Artificial_Intelligence/Machine_Learning/Logistic_Regression_Mortality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Machine Learning Task: Predict In-hospital Mortality Within 48 Hours After Admission\n",
    "\n",
    "For this machine learning task, the 14 features are used to make in-hospital mortality predictions. \n",
    "\n",
    "'Capillary refill rate',\n",
    "'Diastolic blood pressure',\n",
    "'Glascow coma scale total',\n",
    "'Glucose',\n",
    "'Heart Rate',\n",
    "'Height',\n",
    "'Mean blood pressure',\n",
    "'Oxygen saturation',\n",
    "'Respiratory rate',\n",
    "'Systolic blood pressure',\n",
    "'Temperature', \n",
    "'Weight',\n",
    "'pH'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understand MIMIC-III Electronic Health Record Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Homepage: https://mimic.physionet.org\n",
    "\n",
    "- How many variables are present in the dataset?\n",
    "- What is the total number of patients or samples included?\n",
    "- What constitutes the label?\n",
    "- What steps are required to transform the data into a format compatible with the model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MIMIC-III Data Aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "TASK_FOLDER = '/Users/xyz/Documents/data/in_hospital_mortality'\n",
    "features = ['Capillary refill rate',\n",
    "            'Diastolic blood pressure',\n",
    "            'Fraction inspired oxygen',\n",
    "            'Glascow coma scale total',\n",
    "            'Glucose',\n",
    "            'Heart Rate',\n",
    "            'Height',\n",
    "            'Mean blood pressure',\n",
    "            'Oxygen saturation',\n",
    "            'Respiratory rate',\n",
    "            'Systolic blood pressure',\n",
    "            'Temperature', \n",
    "            'Weight',\n",
    "            'pH',\n",
    "            ]\n",
    "\n",
    "N_train = 'all' # (int) number of sample or 'all'\n",
    "observation_window = 48\n",
    "method = 'min'  # method to do aggregation: 'max', 'min', 'mean', 'median'\n",
    "                \n",
    "# load label file\n",
    "train_label = pd.read_csv(TASK_FOLDER + '/train/label.csv', header=0)\n",
    "\n",
    "# aggregation\n",
    "data = []\n",
    "subj_list = []\n",
    "labels = []\n",
    "for idx, row in train_label.iterrows():\n",
    "    subj, label = row['stay'], row['y_true']\n",
    "    \n",
    "    if N_train != 'all' and idx >= N_train:\n",
    "        break\n",
    "    \n",
    "    subj_df = pd.read_csv(TASK_FOLDER + '/train/' + subj, header=0)\n",
    "    \n",
    "    subj_df = subj_df[['Hours'] + features]\n",
    "    \n",
    "  \n",
    "    if method == 'max':\n",
    "        aggregate_data = np.nanmax(subj_df[features].values, axis=0)\n",
    "    \n",
    "    elif method == 'min':\n",
    "        aggregate_data = np.nanmin(subj_df[features].values, axis=0)\n",
    "        \n",
    "    elif method == 'mean':\n",
    "        aggregate_data = np.nanmean(subj_df[features].values, axis=0) \n",
    "        \n",
    "    elif method == 'median':\n",
    "        aggregate_data = np.nanmedian(subj_df[features].values, axis=0)\n",
    "        \n",
    "    \n",
    "    data.append(aggregate_data)\n",
    "    subj_list.append(subj)\n",
    "    labels.append(label)\n",
    "    \n",
    "feature_name = [(ft + '_' + method) for ft in features]\n",
    "data_df = pd.DataFrame()\n",
    "data_df['Subj'] = subj_list\n",
    "data_df[feature_name] = data\n",
    "data_df['label'] = labels\n",
    "\n",
    "data_df.to_csv(TASK_FOLDER + '_%s_day_%s.csv' % (observation_window, method), index=False)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task: to predict whether the cancer is benign or malignant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data set into DataFrame\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(TASK_FOLDER + 'in_hospital_mortality_48_day_min.csv', \n",
    "                   header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subj</th>\n",
       "      <th>Capillary refill rate_min</th>\n",
       "      <th>Diastolic blood pressure_min</th>\n",
       "      <th>Fraction inspired oxygen_min</th>\n",
       "      <th>Glascow coma scale total_min</th>\n",
       "      <th>Glucose_min</th>\n",
       "      <th>Heart Rate_min</th>\n",
       "      <th>Height_min</th>\n",
       "      <th>Mean blood pressure_min</th>\n",
       "      <th>Oxygen saturation_min</th>\n",
       "      <th>Respiratory rate_min</th>\n",
       "      <th>Systolic blood pressure_min</th>\n",
       "      <th>Temperature_min</th>\n",
       "      <th>Weight_min</th>\n",
       "      <th>pH_min</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76541_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>35.277778</td>\n",
       "      <td>60.463814</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12797_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>36.277802</td>\n",
       "      <td>50.299999</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9027_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>34.700001</td>\n",
       "      <td>84.199997</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40386_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>35.611111</td>\n",
       "      <td>61.461716</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22933_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48770_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>94.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>36.611111</td>\n",
       "      <td>76.339534</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14037_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.666698</td>\n",
       "      <td>94.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>75.599998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15656_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>92.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>35.555599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24771_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>36.666698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>83338_episode1_timeseries.csv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>96.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>36.111111</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>7.44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                            Subj  Capillary refill rate_min  \\\n",
       "0  76541_episode1_timeseries.csv                        NaN   \n",
       "1  12797_episode1_timeseries.csv                        NaN   \n",
       "2   9027_episode1_timeseries.csv                        NaN   \n",
       "3  40386_episode1_timeseries.csv                        NaN   \n",
       "4  22933_episode1_timeseries.csv                        NaN   \n",
       "5  48770_episode1_timeseries.csv                        NaN   \n",
       "6  14037_episode1_timeseries.csv                        NaN   \n",
       "7  15656_episode1_timeseries.csv                        NaN   \n",
       "8  24771_episode1_timeseries.csv                        NaN   \n",
       "9  83338_episode1_timeseries.csv                        NaN   \n",
       "\n",
       "   Diastolic blood pressure_min  Fraction inspired oxygen_min  \\\n",
       "0                          45.0                           0.4   \n",
       "1                          47.0                           NaN   \n",
       "2                          44.0                           0.5   \n",
       "3                          33.0                           0.5   \n",
       "4                          36.0                           NaN   \n",
       "5                          63.0                           NaN   \n",
       "6                          47.0                           NaN   \n",
       "7                          26.0                           NaN   \n",
       "8                          54.0                           NaN   \n",
       "9                          33.0                           0.5   \n",
       "\n",
       "   Glascow coma scale total_min  Glucose_min  Heart Rate_min  Height_min  \\\n",
       "0                           NaN        148.0            70.0       168.0   \n",
       "1                           8.0         85.0            89.0         NaN   \n",
       "2                           3.0         63.0            72.0         NaN   \n",
       "3                           NaN         85.0            60.0       168.0   \n",
       "4                          15.0         92.0            69.0         NaN   \n",
       "5                           NaN         90.0            51.0         NaN   \n",
       "6                          13.0         70.0            48.0         NaN   \n",
       "7                           8.0        111.0            91.0         NaN   \n",
       "8                           7.0        109.0            78.0         NaN   \n",
       "9                           NaN         90.0            77.0         NaN   \n",
       "\n",
       "   Mean blood pressure_min  Oxygen saturation_min  Respiratory rate_min  \\\n",
       "0                59.000000                   86.0                  13.0   \n",
       "1                68.000000                   92.0                   2.0   \n",
       "2                57.000000                   90.0                  10.0   \n",
       "3                51.000000                   60.0                   0.0   \n",
       "4                53.000000                   90.0                  12.0   \n",
       "5                74.000000                   94.0                  11.0   \n",
       "6                61.666698                   94.0                   9.0   \n",
       "7                39.000000                   92.0                  17.0   \n",
       "8                72.000000                   97.0                   2.0   \n",
       "9                47.000000                   96.0                  14.0   \n",
       "\n",
       "   Systolic blood pressure_min  Temperature_min  Weight_min  pH_min  label  \n",
       "0                         90.0        35.277778   60.463814    6.50      0  \n",
       "1                        103.0        36.277802   50.299999    7.43      0  \n",
       "2                         82.0        34.700001   84.199997    7.36      0  \n",
       "3                         87.0        35.611111   61.461716    7.00      0  \n",
       "4                         80.0        35.833333         NaN    7.31      0  \n",
       "5                        106.0        36.611111   76.339534     NaN      0  \n",
       "6                         90.0        35.000000   75.599998     NaN      0  \n",
       "7                         75.0        35.555599         NaN    7.00      1  \n",
       "8                        116.0        36.666698         NaN    7.27      0  \n",
       "9                         84.0        36.111111   78.000000    7.44      1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cases:  2423\n",
      "Number of controls :  15480\n",
      "\n",
      "Proportion of case: 13.53%\n"
     ]
    }
   ],
   "source": [
    "# To explore the number of cases (death) and controls (alive finally) \n",
    "\n",
    "n_case = len(data[data['label'] == 1])\n",
    "n_control = len(data[data['label'] == 0]) \n",
    "\n",
    "print('Number of cases: ', n_case) \n",
    "print('Number of controls : ', n_control)\n",
    "print()\n",
    "print('Proportion of case: %.2f%%' % (100 * n_case / (n_control + n_case)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of subjects')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize case-control distribution\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(['Cases (death)','Controls (alive, discharge)'], [n_case, n_control]) \n",
    "\n",
    "plt.ylabel('number of subjects') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subj                            0.000000\n",
      "Capillary refill rate_min       0.981958\n",
      "Diastolic blood pressure_min    0.011953\n",
      "Fraction inspired oxygen_min    0.703458\n",
      "Glascow coma scale total_min    0.419818\n",
      "Glucose_min                     0.000950\n",
      "Heart Rate_min                  0.011953\n",
      "Height_min                      0.810926\n",
      "Mean blood pressure_min         0.012065\n",
      "Oxygen saturation_min           0.006703\n",
      "Respiratory rate_min            0.012288\n",
      "Systolic blood pressure_min     0.011953\n",
      "Temperature_min                 0.019494\n",
      "Weight_min                      0.270960\n",
      "pH_min                          0.174663\n",
      "label                           0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# To assess missing values\n",
    "\n",
    "print(data.isnull().sum() / len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop feature 'Capillary refill rate_min' since high missingness\n",
    "\n",
    "data = data.drop(columns = ['Capillary refill rate_min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>Diastolic blood pressure_min</th>\n",
       "      <th>Fraction inspired oxygen_min</th>\n",
       "      <th>Glascow coma scale total_min</th>\n",
       "      <th>Glucose_min</th>\n",
       "      <th>Heart Rate_min</th>\n",
       "      <th>Height_min</th>\n",
       "      <th>Mean blood pressure_min</th>\n",
       "      <th>Oxygen saturation_min</th>\n",
       "      <th>Respiratory rate_min</th>\n",
       "      <th>Systolic blood pressure_min</th>\n",
       "      <th>Temperature_min</th>\n",
       "      <th>Weight_min</th>\n",
       "      <th>pH_min</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76541_episode1_timeseries.csv</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>35.277778</td>\n",
       "      <td>60.463814</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12797_episode1_timeseries.csv</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>36.277802</td>\n",
       "      <td>50.299999</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9027_episode1_timeseries.csv</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>34.700001</td>\n",
       "      <td>84.199997</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40386_episode1_timeseries.csv</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>35.611111</td>\n",
       "      <td>61.461716</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22933_episode1_timeseries.csv</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17898</th>\n",
       "      <td>50762_episode1_timeseries.csv</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>61.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>36.111111</td>\n",
       "      <td>167.647603</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17899</th>\n",
       "      <td>12348_episode1_timeseries.csv</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.333302</td>\n",
       "      <td>96.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>36.277802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17900</th>\n",
       "      <td>71713_episode1_timeseries.csv</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>92.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>36.666667</td>\n",
       "      <td>78.652853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17901</th>\n",
       "      <td>69395_episode1_timeseries.csv</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>36.388889</td>\n",
       "      <td>83.823802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17902</th>\n",
       "      <td>9231_episode1_timeseries.csv</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.333302</td>\n",
       "      <td>91.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>33.611111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17903 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Subj  Diastolic blood pressure_min  \\\n",
       "0      76541_episode1_timeseries.csv                          45.0   \n",
       "1      12797_episode1_timeseries.csv                          47.0   \n",
       "2       9027_episode1_timeseries.csv                          44.0   \n",
       "3      40386_episode1_timeseries.csv                          33.0   \n",
       "4      22933_episode1_timeseries.csv                          36.0   \n",
       "...                              ...                           ...   \n",
       "17898  50762_episode1_timeseries.csv                          48.0   \n",
       "17899  12348_episode1_timeseries.csv                          35.0   \n",
       "17900  71713_episode1_timeseries.csv                          53.0   \n",
       "17901  69395_episode1_timeseries.csv                          40.0   \n",
       "17902   9231_episode1_timeseries.csv                          15.0   \n",
       "\n",
       "       Fraction inspired oxygen_min  Glascow coma scale total_min  \\\n",
       "0                              0.40                           NaN   \n",
       "1                               NaN                           8.0   \n",
       "2                              0.50                           3.0   \n",
       "3                              0.50                           NaN   \n",
       "4                               NaN                          15.0   \n",
       "...                             ...                           ...   \n",
       "17898                          0.04                           NaN   \n",
       "17899                           NaN                           6.0   \n",
       "17900                           NaN                           NaN   \n",
       "17901                          0.50                           NaN   \n",
       "17902                           NaN                          15.0   \n",
       "\n",
       "       Glucose_min  Heart Rate_min  Height_min  Mean blood pressure_min  \\\n",
       "0            148.0            70.0       168.0                59.000000   \n",
       "1             85.0            89.0         NaN                68.000000   \n",
       "2             63.0            72.0         NaN                57.000000   \n",
       "3             85.0            60.0       168.0                51.000000   \n",
       "4             92.0            69.0         NaN                53.000000   \n",
       "...            ...             ...         ...                      ...   \n",
       "17898        263.0            76.0       173.0                56.000000   \n",
       "17899         87.0            59.0         NaN                53.333302   \n",
       "17900         98.0            53.0         NaN                72.000000   \n",
       "17901         66.0            46.0       185.0                43.000000   \n",
       "17902        141.0            54.0         NaN                50.333302   \n",
       "\n",
       "       Oxygen saturation_min  Respiratory rate_min  \\\n",
       "0                       86.0                  13.0   \n",
       "1                       92.0                   2.0   \n",
       "2                       90.0                  10.0   \n",
       "3                       60.0                   0.0   \n",
       "4                       90.0                  12.0   \n",
       "...                      ...                   ...   \n",
       "17898                   61.0                  14.0   \n",
       "17899                   96.0                   6.0   \n",
       "17900                   92.0                   8.0   \n",
       "17901                   68.0                   7.0   \n",
       "17902                   91.0                  11.0   \n",
       "\n",
       "       Systolic blood pressure_min  Temperature_min  Weight_min  pH_min  label  \n",
       "0                             90.0        35.277778   60.463814    6.50      0  \n",
       "1                            103.0        36.277802   50.299999    7.43      0  \n",
       "2                             82.0        34.700001   84.199997    7.36      0  \n",
       "3                             87.0        35.611111   61.461716    7.00      0  \n",
       "4                             80.0        35.833333         NaN    7.31      0  \n",
       "...                            ...              ...         ...     ...    ...  \n",
       "17898                         92.0        36.111111  167.647603    5.00      0  \n",
       "17899                         90.0        36.277802         NaN    6.50      0  \n",
       "17900                         97.0        36.666667   78.652853     NaN      0  \n",
       "17901                         52.0        36.388889   83.823802     NaN      0  \n",
       "17902                         81.0        33.611111         NaN    6.50      0  \n",
       "\n",
       "[17903 rows x 15 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Diastolic blood pressure_min</th>\n",
       "      <th>Fraction inspired oxygen_min</th>\n",
       "      <th>Glascow coma scale total_min</th>\n",
       "      <th>Glucose_min</th>\n",
       "      <th>Heart Rate_min</th>\n",
       "      <th>Height_min</th>\n",
       "      <th>Mean blood pressure_min</th>\n",
       "      <th>Oxygen saturation_min</th>\n",
       "      <th>Respiratory rate_min</th>\n",
       "      <th>Systolic blood pressure_min</th>\n",
       "      <th>Temperature_min</th>\n",
       "      <th>Weight_min</th>\n",
       "      <th>pH_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17689.000000</td>\n",
       "      <td>5309.000000</td>\n",
       "      <td>10387.000000</td>\n",
       "      <td>17886.000000</td>\n",
       "      <td>17689.000000</td>\n",
       "      <td>3385.000000</td>\n",
       "      <td>17687.000000</td>\n",
       "      <td>17783.000000</td>\n",
       "      <td>17683.000000</td>\n",
       "      <td>17689.000000</td>\n",
       "      <td>17554.000000</td>\n",
       "      <td>13052.000000</td>\n",
       "      <td>14776.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.964459</td>\n",
       "      <td>0.422389</td>\n",
       "      <td>8.767113</td>\n",
       "      <td>95.307206</td>\n",
       "      <td>68.431251</td>\n",
       "      <td>168.548597</td>\n",
       "      <td>53.988573</td>\n",
       "      <td>84.016775</td>\n",
       "      <td>8.605129</td>\n",
       "      <td>85.950386</td>\n",
       "      <td>35.805119</td>\n",
       "      <td>81.416817</td>\n",
       "      <td>6.600238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.032213</td>\n",
       "      <td>0.155498</td>\n",
       "      <td>4.790284</td>\n",
       "      <td>27.783699</td>\n",
       "      <td>14.676653</td>\n",
       "      <td>14.862592</td>\n",
       "      <td>15.226982</td>\n",
       "      <td>14.834182</td>\n",
       "      <td>5.549584</td>\n",
       "      <td>20.012562</td>\n",
       "      <td>2.135860</td>\n",
       "      <td>24.508092</td>\n",
       "      <td>1.955086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>35.555556</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>7.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>36.388889</td>\n",
       "      <td>93.326554</td>\n",
       "      <td>7.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>429.000000</td>\n",
       "      <td>107.333000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Diastolic blood pressure_min  Fraction inspired oxygen_min  \\\n",
       "count                  17689.000000                   5309.000000   \n",
       "mean                      39.964459                      0.422389   \n",
       "std                       12.032213                      0.155498   \n",
       "min                      -13.000000                      0.000000   \n",
       "25%                       34.000000                      0.400000   \n",
       "50%                       41.000000                      0.400000   \n",
       "75%                       47.000000                      0.500000   \n",
       "max                       89.000000                      1.000000   \n",
       "\n",
       "       Glascow coma scale total_min   Glucose_min  Heart Rate_min  \\\n",
       "count                  10387.000000  17886.000000    17689.000000   \n",
       "mean                       8.767113     95.307206       68.431251   \n",
       "std                        4.790284     27.783699       14.676653   \n",
       "min                        3.000000      0.000000        0.000000   \n",
       "25%                        3.000000     78.000000       59.000000   \n",
       "50%                        8.000000     94.000000       68.000000   \n",
       "75%                       14.000000    110.000000       78.000000   \n",
       "max                       15.000000    592.000000      140.000000   \n",
       "\n",
       "        Height_min  Mean blood pressure_min  Oxygen saturation_min  \\\n",
       "count  3385.000000             17687.000000           17783.000000   \n",
       "mean    168.548597                53.988573              84.016775   \n",
       "std      14.862592                15.226982              14.834182   \n",
       "min       0.000000               -37.000000               0.000000   \n",
       "25%     160.000000                48.000000              79.000000   \n",
       "50%     170.000000                55.000000              90.000000   \n",
       "75%     178.000000                62.000000              93.000000   \n",
       "max     429.000000               107.333000             100.000000   \n",
       "\n",
       "       Respiratory rate_min  Systolic blood pressure_min  Temperature_min  \\\n",
       "count          17683.000000                 17689.000000     17554.000000   \n",
       "mean               8.605129                    85.950386        35.805119   \n",
       "std                5.549584                    20.012562         2.135860   \n",
       "min                0.000000                     0.000000        -3.000000   \n",
       "25%                3.000000                    77.000000        35.555556   \n",
       "50%                9.000000                    86.000000        36.000000   \n",
       "75%               13.000000                    97.000000        36.388889   \n",
       "max               29.000000                   157.000000        39.000000   \n",
       "\n",
       "         Weight_min        pH_min  \n",
       "count  13052.000000  14776.000000  \n",
       "mean      81.416817      6.600238  \n",
       "std       24.508092      1.955086  \n",
       "min        0.000000      0.000000  \n",
       "25%       65.500000      5.500000  \n",
       "50%       78.000000      7.240000  \n",
       "75%       93.326554      7.350000  \n",
       "max      280.000000     99.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns = ['label']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13427, 13) (4476, 13) (13427,) (4476,)\n"
     ]
    }
   ],
   "source": [
    "# Split data into Training (75%) and Testing (25%) sets\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "y = data['label'].values\n",
    "\n",
    "X = data.drop(columns = ['Subj', 'label']).values\n",
    "\n",
    "feature_used = data.drop(columns = ['Subj', 'label']).columns.tolist()\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.25, random_state=0) \n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Diastolic blood pressure_min',\n",
       " 'Fraction inspired oxygen_min',\n",
       " 'Glascow coma scale total_min',\n",
       " 'Glucose_min',\n",
       " 'Heart Rate_min',\n",
       " 'Height_min',\n",
       " 'Mean blood pressure_min',\n",
       " 'Oxygen saturation_min',\n",
       " 'Respiratory rate_min',\n",
       " 'Systolic blood pressure_min',\n",
       " 'Temperature_min',\n",
       " 'Weight_min',\n",
       " 'pH_min']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_used\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute the missing values\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# if your sklearn version <= 0.20\n",
    "# Do:\n",
    "# from sklearn.preprocessing import Imputer\n",
    "# imputer = Imputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "imputer.fit(X_train)\n",
    "X_train = np.array(imputer.transform(X_train), dtype=np.float32)\n",
    "X_test = np.array(imputer.transform(X_test), dtype=np.float32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the decision tree classifier using training set\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz # Export a decision tree\n",
    "\n",
    "# define a tree \n",
    "tree = DecisionTreeClassifier(criterion='gini', \n",
    "                              class_weight = 'balanced', random_state=0) \n",
    "\n",
    "# train the tree\n",
    "tree.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tips: before training a model, please check the documentation at Sciki-learn site: https://scikit-learn.org/stable/user_guide.html\n",
    "\n",
    "\n",
    "For more details of decision Tree, please read: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the trained classifer to predict in test set \n",
    "y_pred = tree.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.88      0.88      3881\n",
      "           1       0.21      0.22      0.22       595\n",
      "\n",
      "    accuracy                           0.79      4476\n",
      "   macro avg       0.55      0.55      0.55      4476\n",
      "weighted avg       0.79      0.79      0.79      4476\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the prediction performance\n",
    "from sklearn.metrics import classification_report \n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- macro average: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account;\n",
    "\n",
    "\n",
    "-  microaverage: Calculate metrics globally by counting the total true positives, false negatives and false positives;\n",
    "\n",
    "\n",
    "- weighted average: Calculate metrics for each label, and find their average weighted by sup- port (the number of true instances for each label)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'decision_tree.pdf'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualization of decision tree\n",
    "import graphviz # Run following code in command line:\n",
    "                # conda install python-graphviz\n",
    "\n",
    "dot_data = export_graphviz(tree, out_file=None,\n",
    "                           feature_names=feature_used, \n",
    "                           filled=True, rounded=True, \n",
    "                           special_characters=True)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render(\"decision_tree\",cleanup=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances ranking:\n",
      "0\n",
      "11.85% feature 9: Systolic blood pressure_min \n",
      "1\n",
      "10.63% feature 3: Glucose_min \n",
      "2\n",
      "10.28% feature 4: Heart Rate_min \n",
      "3\n",
      " 9.19% feature 10: Temperature_min \n",
      "4\n",
      " 9.09% feature 11: Weight_min \n",
      "5\n",
      " 8.51% feature 12: pH_min \n",
      "6\n",
      " 8.48% feature 7: Oxygen saturation_min \n",
      "7\n",
      " 7.58% feature 6: Mean blood pressure_min \n",
      "8\n",
      " 6.84% feature 2: Glascow coma scale total_min \n",
      "9\n",
      " 6.61% feature 8: Respiratory rate_min \n",
      "10\n",
      " 6.02% feature 0: Diastolic blood pressure_min \n",
      "11\n",
      " 2.89% feature 1: Fraction inspired oxygen_min \n",
      "12\n",
      " 2.03% feature 5: Height_min \n"
     ]
    }
   ],
   "source": [
    "# Model interpretaion \n",
    "\n",
    "# Importance of features\n",
    "\n",
    "importances = tree.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "print(\"Feature importances ranking:\") \n",
    "\n",
    "for f in range(X.shape[1]):\n",
    "    print(f) \n",
    "    print('{0:.2f}%'.format(importances[indices[f]]*100).rjust(6, ' '),\n",
    "          'feature %d: %s ' % (indices[f], feature_used[indices[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the feature importances of the forest\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "plt.ylabel(\"Feature importances\")\n",
    "\n",
    "plt.bar(range(len(feature_used)), importances[indices], align=\"center\") \n",
    "\n",
    "plt.xticks(range(len(feature_used)), feature_used, rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuning model: search for the optimal model parameters.\n",
    "Parameter to tune:\n",
    "( https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
    ")\n",
    "\n",
    "- max_depth: The maximum depth of the tree.\n",
    "\n",
    "\n",
    "- min_samples_split: The minimum number of samples required to split an internal node\n",
    "\n",
    "\n",
    "- min_samples_leaf: The minimum number of samples required to be at a leaf node.\n",
    "\n",
    "\n",
    "- max_leaf_nodes: Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity.\n",
    "\n",
    "\n",
    "- class_weight: weight of class label (addressing imbalanced data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data set into DataFrame\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "data = pd.read_csv('data/in_hospital_mortality_48_day_min.csv', \n",
    "                  header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cases:  2423\n",
      "Number of controls :  15480\n",
      "\n",
      "Proportion of case: 13.53%\n"
     ]
    }
   ],
   "source": [
    "# To explore the number of cases (death) and controls (alive finally) \n",
    "\n",
    "n_case = len(data[data['label'] == 1])\n",
    "n_control = len(data[data['label'] == 0]) \n",
    "\n",
    "print('Number of cases: ', n_case) \n",
    "print('Number of controls : ', n_control)\n",
    "print()\n",
    "print('Proportion of case: %.2f%%' % (100 * n_case / (n_control + n_case)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of subjects')"
      ]
     },
     "execution_count": 23,
     "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": [
    "# visualize case-control distribution\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(['Cases (death)','Controls (alive, discharge)'], [n_case, n_control]) \n",
    "\n",
    "plt.ylabel('number of subjects') \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subj                            0.000000\n",
      "Capillary refill rate_min       0.981958\n",
      "Diastolic blood pressure_min    0.011953\n",
      "Fraction inspired oxygen_min    0.703458\n",
      "Glascow coma scale total_min    0.419818\n",
      "Glucose_min                     0.000950\n",
      "Heart Rate_min                  0.011953\n",
      "Height_min                      0.810926\n",
      "Mean blood pressure_min         0.012065\n",
      "Oxygen saturation_min           0.006703\n",
      "Respiratory rate_min            0.012288\n",
      "Systolic blood pressure_min     0.011953\n",
      "Temperature_min                 0.019494\n",
      "Weight_min                      0.270960\n",
      "pH_min                          0.174663\n",
      "label                           0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# To assess missing values\n",
    "\n",
    "print(data.isnull().sum() / len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop feature 'Capillary refill rate_min' since high missingness\n",
    "\n",
    "data = data.drop(columns = ['Capillary refill rate_min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Subj</th>\n",
       "      <th>Diastolic blood pressure_min</th>\n",
       "      <th>Fraction inspired oxygen_min</th>\n",
       "      <th>Glascow coma scale total_min</th>\n",
       "      <th>Glucose_min</th>\n",
       "      <th>Heart Rate_min</th>\n",
       "      <th>Height_min</th>\n",
       "      <th>Mean blood pressure_min</th>\n",
       "      <th>Oxygen saturation_min</th>\n",
       "      <th>Respiratory rate_min</th>\n",
       "      <th>Systolic blood pressure_min</th>\n",
       "      <th>Temperature_min</th>\n",
       "      <th>Weight_min</th>\n",
       "      <th>pH_min</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76541_episode1_timeseries.csv</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>35.277778</td>\n",
       "      <td>60.463814</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12797_episode1_timeseries.csv</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>36.277802</td>\n",
       "      <td>50.299999</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9027_episode1_timeseries.csv</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>34.700001</td>\n",
       "      <td>84.199997</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40386_episode1_timeseries.csv</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>35.611111</td>\n",
       "      <td>61.461716</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22933_episode1_timeseries.csv</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17898</th>\n",
       "      <td>50762_episode1_timeseries.csv</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>61.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>36.111111</td>\n",
       "      <td>167.647603</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17899</th>\n",
       "      <td>12348_episode1_timeseries.csv</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.333302</td>\n",
       "      <td>96.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>36.277802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17900</th>\n",
       "      <td>71713_episode1_timeseries.csv</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>92.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>36.666667</td>\n",
       "      <td>78.652853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17901</th>\n",
       "      <td>69395_episode1_timeseries.csv</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>36.388889</td>\n",
       "      <td>83.823802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17902</th>\n",
       "      <td>9231_episode1_timeseries.csv</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.333302</td>\n",
       "      <td>91.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>33.611111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17903 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Subj  Diastolic blood pressure_min  \\\n",
       "0      76541_episode1_timeseries.csv                          45.0   \n",
       "1      12797_episode1_timeseries.csv                          47.0   \n",
       "2       9027_episode1_timeseries.csv                          44.0   \n",
       "3      40386_episode1_timeseries.csv                          33.0   \n",
       "4      22933_episode1_timeseries.csv                          36.0   \n",
       "...                              ...                           ...   \n",
       "17898  50762_episode1_timeseries.csv                          48.0   \n",
       "17899  12348_episode1_timeseries.csv                          35.0   \n",
       "17900  71713_episode1_timeseries.csv                          53.0   \n",
       "17901  69395_episode1_timeseries.csv                          40.0   \n",
       "17902   9231_episode1_timeseries.csv                          15.0   \n",
       "\n",
       "       Fraction inspired oxygen_min  Glascow coma scale total_min  \\\n",
       "0                              0.40                           NaN   \n",
       "1                               NaN                           8.0   \n",
       "2                              0.50                           3.0   \n",
       "3                              0.50                           NaN   \n",
       "4                               NaN                          15.0   \n",
       "...                             ...                           ...   \n",
       "17898                          0.04                           NaN   \n",
       "17899                           NaN                           6.0   \n",
       "17900                           NaN                           NaN   \n",
       "17901                          0.50                           NaN   \n",
       "17902                           NaN                          15.0   \n",
       "\n",
       "       Glucose_min  Heart Rate_min  Height_min  Mean blood pressure_min  \\\n",
       "0            148.0            70.0       168.0                59.000000   \n",
       "1             85.0            89.0         NaN                68.000000   \n",
       "2             63.0            72.0         NaN                57.000000   \n",
       "3             85.0            60.0       168.0                51.000000   \n",
       "4             92.0            69.0         NaN                53.000000   \n",
       "...            ...             ...         ...                      ...   \n",
       "17898        263.0            76.0       173.0                56.000000   \n",
       "17899         87.0            59.0         NaN                53.333302   \n",
       "17900         98.0            53.0         NaN                72.000000   \n",
       "17901         66.0            46.0       185.0                43.000000   \n",
       "17902        141.0            54.0         NaN                50.333302   \n",
       "\n",
       "       Oxygen saturation_min  Respiratory rate_min  \\\n",
       "0                       86.0                  13.0   \n",
       "1                       92.0                   2.0   \n",
       "2                       90.0                  10.0   \n",
       "3                       60.0                   0.0   \n",
       "4                       90.0                  12.0   \n",
       "...                      ...                   ...   \n",
       "17898                   61.0                  14.0   \n",
       "17899                   96.0                   6.0   \n",
       "17900                   92.0                   8.0   \n",
       "17901                   68.0                   7.0   \n",
       "17902                   91.0                  11.0   \n",
       "\n",
       "       Systolic blood pressure_min  Temperature_min  Weight_min  pH_min  label  \n",
       "0                             90.0        35.277778   60.463814    6.50      0  \n",
       "1                            103.0        36.277802   50.299999    7.43      0  \n",
       "2                             82.0        34.700001   84.199997    7.36      0  \n",
       "3                             87.0        35.611111   61.461716    7.00      0  \n",
       "4                             80.0        35.833333         NaN    7.31      0  \n",
       "...                            ...              ...         ...     ...    ...  \n",
       "17898                         92.0        36.111111  167.647603    5.00      0  \n",
       "17899                         90.0        36.277802         NaN    6.50      0  \n",
       "17900                         97.0        36.666667   78.652853     NaN      0  \n",
       "17901                         52.0        36.388889   83.823802     NaN      0  \n",
       "17902                         81.0        33.611111         NaN    6.50      0  \n",
       "\n",
       "[17903 rows x 15 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diastolic blood pressure_min</th>\n",
       "      <th>Fraction inspired oxygen_min</th>\n",
       "      <th>Glascow coma scale total_min</th>\n",
       "      <th>Glucose_min</th>\n",
       "      <th>Heart Rate_min</th>\n",
       "      <th>Height_min</th>\n",
       "      <th>Mean blood pressure_min</th>\n",
       "      <th>Oxygen saturation_min</th>\n",
       "      <th>Respiratory rate_min</th>\n",
       "      <th>Systolic blood pressure_min</th>\n",
       "      <th>Temperature_min</th>\n",
       "      <th>Weight_min</th>\n",
       "      <th>pH_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17689.000000</td>\n",
       "      <td>5309.000000</td>\n",
       "      <td>10387.000000</td>\n",
       "      <td>17886.000000</td>\n",
       "      <td>17689.000000</td>\n",
       "      <td>3385.000000</td>\n",
       "      <td>17687.000000</td>\n",
       "      <td>17783.000000</td>\n",
       "      <td>17683.000000</td>\n",
       "      <td>17689.000000</td>\n",
       "      <td>17554.000000</td>\n",
       "      <td>13052.000000</td>\n",
       "      <td>14776.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.964459</td>\n",
       "      <td>0.422389</td>\n",
       "      <td>8.767113</td>\n",
       "      <td>95.307206</td>\n",
       "      <td>68.431251</td>\n",
       "      <td>168.548597</td>\n",
       "      <td>53.988573</td>\n",
       "      <td>84.016775</td>\n",
       "      <td>8.605129</td>\n",
       "      <td>85.950386</td>\n",
       "      <td>35.805119</td>\n",
       "      <td>81.416817</td>\n",
       "      <td>6.600238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.032213</td>\n",
       "      <td>0.155498</td>\n",
       "      <td>4.790284</td>\n",
       "      <td>27.783699</td>\n",
       "      <td>14.676653</td>\n",
       "      <td>14.862592</td>\n",
       "      <td>15.226982</td>\n",
       "      <td>14.834182</td>\n",
       "      <td>5.549584</td>\n",
       "      <td>20.012562</td>\n",
       "      <td>2.135860</td>\n",
       "      <td>24.508092</td>\n",
       "      <td>1.955086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>35.555556</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>7.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>36.388889</td>\n",
       "      <td>93.326554</td>\n",
       "      <td>7.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>429.000000</td>\n",
       "      <td>107.333000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Diastolic blood pressure_min  Fraction inspired oxygen_min  \\\n",
       "count                  17689.000000                   5309.000000   \n",
       "mean                      39.964459                      0.422389   \n",
       "std                       12.032213                      0.155498   \n",
       "min                      -13.000000                      0.000000   \n",
       "25%                       34.000000                      0.400000   \n",
       "50%                       41.000000                      0.400000   \n",
       "75%                       47.000000                      0.500000   \n",
       "max                       89.000000                      1.000000   \n",
       "\n",
       "       Glascow coma scale total_min   Glucose_min  Heart Rate_min  \\\n",
       "count                  10387.000000  17886.000000    17689.000000   \n",
       "mean                       8.767113     95.307206       68.431251   \n",
       "std                        4.790284     27.783699       14.676653   \n",
       "min                        3.000000      0.000000        0.000000   \n",
       "25%                        3.000000     78.000000       59.000000   \n",
       "50%                        8.000000     94.000000       68.000000   \n",
       "75%                       14.000000    110.000000       78.000000   \n",
       "max                       15.000000    592.000000      140.000000   \n",
       "\n",
       "        Height_min  Mean blood pressure_min  Oxygen saturation_min  \\\n",
       "count  3385.000000             17687.000000           17783.000000   \n",
       "mean    168.548597                53.988573              84.016775   \n",
       "std      14.862592                15.226982              14.834182   \n",
       "min       0.000000               -37.000000               0.000000   \n",
       "25%     160.000000                48.000000              79.000000   \n",
       "50%     170.000000                55.000000              90.000000   \n",
       "75%     178.000000                62.000000              93.000000   \n",
       "max     429.000000               107.333000             100.000000   \n",
       "\n",
       "       Respiratory rate_min  Systolic blood pressure_min  Temperature_min  \\\n",
       "count          17683.000000                 17689.000000     17554.000000   \n",
       "mean               8.605129                    85.950386        35.805119   \n",
       "std                5.549584                    20.012562         2.135860   \n",
       "min                0.000000                     0.000000        -3.000000   \n",
       "25%                3.000000                    77.000000        35.555556   \n",
       "50%                9.000000                    86.000000        36.000000   \n",
       "75%               13.000000                    97.000000        36.388889   \n",
       "max               29.000000                   157.000000        39.000000   \n",
       "\n",
       "         Weight_min        pH_min  \n",
       "count  13052.000000  14776.000000  \n",
       "mean      81.416817      6.600238  \n",
       "std       24.508092      1.955086  \n",
       "min        0.000000      0.000000  \n",
       "25%       65.500000      5.500000  \n",
       "50%       78.000000      7.240000  \n",
       "75%       93.326554      7.350000  \n",
       "max      280.000000     99.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns = ['label']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13427, 13) (4476, 13) (13427,) (4476,)\n"
     ]
    }
   ],
   "source": [
    "# Split data into Training (75%) and Testing (25%) sets\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "y = data['label'].values\n",
    "\n",
    "X = data.drop(columns = ['Subj', 'label']).values\n",
    "\n",
    "feature_used = data.drop(columns = ['Subj', 'label']).columns.tolist()\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.25, random_state=0) \n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute the missing values\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# if your sklearn version <= 0.20\n",
    "# Do:\n",
    "# from sklearn.preprocessing import Imputer\n",
    "# imputer = Imputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "imputer.fit(X_train)\n",
    "X_train = np.array(imputer.transform(X_train), dtype=np.float32)\n",
    "X_test = np.array(imputer.transform(X_test), dtype=np.float32)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train model using Grid Search with cross validation\n",
    "# The goal is to identify the best hyper-parameters of the model\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# set grid search for decision tree clssifier\n",
    "tree = DecisionTreeClassifier(criterion='gini', random_state=0, class_weight = 'balanced') \n",
    "grid = GridSearchCV(tree,\n",
    "                    param_grid={'max_depth': range(2, 10)},\n",
    "                    scoring='accuracy', cv=10,\n",
    "                    return_train_score=True)\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "scores = pd.DataFrame(grid.cv_results_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>split5_train_score</th>\n",
       "      <th>split6_train_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
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       "      <td>0.583023</td>\n",
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       "      <td>0.549516</td>\n",
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       "      <td>0.556971</td>\n",
       "      <td>0.539015</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.016047</td>\n",
       "      <td>0.000973</td>\n",
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       "      <td>3</td>\n",
       "      <td>{'max_depth': 3}</td>\n",
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       "      <td>0.634401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.595498</td>\n",
       "      <td>0.650116</td>\n",
       "      <td>0.622807</td>\n",
       "      <td>0.638779</td>\n",
       "      <td>0.694307</td>\n",
       "      <td>0.500703</td>\n",
       "      <td>0.527265</td>\n",
       "      <td>0.729499</td>\n",
       "      <td>0.637556</td>\n",
       "      <td>0.074272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.019790</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>4</td>\n",
       "      <td>{'max_depth': 4}</td>\n",
       "      <td>0.689501</td>\n",
       "      <td>0.709605</td>\n",
       "      <td>0.682055</td>\n",
       "      <td>0.679077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.679907</td>\n",
       "      <td>0.699768</td>\n",
       "      <td>0.668239</td>\n",
       "      <td>0.648875</td>\n",
       "      <td>0.670391</td>\n",
       "      <td>0.725279</td>\n",
       "      <td>0.754986</td>\n",
       "      <td>0.700455</td>\n",
       "      <td>0.696283</td>\n",
       "      <td>0.030800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.026339</td>\n",
       "      <td>0.002262</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>5</td>\n",
       "      <td>{'max_depth': 5}</td>\n",
       "      <td>0.690246</td>\n",
       "      <td>0.684289</td>\n",
       "      <td>0.627699</td>\n",
       "      <td>0.667908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636958</td>\n",
       "      <td>0.692486</td>\n",
       "      <td>0.676928</td>\n",
       "      <td>0.670887</td>\n",
       "      <td>0.643827</td>\n",
       "      <td>0.717418</td>\n",
       "      <td>0.744311</td>\n",
       "      <td>0.703434</td>\n",
       "      <td>0.690474</td>\n",
       "      <td>0.031794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.032771</td>\n",
       "      <td>0.002358</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>6</td>\n",
       "      <td>{'max_depth': 6}</td>\n",
       "      <td>0.678332</td>\n",
       "      <td>0.667908</td>\n",
       "      <td>0.672375</td>\n",
       "      <td>0.620998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.711437</td>\n",
       "      <td>0.652350</td>\n",
       "      <td>0.704568</td>\n",
       "      <td>0.691824</td>\n",
       "      <td>0.701837</td>\n",
       "      <td>0.688622</td>\n",
       "      <td>0.691105</td>\n",
       "      <td>0.757385</td>\n",
       "      <td>0.701513</td>\n",
       "      <td>0.024850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.032944</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>7</td>\n",
       "      <td>{'max_depth': 7}</td>\n",
       "      <td>0.749069</td>\n",
       "      <td>0.658972</td>\n",
       "      <td>0.664929</td>\n",
       "      <td>0.728220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.723601</td>\n",
       "      <td>0.763241</td>\n",
       "      <td>0.716650</td>\n",
       "      <td>0.741145</td>\n",
       "      <td>0.738414</td>\n",
       "      <td>0.710054</td>\n",
       "      <td>0.728175</td>\n",
       "      <td>0.741498</td>\n",
       "      <td>0.734788</td>\n",
       "      <td>0.019243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.037261</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>8</td>\n",
       "      <td>{'max_depth': 8}</td>\n",
       "      <td>0.723008</td>\n",
       "      <td>0.702159</td>\n",
       "      <td>0.682800</td>\n",
       "      <td>0.702159</td>\n",
       "      <td>...</td>\n",
       "      <td>0.748759</td>\n",
       "      <td>0.752234</td>\n",
       "      <td>0.722443</td>\n",
       "      <td>0.745366</td>\n",
       "      <td>0.745614</td>\n",
       "      <td>0.740422</td>\n",
       "      <td>0.760612</td>\n",
       "      <td>0.757551</td>\n",
       "      <td>0.751521</td>\n",
       "      <td>0.013963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.051826</td>\n",
       "      <td>0.007117</td>\n",
       "      <td>0.000563</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>9</td>\n",
       "      <td>{'max_depth': 9}</td>\n",
       "      <td>0.716307</td>\n",
       "      <td>0.661951</td>\n",
       "      <td>0.699926</td>\n",
       "      <td>0.716307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783102</td>\n",
       "      <td>0.780122</td>\n",
       "      <td>0.778385</td>\n",
       "      <td>0.775488</td>\n",
       "      <td>0.786660</td>\n",
       "      <td>0.753744</td>\n",
       "      <td>0.776500</td>\n",
       "      <td>0.778982</td>\n",
       "      <td>0.775047</td>\n",
       "      <td>0.013159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.014741      0.000878         0.000508        0.000035   \n",
       "1       0.016047      0.000973         0.000402        0.000026   \n",
       "2       0.019790      0.000134         0.000399        0.000016   \n",
       "3       0.026339      0.002262         0.000464        0.000062   \n",
       "4       0.032771      0.002358         0.000482        0.000027   \n",
       "5       0.032944      0.000461         0.000440        0.000024   \n",
       "6       0.037261      0.000244         0.000464        0.000029   \n",
       "7       0.051826      0.007117         0.000563        0.000056   \n",
       "\n",
       "  param_max_depth            params  split0_test_score  split1_test_score  \\\n",
       "0               2  {'max_depth': 2}           0.529412           0.583023   \n",
       "1               3  {'max_depth': 3}           0.695458           0.709605   \n",
       "2               4  {'max_depth': 4}           0.689501           0.709605   \n",
       "3               5  {'max_depth': 5}           0.690246           0.684289   \n",
       "4               6  {'max_depth': 6}           0.678332           0.667908   \n",
       "5               7  {'max_depth': 7}           0.749069           0.658972   \n",
       "6               8  {'max_depth': 8}           0.723008           0.702159   \n",
       "7               9  {'max_depth': 9}           0.716307           0.661951   \n",
       "\n",
       "   split2_test_score  split3_test_score  ...  split2_train_score  \\\n",
       "0           0.583768           0.549516  ...            0.575058   \n",
       "1           0.600149           0.634401  ...            0.595498   \n",
       "2           0.682055           0.679077  ...            0.679907   \n",
       "3           0.627699           0.667908  ...            0.636958   \n",
       "4           0.672375           0.620998  ...            0.711437   \n",
       "5           0.664929           0.728220  ...            0.723601   \n",
       "6           0.682800           0.702159  ...            0.748759   \n",
       "7           0.699926           0.716307  ...            0.783102   \n",
       "\n",
       "   split3_train_score  split4_train_score  split5_train_score  \\\n",
       "0            0.560659            0.600629            0.582340   \n",
       "1            0.650116            0.622807            0.638779   \n",
       "2            0.699768            0.668239            0.648875   \n",
       "3            0.692486            0.676928            0.670887   \n",
       "4            0.652350            0.704568            0.691824   \n",
       "5            0.763241            0.716650            0.741145   \n",
       "6            0.752234            0.722443            0.745366   \n",
       "7            0.780122            0.778385            0.775488   \n",
       "\n",
       "   split6_train_score  split7_train_score  split8_train_score  \\\n",
       "0            0.576961            0.551593            0.556971   \n",
       "1            0.694307            0.500703            0.527265   \n",
       "2            0.670391            0.725279            0.754986   \n",
       "3            0.643827            0.717418            0.744311   \n",
       "4            0.701837            0.688622            0.691105   \n",
       "5            0.738414            0.710054            0.728175   \n",
       "6            0.745614            0.740422            0.760612   \n",
       "7            0.786660            0.753744            0.776500   \n",
       "\n",
       "   split9_train_score  mean_train_score  std_train_score  \n",
       "0            0.539015          0.568689         0.022288  \n",
       "1            0.729499          0.637556         0.074272  \n",
       "2            0.700455          0.696283         0.030800  \n",
