt6hotdjYdcdcVLqk3k1XeIYTm76Ph1YUaRuf8GdXWfTDmEphwJQQnuuxn0mn454VjWbm/nKJaM+eNj+a0Thcbu8PJxiPVPPnDISobrVw9NYpz/bPRbX4Cf0sNL4+5nv+GjuTdvR0XjPJ6C3uKlYeqkYEO1Huz3f0tyKFcPerElb7NtdBJfbwdnS8kzoGR50JIKqx/DqbeDOMvh4jMEzunoPc4HWBphFn3wN6PlTF9yv3IzVVeoIAxQLTUo5pxG1RlKe2HAxPgkrdQWcRNdSiz+UgVKeY9kDnjxA+WMFsZG4e+UQLEHiQ60IeDpaIT0lBmY47yTKDVqGi02IkNMlBU497CdX12Nf/5/iC7Cuu4e0Eas1JC0GtUimZa6R6ozYMxFyv6bTU5sPYJVOGTwW6F3LXw82PQXIVj2u3s8juFP35fyv/OMBK8+1WMhatxJs1DnnYHtoPf0TT6Cl45GEJFSxZPXTaenw9WsCWvhnPGRnFqRjhaS0n39+76YnebwCtoaarHJ3KsUqqx+l9Kd83THlbu815AVnkDb/ySx48Hy5mZEsINs5MZ0Y3+ymBTWmfh/kUZfLGzmDyLnaumJ1LdZKHZYiegVT5AcAI0V0HFAQgboWTQ5W8A/2glGz4svWO7+Blw9Wc4t7wKKq179kD5XmiqgLy1sOF5NA0lPDTmUnYGLOTBMzNYn12Fj07N+eOjqW+xcfVrmzBo1Vw9M4Fv95ZSb7Zzz2lpTEkIZmt+DU+vPExRrZmrpidw3vjo9nlRZ/aVNJAW4cuh1kWQOrONmiYrccEiI1VwbE44A0aSpDslSXoP2AGcD7wKnHmixx2SHPkJPr4RyvcpK0HvXalcTFrx99G6BEjaiAs2MTUpuP39Q7P0TF59LT7ZXysPVWv+BT89CrYOVW6HU+b19bnc+vY2NuVWY7E7eeTL/ZTVd2yzu6iOq17ZyObcGo5UNjFGyiZ0xeVIRZug8jBxax/k9qgDLlk0scEGJicovmwuV2OLGO/mb54zjMtf2sihshN88DeFQtx0d3vMBLj2C5h2C6QugKs+hrP+paTLCgYPlRpn5Fj48REo3w+lu2Hlw4ptmKI1BiIXbIJ1T0FNrpL59fGNmPxE2dtQZuOBPEZoy/unjbRKpXR22PYmNHu2O1ZsoPHEr8MCj3JqhqKP0GyxE2zSUVbfQmKo+wP8pIQgfjxYzo6CWq5/fTPbC2qhbA+8uRjpyE9Qc0QRiS7arGinAXJQEhRvg3eWKO2Hq7JQf/Vrwgq/4/7ZwUR9exPGPW9DbT6q7W8ir3+OZSF3MnmZDZPJxCfbirhz2XaumJbAspumc/m0BKICDGAK6/7eHZQwgP9SghPBx+SP7LDAV/cpE97SXfDpraA3edo1qpus/Gr5Dt7akEdhjZn3txRy05ubu+1EM9gkhhp59Kv97CysI7uiied+zCIhxCTEWPsL30hlLrBvhXL9srdA0baObL42tHqs8bN5NuQP7FFluB8n+VSllOnD6yBvHVTnoPr574yrWMH7W/KpabZSXGOmotHK/R/u5nB5I7uK6rjvg11MSghm45FqrnplE5tyq7ny5Y38kl1FXlUzf/tyP+9vKWifF3VmUkIQe4s7MsjC/fSEB5x8uQeC46M/SpAMwONARmvr6IdlWV7VD8cdUlisNuTN3Shy7/6g/UeNWsU1MxOZGB8IgEqCG2cnMSEukOnJISydGockwQhVkWtqL8Du97FW57E+u5JPtxexMaeqvT1eXlUzW/NqqG+xu6yGbs+vbdeECTbpiK/d4lbOFHfwNc5MNeKjVfGr09KI8NMzJSmIq6bH89G+BraN/bOSBQCg0VM86688tUePzeFE01AAB75UuopUHOz9P1ZNHuz/ArJ/hIUPQ1CSYlfrYNFjEDFWqVHet0J5Vbln4QgGHrvNrmS+dMHZps8zDNGby5B2LXc12sxQLcbgUKXF5uBAhZXUhHhFeLI/8IuAuGmKPpIHiQk0kFPZ1KHLIRhyzBsRxuLx0Xy4rZA7T03Fz0fLkcomFo+Lbh+ukxKCCPfX89dTg9l4kY0t59cTbz+CXLa3XZy3nV3vw4izaZp5P6q4SZC7zu2+H3vgFUab6lF3ua5pKvaSrCpDluGX7ErGxQVSUtdCSXUtFG5Rjp39IzisyqJIW0Cz7d4dPkYJ1O/+EA5926uMmAazjQ05VXyyvYjNR6ppbLEf57+k4Gg4LI2w8133Dw5+PfjOdCGvqokQrY13F8H3p5Xx/iIHmcESOZWeb5G9o6CWP8zy5cvT6/l2YSXPLjTy3d5Syurds9QEvaCuSBlzuz+C0r0QEAOXvA0L/wIz7lD0XRb9HULT3HYtqm3hyR8O86+dOkpm/AmS5sL8/0Oe93tY+Fdl8dvh2r1ItfU1fjcrkD3F9ZwzLopPtxe5HfdwWQOjohRdrf0l9e26MYvHRfPrhelY7U5CfHXcsyC1/Zo8IzmYc8dGUd2otKUO9dXxzOUT2punCATHoj9KkP7VH44MZWRZZvXhSmbpgnFbtzK5qn+nhPvy6rVTyK9uRqdRkRRqau/o8+dzM7l6RiIJ1b+4n0RrZFdJM0uXZ7WbbpubQmWjlSOdblI+2o7uQCZ9x89WuxObrpNwXyt2n2BmpEcREmJn85FqrpuZSIBRxx/OHskV0xKwO5zUpn3Jzj17qLT78NwOmZwqM/9ZYCLxy8uVVTdQuh9c/RnETDz6P1bFQXj7IqjL7/j3ufJD5aKp94eQFGV15s3F0NS6umwKg6tXuNWoCwYWSQKpmzajGpP7SsBwQVZrlRI4c00Xu2hCPVTZk5VHjFSOIXbMsTfuC0mnwPpnlSzH+G6yAQYBXx8NBq2aolozsUEi7XkoEh1o4O9LxpDXeh9fkDGT6mYrwUYdt89LwWJ3sHJ/OTNDm5m04W605a06Kxo9zrOfci8H9fGnZeRF6MLT0er04ON+37fpg3FI3a/g21Q6wIafj5aqxibC/fSMrF4Fn9/WsdG4pcok6YbvlfbsbffuvHXw9pKOSVDsVLjoNQiM7fZcLTYHL689wlMrD7fbfrdoBDfMTkInOh32L5IGDN3cuw0nWEreD/jrJP6RuIXInx5ptyVPuItqveef+c6ONZOx6tdoa5Rn7xE6E2mnv6OU/wn6Rk0+vH8VlOxQ3mv0cO2XyjP/93/q2G7KzYrOS5drl1YtYdRpWJNn5m/GGfw7xoph1V+Va2DFflf9yDZ8/EmNCuKOU43sL6nHz8d92qvXqLhsahx/+mwvhtY51Dljo6hptvL490oDiqdXZfHc5RP59p5TsNgdrVlQGr66ew7VzVYi/H2IDhTBF0HvEVeQfiC/uplfvbeTg/GXufa51xpg9AVu2wcadYyNDSQj0t+lnbKPVsPIKH+MceMgarzLPrY5D3Dvd66TwlfXHeGsMR1pevHBBkZ16hA0KSGIEJNSo9posbNbO9b1ZitJZGXcxn2fHOK/P2Vz7ayk9ppWH62GjCh/RscGEhgeR4Exk9+saianyoyvXsNMaQ+qmk7CpJYGZTLiOMbq1eHvO4IvAE3lsPFFpbwoLB1UamX1rKlTan9ThWITDCpqjQbSF7mP6bhubnLDhGb/ZOxzfutik4OTaQwW2kNDlU0b15Lua1FarvYnaq0iTr3xebB7bjU0PkSUIQ11TDoNmdEBZEYHkBBqYkJ8EAmhJjKi/BkXF8SMlFDSbQc6gi8Adgtyc6VSZtSZhX/DJ3aMEnwBpV1rl/v+kVF38VGOhuYRS1x2rRt5OcuydOjUKiYnBHGgtIFH5weg//4B13PsfBfK9intZWMnK/duSyN890fXFejCTUoJVA9kVzTy9KrDLrZ/f3fIKzIfhhuSWgsTr1G03NrQmSDtdM851UoCxURu/oeLLXTHsyThnq0w2KQ17WgPvgBgbSLl0MsE+wxbJbyBo2hzR/AFlOy9gi3w7YOu221+sdus+tggI789Q9GFOTvGjOGXTuv/ez+B8JEQ0CXYO+/3mDVBvLT6CJ9sL2ZOWiiaTq2ijTo18cEmtuTVMCk+iDExAUQG6EmL8GPN4Y55iCzDn1bswd+gYUxsIP4GLZIktV+vRfBF0FdOOANGAE0WO01WB7f/rOM/c5eR1LgdWaVGl3IKocejW+IfBRe/Afm/KDWNcdM4rM6goNq1w4DF7mRSfCD3LEgjKsCHackhLu1IU8P9WH7zdNZnV1HRaMER6kfpko8JqdyM1taIOWYGh+tiuHuBlenJwYyLDezRpfPHR5MQYmRzbjUjIvwIL1jpvlH5PmUioj6KcFp3pUple5SHNlVrMKpkp/s2orvCoOOwWVHt/RRO/SM0loKkAmOoklo++kJPuzcgNLTYeb18PLee/zr6ks04/WLI9xvP3ppQFid62jtBn5FlNh6pZkJi2MAcPyQFAuJg53sw6dqBOccxiAk0cKC0gfkZov3lcGVKYhDm/FI3u3rjf7Fd+h7a0u1KuU/iLIie5LpRRCZc+5WSndJSh5w4G6c6FSmrjvzkB4hOPxt1xV7kiDHsU2UwtUbHNXP9OVBczzNLJzAruBJauumUY+7SUMDaqDyvdKWpZ52kerPdrcmjwylTbxZlSP2N1WbBJ/tnuPQdKNwMag3ETMaS8ws+qQs86pvG2gDOLr9zWUZrdRc+HWx8GvLcbJqq/UppskboffSJevdrGNZG5d+yK12ykNtYMiGWpFBf4ht3uAtIr/ornP8CFG1V9NniZ0LyXEbpTHx42ww25lSjUUs8f+VENuRUI0kQbNTxwupsUsJ8eeKSscSH+PLODdNZn1Pldu7KRitNFu8QrRYMfUQAph+IDjSQGeXPvpJ6Lv8K/A3j0apULJ8wkuNueBqc6NL1yFjZhK9eQ6Ol4yYVH2xgbGwg87sR9m0jLcKPtIjOAZFoSFPKhAzAub10x9dHy5y0MOaktU5kfBbA5v+5bjT+CqUU6WiMOAO2v9llv8tdV6fHXgrZXQI8Yy7ppaeC/kKt1SEnzITv/gD+McrNrrEUznnS064NGBH+eg41+pC5XMfs1MVklzdR2lDLp7d7PhVa0HecxTvYbo3lsrjAgTtJ+iL45RlIW6j8nQwysUEG9nXToUEwfNCoVW5ZsQBNI5ZgiBwFMccQRo/IbO8gKAEZQEZM29PJCOAiAGa0vgDmjWgtnzabIGYyFG3pOJ5apwQfO+Mbody7N7/sag/vRjCzldggA0FGLTXNHVkz4X564oLFanJ/4+NjpN4/Cf93L1UES61N8NNjWM59FY8X2AbGK51vOmsGGYK8Q9Q5cQ6se1LJ/lVpFX3G8VeCIdDTng09ose52/yjITQdKg912HQmpVtXN/gZtJySHgZ1mco1p7Gs48OaXEWXKmmO236jogPaKwS+2l3CB1sLcDppn1NdPCmW+BClpXRKuC8tdgdqlYTD2REhnpMaSmSAx/9aBMMEUYLUDwQadTx+yTimJyv1tUFGHU8vnUBqa3/4/iAx1MSr104mJUxRrJ8QH8h/r5hEqJ+HIvBx07Cf/pgScFFrsU65lZYR5ymfVR5WOoSseVwRAOzUvYn4WXD6I4rOhloH029X6pIPfK0I/P30D+VGd95/lf9rDTDzHmWloXSPZ77rSYw9+TTkC1+BUUtg7CXIF7+OPd795jZc8NFq+OvCSA5cYeeN+K9ZPe8wu26OJDPaXUdB4P0cXPcZvlqZQMMA6kn4+Ct6MBtfGLhzHIX4YBMHSkQJktfhsCmdh9Y+BVteVTrJ9Qa7FfLWw9onYMvrWEsPsPpQORstCTQsekqZmKrUmEdfjnPidahqc2H7O7D6P3BktTKxPhpOp7JC/MuzSvlv6e6jb28IgMXPKIKXAIGJcPl7EDbSdTu1BmbcCWMubc2WDIElLx21e2FcsJFXrpnCyNZ2w2Ni/Hnp6slKpyVBv+NMOxPn0uVKGXHaQpxXfIScONvTbrVmfb8OUa0T9LCRijBrf3StO1HipsJl78K0W5XFwgteFAuCfaWhTGm8UX4Qzvp3+zWM8VcoWXsXvdrRVS0sA674EPLXK9e03LWuGTIVB2HL60rJ0ZIXIbJ1zESMhqXvQWAcAPaqI5i3LKPpu79TtesbSsrL2w8xPTmY2+am4HDK6DUq7jw1hfkZrnqdIyL8eOnqSUS1BlxOHRHGnxZnYtKLvAVB/yBGUj+REeXPy9dMprLBir+PhmDf/g+MTE0K4cNbZ1JrthFq0uFn8FwbvAq7gf/LnsKUse9iVMu8dcDJuSYrt4zOQvXmeVDfqXb30ndg5DnKz8YgmHknZJ4PVYfgmweVqLXd4pr1Mv12uPZr2Pcp7P8MfnlKCfZc+2XHTVow4Ggbi+HT25SuF4CkNaJd+h6Ep3rYs4Ej5MjnqL75HQBqQBOSinXJGxAz2rOOCfqGw86WAzlkhAzC9SJ+Bqx/Rkntj50y8OfrREyggfyaZlpsDhcRdoGHyV2rCNLKTuW9MVi5f4UfQ08q50d499L2zkU633Cqxr3IvSubSY9I4h9nf0lSkAZTaAKG5jJ46wLXLm1LXoKxR5kgFm6CN87p0GrR+Sp+RY/veZ+ITLjsHWgsVwR3fXso6QtOgvOegVMfAI1BmVgfg4kJQbx783Rqmm0Em7QEGHTH3EdwfPjVHUD1/lXtv3uVzhffy96FEA+XL1qbla44QYmQuhBqc5VFvKgx4BNwrL0HlrpCWHF7R0mMpIKrPu1RWFrQBWsj/Pg35fcJSlON0x5W7plBiaBp/Xu/4gNF79FuVa5/tZ1Kvy5+HUZdoARfXj9b2Q6URdyrVyiZMIZgZX4BUFeE6v2rMJQpwWUTUDrnEfLGX0dCiIlgk55b56Zw7rhoZFmpYlCrXDV9NGoV8zMi+OzOAJosDsL99Rh1Ysos6D/EaOpHfPVafPW9C4o0ttjIq25GrZJICjGh7+WDc5BJR5Cpdw8oRTVmKhothPrq+r1DxsHSer7ZW8Y3nWw5Kw9zY2A9qvouwmk//BkSZioPoG2otfDJrRA5BjLPg09ucd1n4wtKWuK6JztslgY4+I0IwAwWTqdSLtYafAHA1qy0Bk8+xXN+DSDW0gPo1j6urMz4R4PsRNr7CZryXSIAM9TI+ZH18mjSwk0Dfy61BkacCZtaBcVVg3dr1WlURAX4kFXeyOgYD09WBAqt5R3twReA5molI7S7AIy1GWd1DlZLC/oDXyF1FkZpLGeCdAitOo5DZY1c8HYjb98wldl6H8je4Rp8AUUIN2mu0iq9K06Hcm/tLJRrbcS270sqjSOIOpqQpN7v2CXGoGSr9lA+0BOBRh2BRhF4GUgcNiuqba+5/e5VB7/2/P28OlsRXu0qCDTtZkXk2ZMc+dlVj0R2wup/Kx13dCJT65i0ZcS30VQBn98NN//UEXwBJZPUxx/2roDkeUqQ1+mEPR/Bd/9HceBkwop+QdsWfAHl2fSbB+HaL1yuTfaS3WjKXDP7Ijf9g6y4BRCiZO5JktQ+L8qraqK22UZEgN6tjXSYnw9hvbjsCQR9RQRgPEBeVRMPf7aXVQcrkCRYOiWee05LI8K//2oLVx+q4J7l26lpthFo1PLkpeOZmx6GJEnH3rkXtNicbjabQ0a2NrtvbK5xncSD8hAw9lJFgLfrAyQoNzlbN8fqfPEVDCyyE7mpyq3Nqdxc4d76dLjgtMHsXyk6BlXZymRi2m2gFmJ7Qw15x7tstp/D6SGDVGkbOkJpSX3gSyWoPIgkBBvZV1wvAjDegsOmiEB2xVzrbqsrQl75F1S7litaHGmnw7RbYGOHxprW3oRGpcLmUAQgzW33387lvW201LlOsjsjO6DBXQjTXF3MHcu2ce9p6cxJHyDBaoFnkR1KELALUkv3YqeDir3FPfgCyLZmzz9rNHfz79NU7i4aLOgeezfXKOheeBcU2YEjPyuZ8VqDUtZ4ZDXf7i7gTLmCyK7bm6uVrJlOj2hyd3MHayOS0/W6aHM4+XZvKQ98tJtGi50Ifz3PXT6RyYndtGsXCPoZoQHjAT7bUcyqg0ogQZZh2aZ8NnSjuH285FU1ccc729qF7Wqbbdzxzjbyqrq5KB0nKeG+zEoNcRHLW5ARjjpqbEc3ozam3QZ+XS6b/lHKpDZ7FbTUK9kGnYme6F5jDpBxTj99A8ExUWtwjr3UzezIGNzJ5WDiNEUi712hBF9AKY1b9ySSj1gCGVJYm8g/uA1ZrSPcOEiP8JKkCPLuXA7WwdVkiQ82sbOwdlDPKTgKhkCljLYzkgTd6W1k/YC0a3nH+8PfKWKfba2jJRUFxkzMNiX4MjrGv0OTKjxTScPvzNSbey79Uetg6k1u5vyohWwvqOWOZdvIrxItoIcjap0BRl/k/kGKZzsgAdToY7GFu2aYOgMSqDV4gQhvyjx32/TbQTwT9I7gFGVxojMhqe4i3qAEu1b+RQm+gBKkWf0vrJNu5J29FqqCJyglYJ2ZfjuYQlxM2ohM0Lpm/TeMuBhnl3lGdnkj9yzf0S7EW1Zv4e53t1Ne30PQSCDoRzyWASNJUi7QADgAuyzLkyVJCgbeAxKBXOASWZa9IDzff7TYHHyz130Fal1WJeeN71sHjXqzlfU51Xy4tYCYQCNLJsYwNjaQgupmGiyu0fkmq4PiOjOJof2Qjt9cTVLZz7xlWE5TRiL7QhexpiGKiybFoQ72gSs/Qf7p70j1xdSPuYYfpbn47S9nanIwvg25iqZLSz3krlaOt/U1mP9/SpvMkp2Qtgim3wqmcFj8HKx7HLQmmPc7RRBNMCg4nDJfNGZw1pn/Rrv5RWSVGtvUO/iyPoULPO3cAGFvrsWnYIObvaWunP4t4hMMKAe+YqNxLhlaTb9l/fUKv0hFL2PH8m4nugNFUpiJFduLjr2hYPAYuVhZYdnwnBJMmfcgxHRpEd1SBwe/ct+3aCskn4q9tpDSifdSrc5k8bhK7khvIKXkMzRfP68IgibOUTQQfvo7VB+BSdfAuKXuiyCdSVkA5z8Pa/6DQ+1D7ui7+Nsuf2TZTH2LnZL6FuJD+vacsK+4jhU7izlc1sgFE2KYlRpCsElkDXoTNmsL2ooDMPd3SlmHWgdjL8FRuhfN2Is96lt2k56qzEeZHvoeAYU/Uh85g21xV6NtNDLLo56htHW//AP48VGw1MGMuyDjbE97NXTwDYdL3oD1zyoLrknzYNZdim5LV5rKocxdFLzFYuGFKWUklm3Asfg5VFteQWquwjH1FtSjl7gfJzwDy+Wfol7zTzRVB2gYcSENIy8nKcI1s6Ww1uzS5QiguK6Fr/eUkl/dxAUTYhgdE3j8310gOAqeLkE6VZblznm6DwArZVl+TJKkB1rf/84zrg0Meo2K6ckh7O3SNnT8cbRJ/W5fGfd9sKv9/ftbCvjw1hk0WuzoNSos9o4yIZ1aRVh/CQPv/gC+vh8V4AdM83mXKdd9i6otuJM8lzzDSN5Zd4hPN7RQ0VAGlPHdjemkf3sVVOyHwASlc0jRViXL4Ls/Kg+nZ/5LWUVWtw7NiVcqAr6SWqw4DDJqlcT+Jl8eXZvEA7Ofx+aUeOyram46pf+6e3kbGkMAcugIpMqDLna9b5CHPBIcFzvfZb3qfNKDPZDkmTIf1j0FmeeCr1vC9ICQGGLkUHkDNocTrVoktnoFvmEw9UYYvUTRPOtOP2X3R4pwbReaY2fzouV0srQWvv24AZtjNxuuDyfyg8s6SnMPfgkXvKAEXJYuV8qRuqwEd4shEMZfTlnUfB7/IYsV3zfQ0loOoNeoCO1j4CSrvJGlL22kzqxk3K46UM4fzhrJTaf0TQdGMLBodT7IKg3ShuchbaFSpvbzP1DN+72nXSPYpOPKlc2khV7MtLil7ChzsH1PM1/e7QVBPK0e0k+HhBngsHcIvQp6T/hIOPtJsNSCPhA0PWhlOuxKS/LafBez3sdE6tfXKW92vg0jzuLA1EfYY4/hou4COcCKymi+ttxDSqzEt3tsVG3I5aPbohkZ1dHRMqKbLrKBRi351c28sjaX9zYX8uFtM8iIFF0wBf2Ptz2pnQe80frzG8D5nnNlYJAkiUsnxxHfqXRnckIgs9P6Vndd3WTlqZWHXWzNVgdb82p4btVhbpuX0q7qrZLgwbMySOpj9kt+dTOrD1WwNa+6/eGKhlJY/U/XDVvqUHWJWn93uIGXttRR0WBpt1lL9yvBF1AUzgPj3MX6IjI7gi9tGAJF8MUDyLJMYogRo07DvV+Vcf83pYT76wnzVOvzwaCpAmnKDa6TpdEXAu716QIvpakSCjaysSGEkSEe6Aqk94P4mbD1jWNv208YdRrC/Xw4WCraUXuKbu+XoIjPdxd8qS+GlQ8rqfKdhHnl0HQOh53Bk+sq+OJAPTaH0ipVKtzirov28z8VXQ+dqXfBl04Eh4YzPi0ea+tCjVol8egFY/r8nLCvuM71+wLPrDpMSV0PGg8Cj2C1mCFshNIefM9HSiZySBqSwfN6F4khJh5bMpZ9ZWZe3lrPtqIm/rJ4FMmhXrTYo/cTwZcTQaNVOiD1FHwBqNintPvWdhLCnXwD+qyvO947bLBvBcbybTz+3SEqO80x2qhutPD0qsP8mN3Ay1vrKag202x1UFDdzNa8alYfrqCgupnUcF9+t2gEbUmyOrWKW+em8GlrNmmjxc7eonq34wsE/YEnM2Bk4DtJkmTgf7IsvwhEyLJcAiDLcokkSeHd7ShJ0s3AzQDx8fGD5W+/kR7px3u3zCC7vBGNWkVauC8hfcxOkehWswyAotoWPt9ZzN3zU7E5ZbQqidggHzR9WBndVVjLNa9uateROX98NH84eyRh9HRiV1t3m8hdjT//AyZdB6c/otzcwkYo6YrDhKE+TgFWHShjVkoIi8dFI0lQXNvCtrxaLpoU52nXBggZ1j2t6ChIKmXlOncdNPWfRpM3MRzGqBt7P6EoYh7N+RDj6yEJx8RZsPYJqDoMIWmDcsqUMBM7CmqHnRDvUBijXe+XF0yI4fdnZRDm1wth/bWPw4SrIHMxALboKXyT7QuUH3vfnh4CeoFWreLCiTGMiw2krL6FyAAfUsJ8Uan66W/mJItZD4Vxyv7PIXWBEoSRJKgtgIKNSqaxB1GpJM4eG0VGlB8ltS2E++tJDfNFp/G2NeKhjdePUYdd6Xw64w7lvVoHagNsedltU6n1P3IPF5qul8bzxkfz6Y4ivtqtSEAEGbW8ef1UrpuVyOzUUErqWsgqb+St9XlUNXU0Denp+ALBieLJq9ssWZYnAmcCd0iS1Os+eLIsvyjL8mRZlieHhQ1Nxf6oAAOz08KYnhzS5+ALKO2o717g+mBv0KqZlBDELXNTyK5o4okfDvPsqixeXZdLcmjvM0iarHYe+/oANc02Qn11XD0+AKu5kT2FdYrGwdz7XXfQ+0PUeEXXpUVZgZ2RHEKQQculY/y5aLQ/Bq0afdRICMvo2C84GYJTFWHCpDnDKvgCQ3+cSpLEVTMS+XRHMaXlZRSWlvHlrmLOnxB97J2HKNaANJxjLoYjq5WHVHMdlO3FEjrK064NCEN9jHbLjmVsNJ5KZoh6cPVfOqPRQ/KpsPmVQTtlUqgv2/KGlWQa4P1jtMli5+9f728PvgB8sr2I3YV1R9/RPxpO+a0yU9j2ptK2esML6IITuSjTyPSEjtV/i92JM2aScq9NWwhjLgJTqHIvNvaQwWBrUbLBOs9E7BYlmOx0UG+2YbU7yYz259SMcEZG+R/XhDcz2h9/g+ta3p3z047e0noY4u3jVKc3YB9/FRz8Wsm6Umnh4LfYRnhHYwOtWkVGpDIWR0UHoNd6IHtxmOM1Y9TpUK5D9i7dUaPGKcK7+z+HukIo36+UL53yW9fttEYOakZwz4JUwjSW9nlHG8G+eu5ekMq42ACWTIwhPcKX9Ai/9uALQE2zjX9+cxCHDGNiAzl9VCT+Bg1FtR2ZeyadmjHDbEFD4D14LANGluXi1v+XS5L0CTAVKJMkKao1+yWKXi0BnbwsGhVBgM8k3ttSQFyQgQsnxpIZHUBkgA9RgQY+3lpIWoQfF0yIISW896mc9WYbuwrruGWikWv8txF96G2shnAa7PeB41QYc4kioLX9HQhNh3GXQfEOWHsVqHQw937GxM9g/eJadOv/DEg8vOTXSJERcOnbsG8F6AOg8oAiUFiwAeb8Wrn4CryKaVEaNi+uwbDhTzhVGv665Ddoo3TH3nGIUmuxo007Cx/ZgbTheeTAeLjgBXbaEpjhaecEx6YqG2py+UUd6xn9l87ETob89YrOVVfx1QEgPcKX53/KHvDzCFypb7Gxq8A92NL5Qb5Hxi0FvyjYuQzCMmHUYtj4AimHvuHt2OlkzbiBJ3ZrWDwuBmN0ICx5EdY8rozz6XdAfA8SpYVbYPW/oXwvjLkYJlyjCIiufRwKt2BJO4uNAefzxDY795yWzilpoRh0x/c4mBrux7s3TefT7UUcLmvkwkmxzErtWzmUYOCx2h3UmJIJn3UP0uaXkdU6OOOv5KviSfW0c4KTh8rDsOklRXw8bhrM/hVEjlE+i8iEKz9RAtI5P0LUWKjNBXMtXPgKzl0fYPGNITf2PMICEjm1aSO8+h9QaRRx6ZQFoFdKKDOj/IkLNrAxp5o5qaHdBpd3FNRSb7Zhar32nTM2mmCTng+2FJIUauSCCTGMEPovggFCcisLGYyTSpIJUMmy3ND68/fAX4AFQFUnEd5gWZbvP9qxJk+eLG/ZsmXgnT6JsNgd/OXzvdzl8xWRG//e8YFKjfmqrzEkTXPdYd8KeP9qV9tFr8FH17uuvl3+PqSfAZYGeP8ayF7Z8ZkxBG76EYK8oO1g3+jVEvuQHaf7PoP3r3K1XfERpJ3mGX8GmOaGegyrHkTa/naHUeOD4/IPUSfP8ZxjJ8bwHqOdWfUIVBxk1uHLuGeynjg/DwdhSvco3d0WP+PePrOfcTplbn57Cz/dd+pQ1GkasmPUYndw73s7XFZXAd6+YWrftN2aa2DZxVC4ucPmHwM3fAcBsUpQ5dXTldXjNk7/G8y8y/U4FYfgpVPB2thhO/dpWPVXaKro8DvuFB7Q/pZP9jX03deTlyE7TgHkLa8jfXGPq+3i15FGDde+hicl3jtGzbWw7FJl0bUNv0i44QdFE9JcB8svh7y1HZ/7hiuLvns+hpt/VLaHHp5NP4S0hZTUmlny/C+U1HW0k/7zuZk8/Pk+l83PHRfFvy8eh14jsq08gIfSk70HTz2dRgBrJUnaCWwCvpRl+RvgMWChJEmHgYWt708qDpTU89mOYr7fV0pxb1bQBgC9Rs0dUwKI3Puq6wdOh5Lp4mJzdp9mv28FhI10te14V/l/TZ5r8AWguQq6dJ4ReBinE7Z087vd/f7g+zJI6OuzkXa952q0t6ASY9P7cTphxzIKIxfQZJOJ9ZT+S2ciWkvXjvw84KdSqSQyIv3Zkls94OcSdKDXqPnVaelktnbX0Kgkfr0wjTGxPaeul9aZWbm/jBU7ithXXIfTKUPNEdfgC0B9EVRlKT8XbXUNvgBseB4aK1xtFQdcgy8A9YUuwRcAfcFqzohWJihdg0eC4YejpR52vev+QXdt0AUuFNY0893eUj7fWcyhMiF0ftzUHHENvoDS2KOqtaFIba5r8AWgsRx0RmgohrI9iq2HZ1N59wcAZFc2ugRfAH46WMGdp6aiadW4GhXtz13z00TwReAxPFKCJMtyDuBWbyLLchVKFsxJyZbcaq54eWN7++iMSF/+d9VkEkL61pWgP9DpfXD6BKJqLHOxy7ouvkhS99otxmD3h8C27dQ6RdzU4do5Ac3JVTPu9UiSUmrWlWGm1eOCpAGdL5i7aGloeiGmKfAseetAo2N9cyyjQm2e03/pjCRB2umw7Q1ImKVc+waQERF+/JJdxZljogb0PAJX0iP8eOfGaeTXNGPUqUkMMfXYDry41sydy7axLb8WAK1a4vXrpjLLT69kSclO1x3arj26bsqIDUHuY0rbzbWqu3Gn0tAiawAbIb7Dt6xU0Iqkhe46HnlBFyRv5khFI9e/sYUjlU0AGHVq3rlxGhPiRUekPqPp6RrX+uyv1oNK7R5oltSu26lUyL4RbikUZl0Iapsdn26CKmsOV/DHszM4b3w0ZpuDuGAjQUZx3RN4DiEx7iWYrQ4e//5Qe/AF4EBpI1vzaqhpspJb2URDi+0oR+hfgkLCqZ35e1ejbwSq2MmuNkmCKTe5PuBpjZB5HnQO3mgNMPZS5efgJJj9a9fjJM2D8AwEXoQkwZQb3H+3o5Z4zqcBpiEgHfkU16pHOSQFW2hmD3sIvIZtb0DKfNYU2j3TfrongpPAGAYHvhjwU2VG+7Muq3LAzyNwJ8ikY1xsIGnhfj0GXwB2Fda1B18AbA6ZR77aT4kmGsfUW103HnGW0h0QIG6Ke/B7/v+BoUumTcRoRRS/MyFpkDzfxVQ57nZe3idh0qk5Y1RkL76hYCij1htg0rXK4lcbOhOkL/KYT0OB9TlV7cEXgGarg+d/ysZidxxlL0G3BKfAjDtdbWmLOq5xwckw827XzxNmQsVBRd+lbTugZdy1bs+mOwLmk1XeRFq4LwszXa+V185MRKdWER1oYGxsoAi+CDyOJ9tQCzphttpdLvJt5FU1ccMbm9mWX8vkxCAeXjyKUdEDr8qtVklo0hZQd9lnqAs2IJlCkBJmYozqJkgSN1WpU89dq0SqE2dB5Fi4/lvI/UWZyCfO7HgoVGth2i2KMGXRVkXIN24amEQNutcR2/V3O1sRRhum1LXY2aGdySkXv4FUuAX8o2mJnMw3leFc4IVdGwWtmGvh0DfI573A+i0Ofj/Dy25taafDlleVDja63nek6ytJISYqGi2U1ilthQXeR3WTxc2WX9XMqxtKsDWcwR3nTCW4fj/qiJHKvdXQutIemg7XfAF5v0BzNSTMgOhuxJ39o+GSNyB/A1TnKoGbmCmKKHTBRuSKg5jDxrHXlsyFoT5MSwomcxCeKQSepz5yFv6XLVdaT6u1EDuVhqiZCJnRnimocZcCOFzeSIvNIcpX+opGD7PuUYIqxduVrqhx0zo6uWl0SoAmbhoUbVN0YTR6JTM5fprS+a2Vg9qRNM1dRlLTDmRJzWHDOH79k5NnomyMigngL+eN5vzxNRwqayAp1JfNudXM/ffPzEkN5Y/njBTiugKP42VPqScvQSYdF02K5ZlVWS52g07Tvlq2JbeGO5Zt48NbZhI6CCKL/n5+kDFXeR0NSYLoCcqrM93Z2jCGKIK86Wf0j7OCgaGn3+0wJcio4eE9Nq49oGV26jlkVzRRVl/BWzcke9o1wdHY/QFETySrxReV1EyE0QvKjzrjF6G009yxHKbeNGCnUakkxsUG8POhci6dIiKG3kh6hHsAbn5GOGsOV3KgtIk3d+p48rJrWDwqxn3nsBEuq8A9EpSovDpj8IeAWCTACMxtfQlOHr7YX8UfPrGwcORCGi0O1n9dzQtXVrNotMiA6onpycFu3eUumRxLgEFkUBwXplAYcabyOp7PW4kM8OH8tSqaLGNxyjJN1haCjFoSQowARAUYOHusgcyKRs58ag0trdUFa7Iquf+jXbx5/TQCDNqjnUIgGFBECdJgUVeoqHj/+Cgc/BqaXNPEJUni0ilxXDEtHo1KItCo5S/njeKHfa4aLLmVzRR6SJxXcBIiy8pKxS/PwPr/QslOT3s0oNQ02xkfF8jc9DDWZVchy3D/ogwaB7H8T9BHZFlpa5m2kDWFdsaEqbxD/6UrqQsg6wdFWHUAGRMTyPf7ygf0HILjZ3RMAE8vHU+orw6VBGeOjiQ2yMCBUkXc0ynDso35ijBvf1G+Hza9rLSwzt8Admv/HVswJLA6HPy4v5RlZ2n5S/hP/Dt+Pa8u8mFtVsWxdz6JmRgXxCPnj8bfoEGrlrh2ZiLnje8mOCoYVCIDDLx49SSSw0w0WR1kRvnzyjVTiA1SAjBUHIKtbxC96zmWnW5nYnSHxuTOgjpKxDxK4GFEBsxg0FwNX9wLh7/rsM36FZz6ByXlrpXYICMPnTuKW+amoFOrqGxs4U8r9rocSq9R4acXvzbBIFG4GV4/GxytD+xaI1z31bDNiDHpNLy7KZ/4YCN3nJpKdaOV51Zl8fTS4fl9hwV5v4DdDJHj+Hl7M+PCvDQtXO+nlPBteglOe2jATjM+PpA3N+Ritjow6Lz03+IkxkerZvG4GKYlhdBic7DmUDl/XOHaHjUzyh+Vqp+CiOX74fWzlOcQULIar/gYUucffT/BsEKrUvHnCc3EfXZ5ewOEGJ2JtPM+9LBn3o2fQcsV0xOYnxGO3ekkKsCA5igaT4LBY2xsIG/eMI3aJisBRi2BbbouVdnw1vlQX4QemAj8e/4LnFsZRJPVgZ9eg0nMowQeRlxFBoOKA67BF4D1z0B1ttumWo2K+GAjkQE+JIb6ct2sRJfPHzwzg8TQwe+KJDgJkWXY/HJH8AXA1qxkcg1TQv30/PX80WzOrebZVVks25TPxPhARkWLemGvZcNzMOJsLE7YXOJgtLcGYECpfa85omhfDRD+PlpSwnz5+ZDIgvFmIvx9SAgxMSUplDDfjpLiIKOWiybF9d+Jctd1BF9Aua7//BhY3TXnBMMXSXYSfeB11+6T1iaiSlZ6zKehRFSggbhgkwi+eBkBBi0JoaaO4AsoWdtdMk0Tt/+TS0crc6eHFo8iLtg4mG4KBG6IEOBgYG9xtzkdYHcX4+uMr17DPQvSWJgZQVl9C3FBRkZG+aPur5UxgeBoyLJrJ6s2urMNI+amh7HijtlkVzQSaNSSGeVPuL8QNPVKqo8oE8wl17K11EGsnwo/nRdfH9VaGHE2bPgvnP/8gLWlnpoYzMfbilg0WrSj9nZGRPrxwW0z2F9Sj9MpMzLKn+SwblpOHy8tde625krXibhg+CM7UTe5B2U1zaIESTDMsDW7mVSWWi6ZEMG500czMkosqAk8jwjADAYhaeAb4TpxTZgFQUnH3DXQqGNmSugxtxMI+h2VCibfADk/udrHXOIRdwYLrVrFmNgAxsSKziBezy9PK0LeWgOr8s2MDRsCq5PhGVC8FXa9DxOuHJBTTE8OYfnmAmqbra4rgwKvJDHERGLIAGW2Js5Syo7kTpoy0+8EQ+DAnE/gnai1MO1myP/F1T7qAs/4IxAMFJFjQKUBp73DNu02MlLTlGuhQOAFDIGn1WFAYBxc+RGMvggCYmHarXDu02AQEzyBl5M0D5a8pHTeiBgDl7wJ8TM87ZVAAA2lsPtDyDgHgFV5DsaFe3H5UWdGnAP7P4favAE5vEmvYWJ8IB9uLRyQ4wuGENET4YqPIHYKBCfD2f+Bked62iuBJ0g+Fc5/QWlpHjkWLn1HafkrEAwnIsfBVZ9CwmylG9zCv8CEq0TwReBViAyYwSJyjJJybmlQVp5UQ2SiIDi5MfjD2EsgfREggY97C1WBwCOs+Q+kLABDEAX1TqpbnCQHDpE1BUMApJ8Oq/8N5zyhrNb1MwtGRvDi6hyunZkodAtOZjQ6pQNX3DSl7MgY5GmPBJ7CEAjjl0LGWSCpFGFwgWC4oVJB0hyImahIPRiDPe2RQOCGeCobTDQ6MIWI4Itg6OHjL4IvAu+hJg92vQejlwDwQ56NiRFqVENphStmMqj1sP2dATl8eoQf/gYtX+4uGZDjC4YYel8RfBEo+ASI4Itg+KMzieCLwGsRARiBQCAQDC1+eEgRszUoE8pvjtgZP1TKj9qQJBh9AWR9N2BdkS6YEMO/vj2I1e4ckOMLBAKBQCAQCPqGCMAIvI/maqVUSyDwFLIMTZVgdVfTF3iY3LWQtw5GKdkvNS1O9lQ6GOvN7ad7Qu8HYy5Wyqkaivv98GNiAojw9+HlNTn9fuyTGnF9EAxlzLXQUu9pLwSCE8Nhg8YKsIuOboKhhwjACLyHxgrY9DK8vADePA8Ofw92q6e9Epxs1ObDT4/Bi/Ng+eWQv8HTHgnasLXAZ3cr3bm0Smvw73PtjAlVo9cMofKjzgQnK+KY3/8ZLP0/Kbp6egL/W53DwVIR1O4X3K4PGz3tkUDQO8y1sGOZ8oz16iLY9xlYmzztlUDQd8oPwBe/hhdPgc/uhLJ9nvZIIOgTIgAj8B72fwZf/Qaqc5SU/GUXD1hqvkDQLQ4brHsafn4M6gog50clGFi219OeCQBW/RX8oyFhZrvp0ywbU6OHYPZLZ+KnQUgafPdHsDb266HD/X24Ylo8N725hbpmsVJ4QthtsPapLteHxeLhXzA0yP4RPr0NqrKgfC+8fxUUiACiYIjRWAEfXAfb34T6Yti1XAmGNwi9M8HQwaMBGEmS1JIkbZck6YvW98GSJH0vSdLh1v8LxbijUZUD+1bAnk+g4qCnvTkxzLWw4b+uNllWyg0EgsGivhi2vuZqs7dA+X7P+CPoIOsHRXh32q3tpkqzk53lDiYMNf2X7kg/A3wj4ZvfQ0tdvx56TloY4+ICuOa1TTRa7P167JOKhiLY9rqrzd4C5ScYgKnKVu7lez+BysMndiyBoDscNtj0ort976eD7sqQoyYf9n8Buz8UwdbBoOKgMq/Zt0KZ53SlJgcquvweao4o11GBYIjg6TbU9wD7Af/W9w8AK2VZfkySpAda3//OU855NeX74c3zobFUee8TANd8DlHjPOrWcaPWgjFUWZnpjCHQI+4ITlLUWkWXw1zjaldpPeOPQKEmFz6+Beb8RrnWtfLZYRuTI9T4DNXyo85IEmScDVnfw5e/gdMehoCYfjv8ZVPieeOXXC7933peu24K4X4+/XbskwZVD9cHrfH4j1m2V8mya6pQ3huC4JrPIHLs8R9TIOiKpAK/CHe7KXzwfRlKVGXB2xcrk35Q/tav+RxiJ3vWr+FKyU5449yORQi/KLjqEwgf2bGNxtD9vj3ZBQIvxGMZMJIkxQJnAy93Mp8HvNH68xvA+YPs1tDh4NcdwRdQLlZbXleyRvoBp1OmoLqZwppm5H465lHRmWDeA8pDQhvGYEicPfDnFhyVQR8LnkStdcmwACAkRbQy9CTmGnj7IhhzEUSOaTfLssx7B23MivX0OkI/IkmQdjokzICv7utX/SGVJHHtzERGRfuz+Jl17Cyo7bdjnzQExMDCv7rawka6jMs+s3dFR/AFlPG+fdnxH68LNruTvKomimvN/XZMwRBEpVbubepOiwk6Xxh5rud8Ggrkru0IvgDYmmHN42C3eM6n4Yosw5bXXDNAG0qU+U5nQlJh8vWutjGXQGj6wPsoEPQTnnxyfRK4H/DrZIuQZbkEQJblEkmSug3NS5J0M3AzQHx8/AC76aVUHHC3le0Gp931BnsclDe08Nb6PF5cnYNKkrhrfiqXTY0j2KQ/oeMek8TZcP23kL8e9P4QP9016j3EGA7j1GNjwVNYGhVdh9MegrpCJQPLboWG0mPtOSTx+jFqbYJ3LoGI0ZBxjstHuyqc1FlkRoUOQymz2CnKyvT656Bkh/Kwqdad8GElSeKCCbHEBBq55rVN3Hd6OldMS0CSvDeDyOvG6KgLIDAeCjYpAZn4GRAYd/zH6658qXQ3OJ2gOrGxXVTTzAs/Z/PupgKMejW/W5TBeeNi8PUZRkFLL8Hrxmk3VASOo+m8T9AXb8Kp0mCPmU5AwEgCPe2YN1OT626rPAg2M2iG1nOQt49Rq82Crmy3+wdd5zs6I8x7EFJPg9I9EJGp3DN9/Nz3FQi8FI88uUqSdA5QLsvycSmsyrL8oizLk2VZnhwWFtbP3g0Rulu1mHDVCQdfAH48UM4zq7Kw2J2YbQ7++e1BfsmqOuHjHhO1FuKmwqx7YPJ1Qzr4AsNjnHY7FrIHYSx4Cr8oRYfjh4dgz0eKIO/6ZyE01dOeDQhePUatzbDsUqXkaPJ1bh+/vsfCqXEaVF4cPDghghJgxh1Qk6d0fqruv/r2qUnB/OnsTF5ec4RfvbeDFpuj347d33jdGNX7QvJcmPtbGH85BCed2PFGL3G3TbjihIMvAB9tK+KtDfnYnTL1Zjt/+GQP2/Nrjr2joM943Tjthu/2lzNvWT0L14/mtDUjmPtWJeuPDOP7eX+QOMfdNuHqIVke7+1jdHexmbyEi9zsckY38x3fcKVkd97vlPmQX+QgeCgQ9B+eWjqcBSyWJCkXWA7MlyTpbaBMkqQogNb/l3vIP+8nYRac/ohSj67xgVPuh/RFJ3xYp1Pmw62Fbvav9wzPDABBzzh6Ggu7h/FY0PrA/P+DzPOhpRZMYXDZMiUDQzB4WBrg7QuVrI8Zd7qWJgLlzU6+z7VzasIwX8nXGWHcZUqWxbd/gJ3LQe6fYElUoIGHFo+iqtHKhc//Qll9S78cV9BHkk5R9H50vqA1wLzfQ+qCEz5sTbOVD7YWuNk35Vaf8LEFQw+r3cEHW5T7eaPFjrk16Pr9vjJPuuX9xE6Bc55StJnUOph+B4y50NNeDUv2ldTxTs1IKifeo8xr9H6UzfgTdRFTPO2aQNDveOTpVZblB4EHASRJmgfcJ8vylZIk/Qu4Bnis9f8rPOHfkMAYDDPvhFHng+wE/9h+WTFTqSRGRQewOdd1lSwjUqT2nWyoT9axEJoKF/xPKUPSmZSVFsHg0VwNby8B3wiYdpuiXdCFV3ZZmROrwU83TLNfOiNJEDNBybTY8xEUblayL3xPfMVPr1Fz+7wUPttZzHnPruPNG6aSHjHM/769DVOokvU55iJFAyEgVvmdnyBGrZq0cF8Kql21X+KDT0AwWDBk0apVZEb5s6OL9lN6uPh7Pyo+/jD5WkhbqJT4+8eAepgH/j1EiK+e/9vWxA+h87h20kKsTlhf6sOTxhBPuyYQ9DveVjz/GLBQkqTDwMLW94KjERCr1KP3Q/CljUsmxxFo7ChlivDXc/qobtTzj0ZtARRs7r5+VjBkuHhyrPtYGH0SpHpa6qG5SmmP7vTe8oxhR10RvHI6BKcoK43dBF8qmp28u9/KWckn2UOwIVApxQpOhs/vVcQh+wFJkjhvfAwXTorhshc3CHFeTyBJrffyuI7gi80MpXuheDuY+96WXK9Vc+epaRi0HX9DqWG+TEkSguInI5Ikcfm0eC4bG8SLC7W8cJqOJaODWTBSLDD0ioAYpSxUBF8GjPGxgYyPC6SiwUazHZL19Tw614Sf0KwSDEM8PqplWf4J+Kn15yrgxHNvBSdEZrQ/n9w2kwOlDUiSxMgoPxJCTL0/QPYq+OgGZSXbJwDOfx7Sz+zXIJFgcBgVHXBiY2EoUrILPrgGqnOUlOPTHoaJVyvaD4KBo+IQvHW+0gVodM8p3k9ssTArRkOo8SS8nkgqSJqjZMNsfglKd8GUm/pF+2t2ahgGrYZrXtvEq9dOYWJ8UD84LDguGspg9T9hyytKVkziHDj3SaX7Rx+YmBDEijtncbisAb1GzchoP2ICRQbMycpoQzWPOh9HteZ7ABaOuxy1z//h2gtDIPAM0UEG/nflRAylm/H//AZoLINNvnDuU5B5Xr/c5wQCb+EkfIIV9IakMF/OHBPFotGRfZtw1+TBB9cpwRdQ2sl9eD1UZQ2Mo4IB57jHwlDE0gBf3a+0PoybpqQbf/ug0pVEMHAUboXXzoQxFx81+LK7wsHXOTaWpJ/kD2IBsTD9duV6+/X90FzZL4edlBDELackc/3rm91KFQSDSP4vsPllCM+EmEnK++3vKMGYPpIe4cfZY6M5LTNCBF9OdvZ+iip7JURPhMgxqHcugyM/edorgaCdCFUD/l/eogRfAKyN8MnNUHHQs44JBP2MCMAMRyoOwqpH4fVzYMurSlr/YFFfrIiXdsbeAvWD6INAcLw0VYJ/FMy8S2kxmTBDyYAR43fgyPoB3rkQpt+mtJXsgRa7zK9WmVmaqcP3ZNB+ORZaA4xfqmTDfP4rKNvTL4cdHxfETbOTue61Tewrru+XYwr6SNl+OOMRpRTPEAjz/wTVR8DS6GnPBEMVuxXK9ir3M1OIUrp+xiNKxqdA4EnqS2HbW/DGubD+GZh8g2uLb6cD6twFxQWCoYzHS5AE/UxdIbxzMdTmKe9z18DEa+Gsf7pe0AYK3zDQGsHW3GFTaRRBTYHA2/EJUsbqz//ssBmD4ZK3PefTcGbncvjmQZj3oLLa3wOyLPP71WYiTRKzY9x1YU5aJBUkzwO/aFj1CIxbCpnnAicWoJqYEMQ1jkSufnUjy2+eTqoQ6hxcosfB+1crop8AWSvhnCeUrlgCwfGg0UHqfPj0tg7bwa/h4tc95pJAgMMG65+D9U8r74+sVlpKT7sV1j3VsZ1oMy0YZogMmOFGxYGO4Esb29+EmiODc/7gFFj8TEetpkoNZz/e59p1gcAjWBtg2xuutuZqaBjGrbc9gSzD6v/A93+G0/921OALwONbLOwsd3LjWB1SP3SIGXaEpcPUm+HA58q/q/3EW0pPSw7hkslxXP7SRnIrm/rBSUGvKd7eEXxpY/sycFg8449g6OOww+4PXW2ys9/EvAWC46I2HzY972prKFUWckERJT/9EQjLGHzfBIIBRGTADDt6mpwM0qRFkiDzfGVCVV+sRK1D05XVF4HA65G6bwErOh/0H3YrfHkv5G+ARY8pbXh7wCnLPLqhhe+O2Hlwhg8+GhF86RFTCEy9BfavgC/uhVP/oGjFnABz0sKwOZxc9uIGlt88ncTQYa4B5S2outE4UqkYtPu4YHjSTVc5JHFvE3iSHq5pYSPgio+UrPrQEaD1GVy3BIIBRmTADDfCMpQslM5Muh6CEgfPB7UGIjIh7TSIHC2CL4KhQ0AMzLnP1WYKhYjRnvFnuNFYrtR5V2XDGX8/avClvMnJVV82s6HYwR9n+hCoF5PPY6LRweiLFOHWr+6DQ98CfRdu7cz8jAjOGRfFpf9bz+Gyhv7xU3B0Uk9z7/gx+15F90cgOB7UGqWsozMqNYw63yPuCASAokU0/Q5Xm3+MIhSddhpEjRPBF8GwRIS+hxsBMbB0Oez/DPI3Kq3bUhcMjv6LQDDUkSSl5XRgAux+HyJGKV15QlKOva/g6OT8DB/fBCnzFa0Sqfv4v90ps2yflSe2WJmfoOa28TrUKhF86TWSBHFTlTG850PIWwsz7wZT2HEfckFGBHqNikv+t54XrpzEtOSQfnRY4Eb0BLj2a9i5DMy1MOFKiJ/haa8EQ52EWXD1Z7D9LdAYYPzlEDvZ014JTmbUGqWjX3gG7P0EoibC6PMhKMHTngkEA4oIwAxHwtIh7L5jbycQCNwxhcKYC5WX4MSxNMAPD8O+FTDjToiZ2O1mDqfMlzl2Ht/cgp9O4oHpeuL9RZLmceMXoZQkHVkNK+6EURfA6AtAfXzB+NmpYfj7aLnlra386rQ0rpmZKPR4BgqVCuKmKC+BoL/Q+kDyXOUlEHgLfhHKosy4pZ72RCAYNEQARiAQCAT9j90K29+Gn/8OURPg3KdA795Np8Eq89FBKy/vtuKnlVg6UseYMJWY3PcHao3S+SRqHBz+Fg58oWR0pZ0Our7ruYyNDeTP547iuZ+yWLm/nEcuGEN8iOjMIxAIBAKBQNBbRABGIBAIBP1HVTbsfA+2vQ4BcTD3AUWIuxMWh8wvRXZWZNn4IdfO2DA1N4zRMSJYBF4GBFOIUm5QVwS5a2DnuxA/HZLmQuQYUPdepysywIc/n5vJl7tKOOeZNZw7LprrZyeREuY7gF9AIBAIBAKBYHggAjACgUAg6DsOOzSVK20kKw5A0TalpWlLraI1cOofITiJJptMSY2D7Fon+6scbC51sL3MQUKAiimRav51qkEI7A4WATEw7jJoaYCSHbD1NagvgdBUpdNEUBL4Ryl6MT4BoOr+EUGjUnHe+Bjmpofx7d4yLnz+F2ICDcxLD2NCfBBpEb5EBxrQqkUJmUAgEAgEAkFnJFk+sQ4JnkaSpAog7xibhQKVg+CO8KF3eIMf/eVDpSzLi461US/HqbfjDb+3wWY4fOcTHaOhTy/y0d01TRd1tP33OBM4x/r3XjkU4yiSjbK51zcfWZYlSZI8erMarj6oJRmDpm8dEfdWq8yNVsnZblCpJG1InFHqrs1tJwqfv36Xo77c1s1HQ+E6OhyuBTB8vgcM/nfp6zj15n9r4Vvf8Va/oMO3oXAt7Yw3/5v2J+J7utKrcTqcGfIBmN4gSdIWWZY9KvUufPAuP7zBh6HGyfhvdjJ+5654w7+B8EH44GmGy/ceLt8DvP+7eLN/wre+461+gXf7djSGqt99RXxPQVdEfrBAIBAIBAKBQCAQCASCIYEkSXdLkrRfkqR3TvA410qSFN1ffvUGoQEjEAgEAoFAIBAIBAKBYKhwO3CmLMtH2gySJGlkWbb38TjXAnuA4t7ucJznaedkyYB50dMOIHzojDf44Q0+DDVOxn+zk/E7d8Ub/g2EDwrCB88xXL73cPke4P3fxZv9E771HW/1C7zbt6MxVP3uK+J79jOSJL0AJAOfSZJUJ0nSi5IkfQe8KUlSoiRJayRJ2tb6mtlpv/slSdotSdJOSZIekyTpImAy8I4kSTskSTJIkpQrSVJo6/aTJUn6qfXnh7qcJ0ySpI8kSdrc+prVa/9PBg0YgUAgEAgEAoFAIBAIBEMfSZJyUYIndwLnArNlWTZLkmQEnLIst0iSlAa8K8vyZEmSzgT+DzhNluVmSZKCZVmubg2w3CfL8pbOx5VluVKSpMnAv2VZnidJ0kNdzrMM+K8sy2slSYoHvpVleWRvfBclSAKBQCAQCAQCgUAgEAiGIp/Jsmxu/VkLPCtJ0njAAaS32k8DXpNluRlAluXqEzzPaUCmJEltn/lLkuQny3LDsQ4iAjACgUAgEAgEAoFAIBAIhiJNnX6+FygDxqHIrbS02iWgN6U/djpkWnyOch4VMKNTQKbXnCwaMAKBQCAQCAQCgUAgEAiGLwFAiSzLTuAqQN1q/w64vrVECUmSglvtDYBfp/1zgUmtP194lPN8h1L+ROvxxvfWQRGAEQgEAoFAIBAIBAKBQDDU+S9wjSRJG1DKj5oAZFn+BvgM2CJJ0g7gvtbtXwdeaBPhBR4GnpIkaQ1KCVNP3A1MliRplyRJ+4Bbe+ugEOEVCAQCgUAgEAgEAoFAIBhghnwGzKJFi2SUei7xEi9PvHqFGKfi5cFXrxBjVLw8+OoVYoyKl4dfvUKMU/Hy4KtXiDEqXh5+nfQM+QBMZWWlp10QCI6JGKcCb0eMUYG3I8aoYCggxqnA2xFjVCDwLEM+ACMQCAQCgUAgEAgEAoFA4O2IAIxAIBAIBAKBQCAQCAQCwQCj8bQDJzNWu4MjlU00WRwkhBgJ8dV72qVjI8tQfQSaK8AvCgLjPe2RYIAxN9RgLc8CSUIfkY6Pyd/TLgkEnsHpgKosaKmDgDjwjxrU01c3WsirbsagU5MUYkKvVR97J4FAIGjFXF+NtSILJDW6iDQM4n4uGGrYrVCdBZYmCEoE3zBPe+RGbbOV3KpmdGqJpFATBp2YbgtcESPCQ9Q2W3ltXS7P/piFwymTEmbiuSsmkhHpxTdDpwP2fw4rbgdrExiC4KLXIOVUT3smGCAaig+j++53BOSuBKAl/TwaFjyMX0SShz0TCAYZazPseBu+/QM4rOAfDZe+DTGTBuX0B0sbuOvdbRwqa0QlwW3zUrhpTjKBRt2gnF8gEAxtGooPo/v2NwTk/QyAOWMJDac+hF9Egoc9Ewh6SUsdbHgBVv9DmZOEpMIlb0DEaE971k5ORSP3fbCTbfm1AFw+NY5fLUwn3M/Hs44JvApRguQh9hTV8dTKwzicihh0dkUT//n2IGbr0dqNe5jKw/DxTUrwBcBcAx9dD7UFnvVLMGBIBz5H3xp8AfA5tAKyVh5lD4FgmFK+F776rRJ8Aagvhs/uguaaAT+1xebgqR8OcaisEQCnDM/9mM2uwroBP7dAIBgeSPs+Qt8afAEwHPgYjvzkOYcEgr5SvAN+elQJvoCSkfrDX5QFEi/A6ZR5d1N+e/AFYNmmArbkDvxzgmBwkSRpkSRJByVJypIk6YG+7i8CMB6ioNrsZluTVUlNs9UD3vSS+qKOyUcbzdXQUOIZfwQDisPhwPfIN252fe6PHvBGIPAwtfnutrK90FQx4Keuabax5rB714ojlU0Dfm7ByYHV7qS8oQVZFh1ChyM2awu+Od+62bV5qz3gjUBwnNTkuduO/KTMRbyARoudlfvL3ezb8kUAZjghSZIaeA44E8gElkqSlNmXY4gSJA8RFeieijYhPpAAg7Zfjl/VaKG6yYpWLeHno+0ffRnfSFCpOyLPAHp/MHlf/aXgxFGr1TTGnoJvwUYXuyV2BqLoQXDS4RftbgtOVkoxT4DSOjMWu5PIAB/0mu41XfwNGiYmBPHzIddgT1ywoc/nq2q00NBiJ8xXj8lHPAKc7NgcTp74/hBvrM9FLSnPC385bxQLRkZ42jVBP6LV+dAUdwqm4m0udlvMVLylMKLObKOmyUqAUUuQKK0UdEdAjLstZgr4BAy+L91g1KmZkRJCTpfFkdHRin9l9S2YrQ6iAnu+33emoqGFJouDcH89RqEjc9wkPvDl5cCjQDyQD/w+97Gzl53AIacCWbIs5wBIkrQcOA/Y19sDiAwYDzEmJoClU+Pa34eYdPz+zJGY9Cf2B+Z0yqw5XMH5z61j4ROr+e2Hu/hyVwlrD1ec+MpWaBqc9bgShAHQ6OG85yBY6IEMV2wjL8Ae3lFba4+Zij1loQc9Egg8RMQoOOX+jvd6P1j87HELALbY7Hy6vYiznl7Lqf/+iQc/2k1eVfcZLUadht+eMYKwToH0SybHMjY2sNfnczpl1h6u4Pz/rmPev3/ipre2cLC0/rh8FwwP7A4nt761lfXZVfzzwnG8cOUkrp+VyP0f7uLjbYWedk/Qz9hHX4I9dGT7e0vsTOxJ8z3oUQe7Cmu5+pWNzPv3Tyx9cQNbcr0jo0HgZUSNg8k3dLw3hsDpfwUfP8/51AmNWsXVMxJI6LQ4Mj8jnCkJQXy+s5izn17D/P/8xG8/2EXuUTJY7Q4nK/eXsfhZ5X5957JtZJU3DMZXGHa0Bl9eAhIAqfX/L7Xaj5cYoLP+RmGrrdeIcJqHCPHV84ezRnLJ5DiaLA4SQ43EBhl7vX+TxU692Uawr84linqovIHrX9+MzaEEWzbn1mCxO4kLMhDu70N6xAlcpDQ6GH85xE2BhlIIiFUEsATDlk/zDVRE/J3ZI2qQkVhZGUh6sYHLYj3tmUAwyPj4w+x7IeMsRf8qMBFCko/7cLsK6/nVezva33+8vQh/Hy1/PGckDS12rA4H4X4+SJIEwOiYAD69Yxa5VU0YdWpSw33x8+l9xuTh8kauf30LVocTgF+yq/jdR7t44/pp/ZZ5KRha/Of7Q1Q0Wvjt6SPQqJX1uMzoAH63KIOHP99HZrS/dzcGEPSJr4p9iZrxEqN05aBS8UtdKPoaPxb1adrQ/5TWm7nlra2U1LUAcKC0gRve2MLnd80mPrj3z8WCkwBTKCx8GMZfAZZ6CEkZ1G6s1Y0W7E6ZcP+e88ZGRPrz/q0zya5oRKdWkRbuS1ZFI3e9u719m892FmPQqfjbeWPQatxzIQ6UNnDzW1vbdUJXHahQtN+umIhJZML0lUeBrhcSY6v9eLNgpG5sfcpyEL9FD+Lro2VCfN/T13cW1PLY1/vZWVjHvBHh/Oq0tPbASm5lU3vwpY1dhXXMTQ8jt7LpxAIwoARhIkYpL8GwxuZw8tWeUjbnNvDf9ktFA6dbyrlsqmg/LjgJ0RkhekK/HKq77JOPdxRyxuhIHvxkF9VNVq6dkcjSafFEBSiraTFBBmKC+l52BJBb1dgefGljR0EdJbVmEYA5CdldWMfyTfn8fcnY9uBLG3HBRi6ZHMvvPtzFJ7fPQqXq7llTMJSw2h3kVDaRg4bfbHOiVcOlU/SozfUsGh3pUd8Kq83twZc26sw2CqqbRQBG4I7eD2IHp/tgG00WOyv3l/GPbw7SbLVz8ynJXDQplrAeOhtF+PsQ0SlIk9UqoN+ZFTuKuWdBOtGB7vf0I5WN7cGXNn46WEFZXQvJYb4n+G1OOnqasJzIRKYQiOv0PhYo7ssBRAlSP+J0ytQ0WbHaB66TUX5VE9e8ton1OdU0Wx18tbuEe9/b0S7e211LUn+Dhgh/PZEB3evAWO0OapqsOJ1CfE/QgVatIjXc/UKfEm7ygDcCQf/TZLHTYLZ55Nzd6XIlhph4/Zcj5FY2U2+28/SqLD7Y0j+lIAGGbu4NPpoTLnsVDD1kWebPn+3h4klxPQbf5o0Ip9Fi55u9pYPsnWAg0KpVhPv58NKaHKqarJTWt/DUysPtwV1P4m/QoukS5JMkvCow3GxVss4FJxcWu4Pi2ma25lVz9/IdFNWaqWm28Y9vDvLNnlJqm6zYuixsdEeQyf3+mxhiwqjrXgcmsJv7dZifXtyvj49uOigc1d4bNgNpkiQlSZKkAy4DPuvLAQY0ACNJ0quSJJVLkrSnk+09SZJ2tL5yJUna0WpPlCTJ3OmzFwbSt/4mt7KJf3x7gHOfXcuv39/JnqKBaQ96pKqZ2mbXm8De4noKqpUWbBmRflwwwVUs8tcL0/lsZwl/WrGPHw+UY7F1BIj2FtXxm/d3cu6za/nHNweOWpMoOLlwOGXGxQYSaOx4CArz05MUIgIwgqGN2Wbnh/1lLH1pA0ue/4WPtxVS1zy4D9fjYgMYF9shHKhTq7jllGRWHXDtoPDOxjy25lZj78VDXk8U1TRTUN3MwsxwF/tDi0cRJ1aYTzpWH66kstHK3PSe9YtUksSSibE8+cMh0RlpGGBzOvl6j3vHyg05VR7wxpWkEBO/PWOEi+32uSmkhHn+WcNqd/DzwXKufHkjF/x3He9uyqe6yeJptwSDwPb8av759QHuenc7X+92D0S/vSGfx745wP99uodDpUfXZxkTE8CUxI6KB61a4o9nZ3a7aA6QGe3P6ZkdQuiSBH87b5RLVo2g1/we6NqnvLnVflzIsmwH7gS+BfYD78uyvLcvxxjoUNrrwLPAm20GWZYvbftZkqT/AJ0jFdmyLI8fYJ/6nSaLnb98sZdVB5QOFYU1Zn7JruLT22cS38+TVV+9e7RUq5bao6iBRh1/PCeTJRNiKa4142/U8tQPhznQenG47vXNLL95OtOTQyiobubqVzdR1aRkz/xvdQ6Hyxp4ZulE0R1DgFolsbOwliunJ6CWJCQJLDanm7q7QDDU2J5Xy41vbGl//+v3d/LM0vGcO27wxBBigoz876pJ7Cupp7HFTlqEH0cqGt1KSMP89PxvdQ63n5rC+Li+l6za7E5e+DmHtzbksWh0JL9emI7N4WRGSgiTjqMEVjD0eWblYRaPiz5madGEuEDe25zP+pwqZqaEDpJ3goFAI6mICTKwLb/WxR4Z4PkJnVaj4srpCUxKCKKgxkx0gA8jo/wxeIHWxc6COq59fTNtMcgHP96NWpK4ZErc0XcUDGmOVDby6tpcPt9Vgr+Pptt7b5ifnuyKRjbn1rA5t5p3b55OeA8lSVGBBp69fCL7iutoaLGTGu57VH2tEF89j14whiunJ1DdZCU5zCT0uI6T3MfOXpb4wJfQv12QkGX5K+Cr491/QK9usiyvliQpsbvPJEVZ8BLAOyTYT4D86ub24Esb1U1Wsiua+j0Akxrux/njo/l0R0ep2T0L0kjsdJ4Qk5456WE0W+1c8r/17cGXNn48UM705BCyyhvbgy9trDpYQX5NMyOjxB/6yY7N5mB8XCAPf76PZquSNeVv0PB/Z/ep1b1A4HV0V1bx2rpczhgVia4XrSH7i8gAA5GdSgAMWjVxQQYKasyAEgS9YEIMj319gJmpoccVgCmuM/PuJiXT9ps9pXyzR/nuIyL80GsH77sKvIPdhXXkVzfzq9PSj7mtJEksyIjg7fV5IgAzxLHYncxKCeX7fWW02JRsukCjluRQz2eZAJj0GiYnBjM50dOeuLI2q5KuCWAvr83hzDGRfRJBFwwtimvNfNV6r6xvsePvoyHMV09Fo5L9pFOrmJ8Rzl+/VLoOZ1c0kVvZ1GMABtx1YY5FqJ+eU/yOr8uiwJXWYMsJBVz6G0+Gl+cAZbIsH+5kS5IkaTtQD/xRluU13e0oSdLNwM0A8fGeFwPVa1Ro1ZLbyqW+G2XrEyXAoOUPZ49k8bhoiutaSAo1MiYmwE1ED0CjkgjuJr2tLeXNR6siPtiIQavmUHkDsqxk0wyE3ycj3jZO+4pKBZuPVHPr3BT8DRqQodZsY3+JaF07XBjqY/R4Cermuhjiq0cleVZsNDHUxKvXTuGH/WU0WhwYdWpe/yUXm0PG1EOt+LHQqlUY9WrqzXYXu88QCb6crGN0oHhjfS7zM8JR91JYd2ZqKPe+t4PaZmuP6fIC7x+nGpXExpxq7jg1FT+9BhmobbZxsBtxUEEH/gb3aVKwUYdWNfSek719jHoTGpUKk05NfYty3/zvT9ncdEoy0QE+OGVFJPqFn7NdgnP6QVy8EQx9PHkFWQq82+l9CRAvy/IE4NfAMkmSuk3DkGX5RVmWJ8uyPDkszPPRwfhgI3fMc23HPC0pmPTIgelLH+bnw/yREVw5PYFZqWH4dyPWBKDTqLltXgqdn7P89BrmpoeCLDNJc4QvR3zJR1Fv8sVZNqbGGrhtXqpQne8nvG2c9hW1Ws0lEyKYpz/EksJ/ckHx48wzZLOoi46EYOgy1Mfo8bIwM8IloKFWSdw4O6nbQHaPNJTBznfhvatg7ZNQefiYu/SGtAg/0iL8eP6nLP717UEKqs2E+uqYEB94XMeLDjTwwKIMF1tqmC8jowfm/tTfnKxjdCBoaLHxzZ7So2q/dMVXr2FMbEB75pSge7x9nGo1Kq6YEsk83QGWFD7GBSVPMNd4hDNGisymozErNdQlCKOS4M75afgcZ0Dck3j7GO0XinfAd3+Cj26AQ9+C5ejaLD2RGGLk2llJ7e8tdidf7y5h3ohwLpgYQ15VM+UNHVpA54+P9grNIsHQwSMZMJIkaYAlQHsfMVmWLYCl9eetkiRlA+nAlm4P4kVo1CqumZnImNgAtufXkhJmYmpSMKHddLkYbKYkBvPhrTNZn1OFUadmenKIUl5UvB3dW2ejsysXkFEHPuDVJe9gS0ns2yREMKyZrDqEauUVtIX5x+9/B679ChimN2/BScHomAA+uHUG63OqsdgczEgJYWxsYO8PYLfBuqdgw3PK+/2fwbY34ZrPIeDEdWTmpIby7k3T2ZBTRbCvjulJIaSGH3/AZPG4aOKCjGzKrSYu2Mi0xGBiAkWg/WTjq90ljIr273Mmy7TEYD7dUcRlU8Wq+VBmEvuRfriy/f3Efe/AdV8j7uc9kxHpzwe3zGB9ThWNLXamJ4cwLi7Q024JuqN0D7x+Nlhbs7p2fwgXvgJjLurzoSICDO1BlV2FtcQHm5iZEtLeMvq+09M5dUQYe4vryYzyY0pSML6iJE3QBzxVgnQacECW5fb+mpIkhQHVsiw7JElKBtKAHA/512eCTDoWjIxgwUhFtdrucLItr5o1hyvRadTMSQtldEzAMY7S/2jUKiYmBDExoYt2QNYqsLsquftufBIy5gN9TzPOrWpiQ3YVOZWNTE8KYWJCkEhXHurIMtLml3DJsXQ6kHd9gBQ/3XN+CQT9QGZ0AJnRx3lNrjkCm7o06qvOhvL97QEYm8PJzoJa1hyuxKBVMyc9lFFHOV9ZvZnNR2rYUVDLmNgApiYFMy055Pj864Kvj5Y56WHM6UPmg2D48f6WQuYdxxgYFxfIi2tyqGu2EWAUk4whicMOG/7ranPaYd+nEDfVIy4NFUZE+jNCCKB6P4WbOoIvbfz0d0hZAMa+66clh/mSHObLeePdF1XC/X04c0wUU5KC2ZJbzf9W5zAy0o9pySHEBh19caPebGNbfg2/ZFeSEGxiZmooSV6ixSQYPAY0ACNJ0rvAPCBUkqRC4M+yLL+C0i/73S6bnwL8RZIkO+AAbpVluXog/RtItubVcPnLG3E4lcnrUytVvH/LjL6tsg4kssPd5rQBfW83WVJr5ta3tnCgVLnwvbj6CA+emcHNpyQjeVhTQXACyDJOh42uibYOu8Wj4lECgeeRcVNmBJA7WkVvya3mipc30noL4OlVat6/ZUa3gfhGi51/fnOQj7YVtdvOHhvFY0vGCKFHQb9QVGvmcHkDv1qQ1ud9fbRqRkcHsOpgGRdMiB0A7wSDgeyw0fWJzGazIa4wgmGBs7t5jYPjmdf0Bqvdwf9+zualNUfabdOTg3nu8omEHKUC4tMdRfxpRUfH4qRQE2/dMPWYgRvB8GJAa01kWV4qy3KULMtaWZZjW4MvyLJ8rSzLL3TZ9iNZlkfJsjxOluWJsix/PpC+DSR2h5NX1x5pD74AtNicfLe3zINedSFlPqi6TKNn3wu6vkdh95c2tAdf2njyh8PtnTwEQxMnUJJ+patRkihJWOwRfwQCryEoASZc5Wrzj4HwkQBY7A5e+DmbTrcAmq0OfjxY3u3hjlQ2uQRfAL7cVUJ2uRDIFPQPX+wsZmpi8HGXGI+NDeCHfd2PX4H3Y5NVFGdc52qUVJTFLvKMQwJBfxM7BTRdugzNuQ+MwQNyutyqZl5Ze8TFtiGnmsNHuW8X15r51zcHXWxHKpvYXyyaWww1JEl6VZKkckmS9hzP/kLsYwBwIlNjtrnZa5ut3WztIaInwLVfwuiLlWDMZcsgdcFxHcpqd7rZLHYHdoe7XTB0cDrh5fwo9p/2Btbk02lJPZs9p73NWwVChFdwkqPxgbn3w1n/gfjpMPMeuOIjCIwDlOSYum7uAfXd2EBZSevWLq6hgn7is53FTE06/pK28XGBrM2qdFlYEgwd7E6ZF/OjOLDgNazJCzGnncue097m7SJxPxcME6LGKfOa8VdA4ilw8Rsw8pwBO53N4aS7y2F3c6I27A6Zlm7u9+JePyR5HTjuCLaoJBgAdGo1181MZNMR1wqqs8ZEecijblCplYlD3DRltnACLfXSI3zx99G0t2sDuGhSLDFBhv7wVOAhNBoV45IiOfuDMsbH3YPDCbu+qOO/l0d42jWBwPMExMLUG2Hydcr1tBM+WjXXz0ri7uU7XOxtGmFdSQwxMSran72dVsHSInxFXbigXyiobqawxkxm1PHrWIT46gkwaNlTVCdESIcgBp2asUlRnP1RJeNjf4XNKbN7Tz3/u1LoQgmGCZIEsZOVl9N5QvOa3pAQbOSUtFBWH65st0X460kJ8+1xn6hAH66YlsDrv+S220w6NekRQ6Mr4ZDloYDLgUeBeCAf+D0P1S07kUPKsrxakqTE491fZMAMELNSQ3lm6QRGRfszKT6QV6+d4i6E6w1I0glfpJLDfHnnxmmcMzaK5FATvzk9nbsXpKHXDL02fQJXZqeF8diFY7HYlTD/E5eMZ0bywKRzCgRDElX317m5I8J48tLxSoeExCBev24KE3qYuIb46nnqsglcNzORpFATV09P4L+XTyTMz6fb7QWCvvD1nhKmJAahVp2YJtuoaH/WZVUee0OBV3JKaiiPnD+aFruMSlLx9GXjmd5PQt8CgVcxwMEXUMTt/3LeaG6bl0JSqIlLJsfy+nVTj7r4rFWruPmUZH63aAQpYSbOHBXJOzdOI00EYAYOJfjyEpAASK3/f6nV7jFEBswA4W/Qcu64aOZnhKNSSRi03T+kF1Q3c6isAZUkkRHpR1Rg37NGSmrNHCxrwO6QSYvwJSHkBFdNLQ1Qtg8aSyEwHsJGgvboE4ExsYE8ccl4zHYH/kI0ctgQ5qdnYaSZhafUIEkqnGEWAk2eb68uEHgjVruTQ2UN5Fc3E+6nZ0FGOAszI1BLEj66ngPSORWN5FQ0cuaYSG6YnURUoMF1stxQqlyTbc3K9Tg0ZRC+jWC48NXuUs4YdeKZi5nR/qw+XMHtp6b2g1eCwSY8wMDp0S2cMacGSaXGGWbF3yCe1wQnIdVHoOKAooUZkUmtJowDpQ3UNFlJCjWRFuHXbcDabLVzsLSR4joz0QEGRkT6cv8ZI7htbgoGnRptLzS2ogMN3DYvlSumJ+CjUaETi9UDzaNAV4VjY6v9hLJgTgQRgBlgTPqe/4kPlNRz9WubKK9X2kEnhZp4+ZrJR01f60puZRO3vLWFg2WK6FOwScfbN0w9/vaq1mZY/1/46dEO23nPwbjLjxlR1mpUaDUiqWo4UZu7g4CPlqJqKAbAHphIzQXvEJQw2sOeCQTexzd7SrjnvR3tDZJuOSWZu+an4aPv+QFrV2EtV768sb2Ec2J8IE8tnUBcW0eEmnz48Doo2qK89wmAqz6FmIkD+E0Ew4Xy+hayyhv5zcL0Ez7WyEh//vtTNla7E5241w85ao/sIPiji6FREVO2B6dSff6bBMeP8rBnAsEgUrob3jofmpRsPjlsJAenPcNlHyp/FxqVxEtXT+bUDFd9JJvDyXubC3jo833ttv87eyRXzUg4rkCmWKweNOL7aB8UxB3Ug7y/paA9+AKKEvaq/X3rMrD6cEV78AWgusnKWxvyj18or/Kga/AF4Kv7oCbn+I4nGNJo97zfHnwB0NTmoj78tQc9Egi8k/zqZv7wyR6X7tT/W53D4fKGHvex2Bw8szLLRT9rW34tWzrrh+Wv7wi+ALTUwZrHwW5BIDgW3+4rY0J84HF3P+qMSa8hOsCH3UW1J+6YYFBxOhxod73dHnwB0FRnocn+wYNeCQSDjNMBm15qD74ASBX7iapaj1/rgrndKfPgx7spr29x2TWnopG/fbnfxfbo1wfIqWgaeL8FJ0J+H+2DggjAeAibw8n2/Fo3+64+PtgcKHF/uN9ZUIulh64ax6Spyt1mM4O5b34Jhj52ux1T2RY3u0/5Tg94IxB4N/VmGw0Wu5u9qrHnQEmT1c7e4jo3e25Vc8eb6mz3HUt2KKWiAsEx+Gp3CZP6UX9uRKSfW4MBgfdjtVkwlW91s+vKd3vAG4HAQ9hboMj97yCg7iChfh3l9aX1LTS0uN7Pa5pt2LssbjucMjXe1OFW0B2/B5q72Jpb7ceNJEnvAuuBEZIkFUqSdENf9hcBGA+hVas4f0K0m312aigldeZeH2dOeqib7bzx0Rh1x1ldFpQA2i6lcv4xSscPwUmFRqOhPn2Jm92ccqYHvBEIvJuoAB+SQ12vnTq1ivgeNLnyqprIq2zi7LHu3fEyo/2R21JpYqe47zzmIjAK8UzB0alrtrGzoJZxsYH9dsz0cD825IgAzFDDx8dIfZr7/bwl+TQPeCMQeAidCcZe4mYuDZtBQXXHHH1qYjAR/q56hzGBBgKNrmVDAQYtsUFd5UUEXoXS7egmIA+QW/9/Uz90QVoqy3KULMtaWZZjZVl+pS/7iwCMB1mYGcnl0+JRSaBVS1wyOY51WVWc9+w6dhXW9uoYU5OCuXtBGjq1CpWktH8+p5sH+t6y3xJG1oIXwV8JDjlD0rBf9Dr4RR73MQVDl6aE02ge19pmV62lafIdNMXM9rRbAoHX0dbJaESk0s0gzFfPC1dNJLUbTa9VB8o4++m1XPD8enRqFadnKrXmPloVN8xO4oMtBXy9pxSnU1YCMAv/AtpWgfbM82HiNUoHO4HgKKw8UMbo6AB8emgCcDykR/qxPb9GGZuCIYU55Uyax1wFkkq5n0+5m6aoGZ52SyAYXEZdAOOvaP070GGe8RuKAybi66MsXI+LDeAv54/Ct4tGi0mv5qFzRxHb2uUoNsjA00vHExcsAjBez0N1y3ioLpGH6lSt//eY+G4bQoTXg0QHGvjzuZksHhfN6kMVrNxfzsEyJa38Tyv28sb1Uwk4hrBTiEnP3fNTuWBCNE4nxAYbjrv9c4PZxh9W7OVwmY4bx/+PJJOVXXV6FjpSmAbIskxeVTONLXaig3wIFt1whj1v73OgM97IkqXXISPx9kEIybZzi0iIEgjcGBMbyPKbplPeYCHAoCEywL2rXX51M79avoPG1nKl537KZnZqCM9ePoE9RXV8vaeUvKpm1hyuZMTdc0gJD4QZd8HIc8Fhg4B40PW9W57g5OPL3SVM7MfyI4Agow6jXkNOZROp4b1vGCDwPK/sthHgfyuLl96EjMRr+2Rijzi4QdzPBScTgfFwzhMw+1eUNdq56uMyqpqKWDIhBpNeg1OWO0TwO7GjoJYHP97N4vHRhPnpqWywsGJ7EdOTQ4573iU4eREBGA+j16gprjXz359c6/x3FNRS22w9ZgAGQKNWkRR64g9CNc1WtrXq0jyxsU1fwMLIFDPjYu18sq2Yv3yxD7PNQVq4L09dNv74uy0JvB6rzcH6nCq25dfwxI8d9gUZ4dwyV7TBFQi6I8ikI8ik6/HzigaLi+guwNqsKsbHB/HCzx1i5xa7k7KGFlLCfZUOdMHJA+azYPjRZLGzIbuKy6b0f6OHtHBftuXXiADMEKLJYmNDThW7Cuv4Vyf7maMjuWGOx9wSCDyDRg+h6fx8pIBD5UcAeHVdLgBqlcTSqfFuXWyzKxox2xy8t7mg3RZk1HKksomMSP9Bc10wPBjQAIwkSa8C5wDlsiyPbrW9B4xo3SQQqJVleXzrZw8CNwAO4G5Zlr8dSP+8hahuVknHxvq71hrWFSpiuP7RSg1jL6moqcOnuRiDwYAmKOGoaeuBRh3jYgPYWViHTq1i0ehI4kOMJIYYOVjawIOfdIi1HS5v5E+f7eX1a6e4pen1Cw471LUKVAfEg1rECgcbnVbN1KQgzskM4uKEJpBUvHJAj1bf8+Ry2GBphIZi0JogIMbT3ggGAqdTucY4Hco1RtPDdazLtai0yUZTi4MIfz2+cjM0loJPIPhF9Oq0Bq0aX72mPQMGlLaXWrXrtVmvURHeKgooyzJFtWasdicxQcef5Sg4eVh1oJz0SD989f1/70wO9WVLbg2XTI7r92MLBgaTXsuUxGBmJwdwWTo4ZRXPb7eQHOFFi2i1BdBYBsZQCE70tDeCoUBTFTRXKppoJndNTABq85X7uNMOGp1yv1cpChzRgT5um09OCCKwy+J3QXUzmVH+TEkMYnNuTbt9bGwgPx8oJzXM96id5ioaWqgz2wnz0xFgOAmeoQXHZKBnta8DzwJvthlkWb607WdJkv4D1LX+nAlcBowCooEfJElKl2X5ONv5DB1GRftx1/xUnv0xC1mGEJOOhxePVv5IbWbY/zl8fT+YayB9EZz+NwhNO+oxmyx2io4cIHr7k/ge+gi0Ruzz/ohmwhVg6D5S62/Q8vB5o7hz2Taun5XM8s35fLazmM92FPH7s0YSbNJR3dSh9r0lt4bKRmv/B2AaymDD87DhOeX99Dtg+m29nuAI+geHU+bWUU78d72M6u23QFJx9+SbqEm5ytOuDSwVB+GbByF7pXJTP+s/MOIs0IqSu2FDUxVsfQ3W/Fsp65l0Hcy+1z3Y1lAGG1+A9c8CYJl8C+9bFvH0pno+uTCAUdv+jKposxIYP/cpSFmg6CUdBZvTyZ3zU3nyh0O02Jzo1CruXpBKcpgveo0Ki92JXqPiXxeNJSnUl3qzjY+3FfKvbw9itjk4f3wM9y5MF3XngqPy+c5ipiQED8ixU8N9eWtD7oAcWzAwWO0OrhutI2jnC5iWvQYqDf839W7KE67wtGsKOT8pz7kVB5Vsv0X/gPTTPe2VwJsp2AQr7oTKgxCSCuc9C/GdNI0sDbDrfWVMmath7yeg1sKc+5R7vimE0TEB3DQniZfWKFkwYX56/nj2yPZ5TWOLnU+2F/KPbw7SZLVzRmYEt5ySzP9W5xATaGBWaigvrclhyaQ4wvzcnxGdTpm1WZX87qNdlNS1MC42gEeXjGGUqB446RlQEV5ZllcD3crlS5IkAZcA77aazgOWy7JskWX5CJAFTB1I/7wFf4OOO05N5Yu7ZvPuTdP57M7ZTIhvrdsu2QUf36QEXwAOfQM/PQb2nlubAhwsqSf44Hv4HvwAZCdYG9F89wBy4aaj7jc+LojXr53Ky2tyOFTWCEB+tZnfvL+Ty6a4rnYlhxrx8xmAGF72Slj3BDisymvdE4pNMKioVRJ++StRbXlVmaTaLag2PEtg6TpPuzZwWJvh+z93jLfmKvjoOijb41m/BP1L3jpY9VclwO20w+aXYP8K9+1yfoS1j7dfi/Qbn+Fc331cNT6AjI2/U4IvAPXF8O5SqDhwzFP7+2hZtjGPG2Ync+f8VG6Zm8yHWwtJCTPx1d1zWHbTNL68ezZnj41GrZLYUVDLQ5/vo8nqwCnDx9uLeH9LQUeXJIGgCw0tNtZlVTI5sX/1X9pICDGSV9VMs9W97brAO9Fp1Pjnf4dp+0vKNc/egu8v/ySk8ujPhINC+UH45BZlogxQnQMf36g8/woE3VFbAMuXKsEXgKos5R5ck9exTdFW+PFvStBl9wfKuLeZlXt/nvIcG2jU8euF6e3zrxV3zGJMp65xOwtr+b8Ve2m02JFl+GZvGc1WO386J4P5GeH857uDTIgLxM+n+4WX7IpGbnpzCyV1La3Hq+Oe5dupbjr6HE4w/PFkF6Q5QJksy4db38cABZ0+L2y1nRT4aNWMig5gRkoIMUGdSpKqDrtvvO9TaCw/6vGsjZWEHvnUze4s2HxMX+pb7BS3XizaaLI6iAzoSNXz0ap45IIxhPgOQFbA7g97ZxMMKA6bFfWhL93squzvPeDNINFYBoe+drXJsnJzFwwfDn/nbtu5HGyu1z32fOS2WXzhF5yWqEVbvtv1A6ddmTgcg4RgI/eels7zP2Xx7Kosnv0xi2tmJpIYYiIl3JeZKaGkhvuhViklSVvzatyO8cn2Imqabcc8l+Dk5Pt9ZWRG++M3EOXBgFatIiHEyO7CugE5vqD/aWlpxv+g+3OUzhvu5zVHoKHU1dZS16vrqeAkpTYfmipdbeYaxd5GyU6ImQS5a933P9wx7g06DaNjlPlXdKCrJER3HWm/3VvGgdIm3tqQh1Gn5q4Fafhou1+MzqtqwmJ3utiyypsoqW3pdnvByYMnhTWW0pH9AtCdOEm3S3ySJN0M3AwQH9//AnOeoq7Jgs5ag8HoC3pF3M7uE+L+SwpJBV2H+F11owW9VoVJ3/GwpdL5Yg7KwND5YgQQlHhMPwIM2vZU+M6MiQngo9tmUGe2kxBsVMQhB4Loie4ZL9ETB+ZcA8hQH6dqrQ5n6EhU5fsh7XQlk+rQt8ihGd3+sQ4LdL4QmAC1ea52Y4hn/BlghvoY7Q0Wu4N6s51AoxZtW412RKb7hlHjQd2lNjtqgluwpiZwDIUNMhiCOjIT2zCGKLXm5mrQ+4PWvb5cpZI4e2w0GVH+FNeaifD3ITXcF30PrYLjQ9xLjUZE+GHSDYwOjMMpU9NkxaTXYBigc/SFk2GM9jcfbi1kevLAXrOSQk3sLqpj2gCfZ6jg7eNUp9XTHDoWY3UOpC1UrlOHv8UWOhKP91QzBitZCo5OQWVJGrb3XU/h7WO0TxiCQKVRFj7akFSKvQ3/WEU/M2wklOxw3T9ipMvbpsZ6VLYmDIGRLlqZMYHufx1pEb5cMyOBJROiiQ40EB/Ssy5nd4L8vnpNe8trwcmLRzJgJEnSAEuA9zqZC4HONS6xQHF3+8uy/KIsy5NlWZ4cFhY2cI4OEtVNFvbt34Pjx79jeHUejrcvwpq9hrd+OcLLWf6YE+Z3bKzWwZn/BGMQZfVmXvg5m3OeWcvlL21kzaEKbA4laBITFkT+qNtdAjXOiLGoE2d0Pb0biaEm/niO68XprlNTGRHpx6SEYOZnhA9c8AVg9BLwi+p47xel2IYYw2GcOsddrmjwFG2F0l3Ip/wG+4hzPe3WwOEbBmc/rtzY2xhxNkSN9ZxPA8hwGKNHY29xHfcs38FZT6/hj5/uIatcKaskdSEEdeoqZAiCKTe0C/O1M/oC8Ituf+v0jWRbwGn8e30DedP/4ipqPvE6RYz3m9/BC7Phg2uheHu3fuk0KkZG+bNgZASjYwLw6SH4AjAlMZhR0R26XSadmrvmp/YYsDkRciubePSr/Zz59BpufXsrOwrcs28Gm+E+Rvubkjozu4vqmDxA+i9tJIWa2N7aNVHg/eNUpVYjT7gGpt4MBZuhbA/y3N95h85KxBiY+4CrbcbdEDnGM/4MU7x9jPaJkDQ47SFX2/w/KXagrM7MDtKwqIwQPcE1MBOUrDwDoOhl1h5Yjfa9pRhePZWWbx/CUnGkfdOJCUGMi+3QazFo1VwwPobrX9/MygPl3WcJdCI93I/rZia62B5ePIqEowRtBCcH0kDXkUuSlAh80dYFqdW2CHhQluW5nWyjgGUoui/RwEog7VgivJMnT5a3bNkyEK73CbvDidXhxKjre1Tzhz1FTDr0OEG7Xu4wqnX8MPtdbvzWwo0TfTk7rIoRQTLGqAwIzwRJ4r8/ZvHPbw+276KS4MPbZjKxVT+mor4FZ+UhDLVZ6HwM+MSOU8Qie4G1uYEjtXYOVZiJ9PdhZJTfwHQ76omaXCjbq/wcMapXmTseolfJIN4yTvtKw7YP8fvsBhdb3YXvEjDmLA95NAg4HVC+Xyn/8wlSxp/vkH5YGdZjtCeKa82c/9w6yhs6aq3HxQbwxvVTCTTqlBrysr3KClr4SAjpobV6TR6U7cVid5BLDDuagiiosxLtp2aqsZRkVRkqvwgIToVPboYjP3fsawiCm36E4KTj/h6yLFNcayavqpk6s420CF9Sw/2O+3g9YbY6+NXy7Xy7r6zd5qvX8Nmds0gOG/B2wyflGB0InvrhEHuL67lu1vGPud5QUN3Msz9msfr+Uwf0PF7GkB6nDVvew++Lm11sdRe+R8CYRR7yqBPNNVC8TSkhCYiByPHgF+5pr4YiQ3qM9glLE5TvVbJc/GOUZzW9L9ha+GxXMb/+6AB3TPZlQWgV8cEmfB11aHz8lO0ClfX+quzthLy7COwdJUG28degPec/7Z0Ry+pb2F9cT63ZRkFNM6+uPdJeAnzO2Cj+ffHYHkuQAOrMNg6U1FPeYCEhxMiICL8BWUAZYgzbRPreMtBtqN8F5gGhkiQVAn+WZfkVlG5HncuPkGV5ryRJ7wP7ADtwx1DpgLQ9v4aXVueQV93M5VPjOX1UBGF+7qnn3dFic2CpKSJo75uuHzisJDvzgQhe3tbIy+h5aPEoro1IBKCywcJrv+S67OKUYVdhXXsAJszfB/zHAn1Yva8rgn2fotuxjBERoxkx/TaIHt/7/fuLoERvDrqcFFitNky73nCzG/d/BMM5AKNSQ+Ro5SUYsuRUNLoEX0ARwMuralYCMIFx7Q9hRyUoASQVzh0fMmL/Y0QFj2ZX9KXc/LWVFpuDH359hhKgKN3tGnwBpUSp8vBxB2Cyyht5f0sBPx+sYP7IcC6eFDtgwZDCmmaX4AtAo8VOVnnjYARgBP2A3eFk2aZ87lmQPuDnigk0UNFgoc5sI8AwiIszguPCbG7BtPM1N7vh8OfgDQEYYxCkLvC0F4KhhN4EcVOVFygBmQNfIf/yNGehZsRZN/DANj1PbdIBNv563hSuGpfYvntRTTP6iv0uwRcA7a63cc65F1WIct+O8Pchwt+Hb/aU8J/vDrls++XuEu5dmE7KUe6RAQatKNUUuDGgARhZlpf2YL+2B/sjwCMD6VN/s6+4jste3NCul/KHT/dQZ7Zx+6mpvdpfLUk4VFplpbTR9eHXqnIN4vjpO35dBp2a5FAjFV0mGJ236TMOG6x7Gja9oLwv26MIkt648phtrwXDD51Oi9M30s2uFqtSgiFAd/olKkkREO8Tdgus/jeGba8D4F+2h9m53/LQ7Df4z2Ybvm3XXI2Pu44BgPb4FBYqGyzctWwb+0sbADhY1sD67Cpeu3ZKt3XlJ4pOo8KgVWO2ua57eIMOjKB3fLu3jBCTnqTQgU9vV6kkkkJN7C2uY2ZK6ICfT3BiqNVqJF/3e7fGV/zuBMOE3LWwfCkSyuR2RP5a/rrgTc4t0SDLdNyrUbThnlp5mKtD1Lj9BfgEIGnc77GGbrJWjFo1OrUn+9kIhipi1Jwg+0sa3MRqX1idTWld7xSutRoVsbGJ5E3+vYvdGTKCX5o7mkBFBuiZEB9IQbWS/nbzW1uYlRrGrXM7dAwi/PXEBxuwO1z96TV1hbDlZVdbSx2U7+vTYcxWOz8fKufWt7fy2w93siW3Goezh1K3xgrY9T68tURp/1sq2v16C06HAyn5FNB06nSlMyFHjfOcUwJBL0kN9+PMMa4BxJtOSSaxu8lpxSH46R/w5vmw5TWoL+r4rDYftnfJUDTXMNOvghvnJPPr93fyty/28WWhHvOM37hsZk+ez/I832NfB7shp7KxPfjSxo6CWo5UNvX6GH0hPtjIfae7Zk5MSQwiI7L/y50E/Y8syzz742HOHO0eNB8oEkKM7CkSnZCGAmpJhrTTXIXG9X7I3QmSCwReiMXmYF1WJfcu38G+7b9g++b/kN9aoswhmmtg80tu+yQWfU56uB+R/sqCdltXo8JqMx9uLWRtQxTW0FGu5zn1YaQA9ya8GVH+TIgPdLHdv2gEccHuQvl9Jbu8kWdWHubKlzfyzoY8imvNJ3xMgXcjZJhPEK3GPYZl0KrRqHpf3jY+LpAczVmUB8diqtyFJiASTcI0xjYG8Qf/GgIMOiYnBBHiq+euZdtYfVhpvbYuq4pJ8UH8fckYCmvMSBJc/epm3rphKpMTj0OAT6VWJttWexd739KL12dXcf0bHbWln2wr4v1bZjAxIch1Q1mGra/Dj39T3mevhO1vwQ3fKZ2eBB5FkiTI36iI45lrFIV5vS9SmQiSCbyfAIOWh87NZPHYaHIqmxgZ5cf4uED0mi6rWHVFsOxSqGlteZrzI0y6Ds78h3I9VGmUSUuXNGVZpeGRr/YDsDarkphAA5Y55zBr8RjCmw7SZErgldxQnvhG0ZL/ZFsRH9w6gwnxXa6DPaDtYVVNox6Y0mlJkrh4chxpEX7sKaojLtjAxPigXpfTCjzLd/vKaLE53O+zA0hCiIkdBbWDdj7BCSCpIHcdzHtQ6dImqUFnQio/4GnPBIJesfFINVe/uon/zDeR+f110FytfJC9Es59Ruk82AWNIYCLJsdS22zjdx/tRquW+Oi2mfho1WjVKv6xvgGf2Y8yZ2QOppZy9ImTCUyd3u35I/x9eGbpBLbl1VBU28KYmADGxwV0u21fKK0zc/NbW8iuUBZX1mZVcvGkWP52/mihFTOMEQGYE2R0tD+hvjoqG63ttt+eMYJQP/1R9nJFpZJIjQmHmNOA09rtk0NxCaTsKKhpD760sTW/hrkjwnhpdQ7W1syXr3aXHF8AJiAO5j4I3/+xwxaU1CcleqvdwYtrclxsdqfMD/vL3B8M6wph7eOutuYqJQtGBGA8jtPpRIqfhvTFPaDWAzI47cjnPutp1wSCXhHhb+DMMccoASrf3xF8aWPbGzD9VgjLUNqSz7mvI1AMyKHpfFkeDHRkqBTVmslr1vP7b9Ss+s1t/Pq9nWw4Ut3+edt1sLcBmOQwXxZmhvP9vvJ227njokgewPISf4OWU9LDOCV9SItOn3S02Bw88uV+lk6NQyUNnrZhcqiJr/eUDNr5BMeP3eFAmzAT6YtfKeWSshOQxf1cMCRwOmVeb9W9HK3J7wi+tPHN7+Cyd2H/p0ojBQC1lqyIM/nX5wfb50dWh6KVeeGkWG6bl8KTPxzmoTVNaFSRjI5J5/lZk5D0PT8zxAYZiQ068YyXzhwua2wPvrTx4bZCbpyTxIhI96CSYHggAjAnSHKYL8tums7qQxUU1DQzPyOcSfED0/5R6kE02inL7RcX4PgfwCQJJlypBD+yW3VfUub3TqiyC2qVhEmvpt5sb/W9j34IvAJ73gZ0pz0MlYeUFbPgZKxFuzGMv8zTrgkE/cOxrjcqldKiOmIkZK2C8JE0RM/hP8/nuG3afihZEUV3+7wPV0Ilg2c0i0ZXsS2vhskJQUxPCRncbnSCIcGTPxwiKsCH8XGDl/0CEB1ooKy+hUaL3UVfQeCdOHLXo2q7n6u1EJSIpWQ/RlFVLBhSdHMflSRl7nLdN3Dwa1BpsKYs5A8rWrA6LG67q1USV89IYGSUP2sOVTAi0p85aaFEBR6fZtsJ0eNjgZgLDWfEHbMfSI/wIz1i4OvkE0NNnJIW6pIFMzkhiJxOkVO1SmLRidSAG4Mg40zIOJPiWjNb82oo2J3FxPggdNYa4sz78a87gCYsDXX8VLe21jqNmrvnp7E5t5qqJivhfj5sz6/htMwI93MFxMKc38Cqv3Y6f4jSIk7gcdQaDc0jzkf3/kXK71l2QmM5tstW4IFblEAwMIRlQFCyaxbMxGtAa4I9H0F1LkRPgIRTIOMcAHzsDi6bYuetDXntu8QGGag327hjXgo2p8zSaXFsyu1YpdOoJBaM7JuAdUyQgQuDYrlwYmyfv1ZxrZnt+TXkVjUzKtqf8XGBSvcnwbDiu72lfLClkEcu6H2man+hVknEB5vYV1zP1KSBWXgS9A96vZ6GjAvQfnCJ0rLXaYfmSmyXrfC0a17P4bIGtuRW02R1MCkhiDExAWiE8OqxqS+Bwk1QmQWRoyBmMpiOT/RZpZK4blYiqw6Us9sexwhjiJIx38ac+yAghoMtAewLiabF5mSkyo8bTmnm7uU72jcz6dSMjVXKhoJNes4YFckZowZON+tgqTJ2LHYnkxKCiA82sLOwjr3F9SSGGJkYH0RahC8pYSaXLJiLJ8WSGNK/mTYC70IEYIYQAQYtj1wwhh/2l7HqQDlz08OYnxFOca0Zpyzjo1Vz6ZQ4xscFnvC5yutbuGf5djbn1rB0ahyHiqv5o+9nhG5/pmOjEWfDec8pQZtWqhot/G91Dj8fqmi33XlqCqOiukmjkyRlohOYALuWK6VOoy8W5UdehMoQCGc8otSOqzSQMBNJL1rSCoYRATFw+Xuw71PIXw+Z50P8DPj4JuV9G2c8CtNvB0lCo1IxNSkYPx8NOwtrSQoxkR7pR3ywkZhAA9e/vhmTXsODZ2aw8Ug1gUYtl0+NZ1xs4KB8papGCw98tMslWP/rhWncPi9VTByGEd/sKeWBj3dx3+kjPNYKOjHEyP+zd9bhUV3pH//ccY27u0Fw90IptKXUvaXust3ddvtb321X2rV2227dqRt1BUpxd0kIIe4+k8n43N8fN2QymQRCC0SYz/PkgTlz7p2T5Obcc7/nfb/vvuq2gAAzBJBpg+Gsv0DZOsnXKmkqgipgsn00CmvNXPH8Blo6pOp2cpnAGzdNYmqg8tfRsbZKaUH7uwl80+6Fub/zLexwHExKCePNmyezbFslefPfILPuaxSN+xBGXQHpcyioM3P5cxtps0q/K4VM4LUbJ/HMNeP4YFslSWE6LhwbT84pSuspqDFx2fMbujIBFDKBxy4fw73v7EDsjJCdkxXJfy4fw/PXTuCrvTVsKmnm7JExzMmOCvi/DHMCAswQIzFMxw3TU7lhempXW1qkgRmZR8/ZF0WR/TUmCmrNaJRy8uOCSArv20ugsM7MltIWAGKCNKQGNRKx/ukenb6AhnsgeWpXU1Fdu4/4AvDc6sNcODaB9KheHtwNkTDqUukrwKBDvvUF2P8eROeD6IYDnyKbcDekjBvooQUIcOKIzILZv/K+PrTSV3wBWPkXyD4HwlIpb+7g/vd3oZAJZMcY+e5AHW9sKueNmyZzuMHStZO1t6qNvLggLDYXSeE6ZJ3m7Cark73VbVQ0dxAbrGVkfBBh+h+3KO2Novp2P7+wp1YWc+6oONIjAwLqUKe1w8GjXxew/EA995+VPaC/06QwHXsqA5WQBjsupwvF5meh8GOIGQVuJ+z/BNmUX0LKmIEe3qBlfXFjl/gC4PaIPLnyEGMSQ9GqAg/IfdJQ6Cu+AGx4CkZfCT+y8pZaKWd6RgTTM46IX9N83l+7/XCX+AKS79rzq4t5YckEzh4Z2+/PKWuysLfKhM3lJifGSF5skFSU4jhZdbChS3w5Mp63N5czMSWMzZ3+cKsONnCw1syU9HDunpvJ3cf9KQGGKgEB5jRhS2kz17y4ucsrJjFUy2s3TiKtj4WbzeHu+r9bFNEITq+xVXecHT4vrU7/Pk63iN3Vy7EBBjUOhxOVpUaqVlW7u6td1VE7gKMKEOAU0GNe62rrzCV3uD3YXR7swPby1q4uVqebjm5zoEeEvVUmVHIZDpc09zpdHl5dX8p/vjvY1W/J1GR+tTDnhPloWB3+86005sA8PJRxuDy8sq6EZ1YVMzk1jL9dmD/g3ispEXq/TZcAgw+3x43SUivdz2t2dbWrrPVHOSpAs8Xh11ZvtuNye4CAANMnvd1DRQ+4Tl555cZ2u19bvdmO0y2i6uc0ebihnSUvbaKyVap6qJLLePOWyUz8EYVNehtPm9VJbLBvZcHenpsCDH8CAsxQx+OB+v3QVIRHHUytJoNtTQrC9SpyY4MI1auwOlw8/l2Rj1FvRYuVLaXNfgJMu81FQa0Jk83Fgwuz+XZfHQDrmw3MiZ2AqsZbXhpDFMhV2Pd8QqM2jYPuaGKDNUQaVDR0qwo1NS2MxLAeuYyNRVL1EYUKokdKfjABBhUqlZL6kTcSNf46sLVKZSxVBprdOnpx9AkQYGjRWgF1eyUvhKg8CE/3vheZDWoj2L1VjlxZi2iWRROF5M0yNyeSlQXSg6dWKedvc3TMcm/Co5RhmhXMY1usXDM1mQSDjFnBdYSXfUFrbSSV6jT+u6LIZyivbyhjVmYkcplAbqyRmOCf5rKUFqknRKektdvO7fSMcJJ6qd7QbLFTUGOmyeIgNUJPdoyxzxLYAQaOww3t3P7GNgxqJb9blEf8QJhF9kJiqI6y5g5sTjeaQMj8oEWpVNE06iYiJ98C1hbJVF+ppdUTwvE5U51eTM+IoM3qJFyvwt2ZN5IYqsU4QCl/Q4bwTDDGgLnbhl3cOMlvrSdOq/Qc01IKhhgIS4Gmw2Cpl/pHj5CeFTpxuT0U1pkpabQQolOSGxNEuEHN7Kwonv3B1xz/hmmp6HsRqatarByoNeFyexgRayDRUQJNhzC69cxOVPNmq9TP4fbw5Ioinl8y4bjnt7nZUby4psSn7dz8WJ5eVYxSLnD9tFSCNAra7U52lLVQ02Yj3KAiJ9ZIsDbg1zbcCQgwQ52SVfDmpeBxIQOCk+exV38vz223cPH4BH5/bi6HG9qpaPVXo2vbbD6vbU43r6wr4d/ddmbvmZvB3qo2Lhqbyg77X8gNf4ugsm8R4yciZMyFty5F7bQSrwmmYsYr3PapjKeuHsdTKw9RWGtmSlo4oxODabY4MB6p3lG9A14/H2ydYcuRuXDFWxDey8QcYMBwe0TCwiLgwxvAIj1oikHxhF344gCPLECAn0hjEbx5mdd4VxsKSz6B2M5yIBGZcO3HeFY9gqxuL82p5/GdfhH/fm4bS2+cRHZMEH9YNIKU8FK+2FPDSws0jFxxbVdpzCUhycy54jXu+LqG50cWEPftgwCoAdWEu5mZfCarSnzn5F2VrTy58hAjYoN45trxJPUUrY+D5HA9S2+czBMrithV2crZI2O4blqKXwWlFouDv3x+gI92VAEgE+Dpq8ex8DjCtQOcfA7UmLjmxU1cODaeuTlRPyoc/mShUsiIC9ZQVNdOfqe5ZYDBh0v0EBoeDR9c1zVPiSFJhJz/3ACPbHATZVSz8XATB+vaATCqFbx648QBHtUQICQBrnof1vwbKjZC1kLJQ03Xo1KbKMKeD+DTzuSbzPmgDYPd70qvBQEuegnyL+46ZHVRA7e8vg13Z6nBc/JjeOj8kYxNCuGFJRN47LtCzHYXt81KZ16ev7x4uKGdG1/dQmlTB0EaBZ/MN8OKW8HjJhJ4MH0xsvybWLpHSiUub+7A/iME5vgQDX84L493N1dgdbpZPDqWSSmh1I2NJzZEw2sbyqjpfA4L16u4eWYqd761nZtmpPLzM7MwaAKP6MOZwG93KGNpQvzifgSPN8dQX7aCRbOv5Dl0fLitkmsmJ/Hvbw8yLyeaV9eX+hw+KiGEVQX1aJRyMqMN1Jps/Gf5QZ8+z/5QzEd3TGNEXDB2VzS23NF4+D2y8nXw/vXejrY2Rpa/QajmelYfbEAplzE7O5LdFW18uqsag1pBcrgeXE5Y/6RXfAFoOAClqwMCzCBDLhMQ937QJb4ACKYqFIe+gdRpRzly6LOropXDDe2E6lXkxhiJ/okRCQEGGYe+8616ZG2BLS/RdMajHKizIIoiGVEj2JT9dwo1NXxb4qa4oZ1p6eHsrzZR2WIlOVzHb87J5Z4zMghZ9euuhxoAobWM4IqV3DZyIgkb/+Tz0bqtT3HHmXNY1W1jLFSn7ApD3ldjYktp808SYADyE4J58qoxtNvchOiUvZrvFtSausQXkFKmfvfxXkYnhAxMOc4AfjRbHFz/ymaumpzEtEFq/JkcrmdfdVtAgBnEyNxu5Lve7jFPlaMsWTns7+c/hS2lzV3iC4DZ7uKltSXkxwejUgQivo5K7Ci46Hlpva8NlUqf96T5MHzVzX8tYRJ8/1fva1GEL38JCRMhNIl6s43fLtvbJb4AfLmnlisnJjEzK5L5edFMSQ3D5REJ1UtRJFUtVvZVt+HyiMSHaNhaKlUHBLhhtI7UjT/zsVgIKv6UC2ZfxNI90vFXTkoi+EdUENxe3so/vi7gjOwoNEo5b24qZ2+1iaevGstL60q7xBeAJouDovp2UsJ1vLS2hEWjYhmbFHqUswcY6gQEmCGMrcOEpqXEr93oaQOkxbvV6WZLaQtJ4TqunJTIJzurCdIouWduBq+uK+GHTqPGmZkR3Dg9pcuZ+whOt4jN5UYmE9Cq5GhVOunc5Rv9PtfQsp+0EDklDRbKmztYWeDNLS5t7Cyv5rJC7R7/b6axyL8twIDicthRNBb4v9Fb2zBiZUEdd725o+uB+Pwxcdx/VhaJYX2bVgcYYtQd8G+r2ckzKwp4cWM1AElhWu6Yk8EzW6U0pNlZkYTqVPz8Pck/Qa2Q8dJ1E5iRGgw1O/xOp2o6QGRsvhRe3YNkrY3JqRFsK2thRFwQF4yN59/fesXviuZe8ud/BBqlAo2y79t8b/4Gje0O2u2uXnoHGAh+u2wPE1PCBq34Ap1GvFVtXDHQAwnQJx5nRx/384P+bQG66Fq7duNAjRmr0x0QYPqDQi3ZFfSFzeTrF+P2903B2gJ2EwAWu9tHuDhCc4f3XtY9Payg1sSNr2yhuvOYEXFBXDUpsev9JIMbzDV+5wsRTehU0dw4I5Xzx8T3Pf6jUNlixeb08NVebxpWYa0Zh1ukoMbs17+sqYPYEC2lTR209HJvDjC8OKkCjCAILwOLgHpRFEd2a78HuBtwAV+IovgrQRBSgANAYWe3jaIo3n4yx3cqEEWR0qYOqlutRBrVpEXoUchlNJrtHG5sRymXkR5pIOg48knLmixUtVpRejTkpZ2Dvvhzn/ftwWmAFblMIEKv5tz8GN7eXEFqhJ6rJidhsbvQKuX8UNTI7KxIxiWF4BZFjBolYXolzRavb0CkUU18iA46mjpFEkEKz0+bA5ue9fncmpQLWbPTxn3zEvl2f53Pe5NSw6X/aIJg1BWw8iHfn1PqLEoa2qlps/n8nAIMHAqVGjH7HIQeYpuYcRaDJwD+xFLVYuWZVcW8elEMMZ46XEojLxe62FNlCggww4nM+bDjdZ8mc/alfLjaG+1V3mylqM7MtVOTCdOpSA7X+ZiN2l0eHvxwD5/cNY2I0VdB1Xaf8wmjriBXlCPO+wNCcwnsfFMyIVTqqJPHMi87lH9eMooNh5v4TY8dvXGnaOcrNUKPXCb4fPaElFBiepgEBhgY1hc3sqO8lUcvHjXQQzkqKeE6Pt1VPdDDCHAUFNogxJxFCBHZEJIkpXbUH4D0uQM9tEHNpNRwCmrbGZ0YjEcUaTDbSQjVBjw6ThTBCRCWJkXCgFQeXa6UqnQdIXZ0l09kVJCa2VmRPvdiQZDuZT3xeETe2FDWJb4A7KuWPC7HJgYzOzsKZbCAM2U2ytIf6H7ChIx8VozLJdqoQtZcjP1gJWZFBI3qRBIijP0yPx+TGNL1/ylpYVwwJh6dSs6h+nZmZ0fwxR5f4WdiSihvbixHrZBJGQM/kjqTjdJGCxqlnPRIQyCVaZBysn8rrwJPAV0rXUEQzgDOB0aJomgXBKG7NFosiuKYkzymU8rKgnrufkvaTVfKBf5ywUjGJoZyz9vbKewMa5yfF8WfF48krh8h3xuLm7jl9a2Y7S7unptBQ8JtnOHqQFu2EnRhlE3+M5us8UQZK/jbRfmkReq5Z14mJpuLFQX1fLCtkt+dm8cnO6u4cGw8ZpuTx5ZL0Sef7KjmmavH8+uPdnO4sYOsaAOPXjyKOHcNvH0nVHSWZU2eDuf+B+Y/DKv+Dm47bblX8blnKkumxjElLYyLx8Xz/rZKNEo5v5ifxbjkEO83MepSaC2HnUtBoYHZv2avPIfLnljb9XP624X5nD8mHpUiIMIMJLbkOWgm3IiwYykIMsQJN2OLncJPS44YvLRZHfxvtkjUZxdJoqMg8H8Tf0aB4vqBHlqAE0nydJj7e1jzL2mhN+FG1iqn0tLhKxzHBGv4dl8tW8taAciPD+b22WldRn9VrVbarC5kifNRjLmFoN2vgEyB/dz/Itv6AqHFXwMgRmQhzPk/PNvfonDSX3hwVQd/Oi+VpHA9rVYn83Oj+HZ/HTqVgptnpva6mDwZZEUbee6a8fx62R4azHYmp4bx0PkjvH5dAQYMURR59KsCLhmfMOjvg0nheg7WteP2iMhlw1WeH9rYnG4USTNQ7l2GsOttQNr4sobnD9v7+YkgJUKHUi7weOc6OS1Cz5WXjxnYQQ0nDJFwySvw2c+gZicULYeLXoRvfg2main1aNHjoA0BQK9S8LtFubg+9bDuUBMRBhUPXzCSnJggv1NbnW62lrX4tbfbXMzKiuKJFUWIQPCiB5iBiLx0tRStc86/UcfnEytTwIFP4aNbUbtsqOUqLDP/xbN7JnD9jAwiDOqjfmtjEkN4aPEItpW3oJQJ/N9HUvR/TJCGPy8ewc0zU1m6oQyPKHLR2Hia2h0E65Q80vns9mMoqDFx8+tbqWyRIm8vnZDArxZkE2kMbKoMNk6qACOK4urOyJbu3AE8IoqivbPPsK2BV9Hcwc/f29mVyuB0izy/+jCzMiO7xBeA7/bXc/bIJi4ad/RKQB31JbgqCnhgso4ntjvQKOTc9a2Z+Rk/Z+H0n1Fvk7OnQsfP58excHQyUUHSH1xapIGnrhpHTZsVrVJOVJCG/dVtBGuVXeILQFlzB+9vreC926fS1uEiTK+ScijXve4VXwDK1sHhlTDtHsg7n3arjVJHGNNVKnJigpDJBB66YCS3z8lAIRNICNX6mgaGJME5/4Tp94BMSRWRXP3fNT4/p//7aA/5CcG9TqoBTh2P71YRqb+Fa669EoBnCzQoDqq4e5gWrYpW2Qhe9X+S+AIgihg3P0568iwgdUDHFuAEog+HGb+AfMnAnOAkLLtqAa8AE6pTYnW4u8QXgD1VbUxICSXCoKKx3cHohGAijGpeXGNnVdm5XDP5PEYmhBLWtI3YTvEFQGg8iN3cyGu5L/D4N810OMz857tCno4ax9INZaRFGnjiyjgAPt5RRXa00b9yXB80tdupbLGiU8tJCdcfVwUjhVzGmXnR5CcE025zER2k9jPqDTAwbC1roaHdztS08IEeyjExqBUEaxWUNllI71FZMcDgQKdW0n7gG1R13hRwoWQ1zvTtkDJ+AEc2uNlb1UaD2c7P5mUiInKovp0PtlWQFxcUiNI+UcSNkUzw2+slocUQBUlTpPQkY4wUOd+NzCgjz18zgVqzDb1K7ls10O2E5mKwW9CHJrNoVCwFtb7pPlkxRn72zs6u19d/buLacb/lN7f9E40+GII6TeibDsGy28HVGUHjdpC89gEyJ7/DnsoIzsg5ej1Qo1bJkmkpxARruHXptq72WpON1zaUctG4eO6dl8EZ2VHEhWhoanfy4MIcIoxHF3b6wu5y87/vD3WJLwDvb63krLwY5ucdXYBpMNuoarFi1CpJCdcHhPRTwHEJMIIgjAJSuh8niuJHx/mZWcBMQRD+CtiA+0VR3NL5XqogCDsAE/A7URTX9DGOW4FbAZKSko7z408dje12TFbfXPqMKANrDzX69d1R3np0AebQSnQf3sgMawszNMFMn/NvHjnUwm2z0nh1fSnfFnnIiTFy1eRwwg3qLvOpI2hVcp+S09dNTebldWV+H7OuuAkBgfQob1+x6Du/lBPx0EqEKXeytknPvW8fpNlyCKNawb8uG8383GjUCvnRd3EVKgjPAKCurBmTzffn5PaI1LXZhrQAM1Su076w2p1sKW1he3kLf+nWPj93+BahltlaUdTt9mtXtw/P8Pqhfo3+JGQyCE3uejkzI4KrJyfx9uZyPCJcOzWZbb3snhXUmEkO1xOsVfLXC/PRKmWsKmxgT00HD9bAM5clkNu01e84dfka9hvOp8MhCc07K9poaLeTGW3g8901PL2qGJkAF41LwGxz+h3fG4W1Ju5+awdF9e0o5QI/PzOLa6YkH1dKK0B0kIboQTrVnq7X6MtrSzgrLwbZEFkIp0To2VdtOm0FmMF+nZrNZozl3/u166o3Arec+gENEcxWF8nhep5YWYQowtjEEEYnhGCxu36UMetAMqivUW1IV5QLIAkvxpg+u+s1CtI1PeYaqwm2PC9F5ntcEJnHjec/z67KaL7bX4dcJnD+mDisDrfPYaIIr29r4uqZI8gOMnrfaK/z9acBcNmJFFrZXWs+pgBzhN48a/ZUtnFGThTf7qtjZmYkITo1IbofJ7wcoc3qZMPhJr/2Q/XtzM/re6x7q9q4881tlDdbUStk/PacXC6ZkIBOFUhdOpn0W77t9HN5GbgYOK/za9GP+EwFEApMAR4A3hOk8IgaIEkUxbHAL4C3BEHodUkoiuLzoihOEEVxQmRk5I8Ywqkh0qgmVOe7ED5Ya2ZOtv+YxycfJee/pQw+uEEyogKwtZH+wz0sjLfw5Z5a/nPpGO6Zm0FWtJGqlg4sdhcFtSZaO/o2cUqOMDAlLcyvfVZWpM/ivabVSm3MGX79nGlnsruylW/21dLhkMQTs93FvW/v4HB307KWMinP2GbqcyyRRg0hPX5OSrngq2oPQYbKddoXWrWSqWlhTEjQ8595Ov49V09+rM43nWyYodIZEGPH+LcHDV4DzJ/CUL9GTwRmq7PLGO8P5+Xx9X2z+Pq+mdwxO4PZvczVc7IjefiCkbx321RGdlbCODPXm0k7WlGOLCTO/3MS57Kjzjsnj0sOIcaoYaSuhZuzHZyTZcQjwgfbKv12Vj0ekbJGC0V15q7Fo9Xh5h9fF1JUL0VTOt0i//imkL1VbQwnTsdrtKndzpqiRmZkDJ15JzFMN+yuveNhsF+nRqMRT8pMv3ZZYiD65WjI5QK7K1u5Y3Y698zNQK2UUd7UgXYIGvAO9mu0N2pNVgpqTTRbejHn7UnNDlj5sCS+ADTsR7fm7zxxaQ4f3jGVN2+ezM3TU8iK9oosCaEa/jbHwEvnGInV+QozGGJA1UPkUWioFUNBkJ6PjoXF7iI1Qs9ts9JICPU+04xNCkEhEzDZnMdMZeovIVolMzP9f6+Z0X2L4m1WJ7/7eC/lzdL3Ynd5+MOn+/yihgKceI4nfm5K5x/udaIo3tD5deOP+MxK4CNRYjPgASJEUbSLotgEIIriNqAYKVpmyJIQquO/V4zF2GnWpFbIuHdeJpdPTGR0QkhXv/PHxDHlaGHGpmqwtfq2Oa3EC81cMj6Bp384xJMrD9FuczIiLpjznlrLwsfXcM2Lm9hf3fuCqN3mxCOKLBoV29WWFW3gphmpPiHs1W02vnWPx5o4y/vRqfNY1j6SxU+tY/XBBh5cmINeJd2M7C4P1a1WqfLHjjfg2Rnw9BR450poKKQ3EsN0PH75GJ+f0z8vGU36j8yBDHBicLo9XJol49XYj7how8VcvPFi3kz9hvmJQ2NH9segx4Ew+gowdv5dyOQw6RYE0X30AwMMSYrqzNzw2hYWPL6ac59Ywxe7a0gK05EeaeCHg/U0tTuYnOoVqmdmRnD2yFjyYoMI77ZoOn9MPBM6RXSh5TCYaiBrofeDYkbhyLmAis5FTkKolj8vTCZo32tM+3Yxi9ddyL+Fx/jvfGmhVNJNxG7tcPD8msOc9fhq5j+2ml+8t5OyJgstHQ7WFPlHU5afoApKAQaOL/bUMDYpBH0/jB4HCylhenZXtg70MAL0hcuBLH4cxE/wtmUuQAiK7fuYAHQ4XMzMiuSltSU89f0h9GoF0cFqn6o7AU48Ho/IqsJ6Fj+5joWPr+HSZzewq8I/ItWHllL/tuLlNNXV8P7WSq59aROLnlrHD4V1/GFRLgsydLw/dg9XbbuCeSvPI+jz26Cp2HtsWJpURvuICKPUUTLrMfbZIthR3kq9+eiiUFGdmdvf2MaSlzezdGMZ542KY1JqGElhOhaNjqXWZOPh8/vn/9kfVAo5t89OJ6NbBsOSKcmM6fa82ZPmdjs7K1r92k9UJcYAfXM8d/cNgiDkiaK4/yd+5sfAXGCVIAhZgApoFAQhEmgWRdEtCEIakAkc/omfNeDMyork83tnUGuyEaFXkxqhRyYTeOWGiZQ2WlDKZaRF6NEfzaVaHwFKrW85U5mC0VlpjNfKuDQvlVqnDgGBi55Zz5GCFnurTfz6oz28ftMkP8f2XZWt3PXWDianhnHfmZl4RJGYIA1pPdKGlHKBf2yysS/rV5w9806SwnW8dEDOWyslYaesqYOnVxVzyYREXltfilwmoFPJoWYXfHKX90Sla2HFw3DxC9L30oM52VHSz6nNRoRRTWq4fsiEXg9XlHIZYZUrMOx5rastaPsziFEjIT19AEd28uiQGVDvehd57iLQhIAgg8IvsaQsJHigBxfghNLhcPHXLw+wtVRa1JlsLn7x3i5SI/SoFTLufHM7HhEWjIjmvjMz0asVLB4VS3QvkXkpEXpevG4CJY0W9Oat8P0vpOoic/4PRBGPy8Gn5SpeXDIBUYS0SD2p5u3w1QNd59CWrWSOLoZRcZeTHePdodtR0cojX3nLx361t5aUcD13zklnVEKwn8lgoILR0GfZ9irOHGKpnikRep5fcxhRFH093wIMDhQqPPUFyEKTIWOeVDqmajuixT9lIYAXg1rJa+sPdL1ecaCe5DAdIfqAV9YJxdoG1mbQhoE2mMON7dy2dBt2lweA4gYLd721g4/unEZUX4ayQf6Rp5648exqFHlnS0VX239XFvOPi0fx+EwP2rf/6O188Gup2tLCR0GukP5Gss+h5drlNNSU49BGsLElhB0VdRxqMPO7c/P6/Hacbg8vrD7ctUnS4XDzzA/F/O+qscQGa5DJBKakRZDUT6+3/pIdY+SdW6ZQ2mSRrCci9GiPkkoUrFOSHqmnuMG33Hp0UGAdcbI5ngiY15BEmEJBEHYLgrBHEAR/s4RuCILwNrAByBYEoVIQhJuQ0pjSBEHYC7wDXCeKogjMAnYLgrAL+AC4XRTF5h/zTQ02ksP1TE4NJz3K0CUqhOlVjEsOJT8h+OjiC0BYOix+EmSd/WRyWPgo2p0vo/zfOGLfW8RY5y6aLTa6VRMFYFdlG3Vt/irtkfCyTSXNPL68iCdWHOKf3xTS1KP2vNnq5M4zMlh2wMwN33lYVh/DW7t8o2oazHaMGgUyAW6blSaZ6TYX40fhF5LJ1tF+TmnhpEcaAuLLIMDpdBJUtMyvXV/ydS+9hwdlHSr25/8adr0Lq/8JPzxKU8RE9rgGWb50gJ9Mo9nOqsIGv/aSRgulTR1dc+k3++p4fHkRf/3iAK09PL26E6JTMTYplEIhlfZR10PxSlj1CGx6FllQHGd51lLXUM+8vGhSIw3QWOB3juCSL7h9QpBPSurOcv9dv093VeP0ePjdolyf9M3LJySSHx+QCocy9WYbRfXtjEoYWr/HUJ0SmdC730GAgcdisSA79A3s/RB+eFSam4q+hfKNAz20QU1hL6kYKwvqsTs9AzCaYUrVNlh6ATwxBt64CKq2U91i6xJfjlDZYqWm9SjzS+wYGHut97U2FPOsP/LJAX8LhA+3V6Ju8r8Hs28ZWLpFlgoC2pgs1jqyOO+tWv7yZQF7q9r458Wjj2qU32Sx8/X+Wr/23ZVtRBjUjEkMPeHiyxEijGompIQxIi74qOILQJhezd8uzO/KYgC4ZWYqubHGoxwV4ERwPBEwLwPXAnuQ0oaOiSiKV/bx1jW99P0Q+PA4xnP6IJNB3oUQPQLaqqUKHhufhd3vSO+3lMBblzH66m/9Dk0I0aJRyqg32bqqIgHE9rKLmxPjX9veqFXyzpZy7pyTgcsjkh1lQBAk06ojqBUysqKN/GxeJl/sqeGsvGgQe8kvjcgCde9/1M3tkkgUZlCDwyJ5xujCJbPeAAOCUqnEEzMaWcUmn3Yhum/Vf6hj0Ci5Y6uOq8cuJV3RQIfMyOuH1FzM6WksOZzRqxWkROiQCzKmpofTYLaxtbSZ0eEelDI34XqVjyAdrldhPJZYDljkQTwtv4b7LpyFqumAVJXh+78Sb2tlwflvAPlSR6N/6L8nMpfZI1PRB3nn59EJwSyZmsyBGhNbOqN1RsYFoVcrGJMYyqd3Tae0qQODRkFmlCFQQnqIs3x/veQPMMQqrAiCQFqkgb1VbScspD7AiUOtUkn386rtvm9E5AzMgIYIWTFG9Co58/Ni0KpkrCyoZ1RCCFrV0POAGZS0VsBbl4OlczOkaiu8cyU5l33p1zU+REOSugMsLikzoCeGSFjwNxh/nfQMEZ5OcVsQCSE1Pt0iDCouHBuPR1vrH4UQPRI0RkRRpKHdjlohI1ir4urJSUxOC6PRbCcuROtT1KQ3jCol2dHGrnv2EUJ0ykGXWjo5LZzP751BRbOVYK2SjCjDoBvjcOR47vDloih+KopiiSiKZUe+TtrIAvgiV0BUHmSeCQqNV3w5gttBcEc510z27tTnxBj41cIcbnptK2f/dw1PrCiitnN3akxiCNPSvb4zepWcBxbk+P3RZUQZuHBsPP9dUcT/vj/Ec2sOc/cZGT59bpmVxr++KeSx5UXMz40mM9oolZXLXeztpFBLpad1vsa/rR0O3t1SzqIn13L58xtoLNyA+M7Vkm/Mp/f06RsT4BTgcSMkTfV9UAxNQYgcvgu2xDAd98zL5I9rrFy2XMf137qRa4yMDEQVDDvCDWoevWgUYxKD+Xx3NfFaFz8sqCN92bkkvb+QFbOKuThXSsmUywT+dlF+vx4sR8QFEWbUotrwmBRFte7xLg+v4KpV3o7x4yFtrve1So9s/kPog7zRL/ur23hrcwUf76hCp1Lwm3NyCdMruWtuBupOE8ikcD2zsiIZlxQaEF+GAd8dqPXxiBtKJIXp2HMaG/EOZhQyEVnqLDB0S20Lz0CIGJ7pxCeKSSlh/OKsLHZVtrJ8fz3njYrj5pmpqIagCe+gpLXMK74cwVxLmKuGe+Z6nzUuyNHx7awSQt84E56fAzvelKoe9UQTBAkTpTS70BTSogyMiAsmOVyKNlkwIprLJiTy2PKDvFkVhStphvdYlQHm/p7qDhmPLy9i4eNruOzZDXxfWI8gCIyIC2Z2dhSZ0cZjlmnWaxTcf1a2ZMnQyfjkUMYnhfr4xw0WUiMMzMqKZHTi0PIeG8ocz0+5QBCEt4DPgK6clh9RhjrAT0VlAG2otypSJwp9GA8szGHxmDia2h3o1Qque2VzV7TKf747iEImcOcZGcSFaHniirEU1JnosLvJiDL0qujqVApunZXO7Kwo6k02EsN0JIZomZsTRW2bjbgQLUq5wIjYIKKCNGRHdyqn6ihY9DhMugVsbVLJ6cgc6f8KjSTIABsON/Hgh3sA+NscAxEfX+n9vna/I6UyXf2Bb3m6AKcGmRyxoxFh1OVgiJLyYdsqpQilYcz83CjevmUyxQ3thOhUjIgLIj705ISKBhg4nC4Py3ZU8eH2KmQCXBpZjuHLu7veD/n+Qf6y+HnOGj+LpHA9mUeM7VwOcFlB07soF2nUcOnkdNxN2chr9/i8J0Rkel8ExcFFz0H9frC3SxGCkV7f+do2K7e8vpWqzpDrHw42UNZs4Z1bp/pUcQgwfLC73GwuaebKiUMz5TElXM/2XlLmAgwCZEo85jpkoy6XfPgEATqawTmIUsY8HrCbpDWufHA8BNa02Xj4c68HzItrS0gO1zFqiIqkgw5NMH5h9YIMhS6U22alMyc7ikazjdnu9WiW/dLb55M7peeCnHO9bfZ26Vwqr5dliE7FmbnRpEToqG610drh4Pef7APgj6vsVI37FXddeh8GwY4nPBNFVBbvLS/ivyuKAGi2OLjx1S18eMc0xiUdpVptL0xOC+eD26dSUGtGpZCRHmkgJyZw7w4gcTwznBZJeDmrW5sIBASYU01IIo75f0f16e1dTe7cC5BH5RGsVTIpVYpseXHNYZ85DWDpxjIum5hIhEFNhFHNDOOxS9EZ1Aq/Mtlj9b6pQXlxvTyM6MMhtbN6UmuFtBO8802IzIXpP4OECby3tbKre7ayzk9UonKLpJAHBJhTjsvpQt5wEIJiYPe7IMgh73zEpqKBHtpJRSaTMT4ljPEp/mXaAwwfak023t8mzT+ZUUaSqv0zYLW7XmfBkktA3rmLVbUd1j0BdXtg1BUw+nII8X9YDtZrYdpdUPQ1OKQy0e6gRPaoxqKpMZETGyR1NERJX71Q2tTRJb50tTV20GZ1/thvOcAgZ1tZC/EhuiEbyZQaoWfpxtKBHkaAXnA67Sj2L4MeKcXiqKtgbF9uAaeQxoOw9VU4+BWkzoHJt0JU7gAPCtYd8q809+r6Ms4fE0+Qdmj+nQ4qwjNh5gOw+h/ettkPQlgGBmXns4cowtKl/sfufk8SYGwmKF4Bax4DuRJm3Q+ps0ElbZwF65SMTw4jP97N5c/7eh49v72dZiEel1uksK6O/zs7lKUbfZM7RBH2VbUdtwAD0rNRr89HAU57+i3AiKJ4w8kcyOmIxyMiCBx3xYDqVit/2J7ADXPeIdZVhUkeyk5XEhfLgjiirbo9IpFGf/+UKKManUIm7TTI+p+B5vaIyGVC17/96ettcML6p2Dzs9LrxiJpsrxlJcndIgvsQi/h/XIVKAL55AOBQqnAE5WD8GW3XYfq7XD+0wM3qFOIy+VCoRgcu3ABfjqiKCKKdBl8qxUyQnVKrA43WVFa7FGj0Bf2EGFCk73zZONBeP18aYcW4Pu/QFsFnPMv3DKl/7wYPw7nDd9Rd2gb7U6BbfZE/vRhE5HGLXxw+7RjpjOF61WoFTIfI0JBAP0xTPUCDF3WFTUyYgibH0YYVDhcHj/PuQADj1yuxGFMRC3bKpWi9jihegdWfcLAO5x1NMGy2yUzVoDmw1DyA1z/pbQBNIBEGqVo7RFxQehUcnZWtBIXokGlGFoeTYMWpQam3i1t1poqITgRYkaBsluajiBI9+KeHNn8KF0D718v/V8mh7WPgS4CEicC3mcSuUxGYqiWHeWtPqeJCdJwqKGd2jYb722pJMIo+b/pVXJGJYRQb7YRHBDbApxg+r2SEwThNeBnoii2dr4OBf4tiuKNJ2lswxaL3cWmkmZeX1+KViXnumkpjE8ORdlP073ihnaWHzKx/BDAEX+OeibmdaCQC3ywrZJtZa0sGhXLrbNSeX51CQAJISqWngW6L26XXL4n3iypxJqgPj+rpNHC57uq+b6wnmkZEYRolVS2WLlsQiJ5cb7HlTdZ+HJPLd/ur2VWViSLx8SRFmGQ0la2veR7YocF6g5w0fgzeG9bBR0ONx9XBTEq9WwMJV95+81+EMLS+vVzCXBi8bjdCIVf+LULh1fB2KtP/YBOEU21FSgOf4ex4D06QnNwjL6GkLQJAz2sAD8St0dkW1kzr68vo83mZMnUZKamRxAVpOHlS5IxVq4iuexx3OZsycBv1d/BbpbC9CfcKC3+AOoLvOLLEXa+wf60G/jdaitnjYjmnJExJIV7w58LPfEs+qKk85UUCVPdaqO4ob1XAUYURXZWtLJ0QxnVbVYeXJjD6oMNrDoo5cjfOD2VtMhAOtxwZU1RI+eN9i+lOlQQBIH0SAN7qtqYFxBgBhVuBJwTbkMdP0qq0CYPggWP4IyeOtBDg+YSr/jS1VYMzYcGXICZkRHOn84bwbriRtptLu6bl8XUtDA0yoAHzAlDGwypM4/eZ9wSKeLF2SG9VhspjlmIvNFMyuYXpbb4cZB3ARR/D2v+hXPynXzVlsyrG6sZnxzCJeMTuGF6Kt/sq8Pu8qCQCfzirCzqTXbqTQ4uGpeARxSZnBbG1tIWEsN0bDrcRH58cMBYPMAJ53i20kYdEV8ARFFsEQRh7Ikf0vBnQ3ETN7++tev1N/tqee+2qUzoZ8qDuhehRiaARxRZ8vIW6kySRc/28haumpTES0smUGOycX5kDca3FkkRKQCHV8ElL8PIi3v9nGaLnfve3cmuitbO87UyLimE2GAtV724kWV3Tic1QnrYMFmd/PbjvV0177eXt7J8fx2v3jiJCJkSlDpw9zDnU6gZlRDCh3dMY3dlK4Ig0BH7CAbzEmgth8hsaUIdJLnApxuCIODWhPpNEi5NOMO1NpXL6UK961UMG/4FgK58A7qDH9N8xReEJY8Y4NEF+DHsqmjlyhc24e6sK72mqJFnrxnHWblRZFa8j3adFPosq9gAmhDcF72EvL1Wmnti8r0n6q0im0LD1nIz28vNbC9vYd2hRv531biu0HSlXOicm30P620OB9hXbeKK5zd2Rb1sPNzM787NZVRCMLlxQUxODUOjDMyHwxGL3UVRffuQ9/dJDtexu7KVebnRx+4c4JQhEwTE9gb47g/exkPLkV3+ycANqhMnCnqLL3CgGPC1htnu4i9f7MfVOYlvONzEU1eNZdwAj+u0I348nhu/pa14MyabmwOydH61rJ2RsXt5IzQMmUwuiS/drm9l0TckzXuL7eUi28tb+HRXNe/fPpWP7pzG7so2oo1q/vzZPsqarYD0zHRmbhTJYVpSwnU8sfIQAFvLWlhZWM+yO6aTHjXg8WIBhgnHE0Mn64x6AUAQhDCOT8AJADjcbl5Yc9inzSNKIkxflDS089LaEu59ezvLtlcSFaRhRka4T5/rp6VQa7J1iS9HeHdrBamReq6ZkoyxeoNXfDnCmsek3d5eONxg6RJfjrC9vJXMaAOtHU4Kary7wSWNli7x5Qh7q00cbrBASALM/b3vycMzuh5ucmODuHxiEpdNSCQqPgVyzoEpt0P6GX0aXQY4+bjcImVpV4Gs25+5Qk1Z3NkDN6iTjLmhDMPW//k2WluQNewdmAEF+MmsOljfJb4c4dkfiuloKEO7+UnfzrZWOtpNUhnL7uILSOUpI309CWrG/ZLn93jn1DVFjZQ0ek2qUyL0XD8txeeYmRkRRBjVvLe1gnvf3s7SDWVUNEu7ejvKW31SjgBe31BGTZuVRrODMP3gq54Q4MSwvbyFtEj9kE9tSI0wsLMiUAlpsOF02NFuf8G3URRRFHw6MAPqRoUQR1uurw9Ne+oCKmQJAzQiL+uKmrrElyM8vaoYi901QCM6falQpTH9m3jmrkzg9m9tmKwu1h9uxpR/HSROhaLvfA8QRZJrvyWlswJSncnOwbp2RsQFc+WkJMw2V5f4coTlB+rJignihTUlPu0mq4stZc2YAx5sAU4QxyOg/BtYLwjCB0jmu5cBfz0poxrGCAgoe1lgKfrYEa0z2bjzze2UNFkwqBV8uquGG6en8OjFo9lc0sSBWhNT0sIZFR/Sa/lHH1sCWS8hk3IF0Luni0wQEASI0KtpszpxuH0fDGTdTi7rwxemy95m1OUQlgola6R/U2ZCSGKvxwQYHMhk8FxxOBfNeZsM2248gpyD6nxWVITxx2Ea+yYTZJLZMEjmqHYzOK0oAlFYQxZFD6+rCINKqtQm6/a77k5fnlzBCXDlW1C6FpoOYYmdwl83a6ls6fDp1t0LRq2Qc+ecDCalhrG9vIWRccGMTw7l0W8K+XRnNQCf7qphcmoYz1wznt5uA2qFDLVCzhB/Lg9wDDYfbvZW2hrCpEXqeW1DKaIoHre/XYCThyAIiEfuY6Ep0macqWpQRBhbUPOOcAWXzJ5BeMtuWoJz+bwtlRmegU+39PSsZAEoZUKft4kAJw8BcIui34bKbiGbWWf/A5b/wfcAhQa0Ybi7/Q5l3X5xR+YnuUwgTK+ixeLAI4qoFTIUvTzT1LbZ+GpfLZdNCDy7BPjpHI8J7+uCIGwF5iL9HVwkiuL+I+8LghAqimKg/uAxUMpl3DozjbXdokWUcoGz8noP1y2qM3NjrodJslK0HTXUho7jPwfq6XAmcXamnrPU+6D0PTwtKYxJmEpquI6SJu8DwY3TU0kM67yJpc6SfA2cXsW3cdy9bCo0MSVdSXiP3dXoIDW/X5RHYa2ZKKOadruLg3Vm9la1EROsJrdbObXUcB1nj4zhq73eSJ7JqWFkRBposTjYVmZlR0UiI+NuZ3RCMHE9y/q2VUH5RqjfB3FjIXFyn9VBApwa5HI5F46NJcfZhLHGDoKcEeEylAmxxz54iGLVRiOb9zeMjqbO6lthoFDTaMgaeKPCAP2isNbEhsNNmK0upqaHMysrgv99f4iJcWoeGNlOXOtaDFHJaGURtE/7FYZV3RZthihv5EtLGZStg6ZD0nyUOFnyo+r0pLJbHNSv3QZ459tzRsaQ2s0DBiDCqGbhyFiyoo1sPNzEW5vKyY0xUp4Yws7OCMNNJc2UNLYTH6pFr5JjcbgBuHO8jltTalE1rkCuGQNmNRgH1hMhwMlhc2kzs7KOXZVwsBOuVyGKIjVttoBvwiBCrVbTMfUXKMddAzW7pMjWmHxETfxAD43UcB3ldgPnfBdGhGEBzRYHY5PUXBc58HfdzGiDnxn6hWMH/mc27Gk6BKXrJKP75OmQMJGEUD13npHOY995K3HGBmtIjTRCWLRk5ntoOQgy6f9yFQang6cnt/C/olAOtAg+ZaCzog38emYYc3QlhLXtpTUoh2LNSEbGB3PfmZk8/IW3/HiEQYXTLfKfbw8SaVCzvayFCSmhjE0Kod3uZnNJM0X17YxOlDZZej5PHaGw1sTGw820WZ1MSw9nVEIwKkXAT+h05Lik707BZX8fb6+AQFpkf5icGsZbt0zm4x1V6NUKzhsVx+iEkF77pioambL/ZyjaSgGIAh6e/W9cwkQ8O99Cv+I3XX2dEXm8c+UbLDsEuytbOWtEDNMzwr3mvnFj4PovcO7+EJepgZK4c3hoUzAbK3Zw84xUfrUwu2sicHtEPtxexX++O9h1/tEJwdw5J52Dde38amGOj9mkQaPkd+fmMSszktVFDUxJC+eM7CgMGgVPrjzEEyu8E+aklDCevnocEZ3u8lhb4Mv7ofBL7zc+6TaY/5DkkB5gwJgk7kX+4RVdqWuhSi3jL38HmDOg4zppCKBwO2HFn7uaxLB0lCkXDNyYAvSbwloTlz23satUs7Ac3r5pMh/cMZWE8s8I/eZub+eQZOznL8V+zosYij/DHppFS9IC5JpEjKYaqapC9XZv/zP/DNPu7aqKFKZX8a9LR/N9YT0bDzcxKzOSWVmR6DX+t9XSRgvXvLSJ6m5lpX95VhaHG9sxdQpFb24qZ8WBeu6Zm8nhRguZwR6ua/4vqi8+8p5o7LVw9iOgGvgHkwAnDpfbw56qNm6eMfQN548Y8e6qaA0IMIMMrdsEH94Ens70GaUO3eVvDeygkNaPv180gtlZUT7rx1D9QDvAQGWLlV/Mz6Kovh2zzcXYpBC2l7Vw6YSBT48atrSUwRuXQssRu4Z/wvn/Qzb2Gq6ZnExahIGv99aSF2vkrBEx3k3mpGlw/Rd4GosQNj2L0FCAEsgH/nHGX2nMvc5nTmpvN3Gd/U00W6QS15FAWu4FyMc9wcXjEzCoFXx7oI7YIA3RwRqeWnkIg1rBioJ63ugsVf301WN5e3OFjwXDbbPS+OVZWX7CSmGtmcuf30hrh7Q+eWw5vHr9RGZnBzabT0dOZOxhICCvn6iVcqalRzAtPeKYfSPMhV3iyxESt/4dR+50VGv+5tOubNyPuvEAt89Z1Of5HNFj+DQyihcKSji404woStEwr6wv5fKJiWR2GgCWN3fwVKcB1RF2VbahVyu4Z15mr+eOD9Vy5eQkrpyc1NVWXN/OM6t8z7O5tJmDdWavANNw0Fd8AdjyvOTDEB0wPh0oXE4n8h2v+foGOa3IDnwCGXMGbFwnE4OjEe2av/i0Cc3FGFoKICVngEYVoL+sL27qEl8ARBH+vfwgb16Zimrdw76dW8toKdvD4pXhzM2+j8OF7exfVc9z1yaSoN7vK74A/PAI5C6GcO9DclK4juumpXBdD5+XnuytavMRXwDe3lTO2SNjeXdLBfNyovhL527bI18XEB+i5dKZblTrPvI90Y6lUvW6uDH9+nkEGBoU1JqJMKgx9CLeDUVSI/TsqGjl7PzhGy051HDYrCi3v+IVXwCcHQgHv4KMMwZuYJ30tn4cDIxPDuXSZzcQG6JBq5Tz7f5anrhiLHp1oCzxSaNmdzfxpZPv/gDp8wgPiuW80XG9V4tTqiFlBvb2NrQNBT5vBa1/BFfG2YD0jNPW4cBedxDN7qU+/eQHPobpdxOSMJFRiSG8s6WCPZVt1Jslf80lUxP4ZGdVV//iBn//yxfXlnD2yBgqW6yYbS5yYo2MjA9mU0lTl/gC0vrk8eVFTEgJk9KiA5xWnMjfuH+iZICfRIPZjqvNhN8Sxm5G8Li85di647b7t3XD5RFpsjgorPM13nV7RJ8QS4fL4+f5AvgZRB4Lh9uD0+1/aficx2Xzex9RBNfRv5cAJxlRBJu/r5BgbR6AwZwaRLcTHO1+7UJv12iAQYfZ5m+M2GxxIrocvZqNyz0OOhwePt9T09Vmd7pBZvXri8sGnh9nwGfrZd4021ykhOm4Y0462TG+lW+qWq3Ybe7eT3aMOT7A0GNHRSsZw8D/5QhpkQZWFtQN9DACdEMUnWBt9X/D1ktbgC5GJYTw1i2TeXVdKU0dDp6+ahwzMo69eRrgJ+Dq5f5rN/uKh0fB4+xlvebs8NlMdLg9fT9jdLbnxgbxx8UjeGNDKSVNHVwwJp71xY1dYgxIxSp64vaIbCpp5u9fSSKQTIDXb5xEey/rk1arE1cvz1oBhj8nVXITBOFlYBFQL4riyG7t9wB3Ay7gC1EUf9XZ/mvgJsAN3CuK4jcnc3ynkna7iwPVJipbrcQEaRgRZyRI23d4ZXF9O/uq29C64olVaHxECvfEW5FHpGMbeSWaPW96D1IZsARlYDfZsNhdHKgxIZcJ5MYGkdyZLqRTKciJNRJpVNPQbRKZkhpGcpjXlyUxTMuCvGi+2e9dRIXolH0vEhsPQd1eKfcyJl8y2gWSwnTMzY5kZWFDV9cwvcq3lFt4BgQnSrmeR0iYCKGpff58Apx8FCoV7lFXIi/5wafdnXvBsC1/5tDFYB19vW+1CKUWR3j2wA0qQL+ZmhbO72cYmKKrRik6OEQi1tB01KFxMPl2WPsfb2eFGkd4DuC1LtMoZZIYoswFTYjPw4mYdyFCSN+7s8UN7RyoNuH0iMQGaWjpsKNRKmi2OIgwqFDJZT6i9vlj4imqNzMtI5IRsUHkxhg5UOsViYqcUUwLy0DW3C2CMHokhKX/lB9RgEHI9rJm0iL1x+44RMiINPDkyiLcHtHHlDrAwKFUG2H01ZLXXneyzxmYAQ0R5DKBuBAt105Nxu0RiQ7SYNQGol9+NA4L1O6B1nLJzyw6H3Shvn2iR0gGut03vibdCkG9RL30ghCVK6XpdttMs+RejsMQx4oDdVjsLjKiDKijMnBGj0JZt7urnxiaihCe0fV6TGIIoxNG02yxs6OslaxoIyPiglm2o5LiBguxIRq/56nJnab7R/CI8NDn+/njohEIgrS3eYSbZ6QSrBv4VLsAp56TnYL0KvAU8HpXJ0E4AzgfGCWKol0QhKjO9jzgCmAEEAcsFwQhSxTFPrYBhw5Ot4c3N5Xx9y+9IXF3zUnn7rkZaFX+v4K9VW1c9eJGrpuawjubRSLnvk7OwefQmEqoTb8MVf4VhKl0tE/+ORZNDOGHPsIams2+9Fu48a1aMiPbGZ0YwsvrSgHJTHfpTZPJ6kwvGp0Qwj8vGcXbm8vZX2Nibk4U105J9rmp6FQKfnNuLulRBj7bXc3ohBBun53eJeT4ULMbXl8sebkAGGPh2mUQlYtereAP540gK6acL/bUMDYplNtmpZHUTewhOB6uehc2/E8yvcxaCBNv8p+UA5xy6iOnErXoCWRbngOZAs/kO6gNm8hwzX62uQSMuYtAIUhmbsGJeEZdhl0VNtBDC9APxhlbGF9yP7ImybsqS6nDdc3HIEuASbeANgS2vSoZ6c68n+Dg0TywoJr3tlaQHqnnrjMyyI4JosWioWnhG8TtfRZd0z4aUs6jPe8KUpW9e1ocmbNNVheXTUikuL6ds/NjeHx5Ee12V5eh+Vd7a6hutTIvNxqzzcVHO6qJCdZyyfgEnrhyLK9vKGXVwQamp0eQnBzNxuD/MqrqbQxVaxEzzkSYdAvoA7uvw40d5a3cPnv4CGsGjYIwvYqDdWZyY4MGejgBAJfogaiRqM7+hzQHylWIk27FGpzGwNcaGryUNLZz06tbOdxoAUCnkvPWLZMZkxhYnx43HjfseBO+esDbNvlOmPtbUHfblI0eAUs+hdX/guZDMHYJjLq090quvaCLz6Pjyo9QrHsMVVMB5pxLsOddzl3v7GFbeSsgFT956bqJVJ3xX4L3vk5o1fe0xUyjOuc64hXhdJ+1mtod/N9He1h+oB6QCiX+9pxcHC4PM9IjGHVjMC+sPszWshbOyI5iYkoo97y902dM9WY7e6paeXHJBJ79oZhmi5ObZqSwYETAVP90pd8CjCAIS0VRvPYobfN6HiOK4mpBEFJ6NN8BPCKKor2zT31n+/nAO53tJYIgHAImARv6O8bBSkmjhQ37y1m6QEawu4kGeQz/2lHF2fmxjIwP9unr8Yi8uamcmQkqLouqYsHUOiptMSxV/gptpIuOdj1/iZBKoH1TqaLQeRGR6QvZUGlj/WfS7um28lZmdTN1qjPZWX2wAbkgcLixHYNaweiEEMYkhmB3eYg0qLvKSB9uaKe4wYJBLSc7JogHFmRzy6w09Cp5307dO97wii8A5ho48DlE5QKQEqHnwYU53DY7ve/zRI+A8/4rhRlqgvs90QY4uTy5ycyEsGwWzHsEBIFlJQoqqk38epgWAdC6mlGt/KN0s5/1AHjcyAq+RCcLg4TkgR5egJ6010P9fnBYITILecVmaPIah+PsQLHuMYh7Vdo9m/4zaTGn1IBSS4woctdIF7dFtyPTKpFFSfNgQa2JK98xMSXpVrKiZKwscGDeWcWX96YRH+orwoiiNGebrFJ4cUKolv01bWwuaabdLrXVmez88dO9PHrxKDaXNPPpzmoa2qUds+md4eyZ0Ub+eN4IfmF3YVQrUMhltFlDUYydCB4LgjpoUJSMDXBiMduc1JnsJPSsDDjEyYgysKO8NSDADBJkHg+OHW+jypgJ5/wTAKG1AlfBckgYNcCjG7xsKG7qEl8AOhxunl11mP9eOQZ1oHrN8dF8GL77nW/bpqch/xJIGO/bnjQZLl8qVW09ns3Yjiao249O9CDOuAe3x4M+OJ7N9Xq2lXvXBk63yKNfF5AVZeBAzQVMirmYnXVudu9o4oPbzUxI8W667a8xdYkvIEWwPL/6MJ/fM4OoIKlQyL3zMmmxOPjjp/uICdL4RbqcPTIGp1ukyWLnwQU5RAdrvObBAU5Ljmc15+OGKgiCHOj6ixFFsb/GEFnATEEQ/grYgPtFUdwCxAPdYyMrO9v8EAThVuBWgKSkwWXY1Rseh4V/xK8h6od/Sw2CjMzZj1HnGOPX1+XxEKGw8kDQMsI+fo1EYKRMQea8F1jamMU901PQdUbNlDRZUCllPL2xGavTN1DI0c1zQNFZ4/68p9bS0Vne9Ky8aB6+YCTRQd4qQ1tLm1ny8uauPmfmRvHXC/N9+vh/c26o3e3fXu9bLEsQBEKPFWYnV4Ju+EQaDLXrtCcOl5sLEkxMWHsrsh8qAbg8NJNtU58a4JGdRFxWGH05LP+TNz84+2yGqz/akL5GWytg2R1QtkZ6rQmBxU/492sslBZxRyqqdV/MlayBty5BceR3PepyWPBXWjsk4WRjeTsby73dLQ7/HG6nx8OBGlPXa4fbQ0yQhsoW3zx2jwibSprIijayoqCexDAtS6YkkxDqnV8VcpnPPBnclabae0nL04EhfY32gz1VbaRG6Iddqk56pIEtpU1cNchMVU8Wg/06dTid6LLnwef3SZtkAOEZGM7621GPO92paPH3IymsM2NzuoecADPg16jN1LvvSvcN3O4oNcdXCdVcC5//Qop0bS1HKF2DHEBlYPSFbyOXCbg9XlWkrKmDSalhFNRZ6G5Z1Wr19XprtTr8PqrebMfSucGyuaSJ617ews0zU9lTZaLD4eHXZ+fy7pZyGtrtXDw2gXHJIfz9ywKq26S0qvQIPc8vmeBrxxDgtOKYjxWdviy/AbSCIBxZZQqAA3j+R35mKDAFmAi8JwhCGr2nMPVq7CuK4vNHPnvChAmD3vw3VaxEve3f3gbRQ9L63xKaNQ0I9+mrUsi5JtVM2IeveRs9LtI3/prf3/Q98hDvH+v0jAj+9U0hc3Oi+KLTRHJ8ciizMiOICdJwx+x03tpczpS0cF5dX0qHw835Y+JIjdAjilJp1CPiitnm5O9fFjAiLoiZmZHEBGuIMarZeLiJlHA9ubFBqBQy/29OJocxV0F5j0ClvMW9/zBMNdBUBHIVRGQP6zSjoXad9kSlkJPf9BUyc2VXm7KliPz2dUh/vsMPvVaHuPkFhO6LhMKvUI26auAGdRIZrNdou81JUX07FrublAhd79EBFZu94gtIfi073oCUGVC61ts+5ure5xlLE3zxc98F4e53YdQVpEVOQCkXcHlELh6XQHyIFqNGgVYhsKeyjQazDYNGiVyQIvwuHZ/AzopWAHRKOQhw04xUSpssOFwe3tpUjtnuIiXcwOvrS/nz4hGsKqzniRWHCNapSA73zusmq5ND9e1YnW4ywjVEO8qkhWVQHIRnnnZRMIP1Gj1R7K5sIyVi+O2EZkUb+d/3p48R72C/TjUqFWLhlwhmr+E4TYcQanZA9oKBG1gnbo/I4YZ2atpsRAWpSY80oJT3suY8xUxJC6OpoY7z4swocLLdEoEiOLabOD50GPBrNCRRSv9t7lbhSGXo8owE6VnkUH07Ro+JBHclGpkHIjIlv5ijYHe68ZRtQ1v4BZzxW9jZzR/T0U7Y+r+yKPu3fHJAyhSIMqp5cGEObVYnPz8zk2/317Gv2oRaISMl3Hc+ToswIBOkTRSQUpAeXJhNTZuN6jYb6w81YnO5KapvZ1RCMLsr2/jPd4WclRdDuEFFdrSBNquLSyckIghSVsQnO6v5dn8ddwQEmNOWY67kRFH8O/B3QRD+Lorir0/AZ1YCH4miKAKbBUHwABGd7Ynd+iUA1Sfg8wYcta0XddfRjtHjX5UDIEz0rzyDuRa5w4RkjyMxPimUyycm0mJxYHdF4XB5iA3W8NjyIgCCNAp+cVYW8cEa7n57JzfNSGVbWQuf7JR+rF/sqea5ayQFtt3mIkyvJFin4o2NZdwwPYXfLduLw+1BLhP4w6I8rpyU2Hv6UOYCKV1j/ZOSIDPrAUiZ6d+vbj+8cyW0lHqPW/QfCB6ujiJDG5fLhbZms1+7tm57L72HB2rR4bs46MRj96+MFODk0NRu5x9fF/LuVsmUO1yv4pUbJjIqIcS3Y0uJ/8G1u+Hcx6F2r2TAN/4GKaqlN+wmaDrk326pJzPNyEvXTWR3ZStf7qnlg22SCPnR9krmZEfx9KpiDGoFv5ifxT+/KeSPi0dw55x0XlpbQp3JRpRBzS/f3wVAsFbJ/QuyqGmzsbW0mcpWG4V1Zj7cLpWyLOpWka7OZOOvXxzg013VpIVrWDqxDFY/IFV/kCvhgmdhxEUgG/gHkwAnhp0VraRFDB8D3iMkhGpptjhobLcTYTh9I7gGCy5nB6q6Pf5v1O879YPpgSiKfLW3hp+/uxOnWzJu/tuF+Vw0Ln7ARZgp4TamCf9DuUaqCTI5JBnHpW8e46gAvWKIgktfgy/uh8pNEJElWQ+ES/5XjWY7j3xdQK6mhavq/ommsnMjJTwTrngTInsvhmB1uFi6sYyznRUkyuS9VoiV1e3l7kujWFtuxyOK/GJ+Fr/9eA82pwdBgBunp5IQquG6aamkR/qKIjkxRp5fMoHff7yXmjYbDy8ewTtbKnjkq0IA8mKDuH1WOs+tLubBhTlolHI2lzSzo7yFn8/Pwu3x8MKawxQ3SKlsYxJDuGlGKjsr+oj8CXBa0O+ZTRTFXwuCEC8IwjRBEGYd+foRn/kxMBdAEIQsQAU0Ap8CVwiCoBYEIRXIBPyf/oYioYmg6LEACU6QzGd7QRmZLkms3Ykb56cAB2mVLJmawqUTErl5RioXjYvn3a3eaAWTzcWbG8sZkxTC4jGxKGRC1y4twKF6Cx9tl/qHGVSckRPFB9squWhcPP/7vrirYofbI/LQ5/u7Jg8/jNEw5zdw1xa4cxNMv8/fKNLtgs0veMUXgKJvoGx97+cMMOAoFAo8GfP92sXexLVhQq3biD15tl+7STv4QsqHK7sr27rEF4Ami4NHviroCvftIm6M/8EjLoKss+CO9XDPNlj4d2nXrTcMUZDuZ10GYWnIZAKzsiJRyGXs75ZetL/GjMXhJtKgpt3u4tX1pSSG6Vhf3MQvz8pm+S9nMysrkrc2e8ffZnXy/tZKWjucrDrYgFoho3u2ycRuuea7Klr5dJckkP98rIz4NQ96S2+6nfDp3dBc3Pv3E2BIsqeyjbTI4bcLKhMEsmOMbC0NPGQMBlTaINyZC/3a3SlzTv1gelDaaOGB93fj7Czp6/aI/HbZHg43DPzGh7pqE8pD3oKsstYyNNtflNa0AY6f2FFwzQdwzw644WtIntb11u6qVr7cU8M8zQG0ld2iWJuKpOhWsfegnYN17fztywJqFfGSLYLa6N8pdzGZqWl8+bOZvH3rFP71bSE2p/SMI4rw0toSbpmZzrT0CIQez18KuYwzc6P59O7prHngDEw2F3uru68LTNhcbkJ1Kh75ugCtUs4Ht09h2V3T0avl7K8x+zw/7axoRSkXOCc/9sf8BAMME/otwAiC8AiwDvgd8EDn1/3HOOZtJBPdbEEQKgVBuAl4GUgTBGEv8A5wnSixD3gP2A98Ddw1HCogARCeBZe9IS34QQrBu+TVvkPqInPh4pdB2xk2HzUSzntCymvshbgQLe9sqeBQLwJJUX077XYP95yRRVmTvyq85lAjDpeUy6ro3FVVyGVd5pFHcHtE6s02v+O7kMkgNEl62OkpHgE4LVC62r+9Zmff5wwwoLhcLmmCGHGhVF5cJofRVyA6j3IdDHFqrXLWp/8SZ8w4qUEdRMWsf7HJ2r/yhwF+OtVt/jn3O8pbaeuRl038BDjzz1K5SuisnnazdJ0Gx0NoihQ10hcqPSz4q3QeAHUQnP8/iMnv6rKxuMnvsIO1ZpI6Q5TLmzuIDtawsbgJuUwgMVRHc4d/vvjeahPhBjWRBjV/PC+P97ZUopLLuO/MTCakeNOjDnebw8PEFnD3OJfTKhkPBxgWtHU4abY4iA0+Dp+DIURmlIGNh/3/hgKcelotNorC5uAZcbG0RpMp8Iy/kb2qMQM9NJosDj8fQ5dH9CntO2D0FjVUstqnxHGA40QTBOFpoPe1YKhssRIbrCG0eZf/MYe/lzz6eqHOJK1Jn9ivp3r6X6FkrbQRrOxMJUqdDTN/DgoV0UEa3G6Rxnb/+3RLL/fu7kQaNSSG61h7qNHvvaI6M0lhOnRKOfPzosmNDSbCoGZ/tYltZf4idHlzR5cBf4DTk+NJJr8QyD5Svag/iKJ4ZR9vXdNH/78Cfz2OMQ0NZDJpV/aWVZLZlCEaDJF991eoYORF0oOB3QRB8Uf1SmntcHDOqFjabf6K/OiEEML0KgRgZlYEX++r9Xn/zNxoDjdYCNWrSO0Mg7Y63ITqlLR0eB94lHKBYI0SURT91OF+oTJC9tlSmhJID02jLpNSlVor+t6lDjBgKBQKTC45QZoQuPglqfHg11jkBoKPeuTQJVyv4qE9cr4OeYhZ6XaanEqe3ujiz+cPP4+GwUpyL5UBZmZGEKbvkXOvDYFp90LueeC2Q0iyJKq4nVKkndshtamPEl0QlQvXfASmSikXPdS30tW8vChWHWzwaRsRH9SVkpQVbaC8qYMzcrzzeVIvfjVT08I4My+K88fEkRiqY0JKGAqZQFKYDkW3EPvsGO/OnRAUJy0gu4dTq4MkL5gAw4J91W2kReqR/Zh76hAgJyaId7eWH7tjgJNOiF7Dc1XBJEfdy3ljrgdkvHRIh749hDEDPLaoIA1BWkVXJTkAtUJGbLD2KEedIuLH+7dlnyPNxQFOKKnheipbrDSOmUiw8mPIv0x6VnJ0gDHWK6j0ID5UiyDAmjIrl5lyuXN0PslGJWOvvxyn0wEhiQSHSGKP0+VBBB5ckM264qYuMUUQ6EqVtLvclDd14BEhOVyLRun7qDw/L5r1PTZn5ufFMDUtDLVSTlKYrus5KTXcQEuik92VvtYSZ2RHBVIzT3OOJ7nyMHCU7cQAxyQ4HmJGHl186U5oktT/KOJLQY2Jq1/cxK2vb+P9rRX8fH4Wqs4FfXyIlr9cMIIGs50bX9vCnso25udFdx07PSMcj0dk4X/XsPiptSCI/PacXD7dWcXdczMI0Um/br1Kzs/PzOL+93fx9uYKrL1UAjkmMhmMux4Sp4BSC2f+SapA8vYV8NxMOPBZIKRzEKLImodobYEPb4QPb0IU5ChTpg70sE4aieF67l+Yw7pKJ3etsPPwOguXT0wgPXz4eTQMVvITgvnZvMyuqjAZUXp+eVYWGmUv/lMymZQ/HpUniS8dzfDDP+CZqfDMNPjwJmjy9/TxQRsM0SP8xBeAudlRLBjhnTPPyI4EEVo7nMQGa7hyUhJOl4cJyd45ekRcEA8uzEbROf7kMB3njorj4qfXc8XzG1lX3EhGpIG0SIOP+AIwOjGYW2elcd+ZmbxWKKd41n+9C/0jQmg3w8IAQ5u91W0kDeNSpOmRekobO/yj1wIMCNflyVhc/V/0by1G//ZibnS8xcKkgRf/ksJ0PHnlOIK10prToFbwxBVjujYFBxJL9ASsY27oiux2xU3EMvKagA/XSSA/IZhbZqbyRXsmrnP/C4dXwep/we53ICRJSi/qhcwoA/+8ZDQapYzKFhuPbXViD05n/tJaRj9bxbnP7WJbWTONZjv/+q6QC/63jke/KUQhE7h5ZioapYz75mXy6NcFbClt5m9fHGDB46tZ+N/V/OajvVT1qIR1Zl40Z+V61wULRkQzLzeKrJggksP1PpvUk9LCyI42MinVm2q8eHQcMwLRL6c9gthHTl1XB0F4EqkaUTwwGlgBdEXBiKJ478kc4LGYMGGCuHXr1oEcwoBhsbm4+fUtbDjsrQCeHqnnofNHoJTLSAnXE6xT8sAHu/m003h3ekY4E1PCGJ0QzEfbq/hst9cRP0ir4Mt7Z2B1eDDbnCjlAptLWmhot7NsRxV1JunX/uEdUxmf/CPLRVtbpd3p96/z9YORKeC2NRCd9+POO3D0a/UyZK/TTc/CVw/6tl3wjFT5ahhS3dLBb5bt4awRMagVMlQKGcu2V3HVlCTOzD26C/8gZshdow6Xm5JGCx0ON0lhOsL7u1NU+JUk6nZn+n2S4PsjowzabU5Kmyy0WZ18srOGSKOaSIOKML2KZouDlg4HCaE6Lp3gjeJzuj2UNFpobnew/EAdSzeWYXdJ+eZqhYzP75lBZnQveepAh8PJQ58f4J3NFUQa1Nw3XkmswkRORiZxqTk/6nsYAgy5a/REcNeb20kM0zI7K2qgh3LSeOSrA9wzN5Mzu23+DGGG9HVqWvkYQav/5NPWdvYzBE8eHPfziuYO6s12wg0qUgbJpse3+2t57YcCrs10osTFynoDoeFR/PKsrB8XDX7yGdLXqN3pxtRYReQ750Cb10sNuVJ6RojK7fU4j0ektMlCS4cTjULGkpc302TxphTFhWj4v4U53PvOTp/jbp+VRrBOyZubymlst3PrrDSeWOFrzv/H8/K4Ybrvxke7zUlJowVBEEgN16PX9J1QYrG7KG/uoLndjkGjJCPagF51elUz7IVB+cdzKunPFXDkL3QbklFugEFCY7vdR3wBKG6wUG+2c+FYqbJQTauV5fu9pSDXHWpi3aEm/rAot6t09RFMVheVLTampEmheuXNHTz8xQG/zy1t7PjxAow2BFoEX/EFJKPJltKhKMAMXzxu2PuRf3vhV8NWgKk321l1sJFVB31zfGdl9TNqLcAJQaWQkx3zI0K8K3rxbd//MUz/Geh+3Jxl0CgZGR/CZ7uqea+bOfAR7p6bwbf76nwEGKVcRla0kZ2OVl5c61utye7yUNli7VOAabe7u+bshnY7v/3BDsj4tUrJbYHgl2HFvuo2ZmYO753QnNgg1h5qHC4CzJClo8NCULH/El5dugIGiQCTGKYjcZBFhG0rbWFdmYV1ZUdaTKSEu7hlZirBuqFXinqwo1bKifQ0+oov0JlaXNanACOTCV1m5qsK6n3EFwC5TGBTSbPfcSsL60npTH3Kiw1ib5XJr89nu6pZMiUZebeIVYNGSX7Pyox9oFcryI0NpKwF8KU/ZahfOxUDCSDRaLbj8niI6cx9tdhdtFqdhOqU6HoopgaNgpQIHaWNvua6kQavoZ9RqyAvNoitPUygooI0yAQBT2cEVFywhgUjYwjRerPMjBoFWdEGdAqR89IUFLeJfLi/nUjjT8xb1IZI4fS2Vt92w/DdBRySyOSSQ33FJt/2hIkDM55TgEGjICVcR5BaYHG6nFKTwPv7zP7+IwEGJ72VqYwfL/m7HAOruRm31YQqOAqV2v8hIMLgfw3EBWtoarczrjMFqc5kw+MREQRp0RWqVaJXybE4fEOnI4x9X09GtZKR8cGsKvT1nhlsDyYBfhodDhc1bTbiQweBz8VJZERsEEs3lh27Y4CTilqlxhI9CX3Vdp92R/QYhqcF9IkhM9r/3jE2McRvPR7gJ+KwgrUJ1MGgDZNSb+09xJB+PiOEd7u/ymUCZ+ZEMS8vGp1SxpubfD2pMqOMFHdW26ppszI5zXejJlyv4popyTRaHEQHBf5SApw4jqcK0h5BEHb3+FojCMJjgiCEH/sMAY5Gu83Fh9sqOffJNSx4fA3Pry5me1kzt7y+lbn/WsVdb273KYcKEG5Q87cL81ErvL/GyyYkkBfn3Vk1qJX85pxcDGrvzeKsvGjGJ4Xyh/PyEAQpH3HByBg+2l7Fda9s5sNtlbTbXITqVLx5YSSvR73DTTsv4aGWB/n+Yhn5cT+xZGZoMix+QnrAP8KsByFy2IbXD1k6ci7BE+L1xnBH5NCRdtYAjujkkhFl5I2Lolga+wE377yMPzf9kpUXioyIGRzh0AGOQdI0SOrmUaQLl6JfFEcX0CyH1qF46xIMz0/G89HttFfu8+uTFxvMkinevwWVXMaNM1LZV2VibnYkz68+zILHV7P4f+t4a1M5//i6gEaLnX9eOsqn7PQDC7LJjOp7DtWq5Px8fhZBWu+cPTszgrGJIcf+/gMMGQ7UmEkM03VVHxyupEUaqGmzDY6KNqcxcoUCYfRliEHxXW1iZB6kzRm4QQ0BJqeF+3h8hetV3DIrDaVieP/dnlLq9kt+bU+Oh7cvl4SX83o8I5zx2943WHohM8rArxZkY1Ar+NPiPJosDn7/8V4+2FbF3y4c2eXPFmlUc8HYeApqzQC0Wp3MzIwku1N0m5UZwdVTkvn7lwUsfHw1L609TJMlMI8FODEc0wOmq6Mg/ANwA291Nl2BlMPVBswQRfG8kzLCYzBY8xiPlx8O1nPdy1u6Xt84PYXPdtf4LFqSwrR8eMc0Io1eFVYURYob2ilt7CBYpyQr2thlZNadkoZ2Djda0KsVZEcbCNWrsTvdFDe0s/FwEw997ptq9NqNE5mdaoSP74B9y7xvyJVSNaeYkT/tG3a7oKEQWkulqlCROX1XK+lolqomqQblDvCQzrc9Fm9uKsXRXMnckAZEQeCrhgii41O4aFzCQA/tpOCwWxE/vRf1vve8jTIFpmu/JSi1l2oIQ4NhfY360V4P9QfAZZcWbL2Y63bHXLkf4+tngsMiVVnIOBNXWCbOKfegNfoaoJtsTg7Wmqk32wnRKhFkkBahZ01RI/e/v9un768WZPPsD8Usu3M6DreHypYOoowaMqMN/do9LW2ycLi+HZ1KTma0sf8+OEOT0+saBZZuKGVVYQM3z0wb6KGcdB5ffpArJyVxwdj4Y3ce3Azp69S0cSlB5kNSRRlBgKYi2uLnEDx60UAPbVDTaLZTWGfG4fKQEWUY7NGIQ+satTTCq+dBw34IToDk6VLlv/l/BYcZWsvAENP5jND/jbAOh4vCWjM3vLqF1m4VXTOjDNy/IAulTEZWjJEIg5qiunZqTVbigrVkRhtosTg5WG+msd3Bz9/d6XPexy8fzQVjh+f69xQT8IA5jr7TRVGc3u31HkEQ1omiOF0QhF7LSgfoPysP1Pu81qsVfjtG5c1Wypo6fAQYQRDIiDKSEdW7n8ARUiMNpEb6ChxqpZzUSAMPfLDbr//KA/XMDjdL/gndcTuh8eBPF2DkCogZIX31haka9rwPW1+GoASY83/S5DzMdwwHC063h5q6Bi41HiJ5+9N4ULBg5F18UqMHhucNyNFchWH/B76NHhdCQyEMXQHm9MIQdVzpjJ7GQ5L4kjBRKi+6530UDQXIwlJgxPmg8RZdD9IomZDiG6LscLv9wpoB9lS1ERWkoazJwtzc6OPOAU8J1w8aI8oAJ549VaZhXQGpOyPigllZUD8cBJghi9VqxdC8R0oB3/oiyFWQfynq+p1AQIA5GhFGNRE/NfU+QO+0lkniy8SbpddF30JQPDQWQub8H/2soVMpMNtcPuILQFF9O3urTFw4Np6EUGn+zU8IJh/vfT46WE50sIZ73vZN1wN4e3MF542O76rSGCDAj+V4nmQNgiBMPvJCEIRJwJEn+kD94O64HeA6vrKLPfPAFb38ccsEfFKJTgRKmUByuP8iMD5UK0WdaHsxrlQfXew5HmxON6LT5l9eThRh++vw3R8kc96ytbD0AqjZdcI+O8DRUcplXBN1mOS194MhGpkuhLQf7uHKmMqBHtrJQ6UBvb/hrqgOGKgNG3rMN4I2FDLOhHFLYMWfoX4/NB5E9tk9UPz9MU+nEGSk9iKUhBvUtHU4MRylOgKA2yPicPVeXjPA8GVfdRvJp4nANiYxhNUHG3B7+hdxHeDEo1AoIDQV1v4HjHGSsLziIZQR6QM9NF+cNmn9F2D44bT6tyn1EDVCinrZ8iK0lkP5BnjvGqjb++M+x+0Et7PX5yWFTEApl6FRyns5UKrA6HJLFQt72wBJi9QHxJcAJ4TjEWBuBl4UBKFEEIRS4EXgFkEQ9MDfT8bghhwOCxR+DUsvgrcug0MrwNm/fME5WVE+Jo+bS5q5enKST5875qSTGnliF2wKuYybZ6b5+MhEGFTMyYqC4HhY+IjvAcnTISb/J39uVUsHG3btp2XN8wivLMT98Z3Q3RzOXCuVQO6Ox/XjJ+QAx4/HQ1TjJjjjd9LPXpDBmX8msrGXSjPDBEN4AvZ5D/u0eeImIIsbNUAjCnDCMNfBttfglbOl1MqqbWCqxdB6ADqaJLHlrL/4RLyw5UUpXfIoyGQCS6aloO22oAvVKYkJ0jA5LYysPqodiaLI9vIWfvbODi57bgMfbKsI5JefJrjcHoob2k+bCJhIo5ogrZLdla0DPZTTFqUMhMZCmPMbcNtBpoD5DyE0HhzooUk0FcP3f4eX5sPyP0HDIBlXgJ9O0yFY+Rd46SxY8RA0divzHJ4uRbfv6RF57LJLqcTHg9MKRd/BG5fAG5cw2r6FX56R6NPlqslJZEUbiQvx3fRu7XDw8Y5KLn9+I/e+vYOtpc2cPTLGx9JBr5Jz5STf57IAAX4s/Q6nEEVxC5AvCEIwkndMa7e33+v9qNOM0rWSgdQRDq+E6z6D1FnHPDQrxsh7t01lb5UJp9tDbqyRmGANC0bEUNVqJTFUy8j4YNSK3lXbn8K4pFCW3TmNAzVmlHIZI+ODusq5kXse3PA1NBVJhpaxY8AY85M+z+5089q6Em7hIyK3/gsAefUOKPgcbv4OovKk8FhNMFh9qzehPD0WrIMCQUAWkw+f3+dtK12D/MLnBmxIJxuP243Y3gBn/lnyJHI7kLVWINrMAz20AD8FUYQdS2Flp7hWvV0y/suYh2zd451tO+DQcphyB/zwqNRmjPE1AuyDMYkhLLtzGnur2nCLIhEGNXIZXD4xgZA+SpUW1Jr42xcHKG5op6XDyc6K3fzxvDxumB6oNT3cKWm0EGFQo1Wd+Pv5YGVsUgjL99cxNin02J0DnHgEGUSPhC9/6W0rXQMXPD9wYzqCtRU+vQfK1kmva3dLD9JLPg5UxxzqWJrgo9ugqtNvpna3tNlx9QegD5d8JWNHS+t9i2/lPxTHWSGubD28eUnXS3nJKm6/+iNyEsZT2WIl3KgiMVRHbqz/pshXe2v49UfSBu8O4LsDdSy7cxof3D6V/TUmPB6RvLggsmMC0dABTgzHFGAEQbhGFMU3BEH4RY92AERR/M9JGtvQwuOBzb3cyHa/3y8BBqRqAWk9fFpmZfmnQxyNimYLKwvqWXWwkdmZEczLjSIx7NhRM3lxweTFBfu/odRC8lTpq79U75S8W9oqYfTlkDwDNN5Jq7y5gxhZC5E7n/E9ztEOtfskAUYfDvMfhveu9b4flABxY/o/jgA/DVGURLGeHF4Fo6845cM5FXQ0lmGo3SqZt5aukUzhQpIQGwog8Sh+RQEGN6ZqOCK0HCFjnn+UXfeyl3IVTLxFMqvshTqTjTVFDXy1p5ZxSSEsGBnLJRMSe+3rR2s5icVf8ZZ+OQ0pk/lBmMBvVll4elUxi0bF+vh8BRh+7K8x9Zr6O5wZlxTK0g1lPLAwUO1wIHC73cj2f+rbKIqIZetg9GUDM6gjNBV7xZcj1O+DxqKAADPUaTrkFV+OUL1d2tTVdxbQDU2W1vsf3+7tE5YuCTN9UbsH9n4keVKOukyq5rXtNb9uyh1LmX/Zq0cdYovFwdOrin3anG6RrWWtXD8theRwPTvKW3hpbQmiCBePT2BcUiiqQCWsAD+B/kTAHHl6P3HGH8MRQZByGXtyCiv3tFkd/GbZXtYUNQLwfUE9KwrqeeqqsQRrj16G9YRRuxdePVcSU0Ay8b3wWRh9ZVcXuUzA4UF6wMHie7y82yWZOR+u/xLKN4IhUiovGz7I8pWHM4IAql4qUw1jPxRBppQqRKzqlnqnj0Bz/gsDN6gAPx2ZvHO+6YbHKbW5bL7txlg462+QMk2K+OsFh8vNsz8U88q6UgBWFNTz/rZK3rplil9osx82M3zzWwwHpIeh+MPfcnHMBA6P+xNflzgD+eWnAXuq2kgMPb0EmIwoA00WO6WNFlIiTg/vm0GFIENUav1LjxxvlMFJwIWs14cRpyjDv6ZngCGFvI/HTFmP9tzFku1B+SYIipXW+6F9pPs0HITXzvNGyBd8Dhe92PvzVj8qJ8lk9JpdoJRLfy07Klq44oWNXdZEH2yv5O2bpzAlPfyY5w4QoC+OKd+Jovhc579/7u3raMcKgvCyIAj1giDs7db2J0EQqgRB2Nn5dU5ne4ogCNZu7c/2feZBiCDA5FulMM8jyJUw8pK+jznBHG6wdIkvR1hT1MjhBgslDe28sbGMhz7bz/L9dbR2OPp9XlEU2V3ZypMri/j3t4VsK2vpMqnyo2qbV3w5wqpHpVLSnSSG6XDqoqka/4Dv5xhiIKab14ZSCynTYdYvJYPMiMx+jznACUAQYNIt/td0/qm7pk81guiG7T12USyNCB2NvR8QYGhgjIG5f/BtK1kDsx/s0S8O0mbDtLsoVWfx1uZy/vzpPr7dV0uLxTtnVjRbeX1Dmc+hpU0dHKzrR6pay2E44LsTra7dyhnhLdwxO53nfijm4x2V1LT2YlgYYFiwt6rttPF/OYJMEJiQEsZXe2sGeiinJXIBhJxzfCP65CqEhAkDN6hOKmXxtGde4NPWkTiLSsXwrLZ4WhGeCTk9qmxlnwsRWb5tar2ULTD7ARh7zdE3W2v3+NsTfPWAtNHbPWVYJocxxy7Sq5DJuGN2mk+bUa1gQrKULvnu5gofX2hRhLe3+Fc+PEJBjYnnVxfz968OsKG4CZszYLIfwJ9+e8AIgpAFPANEi6I4UhCEUcBiURT/cpTDXgWeAl7v0f6YKIr/6qV/sSiKY/o7pkFH4mS44Us48BkICsk/Jf7Ula49mnH8za9vpbhBijZ5eV0Jvzknl1tmpnalkh2N3ZVtXPbcBuwuSXR5elUxb948mSlpvai/Yi/CjOjxGZxSLuPKiUmUVZ+LcE4cEVUrkIWno8hZGIhwGWwkToYbvpIeGAfgmj7ViIi9/iEFduKGASMukqJbCr+AsDTIWiC9DkuDwi+lBWHWWRCWRm2blTuWbudArZSS9Mr6Un4xP4u7zshALhMQkYTpnvSreEdvcySQHqnnvxuq2VwiidVnj4zh0YtHEaQNXHnDCVEUKagxs2RqykAP5ZQzMSWMT3ZWccecjIEeymmJWL4JYf7DUjEDmRIisnDXHkA2wB7zbW4VH2tvYPGMGUQ2bqEpbCzf2XMZ7dIRcMQa4miCpGIe2WdLHi1JU6V0Ic1PiKTu9TlDlDZpb/gK9n8mCY05i6AfAuO3+2p5c1M5/3d2DvurTQTrlJw/Oq7L78Xdy43d00dFt8JaE5c9vwGTVTLvf+6Hw7x43QTOzI0+jm8wwOnA8dQ0fgF4ADgSEbNbEIS3gD4FGFEUVwuCkPKTRjiUkCulySXpOPxSTiBpkXompYayucSrDE9ODaPBbO8SX47w+PKDnD0yhsR+7MJ9ubemS3wBqWzqq+tKmJQShqxnuHzCeClypXu5uVkPeHM9O4kwqonITgFSgAv7+R0GOOXIlZA0Rfo6DWgUIrCOuZOILd30YW0o9bpMUgZsVAFOCNogyF4gfXUn5xzpqxsFteYu8eUI//v+EItHx5ESoScxTMsVk5J4a5N3FywhRNNnxSMfwjMgcwEUfdPV5IkZw2sHVV3iC8BXe2u5dVZawLR0mFFnsiMIEHIaCmt5sUE8s+oQZU2W06YE92DBKcqoS1hIwhfXQmiKVKp355vUnv8B/XSuOmmkRuj5T6uWZ3ckEB+SRc1+K5lRCi46o5cU6ABDj5BEKapl7LGjUfpFbL5k2mtr87bNegCC4qSvxMn9PlWj2c6jXxdSa7KxvbyV1Ag97TYXk1LCuvpcMTGRT3dV+2ywXDm59/SoLaUtXeLLEf7zbSGTU8Mwak6/OT9A3xyPAKMTRXFzj4iJo9fn7Ju7BUFYAmwFfimK4hHFIFUQhB2ACfidKIprejtYEIRbgVsBkpICJcGOEKJT8Y9LRvP13lq+L6jnjJwoFo6MoaDG5NfX7vLg7kPB7UnPyQSgzerEI4rIemYUx4yC67+A7UuhtQzGXSep3achget06GF1iyw1z+LqmbEklX9MW3AueyLOps4UOiwFmMA12jvOXlIsHW4Prs45U62Qc8/cDEbEBvHZ7momJIexeEwc8aH98FNQG+Gcf0LBHCl3PX0uLcnn8OyzJf0ax+nGcLtG99e0kRKu71f06XBDLhOYlBrGp7uquWfu8EopHuzXqcsj8nxpLFfMe5X00ndxyzUcmnwZX1RF8esxAzu2IK2Shy/I59t9tXy3v46LxsZxzqg4wg3qgR3YMGOwX6P9JjIHlnwGO9+EhgIYey2kz/1Rp3J5RDqc3meckkZps9rRbdN5fHIob948mTc2lCEC10xJZnwfGyNWp//zksXh7vfzVoDTB6G3MOpeOwrCV8DdwPuiKI4TBOES4CZRFM8+xnEpwOeiKI7sfB0NNAIi8DAQK4rijYIgqAGDKIpNgiCMBz4GRoii6K8edGPChAni1q1bj9Zl2HC4oZ391VKZar1aQavVSbRRw8j4IL8bVWO7nX1VJhrabUQZ1Tz3QzHrir27q1dPTuJP541AeRQX7w6bA3vlLmRNhThkWna6krnt0zqSQ9W8d4GBSGsJqIMhdpRULaa/NBVLpeg8bogeAVG5x/2zGET0axU9pK/ThkLJXFkQJIEtYviGjztdHrYUVTFBXoS89TCog2kNzqFMSGBc8pCNRBj+12h3WkqhZrcUhReRKf3bWgohKdJcpZaiVMzVhVI/jwuiR2JMyu86RVmThfP/t47WDicAI+KCWDI1GZkgEGFQMzI+mEhj7w8HzRZp7q0z20gM1ZEeaaC4oZ2Klg6ijRrCDSoO1rWjV8sZFR9MdLCWZoud617ezJ4q7+0uO9rAGzdP6fNzhhmnzTX65MoiiurMXDkpeaCHMiAU1pp5bUMpK385eyiKUEP6Ol17sI4JysMomw6CTIY9LJcdzgSmZ8YM9NAGNeaaIule4bJDdB7G5DEDPaSjMbiv0dYKqNkJjg6IyoHofMkF92h4PFC3B+oLJKPd2DFSVM1PWJsebmhnd2UbNqcbg0bBbz7ag8kmiScXjY3nzLxomi0OIgwqQjRKMmKMRPR4ziprtLC3xoTL7SEnxkh2TBC7KlpYW9SE1eVGrZCxvayV+XlRXDX59Jzvj8KQm/xPNMcTAXMX8DyQIwhCFVACXH28HyiKYt2R/wuC8ALweWe7HbB3/n+bIAjFQBZSlMxpz+GGdp5YUcS28hbOGxXnUzLt4nEJ/GFRHsE6KbyttcPB3744wEc7qrr6/HFRHplRRtYVN3Hx+ATOGxV3VPHF4xGxHVpN2LIrpFBV4IyIPN659H/kquoxvnOOJKCA5Aly6WvShHgs6gtg6QVg7jTiUxngus8gftzx/UACnBqqd8Hr53lDPfURsORTSTgbhigVMiY71iP/+LYuQ4+whEmoFv4XGLICzOlD0yF442JJhAFQaODMP8E3v5Z+n/Mfhil3Yq4uQP/eJci6zUPtVyzDkDYJAKNGye/PzeXb/fW0WOycnR/Lgx/u6fqY+XnRPHJRvp/w3W5z8p9vD/JGZ2pSWoSeC8bG8Z/virr6nJsfS5vVydpDjSSEann9xkmkRRp4/PKxvLOlnO8LG5idFckVkxJPF/HltGJPZVv/UtWGKVnRBqwON3urTOQnBA/0cE4rJssLUb5zFdgls3CdIYoJF78OBASYvjBXHkD3weXIWztN1xUaLFcsQ58xbWAHNhRpLoW3r4CGA9JruRKuWQapM49+XNlaeOOirmcRInPhvMfhrct816bXfgoxx16bFtaauOqFTTR1Gusb1Qr+fnE+T608xLn5seytbuPON7d39f/ZvEy+3l/LfWdmEaKTqikeqjdz7UubqWmTqihqlXLev30Kqw828u/vDnYdu2RKMvNyA6XUA/hzPEXMq4BXgL8C7wDfAdcd7wcKghDb7eWFwN7O9khBEOSd/08DMoHDx3v+4UpRnZmPd1azKD+Ol9f5hqp/uL2Sg/Xe6hsH68w+4kt8iJYOp4sxSSH8YVEel09IOGaofGNTI8a1f/FOeICicT/5YgGGH/7oFV9AqnxUs7Of38g3XvEFpIpJm57zPV+AwcP213zzbC2NsP+TgRvPSaa9+iDy5X/0cVMVKjejbdx7lKMCDBpK13rFF5BKTBd+CckzpNcrH4LmYoSib73iC4CjHfnWFzBZpMVUQa2JX76/m8qWDhaNjuPx5V4BBeC7/XV+HjEARfXtXeILwNn5sTy1stinzxd7apiYKuWXV7ZYWV/cBEB6lIFfn53Lsjun8dtzcsmMOn0f0ocz+2tMp3UZZkEQmJERzgfbKgZ6KKcVTrsdxY7XusQXANrrUR76duAGNRQoXeMVXwBcNpQbHsdh7xi4MQ1VqrZ6xReQni9WPOR7TfbEboblf/Z5FkF0SelHPdemB/q3Nv16b22X+AJgtrv4dl8dZ+VFMzoxhG/21fn0f3ltCUq53KfK4Zqixi7xBcDqdLOnysTjK3zXCq9vLKPOZO/XuAKcXhxPBMwnQCuwHajuzwGCILwNzAEiBEGoBP4IzBEEYQxSClIpcFtn91nAQ4IguAA3cLsois09z3m60maVJh+FXMDm9PcFMFm9k5PZ5s1B1Kvk3DA9hUe+KujyL1g0KpY/LR7hF07ng6MDpcm/zJqsoxGhvc6/v7XNv603mor92xoLpcm1e/m4AAOPxw31B/zbG4v824YJCrcV2mv92gV7P6/vAANLW2XvbXFjpf+7nWA3I2/x1/a1rUWsKKxmSmZ813y6r9pEg9neNf92pzdvrO5zL4BMkLxjeuLq1lbV4l3Iy2RCwKhvGGOyOWlqtxMbpBnooQwoMzMj+eOn+/jNubmoFYH7/qnA5XKgbC31axd6aQvgRWjzFwpVrcVY7TZQn16l5H8y7fX+ba2lUjqSuo8NB0eH1Kc7hmhoPOTft59r0+KGdr+26lYrTe120qP8jZ/Ndhdalcznnl/e5C/AtVmdvXq99FwXBAgAxxcBkyCK4hWiKP5DFMV/H/k62gGiKF4pimKsKIpKURQTRFF8SRTFa0VRzBdFcZQoiotFUazp7PuhKIojRFEcLYriOFEUP/tJ39kwIzVST5BWQVlTB3mxvuXbdCo5qd121FLC9WiV0qJm0eg4XllX2iW+AHy+u4YD1b1b69icbvZWtdEgGrGPvNL//chRuHJ7VC0SBDqC0/ssy+ZDdi+WQWOXgLLvBWlLh4PNJU2sPthAZUtg1+GUIZPDuGv920dccMqHcqqwGRIRs30r4iCT4w4fXoaRw5bk6f5tmfOhbB0AYngGxa5wnBln+XVrzbmCv39bQkljOxONzay7yMUPVxqYlqBmalqYT1+VXEZ6pH8UQ3K4jiCNd1+jts1GeqTvgs6oVvgs0qamR/T57ThcbvabdbMTAAEAAElEQVRXt7GyoI4DNSYfY8AAQ4/91SaSw/X+1QNPM6KCNCSF6Vi+v5cHsgAnBa3eiDvPv+Kkp5e5MIAXZ5L/PcU84mrkukD63HETN9q/bcy1YDhKio4+UjLZFWQw4UaY/aBU2GPUpf59e6xNK5o7+KGwni2lzbRavREv5+TH+R06NT2c5HA9YToV6h72DBOSQxkRG0Rat3v+rOxIv3NkRxtI6JFdEKZXkRweEOoC+HM8Asx6QRDyj90twMlgZFwwj102hsMN7Zw/Jo5p6eFSmfsYI6/eMJG0bov89CgDr944kZwYI5FGNVWtVr/zdQ+/O4Ld6ebNTWVc8ux6KN8o7UzlXyrlaRpj6Fj8PPf+IOM91QXY8q8BmQKC4ime+xxXfmFjdVHDsb+R5Glw7n9AGwpKHcz5NeSc22f36lYrv3h3J5c9t5ElL2/mwv+tZ391IBrhVFEfM4umKf8nefVogmmc8WcaIvpf4m+oYfKo8Uy5C7LPkQSokGTERY9j0qcP9NAC9IeEiXD+01I+uEINU+4GTSiYa/GkzGLT+P9w9gsFvF4Vh2neo13zkHnq/ayWTcbq8JBt20X4G2cS/+USkpctJr/oae6ZHsnsrEgEAdIj9bx8w8RefTySw/W8csMk8uMlkbzebOfRi/OZmSGJLCPigvj7xfl8sK2SML2Kf1w8qk9zZ6fbw4fbq1j05FpufHUri55cy2e7qgPVFIYw+6pNJIcNhsW4CC4rWFvA2iwZVXNqr6uZWZG8uans2B0DnDDcCVMRJ98OKj1oQhBnPYAjcuRAD2tQU6zOo37uf0AXDgoNrePuojbxXKyOQNr8cRM3Di55BYwxIFfBpFthwvWSiW5fyGQw/ka44BkoXQM/PAor/yJFu5zx2661KQv/DileL5k9la0sfmot172yhUuf3cBvPtpDnUlKGcqNNfCzeRkEaRToVHJumJbCuKQQbp2dRp3JxoMLc0iP1CMIMCMjgnm5Ufz2471Y7N7f+YSkUP524UhCdEo0Shm/PCsLQYAbp6cyLikEgFHxQby4ZAIJoYNhzg8w2DhmCpIgCHuQ7swK4AZBEA4jmeUKgCiK4qiTO8QAAFqVgnm50WTHGLDY3VwyPoEOhxujRtFlCtWdyanhvHvrFNpsTnZXtLK6qLHrPUGQomR6cqi+nb98cYAbxgaRs/U+aD4E0SNh6t1ga+Ngh5EfSsz8UALvxV/KwxffzteFbby+3ILZ3sED7+/ms3umExN8FH8ZTTBMvEl6wBXdEBR/1Ml3e3kL3xd6hZ2GdjvPrCrmX5eNDoQunwKW7rawbNckbhj9Li5R5JXNDm7RWLlpmHqK6dxt8PkvQGOA6feBuRbh6/9Dvvg1iO2HyXSAgUVtgLFXQ8Y8qbqRMRbs7Tjyr+CxdU08+1kdogj/Wl3H57F5PHvZchpMVh7b1M76w9U8dX4ihm+XgNMbaWfY8RzJyfOxOQ3cMTudnBgjMzL6jloZnxzKGzdPxmR1EaZXoVcrePba8TRbHARpFRjVSsYmhqCUy4g6SipKSYOF33+8lyN6i9sj8ptlexiTGNJrmHSAwc/uylYSf8puqNsODQfBVN3pm+CRHmQUWqk6iFIrvUYAj1MSVmxtksjSXg/mOrA0SMILSCIlguSVJJNBUIJ0z0+eClF50q7zSWJSShhLN5ZS0dxB4qAQpYY/jt0foSr+WookcDsRdizFIQ9DmxDYW+2LwmaR235I4obRr2NUwtL9TkZ0dPCvtONxcAgASPPTyIsgZYZUUcoYC/J+/BxDk2D3u74pRhufhnP+DXdtBgQI9ka1dDhc/PPbQlo6vKnDX+6p5YIx8Zw1Iob91Wbe2VLBJeMTkMkEvt5bS1WblSevGEtCmJbrX9nCIxflU1RvYXtZC49+XQjA48sP8tRV49Cq5Bi1Sq6aLBnsujygEGDx/9bRYLYzJzuKu87I4FB9O/2tNBzg9KM/M8iikz6KAP0mIdQrnIQfo2+wTkWwTsXvF+Xx+4/3srGkmVCdkofOH0lurP/ubb3ZjihCbqiIfH9nfmXdXukLMMzIB6IB2Fll4ZuqWP632RuN0tBup6XDeXQB5ghBscfugyQK9WRrWQvtNhdqQ0CAOZm4PSLri5uobLHx8Bqv2diWkiZumpE6gCM7eQh2M/LGAulFpbcAm6wXX5gAgxhjt6oe2mAabEqe33Sgu7cyBTVm1tUr+XxXAxsONxOiUzIhWoaspcTvdGprPZtKHGwqaWZaejiLx8Qf9eODtSqCtV5hXK9WoFd7b7fx/dgRa7TYfVJHAewuD4195KkHGPzsqzJx44+ZO9sqYNc7ULFJ8j/QR0oPMwiS0Oh2SF8uuyS8iIBcDnK1FGmq1oM6BEKSQRMCmqBO8aUbDiu010BzCax9HBBh1GWQfuZJ8WdTKWRMT4/g3S0V3L8g+4SfP4AvHR0WDNXrJZPy9U92tatrtw3coIYAZc0dNFkc/Gu9N2rc6YEOu4vgXjY/A/SDo6Uc9UXpav+2g1/BpJv9ms02F7sq/CPlK1ukbICSRgt1Jjsvryvtes/u9GCyuciPD+HhxSPZW2XixbW+a4Ht5S2YbU60Ku98GB0kPe8crDV3me2uLKhnZYGUXnn+GP90pwABoB8CjCiKgRjRIYi9vhibuRG3IY60yARevG4C9SYbNpcHp1vEbHOhVsppMNuobrURrFWSHKZFJZexvgbOj5uEqnqzzznrFTF0D1PW9IhASYvQEX2kbGpbJW5zHSYhmFpZFAkhWoza4zeXzI/3z7NdOCKm16ifACcWuUzgnJExbCtr8WmfmxM9QCM6+cgMETgTp6GsWO/T7g5OHqARnQZ43NB8GKyt0k6+XAZh6f4PiD+SA9VtNFkc/ObsHB5fXoTZ7jXEy4g08MKSCTSY7dicHmyiBXfcBOTVW33OYdUnAlIFhLNHHn/J1to2K3UmO2F6Vb93+2ODtehUcjq6hboHaRTEhvRD4A4w6LA63FS0dPh5BBwV0SPt/O77GFKmw8z7pUiXk4FKC2Fp0lf6XOlBveBL2LsMZvxMKv16gjkjO4p/fFPAfWdmopCfvGibAKDT6TGln0tQ9XafdnvyHE5vS+ijMz4phPz4YM7IiURA4ECNibRIfcAs/UTTXi8Z5muCpTnIXCtVTNWGQVgK5C6Gkh4iTE7v8QGhOiXzcqP4eEcV5+bHkhqpRxRhRGdqcG4PH02A+XlRhOiUWOwu8hODCNIqeWV9KeeNiiMxTIsIeDweQvt49og0qsmKNnCwznfTODGQfhSgDwIxdMMNlwPHnmWov/4larsZghOpX/AsypRJbK9o5Q+f7KPD4SY9UsdfLsjnVx/upqLZik4l56HzR/L8knH8/N1dbJ33GybbH0DeVAQKDY55D/NOUTDQilYp54/n5RGqU6JXybE43MSHaPn3ZWMIM6jh8A/w4U3ILQ2EakNpmv5vHixN5oEFOaRGHt/O7dikEO6Yk87zqw/j9ohMSgnj2qnJyE9zE8NTxYJOAebLvbUIAlwyLoGZWX2nXwx1QkPD8cz9LeLnP0NoOgQKDeLsXyELSxrooQ1P7GbY/jqs+LO0ex+VC2OuhrbXYOYvpN3+H4nL5eHzPdX84dN9mKwuIo1q/u/sHB79ugC7y8P9Z2UzIi4YlULGnqo2frNsLzanm/fP/wNjbPchNB8GpRbz7Id4ar8aMLN4TNxxC5CbS5q4+60d1JvtBGkU/OOSUczPiznmHJYSruPpq8fx83d30tLhJFyv4vErxpAUSNcYkhTUmkgI1aLsr9DgccHqf4KpSkoD1p5C009BgLBUCL1RioBd8RDkLIbRl5/QtKTEMB2RRjUrCupZMOL4hc0Ax4c2cw5izQaEQ8tBEBBHXoo6LpB+dDRyYoMYlRDMUysP4RFhcmoo5+ZnnvZG2ieU6h3w/vWS6KvSw/yHYf8nUPIDqINg8RPYU89EyL0Y1YEPAejIuQRb3GzCejmdSiHnjjnpjE0M4d2tFXy2uwalXEAhE8iJDmJMUgj3zM3gmVXFuDwi45NCuXFGGqWNFn75/k52V5qYlxPJk1eM4b8rDvHxzipUchn3nZmJ3eVGqfCfA0P1Kv55yWjuems7lS1HnqlG8P/s3Xd8ZFXZwPHfmT6ZzEx679ne+7IF6dKrIGIFBF6x967wqqivBRsqiiKigFSlSu8sLFvY3nez6b0nk+n3/eNmk52dyW6STZkkz/fzyQfmzL13nsne3PLcc54zI1N6q4rY1EQfn7Zs2TJt48aNJ15wighWb8X0l9P1J2e9wklFHL7scc68c2df22WLctle3R41HduTn11LUoKZ1m4/RfYeXL5a/QCYUkJTd4Dqth5cNhNFvbMuVTR7aOsJkO226fUM2irgT+/rH2MOYLbz6Ip/scuXwXcumD20E1fQT6j1MJ1+aDBkkJnkwD2MnjSjaFBfZiLvpz3eHryNh1FKYUsvxmYdmZ4J8aizuYbEx29EpRTo9Ym0MOz8N4H3/xTzrHPHO7zhit999PBbcM8xs04VnaoPeVh63UnNuLW5opVr/vwOvt6Zg5SCq+Yn883VTgyWBJxZJRgNil017Vz0uzf7aq1YTQa+tDqJD880gM2FOa2Uwy1eQKMw1UGCZfDPLWrbe7jkDn1c+BFmo+KZz5/K9BhFfGOpavXQ3OUnzWkhN2nSJl/idx8dIfe+fZjX9zXyybUlJ15YC+vJl+5GWHiNXgh/PHk7YNsDkJgNp329t87MyHh9XyM7atr5xycnRHH3Cb2fhl/6IQZvGxSuBTTY+wyh3BUYT7lpvEOLW8/tqOV//hnZa+hTp5Xw9XNnxWsSZmLto55W+PuFUL8zsv3M7+rFdgEMRpo++hLfeKmDj80yUJykqO7S2NyZxI1nzolZD9IfDPHNR7fz2HvVEe33Xr+C981IJxAMc7i5G28wREFKAjazka88tJWnttUCcEpJChajIaJ+JsADN6487syFDZ1eao+MKkhNQB2vwPDUNuV/MdLnc5IJtZZHJF8ADG2HMXZH1rDIS7FHJV8AKlv1gngL8pNwpWVD7hJImwYGA2lOKwvzkyhOT0QphVKKwjQHC/OT+otJtldHJl8AAj3kqGae21lHe08g6jMH1F4F//06xj+eQtLf1jJj7524wzID0pjqqMH+yq0k/20tSX87Fdsb/6d3FZ2kzJ4GVMWbsOV+/QbojV9CyyEMbTISc1TEqLdC+Vv6bAnl66LfG4KqFk9f8gXg9+938r/e/yPlnrUk3Xsmxm0PQKCHqtYeji614guG+enrLVTaZ+HKnY3damF2tovZ2e4hJV8A6jt8EckXgEBI6xuLPhh5yQkszE+azMmXKWFLZRsFKdHF72Pa+gC0V8RH8gX0mjFLP6kX9X3+e72zJo2MlSUpbK1sizlboxg53u4ODAYTeNvgsU/Cv/8HrE4Mvo7xDi2uba5oi2r77446Or1DuJYVA+uqj06+gF4Y/IhwCK21HCNhZvRsovDJD7L6+Yu5ruFnBJsOxdxsqyfAy3ujr1X3N+hDic0mA9MznczPTcJtt9DS7eeVPf3LL8hL4p1DLVHrlzd7otqOluG0sTA/iaI0hyRfxHFJAmYchMIa1a0eGjq9J154iAzOGN14HWlo9sipTtu6/WS6onsypDtP0LvB0wKt5QNfgDnSeosDAu58WPUZWPUZvLZMlhcmk2gdwg3Mrsdh09/0GhFBH7z6E/3mTIydvc/C+jv7Cz2++Ut9iNkkFbC60dJmwOKPwVX3wAW/AEc64eEUjRMnlhjjeJU5D5oPQPZJTLDXWceKFA8zM/SkxapCB6fV3IW94lX9fW8b/OdmqN0a85iX6rAMONZ7QJqmJ407avqakhPMOI855ikFGSc6zopJZ3tVOyXpg0jA1G2FPU/Dgg/HR/LlCKMJ5l8FZhu8dKt+PhgBVpORVaWpPPhuxYhsT8Rmc7jQlIKqTbDyZn0mpL3PgD1pvEOLazOzonsqrihKIWEo17JiYPZkvbfxEUpB6VmQvQjmXt53DFSJWXxmRhs5624BfzdoGo7ucuyN2/TZ3Y7hsplZmJcU1T5QDTaXzRxRc7KsqTvmv31qooXqth7C4Yk9ekSMP0nAjLGath5++t89nPnL17jwN2/y781VdB9VFPJkmbPm4H/ft/objBbqz7gda2o+N5/W3/X5vzvq+N9L5mI9aizjZ86YxuwYBxxAv7k49DrcfS78dhE8eiM07o1eLqUULvoNzLkMZl+k13d4758s8G7gq+/LiDl2MqZAD2x7KLp9/wuDW1+cvHAItj8Y3b77ybGPZYw4M4rRLv4t+Nrh0U/C23egnfsTvOkLxzu0ySlnoZ7sOsLqgkUfhp52fSjSUPm69N5Ld64h6961/GfmS3zlFAcXFJtwHHwmevnmA8zIdPK5M6f1NVmMBn502TxyhlLstrNe7zH1+xXwx9Xw7l3Q00ZhqoP/u3IBpt6u6krBty+YLbMYTTE9/hAVLZ4T1+8J9MAbt+s3HrbBDVEbUwYDzLkclFHf34/pbTtcZ8zM4MGNlYTkpmb0BP2oYA9MP1t/sLXtQb2HVU/0U37Rb2VxCmtK++cczXJbuX5t8eBrOYnjc2bCpb/XH9waTHr9l3AAHvoYNO3TX5/7U0xZsyn07ulfb/XnIXUahv/8D9x1Bux6AgL9vU3tFiNfff8MkhP6k9jnz8uKmZQBfabCb10wq6/Ewct7Grh+bREuW3+i7eIF2bywq56zfvkqt7+wl7p26bUnhk9SuGPssc1V3PWG3mWuscvHlx7ayv032lh9nDGFsbR0+TAaVNQ0eO0hCyy9mVDJWQQ7Gwi5CrCmTSfJYeOzZ07nvHlZNHf7KUxxUJyWwNOfX0tFi4dUh5XpGYkkWE2xt924G+6/Uu+JArDnSfB1wNX3RV4oGgww7wr96dgTn+1rTn3zVsgqgpzLB/cFjVZ9GELtlsj2rHmD/h2Jk2QwQu5yqHgnsj1n0biEMxYCnnZM634He5/WG1oPo/7zP9g+/G/InJxTb48rRzqcexss+bhe78Lm1ve7+VfpvemGqnqj3rOll33D77jpjBQO5F2BdnAGqnFX1OfXtvew/lAzXz5nBv5QGKNSPLKpgtXT0jAbFV3eICkOS98sLR5/kC5fkFSHtb+Q7r5n4ZXbejfaDc98lZAzh5a8szhndgZPf/5Uqts8ZDhtTMtIxGYe+Wl9RfzaWdNOQUrCiW/a3vsHJBVBehxPy2wwwLwr9Zv4zf+ApZ846U0Wpuqzyrx5oInTZqSPQJAiitGMZktCvfnr3oZuePNXcPFvxzOqCP5giDZPALfdjDVOjpG5yQn87sNL2F/fiT8YpiQ9kdyhzGQmTqzkdPifN/RrgKe+rN9vgD406ZXbCH/yJfzKSkpWqd6eMVsfurRDL8hLR7WesLn+eSjoryU1Py+JR29eTWWLB3eCmQynlUzXwHN+LcxP5snPrqGsqRuH1cT0DCdLPpdMTVsPCnjzQDN/fO0gobDGHa8cJCXRyvVr5LpQDI8kYMZQa7eff22ojGrfeLh10AmY5i4fT26r5c+vHSTBauKr75/B6TP1C5ZX9zbyi+f34Q+EuOWSufxnu5G3DtaxujTAZ8+YxqxsFwvzI4ciTctwMi1DT6A0d/t4ZHMVf3rtEHaLka+co2/bbjFB04H+5MsRZa/pBz7brMh2oxm2Pxwd/PaHYd4gEzAGAyy/HnY/Dp5mvS11OpSePbj1xchYdA1sf0g/2QEkFQ449d+k0FqO2ndMT4lwCEPLPuC0cQlp0rO5IX/FyGzr8JtRTdat/0Dlf4BDy79P6XMf7x86Mf1cyF7I4UoP7x5u5d3D/bWr3HYzO6vb+f2rB9hT28lFC7L55NpiGjp9/PL5vexv6OLSRbl8fFUhhUlm2Pz3qM/1bX+cz76WwrLiZK5ZUTCpp28Xx7e1qp2StBMMP2qrgIMvw5ovjE1QJ8No0ntPvPNHSJ8OBatPepOnTk/jgfUVkoAZJUGfB9Oep6LfOPzGiCTRTta++k7++MoBXtvfxIriFD5/5nTm5ERPFzweUhwWVpaknnhBMTxKQdp0vQfrkeTLEb5Oqsr2cMULFfzwtELOzV2KIW85bLkvejsNuyMSMBsOt/DcjjoSrEYe21yN2Wjgy+dM54xZmQOWQyhIdVCQ2n+s7vAGeHVfI49tqiY32c53L5zNH149SGOnj3+9W8EHl+aRKFOSi2GQBMwYspmNFKUmRBVgzDpORhb0mjGt3T7cdjMv7Krn1if0glVmo+Lh9QcochtoCxj5VG+l9g+vKOAHT+3qKxb11LZatla18einVvcXy43h5d0NfP/x/mJYN9+3ua9iOLakGF8oCcwDdKnOmKMnaI420FO9gAcw6GPLj5Y1H254UT+oGkyQORfceQPGL0ZB5lz9qULDLn360cy5kJQ/3lGNGs2coNcl6ayNfMM6hlPAikj+bv3v33T8uindviB2V17UuNpQcgk/f6kcd0Iyn7jwaWztB0lLSSG9dAkkpuO0NUdt6/q1xdx470a6/SEA/v52OYsLknlnz2E8vgBNXX7++mYZjZ0+fnHlPCzps6F6U8Q2OhIKObivi/WHW+jyBvneRXP6etGIqWVzeSvFJ6r/suEvUPw+fRrWicCaCAs/BG/9DlJKYtdzGoLVpWl86cEttHn8JA21/pI4IYPJgpZcjKqKnPlGc+eP+3QkzV0+Pnv/ZvbV6xNDPLujjm1VbTx682qy3XHS2yTo04dlW6QY+qixJurneVuS/lCmvQpq3qO820RTl5+bn27iq6t/wE2zFJay1/R7g6Ml9D9g3lPXwQ1/38D1a4r51Yv7+9o/98AW7rluOafPPHFdP38wxB2v7OfBDVWAPmphd20HnzljGre/sI/StERMRkWnN4BTkjBiiEb1alApdbdSqkEpteOotluVUtVKqS29Pxcc9d63lFIHlFJ7lVITds7XgdgtRr5w9oyIuitFqQmsKI41k71uS2Ur33x0Gx+6az0PvFvB398+DMDCbDtPne/lTu02Zvz3GmY2v8IZxfqJKs1piarUXdnSQ1lz94Cf4/EHuWfd4aj2J7ZW88LOOjwpM6N7Ppz3E0guiL3BBVdFJm0SUvRx7UfzdeqFdu+5CP5xuV7f5djiviklMOtCmHGuJF/GS0oRzLoAZp43qZMvAPsD6Whn/6/+RKaXVrCKFvfscYxqivK0wpYH4O7z4f6r9eLPoeh6WU1dPh54t4Ir71zHW6FZaEmF/W+abDQv/wrfmNPGz7w/YuG7X8OGj78cdNFt1i/WZmY5uWhBdsQ2M5zWvuQLwLdWOzir6Z/8tOXL/DPtXu6/yI7DYuTJbTVUtflgxQ1gPWooZmIGWxJW09Sl97Z54N1K6jpGvui6mBi2VLYxLf04NV3qd0DrYcg/ZcxiGhFJ+Xqtptd+pt+cnoREq4kFee6+aWDFyPIEFYGFH4s6TvUUnzN+QfUqb/H0JV+OqGnzcrhp4GvWMRMK6Oee+6+Gu8+Drf/Sz01iZIUC+hCkS34Pcy6Fpv3gzEa7/M9s6urvffSLde280VMK5/5YH658RPYiyFnc9/JAQxdzcty8ccw00gBPbK2Jaoulrt3HI5sip7H2BcMEw2HsZiMfW1XID57cxeV/WMcvn99L+XHusYQ41mj3gLkHuAO495j2X2ma9oujG5RSc4APAXOBHOBFpdQMTdNO7qweZ5YVJvPvT69mb30XdrOBuTnuAatyV7d4+Oz97/X1mHl6ex2pDv3J0I+WdDHzxev6lk2uuYFvn/kn3qxwYRxg6jP7ccbUmg0Gctw2dtZETknosJr5+qPbuOXiuVx24e2w5BPgaYSUacefpaS1AlbeBCj9ZjYcik6uHHoNHvp4/+v71sHHn4ASGeohxkeKFWgpgzO+q0+DaDSjOutJ1mT68zG39xl4/NP9r8teg+uejehiDPDElhp+8JRe2+W6xxW3nvo7PpDTgl0FIGMOxpZmZr34cb2QOFBc9wWuO+f3mIz6sKdObxCHxdhXA8ZmMgL9xUBX5Du4pudfODc/AICzcQ+rK17m1rV/5wdvebGYDJC+GD75AtTvpMuv8UxTOt97uf94504w68uJKae5y0ebx0920nF6um6+F0rP0If2TDRFq6Fpr16PYcEHT2pTa6el8dDGSj56SuGJFxZDYjUq/dpt1Wf0Y6FSEApg9rWNd2jYzQaU6jtEH9UeB3VgqjfDPy7rLzj97/+By+7Uh2eLkVO9EV68FVJL9UL6AI17UOXr+MQl9/Drdf2LWs1GKD4NbngZGveAxanfjxz1gDDBbKSjJ0BujGL6LquJHn8Iu+X4+5fFpHDZTLR6IqccL0hJ4P4bV/Ltx7azu06f1vp3Lx9gR3U7v7tmsQxJEoMyqmd7TdNeV0oVDXLxS4F/aZrmA8qUUgeAFcDboxXfeFBKMSfHzZycEw9p2F3XGTFc6d3DLXzvwjk0dPoorI2eIaj44D9ZUfhN3jrYzCULcyKyvNcszz/uzBtmk4Gb3lfKq/saCYT0s6DbbiYt0UKrJ8BdbxzinDmrcMx4P6DP5vTixnqe2V7LiuIULlmY01dLBn83vPXr6AK6aPqUczseg55WaNhBlB2PSgJGjJt03yHUm7+MmmLVmDkXiteOU1RTkLcD1h1THFILQ9nrEQmYpk4ff3r9YN/rYFjju691wWVL9Ju4ljJSyv8RdWWfueMu1PLLAQcHGrp4cGNVxPtXLs1jaWESm8rbuHoGuNYdMxtYTyuzjVV884K15CX3JtAzZkPGbNpaPfz19Q34gv0zxHzvwtlkOI8/1PRE2jx+3jzQxIMbKilMTeCDS/NZkJ90UtsUo29LZRvTM50YBngwQt12vcbWwg+PbWAjRRlg3gfg7T9AwSmQNECv2EFYkJfEn984xOGmbopOVDNHDIkW8mPedDeUvxXRbl74YZhzwQBrjY3itESuX1PMX98s62v7wJLcuJgtTjv0KurY2b7W/Vaf5dMahzOVTSB7ajv493vVVDR3c3vGi9gXXA3//VrkQt42nO17KE2fzcHGblaVpDAzy6X3fslZNOCkELNzXBiUYmVJCq/ta+w7HzutJjLdNipbPczIPP6/X5bbzncvnMNXHt7a1zY328Xq0jQONHT1JV+OeGVvIxUtnkHd3wkxXo9bPquU+jiwEfiKpmmtQC5w9HQrVb1tUZRSNwE3ARQUDP9kH++OnbJZ0+Avbxzi9g8uxLw3OWp5Q0Iy184roabdz6J8N5cuyqGipZuZWS5mZDpxWI7/z720MJnHbl7NG/ub6PaHsBgVd76q39y4bea+GT+8gSC3v7CPRzbpNy3vHGrhya213H/jSn28rjLq08keK3M+PPhR/WlZ7lJ9WNKxbJPnwDVV9tNJRZn06RCPScBgnJw1CeJ2HzUMcAw5pj6GyahwWE1AZIFwm9kAnQ3w+GdQmXOjt2Nz68cpiDkzzeNbqvm/DyzgfdPTKU7r1selH9ODrzAjmaIZ0aeovOQE/nbdCt6raKWuw8e8HBcL80/+uPb09lq+8289af3GfnhsczWP3byaWdnxUahytMTtPjpIm8pbKT1e/ZetD+hPcw0TuIeUPQmmnak/eLngF3pSZhiMBsWqklQe21zFl98fxzNBxRDv+6lShtjHVHvSmMdyLJvZyGfOKGXNtDQONHRSnJbIonx3XNTVCBjtHHv2D1tcGAxx0DtniOJpHy1v6uajf11PU5cft91Ma4oFuztfnwE1HFlCwWCycsPaYtwJFhblJ5HuPH49OIBst53ff3gx68ta+NmVC6hp68EfDKOU4i9vlHHu3MHVrDp/fha5yXZ2VLeT4bKxOD+JnCR7VJkHAIOKfT0hRCzjsaf8ESgFFgG1wC9722M9HtJitKFp2p81TVumadqy9PTJWzF/Xo6LFcWRiZaVJakUpiZgX3h5ZFFKgxHDyk9xztwcPrG6iIX5yUzPcGIxGvnPe9W8ub+JPbUd/H1dGV9/ZCtPb6uhqSvypsVgUMzPS+L0men8853D/OrF/XT7QygFnz6jtG/q1PJmD49ujnxiXNbUzYGG3jG8Zhuc+uWIOhpYHHohzaa9+uuazVByRuQYTpMV5l52Ur+zeDJV9tPJxG9NgTVfjGx05xNOLhqPcEZd3O6jFgec9vXINqtLL1J6lKQEC1895kYtOcHMwrwk/VhT/ha4ciITN0qh1n6pr+j3jKxEFuRFJkg+ubaYF3bW85uX9vPd1zqoX/T5yFjSZ+MsWBg1k0JDp5cntlTz6xf30dLt5+zZGawsScVmjp383lPbwe9fOcC3/72dV/c20OWLrnED0Njp43cvHYho8/hDbK+e/EPj4nYfHaQNh1uYNtCT/OYD+uxH2YvGNKZRkb9CL1S677mT2szq0lQe31KDdux4lDgX7/up2WTUa7kdnRwz2SLqZoynFIeVM2dlcNP7SjlnTibpJ9ljcKTUJK+I7OmiFJXzP0N3eOI9lImnfXRPXWdfjbT2ngB7E5dB80FY9bnIBVNLMWTN55qVhVwwP5ucGEOKBlKQ6qAoNYG73yzj1y/u5/evHOT2F/Zx9fJ8CnpLP4TCGpvLW/nJf3fz42d2s+FwC/94+zBff2QrT2yppssb5JSSVG44tYRLFub0lYyYkZHI8qLI+7NrVxdRkCpFmsXgjHkPGE3T6o/8v1LqLuDIvHhVwNEVPvOAwVVKmqRSE638+PL5rDvQzI6adhbmJbGsKJnspARwL9HrIRx4EYJ+mHEO5CztW7epy8eXHnqPTeVtADy0sYpLF+VQ2dLD5opWHtpYxadPL+XL58yImpljTo6b+244hdf3NdLpC3LmzIyIJ7hqoK7URytcA9c+A/uf18dnTj8betr639c0ePfPcNat0NOiz6Y07ey4uRgQU5MWCoCvAy79A1S+A+58SJ2Gt60J6RA/xore138MsSdD6ZmQNS9qsTNmpnP/DSt5ZW8DmU4bp89MZ1qmEw73Hqfe+g2c+lXorIGAF+Zcph+femU4bdxxzRLWHWxid10Hq0pSWV6UQqcvyKkz0thX30XjjGtILV6AqfwNfTa34tPBHdn7xR8MceerB7n7rcOAfsydn+vmr9cuizn8aH99Jx+66x3aeseX37++gl9dvZDLF0cXGzeoyHz2EeM9e4k4vkAozM6aDm56X2nsBbY/DAWrJmbtl2MpA8y+BDbdA4Wrh92btTQ9EX8ozI7qDubnTZ4esfEgXP42xrP/F1oO6Q+/3PkE6/ZiPk45v6nupdYMSlbdw+zuDZj8HVSmreXv+1O5bdF4RzaxHXs++9KrIdadayWhoxou+R1UvgtJhfrQ3qSiYX/OwvxkvnfRHDYebqG8xcMpJamsnZ7Wd9/zXkUrH/rzOwTDGh9cls/3H9/B7lp9aNFDG6u4dnUR37pgFlZTZI+nVKeVX31wEesONbOzpp2VxaksL0qOWk6IgYz5WV8pla1p2pEy95cDRwqBPAHcr5S6Hb0I73Tg3bGObzz5g2G2VbWx4XALLpuZlSUpTMtw9tdWOZpSkLtE/4lhf31XX/LliCe21vCTK+azuUKv4H7XG4c4d24mC/OjhzPNy3UzL7f34kfToGaL/jTZYKK0YBXXrS7qu9EAmJ6RyIyj4zRZ9IuwwtX9bZ11kDEXGnqnum49rE8jd+HtYImTqQbFlNaoUvHbC0juqgdn78w4jbtpzLlYEjBjzWyFojX6z3HYLSZWT0ujNCORzeWtPLmtlrk53azOLiExZ4ne2+6l/4WE1N7kyyr9+HSUgtQEClIju2SnJlopSj36X70Y5hwzE9xRyps9UTPJba9uZ199V8wEzLaq9r7kyxG/fH4f75ueTmpiZBfr1EQrXzx7Ot94dHtfW2LvWPamTh9WTx1a1XoMzfsIZy7EULCCxOTMAWMVY2NXTQcZTmtUTylAr/tS856eHJwsXNl6McxNf4vuSThISilOKU7h8a3VkoAZQb4wNBdfTk7XTkjM0K8htSD12ach80sObHlxClfduZcEy1KsJiN1HV7u/Ghh79BXMVyzspxkuqzUd+g98dt6AlQ55jNj9y2w9X7IWaLXfFv7ZXAe1Vsn6NePmxVvE7I4aUxZxsPlCZRmJLK0MJlMV+S51mIysKwohWVFsWebfWRTFcGw3tsuJ8nGQxsj67rc+/ZhPrKygOm99WJ8gRBbq9rYcLiVVIeF5cUpfHDZ5J4dVIyOUT2CKKUeAE4H0pRSVcAtwOlKqUXow4sOA/8DoGnaTqXUQ8AuIAh8ZrLNgHQi6w42cd09G/rqRaY6LPzrplP6/vCHIhQOR7Vpmt7dbnVpKusONhMMa7yxv4lEm5nS9OMUO6t6V58qurcuhtGcwDc+9hQzsxbw3M46lhUmc968LDLdJ+gy6syCD94Le56CinUw80KYdpYkX0TcCAGEw/oNe69w6gwCuZcPuI4Yf53eALc9vTui8PjHTynk+5f/GdO+/0L5mzD9PJh+TlQdmZES0jTCMUZNhELRx2KAQIxjtC8YJjzA0Ivz52Xhspl5ZFMVyQ4L09IT+cx9m7nt3DzO3X8rlkPP9y3rWf45Amd/B7NVjq3jaWN568Dn711P6LXQzPEx1GLElJ4Fb/0KmvdD6vRhbeKU0jRuf2Ev37lg9uB63IpBUIQwwiu36VP+AlgSCVwcPaGD6BcOh/n6eTPZVtVOly/ItauLTlhPUZxYQaqDez+5kv9ur2VHdTsXLcghtSgNrn0Ktj+iz0a55otQfGrkiodfh/uuBE3DCGQ50iha/Bc+fZ+Hyxbl8MPL5g2pdlD3UcN+Y516w733TUe8vr+JG+/d2Pc6y23l/htOoeR491BCxDDasyDFmqftr8dZ/jbgttGLKH51+wL86oV9EQeA5m4/Gw+3DCsBU5qRSEGKnYqW/sKRq0tTeWFXPSuLU1h3sJn3z8nilb2NZLltAydgNA3evUtPviSkwMqbIRzEWrOBq6edxdXLlw8YgzcQYmd1Owcaukh2WJif5yY7bRqs/SLwxSF/JyFGW3KomeR9D8NZt0CgG4wWDF0NZHn2A3PGOzwxgAMNXRHJF4B/rC/nQytOZc6az8Oazw+w5ontr+9kZ007GjA3x63PnFC/C+q26U+RsxZCxiwKUhK4YF4Wz+yo61s3J8kWuwcjMD/XjdVkiJgt6ebTSgesfeCyW8h02Wjo9LG3vrOvCHpKz8GI5AtAwqY/0DH/g5gLZGzBeHr7YBOzsmIUPvV3w4EXYNVnxz6o0Wa2wbRz4J074cJfMJyBcvnJdqwmI+9VtrGkILqHrhg6k9LI2nNvf/IFwN9Fds2LMP/4vQynsrcOtpDmr+LL2ZVYCLBHK+Afb7tZmOfGlTDx6sCMKl+XPvtp80G9l1XOEnAO3BNzZqaTmcfe3zgXQvbCAbbfCa/8ODJT0t3EQm0XVlMJ/9lSw7Wri1hUkEy3L8iO6nYONXWT4bQyP9dNxjG9Y7p8AZYXp/DkNn1QRqc3QI7bRk27t2+Z8+Zl9dV1aff4+emzuyO2UdfuY2tlmyRgxJBJGjdO+IMazd3+qPYOb+yijCeS7bbz86sW8tCGSvbUdbK0UB+beNcbh1hdmsr1a4oIhjWe21nHxQtzBt6QFobO3rI9p34FXr4NAr3VvxNS4RNPQqxZRoDndtbxhX9t6Xt9SkkKv/nQ4qgugkLEjVAIZpwb0QOG7EWYS84dv5jECR2dxDhC08AbPLlOlDtr2rnmz+/0HYedVhNvX5tK4r8u0y8GQa9P84knSciaz7cumMX8vCSe2lbDyuIUrl6eT25y7F4oc7JdPHDjKdz9VhlVrR4+ekohZ8zMOG483kB04V2LFn3eIBxCC/qi28WYCYc1Nhxu5bJFMSZzPPAipEyLixloRkXOEqhcD2VvRBXOHgylFCuKknliS40kYEaIpoUxehqi3+iK0Sb6vC+llbkvfhpDZzUA2UYLOefdj2+Ano1TlqbpM7o9c9SQylkXwcW/BUfqyHxGKACe5qhmc6Cr72GGNxhG0zQe3VzF9x/f2bfMuXMz+ekV80l29A/vDYY0XtnTwLcvmM0rexrYU9fJLZfM5fV9jWypbOOiBTlctCCbhN4eT75gmPZjhg0DdPun1GANMUJkvqw4keywcMPa4og2g4JlhcO/+ChJc9DY6SPdaeW5nXXc9cYhStMdaOj1YO59uxyjQbHweOOsDUZYfgNkzYeKd/qTL6AfCPc8E3O1uvYe/vfJXRFt7xxqYXdtx7C/jxCjzWazwPo7Ixtrt2AKdo9PQGJQitMc5B2T6Jid7aQw5eRmJHh8S01EEjzDZcO45Z/9yReAnlbY+W8A8lMc3Hx6KY/evJrvXTSHmbF6P/RSSrGkMJlfX72IB29axZVL86NqvxyrON1BblJkArvWlA+JkU8ZA7krMaeVDPZrilGwv6GLBIsx+t9UC8Oux/VaRJOVwQAzz4dNd/cNXR6qFcWpPLO9lnCscX1iyAxGMx3zr41q9864ZOyDmUCmdW3uS74AEPJTuvtO0uU5YqTWMnjh+5Fte56Cxt2xlx+OI73wj6YUlYkL6PAGKUyxU5zmoKLFw0+e2ROx2HM769lb3xXRlpRg4bSZGfzs2T1oaGgafPb+zXxoeR6P3ryam08v7Zv1CPTz//VrIu/TjAbFvNyBz/NCDEQSMHHkwgXZ3HrxHApSElicn8Q9161gQX7SsLeX7rTx3Ytmk+G0YlCKDy7L49dXL8JsVNgtRpYWJvH365YzP/cEhe5KT9dnK+qsjX6vtSzmKt5AmFZP9IVXpzc6eyxEvAj7fdDdGNXu93ljLC3iRabLxl8+sYwrluSS7bZxzYp8fvuhxSdMaJzIocbIC7a0RBPW9gPRCzZFttnMxkHXrjAZDVjNg5s5Idtt5y+fWM5li3LIdtv48IoC5s6eQ9dVD+KdeRm4cvEs+iTeC35DgmuEnjqKYXn7YBNzc2JcmFdvBKMFkgqi35tMUkrAkanXuhmG/JQEbGZ9GJI4eUEtzAu+ObSd+1tIm04ocz5tF/+N5zsLxzu0uGbtqYtqM3WUDzuxOGkFeiIf0B7hHeGHrvMuh/N/BsnFaHnLqbv4Pn6zO5ErluTyp48vI9Nlw+MP0ROI7pXSGWNEwUXzs/nuRXOoafPSEwjx108sZ2aWG9sA5+QPLM3jOxfOJj/FzvKiFP7xyRXMz00a2e8opgQZghRH0p02rl1TzGWLczEbDSNSZX1GpovbLptPhzeAy2bGbDIwPy+JyxflYTEN8jNsbn0a6Y4aqNoY+d7s2E9PstxWzp+XxTPb+09eZqOiNH3o9WyEGCuWlFx6Zn8A+66H+xsNJoKpM8YvKDEos7Jc/PSK+XR6g7jsZszGk3++cMWSPF7c3d9Ff0tlB55LP0zi4TciF1xw1Ul/1mDNznbxsysX0OkN4rabe6fTXIw/6066u9uxOlMxmQdfhFCMjjcONDE7Vg+oHf/Rp56eCsVlp78f3v2zXvx6GNNSLy9K5pnttSw9iZ7AQmcxGlH2JM5/Lo8vrL0bXwh+9XQtv7xKfrfHYyg5Dd68PbJx6fVgk14PEdz5ULBan2DjCIsD0oZXiHtAiZmw8n9g/lUoo4VUUwK/nRHAaTNh6Z0COi9JT45sONzSt1qCxUhJenQB/jSnlWtXF3Hpohwsg7jvynTZuPHUEj6wJA/rYO+hhIhBesDEoaQEy4j+UZtNBlITrZhN/f/cyY5hfMbM8+GcH+k1DxIz4ZI79ANuDDazia+fO4url+VhMxuYne3knutWMCtLEjAiflmsdkKnfp2eBR8Ds51Q2iy6r/oXjvwBisKJuGIx6UM+RiL5Anrh8h9cOpdUh4UUh4VvnT8LNf1sOO+nendoRzpceDsUnXrijY2gI9/TdNT3tFjtOFKyJPkSB4KhMO+WtUT3gGmrgLYyfUjvVJCYrhfU3HLfsFY/MgxJG2BmMDE0p8/I4Lq1xfz01TruWNfAN8+bzfIBpucVvXKXwwf+Aq5csLrg9G/BXJkVMYrNBRf/GuZeASYr5CyFjz468gmYIxJSwJqI2ajf3xxJvgA47WZ+csV8LlqQjdVkYGG+m79fv+K4s70mD/G+K2U491BCHEVN9BPbsmXLtI0bN554QTFyOmr12jCJxy8YCRAIhmns8uGwGHFPzorxg3qMKfvpxBII+PG11mCwJpLgThvvcE6W7KMnqaHTCxqRsyh01gHquLM8iEGbVPvopvJWvvbwVm67/JhEy7rfQjgE084an8DGg78b3vy1nqh0xyhIfByapvH1R7fxh48sYUFe0qiEN0STYj+t7/BiUAw445qIobtRLwLrzI733mvju48Ge4dxW13j3kvIFwzR3OXHaTXhtMuDiTgT139EY0F6wIihc2UPKvkCeu+bnCT7ZE2+iEnKbLaQmFE0GZIvYgRkOG1RU1jizJLki4jptb0N0b1ffB1w+A3IXzE+QY0XiwOKT4WNfx3yqkoplhcm89S2GPXnxLBlumySfBkqRzq4cuI9+TL+TFZw54178gXAajKSk2SX5IuIS5KAESMv6If6XXq9mO6m8Y5GiKHrqofKDdC4F0LDmwpexKGuBv3ftWG3/jRTiFHw8p4GFh5bQH/fs5A+B6xTcBhuwSpo3g9124e86nIZhiTGW6AH6nZA9SbwtI53NJNfMKCfoys3QFf0pAhCTAYygE2MLE8rvP17eOt2vat1+hy48m7InD3ekQkxODVb4aGPQ9thMJrhrFtg6bVT88ZpMqnbAQ9/ApoPgMEEp38bVtwYF0/qxOTR3OXjcHM3MzOPOl6EAvpsQIs/Nn6BjSejubcg75/g4t+CGvyzv6LUBEJhjZ01Hcw70YyNQoy0znp4/Wd6Dy5Ng7yVcNnvR6+2yVTnbYd37oTX/0+/h0ibAVfeA1lzxzsyIUaU9IARI6vmPXjj5/qBE6BxF7z2UwjINL5iAvB2wH+/ridfQL9xev67+s27mLj83fDC9/XkC0A4CC//AGq3jm9cYtJ5aU8D8/OSIgokU/Z67xCG7PELbLxlLdBvYA++NKTVlFIsL0rhaRmGJMZD5Tuw4S/6vgtQtR423QPh8LiGNWnVvAev/rj/HqJpn36u9seY4lqICUwSMGJktRyMbjv4MvS0RLcLEW88zfoF17Haysc+FjFyPC1Q9mp0e+vhsY5ETHLP7axjccTwIw12PAyFa8YrpPigFMy8ADb9HQJDu5laUZzCU9tqZBiSGHvVm6Lb9j6j13QSI6+1Irrt0Cv6OVyISWRUEzBKqbuVUg1KqajHx0qpryqlNKVUWu/rIqVUj1JqS+/PnaMZm4imaRpVrR6qWjyEw8O80EnKj27LWwG2yK7D1W09VLZ4CA33c4QYDbYkyIzR1dWVM+ahiBFkc0POkuh219BmZRkNdR09lDd34wuGItr9wRDlzd3UtveMU2RiqDz+IO8cbGZxfnJ/Y9UG/em5DFnQrw9SSmDrv4a0Wkmag2DvMCRxcvzBMLtrO9hb10FYenGcWOa86LbCtWAZeEpjcRJi9RLMXR51D3GyAsHwSZ1fq1t7qGzpJhiSvyExPKNdA+Ye4A7g3qMblVL5wDnAsanOg5qmLRrlmEQMzd0+/vVuJXe8fAANjU+dVspHVhYMvVJ+zhJY+GHYer/+2pEOZ31XnwkBaPP4eWRTFb96YR/BsMb1a4u4dlURmW77CH8jIYYhIRnPOb8g4ZFrwNsGgG/5zZA2B+v4RiZOhs0F5/0U7rsSenqLKC6/AbIXjltI3kCQZ3fU88OndtHq8XPFkjw+d+Y0ClMdVLZ4+P0rB3h4UxUum4nvXDiHC+ZnkWCRsm3x7NW9jUzPdJJoO/LvpMHWB6BorcyecsSMc+Gt38L0c8Ad44FNDEopVhan8sTWGqkDcxIONnTxz3fKuf/dCkwGxY2nlnDV8jxykxLGO7T4Vbgapp8H+5/VXycVwik3g1GOxaMiZ7Fec2/TPfrrhFR4/w/BNnI1+CpbPfzxlYM8uLESp83Edy6YzQXzs3FYT/xv2tHj59/vVfOL5/bhC4b5+KpCPrm2mOwkuYcRQzOqRxBN015XShXFeOtXwNeBx0fz88XgrTvQzM+f29v3+tcv7icvOYErl+YNbUOJGXD+z2DZ9RDohpTSiF4x75a18KOnd/e9/uOrh8hx2/nYqqKT/QpCnLSOHh/fftfGqoX/YJq5AY/Byb37LdxQD2ukBu/ElrcMbnoNWg6B1QXpM8a1sPK2qg6++OCWvtePbKoi0apfDN63vpx/bagEoNUT4KsPbyUv2c4pJanjFK0YjMe3VLOs6KjeL7VboacdMuePX1DxxuqE0jP0Yv3n/QQYXGLqlJIUfv3ifr51/iyUJLOG5aXd9fxt3WEAfMCvX9pPXoqdK5dKAmZA7jy44k59RsSgH9KmSY/Y0eRIg3N+qBcs93VCaikkFYzY5jVN41/vVnD/u/rz/zZPgK89so3cZDurS9NOuP7G8jZueWJX3+u/vFlGltvGDaeWjFiMYmoY8xowSqlLgGpN02JVPyxWSr2nlHpNKXXqWMc2lT25tSaq7dFNlcMbc21zQv5yKDk9akjSS3vqoxZ/aGMV3kAoql2IsVbZ6uXp7bV857VOrn7RznXPB3mlzMOhxq7xDk2MhORC/eYvb+m4z2q1ty56OMVj71VR1ebh0c3VUe9tr2obg6jEcHV6A7y5v4mVRUeSZBps/icUnwYGKbcXIf8UvSfawVcGvUpBSgIWk2JTuUwDPBw9/iDP7KiLan91T8M4RDPB2JOh4BQoeZ8kX8aCzaU/MCk9Y0STLwAt3X4e3RR9ft1S0Tao9d/cHz0t9kMbK+n2BU42NDHFjOlVgVIqAfgO8P0Yb9cCBZqmLQa+DNyvlIo5P6hS6ial1Eal1MbGRpkjfiTMzIq+GVlWlEyHN0CPf+SSI6Xp0Z8zO9uJ2Tj5LlBlP514Eq0mslw2slw2LlmYwyklKSgFbrt5vEMbFZN9H23v8cdtcjc1MXpQW3GqA6fVRFGqI+q9DNcQh4NOEhNlH/3vjjrm5rr7hx9Vb9aLz4/jMLe4ZTDAnEv12WW87YNaRSnFqpI0HouRnIwH8b6fWk0GitMScFiMnDcvi7NnZ2A1GShKk1omU0W876NjwWhQfHBZHqkOS0R7lntw59fCGOfmmZlOrCbjiMQnpo6xvustBYqBrUqpw0AesFkplaVpmk/TtGYATdM2AQeBGbE2omnanzVNW6Zp2rL09PQxCn1yu2hBDmmJ/Qekj6wsINFq5rLfr+OTf9/AO4eah1+Y9yhnzEwnN6n/QOeymfjYqiKMhsnXpVj204mnMNXBT6+Yz/vnZrK1qg2DUvzfFfNZkDc56w5M1n20vsPLX9441Hf8Wj9Cx6+RtCDPzcKj9iuL0cA3z59FmtPG186dgdXUf3qek+1iSUHSOEQ5/ibKPvrghkrWHOnCroVh490w7Wzp/TIQd56enHrn94NeZc20VJ7ZXhtVsDoexPt+ajAY+OjKQv7ntFIONXZR2+7lq++fyTlzMsc7NDFG4n0fHU3hsMY7h5r59H2beXJbLVcsyeWKJXoR/llZiSwrTBnUdk6dnkZ+cn+9F4fFyA2nlmCahA+Rxega0ypSmqZtBzKOvO5NwizTNK1JKZUOtGiaFlJKlQDTgUNjGd9UNjPLySOfWs3uug5sJgPbqtr5yX/3AFDW1M2Gwy089unVzM9NOqnPmZ7p5IEbT2F3bSfBcJhZWS5KM+QJjIgPwVCYV/c2cu/b+rTT5c0eNle08vhnpvgUshOIpmk8tLGSXz6/D9CPX++WtfDYp9cwP44KeOYlJ3Dnx5ayq6aDbl+QaRmJzM7WO30uL0rhP59Zw/76LhIsRubkuMiRIn9x61BjF4cau/jiWb0zHR18WS9tEmtGNdFv2tl6LZiy1/ShWieQ7rSRl2LnlT0NnDcvxmwp4rjqO73c/sK+vtc7azq475MrxjEiIcbGjpp2PvqX9QR7H8Tc9UYZXzhrOv+4fgWl6YnkJA/u/FqSnsj9N57C7toO/MEwM7OcTM+UAoFi6EY1AaOUegA4HUhTSlUBt2ia9tcBFn8f8AOlVBAIAZ/SNE0mfh9DRWkOitIc1Hd4+crD2yLeC4Q0dlV3nHQCBqAg1UFBjG58Qoy32nYv/1xfHtHmDYTZW9fJzKyYIyJFnKnv9PLXN8si2gIhjd01HXGVgAHIdtvJjjEDnFKK2dmuvoSMiG//fKecU6en609B/d36DB4Lr5GZj07EaIb5V8I7d0L6bL2I/wmsnZbOA+9WSgJmiAKhMH9/uzyq/antdayZPrV6Q4ipZ2d1R1/y5Yh/vlPOR04pIGOIs73mpySQnyKFq8XJGdU+U5qmXaNpWramaWZN0/KOTb5omlakaVpT7/8/qmnaXE3TFmqatkTTtCdHMzYxMIvRQGKM6dhsFhnjKCY3s9EQc6pfm1n2/YnCYhjg+CX/hmIUePxBHt1czdmze5MHW/4JqdOiCtCLAbjzoGgNvPZ/EA6ecPGVxSlsrmiltr1nDIKbPAxKkZpgiWpPcUzO+mZCHC3BGn3+d9lNmGWIqBgnsueJKMkOC988f1ZEW6bLGndPj4UYaVluW9S+X5LmYE6O9ESYKFISrXzrmH/DLLeV+ZO0jo8YXw9tqGROtpN0p02fqvbQqzDj3PEOa2IpWgsovW7OCdjMRlaXpnL/+orRj2sSMRoU160txnRUvT272ci5c7PGMSohxsb8XDcZrsjC918/bxbJjuikpBBjYUxrwIiJ48xZ6Txw40rePtRMhtPGKSUplKRLrRYx+V28IIe8ZDvrD7WQl2znlJJU8pKlu+lEcuasjL7jV6bTxiklqRSnybBHMbICoTB/ev0QN59WCkEvvPELmHkhWGRfGxJl0Icirf8jpJTotWGO46xZmfzsuT187szpWEzyHHGwlhQk8/CnVvH2wWbMJsXq0jTm5khiWkx+JemJ3H+Dfk3Q2OljVWkqi/OTxjssMYVJAkbEZDObWFWaxqojszoIMUUk2kycOj2dU2Vc/IRlt8jxS4y+xzZXkeG06kUY3/oNOLMge8F4hzUxWRJg0Uf1qakTMyFr/oCL5qckkJtk56ltNVyxJG8Mg5zYjAbF4oJkFhckj3coQoy5aRlOpmVIwVwRH+TRgRBCCCHEEHgDIX71wn49AbD/OajdArMvHu+wJjZnJiy4Gl75MbQcOO6i583L5o+vHoy76eWFEEKIE5EeMCJ+hENQuxVqtuhPw3KXQtr08Y5KTDWBHqh5D+p3giMNcpdJQc3x5GmG6s3QfABSiiFnyaBmSxFiNP31zTIKUxOYoZXBxr/B8hvANLTZNEQMqaV6Iuv578P7b9P/5mNYmOfmkU2VvLC7XuqYDEXtVqjZCgYj5C6BjNnjHZEQQ9fdDDVHrgtKeq8LpNeymDgkASPiR/k6+Mdl/TMhJGbBJ56A9JnjGpaYYvY8BY/e0P86ezF86D5w545fTFNV0Atv/gbW/aa/bcm1cO6PwCpdicX4qGnr4c+vH+LWtQnwyk/0XhuSFBw5WfOAMDz/HTjr+5A+K2oRpRSXLc7lF8/t5ezZmRgNMuX3CVVthHsu1I+rALYkuPap4w73EiLuBHrgzV/B27/rb1t6Hbz/R2CVWpViYpAhSCI++D3w2s8ip6HsqoPyt8YvJjH1dNbBs9+KbKt9D+q2jU88U13TgciLLIDN90DT/nEJRwhN0/jWY9t5f6GBrLd/AHMu03ttiJGVtQDmXgYv3goV62IusrQgGbPRwMMbK8c0tAkpHIb1f+pPvgB422DPM+MWkhDD0nwA3rkjsm3T36Bp3/jEI8QwSAJGxIegDzqqotu7Gsc+FjF1Bb3Q0xLd7usa+1gEBDyghaPb/fLvIcbHvW8fpqqunksqfw7zPgCZMoRj1KTPgiUfh7d/D+/9E7RQxNtKKT56SiE/e24vLd3+cQpygtCC0FYe3d4u03mLCcbvAS1G7aeAZ+xjEWKYJAEj4kNCMqy4Kbq9aM3YxyKmLmcOLPxIZJvRDBnRXeDFGEgugrQZkW3ObEiRHgdi7G06WMevntnKZ8L/wrTsE5A+48QriZPjzoNTPg1V78Jz34auhoi3i9McrC5N5VuPbUOLdVMmdEYLLPtkdPvsS8c+FiFORnJx9DWAK1evBSPEBCEJGBE/5l4OZ98KjnRInQZX/1MvxCvEWDFZ4H1fgZWfBnuyXtjto49BxtzxjmxqSsyAq/4Ocy4Fmxumnwsffkjq8Ygxd2Dzq9z019e4KXkT2Ws+Aq7s8Q5p6rA6Yen1+k3Wk5+H3U/qRft7XbU0n711nfx93eHxi3EimHY2XPBzPYmdVAiX/xkKV413VEIMjTMDrv4HzL5Evy6YcT58+EFw5Yx3ZEIMmhThFfHDmQVrvwQLP6w/rUlIHu+IxFSUXKQXeV3zebAkgk2KvY6rzDn6jUJPq1400mIf74jEVFK3gx1P/Z5rD76Pq4t9LJ575nhHNDUZDFByOmTM0RMwe56G5ddD3nIsJgNfPHsGP3hqF1luG+fNk+RYTI5UvafxnMv0WZASUsc7IiGGJ3MuXHGXXBeICUsSMCL+ODPHOwIx1RmM8oQ7nphtYJZ/DzFGQkE4+BLaO3fy8GEbt/mv5vrFNlbkWsc7MpGYAcuuh4Zd8O5dsPlemPcBMgvX8NX3z+Rbj22npdvPNSsKUEpmRopJZuwSk4FcF4gJTBIwQgghhJjaAj1Qvg72PI226wk2mxfxM98HaTS7+c5yK/kuGbEdN5TSn4BnzIaGvbD7CVj/J4qL1vDd5av59asHeHVvI9+7aA75KQnjHa0QQggRQRIwQgghhJgaNA26G6H1sD5tad12qNqIp34/ux2nsM6yiidDt9PpM3FhiYnTC0wYDdKTIi4pgz4LVeZs8LRA3TZydt/Nj7xNPFV1HhfeXs0pWYqL5meycs40MtJSpFeMEEKIcacmetV4pVQjEGNuvQhpQNMYhCMxDE48xDFSMTRpmnbeiRYa5H4a7+Lh322sTYbvfLL7aDz8DiSGyR3DSR9Hp6cYLC98PGFGodtw3HFCDVoSV/puoYKBh7pm+KtCOYGKoDrq+kjTwkalDKEBV5ogJsv3gIG/i8mAcls1o8FsMa4Pz6GdxJjr/8l0O+eaNka0feAhz/7Hdgc7BvjIoe6n8fD3OhCJbejiNS7oj22iXZPG8+90JMn3jDSo/XQym/AJmMFQSm3UNG2ZxDD+McRLHPEQw0QzFX9nU/E7HysefgcSg8Qw3ibL954s3wPi/7vEc3wS29DFa1wQ37Edz0SNe6jke4pjyaBmIYQQQgghhBBCiFEmCRghhBBCCCGEEEKIUTZVEjB/Hu8AkBiOFg9xxEMME81U/J1Nxe98rHj4HUgMOolh/EyW7z1ZvgfE/3eJ5/gktqGL17ggvmM7noka91DJ9xQRpkQNGCGEEEIIIYQQQojxNFV6wAghhBBCCCGEEEKMG0nACCGEEEIIIYQQQowyScAIIYQQQgghhBBCjDJJwAghhBBCCCGEEEKMMknACCGEEEIIIYQQQoyyCZ+AOe+88zRAfuRnvH4GRfZT+RnHn0GRfVR+xvFnUGQflZ9x/hkU2U/lZxx/BkX2UfkZ558pb8InYJqamsY7BCFOSPZTEe9kHxXxTvZRMRHIfirineyjQoyvCZ+AEUIIIYQQQgghhIh3koARQgghhBBCCCGEGGWmsfogpVQ+cC+QBYSBP2ua9ptjllHAb4ALAA9wraZpm8cqxrGkaRp17V6MBkWGy9bXHgpr1HV4sRgV6c7+9vYeP53eIGaDIqhBlsuG0aCgoxYIgzMHlNKX9fjp9AUxKjAaFAZlwOMP4g+FsZmMZCfZqevwYjMaCGkazd0+7BYjmU47dosxIs42j58uXxCn1USnL0iSzURioBEw0GPLoLnbh8VkIBAM47KbcdrMAIQ7G/F4uvElZJDqTOjfYDgMnTUQ9IE5AZxZfXEfT2Onl0BII/PI9xZjqrtmN6Bw5Mwa71DGRFd7C7bOCoJGB7bs6eMdztR15HhhtEBixgkX7/IGaOsJkJxgwWGNcXoLBqCrDsx2cKRBZx0+f4ByvxuTUWE1G0lNtKIUNHb6cFhMJDsssT+svYagplGnJeMPhsl02XBYTfT4QzR0egmFNbJcVtp7gvpX0TScNjMuu36M7PYHaev247KbsZuMHGruxmI0UJTmiPwcXyf0tII9maDJQX2HF1tvnACBUJiGDi8GpQhrGikOa9RxHMAfDNFwou8kRlas/VfToKOGDl+IRpLJcNno8gZxh5pIMBnBndO3ekOnl1BIIznBRFN3ANBwBFoxE8SekofRNPAl3JFzd0aiGYunHowWOozJdHoDfftIc5ePbl8QFKQmWOn0BdHQSLKbaez04QuGAXDaTCTZ9RicVhOBkIYvFO6/DjlJfX+3dgsO25hdlgohRlqgB7obweoCe1LsZboaIOgHVzat3hDdviDpTitWU+95S9Og9TD4u8GeAq7sAe8Tur1BWnv8JNnNJPbefzR0egmGNLJcNryBEJWtHhKtJsxGA6GwRqbLSn2HD4NBkemy0e0LUt3mwW4xkp+sn3/LmroJhsKkJJhp9wWxGA0kWk10+4J9x89ub5Dajh7MRgMFKQmoAWJs7I3HZFR91womY3z1f/AFQye+5hEjaizPdEHgK5qmbVZKOYFNSqkXNE3bddQy5wPTe39WAn/s/e+kUt/h5YF3K7jr9UPYLUa+du4sLpyfRYc3yD3rDnPv24dx281898I5nD07gx3VHdz65E721HWypjSVNdPSyDB7udi4DtNrP4ZQANZ+GW3xR1nfYOTWx3eyv7GL02akc83yfL7/+E5aPH4uWZhDutNKXnIC6w40cuniXB5YX8lr+xspSXPwpXOmMyPTybQMJ5qmsb6shR8/s4tLFuXy0q4GZru8fDbpHXjvDjCa8az4Bj8rm8mBDiNXLcvntb2NfOv9xZS2rcP0wndI7G4gNOcj7J93AzmFM3H4GmHjX+GdP4DFActugMQsmHkeODNj/q48/iAv7Krntqd30+EN8InVRVy7qojsJPsY/6tNTS2Ve0na/yiOd+8EZSC8+vO0lFxGWm7JeIc2aoIVG3Cs/yNqzxMY3floZ36fzoLTcLlSxju0qaWjBjb8Bd75I1idcM4PYdZFYHXEXHxrZRs/fHoXm8tbWV2axrcvmMWcHHf/Ai1l8OavYev94MqBM74N6/+MtXEPyQtv5qHwmViTsjjQ0MkpJal8/ZFt5Kck8L+XzGV1aRqGIzea3Y2w5V/wxs8xKQPWZV/m9qr51PntfOX9M6ho9vCXN8uYmekk1WEhy23jxd0NrC9rZn6um1sunkuC1chtT+3mrYNNLMpP4ro1xdz65E6MSvGls2dw3rxMkh1WqH4Pnv8OVLxNuHANlUu/zaWPduK0mfn+RbOZle3k3rfLyUtO4JFNVeyu7eC0Gel84/xZzMpy9X31sqYu7nj5AI9vqaEwNYFbLp7LmmlpksweTe3V+v67/k6wueDsH0DhasLbHsTw1q9xGc1oq77Je/ZTmNbyKgnv/Qq0MNqpX8U392qePODn/57dg8cf4url+aRZw1zm2E7y+h+BpxnPgmsJrfwfnJmRx2JN03j7UDO3PL6TS0o0rjW/jGXLXWBz0b3i23xrZwEFGWmsnp5Ga7efRzZVsbWqjWWFKVy4IJs7Xt7PVcvyOdzUTUWrh2uWF9ATCBEKhTnY1M22qjY+srKQO187yPnzsrluTRG5yQkD/BJObHtVGz98ajcby1tYWZLCdy+Yw9xc94lXFELEl4Y98PKPYN8zkDEHzv8ZFK7ufz/QA/ueg+e+DZ5m/Iuv4wXrJXz75TYuXJDNF86aTomtCw69Cq/8GDprYfYlMPsiKFgddZ+ws6ad257ezTuHmllSmMx3LphNWVM3P35mNz2BEHd+dCn3rivn5b0NlKQ5+MLZ0ylv6sITCPP3deVYTAa+cNY0fIEwt7+4D7fdzA8unUtFSw+/f+UAoZDGdWuKaO728fDGai5ckE16opUDDZ18+oxpbDjcwl/fLKPHH+K6NcV8fFVhxAP1nt77l39tqOB909P5xzsVNHX5uGZ5AZ88tZj8lOEfN0dSWVM3v31pH09uraUwNYFbL5nLmqOvecSoUJo2PsWIlVKPA3domvbCUW1/Al7VNO2B3td7gdM1TasdaDvLli3TNm7cOOrxjqR71x3m+0/sjGh74MaVbDjcyu0v7Itof/CmU7jung14/KG+tgV5Lv64rIHcZ6+PWNZ/0R2c8kwmLZ5AX9vSwmTcdjMv72kA4CMrC3htXyO3XTaPO145wIbDrX3LWk0Gbr1kDhfMz6a+w8fFv3uTyxbnsv5QM01dfp5eW0bhW9+I+Mw9Z/yZC55NxG42ct3aYs5IKGPpi1dHLNOy8H+oXPoNFlY/CM99K/KXcdYtYE+GZdfF/F29fbCJa+5aH9H2tXNn8pkzpsVcfhwM6gg1EfdTgND6uzD+96uRbZf8AeOSj4xTRKOrs7WRxOe+hNrzZH+jwUj4mocxTD9r/AI7ORNzH33rN/DC9yPbPv44lJwetWhVq4dL73iL5m5/X1tesp3Hbl6tXxAFA/DMV2HzPf0rKQOc+V146QcAVKz5MRevm84Npxbz/M568pLt/HdHHSaD4onPrulP5mx9EP59U8Tn7zvt91z4YgpZbhtnzcrg3+/VcM2KfBo7fWytaudAQ1ffsi6biZtOK+EXz/Uf65MTzHxgaR5/eaMMgD99bCnn5gXhrjOhq77/g9x5/LbkTm5/uwOAn1+5gEON3dy3vpwOb7BvsZlZTh64cSUpDiu+QIgvP7SFp7fX9b1vMige/8yaeLrRnZj76PG8+Wt48Zb+11YnnPV9eOZrEYt5L/4jtqc+A1q4r81z0R9Y8ngK3kB/2xsfspH/nysi1u0+5cskvP97KEP/09Q9tR1ccsdbJCWYeWLxRrLe/UnEOlvOuJd14bk0dfl4cmstjV2+vvdy3DbOnJ3JP98p5/NnTePPrx/CajLyoeX5uO1mWj0BHtlUiccf4lOnlfKbl/bz+TOn8aVzZgz49Pd4atp6uPSOt6Ji+Pdn1pB51I1MHJl8+6mYbMZnH+1ph/uuhKp3+9vMdrjpNUifqb8uXwd/Oz9itabFn+eKvWdQ0dLDRfOz+d2SatRDH9N7wRwx/yqYcQHM7z/+NXR4ufwP66hu6+lrS0u0cOmiXP76ZhlXL8ujrNnDu2Utfe9bTQZ+86HFfOqfmyJi+Mr7Z/CrF/ZhUIovnD2dXz4feR/22TOn8dc3yugJhLhiSS7bq9pRCubmuPn3e9V9y91y8RyuW1Pc9/rI/cs3zpvJ/z27N2KbN55awrfOnzXuSQ5vIMQXH9zCszsirw8irnlGx5TP7oxLHyilVBGwGFh/zFu5QOVRr6t62yaNbl+QBzZURLWXN3v417vR7QcbuyKSLwDeQJi0ymejljVv/QfzcxIj2jaVtzI3p/9J6Iu761ldmka3PxiRfAHwBcP0+MNUt/ZQ1tSFLxgmw2nlcLOH5QWJFJY/HPWZubUvUZqeSLc/hFFBZs/BqGVS9j2Eo6c28ubniOYDsPcZCHij3wM2lrdGtT3wbgUtR91oidHh9/sx7Hosqt2w7+lxiGZs2LsqUXuP+X7hEKoler8Wo6inDTbdE91++M2Yi1c0eyKSLwBVrT1UtHj0F111es+Xo2lh/Ylcr4Kyh1lZ4MAfDLO9up1pGfqxNBjWONjYm0AJh+G9f0R9fn71f5mZ5aSypQenzcyyomTe2N9ETpI9IvkC0OEN4jvqxhqg1RPAbu4fNvReeaveY+fo5AtAexULHf3HxENN3VhMKiL5ArC3rpOqFv271bV7eeaoi6sj3+lAY2RcYgR5WmHz3yPb8pbDtgejFrUeehHSIoc52nY8wIK8/otfu9mIrXFb1LqOHffhaY38tz3Y1IU/FObcEitZB6I/L6d9M2FNw2E1RSQ+AGravSQn6N343y1rYUFeEu09ASwmA83dft4ta2ZFcQq+YJhw7w3SAxsqaTpmO4NV3uyJGUNFs2dY2xNCjJOOqsjkC+jn1+YD/a9rtkStlrb/X1w6TT/mdPZ4oWl/ZPIFYM/T0F4RcZ9Q0eKJSL4ANHX5Segdfru8OCUi+QL6Pc6hGOe9XTUdlKQnMj0zkW1V7VHvv3OwmUX5SQA8v7OeU6ensa++i7zkyJ74D22s1Id09tpc0YbJoOjoiTw/H1m2oSv2fc9Yqmv38txOuT4YD2OegFFKJQKPAl/UNK3j2LdjrBLVRUcpdZNSaqNSamNjY+NohDlqLEYDxanRXegdVhN5MbqjuXvrBRytxx/C744eAhJKmUFdZyCizWk1RTxFy3bbaeryYTEZccUYa202KhIsJly9YymV0rOh9V1BOhNLo5bvTiyk1aPf+BgNBnpM0RnToDMPv9EByTGGrSSk6nVgjNHfE4j5FKwgJSHiZiVeTeT9FMBisUBSYVS7llQ09sGMkZDJBokxhsNZXdFtk0Dc7qMmG6TEOF648mIu7oxxLDMaFIlH2s12fdhR1EL9Y527nCXUdYUwGQ0kWIwEQv3HzSPHQwwGSI+ug9TlLKa5y99bc0vR3OUn02VDQz/mH8tiim47ekx4utMae59TBnpU/3nCaTVhMMTYvtFAQm8NHJvFSJrDGrVM33eKc3G7jx6PyQbHHie7GyG5OGrRcFIBeCJvFPwpM2nq7E8o+kNhgrbUqHWDrgIM1sjrhiP/rtVdGr7Egqh1euxZKKUwxXjyauitGweQ5bLT1KknR0xGAw6rkVSHlcbeuMy9+2t+cnTtuMGK9XdrUPT/3U4gE2k//fPrBznn9td4KMbDQDF5jeo+ak7QSwscy5bU//+O9Ki3A858Knvv9b1B9J6Cx3Ll6Ofqo+4TEq0mYnUeOXIe7fGHYt7jOO3RbelOK63dfpq7/GQ4o8+V2W5bX6I4y22jqcuP1WQgfMzdaVGqI+J8n+G0Egxr2GIcH3OT7djN43+cs1kMpMao+eKeINcHE9mYJmCUUmb05Mt9mqZFP1rXe7zkH/U6D6g5diFN0/6sadoyTdOWpadH/0HHM7PJwI3vK4lIIGS5rczOdvHlc2ZE/PEWpzmYmeXkkoWRNw4XL8xhm2O1XkTyCEsi2tLrKDwmuXPj+0p4cqv+KzQbFZcszKGmrQdfIMSNp0be4Jw2I410p5XC1ARmZrk4d24mz+2o5yOnFLKrtotdeVeB5ageNo50tjpW09Tl56zZGWyraqPJNYdwxtz+ZQxGypd8A7s7HU79kn5heoQrVz9gL7kWDLEv4JYXJVOU2n+BaTEa+OLZ04d9wTeWJvJ+ekRwwYcjbwQTUgjOvGj8Ahpl1uw5aGd+L6Lgm5a7DH/a7HGMavTE7T5qtsH7vgamoy6GkgqhaG3MxUvTE7l+TeTN7efOnEbJkYK2jjR9PLo66pSXNb+/h4nFwe78D4MyUNvWw7Wri3hqmz7y9dRpaczOPupvYPHHIv8m7MlsTzqT2nYvnzqthC5fgC2VbayZlsrLu+v52KrIJObVy/KjEsuXLMzh7YNNgD50anlxit4rYvXnIpZrWvxZ7tqlH/tmZzlZXJDM5vJWzpuXFbHcV8+dSXHvd8902fjfS+dG1DBcVZLCnJyJkVSM2330eCx2OP3rkfuvtwOWXh91Dm3JPRPCRz0htbkJL/gwPYH+nq9mo6ItdRGBlBn9yxnN+E77DvbEpIiPnp3l4oyZ6bx6qJN9sz4dEUPIXci74dk0dfkoa+rmyqWRCc0PLS/gxd31uGwmZmc7OdTUzYXzsylr7CIQDLN2ehqbK1o5dXoae+o6MBsVXzt3JonW4V2sl6Q7uOl9kX+3nz3jqL/bCWSi7Kcv7Krn7jcPc+XSPH7y3z1sitHLWExOo7qPJhfB+2+LbJtzGWQcde2UtxzSZva/NprZP//LPL23E4APn1KIyl2sn5uPUAZYdj0UnhJxn1Cc5uBzZ0b2HLxudRE7q9sA+NPrB/niOTMi3l87LZXC5AQcR90/pCVayHBaae7209DpozTdQcpRCYlEq4n5eUkcaOjCaFB8aHk+z+2s43NnTmN97zkb9F6Kn1xbjPmohyvLi1IoSk2goyfQ16MW9CT3t8+fFfMB+1jLctm59ZK5EW1rSlMjr3nEqBizGjC9Mxz9HWjRNO2LAyxzIfBZ9FmQVgK/1TRtxfG2O1HH2u6p62BPbQdmo4G5OW6K0hxomsau2g721nViNxuZl+smPyWBpk4fO2raqWnz6plKpV+kzzY3YKjfpneLz5oHGbNp7PSxvbqd2rYe0hItuO1mGjp9dPqCpDmsBMNhst02ypo95CXZaPMEqG7zkppooSDFzoxMV9/sIQ2dXnZWd+ALhjAaFJ3eIKe6m0ju2ovBYKLZOZPXmtzYLEZsJgNWkx5zUqAOX8Vmgj2d9CRNx582l9zU3qx23Q6o3aJ3/3flgDMXMo9/c1vR7GFHTTveQIiZmU7m5LiGNd58lEz6MeG+sncwNe4EZSCQPg9b0fLxDmlUtTc34Gzagmo9CFY3/rQ5WPMXjXdYJ2Ni7qOaBvU7oH6nnrjNXggp0T0Ijmj1+NlZ3U5tu5fcJDvzcl247Ec92QkFoG67XijQ7oakAkKN+/D6fDQ5ZrDVm43NYsRtN2MzG9hf30Wyw8K8HDeZ7mN64jXuIVS7HX9Io8kxkzdakshLsTM7y0VFi4fKVg++QJi8FDvdviChsB5fXnICc3NdmA0Gdta0U9XaQ6bLRoLFyK7aDmxmI3NzXMw9Mvba06ofLzuq0dz5VFins7EujMOqH2uzXDZ21nTQ0OnFGwjR5Q1RnO5gXo47oheBLxBiV20HBxu6SEowMy8viaz4qrExMffR44nYf+29+28RNOzCV7UNX1jR4ppFtcohL1xNatderEYw5y6E9FkcbupmZ007vqB+zq5u7WGxu5OMrj367CAZs3EULMZgjH4YUd/hZWdNO93eAKe6G3C17yNstFHvmMHbrU7ykxPIcNmoavbQ2uOnwxsk223DoKDNEyDbbedwcxdWk5EEiwmzSWE3G2ns9GE1G7GbDTR2+fXzcbbrpOoYtHn87Oj9u83p/bt12+N2Fo4JvZ9qmsZ5v36DSxflsLggmVf2NLCrtp1/3nDKeIcmRs747aP+bqjdCi2HwJEBOQujexS3VujnNH83vpSZbPTlUdvuoyQ9kbk5LqxmI9Rshcbd4G0Hdx6klEJGdM/Tjh4/O6o7qG7rIdttY16um3ZPgB017QRCYeZku6ht93GoqYsku5mcJDvNXT4y3TaqWnowGhQzMp00d/vYX9+F3WpkUV4SvmCIHdUdaOg97hs7vPQEwuQk2ej06jM2FaclUNbk4VBjNxoa83PdzM9LioqxssXDjup2bGYj7T0BQmGNGZmJzMlxx00RfF8gxM7aDg41dJGUYNGvLY695hl58fHlx9FYJmDWAm8A29GnoQb4NlAAoGnanb1JmjuA89Cnob5O07TjHiHi9UQnpowJfUEmpgTZR0W8k31UTAQTej/dcLiFLz+4hf/7wAKUUgRCYT7/r/f496fX9PWYExPehN5HxZQx5RMwYzYATdO0NznBL1zTs0GfGZuIhBBCCCGEmPye2lrDqtLUvh7EZqOBlcUpPLO9hs+cMf0EawshhBgp4zILkhBCCCGEEGL0aZrGf3fUsaIospjzssIUntleN8BaQgghRoMkYIQQQgghhJikDjZ2A5CTFFnbYVaWk7Kmbto8/lirCSGEGAWSgBFCCCGEEGKSevtgE3NjTGBgMhqYleXinUMtA6wphBBipEkCRgghhBBCiEnqrQPNzMqKPbXsrCwn646aUlcIIcTokgSMEEIIIYQQk9R7la1Mz0yM+d70jEQ2lbeOcURCCDF1SQJGCCGEEEKISaihw0uPP0SWyxbz/eJ0Bwcbu/AGQmMcmRBCTE2SgBFCCCGEEGIS2lLZxrQMZ1T9lyOsJiO5SXZ21XaMcWRCCDE1SQJGCCGEEEKISWhHdTuFqQnHXaY4zcHOGknACCHEWJAEjBBCCCGEEJPQ9kEkYPKTE9hR1T5GEQkhxNQmCRghhBBCCCEmoV21HRSlOo67TGGqg501koARQoixIAkYIYQQQgghJpk2j59Ob5B0p/W4yxWkJHCgsYtQWBujyIQQYuqSBIwQQgghhBCTzL76LgpSEjAMUID3CLvFSJLdTEWLZ4wiE0KIqUsSMEIIIYQQQkwy++o7yU2yD2rZvOQE9tV3jnJEQgghJAEjhBBCCCHEJLO3rpNs9+ASMDlJNvZLAkYIIUadJGCEEEIIIYSYZPbUdZCXPNgETAJ76iQBI4QQo00SMEIIIYQQQkwyZU3d5A4yAZOXbOdAQ9coRySEEEISMEIIIYQQQkwind4AXb4gKQ7LoJbPdtsob/agaTITkhBCjCZJwAghhBBCCDGJlDV1k+O2n3AGpCMSLCbsFiN1Hd5RjkwIIaY2ScAIIYQQQggxiRxq7CY7yTakdXKTbBxs6B6liIQQQoAkYIQQQgghhJhUDjV2kekaWgImy23jUJPUgRFCiNEkCRghhBBCCCEmkYON3WQNMQGT4bRR1iQ9YIQQYjRJAkYIIYQQQohJ5HDz0BMwWS4bBxslASOEEKNJEjBCCCGEEEJMEpqmUdHiIdM9tARMpstGebMkYIQQYjRJAkYIIYQQQohJos0TIKxpOK2mIa2X6bJR2+YlGAqPUmRCCCEkASOEEEIIIcQkcbhZn4JaDXIK6iMsJgPuBDM1bTIVtRBCjBZJwAghhBBCCDFJVLR4yHBZh7VulstGRYtnhCMSQghxhCRghBBCCCGEmCQON3WTnji8BEyG0yoJGCGEGEWSgBFCCCGEEGKSKG/2kO4cWgHeI9ISrRyWQrxCCDFqxiwBo5S6WynVoJTaMcD7pyul2pVSW3p/vj9WsQkhhBBCCDEZlLd4yHAOrwdMpksSMEIIMZrGsgfMPcB5J1jmDU3TFvX+/GAMYhJCCCGEEGLSqGzxkOkaXg+YDJeNymYZgiSEEKNlzBIwmqa9DrSM1ecJIYQQQggxlfiCIVo9flIdlmGtn+G0UtXWM8JRCSGEOCLeasCsUkptVUr9Vyk1d6CFlFI3KaU2KqU2NjY2jmV8Qgya7Kci3sk+KuKd7KNiIoin/bS6tYe0RCsGw9CmoD4i0WoiFNZo7wmMcGRiPMXTPirEVBdPCZjNQKGmaQuB3wH/GWhBTdP+rGnaMk3TlqWnp49VfEIMieynIt7JPirineyjYiKIp/20srVn2PVfAJRSZLpsVMpMSJNKPO2jQkx1cZOA0TStQ9O0rt7/fwYwK6XSxjksIYQQQgghJoSqVg/pJ5GAgd5hSK2SgBFCiNEQNwkYpVSWUkr1/v8K9NiaxzcqIYQQQgghJoaKZg8pjpNLwKQmWqhskTowQggxGkxj9UFKqQeA04E0pVQVcAtgBtA07U7gSuBmpVQQ6AE+pGmaNlbxCSGEEEIIMZFVtHgoTU88qW2kJVopl6mohRBiVIxZAkbTtGtO8P4dwB1jFI4QQgghhBCTSlVrD6eUpJ7UNtKdVjYcbh2hiIQQQhwtboYgCSGEEEIIIYavuk2fBelkpCdaqZYaMEIIMSokASOEEEIIIcQE5w2E6PIGSUown9R20p1Watq8SCUAIYQYeZKAEUIIIYQQYoKrau0h3WnFoM9pMWwJFhMmo6LVExihyIQQQhwhCRghhBBCCCEmuKpWDxknOQX1ETIVtRBCjA5JwAghhBBCCDHBVbf1kJpoGZFtpTutVLXKVNRCCDHSJAEjhBBCCCHEBFfZ4iE5YWQSMCkOK9WSgBFCiBE3rGmolVJrgFuBwt5tKEDTNK1k5EITQgghhBBCDEZlSw+FqQkjsq1Uh4WKFhmCJIQQI21YCRjgr8CXgE1AaOTCEUIIIYQQQgxVdWsPSwuTR2Rb6YlWNle2jsi2hBBC9BtuAqZd07T/jmgkQgghhBBCiGGpae8hLXFkivCmOWUIkhBCjIbhJmBeUUr9HHgM8B1p1DRt84hEJYQQQgghhBgUfzBMS7efFMcIFeFNtFLb7h2RbQkhhOg33ATMyt7/LjuqTQPOPLlwhBBCCCGEEENR1+4lxWHBaFAjsj2H1UgorNHhDeCymUdkm0IIIYaZgNE07YyRDkQIIYQQQggxdFVtHtKdIzP8CEApRYZLH4bkypYEjBBCjJRhTUOtlHIrpW5XSm3s/fmlUso90sEJIYQQQgghjq+mzTti9V+OSE+UOjBCCDHShpWAAe4GOoEP9v50AH8bqaCEEEIIIYQQg1Pd6iE5YWR7qqQ6LFS3SQJGCCFG0nBrwJRqmvaBo17/r1JqywjEI4QQQgghhBiCihbPiPeASXFYqGz1jOg2hRBiqhtuD5gepdTaIy+UUmsASZELIYQQQggxxqrbRm4K6iPSnFaqWuTyXgghRtJwe8DcDPy9t+6LAlqAa0cqKCGEEEIIIcTgjEYNmLREK6/saRjRbQohxFQ33FmQtgALlVKu3tcdIxmUEEIIIYQQ4sQ0TaOuw0tqomVEt5uWaKWm3Tui2xRCiKluSAkYpdRHNU37p1Lqy8e0A6Bp2u0jGJsQQgghhBDiOJq6/NjNRmxm44huNynBTKc3gDcQGvFtCyHEVDXUGjCO3v86Y/wkjmBcQgghhBBCiBOobushwzmyw48ADErpvWBkJiQhhBgxQ+oBo2nan3r/90VN0946+r3eQrxCCCGEEEKIMVLT1jPiw4+OSHdaqW7roSRdnrMKIcRIGO4sSL8bZJsQQgghhBBilFS39pDiGPkeMACpDov0gBFCiBE01Bowq4DVQPoxdWBcgAwOFUIIIYQQYgxVtnpIdYxOD5gUh4WqVknACCHESBlqDxgLeq0XE5H1XzqAK0c2NCGEEEIIIcTxVLX2jPgU1EekJVqpaPGMyraFEGIqGmoNmNeA15RS92iaVj5KMQkhhBBCCCEGobq1h7NmZYzKttMSrWwsbx2VbQshxFQ0pATMUTxKqZ8DcwHbkUZN084ckaiEEEIIIYQQJ1TbPno9YNKdMguSEEKMpOEW4b0P2AMUA/8LHAY2jFBMQgghhBBCiBPo8gXxh8I4bcN9pnp8qQ4LjZ0+gqHwqGxfCCGmmuEerVM1TfurUuoLRw1Leu14Kyil7gYuAho0TZsX430F/Aa4APAA12qatnmY8U0u4RA0HwR/FyQVgiN14EXDGrtr22npDpBgMWIxGUhxWMhNTjjpMJq7fFS39pBoM1GU6sBgUEPbQFcjtFeC1QkppWAwQHcztJX3tpWAobeWs78bWsr0/08pBovjpOMXw+PvaEQ17kFTBkifhcU58P43WXTVH8TcdhjN6sKQvQCLdXSeLIqx4QuGONzUjVvrJDVYh9nmBHcetJbh8weoM2YTMjsoTHVgPOa45vV04G84CIA1vRSrtwG8HeDOo0FzUdvmJclupiA1Af00NniBUJjy5m56/CHyUxJIShidIpoi/oXDGoebu+nyBslNtpM6iN4M3f4gtS1dpPmrSVReTClFUdcHHT0BKlo8WEwGilITsJgi50uoavWAp4XUQB12hwtSS/vPw8PRXg3N+8HsgKx5YLYPetUef4jDzd1omkZhmgOHZXQSCpNNTVsP6U7rkI8/g2UyGkhKMFPf6SM3afD/nkL0aauErgZIzICk/CGt2tDhpbbdi9tupjA1geYuPzVtPbgTzOQbWjB0N6AlpNLT1YY/pKEll5Cc5I7ciLcTWsv0Y1tKKZhtsT9sAB5/kPLmbgAKUx0kDOPYFAyFKW/20O0Pkp+cQPIoFc0WE8Nwz26B3v/WKqUuBGqAvBOscw9wB3DvAO+fD0zv/VkJ/LH3v1ObrxM23QMv/QBCfsiYCx+4CzLnRi3a4w9y/7uV/OzZPfiCYYpSE/jkqSWgaRSmJnDq9PRhn6B31rTz2fs3U9bkwWoy8L2L5vCBJbnYB3sQqt0Gj1yvX5iZbPD+26BwNTzySWjcBSYrnHULLPk49LTBi7fCjkf0dedfpb83xIO2OHm+6m1YXrsNte9ZUAptzuX41n4Na/ac8Q5t1ATL1uF46nOo5gNgshE+7Zu0zfoQSenZ4x2aGIbGTi9/ePUgZyQ1Ubr1m5iadsGij+hvbr0fq6aRXnQO/8n6PK+78rlqaT4Oq35c66grw/zaj3Dt1o9F4VmXgDsX1v8RLbmYmlN+zVVPeLAYDfzosnlcuCA76gZ3IO09fu59u5zfvrSfQEhjQZ6LX161iOmZzlH5PYj45fEHeXRTFT96eje+YJjSdAe/vWYxc3PcA65T1erhiXf3czkvkvTuTyHkR0ufg7ryL33XB4cau/j2Y9t5p6wFo0Fxw9pibjqthFSHFU3TeHVfI9bmPazY/A3978Jo0c+1Sz+hPxQZqqqN8OQXoH4HGM2w6nOw4iZwnfjYWdPWwy+f38ujm6sBuGB+Ft++YDZ5I/DwaLKrHsUCvEekO21UtXgkASOG7sBL8NiN4GmGhBS4/C6YfvagVt1S2cpn7nuP6rYeEixGbrloDs/sqGV7dQf/OsuD4c0vwfyrUM0HSTjwAglK0Tnrg9Ss+gY5BaX6RloOw7PfgN7rWJZcB6d9A1xZg4qhpq2Hnz27h/9sqQHgkkU5fOPcWeQmD/5vobMnwH3vVnD78/vwh8LMyXbxq6sXMTNLzvdT1XCHIP1IKeUGvgJ8FfgL8KXjraBp2utAy3EWuRS4V9O9AyQppeSOp3YbPP9dPfkC0LATXvxfvYfIMTZXtPHDp3bhC+rdRA83e3hoQyV76jp5Znsd5c3Dq2Lf2RPge//ZQVmTvr4vGOa7/9nB7trOwW3A2wHPfE1PvgAEvfDMV6DibT35AhD0wXPfhvqdsP/5/uQLwPaH9TYx5sz7ntGTLwCahtr5GKayV8Y3qFHU2VyD8aVb9eQLQNCL4aVbcTRvG9/AxLBtONxKZX0Tyw/+Vr/JNFn1JMqW+0DTAEg4/AJnBl7jj68eZHdtR9+66sCL2Hf3H4sMe57QbyxtSajWMuZu+h5XzXXS7Q/x5Ye3sr++a9Bxbatq55fP7yMQ0npfd/C7Vw7gC4ZG6JuLiWJ3bQffe3xn37n7YGM3tzy+k05vYMB1XtpdzzJrBdlv/6Dv+kA17oLnvw/+bkJhjfvWV/BOmX7ZFQpr/On1Q2wubwP064NH3t7L4r2/0v8uQN/O89+B2q1D/xK+Tnjjl3ryBSAUgDdvh6rBjU5/bV9jX/IF4Jntdbywq37ocUxBVW09ozYF9RHpiRaqpQ6MGKqWQ/DwJ/TkC4CnBR65FpoPnXDV5i4fX3loa99+5/GH+Oa/t7OsKIWvLLMw47XP6gsqAxx4Qf9/TcO5+0FCB16iqdOnt+14RE++9L7Pprvh8BuD/gov72noS74APLGlhpf3DO3YtL26nZ/+dw/+3mF8u2o7+OXze/EGgkPajpg8hpWA0TTtKU3T2jVN26Fp2hmapi3VNO2Jk4wlF6g86nVVb9vU1hLjIHXwxf6D2VFiTRO4vbqdpAQLaU4rNe3DO3k2dfvZXNE2qM+LqbsRKt+Jbu+oiW7ztsOep6LbY7WJURUKBjAcejmq3VB23NGGE5rZ04CqWh/VbmiTSd8mqg1lLZyeZ8Re0Zs4TCqExr1Ry2VVP8/ibHvEcc1+6NnoDda8B+kzATA3bGN5un4BpWlQ2Tr4JPehxugk+iu7G2jp9g96G2JyqGyJPjdvLG+luWvgfeGZ7XVkBOui3zj0MnQ309ET4Lmd0e9vrWwD9KKtq7MV9opXo7fReniQkR+lowbKXo+xrbJBrf7y7oaotme216L1JknFwKpaPKQ6RrcHTIrDQlWrJGDEEHXU6MnZo/k6oaM69vJHaez0cfCY86Sm6Q+B801t4OvQe/tVb4xaN63mNdp7/BDogd0xbk8PDf5B4gu7oo+jz8Y4th5PeYz7pdf2NdLSPXCSXUxuw0rAKKX+rpRKOup1cm+Nl5MRa2xMzDOvUuompdRGpdTGxsbGk/zYOOfKiW7LXgzW6K7Jma7oE3BRagJd3gCdPQHSh9lF1WUzUZIW3Q040zXIMZS2JEidHt2eEKOWiNkBRadGt8dqi3MTfT81msyEc5ZGtWs5i8chmrERsLrR0mZEtWuJmeMQzeib6PvoYMzJdbG1SSOQPl9v6KyF5KKo5VoyVrK32U+ms/+41pO7KnqD6TP1ulVA2F3E/o7+YZgZzsGPK4/VlX9hfhIuu3nQ25gKpsI+muGMPjeXpjuOuy+sLk2lzZgS/UbWQrC5cViNLCtMjnp7emYiAGkOK9uaNAIZ86O34RxG5+OEVMiMKu836G0tL47+LmumpY1aXZORNp77aUWLh7QY+9BISku0Dv6hm4hL47KPOtL1oY1HM1r09hNIcphJj7FfW4wGmnHpvVlbDkFG9JD4trQlJFhNesmDwrXRG89bPuivsLI4+l5lVcnQaiFmxbhfmp/rxjVKhbNF/BvuEKQFmqa1HXmhaVorcLJ3ZVXA0UU+8tBry0TRNO3PmqYt0zRtWXr6if+IJ7TshbD4Y/2vbW447ydgj07ALMpL4qOnFPS9TrAY+cTqIgrTHCzMd1OcNrxCtqmJVn58+QISLP21Da5bU8ScbNfgNuBIhYt/E1lId+WnIG8FWI/axpJrIWMWzL448oCaMUdvm2Amw34amvsBtNTSvtda5jyC084dx4hGlzOjmNA5t4Elsa8tvPDDdKXGuLGYBCbDPnoiq4pTqekxs3Phd/Tjp69TL2yes6RvmZC7iA3JF7F2Wjpzco46Js26iEB6f70tLW0m2JOhsw7MCexbeRv3bNWf0H3hrOnMGMJ47gX5bi5e2J9gT04w87XzZkrh0WNMhX10To6La1cX9b12WIz8+PL5pBxnWMlFC3J4pSObtlnX9DdaXXDB/4HdjcVk5KbTSiKSO6fPSGd5kZ7oKE53sHhGIbsWflf/u+ilLf64ft0xVI40OPM7+t/HETMvhBhJ/FjOmZ3B3KOuKaZnJHLRgokzCn0891O9BswoD0FyWqmUBMyENi77aOo0uOjX/YW9lQEuvF1vP4Esl52fX7kAq6n/VvXm00p5t6yZ37wXpnL1j/UHKs5sfRKPXv6MhYRnnEe2295b8+VjkQ9d8lZCyRmD/grnzcti9lHn9llZiVwwf2jHpvl5bj6wpH9Qh8tu4jsXzibRJg9cpio1nO6dSqmtwOm9iReUUinAa5qmxXiUErFeEfDUALMgXQh8Fn0WpJXAbzVNW3GiWJYtW6Zt3Bjd/WxS8XZA4269indqScSB5lhtHj87q9tp6vaTnGDGajKQbLdQlO4YdHHIgRxq7KK82UNygplpGU4Sh5q5bTqgz2yUkKw/RbY69ex18yGwuSB9Nth6D3Kd9dC4R///9FngjNseCIN6PDeR91N//V5Uw26UwUAofQ7WjBOfOCc6b/kmjK0H0ezJ9CTPxp1xohrjcW3S76Mn0tzl41BTN3laHSm+SqyOJL13YWs5PV4fDdYCWoxpTM9IjLog6mquJlS/B9CwpE/D7msGTwvh5CLKyeJwcw+piRamZSQOeWaE9p4A++s76fYFKUlPJD9lyhYcnfL7aJc3yP6GTto8AYpSEyhOTzzhOg0dXlpamsjyl5MQ7saSMS3q+qC6tYeDTV3YTAamZTgjkjr+YIhDjd24eipJ8lZiS0zBkDFLPx8PV+12aNqrJ4Oy5sXuxTuAxk4vBxq6CGt6AiZjsL1sx05c7qfLfvQCt148d1AzZw1XTVsPv3pxH29+48xR+wwxIuJvHw36oWmfPuzIlQtpM8A0uIShpmkcbOyisqX/PFvX7qW82UO208A0VYOpq45wYibe9gb8YQPB1BmkZORFztTaUaMPPTaY9fuPxKEloBp6j01oMG2Yx6aOngD7Gzrp8gYpStNnXZzCJkbXxlE03ATMx4FvAUeqE14F3KZp2j+Os84DwOlAGlAP3AKYATRNu7N3Guo7gPPQp6G+TtO0Ex4dJvMFmZgQ4u9kJ0Qk2UdFvJN9VEwEcbef+oIh5t3yHPdcuyLyhnOE+YNhbrh3A3t/eP6ofo44aXG3jwoRw5Q/iAyrr7OmafcqpTYCZ6L/Eq/QNG3XCda55gTva8BnhhOPEEIIIYQQU0lNm5fUROuoJ0UsJgNOm5n6Tq8+tEMIIcSwDSkBo5RyaZrW0TvkqA64/6j3UjRNO94000IIIYQQQogRUN3aE7OI82jIdFqpau2RBIwQQpykofaAuR+4CNhE5AxFqvf1wMVJhBBCCCGEECOiqtVD2ijWfjlamtNKVaunr5CzEEKI4RlSAkbTtIt6/1s8OuEIIYQQQgghTqSy1UPqcWbLGkmpDgtVLT1j8llCCDGZDXUI0pLjva9p2uaTC0cIIYQQQghxIhXNHnKTx2b2tLREK4ebu8fks4QQYjIb6hCkX/b+1wYsA7aiDz9aAKwH1o5caEIIIYQQQohYKlt7WFSQPCafle60srWqbUw+SwghJjPDUBbWNO0MTdPOAMqBJZqmLdM0bSmwGDgwGgEKIYQQQgghIo1pEV6XjapWGYIkhBAna0gJmKPM0jRt+5EXmqbtABaNSERCCCGEEEKIAfmCIdp6/KQkjFENmEQLTV0+AqHwmHyeEEJMVsNNwOxWSv1FKXW6Uuo0pdRdwO6RDEwIIYQQQggRrbq1h7REKwaDGpPPMxkMJCdYqG3zjsnnCSHEZDXcBMx1wE7gC8AXgV29bUIIIYQQQohRVDmGw4+OyHTZqGjxjOlnCiHEZDPUIrwAaJrmVUrdCTyjadreEY5JCCGEEEIIMYDKFg/pY5yASXdaJQEjhBAnaVg9YJRSlwBbgGd7Xy9SSj0xgnEJIYQQQgghYqho8ZDqGGICxt8Fm/4Gz38XttwPwaEV1U1LtFIuU1ELIcRJGe4QpFuAFUAbgKZpW4CiEYlICCGEEEIIMaDy5m4yXUNIwHia4YkvQFsFZM6D+p3w9FfB1zHoTWQ4rZQ3Sw8YIYQ4GcNNwAQ1TWsf0UiEEEIIIYQQJ1TR7CHDZRvcwuEQvPxDyF4Acy+HzDmw4IPgzoPXfw5og9pMpssmPWCEEOIkDTcBs0Mp9WHAqJSarpT6HbBuBOMSQgghhBBCxFDdNoQivDv/AygoOa2/TSmYcR50N8KBlwa1mQyXlaq2oQ1bEkIIEWm4CZjPAXMBH3A/0I4+I5IQQgghhBBilLR7AgTDGonWQcyl4euA7Q/BrItAHXPZbzDCrIth0z0QPPH00k6riXBYo83jH17gQgghhp2AmdP7YwJswKXAhpEKSgghhBBCCBGtosVDlsuGUurEC+/8tz7kKDE99vtJ+eDOhX3PnnBTSimyk+wcljowQggxbMNNwNwH3A1cAVzU+3PxSAUlhBBCCCGEiFbe0k3mYOq/BHtgzzNQ9L7jL1d8Guz4t14r5gQyXTITkhBCnIxB9F2MqVHTtCdHNBIhhBBCCCHEcZU3e0hLtJx4wYOvQnIROFKPv5w7D2wuqHwHCtccd9GMRCuHmyQBI4QQwzXcBMwtSqm/AC+h14EBQNO0x0YkKiGEEEIIIUSUQ41dg+sBs/cZvXfLYOQtgz1PnzABk+m2cahREjBCCDFcw03AXAfMAsxAuLdNAyQBI4QQQgghxCgpb/YwK8t1/IVayqCnFVKnDW6jmfP04Upd9ZCYOfBiLhvrDjQPIVohhBBHG24CZqGmafNHNBIhhBBCCCHEcVW0eMh0nWAK6oMvQc4iMAyy3KPRDFnz4NCrsODqARfLctkob5EivEIIMVzDLcL7jlJqzohGIoQQQgghhBhQjz9EW0+AVMdxEjBaWE+kZC8a2sazF+mJG7QBF3HbzQTDYVq7ZSpqIYQYjuEmYNYCW5RSe5VS25RS25VS20YyMCGEEEIIIUS/w83dZLlsGAzHmYK6YReY7ZCYMbSNJxWAvwdaDw+4iFKK3CQ7h6QQrxBCDMtwhyCdN6JRCCGEEEIIIY7rcFM3We4TFOAte0Ov6TJUSvUOQ3odlhYPuFiWy0ZZUzdLC5OH/hlCCDHFDasHjKZp5bF+Rjo4IYQQQgghhK6suZsM5wmGH5W/BZlzh/cBmfOg/E2ONwwp02XjYEPX8LYvhBBT3HCHIAkhhBBCCCHG0MGGE0xB3bQfTNahDz86wp0LgR5oqxhwkWy3nf0NncPbvhBCTHGSgBFCCCGEEGICONjYTc7xhiBVrIOM2cP/AGWAjDlQ/vaAi+Qm2znYKDVghBBiOCQBI4QQQgghxARwuKmb7CT7wAtUvAPpJ5GAAciYpSdyBpDlslHd1kMgFD65zxFCiCloTBMwSqnzemdOOqCU+maM909XSrUrpbb0/nx/LOMTQgghhBAiHrV2+wmEwiTZzbEX6KwBX4c+jOhkJBdDZx14mmO+bTEZSHNYqGjxnNznCCHEFDRmCRillBH4PXA+MAe4Rik1J8aib2iatqj35wdjFZ8QQgghhBDx6lBTF7nJdpQaYArqynf13i/qJC/vDUZIm6FvbwC5yXb210shXiGEGKqx7AGzAjigadohTdP8wL+AS8fw84UQQgghhJiQDjZ2k+0+wfCjtBkj82FpM6Fi4DowOW47++qlEK8QQgzVWCZgcoHKo15X9bYda5VSaqtS6r9KqZhz6CmlblJKbVRKbWxsbByNWIU4abKfingn+6iId7KPiolgrPbTffWdZA1UgNffrc+AlFo6Mh+WNgMadkLQG/Pt3GQ7e+o6RuazxKiTY6kQ8WMsEzCx+ktqx7zeDBRqmrYQ+B3wn1gb0jTtz5qmLdM0bVl6evrIRinECJH9VMQ72UdFvJN9VEwEY7Wf7qvvInegAry1WyC5SJ+CeiRY7ODKg9ptMd/OS05gX50MQZoo5FgqRPwYywRMFZB/1Os8oOboBTRN69A0rav3/58BzEqptLELUQghhBBCiPhzoOE4CZjKdyFt+sh+YNoMqHwn5lu5SXYqWj34gzITkhBCDMVYJmA2ANOVUsVKKQvwIeCJoxdQSmWp3spiSqkVvfHFLsEuhBBCCCHEFNDjD9HU5SPTFWsIkgbVm/S6LSMpfSZUbSC6w7o+E1KG08qhJukFI4QQQzFmCRhN04LAZ4HngN3AQ5qm7VRKfUop9anexa4EdiiltgK/BT6kaVr0UV8IIYQQQogp4kjvF6Mhxoj+loNgtIAjdWQ/1JEOyggtZTHfLkhJYHet1IERQoihMI3lh/UOK3rmmLY7j/r/O4A7xjImIYQQQggh4tne+k7ykwcafrRx5IcfASilD0Oq2gApJVFv5ycnsKumk8sXj/xHCyHEZDWWQ5CEEEIIIYQQQ7S7toPsgeq/VK2H1BGafvpY6TP16a1jKEhJYEd1++h8rhBCTFKSgBFCCCGEECKO7anroCA5IfoNXwe0VkBK0eh8cHIxtFeCNzrRUpTmYHdtB1ItQAghBk8SMEIIIYQQQsSxPbWd5KfESMBUb4a0aWA0j84HG02QOr23GG+k5AQzSkFNu3d0PlsIISYhScAIIYQQQggRpxo7ffhDYdISLdFvVq6HlGmjG0D6DKh4O6pZKUVpeqIMQxJCiCGQBIwQQgghhBBxandtB0WpDpQ6ZgakcEiffjp9hKefPlb6LKjdCiF/1FsFqQlsrWwb3c8XQohJRBIwQgghhBBCxKldtR2xZ0Bq3AP2ZLAnjW4AFgc4s6FuW9RbpemJbCpvHd3PF0KISUQSMEIIIYQQQsSpLZVtFKU5ot+oXD8600/Hkj4r5jCkaemJ7KhpJxyWQrxCCDEYkoARQgghhBAiTu2obqckLTH6jcp3IH322ASRMRsq1oMWjmh22c24bWb2N3SNTRxjKBzWeHxLNdf97V3O+uWrXP2nt7n7zTJ6/KHxDk0IMYFJAkYIIYQQQog41O4J0NLtJ9tti3yjswZ8neDOHZtAHGlgskHT3qi3ZmQ62VjeMjZxjJGath6u+OM6/vDqQebmuLnx1BLeNyOd53bWcc6vXmN7lRQeFkIMjyRghBBCCCGEiEPbqtsoTnNgMBxTgLeit/eLGsNL+cw5UPZmVPO0zETWH5o8CZiDjV1c/oe3mJ3t5PsXzWHNtDQKUx0sKUjmi2fP4MqleXzs7vW8daBpvEMVQkxAkoARQgghhBAiDm0ub6U0Pcbwo8PrIGPW2AaTMRfK3wQi673MynKxvqwZTZv4dWDqO7x85K71XLYol0sW5mI4duYpYGVxKp8/czqfuW+z9IQRQgyZJGCEEEIIIYSIQxvLW5l2bAKmpxXaDkNK6dgG48wCZYTGfRHNOW4bwZBGebNnbOMZYb5giE/+fQOnz0zn9JkZx112draL69YUc8O9G2ju8o1RhEKIyUASMEIIIYQQQsSZcFhja2UbpRnHJGAq3tZnJTKaxzYgpSBrHpS9dkyzYl6um7cOTuwhOT96ajeJVhOXLMwZ1PIrilNYVZLKFx/cMil6/wghxoYkYIQQQgghhIgzBxq7SLCYSHFYIt8oe1OflWg8ZC2AstdBi5wJaHa2i1f3No5PTCPglb0NPLezjhvWlqBiDDsayAeW5lHf4eUfb5ePYnRCiMlEEjBCCCGEEELEmXfLWpiV5Yxs9LZD8z5Imzk+QSVmgCURardFNC/Mc/P2wWYCofAAK8avdk+AbzyyjRtPLcFhNQ1pXZPBwE3vK+X2F/ZR2TKxh2AJIcaGJGCEEEIIIYSIM28fbGZ65jEJmPK39OSLyRJ7pbGQswj2vxDRlJRgISfJxoayiTcb0g+f2sXigiTm5bqHtX5ukp3z5mXxrce2y1AkIcQJSQJGCCGEEEKIOKJpGu8camZejivyjQMvQ9b88QnqiOyFULUB/F0RzYsLkvnvjrpxCmp41h1o4vX9jVy9rOCktnPhgmwqWz08vb12hCITQkxWkoARQgghhBAijuxv6MJsNJDhsvU3dtVDewWkzRi/wAAsDkibDodejWheWZTCf3fUEg5PjF4g3kCIbzy2jU+sKsJuMZ7UtkwGA9euLuIHT+6iyxccoQiFEJORJGCEEEIIIYSII2/sb2JebqzeLwvAOLQ6JaMibznsfgroT7ZkJ9lx2c28fah5/OIagt+9fIDcJDtLCpNHZHuzslzMyXHx6xf2nXhhIcSUJQkYIYQQQggh4siLu+tZkJfU36CF4cALkLN43GKKkFIC4QDUbo1oXjstjQc3VI5TUIO3r76Tf75TzsdOKRrR7V69LJ+HN1Wxv75zRLcrhJg8JAEjhBBCCCFEnOj2BdlS0ca8nKOKwtZt03u+uPPGL7CjKQUFq2DHoxHNa6al8fKeBlq6/eMU2ImFwxpff2QbH1iSGz3F90lKSrBw+eJcvvufHVKQVwgRkyRghBBCCCGEiBOv7G1gVrYzsi7Jnmcgd6me+IgXOYuh+SC0HOxrctnMLC9K5r53yscxsOP7xzuH8QZCnDU7c1S2f/bsTBo6fTyxtWZUti+EmNgkASOEEEIIIUSceGZbLUuPrkvS3Qi1WyA7ToYfHWE0Q9GpsPkfEc0XzM/m7rfK6I7DYrQVzR5+9cJ+bji1BMMoJbOMBsW1q4v40VO76fAGRuUzhBATlyRghBBCCCGEiAPdviCv729ieWFKf+PuJyF7EZhtA643bvJXQPMBqN/Z15SXnMC8XDd3vnbwOCuOvVBY40sPbeGiBdnkJtlH9bNmZDpZmO/m58/uHdXPEUJMPJKAEUIIIYQQIg48v6uOWVlOXHaz3uDvhn3PQeHq8Q1sIEYzTD8H1v8RwqG+5g8uy+fet8s52Ng1jsFF+sOrB/AFQpw/L3tMPu/qZQU8vb2WzRWtY/J5QoiJQRIwQgghhBBCxIH711ewujStv2HXE5A+AxJSBl5pvGUvAmWEnf/ua0pLtHLFklw+e/9mvIHQwOuOkbcPNnP3m2V86rRSDIaxqaOTaDPx0ZUFfPWhrXHxOxBCxAdJwAghhBBCCDHODjR0caChi+VFvfVf/J2w+3EoPn08wzoxpWDu5bD9YWje39d8zuxM0hOt3PzPTeOagKhs8fDZ+zfzqdNKSU20julnn1KSSrrTyi+f3zemnyuEiF+msfwwpdR5wG8AI/AXTdN+esz7qvf9CwAPcK2maZvHMsbhaOvxs6+uk+YuH/NdHpK7D2GzmPG4p7G5xYo/pJHn0Mjyl2PorkdLKuCwyqGmI4jRYCDBbCQYDrPA3oRqOUTY6sKQUgLtlZi8zQScBZgD7ST4m9DsKXQZ3Oz0pTLD2k6y1o4x7CPsacFry6DWWkKDJ8wsYzU2fxvKmYG/vR5lttOcUEK3P0iuvxyz5iecVIjWUYsW9NHuKKbekI7NqMgOV5Psq8XoawdnNpqvE2V3oTwtEA6h2ZwQ1tDMdrSgjwAWvJYkaK/GYDRCUgEJHWUYjQaUFkLzdhBMnsbbPXnkJiryQhUYgz4MngaUOQEsCWg9HWC2o/W0YbA5UVoY5c7Vn6g07AKTBbLmQ3LReP9zTznh6vdQzftBGdBSpmHIXTTeIY268OF1qNYysCcRdhVizPl/9u46PI7rauDwb5ZZzAyWZEuWmZ3YiR3mhrlhbpvyV27KnDYNNAwNJw2jEydxyMwg27KYWatlmu+PkddaS2ax7/s8euydnV2dlUYDZ849t2SkQxq/3F3QWgbOFjDGgKTGbcthS5cOhzdIYbyeNH8N2OvAlgrxhfv6IDjboLUMj9tJuz6TBlUi0/TNqDt2gt9DKK6AoM+DOuhCIoSs1oGzDVlnxRedi9/ZhVXtR+qsBmMUsjEOyedADvqRTfFI7g5CrnZCMXmEJA3arj1IBhvukAa/JR1ToAupqwJMiYRsqag6K1Fr9YQ8drAko3K1giwjW5Lx2ptx6ZMoJ5Uok5FEXw0Gvx2t7MHrD+Ky5VIZSkQOQbfHjz8ok2qWyAzVYpXcqP0u/CGZVkM2lcE4dBo1MUYdeYkWdCGP8jN0NEN0JsQX4ApK7G520Obwkh5jIj/RgscfZHdzD+1OHxmxJvITLLh8QXa39NDh9JERY0Ktgqp2F7FmHROSrFj0w3qaMDa07YaOCjBEQeIkMNioandS0epEr1GhUUnUd7nJiTczJT0KlWrfva7GLjduezuxzj2o/A4kaxJG2YXa1UZIawGdGclej2SwIas0hFRa0Ojp6WjGHJOCyt2G7HUQiC2gJWQhIdCIzm9HCnpBH01QkvCpLagCHtyOTqSYHHxoiXfuRhVwEYrKRtJokTr2IKnUBKOz6W6pxRidhBTwoA15kXQmZEczkjUF2dVBABU6a7wy040pHp8umoDbjtoUDT4XGtmPKughEAwSsqbh6W4jpNFjt+azo13NrDgH6vbdoDUgJRRS5TFh9jST5K3GoNfSasih3G3G7vZj1mtIsqhJD9YiddUi2VLQpRRhMFpH7vc9jB79vIKTixLRqHu3mY0vQOJEsCSMbGCHwxQLk86D5b+FM/8K5gQkSeKmE3N58NM9XPzQ1/zj0qnkJ1qGNazWHi9XPbaK86amUpoePazfG0CSJK5fkMNPXtvCSYUJzM+PP/SLxgO/Wzku9TRBVAYkFCrL23ZBVw1YkyGhCAIBaN4M9gZkSypo1Mqx3deDbE4moLXi01owBezQ04Ckt4EhGkJ+6KwCtQ4puRTi86BpC3RWIetsEHARQgVyiB6vjN2STZMqiYYuN7Nj3Zh79qA1xaJXgxRwgL2JkDmZWtNEdnSCxx9kUqKBBE8VUk8DQXMyjdpM2n0q7G4/NoMGSQKjVoPbH6Tb5Sc9xoAnEKK1x4fVoEGtklBLoJIkejwBQkCUUUun00eiTU+3y49GLRFv1VPV5sSi15IabSAvwYLbF2R3i4Mul4/MOBN5CRakw2gaXdN7LDLq1ExIsg76NOvC4Bi2MytJktTA/cApQB2wRpKkN2VZ3t5ntTOACb1fc4AHe/8dtVp7PPz27R28samBB041k/Lxt1F3VQFgSCimOf8PvLQrwEMF64j5+nfKi1Rq4k6+j1tWJNFs91KaHsV/FvmJ+99V4FPGyoaKL0Ql93aPDwWh7G3l/zozMSf/nDnBEFpzDHh74ONfo/I5sQA5pZeTnDIH6wffgdN+B+/fhcnZBoAp60TIWYz203tgzi2wcg3UK/mtGEsS2rOfJeTpIbH5Y/j63+FYufgZePMu5YQTkOLyoeRC0NtApUZjjMPw7p1InVUw7Srk7jokQxSEAlD2DhKg01lYePFTtG5ejyHGCl/eq1x4Lf0VbH4ZsufDV/ft+56Lfgzrn4LSS+CDH4OjBZJL4YL/QNKkofllCv3IVV8ivXG78rsFSCgidPY/UWXNHdG4hpK88z1Ur1wPfhcAqpKLCM67E3XaKJt9YjxwtMKyn8Om55XHOgss+TmaZb9ha/KPeGJbgBfmVMNn3wc5pNxlPeMvMP1aJWHz1negfBkGIM0UR/LFT6F69zdItasAUFsSUV/wH1jxJ5h8EXz8m/A+Vj3xPEyzrofnrlB6LABSyUWQt0SZ5XXX+7D+SaVMVK2Dpb+Ez/4Cni7M878FSSXw5p0Q9AEgL/weUnQGvPt9VLNvhuqvlFlLAKwpmObciumjazCe+EvkuDwsFe8pCZM9y9EBVkM0hote5PZP4cvydgoTDDw5tYwE9x5o3gq1q9ACWeZ46uc9yoM7TSRa9ZxbHMuJXa8hLfuF8r1UakIXPMoL3VO55+0dAGhUEo9fO4vVVR38+5Ny5UetVvHEdTP5dGcrj3xeCYBeo+KHpxdy77Ld9HgD3LYolztOysdi0A7VFjD2VH0Bz14c3j8w43qqpn2Pcx/dht2jHLNPnBBPnEXP//1vC/+8bCqn9/ab2NXUQ0VVBYsr/oph15vK6xf/H2x7Hbx21DOvg0//EO6jIc27E5XPCcjEhIKwuRoqVwCgNcaQec6/kDY9DzvfVd5LZ0F9xp/R1a+HtY9iAjj3PuSNzyPVfAWA6tLn4P0fQXctAJqkEuIWfAeeOQOW/hrkICz/Dcy+GWpWQsMG5UTRmgxzb4fXb8Mw+2Zl5pvqDWBvhJ5G2Pku6t4YjEt+Dh/+DFPuaSTP/xbGF65UZvEBvBknkHHCz4l5+0bU9hoAkpOn8nXub/jB8h5SovS8uaiZ6I/uCv8cnIvvwTfnJnRG0+D/PkeRZruHd7Y08peLpigLumuh/CNY8K2RDexIJJeAuxPe/zGc9nuwJKFRqbjjpHyWbW/mwge/YnZOLGdOTmZyWhRp0abIqbYHWWO3mysfWcXs7FhOmZQ8ZN/nUGxGLTefmMu3X9jIO99eSKJ1FDZTHkx+D6x5DD78qfJYpYaLn4KgH/53475eQafco9xUefkaiEpHOuV3sPU95fwfkNRadOc/iE5jgmU/U6Y9LzxHORd4+ztKAgaQE4qQzrsfPv8bpM5A6qwAlRp1RwVUfUE0EG2KpfukJykxGkl+9TpUJd+AlFII+ODd74HfjQrInH4dD3RfwKbWEK/Or8b6wd3h8w/N4j/yWudcnlhZjyTB7Yvz2VrfRWl6NK9vrOei6en88+PdhGQlxFtPzCMvwcy9H++mrtMNQGGSlZOKEnj5zTpuW5zHb9/ZQWaskQtnZPCPZbu4dn42ZxQn8f62Zp78Svl8eo2Kx785iwWHSN5tqevmmsdX0elSZt46qSiB318wmZSooW04LRy54RyCNBsol2W5QpZlH/ACcN5+65wHPC0rVgLRkiQNT6eso7Sl3s4bmxpIjzEy274snHwB0LZuY4G8lm+Vhohf+ft9LwoFyfj8x3xrmnJSWxIrk/jFz8IXBgCqba8qFR/Jk/clX0C5UFj/NNrG9aAxwLonwhcPAJrNz2P1tSpd6Ss+U7LIe+OpXoFW9ioXOXprOPkCgKOZxO2Pk+6vgpX371tuToSKT8LJF0Dpdu/uhE0vQGc1hHzKBbokQXQmUsWnvXG/0yduB6qPfkWy3g/165SMePH5sOZRKDwNvu7zPUNBJUGTUAitO2DKFcryps2wZ/khfyfCINr+xr7kCyC1liFVjN/fQahpB3z4s30XV4C09RVUnZUjGNU41rRpX/IFlH3ghv+itSVwkn4n35uhIe2LnygnPwCyrFw8tu+GujVQvmzfa13tqL7+N5LU57DmaFFO5DLnwob/RuxjpR1vQOPmiP0nW19Rqu0cLbD+yX3Lgz4lQTz5IuVxQqFyYtmbfAGQgh74+NdKskZj2Jd8AeUCtWkzJE7E/Pk9WOy7ITY3cn/m6cL8+e+wqpT3/PZUiZSv7wFzPPQmlABwtlFa/QQ+r4esODNy606kj3657/lQENXb30bfUxteFAjJbGvsDidfAHzBEJvrusPJFwBvIMSjn1dy7tRUAB78rIKy5h6EXs52ePvuiP0D6x7HW7sxnHwBWLG7jdwEM95AiHve2k5lqwNZlnl5XS0ztFX7ki/meHB3KMe50kvgi39ENDFl5f1gSwFrCkRnhJMvALg7kez1+5IvoGzfqx4Cd7vyWGsCvzucfCFnkfI3071v26B5K3RWgi0NPF2w4i/KNqw1QcOGfev1NCmPk4ph9cPK4+56JTGzfwzrn4GiszG6GtGteSCcfAHQ135OTPt61M7G8DJN00bmyhuwGTR8e5qGhE9/EPFzMH/2SzxNOw756xnr/vnRbhYXJhBl1CqJsC/+CfknKedrY0nOCco56DvfheovARmVJHFacTJ/v2QKWXEmXlpbx7WPr2HKPR9S8LP3mHbPhyz443LOuHcFVz26ih+9spmHV+xhxa5Wuly+Q37LgaysaOe8f3/J/Lw4vjE9/Sg/jKwMA+uuUyrAumvBa1eWH6HS9GgWFSZw6zPr8AVCRxnPGNG2S0mY7BUKQsNGeOtbkfu4j34JbWUQ8MIJP1CO7b3JF0BJ2Hz5L6hdqVyHJE5U9oVlb4WTLwBS3ATl2mTnuxCdppxX2NKUhPlerg7y614jo+JFVD31kDYdUMGKPyvVOr3U65/griIHf1hkxPrRDyPOP2I++ylX5Pn2PuThFXuYkhFNt9vPJTMyeODTPYTk8Op8sK2JTXVd4eQLwM7mHoIh8IdCfLWnnemZ0dR0uGnocpMeY+Spr6po6PaEky+gHJt//L/NtDu8B/yRe/xB/vnxrnDyBeCTslY21nQd8DXCyBnO2uI0oM9Rnzr6V7cMtE4a0Nh3JUmSbgZuBsjMzBz0QI9Ei90DQE6sgfj21f2ej2nfSHx6lvKX2JfXTqJGOfGfHCeh3r2t32sJeAb+pq07oPBMkFTQMsBJScCrnNxXfdn/OUcLxOYoJ0770davhpTJkbHGZCklhPtr261cqOjNSjIGlBM2d9cBY5datsIJd8P23iZtUelKEifo27eD28vnBJUK7A1KvHs1be4fyyg1mrbToxH0uVH3vYjcq2GAZeOF34nUXt5/uaNl+GMZBiO+jfY09V/WvA3yTyG6awfJ8en99yWhoLLPae8/vanUuBHyl0LN1/sWNmyEvCWw8oH+38vT3X9Zb+nzgLEao5X/q3URF5UAaI1KXDE50F3T//Ut25X9cssO5e5gsP9FhaZpI1OKJd7fBVGhLtBbBtz2LC3rKU69Eac3gFXXedDjS/hj+fr3XxhoWWO3h5g+Jcst9gOf8A2HEd9G+/J0KxcW+9G6WoCkiGUubxC1SqKh20OX248/GGJddQc6W/O+laIylYsGUO4Q+yJ/Z8iysp0EfEpF6f72Hnv7at6qzEgDynC0vjdP0qZDxaf9X9O0ValckCQlhuisyCRN+L23KUMGmrcpiZa2nWCO679ey3YoOA3UWtQ1q/o9rW7frQwh6PM3FNOxmSTbVBLUPQP+HOSe0b0PPtbttKzJzrtbGvnTRaXKgi2vKL/79DFabZo5DywpsPZx2Pqqcs6aNh2TMZYlRUksKVL+XmRZxhcM4fYFcfuDuHxBut1+2hxeNtR08eamBva0OEmJMjAvL44F+fHMyYk9aB+X6nYn931czic7W7jxhBymZsQcftxySNmu69crw1k6KpRjjsEGKo3yf69dOf+OzYXkKZAxC+LygEMPD7lgWhr3Ld/N91/exL2XTh22ZsAwzPtSR0v/45KEUrXflyzvW6bRgXeAY7LeCvVrlf/7e88HmrZGrpO/VKmoVanB1aXcaHZ39Hsro+yE+u2gMYLHrrx3n0TOXlGBNoJ6nXI91VcogN7bHn7oD8qEZNjT6iA92oh3v8RaeoyRTXX9P1N1u5Nkm4GdTT3MzollfU0Xu5sdZMSaqOt009UnibJXbYebbrf/gNt+j8fPptr+36uq3TXA2sJIG84EzEB7mf1TyIezDrIsPww8DDBz5swjT0MPoqw4pSR2a6OD+tmnk1YbmYRpTlpEdTCOSWpd5Am3NYVdbitg55O6EBflLEFb8VHkm2sPUG6btUA5yUqYCNkLIzO8oFwI1K+H7AVKgqOvqHTY8AxMPKff23omnI1Wa0Kt0e/b6bTsgJk3QNXnkSunTlMqbFwdEDdBWeZzginugLHLOYuRKlYoF0Ntu5UdaMZs5UCmMUReaJkTlPeLzYWuun3LsxcO/DMZhUbTdno01Dojct6S8HCOsNxFIxPQMJCNsciZ85D6XsCDkogch0Z8G+2bXN0r+wSoX0dL5iXsdFmZa4pV9jN7aU3KiX1Kab+XyhNOQ6rbLxE+4VRl/5V9Qv+LT0vkRTMAUWnK3WdJijyBTCiErt6LUneXcsLdNwnkbFMuXO11EHdZ//fNnA+7P+itLjAq+7z9+PJP5+Na5QSuPhQHfq8Sz346sk7ns8ogF0zT0q5KVN5zv+PLbo9yfAl/LKMWlUT47hxArKn/0KKiZCs1vSdskgSZsSM77GPEt9G+LAkDHnMD0VlAZKLQYtAQDMlMTLGSYjOg06g5rTgFt9VF1N6V2nbC7Fug/GPlIsSSGJlw0+iV46MhSvll7M86QIFw7mKlyhSUZNG8O/Y9t+sD5UK4b2ULQNY8+PSPyrHckgj2eojL7//eWfOVWCWV0q8pbYZyIbO/7IVKDN11BCecjnrNfyKeDqZMRb3uiYhlzYkLqdnmYrcniiUD/BxUsaP7JsaxbKeBYIjvv7SJi2amYzNold/P9tdhzm3KjaixKjYb5t0FLduUyqvVDwOSksjWKBeRUiiIPhRAHwoSjawkOTR6ZZu3JEJ2BsEpWVRJyexo8/P4F5X84JVNxJn1TEq1kRtvJtasIyTLNHZ7WFPZQW2nm5OLEvnThaWYD6uHlayc61Z8qlTsaIwQPwFSpyvnyjpL/78/b49yI7OzAso/UD5X7slQcApYDjzUSSVJ3Loojz++V8Yv39zGPecVH1Zfj8EwrPvS6Azl99g3gRH0KxVzfW+8qHVg7L1ucHWCKUHZv/S9Kev3KL+H6q9AZwJkZR/T99x03ZOw8DtKgsyapCTJzIn9wurWJqCdcA6mL/6gJIHlgHI9s98+sVGTRoPPRo4hWqkM3EtnwWFMAZSb2Ba9hmBQpjQtmk6XnxiTNqICpaLVyTemp7F5vyRMUYqVz3a1clZpCptqlfeflhnN/9bXoVFJJNn6J1mmZ0aTcJDEY7RJx6nFSTy7KvIG0KSUMVZBd5wYzj17HZDR53E60HAU64wqk9Oi+dlZE3H5grzjm4Gr4HzlCUnCNflqXu3K5w9rgrSc/h/lhAXAlkblSffzwFonKgnMZhvlpT8gmNjb6FNrJLjkV7DnM9j1PvIJ31dO2EFp+Fd0Du7iy5GDXqXrfOKk8Os8S/9Ak2lCuBmjnDlfeU6lwTXnOzhUvad+zduRZ1ynZIsBd9bJVGdeQIV+IvLSe/bFqjND+iwo6h1zKUkw8VwIhWDqFUpfFkmNXNSb0Kn5Cnn+t2H3MljwnfBFhpxYjGfhj6mKnQ/GWMg7GXZ/CJPOh6qv4KSf7pti0ZoCJ3wPZEkZG7r5eWWHPP1apYRaGDZyzknIRWcrDyQJefIlyOmjui3TMVHH58LJP0dOKFIWaI3IS39NMDpvZAMbr5KnwOl/2peMSCqBCUtxxxfzUnM6f1/joeKkB/YlSkxxcMnTEJcLabOUXlFqJYngTZ+Pt/Qq5OwTlZN4QM5eCBNOUx7nL1HeH0BjQD7pZ0oSp8/vmqW/3tdc9ZTfhUv/5Zgc5Dm3KlOs6izgcyCf9gfkvU3B9TbktBlKPJZk5cJ3yhXh/Su5i5VqFp+T7rMfoTtxtnJ3dc6tygkoEEidRdeMO5mWnYBWLfHnNT5qlj4IrbtgxnXhz+TNPJEVUedyYkEi2xu6ic8ugYufjDi+yBc9QV7uBGwG5TVp0UZm58Rw76VTsfZekGTGGpmdHcffLp6CubcHQ268iavnZvHe1kYseg3/uGQqBUnD2zBzVNNble01Ye8x1wRn34spYwrTMqKVVTQqbliYw5flbWTHmfjVOcUkRyvH7zMnp/C1KwPHib9QLk58TmSVBkovVW6WLPzuvqSKMQZO/hmoDcjt5crFx+xbwtu7nDEH2RwPC74d+fdT/A0lyaKzgBwi5O1BnnObsv20bFe2+YLTlfUllbKdyrJy9zk6E078gXIDpKUMpl0V3oblnEXKBYunW+nDpDMha3qrvvrEICeVKEnPmpU4iq/AXnI13qze47ZKg3P2t6kwluDMPTMcg2fa9XzqK8IbCPHIRg8tpz8c8XNwnvsY5pSiIfmVjgZ/fK8MnUbFyYWJSkXUZ39Wtom9FXdjmUqlDEmfeqVynrfgW0oPwQmnKdtJ0dnK4ymXKdtiyYWQfwokloBKC03bUK97grzlt3L2nnv4nu1jHl4U4M6FqeQlWGjpUSplNtV24/WHOG9qGv++fBqXzMw4dPKlqxrWPw2vXA8r/qokCGbcoMRYeAYkFil/8wMlSPRW5fnCM2HB3TD5EqVq7M1vK8OYa1crifwB6DVqfnBaIasrO/jhK5vxB8fhcKS4CUrPl73HJWsKFJ6lHKtsyhBXjDHK45RSiM1Tek/FFyj91vQ2ZZ2YbFj8I+SYLOV6ofxjpfVB3snKzx6Uc9OEQuU9Fv8YKlbAqb9Vkiqzbtx3jpA2jz2p57MhaimevDNg26ugNirXG/G9N5J1Zvxn/I1frFLx/WVddJ3z6L7zD3M8HWc/yv0blGrEeIuO7yydgN3jxxMI8tzqGu4+pYAEq5IkiTXr+OaCbDLjTCydqCSDVBKcOyWV+k43UzOiSY8xsqfVyamTkgiEZIKyzI/OKCIr1sSfLizF1Htszksw85vzS7AaD9yPTatWjj1zcmJ7H0vcvXQCU3qPTcLoIsn7l4gN1TeSJA2wC1iCkjpcA1why/K2PuucBdyJMgvSHOBfsizPPtj7zpw5U167du2QxX04giGZ6nYnXS4/8fogcf4GzHoNHmsWlV1B/MEQJq2auEAzKk8nsjWJFjkauzuAurf8UK2SSNK40DjqQWtGm5BLoKMalbebHl0yxlAPtpAdtCbskpnd7ijS9