Early Diabetes Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c4d447a",
   "metadata": {},
   "source": [
    "# Early Diabetes Detection Using Symptoms\n",
    "\n",
    "### <u>Problem Statement</u>\n",
    "Inadequate insulin production or inresponsiveness of body cells to insulin may cause an increase in blood glucose in the body of a person and causing diabetes. Diabetes could cause many other complications, such as loss of vision, kidney infection, and heart problems. Hence, early detection of diabetes can help the patients to keep their sugar levels under control by taking healthy diet with required drugs. \n",
    "\n",
    "### <u>Main Objectives</u>\n",
    "To build an efficient model that can help the patients to detect if they have diabetes. <br>\n",
    "\n",
    "### <u>Dataset Description</u>\n",
    "This dataset is originally from <a href=\"https://archive.ics.uci.edu/ml/datasets/Early+stage+diabetes+risk+prediction+dataset.#\">UCI Machine Learning Repository</a>. It contains the sign and symptpom data of newly diabetic or would be diabetic patient.\n",
    "<br>\n",
    "<img src=\"https://images.pexels.com/photos/5469026/pexels-photo-5469026.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=2\" alt=\"Glucose Meter Surrounded by Sweet Treats on a Blue Surface\"/> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85bd907a",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0352a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "from statsmodels.graphics.gofplots import qqplot\n",
    "from statsmodels.stats.weightstats import ztest\n",
    "# import scipy \n",
    "# from scipy.stats import chi2_contingency\n",
    "from scipy import stats\n",
    "from scipy import interp\n",
    "\n",
    "from collections import Counter\n",
    "from sklearn.pipeline import Pipeline\n",
    "from imblearn.pipeline import Pipeline as imbpipeline\n",
    "from pipelinehelper import PipelineHelper\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.over_sampling import ADASYN\n",
    "from imblearn.combine import SMOTEENN\n",
    "from imblearn.combine import SMOTETomek\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import recall_score, confusion_matrix, classification_report\n",
    "from sklearn.model_selection import StratifiedKFold,cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "from sklearn.tree import DecisionTreeClassifier as DT \n",
    "from sklearn.ensemble import RandomForestClassifier as RF \n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from xgboost import XGBClassifier \n",
    "from sklearn.naive_bayes import GaussianNB as GB  \n",
    "from sklearn.svm import SVC   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb8f4fbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_df = pd.read_csv(\"https://raw.githubusercontent.com/jadanpl/Early-Diabetes-Detection-Using-Symptoms/main/diabetes_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d716b4",
   "metadata": {},
   "source": [
    "# Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc1f695e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 520 entries, 0 to 519\n",
      "Data columns (total 17 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   Age                 520 non-null    int64 \n",
      " 1   Gender              520 non-null    object\n",
      " 2   Polyuria            520 non-null    object\n",
      " 3   Polydipsia          520 non-null    object\n",
      " 4   sudden weight loss  520 non-null    object\n",
      " 5   weakness            520 non-null    object\n",
      " 6   Polyphagia          520 non-null    object\n",
      " 7   Genital thrush      520 non-null    object\n",
      " 8   visual blurring     520 non-null    object\n",
      " 9   Itching             520 non-null    object\n",
      " 10  Irritability        520 non-null    object\n",
      " 11  delayed healing     520 non-null    object\n",
      " 12  partial paresis     520 non-null    object\n",
      " 13  muscle stiffness    520 non-null    object\n",
      " 14  Alopecia            520 non-null    object\n",
      " 15  Obesity             520 non-null    object\n",
      " 16  class               520 non-null    object\n",
      "dtypes: int64(1), object(16)\n",
      "memory usage: 69.2+ KB\n"
     ]
    }
   ],
   "source": [
    "diabetes_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a26ed0",
   "metadata": {},
   "source": [
    "From the result above, only 'Age' column has numerical values as its input, while other columns have categorical inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9117a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_df.columns = diabetes_df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c4efd34e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>445</th>\n",
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      "text/plain": [
       "     age  gender polyuria polydipsia sudden weight loss weakness polyphagia  \\\n",
       "390   47    Male       No         No                 No       No         No   \n",
       "293   53    Male       No         No                 No      Yes        Yes   \n",
       "74    50  Female      Yes        Yes                Yes      Yes        Yes   \n",
       "348   28    Male       No         No                Yes       No         No   \n",
       "445   53  Female       No        Yes                Yes       No        Yes   \n",
       "\n",
       "    genital thrush visual blurring itching irritability delayed healing  \\\n",
       "390             No              No      No          Yes              No   \n",
       "293             No             Yes     Yes           No             Yes   \n",
       "74              No              No     Yes           No             Yes   \n",
       "348             No              No      No           No              No   \n",
       "445            Yes             Yes     Yes          Yes             Yes   \n",
       "\n",
       "    partial paresis muscle stiffness alopecia obesity     class  \n",
       "390             Yes               No       No     Yes  Negative  \n",
       "293             Yes              Yes      Yes      No  Negative  \n",
       "74              Yes              Yes       No      No  Positive  \n",
       "348              No               No       No      No  Negative  \n",
       "445              No              Yes       No      No  Positive  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d952bb53",
   "metadata": {},
   "outputs": [
    {
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       "      <td>46.36</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>48.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = round(diabetes_df[diabetes_df['class']=='Positive'].describe(include='all'),2)\n",
    "neg = round(diabetes_df[diabetes_df['class']=='Negative'].describe(include='all'),2)\n",
    "pos_neg = pd.concat([pos, neg], axis=1, keys=['Positive','Negative'])\n",
    "with pd.option_context('display.max_columns', None):\n",
    "    display(HTML(pos_neg.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a5798ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(diabetes_df[diabetes_df['class']=='Positive']['age'], alpha=0.4, bins=np.arange(15, 90, 5),label='Have Diabetes')\n",
    "plt.hist(diabetes_df[diabetes_df['class']=='Negative']['age'], alpha=0.4, bins=np.arange(15, 90, 5),label='Have No Diabetes')\n",
    "plt.title('Frequency Distribution of Age',fontsize=18)\n",
    "plt.xlabel('age',fontsize=14)\n",
    "plt.ylabel('Count',fontsize=14)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ddb69d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>polyuria</th>\n",
       "      <th>polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>polyphagia</th>\n",
       "      <th>genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>itching</th>\n",
       "      <th>irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>alopecia</th>\n",
       "      <th>obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>85</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>90</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>79</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>85</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>90</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  gender polyuria polydipsia sudden weight loss weakness polyphagia  \\\n",
       "101   85    Male      Yes        Yes                Yes      Yes        Yes   \n",
       "102   90  Female       No        Yes                Yes       No         No   \n",
       "113   79    Male       No        Yes                Yes      Yes        Yes   \n",
       "133   16    Male      Yes         No                Yes       No        Yes   \n",
       "185   85    Male      Yes        Yes                Yes      Yes        Yes   \n",
       "186   90  Female       No        Yes                Yes       No         No   \n",
       "\n",
       "    genital thrush visual blurring itching irritability delayed healing  \\\n",
       "101            Yes             Yes     Yes           No             Yes   \n",
       "102            Yes             Yes     Yes           No              No   \n",
       "113            Yes              No     Yes          Yes              No   \n",
       "133             No              No      No           No              No   \n",
       "185            Yes             Yes     Yes           No             Yes   \n",
       "186            Yes             Yes     Yes           No              No   \n",
       "\n",
       "    partial paresis muscle stiffness alopecia obesity     class  \n",
       "101             Yes              Yes       No      No  Positive  \n",
       "102              No              Yes      Yes      No  Positive  \n",
       "113             Yes               No       No      No  Positive  \n",
       "133              No               No       No      No  Positive  \n",
       "185             Yes              Yes       No      No  Positive  \n",
       "186              No              Yes      Yes      No  Positive  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_df[(diabetes_df['class']=='Positive') & (diabetes_df['age']<=20) | (diabetes_df['age']>=75)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079dca25",
   "metadata": {},
   "source": [
    "* Based on the frequency distribution of age, we found out that most of people who have diabetes have age of 35-39. There are also patients who aged of 16 as well as between 75 and 90 in the dataset. \n",
    "* Based on the extraction of data above, it is found that all of these minority patients sufferred from sudden weight loss and did not suffer from obesity. Hence, it is suspected that sudden weight loss would be an important variable in the diabetes detection analysis, and vice versa for the obesity variable. However, it requires further analysis to confirm these relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b9d022b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1296x1440 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(18,20), facecolor='#F2F4F4',constrained_layout=True)\n",
    "\n",
    "count=1\n",
    "for i in diabetes_df.columns[1:-1]:\n",
    "    plt.subplot(5,3,count)\n",
    "    abs_val = diabetes_df[i].value_counts(ascending=False)\n",
    "    a = sns.countplot(diabetes_df[i], order=abs_val.index)\n",
    "    rel_val = diabetes_df[i].value_counts(ascending=False, normalize=True).values*100\n",
    "    lbs_val = [f\"{a[0]} ({a[1]:.2f}%)\" for a in zip(abs_val,rel_val)]\n",
    "    a.bar_label(container = a.containers[0], labels = lbs_val, fontsize=8)\n",
    "    plt.title(('number of respondents by ' + i).title(), fontsize=13)\n",
    "    count+=1\n",
    "    \n",
    "plt.suptitle('Univariate Analysis of Variables',fontsize=24);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d35f95",
   "metadata": {},
   "source": [
    "# Multivariate Analysis & Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "715e64e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder = LabelEncoder()\n",
    "for i in diabetes_df[1:]:\n",
    "    diabetes_df[i] = labelencoder.fit_transform(diabetes_df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86d29945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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581++XunSpTlw4ADZs2cnefLkZMuWjRQpUqBSqdi/fz+HDh2iffv2zvUzZMjAmTNnKF68uEsn6tXDf17VQVzXiw9JkiShfPnybNmyhe7du7N9+3Y+++wz50WPu3fvMmvWLGrVqvXWiPr7pv7GxmQyMXLkSIoVK8bcuXNdLtJMmjTprfVfvHhBZGSky3T1GzduAMQ6fTZ58uR4eXlhtVrf2q/37t3j3LlzeHl5YbPZuHDhAr6+vlSsWNH5ILCNGzfStWtXfvnlF3r27Pm3fz8hhBD/ntwjKoQQ/0LDhg1Jnz49o0aN4tKlSy5lNpuNgQMH8uTJE7755ht0Oh25c+cmVapULF26lMjISOe6kZGR/Pzzz6RKlYo8efL87Rx58uRxjtJFRUW5vG6XLl3o1atXrKNY4eHhAG899XTFihUYDAasVqtz2avO0rtGPjUaDaVLl2bfvn2cPXvWuVxRFGbPno1KpaJcuXJ/+3f7O+x2O5s3b0an01GwYEHA8WdwLly48NY9lzNmzKBz585cvnzZmf/Va7yuTJky3Lp1i3Xr1lGkSBFUKhXJkiUjR44czJ07l+joaJffq0KFCoSHh7N06VKX11m2bBldu3blwIEDf2u9+FKjRg3u3r3LsmXLePnypcuFk1dTsd88Dnbt2sWNGzdcjoO4MBqNGAwGAgMDXTqh58+f59ChQwAur2m3212e/mu1WlmwYAHe3t6xTl3WarWUKVOGXbt2ceHCBZeykSNH0r59e54/f47NZqN58+YMHz7cZZ38+fMD8T8iL4QQIu5kRFQIIf4FT09Ppk6dSsuWLalXrx41atQgb968hIeHs3nzZs6fP0+1atX46quvAMe02379+tGlSxfq1q1LvXr1AFi5ciWPHj1i8uTJ/+jk+PXXrVOnDvXq1cPT05NffvmFe/fuMXbs2FinDoeGhuLr68uIESO4d+8eSZMmJSwsjI0bN+Lp6enSqX11D9+cOXMoU6aMy5+ZeaV79+6EhYXRrFkzmjVrRqpUqdi6dSsHDx7kq6++ivXPfPxTJ06ccOlcR0ZGsnbtWk6cOEHr1q2deVu3bs2WLVvo0KEDjRo1IkeOHBw9epTffvuNMmXKUKZMGSDmvsc//viDdOnSUaVKFfz8/ChRogQ6nY7Tp09Tq1Yt5/sVLVqUxYsXkz59epffq379+qxZs4YhQ4Zw9uxZ8uXLx6VLl1i+fDm5c+emTp06f2u9+FK2bFn8/PyYNGkSmTJlct6HDI4OaLp06Zg5cyYmk4k0adJw6tQp1qxZ89ZxEBd+fn7kz5+f1atX4+vrS5YsWbh8+TK//PKL8/iOiorCz88PAC8vLyZPnsz9+/fJlCkTGzdu5Pjx4wwYMMA55flNr461Jk2a0KRJE9KlS8fOnTvZsWMHDRs2dP6JoVf3DLdv357SpUtjNBpZvnw5Xl5e1K1b959UpRBCiHggHVEhhPiXcuXKxW+//cb8+fPZvXs3GzduRFEUgoODGT58OHXq1HG5B65q1arMnTuX6dOnM23aNLRaLfnz52fYsGHv/FufcfHqdWfMmMH06dNRq9XkyJGDGTNmUL58+Vi3SZkyJbNmzWLs2LFMnz4dDw8PsmTJwvjx4zl16hQLFy7kyZMnpEyZkurVq7NlyxZWr17NoUOHYu2IZsqUiRUrVjBx4kSWLVuG0WgkW7ZsDBs2zNnpji/Lly9n+fLlgGMarre3N0FBQQwePJgGDRo41/P392f58uVMnjyZzZs3s3z5ctKlS0e7du349ttvnR0jLy8vunbtyk8//cTQoUPJlCkTxYoVw8fHh0KFCnHw4EGXJ8UWKVKExYsXO5+W+4qHhwfz589n2rRp/P7776xdu5bUqVPTuHFj2rdvj5eX199aL754eHhQtWpVVqxYQbNmzd4qmzVrFiNHjmThwoUoikKmTJno3bs3VquVYcOGcebMmb81Wj9p0iRGjBjBqlWrMJvNpE+fnm+//ZZs2bLRsWNHDh48SNWqVQHH/bCjRo1i+PDhLFmyhMyZMzNmzJhYp7u/8upYmzx5MitWrCA6OpqMGTPSq1cvl9+vU6dO+Pv7s2rVKkaNGoVGo6FgwYKMGTNG7g8VQohEpFL+ziMRhRBCCCGEEEKIf0lujhBCCCGEEEIIkaCkIyqEEEIIIYQQIkFJR1QIIYQQQgghhIuTJ0++9UwBgD/++IO6devSsGFDVqxY8Y9fXx5WJIQQQgghhBDCafbs2axdu/atB+dZLBZGjBjBypUr8fLyonHjxpQvX55UqVL97feQEVEhhBBCCCGEEE6ZMmViypQpby2/evUqmTJlws/PDw8PDwoVKsSRI0f+0XvIiOgHZoMuOLEjxIn94Nn3r/QByJz0WWJHiJPnZt/EjhAnz6M9EztCnAQle5jYEeLE3/YksSPEyYmonIkdIU5KsSuxI8RJeNKMiR0hTl4oyRI7Qpx4q//e3zhNLBY8EjtCnPjaXyR2hDjR2K2JHSFOrtuyJnaEODFYdYkdIU4q5fs4zkNe96Ge20cuHuz8E2gADRs2pGHDhs6fq1atyp07d97eLjLS5e87+/j4EBkZ+Y8ySEdUCCGEEEIIIf5D3ux4xpWvry9RUTEXAKOiolw6pn+HTM0VQgghhBBCCPFe2bJl4+bNm4SHh2M2mzly5AihoaH/6LVkRFQIIYQQQggh3EClUyV2hHixbt06oqOjadiwIT179qRVq1YoikLdunUJCAj4R68pHVEhhBBCCCGEEC4yZMjg/PMsNWrUcC6vUKECFSpU+NevL1NzhRBCCCGEEEIkKBkRFUIIIYQQQgg3UGv/f0zNdQcZERVCCCGEEEIIkaCkIyqEEEIIIYQQIkHJ1FwhhBBCCCGEcAOVTsb93kVqRgghhBBCCCFEgpKOqBBCCCGEEEKIBCVTc4UQQgghhBDCDeSpue8mI6JCCCGEEEIIIRKUdESFEEIIIYQQQiQomZorhBBCCCGEEG6g0snU3HeREVEhhBBCCCGEEAlKOqJCCCGEEEIIIRKUTM39f86/aD5ChnfnYKXmCf7edrud1fOGcP/mRTQ6Dxp8M4iUaTI7y88e3cHWNTPQqLUUKVeb4hXqA7D9t9mcO7oDq9VCycqNKFa+LndvnGfV3MGo1RpSpQ2k/jeDUavj/zqK3W5nzvTx3Lh+BZ1OR5tOPUibLoOzfO+ubWz4bQVqtYbMgdn4ut13zhyXL55l8byZDBo5Jd5zxZbz51nDuXPjElqdB83b9Sd12kwu65hMBiYObEvz9gNImyELdpuNhTOG8PDuDdQaNV92GETqNBndku3X+YO5f+siWq0Hdb8e7LLfzx3bwfY1M1BrNBQuW4di5R37fVKfOui9kgCQLFV6GrQezpKp3YgMfwLA8yd3yZg9P006jHNL5h+nT+TG9avodDrad/qetOnSO8v37NzOut9WOvZ7lqy0btcFRVGYPmUs9+7cRq3W0KHrD6RNm/4v3iV+ck6cOYer12+g0+n4vkMb0qdL6yzftf8gS1f+ikoFn1WtTPUqFbHZbIyd+iO3795Do1bzQ+d2pE+bxi3ZVs4dwr1bl9BqdTT8djCp0sQck2eO7mTL6hmoNVqKla1NiYr1sFkt/DyjD88e30Wl1tDwm4EEpM/q3GbNwlGkThtIqcoN4z3vq8yj5i7n8q27eGi19Pn2CzKmSf3WesNn/0xSX286NP485ve5cp2pP//GzP5d3JLtzZxTps/k2vXr6HQ6unbqQPp06ZzlO3buYvVv69Co1WTJEkjHdm2w2myMmzCJ+w8e4O3tTce2bUifPt1fvEv85PxY2s/p06Zy/fo1dDodnTp3Jd1r9blz5w5++/VX1Bo1WQKz0K59B7Zv38b2bVsBMJstXLt2lcVLluLr6+vWnD9Om8SN61fR6nR06NzdpV3avXM7635dhVqjJjAwG63bd3bWZ3j4c7p1asOgYWPIkDHTu97CLZknT//R2UZ169TepY3ave8Ay1auRgVUr1aF/1WtnKDZPtT2882ci38cye0/v99btO9HQFrX72qTycC4ge35qn0/0mbIAsDA777Ay9txPKYMSEerjgPdnnP5nGHcvXERrc6DL9oMfOs8xGwyMGVIa5q0HUSa9I6cES+eMa5vc/qMW4XOw9OtGT8U8tTcd5MR0f/Hsnb7mrw/DkWtT5wP+tkj27FaTHQc/DPVG3Vl3ZIxzjKb1cLaxaP4tuds2vafz8E/fuFl+GOunDvEjUvHaT9wMe36zyf86QMAtq6eQeXabekwcDFWi5nzx3e5JfPhg3swW0wMHzeTJi3asPCnac4yk8nEskWzGTh8MsPGziA6OpKjh/cD8NvKJcyYPBqLxeyWXG86cWgHFouZniMXUqdpJ36ZP96l/MaVs4zt24rHD+84l508shuAHiPmU7NRO36ZF/8dOoBzR7djtZhpP3Ap1Rp9x4afRzvLbFYL6xePpFXP2bTuu4BDf/xCRPhjLGYTAK37LqB13wU0aD0cgCYdxtG67wKadZ2M3jsJNZr0dEvmsAN7sZjNjBo3jWYtvmXenOnOMpPJxJJFcxkyYgIjx00lOiqKI4cOcOTQAQBGjJ1K46ZfMW/29He9fLzZe/AwZrOZaWOG823zJkyfu9BZZrPZmL1gCWOH9GPq6GEsX/MbL16+5MDhowBMHT2Ur5o0ZPpPC9yS7cwRx37vMngJnzXuytrFrp/33xaNok2vWXToP58Df/zCy/AnnDuxB5vNRufBS6hapw0bV0wGIPLlM34c2YazR3e4Jesru46cwmyxMHdwd9o3rsWkxavfWmf1tj1cuX3PZdnCtVsZNutnzBaLW/O9sv/AQcxmM5PGjaFVi+bMmjPXWWYymZi/aAljRgxj4rjRREVFEXboMJs2/45er2fy+LG0b/MtU2f+6PacH0v7eeDAfswWM+PGT6TFVy2ZM2eWS85FCxcyYuQoxo2bQFR0FIcOhVG5chVGjhrDyFFjyJ49O63btHVrJxQc7ZLZYmbU+Kk0/+ob5s2Z4ZJzycK5DB05nlHjphIdHelsk6xWKzOmjMczEU7y9x0Mw2yxMGXcKL5u0YyZP81zltlsNn6av4jRQwcyeexIVqz+lRcvXiZYtg+5/Xzd8bCdWCwm+oyaT71mHVk+b4JL+fUr5xjZ5xsePYj5fn/1Hdpj6Cx6DJ3l9k4owKnDf2Axm+g+fDG1mnRm9cKxLuU3r55lQv+vePzgtnPZuRP7mDq0DREvnro9n/g4SEf0H4iMjKRz5860bNmS2rVr8/PPP3Pq1Cnq1q1L8+bN6dq1Kz17Ok6YFy1aRMOGDWnUqBELFy58zyvHr+hrtzhav2OCvufrrl88RnC+TwDInCM/t6+ddZY9vHeNlAGZ8Pb1Q6v1IEtwQa5fOMalU/tImzGIBRM6MXdse3IVLAtAusAQoqNeoCgKJmMUGq17BvPPnz1FaMFiAASF5Obq5QvOMp1Ox9AxM/DU6wHHF5eHzgOAgLTp+b73ULdkis2V88fJHVoSgKzB+bh59ZxLudVioW2P8aRJH+hcFlqsPM3a9gXg6eN7JPVP4ZZs1y8eI+jVfs+enzvXY/b7o3vXSBGQGW8fx34PDC7I9YtHuX/rAmaTkTkjv2bW8K+4eeWky2tuXTWVklWakjRZKrdkPn/uNKGFigIQHJKLq1cuOct0Oh0jx05x3e8eHhQr8QntOnZ3/F6PHuDvn8wt2V53+vx5ihYMBSBXSBCXrlx1lmk0GhZMn4ivjw8vIyJRFPDS6/mkeFG6d2gNwINHj0nm7+eWbNcuHickfykAAt/8vN99/fOuI0twQa5dOErqtJmx263Y7XaMhig0Gsfn2mSMplq9dhQuXcMtWV85cfEqJfLnAiBvjiycv3bLpfzUpWucuXKDOhVLuSzPEJCSUV2/cWu21505d57ChQoCkDMkhEtXrjjLdDodE8eOQv/nBUebzYbOw4Obt25TpHAhADJmyMCt27fffuF49rG0n+fOnqVQocIAhITk5Mrlyy45x44bj/6Nz/srly9d4tatm3z66f/cnvP82TMULFQEcLRLVy5fdMk5apxru6T7M+f8OTOp9r+aJEvhnjb+r5w5e54izjYqmEuXXduouTOn/NlGRaAoCl5e+gTL9iG3n6+7fP4Eef78fs8WnJcbb32/m+nYcyxpX/t+v3XjEiaTkXED2zG6X2uuXjzt9pxXzx8nV6ijbcwSlJ9bseT89vuJzpFQAJVKTcf+s/D2dX89io+DdET/gZs3b1K9enXmzp3LzJkzmT9/PgMGDGDkyJEsXLiQTJkcUxOuXLnCxo0b+fnnn/n555/Ztm0b165dS7CcD9ZsQbFYE+z93mQ0RKH3TuL8Wa1WY7M58piiI13KPPU+GA0RREU85871szTrPJ66LQfw87QeKIpCqjSZ+XXBcEZ3r0HEi6dky1nULZkNhii8fWKucqs1MZnVajX+yZIDsGndSoxGA/lCHScJxUuVc1vnODbG6CjnFBwAlVrjzAmQPWcBkqd8e/qQRqNl3uR+LJszmoIlKrklm8kQid4lW0wdGt8o89T7YIyOROfpRZnqX9Gqx2xqfzWAZdN/cG4T+eIpV84epHCZz92SF8AQHY23j4/zZ8exanP+/9V+37B2NUajgfyhjpNYjUbDpPEjmDNzCiU/Keu2fK9ERxvw8fGONeerPLv3h/F15+7ky50TjUbjXD5iwlSmzJpL2VIl3JLNaIjE67XPtOt+j3LZ73ovH4zREXjovXn2+B4ju9VgxewBlK7WBIAUqTOQOXs+t+R8XZTBiK+3l/NntVqN9c/6fPL8BbNXbeSHr96eFlyhWCharcbt+V6Jjo7G5y+Oz2TJHBdBfl27HoPRSKHQAmTLmoWwQ4dRFIXzFy7w9Okzl2PFHT6W9jM6Ohof7/fX59q1v2E0GAkNLehcd/mKZTT+ommC5fR2yamJtV1av3Y1RoORAqGF2b51M0n9/Aj9swOb0KINb7RRmrfbqD37D9C643fky5Pb2UYlSLYPuP18ncEQ6fL9/vq5E0COWL7fPT31VKvVlO8GTKN5m97MmtDHZRt3ML4nZ7aQUJK9kTNn/hL4JvF3a64PkUqn+iD/fQikI/oPpEyZkm3bttG9e3dmzJiB1Wrl0aNH5MiRA4BChRxXoS9dusS9e/do0aIFX375JeHh4dy6deuvXvr/Fb2XDyZjlPNnRVGcIx6e3r6YDDFlJqOj0+rt609QvlJotR6kTpcFrc6DyJfP+HXhSNoPWESPcespXLom65aMfuv94oOXlw8GQ3RMZntMZnDcE7Hwp2mcPH6E7r2GolIlzgdZ7+2DySWn3SXnX/mq0xCGTP2VRdMHYzIa4j2bp5frvn29DvVevi7HhMkYhd4nCanSBFKwVA1UKhWp0gbi7etPRPhjAE4f2kJoyeqo1e47YfHy9sb4Vn3GvJ/dbmf+nBmcPH6UHr0Huez3zt/1YtqsRUyfPA6jG+rzdd7eXkQbYt7DrihvnciVKVmMX+b9iMVqZcuO3c7lvbp2YNHMSYybOhOD0Rjv2fRevhgNsX/eHW1BTP0aDVHofZKya+MiQvKVpPeEDXQftYqfZ/RxTjFLCD5eeqIMMXWhKAraP+tze9gxXkRE0mXUdBas3crv+46wfteBBMv2Om9vbwyv7XfHZ8r1+Jw1Zy7Hjp+gf+9eqFQqqlWpjLe3N9/36sPBsMPkyJ7N7Sf9H0v7+WZ92mOpzzlzZnP8+DF69+nrzBkZGcmd23fInz9/ouSMrV2a96pd6jMQlUrF9i2bOHn8KH16dOX6tStMGjeC58+eJUheAG8vL6Jf/0zZ326jSpcswbIFc7BYrGz9Y2fCZfuA28/XeXn5YnzHudO7BKTLTImy/0OlUpEmfWZ8k/jz4vkTt+Z0tPmvfd6VuJ+HCPGKdET/gblz51KgQAHGjh1LtWrVUBSFNGnScOXP6VInTzqmFWbNmpXs2bOzcOFCFi1aRJ06dQgKCkrM6AkqMDiUCyccDfnNyydJkzGHsywgXVaePLhJdGQ4VquZa+ePEpijAFmCC3Lx5F4UReHF80eYTQZ8kvjj7eOH3stxZThpstQYotxzX0lIrrwcO+I42bx04SyZArO6lM+aOgaz2cwPfYc7p0QlhmwhBTh9bC8A1y6eIn3m7O/d5sDO9Wxa9RMAHp56VGq1Wx74FBgUysWTewC4ecV1v6d+Y79fv3CEzNkLcHjXatb/eXHh5fNHmAyRJPF3TMO9fPYAwfnKxHvO1+XMlYejh8MAuHjh3Fv7fcbU8ZgtZnr2G+Lc7zv/2MKqFUsA8NR7olKr3NpZBsiTM4SwI8cAOHfhElkzxzwYIio6ms69+mO2WFCr1Xh5eqJSqdiyYxdLflnjyPnnMo0b9nuWoFDOn3Ds9xuXT5L29c97+qw8fnCTqMgXWK0Wrl04SmCO/Hj7JHXOjPD28cNmtWK3u3fU7nX5g7Ky/4RjCvHpy9fJljHmgTUNq5Vn4fCezOzfhS9rVqZqqcJ8Vtb9oyGxyZ0rJ4cOHwHg/IULBAZmdimfNHU6ZouZgf16O6foXrx0mTy5czF25HBKlSxOmjTufcAKfDztZ65cuTl85BAAFy6cJzAw0KV86pTJWMxm+vUb4JyiC3DmzGkKhBZIsJwhufJw9EhMu5T5zXZpyngsZjO9XmuXho+ZxLDRExk2agJZsmanc7deJEuePMEy586Vk0NHHPdVnrtwkSyBrm3Udz37ONsovd4TtTrhLkZ8yO3n67LnzM/po/sAuHrxNOkzvf/7fe/231g+33Ev6fNnjzEYIvFLltKtObOGFODsMUebf/3SSdJlyvGeLYR4m1y6+AfKly/PwIEDWbduHf7+/mg0Gvr370/v3r3x9vZGp9MREBBASEgIJUqUoHHjxpjNZvLly0dAQEBix08weQpX4tLpA0wZ0AQUhYath3Js33rMxmiKV2xAjaY/MGvktyh2haLlauOXPAC/5AFcu3CESf0aotgV6rToi1qtocE3g1g8pTtqtRaNVkf9bwa5JXPREmU4dfwIfbq3RVEU2nfpxZ6dWzEaDWTLHswfWzcQkjsfg3p3BuB/NetTrKR7O0mxCS1WgfMnDzKy15egKHzZYRBhuzdhMkZTpkrdWLcpWLwi86cOYEzfltisVhq27O6WJ9blLlyJy2f2M23QF6Ao1P92GMf3O/Z7sQoN+KxJD34a9S2KYqdw2Tr4JQ+gSLk6/PJjH2YMdkx5q/fNUOeV1cf3r5M8dYa/est/rViJ0pw4fpSe3TqgoNCxSw9279yG0WAgW45gtm/ZSM7ceenf+zsAPqtZl+IlSzNlwij6/NAZq81Ky2/au9xL5g6lixfl6IlTdPihD4qi0KNze7bt2oPBYKRGtcpUKluazr36o9VoyBqYmcrlSmO2WBg1aTqde/bHarPS/uuv3JIzb5GKXDy9n0n9m6AAjVsP4ei+DZiM0ZSsWJ9aTX/gxxHfoigKxcrVxj95AGX/15ylM/sxeWBzbFYL1Rt1xlPv/d73ii/liuQn7PQFWvUfiwL0b92UzfsOYzCaqF3xkwTL8T6lShTn2PETdOn2AwoK3bp05o+duzAYDATlyMHmLVvJkzsXP/R23AP+ec0a5M2dmwWLlrBy9Rp8fHzo1tn9zwv4WNrPEiVLcvz4Mbp16wqKQpeu3di5YwcGo4EcOXKwZcvv5M6dh969egBQs9bnlCxZirt37pAmTdr3vHr8KV7yE8doZ7cOoEDHrj+wa8d2jEYD2XMEsW3LJnLlzku/Xt0AqFGrDsVLlk6wfLH5pEQxjh0/QafuPVEUhe+7dGT7zt0YjEY+q1aFCuXK8l2PPmi0GrIGBlKxnPtvaXjlQ24/X1ewWHnOnQhjWM+vQFFo2XEAB3dvwmg0UK5Kndh/t4qf89OUAQzv1RKVSkXLDgPcPjqZv2hFLpw6yNg+zUBRaNp+CIf3bMBkNPBJ5Xpufe+PjTw1991UiqIoiR3i/4MlS5bw6aefkjx5ciZMmIBOp6NDhw5/+3U26ILdkC7+2Q+eff9KH4DMSRNuStK/8dzs3qcvxpfn0R/Ho9aDkj1M7Ahx4m9z79Sp+HIiKmdiR4iTUrjnadrxLTxp/P/ZJHd4obj/AVzxwVsd9f6VPgAW3NuBiS++9heJHSFONPbEewbG33HdlvX9K30ADFZdYkeIk0r5Po7zkNftylkgsSPEquz5E4kdQUZE40uKFClo2bIl3t7eJEmShJEjRyZ2JCGEEEIIIYT4IElHNJ5Uq1aNatWqJXYMIYQQQgghxAdCpZGpue8iDysSQgghhBBCCJGgpCMqhBBCCCGEECJBydRcIYQQQgghhHADtUzNfScZERVCCCGEEEIIkaCkIyqEEEIIIYQQIkHJ1FwhhBBCCCGEcAOVWqbmvouMiAohhBBCCCGESFDSERVCCCGEEEIIkaBkaq4QQgghhBBCuIFKI+N+7yI1I4QQQgghhBAiQUlHVAghhBBCCCFEgpKpuUIIIYQQQgjhBmqNPDX3XWREVAghhBBCCCFEgpKOqBBCCCGEEEKIBCVTc4UQQgghhBDCDVRqmZr7LjIiKoQQQgghhBAiQUlHVAghhBBCCCFEgpKpuR8Y+8GziR0hTtTFcyd2hDiZ2HdPYkeIE0PUk8SOECceeo/EjhAnQbkCEjtCnDx44JvYEeLkm+rPEztCnAzYXDyxI8RJcI6PY7/rPRM7Qdw8fmZP7Ahxcmj3jcSOECclyudM7Ahx8kXIycSOECdXH38cn/dZY3cldoQ4qbSubGJH+NvkqbnvJiOiQgghhBBCCCESlHREhRBCCCGEEEIkKJmaK4QQQgghhBBuoJKpue8kI6JCCCGEEEIIIRKUdESFEEIIIYQQQiQomZorhBBCCCGEEG6gUsu437tIzQghhBBCCCGESFDSERVCCCGEEEIIkaBkaq4QQgghhBBCuIFKLU/NfRcZERVCCCGEEEIIkaBkRFQIIYQQQggh3EAtf0f0nWREVAghhBBCCCFEgpKOqBBCCCGEEEKIBCVTc4UQQgghhBDCDeRhRe8mI6JCCCGEEEIIIRKUdESFEEIIIYQQQiQomZorhBBCCCGEEG6gUsu437tIRzQe7N69m40bNzJy5MgEeT+73c7qeUO4f/MiGp0HDb4ZRMo0mZ3lZ4/uYOuaGWjUWoqUq03xCvUB2P7bbM4d3YHVaqFk5UYUK1+XuzfOs2ruYNRqDanSBlL/m8GoE+ED4180HyHDu3OwUvMEf+835Q/yoGYZH2x22HvCwO5jRpfy5EnVtKyVFLUaVMCC9RFEGxXa1EvqXCdTGi0rt0Wy86gRdymY04s6lZJis8Ouw5H8cSjKpdzPV02HxinRaiH8pY0ZK55htigAeOhU9P4mNbN+ecq9x1a3ZQQoEOxJrXK+2O0Ku48Z2HXU4FKe3E/N15/7oVarUKlg3m8vePDURol8eqqV9MGuwJ5j0fxx2PCOd4gfOdLBJ7nV2BU4eU3hxDUl1vWKBKnw1cOOU47yXJlUFA1WoSjwKFxh05HYt4svebJoqVbME7uicPCshf1nLC7lyZKoaFLZi1cf42XbjTx6bqdQsJZyoZ7Y7Qr3nthZ8YcRdyW12+3MmT6eG9evoNPpaNOpB2nTZXCW7921jQ2/rUCt1pA5MBtft/vO2e5cvniWxfNmMmjkFDelc5U7UEPVoh7Y7XDwvIWDZ10/D/6+KhpX8kSjUoEKVvxh5FG4o+Z0Wmhby4tlfxh59Dz+a/PamT8I2zwNlUZL7mJ1yVuygUu5IfIZmxZ2x2ox4uOXmipfjEDn4cWxHfM4c3AlXr7JAajYYBB+KTOy9efevHx2F5vVTNEqbcmWt2K85Lx86g/2bZiGWq0lX8m6FCjtmjM68hlrf3Lk9PVLTfUvHTkBLGYDyyZ+xf+aDyNFmmzObaJePmX+iDo06jzXZbk73Dq/g+N/TEel1hBUuA4hRRrEut6ZfQswRDyhSLVubs3zukK5valfzR+bDXaERbDtQIRLuX8SDZ2bp0argecvbUxd8hizRaFYfh9qV/JDUWDb/gi2H4x4xzv8czfP/cGx7Y56Cy5cl5zFXOvNGPWc7Uu7YbOY8E6amnL1h6P18OLSsd84tesnPPRJCCpUm5Ci9ZzbPLp1krBNY6nRelG857Xb7UyY+RNXb9xEp9PxfYfWZEibxlm+a38YP6/6FZVKxWdVKvJZlYrYbHbGTvuR23fvoVar6dGpLelf2+bfunTyD/aun4ZaoyV/qbqEvvnZiXjGr3P+/Oz4p6bGlyPQeXq9c7s5Qz7H0ysJAP4pM1CjxQjna50JW8eRHYtp0XN5vOUvVSQFLRpnwmaDDVvvs27Lg1jXy5/bj/7dQqjbMgyAhrUy8FmVNIS/cHx/jZ52idt33fsdLz5M0hH9CJ09sh2rxUTHwT9z8/JJ1i0Zw1fdpgJgs1pYu3gUnYcsx0PvxdSBTclVsByP7l3nxqXjtB+4GIvZwM718wHYunoGlWu3JWdoGZZM/YHzx3eRu1D5BP19snb7mvRNa2KLSvxGSKOGRlV9GTL7OSazQu+WyThx0czLKLtzndrlfdh+KJrjF83kzuZB3Yo+TFvxktELwgHIlkFLnQq+7Drmvk6oRg3NavjTd8oDjGaFQe0COHrOwIvImJy1yidl99Eo9hyLom5lPyoW92XTngiyZvCgVZ1kJPdz/8dfo4YvqiVh4I9PMVkU+n6dghMXTS4561ZIwrawaI5dMJEnuwf1KydhyrJwGlVNQu+pTzCaFUZ0SMnB00aije7pOqlVUClUzbwtdsw2+LKimsv3FKJe24VaDfyviIr0KVRcuK04l5XLp2LWJjtWG3xeQkWOdHD5nltiolZDnbJ6xiyNxGyBrg18OH3NSkR0TL1UL+HJ7pNmTl21EpJZQ81SnizYZKB6CT0jFkdisUKLT73InVXLmWvuuQhx+OAezBYTw8fN5NKFsyz8aRo9+jlOiEwmE8sWzWbc1AV46vVMHD2Qo4f3U6TYJ/y2cgm7dmxBr9e7Jdeb1Gr4vLQn41dEY7ZA53penL1uc6nP/xX3YO8pC6ev2QjJpKF6SU/mbTSSMbWa+uU88fd1z0MobDYLu9aMoHG3leg8vFgxsTFZ85THJ2kq5zphv08nuNBn5C5Wh8NbZ3F633IKlm/Bo9tnqdp0FAEZ8zjXPXtwFXoff6o1G4Mh6jk/j64dLx1Rm83C9l9G0KLnSnSeXiwa05js+crj6xeTc9+G6eQq8hn5StbhwOZZHN+9nKKVWnD/5ml+XzKAiPCHb73m5iX90ercfxzYbRYObhhJrfYr0Oq8WP9jEzKFlMc7SUx+q8XI3jX9eXz7JIG5q7g90ysaNbSonYKe4+5iMtsZ2iUdR85EEx5hc67zeSV/dh6KYNfhSBpUS0aVUknZuOsFTWskp8fYOxhNChN6ZeDQ6SgiXvse+7fsNgsH1o+kdodf0Hp4sXbGF2TO5VpvR7dNI3uBzwguXIcTO2ZxLmw5QQVrceT3SdTpvBpPfVI2zPmK9NmLkyR5Bk7snMPl47+h03nHW87X7Q07jNliYfrooZy9eIkZcxcxrM/3ANhsdmYt/Jkfx43AS6+nRYfv+KR4EU6fuwjA1FFDOH76LNNf2+bfslktbFsxgq96r8TD04sFoxqT443Pzp4N08ld7DPyl6zD/k2zOLZ7OYXLN4l1O72342J4s+5vd+If3D7PyX0rUZT4+w7VaFR0/Dob33x3DIPJxozRBdh36CnPwl0vjqZO6UmjzzOg1cYMcgRl82Xo+AtcvBoZb3nEx+k/O1ZsNBrp1KkTjRo1omvXrnzyySdcvHiRZs2a0axZMzp27EhERARhYWF8/fXXtG3blho1ajBjxgwArl69SsOGDWnRogVLly51vu6mTZto2LAhjRs3ZuzYsQBMmTKFli1b0qhRI65evfqvs1+/eIzgfJ8AkDlHfm5fO+sse3jvGikDMuHt64dW60GW4IJcv3CMS6f2kTZjEAsmdGLu2PbkKlgWgHSBIURHvUBRFEzGKDTahL82EX3tFkfrd0zw941N2pQaHj2zEW1UsNnh8m0LQZl1Luss3xLJqctmwHGiYHnjXL7Jp0lYtCGCeGzv35I+tY6HT61EGRRsNrh4w0RIFk+XdRauC2fv8ShUKkjhp+HFnycvWg2MW/CEe48ssb10vEqXSsvDV/Vpg8s3zW/V59LfX3LykgkAjVqFxeqouNsPrXh5qtFpHSOl7pQyKTyPBKMF7Ha4/UQhYyrXdbRqOH1DYd+5mB1rtcGCrY5OKIBapcIaf+d6b0mTXM3jcDsGE9jscO2elWzpNS7rrNlt4sx1x0GpUamwWB05J6yIch6rahVYre47QM+fPUVowWIABIXk5urlC84ynU7H0DEz8Pyzs2mz2fDQeQAQkDY93/ce6rZcbwpIpubJi9fr00bWdK5fi7/tNXH2hmMHq9Ux9abVwNyN7hkJBXj24Cr+KTOh9/ZDo/UgXdZC3L16xGWdu9eOEpizNACBucpw69J+AB7eOcvhrbNYMbExh7b+CECO0GqU/F9n57Yqjetx8089vX+VZKkyofdx5MyQrRB3rrjmvHPlKFlzO3JmzVOGmxccOW0WM3XaTCN5QFaX9f9YOYrQMo3w9UsdLxn/SvijayRNkQlPL0f+gMCCPLxx1GUdm9VE9tBa5C/Xxu15XpchjQcPnliIMjjamAvXTOTM5to5n7/mKbuPRDra+WQawiOs2BXoPPw20UYFXx81KhUYTfF7nD5/VW/er+qtEA+uu9bbgxvHyBjs2O8Zg8tw98oBXj67TYp0Iei9/VGp1aTKkJeHt04CkDRFRqo0c99MiNPnLlI0ND8AuYODuHgl5nxMo1GzYNp4fH28eRkRgQJ46fWULl6Ebu2/BeDh4yck8/eLtzxPHlwlWepMeP352cmYvRC3L7t+dm5fPkq2Pz872fKU4fr5/e/c7uHtC1jMBn6e0JLF45pz99oJAKIjn7Nj9VgqN+gdb9kBAjN6c/e+gYgoK1arwqlzL8mf27V+PHQqurfLwbgZl12WB2f3pWn9TEwfVYCm9TLGa64PkUqt+iD/fQj+sx3R5cuXkyFDBpYtW0aHDh14+vQp/fr1Y8CAASxatIgyZcowZ84cAO7du8eUKVNYvny5c9mkSZPo1KkT8+fPJzQ0FIDw8HCmTJnC/PnzWbp0KQ8fPmTfvn0AZM2alWXLlpEt27+fYmQ0RKH3TuL8Wa1WY7M5zjBN0ZEuZZ56H4yGCKIinnPn+lmadR5P3ZYD+HlaDxRFIVWazPy6YDiju9cg4sVTsuUs+q/z/V0P1mxBebM3l0i8PNUYXht1M5oUvDxdP6yRBkcnNU0KDQ0q+7J2V8yU2AJBHtx9ZOXBUxvu5KVXE22M6fEYTAreXm9/nNUqGP1dWnJl03PphqOzd+mmmWcv3JvvFb2nCsPrOc0K3p6uOSOjY+qzUdUk/LrDcYX0zkMrg9qkYHiHlJy4aHLbaCiAhw5MlpjXN1tAr3Pd70YLXI9l1lGUo1opnEOFhzb2deKL3kOF0fza8WkGLw/XnFFGBbsdUidT83kZTzaFmVDAOcpXJr8OTw8VF2657xgwGKLw9vF1/qzWxLRRarUa/2SOKaOb1q3EaDSQL7QIAMVLlUvQi2F6D1zq02SJrT4dFydS+6uoVcqT3w85LkJdv28nPNJ9x6TZGImHV0xb7qH3wWyMfGsdT71jHQ9PH8wGx/TL4NDqVGwwkLodFnDv2lGundmBh6cPHnpfzMZINsztRMn/dYmXnCZjpHMq4KucJkPkO9fx9PTB9GfODNkLkTR5Wpd1T+1fjXeS5M6Oq7uZTZF46GOOVZ2HD2aj6zRWTy8/MuQolSB5XuelVxFteK39NNrx1sfezo/vmYE82b24cM3RINntUCyfN+N+yMC5q0Zstvg9Vi3GSDz0r+13z7frzWKKWUf3Z7lfisw8f3iF6IgnWM0G7l45gNXsmA2VNW9V1Gr3ff6joqPx9YkZbVWr1VhtMe2gVqNh94EwWnX5gXy5QtBqtM7lIyZOY/KseZQtWSze8pgNb392jIZYPuN/ruP4bEW8czudh57iVVrRuMtPfNp00J9Tes1sWNCHyg1646H3ibfsAD7eGiKjY87dog02fHxc91/XNjlYuuYOT56ZXZZv3/2YsdMv0anPSfLl8qNkkeTxmk18PP6zU3OvXr1KmTJlAMiWLRvJkyfn6tWrDBo0CACLxUKWLFkACAoKQqvVotVqnVPGLl++TL58+QAoWLAg165d49atWzx79oxvv3VcPYuKiuL27dsAzteKD3ovH0zGmM6Poiho/mwwPb19MRliykxGR6fV29efVOmyotV6kDpdFrQ6DyJfPuPXhSNpP2ARaTJkZ9+Wn1m3ZDR1vuoXb1k/FrXL+5Ajk44MAVqu34kZKdR7qmLtAIUE6mj6vyTMWfPSpdNZIp+erWHum2LcoKofwYGeZEqr48qtmIbdy1NFlOHtoTibHb4fd5882T1p1ygFg2c+clu219Wt6EuOTB5kDNBy7W5MfXp5qIgyvp0zJIsHX36WlB9XhfPgqY2MAVryB3nSfcJjjGaFNnX9KJLbk8NnTfGas2xeFRlTqUjtB/eeAry6h9a1g/I+FQuoSJ5Excp97hkOrV7Ck2zpNaRLqeHmg5jjTe/huAjxphwZNDSooGfhZgOPnjsyqYBapT1J7a9mzvpot+R8xcvLB4Mh5j0Ue0wbBY57tRbPm8G9u7fp3msoKncPeb/hf8U9yJpWQ9qUam49jKlPT13s9Zk9vYb65TxZvDXm/lB32b9hAnevHePJvYukyZzPudxsjHI5+QQcHUtTFFoPPWZTFJ5eSVEUhdByXzrXzZKrLI/vniNrnvJEPL/Pup/ak/+TLwgpXONf5dz92wRuXznG47sXSZflr3N66n0xG6PQeegxmaLw9E765ss5ndq/CpVKxY3zB3h05zzr5/WgbrsZLtMV48ORLRN5ePMYzx5cInXGmPwWcxQe+nfnSwiN/peMnFn1ZErnwZWbMW2el179zna+64g75A3yomPTVAyYch+AsFPRHDp9iw5fpKJsUV92hP37aZCHf5/IgxtHeXbftd7MpiiXjimAztMXiykKrU6PxRSFpz4Jnt5+lPisJ1sXd8InaRpSps+F3ifZv84VFz7e3kQbYu63sCsK2jdmBpQpUYxPihVh5KTpbNmxi08rOW5V6tWlPd8+D6fd932YP3UcXv/i9oGdvzo+O4/uvP3ZeX0gAf78jP/52XGUJ8XDy7Hsze2SB2QhWerMqFQqUgRkwcvXn7vXTvDs0U02LRmI1WLiyf0rbFk+jCoN+/zj/N80DSRfLj+yBfpw7lLMxQdvLw2RkTEd0xTJPcify48Mab2gcWaS+moZ+H1OBo45z4q1d4iKdrS9B448I0dWX/YffvaPM4mP13+2IxoUFMTx48epVKkSt27d4vnz5+TKlYtRo0aRLl06jh49yuPHjwFiPUnKmjUrx48fp0yZMpw5cwaADBkykDZtWubOnYtOp2P16tXkzJmTbdu2xesDgAKDQzl3bCcFilfj5uWTpMmYw1kWkC4rTx7cJDoyHA+9N9fOH6Vc9a/Q6TzZs3kxZf/3JS/DH2M2GfBJ4o+3jx96L8dVsqTJUnPj0vF4y/kxWbPD0ahr1DC0XXJ89I6Rp6BMOjbvdz1pDwnU0bhaEiYsCefpC9eTgsxpdVy5/dJtOVf8/sKZc0z3tPh4qTGa7YRk0bN+l+vV6JafJ+Pg6WjOXTVhMCnY3Xv+7GLV9khnzuEdU+Lj5ajP4EAPNu1zfahSSBYPmn6ahLELnznrM9pox2JVMFsVFAVeRtnxiWUk4N/adVoBFNQqaP0/NXoPMFshUyoVYRfiVmH/K6LCZoNf9rhvTu6GA46TUbUa+jTzwdvTMXqXLb2W7UddrzTnyKChblk909dE8zwi5ndoVFGP1Qaz1xnc9pCiV0Jy5eXIoX2ULF2BSxfOkinQderlrKlj0Oo8+KHv8ER5ONrGg446U6uhVxPv1+pTw47jrlPWs6fXUKeMBzPXGlzq011KVu8KOO6TXDS8OsaocHSe3ty9eoRCFVq5rJsuS0Gun9tF7mJ1uHFuN+myFcJsjGTRyM9o3nsjOg9vbl8OI3exukS9fMLqGS0pX7c/mYJL/OucZWrF5JwzsDqGqHA8PL25feUIxSq75kyfrSBXz+wiX8k6XDuzm4zZC73zdZt2X+L8/5JxzajWZGC8d0IBClfpAjjudVw18TNM0eFoPbx5cP0IeT9pGe/v93cs2/gccLSfE3tnxNdbjdFkJ2c2PWv/CHdZ9+v6KThwPIqzV4wYTXYUxXFhsue3aRgy/T5Wm+Oimj2emqciVbsAjnpbMe4zjNHh6Dy8eXD9MPnLuNZbmsBQbl3YRXDhOty+uJs0WQpjt1l5eOskNVsvxm63smFOS4pW+y5+wr1HnpzB7D98lPKflODsxUtkzZzJWRYVHU3voaMZM6gPHjoder0elVrNlh27efz0KU3q1Ubv6YFKpfrXbVa5z//87Fgt/PjaZ+fW5SMUq+L62cmYvSBXzuwif8k6XD2zm4w5CpEyTTaePbr51nYn963k0d1LfNpkIBHhDzEbIsmYvSCtB20AIPzJHdbM/u5fdUIBZi++ATjuEV08vTBJfLUYjDYK5PZj6erbzvWePjPzRdvDzp9/W1iCgWPO4+OtYeHUwjRtdxiD0U7BfP5s2OrGqUQfALXmw5gG+yH6z3ZE69WrR8+ePWnSpAnp0qXD09OTgQMH0qNHD2x/TtUYNmwYjx7FPoI0YMAAunbtyk8//UTy5Mnx9PQkefLktGjRgmbNmmGz2UifPj2ffvppvGfPU7gSl04fYMqAJqAoNGw9lGP71mM2RlO8YgNqNP2BWSO/RbErFC1XG7/kAfglD+DahSNM6tcQxa5Qp0Vf1GoNDb4ZxOIp3VGrtWi0Oup/Myje835MbHZYtiWS75r6o1LB3hNGwiPs+OhVtKiZhGkrXtK4qi9aDbT63HHV/MFTGwvXR5DEW4XR7MYbBN/IuXhdOL2+ToVKpWLn4Uiev7Th46Xm23rJmbDoCZv3RdCqTnLqVALFDnPXJPzVRpsdlm6OoHvz5KhVsPuYgecRdny8VLSs5ceUZeE0+TQJGo2Kb+r4A/DgiZX5616y43A0fVslx2qDR89s7DnhvpFmuwLbjttpXNZxP9XJawoRBseIY/Uiala9Y6QzTTIokFXFrcfQtILji+bwRTsX77opp91xD2i72j6oVHDwrJkXUQrenvBFZS/mrDdQt6werQaaVXU8lfThczv7TpkpnkfH1bs2OtZzTE3bedzxQCN3KFqiDKeOH6FP97YoikL7Lr3Ys3MrRqOBbNmD+WPrBkJy52NQb8c9i/+rWZ9iJcu4Jctfsdvh1z0m2tTyQqWCsHNWZ302rKhn3kYjtct4oNGoaFLJMQLyKNzOih3xOzIfG41GR5naPVkzoxWKopC7eF18/QMwRoWzdVlfarSaStEqbdmypAdnDqzAyycZnzYfh87Tm1KfdWXVlOaO+8eCSpAld1l2rhqKKfolYVumE7ZlOgC1W89G6/HvHgik0eioUL8nyyc7cuYrWZckyQIwRIWzaVFf6rSZSqn/tWX9/B6c3LsCL99k1Gw1Lj6qKF6oNTqK/a8nm+d9g6LYCSpUBx+/AEzR4exZ3Y9KTRPm6c2xsdkd94D2bZsGlUrFjoMRPHthw9dbTdtGqRgz9yEbd72kdYOUKICiwOxfnmAwKew5EsmQTumw2uHmPRN7jsTvQ2HUGh0lPuvBpp++RlHsBBeui49fAMbocHav7EeV5lMIrdCWnSt6cuHQL+h9klGh8VjUGi0ajY7VU+qi0XqQr/RXCTYiWrp4EY6cOEX7H/qhoNCjU1u27dqLwWikRtVKVCr7CZ17DUSj1ZAtMBOVy5bGbDEzavIMOvUagNVmo8PXX+Lp4REveTRaHZXq92TpRMdnJ3+puiT987OzYWFf6rWdSqnqbVk3rwcn9jg+O59/Pe6d2xX4pB7r5vdiwajGjif/thiOWuO+03ybTWHqnGuMH5wXtUrFhq0PePLMTBJfLT07BtFnxLlYt4uKtjFr0XUmD8+PxaJw5ORzDh6V0dD/KpUSn4/Q+ogcO3aM6OhoPvnkE27cuMHXX3/Ntm3bEjsW645+GPdKvo+6eO7EjhAnq/ruSewIcWKIct8TduOThz5+voDdLShX/I+guMODB1HvX+kD8E31j+P4nLvZPU/bjG/BOXzfv9IHQO/5/nU+BI+fJcwFwH/r0O4biR0hTkqUj79bidzpi5CTiR0hTrY+LJDYEeJk1thdiR0hTvauK5vYEf62s7UqJHaEWOX+7Y/EjvDfHRHNmDEj3333HVOnTsVqtdK/f//EjiSEEEIIIYT4f+RDeULth+g/2xFNlSoVixbF/x9MFkIIIYQQQgjx1/6zf75FCCGEEEIIIUTi+M+OiAohhBBCCCGEO6kS4enwHwupGSGEEEIIIYQQCUo6okIIIYQQQgghEpRMzRVCCCGEEEIIN5Cn5r6bjIgKIYQQQgghhEhQ0hEVQgghhBBCCJGgZGquEEIIIYQQQriBTM19NxkRFUIIIYQQQgiRoKQjKoQQQgghhBAiQcnUXCGEEEIIIYRwA5ma+24yIiqEEEIIIYQQIkFJR1QIIYQQQgghRIKSqblCCCGEEEII4QYqtYz7vYvUjBBCCCGEEEKIBCUdUSGEEEIIIYQQCUqm5gohhBBCCCGEG6g18tTcd5GO6Acmc9JniR0hTib23ZPYEeKk7tDSiR0hTirM+zKxI8SJPVuuxI4QJ/dShyZ2hDhJEX07sSPESVhkscSOECfffvo4sSPEyc0Iz8SOECeBSR8ldoQ4eRbgl9gR4qRd+jOJHSFOTvmlSuwIcTLzUO7EjhAn5QsaEjtCnGyqsj6xI8RR2cQOIOKRTM0VQgghhBBCCJGgZERUCCGEEEIIIdxApZapue8iI6JCCCGEEEIIIRKUdESFEEIIIYQQQiQomZorhBBCCCGEEG6gUsu437tIzQghhBBCCCGESFDSERVCCCGEEEIIkaBkaq4QQgghhBBCuIE8NffdZERUCCGEEEIIIUSCko6oEEIIIYQQQogEJVNzhRBCCCGEEMINZGruu8mIqBBCCCGEEEIIAOx2O/3796dhw4Y0a9aMmzdvupSvXbuW2rVrU7duXX7++ed//D4yIiqEEEIIIYQQAoBt27ZhNptZvnw5J06cYOTIkcyYMcNZPnr0aNavX4+3tzfVq1enevXq+Pn5/e33kY6oEEIIIYQQQriBSv3xTUA9evQopUuXBqBAgQKcOXPGpTw4OJiIiAi0Wi2KoqBS/bPpx9IRFUIIIYQQQoj/kOXLl7N8+XLnzw0bNqRhw4YAREZG4uvr6yzTaDRYrVa0WkfXMUeOHNStWxcvLy8qV65M0qRJ/1EG6YgKIYQQQgghxH/I6x3PN/n6+hIVFeX82W63OzuhFy5cYOfOnWzfvh1vb2++//57Nm3axKeffvq3M3x8Y8VCCCGEEEII8RFQqVUf5L+/UrBgQXbv3g3AiRMnCAoKcpYlSZIEvV6Pp6cnGo2G5MmT8/Lly39UNzIi+oawsDCWLVvGhAkT/vFrdOjQgalTp8Zjqnez2+3MmT6eG9evoNPpaNOpB2nTZXCW7921jQ2/rUCt1pA5MBtft/sO9Z9z1S9fPMvieTMZNHJKgmTNH+RBzTI+2Oyw94SB3ceMLuXJk6ppWSspajWogAXrI4g2KrSpFzPcnymNlpXbItl51Ehi8C+aj5Dh3TlYqXmivP+b7IrC8I0HufTwOTqtmgGflSRT8pj6OnPvCeO2HEYBUvp4Max2aTy1moTLZ7czYuGvXLp9Hw+tln4t65IpIOVb6w2Ztwo/H286NXBcTWvcfxK+XnoA0qVKxqCvG7g95/RpU7h+/Ro6nY5OnbuSLl16Z/munTv47dfVqDUaAgOz0K59R9RqNSuWLyUs7CBWq4Xq1WtQperfvxr4d3OOmb2YKzdvo9Pq6NX2SzKmDXCW7zh4hEVrNoFKxeeVylCzUhk27NjLhh37ATBbLFy+cYv1cyaQxMc73rOtnDuEuzcvodXqaNR6MKnSZHKWnzm6k99XzUCt0VK8XG1KVKyHzWphyfQ+PHt8F7VaQ8NvBxKQPisLJnXnZfgTAJ49vkdgjnx82XlsvOZ9lfnH6RO5cf0qOp2O9p2+J+1r+33Pzu2s+22lo/3MkpXW7bo428/w8Od079yagUPHkiFjpne9xb/KFl/1eefGBX6ZMxi1RkOqtJlp9O1g5+8R35l/nDaJG9evotXp6NC5u0t97t65nXW/rkKtURMYmI3W7Tu71Ge3Tm0YNGyMW+rzzZyLfxzJ7RuX0Oo8aNG+HwFpM7qsYzIZGDewPV+170faDFkAGPjdF3h5O6aqpQxIR6uOA92ec+TCNVy6dQ8PnZZ+LeuTMZb2c+jclST19aZTg/85lz17GUnTAROZ9v23ZEmX2u05F/04kts3LqPV6viqQ2z1aWTsgHa07NCftBkCARjQNaY+UwWkp1WnAW7NCRCUQUW5fGrsChy/YufoZSXW9YrnVOPrBduO2V2W1yiuwWBW3loen+x2O0tnD+f2jUvodDqatR1A6rSunwmzycDEQW1o3m4gaTJkwW6zsWjmYB7evYFKraFFh0GkSpPxHe8QX1R4lquNJmU6FJsV4x+/oLx46ixVp86AZ+kagAolOgLjlqVocxRAl7OwY2utFnXKdET+NBjMiXNOJ96tcuXK7Nu3j0aNGqEoCsOHD2fdunVER0c7R1K/+OILdDodmTJlonbt2v/ofaQj6gYJ1QkFOHxwD2aLieHjZnLpwlkW/jSNHv1GAGAymVi2aDbjpi7AU69n4uiBHD28nyLFPuG3lUvYtWMLer0+QXJq1NCoqi9DZj/HZFbo3TIZJy6aeRkV05jXLu/D9kPRHL9oJnc2D+pW9GHaipeMXhAOQLYMWupU8GXXscRpsLJ2+5r0TWtiizIkyvvHZseFW5isNha2/B+n7jxm/NYjTGxYAQBFURi8fj9j65UjU/KkrD5+ifvhkQSm/PtPNfvH+Y6dw2yxsqBfe05ducmEZRuY0PlLl3VW7jjIlTsPKBScFQCT2QLA7F6tEyznwQP7sVjMjBs/iQsXzvPTnFn06z/IkcdkYtHC+Uyd/iN6vZ7Ro4Zz+FAYXt7enD9/jjFjJ2AymVi96he359x96Dhmi4XZw/tw5tJVpixYweieHQGw2exMX7yKeaP64aXX80XXvpQpGkr18p9QvfwnAIyZvZjPKnwS751QgNNHtmMxm+k6ZAk3Lp/kt0Vj+Pp7x0Uum9XCrwtH8d2wZXjovZnUvym5C5Xj5pVT2O02ugxZwsVT+9mwfDItv5vo7HRGR75g6pCWfN68R7znBQg7sBeL2cyocdO4eOEc8+ZMp3f/YYBjvy9ZNJdJ037CU69n3KghHDl0gKLFS2G1Wpk5dTweHp5uyQXxW5+/r5pO1bptyBVahkVTenDu+G7yFCoX75nDDuzFbDEzavzUP+tzBr37DwX+rM+Fc5k8Pfb6nDFlPJ5urM/XHQ/bicVios+o+Vy9eJrl8ybQqfd4Z/n1K+dYOHM4z58+ci6zmE0A9Bg6K0EyAuw8dhaTxcL8/h05feUmE5auY3yXr1zWWbXjAFfu3KdgSLaYrFYbw+atxFOnS5Ccx8J2YjGb6TtqHlcvnmbZvAl0frM+Z4zgWSz12XNYwtWnWgXVCmuYtdGKxQqtqmm4eNtG5GunFFoN1CyhIUNKFeduuXY2C+dQE5AMbjx0b84Th3ZgMZvoOWIh1y6dYuWC8bTrOdFZfuPKWZbMGkb405ggp47sAuCH4Qu4eOYwv8wf57KNO2iz5Ual1RG9cirqgEx4flID44b5znJ9hfoYNi1EefEUXa6iqJIkw3rhCNYLRwDwLFsby7nD0gn9QKnVagYPHuyyLFu2mHamcePGNG7c+N+/z79+hY/E6tWrad++PV9++SU1a9bk999/Z9++fdSvX5+mTZvSoUMHl2HlvXv30qlTJ+fPjRo14tGjR5QqVcq5rGvXroSFhbF69WqaNGlC48aNOXDggHOdQ4cO0bx5c5o3b06DBg24fv16vP9e58+eIrRgMQCCQnJz9fIFZ5lOp2PomBl4/tnZtNlseOg8AAhIm57vew+N9zzvkjalhkfPbEQbFWx2uHzbQlBm1y/J5VsiOXXZDDg6rhar62s0+TQJizZEoMR+AdPtoq/d4mj9jonz5u9w/PYjSmVzjDjky5CKs/efOMtuPn2Jv5cnS8LO0WrBZl4YzAnaCQU4cfk6JfM6pnPky56Zc9fvuJSfvHKT01dvUbdcMeeyS7fvYzSbaTdmDt+OmsWpK65/u8odzp49Q8FCjqu0ISE5uXz5krNMp9MxZtxE50Ubm82GzkPHsaNHCAzMwrChgxg8qD9FixZ3e86TFy5TvEAeAPIEZeP8tRvOMo1GzdJJQ/H18eZFZCSKAl6vXWg6f+UG12/f4/PKZd2S7dqF4+Qs4Gj7AnPk5/a1s86yh3evkTJNJrx9/dBqdWQNLsi1C0dJlTYzdpsVu92O0RCFRuN6bXTTymmUqfoFfslSuSXz+XOnCS1UFIDgkFxcveK630eOneLafno42s/5P82g6qc1SJ4ihVtyQfzWZ/rAnERHvkBRFIzGt+s5vpw/e4aChYoAjvq8cvmis0yn0zFqnGt96l7V55yZVPtfTZK5sT5fd/n8CfKElgQgW3Beblw951JutZjp2HMsadMHOpfdunEJk8nIuIHtGN2vNVcvnnZ7zhOXrlMybwgAeWNpP09ducHpK7eoU9617Zm4bD11K5QgVbJ/9vCQv+vy+RPkLVgC+LM+r5x3KbdazHToOYa06TM7l926cRmzycjYAe0Z1a9NgtRnKj94FqFgNIPNDrceKWQKcJ2eqNXAyat2dp+2uSzPkFJFhlQqjlxy30joK1fOHyd3qOOznzUoHzevnnUpt1rNtP1hPGleOz4LFKtA0zb9AHj2+D5J/JK7PacmbRasNx3nnPaHt9CkjpmNp/JPhWKMwqNAabzqtEGl90YJf+wsV6fOgDp5AJazYW7P+SFQqdUf5L8PwYeRIoFER0czb9485s6dy8iRI+nXrx9Tp05l8eLFFClSxOXv45QqVYpLly7x4sULrly5QrJkyUid+t3TW5ImTcrSpUspUaKEc9nly5cZM2YMCxcupEKFCmzevDnefyeDIQpvn5inWqk1amw2Rw9OrVbjn8zRGG1atxKj0UC+UMdJQvFS5dBoE25A3MtTjcEY04M0mhS8PF2/ACINjk5qmhQaGlT2Ze2umJukCwR5cPeRlQdPXb8cEtKDNVtQ3uwdJ7IokwVffUyHXqNSY7U7viifG0ycvPOYBoVDmNm0Coeu3yfs+v2EzWcw4esd0xnSqFVYbY59+Dj8JT/+upWezT532UbvoaNZtTJM696KPl/Wpu+Py5zbuIshOhofb5/Xcqqx/fmearWaZMmSAbBu7a8YDQZCQwvx8uVLrly+RM9efWnfoRNjx4xEcfNVkiiDEV/vmNFMjVrtUjdajYadB4/SvNtACuQKQquJmYa9YPUGWjWo6bZsJkMkeq8kzp9V6pi2yGiIQu8V0055evlgiI7A