       "3            0.703434          0.690474         0.031794  \n",
       "4            0.757385          0.701513         0.024850  \n",
       "5            0.741498          0.734788         0.019243  \n",
       "6            0.757551          0.751521         0.013963  \n",
       "7            0.778982          0.775047         0.013159  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# visualization of grid search\n",
    "\n",
    "scores.plot(x='param_max_depth', y='mean_train_score',\n",
    "            yerr='std_train_score')\n",
    "\n",
    "scores.plot(x='param_max_depth', y='mean_test_score',\n",
    "            yerr='std_test_score', ax=plt.gca())\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
       "                       max_depth=9, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.70      0.79      3881\n",
      "           1       0.23      0.58      0.32       595\n",
      "\n",
      "    accuracy                           0.68      4476\n",
      "   macro avg       0.57      0.64      0.56      4476\n",
      "weighted avg       0.82      0.68      0.73      4476\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# use the best model to predict\n",
    "\n",
    "from sklearn.metrics import classification_report \n",
    "\n",
    "y_pred_grid = grid.best_estimator_.predict(X_test) \n",
    "\n",
    "print(classification_report(y_test, y_pred_grid))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature importance\n",
    "importances = grid.best_estimator_.feature_importances_ \n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "plt.ylabel(\"Feature importances\")\n",
    "\n",
    "plt.bar(range(len(feature_used)), importances[indices], align=\"center\") \n",
    "\n",
    "plt.xticks(range(len(feature_used)), feature_used, rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k-Nearest Neighor Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data set into DataFrame\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv('data/in_hospital_mortality_48_day_min.csv', \n",
    "                  header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cases:  2423\n",
      "Number of controls :  15480\n",
      "\n",
      "Proportion of case: 13.53%\n"
     ]
    }
   ],
   "source": [
    "# To explore the number of cases (death) and controls (alive finally) \n",
    "\n",
    "n_case = len(data[data['label'] == 1])\n",
    "n_control = len(data[data['label'] == 0]) \n",
    "\n",
    "print('Number of cases: ', n_case) \n",
    "print('Number of controls : ', n_control)\n",
    "print()\n",
    "print('Proportion of case: %.2f%%' % (100 * n_case / (n_control + n_case)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of subjects')"
      ]
     },
     "execution_count": 38,
     "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": [
    "# visualize case-control distribution\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(['Cases (death)','Controls (alive, discharge)'], [n_case, n_control]) \n",
    "\n",
    "plt.ylabel('number of subjects') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subj                            0.000000\n",
      "Capillary refill rate_min       0.981958\n",
      "Diastolic blood pressure_min    0.011953\n",
      "Fraction inspired oxygen_min    0.703458\n",
      "Glascow coma scale total_min    0.419818\n",
      "Glucose_min                     0.000950\n",
      "Heart Rate_min                  0.011953\n",
      "Height_min                      0.810926\n",
      "Mean blood pressure_min         0.012065\n",
      "Oxygen saturation_min           0.006703\n",
      "Respiratory rate_min            0.012288\n",
      "Systolic blood pressure_min     0.011953\n",
      "Temperature_min                 0.019494\n",
      "Weight_min                      0.270960\n",
      "pH_min                          0.174663\n",
      "label                           0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# To assess missing values\n",
    "\n",
    "print(data.isnull().sum() / len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop feature 'Capillary refill rate_min' since high missingness\n",
    "\n",
    "data = data.drop(columns = ['Capillary refill rate_min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Diastolic blood pressure_min</th>\n",
       "      <th>Fraction inspired oxygen_min</th>\n",
       "      <th>Glascow coma scale total_min</th>\n",
       "      <th>Glucose_min</th>\n",
       "      <th>Heart Rate_min</th>\n",
       "      <th>Height_min</th>\n",
       "      <th>Mean blood pressure_min</th>\n",
       "      <th>Oxygen saturation_min</th>\n",
       "      <th>Respiratory rate_min</th>\n",
       "      <th>Systolic blood pressure_min</th>\n",
       "      <th>Temperature_min</th>\n",
       "      <th>Weight_min</th>\n",
       "      <th>pH_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17689.000000</td>\n",
       "      <td>5309.000000</td>\n",
       "      <td>10387.000000</td>\n",
       "      <td>17886.000000</td>\n",
       "      <td>17689.000000</td>\n",
       "      <td>3385.000000</td>\n",
       "      <td>17687.000000</td>\n",
       "      <td>17783.000000</td>\n",
       "      <td>17683.000000</td>\n",
       "      <td>17689.000000</td>\n",
       "      <td>17554.000000</td>\n",
       "      <td>13052.000000</td>\n",
       "      <td>14776.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.964459</td>\n",
       "      <td>0.422389</td>\n",
       "      <td>8.767113</td>\n",
       "      <td>95.307206</td>\n",
       "      <td>68.431251</td>\n",
       "      <td>168.548597</td>\n",
       "      <td>53.988573</td>\n",
       "      <td>84.016775</td>\n",
       "      <td>8.605129</td>\n",
       "      <td>85.950386</td>\n",
       "      <td>35.805119</td>\n",
       "      <td>81.416817</td>\n",
       "      <td>6.600238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.032213</td>\n",
       "      <td>0.155498</td>\n",
       "      <td>4.790284</td>\n",
       "      <td>27.783699</td>\n",
       "      <td>14.676653</td>\n",
       "      <td>14.862592</td>\n",
       "      <td>15.226982</td>\n",
       "      <td>14.834182</td>\n",
       "      <td>5.549584</td>\n",
       "      <td>20.012562</td>\n",
       "      <td>2.135860</td>\n",
       "      <td>24.508092</td>\n",
       "      <td>1.955086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>35.555556</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>7.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>36.388889</td>\n",
       "      <td>93.326554</td>\n",
       "      <td>7.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>429.000000</td>\n",
       "      <td>107.333000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Diastolic blood pressure_min  Fraction inspired oxygen_min  \\\n",
       "count                  17689.000000                   5309.000000   \n",
       "mean                      39.964459                      0.422389   \n",
       "std                       12.032213                      0.155498   \n",
       "min                      -13.000000                      0.000000   \n",
       "25%                       34.000000                      0.400000   \n",
       "50%                       41.000000                      0.400000   \n",
       "75%                       47.000000                      0.500000   \n",
       "max                       89.000000                      1.000000   \n",
       "\n",
       "       Glascow coma scale total_min   Glucose_min  Heart Rate_min  \\\n",
       "count                  10387.000000  17886.000000    17689.000000   \n",
       "mean                       8.767113     95.307206       68.431251   \n",
       "std                        4.790284     27.783699       14.676653   \n",
       "min                        3.000000      0.000000        0.000000   \n",
       "25%                        3.000000     78.000000       59.000000   \n",
       "50%                        8.000000     94.000000       68.000000   \n",
       "75%                       14.000000    110.000000       78.000000   \n",
       "max                       15.000000    592.000000      140.000000   \n",
       "\n",
       "        Height_min  Mean blood pressure_min  Oxygen saturation_min  \\\n",
       "count  3385.000000             17687.000000           17783.000000   \n",
       "mean    168.548597                53.988573              84.016775   \n",
       "std      14.862592                15.226982              14.834182   \n",
       "min       0.000000               -37.000000               0.000000   \n",
       "25%     160.000000                48.000000              79.000000   \n",
       "50%     170.000000                55.000000              90.000000   \n",
       "75%     178.000000                62.000000              93.000000   \n",
       "max     429.000000               107.333000             100.000000   \n",
       "\n",
       "       Respiratory rate_min  Systolic blood pressure_min  Temperature_min  \\\n",
       "count          17683.000000                 17689.000000     17554.000000   \n",
       "mean               8.605129                    85.950386        35.805119   \n",
       "std                5.549584                    20.012562         2.135860   \n",
       "min                0.000000                     0.000000        -3.000000   \n",
       "25%                3.000000                    77.000000        35.555556   \n",
       "50%                9.000000                    86.000000        36.000000   \n",
       "75%               13.000000                    97.000000        36.388889   \n",
       "max               29.000000                   157.000000        39.000000   \n",
       "\n",
       "         Weight_min        pH_min  \n",
       "count  13052.000000  14776.000000  \n",
       "mean      81.416817      6.600238  \n",
       "std       24.508092      1.955086  \n",
       "min        0.000000      0.000000  \n",
       "25%       65.500000      5.500000  \n",
       "50%       78.000000      7.240000  \n",
       "75%       93.326554      7.350000  \n",
       "max      280.000000     99.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns = ['label']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13427, 13) (4476, 13) (13427,) (4476,)\n"
     ]
    }
   ],
   "source": [
    "# Split data into Training (75%) and Testing (25%) sets\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "y = data['label'].values\n",
    "\n",
    "X = data.drop(columns = ['Subj', 'label']).values\n",
    "\n",
    "feature_used = data.drop(columns = ['Subj', 'label']).columns.tolist()\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.25, random_state=0) \n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute the missing values\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# if your sklearn version <= 0.20\n",
    "# Do:\n",
    "# from sklearn.preprocessing import Imputer\n",
    "# imputer = Imputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "imputer.fit(X_train)\n",
    "X_train = np.array(imputer.transform(X_train), dtype=np.float32)\n",
    "X_test = np.array(imputer.transform(X_test), dtype=np.float32)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "\n",
    "*For the non-tree-based machine learning models, such as KNN, logistic regression, and linear regression, data scaling is usually requried to eliminate the effects of magnitude.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data scaling\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler() # z-score based scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "\n",
    "X_test_scaled = scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-7.1436770e-02, -5.5176898e-08, -1.5860262e+00, ...,\n",
       "        -7.4240786e-01,  1.8399764e+00,  2.7079245e-01],\n",
       "       [ 5.9394932e-01, -5.5176898e-08,  1.4351804e+00, ...,\n",
       "         4.2110077e-01, -7.6581484e-01,  3.8183460e-01],\n",
       "       [ 5.9394932e-01, -5.5176898e-08, -7.6206082e-01, ...,\n",
       "         4.7572085e-01, -8.8490957e-01,  4.0202403e-01],\n",
       "       ...,\n",
       "       [ 7.6029581e-01, -2.6942873e-01, -2.5691804e-08, ...,\n",
       "         3.6646190e-01, -1.5431671e-01,  4.1716599e-01],\n",
       "       [ 5.9394932e-01, -2.6942873e-01, -2.5691804e-08, ...,\n",
       "         1.2065358e-01, -4.9904287e-01,  3.7173986e-01],\n",
       "       [-1.4022088e+00, -5.5176898e-08,  1.7098355e+00, ...,\n",
       "         1.4796457e-01,  3.8702145e-01, -9.2743221e-08]], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train KNN classifier with Grid Search\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "\n",
    "knn =KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=5, p=2,\n",
       "                                            weights='uniform'),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'n_neighbors': [5, 10, 15, 20, 25, 30, 35, 40]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = GridSearchCV(knn, \n",
    "                    param_grid={'n_neighbors':[5, 10, 15, 20, 25, 30, 35, 40]},\n",
    "                    scoring='roc_auc',\n",
    "                    cv=5,\n",
    "                    return_train_score=True)\n",
    "\n",
    "grid.fit(X_train_scaled, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# To visualize tuning process\n",
    "\n",
    "scores = pd.DataFrame(grid.cv_results_) \n",
    "\n",
    "\n",
    "scores.plot(x='param_n_neighbors', y='mean_test_score',\n",
    "            yerr='std_test_score', ax=plt.gca())\n",
    "\n",
    "plt.legend() \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 40}\n"
     ]
    }
   ],
   "source": [
    "# best model parameters \n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can predict and plot ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n",
      "[[0.9   0.1  ]\n",
      " [0.8   0.2  ]\n",
      " [0.775 0.225]\n",
      " ...\n",
      " [0.8   0.2  ]\n",
      " [0.8   0.2  ]\n",
      " [0.825 0.175]]\n"
     ]
    }
   ],
   "source": [
    "# make prediction on test set \n",
    "y_pred = grid.best_estimator_.predict(X_test)  # get predicted labels\n",
    "print(y_pred)\n",
    "\n",
    "y_proba = grid.best_estimator_.predict_proba(X_test) # get probabilities that belong to each class \n",
    "print(y_proba)\n",
    "\n",
    "y_proba = y_proba[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "#Create true and false positive rates\n",
    "fpr_knn, tpr_knn, threshold_knn = roc_curve(y_test,y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DSoFMREREKkx+YTGf/5jJS9NWkpG1hw6N4njuop6c2q0J4WEGWSvh9cGQuxP6/gUG3uXNLXaYUyATERGRPy23oIiPF6zl5WkrWb8zl27N4nnlst4M6dSIsDCD3ZuhVkOo0wqOuBB6XQaNugS77CpDgUxEREQO2Z78Qt7/YQ2vzEhnS3YevVvU4bGzuzGgfQPMDHZmwqT7IG0y3LQQajWAk58IdtlVjgKZiIiIHLRduQW8MyeDsbNWsS0nn/5t6jHmwh70a13PC2IFe+H752HmM4CDY/4GUTWDXXaVpUAmIiIiftuxJ583Zq/mrdmr2JVbyKAODbjxhLb0blFqHFjuTnj5ONiRAZ3PhJMe8SZ5lf1SIBMREZED2pKdx+uz0nl3TgY5+UUM7dKIGwe1o1ti/O8H5WyFmvUhJh66nQetB0Cr44NXdAhRIBMREZH92rBzL69MT+eDeWsoKCrmtCOacsOgtnRoHPf7QXt3wLQnYMEbMHIaNOoMg+8LVskhSYFMRERE/sfabXv4z/SVfLogkyLnOKtnM64f2IbWDWr9flBxEfz4Dkx+GPZuh95XQq1GQas5lCmQiYiIyG9WbtnNS1NX8sVP6wg347w+iVw7oA3N68b+8cDiInhjGGTOg6T+cPK/oMkRwSm6GlAgExEREZZv3MWLU1fyzS/riY4I44p+LRl5fGsax8f88cCcLKhZD8LCofNwOPpa6HI2mAWn8GpCgUxEROQw9kvmDl6YksakpZuoGRXOX49vw9XHtqJBXPQfDyzIhbkvwoyn4by3oP1J0P/GoNRcHSmQiYiIHIYWZmzjuclpTE/ZQu2YCG4Z3I4Rx7QkITbqjwc6ByvGw8S7Yfsq6HgaNGgfnKKrMQUyERGRw4Rzjjkrs3h+Shpz0rOoWzOK24Z24PJ+LYiL2c/i3p//FX75CBp0hMu+gDaDKrfow4QCmYiISDXnnGPaii08PyWVRWt20DAumntP7cTFRyURG7WPKJC7EyJrQngEtBkMTXt6C4GH7ye0yZ+mQCYiIlJNFRc7Ji3dxAtTU1m8bhfNEmrwyJldOa93IjGR4ft6Avz0Hkx+CAbcAUdeA90vqPzCD0MKZCIiItVMUbHjm1/W8+LUNFI27aZFvViePOcIzuzZjKiIsH0/ae18GH87rF8EiUdCYt/KLfowp0AmIiJSTRQUFfP5j+v4z7SVrNqaQ7uGtRhzYQ9O7daEiPD9BDHwZtmf9jjUagxnv+Yte6RpLCqVApmIiEiIyyss4pMFmfxn2krW7dhLl6a1efnSXpzUuTFhYfsJVoX5UFwIUbGQdDQceysc9w+Ijtv38RJQCmQiIiIham9+Ee/PW8OrM1ayaVcePZon8MiZXRjUoSFWXgtXyiSYcCd0PAVOehRaD/RuEjQKZCIiIiEmO7eAd+eu4fWZ6WTl5HNUq7o8c34P+repV34Q25oGE++C1ElQr51CWBWiQCYiIhIidu4p4M3vV/Hm7NXs3FvA8e0bcOOgthzZqu6Bn/zje/D1LRARAyeNgiNHQkTUgZ8nlUKBTEREpIrL2p3H2FmreHtOBrvzChnSuRE3DmpL9+YJ5T+xuBjyd0NMbe9bk90vgBPuh7hGlVO4+E2BTEREpIratCuXV2ek894PGeQVFnNKtybcMLAtnZvWPvCT1y2E8XdAXBO44B1vuaPhLwa+aDkkCmQiIiJVTOb2Pbw8fSUfz8+kyDmG92jK9QPb0rZhrQM/efdmb2LXH9+DWg2hz1XeepSaxqJKUyATERGpIlZtzeGlqWl8/uM6zODc3olcN6AtSfVi/TtB+jT46DIo2Av9b4Ljb/O6K6XKUyATEREJspRN2bw4NY2vf15PZHgYlx7dgpHHt6ZpQg3/TpCX7c0f1vgIaHsiDLob6rcLbNFSoRTIREREgmTxup28MCWNCUs2EhsVzjXHtebq41rRMC7GvxNsS4eJ98Cu9XDNVIitC+e9GdiiJSAUyERERCrZojXbeWFKGlOWbyYuOoKbTmjLVce0ok5NP6ehyNsNs56B75+H8Civa9IVAeUsjyRVmgKZiIhIJXDOMTd9Gy9MTWV2WhZ1YiP550ntuaxfS+JrRPp/os3L4Z0zIXsDdL8ITnwQ4hoHqmypJApkIiIiAeScY0bqVl6Yksr81dupXyuae07pxMVHJVEz+iB+DefthuhaULeVt/bk0ddD8yMDV7hUKgUyERGRAHDOkbxsM89PSeWXzJ00iY/hoTO6cEHf5sREhvt/opytMOURSJsMN/wAUTXhvLcCVrcEhwKZiIhIBSoJYqOTU1iyfhfN69bg8bO7cU6vRKIiDmKMV1EBzB8L0x6D/BxvqSNXHLjCJagUyERERCqAc47JyzYzenIKi9ftIqluLP8+9wjO7NmMyPCDHGyfsxXeOg22LIPWg2DYE9CwY2AKlypBgUxERORPcM4xZflmRien8uu6nTSvW4Mnzz2Csw4liOXvgahYiK0HzXrB4PugwymaZf8woEAmIiJyCJxzTF3hBbFfMn1B7JwjOKvXoQSxHJg1Gua/DtfOgvhmcOZLgSlcqiQFMhERkYPgnGPaii2MTk7h58ydJNapwb/O6cbZvRIPPog5B0s+g0n3wa510PVcCDuIAf9SbSiQiYiI+ME5x7SULYxOTuXntTtollCDJ87uxjm9DyGIgTdo/52zYPVMaNwNznkdWvSv+MIlJAQ0kJnZMGAMEA687px7osz+JOD/gATfMXc658YFsiYREZGDsa8gdkjfmixRsBcia0B4JDTpDl3Phl5XqGXsMBewQGZm4cCLwBAgE5hvZl8555aWOuxe4GPn3H/MrDMwDmgZqJpERET85Zxjui+I/VQRQayoEBa+CVMfg8s+g6Y9Yeioii9cQlIgW8iOBNKcc+kAZvYhMBwoHcgcUNt3Px5YH8B6REREDqhkZv3RySn8uMYLYo+d1Y1zex9iEANYNRPG3wGbl0Cr4yGyZsUWLSEvkIGsGbC21ONM4KgyxzwITDKzm4CawIkBrEdERGS/nHPM9AWxRWt20DQ+hlFndeW83s0PPYgBfHED/PQuxDeH89+GTmdoGgv5H4EMZPv6tLkyjy8C3nLOPW1m/YB3zKyrc3+citjMRgIjAZKSkgJSrIiIHJ6cc8xK28ro5FQWZmynaXwMj57ZlfP6JBIdcYjjugpyISLaC14NOsDAu6D/zd4cYyL7EMhAlgk0L/U4kf/tkrwaGAbgnJtjZjFAfWBz6YOcc68CrwL06dOnbKgTERE5aM45ZqdlMTo5hQUZ22kSH8MjZ3bl/D8TxJyDZV/BxHth2GPQ6XQ45uaKLVyqpUAGsvlAOzNrBawDLgQuLnPMGmAw8JaZdQJigC0BrElERA5zzjm+X+kFsfmrt9O4dgyPDO/C+X2bH3oQA9i0FCbcAatmQMMuULNBxRUt1V7AAplzrtDMbgQm4k1p8YZzbomZPQwscM59BfwDeM3MbsXrzrzSOacWMBERqXABC2IA05+EaU9AdByc8hT0HgHhmupT/BfQT4tvTrFxZbbdX+r+UuCYQNYgIiKHN+ccc1ZmMTo5lXmrt9GodjQPD+/C+X2aExP5J4JYcZHXRRkeAXVbQ+8r4YR7IbZuhdUuhw/FdxERqbbmrMzi2eQU5q3ygthDZ3Thgr5/MogBZMyB8bfBERdA/5ug27neTeQQKZCJiEi1M8fXNfnDqm00jIvmwdM7c+GRSX8+iO1cB9/dD4s/hdqJUKdlhdQrokAmIiLVxtx0L4jNTfeC2AOnd+aiighiAD99AN/+3euqHHAHHPM3TWMhFUaBTEREQt4P6d4YsTnpWTSIi+b+0zpz8VEVEMSc8xYBj4iCuq2g7WA46VG1jEmFUyATEZGQNW/VNkYnp/D9ygoOYgBbVnjLHdVrC6c+BUlHezeRAFAgExGRkDN/9Tae/c4LYvVrRXPfaZ25pKKC2N4dMP1fMO9Vb83JDqf8+XOKHIACmYiIhIwFq7fxbHIKs9O8IHbvqZ245KgW1IiqgCAGsHIq/PcvsCcLel8BJ9wHNetXzLlFyqFAJiIiVd6C1dsYnZzKrLSt1K8VVfFBrKgAwiO9sWGNusCQh6Fpj4o5t4gfFMhERKTKWpjhBbGZqV4Qu+eUTlx6dAUGsV0bIPkBr5vyko+9gftXfFUx5xY5CApkIiJS5SzM2M7o5BRmpm6lXs0o7j6lI5ce3YLYqAr6tVWYB3Nfgun/huIC6HejN51FWAUFPZGDpEAmIiJVxqI12xmdnMqMlC2BCWIAm5bAR5fCtnRvwP7QUd7SRyJBpEAmIiJBt2jNdsYkpzI9ZQt1a0Zx18kduaxfBQexokJv3cn4RIhrAqf8G9qeWHHnF/kTFMhERCRofvS1iJUEsTtP7shlR7egZnQF/nrK3QUznoRVM+AvUyAmHkaMq7jzi1QABTIREal0P63dwejkFKat2EKd2EjuGNaRy/tVcBArLoafP4DkByFnM/S8FAr3QnhcxV1DpIIokImISKX5ae0OxiSnMNUXxG4f1oEr+rWs2CAGkL0RPrwY1i2ExL5w8YfQrHfFXkOkAimQiYhIwP28dgdjJqcyZflmEmIjuW1oB67o35JaFR3ESsaJxdaH6Npw5stwxAUQFlax1xGpYApkIiISML9k7mBMciqTAx3ECvPhh5dh4Zswcpo3TuzyLyr2GiIBpEAmIiIV7tfMnYyZnELyss3E1/CC2OX9WhAXE1nxF0v9DibcCVlp0O4kyM/xAplICFEgExGRCrN43U5GJ6eSvGwT8TUi+edJ7bmif8vABLH8PfDpCEiZAHXbwMUfQ/uhFX8dkUqgQCYiIn9a6SBWOyaCfwxpzxXHtKR2IIJYyYz6kTUgMtZbd/Ko6yAiquKvJVJJFMhEROSQLVnvBbHvlnpB7O9D2nNloIKYc/DLxzB1FFz+pbfu5HlvVvx1RIJAgUxERA7akvU7GZOcyqSlm4iLieDWE9sz4tgABTGAdYtg/B2QOQ+a9oSCvYG5jkiQKJCJiIjflq7fxZjJKUxc8nsQu/KYlsTXCFAQcw6+uRUWvgU168PwF6H7xZrGQqodBTIRETmgZRt2MSY5lQlLNhIXE8HfTmzHiGNaBS6IFRd7ocvMGyvW7wYYcLu+PSnVlgKZiIjs17INu3hucirjF28kLjqCWwa346pjAxjEAFZOgQl3weljIOloGPqYF8xEqjEFMhER+R/LN3pBbNyvXhC7eXA7rj6mFfGxAQxi21bBpHth+TdQpyUUFXjbFcbkMKBAJiIiv1mxMZsxk1MY9+tGakVHcPMJbbn62NaBDWIAM5+BaU9AWAQMvh+OvgEiYwJ7TZEqRIFMRERI2ZTNmORUvv11A7WiI7jphLZcfWwrEmIDOLeXc96fZhAeCZ2Hw5CHoHbTwF1TpIpSIBMROYylbMpmzORUxv26gZpRlRTEADb87E1j0ecqOOJ86HejuiblsKZAJiJyGEr1BbFvf91AbGQ4Nwz0glidmgEOYjlZMOURbxqL2LpgvukrFMbkMKdAJiJyGEndlM1zU9L45pf1xEaGc/3ANvzl2NaBD2LgzbI/7p+QtxuOuhYG3gE16gT+uiIhQIFMROQwkLY5m+cmp/G1L4hdN6ANfzmuNXUrI4g557WARcRAkx5w8r+gYafAX1ckhCiQiYhUYwsztvHK9HS+W7aJGpHhXDugDddUVhDbnuFNY9G4mzepa6fTvZu6J0X+hwKZiEg1U1Ts+G7pRl6dkc6iNTtIiI3kxkFtGXFMq8oJYvl7YPZomD0GMEjs621XEBPZLwUyEZFqYm9+EZ8uymTszHRWZ+0hqW4sDw/vwrm9E4mNqqR/7tOnwRc3wK5M6HI2DHkYEppXzrVFQpgCmYhIiMvancfbczJ4Z24G23Ly6d48gZeGdWRol8aEh1VSq1TJOLHo2t63J89+FVoeUznXFqkGFMhERELUqq05vD4znU8XZpJXWMyJnRox8vjW9G1ZB6us7sE922DqY1CUB2c8D816wV9nqHtS5CApkImIhJiFGdt4dUY6k5ZuIjI8jHN6NePqY1vTtmGtyiuiuAgWvglTHoXcndD3L7+3kimMiRw0BTIRkRDgDdTfxKszVv5hoP7l/VrSIC66covZtAQ++ytJEl/5AAAgAElEQVRs+hVaHgfDnoDGXSu3BpFqRoFMRKQKyy0o4tOFmYydtYpVW3NoXrcGD53RhfP6VOJA/RIlLWAxCVBcAOe9BZ3PVIuYSAXw66fZzKKAJOdcWoDrERER9jFQPzGeFy/uxbCulThQv0TBXvj+ecicDxd/DPHN4Pq5CmIiFeiAgczMTgWeAaKAVmbWA3jAOXdWoIsTETnc/O9A/YZcc1xrjmxVt/IG6pdwDpZ/AxPvhh1roNMZULAHomoqjIlUMH9ayB4GjgKmAjjnfjKztgGtSkTkMPOHgfphYZzdqxl/Oa6SB+qXtms9fH4trJoODTrB5V9B6wHBqUXkMOBPICtwzu0o8z8zF6B6REQOGyUD9V+bmc7CjO3E14jkhoFtubx/CxrGxQSnqN/GicVDzlY4+d/Q5yoI15BjkUDy5ydsmZmdD4SZWSvgFmBuYMsSEam+qtRA/RLFRfDjO/DT+3DFN1635LWzICwsOPWIHGb8+cm/EbgfKAY+AyYCdwWyKBGR6ihrdx7vzM3g7Tl/HKg/tEsjIsKDGHzWzIVxt8HGXyCpH+zJgtpNFMZEKpE/gWyoc+4O4I6SDWZ2Nl44ExGRA1i1NYexs9L5ZEEVGKhfWl42fPN3+PVjiGsK54yFrudowL5IEPgTyO7lf8PXPfvYJiIipSzM2M5rM9KZuHRjqYH6rWjbMC64hZWME4usCTvXwvG3wbG3et2UIhIU+w1kZjYUGAY0M7NnSu2qjdd9KSIiZRQVO5KXbeLVGVVooH4J52DFeJjxb7jkU6hZD64cp65JkSqgvBayzcBiIBdYUmp7NnBnIIsSEQk1uQVF/HdRJq/P9AbqJ9apwYOnd+a8Ps2pGV0FvqG4JQUm3AkrJ0P9DrB7oxfIFMZEqoT9/ivhnPsR+NHM3nPO5VZiTSIiIWNbTj5vz1n920D9IxLjeeHingzr0ji4A/VLFBfBd/fDDy97XZTDnvAWAg+PDHZlIlKKP/9ta2Zmo4DOwG/t7c659gd6opkNA8YA4cDrzrkn9nHM+cCDeHOb/eycu9i/0kVEgmf11hxen+XNqJ9bUMzgjg255vjWHBXsgfplhYXDzkzocTGccD/UahDsikRkH/wJZG8BjwJPAScDI/BjDJmZhQMvAkOATGC+mX3lnFta6ph2eFNoHOOc225mDQ/6FYiIVKKyA/XP6ukN1G/XKMgD9UtbOx8m3QNnPA8NOsC5b3jBTESqLH8CWaxzbqKZPeWcWwnca2Yz/XjekUCacy4dwMw+BIYDS0sdcw3wonNuO4BzbvPBlS8iEnjFxY7vlm3itRnpLPAN1L9+YBuu6NeShrWDPFC/tOyNkPwg/PwB1GoM2Ru8QKYwJlLl+RPI8sxrf19pZtcC6wB/WrKaAWtLPc7EWxOztPYAZjYbr1vzQefchLInMrORwEiApKQkPy4tIvLn7Wug/gOnd+b8qjJQv7Q5L8HUUVCU701hcdw/ILoKtdqJSLn8+RflVqAWcDMwCogHrvLjefsaRFF2DcwIoB0wEEgEZppZV+fcjj88yblXgVcB+vTpo3U0RSSgtuXk886cDN6es5qsnHy6NYvn+Yt6cnLXKjJQf1+y10PLY2HoY1CvTbCrEZGDdMBA5pz7wXc3G7gMwMwS/Th3JtC81ONEYP0+jpnrnCsAVpnZCryANt+P84uIVKjVW3MYO2sVnyxcS25BMSd0bMjIqjhQHyBrJUy4C46+DtoMgsEPagFwkRBW7k+vmfXF63qc5ZzbamZd8JZQOgEvYJVnPtDOtyD5OuBCoOw3KL8ALgLeMrP6eF2Y6Qf9KkRE/oRFa7bz6vTfB+qf2bMp1xzXumoN1C+Rl+1N7DrnJYiIga5ne9sVxkRCWnkz9T8OnAP8jDeQ/3PgFuBfwLUHOrFzrtDMbsRbjDwceMM5t8TMHgYWOOe+8u07ycyWAkXAbc65rD/7okREDqS41Iz6CzK2UzsmomoO1C9tyecw/g7YvQl6XAKDH4C4RsGuSkQqQHn/pRoOdHfO7TWzunjdjd2dcyv8Pblzbhwwrsy2+0vdd8DffTcRkYArGag/duYq0qv6QP2y9mRBfCJc+D4k9gl2NSJSgcr71yfXObcXwDm3zcyWH0wYExGpSkJyoP7uzTD5IWh+FPS6HHqPgN5XabkjkWqovEDW2sw+8903oGWpxzjnzg5oZSIiFWBfA/WvOa41R7euggP1SxQVwLxXYdoTULAX6rTytms+MZFqq7xAdk6Zxy8EshARkYq0aI03o/6EJb8P1P/Lca1pXxUH6peW8T18/TfYugLanuitPVm/XbCrEpEAK29x8cmVWYiIyJ9VMlD/tZnpzF/tDdS/bkAbruxfhQfql5W/B4oL4KKPoP1QqKqteCJSoar4CFYRkQNzzvH1LxsYnZxC+pYcmiXU4P7TOnNB3xAYqJ+3G2Y9A+HRMPAOaHcitJ4H4ZHBrkxEKlEV/5dKRKR8CzO28cg3y/hp7Q46No7juYt6ckpVHqhfwjn49VP47n5vlv1el3vbzBTGRA5DfgcyM4t2zuUFshgREX+tydrDvyYs59tfN9AwLponzz2Cc3olEh4WAl18m5fDN3+DNXOgSQ84//+g+ZHBrkpEguiAgczMjgTG4q1hmWRm3YG/OOduCnRxIiJl7dxbwAtTUvm/7zMIDzNuGdyOvw5oTWxUCDX4u2LYtgrOeB56XKppLETErxay54DT8JY5wjn3s5kNCmhVIiJlFBQV897cDMZMTmXH3gLO7ZXIP07qQOP4EBisX1QIC8bClhVw2jPQqDP87VeIiAp2ZSJSRfgTyMKccxll5uspClA9IiJ/4Jzju6WbeGL8ctK35tC/TT3uObUTXZrGB7s0/6RPg/F3wpZl0HoQFOZBRLTCmIj8gT+BbK2v29KZWThwE5AS2LJERGDxup08+u1S5qZvo02Dmoy9og8ndGxYdSd0LW3XBhh/Oyz7ChJaeMsddThF01iIyD75E8iuw+u2TAI2Acm+bSIiAbFh517+PXEFn/+4jjqxUTwyvAsXHplEZFX/5mRpYRGQOR9OuA/63QiRIdC1KiJB408gK3TOXRjwSkTksJeTV8jL01fy2sx0ioth5PGtuWFQW2rHhMA0EM7Bks9h6Zdw7ptQqwHc8rPXPSkicgD+BLL5ZrYC+Aj4zDmXHeCaROQwU1Ts+GTBWp7+LoUt2Xmc3r0ptw/tQPO6scEuzT8bF8P4OyBjFjTuBnu2Qq2GCmMi4rcDBjLnXBsz6w9cCDxkZj8BHzrnPgx4dSJS7c1I2cJj45axfGM2vZISeOWy3vRKqhPssvyTlw3JD8KCNyAmAU57FnpdoUXAReSg+TVxj3Pue+B7M3sQGA28ByiQicghS9mUzWPjljFtxRaa163Bixf34pRujUNjwH6JsEjvW5R9r4FBd0GNEAmSIlLl+DMxbC1gOF4LWSfgS6B/gOsSkWpqS3Yezyan8OG8NdSMjuDuUzpyRf+WREeESKvS6lnw/fPeOLGoWLh2tgbsi8if5k8L2WLga+BJ59zMANcjItVUbkERY2et4j/TVpJbUMTl/Vpy8+B21K0ZIvNx7VgL393nDdyPT4IdGdCwk8KYiFQIfwJZa+dcccArEZFqqbjY8dXP6/n3xBWs27GXEzs14q5TOtKmQa1gl+afokKY9QzMfMZ7PPBuOOZmiKwR3LpEpFrZbyAzs6edc/8A/mtmrux+59zZAa1MRELevFXbGPXtUn7O3EmXprV56rzu9GtTL9hlHZywcFg5BToMgyGPQELzYFckItVQeS1kH/n+fKEyChGR6mP11hyeGL+cCUs20rh2DE+f152zejYjLCxEBuxvWgpTHvG+NRnXGC77XC1iIhJQ+w1kzrl5vrudnHN/CGVmdiMwOZCFiUjo2bEnn+cmp/HO3NVEhofx9yHtuea41tSICpEB+3u3w9THYf7rEB0HW5Z7gUxhTEQCzJ8xZFfxv61kV+9jm4gcpvILi3lnbgbPTU4lO7eA8/s05+9D2tOwdggNeF/4FiQ/BLk7oM9VMOgeiK0b7KpE5DBR3hiyC/CmumhlZp+V2hUH7Ah0YSJS9TnnmLhkE0+MX8bqrD0c164+d5/SiU5Nage7tIOX8b33rcmT/+XNti8iUonKayGbB2QBicCLpbZnAz8GsigRqfp+ydzBo98sY97qbbRrWIs3R/RlYPsGoTOx68513iz7x9zsBbDTRntdk6FSv4hUK+WNIVsFrAKSK68cEanq1u3Yy78nLOeLn9ZTr2YUo87qygV9mhMRHhbs0vxTkAtzXoCZT0NxEbQZ5AWyqBBZN1NEqqXyuiynO+cGmNl2oPS0FwY455wGV4gcRnbnFfKfaWm8PnMVDrh+YBuuG9iGuJjIYJfmvxUTYMIdsH01dDodTnoU6rQMdlUiIuV2WQ7y/Vm/MgoRkaqpsKiYjxas5dnvUti6O58zezTltmEdaZYQgt88XLcQImrA5V9C64HBrkZE5DfldVmWzM7fHFjvnMs3s2OBI4B3gV2VUJ+IBNG0FZt5bNwyUjbtpm/LOrx+RV96NE8Idln+y90J0/4FrY73JnY97h8w4HYID6FWPRE5LPgz7cUXQF8zawO8DXwLvA+cFsjCRCR4lm/cxahvlzEzdSst6sXy8qW9GNqlcegM2C8uhp/e9aax2JMFMfFeINO6kyJSRfkTyIqdcwVmdjYw2jn3nJnpW5Yi1dDm7FyemZTCxwvWEhcTyb2nduLyfi2JigiRAfsAmQtg3D9h/Y/Q/Gi49L/QtEewqxIRKZc/gazQzM4DLgPO9G1Te79INbI3v4jXZ6bzn+kryS8s5sr+rbh5cFsSYqOCXdrB27IcsjfB2a9Dt3M1jYWIhAR/Z+q/HnjSOZduZq2ADwJblohUhuJix+c/ruOpSSvYsDOXoV0acefJnWhVv2awS/NfYR7MfQliEqDPCOh+MXQ+E6JrBbsyERG/HTCQOecWm9nNQFsz6wikOedGBb40EQmkuelZPPrtUhav28URifGMvqAHR7WuF+yyDk7KRJhwJ2xLh+4XeYEsLExhTERCzgEDmZkdB7wDrMObg6yxmV3mnJsd6OJEpOKlb9nN4+OX893STTSNj+HZC7ozvHszwsJCqGsvayWMvwPSvoP67b1xYm1PDHZVIiKHzJ8uy2eBU5xzSwHMrBNeQOsTyMJEpGJtz8lnzORU3p2bQXREGLcN7cDVx7YiJjI82KUdvF3rYe0PMPQxOHKkprEQkZDnTyCLKgljAM65ZWYWgiN9RQ5PeYVFvP19Bs9PSWV3XiEX9E3i70Pa0yAuOtil+a+4GH750Ft/csBt0Oo4uHUJxITgIuYiIvvgTyBbZGav4LWKAVyCFhcXqfKcc4xfvJEnxi9nzbY9DGjfgLtP6USHxnHBLu3gZC6E8bd5s+wn9YNjb4XwCIUxEalW/Alk1wI3A7fjjSGbATwfyKJE5M/5cc12Rn27jAUZ2+nQKI63rzqS49s3CHZZB2f3Fkh+0JvgtVYjOPNlOOICb9C+iEg1U24gM7NuQBvgc+fck5VTkogcqrXb9vDkxBV8/fN66teK5vGzu3F+n+aEh9KA/RJ5u2DpF3DMLXD8bRAdYi17IiIHYb+BzMzuBq4GFuEtnfSwc+6NSqtMRPy2K7eAl6au5I3ZqwgzuOmEtvx1QBtqRfvTCF6FpCbDyskw7HGo18YbJ1YjhNbOFBE5ROX9a30JcIRzLsfMGgDjAAUykSqksKiYD+av5dnvUtiWk8/ZPZvxz6EdaJpQI9ilHZyslTDxHkgZD3XbeC1isXUVxkTksFFeIMtzzuUAOOe2mJkGbohUIfNXb+P+L5ewbMMujmpVl3tP7Uy3xPhgl3Vw8nbDzKdgzosQHgVDHoajroMIfZFbRA4v5QWy1mb2me++AW1KPcY5d3ZAKxORfdqcncsT45bz2Y/raBofw0uX9OLkro2xUFyzsSgfFr0NXc+FEx+AuMbBrkhEJCjKC2TnlHn8QiALEZHyFRQV8/acDEZ/l0JuYRHXD2zDjSe0JTYqxMaJrf8R5o+F08d43ZI3LvD+FBE5jO33X3Ln3OTKLERE9m9uehYPfLmEFZuyGdC+AQ+c3pnWDUJsvcbdW2DKw7DoHahZ3xs31qC9wpiICP7NQyYiQbJpVy6jvl3GVz+vp1lCDV65rDcndW4UWt2TRYUw/zWY+jgU5EC/G2DA7RATYuPdREQCSIFMpArKLyzmzdmreG5yKgXFjpsHt+O6AW2oERWC6066YljwBiT2gWFPeK1iIiLyB34HMjOLds7lBbIYEYHZaVu5/8vFrNySw+CODbn/9M60qFcz2GUdnO2rYca/vQAWHQdXTYQadSCUWvZERCrRAQOZmR0JjAXigSQz6w78xTl3U6CLEzmcrN+xl1HfLuPbXzeQVDeWsVf0YXCnRsEu6+Dk58CsZ2H2cxAW4S111Op4jRMTETkAf1rIngNOA74AcM79bGaD/Dm5mQ0DxgDhwOvOuSf2c9y5wCdAX+fcAn/OLVJd5BUW8frMVbwwJY1i5/j7kPaMPL41MZEh1D3pHCz+L3x3P+xaB93OhyEPQe2mwa5MRCQk+BPIwpxzGWUGERcd6ElmFg68CAwBMoH5ZvaVc25pmePi8BYv/8HvqkWqiekpW3jwqyWs2prDSZ0bcd9pnWleNzbYZR2aH9/1vj157huQdHSwqxERCSn+BLK1vm5L5wtZNwEpfjzvSCDNOZcOYGYfAsOBpWWOewR4Evin31WLhLi12/bwyDdLmbR0E63q1+StEX0Z2KFhsMs6ODlZMO1xb/HvhOZeEIuJh7AQatkTEaki/Alk1+F1WyYBm4Bk37YDaQasLfU4Eziq9AFm1hNo7pz7xsz2G8jMbCQwEiApKcmPS4tUTbkFRbw6I50Xp6YRZsZtQzvwl+NaER0RQiGmqND71uTUUZCXDc16Q4+LNE5MRORPOGAgc85tBi48hHPv6+tU7red3tqYzwJX+lHDq8CrAH369HEHOFykSpqyfBMPfb2UjKw9nNKtMfec2plmobYI+KoZMP4O2LwUWg2Ak5+Ehh2DXZWISMjz51uWr1EqSJVwzo08wFMzgealHicC60s9jgO6AtN849MaA1+Z2Rka2C/VyZqsPTz09RImL99MmwY1effqozi2Xf1gl3VoFv8X8nfDBe9Cx9M0jYWISAXxp8syudT9GOAs/tgVuT/zgXZm1gpYh9fKdnHJTufcTuC330pmNg34p8KYVBe5BUW8NG0lL09fSUSYcdfJHRlxTCuiIsKCXZr/8vfA989B2xO9iV2HPOzNLRYZYi17IiJVnD9dlh+Vfmxm7wDf+fG8QjO7EZiIN+3FG865JWb2MLDAOffVIdYsUqU55/hu6SYe/mYpmdv3cnr3ptxzSicax8cEuzT/OQdLv4RJ98JO3/+/EvtouSMRkQA5lKWTWgEt/DnQOTcOGFdm2/37OXbgIdQiUqWs2prDQ18vYdqKLbRrWIv3rzmK/m1CrHty0xJvnNjqmdCoG5z1CrQ8JthViYhUa/6MIdvO72PIwoBtwJ2BLEok1OzJL+TFqWm8NmMVURFh3HtqJ67o35LI8BDqniyRMhE2LYZTn4HeV2oaCxGRSlBuIDNvtH13vDFgAMXOOX3LUcTHOceExRt55JulrN+Zy1k9m3HXyR1pWDuEuieLi2DhWxDXGDqeCv1u8IKYprEQEak05QYy55wzs8+dc70rqyCRUJG2eTcPfb2Emalb6dg4jtEX9uTIViEWYlbP9ronN/3qrTvZ8VSIiPZuIiJSafwZQzbPzHo55xYFvBqREJCTV8hzU1J5Y9YqYiLDefD0zlx6dAsiQql7cmemt+7k4v9CfHM47/+g8/BgVyUictjabyAzswjnXCFwLHCNma0EcvAmfHXOuV6VVKNIleCc45tfNjDq22Vs3JXLub0TuWNYRxrEhWBr0tofYPm3MOBOb+mjqBBdP1NEpJoor4VsHtALOLOSahGpslI2ZfPAl0uYk55Fl6a1efGSXvRuUSfYZfnPOVj+DezZBr2vgC5nQ1I/qN002JWJiAjlBzIDcM6trKRaRKqc7NwCxiSn8tb3q6kZHcEjZ3bl4iOTCA8LoRnqNy+HCXdA+jRI7Au9Lvdm2FcYExGpMsoLZA3M7O/72+mceyYA9YhUCc45vvhpHY+NW87W3Xlc2Lc5tw3tSN2aUcEuzX97d8C0J2DeqxBdC07+N/S5SssdiYhUQeUFsnCgFvteJFyk2lq2YRcPfLmEeau30T0xntcu70OP5gnBLuvgZaV5YazX5XDCvVAzxCaoFRE5jJQXyDY45x6utEpEgmzn3gKe/S6Fd+ZmUDsmgsfP7sYFfZoTFkrdk2vmegP2j7nFW+rolp8hoXmwqxIRkQM44BgykequuNjx30WZ/GvCcrJy8rnkqCT+eVIHEmJDqHty13r47gH49WOonQh9rva6KRXGRERCQnmBbHClVSESJIvX7eT+LxezaM0OeiYl8NaII+naLIQW0C7MgzkvwIynobgAjvsnHHurF8ZERCRk7DeQOee2VWYhIpVpx558np6Uwns/ZFAnNoonzz2Cc3slhlb3JMDuzTD9SWgzGIY+CnVbB7siERE5BP7M1C9SbRQXOz5esJYnJ65gx558Lju6BX8f0oH42Mhgl+a/LSnw6ycw6G6vS/KGeVCnRbCrEhGRP0GBTA4bv2Tu4L4vl/Dz2h30bVmHh844is5Nawe7LP/l7vRaw354GSJjoeclUKelwpiISDWgQCbV3vacfJ6cuIIP56+hXs1onjm/O2f1bIaFynxcxcXw8/uQ/CDkbIWel8LgB6BWg2BXJiIiFUSBTKqtomLHB/PW8NSkFWTnFjKifyv+NqQdtWNCqHsSoCAHJj8MdVrBxR9DMy0jKyJS3SiQSbW0aM12HvhyCb+u28lRrery8PCudGgcF+yy/Je90euaHHQPRMfB1ZMgPgnCwoJdmYiIBIACmVQrWbvz+NeE5Xy8IJNGtaMZc2EPzujeNHS6Jwvz4Yf/eGPFCvOg3VBo0c8bKyYiItWWAplUC4VFxbz3wxqenrSCPflF/PX41tw0uB21okPoI54yCSbe5S151H4YDH0M6rUJdlUiIlIJQui3lci+LVi9jfu+XMKyDbs4pm09HjqjC20bhlD3JHgD96f4Viq75FNoNyS49YiISKVSIJOQtTk7lyfGL+ezRetoEh/Dixf34pRujUOnezIvG2Y/B/2uhxp14ML3oVZjiAihJZtERKRCKJBJyCksKub/5mQw+rsUcguLuH5gG248oS2xUSHycS4uhl8+guQHYPcmqN8OjjgfEpKCXZmIiARJiPwGE/HMTc/igS+XsGJTNse3b8CDp3emdYMQWrdx3SIYfztkzoemvbxWscQ+wa5KRESCTIFMQsKmXbmM+nYZX/28nmYJNXjlst6c1LlR6HRPlpjxFGzPgOEvQfeLNI2FiIgACmRSxRUUFfPm7FWMSU6loNhx8wltuW5gW2pEhQe7NP8UFcC8V71vTdZrA6c94y17FBNCSzaJiEjAKZBJlTU7bSsPfLWEtM27OaFjQx44vTMt6tUMdln+S5sME+6ErSmQtxsG3gFxjYNdlYiIVEEKZFLlrN+xl1HfLuPbXzfQvG4NXr+8Dyd2bhTssvy3bRVMvAdWfOstd3TRR9B+aLCrEhGRKkyBTKqMvMIixs5axfOT0yh2jltPbM9fB7QmJjJEuidLzHsN0qd5C4D3uwEiooNdkYiIVHEKZFIlzEjZwoNfLSF9aw5DOjfi/tM607xubLDL8o9z8OunkNAcko6GAbdD/5ugdpNgVyYiIiFCgUyCKnP7Hh79ZhkTlmykZb1Y3hzRl0EdGga7LP9t+BnG3Q5r58IRF3qBrEaCdxMREfGTApkERW5B0f+3d9/xUdXpHsc/D6F3pEsHEaQJUkRY1wKLoKsooqCioiire9V1XXV1ZdVFr4tcd70WLChIsWC5lohYUGAtgIBSJCBIFQSld0IIee4fZ9QYIkwgM2cm832/XrzMnDmZ85AfSb7+fmd+D898vIIR05YBcNtZzbjm1EaUKp4ky5O7N8GU++CLsVC2Kpz7KLQbEHZVIiKSpBTIJO6mfr2Be9/OYPXmPfRqVYshv29Bncplwi6rYBa+Dl+Oh87Xw2l/1YyYiIgcFQUyiZtvN+9h6MQMPly8gcbVyzF+UCdObVo97LKit2IaZO2B5mdDh6ug8elQ/fiQixIRkaJAgUxiLnP/AZ6ctpwn/7Oc4sWMO3o15+qujShZPEl2qd+6Gj64Cxa/DXU7QbNekFZCYUxERAqNApnEjLvz4eINDJ2YwZote/l9m9rcdc4J1K6UJMuTWXvg04dh+qNgxeDMIXDKjZBs7ZpERCThKZBJTKzatJt7385g2pKNNK1RnhevPZkuTaqFXVbBrPoEPh4OrfrC74ZCpTphVyQiIkWUApkUqr1ZBxgxdRkjP15ByeLFGHLOCVzZpSEl0pJkefL7r+D7hdD2EmjaA677FGq1DrsqEREp4hTIpFC4O+8t/J7731nMd9v2ckG7OtzZqzk1KpYOu7To7NkCU+6HL56DCrWhVZ9gh32FMRERiQMFMjlqyzfu4t70DD75ZhPNa1XglT+cQqdGx4RdVnQOZAchbMr9sG8ndLwWTr9D7Y5ERCSuFMjkiO3el81jU5Yx6tMVlC6exj3ntuDyzg0onizLkwCblsK7t0ODrtDrQajZMuyKREQkBSmQSYG5OxMXrOe/31nM9zsy6du+Ln/t2ZzqFZJkVmnbGlj6HnS6Fmq2gMHToFYbvXtSRERCo0AmBfLNDzu5Jz2D6cs30/LYioy4rB3tGyTJ8uT+vfDZo8FWFgAnnAsVakHtE8OtS0REUp4CmURlZ+Z+HvnwG8ZMX0XZkmnc17sll57cgLRiSTCr5A6L0+H9IbD9W2hxPvS4L+H3cX8AABZiSURBVAhjIiIiCUCBTA7J3Xlr3joemLSYDTv30a9DPW7v2Yyq5ZNkeRJgz2Z4849QuQFcOREanRp2RSIiIr+gQCa/6uvvd3D3WxnMWrmFNnUrMfKKDrStlyRNtPduhXkvQuc/QrlqcNUkqNES0vRPXkREEo9+O8lBdmTu5+HJSxk3YzUVShfngQta069jveRYnsw5AF+OhY/ug8xtUL8z1Gmv+8RERCShKZDJT3JynNfnfsewdxezeXcWl3aqz609mlGlXMmwS4vO6hnw7m3Bbvv1uwTbWNRuE3ZVIiIih6VAJgBkrNvO3W9l8MXqrbStV5nnBnaidd1KYZcVvQP74fXB4Aeg72ho2UfbWIiISNJQIEtxWdk5PDBpMeNmrKJy2ZIMv7ANfdvXpVgyLE/uzwx22W9/FZQoDZdOgCoNoWS5sCsTEREpkJgGMjPrCTwCpAHPuvuwPM/fAlwDZAMbgavdfXUsa5Kf7cnK5rrnv+TjpRu5vHMDbu3RjEplS4Rd1uG5w5JJ8P7fYOsqKF8z6D2pXfZFRCRJxSyQmVkaMAL4HbAWmG1m6e6+KNdpc4EO7r7HzK4HhgP9YlWT/Gzr7iyuGjObBWu3MfzCNlzcsV7YJUVn4xJ47w5YPgWqN4fL34QmZ4RdlYiIyFGJ5QxZJ2CZu68AMLMJQG/gp0Dm7lNznT8TGBDDeiRi3ba9XDF6Ft9u2cNTA9rTo2USbZD69p/gh0XQcxh0vAbSkmBGT0RE5DBiGcjqAGtyPV4LnHyI8wcB78awHgGWbdjFFaM+Z2dmNuOu7kTnxlXDLunQcnJg3gtwfE8oXx3OexzKVA72FhMRESkiisXwtfO7K9zzPdFsANAB+J9feX6wmc0xszkbN24sxBJTy/w127joqelkHcjhpcGdEz+MrZkFz54J6TfA3HHBsWrHKYyJiEiRE8sZsrVA7huT6gLr8p5kZt2Bu4DT3H1ffi/k7iOBkQAdOnTIN9TJoX36zSYGj59D1fIlGX/1yTSslsDvRNyxHj68FxZMgAq1oc8z0PqisKsSERGJmVgGstlAUzNrBHwH9AcuzX2CmbUDngZ6uvuGGNaS0t5ZsJ6bX55Lk+rlGXd1J2pULB12SYf20VDIeB1+cwuc+hcoVT7sikRERGIqZoHM3bPN7AbgfYJtL0a7e4aZDQXmuHs6wRJleeBVCzbx/Nbdz4tVTalo/MzV3P3WQjo0qMKzV3akUpkEvQl+6ftB8+8azaHb3+G3t0LVJmFXJSIiEhcx3YfM3ScBk/IcuzvXx91jef1U5u48+tEyHv5wKd2a1+DxS0+iTMm0sMs62KZv4L07YdlkaDcAeo+AiseGXZWIiEhcaaf+Iignx/nH2xmMnbGaPifV4cEL21AiLZbv3zgCmTvg4+Ew8ykoXhp6/Dd0Ghx2VSIiIqFQICtisrJzuPXV+aTPX8e1pzbizl4nJGYbpJlPwvTHglmxbvdA+RphVyQiIhIaBbIiJHcrpL/2bM51pzXGEqnB9tovIGc/1O8Mp/wXNO0OddqHXZWIiEjoFMiKiNytkB68sDX9OtYPu6Sf7fwheOfkvOeh4akwcGLwzkmFMREREUCBrEhYv30vl48KWiE9OaA9ZyVKK6TsLJj1NEx7ELIzoeuf4Le3hV2ViIhIwlEgS3LLN+7iilGz2L53P2Ov6sQpTRJo9/1Fb8IHQ6BpDzjrn8Eu+yIiInIQBbIktmDtNgY+N5tiBhMGd6ZVnUphlwSbl8OWFdD0d9DqQihfExqfFnZVIiIiCU2BLEl9+s0m/jB+DlXKlWT8oJNpFHYrpH274JOHYMYIKF8LbpoLacUVxkRERKKgQJaEfmyF1LhaecYN6kTNMFshucOCV+DDe2DnejjxEuh+bxDGREREJCr6rZlknp+5mr+/tZD29asw6sqOVCobciuktbPhjcFwbDu4eDzU6xhuPSIiIklIgSxJuDuPTVnGvycv5czmNRgRZiukXRvh2+nQojfU6wQDXofGZ0CxBOsGICIikiQUyJJATo4zdOIixkxfFW4rpAP7YdYzMG1YsMFrw1Oh7DFwXLf41yIiIlKEKJAluNytkK75TSP+dnZIrZCWT4F374BNS6DJmdBzWBDGRERE5KgpkCWw3K2Qbu/ZjOtPaxJOK6Tt38ELF0GlutD/JWjWCxKpJZOIiEiSUyBLUNv2BK2Q5q/ZxrA+renfKc6tkLJ2w9eToM1FUKkODPg/qNcZSoT4jk4REZEiSoEsAa3fvpcrRs1i9ZY9PHFZe3q2imMrJHdY+H8w+W7Y8R3UagU1ToDGp8evBhERkRSjQJZgQm2FtH4BvPvX4B2UtdrAhaOCMCYiIiIxpUCWQH5shWSE0App/14Yf37w8bmPQLvLoVhI22qIiIikGAWyBBFKK6QD2ZDxRtBzskSZYGPXmi2gTJXYX1tERER+okCWACZ9tZ6bJ8yjUbVy8WuFtPLjYHlywyIoVQGa9YSGXWN/XRERETmIAlnIXvh8NUPeXMhJ9aswOh6tkLZ9Cx8MgUVvQeX6wazY8WfF9poiIiJySApkIXF3Hp+yjH/FsxWSO7x0KWxeBmfcBV1uDJYqRUREJFQKZCH4RSukdnV4sG8MWyG5w9cTg931S5aD8x6BcjWgcr3YXE9EREQKTN2g4ywrO4c/vzKPMdNXMeg3jXjoohNjF8Z+yICx58LLA+DLccGxOu0VxkRERBKMZsjiaE9WNtc//yX/WbqR285qxh9Pj1ErpD1bYNo/YfYoKF0RzvkXnDSw8K8jIiIihUKBLE627cni6jGzmbdmG//s05pLYtkKKf1GWDIJOgyCM/6mJuAiIiIJToEsDn5qhbR5D09cdhI9W9Uu/Iusng5VGkLFY6HbPXD6nUHbIxEREUl4uocsxpZv3EXfJ2ewfnsmY67uWPhhbPtaeO1qeK4XfPq/wbHqxyuMiYiIJBHNkMXQsg07ufjpmbFphbQ/E2Y8Bp/8GzwHTrsDuv6p8F5fRERE4kaBLEYO5Dh/eXUB7s5r13ehcfXyhXuBqffD9MfghPOgx/1QpUHhvr6IiIjEjQJZjDz32Urmr9nGI/3bFl4Y2/B18N8azaHLTXBcd2h8euG8toiIiIRG95DFwKpNu3nogyV0P6EG55147NG/4N5t8N6d8GQXmPz34Fj5GgpjIiIiRYRmyApZTo5zx+sLKFGsGPef3/ro9hnLOQBzn4ePhsKezdB+IJz590KrVURERBKDAlkhmzB7DTNXbOGffVpTq1Lpo3uxL8bAO7dA/VOg1+tQ+8RCqVFEREQSiwJZIVq/fS8PTFpMlyZV6d/xCNsT7VgPO9ZB3fbQ9lIoWxVa9IZY7OgvIiIiCUH3kBUSd+euNxaSnZPDsD5tCr5Umb0v2MLisfbw5nWQkwMlykDL8xXGREREijjNkBWS9PnrmPL1BoaccwL1q5aN/hPdYen78N4dsHUlNP99sI1FMWVlERGRVKFAVgg27drHvekZtKtfmau6NirYJ6+YCi/1g2rN4PI3oMmZsSlSREREEpYCWSG4Nz2D3fsOMPzCNqQVi2J5MXMHfL8AGv4GGp8BfZ6BlhdAWonYFysiIiIJR+tiR+mDjO+ZuGA9N555HE1rVjj0yTk5wTYWj7WHCZfCvl3B/WFtLlYYExERSWGaITsK2/fuZ8ibC2leqwLXnd7k0CevnQPv3g7ffQF1O8HZw6FUIbdTEhERkaSkQHYUHnhnMZt3ZzHqyo6USDvEZOPm5fBsdyhfEy4YGcyI6Z2TIiIiEqFAdoQ+W7aJl+es4brTmtC6bqWDT8jOgm+nB+2NqjaBC5+F48+CUodZ1hQREZGUo3vIjsDW3Vnc9up8Glcrx83dmx58wjeT4clTYPwFsGVlcKx1X4UxERERyZcCWQHl5Dh/fmUem3Zl8Uj/dpQukfbzk5uXw4v94IW+weNLX4FjCrgNhoiIiKQcLVkW0BPTljFtyUbuP7/VL5cqM3fAyNODjV573A+d/gDFS4ZWp4iIiCQPBbICmL5sE/+evJTebY/lspPrB9tYrJwWbOZauiL0HgH1ToYKNcMuVURERJKIliyj9MOOTG6aMJfG1cvzwAWtsXVzYfRZwX1iKz8JTmpxnsKYiIiIFJhmyKKQfSCHG1+ay+59B3jlsvqUe+/mYIPXctWh9xPQoGvYJYqIiEgSUyCLwkMfLGXWyi08fHFrGr99PmxbDV1ugN/eHixVioiIiBwFBbLD+GjxD8z9eCKXdezGBSfVh4rDoXIDqJbPdhciIiIiRyCmgczMegKPAGnAs+4+LM/zpYBxQHtgM9DP3VfFsqaCWLdyMfbyjbxcajb761cD2sFx3cMuS0RERIqYmN3Ub2ZpwAigF9ACuMTMWuQ5bRCw1d2PAx4GHoxVPQWSc4Dsyf+g2thT6cwCtp5yJyXaXRJ2VSIiIlJExfJdlp2AZe6+wt2zgAlA7zzn9AbGRj5+DehmFm6Txx2Z+9m1diHFP/s3Ew+czOfnTKbKWXdA8VJhliUiIiJFWCwDWR1gTa7HayPH8j3H3bOB7UDVvC9kZoPNbI6Zzdm4cWOMyg1UPqY6m0rXY2iNh9nZawRndDwxptcTERERieU9ZPnNdPkRnIO7jwRGAnTo0OGg5wvTFRefH8uXFxERETlILGfI1gL1cj2uC6z7tXPMrDhQCdgSw5pEREREEk4sA9lsoKmZNTKzkkB/ID3POenAlZGP+wJT3D2mM2AiIiIiiSZmS5bunm1mNwDvE2x7MdrdM8xsKDDH3dOBUcB4M1tGMDPWP1b1iIiIiCSqmO5D5u6TgEl5jt2d6+NM4KJY1iAiIiKS6NRcXERERCRkCmQiIiIiIVMgExEREQmZApmIiIhIyBTIREREREKmQCYiIiISMgUyERERkZApkImIiIiETIFMREREJGSWbK0jzWwjsDrGl6kGbIrxNaTgNC6JR2OSmDQuiUdjkpjiMS4N3L364U5KukAWD2Y2x907hF2H/JLGJfFoTBKTxiXxaEwSUyKNi5YsRUREREKmQCYiIiISMgWy/I0MuwDJl8Yl8WhMEpPGJfFoTBJTwoyL7iETERERCZlmyERERERCltKBzMx6mtkSM1tmZnfk83wpM3s58vznZtYw/lWmnijG5RYzW2RmC8zsIzNrEEadqeRwY5LrvL5m5maWEO9aKsqiGRMzuzjyvZJhZi/Gu8ZUFMXPr/pmNtXM5kZ+hp0dRp2pxMxGm9kGM1v4K8+bmT0aGbMFZnZSvGuEFA5kZpYGjAB6AS2AS8ysRZ7TBgFb3f044GHgwfhWmXqiHJe5QAd3bwO8BgyPb5WpJcoxwcwqADcBn8e3wtQTzZiYWVPgTqCru7cEbo57oSkmyu+VIcAr7t4O6A88Ed8qU9IYoOchnu8FNI38GQw8GYeaDpKygQzoBCxz9xXungVMAHrnOac3MDby8WtANzOzONaYig47Lu4+1d33RB7OBOrGucZUE833CsB9BOE4M57FpahoxuRaYIS7bwVw9w1xrjEVRTMuDlSMfFwJWBfH+lKSu38MbDnEKb2BcR6YCVQ2s9rxqe5nqRzI6gBrcj1eGzmW7znung1sB6rGpbrUFc245DYIeDemFclhx8TM2gH13H1iPAtLYdF8nxwPHG9mn5nZTDM71AyBFI5oxuVeYICZrQUmATfGpzQ5hIL+3omJ4vG+YALJb6Yr71tOozlHClfUX3MzGwB0AE6LaUVyyDExs2IES/oD41WQRPV9UpxgCeZ0glnkT8yslbtvi3FtqSyacbkEGOPu/zKzU4DxkXHJiX158isS4nd9Ks+QrQXq5Xpcl4Onjn86x8yKE0wvH2raU45eNOOCmXUH7gLOc/d9caotVR1uTCoArYBpZrYK6Ayk68b+mIr259db7r7f3VcCSwgCmsRONOMyCHgFwN1nAKUJ+ilKeKL6vRNrqRzIZgNNzayRmZUkuLkyPc856cCVkY/7AlNcG7fF2mHHJbI89jRBGNN9MbF3yDFx9+3uXs3dG7p7Q4L7+s5z9znhlJsSovn59SZwBoCZVSNYwlwR1ypTTzTj8i3QDcDMTiAIZBvjWqXklQ5cEXm3ZWdgu7uvj3cRKbtk6e7ZZnYD8D6QBox29wwzGwrMcfd0YBTBdPIygpmx/uFVnBqiHJf/AcoDr0beY/Gtu58XWtFFXJRjInEU5Zi8D/Qws0XAAeA2d98cXtVFX5Tj8hfgGTP7M8Gy2ED9j35smdlLBEv31SL37t0DlABw96cI7uU7G1gG7AGuCqVO/TsQERERCVcqL1mKiIiIJAQFMhEREZGQKZCJiIiIhEyBTERERCRkCmQiIiIiIVMgE5FCZWYHzGxerj8ND3FuQzNbWAjXnGZmS8xsfqRVULMjeI3rzOyKyMcDzezYXM89m19D9aOsc7aZtY3ic242s7JHe20RSWwKZCJS2Pa6e9tcf1bF6bqXufuJwFiCveoKxN2fcvdxkYcDgWNzPXeNuy8qlCp/rvMJoqvzZkCBTKSIUyATkZiLzIR9YmZfRv50yeeclmY2KzKrtsDMmkaOD8h1/GkzSzvM5T4Gjot8bjczm2tmX5nZaDMrFTk+zMwWRa7zUOTYvWZ2q5n1JeiR+kLkmmUiM1sdzOx6Mxueq+aBZvbYEdY5g1wNjM3sSTObY2YZZvaPyLGbCILhVDObGjnWw8xmRL6Or5pZ+cNcR0SSgAKZiBS2MrmWK9+IHNsA/M7dTwL6AY/m83nXAY+4e1uCQLQ20lqmH9A1cvwAcNlhrn8u8JWZlQbGAP3cvTVBZ5LrzewY4AKgpbu3Ae7P/cnu/howh2Amq62778319GtAn1yP+wEvH2GdPQnaG/3oLnfvALQBTjOzNu7+KEFPvTPc/YxIC6QhQPfI13IOcMthriMiSSBlWyeJSMzsjYSS3EoAj0fumTpA0FcxrxnAXWZWF3jd3b8xs25Ae2B2pE1WGYJwl58XzGwvsAq4EWgGrHT3pZHnxwL/BTwOZALPmtk7wMRo/2LuvtHMVkT63X0TucZnkdctSJ3lCFrrnJTr+MVmNpjg53JtoAWwIM/ndo4c/yxynZIEXzcRSXIKZCISD38GfgBOJJiZz8x7gru/aGafA+cA75vZNYABY939ziiucVnuhuZmVjW/kyL9BjsRNHjuD9wAnFmAv8vLwMXA18Ab7u4WpKOo6wTmA8OAEUAfM2sE3Ap0dPetZjaGoOl0XgZMdvdLClCviCQBLVmKSDxUAta7ew5wOcHs0C+YWWNgRWSZLp1g6e4joK+Z1Yicc4yZNYjyml8DDc3suMjjy4H/RO65quTukwhumM/vnY47gQq/8rqvA+cDlxCEMwpap7vvJ1h67BxZ7qwI7Aa2m1lNoNev1DIT6Prj38nMyppZfrONIpJkFMhEJB6eAK40s5kEy5W78zmnH7DQzOYBzYFxkXc2DgE+MLMFwGSC5bzDcvdM4CrgVTP7CsgBniIINxMjr/cfgtm7vMYAT/14U3+e190KLAIauPusyLEC1xm5N+1fwK3uPh+YC2QAowmWQX80EnjXzKa6+0aCd4C+FLnOTIKvlYgkOXP3sGsQERERSWmaIRMREREJmQKZiIiISMgUyERERERCpkAmIiIiEjIFMhEREZGQKZCJiIiIhEyBTERERCRkCmQiIiIiIft/Zq/RledxUf8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot ROC Curve\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,6)) \n",
    "plt.title('Revceiver Operating Characterstic') \n",
    "plt.plot(fpr_knn, tpr_knn) \n",
    "plt.plot([0,1],ls='--') \n",
    "plt.plot([0,0],[1,0],c='.5') \n",
    "plt.plot([1,1],c='.5')\n",
    "plt.xlabel('False Positive Rate') \n",
    "plt.ylabel('True Positive Rate') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5629643663700987\n"
     ]
    }
   ],
   "source": [
    "#Calculate area under the curve\n",
    "auc_knn = roc_auc_score(y_test, y_proba) \n",
    "print(auc_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore And Preprocess Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data set into DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv(TASK_FOLDER + 'in_hospital_mortality_48_day_min.csv', \n",
    "                   header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cases:  2423\n",
      "Number of controls :  15480\n",
      "\n",
      "Proportion of case: 13.53%\n"
     ]
    }
   ],
   "source": [
    "# explore the number of cases (death) and controls (alive) \n",
    "n_case = len(data[data['label'] == 1])\n",
    "n_control = len(data[data['label'] == 0]) \n",
    "\n",
    "print('Number of cases: ', n_case) \n",
    "print('Number of controls : ', n_control)\n",
    "print()\n",
    "print('Proportion of case: %.2f%%' % (100 * n_case / (n_control + n_case)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of subjects')"
      ]
     },
     "execution_count": 57,
     "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": [
    "# visualize case-control distribution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.bar(['Cases (death)','Controls (alive, discharge)'], [n_case, n_control]) \n",
    "plt.ylabel('number of subjects') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subj                            0.000000\n",
      "Capillary refill rate_min       0.981958\n",
      "Diastolic blood pressure_min    0.011953\n",
      "Fraction inspired oxygen_min    0.703458\n",
      "Glascow coma scale total_min    0.419818\n",
      "Glucose_min                     0.000950\n",
      "Heart Rate_min                  0.011953\n",
      "Height_min                      0.810926\n",
      "Mean blood pressure_min         0.012065\n",
      "Oxygen saturation_min           0.006703\n",
      "Respiratory rate_min            0.012288\n",
      "Systolic blood pressure_min     0.011953\n",
      "Temperature_min                 0.019494\n",
      "Weight_min                      0.270960\n",
      "pH_min                          0.174663\n",
      "label                           0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# assess missing values\n",
    "print(data.isnull().sum() / len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop feature 'Capillary refill rate_min' due to its high missingness\n",
    "data = data.drop(columns = ['Capillary refill rate_min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Diastolic blood pressure_min</th>\n",
       "      <th>Fraction inspired oxygen_min</th>\n",
       "      <th>Glascow coma scale total_min</th>\n",
       "      <th>Glucose_min</th>\n",
       "      <th>Heart Rate_min</th>\n",
       "      <th>Height_min</th>\n",
       "      <th>Mean blood pressure_min</th>\n",
       "      <th>Oxygen saturation_min</th>\n",
       "      <th>Respiratory rate_min</th>\n",
       "      <th>Systolic blood pressure_min</th>\n",
       "      <th>Temperature_min</th>\n",
       "      <th>Weight_min</th>\n",
       "      <th>pH_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17689.000000</td>\n",
       "      <td>5309.000000</td>\n",
       "      <td>10387.000000</td>\n",
       "      <td>17886.000000</td>\n",
       "      <td>17689.000000</td>\n",
       "      <td>3385.000000</td>\n",
       "      <td>17687.000000</td>\n",
       "      <td>17783.000000</td>\n",
       "      <td>17683.000000</td>\n",
       "      <td>17689.000000</td>\n",
       "      <td>17554.000000</td>\n",
       "      <td>13052.000000</td>\n",
       "      <td>14776.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.964459</td>\n",
       "      <td>0.422389</td>\n",
       "      <td>8.767113</td>\n",
       "      <td>95.307206</td>\n",
       "      <td>68.431251</td>\n",
       "      <td>168.548597</td>\n",
       "      <td>53.988573</td>\n",
       "      <td>84.016775</td>\n",
       "      <td>8.605129</td>\n",
       "      <td>85.950386</td>\n",
       "      <td>35.805119</td>\n",
       "      <td>81.416817</td>\n",
       "      <td>6.600238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.032213</td>\n",
       "      <td>0.155498</td>\n",
       "      <td>4.790284</td>\n",
       "      <td>27.783699</td>\n",
       "      <td>14.676653</td>\n",
       "      <td>14.862592</td>\n",
       "      <td>15.226982</td>\n",
       "      <td>14.834182</td>\n",
       "      <td>5.549584</td>\n",
       "      <td>20.012562</td>\n",
       "      <td>2.135860</td>\n",
       "      <td>24.508092</td>\n",
       "      <td>1.955086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>35.555556</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>7.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>36.388889</td>\n",
       "      <td>93.326554</td>\n",
       "      <td>7.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>429.000000</td>\n",
       "      <td>107.333000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Diastolic blood pressure_min  Fraction inspired oxygen_min  \\\n",
       "count                  17689.000000                   5309.000000   \n",
       "mean                      39.964459                      0.422389   \n",
       "std                       12.032213                      0.155498   \n",
       "min                      -13.000000                      0.000000   \n",
       "25%                       34.000000                      0.400000   \n",
       "50%                       41.000000                      0.400000   \n",
       "75%                       47.000000                      0.500000   \n",
       "max                       89.000000                      1.000000   \n",
       "\n",
       "       Glascow coma scale total_min   Glucose_min  Heart Rate_min  \\\n",
       "count                  10387.000000  17886.000000    17689.000000   \n",
       "mean                       8.767113     95.307206       68.431251   \n",
       "std                        4.790284     27.783699       14.676653   \n",
       "min                        3.000000      0.000000        0.000000   \n",
       "25%                        3.000000     78.000000       59.000000   \n",
       "50%                        8.000000     94.000000       68.000000   \n",
       "75%                       14.000000    110.000000       78.000000   \n",
       "max                       15.000000    592.000000      140.000000   \n",
       "\n",
       "        Height_min  Mean blood pressure_min  Oxygen saturation_min  \\\n",
       "count  3385.000000             17687.000000           17783.000000   \n",
       "mean    168.548597                53.988573              84.016775   \n",
       "std      14.862592                15.226982              14.834182   \n",
       "min       0.000000               -37.000000               0.000000   \n",
       "25%     160.000000                48.000000              79.000000   \n",
       "50%     170.000000                55.000000              90.000000   \n",
       "75%     178.000000                62.000000              93.000000   \n",
       "max     429.000000               107.333000             100.000000   \n",
       "\n",
       "       Respiratory rate_min  Systolic blood pressure_min  Temperature_min  \\\n",
       "count          17683.000000                 17689.000000     17554.000000   \n",
       "mean               8.605129                    85.950386        35.805119   \n",
       "std                5.549584                    20.012562         2.135860   \n",
       "min                0.000000                     0.000000        -3.000000   \n",
       "25%                3.000000                    77.000000        35.555556   \n",
       "50%                9.000000                    86.000000        36.000000   \n",
       "75%               13.000000                    97.000000        36.388889   \n",
       "max               29.000000                   157.000000        39.000000   \n",
       "\n",
       "         Weight_min        pH_min  \n",
       "count  13052.000000  14776.000000  \n",
       "mean      81.416817      6.600238  \n",
       "std       24.508092      1.955086  \n",
       "min        0.000000      0.000000  \n",
       "25%       65.500000      5.500000  \n",
       "50%       78.000000      7.240000  \n",
       "75%       93.326554      7.350000  \n",
       "max      280.000000     99.000000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns = ['label']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13427, 13) (4476, 13) (13427,) (4476,)\n"
     ]
    }
   ],
   "source": [
    "# split data into training (75%) and test (25%) sets\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "y = data['label'].values\n",
    "X = data.drop(columns = ['Subj', 'label']).values\n",
    "feature_used = data.drop(columns = ['Subj', 'label']).columns.tolist()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.25, random_state=0) \n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute the missing values\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "imputer.fit(X_train)\n",
    "X_train = np.array(imputer.transform(X_train), dtype=np.float32)\n",
    "X_test = np.array(imputer.transform(X_test), dtype=np.float32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler() \n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build The Logistic Regression Classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build logistic regression classifer\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "\n",
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set parameters for GridSearchCV \n",
    "params = {\n",
    "    'penalty':['l2'],\n",
    "    'C':[0.01,0.1,1,10,100, 200, 300], \n",
    "    'class_weight':['balanced',None]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 100, 'class_weight': None, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train model with grid search\n",
    "grid = GridSearchCV(lr, param_grid=params, scoring='roc_auc', cv=10)\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "# print best parameters choosen through GridSearchCV\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions on the test set \n",
    "y_pred = grid.best_estimator_.predict(X_test)\n",
    "y_proba = grid.best_estimator_.predict_proba(X_test)[:,1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      1.00      0.93      3881\n",
      "           1       0.33      0.01      0.01       595\n",
      "\n",
      "    accuracy                           0.87      4476\n",
      "   macro avg       0.60      0.50      0.47      4476\n",
      "weighted avg       0.80      0.87      0.81      4476\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# output the precision, recall, and f1-score\n",
    "from sklearn.metrics import classification_report \n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create true and false positive rates\n",
    "from sklearn.metrics import roc_auc_score,roc_curve \n",
    "\n",
    "fpr_lr, tpr_lr, threshold_lr = roc_curve(y_test,y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10,6)) \n",
    "plt.title('Revceiver Operating Characterstic') \n",
    "plt.plot(fpr_lr, tpr_lr) \n",
    "plt.plot([0,1],ls='--') \n",
    "plt.plot([0,0],[1,0],c='.5') \n",
    "plt.plot([1,1],c='.5')\n",
    "plt.xlabel('False Positive Rate') \n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6522887846197483\n"
     ]
    }
   ],
   "source": [
    "# calculate area under the curve\n",
    "auc_lr = roc_auc_score(y_test, y_proba) \n",
    "print(auc_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "fig.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0.08, hspace=.5, wspace=.15)\n",
    "importances = grid.best_estimator_.coef_[0]\n",
    "indices = np.argsort(abs(importances))[::-1]\n",
    "plt.title(\"Feature importance\")\n",
    "plt.barh(list(range(X.shape[1]))[:], list(importances[indices])[:], align=\"center\")\n",
    "plt.yticks(list(range(X.shape[1]))[:], (np.array(feature_used)[indices]).tolist()[:])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improve Prediction Performance\n",
    "\n",
    "Optimize the aggregation strategy to extract more information from the time sequence data, e.g., more chunks, more aggregation methods like max, min, mean, SD, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data set into DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv(TASK_FOLDER + 'in_hospital_mortality_48_day_chunk_12.csv', \n",
    "                   header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17903, 226)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cases:  2423\n",
      "Number of controls :  15480\n",
      "\n",
      "Proportion of case: 13.53%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of subjects')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7SaerzMzMuuVrf5mZWWWcVMzMrDI9JhVJAyT9slGVMTOz1tZjUslDdJ+X9MYG1cfMzFpYmY76F4AFkmYCf+sMRsQZ3a9iZmYbojJJ5Zb8MDMz61GZ36lMk/R6YFREPNyAOpmZWYsqc0HJfwbuJ91bBUm7S5pR74qZmVnrKTOk+POky6c8CxAR95MuW29mZraGMkllVUSs6BKred8SMzPbsJXpqH9Q0v8CBkgaS7q//O/qWy0zM2tFZVoqpwM7Ay8C1wF/BT5Rz0qZmVlrKjP663ngXEkXpdlYWf9qmZlZKyoz+mtvSQuA+aQfQT4gaa/6V83MzFpNmT6VK4GPR8RvACQdQLpx1671rJiZmbWeMn0qKzsTCkBE/BbwKTAzM1tLt0lF0p6S9gRmS7pC0kGS3i3pMmBWf3YqaZCkGyT9XtJDkt4haYikmZIW57+Dc1lJukRSu6T5uU6d25mQyy+WNKE/dTIzs/7r6fTX17vMF+8p39/fqfwH8POIOFrSxsBmwGeA2yPiQkmTgcnA2cDhwNj82Be4HNhX0pBcp7Zcn3mSZkTEM/2sm5mZ9VG3SSUiDq7HDiVtCRwIfCTv5yXgJUnjgYNysWmk1tDZwHjg6ogI4O7cyhmWy86MiOV5uzOBcaRhz2Zm1gS9dtRLGgScBIwulu/Hpe+3A5YB35W0GzAPOBPYNiKezNt+UtI2ufxw4InC+h051l3czMyapExH/a2khLKAlAA6H301ENgTuDwi9iDdo2VyD+VVIxY9xNfegDRJ0lxJc5ctW7au9TUzs5LKDCneNCI+VeE+O4COiLgnz99ASipPSRqWWynDgKWF8iML648AluT4QV3is2rtMCKmAFMA2trafN0yM7M6KdNSuUbSKZKG5RFaQ3IneZ9ExF+AJyTtmEOHAIuAGUDnCK4JwE15egZwUh4Fth+wIp8muw04VNLgPFLs0BwzM7MmKdNSeQn4KnAur55eClLfSF+dDlybR349ApxMSnDXS5oIPA4ck8veCrwfaAeez2WJiOWSvgjMyeXO7+y0NzOz5iiTVD4FbB8RT1e103xPlrYaiw6pUTaAU7vZzlRgalX1MjOz/ilz+mshqYVgZmbWozItldXA/ZLuIF3+HujXkGIzM3uNKpNUfpIfZmZmPSpzP5VpjaiImZm1vjK/qH+UGj8qjIj+jP4yM7PXoDKnv4qjtDYlDfXt8+9UzMzstavX0V8R8T+Fx58j4pvAexpQNzMzazFlTn/tWZjdiNRy2aJuNTIzs5ZV5vRX8b4qq4DHgGPrUhszM2tpZUZ/1eW+KmZm9tpT5vTXJsCHWPt+KufXr1pmZtaKypz+uglYQbqHyou9lDUzsw1YmaQyIiLG1b0mZmbW8spcUPJ3kt5e95qYmVnLK9NSOQD4SP5l/Yuk2/hGROxa15qZmVnLKZNUDq97LczM7DWhzJDiPzWiImZm1vrK9KmYmZmV0rSkImmApPsk3Zznx0i6R9JiST/I969H0iZ5vj0vH13Yxjk5/rCkw5pzJGZm1qmZLZUzgYcK8xcBF0fEWOAZYGKOTwSeiYjtgYtzOSTtBBwP7AyMAy6TNKBBdTczsxqaklQkjQCOAL6T50W68vENucg04Kg8PT7Pk5cfksuPB6ZHxIsR8SjQDuzTmCMwM7NamtVS+SZwFvBKnt8KeDYiVuX5DmB4nh4OPAGQl6/I5f8Rr7HOGiRNkjRX0txly5ZVeRxmZlbQ8KQi6UhgaUTMK4ZrFI1elvW0zprBiCkR0RYRbUOHDl2n+pqZWXllfqdStf2BD0h6P+lOkluSWi6DJA3MrZERwJJcvgMYCXRIGgi8EVheiHcqrmNmZk3Q8JZKRJwTESMiYjSpo/1XEXEicAdwdC42gXQhS4AZeZ68/FcRETl+fB4dNgYYC8xu0GGYmVkNzWipdOdsYLqkLwH3AVfm+JXANZLaSS2U4wEiYqGk64FFpJuHnRoRqxtfbTMz69TUpBIRs4BZefoRaozeiogXgGO6Wf8C4IL61dDMzNaFf1FvZmaVcVIxM7PKOKmYmVllnFTMzKwyTipmZlYZJxUzM6uMk4qZmVXGScXMzCrjpGJmZpVxUjEzs8o4qZiZWWWcVMzMrDJOKmZmVhknFTMzq4yTipmZVWZ9uknXem/05FuaXQVbTz124RHNroLZesEtFTMzq0zDk4qkkZLukPSQpIWSzszxIZJmSlqc/w7OcUm6RFK7pPmS9ixsa0Iuv1jShO72aWZmjdGMlsoq4H9HxNuA/YBTJe0ETAZuj4ixwO15HuBwYGx+TAIuh5SEgPOAfUm3IT6vMxGZmVlzNDypRMSTEXFvnl4JPAQMB8YD03KxacBReXo8cHUkdwODJA0DDgNmRsTyiHgGmAmMa+ChmJlZF03tU5E0GtgDuAfYNiKehJR4gG1yseHAE4XVOnKsu7iZmTVJ05KKpM2BHwGfiIi/9lS0Rix6iNfa1yRJcyXNXbZs2bpX1szMSmlKUpH0OlJCuTYibszhp/JpLfLfpTneAYwsrD4CWNJDfC0RMSUi2iKibejQodUdiJmZraEZo78EXAk8FBHfKCyaAXSO4JoA3FSIn5RHge0HrMinx24DDpU0OHfQH5pjZmbWJM348eP+wIeBBZLuz7HPABcC10uaCDwOHJOX3Qq8H2gHngdOBoiI5ZK+CMzJ5c6PiOWNOQQzM6ul4UklIn5L7f4QgENqlA/g1G62NRWYWl3tzMysP/yLejMzq4yTipmZVcZJxczMKuOkYmZmlXFSMTOzyjipmJlZZZxUzMysMk4qZmZWGScVMzOrjJOKmZlVxknFzMwq46RiZmaVcVIxM7PKOKmYmVllnFTMzKwyTipmZlYZJxUzM6uMk4qZmVWm5ZOKpHGSHpbULmlys+tjZrYha+mkImkA8J/A4cBOwAmSdmpurczMNlwtnVSAfYD2iHgkIl4CpgPjm1wnM7MNVqsnleHAE4X5jhwzM7MmGNjsCvSTasRirULSJGBSnn1O0sN1rdWGY2vg6WZXYn2gi5pdA+uGX6NZBa/RN5cp1OpJpQMYWZgfASzpWigipgBTGlWpDYWkuRHR1ux6mHXHr9HGa/XTX3OAsZLGSNoYOB6Y0eQ6mZltsFq6pRIRqySdBtwGDACmRsTCJlfLzGyD1dJJBSAibgVubXY9NlA+pWjrO79GG0wRa/Vrm5mZ9Umr96mYmdl6xEnF7DVK0j9Jmi7pj5IWSbpV0g593NYnJG3Wh/VmSSo9+irv56Reynxe0qfz9PmS3ruu9VoXkp7Lf98k6YY+rH+VpKOrr1mP+zxS0hcauc9OTiotqMoPi37UYQ9J3+lm2WOStu7jdtf48Op8Q9cod5qkk/uyjw2BJAE/BmZFxFsiYifgM8C2fdzkJ4CaSSVfLqnfJA0EPgp8v+w6EfG5iPhlFfsvsa8lEdHo5CBJffmcvgX4QF++CPSXk0qLqcOHRV99Bri0Dtvt9sOri6nAGXXY/2vFwcDLEfHtzkBE3B8Rv8kfVF+V9KCkBZKOA5B0UG5Z3CDp95KuzWXPAN4E3CHpjlz2udxKuAd4h6RDJN2XtzdV0ibFykgakL+xd+7zkzXq/B7g3ohYldc5RdIcSQ9I+lGtD8jOVoCkwyVdX4gfJOmnefpQSXdJulfSDyVt3tMTl3+icFfe9xcL8dGSHszTO0uaLel+SfMljc3xk/L8A5KuKWz2QEm/k/RIZ6tF0uaSbs/1WiBpfGE/D0m6DLgXGClpoqQ/5P/Pf0n6Vi47ND83c/Jj//y/DmAWcGRPx1oXEeFHCz1Ib7w7u1m2OXB7fiEuAMbn+BtI31weAB4EjsvxvYBfA/NIw7KH5fgZwCJgPjC9xn62AB4uzG8F/AK4D7gC+BOwdV72r8Bs4P68bECOXw7MBRYCXyjs96Vc9zty7Dngglz3u4FtC/v9MbBPs/8n6+MjP5cXd7PsQ8BM0jD8bYHHgWHAQcAK0o+INwLuAg7I6zzW+T/N8wEcm6c3JV0uaYc8fzXwiTw9C2jLr7WZhfUH1ajXF4DTi6+rwvSXOpcBnwc+naevAo4mjWR9HHhD4fX1r6Rf1N9ZiJ8NfK6X524GcFKePhV4Lk+PBh7M05cCJ+bpjYHXAzsDDxde+0MKdfxhfk53Il2vkFznLfP01kA76Soho4FXgP3ysjfl538I8DrgN8C38rLvF/5Ho4CHCsdxInBpo197bqm0nl1ISaCWF4APRsSepG+qX88tm3HAkojYLSJ2AX4u6XWkN8bREbEX6Zv/BXk7k4E9ImJX4N9r7KeNlJw6nQf8NiL2IL0hRwFIehtwHLB/ROwOrCa90AHOjfRL512Bd0vaNSIuIV0R4eCIODiXewNwd0TsRvpwOKWw37nAu3p6sqymA4DrImJ1RDxF+mKxd142OyI6IuIV0heB0d1sYzXwozy9I/BoRPwhz08DDuxS/hFgO0mXShoH/LXGNocBywrzu0j6jaQFpNfNzt0dUKTWzc+Bf86n0Y4AbgL2I32Q/7ek+4EJ9H65kf2B6/L0Nd2UuQv4jKSzgTdHxN9JX/huiIinc52WF8r/JCJeiYhFvHpWQcCXJc0Hfkm6bmHnsj9FxN15eh/g1xGxPCJeJiWoTu8FvpWPbQawpaQt8rKlpITUUC3/OxVbQ+eL9EDSN53OF+kC4GuSLgJujnQKZBdSgpqZ8g4DgCfzduYD10r6CfCTGvvp+uY/EPgXgIi4RdIzOX4I6RvqnLyP15Ne6ADHKl2TbWDe3k55v129BNycp+cB7yssWwq8tacnZAO2kPQNvpZa18zr9GJhejXdf0a8EBGrS2wPgIh4RtJuwGGkb//HkvpPiv5OavV0ugo4KiIekPQRUkuqJz/I214OzImIlflL1cyIOKG3Onatco8LI76fT/0dAdwm6d9Iz0N36xWf187n60RgKLBXRLws6TFePf6/1Shfy0bAO3JS62pT0nPaUG6ptJ6FpA/qWoov0t2Bp4BN8zfIvUjJ5SuSPkd6oS6MiN3z4+0RcWjezhGk+9TsBczL3/yKur75ofabScC0wj52jIjPSxoDfBo4JLeGbqmxvU4vR27Ls/aHXFPeNC3iV8Amkv7RspO0t6R3k1p8x+V+jqGkLwWze9neStJpz1p+D4yWtH2e/zCp9fMPSgM3NoqIHwGfBfassZ2HgO0L81sAT+ZW9Yk1ync1K2/3FFKCgXTKdP/OuknaTHlQi6SvSPpgje38N+mST3S3X0nbAY/k1vUMUov7dtKXpa1ymSG91PeNwNKcUA6m+xbUbFJrfnB+L36osOwXwGmFeu1eWLYDa55RaAgnldbT04dFzReppDcBz0fE94Cvkd54DwNDJb0jl3ld7nzcCBgZEXcAZwGDSH01RV3f/HeS33ySDgcG5/jtwNGStsnLhkh6M7Al6ZvYCknbkm6y1qmnD6+umvKmaQU5EX8QeJ/SKMGFpL6IJaS+qPmkfqpfAWdFxF962eQU4GfKHfVd9vUCcDLww3yq6hXg212KDQdm5dM0VwHn1NjHz1jztNlngXtI/T+/76V+5JbTzaTX0805tgz4CHBdPs10N6+2bt8O1DruM4FTJc0hvadqOQ54MB/PW4GrI10i6gLg15IeAL7RS5WvBdokzSW9f2oeY0T8Gfgy6bn4Jam/c0VefEbexnxJi1jzdPXBpC9sjdXoThw/+v8gnSe9HvgjqeVyCzCW1Nl3F6mv4TukD//RpFMO80nnyOcAbXk7u5MSwgN5O6eQOgJ/S2rVPAhM7qYOC4At8nRnR/29wMWs2VF/XN7vfNLpq87Ox6ty/W4BbgQ+kuOnk95cd+T55wr7PBq4qjB/L4XOYz9a/0FKeGMbtK/bmn2861DXzfPfgcBPSX2nPZXfFri9GXX1ZVqsT5SGhK6MiJq/VWnA/vcAPhURH27G/q0+JO1IGuF3Z7Prsj6R9DVSp/ympC9wZ0YPH96S9iadOr6/QVV8dd9OKtYXkjYFjomI7kbH1Hv/7wMWR8Rjzdi/mdXmpGJmZpVxR72ZmVXGScXMzCrjpGJmZpVxUjEzs8o4qZiZWWX+P3Ub2AzZu+dGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explore the number of cases (death) and controls (alive) \n",
    "n_case = len(data[data['label'] == 1])\n",
    "n_control = len(data[data['label'] == 0]) \n",
    "\n",
    "print('Number of cases: ', n_case) \n",
    "print('Number of controls : ', n_control)\n",
    "print()\n",
    "print('Proportion of case: %.2f%%' % (100 * n_case / (n_control + n_case)))\n",
    "\n",
    "# visualize case-control distribution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.bar(['Cases (death)','Controls (alive, discharge)'], [n_case, n_control]) \n",
    "plt.ylabel('number of subjects')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subj                                 0.000000\n",
      "Capillary refill rate_0_12_max       0.987935\n",
      "Diastolic blood pressure_0_12_max    0.014299\n",
      "Fraction inspired oxygen_0_12_max    0.740770\n",
      "Glascow coma scale total_0_12_max    0.425739\n",
      "                                       ...   \n",
      "Systolic blood pressure_36_48_SD     0.016310\n",
      "Temperature_36_48_SD                 0.031447\n",
      "Weight_36_48_SD                      0.774004\n",
      "pH_36_48_SD                          0.564710\n",
      "label                                0.000000\n",
      "Length: 226, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# assess missing values\n",
    "print(data.isnull().sum() / len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Capillary refill rate_0_12_max</th>\n",
       "      <th>Diastolic blood pressure_0_12_max</th>\n",
       "      <th>Fraction inspired oxygen_0_12_max</th>\n",
       "      <th>Glascow coma scale total_0_12_max</th>\n",
       "      <th>Glucose_0_12_max</th>\n",