S4S6UAlBwn6PLh1sTRrUrF7gmRr2jAFe/DoYpDcnai0eprUKfiCIdJCTegI4DKmonG3oAr56TSk4VOZQA5hCvWQGGhCG3QR0scg+Z1IOhNSwIUkh5AlNUgqgiodhIJ4JS12dRxGTwsqlQqPOZ1od7UyMwIg+124ovKo8tqINetIklvwu3rQBByACklrRPY5CGlMhPwedFqN0swvKkM5eLftUi5Skkv2JaJGj8O6dTEattOjFWyvROqqBklFKDoLTez4rAbpK9S0Q6lk0FvwJ09Hrx/eaSsH2ejeRkMh5S6iu1u5iFOrCUbnUN3lx+0Pkh5jJMrfptwRN8Upd9b2CvqhoxK/z02zOhmv2kymyYe6dQcEfXhtOYQ8dnQBJ6jUyCodKp+dkNZEKK4QV2slVr0KtaMJdGaCGguqoAdZDiIZY5E9XQQ9DjyWDEKyhMVdh6Q14whpcRvischu9I46QsYYvJY0DF27UWt0eP0B1HoLal8PEiEC+ii8LgceXSw1wVhsBg1RgQ4MgW70qhCegIzTlEab30BIlgmEZEIhGYNWTaLchkV2opWC+GWJDm0qPRjQSCpiLFqSbb37xM4apeTamqx8oUy92uXykxylJ6G36WNNh4tul5+UKAPxvSeK1e1O7O4AqdEGZFkZhhRl1JAZZx6urWB0b6P7c7YrPSH0FqVKU5Lodvuo63Rj0KjwB0O0O/1kxZpI36+CyOUL0NzlIspTi+R3EzDEYQ3Z0QYcBCUNQa0VracVSWtQjrOyREAfjbOrDVNUAhpPB3LAjdeWQ71bS4a6HUPQhRzyI2kM+IMyPq0NSQ7ic3VDVBrtfi3ZgSq0QTdefRySzozWUYskqfGaU3B2tqKxxqMOeDDILlDrCXkdqAwWZL8Hj6zBaDKj7q5FNsbiVEche7pRmeOQ3V0YVCEIBfAFAUsSvp5WZI2RbkMqjT0BCqJRGu5qdJiTJ9DqCNHj6CZNbsJq1FMnJdHqkvCHZIxaFVajhvhQF3JPEypLHNak3BH5NQ9g0LfTp76q5D+fVfCrc4uxdu+ET34HRedCcvExBTruhILKkPTOSuioVIaNRKUp1QtJxcrNOkPUwd/D71KG1NdvUHqK+N29vRZLlaTAYFSiBP1Kcr12lVLVXXg2TFg6YGwef5D7lu9Gp1Zx3xXTwxfux2h07Uu7asHVriQxbL1J1Z4m5avv8bxpK+ydCU5rVKpOgl7Q23CrogiaYrD425Cd7aDWI2kMyid1tiKpdUipU5Ubxj0tShJTUhNSSfgDISSNDrs3hN2QigsjXZ4AWRYZi7se1AaMatCGHODtwWtIoEGTSbvLjy8ok2jRYQu0oXW3ETDE0qpOwB8I4fYH0WvUaNUSGpWELyjj8gVIsOjxBkJ0u/3KjHhqFSFZRkKpPg3KMjq1hNcfwqLX9O7z1EQZtdR2utCr1aTGGEiwGpBlmZp2Fz3eAClRhsOePr3H7aem04VBoyYrzrRvRrXRZfjG3o1Sw5aAAZAk6UzgXpRpqB+XZfl3kiTdCiDL8kO901D/GzgdZRrq62RZPugeYtSckAnHq9F1sBOE/sQ2Kox2YhsVxoJB205DIZn7lu/muVU1/N8ZBSTVfgBbXoaSiyBhwqAEO64F/crFfWel0nOrq1apKrOmKn2JtGalwivgUaq0ehqVoaxR6UrSNL5ASeBIQ3hx2lWrVMK0bFf6L004FVKngrRv1qdQSObVDXV8trOVn589ifOmph7rkCSxLxXGguM+ATOcPWCQZfld4N39lj3U5/8ycMf+rxMEQRAEQRCEsW5Ho51fvLEVhyfAL2f6iPnsR0oiYM4t+4ZiCwen1ipDUeN6q6NkWRke52pXmqsGvUofEWOMknTJXaxUXKjUB33bQRWdoXz5zoTGDcqsn1670qA4cx4kl6BS67l4RgbTMqK5b/luHl5RwZ0n53PKpCS0o7NyQRCEQTCsCRhBEARBEARBOB5VNbZy0QOruTipmaWeN1Fv0UDOiUr/nmFqxjouSb3NfUdj3xydUWlgnTVfadbesg3WPaU0u47NgcRJ5Mflc8+iLNZ1GLn/k3J++toWlk5M4sSCBKZnxZAaZRi2Zr2CIAw9kYARBEEQBEEQhCFmf+oKbP6rie3cyPqoBUpVRhfQVTXCkQnDJxNsmWAOgLMFyhtg875plU8DWuRo1q0v4OV12RGvTNX08PLiTtKW3ja8IQuCMKiGtQfMUJAkqRWoPsRq8UDbMIQjYjg8oyGOwYqhTZbl0w+10mFup6PdaPi9Dbfx8JmPdRsdDT8DEcP4jmEs7EdHw89+MIyXzwHD/1mOdDvtF9/9FyZnfZh+a1QQ9YiWM8jIkoQ0Ki8ARmtswxKXBAY1kl4tq3TqfX0yumQLNXIiRnz8qPM3znMfrizbL5C929pY2Jf2NZ72RwcjPmekw9pOx7Mxn4A5HJIkrZVleaaIYeRjGC1xjIYYxprj8Wd2PH7m/Y2Gn4GIQcQw0sbL5x4vnwNG/2cZzfGJ2I7caI0LRndsBzNW4z5S4nMK+xMdngRBEARBEARBEARBEIaYSMAIgiAIgiAIgiAIgiAMseMlAfPwSAeAiKGv0RDHaIhhrDkef2bH42fe32j4GYgYFCKGkTNePvd4+Rww+j/LaI5PxHbkRmtcMLpjO5ixGveREp9TiHBc9IARBEEQBEEQBEEQBEEYScdLBYwgCIIgCIIgCIIgCMKIEQkYQRAEQRAEQRAEQRCEISYSMIIgCIIgCIIgCIIgCENs2BIwkiQ9LklSiyRJWw+x3ixJkoKSJF00XLEJgiAIgiAIgiAIgiAMpeGsgHkSOP1gK0iSpAb+BHwwHAEJgiAIgiAIgiAIgiAMh2FLwMiyvALoOMRqdwGvAi2H+76nn366DIgv8TVSX4dFbKfiawS/DovYRsXXCH4dFrGNiq8R/josYjsVXyP4dVjENiq+RvjruKcZ6QD2kiQpDbgAOBmYdYh1bwZuBsjMzBz64AThKIjtVBjtxDYqjHZiGxXGArGdCqOd2EYFYfQYTU147wV+JMty8FAryrL8sCzLM2VZnpmQkDD0kQnCURDbqTDaiW1UGO3ENiqMBWI7FUY7sY0KwugxaipggJnAC5IkAcQDZ0qSFJBl+fURjUoICwRDaNSjKWcnDJdgSEYCVCpppEMRBGE/wWAIlUqi9/gpCINCbFeCoAiFZEKyLM6BBUEYFKMmASPLcs7e/0uS9CTwtki+jA417S7e3dLIB9uaOGFCPOdNTSMv0TLSYQnDwOMPsKqygye+rEKrUvHNBdnMyo5Bp1GPdGiCcNxrd3j5bFcrz6+uIS/BwhVzMilNjx7psIQxrsvl44vyNp75uprUaCPXzMtiaka0SMQIx51gSGZ9TSdPf1VFh9PHNfOzmZ8bh9WoHenQBEEYw4YtASNJ0vPAYiBekqQ64JeAFkCW5YeGKw7hyPR4/Pzija18uqsVgA21XXy4vYlnbphDgtUwwtEJQ21VRQfXPrEm/Pijsmaev2kuc3PjRjAqQRAAXl1fz+/f3QHAmqpO3trUwP9un09hsm2EIxPGsve3NvHj/20JP353SyOv3DafyWlRIxiVIAy/zXVdXP7wSgIhpW/ol3va+fcV0zi7NHWEIxMEYSwbtgSMLMuXH8G63xzCUIQjUNXmDCdf9iprcrCnxSkSMONcKCTz1NfVEctkGV7bUCcSMIIwwprtbu7/pDximdMXZHujXSRghKPW4fRx/6eR25U3EGJDdadIwAjHnS/K28LJl70e+HQPJxUmYtaPmkEEgiCMMWLvIRzUgXp+iF4g458kgU7Tf7yzToyBFoQRJ0kSWnX//bBaDBMRjoFKAq2q/z5eLY75wnFIM8Dfgk6tQuxmxxdvIMhjn1eytaGbk4uS+Ma0NHGdIwwpcSUlHFR2vJlzp0aWWs7MjiEvwTxCEQnDRZIkrp2XFXGioVZJnDctbeSCEgQBgESrgbtPKYhYFmPSUpwqqhSEoxdt0nH3KRMilln0GqZnxYxQRIIwck6YEI9BG3mpdOfJ+Zh04v71eOEPhvjm42tYvrOFzFgTD322hx+8sglZlg/9YkE4SmIPIhyUWafhx6cXsTAvnhW7WpmdE8uiwgTiLPqRDk0YBjOyYnnx5rm8sbEBrVrFOVNSmJohTsQFYTQ4pzSFRKuBtzc3kBNv5rTiZNEgXThmJxUl8eR1s3hzUwMpUQbOKElhYooY1iYcf0rSonjx5nm8u6WRDpeP86akMkMkI8eVhz7bg9sf5AenFqJSSczNjeO37+zgya+quG5BzqHfQBCOgkjACIeUGm3kklkZXDIrY6RDEYaZTqNidk4cs3NEzxdBGG1sRh2nTErilElJIx2KMI5Y9BoWFyayuDBxpEMRhBE3JSOaKRnRIx2GMAQ6nT4eXlHB784vCQ850mvU3L44j1+/tZ0zSlJIjhL9LoXBJ4YgCYIgCIIgCIIgCMeNZ76uZnZ2bL9JRVKijJw4IZ77lu8eociE8U4kYARBEARBEARBEITjQigk88KaGpZMHLiC9KzSVN7Y2ECbwzvMkQnHA5GAEQRBEARBEARBEI4L62o60WlUZMeZBnw+yqhlbm4sz62sGebIhOOBSMAIgiAIgiAIgiAIx4X3tzYxMzsW6SBzip9UmMiLa2sJhcSMSMLgEgkYQRAEQRAEQRAE4bjw4bYmpmcefEarnHgzWrXEmqqOYYpKOF6IBIwgCIIgCIIgCIIw7tV2uHB4AwccfrSXJCnTUr+5qWGYIhOOFyIBIwiCIAiCIAiCIIx7X5a3UZIWddDhR3vNyYnjva1NBMUwJGEQiQSMIAiCIAiCIAiCMO59Wd5GUbLtsNZNjjJgM2rYWNs5xFEJxxORgBEEQRAEQRAEQRDGvXU1XRQmWQ97/WkZMXywrXkIIxKONyIBIwiCIAiCIAiCIIxrLT0eejx+UqINh/2aqRnRfFLWMoRRCccbzUgHIAgAtZ0uPtrezAfbmliQF89ZpSnkJlhGOqzjnscfZG1VB/9dWYNGLXHVnCymZ8Wg04jcrSAcyLaGbl5dV8fO5h4ump7OiQUJxFn0Ix2WIBy2YEhmQ00nz62qocvt56q5mczJicOsF6eNY5XbH2BNZSfPrKzCqFFz5bwspmfGoFWL47lw/NhQ00VBkhXVYfR/2SsvwUKT3UOz3UOS7fATN4JwIOJIKow4h8fPb97ezoe95X0rKzp4d2sjT18/mwSr2NGNpNWVHVzz+Orw43e2NPLCTXOZkxs3glEJwuhV3tLD5Y+sxO4OAPBleTs/Pr2IWxblHlbDP0EYDTbXdXHZwysJ9DaeXF7Wwn+unsFpxckjHJlwtFZVdPDNJ9aEH7+9pZEXb57HrJzYEYxKEIbXlrpusg4x+9H+1CqJyWlRrNjVysUzM4YoMuF4ItLewoiraneFky977WjsYU+rc4QiEgBCIZmnv66KWCbL8PpGMR2fIBzIjkZ7OPmy178/Kaep2zNCEQnCkft0Z2s4+bLXQ5/twe0PjlBEwrEIBEM8/kVlxLKQDO9ubRyhiARhZGyq6yInznzEr5uYYuOL3W1DEJFwPBIJGGHEiXvCo5MkMWCJpkr8wgThgKQB9mjiT0YYawbaz6slCUnMxDpmqQb4pYrjuXA8kWWZrfXd5MQfeQKmJDWKr/a0I8tiJygcO5GAEUZcdryZs0tTIpaVpkeRnyh6wIwkSZK4dn52xDKVBOdPTR2ZgARhDJiUaiPGpI1Y9q0lE0iJNo5QRIJw5BYXJqDbrzfIrYvyMOjUIxSRcCw0ahXXL8iJWKZWSZxRknKAVwjC+NPq8BIMycSadUf82iSb0setsk1U5wvHTvSAEUacWa/h/86YyLzcOJaXtTAvL46lE5OIF00rR9ys7Biev2kur6yrRatWceGMdKZlRI90WIIwauUmWHjuprm8vbmB8mYH505NZX5e/EiHJQhHpDQ9mhdvnsv/NtTT7fZx0cwMZmXFjHRYwjGYkxPLczfO4ZV1dRi0Kr4xPZ1pmeJ3Khw/yhp7yI43H1U/NkmSmJRqY1Vlh5gkRDhmIgEjjAppMUaunJvFlXOzRjoUoQ+dRs28vDjm5Ymmu4JwuCam2JiYYhvpMAThqEmSxLSsGKaJpMu4odeqmZ8fz/x8kRAWjk9lTXbSjqEatSDJylflbVw+O3MQoxKOR8M2BEmSpMclSWqRJGnrAZ6/UpKkzb1fX0mSNGW4YhMEQRAEQRAEQRDGp231dtJjjmwGpL4mJltZW905iBEJx6vh7AHzJHD6QZ6vBBbJslwK/AZ4eDiCEgRBEARBEARBEMav3S09ZMQcfQVMcpQBty9IQ5d7EKMSjkfDloCRZXkF0HGQ57+SZXlvWnElkD4sgQmCIAiCIAiCIAjjUigkU9nmIu0YEjCSJFGUYmVN1QEvZwXhsIzWWZBuAN4b6SAEQRAEQRAEQRCEsauh241Jr8akO7b2p3kJFtZWiWFIwrEZdQkYSZJOQknA/Ogg69wsSdJaSZLWtra2Dl9wgnAExHYqjHZiGxVGO7GNCmOB2E6F0e5430bLWxxkHEP/l70KkkQFjHDsRlUCRpKkUuBR4DxZltsPtJ4syw/LsjxTluWZCQkJwxegIBwBsZ0Ko53YRoXRTmyjwlggtlNhtDvet9GKVidJNv0xv09OvJnqdhcOb2AQohKOV6MmASNJUibwP+BqWZZ3jXQ8giAIgiAIgiAIwthW3uIg2Xb0/V/20qpV5MSb2VzbdexBCcet4ZyG+nnga6BQkqQ6SZJukCTpVkmSbu1d5RdAHPCAJEkbJUlaO1yxCYIgCIIgCIIgCOPPnlYHqdGGQXmvvASzmI5aOCbH1onoCMiyfPkhnr8RuHGYwhEEQRAEQRAEQRDGuap2JylRg5OAmZAo+sAIx2bUDEESBEEQBEEQBEEQhMHi9gXpdPmJMx97DxiACUkWNtV2EQrJg/J+wvFHJGDGMVmW6XL58AWCIx2KMMY5vX6couGYIIx5vkCQLpcPWRYnjsLo4/QGcHr9Ix3GuObwiOO5cHyp6XCRbDOgUkmD8n7RJh0mvYaKNsegvJ9w/Bm2IUjC8Kpud/Ly2jre2tzAlPRobj4xl5K0qJEOSxhjHB4/n+5q5YFPytGqVdxxUj4LJ8Rj0oldhyCMNVvru/nPZ3vYXN/NuVNSuXhGOplx5pEOSxBwegN8Ud7Gv5fvJijDHYvzWVSQgMUgjjWDxe7288nOFh76bA96jZo7T85nQX4cRq34GQvjW1W7k+RBmAGprwmJFtZVd5KfaB3U9xWOD6ICZhxy+QL84d0y/v1JOdXtLt7c1MDVj62iut050qEJY8yXe9q587kNbG/sYVNdNzc/s461VaLxmCCMNVXtTq56bBVvbW6kut3FfcvL+eP7Zbh9okJSGHlrqzq45Zl1bKm3s73Bzh3PrefriraRDmtc+bK8jW+/sJEdjT1srO3ixqfWsr66a6TDEoQhV93uJME6OP1f9spPsLBGnA8LR0kkYMahuk43729riljW6fJT3iJK5YTDFwrJPPN1db/lr22oH4FoBEE4FuXNDrpckUM73t3SRF2na4QiEoR9Xl1f12/ZsytrxFC5QeIPhnjy66p+y9/e3Dj8wQjCMKtodZI4yBUwBclW1omZkISjJBIw45BGJaFT9//V6jXi1y0cPkmCOIuu3/I4c/9lgiCMbgPt//UaFdoBjhWCMNziLP0vjmItOiRpcHo2HO9UkkScqf+xO9akHYFoBGF4VbY5SRrkCpiMGBPNdg+dTt+gvq9wfBBnXuNQVpyZO0/Oi1g2PTOawmQxTlE4fJIkcfXcLLTqfSfAeo2Kc6akjmBUgiAcjcJkK1PSI/uA3XlyPhmxphGKSBD2OW9qWkSSUKuWuHJ25ghGNL6oVRLfXJCNpk8TUqNWzWklySMYlSAMj9pON0m2wU3AqFUShUlW1teIKhjhyInOW+OQWiVx9dxsilOjWFvdSV6CmTk5cYM+/lEY/6ZnxvDKrfP4srwdtUpiQX68aOYsCGNQos3Av6+YxurKTspbHczMimF6VgzqQZoVQhCOxZT0KF65dT5f7WkjGJJZkB/PZHGsGVQzsmJ5+dZ5fFXejlYjMT9PHM+F8c8fDNHa4yF+gIruY5WfaGF1ZQdLJiYN+nsL45tIwIwDdZ0udjc7wtnYpCgDMWYdSyYmiZ2CcExUKol4i4HJaVGoVJIYfiQIR6C2w0V5Sw8atYqCJOug34E7UhmxZjJixaxHwrGTZZnyFgdV7S6ijBoKkqxEDzDE5XBJksTk9Cgmp4uEwFBRqyQSrHqmZEShkiQSBhj2JQjjTWOXhxiTDs0QDLctSLLyzhbRR0k4ciIBM8btbLJz7ROraer2Em/RcfMJuWTHm0mJNpCfYMEopgsWjsGu5h5eWVuLUadBkuCznS1cNjuT3ATLSIcmCKPajkY71zy2mlaHF4DLZ2dw7pRUAiGZ7DizGPojjBmyLFPZ5qS200WcWU9+opl11V1c/+QavIEQABfNSOcnZxYRaxYX9aPVziY7r66vw6hVjuef7mzh8jlZ5MSLpKwwftV0uIbs5kd+ooWdTT24fUGMOvWQfA9hfBJX52OYLMu8uKaWpm4vFr2G2xbn8bcPd+HqnVb0O0sncOPCXCwG8WsWjs7Oxh6W72wNz6BVnGpjWmaMSMAIwkEEQzLPrKwOJ19OL0mmy+Xn8kdWARBj0vLkdbOZkhE9glEKwuH5YncbNz2zFo8/hCTB3UsLKGvsDidfAF5ZV8f5U1NZOCFhBCMVDkSWZXY29bBsewuVbU4AStOjmJEVKxIwwrhW0+EiwTo0iWGDVk12nIkNtZ3Mz4sfku8hjE+iCe8Y5g+GWNs7Bdo5U1J47PPKcPIF4N6PdrOzuWekwhPGuGBIZm11R8T05dsa7GxrsI9gVIIw+nn8Qdb3mZ6yONXGe1ubwo87XX5+984OHF7/QC8XhFGjye7mey9vwuNXki2yDH9ftouJqf2HCu1NOAqjjz8YYlVlRzj5ArC5rpuyJnE8F8a32g7XkA6fL0iy8vWe9iF7f2F8OqoEjCRJsYMdiHDkdBo15/bOSBNn0dPQ7em3TlPvMlmWaevx4vQGhjVGYWzb2mDHZtRwwbQ0zp2SikWvEUk9QTgEs14TMVvY3ovXvjbWdtHtHh37Y5cvQGuPh2BIHulQhFGm0+mnpcfLhEQLl83KYEF+HJJExGw6e2XHiUqK0WxLfTfRJi3fmK4cz806dcQNFkEYj6o7nENWAQMwKdXGl+VtQ/b+wvh0tGNTVkmStBF4AnhPlmVx1jZCzihJZnuDnYYuN3kJZva0OiOez4gx0tDl5sU1tTy/uoa0GCM/PK2QOTlxqMTsF8JBqFUSV8zOpLrDyesbGlCrJG46MZc8Ua4sCId07pRUyhrtvLW5EZO2/9jwEybEE3sMTUsHy/qaTv724U52NTk4d2oq18zLIktcSAu9Eq16fnxGETsa7Xy0o5m8BAs/P2sSpelRTE6zsaXejkWv4ZfnTGJiim2kwxUOQKdRc/XcLKraXbyxsR6dWsWti/PISxB/68L4VtvhZnZ23JC9f0GSlXs/2o3LF8Ak+m4Kh+lot5QCYClwPXCfJEkvAk/Ksrxr0CIbB5zeANXtTtQqiew4M/oBTsKPVVqMid9/o4S6DjdtDi/feXETTXYPeo2KX51TTH6ShXs/2s3DKyoAaOnxcvVjq3n9jgVi+kHhkDQqiX99XB5+/I9lu3j46hkjGJEgjA0ZsSb+fNEU7loyAQkIyjL/+ng3gZBMfqKZH5xe2K9pX0OXm9YeL/EWPWkxxiGPsbylh6seXRUeuvrYF5U0dbv52yVTMQzB8UoYeww6NSsr2vl0ZysAbY4OtjfYee2O+fz3hjk0dHsw6zVkHkFT6bYeLw3dbmwGLVlxJiRJ3AwaDipJ4v5P9h3P//bhLh67duYIRiQIQ6++yz2kFTAGrZq8BDOrKjo4qShxyL6PML4cVQKmt+JlGbBMkqSTgP8Ct0uStAn4sSzLXw9ijGNSbYeL37y9nQ+3NyNJcMXsTL61JJ8k2+CfVBu0GvKTrOQnWXnjzgXUd7qJNmnJjjPTZPfwzNfVEesHQkozNpGAEQ4mGFKaPO/vnS2NnFqcPAIRCcLYYtSpKUiyAsrwjNNLknF6A2TGmojbbwrYL3a38u0XNtLu9BFr1vH3S6awqCBhSC9Oy1scEX3DAN7d2sR3T3GTlygabQtQ3+kOJ1/26vEGqGxzkp9oJeoIq7i21ndz5/PrqWpzYdKp+eU5xZw/NXVIblAJ+/iDIV5YU9Nv+QfbmlgyMWkEIhKEoef2BXF4AkSbtEP6fYrTovh0Z4tIwAiH7agSMJIkxQFXAVcDzcBdwJvAVOBlIGeQ4huz3t7cyIfbmwGlad2zq2ooTrVx1uRUooZwR5BkM0RMt6bXqIgxaXF3R55kWw0aWuweutx+Eq16og9xErWnxcHHZS1sb+hm6aQk5uXG9buAEMYXtUoi3qqnJM3GooJEZFnmox3No2LYhCCMNntaHexs6kGrkkiw6vmyvI09rU5OK0lmbk4cUSYtJp0aSaJfdUlNh4vbn12P3aP0hOlw+rj92fW8862F5MQPXSLEOMBFr0mrRqcR/fnHM18gSF2nG5UkkRFrQn2A4ci1HS5a7R4MWlW/PkbNdi+f7mxhVnYsZv3hnUp2u3z86NXNVLW5AHD5gvzo1c0UJVvFjGBDTC1JxFv0lKZHcWJBAqGQzIfbm4kRx3NhHKvvcpFo06Ma4iq7KenR/OezPUP6PYTx5WiHIH0NPAOcL8tyXZ/layVJeujYwxrbvP4g725p7Lf8s12t2N0BrpqXheUwT1iOOoZAEJc3SKxZx8/OnsTtz64PPzcjMxq1SuLcf39Jk91DUbKVP19USml69IDvVd/p5ron11DToZw0vb6xge8smcBdSyYc8MRNGPuCwRDnlKbwcVkLVW0OZBnm5sQyP3/oxtIKwli0tb6bKx5did0d4PbFeby8ti48I8z/NtTzj0un4A/I/Pad7dg9ARYXJvDzsyeRl2DB7QvS4fT2S3q4fEEauz1DmoCZmGJjemY062u6wst+eHoRGUcwnEQYWxq63DzwSTnPr6lFo5K4ZVEe18zNIn6/Ev2KVgfXP7kGlQRXzcni0S8qw89Ny4hmQ00nP3t9K/ddPi2i4fTBtPR4B5xFr6bDFU7AODx+ZMBqGNo71sebQCjEN6an8damRh78dA9qSeKimenMyo4Z6dDCAsEQdk8Aq0GDVi2SwMKxq+10kzAMN4uz4kw4vAEqWh3kJojqUeHQjjYL8DNZll/qu0CSpItlWX5ZluU/DUJcY5pOo2Jubixb6rsjlmfGmrn/k3Lm5cUyJWNwDnp7Wh1srOnC5QswOT2KyWnRbG/o5v5P9rC1oZtzpqRy8Yx0Xrh5LuuqO7Ho1UxIsnLdE2vwBpQ7WmVNPdz53Hr+d9uCfidhyvP2cPJlrwc/28MF09LIEg1Zxy2VSqLD5SM12sjbmztRqyRK0qJweEbHzC1Dxe0LsLmum+2NduItOqZmxIgLUuGAZFnm2VU12HtnNNJpVBHT8S4qSEAtSdz96qbwsk93tpITV8Upxcn86+PdNHZ7OH9qGp0uH6+urweU/ktDOXUmQKLNwL8un8aGmi7qu9xMTo1iSoYYmjreVLQ62FzXRTAk0+708d9VylCUYEjpS1SYZOGs0sgkysqKds6bmkZQlkm06PnbJVOobneikiTaerzh9/jHsl2cMCH+kFW0ANEmLWnRRuq73BHLE216nN4An+1q5d/LdxOUZe5YnM9JhYlYjSIRMxhUwPYGO29uagAgiMxzq2qYmGwd2cB6lbf08ORXVXxS1sr8vDhuOCGHomTR1Fk4NvWdbuIsQ1/lpZIkpmdGs2x7M7csEgkY4dCONgHzY+Cl/Zb9H8rwo+OeJElcPDODD7Y1UdOhnGhMSrEhScrYaac3yLaGbnY09qBTKxe1R5Mx3d3cwxWPrAqf7KtVEs/dNIebnl4bvhh48NM9VLc7uXpuFg9+Wo7HH+LWxXnh5MteNR1u6rvcEQmY+k43zXYPBq2aRKuelp59FxXBkEz/iVWF8cQfCOHwBvj7sn29tXc07uR355eMYFRDb9n2Zr6uaCfeoqfd6eOtjQ386rwSUqOHvimqMLoFQzJV7U7sbj9pMUYSrQb8wRDbG/Yl2/vOCViYZCUz1kRjt6ffeyXYDFzz2GoCvVM/P/pFJd+cn01KlIFmu4d7ziseljtp6TEm0mNEgnG8Km/u4YPtzfgCIbpcPjbUdvVb5+Oyln4JGKtBQ5vDhyRDm9OLwxugNC2KW/67jmCfg78vGCJ0mBNhJlgN/OnCUm58ek14SNNNJ+QwMdnK6sqOiErdb72wkYevniH6jQ0STyDI8rIWlk5MpDhVSbKuruxgTVUHV8/LHtHYOpw+vvPCRrb2Vke9vK6OVZXtvHzLfJKiDId49dCrbHWwtcGONxCkKNlGcapNNI4eI2o7XMSah6ddwoysWN7Z0sgti/KG5fsJY9sRJWAkSToDOBNIkyTpX32esgHj+7b4ESpIsvLkdbN5f2sTvmCI+i43D6+oUIYeSfCNB74KJ0HiLTqeu2luuFnj4Vpd2RFxpzUYktlY0xVOvuz17pYmStOjcHiVPjC6AUo7TTo1UX3uNH3e2xCyw+nDotfwg9MKeeTzCuo6lYTSVXOySBcXpOOaRq3i4x0t/ZZ/taedK+dmjUBEQ6/Z7qHT5efL8nZqOlzo1CqunZ9NRatDJGCOcx5/gFfW1fObt7fjDYRIizby4FXTKU2P5pKZGWyqU5IwkgRWvYYeb4BTi5N48NM9/Oa8yKSlRiXh9YfCyZe93trUwK/PLSbBqicj1oi69yS/vsvF9gY7Hn+IgiQrhaPkrrUwuu1pdfCDVzazobYLlQTfmJbGmSXJbK6LrM4tHmD6aI1KxTMrq2ntvfGi16j47fklzMmJ5as9HeH17jgpP3yBEwrJlDXZ2d3iwKLXUJxqIzkqcr+5ID+Ot+86gZoOJzEmHROSrFj0Gl7bUMf+nl1VzSmTksTF7iDQq9VcMTuT1zbW88+PdwNwWnESiwpHvmlodbsznHzZq6bDTUWbc8QTMOUtPVz12CqaupW/A51axbM3zWFWduyIxiUcntpO15AO4+2rOM3GA5+VU9/lJk2cLwqHcKSDLBuAtYAHWNfn603gtMENbezLTbBwYkECK3a18vLaOvITzTx/02ye+qo6ogKlzeFjxa7Wg7zTwNqdvn7L5AHuROk1KpzefU14P9/dyiUzMyLWuee8YrLilLugtR3OcPIFwOEN8Lt3dvCHC0q4fHYm95xXzC2LctEeolGjPxAkEBR1MmNVSJYHHAIRPwzlnCPF7Qvw2ob68JA7XzDEI59X4PCK/PLxrqyxh5+9vjW8767vcvPjV7fQ7fKxdFISty3OxaxT8/KaWv568RQumZlOrElHICSj06iYnbPvhF2rlogd4O/IatDw0Y4Wvihv49z7vuRvy3ayo8HOnc+tZ9n2FjbUdHLnc+tYX905bJ9bGJuCIZmnvqoKV7yEZHhlfT1RJh0Jfba9nHhzv5k7PL4gZU094eQLgDcQ4q3NDdxzbgnXL8ji0pnp3HfZVE4v2VehsrKynfPu/5Jvv7CRG55ay23/XU99Z+RwI0mSyE+0cHJREtMyY8L98OIH6NOQYNGL5MsgCcgydV1uVlbsS559sK0Zu9s/glEp9JqBZ8DSa0e+D8zXe9rDyRdQzgnuX16O1x88yKuE0aKuwz1s56walYo52bG8ubF+WL6fMLYdUQWMLMubgE2SJD0ry/IRXZFIkvQ4cDbQIstyvzEMknKU/SdKhY0L+KYsy+v3X2+sKUmL4qnrZtPY7aG200Vdp5uaDme/9Rq6+peoH8rc3P4Z+DiLnuJUW0Sju28vnQB98jJrqjrRqlU88c1ZuHzKlKgFSdbwiU5VuyucfNnLFwyxurKTth4vc+dn0+rwsq2hm9RoI/mJ1ogGkjsb7by7tYkNtZ3MzIplfl4c0zJjRMPeMUaSJE4oiOf9bU34g8oGZNCqmJw+jntEyBIbByjR73T1T3YKx5fa/S4kAbY32mlz+MhLtHDW5FTSo02oVBI5CWZ+kT+JHU09/PiMQkKyzJklySzIjycQDKFRScSbdSRZ9TT3uci9dFYG9y0v58Lp6QRkmUc/ryQnzsySomSeW1WN3RPg3CmprKpsZ1Kqrd9sSn1VtDp4f2sTa6s7Oa04iRMLEkiJEnfljhd2t5/lZf0rGDfVdvHHC0sJBENoNSoKk62kRe8bgraz2c6W2m5iTFq+d2oBy7Y3hytmmru9NNrdBGUJuyeAQadG03veYHf7+f07O8LHCoANtV1sqe8iLebQ2915U9N4dlVNOMGpU6u4fM74rLQcCWpJ4rMBbvStr+7kugUjO3FpTryJq+Zm8t+V+6bJPqs0hfxR0Mx0oOGjtZ0uvMGQmDp9DGjoHp4mvHvNz4/n6a+ruXVRnkgeCwd1pEOQXpJl+RJggyRJfUstJECWZbn0IC9/Evg38PQBnj8DmND7NQd4sPffMU+nVfG/DXU89FkFNqOGK2dnsaOxJ2KdxYUJR/y+U9KjeeSamfz5/TK63X7OmZLKV3vamZ0Ty8lFiXS5/JxUlMD0zBic3gA9Xj/Pr64lJcrArYvymJ8Xh+YAw5HMOjVO374Mv0oCjVqirsvF6xvqeaB3ujWVBH+5aAoXTEtDpZJo7vZw+3Pr2dOqJJlW7GqjrCmZ27UqStKij/gzCiMnJMsk2wz83xkTqe10oZIk0mKMpIyCMdlDJdqspSjJSllz5N9nZqxoNn28S7L13+4zY41EmbRsqOnksodXhi8ebUYND105g7tf2kizXUmwTE6zcXpJCk9/VcPlszOYlhHDczfNZdn2Zuo6XaTFGHl7cyMuX5C0aCNdLj+xZh0y8NcPd4a/53Ora7hxYQ5uf/CACZimbjc3P72O8lYHAMvLWrhsVga/PrdYXDQcN2SKU23hYcN7FSRZ+WBbE9nxZm4+ITfiHKDD4WH5jhb+8sFO9o6Ou2FhDsGQzLYGOxdMT+OHr2wJX5S+t7WJf1wyhQump+P0Bahuj2zWD9DuOLzk9ZSMaF69bT4rK9oJhmTm5caN72T/MAvJMnNyYvsNPxsN038bdRq+vWQCJ+QnsLWhm6JkKzOyYrCNggbMUwf4+Vw4PR2bmKVr1PMGgnS7/cM61XphkhV/MMS66k5mimFqwkEcaX3ft3v/PRs4p8/X3scHJMvyCqDjIKucBzwtK1YC0ZIkpRxhfKNSZauTh1dUAGB3B2jodnPdgmyiTVpOLkzgqetmMj1r4FmR/MEQu5p6WFXZ3q+UV69Vc8qkJF65dR4/P3sSb29u4LUN9TzxZRX/+awCtQSLCxKJNulIizHxg1OL+OA7J/LCzXM5sSBhwOQLQHaciW8vnRDuFaOS4NZFebyzpZHTi5N5aMW+ue5DMvz09S1UtSsJl7Imezj5std7W5uoGuDETBjdJFlmeVkrf3hvB3WdLipbnfzunR2srjzYn/HYFm3S8dsLSrAZ9uWmr1+QTXGqmI3heDcx2codJ+WHH5t1av584RRiTTqe+qoqYlhpss3Aq+vrw8kXgC31dgxaFa/dPp+TihJpd/lIijJw/rQ00mNMPPjZHrrdfn5wWiHvbGkElCGlFX32pxa9hsWFCUoV5UH6nu5ucYSTL3u9tLaW6g6xHz5eGLQaTi5KjEiYnzghniijlkSbAV8gRE1n5PZQ2e7mvuXl9G1N9PiXlZw1OYXrF2QTa9L2qwj458e76XL5SLDoOX9a/+moj6S3XUlaFDeekMsti/IozYjGEwgNOKxaOHIhOcTEFBu5fWauLE2PIsk6Om6oJFgNnFaSzPdOLeSs0tR+vYNGSluPl++dWkBatBGbUcM187JweP04vSM/dEs4uIYuD3EWPaphrL6XJImTChN58quqYfuewth0pEOQGnv/2wa4ZVkOSZJUABQB7x1jLGlAbZ/Hdb3LGvdfUZKkm4GbATIzM4/x2w49py8YcULzxsYGilNs3Hf5NF5YXctfPtzFVXOyWDoxKWIWIrcvwAtravndOzsIhJR+HP+5eka/rGqUSceERAu+PhcAapXE+dPTI3Y8IVnGrFNjOUTmPs5iYGF+Ajq1qndKVR/vbG5gV7ODU4tl9usbiccfotOlHIwOdKqkPg5L8cbadrq/kAxuX5DvnVLIRzuaUaskfnR6EbUDDKEbT4xaNTeekIM/KKNVq0iy6tCM0+FzY30bHU5Wo5Y7TsrjtOIkOl0+MmPN5MSb8QWD/S5ks+LMbGvo7vceu5ocrNzTzrLe5tYXTU/jB6cXcsuiXM6flobT6+fZVTU4e3sOeQMhkqOUY8KZk5PJibewYlcriTY95S0OZmbHDFjmPNA1q3yA5aOd2EYPzOn1o1OrB+zHZtSpyYkzc+bkZAqSrDTbveQlmPnlm9toc/jQqCQMGhVXz8vG3NuHxeUL4PJF9raQZUiJNvDyutoBe3WEZOVLo1Zx4wm5OLwB3tjYQIxJxy/OmXRUVSzVbU5e21jPh9uamZcXx2WzMphwhJMUDLfRvp2GQrCqop3vnVpAQ5cHtUoi0aZnQ3Un50ztnzgTFD3eAI+sqOD0khRMOjUf7Wgm0arnzpPH3jnBaN9GB1t9p5tE6/ANP9prUUFCb/WrZ8DKWUGAo5+GegVwgiRJMcDHKI15LwWuPIZYBtqbDXi6KMvyw8DDADNnzhz1p5TZcSbyEswRlSHnTUvlxqfWhu+a/vh/W/i/M/zh6cucngAbajv59Vvbw69pd/r4wSubeOXW+Ri0atodXqwGLTFmHUUpNl6+dR5b6+0EQzKTUm1M7DOzwZa6Lh75vJIdjXYunJ7OOVNSDzoue1KqjfQYI609Hh74dA+7mvfdTTVoVeEpJAHizLrwXbbCJCs58SYq2/ZdkJw6KYmc+ONvCMdY2073p9OqmZUTw/df3hxetqqyg39fPm0Eoxpadrefn7y+Ba1KxZzcOBp6Zy/LjrcwJzdupMMbdGN9Gx1Obl+Q1h4vadFGStOjw8t1ajVXzclifXVXeFm3y88pk5Ioa4ocylaUYuX37+6b7eWV9fUsLkrk7NJUnL4Az6+p5bNdbczPi+MX50wixqTDotcwJSOKtGgT939SrrywHr7Y3cb/bp8fnlK2rwlJFnLizVS27TvmXDg9nay40XFX+UiIbTRSs91Dl8vHlvpuHl5RwYRECzedmDfgUImZ2bEY9RraHV72tDj4+7JdtPUOCQqEZP74/k5m58SFK3Bz480kWPURzXf1GhVWvYbKNhcWg6bf8f9bJ+cT29usPSvOzB8vLOU7SwvQa1Uk2458e7O7/fz09a18Ud4GKH2Wlpe18MJNc0d8RpyDGe3bqcWoY1pWDH/9cBenTkoiEJJ55PMKfnrWxJEObVRbkB/PvR/t5qW1++4P33NuMSbd0V4+jZzRvo0Otvou14ATSQw1s17Dwvx4Hvuikp+cKf6+hIEd7R5EkmXZJUnSDcB9siz/WZKkDccYSx3Qd2qedJRZl8a8OIueB66czj8/2s0X5W3Myo7FqtdGlKwDPPTZHs6floY3EOKet7ZRNMDUkLUdbqrandy7bDefl7eRn2jmd+dPZk5uHPmJVvIT+98lqmh1cOWjq7B7lLuqf3y/jPouF784u3jAO2f+YAiXL4BVr8VmtPJ/Z07knCmpNHV7yEswMz0jmu+/spkOp49km4F/XjY1PEVvSrSRh66ayRsb61lX3ckJE+JZmB8/4GcRRrdQSObNjf3/BD/Z2cLZU8bnHbMOp4+TCxPZVNfNA5+Wk2Iz8K0lE2ixH3mTbGH8KGuy89cPdvLRjhay4kz89vwSFuTFhysMFxcm8Otzi3ng03KmZURTkhZFjyfAiRPiWbG7DbVK4pq5WVS2OSOalILSFHVhfjx3v7CRzfVK1cyeVgdrqjp49sa5xFv1/PWiKVzxyKqI13kDIbbUdw+YgEmJMvLINTN4a1MjqyrbOas0hZMLkzBox95Fg6AMRWt3evl8Vxu/fWcH3W4/Z05O4YQJCTz2RSWf7mzl9TsW9KsSUakkJqdFEQgqQ3ke/ryy33vXd7nDCZi0GBP3XT6Nu1/cSGO3hxiTlj9fWMrUzGj+fcU01lR28M9Lp/HJzhYau91cNjuT+XmRiWm9Rk1W3NHfcKlud4aTL3tVtjnZ0+oY1QmY0c7tC9DY5WZBXhxPfV2FVqXi8jmZ/XoECZFK0qJ46RalV1eny8cZJSkHbBkgjC61He5wcni4nVGSzM9e38odJ+UTNQp6GQmjz1EnYCRJmodS8XLDMb7XXm8Cd0qS9AJK893uPkOexrzCZBv/uHQqnS4fUUYdH25v6reOXqNGLUm8s7mRj3a0DNgc7aq5mfz8jW1s753lqLzFyTefWMM731pI7gE6xu9q7gknX/Z6bnUtNyzMJXu/ypSyJjtPfFHJ6qpOTp2UxGWzMshJsLC4MHKayrfvWki700ui1dCvxK4w2coPTy865M9EGN0kSWmOt78DTRk5HkjIbK7rDs8e0tDt4Y/v7eDRa2eOcGTCSHB6A2yq7eLej3exulKZ+rm63cX1T67h7bsWUpisJJZjzXqunZ/NWZNT2NXcwxWPKsmSEybE860l+cSZ9Zw/NTWime5eJWlRVLY6w8mXvXY2O6hocxBv1RNr1g04JavuAH28APITrdx9yugetiEcWk27i1fW1xJj0kVUxL65qYFLZ2WQFWeiut3FzuaeAw7T2dPq4L0tTWTHmfr1Y0uNjjx+z82N4407FtDc4yHWrAvPkHR2aSpnlyqJ99P6TD092A7Um+5Ay4XDo1ZJaFQq/rtKmWnIQ4iHV1Tw2/P6TUoq7GdyejST+1Q9CmNDbYeL5BFK2iZYDUzLjOHpr6q4a8mEEYlBGN2O9oj2beD/gNdkWd4mSVIu8MnBXiBJ0vPA10ChJEl1kiTdIEnSrZIk3dq7yrtABVAOPALcfpSxjVp6rZrkKCNGnZqStKh+pXE/OK0Qm1HDe1uVvNMnZa1cvyCbve0nbEYNSyYmhZMve7n9wYhS8/1pBzhx0WtU/aaFbuxyc/2Ta3hxbR2VbU7+s6KCn7y+hR53/2ZjqdFGJqdFi/GN41hIVu7s991MtGqJaZnRIxbTUPMEgizfGTl1a0hWpl8Vjj9rqjr4uqI9nHzZyx+U+zUbB4i36qnv3ndH+fPdbfzr43J++eY27J4A18zLJj9xX6L8rNIUZufEHvDicu++O86i5wenFUY8F2PSUipmiRnX3L4gf3p/B4+sqBywUmH5jhbm9Q6NHOg4v1ddp5s3Nzdwzbzs8N1YlQR3L50QTiL2lWgzMDktOmJ66uGSHWfi0pnpEcvm5saSl3j8DWMeTP6APOC05Guqx29TfeH4VtflJn4Yp6De39mlKTz+ZSXu/fpqCQIcZdVK74xGK/o8rgC+dYjXXH6I52XgjqOJZyzKS7Dw3E1z+KSslbpOF0snJTEzKxadRs28vDg213WzvqYTjz/It5ZMIDPWxMzsWEIhGaNWjdsf+Qd9sOn6ilKs/XrQfGdpAen79YApb3XQ0BU51OLrPR1UdziPeQrpYEimy+XDoteIaVDHCLVKwuHx8/OzJoU7NIVCMv5g6OAvHMPUKhVJVgNN+w05Gqj6QBj/Xl5bS5LNQLRJS5crMhF9oLLi5AGS0iWpNmxGDRmxJh66ajrlLQ70GjUFSRZSoozY9FrOn5rK7hYHk1Js7GzuITXKQF6fqsYlE5N46vpZfLS9mdRoEycVJgw45FQYP+o6Xby7tQmVJGHR9z9dS4sx0tLjJTPWeNCZ2qJNOjz+EPd+vIvLZmViNWjIijVTmmYb8H1HklGn4e5TCpiXF8/XFe1MzYhmYX48ceaRu5AaD3Qaiex4MxtquyKWZ8QMf5JNEIZDQ5ebhBFowrtXeoyJgiQrL66p4ZsLckYsDmF0Oqojb+/MR98Hsvu+hyzLJw9OWMeHwmTbgHefLpqezofbmqlsc7K90Y5Oo+Kfl00lM9aELMv87OyJ/PS1reH1L5mZftCpHtOiTTx6zUy+3NPOnlYHC/LiB5w9wxfof2Gtkg5+Z60vWVYuznX7DVGpbHPw9FfVvLu1kSnp0dy1ZAKT08Sd27Fgdk4cn+9u5X/r61GpJC6ekc6UcXzX3aLTcP3CbP7wXll4xpjpmTGi0us4FW3S8eamBq5fkMPfl+0KLz+9JImi5IH3ucWpNq6Ynclzq5VSf6tew6/OLSbKqGNbQzfXPr463Ah1RlY09146jYxYEzcszOGxLyr5bFcrs3NiufmE3IjEukWvYVFBIosKEgf8vsL4o1FJaFUqfMEQDm+AvAQLe3qnF9epVXxzfjY9bj8/PWsi6Qe4kK7vdPHS2hquX5DNE19Vsau5h9K0KP6+bCc6jYq7TylgUUHCsDcV9QWCaNWqAWfxSo4ycv60NM6fljasMY1nKpWK86el8tH2Znp6Z1lLsOqZmxt7iFcKwtgTCIZoc3hHpAlvX2eUpPDoFxVcPS+736gD4fh2tEfcl4GHgEcBUVs1yCYkWXn+5jnsaXGgklRMSLKEy+gkSeKCaWkUJlmp6XCRYNFTnGY7ZJOnnAQLOQfoEQPg8PhZX9PJzKwY1lbvK7e/ck5Wvz4xA9neYOf51dVsquvmwunpnDopiZRoIw6Pn1+8vo3Pe5vqfbi9mdVVHbxxx4JjatQnDI/tDXb++uG+C88/vFfGPy6ZQvExVkSNVsnRRhKtBr53SiHeQBCNWoXD4yczVtwlPB5dND2dl9fW8e6WRr5/qrJNFCVbmZ0TS9wBSptjzXr+74wiLpiWRn2Xmy6Xj2e+rsYXCLG8rCWcfAFYV93Fqsp2jFo13315E7t7Z5t7e3MjW+u7eemWeSSK5N9xKzPOzJ0n5/H3Zbt54stKrp6XzSUz04m36JmYYmViim3ABEZfa6o6eXFNHSVpNr5/aiEWvYZfvrkt/Pxt/13PMzfM5oQJCUP9cQBo7Hbz4bZmXl1fx5T0KC6fncmkARpJC4NLJUFbj5frFmYjISFJ4PWH6PH0H2IuCGNdc4+XKKN2xHtHFSRZMGrVfLarhZOLkkY0FmF0OdoETECW5QcHNRIhQrLNOOAUjoFgCLUkMTM7lpnZg3fnoscT4Ikvq/jG9HTm5cVh1KpIizFh0KhweALoLQceNlTV7uSqx1bR4VQuLDbXdVPR6uBnZ02ittMdTr7s1eXyU9HqFAmYUS4QCPHW5v6zIH1c1sIF09MHeMX4sHRiItsbe6hodRBr1jEh0UKGSMCMa/5giIpWJ50uL6lRRjJ7901TM6N55bZ5fLG7DVmGpROTmJwWFZ796EAsBg2tDi/feXFjeNlbmxv45TnF/dbd2dRDdpw5nHzZq6rdRWW7UyRgjgPdbh8VrU5kGXITzESblLu2apXE1XOzmZQSxeqqdvITrczOjiE5yojhMIfy7q2Y2VpvJ8qoxe3rX+n67pamYUnA+AMh/vNZBU9+VQUo5wrvbGnif7fNP6wbPcLR8/iDPL+6NuIGG8CF09M4Y/L4nNVwMPkDIUKyLIbQjxF1HS4SrCN/7JQkiZOLEnn6q2qRgBEiHG0C5i1Jkm4HXgPC3SllWRbdvIZIKCSzrrqTRz+voKnHwzXzsjm5MJGYQSiva+vx4vAGuGtJPn/9YBeXzEyn2e7lzx8olQ+TUqz86/JpB+w3sKupJ5x82evZVcqYR71GhU6tlFD3ZRA9NUY9lUoi2tS/smq8T6lX1tTDHc+up9XhRSXBbYvzufXEXKzj/HMfrzz+IC+vq+XXb24nEJKxGTU8dNUM5ufFI0kSpenRlB7BDBi7mnv4ek87r2+oj1gekqGi1UFKlIHG7n09hmZlxx7wYtowiDOOOX0Bmrs9mPUaMaRuFKntcPLT17ayYrdyo2J2Tgx/uXAKWb0JiRizjqWTklgyMZH1NZ388b2dNNrdXD03i5OLEok9RG+UqX1mU3R4AgNWbiVYh6dMv67LzX9XVkcs63D62N5oJyjLxJl14eSTMLjUKmnAhqQjNU3vWBEIhlhb3cnDK/bQ6fJz3fxsFhUmEGUUP7fRrL7LTYJldPyO5ubG8eyqGlp6PCSOgqSQMDocbQLm2t5/f9BnmQzkHls4woFsre/mikdX4g8qjSm+V7uJP3yjhMtnZx3R+zR3e9jZ3EMwJDMh0UJ1h5MfvbqFuk430zOj+eM3SqjtdPP86trwa7Y39vD86lp+eubEAe/8DjSuUa2SUEmQFmMKl1DvtSAv7qA9a4TRQZJgSVESH+9owdvbH8ikUzMrO2aEIxs6XU4f9y7bxRVzMgnJMhq1is11Xayt7uSkItF7Yzza3dzDz1/fNyTD7g7w3Rc38cadC444UdFkd3Pz02s5oySFc6emsnBCPJIEz62qoc3hw2rUsCAvnlfW16FVS9xyYi4zsmMwatVcOSeTZ3uniAXlznRewuBUBZS3OPjtO9v5dGcrCVY9vz2vmJOKktBpRCJ8pH1S1hpOvgCsruzk3a1N3LY4L2K9rfV2Lnt43znAptrN/Pb8Yq6am33Q98+MNfG780tod3qp6XAzOyeWz3e3ht/HotdwavHQTSvdl0pSzg0CITlieXW7k9ufXc/EFCu/v2Ay0zLH7zFmpKgkibNKk8mINWLSaZAk6Hb5mZElftYHs76mkysfXUWwd5vdULORv108hQtnjN8q4PGgvtM9apKLBq1y3vzmxgZuPEFcJguKo50FSbRzHmbrajrDJ0x7PfhpBacXpxxWFYwvEKS+y8Otz6xlZ2+pe7JNzzfn54Snt1xf04VVryUzrv9wi093tnLHSXk0dnnodvvJjDOFm/4VJdvIijNR3e4Kr3/b4jwyYkyoVBLXzMumND2aLXXdZMebKU2PoqXHS2O3h+w4ExaDqCwYjSRJIsqo4TtLJ9Dh9CNJSvXLQFUx40Wj3c3JE5P40/tl4aTTKZOScPsDIxyZMFT6VqPs1WT30ObwHnECZk+Lk5K0KMqaenjwsz2AUu33o9OL+PuyXZw6MZm8JAs3nJCDLxDCHwzS7vCRG2/m7qVKM9SdTT0UJFkoSrFR0eZEr1GTE2/q19zcFwj2WzYQly/A799Vki8ArT1ebn12PW/cseCIKnuEwxMKyVS1O7G7/aRGGw84hCwUkgnIIVZVtvd7bnlZM7ecmBtxw2Njbf9zgIc+q+CMkpQD9iPaWt/NVY+tCs/gNSMzmjsW5/HqrfNZW92JRiUxMztm0HuwdLt8lLc6CYZC5CVYwvFlxJi446TIGzLpMcZwX6QdjT3c/Mw63rhjAanR/YdgC0cvJMu4fEHe3NRAs10pXM9PNDPpILNnCbBiV2s4+bLXI59XcHpJEmb9+D0XGuuqO1wH3C+OhNk5cby1SSRghH2OdhYkE/BdIFOW5ZslSZoAFMqy/PagRnccqGl3squ5B5VKIifOjF6rJsGqR6tW0e3yUdPhwqBVD9jJ26BVHbKrdmWrg9c3NrCmqoM5ObHh5AtAk91LTaeLUycl8uH2FgC+rmjnzNL+d8MWFcTz2vp6fvvuDmQZok1aHrt2FjOyYkiLMfL4tbP4ZGcLOxrtLJmYxJyc2PDJY7RJx+LCRBYXJtLU7eYfH+3mpbW1yDKcVJjAr84tFv1gRqFgSOatTQ1cMSeLhi43kiSRbNPz1qYGFheOz7GserWaZ76uCidfAJZtb+b8qWKM/HiVHNX/AjnZZhiwXP9QDFoVp5ckU9bUw/SsaFZVdPBFeRvPrarhhZvmMilVaZpa0+7iuVXVFCTbqGxzcHpxMudNS+PU4mROLU6mvMXBXc9tYHN9N2qVUilz04m5xJh07Glx8L8NdXy+u43TJiVx9pTUg+4/W+xelpe1RiyTZahodQ6YgGmxe2jo9hBl1JAdZz5kk1dhH48/yGvr6/n129vw+EOkxxh54Mrp/X7OG2u7eOqrKqo7nJxbmkZQhve3NoWfP7koqV+16UCzEeq1KjQHOAfwBoI8+Gl5xPTp62q62Fjbxaklyei1EpIkDcqxNxiSqWpz4vQGsBg0/PadHXyxu41TJiUxPTOaWTmxNHZ5MGhVnDMllfwEKx+XNZMTb8EXDPLAJ3vC79Xa46Wu0y0SMINMq1ZR1tTDBdPS0GvUSgWM20+rw3voFx/HBtr/GbRq3P6gSMCMYnWdLvISRk/VckmajQc/Laep2zPgOYdw/DnaIUhPAOuA+b2P61BmRhIJmCNQ1mjn6sdWhw+AGbFGLpqu9F+5dHYGv3xjGxtru9D0noCfNTmZd7bsO0n73ikFEdOU7q/D6ePulzaxsbaLgiQL2xrs/dbZWt/NGSXJGLQa3tzUgE6jojQ9mnNKU3hrcyOg9IA5rSSZS/+zMvy6Lpefn762hRdunku0SUdeooW8xAPPsrTX13vaeXHNvuFNn+xsZfqmBu46ecKhf2DCsFKrJC6dncldz2+gpUfZRtNjjPzlotIRjmzoyMhU9qnk2qvbLWaKGK8Kkqz85vxifv3mdkAZZvf3S6YcVZ+U6nYX33t5U3gK8wunpzEvN46Vle3YjFokSaK63Ul9lxtfSObpr6soTrXR4w1Q2eagIMnW26h0D9salf11MCTzwKd7mJEVw+T0KG5/dl04kb65rpuVlR3cf8X0Ax4LTHp1v74zADHm/utvrO3kjmc3UN/lxqRTc895JZxTmiIaTx6mnU09/N9rW8KP6zrd/PCVzeHjJMCORjuXPfw1Hr+S5F1f3cVdJ+ex3qqnpcfLrOwYzizpfxNkemYMNqMGu3tfNd53lxYSdYCeKS5vkE113f2W725x8PWb23hpbR0qCa6YncldSyYcdV8ghyfA86tr+MsHO/EFQ0xItHDV3CxOmBDPC6trmZkdw1WPrsLuUeKenBbFA1dO48zSFHY29nD6v1aE/172JpOijMM7JfbxwB8MkZ9g4R8f7QpXHGXGmrhjv6FuQqQp6dGYdGpcvn0Tvl4yMx2rqNwe1Rq6PCSMogoYjUrFlIxolpe1cMWczJEORxgFjvYolyfL8qWSJF0OIMuyWxK3yQ5LMCSzrb6bmk4XqyraI+4+1Ha46XL7qWp38siKCjbWdgEQCMnc/+keHrpqBhNTlCmno4xamuxevtjdSml6FLYBGoJVtjnC71Hd7uKMkhQ+3N4csc6s7Fge+bySa+ZloVFJ/OrcYiam2Pj9NyZz4wm5+IMhcuLNfL67rd/7lzX10OXyH1HTvK/29C+3fn9rEzedkHvYszoIwyMYDPHelsZw8gWUC4ovy9uZlxc/gpENnZAMs3NiWV0Z2U88RjSGHLcMWjVLipKIMepo6vGQl2A5qqE5NR0ufvnGtvDFJMCr6+v57ikFmHTqcEVNl8vP019XUdWb6Ftf00V1u4up6cowkPKWHjJjTdxyYi4WvYYX1tRS0+FiW4Mds04TUcUI8PnuNqraB65mAUi0Gvjt+SXc/My6cCn9KZOSKE6JHHbS4fTyvZc2Ud+lDEl1+YL84JVNFCZZmZwupgk+HLWd/ZO3ZU09tPZ4w8fJbQ32cPJlr+dX1/L4tbMIySG63AFW7G4lrdVBaXo08VZluylItvLizfNYvqOZZruXU4qTDtq/I8qo5ezSFB76rCJieXacKTw8LiTDf1fVMCMr5qhnttvW0M3v3t0Rfry7xcGH25vQqVVYDRo+KWsJJ18AttR3s6aqk4xYM1nxJu5eWsC7Wxo5d0oqDm+AtOjDn+FJOHz+QJAt9d3h5Aso+6zmnv5DMIV9CpMs/Oj0Iuo6XHgDITLjTJSmR6EfxAbpwuAKhWSauj0kWEdPAgaUZN6y7U0iASMAR5+A8UmSZERpvIskSXn0mQ1JOLA1lR1c9dgq5uTG4u6TUd+rtsPF3Ny48DSNfdV0OLl6bja/eGMrb2zaNz3wnSflMzU9ivRYE0UpNtp6vLQ7fRHjVr2BEFXtTi6akc5rG+oJyTKnTkrCHwzR4fTR3O3hzTsXhps+Wg1apvSZPSFtgHLg0nQbkgre2dzI+ppOSlJtzM6JIy3mwKXDUzOieXldXcSy+Xlx6EUzyFHHH5TZ0djTb/mu5v7LxguNWuK04iSc3gDbGuxY9BpuWJiDSC+PXx1OH794Yysf7WgJL/v5WRO5bkHOIaeb7nb5aLZ7eysT/PR4+/cK0qgkrluYTYvdQ2q0EVmW6XDtuwgqSrZy/rQ03t3aRHOPl9++syPcl0utkvjJmUX88b0yEiz6A8ZzqKGoiwoSeOOOBVS2OYg26piUaus3Pr61x8eeVmfEMllWkgoiAXN4Egc44U+PMUbcpBhgJBFatYrkKAPPrarh3o/39Ue5cHoavzynOFzdNDHFxsSUfT07/IEQFa0OJEkiPcYYMUxJpZK4bHYme1qcLNvRjE6t4uYTcwGJ751SyPKyFtbXKFMSr9jdFpGA8fiDbKjp4vPdrUxMsTIpxUbeAWZBHCjp9PWedm5dlEd9lzv8Pfqq7N3ODFo11y/IITPWFDFl+8RkG49eO4O0mP796ISjEwiF2NHYvwp6d4tjgLVHRrvDS7vDR4xZOyqmEAbIiDNTmu6j2+1H8vopSLKISSRGuZYeL2a9etQ1mZ+cHsUTX1XiD4YGHFIqHF+ONgHzS+B9IEOSpGeBBcA3Byuo8crlC/CPj3YRCMlsrbdz6awM1td0RaxTnBrF2qoOJiZb+XK/apGsODNb67siki8A/1mxh1sX5XHnCxt45OqZ/PT1LdR0uLl2XiY/Or2QNoePT3e28sbGBr45P4sHrpjGtkY7Kys6+GCbUhEzLTPmoM3YJqba+PEZRfz1g50EQjJJNj1/uKCUhz7dEzFj0slFifzjkikHLIs+oSCBWdkxrKlSTspy481cPDND9BkYhQw6NScWJLC2OvIEenZO7AhFNPRseg1Ob4DsODMnFyXiDYT4eEcz8/PiRjo0YYjsbOqJSL4A/OXDnSyZmER2fP/+GN0uH05fEIfHz3df2sTWBjsJFj33XT6NvARzRBJDp1YRa9bx3Zc2cddJ+XxZ3kZNh5srZmfS1O3lva2NXDAtjT+8V4ZGJWHQqsPJF1AqJv+3vp7bFuWRHmMkP9HCnJxYVvWp0DpvauqAcfalUasoSYuiJO3AiZRok5Ykmz7coHMvMWX14ZuYbOOOk/K5/5NyQBnO9qcLSyPuxE5OiyLapI3ozfK9Uwqwe/zc1/u6vV5dX8+Vc7KYPkClS3O3m4dWVPDM19VIEly3IIcbF+ZENP3NjjNz7+VTqWx1sr3RzsMrKijvveC+dVEu7U4v1e0upmVGR7z3il2t/PG9Mq6cq8zM5fIFuXZeNqdMSuxXbbv/tKoTEi18Y1o6GrXEsm1NLMiL54WO2oh1pveZSc8bCPLn98sint/RZGdrvV0kYAaRSatlUWFCv2FpM7NGx/F8XXUnP3h5IxVtrvBQ59FQabu90c41j60OJ9cf+bySp6+bzQkFCSMcmXAgdZ2uUXncshm0pEQZ2Vjbxazs0fF3J4yco50FaZkkSeuBuYAEfFuW5f5jVIQI3kAoPA6/2+2nx+Pn3CmpvLOlEbUk8Y3paVS0Olixu42nrp9FWVMP7U7lTukls9KZlhHFu316wOzlD8pIEnj8IR79opIEqwGTTkNKlIn/rqqmw+njgmlp/OTMQgqTbTg8AX799nYaupRYZmXHMK/3ArOm3cnuFgdatYrCJCtJvc2iLOogN030c0VOJvVSMjE2Mx1Of0TyBWB5WQvlrQ5mHOCgnhlr4qGrZlDe4iAQkslPtIzKHaUAgWAICZnzpqby9uZGVBJcMC0dfzB06BePUe1OH51OL7fNicPibSKoNVOalkdDl/vQLxbGJJevf9WKxx/C7Y+sUJRlmXXVnWyo7cLu9pMebeT8aalsbbDT6vBy/VNreOK6WfzyjW2UNfWwMDeKXywwUdNWwc9OzeYHr+8IN3fe3mjnm/OzuevkfD7ZqSR/dBpVRJ+BvdocXvISzExIshBr1vHXi0tZsauNNVUdLJgQz8K8eMy6Y++ZkWQz8JeLpnDT02vDcX5rST6F4m7vYbMatdxxUh6nFSfR6fSREWsiNyGyN1p+opXnb5rLsu3N1Ha6OKM4mVnZsVS1O/nWkgkEgiE0KonnV9fSZPfgHGD7BFi+s4UnvqwKP354RQWFyVYu3G8okVmnweMP8sNXNkcsf/KrKr6ztICyJjuL+lxMdrt9/PXDnVw6K4P/ra3i+kkSOpXMa+sr0Kolzp2aFvE+JWk2Lp+dwfOra7l+QTbdbj//Wr6bBKueK+dkEgjKLJmYyPKyFvQaFXcvLWB6n4SPxx+MGBazl+MAn1s4Ov5QCINGzWNXFpNGCzIqauUkqjr7/+yHW0OXm1ufWRcekl/X6eamp9fxzl0LyTpEcnmofVXeRo83QHacCZNOQ1mTnX8t383M7FiMOjEMacjJMtStBXcnZM4Bw6GrMes63UfVRH84TEqx8VV5m0jACEeWgJEkafp+ixp7/82UJClTluX1gxPW+BRj0nH13Ex+965yt+f51bVMTovi+ZvmYNFraHP4aOxy880F2ZSkRvHGnQuoaHVi1KpZXdXOsu0tNNk9/e6eTUqxhqeAbuhyk5doYenERP7Ye1epKNlKlFGLyxtEI0kEQzLP3jiHuk43GpWKwiQLrQ4fn+1s4XsvbwqfDE1KsfHAVdPJ1nTBZ39GveFpbIBtxnVw4vdpCg58d6rvDDIDibPoR9X0cMIByKDTqNnT6uDWRXnIsszHO1ooGcfTVoZkmZsn+olfdg3a5o2gsxB1wj1UJJ460qEJQyQn3oxFr8HRZ/jQ3JxY0vcbSlnW2MNjX1TyXu9sNVq1xK/OKWZJUSIfl7Xg8gXx+oL86cLJ6Lyd5O55Cv2r/6YgFMBfdB4d867nV5/vq47534Y67l5awJT0aKZmRPPQZxUkWPRIEhF9ZC6blcHiokSieisPMmLNXDnXzJVzswb9Z3HChHje/dYJ1HS4iLPoyE+0YBqE5M7xxKTTHLKH0P5DiTqcXh7/sorXNtQDSuXUL86ZRI/Hj0WvwekLRCTZZFnmjY2RlbDpMUZ8/hCvra/DYtBQnBoVnkmobw8WAKtewx0n5dPa48XrD1HW1EO0SUuUUUcgKGPUqklQOXgq5xPiVz0IQT9L8s7mk+47cPmSIraJWLOen5w5kSvmZPK/9fW8ul75DDUdLv74fhkPXDGdhRPi+eFphRi0ajJiTBFD6ZJsBi6dlcEzK6vDy9QqiYLDaOovHD5JJXFGhp/EtX/AvPNVkFSklN5A9uRbRjo0Grrc/WZjcngD1Ha6RjwBEwyF+MmZE9nRaMfhDXDu1FS213cTDIUAkYAZUgEfvHQNNG8DSwK07Yb5d8HCu0F94CbIdZ2uAWeOHQ0Kk6x8Xt7Kt0c6EGHEHemZ1d96/zUAM4FNKBUwpcAqYOHghTa2ODx+Ntd3U9HqJMlmoDQ9asDKjnOnpuIJhHjiyyqijBruOCmP0vToAZvOpceY0Kgkrnp0NeWtDm5fnMeLa+r49pIJvLulkR2NPczOieXSWenc/uwGAE4sSOCdzY1kxirJkStmZ+ILhnjk8woMGjU3npDD+pouGrrc3HvpVIrTothU28Uv3thKWowx4k7U9kY7qyvayZaWw/on9wW29jFInUZ20aVMz4yOGEaVFWskL35wT5zaHV52Nffg8YfISzCTKaasHh4SeAJBTpyQwMtra9GoJC6amY7bP37vTMbpAti++JWSfAHwOYj/+Ltw2btAxkiGJgyy8pYettR34w/KPHzNDB7/ooqVFe1cND2V86als7qygyijloJkKzaDloo2Rzj5Akrl4X3Ly/m/M4r4uKyFjFgju1sd/OHdMt45pRP91/eG19WWvcHpMwt4yDaPJrtSeWjUqtnV3MPzq2spSbNxwbQ0XttQz49OL+LlNbW0u3ycPTkFuyfAh9uaOXNyCmb90CZDJEk67BnthMGzvcEeTr4A+IIh/rNiD/Nz4/jT+zs5ZVIS183PwqjTMCHJikWvYXpmDCsrlKFoeo2K6xZk8/M3thLo7f1WkmbjoatmkB5jIjvOhFmnxtlbYXXjCTk88Gk5dk+AeIuONocXpzfAN6anE2fRc/msDBawhvgN/wrHZNrzNvPjilBLc/rFbzVoSbAYeHW//m6yDJ0uH2dMTjngZ19f00mylY4mGgAAod1JREFUzcBFM9L5cHsTaVFGfnLWRCalir5Dg0mFTEzNB5jLXlEWyEGiNz5MKGUa5OWPaGxRRi06tQpfn+paSYKYUXARXZwaxQ1P7asKXLa9mb9eXIpFzII09Jb/BlztcO6/QKWBniZY/TDseBMufgriBp7Bq7LNOeoa8O5VkGzl/k/LRR8Y4cgSMLIsnwQgSdILwM2yLG/pfVwCfH/wwxsbQiGZF9fU8pt39s0EcMqkJP584WRizJE7gSSbkbtOnsAlMzPQqVWHPMDsanZQ3qqM2f5wezNnTU7mnre3c+KEBC6akU6H08vaqk60amWq6uJUG098WUmyzUC8RYdRp+a5L2oA8AcD/OOj3Xzv1AL+vqyVH726mf/eOJvHvqgk0WZgT4uz3/fvdPkJVb5Kv93E9jeInn41f714Ci+uqWXZ9mYW5Mdx9bzs8LClwdDQ5eYHL28K98OJMWl55oY5B+1lIAwOjVpFfoKFX7+9nSUTEwmG4IVVtfzmgpKRDm3IaNxt6Ks/67dc21WB0upKGA/Kmuxc/vBKOnsrCfUaFc/eOIffnl/M7hYHlz28MnzCfdH0dC6akdavigCgye4hpPSi5ztLJvCz17eREm0gsX11v3WTa95mbvpiXt+uJGAum5XJU19XAbC13s65U1Lx+oPMzIzGrFezq8nBJztbwj1h0mNM4aGiwvgy0BCc2g43CVOVY+my7c0kWvV8UtbCacXJFKVYmZMTx6baTr7c08HSiUm8tKYunHwBZZvaXNdNeowyDOrJ62bzyze3UdZsR6dRYfcEuG1RHm5/kB2NdrY32ClJtVOQbOPUkmQsn6zqF1NMxZsQ/A5o+w9LM2qVRsJ2T2RT16gDTI8OyoXSLc+so9PlJy/BwgXT0vH6g2THmQ/ZWFo4MlIwQPSeN/otN1V9DHOuGIGI9smJN/Pzsyfy8ze2hZd9/9RC8hNGPhG8sbarX1X3M19Xc+bkFFEdOJQ6q2H903DufUryBcCaDCf/HHa+DY8uhfP+DUVn9XtpTYdr1DZKtug1JFr1lDX2iOb2x7mj3XsU7U2+AMiyvFWSpKmDE9LYU93u5M8f7IxYtmx7MzsX5jA3d+As7OH2PZH71KKXtzjIS7Bw99ICmro9TEi0sDA/G6NOzXULcki2GfAEgrx2+wLWVHVw6qQkPt/dGvF+iVY9DZ0e0mOMbG2w0+70U97ioM3h5fSSZHbuN8NNtEmLM3k21qoVkYFlzgUgN0GZou+Ok/Ix6zVHddK0s8nOlrpukCQmp9koTN5Xmr22qiOiGXGny88Dn5Tzj8umimkAh5g/GOKdzQ3cuDCXFrsHSYIbTsjh4+1NnFacPNLhDQm/xkIoNh9VR2QzTNmSOEIRCUPh4x0t4eQLKMMmn/qqil+dO4mfvLYl4oT7lfV1ZMaZmJRi6zc8aFpGNClRBl66ZR56jQq3P0i7w4fdVsj+bVN9KbM4OSsLo7mTzDgTn+9uw6zT4PYF8QZClKRGMS83jhfX1vLB1uZ+JfnrqjsoSraOirvCwuDKjus/nHdWdgxb+zRMXVnRwYzsGNqdPsx6DT99fQs/O2siN5yQi1mn4can1vZ7j64+s23Nyonl+ZvmYPcEWFnRznlTU/mivI0t9cr3WFXZwfZGOzcszCE9xkhsyuR+7ydnzEbSDjzLYZRJx0/PnMR1T65mbx6oMMlC6UEuMspbesJ/h3taHezpvdl0WnEyGbGiAe9g8ofUBJJnY6qP7BTgT57GSHfh06hVXDQzndL0aOq73CTbDBQmW9GPgunIPQMMqQ+EZAJBeYC1hUGz8kHIXwrG/Y6kkgRF50BcAbz1HeiohPl3RqxS3+nu1xx8NMlPtLChtlMkYI5zR5uA2SFJ0qPAf1Gmor4K2HHwl4xfHn9owL4nzgGmJD1SBUlWMmON1HQod0E/2NZEtFHDPeeVDHhwMuk0uHwB/vBeGT8+vQiXL0gwpNydOntKKpVtThKtOq6cncmLa2qxGjRcNjuDX7yxDY1K4tRJSSzb0YxWpeKGhTk02T18bTyRJdGvoO6qAkCOyUWadG74e6pUUniazCO1pa6Lyx9ZFe6/YNFreP6mOUzuHUNf0da/KmdjbRcOTwC9ZeQPzuOZHJJJizESkmW8wRBqSUKWIWWAKcnHC58uGvepf8H8yuUQUCoVvMWX0m0rInpkQxMGUf0ATZWrO1y09nip7ej/nC8Y4u/LdvK3i6fw67e20+32MznNxm8vKKG4d6hEl8tHcaqNbQ12VgSKuTRxCrqWTcobWBJZm3ghL6yr47oFOZQ3O5idE0tylIHMWBMWnYaCJCvvb22irtNNRqyxXwImEJJ58LNyvntK4YBDVoWxa2KKjX9cMoVfvrkNuyfA1IxoTpmUzB/e23datTA/jpx4M8+srOGL8jbOKk1ha303N52Yh0Wv4aKZ6RFNeUHpO7emsoPJ6TYMWg1RJh1RJh0uX4Cmbk+/PjJf7WlnVnYsdz2/gRU3ziE+dTo09F6wmxOwl97Aox/vwazTcMKEeIr3q0RdkB/Ha7cvYFdzDxaDhpK0KDIOMpORUavul9RUqyQMWlGaP9gktYRj4mUYdr+Fyq4MdwvGT8SetojRUCtg1GqYkhHNlIzokQ4lQlGShTtOykOnVhGUZXRqNTaDOuLmqDDIAj7Y/AKc8ZcDr5NQCKf/ET76JUgqmHe78tJgiFaHl3jL6L1RkZtgYU1lB9fMyx7pUIQRdLQJmOuA2yDcR2gF8OCgRDQGpccYmZUdy5qqfVODWvSafrMfHI2UaCOPXTuLNzc1sKqyg3OmpLCkKOmgdwY8/hDBkEyT3cPEFBuhkMyJhQl8/+V9syDYjBoeuGIGiVYDpxUn09rj5ZHPK5iXG8fDV88kLdrAQ5/u4c3NjWhUEj+cdx9zrK3EW/RokyaSGD/w2Msj9b8N9RHNLx3eAK9vbAgnYAa6e3bm5BRiDjDNtTB4tBoVM7PjuOPZ9eHSdr1Gxf1X7N+Le/xQqyT+tiuBM057jXS5Ca/GyjMVJpb6zQx+y1NhOPiDIVzeAFaDNtz889RJSTy3qiZivRMLEnjyqypmZEWzrror4jmDRs2Oph6mZkbzzrcW4vAESIkyENVnPxRt0vH3S6bw+3fL+MXnrYRO+jMXLe6hrt3Oqp54fveeC2/AiVGrIc6i46W1+/plLMyPY3K6jSijls93t/GzsyayrcEeTuxPTLbS4wnw+JeVnD81nUnjuBH28aK+082X5cpsVnNyY1mYH8873zoBpzeAXqvmey9tDCcmEix6ZuUoiZG9y575uprrFmQTDIVQqySuW5CDxx/i5bW1xFl0XDsvm38t382Oxh7+evEULpqxb3akwmRbeEbG/UkSuHxB7l3n47eXPw8tOyDoo1GXxcmPV+L2KdvkfcvVvHTrvHACEpRKhiO5iM5JMHPlnEz+u3Lf3+I352eRO8KNV8cjFRIbPMkEZj3BNEMzsqTmc3s8if5E0g798uNWlEnHq+vqw727dGoV/7xs6lHfdBQOQ+VnYEtThhwdjCURlv4a3v+RkpDJX0Jjt4dokw7NKO6vkp9g4b0tjYdeURjXjnYaag/wj96vfiRJelWW5QuPJbCxxGrU8odvTOb+T3bz4bZmJqba+MmZE8kZpJOICUlWvndqIbIsI0mHHuKTEmXgqjmZrKpsZ2u9naUTE3nyq6qIdezuABWtDhZOiCfJZuDupQVcNisDtUoZx721vps3Nys7iEBI5vdf9gAGfnrWRC6NH7xmpO09PrRqCX+fcs6qPlUv0zJj+M7SCdz/STn+oMyJE+K5cm5mxCwKwtAIhmTe3NgQ0VfAGwjx2e5Wlk5KGsHIhk6ny89TK2t5PCQDesAH+MjPdDFvcHKOwjAqa7Tz6BeVrK7s4JRJSVw5J5PcBAszs2L580Wl/POj3XgDQS6Ylk5Fq4MPtjXz5HWz+M3b29nV7MCsU3PjCbm8u6WRM0uSSbEZDzr1aGGyjQevmk6bw0eUQcOWxh4ue2clsK8vxuT0KO79aHfE674oVyoPUqONxFt0PPDJHm4/KZ9gKERylIHyZgePf1mJLHPAaYmFsaPL7ePnr29lee8U5C+vq+OsySn86cLJZMSa8PqD/Oj0IrbUdxNn1tHU7WFzbTf733R/d0sjty9WGqhmxpq459xivtHbzPmhFXuwu5Vt5Z63tzEvN460PjN7TcmIZm5OLCsr9904mpsby/YGOwCV7U5kSwmSNRlfIMj3n1wbTr4AOH1BPtvZGpGAOVJp0SYun51JYZKV1h4vSTYD07OiSYoav1WWIyUEvLi2luVlbeybvaeNa+aaOXni+DyeD4btDfZw8gWUasjnVtcwPy8uIgEvDKIdb0HG7MNb15IIC+6G12+DO1ZT0xEgyTY6G/DulRatTHjS7fYftEeWML4NVQep3CF631ErP9HCHy8s5Yen+bAaNEPSIf1gyZdut49gSCbaqOPdrQ3MyIrhv713eI1aNS5vsN9r+laeqFQSaX1KhY1adb+u9KBMaT0Ymf9ul5/1tZ2kxRi5bXEe3e4AT/UmifreqYsx6bjzpHzOmZKKLxAiI8Yous8PE5UkYff4+y23u/svGy9CskyMSduvKaZGJPzGnMZuNzc8tYb6LuXk+bEvKtlU18Vj184kyqhj4YR4NtZ2oQLe2FhPS48y5KfH4+fFm+exo9HOnlYHr22oZ1FhApfNyjho8mUvk05DZqxyaFWrJDQqKSKJGTpA6XpQlvnnx7t5/NpZ/G9DHTsa7EzPiuatTY183dsHKzPWSJbojTHmVbQ6w8mXvd7Z0sgti3IpTY/m/9k77/A4qqsPv3d7Ue+9WbLk3ruNG8WAwfTeO4FAyEdICL0FSEggQAgQeu/FNIMBgzHuuDe5SbJk9d61bb4/rtpq17YsW9qVPO/z6LHn7szsmd27M/eee87vbCio5vwXVwJw/JAoRsQHeRW3jwo0Ud9s56fsUoprmhmXEkplg413VrtHd9U2OWi2u48BQi0GHj9nJD9sL2XpzjIyogNxuly80prGdMGEpPYxh8OlUNvkKRRc50WY+kBU1reg02o8xg/D4oJJiwigutFGiMXQrd+YyuEjhKDey/elOnQPTmfnSxuF1U0eY2OVo4SiwK5vYc593T8mdiTEjYWfHiUv7Ca/1n8BOd9Ki7SydX8NU9MjfG2Oio/oLQfMMZkcadRp+1wfo9HmIHtfEVVFOZS36FCCE9hT2oDoVLfo511lXDIpmed+2tPephEwOe3AFTWSwy384YQM/r6oQ1z4xGHRhJj11DXZCTwCJ4zTpfD2qjw34eKRCcFcMTWZzJhApg5yvyHptBoG+YEa/rGGRiO4cGISDY1NXDoEnC54ZRucMTrO16b1Gia9liumpvDEdzvb27JiAglXhU/7HXvLGtqdL22