09ObZ4/vMeK7GiyfNYAy1Zo414l48ZTLZ8IoWu5zt2U2REfj7ROz39Vv7PdX7eeGtasxGg3kDy3MH1s34+fn7+zAukt81meqNJlZPX8EI7rVJCL8KdlzFXFL5ujoaLy9X69PTaz1uX7taowGIwVCC7N962aS+vkRWsg9mWJjMEQ6p4S+yvaqbgFy5CxA8pRpXLbx9NRTrVZTvhswjeZtejNrQh+Xbdwh0mBy3qLwKqdL+7lmKz2au06HW7vnMMmS+FAyb7Bbs73OEB313vpMkcq1Pj089FT7vBndBk7lyza9+HF8X7fXp6eHCpMl5meTBfRvDBobzXD1vms77usF5Quo2RCWMOcfRoNrfarUGpe6yR4S+tbxCaDRaJk3pS/LfhpFoRKV3B/UwxPl9dFMxQ4qR7dC7eWDJm0gllP7Mfw6C02G7GgyZI/ZtHAFzIe2uj+j+OD9p6bmFilSBLVaTcqUKfH29sZqtRIQEOAsGz9+POXKlQNApVJRs2ZN1q9fz507d6hXr95br/f6SWeWLFneKg8ICGDYsGF4e3vz8OFDChYsGO+/k5eXDwZDdEwmu+Jytdtut7N43gzu3b1N915D//Hf+fmnapf3IUcmHRkCtFy/E/MNoPdUEW18+6Q9JFBH0/8lYc6aly6dzhL59GwN+3CmxH4ofDx1RJlivqDsioL2z6tc/l6eZEyWhGyp/AEomS0d5+4/pViWtAmXz8uTKKPJNd+fnaNth08THhFNp/HzePoiAqPZTGDaVFQrXoCMASlRqVRkTpMKP19vnoRHkCaFv9tyenl7YzDEHF92u4LmtU6c3W5n3tw53L17h159+qNSqUiaNCkZMmZEp9ORIUNGdB4evHgRjr9/Mrfl9PHSE2WM+eK32xWXziZAueKFKFM0lCFT57Jp134+q/AJEVHR3Lx3n0J5QtyWzdPLF5Mx5uKRosS0RXovH0zGmHbKZIjCyzspOzcuIjh/SWo07srzJ/eZNrQVPUavQefhycmwrRQs9T/Uavfd0+zl7Y3Rpf20v7XfF879kXt379Cj9yBUKhXbt24CFZw8cZTr164wafwIevcbRrLk8TsCEZ/1uWbBSDoOXEjajNnZ8/tSfls8hnot+8ZrXgDvNz5HsdXnglf12Wegoz63bEKlUnHyxDFHfY4bQe/+8V+fr/Py8sX4jrp9l4B0mUmdJiMqlYo06TPjm8SfF8+fxNohiC++b7Sfyuvt56FThEdE0Xn8Tzx5EYHRZCEwbSrW7j6MSqXi0LnLXLx1jwGzljG+SwtS+rtvdNTL28f1cxSH+kyTPhMBaTPE1GdSP8KfPXmrwxofKhRQkym1ioBkKu4+iTnv8NQ5Op7vkzuzGm9PFU0qavD1UqHTwpMXCieuuufCo97LB6PhtePTbo/zLIavOg7lRdMnjOzZjIGTVuOp93JLRgDMJlS616bTq1SOziigGKOwhz/B/twxHdt66yKa1Bmw3bkCHnrUyVJju3vVfdnER+M/NSJ69qxjesOTJ08wGAxYLBYePXJ8SA4dOkRgYKDL+nXr1mXz5s0cPnyYsmUd09msVitRUVGYzWauXLniXDe2Bz/07duX4cOHM3LkSFKnTu2W0ZKQXHk5duQAAJcunCVTYFaX8llTx2A2m/mh73DnlKiEtGZHFKMXhNN17BNSJ9fgo1ehUUNQJh1XX+uYgqMT2rhaEiYsCefGfdcro5nT6rhy23V9AQUypmbvFcd0rVN3HpMjdUwnKEMyX6ItVm49c0w5P37rkbNTmmD5sgey76Rjet6pKzfJniHmJKNx5VL8PKgTs3u1pkX1clQrHkrN0oX5bc9hJixbD8Dj5y+JMphI6Z8k1tePL7ly5ebIkUMAXLhw/q22YOqUSZjNZvr2G+icopsrV26OHTmMoig8ffoUk9FIkiTunQqXLyQ7B46dAuDMpatkyxTzIJioaANt+4/CbLGgVqvx0ns6n4p34twliuTN5dZsWYNDOXd8DwA3Lp8kbcYczrKA9Fl5/OAmUZEvsFotXL1wlMCg/Hj7JMXL27FvvX39sFmt2O2OC1CXTh8gZ4HSbs2cM1cejh52TA27eOHcW+3njKnjMVvM9Ow3xNl+Dhs9iWGjJjF05ESyZM1O5+96uaXTFJ/16e3rh97LMVLplywV0ZH/7OmG7xOSKw9Hj8TUZ+Y363PKeCxmM71eq8/hYyYxbPREho2a4KjPbu6pz9dlz5mf00f3AXD14mnSZ8r+ni1g7/bfWD7f8RDD588eYzBE4pfs7QcHxaf8OQLZd8oxzfX0m+1nlU9YMrgLs3q1pUX18lQrEUrN0kWY06cds3u3ZVavtgRnSsegbxu5tRMKkCMkP6deq88Mmd9fn3u2rWXZvInAn/UZHYV/cvfU5x8n7MzfYmPMCivJk6jw8nDc/pM5QM3tx+8/Lwu7YOfHDVbmb7Gx94yN09ftbuuEAmQPKcCZY3sBuHbpFOkz53jPFnBw53o2rf4JAA9PPSq1yi0PJHud7f4NtIE5AVAHZML+9IGzzP7iGSqdJyo/x3R7Tbos2J45yjXps2K7fdmt2T44KtWH+e8D8J8aEX3y5AlffvklERERDBw4EK1WS8eOHVGpVPj5+TFixAguX475cAQEBODj40OBAgWcfzunefPmNGzYkAwZMpAuXbq/fL9atWrRoEEDkiZNSsqUKZ2d3vhUtEQZTh0/Qp/ubVEUhfZderFn51aMRgPZsgfzx9YNhOTOx6DenQH4X836FCtZJt5zvI/NDsu2RPJdU39UKth7wkh4hB0fvYoWNZMwbcVLGlf1RauBVp87vjQfPLWxcH0ESbxVGM3uvy/jY1QhJBMHr92j+byNoMCgmqXYePoa0RYr9QoGMfCzkvRasxtFgfwZU1MmR4b3v2g8Kl8oNwfPXqbF0GkoCgxsVZ9NB44TbTK73Bf6us/LFGHAnF9oOWwGqGBAq3pvjfrFtxIlS3H8+DG6d+uCoih06dqNnTv+wGg0kD1HEFu3bCZ37jz07vUDADVrfU7Jkp9w5sxpvuvSEbtip027Di6jP+5QtmhBDp08xze9hwMKfdq35Pc9BzEYTXxeuSxVSxenbb9RaLUasmfOQLXSjlsFbt57QLoA99xn+UreIhW5eHo/E/s1QQG+aDOEo3s3YDJGU7JSfT5v9gMzh3+LoigUK1cb/+QBlKvenKUz+zF5QHOsVgufNeqMp94x9fjR/RukSO3e47VYidKcOH6Unt06oKDQsUsPdu/chtFgIFuOYLZv2UjO3Hnp3/s7AD6rWZfiJd3bOX4lPuuz0beDWDj5e9QaLRqtlobfDHJL5uIlP+Hk8aP06NYBFOjY9Qd27dju/Bxt27KJXLnz0q9XNwBq1KqTYPX5uoLFynPuRBjDen4FikLLjgM4uHsTRqOBclXqxLpN6Yqf89OUAQzv1RKVSkXLDgPcdq/tK+UL5SHs7GW+GjIVRVEY8HVDNh04jsFoeuu+0MRUsHh5zp4MY2iPloBCq44DOLBrMyZjNOWqxl6fZSrVYs7kgQzv1QpQ0bJjf7fXp12BzUdsNKukQaVScfyKnQgDeHk4HlC0fFfi3frzugLFKnD+1EFG9W6OokCL9oM4tGcjRkM0Zaq8PTsPILR4RRZM7c+Yvi2x2aw0+Op7dG5++Jf16hk0GXPgXa89oMK4fTnaoAKodJ5YzoZh/GMFXlW/AFTY7t/AdsNxP6naPxX2156uK/7bVIq7b2r6QKxevZpr167RvXv3v7Vd69at6d27N5kzZ37/yvHg1OX476y6w8SfEztB3NQdmvAnOf9EhXlfvn+lD4A9m3tH1eLLvdShiR0hTlJE307sCHESZo39osGHJnOSx+9f6QNwM8K9FwXiS2DSj+P76JkpYR/C9k/lf/lHYkeIk1N+5RM7Qpz8fjjhZ3n9E+ULfljPlniXQjv6J3aEOEnScUxiR/jb7nR075+g+6cyTFmR2BH+WyOif4fRaOSLL76gdOnSCdYJFUIIIYQQQvz/8eo2GfG2/0xHtE6d2KeHvIter2f16tVuSiOEEEIIIYQQ/13/qYcVCSGEEEIIIYRIfP+ZEVEhhBBCCCGESEgqNz/B+GMmNSOEEEIIIYQQIkFJR1QIIYQQQgghRIKSqblCCCGEEEII4Qby1Nx3kxFRIYQQQgghhBAJSjqiQgghhBBCCCESlEzNFUIIIYQQQgg3kKfmvpvUjBBCCCGEEEKIBCUdUSGEEEIIIYQQCUqm5gohhBBCCCGEG8hTc99NRkSFEEIIIYQQQiQo6YgKIYQQQgghhEhQMjVXCCGEEEIIIdxApua+m4yICiGEEEIIIYRIUNIRFUIIIYQQQgiRoGRq7gfmudk3sSPEiSHqSWJHiJMK875M7Ahx8sdXCxI7QpyUDpua2BHiJInlWWJHiBODp19iR4iTiEhNYkeIk2RejxI7Qpw89vBP7AhxYrJ7JHaEODHa5FQmPnmorYkdIU4unbmf2BHipFT+NIkdIU5UOvkcuY1axv3eRWpGCCGEEEIIIUSCko6oEEIIIYQQQogEJePwQgghhBBCCOEGKpU8NfddZERUCCGEEEIIIUSCko6oEEIIIYQQQogEJVNzhRBCCCGEEMINVPLU3HeSmhFCCCGEEEIIkaCkIyqEEEIIIYQQIkHJ1FwhhBBCCCGEcAOVWp6a+y4yIiqEEEIIIYQQIkFJR1QIIYQQQgghRIKSqblCCCGEEEII4Q7y1Nx3kpoRQgghhBBCCJGgpCMqhBBCCCGEECJBydRcIYQQQgghhHADeWruu8mIqBBCCCGEEEKIBCUdUSGEEEIIIYQQCeo/NTU3LCyMZcuWMWHChPeu27VrVxo1aoTJZOL+/fs0bNgwzu+zevVq/Pz8qFix4r+JGyd2u52fZw3nzo1LaHUeNG/Xn9RpM7msYzIZmDiwLc3bDyBthizYbTYWzhjCw7s3UGvUfNlhEKnTZHR71oI5vahTKSk2O+w6HMkfh6Jcyv181XRonBKtFsJf2pix4hlmiwKAh05F729SM+uXp9x7bHV7VruiMHzjQS49fI5Oq2bAZyXJlDyps/zMvSeM23IYBUjp48Ww2qXx1Grcniuu/IvmI2R4dw5Wap6oOex2O6PmLufyrbvotFr6ftuEjGlSvbXesNk/k9TXh46NazmXnblygyk//8qP/bskSM4JM+dy5cZNPHRavu/Qmgxp0zjLd+0PY8mq31CpVNSoUpHPqlTAZrMzZtosbt+9h1qtpmenNqR/bRt35Zw0YzZXr99Ep9PSvWNb0qdL6yzfve8gS1euAZWKz6pWonrVSlitVkZPms7DR48wW6w0bViXUsWKuCXb+oWDeXD7AhqtB5+3HEKKgMzO8gvHd7Bz7XTUag0FS9ehcLkG2KwW1vzUh/Cn97BazJSt2YacoRW4f/M8axcMRK3RkjJNILW+GoLaDU8ddOz3n7h64yY6nS7W/f7zql9RqVR8VqUin1WpiM1mZ+y0H537vUentgmy3z+Wdv5V3p+mj+Pm9SvodDpad+pJmnQZnOX7dm1l428rUKs1ZArMRqt23dyyf/8q37LZw7h78xJarQdN2g54qz7NJgOTB7ehabuBpEmfBZvVwqLpA3j66B5Wq5lP635LviLl3J5z5MI1XLp1Dw+dln4t65MxIOVb6w2du5Kkvt50avA/57JnLyNpOmAi077/lizpUrs957yZY7h1/TI6nY6vO/QmTTrXY81kMjKiX0e+7dSHdBkCsVqtzJw4mCeP7qNWq/m6Qy/SZQh0a074cM9D7HY7K+YM5e7Ni2h1HnzRZhCp0sQck6eP7GTzqpmo1RqKl69NqUr1sFjMLJnel6eP7qL38qF+qz6kTpuZ29fOsWz2ELQ6DzIEBlO3RU/3tJ+KwsjtJ7j05AUeGjX9Khcko7+vs/zsg2eM33XacZ7k7cmQT4vgqdXwxeLt+HrqAEiX1JuBVQvHe7YPjUol437vIjXzHmXKlPlbnVCAOnXqJEgnFODEoR1YLGZ6jlxInaad+GX+eJfyG1fOMrZvKx4/vONcdvLIbgB6jJhPzUbt+GXeOLfn1KihWQ1/Rsx5xOCZD6lQzBc/X9fDr1b5pOw+GsWgGY+488hKxeKOBi1rBg8GtE1NQIqEu26y48ItTFYbC1v+j84VCjF+6xFnmaIoDF6/n0E1SzG/xaeUzJ6O++GRCZbtfbJ2+5q8Pw5FrfdM7CjsPHIKk8XK3MHd6dC4FhMXr35rndXb9nL19j2XZQvXbmXorCWYLe6/6ACwN+wIZouZGaOH8G3zL5g+d5GzzGaz8+PCpYwf3Jfpo4awbM06wl++ZP/howBMGzWYll/UZ9pr27gt58FDmM0Wpo4dzjdfNmXG3AWv5bQxe8Fixgztz9Qxw1i+Zi0vXrxk687dJE2ahEmjhjJyYB+mzPzJLdnOH9uG1WLi237LqFL/OzYvGx2TzWph09KRfNl9Di17LeTIrl+ICH/MyQPr8Pb15+vei2ne7Uc2LBoKwI7fplG+Vju+6bMEq8XMpZO73JJ5b9hhzBYL00cP5dvmjZnxxn6ftfBnxg3ux7RRQ1n+xn6fOmoIX33RwOVYcZePpZ1/5fDBPVgsZoaO+5HGLdqw6KepzjKzycTyRbPpP3wKQ8bOJDo6kmOH9ydYNoCTh/7AajHz/fBFfN60M6sXuNbNzStnGd+vJU8e3nYuO7R7Az5J/Ok2dD7t+0xn+U8j3J5z57GzmCwW5vfvSMf6/2PC0nVvrbNqxwGu3LnvssxitTFs3ko8dTq3ZwQ4enAXFrOJQWPm0LB5e5bMnexSfu3yeYb0asOjB3edy04c2Y/dZmPg6NnUbtiSFYtmuj3nh3wecurwH1gsJroNW0LNL7qwZuEYZ5nNamH1gtG07/MjnQfNZ//2lbwMf8L+7Svx1HvTbdgS6rXsxS9zhwOwdNYg6rboQdfBC/Dy9uXo3g1uybzzyj1MNhvzG5Wj4yd5mLDrtLNMURSGbjvOwKqFmNuwLCUCA7j/MhqT1QbArPplmFW/zH+iEyr+2v+Ljujq1atp3749X375JTVr1uT3339n37591K9fn6ZNm9KhQwdevnzpXH/v3r106tTJ+XOjRo149OgRS5Ys4fPPP+ebb77h5s2bztceO3Ysd+7coW7durRp04batWs7R1W3bNlC/fr1ady4Md27d8dutzNlyhSWLl2KzWajT58+tGrVijp16jBx4sR4/92vnD9O7tCSAGQNzsfNq+dcyq0WC217jCdN+kDnstBi5WnWti8ATx/fI6l/injP9ab0qXU8fGolyqBgs8HFGyZCsrh2lBauC2fv8ShUKkjhp+FFhKPB0mpg3IIn3HtkcXvOV47ffkSpbOkByJchFWfvP3GW3Xz6En8vT5aEnaPVgs28MJgJTOmXYNneJ/raLY7W75jYMQA4efEqJfPnBCBvjiycv3bLpfzUpWucvnKd2hU/cVmeISAlo7t+k2A5T527QNHQAgDkDs7BxSvXnGUajZqF08bh6+PNy4gIFBS89HpKFy9C9/aOjA8fPyGZv/uPgTPnLlCkkCNnrpAgLl5+PaeG+TMm4evjw8uISFAUvLz0lCtVgpZNGrn8Pu5w6/Ixsud17MeM2Qtw9/oZZ9nj+9dInjoTXj5+aLUeZMpRkJuXjpK7SFUq1unsXE+tccwqSJs5J9FRL1AUBZMxCo3GPSd/p89dpGhofgByBwdx8cpVZ5lGo2bBtPGv7Xec+71b+2+BhNvvH0s7/8rFs6fIX7AYAEEhebh6+YKzTKvTMXjMTDz1esBxAUWn80iwbABXLxwnVwFHfWYJysfNa2ddyq1WM9/+MIGAdFmcy0JLVKFGo/bOn9Vq98+AOXHpOiXzhgCQN3tmzl2/41J+6soNTl+5RZ3yxV2WT1y2nroVSpAqWVISwsXzJ8lfsAQAOULycP3KBZdyi8VM116jSJchZoZE2vQZsdms2O12DIYoNFr3X2j+kM9Drl04Rq4CjvYzS1B+br32GX9w9xqp0mTC29cPrVZH1uBQrp4/yoM718gVWhqAgHRZeHjH8X0Q/vQhWYMLOF4rOJSrF467JfOJe08pGRgAQN60yTn38Lmz7ObzSPz0Hvx87ArfrNjNS6OFwORJuPT4BUarjXar9tJ65R5O33/mlmzi4/H/ZmpudHQ08+bN49mzZ9SvXx+VSsXSpUsJCAhgwYIFzJgxg3LlygFQqlQphg4dyosXL3j8+DHJkiXDy8uLhQsXsm7dOlQqFXXq1HnrPe7evctPP/1EkiRJ+OKLLzh79izr16+nRYsWVK9enV9//ZXIyJiRsfv371OgQAHq16+PyWSiTJkydOnSJV5/b2N0FF7eMVMhVGoNNpvVeeKWPWeBWLfTaLTMm9yP42E7aP39mFjXiU9eejXRRrvzZ4NJwdvr7RNitQpGdk2LTqti9bYXAFy6aXZ7vjdFmSz46mOuJmtUaqx2O1q1mucGEyfvPKZHtWJkSp6UTsu2kyttCoplSfsXr5hwHqzZglfm9IkdA4AogxEfby/nz2q1GqvNhlaj4cnzF8xetZEx333L1oPHXLarUCyUe4+fJljO6GgDPj6x5wTQajTsPnCICT/OpUShULR/fr60Gg3DJ05nz8HDDO7RJWFyens7f9ao1dhsNjR/5tRoNOzef5DJM+dQvHAhNBoNHh4ezm0HjRxLy6aN3ZLNZIhE753E+bP6tbbIURbTTnnqfTAaIvDU+/y5bRTLpnZxdkpTBASyftEQdq2did47CYEhRd2SOSo6Gl+fmPqMfb+HMfHHuRR/Y7+PmDiNPQcPM6hHV7dke93H0s6/Em2IwtvHx/mzWqN25lWr1fgnSw7ApnUrMRkN5AuN/6nif8VoiMLrHccqQLaQ0Le20Xt5O7edPbYbNRt3cHvOSIMJXy/9azljjs/H4S/5cc1Wxnb6kq2HTjrXWbvnMMmS+FAybzDz1//h9owAhugovF7f32q1S30G58r/1jZ6vTdPHt3n+3YNiXj5gu79xro954d8HmI0RLm0ka/X4Ztlei8fDNGRZAgM5szRXeQrUoEbl08R/uwRdruNlAEZuHzuMDlyFeHM0V2YTQa3ZI40W/D1iDlPUqtVzvOkcIOZU/ee8kP5/GT096XLb/vJGeBPMm9PmhbKQe08gdwKj6Tjmv2sblEZbQJOzU8U8tTcd/p/s+eLFCmCWq0mZcqUeHt7o9PpCAgIcJZdvnzZua5KpaJmzZqsX7+eVatWUa9ePa5du0b27Nnx8PBAp9ORL1++t94jJCQEf39/NBoN+fLl4/r16/Tq1YvDhw/TtGlTjh075jIP39/fn9OnT9OtWzeGDx+O2Rz/DZne2weTIdr5s2K3x3n04KtOQxgy9VcWTR+MyeiehqpBVT/6tU5N9xYp8fKMqRsvTxVRBvtb69vs8P24+8xZ9ZR2jRLuCv6bfDx1RJlipoXaFcXZUPp7eZIxWRKypfJHp1FTMls6zt1PuE7Tx8THS0+0weT8WVEU50n+trDjhEdE0XnUdBas3crv+46wbtfBRMnp7e1FtMEYa85XypQoyqq507FYrfy+Y7dzee8u7Vg8YwJjps3GYDTiTt7eXhhey2lX7M5OqDNnyeKsmD8Li9XClh2OKa2PHj/huz4DqFy+DBXLlXZLNk8vX0zGmPutFCWmLXqzzGSMQu/tGK158fQ+c0d9Sf6SNclf4jMANi4ZTqvei+g8ciMFStZk87JRbsns4+3tst/tse73YqycOwOr1eqsT4BeXdqzaMZExk6b5fb9/qG382/y9vLB6JJXcclrt9tZ9NNUTh8/zHe9hqFSJexJmt7LB+Prx2oc6/PZkwdMHPA1xcp8RpHS/3vv+v+Wr5cnUcZ3tJ+HTjnaz/E/MX/DDjYfOM7aPYdZu/swYWcv8+2IGVy8dY8Bs5bxJPzlu94iXnh5u+5vu/L++ty0dil5Q4szbuYvjJi0iJkTB2M2m/5ym3/qYzgP0Xv5YDLE3n7qvXxc2k+jIQovnyQUL18bvZcPkwe15MzRXWTMmgu1WkOTdkPYuuYnZoxoR5KkyfFJ4u+WzL4eOqLMMedJymvnSX5eHmTw9yVriqSO86TAAM4/DCezvy//y5kJlUpF5mRJ8Nd78CTKve2n+LD9v+mInj3rmFrz5MkTDAYDFouFR48eAXDo0CECAwNd1q9bty6bN2/m8OHDlC1blowZM3LlyhWMRiM2m43z58+/9R5Xr17FYDBgs9k4deoU2bNnZ/ny5XTs2JHFixcDsHXrVuf6q1evJkmSJIwbN46WLVtiNBpRFCVef+9sIQU4fWwvANcuniJ95uzv3ebAzvVsWuW4T8zDU49KrXbbgyJW/P6CIT8+os3guwSk1OLjpUajgZAsei6/cYWx5efJyJXNMU3GYFKwx29V/S0FMqZm7xXHNKhTdx6TI3UyZ1mGZL5EW6zceub4cj9+6xHZUvknRswPXv6grOw74fhsnr58nWwZ0znLGlUrx6LhPfixfxe+rFmZqqUKU6Ns8Xe9lFvlzRlM2FHH9KWzFy+TJXPMgzaioqPp1HsQZosFtVqNXu+JWq3i9x27WbzyVwD0nh6oVCq3P3AlT84Qwo44Ro/PXbhE1swxD7OIio6mS8/+r+XUo1apefY8nB/6D+HbFk35tLL77l3PlL0gl086Oui3r5wgIEOQsyxV2qw8fXiT6MhwrFYzNy8eIVP2AkS+eML8sV9TpUE3CpWp61zfy8cPvd4xApAkWWoM0e45kc6TM5iDzv3+dn127j3QpT5VajVbduxmyco1QMLt9w+9nX9TcK68HD/iuKh06cIZMgVmdSmfPXUMFrOZ7n1HOKfoJqRsIaGc/bM+r186RbpMOd67zcvwp0wZ0obPm3ahZMXa7o4IQP4cgew75TgXOX3lJtkzxDwUq3GVT1gyuAuzerWlRfXyVCsRSs3SRZjTpx2ze7dlVq+2BGdKx6BvG5HS371TdINy5uPEEcd9vpcvnCFj5mzv3cbHJynePo7PuE+SpM5puu7wMZyHZA0O5ezxPQBcv3SStK8dk2nSZ+Xx/VtERb7AarVw9fzRP6fvniFbSEE6D5xH/qIVSRngeCDY2WO7adJ2MG17TScqMpyQfCXckjl/uhTsu/EAgNP3n5H9tVuUMvj5YLBYuf3n8zOO331K1hRJ+O3sTSbsOgXA40gDkWYLKX0Svg0QH47/N1Nznzx5wpdffklERAQDBw5Eq9XSsWNHVCoVfn5+jBgxwmVUNCAgAB8fHwoUKIBWqyV58uR07tyZRo0akTx5cry8vN56D51OR+fOnXny5AnVqlUjJCSEe/fu8dVXX+Hv74+Pjw/lypVzdkpLlCjBd999x9GjR/Hy8iJz5sw8evTIOVIbH0KLVeD8yYOM7PUlKApfdhhE2O5NmIzRlKlSN9ZtChavyPypAxjTtyU2q5WGLbuj83Dvg21sdli8LpxeX6dCpVKx83Akz1/a8PFS82295ExY9ITN+yJoVSc5dSqBYoe5axLv3oEKIZk4eO0ezedtBAUG1SzFxtPXiLZYqVcwiIGflaTXmt0oCuTPmJoyOTK8/0X/g8oVyU/Y6Qu07D8OUOjfuimb9x0m2miizhv3hSam0sWLcOTEadr90A8F6NmpDVt37cVgNFKzaiUqlS1Fp16OdiVrYCYqly2N2WJm5OSZdOw1EKvNRsevv8TTw733un1SoihHT5ykw/e9QYEfOrdn+849GIxGPqtWmUrlStOlZz+0Gi1ZAzNTqVxpZvy0gIjIKBYtW8miZSsBGDmwD56e8fuZz1moElfP7mfW0MagKNRuNZyTB9ZjNkVTpFwDPm3Uk4XjvkGx2ylYug5JkwWwYclwjFEv2fnbDHb+NgOA5t1m8XnLIayY0Q21RoNGq6NWiyHxmvUVx34/Rfsf+qGg0KNTW7b9ud9rVK1EpbKf0LnXQDRaDdle2++jJs+gU68BWG02OiTAfv9Y2vlXipQow6njh+nXvQ2KotC2S2/27tyC0Wgga/YQdmxdT0ju/Azp7XhWw6c161O0ZNkEyQaQv2gFzp88wJjezQGFZu0Hc3jPRkzGaD6pXC/WbTavnoMh6iWbVs5i08pZALTvMw0PT/edRJcvlIews5f5ashUFEVhwNcN2XTgOAaj6a37QhNT4eLlOH3iMAN/+AZFUWjduS/7dv2OyWCgQrXPY93m01qNmDV5GIN7tsZqtdCgWVv0+rfPu+LTh3wekq9oRS6cOsD4vk1RFIUm7YZwZO8GTMZoSlWqT+3m3zN9WGsUu53i5WvjnzwArdaD9cunsn3dfLx8ktCkzWAAUqXJzIwR7fDw1JMjd1FyFyzjlszls6cj7OYjvlq2EwUYUKUQmy7cxmC2UidfFvpVLkifjYdRUMiXNgWls6bFYrMz4PcjtFy+C9Wf2/y/n5YLqP4Dv+M/pVLie4guEaxevZpr167RvXv3v7Vd69at6d27N5kzZ37vunfu3OG7775jxYoV/zRmnOw6G/3+lT4AMxc8ef9KH4C5+RYndoQ4+eOrBe9f6QNQOmzq+1f6AER7v/0nDj5ENvXHcS1w39PciR0hTkonO5XYEeLkki3o/St9APw8ot6/0gfgqTHJ+1f6ABSL2pLYEeLkQjL3TN+Pb+N+ikjsCHHyVRP3/nmn+FLywIDEjhAnvm3c/8Tq+BY+yv33lP8T/j0S/5zuP9lFNxqN1KlTh5CQkDh1QoUQQgghhBBCxJ+P43L8e8T2hNu/otfrWb367b9n+FcyZMjg9tFQIYQQQgghxP8fKnlq7jv9J0dEhRBCCCGEEEIkHumICiGEEEIIIYRIUP8vpuYKIYQQQgghxAdHJeN+7yI1I4QQQgghhBAiQcmIqBBCCCGEEEK4gTys6N1kRFQIIYQQQgghRIKSjqgQQgghhBBCiAQlU3OFEEIIIYQQwh3UMu73LlIzQgghhBBCCCESlHREhRBCCCGEEEIkKJmaK4QQQgghhBBuoFLJU3PfRUZEhRBCCCGEEEIkKOmICiGEEEIIIYRIUDI1VwghhBBCCCHcQZ6a+05SM0IIIYQQQgghEpR0RIUQQgghhBBCJCiZmiuEEEIIIYQQbqBSy1Nz30U6oh+Y59GeiR0hTjz0HokdIU7s2XIldoQ4KR02NbEjxMmeYh0SO0Kc5LqwIbEjxIkJfWJHiJOkXtbEjhAnR6LyJHaEOHke+XF89WZNpSR2hDgxWzWJHSFO7qfKn9gR4iQ8yjuxI8RJmky6xI4QJ/6ehsSOEDehJRM7gfgPkqm5QgghhBBCCCES1MdxWVYIIf6PvbuOjuL6+zj+3t1sHIIHgiQElwLBpRRapLS0uFuLu1MsuFtwdwi0WNG20BanSIAQPCSEBHcnyfru88fCJouUtM0m8Py+r3M4pzt3dveTuzN35s69MxVCCCGE+NgoZNzvXaRmhBBCCCGEEEKkKOmICiGEEEIIIYRIUTI1VwghhBBCCCEcQZ6a+04yIiqEEEIIIYQQIkVJR1QIIYQQQgghRIqSqblCCCGEEEII4QAKeWruO0nNCCGEEEIIIYRIUdIRFUIIIYQQQgiRomRqrhBCCCGEEEI4gjw1951kRFQIIYQQQgghRIqSjqgQQgghhBBCiBQlU3OFEEIIIYQQwgEUShn3exepGSGEEEIIIYQQKUo6okIIIYQQQgghUpRMzRVCCCGEEEIIR1DIU3PfxSEd0YMHD/Lbb78xadIku+V9+/alWbNmlCtXzhFf+1bjx4+nbdu2+Pj4vLX8iy++YOfOnbi4uNiW6XQ6tm/fTuPGje3Wbd26NaNGjSJPnjwOzfw+ZrOZrSvHcOd6BE5OzjTsMIZMWX1t5RdP7WPPlgUoVSpKV2lAuc+tf8eswAa4uqUBIH3m7DTpPIG1c/sT+/QhAE8e3iJn3uK07BHkkNwlCrhQt6onZrOFg6c0HAjV2JVn8FLSoZ4XSqUChQJWbHvG3UcmKhRzpVZFD8wWOHQqnr0nNO/4hv/ObDYzcfVWIm/cwdnJieHtGpLLO9Mb641d8TNeHu70avIVAM1HzMLTzRUAn8zpGd2hicMyvso5efl6Ll+/hdrJiWGdWpIza+Y31hu/5EfSenrQs3ld27LzUVeZ8+NWFo3o49CMSZGubDEKThjAseptUjWH2Wxm7rz5RMfEoFar6du7l12bsW//frZu3YZSpSS3X256dO+G2WxmWtB07t2/h1Kpok+vnuTMmdPhORfOm8XVmCuo1c706N2fbD7ZbeUH9+9l+9afUaqU+Pn506V7b5Qv7015+vQJ/Xp1Zcz4KeTImcvhOTcuG8ftaxE4qZ1p1nk0mbMmfOf50P3s2rQQlUpFuc/rU7FaI4wGPWsXDOPRvVu4unvQqF0gWbL5/s23/Ptsm1eM5c61CFRqZ5p0HG3Xfl4I3cefWxagUjpRpmp9yn9hbT/3bFvCxdB9GI0GKtZoRrnPG3Iz5iI/LxuNk9oZH9+C1G0zxFbf/1bkmb389cs8lConildqSEBl+7Yk/sVjti4dgNGgxTNdFr79biJqF7e/fV/c80csG9+AFn2WkylbHu5cu8CGeV3IkMUPgFJVmlO4zNf/KTdY63bNooncuBqJWu3Md92H453NflvT6TRMH9WN77uPIFuO3Lblz58+ZsyAlvQfNd9ueXIxm81sWj6WW9cicXJS06zzmDe2yd9/XoBS5UT5qvWpUK0RJqOBtfMDefzgFkqliqadRuGd3Z+bVy+xcekYlCoVmbP50qzTmP/8u78r8/x5c4iJiUatVtOrd198Eu3vB/bvY9vWzShVKvz8ctOte0+USiUb1v9ESMgxjEYDtWt/S80vv0r2bK/nXL90PLeuWvf3Fl1GkeW1312v0zBnbGdadh1N1uzW3/fFs8cEDWtDYNDPqJ1d3vbRDlXET0XNMs6YLRBy0cCxi0a78nSeCppXc0H58n+9sWGflgdPLQ7PZTabWbVwMtevXsZJ7UyHHoF4Z7M/tuh0WiaP6EGHnsPwyeHHwT2/cGjvLwAY9Hqux0QyZ+VOPDzTODTnpFWbibx+B2e1iuHtm5DzLedL45ZvJK2HO72a1rYte/z8Ba1GzGTewM7k9snisIziw/f/fmpuYGDgOzuh7/LgwQM2btzooET/3cXQPRgNerqP+olazfrx649TbGUmo4Ff1kyi/eAldB62iuN7N/Li6QMMeh0AnYetovOwVTTpPAGAlj2C6DxsFa37zsbVPQ3fthzskMwqJbSolYapqx4zYfljqpZ2x8vTfvNr+EUadofEM2nFY3YcjKVxDWsD2uzLNExZ9ZhxSx9Rq6IH7q6Ou7K079RF9AYjq4Z3p2fjWsxY9+sb62zad4yom3dtr3V6AwBLhnRmyZDODu+EAuw/eRadwcjyMQPo0bwuM9dsfmOdzbv/4sqN23bLVm//k3GL16I3GN9YP6X59+/AJ4vGoXRN+ROQ1x05ehS9Qc/M6UG0a/s9i5cutZXpdDpWrw5m8qSJzAgKIi4+jpDjxzlx4gQmk4kZQUG0bN6clatWOzxnyNHDGAx6pkyfS5u2HVi+dKFdzrWrlzN+UhBTguYQHx/HiePHADAajcyfMwMXZ2eHZwQ4d2IvRoOOvuPW8m3zPmwNnmorMxkNbFk1hW6Bi+g5aiVHdm/i+dOHHNmzCRdXd/qNX0vDtkP4efkEh2S7cHIPRoOOnmN+pHazvuxYa59t+5rJdBq8hK4jVnJs70aeP31A1MXjXI0Mo/uoNXQbsZKnj6z7/6alo6jbZjDdRwbj6u5J2JE324t/wmQ0sHvDRJr3WU7rAcGEHVxP7LMHdusc+nU+Rcp9Q5uBP5I1Z2FOHVz/t+8zGQ38tmYEarWr7TPuXr9IueptaT0gmNYDgpOlEwoQFrIPg0FP4ORVNGzdkw0rZtiVX426yOTADty/e9NuudFoYPXC8Tg7sDNy7uQeDHo9fceu5dsWfdn22ja5dfVkug5dTM+RKzmyZyPPnz7k4ulDmM0m+oxdy5cNu/Dr+tkA/P7zfL5s2IXeo4MxGQxcDDvokMzHjh7BYNATNH0W37dtz7Kli21lOp2O4NUrmTBpKtOCZr7c30M4e/YM4eEXmTptBpMmB/HgwYO/+YbkcfbEXgx6HQMmrKFuy95sXj3NrvzalQvMGNGWB3dv2JZdPH2YueO68OLZI4fnexulEup+6sLC7RrmbtZQoYiaNO725xZfl3fm0FkD87Zo2B2q55sKKXOsCg05gN6gZ+SU5TRt050fl8+yK4++fJHxQzrZ7UefVfuGwPELCRy/EL88BWnVsb9DO6EA+0MvoDMYWTmyJz2b1GbGjzveWOfnvUeJunHXbpnBaGL8ip9xUasdmk98HP62IxoTE0OzZs1o1aoV3333Hffu3SMkJIS+ffva1qlUqRIAV65coWnTpnz//ff89NNPtvK1a9dSr149OnbsyLVr1wAwGAwMHTqUli1b0rx5c0JCQgD49ttvGTt2LK1ataJ169a8ePHC9jkmk4maNWtiNBq5f/8+hQoV4smTJ+j1eurXrw9AUFAQzZo1o2nTpuzcuROwjmJeuXKFx48f065dO1q1asXw4cOpUaOG7bNHjRpF69atad26Nc+ePWPhwoVERUUxd+7ct9bL8+fP6dy5My1btqRZs2YcPXoUgBkzZtC0aVMaN27MypUrbX9/48aNadq0KZMnT07CT/J+MRGnyF/sUwB88xbnZswFW9n929Fk9PbF3cMLJydn/AqUJCYilDvXL6HXaVk6qQOLJ7TlWtQZu8/88+e5VKzZirTp3xxVSw4+mZ2499hEvNaCyQSXr+nJ72vfCP30+3PORFo7zCqlAoPReuXxxj0jbi5K1E4Kh89uOH05hoqf5AegWF5fLsbYnzCdibrGuSvXaVg1YVQ/8sYdtHo93aYupdPkxZyNuubYkMCZiCtULF4IgE/y5SY8+rpd+dnIaM5FxVC/2qd2y3N4Z2JK344Oz5cU8dHXCW3cM7VjAHDhwkVKlyoFQKGCBbl8OcpWplarmR40DVdX64m8yWTC2dmZ7NmzYzKbMJvNxMfH4+Tk+DsdLl44R0CpMgAUKFiYqMsRdjknB83G5bWcACuWLqTW19+SIeObV6sdITriFIWKW7c9v/zFuXHloq3s7q1oMmXNhbunF05OavwLBnAlPJS7N6MpVKIyAN4+ubl7K9oh2WIiTlHgVfuZrzg3ohPaz3u3o8nk/SqbM7kLlCTm0ikizx4mW878rJrRi+XTulO4ZBUAnj2+i1/+gJd/Z0liIk79p2wP714hfZZcuHl4oXJyJmfeUty4fNJunRuXQ8lTxFpPeYp+Rkz4kb99355NkylZpRme6RJGHe5eP0/Uuf2sntqSX1YNRaeN/U+5X7kcfpqiARWt2QoU42qi3x3AYNDTY3AQ2bL72S3fsHImVb9sSLoMjjn+AERfCqNQCev5it/rv/vr22SBkkRfCiVzNl/MJiNmsxmtJg6VyrqPZ/crRHzsMywWC1ptwvLkduHCeUqWKg1AwYKFuHw50lamVquZGjTTrl1SO6s5FXoSP7/cjB83mjGjR1C2bHmHZEvsSngYhQOsdZs7f3Guv/a7Gw16Ov0w0zYSCqBQKOk5YjHunl4Oz/c23umVPHxmRqMDkxli7pjwz2Z/SrztLx0Xr5kAUCnAYHL8aChA5MXTFAuoAEDeAp8QExVuV240Gug9ZCrZcvi98d7oyxe5dSOaL76s7/CcpyNjqFisAACf5PXl4tUbduVnL1/l3JVrNPjCfhucuW4HDT+vQOb0aR2e8YOhVH6Y/z4Af5viyJEjFClShBUrVtClSxeePXv2znVnzZpFr169WLlyJQEB1gPzixcvWL16NRs2bGD+/PkYDNaRo40bN5I+fXrWrl3L/PnzGTNmDABxcXHUrl2bNWvWkCVLFg4eTLjKqFKpKFWqFKdPn+bQoUPky5ePo0ePcvToUSpVqsSBAwe4efMm69atY/Xq1SxcuJDnz5/b3r9w4UKqVavGmjVrqFWrFiaTyVbWsGFDgoODyZ49O4cPH6ZLly7kzZuXHj16vPVvXbBgARUrVmTt2rXMmjWLwMBA63TZrVuZNm0aa9eutR0cNm/eTGBgIOvXrydnzpwYjf99JEqnicXV3dP2WqFUYjJZP1f7WpmLqwfa+FjULm58Vrst7QctoX7bkaybP9D2nthnj4i6cIzSn9X7z9nexdVFgUZrtr3W6C24u9hvfrHxFkxmyJpRRbMv07B1n/Xk6OY9I6O7ZGRCj0ycjtARr3XcwSBOo8PTPWHkQKVUYHy5rTx4+pxFW/9kcOt6du9xdVbTutZnzBvQnsDv6jNs0TrbexyXU4uHu5vttVKptH3nwyfPWPLzbwxq2/SN931RLgAnJ5VDsyXV3S1/YPkARmYB4uPj8XD3sL1WKpW2NkKpVJI+fXoAtm3fjlajpWRAAG5ubty7d5+OnTozc/Zs6tb5NhVyquxypkufAYBftm9Bq9FQIqAUe/7chZdXOkq+7MCmBG183N+0UXG4JSpzdbO2UTn8CnDh1AEsFgtXI8/w7PF9zObk34+0mjhc3RNGCpSJsuniY+3KXFw90GpeEPfiCTdjLtC693QathvJj/MGYbFYyJAlJ1fCTwDWWyL0uv9224BeE4uLW8L3O7t6oNXYdxL12oR1nF090GlevPN9Z45sxj1NBlvH9RUfv2JUazSQNj+sJV2mnBzaMe8/5X5Fq4nDPdFva90+E/bxfIVKkCFTVrv3/LV3O2m80ts6sI6i08Tabk2BN7dJV7dEx003DzTxL3Bxcefxg9tM7Pct6xeP5LNaLQHInNWXzSsnMrF/HV48fUTewo7ZtzSv7e+qd7RLO7ZvRavREBBQiufPnxN1OZLBQ4bRvUcvpk2dhMXi2A6UVhNrt08n3qcA8hQMIP1rv3uh4hXwTJPOobn+jqszaPUJ9aLVW89TEovTgtkMmdMpqFPJhd+P61MkmyY+DnePd9dn/kLFyZjZ+63v3bFpJfWbdnB4RoBYrdZ2SxKAUqG0P1/a8geD2jSwe8/2QydIn8bT1oEV4m8v4zVq1IglS5bQoUMH0qRJYzcS+sqrBu7y5csUK1YMgJIlSxIdHU10