       "      <th>Heart Rate_0_12_max</th>\n",
       "      <th>Height_0_12_max</th>\n",
       "      <th>Mean blood pressure_0_12_max</th>\n",
       "      <th>Oxygen saturation_0_12_max</th>\n",
       "      <th>Respiratory rate_0_12_max</th>\n",
       "      <th>...</th>\n",
       "      <th>Glucose_36_48_SD</th>\n",
       "      <th>Heart Rate_36_48_SD</th>\n",
       "      <th>Height_36_48_SD</th>\n",
       "      <th>Mean blood pressure_36_48_SD</th>\n",
       "      <th>Oxygen saturation_36_48_SD</th>\n",
       "      <th>Respiratory rate_36_48_SD</th>\n",
       "      <th>Systolic blood pressure_36_48_SD</th>\n",
       "      <th>Temperature_36_48_SD</th>\n",
       "      <th>Weight_36_48_SD</th>\n",
       "      <th>pH_36_48_SD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>216.000000</td>\n",
       "      <td>17647.000000</td>\n",
       "      <td>4641.000000</td>\n",
       "      <td>10281.000000</td>\n",
       "      <td>17046.000000</td>\n",
       "      <td>17648.000000</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>17642.000000</td>\n",
       "      <td>17735.000000</td>\n",
       "      <td>1.762200e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>15579.000000</td>\n",
       "      <td>17622.000000</td>\n",
       "      <td>374.0</td>\n",
       "      <td>17596.000000</td>\n",
       "      <td>17568.000000</td>\n",
       "      <td>17555.000000</td>\n",
       "      <td>17611.000000</td>\n",
       "      <td>17340.000000</td>\n",
       "      <td>4046.000000</td>\n",
       "      <td>7793.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.226852</td>\n",
       "      <td>98.887967</td>\n",
       "      <td>0.773646</td>\n",
       "      <td>12.078981</td>\n",
       "      <td>183.095858</td>\n",
       "      <td>101.577233</td>\n",
       "      <td>168.454174</td>\n",
       "      <td>100.991629</td>\n",
       "      <td>156.389715</td>\n",
       "      <td>1.598864e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>14.759953</td>\n",
       "      <td>6.678145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.785811</td>\n",
       "      <td>2.455197</td>\n",
       "      <td>3.445695</td>\n",
       "      <td>11.652259</td>\n",
       "      <td>0.410108</td>\n",
       "      <td>0.164228</td>\n",
       "      <td>0.071292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.419769</td>\n",
       "      <td>1281.199556</td>\n",
       "      <td>0.273719</td>\n",
       "      <td>3.563513</td>\n",
       "      <td>135.325820</td>\n",
       "      <td>22.868323</td>\n",
       "      <td>15.802664</td>\n",
       "      <td>54.012306</td>\n",
       "      <td>7366.806319</td>\n",
       "      <td>1.774439e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>23.902238</td>\n",
       "      <td>14.324916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.490046</td>\n",
       "      <td>28.524254</td>\n",
       "      <td>1.941841</td>\n",
       "      <td>20.557926</td>\n",
       "      <td>15.107534</td>\n",
       "      <td>1.370730</td>\n",
       "      <td>0.235597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.839669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.576888</td>\n",
       "      <td>0.953794</td>\n",
       "      <td>2.247794</td>\n",
       "      <td>7.738643</td>\n",
       "      <td>0.120014</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>96.333298</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.498366</td>\n",
       "      <td>5.527708</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.536863</td>\n",
       "      <td>1.443137</td>\n",
       "      <td>3.104249</td>\n",
       "      <td>10.449282</td>\n",
       "      <td>0.223766</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.976014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.208929</td>\n",
       "      <td>2.192158</td>\n",
       "      <td>4.271189</td>\n",
       "      <td>14.077938</td>\n",
       "      <td>0.366648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>114108.980000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>429.000000</td>\n",
       "      <td>6350.000000</td>\n",
       "      <td>981023.000000</td>\n",
       "      <td>2.355555e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>763.718971</td>\n",
       "      <td>1815.476894</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1763.124428</td>\n",
       "      <td>2875.574702</td>\n",
       "      <td>44.979162</td>\n",
       "      <td>2639.770787</td>\n",
       "      <td>1975.007058</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.586492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 224 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Capillary refill rate_0_12_max  Diastolic blood pressure_0_12_max  \\\n",
       "count                      216.000000                       17647.000000   \n",
       "mean                         0.226852                          98.887967   \n",
       "std                          0.419769                        1281.199556   \n",
       "min                          0.000000                          25.000000   \n",
       "25%                          0.000000                          68.000000   \n",
       "50%                          0.000000                          77.000000   \n",
       "75%                          0.000000                          89.000000   \n",
       "max                          1.000000                      114108.980000   \n",
       "\n",
       "       Fraction inspired oxygen_0_12_max  Glascow coma scale total_0_12_max  \\\n",
       "count                        4641.000000                       10281.000000   \n",
       "mean                            0.773646                          12.078981   \n",
       "std                             0.273719                           3.563513   \n",
       "min                             0.000000                           3.000000   \n",
       "25%                             0.500000                          10.000000   \n",
       "50%                             1.000000                          14.000000   \n",
       "75%                             1.000000                          15.000000   \n",
       "max                             7.100000                          15.000000   \n",
       "\n",
       "       Glucose_0_12_max  Heart Rate_0_12_max  Height_0_12_max  \\\n",
       "count      17046.000000         17648.000000      2204.000000   \n",
       "mean         183.095858           101.577233       168.454174   \n",
       "std          135.325820            22.868323        15.802664   \n",
       "min           32.000000            33.000000         0.000000   \n",
       "25%          124.000000            87.000000       160.000000   \n",
       "50%          157.000000            99.000000       170.000000   \n",
       "75%          202.000000           115.000000       178.000000   \n",
       "max         9999.000000           941.000000       429.000000   \n",
       "\n",
       "       Mean blood pressure_0_12_max  Oxygen saturation_0_12_max  \\\n",
       "count                  17642.000000                17735.000000   \n",
       "mean                     100.991629                  156.389715   \n",
       "std                       54.012306                 7366.806319   \n",
       "min                       31.000000                   42.000000   \n",
       "25%                       86.000000                   99.000000   \n",
       "50%                       96.333298                  100.000000   \n",
       "75%                      109.000000                  100.000000   \n",
       "max                     6350.000000               981023.000000   \n",
       "\n",
       "       Respiratory rate_0_12_max  ...  Glucose_36_48_SD  Heart Rate_36_48_SD  \\\n",
       "count               1.762200e+04  ...      15579.000000         17622.000000   \n",
       "mean                1.598864e+02  ...         14.759953             6.678145   \n",
       "std                 1.774439e+04  ...         23.902238            14.324916   \n",
       "min                 2.000000e+00  ...          0.000000             0.000000   \n",
       "25%                 2.100000e+01  ...          0.000000             3.839669   \n",
       "50%                 2.500000e+01  ...          8.498366             5.527708   \n",
       "75%                 2.900000e+01  ...         20.000000             7.976014   \n",
       "max                 2.355555e+06  ...        763.718971          1815.476894   \n",
       "\n",
       "       Height_36_48_SD  Mean blood pressure_36_48_SD  \\\n",
       "count            374.0                  17596.000000   \n",
       "mean               0.0                      8.785811   \n",
       "std                0.0                     14.490046   \n",
       "min                0.0                      0.000000   \n",
       "25%                0.0                      5.576888   \n",
       "50%                0.0                      7.536863   \n",
       "75%                0.0                     10.208929   \n",
       "max                0.0                   1763.124428   \n",
       "\n",
       "       Oxygen saturation_36_48_SD  Respiratory rate_36_48_SD  \\\n",
       "count                17568.000000               17555.000000   \n",
       "mean                     2.455197                   3.445695   \n",
       "std                     28.524254                   1.941841   \n",
       "min                      0.000000                   0.000000   \n",
       "25%                      0.953794                   2.247794   \n",
       "50%                      1.443137                   3.104249   \n",
       "75%                      2.192158                   4.271189   \n",
       "max                   2875.574702                  44.979162   \n",
       "\n",
       "       Systolic blood pressure_36_48_SD  Temperature_36_48_SD  \\\n",
       "count                      17611.000000          17340.000000   \n",
       "mean                          11.652259              0.410108   \n",
       "std                           20.557926             15.107534   \n",
       "min                            0.000000              0.000000   \n",
       "25%                            7.738643              0.120014   \n",
       "50%                           10.449282              0.223766   \n",
       "75%                           14.077938              0.366648   \n",
       "max                         2639.770787           1975.007058   \n",
       "\n",
       "       Weight_36_48_SD  pH_36_48_SD  \n",
       "count      4046.000000  7793.000000  \n",
       "mean          0.164228     0.071292  \n",
       "std           1.370730     0.235597  \n",
       "min           0.000000     0.000000  \n",
       "25%           0.000000     0.000000  \n",
       "50%           0.000000     0.000000  \n",
       "75%           0.000000     0.020000  \n",
       "max          50.000000     2.586492  \n",
       "\n",
       "[8 rows x 224 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns = ['label']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12532, 224) (5371, 224) (12532,) (5371,)\n"
     ]
    }
   ],
   "source": [
    "# split data into training (70%) and test (30%) sets\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "y = data['label'].values\n",
    "X = data.drop(columns = ['Subj', 'label']).values\n",
    "feature_used = data.drop(columns = ['Subj', 'label']).columns.tolist()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.30, random_state=0) \n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute the missing values\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "imputer.fit(X_train)\n",
    "X_train = np.array(imputer.transform(X_train), dtype=np.float32)\n",
    "X_test = np.array(imputer.transform(X_test), dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler() \n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'class_weight': 'balanced', 'penalty': 'l2'}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build logistic regression classifer\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "\n",
    "lr = LogisticRegression()\n",
    "\n",
    "# set parameters for GridSearchCV \n",
    "params = {\n",
    "    'penalty':['l2'],\n",
    "    'C':[1, 10, 100, 200], \n",
    "    'class_weight':['balanced', None],\n",
    "}\n",
    "\n",
    "# train model with Grid Search\n",
    "grid = GridSearchCV(lr, param_grid=params, scoring='roc_auc', cv=10)\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "# print best parameters choosen through GridSearchCV\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction on the test set \n",
    "y_pred = grid.best_estimator_.predict(X_test)\n",
    "y_proba = grid.best_estimator_.predict_proba(X_test)[:,1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.69      0.79      4641\n",
      "           1       0.23      0.60      0.34       730\n",
      "\n",
      "    accuracy                           0.68      5371\n",
      "   macro avg       0.58      0.65      0.56      5371\n",
      "weighted avg       0.82      0.68      0.73      5371\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# output the precision, recall, and f1-score\n",
    "from sklearn.metrics import classification_report \n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create true and false positive rates\n",
    "from sklearn.metrics import roc_auc_score,roc_curve \n",
    "\n",
    "fpr_lr, tpr_lr, threshold_lr = roc_curve(y_test,y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10,6)) \n",
    "plt.title('Revceiver Operating Characterstic') \n",
    "plt.plot(fpr_lr, tpr_lr) \n",
    "plt.plot([0,1],ls='--') \n",
    "plt.plot([0,0],[1,0],c='.5') \n",
    "plt.plot([1,1],c='.5')\n",
    "plt.xlabel('False Positive Rate') \n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7005112266192041\n"
     ]
    }
   ],
   "source": [
    "# calculate area under the curve\n",
    "auc_lr = roc_auc_score(y_test, y_proba) \n",
    "print(auc_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PdpTC0jfX8x+WtEyPz7Nr2vvUIYuIiIiI+ZUliwufmTXdPVBmv4Dx9e02wLqNrIrd0t7vWF//mFLfq+Uq29Nqv620979vXmx7024Dqwk9TgW+ZfuOWv/rCNoyHNo+DzivUx91v9hLJL0Y+JGkcym/xxsCH7d9paQjgf1t79RlKOdJOoWSsONdwDuBLbsNu9vz2D6WMoPG+PHjO+57i4iIiIjoJQHZomXQae+PBW61/c36fnlKYo6L6hhWAc6StIPtnqnmbd8o6YF6/TTK3q8r6+kJwP5DXH87cIyk7wPT676yTsbRJWNjRERERMT8ypLFRctA0t7Xe30ZeBZlySMAtv9te6VGKvsrgK7BmKTn11k2JD2PkozjLtt/Be6R1ErOsTXQcV9YvfYNjeLTL6QEl/PsOZO0BWX/2Pd7PVtERERExEhlhmzRshfwHUlTKN/9JcxJfd+yN3CypE9RMh/+m6EdC5wr6S+2t2o/KWkN4ABK7bFraix0lO1Oe9h62RzYX9IjwGPAno3Mjh8HTqkZFu8Adu/Rz3uBI2rikkeBd9ueXce1i6TNKVka7wR2TobFiIiIiHiiJCBbhHRLew/8k7LsD7qnvW/vq5n2fnFgQ9sPdmk7TdJXgF0pCTxGtc5J+iTwQUpgNL3+dDMV+A/wTMqs1tKNe0yi1Fj7NrC77X/16OcLwPeAFSh123am7Fm7i1KY2vXnGcCze/QTERERETFfEpAFts+iFEiGUlPsqLqk737g/X10sTdwMtAxIKt+CRwF3Np2/FpKbbEHaz2yr9M5aKT2v6vtWyWtBlwt6bxWintJ4ylB1lC+Rck2+Yt63Usb5y61/cZ6fAPgTEkzbV/QR78REREREcOSgGwRImk08F1gTD20t+3LWpkY66zXvcBMyqzXRZSC0K0ZrVGSJlASaVwNvIeyVLBVh+w+ylK/x2euqvfavqKOYa4Trfpk1RXAe2pq+kPb+rizmTXR9r2S/g6MBu6vBZ4Po2RN3Kne6zvAK9v6OZJSRHpao6+p7Z9VPT5J0sGUfXfzBGSS9qDWKBszZkz76YiIiIiIISUgW/g8o6akb3kOc2a/WnXIfi9pDGWZ3ovbrj8SONL2qZLa95eNA15CCdouo9Yhq8sOt2rs5xqpDwDn9kp73yJpE8rywtvroY8BZ9n+Syvos/2/Xa4F+J2kyymJTn7Yo5D0NcC+nU4k7X1EREREzK8EZAufgdYhGylJ76nj3KKPtqsCJwHvs/1YXb74NrrXEpuL7R9KOg/YHngz8GFJL+t2u376jIiIiIgYiQRki5ZB1yHrSNI2lCyMW9ieNUTbZ1KyP36utQySMnO3FnBbfZZlJd1me61u/di+FzgeOF7SdZRlmJ2kDllEREREPGFSh2zRMrA6ZN1IGkfJeLiD7b8P0XYp4AzgRNuntY7bPsf2Ko16Zg/2CsYkbS9pyfp6FWBFSnbJ9nbrA58HvjP8J4uIiIiIGFoCskXLDpTU8FMk3UCpxXVUW5u9gU9KuoqS/GKWpP2H6Pd3wFRJF7afkLS3pGUlfV3SNMrs1TRJB9Umh1EyI06RNEvSHeo+Zfd2ypLGr0qypJtbQaWk10q6WtJUyj661/QY77bAnyTNpNQaexTYtHWujuMhSnB6J6V+WkRERETEAic7uQgWFZJmtNUA24052RVbx5al7ENr1SF7p+03D9HvlsA+rXTxbefuqvfomvCjBn+foARAvwK+ZfvcLm1fTCkK/b16z4n1+DjgbzX74nrAebZX79LHGsDFlNpp/5Y0Chht+05JJwBn255QA8O9gY8C69l+uNszjB8/3hMndizZFhERERGLIElX2x4/VLvMkAVQUuJLOp2Szv4BSbcBewKXtWbRJK0p6QpJf5R0sKQZjS5GSZog6SZJp6jYizkp8eeZPat9rgo80/YfXP7vwInMSSoyD9s32r65w/Fr674wgOuBZSS1p99vWZmyzHJGvXaG7Ts79GnbRwB/BV7XbUwRERERESOVpB6LlmGlxLf96jqLNqbRZqiU+KdTAqpbgAfquT1tX9plTKvTqAlWX6/TNk6AWbY3pT87UwpO7yPpbW3nTgO+BvwNuFPSBcDPbf+yR3/XAOsAv2geTB2yiIiIiJhfCcgWLU9GSvxNJR0DXGb75LpksVeWwk77xf5je8j09x07k15CKSq9re3bgUO6tNse2BjYmrKXbiPbBw1jjKlDFhERERHzLQFZtAwqJf40YI3G+zUohaeHre4NOwPYtQZjXdXlkVcBV0n6DfBD4KAuzccBF4xkTBERERERvWQPWbQMJCW+7b8A/5X08ppEY1falgb2Q9IKlPpkn7F92RBtV5O0YePQBsDdHdq19sGtCvx6uGOKiIiIiBhKArJo2Yu5U+K37xGDeVPi/7uPfo8Fzu2W1KP6KHAccBtwO9AxwyKApJ1q+vzNgHMknVdPfYxSHPrzkibVn5W7dLMkcHhNQDIJ2IWS5bHlMEmTKfvgNga26pVhMSIiIiJipBb6gEzSKpJ+Iul2STdI+pWkF42wr+MkrVtf3yVppfp6Ru8rn1iS1qkByLU1E+Ll9fhYSdfV11sCFzWvs31CK+W97fts72J7fdvr2v5Is02dgXo98HLbmwA3AxNrm4uaKe9tf8z2CfX1t4HX1jHcKOl6SZ9oG8dE4ATgBcBB7lKLQdKbgS8C9wFTgZ1tb1dPnwhcBiwNLAXs2KPQ9EuBZ1OWWS4FTLB9e62N9lpKoLlsvcdX6964iIiIiIgFbqHeQ1aXwJ0B/Mj2O+qxDYD/ocx+DIvtDy6gMcn2Y8O8bnHbs7uc3hH4he0v1PevGOHYlrD9aJfTK1Bmkd5fn+F+4P19dv0o8Cnb19REIVdL+o3tG+p9n0sJhP40RD8XAGfVGmnrAz+jZD+EEpAdYvs3ta5Yx89X0pKUWbtNbE+rqfHHNpocYfvw2nYX4HeSXmp7ep/PGhERERHRt4V9hmwr4BHb320dsD3J9qWSRkm6QNI1kqbW2ZfWrNJNkn5Ul+9NUCmWjKSLJHUt7jZEnzdKOpqSQv3zko5oXPchSd/o0N+MWu/rSmAzSRtJuljS1ZLOk7SqpNdTlhJ+sLUscDgzdpJ2k3SapF8C53d7Bkqq+FUBU1PiAzup1CSbIumL3e5h+y81GLsSuJQSEJ9VZ/VeChwB7Ff7bo3rO42lh5Pq0sK3NWbPlmu1r7OWS9j+Tb3fDNsP1nNXtvVxDWUW7R+17axOdc3quZ9S9ta9q9/PMyIiIiJiOBbqGTJgPUqh404eAnay/Z+69PAKSa2aXGsDH7B9maTjKQWSD+/Sz3D63N32npKWA6ZI2s/2I8DuwIc79LcccJ3tA+vMzsXAm21Pr7M3h9h+v6TvAjNaMzsjsBmwvu1/SlqiyzPsD6zXSpsvaVvghcAmlLTwZ0l6te1Lut3E9qaSxgKXABvWe+wA/Nn2ZDUyOtr+3059SNoJ+CqluPMb6uEXAfdL+jnwfOC3wP62Z3eqXSbpOOBulRpkZwOn9pixbNUg6zSW1CGLiIiIiPmysM+Q9SLgK5KmUP6AX50ycwNwTyNT38nA5gugz7ttXwFg+wHgd8AbJa0DLGl7aof+ZlMKLUMJ6NYDflNnej7H3Oni58dvbP+zj2do2rb+XMucoOWFvW5SlxKeDuxdg7FlgQOAA/sdqO0zbK9DWab5pXp4CeBVwD6UJBwvAHbr0ccHKfXHrqrXHN9r2D36Odb2eNvjR48e3e8jREREREQ8bmGfIbseeGuXc+8GRgMb2X5EpYDxMvVce1KJfov+9urzgba2xwGfBW6i1MDq5KHGvjEB19verM+xDEdzbL2eoUmUhBff6+cGdYbvdOAU2z+vh9ekzGi1ZsfWAK6RtIntv/bqz/YlNYHJSpRaZtfavqPe60zg5cAPelw/FZgq6STgTroHcOOoyUsiIiIiIha0hX2G7HfA0pI+1DogaWNJWwDPAv5eg46tgOc1rhsjqRX4vBP4fZ/369XnXGxfCTyXsj/p1D76vhkY3RqXpCUlvaTPcQ1Ht2doryd2HiXBx6g6ntXVJc18TQLyA+BG24/vlbM91fbKtsfaHksJrDbsFoxJWqv2hUodsaUoe8H+CDxbUmua6jXADV36GKWScbKlYw2y2nZnyixgP99PRERERMSwLdQzZDUb307ANyXtT9njdRclCcb1wC8lTQQmUWaqWm4E3ifpe8CtwDF93vKUHn128jNgA9v/6uNZHpb0VuBbkp5F+e6+WZ9jQer4DLb/IekylTT659reV9KLgT/UGGkG8B6gU6r5VwLvpcxITarHPmv7V8Mc287ArpIeAWYCu9QkH7Ml7QNcUAO2q4Hvd+lDwH71u51JmR3crXH+/yS9h7p/D3hNMixGRERExBNFXUo+LXQkrUIJYDam1J+6i7KX6Za2dmOBs22v16Ov44Bv2L6hLukbb/s+STNsjxrGmM6mpFm/YJiP062/dYCfUJZYvhU4yfYrms9UZ4f2adYNG+Y9VgDeZfvoEV6/PXAksDhwnO2v9Wj7MUrwvCYw2vZ99fi7gU/XZjOAj9qePJLxNO61A7Bur/H0Mn78eE+cmJWNEREREVFIutp21wztLQv7kkVgrnpkF9le0/a6lP1bnZJVDMn2B1s1tEY4nhUk3QLMHG4wJmnxHqdb9cjG2b7d9ojrkfU4vQIl6+RI+l0c+A7wOmBd4J01ZX03lwHbMO+SwjuBLWyvT0nscexIxtNk+6yRBmMRERERESO1SARkDKMeGfCyOpP0hNUjA75CWSp3nZ6e9cjWVKnrdVi9dl+VemTXS/qr2uqHSVqxXrsJcJvtO2w/TJnNe3OHsexelzb+EDgTWA04tHXe9uWNZZ5X0CXbpKQzJN0g6SFJ/5A0U9LvJG1Tl1/eKmmTxvMfVV+fIOlbki6XdEddKtqp/z0kTZQ0cfr0rGqMiIiIiOFbqPeQNaQeWX8WSD0y4Otd6pGtDtzTeD8NmKdOmO0f0sg8WZeFfrq9XfUB4NxOJ2zvVJdr3gZsSdlv90dKIpXNgR0oM6U7drh81dpmnfpMEzr0fyx1dm78+PGLxtrfiIiIiFigFpWArJdW3a1XA4/Rux7ZXvQXkPXqc656ZJJa9chuZPj1yKDsxfpLvw87hE71yDo9Q1OzHhnAKEqA1ikg61TTa8SBjEoWyA8wdJ24O1ufq6TrgQtqwpepwNgu15xZi0XfIGlES1sjIiIiIoayqARkqUfWnye6Htk0Sqr/ljWAe0cyUEnrUz6719n+xxDNZzVeP9Z4/xjd/xtoXtO1OHRERERExPxYVPaQpR7Z8C3wemSU5YIvlPR8SUsB76AsBxwWSWOAnwPvbc+SGRERERHxdLLQB2Qq6e5PpSy3O1zSA5JuBQ6izM6cAoxXqbv1bjrXI5sCPAdYT3OyAp5V91ZBSaHf1KvPTn4GXNZvPTLKbN+hkiZTaoW9pSbB+DCwoqTL5zy+rquvl1NJs9+v9me4BXhPnY26TNJ1kg6zfT7wY0o9sqmUvVbLt3dW9+DdS/mdO4/y2f4M2E0lecqUmoRjhcY1e0maRplJmyLpV5Kurs88FjixJg2Z2LjmrMYzd/NClQQpvwLWknRQPb6OpOnAm4Bv14QpI8pUGRERERHRj4W6DpnKJqvLgR+1MixK2gBY3valQ1w7lh71yDTC+mN1TKr7k1rHhqxHJmnxxrLF9nP7A8+w/YVuz6A+6o9JWsL2o13OPd5Xt+t7qXvRZgAnNvuoSUF+Z/tRSYcC2O6YwEPSOOBvtu+VtB5wnu3VG+ffQglW1x+ijtzNwNttT1ZJxb92rSm3G+U7/VhttxUlmN/K9o29ni91yCIiIiKiSalDBgwj3b3mTk1/EyV5x1pagOnuJR0NXAN8XtIRmlOPbBXgDR36e7qku58i6Yu97lOzLv6zw/HzG0Fg1xT2te21tlt7zq4HlpG0dB3LKOCTwJf7eOyVqYlQbM/uVlPO9oWULIp79NFnRERERMSwLexJPeY33f1b9QSmuwf2s/2iusSwU0KPp1W6e0mvtn2JSt2xTrN9u3W6uaQzgOfXn/vr8stP2z6vx5h3Bq613Uq+cRglE+QE4Pm1D4CtOyT9OAK4WdJFwK8pM6gPdbnPNXQuRYCkPagxIuphAAAgAElEQVTB2pgxY3oMNSIiIiKis4U9IOsl6e7nWKDp7msAtEH7BXXZ4zxqvbADgPHAWzzEOlqVJCaH1vu3lqGuYfv5jaWV89y/cb+DJZ1Sr38XJWHLlt1u16Of1CGLiIiIiPmysAdkSXffnyc63X1Pkt4HvJEymzVUMLYGcAawq+3b6+HNgI3qWJcAVpZ0ke0tu/VTrz1G0veB6XVWr5NxlAQkEREREREL3MK+hyzp7ofviUh335Wk7YFPAzvYfnCItisA5wCfacxgYvsY26vZHkspEn1Lr2BM0htUpxkps3qzgfs7tNuCsiTx+8N6qIiIiIiIPi3UAVmdbdkJeK2k2yVdz8jS3R/T5y2f7HT3T0RK9o7PMNJ09y2STgX+AKwtaZqkD9RTR9XrflMThny3Wx/Ax4C1KElRJtWfYQeBwHspe8gmAScB727MRO5S+72FMoO581AZFiMiIiIiRmqhTnvfolKL7JvAxsAsSt2wvTsVFdbQ6e6PA75R06TfxQhS3zf6GjLd/TD6Wgf4CWV55VuBk2y/QsNMfT/EPVYA3mX76BFcuwxwCbA0ZVnhhFaa/jpb9WXgbZTZqmNsf2uI/jamZGXcxfaEeuzrlGyViwG/AT7RbQmkpPcD/0f5vBYDDrD9C0knAFsA/wGeUe/xGdt/7jWepL2PiIiIiCYl7X1R/9g/A7jI9pq216XMfHRKVDEk2x/sliZ9GGNqpbufOZxgTKVmVjc7Ar+wPc727bZHNHtWsyx2swIl4+RIzAJeY/tllIQf20t6eT23G2X55jq2X0wJLHuNcXFKUo/zGsdeAbwSWJ+S/GRjSmDV6fo1gAOAzW2vD7yckvWyZd86zrUpSUsulLTUsJ42IiIiIqIPC3tSD+hSiwwer131C+DZwJLA5+osyRtVapFdSUnqcAslicSDNVX6PrY7Tof06HMscC5wISUJxSmUAKd13YeAF9v+ZFt/M4BvANsBn5I0s74fBdxHCWbGUWqRza6p57cazoydSkHkN1CSdywnaYdOz0CjFhklM+O+kvYF3k6Z+fo1NfNhm1bq+VZ9tCXrT2v26qOUmbdWsexxqkWiG+60vVN9/XFK9smNG+ddx78UJeHIksAekr7Z1s+RwGTKnrgZALZnNMY2p8Myu3aEpJ2A19XPJCIiIiJigVkUArL5rUV2mZ7AWmSS9rP9CLA7netdPa1qkQF71SLQ86gzW1dT9oF9pyY2AViTsndrJ2B67aNj2npJq1P2Bb6GRkBm+w8qhbH/UsdylO0Deozjb8Cdki4Afm77lz0+m2uAdWgLyJQ6ZBERERExnxb6JYtDaNXcmgL8lt61yDZfAH3OVYuMkgXyjXX/13BrkU0CPges0ee4htKpFlmnZ2hq1iJrBS0v7HYD27NroLUGsImk1j69pSkp/sdTMhoe32Oc36QUjZ7dPChpLeDFte/Vgdeo1FLrOA5ge8peu1sos2AH9bhnx1pkto+1Pd72+NGjR/e4PCIiIiKis0Vhhiy1yPrzpNUis31/Xfq5PXAdMI05QecZdP8soBSP/knNWr8S8HpJj1ICwSvq8kMknUvZG9Zxtq4uR7wKuErSb+o9D+pyz3HAfCdeiYiIiIhotyjMkKUW2fAt8FpkkkbXLI1IegawDXPKApxJWYIIJRHHPNkvW2w/3/bYWnNsArCn7TOBPwFbSFqiLu3cgi4FnSWtJmnDxqENgLs7tJOkvYBVKfvjIiIiIiIWqIV+hsy2696kb0ran7LH6y5KEozrgV/WmluT6FyL7HvArQyvFlm3Pjv5GbBBv7XIJL0V+JakZ1G+v2/W51iQOj6D7X9IukzSdcC5NanHiym1yKAkxngP8PcOfa4K/Kju31oM+Jnts+u5rwGnSPq/2scHRzDmCZSgbiplNvPXPfaFLQkcLmk1yu/DdOAjjfOHSfo8sCwl7f1WtQ5cRERERMQC9aTVIZM0m/LHcsuOtu+azz53BG5ppaGXdDBwie3fzme/bwJOsL3i/PTT1udHgAdtn9h2vO9aZGrUPVtQ43qySFqREjRtTPlsP1aPLwucRknsMRv4pe39e/TzSUrA9iglkHq/7bsb559JCabPaN2jSz+pQxYRERERT5h+65A9mTNkM7tlzoNS/8r2o8Psc0fgbOAGANsHzsf4mqZSsvUtMM20+/B4keWrgCkLojD008BDwOcpSUnai24fbrtV6+sCSa+zfW6Xfq6lBKUPSvoo8HVgl8b5L1EyUXbVqEO2oe1/1yWXzawc+9qeoDLttzelDtl6mSWLiIiIiAVtoHvIJO0m6TRJvwTOlzRK0gWSrpE0VdKbG213lTRF0mRJJ6kUAt6BsrxskqQ1JZ1Ql/QhaWtJ19Z+jpe0dD1+l6QvNu6xToehjaUsa0TSQfX6iyTdUfcUIWk5SefU8VynkoK+1f+hkq6qP2s1+tmnvr4I2I8S9P2h7q86XdIf688ra7sVJZ1fn+N7dMn2J+md9VmuU63fJel5km6VtJKkxSRdKmlbSV+S9InGtYdI2qu2OVrS9ZLOlvSrxme5kaSLJV0t6TxJq7aeo/Gst0h6VR3zpPYfYBnbv6cEZo+z/aDtC+vrhynZGteQtHuHfr5j+0LbD9bLr6CRZVLSRpRskOc3jl3ZYSyvoK0Ome072z9XF0cAf6XUIWv/3PeQNFHSxOnTp3f6aiIiIiIienoyZ8ieUf8YhrmL/PZT/2pdyozGK23fJ+k5tf1ZwNm2JwCUCQ2QtAxwAqUg8S2STqQUH24VCb7P9oaS9gT2Yeg9S+tQCkwvD9ws6RhKhsB7bb+h3vNZjfb/sb2JpF3rPd/Yoc8VbG9Rr/0xZdni7yWNoSTLeDHwBeD3tg+W9AZqzasmlX1QhwIbAf+iBLY72j6zBmffpRS4vsH2+ZJuAX4OHClpMeAdlDpib6EEoi8FVqYs+zteJUHGt2mrfQa8vw5hifqsrwe+YHsbSpKMYVOZNXwTcKTtO+idbRHgA5Ri29Rn+X/Ae4GtWw1sb9rhPovXa+erDpntY4FjoSxZHGKsERERERHzeCosWexU/+rVwGPMqX/1GmBCa+9Uo303a1OCvla2vh8B/8ucgOzn9d+rKYHIUM6xPQuYJenvdUxTKYkhDqUEhZc22p/a+PeILn3+tPF6G2DdVkAJPFPS8sCrW+OzfY6kTok/NgYusj0dQNIp9bozbR8n6W2UhBUb1H7ukvQPSePqc1xbk3VsDpxm+zHgrypFlmHu2mcAizP3cs7mZzm2y7MOqQbjpwLfqsHYUO3fQ0mBv0U9tCfwK9v3ND7HjmzPlrQ95bPbmlKHbCPbB3W7XX9PERERERExPE+FLIv91L8S/dcBg6H/gJ5V/51Nf5/BrMbr2ZRZoVvqErnXA1+VdL7tg2ub5li7jbv53IsBm9me2WxQA4uhnrvrs6okzGgt6RtFWaYHpf7ZbsAqzCnC3K2foWqfDfez7OZY4Fbb3xyqoaRtKDOmW9RAGcpM66vqrOcoYClJM7olCEkdsoiIiIh4Kniq1SHrVv/qAuDtKpn6kPScery9LlbLTcDY1v4tyjK2nokehqsuFXzQ9snA4UCzrtUujX//0Ed35wOPZwSU1JpJvIQSpCLpdcCzO1x7JaX+1kp1Kd47mfOsh1JS2B8IfL9xzRmUJZcbU5ZHQqmztnPdS/Y/wJb1+BNe+0zSlynf/d59tB0HfA/Ywfbj6fVtv9v2mFqfbB/gxG7BmFKHLCIiIiKeIp7MGbLlGnvIoGRIbNet/tX1kg4BLlZJn38tZYbnz8AB9Y/mt1L/sK4Z8nYHTqtL4f5I2UvVr7WBoYKOl1ISijwGPELZo9aytKQrKQHvO+ux8cyZoWq3F/AdSVMo38kllGWGXwROlXQNJciaDTwHeDztve2/SPoMcCFlNutXNX37FpSA65V1id7Okna3/cNaz+xC4H7bs2tXp1OW711HKcx8JfDvBVX7TNJrKRkxFy9vtQtz0ssfQPmu/6WSafHjto/r0tXplGLaUyQ9AvzB9nb1HlvWsbUyJnZLe78k5XdjDcrS2EeB99Vz2wPvknRSHesdlP1zybAYEREREQvck1mHbIbtUT3ODzvtvUrNqMeTejwVaJi1wobz3MPtu0c/i1ESVbzN9q2N46Nsz6gzkVdRgrm/zs+9Gn2PA/5m+15J6wHn2V69cf4tlKB6fdvtafGb/TzT9n/q672AdW1/pCYEuRzY3vafJK3cnEFr62Mz4BvAlrZn1QQyS9WxXQTsY3tiDQ6/SvnMt+jUV0vqkEVEREREk/qsQ5a09x3S3kvaUqVg87DT3lP2bB2oIdLeS/qKpIuBT+jJTXt/DPB34ALbt6qR9p6SQXImJeX/DGDz2lffae+7fde2r7V9b317PbBM4zsZBXwS+HK36xv9/Kfxdjnm7LF7FyVb4p9qu47BWLUqJdPmrNr2vsbYmvd6mFKeYIyklw01toiIiIiI4Ura+wWf9n4aZSboqZz2flPbn9K8ae8n17G20t6jYaa9l3RYHU9T8/sG2JmS3bGVkONLlJT1rfpiSDoAeFtbP6fZPkRl+equwL8p3wvAi4Al6wzX8pTU+SeqLB1duq2fPYDn1s/it8BPbXfcY1iXe06m/A5Mbp6TtEftizFjxnS6PCIiIiKip6S9T9r7BZr23vZ5zEkUMg+VhCCHAtvW9xsAa9n+P0ljW+1sH0IJ/OZh+wDK3sHPUPaJfYHyu7wRZR/cMygFt6/oVIes3ncj4FWUgO6nkva3fUK3YXcZR+qQRURERMR8Sdr7pL1/0tLeqyTROAPY1fbt9fBmwEb1u14CWFnSRba37NVX9WPgHEpANo0y8/kA8ICkS4CXURKUzKMmM7kIuEjSVEpSjxM6jHlxSgKXG/sYT0RERETEsCTt/Qgpae+HRSXpxjnAZ2xf1jpu+xjbq9V09ZsDt/QKxiS9sPF2B2omTuAXlDpkS9RAdFO6BFGS1m7rp1va+yUpST3usT1l6KeMiIiIiBiep8IMWdNw097/BPi+5qS9p7Z/SPOX9r4fw01730u/ae//1H7h0yXtPSXgXAv4vKTP12PbDpF8o5OvSVqbsqT1bsrnhO0bJf0amFLPHWf7ui59jAK+XYPER4HbmHt/3imSZlH2nv0WePO8XUREREREzL8nLe39E0XSKpQAYWPK8rm7gL0b+8eG09dxwDds36BGinkNkbK/rY/Hrxvu/Xv0uQ4l+DQl8DzJ9ivqnquzba+nUoNrH9udEoi09zdP2vsanOxu+wiNIO29pO2BIyl7zI6z/bUebT9GKQK9JjC69VlJejfw6dpsBvBR25M79/LUkrT3EREREdGkp0Pa+/mlssnqDEpSizVtrwt8lpKsYthsf9D2DQtiaMO+oCw37GZH4Be2x9m+3fYrRjSospxvXcqM0AXNGmTACsCXaybMS4EvDSMYWxz4DvA6SkbMd9b7dHMZJZFJ+zLBO4EtbK9Pybx4bD/3j4iIiIh4unpaB2SUDHmP2H58OaLtSbYvVZeaZpLGSrpJ0o9U6ppNqHuOWnW1ukaxQ/R5o6SjKann95R0ROO6D0n6Rof+Zkg6uC5v3Ewd6n2ppJPfG/hgK/OhpBn9fkBqq/VGWfZ4J7CV5q719jXm/D6cY/sESfuq1EWbIumLPW6zCXCb7Tso38lo4Lcq9eEmSTqj2bjWJLurvRPbl9tuZZLcGRjX6GNSXYbaeq7W93icSv21UyRtI+kylfprm6jUcZuiklXywfpzRz0+VqU22zX15xW13y3r78GE2v8pNfDv9NnuIWmipInTp0/v+T1ERERERHTyVNtDNlzrUdKtd/IQnWuaQUnl/gHbl0k6HtiTkphjKEP1ubvtPSUtB0yRtJ/tR4DdgQ936G854DrbB6okkLiYtnpftt8v6bvADNv9jLGTfmq97Q+s1ypNIGlb4IWUYEvAWZJebfuSDv2vDtwDYPs8SZ+m1Dr7WIe2/boJONF2rxpxa1Fqle1B2Sf4LkpikB2Az9reUaXA9w22T67LMq+ifI8zgdfW/YYvpJQoaAXj44CXAPdSZvNeSUl6MpekvY+IiIiI+fV0D8h66VbTDErWvFamv5MpSTX6CXZ69Xm37SsAbD8g6XfAGyXdCCxpe2qH/mZTkmnA0PW+5kc/td7abVt/rq3vR1ECtE4BWacZpBEHKCoZNj9ACa56ubP1uUq6nrIM0ypp7MfWNtsCO0jap75fBhhDCbaOUsloOZtSWLrlKtvTar+Tal/zBGQREREREfPr6R6QXU8ju2KbbjXNYN5god/goVefD7S1PY6yn+0m4Idd+nuokeVwqHpf86OfWm/tBHzV9vf66H8a8NzG+zUoAc+wSVqf8tm9zvY/hmjerA/3WOP9Y8z53Raws+2b2+5zEPA3Sq2yxSizZp367bdWXURERETEsD3d95D9jpJi/kOtA5I2Vkn53q2mGcAY1bpalLT0/c5+9OpzLravpAQp76IshxvKAqn31Yduz9Be0+084P2SRtXxrC5p5S59/hF4oaTnS1oKeAdwVpe2XUkaA/wceO9IsmR2cR7w8dY+MEnj6vFnAX+x/RilTl2vpCoREREREU+Ip3VA5pKzfyfgtZJur8vWDqLMzpwCjFepafZu5hQQhlIw+H0qdb+eAxzT5y179dnJz4DLGokqej3Lw5TZvkMlTabUYRtRNsUhdHyGOht1WU2QcZjt84EfA3+oSwAn0LkIN7YfpdQZO4/y2f7Mdtc6ZZL2kjSNMpM2RaXcAJQC1isCR9ckHgsij/yXgCXrfa6r7wGOpvwOXEFZrtg+wxkRERER8YR70uqQqRR0bu6j2rFTpr1h9rkjcEsrVb2kg4FLbP+2xzVjqbW7erQZD+xqe6/5HN/ZwBG2L5D0EeBB2yfOR393sYBrnD1ZJG3CnDT2Ag6yfUY9twJlmeJ6lOWj77f9hyH62wc4jFrHTKVo9cmU/WFLAIfb7rhUVKUO2zeB19T7PQS83fad9TP+b226OGXG7ku2Z3XqqyV1yCIiIiKiSX3WIXsy98bMbGXw60TSEnWmZTh2BM4GbgCwfeB8jO9xticCI/7rupHNb7LtC2qf3+3SdiTP/XR0HSWYfFTSqsBkSb+sz34k8Gvbb61LHpft1ZGk5wKvpaTwb/lfSjbFN0kaDdws6ZQ689huF2A1SubJxyStwdwzZFvVIG8UJYg8FnjfyB47IiIiIqK7gS5ZVFuNLHWp81Xb7qpSU2qypJNq3agdgMPq8rY1JZ0g6a21/daSrq39HC9p6drVRcDpjXus02FcW9bZLSQdVK+/SKWG1V71+HKSzqnjua6mqW/NYn0GuB94nqS1Gv3sU19fJOkrki4GPiFptKTTVWp+/VHSK2u7FSWdX5/je3QpOC3pnfVZrpN0aD32PJV6XCtJWkyl5ta2kr4k6RONaw+pSwgXk3S0pOslnS3pV43PslUfbZKk/9T7TFKpo/a3+lneIulVbeM6o7abBFwOTJS0HSWJiGubZwKvBn4AZemm7fsbfayouWuRTaLMtB7C3MlYDCwvSZSMkP8EugW6qzJn/xi2p3VaVmp7BvARYEdJz+nwuacOWURERETMlydzhuwZ9Y9pKOnKd6qv+6mRtS5wAPDKOnPxnNr+LMrywwkAqvV7JS0DnABsbfsWSScCH6UsUwO4z/aGkvYE9gF61boCWIdS8Hh5yszLMcD2wL2231Dv+axG+//Y3kTSrvWeb+zQ5wq2t6jX/piytPH3KoktzgNeDHwB+L3tgyW9gVJvay6SVgMOBTaiFKU+X9KOts+swdl3gSsps0fnS7qFsgzvSJWle++g1Bp7CyW9+0uBlSl7wY5XqY/2beauj7ZdrY92EXC17U+pFLD+ArBNa2yN77g11k2B4ymJRN5bZ8teAEwHfijpZZS6cp+w/UDt4x/ABo0+dqB8r5do7nrNR1ESidxbv6ddWgFXBz8Dfl8DyAuAk21f26lh/V28k5Ly/8q2c6lDFhERERHz5cmcIZtpe4P60/xDvVONrCnAb5lTI+s1wITW3qlG+27WpgR9rUx9P6LMwrT8vP57NXPqVfVyju1Z9f5/r2OaCmwj6VBJr7L970b7Uxv/dktj/9PG620oNbEmUYKKZ0pavo75ZADb51ACrnYbAxfZnl6X/53Selbbx1GCk49QAk/qvr1/qGQb3Ba4tgY9mwOn2X7M9l+BC2v/zfpok4DPUZJxtPT9Wdq+0vZL6pg/UwPnJYANgWNsj6MsHdy/0/WSlqUE5p2Wpm5HSYSyGiWAO6rOvnUax7T6XJ+hpMi/QNLWPYbecWYyIiIiImJ+PRXqK/VTI0sMr9DwUH9AtxI09Ftjap66VHXmbSPg9cBXJZ1v++Dapn0pXSfN514M2Mz2zGaDOgM01HN3fdYawLSCp1HMSVZxHLAbsAplxqpXP0PVRxvuZ4ntGyU9QAn0pgHTapkAKNkcOwZkwJrA8yn7z6A82zUqCUN2B75WM2/eVme11qHs5es0hlnAucC5kv5G2Y94QXu7GhiPBRZUGv6IiIiIiMc91dLed6uRdQHwdkkrAjT287TXzmq5CRjb2r9FqTN18YIcaF0q+KDtk4HDKbM8Lbs0/u2ZLbA6n5I2vtV3a4neJZQgFUmvA57d4dorgS3qXrHFKXXVWs96KGXG7EDg+41rzqAsudyYsjwSSi22netesv8BtqzHF0h9NJUaZUvU18+jzFDdVWfj7pG0dm26NTVJSzvbU22vbHus7bGUYG7D2sef6rXU8a8N3NFlLBvW76+VcXF94O4O7UZR0uOf2U/pgoiIiIiI4XqqBWTdamRdT0nicLFKja5v1PY/AfZVSXqxZqsT2w9RZkxOU6mh9RhlL1W7FYF1VWqY3VATWbyoz7G+FLiqLuM7AEDSuvXcpyVdDXyCstxyKHvV554i6QbKEkOALwKvlnQNZXnhn9ovtP0XytK7C4HJwDW2f6FSHHtj4ExgX2BzSftJurxmHpwILG97tqQtKVkEp1GyIX6PEuj9u5/6aCpZJXcf4hk3p8xsTaIEhHs20vefWs/NAt4GfKVbJ5I+Juk2SWbu39/rgE9JmgncCXynR3mAlYE/SnoIeBB4A3P2h60C/Lme+wdlL99+QzxbRERERMSIPGl1yJ5qaja+y4EftVLS15mp5W1fOh/93kUJFDasCUhm2B41jDGpRzKKbtctbnt2l3P7A8+w/YXGscUoAcwStl9UA7J9gHfYnlFnIq+iJFH5a72ma3p+9VHbrdfYKcsBX0sJCP8IvLNVW65D+3GUvXQX0ajJppJ180bb/6qziQfZ3rRLH5tRgvotbc+qCWSWsn1vTVSyj+2JKin4v1rvs0Wv50gdsoiIiIhoUp91yJ5qM2RPpq2AR5r1wWxPsn2puqTflzRW0k2SflRnsybUfVqtVPZdP/Ah+rxR0tHANcDnJR3RuO5Dkr7Rob8Zkg6WdCWwmeakpr9a0nmSVlXJfLg38EFJF9brHgBuAy4D2mt0nV1nsC4FvgRsr/7KEnwNWFMlLf1h9T77qqTwnyLpiz2+h02A22zfUWfjfgK8uVtj29e6Q0Fx25c3lhVewdyJR9qtSsm0Oatee5/tezv0+TBldmyMSgbIiIiIiIgF6qmQ1GNQ1qNkBuzkITqn34eyN+kDti+TdDywJ2UPGQC2x9ZZsuH2ubvtPSUtB0yRtJ/tRyhLAT/cob/lgOtsH6iSmv5i5k5Nf0hNTf9dYIbt1hht+wWtWa1mh7a3bL6XtBv9lSXYH1jP9gaStpN0O2U/4DTKctE9JF1g+5IOz7E6cE/j/TRgU0nfAV7Z1vZI2z/s0Ee7D1CSdbwUOKnt3CzKXrMDVUoA/Bb4qe2Oewzrks7JlAQhk5vnJO1BLUUwZsyYPoYVERERETG3RTkg66WVfv/VlICilX4f4B7bl9XXJ1P2fx0+bxfD6vNu21cA2H5A0u+AN0q6EVjS9tQO/c0GTq+vm6np+f/svXe0XVXV/v956EiQKIQuRgEpIgYI0QDSRF4LUgRFQekgAl/0p6C8+opUFaOCjS5NQZqEjgktCJEWIJUSjQRFQQLSQic8vz/mOsnOyan33kAS5meMO7LP3mvPvdY+lzHuZM71PMDCwGOdLrYNjWwJGq2BMv8RkoYT+85qvED4eDVKyBqpO9r2IQ3Ot0UhBrMvsFm9h1nduI2AjxGV0oskHWH7nGZhG51MH7IkSZIkSZKkt7ydE7JJzJ40VGkmvw9zytB3+od4q5gv1I09E/guIWrSrCL0cmXfWDtp+t7QiS1BPQJ+ZPu0DuI/Cryn8nlVwty5ayStT7y7T5VkrCnl3Y0CRimEX/YkzMTrYy5MCLg80JM5JUmSJEmSJEkr3s57yG4CFpe0f+2EpI0V6oTN5Pch9hPVEp8vEXLxndAq5mwUT673ALsxy2S6FX0iTd8BzdZQbz8wAthHIRuPpFUkLd8k5t3AmgpZ/MWALxLm2F0haTXCpPorFUPwZmPXkrRm5dQgGsveL0qIevzT9vhu55QkSZIkSZIk7XjbVshsW9JOwElFifBlYCohgjEJuEohvz+WIr9feADYU9JpwF+BUzp85PktYjbiYmBQJ/5Xtl+VtAvwS0nLEN/rSWUdfUnDNdh+StJoSROB62wfLmkd4PbSQjkd+DLwRIO5vy7pECKJWxg4q9gcNETSoYTQxorEXrtrbe9HeK0tC5xcnvl6C1WbfsCvFHL9rxMiJwdU16mQ4F+c2GPWVGQkSZIkSZIkSXrD21b2vicUIYzrCGEIiKRgBjCtfB5SlPn64llXAyfavrEPYu0DXFuTsJ8blD1ZvyGSnTeAY2xfWjfmFELSvn+LOIcQIiYziMrb/rYfLNcGEX5yS5dnbFiET95yUvY+SZIkSZIkqaIOZe/fthWyXjDD9iAASUcxu4JhrylVm7uAcd0kY2rhRQbsQ0jqd5yQqYXvWBOmA7vbniJpVWCMpBG2nwiYAkcAACAASURBVC/xPkIka+04z/avyz2fIwRTtivtg78DdrM9oag8NltvkiRJkiRJkswXvJ33kHWN7amtzI8l7SnpruLHdbKkhSQtIukZScOKf9cISR9ReIb9vXiFIWm/ok54ESEUMq7DuMdJugsYIuno4v01UdKpCnYl9khdVO5fTNKjJfFD0kcl3VCOj5N0mqTrgbPLM35enj1e0n4t3s1DtqeU40eBp4DlStxFgJ8BGwJLl3nUfpati/NcuWc4cCLwMYU32kPAYzXFyeId1tBAu/JufiXpRUnPSZqs8G57pbwTJK0u6VZJ9yn82z5Szn9e0ohyvEq5d449cJIOkDRG0php06bVX06SJEmSJEmStmRC1kdIWg/YCdikVNAWIQQqIMQwRtrekDBjPorwwvo8cEwlzJByz4bAbpIGdRD3XttDbN9O+HRtTKgCLgN80vZFxH6vXW0P6qClcgPgs7a/QuyresL2EGBj4OAintHuXWxSDqeWf79OJJofBp4v86j9zKGGWPaJrU+oNW5Y1n0y8JikkSWx/VabaSwDXGn7HYRH2xSgP7AJcHgZ8xjwCdsbEAqSvwSwfQnwtKQDCdXG79lutP/tdNuDbQ8eMGBAu9eSJEmSJEmSJHOQLYt9xzZE0jKmiEosySzD45dsX1+OJwDPFjGLCcDASowRNREPSZcDmxHfUbO4rwLDK/d/XNLhhBT9coTx9XVdruMK2y+X422BdSRVE8A1gX80u1nSKoR8/O5FOGVVYEdgS5r4edVj+5eEQMkehPz/vsR72BT4CCHAcrOkMc0MnensnS8O/FrShwlxj9Ur9x8MTAT+XBK0JEmSJEmSJOlzMiHrO0QoBH5/tpPRrletSr3BLFGQN5j9O2jkcdYq7ksuqiyS3gH8mqgo/UvScTT2CINIPmrV0foxVd8xAQd1updNofB4DfAd23eX0xsSSdyU8vmdkh6yvVYHIS8AfkEkZI8Co2oVNUnXldjNErJO3vm3iOT2y8CixD64GqsSe9RWlKTae06SJEmSJEmSviRbFvuOG4AvFLEJJC3bSXtfHdtK6l+Sqx2A0V3EXZJINp6UtDSwc+VavU/YVGCjclwdV88I4KCS/NX8u5ZsNFDS4sAVwG9tz6za2b7S9oq2BwJrAM+1SsY0uz/YZ4m9YxCVvg0kLVnmszlwf4u5d8IyxL40E8bQKnNYFDgL+ALwd6LlMkmSJEmSJEn6nKyQ9RFF+e9o4AZJCwGvAQcC/+4izG1EVWh14He2xwJ0Erd4gZ1LtNk9AtxZuXw2cKakl4h9akcBZ0h6nFB0bMZpwGrA2NIu+QSwg6RRwGG2x5T5DSxzXx7oL2nfcv9XyntZFriUaL1ctBa8JJ6XlPXOAK4iRD+2LOt8Cti7sr6HgGeIyuHj1Hm5SXon4RN3eYs1ASxWWhcXA/aXdFCZB5IeJpLbxYj3/EPgCoXfWUvD6SRJkiRJkiTplvQhm0coCobr2f7GWz2XdjRJyK5upkApaSlCLGQ9Yo2HlPPvAD5i+2ZJiwE3Aj+03XDfm6StgDttvyjpa8CWtnetXP8FMAD4b+0ZDWKsSrQ5bmj7WUn9gAG2H5Z0TlnHpYoM9BvA18qcW4qhpA9ZkiRJkiRJUkUd+pBly2LSFEkDJT0o6dwie39pSaK6wvYLtm8jxDiq51+0fXM5fpXwSlu1RZybbb9YPt5RHaswpl4BGNlmOssTLZzTS8zpth9u8CzbPpGoxH2qTcwkSZIkSZIk6RHZsjiPYPvMt3oOTVgL2Nf2aElnAccBg4HbJNXKqyb2WnWMpCOBz1VOLQysTLQ1dsK+wHWSxhC/x2sQrZqbALe2uG8c8B/gYUk3ApfZvqrF+HuBtYn9cfVrOICwBmC11brdLpgkSZIkSZIkWSFL2vNP26PL8e+B9wNjgM1sL2l7SWDdboPaPqbmRUYkeI8Cx9pum9hJ+nK5Z1gpA58JHGP7g8CRwNMtnjsD+CSwCzAZOFHSUa0e1yJW+pAlSZIkSZIkvSIrZEk7Gknx9zWnA3+1fVK7gZK2Ab4HbGG7JmU/FPhYEefoR4h2TLd9RKMYRVXxLuAuSdcToidHNXnkBsTetiRJkiRJkiTpc7JClrRjNUlDy/GXCDXFPqP4pS1DCGi0G7sBofy4ve0naudt7257tSKtfxhwXrNkTNLKkjasnBpEtDrWj5OkQ4GVgD91saQkSZIkSZIk6ZiskCXteADYU9JpwF+BUwh/sK6QNBV4J1G92hHYFniOqHY9CNxbpPV/3WI/3TCiAnZJGfsP29t3OZVFgZ9KWpkQGZlGyNvPfIak7wPvIIRDtmqnsJgkSZIkSZIkPWWBkL2XtCJwEiEI8QphfPyNnvhGSToT+Lnt+0sSMdj2k6UFrl8fTrvbea0NXEi0DO5C+JRtUpWcL/5dh9nerofP6A/sZvvk8nlm7A7ufQ9wHrAiYVB9uu1f1I05jEiqBth+skmcHYBjS4zXie/xtnJtNWK/2HuI9/Bp21O7XuhcIGXvkyRJkiRJkipvG9n74hc1HBhle3Xb6wLfJSTQu8b2frbv7+2ciolzt/ct3OLyjsAVtjewPcX2Jj2cW6uqaH/goJ7EJZKnb9leB/gocLCkmWIfJWH7BPCPNnFuBD5cxD72IRKwGucRQh7rEAbXTzS4P0mSJEmSJEnmG+b7hAzYCnjN9qm1E7bH2r5VUj9JN0q6V9KEUn1p6a8laZSkpplsm5gPSDqZkEr/vqQTK/ftL+nnDeJNl3SMpDuBoZI2knSLpHskjZC0kqRPE3us9pN0c+2+Tl+QpL0kXSLpKmBkszUAPwZWlzRW0rBSfTpX0t3lPR3d7Bm2HwMGSBpLyM6vAFwpaXgZciLwbdqIgtieDny3xLkUGFjmcxKwiO3ra+MqnmSN1jxV0r8kvSDpRUkPSXpO0j8lHVjGNPsuNy7rXULSUpImSWpbJUySJEmSJEmSblkQ9pCtB9zT5NrLwE62n5O0HHCHpCvLtXp/rYOAn3bwvHYx97Z9kKSlgPGSvm37NWBv4KsN4i0FTLR9pKRFgVuAHWxPk7QrcLztfSSdCky33ckcGzEUWN/2f0uVrNEajgDWK9UpJG0LrElUo0QkWJvb/nOjB9geAYworY5/BjYsz9ge+JftcWXvVzvuB5YAliP2cN1e9p2tIeky4H3ADcARRca+GcfZPqUkxh8HVilxJwGn0uS7tH13eR/HAUsCv7c9sT640ocsSZIkSZIk6SULQkLWCgE/lLQ5sSdpFWa1Mtb7ax1KZwlZq5iP2L4DwPYLkm4CtpP0ALCo7QkN4s0A/liO1yISzOtL4rIw8Fini23D9bb/28Eaqmxbfu4rn/sRCVrDhAyi6kSs5xsl0XkHIdyxbacTtT0cGF7mdyywDfG7+jFChv4fwEXAXsBvW4SqJcoTgH62nweel/Ry2S/3Ao3fw+PAMcDdRNJ2aJN5nk5I9jN48OD5fzNmkiRJkiRJ8qazICRkkwiRi0bsDgwANrL9mkKkY4lyraf+Wq1ivlA39kxiP9uDhNdVI16uVHkETLI9tMnY3lCdW6s1VBHwI9undfKAUuH7I3C+7cvK6dWJilatOrYqoag4xPbjreLZ/rOk1Uv16lHgvppxtKTLib1qrRKymk/ZG5Xj2udFaP0e3k0koIuWc/XfbZIkSZIkSZL0mgVhD9lNwOKS9q+dKHuAtiD8rZ4of2xvBby3cl9P/bVaxZwN23cSioC7AX/oIPZDxD6soWUdi0r6YIfz6oZma3geWLoybgSwT6l6IWkVScs3CqjItn4LPGB75l452xNsL297YPEJe5RoZWyYjElao8RC4Re2GPAUUa16l6QBZejWRGtjb2j1XZ4OfB84Hzihl89JkiRJkiRJkobM9xUy25a0E3CSpCOIFrOphAjGJOAqSWOAsUSlqkYjf61OOL9FzEZcDAyy/XQHa3lV0i7ALyUtQ3w/J5V19CUN12D7KUmjJU0ErrN9uKR1gNtLjjQd+DKN1Q03Bb4CTCiCHADftX1tl3PbGdhD0mvAS8CuDm+GGQrZ/BtLwnYPcEaXsetp+B4k7QG8bvsChfLlXyRtbfumXj4vSZIkSZIkSWZjvvEhU996jV1ItKmtqbnsNSbpauBE2zd2MPZN9xrr8t63tddYK9KHLEmSJEmSJKmiBcmHrFRE+sxrjFATfKXtqDZzUguvMUn9JU0GXqomY0qvMUivsSRJkiRJkiQB5pOEjD72GgPOIRT6GtImZkdeY7afsf0B25/XPOw1Vu49XJ17jT2iOb3Gxkpalu68xvbSnF5jF9C919hDmtNr7N8qXmOt1ifp8vIdTFJI2NfOT5d0vKRxku6Q1DDxl3SApDGSxkybNq3VkpMkSZIkSZKkIfNLQtaJ19iGROL2s1JRg5CRP932+sBzdF4ZahfzPNsbEDL52yvUBSG8xhqpKda8xj4C3An8CtjF9kbAWYTX2LWEN9aJtrfqcJ71DAX2tL11izUcAUyxPajsEat6jQ0CNlLIwDfE9lOlsrUj8Cwh0DGI2EP2L9vjOpzrM8zuNTaI2G/3jKTLJN0naVibiiLASbaXAk4jqp6rAB8mZOvrvdTq17dP+Q4GA4eWpBLi+7rD9ocJif+ZgjF17+J024NtDx4wYECjIUmSJEmSJEnSkvle1IP0GquSXmNzeo21Wt+hCkEYiD1raxKKjq8CV5fz9xBtmEmSJEmSJEnS58wvCVl6jXVGeo3NouY11nB9CnGUbYChtl+UNIpZ7+c1z1K7mcH8899JkiRJkiRJMp8xv7QsptdY96TXWOv1LQM8XZKxtYnEL0mSJEmSJEneVOaLhKxUK3YCPiFpiqRJwFHAvwkvqcEKL6ndCU+xXwG3EK1nF0p6EHg33XmNDZY0DTiYqH7dBrwLGoptXAyM7tRrjKj2nSBpHOF/1a2a4lJFBOO+Ul36Szm/tMJDDMIW4MuV9zLTawwYLWmipGG2RwIXEF5jEwiRjaUVKpH1e+5qXmNbl+ePVYiR1LMwcLlCAGWSpK/XXd8ZeEySib1fuzqYAdS8xiYAqwEHlueMkbRZLYBCHn8FwiPsfmIv2hw0Wx/wJ2ARSeOJlsl7gaaCJkmSJEmSJEkyN5hvfMg6oVRd/gKcS/zBfTVhZLy07Vt7GXsqTfzK1MJrrMxJtt/o8nkLV9oc668dASxp+wd15wfShV+ZpEVsv97k2sxY3cy73LsSsJLteyUtTezD2tH2/eX6e4hWz7WJlspmfmX9gBdsW9L6wMW21y7XRhFiKNeXcW+0UmSc26QPWZIkSZIkSVJFC5IPWRf0qTy+pFGSmr7E0v72IlE5Okk9kMevi7fAyOPbvrccPw88QIiK1OhYHr+yl2up2niF91m38vg/lHR7qbRtWN7nFBV5/PKdTay8o8sk/UnSXyX9pNU8kyRJkiRJkqSnLGhiBTPl8W1PLZ9r1GTgn1MISNwhqabQtxawr+3Rks4i5PE7UWP8D7Bii5h72z5I0lLAeEnftv0aIY//1QbxavL4RyrEM24BdrA9TdKuREVoH0mnAtNtdzLHRgwF1rf9X4WJdP17uQ1YvzL+E5KmEG2bQwihjCslbW67lRrj3sDhwBrA+yS9AfwL+JvtmgBISxQqiOcBSwJ/V3iXLQM8IekyQkzkBuCIZhXFwj9tDy2J8TlEEr0EIRhzaoPxgwi1x1eAhyT9yvY/6+Z2AHAAwGqrrdZ2LUmSJEmSJElSz4JWIWtFTQZ+PPEHfCt5/M0a3N9tzNnk8Qlhku0UAhLdyuOPBf6PUC/sCxrJ41fXsCjwaWb5lQ0ChgNbEPLx9xLthmu2ec4lwEvAF4sX3CbEXq8jO52o7eG2lyYEPv5R5nJ4ef5hwMbA+2lh9F2oyuPfaft529OAmjx+PTfaftb2y4SwyBzCLulDliRJkiRJkvSWBa1ClvL4nZHy+LOoyeM3Gw8pfZ8kSZIkSZLMJRa0ClnK43dPyuMnSZIkSZIkyVvEAvV//Ysa306EwMYRxL6xqYQIxiTgKoUM/FiKDHzhAWBPSacRsvndyOM3i9mIi4FBncrjS9oF+KWkZYjv6qSyjr6k4RpsPyVpdBG6uM724ZLWIeTjAaYTCpZPNIhZk8efUNotAb5r+9ou57YzsIek14jWx12LyMcMSTV5fBH7Bs/oMnaSJEmSJEmSvOUsULL39UhakUhiNiZa0KYC37A9uTJmIB3Iu0s6E/i57fvVQgK/TYym8vg9pexJu5Bos9wF+J3tTdSlBH6bZ/QHdrN9cg/v/yTwC8Kf7EzbP24x9hAigV4dGFCTxJe0O/CdMmw68DXb43oyn7lByt4nSZIkSZIkVfQ2lb2fSamcDAdG2V7d9rrEHq4VWt/ZGNv71Xy0ejCX/pImE1Wem3tw/8ItLu8IXGF7A9tTbHdrMl17RqtqaX9CebIncRcGfgN8ClgX+FKRrW/GaGAb4JG68w8DWxRxkGOB03synyRJkiRJkiSZl1hgEzI69CQDrgK+B732JGvqcwbcTigYrsGC5Ul2r6SXJD1ertV+lq08Zgghc/93268S1bwd6uaxd+1eQvDkcuDd1TG2/1Jp9byDBoqTkoaXOPdLelnSUwoPsvMlbVNaMP8qaUgZP0TSXyTdV/5dq5z/psL+AEkfkjSx9ntQ97wDFL5mY6ZNm9bpa0+SJEmSJEmSmSxQe8jqmOlJ1oC54UnWLub86kl2JXAEsF6RnEfStoTk/UYUTzLgJ008yVYBqv5djwIfqQ6wfTZ1ypOlLbQZ+wLX1Z+0vVO5dyDwN2BLYs/d3YSYymbA9kSldEdiv9zmtl+XtA3wQ2Lf2knAKMV+xO8BX21kPG37dEqlbvDgwQtu72+SJEmSJEky11iQE7JW1Ly3Nidkz1t5kh1KZwlZq5izeZJJqnmSPUD3nmQQe7Ee63SxbWjkSdZoDVW2LT/3lc/9iAStUULWyP25x8mLQglyX9p7xT1ce6+SJhG+YpY0ARhYxiwDnCtpzTKnRQFsvyFpL2A8cFrl9yFJkiRJkiRJ+pQFOSFLT7LOmNueZI8Scv81VgX+3ZOJSlqfeHefsv1Um+H1XmNVH7La7/2xwM22dypVtVGVe9YkxENW7slckyRJkiRJkqQTFuQ9ZOlJ1j197klGtAuuKel9khYDvki0OHaFpNWAy4CvVFUye8kywL/K8V6VZy1DqEJuDiyrsB9IkiRJkiRJkj5ngU3Iil/VTsAnJE0pbWtHEdWZ84HBCu+t3WnsSTaeEJboxpOsWcxGXAyM7tSTjKj2nSBpHOEX1iM1xTY0XEOpRo0u4hbDbI8ELiA8ySYAlzJ7wlad++vAIUQS9wBwse2mXmqSDpX0KFFJG6+wGwA4ElgWOLkId/SFxvxPgB9JGk20gdY4ETi5JH77Aj9ukXAmSZIkSZIkSY+Z6z5kkmYA1T1SO9qe2suYOwKTazL0ko4B/mz7hl7G/Sxwju1l2w7uPOaBwIu2z6s737EnmSq+Z301rzcTSf9LJDYzgENtj2gx9ixgO6JSt17l/DDgs8CrwBRCJOWZuTrxLkgfsiRJkiRJkqSK5iEfspdsD6r8TK1eVGv/q2bsSHhaAWD7yN4mY4UJ9J1YBgC2T60mY5rlSfZyXxpEz6soPMe+CHwQ+CRR4Wrlq3ZOGVfP9YTS4/rAZOB/+3iqSZIkSZIkSfKm85a0LKpz/ysk7aHwBBsn6XeSNiGky4eV1rXVJZ1T2+cj6ePFV2qCpLMkLV7OT5V0dOUZazeY2kBgahl/VLl/lKS/Szq0nF9K0jVlPhOLBH0t/gmS7io/a1TiHFaORwHfJpK+2yUNkPRHSXeXn03LuGUljSzrOI3GSoVI+lJZy0RJJ5Rz71V4bS0naSFJt0raVtKxkr5euff40h64kKSTJU2SdLWkayvvcg7vs9o6KmudLOljZc5j63+IfXgX2n7F9sOEHP0QzfIMq/78T5HO/2/9Wm2PLO2PUHzImj1T0sGSLpd0laSHJR2i8Ba7T9Idkt5d1rF/ee/jyvdQ85y7QtIe5firks5v8v7ThyxJkiRJkiTpFW+GyuKS5Q9zCCnyncpxJ/5X6xI+UJvaflLSu8v4K4GrbV8KoJCCR9ISRIXl47YnSzoP+BrhKwXwpO0NJR0EHAbs12buaxMG00sDD0k6haje/Nv2Z8ozl6mMf872kPLH/ElE6109/W1vUe69gGhbvE0hWjECWAf4AXCb7WMkfQY4oD6IpJWBEwgvsKeJxHZH25eX5OxU4E7gftsjFVW5y4BfSFqIqFoNAT5HJKIfApYn9nmdpfA++xV13mfAPmUKi5S1fhr4ge1tgEEN5vlrQpSkxqPAKpXfg56wD3BR2dvW6Jl7ETYBGxAqkX8DvmN7A4Upd+37ucz2GeWe44i2yl8R73u0pIeBbwEfbTSJ9CFLkiRJkiRJesubkZC9VDMUrqMT/6utgUtre6cq45uxFpH01VT4zgUOZlZCdln59x4iEWnHNbZfAV6R9ESZ0wTgpyXpudr2rZXxf6j8e2KTmBdVjrcB1q0llMA7JS1NqPt9DsD2NZIaCX9sDIyyPQ2gVHE2By63faakzwMHUhIW21MlPSVpg7KO+2w/JWkz4BLbbwCPS7q5xG/nfVZ9lwObrBX63ofse8DrhABJK262/TzwvKRngavK+QnA+uV4vZKI9Se81EYA2P6PpCOBm4n/UdDu9y5JkiRJkiRJesRb6UPWif+V6O6P94atfRVqXlQz6GztVS+rGURVaLKkjYBPEwp9I20fU8ZU59ps3tV1LwQMtf1SdUBJgNqtu+laS+vdquVjP0K2HsLDay9gReCsNnHaeZ91+i770odsT6Lq+HG3V6PpxIfsHEJkZlypqm1ZuedDwFOkD1mSJEmSJEkyF5lXZO+b+V/dCHxB0rIAtb0/zOmLVeNBYKDK/i3gK8AtfTnR0ir4ou3fAz8FNqxc3rXy7+0dhBtJSMLXYtcqiX8mklQkfQp4V4N77wS2KHvFFib2atXWegJRQToSOKNyz3Ci5XJjSjWI8FnbuewlW4FZSUlfeZ9dCXxR0uKS3kcYLt/VbRBJnwS+A2xv+8UezKMRSwOPlfbM3SvPGgJ8imh5PKzMO0mSJEmSJEn6nLeyQlblfOAqhbfUWGb5X02SdDxwi0I+/z6iwnMhcIZCaKNq2tufkESfJMnANGBYD+e0PXADzJSdr1WxpgB/lfQG8BqxR63G4pLuJBLdL3XwjEOB3yg8zxYhErEDgaOBP0i6l0iy/lG7QSFGciFRQTsReAR4mEisjpf0DPBx4O+2fyhpZ0l72z7b9qulJfEZ2zNKyD+W8RMJ9cI7iZbA/Yh3+8uyT24RovWzqYdYZY6zSddLuhi4n0gsXwDuk9RQul7SH4ikcICkV4EngP8QVb03mNVCeQdRvXp/VR6/S75f1vsI0aL5L4UIzBllbv+W9C1iT93WHVTlkiRJkiRJkqQr5roP2ZuF4q/0vwDn2j61nBsELF23z6snsadSfMAkTbfdr9WYujmp7M/q5nkLVxKm+mtHAEva/kHd+YHEnrb1JG0JHGZ7u7oxCwH3Ap8n9tq9Xs73sz29VCLvIgQ/zu5polP2Ak4HzqvzEtsWuMn262UPHra/0yTGBsB/SlK0HjDC9iqV658jEsb1e5GQ9RnpQ5YkSZIkSZJU0TzkQ/ZmsRXwWi0ZA7A91vataiKrL2mgpAclnauQ1r9Us6TPR0lq+gLrYwJLVmI+IOlkIvn5flH2q923v6SfN4g3XdIxpcI2VA0k5xWKht8A9quJb0ia3snLUfiBPV7m+XMqdgNE295LRAXyWEJZcHWFhPywcv/hCon48ZKObvWsbqTrW8S4z3Ztr9kkYAnNsjDoB3wTOK6DdY+SdKKkP5fvZWNJlymsAY6rjJte/t2y3HNp+d04vyTWSZIkSZIkSdLnzCsti33BeoTiXyNeprGsPkSr2r62R5dWu4OIvWHtmCMmIQLRr8Tc2/ZBkpYCxkv6tu3XgL2BrzaItxQw0faRZU/TLdRJztveR9KpwHTbncxxJrbvl/RtIonZs5ndAKFMOYowYR4EMytbaxIy+QKulLR5SbwoY/6H2LtWYzGaJFylEncO8IxmWSJAiHU81eCWnQlVyJowx7HAz4DBlMSxMrZqrVDjVdubK3zYriCsAv4LTJF0YoNnbkAYWf8bGA1sSrSE1q/jAIolwWqrrdZoqUmSJEmSJEnSkgUpIWtFM1l9gH/aHl2Of0/s6+ok2WkV8xHbdwDYfkHSTcB2kh4AFrU9oUG8GcR+LmgvOd8bOrEbqGfb8nNf+dyPSNBmJmS2RzBLKGRmC2WTORwI3AR8rt2+LIWQyAnl+bU21DVs/3/lGVOa2CpUqSXfEwjlyMdKrL8TCpD1Cdldth8tY8YSsv5zJGTpQ5YkSZIkSZL0lgUpIZvE7AIfVZrJ6sOc8vKd/mHdKuYLdWPPBL5LiJWc3STey5V9Y+0k53tDJ3YD9Qj4ke3TevtwdSFdL2lVQhlyD9tTyumhwEZlrosAy0saZXvLFqGqkvf1cviN/huYw+6g1TyTJEmSJEmSpKcsSHvIbiJUDvevnSj7hbaguaw+wGoq0u6EMuIclZAmtIo5G7bvJCoxuzHLPLoVfSU5345ma6i3FRgB7FP2biFpFUnLd/swdSFdL6k/cA3wv5UKJrZPsb2y7YHAZsDkNslYkiRJkiRJksyzLDAJWam27AR8QtIUSZOAo4h9QOcDgxWy+rtTZPULDwB7KqTn3w2c0uEjW8VsxMXAaNtPd7CWV4lq3wmSxhFWAJt0OK9uaLiGsqdqtKSJkobZHglcANxeBEwupbEPHDBTuv52YC1Jj0rat1z6dbnv+iIYcmqzGIQ/2xqEKMrY8tN1EpgkSZIkSZIk8zLzjey9pBUJH6yNiZayqcA3bE/uQawzCaXBFwnvrZVbSdr3BZKuBk60fWOLMVWPsV2A39neRB1If3zplQAAIABJREFU2ncxj/7AbrZP7uH9s3mMVc4PAz4LvEp4tc3hMVYZ+wngx4Twx6vA4bZvqhtzJb3zGHtTSdn7JEmSJEmSpIoWJNn7Ijs+HBhle3Xb6xJ7shoJULTF9n627+/tnBS+Xu3G9Zc0GXjJ9o2SFm4xfEfgCtsb2J5iu0dVsaKe2Iz+hJJkTzkH+GSD89cTyozrE0nu/7aI8STwWdsfAvYEfle9qPAY60jOP0mSJEmSJEnmZ+aLhIy55DFmeyrR0jgbbWJ25TFWqkQrAw9oLnmMlbF7SbpE0lVUPMbq10BUpvrCY2zhSivhWOAnwBiFpH03HmOrACtIGldijQfOoHuPsf9KekjSM5JekfS4pL3LuC9LuqvEP62WFEs6RdIYSZOq65Y0VdLRlXe3dpPnH1DuHzNt2rR2002SJEmSJEmSOZhfErJOPMY2JBK3n5WKGoR8/OmlavMcnVeG2sU8z/YGhDz+9grfMAiPsUYqijWPsY8AdwK/AnaxvRFwFuExdi1wKtHWuFWH86xnKOExtnWLNRxBkYq3fbhm9xgbRCgYbt7Bs2aUGPU/TwH7ANd1OOelicrnh4t8/Y3AfkQ7aSe8antz4OgSax3gncDrhF/aOsCuwKYl/gxivxzA90oZeX1gC0nrV+I+Wd7dKcBhjR5s+3Tbg20PHjBgQIfTTZIkSZIkSZJZLAhy3ukxNou54jHWDZK+RyRD53cwtp3HWCe08xjbjDCCvru87yWBJ8o9X1CYOy8CrASsC4wv1y4r/94DfK7DuSRJkiRJkiRJV8wvCVl6jHVGeozNouYxJuBc27PtaZP0PqLytbHtpyWdw+zvpxYrfciSJEmSJEmSucb80rKYHmPdkx5jwY3ALrU1SXq3pPcSbY0vAM9KWgH4VC+fkyRJkiRJkiRdM18kZOkx1iPSYwwoapr/RwidjCfUIFeyPY5o05xE7OMb3TxKkiRJkiRJkswd5hsfslaosUfZMOBX3fpYqXiU2b6/tM4NbudRpg48xnqL0qOsOmae8yhLH7IkSZIkSZKkihYkH7JWFOXARh5ly/UkXjceZarzGKvOSR14lDWIlx5l6VGWJEmSJEmSvI2Y7xMymnuUXQJ8VD30KGv2MFX8vYBbiQrO59UDj7JyfrqkYzRveZRNkPREaSX8l6QXJb0k6SetnlPxKKs99380u0fZWGALOvcomwQsofALq3mUnQdsQCSPrdZc9Sh7oOw5vEzSXyUdVxl3eXnXkxSKi0h6bxm3nKSFJN2qsAeof0b6kCVJkiRJkiS9YkFQj+vEo+w5ScsBd5R2Nwj5+X1tjy6tdgfRmSR+u5h72z5I0lLAeEnftv0a4VH21Qbxah5lRyr8zG4BdrA9TdKuhEfZPmVP1nTbncyxEUOB9W3/t1TJGq3hCKKS9SGAkoTsUuYtwtdr85J4tcX2CEI0ZCYlKezUo2xn4D7bXyv3ngj8gNj7dXUH979qe3NJXweuIOTv/wtMkXRizTOtvJMlCWn8P9p+RNIJhC/cncD9Za9d/fpOB06HaFnscE1JkiRJkiRJMpMFISFrRXqUzSI9yub0KHsKOFTSTmXce4j1PWX7TEmfBw4kDLOTJEmSJEmSpM9ZEBKy9CjrjPQom8UbwCIKEZRtgKG2X5Q0ivIeSgtrrbWyH2EXkCRJkiRJkiR9yoKwhyw9yronPcqCZYCnSzK2NvDRyrUTiErekcAZvXxOkiRJkiRJkjRkvk/I0qOsR/TGo+xKVURPFGImE8txM4+y84lWwH+W76iVR9mNwPrA71XxKJM0TCHEMh44jb753f0TUSkbDxwL3FHWsQVhoXCC7fOBVyXt3QfPS5IkSZIkSZLZWCB8yLpFFe+uN+FZc92j7M2ktPUdZntM+TyQFu9S0rpEdXAIsDJwA/CBSptm/fjNCVn78+q8zLYFbrL9ehHcwPZ3+mhZvSZ9yJIkSZIkSZIqerv4kM2rqIlH2fyCWlgDdMkOwIW2X7H9MPA3IjlrSL10fuX8SNuvl4930EI6XyHzf7mkqyQ9LOkQSd+UdJ+kOyS9u4zbX9LdksZJ+qNmWR9cIWmPcvxVSW1FSJIkSZIkSZKkJywIoh5dY3sqoWY4N5/xDPCBufmMN4FG1gCLALdJqpVWBVjSsqXlsZ5VKK2AhUeBVST9D7FPq8rDtneiPfsAFwFI+g2wad31McT3uwEh0vE34Du2NyjS+XsAJwGX2T6jxDkO2Bf4FXAA0br5MPAtZt9bNhOFb9kBAKuttloH006SJEmSJEmS2XlbJmRJxzSyBngd2KxBy2KjZAwiYavHjTzKOqFeOt/2wQ3G7FWe8TzwvKRngavK5QnEHjWA9Uoi1p9QUhxRYv5H0pHAzYRf2xwVuzIufciSJEmSJEmSXpEJWdKKnloDVHmUUJqssSohuNI13UjnM6fMfVUCv/Z7fw6wo+1xJYnbsnLPhwifspV7MtckSZIkSZIk6YTcQ5a0oqfWAFWuBL4oaXFJ7yPUFu/qNkg30vldsDTwmKRFCbXJ2rOGAJ8iWh4PK/NOkiRJkiRJkj4nE7KkFT21BpiJ7UmE9P/9hMz8wc0UFqGldP6viQTq+iKF30o6v1O+D9wJXE+R/pe0OOE7to/tfxN7yM6S1Kj1MkmSJElmY+AR17zVU0iSZD7jbSl731MkLUv4ZAGsCMwAppXPQ4qP2DyFpH2Aa20/3uV9A+nCGqAkTkeUj0fb/n2LsT8Gvgz0s92/cv7bwN7Aq8ATRFL0z27m/VaRsvdJkiQJREI29cefeaunkSTJPEDK3s8FbD9le5DtQcCphL/YoPLzliVjkhZucXkfInnsJl5XewslLQd8lzBT/ihwrKRlWtxyBY2VC8cAG9r+MNHq+ONu5pEkSZIkSZIk8xuZkPURkvaUdFdppztZ0kKSFpH0jKRhku6VNELSRyTdIunvkj5d7t1P0vBy/SFJ/9dh3OMk3QUMkXR08dSaKOlUBbsCg4CLyv2LlTbA/iX2RyXdUI6Pk3SapOuBswkxjpHl2eMl7ddi+Z8CJgGjiAriO4GxkoY3GT8ZuBpYusxrrKSxwDjbL5Ux7bzGtin+Yc9IekXS45IekfR0me/AMm4FSZdJGlPW8tHK2m8v3mSjJa1Z+S4uLd/FXyX9qMUcDihxx0ybNq3ZsCRJkiRJkiRpSiZkfYCk9YCdgE1K9WwR4Ivl8jLASNsbEq14RwEfBz4PHFMJM6TcsyGwm6RBHcS91/YQ27cDv7C9MaEOuAzwSdsXAWOBXTus4m0AfNb2Vwh/rSdsDyEqXwdLama2tQrwl0r18NdlPg09xYpE/mDg+UqFcVCddP6+wHVt5vsewiutVo37re13AecCh5RzvwR+UsrFXwDOLOcfIOT7NwCOBY6rxP0wsAshj/9lSQ2VFm2fbnuw7cEDBgxoM9UkSZIkSZIkmZOUve8btiGSljFF+2FJoLb36SXb15fjCcCztl+XNAEYWIkxwvbTAJIuBzYjvp9mcV8FqhWoj0s6nDBCXg64h/YJTT1X2H65HG8LrCOpmgCuCfyjwX2ibyTyI1jI23+I8D1rxZ22/1Pu+TuzfM0mADV1yG0IgZDaPe+StCThPXaepNUbxL2heJgh6UFgNXoo1Z8kSZIkSZIkrciErG8QcJbt7892MvZiVatSzfywoHFC0yruSzUvLknvIKpSG9r+l8LseIkmc32dWZXR+jEv1K3pINs30p5HmX1P2KrAxA7um4Mib/9tYIsOKnqdeI2JBoIrko4nkuCTJa1BKEA2ijuD/O8kSZIk6ZAU9EiSpFuyZbFvuAH4QhG3QNKyLdr7mrGtpP4ludoBGN1F3CWJJORJSUsDO1euPU/IxdeYCmxUjqvj6hkBHFQT+JC0VqksNeJPwKfK/JclWjJHtojdEEmDgd8QXmNPdnt/E24ADq48Y1A5XAb4Vzneq4+elSRJkiRJkiRd8Zb/n39JM4gWsxo72p7ay5g7ApNt318+HwP82fYNvYw7GNjD9mytdLYnSDoauEHSQsBrwIF00OYm6UCiLfE24AJgdeB3tseW653EvYfw+poIPEJ4a9U4GzhT0kvEPrWjgDMkPU5rg+bTiFa9saXd7wkiUZwD29OK+EVN9/1I28+2WPPPif1c75T0KOFR9n5gZeBl4I/lmQ/X70OTdBawHVHNG1e5NBAYLul54Dmg9vyDgVMk7U38vt9cWjsXBy6W9DrwFPFuAU4HnpL0CaKauDhRZUuSJEmStqTsfZIk3fKW+5BJmm67X4vri9h+vcuY5xAeWpf2dn5vBkXBcD3b36ic63jdkqYCg/uwqvSmIWld4A9EsrgyUdH6QDPzaEmbA9OB86oeaZK2BW4q+/NOALD9nSYx/hcYYPub5fNawFTbr1R/HyUtTyTJo23/oNU60ocsSZIkgUzIkiSZheZnHzJJe0m6RNJVhPR6P0k3KqTjJ0jaoTJ2jyJzPk7S7yRtAmwPDCty6qtLOkfSLmX8x4vU+QRJZ0lavJyfqpCOrz1j7Qbz2lLS1eX4qHL/KIWE/aHl/FKSrinzmaiQnq/FP0EhvX5X2beEpKMIAQ1KrB9KugX4uqQBkv6okLO/W9KmZdyykkaWdZxGkwqOpC+VtUysJSmS3quQc19OIaF/q6RtJR0r6euVe4+XdGgZc7KkSZKulnRt5V1upJDwv0chE79SZR21tU6W9LEWX/cOwIW2X7H9MPA3IjlriO0/A/9tcH5kJYFtKZkPrMSsdkVsP2T7lfpBtp8g1CYPkZRVsiRJkiRJkqTPectbFoElFR5UMHuL2lBgfdv/Vexj2sn2c4r9VHdIuhJYF/gesKntJyW9u4y/kkqFrPa3tKQlgHOAj9ueLOk84GvASeWZT9reUNJBwGFAK+8tgLWBrYg9Wg9JOgX4JPBv258pz6waJD9ne4ikPcoztyvn77L9U0mjgP62tyj3XkCYT9+m2Ds2AlgH+AFwm+1jJH2GSBpmQyHVfgKxX+xpIrHd0fblJTk7lWhtvN/2SEmTgcuAXyjaI79IJEafI9oBP1TWOoJQX/w/QnVxku2NSuJ5PGFEDfG7tRCwMHCtpCnl/G61VtLCKkQCVePRcq62jkHEd1alYfWswj7ARS2un1Xexy6Eb9q5tv/aaKDtv5f3sTzwn+o1SQdQ3v1qq3W7ZTBJkiRJkiRJ5o2E7KXiXVXP9bZrlRABPyztam8Qf7CvAGwNXFpr1auMb8ZaRNI3uXw+l9hjVEvILiv/3kMkIu24plRWXpH0RJnTBOCnJem52vatlfF/qPx7YpOY1URiG2DdSnHmnQrRjs1r87N9jaSnG8TZGBhlexqApPPLfZfbPlPS54n9aINKnKmSnpK0QVnHfbafkrQZcIntN4AbS7J7AfAg8BdgsZJQLww8Vnn+Zba/JWkFouWv0XcMjat7M/toy1662e5VmD5f3TCY9D1i79f5TZ6H7bGS3k9UJrcB7pY01PYDXcwR26cTe84YPHjwW9v7myRJkiRJksyXzAsJWTOqEuy7AwOAjWy/ptgztQSN/a9a0a7trNa21qnU+Rzy6KXythHwaeBHkkbarhlAV+fabN7VdS8EDLX9UnVASdDarbvpWhVKjrWWvn6EEiOEafJewIpEFalVHBHVsaFNrnf6Lh8lDJ5rrEoPPb8U/mXbERXQlu/H9nQiAb9M0hvE9zVHQlYStxmEqEmSJEmSJEmS9Cnz5B6yBiwDPFGSsa2A95bzNxKy8MsCSHp3OV8v9V7jQWBgbf8W8BXglr6caGkVfNH274GfAhtWLu9a+ff2DsKNBA6pxK5Viv5MJKlI+hTwrgb33glsUfaKLQx8iVlrPYGoIB0JnFG5ZzjRcrkxs0yWbwN2LnvJVgC2LOcfAgZIGlrmsaikD3awpnquBL4oaXFJ7yPaIFupPzZE4V/2HUIy/8U2YzeV9K5yvBjR+vpIg3EDiNbOX7dL8JIkSZIE0ocsSZLumZcrZFXOB66SNAYYSyRW2J6kMPi9RSGffx9R4bmQkHY/FNilFsT2ywr580vKvrS7iT+4+5IPEYIibxBS6l+rXFtc0p1EIvylDmIdCvxG0njiu/oz0WZ4NPAHSfcSSdY/6m+0/ZhCTfBmopp1re0rJG1BJFyb2p4haWdJe9s+2/arkm4GnqmoHP6R8BWbCEwmEr1ny9hdgF+WfXKLEK2fkzp+U8z8Di8mpO9fBw5uprAIIOkPRFK4nEIy/we2f0sYYy8OXF8qiHfYPrBJmNUJKXwR38U1ZZ0wa0/jomU+vwN+3s2akiRJkiRJkqRT3nLZ+75A0opEMrAx0So3FfhGZa9YN7HOBH5u+35V5OTVRp6/g7gzY/Xw/rWJRNNEkvk725vU9lPZXk/SlsBhtrdrGqj1M94FjAe2ropcSOpne3qpRN5FJHOP1927BJEwLk4kZ5fWpOJL4nMc8Hmi/e8U279sM5eNCbGPXSviLD8BPkMkUdcDX59XKlcpe58kSZIkSZJUUYey9/NLhawp5Y/94YRS3hfLuUGEMEXXCZntdsqKnc5JRQijm/sWblEd2hG4ouKHtUkP59bQ30zhB/YnYMkGioNXS+oPLAYcW5+MFV4hErnpkhYFbpN0ne07iKrle4C1bb+h8PdqNceFibbKEZVzmwCbAuuXU7cBWwCj2q05SZIkSZIkSeZV5pc9ZK3YCnjN9szWQ9tjbd+qJv5lkgZKelDSuQoPs0uL0EXNQ6tpJtsm5gOSTgbuBb4vqaqkeDzw3Qbxpks6prQyDlUDby9Jnwa+AexXWgqRNL3TF6QOfN2KFP1fgKUU/m3Dyr2HA0sRvyuX2D6n0TMc1Oa0aPmpVa++RrRO3lvaAUeWZwxvMuXvAB8APkrxkyOk75ciksLFS/z/NLm/9l4vlvSipOcVfmjTJb0gafsyZqDCh+3e8rNJOb+TpBsUrFTuXbHNa06SJEmSJEmSrpnvK2TAeoRMfSNeprF/GYQE/r62R0s6CziIEOFoR7uYe9s+SNJSwHhJ37b9GrA38NUG8ZYCJto+slSWbgF2sD1NxdvL9j6STgWm2+5kjo3oxNftCGC9mkS9pG0JkY0hxD60KyVtXsyZ56BUtu4B1gB+Y/vOcml1YGViT9Y04NBmvl+SVgH+hxBu+S2z+8n9lJDWFyG00UymHuK9nm37CyXxWwr4ICHgcS4hJvIE8Imyt3BNwo5gsO3hknYmLBE+SexTm6MqqPQhS5IkSZIkSXrJgpCQtaKZfxnAP22PLse/JwQ0Okl2WsV8pLToYfsFSTcB20l6AFjU9oQG8WYwS1BiLSLBrAlT1Ht79YZOfN3q2bb83Fc+9yMStIYJWWm3HFTaG4dLWs/2RKKi9bLtwZI+R0jqf6zJPE8CvlMER2aeVChjrsMsuf7rWyWHwKtECyaEN9wrRaVzAmF0DVFl+3VpcZ1BVOVq/D9CyOQO23+gAelDliRJkiRJkvSWBSEhm0RFSbGOZv5lMKePV6d/ULeK+ULd2DOJNsUHgbObxHu5sm+snbdXb+jE160eAT+yfVo3D7L9jKRRRHVpIuE1Vks6h9P8XQAMBi4sydhywKclvU4kgnfU2iIlXUe0NDZLyF6rCH68QfFFK3vYar/3/x/R9vhhoiXz5cr9q5T7VpC0ULf7AZMkSZIkSZKkExaEPWQ3EXLy+9dOSNpYIe/ezL8MYDUVDy1Cgv62Dp/XKuZslJa99wC7Ee1w7egrb692NFtDvX/bCGAfSf3KfFZRE0EOSQNKZQxJSwLbUOwJgMuBrcvxFrQQW7H9PtsDbQ8ELgUOsn05Ie2/haRFSmvnFjQwcu6SZYDHSrL1FaIiSUnYzia+tweAb/byOUmSJEmSJEnSkPk+IStVkJ2AT0iaImkScBTwb8K/bLDCv2x3ZiUIEH9o76nw+Ho3cEqHj2wVsxEXA6NtP93BWl4lqn0nSBpHeK71SE2xDQ3XYPspYLSkiZKG2R4JXADcXlr9LqWx4TbASsDN5X3eTbRIXl2u/Zgwl54A/AjoiZLlpcAUov1wHDDO9lU9iFPlZOJ34A6iXbFWRfwucKvtW4lkbD9J6/TyWUmSJMnbgIFHXPNWTyFJkvmMBcKHrFtU8e56E551NXCi7Rvn9rPmFqX98DDbY8rngbR4f5I+QSRhixF7uQ63fVPdmCuB97f6DiQdC+xAtA4+Aexl+9/l2pbEfrNFgSdtb9HzFUJRXlzX9o97cn/6kCVJkiQQCdnUH3/mrZ5GkiTzAOrQh2y+r5DNq0jqL2ky8NL8nIz1kCeBz9r+ELAn8LvqxSLs0Yls/zDb6xfVx6uBI8v9/Ynq1va2P0gYTvcK21f2NBlLkiRJkiRJkp6yIIh6dI3tqYSa4dx8xjPMrto3z1MqX38C7gQ2IPZ67VE3ZlngWuB9xR+sxsdLyyO276ucnwQsIWlx26+U/WjfBK4DdqqLMdr2wbUPtp+rXFuKWcIruwGX2f6Hwr9t8aoiI/CVmqJlZU23ESIg44j9YUcDywO7275L0l6E5P0hks4BniMERlYEvl2T3q97Fyl7nyRJkiRJkvSKt2VClrSkkT8bwPmSXqqMm1LzK2vDzsB9tl8pn48FfkZI6X+pXQxJxxNJ4bOECThEortoaaVcBPi57fNahFmDqKIdQOxv2w3YDNie2C+2Y4N7Vipj1iY8y+ZIyFL2PkmSJEmSJOkt2bKY1FPvz7ZZOd7d9qCSQH26k0BFIfIEiiF28ftaw/bwTidj+3u230MIkRxSTi8CbAR8hjCR/r6kVtXIh21PKGqKk4AbixhM1ZOsnsttv2H7fhp7tCVJkiRJkiRJr8mELKmnp/5ssyFpVcJzbA/bU8rpocBGxffsNuADpcrVCRcQ1TYIX7M/2X7B9pOEF9mHW9z7SuX4jcrnN2heJa7eoyZjkiRJkmQ2UtAjSZJuyYQsqaen/mwzKaIb1wD/W6m2YfsU2ysXj7HNgMm2t2wRZ83Kx+2ZZTFwBfCx4kn2DuAj9N6TLEmSJEmSJEnedOabhEzSipIuLF5j90u6tk2bWqtYZ0patxxPlbRcOe5E+W+uIWltSWMl3SdpdUl/KecHSppYjrcsUvo9fUZ/SQe1GNLUn03SEpLuIkQ91pB0dOWaJB1flCUnE3uvvl/WM7aJofSSkmZI2qUS5yeSJkl6ALiueKKNB7YFvg5g+wFCqGM8cBdwpu2JPXohSZIkSZIkSfIWMl+Ieigk9IYD59r+Yjk3iNjbM7nbeLZ7YkzcaE4q+5K6uW9h2zOaXN4RuML2D8rnHplCS1rE9utNLvcnhDpObnL9DdsH1p3bssQVsLXt6ZIWBW6TdJ3tO4C9gPcAa9t+Q9Lytp9oMc1/Ao8AD1XmvQmwKbB+OXUbUWUbVX+z7WHAsBbxa+OmUlHUtL1Xo2u2zwHOqR9TPvdr95wkSZIkSZIk6QnzS4VsK+A126fWTtgea/tWSf0k3SjpXkkTJO0AM6tKD0o6V9J4SZeW9jYkjZLU1KStTcwHJJ0M3EtUgE6s3Le/pJ83iDdd0jFFon2opI0k3SLpHkkjJK0k6dPAN4D9JN1cu6/TFyRpL0mXSLoKGNlsDYRh8+qlajWs3Hu4pLsJKfpGlazaO7ft2pwWLT+1PWZfA46pJahtkjGA/wf8kTB8nvkIYAnCUHrxEv8/LdY8XdIJ5T3eIGlI+W7/rjB6RtLCkoZJurv8HtQERtp9x2eUSt1ISUu2WUuSJEmSJEmS9Ij5JSFbD7inybWXgZ1sb0gkbj8rlRwICffTba9P+Eq1atXrJuZ5tjcAfgpsX6pFAHsTHlf1LAVMtP0RwuPrV8AutjcCzgKOt30tcCpwou2tGsTohKHAnra3brGGIyiS9bYPl7QtsCYwBPggcJekzZs9oCQ4Y4lE6m/AaeXzhsCtkp6WdJ1m3/9VH2MVYKeyXogkdCzRHvkewjT6SWBEaU9sxlLAqPIeXwFuICqArwIXlZiHAs/a3hjYGNhf0vtavB/K+/hNMZ1+hlliIvXrOEDSmP+fvTMPt3O8+v/nS0w1REso1YhZSTSI9FVjUXSgplbVW1P7qtbUgbf81DyUl1apodVUaaUURZFWgsZYUxASMZOSKoKaJZH4/v5Y9855srP3OWefIRLW57rOdfZ+9v2s576fZ+e6zspa9/craczkyZPbmWaSJEmSJEmSNGZeScjaQ8BJZZ/RDcAnaJMpbybh3p2Y/ywteth+C/g78GVJawIL1AyJ65hBVIMgErqBwPUlYfgJsEIn59UR19t+pRNrqLJ1+bmfqPqtSSQkDbE9o0jfrwD0A/67vJ8CnGb7o8BviESzGb8Aflxp3RxWYuxC+IT1JSp1W7SXHBKJ13Xl9b3AKSXOWsDU8nojYI9yr+8Clirra+/+PG17bCXugCb34jzbQ2wP6devXzvTTJIkSZIkSZLGzBN7yAjvqF2afLY7kRisb/tdhaT6wuWzrkq4txfzrbqxwwhz4UdoXB0DmFJJPgQ8ZHvDJmO7Q3Vu7a2hioCf2v51Kxey/apCsn5bYDwhRV9LOq+k+b0AGAJcUgpSSwNflDSdSJTurLVFSvob8F+ErH0j3i1+YlCRsy972GrfbQEH2h5ZPVHSXjS/P1XJ+xlAtiwmSZIkSZIkvcK8UiH7O7CQpP+pHZC0gaTNiGrKi+WP6s8BK1bO66qEe3sxZ8H2XUSb3TeAizsR+1GgX21ekhZQGCj3NM3W8AaweGXcSGAfSYuV+XxCjRURkdRPIWlP2Ve1FW1S9FcBW5TXm9GO2IrtlWwPKPL3lwPfs30V8AywmULOfoESp7ty9iOB79baSiWtLmlRWnjGSZIkSZIkSdJbzBMVMtuWtCPwC0mHEe1xEwkRjIeAaySNAcbSliBAm4T7r4HHqUi4d8DwdmI24lJgsO3/dGIt0xQy72dK6ks8g1+UdfQkDddg+2VJtytk9P9W9pF9CrijVKzeBP6bWcU2aiwHXChpfiKZv9R2TYL/ZGDFF8J3AAAgAElEQVS4pB+UGF1RsrycSOrGEdXM62xf04U4VYYRLYf3lT1ikwk1y1afcZIkSZIkSZL0OGrr+PpgIOnjRIKzIbEn6O/A9223LI8vaRjwc9sTSkvbENsvSXqzKoWu8AU73faNPbKIjue1JnAJkbTsAvzB9mclDQCutT1Q0ubAIba/3MVrLAl8w3Yzefz2zl2YaDNciEg4L69J+Zek6ATgq0Q74Lm2z+wg3gbAncCuti8vx/4P+BKRGF4PHOxufJkVqpt72D6oK+cPGTLEY8aM6erlkyRJkiRJkg8Yku613VTZvca80rLYKcof+1cCNxHtbk8Q+7saiVl0iO1v257QzvWWVBghv9MsGVPQ8n0uVahm1PzK1rX9pO0u+5W183HNr6wrTCX8yj4NDAa2lfRf5bO9aPMr+xSRWLY3x/mBU4jWw9qxql/ZQEI9cbMuzhUA22O6mowlSZIkSZIkSVf5QCVkVPzKbE+0PbA3/cqA6YTB8SoNYs6zfmWSliJUF9eS9I6kFxW+ZUerzc/r2GbXqPmVSdobGAMMAn6vUDo8mdb9ylYixD1OLTEuIBLGnvQr27xUOpF0jKTzK2MaJmpK2fskSZIkSZKkm3zQErL0K+sc7fqVAa8A6wITbC9iexngfwlp+KFE1Wt9deBXBhwMrAL80vbqRYa+D7BrSWQ661e2KnA1cGjxT1udMLH+d/lpxa/sDaJl8vMl9nFNzlkT2Kas9+jKs5tJyt4nSZIkSZIk3WWeEPXoIWq+U5sSEunt+ZUdRCRR3Yk5i1+ZpJpf2cO07lcGMD+RfPQEjfzKGq2hStWvDGAxQqa+oSR9kfkfXPaiXSlpoO3xREVriu0hknYiEs1Nmsxzpl9ZW54LklYFPkWbf9v1kja13Uwev+pXNo7wKHtX0jiaeIwBI2xPBaZKepG4J5OajE2SJEmSJEmSLvFBS8jSr6xzpF8Zs/mV1VPvRfZB+7eSJEmSJEmSzAV80FoW06+sddKvLEmSJEmSJEneJz5QCVmpguwIfF7Sk5IeAo4BniN8p4YofKd2p7Ff2YPAx2jNr6xZzEZcCtzeWb8yotp3iqQHCK+sLqkpdkDDNdh+Gbhd0nhJp9oeBfyR8CsbRyRIizeJuRwwutzPe4gWyapf2c4lxk/pul/Zk0T74QPAAz3gV5YkSZIkPcKAw0a831NIkmQe4gPnQ9Yqqnh3zYFrzVG/st6ktCEeYntMeT+Adu6jpKHAebW3wDG2ryyfLUm0dA4k2kX3sX1HB9c/BDgV6Fe84foS+//6E+2Fp9lu2A6psCH4BVGpMyFs8jXbT5eWzTfK0PmBK4Djy36ypqQPWZIkSVJjwGEjmHjyl97vaSRJ8j6jTvqQ5b6YOUBJOO4mKjnzfDLWRcYTxtrTJS0HPCDpGtvTgTOA62zvImlB4CPtBZL0SUIl8ZnK4f0JVcjtJPUDHpU0vFQa69kVWB5Yp+wjW4FZ99V9riR5ixFJ5HnAnl1bdpIkSZIkSZI050OfkNmeSFRmevMarwKr9+Y1eotS+bqOkOFfl9jztUf5uG/xBYPwBFupvN+ytDzOxPbblbcLU4RTJC0BfBH4tKQDKte93fb+TaZ1OiHD/5fqJYC9JG1T5rIocLekbzZQtFwO+HfFC62hemLxUtsPeFbSxyrKlLU57gvsC9C/f/8mU02SJEmSJEmS5nyg9pAlvUYzn7Zz6sY9WXzCXqYBkj5T9vWNA/Yr1bGVgaeJPXImjKQ3apaMFSPnf9l+oO6js4AJhDx9f2CnMpdG9gKXAtsVs+ufSVq32cJtv17mN5tfWvqQJUmSJEmSJN0lE7KkM9T7tG1cXu9ekp7BRJWrXWzfZXttYAPgcEkLE1Xa9YBzi4n2W8Bhjc6X9BHgCOCoBh9vQyR1yxPG1WeV6lujeUwikszDCRn8GyVt2c7U1c5nSZIkSZIkSdJlMiFLOkNXfdoaB7MfJhKvgYQv2aRiCwChoLhek1NXAVYi9p9NJIyh75P0cWBv4AoHTxBVrTXbmcNU23+zfShwErBDo3GSFifMo5vK8ydJkiRJlRT0SJKkFTIhSzpDV33aZiJppZoJs6QViQrVRNvPE3u01ihDtyRaD2fD9jjby1S8ySYB65UYz5RzkbRsif9Uk7msJ2n58no+YB3gnw3GLUa0ZV7VGauCJEmSJEmSJGmVTMh6EElLlX1JYyU9L+lflfcLvt/za4SkfUqFqT266tNWu8b6wPXAm5LeAUYTJs8vlSEHArdKmkG0G57UJM4BksaV+3krsEDl4yuA70uaQiRX/68Sv549gCfLXN4mRD7OknQR8AkiQZxKJHyvAN9pZb1JkiTJh5v0IUuSpBU+9CqLPUkRsxgMIOkY4E3bp72vk4q5zG97RpOP9wHuA55vJ8R7tverxOtje/PqgA7UKt8EtrH9ZJGYHwNU5f8XAkYC29lu2DpY+L3ts8ocdiL8yl6StADwc2AT2+MkLU0kUrNRqnNfBJax/UZpSVzK9hRJAF+1fVWpnP2QMK4+pJ05JUmSJEmSJEmXyQrZHELSnpLuLtWdcyTNJ6mPpFclnSrpPkkjixLhzZKekvTFcu63JV1ZPn9U0k86GfcESXcDQyUdK+keSeMl/UrBrkQC+adaFU/SpOKbhqT/IkQ8KLF+Lel64HflGj8v135Q0rebrd32o7afLK8nAS8DS5e4fYBTaCLkURfn9crbRWnby/YF4N6aoqLtl2qS9g1YllCKfKuMfaMkk/XXeq8k068AW3c0tyRJkiRJkiTpClkhmwNIGgjsCHy2GCOfB3ydkF/vC4yyfaika4BjiL1QnwZ+Dfy1hBlKVKCmAfdIuhaY3kHc+2z/pMzhUdtHK8pAfwS2tf0nSQcCB9geW8bVT3+K7YGSTiB8yDYt1aTvAS/aHippIeBOSaOATxEJVpWnbe9Y4n+2HJtYfh8M/Bl4oe6enQ1sVBfnDGDxcs4CwOfK8dXjFI0iEr3htn8maRDwh7oYU4FXgacl3UgIgVxbv+gK9xHiILP1nyh9yJIkSZIkSZJukgnZnGErQup9TEl4FgGeLZ+9Y/v68noc8FpJrsYR6n41RtaEJSRdRUjP92kn7jTgysr5W0o6lDBlXhq4F/hbi+v4i+0p5fXWwKckfb287wusZnsk0X44G5I+AVxAyOW7tC/uAGxOnbR8O6bQAGdK2gP4f8C3iPuwEfAZYAowWtIY2zdTWkjr5qEydosSa7DtE5pcq6nkve3zgPMAhgwZ0i3lySRJkiRJkuTDSSZkcwYB59s+cpaD0a43rXLoPaKCU3tdfT6NpOfbi/uObZf3HyGMk9ez/a9S7Vq4yVyn09bKWj/mrbo1fc/2jXQCSX2JKtOPbd9TDq9HGC4/Wd4vUSp5azSKUccfiYrZtwjxjZtqhtSS/lZi39zoxHJf7iSqen8nREqaJWSDaVAdS5IkSZIkSZKeIPeQzRluAL5WxCZqaoyt9rhtLWnJklx9Bbi9hbiLEAneS0XEYufKZ28QbYA1JgLrl9fVcfWMBL6nNin7NSQt0mhgaWn8C/Bb2zOrdravtv3xImG/KvB6e8mYpNUqb7cDHi2v/wasK2mRMp9NaSKdL2kFSdWq2WAaS95L0g+ApQiFyCRJkiTpFOlDliRJK2SFbA5QlP+OBW4o6n3vAvsBz7UQ5jaiKrQK8IfKnq8O49p+WdKFwHgi+bir8vHvgGEKCfihxB6230h6Hri7nfn8GugPjC3tki8SiWIjdgM+Cywp6Vvl2DdrIhwt8H1JmxPrfJkwg66t70yiDfM94JrSOtmIBYDTJS1HVCNfYFZZ+9PLPV0EuAPYwva7Lc4zSZIk+RAz4LARmZQlSdJpVLrauh8oPKSqf2Dv0Ei9rsWYOwCP2Z5Q3h8H3GL7hm7GHQLsYfug7sSpi7kf8Lbt33cjxkRgSL1/VlEwHGj7+92bZe8haShlPxXRznhMrRpWVBuHEaIkJuTq7+gg3iHAqUC/Im3fl1B87E/8R8Jptn/XK4vpAkOGDPGYMWPe72kkSZIkcwGZkCVJAiDpXttDOhrXkxWyd2zPJqBQmVAf29NbjLkDcC2l/cz2Ud2Y30xsjyG8sHoM279qdLyL654XGU8kk9NL9ekBSdeUtZ8BXGd7F4VB9kfaCyTpk8DngWcqh/cHJtjeTlI/4FFJw21PaxgkSZIkSZIkSeYBenUPmaS9JF1W5NxHSVpM0o0Kz61xkr5SGbtH8bN6QNIfijz69sCpxSNrFUkXSNqljN9S0v0lzvllnxKSJio8t2rXWLPBvDYvsvFIOqacf5PC++ugcnxRSSPKfMYrPLtq8U9R+G/dLWnVSpxDyuubJJ0k6WbgYEn9JP1Z4QN2j6SNyrilJI0q6/g1zRX93iJUEsdLOqWcu6KkxyUtrfAeu1XS1pKOl3RwZa0nSjqojDlH0kOSrpX018q9XF/hfXavwutsuco6amt9TNImzZ617beBgZLGEqbPHyXUH+8i9nT9toybZvvVpl+a4DpgRWA5QjFxbImxuKI/cjHCH6xholue782SXpE0RdILkv4p6a2yjlXKuO0k3VXu/w2Sli3Hz5R0VHm9jaRbFC2h9dfZV9IYSWMmT57cwZKSJEmSJEmSZHZ6MiFbpCROYyVV5dY3BPa0vQUhSb6j7fUID6mfKVgbOILYr/Np4GDb/wCuBg61PbhmLAwgaWFCPn1X24OISt93K9d8qVzjXOCQTsx9TWAbYg/V0ZIWALYFnrP9adsDiSShxuu2hxLKhb9oEnNJ25vZ/hlRITrd9gaEUMawMuZo4Dbb65a1zibIIWl5wtdrC0KAYgNJO9j+Zzn+K+BHRPVoFJH47FnOnY/wJRsO7ETI6A8Cvk08F8pafwnsYnt94HzgxMoU+pS1fr/Mtz0WIvZo9Qe+Viqm3wEmE2bS90saJmnRZgEkbQ/cYHtNYi/c50qcrxIeZ88RrbEHt2P+DOHjtjYhxz8duMD2osDZwIFlzG3Af5X7fwnwv+X4YcCukj4HnAns3ehats+zPcT2kH79+nVwa5IkSZIkSZJkdnoyIXunJE6DaybAhettv1JeCzhJ0oOEQuAngGWJZOPy2t6pyvhmrEGYDT9W3l9IVFBqXFF+38usXl7NGGF7arn+i2VO44CtSoVoE9uvVcZfXPm9YZOYf6q83go4q1R6ribk3Rcvc74IwPYI4D8N4mxASLpPLu1/w2trtT2MUEjcj5J4ln17L0tal/AKu7/IwW8MXGb7PdvPA6NL/DWIvV3Xl/n9BFihcv1O30vbd9leu8z58JI49yEk6M8tic9bRMIzGwoFySOARq2p2wBjgeWJxPQsSUu0M517bP/b9lRCVn9UOV71d1sBGKnwfDuUSOBq1b7/IdQVz6r+Z0CSJEmSJEmS9CRzQva+6l21O9APWL9UPV4gvK7E7D5b7dHUrLdQ8/KaQef2yU2tvJ5BVIUeI+TfxwE/rbWwFdzkdZXquucDNqwkrJ+w/UYH59doutaSwNSSp8UqHw0D9iJUCM/vII6AhypzG2R768rnrd5LbD9MrH8g4RE2yXZN2fFyIkFrxCrASsT+s4nE2u6T9PGyliscPAE8TVQ2m1F9ps383X5JJFyDiEpe1XdtEKHkuHz7q02SJEmSWUlBjyRJWmFO+5D1BV60/W5pB1uxHL+R8NNaCkDSx8rxeo+sGo8AA2r7t4Bv0sQEuKuUVsG3bV8EnMasScSuld/tqgUWRgEHVGLXxE9uIZJUJH2B2HdVz13AZmWv2PyEhHxtracQFbOjgN9UzrmSaLncgPALg2jP27nsJVsW2LwcfxToJ2lmC2NpIW0JSSupzZNsRaLyNrFU456VVPMX25ImHmG2x9lexvaA4k02iTCzfp4Q+NiyxF+2xH+q1XnW0Rf4V3m9Z2UtKxJtoOsCX5D0mW5eJ0mSJEmSJEkaMqcTsuHAEEljiETkEQDbDxH7lm6W9ADw8zL+EuDQsvdolVoQ21OIisllpd3sPWIvVU8yCLi7tPEdAZxQ+WyhIlZxMPCDTsQ6iFj3g5ImEC2GAMcCm0q6j2gvfKb+RNv/Bg4nWgwfAO6z/RdJmxEJ1ym2hwPTJNV8uaaV8ZfanlFC/ZlIcMYTHmJ3AUcC6wC7AKeUub1BeIY1Yj5JoyW9Kems2sFSqbsSeEvhZ3YX8L2KfP+BwPDSqjoYeFXShHI/biwJEJV4S0j6F/CxyuHjgc+W530j8ON6e4AucAzxHboVeKlcW8Q+vENsPwd8i/BpW7hplCRJkiQh5O4HHDbi/Z5GkiTzGD3mQ/ZhQU28wuYmipjHfcBXbT9eOb6Y7TdLJfJuomX0oGIDgKQBwLVFxKRR3EWJqtFAwhftgHL8I8BnbI9WyNrfCJxk+29N4nwOuMv225K+C2xue9fK52cQra2v1K4xt5M+ZEmSJEktGcuWxSRJoPM+ZHO6Qpb0MpLWAp4AbqwlY5IGSHoEeKJUsJ4m2h1b8vCy/Zbt2wi1zOrxt22PLq+nEcngCg1C1MaPLsIZAHdWx0panxBVGdXo3CqlUneKQq7/BklD1WZfsH0ZM7+kUxV2Aw9K+k453tCCodyrhyX9RmERMErSIi3cpiRJkiRJkiTpND1pDP2hoOxtmmuxPQFYucFHawAb275d0vlATaFweEnSABYk2j+bImkb4DjC5mDjcvjpmrKmpCWB7Qip//binA1sRChtTi+toWcCexB7Ardsd6HBokS75RvAEEK58xFgR0J582qi5fA12xsovOpulzQKeJawYHhd0tLAnZKuLnFXA3az/T+SLiWsCi5qsIZ9gX0B+vefzbEgSZIkSZIkSTokE7IPD8/avr28vojY1wawe33LYntBbI8sipND6tsJi6jHxcCZttsV3LC9v6T/JsRONrM9VdIBwF9tPxtbuTpkGrCqbUs6Dphq+8TSsjmgjNkaWEfFBJsQ8liN2E93kqRNiSS0ZsEAkWCOLa+byv3bPg84D6JlsTMTTpIkSZIkSZIqmZB9eKhPGHojgTgPeNx2M7PsmUjaihBL2ax4hUF4um0i6XuEjP+Ckt603dC3DHjXbZsgZ0rb236vpvhIyPofaHtk9URJe9FmwfBu2RtYE+6ot0HIlsUkSZIkSZKkV8iE7MNDf0kb2r6DkM6/jWgt7BEknUBUn77dibHrEkqP29p+sXbc9u6VMXsRVbhmyVhnGQl8V9LfS+K1OiF138yCIUmSJEm6RIp5JEnSFTIh+/DwMLCnpF8DjwPn0oWErFSSliCqVzsQLYGvE9WuRwgjZwjD5WFNwpxKVMAuK2Ofsb19q3PpJMOIlsP7iqT9ZGAHwoLhmmLBMLbMPUmSJEmSJEnmKD0mey9pBjCucmgH2xO7GXMH4LEiVEHZJ3SL7Ru6GXcIsIftgzoc3PmY+xFG0r/vRoyJ9IKkfkdy9j14ncMJEY0ZhJz+yHbGng98mahSDawcP5VIFKcBTwJ72361N+fdE6TsfZIkSVJjwGEjslqWJMn7Inv/ju3BlZ+JdRPqSjVuB2Ct2hvbR3U3GStxxvRkMlZi/qpRMtbFdc9zFLn9rwNrA9sC50iav51TLijj6rme8DhbB3iMMMVOkiRJkiRJkg8kvepDJmkvSZdJugYY1cz7qYzdo/hEPSDpD5I+C2wPnCpprKRVJF1QU8uTtKWk+0uc84ukOZImSjq2co01G8xrc0nXltfHlPNr/lUHleOLShpR5jNe0q6V+KdIurv8rFqJc0h5fZOkkyTdDBwsqZ+kPxcvrHskbVTGLVV8ru4vrYQNpQUl7VbWMl7SKeXYipIel7S0pPkk3Sppa0nHSzq4cu6J5T6uI+kchbfWtZL+WrmX60u6WeHnNbJcb6zC5+sFSW9JekPSJu087q8Al9ieavtpwgvt7BKn+nMEgO1bgFfqg9geZXt6eXsnsIKkuxrEGVS+X1dJukbS05IOkPTDcj/vlPSxsr5VJF1X1ndr7TshabsS+36Fj9my7X0nkiRJkiRJkqSn6cnqzSIKLymo+FIRynnr2H6lVIsaeT+tRexB2sj2S5I+VsZfTbTaXQ6gIoUuaWGiwrKl7cck/R74LlBT93vJ9noKtb5D6FhoYk3gc8DiwKOSziWqN8/Z/lK5Zt/K+NdtD5W0R7nmlxvEXNL2ZuXcPwKn275NUn9CaOJTwNHAbbaPk/QliqdVFUnLEybO6wP/IRLbHWxfVZKzXwF3ARNsj5L0GHAFcIZC/v3rwFBgJ2Iv1SBgGWJP2fmSFgB+CXzF9uSSeG5je7Ckm4B7bf9I0hfLfLdqcg8/QSRQNSYBN9jer8n4zrAP8Cfbs3mAwUwT6YHAuoRC4hPAj22vK+l0wtPsF4T64362H5f0GeAcYAtC2OS/imz+t4H/BX5Uws/2nbD9bt3104csSZIkSZIk6RY9mZC9Y3twg+PX265VQkRj76ctgMtre6cq45uxBpH0PVbeXwjsT1tCdkX5fS+RiHTEiCK9PlXSi2VO44DTStJzre1bK+Mvrvw+vUnMP1VebwWspTZvrSUkLQ5sWpuf7RGS/tMgzgbATbYnA0gaXs67yvYwSV8F9gMGlzgTJb2sUDJcFrjf9ssKE+fLbL8HPC9pdIm/BpHUXF/mNz/w78r1q/dyQJO1QuPqXpc3KJZK2nRCfKM9Rtt+A3hD0mvANeX4OKIquBjwWdoERAAWKr9XAP4kaTnCFPvpStxG34lJ1QunD1mSJEmSJEnSXebE/qa3Kq93p7H3k2jtj/eOXINrPlIz6Nwa632n+pTK2/rAF4GfShpl+7gypjrXZvOurns+YEPb71QHlASho3U3XaukjxBJBYRq4Rvl9TBgL+DjwPkdxBHwkO0Nm3ze2Xs5Cfhk5f0KwHPtjG+KpD2JquOWFZ+xZlSf3XuV9+8R850PeLXJfxb8Evi57aslbQ4c0yRuZ79HSZIkSZIkSdISvbqHrAHNvJ9uBL4maSmA2t4fIsFYvEGcR4ABKvu3gG8CN/fkREur4NulXe40YL3Kx7tWft/RiXCjgAMqsWvJwS1EkoqkLwAfbXDuXcBmZa/Y/ISHWG2tpxAVpKOA31TOuZJoudyAaI+EaM/buew3WxbYvBx/FOgnacMyjwUkrd2JNdVzNfB1SQtJWglYDbi71SCStgV+DGxv++0uzGMWbL8OPF0qiSj4dPm4L+FJBrBnd6+VJEmSJJB+ZEmStMacTsiGA0MU3k+7U7yfbD8EnAjcLOkB4Odl/CXAoUV0YZVaENtTgL2JNrRxRDXkVz0810HA3WVf3BHACZXPFpJ0F3Aw8INOxDqIWPeDkiYQLYYAxwKbSrqP8PN6pv5E2/8mlAZHAw8A99n+i6TNiITrFNvDgWmS9i7nTCvjL7U9o4T6M1HFGk+YMt8FvFbG7gKcUu79WKLFryXKM7wUmABcB+xfufZsSLqYSGbXkDRJ0rfKR2cRSfj1RbyjJ57r7sC3yvoeIgRIICpil0m6FehRq4EkSZLkw8uAw0a831NIkmQeomUfMqXf2MvAkbbP6UaMifSC31gl/nzAfcBXbT9eOb6Y7TdLJfJuQkTl+RZjD6XsmyLaHY+xfWX5bEmiXXIg0Yq5j+12K4gKZcpTgX5F0KUvcBHQn2gTPM3271qZ4/tB+pAlSZIkNdKHLEkS6LwPWVf2xTQT76hduE9Ftryz7ABcS1RXsH1UF+Y1G7bHAD39V/IbRCVoFrq47h5H4Qd2LXBlNRkrXFuSpgWB41tNxgrjiWRyehHDeEDSNWXtZwDX2d5F0oLARzqY6yeBzzNrZXB/QjFyO0n9CIXD4aWSlyRJkiRJkiQfKHqkZVEfIr8xQm5/rzJmrvIbK2t6hGgZ3FZ1fmOEpPtrwDvAbiWhqq2jttbH1L7f2CbAmNLKeSOx7+1ySUsQ6o+/hZltk/Nrdv+wsaVCB6FQ+b/MLpKyuCQRQiWvAFs0iHFleb43S7q0zPtkSbuXdYxTaXNt57kMlfSP8lz+IWmNcnwvSVcovMsel/R/TZ7XvpLGSBozefLkdm5ZkiRJkiRJkjSmKxWy9BublXnZb+xEwusLQllyqDrwG7M9UtKrhHpjf+Brtq9UCJVMBn6nEM24Fzi4WTVV0vbAv2w/IM2Sn55FCIQ8RzynXW1fRySZ9TE2Bz5d7vErwFPAsLKOg4EDge8TlbtGz+URYNNS7dsKOAnYuYQfTPibTSW+K7+0/WzdvUjZ+yRJkiRJkqRb9GTLYvqNfTj8xrB9F7C2pE8BF0r6G/FdWg840PZdks4ADgOOrD9fIdd/BCFkUs82hLDIFsAqZb63FrXERtxThE+Q9CShaAnxXD9XXjd7Ln3L/FcjKnMLVOLeaPu1EncCoQg6S0KWJEmSJEmSJN2lJ72V0m/sw+E3NhPbD0t6i0j0JgGTSrIGcDmRkDViFWAlYv8ZxNruUwiG7A2cXPzHnpD0NFHZbCah35EPGTR/Lr8kjKV3lDQAuKlJ3PQhS5IkSTpNCnokSdIKvSV7n35jfDD9xiStVFpSkbQiUXmbWARCnq3twwK2pIi01GN7nO1lbA+wPYBI5tYrMZ4p51LmvwbRitgdmj2Xqg/ZXt28RpIkSZIkSZK0TG/9r/9w4BqF39hYKn5jkmp+YzOA+4k/hC8BfqMQpagJUGB7isJb67KSBNxD7/iNnSrpPeBdYo9ajZrf2HxEgtQRBwFnS3qQuLe3EG2GxwIXK/zGbqaJ35ikmt+YgL/W+Y1tZHuGpJ0l7W37d7anlZbEV+v8xrYk1BAfo+I3phD3OLPsk+tDtH4+1Ok7FWwMHFaSsUnA98p+wAHAcsBwhcLiU0S1CwBJnwdOJhQepwGH2v57NXDZS7g68IzCX07Aj6v2AJKOJ3zEas/r1UqIJcv1p5fPJpXjsz0XSc8S+8++KOldSuuppJuIJHCBsq/sBrI6liRJkrRAyt4nSdIKLfuQfVhQL3uF9QTqRb+xTlz7JuCQYi1ASciutT2wyfh1gRGKxO4AACAASURBVBdsPydpIDDS9icqn+9EJOPrNItRxi1R209WEvi1bO+nkPP/B7Ct7WckLWP7xSYxNiTMxze3PVUhPLNgmdvMdZXE8qfE92Cz9u5H+pAlSZIkNTIhS5IEOu9D1lsti0kvo/Abe4IQn2jkNzYWuJWu+43VrjNA0iOSLlTYFVxe9rS1hO37bT9X3j4ELKw2C4PFgB8CJ3QiTlXcY1Ha9uZ9A7jC9jNlXMNkrLAcodA5tYx9qTK36rWmEbL8/YtyZJIkSZIkSZL0KNmK1YSyt2muxfYEYOUmn23e3fiStiH2ri1ItPDNT7RBvg58rwwbLqkmlLEg0UZYH+dsYKO6w7cTqpA14YzjgZ8BbzeZyyDgD5VDHyf24T1Om5Li6kSb4U3EfsQzbP++yfJGAUcprANuAP5ku+HexNIm+gAhLPJA3bz2pVgY9O/fv8mlkiRJkiRJkqQ5mZAlDbE9EhhZWhFvsb0agKQtiD1ZALvXtyw2iLN/9X0RErmaInlfBDZWtf2DEqPRXMZR5P7rYh1OiHUcTXyX1yf2zy0C3CHpzoplQjXem0VZcxMiofuTpMNsX9DkdjRUrkwfsiRJkiRJkqS7ZMti0hnqk40uJR+SViCUIfew/WQ5vCGwftmzdxuweqlydYY/0mbkPAm4zvZbZd/fLYRoR0Nsz7B9k+2jiaRu50bjiuLlIMJgO0mSJEmSJEl6lEzIks7QvyaXT6hN3tZqgCK6MQI43PbtteO2z7W9fGkR3Rh4rL2Wy2LiXGN7ioIn8BdgE0l9yh63z9AkiZK0Rl2cwcA/G4xbgBD1eNb2gx2vMkmSJEnShyxJktbIhCzpDA8DexbZ+I8B53YhxgHAqsCRksaWn2W6EOdkSePLXLYGDoYwqQauAx4klCWH2R7fJMZiwIWSJpQ4awHHVD4fXo6PJ4RDvtKFeSZJkiQfUgYcNuL9nkKSJPMQvbKHrHiMjasc2sH2xG7G3IGonkwo748j9jbd0M24Q4gWuoM6HNz5mPsRZtPNRCU6E2Mic4/s/nu296s7tnn1TXm+AwEkDaXsrSL2Xx1j+wTghFIpG1bG3ixpH9t31MeoIukQ4FSgn+2di4/aRUB/YJSk04ov26llXLvYvhf4bN019pP0dk8IoiRJkiRJkiRJZ+ktUY93bM8mwlBDUh/b01uMuQMhGjEBwPZR3ZjfTIooRY8aSNluaF7dxXXPi4wnksnpkpYDHpB0TVn7GcRer12Kz1e7EvqSPgl8nlnNtPcHJtjeTlI/4FFJw4tMfZdo9sySJEmSJEmSpDeZYy2LkvaSdJmka4iqxmKSbpR0n6Rxkr5SGbtH8bx6QNIfJH2W2C90aml1W0XSBZJ2KeO3lHR/iXN+xd9qoqRjK9dYs8G8Npd0bXl9TDn/JklPFeNhJC0qaUSZz3hJu1binyLp7vKzaiXOIeX1TZJOknQzcLCkfpL+LOme8rNRGbeUpFFlHb+mibKfpN3KWsZLOqUcW1HS45KWljSfpFslbS3peEkHV849UdJBZcw5kh6SdK2kv1bu5fqSbpZ0r6SRwFTbA8s6amt9TNImTea3DWHQPEbhhXYjsET5bAlgU+C3ED5ftl9tEufscv44YEXCO2y38rGBxSWJaD98BZgt0S339AlJb0r6j6Qpkl6Q9J2yjnGSVmnyzDpca5IkSZIkSZJ0l95KyBZR2z6hKyvHNwT2tL0FMAXY0fZ6hPT4zxSsDRwBbGH708DBtv9BSKUfantwRaEPSQsDFwC72h5EVP2+W7nmS+Ua5wKHdGLuawLbAEOBoxXCDtsCz9n+tO2BxF6lGq/bHgqcBfyiScwlbW9m+2dEheh02xsQyn7Dypijgdtsr1vWOpuxlaTlCW+wLQghig0k7WD7n+X4r4AfEdWjUUTis2c5dz7g68BwYCdgAKEe+G3iudRELH4J7GJ7feB84MTKFPqUtX6/zHc2bI8s1dHvAAuUdexWqmMrA5OB35XEc5ikRZvE2R84CrjQ9prAc8DF5eOzgE+VY+OI78hsHmi2Xy7rm07sE+tbXi9f1jEMOLDR9TuzVkn7ShojaczkyZObhEmSJEmSJEmS5vRWQvZOSZwG296xcvx626+U1wJOUogn3AB8AliWSDYur+2dqoxvxhrA0xW/qQuJKkyNK8rve4kkpCNG2J5arv9imdM4YKtSNdnE9muV8RdXfm9IY/5Ueb0VcFap/lwNLCFp8TLniwBsjwD+0yDOBsBNtieXBGd4ba22hxGGyPtREs+yJ+tlSesSAhj3lyRlY+Ay2+/Zfh4YXeKvQezhur7M7yfACpXrd/pe2r7L9tplzoeXxLkPsB5wbkk83wIOa3S+QinxCCIpq2cbYCywPJGYnlWqb824x/a/ixH1k4QxNMRzbbaODtdq+zzbQ2wP6devXzuXT5IkSZIkSZLGzGlj6Lcqr3cH+gHr235XIWKxMJGoteJz1bC1r8LU8nsGnVvv1MrrGUSl5DGFkfAXgZ9KGmX7uDKmOtdm866uez5gQ9vvVAdE912H62661pLA1JKnxYA3yuthwF7Ax4mKV3txBDxku1li2eq9xPbDkt4iEr1JwCTbd5WPL6dJQgasAqxE7D+DWNt9CsGQvYGTbRt4QtLTRGXz7g7mDfBe5f177ayj5bUmSZIkSZIkSau8n7L3fYEXSzL2OWKfEMSeo69JWgpA0sfK8TeIClA9jwADavu3gG8CN/fkREur4Nu2LwJOI6o8NXat/L6jE+FGERLwtdg18ZNbiCQVSV8APtrg3LuAzcpesfmJPVW1tZ5CVMyOAn5TOedKouVyA2BkOXYbsHPZS7YsbYqJjwL9VDzHJC1QWkhbQtJKkvqU1ysSlbeJpRr3rKQ1ytAtKSIt9dgeZ3sZ2wOKR9kkYL0S45lyLmX+awBPtTrPJEmSJOkN0ocsSZJWeD//5384cI2kMUT72SMAth+SdCIhiT4DuJ+o8FwC/EYhtLFLLYjtKZL2Bi4rScA9xF6qnmQQISjyHvAus+5RW0jSXURyu1ujk+s4CDi7tGr2IRKx/YBjgYsl3UckWc/Un2j735IOJ1oMBfzV9l8kbUYkXBvZniFpZ0l7Fyn4aZJGA6/anlFC/ZlIaMYDjxGJ3mtl7C7AmQpp+T7EvriHOn2ngo2BwyS9S1ShvleR7z+Q8PlakEii9m4xNsDxwAWSxpX78OO5xB4gSZIkSRhw2IhMypIk6TSKrq8eCPQh9B5TxStMc6n3WBHzuA/4qu3HK8cXs/1mqUTeTSRzz3fjOp8HTgYWBKYRAix/rxtzNbByEUZpFud4woj5PWIP3162nyufbU4kiAsQYi2bdXW+Pc2QIUM8ZkyPuickSZIk8yiZkCVJAiDpXttDOhrXkxWy9B5rQBfX3SNIWou4f1dWk7HCtQqT5gWB47uTjBVeAraz/ZykgUR75Ccqc9kJeLMTcU61fWQ55yCiBXO/MtdzgG1tPyNpmW7ON0mSJEmSJEned3p1D5k+4N5jRAVnyUqcucp7rFQWLwb+qTrvMeBt4ATbawHjVPEeU5g5d+jHJWmb8mzGAr8D/qqwOXgIWLjyTBYDfgic0GR9Z1fi3FJe7w0sSpvQyTeAK2w/U9b2Yl2MQWqzWpig8Bx7sdyz4ZK2knR7uWdDyzlDJf2j3P9/qOxtk/RDSedX4o5XiKbUzztl75MkSZIkSZJu0ZMVskXKH9QQMvQ1ufsNgXVsv6LY47Wj7dclLQ3cqWhjW4uQON+otP99rIy/GrjW9uUwU4mw6j22ZVFA/D2xr6vmA/aS7fUkfY+QgP92B3Nfk/BCWxx4VNK5tHmPfalcs29l/Ou2h0rao1zzyw1iLllrqZP0R8J77DZJ/Ynq0ado8x47TtKXgH3rg6jNe2x9Qgp/lMJ77KqSnP2K2AM2wfYoSY8Rku1nqM17bCizeo8tAzwMnK8277Gv2J5cEs8TgX3KFPqUtX6xzHer2txsj6RNKKQ6510Iif2aUuHxwM+IJHA2iudY9fwTgeOA14jnArA6sICkm4jndEa1PdT2OEICH0kDgCeIfXIPEfsKv0Hsbdse+H9E9fURYFPb0yVtBZxEeMP9ArhJ0o7E9/I7tmebu+3zgPMgWhYbrS1JkiRJkiRJ2mNOtCw28h7blNgj1JPeY/vTlpBVPaR26sTcR5TkYaqkqvfYaSXpudb2rZXxVe+x05vErPceW6uWUDKr99hOEN5jktr1HgOQVPMeu8r2MElfJURBBpc4EyXVvMeWpXiPSZrpPQY8rxD6gFm9xwDmB/5duX5LPm4KVcZTCN+zmorkqrZ/UBKlDrF9BHCEQsDkACIR7EMkpVsCiwB3SLqz8h2o5+mSpCHpIeBG21YIgdTm0Re4UNJqRCVugXL99yTtBTwI/Nr27Z2Zd5IkSZIkSZK0ypxQWUzvsQ+J95ikFQiZ/T1sP1kObwisX551H2AZSTfZ3ry9WIU/AiOIhGwSUfl8C3hL0i3ApwmVyPbmDc29x44HRtvesSSLN1XOWY3Y87Z8J+aZJEmSJDNJQY8kSVphTvuQpfcYH1jvsSWJ5OnwakXJ9rm2ly9eYhsTqpmbN44CpVpVY3uKHQLwF2ATSX1KIvoZou2yO/QF/lVe71WZQ1/gDKISuVRpwUySJEmSJEmSHqelhEzSjIpwwtjOtqBVGA4MUXiP7U78sb01USWqeY89T1tL4CXAoUV0YZVaENtTCP+qy0oL2nvUeY8ppO0PaXF+VQYBd5d9cUcAJyik7RelzXvsYOAHnYh1ELHuByVNA75fjh8LbKrwHtuaJt5jQM177AHgvjrvsVNsDwemFSEMbE8r4y+t8x6bRHiP/ZqK9xjh63aKpAcIT7jPNlnHwgrhj3Hl9xaVzw4AViX2pb1Tvh8NlRAVwiMPljGjSvJbY1g5fwpwDHGPsf0wcB3RRng3MMz2+Cbz7Cz/R1Q/bydaNWucDpxT2iG/BZzcbC1JkiRJUs+Aw0Yw4LAR7/c0kiSZR2jJh0zSm7YXa+fzliXeJV1ARbhjbkcNvMJaWXej83sa9ZL3WNmX9kJV2t52vbT9LoSIS3teY0vYfr28PghYy3ZN2v4fVKTt69UU51bShyxJkiSpUUvGsnUxST7cqJM+ZN1uWdQHXNq+/KxaiwMsUV7PFdL2ZdyJkg4qYy4GplCUCCv3cn1gkqS3gX8CZ9p+Xh1I21exfX/NpJkWpe3r4rxeedtpafu6+zVA0iOShql9aftFy7O/pzyDr1TOv7V8h+4r38Xa9+YmSZeX+MMldbRnMUmSJEmSJEm6RKuiHiltDyeV+cPcKW3flxBKaSRtv1pF2n6byhSaSts3mOs2Za59CVPpuyQ9DUykHWn7BnHGAOsQYiFPlO/Vi8DjaiJt34DaHsIpwI7E83yWaAWtSdsfAfzd9j6KCtzdkm4o1/q87SmKfWsXA7X/wVgXWBt4Drgd2IjYg1e/hn0pz7N///6dWXaSJEmSJEmSzEKrCVlK28/Kh07a3vZISZOAq4HBtp9UiJQc36K0/ZCy1sOBhW0fLeksIilqRdp+zRLn90QL5XBJK9O2f3BrYHsV024iWe1PJFtnlbnPIHzOatxte1KJO7bck9kSsvQhS5IkSZIkSbpLT8nep7R9StvPrdL2Ana2/WjdOo4BXijx5yOqbI3idvb7lSRJkiRJkiQt0xuy9yltT0rbz0XS9iOBA2v7wEpVEeJ7+u9STfwms6osJkmSJEmXmXjyl1LQI0mSTtMb//M/HLim7BEaS/lj2/ZDkmrS9jOA+4kKzyXAbxRCGzP9nsrenpq0fR/gHuqk7XuAQYSgyHvAu8QetRo1afv5iASpIw4Czpb0IHFfbyHaDI8FLlZI299ME2n70ro3mqjo/LVO2n4j2zMk7Sxpb9u/sz2ttCS+WidtvyUhbf8YFWl7hbjHmWWfXB+i9fOhTt+poCZtf6SkI8uxrbughHiypDWIStY/ifuE7Ycl1aTt36NnpO2PJ9b6YEnKJhL7Ac8B/lzaQUcza7UzSZIkSbpMqiwmSdIKLcned/tikYiNqxzawfbEbsbcgajITCjvjwNusX1DN2JOJPynvmL7oO7Mry7ufkRFrj2his7MbQjwCr0gbd/blMTzW0Qr4EG2R7Yz9nwieXqxKqMv6VRgO2Aa8CSwt+1Xuzmvbn1vUvY+SZIkqZEJWZIk0HnZ+zm9N6aZKAjQNR8zQknvWmACgO2jujG/Kg/YvrGHYgFgu2GFrwvrXh24CLiymowVri2thQsSQhtzUzK2FqEIuTawPHCDpNUrFb56LgDOAuoT2OuJtsnpRZDlcODH3ZlbD35vkiRJkiRJkqTT9MYespbQXOhjVvZCDVQv+JjV1P7UPR+zx2yvbPtHlXuzm6RxwNKE2uBawGi17mN2Tpn/a5Jel/R0ubejJd0s6V5JIyUtV1lHQx8zSWeXc8cq1ApvAB63PdX208AThFx/Q2zfQrQurlIX5/+IPWAAd9ImetLs+3WVpGvKWg6Q9MNyP+9U2ctY973p0OeujNtX0hhJYyZPntxsCkmSJEmSJEnSlDldIUsfs1mZG33MBgAr0+ZjdightnEz0cJZ8zE7EdinTKGhj5nt/evmeRaRQNWYRNgitMerwJPtVFb3YVb7gUYMJLzFFiaSwB/bXlfS6UDt+dTT4fcjZe+TJEmSJEmS7jK3tCymj9mHw8eskSx/lxMZSUcA0wkhmfYYbfsN4A1JrwHXlOPjCHPqRrT6/UiSJEmSJEmSlplb/JXSx+zD4WM2Cfhk5f0KhEFzy0jak6g6bumOlWk641fW7Jz0IUuSJElaIsU8kiRphfd9D1kD0seMD6aPGXA18HVJC0laCViNUIJsCUnbEiIe29t+uwvzSJIkSZIkSZK5grnxf/7Tx+wD6mNWnuGlhCLmdGD/dhQWkXQxkRQuLWkScLTt3xLKiwvR1kJ5p+39WplLkiRJkvQWKXufJEkr9IoPmeYRv7ESZwiwRw/7jb0MHGn7nG7EmAgMqe2Z62mKmEev+JhJ+jxwMiG9Pw041Pbf68ZcDaxc9RdrEOd44CtEa+GLwF62nyufbU4khQsQAhybtTLHBtfaHljL9sldOT99yJIkSZIamZAlSQLvvw/ZPOM3ZnsM0NN/Sb8BXFp/sIvr7nEUfmDX0ns+Zi8B29l+TtJAoiVyppqipJ2ANzsR51TbR5ZzDiLaLvcr8zsH2Nb2M5KW6cIcZ8H21URLZZIkSZIkSZLMMebYHjLNhX5jZczm6mG/MUJuf68ypjt+Y43u425lLeMV6o5IWlEt+I0RbaDXAdtKulbSX2v3EvgR8BrwDrCbOuE31oBlgL8qLA4uApaV9JcSZzHgh8AJlXWPbfCzlO3XKzEXpU3c5BvAFbafAbD9oqRtGsS4UtIASY9IGlbu2XBJW0m6vdyzoWUeeylk+WueZGdK+kf5HuxCA5Q+ZEmSJEmSJEk36a0KWfqNzcrc6jc2iDa/sfMlLQD8khb9xuqxPbKsi5LM7Ge7lnAfD/wMeLuMfZkix98Ixb7BPYgk8XPl8OrAApJuIp7TGbZ/T5s4SfX8AcCqwFeJe3oPkdBtTCT5/4+ovtazXBmzJlE5u7zBOtOHLEmSJEmSJOkWvVUhe8f24PKzY+V4I7+xB4Eb6Fm/sU0rn3fWI6vGCNtTy/WrfmNblQrRJrZfq4yv+o01k4Wv9xs7qySsVzOr39hFEH5jRMJVz0y/sdL6WPMbw/YwIjnZj0g8Kfv2an5jW1P8xohE4zLb75WWxEZ+Y2OBn9AmmQ8t3kuFEuMpwHfK+8HAqrav7OjcGraPsP3JstaaCmUfIin9ErANcKSk1dsJ87TtccVf7SHgxiKVP66ddVxV7s8E4juQJEmSJEmSJD3OnFZZTL+xD4ffGJJWIKT197D9ZDm8IbB+edZ9gGUk3WR78/ZiFf4IjCAqc5OIyudbwFuSbgE+TShDtjdvaN2HDDr+jiVJkiTJTFLMI0mSVng/fcjSb4wPpt+YQnRjBHC47dtrx22fa3t52wOIKt1j7SVjklarvN2eYoEA/AXYRFKfkoh+hmi7TJIkSZIkSZJ5ijnuQ6Y2SfyFgU8q5OxHEn9sDwI+T+xbulmd9xs7ABgt6VXa9xtbWNJ42wNV5O5pa8Nrj0GEN9qjwBRm9RvbRtJ3ifbG7viNnQ/cqlAgnKf9xoiEc1WilfDIMteniXbIl2lLYmejJOKXl/U8L2kKUcmaBCwo6RGiQvcC8GD5bJjt8S3OMUmSJEl6hZS9T5KkFXrFh6zdC0pv2l6svF6GaEW73fbR3Yg5gBD8aOpp1cq4JudOpM4XrBy7lKj0ndZqzJ6aWzl//kYmy+oFv7Fm12pn/PcIMZf9JH2dEHNpmJRJWhRYl0jeBto+oBz/CPAZ26MlLUhUUk+y/bfOzqM3SR+yJEmSpEYmZEmSQOd9yN7PlkVsv0go3x2goCpBP7TIjt9ffq9Rjq+tkF4fq5DGX40wIV6lHDu1xDpVIXM+rqgFzkLdtRaT9Lsy9kFJOzeZ8qGaXeK+GnOwpDtLjCslfbSD4+srpPTvAPZvdMEyz1vKeRMk/aokWUh6U9Jxku4CNizxbpZ0r6SRkjYFngBeAf5Srn9JCX2rpHeAfxGVsLeq96TEP0vSXuX1RElHSboN+KrCeuC6cq1b1cBSoMJXCLEViOrXlpIa7suy/Zbt24hKZPX427ZHl9fTiCRzhQYhanO/QNK5kkYrpOs3U1gaPCzpgsq4cxXS9Q9JOrYc6yvp0cp37mJJ/9PO+pIkSZIkSZKkS8zxlsV6bD9VEox6c99HgE1tT5e0FXASsDPR3neG7eGlUjI/cBhRTRkMUBKqwYTQw9LAPQrhh2YcSbTsDSrnN9q/BXUS97YHSDqm8vnvgQNt3yzpOEKA4vvtHP9d5fip7cxvKGEH8E/CP2wnIrFZFBhv+yiFZP3NzCpZv5ftlSU9B6xke6pifxdEC+ABtm9XeINNqb9oA6bY3rjcoxsJVciDCRXCeyQ9yaw2BzU+ATwLUJ7na8BShIE0ks4GNqo7Z0yzOZU1bAecUTm2FFE1q9GfaJVcldivdk25xrfLXAfbHgscUWwV5gdulLSO7QcVbbAXSDoD+Kjt6r682jX3pdgT9O/fv9k9S5IkSZIkSZKmvO8JWaFRtaQvcGGpgBlYoBy/AzhCoeJ3he3HGxRbNgYuLm11LyhMmTcg9hw1YivCowsA240k52FWifvTZ1lA7Lla0nZNZONC4LIWjv8B+EKT695t+6lynYvL+i4n9lL9uYypStZDJKr/Lp89CAyXdBVwVTl2O/BzScOJ+zipSdGqyp/KHBYDPkskVRCeYpNqCXEDGgWe2Stre7bqYKnMzVbiVfjXXQycWbsnJcYsfmalCna97ZcljQNesD2ufPYQIXc/lhCQ2Zf4t7Ackfg+aPt6SV8FziYS+9kXkD5kSZIkSZIkSTd5X1sWASStTCQWL9Z9dDwwuuyp2o4QAcH2HwnFvXeAkZK2aBS21WnQOan9zkjc9/Q1G12r9n5KZS9XTbK+5v82yPbW5bMvEYnF+sC9kvrYPpmoFi1CmHKvCUxn1u/EwnXXrcn3z0eIhAyu/HyqnflPAj4JMxOqvkQbZVc4D3jc9i86HDmrvH299H0fSSsRnm1b2l6HUIZcuMxzPsKw+x3gYyRJkiRJkiRJL/C+JmSS+hGKiGd5dnWRvsT+JgiVxdo5KwNP2T6TMFZeh9kl8W8BdpU0f7nGpoRwRTPqpeibtSw2lbgvZtH/kbRJOfRN4OZ2jr8KvCZp43J893bmN1TSSiVJ2JWQra+noWR9OeeTZf/V/wJLAotJWqWYJZ9CtAeuSbREriVpoVLB27LRZGy/DjxdKkgoaFhFKlwN7Fle7wL8vcHz7hBJJxDfi++3em4TliCSzNcU8v/VCuUPCCn93YDzS0tokiRJknTIxJO/lIIeSZJ0mvejZXERSWOJFsTpRKvezxuM+z+iZfGHwN8rx3cF/lvSu8DzwHFlD9DtksYDfyMSjw2BB4hq0v/afl6hZNiIEwgp+vFEte5YGsvhL6QQ0JiPxhL3ewK/UigCPgXs3cHxvYk/9t+mzSOsEXcQwiWDiGTzyvoB7UjWPwZcVI4JON32q5KOV/i/zQAmAH8re8wuJVocHycsB5qxO3CupJ8Qz/IS4n434rfAHyTVBEa+3mQcMFO9cglC5n4HYGvgdeAIYm/hfaW98izbw9qL1R62H5B0PyHr/xTRxomk1Ynq4VDbb5T9hz8h9v4lSZIkSbukymKSJK3QK7L3avMaq7GD7YndjLkDYSQ8obw/DrjF9g3djDsE2MP2Qd2JUxdzP8JI+vfdiDGR2EM1EDjE9pd7aHq9jmb1ErugTrr+MmAVIhG8xvZh7cT5IZEYTQcmA/vY/mfl8yWIKtaVtWt0Y87d+h6k7H2SJElSIxOyJEmg87L3vVUhe6cdgQfKHqbpLcbcAbiWqOZg+6huzG8mtscQLXs9hu2GxtRdXPe8yBRCuXJg+alyWtVLTP+fvfOOlqus3vDzQuihqIAKGqM0EQgJBPjRIyCgIAJSRToiohQVbAjSq4goHaSDIk2RYpBAKAECIYSELiX0FpBek7y/P/Y3uSeTmblzc5OQkP2sdVfOnPPVc4e17mbv732lb7bwEruX8H57V2G+fSwTm0ofRihLdpup8T1IkiRJkiRJks6YZmfIJO0k6VJJ/wKuV3h/DZI0XOH/9Z1K2x0Unln3SbpA0mqEkMdxCq+xxRQ+U1uU9usq/MpGKbym5ij3R0s6pDLHJF5ZmtiP7ODSf7DCu2rvcn8eSdeU9dyv4mtWxj9Gdd5kZZz9yvVgSUcqlB73kbSQpMsl3V1+Vi/tPiPp+rKP0ynCJLYHV7NjkrYte7lf0jHl3pck/VfSgpJmUfiCrV/KEvep9D1C0t6lzSkK762rJV1beZf1Xmafr+yjttdH1XEmrvouD1CUow4BcD975QAAIABJREFUTgJ+QwiJUPYywUsM+Dpxbu308jsdIWmiUkzbN9l+t3y8k4rvmKQVCbn92YnzgiMqPwfUrevtsvZ7JN2g8Lir/Y43afd70GC/uys8zIa98sorjZokSZIkSZIkSUumVoasdk4MJvalWhXoU8589QA2s/2mpAUJpb+rCNnxA4DVbY+R9OnS/irgatuXAahItEuaEziXUMp7VNL5wI+I81MAY2yvIGlPQlFvt07W/lUiWJgXeETSqcCGwPO2Nypzzl9pP5E3GdCotHAB22uXvhcT57huk9SLODu2NHE+6Tbbh0raiOJvVUXSIsAxRJDzPyKw3dT2P0pwdhowFHjQ9vWSHiXOwp2oEPfYhvA025yQfV+O8H97iA7hij8zsZfZEcAuZQk9yl6/Vda7XnV9to8o7Wvr3YkG0vWFoYQYy3pV+foW7EqcD6wpIB5PCKSsC7zYScniPMBg278sQd/hwDeI79p5hOhIPZN8D2x/VG2QsvdJkiRJkiRJd5nWJYv/sV2TOxdwpKS1CBnyRYmMxzrAZbbHAFTaN2MpIuh7tHw+D/gxHQFZTZzjHiIQ6YxrbH8AfCDp5bKmUcDvS9Bzte1bK+2bepNVuKRyvR6hZFj7PJ+keQklyM0BbF8jqZEX2kpEYPEKgMJDbC3gH7bPUqge7kHx47I9WtKrkvqVfdxbfLnWAC61PR54UVIta9XKywwmfpe9m+y1U9TES6xF++8Tgd3a5daewLW2n1Hn3mkAHxKG2hC/yw9sf6TwJ+vdpE+j78Gz7UyWJEmSJEmSJO0yrVUW36lcbwcsBKxY/jgeTXhAdcWfCzr3HKv5T42jvf1W/arGEVmhR0uJ3LeAoyRdb/vQ0qYdb7LqvmcBVrX9XrVBCSw623fTvSoEM2olfT2J7BPAWYRtwOeAszsZp+ZltmqT5119l81o20tM0npExnTtEiBBZFrXLFnPnoQa49stBEI+qsjsT/Aksz2+BIeNmOR70NlakyRJkgRSzCNJkq7xcfqQzQ+8XIKxrwNfKvcHAVsplPqQVDPlrfcaq/Ew0Fvl/BbF52tKLrSUCr5r+0Lg98AKlcdNvcmaUO95Vssk3kLxIpP0TaCRF9pQYO1yVmxWQnq/ttdjgIuAg4AzK32uJEouV6JDWv824LvlLNlngQHlfkMvszb21DbqgpdYyeydDmxie4JxuO3tbPey3ZsoQz2/lVpjkiRJkiRJkkyvdOn/+mvKytlfBPxL4U01ggisIAKbK4Gby3z3EhmevwFnFoGFLWqD2H5f0s7ApSXbcTfh+dUfqAVz3WU5QlDkU8BHwFaVZ515k9WzBnHGbiTx/m8hygwPAf4qaTgRZD1d39H2C5J+DdxEZLOutf1PSWsTAdfqtsdJ+q6knW2fU/zJbgJetz2uDHU5cfbqfsKnbCjwRgsvswdKv/nLWCsRmSagsZw9cV6tmZfYy8C7kgyMBjZsImc/nhDtuLRkEJ+2vUkb7zhJkiRJPjZS9j5Jkq7QJR+yUhbWs8XzLsu6SzqXiljHjIQ6vMJeb3fftT61M3JTmyKAMRzY0vZ/K/d72n67ZCLvIoK5FzsZax6gH0XOvs5fbJWqnD1wZDM5+5IRHVqRsx9ge+vK8xOJctbXuusvNq1IH7IkSZKkRgZkSZJA+z5k3S5Z1EwsZw8sAPyTyZCzb7DeqSFnfwshyjGIEA+5WUXOvvyuRhCZuIeBq9REzr6G7Xds30b4jFXvT5Czt/0hEQB+ocEQtfbtyNlf36x/pW07cvazSjqu/E5GSvphud/weyqpt6SHJJ1Z3uP1kubqbC1JkiRJkiRJMjl0Vagg5ewn5mLbe5a+06uc/Q+IoPFmJpaz38D2LiWwfNj2RmoiZ98MSRuUdVd5lvhdn9jOGISc/buV79XiwFPEd7Nlxo5J5eyvJ0ogPwQukfQIsfc3bK9UAvohkq4HnqHx9xRgCWBb2z+Q9Hfgu8CFDfa/O+X32atXrza3myRJkiRJkiQddDUgSzn7iZmp5extD6RDKKQmZ/8vJkPO3vYHkn4CzG37WLX2MKtRL2c/zPYRJUh9zXZfSZcBfVSyroSgyBJE4NjoewrxvasFiE3fSfqQJUmSJEmSJN1lSkl5p5x9ytnD9ClnL2CvEjxW59+Jxt9TmPS7kiWLSZIkSZIkyVRhasjep5w9KWffSdtpKWc/EPiRpNnK3EsqxEmafU+TJEmSpFuMPnqjFPRIkqRtpobZbU3OfhgVOXvbD0g6gm7K2U/htdbk7McTcvY/qjzrqpz93sDJmjHl7NumZJKaydk/DAwvWcGTbJ/VZJjjiAzYtJCzP4soORyumOwVYFOafE+TJEmSJEmSZFrSJdn7lgNNWY+y2pibAo/afrB8PhS4xfYN3Ry3P7CD7b2bPB9NF6XpJe1BZNvO78a62p5XU1DOfkpQ5ruMCCDPrZPEn8ijrFXWS9LPCIGWsUTwtEsTj7IrpydJ/JS9T5IkSWqk7H2SJNC+7P2UzJA1E/yoLajLHmVEJuNq4EEA2wd1Y30TsD0MmKJ/PdtumL2bzH23RNLXiPdyZTUYK1wtaQHCUPmwaRGMFd4HDqR4lNU9+33Vo0zSN5t5lBGZ0/4Vj7Jj6SgfBTiMKVy6miRJkiRJkiQfF1PjDNkENIN6lJUzTO+pGx5lko6UdDNTwaOsZAy/DmyiOo8y4FbgHNtfs32umnuUXVt5lyuq4lFW5huh8Pl6SdI7kt5SFz3KJB0A3A6coJC1v4tQeOySR5mkoWU9jwA7AGsAn242Rpm7HY+y3uW9DS8/q5X7m5U+kvR5hT/b51rNlyRJkiRJkiSTw5QMyOYqfzSPUHhC1VgV2NH2OsQf65vZXoEIKI4vf/QuQ5xBWsf28sA+tm8HrgL2t93X9uO1AdXhUba17eWITF/1/NeYMsephDhEZ3wV2IDw8vqdQgCi5lG2vO1l6ZBXh+JRBpxEhwx/PQvYXtv28YQn1wm2VyI8rWpnq2oeZf3KXicxs1KHR9k6hOz9SgqPsqfK/dOAn1M8yoC/ADuWvjWPsouY2KNsN+L3Qtnrn4EtbK9IKDZ+o2Q7hwEX2p6HyFL9ro13OQHbR5TfXd8y3gBCFXJQm0PsClxnexVCcOUFoA8hcNKZbULNo2xFQjjmcOAbwGZATUnzZWKvKxD7+1NZ95WEB9qPCSGV3zXKNEraXdIwScNeeeWVNreUJEmSJEmSJB1Mi5LF9CibCT3K6lEIs/yVyfAoK7f2JMROnqm8x1bUe5R9UBQVR9Gxj9mAkxSKmOOAJSv99yLEUe60/VcakD5kSZIkSZIkSXeZGiqL9aRHWXqUwfTpUfZT4CVgeeJ39H6l/6Kl32clzVIC2SRJkiRJkiSZokzVM2QNSI8y0qOsk7bT0qNsfuCFEmxtT2QHa9m8c4DvEYqOP+vmPEmSJMlMRPqQJUnSFaZFhqxKepSlRxlMPx5lpwCXl7LPm+jIav4GuNX2rUWM5G5J19h+aCqtI0mSJJnBqUndQ8rdJ0nSNaaYD9nMgibDo2wKzXsC8FSt5E/SQOAZ27uVz8cDz9n+gxp4lEm6HVi/lUdZs71JGgB8WIRWmq1viviHSfo38HkiMLwV+HEtqJS0F5FpHEuc+/tFJ69tmpE+ZEmSJDM3GZAlSVKP2vQhm9Yli8nkcztQk2WfBVgQqJYWrgYMUXiUPQYMqnqU2V6N8CgbQQQ6XfEoG1CbuwU1/7A+hEH0sXXP2/UP26oobS5LnDfcEqCUuH4H6GN7GaKMNEmSJEmSJElmaKZ1yeIMTznD9HEwhA5Fx2WI0sPPS/oU8C6wNBEU7QO8CnxD0iG2fwfhy2W7ZwnmTgL2L6WKswBn276sjL2XpG8TCoRbEkIXewDjivLhBcR5qypP2t6s8vlO4Pu1D0Uc5bOE6mH/cu+AMn6VS20fUa57EObWtRTuj4CjayIftfNlpXR0jrpxLivzzEoEdseXsbYnxD2+Zfs1ST8Adi/PHgO2L4bU/wQut32+pB8Ca9nerm4OJO1e+tOr1ySOBUmSJEmSJEnSKRmQzSDYfl7SWEm9iGzVHYQS4KrAG8BIIpO1BOGnJuAqSWvZvqUyVNWPbGGijPDsyvMxtlcoaob72d5N0mnA27ZrWanjO1nursB1MCGbdzwRDK1b2c8RwBGNOpdyzJXLGLVAcUlCZfEIIkjcz/bdxaOsvv9OxBnEfoSK52PAL233K6WfOxDn5K6wfWbpc3hZ95+JIGuIpCcJj7f/a7TOlL1PkiRJkiRJukuWLM5YDCGCsVpAdkfl8+2EiMb6RKZsOGF4vUTdGBP8yErJ4k11z7vlO1bxDzuu3JrgH9buGLY3IM6RzUF41EH8z4NPEcHR/sDf1dqQ7CbbbxX/tjeAf5X7VR+yZSXdWrzJtqOUgNp+iVCuvAn4eRu+eEmSJEmSJEkyWWSGbMaido5sOaJk8Rkig/MmkeUaABxl+/QWY0xpD7eOgaeMfxgwQUnzKuLc2H+AZ4mMloG7ivrlgoSASKt9QMWHrFzX9nUusKnt+0pWbUClz3JE6eciLTedJEmSJKSQR5Ikk09myGYshgAbA6/ZHlcyNwsQQc8dhOfYLpJ6AkhaVNLCdWM08yNrRTM/uAlMCf8wST0lfb5c9yBMuR8uj/9ByZZJWpI499Vdpct5gRckzUbxgyvjrwx8kyh53E/Sl7s5T5IkSZIkSZI0JDNkMwjl7NPTRFbo4prsPVGC1xP4NfAccDFwR6nme5sQ13gZmKsM1dCPrDz7AvBpJg10XgJ+JOk7wF62b22wxIuBXsDI4iV3l+1aANWHCNZ6AXNK2s/2+w3GmIc49zYHUVb4ER2B0jDgOkn7E1mu/d19z4YDif0/RbzHecvcZwI7l3N7PwfOlrTOFJgvSZIk+YSSsvdJkkwu6UM2g1DMi7e0vVURyrib8AZbtTy/A9jX9tA2xurZyI+shQ/ZwUws6tFozPlsv1muNwH2tL1hyXQNJxQM7ytzVg2rG421OWEE3sf2suXe9cAJtq+T9C3gF7YHdLbXaUX6kCVJkszcZECWJEk96UP2yaMm6AEdsvdvSfpUyeosDdwraX9Jd0saKemQWmdJb5d/ZwEekfQeMJrIoq1RmWcvScMljZL0VUm9Cdn7n0oaIWnNRourBWOFeeiQq18fGGn7vtLu1U6CsZ7Az4DD66cA5ivX8wPPtxjjYEnnSbpe0mhJm0s6tuzp36VEEUkHlXd1v6QzFPQo9waUNkcVZcckSZIkSZIkmeJkQDaDYPt5oF72fihxfqw/k8re9wVWlLRW3VCbA/cRQdMSRBlhlTG2VwBOJaTlRwOnEdmpvsBaJTCr/hwAIOnHkh4nTKH3LuMtCVjSwBLo/aK0HdpgnOUIA+njCW+1KvsCx0l6hjCF/rWkDRqMcWVpvxiwESEKciGhurgc8F65D3CS7ZVKFm4uYGPbYwnJ/FMlfQPYEJgQ2FaRtLukYZKGvfJKM22RJEmSJEmSJGlOniGbsajK3v+B8CFbjTgDVi97D3G2bAmg6kM2QfYeeFFSK9n7zesX0Mo/zPbJwMmSvgf8FtiR+I6tAaxEBFmDSvq2kX9YX2Bx2z8tmbkqPwJ+avtySVsBf7G9HiFkUj/OwcB1tj8qkvazEqbUMLHs/ddLgDg3cXbuAeBfth+QdAEhlb+q7Q+b7Dd9yJIkSZIkSZJukRmyGYt62fs7iQzZakSwJkL2vm/5Wdz2X+rGmGqy9xX+Bmxarp8FbrY9xva7wLXACk36rUpk9UYTapBLShpcnu1IR7B4KZEFbMUHACXw/KgiyDEe6CFpTuAUYIuSOTuTMJGusRzwOvDZTuZJkiRJkiRJkskmA7IZi+lZ9r5qQL0R8N9yPRDoI2nuIvCxNvBgozFsn2p7kSKRvwbwaEW44/nSF0L+/r+TjtAlasHXmPK+tqjsZXPgM8BawJ8kLdDNuZIkSZJPOKOP3mjCT5IkSVfIksUZi1EU2fu6ez2LMuL1kpamsex9jVay9834F3BZJ7L3P1EYQ38E/I/IaGH7f5L+QKhCGrjW9jUN+nfGD4ATS1D3PrD7ZIwxAduvSzqTeH+jy/qQtCBwNLCu7WcknQScWNtPkiRJkjQiVRaTJJlcUva+AUWafVD5+DmifK+m2rByszNFHyeSdiGCnRfbaNtQ9r6NfisCJxNn08YDh9q+rK7NqcC2tptmlST9BPgh8V7fAn5g++HyrC8hIjJvmWMF2x91traPm5S9T5IkmbnJgCxJknralb3PDFkDbL9KqBS25cE1rZA0awvJ+F0Iv69OAyvg6lKGNztwWDvBWOFtYDvbj0v6AjBM0kDbb5X1rUIEa51xvu2TSp/NCdXEjYsc/QXA92yPKtmqphL5SZIkSZIkSTKjk2fIuoikHSXdVSTWTylnsXpIel3ScUXafaCkVSTdLOkJhZExknaTdGV5/oik37Y57uGS7gJWlnRIxTvrtOKdtTURQF5S+s8u6dna2SdJ/yfphnJ9OPAIkfG7B7hQ0h/K3CMl7dZs77YfAbaRNAK4mvADGyrpgFJKeAzwq87eoe03a7L3wAnAmuV6D+Ae26NKuzFFlKPR76GHpHclvVz+fVPSo5Keq3vnPRrtT9J8km4sv6+RkjYu9xcv7/Yvkh6QdF0RAGm0hpS9T5IkSZIkSbpFBmRdQNKywGbAasWTqwewTXk8P3B98fD6EDiYOKu1JXBoZZiVS58VgO9J6tvGuMNtr2z7DuBE2ysRKoDzAxvavgQYAWxd1BU7K6nsB3zb9vbEWayXba9MSNP/WOF11hDbR5Q17gk8ASxTpPD3Ic6nvdTJ3DUuIsoSRZQl9gXmAKQwdB4u6eedjDEXkbGbG7gZeBz4EhO/82b7ew/4Tvl9rUcEhjWWAv5oe5nSblMaYPsM2/1t919ooYXa3HaSJEmSJEmSdJAli11jPeKP+mFFNGMu4Jny7D3b/ynXo4A3bI9V+GD1rowx0Pb/ACT9g1AT7NFi3A+BKyv915W0P6ESuCCR5bqui/v4p+33y/X6wNKSqgHgEsDTzTpLWhQ4lwiGXMoXNyUUGzuT1QfA9p8IBcMdgN8AuxLvYXVgFUK44yZJw2zf3GSYdt55s/29CBwjaQ3irNoXS4kkwGO1LB3xfmtjJUmSJEmSJMkUJQOyriHgbNsHTnQzyvWqWanxdPh5jWfi91yvouJOxn2v5qElaW7gJCKj9FwpP2xYTgeMpSMDWt/mnbo97Wl7EG0gaX7gGuCXtu8ut1cggpzHy+f5JD1ie6k2hryYUDHclfAsG1zO8CHpujJ2s4CsnXfecH+ldHF+4l2OlfQsHe/pg0rT7vixJUmSJEmSJElLsmSxa9wAbFXLpEj6TKvyviasL2mBElx9h/AWa3fcuYhgY4ykeYHvVp7Ve4WNBlYs19V29QwE9izBH5KWkjRXo4aS5gD+CfzF9oSsne2rbH+u+IctDrzZKhjTxJ5l3ybOtEFk+vpJmqusZy2aeJZ1gWb7m58oZRwr6RvAot2cJ0mSJJmJSR+yJEkml/w//12gKP8dAtwgaRbCc2sPwrS4XW4jskKLARfYHgHQzri2X5V0HuEh9hThIVbjHOAsSe8R59QOBs6U9CIhbd+M04FewIhSLvkyESg2YltgNWABSbuWe9tXyvvaZV9JA4h9vgrsXNnfn4gywfHAv2wP7OLY9TTb3wXAvyQNI9Qpu2s0nSRJkiRAhwR+BmdJkrRD+pBNQ0qZ3LK29/241wIgaTCwn+1h5XNv4GrbyzZp/w3CNHl2olxwf9s31rW5CvhKszFKm8OIoGg8ESDtZPv58mwA8EdgNmCM7bUnf4fTjvQhS5IkSWpkQJYkCbTvQ5Yli0lXGEOoMy4H7EhkmSag8BR7u41xjrPdpygrXg0cVPovAJwCbFIUDreckotPkiRJkiRJkumNLFmchtg+6+OYt2S+/k2UOPYDHgV2aNG+L6GiWOVd26tVPj8AzClpDtsfSOoJ/IyQmf97GecgYPO6cf5m++jK53noEDr5HnCF7acBbL9cxhnGpN/V/QiBk9uA/wPuI8o2DwEWJhQg75I0D/BnwiagB3Cw7X+Wd3JBmR/gJ7ZvLxm6g4ngc1mifPL7bpBKlrR72S+9enX1KGGSJEmSJEmSZEA2M7EUsKvtIZLOJnzEAC4q584gShHHl3NtfTsZ77vAvbZrioSHAccD79Ya2D6UiT3YJiDpCCIofAP4erm9JDBbKaWcl/BcO79RqrcEVIsTWbTdgbuJgG4NYBNCSn9T4ADgRtu7lAzcXQqT7JeBb9h+v4iM/BWozdMPWIY4wzeEkOK/rX4Nts8AzoAoWWz+qpIkSZIkSZKkMVmyOPPwjO0h5fpCInCByCT1LeWD32pnIEnLAMcAPyyf+wKLV5UXO8P2Aba/SBhE/6Tc7kEoQ24EbAAcKGnJFsM8aXuU7fFExm5QyWTV+5D9StIIYDAhbd+LOKN2ZvEsuxT4WmXcu2w/W8YdQfqQJUmSJEmSJFOJzJDNPDTyP+syxQT6SmAH2zXfsVWBFSWNJr5TC0sabHtAG0NeTPia/Y7wIRtj+x3gHUm3AMsTJZaNqPqFtfIh+67tR6odJR0MvFTGn4Uwom40bvqQJUmSJF0ixTySJOkKmSGbeegladVyvS0NSvA6o5T8XQP8upJtw/apthcpPmRrAI+2CsbqfMg2AR4u1/8E1pTUo/i0rQI81NV11jEQ2EtF815Sv3J/fuCFkgXbHpi1m/MkSZIkSZIkSZfJgGzm4SFgR0kjgU8Dp7bbUdJgSf2J0sLFgcMkvSdphKSFG3SZqzwbIek+SZtVxloAuEnSB5LeB7YG9gGw/RAhPjKS8E47y/b9kvaT5Ipx9vzAWcBikh6QtHOL5R9GlCeOlHR/+Qyh5rijpDuJs2sfSGoqdJIkSZIk7dL7V9dM+EmSJOmM9CGbCejMX6yN/oPpml/Z3MCHtsdK+jyhgLhI+XwecKvtsyTNDsxt+/UWc3+RCL6+Cqxoe4yk3wDz2/6lpIWAR4DP2f5wcvY3JUgfsiRJkqRGNRDL8sUkmXlJH7Kky0jqLelhSedJGinpshJcdQnb79oeWz7OSTmvJmk+YC3gL6Xdh62CscIJwC+Y+MybgXlLGWJP4DVgbIO+SBog6WZJf5f0qKSjJW0n6S5JoyQtVtodLGm/cj1Y0jGlzaOS1uzqO0iSJEmSJEmSdkixgpkA26MJT612WAo4nRC7+DrwX+K81U2SaiIesxPCGU2RtApwXen7NDAMmIsIps6RtDzh8bVPEfFoNMYmwKvAecAiZQ3jiP+R8AYhSz8vsHU5C9aM5YGlicDtCaIUcmVJ+wB7Afs26NOjtPkWITiyXoP1pQ9ZkiRJkiRJ0i0yQ5bU84ztE4oM/paEv9cw4Otdkce3PdT2p4lA8BXCvHk7QnL+VNv9gHeAXzXqXzJzBwC/KHM+X1sD4W02jAjS+gInlexbM+62/ULxTHscuL7cr8rj13NF+feeZm1sn2G7v+3+Cy20UIvpkyRJkiRJkqQxGZAl9UwRefwJnUOo4x0iMHsWeNb20PL4MmCFJl0XA74M3Ffk9L8ADJf0OWBn4AoHjwFPEmfMmtGOPH6zPil7nyRJkiRJkkw18g/NpJ5ekla1fQcd8vjf7soAkr5MZNrGSvoSUQY5ughyPCNpqeILti7wYKMxbI8CFq6MORroX8Z4uvS9VdJny/hPdHmnSZIkSTIVSCGPJEm6QgZkST01efzTifNjp9LFgIzwIvuVpI+ILNSetseUZ3sBFxWFxSeIbFdXOQw4V9Iowvj5l5XxkyRJkuRjJVUWkyTpClMtICuZixOIs0P/Az4EjrV9paQBhIz6xlNr/pkNSW/b7tmF9r+xfWSDR+Nt71HaLADsVG/y3EgkpDq/7QuAC+qe/5v4LtxWlf+UdFHxOPuI8B77oe2P6hdlu3dRR/xlufU28H3b91XGmpU4W/Zc7btlezAwuDLOAEkbS7qXKNmdTdIPbR9clBafI868/bUEfL8thtdJkiRJkiRJMsWZKmfIihz5P4BbbH/F9orANsQ5oGT64DdttFkA2HMKzXccsH2D+xcR57+WI1QYd2sxxpPA2rb7EFmyM+qe70Nk+JoiabbS79u2lwf6UQnYgBOKeMkSwCXAjcXrLEmSJEmSJEmmOFNL1GMdwhj4tNoN20/Z/nN9Q0krS7pd0r3l36XK/WWKD9SI4om1RLm/Q/l8n6QLyr0vSRpU7g+S1EvSrJKeULCApPGS1irtb5W0eN06ZpX0++JNNVLSXuX+umVtoySdLWmOcn+0pCMl3SFpmKQVJA2U9LikWoapZ1nP8NL/Ow32P6ukcyXdX9r8tNxfXNINZZ/DJS3Wznil7/6S7i77OKTB86OBucq7vajc+xlwdbmuycAfDSxW2h3XYP6Dy7MRlfGubLQm24OAtxrcvxY4CbiX8Cj7XRlnklJG27fb/l/5eCeVAF/SesAhwMrAmmWMofVjEDL5PQg5fWx/UM6zNVrzJYQi4/caPZe0e/ndD3vllVcaNUmSJEmSJEmSlkytksVlgOFttn0YWKsIQKwHHAl8F9gDONF27bzRrJKWIaTQVy/iDp8uY5wEnG/7PEm7AH+yvamkR4GvEWp99xB/qA8FvlDU+arsXtr1K2v5tKQ5gXOBdW0/Kul84EfAH0ufZ2yvKumE0m51wgj5AeA04H1gM9tvSloQuFPSVbaryoV9gUVtLwsTygQhMkdHlxLPOYng+cPOxpO0PrAEEZgIuErSWrZvqbWx/StJPykS8khakTjLtUrpM1TSzYQk/bKVdj3q5weWsG1FyWLfpr/lFtj+cclcDSV8yW5to9uuhM9ZjT2AAUTA1bQc1vZrkq4CnpI0iAhC/9rCx2w4TRQcbZ9BydL179+/W2qUSZIkSZIkyczJNJG9l3RyyfTc3eDx/MClku4nzpwtU+7fAfxGcWboS7bfIzJG0XWqAAAgAElEQVRvl9UEHGy/VtquClxcri8gRCUAbiWyLmsBR5X7KxHeWvWsB5xme2xl7KWAJ20/WtqcV8aqcVX5dxQw1PZbtl8B3i+BlYAjJY0EbgAWBT5bN+8TwFck/VnShsCbkuYlgrQry1ret/1um+OtX37upSOYWKLBfqusAVxp+x3bbxMeXGs2aNfO/JPLKUSJa6fBmKSvEwHZL8vnjYGXbd/TzkS2dyNUGu8C9gPObjVdO2MmSZIkSZIkyeQwtTJkDxBZLmBCBmRBQnChnsOAm2xvJqk35TyP7YtLNmsjYKCk3Yg/jtvJRNTa3EpkThYBDgL2J7IotzTo02jszv4Yr/pZ1Xtd9SCMkBcCVrT9kUK6fc6JFmr/T9LywAbAj4GtgH1pTKfjlTUfZfv0TtZe36cd2pm/y0j6XRn3h2207QOcBXzT9qvl9urAJpK+VdYzn6QLbX+/2ThFVn+Uouz1SWCnJk370fh7myRJkiQNSWXFJEm6wtTKkN0IzCnpR5V7czdpOz/wXLneqXZT0leAJ2z/ichE9QEGAVtJ+kxpUytZvJ0QDYEIGm4r10OB1QjlwPeBEcQf/Y2yMNcDe5SyvNrYDwO91XHebHvg5pY7n3RvL5fg5evAl+oblEB1FtuXAwcCK9h+E3hW0qalzRyS5m5nPGAgsIuknqXvopIWbtDuo1ImCBGgbippbknzAJsR7+gtogSw7f10lRJobwBs26JssNa2F5G9276StcT2r21/oaghbgPc2CwYK+fgBlRu9QWeatL2u0S28a/t7yhJkiRJkiRJ2meqZMjKmaJNgRMk/YKQEX+HUmJWx7HAeUVU4sbK/a2B7yu8rF4EDi3nf44AbpY0jijL2wnYGzhb0v5lrp3LOj6Q9Axx1gkiyNiWKDGs5yxgSWBkmfNM2ycVcYlLS6B2N3E2rF0uAv4laRgRDD7coM2iwDmSasHxr8u/2wOnSzqUkITfsp3xbF8vaWngDklQ5OGBl+uanlH2Otz2dpLOJUr4AM6yfS+ApCGlnPQ64Jg29tMQSbcS5ZM9JT0L7Gp7IPE+n6qs9wrbhzYZ5iDgM8Appe3YqoR+u0sBfqHwWXuP+F7uVHn+U0nfB+YB7gfWKWWoSZIkSdIW6UOWJElX0MT6EsmMSBEVecr2H8vngYTgyG7l8/GEN9cfmvS/3fZqncwxGuhfb8Bcsk0f2r69Rd+1CCGUPsA2ti8r9/sSxtPzAeOAI4qyYbNxLgKaepZJWokIvreuzdFgjFnKWtYhSlTfB7ay/WTZY00JclYiG3eY7Q8ajVWlf//+HjYsKxuTJEmSDMiSJAkk3dNO8mCaiHokU53bidLMWsCxIB3iKJRnQ5p17iwY64QBtblb8DSRhbq47v67wA62lwE2BP5YUZlsRFPPMoUp9DFEyWYrtibOFPaxvRxRnvl65fnXy/2Vga8wqddZkiRJkiRJkkwxppaoRzJtGUIoVEIEYvcDn5f0KSLoWRq4t5R0bgXMQagq/g6gSNb3LMHcScDahNDFLMDZlWzTXpK+DcxGlFC+T4imjCtlficAP61b2we2VynzTHRGrO4c2POSXgYWkrQZYfI80R5t/7j2QdJdTGw0vhdwOaGiWWtzJWFlUGUo8ELtvJrtZ2mA7bcVfnLPSPp0RdFzApJ2J+wS6NWrV6NhkiRJkiRJkqQlGZB9AijBzNgierEaYRmwKGEH8AYwkshktfQnAzYHehMZqIWBh5hYEn6M7RUk7Ul4fe0m6TTgbdu/L23Om5w9SFoZmB143PZ/gXNatJ2NOGO3T/m8KJHpWodKQGZ7swZ9vwDcJmlNQiTmwtp5uXqK39qTxHubxGQ6fciSJEmSJEmS7pIli58chhDBWC0gu6Py+Xba8ydbA7jU9njbLwI31T2/ovx7DxG4TREkfZ7wj9u5M6XFQr1n2R+BX9oe11nHkhFbihBPGQ8MkrRuq+W1sZ4kSZIkSZIkmSwyQ/bJoXaObDmiZPEZ4OfAm0SWawCd+5O167s2jin03ZE0H3AN8Fvbd7bRvpFnWX/gb0V5cUHgW5LG2v5HozGKSMd1wHWSXgI2JbJl9XPNSwSej9Y/S5IkSZJmpJBHkiRdITNknxyGABsDr9keV848LUCULd5Be/5ktwHflTSLpM8SQVxn1HuVtY2k2YErgfNtX9pG+4aeZba/bLt38SG7DNizWTAmaQVJi5TrWQjlx0l8yMp7OgX4h+3/dXlzSZIkSZIkSdIGmSH75DCKyA5dXHevZ5Gqb8ef7HJgXSLDVjtD9kZ51oPwcVu6bt5/AZdJ2o7InH1Yfva3fSNMkKO/Evgc4S33SFFW3ApYC/iMpJ3KeHcB/0eUE74M7GT7+fLsdGAs8FrxoTu2hWdZMxYGzpQ0R2W+kyrPb1K8oFnKmg/r4vhJkiTJTE7K3idJ0hXShyyZCEk9i8LgbUS5Xn/bL0rqDVxte9km/foBLxWBkWWBgbYXrTzfHNiCkJtvOEZpN5/tN8v13sDXbO9R5PBvBza0/bSkhW3Xm11/bKQPWZIkSVIjA7IkSSB9yJI2kdRb0sOSzpM0EnhS0n1AP+CsIu7RKbbvrWSyHgDmrGWhSvnfz4DD2xjnzcrHeQjzZoDvAVfYfrq0axqMVfZ0lqT7JV0kaT1JQyT9tyg6ImllSbdLurf8u1S5/zNJZ5fr5coYc7fzHpIkSZIkSZKkK2TJYgKhOrir7SElEHmQOI+2raRNS5vZiTLCpkg6gPAnm7+0HyrpUqKU8njCE61TJD1C+IeNAx6TNILwRXte0mDizNqJts9vMczi5d/3CUn8DYHHgP2A3xBCHg8Da9keK2k94Ejgu4Rq4+Dih3YA8EPbk6w9fciSJEmSJEmS7pIZsgTgGdtDyvWFhPw9wHa2+9ruC3yrs0FsHwFsRwRutX7XAIvbvrLdxdheyvbswKFEVqwv8BywIrARIexxoKQlWwzzpO2vlr6XAXsXg+pRdEj2zw9cKul+wtR6mTL/eGAnQor/5sq7qV/nGbb72+6/0EILtbu9JEmSJEmSJJlABmQJdJQFNvvcFsV0+UpgB9uPl9urAitKGk2oOC5ZslztcDGRsQJ4Fvi37XeKSMktwPIt+n5QuR5f+TyejszwYcBN5Uzbt4E5K32WIIRPFmlzrUmSJEmSJEnSZTIgSwB6SVq1XG9LBE5doohuXAP8uppRsn2q7UWKJP0awKO2B7QYp2pWvQlRVgjwT2BNST3Kea5VCBXI7jA/kXmDyIjV1jA/cCIdCpBbdHOeJEmSZCZi9NEbTfhJkiTpjDxDlkAENjtKOh34L3AqkTHqCj8hzm0dKOnAcm/9yVBCPLqIa4wn/MH2ALD9kKR/AyPLs7Ns39/Fses5FjhP0s+AGyv3TwBOsf2opF0JKfxbpidVxyRJkmT6oaqqCKmsmCRJ15guAzJJBi60vX353AN4ARhqe+OpOO9gYD/bw+ru70TIv/9kCszxtu2e3R1nCjPe9h519wZUP9geDTSVqyfOaW0ErAAcYPv3AJK+CJxPeJCNB87oZC1PEGe5PiQ8x96prOE4SZcQoiNNv7v1a7W9U6Nntu8AqufQDiz3d6m0f4YOgZAkSZIkSZIkmaJMryWL7wDLSpqrfP4GHaVlMy2SZp2Gc3U1WH8N2Bv4fd39scDPbS9NGD7/WNLXWozzH2BZ232AR4Ff1z0/Abiui2tLkiRJkiRJkumS6TUgg/iju5bz3xb4a+2BpHkknS3p7uIh9Z1yv7ekWyUNLz+rlfsDJA2WdFnxp7pIkprM+/3iSXV/za+qiqQvSRokaWT5t1cn978s6Y6y1sMaTVjvBVbWOXd5NlrSQcWoeUtJi0n6t6R7yl6/WtptWdZ8n6Rbyr1lJN0laUQZd4kyV7XUbwsiu0V5R0dKuhnYR9JCki4va79b0uqSNijjVX+utP2y7buBj6p7s/2C7eHl+i2iPHJRSSc3GGdn29fbHlu63wl8ofKeNiUyaA+Uz59pMMYISX01DXzIJO0uaZikYa+88kqTr1OSJEmSJEmSNGe6LFks/A04SNLVQB/gbGDN8uwA4EbbuyjEJO6SdAPwMvAN2+8rxCH+CtTcsfsRpXDPA0OA1WksXjGP7dUkrVXmrC/TOwk43/Z5knYB/kR4WjW7fyJwqu3zJf24xX7rvcD2pCPb9L7tNQAkDQL2sP1fSasApwDrAAcBG9h+rrwTiPNXJ9q+SNLswKzAZ1usAWAB22uXuS4GTrB9WwkwB5ZM18BOxmiIpN7E72Go7f+00WUX4JLSdx7gl0S2dD8A268CfZvMszjhibY7cDdhLL0GIRQyRXzIbJ9BKcHs37//ZClTJkmSJEmSJDM3021AZntk+cN6W+DausfrA5tI2q98nhPoRQRbJ0nqS5gKV88H3WX7WQCF0XBvGgdkfy3z3yJpvkpwU2NVYPNyfQEhDNHq/up0SLdfABzTZMv1XmDV8r9aUNITWI3wzqr1m6P8OwQ4V9LfgSvKvTuAAxRy9FeUIK7J9BO4pHK9HvC1Sp/5JM1bMl1doqz9cmBf22+20f4AotzxonLrECI4fLuNPUD4kI0qYz0ADLJtSfU+ZOeV4N3AbBA+ZIpzgyOB05v5kCVJkiRJkiRJd5luA7LCVURQMgD4TOW+gO/afqTaWNLBwEuEP9UswPuVx1VfqnE033tXPbmaPXcbbdqdtyZsMQvwejE7nrixvUfJmG0EjJDU1/bFkoaWewMl7Uacy6qWqs5ZN9Q7letZgFVtv9fG+psiaTYiGLvI9hVttN8R2BhY13btPawCbCHpWGABYLyk922f1GSYrviQbVaC/8GVPulDliRJkrRFqiomSdIdpuczZBAlg4fWMh0VBgJ7qaRKJPUr9+cHXrA9HtieKNHrKluXMdcA3rD9Rt3z24FtyvV2dGTZmt0fUne/GZ16gZXM0pOStixrlKTly/VitofaPggYA3xR0leAJ2z/iQhu+xAB68Ll/NUcRODTjOsJOXvKHJMEgp1Rfkd/AR6y/Yc22m9IlCZuUi0TtL2m7d7Fz+yPwJEtgrF2SR+yJEmSJEmS5GNlus6QlRLDExs8Ooz4o3xk+YP/RUnvEJmxz5dSxvOZONvTLv+TdDswH3GGqZ69gbMl7Q+8Auzcyf19gIsl7UNkiZpR9QJ7jgjkGrEdcKqk3xIldn8D7gOOK6V3AgaVe78iREo+IoLTrW1/JOlQOsQxHm4wR3WvJ0saSXxXbqH4gtVTzpg9TGTcxkv6OXEurg8RHI8qpaIAR5VxVgLOrdkJFOGMy4HZgaclvQlc2UCSf0qRPmRJkiRJt0kfsiRJuoM6KsJmTEpANpQQzjhHIQ1/BvCa7f0/3tW1RymXu9r2suXzAMIPrUuea5JmtT2uybPBNPBYm1JI2hPoU0ontwE2s711k7bzEOIeyxIS99WAbBXbNxURkkFEJmy6l7nv37+/hw2bKq82SZIkmc7JgCxJkkZIusd2/87aTe8li+2wDqFCeA5ACUh+Cuwiae5mEuYK+fOFyv1ZJD0maUGFrPydCpn3QyW9XZtI0v7l/khJh5R7vSU9JOlMSQ9Iul4d/mlU+jaSpe8t6VbgamAxFZl+4GhgTYWE+08l7STppMpYV5egDUlvl3UOBVZVSOTfXeY6o5Q1bkGoTV5UxpxLIXHfv4yxraRRpc8xlXnelnREWfOdklopNH4HOK9cXwasWysprcf2O7ZvY+Izfth+1/ZN5fpDYDgV2fsG7/RcSadKuknSE5LWVtghPCTp3Eq79RXWA8MlXaoQGKHRuyr3B0s6RmEZ8KikNZssIUmSJEmSJEm6xSchIFsGuKd6o5y1epqQPv8jsLhCwvwcOiTML6TjTNd6wH22xxAlkifaXolQbQTij3pC6GFlQmp9RYU0PuX+ybaXAV6nQ1WxSk2WfnlCeh06ZPqXJUr7/lTu/wq41XZf2yd0sv95gPttr1KCnJNsr1TGnAvY2PZlwDBguzLmBJEOSYsQyo/rlH2tpPD7qo19Z1nzLcAPJO2sSX2/TgYWBZ4p738s8AYTC7FMROlzKLB1ZZydK88XAL5NZMmaMQdxPu9T5fONhArlmsByCj+yBYHfAuvZXqG8h5+V9pO8q8rYPWyvDOwL/K7JHtKHLEmSJEmSJOkWn4SATDRWMRTgIvCxEyE5f3NFwvxsYIdyvQsRrEHI119ari+ujLd++bmXyNx8lQjEICTWa+ej7qFDVr1KTZb+B3SIjcwGnKmQYr8U+Fone23EOCY+m/Z1SUPLmOsQAWsrVgIG236lBFIXEWIWAB8S2Tso+7J9Tgnqqj8/Jt53PU3rYUufg4BLKuOcAyCpB2E/8CfbT7RY+wfAT4rq5HqEgMlXS2D9APF7+D/ivQ5RnGHbEfhS6d/qXdXUIJv9PrF9hu3+tvsvtNBCLZaZJEmSJEmSJI2ZrkU92uQB6jJSkuYDvgg8Xm5NImFu+xlJL0lah5BUb6WACBFwHGX79Lq5ejOppP4kJYuNZOmBvWgu019lLM2l6t+vnRuTNCdhFN2/7O9gJpW1b7SvZnxUkZ1vZRUA8Czxzp8tAdX8wGudzN2MM4D/2v5jG22rcvb1Uvc9iHX/x/a21U5tvKvaWJ3tO0mSJEmSJEkmm09ChmwQMLekHSCELYDjCfW+d9VawvwsonTx7xUxjDvpCPC2qbQdSJxLq50/WlTSwu0uUg1k6Wku0/8WMG+l+2igr+Ks2xeJsslG1AKKMWWd1b3Wj1ljKLC24vzcrITk/s3t7qvCVUT2iTLvjZVgrm0kHU68l30nYw2NuBNYXdLiZfy5JS1J63eVJEmSJG0z+uiNJvpJkiTpCjP8//m37XI+7BRJBxJB5rXAb0qTVhLmVxGliudUhtwXuFAh234NcRYK29dLWhq4o2g/vA18n8igtEMjWfpTgMsVvmI30SHTPxIYK+k+4FziHNyTwCjgfqJkstG7eF3SmaXdaODuyuNzgdMkvUeUZdb6vCDp12V+Adfa/mebe6ryF+ACSY8RmbFtWjWWNJqwFpi9nFlbH3gTOICQzx9e3vNJts+ajPUAYPsVSTsBf1X4rgH8tnwfmr2rJEmSJGmLeoVFSJXFJEm6xgwve98disrgCbbXrNybG3ivBHrbANva/k6T/p+hQ3Tic0RwVlN3WLkoBU5XSNqFCLpenIpzfJk41zYLEfSfaPvM8mwO4GRCeMPAr2z/o5PxtiHOlPWzPUIhiX82UerZAzjH9rHdXPNmwOK2j5uc/il7nyRJMnOSAVmSJM1Qm7L3M3yGbHKR9CvgR0x6dmxF4KQigf46jc2hAbD9KqFMSDmD9Lbt30+VBXcBtfAjI/YzHGg7IJPUowh+tMvzwKq2Pyjn+e6X9M+SlTwIeNb2UpJmoUMhsdnc8wF7EuqINbYBsL2cwtPsYUkXFyPxycL2lZPbN0mSJEmSJEkml0/CGbLJwvbRtr9UpOKr92+1vbztPrbXsv3Y5IwvacfiYzVC0inl/FcPSa9LOq54Yg2UtIqkmxU+Wt8qfXeTdGV5/oik37Y57uGS7gJWlnSIOjy2TlOwNRFAXlL6zy7pWYXEPJL+T9IN5fpwSadL+g9wTpnjD5KekfSepKfVIVd/QN07/KAEYxsQ6pKfA25UqBz+jJDZx/b4EtS24kjgCWDp2rqBw4B+5czbXIQYyltNfg+Ll3dwtsIn7nxJG0i6XeExVvNi203SH8v1hZJOLG2eKNmzJEmSJEmSJJnizLQB2dRE0rLAZsBqRZK9Bx1nquYHri+eWB8CBwPrAlsSvlw1Vi59VgC+p/DU6mzc4bZXtn0HHV5qy5VnG9q+BBgBbF1k5jsrqewHfNv29sDuwMu2vwgsALwKbFLGOaLBO+gNHAcsBuxdvL7WIzJzR5WA9BIVc+4m73ElYGHbO1XXTfjLPQi8ADwFHG37jRb7WAr4fXkXfYAtbK8G/JrwfGvEwsDqwKbAUU3Wlz5kSZIkSZIkSbfIgGzqsB7h7zWsZHTWJgITiPNp/ynXowgPsLHlundljIG2/2f7HeAfwBqdjPshUC27W7dky+4r7TrzI2vEP23XpPjXB3Yu8w4lgrIlmnW0Pdp2n9JmV4VBc4+yx5tKQHoP0PDsVylnPB7Yr8HjVYms2KLAV4BfSvpSg3Y1HrP9YFGzfBC4odyvf+dV/uFgZJmn0R7ThyxJkiRJkiTpFjPtGbKpjICzbR840c3w56pmpareWTXfrBr1aivuZNz3ajLzRZjkJGAF288ppOSb+ZFVPc7q27xTuRawp+1BdIEy/8NEQHkV8G75F8IMu5mgxwKEofMtRW3xc8C1kjYizv1da/sj4CVJdxJn/55qMla9P1mzd96sTyuvtiRJkiRJkiSZbDIgmzrcAFwm6UTbY4oa4zyE2EW7rF/Odn0IfIcIQsa1Oe5cRLAxRtK8hK/aReVZI4+zFYH/UGewXcdAYE9JN9seK2kp4Gnb79U3lPQFYIzt98saVyVMtcdLuo5QWLyFKNV8sNFktl8DFqyMeRvwk6Ky+DSwDiFl35Mw9j6mxdqTJEmSZKqQiopJknSXDMimArZHSToEuKGU3n0E7EHXArLbgIuJksQLbI8AaGdc269KOo/wLHuKKDGscQ5wlsKPbGXiDNuZkl4E7mqxntOBXsCIkrF6mQgUG7Es4bs2nsguHWW7FnjtD5yvMOx+Gdi5k/fQiD8B50p6oIx/mu0HJmOcJEmSJJni1EvhZ9CWJEkrZmofsqmBpoA3maTdgGVt7zt1VjnJfDOdN5mkg4Ctid/POGB323eXTNxCRMni7MD1wIGdiIakD1mSJEkygQzIkiSB9CH72EhvsqZMN95kktYkREr62f6wKD1W/1vYuhLkHQtcQZRXJkmSJEmSJMkUJVUWpyFq05uMUP/rp+nfm2xFSS9Lekcd3mS3N9p7zZusfJyD+O7VxDL2AjYrCo7DgUEK4+5mHFl+qsIb8wIblzHuJGTrr63to47PA6/UspW2X7H9QoM1f0ioPC4haRKVypS9T5IkSZIkSbpLBmTTCH0yvclWAf5gex46vMm2adZRUm9JI4lzbYfbfqnI4b8K3EQIkTwCfMP20U3GqHmT/bvu0RmEMMkihBT+nsXc+/UGw/wbWKwEtieXjFlDShZwJPDVBs9S9j5JkiRJkiTpFhmQTTvSm2w68Saz/SYR1O5BBIOXSdq+xZ5T9j5JkiRJkiSZKuQZsmlHepPVFj0deJOVgPcm4CZJDxICHxfUtyvvcVngoa7sMUmSJJl5SRGPJEm6QmbIph03AFuVrBCSPiOpVxfHWF/SAiW4+g4wpAvjNvImq9HMmwza8ybrUeZeStJcjRpK+oKkOWtrJDJaj9oeD9S8yaATbzLbC9rubbs3IerxLdv3AjVvMtThTfZIk7UsLWnxyq3laRC4FVGPY4DHKrL9SZIkSZIkSTLFyAzZNGIqe5M9Dtwn6dUy7odUghpJxwPPAc28yb5CJ95kkkYTsvVVTieyXI+Uvg29ySTtAfwS+JykcWUtR9l+UFKfMv915b3cAezY6iVIuqr0eb187lvmXVLS94EXgD838iaT9FnivNkKJZD8gJC2P6zsYRwwtKzzDeAyYPNW60mSJEmSKil7nyRJV8iAbCpi++C6zxcTAVU9C1Ta/LZyPbb6DHjJ9rYN+p8DbGl7qxLU3A18zXat72rAvraHApOoF9pequ7WYOrOgpWA7CjbYyr9xkl6lBAOaSXtf7Ht08o4mxBljn8pAdGFwPa27yuZs9dbyPMjaXPg7TL/GuXe9cDvbF9XVCl/YfuEJkMcClxme83St4/tkZJ6A4/bXrbc/wohdz+8Mw+yJEmSJEmSJJlcsmTxk8EQIuiCEOq4H3hL0qcUpstLA/dK2r/I3o8s2ToAJL1d/p2lyOY/IOlqSddK2qIyz14Kaf5Rkr5agpg9gJ8WyfyGaoVFRKPGPHSchVsfGGn7vtLu1U6CsZ7Az4DD66cA5ivX89M66/h5YII3me2RTdb8RJlr7xZjJUmSJEmSJEm3yAzZDILts1o8e17S2HJ2bDWi7G9R4pzWG4Rs+wAi67UyIcZxlaS1bN9SGWpzQvFwOcLH6yHg7MrzMbZXkLQnsJ/t3SSdRsX8upQPnlu3xHeBi4gAZ3bKWS9gScAK77WFgL/ZPraMcxCTlgq+Ragsvlt3f19goKTfE/+TYbUyxsJEOWKVeYGzJf2EOH93ju1mAdxwGsjd15C0O7A7QK9eXT0OmCRJkiRJkiQZkH2SqGXJVgP+QARkqxEB2e1ENmp94N7SvicRoFUDsjWAS4vQxouSbqqb44ry7z00OVdVzrX1bbLGkyV9D/gtcU6sR5lzJSLIGiTpHtuDbB9KxYOtBHqH2b6yZOaq/Aj4qe3LJW0F/AVYz/bLjdYi6dPAhsA3iczhsk3W21Lu3vYZxHk0+vfvX6+AmSRJkiRJkiSdkiWLnxxuJwKw5YiSxTuJDNlqRLAm4gxY3/KzuO2/1I3Rmd9WTY5/HJMfzP8N2LRcPwvcbHuM7XeBawl/sEasCqxYzrLdRgh4DC7PdqQjWLyUyAI2pag1XlzMre8G1mrStB8pd58kSZIkSZJMRTIg++QwBNgYeM32ONuvEYIg/8/emYddVVbv/3MjDjhh5ZAzhlMOiIoY5pSamWOWZUnmmJlTWZqW5RdTy9IcccycTU3S0jJxyFlEEVHUUlNJcJ4VZRC4f3+s58jmcM55J0h+sj7X9V7vPns/ez3P3uf1uliu9dz3AKKFcSiwd9mHhaRlS0tflbuBr5W9ZEsRbY5tUS+ZPxOSqgIh2wFPleOhQB9JCxaBj81oLnl/tu1litz9xoRkfm19L5R7Idohn5o5wodr2aLYBlDk/3sTkvn143oBJwFntHq2JEmSJKlnzAnbzfCTJEnSimxZ/PgwGlicGVUcRwMLF2XEmyR9FhhWTJXHA98mpOpr/JnwAXsUeJKQxm9LYfB6YIe1PPsAACAASURBVIiknYCDbd/VYMxBkrYiJPnfpMja235T0slElcqEsfPfG9zfFt8FTitJ3UTKvq4mrA8MllQzvz7f9gMlAest6SHCDPtdQjr/wk6sJ0mSJJmLSdn7JEk6guzc+jK3Ulr+DrM9onzuRSRFaxQJ+vuBz9t+qVzvT9kzRbQ3DrJ9bbm2GHA+sBaRXO1te1gb8x8GnAgsYfs1ST0JGfwViP9ZcNL/LwlRv379PGLEiI96GUmSJMkcQCZkSZIAFG2Efm2NywpZUs+KkkYRaojH1pKxwqNAP9tTJC1NmFFfX/zSTgNutL2LpPmABVtNIml54IvM2C54IPC47R0kLUEYTl9ue/IsfL4kSZIkSZIkmWPIhGwuoFS+biRaENcl2hG/02T4s7YbqiQW4Y0aC1D8xCQtSghjPFmSudq8EKqNxzcIdwrwE+Cv1SmARSQNJ/alLQTcX+Lsbnt0JfbmwDHAy4SS4jVEi+YPgB7ApcBuwDzA8kSC+S7wVdv3lGrfqWXsBGAv209I2hPYkUgoewPX2v5Jo/eRsvdJkiRJkiRJV8mEbO5hNWCfkoxcABxQzl8uaUI5ng+Y1iqIpA0Jb7IViSRpiqTPAK8SMvomZPF/YPu9JjF2BJ63/XBJtmoMBq4jWhYXIZKnVnvK1iFMr98AniH2g/WX9ANgJdt9Jf0R+L7tu4tP29Byz7+BTcv6twJ+BXytxO1LJK6TiCrdGbbH1k+esvdJkiRJkiRJV0mVxbmHsbbvKceXEUqFAANrUvjAtm0FsT3c9pqEd9hPJS1AJPbrAWfbXhd4Dziy0f1F4fAo4OgGl78EjAKWIZKiwaX61owHbL9oexLwNNNNoEcTBtcAW5U4o4hkb9GirtgTuFrSo0S1bs1K3Fttv217IqH6uGKLNSRJkiRJkiRJp8kK2dxDfQWnSxUd2/+S9B4h4jEOGGd7eLk8hCYJGdEGuBKx/wxgOWBkaSHcCzjBoTTzH0nPAqsT4iKNmFQ5nlb5PI3pf9vdgAG2J1RvlHQGcJvtnUtL5+1N4nbFcy1JkiSZC0kRjyRJOkJWyOYeVpA0oBx/i/Ac6xCSVirS8khakWiDHFOEP8ZKWq0M3ZLmfmKjbS9pu1fxFBsHrFdiPFfupfigrUa0InaFm4CDKs9Q2x/XE3i+HO/ZxTmSJEmSJEmSpFPk//mfe/gXsIekcwnj5LOBHToYY2PgSEkfEFWoA4rHGcDBxH60+Ygkaq9OrPFY4CJJowlZ/SMq8TvLIcCZkh4h/t7vBPYHfgtcLOlHwD+7OEeSJEkyl1IvcQ9ZIUuSpGOkD9lcQGnJ+5vttTpwz+3M7FHWMoaknwL7EG1+h9ge2mLsBcD2wCvVmJJOJBLFycS+sL1sv9XedX9UpA9ZkiTJ3EkmZEmSNKO9PmTZspjMEiStAXyTEMfYBjhL0jwtbrmojKvnZmAt230Ief6fzuKlJkmSJEmSJMkcQ7YszgXYHkOIb8xEE4+yS4B+zCiJ/3Ib0+wEXFkUD5+V9B/gKkkr1407zfaFtu8sc9ev9abKx/uAfareZoVJtjcsnmFfIbzG1gJ+R0j3704Ic2xr+w1JvYEzgSWA94Hv2v63pB2An5d7XicUJ1+WNIiQ3v9M+X2q7dPr15o+ZEmSJEmSJElXyYQsgZk9ylYFRgBLV8YsT2uPsmWJBKrGOOAftod0YV17E0neZS3GrEUkkgsA/yH2na0r6RTC/PpUwitsf9tPFR+1s4AtCGGTz9m2pH0Jo+ofl7irA18g/NCekHS27Q+qE6cPWZIkSZIkSdJVMiFLYGaPskPK8cD6PWQtYqjBuU4nKZKOAqYAl7cx9Dbb7wLvSnobuL6cHw30kbQwsBHhOVa7Z/7yezmiirc0USV7thL376XaN0nSK8BSRJKZJEmSJEmSJLOMTMgSmDUeZeOIKlqN5YAXOrMYSXsQgh9bum3Vmba8yLoBbxXj63rOAE62fZ2kzYFBTeKmF1mSJEnSkBTwSJKkq6SoRwKzwKMMuA74pqT5Ja0ErEJzQ+emSNoGOALY0fb7nVjHDNh+h9jT9vUSX5LWKZerXmR7dHWuJEmSJEmSJOko+X/9GyDpU8Ct5eOniQrJq+Vzf9uTP5KFtUDS3sANxWC5o7TXo2xeSSOJRL47IdDx+3LtP8AHwLtEhe03tqe2WO8VhMriYpJeBn4GXApcTbQUjpX0BnCd7f078UxVBgJnS/o5MC9wJfAwURG7WtLzxP63lbo4T5IkSZKkFH6SJB0ifcjaoCjujbd90hywlnmaJTmS7gYOsl2vSNgqXneitbBdHmWS5gewPUnSosCjQD/br0g6HvjA9iBJ3YBP2H69RaxFiT1pPQjVw1GSvgNsbfvbkhYC/g0MsD3H791KH7IkSZKkRiZkSZJA+pDNNiTtIel+SaMknSWpm6Tukt6SdKKkkZKGStpQ0h2SnpG0bbl3X0nXlutPlIpNe+IeJ+l+oL+kYyQ9IOlRSeeUFrxdgb6EQMUoSfNJGidpsRL7c5JuKcfHSTpX0s3AhYRk/KfL3I8UtcGG2J5UhC4gqljdmC7msSfwmzJuWqtkrPCr8lPdq2VgoeJf1gOYSFTcGn0PK5d3cIGkxyRdIulLku6V9KSkfmXcwpIuKs/3UJG6R1JvSXeVcw8W9UUkbSXpVknXlO/okjaeI0mSJEmSJEk6TSZkHUDSWsDOwEZFJKI7YYYMsR/pJtvrAZOJdrgtga8Dv6yE6V/uWQ/YTVLfdsQdabu/7WFEm+AGwNrl2ja2rwJGAbva7tuOlsp1gR1s7w58CTjJdn9gA+BASU1NtSR9R+FN9jKRQA2V9PfyzL8uCelVkpaQ9KmSINb/bAUsafvGuvBXEsqKLwL/BU6w/XZJtGaIQUjXrwacVN5FH2AX2xsRZtJHlphHAzeW59sC+J2kBcocX7S9LtHSWPUZWw84EFgD+KykzzV5F/tJGiFpxKuvvtpoSJIkSZIkSZK0JPeQdYytiKRlhEJCvQcwtlybYPvmcjwaeNv2FEmjgV6VGENtvwkg6S/AxsT30CzuZODayv1bSjqc8N1aHHgQ+EcHn+OvtieW462JpKOaAK4CPNfoRtuXAJdIWhb4C/Dlsv4XCQn6H0r6CfBb23sRlbsPKe2MtwPfbhB+AFEVWxb4JHCXpFtsDwWG1sVZGbje9uPl8+PALeXyaCIpqz3flyXVErQFCLPnV4HBCoGPKUDvSvj7bL9Y4o4ivr+qx1rtXaQPWZIkSZIkSdIlMiHrGAIusP2LGU7GXqxqVaqR/HqNRhLzreJOqEm/S1oQGAysZ/t5SccRCUYjpjC9Alo/5r26ZzrA9q10gDL/v4mE8jrg/fIbQpjjL01uXYyoPN1Zks9PAzdI2o6oVN1QDJhflnQfsD5RLWtEW5L3tef7iu2nqzeWdzeWSAznBcY3iZuS90mSJEmSJMlsI1sWO8YtwDckLQ6hxtiqva8JW0tarCRXOwH3dCBuDyLZeE3SIsDXKtfeBRapfB5DJDPUjatnKHBASf6QtJqkHo0GSlqutPvVlCgHAE/ankZU6TYpQ7cEHm8Uw/Ybthe33ct2L2AEsK3th4iq3BYl/sLAhsATLdbeHoYy3egaSeuWw57AiyXZ3YPGxtZJkiRJ0mHGnLDdTD9JkiTNyP/z3wFsj5Z0DHBLab37ANifjhkg3w38kWiRu7SmitieuLZfl3QxoW74X2B45fKFwPllf1d/Yg/b7yW9RGs/sHOJFr5RpWL1CpEoNmIt4ERJ04gE5te1lkHgcKKVsWeJsVcb76ERpwMXSXqsxD/H9mOdiFPlGODU0jrajZDn34moNA6R9C0iIZ7UPESSJEmSzEwjNUVIRcUkSTpGyt7/DykKhmvZ/mHl3HjbC1c+70lIyR80C+brRQiF/LHJtX8RFaj5iErVPqVdsMPx2rGWFYFrCFXHeYEzbJ9Trs1HJEibExXAo2z/uY14uxCtkRvYHiFpXuB8QpCjO3CJ7V+3uP8oYDeiJXEa8D3bwyXdDixNJGjzEcnaz22/1Wo9KXufJEky95EJWZIkrVDK3s/dlBbEXkTS0Yyni6rj2oQf2TfaCNtWvFa8yHQVyQ2BIyUtU64dBbxie1Vif9kdrQKVds1DmLFC+HVgfttrE62a3ysJZKP7BwDbE3vx+hBiLWMrQwaW832IxOyvHXjOJEmSJEmSJGk32bL4P8T2+R0ZL2kJ4ByipRDgh7bvkdQfOJXYUzYB2Mv2E6W6th0h4rEQsCChoDgKuNj2KU3WNVXhc7ZsmXdb4E9MT9jHAa+Vzx/GI1oMTyAqW/MDZ9o+t7L+o4Gv1j3TlcDvmfF/BuwNrF7WMq3MVRu/JHBT3ZKXBQ4Cvl99DMLDrHt5L5OBdxo9L1EBe63mqWb7tUaDbE8uipH/kbSO7YfrnmU/YD+AFVbo6FbCJEmSJEmSJMkK2ZxAjzp/rapn2WnAKcV37GtESx7Av4FNi4fW0YTBco0BwB62tyC8uO4q3mQNkzGAItSxIVDzBbsdWNz2gsA6wDvF36s+3j6EvP8GhGz/dyWtVItr+5dlbN9SGduBqLCNBX5j+wUV82rg2OJhdrWkpSoxXqmLsRdwZ/FeqzKEUI98kRAHOcn2G00e+SZgeYWB9FmSNmv2bmxPBR6mJIx1186z3c92vyWWWKJZiCRJkiRJkiRpSlbIPnomlEQDmL6HrHzcClijiG0ALFra9XoCF0tahagMzVuJd3OLRKSe3iUJXAUYYvuRcn5ewqOrL7HHatUm928N9Cn7uWC6h9mzjQbbHlvGLwP8RdKQEn854B7bP5L0I8Lseff6+4vgySnAng3C9y+xlgE+wXQPs2carGO8pPUJVcgvAFdJOtL2RU2eMxUYkyRJkiRJktlCJmRzNt2AAbYnVE9KOoMwYd657JO6vXK56jHWFk/b7itpaeB2STvavg44FHiZqI51I8yaGyHg4GLc3G5KZewxIiH6M+FhVjO/vpqovDViEULp8faKh9l1knYkKm83FlGSVyTdQyS2MyVkZQ1Tifd2e1Fg3AO4aKYHlOYh9tj9qyPPmCRJknz8SfGOJElmBdmyOGdzE7FXCoBSsYKoRD1fjvdscX+9N1lDbL9ItCP+tBL/xbKfa3dCGbFRvKHA94vCIZJWlbRQozmKh1mPcvwJ4PPAE8UH7HpiHxq09jB7u87D7D5gR9sjKB5mChYCPke0djZay2qlulijLw3Mp8tz/RoYW6keJkmSJEmSJMksIytkczaHAGdKeoT4ru4k/Ml+S7Qs/gj4Z4v7HwGmSHoYuKjVPjLgL8AgSZsAZwF/lvR14DZgmqR+xF6qKZIeJ9oClyWUF0cqSlavAl8pptFDiH1lFxUJ/88CJxf5+/mAN4CBRCJ4BHCppFNLjL0AyvPtC0wp5/e2XU2c5gFukPSnEudCwqNNwIUtkqiFgTPK/rUphDfZmZJOL9cvlzSJECq5hea+bEmSJMlcTMreJ0kyK0gfsqRNijfXYaUSVfMj+5vttZqMXwhYl2gvXKvmqSZpQWBD27cV77FbgV/Z/keTOF8Ahtt+X9L3gc1t71q5fhqwBPDGrPBt6wrpQ5YkSTL3kQlZkiStSB+ypMNI6iXp35IulvSIpCElieoQtt+zfTd1e89sv2/7tnI8GRhJCHo0i3Ob7ffLx/uqY4sox1LMLInf6LnGS/qNpAcl3SKpv6TbJT1T9p8haXNJfyvHgyRdUBlzSIdeQJIkSZIkSZK0k2xZTOpZDdin+J1dABxQzl8uqSYuMh8wrdHNktYGLi0fP0nI+m9ge8O6cYsRMvinNYlzFGH2XGNZYFS51g34HbG/bctmD1JaJ28lPNl2IfbA9QN+A3yRMKG+GLiuwe2rEwqMiwBPSDq7CIZU46cPWZIkSZIkSdIlskKW1DPW9j3l+DJg43I8sOIFtm2zm22Prow7GriqQTLWHbgCOL2RLH2Jc3wlzknA08D25fIBwA1FRr8ptl8v908GVi7HpwO3lORqNLEHrhF/tz2pmEa/QlTj6uOnD1mSJEmSJEnSJbJCltRTv6lwdmwyPA94yvapbQ2UtBVwFLCZ7Unl9ABgE0kHEAId80kab/vIJmE+8PTNktOASQC2p5XksBGTKsdTyf9WkiRJkiRJktlA/iMzqWcFSQNsDwO+BdxNtBbOEiQdR8jq79uOsesC5wLb2H6ldt72wMqYPYF+LZKxJEmSJJktpHhHkiSzgmxZTOr5F7BHkdr/JHB2Z4JIGgOcDOwpaZykNSQtR1S71iCk8kdJapWYnUhUwK4uYxvt9UqSJEmSJEmS/29J2fvkQ6py9p2Quu9PtCJC+IANsn1tubYYcD4hg2/CT2xYG2s5jEjIlrD9mqSexJ62FYjK7km2L+z0w85iUvY+SZIkqdFIDj+raUky99Fe2ftsWUxmFY8SrYNTJC0NPCzpettTCCXFG23vUvzHWkrpS1qeUEF8rnL6QOBx2ztIWoJQPry8yOcnSZIkSZIkyf+XZMviXE7Ve4yQf/93e73HJH2ptBKOAu4FRki6FliAIgYiaVFgU+APEP5jtt+qi3NULU6JNRp4khkFRQwsIklEG+MbwJS6OMNLjP8U77E3JY2RdIKkgZLulzRaUu8yfodyz0PFn2ypcv50SUdXnvHOIrWfJEmSJEmSJLOU/EdmAuE9dp7tPsA7zOg9VkuSbqi/yfbQmjR9kZT/HrAqkVDtX6pjnwFeBS4sic/5khaqi1OVuD8auNj2wXXTDQY+C7xQ4v/A9rS6OBuWGPsSydoa5dl2B1a13Z9onazFvhv4nO11gSuBn5TzRwK7SvoCIZO/V/1cED5kkkZIGvHqq682ebVJkiRJkiRJ0pxMyBLoovdYDdvDba8JbAD8VNICRFvsesDZJfF5j0h4ZqJU5o4ikrJ6vkQYQy8D9AUGl+pbMx6w/WKRyn8auKmcr3qPLQcMlTQaOBxYszzH+8B3gZuBwbafbvK86UOWJEmSJEmSdIlMyBKYxd5jtv9FJF5rAeOAcbaHl8tDiAStEb2BlYj9Z2OIhGmkpE8DewHXOPgP8CyweotlVH3EplU+T2P63skziIRrbaK6t0DlnrWB14kEMEmSJEmSJElmCynqkcAs8B6TtBJRaZsiaUWiVXBMUUgcK2k1208AWwKPN4phezSwZCXmGEIo5DVJz5V77yp7vVYDnunwk85IT+D5crxHZd4VgR8D6wI3SPpLJaFMkiRJkpakomKSJB0hE7IEpnuPnQs8RXiPddQMemPgSEkfEFWoA2y/Vq4dTOxHm49IovbqxBqPBS4q7YUCjqjE7yyDCI+z54H7gJWKaMgfCMn/FyTtU+bdwPbELs6XJEmSfIxoJG8PmZAlSdIxMiGbg5B0CvBf26eWz0OJqtO+5fPvgOdtn9zk/nttb9TGHGMoVafK6WmEqMUltu8t5zav3md7jKTBJSGaCowH9rNdq3Y9TAiCLFri3Vi5dxQwkwdDMXr+TM3bTFJf4ByidXAK8I3aOm2/AGzd6tkq890O3F75vHmja7b/Cvy1QYitKuMfJNoXkyRJkiRJkmSWk3vI5izuBTYCKDLri1OEJgobAfc0uA+AtpKxNti8NncL/mh77SLy8Vvg5LLW7oQYyP5F1GNz4INWgSR9lUjqqvwWOKaitvjbDj5DkiRJkiRJkvx/RSZkcxb3MD0pWpMwW35X0ickzU/Ivj8k6XBJD0h6RNIxtZsljS+/u0k6S9Jjkv4m6QZJu1TmOVjSyFLtWgDYHtgfOLTI3G/SaHG236l5jxFy8BuV4zuBR2w/XMa9bntqs4eUtDAhY78u0Lsirb80UWGD2N/1QosYgyT9VdI7kiZLelbSy5Lel3SjpHnLuPUl3SHpQUlDi2k1kr5b3uHDkv5c816TdFHxIbtX0jN1761+DSl7nyRJkiRJknSJTMjmIEpb3hRJKxCJ2TBgODCAaPl7hKg+rQL0J+Tf15e0aV2orxLS7msTnlwD6q6/Zns9Yq/YYbbHEK2CpxSZ+7taLHNlYBFgHmD9Us36E+CS8IyU9JMW90PsBzsQ+DLwdEVa/xvAiZLGAicBP20jzqfKzwbAUsCethcEJgDblaTsDGAX2+sDFwDHl3uvsb2B7XWIPXT7VOIuTeyJ2x44odnkKXufJEmSJEmSdJVMyOY8alWyWkI2rPL5XmIf1dbAQ8BIQvp9lboYGwNX255m+yXgtrrr15TfDzLdk6td2D7Tdm/gCODn5XT3MufA8ntnSVs2ur/sE1vZ9rUNLn8fONT28sChhLhGK/5h+wPCW2wepu9bq3mNrUZI799cKnA/J6T0AdaSdFepEg5kxtbQv5R39ziR6CVJkiRJkiTJbCFFPeY8avvI1iZaFscSEuzvEBWezYFf2z63RQy1MUfNk2sqnf8buJKosEF4jd1RE+CQdAPhNXZrg/sGEFW9MWXuJSXdXoQ39gB+UMZdDZzfxhomAdieJukD2zX/tJrXmIDHbNdXCAEuAr5i+2FJezKjiEnVw6ytd5kkSZLMpaSaYpIks4KskM153EO0yr1he6rtN4DFiERmGDAU2Lvsw0LSspKWrItxN/C1spdsKeoUE5vwLtGK2BRJ1UrcdoREPmVNfSQtWAQ+NqO519jZtpex3Yuopj1ZUUF8odwLsEUlfmd5AlhC0oCy/nkl1SphiwAvlrbGgV2cJ0mSJEmSJEk6RVbI5jxGE+qKf6w7t3CpQN0k6bPAsLDMYjzwbeCVyvg/EybKjwLzAS8Bb5drSwGnALuXz70l/Qi4HhgiaSfg4No+sjop/YMkbUUoKL7JdDPlh4i9Vg8ABm6w/XdJmwOTK1L6jZCkq4D1icrUGZKmAROB/eoGfgoYQuwZe5zYu0YR5FhA0r+Jqt+bRNvh5CLKcbqknsTf+6mSpgBvENXHKcCzwPNlvbsBm0k6HniZaIVMkiRJkplIH7IkSWYFmZDNYRR1wkXrzu1Z9/k04LQG9y5cfk+TdJjt8ZL2IoQtRhcp/ccJYQ5sj5A0DrjH9pNAnwYxN6oc/6D+eoUhts+pO7c5kTA2TMiKt9mZQB/bK0v6JrCz7V2bzDER+AWxL2wt2ydVrn3R9m3FfPpW4LEyxyhgBtGT4u92ePEhQ9LatkeXhOwm29uX832Bv0ja0naj9sskSZIkSZIk6RLZsvjx5W9FyOKnRJXqJf5HUvqSVpfUi3ZI6QM7AReX4yHAliqlv3psv2f7biIxq55/3/Zt5XgyIXayXIMQNZYm9r3V7h/dZL5RwC+Bg1rESpIkSZIkSZJOkxWyjymVfVlIGlMnpb8ssSftbWaW0hfwuKTdgPeAHiWxe5zYy7Y2sCQhFX9BZcrXbK8n6QBCSn/f0hpYS67OKHnW7nUJ0LJE6yC2p0h6m5CyrwmE7MV0oY8ar5f5Z0LSYsAONKggVjgF+Keke4GbgAttv9Vk7Ejg8CZz7Udpq1xhhRVaTJckSZIkSZIkjckK2dxBR6X0uwMnFG+wCeX3K3RcSv98pnub1X7qq1GNqmH+8MC+sO7+vsCljR6yCIpcAZxu+5lmL8P2hURl8GoiGb2vVAsbhm0RJ33IkiRJkiRJki6RCdncQb2U/n1EhWwjIlkTIaVfS3pWtl3vATa7pPTHAcvDhwlVT0JwozOcBzxl+9S2Btp+wfYFtncihD3WajJ0XZpU45IkSZIkSZKkq2TL4tzBPYSX2TNFNOSN0tq3JvBdIpk6VtLlRQhkWeAD21XlxruBPSRdDCxBVJaqSpCNeJc6gZIGXEeoNQ4DdgH+WfETazeSjiOSuX3bMXYb4FbbH0j6NNEi+Txhsl0d14cQEWkzZpIkSTL3kWqKSZLMCjIhmzuY1VL6TwLDmS6l34yGUvp1/AG4VNJ/iMrYN1sFLIbSiwLzSfoK0Wr5DnAU8G9gZHmGwbabGUtvDZwmqSYOcrjtlyStDmwi6SFgQeL5D0mFxSRJkqQjpBx+kiQdQZ0oRrQdVJpK/IN/XqId7GLg1CLH3g/4ju1DOhhzMWA322e1Ma4X8Dfba3V0rvKP/X4lSameHwSMr5NZ7zDVtXUlzkeFpIVLBe1TwP3A58t+MiTtDxxItCyOB/az/Xi51gc4l0ikpgEb2J7YaI7KXNcBn6m9qyJBfw6wAPE3dYDt+2fDY3aKfv36ecSIER/1MpIkSZI5gEzIkiQBkPSg7X5tjZtdFbKaEASSliQqMz2B/7M9AujMv1wXAw4AWiZkVbow1xyJpHlKy+FHNdffSmI8H3BsLRkr/LHmQyZpR+BkYJuyL+wyQl3x4ZLMfdDG3F8lkroqvwWOsf0PSduWz5t38vGSJEmSJEmSZI5gtot6lH1I+wEHKdhc0t8AJPWXdK+kh8rv1cr5NSXdX/yrHpG0CnAC0LucO7HEOlHSo8X7aiYz4bq5FpZ0YRn7iKSvNVny4WXu+yWt3CBmX0n3lRjXSvpEG+fXl/SwpGFEBWkmyjrvLPc9LukchYkzksZL+qWk4cCAEu8OSQ9KGipp6TLukHLvI5KuLOc2K+9rVHnHi1TfSRkzWNKe5XiMpKMl3Q18XVJvSTeWue4C9i+iH2vYvqjue36n8nEhpislbk1I62+vkM+/FXiwrOmoBu9iYcL7a12mf9+jgHWYvh+tJ/BCo3dZYgySdLGkpyRNlvSspJclTZD0pKR5y7ijFd5rj0o6r/xNdS/nNi9jfi3p+GZzJUmSJEmSJElX+J+oLBYJ8m6Ef1WVfwOb2l4XOBr4VTm/P3BaqbL1I5T4jgSeLgnB4cBXgb7EP9S3Ak6sJSdN+AXwtu21bfcB/tlk3Du2+wODgUZqfZcAR5QYo4H/a+P8hcQ+pAEt1gbhAfZjQgmxd3k+iOTmUdsbEvu2zgB2sb0+4QNWSxaOBNYt8+9fzh0GHFje4ybAhDbWADDR9sa2ryRUCw8ucx1GG9VJSQdKepqoXtXaRFclkrNNiXbFP1bUHBslOscS39WXmf59USSn0AAAIABJREFU9yWqYSdKGgucRBhet6I3sAawAbAUsKftHsBjQK1nZLDtDUpbZA9ge9tTgD2BsyV9EdgGOKY+eHne/SSNkDTi1VdfbWM5SZIkSZIkSTIz/0vZ+0ay6T2BqyU9Spj1rlnODwN+JukIYEXbjRKJjYErbE+1/TJwB/GP72ZsBZxZ+2D7zSbjrqj8niGJktQTWMz2HeXUxcCmHTjf0D+rcL/tmgriFeX5IPZk/bkcr0bIs99cqkY/B5Yr1x4BLpf0bWKPFYS64smSDinrqJ1vxVXlWRcmZPGvLnOdC7RKeLF9pu3ewBFlbRBtsRsDA8vvnSVt2eh+xT6xlW1f2+Dy94FDbS8PHEqIgbTiH7Y/IJLjeYAby/nRTPdJ+4Kk4ZJGA1tQ/v5sP0Z8V9cDe9ue3OR504csSZIkSZIk6RL/k4RM0meIxOKVukvHAreVCsUOhGADtv8I7EhUdIZK2qJR2I4ug4rhcAvc5LgztHfORnPVPk+s7OUS8FilwrS27a3Lte2IhHN9oiWwu+0TCMn2HoT58epEslb93heom/e98rsb8FadKfNn2/ksVwJfKcfjgDtsv2b7feAGYL0m9w0A1leIq9wNrCrp9nJtD6abT19NVBRbMQnA9jRCwr/2PqcB3SUtQFT8drG9NvB7ZnwXawNvEdW1JEmSJEmSJJktzPaETNIShDre4Ab+Uj0J/yeINrHaPZ8hPLNOJ3yq+hCeVotU7r0T2FXSPGWOTQnlv2bcBBxUmeMTTcbtWvk9rHrB9tvAm5I2Kad2J5KNZuffAt6WVKt2DWyxvv6SVip7x3YlEpJ6ngCWkDSgPMO8iv123YDlbd8G/IQQQFlYUm/bo23/hhA3WR34L7CGpPlLBa9htarsCXtW0tfLXJK0TrPFK/b51dgOeKocDwX6SFpQIfCxGfB4kznPtr2M7V5ENe1J25uXyy+UeyGqWU/NHKFD1JKv10o1cJfKs3yV8CbbFDhdIWSSJEmSJO1izAnbNfxJkiRpxOxSWexR2txqsveXEqp79fwWuFjSj5hxT9euwLclfQC8BPzS9huS7intjf8gEo8BwMNENeknxUuqV5M1HQecWe6fSuwLuqbBuPmLgEY34FsNru8BnCNpQeAZYK82zu8FXCDpfSI5acYwQrhkbSLZnKltz/ZkSbsQSUJP4vs7lfAFu6ycE3CK7bckHSvpC+V5Hyfa+CZJ+hPR4vgU8FCLNQ0k9lL9nPguryTedyMOkrQVoaD4Znkf2H5T0snAA8T3dIPtxnrArfku4R3WHZhICMV0mvJ+fk+0MI4p60PS4sT3sKXtsZIGA6fVnidJkiRJ2qKZ7D2k9H2SJDMzW3zIkplRyL3XDIY/TSRJNSWIw4Af2t7+o1hbMyTtTSRQL7U5uGvz7EOIkkBI21/WYuwJhGn1wrYXq5z/CZH8TiZaY/e2PbZJjHmA04mKm4nW2K/b/q+kcURCqfLzZ+B425NaPUP6kCVJkiQ1MiFLkgTa70P2vxT1mKux/XpFMfAcoopV+9wesY3ZQklOmrE3kTx2JF6Hqq6lIvUzQpDlc8CxpdLXjL+WcfWMANazvQ7R5npCixi7ES2Jfcr+sV2AtyvXNyn7GgcQQirt9r5LkiRJkiRJko6QCdmcwYrAksVz6yxJ3Yof1lsKr7WRCs+xDRUeZM8ozJGRtK/Cv2yopCdKeyHl2h6a7udWH/c4SfcTe9eO0XQ/rnPKfrFdCVuBq8r980kaV9ogRyn8vN4tx7dJOlfSzcCFZY6Ty9yPSNq3/oEV6oajgAcJf7HbgWWI1tWt68fXsD2MaGNF0l6a7lN2MjBM0pnAfUxXn2zE0sCLRfAD28+V/X71c71DtEZ+o1GSqJS9T5IkSZIkSbpIJmQfMZLWAnYGNirVsu7AN8vlnsBNttcjWvEGESIcXyfMk2v0L/esB+ymMKluK+5I2/1LgnOa7Q2I/Ws9gW1sXwWMAnYtlbya9PvvSrzvAMPL8T2EkfMOtncnkphXip/bBsCBklaoPrftDcu9Z9di2h5NqDIu2553Z/vCOhXIvrYPBPYh9hk240rgqwqz7JMUcvvN5nibEEKZySQ8Ze+TJEmSJEmSrjK7RD2S9rMVkbSMkAQhUV/b+zTB9s3leDRhbD1F4ZvVqxJjaM1XTdJfCIXC7i3iTmZG0ZAtJR1OKA8uTlStWiU0jfir7YnleGvgs5KqCeAqwHMN7mtkDdDpjY2S9iASy0OajbH9nKTVCLXGLYDbJO1s+/ZmYTu7niRJkiRJkiRpRSZkHz0CLrD9ixlOxl6sqiHxNIq3VjmufneNEppWcSfULAiKKuRgYv/V85KOY2ZvshpVD7Nm/mW1ZzrA9q20zThm3BO2HPBoO+6bCUnbEOqbmzUzc65RkscbgBskvQbsRLRN1sfsCSxP12X2kyRJkrmEFO5IkqQjZMviR88txB6lxSHUGOvb+9rB1pIWK8nVTkQLYXvj9iASvNckLQJ8rXKt3vttDGE8Td24eoYCB9QEPiStJqlHk7E3Al8u6/8U0ZJ5U4vYDZHUjzDG3tH2a22MXV/S0uW4G1FR+2+DcYsQLZVXl/1kSZIkSZIkSTJLyQrZLKINWfv+zSo2tkdLOga4pSQHHwD7E0bI7eVu4I9Ab+BS26PKmlrGrcnaAxcTVan/AsMrcS8Ezpc0gdinNgj4vaSXaG3CfS6wAjCqtEvOA8wrydTJ2tt+VdKvCZVEyjoek9RM1n5xIkmcr0jUn2P7OOAkYCHgz2XOZ23v3GR9ny7PMR9RzRtGJF417ioxuhFedce1eNYkSZIkmYGUvU+SpCOkD9lsQNIgYLztk/4Hc+0LrGX7h02uz2N7apNrdwMH1RK4ds7X3Xa7ZfpLhW44UVmbh0i8+haxjEbjBxB73R6tS8i2AIbZniDpYOBztge2dx2zm/QhS5IkSWpkQpYkCaQP2RxFG/LzXZK1B44CBjaI21lZ+8VK7M9JuqUcH6cOytpX+DJwo+23bL9OB2Tt687/0/aE8rGlrL2krRRS/EMkPVXW/53yDh6R1KuM20khv/+QpJskLVnOnyXpZ+V4uxIrhT2SJEmSJEmSWU4mZLMZzX5Z+1VsL9Egbmdl7ZvRIVn7CssCY8uaRwHbAaeWJPDeFvMtXMZUf9Yo19qStQdYhzB0fh84AvgNMC/heXZQGXMnUWlbl2hN/HE5fzjwbUmbA6cAe7tBKVnpQ5YkSZIkSZJ0kdxDNvtJWXtwaYvsW/a1vWH7tDbmG18S2BmDtUPWvjDc9j+BdUrid6jt4ZK2JhJKiH1uf5L0aWB+4Elise9J2p+o5h1s+9lGE9g+DzgPomWxjfUkSZIkSZIkyUxkQjb7SVn7j0DWnunvEpq/2zOBX9m+QdJWwJGVe9YGXgeW6cxakyRJkiRJkqQ9ZEI2+7kFGCLpNNuvKdQYF6JjKopbl71dkwlZ+4GEimN74jaStb+8XGsma38z7ZO1v6NU9FYDnqvs8apyI3BMWf88REvmj9r53B+i6bL2W7cla98BegLPl/1he1Tm+gxRgesLDJX0V9up2JEkSZK0ixTuSJKkI2RCNpv5KGXty/yvS5rdsvavEIniTDSQtT+6mcJieaaTgW8Ai6rzsvbtZRDR2jmOeN6lS3J2AdHi+GIRLPmDpP62JzUPlSRJkswttFJRhEzIkiTpGCl7P4ejNmTtZ+O8twOH1SpDRZnwb7bXajL+U8AQYl/bRbYPKucXBK4mksmpwPW2j2wUo4z/EbAv0T75KiGo8d/K9UWBfwHX1uaYE0jZ+yRJkrmHTMiSJGkPStn75H/MROAXwGENrp1ke3VCqfHzkr7cIs5DQD/bfYgE77d1148F7pgF602SJEmSJEmSj5xsWZzDsX3+7IxfKl83Eq2M6xJKg9/pRKhVCPGQTwI9JG0MvG97I+A2ANuTJY0ElpM0gpn//nazfVvl833AtytrXR9Yqqy3n6S+wEV1Md63vZGk8cSes62AN4GfEcndCsAPbV9Xnv1SohUSwiT7Xkk7AwcCXwQ+TSSAm9qewR9N0n4UxcYVVmim+p8kSZIkSZIkzcmELAFYDdjH9j2SLgAOKOcvL/vLAOYjxEEaUpG135OocM3UTliEPXYgfNF+3451feg3VvbJ/Q7YnRAG+XDOJvcuBNxu+whJ1wLHEQnWGsDFwHXE3rcv2p4oaRXgirL2ayV9jUjKtgH+rz4ZK/On7H2SJEmSJEnSJTIhSwDG2r6nHF/GdI+vgfV7yDo7QZHjvwI43fYz7Rj/baAfsFk5dQBwg+2xRdSjLSYTlTQIj7dJtj+o83ibFxhcKm1TgVUr9x9MCKHcZ/uK9kyYJEmSJEmSJB0lE7IEGvuczWrOA56yfWpbA4sn2FGE31hN2XAAsImkA4CFgfkkjW8hEPJBzYuNig+Z7WklOQQ4FHgZWIfYTzmxcv+y5b6lJHWz3bQ6mCRJksxdpGhHkiSzkhT1SABWkDSgHH+LkNmfZRQz6p5Am0qRktYlZPV3tP1K7bztgbZXsN2LEA65pJVaYzvpCbxYkq3dCZ+0WjXvQmA3QtGxw75pSZIkSZIkSdIeskKWQCQde0g6F3gKOJvY69UhJI0BFiWqV18BtgbeIapd/wZGlnbDwS3ESk4kKmBXl7HP2d6xo2tpJ2cRvmZfJ4RH3ivnfwbcZfsuSaOAByT93fa/ZtM6kiRJko8RKYufJElHyIQsAZhme/+6c5tXP9geAzT0IKuM6dXEv+yxZv5lZcxPCQGPqcAhtoe2GHsBsD0hyFE9fyKRRE4GngaWq6xrUF2YJSVdDvQBRHieHW/7p5KmEnvO5pX0MCEAska2LCZJkiRJkiSzg2xZTD5SJK0BfBNYk1A0PEvSPC1uuaiMq+dmwkC7DyHd/9MWMX4AvGx77ZIo7gN8UK5NsN3X9pqEKuO2wP914JGSJEmSJEmSpN1kQjaXY3tMq+pVPZK+JGmUpMclTZT0hqS3JQ2RtGAH4hxV2gFvAZYgfNB2A/4D9G+x3juBNypxhpc4vwVGlOMXqFTIGrA08Hwl5hMV8ZDqXK8QPmMHqYG0o6T9JI2QNOLVV19t+bxJkiRJkiRJ0ohsWUw6RGknHFpaEZ8FtuyMf5nt44HjJQ0mpOUvA5D0GULhsL3r2bD+nKTrgata3HYBcJOkXYBbgYttP9Uk/jPFA21JQpGxei19yJIkSZIkSZIukRWypCvU+5dtXI4Hlra/vkTLXysamYp1OrmRdBSxJ+zyZmOKofRnCAGRTxKiHZ/t4BqTJEmSJEmSpMtkhSzpCrPCv2wcsHzl83JEy2GHkbQHIfixZcWDrCG2xwPXANdImkYkjjOpKJaK3VTqRESSJEmSpBmpopgkSUfIClnSFWaFf9l1wDclzS9pJWAV4P6OBpG0DXAE4V/2fhtjPy/pE+V4PmAN4L8Nxi0BnEPI9GdLYpIkSZIkSTLLyQpZF5D0KWIPEsCniUpKTd2hv+3JH8nCWiBpb+AG2y/NgnDN/MtWL3vDFibMlhdqsI6zgW/ZXkzSn4DHiVbDA21PLWMOAr5HvNd3ge8SioebA4tLmgS8Roh89ADmB24u+hv3NZDyr9EbOLsIdXQD/g6MlXQK0KMIg8xb1nMpcHKn31CSJEnysaItjzHIClmSJB0jE7IuYPt1oC+ApEHAeNsnfaSLirXMU0tqGrA3MBJod0ImqbvtKQ0uNfQvk7QasY/saUnLEeqHi9h+t8TbkEjWgOkCHw3iX2J7cLnnq8BJtreXNG95ht1sj5a0OPBGe73CbF8CXNLg0nDg0PbESJIkSZIkSZJZQbYsziYk7SHp/iIRf5akbpK6S3pL0omSRkoaKmlDSXdIekbStuXefSVdW64/Ienn7Yx7nKT7gf6SjpH0gKRHJZ2jYFcigbyq3D+fpHGSFiuxPyfplnJ8nKRzJd0MXFjmOLnM/Qiwa7NnLzLyT5fjccDrwOIlbnfgN8CRbb1D2+9UPi7E9D1qXwYetD26jHutWTLWgXe+laS/VJ79D5UxB7a11iRJkiRJkiTpDJmQzQYkrQXsDGxUlAa7E+bHAD2Bm2yvB0wGBgFbAl8HflkJ07/csx6wm6S+7Yg70nZ/28OA02xvAKxdrm1j+ypgFLBrUUFsq6VyXWAH27sTflyv2O4PbFDmbUtBEUkblcNVSyvgf4GViTbBhr5lkj5VEsZRJWGcBPyhvCuAVWOYbipJ1o+bxPkSMKI8/87E33sfmr/zKqsSxtCfA36pBmbVSh+yJEmSJEmSpItky+LsYSsiaRlR9jP1AMaWaxNs31yORwNv254iaTTQqxJjqO03AUrlZmPi+2oWdzJwbeX+LSUdDixAVKceBP7Rwef4q+2J5Xhr4LOSqgngKsBzzW6WtCxwEdG++ICkx4AriD1gIvZ/zUS1FbQS6zuEz9k+xHv4PLAhMBG4TdII23fUxRkq6VbgHdsrlzi/ovk7r/K3krC+IukNwrx6hjbP9CFLkiRJkiRJukomZLMHARfY/sUMJ6Ndr1qVmgZMqhxXv49GkvKt4k6oKQFKWhAYDKxn+3lJxxGJWSOmML1SWj/mvbpnOsD2rbQDST2JKtgRth8op9cjkriny+dFJT1he7V2hPwjcBqRkI0Dbi+JG5L+UWLf0eTe9r7zKpMqx1NbjEuSJEmSJEmSTpP/yJw93AIMkXSa7dcUaowL0TF/ra3L3q7JwE7AQCIxaE/cHkSy8ZqkRYCvMd0o+V1gkcrYMcD6wM1lXDOGAgdIuqNUl1YDnrM9oX6gpPmBvwJ/sP1h1c72dYTMfS2JfK1VMiZpFdtPlY87AE+U438Ah0rqAXwAbAqc0GLtSZIkSTJLSAXFJElmNZmQzQaK8t8xwC2SuhFJw/50LCG7m6gK9QYutT0KoD1xbb8u6WLgUWLP1vDK5QuB8yVNIPapDQJ+L+klWvt/nQusAIwq7ZKvEIliI74FbAQsJmmfcm73mghHB/ihpM2J53wd2KvyfKcTbZjTgOttD+1g7CRJkiTpEO2RvIdM2pIk6Ria2/1uJU0l9nJ1B54lEoe3ZmH8/YH3i9R6e+85EVjBdlMlw9mNpF6EeMgfO3n/jYQgxt22t6+cvxzoRyRZ9wPfs/1BkxgDCbNngPHA920/XLk+DyHa8Xx1jk6udxngdNu7dOb+fv36ecSIEV1ZQpIkSTKHkwlZkiQdQdKDtvu1NS5VFmPvVV/baxEGw7NU4tz2OY2SsdKy14zVgKU7Mk8b8TpzTy9gt47GrHAisHuD85cDqxPqjz2AfVvEeBbYzHYf4FiKgEaFHxDm1F3G9gudTcaSJEmSJEmSpLNkQjYjw4Blax8kHV68vB4prYJIWkjS3yU9XDy+di3nx0j6TfHpul9STdVvkKTDyvHtkn4l6Q7gB5J2kDRc0kOSbpG0VKlM9Qd6F9n3TSStKOnWso5bJa1Q4l2k8Aa7DThR0lOSlijXukn6j8I0mcozDZJ0nqSbgEsk9ZJ0V5GPH1mRqT8B2KSs4VBJ8yi8vGrv43sKKf5RdT/3AhTxj3frX7DtG1wgKmTLSTq6QZwjbd9bU5oE7gOWqzzHcsB2wPmVcyMaxFmjfDe/kjSsjFlP4Uf2dKlgUt7Do+V4T0nXSLqxvNPfNvpjUcreJ0mSJEmSJF0k95AVSvvbloTfFZK2JhQB+xMKg9dJ2pSQP3/B9nZlXM9KmHds91dItJ8KNGqjW8z2ZuXeTwCfs21J+wI/sf1jSecA422fVMZdD1xi+2JJewOnA18p8VYFtrI9VdJbhPjHqYT0/sO2G0nLrw9sbHuCQpHxi7YnSlqFkKXvRxg3H1ZrBZS0HyEXv4FCtOMewk+tb4P4bSJpXqKC9gPbd9HcD6zGPswo238q8BMqAiXNSsJlz9tY2wMknUJI8X+eUJV8DDinwW19CR+2ScATks6wPbY6IGXvkyRJkiRJkq6SFTLooTAsfh34JKE2COG7tTXwEDCSaLNbhdhvtlWphm1i++1KrCsqvwc0me+qyvFywFCFH9bhwJpN7hlACHwAXEp4ktW42vbUcnwB8J1yvDch4NGI6yrqiPMSoh6jgauBNZrcszXwnfKuhgOfIt5HZzkLuLMkYy2R9AUiITuifN6eMKl+sAPzXVd+jwaG237X9qvARIWaZT232n67+LA9DqzYgbmSJEmSJEmSpF1khazsISuVrr8Re8hOJ6piv7Z9bv0NktYHtgV+Lekm27XqTrVK0qxiUvX2OgM42fZ1CjXBQe1cczX2h/Fsj5X0sqQtCNPkge1Yw6HAy8A6RII+seEd8T4OnhVqhpL+j6g0fq8dY/sQbYlfrvmOEdWtHSVtS1S5FpV0me1vtwhV9R6reow18yJLH7IkSZJkBlKsI0mS2UFWyAql0nUIcFhppxsK7C1pYQBJy0paUqHG977ty4CTCEPiGrtWfg9rx7Q9gefL8R6V8/VeYfcC3yzHAwlJ/GacD1wG/KlSOWtrDS/anka0EM7TZA1Dge+Xd4OkVSUt1I74M1BaM78EfKvM2WrsCsA1hPLlk7Xztn9qeznbvYj38s82krEkSZIkSZIkmSP52P9f/yL+cCbRiteNqIIdbnty/VjbD0l6GPim7UslfRYYVvYgjQe+DaxMCGhMI6Tbv18JMb+k4WWeb7VjeYOAqyU9T4hWrFTOX08YQO8EHEwkihdIOpxIlK5sEfM6olWxWbtiPWcB/5S0G3AT8J6k84kq4ZTyPi4CTiOUF0cqXsirTN/HNhOS7iLaPBeWNA54mhBM6Q08x/T3ek2lwojC7HoIsAHhofYp4Kwy54rAS0TF6nrbR7bzGZMkSZKky6TsfZIks4OPtQ9Z+Uf8cOBs2xcW4Y7zgDdsHz6L5xoD9GsiojEr59mcithGg+v9gFNsb1J3fp5mFTNJt5eYs8VIS9IBQB/b+0v6JrBzM4+1UnVbF1gLWMv2QeX8gsCGtm+TNB9wK/Ar2/9oFOd/TfqQJUmSfPzJhCxJko6g9CEDYAtgou0LAUpCcijRirigpB9JugBA0toKGfsF1UQ+XlJvSfcppN9/KWl8Za5FgZs1o0R+L0n/kvR7SY9JuklSj/pFSvp6mfthSXdW7m2PHP2ekgaXe44E7gT+VD6PL+scDgxQyMs/UOY6T8EuhKri5SVmD4U8f78S41uSRpd7flNZ83hJx5c13ydpqRbfw07AxeV4CLBlSZZnwvZ7tu+mbi+b7fdt31aOJxNCK8s1CFFb30WSzpZ0m6RnJG0m6YLyfVxUGXe2Qrr+scr31lPSE5JWK5+vkPTdFs+XJEmSJEmSJJ3i456QrQnMoMRn+x2iZW5lQjp9ZUk7Ey1+37P9PrEHqyaIUZWPPw04zfYGwAu1mAqJ/CHEfrK+wPoKiXwIJcIzba8JvAV8rcE6jwa+ZHsdYMdy7hVCjn49Yk/a6eX8kcBdxcz6lLpnOwH4J6EkCLAQ8KjtDUuSM9j2BsUEuwewve0hwAhgYIlZU1+k7Jf7DZHY9gU2kPSVSuwXCYGRlYCa79fwBs+3LDC2rHEK8DbRilibZy/V+YfR2FS6Nn4xYAeiSlY9f23l/h2J/WUnEEn49cApxN/E2pJqcv1Hlf9z0QfYTFKfsp/wIOCiUtH7hO3fN1hH+pAlSZIkSZIkXeLjnpCJxmqHAlxEJfYkpOTvsH1Pud5MPn4AIQ0P02XooblEPsCztkeV4weJfVj13EP84/+7TBfVaK8cfSumAn+ufP6Cwoh6NJFkNZPZr7EBcLvtV0sidTlQSzQnE4lmX2KP299LQrdhgziNqmEffi+2Lyz3fvhDfCczB5K6E7YCp9t+ZoaA9s6V+68DDiqqkKOBl22PLt/5Y0z/Hr4haSTx3a1Jec+2by73nQns22gtts+z3c92vyWWWKLRkCRJkiRJkiRpycdd1OMx6ipSkhYFlidEJiASp/HAMrUxHZCP/zAsDSTyJfViZvn0mVoWy96qDYHtiEpTX0LMoz1y9FOYMbFeoHI8sbZvTNIChIBHv/J8g+rGNnuuZnzg6RsQ25KFH0e883EloeoJvNHG3M04D3jK/4+9Mw+3c7ze8P0QU6Vo0YlG1FRFBBFDUUVVq6WTUmqsqpqKmlqqZn6lVM2q5ilFTa02McWYxBiJ1lhSQtU8hBjz/P5Y7+d8Z2fvffbJoMK6r+tcZ+/3e793+PbJde2Vtd7nsX/XQd+2UveSFgb2AFa0/UIpZZwdolQVWBKYSPjTjZ/C9SZJkiRJkiRJSz7oAdm1wBGStrB9tkLU47fAmbZfU3iPHUtkfY6X9L1Swgdd8vHn1MQwRhIB3hC6ZOghJOEPlnSe7QmSFiAUGDtC0iK2RwGjJH2TCF7mBsbbniRpS1rL0Y8DdigBxALA4BbTVMHXswop/+8RZZbNxqwYBRwraT7gBUI58rhO91XjCkLWf0SZ97paMNcxkg4hnkvTjNUUMBfhyfZSOQP3NWB4ubYbcB/wS0LhchXbHX+mSZIkyQePFOtIkmR68IEOyGy7nA87UdKviEzSVcSXbIgzRSfaflDSj4DrJd1o+2may8fvCpwr6efAX4mzUNgepuYS+Z34gEHI6C9GZKSuBe4hslmXSNoIuJ4uM+cxdJej/x3wKFFedy9RMtnsWbwo6Q+l3zjg9trlM4GTJU0kyjKre/4j6RdlfgFX2b68wz3V+SNwjqSHiczYJu06KxQr5wJmLWfW1gVeBvYF7iek9yHOxJ02BesBwPY9ku4mMqmPEKWjSFqcCPoG235FIbSyH/DrKZ0rSZIkSZIkSZrxgZa9nxrURD5eIb0+sQR6mxDmxhs2uXdeugQnPkUEZpXqw+BmHmj/ayRtQwRcT03neX5ECJMAHFgMtlv1PYIIbPvanqfWvhewNXGO7WlgG9uPT+W6dgRetH3elNyfsvdJkiQffFL2PkmS3qAOZe8/0BmyKUUhH/9TJj87tgJR2ihCMXGbZvfbfo5QJaSc1Zpg+6jptuAJcoJMAAAgAElEQVQOURsvMmIvdxHGy52O16eIfXTafz4iO7kCUYJ5h6Qri6phMy4Hjicyf3XuAI6zPVHSzoSSYk/n/Npi+4SpuT9JkiRJkiRJpoQPusriFGH7CNsLFan4evtNtpe1PcD2GrYf7u3YkraUdFuRZz9R4XPWR9KLko5UeI4NlbSSpBuKh9bXy73bFmn3ocUna78Oxz1E0m3AYEkHqsuL7GQFGxMB5JBy/6ySxhd5eSStLOma8voQSadIuho4o8xxdJn7KUmPqbuE/b617X8N+LvtFwnvs7nokssfLenShuc9giYBou3ravL8SwIbtppT0joKL7KLFf5yh0jaojyDMQrhlWpfu5bXN0s6ouzpAXV5wCVJkiRJkiTJNCUzZO8hkpYGvg2savttSacS56n+RIhVDLO9p6QrgQOAtQmVxVOIs28Qoh1LE+V6t0v6C6G02G7cu2zvV9bwgO1flyzf+cB6toeUTNNOlUS/mvs2VywHrGH7dUk7AE/bHixpNkL4ZEPbjzW5r+5HNlRhaP1Ch4qJrZgJOKR4sLViWSJwe4k4P3ei7RXLWcCdCKXFRlT2tAHhE7feZB2k7YDtAPr16zcVW0iSJEmSJEk+rGRA9t6yDuHtdUcJeOagBCjE2bSry+uxwEsluBpLd++yobZfAJB0GbAa8Tm2GvdNoJ55WlvSnoTq4nyEN9rfermPy21XMvzrAkuWM3UQAeBihPl2I8184ab4EKNCfXIZwgetHaNs/7fc8wihignxnFdpcc+fy+9W3nHYPpWQ4WfQoEF5GDNJkiRJkiTpNRmQvbcION32r7o1hjdXXeij7ps1ie6fU7OApt24EyuJ+SJKcjywvO0nFDLyrbzI6v5mjX1erb0WsIPta+mZ8cDKtfcLMvn5sI6QtB6wF/ClDkRSGj3IWj3bZvf05LGWJEmSJEmSJFNMftF8b7kGuFjSsbafLWqMcwJP9mKMdcvZrjeBDQkxi3c6HHcOIgh5VtJHCU+1SlWwmb/ZCsDVNJhrNzCU8EG7oWT0lgAeq53xqvN34MCy/pmJkszdO9z3uygUME8A1rX9bG/vT5IkSZIpIdUTkySZHmRA9h5ie6ykA4FrFEbObwHb07uA7Gbi7NcihGl1dearx3FtPyfpLCIr9W/C+LniDOA0hRfZYOIM2x8kPQXc1mY9pwD9CHEOCBn6yawAyvzPSDqcUEkE2L+NwiKSjga+D8wlaTxwsu1DgKOIgPOSMuejtr/dZo1JkiRJ8p6R8vhJkvSGaepDVtTtNiUyNpOAn9ge1f6uycbYihC3aBukSBoO7GH7DklXAZsW9b6exj+AJjL0RW3vL7aX7s16e1rb1I7VMO62wNK2d52W4/Zi/oHASYQ64jvAobaHNPQ5Dtjadt8242wP7FjGmABsZ/uf5doAIsibi/gbWrF2Xu19S/qQJUmSJBUZkCVJAv8DHzJJqwDfIM4nvaHwnJp1CobaisjgdJw1sv31KZjnfYl66e31Hs/1GrCF7YckfQa4U9LQKhAupYTztB0hON/2yeWeDYCjgfXKmbdzgc1t31NKL9/qzZ6SJEmSJEmSZEZiWvqQfRp41vYbALaftf2kpLXr/lKSviLpz5JmlnSmwg9rrKTdJH2P8Kc6r/hJzVHuv7v0Ob1Iq3dD0rgSAFI8psZIukfSOS3Wuqyk64ov1Y+bjDe7pDPKnHdL+nIP7XNIurDMO4Q4qzUZZZ3/V/ytbpO0aGk/U+HldT3wf5LmLHu9vcxTlQCOAFYtz2aMpMVK37+W/d6r8BRrfCaDStYOSQdIOlXSMODs8jkcqS5frp+0+oBtP2j7ofL6SaI8cf4y7szAkYTQxkzq7gs2WtKttXFerg07J7CKpNHAA4TQx1mSvmD7uTZG1kiaUJ7nnZJGSnqwtL2h8G+7tdX+JPWVdK3C921s9Ywl9Zd0n6Q/SPqHpGGSWn2e20m6Q9IdzzzzTKtlJkmSJEmSJElLpuUZsmHA/pIeJMQrhti+AbgOOEHS/LafAbYmzisNBBaoSgQlzWP7RUk70VWKODtwJrC27QclnQ38FGjqWyVpKWBf4ItF3OLjLdY6gFD7mxO4W1JjbcGOALaXkfR5YJikxdu0/xR4zfYARcndXW2e08vF32qLso9vlPbFgXVsvyPpMOA629soBDBuUxgzbw8ca/s8SbMSwhhfB560vX55BnO3mbtiBWA12xMVXlovFV+u2YBbJA2z/Wi7ASQNJjKg/ypNOwFX2P6PpEm2B/Zw/46EoMeswKCSddu1rO0TwLmSLrT9mzbDzAkMt723Iuh/GVgK+AJwlu1VW+2PsAX4tu2XS+A6UtIVZdzFgB/Y/rGkPxGiJuc2Tp6y90mSJEmSJMnUMs0yZLYnEF+mtwOeAYZI2qpIrp8D/LAEF6sQvlePAJ+TdJxCwvzlJsMuQQg2PFjenwWs0WYZawEXV8p7tp9v0e9y2xNLv+sJEYs6q5U1Y/t+QgBj8Tbta1C+sNseA4xps8YLar/rHlgX1bJB6wL7lKzRcEJ2vh+RIfulpL2BhYqS4VhgnZIpWr2dSEaNK2oqiOsCW5S5RgHzEgFJSyR9mngOW9uepChf3Ag4roO5AbB9gu1FgL2B/UpzH+IZb1Z+f1vS2m2GeZNQboR4DjfYfqu87t/D/gQcJmkM8R8ICwCfLPc8Woml0MaHLEmSJEmSJEmmlmmqslgCiuHAcIWh8ZZEhusM4ErgdSLweBt4QdKywFeJzNP3gW0ahlQvl9DMeLjpUnt432reduvpNEPiFq8bvb2+a/uBhnvvkzQKWB8