sza0ir6KRkQkGYoNMxIeY+Uen+1CoRU9WTBChVgOT0sKJCDSSER1AcriV2B6syqdFWrliagquxgpGh9rYW6/HrNcyJimE9fuq2/dLDDNT2WCnxeGiyebkjpOyMOq1bC2sYcWeCqwGLVMHhfPHEzOJ6qaAu4r/4jyAgGebsOdP2R3i+Ut3ljM+OYyK+hbiQ8zt+kUaATfPSefaN9awu6yxff9XrxiPUadx06WbkxVJbIhnv0kOt3LV9FTOGy9LQP990U4iA43cMiedGRkdz2GLQcdDJyWyL28PBU16XtzQTE2TndlZkYe81rK6ZhZuLOLlX/YSbNbzfydlMiM9wi2N2mzQYjaoUS+9iRBwzvgEXA4bl2SBwyV4eauLOVmqwPzBmDoo3ENb6ayxCUT6+SS/31KxGxQXBCccet/OjL4IPr+J3KyziQzw/+8mOczCJtUBc0zTpzXUhBDzgH8j4x9fUhTlsS6vByOFfZNabXtCUZRX+9JGX1FXV4coWo+hfCu6gEg0ieMhNOWA+7fYnewpayC8KYehP92JseBXMIWwb9K9kHYcuyrsJISaKahqorbJwe7Sem4/cTBfbioi1KLnuuPSSA7zHPDYHC62Fdawu6yekfFBvHrFBPaW1aPVCjYX1HDas78yIyOCh88YTnJ4z1Ks9lU28O8f3EPwNxXUcNvxg5mtDgb8imEBdXyY/Dna5a+D0HDGhOspMCX52qxeQys0aATccVImDTYHBq2GhhYneq0aAdPfMOg8c8A1Qubwg1wRvmBCIvEhZj5dX8CQ2CAWjI4jLTKAFruTT9bt5/4vtqIoshrc85eMY0RCMDnlDdQ02UkIMXs63JtrsOWtRSnbjghJIixpAn8cVo/pq/9Dsy0bguJoynqaecOn8OryXJbuLGdYXBBDYoP41+KdXHtcGp9t3E9Ns51xyaEMiwvmuYvHUd1kI8R88OiAumY7tU12wqxGNYrAz0mLtDI0NohtRbXtbWOTQkiNlM/UoE5Rnjani0Vbizl3XDw3zhpEs91JWzBsYVWjm/MF4PFF2bx8+Xge/WYHu0vrmT8ylptmpx+0ZG5lo53YYDP/u3wcYRaDWzQsAEWbGbXkJkYXbQRrJAtOfJz9kdMZ3ar14qjIwZm/FhrKELGjMSSOpbQJskvq2LK/lscX7ZD21jRzzetr+fD6KUxIDevpx6fSA7RCMCG0kXMSP0bz61ug0XHmpBvZYx24z/OjwdRB4fz9nJE8uXgn9c0OLpuazOmjBu4ilM/JWQqxo6AbcgtumEMh/Xjydm8nc9Tk3rHtKJIcbmVTQbWvzVDxIb3lgPH45QghtMB/gBOAAmCNEGKhoijbOu12E7BNUZTThBCRQLYQ4m1FUTxjX/s5+ZWN7CypQ6fREBtiJHD3l8QuvrH9dSVqKOKi9yEkCaryYP9v0FgOMSOwRY7ivfWlbM0t4mHlaQwFv8qDmqtJ+vmPVJ/0Ed9VhHDBhCT2lteTXVzHsLggJqaGYdZr+XV3OXd9uoXfzU4nMtDArMFR7atRS7JLueGt39pzzaenh3P6qDju+Li96ji/7Crny01F3DQ73eO6CqubKK1rIdxqkGUkq/aBvRGC48EotfbtTsVr1Sin65gMnPJbnC6F2MLFaNe90taCbuWzxIYNhpSBmWWooDAzNZCwum3oSrfitERgSxvDHpvaN/sbGVEBzMmKYkl2KRdOSCIqyEiY1YCxk2MmPMDIGWPiOWOMuwxldkkdZYU5LDy+ioCWUkpMaXywfBsN4zLYkF9Di9NFoEnHlNRwRrYJjrqctKx8CeNPD7afx3nCI1jWvADVrSkhtYWYP7mUhGt/ZnJqGMdlRGJt2k9zUxH/PCODpbmNmPUaft5Zxob8ak4eFoNepyEl3Irei0OpjfX7qnj4y+1s3l/DzMER3H5SJpkxqlCvP2JzONm6v4Y/HJ/BDztK2bK/hlEJwZw2Kq5dXH5mZgTPLdFR15omvKmgmj+dlMm09Ajqmuxc/foaVudWcctcz2dwTnkDMcEm7jttGM12J+EBBuJDzORXNvDr7gryKhq4clAdYbXb0Gj0lAcP44s8Ky5g0ZZiJqaEcfuMCKy2CrCEgdYIn92AKNki36ChjPjvriP++qWgi6alPAfdexdiLO+o2NRy5qvcvyUZrRBsKaz1sHHF3grVAdPHOFwKSUXfoln/hmxw2tAu/zdpEZmAKnB2YAR6jeDUkbFohSDQqEPNSO5Fcn+ByCE9OzZrPnnry5lh8f8vKCXCytdbVCHeY5keO2CEEGYgSVGUbC8v/9lL20Rgt6Ioe1uPfw9YAHR2wChAoJCJxwFAJeD3CaoV9S1kF9dR12wnLTKAjOiDF/XbXlTLpS+vateZeGBOBJdvftBtH1G6DYo2AQLeuwjaBj+AcuYr/GtxAA/PCsLw0zce509SCjl5WBZr86rIjA5kcqosX3neiyvcRPxeXLqXSWlhhFlkhYXyuhbu+3yr2z7l9TaW7ir3eI/vthZz7XGpGLQdK63Ldpdz67vrqWiwMTM1gOfH7sP8w13QXAMpM+DUf0JkJvGhZk4YIstdtxFk1pEepaYZ+RNaXGizF3q0G/YsgomX+8Ci3seghYjSHwn6usMZao8aiTjlFUAVKuxPhFgMPHzGcHaV1PHoN9vZUSyFcEMtet66ZhLD4oJpaHGwIb+atbmVxIWYmZgSRnKEFX1TBddU/B3LxmUAxAOps/7Gw+stfLy+o3TvnSdnkRRmIcRqoLF4F5Zf3II60bZUdThf2rA30VK2l/SQ0Vh2fEjcmkfBVk9TygmkjPkLN35dw4LR8by0bC/RgUaqm+wEmvQMiwtiWHwQZr37YzuvooHLX13dLri6eHspBdVNvH3NZMLU1Dm/Y0thLatyq3hlWQ4RgQbSIwP5eWc5X2ws4stbppMcbmVobDDv3zCFFXvKaWhxMi09nJGtlQFL61pYnVsFgE6j8dBqe+D0Yfx9UTbfbet4vj55/igWbixkyY4yPjlNT9QHl4JDpudFmULImvwKVy9q5ta56ZwUsh/L21dA+U5ZiWTBf9zGHwC4HFCxF6KH4dy/wc35AmD84S7SUl5kc7WRUIuenC6fgdov+x6tqwXt9s882/cshrEX971B/YQVeyu47YONbm0uaBe/VjnK7FsFx8/r0aGKNZJ9iobo6nUQe8JRNuzoEhdiorimmYYWB1ZjnyajqPgJPfrWhRCnAU8ABiBVCDEaeFBRlNMBFEX5zsth8UB+p+0CYFKXfZ4FFgKFQCBwvqIofq10VVLbzJ2fbOLHHTJn26TX8MZVE5mY6l1fxeVSeHtlnpvIp8Zlh8YKz51tDVC00WPwY/z+Li4f+T+EMRBnWDraCvd0Hpclgj+9v4mIACP1zQ6a7E7uP22oRwWFfZWNnDYqjp2ldUxIDaPJ7qTR5uCCCYmEBxhZv6+SE4bGUNPoKbo6IyPSzflSUNXIze+so7p13xuzGjF/dXPHAbm/wI8Pw1kvYjWYuXv+EFIjrXy1qYjhcUHcPCedFLX0pF9hdzjRhQ9G7Fvp/kL4wB14GJrKCfr5Hrc2fekmAiq3QUqGj6xS6SlxIWZ+zi5rd76ArHT16q+5PHbWCL7cVMSfP97U/tqgSCuvXzWRRPteLAXL3M4VuvxvjB79Dh8jK87MGx5DfbOd8voWQqwGmprqsTi7BGu6XKC3yCjATmypNhDesIG4X+9qbzPnLiYjMIb0iAsx6DQkh1kpq2/h3z/sbt/ngdOHcdGkJPSdSmzmlje0O1/a2F5UR0FlozrR9UOyi+sAaLI7ya9sIr+yqf214prm9tTeobFBDI3tiGJyuhSW7y5nVU4FGVEB7Cqt562Vefzl5Cy+2VJEUU0zF05MJCrI6OZ8yYwOJK+8gfkj4nDaHWTmvdjufAGguZrhdcuIDJyGoamUoZtvgtpWJ2Ptftj9gwzvb6pyvxBrBDtL6jBXVuEhC91QTpRZsGxtOX89JYuNBTXtEa7hVgOT1OiXPkdRFJTILMT+de4vhA3MaNajxfLdnguQH/9WwHnjE4kI8O9qO/2O2kL5rAzsWYpXeZOCTqslIHcxDPFvB4xOoyEpzEJ2SR1jk0J9bY6KD+hpofT7kREt1QCKomwAUg5xjLeYsK5x/ScBG4A4YDTwrBDCI45aCHGdEGKtEGJtWVlZ15f7lM37a9qdLwDNdhcPf7XtgJVibE4X6/Or3drez7bTMPwS9x21eojKAls9HjSWM2dQEAXNJnaOux+0HYPsutST2SFSURQoq2uhqbXygcWLJsCgSCsZUQEUVTfz4Bdbyato4J/njWT9vmpMzaX8caSTkaF2muxO5g2L5v9OHMzNc9K565QsThsV63au4prmducLQIy9wNPuHV9Cvaz8kBxu5S/zslh48zSevmgMI1pX9wYS/tRPe4IQAjF4HgTGdDSGJKEkTfGdUb2MTrF5TjQAnbPRy979n/7eR7vDzpI6j7aN+dUUVDVSVtfMrXMzOHdcAkLAnrIGthXWYqbJ80S2eqYkW7lschKzMiN5c0Uer/yay7fbSqhsaKHJkkBLQpffRvYiHCc+6tZUOuYW/rNFg6l6J12x7FrInTPDef7nPSwYHcczP0rnS1qEmUdnWQlrzKG0wr1/Bpg811EMWs2AWVXrj320utFGQVUjLQ7P6oPBZj06jfDQlTLpNUQEek7obA4nmwuq+WRdAfmVjUQFmvjd7EFcPT2F6kY7D3+1jdNGxfLB9ZO5aXZG+zMf4JLJyQyPD+K5n/by8FfbOHNMLObG/R7vYW4qJsikJ9Nc2+F8aWPTu9hPeAREx3BRmXAtRA/ls/X72SMSQePe11pGX8FHu5w4XQqvL8/j6bMz+eaiSH64MpFPbpx8yCjh/oi/91PF5URkngrWTsLJoamQMMF3RvUDwgM8ndjRQSZ0h6tR4gf4ex+lYC1EZh2+/ksrebUuYgN1UJUrJRv8nMQwCzuKPMcnKscGPR2hORRFqTlYWWQvFACJnbYTkJEunbkSeExRFAXYLYTIAbKA1Z13UhTlReBFgPHjx/tUnKG8tXRpZ7KL66lvcXgt12zSa1kwOo6tnfKitxQ1snvOlSRqTIRlv48jOAXX3HsxRI+QO2h0MuS3lZohF/HIshounhLOCzviOHHqeyRTRKPGyhfFYcwxRkCnoN/RiSEMjQviL/MyefzbbBRFlpm+46RMbv9wU3ue+Su/5vLgaUN5ZlIVg1f8GdYX4QpLJ2LmP1kRkcZfP91Mm0zL1sJa7j1tKGFWOWAMsxow67Xtg79abYjnhxWe0a4DA7LKTvgAXkHwp37aIzRaHNWF6E76G1TmyAF4aAqO+nIG6rq63RJF87ALMW1+q6NRq8cRPth3RvUi/b6PdoMpg8J5tbXEfRvzR8byzI+7+HhdIUEmHfNHxvLAacO4d+FWmuxOtPFZYLDKKMRWmlJO4J+rmzhlbBy/f3d9e/s/vs0mOczC7CFRFM94jNANzxOUu4jmyJGUT7yDxpBMyubEk6gpp1KE8sxWAz/nNGIbFkNXbGGZ/LC3iRtnDiI2xIxLgdMHW/hr7Cpi1j4JjmacZafBSQ9BWCoAGVGBnD02gY/XdTi9/3jiYJLDB0a56v7UR10uhRV7K7j38y3klDdwyohYbjt+MIM6pdeOTAjmw7X7+N2sdP6zZDcOl4JeK3j87JGkehG2/3ZrCbe8t55TR8TiUhS+3lwMQEKomWcuGkN4gIGhMUFYWh1ugyIC0GsF4VYjGgEfr5MOF1uji7sWZjP7lMsIKXAbUpEfczx719fTqAkAnck9QqahnN+aE6g/7gNGW6vY02imKiCDmcLKb3lVLKwS/O/410nf9AT62n3UDr0I19gr+POQEO5fuJWrhgrmZt+HaecX8twz/wzjrgTLwFr19fd+6tSacDla0Mx7DKpyQGghNAXF5fK6OqoimZgaRmyQidgQM2aDhk0FNVw5LYWQfhhd6O99lIK1EN5zPaKcGhcxVg0ED4Hc5TD09KNo3NEnPsTMtsIaX5uh4iN6GgGzRQhxEaAVQmQIIZ4Blh/imDVAhhAiVQhhAC5Apht1Zh8wF0AIEQ1kAnt7aGOf0Fa1oDMnD4/x6jVv49QRsZzTuuKq1wp+N2sQwbGDyB/zJ4ou+hHbhR9hSJ8FGg1Ej4BLPkWJGwOWMCpHXc8n5rNZs6+Oexdu5ZzxiXy2P5j534fyh1XBTBkxmLHJIbxw6VhOGBLFY2ePIDrIyD2fbyU62MTCm6bx3MVjeeGSceRWNLY7X9pIUAoZvOR6qJPiUJrK3ST/8Dt0jaUEd3IofbahkO2dPLcp4VYePWsE2lZ1si9KI7ENnt9xYp0RTnlCivqp9At0Wg2ExMPSf0DlXijPhl/+iQiMPfTB/ZTSRvgs8AIqR14H5lDsMWPYNvcNltao+i/9lfEpofxhbgYGrQYh4PRRcYyID+bjdYWcPTaeiyYlsaWwlrV5VTy8YDhDYgIhcjBc+imOxKlgDqVm6CUsSvg932TX8u3WYrJi3Ffwv9xUiNWgQxOZwcbR97LmlK/4bcqzNESMZHBCJCkjplOXegqPbgnmh70yuqY8cCgtScd1nERvYcfw23lyaRFJ4RZMOg3D44L4XUYVMasebZ8Ua7O/gHWv05ZTGmTWc+cpWbx25QQeP3sE7147iYsnJsnfr0qfsrOkjiteXc2esgZcCny5qYiHvtpGg63jOZsQauGhM0YwOjGYFy4dx3MXj+WLm6dz6ohYNF3UPQuqGrn7M5mCnBkT2O58ka818cGafAZFWNudLwCDowN55YoJnDEmlr1lDeg6nbPR5uQn5yhqZj8KQXEQmkLB7Kd5Ynso8SFmtBHplB/3iJsNpeP+yFPrnVzzrY0bfkvgmd1R3PDRHnaW1rFgdBwF1S2c/pWGP1sf4smM1/kl8UZC4tKZlh7BRzdM5jztj9L5ArIP//AA7F971D5zle4hhAaXOaw1OqASGkqhthCHYeBFIx1NRsaH8MiZwwmx6Gi2ubjn1KEMj1MFznuF/WvlQm0Pyal2EWnRQNQQyFt26AN8TFKYha1FniLlKscGPY2A+T1wF9ACvAt8Czx0sAMURXEIIW5u3VcLvKIoylYhxA2trz/feo7XhBCbkSlLf1YUxa/jyEbEB/PomSN45Ovt1Lc4mJ4ezu/nZGDUHbgMaHyohUfOGM71M9PQCkFSmKV1sGwFujgoNBpIO44dJ77F4o05fL7Lzp5ymZZU2+SgtK4FrQYePH0YI+NDGJkYjBCCk4bFEh1k5rznV7QL9K3fV81jZ43ggomy7OCOEs8ffoSjGOxdwu9r95NpquaMMfGEmPU899MeWhwuKhs66di0qsRnxQZSVN1MVJAREfAUlF8nRXgj0nuubK7iE5xOJ7rVz0H6CaA3y7BQcyiaDW9Bpn/n1/YUl6Jw39J6Xg49hdOzTmdvjcKXX9XzwOmeqQQq/YMwq5Gb56SzYEw8DqeLxDAzd3+2lbQIK0adlud/lj7+TQU1LNlRyie/myoPTJzE66l/pymiim9znWxeJ++7TTanR4nrtpSKxDAriWFWGlqiMOm17Q7p+FAz8aFmfj8ngzWvrEKrEfxYqKco8yFS0nPR2BvYq8Rx/w8OnC4H9S0OLAYt45NDiW/4yfOiNn8EU24GawQAEQFGZmVG9cKnp3I47Cmvx+50X1j+KbuM4upmtyiYhFALCV1LPXuhvtlBTZMdq0Hr9rxtY0N+tcf7aTSC+BAzEQEmQq3N/H5uBtsKa/h2q9SFadKHsC7sbPYOG4dWq2NwQjKXR8tS13/6eDPp4ancOfdjUnUV7GkO5KWdJlbmyzFBk91JsEUuxBRWNzMnK5rLptTx9qp9LNxex5XTUhjfSf8umHrY9qnnheWvgoyB+QzxVxRHC7qlj8GguRA3FlCgZAvKurdg0HGHPP5YZWtRLde++Vu7htFv+6p4+oLRnD56YEQY+g2KAsWbYdKNh973AOyudpEZppHzjS0fgq0O/NjBmBRmYVdJPYqicJgZJSoDgB45YBRFaUQ6YO461L5djvsa+LpL2/Od/l8InNgTm3yFxaDjwklJTM+IoMnuJD7YjNVLTn5XjHotGVHdvzEIUxBPr6rD0alUc4hFz4SUMGYOjiSvopHskloKa5oYkRBMQqiFDfnVbtURAP7z025OHBZDmNXAhJQwdBrhds7AcM+wePQWHKYQXl+eS2SgkaumpfLC0j2kdhHN1Ws1ZMUEkdW5/GnQzG5fo4p/IYSGutCRBC5/2q29durdDKzg8Q5iAo3cPDsdrUbQZHeSGqDhomAbgyLVCl39CYfTxbbCWnaV1hNg1DI8PtjtfjUpLYyIAAOv/prrdlxdi4Ps4joyogMpqW0iNjKC333nXsXovPGJ/OWTDuHecKuBU0e6R4UdSH9lUloYH94wheV7KgizGnh2eQlZMcks3FgISJ0hq0GLWa/lhrfWodcKLp0Tj8eTIna0Wzqnim9xOF1sK6qlqsHObScMprrRxuvLc3Ep8jlt9qLB1h1ig00Mjwtia1GtV0HlaekR7Q6RNgqrm7j69bXklLelzxVx2ZRkUiOsKIpCVKARk0HLaVNGYTFoCTDp+eMHG/ikNVVpU5GTC7+Cx86awl+/24xL6ViQOXFYDP/9aTdCSP2CmGATd586hCumpgCyrbNANIYAiBkloy46cwSr3Co9w2wyY4sahWGJe4STa87DPrKof/DrrvJ250sbz/+8h7lDorAaPWUGVHpIVS7oTWAO6fEp9la7mJWkA60OQtNg/3pI9V/nYpBZj14rKKppJi7E7GtzVPqYnlZBGgzcjhTebT+Hoihzjo5Z/Y/EsN71hqeHm3jnipHc9MF2yuptBBh1PHX+aBLDLPy4o4RrXl/brs8yJCaI/10+DoPW06Nq1GlpGx+NTAjhjasn8vIvOZTXt3DisBiqTWYapv4Z6/LH5U5CkD/lQf6x2sEVU1N45ddcrEYtL1w6jswYdQIwkBEC9safxoidn6Kpl3JNjpBU9kXOGrAOGEUIQq0G/rU4mylJFvbXOtFodRw/NPLQB6v4Db/uqeDKV1e33xOHxgbx4mXj2qMOpqZF0GJ3YtBqaHG4O6m1WkFFfQt/+WQzZXUt3HVKFmXVdQghmJQew5RB4Xx041S2F9WiEYIhsUEezugDoddqGJccxrjkMPIrG3lrRR6DogIYHh/Elv21aATcM38oz/+8BwC7U2FJ4yDiY8ZjKm5N2zAFw3H/J9M6VfyC5XsquPK1Ne0TtfSogPbn5X3zh/Z4cB1sMfCPc0dx16eb2V5Uyxmj4/hsg7wXp0ZY+N3sQR7RttuLajs5XyTvr8nn2YvGsHJvJde1rubfdcoQrpoudYQsek8H0Y7iGt64aiIvLcuhpsnOCUOjWb5bVmv8+9kjyWiN6DHotKR1dVDbG0FjkH10xm2Q8zM0V8vX4sfDABZy91cUIajKPJ/I7Z+jaZCpbI6wDCriZpHgY9vaURTZd/SWHguxHm26RjuC1HJUIxaOMkUbj6jCptOlsK/ORay19XuJSIeC1X7tgAFZkCS7uE51wByD9DQF6UPgeeAlQI3NP4oU1TThdCnEBZvb88HteavRrPovEyt3s/z4yyiKnYsSEE1yuJWqBhsPf7Wdzg767cW1rMurYlRiCIFGnZvOy23HDybYbKCh2c7i7aX887tsksIsnDMugfEpoeRXNmHNupJ66zj0jaWU66L51wYtm0sqmDRIhhbHh5g5YaiXSBmVAYUQgvrgDN4Y+jyjjcUoQsPaxhhGBQ/cspXVTXYayvexcOI2EnI+oj48g21JF1PU4L2qmYp/0GRzUljThFGnIdCo45GvtrndE7cV1bKpoKbdARMfauaM0fE0tDh59Jsd7fvFBBnJiApgR3EdS3aUMT7exCzNelLLXwFFwZl2M6t2DWdjsY3RiSGMTgwh0IvYendIDLPw0uXj2bK/lpEJwehbRcnDrQaeXdJRevrhX+upmnAfl01qINqsSH2aAVwKvr9R02Tjka+2ua2S7y6t5+rpqXx84xSGxQUf8hwOp4uCqiYUpLhu5yiSIbFBvH7lRErrWjAbNFw7I40mu5PkcAuRgSa389gcTppsnkMyh0thQ34NLy/rEOd/4rts5g6JIi0ygDPGxvPemvz2aFiNkOl0T/+wi3vmD6W+xU5lvY2Mqcncc+oQMqIDPfRqAKgvg+yvYe3LEJIs0+QSJ8K1P0JZthThjR7qXllPpU9wOJ08+puGscNeZJSpCBdaVtRHU5Ot5U5/uJ2U74GNb0P2NzBoDoy9DCIzfW0Vk9PC3QpMANwwcxAWw8CoMuc3FG2U94weUlivEGwQmHSt96XwwfI+hIL3Irz+QXyImR3FdczOUlOIjzWOpArSf4+qJcc4tU02PttQyBPfZdNid3H19FQum5KMoXIHYe8skKsCgH7R7cTO/CuLwy9lQ341ya3OkyabE6NOy8frCsgpb2BnaT3fbCnmlSsmsHxPOSV1LZw8LIZxKTJ2YeXeSv7w/gYA8qua+HVPBTfOTOOOeVmU1DZzzqtQ2xQG2Fv/oMXuJNisZ3j8oQeURdVN7C6tR6fVkBEdQMQArnY0kKlrtuMMSmKFIx4BaIMEDXbHIY/rr+iFiwuURQSveBaAgOLNTMxbTN6ZC4F43xqn4pXc8gYeX7SDRVuLCTDoeOqC0RRWN3vsV9Pk7kTT6zQ4XS7+78TBbCusJSHUzOjEEFbnVKIA/zpvFMdptxDx6dXtx+jylxM59zWe/N6AS4EHFwzj7DHxZJfUU9dsJzncQkywGZOXiAJvxIdaiPeiBXLjzEHc1Sq+CvC/dQ3MmzSF6ISQ7n0oKn1Gk81FUY1nf1MUhXHJhxadL6tr5uVfcmiyOwkPMKLTCkYnhDA2ObS9HwWa9e2OvrgQ7+fZV9HAK7/mMDQ2iNmZEfy0s7xNp5lzxyXw885St/1bHC4aW501YxND+eD6KXyzpZiqRhuDowN5Y0UeeRWNvLEyj/HJoeRXNWHSaSmva2nXPNpbVk9eRSPBZj2DowII2PgeLL5bvkHRRtj1LVz9PcSOPKLqJipHjkuBqgYb1ZFxfO+IQSMEilmh0kvf7XMaq2DhTbBvhdwu3Qa7f4DLPodA3wrgN9ud3Do3g7zKBhpanAyNC6K2WV2QOeoUbTiiyLjd1U7iAjo5WqzhoNFCVR6Ephyxeb1FfKiZHaoQ7zFJTx0wXwghfgd8ihTiBUBRlMqjYtUxyNq8Ku79fGv79gtL9zI2KYSRVZvbnS9t6Fc8zf5R03jklxoiA4xcMjmJZ37cjUbAX04ewpcbChgUaeW7LUVoSrdwc0Qh2jgLRAaCIZKGFgfr86sAmJYezujEUEpqm1m4sZArp6USGWji/PGJ/O+XjtUyIWSlozeumtg++DoQ2cW1XPXaGva3ToLGJ4fw5Pljej1NS+Xo4nQprMmp5MK0ZoJqdwAaqoKy+DynhdmZA7MqUAwVWDf+z72xpY7Ipj3AWJ/YpHJgHE4XryzL4ZstMqS+rsXBY9/s4Oyx8by+Iq99PyEgs8t9q6S2hSe+24kQgsHRAZw0LJr7Fm6lvN7GGaPjMegE82zveLxnat4HDI+/CTM2hjq2w8afidWGYTem8/t3d2LSa7hpdjpT0sIxdtMR05VTRsRiMmh5c0UuscFmrp6e2i3Ht0rfExlo5Lzxiby0zP15mRnTvUopP+4opabZzub9NWzZ3zEQf/qC0UxMC8PhVIgNNrcLOnvD4XSxdGcZZr2OV37NJSbIxBPnjOLT9QXMzopm6qAwznt+pdsxoxKCSQyVYe82p4uxyaFoNPD7d9fz0W+ypPlJw2Ioq2vhzx9vbj/ulrnp7K9uorC6iStfW9PuxHn1nCRmL3+qi2EtULxROmBUfIpBp+XyqSnYireRIfJwoWEbKYwfmeVr02SVxTbnSxtl26Fit88dMEt3lvHh2gJuPT6dQKOOF37ZS7PdxYlDYwjqYfSjihdKtsCoi3p8+O4qF3EBXdLFwgZJx44fO2ASQy0s3VnmazNUfEBPHTCXt/77p05tCjBwcxN6mSU73Fenzh2XwN++3sELY8EjME1vpqk1CKGsvoW6FgdBJh0tDhd6rWBYQgiv/ZrLa8crxC08Rw6CQIpSXfIRdnMiJr2WP8/LZNnucv6zZDfJ4RZunJWOSa9BqxFcNiUFu1Ph3dX7iAw0cvepQ5iZGYlZf/Au43IpvLNqX7vzBWBtXjXLdpdzYWv1JZX+gVYjuCGznshPzpWVrIAoSziXn/mhjy3rPRodYNUaO34zrdiVnk2kVXqXygYbX2wqdGvbVVrP7SfK0PX31uQTFWTk3vnDGB7vPiE26zVEBBhRFLhscgrr91VTXi8rzaRGWvliYyGOBE/xZYc+kCCTnsfTthL/wx2ArF8XOvh0bpr0F274JJcrXl3DS5eNRyMgJcLqqZFxCEKtBs4em8D8EbFoNUItKe3HaDWCy6em4HB1PC/vnT+UEfHdc8B8u7WEUYkhvLs63639wS+3ceaYBN5YkcsV01K4cmoqMcEmr+eobLCRU9HAy8tyAdhRXMfyPRW8eNm49upYr181kb99vZ3N+2uYmxXFH04YTEWDjf/9ksNPO0uZmxXN9PRwijo9u4fGBfHk4p1u7/XKslzmZEVx12eb250vANuKG5it86ZjoPZdf6CxxU5Sy07SV14KLXUAZFgj2Rn5Nl5GmX2L9gDjygO19yGjE4PJjAnik3UF1LU4OHdcIkEmHUYv2jAqPaShAmyNENBzZ9uOSpd7BAxAWBrsXwdDzzgy+3qRhFAzOeUNOF3KQZ3sKgOPnlZBSj3ahhwTOFqguRbMoR4PlpQI9wF6dJCJveUNrLcnM8ga0y6aBpA/9k+8vryjMkF1o50Ao44LJ8bxnyW7Ka+3cXx6IOFrn3SfSFbJVYaQMYMYlxzKP77NZt2+agDyKhp54IutjE8JJchsIDHMwt2nDuHiSUk02Z0EmHQYte6T0JzyBpbsKGXz/hrmZEUxOS0Mq1HHqhzPQKj1+6o4aWgMYQGelRzaaayEpiqwRIBZXe31NQ6Hi6Bt77Y7XwBorCAo52vIGOc7w3qRMhGOfdztxC2/t73NGZRIsTkDtUf6H1ajjismRFJTW8/CXc2U19sYnxyKQafh/AmJXDAxkf1VzXy/vZjqRhtDIg00lhcgDBbCouN54PRh7Ciu4901+4gK7EiTdDhd7C6tZ/ekBYzd8T64Wj3eGi27E87ipJpm4lc95GaLaedCElMuYN7wGBZtKebzDfvZWlhLcW0zr105gYmdyvN2l55G0Kj0LW3Py2uPS8Os0xB2kJTbvWX17CiuQ6sRjE4MJjXC4nUyV9lgw6CTItEv/LwXvVZDbLCJKWnhHg69FoeLT9e7OyJtThc5ZQ0MjW0mKsjE2Bgdb12UQTUBmLTgrM5n2Z4qPttQT0FVM1v217JsdzkPnD6sPf3N0aWKIkB9iwOXC3aXSqHftoiv1EAnSsjViO/v79jZEg5BsR7nUOl7hKIQu+udducLAA1lRBUugRGTfGcYyPS0kefDpvc72tJPhIjBvrOpFY1Gw20fdCpDnVfFgwuGqffmo0nJZhmtcgTCxruqnJyZ0SUiKXwQbP8cXE6ZjuSHmPRaQi168ioaDnuhRqV/09MqSHrgRqBNXvon4AVFUdTEyANRtAl++adU5c48BSbdABEdpRhnDo7g5RBTu3aBrtUTes8vjVhnP89o21oCGguwp53Ag2stVDR0OGDSIwP46LcCTHpt+wpuQoDAWJzraUeNLDUZYNS1O1/asDsVcsob2stIb8iv5sa31lFW34JRp+HBBcM5Y3QcRr2Wouomrn19LbvL6gH4dP1+rj8ujdtPyuTk4THsKK5zO3dssJm7PtvMQwuGExHoZXC6bwV8cZsMO02YAKc8AXGju/3xqhx9BC4Mldke7Yaq3V72HhiY9Vq+085k4qz/EVe2jPrAFNbrxxKqjcH3coAqbjhsWPN/4ff5j6BpquCaCZez0jyLX4oEV72+BkWB4XFBnDAshvfWFDAxuIb0XS9j2vk5WKPIm3Q/I7NOpsXh5D9Lapk7N4Pvt8tIRK1GYNJr+P1SLU/OfofUyl9kVbnB83hrvYmTo2vdJzKtOBtryIzOYNGWYswGHc12J402J/ct3Mp7104m2HIQB7RKv0an1RB/iEoWW/bXcNFLK6ltDWFNCTdz/2nDabQ70WsFdmeHkO/cIdGs2FPRvv3tlmLSIgN47ddc3rhqIrGd3sti0GIxaKl0L36EC4U73lvLf6c3YP7lUUzNVUTOfQD7vrWY1r3IAo2eSWP/wN9LxvPJ9kZ+y6vi2hmp3H5iJk12J0NigzDq3CuFjUsOITHMzMSUMFbnVnLVtFReX57L1aNMiOpf4Pj7oWSrdL5YwtvHHCq+xawDbc1Oj/bAhhwve/cxhgCYez+kHw95y+UYMHWGXKz0MatyKj3KUL+7eh8nD4/xEMFW6SElWyG05wK8iqKwu8pFfGAXR7bBCqYQmcrmB4LOByIh1MLOknrVAXOM0dMYuv8C44DnWv/GtbapeKNqH7x9Nmz7DGoLYc1L8PnN0FTdvkt6VCDvXjuZ/14ylmcuHM1Jw6I5YWgULQ4XNy1u5JTVo3hKfx354VMZm5FEiEVPfIiZBxcMY/2+KrQaQYilw/u7aK+NkvTzPW1JliJXWo0gyOzpfzO1rsSV1TXzh/c3UFYvI2haHC7+8skmdpVKh8vOkvp250sbLy/LIb+ykTPGxDMnS5bt1QhYMDqOvIpGvtlSzDZvYlOVOfDO+dL5AlCwBj64DOpKuvXxqvQOQqNBGbrAo12TPtcH1vQNLQ4FhyGIF4ozuHD/OdyydxJlxmTsXQZgKn5A4Xp4+2w0hb9BVS5xKx5gnvIrv+zqEB/dUlhLTlkDC0ZEckLZG5iyP5OlTutLSP7xdxjLtxIXYsbhUtiYX811x6URZNLx0W8FPHHuKCICTVyxWOEZ5Xy2Zt3C8e83MD41klJNJI6Eye726EzkEYsQYNRpSIu0UtgqcLmjuI7a5oErXq1yaBRF4e1V+9qdLwC5FU3UNNtosjm48+QhZMUEYjVoOWdcAoOjA1m3r6p93/hQM2V1zewqrSe7xN35Fx5g5I6T3CcYEQEGqhrt3JxVi/nD86FoPTRVoy3egGn1MzI61lZPzMqHuT65qH3RJzrIxOS0MNKjrEQE6HnligkMjg5ACJibFcVjZ40kMtDE/QuGMSjSikGnobzexqe7HFRaU+GHB6BgrYxmWPKI20KTiu+wuQTK0DM92jWpM31gjReC42DkeXDaUzDmYgjxj5R1s5dIF6NW41H+XeUIKNp0RN93Qb2CWS8INHiJoAlNheJNR2Bc7xMXYmJnieeCjsrApqcJlhMURRnVaftHIcTGo2HQgKCuGIQGAlrzait2Qb27xgv5K6EqF8yj25uSw60kh1vlhr2Jp2fpqB+lI88VQbUhlslBVQTYNzFmaDSnjZjCqyv28d+f9jBrcCTPXjSGPaX1pIRbyK1opLi2mQ/t07hqws1Y1r8EpiA44WGIHwfNtQQ6a3h0fga66j3oFAc/lgZQ0GSQomIuJ9QXMy5WT3FNc3tpSkWB/VVNDI8PxunyDE12KQpORSEtPIB7Th3SLhr5y85y1udXA1DdZPP8vKpy3NNcAKrzoHqfzwXYjmlcTkR9MUy+Eda/BRqdLA1Zk3/oY/spLQ4nL/+SwwdnBhEzrBy7PoRntpeg16plU/2O/FW0e1pasax/kdMHP8cr6zruM7/tq+Rvc0IJ/OYz9+MVBXPNHkZHxLL9Eqh0NfKvTXbOGZfAcYMjOS4jAqdLQSNg1d5KHvlqK3ceF0ZeWTnPLS9mxLkPkmV+Ct3uRbgistgz4T6W5YUyKsnIEwsyCGncyzvHt5DdEsHqKgvhB0u/VBnw2J0uthXWeLT/srOcCyclceGLqzh+aBRTB4WTFGZxE8G3GLTMyIjgoS/lIoWHP7ixipOTXHxy/UQ+31SCTqshyKzn3VV5XDpyfcfvJH4c5PzsYUNK2U/MH34NLS4t6VEBBJr0jA9rhortYLLwydUjqHKYiAgwYtRpyKtoQK8RvHPNZFbsLef68UEoCL61nsEpw+wEb39Xjn9Oexpix3j/QJpqoHKPfK6Ep8koCJXew9mCaCiDidfBhndAq4dxV0BN3iEPPZaZnh7Bi0v3upWhvu64NFWA92hSvBnGX9Xjw3dUOEkOOkA8QWiqFOIdcW6Pz9/bxIdY2FGsVkI61uipA8YphBikKMoeACFEGuA8xDEDn4Zy2PQB/PIPOaiYfRcMOxP0XsKSNVrQHSBPvKUeVj2PecnDmBWFSGMQnPk8vHOT1EjR6kk47RlGxk3l5WXNvLsmn9pmB1ajlvkj42iyOympbSY+JZqWjElYpt0oH7amENi7BH58mMSkycTbGtBsehcUhZnxE9k49hEydOXw7eNEbnqfJ0IHce3827lyiZ6yOjmhaRMBHBwTSFSQkdLaDo2Zs8cmkNRa6Sg62Mymghp+yu5Q99YISIvwMsgyhXi2aQ3SaaTiMzQ6PUp9GeQulY4Xl1P27+Hn+Nq0XiPcauSXcxR0n50HjZXogTsmXMu+mOt9bZpKV0yeqjwucwRVXaqqDo8LZnu5g6nByWgr3dPnLBYL4tXjobGSeOCJyb+jcMRNhEeFodFoQIHfv7eBf84N4Pa4jwhZ+SWO8EyOP+12REsNOq0Wpt+GpmY/sa5S/jh3CsE6O8ov/8S85jkAplrCOf/cd7EYfC8oqeI7DDot545PZGOBuxPm+KExDI0L4sEFw3jwy2002pwMiwviuYvHUljdxP7qJhpsTp75UfbdqCAjg6Nbn6NOh3SoLLoTfW0BY0ddTNLYa5nzcg61zQ7Mei3GgE6/k7pCiBwihSk7YQwM5++293COuwqzSS8nRO9e2O5sDxh1IQHHP0CdU8/bq/J44rtsmu0u/jo7hstDNnLGvidAaNg36lb+Iy7ktLOuZXhyNCLgAOKulXvhyz/K8QhI/Y+590Fw/JF/0CpeMZvMKHVFiH0rpePFZZeOmDGX+No0vyYjOoDnLh7Dst3l1DY5mJkZyfAEVRHuqOF0SEdsSM9TkLZXOEnoKsDbRlgqbP1I6rhp/PMZnBhmYfF2NeL/WKOnvfFPwBIhxN7W7RTgyqNiUX9m9w/w7Z0d21/cKleBEidD5smQ/U3Ha1NvkaJT3ijdCj92EnhsqYVv/wpZ82H9m+C0wxe/Z97VS3nq/NG8uTIPrQYunZyMSa9FKyDEaiDM2ubgSZD/5C2Hd8+XN6Eh89Gs6Si3q9u/mpEJn6MrU2DV8wDom9YyvPRyHpr6Hr9bDNdMTyMuRDpgEkItvHHlRD5Ym89veVWcPjqOecNi28MyLQYdd586FA3b+DG7jJggEw+fMZysGC8lrCMy5eex/OmOtuMfOPDno9J3DD8Tsr+CFf+R2wHRkD7Htzb1IgmaSpQf7pWC0K2INf8jKfU4QA2l9yuSJoM1EhpanbxC4Jx1J7a1FkCuJiWFmTltVCyv/JrL2TMfJvzzS9oFdZXkaYjybPfveuVzxCdPh3h5z5yWEcHTZ2dyYvY9mHZ/DYCuYCUjYr5C2bNERjHukMcGbH4fJXooJqUBWp0vADRWYPn+r3DJJ6q4+DHOCUOjyK9M49Vfc9FpBbfMzWBSahhmvY7zJyQyJS2cuhYH8SFmQq0GRiWGsL+qiUVbi0kOs3DmmFDOH59IQqhc6KB4M7xzrnSOA6x5kTCXnTtO+D13f7EDu9OFLjJDarE0VkDpdhh9Mez5EZqr5TFBcWAKxrDyUdj3K1z2OSx9wj3SceO7MOQ0Nmon8fBXMgrHqNMwx7AN4ze3te+WtPR2frfgDexJYxABB9HH2PJJh/MFZLpS2iwY3fMytCqHQKuTUQA7v4UVz8q2oDhImeFbuzqhKAqNNgdmvQ6Nn1SEWZtbxTVvrCU6yIhZr+XD3wq4ZW46tx0/GHEEorEqrVTsks9xfc/1dLaWu0gPPUAEjMEi738VuyHSD0queyEuxMS+ykYcTpda8fAYoqcOmF+BF4A2MYgXgBVHxaL+itMJv73m2b7lYym6e+o/YeQFUL4LYkdC/HjQHSAk3ZtoXVUujOgUeeC0Y2wu5YwxQzllZAw6oUGjEWwqqOblZTnsKKrj3PEJnDIilrg2sb69raHHgTHyfF3Q7/4GYka6NzpaGGcp5da5Y/luWwlnjYtvFx7Lig3invlDsTtdGLzkw6ZHBfDsxWMprW3BatAS6SiW0RTmYKlub2hNtzJaYcb/QcaJUiMnNAmiR/hFCcJjGaetBe26N2UYeV1RR1rdxvdg0AB1wtSXIkq2ejSL2iIfGKNyUCIz4YqvYN9KOZlMmow+dgx/SxJcNT2NFoeTtEgrMUFmjh8SjUEDRC3BVZaNMAUjLBHw0mzP89YWtP/XatAyIbSx3fnSjiUc0fUeqii4qvaBaMKD/WuljaoD5pgmOsjMn07K5OJJSWg0gvgQc/skTghBcoTV45j4UDNXT0/l0slJ6LUa90lf2Y4O50srmg1vcf7vbmNk8jTsTifaXS/AxGvBYQNHEwgtzP4rTkMg2po8sDfJAgEA5Tuk4yVvmafx5bvYJYa0bw6PDyYl73mP3UJ2fgxjPLXD2nG0wI4vPdv3/qw6YHqThgrEujfh9Gc6nufWSMTmj2CQl/tgH5NfVktdUTb2qgL0wXFYYjNJiQ7xtVmsyZUO+pJO0d4LNxRy9bRUVVT9aFCyVUapHAFbyp2ckHKQ+UJIitSB8VMHjFGnJSLAQG5FI+lRairmsUJPZ7hvIJcY28I0LgTeBPw3ya630WohPB32LXdvD22N4giKh2HdDK/1FooXnu7umNGZIDAOAENreejdpXVc/L9V1LXIFd6Hv9pOUU0Td548RHpVLa1lUOtLITjB8z1SZsgVsi4UNml58vtdhFsNBJnc816FEF6dL21YDDpSInRykvTO+R2rbtP/CNP/0JFGYA6RqvcqfoPWYEQJikV8cKnMz1dcYG+EWXce+uB+issciiZujBR47YQSHIe61uWHRGZ6VDcI1sG4ZPfqGe33qNiRaGJbnczluyFuLBS6p2MQ0KE7tSa3ipzd1VxuDHSvetRULR3ZdcXuxwbGgt5LNm7ydLCEHc6VqQxQdFoNSeGejpZD4fU5a/QSURoQg95oYWR4CB+uzceqTSHrpxvlmEFrkBG1E65BO+4K+PxGz+PrS2HQXPeSwABRWUQ3d6RT1zTZaYxIwCNR+FDVTHRGSJvtcY8l0celkAc6xkAITYGuz/OTHvG1ZdQ0tqDb9TVDf/g9OG2g0VE08+9UmM8nPMi3E1JvE+JRiSFqSunR4ggFeGtaFCqbFWIPlIIE0sFTuAFGnNfj9+ltEkMt7CypUx0wxxA9jXXKVBTlGkVRlrT+XQcMPpqG9UvGX9kR1QGyhN7Q0w//PFFD4OS/S90WkFEH8x6TKU4g3+OsF6VTphPZxXXtzpc23liRx/7q1hXZ1BnSCeO0yXDkzqGnoakw8XoYe7nb8c0x4/mxKhqtRvC3M0d0RNMcDo2VssR0m/MFYNm/ZPi0in+TeYrsj7Z6OViLGwMp031tVa9RrI3GNec+6TAF0GhRpv+RKquaDjfgiEiXzsQ2Z7RGC1NuhvCOR9kHa/N5YaOd/RP+6n5s2U6Yc0/H/V4IaqffhS5mmIwinH23PB9AcCLM+5v3ybKKypEQO9pd5FYImPc4BERS22znhaV7eb8kjuphl4KjGVpqcUYPl0KsoSmeESfDz4YVz8CYy6RWTNs5J90I8eMZnRTClDTpSNxdWs/O+LNAb+k43hgoz3EoRl3YcX6A5BkwgKvr+QU6g0yFT5zU8TwfPA/iJ/jaMhzle4hdcpscmwK4HMT+fId0kvuYyWnhjOyk+RJi0XPtjDT0OjVV5KhQvBFCeh4Bs7XcSUqwBs3B0sFC06A8W8o3+ClxwSayVSHeY4qeunDXCyEmK4qyEkAIMQmZlnRsEz8Wrv5ehroJAbGjDjvkrcXhpKhWwZJ5CVGpM6XobkiinCRc/Z1ccQ2IgrA0+R6d0HvJHTTqtB05hVFD4IqvZfUQW70cZDlawGWD8AwIipWrV6EpULIFpzWKqsDhDKkL4MspVjJ66pltqoKybZ7t3lKt2nC5oKY1FSA4ATTqw67PcTkRa1+Dkx6D+mI5obREIDZ9MGCdMALBbcsNPLzgVSxNhbiMQSyvDKGpNox5vjZO5ZBUNtiobrQRbjV0Lzw8PBPOfAFq94MxSDqiozru2UEmHYU1zdy5K4vnz3gFS+kGMAZAXYkstzv5RloiR9FojUMflYk1oNXJMu1WyDpFRs2EpshoGRWVThRWN2FzuIgNNmH0Uuq2W4QkwPlvymiS5mrp1IiVBSq1QhBg1PHqhmr2pJ7DJTMWoFdasMQOZlJkq5Nx6h9k/2woA2sUlG6DfStkFcLLv5AVCvVmudijNxMHPHPhWHYU19FkdxARFQBpi2UUmRAQNw7CB0Fljoy4CYr1bnfkYKk1U75LPlciB3dE6Kr0HkmT4ZR/yLGXRiujqGNH+NoqDM2V0iHUGZcDQ1OZ9wP6kMQwC69cPp695Q04nQrxYWaSwg4/gk3lAJRslXORHrKh1EFa8CHmBwYzWCKhfCdED+vxe/Um8aEWdhSrpaiPJQ7LASOE2AwogB64TAixr3U7GfAywz4GiR4q/3pAfmUjz/y4i4/X7cdq1PKXeVmcPjqdAGPr1xSWetBcySGxQSSHW8ir6HiQ3XZCBvGdo1aistwmGB4YrJAyDVKmoQViq/KItW8DuxVsWTJfvKVWhtp3t0qRJUIOzAp/c28/UKhyfanU01n2L7k9/Y9Stf9AFRVUegeNFmKGw5sLWtXjFak3cOq/fG1ZrxEbYmbi4AS2lu8ny6Cl0QlLC+xcOlONXvB31uZW8pdPNjE5RsO5yfUMCtUREDdUTlK70lgFG9+Bnx6VTujpf4BxV3lMGM8el8iHvxWwNK+J9UMsTFv9glylbSvru+MbjJNvwmgJA4cdKvZIbYWQZL8d6Kn4lma7g282F/Pgl9uobrJz6ohYbj8xk5Q2/ZfGSukQMYd6PvOaqqAsG2wN0ikSmiwXaEISPd7HatRxy9x0rnptLUtzGliaAwFGPR/e0MkZGBwPeSvls7mltqNfhyRCQKT860JEoJHpgZ0rOA6XzwmQ1Y0W3gKbP5Apd/Mek8UDvFWCDIyWfyp9hxBgDoe6Mrmo5SdjKlOYFIKmuVOFMJ0RY5hnv+5rapvsfLmpiCe+20mz3cn5ExL53axBxIdaDn2wysGpL5NzCqvnfaa7/FbsYlhENxZow1rLUfvpczkxzMKXmwp9bYZKH3K4ETDze8UKFRRF4d3V+/hgrYz6qG1y8NdPt5AUZmV6RkS3zpEYZuHVKybwy65y9pbXMyM9kgkpR6A7ULge3jpbpisBZJ0mB0xrXoKEiXDakxA93PO4hnKpJWNrhIgMCE+D+f+E9y+RUS1avSw5GT1cRrqUbZehpgHRchBYtgOWdMpLXvKInNCMOr/n16LSM6KHQfI0yGsNcEs/Xq6YDlCabE7OC9uF/uMroKWOEODusZdTYrsdUFe9fEL1Pti1WP6lzZRh810c0fsqG7nq9TVcN9LIlVX/wrpYVlhRAuMQF3/YMUFsP2C5rCzXxs9/B2OwPG9QgowW1BkYkxjCB9dPYUl2KQUGsM1+AMOP90onTGCsFLS0hMkV5V//DWtfls7K6bfBhKsPPLB02OR9riZfrkJHZXmfpKr4Hw3lsvTzlk/lYsvQMw5r0WVzQS23fbCxffvLTUUEmfU8cPow9MUb4PObZSXEkBRY8AykHid3rC2CRX+GbZ/LbWsEXPCujLISQkbbBsW5vde0QRG8d+1kfswuIcRsYGZmJENiOy2cGAPgxAfh/Uvl5FejhZl/gZjRh74Ql0tGzFTsAUsoRA2DFc/Bpvc6PqePr4ErF0HylG5/Piq9yP718PFV0lEGUgNrwbM+n5TqjVaY9VfpEG+ulho1M+/AYPb9PXHdviru/6JjffntVfuICzZz05z0gxyl0i1KNktHcg+rSSmKwsYyJwsyujGVDRsko/tGX9yj9+pt4oJNFNY002x3YuppRKRKv+KwHDCKouT1liHHOlUNNj5Z55mSszG/utsOGIC0yADSIo+CiJOtCZb8rcP5ArDjC5h9l7xZFqyGhbfivOgjNpTD0p1lGHUazs7QEv3THbD7O3mMKQQu/QTix8E1P0BVnlzpCB8kHTG7f+gojX3cHZD9tXz4dmXzB6oDpq9xOVGWPYWwhMLMP7d+72tQfnsdMUDz9V21Rei++6ub4KpY9zpR6cdDXM+F4lR6SHMdfHMnZLdWTdn5DWz/QqZddEpZyK9spMnmZF5QDtaNHeVtRV2hLLl62jOg6yQgvvNbz/fa/KFc8d/9PZz1Mow4GyFAIwQhZj31TsgZdD4ZV09D01wpB3RtkQc7voTVL8j/uxxyIhGZCcPO9Hwflwu2fASf3ySFMAFOfAQmXHNEpThV+gBFgfVvwvf3y+0dX8Bvr8JV38q04G6wq9QzzHzhhkLunB6C/oNLO1Jvq3Ph3Qvg+qVykrL/tw7nC0gHx5K/gcsOuctkZcEL3pGLHq0Y9VomDwpn8qCDpPfEjJD2V+dJDZewdPffClDZ0MLa3CpW5VQyODqAqWkRJFatgHfOay/rzin/8hTuBbkY0xMHTEudHH+YQqRIv8oRo2x6F9HmfAGZOrZrsc8dMFTlwtK/w9jLpFaNywkr/ys1jrwVpehDVu2t8Gj7aF0Bl0xJItisVkE6Ioo2HdGCXkGdgqJAhLkbDpywFNj4rkx10/tf9JJOqyE2yMSesnqGxanVEo8FVBlvP8Fs0JEeZaW4ttmtPS7ERwPylhooWOPZ3lwtc7vtTbB/Lc7SbVz8Si3NdhdCwPGihug250vb/j8+Aue/JXUQOmsh1JfBl3+UwlgZJ8HWT+QqXKqXFKmu5bFVeh+NVjrK1r8JO77qaJ96q+9s6mXMzSWI8myPdrUMtY+o3N3hfGkj71epHZHUMakMNusJtRoIrvMi2rhvBdjqQNcpGrDTJLWdkCS5Mqwo8NUfIXECG2oDOf+FldicLhJCjMzQ1iPW3C8nx5nzYe69cgDZturfmexF3h0wlXvk+ducLwCL75HRPTE91GOoL5Ph1XXF0p7YUd1PEVXpPjUFsPQf7m31pVC8pdsOmPAAI3dPC2BGwH5Mrgbydcm8lRuMqbGow/nShq0BKnOlA6bzxLmNks0yxSd3mdQ32P4lzLit25djd7rYU1ZPSa0gNngIaeHWDs24VpwuhTdW5PHU97va2+6fG8XlW/+IaHO+AOT+LPte8Sb3N7F2fwGpneLNsOgv8rpiRsCp/1SrJB0pzXWIfM8xnbL/N99X+DMFybHf8qe7tPt+IprspWJZVnSgGqVwNChcL3XXesjqYgdZ4RpEdyJotAYITZJOn6TJPX7P3iQhzMzOkjrVAXOMoCqb+glmg5bbTsjE3OmmPjQ2iHFdUohsDifbCmv4YXsJWwtraLF7KXl6VAwKk04Rj/ZQ6XwBsISjy/mZq8fIiBWTTktAk5ccxqL1MrWoK7Z6OZEBqZvgcsp0g7A090GbNQISJ8pBrkqfoTidiPjx8jtvIyDao/rWQEKjt0CCl6oQBxKTVOld2jQpPNpdbpvpUVYunJBIebCXlMjMU+Uquq0Rcn6Bpf+EiEwI7hTRZAqW95iS1ntMczU01fDRbwXYnPK97psEg7+/GlGVI+3a8QWuRXeSXViFI2aMx9t6pD210VjZcQ/tfD0NPRScbK6B7++Dt8+BhTfD6/Nh7SvgdBz6WJXDQ1E8+p5s99J2AKaFN3JF3l/I/PlGkn+5nek/nc+/xleht4bKxY2utEVFRQ3xfC1pqiyv2kb+ym7b4XC6+Hz9fk59ehmXv7KGU/79C19tLsLlcv/N7ats5Lkle9zarDQh2p7dbWR/A8f9qaN6I8hU5Tgvv42DUV8KH1wmnS8gnTFvn+vdAaXSbZxoOtLZOuMHji0lbBDNM+5ya2ue/AccYV4c5X3M5LRw0jtFlVsNWm6YNQijt7LwKodH8aZuO669sbLQSXroYUxjw9Jh/9oev19vEx9iZnuRKsR7rKBGwPgR45JD+fzmaewsqcNi0DIkJojYTgK6TpfC5xsK+fPHm3ApMiPk4QXDOXd8IoajXRJPZ4Bpt8jSbYXrZYrQhGtlBSWQDpMpN6NZ8Sxjx8qqC012J2XmNOK6nMo++DQq7FY86oAEREH6CbB7Mez5Aab9QeaUL/07TP6d9Fhbo6C2AD66UtpwxVftVR5UehcFoLEMMeHqVhFepGBp88AtlWczBGOY+RdYdIfUNtAZYeqtKAExvl8lPBYJGwRpc2Dvjx1tceM8IlhMeh3XTE+jvNRES+PvMK59HhQXSvJ0qoddwsad5Uxt/gnDZ9fKA7R6lOPuoNaajMZpJ7ApX+rAtBE5BHQmpkfX8YVZR22Tg0TX/o50i1Y0e75na8x2ioPnM9PyWUfKZmiqdwc2SOFTa4RMIWlDb3Z3CB0OpTtgw9vubUsekSXk26rdqBwdghPkc+qnRzvaLGHetdAOQEDFJijvVLNAcWH+6T6plTLvMfjyDx2vjb0cfn2KZn0wpuAEmHE7/PqkXKyIHSOfhdsXduyfdVq37cgpb+DOTzfjbHW4xIeaaCjZS11xE8Exae2VBx1OF3aXu4NpyX7BWYOOR7vn+45Gpw17UCL6a3+S+kbGQBm9EtR1RHAIqvd5Oluaa2Qk0BFM1o51FI1GargVbYC9P8kB5JAFh+8g6wVyq2w8umcsV816hwhHCVW6CN7YG8TN1U4yfVxALiXCyutXT2B7YR02p4uMqAAyolVR/iOmpQ5qC2XkaQ9ZUejglrHGQ+/YRmQmrHsTpijghyO6xFALq3MrfW2GSh+hOmD8jMHRgQw+wM09p7yBuz/bQtsClaLAfQu3Mj4llMyYoxxuXpYNi++H9BNh5PlyYhEzUg42Y0eBziwH/S211CtmQK7ovrAnlCdn3Ytx2ePgaKElYRrfBZ3DA8+t5PWrJrqH1hmscOLD4GiSq137f4Mz/ivzyMt3waC5gCLDUp12eUz2ItUB00dotFoUU6jU+9nysXS6jboAJTTVDx9dRwdbcyMGp13qGTSUyT5qDMZWV4aqzuEDzMEw/19S9yX7azmBGHaGV3HbQLOewORkiL8fJlyK097CsopArngum99PrGTW3ns7dnbaEUseoXjmf7h6VQyvTjeToTfLSL3oYbIs5ur/cjKCwXMmcv2qcBo0Xu7LlnBKmjT8dWkT909/ldNiq7EaDXJC7qUyDSAn8ee9KQVKa/fLaznjeZnu1xNavKyYOW0ywlDl6KLRwPgr5Xe7/h3pYBh9MUQcRlRg50ovrYjaQqlNkHWaFGhuLJXP2JyfIWcphoSJcmEieQZc/ImM2DKHwNInWgUsBYy9AtLndNuMsvoW7E45mLh4uJVbQ38lau0zsEaBqbdITaLAaBJCLcwfEcsXmzrSMMOCQ1gd+0fGNjdg3L8CzKHkTXqQKnscoxOiDxz91R2MgTKKpu2Z34YfpKP0Z3RCoLgciOPugHFXyue5JQIczYc+uJepaLDx3e4GvtsN0FYZq4HzZ7T43AEDEB9iIT7E/3RD+jXFm6VDVdOzSKKCOhf1NoWEwMMYjVojpd+lMvegFWV9RVKYhTdXqlKrxwp96oARQswD/g1ogZcURXnMyz6zgKeQpa7LFUWZ2Ycm+jWVDS20ONxXohwuhfJ6G5lH841sjbD4Ximkm7cMptwk895TpsvKH5s/bN9VmX03o5MSGbGjGJNey3nT0xGp08iLO4Gt+0pZXGjgs8V1KAq8sHQvT5wzEkPn0M2oLLjwPVlFxGmDD69oXR0ukyt7ERkw/BwpngU9D9NXOWycTieagCj53Uz7A6BA3grEgVb2BwAucxjK0vcRBrPME67bD2teQZz69KEPVukdwlJlNN6Um9tX5Q+KzghRQ9mxv4ZvNq7js5OaSQ3XwrZqj12DtA4Kqlu4YEkg3y14hvDy1VCZA4vvlhPA6X9k0LI/8fjs11lfHc+o9JPQ7e4Q8M2d9AD/W9pCs93FX5bUE3LJROZldiNdLXkqXPujTLewhMuomJ4SPkimWDV3ur6o4T4XrxywBERLp8vIC7vXH7sSNUQ6TTqn1429TOqjNVbC8n9Lx1wnNM01oNHLSLC8X+D8t+U5TvkHTL0ZEFJk0lsK0wGICTJh0mtQFLgyLpeopZ0iwJb+XZa4HnMJZoOWP83LIj0qgIUbixiVEMw54xM567lfOS3rr8yZ5qCsRcfzy1q4K9jO6MP/RNwJGySrJH53d0fbxOvVaK4jxK4IdLZ6yFkmK8nhkuOqrFN9bRrRQSaCWiMN2zDqNMQG+74KkkovUbhe/tZ7yLICB8MjtN3Tf2lDCBndum+lXzpgIgKN1Lc4qG60EWJRBZ4HOn3mgBFCaIH/ACcABcAaIcRCRVG2ddonBHgOmKcoyj4hRFRf2dcfiA02ezykLAYt8SHeH1LNdgdVjXZCzAbMhsPwMtcVw85F8v/2RinuN+VmGfESEIPrzBfR1OSDEIjd35O840s+ufgNnAEJmFrf58McI88uaQFa2k+7Lq+KhhanuwMG5IpXVBbs/E4KVFZ2yjcv3yXLfLaRpVZC7yu0ApTfXoOUqYCQD6/kqbL86pCB+T1omioQEYNkH/zmDrnSPel6aPCshKDSxxzmZFdpqePPxo8J/enNDkfu+jc7dtAaCEgcSVJYNVajDvPml2DX5+4nyfsV4kYzxlrBkLEz0LU8A8WbcNSXsakpkvtWaahskCvIGgGJYYexStpVlLynhKXCJR/Dt3fJqibpJ8Ccu8F6kMo3KkdOT5wvICu7XPAuLLoT6ktkmtHE60CjpV4bgnniDWi/v6djf6GRixL2RrnttEuB/GVPwqWfetf16AapEVaeuXAsLy/bS8L+1z132PCudDQJQVKYhVuPH8yV01IxG7S0OJxMSAnj8+2VfL6945CkME/B0sNGq4NxV8jKiVW5MoUpZpQcJ6j0GOGyI1a/CElTIH+FfJ5bI2Dbp5A5z6e2JYZZeLa1L6ZEBFBQ2cB545NIjTgK/UnFPylYe0ROkB/yHIyI7EH0TNQQOb8ZfWGP37u30AhBSriVbUW1TB3UA/FylX5FX0bATAR2K4qyF0AI8R6wAOiUDM1FwCeKouwDUBSltA/t83sSwyw8d/E4bntvA2X1LYRbDfzzvFEkh3sO+rcX1fLk4p2s2FPB5LRwbjtxMENju5mmZAiAoPiOVbhtn0NQHM7z36FeH0nQN7+TaSmd0JdsQh/WseI6KjHE47SnjIgh2Kz3aG/HW6lJvUXqI0SPgFl/kUKZKn2DRosSNgjRWRsDcM36KwNVfs6k08qS5+YwmHitdEb+8CC60//ja9NUDpNUCrBua3W4lO+ClBnyO81ehBKagph9J4FJY/j4xhaKqpvR7xnh6YAJSYKiDWgs4VgMOjBEQ+AJ6ABtfhXVy9YDEGjU8fCZw8mI8tEkMWE8XPyhTG+xRIBBXTn2W3QGyDxZCtQ6miAgBkWjZdXeCh79Zjuz48Zy7eyHsK5/Cac1Cu2Is2HN/9zPoTVIPaIfHoJLP+mRc0IIwQlDoxkaG4hm3UjY8437DnGjWtObOghqfX7rtRruP30Yt7y7nl2l9Zj0Gu6ZP5ShsUep/xsDpbM/eerROZ8KOp0JJTgB8fPj7i8cd4dvDOpCVJARs17HJ78VMC45lIQwMxrNQE12VmH/bzCo+ymTnbE5FVYWOTgrswfPuZBk+ZysyYfgA6QJ+5DEUCnEqzpgBj596YCJB/I7bRcAXeXXBwN6IcRPQCDwb0VR3uh6IiHEdcB1AElJPRdw6o9MT4/g85unUV7fQkSAkTgv0S+ldc1c/+Za9lVKXZbF20vYXlzLxzdOJTqoGyHKgVEw/0l470Ip9gcQOwZtRDrBTltHpZBONNVWsqugmsHRAZj0OsYlh3Dz7EE8//NeHC6F4zIiuHBi8sEfqJFZUui382Bz3qMw9EyYcB2Y+tcK2EDop5pxl8KWD6GpCgAlMBbt0NN9bFXvITQauRr9/X1SrBAgZQYuQ9CAdDoNhD56ICyuRveGta9AUDy75r5IkTaR45Klwzgy0ERkoAmMp8FvL0Ndq9aFOVRqfDhsXkVWRyWG8smNUymqaSbYrHcrV1rXbGdnSR11zQ6Sw619s5JrChqQpacHbB/tFKG0o6iWy15ejc3pYmM+FE+YxfTJc1i5r4FrW3aS3FmUNmt+RyWP6jywNR1RdEh8qAWGnwHrX4f6YtloCaM8/WyK99eQERWA0UvJ3SGxQbx//WT2VzURYNSRHG49pifMft9PhQsxdIGsVtX6PCcoznvVvz6mor6Fm95ex56yBgB+2lnGtqJaPr9pmlshCpUjw2/6aGOlFKHvoQNkVZGT+AANIcYe3G80GogdCXuWyNRPPyMxzMKW/dW+NkOlD+hLB4y3X0rXGqM6YBwwFzADK4QQKxVF2el2kKK8CLwIMH78+APUKR24xIWY3R0vjha5Ita6WpVX0djufGmjoKqJ3PKG7jlgQArgXrsEKnbLaIDo4RAQKUubjr7Y3UkiBOuaorj42V/587xMrpyWQpjVyK3HD+aMMQnYnS4SQ80EmLpEv1TlyQpHlghZ2tgUBLPvgqGny8iD0BQ5AdL3zwfwgOinMSPhiq+hZDMIDSJm1IDOxS8XoQSU5WGd+RepSaTVQ8UeKgjyrOI1ABgQfbQrtnoo340wBkgnSttkA3DqLHy0S2Hp/jzGZcRjNXZ6BEZlwZXfSHFAe6MU7NPoYdRF8t7nhXbnTScqG1r4x7fZvLtarjcEGHW8duUExqeE9fya7M0d5YiPMQZkH+3CrpL69nLnAO+tyWfL/iAeOXM4Tn0GjrSh6Mq2yudi0QbY2apDNPZy733TYYfKXdBULSc53gShnXZAyJSf6KFw1SKUki3UNdpYXBnB3a+V0ewo5t75Q7loYpJXJ0yY1UiY9TCqkAxg/L6fag3YGmoxnPovWVRBaMAYiN3hxNdqE3kVje3OlzZK61rIqWhQHTBHEb/powVrZUWiHgrwfrPXzpjoI1gSix0jJRXGXAzCv5bWUiKsLN2lal0eC/SlA6YA6DwKSAAKvexTrihKA9AghFgKjAJ2ouJJbSHs+Ao2viPzysddCbEjsei1Hhp/ABbjYXzdWp2sNtS14pBWJ0X/FBesfxNXYBw54+7i4TVGoInHF2UzLT2CkTEW9E1VpIeFSFFMAJcLijdCyXYwBkih34YyOdE95Z8w6gKwhPY4p12lF6jYA3nLZalwoQFbA2iN0CndbCDRrOhYHXQGs4vfIGDnJ7iCk9kz7h5yHIkD0gHjdzSUy5Ln3tIRu0NdMfz4sNR6MQbBiQ+jrHsTUbSOpsTjWJvxB175po7jBkei03pZEwhLPWJxvh3Fde3OF4D6Fgf3LdzKO9dMIvhwhfWKt8CmDyB3KaTNhhHnysmyyoDCavScBFQ22kixOgg2CrCOg5ihUrvgt9ek2O74a+QKblcRSlsD/PY6LL5HpilZI+G0p8HeJJ/ngbGyP638rzzPlJukLkhYKvtcUZzy9C80dKqg9dCX25iYGuZewVClX1ITlEFE3leIje+B0KJMvIaKmGF0Qza8VzEbtGgE7RU+27AY/KRQa/kuqa9lb5bREzGjeq4BpQL5q6QuWw9wuhQW5Ti4e8oROH6DYqW8wf51fhEB1pnEUAt5FY20OJwYu+plqgwo+vLutgbIEEKkAvuBC5CaL535HHhWCKEDDMgUpSf70Mb+g9MuyzOv/K/c3r8Otn4K1/xAWmQyV05N5ZVfc9p3v2xKMoOOVhh8aAqc/DjlY27ifyuKeGtxPQ02GXGj1QjSnDmwbo0MMwxOgLhxED0E8lfCG6fD9D/Kqh0ZJ0jnTGAsrH4B4kbLh5uK/1C0UUYUhKZIB0xjpYyGGaAOGK2AQTEhmGLPhvEXIhw2QpVAyrS+XiMc4DSUwdbP4Nd/y9Lfc+6WJaeFRuZqawwQ2o2Q6fxV0vmi1YPLDl/9EdeJj/Dj0Ef4rVzLdJ2dL+fsIz5Gi7GpHAKjD33O7uByyQF67jJGOzW8evIwbvvZSXWjLKW7tbCW2mbH4TlgavfDpzfI3xvIqhG5rRVwjpbdKr1LWTaUbm/VMRt+wGpXI+ICeOPsWGqb7fxnbSPhZsF/J1cT+O586VCZdqssvz7sTEieJtPigmK9ryCXbINv7+zYbiiTzpiE8fD9/XDiw/Bhp9D73d/JKMfkqVQ22miwOd1O51JA01wNO1dK8d/wdEia2r3fo4rfYHM4CWvYg7CEw8jzQatDAJEtucAwn9qWFmnl2uPSeOHnjlS7CyYkkh4Z4EOrWinLloLUehMoTqgrBGcLJE72tWX9l7xfoYfVNFcVOQk1CWIDjtABljhRVlr1MweMQachNtjEzuJ6RiSoTu+BTJ85YBRFcQghbga+RZahfkVRlK1CiBtaX39eUZTtQohFwCbAhSxV7Sk4oiI98mtecm9rqoLSbZjDB3HznEEcNziC3IpGksMsjEwIPqwIGLvTxf6qJoSAhFAL2q653Vo9rsB4Fu7KdRuwvbkgDOu3f5STEZAOljP+Kyfxq16UkTPh6fDp9fL/AKZgmPeY1F6IHq6uLPgLDocc/C95uDVcHTmROHXg+kTDzBriRQGiZBMUbUQExRIRNQxrpBnwnoaichTI/ga+vr1j+/1LcF70EWz7HO3Gt+Vq1Zy7ZTqQ+SCDkuItMPPPcqDssIElDO3en5kzeRhza79Gk18sHb2/vAjWKDjjP9IBfKTkr4LX54PLgQWYbbDy7Kw3uORr+fKMjAjCAg43+mVrh/OljYI1ULpVdcD0BwrWwhsL5LMP5LPt/Lc8I6yqC4jKX0VUwXcgtJx60jyUmkI0NTUQN0aWCv76dvksHXsZBByiOGRNvmdbxW7pwAlN8hT0VRTp/EyeSkyQiYgAA+X1tvaXh0Rbydj3Ifz0UMcxCRPltaj9sN9gcLWA4pDlvV2tlTQNVrSnPe1bwwCjTssNxw1ialo4ORWNJIWZGZkQQoDJDyJgKnZD9pdyzA1Sbyk4CWJGH7NpoUeE0y4X9qbd2qPDP9lpZ3LsUYgMiR0lI7ur86Qwrx+REmFlS2GN6oAZ4PTp3U1RlK+Br7u0Pd9l+x/AP/rSrn5JfYkM1W+bGLfRuiIWZjUyK7NnVbxLapt46ZdcXlueg0YIrp85iMumJBMR4B7yFxVk4pkLx3DTO+soqW0h0KhjoiEX0eZ8AalPs/wZCE2FlOnQXCXDo5WOfHeaa6TuwuYP4IL3ING/PNLHLgrKrsWIzn3M3oSSvwrhhyX8jgaWxiLIXy4jMdoIjMF0xn99Z9RAp6UeVr3g0Syyv0GTs0TeK2z1sOgv0nmbccKBz5U0GT69TqYygbwfnv822nfOAYcsF83WT2QUwA8PQOFGyDxCB4zLKSMR2yY1ALYGsqp/IiZoNkFmHXedMgTrYYfTHyBF3//UJVS64miBpf/scL6AFK/PW+7ugCnbBaVb4NNr2wXvxcZ3ESc+BEv+JqPAMk+WDspVz8Ows8F4iEhWb1E2YWlQWwROO4ox0FOQTycnkrEhZp69aCy/f3c9ZXUtBJl1vHR6BLp33SvhUbAaSrepDpj+hNCgbP8C0eU+ReF6GHGO7+xqJdRqYGZmFDN9bUhXynd1OF8AWupg22cw+GTVAdMTCtfLe5Th8KObmh0K3+baeey4o/C5a/Uy9XLje3LRxo9ICbeyYV81F05UowwHMmqoQX9l12IYc6l7W3AiRA454lP/sL2U//2yF7tTocXh4ukfdrF8d7nXfcenhLHw5ul8ftM0vrplBrqWKs+dKveCNUIO2EJSoLnacx9bvXQobfvUvb2pRq5qV+yRYf4qfYdGK51jXWmp63tb+ormGlktpzN1xYjqfb6x51hAq/cahaIxWOQEoTP7Vhz8XOU7O5wvIEV4c5Z2OF/a2P6FLEvd2OW+5miBsh3yXmXvJGTeVA27voPvH4D1b0NVbsdrikumeXQhVKnljasm8N51k8mK7UF1oshMSJ7u3pZ+PET2LHdepQ+xNUDZNs/2qo60YOzNUtMl++uOaoMg+1PuMqnrtvt7+S9AYJz8rRyIumK5shwQC3Pvk+l7INN9J14nHY8Fa3COu9JdN0ZrkML3rUxOC2fhzdPkM/33M4gP1MrfRVfszZ5tKv6L4pLiu11p8D62U2mlrtizrXKPTENSOXxyfoGonqW8fbPXTkaoljDzUZq6Jk6WDiE/G9+lRVjZVFDtazNUehk/iO9T6REBUTIcffZfZfRIcIJU9g5NOaLTulwKn6zb79G+JLuU00d7z1+PDjJ1VFeq9SzXSvrxMuczNFUK7AbGeJayDs+AdW+4D+rKsmHhLVI7RmeSg8qxlx5RyU2V7uNEYB95Caacn9zamzLPxOIbk3ofofGMKgNPoUuVo4fOKFegGsrkfcHlAHMojvAMdE1dHLqhaQc/V1MXh6E4wOTRaZcO34hOFb1qi+GXf8LalwAFRpwn056CE2Hdm7D47o59Y0bCRe/LMq5aPUy6DvYtd3sL7fCzGBxzBGWhQ1PgxAdh1/dQvElOxDOO73HpTpU+xBwKIy+Anx9zb0/qpBvRUAp7vgdLOB44bVLwHuTvQ6OH6X8A3QHS2HKXwcfXyDTegCg45zVs1/xMaWEuYXoHlqUP4QqII3fMn9jRkMkpl38F2z6X5x56OsSNdTtdbLCZ2ODW6jO2ZBhyutRL6Hx9kZmH95mo+BSHy4ku61RPJ3bKdO8HqEjix3m2DTnd++9W5dDs+VEKyveAt7fbOS7hKArT6k1yIea3V+X8wk9IDreSU9FAs92JyUv1OZWBgRoB01/JOBGKNsHPf5fhkTu/haihcqJYvht+fRrev0yG19UWdfu0Go1gdGJI+/ZxyWY+PdnBQxE/wuaPZOnoNoq3yNSBZU9C/mpZojp+rKy4YAqRtmScIEOuS7bCkNNkOpIpCObeL1XQ48bIdICN78r9h58lz+1okdeWv7J1u1kKCxZuONJPTqWbaDUCl8sJc+6BqCFy0nn8A7icjkMf3F8xh8nV4s6YgiEs3Tf2HAuUbJURfaGpMP9Jms9+na/Gv8omR5Kbs1WJGgopUw9+rtQZ7tv1JZA81VOsdOhpMOwsmd+/7N+QtwKyv4I1rTpVigKb3pdV5qpy4ae/uR9fvEna3cagOXDmixCZJXPLL3hHivyVbJP3za2fQcVeDpv4cTD19zD/SVl9Lm7M4Z9Dpe8RAkZfBKMvaS/3y8mPQ/zEjn0MgVBXCoPneR6fOksK6wdEgzEYzntDLrB4o3ofvH+JdL4A1JfCO+ehw8UjO+I5/ptgnkp6modinua070NpECZImQan/B1OfEiKULbprtUWQfYi+TwuWCt1lAwWudAz+SaplZB1Klz4HoQfwhmq4ldodSaUsDSY9Rd5n4oZCcfff3Q0sAYysaNk4QhDgHyODDsbsuYf2BmqcmDsTVIfMsbLQu0h2F3lZG+1i3ExR9khkTRZjgOKNx963z7CoNOQGGpha6GXCHSVAYMaAdNfKd4kV66MAXKyEBgrqyLU7of3LpSh+ADbP4eJN8iBVjcfGGePS+CzDfvRCMHfBu8kYUmn/MjY0XDBu9BUAa+d2pGiotHCpZ/LCdCIczsGndsXyupMs/8qV6OHnw3f/lU6aMZfKUPsVz0PIUlSiDehdYDaUA47v/E0rmK35yRLpXdwObFseEVONNPnyjD5nx/DmjYXRp/ha+t6ByEgdaZMmdu+UEZmDTsDhHqr7BXKd0nx2sZKub3tU3QnPc5XxYks213OQ9PeJFOzn4TIUAKSRh+wikw78ePk5PDHh2Wq49jLpUDuCQ/B3iVga4IJV0HYIPjgMinAB/K+Fpzgeb4tH8OgueBo8nytc1qTKRhGnS/1OoSQ976CtbLqW1saVWAcXPbZ4UcOGCzyT6V/EZoM8/8FM/4o03xCukQuWULhlMdlWtsZz8OWj2Sa7bAF0mGXNlved7/7q5y4XPg+ZHpx1tQUSAH+ztjq0exezMMz5zPz5XKeWin76tDYICalHGDlvrZYVt1qi3gUAs59Q042s7+RqVJJk+QizHf3wvlvqhow/QjhckgnQkiK/E6FRjrUtEdQzvdYIHIwTLxWOtldTvk7Dh/ka6v6J3m/Sj0qw+FXZH19i41ZiVp0XQuCHClavVzQXv2CXDwW/hGXkBZpZf2+asYlh/naFJVeQp1V9Eeq9sE3d3jqc4SlyfD9NudLG2v/Jycd3Rz4D4kN4uMbp6JUF5Dw4dXuLxZtkBoJxZvc39/lhGVPQeIkOVnQ6uCLW2HwSTK8efmzcmI0+CQ5iKwrkQPUyKGQNkueQ9upO5qCIHok5HcJlw2M6dY1qBwFhAZiRsgJ7LbPO5qjsnxoVC9jb4APL5e/pRHnQmUOvHcRnP4sJE/ytXUDj+JNHc6XVnRLH+MfV//CbxOSqG9xICLHY40O6F4amN4knSBJU2UZap1JpjXVFcPxD8p7jsEKPz3e4XwBqNkHg2Z5ni9pqjxmxHkyIqYNU7B3vS1Ta8qR0wErn3PXsKkrhN0/qKkbxxI648EnaykzZIRh3q8w9AxZwWjrZzDqAlj3ulysaOPXJ6Vz2GB2P4clXDp4nB2Vi9BoQXEQXruNz286iV0ldRj1WobEBhIT3OX4Nko2dzhfQC7sfH27vBf+/Lh0OHbWsFFFePsXioDlT0udoEnXyyjjhbcgRp0Paeqi1kEJipN/KkfGzu96FMVZZ1P4bLedR4+G+K43YkbCvpWwZ4l0evsBgyIDWJNbyTUz1EjDgYrqgOmP2Bu9i6M218gywV1pC6k/DJLDrYAOWmo9X3S24Gqo9MxfayiTkx4MkDFPruYtf1pOVs55BeLHg94olccPhTFQ6h+8dVaH6OuQ0zxy1VV6EZdTiqWFJHWIlIWne67kDiRcTlnCuHiT/GvDmy6MypHjTVjb5cRq0HLc4CMo+925VHVn3Y02uormNlaCNVpqwrQ5sEOSYfSFchI9+y6ZIrX5A4geIfU4Ig6Slua0eTrCQTr0ekiz3cnG/GrW5FYSbjUyMTWMQVGHX0lCxX9otDvJ0Q4mNriSkDX/RriciNmtqbZ57ppCOO0gvDzHw9Ph5L/DV7d1POen3Cyd5pNuZFBUQPf6iTdx/IZS6XjpKmIN7iLVKv0Au1ygayiTEYJtdBaAVvFKdnEtq3MqqW9xMjE1lJEJIei1/hEp0W9QFBnVPv3/DvvQ97fbGBGpJfxoie92RQiZCrruNZmeqfN9davM6EA+WJuPoigIVYNwQKI6YPojwfFy5Sz3l442nVFOCAyBEJwkV3TbGHVxz8R5gxOkkODGdzra9GZcIcmUxuiI4d9uuzsmXo+uLbTQEgpjLpZOE422RyGHJE6Ea3+SaUfGALnibFWFz/oMrU72q8xTpBNNCJkaZjwCYVF/JywdZfzViBXPdrSZQ3FEDFFvlr1BzAjZnzo7emf8X++vNg6ZD2v+597WUifTPoafJW3KPKWjZHBoMsy+EybdICP8dIcI2zdYZPrT17e7tx+shPYh+Dm7jOvf+q19OybYyLvXTiE1ogf3VhW/YPG2Em59bwNGnZmTMh4mOsjIZZHDSbRGwrJ/uZc2n/YH0HtJRdNoYdSFss+WbZMpJTu+lA7AmBHdNyZ8sDxX5wn5kAXSweNVhHcAR0IORIyBUpdo708dbUJzRPekY4Hs4lrOe2ElNU1yEUYj4M2rJzEtPcLHlvUzynZITamww4vocLgUXtli48bRvay5E5oMQQmwbSGMPK9336sbRAYaURTIr2wiKVxNQR6IqC7c/ogxEE59ArJOk5PiiMFw0YfSQRGSCBd/ANNvk+lApzwhJw76Hnh0dUaY9WeYeqsUA0yZAZd+xj5NMjct1bLz+FdwxIyB8HT2Tf87OWFewlhNQT1zvrQRkS5TllKmq84XX5AyQw7At30O27+E2JEyLWOAUtzo4rfw03Ac/xAkTUEZdRF1p7/CypYUX5s2MInKgsu/gPFXQ/I0OPMF6bjtbRInSS2r2NHy/jn/KRmSv+1TaKiAjJM6nC+dsYQe2vnSRtZpcNwdMirRHCqFdBO9RON0g6oGG48t2u7WVlzTwqb86h6dT8X3lNU188hX8jttcbhYuL2W/60qY3NhjdQyuvxLGH6O1J648D0YdJDKIXoTJE8BnVlW9NAY4JJPpIBod4keJnVmQlPlxHzEuTD3HjCHwAkPSgdQSJJ0xlz6qSrC2x+JGSPFwgfNgcEnw7mvQ8xh9JFjkF/3VLQ7XwBcCjy7ZDfNNjVy6LDY9rl87h5mNMfXex2EGgXpoX1QDSj9BNj6Cdjqev+9DoEQgqyYQFbleCkdrzIgUBd1+yuRWXD2/2TFA2MgWDoJNUUNker2Lqdn9Y/DJTRFnmvqzdKRYrDSVFTLb/ubmF9sYd7gh7AGCdZsdPBSul1qu6h54QOH0CSYcI0UKUPI7QGM3eniwk8rOWnIJG6eNIM9NYI/vVvMY2epKUi9Rtxo+Xc07ldt1LemQwbGeh/w6c2QdYoU9FZcMsLL1iQjXCxh7npUPSUoBmbdKSNhNFopkt5D7E4XNU2e1cca1UlAv8XuVKht9ryvNNtbfwfJU2T6nKJ0VCk6GEFxcNztsr/pTW4VxDpOXiujzSwRnosyWp2Mhrjme7A1yud4m7MxLLV1HHCLjEbtrhNSxb+IGgyRGfJ+ixYi1ep+h6K60ebRVl7Xgt3lwoRaIrjbbPkEJlx96P06oSgK/1nfwunp+l4yqgsBEXJuteUTeR/1MVmxQfy6u5xzxw/gtP9jGNUB05/Rm2XY3IE4WpMZjQYCoto3E8PMHJcRwdJd5SzcXssFw6y8PWId0R9cCRodzLpLVo4xh3g/X1WeLPnmcshVt4iMo2OnSu9QsVcKmWo0Uh/FW2TAACEm2MR750STvvtVgj/7mIzgJDLm3YU2RtXa6HWOxv3K1iArtnx/n0wpmvw7OZDy5vzwdh/qKnB6pGg0EOKlutJhEhVk4qppKTzxXYeujE4jGBY/gNMBBzgxQSaumJrC8z93lCc3aDVkxnRynAjh7kCs2S+1qWyNMnosaqingzHgANpJ+avh27vk8RknSl0jb4Lq1gjwFrQqhBqF2t+pL4ON78Gyf0rh5jn3yDQzs3ofORDT0yMJbilmsqUQnWJjF0nYQwcRaOojp8BAoGQrNFcddtriT/kOmh0Ko6P6MFlj0GxY8R8YdqbP0+2HxQbx2KIdqg7MAEV1wKgcNgFGPQ8sGMZbK/fx4/YSbkvNI/r7Bzp2+PJWOQjMOtXz4LJseOucDo0aUzBctrB1RUbF7yjaJEvptpU5DYiRpXSjvFSAGQDoFTujc15Eu/U9ALTlOxj8/ZVw7Y+AGqrt9xSshY87rbL99KgsvTr1Zvf9+uF96NzxCRj1Wt5YkUtskJnbTshgWFzwoQ9U8Us0GsHlU1IINOl4d3U+SWEW/nB8BkNjDzDor8qTpdOLNshtnREu/QySu5ESWrEH3jq7Q2tp+0JZvvqST2Rancqxwa7FsPjuju2FN4M10nt5cxUAxgZUMH7v/6Gp3AVApsGK/ZLPgSN3rB8zbHhXVnA7jBLPiqLw9G82TkvXo+lL54M5VDq2ty2EMZf03ft6ISbYhALsKWsgXRXcH3CoGjAqPSI1IoC/njKEz343hahd73vusOUT7wfu+s5dILi5Blb/T1Xi91fWv93hfAGoL4ZtX/jOnt6mtgjt5g/c21wOOWFX8X9yl3m2/fYKNFW7t/XD+1B0kJlrZ6Sx8KbpvHbVBKYMikCrUVfF+jOxIWZump3Bwpun8/LlE5iYGn7glc6CtR3OF5CaRT883FEl8GBU7vGsaFi4rqO6ncrAx2mT+kBd2T6An+dHAd2+X9udLwDYGtCveFr+/lQOjdMBm96HtDmHddjy/U5KG11MifNBmlfqTClkbmvo+/fuhBCCUQnB/Lyz7NA7q/Q7VAeMSo/RagTBVhMi3Ese8YGUzst3ebaVbIFm34teqXTB5XQvxdxG6da+t6Wv0JvlimBXBnLlp4FEoJdUo+DkDs0KRZEiu2VeSkSXbOkX5cZDrQYsBjV4dSARZjVgNhxiolFX5NlWuUumI7XUSz24tjLUXTF40YPRGWW1LpVjA6GD8EGe7T2pkNlbOGxQV+pfzo2qXM+20q1qGfbusnORlDAI6b6OiaIo/HNtCwv6OvqlDWu4/K1kL+r79+7CiPgQvt9e4mszVHoB1QGjcuSMu0KG+bdhDoWhp3vfd/BJnm1ps+Hja+TqtcvVKyaq9ACNVpat7MrQBX1vS18RGA0nPuLeFj/+8Mq5qviO1BkQFN+xrTXAzNulY606X6YkvTADIjM9jx19cc+qxamo9AXe0uNGXiijWN46S/brnx6V/bwrkUNkRaXOzLnnsEvCqvRjNBqYcK17KXNLGGSe4jubOlO6A764FZ6fCp9cB8VbfG2RJMVLdc8xlx5Y41DFndUvHnap818KZPTLtAQfihynHCerIjo9RZj7khHxwWzMr/Yq2K7Sv1GX0VSOnPixsnJC0UYQWlmq2NsEB2QJ45P+JgeKThuMOA8ay2DPYsj9Ca75UR6v4h9knChL6S5/WjpkZtwOqcf52qrew2GHit0w9z6wN4DWCA1lMg0rOP7Qx6v4logMWb63aAM4mqW4bsxIGc216gVY8YzcL3cpTP09/PaavA9NugGGzPel5SoqByduDJzxX/juLpkyN+oi6Qx/9aSOyK2fH5fRMCc86F7JyxIC8/4GI8+XkTRhabJE9dES6lfpHySMk2O14s3yu48dBRGDfW0VNJTDh1dC2Ta5ve0zmXJ3zWJZ3cuXJEyAU/8FPzwA9kYYdxUMP9e3NvUXynbKvjbt1m4foigKj69q5qzBPop+aSMoVmoe7vkRBvtOI8ls0DI0NoglO0pZMFodgw4kVAeMyuFRUwC1RTJEr/PqWdSQ7gmzWkJhyk0waA5s/hB2fAVlO+RrTjuUblMdMP5EYLScqA6aK6tgxAxzj3YaaNQVwi9PSN2XziROgpjhvrFJ5fAIT5N/nakpgjX/69je+S2UbINzX4PwdAhO9JyMulxQuReaqyE4AQJjettyFZUDY7DKiMTUmdJpGBQPWz/xTJtb+xJMvtEz5D8gGgafeOR2OGxQuRvszTJ9xRJ25OdU6TtCkqQuh0YDQX4iJFuZ0+F8aaO2QIpH+9oBYwqS5ZMzTgKXXT4LtGoFpG6x4hkZ9a41dPuQr/Y6aHbCpFg/cA6nzoBNH8gIHuE7e8anhLFwQ6HqgBlgqA4Yle6Tuww+vEJGBBgD4fRnIOs095W27mIJhw1vQ12xe7veW/1LFZ9RlQeL7oTsr+T2iPPg+PvkIGQgojXIijiNFe7telUroV+jM8iJYm1hR1tNvtTP8KaBYG+WDuJv/iRz/YPi4bw3IGF8n5msouKVzpF43u5L5rAOzaOjTWMFLP8PLH9KRpXFjoGzXjhwxKuKf1GZA9/cIUXIQUZRzbkHgn3s5NCb5QJPVw0jf3ruhgzQMU9vUV8KWz+DBc91+5AWp8JjK5u5fITBt9EvbYSmSJ2s3GXS8e0jJqSE8uaKXKobbYRYuu/MUvFvVA0Yle5Rsx8+ulI6X0BWXvjkWqjwImbZHQKi4MS/ubdFZMqQWBX/YfvCDucLwOYPYPcPvrOntwmK9dSAiRoGsaoGTL/G6/1mMMSO9r5/6XZZorVNaLF2P3x2owyVV1HxF2JHy+dmZ076m+zvvcH+dbDsnx3VworWw7KnZOqmiv+z9bMO5wvAxncg5ydfWdNB+CCYdKN728jz/SM9SqVnLH8W0mYellbOK5taiA3QMDzCD6JfQDoFU2fKMtqK7/QpLQYdY5ND+XT9fp/ZoHL0USNgVLpHXbH0aHfGaYfqAoga2rNzZp0CV3wty2FaoyBxIoQmHbmtKkcHl9N7icrd38O4y/venr5i6OkywqdwnayqkzBRpqio9G8y58GVX8tJpDUKEicc+H7jrTxv+U6oLwFrRO/aqaLSXUKT4KL3IX81NJRC3FipydZblG73bNv9HTRVynRVFf/FYZMLKl3Zs8S72H5fojfDjP+TE/bSHTItNGECmLxU71LxfxrKYd1rcOqT3T6kuMHFCxtt3DfNz4TwIwZLHZi85ZAy3WdmzBocyZsr8rhiagrCH6KDVI4Y1QGj0j2s4TI1o7mmo02IIxt06c2QMk3+qfgfGi2kzoL8Ve7tSVN8YU3fYbDK3N9UL9UPVPovejMkT5N/h8Kb3ktQHJjDj75dKipHQliq/OuT9/JSNSlxstTJUPFvdAbp4Chc596eOMk39nTFGiHFTn0oeKpylFj6hCzWcBiRePcua2ZOso4Yq58lZggBg2bD+jcgeYrPtGCGxAahAD/vLGNWZi9FOKr0KX7W01X8ltAUWPCfDjEtoYF5j3mGP6sMLEac4x7hFD9eRhIMdBqroGizFGHtmpeuMvCJHgqz/tqxrbfI+19Qq2PG3iSjAUp3yGpLKirdxdYoBaDLsmVUQn8iYTwMO7NjOyAKZv5ZOjdV/J9RF7qP2RInQ/rxvrOnv+C0y4o+JVtllTGVA1OVJ1PbRpzX7UMW5djZWu5kQbqfihtHZMqKmHt+9JkJQghOHRHLU9/vQlHHpAOCPo2AEULMA/4NaIGXFEV57AD7TQBWAucrivJRH5qocjBiR8tyfNW58mbUUi8jYvR+FjKocvSIHAyXfgbl2dLpFpEJAZG+tqp3KdkKn94AxZtkNMxJj8KIc6UYm8qxgTFQVv8afCI0VEgHdPgg+Vp1Piz5G2x6FxAw9gqY+SffV+tQ8X+q98H398OWj2WE4YRrYfpt/afCVmAMzH9K6nXYG2WqSNdqSyr+S2QmXLZQplNqtPL5bh3gz/MjpaEcVv4Xfn1KVkccfLIs6e4tGkwFvrsbsuaDObRbu5c3ubhraTM3jzVg0Pppao0QsprTb69D8nSfOZynpIXzxaZCvt1azLzhsT6xQeXo0WcOGCGEFvgPcAJQAKwRQixUFGWbl/0eB77tK9tUusm2z+TNtTMhyTDyXJ+Yo9JHBEYfO/n9LfWw6K/S+QJga4AvbpEl1hMn+tY2lb7FYIG4MZ7tOxfJFT4AFPjtFRkZMObiPjVPpR+y7XPpfAGpsbXqeRlV2J+eoeYQSPKTtBWVwycopiOST+XQ5K+CX57o2N75DUQPgzl3y4m5Sgd7f4KC1XD6s93a3aUo/N+PTUxP0JIV7ifCuwciJEmmem58F8Zf5RMTNBrBpZOTuW/hVqamRxBk8tOIIZVu0ZcpSBOB3Yqi7FUUxQa8Byzwst/vgY+BUi+vqfgKhw22fOLZvntx39uiotJbNJR5rwpRmdPnpqj4IS5XxwS6Mzu+8mxTUemMo+UAz9Dv+94WFRWV7tFVAw9g6yfueogqMrVy4a0yqk/Xvaj4/6xvoaRR4ZzMfuJIGDxPVhGr2O0zE4bFBTM6MYT/+2AjLpeaitSf6UsHTDyQ32m7oLWtHSFEPHAm8Hwf2qXSHbR6KUDVlfhxfW+LikpvYQyC8AzP9t4q66rSv9BovIv4qtFRKodCazjAM7QXqxapqKgcGZFDPNsSJoDe2ve2+DOL74HwtG6LOi/KsfP6Fju/H2tAp+knkUTGQJmCtvQJn2q/XTwpmf1VTdz5yWYcTt+Vx1Y5MvrSAePtF9bVffcU8GdFUZwHPZEQ1wkh1goh1paVlR0t+1QOhhAw5lIISuhoixmhCrgdBLWf9kOs4TD/Sfcc39GXyL4+AFH7aA8YeZ7UhGkjIhOyTvWZOQOdAdNH1WfogGbA9FMVd5KnuVd+tEbAlJtB10+iNjrRa31020LY8SVMuK5buy/f7+AvPzdz23gjYeZ+VgsmbrTUQVz+LJ5T2L5Br9Vw+4mZZJfUcd4LK9iyX43G6o+IvlJTFkJMAe5XFOWk1u07ARRFebTTPjl0OGoigEbgOkVRPjvQecePH6+sXbu2t8xW6Up1vqzeoNHKlQE1l7hbrnu1n/YjFEWKFFbulXoHkUPBHOxrq44EtY8ebWr2Q9kOOamOzFIFeI+cY6ePuj1DsyBIFVPsRxw7/VSlg/pSWfXO2SId7qHJvrboYPRtHy3aCG+cAXPugQgv0cNdWLLPzh9+bOKWsUaGRvi57suBcNhgzUuQMk061X2Ey6Xww44SFm4sJDzAyMSUMJLDLYRaDJj0Wgw6DcbWP7NBi1mvxajTotcJNEK0dxSNRmDWa7EYtIi+0zXqJ2FPvUdfVkFaA2QIIVKB/cAFwEWdd1AUJbXt/0KI14AvD+Z8UfEBIYlq1QOVgY0QslpEpFpiXeUABMfLPxWVw0V9hqqo9C8CotQ0ZG8Ub4a3zoZJNxzS+aIoCv/bZOO/G2z8cbyRwWH91PkCoDPA2EthzctSF27cZfjCn6DRCE4YGsOcrGh2ldaxp7SB3/KqaLI5aXG6cDhdOJwKNqeLFoeLFocTu0PB7nLROfbC6VKwOZw4XAoxQSYGxwQyISWM4zIiGRIb2JdOmWOKPnPAKIriEELcjKxupAVeURRlqxDihtbXVd0XFRUVFRUVFRUVFRUVf2XrZ/DlbTDxOkieetBdd1c5ueuXZqqaFR6YZiTS0s/SjrxhDIQJ18CGt6EqB6bd2u3S20cbrUaQFRNEVkzQEZ3H5nBRVtdCXmUD6/dV8cbyXBTg1JGxLBgVz/D4INUZcxTpywgYFEX5Gvi6S5tXx4uiKFf0hU0qKioqKioqKioqKioqB0BRYP86+PkxmZI1526IGOx1V4dLYXWRk7e22vi10MHpg/SclKpD218Ed7uDMQDGXw17foTPboSs+ZB5MljCfW1ZjzDoNMSHmokPNTN1UASKorCvspFVOZVc/+ZaNBrBycNjmZMVxdjkEIy6fhzF5Af0qQNGRUVFRUVFRUVFRUVFxQ+xNULecrDVQWMl1ORDyRbIXQYuJ2TNxzHrXtZWGmisqKDBIaixQVmTYF89bKsSZFdrCDUqTItxcc9YF2atnZqBqhUbOQkCB0PBatjwpYyEiRgEgfHy/4YA0JsgLBUMgb629rAINOk5fkg0c7OiyK1o5Le8Sj5eV0Blg42YIBOZMYGkRliJCTYRZjEQYNJhNmix6LWMSw5Fpx0A0U69RJ+J8PYWQogyIO8Qu0UA5X1gjmpD9/AHO46WDeWKosw71E7d7Kf+jj98b33NQLjmI+2j/vAZqDYMbBv6w33UHz77o8FAuQ7o+2s53H7qz5+1atvh4692QYdtR3wvvW2yIeJfJ5m8Kg2XN7ocAGsZKm7Q3HvAEAiN4iKE2m4ZPpAQ4sDlhRscwlVnEwOibrTQGzQavemg3pXSjx/a2bR7Vd0BXu5WPx3I9HsHTHcQQqxVFGW8aoPvbfAXO/zBhv7GsfiZHYvX3BV/+AxUG1QbfM1Aue6Bch3g/9fiz/apth0+/moX+LdtB6O/2n24qNep0hU1NkhFRUVFRUVFRUVFRUVFRUWll1EdMCoqKioqKioqKioqKioqKiq9zLHigHnR1wag2tAZf7DDH2zobxyLn9mxeM1d8YfPQLVBotrgOwbKdQ+U6wD/vxZ/tk+17fDxV7vAv207GP3V7sNFvU4VN44JDRgVFRUVFRUVFRUVFRUVFRUVX3KsRMCoqKioqKioqKioqKioqKio+AzVAaOioqKioqKioqKioqKioqLSywwoB4wQIlEIsUQIsV0IsVUIcWtre5gQYrEQYlfrv6F9YItWCLFeCPGlD20IEUJ8JITY0fqZTOlrO4QQt7V+F1uEEO8KIUy9bYMQ4hUhRKkQYkuntgO+pxDiTiHEbiFEthDipKNpy0BACDGv9bPZLYT4i6/t6Qu89aGBjnr/dHv/Y/Le2fq+x9z982C/dyHE7UIIRQgR0anNb6/5QNcihPh9q71bhRB/79Ter65FCDFaCLFSCLFBCLFWCDGx02t+dy1CiHNbP3OXEMLn5Vn99Xnuz8/cAz0bfU3rM2G1EGJjq10P+NqmA3GofieEmCWEqGn9XW8QQtzrCzuPhEP1YSF5uvUz2CSEGNvXNh4NunGd/f677AsGlAMGcAD/pyjKEGAycJMQYijwF+AHRVEygB9at3ubW4HtnbZ9YcO/gUWKomQBo1rt6TM7hBDxwC3AeEVRhgNa4II+sOE1YF6XNq/v2do/LgCGtR7znBBCe5Tt6be0fhb/AU4GhgIXtn5mA53X8OxDAx31/tnBsXrvhGPz/vkaXn7vQohE4ARgX6c2f7/m1+hyLUKI2cACYKSiKMOAJ1rb+921AH8HHlAUZTRwb+u2P1/LFuAsYKmvDfHz5/lr+O8z90DPRl/TAsxRFGUUMBqYJ4SY7FuTPDmMfveLoiijW/8e7FMjjw6vcfA+fDKQ0fp3HfDfPrCpN3iNQ/9W+/t32esMKAeMoihFiqKsa/1/HXLQHI8ceLzeutvrwBm9aYcQIgE4FXipU3Nf2xAEHAe8DKAoik1RlOq+tgPQAWYhhA6wAIW9bYOiKEuByi7NB3rPBcB7iqK0KIqSA+wGJqLSxkRgt6IoexVFsQHvIT+zAc0B+tCARr1/tr//MXvvhGPz/nmQ3/uTwB1A52oFfn3NB7iWG4HHFEVpad2ntLW9P16LAgS1/j8Y+bsAP70WRVG2K4qS7Ws7WvHb57k/P3MP8mz0KYqkvnVT3/rnj5VV/LbfHU260YcXAG+0fm8rgRAhRGzfWHf08Offan9iQDlgOiOESAHGAKuAaEVRikDeSIGoXn77p5CDNlentr62IQ0oA14VMpT/JSGEtS/tUBRlP3KlbR9QBNQoivJdX9rQiQO9ZzyQ32m/AvzgwepHqJ/PMcgxfv9U752eHHP3TyHE6cB+RVE2dnmpP17zYGCGEGKVEOJnIcSE1vb+eC1/AP4hhMhH/kbubG3vj9fS16if0RHS5dnoc4RM190AlAKLFUXxC7u60N1+N6U1neobIcSwvjGtTzmWfn8D/bs8YgakA0YIEQB8DPxBUZTaPn7v+UCpoii/9eX7ekEHjAX+qyjKGKCBvkkdaEdInYAFQCoQB1iFEJf0pQ3dQHhp88cVBF+hfj7HGOr9U713HgYD8v4ghLAAdyFTXDxe9tLm79esA0KR6RN/Aj4QQgj657XcCNymKEoicButkWr48FqEEN8LqdXU9c/fVvn74/ftN/jy2XggFEVxtqbjJQAThRDDfWySN7rT79YBya3pVM8An/W2UT7gWPn9HQvf5REz4BwwQgg98gb5tqIon7Q2l7SFebX+W3qg448C04DThRC5yDC7OUKIt/rYBv6/vbuPlqsq7zj+/dEABqGAQhUMNK4oKCQQDdIQeQlJShV0dVFpg1IJBVvjqoJoilisRVhWqC5Sl2gUaAwYCxQKIVRL5EWSFIjhLclNgiTVYIG0LFHeFKQIT//YzyQnk5n7EjJz78z9fdaadc89c172mdmzz9nP2XsfSmT1sUo0/HpKpaKd6ZgGbIiIn0fES8ANwKQ2p6Gm2T4fA/arLDeKzU2azZ/PsOLyE3DZ2chwKz/HUIJfKzMvjgIekPRGOvOYHwNuyKbvyymty/aiM49lBuX3AHAdm7sZDdqxRMS0iBjb4HVTO/Y/AJ34fQ8JTc6NQ0Z2k72ToTmOTp/5LiKerXWniojvAzuqMvB5lxgWv79h8l2+al0VgMk7Ov8MPBQRl1TeWkg5aZN/W3ZSjIjPRsSoiBhNGRDujoj483amIdPxv8Cjkg7MWVOBtW1Ox38DEyXtkt/NVErf2bZ+FqnZPhcCJ0vaWdKbKYNjLW9DejrFvcBbJb1Z0k6UPL1wkNNkLeDyc1MaXHZubViVnxHRExG/FxGjMy8+Brwz80YnHvMCYAqApAOAnYAn6cxj2Qgck9NTgPU53YnH0m4+n2+DXs6Ng0rS3pL2yOmRlMD9jwc1UY31me8kvTE/Z1SebLYD8Iu2p7S1FgKnqphI6Vr8P4OdqO1tmHyXr15EdM0LOJLSnGsVsCJfxwOvpzy5YX3+fV2b0jMZ+PecbnsaKKOi35efxwJKE+S2pgP4AuWEsBr4DrBzq9MAXE0ZN+ElyoXzGb3tk9LU/CfAw8B7BzsfD7VX/obW5Wd03mCnp03HvFUeGuw0teGYXX5u3vewLDtzv8Ou/Ozr9w48AuzVCcfc5PvbCZifeekBypNTOvVYjgTuB1ZSxuGYMJSPBTgx0/4i8ASwaJDTMyTP50P5nNvs3DgE0nUI8GCmazXw+cFOUy9p3SrfATOBmTn9cWBN/q6XAZMGO83bcIyNyqvqMYryNKifAD2UpxwOerpbcJwd/12246X8sMzMzMzMzMzMrEW6qguSmZmZmZmZmdlQ5ACMmZmZmZmZmVmLOQBjZmZmZmZmZtZiDsCYmZmZmZmZmbWYAzBmZmZmZmZmZi3mAIyZmZmZmbWcpDslHZbT35e0xzZsY7KkSZX/Z0o6dTsm08ysZRyAMTMza0DSFZIO6mOZeZJOajB/tKQP9bHuaZIufbXptM6X+WX1AJbvs8LZW/6S9LcDTaPZ9hYRx0fE09uw6mRgUwAmIr4ZEVdtr3TZ0DbQ8rJFadgUSNxO29tX0vU5PV7S8dtr2zb0OADT5SQtkHS/pDWS/irnnSFpXRYel9cu0CTtLenfJN2br3cPbuptOJD0Wknfk7RS0mpJ0yVNkLQ48+4iSftI2l3Sw5IOzPWulvSXg51+614R8ZGIWLuNq48Geg3AmG2r7VDhdADG+i0rvD/OoPRqSd+VNE3SXZLWSzo8z+Vz8/rxQUl/nOuOlHSNpFWSrgVGVrb7iKS9cvrUXGalpO/kvPdL+lFu7zZJb5A0GpgJnC1phaSjJJ0vaVauM17SstzWjZL2zPl3SrpY0vK8Bj6qvZ+iWXMRsTEiajdzxgMOwHQxB2C63+kRMQE4DDhT0puAvwMmAn8IvK2y7FeB2RHxLuADwBXtTqwNS+8BNkbEoRExFrgF+BpwUubducAXI+IZ4OPAPEknA3tGxOWDlmrrGJLOkXRmTs+WdEdOT5U0X9Jxku6R9ICk6yTtmu9Xm8o3DFynoyXdLemnldYwFwFHZQXh7H6k8YRMw16SxmQF4l5JF0j61Xb9QGyo+p3MW2sk/SArrmMk3ZLB6KWS3gZQV+F8V1Y275H0ZW15Z3jfXH+9pH/M5S8CRmbe/G6zxDSpEM+TNEfSDzO/H5OV7ockzWvZJ2NDwVso14mHUK4dPwQcCcyiBPTOA+7Ia8hjgS9Lei3wMeD5iDgE+CIwoX7Dkg7O9adExKHAWfnWfwITI+IdwDXAORHxCPBNyvXq+IhYWre5q4DP5P56gL+vvDciIg4HPlk33zrPCElXZhl1vaRd8pz+oKSeLJd2hk2Bvi/kOb6nUo42DBo20kcgsdk1RLP9HpPl74rc727KVj2SdgIuAKbn+9Oz/N47191B0n8pA5fWmRyA6X5nSloJLAP2Az4MLI6IX0bES8B1lWWnAZdKWgEsBH5X0m7tTrANOz3AtLwzdRQln44Fbs28+DlgFEBE3JrLfx34yOAk1zrQEqB2t/MwYFdJO1IqDz2UPDYtIt4J3Ad8qrqypH1pHrgG2Ce39T5K4AXgXGBpVhBm95Y4SSfm8sdHxJOUSs5XsyKzceCHax3qrcDXI+Jg4GnKjZDLgE9kMHoW8I0G630bmBkRRwAv1703HpgOjKNc0O8XEecCL2TePKVRQnqpEAPsCUwBzgZuBmYDBwPjJI0f6EFbx9gQET0R8QqwBrg9IoJSho4GjgPOzfP2ncBrgP2Bo4H5ABGxCljVYNtTgOuz/CMifpnzRwGLJPUAf0PJZ01J2h3YIyIW56wrc/81N+Tf+zPN1rkOBC7LQNuzlPP2PGB6RIwDRlCCfzVP5jl+DqUsheZBw0YaBhIzENLbNUSj/c4C/joixlOuTV6oLRwR/wd8Hrg2y+hrKb+fWlk9DVhZ+61YZ3IApotJmkz5oR6RF1APAg/3ssoOuez4fL0pIp5rfUptOIuIdZQTWQ/wJUqlY00lH46LiOOgRP6Bt1NOVq8brDRbx7kfmJAB5ReBeyiBmNqFz0HAXVlxmAH8ft36h9M8cA2wICJeye5Kbxhg2o4FPgOcEBFP5bwjKvv4lwFuzzrXhohYkdO1CuIk4LrMm9+iBPs2URnAdLeIuDtn1eeX2yPimYj4DbCWrfN2M80qxAA3VyreT9RVykf3c/vWeV6sTL9S+f8VSmVXwAcq5+79I+KhXCb62LaaLPM14NKsUH+UEtR5NWppfjnTbJ3r0Yi4K6fnA1MpZei6nNef4FuzoGEjzQKJE+n9GqLRfu8CLlFpmbtHRPy2j2OdC9TG/DqdEnS3DuYATHfbHXgqIp7PZm8TgV2AYyTtKWkEpbJb8wNKFw+g9KNtZ2JteMrWBc9HxHzgK8AfAHtLOiLf3zHvxkK54/oQ8EFgbrZiMOtVBk0eAf4CuBtYSgl8jAE2ALdWKg0HRcQZdZtQH7uoVkz6WrbeT4HdgAMGuJ51n2o+epkSZH66kjfHR8Tb69YZSN4cSKWzWYW4us1qJbz2vyu1w9ci4BOSBCDpHTl/CXn3XtJYShemercDfybp9blc7QbL7sDjOT2jsvxzlHJzC9lV+SltHt/lw8Di+uWsK/QV1KvXKPjWW9Cwv/sUvV9DbLXfiLiI0op7JLCs1jWp6U4jHgWekDSFco38H70tb0OfAzDd7RZKH8lVwIWUbkiPA/8A/Ai4jXJH7Jlc/kzgsOzfuJYyyJlZq40Dluedg/MoTS9PAi7O7nMrgEmSDqCcsD6dfb6XUJp9mvXHEkqz3yWUAMxMSt5aBrxb0lsAsh95fTBkOc0D1800rCA08DPgT4CrKoHGZZV9nNyPbVh3ehbYIOlPAVQcWl0gW009J2lizupvfnmpjwB2swqxWTMXAjsCq1TGIbow58+hdPtcBZxDKU+3EBFrKN06Fud5/5J863xKC7ClQLXLxc3AiTlGRv1gujMoXUlWUbrgXbAdjs2Gnv1rN+ooN+VuA0bXzuX0L/jWLGjYSLNAYn+uIbYgaUy2HLyY0mWpPgDT6PrhCkoLnH+NiPquptZhfKeii0XEi8B76+dLui8iLsuKxI2Uli9kU+Pp7U2lDXcRsYhyEqx3dIN5m+7+RsSnGrxv1sxSSoDvnoj4taTfUMZo+bmk04CrlQP2UQJ7tWbMRMTjkmqB641sGbhuZhXw26xMzOttHJiIeFjSKZSKxvspA0TOl/Rp4Hv92Jd1r1OAOZI+R6ncXgOsrFvmDOBySb+mNKPvT365jFJRfqDRODARsUZSrUL8MqUL82nbfBTW0XLg27GV/09r8t5HG6z7Ak0CgxExujJ9JaXbSPX9m4CbGqy3ji1b0iytvLeC0uK7fp3JlekncXe5TvcQMEPSt4D1lHGqllHOoyOAeymDNffmQuCfKGWhKC1l39dk2TnAtzOwt4IMJPbnGqKBT0o6ltIqZi2lRUu1e+kP2dw16ks5DsxCStcjdz/qAirdeG04kfQVytgwr6EEX84KZwQzs6Yk7RoRv6oErudGxI0t2tculEFSQ+WJXx+MiKZPZ7DhrZY3c/pcYJ+IOKuP1czMrEOoPJFxdkT48eldwC1ghqGImNX3UmZmVnG+pGrgekEL9zWB8kQ6UZ6Gc3oL92Wd7wRJn6Vc0/0Mt1QxM+saGVj/GJufhGQdzi1gzMzMWkjSHwEX183eEBEnDkZ6zGpyjJfbG7w1NSJ+0e70mJkNJp+vrR0cgDEzMzMzMzMzazE/BcnMzMzMzMzMrMUcgDEzMzMzMzMzazEHYMzMzMzMzMzMWswBGDMzMzMzMzOzFvt/BDQ2ykKYr/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1127.62x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's run a pair plot\n",
    "import seaborn as sns\n",
    "sns.pairplot(df,hue='fracture')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "578cc395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>medication</th>\n",
       "      <th>bone_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.052768</td>\n",
       "      <td>1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>155.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.741225</td>\n",
       "      <td>1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.778900</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>170.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78.247175</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.191877</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>77.982543</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>50.285303</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>46.359721</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>54.788368</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>69.994822</td>\n",
       "      <td>1</td>\n",
       "      <td>68.5</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  sex  weight_kg  height_cm  medication  bone_density\n",
       "0    57.052768    1       64.0      155.5           1        0.8793\n",
       "1    75.741225    1       78.0      162.0           0        0.7946\n",
       "2    70.778900    0       73.0      170.5           0        0.9067\n",
       "3    78.247175    1       60.0      148.0           0        0.7112\n",
       "4    54.191877    0       55.0      161.0           0        0.7909\n",
       "..         ...  ...        ...        ...         ...           ...\n",
       "164  77.982543    0       74.0      164.0           0        0.7941\n",
       "165  50.285303    1       59.0      161.0           0        0.7971\n",
       "166  46.359721    0       67.0      169.0           0        0.8037\n",
       "167  54.788368    0       70.0      166.0           0        0.8072\n",
       "168  69.994822    1       68.5      165.0           0        0.8664\n",
       "\n",
       "[169 rows x 6 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['age', 'sex', 'weight_kg', 'height_cm', 'medication',\n",
    "       'bone_density']]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5bd392c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "164    1\n",
       "165    1\n",
       "166    1\n",
       "167    1\n",
       "168    1\n",
       "Name: fracture, Length: 169, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['fracture']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f76f6571",
   "metadata": {},
   "outputs": [],
   "source": [
    "#before we learn the data, let's split the data into training set\n",
    "#and test set\n",
    "#need to import the package\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cbcf1470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "678"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "623e729c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a2960a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the classifier\n",
    "#first let's create a model object\n",
    "lrm = linear_model.LogisticRegression()\n",
    "\n",
    "#Recall that scikit-learn datasets have to be \n",
    "#a matrix\n",
    "\n",
    "lrm.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7fb7e0be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.05235865, -0.73845484, -0.06519163,  0.01428253, -0.41415145,\n",
       "        -2.4808583 ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c8cb49da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.07210364])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f38c867d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make predictions over test dataset\n",
    "predictions = lrm.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "79ab8348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>medication</th>\n",
       "      <th>bone_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>69.977701</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>54.912534</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>83.093647</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>75.015480</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>81.979559</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>50.276059</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>50.285303</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>67.573380</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>68.571308</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>63.673184</td>\n",
       "      <td>1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>66.825462</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>51.048370</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>164.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>76.745516</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>82.423159</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>51.596540</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>44.239679</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>54.992184</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>67.925528</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>55.411710</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>44.683127</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>57.802298</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>57.934593</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>70.028503</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>69.048179</td>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60.555417</td>\n",
       "      <td>1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>66.256648</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>39.254947</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>58.812410</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>56.545818</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>62.065237</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>66.644866</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>61.184672</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>61.644504</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>52.478311</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>150.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>61.170085</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>88.024151</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>151.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.778900</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>170.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>61.600414</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>72.270137</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>68.683288</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>69.086638</td>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>57.569180</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>70.198573</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>71.471721</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>68.017748</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>60.221386</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>47.024651</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.6496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>60.831357</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>81.087385</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>149.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>65.795352</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>63.024697</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>84.721764</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>72.772387</td>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>149.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73.970471</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>54.406178</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>77.819623</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  sex  weight_kg  height_cm  medication  bone_density\n",
       "138  69.977701    1       85.0      148.5           0        0.6264\n",
       "30   54.912534    0       69.0      168.0           0        1.0909\n",
       "119  83.093647    1       52.0      156.0           1        0.4586\n",
       "29   75.015480    0       69.0      173.5           0        0.9364\n",
       "143  81.979559    0       66.0      166.0           0        0.5615\n",
       "162  50.276059    1       60.0      170.0           1        0.7399\n",
       "165  50.285303    1       59.0      161.0           0        0.7971\n",
       "51   67.573380    0       39.0      159.0           0        0.5514\n",
       "105  68.571308    1       48.0      161.0           0        0.7096\n",
       "60   63.673184    1       63.0      158.0           0        0.7095\n",
       "15   66.825462    0       76.0      171.0           0        0.8033\n",
       "157  51.048370    1       68.0      164.5           0        0.6904\n",
       "133  76.745516    1       52.0      146.0           2        0.5603\n",
       "45   82.423159    1       74.0      156.0           0        0.8171\n",
       "68   51.596540    1       54.0      154.0           0        0.7755\n",
       "85   44.239679    0       81.0      177.0           0        0.9824\n",
       "24   54.992184    0       75.0      172.0           0        0.8126\n",
       "109  67.925528    1       62.0      159.0           0        1.0176\n",
       "75   55.411710    1       59.0      160.0           0        0.7510\n",
       "108  44.683127    0       67.0      172.0           0        0.9010\n",
       "19   57.802298    1       50.0      152.0           0        0.8254\n",
       "16   57.934593    0       67.0      160.0           2        0.7978\n",
       "31   70.028503    1       59.0      155.0           0        0.8079\n",
       "18   69.048179    1       72.0      154.0           0        0.7861\n",
       "12   60.555417    1       76.0      155.0           0        0.9760\n",
       "9    66.256648    0       72.0      159.5           0        0.9466\n",
       "82   39.254947    0       76.0      173.5           0        1.0502\n",
       "98   58.812410    1       74.0      149.0           0        0.8307\n",
       "76   56.545818    0       71.0      166.0           0        1.0839\n",
       "55   62.065237    1       85.0      148.5           2        0.7999\n",
       "42   66.644866    1       60.0      153.0           0        0.9630\n",
       "56   61.184672    1       67.0      149.0           0        0.8013\n",
       "153  61.644504    1       55.0      162.0           0        0.6880\n",
       "111  52.478311    1       59.0      150.5           0        0.7314\n",
       "69   61.170085    1       70.0      152.0           0        0.8599\n",
       "137  88.024151    0       52.0      151.5           0        0.6204\n",
       "2    70.778900    0       73.0      170.5           0        0.9067\n",
       "93   61.600414    1       68.0      159.0           0        0.9139\n",
       "135  72.270137    1       50.0      154.0           0        0.6001\n",
       "150  68.683288    1       48.0      154.0           0        0.6448\n",
       "90   69.086638    0       77.0      162.0           0        0.8509\n",
       "114  57.569180    0       74.0      176.0           1        1.0287\n",
       "26   70.198573    0       68.0      167.0           0        1.0406\n",
       "145  71.471721    0       62.0      177.0           0        0.6297\n",
       "136  68.017748    1       58.0      153.0           0        0.6103\n",
       "97   60.221386    0       94.0      166.0           0        1.0617\n",
       "65   47.024651    1       47.0      148.5           2        0.6496\n",
       "36   60.831357    0       70.0      159.5           0        1.0265\n",
       "126  81.087385    1       50.0      149.5           0        0.5515\n",
       "22   65.795352    1       74.0      145.0           0        0.7738\n",
       "101  63.024697    0       71.0      161.0           0        1.0366\n",
       "160  84.721764    0       52.0      161.0           0        0.7085\n",
       "96   72.772387    1       81.0      149.5           2        0.5090\n",
       "11   73.970471    1       68.0      164.0           0        0.5793\n",
       "67   54.406178    1       69.0      160.0           0        0.8819\n",
       "132  77.819623    1       48.0      151.0           0        0.5547"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = X_test\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6d96adfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>medication</th>\n",
       "      <th>bone_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69.977701</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.912534</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83.093647</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.015480</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.979559</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.276059</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50.285303</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>67.573380</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>68.571308</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>63.673184</td>\n",
       "      <td>1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>66.825462</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>51.048370</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>164.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>76.745516</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>82.423159</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>51.596540</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>44.239679</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>54.992184</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67.925528</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55.411710</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>44.683127</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>57.802298</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>57.934593</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>70.028503</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>69.048179</td>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>60.555417</td>\n",
       "      <td>1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>66.256648</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39.254947</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>58.812410</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>56.545818</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>62.065237</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>66.644866</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61.184672</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>61.644504</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>52.478311</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>150.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>61.170085</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>88.024151</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>151.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>70.778900</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>170.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>61.600414</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>72.270137</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>68.683288</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>69.086638</td>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>57.569180</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>70.198573</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>71.471721</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>68.017748</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>60.221386</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47.024651</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.6496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>60.831357</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>81.087385</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>149.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>65.795352</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>63.024697</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>84.721764</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>72.772387</td>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>149.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>73.970471</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54.406178</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>77.819623</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  sex  weight_kg  height_cm  medication  bone_density\n",
       "0   69.977701    1       85.0      148.5           0        0.6264\n",
       "1   54.912534    0       69.0      168.0           0        1.0909\n",
       "2   83.093647    1       52.0      156.0           1        0.4586\n",
       "3   75.015480    0       69.0      173.5           0        0.9364\n",
       "4   81.979559    0       66.0      166.0           0        0.5615\n",
       "5   50.276059    1       60.0      170.0           1        0.7399\n",
       "6   50.285303    1       59.0      161.0           0        0.7971\n",
       "7   67.573380    0       39.0      159.0           0        0.5514\n",
       "8   68.571308    1       48.0      161.0           0        0.7096\n",
       "9   63.673184    1       63.0      158.0           0        0.7095\n",
       "10  66.825462    0       76.0      171.0           0        0.8033\n",
       "11  51.048370    1       68.0      164.5           0        0.6904\n",
       "12  76.745516    1       52.0      146.0           2        0.5603\n",
       "13  82.423159    1       74.0      156.0           0        0.8171\n",
       "14  51.596540    1       54.0      154.0           0        0.7755\n",
       "15  44.239679    0       81.0      177.0           0        0.9824\n",
       "16  54.992184    0       75.0      172.0           0        0.8126\n",
       "17  67.925528    1       62.0      159.0           0        1.0176\n",
       "18  55.411710    1       59.0      160.0           0        0.7510\n",
       "19  44.683127    0       67.0      172.0           0        0.9010\n",
       "20  57.802298    1       50.0      152.0           0        0.8254\n",
       "21  57.934593    0       67.0      160.0           2        0.7978\n",
       "22  70.028503    1       59.0      155.0           0        0.8079\n",
       "23  69.048179    1       72.0      154.0           0        0.7861\n",
       "24  60.555417    1       76.0      155.0           0        0.9760\n",
       "25  66.256648    0       72.0      159.5           0        0.9466\n",
       "26  39.254947    0       76.0      173.5           0        1.0502\n",
       "27  58.812410    1       74.0      149.0           0        0.8307\n",
       "28  56.545818    0       71.0      166.0           0        1.0839\n",
       "29  62.065237    1       85.0      148.5           2        0.7999\n",
       "30  66.644866    1       60.0      153.0           0        0.9630\n",
       "31  61.184672    1       67.0      149.0           0        0.8013\n",
       "32  61.644504    1       55.0      162.0           0        0.6880\n",
       "33  52.478311    1       59.0      150.5           0        0.7314\n",
       "34  61.170085    1       70.0      152.0           0        0.8599\n",
       "35  88.024151    0       52.0      151.5           0        0.6204\n",
       "36  70.778900    0       73.0      170.5           0        0.9067\n",
       "37  61.600414    1       68.0      159.0           0        0.9139\n",
       "38  72.270137    1       50.0      154.0           0        0.6001\n",
       "39  68.683288    1       48.0      154.0           0        0.6448\n",
       "40  69.086638    0       77.0      162.0           0        0.8509\n",
       "41  57.569180    0       74.0      176.0           1        1.0287\n",
       "42  70.198573    0       68.0      167.0           0        1.0406\n",
       "43  71.471721    0       62.0      177.0           0        0.6297\n",
       "44  68.017748    1       58.0      153.0           0        0.6103\n",
       "45  60.221386    0       94.0      166.0           0        1.0617\n",
       "46  47.024651    1       47.0      148.5           2        0.6496\n",
       "47  60.831357    0       70.0      159.5           0        1.0265\n",
       "48  81.087385    1       50.0      149.5           0        0.5515\n",
       "49  65.795352    1       74.0      145.0           0        0.7738\n",
       "50  63.024697    0       71.0      161.0           0        1.0366\n",