dDR58+a1XZV/VR4ZGUloaChnz54FrNPGnjx5AkDhwoUByJYtGzqdzu67atasaetw9u3blz179qBUKmnUqBEhISFcuHCB1q1b2z7z9u2EaYlXrlyxjZyWLl3a7nOLFi0KQKZMmdBqtX9fYy8/69tvrSed3t7eeHp68vjxY6ZPn8706dN5+PAhlStbD/wTJ05k+fLlTJs2jRIlSiTLAcHFzROdJs722mK22K7Iurp5otMmlOm0cbh6pCFzVj8yeedCoVCQOZsf7p7pePH0AekyZuPc8T8IqFgbpTL5OygNq3mSL5czOb2diL5lsC13c1YQl6hj+krB3M58901aFv38lLuPTOT0dqJ4fhcGzHiAVm+hS0MvyhRx4cQF3RvvTQ4ebi7EaRM+22yx4KSy1svuE+d4+iKeXtNX8OjZC7R6PX7ZMlOrfAlyemdCoVDgmzUzXp7uPHz6gqwZ0zkkozWnK/GahJyWxDlDwnj6Io7ek+dbc+r0+Pl4820Vx18Z/1i5u7uj0SR0IixmMypVwv5gNptZtnw5N2/dYljgUBQKBZu3bKVUyZK0a/s9Dx48YNCQoSycP8/W3qVWzlXLF3Pr1k0GB45CoVCw+49dKBQKzpw+RUx0FDODJhE4YhzpM2RwWE5Xdw+7dshiMSdqozzQJmq/tJo43DzS8EmZL7h7K5q5Y9qRu0AAOf0LO6RNcnV7PVtC++nibt+26rTWTqu7Zzoy+/jj5ORMFp/cOKmdiX3+mKadx7Ft9UT27VhOTv+iOKn/3W+/f+sMbkSd4v7NCHxyF7Mt12vtO80Azq6e6LVxqJ1dX5anxdnNuuz1953YG4wCBTHhR7l3I5ztKwbRpPsCCgTUwNXdOhpRIKAGf6wb+69yv87VzQPtO373d/lrzzYUKAg/E8L1mAiWzRpBz6Ez8EqfvKP3Lq8dGxP/7tZtIt5WptPE4eaelv2/BVOgeEW+bd6XJw/vMG9cewZN2cKWVZPoOWo12XLm5dDvP7FtzVQatRuWrHkB3F7b381myxv7+4rlS7l16yZDAkegUChImzYtOXLmRK1WkyNHTtTOzjx79pR06dIne75XXN080WoS6i8pv3tq+aqcM/4+KrJlVHL9XsKFLldn0OrePD/Lm11FoyourN2dMveHAri527eR5kTb6t+Ji33B7ZvXKFys9HvXTQ6erq5250t25yHHz/A0No7eQUt5+OwFWp0BP58sbD94HAUKjl+IJOL6bUYu/onpfdqSKd3/0OiosPO3I6J79uyhVKlSrFq1ilq1arF06VJcXFxs9xzcunXLNkrq7+9PWFgYAOfPnwcgZ86cREVFodVqMZlMhIeH29atXbs2wcHBLFmyhFq1auHlZZ2eofibuZeVKlXixIkTPHnyhCpVqnDhwgUuXbpEsWLF8Pf3p1y5cgQHB7Nq1Sq++uorcuTIYXtv/vz5bflOnz5t97mvf6dSqcRsfrOT9EqePHk4edI67enevXs8f/6ctGnTsmvXLqZPn86qVavYsmULt27dYsOGDYwePZo1a9YQHh5uy/Bf+OUPIOLMIQCuRZ0ha858trIsPv48vHuN+NinGI16Yi6dxDdvCU4c2Mwva633kj5/ch+dJpY06azToC5fOEqBYp/951xv8/OeWCateEyvKffJkkGFh5sClQoK+Dlz5YbBbt2CuZ1p9VUapq1+zNXb1qt/8VozBqMFvdGCxQLP48x4uDpuOkGJvH4cPmOd7ng26hp5cyRcxW1eoxI/ju7FkiGd+b52VWqVD6BO5dJsO3SCGeusDwl48OQ5cRodmdI59t6M4vn9OXzaOrXs3OUY8uRMuA+6Wa2qBE8YxKIRffiuTg2+rFRaOqHvUaRwYY6ftI5shV+6hJ+fn1357Dlz0esNjBw+3DbbwdPTEw8PdwDSpEmD0Wj823YjORQqXJTQk9ZbGSIuXcTXz/6hLvPnzECv1zN0+BjbFN2JU2cyYcoMxk+eTm7/vPTpP9ihnVCA3AUCuBhmbaOuRp7BJ1dCG5U1uz8P7l4nLvYZRqOBK+Gh+OUvzvUr5/EvWJKeI1dQrEw1MmbJ8a6P/0/8CgRw6bR1ts21y/btp/dr7Wd0eCh++UqQu0BJIs78hcVi4dmT++h1GjzSpCM87ABNOo+jw8AFxMc+JX/RfzeqV7VeX1oPCKbPtMM8eXAdTdxTTEY91y+fJLt/gN26OfOWJOr8AQCunD9IznylyJQ1D4/vX3vjfW1+WEvrH9bQekAw3jkLUaftZDy9MvPTzPbcirFeCL566ShZcxX5V7lfl7dQCc6GHrZmizhL9lx53/ueweOXMWj8UgaOW0Ku3AVo33tMsndCAfwTb5OXz5At8e+e3Z8Hd68lbJOXrNuku0da3F5eCHD39MJkNGI2m3D39MLVzTpS6ZU+M/Gxz9/8wmRQuHARTp48DsClS+FvtEtz58xCr9czbPgoW7tUuHARTp08gcVi4dGjR+i0WtKkcexJvn/BElw4Za3bmNf29w/NzhA987ZoGLE8jkxeStxdrM+w8PdRcfWuffudN7uK+pWdWbRDw437jm3bE8tfqDinQ48AEBVxjpy+SXtAZsSFMIoUT7mZL8Xz+3H4zCUAzkVdI2/OROdLNSuzdkxfFg/txvfffEGtCgHUqVyGpYHdWRLYjcVDu1Eglw+jOzX/3+iEKhQf5r8PwN9eYilatCg//PADc+bMQalUMmTIEAoUKECaNGlo3LgxefLksXX2Ro4cSd++fVm2bBkZMmTAxcWFDBky0Lt3b5o1a0aGDBlwc7NOJWzWrBnDhg2jVatWxMbG0qJFiyQ9cc7Z2ZmsWbPi4+ODUqkkd+7cZHh5QvXFF19w/PhxWrRoQXx8PNWrV8fTM2FqQ8eOHRk4cCA7d+4kS5Ysf3s/V8aMGTEYDEydOpUffvjhjfLOnTszdOhQfv/9d7RaLWPGjMHZ2RkvLy/q1q2Ll5cXlSpVwsfHhwIFCtCoUSPSp0+Pt7c3xYsXf+/f+T5FSlfn8vkjzBvdAiwWGncaT9iRX9Br4yn3RRO+aTmIZZM7YbGYKV2lAV4ZvClTtQEbFwWyYEwrABp1HGe7wvbgTgwZHHTS94rJDD/tesGANhlQKuDgKQ1PXpjxcFPQrq4Xc9Y9peVXaVCpFHRskA6Auw+NrNzxnH0n4hnWPgNGE9x/bOLQacc9NffzUkU4duEy34+bh8UCo9o3ZufRMOJ1erv7QhOr91kZRi7dSLvxC0ABI9s3sl0VdJSqZYoTcu4S7UYEARZGdG7FrsMniNfqaPDafaHi/SpWrMCpsDD69u+PxQL9+/Zh3779aLQa8uXLx+9//EHRIkUYNGQoAPXq1qFB/XpMnzGT/j8MxGgw0Pa772wng45SvuKnnA4LZWD/nmCx0KvvQA7s24NWqyFvvvzs/mMnhYt8wvAhAwD4pm4DKlRM+e2hWJlqRJw9yozhrcBioUXXsZz861f02ngqVm9M/TY/sGB8ZywWM+U/r0+6DN44OTnz2/q57NuxEjePNDTvPMYh2YqWrk7kuaPMGdkSLBaadh7HqcPW9rN8tSZ822ogiyd1wmK2ULZqfbwyeOOVwZvoSyeZNbwpFrOFBt8PQ6lUkSmrL8umdEHt7EbewmUpFPDfLuipnNRUbzyYn2a2x2KxULxSQ9Km90YT95RfVw+jUde5VKrdlR0rBnH60AbcPNNTr0PQO9/3LrVajuL3n8aiclLjmTYTX7dOnhHRkuU+5+LpY0wY/D0Wi4V2PUdx7OBOdNp4qtRsmCzf8W99UqYaEeeOMHN4SyxAiy5jCf3rV3Qvt8l6rQeycEInLBYL5apat8mqtdvw08LhzB7ZBqPRwDfNeuPi6k6zTqNZPfsHlConVE5ONO042iGZK1SsRFjYKQb074PFYqFP3/7s37fXtr//+ccuihQpytAhAwGoU7ceFSt+yvnz5+jXpydmi5ku3XrYjaI6QvGy1bh09hjTAluDxUKr7mM5cehXdFoNn9Zo5NDv/rfMZus9oJ3ruKFQQEi4kWdxFtxdoOkXrqzYqaVeZWdUKgUtqlvb9ftPzGzc75jZWImVKl+V86dDGD2wPWChY68RHDmwC61W87f3ft65dY0sWbO/szy5fV6qKCHnI2k7Zg4WC4zs2JSdR06h0elp8Llc/BZJo7A4+uaBD8SBAwdInz49xYoV48iRIyxcuJDVqx3/lMt/ausJx95bmFy27HD8k/iSw/yvjqV2hCQxuXi8f6UPwKFyb79v+kNT+NJ/e3ppStHh2I5rcrn64uN4vL7B9GE8fOF9nsR+mNMWX+efOe79K30AXuhS5mnQ/1XetLffv9IH4Fpc1vev9AH49aDh/St9AJpX/zjO6wo/c8zTn5ObZznHP48hucWvcsyF1f/K/bsRqR3BMf8f0Q9Rjhw5GDp0KCqVCrPZTGBgYGpHEkIIIYQQQvw/pvhAnlD7Ifqf6YjmyZOH9evXp3YMIYQQQgghhPif9z/TERVCCCGEEEII8ffMZjOjRo0iIiICZ2dnxo0bh6+vr6387NmzTJpk/d9DZc6cmalTp+Li4vKPv0c6okIIIYQQQgjhCIqPb2ru7t270ev1rF+/ntOnTzNp0iQWLFgAWP9XPcOHD2f27Nn4+vqyceNGbt26hb+//z/+HumICiGEEEIIIYQAIDQ0lMqVKwNQokQJ2/+aEyAmJoZ06dKxatUqIiMjqVKlyr/qhIJ0RIUQQgghhBDif8r69evtnp/TtGlTmjZtCkBsbKzd/wZTpVJhNBpxcnLiyZMnhIWFMXz4cHx9fenSpQtFixalQoUK/ziDdESFEEIIIYQQwhGUitRO8FaJO56v8/T0JC4u4X/hZTabcXKydhvTpUuHr68vefPmBaBy5cqcP3/+X3VEP75Jy0IIIYQQQgghHKJkyZIcPGj9f8uePn2a/Pnz28py5sxJXFwc165dA+DkyZPky5fvX32PjIgKIYQQQgghhACgRo0aHD58mGbNmmGxWJgwYQI7duwgPj6epk2bMn78ePr374/FYiEgIICqVav+q++RjqgQQgghhBBCOIDiI3xqrlKpZMyYMXbL8uTJY/vvChUqsGnTpv/+Pf/5E4QQQgghhBBCiH9AOqJCCCGEEEIIIVKUTM0VQgghhBBCCEf4QJ+a+yGQEVEhhBBCCCGEEClKOqJCCCGEEEIIIVKUTM0VQgghhBBCCEf4CJ+am1KkZoQQQgghhBBCpCjpiAohhBBCCCGESFEyNVcIIYQQQgghHEEhT819F+mIfmDyp7+X2hGSJH9h79SOkCS3swSkdoQkSWN4nNoRkqTwpV9TO0KSXCxYO7UjJIn3+WOpHSFJnms+jkNF+UwRqR0hSZRpTKkdIUmuGf1SO0KSBLidT+0ISRKn8ErtCEni63E3tSMkyZefZkvtCEly8/nH0X6mzfRxnC8VTO0AIlnJ1FwhhBBCCCGEECnq47hMI4QQQgghhBAfG6WM+72L1IwQQgghhBBCiBQlHVEhhBBCCCGEEClKpuYKIYQQQgghhCMoZNzvXaRmhBBCCCGEEEKkKOmICiGEEEIIIYRIUTI1VwghhBBCCCEcQalI7QQfLBkRFUIIIYQQQgiRoqQjKoQQQgghhBAiRcnUXCGEEEIIIYRwBHlq7jtJzQghhBBCCCGESFHSERVCCCGEEEIIkaJkaq4QQgghhBBCOIJCnpr7LjIiKoQQQgghhBAiRUlHVAghhBBCCCFEipKpuUIIIYQQQgjhCEoZ93sX6Yi+VKlSJQ4fPpzaMf4xs9nMovkzuRpzBbVaTfdeP5DNJ7ut/ND+PezYtgmlUoVvbn86d+uDxWJh/pxp3L55A6VSRY++A8mWLfvffEvyyOcDnxZRYrbAmWgLp6Mtb12vTH4Fnq6w76y1vHAuBWULKLBY4P5TCztPvv19ycFsNjN/3hxiYqJRq9X06t0Xn0T1eWD/PrZt3YxSpcLPLzfduvdEqVSyYf1PhIQcw2g0ULv2t9T88iuHZXyVc8bC5URdvYaz2okfenQmR7asCTmPhLD2520oFAq+rVmNb2p+gclkZuq8xdy4dRulUsngXl3Inug9jso5d958omNiUKvV9O3dCx8fH1v5vv372bp1G0qVktx+uenRvRtms5lpQdO5d/8eSqWKPr16kjNnTofmfJ90ZYtRcMIAjlVvk6o5zGYzKxZO5XrMZdRqNR16DCWrj33d6HRaJg7vSadegfjk8MNoNLJw5hge3r+DUqmkQ48h+OTwc0i2X1aP4e6NS6icnKnXbiwZvX1t5ZfC9rF/+3yUShUlKzegdNUmmIwGtiwL5Omj2xgNeqrU6UKhgC+4cy2c7atGoVQ5kSmrH3XbjkXpwAO52Wxm9vxFRMdcRa12ol+vHmT3yWYrP3T4COs2bUaBgq9r1eTrL2s4LMu78s1asIQrMVdxVqvp37OrXb6Dh4+ybtMWUCio/WUNan9Z3VYWHhHJkpVrmD5xTIrkXLNoEjeuRuKkdub77sPxzvb69qkhaFR32nYfTrYcuQEY1a8Fbu6eAGTy9qF9z1EOz/lP289Xnjx9Rsd+QwgaE4hvDsceN81mM/PmzSMm2no86t2nj137uX//frZu3YpKqcQvd266d+/Onj172P3nnwDo9Xqio6NZ++OPeHp6OjTnx3Lc3LhsHLevReCkdqZZ59FkzprLVn4+dD+7Ni1EpVJR7vP6VKzWCKNBz9oFw3h07xau7h40ahdIlmy+f/Mt/z7btlVjuHP9Ek5OzjToMJZMidrP8FP72LN1PkqVitKfNaDs501sZbHPHjF3RCPaDVpGFh9/Yp89YvPyEWjinmExm2nceRIZvXO97Wv/c+aF82a9PP90pkfv/nbnnwf372X71p9RqpT4+fnTpXtvWzv+9OkT+vXqypjxU8iRM/mziY+HdNE/ciFH/8Kg1zM5aB6tv+/EiqXzbWU6nY61wcsZO3EGk4LmEh8Xx8njRzl5/CgAE6fNpXmrtqxYMv9dH59slAqoHqDkp/1mgveaCcijwMPVfh0nFdQpr6B0PoXdsqrFFKzZa2bVbjMuamuH1lGOHT2CwaAnaPosvm/bnmVLF9vKdDodwatXMmHSVKYFzSQ+Po4Tx0M4e/YM4eEXmTptBpMmB/HgwQPHBXzpr5CT6A16FkwZS6c2LZi/PNhWZjKZWbT6J6aPGcb8yWNZt2UHT58/58iJUADmTR5DuxaNmZfoPY5y5OhR9AY9M6cH0a7t9yxeutRWptPpWL06mMmTJjIjKIi4+DhCjh/nxIkTmEwmZgQF0bJ5c1auWu3wnH/Hv38HPlk0DqWrS6rmAAg9dgCDXsfoqUtp2qY7a5fPtiuPvhzO2CFduH/3lm3Z6ZNHMJtMjJqyhPpN27EheKFDsoWf2o3RoKPT8HXUbNyPXeum2MpMRgM7f5rEdwOW0m7Iak4e2MiLpw84c3QH7p7p6DB0DW36L+LX4HEA7Ns2j8/rdqNj4FqMBj2RZw44JPMrh4+FoDfomR00mfbft2HRshUJ2U0mlq4MZsq40cyaNomNm7fw7Nlzh+Z5M99x9Ho9c6dNpMN3rVi4fJV9vlVrmDJuJHOmTmDDlm22fOt+3krQnAXoDfoUyRkWsh+DQUfg5JU0at2T9Stm2JXHRF1kUmBH7t+9aVtm0OsAGDRuMYPGLXZ4JxT+XfsJYDQamTZ/KS4uzg7PCHD06FEMej3TZ8ygbdu2LF2yxFam0+lYvWoVkyZNImj6dOLj4jh+/Dg1atRg8pQpTJ4yhbz58tGlSxeHdkLh4zlunjuxF6NBR99xa/m2eR+2Bk+1lZmMBrasmkK3wEX0HLWSI7s38fzpQ47s2YSLqzv9xq+lYdsh/Lx8gkOyXQzdjVGvo9vIddRq2o/ffrRvP39ZO4n2g5bSKXA1x/dZ209b7hUjcXJOOD7tXDeNEhW/ofOwNdRo1JsHd6Idkjnk6GEMBj1Tps+lTdsOLF+acGzR6XSsXb2c8ZOCmBI05+Xvfgyw7kfz58zAxTll9iPxYfvoO6L169fn0aNHGAwGSpYsycWLF23LV61aRdOmTWnWrBmrV1tPZiMjI2nXrh3ff/89DRo04NSpU3afN336dEaPHo3FYqFmzZoMHjyYpk2b0q1bN0wmEwaDgaFDh9KyZUuaN29OSEgIADNmzKBp06Y0btyYlStXArB27VoaN25M06ZNmTx5skP+/vCL5wgoVRaAAgULcyUq0lamVquZNG0OLq7WHp/JZMLZ2ZlyFT6lW88BANy/f5d06dI7JFtimdLCk1jQGsBshhsPLeTMbL+OkxLOXbVw+GLCiKfRBKv+NGM0WV8rFQqMZsflvHDhPCVLlQagYMFCXL5sX59Tg2bimqg+1c5qToWexM8vN+PHjWbM6BGULVvecQFfOnvxEmUDSgBQpEA+IqISDjQqlZLV84Lw9HDn+YsXWLDg5upK5fJlGNC9IwD3HjwkfTovh+e8cOEipUuVAqBQwYJcvhxlK1Or1UwPmmZXn87OzmTPnh2T2YTZbCY+Ph4np9SduBEffZ3Qxj1TNcMrEeFnKF6yAgD5ChYlJuqSXbnBoKfvkMn45Ei4kp4te05MJiNmsxmNJg6Vg+rz+uVT5P3kUwBy5i3BrZjztrIHd6LJkCUXbh5eODk5kytfSa5FhlKkzJdUa9Dbtp5SpbJm9i1EfNwzLBYLOm0cKpVjt4ELF8IpU7IkAIULFiAy0XaqUqlYvnAuHh4e1v3JAm5uru/6KIc4dzGcMqUCXubLT8TlK3b5ViyYjaeHB89fxGKxWGz5fLJ6M2roDymW83L4aYoGVAQgT4FPuHrlol250aCn5+BpZMvuZ1t2/WokOp2WoFHdmDK8M1cizjk8579pPwHmr1hD3VrVyZTB8cdMgAsXLlDqZftZsFAhLl++bCtTq9UETZ9u336q1bbyyMhIrl+7xldff50COT+O42Z0xCkKFbe2UX75i3Mj0fZ591Y0mbLmwt3TCycnNf4FA7gSHsrdm9EUKlEZAG+f3Ny95ZhO3dXIU+QvZs2W67X28/7taDJ6J7SffvlLcjXCemH5t5+mUu6LZqRNl8W2/rXLp3j2+B5LJ7Xl9JEd+Bcs65DMFy+cI6BUGcB6/hl1OcJWplarmRw0+43zT4AVSxdS6+tvyZAxk0NyfZAUig/z3wfgo++IVqtWjUOHDhEaGkqOHDk4fPgwUVFR5MqVi127dvHjjz/y448/snv3bqKjo4mKimLQoEGsXLmStm3bsnnzZttnTZ48GaPRyMiRI1EoFNy4cYPevXuzfv16Hj9+zLlz59i4cSPp06dn7dq1zJ8/nzFjrNOdtm7dyrRp01i7dq2twd28eTOBgYGsX7+enDlzYjQak/3v18TH4+7hYXutVCoxmUy2/06XPgMAv27fjFaroXiA9WChUqmYNX0iSxfOoeKnVZI91+uc1aAzJHQw9QZwVdvvBFoDxNx9871x1gvmlM6nwNnp7eskF018PB7uCfWpeq0+06e3noDs2L4VrUZDQEApnj9/TtTlSAYPGUb3Hr2YNnUSFovjpg8DxMdr8PBws71WKpUYX+YEcFKpOHj0OO36DKJ44UI4vTyRd1KpmDBzPrMWr6RqxXIOzWjNaV+fr2+fr+pz2/btaDVaSgYE4Obmxr179+nYqTMzZ8+mbp1vHZ7z79zd8gcWQ/Lvu/+GJj4Otzf294RsBQoXJ2Nmb7v3uLq68/D+HX7o1pSlcyfx5TdNcASdJhZX9zSJsqls2axlCaMyLq4eaDUvcHH1wMXNA50mjnVz+9g6pRm9/fht7QRmD6lN3PNH+DnoROqVOE08Hh7uCdlVCdspWNvLQ0eO0qVnXz4pWhjVyw5zSomP1+DhnpAvcbuUkO8YnXr1o1iRhHyfVapg2/dTgkYTa5tiC29un/kKlSBDJvvbAVxcXKlVtxX9Rs6jTZehLJ4RaPceR/g37efOPftJ55WWsiWLOzSbfc6/P76/aj+3b9uGRqsl4OXFFIAN69fTomXLFMn5sRw3tfFxdu2QItH2qdXE2W27rm4eaONjyeFXgAunDmCxWLgaeYZnj+9jNpve+Oz/6vX2U/F37aebtf0MPbgFjzTpbR3YV548vI2bR1o6DF5BuozZOPDrUhzhzeO76q3nn79s34JWo6FEQCn2/LkLL690lHzZgRXio++I1qxZk4MHD3Lo0CH69u3L0aNH2bt3L19++SW3b9/m+++/57vvvuPp06dcv36dLFmyMH/+fAYNGsTvv/9u6xw+fPiQiIgI4uPjbZ+dPn16smWz3oeTLVs2dDodkZGRHDx4kNatW9OrVy+MRiNPnjxh+vTpTJ8+nfbt2/P85TSeiRMnsm7dOlq1asXt27cd0si6ubuj1SRktpjNdidJZrOZlUsXcCYslEFDR6NIdAWkd78hzFsczPzZQWi1mmTPBlDlEwWtvlDSpLISF6eE73ZWg1af9PqoVkJB7qwKNh124HAo1vrUaBLqwmy2vFGfy5YuJizsFEMCR6BQKEibNi0BpUqjVqvJkSMnamdnnj176tCc7u5uxGu0ttcWiwWn106OP6tQlp+Xz8dgNPL7voO25UP7dGPNghlMnbcEjVaLI7m/Vp9v2z6XLF3KqbAwhgUORaFQsHnLVkqVLMmypUtYMG8u06bPQK9PmamFHzo3dw+7/d1sMb93tHDn9p/4JKA8QQs3MnFWMAtnjkH/cjpkcnJx80SnjbO9tiTK9nqZThuHq3taAJ49usPyyd9RvGIdilf4BoDf1k6g/dBgek/6jRIV67BrnWNmlLzi4fb6dmp5o7NZuWIFflq1DKPByJ979zs0z+vc3d3s2yWL+S35yrN+5RKMRiN/7nPsVOZ3cXPzRGu3DVjeu316+/hSocrXKBQKsmb3xTNNOp49eejQnP+m/fxt935Onj5H78DRRMVcY8LM+Tx68tTBOV8/Hr3Zfi5dsoSwsDACAwNtx/fY2Fhu3LhB8eIp02n+WI6bru4e72yjXN080GoSyrSaONw80lDu8/q4unkwd0w7zp86QE7/wiiVyX8h6o320/xa+5kom05jbT9PHvyZqPNHWDy+DXeuX2LjosG8ePoAd890FAr4HIBCAZ9zM/o8jpCU4/uKpQs5HRbK4MBRKBQKdv+xi9NhoQQO6kdMdBQzgybx5PFjh+QTH4ePviOaP39+bt68ydmzZ6lSpQrx8fHs2bMHf39/8ubNy+rVqwkODqZBgwbkz5+f8ePH06tXLyZPnkz+/PltncNMmTKxbNkyoqKiOHjQetKueMuwtb+/P7Vr1yY4OJglS5ZQq1YtPDw82LVrF9OnT2fVqlVs2bKFW7dusWHDBkaPHs2aNWsIDw8nLCws2f/+QoWLEnrCOj044tJFcvn525UvmDsdvUHP4OFjbVMk9u/9g583rAXAxdUFhVLhkIYV4MA5C2v2mpm51Uz6NODqbH14WK7MCm49StpnfF1GgZMSNh5KmKLrKIULF+HkyeMAXLoUjp+fn1353Dmz0Ov1DBs+yjbyXbhwEU6dPIHFYuHRo0fotFrSpEnr0JyfFCpASKh1e7oQcZncvgkPBImLj6fX0NHoDQaUSiWuri4olQp+33eQNZu2AuDq4oxCoXDoA2AAihQuzPGTJwAIv3TpjfqcPWcuer2BkcOH2+rT09PTNjqVJk0ajEbrtFIB+QsV4/TJIwBcvnSenL553vseD4+0uHtYr6Z7pElrm6ab3HLlLcnlM9a280bUabxz5LeVZc7mz6N714iPfYrRqOdaxEly5S1B7LOHrJzWgZpN+lPqs4a29d08vHB1tWZOkz4LmnjH3pNZpHBBQk5ap7pdvBRBbr+Eqc1x8fH0GxyYaH9yRalM2SlNRQsVJOTkqZf5Isnta5+v7+Dhdvv7245dKSFvoeKcC7U+9O9KxDmy58r73vf8tWcb61da7yV98vgBGk0sXukdO2Xv37SfcyaOYvaEkcwaP5K8uX0Z2qcbGdOnc2jOwoULc/KEtf28FB6OX+7cduVz5sxBbzAwfMQIW/sJcP78eQICAhyazT7nx3HczF0ggIthhwC4GnkGn1z5bGVZs/vz4O514mKfYTQauBIeil/+4ly/ch7/giXpOXIFxcpUI2OWHA7J5pe/JBGnre3n9ajTZM2Z0H5m8bFvP2Netp+dh62h07BgOgWuJluugjTuPIk06TJbP+tlWxwTcRLvHPne+p3/VaHCRQk9mXD+6etnv33On2O9iDx0+Bjb+efEqTOZMGUG4ydPJ7d/Xvr0H0z6DBkcku+DolB+mP8+AP8vnppbpkwZbt68iVKppEyZMkRFRVGwYEEqVKhA8+bN0ev1FCtWDG9vb+rUqUO3bt3ImDEjWbNm5cmTJ7bPUSgUTJgwgfbt27Nhw4a3flezZs0YNmwYrVq1IjY2lhYtWuDs7IyXlxd169bFy8uLSpUq4ePjQ4ECBWjUqBHp06fH29vbIVcny1WobL3a1L8HFiz07DOIg/t3o9VoyJOvAHv++I1CRT5hxNB+AHxTpyHlK1ZmzozJBA7sjdFkpF3H7ra5+45itsDuMDPNqyhRKKxPzX2hsXZMa5dR8vM7RjqzpocS/gquP4BWX1hPrk5EmIm49dbV/7MKFSsRFnaKAf2tTxfu07c/+/ftRavVkDdffv78YxdFihRl6JCBANSpW4+KFT/l/Plz9OvTE7PFTJduPRw+da9y+TKcPH2ObgOHYwEG9+rCnwf+QqPVUufL6lSvUoleQ0bh5OSEv18ualSpjN6gZ9LshfQcMgqjyUTPDt85/GEBFStW4FRYGH3798digf59+7Bv3340Wg358uXj9z/+oGiRIgwaMhSAenXr0KB+PabPmEn/HwZiNBho+913didZ/8tKl6/KudMnGDWwIxaLhc69h3H4wO/oNBq+qFXvre/5qm4zFs8ez5jBnTEaDTRp3RVXV7e3rvtfFCpVnSsXjrB4XHOwWKjffgJnjv6CXhdPmapN+KrZYFYHdcRiNlOycgPSpvfm17UT0MY9Z/+2BezftgCANv0XU6/dWDYs6I9SpULlpKbu92OTPW9ilSqUJzTsDL0HDMJigQF9erJ3/wE0Wi21a31Jtaqf0X/QUFROTvj7+VKtquNvZ0js0wrlCD19lp4/DMVisTCwd3f27D+ERqvhm1o1qVa1Mn0HD8dJpcLfz5fqVT9L0XyvlCz3ORdPhzB+cFuwWGjXcyTHDu5Eq9VQtWaDt76ncrV6LJszkglD2qFQKGjXY6TD7wn+N+1naqhYsSJhYWH079cPi8VC33792LdvH1qNhnz58/PH779TpEgRhgweDEDdunWpWKkSN2/eJGtWxz4RPbGP5bhZrEw1Is4eZcbwVmCx0KLrWE7+9St6bTwVqzemfpsfWDC+MxaLmfKf1yddBm+cnJz5bf1c9u1YiZtHGpp3dszTpwuXqs7l80dYMLo5Fiw06jiB00d+Qa+Np+wXTajdYjDLp3TEYjFT+rMGeGXwfudnfd1iIJuXDidkzzpc3dPQrNvUd677X5Sv+Cmnw0IZ2L8nWCz06juQA/v22H733X/spHCRTxg+xPpMkm/qNqBCxU/f86nif43C4uhJ+eIfuRh1O7UjJMmWk+9uBD8kTcrcfP9KH4A0ho9jaopGneb9K30ALhasndoRksT7/LHUjpAk0Y8d/2Cr5FA+U8T7V/oAKC0OntqRTK4Z/VI7QpLkUV5+/0ofgDjnj2M/MltS9h7of+vKi2zvX+kDEK//OMZ8Cmf8OM4/C+ZxzKi0I2l/W/z+lVKB69edUjvC/48RUSGEEEIIIYT44Dj4NqiPmdSMEEIIIYQQQogUJR1RIYQQQgghhBApSqbmCiGEEEIIIYQjpNKTzD8GMiIqhBBCCCGEECJFSUdUCCGEEEIIIUSKkqm5QgghhBBCCOEIChn3exepGSGEEEIIIYQQKUo6okIIIYQQQgghUpRMzRVCCCGEEEIIR5Cn5r6TjIgKIYQQQgghhEhR0hEVQgghhBBCCJGiZGquEEIIIYQQQjiCUsb93kVqRgghhBBCCCFEipKOqBBCCCGEEEKIFCVTc4UQQgghhBDCASzy1Nx3khFRIYQQQgghhBApSjqiQgghhBBCCCFSlEzNFUIIIYQQQghHUMi437tIR/QDk870MLUjJMndu56pHSFJMsbfSO0ISaJx8UrtCEmiwzW1IySJ9/ljqR0hSe4VLZ/aEZLk/rZLqR0hSSyZP46D/VVDrtSOkCQZXZ6mdoQkeUGG1I6QJBnj5HiUnK7c8U3tCEmSOZ05tSMkSXr9vdSOkEQ5UjuASEYfx1FbCCGEEEIIIcT/GzIiKoQQQgghhBCOIFNz30lqRgghhBBCCCFEipKOqBBCCCGEEEKIFCVTc4UQQgghhBDCASwKRWpH+GDJiKgQQgghhBBCiBQlI6JCCCGEEEII4QjysKJ3kpoRQgghhBBCCJGipCMqhBBCCCGEECJFydRcIYQQQgghhHAEeVjRO8mIqBBCCCGEEEKIFCUdUSGEEEIIIYQQKUqm5gohhBBCCCGEIyhl3O9dpGaEEEIIIYQQQqQo6YgKIYQQQgghhEhRMjVXCCGEEEIIIRzAIk/NfScZERVCCCGEEEIIkaL+X4+IhoSEsG7dOmbMmJHk98yZM4dMmTLRvHnzf/2948ePp23btvj4+Pzrz0gqs9nMzIVLuRJzFbVazQ89upDdJ5ut/MCRY/y0aSsKBXzzZQ1q16yGyWRi2txF3Lh1G5VSycDe3cieLavDsxbN7UStci6YLRaOXTBw5LzBrjx9GgUta7jZ7ulet0fL/SdmShVwomqAC2azhdsPzWzYq8XioIxms5mpS9YQde0Gaic1Q7p+R85s3rbyfcdOErxlJygU1Kv+GXWqf8av+/7i131HANAbDFy+ep1fls4gjYe7g1Jac85asIQrMddQq50Y0LOr3e9+8PAxftq0BRQKvvmyOrW/rI7RaGTKrPncu38fvcFIq6YNqVSujMMyvsq5cN4srsZcQa12pkfv/mTzyZ6Qc/9etm/9GaVKiZ+fP12690b5cgN4+vQJ/Xp1Zcz4KeTImcvhOVcsnMr1mMuo1Wo69BhKVp+cduvodFomDu9Jp16B+OTww2g0snDmGB7ev4NSqaRDjyH45PBzaM73SVe2GAUnDOBY9Tap8v0x5/dy/I/5KJUqCpVrSNEKTezKNbFP+D24P0aDDg+vLFRvPgG1sxv3rp/j0NZJgAX3NJmo2WoqTmoXAOJfPGL99IbU7bKcDN7+yZrXbDYzZ/5ComNiUKvV9O3Vg+yJ2u19+w+wedsOVEoluXP70bNbF4wmE0EzZnHn7l3c3d3p2bUL2bM7tq03m82sWTSRG1cjUaud+a77cLyz2e8TOp2G6aO68X33EWTLkdu2/PnTx4wZ0JL+o+bbLXdUzkUv93cntZoevQe8tr/vYYdtf89D59f29/69ujB6/NQU2d/nz5tDTEw0arWaXr374pMo54H9+9i2dTNKlQo/v9x0694TpVLJhvU/ERJyDKPRQO3a31Lzy68cnlOOR/9d9Pm9HP99HkqlE4XLv61desyu4AGYDFo80maheouJqJ3dOLVvBRdDNuHmkQGAL5qMxiuzL3vXDePJgxgUChXVW0wkXabk3V7NZjO/rB7D3RuXUDk5U6/dWDJ6+9rKL4XtY/92aztbsnIDSldtgtlsYtuKETy8E4NCqaRBhwlkyOL4/Wj6ohVcuXoNtZOagT06kiPRueT+I8f5cfN2UECdmtX4psbntrInT5/RoX8g00cPwTdH9rd9vPgfISOiDhAYGJginVCAv46dQK/XM2/qBDq1acn85attZSaTiSWr1jJt7HDmThnP+i3bePb8OUdPhAIwd8o42rZsyvxlqxyeU6mEBlVcmbcljlkb46lY1Jk07vZTFWpXcOHgGT2zN8XzxwkddSq5oFZB7QquzN4Ux4wN8bi5KCji77jrJwePh6E3GFgyIZBurRoyZ9UGW5nJZGb+mp+ZPaI/S8YPZe32XTx9/oLan3/K/DEDmT9mIAX8fenbroVDD/oAfx07jl5vYO60CXT8rhULlif8htbffQ1Tx41g7tTxrN+ynWfPnvPn/oOkTZuGWZPHMWlUIHMWLnNoRoCQo4cxGPRMmT6XNm07sHzpQluZTqdj7erljJ8UxJSgOcTHx3Hi+DEAjEYj8+fMwMXZ2eEZAUKPHcCg1zF66lKatunO2uWz7cqjL4czdkgX7t+9ZVt2+uQRzCYTo6YsoX7TdmwIXvj6x6Yo//4d+GTROJSuLqny/SaTgUPbJlG3yzIa9AjmwtENxD1/YLfO8T/mUaDUNzTqtZbM2Qtx/sh6LBYLe9cPp3rzCTTq9SO+hSrz4skt22fu2zjS1ilNbkeOHkOv1zMraCrtv2/D4qXLbWU6nY6VwWuZOnE8M4OmEBcXR8jxE+zc9Tuurq7Mnj6N7l06MXfhIodkSywsZB8Gg57Ayato2LonG1bYX1y9GnWRyYEduH/3pt1yo9HA6oXjcXZOmW0i5Ohf6A16Jk+fS5u2HVmxdIGt7NX+Pm7SdCYHzSU+PpaTx4++zGlkwZzpuKRQzmNHj2Aw6AmaPovv27Zn2dLFdjmDV69kwqSpTAua+bJdCuHs2TOEh19k6rQZTJocxIMHD/7mG5KHHI/+O5PJwKGtE6nXdTkNewZz/sj6N9ul3+dToOQ3NOr1I5lzFOb8kfUAPLh5gZotJ9OwZzANewaT3tufmPP7AGjcex3lv+rFoa0Tkz1z+KndGA06Og1fR83G/di1bkrC32M0sPOnSXw3YCnthqzm5IGNvHj6gEth1lwdh/1Itfq92PnT5GTP9bpDISfR6w0smDyGzm2aMW/F2oScJjOLgn9i+uihLJg0hp+2/MLT588B6/4+bcEyXFxS5vj+QVAoP8x/H4API8U/tHnzZrp37853331HnTp1+P333zl8+DCNGzemVatW9OjRg+cvN3iAv/76i169etleN2vWjPv371OtWjX69etHo0aNGDJkCGazGYA9e/bw3XffUbduXfbu3QvAmjVraNOmDS1atKBz587o9Xq0Wi29evWiWbNm9O3bl08//RSA1q1bc+XKFe7evUuXLl1o27Yt9evXZ/fu3cleF+fCwylbMgCAwgXzExl1xVamUqlYNX8mnh4ePH8Ri8UCbq6ufFq+LAN6dAbg7v0HpE/nley5Xpc1g5IHT81odGAyQ/RtI3myq+zW2XJQx/kYozW7QoHBCEYTzNgQh8G6GKUCjEZHjYfCmUuXKV+iKABF8+chPPqqrUylUvLTrHF4erjzLDahPl8Jj7pKzI3b1KtRxWH5Xjl/8RJlSpUArL97xOXoRDlVrFwwy/a7Y7Hg5uZK1UoVaNeymd3f42gXL5wjoJT1KneBgoWJuhxhK1Or1UwOmo3Lyzo0mUw4v+x4rli6kFpff0uGjJkcnhEgIvwMxUtWACBfwaLERF2yKzcY9PQdMhmfHAlXpbNlz4nJZMRsNqPRxKFySt0JJvHR1wlt3DPVvv/JvWi8MuXC1d0LlZMzPrlLcTs61G6dO9GnyFWwMgC+hT7jRuRRnj6IwdUjHacPruLnua3Qxj0jfRbryOfhbVMoWrEpHmmzOCTz+YvhlC5VEoBCBQsSGRVlK1Or1cycNhnXlx17k8mE2tmZa9dvUKZ0KQBy5sjB9Rs3HJItscvhpykaUBGAPAWKcfXKRbtyg0FPj8FBZMvuZ7d8w8qZVP2yIekyZHZ4RoDwC+cp+bf7+xy7/V39cn9fuXQhtb6uQ/qMGVMk54UL5ylZqjQABQsW4vLlSLucU4Nm4mqXU82p0JP4+eVm/LjRjBk9grJlyzs8pxyP/rsn967Yt0v+pbgdfdJundsxofgWStwuWUeU79+4wIndi9k4qzkn/rRecMpTrDpfNB0LwIsnt3FPk/zHqOuXT5H3E+v5ZM68JbgVc95W9uBONBmy5MLNwwsnJ2dy5SvJtchQCpeqTp3vRwPw9NFtPNM6fl86Fx5BuZLFAChSIB8RUYl/dyXBc6fh6eHO8xcvsGCxbZ/zVq6lbq1qZEqf3uEZxYfvo+yIAsTHx7NixQqWL1/OpEmTGD58OHPnzmXNmjWUKVOGBQsSrsRWqlSJyMhInj17RlRUFOnTpydLlizcu3eP3r17s2nTJuLj420dRW9vb1atWsXQoUP56aefMJvNPH36lJUrV/Ljjz9iNBo5d+4c69evJ0eOHKxbt44ePXrw6NEju4zR0dG0bduWFStWMHz4cNauXUtyi4/X4JHoaqdSqcRkMtleq1QqDh4JoUPvARQrUgiVSmVbPnHGXOYsXk6VShWSPdfrXJ0VaPUJHUitHtyc7UdE47QWzGbIkl5Jvc9c2BmiwwK8iLe+77PialycFVy6bsJR4jRaPN0T6lOlVGJMVJ9OKhX7j4XSpv8oShTOj5MqoTO9avOvtG9Sx2HZEouP1+DxWs43f/djdOzVn2JFCqNSqXBzc8Pd3Y34eA2jJ02jXat/P/086Tnj8XD3sL1WKlW2nEqlknTprVOeftm+Ba1GQ4mAUuz5cxdeXulsJ7QpQRMfh5tH4pxKTCaj7XWBwsXJmNnb7j2uru48vH+HH7o1ZencSXz5jf10r5R2d8sfWAzG96/oIHptLM6uaWyv1a4e6LUv7NfRxeLych1nF2u5JvYpd66GUaxSC+p1XcHNy0e5EXmU8OObcfPMgO/LjqsjxMfH4/HG756wfaZ/eaK0dfsvaLRaSgWUII9/bkKOn8BisRB+6RKPHj222/ccQauJw93dM1FOld32ma9QCTJksr+94q+920njld7WgU0J8fHxuCdpf9+MVqOlREBp9vy5i7ReXrYLVilB81q7pHrH775j+1a0Gg0BAaV4/vw5UZcjGTxkGN179GLa1ElYLI67KApyPEoOem1CmwPWdkeniX3nOtZya7uVv2Rtvmg8igbdV3EnJpSYC9ZRR6XKiT/WDmL/z2PJW/zLZM+s08Ti6p6QOfH+bi1LaAtcXD3QvsyrUjnx85LB/LpmHEXKJH+u18W99rsr37J9Hjh6nLZ9h1C8cEGcVE7s3HOAdGnTUjaguMPziY/DR9sRLVOmDEqlkkyZMuHu7o5arcbb29tWdvnyZdu6CoWCOnXq8Msvv/Dzzz/TqFEjALJly4avr3WEIyAggJiYGACKFCkCQKZMmdBqtSiVStRqNf369WPo0KHcvXsXo9HIlStXKFnSejU9T548ZMiQwS5j5syZWb9+PT/88APr1q3DaEz+k0R3dzfiNRrba7PFYutsvvJZxXJsXLEIg9HIH/sO2pYP6duD4IWzCJq7EI1Wm+zZwDrdtlcjdzrVccc1UcfT1Rk0ujcP4vlyqOj4rRurd2m4/8Q6Qq0A6lV2oWAuJ5b+Eu+QnK94uLkSl6guzGaL3cEdoGr5UmxfPA2DwcjOA9Yrpy/i4rl2+w6lihZ0aL5X3N3d0GgS5bSY3/K7l2fDysUYjAb+2HcAgPsPHtIvcCQ1Pv+MalUdd4KfkNMdTaLt02K2z2k2m1mxdCGnw0IZHDgKhULB7j92cToslMBB/YiJjmJm0CSePH7s0Jxu7h5oNQnblrU+/36Ec+f2n/gkoDxBCzcycVYwC2eOQa/XOTTnh+jobzPZPLc1vyzrhl6bcIJn0MbZnQACOLt4otfFAaDXxeHilgZXj3Sky5SLDFnzolKpyVWwMvdvXOBiyGauRx5m89zWPLh1iT9/HPTGlLr/6s3t0/LG9rl46XJOhZ1mxNAhKBQKatWsgbu7Oz8MCeRYyAny5c3zxr6X3FzdPNBq4xJyJmH7/GvPNi6ePsaUYR25HhPBslkjePbkoUNzJm1/X8CZsFAGvdzf9/yxkzNhoQQO6ktMdBSzgiamwP5un9P8lt992dLFhIWdYkjgCBQKBWnTpiWgVGnUajU5cuRE7ezMs2dPHZpTjkf/3tFfZ/DznNbsWGrfLr1qdxJzdn29XUqLxWKhRJXvcPPMgMrJGb/CVXhwM2EmQs2Wk2kT+Dt71w/HoEve8xIXN09079jfXy/TaeNwdU9re92w4yR6T9rJ1hXD0Sdzrtd5uLsRn+h3t1je3D6rVCjL5mVzMRqN/L7/IL/uOcDJM+foFTiWqJhrjJ+1gEdPnjo054fAolB+kP8+BB9Gin/hwoULADx8+BCNRoPBYOD+/fsAHD9+HD8/P7v1GzZsyK5duzhx4gRVqlinqty7d892n8epU6fImzcvYO24Jnbp0iV2797NzJkzGT58OGazGYvFQv78+QkLCwPg+vXrPHnyxO59s2bNom7dukydOpVy5co55Opp0UIFCTl5CoCLlyLx9024OT0uPp7eQ0agNxhQKpW4ubigUCj4Y98B1m7cAoDLy2UqpWM2hV+P6pi9KZ6hi1+QyUuBuwuolJAnuxMxd+xHEfLlUNGwiivzt8Rz477ZtrxZNVfUKgVLdmhw9IBPsYJ5OXrqLADnI6+QJ1fCTfRx8Rq6jpicUJ+uLiiU1m3l9MVIynxS2LHhEnnf795ncMLv7urqilKh5PGTpwwcMZZO37fiqxrVUiRnocJFCT0ZAkDEpYv4+tk/LGX+nBno9XqGDh9jm7I3cepMJkyZwfjJ08ntn5c+/QeT/rWLPMktf6FinD5pPYm7fOk8OX3zvPc9Hh5pcfewXpn2SJPWNk33f02Fr/vQoEcw7cf8xbOH19HGPcVk1HMr+gRZ/QLs1s2WO4Br4daT0GvhB/HxL41XxhwYdPE8fXANgNvRoWTImpeGPdfQsMcaGvQIJnP2gtRoMRmPtMk7xbRI4UIcP2Gdphd+6RJ+fr525bPmzkdv0DNq+FDbFN2IyMsULVKYaZMmUKliebJmdfyD3vIWKsHZ0MMAXIk4S/Zced/7nsHjlzFo/FIGjltCrtwFaN97DF7pHTvVveAb+7v9w6UWzJmOQa9nyPCxtv19wtRZjJ8yk/GTZ5DbPy+9+w9x+P5euHARTp48DsClS+FvnC/MnTMLvV7PsOGjbFN0CxcuwqmT1pHwR48eodNqSZMm7esfnazkePTvVajdl4Y9g+kw9jBPE7dLV06S7Y12qSRXLyZul0qh18aydvI36HVxWCwWblwOIUvOooSf2Gqbpuvk7IZCoUChTN4LUbnyluTyGeugwY2o03jnyG8ry5zNn0f3rhEf+xSjUc+1iJPkyluC04e3ceAX673Oahc3FAolCoVjL5AVLViAY6GnAbgQcRl/34QH/MXFx9MzcEyi390FhULJ3AkjmDN+BLPHDydvbl8Ce3clY/p0Ds0pPmwf7VNzHz58yHfffceLFy8YNWoUTk5O9OzZE4VCgZeXFxMnTrQbFfX29sbDw4MSJUrg9PJeLmdnZ8aOHcudO3coXrw4X3zxBRcvXnzju3x9fXFzc6NBgwY4OzuTOXNm7t+/T6NGjRg8eDAtW7bEx8cHFxf7By3UqlWL8ePHs2jRIrJly/ZGRzU5VC5fltDTZ+kxMBCLxcKg3t3ZfeAQGo2Wb2vVoHqVyvQeMgInlQp/P19qVK2M3mBg8qz59B48AqPJSPcObW335jmK2Wy9B7RbfQ8UCjh2Qc+zOAvuLtCihhtLf9HQsIorTipo/aUbAPeemDl8Vk/5omqu3DLRs5F1Csj+MD1nrzimR1qlbEmOn7lIx6ETAAuB3dvx+6FjaLQ66tWowpeVy9N1+GScnFTk9c1BrcrWac3Xbt/Fxztl7sMC+LRCWUJPn6HHD0PBAgN7d2fP/kNotFq+qVWD6lUr02fwcJxUTvj7+VK9amUWLFvFi9g4gtdtInjdJgAmjQp8Y7tNTuUrfsrpsFAG9u8JFgu9+g7kwL49aLUa8ubLz+4/dlK4yCcMHzIAgG/qNqBCxU8dluddSpevyrnTJxg1sCMWi4XOvYdx+MDv6DQavqhV763v+apuMxbPHs+YwZ0xGg00ad0VV1e3lA3+AVGp1FSuO4htizpgsZgpXK4hnum80cY9Zc/64dRuN4cyNbvy54+DuXB0I66e6fmy1TRUTs580Ww8v68ZABYL2fwCyF2kaopkrlShPKfCTtOn/0AsWOjfpzd79x9Ao9GQP18+dv3xJ0WLFGbg0GEA1KvzLZ8UKcKq4LVs2rwFDw8P+vd2/H25Jct9zsXTx5gw+HssFgvteo7i2MGd6LTxVKnZ0OHfn1TlK35qHe3s3wMs0POd+3t/AL6t24DyFR0/M+N1FSpWIizsFAP698FisdCnb3/279try/nnH7soUqQoQ4cMBKBO3XpUrPgp58+fo1+fnpgtZrp06+HwkXA5Hv13KpWayvUGs3VheywWy2vt0jBqt5tL2Zpd+WPtIC4c24CrR3pqtQ5C7eJOhdp92Ty3DSonZ3Lmr4Bf4SoYdPH8+dMQNs1uidlkpHL9ocn+MLVCpapz5cIRFo9rDhYL9dtP4MzRX9Dr4ilTtQlfNRvM6qCOWMxmSlZuQNr03hQuXYPNSwNZOqEVZpORr1sMQe3gh399Vr40J8+co+ugkYCFwT078+eBw2i0Wup8WY0an1Wi59AxODmpyOObi5pVUv7YLj58Coujb3JwgM2bNxMdHc2AAQP+0fs6d+7M0KFDbdNxK1WqxOHDh/91jlOnThEfH8+nn37K1atX6dChw39+INHtiLP/6f0pZeJOv9SOkCSjq30c9alxcfwDo5LDC9XH8XCBWKPH+1f6ANwr6vgHniSHmG2X3r/SB+Db/JHvX+kDcEOfI7UjJElGl6epHSFJnEi9e6P/iYzxjn+wVXL4WI5HW698ktoRkiRzuo9jpkyVdGGpHSFJvAuVSu0I/1hsyI7UjvBWnuW+Te0IH++I6D+h1Wpp0aIFlStXtnVCk0POnDnp168fc+da57+PGDEi2T5bCCGEEEIIIf6/+ig7og0aNPhH67u6urJ58+Y3lv+X0VCwPowoODj4P32GEEIIIYQQQvyv+Sg7okIIIYQQQgjxoftQnlD7IZKaEUIIIYQQQgiRoqQjKoQQQgghhBAiRcnUXCGEEEIIIYRwBIUitRN8sGREVAghhBBCCCFEipKOqBBCCCGEEEKIFCVTc4UQQgghhBDCEeSpue8kNSOEEEIIIYQQIkVJR1QIIYQQQgghRIqSqblCCCGEEEII4QAWeWruO8mIqBBCCCGEEEKIFCUdUSGEEEIIIYQQKUqm5gohhBBCCCGEI8hTc99JakYIIYQQQgghRIqSjqgQQgghhBBCiBQlU3OFEEIIIYQQwgEsfHxPzTWbzYwaNYqIiAicnZ0ZN24cvr6+b6w3fPhwvLy8GDBgwL/6HumIfmBOxxVK7QhJ0rH2k9SOkCQhseVSO0KSvIhVpXaEJEnrZkztCEnyXPNxNG33t11K7QhJkrtuwdSOkCTLl51P7QhJ8nnJj2M/eqpPk9oRksRg/jjaz4WHMqV2hCTx9nZL7QhJUrPYg9SOkCQxTzKkdoQkOWcomtoRksQ7tQP8j9i9ezd6vZ7169dz+vRpJk2axIIFC+zWWbduHZGRkZQpU+Zff49MzRVCCCGEEEIIAUBoaCiVK1cGoESJEpw/b3+hNywsjDNnztC0adP/9D0fx7CBEEIIIYQQQnxkLB/oU3PXr1/P+vXrba+bNm1q61jGxsbi6elpK1OpVBiNRpycnLh//z5z585l7ty57Ny58z9lkI6oEEIIIYQQQvwPSdzxfJ2npydxcXG212azGScna7dx165dPHnyhE6dOvHgwQO0Wi3+/v40aNDgH2eQjqgQQgghhBBCCABKlizJvn37+Prrrzl9+jT58+e3lbVp04Y2bdoAsHnzZqKjo/9VJxSkIyqEEEIIIYQQjvGBTs39OzVq1ODw4cM0a9YMi8XChAkT2LFjB/Hx8f/5vtDEpCMqhBBCCCGEEAIApVLJmDFj7JblyZPnjfX+7Uio7Xv+07uFEEIIIYQQQoh/SEZEhRBCCCGEEMIBLApFakf4YMmIqBBCCCGEEEKIFCUdUSGEEEIIIYQQKUqm5gohhBBCCCGEA1g+wqfmphSpGSGEEEIIIYQQKUo6okIIIYQQQgghUpRMzRVCCCGEEEIIR5Cn5r6TjIgKIYQQQgghhEhR0hEVQgghhBBCCJGiZGquEEIIIYQQQjiAPDX33f5fdEQXL15M+fLlKVCgANu3b6dx48bvXPeLL75g586duLi42JadOHGCNGnSULBgQSpVqsThw4eTJdfmzZuJjo5mwIAByfJ5r5jNZjYtH8vt65E4Oalp2mkMmbPmspWfD93PH5sXoFQ5Ua5KfSpUa4TJaODHBYE8fnALhVJF046j8M7ub3vPltWTyZLNj0o1miZr1sSZl86fztWYKNRqNV16DSKbTw5b+V8HdvPrtg0olSp8/fLQoVs/lErrjns54gJrVixk9KQ5Dsu2aflYbl2z1mezzm/W5+8/W+uzfNWE+lw731qfSqWKpp2s9blq1gCeP30IwOMHt/HLV4zvek9Ltpy/rB7D3RuXUDk5U6/dWDJ6+9rKL4XtY//2+SiVKkpWbkDpqk0wGQ1sWRbI00e3MRr0VKnThUIBX3DnWjjbV41CqXIiU1Y/6rYda6vv5GQ2m9m4bBy3r0XgpHamWefRb9Ttrk0LUalUlPu8PhWrNcJo0LN2wTAe3buFq7sHjdoFkiWb7998y7/P9rHUZ8z5vRz/w5qlULmGFK3QxK5cE/uE34P7YzTo8PDKQvXmE1A7u3Hv+jkObZ0EWHBPk4marabipLa2ffEvHrF+ekPqdllOBm//t3yr46QrW4yCEwZwrHqbFP3et8mfQ0HVYkrMFgiLMhN62fLW9coXUuLpBrtPmQGoUEhJyXxK4rTW9XccM/HouWMyms1mfloygRtXI1Gr1bTuOpIs2XLZraPXaZg5ugttuo0ia47cmE0mgheO4d6tqyiUKr7vMZrMWXM6JmCinKsXTebG1cs4Oalp12MY3tnsv1On0zJ1ZHfa9RiOTw4/AH7ZtIKwE4cwGgx88VUjqtSo6/CcaxdP5ObVSJzUznzXbfgb9anTaZgxqhvfdR9Bthy5bcufP33MuB9a0nfkfLvlKaGwr4oaZdSYzXA83EhIuNGuPJ2ngqafu6BUggLYeEDHg6dv354d6Xr4PsL2zkehVJG/dAMKlmny1vXOH16F5sVDytTqn8IJrdvAsvlBXHt5TtK512CyJjonOXzgT357eU6Syy8P7bv1d8gxMnGebavGcOd6BConZxp2GEOmRMeji6f2sXfrApQqFaU/a0DZz63nubOGNcDVLQ0AGTJnp3GnCbb3nD7yC0f+XEu3kT85LPP6peO5ddV6fG/RZdRb26U5YzvTsutosma37i8vnj0maFgbAoN+Ru3s8raPFv9D/l900Tt16kSxYsV48OABGzdu/Mfv//nnn7l//74DkjnG+ZN7MBr09Bmzlm+a92X7mqm2MpPRwLbgyXQZspgeI1ZydO9Gnj99yMXThzCZTPQes5YvG3Thtw2zAYh9/phFk7pwIXSfQzOfOHYIvUHHhKCFtPy+C6uXzbOV6XQ61gUvYdSE2YyftoD4+FhCTxwBYNumtSyYPQWDQe+wbOdO7sGg19N37Fq+bdGXbcH29bl19WS6Dl1Mz5ErObInoT7NZhN9xq7ly4Zd+HW9tT6/6z2NniNX0r7/LNw80lCvzaBkyxl+ajdGg45Ow9dRs3E/dq2bYpdz50+T+G7AUtoNWc3JAxt58fQBZ47uwN0zHR2GrqFN/0X8GjwOgH3b5vF53W50DFyL0aAn8syBZMuZ2LkTezEadPQdt5Zvm/dh62t1u2XVFLoFLqLnqJUc2b2J508fcmTPJlxc3ek3fi0N2w7h5+UT/uYb/r2PpT5NJgOHtk2ibpdlNOgRzIWjG4h7/sBuneN/zKNAqW9o1GstmbMX4vyR9VgsFvauH0715hNo1OtHfAtV5sWTW7bP3LdxpK1TmpL8+3fgk0XjULqm/gmIUgG1SqtYvdvEit9NlMqnxNPVfh0nFTT4VEXZAvaHy2wZFWz+y8jKP0ys/MNxnVCA08f3YdDrGDxxNfVb9WbTqul25VejLjB1eHse3LtpW3b2pHUbHDhhFXWadWXjyiDHBXzpVMh+DHodwycvp3GbHqxbMdOuPCbqIhOHduL+3YSc4edCuXzpLIETlzJk/CIeP7zn8Jynj+/DYNAzZNIqGrTqyYaVM+zKr0ZdZOqwDnb1CWA0GgheOD5VTp6VSqj7qTOLd2iZv1VL+SJOpHGzfwBKrbJqDp83sGCblj2nDHxd3jnFc5pNBo79Oola7ZZSu+NqIo5vJP6FfXtlNGjZv2Eg4cd+TPF8r5w4dgiDQc+4oEU0/74Lwcvm2sr0Oh3rg5cwYsIcxk5bSHx8LKdenpM4ysVQ63lIt5E/8VXTfvz6o/3x6Ne1k2g3aAmdAldxfJ/1eGTQ6wDoHLiKzoGr7Dqht6+Fc+LAz1gsjrsQcfbEXgx6HQMmrKFuy95sXm1/0f3alQvMGNGWB3dvJPydpw8zd1wXXjx75LBc4uOS6h1RrVZLr169aNasGX379uXTTz8FICIigtatW9O6dWt69uzJixcvCAkJoUOHDnTt2pVvv/2WBQsWADB48GAOHjzIwoULiYqKYu7cudy9e5cuXbrQtm1b6tevz+7du9/6/efPn+fQoUNMnTqV27dvo9fr6d+/P82bN6dr164YDAbmzJlDu3btaNasGVeuXKFJk4Sre02aNOHmzZuEhobSpEkTWrRoQZcuXYiNjQXgzJkztGvXjnr16rF+/fpkqbPoiDAKFq8EgF++4tyIvmAru3crmkzeuXD39MLJSU3uAiWJvhRKlmy+mM1GzGYzWk0cKpV1MFynjadWo26UrvxtsmR7l/ALZwkoWQ6A/AWLcOXyJVuZWq1m3NQFuLhazwBNJhPOausB1Dtbdn4YOs6h2aIvhVGoxN/UZ9aE+vR/WZ+Zs/liNr1Zn6/s3DSPz75sgVf6zMmW8/rlU+T9xLp/5Mxbglsx521lD+5EkyFLLtw8vHByciZXvpJciwylSJkvqdagt209pUoFQDbfQsTHPcNisaDTvpk/uURHnKJQcWtmv/zFuXHloq3s7ut1WzCAK+Gh3L0ZTaESlQHw9snN3VvRDsn2sdTnk3vReGXKhau7FyonZ3xyl+J2dKjdOneiT5GroLXOfAt9xo3Iozx9EIOrRzpOH1zFz3NboY17Rvos1pHPw9umULRiUzzSZkm2nEkVH32d0MY9U/x73yazFzx+YUGrB5MZrt+3kMvb/uTeSQVnrpg5eM5kt9wng4LKn6hoV0tF5aKOPZRGhYdRJMDaRvnnL8a1Kxfsyo1GPV0HTidrdj/bshLlvqBVl+EAPH5whzReGRyaEeBy+Bk+KVkRgLwFPiEmKtw+p8FAz8FTyJYo5/nTR8npm5c5k35g5vh+lCjzaQrkPE3RAGvOPAWKcS1Ru2TNqafboCC7+gTYtGomVb5sSLoMydeuJ5V3egUPn5nR6KzbaswdM7l97Le7HUf0XLxm3U6VSjAa3/ZJjvX0fjRpM+bCxc3aXnn7leTeVfv2ymTUkTegLsWrdkn5gC9FXDhLcds5SVG7cxIntZoxUxfanZOo1Y7t1F+NPEWBYtZtP1fe4tyKSdjH79+OJqO3L+4vj0e++UsSExHKneuXMOi0LJvcgcUT2nI96gwAcS+esmv9DL5tNcShma+Eh1H4ZbuUO39xrr9lP+r0w0zbSCiAQqGk54jFuHt6OTTbh8aC4oP89yFI9Y7o+vXryZEjB+vWraNHjx48emS9SjJ8+HBGjhxJcHAwn332GUuXLgXg9u3bzJkzh/Xr19uWvdKlSxfy5s1Ljx49iI6Opm3btqxYsYLhw4ezdu3at35/0aJFqVy5Mj/88AM+Pj7Ex8fTt29ffvrpJ2JjYwkPtx5I/f39Wbdund2U3sR2795NjRo1WLNmDY0aNeL5c+vlcScnJ5YtW8bcuXNZtWpVstSZVhOLm3sa22uFUonJZHxZFoeru6etzNXNA238C5xd3Xn84DaT+n/LhiUjqVyrJQAZs+TAN2+xZMn1dzSaONw9EnIpVQmZlUol6dJbT5J27tiEVquhWEAZAMpXqorKybEzyHWaWNvUFnhLfbol5HZx80AT/wIXF2t9Tuz3LesXj+Szl/UJ8OLZIy6fD6Fs1XrJnzPR765Uqmw5rWWJcrp6oNW8wMXVAxc3D3SaONbN7WPrRGX09uO3tROYPaQ2cc8f4VewbLJmfUUbb789vl63bm9sq7Hk8CvAhVMHsFgsXI08w7PH9zGbTW989n/1sdSnXhuLs2tCTrWrB3rtC/t1dLG4vFzH2cVarol9yp2rYRSr1IJ6XVdw8/JRbkQeJfz4Ztw8M+D7suOa0u5u+QOLIRXOkN/CxVmBzpDwWmcAV7X9Olo9XLnz5qjC+atmfjlmYtUfJnJlUZA/u+MO6q/vK4pE2ypA3oIBZMiU9Y33qVROrJgzjHXLJlOqQnWH5XtFEx+Hu7uH7bUy0f4OkK9QcTJmts/54vlTYqLC6f7DJL7rOphF04c7dBQHrO1S4vpUvl6fhUq8UZ+H927HM216Wwc2pbmqFWgTTQzS6S24Odtvc3FaMJshczoF31Z05o+TjptJ9C56XSzOrgl1q3Z+s71ycfMiR75KKR3NTrwmDnePRNvq35yT6BKdkziK9rVjTuJj5RvHIzcPtJpYnF3c+OzrtrQbuIT6bUeybsFAjAY9Py8dxjctB+Hs6vHG9yR3Zvv9yH5/z1MwgPSv7UeFilfAM006h+YSH5dUv0f0ypUrfPbZZwDkyZOHDBky2JaPHj0aAIPBQO7c1isq+fPnx8nJCScnJ1xdXd/+oUDmzJlZsGABmzZtQqFQYEzipUEvLy9y5LDeJ5ApUyY0Gg2A7ftf9+qA2aVLFxYuXMh3332Ht7c3xYpZO3eFCxdGoVCQOXNmtFptkjK8j6ubJ1pNnF2GVyMwrm4e6LTxtjKtJg5Xj7Qc+C2YgsUq8k3zvjx5dIf549ozcPKWFJti5ObmgUaTkMtittiNGpnNZtasWMDtWzcYMGQcihT8fy65uHmi0yatPnWaONzc07L/t2AKFK/It8378uThHeaNa8+gKdb6PBPyJyUrfY1SqXJwTrMt5+tlOm0cru5pAXj26A4/zulJ2S+aU7zCNwD8tnYC7YcG4509HyG717Jr3WS+bTMiWfMCuLp7vDOzq5uH3Xas1cTh5pGGT8p8wd1b0cwd047cBQLI6V842esSPvz6PPrbTO5Eh/LwTiTeuRIuFhm0cbZO5yvOLp7odXE4Obui18Xh4pYGV490pMuUiwxZ8wKQq2Bl7t+4wNWL+0EBNyKP8ODWJf78cRDftJ+PR9qUH+VJLV+UUJIriwLv9ApuPUzo9LiosTvZ/ztHw822TmzkLQvZMiiIvOWYDtTr+4rFbE7yqHvbnuN41uohkwa3ZtSszbi4ujkkI4CbuwfaxO28xfLenJ5pvMiW3Q8ntZps2f1QO7vw4tkT0qZz3Aiuq7t9fZqTUJ+H925DgYLwsyHciIlg+ewR9BgyA6/0mRyWE6zTbXNnU+GTUcm1ewkX5FycFWh05jfWz+OjpGEVF37cnbL3h578Yyb3rp3i8d1IsuRM1F7p43B2TZtiOZLK3e21bfUt5yRrV8znzq0b9Bsy3uHnJK6vH48S5XFx80SXaHu1noekIVNWPzJ657KeY2bzw90zHdejzvDw3jW2rByD0aDj/q0r7Fgz0SGjo9Zz0cT7e9LbJSFeSfUR0fz58xMWFgbA9evXefLkCWDt+E2ePJng4GB++OEHqlSpAvC3jYFSqcRstjbMs2bNom7dukydOpVy5cr97RVWhUJhK3/X57+6Sd3FxYVHjx5hMpl4/vw5N29a7yHZsWMH9evXJzg4mHz58rFhw4b35v23cucPIPz0IQCuXj5Dtpz5bGXe2f15cPcacbHPMBoNRF8KxS9fcdw90tpGf9w9vDAZjQ4ZZXqXgoU/4dTJowBEXrpALj/7h6MsnjsVvV7PwGETbNNhUop/gQAuhiWtPq9cCsUvv7U+X41Ku3va12fkuaO2qaXJKVfeklw+cxCAG1Gn8c6R31aWOZs/j+5dIz72KUajnmsRJ8mVtwSxzx6ycloHajbpT6nPGtrWd/PwwvXlVes06bOgiXfMDW65E9dt5Bl8ciXUbdbs/jy4ez2hbsOtdXv9ynn8C5ak58gVFCtTjYxZcrzr4/+TD70+K3zdhwY9gmk/5i+ePbyONu4pJqOeW9EnyOoXYLduttwBXAu33hN4LfwgPv6l8cqYA4MunqcPrgFwOzqUDFnz0rDnGhr2WEODHsFkzl6QGi0m/091QgH2njaz8g8TUzcYyZBGgZszqJTg663kxoP3n7y7qKF7HSecX55z5c6q4PZjx5305y1YgvOn/gIgOvIs2X3zvecdcGz/L+zcvAwAZxdXFEqFQx+2Ys1ZnDOh1of9RUWcI4dvnve+J3+hEpwPO4rFYuHJ4wfotBo80zh22l7egiU4d8qa80rEWXL45n3vewaOW8YP45byw9gl5MxdgHa9xji8Ewqw67j1ns+RK+PJ5KXEzcW6rfpnU3L1nn1HNI+PknqVXViyQ8vNB292Uh2pdM0+1O64mpZDD/H80TV08db26m7MSbLkKpGiWZKiQOFPCDt5DIDIS+ffOCdZMncqBr2eAcMmpsg5iW/+AC69PK+7HnWGrInOQ7L4+PMw0fHo6svj0ckDm233kj5/ch+dJhbf/AH0m7SDzoGraN49iCzZ8zhsiq5/wRJcOGXNHPPa8V3YsyiUH+S/D0GqX7po1KgRgwcPpmXLlvj4+Nimvo4aNYpBgwZhMllP7sePH//eBwplzJgRg8HA1KlTqVWrFuPHj2fRokVky5bN1sF9m+LFizNt2jTbSOjfyZw5M5UqVaJRo0bkypULX1/rU80++eQTBg8ejLu7O2q1mjFjxnDixImkVsM/8kmZakScO8KsES2xAM07jyX08K/otPFUrNaYuq0GsmhiJywWC+Wq1iddBm+qfN2GnxYOZ/aoNpiMBmo3642Lq7tD8r1N2QqfcTbsJIEDumKxWOjeZwiH9v+JVqshT94C7P3zVwoWKcboodapjl/XaUy5ip+lSLZX9TlzuLU+W3QZS+hfL+uzemPqtR7Iwgn29Vm19sv6HNkGo9HAN4nq8/6dqw7pPBUqVZ0rF46weFxzsFio334CZ47+gl4XT5mqTfiq2WBWB3XEYjZTsnID0qb35te1E9DGPWf/tgXs32a9p7pN/8XUazeWDQv6o1SpUDmpqfv92GTPC1CsTDUizh5lxvBWYLHQoutYTv71K/qXdVu/zQ8sGN8Zi8VM+c+tdevk5Mxv6+eyb8dK3DzS0LzzGIdk+1jqU6VSU7nuILYt6oDFYqZwuYZ4pvNGG/eUPeuHU7vdHMrU7MqfPw7mwtGNuHqm58tW01A5OfNFs/H8vmYAWCxk8wsgd5GqyZbr/wOzBXadNNG6ugqFQkFYlJkXGnBzhjoVVKw/8PaLdToD7A4z8X1NFUYzxNyxcNlBo6Fgvd8z/OwxJg9tg8UC33cfzfFDv6HVxPNZzUZvfU9A+WqsmjuCqcPaYTIZadL2B4fPgClVvioXzoQwblA7LED7niM4emAXOm08Vb9s8Nb3lChTmYgLYYz54TvMZgutOw+03XvtKAHlPufimWNMGvI9FouF73uMIuTgTnTaeD6r2fD9H5AKzGbYflhPp29dUQAnLhl5HmfBzQWafO7Cql066n3qjJMSmlWz/s4PnprZdCBlp+cqVWrKfT2YXSs6YrGYyV+qAR5e3ujin3Jo83Cqt3LM0+//qTIVPuNs2AmGD+iCxWKha5+h/LX/D7RaDf55C7Lvz18oWKQ4Y4f2AuCrOo0pW7GKw/IUKVWdqPNHmD+6BWChUcfxnD7yCzptPOW+aELtFoNYPqUTFouZ0p81wCuDN6WrNmDj4kAWjG2FAmjUYVyKjkgWL1uNS2ePMS2wNVgstOo+lhOHfkWn1fBpjbe3S0K8TmFx9M0Y73Hq1Cni4+P59NNPuXr1Kh06dHjng4X+F/x2yvD+lT4AOdK8u2P/IbkVmz61IyTJC61jT7ySS1q3D+Pevvd5rkn1a2xJcv/Jh/GwgPfJXbdgakdIkuPLzr9/pQ/A5yU/jv3IRfVxHI8M5o+j/dzmmIeTJztvb8dN305ONYt9HE9ejXni+AeFJYc0rh9Hu1S9WOo/cf2fenAhJLUjvFXmIuVSO0Lqj4jmzJmTfv36MXfuXIxGIyNGJP99akIIIYQQQgiR4lLwuScfm1TviGbOnJng4ODUjiGEEEIIIYQQIoV8GHeqCiGEEEIIIYT4n5HqI6JCCCGEEEII8f+RRcb93klqRgghhBBCCCFEipKOqBBCCCGEEEKIFCVTc4UQQgghhBDCASzy1Nx3khFRIYQQQgghhBApSjqiQgghhBBCCCFSlEzNFUIIIYQQQggHsChk3O9dpGaEEEIIIYQQQqQo6YgKIYQQQgghhEhRMjVXCCGEEEIIIRzAgjw1911kRFQIIYQQQgghRIqSjqgQQgghhBBCiBQlU3OFEEIIIYQQwgHkqbnvJjUjhBBCCCGEECJFSUdUCCGEEEIIIUSKkqm5QgghhBBCCOEAFoU8NfddpCP6ganEgdSOkCQjd5VP7QhJ0umrB6kdIUnSu91P7QhJcjKuaGpHSJLymSJSO0KSWDJ/HJNSli87n9oRkqRs+49j+/QL35HaEZLk/HP/1I6QJN4esakdIUmGfBae2hGSxFn3PLUjJMkv92qmdoQkyZJWn9oRkiSnx8dxHgI5UzuASEYfx1mQEEIIIYQQQoj/N2REVAghhBBCCCEcwIJMzX0XGREVQgghhBBCCJGipCMqhBBCCCGEECJFydRcIYQQQgghhHAAi0LG/d5FakYIIYQQQgghRIqSjqgQQgghhBBCiBQlU3OFEEIIIYQQwgHkqbnvJiOiQgghhBBCCCFSlHREhRBCCCGEEEKkKJmaK4QQQgghhBAOIE/NfTepGSGEEEIIIYQQKUo6okIIIYQQQgghUpRMzRVCCCGEEEIIB5Cn5r6bjIgKIYQQQgghhEhR0hEVQgghhBBCCJGiZGquEEIIIYQQQjiAPDX33T64jujBgwe5c+cOTZs2/c+f1bp1a0aNGkWePHn+dllISAjr1q1jxowZ//g7Fi9eTPny5SlWrNh/zvtvmM1mJi9fz+Xrt3B2ciKwUwtyZs3yxnoTlvxIWk93ejSvZ1t2PiqGuT9uY+GIPimStYifii/LOmM2w7FwA8cuGO3K03kqaF7dBZVCAQrYsFfL/acWANRO0LWuG+v2arn/xOKwjGazmUXzZ3I15gpqtZruvX4gm092W/mh/XvYsW0TSqUK39z+dO7WB6XS2sA8ffqEAb07M2rcNHLkzOWwjK9yzli4jCtXr6FWq/mhR2dyZMtqKz9wJIQff96KQqHgm5rV+KZmNUwmM9PmLeLGrdsolUoG9epK9kTvSc5sm1eM5c61CFRqZ5p0HE2mrL628guh+/hzywJUSifKVK1P+S8aA7Bn2xIuhu7DaDRQsUYzyn3ekJsxF/l52Wic1M74+Bakbpshtvp2BLPZzOz5i4iOuYpa7US/Xj3I7pPNVn7o8BHWbdqMAgVf16rJ11/WcFiWd+WbM38h0TExqNVq+vbqQXYfH1v5vv0H2LxtByqlkty5/ejZrQtGk4mgGbO4c/cu7u7u9OzahezZff7mW5JH/hwKqhZTYrZAWJSZ0Mtv32/LF1Li6Qa7T5kBqFBIScl8SuK01vV3HDPx6LnD475VurLFKDhhAMeqt0mdAC9Zf/cFxLz83fv06vnG775l23aUtt+968vffSZ3797D3d2NHl27OuR3N5vNbF4+ltvXI3BycqZJp7fs75sXoFQ5UbZKfcpXa8zxA1s4eWAbAAaDjtvXLjFqwQE2LRvDi6cPAXj84Ba++YrTutc0h2ReuXAK12Mu46R2pkOPoWT1yWm3jk6nZdLwnnTsFYhPDj+MRiOLZo7m4f07KJRKOvQYik8Ov2TP9nrOaUtWE3X1Bs5qJwZ3bUeObN628n1HT7Bmy28oFFCnRlXqVK8CwOrNv/DXiTCMRiP1v/yCb18ud2TOySs2cvnaLdRqJ4Z1bE7OrJnfWG/8knWk9XSnZ/M6tmXno64y56ftLBreyyHZLGYzv64Zzb0bl1Cpnanz3TgyeCdsnxGn93Jwx3yUShUlPm1IqSpNbGU3o8+we9M0vh8YDMCdaxf4aXZX2/tLV21O0bJfJ3tms9nM+qXjuXU1Aie1My26jCJLNvtzCr1Ow5yxnWnZdTRZs+cG4MWzxwQNa0Ng0M+onV2SPdfbci6cN5uYl+dLPXr3xyfR+dKB/XvZsXUzSpUSPz9/unTvhVKppHePznh4eADg7Z2N3v1+cHhW8eH64Dqin332WWpH+Ec6deqUqt9/4ORZ9AYDy8cM4NzlGGat2cy0AV3s1tm8+xBRN25TslBe27LV2/9k51/HcXNxTpGcSiXUq+zC9A3x6A3Qu5EbF2JMvIhPODn9urwzf501cC7aRMFcKmpXdGHFb1pyZlHSuKoL6Twdf7N3yNG/MOj1TA6aR8Sli6xYOp+hI8YDoNPpWBu8nFnzluHi6krQ5LGcPH6UsuUrYTQaWTh3Os4p0PgD/BVyAr3BwPwp47gQEcmC5cGMD7Q25iaTmcWrf2RR0ETcXF35vkc/Pi1fhnMXIwCYO3ksYecuMD/Re5LThZN7MBp09BzzI9cun2HH2qm07T/Xms1oYPuayfQeux5nVzfmjmpF4ZJVuX87hquRYXQftQaDXsP+X1YCsGnpKOp9NwS//AHs3DCLsCO/UurTb5M98yuHj4WgN+iZHTSZi5ciWLRsBWOGD7VmN5lYujKY+TOn4erqSoduPalUvhxeXmkdlud1R44eQ6/XMytoKuGXLrF46XJGjxgGWLfPlcFrWTRvDq6uLkyYPJWQ4ye4/+ABrq6uzJ4+jRs3bzJ34SImjh3t0JxKBdQqrWLxb0YMRmhfS0XEDROx2oR1nFRQp4KKHJkUXLxuti3PllHB5r+M3Hns0Ijv5d+/A9lb1cEUp0ndIFh/d4Nez8ygaW/93VcFr2HhvDm4uroyMdHv7ubqxqyXv/u8hQuZMHZMsmc7f3IPBoOOXi/39+1rptJuQML+vi14Mn3GvdzfR7aicKmqlK1Sn7JV6gPw8/KxlK1aHzePtLZOZ3zsMxaMa0vd1oOSPS9A6LEDGPR6Rk1dRtSlc/y4fBb9hiV0eKMvh7NiwSQeP7xvW3bm5GFMJhMjpyzlXFgIG4MX0HvIZIfke+Xg8VPo9QYWTxzO+cgo5qxax+TBvQFrO79wzSaWTRmJm6srLfsM5bOyJYm+fpPzly6zcHwgWp2en7bvdGhGgP0nz6EzGFg+ph/nLscwc+0Wgvrbnxtt3nOYKzduE5D4PGTHbn7764RDz0Muhe3GaNDRPnA9N6+c5o8Nk2nWcz5g3T5/Xz+JjsM24uzixvKJLShQ4nM8vTJzeOdSzh7dhtrF3fZZd65dpHzN76n4ZTuH5QU4e2IvBr2OARPWEBN5hs2rp9Fl0Gxb+bUrF1i3eCxPHt2zLbt4+jDb1s7ixbNHDs2W2LGjh9Eb9EydPodLly6yfOlCho0YC7w8X1q9gjnzl+Di6srUyeM5cfwYASVLAzBh8vQUyyk+bKk6VtyjRw+OHz8OwNmzZ+natSubN29m2rRp6HQ6unTpQqtWrWjUqBEhISEAVKpUyfb+vn37EhISQmxsLL1796Zdu3bUr1+fH3/88W+/d/bs2bRp04YOHTrw+LH92c7bPn/z5s20bNmS5s2bc/ToUT7//HPat2/P+PHjGTx4MAcPHmTz5s307t2bzp0789VXX7F582bb39WwYUPatGlD3759GTx4cLLU3SunI65QoXhhAD7Jl5vw6Ot25WcjozkfdZUG1SrZLc/hnYnJfTsma5a/451eycNnZjQ6MJkh+rYJfx/7zW/bXzouXDUB1o6r0WjtpDqpYPlvjh0JfSX84jkCSpUFoEDBwlyJirSVqdVqJk2bg4urK2DtmDg7Ww+gK5ct4MuvviVDxowOzwhw7mIEZQOKA1CkQH4ioq7YylQqJavmTcfTw53nL15gAdxcXalcvgz9u1tPDu49eEj6dF4OyRYTcYoCxT4FwDdfcW5EX7CV3bsdTSbvXLh7euHk5EzuAiWJuXSKyLOHyZYzP6tm9GL5tO4ULmm9gv/s8V388gcA4Je/JDERpxyS+ZULF8IpU7IkAIULFiDycpStTKVSsXzhXDw8PKz1agE3N1eH5nnd+YvhlC5lzVeoYEEioxLyqdVqZk6bjKur9WKIyWRC7ezMtes3KFO6FAA5c+Tg+o0bDs+Z2Qsev7Cg1Vv39+v3LeTytr+Q5KSCM1fMHDxnslvuk0FB5U9UtKulonLR1DtExUdfJ7Rxz1T7/sQuXLxI6VLW37BQwYJcjrpsK1Or1cyYNgVXu3ZJzfXr11Pkd4+JOEXB4u/Y32+9fX9/5caV89y7eYUK1ZrYfebvm+bx6ZctSZv+zVG15BARfoZiJcsDkLfgJ8REXbIrNxr09BkyhWyJRjyzZs+F2WTCbDaj0cShcnL8dfyz4ZcpH/AJAEXz5+XSlRhbmUqlZO3sCdZ2PjYWLBbcXF0IOX0ef9+cDJkyh4ETZ1KxVAmH5zwTcYWKxQoBr85D7Le1s5ExnLt8lfpvOQ+Z0qe9Q7NdvxxK3qKVrd+XpwS3r563lT28E02GLLlw8/BC5eRMznyluBYZCkD6zDlp0n2O3WfdvnaBy2cPsGJSK7atCESniXVI5ivhYRQOsNZV7vzFuX7lol250aCn0w8zbSOhAAqFkp4jFuPu6Zjj+tuEXzhPyVJlAChYsDBRl+3PlyYHzX7jfCkm+gp6nY4RgYMIHDyAS5cuvvWz/7+xoPgg/30IUrUj2rhxY7Zs2QLAli1baNIk4WB0/fp1Hj58yMKFCwkKCkKr1b7rY7h27Rq1a9dm+fLlLFy4kJUrV/7t99asWZPVq1fz+eefs2jRoiRlTZs2LT/99BMVKlTgzp07TJs2jcDAQLt1YmNjWbRoEQsWLGDx4sUAjBw5kkmTJrF69Wpy5Ur+6ZpxGi2e7m6210qlEqPJemL38Mkzlvz8GwPbvjnN+YtyATg5qZI9z7u4OoNWn9CR1BnAzdl+J4jTgtkMWdIpqFvJhd+P6wGIuWPmaazjO6EAmvh43F9OGQFrfZpMJtt/p0ufAYBft29Gq9VQPKA0e//chZdXOlsHNiXExcfj6ZFwpTbx7w7gpFJx8GgI7fsMpFjhgjipnGzLJ86cx+zFK6hSsZxDsmk1cbi6p7HLZjJZp2Hr4mPtylxcPdBqXhD34gk3Yy7Quvd0GrYbyY/zBmGxWMiQJSdXwk8AcPHUPvQ6x45OxWni8Uhcr6qE3x+sndFDR47SpWdfPilaGJUq5fYhgPj4eNuUJnhz+0yfPj0AW7f/gkarpVRACfL45ybk+AksFgvhly7x6NFju7/JEVycFegMCa91BnBV26+j1cOVO2/u1+evmvk/9u47rqnr/+P4KwkBArgH4GK4R1Xco1prXR1aq3XVPereeyFq3XvvjbZqrVY7bK17496igLj3Fsgk+f0RDUSh0pYE/f4+z8ejj0dzz73J2zvPuefcy6+H41m5LZ482RUUyJk2F8u7m7ZhMZrePqMTWLe7/fGe1HbfvOUXtDotpYKCyBsY6JTt/nfHu0772vGu8UQb98L2ecfmxdRq2MXu+148e0TEucOU/ah+qmd9RRsXi4enV5KZAQoUKUGWbN52y7i7e/Dg/h0Gdm3M0jnjqfXFf3986G1itVo8PRK2uyqJ8/zuw8do3TeYEkUK4qJy4dmLF4RHRTOmXzcGdGrNqJkLsVgce/2M1erw/Nt6yFYGtW30xnLVy5V0eD1Er4vFLdE+qFCqML+6Huli0ENkxAABAABJREFUcNfYX4/0Wuv+WaRMbVQq+5sNOQM+oGajAbQdvJpM2XKzZ8tch2TWaWPQeCS/f+YtFESmrPaP1RQuURGvdBkdkic5cXFxeHq8/Xr065ZN6LRaSgaVxs3NjfoNGzFqzAS6du/NtEnjHX49Eu+2NG2IVqlShbNnz/L06VOOHTtmNyw3f/78NG