YKmlb29dJWoHIlB1eslsH0TsfsZ1tD6UDJM0F/BXYz/bI0rwcsCjwsEL18COSHra9aAdDXkgIhUD4lN1QBdQKsZblgVYeZ29VHmvU/MVKkFj9bTfdn0I8Zn5gBdtvSRpH13Oq+5a9Q4sS1CRJkiRpRop1JEnSG6ZZhkzSEpLqmZWBRAapOm/0JJEJObP0nw+YyfYlwK+IL97Q3Q/rfqC/ylkrYHPghjbLuBb4vkIMgjYlixsqzoPNC6wJ3N5w/UYiS0MpSexHnG/qpH1poiSyFRvXfo9o0WcosLNKdCNpufL7c8Ajtn8PXAEMKNmp12yfS8jBV89xHJGxhJ59xH4qaZZqX5LmbNaxlEleCpxt+6Kq3fZfbX/Kdn/b/ct6WgZjDX8n6wMP1dYyQNJHSkD1JeCfbdbeCa32NzfwdAnGvgwsNJXzJEmSJEmSJEmvmZYZsr7AcaUs8W3gYWBfSecT57UgpMyXIL5kbwgcLak6q/SL8vtM4GSFH9YqxJmzi8oX9NuBk1stwPY/JB0K3CDpHeBuQrWxkduILE8/4OAiPtK/dv3EsoaxZS9bFeXIVu0nAWeU8rfRtPftmq1kuWYCftCiz8HE+bIxJSgbR5w125go/XwLeAo4CFgROFLSJGAZYKUyxoHAHyX9ku5+Y+9Srh1BlOTdVeZ6BvhWrc88hKXAiUQWcw1g3pJhojyDygttQiu5e3WXzP9k+XyfA14gMqmU/XyB+JwNXGX7rw3j1CXz51CIf1RB26cljShzzFHOIJ7WYn8GtpH0IyJzO76McRSwuKR7iMzYy7T/T4AkSZIk6UansveQ2bQkSaaxD1m3gePL762EuMLJko4HHgMm2j5O0pqEeMc32o3zQaKUxQ2qSvKmw/gtA6Ip7a9e+LP1EJAtDrgumQ8s2SCZ/zNCaKOdh9lclUqjQjJ/B9uVZP5ddJfMf7GZSqOkBYkga3nbL0nqC8xv+1FJZ5b9Xlz+hnclRFuWtv1mq3WlD1mSJElSkQFZkiTQuQ/ZtJS9b2Qt4M0SjN1JlPEda3sy4QdJgxUS5XeX30uU9qUU8vDvyryX9smk7SUtpJAxH1N+91NInj+iYB5JkyStUfrfVCuFrNYxs6SjFDLoYyTtXNqbSu8rpOUPkzRCIX++vKShkv5VMjnd5NWBzwDrNdn/ZBYApX1RSdeUfd4laRG1kGtvMuae6pJ6P7DJ9SOILNJoSeeVtt3LGu5VKB5CZNAWKf2O7HT+Rnohmd/TOI2S+dX/KKwLjLF9T+nXTjL/E0Rp7ITSd0IzVUkHxxDZyK/1uMkkSZIkSZIk6SXTVNSjgaUo8u+2V+ih7/3AGrbflrQOcBhx7mkymXe1lrY/njjbdJakbYDf2/6WQob/C8DCRFZmdUXJ4IK2H25Yx3al33JlLR9Xz9L7j9teRdIxpd8XCXGIfxDlla8zubz6eTUxCmhiAVDazwOOsH1pWcdMhLLgZHLt9fEkrUsoCQ4mRC2ukLSG7RurPrb3kbRTJU+vEAbZmih5XIoo+/wpURoI8IbtPUsmqu38tXXsS6gv1rkIuJrWkvmNw1A+r7r/3Hx0BWJrld+LA5Y0lAj02knm3wP8F3hU0rXAn21f2aIvxN/x54HLG9a1HfE3Q79+/drcniRJkiRJkiTNmZ4BWTcknUBImb9pe8WGy3MTZsCLEV+0ZyntI4hzaAsSX5ofktRK2n4V4Dvl9TlA9WX8JuLc08LA4cCPiXK1RiEPgHWAk4sKJLafVyhBNkrv70hXQFZ5V40F+tp+BXhF0uslsHqVkFdfg1ACrOTVn6rN+64FAHG2bZikjxJB2qVlLa+X5zhLB+OtW37uLu/7EgHajbRmNeBS268SvmdHEeetriBK+KqzaZVcfLv5KWs+FDi03qaQzB8ObNkgmb9mq4XV5u6GpE0JoZgt6ZLMXxF4DbhWkSaeTKHR4fW2Xum7NnCMpBVsH9BiCU3VNZ0+ZEmSJEmSJMlUMj1LFv9Bl+IftnckvvzO36TvwcD1JUP0TYr8uO3zgQ2AiYTM+1r0Xtr+JmB1Ilt0FTAP8eW/WXDSbOyepPcrifRJdJdLn0QECZvRJa8+kMjMdJOht/0CsCwRqOxICFG0mrfH8cq9h9seWH4Wtf3HHvbRqcVAJ/M3n6BnyfxxFMn8DtdyIV0CJO9K5tt+jfisl291YylHvM324cAmtFeiXA64r8M1JUmSJEmSJEnHTM8M2XVEJuWntiufqY+06Ds38ER5vVXVqJrMe3k9gCh3u1TSMbafk/TxkiW7lfhifQ4RNNxchhkFnF3GeV1hEPwTQrWwkWHA9pKGVyWL1KT3S4ljT9L7zfbWVl69lP69afsSSf8CziwlgeMlfcv2ZeXc2sydjEdIvR9cSiMnSFqA8Ox6uqHfW5JmcZgp3wicWc6WCfh22WvdhqCj/TRDbSTzgU/V+k1wD5L51Vk0JpfM30vSR4iyzi8Bx7QY4zPAp2zfVZretWho6CdgZ+DTdBlQJ0mSJElbUqgjSZLeMN0CMtuW9C2iHGwvovztVWDvJt1/Q5Qs7k4EchWTybyXMsJm0va7AKdL2rPMtXVZxxuSHgeqjMxNhNz82CbrOI04izSmzPkH28dL6lh6vwnnAVdKuoOQxL+/SZ8FCNn8KmNZWQBsDpwi6SDgLaK0r8fxbA+TtCQwopzJmgD8kBDSqHNq2etdtjdTKAxWkv2n2b4bQNItku4F/gb8Xwf7aUZbyfxesFM5Z/gWNcl82y9IOpo2kvk1ZgGOKoHZ68Tfy/a160dK+hXxHwgjgS+3U1hMkiRJkjqpspgkSW+Y3mfIJpUfE9md2YnsRTdsjyACoYpflfbDiXNfjf3PIs5y1dvG0SXw0Nh/9drr84HzW/R7G9i9/NTbryXK1hr796+9PpNiet14jTjf1pKiDDhZeV3JBDXbU9vxyr3HAsf20O0l20vW7jkaOLreoZyDu9n2pr2Zvwk3AWOIv4NZgOPc5V82KyHKsiYwXtJ3HYbhk2H7Z+We7xECIXOU97MAX6GrVPSFVgux/e8i5rEpUbY7PyEU8i/Cs+xNIkv4DvASRY0xSZIkSZIkSaY10+0MWSn3ugy40fbnitLiJsCC02vOpNf8soM+8wA7TIO5/gOsWs6drQTsUzJUEKqZT9tenFDEbFsSWgRPdqG74fVGwGy2lwFWAH6i7mbf9ftXIUpWl7c9gBBzebzWZbPSPoA4F3j55KMkSZIkSZIkydTznviQVQ22/+0PsQ+ZWvh26QPkQyZpmdK3/jPK9pu2K9GT2ej+t7cNJRNqe5LDzmDfJuPsW/ofTJS5vl4bw8Ccpax0DiLL9bKkUY3jEAHhs9V6ihDIk43PqJQp7gX0U6htNj7D7crnfsczzzzT7GNIkiRJkiRJkra8Jz5kHfBh8yFr9O16v/mQCRgl6QZgH2DpWr+2PmS2x5b9TIakzxIqi4sCe9p+srbXgyWtSZQN7tRMMr+MsRzwWdt/kbRH7dLFwIZEJu4jwG5F7GUyyXxJfYGtyt/GNcAQ202zckUi/x7Ch+yehmspe58kSZIkSZJMFdMzQ9YNSSeUTE8z/6+5CdGMewllvKVK+wjgl5L2BhayPZHIvLXyIavOhp1DeFJBlw/ZGkQWpvKq6siHDFiCyX3I1qjdU/chG2X7FdvPAJUPWeXbNYb48l/5dtV514dM4Y/1spr4kBU5907Gq/uQVabGizXZb513fchsTwD+TNgFNNLJ/E2x/XgpBVwU2FLSJ4n/FFgQuMX28sRnflSz+xWiJ8cAP29yeTBx5uszRFD9c4UyZ7N1TCDKGrcjBD2GqEtopOnUPe8uSZIkSZIkSXpP+pB1J33IemaKfcgqSnngP4jP5TnCyPnScvkiWvuHfRRYGhiu8Cxbmcj+DSIEOv5uu5L3vwUY1GYN79gebvvXwE608CGTNDOwDOlDliRJkiRJkkwH0oesO+lDNv18yBYEnrM9UdLHiNLOo21b0pVEkHwdEbT/s9kYtl8i1BCrMYcDe9i+Q9LawFqSziX+zlamq6y0cS1LAJPc5WfWyodsFqJs8nHbYzrZZ5IkSZKklH2SJL0hfci6kz5k08+HbEngt5JMBHxHlfNmEH8T50j6HbXPrpecAJwB3FvGP6NNENUXOK6UlL4NPEyUL1acJ+kNQnzkGuJsWpIkSZJ0RG98yCADuCT5sKPu2hJJT0iaF7i2vP0UcW6pktgb7PehgXARObnK9lPTcY6FgUuIMtg+hBjLH8q12YiAaXWiJHQf25f1MN4mwAWEwMroIupyOlHa2YcIuH4zvfbTWwYNGuQ77rjjf72MJEmS5H1ABmRJkgBIutN2yyM0FdPbGPoDh+3nKCqCkg4AJthuKkLxXiJpZtvvtLi8DSHu0XFAJqlPJW7SIU8Cq5SM5FzAvZIuL2WS+wPjbS9RsoAf62HuuQjvs3qEswmA7WUkzQncL+l82+N7scYkSZIkSZIkeV/xnqksfhiQtKW6fNNOlDSTpD6SXlR4eN2l8ClbSdINCo+0r5d7t5V0abn+gKT9Ohz3EEm3AYMlHajwHrtX0skKNiYCyCHl/lnL2bR5ytgrS7qmvD5E0imSriZKKPtIOrrMPUbStq32bvuNEowtQwhqfAq4rpzZ25Modax8xp7r4VEeRihPLlmtmxB+WU4hsjEHYSfwSovPYdHyDM6TNFHS8wpvuFclvV7Om1V7H6HwmLtFXT53e0k6tbweqPBbm6PJPOlDliRJkiRJkkwVGZBNIyQtTQhhrFrUB/tQsjqEEMawIuv+JnAAIV6xEXBQbZjB5Z7lgU1LMNDTuHfZHmx7BFEmuCKhCjg3sJ7tIcRZr42L4mJPJZXLAd+0vTlxrupp24MJq4AdJfVr8wz6E2fcFgF2KaqZ6xCCLYeXgHSIpGZKm9UYKwKfsL1Vfd2EVP4/CZ+xfxP+bC+12ccShCDHnMBjwHW25yQEX35a+twHrGZ7OSLgO6S0HwUspTC9PgP4cbFc6IbtU20Psj1o/vlbbilJkiRJkiRJWpIli9OOdYig5Y4ipDEH8Hi5NtH21eX1WOClouI4FuhfG2NokcBH0mWEN1ifNuO+SZdcPMDaClGT2Qk1wjsJIY7ecLnt18vrdYEly3kuiCBvMSLAmQzb44ABClXHyyRdXNbfn7A12FUh8PIbmgh3lHLG3xICJI2sQmTFFgA+Dtwk6Rrbk6kjFh62/c8y7j8JcQ6I51+JpswDnC1pkYZ9TFL4ko0Gjrc9kiRJkiRJkiSZDmRANu0QcLrtX3VrDGXGelaq7ldWeZVVNCqsVIqErcad6KLKIukjwPHA8rafkHQIrf3B3qYrO9rY59WGPe1g+1p6QZn/fiKgvILwGasMtC8CWgl6zAN8AbixBJ+fAq6StD6R2bqqSPT/V9JIwty5VUDW6AnX7JkfSgTBJ0paFPh77Z7FCHXKz/S84yRJkiTpIkU6kiTpDVmyOO24Bvi+wlMMSfO2K+9rwbqS5inB1YbEWaxOx52DCDaelfRRuhsdN3qJjSOCGWhhiFwYCuxQgj8kLdHsLFW5tqCk2as1EhmtB21PIrJ0q5eu7XzGnrc9n+3+tvsToh5fL/L7jwFrlfH7AisBD7RZeye08r+bBziG8EpbQGHfkCRJkiRJkiTTnMyQTSNsj5V0IHBNKb17C9ieUB/slJuB84kzWOfYHg3QZNwHqCkQShpKlDGeRfhwzQr8tzbuGcBpkiYS59TmA06U9BRdvmPdkDSu9O0HjC4Zq6eBIyW9ZPvWhluWLtcmAfOWtvMlTQAOBA6VNDeRLZtF0j+IAHLFWolkUyRdQZwhu6/cNzthkn2epLeJLF7TffTA/xHedXsRZtKzlPbfE+fxHlZ40F0j6Wbbz07BHEmSJMkMTG8l7CEzZEmS9I70IXufUBQMl7a9awd9NwI2sv39EqTdDrxpe5VyfQSwq+1RU7GeccCgxiBEHUj9S5rL9svl9QZEwLReybTdBWxu+56SSXuxjVw/kr4DfA8YUERCkDQMOMb23xQqlXvZXnNK9zotSB+yJEmSDyYZkCVJMqWoQx+yLFmcMbkFWLW8XorIir0i6WMKE+Ylgbsl7amQwR9TsmwAlKwVCvn8EyX9Q9JfJF0l6Xu1eXYuyohjJX2+qChuD+ymkNBfnSZUwVhhTrrOxq0LjLF9T+n3XA/BWF9gd7rUD9+dApirvJ6bNllISQdIOkvSMEnjJH1H0m/Knv4uaZbSb7ikQdXzkXSopHskjZT0yVbjJ0mSJEmSJMnUkAHZ+wTbp3WSHSt9nwTeLmfJVgVGAKOIc1uDgDHAmoQwxWDCh2wFSWs0DPUdQgFxGWDbcn+dZ4tU/0nAHkVFsS7IcVwJzBrLF5G0o6R/EYqKu5TmxQErvNaeLD+jaz/7NAxzMKG6+FpD+65EeeQTRJnmsg3jzNPQfxFgfeJc3rmE4uMywMTS3sicwEjbywI3Aj9u0id9yJIkSZIkSZKpJgOyGZcqS1YFZCNq728lslHrAncTZYKfJwK0OqsBFxWz5qeA6xuu/7n8vpMuef6niHLBgbWfVRvuw/YJthcB9gYqk+s+Zc7NiDNh/wZ+XhvniOp+SQOBRW1fyuT8FNjN9gKERP5/GtbzYkP/vxV1xrHE2bNKTbHRdqDiTeAvTfbeuMf0IUuSJEmSJEmmigzIZlxuJYKvZYiSxZFEhmtVIlgTcHgtSFnU9h8bxlAPc1RS8e8w5QIwFwKVSuF44Abbz9p+DbiKMMFuxipEVm8cIXayuKTh5dqWdAWLFxFZwHa8AeEvBrxVWQUwue1ARb3P1Ow9SZIkSZIkSdqSXzRnXG4Bfg48Us5hPV9K9ZYiSuzeAA6WdJ7tCQqz5rdsP10b42ZgS0lnAfMTZY7n9zDvK3Sd32qKpMVsP1Terg9Ur4cCexVZ/zeBLxHy8pNh+ySiVJJydu0vNeGOJ8u9wwkp/IcmGyBJkiRJpgEp0JEkyfQmA7IZl7GEfP35DW19izLiMElLAiOKZP0EoryvHpBdQviC3Qs8SJxDe6mHea8ELpa0IbCz7Zua9NlJ0jqERP8LREYL2y9IOppQhTRh9Nx7+aoIOI8tqo2vA9tNwRhJkiRJMl1IZcYkSXpDyt43ocixX1vefoooW6tUGwbbfvN/srA2SNqGCHCe6uV9fUsGbV7Ck+yLrcYoEvt/I0yZh9v+Vu3ahcByRBA2Etje9tstxtkC2JMIyl4pfcfWrlfy+I/U53g/k7L3SZIkSUUGZEmSQMreTxVFjn2g7YHAyXQXsfifBWOSZm5zeRsieOzNeH2Av0gaDdwEHNxDQGdCNXGrJtfOJoRDBhBS9Fu3GedfwOq2BwBHEM+4zu5E1i5JkiRJkiRJPtBkQNZLJG0p6bYir35i8fLqI+lFSUcW366hklaSdIOkR4p5MZK2lXRpuf6ApP06HPcQSbcBgyUdWLzF7pV0soKNCWn7IeX+WSWNr+TfJa0s6Zry+hBJp0i6GjgDWAe4jihp3F1hUN0UB9eWvkjat5KaBw4jFB1/QWTaFmwzzi01JcSRwKCaZP0/gF8xueJjs89ifPELGynp0fJMX5b0hqTHJJ1Q+u1Tnu0YSfvX7r9S0p0KH7ZtS1v1zI8oPmQjJH2ixfwpe58kSZIkSZJMFRmQ9QJJSwPfBlYt2bM+wCbl8tzAsOLb9SZwAHE+ayPgoNowg8s9ywObShrYwbh32R5sewRwrO0VCXXFuYH1bA8BRgMbd5jFWw74pu3NifNXT9seDKwI7KjwN+sR24c2yM0PBI4kZO3/3sPtFT8Czqjd/yCwOvBoh/ePs70yIVP/GvBpoB8wq+0dSzDcjyizHAisKqmS6d/S9grEvneX9LHSPjehBrksYSewTYv9p+x9kiRJkiRJMlWkqEfvWIf48n5HEcqYA3i8XJto++ryeizwku23JTV6XQ21/QKApMsIX64+bcZ9E6h7ca0taU9gdkLU407iXFdvuNz26+X1usCSkuoB4GLAY70cs+Jk4JoSPLalCH9sTjwDJH0LeNz26HKtE64ov8cCfWy/CrwqaZKkvsT+vkZk7wD6EgbVtwK7SdqgtC9IGEiPJj7L6pneSQSISZIkSZIkSTLNyYCsdwg43favujXGWax6VmoSXR5ejV5XjSoq7mHciZUnVpGLPx5Y3vYTkvj3cMUAACAASURBVA4hArNmvE1XBrSxz6sNe9qhlCJOFZIOJgK6lmWPtb4DgVOAr1YBKuGh9p0SJM0OzCXpLNtbthmq/pzfqLVXz13AIY0ebCXgWwNY2fZESTfT9Zzqn2X6kCVJkiS9IgU6kiTpDVmy2DuuAb4vaT4INcZOy/tqrCtpnhJcbUj4iXU67hxEoPGspI8C361dewX4aO39OGCF8rrer5GhwA4l+EPSEpLm6OWekLQ94WO2WTFgbte3P3AxsKnth6t223vZXtB2f0Kif1gPwVgnDAV+JGnOMveC5TnPDTxfgrGliAxlkiRJkiRJkryn5P/89wLbYyUdCFyjkIB/C9ieMCrulJsJ77BFgHNsjwboZFzbzylMnO8F/k34hlWcAZwmaSJxTu0A4A+SniJENlpxCnHGanQpl3yaCBSbImkEsCjQV9J4wmNsOJG5GweMLONcZPvQFsMcAHwcOKX0fcP2Sm3WOMXYvkrS52vregXYFPgrsJ2ke4D76f4skyRJkmSKmRLZe8jMWpJ8WJkhfcgkGTi3iFJUpX3/AUbZ/sZ0nHc4sIftOxratwIG2d6ph/u3BZa2vWubPhNs950Gy31fUYKiMwgxk31tH1XaP0tI5n+KyP6davvYNuMcCXyTKCv8F7B1TbGRkln8J3BANUeLcfYlArN3yrw/sT2qfMafJsofZyWyl/vV52hG+pAlSZIkFRmQJUkCH3wfsleBpWuldV8Bnvgfrud9gdr7lE3ruXqbXX0e2AVoDJLeBn5ue0lgZULl8QttxrmaCGoHEIqMv2i4fgw9iJxIWgX4BnEWbwAh1vJ4rctmpX0AEZhd3m68JEmSJEmSJJlSZtSADOJLd/VfST8ALqguSJpT0ukKv667JW1Y2vtLuknhFXZXJX8uaU1JwyVdLOl+Seep1Lc14YeSblX4gA1uvChpIUnXFs+ra6uzYJIWKutcq6F94eJ1dXsRxZiMsu77JZ1Vxr24nEFD0jhJ+xdRio0kLSLp7wp/rZtKZgpJG5U13yPpxtK2lLq8z8ZIWqzM9bC6fMGekPRU2fNwSYdJugH4maT5JV1S1n67pC82rHtbdfmUDQP+QAQ/72L7P7bvKq9fAe4DFmgY54raOL8h1CjXITzMFqz1+xbwCPCPFp9dxaeBZ22/UeZ91vZkZafFPmAvoJ+kZZt8LulDliRJkiRJkkwVM3JAdiGwiaTZiUxG/QzQvsB1xa/ry8CRRdThaeArxStsY+D3tXuWA3YFvgB8DugWXNSY0/aqwA7A6U2uHw+cXTIs59XmaNV+LHBSWetTbfa7BFHONwB4ucxf8brt1WxfCJwK7Fz8tfYATix99icUDZcFKqn37Qlfs4HAIGB8bbzKF+wY4OSyZ4B5bH/J9m/L2o8pa/8ucFp9wbZPa+JT1jJ7VcQ+lqPhPJftDRrHsX0N4Q/2t3LvnMDewIFtnmHFMOCzkh5UmHB/qVVH2+8A9wCfb3ItfciSJEmSJEmSqWKGDchsjyH8vX4AXNVweV1gn5JRGU7ImfcDZiGELsYCFxHBV8VttscXhcDRdPcOq3NBmf9GQpZ9nobrqxCiHQDnUDy22rR/ka7s3jktNxz+XLeU1+fW7gcYAqDw3VoVuKjs/RQiGwSh5nimpB8DVWnjCOCXkvYGFrI9sc383eYqrAMcX+a6gngeH21+W3vK2i8BdrX9cgf99yXKHc8rTQcSweGEnu4tfVYgTLGfAYaUc4Atp+tpzCRJkiRJkiSZEmZ0lcUriDNJawLz1toFfNf2A/XOkg4A/gssSwSjr9cu1z2s2nlPNfMRa0er6+6gT6fzVr5iMwEvlkxU98729pJWIso8R0saaPt8SaNK29AiOvIg3QP1dh5mMwGrdBjItUTSLEQwdp7tP3fQf0viDNjalUcbsBLwPUm/AeYBJkl63fbxzcYoma/hwPASoG8JnNlkrpmBZYhSyiRJkiTpkRTnSJKkN8ywGbLC6cBBtsc2tA8Fdq7OgUlarrTPDfynZME2pytT1Bs2LmOuBrxk+6WG67cCm5TXmxEy9+3ab2lob0W/IkYBkRW8ubFDySw9KmmjskZVZ58kLWJ7lO39gWeJkr3PAY/Y/j0R3A4gAtZPKLzQZiMCn1YMA95VllSYPfeK8hn9EbjP9tEd9F+PKE3cwPZrVbvt1W33Lx5mvwMOaxWMKbzWFqs1DSRsBBr7zQIcTmQnx/RiW0mSJEmSJEnSEdMlQybpHWAsUSL4NnAW8DvbkyQNArawvUsvx5yHMBKuzkRhezxxjqnerz/ho3Uz8JCkjxGled8gzlNdUgKW6+me7UHSOOIsVePcBwBVKdwLkm4F5iLOMDWyC3C6pD2Jcrita+3nS/oDEYRV7T8r7T8jskStuA/YUtIpwEPASS36bQacJGk/4vlfSJyBOrIEIQKuLW37ECIlbxHn1w6y/Zakg4hzXI8SHl2t2AU4QdKY8jzmLM97E9sXw7tB2kmE79jCxN/Dm5Kq83oDiOB4bCl97Ae8BrxI+Kf9xPZbtTn/QAh53CbpJWCk7e3brLEZfYHjyt/U28DDRPlixXmS3gBmI2TvW/qyJUmSJB88plS2viIzZEmS9Ibp4kOmmpeWpE8QZ6dusf3rqRizP/AX20tPi34t7h1H+Ik929B+ADChna9Vh+NP0dqq+4BlS6nddEfSzL2Zq6xxLkJI5IpaQLY4YNsPSfoMcCewZCtfL0lfp0v443zgRtsnVWsiZO9fB06v5ng/kD5kSZIkHxwyIEuSZFqg94sPme2niezDTqWEbk1JfymLHFzk1O8uv5co7ZPJsQNHAIuUtiPLWEcqpNzHStq4ce6GufpKOqP0HSPpuy2WvGeZ+zZJizYZc6CkkWWMS0tGqF37Cgqp+RHAjs0mLOu8sdz3T0knS5qpXJsA7EYoP65SxrtBIWs/VNKnS79dyr1jJF1Y2r6kLvn6uyV9tP5MSp/jVQQt1KGEfovPeVwp65vU0P6g7YfK6ycJpcuWkoS2r3KByJAtWLu8M5FFfLrV/bXneYOkPymUFI+QtFn5TMdKWqT0+6akUeXZXCPpk6X995L2L6+/Wj6bGb28N0mSJEmSJHkf8p6Ieth+pHyh/UTDpfuBNWy/rfCVOoyQT6/k2M+TNCtx1msfwhB4IEAJqAYSAh3zAber+Gu14FfEma9lyv0fa9HvZduDJW1BnEVqPEN1NiErf4OitO/XhFx+q/Yzau1HtlnfYKKE79/A34HvABcDcxLZxZ8pzjTdAGxo+5kShB5KlE7uAyxs+w11KT/uAexo+xaFiuHr9Mzrtlcrz+haYPuS3VqJKPlcq4f7FyXKI/ertd1ie0eFb9uswL96WoSkkwihjSckrU+UX84MLAWs2ME+liUUJy8AdieMqZ8CPgbsSfyN3QysbNsKQZO9gJ8Tz/J2STcR9gRfL+cOG9e4HaXUsV+/fh0sKUmSJEmSJEm6816qLDaTDp8bOKtkwEx86YY487WvpAWBP5eAoPHe1YALSlndfxVmxSsCrcQX1qFLPAPbL7Tod0Ht9zHdNiDNTfhw3VCaziIk5jttPwf4Wot5b7P9SJnngrK/iwnFx+ps2RLA0sDV5XnMDPynXBtDnH26DListN0CHC3pPOI5jm/yHBtpJqFfXZutp5uJ81i/aywnLJm8c4AtmwU3TehD+K7tWu6/CPit7Xc62APA7bb/CSxbAvVflMB0LeLsG0T2bUhZ26zEmTlsv6awB7gR2M120wDS9qmE7xuDBg2a9rW/SZIkSZIkyQee96QMS6Hm9w6Tl5odDFxfzlR9kyKxbvt8wrx4IiHH3iwr01tvKNF7efmp/ZLd6ZzN5qrev147yyXgHzVz5GVsr1uurQ+cQPhr3Smpj+0jgG2BOYCRpeTwbTqTtX9XQr/2s2SHe+mGpLmAvwL72R7ZQf9fE2WNu9eaBwEXKs75fQ84UdK32gxTtzGYVHs/ia7/iDgOOL5kTX9C92exDPAc8Jme1pskSZIkSZIkU8p0z5BJmh84mfji64bsxtzAE+X1VrV73pVjL68HEKqAddPhG4GfSDqLUPBbgyhFawwwKiqJ9irj8rEWWbKNifNqGxOZunex/ZKkFyStbvsmQh3whjbtL0p6SdJqtm+mvaz9YEkLEyWLG1MyLw08AMwvaRXbI0oJ4+KEAuNnbV9fzn9tCvSVNG+xBBirkMz/PCGq8QWFpP3swNq0kNCX9KikjWxfpPjgBti+p80eJqOUnF4KnG37og76bwt8lfAYezeTZnvhWp8zCXGUyyYfoVfU//62rI2/EFG6uBxwlaTLbI+ayrmSJEmSGYQU5UiS5L1kegVkcygkzCvZ+3OAZh5TvyFKFncHrqu1b8zkcuzPS7pF0r2ECt9ewCpEoGZgL9tPKdT+mnEIIdF+L5GtOxBoZkI8m8IseSbC76uRLYGTJX0EeIQu+fpW7VsTMvivEf5orRhBBILLEMHmpY0dbL8p6XvA70s5ZB/inNuDwLmlTcAxJRg8WNKXy37/CfytnDH7E1Hi+BBwd5s1tZLQnwxJK5Y1fwz4pqQDbS8FfJ8IludVEQ8BtrI9usWcJxNB6YgSvP/Z9kFt1jg1HECUZD4BjAQWLoHnH4E9bD8p6UfAmZJWtN3JGbwkSZJkBmZqFRYhA7okSXrHdJG9/zAgaV7CzwvgU0TQ80x5P9j2m70Ya00iAGhnwjzVSNoGuMr2U9NxjpmIgHklYLjtb9WuXUhknd4iAqDtbb/dYpwtiIyngVdK37G1632Au4hMarvSxU7W/FngKNuTKXV2SsreJ0mSfDDIgCxJkmmF3i+y9x9UbD9Xna0isjrH1M5adRyMTWsUXl2t2IYIHnszXm+zqCYyn1s1uXY2UTY5gCgX3LpJn4p/AavbHkBkDk9uuL47cG8v19YU249PTTCWJEmSJEmSJFNKBmTTAUlbqstH7URJM0nqI+lFhXfaXQoPsZWKOuTphKQ8krZV+JENlfRAXT6+h3EPkXQbcRbtQEm3KzzaTlawMWETMKTcP6uk8SoS+ZJWlnRNeX2IpFMkXQ2cUeY4usz9lKTH1OVvNlrSvtUai4XYtcCExudS8xibBCwJHNwwzldrfW9xl3n0SGp+ZOWM11eA64EvN4zR7axX2eOhCo+42yUtL2mYpH8plBSRtGgpsa2e/8Xl+T8k6fA2n/N2ku6QdMczzzzTqluSJEmSJEmStOS9lL3/UCBpaeDbwKrFX+1UQm7/T0RWaJjtPSVdSZxhWpvwzDoFuKoMM5iQt3+T8MP6C3EWr924d9ner6zhAdu/LuehzgfWsz1E0s7ATtX5LbWXj1+O8Ih7XdIOwNPFn202IkDa0PZjU/iMZq32Y3tET/2BHxFlkBW/I8oZ5yNUOnsqWRxne2VJxxHnw1YD+hLn4f7QpP+ywPJljQ9KOs5hat2NlL1PkiRJkiRJppYMyKY96xB+aHeUgGcO4PFybaLtq8vrsYRR9duSxgL9a2MMrRQgFb5iqxGfVatx36S7CMjakirFyfkIZcV6QNMJl9dELNYFlpRU+bjNDSwGTFFARpQfXtNJMKYwDN+ceAYopO4ftz26XOuEK8rvsUAf268Cr0qapPBba+Qa26+U+e4H+gGTBWRJkiRJkiRJMrVkQDbtEXC67V91a4yzWPWzZa28saC5J1m7cSe6qLMoVB6PB5a3/YSkQ2htBVD3JGvlR1btaYdSijhVSDqYCOi27aDvQCJz+NWaRcGqwHckbVDWPJeks2xv2Wocuj/nRn+yZv8G6n3eadEnSZIk+QCSghxJkrzX5Bmyac81wPclzQehxiipXy/HWFfSPCW42hC4pRfjzkEEGs9K+ijw3dq1V+ju5TaOMJKmoV8jQ4EdKoEPSUtImqOXe0LS9sCawGZ1j7EWffsDFwOb2n64are9l+0FbfcHfkiUgLYLxpIkSZIkSZLkfUv+z38Dkt6hlLYBjwKb18QlesT2WEkHAtcoJODfAranlLyVoOS1Hoa5mTj7tQjh4TYPkV1rOW5t/ucUZtn3En5edZGLM4DTJE0kzqkdAPxB0lPAbW3Wcwrhj/aopJeAp4lAsSmSRgCLEubU4wmPtj0Jw+fXgJGl7PIi4AvAoLKf24Cf2H6rrO3jwCml7xvA74G9yzQTgDMb5p0ZuAN4op2FgKRvAAcDnyDOw51b2g8Afga8LWl94u9gzjbPJUmSJEkmY2ql8zNLlyQfLtKHrAFJE2z3La/PAh60feh7MG+fcp5sW2Bp27vWrh0ATLB9VG/Hm5I1tLi2JlPhlSZpbeAjRMD1jVr71+k633Y+cKPtk1qMsSpwn+0XJH0NOMD2SrXruxPB3Vyt1ilpFiJQHWx7fBEp6W/7gcbnrFCmPBZYxnZbGcX0IUuSJEkqMiBLkgTSh2xaMQJYoHojac8inT6mZMGQNKekv0q6RyEzv3FpHyfp/xRS8bdJWrS0HyBpj/J6uKTDFNL3P5P0TeCXwKaSrpH0yVK6tz2wW5F1X13SQpKuLeu4tipdlHSmQp7+euDIIts+f7k2k6SHq5LH2p4OkHSqpGHA2ZL6S7pJIc1/VwmCILzAVi9r2E3SzAoJ/+p5/KTdgyznz15p0l5J4ZvIkC042c1dfW+tnSVrlMJfEFgfOK3dOoiSzT7Ac2XMN2w/0GK+IcAwYNMexkySJEmSJEmSKSJLFltQyt/WJmTSkbQuoSw4mBC5uELSGsD8wJO21y/95q4N83KRit+CkGpvlrWZx/aXyr0fAxax7ZIp28v2zyWdTPfMzZXA2bbPkrQNUcpXSb8vDqxj+x1JLwKblbnXAe6x/WyTNawArGZ7Yjm39pUid78YcAGRddqHWoZM0kHEGa7/EqIjx0jazvYKTcZvSdnnTrW1PyHpo7Z36eHWRin824iA71RgfoWv2B62r6nfZPt5SVcA/5Z0LfAX4II2Z9ruIsysm619O2A7gH79entMMEmSJEmSJEkyQ9aMOcqX+eeIM0yVTP265eduur6kL0acM1qnZMNWt/1SbawLar9XaTHfkNrrBYGhChn8PYGlWtyzClHeB3HGbLXatYtsv1Nenw5sUV5vQ5wha8YVtieW17MQ58rG0nXGqxlfoCvjZSIw+0WLvi2xfZrtgcDtwKm2F+spGJP0ZSIg27u8/wZwme0lCPXGm2wPbAzGanNuSwTbtwF7EM+p5XRt1n6q7UG2B80///ztlpwkSZIkSZIkTcmAbHImlgBhIWBWYMfSLuDw8kV/oO1Fbf/R9oNEhmkscLik/WtjucXrOnV5+eOA420vA/yE1nL1jdTHfnc8248D/5W0FrASrb3I6mvYjQiuliUyY7O2uEfAzrXnsbDtYR2ut/tA0q+JTOPuHfQdQJQlbmj7udL8RWADSeOAC4G1JJ3bbhzbY20fA3yF9gqTywH39biJJEmSJEmSJJkCsmSxBbZfkrQLcLmkkwjp94MlnWd7gqQFCGXAPsDzts+VNAHYqjbMxsTZq42J82g9MTfwRHldl3J/BZir9v5WYBMiO7YZocrYitMIFcFzapmzntYw3vYkSVsCM9fWUJfMHwr8VNJ1tt+StDihbvgqvaCULH4VWLsDKfx+wJ8J5csHq3bbv6Bk52riIz9sMUZfYJDt4aVpICHy0azvd4ms6M97saUkSZLkQ06KciRJ0hsyIGuD7bsl3QNsYvscSUsCI4oM+wTiDNWihIDGJCJA+2ltiNkkjSIykT/oYMoDgIskPUGIVixc2q8ELpa0IbAzsAtwuqQ9gWeArduMeQVRqtiqXLGRE4FLJG0EXE9X9mwMIQd/DyE3fyzQH7hL8UCeoesc22RIuoko86yk8H9keyhwMhEQVc/1z7YPajHM/sC8wIml79udKNc0LgXYS9IpwMSyv61q13eT9ENC7v5eYK2eFBaTJEmSDwZTq45YkQFZkiS9IWXve4F64VFWyucGtRDRaDX+9sBrts/uxT1rAm/avrXF9UHAMbZX73TM3lKUIFe1fX4PXVvdvyWwX3l7iO2z2vQ9lDgX97HKnqC0706cH3ubCA63sd0q8zUTIXSyFlHu+TrwfduPls+tOhs3M5GRO9j2G+32kLL3SZIkMz4ZkCVJMi1Ryt5PFyaW81JLA8/Tdb5smmD75GbBmKR2mcw1gVWbXZC0D3AJDWIbPYzXlB7u6c8USsNL+jjwa+KM22Dg10VtshVXln6N3E0EwAOAi4HftBljY+AzwIByXu/bQD2w/nJpHwx8jlBuTJIkSZIkSZJpTpYsTjkjgAHVm1I++H1gNuBS2/1VPMoI9cSZiUzLkJKFGQJ8udy+qe2HVTMmljScOCv2RUJi/0EiizQroQC5GTAH4VH2Timz2xl4jFANnJ/IFK1u+zFJZxJB5HLA6KJMuKrtZ0rG6EFg5XpGr6znM0TA9aykXxLn1uYsXXYqmbkjgCUl3U+cM3um3FdlsHazfUqL5/hV4Grbz5c5rybEUVZu6HeL7R1tjyz9ul20fX3t7Ujgh5Iupavss2Jv4NPAf6oza7bHN1tYOSu4PfC4pI9Xa0ySJEmSJEmSaUUGZFPA/9CjbOUZxKNsO+ATtg+RNBtwi6Rhth9tMv4CwOO19+OJTOT2Tfp2yo+Av9k+pNlFSf8Abpa0OnAtcK7tu5v1tf2ypEeJz3dUwzjpQ5YkSZIkSZJMFVmy2DvSo6wzj7J1gS3KsxpFCHEs1qJvM5+vKT7YWDKFg4AjW/UpGbEliFLOScC1ktZuN2yLcdKHLEmSJEmSJJkqMiDrHelRNu09ysYDn629XxB4stVm2iFpHWBfYIOeRDhsv2H7b7b3BA6jhUKkpI8SJZsPNrueJEmSJEmSJFNDlixOAelRNk09yoYCh9WEPNalQYSkEyQtB5wCrGf76R76Lg88ZfvJcn5uACHr39ivL2EDcJntF3q7piRJkmTGItURkyT5X5AB2RSSHmXTxqPM9vOSDgZuL00HtRPPkPQbQtHxI8XP7DTbBxAlin2JZwTwmO0NWgzzCaL0crby/jbg+Nr168u6ZwIuBQ5utZ4kSZLkg0PK3idJ8r9ghvQhk2RCiGHz8r4P8B9gVCUsMZ3mHU6IV9zR0L4VIbm+U4fjjKOFR5mkCXV/renJe+FRVptrM0LhECJg/ante2rXZwbuIDJpLT9DSX8kyiVFlBFuZXtCufZ9InA1IVLSVIo/fciSJEmSZmRAliTJtOSD7kP2KrC0pDnK+6/QVc73oaUENZ32bepR1ov7e5tdfRT4UvEJO5jJvb1+BtzXwTi72V62jPMYsFNZz2LEXr5oeylg1zZjpA9ZkiRJkiRJ8r5gRg3IIEQoqv+C+gFdqoUU/6/TJd0u6e5Szoek/pJuknRX+Vm1tK8pabikiyXdL+k8NRpddfFDSbdKulfSZAbFkhaSdK2kMeV3v8Z24F/AR0r7wpJGlLU2LY0r675f0lll3IuLBD2SxknaX9LNwEaSFpH0d0l3lr1+vvTbqKz5Hkk32j4C+DpwtKTRZdzFylz31ubeQ+FHRnlGh0m6AfiZpPklXVLWfrukL7b5vF4hSgFHE6bNy5WSTSQtWD7L02rzjirrqv8sY/vlcl2ED1uV4v0xcEJ11sv205KWaTLGKJr4kDU7I1Yyb9sD31IYWDd+LttJukPSHc8880ybrSdJkiRJkiRJc2bkgOxCYBNJsxOiDHWPqH2B62yvSJgvHylpTuBpwkdreSJL8vvaPcsRWZUvEFmRVsHFnLZXBXYgpOMbOZ7wARsAnFebo1X7scBJZa1PtdnvEsCp5f6Xy/wVr9tezfaFRDZnZ9srAHsQ574A9ge+antZoDpbtT1wbFGOHEQoHvbEPLa/ZPu3Ze3HlLV/l1pA1YjtsZXqInBWeRYrlcu/A/YiJOir/ivVVBqrn7EAks4gntXnCfVJCI+1xSXdImmkpPXqc9Z+VgL+BHyzBGi/VQiCtFr3y0R2bzLZ/pS9T5IkSZIkSaaWGTYgsz2GEI74AXBVw+V1gX1KNmY4IRHfj/Y+WreVTMkkYHQZuxkXlPlvBOaSNE/D9VY+YK3av0hXdu+clhuGx23fUl6fS3d/sSHwrirgqoSwxWhCdfDTpc8twJmSfkyXOuII4JeS9gYWqvmNtaPujbYOcHyZ6wrieXy0+W2BpC8Txs17l/ffAJ62fWcHcwNge2ui5PA+IrCGEKhZDFiT+Js4rclnU90/TXzIkiRJkiRJkmRqmdFVFq8AjiK+hM9baxfwXdsP1DuX0rvKR2smQsyhoi7a8A6tn02jCkpPqiitrnfiQ9bpvJXa4UzAiyUL1b2zvb2klYjSwNGSBto+v5TwrU+YTm9LCGXUA/VGv7O6dP1MwCodBnJIGkBk0b5m+7nS/EVgA0lfL3PNJelc2z9sN5btdyQNIUyyzyCyeyNtvwU8KukBIkC7vcX9bxBlr3+T9F9CBfLaJmtOH7IkSZIPCSnGkSTJ/4IZNkNWOJ2QSR/b0D4U2Lk6B1YrSZubrrNDm9OVKeoNG5cxVwNesv1Sw/XKBwy6+4C1ar+lob0V/SStUl7/gCb+YlV5nUKWHgXLlteL2B5le3/4f/bOPOyu6Wzjv9s8z/RTbcQQHZAmvKEUTfHRKi1fqZrHqrmllJaqUq3SinmqIogipi9VrfhSEVNCkImqKSlKax5SQYb7++NZx7tzcs553zeJZujzu65c7z5rr73W2nsf13Uez7Pum1eBT0paE3jW9rlEcNuTCFhXkbSiQha+lWrlYIqoRpljhkCwcq4boVi4VzHMrq35h7Y/Ybt7eQ5/bhaMlftZu3YM7AA8UU7fSpSnImklooTx2SbjbCDp4+W45kP2twb90ocsSZIkSZIk+UiZpzJkRfzhAsKD6hngNiJDUs+pxL6kMeWH+wQisGjmo9UV3pB0P2HGvH+D8818wGZol9SdyMocJum7hOphM/4C7CPpEuAp4KIm/W4GDpV0IlGiOUnSXsDPFEqEKnOOBo4nkx+1hQAAIABJREFUREomE3uyTilGzqcQe/LG0x7w1Nhd0lXAFEKZcOky/kLAMEk/KGu9pc4G4CQii3lhiZPXIN7LQsA9wGG1jpKOIAK9KcAfbP+gdgroL2mZcjyadm+3O4BtJD1OZDiPrWTh6kkfsiRJkuQjI+XzkyTpCvOMD1n5gTyCEMC4QiHxfinwuu1GQdlcj6S+hK9ZS++0ErjdZnu98nlB21Ob9B1KA6+02UXZAzbC9ruSDgH62t61cv4cYGXivTT1ZZO0jO23y3u9ERho+7oy/gnAV22/L2kV2y9/FPcyO0kfsiRJkqRGBmRJksD86UO2JaEmeAXEHiLgKGB/SUtIOlrS5QBF7nxcaX9K0sqlfQFJT0taSSEPP1wh136KpIm1iSQdW9rHSPppaesu6S+SfiPpMUmD1e6DRuXa6eTlK9fOILcPnA5srlD7O0rSvpLOr4x1WwnaAD5T1jkC2EQhdf9QmevSUs63M6GWOKCMubhCqr6tjLebpLHlml9W5pko6bSy5uGSPtbsJdi+y/a75eNw4BOVcTYEPkaUMrakJl9PZMgWoX1P3CHA6TUj5lbBWHlet0r6vaTxkg4v34NHy32sUPo1swLYQSGv/6ik/6vdt6STFbYJQyU9K+nIju4nSZIkSZIkSWaGeSkgWxeYTomv/Kh/DlibKFFcW9JOhMjDd0rgcA3te7O2BkbbfpWQbD+nSLa/WBtT0jaEGMRGQC9gQ0lblNM9CK+rdYlyvW80WGcjeflmcvvHA/cUOfZ+zW7c9gTiXY0rcvD3Aufb7lOyZosD29u+ERgJ7FHG/FBsQ7Fn6pdEYNsL6CNpx3J6SUIQ43PAMMLTqzMcQAhj1PZi/ZoQ0TgF2FXt3l8XNLpY0h3l2bxDZMkg9n5tLun1Eij+tTLOtg2GWQ/YnXhfpwHv2u5NKEgep1CAHEWobC4ILE+7FcC9wOdL/+sI6f0anwa2LeP+RNLCDdafPmRJkiRJkiTJLDEvBWSisRqhABehjn0J6fi7KxLxlwN7l+P9iWANQoZ+YDmuydFDSOZvAzwKPEL8MK95UI23PaocP0xjafxG8vKt5PY7y1Sm32P2pZLdGUsEWet2cH0fYKjtV2xPIbzQaoHmB8R+PGh+X9MhaU8iG3dmaToUuL34k50EXF/x/jqs0Ri2tyVk+Rct9wCRMVue2G+2JZE9613GuaPBMHfZfsf2K8BbwO9L+9gy7mZlzA9K+xTarQA+QahLjiX2Ilaf4R9sv1+C95eJzF/9+tOHLEmSJEmSJJkl5iVRj8eoy0gpxB0+CTxTmnoAEwmPKgBsPy/pn5K2BDamtZIhRID3C9uX1M3VnRml8WcoWWwkLw8cQXO5/SpTaC45/15t35jCDPtCoK3c38nMKE/f6L6aMdntmwlbSf5T5t+a2Of1xVppIRHgbi7pUGApYBFJE20f32os2+9JGgR8HbiTkK+/uaznQUnTgJUIIZRGVN/JtMrnaeU+mloBEKbSZ9keVEpDT24ybofPJEmSJEmSJElmhnnpR+YQ4HRJe9u+SiHq8WvgyiIwsSxRhrgFYVa8cynhg/C+uga4uiKGMZwI8K6nXXYeQq3vVEkDbE+UtBowubOLVJGXB0ZI2oEIGJcFXrA9TdI+tGfO3gGqRsoTCIXEBYDViHK5RtSCr1cV0uw7017yVz9mjRHAOQpJ+DcI6fzzOntflfvrTRhOf7m6v8v2HpU++xLBYsNgrKx5adsvSVoI2I5QWoSQr98SGCppHSJD9mpX11lZ19tlf9kutgdKEtDT9mjivfy9dN1nZudIkiRJkiopxpEkSVeYZwIy2y77wy6U9GMi83E78KPSpR9woe0nJR1ASJcPK0HDIKJU8YrKkN8DrpH0feAPRLkbtgdL+gzwQPx2ZyKwJ5El6QxnakZ5+WZy+2OAKZJGA1cS++DGE+V244iSyUbP4k1Jvyn9JjC9+fGVwMWSJhFZq9o1L0n6YZlfRHnh/3bynqa7PyIDNrA8n+dsf631JTOwJDBIITu/IPBn4OJy7nLCHmAcUWa4TyV7N7PsAVykdiuA64j3cnK5j78TAfoaszhPkiRJMg+T6ohJkswJ5hnZ+1mhqAz2s715pW0JYFIJ9L4F7Gb763NskfMgqpPYV508f5NrfkiIgUwFjmyyL6zW93LCP+7l6piSziRMoT8gylX3s/3mLN7LKcAw2/83M9en7H2SJMm8TwZkSZLMTjQfyt7PFJKOJ8Qwflh3akNij9cYQpDi+//utf2nIemzRHnousCXiWzngi0uubL0q+dOYD3bPYEnmfHddhnbJ81sMJYkSZIkSZIkM8s8U7I4s9g+nfD7qm+/hxDZSBog6QRgl/JxEWBN4HHiO/Mk7cqVHY0zglA7BFil/F3H9lhJTxP75B5odK3tYSXrtlSRr68yHtiJKDXcucX8+wI7EqWR6xH7DhcB9iKEO7az/bqkK4ns3o2SJgD9iSzcwsAutp9oMPZBwEEA3bp1a/oMkiRJkiRJkqQZ831Alswctk8jfL1qpYjjgSNs31dKCQ8tXQeU/WoQgc60unE2rh0rTK+H2x5bml4gxEs6YmITlUQIK4PrO7h+PaA3IYbyNHCc7d6S+hGB5dkNrnnV9gZFNfIY4MD6DrYvBS6FKFnsxH0kSZIkSZIkyXTM9yWLyWzj+Yq32zWEvxe0m1D3ItQSW9FIen+mA5mSxat5qrWilVdZ9ybX3Fz+dsqXLUmSJEmSJElmhsyQJZ2lPnCamUDqBcIGoMYngBdnZjHFPmB7YKtOqDB25FXW6pr0IEuSJEmSJEk+MvKHZtJZuknaxPYDhIfZvcQeq64wCLhW0lmEeXcP4MGuLkTSl4HjCGPqd7t6fZIkSZI0ItURkySZE2RAlnSWvwD7SLoEeAq4iC4GZLYfk3QDIQ4yBTisYtQ9A5J+B/QFVpL0AvAT278FzieEQu4sXmjDbR/c9VtKkiRJktnP7JLPhwwSk+Q/gQzI5jCSJtpeqvJ5X6DN9uGzYezuwKa2r21y7i/AXwkxjpHAAbYnNxluGqFWWR2vb7WD7QmEgEajtfQigrhlCGPs02z/sa7PeYSn2FJlvN0ajHMwMIkw7J4IHGT78XKuJ3BJmWMa0Mf2lYR8fm2N3Uvf/YGjABcT6hNs/29RWzQwRNLihIrjHk2eSZIkSZIkSZLMEinqMZ8iaSFCjGL3Ft2eKWIc6xP7ub7ZwbAdjdeKd4G9bdc8yM6WtFxlvW3Acs0urnCt7fXLus8AzirXL0SIjRxc5ugLNAwuJX0COAHYrHiZfR4YU+lyrO3PAZ8CHgXukrRIV242SZIkSZIkSTpDZsjmYiStDFwM1Eyuvldk5zcipNoXJ7JF+9n+a8mufZWQd18SWAL4TPHw6m+7X6N5bE+V9CBFgr5kz64uYwAcbns9ScOr4wHnElmzvkQJ4QW2L5G0LfDLumnG296pzPeipJeBlYsx9BBgLeBvwOJl/K1sv9ZgrW9XPi5Ju7jI8URQ2b+UMUK7V1k9qwDvEBk2bNeybfVzGegnaSfgK8D/Vs+nD1mSJEmSJEkyq2RANuepBSA1ViDELwDOAfrZvldSN+AO4DPAE8AWtqdI2hr4OfCNcs0mQM9idtwXOMb29q0WIGkxYGPgu6XpZeC/bb8nqQfwO6CNCHo+HK8EJG/Z7iNpUeA+SYNt31HW2my+jYgyyWdsT5N0BbCA7X6lhLOZ51jt+sOAo8sYW5bmicAfiGBrZeA622c0GWI08E9gvKQhwM22f9+kL8AjwKepC8jShyxJkiRJkiSZVTIgm/NMqgYgtT1k5ePWwGcrGZ9lJC0NLEtkgnoQGaKFK+Pdafv1Ts69VgkGewA32q6V7S0MnF/2fU0F1mly/TZAT0k7l8/LlrHGN5tQ0qpE9m2fEox9HNiFuv1orbB9AXCBpN2BE4F9iO/yZkAfojxyiKSHbQ9pcP3UotTYB9iKyIJtaPvkZsvu7NqSJEmSJEmSpCtkQDZ3swCwie1J1cYifnGX7Z1KeeHQyul/dWH8Z2z3KkHSUElfsz2IELv4J/C5sob3mlwv4IiSEesQScsQWawTbQ8vzb2BtYGnS+C5hKSnba/diSGvI4RCIDzO7rb9apnrdmADohxyBko54oPAg5LuBK4ATm4yT+9m4yRJkiRJPamMmCRJV0hRj7mbwcCHaoslYwWRifp7Od63xfXvAEt3NIntl4hyxB9Wxn/J9jRgL2DBJuPdARwiaeGyvnUkLUkDiijGLcBVtgdW5v6D7f+y3b0oIL7bKhgrWcEaXyUk+Gtr6SlpiSLw8UVCXr/RGB+XtEGlqRexf62+nyQdCawK/KnZmpIkSZIkSZJkZpljGTJJBq6xvVf5vBDwEjCioz1PszjvUGIf1Mi69n2ZfXLz00nZzwJHEqV5Y4h3NQw4mFAX7C/paODPLa4fA0yRNBq4spmoR+FW4GRJmwMXAjdJ2gW4i/as2/tAL0nTgN8TghndgbFFuVDAc5Iutn1O3fjfBLYAVpT0I0Ka/jngMUKU5M1ax7Jf7nHgZNu/qhvn8LJvbjLwBlGuiO03iuH0Q0QZ5+22mxnBLAz8qpRLvge8QjzXGmdK+jEhijIc+JLtD1o8uyRJkiT5kNnpQwaZcUuS+R1F5dYcmFiaSGQ3NrU9SdJXgF8AL/wnB2SSFmxlljw7kbSQ7Sld6L8KsDqwI/BGLVgqJY+r2n6k7HF7GNix5g/WYJxtgD8XUZJfAtg+rnL+JsJHbESDgGyupK2tzSNHjuy4Y5IkSTLfkwFZkiQARc+graN+c7pk8Y9E2RnAboSaHwCSlpR0uaSHJD0q6eulvbukeyQ9Uv5tWtr7Shoq6UZJT0gaoIoaRh17Srpf0rii+DcdklaXNETSmPK3Wwfta0h6oKz11EYTlnU/Ial/uf5GSUuUcxMknSTpXmAXSWtJ+pOkh8u9frr026WsebSkYaVtXUkPShpVxu1R5hpXmfsYSSeX46GSfi7pbuC7klaWdFNZ+0OSvtDsZdl+2fZD1Pl72X7J9iPl+B3CcHq1FuMMrgSCwwm5+tpadwSeJTJnTak8z8vKMxkgaWtJ90l6qvZeP+LvUZIkSZIkSZLMEnM6ILsO+JZCdr0nMKJy7gQii9IH+BJRRrYk7ZLsGwC7El5YNXoD3wM+C6wJNAsulrS9KXAocHmD8+cTe516AgMqczRrPwe4qKz1Hy3u91PApeX6t8v8Nd6zvZnt6wgp9SNsbwgcQ5QQApwEbFtMi79W2g4GzilKjW2EuEVHLGf7i7Z/Tbu0fh9COv+yTlzfFIUH2VeAX5cgcZSkES0u2Z8IzCnv9zjgp8DmwFGVMUZJOqHu2rXL+nsSsvS7E0qLxwA/Kn2eJJQUFybKPq8vwdosf48kHSRppKSRr7zySkePJkmSJEmSJElmYI6qLNoeo1AJ3A24ve70NsDXJB1TPi9GGCS/SHNJ9gdtvwCgkHPvDtzbYOrflfmHSVpG0nJ15zcB/qccX03s2WrV/gXafcCuZkZT5BrP276vHF9D7BGrleRdX9a9FLApMLCSmFm0/L0PuFLSDcDNpe0B4ATFHq6bbT/ViYTO9ZXjhtL6JdPVJcrafw58y/bNneh/AjCFCG4hArF+tidKugf4Ywcli+Ntjy1jPQYMsW1JY4l3D7EvcbFyPJUIxN6htbR/p75H6UOWJEmSJEmSzCpzg+z9ICIo6QusWGkX8A3bf612LqV3zSTZ368cT6X5/dX/eO7ox3Sz8+5En87OWxPOWAB4s5E5su2DJW1MlHmOktTL9rUlA/VV4A5JBxJZoWr2c7G6oarS+A2l9buKQmnxJmBAJ4OxfYDtga3cvpFxY2BnSWcAywHTJL1n+/wmw1Tf97TK52m0v/uP8nuUJEmSJEmSJLPE3PBD83LgLdtjJfWttN8BHCHpiJL16G37UUKS/YViKrwP7ZLsXWFX4C5Jm5W536rLKt0PfIvIdu1Be3akWft9pf2a0t6MbpI2sf0AkRVslHV5W9J4SbvYHlj2L/W0PVrSWrZHACMk7QB8UtKywLO2z5W0JlG+dw+wiqQVgYlE4NNMtr0mrX8mhLS+7VEt7mEGyhp/C/zF9lmd6P9lojTxi7bfrdz75pU+JwMTWwRjneWj/B4lSZIkyQykCEeSJF1hjgdkpTSsXiId4FTgbGBM+cE/gQgsmkmyd4U3JN1PSK/v3+D8kcDlko4lJNH366D9u8C1kr5LZIma8RdgH0mXEAqTFzXptwdwkaQTidK664DRxD66HkTWZ0hpO54QKZlM7F87xfZkSacQe/LGA0+0WFMzaf0ZkPRfwEjiuU2TVNtn1ZPwKxtbSvwAfmS7vgy1xvlEGeadJRAebrvhnLOBj/J7lCRJkiQzkCqLSZJ0hTkmez87KfunLiCCgwWA24Bj5ybvqLJX7jbb63Wy76a2r52N8+8LDLb9Yvl8GXBWM2n6mRh/CyLw6UnsIbuxtPciAs9liPK/02xf32KcAYQ4yWTgQeA7tidXzvchlBl3rc0xN5Cy90mSJEmNDMiSJIF5R/Z+lilZj5uBW233IMQZlgJOm6MLmzW6E4qBXUJSq7K7fYGP1z7YPnB2BWOF58oc9UHku8DettcFvgyc3UBEpcoAQjFxfWBx4MDaiXJ/vyTKEJMkSZIkSZJknmeeD8iALQnJ+CsAiqnyUcD+kpaQdLSkywEkra/wrFpC4VW1cmlfQNLTklZSeIANV/hWnaIwsKb0O7a0j5H009LWXdJfJP1G0mOSBktavME6+5T+VQ+xhl5YwOnA5kXq/ShJ+0r6cC+VpNtq++0kTSzrHAFsovAze6jc56UKdiayTgPKmIsrvLbayhi7SRpbrvmlpP1Kv6mS/iFpUvn7sWYvwfYE22MIQY3aOi8AbiAUI0cRSpqTgZVbjHO7C0SGrEdZyygi6FsH+DwRdDdE4SV2t6QbJD0p6XRJeyj82sZKWqv020HSCIU/2f/V7k/SuZJOKsfbShomaYb/VpSy90mSJEmSJMksMj8EZOsCD1cbbL9N/HhfmyijW1vSTsAVRAncu0wvwLE1MNr2q8R+tnOKL9eLtTElbQP0ADYCegEbljI9SvsFJQv0Ju0S+FUaeYg188I6HrjHdi/b/Tq4/yWBcbY3tn0vcL7tPqU0cnFg+1LaNxLYo4z5oaKipI8TWacty331Ad4oKo8LAN+2vThwFfDtDtYyHbYPK/P1KuMdROzVeqajaxWqjXsBt5Rrvwo8DaxOKHNObHE5hHrid4lM217AOrY3InzWjih97gU+b7s3sU/vB6X9eGBXSV8i3sl+tqdRh+1LbbfZblt55aYxZpIkSZIkSZI0ZX4IyERjyXkBLj+k9yWUEe+u+IBdDuxdjvcngjUIr7GB5bhafrdN+fco8AhRVtejnBtfUSZ8mHYPrCo1D7Fv067otzDwG4Vv1kBiD1xXmcr0QiJfKlmfsUSQtW4H1/cBhtp+xXbNE6wWaH5A7MeD5vfVKSStSryDhsFNAy4Ehtm+p3w+GziuZEA7w0O2X7L9PhEADi7tVY+yTxBWAWOBYynPqgTs3wbuJALcDgPIJEmSJEmSJJkZ5rjK4mzgMeoyUpKWAT5JeyamB5FRqe6hel7SPyVtSfhftZKrhwjwfmH7krq5ujOjb9UMJYuNPMSITE0zL6wqU2juK/ZeLUiRtBgRyLSV+zuZGT3IGt1XMyZXPMJm2o+rvI8/ACfaHt6J/j8hyhq/U2luA66LLYOsBGwnaYrtW5sM0xmPsvMIYZNBpQT05Mo16wOvUfnOJEmSJElnSBGOJEm6wvyQIRsCLCFpb/hQ+OHXwJW231X4dJ1DZH1WLPupalxGlC7eUMm8DKc9wPtWpe8dxL60pco8q0lapbOLVPEQs30S8CoRMC4LvFQyRnvRnjl7B1i6cvkEoFfZ6/ZJomyyEbXg69Wyzuq91o9ZYwTwxbJ/bkHCH+3uzt5XR0haBLgFuMr2wE70PxDYFtitmkmzvYbt7ra7AzcCh7YIxjrLssDfy/E+lTWsDnwf6A18pQTSSZIkSZIkSTLbmSMZsiKe0I8QZ3iDKI07w/YtJVNxjO3tOzNWMfvdCbhQ0o+JIPN24EelSz/gQttPSjqAMIQeZvtlYi/SFbSXKwJ8D7hG0veJrM5bZZ7Bkj4DPFCyNBOBPYnMUWdo5CHWzAtrDDBF0mjgSqJcbzxRbjeOKJls9CzelPSb0m8C8BCE8AcRcFwsaRJRllm75iVJPyzzqzy7dYH/bXUzCqXE3W1fWD73IQKv5YEdJP207Kn7JlE6uaJCeh9g36r5tMKY+cTycc2y9tpzvtn2KZW+pwG7EHvubqy0H00oMk4hhEPe7GD95wIrlHneB35X2icQGbhXgf8jzMB/K6nNdrMMZpIkSTIPMrvl6WtkhixJkq7wb/chU/zKvh/ob/vi0rY68DXb53U1IJvFtbQB/WxvXmlbAphUAr1vEZmar3/Ua/kokTTRdlNVwpnpr675qjUdT9IKhOBIG7EX8GFgQ9tvNOn/eeBvwFPVMYsAx4iSFT0E6Gt71yZj7EZkQb9pe5rCx+5ftt8oAVmb7VqW8VKidHOfRmPVSB+yJEmSeY8MyJIk+SjRXOxDtiXwQS0YA7D9N9vn1XeUtJGk+4ss+f2SPlXa1y0S5qMUEvQ9Svve5fNoSVeXttUlDSntQyR1k7SgpNcIMYzTJE2rKCY+ADwuaQxwKPD90v9XCsn0MZKOKGNvVdY2VtLlkhYt7RMk/VzSAwpZ9A0k3SHpGUkHlz5LlfU8Uq6fIegr816pkKMfK+mo0r62QqZ9dLl+rc6MV66dQbq/7vzpwOLl2Q4obUeXNYyT9L3S9XRgrdLvzM7O34BtgTttv16CsDsJv7KG2B5u+6UG7XcVMQ6IstNPtJhzVdpLRbH9QqMA0PZE4GBgxxI4JkmSJEmSJMlsZU6ULK5Lk5K7BjwBbGF7iqStgZ8TmY2DCWn6AYo9SgtKWhc4AfhCyW7UfkCfT+xf6i9pf+Bc2ztKeojYJ7QGkZXZXOHltYztNaqLKBmXNYDeZS0rKAQ0rgS2KuWQVwGHEOWFAM/b3kRSv9LvC8Qer8eAiwkBj51svy1pJWC4pEEVEQ0IGfrValkotRsqDwBOLyWeixGB9QcdjafppfsFDJK0he1htT62j5d0eJGaR9KGwH6E8ImAZ8rzqJZqvtnJ+6k+01vKM10ZWECxT+s44AVgtUbXNBhjfUK5scr7RKnmH1tcegNwr6TNifLRa2w/2qhjuZ/xxHMbUTf/QYSUP926devMkpMkSZIkSZJkOua4yqLCPHgzImvWp+70skD/kgEzIRMPkcU6oZSa3Wz7KYVa4o0OLzFsv176bgL8Tzm+GjijHN9DCH2sAfyCkDm/m7Lvqo6tgYuLLDy2X5f0OULu/snSpz9wGO0B2aDydyywlO13gHckvVcCq38BPy+ZuWlEEPIx4B+VeZ8F1pR0HrGfbbCkpYkg7ZaylvfKc1y4E+NVpfshzJV7AMNozmaEF9i/yjyXAq+U+7utErh1Zv4Psb1Tue5YYFHbPyufN6KxjUGjMcYSQeuHSNoTOBz4YovrXijZ1i3LvyGSdrE9pMklDZUobV9KlDTS1tb27639TZIkSZIkSeYL5kTJ4mPABrUPtg8DtiIyJfWcCtxVMkQ7UFQEbV9LmCtPInyktqS5H1k9tT73AJsT2aLbgeWAvjQOThqN3UouHqaXWa+XYF+IkNlfmdgv1YuQv59Oor6U0X0OGEoEe5e1mLfD8WiX7q+ZNa9t+7cd3EdH99mV+RvxAqE4WeMTVAy5u0LJop5A7Ed8v1Vf2+/b/qPtY4nM645Nxlya8C17stH5JEmSJEmSJJkV5kSG7M9EJuUQ2xeVtiWa9K3Kku9ba5S0JvCs7XPLcU9i79EtkvrZfk3SCiVLdj8hX381ETTcW4YZAVxVxnlP0ijC96qRmMhg4GBJQ2sli0Q5ZXdJa9t+mpCt74pc/LLAy7YnKwQpVq/vUEr/PrB9k6RnCCn/tyW9IGlH27eWfWsLdmY8Qrr/VEkDbE+UtBohWPFyXb/Jkha2PZkIUK8se8sE7FTutV5GvzPzN+IO4vuwfPm8DfDDTl77IZJ6A5cAX25wP/V9NwD+YftFSQsQ358xDfotRShh3tpMZCRJkiSZd0nxjSRJ5gb+7RmysqdoR8L7arykB4lyv+MadD8D+IWk+2j36IKQPB9XgqhPE3vEHgNOA+5WyMWfVfoeCeynEOnYC/huWcf7wPOEAARExmxposSwnsuA54AxZezdS6ngfsBASWOJzNfFDa5txgCgTdJIIlB8okGf1YCh5T6vpD1Q2Qs4stzT/cB/dWY824OBawmp97GEbHwjb7JLy70OsP1ImftBIoi9zPajtl8D7itCH2d28n5moATNpxKlog8Bp1TKTWdA0hmSXiC8515QmF8DnEmUYA4sQiODmo0BrAL8XtI4isUAsdewxl3l3IPEe//OjEMkSZIkSZIkyazzb5e9nxXKnrELgM8SweRtwLG2P5ijC5tJFNLxm5YSzNk15r7AYNsvls+XAWfZfnw2jb8FsU+uJ/At2zeW9l7ARcAyhODHabavbzHOAELqfjIR+HynZORq5/sQwfKutTnmZlL2PkmSJKnxUcnpQ2b1kmReQnOx7P1MIUnAzUT5WA9gHSIjctocXdis0R3YvasXSVqwxel9gY/XPtg+cHYFY4Xnyhz1QeS7wN4OM+gvA2dXVCEbMYDIbq4PLE6YOgMf3t8viXLGJEmSJEmSJJlvmWcCMkIN7z3bVwDYngocBewvaQmFV9blEHLopZRuCUlPSVq5tC8g6WlJKym8u4YrPLlOkTSxNpEaeHVJ6i7pL5J+I+kxSYMlLV6/SEm7lLlHSxpWufYehUfXI5I2Ld1PJ+T2R0k6StK+ks6vjHWbwigbSRPLOkcAm0g6qaxxnKRLFexMZJ22rLzRAAAgAElEQVQGlDEXlzRUYYCNpN0UHmHjJP2yMs9ESaeVNQ+X9LFmL8H2BNtjiBLNavuTtp8qxy8CL1MRainvZFTtHyGkMbyUsD7I9L5hRxDql58Hzqxct37ds+4r6W5JN0h6UtLpkvZQeNSNlfSVct1YSW9Kerfc6xfK9c187vaVdLOkP5XvzxkkSZIkSZIkyUfAvBSQrUv4hX2I7beJjM3aRBnd2pJ2Aq4gSuDeBa4h9jRByNePLtL45xBeZn2oqPppeq+uXsCGajeN7gFcULJAbxKeaPWcBGxr+3OEEiREcPLftjcg9r+dW9qPB+4piof9Orj/JYFxtje2fS9wvu0+RYFycWD7Uto3EtijjDmpcl8fJ7JOW5b76iNpx8rYw8uahxEWADONQrp+EeCZWpvtsRV1x9q/jRVy+XsBfyrXrkYIh6xNSOsfW+nfaH/f54h9geuXcdaxvRGx72/bovg4tjyfJYhy18vKtTWfu97Ee/t5ZdxexLtaH9hVUlUJsnafBymMv0e+8sorM/WskiRJkiRJkv9s5qWArJmsvQitkGlEKd3VwN227yvnLwf2Lsf7E8EahD/ZwHJcLb+renU9QpTV9SjnxtseVY4fJkoO67mPUCX8Nu1CJAsDv1EIaQwkgoKuMhW4qfL5S5JGlDG3JALWVvQBhtp+pfipDSB82CBMpW8rx83uq1NIWpV4B/uVd9IRFwLDbN9TPp8NHFcyoJ3hIdsvFZGWZwhFTIggrHs53ho4v2TmBgHLKOTslyVEQMYB/Zj+GQ6x/VYRb3mcBqqRti+13Wa7beWVG7k2JEmSJEmSJElr5rgxdBd4jLqMlKRlCA+rWiamBzCR6fdQPS/pnwqvso1pz5Y1o+bVdUndXN2Z3k9sKpGZmg7bB0vaGPgqMEohdnEE4cv1OSIIfq/J3FOYPkiu+ni9VwtSJC1GBDJt5f5OpmPPr1Z+YpPdru4ylZn8XpT38QfgRNvDO9H/J0RZY1XFsA24ThLASsB2kqbYvrXJMPUeb1X/t9p9LABsUs0YlvnPI3zudirvd2iTcWf6mSRJkiRJkiRJK+alH5lDgNMl7W37KoXww68Jb653JS1LlCFuQWRDdq6o811GlC5eXcm8DCcCvOsJn7IaDb26OrtISWvZHgGMkLQDETAuC7xge5qkfWjPnNV7eU0ADlV4Y61GlE02ohZ8varwytqZkLBvNGaNEcA5Cm+zN4DdgPM6e18dIWkR4BbCgmBgJ/ofCGwLbFXNpNleo9LnSuC2FsFYZxkMHE5I4yOpV8l0NvS5S5IkSZJZIZUQkyTpCvNMQGbbZX/YhZJ+TGQ9bgd+VLr0Ay60/aSkAwgvqWHFJHgQUap4RWXI7wHXSPo+kdV5q8wzWNJnCK8uiIzbnkSWpDOcKakHkZEaAowmslk3SdoFuAv4V+k7Bpii8Da7kijXG0+U240jSiYbPYs3Jf2m9JtA+HfVuBK4WNIkoiyzds1Lkn5Y5hdwu+3/7eQ9fYhCjv4WYHlgB0k/LXvqvkkEwysqpPcB9q2UeNZzMfA32p/zzbZP6ep6OsmRwAUK37aFiH1yBxM+d/0lHU0YlidJkiTJLJOy90mSdIV5yodsZpG0HbGv6XXa/ctOAt4ugd63gN1sf30OLrPTKP3LZsm/rGQgzyb23pkoIf2m7fGSJhBZRohM5s3AqWWPWlPShyxJkiSpkQFZkiQwH/qQzSySjicyOpfU+ZddROzxGgMcCnx/zq2yy3Qn/ctmxb9sV+Jee9pen1B1fLNy/kulfSNgTeDSDsZLkiRJkiRJkplivg/IiHK+EbZ/BNP5l21LlPRdCTxt+2mlf1nVv+xZSX8tnydImlTEUbrkXybpFoW64a3AVcD2hLgJ5X209C8rY3zoYUZI0z9KZMEa+ZfdVMZoxarAS7W9a7ZfsP1GfSfbE4nSxh0lrdDg3lL2PkmSJEmSJJkl/hMCsvQvmzn/sufK/W9HfE+6EUIjXfIvs71T1XuM2M83utFC1cC/rIwxg4cZsBmN/csu7uB5ANxA7H8bJenXkno361i+K+Nptz6onkvZ+yRJkiRJkmSW+E8IyNK/LP3LpsP2C8CngB8S8vhDJG3VanmdWE+SJEmSJEmSdJl5RmVxFkj/svQvm4Ei0vFH4I+S/gnsSKhi1s+1NBFoPtmVe0qSJEn+c0nhjSRJusJ/QoZsCLCEpL3hQ+GHZv5lK5b9VDVq/mU3NPAvgxn9y/ZX+IIhaTVJq3R2kSr+ZbZPAl6l3b+sttdpL1r7l/Uqe90+Sdf8y2q08i/7Ytk/tyDhX3Z3Z++rIzTz/mW71fuX2e5uuzvhyXZos2BM0gaSPl6OFyCUH//WoN9SRAB7a6M9ZkmSJEmSJEkyq8z3GbJ53L/seiKDcxph5jyt3MvbwLqau/zLVgK2L8IbCwPn2b4YPgy6ricEPRYEvqEO/MuI/X0DgT62R0pamAiQ9yYk75+R9DIz51+2ClEKumj5/CBwfuX8XYqXuAARLJ7axfGTJEmS/2A+Stl7yAxcksxv/Ef4kM0sktqAfrY3r7QtAUz6qP3LSkBwP9C/EtisTgh+jAWOsb397J53ZilBl2y/XzJL4wivtBcVipML2j6xZKRWKAIpzcZamgh2FwEOLwHZ7sDXbH+rvIPHgb62J3zU99YZ0ocsSZIkqZEBWZIkkD5ks4zCv+wmQvihyob8e/zLtgQ+qAVjALb/Zvu8unWeLOmYyudxZd8akvZWSPCPlnR1aVtd0pDSPkRSt9LeSHZ/QUlnql3Kv7pnazpsf1AxT16U6b9b+wO/KP2mtQrGCqcCZzD9njkDS0paiNiD9wGRKZwBhV3AE5IuK/c0QNLWku5T2BlsVPptJOl+SY+Wv58q7UdLurwcf2iF0MGakyRJkiRJkqTLzPclizOL7dMJv6/69nuo+Gh9hKxLk9LDziBpXeAE4Au2X1W7j9b5xH6t/pL2J6T0d6Rddv/vajdmPgB4y3afUt53n6TBtsc3mfOTRBnkKoQlwO1l39nSwKkKb7RniKzXP5uM0ZvYP3cA7d5ok8rpN4GXgCWAo2y/3uIRrE2UpbYRcvhfBp4nVBV/VO75CWAL21MkbU14nH2DKAEdWspDT6DdCqF+rQcBBwF069atxVKSJEmSJEmSpDGZIZtHkHRByV491HFvIDJsN9ayUZXgZRPa5fqvJvy8oLHs/jbA3mVf2AhgRRr4cdWw/bzt/yLMml8kxDe+RIiT3Ff81B4AftXkHhcg9vR93/ZOVLzRgMOAvxNKmGsA35e0Zov7H2/75nLtjcCR5fgrtMvzLwsMlDSuzLtuuY9mVgj195s+ZEmSJEmSJMkskQHZ3MtjwAa1D7YPA7Yi5N6rNJO8b+a/Vo/L+AcDJxLZqVGSVixjHFExZF7D9uAOB7RfLOvfHHgNeJcQx4AQ6tigyaVLA+sR2akJwOeBQWUv3+7An2xPLoIr9xHZr2ZUrQamVT5Poz0zfCpwVzHJ3oHpLQBmsEJIkiRJkiRJktlNlizOvfwZ+LmkQ2xfVNoa7WOaQKgXImkDInsEodR4i6R+tl+TtELJkt1PyPVfTXir3VuuXcv2CGCEpB2IwOwO4BBJf7Y9WdI6wN9t/6t+EZI+Abxme5Kk5YEvAGcV8ZPfA33LPW1FCHLMgO23CLXG2phDCfGSkQrj5i0lXVOew+eJ0sJZYVki6waREavNW7VCOF/SzrZvnMW5kiRJkv8QUnQjSZKukAHZXEoJZHYE+kn6AfAK8C/guLquN9FeVvgQxcDY9mNFLv9uSVOBR4mg40jgcknHljH3K+M0kt0fQ5T3PVJUH18h9l414jPAryW5jPEr22PLueOAqyWdXTdnV7iAsB8YV8a/wvaYmRinyhlAf0lHE8FijVZWCEmSJEnSko9aZREy6EuS+Yk5IntfAoSxhF/VFKA/cLbtaaU8bW/bR3ZxzOWA3W1f2EG/7sBtttfr6lyljK6tXiVQ0snARNsN90Z1luraZmWcuRFJBxP7wKYSpYAH2X68nOsJXAIsQ5QU9rH9XrOxyjWDgDVrz0pSL+BiouxwCmEM/WCTaz8G/JbIAi4MTLC9XXn+fyHEPhYjzLIvsN2/o/tL2fskSZKkRgZkSZJA52Xv51SGbFIRWEDSKoTIxLLAT2yPJMQcuspyhAx9y4CsyizMNVciaUHbnTWi/nfPdW3FT+1rwFnAl4uM/TXAXrZHl71rkzuY+3+IoK7KGcBPbf9R0nblc98mQ5wC3Gn7nDJez8q5Z2z3Lu1rAjdLWsD2FQ3GSZIkSZIkSZJZYo6LepQysIOAwxX0lXQbtPSJWlfSg5JGFX+sHoRE/Vql7cwy1pnFQ2qspF3r566baylJV5S+YyR9o8mSjy1zPyhp7QZj9pI0vIxxS9lP1ap9w6Ke+ACRQZqBss5h5brHJV1cFAmRNFHSKZJGAJuU8e6W9LCkOyStWvodWa4dI+m60vbF8rxGlWe8dPWZlD7nS9q3HE8onycCz5Tx3pb0L0n3SPp0i/dc9QxbElizlFn+lVBl7C/pBNuvtQr0FKbTRwM/AxasrZ/YU3ZROV6NUHlsxqrAC5W1NSx9tP1smatL2dokSZIkSZIk6SxzPCCDD3/4LkD4V1Wp+UT1Jnyyfl7aDwbOKVm2NuLH9fFEdqOX7WOB/wF6EZ5hWxN7pFZtsYwfE55b69vuyfR7iqq8bXsjws+rkajEVcBxZYyxwE86aL+CkGTfpMXaADYiTKjXB9Yq9wcR3IyzvTEhTX8esLPtDYHLgdNKv+OB3mX+g0vbMcBh5TluDtT8vlrxnO2lbHcnPME2tL1kGaujctHDJD1DZK++WuY9D/gD8E/gG4r9cq04Ffg1odw4taYACWxMfIdWBE5mRkPvKhcAv5V0l6QTJLVSUnwEaBhoSjpI0khJI1955ZUOlp0kSZIkSZIkMzJXBGQFNWhr6BNFeFn9SNJxwOq2GwUSmwG/sz21mBDfDfRpMf/WxA91AGy/0aTf7yp/pwuiFOp8y9m+uzT1B7boQvvVLdb3oO1nS/bod7T7h00lhD0APkXIxt9ZMkUnEtknCIGOAZL2JPZYQUjHnyXpyLKOWnsrri/3uhSwKfF+RhF7wFoFvNi+wPZahMjHiaV5oXIve5S/OykUFWeg7BNb2/YtDU4fQphFfxI4itgj1mwddwBrAr8hgq1HJTUzEmv0vayNkz5kSZIkSZIkySwxVwRkZa/OVKBexa6hT5Tta4GvERmdOyRt2WjYri6DLvh2NTieGTo7Z6O5ap/fq5T4CXis4hu2vu1tyrmvEgHnhsDDkhayfTpwILA4MLyUHDbzNatRk7xfAHizMlcv25/p5L1cR7ta4wuE+fKrtt8Fbqe5T9kmwIYKcZV7gXUU0vgA+wA3l+OBREaxKbZft32t7b0IdcotmnTtTQh9JEmSJEmSJMlsZ47L3pfMxMXA+UXqvXq6mU/UmsCzts8txz0JmfalK9cOA74jqT+wAvGD+1hmDDBqDAYOB75X5li+SZZsV2K/2q5Epu5DbL8l6Q1Jm9u+B9iLCDaatb8p6S1Jm9m+l8gSNWMjSWsAfytzX9qgz1+BlSVtYvsBSQsD6xABxSdt3yXpXsJkeSlJKxZp+rGSNiGyRQ8Dn5W0aHlWW1G8yuru9W1J4yXtYnug4sX1tD260eIl9bD9VPn4VaB2fAfwA0lLAB8AXySyoTPg8GO7qIzXnVCk7FtOv1iuHQpsWRm/0Vq2BIbbflfS0kQJ6HMN+nUHfkWUVSZJkiRJp0gFxCRJusKcCsgWL2VuNdn7qwnVvXqa+UTtCuwpaTLwD+AU269Luq+UN/4R+AGRURlNZJN+YPsf5Ud2I34GXFCunwr8lPaMS5VFi4DGAsBuDc7vA1xcAoxnaffcata+H+EL9i4RnDTjASIQXJ8INmco27P9gaSdgXNLOeRCxD63J4FrSpuAfiUYPFXSl8r9Pg780fb7km4gShyfIvzLmrEHIaRxIvEuryOedyMOl7Q1oaD4Rnke2H5D0llElsrA7bZnRi/428A5CtXG9wihmGZsSBg+17KBl9l+qHw31pL0KO2y9+elwmKSJEnSFf4dsvc1MvhLknmfOeJDlnQNSX2BY2xv3+R8P+Bvts8un+8Anrd9YPn8a+DvthsFvUi63/amHaxhAo092PoCH9i+v8W1RxOlkVMIY+j9bf+tcn4ZIot3i+3DW4zzJ2Kf2kLAPYQgydRy7ggiwzkF+IPthuIgJSD+DZFVFfAm8GXbE9XCH6/ZmmqkD1mSJElSIwOyJEmg8z5kc8UesmSWuZ8Q2EAhh78S7QIolHP3Nbu4o2CsA/rW5m7Bo0Qw1xO4kch8VjmVEF3piG/a/hwhXLIysAtAyfJ9nSiZXJcoM2zGd4F/lv116wEH0O57NqnshVsX+G9gO9rVMJMkSZIkSZJktpMB2TyA7aHNsmOF+2gPitYFxgHvSFq+7AX7DKEkeKykhxReZD+tXVx8xZC0gKQLJT0m6TZJt5cSyBpHSHpE4dX26VLidzBwVPED27zIyI+q/gM2LYIdAMNpV35E0obAx4g9fFTaRzQYZ/VyeiFgEdqFTQ4BTrf9fnleL5cx9mswxm6070vE9l9r19U98+n88Ro9dKXsfZIkSZIkSTKLzHFRj2TWsf2ipCmSuhGB2QOEOfImwFvEfrC+QA9CfVDAIElb2B5WGep/gO7EPrVViDLCyyvnX7W9gaRDiRLKAyVdDEy0XctK3UO791kjDiD2+NWyeb8mRE6mk7ovvmozUMoxNypj3Fia1wE2l3QasX/sGNsPlb1fV9Rd3wsYXALNIUD/itjIdNh+tqxxFcInrf78pRRxlba2tqz9TZIkSZIkSbpMZsjmH2pZslpA9kDl8/3ANuXfo7SbHfeoG2MzYKDtabb/AdxVd74mcvIwEbh1CYUHWhtwZmk6lBDxeL6zY9jelthHtiihpgjxPxaWBz5PKGne0CyrZXsU4UF2JqG++ZCkVnL9XbVPSJIkSZIkSZJOkxmy+YfaPrL1iZLF54HvA28TWa6+wC9sX9JijI6Cj1pp31S6+N0pCosnAF+slAhuQmS2DgWWAhaRNNH28a3Gsv2epEHEvrE7CS+zmx0KNQ9Kmkbso2tYR2h7IhFc3lz6bkcDrzE198dLkiRJkqak0EaSJF0hM2TzD/cB2wOv255q+3VgOSLoeYCQ1N9f0lIAklaTtErdGPcC3yh7yT5GBHEd8Q7T+7/NgKTewCXA12r7uwBs72G7m+3uwDHAVc2CMUlLSVq1HC9EBFFPlNO3UrJlktYh9pe92mScL0havhwvAnyW8Har7zedP16r+0uSJEmSJEmSmWWuzJBJMnCN7b3K54WAl4ARHYhbzOq8Q4n9RyPr2vclVAKbSrJ3YY6Jtpea1XEaMJbICl1b17ZUkaofXErzHijVfBOBPZk++3MTsZdrHOFdNoLYg9aK3wM3lmc0GXi3jH1IxST6TCIDNrp4x91t+2uNBpP0W6KsUWUN+5aM1pLAPZJqgiB/o91I+3LCy20cYS69T4sgai3CO03E/5D4Q7lv6Lw/XpIkSTKPkVL0SZLMrcyVARnwL2A9SYvbnkRIkP+9g2vmeyQtWPPdqqe0L1PXtm/d53OAcxpcu1T5O03SMcWTaxUisza2nOte6T+Skj2z/STQU9KmwF+K0fNXCLGLjUufrYsXWRuwTKNgzPaVwJWSlrH9drnfswhvsdPLvb0DrFrmWMX2lHLtB0Rw2SG2rwKuanJuwc6MkSRJkiRJkiSzi7m5ZPGPQO1/Me0G/K52QtKSki4vEu6PSvp6ae8u6Z4izf5ICRKQ1FfSUEk3SnpC0oBmog/AnpLulzRO0kb1JyWtLmlIkY4fUpQNW7WvIemBstZTG01Y1v2EpP7l+hsVBsZImiDpJEn3ArtIWkvSnyQ9XO7106XfLmXNoyUNK23rSnqwSL6PkdSjzDWuMvcxkk4ux0OBxxQy+GOAs4ELytofkvSFZi/L9v223ygf66XtP0G8y8uaXV8ZpxaMCVicdmn7bwMX1Oaolj42eJ59Jd0t6QZJT0o6XdIe5VmMlbRW6beDQl7/UUn/V8o0kXSupJPK8baShinUFpMkSZIkSZJktjI3/8i8DviWpMWAnkT5XI0TgD/b7gN8CThT0pJE+d1/294A2BU4t3JNb+B7xJ6hNYFmwcWSxSj5UKaXfK9xPrHXqScwoDJHs/ZzgIvKWv/R4n4/BVxarn+7zF/jPdub2b6OyDwdYXtDYt/VhaXPScC2xTi5loE6GDjHdi8iO/VCi/lr/MH2Urb/i9h/1q+s/Rt0EFCpeJARJY+LlUDwBCKw+wEwrRPzI+kVovRwT2D3MmYbsI6k+yQNl/TlDobZkHjXk4CjCXn9v5d7OKL0uRf4vO3exPftB6X9eGBXheH0ucB+tmdYu9KHLEmSJEmSJJlF5tqAzPYYQlp9N+D2utPbAMeXH+pDgcWAbsTen99IGgsMJH6Q13jQ9gvlh/Uomsu2/67MPwxYRtJydec3oX2f1tWEVHyr9i/Qnt27uukNw/O27yvH11SuB7geQtiCUFIcWO79EkICHkLU40pJ3wZqpXcPAD+SdBywein/7IjrK8dbA+eXuQYRz6OpgIft04CjiIBynRIIjgZetv1wJ+aujbMy8U6vBs4u47xLyPT3Jb4TlzV4N1UesL1eCVCHA9+wvRNRgtm99PkEcEf5vhxLmGpTTKy/TSg4nm/7mSbrvNR2m+22lVdeubO3lyRJkiRJkiQfMrfuIasxCPgV8SN8xUq7iB/Yf612LqV3/wQ+RwSb71VOv185biXbXi8G0ZHCXrPz7kSfzs77r/J3AeDNEqBM39k+WNLGRGngKEm9bF8raURpu0PSgYRQRjUQX6xuqH9VjhcANulkIIeknkQG6iu2XyvNXwC+Jmm7Mtcykq6x3XLPl+2pkq4nAqUriOzecNuTgfGS/koEaA81GaL6vqdVPk+j/d2fB5xle5CkvsDJlWvWB14DPt76rpMkSZIkSZJk5pnbA7LLgbdsjy0/mGvcARwh6QjbltTb9qPAssALRZxiH9ozRV1hV+AuSZuVud+q2252P/AtInuzB1H21qr9vtJ+De2qgI3oJmkT2w8QGaB76zvYflvSeEm72B5Y9ln1tD1a0lq2RwAjJO0AfFLSssCzts9VeGr1BO4BVpG0IqGGuD3wpyZrGkyIapwJUIK8UY06lj1zNwN7FaGP2pp/CPyw9OlLqFg2DMbK/axl++lyvAPTS9vvRmQBVwLWAZ5tsu7OsiztYjH7VNaxOuHh1hu4XdKt5dkmSZIk8yipfJgkydzKXB2Q2X6BBqqAwKnEvqQx5Yf7BCKwuBC4SdIuwF1Mn+3pLG9Iup9Q9du/wfkjCYn1Ywnj4f06aP8ucK2k79Iur96IvwD7SLoEeAq4qEm/PQjZ9hOJEs3riLLAMyX1ILKHQ0rb8YRIyWRi/9optidLOoXYkzee9oCnEUcSoh5jiO/KMGJfWiNOIrKYF5YAdortthZjN0JAf0nLlOPRwCHl3B3ANpIeJzKcx1aycDPLyUT559+JssY1yvfpt0Tg+KKkA4ggsI/t91qMlSRJkiRASuwnSdI1lJ63jZE0ldhvtBARuOxl+83ZOP7BwLu2r5LUHbjN9nodXNMX+MD2/bNrHV2lrHVT29d20LXZ9fsAJ5aPP7Pdv0Xf04C9geWr3m0KCf0DCa+wV4D9bc9g7tzFdX34Pmbm+ra2No8cObLjjkmSJMl8TwZkSZIASHq4MwmKuVbUYy5gku1eJUh6HThsdg5u++JGP/4VJtjN6EuIenSaDsabmWu6A7t3dcwy7grATwh/so2An0havsUlvy/96nmUMOruCdwInDEz66nS7H0kSZIkSZIkyUfJXF2yOBfxALH/CoBSlvhNYFHgFts/KbL7NxDKfQsCp9q+XtIEQrnwS+Xy3cseqZOBibZ/BVwJDJJ0d/n7JJFFWoQQltiD8OQ6GJgqaU9Cuv05Yp/dypQySdvPSbqSCCJ7EwIf2xNZrVeKn9aThNz7q5V7OpkQsOgOvCrpR8R+uCVLl8NLZu63QHdJk8ocrxACJFPK87jA9iVNnuO2wJ22X5d0C1EW+qikWubxONt31DrbHl7WNt0gtu8q7esTVgarFSVIgPdtb1y5r77ATwmxl17EPrexRCnp4sCOtp+pvg+FH9sI4p0tBxxg+576m5F0EHAQQLdu3ZrccpIkSZIkSZI0JwOyDpC0ILAVEYggaRtC3W8jYp/TIElbEEHRi7a/WvotWxnmbdsbSdqb2Pu2fYOplrP9xXLt8kTA5KKM+APb35d0Me1BHJJ+T3if9Ze0P+GZtWMZbx1g66JW+CYR1J1NSNmPrgZjFTYENrM9SWFM/d+23yt7035HeIEdQOyv2r6s4SBgFds/k7QocJ+kwbbHNxh/NeB5ANs7SfoxkYn8VeOn35oi9nIn8A/bP2vR9XPAZ4gA8lngsvI+vksEtt9rcM1Cpc92RFZv6wbzX0r4wtHW1pa1v0mSJEmSJEmXyZLF5ixesi6vASsQnlQQHmjbEGVzjwCfJgK0scDWkn4paXPbb1XG+l3l7yZN5qv6fzX0x2pAM+8zgIG2p5bjy4m9WBBCJVc0GW9QReK+ladblW2AvcuzGkEIe/Ro0lcN2mY6kCmZwjaKCmQLHrL9ku33gWcI9UiY3pOsnpvL34db9EmSJEmSJEmSWSIzZM2ZZLtXyXTdRuwhO5cIKn7RqCxP0obAdsAvSpbolHKqM55kVUXIVv5YrWjkXYbt5yX9U9KWxP6tZvL71TUcRXNPtyqC/2fvvKPtqqr2/bz0ErpBEQiRjpAQIARDDUVQRBApUWroIE0RED8UIojSPoI0EfnRezfUwBcIoYQUCCl0aRLpAoEYAiSZvz/m3Nx9T84+99wkkIvOZ4w7ztlrr73W2vucjHFm5prvyxkiyZ0AACAASURBVOHlrYYNmIDXwRUshxt7txtJWwHHA5tFoNWIZjzJqq5p5FmXJEmSJDOQQhtJkrSHzJC1QWS6jgCOljQvLr++r6ROAJKWlbS0pG/iKn1X4WbW65aG6Vt6HdbEtHX9sYCPgEVKx4X3GbT2PqvHxbgX2g2lzFlba3jDzKYDe9Li6Va7hkHAIfFskLRq1NPVo5CuXyK2ZW4dbe1C0jrAX4Dtzezt9l6fJEmSJEmSJB2F/J//JjCz0ZLGAD8xsyslrQEMC7GJScAewMq4F9h04DNa/LMA5pc0HA+Af9rElP2p8ceK9tuBmyTtgNc+VXmf1WMgvlWxartiLVWebmOBqfE8LsN94roCT4SH1zu01LG1IsQ8TgZGRtNJZvZe1QIknY4rOi4kaQJe+9Uf36LYCX9GAP8ws+2bvK8kSZLkv4wvU4YeMkOWJEn7SB+yL5hQWexZIaIxK+N+HRgAfAd4H/gUl39/n5LoRql/T2CAmW0yO9fR5FpXwGuy5sZr0841swvj3HzAefhWxunA8WbWyEAbSTvjdW3rm9moyM5djGcl58GFTv7Y4Prj8UBvWsx5kJkND3XFZfDtivMB/wf8phn/ufQhS5Ik6bhkQJYkyZygWR+yzJB9BYlM1G3A5Wa2W7StAGyPB2S1/Y/DM3ZVtWNfNG/gsvufxFbP8ZIGmtnreB3Y22a2akjyL9loIEmL4JnB4aXmXYD5zaxbqEM+LelaM3ulzvW9cZXLdWM9X8ODr4LdI8ibD/gj8Ddgs5m98SRJkiRJkiRpRNaQfcGYWdfZnR0DtgA+LbJMMc+rZnZuuZOk/pKONrNTzWwF4EJJXePcXpLGShoj6cpoW0HS4GgfLKlLtO8iaXz0HRptc0s6Q9LI6H9Qad5ukp4s/oARwNA4PT+tv3f7AttEvyeA/4vrulXc+8l4JrAsMmLAN0pKj98Abo9torUsA7xbCIGY2bsRGLbCzD4FjgW6SFq73kIkHShplKRR77zzTsVykyRJkiRJkqSaDMi+mqyJBy8zhaQ18czUFma2Nm6SDL518Aoz6w5cjatKApwAbBN9i1qt/YCJZrY+sD5wgKRvgfuDmVmP8h+ws6SxuA/ZaWb2uqTFY6yH8K2DL8Q8PcxsXJ11rwMsb2Z31Jy6CXgT9zlbETjCzLqVDaJL3AssL+l5SRdIqsx+hfjJGNzaoN75i8ysp5n17Ny5c9UwSZIkSZIkSVJJBmT/AUg6P7JXI9vuDXiG7aYic1cS1qjyNXsEuEzSAbSoLbbHfwwzey0CvZWBvaMGbh5c+v4RM1sXV6CsaxId2xkHAL+sc7oXXg/2TVwA5ZeSVqxYxyTcAPtAXIDkekn9qtZNfe+0JEmSJEmSJJktZA3ZV5OngJ2KAzM7NGqhalUlptI66F4gXkVzhswW4x8saQPgB8CTknrQPv+xlgE9M/YUsAlwMzAZuDVO34hn3uqxCLAWMCSUFb8BDJS0PS7QcY+ZfQa8LekR3DD6pYo1TMP9z4aE8fXeuGJkKyTNDXQDnmnPPSZJkiQdixTZSJKkI5MZsq8m9wMLSCpL6y9Up98rhB+apHVpkc8fDOwqaak4Vwhp1PU1k7SSmQ03sxOAd4HlaYf/mKTlJC0Y75cANgKeM5f4vJ0Ws+gtgafrjWFmE83sa1GT1xW3A9jezEYB/wC2kLMwrjz5bMVaVpNUzuT1AF6t029eXNTjNTMbW2+sJEmSJEmSJJlVMkM2B5E0ycw6lY774RL5hzW6zsxM0o+AAZKOxbfe/Rv4VWmsrvj2wiVjW+FI4Pk4/W/cO2xCKDZOCG+1Kl+zMyR9G1gYl68fg/uRdaUJ/zFgDeCWUED8Ny7LX9SILQDcEVsS36Nlm+QMSNq9dI8r4FskRwHn4/5q4/EaspcbBFGdgKsKcRPcM65/vO+K+8tNxZ/da3j2LUmSJEmaJmX2kyRpDxmQfUUxszdoyWa1QtLDeGCzi5ltXed8V+B5M1srtuXdB+xqZlfj9WW1c/1YUh88kCoEQAz4n/hra633RQC5EO75dVHp9EXAjvH+GmAb4M8VQ70MbGZm70v6Ph5IXRt1YbtIOgrfqrhog+WMBRYDVjGzCZLmxwMx8G2Lk8zsTABJfYG/SepmZimjmCRJkiRJksx2cstiB0VSZ0k3h6z8SEkbRXsvSY9KGh2vq0V7P0k3SrodVxI8FdgkJOR/UTVP1FONwBUKkdRV0kOSnoi/DaNrq/Eayd5XzDMY+KhO+10WxDqWazDGo2ZW+Kw9Vu4raTm8xu3iRuvAa9HmAf4VY35iZs9VzHc9/iwzS5YkSZIkSZJ8IWSGbM6yYGwnLFgSGBjv/wQMMLOHww9sEL7171lgUzObKmkr4A+0CHz0Brqb2XuljNZ2jRYgaQFgA1qk798GvmtmU6LW6lo863RceTxJBxKy95FlekTSq3jgVuaTCvn52nXMC+wJHCnpeNzsucyNZnZK6Xg/4O7S8dm4b9giMd5SeK1cLVviz/hVSYOBO/As2/SKpT1Bhex9PIMDAbp06VJ9c0mSJEmSJElSQQZkc5aPw6MLaKkhi8OtgG+HoiDAopIWwbfbXR7BkgHzlsa7ryRh3xYrRTC4Ci6BX9RczQucF0qK04BVK67fGuguaec4XgyYXr6fdnIBMNTMHsJ9yU6p6ihpczwg2ziOtwPeNrPHIxDFzP6FC3bUY3+58fRWwNHAd4F+VdNVrSO2Xl4E0LNnz2ZUK5MkSZIkSZKkFRmQdVzmAnqb2cflRknnAg+Y2Y5RCzakdPrf7Rj/RTPrIWkZXP59ezMbCPwCeAtYO9YwpeL6mZK9rzuQdCLQGWi47TH6dse3JX4/gi5w1cbtJW2Li4QsKukqM9ujapwQFRkn6Uq8Nq1fRdd1mNFOIEmSJEmSJElmCxmQdVzuBQ4DzgCQ1MPMnsQzUf+MPv0aXP8RsX2vEWb2hqTjgF/jW/kWAyaY2XRJe9NiBF07XiF7f7+ZfSZpVeCfZtaeoBBJ++NCHls22DZY9O2CqzzuaWaFYiRm9utYP6WtmnWDMUmdcCXLIdFUV/Y++u6EZwLrmVEnSZIkSV1S9TBJkvaQAVnH5QjgfElj8c9pKHAwcDq+ZfEo3I+sirHAVEljgMvMbECDvrcB/SVtgm8dvFnSLsADtGTdWo2H17h1pTnZeyQ9hNdidZI0AdgvsmsX4gHRsNieeYuZnVQxzAnAUsAF0XeqmfWs6Fu5FOBYSX8BPo7761c6/wtJe+AS/+OBLVJhMUmS5KvLly1BDxmQJUnSPuTidklHQNIA4FUzOzuOB+HGxPvH8f/iWaizKq5/1Mw2rHeu1OcVPEP0bk17H+BTM3u0wbVHAfsDU/EAbF8ze7V0flHgGeDWRl5qku4BlsEDzYeAQ0PtEUmH45nBqcCdZnZso/vpKPTs2dNGjcqdjUmSJB2NDMiSJJlTSHq8meRByt53LB4FNgQIo+SvAWuWzm8IPFJ1cVvBWBv0KeZuwGg8mOsO3IRn68qcDDzYxFy7mtnawFp47dgu8LlYxw64UuSawJlNrz5JkiRJkiRJvoJkQNaxeISWoGhNfMvcR5KWCGn5NYDRko4p+X/9rrhY0qR4nUvSBZKeknSHpLtKaogAh4fH2DhJq4c4yMH4dr0nY+viDJjZA2Y2OQ5rfcDWwxUZNwb6xjhPShpeZ5wP4+08wHy4WiTAIbhs/m6hAHlvaZzzy2OE79ptkm6X9LKkwyQdFf5sj0laUtKtkp6W9KGkyZImSdovrv+hpOHR//8kfT3a+0u6RNIQSS9JOqLis0LSgZJGSRr1zju5qzFJkiRJkiRpPxmQdSDM7HW8TqsLHpgNA4bj/mI98TquPrhUfS9ckGI9SZvWDPVjvL6rG77FsHfN+XfNbF3gz7gAxit4LdcAM+sR0vNt8bkPWGTz/hf35DoBuD7G6VHlQRbbMd/GxUJuiuZVgU3w4HAicEBpnEPrDLMWbtrcC5fJn2xm6+DPbS8z2xF4A1jPzBbCPch2j2sfBr4T/a/DPcwKVseFRnoBJ4ZH2gyY2UVm1tPMenbu3LnRs0qSJEmSJEmSuqSoR8ejyJJtCJwFLBvvJ+JbGreOv9HRvxMeoA0tjbExbqQ8HXhT0gM1c9wSr4/jwVu7CNGLnsBm0fQz4C4ze63km9YQM9tGbkp9NbAFcB/+fVwC+A6wPnCDpBWtutDxATP7CM8iTgRuj/ZxuEdaJ/zZ3Vha1/zxuhxwfcj+z4dL3xfcaWafAJ9Iehv4OjChqRtLkiRJkiRJknaQAVnHo6gj64ZvWXwNl13/ELgEz5D90cz+0mCMtqKiT+J1Gu38DkjaCjge2CyCFvAM3CaSfoYHiPNJmmRmxzUay8ymSBqI143dhwc9t0QANkLSdLyOrmo/4Cel99NLx9PjvuYCPqgwqz4XOMvMBoagSf+Kcdv9jJIkSZKOQwpsJEnS0cktix2PR4DtgPfMbJqZvQcsjgc9w3D/r30j+4OkZSUtXTPGw8BOUUv2dTyIa4s2fcskrQP8BdjezN4u2s1sdzPrYmZdgaOBK6qCMUmdIiuFpHmAbYFn4/RteLaM8DWbD3i33jjNELVqL4eEP3LWjtNlP7e9Z3aOJEmSJEmSJJkV5sj//Euahm8rmxeXN78cODvMiHvi9T+VYgoVYy4O7GZmF7TRrytwh5mt1d65GkjG9wcmmdksqQLG2q7Cs0LXlE6NAzrFvPdKWoMW365JwB54PVbBzXi91HjgebwObWIb098O3CRpB+DwijqyM/AMWLEF8B9mtn2T97YpcDbQHXhF0mTcdHossLukQ/DM1geSxgOfAnvXbleUdDW+XXJh4ENJ85rZZ6Xz6+OZxEHRtDvwZ0m/wb9v10kah2fjhkn6DM8+FvL9Pwc+Vosp9tfwwDBJkiRJmiKl9pMkaQ9zaivWx8U2ssjuXINnLE40s1HAzBg6LY7XMjUMyMrMwlxfKGa2aM1xv5rjP+HGzLUsFuenSzrazCZJWgoYgQd1RBarGGcUkT0zs+fxYKnRurYq3kuau/AOq+lzGW4cXcs/cAPmo4GBZnZTjLOqX2YvSPomXte2hpl9ULGMq/EAFPx7sz/wZzPrKmlu4AbgHjwow8xeBr5XHkDST/FtiQvEs1qOFgPsD4igO7KQF+FbNDOLliRJkiRJksx25viWxdj6diBwWGwp6yPpDgBJvSQ9GtLkj0paLdrXlDQi5NDHSloFl0tfKdrOiLHOkDReLu/et3bumrk6Sbo0+o6VtFPFko+JuUdIWrnOmD1Cdn1syK4v0Ub7epLGSBoG1FMSLNY5VC0y7heGsiFyKfeT5PLyvWO8B4E3JH2Ib3M8Gdg1rh0r6bq4djO1yMqPlrRI+ZlEn/Mk9Yv3r0g6QdLDwC6SVpJ0j6THJT0kafUGn/MrZjYWz4KV2583sxfi/et4pq9SstDM7rIADzSXK50+HM8Ovl334haWAd4I0RPMbIKZvV9nrkm44uOPJC3ZxphJkiRJkiRJ0m46hFiBmb0UAUZtLdSzwKZmNlUuJvEHYCf8R/KfzOxqSfPhW8uOA9YqZd52wmXh18a3nY2UNJRqfgtMNLNucf0SFf0+NLNekvbCt+BtV3P+CnzL34OSTgJOxLfBVbVfWmo/o8H6egHfxrfW3YOrI96Eb90bb2YnyOXZHwR2MLN3Igjdxswuk/Q68C0z+0S+vRM8W3WomT0S2aAp0b6y3AcMXOVxe0nLxvEUM9s4ntFg4ODIbm2AZye3KBYcQWKhaljwStUNSuqFbw98sdS2DXBaTdeXgV2BPYEjo9+ywI4x//pVcwQ3AA/L/dYGA1eZ2eh6Hc3sQ0kv40qWrTzVJB2I/2cCXbp0aWPKJEmSJEmSJJmRDhGQBfWUARcDLo8MmOE1QOBZn+Njq9ktERDUXrsxcG1sq3srskbr4zVL9dgK+ElxUC9jElxbeh3Q6gakxYDFzezBaLocr7dqtv1K4PsV844ws5dinmvj/m7CVQBvjj6r4d5c98XzmBv34SLu+2pJt+HiGeACImfJ67JuMbMJcd3fzWy7mOs8YFQEdQcA10d7I0l5AOp5kEm6rN7NyYU+rsTrxj7PopnZIFrqwcr9/woMLdW6nQ38ysym1fkutCLuczU8eNsCGCxpFzMbXHFJ3QHN7CJ8SyM9e/askuZPkiRJkiRJkko6REAmaUU8sHgbWKN06mTca2pHueDFEAAzuyayLz8ABknaH3ipdtj2LgMP+trCKt7PDM3OWW+u4nhKqZZLwFNmVmsEDf6sNgW2B34raU0zO1XSnbjS4WORhZxK662sC9SMU9RaNZKUbxeSFgXuBH5jZo810f9EfFvjQaXmnrhgB3hGdFtJU83stjpDEJL9dwN3S3oL+BGeLaudaxHcZPv59txTkiRJkiRJkjTDHA/IJHUGLgTOMzOryW6Upcn7la5ZEXjJzM6J992BMbSWbR8KHCTpcmBJPBg5hhkDjIJ7gcPwbYRIWqIiS9YXr1fri2fqPsfMJkp6X9ImkbnZE3iwQfsHkiZK2tjMHsYVAavoJelb+JbFvkRmpobngM6SepvZsNjCuCrwDLC8mT0Q9V+7AZ0kLWVm44BxknoDq+OiGt+WNH88qy1xGf1WFFv5IrN0o/yD625mYxrcwwzEltNbcan8G5vovz+wDbBlTSbtW6U+l+FKmnWDMUnrAm+a2euxVbY7dTKnkQW8ALitQcY0SZIkSVqRiodJkrSHORWQLRg1SoXs/ZXAWXX6nY5vWTwKuL/U3hfYQy5Z/iZwkpm9J+kRuWT63cCxuHfXGDybdKyZvRmZtnr8Hjg/rp8G/A64pU6/+SM7Nxfw0zrn9wYulLQQnrXbp432fYBL5DLwM2zNKzEMDwS74cHmrbUdzOxTSTsD58R2yHnwrXzPA1dFm4ABEQyeLGnzuN+ngbujxuwGPEB5AahbWxXMICmPP+8ZkMvR3wosAfxQ0u/MbE28FmxTYKlCPAToZ2ZP1hsHD95fpUX2/xYzO6nBGuuxNPDXCDrBxUHOK51/IALMuWLNJ7dz/CRJkiRJkiRpCtXYPCVzAEmTzKxT6bgfLr1+WBz3AY4u6rraOXZXYEMzu6bi3DN4Zm0+3AJgv7KvV3vGa3I9ewO/icPfm9nlDfqeAuwFLFHzfI7C5e6nAu8A+5rZq/VHaXpdBwOTzeyKmbm+Z8+eNmpUh3NQSJIk+a9lTniBFWSGLEkSAEmPm1nPtvrNcdn75ItD0jx4/dNuDbq9GHVg3XAJ+V3bGLat8RqtZ0lcXXIDXDXyxAZqluBm1b3qtI/GA9buuLDJ6TOznjJmduHMBmNJkiRJkiRJMrNkQNbBiRq7w4GvSxopaaNor/Jo6yfpRkm343VxpwKbyL3GflE1TwiDjMBl7pHUVe4t9kT8bRhdW40naW6539tIucfZbWrxNiv+jo9rtwHuM7P3oibrPmpMm0v3fSu+PfFuYourXAIfM3vAzCZH18do7UVWHqObpL/LvdrelzRF0uuSdpf7yI2TtFL07S/p6Hg/RNJp0ed5uTx+kiRJkiRJksx25rioRwK01NQVLAkMjPd/wmu+HpbUBa8zW4Nqjzbw2rnuUVfXhya2O0paAM9cHRlNbwPfNbMpctuBa3Elw+PK48m9uCaa2fpRk/UIsKOZvVxnmmWB10rHE6JtBsxsx9LaJjVQc9wPD9rqjTEuREBuwz3c3sPr91YNL7kj8WD353Uunyf6bItn9baq7aD0IUuSJEmSJElmkQzIOgYflwOOooYsDrfCVQ+L04vKpdirPNogslBNzr1SBIOrADeZWaE2OC9wnqQeuOjHqhXXbw10DzERYl2r4ObNtdSzIpjpIkZJe+DPabM2uo40szfimhfxzCHAOGDzimsKQZfH8W2aM5A+ZEmSJEmSJMmskgFZx2cuoLeZfVxulHQudTzagn/TPC+aWQ+5MfMQSdub2UDgF8BbwNqxhikV1ws4PAyc22IC0Kd0vFzNupsmsoLHA5uFp1gjyuenl46nU/1voOgzrUGfJEmSJEmSJJkl8odmx6fwRzsDQFKPkISv69FWh49o7c9WFzN7Q9JxwK/x7ZKLARPMbHooI85dMd4g4BBJ95vZZ5JWBf5pZvWCwkHAH0pCHlvHfO1C0jrAX4Dvmdnb7b0+SZIk+c8nlQ6TJPmqkAFZx+cI3B9tLP55DQUOptqjrZaxwFRJY4DLzGxAg763Af1DxOIC4GZJuwAP0JJ1azUeXuPWFXgivLveAX5Ub/CoaTsZGBlNJzXaWinpdFzRcSFJE4CLzaw/Hpx2Am6MrZz/MLPtG9xXkiRJ8l9Gyt4nSfJVYY74kEmahtfvFMbQlwNnRzamJ7CXmR3RzjEXB3Yzswva6NcVuMPM1mrvXJJeweXW361p7w9MMrMz27PmRmublXE6Im15h0laFPdEu7XwX6sY5x5gGTw4fQg4NBQikXQ4nk2cCtxpZsdWjLEQ8FegO77l8gM82zap0Xez0f2lD1mSJEnHIgOyJEnmNM36kM2pDNnnIhaSlgauwbfInWhmo3CD4vayOPAzPLPTFLMwV4dE0txFcNIB5yq8wyZLOgTP8PUtnT8ZeLCJcXY1sw8jG3cTsAtwnaTNgR1wdclP4ntVxZHAW2bWLe5lNaAww678bjZ7o0mSJEmSJEnSLHPchyxqgA4EDpPTR9Id0NBra83wiHoyvK9Wwf2xVoq2M2KsMySND7+pvrVz18zVSdKl0XespJ1q+wfHxNwjJK1cZ8wekh6LMW4t6qUatK8naYykYcCh9SaMdQ6N656WdKGkueLcJEknSRoO9I7xHpT0uKRBIdaBpCPi2rGSrou2zdTiFTZa0iLlZxJ9zpOrPiLpFUknSHoY2EXSSpLuibkekrR69OumGi8y4NQq7zBJ44C9gI2BvnFNt3rPwsw+jLfzAPPRotJ4HL51cnjMd288k3osQ0v9HWb2XD1hkNrvZp3P5UBJoySNeueddyqmSpIkSZIkSZJq5nhABmBmL+Frqc1qFF5b6wAn4F5b4DVUf4pMRk9cve84QjHQzI4Bfgz0wFUCtwLOKIKTCn6L+2l1M7PuVNdlfWhmvYDzgLPrnL8C+FWMMY6WzEpV+6XAEWbWu8HaAHoBvwS6ASvF/QEsDIw3sw2A4cC5wM5mth5wCXBK9DsOWCfmPzjajsa3/PUANgFaKTlWMMXMNjaz63DJ98NjrqOJ7KSZjYvPofy3QWmMz73DIrD8F7598ATg+ug/rmoBkgbhPmkf4VkygK8DV+PqiBOBA2rmLHMJ8CtJwyT9PgL6ujT4bmJmF5lZTzPr2blz56ohkiRJkiRJkqSSDhGQBfU8qhbDhRvGAwOANaN9GPA/kn4FrFArCR9sDFxrZtPM7C18O9z6DebfCji/ODCz9yv6XVt6bRVESVoMWNzMiq13lwObtqP9ygbrG2FmL8U2wWvj/sBl2W+O96sBawH3RZboN7RkosYCV8u9u6ZG2yPAWZKOiHUU7Y24Pu61E7Ah/vk8iaseNgp4iesK77AzoulnwF1m9lr1Va0xs21irvmBLaJ5HmAJ4DvAMcAN9bJacf2TwIqxhiWBkZLWaLTsZteWJEmSJEmSJO2hQ6gsSloRDyzeBso/jE+mjteWmV0T29F+AAyStD/wUu2w7V0GzZkUW8X7maHZOevNVRxPKdVyCXiqItv2A2BTYHvgt5LWNLNTJd0JbAs8Jvf2mkrrQH2BmnEKtcW5gA/KhtZtofreYb2BTST9DFdOnE/SJDM7rtFYZjZF0kC8buw+PEt6i7lKzQhJ04Gv4QIi9a6fhJs/3xJ9t8VFRWrXXP5uJkmSJF8RUlgjSZKvCnM8QyapM3AhcJ7NKPlY12srfiS/ZGbn4J5Z3ZnRH2soXo80d8yxKTCiwVIKv69ijiUq+vUtvQ4rnzCzicD7ctl4gD2BBxu0fwBMlFRku3ZvsL5ekr4VW/z6Ag/X6fMc0FlS77iHeeX1dnMBy5vZA8CxuABKJ0krxfbC03Bxk9WBV4FvS5o/Mnhb1ltM1HK9LJfFR87aVYtXi3fY9mXvMDPb3cy6mFlXfNvjFVXBmLzOr6iJmwcPop6N07cR2TK5F9p8wLsV42xUquGbD/h23Hdtv0bfzSRJkiRJkiSZZeZUhmzB2OZWSItfCZxVOv8DSVfS2murBy1Zr77AHpI+A94k/KwkPRLbG+/GA4/ewBg8m3Ssmb0ZmbZ6/B74u6Tt8CzQ7/AMCiFqUUhWzh/ZubmAn9YZZ2/gQrm0+kvAPjXt6wO3l9r3AS6RNBk3Tq5iGC5c0g0PNm+t7WBmn0raGTgngql58Dq354Grok3AADP7QNLJcnXCacDTwN2hUHgDvsXxBVwdsYrdgT9L+g3+WX4o6WvAZKCfmT1R6lvrHfZNPGh62My2K/X7rqTncNXDEcBBZlYoIC4MDJQ0P25UfT8eMIHXhV0Sn/+nwN4NgqiVYt3CP8c7adn22dZ3M0mSJOngzEnJe8jsXJIk7WOO+JC1haRJeDCwoZl9LOn7wB+BCTU/3mf3vEOAo0MOv9zeD5dsr/THascck8ysUzuv6YNnj3b4EmXt52mypqzovy1wOJ612gAXXakS1UDSlsBCeMC1Xal9W0LwA5ecH2pmf56JW/hSSR+yJEmSjkMGZEmSdATUpA/ZHN+y2IC78bon8ExUIaaBpIUlXSJppFyufYdo7yqXX38i/jaM9j6Shki6SdKzkq6uEnzAM2+PyuXye9WelLSCpMFy+fjBkrq00f4tuZrfSEkn15sw1v2spMvj+psiw1aYUe+FC2g0kprfJdY8RtLQaJvBHiDmGl+a+2i5sTXxjP4g6UHgSEmdJd0cax8paaMGn9cO+HZDM7PHgMXVQNXSzAbj20xr2++KMQzPkC03w8Uta+8fz+xeuST/jyWdLrcuuEfSvNGvygrggLivMXGfxTO/TNI58T14KbKOSZIkSZIkSTLb6cgBsqzU7QAAIABJREFU2XXATyQtgNeIlT2ljgfuN7P1gc1xSfuFceGF75rZuvi2xnNK16wD/ByvF1oRqAouFjazDXH1v0vqnD8PDzy64zLr57TR/ifgz7HWNxvc72rARXH9hzF/wbNmtmQjqXlcMn4bM1sbF+6A+vYAbbG4mW1mZv8bax8Qa98JuLjBdcsCr0k6Prb8fRMYEsHg8U3M2wpJI3DJ/l3V4mdWz5tsJTxw3wG4CheB6YZL+BdrGYqrKc6NKzEWVgC3mNn68cyeweX4C5bBlSy3w7eK1ltj+pAlSZIkSZIks0SHDcjMbCxu9PtT4K6a01sDx8WP7SG4EmAXvO7nr3Kj4Rvx4KtghJlNMLPpwJMxdj2ujfmHAotKWrzmfG98Kx14fdHGbbRvREt2r5Gs/Wtm9ki8v6p0PTQnNf8IcJmkA/DAA5qzB6jl+tL7rYDzYq6B+PNYpP5lXt9nZqdEADgC2C08xU6puKYRY/AAdZWSl1k9b7K7o8ZsHH7f90T7OOADYA+8Rq7Y6rkgLVm3tSLLOA6vh1uTFm4zs+lm9jTucTYD6UOWJEmSJEmSzCodQva+AQOBM4E+wFKldgE7mdlz5c6x9e4t3Ax6LmBK6fQnpffTqL73Knn5KqrOt1cev9G8bUrNm9nBkjbAs0VPSupRYQ/wPM3J2hfz9W4ykJsALF86Xg54vYnrZkDSiUBn4KAmun8CYGbTJX1WEvKYjn/GjawALgN+ZGZj5HWCfWrHLZbUrhtIkiRJkiRJkibp6AHZJcBEMxsnF7YoGAQcLulwMzNJ65jZaFwmf0L8ON+blkxRe+gLPCCXop9oZhNrys0eBX6CZ7t2p0V+vqr9kWi/isay9l0k9TazYXhWcAZZezP7UNLLknYxsxujDq57BBQrmdlwYLikHwLLy1UVXzKzc+RWAd2Bh4ClJS0FTMK35N1TO1dQWAGcARBB3pMVfQcCh0m6Dhf1mGhmbzS437pE0LgNsGVkM2eVz60AzGxY1JWtamZP4TYJb0Tb7rRYLCRJkiRfYVJUI0mSrxIdOiAzswl4HVMtJ+Ny7mMjKHkFDywuAG6We2M9QOtsT7O8L+lRYFFg3zrnj8Dl1Y/BTYf3aaP9SOAaSUfSIq1ej2eAvSX9BVeYrFIWrJWavw7f3neGpFXwbM7gaDuOGe0BPpN0El6T9zItPl71OAI4X9JY/LsyFK9Lq8dduMLi33HZ+30q+gEg6SHc96yTpAnAfmY2CJexfxUYFoHwLWZ2UqOxGtHACuAp4Lf4c3gV3+JYtR0zSZIkSZpmTqs8QgalSfJVokPJ3kuahv8wLjygLgfOjoxXT2AvMzuinWMujtcyXdBGv67AHWa2Vnvnkish9jSzd2va+wOTzOzMZueemfNfZeSeYlcA6wH/Avqa2SsVfZcCbgLWBy4rbAhCHfFGXOBjGnB7lbl09F8Nr79bHJgfeMjMDows7N9w/7iF8O2vp5vZHW3dR8reJ0mSJAUZkCVJAs3L3ne0DNnHRX2UpKVxkYzFgBPDG2xmfvEujisWNgzIyszCXB0SSXN/if5l7Z1rP+B9M1tZ0k+A0/Bto/WYgme11oq/Mmea2QOS5gMGS/q+md09wwjOObh65N9izWX1xocKXzRJPYDbJH0cMv1JkiRJkiRJMlvpyCqLbwMH4nVJknuJ3QEgqVd4RI2O19WifQbfLVyyfKVoOyPGOkPu2TVO0gw//mvm6iTp0ug7VtJOFUs+JuYeIWnlOmP2kPRYjHGrpCWKdnzb4fSa9vXk/ljDgEPrTRjrHBrXPS3pQklzxblJkk4KUY/eqvbiOiKuHRv1X0jaTC1S86MlLVI8E0n7RPs7kv4R7z+UdIKkh2ntlfZUrOOZ0njDa25jBzwTCp792lJqXbRXzInX450H/A+waXHezCab2QPx/lPgCRr4l+HKlJ9bAFSoNxL1cifhdXT1nn/K3idJkiRJkiSzRIcNyADM7CV8jUvXnHoW2NTM1sH9t/4Q7fV8t44DXgzZ9GOAHwM9cCXGrfDaq0oDYzwjM9HMuoVH2P0V/T40s154wHB2nfNXAL+KMcYBJ7bRfilwRIU6YJlewC+BbviWvR9H+8LAeDPbAK+TOhfYOfzLLqHFi+s4YJ2Yv6gPOxo4NJ7jJrinFwBmdmm0Xw+cEO/fA6aY2cY1XmlrAlsCb5Sk6zeoWf+ywGsx9lRgIq0VNT+fs/jDP/Oh9R5GbFH9IV5HV8UA4H5Jd0v6hWa0NijzBF7rNgMpe58kSZIkSZLMKh06IAvqSY4vhntxjcd/XBf+Uc34bm0MXGtm08zsLeBBvCapiq2A84sDM3u/ot+1pddWQZRcTGJxM3swmi4HNm1HeyP/shFm9lJsE7yWFv+yabSIiKyGb/G7LzJNv6ElgzQWuFrSHnjdHngm6ixJR8Q6ivZGNOOVVo96n+9MFTZKmgd/BudEMF8XM7sUWAOvO+sDPCavZWt2fUmSJEmSJEkyW+jQAZlcqn0a8HbNqZOBB0Lk4oeEl5aZXQNsj2d0Bknaot6w7V0G7fcRm1WllGbnrDdXcTylVMtVeHEVWaZuZrZ1nPsBHnCuBzwuaR4zOxXYHzdRfkzS6niw1ox/2edeaaW/NRqs/3P/sgioFsMzbjPDRcALZlYvQ9kKM3vdzC4xsx3we6sSTFkHV8BMkiRJkiRJktlORxP1+BxJnXEJ9PPCa6x8ejFaPKP6la5ZkRl9t8bQWs58KHCQpMuBJfFapGOYMcAoKLy4fh5zLFGRJeuL16v1xTN1nxNeZu9L2sTMHgL2BB5s0P6BpImSNjazh2nsX9ZL0rdw6fa+eFBSS10vLjzQWD7EMB4GdsNl6JeKuqpxknrjW/YeB74dmaQF8K2I7fJKq1j/QGDveGY7A/fbTEh/Svo9/r3Yv4m+3wMGhwXAN/Atkv+kZmuipO74ltU2x0ySJEmSglQ4TJKkPXS0gGzB2OZWyN5fCZxVp9/pwOWSjqJ1TVdfZvTdek/SI7G98W7gWHxL4Rg8m3Ssmb0pl5avx+9xL67xeLbud8AtdfrNH4IVc+HGzrXsDVwol2h/iRafrqr2fXBfs8m4EXYVw/BAsBsebN5a26GBF9fzwFXRJlx58ANJJ0vaPO73aeBuM/tE0g34FscXgNEN1lTllVaP/wdcKenveGbsJw3GLSwGFgXmk/QjYGvgQ+B4vLbwiQjezzOziyuG2Rr4k6QpcXxMfAdWBzaRNBqXvX8br+NLhcUkSZKkaTqC7D1kYJgkXxU6lA/Zl42k5fDtet/GA6k78B/nn87RhTWJ3Dfr6JJMe1dgw9i6Obvm6Afca2avx/HFwFlm9vRsGj99yJIkSZL/KDIgS5IEmvch69A1ZF8ksZXuFuA2M1sF38LXiRb1wa8iXfFth+1C0twNTvcDvlkcmNn+sysYCz73IcMFWk5r0LfwITu6zrkzzWx1vOZrI0nfbzBO4UNW1LedWzr3kJmtY2arAUcA50nash33kyRJkiRJkiRN818bkAFb4MIXlwKEAMYvgH0lLSTpKEmXgBsHy33LFpL0QtS3IWkuSX+X9LXw3npM0ki5/9ekYiJJx0T7WEm/i7au4c/11/DrulfSgrWLlLRLzD1G0tDStQ/h2zm/KWnD6H4qvuXuyZBz7yfpvNJYd0QWqJ5P2QmxxvGSLpKzM24fcHWMuaCkIZJ6xhg/lfuzjZd0WmmeSZJOiTU/Junrko5XixdZ8Xc89X3ItqnT91Yz+3fU1BVbDYnPrq4PWYM504csSZIkSZIk6RD8Nwdka+JCFZ9jZh8C/wBWxmusVpa0I+4JdpCZTQauokVkYytgjJm9C/wJ90BbH3i9GFPS1sAquF9YD2A9SYWp8SrA+eHX9QFQz3T6BGAbM1sbV5AEr236rpmti9fNnRPtx+EZnh5mNqCN+//cpyyCnPPMbP1QrlwQ2M7MbgJGAbvHmJ/bCEj6Jp7N2iLua/2o6SrGfizWPBQ4wMxOqVFe7GFmp1Dfh+zxOn13bON+inV97kPWYM70IUuSJEmSJEk6BP/NAVmVtLwAM7Pp+Ha9K3Hlw0fi/CXAXvF+XzxYAxcKuTHel2u4to6/0bT8uF8lzr0cWRjw4LBrnfU8Alwm6QCg2Fo4L/BXSeNizm+3ca/1KPuUAWwuaXiMuQUt3m5VrA8MMbN3IpC6GlesBPgUr8eD6vsqSB+yJEmSJEmS5L+Wjqay+GXyFDUZKUmL4p5YL0bTKsAkWtdQvSbpLbnH2QY0lqQH/0H/RzP7S81cXYFPSk3T8MxUK8zsYEkb4H5hT0rqARyOC06sjQfVU2qvCxp5h33uUyZpAeACoGfcX3+qbQDK91XFZyXp+mk0/p4VPmQT9CX7kOHB9SVyBc30IUuSJElmCymmkSRJe/hvzpANBhaStBd8Lmzxv7h632S5FPyf8KzPUlFPVXAxvnXxhpL58mO0BHhl6fZBeF1ap5hnWUlLN7tISSuZ2XAzOwF4Fw9eFgPeiCzenrRkzj6itefaK0CPqHVbHt82WY8i+Ho31lm+19oxC4YDm0X93Ny41P+Dzd5XicKHDGaPD9nPm+j7PbkXG2rtQ1bbr/AhO7+960mSJEmSJEmSZpijGTJJBlxlZnvG8TzAG8DwQsr9C5p3CK7UtyNwgaTf4sHpS7jHFnid0QVm9ryk/YAHJA01s7fxIOJSWrYrggcCV0n6JXAnLpuOmd0raQ1gmNwfaxKwB545aoYzJK2CZ6QG435eFwA3S9oFeAD4d/QdC0yVNAa4DK+DexkYB4zHt0zOQHiP/TX6vQKMLJ2+DPdJ+xjflrk0vi3wYzzDNyxe7zKzv0VwtqCkOxp9hpL+Hy4YMhewhKQXcdn7n0jaFeiPb10cY2a7la57hfQhS5IkSTowHUX2HjJblyRfBeaoD5lcifAF3DvrY7lU+R+BCV9GQGZmo2ra++Hb9uqq6pX69cRl0zcptS0EfGxmJukneKA5UwGvpLlLmbcvFEnzRA1Ys/03BJ4xs/fj8+pvZhuUzh+FB1qLthGQLRoiKkg6C3jbzE6N4PMGYIuYY+kIgjs06UOWJEmSFGRAliQJfLV8yO7G66PAt71dW5yQtLCkS+Ry7KMl7RDtXSU9JOmJ+Nsw2vvIZdlvkvSspKsV6ZI67CHpUblk+wxb+SStIGmwXKp+sKQu0X4q8BCwTLkd2A6YJGkyrj74SZ0xu8a6Lo9xb4pADkmvyKXnHwZ2kcvo3yPp8bjX1aNfPRn8NSWNkMu6j5W0Ssw1vjT30VEbRjyjP0h6EDhSUmdJN8dzHilpo6oPy8weNbP34/AxYLnSHMvhn2VVZqo8ThGMCa+dK/5n4ABcefL96FcZjMXn/aCkGyQ9L+lUSbvHsxgnaaXoV/f+JPWK78DoeF0t2vtJuiWe/wuSTm/rfpIkSZIkSZJkZugIAdl1+Da1BYDueG1SwfF4TdH6wOb49r2FqZZ9Bxdh+DmuPLgiUBVcLGxmGwI/w8UdajkPuMLMuuMKgsUcawIHh5FxuX0P4BAzWwg4nWqlwNWAi2LcD2P+gilmtrGZXYcLVBxuZuvh2ysviD71ZPAPxiX3e+DZqQm0zeJmtpmZ/S9eKzcgnvNONBFQBfvhAXXB2cCxwPTajqrvCfY48CauPFmYM68KbC33Mvu3pBej760Va1gbOBLohtfTrWpmveIerpD0JPA8/t2aF9/iWdzfs8CmZrYO/lz/UBq3B/7d6gb0ldfg1d5T+pAlSZIkSZIks8QcD8jMbCwui/5T4K6a01sDx8WP6iG4+EQXGsu+jzCzCSF48STVkuvXxvxDgUU1oxdVb1rk668ENm6jfSNasntXVt4wvFaS0L+qdD3A9QByYY0NgRvj3v+CmxlDfRn8YcD/SPoVsELZL6wB15febwWcF3MNxJ9HPSGPz5G0OR6Q/SqOt8O3HT5er3+FJ9h6uILlM3jwA17XOC+wBK58OD/Qp4EP2Ugze8PMPsHVMe+N9nHAOxGkfkZLnd3WpftbDH/G4/GawbLU/2Azm2hmU4CngRXq3FP6kCVJkiRJkiSzREeRvR8InIl7Qi1Vahewk5k9V+4cW++qZN9rpeSr7rE2g9VWMV3VeWuiT7PzFkHDXMAHEUy07lxHBt/MrpE0PNoGSdofzwpVSd6X5yrm691kIFeoD14MfN/M/hXNGwHbS9o25lpU0lVmtkejscxsmqTrgWNwkZQJuKn0Z8DLkp7D7QdGVgxR/rynl46n0/LZ170/SecCD5jZjnIbgiEV47Yl3Z8kSZIkSZIkM0VH+ZF5CTDRzMZJ6lNqHwQcLunwEMtYx8xG45mNCWY2XdLetGSK2kNfXDlx45h7Yk252aO4fP2VuNfYw220PxLtV9HYm6yLpN5mNgzPCj5c28HMPpT0sqRdzOzGqLPqbmZjFDL4wHBJPwSWl0v0v2Rm50haEd+e9xCwtKSlcGXH7YB7KtZ0L3AYcAZABHlP1usYNXO3AHua2fOlNf8a+HX06YOLptQNxuJ+VjKzv8f7H+LbBwFui+dymaSv4VsYK02em6Tq/hajRe6+3yzOkSRJkiRACmkkSdI+OkRAZmYT8DqmWk7G65LGxg/3V/DAokr2vT28L+lRXEJ93zrnj8BNg48B3gH2aaP9SOAaSUcCNzeY9xlgb0l/wRUm/1zRb3fgz5J+g2/huw6XvK8ng38cLlLyGV6TdZKZfSbpJLwm72VaAp56HAGcL2ks/p0Yitel1eMEPIt5QQSwU5tRj6lBwOVyI27FPRwS5wbhNWRP45mpY0pZuJml6v5Oj3UcBdw/i3MkSZIkCdCxVBYhA8Qk6ejMUdn7joqkaXgN0jx4MLOnmX0wG8btCtyBC4ZMNrMr2nFtH+BTM3t0Vtcxs8T6NzSza9roWu/aHnjwuSgeaJ1iZtfX9DkX2MfMOjUY52Dg0BhjEnCgmT0d57rj9XaL4lsW148asHrj7Av8At8yOhdwfPioXQZshguuLIgrSf7azGYwji6TsvdJkiRJQQZkSZLAV0v2viPycYhOrAW8hwcAsw0zu7BeMCY3xq6iDy700TRtjDcz13QFdmtwvhGTgb3MbE3ge8DZZSEVubdbrbBKPa4xs25RX3c6cFZp3VfhCphr4s/rs3oDyOX5jwc2DrXL7+Cm2gXHhIrlasBofGvrfO252SRJkiRJkiRphg6xZbGDMwyvyQIgtiruiqv/3WpmJ4YU/w24J9fcwMlmdr2kV3A1w83j8t3MbK0QJZlkZmfKTaofxUUxBkp6HvgNMB/wL3zr4oL4FrtpkvYADgf+gdfedSa2TprZPyLD8x4u//9kqB9uaGbvSJoLF/v4jpm9W7qn/rjaYVfgXUn/g9ddFR5jE/CA6hvAgqHGeDku+X8qHvzMj/uH/aX88EKu/lulY3Blxrdj7R9Imhuv79oNmEFNUVI3SsqVMcYn+HbWIsW7NTDWzMYAtLHNcWngIzzDhplNKt6XMU8fD5C0I/B94G816zoQOBCgS5cutZcnSZIkSZIkSZtkQNaACBS2BP5fHG+NK/71wmufBkraFA8sXjezH0S/xUrDfGhmvSTthQcQ29WZanEz2yyuXQIPmCzUEo81s19KupAI4qLf7bhP2uWx/e4c4Ecx3qrAVqFg+AEe1J2Ny9uPKQdjJdbDM0Yfy82qu5vZlKhXu9bMepbEOraLNRyIC6KsL2l+4BFJ95rZy8Wg9eTq5Ubc8+Ey9eCCGwPN7A3V8fE2s3G4L1hx/aHAUXiGbIvSPZukQfjncZ2ZVRk6j8FVOl+WNBi4xcxur+gL8ATuldYqIDOzi3C/OHr27Jl7f5MkSZIkSZJ2k1sW61Nkgf4FLAncF+1bx99oWn6kr4LXm20l6TRJm5jZxNJY15Zee1fMV66lWg6Xrh+HS8GvWf+SSj80gBvNbFq8vwTYK97vi0vL12NgSRa+kc9bma2BveJZDcfFPlap6AuApGVivfuESuY3gV1oMYZuEzM738xWwjNtv4nmefBnsHu87ihpy4rrp+HbJnfGM4YDIktYuexm15YkSZIkSZIk7SEzZPX52Mx6RKbrDryG7Bz8h/kfa7flAUhaD9gW+GNkiU6KU834lJVVIs8FzjKzgZGR6t/kmuv5mWFmr0l6S9IWwAZUS/KX1/ALqn3eygg43MwGNbPAUFW8E/iNmT0WzesAKwN/j+zYQpL+bmYrNzHkdbSoVE4AHiyyf5LuAtbFlShnILYjjgBGSLoPD1T7V8yzTtU4SZIkSVJLimgkSdIeMkPWgMh0HQEcLWleXJJ9X0mdACQtK2npyPJMNrOrcIPrdUvD9C29Dmti2rI31t6l9o+ARUrHhR8atPZDq8fFuODFDaXMWVtreMPMpgN70uLzVruGQcAh8WyQtGrU081AiGLcim+zvLFoN7M7zewbZtbVzLriz7EyGIstlAU/wK0DirV0l7RQCHxsBjxdMcY3JZU/ox7Aq3X6SdIRwDJUe7glSZIkSZIkyUyTGbI2MLPRksYAPzGzKyWtAQyLbM4kYA88w3OGpOm4st8hpSHmlzQcD35/2sSU/YEbJf0Tl1wvBDFuB26StAMu6lHlh1aPgXgGqGq7Yi1VPm9jganxPC7DveO6Ak/IH8g7tNSx1bIrsCmwlKR+0davyoC6AYdJ2gp/zu8TQauZvS/pLGAkni28y8yqdIfnBc6MQHpKrLvsu3aGpN8CC+GfweZm9mk715kkSZJ8CXQ0iXnIDFmSJO0jfchmA5Imlb2zIuDoiQt49KwQ0Wh27K5UeH/FuWeA53CRjFHAfmb2WU2/nsAAM9tkVrzEYqx7cJn4hwtxj2i/Gr/nz/CtgAfVrqPUd3e8/gs8qD2kUEeM83PHvfyzPEdHJn3IkiRJ5gwZkCVJ0lFJH7L/AGLrXVcae3+9GJ5c3XBBkF1rxjgOuBn4dTS1NV5bnIFvY6zlalzkpBsu079/gzFeBjYLD7CTCaXCEkfigWaSJEmSJEmS/EeTAdkXSNRESdLNkkbG30Z4Yy9Jj0oaHa+rRXs/STeGrP29uM/XJpKelPSLBnNNwzNTy8Y4XSU9hAdo/wKmR9dW40maW9IZsbaxkg5q454G47Vkte13hVDGWvjWxBNjjidjy2a576Nm9n4cPkaL31lh2vwD4OPSOou/48vjSHpF0h8kDZM0StK6kgZJelHSwdFnuKR/Spos6WNJb8p9zZB0m6THJT0VEv7FuJMknSJpjKTHJH293rOQdGDMO+qdd95p9NiSJEmSJEmSpC5ZQzZ7KGTyC5bE67bA66wGmNnDkrrg4hNrAM8Cm5rZ1KiJ+gOwU1zTG/cBe0813l9VSFoAV1E8MpreBr5b9hLDtxQeRzu9xNrJs7hC45Fm9lAT/fcD7i4dnw0ci4uHfL2JLYuvmVlvSQPwuraNgAWAp4ALgd/i8vYHEd5xwBJx7b7xjBcERkq6OQylFwYeM7PjJZ0OHAD8vnbi9CFLkiRJkiRJZpUMyGYPH8e2QaBVDRm4GfO31WJ4vKikRXAlw8sjWDJcaKLgPjN7r8m5V4pgcBXgJjMbG+3zAudJ6gFMw42T67E1rk64cxwvFmPNbEB2ATC0mWBM0uZ4QLZxHG8HvG1mj0cg2gxF4DsO6GRmHwEfSZoiaXFae8cBdMLvbyhwhKTCuHr5aP8X8CludwDwOPDdJteSJEmSJEmSJO0iA7IvnrmA3iXTZQAknQs8YGY7htDGkNLpsidYW7wYnmnLAEMkbW9mA/mCvMQaIelEoDOejWqrb3dcjv/7kZUCz25tL2lbPMu1qKSrzGyPBkN9Eq/TS++L43mo8I6LgG8r/LOZLGlIzAnwmbWo3Uwj/50kSZJ0WFJAI0mSrzpZQ/bFcy9wWHEQGSto7TfWr8H1td5fdTGzN/DtiIV4x2z3EmuEpP2BbYCfxpyN+nYBbgH2NLPnS/fwazNbLmrvfgLc30Yw1gx1vePw5/N+BGOr48qRSZIkSZIkSfKl8qX9z38IMuyGZxym47LowxtfNcMY/YB7zez1NvoNweukRkm6C9jNzD5oYvz+wCQzO7OmvStwh5mt1Z71BkcA50saiz/vxSVtD5yOb1k8Cri/wfWtvL/MbECDvrcB/SVtwhfjJUYIhawOdJI0AZfZH4TXa/0L+CC2Z95pZj+uGOYEXHxkSEjcjy4kQeN57A/MDywmaQUzm8G0uVnM7F7V9467Bzg4PpfncHGRJEmSJJllOpoUf2YRk6Rj86X4kEnqDZwF9DGzTyR9DZivrcCqzjhDiEBrdvSrc11/Zn9ANlvWFtfOY2ZTZ3UNX8RckpbEvcN64jVxjwPrldQUa/t/B3gVeKHGw21zYHhkrg7BvzN9Z+FWvhTShyxJkiQpyIAsSRLoeD5kywDvmtknAGb2rpm9LmlLSbcWnSR9V9Itcin2yySNlzROLs++M/5j/+qQQF8wrh8dfS4JlcBWyKXRvxbv95JLu4+RdGXFWteWdL+kFyQdUGe8BSRdGnOOjgCiUfuCkq6Lea/HPbpmINZ5mqQR8bdytF8m6SxJDwCnSVo47nVkzLND9Fszrnsy5lol+t4Z9zteUt86z6RnBIlI6i/pIkn3AleofZL42xBiJBGE3Qd8r6qzmT0W2yxr2x8ws8lx2EoSv84z6yPpQUk3SHpe0qmSdo/nME7SStGvs9pvPXCLpHvie3B6g/tOkiRJkiRJkpnmy9qyeC9wgqTngf8DrjezB/GteudL6mxm7wD7AJcCPYBli4yUpMXN7ANJh9GyFXEBfMvdlmb2vKQrgENw2fQZkLQmcDywkZm9GxmdenTH64kWBkZLqv1vrkMBzKybvPboXkmrNmg/BJhsZt3lQhZPNHhOH5pZL0l7xX0Uku+rAluZ2TRJf8Brq/aVqwiOkPR/wMHAn8zsaknz4TVj2wKvm9kP4hks1mDugvVwUY6LgVfw78hbuCjIAaqWxF8WeK10PAH4kaRf1fR7xMwObWIdEJL4EbR/q+bPkIw1AAAgAElEQVTcr3ARj7VxG4H3gJeAi+MZHgkcDvycmbMe6AGsE3M8J+lcMyvfX2EZcCBAly5dmrylJEmSJEmSJGnhSwnIzGySpPWATYDNgeslHWdml0Wmag9Jl+L+W3vhohMrypUI78QDulpWA14uiUJcjgdFdQMyYAtcFv7dWFOVrPzfQhHx48hK9QLKHmMbA+fGGM9KehUPmKraNwXOifax8pqlKq4tvZZrxW4M42dwCfftJR0dxwsAXYBhwPFyY+VbzOwFSeOAMyWd9v/ZO+8wO8tqfd+PICUgKBg5ikKOFAvFAAEEREL3p0hROagoICqCdBTsSJEiKCgd5FCkWGgeBCQoHYRQEwIiqIDSFJCikZ48vz/W2syXnb33zGQCZMK6r2uu+fb7ve37ZnJds7LW+zxEyeVAfMHOz3LK0ZLOJgJUgLnoLYmvDm03zGi5oaTPEBnRtVqZ1Q59xgI3tjJtkv5C3+/KJOJ3DWbMeuBS20/lvH8AFmfagLN8yIqiKIqiKIoh84qJemRAcQUh5DAJ2JrIcJ0M/JrIwJyV55aekPQ+ogxuR+B/gG3bpuwUAPRCxB/d/W61n8/d1u21n4H+se4u100ZfAEft31X29g7JY0HPgKMk/QF25dlIPxh4KDMbu0HvEhfueo8bfO0rzVQSfwHgLGNz29nWin/AZPZqm/RIxhr0C5135TBb/1+z4j1QHPekr4viqIoiqIoXhZekT8y82zOVNt/yqbRhKADeZbsIeDbpAFvnm963vY5mfU4Jcc1Jdv/CIyStKTtPxPS7lf22MalwHmSDrf9T0kLdcmSbSLpIKJkcSwhJT9X4/5VwJbAZVmSuBih0tdf++WSlqUv49SJLYCD8/t1XfqMA3aWtLNtS1rB9q2S3gncY/uIvF5e0h+Bx22fLmkyffL69xGlib+hr0Sv21o7SLrM9gv5XA/a7uSTNg44UNKb8vMG9EnwDxhJKwDHAx+y/chgx3ehZT1waK4x2vYEBm49UBRFURQDpkQ0iqIYDK/U//rPDxyZZ55eBP5Mnr1JzgBG2v5Dfl4UOFlSK4vT+sP+FOA4Sc8Q5Y2fA86SNCdwIyG93hHbd0g6ALhS0hTgVjr/EX4DUSa5GLB/BoyjGvePyT1MymfZJpUju7Ufm89yG1H6eEOP9zR3ZrleB3yqS5/9ibLM2xQ1ePcRZ822IEo/XwD+DuwHrAwcKmkq8AJxng1gX+B/JX0T6GU9cCIDlMS3/bik/YmfA8B+PcpCSaGMTwMjFPL5J9rehwia5id+rgB/s71xjz0OhHbrgauIM3cDtR4oiqIoZlFmNUVDqICsKIrB8YrI3ve7Cekowovqf2dw/NuBo4H3EsHMBcCetp+febt8eZF0HzAmBUdGAavbPnMmzr8NDQ83SScChzWC4KHOvz1RXjqF8PrarjV3ipkcDyxAlBKubPvZfuY7H3hnQ9hlNBFwz0MEvF+23Su4fUUp2fuiKIpXhwrIiqKYVdEsJnvfFUk3E2V8p8/geAHnAr+yvRQhpDE/cMBM2+QrzygiezQoFCbL3dgGeFvrg+0vzKxgLDnT9nK2RxOZp8NyT3MSP9vtbS9DlIG+0GsiSR8jgromhwD75vx75+eiKIqiKIqiGNa86gGZ7ZVsf3AA4g3dWAd41vbJOd8UYHdgW0kjJO0h6SQAScsp/LhGKPylRmb76yT9WdKbJS0h6XqFX9V+efaK7Len+jy59s22UZLulPQTSXdIukTSdF5jkjbPtSdKuqox9mpJtxCy7Utn94OBNRWeYrsrfLGOasx1QSoMImly7nM8sJqkvXOPtys8xaTOHm5XSBqTc3xK4dt1u0KRkcbcB+Ser5e0SL7DCW1f423/q/G489EnSnI7IfBxqqQJxFm+93b7YUqaH9gD+F6jbTnCiuDYnOMYoKtJt8JP7dT8Wdwn6WOSDslnvFjS67Nfp3c1Z7a13u9BilLXTutsJ+kmSTc9+uij3bZTFEVRFEVRFF151QOymcAywM3NhgwO/gYsSZy3WlLSZoSi45fSePh0QmwDQhZ9Ykri/5jw81oZeKg1p6QNCMn3VQhRkpUkfTBvLwUcnRmgJ+kslLE3sKHt9wGtM1GPAOvbXpE4A3ZEtn8duNr2aNuHTz/VNMwH3G57VdvXAEfZXjlL/eYFNrJ9NnATsGXO+ZLaoKS3Ad8nAtvRwMqSNm3MfX3u+Srgi7Yn5RzNr1Vzrh0VIiyHEOe2IMoMLyS8zKYSmbRJPZ5nf+CHQMscmuy/KvH7ujAhT79SP+9lCUJxchPiZ3257eWAZ7KdLu/qRSKbeKyk9Qlz6307LWD7BNtjbI8ZOXJkP9spiqIoiqIoiumZHQKybnL2Amx7KvEH9mnAlbavzfsnEZ5nEJL6J+f1asBZed08w7VBft1KmDu/mwjEIPzQWl5lNxMlh+1cC5wi6YuEaTNEYPEThRDIWfTIHPVgCnBO4/PaksbnnOsQAWsvVgausP1oBiNnEN5pAM8T5/Gg+3O9hO2jbS9BmDZ/O5vnJDzatszvm0lat9P4PCe2pO3zOtzeAdjd9juIDGh/5w1/Y/sFwo9sDuDibJ/UeI6O78r2HcTvy6+BbYfTWcSiKIqiKIpieDE7eCvdQVtGStICwDuAv2TTUsSZpOYZqvsl/UPSOkT2ZUt6I+Ag28e3rTWK6T2rpitZtL29pFWJ7MyEDD52JjJH7yOC425CF03fMJjWO+zZlmm0pHmIcr4x+Xz7ML3PWKfn6sYL7lN9GYwX18+BY/P6ASIQfiz3eBGwIlG62M5qRObxvlzrLZKusD2W8K3bNfudRShA9uI5ANtTJTWfYyow5wDe1XJEtnORAT1xURRF8apQAhpFUQx3ZocM2aWEdPpW8JKwxQ+BU2w/LWlBogzxg8DCeZ6qxYlEOdsvW0ENcD19Ad4nG33HEefS5s91FpX0loFuUtIStsfb3ht4jAgYFwQezizeZ+nLnDX91iCk7Ucrzrq9gyib7EQroHgs99l81vY5W4wH1lKcn5uDkNvv5efW7fmWanz8CNDynBtHeKKNUAh8rAV0FBOxfaztt9keRWTT7s5gDKJ8dK28Xqcx/4zS9V0pREUWJn5njlDYNRRFURRFURTFTGfYZ8jSHHkz4BhJ3yGCzIuAb2aXw4FjbN8t6fOEQfNVaTp8PlGqeHJjyt2A0yV9hTj79FSuc4mk9wDXKfyxJgOfITJHAEhamCinm0MhAz+F8O4CuFfSkkRG6lJgIpGhOUfS5sDlQMtw+TbgRUkTCe+1HwH3EuV2txMlk53exZOSfpL97qPPEwym93Br8f8IEZHLc28X2f6/TvP3w06S1iMUFJ8gMlrYfkLS4cR5uXkJ8+qXNIol/RxYIcddT6gxvthh/i8Cp0n6L6JE9W5JyzXPo2XAdwth5N0rg7YMcEVeP0X44o0H1pH0JeBNwMPAQcAviYB+64G/iqIoiuK1zKwoxQ+VTSyKWZVZwofs1SJVBg+3vWajbQTwTAZ6nwQ+ZXuTGZh7H2Cy7R/MtA3PIJLmaGQA2+9dA+zUOAM3kPnm7BI0desvIqv1BsIwe9PGvQ8DvyGCwV8QXmk/6TLPGsAdGXh+FPi67TUa9/cihElGNNdom2NuIrgdk6bfcwOLZ8D+PeAx2z/Kvp8i5PuXtf3PXs9YPmRFURRFiwrIiqKAYeRD9moh6euEGMY32m6tRJzxug34MvCVmbTe1pJuSJn4Y7L8cE5JT0o6VNItksZJWlXSlZLuyWAFSV+QdF7ev0vStwc47/ck3QCsImlf9Um8H6dgCyKA+UWOn0vSA60SPUnvl/S7vP6epOMl/RY4Odc4LNe+TdIXuj27g0uZ3lsM2xfl/anADYREfrd5rrX9ZH68vtlX0uLA+kyb7ezEgkTw93jO+Zztu7us9zMic/jJTveLoiiKoiiKYqi8ZgMy2wfbXjyl4pvtV9t+n+3l0x/tz0NdS9KywGbA6g5j4znp+yN/QSIrtCKhargPsC6wObBfY5pVcsyKwKcljR7AvLfYXsX2dfRJ+S+X9z5k+xfABGCLlK/vT01wBeCanOPeXGsu4GxgR0mLDeKdjNe0XmYTgc/Tp4bYH58H7mmNJ0pAFwN26jUoS1XHAX+VdKbCg63Xv4OWomanZygfsqIoiqIoimJIDPszZMOE9Qh5+Zvy/Nm8wP157xnbv83rScBTtl9USLGPaswxzvYTAJJ+RYhezNlj3ueBpnz8upL2JMQs3kzI2P9mkM/xf7YPAPbNPbwn2z9GBHlLEf5v/dLyLmuhMO9+PIPHnuRZtc8CH8gzapsC69jeJe/1F5RtI2l54ufydSIA7pbh66pCafsE4ASIksX+9l0URVEURVEU7VRA9sog4CTb35mmMUQomlmpqfRJ6E9l2p9P+x/87mfeZ1pS73ku7ihgRdsP5lmpbnL4TYn99j7/aVwL+HKWIg4JSfsTAV3XssdG39HA8YTJ9hPZvDrwMUkb554XkHSq7a5CHLZvA26TdCZwZ4+1W1nBoiiKoiiKopjpVED2yvA74GxJP7b9WKoxzkdIuQ+UDfJs1/PAJoRv2pQBzjsvEeA9JukNhKz/GXmvk8T+SsBvafN3a2Mc8GVJV2ZG713A32w/M4hnQqFGORZYP8+R9eo7iiiP/HSzlNT2XsBe2Wc9QqSkYzCm8KgbbfuqbBoN/LVL3/8B1ib84oqiKIpiQJR4RlEUg6ECslcA25Mk7Qv8Ls8rvQBsz+ACsmuAM4ElgNNaqogDmdf2PyWdSkjm/5WQeG9xMnCiQg5/FeIM208k/Z0Q2ejG8cSZrQlZLvkIESh2RNJ1wJLA/JIeIGTkryAyd/cB1+c8Z2VZZCf2ARYCjs++z7WXPg4AAd9Q2AM8QwiNbNu4v6ekbYjAdhKwdn8Ki0VRFEXRZFZVWYQKFotiVuQ1LXs/XEgFw2Vt79bWvgjhs/Z+wvvreeCQvP6q7Y1e6b32QtLWQEsh8nu2T+3R9wBgK+BNtudvtO9BlBe+SHi8bWu7Y4ZrEPvaHnja9k9ndI6SvS+KoihaVEBWFAWU7P1sjyJF9CvgKtvvtL0SoXrYVTb+1UTSQsB3gVWJTNx3Jb2px5BfZ792biU8xJYnyhcPGerebB83lGCsKIqiKIqiKGaUCsiGAbZPbM+OEUbLz9s+rtHvr7aPbHaStI+krzY+355nsZC0VXqITZR0WrYtLunSbL+0JWUvafMcO1HSVdk2h8JD7cbsv3+blP0ESb/PpTcEfmv78RTj+C3woU7PmxnB4wgVyHlzniPyGS+3/XR2ncaLrMM810qaLOkJSc9K+oekA9M7bZKkJdrfkaQrJH0/+9wtac1u8xdFURRFURTFUKkzZMOXZQiPrBlC0jLAt4A1UhBkobx1FPBT26dK2hY4AtgU2JtQNnwwxUUgvMCesr2ypLmBa4HNbN/bYclF6ZPkB3gg26bD9onAibnPyemx1onP01u6/1tEFvG9hBH0PcALtleRtCsh1tEe6ALMmX0+TGT11us0uaTtgO0AFltswBZsRVEURVEURfESlSGbTZB0dGavbhzgkHWAs20/BmD78WxfjRAPATiN8DuDCLZOkfRFYI5s2wDYKo2ZxwMLE15kHbfYoW2GDzBK+gwwBji0n6432n7Y9nPAX4BLsr3d563Jufn95h59sH2C7TG2x4wcOXKgWy+KoiiKoiiKl6gM2fDlDhqy9LZ3lPRmoF1ZoukrBn3eYmJgAZFz/u0lrQp8hFBWHJ1z7Gx73ADmeYCQt2/xdkJlcdCktP23gLUy0OpF834vn7dOY6b06FMURVEURVEUQ6b+2By+XAYcKGkH28dm24gO/e4DNgKQtCLw39l+KXCepMNTFn+hzJL9nhAHOY3wOrsmxy5hezwwXtJHgXcQXmQ7SLrM9guSlgYetN00kG4xLvfbEvLYAPjGYB9a0gqE5P6HbD8y2PFFURRF8XJTSoZFUQyGCsiGKbYtaVPgcEl7ERLw/wG+1tb1HPrKCm8E7s7xd6S0/JWSphDqhdsAuwAnSdoz5/xcznOopKWIrNilwETgNqKk75ZUfXyUOG/Wab+PS9o/9wCwX6NMcjokHQJ8GhiRvmUn2t6HKFGcHzgrvcj+Znvjfl5XURRFUbxizMqy91ABY1HMapQP2WxKimE0/bu2IeTid+oxZmPgvbYP7tFnLF08ziTtBpzQUEHsNH4l4BRgXuAiYFd3+SWUtDlhBv0eYBXbN2X7+sDBwFyE99qeti/rtuZAkDQG2Mr2LjMyvnzIiqIoihYVkBVFAeVDVswAts/vFYwNgN3oXDbZ5FhCmXCp/OoofZ/cDnwMuKqt/THgo7aXA7YmyiuHhO2bZjQYK4qiKIqiKIoZpQKy1yCSRko6J/3DbpS0RrZvI+movF5C0vV5fz9JkxtTzC/pbEl/lHSGgl2AtwG/l/TvNi+y8TnnW4EFbF+XWbGfEsqN7d5lywHYvtP2Xe37t32r7Yfy4x3AfKkwOd2aue7k9Ba7WdLvJK2SfmP3ZFYQSWMlXZDX+0g6qdGnY6AmaTtJN0m66dFHHx3Kj6QoiqIoiqJ4jVJnyGZf5s1zYy0WAs7P6x8Dh9u+RmH8PI4oC2zyY+DHtn8mafu2eysQPmgPEXL4a9g+QtIewOotKf0OLEqoLbZ4gJCln678cRB8HLjJdkevsGQ+4ArbX5N0HvA9YH3Cn+xU+t5Lk3cDawNvAO6SdKztF5odbJ8AnABRsjiEZyiKoiiKoiheo1RANvvyTNNQuXWGLD+uB7w3RTEAFpD0hrbxq9En0HEm8IPGvRtsP5DzTiCEPa4ZwJ5mthfZMsD3CcXGXjwPXJzXk4DnUhWylxfZhSmp/5ykR4BFmDaYLIqiKIqiKIohUwHZa5PXAavZfqbZ2AjQ+qPp7TUYr64HCP+xFm8nsmyDRtLbgfMIIY6/9NP9hYZwyEteZLanSurPiwzKj6woiqIYBCWaURTFYKgzZK9NLgFeUltMk+d2rqfPePqTA5z330SJX0dsPwz8W9L7UyZ/K+D/Bjj3S0h6I3Ah8A3b1w52fFEURVEURVHMKtT/+s9GDELqfhfgaEm3AW8kslTvb+uzG3C6pK8QwhlzdFn2fYQfGcR5qt9Ietj22o19NKXubwJOzOvf5FfzGZpS93vlPkYCv5X0OuBvxHm4hYDvSPpODt2gjKKLoiiKWYGSvS+KYjCUD9lsxIx4j/WYawRxDs2S9gO2s/1fHfrdl2t0E/JA0g3ArkTW7SLgCNu/6dL3PURZ4fGE31nLe2wF4B+2H5K0LDDO9qKDfa6Xi/IhK4qiKFpUQFYUBZQPWdHGYKXugWOAFzOLtgnw1x5S95dLurzLup2k7jft1BcGJXU/j6S5ezzvQKTuR0m6WtIt+bV6tm+WYyTprZLuljRdMFoURVEURVEUQ6VKFmcvZrbU/SdsLy9pLHHW6+N0lrpfuz1Dlj5gcxOliW/LfX2WEPZYtK3v0cAabXuZp8dzfhy4NVUQO60JIXX/CSL424/OUvePAOvbflbSUsDPiGzfeZI+DuxIGFd/1/bf2zchaTvC5JrFFlusx3aLoiiKoiiKojMVkM1ezDJS97ZXzb4rAwe1fMIkrUmb1L3tHdvHS7qi07y9pO5ba2a/54Als+Sym9T964GjUtRkCrB0Y7qdgduB623/rMszlg9ZURRFURRFMSQqIHvtUFL3TCd1vzvwD0KY5HXAs43xi+a4RSS9zvbUGdlzURRFURRFUfSiArLXDi2p+0MhpO5tT2jr05K6/wWDl7rvKOph+2FJ/5b0fmA8IXV/5GA3/zJJ3S8IPJBB2takkmQGbCcDn8797sG02cKiKIqi6EqJZhRFMRgqIHvt0JS6nxO4Cti+rU9T6v5C4KkBzNtR6r6NHeiTvZ9O6r6JpM2IgG0kcKGkCbY3JILJJZm5UvfHAOek1P7lwH+y/ZvA1bavzvLMGyVdaPvOIaxVFEVRvEYolcWiKAZDyd4PM2ZE2j5VBd9r++AefcYSvl8fyXNXnwQ+ZXsTSbsBJ9h+usf4ptfYRcCu7vLL1eY1tkpD2n594GBgLuB5YE/bl3Vbc1aiZO+LoiiKFhWQFUUBJXtfNLB9fq9grMEbgQmZRfsy8JVs3w0Y0c/YYwnFwaXy60M9+t4OfIzI0jV5DPio7eWArYHTBrDnoiiKoiiKohi2VEA2GzFYrzFJ+0ma3JjieeBPRIbqfuAvg/EaA34E3Epkvk6TNEHScu39s/RvF0IB8ozsNwEYPQNeYw9JejrPqd2dbfc3vMbmkHRoPu9tkr6U7fNLujT9xyZJ2iTbR0m6U9JPJN0h6RJJ83ZZfztJN0m66dFHH+22zaIoiqIoiqLoSgVkw495WwFMBjH7Ne61vMZWJsQ5TuwwvuU1tjLTqx2uQGTD3gu8k/Qay35r9zgjtighjrFqyu5/lpCLH217UqcBKXV/E7Bl9htt++RGl45eY23MB3ze9gjgd8B9wJuAjeh7L58HnsrnXRn4oqT/JhQVN7O9IrA28EP1SU4uBRxtexngydxLp2c4wfYY22NGjhzZY5tFURRFURRF0ZkS9Rh+zDJeYw06aefP8OHEXl5jbTwPXJzX3bzGNgCWl/SJ/LwgEXA9ABwo6YOEvP2iwCLZ596GAuXNjbmKoiiKoiiKYqZSAdnsRXmNMZ3XmICdbY9rW2cbQslxpQzi7gPmydvt76FjyWJRFEVRFEVRDJUKyGYvymtsesYBO0i6LAOvpYEHiUzZI9m2NrD4TFqvKIqieI1TKoZFUQyGCshmL8prbHpOJEoOb8kzYo8SJZtnAL+WdBMwAfjjENYoiqIoipeY1WXvoYLGopiVKB+yYchQvMiAI4hzaNN4jWWfscBXbW/UYfxAvMguBt5KBINXAzvanpL3diYCrheBC23v1c8zLgb8AdjH9g+ybXfgC8T5tEnA52w/22ueV4ryISuKoihaVEBWFAWUD1nRRsOLbCU6e431x0C8yP7H9vuAZYns1+YAWRK4CbB8Khf+oPsUL3E4jSybpEVJqXzbywJzMPCSy6IoiqIoiqKYJamAbDajPy8y21cTpsxPE+WFW7V5kc0v6WxJf5R0hoKmF9m/mrL7Ta8x2//KOeYkvMw+l2qN5xJlg+Mlfa6/EkRJmwL3EF5kSBpPnI9bFLgh51yEHsIhku6TdKCk69IrbEVJ4yT9RdL22aebF9nK6Vk2j6T50o9s2Q5rlA9ZURRFURRFMSTqDNnwZN4MSlosBJyf1y0vsmuy7G8cYdTcpOVF9rNWcNJgBWAZIti5lvQik7QH4UXWUdijhaRxwCpEduuztqfkXv8P+BCwjaTbbd/YZfx8wNeA9YGvAtheNe/tChwAPAPcYfuSXnsB7re9mqTDifNtaxBKincAx9HnRfYvSW8Grpd0vu0bJZ0PfI8IWk+3fXv75LZPIM7XMWbMmKr9LYqiKIqiKAZNZciGJ880zJRHA3s37q0HHJVB0Pl09yI7K6/PbLt3g+0HbE8lxC5GDWZjKc7xVmBuYJ1snpMwbH4/sCfwy4YJczv7EgFlM2uHpDcRZY//TWTr5pP0mX620wpSJwHjbf/b9qPAs6noKMKL7DbCWLrpRbYfERSOAQ7p98GLoiiKoiiKYgaoDNnsx6vlRfYStp/NDNMmwG8Jn7Jz0zPsBklTgTcTioftrAp8QtIhwBuBqZKeBf5BGDY/ms9zLrA6cPoAnmUq0z7X1HyuLenuRbYQMD/w+mz7z8DfQFEURfFapgQziqIYDJUhm/1oeZEB4UXWoU/LiwwG70XWkTyP9da8nhP4MH1S8r8is2XpAzYX3T3N1rQ9yvYo4EfAgbaPAv4GvF/SiMyurQvcOcC9d6OXF9kJwHcIefzvD3GdoiiKoiiKouhIZchmUYYgbf9nYEw3L7KUtp8X2KODF9kn6B6kt7zIRhIZr3Zp+/mIM1itkr87iAwUwEnASZJuB54Htk7Z/f6k7U0ERNgeL+ls4BZCOv/W3NNQ6OhFJmkr4EXbZ0qaA/i9pHVsXzbE9YqiKIrXACV7XxTFYKiAbBjSDNTy8ymEaAX0nZtqH3MKcEoGZM8C6za8yFoGWhsRZ6ZaY3ZqXB8JHClpgRTBEHA2IW3/c8Lj7E/A0rafk/QW2y/m2OeBTue9uknbv9f2M5J+Cfy9sYfvAt/t/mamed5R7c/e6R5xnq6d+4CfZt8pRBllURRFURRFUcx0qmRxGNKftH1eLyHp+ry/X5u0/VuBJ/Js1o+Br7RJ21/ebe0O0vYtdcEdgINtP5f9BiVt32BOQkVyTsL3bEjS9tlvz3wPt0nat9H+K0k3p6z9do32yZIOkDQx3+Ei7WsXRVEURVEUxcygArJZl3nV8PoiVP9atKTtVybOgp3YYXxL2n5lpg9q3kmYN48ggqL/sn1E9lvb9tq9NibpSUJ6/gPAN3J/ywNrShov6UpJK/cYPx9wILAhUU65e85xFGEa/TfgYeCpAUjbf5gowVyMkOl/K/AN8n1J2gBYipDiHw2sJOmDOXZb2ysRWcFdJC2c7fMB16fJ9VXAF7s8R/mQFUVRFEVRFEOiArJZl1lZ2v6NhALhZcCeub8XGZy0/T62lyf8wA7PObZl8NL2H8mxXyf8wpa3/Uv6pO03yK9bifNn7yYCNIggbCIhcvKORvvzwAV5fTNd3o/tE2yPsT1m5MiR/WyzKIqiKIqiKKanzpANT0rafvpn6SZtL+Ag28c3B+VZuvWI9/i0pCvok7x/IZ8DZvD9FEVRFEVRFMVAqD80hyctaftDIaTtbU9o69OStv8Fg5e27yhJL2l+4A22H25I21+dt1vS9lcMRNq+Mec+wGTbR0lalZS2J0oi16VPcGRGGQfsL+kM25NTOOQFQvL+iQzG3k1k9oqiKIpiyJSCYVEUg6ECsuHJLsDR3aTtk92A0ztI2/eiJW3/cJdzZPMB50uaG5iDKFk8Lu91lLYfzGpYvagAACAASURBVEO9HNL2ti+R9B7guswgTiYUHy8Gts93eBcRwBZFURTFkCnZ+6IoBoMG+TdzMYvRza8M2Is4h9aStv+U7U2yz8aEtPzBPeYdC3zV9kYd7u0GnGD76R7jLyYENtr9ypC0M5HhexG40PZe/Txjf35lk4DP2X621zz9rPE24Ajbn5iR8WPGjPFNNw01mVcURVHMDlRAVhQFgKSbbY/pr1+Jesy+rARMyAzQl4GvtG7YPr9XMDYAdiMUGnvxP6lSuCwwkvArQ9LaxLmz5W0vQ6gq9kc3v7IxtpclsnUDLcvsiO2HZjQYK4qiKIqiKIoZpQKy2RTbVxOiFX8iZOF/Nki/smOAsZKelPSspMclLTcYvzJJGxJlh+sBB6cq5GkM3q9sOWBN+uTxLyFUHmeaX5mkUVlu2Xo/50q6WNKfUnyk07wle18URVEURVEMiQrIhj8vl1/ZlwmFwZZf2V3AgoPxKwP2ABYnzrAtkfL0jzE4v7KvASvSkMfPzNr3GJxf2f22VyPKJ08BPkEIeezXpf9oYAsiGNxC0jvaO5TsfVEURVEURTFUKiAb/szKfmUbEufI5iYUGCHOlA3Gr+xw25ObjZLexOD9ys7P75OA8bb/nfL6Lb+ydi61/VSeS/sDEVgWRVEURVEUxUylVBZnb8qvbPpn6eZX1q0/lBdZURRFMQhKMKMoisFQGbLZm5ZfGRB+ZR36tPzKYPB+ZR2RNL+kt+Z1y6/sj3m75VfGQPzKbI+yPQr4EXCg7aOIUsX3SxqR2bV1gTsHuPeiKIqiKIqimGWo//V/lZG0CKEi+H7gCcLD65C87ig7Pwhmtl/ZwpJuJkoO/yjpPtvvyueYCzgKGAsomvQ0vf3KRgBvJxQhb5L0euKc24q535/aPqh9Ey+HX1lRFEVRzCxK9r4oisFQPmSvIpnd+T1wqu3jsm1xYGPirNNQA7KB7GEEXfzKOvSdi/ideU7S/MDtwOq2H5K0LzCH7W9Leh2wkO2Oma+c6w1EADgXsJPtmyR9GtjY9idzX38Axtq+b2Y+88tB+ZAVRVEULSogK4oCyodsuLAO8HwrGAOw/VfbRzY7SdpH0lcbn2+XNCqvt5J0m6SJkk7LtsUlXZrtl6axMpI2z7ETJV2V060M/COzWScCN3bbrO3nW3L1hFBH8/dnW+Cg7De1VzCW7E9kAptmziYEOuYkpPqfB/7VaXDK1P9R0on5TGdIWk/StSlVv0r2m0/SSSnrf6ukTRrjr5Z0S36tnu1jJV0h6eyc/4weoiNFURRFURRFMSQqIHt1WYYou5shJC0DfAtYJ02Yd81bRxHlfssDZwBHZPvewIbZd+NsexdwhO0RwMLAppL+u8ea78gSyPuB7wPnSZoELALcK+np9PhapMccKwDvsH1BNr0/lSC/BnyACNL+AfzA9uM9XsGShGz/8sC7iYByPqJM8rc554nAZSnrvzZwaMrpPwKsb3tFQt7+iMa8KxClnO8F3gms0eU5yoesKIqiKIqiGBIVkM1CSDo6s1dds1RtrAOc3cpGNYKX1eiTsD+NCHIArgVOkfRFImgB2ADYKoOX8URQtlS3BW3fn4HeksDWRGC3NvB6YIcM7MYBP+jyjK8jzsx9pdF8fUr27whcRGTH3gZ8RdI7ezz/vbYnpSz/HcC3cp718t7ofJav5/NdAcwDLJb7/UkGk2cRwVeLAcn9lw9ZURRFURRFMVRK1OPV5Q76FA6xvaOkNwPth5FeZNrgeZ78LqLMrz+c828vaVXgI8CEVF0UsLPtcYPZeJ4buwNYEzgHeBo4L2+fBXy+y9A3EGbTV2Ql4H8B50vaGPg0cLHtF4BHJF0LjAHu6TJXu3x9U9q+9bst4OO272oOlLQPkYV7H/Fum6WTJXlfFEVRFEVRvCLUH5qvLpcBB0rawfax2TaiQ7/7gI0AJK1IGCIDXEqUDB5u+5+SFsos2e8JCfvTgC2Ba3LsErbHA+MlfRR4B5HN2kHSZbZfSCn6B23/p30Tkt4O/NP2M2nOvAZwWAqC/JpQWLyMkKH/Q6cHtv0U4TvWmvMKQrzkJknrAutIOj3fw/sJufuhMA7YWdLOuc8VbN8KLAg8YHuqpK3pyxgWRVEUxZAowYyiKAZDBWSvIhkgbAocLmkvwhz5P8RZqibn0FdWeCNwd46/Q9IBwJWSphDy79sQcvcnSdoz5/xcznOopKWIrNGlwETgNqIk75YUr3gU2LTLlt8D/FCSc44f2J6U974GnCbpR21rDoajgZMJ9UYBJ9u+bQbmabI/EdTdls93HxHcHgOcI2lz4HLivRdFURRFURTFK8qwlL3PgOB025/Nz3MCDwPjX06Z+GY2p619G2CM7Z06jRvkGpNtzz/UeWY1Ut1wf6Kc8EVgN9utzN1ihPjGO4jyyg/3J3Uv6Ujgc613lXOcCryRyHZ93fZFXcaOAH5CiIEIeBL4kO3JGdhOIs6YvZhz/ijPk3WlZO+LoiiKFsNB9h4qk1cULzcDlb0frhmy/wDLSprX9jPA+sCDr/KeXnUkzWF7yiu01py2XxzEkEuB8zMruDzwS0IZEeCnwAG2f6vwN+sZ/EgaQwReTb4N/NL2sZLeS4iDjOoyxa7AP2wvl/O9C3gh7z2TYiBIegshjrIg8N0BP2lRFEVRFEVRDJDhrLL4G0KcAuBTwM9aN15m76nPSPp9el+t0n5T3T3AurX/t6Trcq/7d1pQfZ5bp+b4szPLg6T7JO0t6Rpgc0lLSLpY0s35rO/OftN5kElaRtINkibkvEvlWn/KtgmSHpT0d0nj8x0dKOlKYFdJIyWdk3u/UdIabfv+Vmse4hzbrZK+RUjTO/u8F5jT9m8BbE+2/XRjjoUbe2nNdTnpedbAwAJ5vSDwUJefH8BbaQTwtu9q+KvRaH8E2A7YqcfvQ1EURVEURVHMMMM5IPs58ElJ8xClZ+Mb977Fy+Q9Bcxne3Xgy8BJHe538wDr1v5j4Njc6997PO+7gBNy/L9y/RbP2v6A7Z8DJxCqiSsBXyXOSkFnD7LtgR9nRmgM8EC2P2d7dLYfDhxne9W890bba9n+Ye798Nz7x4myw5ewfUBrnpxrX+CzwIWEkTTA0sCTks7N4PlQSXM05vhn2xwnA3vbbhcN2YcIlh8gsmM793iXJwFfy0D4e3muriO27yH+nbyl/Z7Kh6woiqIoiqIYIsM2IEuxh1FEdqz9rNAGvEzeU2QmzvZVwAKS2kvnunmAdWtfg77s3mldHxjut31tXp/eGA/wC4As91sdOCuf/XgiGwSdPciuA74p6WvA4ln+2R+/aFyvBxyVa51PvI83dBto+zzb7yZEQ1rZwDkJ6fyvAisTwfA2ncZLehuwOXBkh9ufAk6x/Xbgw4TASMffb9sTcp1DgYWAGyW9p9u+iXNmneYpH7KiKIqiKIpiSAzXM2QtzicMiMcShsYtXk7vqXYVlP5UUbrd9wD6DHTdlkLg64AnW2egpuncwYPM9pmSxmfbOElfIBQcO3meta/VWm+1AQZyzb1claWVbyaycrdmJgpJvyLk7v+3w9AVCEPqP2cF4QhJf7a9JOF79qGc/7rMnL6ZyIp22sNk4FzgXElTiSDuzvZ+CmPqKd3mKYqiKIqiKIqhMNwDspOAp2xPkjS20f5yek9tAVwu6QO59lNtx4s6eoD1aL8220/P9m4sJmk129cR2aBr2jvY/pekeyVtbvusPPe0vO2J6uBBJmlB4B7bR2TgsTxwNfAWSQsDkwmJ+Iu77OkSYCci00QGeRM6dZS0JPCX/HmsCMwF/BN4AniTpJG2HwXWYXpj7NbzXUgYSbfmnJzBGMDfCP+zUzLbNQ8hv99pL2sAf7D9hKS5iEzpFR36jQSOA47ycJQjLYqiKF4VSr2wKIrBMKwDMtsPEOeY2nk5vaeekPR7QkBi2w73u3mAdWvfFThT0q6E31g37gS2lnQ88Cfg2C79tgSOlfRtokTz54TfWCcPsq8T565eIM6v7Zfm0PsRZ/LuBf7YY0+7AEdLuo34XbqKOJfWiY8TXmovAM8AW2SQM0XSV4FL82d1MyFJP1i+QpSj7k5kD7fpEUQtQbwjEVm+C+l79/NmCWZL9v404LAZ2E9RFEUxExkuUvJQAVlRFINjWPqQzUzU5zs1JxGAfNb2kzNx/u2Bp23/dBBjxgLP2/59fh4FXGB72Zm1rwHsYRSwuu0z++naaexoImBcgCj3O8D2L9r6TOMj1mWe7YEdc47JwHYtMQ+FdP7xucZUYGXbz3abawB7HgNsZXuXGRlfPmRFURQvLxWQFUUx3NAAfciGrajHTOSZVPBbFnicCABmGraP6xSMKcysuzGWEOcYMP3MNyNjRgGfHuycydNEcLMMca7rR03xE3X2EevEmbaXyzNxh5CZqtz36cD2ucZY+nzEZgjbN81oMFYURVEURVEUM0oFZNNyHbBo64OkPdNf6zZJ+2bbfJIuVPh53S5pi2y/T9L3Fb5eN+SZKSTtkyV5aHofr48q/L1ulfQ7SYtkZmp7YHeF79aaRAnePzS9h9kpkg6TdDlRkvinPPeEpNdJ+nMKZ9B4pn0knSDpEuCn6uLNBhwMrJl72F3SHApJ+tb7+FKnFyjpc4Tpc0vp8SKi/K+1rzmIM2d79fpBSDoPuEp93mNH0CfcsgFwm+2J8JI0/nSG2JI2zPFTJP1D0tOSHpG0Sv4s7pG0cfYdK+mCxjs6qdGnY6Cmkr0viqIoiqIohsiwPkM2M8lAYV1S3U/SBsBSwCrEuavzJX2QCCwesv2R7LdgY5p/2V5F0lbEGbaNOiz1Rttr5dg3Ae9PoYsvAHvZ/oqk44DJtn+Q/X5NeJidKmlbIjjZNOdbGljP9hRJTxJnyH5ESNJPtP1Yhz2sBHzA9jMKg+n1bT+bZ8x+RniSfR34qu2Ncg/bESImK0uaG7hW0iW2721ObPtkwius9V5XAU4F/pJNOwHn235YPbyWbW+W43cE9iAEWFoZu6UBSxpH/Dx+bvuQDnOMI9QjW2fKfpOB3veA9Qkxj1MJtc523k142L0BuEvSsbanycLZPoHwfWPMmDGv7drfoiiKoiiKYoaoDFmfiMM/CU+q32b7Bvl1K3AL8Qf6UsR5s/UyG7am7acac/2s8X21Lus1z1K9nQgYJgF7Ast0GdPNwwzgrEZ26CRgq7zelkZg1Mb5Dan6Xt5sTTYgRDkmEIIfCxPvoyuS3pr7/VwqW/byEeuI7aNtLwF8Dfh2Ns9JvIMt8/tmktbtMc3z9ClFTgKuzOBqEt395i60/VwGtI8Aiwx0z0VRFEVRFEUxUCpDlmfIMtN1AXGG7AgiK3aQ7ePbB0haifCtOiizRPvlrYF4izWVHY8EDrN9vkLIY58B7rmTBxm278/SvHWAVekuo9/cw+5092ZrImDnzDr1i6QFCPXCb9u+Ppt7+Yj1x8/pU5Z8gAiqHsu1LgJWJNQjO/FCQ3FxKuk5l0Fit38DA/WlK4qiKF4BSiijKIrZlcqQJZnp2gX4qqTXE15m20qaH0DSopLeklmep22fTphSr9iYZovG9+sGsOyCwIN5vXWj/d9EqVyLlocZTOth1okTCcGLX3Y6V9VlDw/bngp8lj5vtvY9jAN2yHeDpKUlzddpQoW313lEmeVZrXbbF9r+L9ujbI8i3mPXYCxLKFt8hJD7b+1leUkjMqBaC/jDAJ61KIqiKIqiKGYp6n/9G9i+VdJE4JO2T1MYDF+X2ZzJwGeIDM+hkqYSyn47NKaYW9J4ItD91ACW3IcQv3gQuB7472z/NXC2pE2AnYH9gAsknUhkbv4kaTPCJPnrwNmNOc8nShW7lSu2082b7TbgxXwfpxB+b6OAWxQv5FH6zrG9hELy/iziPa0uaTfgSeIM14TscyR9PmwdUUjefy+DvinAHfQFre8ggsXH8/PRaRo9QyhUH3ee0fFFURRF0aQk+ouiGAyveR+ymYWk+4AxXUQ0hjKviAzZqbaPy7bFgY2JM1AvCW/kvTHA4bbXnJn7GMR+lwZs+0+ZTbwZeE/L2y33tyuwWT8eZAvY/ldebwx82faHMiN2C+EXN1HSwsCTA8wGvmyUD1lRFEXRogKyoiigfMhmJ9YhTKKPazXY/qvtaYQxUqr9QuAc4BsKSf5ReW+rlKqfKOm0bFtcIaHfLqW/eY6dKOmqbBuQ5H3u7W7bf8rrhwhBjEFJ3ufYfzU+zkffubkBSd433svkFGC5WWEtMFMk74uiKIqiKIpiZlAlizOJPBP1crAMkREaCJc35PjJ78sA3wLWsP2YpIWy71F0ltLfG9jQ9oPqM3P+PB0k74H5CRXFJs/ZXjXXXgWYi34k7yV9i1BfbHIWUeq4R86xTrZ3lbzPctG52+aZD7jC9tdmpuR9rrcdsB3AYost1mGaoiiKoiiKouhNBWTDDElHE1LvzxNS+f2xDnB2q5TSduvc1WrAx/L6NKDl43UtcIqkXwLnZtsGhIjGJ/LzgsBSti8BRnfZZ0vyfus2yfux7X1tHwAc0GX/R0v6NCF5vzV9kvcrA08Dl2Y6+NJWINi2j+eYVvL+OdsvpMz/qC5rXmj7OeA5SS3J+wc67Lt8yIqiKIqiKIohUQHZrM8dwMdbH2zvKOnNQPuBpReZtgR1nvwuukvwN3HOv72kVQlVwwkp0lGS90VRFEVRFEXxMlB/aM76XAYcKGkH262gZESHfvcBGwFIWpE+xcZLgfMkHW77n5IWyixZS0r/NBpS+pKWsD0eGC/po4SiYUvy/rLMLi0NPGi76WdGju8qeU+oQrb6Te5P8r51Fo3pJe/3kjSCyBKuBRzebZ6iKIqieKUpoYyiKAZDBWSzOLYtaVPgcEl7EXLz/wG+1tb1HGArSROAG4G7c/wdkg4ArpQ0BbgV2IbwXDtJ0p45Z0uG/tD0/xIRzE0kJPBH0Y/kffI/wAeBhSVtk20vSd4Pgp0krUdYCzxBSt7bfkLSYfmMBi4aiuR9URRFUcxshpPKIlQAWRSvNjNV9j7FGT5NlHlNBb6U2ZbBzLENcEkq9PXqdwUh+X5Tlq19uiWt3s+4fYDJtn/Q1j4KuMD2soPZb397G+pcsxqSLgbeD1zTJrd/BjCGCKBuIH720wlhZN8t6QsoJwM7tFQT8/4cREnmg801ZmVK9r4oiqJoUQFZURTwKsjeS1qNKJlb0fbywHrA/TMw1TbA2wYzwPaHBxKMDQd6nGuaVdY6FPhsh/YzCHXC5YB5gS/0mONeYK38PdmfFMZosCtw5wzsrSiKoiiKoiiGFTPTh+ytwGOpToftx2w/JGndlBsHQNL6ks5Nb6tT0vNqkqTdU8VvDHCGpAmS5s3xt2afk1J2fRok3ZdCFx09tzrwPkmXSfqTpC92mG8eSSfnmrdKWruf9nkl/TzX/QURkExH7vP7km7IryWz/RRJh0m6HPi+pPnyWW/MdTbJfsvkuAm51lLZ98J83tslbdHhnYzJrF3LZ+sEhWz9TzUIj7H8uV4K/Bt4Q+5jQpZJHghcnwIaNwBv7zHH720/kR9XB1ZozHUHsCNwYq99NJ7xQEnXKfzG7pL0L0nPSbpf0nLZb8/G8+3bGP8rhT/ZHQoJ+1b7ZEkH5Du9XtIi/e2lKIqiKIqiKGaEmZmNuQTYW9LdwO+AX9i+khClOFrSSNuts0onE3Lpi7ZKBCW90faTknairxRxHuAUYF3bd0v6KbAD8KNOG1B3z612lifK7uYDblUYKjfZEcD2cpLeDVyiELLo1r4D8LTt5SUtT2/fsH/ZXkXSVvkcrZK8pYH1bE+RdCBwme1tFV5gN0j6HbA98GPbZyjEM+YAPgw81PAfW7DH2i1WAj5g+5kMRKbzGLN9bz9z/Nv2Wu2Nkl5PZNB2HcA+AJ4iBEC+kOPPBg4iPMAGwv22V5N0OLAusCihMHmH7UmSNgCWAlYhzsWdL+mDtq8CtrX9uKR5gRslnWP7n8TvxfW2vyXpEOCLhH9Z+7OWD1lRFEVRFEUxJGZahsz2ZOIP/e0I0YdfSNomMyanAZ/J4GI14DfAPcA7JR0p6UPAvzpM+y7gXtt35+dTCcGIbnTz3Grn/2w/k/0uJ/5Yb/KB3DO2/wj8lQiYurV/EDg9228jRDC68bPG99Ua7WfZnpLXGwBfz8zTFUSAsRhwHfBNSV8DFrf9DOGttV5m3ta0/VSPtVucn2Nba7XEQMYDCxMBzIxyDHCV7av765gZxs+T58kkbQQ8YvvmQazXMnaeBIy3/e8M/J/N37cN8utWIlB+N33Pt4ukicD1hJpkq/154IK8vpkufmW2T7A9xvaYkSNHDmLLRVEURVEURRHM1PNKGVBcAVyhMN7dmshwnQz8GniWCDxeBJ6Q9D5gQyLz9D/Atm1TapBbGJTnVo/P3dbttZ+BqqO4y3VTQl7Ax23f1Tb2TknjCRn4cZK+YPsySSsRmbKDMru1H9P6ks3TNk/7WgP2GOuFpO8CI4GeZY/Zd3miLPH/ZVYKYA1gY0kfzj0vIOl025/pMVXLM2wq0/qHTSV+vwUcZPv4tvXHEuccV7P9dJZ0tt5T07usfMiKoiiKoiiKl42Z9oempHcBUxveUaOJDBJ5luwh4NvA+tn/zcDzts+R9BcicIM8n5TXfwRGSVrS9p+JUrgre2yjm+dWO5tIOogoTRsLfB2Yq3H/KsKb67IsSVwMuGsA7ZdLWpYoiezGFsDB+f26Ln3GATtL2jll71ewfaukdwL32D4ir5eX9EfgcdunS5pMiKJA+JKtRGQjP95hjeZaA/IY64WkLxDB9bq2p/bTdzHgXOCzjewntr8BfCP7jCVKV3sFYwNhHLC/pDNsT5a0KKEEuSDwRAZj7yZKWIuiKIpiyJRqYVEUg2Fm/s///MCRWSb2IvBn8nxNcgYw0vYf8vOiwMmSWlmcb+T3U4DjJD1DlPR9DjhLoQh4I3Bctw308Nxq5wbgQiKg2j8DxlGN+8fkHibls2xj+zlJ3dqPzWe5DZiQ83dj7sxyvQ74VJc++xPny26TJPpMn7cgSj9fAP4O7AesTHiHTSUCjR1yjn2B/5X0TaIUsRsnMnCPMSRdTZT9zS/pAeDzmV07jgjAr4tpODczdZ3YmyiNPCb7vjgQSdAZwfYlkt7T2Ndk4DPAxcD2+TO7iyhbLIqiKGYxhpuEPFRAVhTF4JipPmQ9F5KOAm61/b9DmOPtwNHAe4mA5gJgT9vPz5xdvrxIug8Y0zrjlkHg6rbPnIlrbEPDx03SicBhjUB4qPNvT5SYTiGCm+1ac2cZ4vHAAkTJ4Mq2n+1nvvOBdzbEXUYTwd08RND7Zdu9AtxZgvIhK4qieHmogKwoiuGKXmkfsv42Q5TxnT6EOUSUuf3K9lKEmMb8wAEzZZOvDqMII+1BoTBO7sY2NHzcbH9hZgVjyZm2l7M9GjgEOCz3NCfx893e9jJEKWhHY+gWkj5GBHVNDgH2zfn3zs9FURRFURRFMVvyigRktley/cGWR9kMsg7wrO2Tc84pwO7AtpJGSNpD0kkAkpZTeHKNUHiNjcz210n6s6Q3S1pC4TF1o6T98vwV2W863ypJoyTdKeknCt+qS1IufRokbZ5rT5R0VWPs1cDjhFT+6tn9YGBNhf/W7pK2yUxia64L8ixVyxtrvyx3XE3S3rnH2xW+YlJnH7crJI3JOT6l8FC7XdL3G+u0+26tpYbHWH6Nz/feVMOcjz5hkg2A22xPzH7/JNUb276OzjXnB/Zgejl5Exk2iHNeD0k6r8M8Gyo81U7Nn8V9kj4m6ZB8xosVEvxIWknSlQrPsXGS3prtX8x3OFHSOZJGZPspko6Q9HtJ9+R7nQ5J20m6SdJNjz76aKcuRVEURVEURdGTVyQgm0ksQ0iQv0QGB38DliTOXC0paTNC1fFLtp8msjZb5pD1gIlZMvhjwtNrZeCh1pya1rdqNLCSpJbU/lLA0ZkBepLOYhl7Axvafh+wcbY9Aqxve0XiHNgR2f514Grbo20f3s/zzwfcbntV29cAR9leOUv95gU2sn02cBOwZc7ZkrZH0tuA7xOB7WhgZUmbNua+Pvd8FbBmjm9+rdqYa0eFEMshwC7ZvDTgDHhukbSX7ZM7zLNj9t8f+CHwdNtz7kacibsf+AHwDdubdZinpQq5BKE6uQnxs77c9nLAM8BHMig7EviE7ZWAk+jLqp6b7/B9wJ2EBH+LtxI2BxsRgfN0lOx9URRFURRFMVSGU0DWTdJegFPZbxvCJ+xK29fm/ZOArfJ6WyJYgxAMOSuvm2e4evlW3Wt7Ql5386e6FjhF0hcJ42aA1wM/UYiBnEWcgRssU4BzGp/XljQ+51yHCFh7sTJwhe1H03bgDPo83Qbku9XC9tG2lyD8w76dzXMSAcyW+X0zSet2Gq84J7ak7fM63N4B2N32O4gMaH9nDn9j+wXCh2wOQqyD/DyK8LJbFvitwmvt28Dbs8+ykq7Od7gl077DX9memuWei/Szh6IoiqIoiqKYIYaTv9IdtGWkJC1AGPr+JZuWIs4kNc9Q3S/pH5LWAValL1vWjW6+VaOY1udqCpGZmgbb20talcjaTMjgY2fgH8D7iCC4m9BF0zsMpvUPezbLNJE0D6EEOSafbx+m9xrr9FzdmFHfrZ8Dx+b1A0Qg3BIsuQhYkbAiaGc1IvN4X671FklX2B5LeNftmv3OIlQge/EcgO2pkprP0fQhu8P2ah3GngJsanuiQgxlbPu8yWD98IqiKIqZRAlkFEUxuzOcMmSXAiMkbQUvCVv8EDglvaQWJMoQPwgs3Hbu50SinO2XraCGkDlvBXifbPQdR5xLmz/XWVTSWwa6SUlL2B5ve2/gMSJgXBB4OLN4n6Uvc9b0XIOQtx+tOOv2DqJsshOt4Oux3GfzWdvnbDEeWEtxfm4OQnK/l6dbt+dbqvHxI0DLd24c4Ys2QiHwsRbQUUzE9rG232Z7FJFNuzuDMYjy0bXyep3GE9YvggAAIABJREFU/DPKXcBISavl/l8vqZUJewPwcJY19heoF0VRFEVRFMVM51XPkEn6FqE0OIXIanzJ9nS+Wbad58OOkfQdIpi8CPhmZjc2BI6xfbekzxMmzVfZfgQ4nyhVXFvSGNs3Eeem9pT0FcKT7Klcp5Nv1Q1MrwYI8EZJt7ck25NDM2gREUROJLJZ50jaHLgcaJku3wa8KGli7ueTwL1Eud3tRMnkdNh+UtJPst99hD9bi1OY1setNeZhSd/I9QVcZPv/Os3fDztJWo9QUHyCyGhh+wlJh+VenPNfCCBpa/pKG79n+9Qe899ACJ/MQZyH2y7n2AP4ApFFfJQoP+0X289ncH5EBu1zEucN7wC+QwSqfyXeZadAtiiKoigGxXCU6ofKRhbFq8Ur5kPWcfHIWhwGjE2D5TcDc7U8tAYxzxXAVzPQ6nR/DP+fvfOOtquq2vfzQiiSUAX5KRiiFAsQgiRgKBK6AoJ+fohIC0WKNEVQUD9AEAVRQgcR6SAYmqEGhYROCBBSUDpRKUIizUgP7++PuQ5335Nzzj03hRTmM8Yd2Wftvddae5/LGHcy53xfGEIEfYfYvr8o6r1RAr1vATvY3rbJ/UcBU2z/um68D3BdXUA2XXT1DF3c26P0hc1yuruWpKWIwKo/Eag9AKxl++Um13+RCJAet92rMr4RMKpkQ/clfme2n4FHmamkD1mSJElSIwOyJElgDvMha8HHgck1OXzbk20/J2kTSe8LPkjaTNJVkuYvkuQTirT599VY6n0TSWPKNaMJMYzD69Z+EhgvaRxwFPCZIn9+UZO9riHpVoWM/nfqT0paWNJ5Zc0xJYBoNf4RSZcppPUvp0E/WrluoqTjJd1XflYq4+dLOlHSCOB4ST0lnauQcR8jadty3arlvofKWiuXa68vzztB0vaVtZYux/1LkIhCXv5sSTcDF5bv4QR1WAPs3eI73gL4s+2XShD2Z+DLzS62fa/t5xuMjyiqmRDlpsvXX1N5Z4MUMvd/lPSYpOMk7Vjew3hJK5brvqoQRhkj6S+Sli3jp0g6ohxvIel2SbP7v5UkSZIkSZJkHmR2lyzeDBwh6THgL8Dltm8DbgVOl7SM7UnAbkTJYT9guVpGStISpXxvfzoyXwsTZXublPLFC4FLbN9ZShBrvEWIOCxLGE6vZ3tyyeg0oi/wRaK0cIyk+v/9tR+A7dUlfZYou1ulxfi+wOu2+0rqS5PyxMJrttdW9M+dREixQ0jNb2p7qqRfALfa3l3SEsB9kv4C7EPI+18iaUGif21L4DnbW5X3uHiDNXcH+iuUCf8f4Q12vO2fSdoLeNX2AEkLAXdJutn20w3mOQ5YUCF0AvG+/wP8ocXzdkLSbnQIfQAsR4eQSzPWAD5HeL89BZxT3uFBhMjK94A7gS+WLOmewA+BHxB2BKMV3nGnAFuW/r/6fe1FKans3bt3u4+TJEmSJEmSJO8zW/+vv+0pwFrEH7WTgMslDS5KeRcBO5XgYiBwI/GH9aclnSrpy8BrDab9DCFP/1j5fAEd8u6N2Bi4oqYOaPulJtf9yfYb5boRTCu4sX7ZM7YfIcruVmkx/iVCaATb44h+smb8ofJvVS1waEWkZHPgsBJAjSSEP3oD9xB9dj8CVnB4k40HNi2Ztw1sv9pgzXOB+233A86iBGOVtXYpa40CPkqHNUA9pxHebf3KXGcA02TAWuGKnxnhTfYkHcIfzRht+/mSfX2SCP6hQw4fIss2XCF7fyhF9r5k4r5DZPNOs90w+HP6kCVJkiRJkiQzyGwvw7I91fZI20cC+9OhfHgesBOhBjjU9rul5G0NIuDYj8aS6N2VKG/mbzbNVrv43GzdVvtpt4HPTY7/WzkW8A13GCf3tv0325cSBtVvEMHHxiVYXYsITn5ZK8+js+x+vYx+/VoHVNb6lO2bacwzhNJkjeWpGHF3B4WYyE+AbWplri2onn+v8rkmhw9hGH2aw0h6bzo/8+rAv6lYKCRJkiRJkiTJzGa2lixK+gzwnu2atHk/IoNE6SV7jlDn26xcvzTwtu0rJT1JlCZCZ6n3R4A+klay/QQhM99K3v0W4GpJQ2z/W9JSTbJk20r6JVGyOIgoa1uwcv52Qjr91lKS2JuQXO9qfISk1YiSyGZsT5T+bU9kvBoxHDhA0gGlBG9N22MkfRp4yvYp5bivpEeAl2xfLGkKYagNodi4FpGN/EaDNapr7SvpVtvvlOd61vZ/m1z7C0lLls+bM20/X5dIWhP4LfDlopw5M1gceLYc71pZawWidHFN4AZJ1zRS/kySJEmSRqQ4RpIk3WF295D1Ak4tZYnvAk9QenIKlwDL2K75WS0HnFcRWKj9YX8+naXedwOGKvywRhMldw2x/bCkY4HbJE0FxtARoFS5j5DH7w0cUwLGPpXzZ5Q9jC/PMrgoRzYbP7M8yzjgoTJ/MxaSNIrIXu3Q5JpjiP6ycYpmuYlEr9n2ROnnO8C/gKOBAYQ8/3uEfP2+ZY6fAb+X9GOiFLEZ5xBlfw+WtSYBX2t0oe2XJB1DhzT/0S3KQpH0K8IGYRFJzxC9X0cBJxC/L0NLL+A/bG/TYo/tcFSZ71lCKORT5Xl+T/QkPqewUDhf0gDbzQy9kyRJkpnM3KpUCBmQJUnSPWar7H1XSDqNKE282PbOZawH0YM0yvbWre6fwbVH0kCGXuF51t/2/jNhjSlVafcm10ws602e0fU+KCTtCPyofJwC7Gt7bOV8zWPs2VbfoaTfEwqaAh4jgtkp5dw3iYDKwFjb357BPe9DiKxcOD33p+x9kiTJzCUDsiRJ5nbUpuz97M6QNUXSA0Tf0n+B1SR9pAhSbEZHmdmHFknzVwQ9ZvVa3fU5exrY0GEW/RXgbGCdyvmDgL8Ryo2t+L7t18oeTiR6DI9TGG8fTihjvizpY93YW0NsN82iJkmSJEmSJMmsYraLejTD9lq2a+qINwK1/920AxXJdDX33+oj6Q5JD5afdcv4IEkjJV0h6RFJl5QytUbsJOluhVdXvaoiklaQdIvCi+sWSb27GP+UpHvKXo9ptGDZ9yOSLijljPcDr5dzEyUdIelOYDtJK0q6SdID5Vk/W67brux5rKTby1gjP7I+kiZU1j5EYYJNeUe/kHQbcJCkZSRdWfY+WtJ6dftevcz9EFG+OaKUWXbyDJO0fPku1wQ2qN1TflavzlkJxkT4tNXSud8BTgeWL+vdXJmjU6ml2vckO0rSIZVnr3m/PSZpgybf1V6S7pd0/6RJkxpdkiRJkiRJkiQtmWMDsjouA76l8BjrS+f+pp8Q/lsDgI2I3qiewIvAZra/QPRRnVK5Z03Ch+rzwKeBTsFFhZ621wW+S8jA13MacKHtvkS/2yldjJ8MnFn2+q8Wz/sZ4Oxy/2tl/Rpv2l7f9mVE5ukA22sBhxCBEMARwBa21yAUFqHDj6wfUQb4TIv1ayxhe0Pbvyl7H1L2/g3qFC5tj6+oLtZ+1gH2IALqGicRfl/7AnfUXT++fgOSziPe1WcJVUQI24BViN7AN4HD6tasZw0iK7c6IfKyiu21yzMc0OTZe5Rrvgcc2eiClL1PkiRJkiRJZpS5IiArPl19iOzYDXWnm/lvLQD8TiGmMZQIvmrcZ/sZh9nvQ3T4UtXzh7L+7cBiCvGRKgOBS8vxRYTnWKvx9ejI7l3U9IHhn7bvKscXV+4HuBxAUi9gXUKU4iFCgfDj5Zq7CCGK7xBG0NDYj6wrLq8cbwqcVtYaRryPRRvfFkjaiAjIflQ+bw28aPuBNtYGwPZuhPT834jAGqLUdmVC7XIH4JwG302VdjzJ6rmq/PtAi2uSJEmSJEmSZIaYY3vIGjCMMAUeRBgR16j5bz1avbiU3r1AZEfmIzIpNaoeVVNp/h668h7r6vpG4zPqeVaTlp8PeKVkvDpfbO8jaR2iNPAhSf1sX1rK+bYi/Mj2JIQyqkF5K++x+YCBbQZySOpLZKC+YvvfZXg9YBtJW5a1FpN0se2dWs1le6qkywnz5vOI7N69tt8Bnpb0KBGgjW4yRTueZM3uafX7kSRJkswiUhgjSZIPC3NFhqxwLiGZXl/WVvPfErzvVwXhMfV8yYLtTEemqDtsX+ZcH3jV9qt15+8GvlWOdwTu7GL8rrrxZvSWNLAc71C5/31Kf9XTkrYre5SkNcrxirZH2T4CmAx8UhU/MiK47UsErB+T9FFJCxEy+c24mRDVoKwxTSBYOdebyDDtXEyoa3s+3PbytvuU93Brs2CsPM9KtWPgq4THHMA1RHlqzZtuFeCpFntPkiRJkiRJkjmSueb//Nt+huhjqqeZ/9YZwJUlYBlB52xPu7ws6W5CDXD3BucPBM6VdCjhxbVbF+MHAZdKOgi4ssW6fwN2lfRb4HHgzCbX7QicKemnRInmZcBYoo9uZSJ7eEsZO4w6P7Ji6nw00ZP3NB0BTyMOBE4vQiM9CGPrfZpcewSRxTyjxMnvtiP5WYeACyQtVo7H0uGXNhzYXNJfiQzWoZUsXJIkSZLMVuZmyX7I7GSSfNDM0T5k7VKU+04n+sTmA64j/kh/e7ZubDpQmE0PB35m+9LWV3dr3sHAzbafK5/PAU6smG7P6PwHA3sS5teTgN1t/71yfjEi0Ly6lYebpJuIXrgewB3AfjV5f0kHEFm6d4Hrbf9wZux9ZpA+ZEmSJEmNDMiSJIH2fcjmppLFhpSs2FXANbZXJsrXegHHztaNzRgLAt02OlYYLjdjMCGOAYDtPWdWMFYYQxhY9wWuAH5Vd/4Y4LY25vlmUYdcDVgGqJVkbgRsC/S1vSrRT5gkSZIkSZIkczVzfUAGbExIwZ8HIQABfB/YXdIikg6WdC6875U1oYw/LmmZMj6fpCckLa3w9rpX4bV1tKQptYUkHVrGx0n6WRnrI+lvkn4n6WFJN0v6SP0m1dgbbBqvNNsTid6umkfX9yUNlnRaZa7rJA0qx1PKPkcBAxU+ZaPLWmeXXqz/JaTuLylzfkThtdW/zLGDwpNrgqTjK+tMkXRs2fO9kpate6bd1OE9NgS4W9LpTOs9thawLB3qhvXv5urKPLeXf79CBKa1FO6+wHFFKRHbL9bN8b4PmqR/SHpV0suSnpa0f/k9GFOeY6lyz3fKuxqr8FhbpIz/SdIu5XhvSZc02Xf6kCVJkiRJkiQzxLwQkK1KSJO/TxG8+AewEtFftpKkrxMKfXvbfp2Qk68Ja2wKjLU9mehTO7n4bT1Xm1PS5oSS39pAP2AtSTXj6pWB00vm5hXCp6ueRt5gzbzSDqPDo2tIF8/fE5hgex3bdwKn2R5gezXCTHlr21cQBtM7ljnfV0qU9AngeCKw7QcMkPS1ytz3lj3fThgyv4/t8xp4j+1HxXtM0nzAbwiFxIbY/np1DiIgvQT4D5Ftg8h8biBplMLoeUDdHOMr9x9BlE32Jr6vY4HXba9JyP/vUm67qryrNYhyyj3K+F7AEQpD6B/QxKssfciSJEmSJEmSGWVeCMhEYyl5AS4qi4MJ36/bKv5e59Lxh/nuRLAG4SE2tBxXe7g2Lz9jgAcJo+KVy7mnbT9Ujpv5VjXyBmvlldYuU+ksELJRCVrGE0HWql3cPwAYaXuS7XeJQKgWaL5N9ONBm35cknYisnEnlKHvAjfY/mcbzwKA7S2IPrKFyjNA9JQtCXyRCO7+WMpVmzHC9n9sTwJeBa4t41XvsdVKhnI8EZyvWtZ/gQjqRgA/sP1Su3tPkiRJkiRJku4w16gstuBh6jJSCgGJTxImwBCB0xQ691D9U9ILkjYG1qG1DD1EgPdL27+tW6sP0/qaTVOy2MgbjMi8NPNKq/Iuzf3C3qyIXixMqEv2L893FNN6izV6rma84w7Vly79uCRtCvwE2LBWWkgEuBtI+i7R27egpCm2D2s1l+03JQ0j+sb+THiPXVX2c5+k94CliUxYI9rxHjsf+JrtsQrRk0GVe1YH/k3ldyZJkiRJ2iFFMZIk6Q7zQobsFmCRSs/P/ESJ3Pm2X5e0OFGG+CXgo6WfqsY5ROniH2tBDdH/VAvwvlW5djjRl9arrLOcpI+1u0k18AajuVfaf4BFK7dPBPopet0+SZThNaIWfE0u+6w+a/2cNUYBGyr65+YnfM/aEd+of741gd8C21T7u2zvaLt38R47BLiwWTAmqZekj5fjHsCWdPYe27icW4XoL5vc3X3WsSjwvKQFqATkktYmetjWBA6R9KkZXCdJkiRJkiRJGjLXZ8hsu/SHnSHp/4gywPeA90ov1JvAb20/JmkPYISk20vQMIwoVTyvMuX3gIsl/QC4nih3w/bNkj4H3FMq5aYAOxGZo3Zo5A3WzCttHPCupLHAn4ig5Gmi3G4CUTLZ6F28Iul35bqJwOjK6fOBsyS9QVgELFjueV7SU4SZ9dtEeeGf2nymTs9HZMCGSupJKCT2Ar5Vetggerq+oVBMnAoca/vyyhw9gWEKk+r5id/PfpL2IXrgFpA0oexzV6C/pHuB7StrdIf/IwLSvxNlryuWtX8H7Gb7ufJ7cK6kjSvZwiRJkmQWkrLxSZJ8mJgnfMhqlJ6iUcCZts8rGZ+zgZdsTyMqUVQGh9jeoDK2CPBGCfS+Bexge9sP6BGmQaGmeIjtrbt53/yVrF/9uZFlzllinFXKOBcjMmLDasFSyWzZ9uNFTOQB4HO2X2kyz5YUcRCin+9222eWc/MTpYxvAudOZ0A200gfsiRJkplHBmRJkswL6MPiQ1ZHdyTwhxBZoaNUkcAnBCnelPQwcDDQW7NJAr9cfhxzgQR+FdsTbY8jMpXV8cdsP16OnyNUJpvKE9q+wQXgPipS+kT/3ZVljqZIGqRQZfyjpMckHSdpR0n3ledcsVx3lKRDyvFISceXax5TqC0mSZIkSZIkyUxnXgvIuiOBvwGwke1b6CyBvzBwXZGwfxH4VUrgvz/3vcAfgU8RwiS1n590sa9pKKWHnyFKHGvzrN7k2gWIHrubyuflgK8DTxHv74TKHFc3mGIN4CBCqGNnYBXbaxM9hA0l7YEe5ZrvAUc22Vf6kCVJkiRJkiQzxFzfQ1ZHlxL4CjW9cURfWVUC/09EwFYvgV8LSC4Ffl2OqxL4EL1SKxOBX3ck8P8IXFXGFgBOU6gvTiV8t7pLIwn8HwKLAEsRipTXNrqx8L4EPoDCEPlLhKDG20Sgeq2kJ4jgcc/p2CMK4Y4FgEG2723jljOIcsU7yueTgB/ZvlehxHhdFyWLo20/X9Z+kg6D6vHARk3uqX0vTeX+bZ9NlMTSv3//eaf2N0mSJEmSJPnAmNcCspTAn0Mk8JsuEN/H9cBP2wnGJB1JlDXuXRnuD1ymEFdZGthS0ru2r2kyTTsS+M3ume5nTZIkSZIkSZKumNf+0LwFOE7SLrYvVGsJ/NMk/W8ls1KTwL+ogQT+5UwrgX+MpEtsTykldO+0u0kVCXxglKSv0iGB/0zJ4u1Kawn870qaD1iO7kng1561lQT+yZKWBl4mJPBPbfe5ukLSgsDVhPT90Dau3xPYAtikWAMAYPtTlWvOJzJkzYKxJEmSZC4jRTGSJPkwMU8FZA0k8OcDbgB+XC4ZApwxF0vgn0+U681MCfyBlXuel3R4WV9MpwS+pAFE4LUk8FVJPys9dd+kww9ucLl8cKXEs56zCEn62nu+yvbR3d1PkiRJknyQpEpkkiTdYZ6SvZ8RNJsk8Ita4RDgi0RW6m3gV+W423L3s5JSWnkmIWnfyEcMSacSHl69WsyzD7BfmWMKsJftv5ZzfQmD6cWIksIBtpuVb84xpOx9kiRJUiMDsiRJoH3Z+3kqQza9SDoM2Jdpe8fWIkobRSgm7j6T1xUhmHGB7W+XsRUI5cCXZ+ZaM4nXgV2qPmKShtd8xEpQu0Qb81xq+6xyzzbAicCXJfUgykZ3tj1W0kfpRilokiRJkiRJksxtzGuy99OF7eNsr1Ck4qvjd9hew3Zf21+y/cRMXnpj4O1acFLW/LvtTn1bVY+s8nlCERBB0i4KL7Sxki4qYytIuqWM3yKpdxlv5H82v6QT1OGpVhXP6EQDHzGAu2uS80Sp4zNdPXSxIqjRE+hX7n+U8Bq7QNIWtv/dzNy67H1K8Qt7QNI9xTNsiqS3JD0laVSz55PUq7ybBxV+ZNuW8ba85Mq1KXufJEmSJEmSzBAZkM1eVqVJD1g7SFoV+AmwcfE0O6icOo0QzugLXEKHp1kj/7M9gFeL19oA4DuS3hfNaLH22kTWcDXb/YjeuyNsH97m3vcrEvS/AjYoc5xK9Oq9APxSIdnfip6ETP9awL+IXrklCaGT12yv0+L53gS+XnzfNgJ+UzKW0J6XHLbPtt3fdv9llmnqb50kSZIkSZIkTcmAbA5C0uklezW666uByLBdYXsygO2XyvhAwjcN4CJg/XJc8z/7Dh0qjpsDu5QM1Sjgo0RA0mqfHy/z7lZUIT8BbEc3FBltn257ReBHwE/LcI+y1x3Lv1+XtEmLad6mmEUT4iW32X6nHPfp4vkE/ELSOOAvhGLlsuWedrzkkiRJkiRJkmSGyR6y2Usn3zTb+xXJ+Xp1iGbeY82MsOtxmb+R/5mAA2wPb2fDauwjtiawEvBESTItIukJ2yu1MeVlhFAIRLnjbbUAU9INwBcIJcpGVL3R3vcXK0Fi7Xe74fMVlcdlgLVsvyNpIh3vtUsvuSRJkiRpRopiJEnSHTJDNnu5FVhY0r6VsUUaXDeRCEyQ9AWgVlJ4C/DNIn6BpKXK+N10+KbtCNxZzq9oe5TtI4DJhP/ZcGBfSQuUa1aR1LPRZtXER8z29bb/n+0+tvsAr7cKxorkf42tgMfL8XCgr6RFSkC1IfDXZvO0SbPnWxx4sQRjGwErzOA6SZIkSZIkSdJt5qoMmSQDF9veuXzuATwPjJqV8vCSRhIS9PfXjQ8G+tvef3rmLXL6XwOGSDqdyIz9lyjjq3IlHWV3o4HHyv0PSzoWuE3SVGAMMBg4EDhX0qHAJGC3Mk8j/7NxREneg6WHahLwtSZb7q6PWE1J8mRgS0KlcTCwq6RNCQXFl4Fdy/O8rDDz/k95D2fZvr7McwnQv9xzH9BUfKSOc5o83yXAtZLuBx4CHmlzviRJkiRpydwuew+Z5UuSD5K5yodM0hQim7Ku7TckfQX4JfDM3BiQ1c01pZV3Vxf3zt9KjXBmIqmH7Xe7cf2WwAFEQLYOcHIR22h2/SZElnDv6nda5rmxfLwUuN32mQ2mmC2kD1mSJElSIwOyJEmgfR+yubFk8UaizA1gB+APtROSeko6t0icj6mTMr+jSJw/KGndMj5I0khJV0h6RNIlFaW9enaSdLdCNn7t+pNqLjXfbPxTCqn20ZKOabRg2fcjki4o91+hMKtG0kRJR0i6E9hO0oqSblJIwN8h6bPlukZS96tKuk8hVz9O0splrQmVtQ+RdFQ5HinpF5JuAw6StIykK8veR0tar8X3tS1R4ujSc7aEQhSkIbZvITJk9eM3lDlMZMiWbzaHwibgAoVk/URJ/yPpVwp5+5sq5YtHlP1PkHS2gh5lbFC55pclC5kkSZIkSZIkM525MSC7DPiWpIWBvoRyXo2fALcWifONiBK9nsCLwGZF4nx7OmTgIQQpvgd8Hvg00Cy46Gl7XeC7wLkNzjeTmm82fjJwZtnrv1o872eAs8v9r5X1a7xpe33blwFnE+IVawGHAGeUaxpJ3e9DZKr6EWWAnbzDJK0OfB/Yp5RJ9gf2sL2h7d+UvQ8pe/8GURY4DZJ+QvSyHa0Or7IehKJh2yj8xB6qzHEspWyzBSsSgfu2hNn0CNurA2/QEdCfZnuA7dUI4Y6tS/ZvMHCmpM2ALwM/a7Kv9CFLkiRJkiRJZoi5LiCzXet52gG4oe705sBh5Y/2kYRqXm9gAeB3ksYDQ4ngq8Z9tp+x/R7RS9SnydJ/KOvfDiwmaYm6882k5puNr0dHdu+ipg8M/7R9Vzm+uHI/wOUQJsfAusDQ8uy/BWpZqEZS9/cAP5b0I2AF229UF7Q9HhhC9HD1I3rbvlm5ZFPgtLLWMOJ9LFq/cdvHEoIiu9nuV+aaSHvKkNV51qncP5oIUC/o4rYbKxL489NZHr9POd6oBHvjCQuBVct6DxPfybXA7rbfbrKv9CFLkiRJkiRJZoi5StSjwjDg18AgwleqhoBv2H60enEpvXsBWIMIQt+snK6XOG/2TuqDiK6Cimbn3cY17a773/LvfMArJWDpfHEDqXvbl0oaVcaGS9qTyDg1ktavX6u23sD6QK4JzxBqjjWWB55r475pkHQkIVXfjqBHVQK/Xh6/R8mwnkH0AP6z/I5Un3l1whR6WZIkSZIkSZJkFjG3BmTnAq/aHl/r9SkMBw6QdEBRMFzT9hhC4vyZ8sf5rnRkirrD9sAISeuXtV+tazerSc1fREVqvsX4XWX84jLejN6SBtq+h8gK3ll/ge3XJD0taTvbQ0sfXF/bY1Wk7oFRkr4KfFLS4sBTtk+R9Gmi9PMO4GMKCf0pwNZ0ZJXquRnYHzgBoAR5zZQWhwH7S7qMEPV41fbzLZ63ISVo3ALYpGQzZ5Ra8DW5ZBj/F7iirPU/RKD/JeA6SWvbfmUmrJkkSZJ8CEhBjCRJusNcV7IIUEoMT25w6hiiPHFcEaioiWWcQUit3wusQudsT7u8LOlu4CxgjwbnDwR2kzQO2Bk4qIvxg4D9JI0mAsZm/K3sfRywFB0myvXsCOwhaSxhOL1tGT+hiFlMAG4npO63ByaUksPPEj1u7wBHEz1519FaBv5AoH8RBPkr0ZPWjBuAp4AngN/RuQduGiTdQZSVbiLpGUlblFNnEdmqe0ov2RGt5umKEmD9jihhvIYohURhzH0c0TP3GNED2Oh3LUmSJEmSJElmmDle9r4IQ3ybKCd8j5BDH9X6rmnmGAzcbLtlqZwq8vaSbgC+3U5mpJS7TbH967rxPsB1RTSi21TvVxPp/XmBkrX8aflrbLEbAAAgAElEQVT481b9YUXxcBdgyapNgKSDgT2Bdwmvsd1t/73JHPMBJxF9YyZKWL9p+2lJE+lQeZwfuAo4xvZbjeaqkbL3SZIkSY15QfYeMtOXJDOK5gXZe0kDidK5LxSVwU2Bf07HVIOBT3TnBttbzitlagoD7TlyLUlLAUcS5YxrA0dKWrLFLdeW6+oZQ/SD9SVKD3/VYo7tid+HvkV58etEv1iNjcr42oTy5tltPk6SJEmSJEmSdIs5OiAjlAIn17ITtifbfk7SJpKurl0kaTNJV0maX9L5xVdqvKTvS/pfQrb9klLq9pFy/5hyzbmSFqpfWOFftXQ53qWU542V1EwRcQ1Jt0p6vCga1s+3sKTzyppjJG3UxfhHiNK59yRdTsiyT0PZ5/EKX7H7JK1Uxs+XdKKkEcDxau7R1siTrKek68vzTpC0fYN30r9k7Wq+XyMlvQZMKnO9KOmFMufedXteXR0S9g8CixGqiC8Dfyak5hti+95aD5qk3SrzDAHulnQ6cC8tfMqI36vna71opQT25QZrTSHKMb9WAsckSZIkSZIkmanM6aIeNwNHSHoM+Atwue3bgFuB0yUtY3sSsBtwHtAPWK5WIihpCduvSNqfjlLEhYHzCXGIxyRdCOxLlLBNg6RVCX+z9WxPbvGHeV/gi0BPYIyk+nqF/QBsr64wbb5Z0iotxvcFXrfdV1JfInBpxmu215a0S3mOrcv4KsCmtqdK+gXh0ba7QrL/Pkl/ocOT7BJJCxJlelsCz9neqryDVj1uNRYFlrX9hqS9gI/Z/nkJdu+SdLPtp8uzjie+KyQdAixs++dlnmdo06fM9nnE994JSacRBuLN+CNwp6QNgFuAi4v4S6M1XpP0NLAynT3vKM+5F0Dv3r3b2XKSJEmSJEmSdGKOzpCVDMVaxB+9k4DLJQ0uEuYXATuV4GIg8Qf4U8CnJZ0q6cuEkXI9nwGeLoINABcQanrN2Bi4wvbksqeXmlz3J9tvlOtGMG1Z3fplz9h+BPg7ETA1G/8SocBY814b12KPf6j8O7AyPtT21HLczKOtkSfZeGDTknnbwParLdauMawig785sEtZaxShWLhyk/vUYGy6Gxsl7URkRE9odo3tZ4jfg8OJvsRbJG3Satom86QPWZIkSZIkSTJDzOkZMkpAMRIYqTDw3ZXIcJ1H9BO9SQQe7xJKiGsQ8uj7EWbGu9dN2fCP6xaIGfcLa7Vuq/20G5g08zarqkk29GgD/qY6TzLbt0pai8iU/bJkt44mBDNqQXwrnzIBB9ge3sbenyH85GosT3zf3UbSpkQ2c8OuRDjK+RuBGyW9AHyNyJbVz7koYST9WP25JEmSJEmSJJlR5uiATNJngPdsP16G+hEZJEov2XOEOt9m5fqlgbdtXynpSSJwg1DNW7QcPwL0kbSS7ScIKfrbWmzjFuBqSUNs/1vSUk2yZNtK+iVRsjgIOAxYsHL+dkKa/tZSktgbeLSN8RGSViNKIpuxPdFvtj2R8WpEQ482hQ9ZJ08ySY8AL9m+WNIUQhQFYCKRsbwR+EaL/QwH9pV0q+13ynM9a7uR3cBw4BfqEPLYnMhcdQtJawK/Bb5s+8Uurv0C8K/yOzQf8W6nyUAq/MnOAK5p1GOWJEmSJI1IdcIkSbrDHB2QAb2AU0tZ4ruEl9VelfOXAMvY/mv5vBxwXvkjGzr+sD8fOEvSG0RJ327AUIUi4GjC46ohth9WSK3fJmkqoeY3uMGl9wHXEwHVMeWP/T6V82eUPYwvzzLY9luSmo2fWZ5lHPBQmb8ZC5Us13yEeXQjjiH6y8ZJEhFcbU0EcTtJegf4F+FFNoDwL3sPeIfoZwP4GfB7ST+mrp+qjnOIrNKDZa1JRAZqGmy/JOkYig8YcHSLslAk/YqwQVhE0jPAObaPIkoUexHfK8A/bG/TZJqPAb9Th5jLfYTfWI0RZd/zAVfT4WeXJEmSJF2SsvdJknSHOd6HrBVFvGGM7d93cd3ywOnA54k/sq8DDrX99qzf5cynBHrr2r5U4ZvVv9bjNgNzDqbi1SbpHODESrA7Q6gLnzBJixEm2Ffb3r/FPDcRKok9gDuA/Wp9cpIOAPYva1xv+4dN5liEMIXuS5RXvkJk1qaUoHs8YTD+LtFjeFJNkbEZ6UOWJEmS1MiALEkSmEd8yFoh6QHiD+qLu7hOhLnvNbZXJgQzegHHzvJNzjr6EFmibiFp/hanB1PxarO958wKxgpd+YQdQ+vS0RrftL0GsBqwDLAdgMIuYFvCW2xV4NfNp+Ag4AXbqxdFzj2ITCDAG7b7lTk2I/rojmznAZMkSZIkSZKku8y1AZnttWx/qSvxBkIl8c0ikV4TCfk+sLukRSQdLOlceN8fa0IZf1zSMmV8PklPSFpa0oqS7lX4eR1deqwo1x1axsdJ+lkZ6yPpb5J+J+lhSTcrPMY6IWm7svZYSbdX7r1D0oPlZ91y+XHABkXF8GRg65ItrM11naRB5XhK2ecoYKCkI8oeJ0g6W0Ejr7aRkvqXOXZQ+KRNkHR8ZZ0pko4te75X0rItvofJhE/YQ8D3gK3KnlAIiCxL2BzU5h5V9lL9Wd12TTmzB9GjV0vx7gscV/Gse1EVv7PKzygiw/ZsbS3bjzb6PSq9aHsB+5fAvv4720vS/ZLunzRpUotHT5IkSZIkSZLGzLUBWTdYFXigOlD+qP8HsBLRV7WSpK8Tyo17236dyLztWG7ZFBhbygJPJny7BgDP1eaUtDkh7b42IT6ylqSanP7KwOkl6/IKjQUxjgC2KNmfWu/Ti8Bmtr9A9HqdUsYPA+4omZwhXTx/T2CC7XVs3wmcZntAyQx9BNja9hXA/cCOZc6afD2SPgEcTwS2/YABkr5WmfvesufbgWkMsWvYHl/m7keYPx9nex1Fv99vgEPrrl+ndn3lZ3zZ0/Dybv5DZNsgMp8blEDuNkkDqmtWftYBzgV+JOkeST+X1EySH9tPEf+dfKzBuZS9T5IkSZIkSWaID0NA1ky2XoBLb9BgwgvsNtt3lfPnAruU493pMCAeCAwtx5dW5tu8/IwhTJw/S4f31tO2HyrHDxAlh/XcBZwv6TuEOTNEH9PvFIIfQ4keuO4yFbiy8nmjErSMJ4KsVbu4fwAw0vakYi1wCR2+bW8T/XjQ/Lk6oWl9wr4L3GD7n208CwC2tyCyXAuVZ4DImC1JmHMfCvyxUVar3P8Q8Omyh6WA0ZI+12rb7e4tSZIkSZIkSbrDnK6yODN4mLqMVBGQ+CTwZBlaGZhC5x6qf0p6QdLGwDp0ZMuaIeCXtn9bt1YfoFoON5XITHXC9j6S1iH8wB6S1A84AHgBWIMInt9ssnbVHww6e4S9WRG9WJhQe+xfnu8opvUTa/RczXjHHaowU+ni90mNfcIGEpmt7xK9fQtKmmL7sFZz2X5T0jCib+zPhJ/ZVWU/9ykUIpcmBEQa3T+F6C28qly7JSEqUr/nT5dnaymlnyRJkiQ1UgwjSZLu8GHIkN1CSKTvAu8LW/wGON/265IWJ8oQvwR8tPRT1TiHKF38Yy2oAe6lI8D7VuXa4URfWq+yznKSpilza4akFW2Psn0E0W/1SWBx4PmSxduZjsxZ1VcNQsK+X+l1+yRRNtmIWvA1ueyz+qz1c9YYBWxY+ufmJ2T12xHfqH++mk/YNlWfMNs72u5tuw9wCHBhs2BMUi9JHy/HPYgg6pFy+hpKtkzhe7Yg8R4bzbOeiu+ZpAWJzOPfG1y3DGGJcFol8EySJEmSJEmSmcY8nyErJshfB86Q9H9EEHoD8ONyyRDgDNuPSdqD8KC6nJA+70Eo+V1RmfJ7wMWSfkD4jr1a1rm5lL3dUyrlpgA7EdmVevpL2sX2hZWxE0ovk4ggciyRzbpS0nZENq/W2zUOeFfSWMJj7STg6bLnCUTJZKN38Yqk35XrJtLh/QXTerXV7nle0uHACCLIecz2nxrN3wWnEh5t48r7eaz0ntWCotOI3rmFJI2wfWWDOXoCwxT+YUsCywMnlnMXAuMl/ZAoUb24RRC1IvFelyifXwVqJZM9JVUzkfcT7zdJkiRJ2iJl75Mk6Q5ztQ/ZrKKUzPUqKoPXA6fYPracW4SQRrekbwE72N52Jq3bo/RpNTp3FDDFdis597bnm849DAIOsb11d+Ys9y5I/L69VbJzEwgvtecUipTz2/5pEflYyi181SQtSnwvCwL7275f0reJ7Nu3ynf0V2CQ7YkN7h9IBHKDyn6WBhYsexlZnvH+sudfEiWeG7Z6vvQhS5IkSWpkQJYkCXwIfMhmNZIOI8QwLgSWq5z6NfDfkkk6HviBpJ6SrlfIv0+QtH2ZY6Kk4yXdV35WKuNHSTqkHI+U9AtJtwEHSfpqEd0YI+kvkpYtfWj7AN9XSLdvIGkFSbcoJPZvkdS7zHe+pBMljSCybg3l++ue9SiFBP7NwIVqQ25f0vclzS/pBHVI/e/d7H3afrvSN7YQnX/3dicCH2y/1yoYKxxD+JhVM1kmsls9iB69t4HXGtwLIQgyuSKRP9nFELt+z8APgd6S1uhiT0mSJEmSJEnSbeb5ksXpxfZxkk4ALgN+D+9L2/cgSucEDCOEQNYAnrO9Vblu8cpUr9leu/SwnQQ0yi4tUcvAlN6mL5YM3J7AD23/QNJZVDJkkq4l+q0ukLQ7IYlfk6NfBdjU9lRJrxCCJCfRWb6/nrWA9W2/UTJMmxXhjJWBPxDKiIdRyZBJ2gt41faAUkZ4V7l+07q5h9o+tvS3XU/YDRxaMlK1ssFjSgbuSWD/8m4Xqptn5/L+P2n7ulpQW7iCEPh4HliE8JoboIpvWuHpMs8Rkh4D/gJcbrthX1x5h2MJ1cyx1XPl+fcC6N27d6PbkyRJkiRJkqQlGZA15iMKA+M+hJz7n8t4VdoeQhVwZeAO4Nflj//rbN9RmesPlX+beYZdXjleHrhcIV6xIBFANGIg8D/l+CIiY1RjaEWE5FzgT0RAVpXvr2dYxX9sAeA0hdLjVCLAa8TmQF91CKEsDtxs+5BGFxdp+74Kb7NrJF1R5l8euMv2wZIOBn5d/MI6UcoZbyVsCupZu8z1CaK/7A7gK8X3bBoUZtQbABsR7/sw2+c3ec5m8vlnA2dDlCw2uTdJkiRJkiRJmpIli415o/whvwIRFO1XxmvS9jWT4ZVs/972Y0SGaTzwS0lHVOZyk+Mq/60cn0qo+q0O7E3XsvSN5n5/vhIEVeX7b2xjD9+nQ26/P/EOGiHggMr7+JTtm7vcaJQHPkwERP8GXgeuLqeHAl9ocuuiwGrASEkTCc+xYaXX79vATbbfKSqOd5W9N9vDVNsjbR9JZOQamXXXVDlXp4EkfpIkSZIkSZLMKJkha4HtVyUdCPxJ0pmEtP0xki6xPUXScsA7xHt8yfbFkqbQOYOzPdF7tT1wTxvLLg48W453rYz/B1is8vluQnb/IqIk8c4Wc9bk+y+qZM662sMztt+TtCvN5faHA/tKutX2Owq5+Wdt/7duPiQtD/y7lEQuCawHnFhKM68FBhHZr00IQY5psP0q4S1Wm3MkHQIcmwAbS7qYKFn8Ik3UESV9BnjP9uNlqB+NZe8XAI4F/ml7XKO5kiRJkqSeFMNIkqQ7ZEDWBbbHlB6ib9m+SI2l7VciBDTeIwK0fStTLCRpFJGN3KGNJY8Chkp6lvA8+1QZvxa4QtK2hGH0gcC5kg4lzI93azHnMKJUsVm5Yj1Vuf0RdGTP6uX2TybKOh9UvJBJdPSx1fM54DeSTGTWfm17fDn3I+AiSSe18SzNOJ14vgll/vNaBFG9gFNL/9q7wBOUXrDCJZLeInrY/kL0piVJkiRJW8wrKouQwWWSfBCk7P0spJTVLW27V2VsMCGjvv9MmL8PIR1/aZNzfwMeJTJrPYHlbb8zPfO1uZ+biMzUnVVpfEmXEOWD7wD3AXs324ekHYkADSLg3df22Mr5+QlvsGenR35/VpGy90mSJEmNDMiSJIGUvZ/nKfLufYjeqWY8SahEzg88A3yzi2m7mq8rTiAUDOu5hFApXJ2QpN+zxRxPAxva7kvI259dd/4gsp8rSZIkSZIkmUfIgGwWYrtPq/OSlpF0ZfHxGi1pvTK+tqS7ixfZ3aXnCUmDJQ0tPVc3U+cL1mQPx9legVCKXK7MM9N9xspatxA9XrU5HipqlUe6QGTIlm8xx922X5b0E0I5cs3KXMcDWxE9cS1ReMANkfRfSa9LelTSa5LekvSDck0vhYfbg5LGl3JQJA0oz7uwwmPuYUmrNVhjL0n3S7p/0qRJXW0pSZIkSZIkSaYhe8hmPTUJ/RpLET1dED1YQ2zfqTB2Hk70Wj0CfMn2u5I2BX5BhwrgQKCv7ZeKb9chXZXuSVqYUFg8qAy9yPT7jN1su5kUP0SG645GeyoiGTtX9tGU4lv2FvBZ23uW+68gjJoXbXlzB4/Z7ilpCCEWshyhWvkw8BvCWPrrtl9TmGXfK2mY7dGShgE/JzJ6F9ue0GCPKXufJEmSJEmSzBAZkM16ahL6QEcPWfm4KfD5IhACsJikRQmVwwtKsGTCF6zGn22/1ObaK5ZgcGXgiorIxYz4jK1Mc2+0rjgDuL3Op60hkjYC9gDWL5+3Bl60/UAJRNuhFviOB3rZ/g/wH0lvFkGP/wK/kPQl4D0iYFsW+BdwNDCaCNoObHO9JEmSJEmSJOkWGZDNXuYDBlYMmQGQdCowwvbXi9DGyMrpaSTlW/Ck7X4Kk+mRkraxPYzOPmPzEUFHI2o+Y8O7sWbjiaQjgWUIb7Wuru1LlCV+xfa/y/B6wDaStiSyXItJutj2Ti2meqv8+17luPa5B2EXsAywVpHtn0iH79tShBrjAmWsO+89SZIkSZIkSdoiA7LZy82EKfEJAJL62X6Izl5kg1vcX+8L1hDbz0s6DDicyBrNdJ+xVkjaE9gC2MT2e11c2xu4Cti5GG7XnuHwsn8qpZqtgrF2WJzIur1TMnIrVM6dDfwfYTtwPPE9JUmSJEmXpDJhkiTdIQOy2cuBwOmSxhHfxe3APoSYxQWSDibMkpvRyRfM9pAW114DHCVpA2aNzxiS7iDUFHtJegbYo2TXziKMl2v+bVfZPrrJNEcAHwXOKNe+245c6HRyCXCtpPuBh4jePSTtUta9tMjs3y1pY9utvoskSZIkAVL2PkmS7pE+ZLMYSVPmAB+yBQnvrj1msQ/ZrsBPy8ef276gxbXHArsAS9a9n4MJWfx3iQBwd9t/n579fJCkD1mSJElSIwOyJEkgfcjmedSmD1kRFFmdkJqfZT5kkpYCjiTUHNcGjpS0ZItbri3X1TOGCFj7AlcQ2cIkSZIkSZIkmSfJgGw2og/AhwzA9lTC/2uGfcgkra6Kx1j5GUX0iP3Z9ku2XyZ8z77c4vE/B9xIsQUoP6fbHmH79XLNvbTwLJM0SNJkSS8X5cQXJP1d0iMKX7EVZ+A9XyXpJkmPS2oYFCp9yJIkSZIkSZIZJHvIZj3zlA8ZcHNVxr+yxiHAPytDz1ACwEbYPg84r5R0TjNfYQ8iaGtFD+KdvQQ8RfTSHSnpIOAA4HtM33vuB6xJqDM+KulU29XnSx+yJEmSJEmSZIbJgGzW82HxIVODsekOUiTtRLynDbu4dLTt58s9TxKZQwjvsY3K8fS851tsv1rm/SuhwNgpIEuSJEmSJEmSGSUDstnLvORD9gwwqPJ5+bp9t03JVv0E2ND2W11cXu8vVvUeq/1+T897rs47lfxvJUmSJGmTFMJIkqQ7ZA/Z7KXmQwaED1k5nOk+ZEQ54uGV+Z8vnmA707UP2QJlf6tI6tlkmeHA5pKWLGIem5exbiFpTeC3wDa2X+zu/U2Y0fecJEmSJEmSJLOEWf5//SVNJcrHFiCkzC8ATiqmxP2BXWwf2M05lwC+bfuMLq7rA1xne7XuriVpIqH2N7lu/Chgiu1fd2fPTfa2JjC/5gEfstLTdgwwGliCyDCNlDSNdL2kxYhASMAiCs+yc2wfRZhk9wKGSvo0UUr4DHAHsF8RKEHSAcChwJKSfmX7hy2evR2/t6nAwi3mSJIkSWYR85JMPGSGLEmS7jHLfchU8eGS9DHgUuAu20fOwJx9KIHWzLiuyb0TmfUB2XTtrdw/fy04mdV0dy1JGwGjbL8uaV9gkO3tK+dPBpYBXmrlxyZpMduvlWDwCmCo7cvK/D8BtrL9lqSPzcRs2nSRPmRJkiTTTwZkSZLMi2hO9CErfzTvBeyvYJCk66ClBPmqku5TyKKPKwIMx1EEKxSy7Cr/TlDInW9fv3bdWr0knVeuHSfpG/XXFw4ta98naaUGc/aTdG+Z4+pSqtdqfC1JYyXdA+zXaMGyz9vLfX+VdJak+cq5KZKOVsjMDyzz3SbpAUnDS68Ykg4s946TdFkZ21Ad8vJjJC1afSflmtMUoiNImijpCEl3AttJWlEhA/+AQjL/sy2+56bS9ZLWApalQ3yjKbZfK4c9CHPr2v892Bc4rtZf1ioYU0jYXyPpWklPS9pf0sHlHdyr8E9D0vkq4iXl2X+msAQY3+pZkyRJkiRJkmRG+MCFCmw/VQKMj9WdaiZBvg9wsu1LJC1I9DsdBqxWUy8sAVU/QqRiaWC0pNtbbOP/CDn31cv9zQyMX7O9tqRdgJOAenn5CwnRi9skHU0YI3+vxfh5lfETWuxvbeDzwN+Bm4D/ITJEPYEJto9Q9HXdBmxre1IJQo8Fdi/v51Mle7REmfMQouTvLkm9aC7kUeVN2+uXd3QL8V0sDFxJvOMny3Vv2V6nyRw3AlaH9P9KRNljU3+xKpKGA18kRDo+Kelw4DPAupKeLc9xiO3RLaZZjSgPXRh4AviR7TUlDQFq3209k21/QdJ3iXe3Z4O97UX8DwZ69+7dzuMkSZIkSZIkSSdml6hHI4n0xYm+oQnAEGDVMn4P8GNJPwJWqFfKK6wP/MH2VNsvEIHKgBbrbwqcXvtQjIwb8YfKvwM7PYC0OLCE7dvK0AXAl7oxflGL/d1n+6lSJviH8nwQfU5XluPPEIHGn0uw81M6gpxxwCUK6fh3y9hdwImSDiz7qI234vLyrL2AdYGhZd+vA8/Y7ld+GgZjZf2JQJ8SPJ8DHG37L22sDYDtLYiM2q3AoWWeJ4GriEDtUOCPkhr9TtUYYfs/ticBrwLXlvHxRI9cI64q/z7Q7BrbZ9vub7v/Msss0+4jJUmSJEmSJMn7fOAZMoVQw1TCnPhzlVPH0ECC3PalpURvK2C4pD0JA+BO03Z3G7TnkeUmx9NDu2s2Wqv2+c1KL5eAh20PZFq2Ar4EbAP8n6RVbR8n6XpgS+DekoV8l85Beb2oRU3sYz7glRYGztOgxtL1A4ENStapF7CgosfwsFZzFQPrYcC2wJ8JkY+rHA2Q90l6j8iMTmoyRTvS+M3uScn7JEmSJEmSZJbxgf6hKWkZ4CzgNNuuS2o0lCAvAdxTtk8px32BsXSWZ78d2FvSBcBSRDByKM1V82oy6N8rayzZJEu2PdGvtj2RqXsf269KelnSBrbvIOTjb2sx/oqkVyWtb/tOYMcWr2ptSZ8iSha3B85ucM2jwDKSBtq+p5QwrgL8Dfik7RGl/+vbQC9JH7U9HhgvaSDwWSL783lJC5V3tQlwZ/1CRVjjaUnb2R5aslF9bY9ttHl1SNd/udrfZXvHyjWDCdGUhsFYycotavt5ST2IQPKOcvoaYGNCxXEVor9scqN5kiRJkjmfFMFIkuTDzAcRkH2klNTVZO8vAk5scF0zqfftgZ0kvQP8iyh5e0nSXaW88Ubgh0T2ZSyRTfqh7X+VTFsjfk7IoE8gMiA/o6NErcpCJTs3H7BDg/O7AmdJWoTI2u3WxfhuwLmSXqe1R9c9RCC4OhFsXl1/ge23iwjFKaUcsgfRC/UYcHEZEzCkBIPHKNQJpwJ/BW4sPWZ/JEocHwfGtNjTjsCZkn5KfJeXEe+7EVXpeoB/2N6mxdyN6AkMK8Hi/MTvxFnl3LnEe5wAvA3s6lktF5okSZIkbTKvqUZCBs1JMiuZ5bL3SQeqWACUz4OJLFHVtHgQIVJRLyDS1dx9gHVtX9rk3N+IrNqCwP3AHrbfmZ752thLP+BMYDEiADzW9uV115wK7FZ9Hw3m2YdQo5wKTAH2sv3Xcq4vkYVbjCg9HGC7HaGSZmtNlydejZS9T5IkSWpkQJYkCcyhsvfJrKGU9PUhyhOb8WTpAVudEP/4ZhfTdjVfK14ngptVgS8DJ1XUHmvBzxLNbq5wqe3Vy75/Rcmslue9GNinrDEIaBpctoPt+6c3GEuSJEmSJEmS6SXFCuYQKv11vcvn9YpE/dpEKeJHgDeIrNKjJbu2FdH71RNYBPhcKQ+9wPaQRuvYnirpPmC5sk4fooy0Z7lkf9t3EyWT788HnFLGBgELESqVSwPb1S0x1PaxlfWek/QiYQT9iqT5iZLGbwNfL3sYVeassnPpeavRkw5xk82BcbUeNtv/lrSFpOPr5ni6iMRMKfvdFHgZ+DER4PUGvmd7WDUzqTD/7g18uvx7ku1T6t9lyt4nSZIkSZIkM0oGZB8stX66GksBw8rxyUS/152SehM9Zp+juT8bRN9c39JTN4g2Sh0lLQysAxxUhl4ENitKhisTMvv9CS+z9+crwcertgeUvq67gO2qwVeT9dYmyiRrnmX7A8OKWAcALTzMkLQfcHCZY+MyvArhbTacCPQus/0rmvfl9QRG2v6RpKuJHsLNCK+3C+j4Dqp8FtiIEI95VNKZ9SWets+mCK70798/a3+TJEmSJEmSbpMB2QfLG1Xp+FoPWfm4KaF4WDu9mKRFCfXJC0qwZEJQo8afbb/U5torlmBwZeAK2+PK+ALAaaXvayoR7DRic6BvERKh7Gtl4OlmC0r6OJF929X2e5I+QWTUBrW5Z2yfTgiwfJvwWtuV+L1dn/Caex24pdTo3tJkmrcJg20I77G3bL8jqZUP2fVFrrGpYOEAACAASURBVP+tkuFblpDbT5IkSZIkSZKZRgZkcw7zAQPrja+L+MU0/myF/9I+T9ruV4KkkZK2sT0M+D7wArBG2UMzYQwBB9hupQ5Z3fdiwPXAT23fW4bXBFYCniiB5yKSnrC9UhtTXkYIhUAERrfZnlzWugH4AtAsIHunosL4vg9ZCRK78iGD9CJLkiRJukEKYCRJ0h1S1GPOoeaNBryvVAhN/Nka8B86e7M1xPbzRDni4ZX5n7f9HuGZNn+T+YYD+xa/MyStIqknDZC0ICHVf6HtoZW1r7f9/2z3sd0HeL1VMFaygjW2IqT5a3vpK2mRElBtSEj5J0mSJEmSJMlcRf5f/zmHA4nSvHHE93I7sA/N/dnqGQe8K2kscH4zUY/CNcBRkjYAzgCulLQdMIKOrFun+Ygetz7Ag8UYehLwtSbzf5Mw5/5oKcsEGGz7oSbXN2P/0jf3DiHGsSuA7ZclnQiMJso4b7A972kMJ0mSzKPMi7LwVTJDliRJd0gfsrkMSUOAv9s+qXweDvzT9p7l82+AZ203Mt9G0t221+1ijYmEP9rkuvFBwNtFhbHZvQcDexIm4JOA3W3/vXJ+McIT7eqq/1qDeW4CPk4Ep3cA+9meWs4dQGQT3yV6vX7Y6nm6QtLRwO22/zK9c6QPWZIkSftkQJYkyYeB9CGbd7kbWBdA0nyE9PyqlfPrEgqIDekqGOuCQbW1WzCGCOb6AlcQGb4qxwC3tbHWN22vAaxGKCluByBpI2BbQl1yVeDXbe++CbaPmJFgLEmSJEmSJEmmlyxZnPu4C6iVI64KTAA+LmlJQnHwc8AYSYcSpYMLEdmoIwEkTbHdqwRzpxH9V08Twfm5tq8ocx8g6auECuN2hNjHPsBUSTsBBwCvECqKVd6qyNjfC+xUOyFpLUKt8CY61CWR9BMa+JkBxxK/owvS4UG2L3BcUUDE9ouVeer9zJYCniDKMFcDflPm2pkQ7diyWAac///ZO+9ou6qqfT8vnRA6kY8epCg9QAICAhEQLIiiIgICAaRJk6LCD0UEQRA0dBCQIu1DqkHFgAGkh9BClyJRQg2d0Ene3x9zndydk3POPffe4HfDnc8Yd9yz9157rbX3uYyRyZzzfYE/2768ZAfPB6Y8u+3HaED6kCVJkiRJkiQ9JQOyGYxitPxR8SpbF7iDMHleB3iD6P0aSkjSr0WoI46QtIHtmytTfZPoCVsF+BRRRnhO5frLtteQ9APCj+z7ks4AJtquZqUG0ZxdgGthSjbvN0QwtHHdMx1FBF9TUcox1ypz1ALF5YH1JR1FBIkH2R5T5lm77v5hhFT+6oSB9pPAT2yvXko/dyBMt+uZ6tmJEsxpSB+yJEmSJEmSpKdkyeKMyW1EMFYLyO6oHN9OeIZtSpQP3kuYHC9XN8fngctsT7b9AiHoUeXK8vsemnt1NaVk0QYDx5VTPyDEN55pdw7bmxF9ZLPTYQo9CzA/8DngR8AfVTFva8CNtt+yPYEIWK8p51t5kPXo2ZMkSZIkSZKkXTJDNmNS6yNbhShZfAY4EHiTyHINBX5l+3ct5mgVxECHD1eXPbiKMuKhwIa10kIig7d+yTr1B2Yr5ZMHt5rL9nuSRhB9Y9cTHmRXFl+xuyRNJvroJnTyHFDxICufO/MgS/+xJEmSj4EUvUiSJOkgM2QzJrcBmwOv2p5k+1VgPiLouYPw6dpZUn8ASYtJ+lTdHLcC35I0k6SFiSCuMzr1OpO0OvA7YItqf5ft7WwvWfzHDiI8yhoGY5L6FwNris/YV4BaH9fVlGyZpOWJnrCXG82TJEmSJEmSJL2d/L//vZSa+EbleBihXrg3UW63EHBx5ZYHgf5Fqv46SSsAd5RqvomEuMZLZa6BRCAznsiwPQ6MBt4o15YEbpQ0MyH4UcumXQNcLunrwD62b6nMt67ti4kSxf7AZWXt/9jeopNn3ZHo9QL4JSH6MULS7IRR9Q3AGeX6UsC+RQhkLLCjbTeS22+x3kzAdsAakjYkyiLvKpcXB26WNAmYC5hZ0uyVTF+SJEmStCRl/ZMk6QoZkM2AFD+ueerODas7PpEwc65nPqJ/bBvgu7YnSlqQCEgeJMQvHrG9SgnIrqcIV9h+HFi1wZwDgW2Bi21v0sb+zyPMppG0APBzot/MRN/WCNtDmtz+J+Ak4Anba1TO1+T235G0J/Br21vX1inrDixrbkP0kw2wPVnS4nQYYo8HNrD9cskw1oQ7duzsuZIkSZIkSZKkq2TJ4gyIpAGSrpA0pvysV86vJel2SfeV358p54dJukzSNcB1wDHA+sALkp4ljJePLOIeUyiB312EiiOSBkq6RdK95afmSXYM0R92v6T9Jc0s6biytwck7d7icTYDrrf9qu3XiADwS80G277T9vMNzt9o+51yeCeR6WrGIsDztieXe8eXtevnnEhI/X+jBI5JkiRJkiRJMl3JDFnvZU5J91eOFwBGlM8nAsNt31rk70cS/mOPEdmdj4qwxtHAt8o96xBmyq9KGkrIxW/eagOS5gDWBvYrp14CvliENn4L/F3S40SJIoRq4/Diz/WG7SGl7PA2SdfZfrrBMscQAh81yfqFiV61Szp7QZV9bgYcWzm1GM1FPgD+CNwqaX1gFHCh7fsaDbT9pqSnCZXK0XXrpg9ZkiRJkiRJ0iMyIOu9vGt7isdXrYesHG4CrFhRe59H0tzAvMD5kpYjyv9mrcx3fRH/aIdlSjC4HHC57QfK+VmBUyQNIhQIsT2oEuDVvMQ2BVaV9O1yPG+Zq1FAdgowu+1fluf8GWFw3Ta2RxJBaU1uf2/C8LrZ+PEle7hR+RklaSvbo5rc0lCRMn3IkiRJkiRJkp6SAdmMyUzAOrbfrZ6UdDLhu7VlEdq4qXL5bdrnqRJoLQLcJGkL2yOA/YEXgdXKHt5rcr8I0Y+Rbaw1nqkVHhev23fbNJHbb0i5fi1wraQXgW8Q2bL6OecmeuQe786ekiRJkiRJkqQVGZDNmFxHZIGOA5A0yPb9RCbq2TJmWIv7O5WvB7D9vKSDgUOIcsl5gfFFCGNHQgGx0XwjgT0l3WD7wyJP/6ztRkHhSOBoSfOX403Lel2iIrf/parcfpOxawAv2H6uKC6uCjzQYFx/4DTg6kY9ZkmSJEnSiFQhTJKkK2RANmOyL3CqpAeI7/BmQnzi10TJ4gGEVHwzHgA+kjQWOM/28BZjrwYOL/1WpwFXSNoKuJGOrNtU8xE9bgOBexV1lROIDNQ0lJ62I4Ex5dQRrUorJf2aUHTsJ2k8cLbtw+ma3P6ngLNKfxuEcMkples3ln3PBFwFHNlsP0mSJEn7fNLl4GtkQJYkSVeQna0vvZ1i3Dwc+BzwGvABEXy9RhviHP9NJC0FXElkz2YFTrZ9Rrk2GxH4DAUmA4favqKT+b4NXAYMsX23pFmBs4E1iGD0D7Z/1cM9bwGsaPuY7s4xePBg33333T3ZRpIkySeeDMiSJOlLSLrH9uDOxmWGrJdTMjVXA+fb3racWwrYggjIehvPEybR75eSv4ckjbD9HNHf9ZLt5UupYEsp+dK/tS9TqxtuRYiArCKpH/CIpEtsj+vuhkt/3IhOByZJkiRJkiTJdCZ9yHo/GwEf1LJMALb/bfvk6iBJh0s6qHL8UBH2QNIOxQ9srKQLyrmlJI0q50cV+XwkbVXuHSvp5nKubV8x2x9UBDVmp/yNSVqF6A3boig43gs0/V+lkkYD/yJEPlYGLipzGJhL0izAnES28M0mc2wm6T1Jr0h6V9Krkh6VdJukJyStVcYNk3RK+XyepJMUPm7/qihFNpp/N0l3S7p7woRWKvtJkiRJkiRJ0pgMyHo/KxHBS7eQtBKRmdrI9mp0eIqdQpT7rQpcBJxUzh8GbFbG1nqwdqH4igFDgF0lLd1izSVKf9szwLElO/YModD4d6Jc8YnK/I3YA7jZ9qeBu4HtbD8IXE70rj0P/Ac4vkXP2T+JLPBQYC5Cdv8O4PPAQcD/a3LfImXM5oRPWkNsn2l7sO3BAwYMaPEoSZIkSZIkSdKYDMhmMCSdWrJXYzofDUSG7XLbL0OIaJTz6wAXl88XEAEIwG3AeZJ2pUNFcVNgh5LZGg0sSPiKNcT2MyXQWxbYsfTAzUJku26zvQYRGB3f5BlnInrmDmxweS3CA21RYGngQEmfbvH8T9t+0PZk4GFglKNx8kFCeKQRV9uebPsRwqg6SZIkSZIkST4Wsoes9/Mw8K3age29JC1EZI2qfMTUAfYc5beIMr/OcJl/D0lrA18F7i8m0F3xFeuYMGTlHwbWB64gDJ+vKpcvIzJvjZibKFO8qSgm/g8woohvbAv8zfaHwEuSbiMMs//VZK6qH9nkyvFkmv/9V+9paAqdJEmSdJ0Uu0iSJJmWzJD1fm4A5pC0Z+VcvwbjxhHKgzWfrVpJ4SjgO5IWLNdqQhq3A98tn7cDbi3Xl7E92vZhwMvAEnT4is1axiwvaa5Gm5W0uKQ5y+f5gfWAf5as1DV0mEBvDDzSaA7bb9heyPZA2wOBO4EtbN9NlClupGAuQnnysUbzJEmSJEmSJElvp89nyCRNIsrXZiF6jLa3/fp0nH8P4B3bf+jCPUMJIY/bbVvSN4Dhkn5MeHq9Dfyk7rYr6CgrHAM8DmD7YUlHAf8oz3ofYRq9L3COpB+VOXcq8xwnaTlgNuJ9jCV8xgbShq8YsAJwUlGCFKEEuR7xjn8CXCjpMkKQ4zlJ3+pM+h4YAIyRNAQ4lfA6e43IAr5BZPOmMXbuIgMVJthJkiTJx0TK3idJkkxLn/chkzTRdv/y+XzgcdtH/RfWncX2R02uHQ5MtN2wx6qr83VzD0PppsdZ8RtTVfqekMJ/TtIvgJlt/7QmfV/rb2sy19yEGuNswN7Fi2xbImP23Zr0PTC0J9L3PSV9yJIkSTonA7IkSfoS7fqQZcni1NwBLFY7kPSjitT7L8q5uST9pQhrPCRp63J+nKRjJd1VfpYt56fI0Uu6SdLRkv4B7Cfpa5JGS7pP0t8lLayQqt8D2F/S/ZLWV3OJ+vMk/VbSjURm6wlJA8q1mSQ9WfrNqDzT4ZLOlHQd8AdJAyXdIune8rNuGXoMsH7Zw/6aDtL3hZ2BX5Vxk1sFY4UjCRPs96pL0L70/UBJj0k6u3xfF0naRNNJ+j5JkiRJkiRJekKfL1msIWlmoq/p9+V4U0JJcC2i9G6EpA2I8rnnbH+1jJu3Ms2btteStANwAiGbXs98tjcs984PfK6UJX4f+LHtAyWdQSVDJukaQqL+fEk7ExL1tZLB5YFNbE+S9DrRD3YCsAkwtknAsybwedvvlgzTF22/V0oVLyFEMg6mkiGTtBtF+l7S7ET54n5EMFTjfdtrl/FLEJmtZYEflezYfGXckSUD9xQhf/+Vuv1dZvsoSasDS9j+syoea4T0/dcJ6ft+wE+BG6Kacio2Lr+XJQyldyPKObclVCW3IKTvG5Vf1qTvP0uYRl9eP6C8k90AllxyyQZTJEmSJEmSJElrMiCDOUvf1UDgHuD6cn7T8nNfOe5PBGi3AMdLOhb4s+1bKnNdUvk9vMl6l1Y+Lw5cKmkROnq2GrEO8M3y+QIiY1TjMtuTyudzgD8RAdnOwLlN5hth+93yeVbgFIWa4iQiwGvEpsCqlWxRP2B329c1Gmz7mTJ+UeBqSZeX+WvS9wdIOgBY3fag+vvVIX0/rMH0Ven7+Ynv5Mu2p1FaLCWPTxcPMxSqj6NKENyp9D3wiEK2v9EzngmcCVGy2GSeJEmSJEmSJGlKlizCuyUgWIoIivYq5wX8yvag8rOs7d/bfpzIMD0I/ErSYZW53ORzlbcrn08GTrG9CrA7HVL1nVGde8p8JQh6UdJGwNrAtW3sYX/CsHk1IjM2W5N7atL3tfexdLNgbKqNhil0Tfr+FaaVvl+jya1V6ftxhJriCEmDqUjf236J8E5rVZ+b0vdJkiRJkiRJryQzZAXbb0jaF/iTpNMJqfcjJV1ke6KkxYAPiXf2qu0LJU1k6gzO1kTv1dZEP1pnzAs8Wz7vWDn/FjBP5bgmUX8BFYn6JpwNXAhcUMmcdbaH8bYnS9qRDjPot4igqEZN+v4G2x9KWh541vbbdfMhaXHglVISWZO+/23JStWk72+gE+l7YEr/m6SbiBLKuyVtTEjfX0hk6j5HZAWTJEmSXkyKXSRJkkxLBmQVbN8naSzwXdsXSFoBuKP0Jk0Evkf0Ix0naTIRoFX9wWaXNJrIPG7TxpKHA5dJepbw2qp5h10DXC7p68A+NJeob8QIolSxWbliPacBV0jaCriRjuzZA8BH5X2cB5xI16TvfyPJRHbp+FrJICF9f4GkE9p4lmacSjzfQ2X+c233VPY+SZIkSZIkSf7r9AnZ+5KxORVYkQiW/kwITXzQ8saurTEOGNyGauD0WGsgISN/cYNrg4Hhttfv4pzDgOtKiSGSziayWg0zWF1F4ce2F9H7NRHYrTa3pFWB3xFZwcnAENvvNZur3DMC+LTtlcvxIOAMouzzI+AHtu9qcu/ChHjLEkQP3TjbXynv9VHCaHoOIkt4qu3zO3u+lL1PkiRJaqS8f5IkkLL3UyjZnCsJkYblCNGK/sDH7jX2MTKQ6KOaCoWx8RXAIY1uKkqSzRhGiGQAYPv70ysYK1xse5XSr/dr4LdlT7MQJZZ72F6JKGf8sNVEkr5JBHVVfg38osx/GFMLn9RzBHC97dVsr0goStZ4yvbqtlcgykT3l9SdLF6SJEmSJEmSdMonPiADNgLes30uQOmr2h/YWVI/SQdIOgdA0irFq6qfmnh6SVpG0p3Fj+uI0keG7YHATprWt2ygpEclnSXpYUnXSZqzfpOStiprj5V0c+XedjzChkk6xfYxtpcCDi6y8kiaWPY5GlhH0mFljw8p/MhUlBMHAxeVOedUeKYNLnNsI+nBcs+xlT2/I+kFSe9KertcH93oS7Bd9Qmbiw5hkk2JrNj5CrXLUcA9kg5tNI/CaPoA4Jd1l2YBTi9znAasVp5lwQbTLAKMr+ytYbljUW08gCgZTZIkSZIkSZLpTl8IyFYi5OynUIKD/xD9YCcAy0rakuhL2t32O0TWZrtyS9XT60TgRNtDgOdqc2pq37JBwJoK3zLK+VNLBuh14FsN9nkYsJnt1Qh/LICXCI+wNQihkJPK+YOBW4raYTN5/RpzAQ/ZXtv2rYSq45BS6jcnsLnty4G7ge3KnDVJfBSy9ccSge0gYIikWu/YnMCutuckSkL/t+ZD1ghJe0l6ishe1YKc5QnFyheJwOzisodmGcwjgd8Qao1V9iL+nhckyhBrapCvNJjjVOD3km6UdGh5xmbcS3iRNXqe3STdLenuCRMmtJgiSZIkSZIkSRrTFwIy0ViCXoCL19QwQsHwH7ZvK9fPAXYon6ueXusQcu0A1R6uqm9Z7R/xy5VrT9u+v3y+h8beV7cB50nalQ6lw1mBsxR+WZcRPXBdZRJRxljjC5JGlzk3IgLWVgwBbrI9wfZHwEVALdD8gOjHg+bPNQXbp9pehhD2+Gk5PQthwLxd+b1lUVGchtIntqztqxpc3hPY3/YSRAb09y32MRL4NHAW8T3dV8uGNlq2xTxn2h5se/CAAc1uT5IkSZIkSZLm9IWA7GHqPKokzUMIOjxVTi1H9CRVe6ja9fSaMi0NfMvKtaqn1SQaqFva3oMIUpYAaqV27XqEfcTU32XVz+y9mvy9pDmIcr5vF++zs+jc+6yVB9eH7lCFafhcTfhfOhQaxxOB8MslM/lXmnuTrUNkHscR0v/LK+TwIWwDriyfLyMylU2x/arti21vD4yhI8isZ3VC6CNJkiRJkiRJpjt9QfZ+FHCMpB1s/6EIW/wGOM/2O5LmJcoQNwBOkfTtUsIHjT297iRKDi8lRB9qNPMtawtJy9geDYyW9DUiMGvXI2wc8ANJMwGL0TwYqQVfL5derG8DtWetn7PGaOBESQsBrxFy/ie3+1yV51vO9hPl8KtA7fNI4MeS+hEZtw2BhmWYtk8HTi/zDQT+bHtoufxcufcmIvP3xDQTdOxlI+DO8v3PDSxDlLDWjxsIHE83njdJkiTpu6T6YJIkXeETH5AVM+ItgdMk/YzIJP0V+H9lyHDgNNuPS9oFuFHSzbZforGn1w+BCyUdCPwFeKOsc50a+5a1Y84M4W22HJGRGgWMpX2PsBOAp4lerIeIkslG7+J1SWeVceOIzFCN84AzJL1LZKJq9zwv6ZCyvoC/2v5Tm89UZW9JmxBB6msUI2zbr0n6bdmLy/zd0QvelQgcZwHeA3ZrMXZNIviuZRbPtj2mBGDLSLqPDtn7k2uCMEmSJEn36Csy8DUyIEuSpCv0CR+y7qIGnl4lk/NuCfS+C2xj++sfw9oLE8Hi54gA5gNCDOM14CDbm0/vNbtL6e06nfARmwQcZfvSujEnAzvZ7t9inunqVfbfJH3IkiRJmpMBWZIkfRG16UP2ic+QdReFp9eedCgt1qhlV0QoJu78Mawt4GrgfNvblnNLEeqLr03v9aYD7wA72H6iKBbeI2mk7ddhSmA7XxvzXGz7jHLPFoRX2ZfU4VW2ve2xpb+u7XLQJEmSJEmSJOmt9AVRj25R8/QqUvHV87cUQ+FVbW9g+8mPYfmNgA9qwUlZ99+2p+plknS4pIMqxw+Vsjsk7aDwQxsr6YJybilJo8r5UZKWLOcbeaDNLOk4dfiq7d5ss7Yfr/SH7UQEX7cXH7D7gT8CP+7soeu8yn4LrFvu/yewOJEZw/YrlZ6+aVB4rx0raZyktyQ9Xs69L+kvZUxDjzdJW0r6u4JFyr3/02SdlL1PkiRJkiRJekQGZL2TlWjSB9YOklYCDgU2Kr5m+5VLpwB/sL0qIV9f8zVr5IG2C/BG8VsbAuwqaek2lr+e6E9b2fYgov/uZNvPt7n3mlfZ7MCaZY6TiX6940vw1FlwNxch1T8Q+HvZz/yE2MliZUxDj7ciqf8CUTp5FvBz2y80WiRl75MkSZIkSZKekgHZDICkU0v2akzno4HIsF1ejKyx/Wo5vw4d3mkXEL5f0NgDbVNgh5KhGk0YLtd81Zrtc5Ey705FGXJRYCu6oFLYU6+ywgfA38rnBwlZ/Q/L54HlfCuPt32AQ4D3bV/S7t6TJEmSJEmSpKtkD1nv5GFCWh8A23sV2fl61Yhm/mPNzLDrcZl/D0lrE3L09xeRDgH7FBPlTlF4u/0F+KntO8vp1YFlgSeL8mQ/SU/aXraNKf+XInFPxausrFXzKhvV5N6qP9pkig9cCRJrf/NVj7eZCGXGGouV+xaWNFMxD0+SJEm6SYpcJEmSNCczZL2TG4A5JO1ZOdevwbhxFBNlSWsAtZLCUcB3ivgFkhYo52+nwzttO8JceYoHmu3DgJcJD7SRwJ6SZi1jlpc0V6PNSpoNuIooh7ysdt72X2z/j+2BpXzwnVbBWJH9r1HvVbaqpH4loNoQeKTZPG0yL/B8Cba2p2QGy/znAtsShtAH9HCdJEmSJEmSJGlKr8yQSTJwoe3ty/EswPPA6I9T7l3STYSk/N1154cBg23vPR3WmNhK+h2meKd9Axhe+qUmEB5kP6kbegUdZYVjgMfL/Q9LOgr4h6RJwH3AMGBf4BxJPypz7lTmaeSB9gBR3ndvUX2cAHyjyZa/QwRJ60r6FZF5+4Ht88ozL0mYbPeT9AjwFdvjGsxT9SpbCFigPM9rks4ve5iJ8AfrqV9DM4+3/wfcYvuW2nuV9Bfbj/ZwvSRJkqSP0Ndk/iGzoEnSE3qlD5mkiUR2ZF3b70r6MvArYHxfCMha3DtzK3XB6YmkWWx/1IXx/YG3SzC5KvBH258t124ivMmuL+Mm236nxVyDCSGSLWvvStKZwH22T5e0ImEgPbC7zze9SR+yJEmSpEYGZEmSQPs+ZL25ZPFaomwNYBtgiriCpLkknVMk2e+T9PVyvpmU+VBJN0m6XNJjki4qWZ9GfE/S7QoZ+LXqL6q5dHyz80tLuqPs9chGC5Z9Pybp/HL/5QoDahTS7YdJuhXYStIykv4m6Z7yrLWgp5F0/UqS7lLIzz8gabmy1kOVtQ+SdHj5fJOkoyX9A9hP0gBJV5S9j5G0XrMvy/bESt/WXJQMVgmeZrF9fWVcq2BsZuA4ppXJN2EKDVFu+FyLOYZJulrSNZKelrS3pAPK38qdKiWcknYtzzW2PGftnf9J0g7l8+6SLmq2VpIkSZIkSZL0hF5Zslj4X+AwSX8GVgXOAdYv1w4FbrC9s6T5gLsk/Z0OKfP3SgneJUAtKl2dkJN/jlAVXI/SQ1XHXLbXlbRBWXPluus16fjzJe1MyKV/o8X5E4HTbf9B0l4tnvczwC62b5N0DvAD4Phy7T3bnweQNArYo5gwr02U3m1Eh3T9s+WdAOwBnGj7IkWf18zAwi32ADCf7Q3LWhcDw23fWgLMmyS9WTf+fdtrl/FbEpnMT9ERTC8PvC7pSqLH7e/AG8C36+a5zPZRwN7ACNvP18XMhwPXSdqHCPg2kTSakMevsn35vTLxnc8BPAn8xPbqkoYDOwAnAFfaPqvs/ZeE1P/JwG7AbZKeBg4EPtfoRUnarYxlySWXbDQkSZIkSZIkSVrSawMy2w8oTI63Af5ad3lTYAt1mCLPASxJBFunKFQCJxHBQI27bI8HUPQGDaRxQHZJWf9mSfNUgpsa6wDfLJ8vAH7dyfn16FBMvAA4tskjP2P7tvL5QqLfqxaQXVr23R9YF7isEqzUApKadP0fgSvLuTuAQyUtTgQfTzRPDE7h0srnTYAV69Za3/ZbjW4sHl5XlWD2yHL/LEQgvTrwnzL/X4u/2FSoQyZ/aIPptwHOs/0bSesQ73LlRgqIktYEbiz7fEvSG8A15fKDRIAPsHIJxOYD+hPiIdh+UdJhRG/ZlhXbgPrnPRM4E6JksdGYJEmSJEmSJGlFrw3ICiOIoGQo4YNVQ8C3bP+zOriUAqnJzgAAIABJREFU3jWTMn+/8nkSzZ+9/h/Wnf1Du9l1tzGm3XVrghMzAa83CmYaSdfbvrhkkb4KjJT0fUL4o5FUfv1atfXWsf1uG/uv7uXmUlq5ECFZf5/tfwFIuprIOP2+wa2tZPJ3Ab5U5r9D0hyE8MdLTbZR/b4nV44n0/Hdnwd8w/ZYRZ/g0Mo9qwCvAIu2+dhJkiRJkiRJ0mV6e0B2DvCG7QclDa2cHwnsI2mfIiKxuu37iN6i8cVvakc6TI67wtbAjZI+X9Z+oy6rVJOOv4CKdHyL87eV8xeW881YUtI6tu8gskHTZO9sv1l6orayfZliY6uWgGIZ26OB0ZK+BiwhaV7gX7ZPkvRpIjN0C/AphST+RGBzOkyU67mOKCE8DqAEefc3GihpWeCp8n2sAcxGBDSvAfNLGmB7AlFe2VD9wvZfgP+pzDmxIpP/H2BjIgu4AhFITmiy73aZG3heIe2/HfBsWXct4MtEgPgPSdfZfrqHayVJkiR9hBS4SJKkK/TqgKyUGJ7Y4NKRRA/QAyUoGUcEFs2kzLvCa5JuJwQkdm5wvZl0fLPz+wEXS9qPkKlvxqPAjpJ+RyhMnt5k3HbA6ZJ+CsxK9NqNpbF0/cGESMmHwAvAEbY/lHQEMBp4GnisxZ72BU6V9ADxt3Iz0ZfWiG8REvwfAu8CWxeRj0mltHRU+a7uAc5qsWYzDgTOkrQ/kT0cVhER6S4/I97Dv4lSxrklzV72t5Pt5yQdSHyvG02H9ZIkSfoUfVFtEDIgS5Kka/RK2ftPKqqTvC9lcoOJssw/264XEOnK3AMJm4CLm1x7FPgnkbm6mxAQ+bA787W5n78RpYm3Vq0KimLhYMJr7C5g92b7kLQdHd5rE4E9bY+tXJ+5PMuzPbVDKP1rJ9muFxtpi5S9T5IkmZYMyJIk6cvoEyB7n7SJwjh7ILBti2FPld6zVYDFCTPnVnQ2X2ccR4fiYZWLgM+WfcwJfL/FHE8DG9pelciKnll3fT8i0Owxtp/rbjCWJEmSJEmSJN0lA7JegO1xwBfUwPNL0loKX7T7yu/PlPPDJF0m6Rqi1+sYYH2F59j+LdaaRGSmFivzNPRuq59P0sySjis9bO9KeqZcu1/SqQ3WGQVMo8Zo+6+l9O9KYAPg55V5Nqsbe7vt18rhnUQgSdn34kTAt1Zln/dLuqp+TYWX29EKP7i7Ja0haaSkpyTtUXkPD1Xe7ZUKv7cnJP26fs4ybrcy390TJvS0nS1JkiRJkiTpi/TqHrJPIHMqJPdrLEAoSUL0ylU9v0YCKxA9XhvY/kjSJsDRdMjor0OIerxaRE8O6qx0r6gTrk1kl6C5d9vB1fkUnltv2F669FndBmzVA7GL7xD9W/vZvqWN8bsQZuE1TiCya3PTxnMTtgLrKHzIziPsCOYAHgbOaDB+ECHq8T7wT0kn236mOiBl75MkSZIkSZKekgHZf5d3q5L1lR4ymNbzax5JcxPKkeeXYMmEkEeN65t5ZDVgmRIMLgdcbvuBcn5Wmnu3VdkUWFVSraxv3jJXdwOy04Cb2wnGJH2BCMhq5tibAy/ZvkdTq2+2ohb4Pgj0r3iUvadpveYARtl+o6z3CLAU8EyDcUmSJEmSJEnSbTIg6z009PySdDJhcrxlEdq4qXK5KyqST9keJGkR4CZJW9geAexPc++2qbYC7GN7ZBfWbDyR9HNgALB7G2NXBc4Gvmz7lXJ6PcIY/CtElmseSRfa/l6Lqao+ZPUeZY3+O2jXty5JkiRJkiRJuk3+I7P30Mzza16KPxYwrMX9bxHley2x/bykg4FDiKxRM++2+vlGAntKuqFI5y9PqBt2yVpAYU69GbCx7cmdjF2S6DXb3vbjlWc4pOyfSqlmq2AsSZIk+T8g1QaTJEk6JwOy3kMzz69fEyWLBwA3tLj/AeAjSWOB82wPbzH2auBwSevT3LttqvmIHreBwL3FT2wC8I1mC0i6hVBT7C9pPCGzP5Lo1/o3cEcpz7zS9hFNpjkMWBA4rYz9qB3p0CRJkuT/lr4qd18jA9EkSbpC+pDNoEhaGBhOeH29BnxABG+v0Z7IxX+V3uRLVnrQjiRKNGcFTrT9O0mHA7sSweZcRL/ZT20/0tnzpQ9ZkiRJBxmQZUCWJEn6kH2iKRmqqwlRjE/bXhP4LhVZ+F5Ir/AlkzRrue9rtlcjlBRvqgwZbnuQ7eWAS4EbJA1oNWeSJEmSJEmSdJcMyGZMNgI+sD1Frt32v22fXB0k6XBJB1WOHyrCIEjaQdIDksZKuqCcW0rSqHJ+VOnhQtJW5d6xkm4u52aWdK6kd+p8yUY32nArXzKiN+4+pvYla+Rt1pkv2cFM60u2Wd00cxMloa+UOd+3/c8me76U6O1raJCdPmRJkiRJkiRJT8keshmTlYB7u3uzpJWAQ4H1bL8saYFy6RTgD7bPl7QzcBLRJ3YYsJntZysS8bsQyo39eupLZvtcSRfSc1+yL9OJL1nxbBsB/FvSKODPwCUtBEbuJTJ4jeZKH7IkSZIkSZKkR2SG7BOApFNL9mpMm7dsRHiRvQwRpJTz6wAXl88XUHy/iGDrPEm70qHCuCmwQ/E2G02IbyzXg8foji/ZT8rxFF+ydhay/X1gY6Jn7SDgnFbLtTNnkiRJkiRJknSHzJDNmDwMfKt2YHsvSQsRghZVPmLqoHuO8luEyXRnuMy/h6S1ga8C9xcT6Rnal8z2g8CDpVzzaZpbCqzOtO81SZIkaUGKWiRJkrRPZshmTG4A5pC0Z+VcvwbjxgFrAEhaA1i6nB8FfEfSguVarWTxdkIcBGA74NZyfRnbo20fBrwMLEGHL9msZczykubq6oNUfMm26Ykvme3FbQ8s+7+hWTAmqX/xLqsxiJDhbzT2W0Qm8JL2nyhJkiRJkiRJ2iczZL2UzmTtid6u4ZJ+TMi0v02HJHyNK+goKxwDPA5g+2FJRwH/kDSJENQYRnihnSPpR2XOnco8x0lajsiKjQLGEj5lA+nwJVsEmFlSM1n7xSn/A6CFL9n8wOzASzSQtQd+Xta8VtLTdM+XTMCPJf0OeLe8t2GV6/tL+h4he/8QsJHtVOxIkiTpAil7nxnCJEnaJ33IeiElwLkdOL+mpChpKWALwhurN/qMbUxk6XavC8i+Qof4xsVEn9jpTeZYF3jU9muSvgwcbnvtyvUDiOBunt72/OlDliRJ0kEGZBmQJUmSPmQzOr1F1v44SWPK+Jb9Xa1k7V0gRDSaeqW1IWv/VaJ/rCWSxkk6WtIdRZZ+DUkjJT0laY8ypn95B/dKelDS18v5IeV555A0l6SHJa3c2ZpJkiRJkiRJ0h2yZLF30ltk7d+wPaQmay/puk5k7ZemeIBVzr1ve+3Sa7Y9Yd7c2f53An5J9MnV5uoPbE3I2rfDM8ALhODHbcATwDvAMUSZ5HvAlrbfLIIod0oaYXtMkcX/JWFUfaHth5rsczdgN4All1yyzW0lSZIkSZIkSQeZIZsBmIFk7Z8GbrE9qPJTKzlsW9aeECN5E1je9iDgp8B17craF0bY3pIwi77Q9qq2VwHeLEGngKMlPQD8HVgMWLjcewTwRaI88tfNFrB9pu3BtgcPGDCgC1tLkiRJkiRJkiAzZL2TlLXvoaw98H75PbnyuXY8C6EiOQBY0/aHksbR8f4WIDJys5Zzb3f6kEmSJEmSJEnSDTIg653cQGRv9qwIYDSTtd8cGsraXyVpuO1XJC1QsmQ1WfsLaCBrD4yW9DWmlrW/oQQsywPP2u5ScFKRtd+4J7L2wCFlzFBC1KRVMNYO8xJm0h8qjKaXqlw7E/gZ8T6PBfbu4VpJkiR9ihS1SJIkaZ8MyHohti2pt8naTyD6zRoi6Rbgs0D/FrL2AFfaPqLJNIcRpZGnlbHdkbVvl4uAayTdDdwPPFaeY4ey7sWSZgZul7SR7Rs+pn0kSZIknzD6usokZFCeJF0hZe9nQCRNtN2/cjwMGGy7aSZH0hbAiraPaTFmKE0k9SX9EDjT9jtN7u0HXAYsA0wCrrF9cN2Yb5cxQ2w31IiXtBaRoYIIEA+3fVW5Nh9RzrgyUW65s+07mj3Pf5OUvU+SJElqZECWAVmSQMreJ3XYHtEqGGuDH9K4bLLK8bY/C6wOrFe8xACQNDeRoRvdyRwPEcHlIOBLwO8k1TK5JwJ/K2usBjza9cdIkiRJkiRJkt5DBmSfMCQNkHRF8Q8bI2m9cn6YpFPK52Uk3VmuHyFpYmWK/pIul/SYpIsU7AssSpTvvSXp/srPaADb79i+sXz+gJDtr3qOHUkoFr4HfK1ujvslnVqZ56NyzxwU4RFJ8wAbAL+vrHFug3k2K+NvkjRc0s2SHi3+YldKekLSLyvv62pJ9xS/sd3KuaXKuIUkzSTpFkmbNnjXuyl8zu6eMGFC97+0JEmSJEmSpM+SPWQzJnPWeX0tAIwon08Ehtu+tYhkjARWqLv/ROBE25eoGCVXWJ3wQXuOkMNfz/ZJkg4A1q1J6beilBZ+rayDpNWBJWz/WWFkfY3tn7e4f23gHEJoY3vbH0n6NNHHdq6k1YB7gO91IjLyge0NJO0H/AlYE3gVeKomeEKUPb4qaU5gjKQrbP9b0rFE/9to4BHb19VPbvtMSnnl4MGDs/Y3SZIkSZIk6TKZIZsxebfq9UWIYdTYBDilBGwjCIn4ejPldYheLujwJatxl+3xRRHxfkLYo21KeeElwEm2/yVpJmA4cGC7c9gebXslYAhwiKQ5iP95sAZwuu3VCZGTg1tMAx1B6oPAw7aft/0+8C9CSRJgX0ljgTvLueXKHs4mTKj3AA5qd+9JkiRJkiRJ0hUyQ/bJYyZgHdvvVk8W1cJ2qHp2TaLrfyNnAk/YPqEcz02IcNxU9vA/wAhJWzQT9qhh+1FJb5f7xwPjizw/wOV0HpC19CIrIiabEO/rHUk3UbzIikhJreSyP/BWJ2slSZIkCZCCFkmSdI3MkH3yuI6Kb5bC5LmeO+kwnv5um/O+RQRXTSm9WfMSAiAA2H7D9kK2B9oeWNZuGoxJWrom4iFpKeAzwDjbLwDPSPpMGbox8Eibe2/GvMBrJRj7LPC5yrVjCWn8w4CzerhOkiRJkiRJkjQkM2QzME3k798BBkt6gPh+bybK7mpjtiACmQMkHQj8BXijbt6hRJneuMrpM4FrizfXavXy95IWBw4F/gm8Jml24BXggnr5e2AjSWNoLH//eeBgSbMSGaoXgVGSDgf2AS4qJYzzA29L2pzuy9//DdijvKtFiWARSRsS5ZLr2Z4k6VuSdrJ9bjfWSJIkSfoYKXufWcIk6QrpQzYD0x0/sjKuH9GHZknfBbax/fXK9aE09yMbV9ZoKO5R5l7b9o2SZiOMpo+2fW25PjcRBM4G7N0iU9aPEOX4SNIihFn1ouX4fOAW22eXNfrZfr3VM3/cpA9ZkiRJUiMDsgzIkgTSh6zP00r+HrgAuF/SY0Tma+kuyt/fKOnGRut2Uf6+KV2Rv28VjHVB/n5i+T203DPVs7faa5IkSZIkSZJ0lwzIZmzmVMWDCziicq0mfz+E6Bc7u3LtedurAU8Cu9telZC5r7I60Qu2IvBpivx9GfcBML+m9v9apX5zFfn7UeV4ivw9UXJ4Ud0cV9Xdv7akhwmVxD1KgFaVv79P0iOSHqibZ6e6rXxgewNCxv5PwF6EUMgwSQs2eK/TPHuDMelDliRJkiRJkvSY7CGbsXm3yN4DHSWL5XATYMVKcqeZ/P03yueLgeMr1+6yPb7MW5O/v7Vc26wzP7IW8vfDypDXiLLIpnV+RVFxJUkrAOdLupYO+ft9bI+WdCLwpu2ftdjONPL3ZY81+ftX6sa3evbq/tKHLEmSJEmSJOkRGZB9ckn5+w5ayt+3GA/de/YkSZIkSZIkaYv8h+Ynl5r8/XEQ8ve2768bU5O/v5Suy983zZBV5O+/Xztn+w1gocqYm2iRIZO0NPBMEfGoyt+/LOkZSZ+x/U+mj/x9kiRJkkw3UtAiSZKukD1kn1z2pcjfS3qEivR9hR8S8vd3AYtQJ3/fhJr8fUNRj4r8/YrAvaWn6/uNxnbC54GxpWTwKuAHlTLJmvz9A8Ag4OhuzJ8kSZIkSZIk/+ek7H0vpTuS9sVjbEXbx7QYM5Qiad9A/v5QQrL+nSb39gMuA5YhSvmuqfcYk/TtMqaRx1htzFqU3itAwOG2ryrX5iMESFYmlBW76zH2XyVl75MkSZIaKXufWcIkgfZl77Nk8ROE7RF0CFi0w5rAKUXW/XVC+bAfYS7djOOrHmOSvlznMbYvMLrF/QAPEcHlFI8xSdcUFcUTgb/Z/nbNY6wLz5MkSZIkSZIkMxRZsjgD0spjTNIp5fMyku4s1xt6jAFnEcHRaoQ4xqfomsfYcsDvKrL7/yL60briMfZFIhC8u5Qgfhf4am2NLniMvSrpn5Jel/S+pBdq8veSvifprrLP30mauZw/vcjWPyzpF5V5x0n6haR7JT0o6bOtnidJkiRJkiRJuksGZL2X7nqMVcecWMZ0xWPsC7a/0NnmSmnhO8DQIr2/E3Cz7VPbebiKx9hpwHfKHDsAY4E3isfY2ZLm6mSqmsfYLwixkRWAeYCPCBXHFYCtyzMOIkottyv3HlrSyKsCG0patTLvy7bXAE4HDmryDOlDliRJkiRJkvSIDMh6L+/aHlT7AQ6rXNuEKDW8nyhRbOYxdln5fHHdtbtsj7c9Gaj5bLVNC4+xA9udw/Zo2ysBQ4BDJM1Bh8fY6bZXB96mc0n7aTzGbL9PZOuWIFQY1wTGlPe1MRGEAnxH0r3AfcBKRIBa48ry+x6avB/bZ9oebHvwgAED2nzyJEmSJEmSJOkge8hmTNJjrIPOPMYEnG/7kOpNRVb/IEJ85DVJ5wFzNJg3fciSJEmSJEmSj438h+aMSXqMtc8o4E+Shtt+SdIC5RnnITJwb0haGPgycFMP10qSJEmSVBhMkqRLZEA2Y7IvcGoRwZgFuJlpfcZ+CFwo6UDgL3TNY+z5Rn1kFY+xxwiPMYBTbDfqYWvF54GDJX1IZLIaeYzNRpQd7tTFuafC9iOSfgpcV0orPwT2sn2npPuAh8s6t/VknSRJkiTl3mtkQJYkSVfoMz5kJZg4legTmgn4M/CjohY4wyFpILCu7fr+sNr1eo+xbWx/vZM5hwHX2X6uHJ8N/NZ2T7NUtflnB/5A9HS9Amxte1yTsQsSJYtDgPNq/mvteKF1c29/BbZtperYivQhS5IkyYCsRgZkSZJA+z5kfULUo/hsXQlcbXs5YHmgP3DU/+nGesZAYNsW19cE7i9ZtB9QBDdqku9NGAYsWjuw/f3pFYwVdgFes70sIQJybIux7wE/o7HC4fG2P0uoRa4n6cs93Zjtr3Q3GEuSJEmSJEmS7tInAjJgI+A92+cC2J4E7A/sLKmfpAMknQMgaRVJD5XzT0gaUM7PJOlJSQu18viS9KNy/oGat5WkgZIelXRW8by6TtKc9ZuUtFVZe6ykmyv33lI8se6VtG4ZfgywfpHF318VD7LCT4D9bK9KKBfuIGk0sI6kw8oeH5J0poJvA4OJcsH7i2/XRIW31/3Fm+vxcs+xlT1PlHRU2fOdpR+rdm2zOun+YwhZeojs18ZqokRi+23btxKB2QaVOW4HhkvaqWQ37wUWb/bFSzpP4Td2o6R/SdpQ0jnl+zivMm5c+W7b+q7KPSl7nyRJkiRJkvSIvhKQrUTIl0/B9pvAf4BlgROAZSVtCZwL7G77HeBCOjyrNgHGll6nhh5fkjYlzJLXAgYBa0raoFxeDji1SL2/Tghu1HMYsJnt1YAtyrmXgC8WT6ytgZPK+YOBW4os/vBOnn8u4CHba5cg5xTbQ2yvDMwJbG77cuBuYLsy51q1Y+ArxN/KeuW5hkj6RmXuO8uebwZ2rbzjkXXS/f+pvc9iDP0GsGAne4fwNxtU93Ouwgvta4RwRyvmJ4Ly/YFriOzcSsAqkgY1GN/Od5Wy90mSJEmSJEmP6SsBmYBGzXICXPy4hgEXAP+wXRN4OIcwKwbYmQjWoLnH16bl5z4ic/NZ4h/3AE9XlBCbeVvdBpwnaVegVlo4K3CWpAfLmis2uK8zJgFXVI6/IGl0mXMjIjhpxRDgJtsTSiB1EVALND8g+vGghWdXoVE2rFtNjKrzQutk+DWOZskHgRdtP1i+84eb7Led7ypJkiRJkiRJekxfUVl8mLosh6R5COPgp8qp5YCJTN1D9YykFyVtBKxNR7asGQJ+Zft3dWsNZFrvr2nK4GzvIWlt4KtE/9cgQnXwRWA1IoB+r8naHzF1gF311HqvlGmiMGA+DRhcnu/wurHNnqsZH7pDGaYzz67xxDsfXwKqeYFXO1m7GfVeaK3ozKus2Xho8l0lSZIk05JiFkmSJF2nr2TIRgH9JO0AU4QtfkOo970jaV6iDHEDYMHST1XjbKJ08Y+1oIYOjy+Y2uNrJNGX1r+ss5ikT7W7SUnL2B5t+zDCC2wJImh5vmR0tqcjc1bzDKsxDhhUet2WIMomG1ELvl4u+6w+a/2cNUYDG5Yeq5mBbYB/tPtcFUYAO5bP3wZuqARzbaMOL7QfdmMPSZIkSZIkSdJr6BMZsiL9viVwmqSfEX1jLwMvSVqe6GU6zfbjknYBbpR0s+2XiCDiXDrKFaGJx5ft6yStAPxT0mQiI/Q9IsvSEklDgcMVcu8igsixRDbrCklbATcSZsYADwAfSRoLnEf0wT1NlOU9RJRMNnoXr0s6q4wbB4ypXP4LIerxElGWWbvneUmHlPUF/NX2nxpMfyiwkqSFbG9eebaLCMGQj4jA+EkiMzaNYbWk7QhBEgg1zPeBmUvP2qZEFvPQ8rsnXmhJkiRJ8rGQ8v9BZkyTpD36jA9ZFUkTbdeyWOcDj9tuKIEvaTAw3Pb6lXNd9vgq981SerAaXTscmGj7+C48R9P5urmHocBB1WCqi3NvDPQjRFGqAdlXgGvL4cWESMfpTeZYF3jU9msKOfvDba9duX4AEdzN0919fhykD1mSJElSIwOyIAOypK+j9CFrmzuAxWoHmlq2/u+EGMYvJP2lSLs/BPyI6PH6ADgOGFhk4pctcxwu6aDy+SZJR0v6B7CfpK8VQY37JP1d0sKlx2wPYP8i776+pKUkjSr7GCVpyTLfeZJ+K+lG4Dg1keavPmDZz5mSrgP+oPal9GeWdFzlfeze6kXaHkWUPdaf/6sLwF20kKm3fbvt18rhndWxCnPvrxJlpC1RyNgfLekOhTT9GpJGSnpK0h5lTP/ybu+V9KCkr5fzQ8rzziFpLoX8/cqdrZkkSZIkSZIkXaVPlCw2o/RDbQz8vhxXZetFlCtuDwwAnrP91TJuXtu/kDQOOMv2UaU/7QSgUdZmPtsblnvnBz5XsmvfB35s+0BJZ1DJkEm6BviD7fMl7UzI3dek5pcHNrE9SdLrhNjICUwtzV/PmsDnbb9bMnxftP2epOUItcLBhJT+lAyZpN2AN2wPkTQ7cJuk62w/3cmrXVbhGVblMuDX5X3uVzspaTOmNYh+2vaWhJH0tZXzJwA/pkGfm6RDga0qpxYFVrK9jqThRFnnekQP3cPAGYRAypa23yxB7J2SRtgeI2kE8EtC0ONC2w81WHM3YDeAJZdcssXrSJIkSZIkSZLG9NWAbM4SMAwkZM2vL+ersvUA/YkA7RbgeIUh8p9t31KZ65LK72Z+YJdWPi8OXCppEWA2ou+rEesA3yyfLyCCmRqXVQRGzgH+RAQrVWn+ekbYfrd8nhU4RaHiOIkI8BqxKbCqOkRO5iXeR2cB2ZONyglL79rN1fdneyQhhlI/9gtEQPb5crw58JLte0pp5VSUktOjKvePA35QDh8E+tt+C3hL0nsKD7O3gaMVXnGTiUzpwsALwBFEf917wL6NHtL2mYTaI4MHD+57tb9JkiRJkiRJj+mrJYvvFqPipYigaK9yviZbXzMfXtb2720/TmSYHgR+Jemwylxu8rnK25XPJxMiFKsAu9O55HyjuafMZ/sZoCrNf239jQ32sD8dUvqDiXfQCAH7VN7H0rava3O/U08k/ZzINB7QxthVibLEr9t+pZxeD9iiBFr/C2wk6cJOpupM7n67sqc1y9/Di3R8HwsQAfnctP8dJUmSJEmSJEmX6KsZMgBsvyFpX+BPkk4nMjVHSrrI9kRJiwEfEu/pVdsXSppImEjX2Jrovdqa6EfrjHmBZ8vnHSvn3wLmqRzfTqgQXkAEDre2mLMmzX9BJXPW2R7G254saUeaS+mPBPaUdIPtDxWKlM/afpsuUEozNwM2LvL9rcYuCVwJbF8CYQBsHwIcUsYMJUorv9eVfTRgXiLr9mHJyC1VuXYm8DNgaaKkcu8erpUkSZL0EVLMIkmSrtCnAzIA2/cppOO/a/sChWz9HQo59YmEbP2yhIDGZCJA27MyxeySRhPZxm3aWPJw4DJJzxKiFUuX89cAlxdhiX2IMrlzJP0ImADs1GLORtL8rWhXSv9EoqzzXsULmUBHH9s0SLoF+CzQX9J4YJdSkngG8G863uuVto9oMs1hwIKERQHAR+2o03STi4BrJN0N3A88Vp5jh7LuxaXP8HZJG9m+4WPaR5IkyQxPKgt2kAFZkiRdoU/K3jdD0iSiLHEWok9qe9uvtxg/DhjcRESj0fg9gHds/6ELexoKfGD79hZjppHmn54oVCDXtX1xN+/fEfhpOfyl7fNbjD0K2AGYv2ZNUM4fAHyf8DKbAOxs+9/d2c/HQcreJ0nS18mArIMMyJIkgZS97y7vll6plQnj4r06u6Er2D6jUTAmqVWmciiwbrOLkg4mpPkPaXO+ZvO0umcgsG1X5yzzLgD8nOhvWwv4eVGabMY1ZVw99xHB76rA5UwtcpIkSZIkSZIkMyR9vmSxBXcAq9YOSungd4DZgats/xxYCfhj8ceaGTjS9qUlc3Yp8IVy+7a2n1TF/FnSTUSf2HrACEmqIgIpAAAgAElEQVSPE1mk2YBXiL6xOQl/skmSvkeUMv6HUFYcQClltH2Mwp/sm8DqhEfa5kRWa4KkmYDHCbn9Kdm8sp9FiYDrZUn/j+hZm6sM2btk5o4BVijKlNcR6ouLEqIXAl6wXSu9rGcz4Hrbr5Y1rwe+JGkOKvL3hdts71XGTXXB9o2VwzuJUlIkXUVH2WeNiwj7gReBQURP2oNlvTmBb9h+StLXqHvntl+UdBLwsu0jiiz/ocDQzvrfkiRJkiRJkqSrZEDWgHb8yYpU+jT+ZJVp3rS91ifUn+xJ4FO2f1nxJ1u6iT/ZYsAzlePxwGLledrteatnij9Z8SubilLmeSiwApHp/Bdwdvk+9iMC2x8SQilTvXPgwPK8Y0pP3EnAVxoFY+lDliRJkiRJkvSUDMimJv3Jpr8/mRqc63bjYskUDgY27GToGNvPl3ueIjJ7EJmyWuay4Tu3/Y6kXYGbgf1tP9VogfQhS5IkSZIkSXpK9pBNTfqTTX9/svHAEpXjxYHnmj1MKyRtQmS+trD9fifD633Hqp5ktf8R0eqdr0KUMS7anb0mSZIkSZIkSTtkhqwB6U82Xf3JRgJHV4Q8NqUiQNIuklYHfgd8yfZLXb2/CQ3fuaSliNLF1YG/Srra9ujptGaSJMknklQWTJIk6R4ZkDUh/cmmjz+Z7VclHQmMKaeOqAl8NELSrwlFx37Fy+xs24cDxxGlopeV7+A/trdo87macTh177w8z++JnrnnJO0CnCdpiO33erhekiRJ0gdIC4AOMlBPks5JH7KPga76k/VgnYl1Xl3Dyrp7l+Np/MkkbQGsaPuYFvMOpSLiUXfth8CZtt9pcX+PvcRKH9vpRHZwEnCU7UvrxpxMqEz2bzBFl5D0V0INs6nvXCvShyxJkiSpkQFZBxmQJX2Z9CHr4zTyJwOwPaJVMNYGPwT6dTJmeniJvQPsYHsl4EvACZLmq10sweZ8zW7uKra/0t1gLEmSJEmSJEm6S5YsfgzYHvh/vQei7G4IMLyU+P3Q9m3VLJqkZQjPrpkJ0Y8DKtmm/pIuB1YmFCdrPmiLAjdKetl2Ta0QSasQfW1V5qwe1HmJ/YAoEby/cm572w+WsY9X7ntO0kv8f/bOPNzO8Xr/n9scYirpoGnEPEdCRGMMVW1R6tuqoghaQ0sNVdVSlBZt1FBjVc1jjT9KxRhDRAgiMY9RQYm5qTFx//5Yz3be7Oy9zzk5UUmsz3Wd67z7eZ/pfc92XVnWs+47bAbeLLYEQ4DDgW3r5njf9pqVfZ0NvAssT4i17ETUiw0ERtoeXPqNI4RMupd3cSdhyP0CsEVFifJjUvY+SZIkSZIk6SoZkM3cdKsLRj5H1I1B1HkdZ/tOSb0IcY0V6safAJxg+yJJu9fd60cYX78IDAfWtv3ncuxwg/rjmCWQ6lttK0InzbgNuMT279p7SEkDCMXHmvz8noRc/62SasqYrVgY2BDYnMjerU0cnbxXUl/bo+v6LwNsY/vHkv4OfJcQR5mClL1PkiRJkiRJukoGZDM3UwQjtexX+bgRsGLJjgEsIGn+KYczkDYxjguBYyr37rE9vsxb82ZrpejYYTrhJUbxCDsP2LGoPy4GbAUM6sSS1xTz57HAy7UsnKSHieeqD8ierQRp95U+SZIkSZIkSTLdyYBs1mU2YGD9UbtKgNYeVR+vyUyn70rFS2z99rzEJC0AXAscbPvu0tyPULd8qjzLvJKesr10i6mqHmT1/mSNnqv+2bs16JMkSZIkDUkhiyRJOkOKesy63EAc7QM+Vi2s527iOB6Et1lHqPck6zAVL7HN2/MSkzQXcCVwru1La+22r7X9Rdu9S63eO+0EY0mSJEmSJEkyw5IZshmU9iTtm9AXWK1c/ww4WdIY4u98O7A7IW6xKRGs7QOcL+nnRCZqkqT2FBRfBJ6QJNsff3/qJO0XJGq+mnmJ/UNSTyL7NI4GkvbAP4i6r0XKswMMblDvlSRJkiQzFCl730ZmC5OkfTIgm4mp99+yvU/l+lVg6wbDrieUEyEUBL9a6qt+QAmibA8DhlXmqgaBhwKDgSfr5q1J2r8jaQ9gkO0p1re9EYCkZeOjnyw1YfdJGlqTnS+S9i8D/21PsKM9D7KaimK5Hld59vp7vcvlq3V9qnV1SZIkSZIkSTJdySOLMyGSeki6XNK95Wft0j5Y0knleilJd5f7h9cpHtYk7R8FXi9ZtN8RAfqtkm6tX7OG7bttv9Sg/daKWfTdQM8Wczxh+8ly/SJQk7SnIml/QAfew9mSTpV0q6RnJK0v6UxJjxa5+1q/jSWNkHS/pEsldS/th5T385Ck01WK0iQNk/QHSfdIekLSuk22kCRJkiRJkiRdIgOyGZdukkbXfgjPrRo1Sfs1iBqwMxqMr0nar0EcM6zSjziuuATwGPCTUof1IiFpvwEdQNLI6h7LzyrALoSXV0fm2AZYDri0POdzwJKNgr4m1CTt7wJuIbzD3ge+L+lESYsCBwMb2V4NGAXsV8aeZHsN2ysTwh2bVeadw/YA4j0d2mTvu0oaJWnUhAkTOrjdJEmSJEmSJGkjjyzOuMzwkvZVA+bKPjsraX8Ycbzx7nJ88e9Mm6T9IcCatpcvc58L3Ax8FVgRGF7e11zAiDJ2A0kHAPMSHm4PEz5lAFeU301l79OHLEmSJEmSJOkqGZDNnKSkfRvtSdpPBm60vU3d+vMApxB1b89LOgyYp8G80+39JEmSJEmSJEk9+Q/NmZOapP0QCEn7BuqDNUn7S+i8pP2rnd1QRdL+m12RtAe+WOk3cTpI2t9NqE0ubfupoiLZk6hbA3i11JR9D7isi2slSZIkSSoLJknSKTIgmzlpJmlfpV7S/q0OzHs68E9JLzWrI5P0R2BbmkvaX1qyW/+yvXmTdb4PrMf/QNLe9oSyxkWS5i7NB9t+QtJfgbGE9P6903vtJEmS5LNJyt63kcFpkrSP7Cx9mdnoiEdZyQS9W5G03xe40vbRLeYdBOxve7MG9/YBTq8oKTYa/3tgB2Dhuv1VPcomADvbfq7JHH2BU4EFiOOCU3mUSToR2Kk9yfv/Jf379/eoUaM+7W0kSZIkMwAZkLWRAVnyWUbSfbb7t9cvVRZnXVYHRpcs2k+A7VoFYx1gH0L8ohXXAAMatNc8yvoQxwL/2GKOd4AdbK8EfBM4XtJCtZvFo2yhZoOTJEmSJEmSZGYijyzOYkjqAZwG9AI+APaxPbx4lO1je09JSwEXALMT8vT7VbJNNY+yTYF3gX8BiwKLAXdJeqHZccaaOEdNXKRI4J9X2Rtlzbeb7d/2E5XrFyW9AhwiacPSvBQhjT9nO+/h7LL/5YHFgZ2AHQn1yZE1U2hJpwJrELL3l9k+VNKCwD3A5rYfl3QRcIvtv9atsSuwK0CvXr1abSdJkiRJkiRJGpIB2cxJtyJXX+NzwNXluuZRdqekXsBQYIW68TWPsosk1dee9QNWIuq8hgO/KHONA9ay3WHBD9tjgb7VtmJc/e+OjJc0gJCp39/2R5L2BmazfVyd0XUzah5lmxPZu7WJo5P3VoRQDrL9ejGkvllSH9tjJO0JnC3pBOII5l/rJ0/Z+yRJkiRJkqSrZEA2czLDe5Q1Yho8ys4DdizB2GLAVkybR9lY4OUSICLpYeK5RhMG0rsS/y18ifAsG2P7RklbAScDq3ZizSRJkiRJkiTpMBmQzXqkR1kbLT3KJC0B7A+sYfuNcsxxnrKH2YjM4rtEBnJ8R581SZIk+WyTQhZJknSGFPWY9ah5lAEfqxbWU/Mog857lHWaikfZ5l3xKLP9Rdu9bfcG3pkOHmULAP8F3pL0BeBblXv7Ao8C2wBnSmpZs5YkSZIkSZIk00JmyGY90qOsg9h+UNIDwMPAM0TNHJKWJWrNBtj+j6TbgYOBQ6f3HpIkSZJZj5S9n5rMGiZJc9KHbDrQEV+wLszdmxDTuLDJvUeBxwnxi1HALrY/bGe+QcA5FY+ybWxv0YG9TBePsCIk8tMyx0RgV9uPlHt9iGzaAsTRwjVsv9fe3j5t0ocsSZIkqZEB2dRkQJZ8FkkfslkASXMQ4hPbtuj2dBH4WAXoSWSYWtGbkGqvepT9vINbml4eYRfaXqXs+4/AsWX8HMD5wO5ljUFA0+AySZIkSZIkSWZ2MiD7hJHUQ9Llku4tP2uX9gGS7pL0QPm9XGkfLOlSSdcQ9WBHA+tKGi1p32br2J5MeGd9uczTW9Idku4vP2uVrkcTsvYGzgI2AHYrexsjabcWazxh+0lJI4HriODrrrK3VYmjiQe0905svy1plaLi+GdgrXL9EKFw+GDp91p5rmbvdqKk2yW9I+k/kp4oba9L2rz0mV3SkPrnk9Rd0s3l3YyVtEXlvT0q6a+SHpZ0g6RuTdbfVdIoSaMmTJjQ3mMnSZIkSZIkyVRkQDZ96FaCktElsDi8cq/mC7YGIaRxRml/DFjPdj/gEODIypiBhNz7hsCBwB22+9o+rtkGJM0DrAlcX5peAb5uezVgayLwocF8uwBvlf2tAfy4qA82xfaaRJZtHLByyXQNAq62/VKrsRXWI0RCZgdWL3OcBljS0BIotRfczQccZXte4Kayn4WJILP2N2j2fO8BW5b3swHwJ7VJUS4DnFyydG/SJoBS/x5Ot93fdv8ePXp08LGTJEmSJEmSpI0U9Zg+TIsv2ILAOZKWIbJVVRW/G22/3sG1lypB4DLAZbbHlPY5gZNK3ddkYNkm4zcG+kj6Xvm8YJnr2WYLTg+PMNsnE+Ij2xKCGTsS38d1iMDpHcKo+T7bNzeZ5gPaAtCxwPu2Pyy+Y73beb7xwJGS1iNq1b4MfKH0ebYiInJfZa4kSZIkSZIkma5kQPbJ08wX7ETgVttbFqGNYZXb/+3E/E/b7luCpGGSNrd9NSHb/jJhajwbkRFqhIC9bA/tyGLT0SOsxsWEUAhEkHSb7VfLWtcBqwHNArIP3aZK87HXWAkSa9/ths9XguYeRHbuQ0njKB5kTO3F1vDIYpIkSZI0IgUskiTpDHlk8ZOnmS/YgsAL5Xpwi/Ed8v8qRwUPBH5Vmf8l2x8B2xNHAxvNNxTYo+azJWlZSfM1WmN6eYSVrGCNTYEnK3vpI2neElCtDzzS3rO3Q7PnWxB4pQRjGwCLd3GdJEmSJEmSJOk0n2qGTJKB821vXz7PAbwEjLS92Se47jBgf9uj6toHM53k6okaqz1p7gv2R+LI4n7ALS3mGQNMkvQgcHarOjLgKuAwSesCpwCXS9oKuJW2rNsU8xE1br2B+0sN1QTgO03m/z4RJK0l6SjiqOVPbJ8NIKkXUSM3r6RHgE1sj2swz56SNiIUFBcFPgdg+w1J55Q9zEYEj131ZTijyfNdAFwjaRQwmqjpS5IkSTpJSrxPTWbIkiTpDJ+qD5mkiUR2ZC3b70r6FnAUMH5mD8hU503WybGzt1IXnJ5ImsP2pE707w78t3iY9QH+bnv5cm8Y4U12Y+n3ke13WszVH9ibENfoXtpOBx6wfaqkFYHrStZthiZ9yJIk+aySAdnUZECWJAnMXD5k/ySOrQFsA1xUuyFpPklnFsnyB+qkyaeSdJc0SNIwSZdJekzSBRXlvHp+qJCbf0jSgPqbkhYvsuhjyu9e7bQvIWlE2esRjRYs+35M0jll/GWS5i33xkk6RNKdwFaSlpJ0vaT7yrPWgp6typ4flHR7aVtJ0j0KlccxkpYpaz1UWXt/SYeV62GSjpR0G7C3mkjzN8L2xErd1nyUDFYJnuawfWOlX6tgbHYay+SbMIWGOFb4Yos5Bku6StI1kp6VtKek/cp35W5Jnyv9mr3Lb0saWfrfJOkLpf2w8r0bJukZST9rtockSZIkSZIk6QozgqjHxcAhkv4B9AHOBNYt9w4CbrG9s8KA+B5JN9Em6f6eoh7pItpUDfsRPlsvAsOBtYE7G6w7n+21FCp7ZwIr190/iaiVOkfSzoRs/HdatJ8AnGr7XEk/bfG8ywG72B4u6UzCmPmYcu892+sASLqZMEh+UtKaxBHEDQmJ/G/YfkFtpsy7AyfYvkBR5zU7bYqBzVjI9vplrQsJaf47S4A5TNLbdf3fL3L3SNqSyGR+nrZgelngTUlXAEsQMvRvAd+rm+dS278njnNebfulupj5MOAGSXsRAd9GCt+zuevm2b78Xpn4m88DPAX80nY/SccBOwDHA6fT+F3eCXy1ZPt+RASHNZPs5Qk5/PmBxyWdansKk2pJuxJHU+nVqxdJkiRJkiRJ0lk+9YDM9hiFyuA2hNlwlY2BzSXtXz7PA/Qigq1mku732B4PoJCD703jgOyisv7tkhaoBDc1BgL/V67PI2q+WrWvTZtf1XnAH5o88vO2h5fr84kas1pAdknZd3dgLeDSSrBSC0iGA2dL+jtwRWkbARwkqSdwRQk8miz/MZdUruul+ecG1rX9n0YDbV8JXFmC2SPK+DmIQLof8K8y/3VVO4Aaai2Tvw1RK/cnSQOJd7lyESepn2d1QqnyP8B/JL0FXFNujyUEQlq9y57AJQqFyrmYUur/WtvvA+9LeoUIcMfXvYfTiWCP/v37f3pnf5MkSZIkSZKZlk89ICtcTQQlg4BFKu0Cvmv78WrncvSumaR7vWR5s2es/wd0e/+gbnbfHejT0XVrwhuzAW82CmZs716yPJsCoyX1tX1hySJtCgwt2Z4nmPJI6jx1U1Wl9RtK87f7IBHMLiVpUSJYecD2MwCSrgK+CvytwdBWMvm7AN8s849QGF4vSmRFG1H9e39U+fwR8bdv+i6BE4FjbV8taRCRnWs0b6vvUZIkSZIkSZJMMzPKPzLPBN6yPbb8w7jGUGAvSXuVY2X9bD9A1BaNL35TO9Im6d4ZtgZulbROWfutuqzSXcAPiAzNdrRl2Zq1Dy/t55f2ZvSSNND2CCIbNFX2zvbbpSZqK9uXKjbWx/aDkpayPRIYKenbwFckLQg8Y/vPkpYkjn7eAXxe0iLARGAz2kyU66lJ8w+BkOavGCNPgaSlCe8zS1qNyCy9BrwBLCyph+0JxJHAhioXtq8FvliZc2JFJv9fwNeILOAKRCA5ocm+26XVu2RK64Edp3WNJEmSzzIpYJEkSdI1ZoiArBwxPKHBrSOIGqAx5R/S44jAopmke2d4Q9JdhIDEzg3u/ww4U9IviIBgp3ba9wYulLQ3cHmLdR8FdpT0F0Jh8tQm/bYDTpV0MDAnUWv3IDCk1M2JMEx+kPAf+6GkD4F/A4cXf63DgZHEUbxWsu7NpPkb8V1gh7LWu8DWReRjcjlaenP5W90H/LXFms34OfBXSfsS2cPBFRGRaaXZuzyMOMr4AnA3UfuWJEmSdJBUWGxMBqlJknSGLsneS5pM1OrMCUwCzgGOL5mr/sAOtjulUFdquba1fUo7/XoD/7C9cmfXkjSOkLd/ta79MGCi7WMajeso1b11pH1motSNHU9k4X5g+7LS3pcILhcgjvj93vYlLea5gBBi+RC4B9itKpohaQ0iSNq6tsaMTMreJ0nyWSQDssZkQJYkCfzvZO/ftd3X9krA14FNgEMBbI/qbDBWWIhQHuwwXVhrhkQhCT+jrvUvYDBwYV37O0RQvBJRA3Z8A6GUKhcQSoarAN2AH9Xt6Q/EkdUkSZIkSZIkmWWZbj5ktl8hJMD3VDBIIWWPpAEKz68Hyu/lSvtU/lnA0cBSpW1ImWuIwntrrKSt69euW6u7pLNK3zGSvlvfv/CLsvY9pS6qfs6+Ci+rMZKulLRwO+2rK7zBRgDNZO97A6+XcY9IOk3SbGX8REmHF3GOgWW+2xTeWUMVSoBI+lkZO0bSxaVt/fK+Rpd3PH/1nZQ+JymMrzvkeSZpp8qctZ+TbY+zPYYQzaj+/Z+w/WS5fpEQ4ehRnrV+nm/Yvs4FIkPWszLdXsSxz1fKfr/RYI4ryzPeJunvkp6QdLSk7crfdKykpcr4hj5rLb6XgyVdUd7Jk5L+SAMk7SpplKRREyZMc5lbkiRJkiRJ8hlmutaQ2X6mBBifr7v1GLCe7UmSNgKOJGqRGvlnHUjInPcFKAFVX0JRcVHgXhVD5Cb8hhDpWKWMX7hJv7dtD5BU86rarO7+ucBetm9T1GIdCuzTov2sSvuQFvsbAKwIPEeIbPwfcBnhufWQ7UMkzQncBmxhe0IJQn9P1LodCCxh+321ZaD2B35avM26M6XqZDNaep7Z3rA8U6dRGG3PRYh/bNlO3zkJT7G9y+cvA1sSoiBrANgeSoNsmUIAZlVgBeB14BngjPJ33ZsI7PYh6hOrPmtDy5hm30uI71w/Qm3xcUkn2n6+un7K3idJkiRJkiRd5ZMQ9WhkgLUgcI4iA2ai5gw65p+1DnCR7cnAy5JuI/6hPqbJ+hsRaocA2H6jSb+LKr+Pm+IBQrVwIdu3laZzCPGHjrafB3yrybr3VKThLyrPdxlRd1UTA1mOMDy+sbyP2YGXyr0xwAUKWfmrSttw4FhFXdYVtsc3eI/1dMTzrNOUTN55wI6NvMMacApwu+07yufjCXPnyR14BoB7bb9U1n6aUIyEqG3coFzX+6wtIGl+mn8vAW62/VaZ9xFgcWCKgCxJkiRJkiRJusp0DcgUkuuTiaNmK1RuHUEY+G6pELYYBtDEP+uZ+mk7uw067wfW1exGR9dstFbt83sl6KzN97DtgQ3GbwqsB2wO/EbSSraPlnQtUcN3d8n2TKJjPmStfLo6haQFgGuBg23f3YH+hwI9gN0qzf2Bi0vwtCiwiaRJtq9qMAW070MGTXzWJJ1Ig+9lg3nThyxJkqQBKV6RJEnSdaZbDZmkHsBpwEkNZMqrfk+DK2OWpPhnEebQfYD/APNXxt4ObC1p9rLGekTNUTNqnlq1NZodWdy68ntE9UbJjLwhad3StD1wW4v2N4G3FJ5m0NqHbICkJcrRzq1p4EMGPE7UXw0szzCnot5uNuArtm8FDiAEULorvMnG2v4D4f21PHEkckVJc5cM3tcabcb228CzCgsBFKzaYv8NKUdOrwTOtX1pB/r/CPgGsE01k2Z7Cdu9bfcmMoc/aRGMdZT670Qt+Gz4vUySJEmSJEmS/xVd/b/+3SSNpk32/jzg2Ab9/kgcDdsPuKXSvjVT+2e9Lmm4pIeAfxKBx0DCN8rAAbb/XTIajfgd4an1EJHZ+C1wRYN+c5fs3GyEQXM9OwKnSZqXyNrt1E77ToQ/2Tu0VgccQQiXrEIEm1fWd7D9gaTvAX8uwdQcxFG+J4DzK20vAfcTQVl34OWyp11tbyLp78QRxyeBB1rsqZlP11Qo5OivBBYGvi3pt0VZ8ftEsLyIingI4SE2Wg0k8Yng/TlgRMmGvUfUDnZvtklJuxOCKZMJs+u/Vu71IWq+Lil/g59Xhtb7rL0saVFC3fFCSc8Qwd+ikp4ljojOXzJ+v2rx3pIkST6TpNx9azJzmCRJZ+iSD1nSOYoIxf626wVEOjuPgLuAc2yfVtoWJ44xjp0ea0xPJC0LuNQILkaYRq9QMosofOT2BrZsJyBboGT0kLQ5kT37pqQ5iMB0e9sPSlqEOIY5ucEcPQnBlNVsv1UC2R62n5V0NuETd1l5x/sAexCB4getnjF9yJIk+SyRAVlrMiBLkgT+dz5kyafDhsAHtWAMwPZztk+sdpJ0mKT9K58fqmUWJe2gkM5/UNJ5pW1xSTeX9puLIiGStipjH1RRuCxHSIcoZOTHSKrWgU1BM0n82jzAECIT2pJaMFaYj7b6u42BMbYfLP1eaxSMFT5PHIudWPpOtP1sg7Vs+zgic9tMoCVJkiRJkiRJukQKFfwPsT2MKYUjppWViIzQNCFpJeAgYG3br0r6XLl1ElEDdo6ky4Gx5QjfcsRRyL8DJ5e+uxD2AmtImhsYLumG+uBG0pXAEpWmeQmBkafL5z2Bq22/1ExVUdIqxHFYCKGPHuV65fJ7WcCShpZ7F9tu6B1GHMV8maibu5lQpbymSV+I97w88P8a7GtXwnuPXr16tZgiSZIkSZIkSRqTGbJZAEknl+zVvR0csiFwme1XAWy/XtoHAheW6x8AHxb1xXOAWmZr9nJ/Y2CHUkM4ElgEWKZ+Idtb2u5b5vkWkdX6vu2PyvHFrYAT68fVzTG2NoftnrbnJmr2Di5d5iDsA7Yrv7eU1EzEZDLwTeB7RE3ecZIOa7F8U5VP26fb7m+7f48ePZp1S5IkSZIkSZKmZEA2c/IwsFrtg+2fEiqK9VFBM+n7TlkD2N6dCH6+AowuNVoijLBrgdIStm9oNlETSfx+wNLAU5LGAfNKeqoD+4IQHvlOuR5PqF2+avsd4Doq72eqhwrusX0UEXh+t1nfssdHO7inJEmSJEmSJOkUeWRx5uQW4EhJe9g+tbTN26DfOGAzAEmr0XZ08GbgSknH2X5N0udKluwuIkA5j8g23VnGLmV7JDBS0reJwGwosIekW2x/WIQ7XrD9X+poJolv+1rgi5V+E20v3eyhJS1Tq0Uj/Nhq10OBA4ry5QfA+tSZfVfmWAz4ou3akc++hNpjfT8BewFfAq5vtqckSZLPIilakSRJMv3IgGwmxLYlfYc4bncAMIEwev5lXdfLaTtWeC9xRA/bD0v6PXCbpMmEJP5gQh7+TEm/KHPWJP2HSFqGyIrdTNRhjQF6A/eX4GUCbRmreppK4nfy0fdUmF5/CLxBWBBg+w1Jx5ZnNHBdCfYaMSdwTAnM3iv73r1yf4ik3xAB7t3ABu0pLCZJkiRJkiTJtJKy958wJevTvfJ5MNDf9p7NR3V47t7AWrYvbHLvUcJkei7CMHoX2x9Oy3wd2MvihN/b7ETQc2JFkn8uQjBkEPARcJDty9uZ73vApcAatkdJmhM4gziKOAeRbTuqs/v8pEjZ+yRJkqRG2gK0T2ZZk88CKXs/i1O8t3oD27bo9nQR01gF6ElkqlrR3nyteIkI5voCawIHliwUhKLjK7aXBVYkfMCaIml+Ils3soE8PEAAACAASURBVNK8FTC37VWA1YHd1NwcPEmSJEmSJElmCjIg+xSR1EPS5cXL615Ja5f2AZLukvRA+b1caR8s6VJJ1wA3AEcD60oaLWnfZusUZcF7gC+XeXpLukPS/eVnrdJ1ivk66TX2AbBsOR45AlgMuEHSSGBn4KjS76OaumMLricCyJWBC8qcWwDzlUC0G1Er9najweX5HpP0iqR3Jb0u6SlJEyU9J2lAO+95P0lnlutVigdboxq9JEmSJEmSJOkSWUP2ydOtBBQ1PgdcXa5PAI6zfafChHkosALwGLCe7UmlZupI2pQABwJ9bL8uaRCwv+3NWm1A0jxE1mrv0vQK8HXb75XasIuA/sCB1fmKz1a7XmMV3iSC/KWAfW2fLGkhYCxwRNnv08Cetl9ustd+wL9try1pWNlP7cjieUQmbt4y/+uN5igsTSgkPkzUlj1IeKdtDvyaqHdr9p6PB4ZJ2pLI7u1W1Bvr95o+ZEmSJEmSJEmXyIDsk+fdcowPaKshKx83AlZUmyHyAuW43oLAOSVYMlGTVePGdgKRKkuVYHAZwndsTGmfEzhJUl9gMmGs3IiNgT6lnouyr2WAhgGZ7edL/8WAqyRdVubvCQy3vZ+k/YBjgO3rx0uajVBHHNxg+gFlrsWAhYE7JN1k+5kme3/W9tgy78PAzUUMZSxxNLP2PFO95+KRNpgQLvmL7eFNnvd04HSIGrIm+0iSJEmSJEmSpmRA9ukyGzDQ9rvVRkknArfa3rLUSQ2r3J5KVr4FT9vuK+lLRMZnc9tXA/sCLwOrlj2812R8zWtsaCfWxPaLJQhal1B6fIeQvYcQ6tilydD5iWOKw0qQ+kXgakmbE7Vt1xdRklckDScC22YB2fuV648qnz+i7Xt/BM3f8zLARCIATJIkSZIkSZJPhAzIPl1uAPYEhgBI6luk4BcEXih9BrcY/x8iiGmJ7ZckHQj8ijguuSAwvmSCdiSUERvN1xmvsZ7Aa7bflbQwsDZwbMlKXUMoLN5CGFg/0mSfbwGLVuYcRtuRxa8BG0o6nziy+FXiaGFXaPieJS1IHCddj8gkfs/2ZV1cK0mSJPmMkAqCSZJ0hgzIPl1+BpwsaQzxt7id8MT6I3GUbj8iiGnGGGCSpAeBs203NEMuXAUcJmld4BTgcklbAbfSlnWbYj4iKOlNx7zGVgD+JMlEZu2Y2pFBwh/tPEnHM6W/WWc4GTgLeKjMf1blCOa00uw9HwecYvsJSbsAt0q63fYrXVwvSZIk+QyQsvcdIwPXJAnSh6yTSFqEMEeGOFI3mQgyAAbMiCbCknYmzJL//QmuMRvwT0I8ZJjt71TuXUwIbHxImC3vbntSk3l2AH5B1HT9p/QdW7k/B3A/8Ex1jU+b9CFLkiRJamRA1jEyIEtmdZQ+ZJ8Mtl+z3bcIdZxGqCT2LT+fWjAmafYWt3cmgsfOzNfZ7KmJjNPgBvfOBZYH+hDHBFtlyJ4G1rXdh5DhP63u/n5ElixJkiRJkiRJZnoyIJuOSNpR0j3Fx+sUSbNJmkPSm8XP635JQyWtKek2Sc9I2qSM/ZGkK8v9xyUd3MF5fyfpHmCApN8qPMMeknSagq2BvsAlZfxcksYXOXokfVXSTeX6d5L+IulG4KyyxrFl7TFlj6uUeao/Ix3cTAhhTIHt68r9jwg/tJ6SDmowz0G2h9t+swy9m1BorL2HxYGvE0cXkbRIgzlGl/bxkn4v6e7yTlaTdIOkpyX9uIxfQNIt5e8yRlJN7n9g5V11l/SIpBUa/L13lTRK0qgJEybU306SJEmSJEmSdskasumEpJWBLYG1iq/V6cAPgL8TWaEbbP+iCFwcRohbrAr8BbiuTDOAUBn8ALhX0j+ASe3Me7/tg8seHrd9aKn3uhD4pu1LJO1FeH+NLv1aPUo/wpvrPUk/AV6xPUDhQ3Z3eY6+rSZo8Y7mArYD9rA9Avh9O0N2IY5B1jieOM64KES2kgg2G60FMM72VxWqlX8D1gG6E55kfwXeBbaw/R9JnweGA/+wPULS9cDhhMT+WbYfrV8jZe+TJEmSJEmSrpIB2fRjI2ANYFQJBroBz5d779q+sVyPJcyWJ2lKTyyAobbfAJB0FRFAzNFi3g9ok5MH+JqkXwDzEEHLfUwZ0HSE/2e7JoO/MbCCpB+UzzUfsn91cs4apwE3lWCsJQqj5u2Jd4Ck7wDP2x5d7nWEmgH3WGCOog75X0kfSepOvL8/SFqHkMP/iqRFbb8KHEq8v7eBPTr+iEmSJEmSJEnScTIgm34IONP2b6ZojFqsam1ZM08siDos6j63mvddF1UWSfMCJwGr2X5B0u+IwKwRk2g7rlrfpyppL+An5Shil5B0BBHQ/agDffsSmcNv1AJUYC3g/xSeZPMQJtrn2N6xxVTV91zvSzYHkWlckHhnkySNp+19LErI6wPMTWTTkiRJkqRdUqwiSZLOkDVk04+bgO9LWhQ+rm/q1ck5Npa0UAmutiCO0HV03m5EoPGqpPmB71bu1fuLjQNWL9fVfvUMBX5Sgj8kLSepWyefCUm7Ez5k25U6slZ9ewOXAdvafqrWbvsA2z1t9wZ+SBydbBWMdYQFiSOZkyR9Hfhy5d7pwIGEkfVRXVwnSZIkSZIkSRrSpQyZpMnEcbA5iazLOcDxxXC4P7CD7Z91cs6FiH+Mn9JOv95Evc/KnV1L0jigfzmaVm0/DJho+5jO7BnA9lhJvwVuKvVWvQlj4Rc7Mc2dRO3XUsB5lZqv2ryzEdLxu9fPa/s1SecQCoTPASMrt88CzpD0LlGndhjwV0n/JkQ2mvEXoBcwuhyXfAW4UdL2xN97ArCz7efKPkcQRxoXkTSRCCqHEZm7ccDdZZ5LgXWBLxHfwTuAn9qeXPb2JeA2hafZG7YX69Db6zznAddIGkVI6T9ZnmNn4L+2/16C0RGS1rd92ye0jyRJkhmalHHvHJkhS5KkM3TJh0zSRNvdy/XniWBiuO1DuzBnb0qgNT36NRk7jukckHV1b5J+BKxsex9Js5fg5BOns2tJ2gAYafsdSXsAg2xvXbl/AtADeN32ni3mWcD220WA5DLgUtsXl/kPAja1/b6kz88MhszpQ5YkyaxMBmSdIwOyJEngU/AhK/9o3hXYU8GgohKIpAGS7pL0QPm9XGlfSW1y7mMkLUN4Ty1V2oaUuYYopNzHKmTc6x+2ulZ3SWeVvmMkNTuS94uy9j2Slm4wZ1+FZPoYhRz9wu20ry7pwZIl+mmjBcs+by/jHlFI09f+BicBX5U0EhhY5rtN0n0KKfwvlTl+VsaOURguI2l9tUm+PyBp/uo7KX1OkjS4XI+TdIikO4GtJC0l6fqy1h2Slm/xd77V9jvlY70s/erAF4Abmo2vzPN2uZwDmIu2+rk9gKNtv1/6NQ3GJA2WdJWkayQ9K2lPSfuVd3C3pM+Vfg2fT9K3JY0s/W+S9IXSfpikMyUNU1gTdCrLmyRJkiRJkiQdZbrWkNl+psz5+bpbjxFS6v2AQ4AjS/vuwAlFRr0/MJ6o23m6GC3/Avg/Qtp8VULJcEgtOGnCbwgVw1WKufAtTfq9bXsAEQgd3+D+ucAvyxxjCdW9Vu1nAT+zPbDF3iCODP4cWIU4mvh/pX1u4FjbaxLHDU8Evmd7deBM2iTiDwT6lfV3L237E0f++hJHATsiQPGe7XVsX0zUS+1V1tofaHpctASkoyWNJgKvJUuQPRvwJ0KWvl0UnmZvl72uA/xK0p+BZYF1S6B0m6Q12pnq68DihP/ZCcC+ZQ8jgB1Kn2bPdyfw1fK9vBg4oDLv8sA3iL/XoZLmbPAM6UOWJEmSJEmSdIlPQmWxkcnVgsA5JQNmouYM4h/NB0nqCVxh+0lN7ZG1DnBROVb3sqTbCBn4MU3W34hQzwOgotJXz0WV38dN8QDSgsBClZqhc4BLO9F+HvCtJuveUwJXJF1Unu8yYDJweemzHOFHdmN5H7MDL5V7Y4ALFLL4V5W24cCxki4g3uP4Bu+xnkvKHroTCoaXVsbM3WxQqWvrK+mHwJ7A+uVo4Z7Adbaf78Da2D6DqGubB7gAOM32jZIeIry/vkr8nf8uacmammQDLrRdM3r+FzCwqEz2Avq083w9CcPsLxFZumcr815bsnTvS3qFyPyNr3uG9CFLkiRJkiRJusR0DcgkLUkEFq8AK1RuHQHcantLRX3VMADbF5YjepsCQxV1VM/UT9vZbTC1fHwj3OR6Wujomo3Wqn1+r1LLJeDhJtm2TQmxkM2B30hayfbRkq4FNiGEMzZiSml7aC5vPxvwZmfMnsv8B1GCsdI8kMhs/YQwX55LUWN4YKu5igH11YQAyI1E0HNFCcDukfQRIUHfLAVVL2dfbynQ6vlOJLKSV0saRAiKNJp3MmkRkSRJkiRJknwCTLd/ZErqQRj/nmTbdVmSBYEXyvXgypglgWds/7lc9wEeZEqJ9tuB3RQKgp8jgpGa+XEjbiAyN/uUNRZukiXbmqhX25rI1H2M7bckvSFpXdt3EAbFt7Vof1PSW5LWsX0nsF2LVzVA0hKEEuLWlAxLHY8DPSQNtD2iHJdbFngU+IrtW0v917ZAd0mL2B4LjJU0kDhudx+wokLxcR7ga8QRvSkowhrPStrK9qWKP1wf2w822rykfoT64jer9V22t6v0GUyIpjQMxkrWan7bLylUDDchlBYhsn4bAsMkLUtkrl5tNE9HaOf5qt/LrkroJ0mSzLKkSEWSJMknR1cDsm6llqgme38ecGyDfn8kjizux5Q1XVsDP5T0IfBv4HDbr0saXo6u/ZOo6xlIBGoGDrD975Jpa8TvgJPL+MnAb4ErGvSbu2TnZgO2aXB/R+A0hSfYM8BO7bTvBJwp6R3Cv6sZI4hAcBUi2LyyvoPtDyR9D/hzOQ45B1Hn9gRwfmkTcFwJBo9QqBNOBh4B/lmOEf6dOOL4JPBAiz1tB5wq6WDib3kx8b4bMYTIgNWOAP7L9uYt5m7EfMDVJVicnfhOnFbunUm8x4cIQ+0dWxxX7CjNnu+w8hwvEAIlS3RxnSRJkiRJVcppJAP/5LNKl2TvZzQUvlXn296+fJ6DqL0aaXuzT3DdYcD+tkfVtQ8mMkV7ls+DSr9O70UVi4FZiZKxOoHIkr0DDLZ9f4v+1xP1ZXdW32Opn+tP+LTdA+xm+8Mmc3wB+BvwFSJAG2d7kxLkP0qI0MxDGGqfbPuc9p4jZe+TJEmSGhmQTRsZkCWzGvpfy97PIPwXWFlSt/L567QdSfvMImn2/+Fanc26foswk16GsE04tZ3+Q4ijovVcQBzVXAXoBvyoxRyHAzfaXtX2ioRyZY2nbfezvQIhDrOvpJ0azpIkSZIkSZIkXWRWC8ggjjnW/hfLNrSpKSJpPoW/1L0K76ktSntvhT/V/eVnrdI+SOFFdZmkxyRdoOYSgj9UyL8/JGlA/U1JixOS/L0k3VxUAJG0ePk8pq59CUkjyl6PaLRg2fdjks4p4y8rRyk77DUmaauy5wcl3V7aVpL0vKR3y88j5eflytr7K4y0Ke/oSIUC5t6Seki6vOx9oqQn1eaTNlrSKpXH2AI418HdwEKqszWQ9A21Se3/iVDFXL3ax/Z1ZQ4TGbKeNOdLVBQTbTdU7CxqmPsBDX3IlLL3SZIkSZIkSReZFQOyi4EfKOTU+xCeXjUOAm6xvQawAeFpNh+hCvl126sRdW1/rozpRwiErAgsCazdZN35bK8F/ISog6rnJCLw6ENkc/7cTvsJwKllr/9u8bzLAaeX8W+X9Wt0xGvsEOAbtlcllBsh/M0OtN2NEL5YnThS2CrqWMj2+rb/VPZ+XNn7isAkh69c7WdsZdyXgecrn8eXto+xPbQ6nsh+3ddoEwoBlO2B61vs9WTgb5JulXSQpMVa9L2fyLxNhe3Tbfe33b9Hjx4tpkiSJEmSJEmSxsxyAVnJdvQmsmPX1d3eGDiwZFqGEXVCvYg6or9KGgtcSgQRNe6xPd72R8DoMncjLirr3w4sIGmhuvsDgQvL9XmE/1ir9rVpy+6d1/SB4Xnbw8v1+ZXx0NhrbDShkljLQg0Hzpb0Y0JgA0J45NeSfgksbrsjRtOXVK43Ak4qa11NvI/5Gw9raGvQlcLGU4DbiwpmQ2wPJYLrvxLB1gMKldCO7i9JkiRJkiRJpguzqrfS1cAxwCBgkUq7gO/afrzauRy9exlYlQhS36vc7qgfVTN/sWY0u99Zf7RW67brNWZ7d0lrEsc8R0vq28Qf7gk65mtWW29gBwO58YS4Ro2ewIsdGDcVkg4FegC7tdfX9utEIHyhpH8QdgqNsm79CKGPJEmSJEmSJJnuzKoB2ZnAW7bHFmXDGkOBvSTtVbzS+tl+gDiWN972R5J2pC1T1Bm2Bm6VtE5Z+626crO7CJGI8wgZ9jvbaR9e2s+nta9ZLxW/MiIr2CmvMUlL2R4JjJT0beArCln9en+4O4DPS1oEmAhsRvNjgTUvuCEAJcgb3aTv1cCeki4G1iTe3UstnrchJWj8BvC1ks1s1XdD4G7b75TM3VLAvxr0600E9id2dj9JkiTJZ5dUC0ySpDPMkgGZ7fFEHVM9RxB+XmNKUDKOCCxOAS6XtBVwK1NmezrKG5LuAhYAdm5w/2eEv9YviFqsndpp35vI3uwNXN5i3UeBHSX9hfAba6ZS2MyLa4ikZYjs4c2l7UCm9of7UNLhRE3es4Q0fDN+RnjBjSG+Y7cTdWmNuI6oT3uKkL1vqWgo6Q7imGF3SeOBXcoRxNMIs+0RJRC+wvbhTaZZnThSOYnI5p1h+94SgC0l6QHaZO9PtH1Wqz0lSZIkSZWUvZ92MphNPovMUj5kMzqq8xJTnU/ZNMzXG/iH7ZXL9Vq2L2zS71HgcWAuYBQRyDT06aqMaThfB/a1OGHGPTsR/J1o+7Ryby5CyGQQ8BFwkO1WAScKk+xLgTVsjyrCHWcAqxEB37m2j+rsPuvW2BxY0fbR0zI+fciSJEmSGhmQTTsZkCWzEvqM+pB9JlF4f/UGtm3R7elSQ7YKUaf1/XambW++VrxEBHN9iWOIB1aUDA8CXrG9LCGecluricqRwp8xpVrmVsDctlchsl27lQBymrF99bQGY0mSJEmSJEkyrWRANoNQ5911r6S1S/sAhb/ZA+X3cqV9MFGj9SxRs3U0sG7x69q32Tq2JxM+XV8u8zT0YKufT9LskoaUvY2R1FQ4w/YHtmtiKHMT37Oti+rir4DNy/WJtl9t8C5WqfiOPUMEkFXlSwPzlUC0G/ABIflfP89OCv+09yS9pvBUe1zSRpKGK/zRBtTep6STyvXZkv5c3vczJUM3FUofsiRJkiRJkqSLZED2v6WbKgbJQLXGqerd9V3iSB5ErdZ6tvsRnmFHVsYMBHa0vSFR93VH8eo6rtkGFP5sa9ImyNHMg61+vl0IwY01gDWAH0taosU6Xyk1ZM8DfyhzDCLULG8ijit+XtIX6sfaHluyazsREvZLEn5gNS4j6vxeIsQ4jimqifXznEXUp81R1p6PECTZlrAH2B/4dZNH+FLpsxkRnE5F+pAlSZIkSZIkXWWWFPWYgXm3Kj1fqyErHzcCVqwoM9a8uxYEzinCGyZqsmrc2CgQacJSJQhcBris+LVR5jtJUl9C1n/ZJuM3BvpUskULlrmebdTZ9vOl/2LAVZIuK/P3BIbb3k/SfoSK4fb14yXNBhwHDG4w/YAy12LAwsAdkm6y/UyTvT9bM6OW9DBwc1HZHEtzX7mrilrjI42CxiRJkiRJkiSZHmRANuPQ0LtL0onArba3LHVSwyq3O6MG+bTtvpK+BAyTtLntq4F9ae7BNsVWgL2KomGHsf1iCYLWJdQi3wGuLLcvJTJvjZgfWLnsFeCLwNVFfGNb4PoiSvKKpOFEYNssIKt6yX1U+fwRzf8bqI5Jc+gkSZKkw6QwRZIknSGPLM441Ly7gPDuKpcLAi+U68Etxv+HCGJaUjy+DiRquWrzv1SyQdvT5sFWP99QYI+icIikZSXN12gNST0ldSvXCwNrA487JD2vIY4PAnwNeKTJPt+yvajt3rZ7A3cDm9seRRxT3FDBfMBXaS3DnyRJkiRJkiQzJDNMhkzSQUTmYzKRuditGBZ3Zo7BwA22X2yn3zBg/yKhfh2wre03OzD/YcBE28fUtfemyM93Zr911Ly7JhJ1XTcQ3l1/JI4s7gfc0mL8GGCSpAeBs1vVkQFXAYdJWpfmHmxTzEfUuPUG7lekrCYA32ky/wrAnySZyC4dA8wuaQSwCJHpeo3wHvvYd6xkA3eqWgPUI2l3YAdClOT7RB3ZX2yPkdQH+AvhBfcRUevWbJ6dgQOA3pIeIjzYkHQ2UcM3SNKRRCCYGbIkSZKkw6Ts/fQhM43JZ4UZwodM0kDgWGCQ7fclLQrM1V5g1WCeYZRAa3r0azDuMD65gKxLeytj57A9qat7+CTWkrQsYNtPlrqy+4AVaoGwpP6EGfaW7QRkC9h+u1xvDvzE9jeL4uL9wPa2H5S0CPBmUZWsn6MnIbe/mu23JHUHeth+tgRk/7B9WQk89wH2AFa2/UGzfaUPWZIkSVIjA7LpQwZkycyOZjIfsi8Br9ak0m2/WmqPviapVm+EpK9LuqJIsJ8t6SFJYxWy7N8j6oguKCqG3cr4B0qfMyXNXb+wpHElAETSDgpJ9wclnddkr6tKuqVIpv+4wXzzSDqrrPmApA3aae8m6eKy7iWEjPtUlH3+QdI95Wfp0n62pGMl3Qr8QdJ85VnvLetsUfqtVMaNLmstU/peW573IUlbN3gn/UuQiKTDJJ0u6QbgXHVOCv8J20+W6xeJLGCPMu/shIT/Ac3GV+apytvPRwidQIiOjLH9YOn3WqNgrPB54kjmxNJ3ou2pxEkcHAf8G/hWe3tLkiRJkiRJks4yoxxZvAE4RNIThCT6JbZvI47onSyph+0JxPG2s4C+wJdrGSlJC9l+U9KetB1FnIc4avc1209IOpfIdBzfaAOSViJMi9e2/aqkzzXZax+iZmk+4AFJ9f8b7KcAtleRtDxwQ8kONWvfA3jHdp9y5O5+mvO27QGSdijPsVlpXxbYyPbkcszuFts7S1oIuEfSTcTxxxNsXyBpLqJWbBPgRdublnewYIu1a6wOrGP7XUm/BX5IiIIYOE7SHlUlyUYovL/mAp4uTf+vPMM/KdYAwKW2f99k/E+B/cocG5bmvsAmkt4mvtdvEkHf12y/VjfFg2XPz0q6GbjC9jUttnw/sHzZZ3UfuwK7AvTq1avVIydJkiRJkiRJQ2aIDJnticQ/9HclapMukTS4iECcB/ywBBcDiX+0PwMsKelESd+kgSkwsBwhd/5E+XwOsF6LbWxIyMG/WvbUTE7+/9l+t/S7lZBgr7JO2TO2HwOeI4KNZu3rAeeX9jFE7VYzLqr8Hlhpv7SSDdoYOLAENcOAeYBewAjg15J+CSxe1BzHAhuVzNu6tt9qsXaNqytKkCsRmSaIgOxl2slyKVQezyNqxT4qxxcXKnvqS7EGaBaMAdg+2fZSwC+Bg0vzB8BbwJLAosT7/XmDYKxmjv1N4HvAE0QgeVirbTfZR/qQJUmSJEmSJF1ihgjIIP6RbHuY7UMJtcHvlltnEVmYbYjAY5LtNwiZ9mFE5umMBlN2VohBtB1/a7nVdj43W7fVfjpayOcm11X5ewHfLUFNX9u9bD9q+0Jgc+BdYKikDUuwujoRmB0l6ZAyxyTavhvz1O2hfq29KmstYfuGZpuXtABwLXCw7btLcz9gaeApSeOAeSU91fIttHExbcIi44HbynHXd4DrgNWaDSzHEe+xfRTwA9q+b43oBzzawT0lSZIkSZIkSYeZIY4sSloO+KhWY0QcP3sOPvaxepHIhHy99F8U+MD25ZKeJo4mwpRS7Y8RCnpL236KkHS/rcU2bgaulHSc7dckfa5JlmwLSUcRRxYHERLyc1Xu3w5sB9xSjiT2Ah7vQPutklYmjkQ2Y2vg6PJ7RJM+Q4G9JO1l25L62X5A0pLAM7b/XK77SHoMeN32+Qp1x8FljnFEoPZPWgcqNSn8W2x/WJ7rBdtT+aOVY5JXAufavrTWbvtawmOs1m+i7aWbLShpmcr3ZFOgdj0UOEDSvES2bH3CWLrRHIsBX7RdOx768fetrp+AvYgax+ub7SlJkiRJqqQYRZIknWGGCMiA7sCJ5VjiJEIOfdfK/QsIFbyaZ9WXgbMk1bI4NU+ts4HTJL1LHOnbCbhUocB3L3Basw3YfljS74HbJE0GHqCx79c9RJanF3BECRh7V+6fUvYwtjzLYIdyZLP2U8uzjAFGl/mbMbekkUT2apsmfY4g6svGlIBiHFFrtjVx9PNDQqTicEIWfoikj4APiXo2gN8Cf5P0a6CV9cAZdFwK//vE8cxFFPYEEO9gdIv5G7GnpI3Kft8AdgSw/YakY4m/s4HrSrDXiDmBY0pg9l7Z9+6V+0Mk/QaYl5C936CVwmKSJEmSVEmVxU+HDISTmZUZRfa+pQeZpJOAB2z/rcUcg5l5Pcim2luDe+OA/rUat5kNSdcTYih32t6s0n4BoY75IRGM7mb7wyZzbEfUjUEoJO5RU1Us92cHRhFZus0aTFHrtxkRuM5GBGcn2P5L+Rv/mAjQ5iOOch5c+R8BTUnZ+yRJkqRGBmSfDhmQJTMamllk7xUeZJsRnlB9gI2A5yv37yOO8Z3fzlSDgcU6s7btTToSjM0MlCzgjLzWEOLYaD0XEAqGqxCS/z9qMcezwPrle3IEcHrd/b1pp9ZL0pxl3Ldtr0rUhw2rdDmu1MMtA1xCHDFNxY4kSZIkSZLkE+FTD8hox4PM9uq21wPW02fYg4wIDq7TDO5BJmmVsk71Z6Ttm2lTZPwY29cVgQ0TGbKeZZ6D6uchjg6+UYbeXetb+vckasqmEHiRNLJujvuBuYHXyvrv23680Xu3fQlhybBtk7/LrpJGSRo1YcKERl2SwMfD6gAAIABJREFUJEmSJEmSpCUzQg1ZepDNnB5kuwJv2V6jBLvDJd1geyzxN+oUJXO1PZHlosjeN5W+B3YhREdqHE9I7s9f7WR7zQZrnQE8p/Ag+wdwke2PmqxT8yCbCtunU7J0/fv3//TP/iZJkiRJkiQzHZ96hszpQTazepBtDOxQ1hoJLAIs04E5mnEKcLvtO9rrWDKMu1DqyRQ1Ya/Yvq8jC9n+EfA1IiO3P3Bmq+U6MmeSJEmSJEmSTAszQoasZtQ7DBimUCHckchwnQVcQyjhXWp7EvCGpFWBbxCZp+8DO9dN+Vn3IKs/gveoQp1xU8KD7Ee2b5G0OpEpO6pktw6n8x5kQzu4/6ZIOhToAezWgb59iGOJ33Kb6fPawOaSNil7XkDS+bZ/2GyekskbW46nPktjRU2IGrNU60iSJEk6TIpLJEnSGT71DJmk5SRVMytTeJABNQ+ys0v/RYHZbF8O/IY289+GHmTlc0c8yL4vaZGyRrMji1uUerBFCA+ye+vu1zzFUGOvsVbtHfEgq/1uz4NMZc5+5ffHHmTA1YQH2WLEccnzgWNoe4/jiIwldMyDbM7ac0mar0X/hkj6ERFcb9Pi2GCtby/gCmD7SvYT27+y3dN2b8Lk+ZZmwZik7pIGVZoaepCVvt8lMoEXNbqfJEmSJEmSJF1lRsiQpQfZrO9BhqQ7iFqs7pLGA7uU7NppREA0osSRV5RMXSMOIY5GnlL6TuqIlGj9VggD6b8A7xJZv8GV+/tK+iFRJ/gQsGGpYUySJJkpSMn1T5/MkCVJ0hk+cR8ySV8AjiPEMN4APgD+aPvKkqnYvx3PqHY9yGZ11AkPMkkTbXfvxNy/tn1kO30WIvzaTunq+pJ2JDKeAL+zfU6Lvr8HdgAWrs4paT9CHn8SEQjubLthlqujSNqdyBieOy3j04csSZIZhQzIPn0yIEuSBGYQH7KSObmKEGtY0vbqxJGynq1Hfjy+ox5kybTz6w70WQj4SVcXKkdBDwXWJARRDpW0cIsh1zC1cApEBrN/8SO7DPhjV/dm+7RpDcaSJEmSJEmSZFr5pGvINgQ+sP3xcUHbz9k+sb6jpAGS7lL4Z90labkSwO0B3KGKh1bpP5VvmKTFJd1c2m+W1Evhl/WMgoUkfSRpvdL/jkqdWW0fs0s6RuEZNkbSXqW9oa+ZwrfrSEkjFJ5Uq0kaKunpknWp1S3dLOn+Mn6LBs8/lb9aaV+aOMZZG79UR+YrY3+hNp+w3za4fzTQrbzbC0rbfmUPD0nap3Q9Gliq9BvSbH1Jq1Tm+9iDrLLkN4Abbb9e/MRuBL7ZYF87KdQbTyOUNbtJOrl23/attt8pH6fwI6ub50pJT0n/n73zjrazqtb380IA6U1sIIRqoUsAQZoQ0Xu5IhYuKlJERVCKKCBXFBEuRcKPLiJGWgio1BtEKdKlI5ACIiigIgiRaoRIyfv7Y87N+bKz9z7nJCQGMp8xMs7ea69vrfWtbzPGnsy53leTJD0tabKkn0naXuHLNl7Sitn3YEn75utrFeqTt0m6X9LGXcYvH7KiKIqiKIpihpjZZ8hWpbe3VpP7gE1svyxpOHA4ISoxjYeWuvuGnQScZftMSbsAJ9jeRuFx9l5geeC3wMYZKCxj+w9t69g1+62da1lC/fua/cX2BpKOzX4fINT+7iGCisnAx20/pxAluUXSGE9dLzqNv1q2jwaOzBLPNxFB9Iv9jSdpS0KGfj3i3NQYSZvYvr7Vx/YBkvawvVZesw5x9m79vOZWSdcBBwCrNfoN6TL/eEkvtPp1YGngL433j2TbVNg+nVDYbN3LJNtf7TJmux9Zc5yPK8piLyae/1OEbcJ96em2N7An8LUOlw/JPv9JZPWGdxi/fMiKoiiKoiiKGWKWqixK+oEio9WuTgiwKCHCMYE4c7Zqtnfy0OrmG7YBcE6+HkX4fwHcQHh+bQIcke3rMq1KIsQP71NSYr81dn++ZmPy73jgVtv/SCGIyRlYCThcId7xayIIeWvbvNP4q0lamAjSLsq1TM7M0EDG2zL/3UWfuXF/PmEbARfZ/mf6w10IdMoODWT+TnSS/5/uQEYhvjEMGNFP19ttP2b7X8AfCTNyiOc1tMs1F+bf3/boUxRFURRFURQzxMzOkN1DQzrd9lczo9JJ/eBQ4JrMagwlfMmwfY7aPLQYvG/YDUSm7R2EUt9+hGz99R2u6TR2f75m/8q/UxqvW++HENL2SwHr2H5JIdIxlceX7U7+ap0yNwxkvFzzEbZ/1M/a268ZCAOZvxOPEPveYhnyOQ+WzKIeCGyagVYv2p9J83l1+2+g1eeVHn2KoihmO0pQoiiK4vXFzM6QXQ28SdLujbYFuvRdFPhrvt651agOHlp09w27iRANgQgafpOvbwU2BKbYnkxIzH+ZCNTauQLYLcvyWmMP1tes0709kcHLB4Hl2juog7+a7eeARyRtk33mk7TAQMYjfMJ2kbRQXru0pLd06PeS0kuMCFC3kbSAwlPs48QeNT3eBnQ/Xbgc2FLS4goxjy2zbVAo/NV+BGxt+4nBXl8URVEURVEUswsz9f/823YGE8dK2p+QKP8n8M0O3Y8CzlRIml/daJ/GQ8v2U+rsG7YXcJqk/YDVgXuzBPIhwmD6lhzzBsLLa3yHdYwEViG8vF4Cfmz7JEntvmavSNpxgFsxGrhE0u8JQ+j7OvTp5q+2A/AjSYcQfmHbNsa7gwgupxnP9hWS3kOfv9ckYh8XADa03SrtPDXv9U7b20s6gz4/tJG27wKQdKOkPwDzEwHaO/N81TVt88+rLtL3+dwOzT1YFHicONu2a8tnTtIaRLC1CPAWIlO1gMK7bKTtg4kSxYWI5wHwZ9tbd9jToiiKopjllPXAv5/KFBevJ2a6D9m/i2ZQIOlM4H7bh82CeYe0zp91+OxgYJLto1+L8aZzDZvRj/dbj3FXIeLsByS9gzhf9R7bz+Tnw4C9CcGPXl5ki2T2D0lbA1+x/ZEMdu8EdrA9NjOgz9h+ZbBrndWUD1lRFEXRogKyfz8VkBWzA5odfMhmI26moeanDnLwkhaUdGmKjkyQtF22P6w+CfTbWmWLmlYm/fBUJNxb0kcl3aqQyf+1pLfmubjdgH0UcvAbq4NMf453hqRjJF0DjJD0gKSl8rO5FFLub27eYK7nVElXAGdJGqqQ9b8z/22YXY8kVCbvlrSPQm5/RGM/vtxtE23fb/uBfP0o8ARxlgxJcxOZq/37exitYCxZkL4ze1sC42yPzX5P9grGFHL235f029zn9fJZPJiBHt3uT92l+4dK+p2kH0u6R9IVkubv756KoiiKoiiKYnp4w4sVZKCwBfCTfN9RDp4ILB61vVX2W7QxzHMpgb4jIXXfKbu0mO1N89rFgfdnyeYXgf1tf0PSKTQyZJIuoU2mH9gmx1sFGG77FUnPEGfijiNUIMe2FCbbWAfYyPYLWZr4IduTFd5t5xKKhAfQyJBJ2hV41va6Cm+1GyVdYfuhfvZ1PWBeQrUQYA9gjO3HMmi8u+2Sf9lev3H9V4Gv5xhPZv+lCM+x5wgBkDNs9zJ9XhC41vY3JV1FKD4+SNgC/EyhAvmTTvdHyO9PI92f464MfMb2lyT9nBCmmcacPPduV4Bll12213YVRVEURVEURUfeyBmy+fNH/pPAEoQJMXSXgx8PDM+My8a2n22MdW7j7wZd5vtZ4/UyhCLkeELRcdXOl3SV6Qc4r5EdOg1onVfbhYZHVxtj0hYAYB7gx7mG8wgfrk5sCeyYe3UrsCT9yONLenuu9/O2p2T54rZAy/B7iu212v6t3xzD9g9sr0icJxyb3mXHAs8CKxDB48clbdFjKS8Cl+XrG4Hv5zjvpS8A7HZ/vaT7H7LdCii7yt7bPtX2MNvDllpqqV5bVhRFURRFURQdeSNnyF6wvVZmun5BSMmfQA85eIUx8n8CR2SW6JD8qHnQrtuhu382Xp8IHGN7jOLM1sEDXHNz7FfHs/0XSY9L2pwwbd5+AGvYhxDNWJMIvCd3uUbAnrYHpHYoaRHgUuDbtlsiKWsDKwF/UIhsLCDpD7ZX6jJMk58CP8zXjwDXtbJ/kn4JvI9Q1ezES+47BPmqnH0Gia3vdsf7k7Qz3aX7mzL5rxBCJkVRFEVRFEXxmvNGDsgAsP2spL2A/5P0Q0Jm/VBJo21PkrQ0oV44BHjK9tmSJtGQ3ieUHo/MvzcPYNqmhP9OjfZ/EOqBLVoy/aOYWqa/EyOJsrlRAxS5WBR4JIOTnYC5G2toSthfDuwu6eoMTFYB/mr7n23jIWle4CKizPK8VrvtS4G3NfpN6hWMSVq5dRaN8Jdrvb4c2D/LLV8ENiWyZjNCx/tj+qX7i6IoiqInJShRFMVgeMMHZAC275I0Fvi07VHqIAdPZHhGSJpCBGhN77T5FObUcxFy+f1xMCHJ/ldCan/5bL8EOD8FJPZkapn+icDne4w5hihV7Fau2M7JwAWStiWk6VsB1jjg5dyPM4DjiZK8OxUbMpG+c2zt/DewCbBkZpgAdm6U9w2UPRTGzi8BT5NBa5pjH0PYChj4ZQZ7M8JIOt9fv9YBRVEUswul2vf6ogKyoigGw2wvey/JwNm2d8j3Q4DHgFunR7p9EPNeC+wLnA8Ma5TR7Zzv93gN5ujo19Wl7zDgWNsbz+i8MxtJ7yYCx/cBBzZETN4JnEVk06YAp9o+vsc4I4CPEtmyPxJn1p5pfL4scC9w8GCsBLrMtTXwXttHTs/1JXtfFMXMpAKy1xcVkBVFAW8s2ft/Aqs1pMc/RF854ByBpAOAC+gzi26pR86q+QebSX2KyP61B0kvA9+w/R7g/cBXJXUTG4EQYlnN9hrA/TTuPzkW+NUg19YR22OmNxgriqIoiqIoiunl9RCQQfzobv3vps/Qp3rY8g87LX2m7mrzk5rGh0vSZgqvqvMl3SdpdJaydeJzwKPAtSnzPhXq7iPWrX15STfnWg/tNGGu+z5JZ+b15wMn2F4OOFvSQZJ+A2wraUVJlyl8uG7IzBSStlV4qY2VdH22rarwUbs7x10555rQmHtfhZ/Z6gqPr8fzPN2fco4Lcu23S/pAt4dl+wnbtxNBV8t37e58jv+Rff4B/I6GP1yHca6w/XKWi/438KXWWHku8EHgnm7Xt+3nyNyT0ZKGS7pR4e+2XvbbWdJJ+foMSSdIuknhafapLmPvKukOSXdMnDix1zKKoiiKoiiKoiOvl4Dsp8CnJb0JWIOQL29xIHC17XWBDxLnwBYkTIs/ZPt9hBjHCY1r1ga+RsijrwB0Cy4WtL0h8BVCer6dkwiBizWIM0kn9NN+PPDDXOvfetzvu4hyvjWA53L+FpNtb2T7p8CphILgOkR55cnZ5yDgw7bXBLbOtt2A41MWfhihaNgR2+OBO4ALbC9ke2ng90TJ5LqEL9fIHutvcUNe05S/PwwiUCKew609rm+tZ31gArBXrv8DRGD+vQGsAeJ84PHEd+fdwGcJi4F9gW91uebt2ee/CEGXTusq2fuiKIqiKIpihnhdBGS2xxHCDJ8Bftn28ZbAAZmBuZaQLl+W3j5ct9l+xPYUQtBhaJepz835rwcWkbRY2+fdfMS6tX+AvuzeqK43DH+xfWO+Ppup/cl+BiBpIWBDQjzkbuBHRBAB4cl1hqQv0aeueDPwLUnfBJZr+JX1oumtNhw4KecaQ+zHwp0v602u/QLga7afG0D/A4lyx9HZ9D0i0Js0wCkfsj0+n/c9wFUplz+e7s/+YttTbN9Lnz9ZURRFURRFUbymvJ5UFscQZ5I2I8x9Wwj4pO3fNztLOpjuPlztPlPd9qFd8aQ/BZRunw/Ex2yg87bUEucCnsmM0dSd7d0krU+Ued4taS3b52Tp31aEafUXiXNZzaD8TW1DNaXv5wI2GGAg1xVJ8xDB2GjbFw6g/05ElmqLhufY+sCnJB0FLAZMkTTZ9kldhmk+7ymN91Po/uyb13QraS2KopgllEhEURTFG5fXRYYsOQ04JMvpmlwO7Nk6ByZp7WxfFHgssyI70JcpGgzb5ZgbAc/afrbt85aPGEztI9at/ca29m4sK2mDfP0ZOviTZWbpIYWsPQrWzNcr2r7V9kHA34F3SloBeND2CURwuwYRsL5F0pKS5iMCn25cAbyqLClpmkCwP/IZ/QT4ne1jBtD/I8A3ga1tP99qt72x7aG2hwLHAYf3CMaKoiiKoiiKYrbldZMhs/0IcQ6onUOJH+Xj8gf/w0Rg0c2HazA8Lekmwsx5lw6fd/MR69a+N3COpL2JLFE3fgfsJOlHhGnyD5sfSnoroTC4DDBS0hmEsuGVkt4CvChpZSKzcxUwFjgA+Jykl4jza4ekKfIhxDmuh+jtxbUX8ANJ44jvzfXEubRpkPQ24gzaIkT26luE39i8uebxWfoI8C3bv2xcexiwI7B4rnO+vK+lgPmJs28TgV1s/6nHeouiKF73lNz965PKaBZFMRhmex+yOY0Uu/iF7dW6fC4iA3em7VOybTlCvGM8sO/M9GcbLJKWIIKzYUTp5W+BdWw/3aX/+4E/AQ80PdokfZDwnnte0u7AZra3m+k3MEDKh6woiplBBWSvTyogK4oC3lg+ZMXUbA682ArGAGz/yfaJzU4pX79v4/2EDPaQtGNK34+VNCrbukn1d5LQn1vSiJS/Hyfpyz3W+2HgSttPZRB2JfCRbp1t32L7sQ7t1zTKFm8hMm0dUVgbXCfp55Lul3SkpO0Vsv/jJa2Y/T4q6VaFXcKvM/NISt4flK8/LOl6SfXfSlEURVEURfGaUz8yZzNsP9wtO5asCtw5veNLWpWwCtg8ZfH3zo+6SfV3ktD/AvAsURZq4FhJ9yr8wX7QNuXSwF8a7x+hzXtM0g/U5y/W8izrVU77BdoMofMcXOvakYQy5XuBTYkzhKvYXi8/2zMv+w3wfttrE9YK+2f7AcB2mZU7Afh8nkWcCpUPWVEURVEURTGDvG7OkBWdyQBoI+BFYL8BXLI5cL7tvwPYfirbNwA+ka9HAUfl65aE/s+BliriloQoyPNEQPY4IWF/Racldmibqk7W9lc73FdHSXtJnyPKHzdtG+NJYK3ssxlwoO0P5fs/EqIkEGWdH8zXywA/k/R24nzbQznW82kZcD2wj+0/dlqL7VMJLziGDRtWtb9FURRFURTFoKkM2euPe4D3td5kMLMF0O5M/DKdJe3FIKT3be8GfBt4JyGhv2SOsWfD7Hn5LsEYREbsnY33ywCPDmD+aZA0nMjubW37X/10H4jU/YnASbZXB77M1LL/qwNPAu+YnrUWRVEURVEUxUCoDNnrj6uBwyXtbrulvrhAh34PkzL2kt4HLJ/tVwEXSTrW9pOSlsgsWUuqfxQNqf6WhD5wq6SPEsHV5cDukq5OpcZVgL/a7qRkeXmud/F8vyXwP4O96bQz+BHwEdtPDPb6LiwK/DVf79SYazngG8DawC8lXZx7UBRFMUspcYiiKIo3PpUhe52R5sjbAJtKekjSbcCZhF9XkwuAJfJM1e6ECTS27wEOA66TNBZo+YHtBXw+Ze13oO9s2YgUwphAlPCNJc5h3Qvcme0/oktwn8HeocDt+e+QRpnkNEg6StIjwAKSHlEYfAOMABYCzsuzYmP626sBcHCOdwPh19b0StvX9qPEebWRktpNs4uiKIqiKIpihpkjZe8lvUKcJRpCnBvawfYzr+H4uwHP2z5rENdsRqgn3vRarWOwpArjhrbPmY5r1yL80hYBXgEOs/2ztj4nEgIZC3UYotVnN+CrOcYkYFfb9+ZnaxDB3yJE6eG6ticPdq0zg5K9L4qiKFqUXcHrj8pGFzODkr3vzQt59mk1wlB5GlGJGcH2KZ2CMUm9SkQ3AzYczDz9jDc91wwFPjvYMZPngR1tr0rI2h8nabHGvMOAxbpd3OAc26vbXosQFjmmse6zgd1yjs2Al6ZzrUVRFEVRFEUxWzCnBmRNbqYhwy5pv4a/1veybUFJl6YX1wRJ22X7w5K+n/5Wt0laKdtf9QCTdK2kwyVdB+zdyfsqM1O7AftkOd7G6u4LdoakYyRdQ5QTPiBpqfxsLkl/kPTm5g3mek6VdAVwlqShkm6QdGf+awWCRwIb5xr20eD8xuYjywmBXxLB17U5/9xEyWFLVp7cg7vb/q1u+7nGmAvSJ0CyJTDO9lh4VVXxvR3GuDXHn5TP5re5z+vls3hQ0tbZp+M+SPp4XiNJb1d4mb2tx70XRVEURVEUxXQxR4t6ZKCwBXFmCElbAisD6xFKgmMkbUIoGD5qe6vst2hjmOdsrydpR+A4UkijjcVsb5rXLk54X1nSF4H9bX9D0inAJNtHZ79LCF+wMyXtQvhhbZPjrQIMt/2KpGcIEY7jgOHA2JakfRvrABvZfkHSAsCHbE+WtDJwLiElfwBxdqolBrIr8KztdSXNB9wo6QrbD7UPbns8fbLz6xHn2lpqkHsAY2w/Fke0wPb6HdZIXv9V4OuEFP3mjXu2pMuJ5/FT20e15uzAgsC1tr8p6SLgf4EPEd5kZwJjgCc67YPtiyR9ksicfgT4ru2/dVjnrsCuAMsuu2y32ymKoiiKoiiKrsypGbL5M5PzJLAEcGW2b5n/7iLMl99NBGjjgeGZcdnY9rONsc5t/N2gy3zNs1TLAJdLGk/4hq3a5ZoNgNZZrlGE11iL82y/kq9PA3bM17sAp3cZb4ztF/L1PMCPcw3nEUFKJ7YEdsy9uhVYktiPrig8vUaRZsqS3gFsS0jMDwjbP7C9IiFU8u1sHkLswfb59+OStugxzIvAZfl6PHCd7Zfy9dBs77UPexJqkP+yfS4dsH2q7WG2hy21VLvrQFEURVEURVH0z5wakL2QZ5SWI7IwrTNkAo5o+GutZPsntu8nMkzjgSMkHdQYy11eN2nKwffyvupFc+xXx7P9F+BxSZsD6wO/GsAa9iHMnNckMmPzdrlmMH5jSFoEuBT4tu1bsnltYCXgD5IeJtQT/9BtjDZ+Sl9W8BEiqPq77eeJssj3db0SXnKfYs2rPmS2mz5kvfZh6bzurZLm1P9OiqIoiqIoipnMHF2yaPtZSXsB/yfph4Rn1qGSRtueJGlpQjhiCPCU7bMlTQJ2bgyzHXH2ajviPFp/dPS+Av5BqAe26OgL1oWRhODFqEbmrL81PJIZrJ2AuRtrWLjRb8B+Y5LmBS4iyizPa7XbvhR4W6PfJNsrdVuYpJVtP5BvtwJary8H9s9yyxeBTYFjB3Cvvei4DwoBkdMJgZMdifLJo2dwrqIoimIOoRT7iqIYDHN0QAZg+y6FH9enbY+S9B7g5jzrNAn4HJHhGSFpChGg7d4YYr4UkpgL+MwApjyYEL/4K3ALfYbNlwDnS/oYUS63F3CapP2AicDne4w5hgggupUrtnMycIGkbYFr6MuejQNezv04AzieKO+7U7EhE+nLWLXz38AmwJKSds62nW3fPcA1tdhD0nBin58mg1bbT0s6hvAyM/DLDPZmhG778C3gBts3ZLnm7ZIutf27GZyvKIpiwJR0+uuXCsiKohgMM92HTH2eX/MALxOCCsdlVmIYIZW+1yDHXAz4rO2T++k3FPiF7dUGO1eW1w1rF8hQGBVPsn10tz4DHP/VtQ322g5jDQOOtb3xjI71WpBCKMcBaxCB7vnZ3q9XWds4o4lSwpeA24Av5zmw1ufrEkHtdq05ZmDNg/aOa1I+ZEVRvNZUQPb6pQKyoihg9vIha3l+rUqo3P0n8F0A23cMNhhLFgO+MpgLZmCu2ZJUiETSAcAFhADFTJ1rEPyZKOtsN5ju6VXWgdGEsMrqwPzAF9vW9H2ilHGG6eYdVxRFURRFURQzk1kqVmD7CUImfI/0eNpM0i8gpNIl3aTw57pJ0ruyfVWFx9fdCi+slYkzWytm24gca4TCI2y80iesSdtcC0k6PfuOS4nzTuynNo+xNrYBfpFjXJSS9khaS9ItHdrXUXiZ3UwXM+pc5/V53b2STmmJSii8tQ7JEskNJK0D/Afwd+A7qXCIpL3y2nGSfpptm6rPq+suSQs39yT7nNQqN1R4rB0k6TfAtpJWlHSZpHtyHb9Tm/dX4zk/bHscIYrRbL+/cT7s80RgfVNjnAPb+v/SCZEhW6bx8Z5EILouUU46lZ9Z235eJ+nnCj+xIyVtn890vKQVs1+7d1zLX+5+SR0zj5J2lXSHpDsmTpzYqUtRFEVRFEVR9GSWnyGz/WAGGG9p++g+YBPbLyvOEB0OfJIwTD7e9miFcMTchF/WaqmUSAZUaxFqeW8mzvxc32MZ3yH8tVbP6xfv0q8/j7GzCBXC6yQdQmT+vtaj/fRG+4ge61uPkGD/EyHd/gngfMJba4LtgyTNA1wHfMz2xAxCDyOk7w8Alrf9r0YGal/gq7ZvlLQQMLnH/C0m294o9+gqYDfbD0han1Cj3Lz35T25kjift1oqH3Yl73UHYO98vzTwccKjbF2i9LNXyeKawHuAp4AHgZH5XPcmAruvdbhmSPZpZXSHt3ewfSpwKkTJYq97KIqiKIqiKIpO/LtEPdShbVHgzMyAmThzBqFceKCkZYALMyBov3Yj4NxUGHxc0nXED/VxXeYfTigYAiEY0aVf02NsKkU/hTn0Yravy6YzCbGOgbaPIrJbnbjN9oM5z7l5f+cT564uyD7vAlYDrsz9mBt4LD8bB4yWdDFwcbbdCByjOJd1oe1HOuxjOz/LNSwEbJj30fpsvv4u7ob6vMp26i8YS04Grrd9Q74/Dvimwxh7IFPebvuxnPuPQEu6fzzwwS7XXJh/f0ufb1lRFEVRFEVRvKbM8oBM0gpEYPEEkbVocShwje2PKwQvrgWwfU6WxG1FGCp/kchyTDXsYJdBd8+wJgPxGHut5+w0V+v95IasvYB7bHcyo96KUDzcmihlXNX2kZIuJc7w3ZJZyJeZumy13ROtpTo4F/BMKyM5I6izV1mv/t838+zJAAAgAElEQVQFliI821oMA36awdibgf+U9LLtizsMAelBlkxpvG96knW75pUefYqiKGYaJQxRFEUxZzBLz5BJWgo4hTBGbg86mv5cOzeuWQF40PYJhLz7Gkzrl3U9sJ2kuXOOTYgzR924AtijMUe3ksXtGn+n8hiz/SzwdON80Q6EcXG39meAZyVtlO3b91jfepKWz9LO7ejsQfZ7YClJG+Q9zKM4bzcX8E7b1wD7E+e0FpK0ou3xtr8P3EGIZfwJeK+k+TKDt0Wnxdh+DnhIIQ+PgjV7rL8j6uJV1qP/F4EPA59pZtLSoHqo7aFE5vArPYKxoiiKoiiKophtmRX/539+hZdTS/Z+FHBMh35HESWLXweubrRvB3xO0kvA34BDbD8l6UZJE4BfEYHHBsBYIpu0PxHc/R5YQdIlwI8aY/4v8IO8/hXge/SVqDXp6jGmkEk/jxCUWIDI2rW8wnYCTunQfhJwuqS/01sd8GZCuGR1Iti8qL2D7RclfQo4IYOpIUQp3/3A2dkmQg7/GUmHSvpg3sszwK/yjNnPiRLHB4C7eqxp+1z7mTnGc5IOsn1K7se8eX+bAfMSAfObgI9K+l4qK7Z7lS1KlAOua/uOPCs2Enhf3s8qwMP0+cJdaPuQHmssiqIoin87ZVnw+qUy08W/g5nuQ/bvQtIk2wvl6zOB+20fNgvmHWL75S6fHUx6mPW4fjNgX9v/1d9407mGqcYf5LjzEt+Zf+W5sgnAhrYflfQ9YG7b384s3RK9/NkkLUyULs4L7JEB2WeBrW1/OoPZe4HNbD882LXOasqHrCiKomhRAdnrlwrIitcSzUY+ZLMDNwNLt95I2k/S7QpZ+O9l24KSLlXI0k9I1cKW/HtLAv1V+XtNK5N+eIqJ7C3po5JuVcjL/1rSW/Nc3G7APgpp9o0lLSfpqlzHVZKWzSWuKekYSdcQGbgHshQTSXNJ+oOkNzdvMNdzqqQrgLMkDZV0g6Q789+G2fVIYONcwz5Z5jmisR/Ns1pTYftF262zVfMx9fdnF+CI7DdlAGbZhxJZ0abao4EFJQ0hfMdeBJ7rdHHe332SRubzGi1peGZOH5C0XvZbUNJpeX93SfpY4/pp9kchk3+tpPNz/NHSwJRDiqIoiqIoimKwvOHFChQGwlsAP8n3WwIrE9LyAsZI2oQQjnjU9lbZb9HGMP3J30MoKG6a1y4OvN+2Feeg9rf9DUmn0MiQZSnlWbbPlLQLcILtbRQS86sAw1NJ8BmiZPA4QiFybJeAZx1gI9svZIbpQ7YnK5QrzyXEMA5g6gzcroQFwLqS5gNulHSF7Ye67Oc7iczWSsCvgV/mHr+VOGf2MqHouIftx7uMsTZxzu0XGdSOUN85vuWIIO1lYC/bT3UaI1kJ2JbwthtPqEL+hVCcvFLSfcBVwNW2d1FYANwm6deEqEyn/QFYG1gVeDTv5QN0OMeXe7crwLLLLtv+cVEURVEURVH0yxs5Q9Y6u/YksAThewWwZf67C7iTELdYmfhBPzyzYRunOEeLpvx9J1VDSIn4ZBlCEXI8sB/x474TGwDn5OtRhLx9i/MaioqnATvm610IP7NOjLH9Qr6eB/hxruE8wtesE1sCO+Ze3QosSexHR2z/xfYaRDD0NkJ044M53+6230FkJDuWZWY547HANxrN+6WC41eBXxLZsWWBbyhEXbrxUAqVTCHk6ffKcYbnZ+vn/R2Q93ctca5t2X725zbbj+S4d9NF9t72qbaH2R621FJL9VhmURRFURRFUXTmjZwhe8H2Wpnp+gXxY/8EIit2hO0ftV8gaR1CFv6IzBK1BCQGIn//z8brE4FjbI9RnNk6eIBrbo796ni2/yLpcUmbA+vTXaGxuYZ9gMcJU+S56G4ELcKsupfIyLQLjXNj9wAbE95oz9MnPnIe8IUuly5M+Kddm5WAbyOylFsDnwUus/0S8ISkG4msVbvNQYuByNkL+KTt3zcvVJzn67Y/zXFL9r4oiqIoiqKYabzhf2jaflbSXsD/SfohoW54qKTRtidJWhp4idiLp2yfLWkSDel9QunxSDrI33ehKeG/U6P9H8Aijfc3EQbVo4ggq5O8fYuRwNnAqEbmrL81PGJ7iqSdiDK+1hqalgGXA7tLutr2S5JWAf5q+59t46Ew534ySyIXJ0r5jsnSzEsIhcWriRLRezstKjOPr55/k3QtUUJ5h6QtgM0lnQ0sALyfKNOcES4H9pS0Z65zbdt30X1/iqIoimKGKGGIoigGwxs+IAOwfZekscCnbY+S9B76pNQnAZ8jSvBGSJpCBGi7N4boKn/fhYOB8yT9FbgFWD7bLwHOT2GJPYG9gNMk7QdMpE8evxNjiFLFbuWK7ZwMXKDwDruGvuzZOODl3I8zgOOJkrw7U7xiIrBNlzHfA/w/SSYyT0fbHp+ffRMYJem4AdxLN35A3N+EHP902+OmY5wmhxJB3bi8v4eJM4Dd9qcoimKmUep7cwYVkBVFMRhmiuy9pFeIM1kt77EzgeMyGzEM2NH2XoMcczHgs7ZP7qffUOAXtlcb7FySHgaGNQUzsu3nwBO95OoHOP6ra5uOa4cRnmIb99v530AKgpxFCIs8CWzXTa5e0pKEofO6wBm298j2BYhyxxWJUsFLbB8w81c/45TsfVEUA6ECsjmDCsiKooB/v+z9C7bXSjPgDxHnsr4LYPuOwQZjyWLAVwZzwQzMNVsh6QDinNaBs3DOwZbwfQF42vZKhGjH93v0nQx8B9i3w2dH2343oXT4AUn/Mch1FEVRFEVRFMXrhpmusmj7CUIafA8Fm0n6BYCk9STdlP5QN0l6V7avqvD8ulvhjbUycYZrxWwbkWONUHhQjVf6hjVpm2shSadn33GSPtllyfup4TlmeyghWNEacy1Jt+QYF7Xk2nu0r6PwNruZEBaZhlzn9XndvZJOSTVC8jzbAsDfgCk53nWSfivpcklvz3575bXjJP002zbN/bo793jh5p5kn5Mk7ZyvH5Z0kKQ7gT/meM9Jej7/vrvHo/4YkQmFyH5tIenAxvytfwfa/qft39AmNGL7edvX5OsXCRXMd3UY425JS0o6Q9IPJV0j6cG839Mk/U7SGY173FLSzQq/sfMUptbkvd6e36FTpahhVfiQtbzn7pfUMSspaVdJd0i6Y+LEiT22piiKoiiKoig6M0tk720/mHO9pe2j+4BNbK8NHAQcnu27AcenhPkw4BHCP+uPmXnbD/gEsBahkjecOP/19h7L+A7ht7V6yrZf3aXfc7bXA06is6DEWcA3c4zxZOavR/vphBx7N7n8FusRUvCrEyV7n8j2BYEJKeF+K6Hg+Cnb6xBy+IdlvwOAtXP+3bJtX+CruY8bAy1J/F5Mtv2+DEQfA9axvQCR6exVLro04QGG7ZeBZ4Ef5fNq/jusxxivoihR/Sgh5d8+xlq2n8yuiwObE6qSlxDZuVWB1TNIfjPwbcLT7X3AHcDX89qTbK+bJaTzM7W/3JD8HnyNvmc5FSV7XxRFURRFUcwos1LUQx3aFgXOzAyYiTNnEEqGBypU/S60/UAmL5psBJybioOPS7qOOJPUTQRiOKFoCIDtp7v0a3qOHTvVDYSE/mK2r8umMwnxjoG2jwK6leDdloErks7N+zufOEt1QfZ5FyEZf2Xux9xE0ETe92hJFwMXZ9uNwDGSRhP7+EiHfWznZ7mGhYAN8z5an83X47pOA0/XAUVJQ4j9P6G1Jz24JNUTxwOPt0RGFJL8QwlPuPcShtcA89KnlPlBSfsTGcglgHuIoA7gwvz7W7r4kBVFURRFURTFjDJLAjKFue8rwBOEUl+LQ4FrbH9cIXhxLYDtcxSqhlsRBstfZFovqn4jiw79BxIgDMRz7LWes9NcrfeTGzL3Au7pkm3bCtgE2Br4jqRVbR8p6VLiDN8tkoYTIivNzOib2sZpqQ3OBTyT2bWB8AjwTuCRDKgWBZ4a4LXtnAo8YHsgkvdN77F2X7IhxPfuSttTqWNKehOR8RuWPm8HM/VetMYqH7KiKIqiKIpipjHTf2hKWgo4hSgPc1uGpunXtXPjmhWAB22fkK/XAMYytX/W9cCXJZ1JZDc2AfZj2gCjxRXAHkQJGpIW75Il6+o5lp5mT0va2PYNwA7AdT3an5H0rKSN8sxUN0NngPUkLQ/8Kec+tUOf3wNLSdrA9s2S5gFWAX4HvNP2NZJ+QxgsLyRpycwYjZe0AfBuIuPzXoUq4psIz7Bp/M9sPyfpIUnb2j4vz1etYXtsl/WPITzXbgY+BVzt6ZDwlPS/xPfii4O9tgu3AD9QnAf8g0LJcRnifw4A/D2zgZ8iMpJFURQzjVLfK4qiKNqZWQHZ/JLupk/2fhRwTId+RxEli19n6jNd2wGfk/QSIWZxiO2nJN0oaQLwK2B/YAMiUDOwv+2/ZaatE/9L/DCfQGQ9vkdfWVqT/jzHdgJOyR/2D9Lnt9Wt/fOE19jzhElxN24mAsHViWDzovYOtl+U9CnghCyHHEKcc7sfODvbRMjjPyPpUEkfzPu9F/iV7X9J+jlR4vgAcFePNW0P/FDSt4ln+VNivzvxE8KH7A9EZuzTXfoBr9oJLALMK2kbYEvgOUJJ8j7CFw0ikB/Za6xe2J6oEC05N4NQgG/bvl/Sj4nzfg8Dt0/vHEVRFEXRpOwN5hzqf7IUrwUzxYdsepB0IJHZeYUoN/uy7VsHOcbOwBW2H+2n37XAvrbvkPRLwt/smQGMfzAwqd2PTDPgL5bXb5br+a/m2qZnrNkVScsRAfDcRHB3ou1T8rN5CRGVzYhnf6DtC7oM1RrvU4Rn2br5HOcBRgLvIwLVs2wf0eP6jt+33P+3EyWL8wK/JgK4nt+P8iEriqIoWlRANudQAVnRCw3Qh2y2OBuT5XT/BbwvMzhvJn4MD5adgQlAz4Csie3/nI55ZkskDUmFw9lxrseADfP5LgRMkDQmg+cDCePtVRRy/0v0M/fCwF6E6mSLbYH5bK+eWcp7JZ3rDubUA/i+bZ9B3rzAEcD/AZsO4l6LoiiKoiiKYkDMEtn7AfB24O+2/wVg+++2H5W0haRXS/ckfUjShZLmVnhQtTzI9smMyTBCafBuSfPn9Xdln9MaJWs0xnw4f5AjaUeFj9dYSaO6rHVNSVdLekDSlzqM9yb1+Z3dlSWDvdrnJ2Tql5X0M0J+fRpynS1vrNskrZTtZ0g6RtI1wPclLZj3envO87HsN423W/a9NO93gtLLrW1PhmXWCEkH57jPARNzrCckPZJjfrltzR/OPncDtwG35vOcj6m/e7sQgQ+2p9j+e9s4U/mZESWhDzO1j5mBBRWCIvMDLxIlkJ3o+H1r75ReaPvns1mzwzMpH7KiKIqiKIpihphdArIrgHcqTHhPltTKRlwNvEchDAJxHut0wn9sadur2V4dON32+YTH1PapDGjgDGC77DME2L3bAiStSmRqNre9JrB3l65rEIqGGwAHSXpH2+dfBcg5P0OckXtTj/bdgefTP+wwYJ0e+9TNI20VwmfrG3kPV9teF/gg4c+2IJ293T4CPGp7zSy3vKzH3C3mAt5qe3FCpfAE28sQlgNfUgiTkPd6edM7jPAVW5HwK/t+Bt2LZfdD1Wfc/NbmhLYPa4zxeeB62zu2ret8QiHyMeDPwNG2u6k8dvu+TUMqXI4lBFHaPysfsqIoiqIoimKGmC0CMtuTiEBkV2Ai8DNJO6dK3yhC4GMxIgj6FZEhWUHSiZI+QudMyLuAh2zfn+/PJJQYu7E5cH4rO9Pjx/z/2X4h+11DGDo32SjXjO37CNXEVXq0bwKcne3j6O6jBlN7pDWl789rSONvCRyQmaRrCSXFZQnRkG9J+iawnO0XCEGL4Zl529j2sz3mbjEmr23NtWPOdSuwJLBytwtt/yUDz5WAnTLwGkKoHt7oMG6+GTi60/VZzngsYaDdznrEebB3AMsD31AodHZaR8fvW497HqzFQlEURVEURVEMiNniDBm8mom4FrhWYfK7E5HhOp0w651MBB4vA09nCdmHiczTfxNlb01mhU9Zp/fd5u21nunxKmu+/mfjtYBP2v5927W/U5u3m+2rJa1D+JQdIekK24cwtVdZN5+y1lx72u6lHjntTURm7B5gY8L0+nn6VCXPA77Q5dKFCWPsaxUKjG8DxkjamhDouMz2S8ATkm4kMoEdjaV7fN+mQtLchPLl7wZzj0VRFMWcSwk9FEUxGGaLDJmkd0lqZlbWIjJI5NmeR4Fvkz+Y83zTXKnE9x1CWQ/gH/R5ld0HDG2dtSK9wXos4yrgvyUtmXN0E5b4WJ4HW5JQBWyXS7+e9BuTtAqRnfr9ANtXI0oiu7Fd4+/NXfpcDuypjFgkrZ1/X/V2IzzD1shyy+dtn01kpVr7+DB9pZOf7LGey4HdFQqHSFolyyOnQdIyeV4OSYsDHwB+n1nQS4i9hPBFu7fTGLaftf1m20NtDyU8xrZORco/A5srWBB4P/Ed6LSWrt+3tn7zEGfb/pLZy6IoiqIoiqJ4TZkpGTJJrxDlcC0fsjOB42xPkTQM2NH2Xo1LFgJOzLLEl4E/EOVkLUYT2ZDNiB/rSwOnZwkbwP/k3zOAkRloLEH8mL5J0uNE4HRKtzXbvicDhd9IepHw59o57+dgYFJ2vQ24lAioDs1sz9DGUCcTfmTj8152TiW/kwmvsMlEtqXV/sO8l3FAS/yiG/15pAEcSpwvG5dB2cOEouA03m7Eua8RkqYAL9F3xu57wE8kfYuplQzbGQkMJTzD3kz4ij2U+72L7WaQsw5wvqRniQD7aIdpNcA3CQ+z44hn+3hm0G4Avtoqx5S0J2Hu/TLxDJr8gMimTiAyd6f3CKL6/b5J+hchPvJr4GM99qAoiqJfSgZ9zqIyZEVRDIaZ4kMmaZLthfL1W4BziDNC353O8U4iMiA79uf1pRnwBFOYFQ/roPJ3MB38x6Zj/Bld23q2n5iRNQxivrkb59IG0v+DwK22n5e0O7CZ7e0anx8PLAU8ZXuPHuMsYvu5DCbPJ8pUf5rjHwhslYHsW2bVXgyE8iEriqIXFZDNWVRAVhQFDNyHbKaXLOaP5l2BPbKcbDNJv8hFrifpJoU8+02S3pXtTYn25wnBhmHAitk2IscaoT7p++3a526bayH1yc6Pk9StFG8/tUnLt425lqRbcoyLMqvWq30dhaz8zaTSYpd1Xp/X3SvplFb2T9IkSYcQUu3r5njXSfqtpMslvT377ZXXjpP002zbVH1y8XdJWri5J9nnJKWghULu/iBJvwG2lbSipMtyrhskTaM02HjO19h+Pt/eQgh1tOZYB3groW7YE9stgZYhhDdY6/8Y7A4c2ZCq7xqMSdpZ0sWSLpH0kKQ9JH099+AWZTmqpC8pZPzHSrpA4V+GpP+TtGO+/rKk0f2tuyiKoiiKoiimh1ki6mH7wQww3tL20X3AJrZfljQcOJw4s9SSaB+tMOedm/hB/96UPicDqrWANYE3A7dLur7HMr4DPJuy861zTJ14zvZ6+YP8OKLcr8lZhJDFdRkofRf4Wo/20xvtI3qsbz3gvcRZpsuATxAZogWBCbbnU5xpug74mO2JGYQeRgiaHAAsn9mjlpT8vkTJ340KM+bJ9M9k2xvlHl0F7Gb7AUnrE+WYm/e6WNLqROD1skJ9EWAFYFXifFi/SLqcUJ+cTMjT/w+hmvlmSYdl+76228/vNVkt5/sFcDxRJvlkrmU34rt2oe0f55z/S4iJnEj8D4QbJT1EKDq+v8s6d82+LLvssgO5taIoiqIoiqKYilmpsthJZXBRwo9rZSITMk+23wwcKGkZ4kfzA9I0l28EnJtldY9Luo44E9Xt3NBw4NOtN7af7tKvKS1/7FQ3IC0KLGa7JQ5yJnDeINpHAf/RZd7bbD+Y85yb93c+IeV+QfZ5FxFoXJn7MTfhu0Xe92hJFwMXZ9uNwDGZ4bnQ9iMd9rGdn+UaFgI2zPtofTaNsXYH1gT+CGyaweEewAK2/zKAuQGw/WGFR9to4BTbV0qaQJwP25t4zj+XtIK719xek2fYVpf0Z2AD23+VtAt9wimrZSC2GHGu7PKc/3FJBxG2Bh/vZoFg+1TgVIiSxQHdXFEURVEURVE0mCUBmULh7xXgCeA9jY8OJX44f1xxvupaANvnqE2inWnly2eFrP2M/sge6Jyd5mq9n9w4yyXgHtsbMC1bEVmlrYHvSFrV9pGSLiVk7W/JLGRT0h66y9rPBTzTykgOhBz/QDIYy+YNgI0lfYUIeuZVnDE8oNdYtidLGkMIalxJGFlfmAHYbQohkjcTPmKd+Ffj9ZTG+yn0fe/PALaxPTbLNjdrXLM6kVFrN/4uiqIoiqIoiteMmR6QSVqKUDc8ybbbsiSLAn/N1zs3rnlVoj1frwGMpU/SHkIu/suSziQUFTcB9mPaAKPFFYRC39dyjsW7ZMm2A46kg7S87WclPa0wUb6BlNLv0f6MpGclbWT7N6S8fRfWk7Q8UbK4HZl5aeP3wFKSNrB9c5YwrkKoNr7T9jV5/uuzwEKSlkwlw/GSNgDeDfwWeK+k+XKvtgB+0z5RCms8JGlb2+cpHtwatsd2WrxCXv9HwEea57tsb9/oszMhmtIxGMus3MK2H5M0hAgkb8iPLybKJa9V2AbMC/y90ziDYGHgsdzH7cnvoqT1iEzm2sB1Cn+2h2ZwrqIo5mBK5KEoiqLoxswKyObP80Mt2ftRwDEd+h1FlCx+Hbi60T6NRLvtpyTdmKVrvwL2J7IvY4ls0v62/6apJeib/C/wg7z+FULa/cIO/fqTlt+JkLVfgMjafb6f9s8DpynESXoZKN9MBIKrE8HmRe0dbL8o6VPACVkOOYQ453Y/Iam/KJFFOzaDwUMV6oSvEHYBv8oywp8TJY4PEPL+3dge+KGkbxPP8qfEfndiBJEBa5U4/tn21j3G7sSChNHzfEQ55tX0WRWcRuzjBOBFYKce5YoD5TuErP+fCJuGhXPuHwOfT0uDb+S8m78G8xVFURRzAKWqOWdR/8OlmFFmiuz97ID6vNCGAA8BO9h+5jUcfzfCVPmsQVyzGfCi7Zs6tO9ru11A5DUnA9YNbZ8znddfRohc/Ka53jynNozwM7sN+LLtl7qMsT3hPQbh77Z7M/MmaW7gDuCvs2JPXgtK9r4oiqJoUQHZnEUFZEU3NLvI3v8becH2Wun59RRdJOenF9undArGstSuG5sRQhkDpp/xpueaoURJ4/QygijJbGc0URK5OjA/8MUeYzxEnDNbgzhH2F6euTdRhlkURVEURVEUb2jeyAFZk5uBpVtvJO2n8J8aJ+l72bagpEsVnlQTUlK+5c31fbV5k0k6WNK++fpaSYen0uPekj4q6VaF79WvJb01M1O7AfsofME2lrScQlr+BKLMc9kc7wxJx0i6Bhgh6YE8i4ekuST9QdKbmzeY6zlV0hXAWZKGKrzD7sx/rUDwSEJk425J+0iaW+Hn1tqPL/ezl+8n1CdbY9wt6UDbv3RCZMiW6TZAZggvy7LWo4C1c5zVFcqaWwEj+1kHkv4m6XFJ/5T0vKTf5/s/Zgaz1W+a553tFys81u5RSNi32idJOiy/C7dIemuX+XeVdIekOyZO7KYtUhRFURRFURTdmZWy9/8WsvxtC+An+X5LYGXC90vEmaVNgKWAR21vlf0WbQzTnzcZhLz9pnnt4sD7U8Tki8T5tm9IOgWYZPvo7HcJcJbtMxVy7CcA2+R4qwDDbb8i6RniPNdxhHz/WNudBC3WATay/UKeZftQqhWuTMj4DyP8yl4tj8xA5Fnb6+b5qRvVQ8TC9mGSbqRLiaVCIGMHIsvVFdvrZ/99gXfb/mK+P584H7hwj8tbTAa+b/uHko4lnvNKhFjJPcSZvo7P2/b1wC55NnF+wsfuAttPEmfZbrF9oKSjgC8RZxDb76Fk74uiKIqiKIoZ4o2cIWsJizxJqDBeme1b5r+7gDuJMruVifNmwzMbtrHtZxtjNb3JOknOQ/p3JcsQcv3jCeXHVbtcswHQOss1ivAea3FeQ+7+NGDHfL0LYTbdiTG2X8jX8wA/zjWcR5hOd2JLYMfcq1uBJYn9mF5OBq5PtcmeKARHvkCeJ5P0X8ATtn87iPnG5N/xwK22/2F7IjBZYZDd7XkD7CVpLHAL8M5G+4uEoTSEKuXQQaynKIqiKIqiKAbMGzlD9oLttTLT9QviDNkJRJbkCNs/ar9A0jqE1PoRmSU6JD8aiDfZPxuvTwSOsT1GIdhx8ADX3Bz71fHSVPlxSZsD69NdPr+5hn2Axwmj5rmIbFInBOxpu5cC5ICQ9F0i09hf2SOS1iDKEv8js1IAHwC2lvSfRJZrEUln2/5cj6Ga/mLt3mND6PK887kMJwyjn5d0LX2WCS81FBVf4Y3930lRFEXxGlMiD0VRDIY3coYMCO8wYC9g3yynuxzYReF5haSlJb1F0jsI1cSzgaOB9zWG2a7xdypvsi40/dV2arT/g6lL8W4CPp2vt6eDH1iDkcDZwM8bmbP+1vCY7SlECeHcXdZwObB77g2SVpG04ADGn4oszfww8Jmcs1ffZQnLgR1s399qt/0/tpexPZTYl6v7CcYGQsfnTezP0xmMvZs4G1cURVEURVEUs5QZ+j//KXZwLPFj9mmi1Oso2xdpFkq594ftu7I07dO2R0l6D3Czwi9rEvA54uzRCElTCOn23RtD9OdN1s7BhB/XX4lyuOWz/RLgfEkfA/YkAsXTJO0HTKTPu6wTY4Dz6V6u2M7JwPWStgWuoS97Ng54OffjjPz3CeBOxYZMpO8c21RImkSU/r2bMJ5+BPhCZtdOIcpDn8l9vdT2J7qs7SBCZOXaPON3V0sSVOFJ90VgPmBRScvZ/tMA73kabF/R5XlfBuwmaRxhuH3L9M5RFEXRiZI+n3OpDFlRFINhun3I8sf7TcCZtk/JtuWArW2fODsFZDOCpLRh6gwAACAASURBVIeBYV1ENGblOoYRZ6Tm7rdz3zWTbC/UT5+hwC/SHmC6x5O0BOEdNowovfwtsI7tp7v0fz9hyPxAc8w8V3ZrZq52BzazvV2nMWYnyoesKIp2KiCbc6mArCgKmDU+ZJsTJsentBps/8n2iR0Ws56kmxQy8DdJele2r6qQkr87JclXzvYd8/1YSaOybTlJV2X7VZKWVUi2P6hgMUlTUjERheT7Sm3rmFvS0ZLG5zh7ZvsWubbxkk5LtcFWMLYY8CuFvPn7JF2uhqy6pIVyPXfm9R/rcP9zK6TsJ2SffbJ9JYUs/ti8fsVO40k6ALiAxhkpdZFyb3x+JClsojBtRtLXcw0TJH0tux4JrJj9RgzkfrrwYeBK209lEHYl8JFunW3fYvuxDu3X2H4+395CD/l8SZtJuk7SzyXdL+lISdvnd2q8pBWz31KSLsj9ul3SB7K92/dyZ0kXSrpMYTlw1AD3oCiKoiiKoigGxYyULK5KqNYNhPuATWy/LGk4cDjwScKX63jboyXNC8wtaVXgQOADtv+emReAk2iTiLe9jaT7CQXB5YmszMaK8sJlbP+hbR27Zr+1cy1LSHoTUba3he37JZ1FlCsel9f8T0NW/QxCeOJVWXVCLOPjtp9TeIPdImlMQxQCYC1g6VYWSqH+B2GmfGSWeL6JCJBfbB8PWNn2kYqSwa7S/SnlDoDtAyTtYXutvGYdoiRy/bzmVoVv2gHAao1+Q3L/W4H2zyX9jqkFMzqxNPCXxvtHgG0kfbOt3422B2rS/QUiGL6IvrLPFt/MNa0JvIcw/34QGJkWBXsTZaFfA44HjrX9G8X5tcvzmm7fS4hntnbO8XtJJ9pu3l/LMmBXgGWXXXaAt1QURVEURVEUfbxm6nGSfkDItr9oe922jxcFzlRkwExIskMIZByoMAO+0PYDCiXB81slgrafyr4bEGedICTiW1mLG4BNiB/sRxCeUdcBt3dY5nDgFNsvt8aWtCbwUENc4kxCkbEVkDVl1Rey/Q/gH5Jasur/BA7PzNwUIjB5K/C3xrwPAitIOhG4FLhC0sJEkHZRrmVy7uM8AxivKeUOsBARoF1PdzYCLrL9z5znQmDjxv21EHHGai5CYXAK8BHbf2sFhF1Qh7bbprfcUNLniPLHTW13DAYVZbG3tzJtkv4IXJEfjwc+mK+HA++VXl3iIrn/3b6XAFe1rA8k3Qssx9QBZ/mQFUVRFEVRFDPMjJQs3kNDiTCzHlsQsuftHApckxmij5Ly4rbPAbYGXiB8uzYnftgP5Mdtq88NRGCxHvBLosRwMzoHJ53G7hRINOlPVn174p7XySzT4/TJp8dCo4RvTeBaItgb2WPefsejT8p9rfy3ku2f9HMf/d3nYObvxCOEl1eLZYBHBzjnVGS26kDiPGJ/mbn2Z9J8Xq3/4TAXIW/f2q+lM7Du+L3sMG5J3xdFURRFURQzhRn5kXk1kcnZ3fYPs22BLn2bMvA7txolrQA8aPuEfL0GcfboIknH2n5S0hKZJWtJxI9iaon4W4GzcpzJCoPjLwOdxESuIJT1rm2VLBJla0MlrZQljjsQGbaBsihhZvySQpBiufYOWXr4ou0LMotzRpYkPiJpG9sXK86tzT2Q8YiSu0MljbY9SdLShHfWE239XpI0j+2XiAD1jDxbJuDjea/tMvgDmb8TlxPfh8Xz/ZbA/wzw2leRtDbwIyIr134/08sVwB7AiJxjLdt30+V7WRRF8VpQwg5FURTFQJjuDFmekdoG2FTSQ5JuI8r92s8MQZQXHiHpRvr8sCB8vSZkEPVu4ozYPcBhwHUKafZjsu9ewOcVMuU7AHvnOv5FlJK1ZMtvIAKM8R3WMRL4MzAux/5slgp+npCpH09kVk7pcG03RgPDJN1BBIr3dejTkni/mziH1gpUdgD2ynu6CXjbQMazfQVwDiHlPp6Qw1+4vR9RTjcuA7c7c+7biCB2pO27HKbMNyqEPkYM8H6mIYPmQ4lS0duBQxrlptMg6SiFbP4CGZgenB+NIEowz1MIjbSXVE4PexH3NC7LD3fL9m7fy6IoiqIoiqKYJUy37P2AJ5BeIYKjeYCXiaDtONtTFFLuO9rea5BjLkYEUyf3028oKek+2LnURe4+A4dJto8ezJp7rW1GxpkdUShQfpUo9ZsE7Gr73vxsDSIDtggR/K7bOj/XY7wxwAoNUZS1iKD5TcR36iu2b5vBNY8Ejmmtc7CU7H1RFE1K8n7OprKjxf9n78zD7ZzO9/+5zUNqTvsrGmmRVkkEQaOGFKVfWqqtamuKmRpaig58FSlVWlFzVUlMNfONMRQRYwwNCVXUVIqKGlOCJPfvj2dt5z07e+9zTk4ig+dzXec677ve9a613nfvXNd58jzrvpMEOi97/1Hsi3m3ouD3SSKzszjwS9sPEN5VXWUJ4IeE+XGn6MZcsyWS5rU9ZTad6yK3edNtRWQ5v1YUHC8AdrT9sKSlCRPuVnN/iwjqqhwPHGX7BklblPNBXVjfNNjevTv3J0mSJEmSJMn00B1Rjy5T9gTtCeynYJCka6HLXmX13lkqv2s+X9Mo+9XN1UPSuWrzI/t2ff/CIWXu+1TnaVbG6S/p3jLGVbX9Uy3a11J4jt1DZJCmoaxzdLnvb5LOlDRPuTZR0tEKWf+BZbzbJT2o8Ef7dOl3QLl3nKSLS9tG5X09VN7xJ6rvpPQ5VdLgcvyspCMk3Qlsq/BIu1HSo2Udj1XGG1P3Ob9VOT0RWK+Uaz5OiH1MLf3+0yrQk9QDOAi4hLbP+yHgS8DJpdvitBAPkXSkpOGSbirP9C1FueT48jzzl36jSha19p6PKZ/VvZI+1Wz8JEmSJEmSJOkOH2lABmD76TLvJ+su1Tyh1gCOIDyhoM2rrD8hg/4C4Z31VFHMO4SQw+9PKBluCpxQC06a8L/Am7b72u5HCJQ04i3b6xAeaCc1uH4e8NMyxnjglx20nwscYHtgi7VBKEb+BOgLrEib3P+iwCO21yX2gZ0CfMf2WsA5xN47iPezRpm/tl/qYGDf8h43IJQtO2KS7fVtX0zsR9vf9qqEmuZLFdXCdetvlLSvQsBkQdoUG2uy/79VGE8f2sH8Q4DfAQ/T9nn3J7zUFpT0PPBbOhYPWRHYEtiayNDdZrtveQeN6koWBe61vTohhrJHo0El7akwDH9gwoQJHSwhSZIkSZIkSablIw/ICo0k2BcnhBweAYYSxtMQXmW/UBgMr2C7USCxPvBn21Ns/5tQSaz3QquyKXBa7aTI0jfiz5Xf7YIoSYsDS9iuKTIOBzbsQvv5LdZ3n+2nS/boz+X5IPZkXVGOPw+sBtxcskaHE9kngHHAhQovr8ml7S7gREkHlHXU2ltxSXnWHsB6FKENYg9Yq4AX26fZXpEQeTm8NM9XnmX78nsbSZs0ul+xT2ylmk9bHfsAB9r+DHAg0JHk/w1FaXI8Id5xY2kfD/Ru0P99oJY5fLBJH2yfZXuA7QE9ezZye0iSJEmSJEmS1nzkAZlC3n4KUC9p3hWvsmmG7eoy6JrXWf3x9NDZORvNVTufVCnxE/BoJUvV1/Zm5dqWRMC5FvCgpPlsHwfsDiwM3CvpC0SwVv0O1PuN/bf8ngd4ozJXf9urdPJZLibUOCGym7fbftX2O4Rv3JpN7hsIrKUQV7kT6CNpVLm2M3BlOb6MyCi24j0A21MJe4Da+6x6lVWp9kkPsiRJkiRJkmSm8ZH+oSmpJ6GOd6ptS+3iqK54lT1Me5n30cBekoYDSwEbAofQ3NC45kv14zLHkk2yZNsR+9W2IzJ1H2L7TUmvS9rA9h0U/7IW7W9IelPS+rbvJLJEzVhH0meB58rcZzXo8zjQU9JA2/eUvVB9gMeAz9i+rez/+gHQQ9LStscD4yUNJGwGHgS+qPBAW4goRbyzfqLimfaMpG1tX6b44PrZfrjR4iWtbPvJcrolUDseCRwqaREiC7URkQ2dBoe33RllvN6EIuWgcvnFcu8oYOPK+EmSJLMFqbKXJEmSdJaPIiBbuJS51WTvz6fNW6zK8cBwSQfRfk/XdsAOkj4AXqb4W0m6q5Q33gAcSmRUHiaySYfafrn8Id+IXwGnlfunAEfRlnGpsmARrJgH+H6D6zsDZ5YA42nCz6xV+y7AOZLeIYKTZtxDBIJ9iWBzmrI92+9L+g5wcimHnI/Y5/YEcEFpEzC0BINDFEbPU4C/EWV870m6lChxfBIY22JN2wNnSDqc+CwvJt53I/aTtCmhoPh6eR/Yfl3SiYRPmYHrbU+PNvQewO8Vqo2TCKGYJEmSJJktSNuDjy/5nzHJ9DDTfcg+7kiaaLtH5Xww4W+2X5P+g4CDbX+9E2P3BtYrZZ2Nrj1GZNIWICT/dyt7qbo8XifW0p/IaC1GBH3H2L6krs8pwC7V99FgnBnqYfZRkT5kSZIkSY0MyD6+ZECWVFEnfchmlahH0k1Kdqg3UZLYjKeKKmFfQvDjux0M29F4rXiHMN5eFfgacJLCwLu23gGEf1xHXFT2w/UnsqYnlvtrHmZ7lzkG0YGHWZIkSZIkSZLM7mRANguR1FPSFZLuLz9ftj0KOFqNPdkGS7pM0jXEPrjjgA2KP9eBzeYpQiD3AcuVcXpLuqNIz/9V0nqla7vxJM2r8He7X+Fptlfd+g9TmzfYpYQK42G2XyREW3qWfvMCJxClpY3ew5jKOKPLcV9Cfr6Wwt0MGFfbt1bvYSZpF7X5oj0kaUp5tgcl/UXhczdK0tMKs+qm70HSNuUeSfq0pCck/b8G607Z+yRJkiRJkqRbpHrczKe2h67GUsCIcvx7Yo/XnZJ6EfvKVqHNk21y2Yt1LFAzrx5ICGq81tnyRkkLEd5dPypNrwBftT1JYbT9Z8Lj7WfV8STtSfi1rV2EP+6SdJPtZwBsH0Ob91l1vnWIMsmnStN+wAjbL0nTCmLW+5hJ2he4uoxRU9XsA1jSSCLQu9j28ZUxziV83mpjGDjM9g2SriL2DX4V+CJhRTCi2XuwfZXCLHxfItv3S9svN1j3WRTBlQEDBmTtb5IkSZIkSdJlMiCb+bxbyu+Atj1k5XRTQuWwdnkxSZ8gFCeHlyDBhIhGjZttv9bJuVcsweDKwOW2x5X2+YFTy76vKUSw04jNgH5FPISyrpWBZ5pNqDDkPh/Y2fZUScsC2xIlhp3C9mmE6MoPCA+znWnzMFubKI+8pdTl3tJkmPdp7zf2nu0PJFW9x1q9h/2BRwiD6D+TJEmSJEmSJDOBDMhmLfMAA+vNrov4xW22tylCG6Mql/9L53nKdv8SJI2StJXtEYSZ8r+B1csamgljCNjfditFyOq6FwOuAw63fW9pXgNYCfhHCTwXkfQP2yt1YsiLKdL3VDzMylw1D7NmAVm939iHXmRlPxq0fg/Llfs+JWme4mGWJEmSJB2Swg5JknSF3EM2a6n5oQEfKhVCE0+2BrxNez+2hth+iShH/Hll/JdKkLEjMG+T8UYC+yg8zpDUR9KijeaQtAAhz3+e7csqc19n+//Z7m27N/BOq2CsZAVr1HuY9ZO0SAmoNiLk+7tDw/dQxj+XEDh5DDiom/MkSZIkSZIkSUMyQzZrOYAozRtHfBajgb1p4MkmaSjwKeC1cj6SyBpNlvRwab/GdiOPNwij7EUkbQCcDlwhaVvgNtqyblcA/yzjDSP2uPUGHi97sv4JfLPJ+P9L7PfaWNKPgDdpCyarcvgdfeeuUZhiTwXeIPZwVT3MxgOfA/5vOj3MqjR7D78A7rB9Ryn5vF/SdbYf6+Z8SZJ8DEjJ8yQzZEmSdIX0IZtDKEHDtra/K2kewlz5fdsDy/V7gB/bHtONOZ4lRC1erWs/Epho+7ct7u1NBF0HEwIel5f2PoBtP1n2kz0IrGL7jSbjbEGYfQNcBIy2fUa5Ni9wM1FaeE5tjtmB9CFLkqRGBmRJBmRJkkD6kM2N3AXU5OlXJQQn3pa0ZFFAXAUYK+mQikz9UbWbJU0sv+eRdLqkRyVdK+n6imgHwP5FAn68pC+UQGtv4MAiJ79Bo8XZfraIhkyta3/C9pPluJ0cfpNxrneBkOpfvro2Iov3SqsXJWmQpNslXVok64+TtL2k+8pzrVj6fUMhuT+2yNx/qrSfLOmIcry5pNElCE6SJEmSJEmSGUqWLM4h2H5R0uQij78ecA8hPDGQKA8cRygZrgysQwhyjJC0oe3RlaG+RZQh9gU+SeyROqdy/VXba0r6ISGBv7ukMykZMkl962T8IRQM16UDqnL4kg4j1BerXFak9Cn71nakSPVLWg7YhiiLXLsy5hhgwbpxfk8IdaxClHI+DZxte51STrk/8GPgTuBLti1pd8In7SfEfrv7Jd0BnAxs0UjUo9gC7AnQq1evjh4/SZIkSZIkSaYhA7I5i1qWbD3gRCIgW48IyO4mZOo3A8aW/j2IAK0akK1PBD5TgZcl3VY3x5Xl94NE8NYO2+OB/vXtHVEvh0/4l03jYVbhdKJc8Y5yfhLwU9tTql5mjQJBhT/b/UXMBElPEQIqEHvQvlKOlwcuKWtbgCLnb/sdSXsQ7+1A20/RgPQhS5IkSZIkSbpLBmRzFncTAVhfomTxeSKj8xaR5RoE/Nr2H1qMMa0zc3veK787I8DRKZrI4bfq/0uirHGvSvMA4OISjC0DbCFpsu2rmwzzXuV4auV8Km3PdQpwou0RJYg7snJPX+A/wLIdrTdJkiRJkiRJppcMyOYs7iICsKdtTwFek7QEsadsDyLoGCLpQtsTS5nfB7are67uBHaWNJwIegYR4hmteJsQ7OgyzeTwW/TfHdgc2KRaJmj7s5U+w4BrWwRjnaVqL7BzZfwViPe8BnC9pKu7I5aSJMnHixR0SJIkSbpCBmRzFuOJ7NBFdW09ijLiTZJWAe4pmaSJwA60F8G4AtiEyLA9AYwhSh5bcQ1wuaStCaPoO+o7SFqbCLyWBL4h6SjbqwLfBTYElpY0uHQfbLt+H1qNM4HnKs9wpe2jO1jf9HIkcJmkfwH3Ap9VTPonYv/ci5J2A4ZJWtt2MwPtJEmSVFdMPiSD8iRJukLK3s9GFK+x52yfVM5HAs/b3r2c/w74VzOvMUl3216v0bVKn2eBjWw/J2lpQsnwy8AXCBn9u1vcuyGxl6sf8L2KtH1/2nuNHWP7khbjXEiUIH5Q5t/L9geV62sTAdJ2s5O0fStS9j5JkgzIkhoZkCVJAil7P6dS2yNGkVlfhihHrLEeUbbYkI6CsQoXFaXEO4Ahtl8mShc7uv+fhNlzfYnjO8BOJSP2NeCkUkrZjAuJALAvsDCwe+1C8Rr7DTCyk8+SJEmSJEmSJHMsGZDNXnxUXmN/IcQtpgD3dsVrDPgG8HXghNL3IcKwukteY4Sa41iinPGXZazN6ZrX2AOSXpc0SdK/JT0naWKd11hPSVeU93W/pC+X9nUk3V08yO6W9PnSPljSlZJulPSkpONbrSNJkiRJkiRJukMGZLMRJZip9xobQ3iNDWBar7H+wFqllLBK1Wts93J/lVdtr0mUGR5cAq0zgaG2+zfaI1ZZ4zHACOCQ0rd/zTsM2nuNdfCs2xB+Yv8mArr+RAC6TVlLZ1gJ+CIhzjEZGGa7B3A2EdhBeJINtb028O1yDeDvwIa21wCOAI6tjNsf2I54f9tJ+kyjySXtWYLCByZMmNDJJSdJkiRJkiRJGynqMfsxy73GppcGXmMd0SmvsRZ0xmtsU+CLlfEWk/QJIogbLmllwMD8lXFvsf1mGfdvwAqExUA70ocsSZIkSZIk6S4ZkM1+pNfYjPUamwcYaPvduvlPAW6zvU0p2RzVZNwZ9o6SJEmSJEmSpJ78Q3P2I73GmKFeYzcB+wEnlHH7F8n9qgfZ4G7OkSRJksp6SZIkyXSRAdnsR3qNzVgOAE6TNI74vo8mBEyOJ0oWDwJunUlzJ0mSJB9D0gIhqSf/wyZpRfqQzWVI+hQwlBDyeI0o3/sM8ENgV9tfn4XLmwZJOwOHl9Nf2R7eou8xwE7AkkW8o9Z+ECFeMhmYQDznc03GmIfYq7YxsXdsEvBd288Uj7a3S9d5ib12Q2y/12isKulDliRJktTIgCypJwOyjyfpQ/YxRJFuuprIAj1HBBVLEDL3r83CpTVE0lLAL4F1CdXIX0passUt15R+9YwFBtjuB1xOZL+asR2wLNDPdl9C1fGNyvWvlPZ1gM9RRDuSJEmSJEmSZGaQJYtzFxsD79s+kzrpeEmDKsdHAhNt/7acPwJ83fazknYiAprFgXcJM+j5gYWA/xIZqF1s/1PStkRANQV40/aGxdj5OGLfWh9CjOQ/laX81HbN9Hlz4Gbbr5V13EwYS/+5sta+hHJjlYWrJ7arKpL3EiWczfg08FJt75rtFxp1Kvvz9gael7RUbY1VJO0J7AnQq1evFlMmSZIkSZIkSWMyIJu7WBX46/TeLGlV4DBgNduv1gIRSdcAl9seLmlX4GTgm4R/1+a2/1WERwB2I4KztYuZ9V2Ez9gzDaZcjvZy8i+Utg+xPZ7wBauuc2KLx9gNuKHF9UuBO4v59S3ABbbHNupo+y1JzxC2AmMaXE/Z+yRJkiRJkqRbZMniXIyk0yQ9LOn+Tt6yMRF4vQpQyQoNpE1k5HzC5wwi2BomaQ+iPBLCI20nSQ8RQczSREDTcIkN2qY7sJG0AyGdf0KzPiUj9nng58T+ulskbdJq2OldT5IkSZIkSZJ0RGbI5i4eBb5dO7G9r6RlgHq1icm0D8YXKr9F5wIil/H3lrQusCXwkKT+ZYz9K2WJrXiBKG2ssTzt/cA6jaRNiezeRh2JcJTrNwA3SPo3ke27pcGYnwB6E0qVSZIkSdIpUsAhSZKukBmyuYtbgYUk7VNpW6RBv2eBNQEkrQnU/L9uAb4raelybanSfjfwvXK8PeFzhqQVbY+xfQTwKqHmOBLYR9L8pU8fSYs2We9IYDNJSxYxj81KW5eQtAbwB2CrOj+2Rn3XlLRsOZ4H6EcIoNT36wGcDlxt+/WurilJkiRJkiRJOkNmyOYwJA0FnrN9UjkfCTxve3fbljQW2FPSoYQAx3+Bn1buv5vwKKuVFd5PyQDZfrRIy78k6QkiszaY8PI6p4iBvA58qwx3gqSViazYLcDDhBjI0cD7kp4HngK+WbJnZxDm01OAY2xfImlIWQPA0RWBjwuJ8sMPgPuAvYBjgB8Ai5TMVk/CA21voAdwWfE1+6ftrZq8wk8Cfyz72yhjTypiJgC3FbXKeQjPtSHNP40kST6upKx50orMkCVJ0hXSh2wOoygbbmv7uyXDcz+hrDiwXL8H+LHtaUQoujDHs4SM/Kt17UdSUWdscm9vIug6GBhh+/LS3gew7SdLhupBYBXbbzQZZwvaxDkuAkbbPqNcmxe4mfAQO6c2x6wkfciS5ONFBmRJKzIgS5IE0odsbuYuYL1yvCrwCPB2KftbEFgFGCvpEEn3Sxon6ajazTWFQknzSDpd0qOSrpV0vaTvVObZX9JfJY2X9IUSaO0NHCjpoaJSOA22n7U9jhDMqLY/YfvJcvwi8AqR4WqI7etdILJYy1fXBlxRxmiKpEGSbpd0qaQnJB0naXtJ95XnWrH0O1LSweV4lKTflD5PNHvOJEmSJEmSJJkRZMniHIbtFyVNltSLCMzuIaTiBwJvAuMIoYyVCXNjASMkbWh7dGWobxGCFX2JMr7HgHMq11+1vaakHwIH295d0pl0kCFrRAMvsUWI/WZPNek/Bliw0tQH2KdcW44wc94YWLuDOc8m9sc9RniqHQT82vY6kn5EBHY/bnD7fKXPFoTP2qZN5kgfsiRJkiRJkqRbZIZszqSWJasFZPdUzu8mxDE2A8YSvmRfYFrp+fWBy2xPtf0ycFvd9SvL7weJwG26sT3edn/b/YH/IVQav1IzZ27Qf91K//uBs2wPL5dPIsylp3Q0J7A7cKvt1WyvTphG31S6jG/xXJ16dttn2R5ge0DPnk2TfUmSJEmSJEnSlMyQzZncTQRffYmSxeeBnwBvEVmuQUQm6A8txujIX6smHT+FGfQ9kbQYcB1wuO17O9H/l0RZ416V5gHAxUW8YxlgC0mTbV/dZJiqBP7UyvlUmj/XDH/2JEmSJEmSJGlE/rE5Z3IXEYA9XTJFr0lagthTtgcRUAyRdKHtiaXM74M6Sfg7gZ0lDSeCnkG0mT83421CsKPLSFqAUC08z/Zlnei/O7A5sEk1k2b7s5U+w4BrWwRjSZIkM4UUbUiSJElmFBmQzZmMJ7JDF9W19SjKiDdJWgW4p