       "51  84.721764    0       52.0      161.0           0        0.7085\n",
       "52  72.772387    1       81.0      149.5           2        0.5090\n",
       "53  73.970471    1       68.0      164.0           0        0.5793\n",
       "54  54.406178    1       69.0      160.0           0        0.8819\n",
       "55  77.819623    1       48.0      151.0           0        0.5547"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = results.reset_index(drop=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2439843c",
   "metadata": {},
   "outputs": [],
   "source": [
    "results['prediction'] = predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a0b514aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>medication</th>\n",
       "      <th>bone_density</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69.977701</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6264</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.912534</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0909</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83.093647</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4586</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.015480</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9364</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.979559</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5615</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.276059</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50.285303</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>67.573380</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5514</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>68.571308</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7096</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>63.673184</td>\n",
       "      <td>1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7095</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>66.825462</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8033</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>51.048370</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>164.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6904</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>76.745516</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5603</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>82.423159</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8171</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>51.596540</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>44.239679</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9824</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>54.992184</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8126</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67.925528</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55.411710</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7510</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>44.683127</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>57.802298</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8254</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>57.934593</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7978</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>70.028503</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8079</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>69.048179</td>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7861</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>60.555417</td>\n",
       "      <td>1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9760</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>66.256648</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9466</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39.254947</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0502</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>58.812410</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>56.545818</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0839</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>62.065237</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>66.644866</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9630</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61.184672</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8013</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>61.644504</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>52.478311</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>150.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7314</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>61.170085</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8599</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>88.024151</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>151.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6204</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>70.778900</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>170.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9067</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>61.600414</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>72.270137</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>68.683288</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6448</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>69.086638</td>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8509</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>57.569180</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0287</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>70.198573</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0406</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>71.471721</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>68.017748</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6103</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>60.221386</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0617</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47.024651</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.6496</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>60.831357</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0265</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>81.087385</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>149.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5515</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>65.795352</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7738</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>63.024697</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0366</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>84.721764</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7085</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>72.772387</td>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>149.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5090</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>73.970471</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5793</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54.406178</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8819</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>77.819623</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5547</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  sex  weight_kg  height_cm  medication  bone_density  prediction\n",
       "0   69.977701    1       85.0      148.5           0        0.6264           0\n",
       "1   54.912534    0       69.0      168.0           0        1.0909           0\n",
       "2   83.093647    1       52.0      156.0           1        0.4586           1\n",
       "3   75.015480    0       69.0      173.5           0        0.9364           0\n",
       "4   81.979559    0       66.0      166.0           0        0.5615           1\n",
       "5   50.276059    1       60.0      170.0           1        0.7399           0\n",
       "6   50.285303    1       59.0      161.0           0        0.7971           0\n",
       "7   67.573380    0       39.0      159.0           0        0.5514           1\n",
       "8   68.571308    1       48.0      161.0           0        0.7096           1\n",
       "9   63.673184    1       63.0      158.0           0        0.7095           0\n",
       "10  66.825462    0       76.0      171.0           0        0.8033           0\n",
       "11  51.048370    1       68.0      164.5           0        0.6904           0\n",
       "12  76.745516    1       52.0      146.0           2        0.5603           0\n",
       "13  82.423159    1       74.0      156.0           0        0.8171           0\n",
       "14  51.596540    1       54.0      154.0           0        0.7755           0\n",
       "15  44.239679    0       81.0      177.0           0        0.9824           0\n",
       "16  54.992184    0       75.0      172.0           0        0.8126           0\n",
       "17  67.925528    1       62.0      159.0           0        1.0176           0\n",
       "18  55.411710    1       59.0      160.0           0        0.7510           0\n",
       "19  44.683127    0       67.0      172.0           0        0.9010           0\n",
       "20  57.802298    1       50.0      152.0           0        0.8254           0\n",
       "21  57.934593    0       67.0      160.0           2        0.7978           0\n",
       "22  70.028503    1       59.0      155.0           0        0.8079           0\n",
       "23  69.048179    1       72.0      154.0           0        0.7861           0\n",
       "24  60.555417    1       76.0      155.0           0        0.9760           0\n",
       "25  66.256648    0       72.0      159.5           0        0.9466           0\n",
       "26  39.254947    0       76.0      173.5           0        1.0502           0\n",
       "27  58.812410    1       74.0      149.0           0        0.8307           0\n",
       "28  56.545818    0       71.0      166.0           0        1.0839           0\n",
       "29  62.065237    1       85.0      148.5           2        0.7999           0\n",
       "30  66.644866    1       60.0      153.0           0        0.9630           0\n",
       "31  61.184672    1       67.0      149.0           0        0.8013           0\n",
       "32  61.644504    1       55.0      162.0           0        0.6880           0\n",
       "33  52.478311    1       59.0      150.5           0        0.7314           0\n",
       "34  61.170085    1       70.0      152.0           0        0.8599           0\n",
       "35  88.024151    0       52.0      151.5           0        0.6204           1\n",
       "36  70.778900    0       73.0      170.5           0        0.9067           0\n",
       "37  61.600414    1       68.0      159.0           0        0.9139           0\n",
       "38  72.270137    1       50.0      154.0           0        0.6001           1\n",
       "39  68.683288    1       48.0      154.0           0        0.6448           1\n",
       "40  69.086638    0       77.0      162.0           0        0.8509           0\n",
       "41  57.569180    0       74.0      176.0           1        1.0287           0\n",
       "42  70.198573    0       68.0      167.0           0        1.0406           0\n",
       "43  71.471721    0       62.0      177.0           0        0.6297           1\n",
       "44  68.017748    1       58.0      153.0           0        0.6103           0\n",
       "45  60.221386    0       94.0      166.0           0        1.0617           0\n",
       "46  47.024651    1       47.0      148.5           2        0.6496           0\n",
       "47  60.831357    0       70.0      159.5           0        1.0265           0\n",
       "48  81.087385    1       50.0      149.5           0        0.5515           1\n",
       "49  65.795352    1       74.0      145.0           0        0.7738           0\n",
       "50  63.024697    0       71.0      161.0           0        1.0366           0\n",
       "51  84.721764    0       52.0      161.0           0        0.7085           1\n",
       "52  72.772387    1       81.0      149.5           2        0.5090           0\n",
       "53  73.970471    1       68.0      164.0           0        0.5793           0\n",
       "54  54.406178    1       69.0      160.0           0        0.8819           0\n",
       "55  77.819623    1       48.0      151.0           0        0.5547           1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "86172d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138    1\n",
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       "29     0\n",
       "143    1\n",
       "162    1\n",
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       "82     0\n",
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       "42     0\n",
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       "153    1\n",
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       "137    1\n",
       "2      0\n",
       "93     0\n",
       "135    1\n",
       "150    1\n",
       "90     0\n",
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       "26     0\n",
       "145    1\n",
       "136    1\n",
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       "65     0\n",
       "36     0\n",
       "126    1\n",
       "22     0\n",
       "101    0\n",
       "160    1\n",
       "96     0\n",
       "11     0\n",
       "67     0\n",
       "132    1\n",
       "Name: fracture, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ead3948b",
   "metadata": {},
   "outputs": [],
   "source": [
    "results['actual'] = y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "30bf20ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>medication</th>\n",
       "      <th>bone_density</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69.977701</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6264</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.912534</td>\n",
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       "      <td>69.0</td>\n",
       "      <td>168.0</td>\n",
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       "      <td>1.0909</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83.093647</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.015480</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9364</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.979559</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5615</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.276059</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7399</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50.285303</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7971</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>67.573380</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5514</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>68.571308</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7096</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>63.673184</td>\n",
       "      <td>1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7095</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>66.825462</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8033</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>51.048370</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>164.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6904</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>76.745516</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5603</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>82.423159</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>51.596540</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7755</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>44.239679</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9824</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>54.992184</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8126</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67.925528</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0176</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55.411710</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7510</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>44.683127</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>57.802298</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>57.934593</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7978</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>70.028503</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8079</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>69.048179</td>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7861</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>60.555417</td>\n",
       "      <td>1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9760</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>66.256648</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9466</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39.254947</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>58.812410</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8307</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>56.545818</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0839</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>62.065237</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>66.644866</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61.184672</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>61.644504</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6880</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>52.478311</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>150.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>61.170085</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8599</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>88.024151</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>151.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6204</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>70.778900</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>170.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9067</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>61.600414</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>72.270137</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>68.683288</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6448</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>69.086638</td>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8509</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>57.569180</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0287</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>70.198573</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0406</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>71.471721</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6297</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>68.017748</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6103</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>60.221386</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0617</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47.024651</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.6496</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>60.831357</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>81.087385</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>149.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5515</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>65.795352</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7738</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>63.024697</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0366</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>84.721764</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7085</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>72.772387</td>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>149.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5090</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>73.970471</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54.406178</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8819</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>77.819623</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5547</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  sex  weight_kg  height_cm  medication  bone_density  \\\n",
       "0   69.977701    1       85.0      148.5           0        0.6264   \n",
       "1   54.912534    0       69.0      168.0           0        1.0909   \n",
       "2   83.093647    1       52.0      156.0           1        0.4586   \n",
       "3   75.015480    0       69.0      173.5           0        0.9364   \n",
       "4   81.979559    0       66.0      166.0           0        0.5615   \n",
       "5   50.276059    1       60.0      170.0           1        0.7399   \n",
       "6   50.285303    1       59.0      161.0           0        0.7971   \n",
       "7   67.573380    0       39.0      159.0           0        0.5514   \n",
       "8   68.571308    1       48.0      161.0           0        0.7096   \n",
       "9   63.673184    1       63.0      158.0           0        0.7095   \n",
       "10  66.825462    0       76.0      171.0           0        0.8033   \n",
       "11  51.048370    1       68.0      164.5           0        0.6904   \n",
       "12  76.745516    1       52.0      146.0           2        0.5603   \n",
       "13  82.423159    1       74.0      156.0           0        0.8171   \n",
       "14  51.596540    1       54.0      154.0           0        0.7755   \n",
       "15  44.239679    0       81.0      177.0           0        0.9824   \n",
       "16  54.992184    0       75.0      172.0           0        0.8126   \n",
       "17  67.925528    1       62.0      159.0           0        1.0176   \n",
       "18  55.411710    1       59.0      160.0           0        0.7510   \n",
       "19  44.683127    0       67.0      172.0           0        0.9010   \n",
       "20  57.802298    1       50.0      152.0           0        0.8254   \n",
       "21  57.934593    0       67.0      160.0           2        0.7978   \n",
       "22  70.028503    1       59.0      155.0           0        0.8079   \n",
       "23  69.048179    1       72.0      154.0           0        0.7861   \n",
       "24  60.555417    1       76.0      155.0           0        0.9760   \n",
       "25  66.256648    0       72.0      159.5           0        0.9466   \n",
       "26  39.254947    0       76.0      173.5           0        1.0502   \n",
       "27  58.812410    1       74.0      149.0           0        0.8307   \n",
       "28  56.545818    0       71.0      166.0           0        1.0839   \n",
       "29  62.065237    1       85.0      148.5           2        0.7999   \n",
       "30  66.644866    1       60.0      153.0           0        0.9630   \n",
       "31  61.184672    1       67.0      149.0           0        0.8013   \n",
       "32  61.644504    1       55.0      162.0           0        0.6880   \n",
       "33  52.478311    1       59.0      150.5           0        0.7314   \n",
       "34  61.170085    1       70.0      152.0           0        0.8599   \n",
       "35  88.024151    0       52.0      151.5           0        0.6204   \n",
       "36  70.778900    0       73.0      170.5           0        0.9067   \n",
       "37  61.600414    1       68.0      159.0           0        0.9139   \n",
       "38  72.270137    1       50.0      154.0           0        0.6001   \n",
       "39  68.683288    1       48.0      154.0           0        0.6448   \n",
       "40  69.086638    0       77.0      162.0           0        0.8509   \n",
       "41  57.569180    0       74.0      176.0           1        1.0287   \n",
       "42  70.198573    0       68.0      167.0           0        1.0406   \n",
       "43  71.471721    0       62.0      177.0           0        0.6297   \n",
       "44  68.017748    1       58.0      153.0           0        0.6103   \n",
       "45  60.221386    0       94.0      166.0           0        1.0617   \n",
       "46  47.024651    1       47.0      148.5           2        0.6496   \n",
       "47  60.831357    0       70.0      159.5           0        1.0265   \n",
       "48  81.087385    1       50.0      149.5           0        0.5515   \n",
       "49  65.795352    1       74.0      145.0           0        0.7738   \n",
       "50  63.024697    0       71.0      161.0           0        1.0366   \n",
       "51  84.721764    0       52.0      161.0           0        0.7085   \n",
       "52  72.772387    1       81.0      149.5           2        0.5090   \n",
       "53  73.970471    1       68.0      164.0           0        0.5793   \n",
       "54  54.406178    1       69.0      160.0           0        0.8819   \n",
       "55  77.819623    1       48.0      151.0           0        0.5547   \n",
       "\n",
       "    prediction  actual  \n",
       "0            0       1  \n",
       "1            0       0  \n",
       "2            1       1  \n",
       "3            0       0  \n",
       "4            1       1  \n",
       "5            0       1  \n",
       "6            0       1  \n",
       "7            1       0  \n",
       "8            1       0  \n",
       "9            0       0  \n",
       "10           0       0  \n",
       "11           0       1  \n",
       "12           0       1  \n",
       "13           0       0  \n",
       "14           0       0  \n",
       "15           0       0  \n",
       "16           0       0  \n",
       "17           0       0  \n",
       "18           0       0  \n",
       "19           0       0  \n",
       "20           0       0  \n",
       "21           0       0  \n",
       "22           0       0  \n",
       "23           0       0  \n",
       "24           0       0  \n",
       "25           0       0  \n",
       "26           0       0  \n",
       "27           0       0  \n",
       "28           0       0  \n",
       "29           0       0  \n",
       "30           0       0  \n",
       "31           0       0  \n",
       "32           0       1  \n",
       "33           0       0  \n",
       "34           0       0  \n",
       "35           1       1  \n",
       "36           0       0  \n",
       "37           0       0  \n",
       "38           1       1  \n",
       "39           1       1  \n",
       "40           0       0  \n",
       "41           0       0  \n",
       "42           0       0  \n",
       "43           1       1  \n",
       "44           0       1  \n",
       "45           0       0  \n",
       "46           0       0  \n",
       "47           0       0  \n",
       "48           1       1  \n",
       "49           0       0  \n",
       "50           0       0  \n",
       "51           1       1  \n",
       "52           0       0  \n",
       "53           0       0  \n",
       "54           0       0  \n",
       "55           1       1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "540fba9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>medication</th>\n",
       "      <th>bone_density</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>69.977701</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6264</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>54.912534</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0909</td>\n",
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       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>83.093647</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4586</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>75.015480</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>173.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9364</td>\n",
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       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>81.979559</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>50.276059</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>50.285303</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7971</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>67.573380</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5514</td>\n",
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       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>68.571308</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>63.673184</td>\n",
       "      <td>1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  sex  weight_kg  height_cm  medication  bone_density\n",
       "138  69.977701    1       85.0      148.5           0        0.6264\n",
       "30   54.912534    0       69.0      168.0           0        1.0909\n",
       "119  83.093647    1       52.0      156.0           1        0.4586\n",
       "29   75.015480    0       69.0      173.5           0        0.9364\n",
       "143  81.979559    0       66.0      166.0           0        0.5615\n",
       "162  50.276059    1       60.0      170.0           1        0.7399\n",
       "165  50.285303    1       59.0      161.0           0        0.7971\n",
       "51   67.573380    0       39.0      159.0           0        0.5514\n",
       "105  68.571308    1       48.0      161.0           0        0.7096\n",
       "60   63.673184    1       63.0      158.0           0        0.7095"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3da66df3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138    1\n",
       "30     0\n",
       "119    1\n",
       "29     0\n",
       "143    1\n",
       "162    1\n",
       "165    1\n",
       "51     0\n",
       "105    0\n",
       "60     0\n",
       "Name: fracture, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "db2b61e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8392857142857143"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use score method to get accuracy of model\n",
    "score = lrm.score(X_test, y_test)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0be2fdcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#83.9% accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3c0e16f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>78.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.778900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>170.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78.247175</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.191877</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  sex  fracture  weight_kg  height_cm  medication  bone_density\n",
       "0  57.052768    1         0       64.0      155.5           1        0.8793\n",
       "1  75.741225    1         0       78.0      162.0           0        0.7946\n",
       "2  70.778900    0         0       73.0      170.5           0        0.9067\n",
       "3  78.247175    1         0       60.0      148.0           0        0.7112\n",
       "4  54.191877    0         0       55.0      161.0           0        0.7909"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Why did I take a random sample?\n",
    "#Let's look at the raw data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dad10efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x274a90f6e80>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['fracture'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "adb82517",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x274a9155340>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is the issue with this data?\n",
    "#In first 2/3 of the observations, fracture is 0\n",
    "#and the last 1/3 fracture is 1. Need to take\n",
    "#random samples for training data and for testing \n",
    "#data; otherwise, the model will not be valid.\n",
    "\n",
    "plt.plot(y_train,'bo')\n",
    "plt.plot(y_test,'r+')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c05d0dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fracture', ylabel='Count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=y_train,stat='count',cbar=True,discrete=True,color='b')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d09a2da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fracture', ylabel='Count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=y_test,stat='count',cbar=True,discrete=True,color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "14dc554a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fracture', ylabel='Count'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0J4HjJ92+x7oCXAC8YZFlm8amXwK8Yxdq2QB8DTiUOz4UevCCdZ7MnT+A+8yk2/Zc1wOArwBHTfqeTqmu+3DHTZCPAr7RvXaDvl7devdg1Ie8/zRer26fm1n6w8qpH1sT1jX1Y2vCuno9ttbsHzHkA/hN4MPAjd3Pg7r5BwPv66YP616ka4DrgbOW235KdR3D6E+1a4Gru8eJ3bK3A5/vll3C2C+CVdZzIqOrhr66498PPBd4bjcdRl+S89Wu3dmdbbuG799ydZ0DfG/s9Zlb7j2dUl3P79q9htEHg0ftbNtp1dU9fzYLThT6fL0Y/WWxDfgFo7PS03eTY2u5uoY6tparq9djyyEbJKkx6+WqHknShAx+SWqMwS9JjTH4JakxBr8kNcbgV5OSvDCjEVEv3MX9PDvJwWtVlzQNe8ydu9Iaex6jOzW/vmNGkg1VtX2F+3k2cB0ruHtyle1Ia8YzfjUnydmMbtC5pBvzfEuSS4ELkmxO8vEkV3WPo8a2e3k3Pvs1SV6T5GnALHBhN2b6Xbsx3Dd2688m2dpNv2pBOzNJ3pXks93j6Km/EGqWZ/xqTlU9N8nxwOMZ3SF5EnBMVf20G9HySVX1sySHM7rDcjbJCYyG1X50Vf0kyUFV9d0kz2f0nQlzAMliY2j9v98Za+ffgH+oqk8keQDwQeBBPf2TpTsx+CW4pKp+2k3vDbwpycOBXwK/3c0/DnhbVf0EoO484uVq2jkOOGLsF8XdkxxQo+9kkHpl8Evw47HplwC3AQ9j1BX6s25+mGz42+3c0YW6707auQvw2LFfBNLU2Mcv3dk9gG1V9SvgWYy+6g7gUuA5XVcQ6b7DFvgho6/M3OEmRl06AH+4k3YuZdTNRLe/h+9q4dKkDH7pzt4MnJbk04y6eX4MUFUfYDRC6lySq4GXduufB5y948Nd4G+ANyb5OKOuoqW8kNFnB9cm+QKjkRmlqXB0TklqjGf8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ15v8ARQR2mExpcl8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##before we learn the data, let's split the data into training set\n",
    "#and test set\n",
    "#need to import the package\n",
    "from sklearn.model_selection import train_test_split\n",
    "#test_size= proportion use number between 0.0 and 1.0 or absolute number of observations\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=20, random_state=42)\n",
    "sns.histplot(x=y_test,stat='count',cbar=True,discrete=True,color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e2a8f702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fracture', ylabel='Count'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#traint_size= proportion use number between 0.0 and 1.0 or absolute number of observations\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    train_size=0.8, random_state=42)\n",
    "sns.histplot(x=y_train,stat='count',cbar=True,discrete=True,color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b2c3f05d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Controls the shuffling applied to the data before applying the split.\n",
    "#shuffle=True True is default\n",
    "#\n",
    "##Pass an int for reproducible output across multiple function calls.\n",
    "#random_state=42\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e133cb11",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}