2SSJgI70F4E4wpC/v4RQvZ+DLEHrRXXAJdL2powf76jvoOktYnAa0ngG5KOsr0qIU+/IbC0pMGl+2DbDzWZ60zCH6z2DFfaPrqD9SVJksxUUl0x6QwZsCdJ0hVmK9l7SYcRPlNTiJKyvboq317+2L+pKPm16jeKEKt4QNL1wA+aSbDX3XckDYQtigrhtbZX68p6O1pbd8fqYJ4eJYO2NKFk+OWyn2xmzrkzcHg5/VVlb1ijvscAOwFL2u5RaT+I2B82mfBa29X2NObOXVzX3sA7ts+bnvtT9j5JPh5kQJZ0hgzIkiSBzsvezzYZMkkDga8Da9p+T9IywALTMdRgIuvTMiCrYnuL6ZhntkTSfLYnd7L7taXUcQFgSFeDsS7OhaSlCNXCAYSwx4OSRth+vckt1wCnAk/WtY8lfNLekbQPcDywXVfWXo/tM7tzf5IkSZIkSZJMD7OTyuKnCan19wBsv1ok3jeRdFWtk6SvSrpS0ryShkl6pHhKHVh8tAYAFxavrIXL/WNLn3OKV1c7JD1bAkAk7VS8ux6WdH5938Lqkm6V9KSkPRqMt5Ckc8ucYyV9pYP2hSVdXOa9BFi40aRlnTWPrPskrVTah0k6UdJtwG8kLVqe9f4yz9al36rlvockjSP2m30ZeIbwF3tE0nYN3smAkrWreXadJelJYEIZ6xVJURorIAAAIABJREFU70h6WdJe0yy8jc2Bm22/VoKwm4nyyofqfjYHsH2v7ZfqB7F9m+13yum9QJ8GY4wp651pXmSS9pT0gKQHJkyY0OKxkyRJkiRJkqQxs02GjJAjP0LSE8BfgEts3w7cCpwmqaftCcAuwLlAf2C5WomgpCVsvyFpP9pKERcChhHCEE9IOo/wszqp0QIkrQocRpTuvVoyOo3oB3wJWJQwYa6vYdkXwHZfSV8ggo4+Ldr3Icrl+knqR0jVN+Ot4pG1U3mOr5f2PsCmtqdIOpaQe9+1ZMDuk/QXwtj597YvVIhszAtsAbxoe8vyDhZvMXeNtYB+tt9VeHF90vavSrB7l6SbbD/T4L7lCEXIGi8AT3bV16yO3YArbP+qRZ/VCcPs14CngbNnhBeZ7bOAsyBKFrvxDEmSJEmSJMnHlNkmQ2Z7IvGH/p7EvqBLJA12bHI7H9ihBBcDgRuIP6w/J+kUSV8jJN/r+TzwjO0nyvlwQliiGRsDlxdhDGy/1qTf/9l+t/S7jTBgrrJ+WTO2/06IU/Rp0b4hcEFpH0eYOzfjz5XfAyvtl1W8uTYDfibpIWAUsBDQi/Ar+4WknwIr2H6XEAPZtGSENrDdkbAHwIhyb22uncpcY4ClmdbzrEYjqf3pDmQk7UBkRE/ooOv9tl8q2denmIFeZEmSJEmSJEnSHWanDBkloBgFjJI0HtiZyHCdS+wnmkQEHpOB1yWtTpTB7Uuo+O1aN2RHXlv1iM4FCPV96s+bzdtqPZ0NTNzk+L9183zb9uN19z5WSvm2BEZK2t32rZLWIjJlvy7ZraMJwYxawL5Q3Tj1c+1ve2Qn1v4CIa9fY3ni8+4ykjYlspkb1cpcW5BeZEmSzBBSrCFJkiSZ0cw2GTJJn5dUzaz0JzJIFMXEFwl1vmGl/zLAPLavAP4XWLPc9zbwiXL8d6B3ba8VsCNwe4tl3AJ8V6E6WBOhaMTWZT/Y0kSAcX/d9dHA9mWMPkR26vFOtq9GlEQ2Y7vK73ua9BkJ7C+FXrykNcrvzxHeZScDI4B+kpYlyiUvAH5L23t8lshYAny7xXpGAvtImr/2XJIWbdF3M0lLSlqSyK51JpBrR3mePwBb1XmrJUmSJEmSJMkcxez0P/89gFNKWeJk4B9E+WKNC4Getv9WzpcDzpVUCyp/Xn4PA86U9C5R0rcLcJmk+YjAqamanu1HFVLrt0uaQqj5DW7Q9T7gOiKgGlLER3pXrp9e1jC+PMvgohzZrP2M8izjgIfK+M1YsGS55gG+36TPEGJ/2bgSlD1L7DXbjij9/AB4GTgaWBs4QdJU4ANiPxvAUcCfJP2CKEVsxtlESd9fy1wTgG826mj7NUlDaAtgj25RFoqk4wkbhEUkvUDs/TqSKFHsQXyuAP+0vVWLNSZJknSblLxPOktmUpMk6QqzxIdM0+E3JulUYKztP5XzwXzM/MYkPUvIvb/a3blnNpJuJIRP7rT99Ur7hcS+rw+IwHMv2x80GWN74KfldCKwj+2HK9fnBR4A/lWdYzrXuyxwsu3vTM/96UOWJHM/GZAlnSUDsiRJoPM+ZB95yaLa+431I9Trnu/gngeJMr4LKs2DgWW7MrftLToTjM0JlIzf7DzXCUSJaD0XAl8A+hLy/ru3GOMZYo9YPyLrd1bd9R8Bj03H2qbB9ovTG4wlSZIkSZIkyfQyK/aQddlvjFDDWwp4QB9jvzFCAOMX6qLfmKSVS9/ryvN2xW/sJuA8he/bCWWucWrhNyapL/A7YCiwgSq+YLavd4HIkC0vaYym9RHra/vuimn0vYQISG2O5QlxkrNrczYYo+ZF9qykYyXdo/ANW1PSSElPSdq79Okt6ZFyPLh8924sn/3xTZ4zfciSJEmSJEmSbjEr9pCl39ic5Te2fsVv7E3ba6sDvzHb44H+kgYRn9E05YQKEZAdgR/ZPqwTa9mNsDuocRJwKEXApTZni/uftz1Q0lDiu/JlQj3yURrvK+wPrEGoLT4u6RTb7TK56UOWJEmSJEmSdJePPEOWfmMfC7+xznA6MNr2HR11LBnG3Sj7ySR9HXjF9oNdmG9E+T0eGGP77RL4Tyrft3pusf2m7UnA34AVujBXkiRJkiRJknSKWaKymH5jnWJO9htriaRfAj2BpmWPlb79iLLE/7H9n9L8ZWArSVuUNS8m6QLbO7QYquo9Vu9L1ujfQbVPepElSZJCDUmSJMlMYVaIeqTfGHO931hTJO1OBNfftz21g769gCuBHSvZT2z/3PbytnsD3yPKNlsFY0mSJEmSJEkyW9Kt//VXeHWNB+YnMi3DgZNsT5U0ANjJ9gF1t3XkN3YVsFon/MauA26U9BiROXuATvqNlXtPoM5vTNNK3U+v39h1wP9KWoe5z2/s2RKgfgA8Imm32mdVsll/ABYrfd8Beig8xHYr2bUziQD8nhJHLgRMruwR7F/6LER89gsCp5e+kzsjHZokSTKjScn7pCtkNjVJkq7QLR8ySRNt9yjHnwQuAu6y/ctujDmcEK1YroN+vZlOPzA18fNqEJBNF91ZW6v1zQwkzVvZk9aZ/ovZfqscbwX80PbXSiD8VyKb9XAJ2t5oNbakbwHfAfpVArKbgKG2bygliYfaHjTdD/gRkT5kSTJ3kwFZ0hUyIEuSBGaBD5ntV4hM134KBkm6tixmHUl3K6TZ75b0+dJeL8/+CPA/wFKl7YQy1gkKqfbxKnLtdQ9bnauH2iTnx0lqVoZ3iKaVla+O2V/SvWWMqyQt2UH7WgpJ+XsoKotN1jm63Pc3SWfWMn+SJko6umTFFiQk92+X9KBCov3Tpd8B5d5xki4ubRupTep9rKRPVN9J6XOqwky7JgN/hKQ7gW0lraiQeH9Q0h0KZchmn3NVVGVR2va3bQaMqxk32/5PB8FYD+Ag4Ff1UxAZNoDFiRLWZmMcKWm4pJvKM31L0vHls79RbeWVazV5l3soZPwflnSFpEVK+zBJJ5fv6tMKm4UkSZIkSZIkmeHMUKEC20+XAOOTdZf+Dmxoe7KkTYFjif1KjeTZP0Vkl/oDlICqP7A6sAxwv6TRLZbxv4Q8e99y/5JN+jWTla9xHiFicbuko4FfAj9u0X5upf2EFutbB/giUbZ3I/At4HIiuHnE9hElkLgd2Nr2hBKEHkOImfwM+GwpgaypAx4M7Gv7rhLoTGoxf41Jttcv7+gWYG/bT0palyjF3LjZjZL2JYKpRYHXFMqLPYGFJb1BKGNebLuhf1dhCOFV9g6wRBkDIhg9X9LZhKLmeh08x4rAV4h3+iDxXt8m7AqeKGs9nMbv8krbfyzP9CtCyfGUMu6nCbXMLxD78C5v8B72pJTb9urVq4NlJkmSJEmSJMm0zAxRj0YKg4sT+7seIcyCVy3tjeTZ61kf+LPtKbb/TQQqa7eYf1PgtNpJxVi4nmay8jWPriWKPxoUGf0utDczmga4z/bTJXv05/J8EEp+V5TjzwOrATeXQOVw2kyRxxGG2DsQ+9MA7gJOlHRAWUetvRWXlGftQQQ9l5W5/kAEI02xfZrtFYmg7P4SPA8F3gRWKs+0jaRNGt2v2Ce2ku2aEfgbtvuXcW4mBD8+ARwI/KmD57jB9gfEXsYpZdz+wMnA74F/0vxdrlYyguMJsZVVK+NebXtq2R/3qSbv4SzbA2wP6NmzZwfLTJIkSZIkSZJpmaEBmULdbwrwSt2lIcBtZZ/QNyjy6rYvArYC3iXk2RtlZT4KSfvumvp2ds5Gc9XOJ1VK/AQ8WgtSbPe1vVm5tiURcK4FPChpPtvHAbsDCwP3lpLDqpw9NJe0n4dKQFR+Vunks1wMfLMcvwDcbvtV2+8A19Om5FjPQGAtxV65O4E+kkaVazsTyooAlzGt91s97wEUxcYP3LYpsiZn3+pdDgP2K9nUo2j/jqqy9139DiZJkiRJkiRJp5hhJYuSehLqeKfattTub9jFgX+V48GVez6UZy/H/YCHaZOzh5CK30sh9rEUYa58CNMGGDVuAvYjygiRtGSTLNl2wHE0kJW3/aak1xUGyndQZPRbtL8h6U1J69u+kyJt34R1JH2WKK3bDjirQZ/HgZ6SBtq+p5Qw9gEeAz5j+7ay/+sHhIrh0rbHA+MlDSTK7B4EvihpwfKuNiGCn3bYfkvSM5K2tX2Z4oPrV9sLVo+klW0/WU63BGrHI4FDyz6s94GNiKzZNNg+AzijjNebKFEdVC6/WO4dRZRNPjnNAF2j4bu0/SjxPXuptG1P23c0SZKkHSnSkCRJkswsuhuQLVzKwGqy9+cDJzbodzwwXNJBwK2V9mnk2W2/JumuUt54A3AokVF5mMgmHWr7ZbWXn6/yK+C0cv8UIvNxZYN+HcnK70xI2i9C7InapYP2XYBzJL1DBCfNuIcIBPsSweZV9R1sv1+EJE4u5ZDzEfvcngAuKG0i1AjfkDRE0lfK8/6NKON7T9KlRInjk4S0fzO2B86QdDjxWV5MvO9G7Ff2AX4AvF7eB7Zfl3QiYTlg4Hrb0yNLtgfwe4Vq4yTaWyJ0mRbv8lFiv+EYIjgeT/v/CEiSJAFSYTHpOhnAJ0nSFbolez87Iml5oqTvi0SwdS1wiO33Z+nCCJVF4GDb9QIire7pDaxXyjtn1DoGAzc5jLgpAhonus37rbvjH0SUUE4GJgC72n6ucn0xItt3le39WoxzI7GfbT7gDkK4ZEq5tj+RCZ0MXGf70G6u+WhgtO2/TM/9KXufJHMvGZAlXSUDsiRJYBbI3s8OlHK7KwlBhpWJMr8ehKrenEpvojSxS0iat8XlwcCytRPbu8+oYKwwlvBR60eoE9arLQ4hxFk64ru2VydEOXoC2wKUbODWRGnlqkC3fOMAbB8xvcFYkiRJkiRJkkwvc1VARuw5mmT7XICSTTkQ2FXSIpIOknQOgKS+Cm+zRSQ9WfbAIWkeSf+QtIzCn+tehVfV0ZIm1iaSdEhpHyfpqNLWW9Jjkv4o6VGFP9bCtXtsj7L9dUnblrkfVpHwL/feIemv5acm934csIHCY+xASYMlnVpZx7Ul81bvZTZQ4TV2f5nrLAXfAQYQSo0PSVpY0ihJA8oY31f4eP27/NT8zaaUfg+Xd9JQebA8521F2APgh8CWlXEeJwLlmzr6MCueZ7sR6pnHlRLZK4H/2q4JetSLyHxIeV9XS7qm7JXbr3wPxpbnWKr0G1beTc2n7ajyOYxXE182SXtKekDSAxMmTOjocZIkSZIkSZJkGua2gGxVQsziQ8of9f8k5NhPAlaStA3hG7ZXCRwuoE2IY1PgYduvErLpv7e9NhWDYkmbASsTCoD9CcXADcvllYHTSubmDcJvrZ4jgM1L9mer0vYK8FXbaxJ7604u7T8D7igKgQ1FMirUvMzWLeIip9peu6hbLgx83fblwAPA9mXMD60GJC0L/IYIbJcj9lkdWWTk5wF+V9Y8mtjr1RluB44rY6wJvEQX9oVJGgmcAFwHrFjGeQ54SNIYheFzKxsEiAzbD4jP6xjgHdtrEPv5dmpyz6vlsziD8HmbhpS9T5IkSZIkSbrL3BaQNZOfF+AijT6YEB+53fZd5fo5tP1hvisRrEGIiVxWjqt7uDYrP2OBvxKqhiuXa8/YrpkcP0iUHNZzFzBM0h6EGTaEmMYfFZ5YlxF74LpK1csM4CslaBlPBFmrNr7tQ9YGRtmeULzMLiRULSGUE68tx82eqx0Kr7QBREAFkS273vbznXgWAGxvTuwjW5A2s+r5gCUJ8+dDgEtLuWozbrP9tu0JhFfaNaV9fIvnqAnBdOpZkyRJkiRJkmR6mGGy97MJj1KXkSoCEp8BnipNKwMTab+H6vlSnrcxsC6tZeshArxf2/5D3Vy9ae9fNYXITLXD9t6S1iVk4x9SGCXvD/wbWJ0IlCc1mbuVv9iHXmaSFgJOJ/ZyPS/pSJpbBVSfqxlVj68pdPDdUSgxHgZsVCstJALcDST9kNjbt4CkibZ/1mos25MkjSD2jd1MeJ5dWdZzn6SpwDKEgEgjqp/J1Mp5zaus1T0dPmuSJHM3KdCQJEmSzEzmtgzZLcAiknaCD4UtfgcMs/2OQvb890TWZ+nanqHC2UTp4qVuM2i+l7YA73uVviOJfWk9yjzLSfpkZxcpaUXbY2wfAbxKBIyLAy+VLN6OtGXO3qa9HPuzQP+y1+0zNDdOrgVfr5Z1Vp+1fswaY4CNyv65eQk7gM6Ib9Q/3xrAH4Ctqvu7bG9vu5ft3kQZ4HnNgjFJPSR9uhzPB2wB/L1cvpqSLZPUB1iAeI9JkiRJkiRJMkcxU/7nvwg+DCVKyl4nyt2Ot32VpkP6vbMUQ+ptgNMl/S8RcF4P/KJ0GQqcbvsJSbsBt0kaXYKGEUSp4rmVIX9M+H79hNjD9GaZ5yZJqwD3lEq5icAORDalM5wgaWUiI3UL4fl1OnCFpG2B24D/lr7jgMmSHgaGEfvgniHK7R4hSiZvoC4TV/zJ/lj6PUv4g9V4i/BSe5fIWtXueUnSz8v88wIv2P6/jh6mZLl6VJ+PyIBdVt7PVNoCwF/ZHt5irGOI8tGlgL8pjK3nBV4DfihpTyL4elPhNfc+sHMle5ckSTJdpLx9MqPIrGqSJF1hhvuQlb08dwPDbZ9Z2lYgsiWnzMyArDsUlcGhtjeotC0CvFsCve8B37e99SxbZBMaBETd7l/KL68tgiDTPV5RMXyA2EtmYk/WWrZfb9L/S4Rox5PVMRVS92NKpnMfYJDt7Tpa20dF+pAlyZxPBmTJjCIDsiRJYNb6kG0MvF8LxgBsP2f7lPqOktaRdHeRIL9b0udL+6qS7isy6eNKNglJO5XzhyWdX9pWkHRLab9FUi9J80p6WsESkqbWVBAV0vIr1a3j50Smatkyzv7l0p7AOyWTdBLw89L/WUnHSrqnyJ6vKWmkpKck7V369CjrqUmnTxPIlXUOU8jSj5d0YGlfSdJfynP+VSG/3+F45d5p5Pjrrh8HLFze7YWl7aCyhkck/bh0PQ5YsfQ7obPzN2Bz4Gbbr5Ug7Gbga806277X9ksN2qtS+vcCyzcbQ9IghfripZKekHScpO3Ld2q8pBVLv28oRE/Glvf9qdJ+sqQjyvHmkkZLmtvKe5MkSZIkSZLZgJlRsrgqUUbXGf4ObGh7skIE4lhiz9behNz8hZIWAOaVtCohEvFl26+WzAvAqcRepOGSdgVOtv1NSU8QSoWfJbIyGyj8uZa3/Y+6dbwB/AXYrqxlKYUoxk+A1UuJ43mEsmLNQPl52wMlDSVKCb9M7Nt6FDiTEOXYxvZbkpYB7pU0oq60rj+wXC0LJWmJ0n4hIRV/VVnHPERpXsvx1F6OX8AISRvaHl3rY/tnkvYr8vFIWgvYhRAzETBG0u2E3P5qlX7zNZh/NcKseWGFPxjAZbarRtzLAVVVxRdK24eUz2XBus+kVQC0G1GmWR1jc0KyH6Jc8rPEXr++wNPA2bbXkfQjQkDlx8CdwJdKBnR34FDiM/8ZcL+kOwj7gS3K3r52KMon9wTo1atXi+UmSZIkSZIkSWNmunqcpNOA9YmsWb1f1OLA8JIBMyH9DuEPdZik5Qk1vScVCoiXO/zBsP1a6TsQ+FY5Ph84vhzfQYh3fBb4NeGbdTvt91LV2BQ4s0i9Y/s1SasTEvZPlD7DgX2JTBnEnjOIPVo9bL8NvC1pUgms/gscWzJzU4kg5FPAy5V5nwY+J+kUYo/aTZI+QQRpV5W1TCrvcf5OjFeV44cITFYmfMOasT5wle3/lnmuBDaoPF8NNZj/T7aPUZQs9m8yfiPlxnZ1srbXneamigl3XXtNSn+jujFGEgEYirLYw2xvUc6fos2IejzwlXK8PHCJQjxkAWJvHqUscg/ivR1ou6bQ2f4h7LOAsyBKFhv1SZIkSZIkSZJWzIwyrEcJA2AAbO8LbAI0cs4dQnhErQZ8g6IMaPsiwjD5XWBkCcaaeYzVU+tzBxFYrEMIeywBDKJxcNJo7FYS8NBeOr1eVn0+Qjq/J7Ffqj8had9Odr6U8K0OjCKCvbNbzNvheLTJ8fcvPyvZ/lMHz9HRc3Zl/ka8QKhI1lieisl2V1CblP5WFSn9ZnRG6v4Uwjy7L7AX7Z+nL/AfKvYISZIkSZIkSTKjmRkZsluJTMo+ts8obYs06bs48K9yPLjWKOlzwNO2Ty7H/Yi9R1dJGmr7P5KWKlmyuwlJ+vOJoOHOMswY4LwyzqRSUrcX0EhM5CZgb0mjaiWLRDllb0krlRLHHemaBPziwCu2P1AIUqxQ36GU/r1v+4qSxRlWSgJfkPRN21erTWWww/GIDNEQSRfanihpOcI/7JW6fh9Imt/2B0SAOqzsLROwTXnWemn8zszfiJHE92HJcr4ZZS9eV1CblP7XGjzP9FL9/u1cmWsFonRxDeB6SVfbHjOD5kySZDYlhRiSJEmSWcEMz5CVPU3fJPysnpF0H1Hu99MG3Y8Hfi3pLtp8twC2Ax4pQdQXiD1ijwLHALcrJOBPLH0PAHaRNI4IJH5U1vEesXfp3tLvDiLAGN9gHWcD/wTGlbF/UEoFdyGk28cTmZUzG9zbjAuBAZIeIALFvzfosxwwqjznMNoClR2BA8oz3Q38v86MZ/sm4CJCjn88cDmN/cbOKs96oe2/lrnvI4LYs22Ptf0f4K4i9HFCJ59nGkrQPIQoFb0fOLpSbjoNko6X9ALhJ/eCwtAa2kvpP6Qwiu4uR5bx7qD4mEkS8CdCCfRFYr/a2WUvX5IkSZIkSZLMUGa47H2nJpUOA35A+HZNBfbqagZC0mDgpvJHc6t+o4g/rh+QdD0RbL3RifGPBCba/m1de286KQffiTk+XFt3x5qdkNQfOANYjPiMj7F9SV2fU4BdWsnvKxQr9y1jTAT2tP23cq0fkTFbjPgOrV3bbzedax4A7GT7gOm5P2Xvk2TOJiXvkxlJZluTJIHOy97PdFGPeiQNJMoG17T9XinbW2A6hhpMGCN3ej9STeRhbkDSfDURktlwrneI4OZJScsCD0oaWQuES/CzRMsRgovc5mW3FZEV/VpRfLwA2NH2w5KWBj7oyjPVU4LijKiSJEmSJEmSj5RZ4a30aeDVmiiD7VdtvyhpE0lX1TpJ+qqkK9XAq0vSdwilvQtL+drC5f6xpc85Ze9VOxT+YcuU42k8zRqwuqRbJT1ZVPfqx1tI0rllzrFlb1Wr9oUlXVzmvQRYuNGkZZ2/Ufhm3afim1bew4mSbgN+I2nR8qz3l3m2Lv2m8XErfa8rz/uIpO0avJMBJWuHpCMlnSXpJuC88jmcoDaPs73q1ty3zPcQcClRCjimZDBfoYi6SJqXKD9clTY/tNpP3+qYtt+qnC5Km/DKZsA4YGqZ7xYi6HtIIaFf/z4nlvf5oMJvbB1JoxRedVuVPoMkXVt59nMqfaYra5YkSZIkSZIkHfGRZ8gIAY0jFD5hfwEusX07IQZymqSeticQ+7fOpYFXl+03JO1HWyniQsQ+qE0qnmH70CZR3w419zSrpx/wJSIYGCupvqZlXwDbfSV9gZCt79OifR/gHdv9SsldK7+2t4pv1k7lOWpiJH2ATW1PkXQscKvtXRVS+/dJ+gsNfNyALYAXbW9Z3sHiLeausRawvu13FZ5bb9peuwS7d0m6yXZNKn488Vm1Q9I6RAa0Jh2/HzDC9lC1lsuv3b8vcFAZY+PKOzDwW6Jc8WLbxzceAYjPb5Ttn5ag/1fAVwmfuuFMK/EPsXfxK8QevMclnVFEUKprSx+yJEmSJEmSpFt85Bky2xOJP/T3BCYQPlCDixjI+cAOJbgYSJj/fujVJelrwFsNhv0803qGbdhiGc08zer5P9vvln63ERL6VdYva8b234HniGChWfuGRKkdtscRWZ5m/Lnye2Cl/TLbU8rxZsDPSpZoFCHb3ovwcfuFpJ8CK9h+lxAz2bRkijaw/WaLuWuMKPfW5tqpzDUGWJrwOGuKwt/rfGKv2FRF+eK2hNx8p7B9mu0VCVGYw0vzfMQ73r783kbSJi2GeR+4sRyPB24vwdV4oHeTe66z/V757F8hPN/q13aW7QG2B/Ts2cjVIUmSJEmSJElaMysyZJSAYhShMDiekBwfRmTErgEmEYHHZOB1hUnz5kTm6bvArnVDdtZLq9q/K55mzc6bzdtqPZ1VUXGT4//WzfNt24/X3ftYKd3bkvBx2932rZLWIjJlvy7ZraOBybQF5vVKgvVz7e8wYO4QSYsRZteH264pXa4BrAT8QxKEkuI/bK/UiSEvJoRCILzNbq8F1AqxljWJ0sVGfOA29ZoPPclKkNjs30DVx2wKs+jfSpIkSZIkSTJ3MytEPT4PTLX9ZGnqT2SQKHvJXiQyIV8t/afx6ir3VX2yuuoZdguNPc3q2VrSr4mSt0HAz2gvQDKayNLcWkoSewGPd6L9NkmrESWRzdgOOK78vqdJn5HA/pL2t21Ja9geqwY+bpL+Drxm+wJJE2nzfXuWyFjeAHy7xXpGAvtIurV4kfUB/mX7v/UdS5nkVYRdwWW1dtvXERL+tX4TWwVjklaufE+2BGrHI4FDJS1CZL82Aoa2WHuSJElLUhUvSZIkmVXMiv/17wGcUsoSJwP/oOzDKVwI9KzJmxNeXedKqmVxal5dw4AzJb1LlPTVPMPmI/yumnqG2X5UUs3TbAowlooxdYX7iCxPL2BICRh7V66fXtYwvjzL4KIc2az9jPIs44CHyvjNWLBkueYBvt+kzxBif9k4RcrpWWKv2XZE6ecHwMvA0cDawAmSphKKhPuUMY4C/iTpF0QpYjPOJsr7/lrmmkD4zTXiu0R55tIKewKId/BQi/EbsZ+kTct6X6eYN9t+XdKJxOds4PoS7CVJkiTJLCdtFJKZTf4n0tzFLPEha4WkU4Gxtv80E+eYQuwfmg94hpBP79CbrAvj702Id5zXhXsGEZnAuyU9CwyoleR9VJRgcz3bF03HvSsAVxICIvMDp1SYdWpMAAAgAElEQVQk6xcATiWyjFOBw2xf0cF43wEuI/zFHpA0PxEUrkl8bufZ/nVX1zmzSB+yJEmSpEYGZMnMJgOyOQN10odsVsjeN0XSg0QZ3wUzeap3bfcvyo2vUVQRZxS2z2wUjLXYrwQRrKzXlXk6GG967ulNGHZPDy8RwVx/YF1CbGTZcu0w4BXbfQhlw1blpEj6BHAA7TN22wIL2u5LlFjuVZetTJIkSZIkSZI5jtkqILO9lu0Nax5lHxH3EGWRAEg6RG1eW0eVtlYeXo38wo6UdHA5HiXpWEm3Az+S9A1JYxS+YX+R9KkSWOwNHFhUDHcEFpV0S1nHLZJ6lfGqXmQnKDzSah5f80j6R9l3R+WZ6j3Feku6Q9Jfy08tEDwO2EDh53WguuY9dh8wppRZLkj779auwK8hhDTqM3+SDlPFj4xQ1nyWEHep4fJO5iP8296nTnGzvNeHJP1N0iRJ/ynv50JJm0q6q5yvU/qvI+nu8lncrdjfiKSDJJ1TecZHyn416ubbU9IDkh6YMGFC/eUkSZIkSZIk6ZCPtXKcwqR4E+BP5XwzQsp9HUJVcISkDQlT42YeXs38wqosYXujcu+SwJeKCMfuwKG2fyLpTGCi7d+WftcQZXnDJe0KnEzbnq2qF9kbhFDIScCmwMNNSh2rnmKLAF+1PUnSyoS0/gBCtORg218va+iS95ikzxB77p4HDil77pYol4eUssyngP1s/7t2n+1jgGPKGGsQyow7qZhUFy4HtiYycYsAB9YLsdhet4zRm9ibOAh4lNhr9gNCIn8r4BflXf4d2ND2ZMVetWMJYZOTCAXQbYjs3l6236l/obbPAs6CKFls8M6TJEmSJEmSpCWzVYbsI2Thkon5D7AUcHNp36z8jCVMm79ABGitPLya+YVVuaRyvDwhRT8eOARYtck9A4HaXq7ziWCiRtWL7Bxgp3K8K2Ed0Iiqp9j8wB/LGi4jyggb0SXvMdvP2+5HSNvvLOlTRNC/PHCX7TWJjORvG92vEG4ZCvykweV1CPn5ZYHPAj9RKEg24xnb421PJYKyW4r0fdV7bHFCCOaRMu+q5TmmEiIv5xPy+ne1mCdJkiRJkiRJppuPa4bsXdv9S6brWmIP2clEVuzXtv9Qf4Mae3hBc7+wKlVp+FOAE22PKBmjIzu55oZeZLafl/RvSRsTe7e278QaDgT+DaxOBOWTGt7RRe+xyppelPQosAFwBfAOIYMPEQDu1uTWTwCrEdkpCIn8EZK2IjJcNxZD51ck3UVk9Z5uMla17HVq5Xwqbd/7IcBttrcpWbVRlXtWBiYSAWCSJEmSdJoUXEiSpCt8XDNkAJRM1wHAwQoVv5HArpJ6AEhaTtInFeIU79i+gMjurFkZZrvK72Z+YVUWB/5VjneutFd91QDuBr5XjrcH7mwx5tmEEMqllcxZR2t4qWSCdiSUERutoeY9Nj+ApD6SFm00oKTlJS1cjpcEvgw8XrJS1xDlgxAlon9rNIbtN20vY7u37d7AvcBWth8A/glsrGBR4EtEyWF3qH4WgyvPsjjwe9qk+7/TzXmSJEmSJEmSpCEf1wzZhxQj5YeB79k+X9IqwD0lQzMR2IEowWvk4QWd8wurciRRJrcCcCJRfgfxWewraWtgfyJQPEfSIYTn1y4txhxBlCqeC63l68u1fQBLGkLs6aplz8YBk8v7GEYEJb2B8SUI+gfNvcdWAX4nyURm7bdljxkUP7dSkvgP4H+aPYjCH24nYEmgqiMvYn/X94nSxZNsj2s2Tic5Hhgu6SDg1kr7UOB0209I2o0w8h5t+5VuzpckSZJ8DEjZ+2Rmk1nYuYvZzodsTkLd8AuTNNF2j8r54DLWftMx1gBgqO0Nigrh+lTEOer69gautb1aETW5GfiT7QtbjD+o2XidWNtSRGA1gCi7fBBYy/brTfp/CXgOeLLu/XwFGGP7HUn7AINsb9dojFlB+pAlSZIkNTIgS2Y2GZDNGWhO9CFLAkk9JV2hkJu/X9KXS/s0Mu2SfgbcCHxQlBlvok6+vtk8pbzxPorsv2aQHH4dmwM3236tBGE3A19rsaZ7bb/UoP22itLhvYRQSEMkDZJ0u6RLJT0h6ThJ2yusCcZLWrH0m8aCoLSfLOmIcry5pNElu5ckSZIkSZIkM5SPfclidyj7nKaXmtJjjaWI0kOIUsGhtu9U+I+NJEoCp5Fpt/1tSS8DvwL62X6tsxktSQsRQiA/Kk2v0IEcvqS+hEfYfIQwiIlSxQ/l8OtYjpDBr/ECsJykwwiz5yqXFQn8jtgNuEHS0sAtDa4fQQiWrEIYfz8NnF3sCX5ElIT+mNiX186CgFB4/Blwv6Q7CLGXLcp+u3YobAH2BOjVq1cnlp0kSZIkSZIk7cmAbNbxru2qh9dgIviB8BP7YtnHBrCYpE8QIhTDS7BkQr6+xs31vlwtWLEEgysDl1f2Ys0PnCqpP7FPq0/9jbbHlz1z/WpNxB63lYFGAZkatLnqPdYVJO1AvKeNioF4/wZ9BgH31zJtkp4iMocQsvdfKcfLA5dI+jSwQG39pSxyD2A04Xf2VKO1pA9ZkiRJkiRJ0l0yIJs9mQcYWPENA0DSKTSXaa/K2nfEU0X2/9OExPxWtkcwc+TwX6BNYREiCBrVsGcHlKzgYbQFY63ojOx9KwuCvoRPXcreJ0mSJEmSJDONDMhmT24C9gNOAJDU3/ZDNJFpb0C9fH1DbL9U9qD9nCiXXBx4wfZUSTvTsRz+rbY/kNQH+JftRkHhSODYIoUPYTb9847WVo+kNYA/AF+bgWqHDS0IigLmT4A1gOslXW17zAyaM0mSJJnLScGFJEm6QgZksycHAKdJGkd8RqOBvWku015PO/l620Nb9L0aOFLSBsDpwBXS/2fvzMPums73/7lNNaTmtN+iETWUIoKgMRfVSQ011xQzX1QprVZ/qhQtvqU1VpUQoWosqpJKjAkxJYSaiqjQVgxFTBHu3x/POt6dkzO8b96IJJ7Pdb3X2Wfttddae5+T6zpPnmfdt7YHbqG9HP4DirrKCTSRwy972o4H7i1Nx7UqrZR0MmECPb+k8cTer2OJ4LQHYRkA8E/bW7a4r85wbBnveUIoZJlyP38g9sy9UGTvB0pay3azjGGSJLMAqXyXzCgyIEuSpCvM9LL3Cl+rS2zvVt7PBfyLkEDvsgx7F+a9lfhRfl9d+wCmUZ6+wRxTSN/PLii81I4nygMnA9+3fWc514swsv48sf/sm7bHtRnvDGDP2rMqY1wELExk8Y6yfWM313wccLvtm6fl+pS9T5KZnwzIkhlFBmRJkkDnZe9nhQzZm8AqkuYre6q+SkeZ2ScWSXMW2foZMddctid34ZJhwHVFvbAP8CdgxXLuYuAE23+T1IMI2lrN3Y8IvKr8FPiT7XMkfQm4kcjYTTO2j+nO9UmSJEmSJEkyLcwq3kp/BWr/3bQzIccOgKQFJF1QPLFGl+xMU0+t4lF1q6QrJT0mabAqcoZ17Krw+3pY0tr1JyUtLWlY8eIaVjI3rdqXkXRXWevxjSYs635M0kXl+islzV/OjZN0jKQ7ge0lLSvpJkn3l3tdsfTbvqz5QUm3l7aVFT5cY8q4y5e5Hq7MfYSkY8vxrZJOlHQbcKiaeKNVrl21jD2GkJMfrVBjXIDIhFGCp7lKMDaq9BtZu04hqV8dc06iVPGHdY/JhPLkqkTJZc/KGKPqxhgg6VpJ10t6RtLBkg4v35W7FcbVSBooabvKc/55+d6MrT3XBp/VfpLuk3TfhAkTGnVJkiRJkiRJkpbMKgHZH4GdFL5ZfYDqj+6jgeG21yLkzE+RtAAdnlprADsSflI1Vid8qL4EfAGYIriosIDtdYH/BS5ocP5M4GLbfYDBlTmatf8GOKes9d8t7veLwHnl+tfL/DXesb2+7T8SkuuH2F4TOILYAwbhw/U126sBtX1WBwC/KVL7/Qj1w3YsbHsj2/9HhzfaWsC2RNnhh9gea7tv7Q/4OSGa8Rdgr9JtBeC/kq4mZOb/BqxZuW5s3fwHE5m2eqPoY4FdiUC9J+HNVhtjnQb3sQqxL21tQmr/LdurA3cBuze595fKd+cc4tlOhe3zbPez3a9nz55NhkmSJEmSJEmS5swSAVnxyepNZMfq9wptDhxVMjO3AvMCvQhPrd9LGgtcQQRfNe6xPb6Y/Y6hebnbZWX+24mMTH3pXH/g0nI8CFi/Tft6dGT3BjW9YXjO9ohyfEnleoDLAUq537qEKMUYQoHwc6XPCEKIYl86lBLvAn4i6UfA0vWS+k24vHK8GeFRNoZQZKx5ozXE9jW2VyTEPmrZwLmADYgAZy0iGB7Q6HpJSxDG0Wc0OL0zIVayFPBNYJCkVt/lW2y/YXsC8BpwfWkfS/PP/uryen+LPkmSJEmSJEnSLWaFPWQ1rgNOJTytFqu0C9jW9uPVzqX0rpmnVtWj6n2aP4d6xZN2CijNzrsTfTo7b035cA7gv1Vz6Q872wdIWoco8xyjkM2/tJTzfQsYImkf4AmmDMrnrRuqKmPf0But7Y3Yt5fSysWJrNxo208DSLoW+DKhaljP6sBywD9KRen8kv5hezlgb+DrZfy7SuZ0cSIr2ojOeJI1u6bV9yNJkiRJkiRJusWs9EPzAuA122MVJr41hgCHSDqkiEisbns0zT21usKOwC2S1i9zv1a33WwksBOR7dqF2BPVqn1Eab+ktDejl6T+tu8iskF31new/XrZE7W97SvKPrg+th+UtGzxzRol6dvA5yUtBDxt+7eSvkCUft4BfEbSYsBEYAvgpiZrauaNNhWSliPMpy1pDaI88WXgVWARST1LtmoToKE0oe2/AP9TGXNiCcYA/glsSmQBVyICydzElSRJS1L5LkmSJJkZmWUCMtvjiX1M9RwPnA48VIKScURg0cxTqyu8KmkksCAd+6CqfA+4QNKRRECwZ5v2Q4FLJR0KXNVi3keBPST9DniS2MfUiF2AcyT9lCjR/CPwILGPbnkiezistB1FiJS8R+xfO66YOh9H7Ml7BnisxZqaeaM1Yltg9zLX28CODn+F9yUdAQwrn9X9wO9bzNmMHxDlqIcR2cMBntn9G5IkSZJPDGmxkMwo8j+aZg9meh+yTxqSegM3EBmg04iSvleBSYQx9KuEP9pH5sE2LZQs5E/L21/YvqhF3xMIMY1Fqj5sCsPrfQjvsgnAXraf/ehWPf1IH7IkSZKkRgZkyYwiA7KZG3XSh2yWEPX4hHItYVT8haKiuBOw1Me8poYU6fifAesQSoY/k7RIi0uuL/3qGU2YbvcBriQC0CRJkiRJkiSZbcmAbCbD9jiitHGS7XMr7c/ankJxUNKxpQSw9v7hkmFD0u7Fb+xBSYNKWzN/tEa+ZXNKOqV4jj0kaf/6tUras6guPkCUdQ4HfkHI2X+9xT3eXZWyl3RNGec0ii8ZUW7ZNABV+MndJulPkp6QdLmkZyW9KeltSX8v4zb0T5O0tsJjbnR5/WJpHyDpaoW/25OSmgaFSh+yJEmSJEmSpJvMMnvIPmGsTAQ504SklQl/tvVsv1QyWNDhj3aRpL0If7St6fAte14d0v57E0Ima0n6FDBC0lDbz9TmsX0hcGEJCue1/Ysy//8Dluzsem1v0+AeziR8xlqxGrAS8ArwNHC+7Z+VPXrL2P6+pEsJ/7Q7SwA6pFzzGOFfNlnSZsCJxN43gL6EyuO7wOOSzrD9XIN1n0d4wdGvX7+s/U2SJEmSJEm6TAZkswCSziK8yCYBR3bikk2AK22/BGD7ldLeH/hOOR5ER0lgzbfsT3T4b20O9JG0XXm/ELA8If4x1RIbtE1zgCJpV8K8eqM2Xe+tZdokPUUoQUL4i32lHG8GfKmijlnzT1sIuKiIn5gQRakxzPZrZdy/A0sDUwVkSZIkSZIkSdJdMiCbOXmEjmwNtg9S+HjVq0ZMprGPmOiC31kj37IyxiG2h3RinPGEP1yNpQiT7i5TslVHAxvZfrdN9874izX0T5N0BmEYvU0p86yut7M+dUmSJEkyFSm0kCRJV8g9ZDMnw4F5JR1YaZu/Qb9xwBoAxe9rmdI+DNih+IvVRDegwx8NKv5oKr5lto8BXgI+T5T2HShp7tJnBUkLNFnvEGBzSYsUMY/NS1uXkLQ68DtgS9vNTJ67Ss0/rTZHzUh7IeD5cjxgOs2VJEmSJEmSJF0i/+d/JqQYKm8NnCbph4QE/JvAj+q6XkX4fY0B7gX+S0jFH1Ok5cdJmkSoGg4A/gMc08AfreZbtizhC/Yg8BDQG3igeIZNALaWNI5QQnypst5XSrnjWOAdwuOsVib5IWUv2sVEwDY/MLek8cTer2MJ0+kewBWS5gQ+R2T9Bto+uIwxP3AFsCphMv1L20e1eJzN/NNOJkoWDycC4M9U9s8lSTITklLiyaxCZsiSJOkK6UM2G6Ewwd7e9g6S5iCCtEm2+5fzdwHftz2qG3OMoy4gK+3HAhNtn9ri2v8F+pQSyZ2AbWzv2KTvAoSwxirAKnUB2Tq2b5E0D5ENPNF2OwGQj5T0IUuSj54MyJJZhQzIkiSB9CH7pDICWLccrww8DLxRSgk/RagLjpZ0ZEXO/ue1iyVNLK9zSDpb0iOSbpB0Y0XcA+AQSQ9IGitpxbIH6wDgMEljJG3QZH1bATXD6CuBTVVR26hi+03bdxIZt2r7W7ZvKceTCDXKVvL4AyWdI+kWSU9L2kjSBZIelTSw0m+cpMUl9S7nfl/uf6ik+ZqNnyRJkiRJkiTdIUsWZyNsvyBpcpF3Xxe4i5Cf7w+8RpQhbkyoJa5NCHdcJ2lD27dXhvoOUa64KvAZ4FHgAkmrAksQkvgvAQsT5tWfkXQudRkySaOAT1XG/SKwQFnrZEmvAYuVsZqxFLCjpPUrbe/aXqeUGH4b+E2bR7MIoTy5JVG+uR6wD3CvpL62x9T1Xx7Y2fa+pRRzW+CS+kEl7QfsB9CrV682S0iSJEmSJEmSqcmAbPajliVbF/g1EZCtSwRkI4n9W5sDo0v/HkQAUg3I1geusP0B8G9JtYzUWEkvEP5mzxdlxhOaLcT2OtX3kh4h/L+m6NbmfsYDl9dKFitjzQVcBvzW9tNtxri+7MsbC/zH9tjKenoD9QHZM5Ug7f7SZyrShyxJkiRJkiTpLhmQzX6MJAKwVYmSxeeAHwCvAxcQGbKTbP+uxRgNywgr1GThuyoJP55QcBxfAqqFCFPnaeE84Enbp3eib1UOv14qv9H662Xvs2QxSZIkSZIk+UjIgGz2YwQRgD1t+33glVLatzKwLxFsHC9psO2JkpYE3quTmb8T2EPSRUBPIoi7tM28bwALtulzHbAHUUq5HTDc06AqI+kXRDC3T1evTZJk1iWFEpIkSZLZkQzIZj/GAoszZQA1FuhRlBGHSloJuKvoaUwEdgWqAdlVwKZEhu0JYBRR8tiK64ErJW1FGErf0aDPH4BBkv5BZMZ2atDnQ4qi44LAPMUGYHMi03c0Ufr4QLmHM22f32Z9SZIkSTJDSEXQZFYi/7Pr4ycDsumApIm2e1TeDyCk4Q9uflWnx+4NrGt7qgxVOfco8DgwD3AfsLftKTJVtgfUXTPB9qr149XuwfYHko4oGbTFgHuAsZKWBl4Gbi6G0WfY3rhcPg64m8im/V7S0bavqhv/HWD7ylq2k/QUsJbt+8qY5xNm13MBv7N9UqPH0qCtIdV7tz2OkNFvdK53OXyprk9TGf8kSZIkSZIk6S4pez8TU/ZZ9Qa+26LbU7b7EnvGlgJ2aDNsu/Fq3FAMp+8Ajrf9b+BfRHDYF1gHOErSEqX/0cCLtlcAvgTc1mpwSZ8mTJurnmjbA58qweKawP4lgEySJEmSJEmS2ZLMkH3ESOoJnAvUdNG/b3uEpLWB0wnBiLeBPW0/XrJr3wLmJSTi5wdWKsHRRbZPazSP7fcl3UOoKtYyYYPKGAAH2x4J/LI6HvDb0rYxIVF/lu3fVTJf1TkmVd5+iikD+r2AFcvxj4Ht6yzGrrBdVWQ8HjiZUGkcLOltQkZ/EUnXEtL6k4gSxako93cTsd/ty8BkQjHyf4C5gWfL/Z0OnEEErHMBx9r+c7PnI2lj4Fg6MmX3A7s22uuWsvdJkiRJkiRJd8mAbPowXwlwaixKCFhAeGSdZvvO4g82hDBofgzYsPhxbQacSPhdQfiG9bH9SgkQjrC9RasFSJqXyFodWppeBL5q+x1JyxMS8f2Ao6rjlaDiNdtrFfPoEZKG2n6myTyfB/4CLAccWbzPFi6njy/rfQr4mu3/NBljdeDztm+QdERZT61kcRCxf+2fwGG2W6kwLkdk1fYD7iWk+/cm/Mb2tH2CpBMJ8ZC9yjrvkXRzi+cDsDohgvICIZKyHhH4TUHK3idJkiRJkiTdJQOy6cPbpYwP6NhDVt5uBnypki1asJTrLQRcVIIBE1mdGn9rE4hUWbYEg8sDV9p+qLTPDZwpqS8h3b5Ck+s3B/pI2q68X6iM1TAgs/1c6b8EcK2kK8v4SwEjbB8u6XDgVGC3+uslzQGcBgxoMPzaZawlCDPnOyTd3MJn7Jk6T7FhFb+x3pX727IEfhCZx15EsNXs+dxje3wZd0wZa6qALEmSJEmSJEm6SwZkHz1zAP1tv11tlHQGcIvtbUr53K2V0292YfynbPeV9DngVklb2r4OOAz4D7BaWcM7Ta4XoYo4pAtzUjJjjwAbEKqMbwHXlNNXEJmqRnyaKAW8tQSp/wNcJ2lLYm/bTbbfA16UNIIIbJsFZPWeYlW/sdp3W8C2th+vXijpWJo/n3ofsvx3kiRJknSaVK1LkqQrpKjHR89Q4EO1xZKRgchEPV+OB7S4/g0iiGmJ7X8R5Yg/roz/L9sfEJmqOZuMNwQ4sJQLImkFSQvQAElLSZqvHC9ClPI9XvZXXU/sQ4MoOfx7k3W+Zntx272LsuHdwJa27yPKFDdRsACxN+yxdvfehiHAISrRXymXhObPJ0mSJEmSJElmGPk//x893wPOkvQQ8bxvBw4gBC0uKuV9w1tc/xAwWdKDwMBmoh6Fa4FjJW0AnA1cJWl74BY6sm5TjEfscetNeHoJmABs3WT8lYD/k2Qi83RqrWQQ+BHhMXZ6GWPPFutsxlnAhYT/mYALKyWY08rxhLDHQ+X+xgFb0Pz5JEkyk5GeTsmsRmbIkiTpCmogHjftg0lHE2Vn7xNlY/vbHtX6qqnGGAAMtf1Cm3630iEGcSPwXdv/7cT4xwIT6/2lStngDbZXaXBZl6iurbtjzUwUH7KriWxSzYfs3HJuHuBMIkv2ATCVD1mD8bYjyhub+ZBd3MSHbKajX79+vu++2erjTpKZhgzIklmNDMiSJAGQdL/tfu36TbcMmaT+ROZhDdvvSlqcMCvuKgOIDEnLgKyK7W9OwzwzJZLmsj15Jp2r5kP2rqQewMOSrivB84c+ZEW4Y9E2c7f0IZM0P/B3SZcVQ+ckSZIkSZIkme2YnnvIPge8ZPtdANsvFeGHTSXVxB6Q9FVJV0uaU9JASQ9LGivpsJIx6Uf4Uo2RNF+5fnTpc0GRZp8CSeNKAIik3SU9JOlBSYOarHU1ScMlPSlp3wbjzSvpwjLnaElfadM+n6Q/lnkvJ7zFpqKs81eS7il/y5X2gZJ+LekW4FeSFij3em+ZZ6vSb+Vy3Zgy1/Kl71/K/T4saccGz6Rfydoh6VhJ50kaClxcPodTylwPSdpf0qpljurfKNuTap8vjX3ITiqf/Qe2X5J0dINxji79az5kVTENAwsoDLHnI3zI5mwwxhhJfSU9Jun8ct+DJW0maUT5XNcu99vsWfaWdIekB8rfuqV9Y0m3SrqyjD9YmtJQrfJ57ifpPkn3TZgwoVGXJEmSJEmSJGnJ9NxDNhQ4RtITwM3A5bZvI/ZHnSWpp+3a3qILgb7AkrUSQUkL2/6vpIPpKEWcl9jntKntJyRdDBxI7AmaCkkrE5ma9UpA0CxL04cQjFgAGC2pvh7mIICSqVkRGCpphRbtBwJv2e4jqQ/wQIvn9LrttSXtXu6j5i+2ArBZMXhu5p11APAb24MVJYJzAt8EXrD9rfIMFmoxd401gfVtv60GPmREyWjfRheq8z5kBxcj6BMajFHvQ1bjSmArIhM3P+FD9hTxXakfozdT+5B9F1if8CH7CbEX7mjShyxJkiRJkiSZSZluGTLbE4kf+vsRog6XSxpQFPgGAbuWH8T9gb8SUuZfkHSGpK8DrzcY9ouE19QT5f1FwIYtlrEJ4cX1UllTMy+vP9t+u/S7hfC/qrJ+WTO2HwOeJQKmZu0bApeU9ocI4YxmXFZ57V9pv8L2++V4c+AohQfWrXR4Z90F/ETSj4Cli5T+WGCzknnbwPZrLeaucV1Fhn9zYPcy1yhgMcKHrCG2n7PdhwiG9pD0WSKwr/mQrVHWeWqj69XhQ/aDBqerPmTLAD+Q9IUW9/GM7bFFKfFDHzLimfSu3F+jZzk38HuFZ9kVwJcq495je3wZd0xlrCRJkiRJkiSZrkxXlcUSUNxKeEyNBfYgMlwXErLo7xCBx2TgVUmrAV8jMk87EGVvVRqWirVARNlb26W2ed9s3lbr6WyGxE2Oqyp/Db2zgEcljQK+BQyRtI/t4ZLWJDJlJ0kaavs4YDIdAfe8dePUz5U+ZOlDliQzLSmQkCRJkszOTLcMmaQvltKvGn2JDBJF9OEF4KdEgEbZ3zRHUeL7f4SyHkzpk/UY0FtlrxXhF3Vbi2UMA3aQtFiZo1nJ4laK/WCLEaqA99advx3YpYyxApFRebyT7asQJZHN2LHyeleTPg29s0q26GnbvwWuA/pIWoIol7yEyErVnuM4ImMJsG2L9aQPWfqQJUmSJEmSJB8T0/N//nsAZ5SyxMnAP4jyxRqDgZ62az/UlwQuLCVs0GFoPBA4V9LbREnfnsAVRejhXuDcZguw/YikE4DbJL0PjKax6XIfoqxyHuIH/1tMqfiEPVEAACAASURBVAh5dlnD2HIvA4qyYLP2c8q9PESUuD1LZKwa6aB/qmS55gB2rrSvLOkF2yNp7p21I1H6+R7wb+A4YC3gFEkfAO8R+9kAfg78QdJPmFLJsJ7z6fAhm6fc17pN+rbzIRta9na9T+zdakr5nHYnyhNrnEXs1aqVUz4HdKYEsxXpQ5YkSZLMUNKqIZnVyEqEj5fp6kPWciLpTGC07T/MkAlbr2Wi7R7l+CLgiSJA8VHPOw7oV9vjVnfuWBr4o7UZr8sS+a2uKYIcR9jeotH5NuMuSgSg/YhSzPuBNW2/2qT/l4nA9cnaZ1HavwKMsv2WpAOBjW3v2GiMmYn0IUuSJElqZECWzGpkQPbRoE76kE1P2fuWiyGyUpfMiPm6yF1Etg4ASUeqQwL+56WtlbR8Ixn7Y2vqgQoJ9RMl3QYsCGwuaZRCgv1mSZ8tWaUDgMMUku4bSFpa0rCyjmGSepXxqhL5pygk3nuWc3NI+kcpB6VyT/VS9w0l34FfAhuUNRymBpL4LZ7j14C/2X6lBGF/A77erLPtu23/q0H7LbbfKm/vJsRCGqKQqL9N0p8kPSHpl5J2KZ/FWEnLln49JV1V7uNeSeuV9rUljSyfxUhJXyztAxTWDDeV53tyi/tOkiRJkiRJkmlmhogV2F6zfa8Zj6Q5if1OfyjvNycUBtcmSvKuk7Qh0JPm0vLNZOyrLGx7o3LtIsBlti1pH+CHtn8g6VwqGTJJ1wMX275I0l7AbwkZd5hSIv+/xP6104HNgAcbZeCYUup+fhpLvh8FHEGUjw4CDie+I/8hRC/2VYiGPNNg/CWJEsMa4wn1xh/V9buiC9nIvQnxjzENzm1aXlcjSilfIcQ/zi+fx6HAIcD3gd8Ap9m+swS2Q8o1jwEb2p4saTPgRDr22/Ul5O/fBR6XdIbt6v2hsAzYD6BXr16dvKUkSZIkSZIk6eCTqh43X/mR35sorftbad+8/I0u73sQAdodwKmSfgXcYPuOylhVGfvTmsx3eeV4KcIS4HPEvrVGwQ3E/rnvlONBhIlyjapE/gXAn4mAbC9C0bIRVan7uYEzJfUl9nutUO1Y9oX1lXQlHQIl8xBCGMs3WXMjBcqbbf9fk/W0RNKuRJC4ke2G+9FiSxj31jJtkp4i/PAgpO+/Uo43A76kDn/nBSV9mrifi0pQauK51BhWsxCQ9HdgaaYMONOHLEmSJEmSJOk2M6RkcSbk7WJ8vDQRaBxU2gWcZLtv+VvO9h+KD9qaxI/8kyQdUxmrmYx9lapgxBnAmbZXBfZnakn6ZjSUyC9Zm/9I2gRYh/B4a7eGw+iQfO/HlIImVWqS+LXnsYztoU36jgc+X3m/FKGs2WVKtupoQn3x3TbdOyN9PwfQv3IfS9p+gxD8uMVhTv5tpvwsUvo+SZIkSZIk+cj5RP/ItP2apO8Bf1YoJQ4Bjpc02PZESUsSyoVzAa/YvkTSRKZUbtyR2HvVSsa+ykLA8+V4j0r7G8QesxojgZ2I7NguhPpgM84n9ucNqmTO2q1hvO0PJO1Bh+R71XIAOiTxh9t+TyH1/7ztRoqEQ4ATS0kmRKbxxw36tUQhS/874Ou2X+zq9U0YChwMnFLm6Gt7DFN+FgOm01xJkiTJJ5wUSEiSpCt8ogMyANujJT0I7GR7kKSVgLtKedtEYFdgORpLy0NzGftmHEvI+D9PiFYsU9qvB66UtBWx9+l7wAWSjiQk+vdsMeZ1RKlis3LFeppJvj8ETC7PYyCx96o3IYmvso6tpxoNsP2KpOPp8HQ7zvYrzRZQhDK+C8wvaTyx9+tYImjqQTwjgH/a3rKT99WM7wFnKWwJ5iJ84w4gykAvknQ4MLybcyRJkiQJkCqLyaxP/qfCjGWGyd7PjqiFjH0Xxvgssffsy8CrwCQiUHiVTkrQS+pHiFZsMK3r6CySbiLWemd1bZIGE+WP7wH3APvbfq/JGLsQvmUQQe+Bth+snJ+TkNB/flok+D8OUvY+SZIkqZEBWTKrkwHZ9EEzk+x90piSdboWuN32F4oa5U60kHpvMMZRwFVMQ3ngNHIKsFuD9sHAisCqwHzAPi3GeIYQ6+hD7OM6r+78ocCj3V9qkiRJkiRJkszcZEDWDWz37k52DNgEmGT73MqYz9o+o9pJFV+z8v5hhXcZhHDGa0RJ3qByvpmH2fbl2gcl3V7auuI1BvAikdGr+ZWNkTTK9o0uEBmypcr4oyr9xhR1yzcqhtFTeI1JWgr4FrEvrta2av0YZdxxCo+3uyTdJ2kNSUMkPSXpgMr1U3nLlfZrJd0v6ZEiYV9rnyjphPKc7i5ZzKmQtF+Z974JEya0eWxJkiRJkiRJMjWf+D1kHzMrAw9M68WSVibUCNez/ZKkRcupM2nsYXYM8DXbz0tauPTdG3jN9lqSPgWMUHOvMWyPVfinNSynlDQ3kUE7tPRfp81t7M2UypCnAz+kIi5Sk+FvMNc44Dnb/SWdRux7W49QS3wEOFdNvOVs3w7sVfa+zQfcK+kq2y8DCwB32z667HXbF/hFg2eRsvdJkiRJkiRJt8gM2UyEpLNKVube9r2ByLBdWcvSVUQ0+gOXluNBwPrleAQwUNK+dCgrbk4YOI8BRgGLEQHMtHI2UYJ5R7uOkr5CBGQ/Ku+3AF60fX8X5ruuvI4FRtl+w/YE4J0SdFa95R4gyipr9/e9ImByNyHZX2ufBNxQju8nhE2SJEmSJEmSZLqTGbKPl0eAbWtvbB8kaXFC0KLKZKYMnmt+WaK591kVl/EPkLQOURI4RmEMXfMaGzJtt9CBpJ8BPQl/tXZ9+xBlid8oWSmI7NaWkr5J3OOCki6xvWuLoaq+Y/WeZHPR4S33u7r5NyYMo/vbfkvSrXQ81/fcoXaTHmRJkiRJl0hBhCRJukJmyD5ehgPzSqrK6M/foN84YA0ASWvQIZU/DNhB0mLlXK1kseZhBhUPM0nL2h5l+xjgJSIrVPMam7v0WUHSAl29kVLG+DVgZ9sftOnbC7ga2K2YbgNg+8e2l7Ldu6x/eJtgrDMMAfaS1KPMvaSkzxAeZK+WYGxFQjkySZIkSZIkSWYo+T//HyO2LWlr4DRJPyR8vt6kQxK+xtpA31JWeC/wNvBL2ztJOgF4XNK7wN8Ig+N6D7OFyjinSFqeyBoNAx4kvMd6A29Kegr4NxWvsZJJmmR7ZHl/B1H210PhH7Y3cCuxl+p94CVJzwJ/tH1c/T2X4PFu4HOEIffzRAZwQ+AKYNkyzoP1104LtoeqsbfcTcABCm+yx8uakiT5GEmp8GR2ITNkSZJ0hfQhmwVQGDhvb3sHSXMQQdkk2/3L+buA79se1Y05xtHAU03SscBE26e2uPZ/gT6lJHInYBvbOzbpuwCwOrAKsIrtg0v7/MA6tm+RNA8RMJ5o+6+NxpnZSB+yJOk+GZAlswsZkCVJAulDNrsxAli3HK8MPAy8IWmRooy4EjC6hbz7xPI6h6Szi8z7DZJulLRdZZ5DJD0gaaykFRXS+gcAhxWp+WbG01sBF5XjK4FNVdJR9dh+0/adwDt17W/ZvqUcTyIEOJr6sUkaKOkcSbdIelrSRpIukPSopIGVfpsXWfwHJF1RKV08pjyrhyWdV1uvpFsl/UrSPZKeaHHPSZIkSZIkSdJtMiCbBbD9AjC57L1aF7iLUETsD/Qjyg43pkPevS+wpqQN64b6DlGeuCph3Ny/7vxLttcA/kwEgddWzr3bQjlxSeC5stbJhC/aYpL21NT+YWe1u9+ijvhtIkuGpGvqxwGWABYhlCYPA64n/NFWBlaV1LcIpPwU2Kzc133A4WWaM22vZXsVwsi6KuE/l+21ge8DP2uxzvQhS5IkSZIkSbpF7iGbdahlydYFfk0EQesSwc9IppR3B+hBBGi3V8ZYH7iiiG78W9ItdXNcXV6vB75se7POlCwSe9Lqse0LgQs7d3tlIGku4DLgt7afLgNt06DfQOBvZR/eWOA/xa8MSY8QgedSwJcIbzWAeYhgFuArZd/e/MCihOLl9eVc7Tm0lLxPH7IkSZIkSZKku2RANuswkgjAViVKFp8DfgC8DlxAZMimknevo2EZYYWabHxXpd7HE4qN40tAtRDwSutLmnIe8KTt0zvRt53k/ftE0LZz9SJJ8xJ+af1sP1eCznkrXab1OSRJkiRJkiRJl8gfm7MOI4gA7Gnb7wOvlNK+lYF9iSDieEmDbU+UtCThp/ViZYw7gT0kXUT4hW1Mh4F0M94AFmzT5zpgDyL7tB0hV9/ljJGkXxDB3D5dvbYJdwNnSVrO9j+KcMhSQO2ZvFT2lG1H7H1LkuRjJIUQkiRJkk8iGZDNOowFFmfKAGos0KMoIzaTd68GZFcBmxIZtieIfWivtZn3euBKSVsRBtKN9pH9ARgk6R9EZmynBn0+pCg6LgjMU2T/NycyfUcDjwEPlHs40/b5bdbXFNsTJA0ALiviJwA/tf2EpN8Tz28coVqZJEmSJEmSJDOclL2fBZD0WUKw4svAq8Ak4ORyfITtLVpcXj9Wj5JBWwy4B1jP9r+n83pvKmu9s7o2SYMJEZL3ytz7236vyRi70OHHNhE40PaDlfNzEiIdz3fl/pvMtQSxZ227tp2bkLL3SZIkSY20cEhmV7KSoWuk7P1sQpFjvxa43fYXbK9JZKCaSsK34YaiUngHcPz0DsYKpwC7NWgfTJhKr0ooG7YqTXwG2Mh2H+B4inhGhUOBR7u/1FCx7E4wliRJkiRJkiTTSgZkMz+bECbQ59YabD9r+4xqJ0nHSjqi8v7h4iOGpN2LN9mDwHO2+wLfAHYr7cOKpD6Sti/XPijp9tI2p6RTJI2X9Lak5yoS9EfXL9j2MCLw2qBOqn5fF4gMWdOg0vZI26+W8U8GVq+M9SvgW0DbckZJ4ySdWLzI7pO0hqQhkp6SdEDp01vSw+V4gKSrJd0k6UlJJ7ebI0mSJEmSJEmmldxDNvOzMmGSPE1IWpnYm7We7ZckLVpOnQlcbPsiSXsBvwW2Bo4Bvmb7+SIaArA38JrtpcperBHA9rafaTH1vcAdjcoJJc1NZNAObbd+2ydIehdY0fY+5forgR8Cn277AILnbPeXdBowEFiPUFV8BDi3Qf++wOqEUMrjks6w/VyD+9gP2A+gV69enVxKkiRJkiRJknSQGbJZDElnlexVZ4UoNgGuLMIf2K7J0fenQyBkEOFRBhFsDZS0LzBnadsc2L1kuUYBixEeZ9PK2UQJZjOj6Q+R9BUiIPxReb8F8KLt+7sw33XldSwwyvYbticA71SCzirDbL9m+x3g78DSjQa1fZ7tfrb79ezZswvLSZIkSZIkSZIgM2QzP48A29be2D5I0uKEoEWVyUwZYNd8tQR0RrnFZfwDJK1DlASOkdS3jHGI7SHTdgsdSPoZIbm/fyf69iHKEr9h++XSvB6wpaRvEve4oKRLbO/aYqh2fmXN+kN6kSVJkiRJkiQfIflDc+ZnOHCipANtn1Pa5m/QbxywBYCkNYBlSvsw4BpJp9l+WdKiJUs2khAHGQTsQniUIWlZ26OAUZK+TRg+DwEOlDTc9nuSViDUDd/syo1I2gf4GrCp7Q/a9O0FXA3sZvuJWrvtHwM/Ln02JlQmWwVjSZIkSTJDSSW6JEm6QgZkMzm2Xby6TpP0Q2AC8CYdkvA1rqKjrPBewmcM249IOgG4TdL7wGhgAPA94AJJR5Yx9yzjnCJpeSIrNgx4EHgI6E34g6n037rZmiXdQYh69JA0Hti7ZNfOBZ6lwyvtatvHNRnmGKI08uzSd3JnZEOTJJm5STnw5JNABmRJknSF9CGbxSlCFc/aPr28H0KIWNQEMP6PyGb9usn1I22v22aOcUC/2j60SvvGhALkyBbXfgq4GFgTeBnY0fa4Jn0XA64E1gIG2j64tM8PXAEsS5QQXm/7qFZrntGkD1mSdI4MyJJPAhmQJUkC6UP2SWIksC6ApDmAxQllxhrrEkIdDWkXjLVh49rcLdgbeNX2coS59a9a9H0H+H/AEQ3OnWp7RUL9cD1J3+j6cpMkSZIkSZJk5iIDslmfEXQERSsDDwNvSFqkZKdWAkZLOlLSvcV37Oe1iyVNLK9zSDpb0iOSbpB0o6SqWfIhkh6QNFbSipK+Rsjpn1i8yZ6UNKrB+rYCLirHVwKblrLH2vx7VnzKRhBy/NtXB7D9lu1byvEkwgZgCg8zSddUfMpekfRSUaN8WtJGki6Q9KikgZVrzineZI/UnomkhSQ9LumL5f1lRXFyKiTtV66/b8KECY26JEmSJEmSJElLcg/ZLI7tFyRNLiIY6wJ3AUsSsvavEfu/NiZk6tcm9oZdJ2lD27dXhvoOsU9sVeAzwKPABZXzL9leQ9L/EkIa+5S9aRNtn9piiUsCz5W1Tpb0GrE3rCbDfyFwYfUCSQOAhundIlP/beA3dc9hm0qfgYQC487AlsD1hDrjPsC9kvraHgMcbfsVSXMCwyT1sf2QpIMJ6f/fAIvY/n2jtdg+DzgPomSxxTNIkiRJkiRJkoZkhmz2oJYlqwVkd1XejyR8xDYnBD0eIAQ36n3E1geusP2B7X8Dt9Sdv7q83k8Ebp1FDdqmKXiRNBdwGfBb20+36X69Y4PkWOA/tscWZcdH6Fj/DpIeIJ7LysCXAGz/rVx3FhHEJUmSJEmSJMlHQmbIZg9q+8hWJUoWnwN+ALxOZLk2Bk6y/bsWYzQKnKrUvLm66ss1npDOH18CqoWAV1pf0pTzgCdrAiZtaOk9JmkZYq/aWrZfrWTVanvxVgLeBhYt95AkyXQgxQ6SJEmSZEoyQzZ7MILwIHvF9vvFZ2xhomzxLsJHbC9JPQAkLSnpM3Vj3AlsW/aSfZYI4trxBvDpNn2uA/Yox9sBwz0N0p6SfkEEc9/v6rVNWJCwD3it3G9VJOQwomRzZ8IaYO7pNGeSJEmSJEmSTMEMy5BJOhr4LpFh+QDYvxgQd2WMAcBQ2y+06Xcrsc/pPkk3At+1/d9OjH8sDfZESeoN3GB7la6st93aujtWhbGEuuKldW09ilT9UEkr0eH/NRHYFXix0v8qYFMiw/YEMIrYg9aK64ErJW0F/IIwbJ4TmBs4w/a5wB+AS8resXmAFyRta/uqRgMWif0FgfkkHQTsQASVR5f1vFlEQf5ie9t2D6YZth+UNJooYXyaokRZTK/3Ada2/Yak24GfAj+b1rmSJEmSTxZp75B80slqiK4xQwIySf2JDM4att+VtDjx47yrDCAChpYBWRXb35yGeWZKJM1le3J9u+33iSCm2jag7v1vqBPCKO09yusHko6wPbH4gd1DBHXY7l3pfx8le2b7CaBPWds8xL6td0sm7mFJ1xXRkUeAx2z/tJQDLtrsHm33lvRp4C/Ed+QZ2+Ml7QJsaXun4kv2d0m9G3maVe+9nF+lybkpnlGFlSp9Dm+21iRJkiRJkiTpLjOqZPFzhErfuwC2Xyo/1DeVdE2tk6SvSrpa0pySBkp6uMisH1Yk2PsBg4u0+Xzl+tGlzwVF5n0KJI0rASCSdi+y7w9KGtRkratJGl5k3KeSO5c0r6QLy5yjJX2lTft8kv5Y5r0cmK/RpGWdv5J0T/lbrrQPlPRrSbcAv5K0QLnXe8s8W5V+K5frxpS5li99/1Lu92FJOzZ4Jv1K1g4iiHoJeJbInk2QdIo65PL3b/YB255U+3yBTzHld2sv4KTS74N6g+kGHA+cTPiSfTgFsEDZhzYfMInYI9foWfaW9Jik88t9D5a0maQR5XNdu/RbW9LI8hxHqkPq/nBJF5TjVcsY87dZc5IkSZIkSZJ0mRkVkA0FPi/pCYXX1UalfTiwkqSe5f2ehAR6X2BJ26vYXhW40PaVwH3ALrb7Ej/QBwI7lj5zAQc2W4CklYmyt01srwYc2qRrH+BbxP6rYyQtUXf+IIAy587ARZLmbdF+IPCW7T7ACcCaLZ7T67bXJry4qsIVKwCb2f5BuYfhttcCvgKcImkB4ADgN+XZ9COEKL4OvGB7tVJueVOLuSGe57NAT9v9CVPn18pcawH7KsQwpkLS0Qo/r7eBCcR3a0+FTD3A8Qofsysk7agOz7Da3zVlnNWBz9u+oW6KK4k9X/8C/gmcChzYYJyjS//liIxgH0JV8ruEkuQRwE9Kn8eADW2vDhwDnFjaTweWk7QN8X3c3/ZbDe45fciSJEmSJEmSbjFDAjLbE4lAZD/ix/rlkgYUcYdBwK7lh3t/4K/Enp4vSDpD0tdpnAn5IlHO9kR5fxGwYYtlbAJcWcvOFOGLRvzZ9tul3y2Ed1eV9cuasf0YEcCs0KJ9Q+CS0v4Q4QvWjMsqr/0r7VeUskQI+fqjFEbKtxLKgL2IfVY/kfQjYGnbbxMlh5uVzNsGttvtCQO4rlxbm2v3Mtcowj+sXi6fcm8n2F7Z9nyEafMLwPlEoLwUMML2GmWdW9juW/e3jaKc8TRCIbKetYn9h0sAy5Q+lzUY54TS/5k6qfthFRn83qXPQsAVkh4u865c7uUDojx2EHCb7RFN7vk82/1s9+vZs2ejLkmSJEmSJEnSkhmmsljU/261/TPgYKAmyHAhITCxMxF4TLb9KrAaEXAcRPywr6edTHuj/p1R96vvU/++2byt1tNZVUE3OX6zbp5tKwFIL9uP2r6UMEF+GxgiaZMSrK5JBCEnSTqmjDGZjs9+3ro11M91SGWuZWwPbXsTIbryCLAB8DLwFlArTb0CWKPJpZ8m9nvdqhD3+DJhYt2PyHDdZPs92y8SIhwNzaML9VL3VRn82t7J44FbSvbw20z5LJYnxE/qM6RJkiRJkiRJMt2YUaIeXwQ+sP1kaepLZJAoe8leIJTsvlr6Lw5Msn2VpKeIUjqYUmb9MaC3pOVs/wPYDbitxTKGAddIOs32y5IWbZIl20rSScAChHjFUUwpQHI7sAswXKHI1wt4vBPtt0hahSKC0YQdgV+W17ua9BkCHCLpENuWtLrt0ZK+ADxt+7fluI+kxwgp/EskTSSyPgDjiEDtr3QExs3mOlDScNvvlft63vab9R0lLQW8bPttSYsA6wG/Lmu8nniWwwklx783mqxk8BavjHkrHWqZmwKbSLoEmJ8I1jrjR9aKhYDny/GAyrwLEeWOGwJnStqulMwmSZIkSVtSYS5Jkq4wo2TvewBnlLLEycA/iPLFGoOJfUu1H+pLAheWEjYIOXWIwOzcsk+pP7Hn7Ioi9HAvcG6zBdh+RNIJwG2S3gdGU/kRXuEeQuGvF3B8CRh7V86fXdYwttzLgKIs2Kz9nHIvDwFjyvjN+JSkUUT2aucmfY4nApGHJIkIrrYggrhdJb0H/Bs4jtj3dYqkD4D36Nhj93PgD5J+QpQiNuN8orzvgTLXBGDrJn1XAv5PkonM2qm2x5ZzPwIGSTq9jLFnizmbcRaRTX24jH9hKQHtDicTe/0OJ4LFGqcBZ9t+QtLeRDB9e8nMJUmSJElLUvY+SbrOJ/k/MuSue/RO20QtfMgknQmMtv2HNmMMYDb1ISslev06oUA4UyKpL3AOIb//PnCC7cvr+pwB7Okitd9knAOIMtX3iZLB/WqBuqQ+wO/KHB8Aa9l+p9lYM5J+/fr5vvump7VckiRJMquSAVmSdJ3ZMSCTdL/tVltsgJnAh0zS/cS+pUZCDvUMIH3IpvIhm0nmegvY3faTRZnyfklDaoFw2Qe2cMsRgksdhtJI2hL4NfD1kgW9BNjNYeq8GJH1S5IkSZIkSZJZlo/dhwz4p+0NS6D2ifUhKxypj9GHTNKxks6TNBS4uHwOU/iQKXy56qXmR9l+orZHsGQwXwR6lnHnBE4Bflh5jkc3GOdo21VFzQXoEDfZHHjI9oNljpeBhRuMMUbSYpImKtQl75d0s8Jz7FZJT5dAr+ZXdodCjv8BSeuW9m3KNZL0OYVdw/80+C6k7H2SJEmSJEnSLWbUHrKhhKfXE8DNwOW2byP27Zwlqaft2t6iKXzIACQtbPu/kg6moxRxXmJP2aZlr8/FxB6phkIP6vAhW8/2S5IWbbLWPoRgxALAaEn1dQcf+o1JWhEYqhC7aNb+oQ+ZouTugRbP6XXba0vavdzHFqW95kP2vqQTCR+yvRR78u6RdDMdPmSDJc0DzAl8k/Ah+1Z5Bgu1mLvGmsD6RZxjP4oPWQl2RxAlo31bDaAwXp4HeKo0HUzI6f9LCjHKIk9/QpPrDwIOL2NsUnkGljSECPT+aPtk4rvSaIwFgFtt/0jhcfYLQjTmS4RFwnVE0PhV2+9IWp6wG+hn+xpJ2xKf6deBn9n+d/0cts8DzoMoWWz1TJIkSZIkSZKkEelDNjXpQzYNPmQ1JH2OeA572v5AUb64PXBGJ+YGwPZZtpclxEB+WprnIp7xLuV1G0WGtRmT6DDCHkv4ib3HlD5kcwO/VwixXEEEazUOIcRk3rV9GUmSJEmSJEnyETCjMmSUgOJWwmNqLLAHkeG6ELgeeIfiQwa8Kmk14GtElmIHYK+6IT/pPmSP1137qEKh8VuED9k+todLWpPIlJ0kaajt4+i6D9mQzixe0oKEQuVPbd9dmlcHlgP+UbJj80v6h+3lOjHkHwmhEIDxRFD1UpnrRsLPbFiTa99zh2LNhz5kJUisfe8PA/5DeN7NQXwHayxZrvuspDkcZtFJkiRJkiRJMl1JH7KpSR+yKefqrA/ZPIT588W2r6i12/4L8D+VfhNbBWOSlq98T74F1I6HAD+UND+R/dqIkKfvDgsB40uQtgdR5kkJ2C4kVEF3J8onT206SpIkSZJUmB3V4pIk+ehIH7KpSR+yDrriQ7YDUZ65mMKeAOIZjGkxfiMOlrRZWe+rRCYV269K+jXxORu4sQR73eFs4CpJ2xPlqbVA8yfAHbbvKOWa90r6i+1Huzlfksw0pCx3knx0ZECWJElX6JYPmaTPElmKLxM/nicBJxdRhI0JAY4tWgxRG6dTPmSzM+qED1nJLjX18GrQHsEVVAAAIABJREFU/ye2T2zTZ2HCp+3sTozXcn5JNxHfhTurn7ukwYRC5ntEQLp/2c/VaIxdiL1jED5kB9aUFcv5OYH7iExd2+/WjCJ9yJJZjQzIkuSjIwOyJEmg8z5k0yzqUTIm1wK32/6C7TWBnYClujjO/UQZ3yXTupakKT/pRJ+Fgf+dTvOdQpSO1jMYWBFYlZD936fFGM8AG9nuQ2QDz6s7fyiQmaokSZIkSZJktqA7KoubEPu8PiwTtP2s7anU9BQeUCMVvlkjy56ymhT9+8CCRFnY8qV9Kr8wSUtLGlbah0nqpfDJelrBwpI+kLRh6X+HipdXZR1zSjpV4RX2kKRDSntDPzOFX9eJku5S+E2tIWmIpKckHVD69CjreaBcv1WD+5/KV620L6fwu3oQeAVYqDPjlWuPVIc/2M8bnP8lMJ/Cl2twaTu8rOFhSd8vXX8JLFv6ndJufjXxISuf/zBin98U2L7RBSJDtpSa+5CNtP1qufRuKgG+pKWIfWXnV9pGNRhn1e58dpLWKs91XoWX2yOK/X/1zzh9yJIkSZIkSZJu0Z09ZCvT2lOrymPAhrYnK/YHnUiISUzlnaXmfmFnEoIRF0naC/it7a0V3mZfApYB7gc2KAHCUkXso8p+pd/qZS2Lqr2f2XO2+0s6rfRbj1AmfITYs/YOsI3t1xViJHdLuq6i8AcNfNVK+2Dgl6XEc14iQJ7UbjxJmxPy82sTSojXSdrQ9u21PraPknRwzTNMoba4J7BOuWaUpNsI0ZJVKv3majW/7bE08f5qh6S5iQzaobbvoIkPWYW9CeGRGqcT5tI1YRdsr9NkLpj2z+5eSdcR3mXzAZfYfrh+jvQhS5IkSZIkSbrLdBP1kHQW4Q81yfZadacXAi5SZMBM+D9BKAkeXTIfV9t+UlIzv7D+wHfK8SDg5HJ8ByEmsQxwErAvobZ4b4NlbgacW6T1sf2KQl7/GU/pZ3YQHQHZdeV1LNDD9hvAG5LeKYHVm8CJiszcB4QgyWcJYY0aH/qqEYIhQyV9mgjSrilreac8x7k7Md7m5W90ed+DCNBupznrA9fUFBIlXQ1sULm/GurE/NPK2USJ6x3tOkr6ChGQrV/ebwG8aPt+xf7EztCdz+444jv0DvC9Ts6XJEmSJEmSJF2iOwHZI1Qk020fVLIMjZQNjgdusb2NQrHw1nLNparzzqLrfmF3EJm2JYBjgCMJufpGwUmjsdv5mb1bXj+oHNfez0VI2vcE1izS8OOo8/YqCoH1vmrfpzFtxytrPsn279qsvf6aztCZ+buMpJ+VcffvRN8+RFniN2y/XJrXA7aU9M2yngUlXWJ71xZDdeezW5QIdOcubVNJ/SfJrEyKDiRJkiTJzEF39pANB+aVdGClbf4mfRcCni/HA2qNqnhnEdmMPoRf2A6SFit9aiWLIwnREIgf0neW41HAuoTP2TuEtPz+RKBWz1DggFKWVxv7Qz+z0qedn1mje3ux/KD/CrB0fYcSqM5h+yrg/wFr2H4dGC9p69LnUwqPrbbjEZ5ce0nqUa5dUtJnGvR7r2TcIALUrSXNL2kBYBviGVW93Tp1P12lBNpfA3ZuZ7AsqRdwNbBbJWuJ7R/bXsp2b+J7MLxNMNYZWt3recRnNRj4VTfnSZIkSZIkSZKGtM2QqbW0/a8If7EfEh5Vb9IhWV7lZKJk8XAikKsxlXdWKSNs5Bf2PeACSUeWufYEKF5fzxECEBBBxs5EmVo95wMrEB5e7wG/t32mpE77mTVgMHC9pPuIYPCxBn2a+artBvxO0nGEJPz2xL6qsa3Gsz1U0krAXZJ6EsqEuwIv1nU9r9zrWCIrOZAOH7TzbY8GkDRC0sPEfq0DW82v1tL221BMtCWNB/a2PYR4ns+W9UL40K1QLq2Xtj8GWAy4uXxGT3ZGLnQaafjZSdodmFwyuHMCIyVtYnt4i7GSZKYlJe6TZMaSGegkSbpCSx8yxa/nkcBFNTVFSUsDW9o+Q13wGks6h7ruNda2fykTvaEmKtKd8SRtSmRC968LyL5JhwDHpcResXOajLEu8Ggp5fwGcGxVnKME7v2ABWeV71b6kCUzMxmQJcmMJQOyJElg+vmQTRdpe0n3KOTIH9InWNq+XL9sZ8Yr134s0vat6Iq0fYsxuiRt34zOfHalX8PnKOlaSfcrZO33q7RPlHRC+czuVmSJkyRJkiRJkmS6065kMaXtP4HS9q2QtCoRLPWUNKY0v1vLcKkibd9mnD1Ln57EXsQxwAhC5XAKafs2fLO8LlGuf5IoCT0TOLfNc9yrlMjOR/jgXVVERBYA7rZ9tKSTCeXOXzS4h/2I7xu9evXq5HKTJEmSJEmSpIMuiXpIOqtkDRpJyi9E7MN6mNhztnJpvwv4iaQfAUvbfpvIvDWTtr+0HA+iSJ7TIW2/ISFtvz6wFp2Utge+yNTS9htWrqnKo4+y/YbtCUBNHr0mBf8QcDMd8uhVPpS2l/R14HU1kLa3/VYnx6tK2z8ArEgEFq34UNre9kRCHGODBv06M39DHD5k+wB32O5b/qpeYJ2Strd9IXAY8DqwQgkW/0qRtu/MWgrfKtceRfiF9bH9Jzo+u1bP8XsKU+67gc9X2icBN5Tj+4HeTe7hPNv9bPfr2bNnF5acJEmSJEmSJEG7gOwRYI3aG9sHAZsSWY16atL2qwDfpsiH274U2BJ4m5C234Rpk7bfgMhy3AgszMcnbd8X+A8NpO2B1QjxjIOILFKzeduOR4e0fS3oWc72H9rcx7RI2zebv8uoQ9r+8E70rUnbb9VA2n4c8EdgE0mXtBmq3WfX8Dkq9j9uBvS3vRoRsNWewXuVbOH7TEe/viRJkiRJkiSp0u6H5nAik3JgRaBhmqXty3Ef4G/ANZJOs/2ypEVLJqsmbT+IqaXtLy7jvFPK2/YHGgk+1KTtb62VLFKRti8ljh+VtP0k21dJegoYWEoCx0va2va1in1rc3ZmPELa/nhJg21PlLQkESjUKym+J2lu2+8RAerAsrdMhOrhbsxYaftN3Q1pe4oCpToEY7orbd/wORLP4FXbb0lakVCOTJLZkhQYSJIkSZKZl5YBmW0rfLJOU0rbT09p+7bjeUppewh5+FbS9g/Y3kXSQNpL2/+qE/fTEEl3EGV/PdRa2v5q2/+fvTuP+3yu9z/+eJqRNWRps01CThiDoQgJabecQifJUlkO2pWoThHZSiehpKwjDtKZUJQ9MZJlhmTXMYdfhmNpQrbX74/X+zPXZ77zXa/tew3P++123a7v9fl+Pu/Pcn3T9Z73+/18HdKimSra/oSy7wvdJNAMRpvn+Buy4z4duJOBz5bZy45TFs1Gl/8RxMx60Tb2fkRPnB2xGcCCwAvkuq7vR8RLkiYDn4iIz/TY5lLAxyLihA77TaDEwPd6rjKdbnK1/q22/ZvA7Ig4ppdrbndtQ2lnLCojhKcD6wOPATtFxAMt9l0GOI9cK3hqROxXti8KnAu8mZxO+KuIOHAYru1i8rPzxGCOd+y9jWXukJmNLnfIzAyGL/Z+JD1T1vSsCbybTMv7D4CIuLHXzlixFPDvvRwwhHONScpCxmP1XJ8kpwmuSga/HNlm32eBrwNfavLeMRGxBrAu8A5lLbMhiYj3D7YzZmZmZmY2WP3skM1R1kXtCeyntLmkC6Hn+maN9bZUvle1wXZqPHfDuRaXdIoGaph9uMUlH1DOfYMa6qCVdiYp61dNl3SBpNd02L6+Mr3yOjIQZB7lOq8ux/1Z0o+q6ZHKulmHKEsBbFTau0pZY+sSSW8o+32mHDtd0tll2zvL87qlPONXS9pd0pO17bOU6+Kq2l/fkPR7YAdlXbXflHNdo1yPVV3z7rU2bim/n2XL2+cBW6rMI2zymfhHRPye7Ji9r9bGH8gptO+JiOfI5MSWNc+UteFOlHSFsp7dO5V16O5QTu+s9ntA0rKSJpT3fqKsT3apMha/Wdt7Kuuf3Thr1qxWl2BmZmZm1tKY6JABRMR95PW8tuGtqr7ZuuTao8PL9qq+2SRgMjCTjD6/t4y8HQD8K1kfbB0yUe/oqnPSwteBJyNi7YiYyNxr4eqeiogNyVpX32/y/unAV0obMygjf222nwJ8JiI2anNtkCmTXwTWJqfs/WvZvhhwW4mfnwYcB3wkItYHfgYcVvY7kKzPNpF8fpAjUPuW57gpmYZ5P7VYe+AcMkWz8mxEbBIRZ5Nr2PYv5/oSGXsPZLR9Ld1wEvA/wOfKey8AT5JryTr5db2d8nWJcorqh4DLOhz/GrLUwueBXzFQlmFtSZOa7L8acHwZvX2CrKc3D8fem5mZmdlQjbU472ajJUuSYSGrkXH2C5bt1wEHS1qBDJC4u8lgyybAzyPiReBvyiLJGwDTW5x/KzLlEZgTZd/Mz2vfj53rBqQlgaUiokpxPI0ME+l2+xlAqyl4N5SOK5J+Xu7vPHIt1flln7cAawG/Lc9jHPBweW86MEXSL4Fflm3XAt+TNIV8jjNbDFrVnVOuYXFg43If1XsLtTmuWcODWsSoDGf5OVk8/L4Ou/+qBNTMAP4WWUsNSbeTNcZuadj//oiotrWsQ2ZmZmZmNlRjpkOmjMR/kUwR/JfaW1V9s+2VgRdXQtY3K1P0PkDWN/sUWZx5rmZ7vQx6q4/W+Howuj1ns3NVPz9bOp1Ve7e3GG37AFkQexvg65LWjIgjJF1EruG7XtJWZMhKffS0sUbZP8r3BYAnyuhXN2aSBZhnlg7VksD/tT+kpZOAuyOi2Qhlo061ylrtD/mZbDpl0Wx+4YABMzOzsWtMTFmUtBwZm/7DWkHeSsf6ZsBUsr5ZY72tq4GdJI0r59iMgUj4Zi4F9qud4zUt9tup9v26+hsR8STwuKRNy6ZdgKvabH8CeFLSJmX7zm2ub0NJbyprx3ZioE5b3Z3AcpI2KvewoHK93QLAihFxBfBlMgBlcUlvjogZEXEkcCMZaf9X4K2SFiojeFs2u5iIeAq4X9IO5VyStE6b658K7FpefwS4vMnvuyNJ3yY/F5/r9VgzMzMzs7GknyNki5SQhir2/gzge03267W+Wb3e1peBjYBbydGkL0fE/ysjbc18Gzi+HP8i8C2ygHGjhcro3AJkPbRGuwI/Uka030epp9Zm++5k/bWnyULGrVxHBmOsTXY2L2jcISKek/QR4AelMzWeXOd2F3Bm2Sbg2Ih4QtKhyuLQLwJ/Jtdr/VPSf5FTHO8m68S1sjNwoqSvkb/Ls8nn3cxPgTMk3UOOjH20xX7AnBIDSwCvUtbD2xp4CjiYXFt4U5kq+cOIOLldW2avNI66N+sfj0qbWS+GrQ6ZpNeR66neDjwOPAccFREXSNoc+FJEfHBYTvYK1PgMJc2OiMV7OP6giDi8wz5d1XHr5vySfkN+Fn5f/72XtWqTySLZNwB7RcTzLdrYmYEi5LOBfSLi1tr748hRvf8d6mdL0hvJ9WgfGczxrkNmY407ZGb94w6ZmcEo1yEr0eW/BK6OiFVK4t5HaRNHbqPuoC726bmOWxtHk9MyG00hp0WuTa7N+lSbNu4H3llSIQ8l143VfRa4Y+iXChHx0GA7Y2ZmZmZmgzVca8i2AJ6LiB9VGyLirxFxXOOO6q2uGJI+UX6+VdIZZdvKki4r2y+TtFJZJ3ZfWce0lKSXJG1W9r9GDfXCyv7HaKDm2P5l+5bl2mYo61UtVLY/IOlwSdcpa0+tp6zxda+kvcs+i5fruakcv22T+x+nrI1V1Ub7fNm+qqTflfu8SVnfa057ZJT9T5o9fEkHSPpjuY9vNXn/CMoU0TJChaQvlGu4TVK1FquxjlvH+2lxPQcD3yVHTDct7R0MEBEXR0GOkG2mWq2y8nVB2fcPtaTL66l18JXpmh8ATq7O2aSdg3v43U1QTlVF0m6SfqGsr3a3pKNa3KfrkJmZmZnZkAzXGrI1yQK93ajqir2gTPQ7nKzzVNUVmyLpVcA4SWuS64XeERGPSlq6tPFD4PSIOE3SHuRUs+0k3QW8FXgTGVe+qXKt1woRcU/DdexZ9lu3XMvSkhYGTgW2jIi7JJ0O7MNArbEHI2IjSceW/d5BJhDeToaSPAtsHxFPSVqWTC2c2hBcMQlYPiLWgjnTBCFHjo4oUzwXJjvLz3VqT9LWZN2sDcm1YVMlbRYRV1f7RMSBkvar0hAlrU+uW3tbOWaasiTAgcBatf3Gd3E/84iIw4DD1GaqqqQFyRG0z0bENe3aKz5JrgusfJ9cI/jq+jnbHN/pd9doErAumbh4p6TjIuLBhvs8iTJqN3ny5OGZ+2tmZmZmrygjkrIo6fgy0vPHJm8vSdatuo2BAr2QgRUHSfoKsHJEPEOOvJ0XEY8CREQVkb4RcFZ5fQZZjwvgGjJJcTPgO2X7BkCz69gK+FEpUFy1/RayBtVdZZ/TSluVqeX7DGBaRPw9ImYBz5aOlYDDJU0HfgcsD7yu4bz3AatIOk7Se4GnJL2a7KRVI0PPRsTTXba3dfm6mewUr0F20NrZBLggIv4REbPJ4JJNm+zXzfkH6wRyimvHzpgydOSTlPVkkj4IPBIRf+rhfJ1+d40ui4gnI+JZMuxk5R7OZWZmZmbWleEaIbudHOUCICL2LSMqzVIOeqkr1mtdsGvIkbY3At8ADgA2JxMJGzVru1Pdsk71rHYGlgPWj4jnlSmBc9XwiojHldHw7wH2BXakdXx7x/bKNX8nIn7c4dobj+lGN+fvmaT/KO3u1cW+E8lpie+LiMfK5ncA20h6f7meJSSdGREfb9PUUGuRjZmafWbdcKiAmZnZ/GG4RsguBxaWtE9t26It9u2lrthlwI6Slin7VFMW/8BAZPrODNTjmgZsDLxURjZuIf/obzYKcymwd5mWV7X9F2CCBtab7QJc1fbO5723R0rn5V00GVUpHdUFIuJ84OvAeqWe10xltDvK+l+LdtMeGZO/h6TFy7HLS3ptk/2eL9MEITuo20laVNJiwPbkM2qs49bN+XtSOtrvAf4tIl7qsO9K5OjdLrVRSyLiqxGxQkRMID8Hl3fojJmZmZmZjUnD8q/+ERGlM3GspC8Ds4B/MBBZDrCqpNuBhYApkr7BwDQyaF1X7DDgKkkvAn+XtCPwGbJu1wHlXLuX6/inpAfJYIrJZCfjU8D/NLnsk4HVgenlnD8BlgUuJqdUjienOv6ojOS9sYtHMQX4laQbyc7gX5rs82vg1ZKeLT9/tXzfBfixpEPISPgdumkvIi6V9C/AdcqaXLOBjwOPNOx6UrnXmyJiZ0mnMlAk++SIuBlAc9dxO7KL+5lDtah7sjO3Bll8+mngMeBJco3f/bXr/UVEHFJrox51/zoyifGEsu8L5Lq3YYm6NzMzGwkuO2E2toz1WSPDVoes7Umkjciiz5uXTtOywKsi4qEe27mSDIloW/Cp2/2aHPdNYHZEHNOwfQJwYRXEMRSDvbZy7PhqzdtIG8y5JG1Jjozu1VB77P0MBHKcRa4dO7FFGxsDd5Spne8DvhkRb6u9/wWyjtkSY6lD5jpkZmZWcYfMbGzpV4dMo1mHrAtvAB6NiH8CRMSjEfGQMmL+gmonSe9Wxo3PEw0v6SPkH+JTlJHmi6hFRH2dMvJ82fJ6ngj9JtaRdLky7vzTTdpbWNIp5Zw3l6l87bYvIunsct5zyBGfeZTrPFIZ/X9DNW2yPIfvSboCOFLSYuVe/1jOs23Zb56yAWXfi8r93iZppybPZHLpJCLpm5JOknQpcHr5PRytgUj9tmu+IuIyctpj4/bGqPuW9el6ibpvR0MoUyBpg3K/C5dneLukIXfGzczMzMwajVZQwaXAN5Sx9L8DzomIq8i1Z8dLWq4k3u0OnEKTaPiIeELSfpTRJXWOqJ+LWkfoN5pITrtbDLhZUuM/c+0LEBFrS1oDuFTS6m227wM8HRETlQEV7coDPBURG0r6RLmPagRodWCriHhR0uHkmqk9lOmAN0j6HU3KBgDvBx6KiA+UZ7Bkm3NX1gc2iYhnJO0JPBkRG5TO7rWSLo2I+0t7a5Mpl3WvIpMk56Fa1H3D9t0btwHXklMbW0bdN2n/ArKUAeQU013IKavvpbcyBX+UNBX4NtmBPjMibmtyvj3J8gmstNJKzS7JzMzMzKytURkhK9Hq65N/vM4CzpG0WxkxOYNcO7YUGWf/a5pEwzdptlNEfaNWEfqN/jsinin7XUHW96rbpFwzEfEX4K9kh6nV9s2AM8v26cD0Ntf489r3jWrbz42IF8vrrYEDJd1CJlQuDKxE87IBM4CtysjbphHxZJtzV6aWY6tzfaKcaxqwDLVI/YiYERGT6l/Av7dpu2nUfUSc0qSd8+gx6j4itq8d/xDw9oi4hMGVKTgEeDc5Ktu0MHREnBQRkyNi8nLLLdfmts3MzMzMmhu1KO/SobgSuFLSDGBXctTiFOBX5GjFuWXdUrNo+D0amuw2ur2+fy8R+q1+bnXedtfT7UK9aPH6Hw3n+XBE3Nlw7B1qKBsQEZcri0C/H/hOGd06hAzHqDrjjTH2jefav3RqhkRjM+q+Xaz/0sDiwIJlW/25mJmZmZkNi1HpkEl6CxlFf3fZNIkcQaKsJXsI+Bo5IlFFwz8XEedLupfsuMHcsexzIuoj4h46R9RfBlwg6diIeEzS0i1GybaV9B1yyuLmwIHkNLzK1eQf8peXKYkrAXd2sf2Ksg5pYptr3Ak4ony/rsU+lwD7S9q/pFuuGxE3q1Y2oLyeKOkvwP9FxJmSZjNQZuABcsTy19Tqx7U41z6SLi8dltXJdMOeOicaiLrfcihR95RESkmbk1NXhxp13y7W/ySyLMGbyLTJ/YZ4LjMze4UY64luZja2jNYI2eLAcWWa2AvAPZS1N8UUYLmI+HP5eXngFEnVKE4VDX8qGUP/DDmlb3caIupbXUBE3K65I/RvplYHreYG4CKyQ3Vo6TBOqL1/QrmGGeVedivJka22n1juZToZHX8DrS1URrkWAP6txT6HkmuppksS2bn6IE3KBgAbAEdLeomM0q/qxH0L+Kmkg8ipiK2cDEwAbirnmgVs12pnSdcwEHU/E/hkGV37EdkBbxp13+Ab5NTIOVH33aTTDFLTsgJlDd8LkcXKxwF/kLRFRFw+QtdhNg+ntJnNv9whM7NejErsfceLkH4I3BwRPx2GtlYAjifrXS0AXAgcEBHPDbXtkVSmy02u1rjVtk8ANo6Is4bxXLsBl0YpOyDpZOB7tQ7xUNvfm5xq+iJZF23Pqu0yHfHHwBLk1MENIot4t2tvKrBKLeRlEtnJW5js/P57RLTr6HZzzUN6Bo69t+HmDpnZ/MsdMjODsRd735KkP5HT+M4chrZETnf7ZUSsRoZqLA4cNtS2+2gC8LFeDyojO63sRq3QdUR8arg6Y8VZEbF2Cdc4iqxBRxnJPBPYOyLWJKeEPt+uIUn/Snbq6o4CvlXa/wYtQjd6MQLPwMzMzMyso753yCJi/YjYLEqNsiHaAng2Ik4pbb8IfB7YQ9Kikr4g6WeQke3K2lyLKmuOLVe2LyDpHknLSnqzpOuVdbgOKeuwKPsdoIH6XN8q2yZIukPST5S1qy6VNE/dMUk7lHPfKunqsnlzco3bTeVr47L9CGBTZX2xz0varYwoVm1dWNZUIWl2uc5pwEaSvlGu8TZlfTGpeT23KyVNLm38m7Im122SjqydZ7akwyTdKekf5f1bytdc0x4jop6KuRgDASVbA9Mj4lZl1P1lwJ9q7Rzf8JwWB75Axs/PdQpyhA1y+uU6tTZukfSeWhvflHRa+V08IOlfJR1V7vE3yih+Gp5Bda+3lt//62hC0p7KGmc3zpo1q9kuZmZmZmZt9b1DNszWBOaKRS+dg/8BViXXXq0qaXsy3XGviHiaHLXZuRyyFXBrmTr4n2Rtrw3IGHUAJG1Nxr9vSAaUrC+pitxfDTi+jAA9QfPQjG8A74mIdYBtyrZHgHdHxHrkerAflO0HAteUOPdjO9z/YsBtEfG2iPg98MOI2KBM9VsE+GBEnAfcCOxc2qwi7pH0RjLAYotyXxtI2q7W9vUR8RZySujZtZj6tzVeiKR9lYEsRwGfKZtXB0LSJcD+5EhaPe5+34ZmDgW+CzzdsP1z5Nq4B8mRvkkN7TSmQr6ZTJ/clvxdXxERawPPlO3NnuP15fdzNTBPgXBw7L2ZmZmZDd3LrUPWKtpeQJSEv93IemFXRcS15f2fAZ8or/cgO2uQwSHnltf1NVxbl6+byULPazBQn+v+iLilvP4TOeWw0bXAqZI+TRZwhoxX/4kyFORccg1cr14Ezq/9/C5J00qbW5Ad1nY2AK6MiFml/MAUBmq7PUeux4PW9zVHRBwfEW8m64h9rWweT9Zr27l8317Sls2OL+vEVo2IC5q8vQ/w+YhYkRwB7bT28NcR8TxZj2wc8JuyfUaL++jpXs3MzMzMBmvU6pCNkttpGJGStASwInBv2bQauSapvobqQUl/k7QF8DYGRstaEfCdiPhxw7kmMHe9qxfJkam5RMTekt5Gjs7cUjof+wN/A9YhO8qtgi7qNcRg7jpiz5ZpmkhamEyEnFzu75vMW3Os2X218nwMJMC8SPefnbOBE8vrmWRH+NFyjRcD65FTFxttRI48PlDO9VpJV0bE5mQNu8+W/c4l0yDb+SdARLwkqX4fVT2yRoO9V7Nh41AAMzOzV4aX2wjZZcCiytjyKtjiu8CpEfG0pCXJaYibAcuU9VSVk8npbP9VdWqA6xno4H20tu8l5Lq0xct5lpf02m4vUtKbI2JaRHwDeJTsMC4JPFxG8XZhYOSsXnsNMuZ+UlnrtiI5bbKZqvP1aLnO+r02tlmZBryzrJ8bR0bvt6vt1ur+Vqv9+AGgqj93CVkfbVFlwMc7gaZBGhFxYkS8MSImkKNpd5XOGOT00XeW11vU2jczMzMzm6/Mt//yLymAMyNil/LzeOBh4FZgB0lfJzucFwMHlcOOBU6IiLvfOmyvAAAgAElEQVQkfZIs1nx1RDwCTCWnKp5SO83ngDMlfZGsTfakpCuBL5FTGKu6WrPJdMe1urz8o0unRWQn8lZyNOt8STuQo0ZV8eXpwAuSbiXrsH0fuJ+cbncbOWVyHhHxhKSflP0eIOu0VU5l7npu1TEPS/oqcEW5tosj4r+7vKe6/SRtRSYoPk6OaEGuqZtVtgVwTkS0zPaW9Bvg7TSsCwQeBH5TPgN/Bz40iGts9BayCLSz623EOMre7JXBI9xm1osxUYdsMJSJh3eTNbqekfQ+4DvAzIj44CDamwwcGxGb1rYtCjwTESHpo+SI0ZLAlyLixobjdyOnB+436JsaaGt2RCw+yGPH1Ub4RpSk8WWtWbf7v5+cmvl+cmrofzYLBKntvyWwKBm+8sHa9vcDvy4/ngVcHREnNmli1LgOmXXDHTKzVwZ3yMwM5qM6ZEP0awZS8v4N+Hn1hqTFJP1MGft+s6Rty/YJkq5RLV5e0oFkiMPiks6T9BdJU4D1yTVe04F/B75Ymv+4pD+U6Pd5pgxKWlnSZcpI/MskrdRh+5skXVeu9dBmN1qu+y8lwn16uc5Fy3sPKCPuf0+ODr5ZGen+p3Kva5T95onbl7SmpBuUcfHTJa1WznVb7dxfKmvQqnj4wyVdBXxW0nKSzi/X/kdJ72jz+9oWOD3S9cBSkt7QaueIuIwcAWvcfnFpI4AbgBVataERjL03MzMzMxuq+b1DdjbwUWWAxURyDVTlYODyEln/LnKa4GI0iZePiCPINWKrkNMU31peR0SsExETS620e0rbi0XExmQn7WdNruuHZMdjIplU+IMO2/8TOLFc6/9rc79vAU4qxz9Vzl95NiI2iYizgZOA/SNifXJ65Qlln2Zx+3uTI1WTyPpkM9ucv7JURLyTXKd2F/nsFyRHs5qlIlaWJ6cbVmYCyyuTIG9p+Fq700WUztQu5PTF3Zu0UdU1G5HYe7kOmZmZmZkN0XzdIYuI6WQk+b+Ra8XqtgYOlHQLcCXZeViJ9vHyN0TEzBKscQut485/Xs5/NbCEpKUa3t+IgZj8M8hQinbb38HA6N4ZLW8YHqxF9Z9ZOx7gHJhTTHlj4Nxy7z8GqlGoZnH71wEHSfoKsHK9Llkb5wBExGHkOrFqvdvzwPOSmgWGQPMUxyh10xpric3o4jpOIKcrXhMRpzRpo6prNiKx965DZmZmZmZDNd+GetRMBY4BNgeWqW0X8OGIuLO+c5l61ypevjGyvtXzaVx412khXqv3o4t9uj1v1SlaAHiijHjNvXOTuP2IOEvStLLtEkmfIke9WkXr189VnW+jLjtyM8lEycoK1Apu90LSfwDLAXt1sbtj783MzMxsTHo5/KH5M+DJiJghafPa9kuA/SXtX0I51o2Im8lQjpnlj/NdGRgp6sVOZELjJuXcT0pzDf78gZwCeQZZ0+z3HbZfW7afSfsaaCtJ2igiriNHBX/fuENEPCXpfkk7RMS5ygubGBG3qsTtA9MkfQhYUVkK4L6I+IGkVcjph9eQdb+WIRMkP8jAqFKjS4H9gKMhCzrHQGHsRlPJBMazyVCPJyPi4Tb321TpNL4H2LKMZprNF7zQ38zMzBrN9x2yiJhJrsFqdCgZET+9dEoeIDsW9Xj5K5h7tKdbj0v6A7AEsEeT9z8D/EzSAWTM++4dtn8WOEvSZ4Hz25z3DmBXST8mEyZbJQvuDJwo6WvkFM2zyWj9ZnH7B5IhJc+T69cOiYjnJR1Crsm7H/hLm2v6DHC8MvhkPLnmau8W+15MJizeAzxdu/+mJF0DrEGGrcwEPhkRlwA/Av7KQNmBX0TEIe3aMhstTlI0M//ji5n1Ykix9yV97liyVtTj5NqboyLigjJa9aXBRNDbvCRNAO6NiK5H9CQdFBGHd9hnKeBjEXFCu/3Kvi3j+CVNIjuIS5DT/A6LiHMa9jkO2L1dpL+kvYF9SxuzgT0j4s/lvYnkmrglyOmGG0TEs63aGk2OvbeKO2Rm5g6ZmcEoxN6XUadfkqEKq5REv4/SJoLcRt1BnXdhKeZOaxysp4FPRMSawHuB79fDTkqkfGP4STNnRcTaZQ3cUcD3yvHjySmde5dzbE6GiJiZmZmZzbeGkrK4BfBcRPyo2hARf42I4xp3lLShsm7XzeX7W8r2eWpgle2fKD/fKumMsm2eGl6Sxkm6T2kpSS9J2qzsf42kVRuuY5ykY0oNqumS9i/btyzXNkNZu2yhsv0BZc2t60q8+XqSLpF0bxnJQdLi5XpuKsdv2+T+x0k6VVkDbIakz5ftq0r6XbnPm5T1w5q2FxEPkDHtVZsHKOt+TZf0rSbnPAJYpDzbKWXbF8o13Cbpc2XXI4A3l/2O7uZ+momIuyLi7vLje8jO1x9Ku7eQnfcvN7nOtVWLqgeuVoaMQMbPV0O4WwPTI+LWcr7H6gWwNW/s/YvlHv5UnvGGylpj90naphwzT026sn37cowkvUHSXZJe381zMDMzMzPrxVDWkK0J3NTlvn8BNouIFyRtBRwOfJiBGlhTJL0KGCdpTbKG2Dsi4lFJS5c2qhpep0nag6wftp2ku8jo+jeREeWblj/oV6jVDavsWfZbt1zL0soaZqeSARF3STod2IdcfwYZNb+RpGPLfu8gUwdvJ9cyPQtsX8I0lgWulzS1ltIHMAlYPiLWgjnTBCFrkR1RpnguTHaQn+vUnqStgdWADcn1YFMlbVZi+AGIiAMl7VelLUpan1yz9bZyzDRlcecDgbVq+43v4n46uZ1cs7dWCU/5LLBARDysucNPKPH2cyVCStpX0r3Aq8iOP8DqQEi6hExXPDsijqq1cwpwSq2NAA6OiF9LugD4NvBu8rNyGhkwUtWke1b5jwE/ByaX38eHyamT7wX+IyLmqQ8naU/yM8VKK63Uw+MxMzMzM0vDVodM0vFlpOePTd5ekqyLdRu55mzNsr1ZDawtgPMi4lGAiPi/sm+rGl7XAJuVr++U7RsAza5jK+BHEfFCre23APdHxF1ln9NKW5Wp5fsMYFpE/D0iZgHPlo6VgMOVoRa/I4sfv67hvPcBq0g6TtJ7gaeUtbqWj4gLyrU8GxFPd9ne1uXrZrJTvAbZQWtnE+CCiPhHRMwGfgFs2mS/bs7fkqQ3kL+f3Utn7I3ADsA8I6etRMTxEfFm4CvA18rm8eUedi7ft5e0ZZtmnmPuemNX1WqRTSjb29Wk2x/4KvDPiPg5TbgOmZmZmZkN1VA6ZLcD61U/lCK8W5KjF40OBa4oI0QfotS1ioizgG3IqXiXSNqC7BD0UpPrGrJjsSGZ4rcUub7o6ibHNGu7WbHiuqo22UvMXaesqmG1M3nP65dRpr/RULcrIh4n655dSY66nNzmvB3bK8d+p1YAedWI+GmH++h0n72cv/kJpCWAi4CvRcT1ZfO6wKrAPZIeABaV1Dhy2crZwHbl9UyyU/Vo6bheTO3z10RjvbE5tcgYGBn+PAM16SaTI3KV5ctxr5M0XxdQNzMzM7OxayhTFi8nR1L2iYgqfn3RFvsuCfxveb1btVFZ96qxBtZvgQskHRsRj0lauoxktarhNQ04vbTzbFmHtBcZcd/oUmBvSVdWUxbJ6ZQTJK1apjjuAlzVw3NYEnikRMW/C1i5cYcy9e+5iDi/TMU7tUwJnClpu4j4pXLd2rhu2iNrrB0qaUpEzJa0PNkBeaRhv+clLVhGhq4GTi1rywRsX+7178Cre7mfZsqU0wvIaaXnVtsj4iLg9bX9ZkfEqk2aqN5frbYW7QNkvH91z1+WtCg5+vVOcrR1KJrWpCvTNk8BPgZ8AvgCWXzcrCOnq5mZmVkvBt0hi4iQtB1wrKQvk3W1/kFOM2t0FHCapC+QHbnKTsxbA+v/JB0GXCXpRXJa3m60qOEVEf+U9CBQjchcQxZNntHkOk4m1yJNL+f8SUT8UNLu5JTK8eRUxx81ObaVKcCvJN0I3ELzml3LA6fURlq+Wr7vAvxYWfPreXJqX8f2IuJSSf/CQB2u2cDHyTVRdSeVe70pInaWdCpwQ/UsIgtlI+naMp3018CRXdxPMzuSUz2XkbRb2bZbtC4S3cp+ZZ3h82QphV3LPT8u6Xvk7yeAi0tnbyha1aQ7CLgmIq4pHfw/SrooIu4Y4vlsPuHoejMbCv/DjJn1omMdMrnW2KhSm1pfLfZ3rbExwHXIXl7cITOzoXCHzMxgmOqQSa41Nh9wrTEzMzMzs/lUp7AC1xpj9GqNNWlzzNUaAxYu0/iqAJU/SJomaRxwNE1qjTVp5ylJB5d2fgBsXF7/lDa1xprc/2xJD0l6WtLflfXCZkt6UAO1xsaVe66e415le9NnoKxNdoekn0i6XdKlkhZpcf49y2fmxlmzZnXzCM3MzMzM5tKpQzaYWmPrAt8ga43BQK2xSWSS3UwN1BrbIiLWAT5b9q1qjU0k11L9oPxBXtUa24SBWmML0bnW2ERgigZqje0UEWuTa+f2qR3zYERsRK4/OxX4CDlF85DyflVrbD3gXcB3y+hh3ZxaY+UcVU2sKcDx5T43Bh7upj3NXWtsErB+1RGtRMSBwDMlaXFnzV1r7O3ApyWtS9Yau7fsd0CX99NURMwov8s9Gag19jZgP2BqRDzcTTvAE2SYyDgGEh1vptQaKx2lTp27xYBPRsSiZET/A8BryECX6nf3SeDJiNiALIfwaUlvov0zWI38na1ZrvPDLZ6FY+/NzMzMbEh6CvWQdDzZKXqu/IFbtyQZ3LEaGbqwYNl+HXCwpBWAX0TE3cp4+1a1xv61vD6DnM4GA7XG3kTWGvs0mYTYVa0xSeswb62xfRko/lyvNbZ4RPwd+LukqtbYP8hEyc3IdU1Vba56seA5tcbI6PdL1aTWWHmOC3bRXr3WGMDiZEehWZx/ZU6tsXKeqtbY1Ib9qlpj7c7fkgZqje0ac9ca27yb4yFrjQHHS/oYWWtsVwZqjW1ATo+8TDn39rIWzTTWGvtnSYes1xrbGpgo6SPl5yXJ5ziT5s8A8rNShZH8qdaWmZmZmdmw6tQhu53a6EBE7KuMcG+WXlDVGtte0gSy5hYRcZakaWSE+SWSPsXgao3tDbyRHH07gP7VGnteWU9rnlpjpeP3HrKztyPwOZrr2B4DtcZ+3OHaG4/pRjfnb36CzrXGoNQaaxdvX3M2GRQCtVpj5VxVrbFWHbKWtcaU69Egn8n+EXFJw33sRutnUP8MvAg0nbJoL19ekG9mZmajpdOUxcvJNUP16X2DrjVGjtRMJP/A3lHSMmWfpcvuVa0xmLfW2MbAS2WUqao1dk2T66hqjY2vtT2n1ljZZ6RqjS0QEecDXwfWi4inyCma25V9FlLW0eq21tgekhYvxy4v6bVN9nu+jLhBdlC3k7SopMXIWmPXMAq1xiLi9RExISImAE93qjVW+7Gx1tjEcv3jyVpjf+7m2tq4BNinekaSVi/PZlDPwMzMzMxsOLUdIeui1thrgHUl3UdOHztT0reB88iO3IXAtbjW2HDVGptIrpuqa1dr7IEuao39mS6nKgJPkWu+5qk1Jmll4Bfl/UUk7V2FwZSO3A/JUc2XyJG0N9G+1tirgRXJ9W/VNM+TyRGz8WQx8G6cTE45vKmsEZsFbEd3v1N7BXDEvZkNN4+ym1kvOtYha3lg/nH7B+C02h/eKwPbRMRxco2yYafea5R13L9ML70wItYaSnul06XSeV4cuA3YOCIeUiZEjouIr5UO69LVtMQWbb2anBb5KmC/iLixrDXbJiI+WkYZ/wxsHhEPdLru0eA6ZPMvd8jMbLi5Q2ZmMEx1yDpwJD6OxK9ExHMRUa29Woi5P1t7kGEsRMRL7TpjxaFkoEu9IHQAi5URzkXIEdmnmh2sjK7/i6STy71OkbRVGSW8W9KGZb9Wn8svSPpZeb12aaPVVF0zMzMzs0HrKWWxwWAi8V+QtBUZif9hBiLxp5QRlnEaiMR/R0Q8qoH1ZVUk/mmS9iAj8beTVEXiv4mBSPxpdI7Ef0HS0hqIxN8yIu6SdDoZiV8lMD4YERtJOrbs9w4y/OF2ctpjFZ/+lHId2fWSptbCJqAWiQ+ggWLKU4AjIuKCch0LkB2Ntu1p7kh8AVMlbRYRc0JOIuJASfuVOHk0dyS+gGmSriKnBK5V2298ef5VR/u/JN3B3EEXTUlakRzZWpUMXtld0kfJ9ML7lSNn9wJbRcTfWrSxLrBiRFwo6Utl2zSyk7dyed4Cvl1L52xmVXJ66J7kFMiPkQmO25DFtLej9efy+8CVkrYvz2KviHi6ybXuWdpnpZVW6vR4zMzMzMzmMZQRsrlIOr6M9DSLol+SXL91G3As2ZmDjMQ/SNJXgJUj4hly5K1VJP5Z5fUZ5B/XMBCJvxk5ClPFpncViQ+8hXkj8ev1vuqR+NMi4u8RMQuoIvGrCPnpZC2senx6ZU4kvqT3Ak+pSSR++aO/m/bqkfg3AWuQHbR25kTiR8Rscr3Xpk32E/Bx8rPxIrnm672RtcbaiogHI2u/rUquCzuZrPG1ILBPZL2wU4Bjmh2vnM54LPDFhnbfRiZXXkyOjr0B+KgyMKaV+yNrpr1Edp4vK53aeiR+089lOWY38nN2VURc2+J+XYfMzMzMzIZkKB2y28mABSAj8YEtySjxRlUk/lrAhyjx4hFxFjli8QwZib8Fg4vE35QcLboYWIr+ReJPAv5Gk0h8YB2yFMC+ZEel1Xk7tsdAJP6k8rVqRPy0w30MJhK/1fnbioiHyM/HpsBjZE2xC8rb51L73DR4NbAWOTr1AFnceqqkyeQI128i4vmIeIQMi2k3J7fxd1X/PVYjw00/l8VqZJDKG9verJmZmZnZEAxlyuLl5EjOPhFR1ZEadCR+eT0R+C1wgaRjI+IxSUuXkawqEv8M5o3EP72086ykKhK/WZhIFYl/ZTVlkVokfpniOFKR+M9FxPmS7gVOLVMSZ0raLiJ+qVy3Nq6b9sgo90MlTYmI2ZKWJ2tyPdKw3/OSFoyI58kO6qllbZnISPxdGL5I/BWAxyLiGUmvIad2fq8kdf6K7CRfTnbam0bZR8STwLK1Nq8kg2FulLQlsIWkM8nP2dsZmFY6WK0+l0sC/0mOlP5Q0kci4rwhnsvGKC++NzMzs34adIes/KHdLhK/7ijgNElfIP8or+yEI/GHKxL/40Bjh6xdJP7J0TkSv5c4+H8BvispyA7fMRFR/Q6+Apwh6fvUfnc9Op6c7nhbaf+UiJg+iHbqWn0ujwVOKGsKPwlcIenqJh1em884UdHMRoP/ocfMejHo2Pt5GpJeR/4h+3ayttRzwFElsGJzHIE/rNR7BP5BEXF4h32WAj4WEScM5fySJgEnAkuQ69AOi4hzGvY5Dti93T0okyz3LW3MBvaMiD+X9yYCPy7neAnYILJo+KCUaZGfiIjPDOZ4x97PH9whM7PR4A6ZmcHoxN7XTybgl8DVEbFKRKxPTi9cYTjat2FxUBf7LAX8+zCc62myc7Mm8F7g+7Vkyarzs1Srg2vOioi1y1q2o4DvlePHA2cCe5dzbE6OMA5aRNw42M6YmZmZmdlgDVfKomuS8cqoSaasy3VLrb1blLH0c0TEXRFxd3n9EDmVcrnq/oGjgZkNbdwi6eCGdup1xhZjIJBla2A6MLNcy2XAn2rtLNNw/7MlHSnpT+UZbyjpyvJ52abss7mkC8vrb5bffbVP046apD3LZ+HGWbNmNdvFzMzMzKytoYR61Lkm2cu3Jtlc5wdWi4hJyimLk1r+lgeub0PgVWT9MYD9gKkRcayk/Tu1IWlf4AuljS3K5tXJztlZ5HTFsyPiqDbNLAZcGRFfkXQB8G3g3eRn5TQGShvUrUFG9r8auFPSiSUcZY6IOIlcp8fkyZOHZ+6vmZmZmb2iDFsdsjq5JtnLqSZZp/O3JOkN5O9n94h4SdIbyeCSeUZOW4mI4yPizWQwyNfK5vHlHnYu37dXpjC28hzwm/J6Bllb7HnmrknW6KKI+Gf57D1CD/dtZmZmZtat4Rohu50c5QKyJlkZUWmWclDVftpe0gSyNhcRcVYZzfoAWZPsUwyuJtneZO2obwAH0L+aZM8ra2nNU5NM0jrAe8jAih2Bz9Fcx/YYqEn24w7X3nhMN7o5f/MTSEsAFwFfi4gqAXNdsmj0Pcp0yEUl3RMRq7Zopu5sMigEcrrjVVVHXdLFZG2zy1oc+3xtVHHO7650Elv9b6D++32R4fvfivWRF9qbmZnZWDNcI2SXAwtL2qe2bdA1yciRqInkH9g7VmuCalMWq5pkMG9Nso2Bl0riXlWT7Jom11HVJBtfa3tOTbKyz0jVJFsgIs4Hvg6sV9ZKzVSWEUDSQpIW7aY9sibZHpIWL8cuL+m1TfZ7XtKC5fXVwHaSFpW0GFmT7BqGrybZq8hC0KdHxLnV9oi4KCJeHxETImIC8HS7zpjKOsLiA8DdtXueWK5/PPBOWtQ2MzMzMzMby4blX/1dk2wO1yRLO5JTPZeRtFvZtltE3NLl8ZX9yjrD58lSCruWe35c0vfI308AF0eE88xfwRxnb2ZjiUfjzawXHeuQyfXFRpXGdn2xlcn1ZuOABYHjqmTNMir2Q3KK6EvAwWUUsN25PgKcS9YQu7GM4J1MTj8cT46wfafTNY8FrkPWX+6QmdlY4g6ZmcEw1SGTXF9sPjCa9cUeBjYuyYhvAw4sQR2QaZiPRMTqZHph26meJcjkM+Q008oOwEIRsTawPrBXWWdoZmZmZvay1GkNmeuL8cqoL9buQ1CJiOciogq7WKh8XaqsBfZVYBtJ0yLipSpwo41Dyemrz5Jr4G4hSyC8r7w+iByNfarZwZImSPqLpJPLvU6RdIey5tg/Jd1V7nfD8vv+Y/n9b1s7/pryDG6StHHZvrmy/th5pf0p5R8mml2D65CZmZmZ2ZB06pANpr7YumTCYTWNrqovNgmYTIZXVPXFtoiIdYDPln2r+mITyfVTP4iIF4GqvtgmDNQXW4jO9cUmAlM0UF9spzL6Mp6sL1Z5MCI2ItecnQp8hJyieUh5v6ovth5Zm+q7Tf5In1NfrJzjlLJ9CnB8uc+NyVGmju1p7vpik4D1q45oJSIOBJ6JiEllXVi9vtjbgU9LWpesL3Zv2e+ALu+nKUkrKmPwHwS+XeqpbQ78jYzGX1DSucqprq3aWBdYMSIuLJt+Vj4fq5Fr15YHPg8cUyt10MyqwH+SATBrkKUTXk2uYftzaXM74PKI2KDc69HKIJNHgHeXZ7AT8INau+uSyZdvBVYh683NIyJOiojJETF5ueWWa3OZZmZmZmbN9ZSyKNcXe8XXF4uIB0tHd1Vg19LxGk9OY722dHCuA45pdrwyzORY4ItN3t6QjJh/I9mp/qIyfbOV+yNiRkS8RJZeuKzE29fri21NTq28hSyxsDCwErkG7ieSZpDr2N5aa/eGiJhZ2r2F1rXKzMzMzMyGpFPKouuLub5YUxHxkKTbyQ7f+cDTZNQ9ZAfnky0OfTWwFnBlGZR7PTBV0jbAx4DfRBZtfkTSteSo6n0t2mr8XdV/j9VnW8CHI+LO+oGSvkmO6q1D/sPEsy3adQ2y+YAX0JuZmdn8qtMImeuLDdyb64tJK0hapLx+DTmV784yKvUrspMMsCUt6oJFxJMRsWytFtn1wDYRcSPwP8AWSouR0y67jdpv5RJg/2pKZpkuCfkMHi6jYLuQyZFmZmZmZqOq7b/891hf7CfA2SVc4nHgNZK2J6fa7Vk6Rtfh+mLDUl9MA/H03dYXe1wd6ouV6ZntPhP/Qq43C3KUa5+IqH4HXwHOkPR9SgewTBPsFI//Qq3948m1d7eRI1unRMR0NY/HPwZYVdIdwOltrvlQ4PvlGQl4APggcAJwvqQdgCvI6akHMvAZsz5xhL2Zze88am9mvehYh6yrRvIP3T8Ap9X+8F6ZHPk4Tq5XNuzUe72yjvuXqaYXlqCOQbdXOl0qHenFyQ7WxmWa47eAcRHxtdJ5XbpdImNZh3cR8Cpgv9Ih+xj52fpoGXH8M7B5RDzQ6bpHiuuQDR93yMxsfucOmZnBMNUh64Hj8XE8fqVJPH79c7YHGczCIOLx55wCWKyMdi5C7/H4W0m6VtLdkjYs++0m6Yfl9amSflA+n/eVETozMzMzs2E3XGEFg4nHf0HSVmQ8/ocZiMefUkZYxmkgHv8dEfGoBtaaVfH4p0nag4zH305SFY//Jgbi8afROR7/BUlLayAef8uIuEvS6WQ8/vfLMQ9GxEaSji37vYMMw7idnAJZxck/pVxTdr2kqTH3MOSceHyYM00QchrjERFxQbmOBciORtv2NHc8vsiAjM0iYk7gSUQcKGm/EgOP5o7HFzBN0lVkPP5a5DTLM4B3l+t4qVzLd5vcT1OSViSnhr4WeAi4WNI4ci3bocpR03vJUa+/tWhjXeDN5PTGVckSBs+Ut+8jSwgsCny+i3j8Hcjf+R/J8JBNgG3IemfbNTnmDWWfNci1j+c1ub49S5ustNJKbU5vZmZmZtbccI2QzUWOx5+v4/FLlPwk8tldTX5OxrU4f1MlHv/1ZBz+Q2T65LvI338v8fj7l2u5Edi5vN4XeIbhjcdv9MsygvfnVvccrkNmZmZmZkM0XB2y24H1qh8iYl8yaa/ZX6lVPP5awIcocesRcRY5YvEMGY+/BYOLx9+UHC26GFiK/sXjTyJj1eeJxyej1q8kOxYntzlvx/YYiMefVL5WjYifdriPwcTjtzp/WxHxEPn52BR4jHnj8ddrcWg9Hv8BMnFxqqTJ1OLxI+IRoIrHb6WbePx2x3T7vMzMzMzMejJcUxYvJ0dy9omIE8u2Qcfjl9cTgd8CF0g6NiIek7R0Gcmq4vHPYN54/NNLO88qU/72IlP1GlXx+FdWUxapxeOXKY4jFY//XEScL+le4NQyJXGmpO0i4pdl3dq4btojY90PlTQlImZLWh6oOip1z0taMLLG19XAqWVtmch4/Bde7egAABC9SURBVF0Yxnh84LGIeEYD8fjfK6mdVTz+5XSIxweWrbV5JRkMc6OkLcl4/DPJz9nbGZhWavM5L4Y3MzOzV5Jh6ZCVP7S7jcc/CjhN0hfIP8orOwEfV0bV/z8cjz8s8fg13cbjX6sO8fhdqMfjCzimRTz+nN9dj5rG4w+infma0wjNzMYm/8OSmfViWGLvez6pdDA57exFctrYXhExrcc2dgMuLVPi2u13JQMjKxcDH4uIJ7po/5vA7Ig4pmH7BLqMhu/iHHOubahtjSXKkge/IEf5OtUhOziymHa79prVITuZnO44ngx4+c4Qr3kb4K0RccRgju9H7L07ZGZmY5M7ZGYG3cfeD9eUxa5J2oicQrheGdFalqwx1avdyBGSth2yuoh4/yDOMyZJGl8FkozBcz1M1h2bU4espDM+RKZmPhIRq5dRwqXbNVQCTz5DTket7AAsFBFrq9Qhk/TzGEIdsoiYykBoi5mZmZnZqBiRlMUO3gA8GqVOVUQ8GlkweEtJVdgDkt4t6RdqUrerjJhMJmPQb5G0iFrUD6tT1hJbtryep75ZE+tIulxZr+rTTdpbWNIp5Zw3l3VW7bYvIunsct5zyBpa8yjXeaSyLtsNKjXUynP4nqQrgCMlLVbu9Y/lPNuW/eap6Vb2vajc722SdmryTCaXUTskfVPSSZIuBU4vv4ejNVDvbC9Ja5dz1L+mRY91yCQd3KSdg8v+3dYhG9ekjVskTdII1SGTtKeyJt2Ns2bNaraLmZmZmVlboz5CRoZpfENZM+x3wDkRcRW5nux4ScuVKPndyXVC89TtiognJO3HwFTETvXD5qLW9c0aTSQDIxYDbpbUOEdsX4AyUrMGcKmk1dts3wd4OiImSppI+9ptT0XEhpI+Ue6jCiZZHdgqIl6UdDhweUTsoYzdv0HS72hS0w14P/BQRHygPIMl25y7sj6wSQnn2BN4MiI2KJ3da8kpo5OaHaisQ3YRWQPsgNLprmquNdYhOww4rEkb6wIrRsSFkr5Ue+s8YFvmrkN2L/lZaWxjAiNUhywiTiLX5jF58uTRn/trZmZmZvO9UR8hK3Wv1if/OJ4FnCNpt1IX6gwy2GMpstbYr2lSt6tJs53qhzVqVd+s0X9HxDNlvyvIOP26Tco1ExF/Af5Kdphabd8MOLNsnw60C6L4ee37RrXt50bEi+X11sCByjTJK8lI+pVoXtNtBrBVGXnbtKQYdjK1HFud6xPlXNOAZWhT7yyyDtlEsjO0q6TXkf8AsAK91SH7YpO3NyTXH/a1DpmZmZmZ2VD1Y4SM0qG4kqwxNQPYlRzhOgX4FTk97dyybulxSeuQhYX3BXYkp73V9Vonqtf6Zq1+bnXedtfT7UhKtHj9j4bzfDgi7mw49g5J04APkDXdPhURl0tanxwp+46kSyPiEOAFBjrmjTXGGs+1f0Rc0uX154XnyFhVh+x85q1D9skWh9brkAG8nqxDtg21OmTAI5KqOmT3tWjrZVmHzIvGzczMzOZ/oz5CJuktkuojK5PIEaSqiPBDwNfIDlpVt2uBksT3dQYKCddrZs2pH1Z+7lQ/7DJgR0nLlHO0mrK4rXI92DJkKuAfG96/mqyDRpmSuBJwZ5fb1yKnRLayU+37dS32uQTYX6XHUqb4zVXTjZxuN1HSG8npkmeSo1LVc3yAHLEE+HCb67kE2EeZcIik1SUt1mxHSStIWqS8ruqQ3VlGpao6ZNChDllELBsREyJiAlnKYJuSSPk/ZB0ylWt4O91H8puZmZmZjRn9GCFbHDiuTEt8AbiHnL5YmQIsV6aKQeu6XacCP5L0DDmlr+v6YRFxu5rXN2t0A7kOaiXg0DLaM6H2/gnlGmaUe9mtJAu22n5iuZfpZF2vG2htoTLKtQBZS62ZQ8n1ZdNLp+wBcq3ZPDXdgA2AoyW9RNY526e08S3gp5IOYu4kw0Ynk9P7birnmkXztVfgOmRmZmZmZl3pSx2ydkrS3c0R8dN+X0u/SHoAmFytcbOxrx91yMzMzMxs7NJYrUPWjqQ/keuWmgU5mJmZmZmZvayMqQ5ZRKzfea+Xv7JmasyTtDYlTbLmnxHxth7bOZiMpa87t8Thd9vGMuTawEZbRsRjvVyPmZmZmdloGVMdMpu/lHVhTeuQ9dhO0zpkPbbx2HBci5mZmZnZaBr1lEUzMzMzMzNL7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfeIOmZmZmZmZWZ+4Q2ZmZmZmZtYn7pCZmZmZmZn1iTtkZmZmZmZmfaKI6Pc1mM33JM0C/tri7WWBR0fxcuZ3fl698fPqnZ9Zb/y8eudn1hs/r975mfWmX89r5YhYrtNO7pCZjTBJN0bE5H5fx/zCz6s3fl698zPrjZ9X7/zMeuPn1Ts/s96M9eflKYtmZmZmZmZ94g6ZmZmZmZlZn7hDZjbyTur3Bcxn/Lx64+fVOz+z3vh59c7PrDd+Xr3zM+vNmH5eXkNmZmZmZmbWJx4hMzMzMzMz6xN3yMwGQdLSkn4r6e7y/TUt9tu17HO3pF1r2w+T9KCk2Q37LyTpHEn3SJomacLI3snoGYZntr6kGeXZ/ECSyvZJkq6XdIukGyVtOFr3NJJG6nmV9/aXdKek2yUdNRr3MxpG8pmV978kKSQtO9L3MhpG8H+TR0v6i6Tpki6QtNRo3dNIkPTe8r+XeyQd2OT9lv/dlvTVsv1OSe/pts353XA/M0krSrpC0h3lv1ufHb27GXkj8Rkr742TdLOkC0f+LkbPCP1vcilJ55X/dt0haaPRuZsiIvzlL3/1+AUcBRxYXh8IHNlkn6WB+8r315TXrynvvR14AzC74Zh/B35UXn8UOKff9zqGntkNwEaAgF8D7yvbL629fj9wZb/vdYw/r3cBvwMWKj+/tt/3OtafWXlvReASst7gsv2+17H8vICtgfHl9ZHN2p1fvoBxwL3AKsCrgFuBtzbs0/S/28Bby/4LAW8q7Yzrps35+WuEntkbgPXKPq8G7nq5PLOReF61474AnAVc2O/7HOvPCzgN+FR5/SpgqdG8L4+QmQ3OtuT/eCnft2uyz3uA30bE/0XE48BvgfcCRMT1EfFwh3bPA7Zs/Ff6+dign5mkNwBLRMR1kf+1PL12fABLlNdLAg+N1A2MspF6XvsAR0TEPwEi4pGRvIlRNlLPDOBY4Mvk5+3lYkSeV0RcGhEvlOOvB1YYyZsYYRsC90TEfRHxHHA2+dzqWv13e1vg7Ij4Z0TcD9xT2uumzfnZsD+ziHg4Im4CiIi/A3cAy4/CvYyGkfiMIWkF4APAyaNwD6Np2J+XpCWAzYCfAkTEcxHxxCjcyxzukJkNzuuqDlX5/tom+ywPPFj7eSad/w9kzjHlD5ongWWGfLVjw1Ce2fLldeN2gM8BR0t6EDgG+OowX3e/jNTzWh3YtEzjuErSBsN+5f0zIs9M0jbA/0bErSNx0X00Up+xuj3I0bP5VTf/HW/13+12z67X/2+Yn4zEM5ujTD9bF5g2jNfcTyP1vL5P/iPSS8N/yX01Es9rFWAWcEqZ4nmypMVG5vKbGz+aJzObn0j6HfD6Jm8d3G0TTbZ1+tf1wRwzZozgM2v3XPYBPh8R50vakfwXrq26PF9f9el5jSennr0d2AD4L0mrlFGOMW+0n5mk/9/e/YNGEURxHP8+iEFEBEUEIUVUoq1EEQvBECFIkFQWFqJoL9iJBEEsLQTBwkK7WPkHTWeh2PoX/6GICQgeGkUsFKzEZzFzeHfm9C53s7N3/D4wJNnszu48JnP77mYnK2LdEy3WXyqZ+lj13NPAT+BKi+cqo1bG5HZjtNib4T3x99eiFDELB5mtBK4Dx93925KvsFy6Hi8z2wd8dvfHZjbW4fWVTYr+NQCMAsfc/b6ZnSdM4z7VyYW2QwmZSBPu3vSm3sw+mdl6d/8Yp+4sNu2rAozV/DwE3PvPaSuEZ1UqZjZAmIL3tZ3rzilhzCrUT3sa4s/UxMNA9QHvq/TQ9IxM8aoAN2IC9sDMfgFrCe8Oll6GmG0iPGvwLM4eHgKemNkOd1/ooCmFyNTHsLD4xz5gT68k+01Ux+SqunY27NM4bv/r2P/V2cuSxMzMlhGSsSvufiPNpWeRIl5TwJSZTQLLgVVmNuPuB9M0oVAp4lUBKu5e/dT1GiEhK4ymLIoszSwhESB+vbXIPreBCTNbbWH1som4rdV69wN3e/xmptaSYxanU303s51xHvihmuM/ALvj9+PA21QNKFiqeN0kxAkz20x4ePlLumYUqusxc/cX7r7O3YfdfZjwwj3aC8lYC5L0MTPbC5wAptz9R+pGJPYQGDGzDWY2SFggYLZhn2bj9ixwIK74tgEYISyE0kqdvazrMYt97DLw2t3PFdKK4nQ9Xu5+0t2H4ph1IO7fD8kYpInXAvDezLbEY/YAr1I3pE47K4CoqKiEQpiLfIdw838HWBO3bwcu1ex3lPDQ6BxwpGb7WcKN3a/49XTcvpzwKc8c4YV7Y+62lihm24GXhFWRLvDnH9vvAh4TVk66D2zL3daSx2sQmIm/ewKM525r2WPWcI539M8qi6n62BzhOY2nsVzM3dYO4zRJWNVvHpiO284QEs5/jtuEqaHzwBvqV+38q85+Kt2OWRznHXhe068mc7ezrPFqqHuMPlplMVW8gK3Ao9jHbhJXky2qVAdPERERERERKZimLIqIiIiIiGSihExERERERCQTJWQiIiIiIiKZKCETERERERHJRAmZiIiIiIhIJkrIREREREREMlFCJiIiIiIikokSMhERERERkUx+A9oWwZLAbcOxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize feature importance\n",
    "fig, ax = plt.subplots(figsize=(12, 30))\n",
    "fig.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0.08, hspace=.5, wspace=.15)\n",
    "importances = grid.best_estimator_.coef_[0]\n",
    "indices = np.argsort(abs(importances))[::-1]\n",
    "plt.title(\"Feature importance\")\n",
    "plt.barh(list(range(X.shape[1]))[:], list(importances[indices])[:], align=\"center\")\n",
    "plt.yticks(list(range(X.shape[1]))[:], (np.array(feature_used)[indices]).tolist()[:])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}