/enL59+zJq1CjMZvMby786uWbNmpXt27fTv39/5s+fj8n095WHMmWsQwNKlSpFdHR0svMlPnkHBCTcecqUKZPtAEusUKFCAPj6+mIwWBtR9+/fJ3/+/ACUfnlHOzV5atyJ1SY00i0WCy4vK8c7wk7w7EUMvSfOY+WWv/jzwDF+3XMo1TP8nc8quNL9Kw0dvtDgnqjh6aYGrf7Ni2O+nCraf65h9V8Jz4c6k8bDA502zvbZYjbbNTbMZjMrlszn9MnjDBo6CoVCwY6/tnLq5DGGD+5N9JVIZk4bz5PHjh1X6OnhQVyi7W5OtN1fqVqxPBuWWY+Hbbv22KYP6d2N0PkzmDJ3Edq/ucHzb7lrPNHrYm2fLRaL7YLu5uGFXptQptdZK7EeXhkpULwyLi6uZM8RgIvalZjnj2nSaQw7Ny9myaQueKXPgqeDL7SeGg+02oTGrsVseaOxWaVSRX5YuRST0cRfO3c7NM/rPDz+Pp/ZbGbRkmWcOHmKEUOHoFAoqFOrJh4eHgwYMozDYUfJny+vwxrQ1UsqaVNLRbOPVbglani6qa0Nz5Q4dNFM3MuRE5dvWfDN/G7ctU1LHh4exL11uy/lxMlTBL/c7rVr1cTDQ8PAIUM5HHaEfA7a7u4aT7tjOvHx7q7xsjsX6LWxaDytFX9t7HPu375CvqL2N8TOhG2jVOXPUSod10DReHiiTXSeN1vMbzQ6Xrd1yw8UDyrPlAUbGDdzNQtnjMJg0DssI4CnRmN/nje/eZ6vVqEMPy+ejslk4o89B8jg5UX5ksVQq13wy+mLm1rN0+cvXv/qVM7pTpwucT3EbMu5PewUT1/E0GvSAms95OBxftkT5tA8ibm5e2Kwux6ZUb66Hrm/tn/q7G+qvK5wqZrk8C/28v9rcPf6RYdkdtd42ddDUrB/pgXr9ShxfenN89KyJQs5dfIEQ4aFoFAoyJkrF9U+rmH7/3Tp0/P4sfOGE4t3T5o2RJVKJXXq1GHkyJHUqFHDbge+dOkSsbGxLFq0iAkTJvDdd9Zx5yaTidjYWAwGA5Evh6UtW7aMkiVLMmXKFOrUqfPWu39nz54F4NixY7ZG4itJff+rrEn9f2IKxZsVJh8fH9v3nD59+m9z/RslCgRy8JR1KNTZiGjy5k54GUWTOh+zatxgFozoTet6NalduQxffFQx1TP8nd8PG5izSUvw0liyZlDi4QYqJeTNqeLqXfubC/lyqmhQ1ZUFW7TcuP/mjQdnKFykGMePWi+Sl8IvkMc/0K58/pxpGIwGBgd/ZxtyMnbSTMZOnMmYCTMICMxHr75DyJQ5s0NzFitckMPHTwJw/tJlAv0Settj4+LoNXQkBqMRpVKJu7s7CqWSbbv2smaDdQSCu5srCoXCIS/+8S8YRPipvQBciziNT+6EY8w7RyAP714jLuYpJpOBKxeP45+/JAEFS3Hp9H4sFgvPntzHoNfimS4jF0/uoXGnMXQYOJ+4mKcUKFYp1fMmVrRIIcKOWYdmXQi/RIB/wkstYuPi6Dt4mN16VSqd20gqWqQwR44eA+BieDj+ifIBzJwzD4PRwMjgobYhupcuR1CsaBGmTBhH5UoV8PFJ/RdUvbLzlJkV2+KZvN5E5nQKNK7W493PW8mNB2+/seSmhm71XHB9WecK8FFw+7Hzb0i9a4oUKczRv93uczEYjYQED7MN0b10OYKiRYswecJ4KleqiK+DtntAgSAuJjrefRMf7zlfO97Drcc7QNTFY+Qv9ub1KOLcYQqVrOKQrK8UKFyc08cOAhAZfpbcfvnesgR4eqZD87IX1TNdeuJfDtN1pA8K5ePQCWu94dzlSPL65bKVxcZp6RY8PuF85OaGQqGgeOECHD55DovFwoPHT9Dq9aT38kruJ1JFiYKBHDhlHWL5ej2kaZ2PCB03kIXBPa31kEqlqfuRY0bjJCV3vlJEnLHeiL0ZdQrvnAVsZVl9A3l87xramKfEmwxcv3yUXHmDkv2u1dM6cOvKGQCuXDyEr39Rh2QOLFSS8yf2ARB9+TQ58uR/yxJpo3CRohw7Zn28Ljz8An7+AXbl82ZPx2AwMDR4lK2+9Ne2P1i2ZAEAjx49JC4ujsyZnfNIU1qyKBTv5H/vgjS/xdKwYUNq1KjBn3/+aTfd39+fuXPn8vPPP6NWq+nZ0/pGtVatWtGkSRNy5cpFjpdvDfz4448ZOXIkv/zyCxkzZkSlUtl6JJOyfft2Vq5ciaenJxMnTiQ8POH5kKS+/78ICQlh6NCheHh4oFar8fb2fvtC/0C1siUIOxtO+xFTsAAjOrXgjwNH0er0fPXJh6n6W/+F2Qw/79PT+UsNCgWEXTDxLNaChxs0+cSd5b/r+KqqKyqVguY1rCes+0/NrN/l2DvOrytfsQqnTh5ncL/uWLDQo/cg9u7ejk6rJW/+guzY9juFi37AiKF9AfiiXkMqVHJspSkpVSqU5dipM3QbGIwFC4N6dmH7nv1odTrq1q5BjY8+pNeQkahcVOT1z0PNj6pgMBqYOGs+PYeEYIqPp3uH1ri5pv5LIoqVqcHls4eYHdIcLBaadBrDiQO/YtDFUeGTxtRtMZBFEzpiMVsoV+0rMmT2JkNmb66EH2NmcBMsZgsN2gxHqVSR1cePpZM6o3bVkK9IOQoHOfZlZpUrVuD4ydP06j8IiwX69+7Bzt170Op0fF6nNp9Uq0q/QUNRubgQ6O/HJ9Uc+zbKpPKdOHmK3v0GYsFCv969rPm0Wgrkz88f2/6iWNEiDBxqfZFN/Xp1+aBoUVaGrmHDxk14enrSr5fjn3s0W+CPY/G0rKFCoVBwMtLMCy1oXK0vKFq3J+mhWHojbD8ZT5taKkxmiL5jIeKWNEQrV6z4crsPACz07d2Lnbt3o9PqyJ8/H3++3O6DhlofF6lfrx7FihZhZehqfnq53fv2csxbSYuVtR7vs0Y0BxKOd70ujoqfNKZei4EsGt8Ri8VC2ZfHO8CDO1fJkj3XG993/3Z0ktNTU5kK1Th36gijBnbAYrHQsVcwB/f8iU4bR/U6XyW5zKdfNmPRrDGMHtyReJOJxi274O6uSXLe1PJR+dIcPXOeTkPHYLFYGNatPdv2HUKr1fNlrWrUqlKRbsHjcVGpyOuXm9pVK6FSKTl14RIdBo3GYjHTr0NLVCrH9jlUK1OcsLOXaBcyDSwwolNz/jhwjDid/o33Uzhb4VI1uXLhIEvHNQWLhS/bjefs4V8w6OMo/VETajUZxOrpHbBYzJT8sCHpMyVfR/u8ZQi/r/kOlYsar/TZqNs69V/+BVCi3CeEnznMlGEtwWKhRbfvOLrvN/Q6LR/W/Nohv/lvVKj0IadOnmBgv55YLBZ69RnAnl070Oq05M9fkL+2/UGRoh8wfEh/AOp+2YCatT5l5rRJDOrfC4VCQc/e/Z3+iIt4tygsjn544P+5NWvW8Omnn5I5c2amT59ufcV19+7Jzv/sxHYnpvv3Qg5USOsIKdLx0zT6uw//UCbT/bfP9A44FlssrSOkSIn0EW+f6R3wvvxtsWUHA98+0zugXPv3Y/8sevGXtI6QIueevx/b3dvTMS+NSW0BRscM5Uxtrvr347r5q7ZWWkdIkezpU/hcQhrL7fl+1EMK5s399pneMVFXrqR1hCTlDUz7c3ya94j+r8uSJQvt2rXDw8ODdOnSMWHChLSOJIQQQgghhHACi+XdGAb7LpKGqIPVqVOHOnXqpHUMIYQQQgghhHhnSENUCCGEEEIIIRzAkrbvhn2nyZoRQgghhBBCCOFU0hAVQgghhBBCCOFUMjRXCCGEEEIIIRzAgrysKDnSIyqEEEIIIYQQwqmkISqEEEIIIYQQwqlkaK4QQgghhBBCOIAMzU2e9IgKIYQQQgghhHAqaYgKIYQQQgghhHAqGZorhBBCCCGEEA4gQ3OTJz2iQgghhBBCCCGcShqiQgghhBBCCCGcSobmCiGEEEIIIYQDyNDc5EmPqBBCCCGEEEIIp5KGqBBCCCGEEEIIp5KhuUIIIYQQQgjhABaLDM1NjvSICiGEEEIIIYRwKukRfcc8TZ87rSOkSMH8XmkdIUWuvXBL6wgp8sA1Y1pHSJEnMe/HKUOZLj6tI6TIVWOetI6QIh+XMqV1hBTxv/hLWkdIkfOF66Z1hBTRnDid1hFSpMijnWkdIUUOp/ssrSOkSPYMz9I6QoqUy3g1rSOkyKFbAWkdIUVuPHpP6p950zqBSE3vR61SCCGEEEIIId4z8tbc5MnQXCGEEEIIIYQQTiUNUSGEEEIIIYQQTiVDc4UQQgghhBDCAWRobvKkR1QIIYQQQgghhFNJQ1QIIYQQQgghhFPJ0FwhhBBCCCGEcAAZmps86REVQgghhBBCCOFU0hAVQgghhBBCCOFUMjRXCCGEEEIIIRzAYpGhucmRHlEhhBBCCCGEEE4lDVEhhBBCCCGEEE4lQ3OFEEIIIYQQwgHM8tbcZElDVAghhBBCCCEEAGazmZEjR3Lp0iVcXV0ZM2YMfn5+tvJff/2VlStXolKpKFCgACNHjkSp/OcDbWVorhBCCCGEEEIIALZv347BYGDdunX069ePCRMm2Mp0Oh0zZsxg1apVrF27lpiYGHbt2vWvfkd6RIUQQgghhBDCASzv4dDc48ePU6VKFQBKlizJuXPnbGWurq6sXbsWjUYDgMlkws3N7V/9jjREhRBCCCGEEOL/kXXr1rFu3Trb5yZNmtCkSRMAYmJi8PLyspWpVCpMJhMuLi4olUqyZs0KQGhoKHFxcVSuXPlfZfh/3xDdu3cvd+7cAaBBgwao1eo35gkLC2Pt2rVMnz7dbvrYsWNp27YtOXLkcErWpJjNZmbPW8CV6GjUajV9enYnZ6I8u3bvYePmX1AplQQE+NOja2dM8fFMnT6TO3fv4uHhQY8uncmZ0zH/hivndhL2x1wUKheKlm/IB5Ua25VrYx6zdVV/TEYdnhmyU+ub8ahdNZzYtZxzhzeg8coMwCeNR5Eha27++n4ozx/fIt5koFytLuT94JP/nNFsNrNh2XfcunYZFxc1TTuNJptPHlv5ueO7+fOn+ShVLlSo9hUVP/maeJORNfOG8fjBLZRKFU06jsQ7ZyA3r4bz45LRKFUqsvn60bTj6H81Zj4lmb9fNI6bVy/jonalVdcRZPfNYzePXq9lxsgutOoWgm+uAMzx8aya/x33bl1FqVLSuvsosvvkTrVMl0/vZP+vc1GqXChRuSFBVey3ddyLx/y8xLqtvTJmp27r8ajdNH+7XOzzRywd24Bvei8jq29e7lw7z/q5ncmc3R+A0h81o0jZz/5zdrPZzMz5i4mKvoqrWk2/Hl3ImcPXVr73wCHWbtgECgWf167J57Vr2MouXrrM4hWrmTZ+9H/OkZKcqxeO58bVy6jVrrTuFox3Ett92siutOk2At9cAbbpz58+ZnT/5vQbOc9uuqNy/rB43Mucalp2CXlj/zTotcwY1ZlWXUfi83L/DF0wmnu3rqJQqmjTfRTZUnH/TC7n7HnziX55/uzds8cb589Nm7egtJ0/u7w8f87g7t17eHho6N6li8POnymVsVxxCo3rz+EardI0h9lsZu3isS/Ppa40T2a7zxrdmRZdR+KT07ofvnj2mCnDWjN82gbUrv/urvo/zTk+dDOXb9zB1cWF4LYNyOOd9Y35vluxkQyeHvRsVAeAZiGz8NK4A5AjW2ZGtf/a4Tn/6fqMNxkJnRfCo/u3MZkMfNqwI8XLVnN4zsXzpnEtOgoXtZouPQfimyOXrXz/7u38uvlHlEolfgF5+bZrX8xmM/NmTOD+/buYjAYaNmlF2QofOjznvLlziI6+glqtpmevPnb1t927d7H5559RqpQE+AfQtVt3duzYzo7tfwFgMBi5ciWK1Wt+sKug/1sWs5mta0Zy7+YlVC6ufNF6DJmzJzyDd/n0Tvb9knBtLFW18VuX2bZuHFm8AyhdrRkAB7cu4vyR33DVeFGpdgfyl/j4P+eOOLOTA7/NRal0oXilhpR8/Vof85gtS19e6zNk5/PW1nodgNGgZe2MtnzWaixZfPJiNsezdfVwHt+NRqFU8Xnr8WTKliepnxUOkLjh+TovLy9iY2Ntn81mMy4uLnafJ0+eTHR0NLNnz0ah+He9vv/vnxGtWrUqTZo0YeHChZjN5n+07LBhw9K0EQpw8NBhDAYDM6dOpn2bVixassxWptfrWRG6hsnjxzJj6iRiY2MJO3KUrX/8ibu7O7OmTaFb547MWbDQIdni443s2TSer7ouo1GPUM4dXEfs8wd284T9OY+Cpb+gca/vyZ6zCGcPWO/M3L9xntotJtKoRyiNeoSS2TuQ8KNbcPfMSONe31O/82J2b/guVXKePbYDo8FAn+/WUPebPmwOnZzwbzAZ+XnVRLoMXUSPkBUc3PEjz58+5MKpfZjN8fT+bg21G3bmt3WzAPjzp3nUbtiZXqNCiTcauXByb6pkfN2pI7swGg0MnrCKBi168uOKaXblVyPPM2V4ex7cu2mbdvqYNcug8Suo17QrPy6fmmp54k1Gtq8fT7Pey2jZP5STe9cR88x+W+/7bR5Fy39Bq4Hf45O7CCf2rvvb5eJNRn5fPQK12t32HXevX6B8jba07B9Ky/6hqdIIBThw+AgGg4E5U8bToXULFixbmfBvi49nycrVTBoTwuzJ41i/aTPPnj0HYO1PPzN19nwMRkOq5Hibk2HW7T5s4koatuzB+uX2N8euRl5g4rAO3L970266yWRk1YKxuDqhkg8v90+DnsHjV/FVi15sWPnm/jk52H7/PHNsDwADx62kXtMu/Lgi9fbP5Bw8dBijwcCMqVNo16b1G+fPlaGrmTR+LDOmTiYuNs52/tS4a5g5bQpdO3di7oIFDs/5dwL7deCDhWNQujtn2/6d00d2YjIaGDAulPoterFxpf02vBZ5nmnB7Xh474Zt2oVTB5j9XWdePHvktJy7TlzAYDSxcnhXejSqw/S1v78xz4ZdYUTevGv7rDcaAVg8uCOLB3d0eCMU/t36PLL3NzzTZaTfmBV0GzaPdUvHOzznkUP7MBoMjJs6nxZtOrFyyVxbmV6v54fQJYwaP5NxU+cTFxvL8SMH2btrG+nSp2fMpDkMGzWZpQtmODznoUMHMRgNTJ02gzZt27FkySK7nKGrVjF+wkSmTp1ObFwsR46EUbNmLSZMnMyEiZPJly8fnTp3SZVGKMClU9sxGQ20HbKO6g36sX19wjN48SYjf60bzzd9ltFqQMK1MbllYl885oeZHbh8eqftO+7fvMS5I7/Sduh6mvdZxp4tszDqtf8pc3y8kR0/jqdpz2U07xfKqf1vXusP/DaPImW/oEX/7/HOXYSTe631ujvXzrJmSnOePkzYXyPPWJ8rbDlwLVXq9mTHj47fX9OCxaJ4J//7O6VKlWLvXmu98dSpUxQoUMCufMSIEej1eubNm2cbovtv/L9viG7cuJEKFSrw4MED+vTpg9lsZvTo0Xz99dd8+eWXbN++HYBr167RoUMHGjRowOzZswFo2bIlUVFRzJ49m0GDBtGhQwc+++wz9u3bB8CuXbv46quvaNmyJd27d7ctl5rOXbhImdKlAChcqBCXIyNtZWq1mhlTJuL+soISHx+P2tWVa9dvULZMaQBy58rF9Rs33vziVPD4bhQZs+bB3SMDKhdXcgSW5lbUMbt5bl05jn9h6xh0/yJVuX75IAD3bp7n6F+LWD+jGUf+sjaU8wfVodJnvWzLKlSqVMl5JfwkhUtahxT45y/BjSvnbWX3bl0hq08ePLwy4OKiJrBgKa6EHyebrx/meBNmsxmdNhaVynqXKKd/YeJinmGxWNDpEqantsiLJykaVAmAwILFuRZ1wa7cZDTSZdA0fHL626YFlf+Yll2GA/DowW3SZ8ySanke3o0iU/Y8aDyt2zp3vtLciLDf1jcijpO3qHVb5y1WleiLB/92uR0bJlLqo6Z4Zcxu+467188ReXY3qyY359eVQ9HrYlIl/9kLFylbOgiAIoUKcCkiylamUqlYPn8WXp6ePH8Rg8ViQfOqV8THm5FDB6RKhpSIuHiKYi+3e96Cxbn62nY3Gg10HzwV30TbHWD9ihlUq92QjJmzOSWndf+0HlOBBYpzLeq8XbnJZKDLQPv9s2T56rToHAzA4wd3SJchs8Nznr9wgTKlrefCwoUKEREZYStTq9VMnzIJd3frto6Pj8fVVc3169edcv5Mqbgr1zneqEeaZnglKvwkRUpa98+AAsW5duXN7d5x4HS8cyT0yCsUSnqGLMLDK4PTcp6KuEqlD6yVquJ583Dh6i278tOR1zh75QYNq5W3Tbt8/Q46g5GuU5bSceJizkRdd3jOf7M+gyrWom7TbrbPSmXqXCf/TviFs5QsbV1XBQoV5UrkJVuZWq1m7JR5uCU6jtSurlT8sBpNW3Rwas4L589TunQZAAoVKkxkhP3xPmXqtNeOd1dbecTly1y/fo1PP02dm5/w8ppYzHpNzJW3JHeuJTyD98a1MX9prkccS3YZgy6WqnV78EGFLxO+404UfgXL4aJ2w0XtRubsfty7eYn/4tGdKDJly4P7y1y58pbmZqT9tf5m5HECX17rA4tV5Vq4tV4XbzTQoPNcMnsH2uYtULIGnza3dio8f3wbz/RvjkwQaaNmzZq4urrStGlTxo8fz5AhQ/jll19Yt24d58+fZ8OGDVy+fJnWrVvTsmVL/vrrr3/1O//vh+YCfP311/z+++9Mnz6dHTt28OTJEzZs2MCDBw9YvXo1lSpVsrX64+PjqVatGj162F/4XV1dWbJkCQcOHGDZsmVUqlSJMWPGsG7dOrJmzUq/fv0ckj0uLg5PT0/bZ6VSSXx8PCqVCqVSSaZMmQD4ecuvaHU6SgeV5N69e4QdOUrlihUIv3SJR48e25ZJTQZdDK6adLbPru6eGF5rOBh0Mbi5W+dxdfPEoH0BQMGgzylR5Rtc3b34ZWl3rpzbRWCxj23L/LasJ5U+650qOfXaGNwT5VQolcTHm1CpXNBpY3HXJNz9dNN4oo17gZubB48f3GZ837rEvnjCtwOtd4Cz+fjx07IxbNu0CHeNF/mKlE2VjK/TxcWi8UjIpVCqbJkB8hUumeRyKpULy2cFczJsF50GTE5ynn/DoI3B7bVtrdMmsa1fzuPq7ole+yLZ5U4f3IhHuszkLVqFg1sT7lrn8C9OyQ8b4etXjP2/zWffL3Op0WjQf84fF6fF08PD9lmV6DgCa2N038HDzFqwmPJlStumV61ckbv37v/n308pnTYWj0TbXfnads+fxHbfv3ML6TJkolhQJX7/abnTcv7t/lkoKMnlVCoXls8ezqmwXXTqn3r7Z3Ks58+E7Z7c+XPzll/Q6rSUCgri3r37hB05SiUHnz9T6u6mbWj8cqbJb7/Out0TjufX98+8SWz3wiUqOi3fK7FavW2ILYBKqcAUH4+LSsWDp89Z+PMOpvZowV9Hz9rmcXd1pWWdKnxVtSzX7z2kx7QVbBzfFxcHbvd/sz7dNR62ZRdP6Ue9Zt0dlu8VbVwsHm/UQ6w5lUolGTNZbyr9vuUndDotJYLK2obxaePimDJuBM1adUjyu1NTXFwcnh5vry9t2bIZnVZHUFAp27zr1q+l2TctUjWPXheDm8b+PGmON6FUubxRL7FeM2OSXSZTttxkypabyHMJo7Cy5yrIga2L0OtiiDcZuRF1kqCqSQ/D/GeZ38yV3DxubtZrPUCufKWT/E6lyoVfVwzi8qm/+KrjrP+UT6QepVLJ6NH2jxzlzZvX9v/h4eGp8jvSEH1NdHQ0JUuWBCBbtmz06dOHsLAw8ufPb7s7lniM9CuFCxcGwMfHB4PBwOPHj/Hy8rI9zFumTBkePnyY6nk9PDzQahOGWljMFrsKkdlsZsmyFdy8dZsRQ4egUCioU6sm12/cZMCQYRQtXJj8+fKmaiXq4G/TuXXlBA9vX8LHr7htukEXa3cCA3B198Kgj8XF1R2DPhY3TXosFgtB1Vrb5g0o8hEPbl0gsNjHvHhyh1+WdqPEh99QqEzdVMnrpvFCr0sYB2+xWGwXeneNJ3pdnK1Mr41F45Ge3b+HUrBEJeo268OTh3eYO6Y9gyZtYtPKCfQYuQrf3PnY9+cPbF49ma/bDU+VnIm5e3ii1ybkspjNKe59bdvzOxo8ecj4QS0ZNWsjbu7/fkjF7p+ncyPyBPdvXiJHgP22dvdIYlvrYlG7ur8sT4+rxjrt9eWO7gxFgYLoi4e4d+MiW5YPonG3+RQMqom7R3oACgbVZNva1Bme7eGhsTuOzBbzG8dElUoVqFyhHJNmzOGvXXuoU6N6qvz2P+Gu8URnt6++fbvv37EZBQoung7jevQlls4cQY+h08mQyXF3nt01nui0iXL+k/2zxxietXjIhMEtGTnzv+2fb+Ph4UHcW8+fy7l16zbBL8+ftWvV5PqNGwwcMpQihQuTL5XPn++zN/bPf7DdnclT40asTm/7bLZYbA3K7UfP8jQmlp7TV/DoWQw6gwF/32zUKV+C3N5ZUCgU+PlkI4OXBw+fvsAnS0aH5fy36/Pxw7ssmtSHqrUbU7ZK6vXgJUfj4Yku0fXIbLbY5TSbzYQum8+dWzcYMPQ7WyP04YN7TBoznNqf16dKtZoOz/l6fcmcxPG+bNlSbt26ydBhw205Y2JiuHnjJiVKlEjVPG7u9tc/i9mM8uV6e71e8ura+HfLvC6rb17KfNycH2Z+S+ZsfuQMKIGHV6Z/lXXvZuu1/sGtN6/1r9fr3BJd6/X6WNxeXrP/zhdtJhLzrD+rJjamQ8hvuLp5vHWZ98n7+NZcZ/l/PzT3FYVCgdlsJjAwkLNnrXdBX7x4Qfv27W3lb1s+sSxZshAbG8vjx48BOH36tANSQ9EihTly1Dos4mJ4OP7+fnblM+fMw2A0MDJ4qG2I7qXLERQrWoQpE8ZRuVIFfHx8UjVTpc/70KhHKB3HHODZg+voYp8SbzJwK+oYvv72d3BzBJQi+oL12bCrF/aSI29pDLoYQid8gUEfi8Vi4UZEGNlzFSP2+UM2zm/Hh3UHULRC6j2fE1gwiAsnrcOpr0acxjd3fluZd85AHty9RmzMM0wmI1Hhx/EvUAIPz/S2O9UeXhmIN5kwm+Px8MqAu8Z6xzVDpmzExTxPtZyJ5S1UkrMn9gNw5dIZcvrle+syh3b/ytaflgLg6uaOQqn8zy9Sqla/Dy37h9J7ygGePLiO9uW2vh5xjJyB9ts6d75SRJ6zbuuoc3vJnb80WX3y8vj+tTeWazVgDS0HrKZl/1C8cxemXtuJeGXIxg8z2nMr+gwAV8MP4ZOn6H/K/0qxwoUIO3YCgAvhlwlI9EebY+Pi6DM4GIPRiFKpxN3d7V8/lP9f5StckjPHDwAQdekMOfO8fbsPHruUQWOXMHDMYvIEFKR9r9EObYQC5CtUknOv9s/LZ8jpl/8tS8Dh3b+ydWPi/VPhkBd9JVakSGGO/u35cy4Go5GQ4GG2IXuXLkdQtGgRJk8YT+VKFfFN5fPn+yxvoSDOv9zu0ZfPkCPP27d7WiiZ358DZ6xDFM9EXSdfroRt2KxmZb4f2YPFgzvS5vOPqFOhJPU+LM3mfceYvvY3AB48eU6sVk/WjOmS/P7U8m/W5/Onj5j9XWfqt+hNpU++cmi+VwoVKcaJo4cBuBx+njz+gXblC+dMwWg0MDB4nG2I7tMnj/lueD9atO3MJ7U+d0rOIkWKcvTYEQDCwy/i7+9vVz5n9iyMBgPBwSG24x3g3LmzlAwqmep5cuUrReRZaw/mzahTZM+V8AzeG9fGy9Zr498t87rYF4/RxjyhzaAfqNV0GM+f3CFbzn93TFb9sg/N+4XSY/IBntxPuNbfiHzzWp8zbymiXl7rr5zbS+5kekIBzh3+mUN/WB+/UrtqUCgUThmmLd4d796tyjRSpkwZOnbsyKpVqzh06BDNmjUjPj6ebt26vX3hJCiVSoKDg/n2229Jly4dZrMZPz+/ty/4D1WuWIETJ0/Ru99ALFjo17sXO3fvQavVUiB/fv7Y9hfFihZh4FBrr1z9enX5oGhRVoauYcPGTXh6etKvl2OeL1Kp1FT9ajCb5rfHYrFQtEJDvDJ6o4t9yl9rh1O3/RzK1erCtjWDOHdoPRrPTHzaaipqNw8qf9GHn2a3sj4bUaAiAUU/YvdPY9DHPSds2zzCts0D4KtOi3FxdX9Lkr/3QdlPuHT2IDOCm2MBvun8Hcf3/4ZeF0elGo2o33IgC8Z1xGKxUL7aV2TM7E21z1vxw4JgZoW0wmQy8kXTXri5e9C04yhWzRqAUuWCysWFJt+OSoU1+aag8tW5ePowE4a0BouF1t1HEbZ3K3pdHFVrNUxymVIVPmHFnBAmD29HvMlEk3b9U+0NlSoXNTUaDeaHGdZtXaJyQ9Jn8kYb+5TfVg3n6y5zqPx5F35ZPohT+9aj8cpE/Q5Tk10uOXWaj+TPH75D5aLGK31WPmuZOj2iH1Ysz/FTZ+gxYCgWi4WBvbqxY/c+tDotX9SpxSfVqtBncDAuKhWB/n7UqFY1VX73nypV/mMunDrMuMFtsFgstOsxksMvt/tHyWz3tFCyfHUunjnMxKGtsFigTbdRHNn3OzptHFVrJX0TKajCJ6ycM8K6f8abaNx2gMPfoFq5YsWX588BgIW+vXuxc/dudFod+fPn48+X589BQ4cBUL9ePYoVLcLK0NX89PL82bdXT4dmfJ+UKFedi6cPMXloK8BCy26jObrvd/S6OD6s6fiX+6TUx6WKcPh8BG3GzMeChZHtv2broVPE6Q00rFYuyWXqVy1DyJINtBu3AFAQ0q6hQ4flwr9bn39sXII29jlbNyxi6wbrYw3dhs3F1e2/XSf/TrmKVTl98hhD+3Wx/l7vwezb/Rc6rZa8+Quyc9tvFC5anJFDewPweb2vOX/2JLExMWxYu5INa60vhxs2avK//luEKVGxUiVOnjxBv359wGKhd59+7N61C61OS/78+dm27U+KFi3G0CHWxz3qfVmfSpUqc+vmTXx8fN/y7f9coaCaRF84wIoJTbFYLNRtM45zYb9g0MdRqmoTajYezPfTrdfGkh9ar43pklgmOR5emXjy8CZLxzRE5aLmk68H/udGnkqlpnqjwaybZc1VvFJD0r281m8NHU6DznOo/FkXfl0xiNP7rdf6eu2Tf/FcgaBa/L5yCKunNMccb+KTRkNxUaf9i9eE8ygsFoslrUP8r1q4cCFt27bF1dWV/v378+GHH1K/fv2/XeZa5H97kNxZfo8smNYRUsTf25jWEVLEw/X9yHnt4fsxXKa677m3z/QOuGpy7J9RSS0m8/sxeMbfPW1fHJRS5wunzmMFjuZ+wjEjeVJbhRdvvvX2XXQ4neOHyaaG7JpnaR0hRTSKuLfP9A44dOs9Oc/Hp3WClGn73/8CjdMdu/QkrSMkqUzBfzdUOzVJj6gDeXp60rhxY9zd3cmZMyefffZ+XISEEEIIIYQQwpGkIepALVq0oEWL1H3LmhBCCCGEEEK876QhKoQQQgghhBAOIG/NTd778eCPEEIIIYQQQoj/GdIQFUIIIYQQQgjhVDI0VwghhBBCCCEcwGKRobnJkR5RIYQQQgghhBBOJQ1RIYQQQgghhBBOJUNzhRBCCCGEEMIBzGkd4B0mPaJCCCGEEEIIIZxKGqJCCCGEEEIIIZxKhuYKIYQQQgghhAPIW3OTJz2iQgghhBBCCCGcShqiQgghhBBCCCGcSobmCiGEEEIIIYQDWJChucmRHlEhhBBCCCGEEE4lDVEhhBBCCCGEEE4lQ3OFEEIIIYQQwgHkrbnJkx5RIYQQQgghhBBOJT2i75hnlkxpHSFF3N3SOkHK+Ke/n9YRUkRvdk3rCCkSmM2S1hFS5JrJP60jpEgWt6dpHSFFnhrSpXWEFDn3PDCtI6SI5sTptI6QIrpSJdI6QoocPXkqrSOkiEZlTOsIKWKyqNI6QoqceeyX1hFSJHdmbVpHSJFAt6tpHSGFCqd1AJGKpCEqhBBCCCGEEA4gb81NngzNFUIIIYQQQgjhVNIQFUIIIYQQQgjhVDI0VwghhBBCCCEcwPx+vF4jTUiPqBBCCCGEEEIIp5KGqBBCCCGEEEIIp5KhuUIIIYQQQgjhAPLW3ORJj6gQQgghhBBCCKeShqgQQgghhBBCCKeSoblCCCGEEEII4QAWiwzNTY70iAohhBBCCCGEcCppiAohhBBCCCGEcCoZmiuEEEIIIYQQDmCxpHWCd5f0iAohhBBCCCGEcCppiAohhBBCCCGEcCoZmiuEEEIIIYQQDmBG3pqbHOkRFUIIIYQQQgjhVP+zPaJ79+7lzp07NGnS5K3l69ato0GDBqjV6iTnHTx4MJ999hlVq1a1TXvw4AFz585l5MiRVK9ena1bt7Jy5UoqVKhAwYIF2bJlC40aNXLIvy0xs9nMknnTuBodiVqtpnPPQfjmyGUr379nO79tXo9SqcLPPy8duvZFqbTef4i4dJ7VyxcwasJsh+WLOLOTA7/NRal0oXilhpSs0tiuPC7mMVuW9sdk1OGVITuftx6P2lUDgNGgZe2MtnzWaixZfPLalol9/ogV4xvQtNcyu+mpwWw2s3DuTK5GR+GiVtO9V398c+S0le/dvYNffv4JpUqJv39eOnXrZVufT58+oV/PzowaO5lcufOkaq6/y7t03lSuvdz+nXoOxifR9j+w5y9+f7n98/jnpX3Xfra8zsq3euF4bly9jFrtSutuwXj72q8bvV7LtJFdadNtBL65AmzTnz99zOj+zek3cp7ddMflnMCNq5dxUbvSplsw3r6538g5dWQ32nYLtuUZ2fcbNB5eAGT1zkH7HiMdnvN92D/NZjOrFk7kxtUIXFzUtOs+PIn1qWNySDfadQ8mRy5/AH7dsJyTR/dhMhqp/unXfFTzS4dk27jsO25fv4SLiyuNO44iq4+frfz88V38tXE+SpUL5T76igqfNOLInk0c27MZAKNRz+1r4Yycv4cNS0fz4ulDAB4/uIVf/hK07DnFIZnXLh7LrWuXcXFxpXmXELK/dhwZ9Fpmje5Mi64j8clp3T9fPHvMlGGtGT5tA2pXt1TP9U9lLFecQuP6c7hGqzTNYTab+WHxuJfnJTUtk1mfM0Z1plXXkfjkCsAcH0/ogtHcu3UVhVJFm+6jyOaTO5lfSL2coQsn2I6jtt2TOi/pmBLSlXbdR+D78jgK6ZNwXsrmnZP2PUMcmvP1zMvmTeFadCQuate/uSYp8fPPS7uu/R16TTKbzWxeOZo718NxcXGlQYfvyOqdcLxfPLGLHT/PQ6lSUaZqA8p9nFBHiXn2iDkjvqbdoKVkzxFom37q4K8c/Gs1XUPWOizz+7B/vp551ryFXIm+ilrtQt+e3cmZw9dWvu/AQdZu2IgCBZ/VqcVntWs6LZt4t/3P9ohWrVo12Ubo6+ULFy7EbDb/o+/Pli0bI0eOtJvWsWNHihcvzoMHD/jxxx//ceZ/4+jhfRiMesZNXUDzNp1ZtXSurUyv17M2dDEjx81i7JT5xMXFcPzoQQA2b1jD/FmTMBoNDssWH29kx4/jadpzGc37hXJq/zpinj2wm+fAb/MoUvYLWvT/Hu/cRTi5dx0Ad66dZc2U5jx9eOON7/xjzQhc1O4OyRx2aD8Go4GJ0+bQqu23LF8y31am1+tZs2oZYyZMY+LUOcTFxXDsyCEATCYT82dPw83Jlb2jh/dhNBoYM3Uhzdp0JnTpHFuZQa9nXehiRoybzXdTFhAXF8OJl9vfWU6G7cJoNDBs4koatuzB+uXT7cqvRl5g4rAO3L970266yWRk1YKxuDppfZ4M243RqGfYxBV83bIH617LGR15gQnDvrXLaTToARg0ZhGDxixyeCMU3p/980TYbowGPcETl9GoVXfWLp9hVx4deYHxQzvarc+LZ48TEX6GYeOXMGTsQh4/vOeQbOeO7cBo1NNz9Pd83qwPW1ZPtpXFm4xsDp1IxyGL6TpiBYd3/sjzpw8o99FXdB2xgq4jVpAroAj1Ww9B45melj2n0HXECtr0nYnGMx1fthzkkMynj+zEZDQwYFwo9Vv0YuPKqXbl1yLPMy24HQ/vJZwvL5w6wOzvOvPi2SOHZPqnAvt14IOFY1C6p32D+NSRXRgNegaPX8VXLXqxYeU0u/KrkeeZHNyeB/cS9s8zx/YAMHDcSuo17cKPK+y3gSNYjyMDwycup1GrHqxN6rw09Fvu371lm/bqvDR47CIGj13k1EYowLHDezEYDXw3dRHftOlM6NKEG93Wa9Kil9ekhcTFxXLi6AGH5rlwfDsmg56uIWup06Qvv38/yVYWbzLy65oJtB+0hI7DVnFk14+8ePrAVrZpeQgur50zb1+7yLE9Pzn0Najvy/6Z2IHDYRiMBmZNnUj7Nq1YuHS5rSw+Pp4lK0KZNGYUM6dM4MeNm3j27LlT86U1i0XxTv73LvifbYhu3LiRPn36ULduXVq2bMnixYtp2bIlPXv2pE2bNvz4449MmTKFH3/8kQcPHtCnTx/i4+MZNmwY7du3p0GDBsyYMcP2fd9//z2tW7emRYsWXLt2jZs3b9K4sX3v3uDBg9m7dy8LFiwgMjKSOXPm0LRpUyIiIgDYs2cPo0aNStV/58XzZwgqVR6AAoWKEhURbitTq9WMmTwfN3droy0+Ph5XtSsA3r45GTB0TKpmed2jO1FkypYHd88MqFxcyZW3NDcjj9nNczPyOIFFqwAQWKwq18KtDaV4o4EGneeS2TvQbv6dGyYSVLUpXhmyOyTzxfPnKFW6LAAFCxUhMuKSrUytVjNx6my79al2ta7PFUsWUOezemTKksUhuZJz6fwZSti2fzG77e+iVjN68gL7vC+3v7NEXDxFsaBKAOQtWJyrURfsyo1GA90HT8U3p7/d9PUrZlCtdkMyZs6WBjk/eCOnyWigx+ApdjmvX72MXq9j6siuTAruRNSlsw7P+b7snxEXT/NBKev6zFfwA6IjL9qVm4xGegyeZLc+z506RG6/fMyeMIAZY/tSsuyHDskWfekEhUpYv9svfwluXDlvK7t36wpZvfPg4ZUBFxdXAgqWIjr8hK38RtQ57t2MouIn9uf+PzfM5cPazUmfyTH7a1T4SYqUtK7PgALFuZYoM4DJZKDjwOl450gYOaBQKOkZsggPrwwOyfRPxV25zvFGPdI6BgCRF09SNKgyAIEFinMt6s312WXgNHwS7Z8ly1enRedgAB4/uEO6DJkdnjPi4ik+KFUReHleeuM4MtB98GR8cyb08F2/GoFBr2NKSDcmBnd2ynkpsfDzZyhZqgIA+QsV48ob16SFTr0mXb18ggLFrcd7nnwluRV9zlZ2//YVsnjnQeNpPd79C5Ti6qXjAPz+w2TKV29K+owJdY3YF0/4Y900vmgxxKGZ35f9M7Hz5y9StlQpAIoUKsjliEhbmUqlYtmCOXh6evL8xQssFtBoHNOZIN4//7MN0VcePHjA0qVL+fbbbwGoW7cuK1asQKVSAdCoUSOyZcvG9OnTuXPnDiVLlmTp0qX88MMP/PDDD7bvKVWqFCtXruTbb79l8uTJSf7WK507dyZfvnx0796dRo0asWnTJgB++uknvv7661T992m1sXh4etk+K1VK4uNN1v9XKsmYyXoy2vrLBnQ6LcWDrJXYCpWroXJx7MhsvS4GN00622dXd0/02phk53Fz80SvfQFArnylSZ/Z127eMwc34pEus63h6ghxcXF4eHjaPiuVKuLj41/+f8L6/HXLRnRaHSWDyrDjrz9InyEDQS8bCM4Up43FwzNR3r/Z/vpE299ZdNpYPDwS7Z9KlS0fQP7CJcmc1cdumf07t5AuQyZbw9AZtNoY21A2a07lW3O6ublT58sW9A2ZS6vOQ1k0fZjdMo7wvuyf2rjY13K+vj5LkCWb/fp88fwp0ZEX6TZgAq27DGbhtGAsDuh10GljcfdIOC8lzqbTxtiVuWk80ca9sH3esXkxtRp2sc/97BER5w5T9qP6qZ41cWaNXWb74yhvoaA39s/CJSrilS6jwzL9U3c3bcNidOzxkVLW9ZlwvCteW5/5klifACqVC8tnD2ft0omUrljD4Tm1cbFvPS+9fhy5urpTp35L+o2cQ+vOQ1g4bbjDz0t2mbWxaOyuSaokr0l//PLjyzpJOYfm0b92TCfe1tayhPXrpvFEp33B8b2b8EyXydaABTCb49m4JJgvmg/GzT3h3+cI78v+mVisNg5PTw/bZ2tdJD5RNhX7Dh6ic48+fFCsiK0OLsT/7DOir+TKlQtX14Q7bgEByT9rljFjRs6ePcvhw4fx8vLCYEgYtlqmTBkAgoKCmDRpUnJf8YbPPvuMr776ivbt23P37l2KFi36L/4VydNoPNFq42yfLWYLKlXCZjWbzaxePp/bt27Qf8gYFArHd8Xv3TydG5EneHDrEjkCitumG3Sxdg1TADd3Lwy6WNSu7uj1sbh5pE/2e88c/AmFQsHVi4e4f/Mivy4fRMOu8/HKkHq9EB4eHmi1Wttni9lsd8I0m82sXLaQ27duMmjYSBQKBTu2bUWhUHD61Amir0Qyc+p4ho4YS6bMjr8j6aHxRPeW7b9m+Tzu3LpB3yFjnbL9E3PXeKLTxSbks5jt8iVl/47NKFBw8XQY16MvsXTmCHoMnU6GTFkdllOj8Xotp+WtOb1z+JHdJzcKhQKfnH54pcvIsycPk6wgpJb3Zf/UeLy2X6ZgfXqly4BvTn9c1Gp8c/qjdnXjxbMnpM+YujndNZ7otUlva3eNF/pE+4FeG4vG03rO0sY+5/7tK+QrWt7u+86EbaNU5c9RKh1XsXrjODK//TgSyXPXeKLT/rv12bbHGJ61eMiEwS0ZOXMjbu4aR8X8V8eRT848ePvmSjgvpc/A08cP32iwOormjWuSOclr0t1b151yTXJ77ZhOnMdN42V3LtBrY3H3SM/BbaEoUBB5/hB3rofz48LBfNZsAA/vXeXnFaMwGfXcvxXFL6vHUbfF0FTP/L7sn4l5al6/NlneaGxWqVSRyhXKM3n6LP7auZs6NT9xSrZ3gQNHcr/3/ud7RF9/CD6pk55CobC+wGLjRtKlS8fUqVNp164dOp3Odkf+zJkzABw7doz8+fO/9TdfPXOq0WgoX748Y8eO5csvU//FG4WKfMCJY9bnwC6HnyePv/1Q1kVzJmMwGBg4fJxtOIyjVf2yD837hdJj8gGe3L+ONvYp8SYDNyKPkTMwyG7enHlLEXXO+mzDlXN7yZ2vdLLf26L/Gpr3W03zfqFkz1WYL9pOTNVGKEChIsU4fiwMgEvhF/B7bX3Onz0No8HAkODvbOtz3OSZjJ00g7ETpxMQmI9e/YY4pREKULDIB5w8dhiAy+Hn3tj+i+dMxmgw0H/4eKdt/8TyFS7JmePWZ4CiLp0hZ558b11m8NilDBq7hIFjFpMnoCDte412aCPUmrMEZ205z6Yo5/4dm1m3wvrM1pPHD9BqYxye833ZP/MVKsHpl+sz8tJZcvm9/aViBQqX5NzJQ1gsFp48foBep8UrXeoPKw0oEMTFU3sBuBZxGt/cCedz75yBPLx7jbiYp5hMBq6EH8c/f0kAoi4eI3+xim98X8S5wxQq6bhRGmDt8Tx/Yj8A0ZfPkCPP31+DxN/LV6gk516uzyuXz5DT7+3r8/DuX9m6cSkArm7uKJQKh7/4LX+hEonOn2fJ5ff289K+7Vtsz2Q/efwAbVwsGTM79ryUmPWaZK2TRISfI7e//bG/ZM4kjAY9/YZPcMo1yb9AKS69PN6vR57CJ3cBW1n2HIE8updwvEdfOkaefCXpNHw1HYeH0nHYKnzzFKJRpwkEFCpLnwm/0nHYKpp1m0b2nHkd0giF92f/TKxokUKEHbMOa74QfokA/4Th4rFxcfQdPAyD0YhSqcTd3R2l8t14PlGkPbmlirW3s2PHjowYMYK+ffty/PhxNBoNfn5+3L9/H4DTp0/TqlUrFAoF48aN+9shY1myZMFoNDJ58mQGDBhA48aNadas2RsvN0oN5SpW5czJYwzr3wWLxUK33kPYt/svdDotefMVZOdfv1GoaHFGDe0FwGf1GlG+UtW3fGvqUKnUVG80mHWz2mOxWCheqSHpMnmjjX3K1tDhNOg8h8qfdeHXFYM4vX89Gq9M1Gvv3AfsX1eh0oecPnmcQf26gwV69BnInl070Om05MtfgO3btlKk6AcED+kHQN0vG1ChkmMroX+nbMWqnDl5lOD+nbFYLHTpPZT9u7eh02kJzFeIXX/9SqGiJfhuaE8APq3XiHKVPnJavlLlP+bCqcOMG9wGi8VCux4jObx3K3pdHB/Vaui0HG9jzRnG2MFtwWKhXY8QDu/dik6npVqtBkkuU+WT+iydHcK4Ie1QKBS06x7i8F6q92X/LF2hGudPhzFmUDssQPseIzi05w/0ujiq1U56fZYsW4VL508yekBrzGYLLTsNROmA4VvFytbg8tlDzBrRHLDQpNMYThz4Fb0ujoqfNKZei4EsGt8Ri8VC2WpfkSGzNwAP7lwlS/Zcb3zf/dvRSU5PTSXKVefi6UNMHtoKsNCy22iO7vsdvS6OD2um7uMe/x+ULF+di2cOM3FoKywWaNNtFEf2/Y5OG0fVWkmvz6AKn7ByzggmD29HfLyJxm0HOPxNxKUqfGw7jsBC+x4hbz2Oqtb4kiWzRjJuSHtAQbseI5zae1624kecPXmU4P6dwGKhc+9htmtS3jS4JhUpXYOIcweZP6oZFix8/e04Th38FYMujnLVG/P5N4NZNulbLBYzZao2sB3vael92T8Tq1yxAsdPnqZX/0FYLNC/dw927t6DVqfj8zq1+aRaVfoNGorKxYVAfz8+qea8eoh4tyksjngIR9g5c+YMq1evTtGQ3jMR952Q6L87ftMxLwtKbRXy3Hr7TO8Avdm5LxH6t2KMHm+f6R2g4P04rWV2e5bWEVLkqSHd22d6BzzSvh/7p0b9bjwr+Ta6UiXSOkKKeJ48ldYRUsRV9X5sd42LPq0jpEj0E+e+kOffyuzxfqzPQLeraR0hRfLkL5zWEf6xbacd9xcq/otaJdK+7ik9og62evVqfvrpJ2bNmpXWUYQQQgghhBDinSANUQdr0aIFLVq0SOsYQgghhBBCCPHOkIaoEEIIIYQQQjiA+f14WihN/M+/NVcIIYQQQgghxLtFGqJCCCGEEEIIIZxKhuYKIYQQQgghhANYLPJ3U5MjPaJCCCGEEEIIIZxKGqJCCCGEEEIIIZxKhuYKIYQQQgghhANY5K25yZIeUSGEEEIIIYQQTiUNUSGEEEIIIYQQTiVDc4UQQgghhBDCAczIW3OTIz2iQgghhBBCCCGcShqiQgghhBBCCCGcSobmCiGEEEIIIYQDyFtzkyc9okIIIYQQQgghnEoaokIIIYQQQgghnEqG5gohhBBCCCGEA1gs8tbc5EhD9B3joYxN6wgp8uCxOa0jpMhj7wxpHSFFdPHvx6FoMKnSOkKKBGnOpXWEFHlB5rSOkCJG8/ux3b09Y9I6QooUebQzrSOkyNGTp9I6QorEBpVM6wgpku78obSOkCIeSm1aR0iRUpkj0zpCivxyKX9aR0iRx9kKpnWEFMmT1gFEqno/ar9CCCGEEEII8Z4xy8uKkiXPiAohhBBCCCGEcCppiAohhBBCCCGEcCoZmiuEEEIIIYQQDiB/RzR50iMqhBBCCCGEEMKppCEqhBBCCCGEEMKpZGiuEEIIIYQQQjiABfk7osmRHlEhhBBCCCGEEE4lDVEhhBBCCCGEEE4lQ3OFEEIIIYQQwgHM8tbcZEmPqBBCCCGEEEIIp5KGqBBCCCGEEEIIp5KhuUIIIYQQQgjhABYZmpss6REVQgghhBBCCOFU0hAVQgghhBBCCOFUMjRXCCGEEEIIIRxAhuYmT3pEhRBCCCGEEEI4lTREhRBCCCGEEEI4ldOG5ur1ej799FN27tyZZHlYWBhr165l+vTpqfq7U6ZMITAwkAYNGtimzZ49m6xZs9KsWbN//b3Vq1dn69atrFy5kgoVKlC8ePHUiPuPmc1m5s2dQ3T0FdRqNT179SFHjhy28t27d7H5559RqpQE+AfQtVt3duzYzo7tfwFgMBi5ciWK1Wt+wMvLy2m5r1/cxcmd81AoVRQo04BCZRsnOd+5AyvRvnhI2Tr9nJLLbDazeuEEbly9jIvalTbdgvH2zW03j16vZerIbrTtFoxvrgAARvb9Bo2Hdf1l9c5B+x4jHZ5z7eKx3Lp2GRcXV5p3CSG7bx67eQx6LbNGd6ZF15H45Awg3mQkdF4Ij+7fxmQy8GnDjhQvW80h2TYs++5lNjVNO40mm09CtnPHd/PnT/NRqlyoUO0rKn7yNfEmI2vmDePxg1solSqadByJd85Abl4N58clo1GqVGTz9aNpx9Eolal//8xsNjN9wTIir17DVe3CgO6dyOXrYyvfczCMNT9tRqFQULfWJ3xRq7qt7MnTZ3zbdwhTRw/DL1fOVM/2es55c2e/drwn/Oae3bvY/PNGlCoV/v4BdO3WA6VSyfp1PxAWdhiTycjnn9elVu1PHZ5zzaLx3Hx5HLXuGvzG/qnXa5k+siutu42wHUcAz58+ZsyA5vQJmWc33VE5VyyYxPXoCFzUrnToPhSfHK8f7zomBPfg257DyJHLH5PJxMIZo3h4/w4KpZIO3YeSI5e/w3OOD93M5Rt3cHVxIbhtA/J4Z31jvu9WbCSDpwc9G9UBoFnILLw07gDkyJaZUe2/dnjOHxaP48bVy6jValomc16aMaozrbqOxCdXAOb4eEIXjOberasolCradB9FNp/cyfyCc2QsV5xC4/pzuEarNM1hNptZPn8y16IjUavVfNtjyJv7p07H+BE9+bbHUHLm9sdoNLBwxhju37uNRuNJmy798c3h2PVpNptZMHcW0dFRqNVquvfq99p5aSe//LwRpUqJv38gnbv1RKlU0qt7Jzw9PQHw9valV98BDs85e958oqOjUavV9O7Zg5yJ6ku7du9h0+YtKJVKAgL86dG1C6b4eKZOn8Hdu/fw8NDQvUsXcubM8Te/8u9Fn9/J0W3WelGRcg0pWtG+XqSNecK21f0wGfV4ZsjOJ03HoXbVcO/6WfZvngBY8EiXlZrNJ+OiduPY9oVEn9+JOd7IB5W+oUiF/378m81mNq8czZ3r4bi4uNKgw3dk9fazlV88sYsdP89DqVJRpmoDyn2c8G+IefaIOSO+pt2gpWTPEcgPc/ry4tlDAJ48vEWevCVo1n3af874LjJbFGkd4Z0lz4j+Rx07dkzT3z906CAGo4Gp02YQHn6RJUsWMWLESMDa+A9dtYq58+bj7u7OxInjOXIkjJo1a1GzZi0A5s2dQ81atZzaCDXHGzn82wS+7LYeF7WGXxc2J0+hj/FIl802j8moY/+mETy4cRr/orWclu1k2G6MRj3DJq4g6tJZ1i2fTs+hCSfG6MgLrFowjieP7tumGQ16AAaNWeS0nKeP7MRkNDBgXCjRl8+wceVUOg+eaSu/FnmeHxaN4enje7ZpR/b+hme6jLTpOY6YF08ZP6CJQxqiZ4/twGgw0Oe7NVyNOM3m0Ml0GDAbgHiTkZ9XTaTv2LW4unswc0QLipauxrXIM5jN8fT+bg2Xzhzkt3WzaNd3Bn/+NI/aDTtTJKgqobMHceHkXoqVTv3M+8OOYTAamD/pO85fimDeslDGDbNWiuLjzSxc9QOLpo5D4+5O6+79+LBCGTKmT4/JZGLKvCW4ubmmeqakHD50EKPRwNRpMwkPv8jSJYsIHjEKeHW8r2DOvIW4u7szaeI4jh4JQ+PhwcWLF5g8ZTp6vZ6NP/3o8JynjuzCaDQwZMJKoi6dYf2K6XQfknCT8WrkBVYvHGt3HAGYTEZCF4xF7erm8IwAxw/vwWgwMHLyUiLDz/L9spn0HT7FVn4l4iLL50/g8cOEnKePHSA+Pp6QSUs4ezKMH0Pn02vIRIfm3HXiAgajiZXDu3Im6jrT1/7O9F72jaQNu8KIvHmX0gUDAdAbjQAsHuy8a9SpI7swGvQMHr+KK5fPsGHlNLoOnmErvxp5njWLxvL0UcJ56cyxPQAMHLeSS+eO8uOKqXbLOFtgvw7kbFGP+FhtmmV45djhvRgNBkZPWUxE+DnWLJtNv+GTbOVXIi6ydN4ku/1z55+bcdN4MHrKEm7fvMaKBVMZMnqGQ3MePnQAg9HA5GmzCQ+/wLIlCxg+4jvAel5as2o5s+ctxs3dnckTx3L0yGGCSpUBYNxE5zU8Dh46jNFgYMbUKVwMD2fRkmWMGjHclnNl6GoWzJ2Nu7s74ydOJuzIUe4/eIDGXcPMaVO4cfMmcxcsYNx3o1M9W3y8kf0/T6BRnx9Ru2r4afY3+Bf9GM/0CfWio9vmUqDUFxQu14DjOxZx/tA6SlRtza71wdRpPZOM2fw4f/hHXjy5RezzB9y9epKve/yA0ajl5K5lqZLzwvHtmAx6uoas5XrkKX7/fhKt+sy1/htMRn5dM4Huo9ejdtOwYHRzCgd9TLqM2Yg3Gdm0PASXROf2V41ObewzFo9rw+ctBqdKRvF+cejQ3NjYWLp06ULz5s0ZOXKkbfqlS5do2bIlLVu2pEePHrx48cJuudWrV9OqVSu++eYbOnXqhMFgoF+/fuzevRuAqKgoOnbsiNFoZOjQoTRv3pxmzZoRFhYGwJ9//kn9+vVp164dp0+fTjLbjh07aN26NV9++aWtl3br1q00adKEZs2aMWWKtTJy9+5dOnfuTNu2bfnqq6/Yvn273fcMHjyYvXv3snHjRnr16kWnTp349NNP2bhxIwBnzpyhYcOGtGrVij59+jB4cOoeaBfOn6d0aesJvVChwkRGRNjK1Go1U6ZOw93dekc8Pj4eV9eECnPE5ctcv36NTz/9LFUzvc3T+1dInyUPbpoMqFxc8fYvxb2rx+3miTfpyRf0JSWqdXZqtoiLpygWVAmAvAU/4GrUBbtyk9FAj8FT8M3pb5t2/epl9HodU0d2ZVJwJ6IunXV4zqjwkxQpac0ZUKA4166ct89pMtBx4HS8cyT0KAVVrEXdpt1sn5VKlUOyXQk/SeGSlQHwz1+CG4my3bt1haw+efDwyoCLi5rAgqW4En6cbL5+mONNmM1mdNpYVCrrPbKc/oWJi3mGxWJBp0uYntrOXAinXFBJAIoWzM+lyCu2MpVKyaq5U/Hy9OD5ixdYsKB5eUzNW76aL+vUIGvmTA7J9brz589RKtHxHhFx2VamVquZPHWG3fGudlVz4vgx/P0DGDtmFKNHjaBcuQoOz2l/HBXnWhLHUddBU/FJdBwBbFg5g49qNyRj5mw4w6WLpyleyro+8hX6gOjI8Ddy9h4yCd9EPZ4+OfNgjo/HbDaj1caicnH8/dxTEVep9EEBAIrnzcOFq7fsyk9HXuPslRs0rFbeNu3y9TvoDEa6TllKx4mLORN13eE5Iy+epGiQ9dgPLFCca1Fvnpe6DJxmt91Llq9Oi87BADx+cId0GTI7POffibtyneONeqRphlcuXThN8dLW/TN/oWJcibhoV240Gug7dAI5ciX0SN26fpWSL5fJkcuP2zeuOjznxfPnKFW6LACFChUh8rXz0sSps3B7rR4SfSUKg17PiGGDGDa4P+HhF5L87tR0/sIFypQuDUDhQoWIiLSvL02fMum1+pKa69evU7aMdZncuXJx/cYNh2R7cu8KGbLmwd3DWi/yDSjNnSv29aI70SfIU6gKAH6FqnLj8iGePojG3TMjp/euZOOcFujjnpEpeyDXw/eTxbcAvy/vzm9LuuBftFqq5Lx6+QQFin8IQJ58JbkVfc5Wdv/2FbJ450HjmQEXF1f8C5Ti6iXrv+H3HyZTvnpT0mfM/sZ3/vXTHCrWbJ5kmfjf59CG6KZNmyhQoABr1qyhadOmtunBwcGEhIQQGhpK1apVWbJkia3MbDbz9OlTVqxYwffff4/JZOLs2bM0atSITZs2AbBhwwa+/vprfvzxRzJlysSaNWuYN28eo0db71JNnjyZ5cuXs3TpUttJ5XXe3t6sXLmSoUOH8sMPP/D06VNmz57NihUr+OGHH7h37x4HDhzgypUrtG3bluXLlxMcHMyaNWuS/ffGxMSwcOFC5s+fz6JF1t6xkJAQJkyYwKpVq8iTJ0+yy/5bcXFxeHp42j4rlUri4+Nt/58pk7WCvGXLZnRaHUFBpWzzrlu/lmbftEj1TG9j0Mfg6p7QA6t29cSgs78Z4abJQK78lZ0dDa02xjbEFl6tT5Ptc/7CJcmc1cduGTc3d+p82YK+IXNp1Xkoi6YPs1vGEXTaWDQe6RLlVNn9Zt5CQW/kdNd44K7xRKeNZfGUftRr1t0h2fTaGNw1CdkUidahThuLuyZh/bppPNHGvcDNzYPHD24zvm9d1i0KoWqd5gBk8/Fj44rxjO9XjxdPH5GvSFmHZI6L0+LpqbF9ViqVmF4eRwAuKhV7Dx2hXe9BlChSGBeVC1t37CZjhvSUK1XCIZmSon3teFclc7z/suVndFotQUGlef78OZERlxk8ZDjduvdkyuQJWBz8Cj9dXOxrx5H9/pkviePowM4teKXPZGvAOoM2LhYPz+SP9wJFSpAlm7fdMu7uHjy4f4eBXRuzdM54an3RxOE5Y7V62xBbAJVSYds/Hzx9zsKfdzC4RT37nK6utKxThbn92jGsdX2GL1xnt087gvW8lLA+Fa9v9yTOSwAqlQvLZw9n7dKJlK5Yw6EZ3+bupm1YjI49f6eUNi4Wj785jgomsX/6Bebn5NEDWCwWIsLP8fjxA8wO3u4prYf8umUTOq2WkkGlcXNzo37DRowaM4Gu3XszbdJ42zIOzenp8dacm7f8glanpVRQEHkDAwk7chSLxcLF8HAePXrskJwGXQyuia6brm6e6F+rFxl0Mbi5W+dRu1vrTbrYp9yJPskHlb/hyy7LuRFxiBuXD6GLfcr9G+eo03oG1RqN5K/VA1LlvK/XxuDukfj6nrBPWsvsr+867QuO792EZ7pMtgZsYjHPHhF14RClq371n7O9yyyWd/O/d4FDb+VGRERQpYr17k2JEiVweXnnOCoqilGjrMPJjEYjAQEJvTZKpRK1Wk3fvn3x8PDg7t27mEwmypcvz9ixY3n06BEHDhygb9++jB07luPHj3PmzBkATCYTDx8+xMvLy3ZCCQoKSjJb0aJFAciaNSs6nY7r16/z+PFj21Db2NhYbty4QenSpZk/fz4bNmxAoVBgMiV/gSpUqBAAvr6+GAwGAO7fv0/+/PkBKF26NL///vu/WJPJ8/DwQKtNGEJkNltQqVSJPptZtmwpt27dZOiw4SgU1nHqMTEx3LxxkxIlnFeJPrZtBveuneDx3ctkz53wTK3REIure3qn5fg7Go0XOl2s7bPFYnlrL5x3Dj+y++RGoVDgk9MPr3QZefbkYZIVrtTirvG0z2k2p6i38PHDuyya1IeqtRtTtopjesLdNF7ok1mH7hpP9Lo4W5leG4vGIz27fw+lYIlK1G3WhycP7zB3THsGTdrEppUT6DFyFb6587Hvzx/YvHoyX7cbnuqZPTw0xGl1dpldVPY9xlUrluPD8mUYP3M+f+7ay9Ydu1EoFBw/fZbI6GuMmzGPccMGkCVTxlTP94omBcf78mVLuHXrJkOGjUChUJA+fXpy5c6NWq0mV67cqF1defbsKRkzOq4X193DesMjca637Z8Hdm5GgYKLZ8K4EX2JZbNG0H3IdDJkevNZyNSi8fBEq03YH82Wt+fcuuUHigeVp0nrbjx6cI9xw7syfvb3uDpwOLGnxo1YnT5RzoT9c/vRszyNiaXn9BU8ehaDzmDA3zcbdcqXILd3FhQKBX4+2cjg5cHDpy/wyZLRYTlf3eh6JaXnJYC2PcbwrMVDJgxuyciZG3Fz17x9of9xmteOI0sK9s9qNb/g9o2rjBnajQKFixOQtyBKlWNGv7xirYckHEeWJM5LK5Yt5vatmwwZFoJCoSBnrlz45shp+/906dPz+PEjsmVzXK+Yh4cHcYnOn0nlXLJsObdu3SZ46BAUCgW1a9Xk+o0bDBwylCKFC5MvX167Zf6rw7/P4Hb0cR7dvoy3X0K9yKCPxS1RwxTA1d0Lgz4WF1d3jLpYXDXpcPfISIasecjskw8Av0JVeHDzPO6eGcnkHYDKxZVM2QNRubihjXmMR7os/ynvG9f3RMe4m8YLfaL9Va+Nxd0jPQe3haJAQeT5Q9y5Hs6PCwfTqs9c0mXMxtmjf1Ky4hcOG6El3n0O7RENDAzk1KlTAFy4cMHWiAsICGDixImEhoYyYMAAPvroI9sy4eHhbN++nRkzZhAcHIzZbMZisVhfElK3LmPHjqVy5cqo1WoCAwP5/PPPCQ0NZfHixdSpU4f06dPz4sULHj9+DMDZs0kPk3zVIHslV65c+Pr6smzZMkJDQ2nRogUlSpRg5syZfPnll0yePJny5cv/7R2l178TwMfHh8jISIBkhwn/F0WKFOXosSMAhIdfxN/f3658zuxZGA0GgoND7HqHz507S8mXQxGdpUyt3nz+7SqaD93H80fX0Mc9Jd5k4G70MbLncW6W5OQrXIKzxw8AEHXpLDnz5HvrMvt3bGbdCuvzb08eP0CrjXFo5RmsPZ7nT+wHIPryGXLkyf/WZZ4/fcTs7zpTv0VvKn3iuLuPgQWDuHByHwBXI07jmzshm3fOQB7cvUZszDNMJiNR4cfxL1ACD8/0th5eD68MxJtMmM3xeHhlwF1jvdOeIVM24mKeOyTzB4ULEnb8JADnL0UQ4Jfwco/YuDh6Dh2FwWhEqVTi7u6GUqlg9viRzBoXwsyxIeQL8GNo764ObYSC9Xg/9rfH+0wMBgPDg0fajvciRYpy4pj1jv6jR4/Q63SkS+fYGz/5CpXk7IlXx9EZcvm9/TgaOGYpA8YsYcB3i8kdUJB2PUc7/DgqULg4p48dBCAy/Cy5U5DT0zMdmpe9qJ7p0hP/cpiuI5XM78+BM5cAOBN1nXy5Em5yNatZme9H9mDx4I60+fwj6lQoSb0PS7N53zGmr/0NgAdPnhOr1ZM1Y7okvz+15CtUknMvz0tXLp8hp9/bz0uHd//K1o1LAXB1c0ehVDjkhWTvo4KFi3Pq2CEAIsLPkdsv71uXiYq4SMEiJQgeP4+yFT8iu49jX6AGUNjuvHQBP3/7l4zNmz0dg8HA0OBRtiG6f237g2VLFgDw6NFD4uLiyJz5vzWS3qZIkcIcPXoMgIvh4fj7+9mVz5wzF4PRSEjwMNv589LlCIoWLcLkCeOpXKkivj6pe4O5wme9adAtlHaj9/Ps4XV0sdZ60e0rR/Hxs+9I8QkI4tpF6zPV18L3kiOgDOmz5MJoiOPpg2sA3L5ynMw++fANKMX18P1YLBZint3DZNDi7pnxP+f1L1CKS6f2AnA98hQ+uQvYyrLnCOTRvWvExTzFZDIQfekYefKVpNPw1XQcHkrHYavwzVOIRp0mkC6j9fGLqPOHKFCiyn/OJd5fDu0Rbd68OUOGDKFZs2YEBgaiVqsBGDlyJIMGDbINbxg7diz371sftvfz80Oj0dCgQQNcXV3Jli2braxBgwZUq1aNzZs3A9C0aVOGDx9OixYtiImJ4ZtvvsHV1ZXx48fTvn17MmTIYOuFfZvMmTPTpk0bWrZsSXx8PDlz5uTTTz+lTp06jB07loULF+Lr68uTJ0/+0ToICQlh6NCheHh4oFar8fb2fvtC/0DFSpU4efIE/fr1AYuF3n36sXvXLrQ6Lfnz52fbtj8pWrQYQ4cMAqDel/WpVKkyt27exMfHN1WzpJRSpab8Z4P5Y/m3WCxmCpRugGcGb/RxT9m3MZgaLWanSS6AUuU/5sKpMMYObgsWC+16hHB471Z0Oi3VajVIcpkqn9Rn6ewQxg1ph0KhoF33EIc9y/hKiXLVuXj6EJOHtgIstOw2mqP7fkevi+PDmkm/Ge+PjUvQxj5n64ZFbN1gHTrebdhcXN2SHr7+b31Q9hMunT3IjODmWIBvOn/H8f2/odfFUalGI+q3HMiCcR2xWCyUr/YVGTN7U+3zVvywIJhZIa0wmYx80bQXbu4eNO04ilWzBqBUuaBycaHJt6NSNesrVSqU5dips3QdGIwFGNyzM3/t2Y9Wp6Ne7RrU+KgyPYeMxMXFhUD/PNT8KG0unBUrVebkyRP079cbi+1434lOpyVf/gL8te2Pl8f7QODV8f4h586dpW/vHpgtZjp37Z6qd/STElT+Yy6cPsyEIW2wWCy06T6SsL1b0eviqFqroUN/+58oU6Ea504dYdTADlgsFjr2Cubgnj/RaeOoXifpmzWfftmMRbPGMHpwR+JNJhq37IK7g3vvPi5VhMPnI2gzZj4WLIxs/zVbD50iTm+gYbVySS5Tv2oZQpZsoN24BYCCkHYN3+jlT20ly1fn4pnDTBzaCosF2nQbxZF9v6PTxlG1VtLnpaAKn7ByzggmD29HfLyJxm0HOO1lVe+6MhU/4uypI4QM+BaLBTr1GsaB3X+i02n5pE79JJfx8c3Nj6sX8eum7/H09KJjz6EOz1mh0oecOnmCgf16YrFY6NVnAHt27XhZDynIX9v+oEjRDxg+pD8Adb9sQM1anzJz2iQG9e+FQqGgZ+/+Dj8vVa5YkRMnT9G73wDAQt/evdi5ezc6rY78+fPx57a/KFa0CIOGDgOgfr16FCtahJWhq/lp4yY8PT3p26unQ7KpVGo+/HIQWxZ1wGIxU7hcQ7wyeqOLfcrO9cF81nY2ZWt2Yfv3gzl/+Ec0npmo1WIKKhdXPmkylm2r+wMWfPyD8C9SDYDbV47x44xGWCxmqjYMTpVexyKlaxBx7iDzRzXDgoWvvx3HqYO/YtDFUa56Yz7/ZjDLJlnrdmWqNiBD5r+v8z64E03mbGn7lmxneFeGwb6LFBZHPyyUiu7du8fAgQNZuXJlWkdJsTVr1vDpp5+SOXNmpk+fbn21effkn82LjIp2Yrp/b+Mpv7fP9A6oXDju7TO9A3Tx78cLrA2m92P4TJDm3Ntnege8UKfti1lS6o7OsT2TqUXjYkzrCClS5FHSf8bsXXM0vWP/zE9qiXXy6J5/y+f8obSOkCJeqti3z/QOcLOk/ZuNU+KXS28fGfAuyJnt/WgONCj3/o2Y+H7/u7luv/kw7f+szPtR+8X6Jtw5c+YwduzYtI7yj2TJkoV27drh4eFBunTpmDBhQlpHEkIIIYQQQog09d40RGvXrk3t2rXTOsY/VqdOHerUqZPWMYQQQgghhBBOZn43O0TfCe9f/7YQQgghhBBCiPeaNESFEEIIIYQQQgDWP2c0YsQImjRpQsuWLbl27Zpd+c6dO2nYsCFNmjRh/fr1//p33puhuUIIIYQQQgjxPrFY0v6lQP/U9u3bMRgMrFu3jlOnTjFhwgTmz58PgNFoZPz48WzYsAGNRkOzZs34+OOPyZYt2z/+HekRFUIIIYQQQggBwPHjx6lSxfqn6kqWLMm5cwl/jSAqKoo8efKQIUMGXF1dKV26NMeOHftXvyM9okIIIYQQQgjx/8i6detYt26d7XOTJk1o0qQJADExMXh5ednKVCoVJpMJFxcXYmJiSJcuna3M09OTmJiYf5VBGqJCCCGEEEII4QCWd/StuYkbnq/z8vIiNjbhbwqbzWZcXFySLIuNjbVrmP4TMjRXCCGEEEIIIQQApUqVYu/evQCcOnWKAgUK2Mry5s3LtWvXePr0KQaDgWPHjhEUFPSvfkd6RIUQQgghhBBCAFCzZk0OHDhA06ZNsVgsjBs3jl9++YW4uDiaNGnC4MGDad++PRaLhYYNG+Lt7f2vfkcaokIIIYQQQgjhAOZ3dGju31EqlYwePdpuWt68eW3/X716dapXr/7ff+c/f4MQQgghhBBCCPEPSENUCCGEEEIIIYRTydBcIYQQQgghhHCAd/Wtue8C6REVQgghhBBCCOFU0hAVQgghhBBCCOFUMjRXCCGEEEIIIRxAhuYmT3pEhRBCCCGEEEI4lfSIvmOMuKZ1hBQ5svdqWkdIka45z6V1hP8pd7KVSOsIKRKryJDWEVIkS+yNtI6QIgv2ZU3rCCkypOrFtI6QIofTfZbWEVJEozKmdYQUSXf+UFpHSJG7RSumdYQUCbi4K60jpMjUX33SOkKKlC6pSOsIKVKT39M6Qgp9ntYBRCqShqgQQgghhBBCOIBZhuYmS4bmCiGEEEIIIYRwKmmICiGEEEIIIYRwKhmaK4QQQgghhBAOIG/NTZ70iAohhBBCCCGEcCppiAohhBBCCCGEcCoZmiuEEEIIIYQQDmA2p3WCd5f0iAohhBBCCCGEcCppiAohhBBCCCGEcCoZmiuEEEIIIYQQDiBvzU2e9IgKIYQQQgghhHAqaYgKIYQQQgghhHAqGZorhBBCCCGEEA4gQ3OTJz2iQgghhBBCCCGcShqiQgghhBBCCCGcSobmCiGEEEIIIYQDmGVobrKkR1QIIYQQQgghhFO9lz2iR48eJV26dBQqVIju3bszZ86cJOe7efMmffv2Zf369U5OyN/mSk1ms5mFc2dyNToKF7Wa7r3645sjp6187+4d/PLzTyhVSvz989KpWy+USuv9h6dPn9CvZ2dGjZ1Mrtx5HJ61dFEPGtXJSHw87Ap7wfZDL+zKM6ZT0atVdlxU8OR5PHPWPMBgtFC+hCdf1ciAxQLbD75gx+EXyfzCf2c2m5mwahOXr9/GVe1CcLtG5PbO+sZ8Y5ZtIL2XBz0bf2ab9vh5DC1CZjB3QEcCcmR3WMb3Lee8ubOJjr6CWq2mZ68+5Ei0f+7ZvYvNP29EqVLh7x9A1249UCqVrF/3A2FhhzGZjHz+eV1q1f7U4Tnnzp1L9BVrzl69e5MjRw5b+e7du/n5559RKZX4BwTQrVs3duzYwfa//gLAYDBw5coV1nz/PV5eXg7NOXnxaiKv3UDtomZIl9bk9vW2le86fIzQTVtBoaB+jarUq1GV33bt57ddB605jUYirl7n1yXTSefp4bCcryvip6JmWTVmMxy5aCLsosmuPKOXgiYfu6FUggL4cY+eB08dfwvZbDYzZfEqIq/ewFXtwuAu7ciVeH0eOsrqTb+jUEC9mtWoV+MjAFZt/JX9R09iMpn4qnZ16r6c7sicaxeP5da1y7i4uNK8SwjZfe3P2Qa9llmjO9Oi60h8cgYQbzISOi+ER/dvYzIZ+LRhR4qXrebwnKELJ3DjagQuLmradg/G2ze33Tx6vY4pIV1p130Evrn8AQjp8w0aD+txk807J+17hjg85/L5k7kWHYlarebbHkPwyfFaTp2O8SN68m2PoeTM7Y/RaGDhjDHcv3cbjcaTNl364/vaMs6WsVxxCo3rz+EardI0h9lsZuG8GVyNjkKtVtOt5wC7esi+3Tv4ZfMGlEoVfgGBdOraG4vFwrzZU7h98wZKpYrufQbi65vzb34ldRQLcKFOeTfMFguHzxs5eM5oV54pnYLmNTW8rCaxdoeO+0/MlC7oQrUgN8xmC7cfmlm/U0dqnKEiTu9k/29zUSpdKFG5ISWrNLYrj4t5zOYl/TEZdaTLkJ3P24xH7apJcjmT0cBvK4fw9OENXN29qN1sBJm9/W3fdf7ILxzbuZrWg9elQvKX9ZCVPxFx/TZqFxeCOzQmt3e2N+Ybu3Q96b086NHkC+LNZsYsXc+1O/dRKZWEfNuUXEnUXcT/H+9lj+hPP/3E/fv3AZzS2Ps3nJUr7NB+DEYDE6fNoVXbb1m+ZL6tTK/Xs2bVMsZMmMbEqXOIi4vh2JFDAJhMJubPnoabq5tTcqqU0OarLHw37y4hs29To1I6MqZT2c1Tv0ZGdh95QfCsO9y8a6RW5fQoFdCibmZGz73DsOm3qVc9A+k8Hbfb7j5xHr3RyIoRPejR6DOm//DLG/P8tOsQkTfv2E0zmuIZu3wDbmq1w7K9jzkPHzqI0Whg6rSZtGnbnqVLFtnK9Ho9oatWMG7CZKZMnUFcXCxHj4Rx5sxpLl68wOQp05kwcSoPHjxweM5Dhw5hNBiYNn06bdu2ZcnixXY5V61cyYQJE5g6bRpxsbEcOXKEmjVrMnHSJCZOmkS+/Pnp3LmzQxuhAHuPnMRgNLJ43DC6tmjI7JUJN9ni483MW/0Ts0b0Y/HYoazZ8gdPn7/g848/ZN7ogcwbPZCCgX70afeNUxuhSiV8+aEri37RMe9nHRWKupBOo7Cbp045NQfOGZm/WceOE0Y+q+DqlGx7j5zAYDCyaHwwnVs0YvbKtbay+HgzC1ZvYGbIABaOC+b7zVt5+vwFJ85d5Fx4BAvGDmPO6CHcf/TY4TlPH9mJyWhgwLhQ6rfoxcaVU+3Kr0WeZ1pwOx7eu2GbdmTvb3imy0i/MSvoNmwe65aOd3jOE2G7MRoMDJ+4nEaterB2+XS78ujIC0wY+i33796yTTMa9AAMHruIwWMXObwRCnDs8F6MBgOjpyymaeuurFk22678SsRFRg/pwr07CTl3/rkZN40Ho6csoXWnvqxYMPX1r3WqwH4d+GDhGJTuzrmG/52wQ/sxGgxMnDqXlm06snzJPFuZXq9nTegyvhs/nQlT5xAXG8uxI4dsdZHxU+bQrEVbli+el9zXpxqlEhp85M7cTbHM/DGOSsVcSedhfy76vKIbe08bmLUhjm1H9dSr7IZaBZ9XdGfWhlimr49D46agaOB/78eJjzey/cfxNO21jBb9Qzm5bx0xz+yvd/t/nUfRcl/QcsD3eOcpwsm965Jd7tT+9bi6edB68HpqNR3OtrXf2b7n3o2LnN6/AVKl+Wy1+/g5DAYTy0N60aPJ50z/fssb8/y086BdPWTfifMALBvRk04N6zDt+82pluddZrFY3sn/3gVp1hDduHEj3bp1o3Xr1tSrV48///wTgD/++IOWLVva/nv8+DFhYWE0atSIb775hjlz5rBv3z4mT57M7du3qVy5MgBHjhyhVatWtGrVisaNGxMdHZ3k7968eZOGDRvSuXNnvvrqK6ZPt14oL1++TLt27WjTpg0NGjTgxIkTAHz88ce0b9+esWPHcufOHTp06EDLli3p0KEDd+7cQa/X07lzZ1q0aMHXX39NWFgYgC3XmjVraNSoEU2aNGHixImpvh4vnj9HqdJlAShYqAiREZdsZWq1molTZ+Pm7g5AfHw8aldrBW/FkgXU+awembJkSfVMScnl48rdh0ZitWZM8RB+RU/hvO5286zY9Ii9x2JQKCBLJhVPX5gwW6DXuBvE6Sx4eSpRKECnd9zBc+pyNJU+KATAB/n8uBB90678TORVzkZep8HHFeymz1j7Kw2rVyRbpvQOy/Y+5jx//hylSpcBoFChwkREXLaVqdVqJk+dgbvd/qnmxPFj+PsHMHbMKEaPGkG5chWS/O7UzXme0qVLW3MWLkxERIRdzqnTptnldE3UkL98+TLXr13j088+w9FOh0dQoWQxAIoVyMvFK1dtZSqVkh9mjsHL04NnMTFYLKBxTzjGLkZeJfrGberXdGzv3eu8Myl4+MyMVg/xZoi+YyYgh/2l55eDBi5ciweslUWTKalvSn1nLkZQIegDAIoVyEd4VMJ1Q6VSsmbWOLw8PXgeEwMWCxp3N8JOnSPQLzdDJs1m4PgZVCpd0uE5o8JPUqRkJQACChTn2pXzduUmk4GOA6fjnSPANi2oYi3qNu1m+6xU2t/4c4SIi6f4oFRFAPIW/ICrkRftcxoNdB88Gd+cfrZp169GYNDrmBLSjYnBnYm6dNbhOS9dOE3x0tbzSv5CxbgSYZ/TaDTQd+gEcuRKyHnr+lVKvlwmRy4/bt+46vCcfyfuynWON+qRphleuXjhLEGlywHWekhUpP15fsIU+3qIq6sr5St+SNce/QG4f/8uGTNmcnhOn8xKHjxNOBdduW0ib07742LTXj3noq0nIJVCgdEEpniYvj4W48vzklIBJtN/r4c8uhNFpmx50HhmQOXiSu58pbkRecxunpuRxwksWgWAwKJVuXrxYLLLPbwTSWCxqgBk8Qnk0Z0oAOJinrBr4xRqNBn6nzMndupyNBWLv6qH+HMx+oZd+ZmIq5yLvEaDjyvaplUr8wHD2jUC4O7DJ2RJny5VM4n3T5r2iMbFxbF8+XKWLVvGhAkTMJlMXL16lUWLFhEaGkpAQAD79+8HrHfVvv/+e7p3706VKlUYMGCA3dC5iIgIJk+ezKpVq6hevTp//PFHsr9769YtJkyYwIYNGzh8+DDnz58nMjKSQYMGsWLFCtq2bcvGjRsBuHPnDlOmTGHYsGFMnDiRli1bEhoaSvv27ZkyZQrXr1/n4cOHLFiwgKlTp6LT6ex+a+PGjQwbNox169aRO3duTKlcw4qLi8PDw9P2WalUER//qkKnJGOmzAD8umUjOq2OkkFl2PHXH6TPkIGglw1YZ9C4K4jTmm2ftTozHu5v7n5KBUwbnIti+TSEX7HeKTeboXxxD6YOzMWFKB3x8Y5riMZo9XhpEirvSqUS08v1+eDpcxZu+otBrb6yW2bLvqNkSudJpQ8KOizX+5pTGxeHZ6L9U6VU2u2fmTJZKx+/bPkZnVZLUFBpnj9/TmTEZQYPGU637j2ZMnmCw+/cxcXF4eGZ+DhKOueWzZvR6nQElSplm3f9unV807y5Q/O9EqvV4eWR0JupSrTdAVxUKnYfPk6rfiMpWaQALqqEStbKjb/RvnE9p+RMzF2tQGdI+Kw3WNC42vdCxOqsx3m2jArqVnJl2zEDzhCr1eL51vV5jNZ9gylRpCAuKheevXhBeFQ0Y/p1Y0Cn1oyaudDh+6dOG4vGI6HCZj3PJ1xL8hYKInNWH7tl3DUeuGs80WljWTylH/WadXdoRgBtXKxtiK01p9IuZ/7CJcmSzT6nq6s7deq3pN/IObTuPISF04bbLeOonB52Oe3XZ8EiJciSzdtuGb/A/Jw8egCLxUJE+DkeP36AOdG+4mx3N23DYnTSHZu30L7l/PmqHvLblo3odFpKBFlvTqpUKmZOG8+SBbOp9KHjb5C5uyrQGRKOVZ2BJM5FFsxmyJ5JSf2qbmwN02MBXsRZl6taQo2bq4Lw6/992+t1MbhrEo5rV3dP9NqYN+ZxezmPtfxFsst55ypM5NldWCwWbl05xYun9zCb4/l91TBqNB6Kq5snqcl6PUq6HvLw6XMWbfqTQa0bvrGci0pFyMLvmbxqI5+UK5GqmcT7J02fES1btixKpZKsWbOSPn16Hj9+TJYsWRg0aBCenp5cuXKFkiVLAhAQEPC33+Xt7c3YsWPx8PDg3r17lEpUUXxdoUKFyJgxIwDFixcnOjoaHx8f5s2bh7u7O7GxsbYhdpkyZbJVQi9fvszChQtZsmQJFosFtVpN/vz5ad68OX379sVkMtGyZUu73xo/fjzLli1jypQplCxZMtUrLB4eHmi1Wttni9mMKlHl02w2s3LZQm7fusmgYSNRKBTs2LYVhULB6VMniL4Sycyp4xk6YiyZMmdO1WwATT/LROFAd/LkcCXymt42XeOuJDZRw/SVeDP0GX+TDwpo6NEiGyGzrUM6ws7EceTsdbp/k42PynmxKyzmjWVTg5fGjVhdQk6LxWKrzG8/coanL2LpNW0pD5+9QKc34u+bjS17j6JQKDhyIYJL128Tsmgt03q3IWtGx/U6vi85Na/tn2az5Y39c/myJdy6dZMhw0agUChInz49uXLnRq1WkytXbtSurjx79tShd8xfP47MSRxHy5Yu5datWwwbNgyFwlp5iYmJ4caNG5Qo4ZyLqafGndhEN7vMZotdYxOgWoXSVC0XxHdzlrF1z0G+qP4hL2LjuHb7DqWLFXJKTrAOtw3wVZEji5Jr9xIqbW6uCrT6N4/9vDmUNPzIje+3O+f5UABPjYY47dvWZxmqlivF2DlL+GPPATJ4eeGX0xe12gW/nL64qdU8ff6CTBkcdxy5azzR6WJtn63n+bdfvh8/vMuiSX2oWrsxZas4vsde4+GJThtn+2yxWN6a0ydnHrx9c6FQKPDJ6YdX+gw8ffzwjQZr6udMtD4tb1+f1Wp+we0bVxkztBsFChcnIG9BlCrH9zK/DzQeHvbbPYnz56pX9ZCho2znT4BefYfwpM1jBvXtyqwFy3F316R6vs8rupE3p4ocWVVcu5twLnJ3BW0SI6zy51LRuLo7q/7Qcv+J9VylAL6s4kb2jEqW/Br3xjL/xJ6fp3Mz6gT3b14iR0Bx23SDLtaugQng5u6FQReL2tUdgy4WN4/0uLl7odfHvrFcgZI1eHQ3iu+ntSJX3lL4+BXl7rXzPLl/jT+/H4nJqOfhnUj+WjeWmk2G/ad/A1ivR3GJ6yGJzp/bw07x9EUsPacs5tGzF+gMBvx9s1O3qrXnfFSnb3jY5DltRs7kxwkD0bwDQ8wd6R0ZBftOStOG6Pnz1uFFDx8+JCYmBo1Gw6xZs9i9ezcAbdu2tTXcXr1gB0ChULzRoBs+fDjbt2/Hy8uLQYMG/W2DLyoqCq1Wi6urK2fOnKFhw4YMGTKEKVOmkDdvXmbNmsWtW7fe+N3AwEDatWtHqVKliIqK4ujRo1y6dInY2FgWLVrE/fv3adq0KR9//LFtmfXr1zNq1Cjc3Nxo3749J0+epFy5cv9txSVSqEgxjh45xIdVq3Ep/AJ+/oF25fNnT0OtVjMk+Dvbv2Xc5Jm28mGD+tClex+HNEIB1v7+BLA+IzpjaG68PJTo9GYK53Vny86ndvN2aJSFQydjOR+pQ6c3W4cWuikY3NGH7+bdwRQPOoP1bqWjlMjvz95TF6hVvgRnI6+RL1dCZahZrQ9pVutDwNq7ePXOA+pVKUu9Kgk9yx3Hz2dI64YObdy9TzmLFCnKkSOHqVL1I8LDL+Lv729XPmf2TNRqNcODR9r2zyJFirJl8ya++qohjx8/Rq/TkS6do3MW4UhYGFWrViX84kX8X7vxNXv2bNRqNcEjRtidE86dO0dQUJBDsyVWvFA+9h87TY1KZTl3OYq8eRJe7hEbp6X/hFnMDO6Lq1qNxt0NhdJa4Tt14TJlPyjitJwAfxwxAkaUShjYVIPGDQxGCPRVsvuU/QtC8uZQUr+KG4t/0fEkxnlX7A8K5ePAsVN8Urkc5y5Hktcvl60sNk7LwPEzmD6iP65qNe5ubigUCooXLsD63/6iad06PHzyFK1eT3oHPxuct1AQZ4/toXSl2kRfPkOOPPnfuszzp4+Y/V1nmrQfQqHi5R2a75X8hUpw6ug+yn1Yk6hLZ8nll++ty+zbvoWb1yJp1XkwTx4/QBsXS8bMjn15ScHCxTlxZD8VqtQgIvwcuf3yvnWZqIiLFCxSgpbf9uZKxEXuJXrO9f+7wkWKcTTsEJWrfMyl8Avkeb0eMsdaDxmcqB6ye+c2Hj18QMPGzXF7ea5y1PDx3w5ZG0tKJQxr6YmHG+iNkDenCzuO24++yJ9LRcOP3Jm3KY4nLxLORU0/cccUD4t/0f7npyw/qt8HsD4junjk52hjn+Lq5sGNiGOUr9nebt5ceUsRdW4PxSs14Mr5veTOV5osvnl5cv/aG8vdvnqWXPlKU6PxUO5cPcuTB9fJEVCcb0f+BsDThzfZvKRvqjRCAUoU8GffiQvULF+Ss5FXyZfb11bWtHZVmta2DhP+Ze8Rrt65T92q5fht/zHuP35K23o1cHdzRalQ2F1Txf8/adoQffjwIa1bt+bFixeEhITg5eVFqVKl+Oqrr/Dw8CB9+vTcv3+fXLly2S1XokQJpkyZYjf9yy+/pHHjxqRPn56sWbPaXmaUFLVaTa9evXj48CF16tShUKFC1KtXj65du5IlSxZ8fHx48uTJG8sNGjSIkSNHotfr0el0DBs2DH9/f+bOncvPP/9sfStoz552yxQsWJCvv/6aTJky4e3tneo9JxUqfcjpk8cZ1K87WKBHn4Hs2bUDnU5LvvwF2L5tK0WKfkDwkH4A1P2yARUqVUnVDCkRb7Y+Azq8iw8KhYJdh1/w+Fk8Xh5KujTNxuRl9/h9z3M6Nc6KBevdo8U/PkSrt7DvWAzf9cyByQzXbuvZd8wxvaEAH5cuRtj5CNp+NweLxUJIhyZsPXQSrU7/xvOWael9yVmxUmVOnjxB/37WtyT27tOP3bt22vbPv7b9QdGixRg6ZCAA9b6sT6VKH3Lu3Fn69u6B2WKmc9fudnfXHaFSpUqcPHmSfn37YrFY6NO3L7t27UKn1ZK/QAG2/fknRYsWZcjgwYD1fFOpcmVu3ryJj4/jem5e91G5Uhw5fYFvh44DLAzr1o4/9x1Gq9NTv+ZH1K5SgS7BE3FxUZHPLxd1qlifzbl2+y45kniboTOYzbDlgIGOdd1RAEfDTTyPtaBxg8Yfu7HyDz31P3TFRQlNP7HeFX/w1MyGPY4fnvtR+dIcPXOeTkPHYLFYGNatPdv2HUKr1fNlrWrUqlKRbsHjcVGpyOuXm9pVK6FSKTl14RIdBo3GYjHTr0NLVCrHVqRKlKvOxdOHmDy0FWChZbfRHN33O3pdHB/W/DrJZf7YuARt7HO2bljE1g3Wl4R1GzYXVzf3JOdPDaUqfMz502GMGdQOsNC+RwiH9vyBXhdHtdoNklymao0vWTJrJOOGtAcUtOsxIkW9vf9FmYofcfbUEUIGfIvFAp16DePA7j/R6bR8Uqd+ksv4+Obmx9WL+HXT93h6etGxZ+o+b/c+K1+xCqdOHmdwv+5YsNCj9yD27t6OTqslb/6C7Nj2O4WLfsCIoX0B+KJeQypUqsLs6RMZNrAXpngT7b7thqurY19SZjZbnwHt+pUnCgUcPm/gWawFDzf4pqaGJb9qafiROy4qaFnb2jN774mZA2cMVCimJupWPD2+tg7l333SwJmo/zY0WqVS88nXg1k7sz1YLBSv1JB0mbzRxj7l91XDadhlDpU/78Ivywdxav96PLwyUa/91GSXU6nV7Nsyk7Bty3D3SMdnrcb+53X2dz4u/QFh5y7TbtQsLFgI+bYpfxw8TpzOQIPqFZNcpnqZDxi1eC3fjpmDKT6evi3q4+bqnJcnineTwpJGr03auHEjV65coX///k793bT8ky4pcTHq/bjLGjxL//aZ3gErmp1L6wj/U+5kez+e51Aq0u7ZrX8iU+zttI6QImP3Oa/X978YUvVMWkdIkdOW92N9alyMb5/pHeCmej9y3i2adOX8XRNwcVdaR0iR+b86diRCaild0jkvB/yvGnr8ltYRUiRduc/TOsI/NvOXd3Nsbq+6irfP5GDSHy6EEEIIIYQQwqnSbGhugwZJD9NxtFy5cr2zvaFCCCGEEEII8f9Bmj4jKoQQQgghhBD/q+StucmToblCCCGEEEIIIZxKGqJCCCGEEEIIIZxKhuYKIYQQQgghhAOYZWhusqRHVAghhBBCCCGEU0lDVAghhBBCCCGEU8nQXCGEEEIIIYRwAHlrbvKkR1QIIYQQQgghhFNJQ1QIIYQQQgghhFPJ0FwhhBBCCCGEcADLO/vaXEVaB5AeUSGEEEIIIYQQziUNUSGEEEIIIYQQTiVDc4UQQgghhBDCAd7ZkbnvAOkRFUIIIYQQQgjhVNIQFUIIIYQQQgjhVDI0VwghhBBCCCEcwCJDc5MlPaJCCCGEEEIIIZxKekTfMV7mZ2kdIUUqflw4rSOkyJkM2dI6Qoq4Kk1pHSFFnsZ6pHWEFPHzvJvWEVJE65YhrSOkiLe3Jq0jpIir/nlaR0iR7Bnej/O8yaJK6wgp4qHUpnWEFAm4uCutI6RIdOGP0zpCiny0/2JaR0iRnOnfj/PSMVP1tI6QIu/H3ilSShqiQgghhBBCCOEAZnltbrJkaK4QQgghhBBCCKeShqgQQgghhBBCCKeSoblCCCGEEEII4QDy1tzkSY+oEEIIIYQQQginkoaoEEIIIYQQQginkqG5QgghhBBCCOEAMjQ3edIjKoQQQgghhBDCqaQhKoQQQgghhBDCqWRorhBCCCGEEEI4gFnG5iZLekSFEEIIIYQQQjiVNESFEEIIIYQQQjiVDM0VQgghhBBCCAewmNM6wbtLekSFEEIIIYQQQjiVNESFEEIIIYQQQjiVDM0VQgghhBBCCAewyFtzkyU9okIIIYQQQgghnOq97BENCwtj7dq1TJ8+/V8tf/v2bcLDw6levTpjx46lbdu2eHl58e233+Lp6Unv3r0ZMmQI1atXp1+/fqmc3jHMZjOz5i0kKvoqarWafj27kTOHr61874FDrN2wEQXweZ1afFa7plNyXbuwkxM75qFQqihYpiGFyze2K9fFPmHHD/2IN+rxSJ+dao3G4eKq4fKJzZzZsxRX93QUKP0Vhcp9bVvm/vXThG2dQt1Ooame12w2E7pwAjeuRuDioqZt92C8fXPbzaPX65gS0pV23Ufgm8sfgJA+36Dx8AIgm3dO2vcMSfVsr+dcvmAy16MjUKvVdOg+FJ8cb+YcH9yDjj2HkSOXPyaTiQUzRvPw/h2USiUdug8hx8v8jsy5bslYbl29hIvalW86jyS7bx67eQx6LbO/60TzLqPwyRkAwItnj5k6vBXDpv6E2tXNoRlf5Zw3dzbR0VdQq9X07NWHHDly2sr37N7F5p83olSp8PcPoGu3HiiVStav+4GwsMOYTEY+/7wutWp/6vCcM+cvJir6Gmq1C/17dHntOD/MDxs2gULBF7Vr8HntGphMJibNnMe9+/cxGE20aNKQyuXLOjTn665f3MXJndbzQIEyDShUtnGS8507sBLti4eUreOc867ZbGbi8h+JuHYLtdqF4d82I7dPtjfmG7t4Lem9POjRrF5C1sirzP5hCwuDezol5+J507gWHYWLWk2XngPxzZHLVr5/93Z+3fwjSqUSv4C8fNu1r3WfnjGB+/fvYjIaaNikFWUrfOjwrK/yLps3hWvRkbioXenUczA+ifIe2PMXv29eb83rn5d2XfujVDrvvrjZbGbB3FlER0ehVqvp3qvfa8f7Tn75eSNKlRJ//0A6d+uJUqmkV/dOeHp6AuDt7UuvvgMcnnPhvBlcfZmzW88B+CbKuW/3Dn7ZvAGlUoVfQCCduvbGYrEwb/YUbt+8gVKponufgfj65vybX3G8jOWKU2hcfw7XaOX03zabzWxZOZq718NxUbvyVfvvyOLtZyu/eHIXu36eh1KponTVBpT9OOHcFPP8EfNGfE3bgUvJliPQNv23NePJ6htA+epNHZZ51cKJtnpIu+7Dk6yHTA7pRrvuwbbr+K8blnPy6D5MRiPVP/2aj2p+6ZB8iXP+sHgcN69dxsVFTcsuIUle32eM7kyrriPxyRlAvMnIqnkjeXT/NkaTgc8afkuJstUcmlO82/5f9ogePnyYEydOADBs2DBy5MjB5cuXyZ49O8uWLWP//v00bdr0vWmEAhw4HIbBaGT21Il0aNOSBUuX28ri4+NZuiKUSWNGMmvKBNZv/Jlnz547PJM53sihXyfwWful1O0USviR9cS9ePB/7N11fFPX/8fxV5Km3kKxIsWKw3Bnw2XCYLj7YLgOZ7jrgOHDi8NgG7Yhw4a7Q6ngTimlbbTJ749A2tAy+vt+mybs+3k+Hns81px7kzf35p57zz3nntgsc3bffPKW+Jr63deSIWshrp3ciDbmJWf+nMPXXVdTr2sQIRe28zriPgAXDi7l0C8/EGfQ2yXzuZMHMej1/DB1BU3b9WbDCtubHeEh15gyvAtPHz+wvmbQ6wAYOnEJQycusXsjFODsiUMY9DrGTl9K83Y9Wbt8rk152K3rjB/WzSbnhTPHMMXFMWbazzRs3olNQYvsnvPS6b8w6HUMnLSGb1r3ZevqGTbld0Kv8uOojjx7fM/62rULR5k3oRuvX72we763Thw/hsGgZ+asOXTo+C3Lli6xlul0OoJWr2TSlOnMmDmb2NgYTp86yaVLF7l+/RrTZ/zIlKkzefbs2T98Qsr4+8Qp9HoD82ZMokv7NixcvspaFhcXx8+r1jB9wijmTZ/Ixm2/8+pVFHsPHsbX14c5UycwZcwIflq0zO45EzLFGTixcwpfdFpK3S6ruXlqc6J6wGjQcnDTYK6fWJeq2Q6euYzOYGD5uAH0alGP2Wu3JVpm6/6jhN57aPPa6u37mPDzevQGQ6rkPHX8CAa9nkkzF9KmQ1dWLZ1vLdPpdKwPWsrYyXOYNHMhsTExnD11jMMH9uDj68uEafMYMXY6yxbNTpWsAGdOHEZv0DN+5hJadehG0LKfrGV6nY6NQUsYNeknxs9YTGxsDOdOH021bAAnjh9Fb9AzfdZPtOvYmeVL4+tCnU7H2tUrmDhlBtNmziUmNobTp06g11vOOZOmzmLS1Fl2b4QCnDz+Nwa9nqkz59O2w3esWLrANmfQcsZP/pEpM+cRGxPDmVPHOXPqOACTZ8yjZZuOrPh5wfvePlUEft+ZoosnoHS3/w3FpFw/uw+jQUe30Ruo02wAu9ZNs5bFGQ3sWjuFjoOX0nnEak4f3MzryGfWsl9XjMZFHZ87JiqClTO+48b5A3bNbLkO0TFy6nKatuvFhhWzbcrDQ64xefh3PH18P/7fefkst25cYsTkpQybuJiI50/smhHg4qkDGAw6hkxaTcM2fdmyapZN+Z2Qq8wY+S3Pn8TnPHl4J14+aRg4YQW9R8xnw7Ipds/pDEwm5/zPGdilIbp161Z69+5Nly5daNCgAVu3bqVnz57UqVOHffv2AfDpp59al+/fvz8nT54kPDycFi1a0KZNG9q3b8+TJ08wmUyMGzeOJk2a8M0331jXf2v37t00b96cli1bMmOG7UUuwNq1a2natCnNmzdn6tSpxMXFsWTJEnbs2MH+/ftp27YtN27cYPz48Zw5c4bOnTuzZcsW1qxZw969e6lXrx7jx4+nTZs2tG3bltevXwMwc+ZMWrRoQfPmzdm9e3eSnwWwZ88emjZtSsuWLRk4cCAmO+35K1evU7ZUSQAKFyxA8K1Qa5lKpWL5op/w9vIi6vVrzGYzHh7udsmR0MunYfimz4GbZxpULq745yrN4/CzNss8vn2O7AUqA5C9QBUehBwnKuIe6bMWxN0zLQqlkowBRXly9yIAvumzU6ftT4k+K6Xcun6BoqUqApCnQFFuh1y3KTca9PQaOp0s2eLvqN69fQu9TsuM0T2ZOrIboTcv2y3fWzevX6T4m5z5Cn5CeMgNm3KDQU//YVPJGhCfM0u27MTFGTGZTGg0Mahc7D8gIvT6eQqXtBzrufMX527oNZtyo0HPd4NmW3tCARQKJb1HLcHTO43d87119eoVSpUuA0DBgoW4dSvYWqZWq5k+czbu7pZjJi4uDrWrmnNnz5ArV24mThjLuLGjKFeugt1zXrl2g7KlSwBQuGB+bt4Ks5apVCpWLpzz5jiPhjfHebVPK9KpdYsEy6Xu/cfIt/WAx9t6oBRPbtvWA3FGHXlLfkPxat1SNdvFm6FUKlYIgKL5cnM97J5N+aXgcC7fuk3Dmp/avB7gn4Fp/b5NtZw3rl2mROnyAOQvWISwkJvWMrVazcQZC3Cz+X66UvGzarRo09m6nFKpSr28Vy9RopTleMhX8BPCbsXXTy5qNeOmL7bNq3ZNtWwA169eoVRpy6iAggULE/LO8T515lybfK6uroSHhaLX6Rg1Yggjhg7kxo1rSb53iua8dpmSpcsBUKBgYUJDbHNOmfFTopzlK35Gj94DAXj69DFp0/rZPec/iQ27y9mmvR32+XeCz5G/mGUkQI68JXhw+4q17NnDMNL758DDKw0uLq7kzF+K28GWumn3humUr94CX79M1uV1ulhqNuxJiUr1sadb1y9StFQlAPIWKEp4ousQA72HTiNLtlzW165cOE72nHn5acogZk8cQImy9h/9EHLjPEVKWOrGwPzFuBN21abcYNTTbfAs/LPG5yxVsQ71W/S0/q1KxXpJOCe7XZHExMTw888/06VLF9avX8+8efMYN24cW7dufe86x44do0iRIqxYsYJu3brx6tUr9u/fz8uXL9myZQtLly7l8uX4i/zIyEh++uknVq5cyfr163ny5AlHj9reWd26dSsjRoxg48aNZM+eHbPZzHfffcfXX39NzZo1AUuFPnz4cCpUqMDSpUtp2LAhHTp0oHbt2sTExFC3bl3WrFlDpkyZOHz4MIcOHeL+/fts2LCB1atXs2jRIqKiohJ9ltFoZMeOHXTo0IH169fz2WefER0dbZftHavR4OXlaf1bqVISFxdn/VulUnHk2HG69h5AsU+KoFLZ/+A3aKNxdfex/u3q5oVe+9p2GV38Muo35WnS5+TlkxBiXz/HqNfwIOQ4Rr0GgMCin6NU2q8BpYmNsQ6xBVAqlcTFGa1/5ytUgvQZM9us4+rqzhcN2vL9mHm07zaMxbN+sFnHbjnfDBFLKmeBwsVJn9HfZh13d0+eP33EoB7NWTpvCp9/nfTwyJSk1UT/4/bMU7Akfhlst2eh4hXx9klr92wJaWJj8fKM354qZfzxo1Qq8fOzXMxt//1XtBoNJUuWJioqipBbwQwd9gM9e/VhxvQpdp+QIDZWg5dn/HGeMCdYjvPDx07Qpc/3FCtSGJVKhYeHB56eHsTGahg7ZQad2rS0a8Z36XXRuLrHfwfUronrATePNATk+/TdVe0uRqPFy9PD+rdSqcT4Zns+f/mKn3/ZzZCOTROtV6NcCVxcUu8CShMbg+d7jnelUklav3QA7Pr9F7RaDcVLlsXDwxMPT080sbHMmDSKlu06J/nedsmread+UqmSzPvH9s1otRqKlSyXatkAYt853pXvOd53/L4NrUZDiZKlcXNzo0HjpoydMIUevfoxa9pkm2PPHjSxsUns9/icb7fjzt+3vtnvlptpKpWKObMms3TRT1T6rKpdM37I4217MBvsez78J1ptNG4e8dchSkX8d1GricbdI75ucnP3Qhv7mnNHtuHl40e+YraNuXQZA8iep7jdM2tiY/BM9P1MeB1SPNF1yOuoSMJDrtNz0BTadx/K4lkj7X4+0mrevV5S2eTMW7Ak6d45v7t7eOLu4YVWE8OSGQOp37In/wvMZrNT/ucM7NYQLVTIcpfZx8eHPHnyoFAoSJMmDTqdLtGybzdGkyZN8PPzo3PnzqxduxaVSkV4eDglSpQAIGPGjPTv39+63t27d4mIiOC7776jbdu2hIaGcu+e7R3tyZMns2HDBtq0acPDhw//ow1fuHBhALJkyYJOpyM4OJirV6/Stm1bOnfujNFo5OHDh0l+1rBhwzh9+jRt2rTh3LlzdnsOxtPDg1iN1vq32WRO1NisXKkiG1YtxWAwsvevg3bJAXD6z9lsX9yWP1f1wKCNb3jrdTE2DVMAtZs3Bl0MAAZdDG7uPrh5pqHi10PZu6YPBzePIEO2wrh7pc5dXQ9PL7SaWOvfZrMZleqfG76Zs+WgYtUvUSgUZM6WE2/fNERGPE/VnCaz6YM5d/++nqIlKzBz0WYmzwli0exx6PWJj8eU5O7h/c72/HBOR/Dw9ESj0Vj/Nr1z/JhMJpYtXcL58+cYNmIUCoUCX19fSpYug1qtJiAgO2pXV169irRrTk9PDzQJjnPLfrc9zqtUqsCmlUswGA3sOXAIgKfPnjNgxGhqV69CzWqV7ZrxrTN7ZrPz53bsDeppPcYBDPoYXN19UyXDh3h5uBOrTVBvmk24vNme+05eIPJ1NH2nLWLV73v589hZth866ZCciY53k229ZDKZWLV0PpfOn2bQ8PEoFAoAnj97wuhhfalSow6Vq6XOvAAAHh7v1KMmU6K8Qcvmcfn8aQYMm2jNm1o8PT3R2ORLfLwvX7qYC+fPMWzEaBQKBdkCAqhWvZb1/318fYmIsO/jAx6enklsR9ucK5cu5OL5swwZPtZmO/YdMIz5S4JYMHcmWq2G/1Xu7t7otfH1T8JzkLuHN7oEZTptDB6evpw9/AshV46xdFI7Ht29weYlQ61DdlPDf3Id4u2Thk9KVsBFrSZLtlyoXd14/eqlXXO6e3ihTbhtTck7v0c8f8ys0V0oX+VrylX+yp4RxUfAbg3RD51YjEYjMTEx6PV6QkJCANi/fz+lS5dm1apVfPHFFyxdupTAwEBrL+jr16/59tv44VABAQFkyZKF5cuXExQURJs2bShe3PZu1aZNmxg7dixr1qzh+vXrnD9/HqVS+f8aIvvuvyUwMJDy5csTFBTEqlWr+PLLLwkICEjyszZu3Ejv3r1Zs2YNAHv37k325/5/FClciFNnLENKrt24Se5c8Q+Mx8TGMmDoCPQGA0qlEnd3N5RK+534y37ej3pdg2g78m9evbiLNjaSOKOex+Gn8c9Z0mbZzLlKcveG5WL53s3DZM5dBlOckSd3L1K/6xqqN59C5LNwMucqZbe8CeUrWJxLZy296qE3LxOQM+8H1zmy73frMxwvI56hiY0hbboM9oxJ/kLFuHDmGAC3blwhe848H1zHy8sXTy/L3UsvH1/rMF17CixYgqvnjgAQHnyRrDny2fXz/lOFCxfhzJlTANy4cZ1cuXLZlM/7aQ56vZ4fRo6xDtEtXLgI586cxmw28+LFC3RaLT4+9m1gfVKoICfPWJ5vv3YjmMCctsd5v6GjEhzn7igVSiJeRjJ41Hi+69CGL2vXtGu+hMrU6UfdLqtpPfwIUS/uoLPWA2fIlKNEquX4J8ULBHL0gmWI5eVb4eTJntVa1uKLqgRNGszikX1oX782n1cqTb2q5R2Ss2DhTzh3+gQAwTeukiNXoE354nkzMBj0DB45yTpUM/JlBON/+J42HbtRs07dVM1boHBRzp+xPKt468YVsueyrZ+WzpuGQa/j+x+mWPOmpkI2x/s1cubKbVO+4Kcf0ev1DB851ppv754/rM+SvnjxnNjYWNKlS2/nnJ9w9rTl5sfNG9cS7feF82ahN+gZOnK8NefBv/bwy6a1ALi5u6FQKlJ1WLazyZG/FDcvHgbgbsgF/LPnt5ZlzBrIiyd3iI2OxGjUc/vmGbLnLUGXEWvoMiKIzsNXkyVHQZp+NwWftIknMbOXvAWLc/HNdUjIzcsEJOP8nr9QCa6cP47ZbOZlxDN0Wg3ePvZ9vCVPwRJcOfc3AGHBl8iWjPN7VOQL5o7vTqM2ffm0ZgO75hMfB4d1TbRr147mzZsTEBBA1qyWk/8nn3zCoEGD+Omnn1AqlQwbNozChQtz/PhxWrZsSVxcHD17xnfjp0uXjg4dOtC2bVvi4uLIli0bX35pO2tlgQIFrD2t/v7+FC9eHG9vbxYuXEiRIkX+o+w1atTg1KlTtGrVitjYWGrVqoW3t3eSnxUdHU3Hjh1JmzYtXl5eVKtW7T/eZv/ks4rlOXf+An0GDsVsNjOoX2/2HzyMRqvl6y/qUKNaVQYMGYHKRUVgrlzUrGb/4TpKlZqKXw9h97LOmM0mCpRpjFcaf7SxkRzeMpI67X6iZI3uHNw0lBunNuPu5UeNljNQqlxQqdRs/akxKhdXilXumGo9oqUqVOfqxZNMGNIJMPNt79EcP/QHOm0s1T5vlOQ6VWp9w9K5Y5g07FtAQafeo+ze61emQjUuXzjNmMFdMJvNdO37A0cP/YlOo6HGFw2SXOfLb1qwZO5Exg3titFooFnb7ri7eyS5bEopXq4mNy6dYMaItmA206bneE4f2YlOq+Gz2k0+/AappGKlTzl//hwDv7fMOtmv//ccPPAXWq2GvPnys3fPHxQp8gnDhw0GoP43DahU6TOuXLnMgH69MZlNdOvRy+5D3j+rWI6zFy7Sa9BwMMPgvj3Zf/DIm+O8NrWqVabf0JG4qFwIzJWTWtUqs3DZKl5HxxC0YQtBG7YAMGXMCNzcUmfyEKVKTfmvhvLHii6YzSbyl26EVxp/dLGRHNk6klpt7PfM94dUK1OMk5dv0mn0LDDDqK6t+ePoGWK1OhrVTP2hwu9TrmIVLp4/w/DvuwPQs99Qjhzci1ajIU++Avy1ZyeFihRjzPB+ANSt34Srl88TEx3Nlg2r2LLBMqnViLHTU2W/l61YlcvnTzNyYFcwm+nWbwR/H9yDVqshT96CHNi7g4JFijN+uGXG4S/rN6VcpdQbQlqh0mdcOH+Owd/3wWw207f/IA4d2I9GqyFfvgLs3fMHhYsU5Ydhlmct633TiNp1vmTOrGkMGdgXhUJBn34D7X68l69YmQvnzzL0+16YMdO73xAOH9xn3e/79+yiUJGijBo+AICv6zemQqXK/PTjVEYM7osxzkinLj1xdU3dZ3CdSeHStQi5cozF41piNptp3GUSF4/tQKeLpVz1ZnzZaigrp1vqptJVGpEmnf+H39TOSleoZr0OMQPf9h71weuQEmUrc/PqecYNao/JZKZt18Eo7fz9LFGuBtcvnmDa8HaYgfY9x3LqyC502lgqv+f8vnvrUmJjoti5ZQk7t1gmBew9Yj6ubql/Qyo1mZxjFKxTUpidZZCwAODeLftPgJASNl0t5OgIyVKxgH2eyU1prkrHPUPz/xGp8/zwQk4gp9djR0dIFs84+89enRLWXv7Pbtqltm657DPiJKXdSVPC0RGSxWj+OHrSPJUfx9DTuI9ke4YXqu7oCMmi+fv6hxdyAtl8P47rEK1R7egIyVK9qH1voNvDDyvt80sP/60JHRx/k+p/8udbhBBCCCGEEEI4jvPNGiKEEEIIIYQQ/wJmGZv7XtIjKoQQQgghhBAiVUlDVAghhBBCCCFEqpKhuUIIIYQQQghhBzIt7PtJj6gQQgghhBBCiFQlDVEhhBBCCCGEEKlKhuYKIYQQQgghhB2YZNbc95IeUSGEEEIIIYQQqUoaokIIIYQQQgghUpUMzRVCCCGEEEIIOzDLtLnvJT2iQgghhBBCCCFSlTREhRBCCCGEEEKkKhmaK4QQQgghhBB2YDY5OoHzkh5RIYQQQgghhBCpShqiQgghhBBCCCFSlQzNFUIIIYQQQgg7MMmsue8lPaJCCCGEEEIIIVKVNESFEEIIIYQQQqQqGZrrZFQmo6MjJEurghcdHSFZFp0q4ugIyRJ85ZGjIyRL5hxqR0dIls8/y+LoCMkS+iinoyMkS51izxwdIVl2PKnj6AjJUi7tbUdHSJZLER/H97NUuhBHR0iWmTsyOzpCslT9+7qjIySLx2eFHB0hWe4d/ji255/7Xjg6QrJUL+rh6Aj/b+Z/ydBcrVbLoEGDePHiBV5eXkydOpV06dLZLLNy5Up27twJQNWqVenVq9c/vqf0iAohhBBCCCGEeK/169eTP39+1q1bR4MGDViwYIFN+b179/j999/ZsGEDGzdu5O+//+bGjRv/+J7SEBVCCCGEEEII8V5nz56lcuXKAFSpUoXjx4/blGfOnJmlS5eiUqlQKpUYjUbc3Nz+8T1laK4QQgghhBBC2IHJ5JxDczdu3MjGjRutfzdv3pzmzZsDsHnzZlatWmWzfPr06fHx8QHAy8uL169f25Sr1WrSpUuH2Wxm2rRpFC5cmNy5c/9jBmmICiGEEEIIIcT/kIQNz3c1bdqUpk2b2rzWq1cvYmJiAIiJicHX1zfRejqdjuHDh+Pl5cXo0aM/mEGG5gohhBBCCCGEeK9SpUpx6NAhAA4fPkzp0qVtys1mMz169KBAgQKMGzcOlUr1wfeUHlEhhBBCCCGEsIN/yaS5tGzZkiFDhtCyZUvUajUzZ84EYMWKFeTIkQOTycSpU6fQ6/UcOXIEgAEDBlCyZMn3vqc0RIUQQgghhBBCvJeHhwdz585N9HrHjh2t/3/58uX/13vK0FwhhBBCCCGEEKlKekSFEEIIIYQQwg7MTjprrjOQHlEhhBBCCCGEEKlKGqJCCCGEEEIIIVKVDM0VQgghhBBCCDsw/VumzbUD6REVQgghhBBCCJGqpCEqhBBCCCGEECJVydBcIYQQQgghhLADmTX3/aRHVAghhBBCCCFEqpKGqBBCCCGEEEKIVPU/OTS3bdu2jBkzhjx58tjl/fv378/UqVNxdXW1y/snZDKZmL1oKaHht1Gr1Qzq1Y1sWbNYyw8dO8H6Lb+iUMDXn9embp2axMXFMWPeYu49eIhKqWRw3x5ky5LZ7jl/XLSM0Nt33uTsSkCCzzx07CTrfvkVhULB13Vq8nWdmsTFmZgx35JTqVQypE93u+cEyB+goFoxJSYznA8xcfZW0kMqKhRS4u0B+86ZbF6vV0GFRm9O9HpKK1XIg0a1fIkzwaHT0fx1KsamPI23kl4tM+DiApFRcSzcFIHeYPm3uKoVDO+SiSWbX/DwmdGuORMqkktFnbKumMxw8pqBE9dsPzutt4KWNd1QKhUAbDqg5Vmk/Ye0mEwmNi+bwMM7N3FRu9Ki61gyZs5hLb9y9iB/bFmESqWifPWGVKrZBKNBz9qFP/DiyQPcPb1o0mkEmbLktEu+sCt/cerP+SiVLhSu0JhPKjazKddER/BH0EDiDFq8fDNRq9Vk1K4enDuwgmsnt+DhlQ6AGs3GkiZjTv7a8AMvn4WjUKio1WoyaTPkSOpj/2smk4llC2ZyJzwEtVpN1z5DyZw1wFp+9NBedv22CaVSRY5cefi2x/colfa9P2o2mdi5ZixP7t1ApXalfvsJpPOP3283L/zF4e0LUCpVlPisMaWrxm/r+2EX2bdlBh0GBwHw6M5V1s/tbl2/TLWWfFLuqxTPbDKZWDB/HuHhYajVavr07U/WrFmt5QcPHuC3X39FqVKSO1duevTsxf79+9i/by8Aer2BsLBQ1qxdj7e3d4pn+23VOB7dvYGLiyuNOo8nQ4Ltef3cAfb/ugClSkWZKo0oVz1+e0a/esG8UU3oNGQZmbIGWl+/cGwHx/auocfoDSmaNWHmnxYsJDw8HLVaTb8+vcmWYHseOHiIbb/9jlKpJHfuXPTu0R1jXBwzf5zN48dP8PT0oFf37mTLlvUfPiVlfJLbhS/Ku2Eymzlx1cCxKwabcj8fBa1re/D2sNmwX8vTlyZKF3ChWkk3TCYzD5+b2PSXlpSsSU0mE7+vGsfjuzdwUbvS8NvxpE+4388f4MCvluOodJVGlE2436NesGBUEzoOXkbGBPt959rJZMiSm/I1WqRg0uRLW64YBScN5EStdqn+2SaTiR2rx/H43g1ULq406GS7PW+cP8DB3y3bs1TlRpSp1ow4o4Fty0YQ+eIhRoOeqvW7UahkDR7evsrvq8bionYlc/aCfNV6uN3q1eL5XKlXxROTCf6+oOXwea1NeTpfJR3r+aBUgkIBq3ZGo9Ga6NrI17pMjswubNkfw6Fz2nff/l9Dhua+n/SI2sGPP/6YKo1QgL9PnEav1zN/+iS+a9eaBctXW8vi4uL4edVaZowfybxpE9m47TdeRUVx/PRZAOZNm0DH1s1ZsGyV/XOePI3eYGDBtAl8164lC5cHJchpYsnqdcwcN5L5Uyewcdt2IqOiOPY259TxdGzVjAUJ1rEXpQK+KKNi9b44VvwZR+l8SrzdbZdxUUGjz1SUK5D48CmTT4m/n91jolJC23ppmbz0KeMWPaFGeW/SeNvm+aa6L4fPxjB24VPuPzVSs4LlAjQwwJXR3TPhnz5170MplfDNZ24s+l3DvK0aKhZR4+OpsFnmqwquHLlkYP42DfvO6vm6oluqZLt8+i+MBh39J6ylXst+/Bo03VoWZzSwbdU0eoxYTO8xKzm2bwtRkc85tn8Lbu6eDJi4lsYdh/HL8kl2yRYXZ+DIr5Np0H05jXsHceXYRmKintksc+rPBRQo9TVN+qwjY0BhrhzbCMCz+1ep03oqjXsH0bh3EH7+gYRfOQBA074bqPBlH478OtkuuQFOnziCwaBnwszFtOzQjaBl86xlep2OjUE/M2rST4yfsYjY2GjOnT5mtyxv3Ti/D6NBx7cjNlKr8ffs2TTVWhZnNPDnxim0GbCMDkOCOHd4E9GvLNv66O6lbF/5A0aD3rr8ozvXqFCnAx0GB9FhcJBdGqEAx48fQ2/QM3PWbDp07MTSpUusZTqdjqDVq5k8ZSozZ/5ITGwMp06dpHbtOkyZOp0pU6eTN29eunbrnuKNUIBrZ/dh1OvoMXoDXzQfwK5106xlcUYDO9ZO4dshS/luxGpOHdjM68hn1rJtK0bj4mp7jD+8c50zh34BO/7cwbHjJzDo9cyeOYNOHdqzZOlya5lOp2NV0BqmTZ7I7JnTiY2J5eSp0+z+40883D2YM2sGPbp1Zf6iRXbL95ZSCY2qujN/WwxzNsdS6RPXRHVm3YpuHL6oZ+6WWPac1lH/UzfUKqhb0Z25W2L4cVMsHm4KigSmbH1//azlOOo2egN1miXe77vWTqHj4KV0HrGa0wdt9/uvK0bjoo7f7zFREayc8R03zh9I0Yz/H4Hfd6bo4gko3VPnnPOu6+cs2/O7kRuo03QAf2yw3Z6710+h/cCldBq2mjOHLNvz4vHteHqnpfPwNbT7fjE7gyYA8NvK0XzVahidh6/B3dObSyd22CWzSgnN63gza+0rpq6KpEopd3y9bL+fDap58dcZDdODXrHz71ga1/AiKsbM9KBXTA96xda/YrjzyJioASv+d/zrG6LR0dH07duXTp060bBhQ9atW2cti4qKomvXrrRu3ZoWLVpw/PhxAL766itGjRpFy5Yt6datG7GxsRgMBoYPH07r1q1p2bIlJ0+eBODAgQM0btyYxo0bM3LkSEwmEzVq1ECn0xEcHEynTp3o0KEDjRo14ty5cyn+77t8/TrlSpUEoHDB/ASHhFrLVCoVqxbMxtvLi6jX0ZjN4OHuzmcVyjGwV1cAHj99hl/aNCmeK1HOazcpV7I4AEUK5OemTU4lq+bPwtvLk6jXrzFjyVm5Qlm+7/kdAE+ePU+VnBnTQMRrM1o9xJng7lMzOfxtK1YXFVwMNXH4cpzN6wEZFARkVHAm2L49oQDZMql58sJIjMZMXBzcvK2jYG7bE+jq7ZH8fT4GhQLSp1Hx6nWcNf/MVc95+NSQ1Fvbjb+fkuevTGh0lm0b/iiOwCy2VdBvf+u4dseSU6UAQ1zq3EUMu3mOQsU/AyBX/uLcC71mLXv8IIwMmXPg6Z0GFxc1gQVLEnr9LI/vh1GoRGXLvy1rbh4/CLNLtpdPQkmTIQfunmlQubiSNbA0D8PO2CzzMPwsOQtZsuQsVIV7wZYG3dN7Vzm9bwmb57Tk9N7FAOQpVosazccD8PrlQzx9MtglN8DNq5coXqo8APkLfkLorRvWMhe1mnHTF+HmbrnTExcXh1pt/xt4d2+dJe8nlm0VkKcED29fsZY9fxRGukw58PCybOvs+UpzJ9hyQ8wvY3aa9fzJ5r0e3rnKrUuHWDGlDb+tGIFOE22XzNeuXqV06TIAFCxYiJBbt6xlarWaGTNn4Z5gOya8EXorOJi7d+/w5Zf2aSTfDj5H/mKWYydH3hI8CI/fnk8fhpHe37I9XVxcyZW/FLdvWrbnrvXTKV+jBb5pM1mXj3n9kj82zuLrNsPskvWtq9euUaZ0aQAKFSzIrRDb7fnjjGnvbE81d+/epWwZyzrZAwK4e++eXTMCZE6n5FlkfJ0Z9tBInmwqm2W2HdZxJdwyskSlUGAwgjEOftwUg+HNgBOlAozGlK1L77y73xMcR8/e2e8585fi9pvjaPeG6ZSv3gJfv/j9rtPFUrNhT0pUqp+iGf8/YsPucrZpb4d9/t1b58hb1LI9s79zHD1LUC+5uLiSI18p7gSfpUjZz6nZqK91OaXK8t2IinhCjnyW68Ic+UpxNzjlrz0BsmRQ8TQijlitmTgT3LprIH8Otc0ym/ZGc+mW5eadSqnA8M73sNUX3gTtjrbnfSfh5P71DdE7d+5Qt25dli9fzqJFi1i5cqW1bOHChVSqVIm1a9cyZ84cRowYgclkQqvVUq9ePdavX09gYCAbN25k8+bN+Pn5sXbtWhYsWMC4ceMwGo2MHz+eJUuW8Msvv+Dv78/jx4+t7x8SEsKQIUNYuXIlHTt2ZOvWrSn+74uN1eDl5Wn9W6lUEhcX30BSqVQcPnaSzn0HUqxIIVRvKiqVSsXkH+fx05LlVP20YorneldMbCze7+Q0JsjpolJx+PhJvu03mGKFC+KicrG+Pnn2fOYuWUHVSuXtntPNVYEuQftMZwB323oVrR5CH9nWmt4eUL2Ekp0nbRun9uLhriRWG9/g1ejMeHokPpyVCpg2IAuF87gTfFsHQPAdPRGvUidnQu6uoNXHbzetHtzdbBv5MVowmSBjWgX1P3Xjz1P6d9/GLrSxMbh7xvcWKZRK4uIsV3FaTQweCcrcPbzQxkYTkKsAV88dwmw2czv4Iq8inmIypfx21WujcXP3sf7t6uaVqMGTcBlL+WsA8peqS42mY2jUcxWPws8SftXS46BUubBn7RAO/jKevMU/T/HMb8VqYvD08rL+rVTFb1elUklaP8uQ4d3bt6DTaihWsqzdsryl08bg5hm/PRVKFaY3mXTaaNw94svc3OO3ZeEyn6NS2fYqZctdlNpNB9Fx6Br8Mmbn0O/z7ZI5NjYWL88E2zFBPa9UKvHzswzD+P3339BqtJQsWcq67MZNG2jZqo1dcgHoNNG4v7M93+5jS1n8sePm4YVW85qzh7fh5eNnbcgAmExxbF06kq9bD8XNPf7fag+xsbHvPW8m3J6//b4djVZDqZIlyRMYyMlTpzGbzVy/cYMXLyJszrX24O6qSFRneri+W2eaMZkgk5+SBlXc2H1Shxl4HWtZr0pxNW6uCm7cTdmsWm00bgmOFaVClaDOjMbdI8F+d/dCG/uac0cs+z1fgv0OkC5jANnzFE/RfP9fj7ftwWxIvcdU3vXucaT8p+PI3XIcubl74ebhhU4Tw4Z5/ayNUr9MAYTfOAXAzQsH0Oti7ZLZ3U2BRhd/HaLVm/Fws70OidZYGqn+6VU0re3F74fjsxTP78qDZ3E8eZH61yOpzWR2zv+cwb/+GdEMGTKwatUq9uzZg7e3N0ZjfEUTGhpKvXr1APD398fb25uIiAhcXFwoW9ZyQVSqVCkOHz6MUqnk7NmzXLp0CQCj0ciLFy/w9fUlffr0APTq1cvmszNlysSCBQtwd3cnJibGLsOiPD09iNVorH+bzGZrY/OtKpXK81mFskyZM589Bw7zZa3qAAzr34uIly/pMXA4K+b/iIf7O2NQU5CXpyexmvihFyazGZd3c1Ysz2flyzJlzgL2HDgUn7NfT757GUmPQSNYOW+mXXLWKKEkRyYF/n4KHjyPPzrd1JaT/4cUyanE001B65oqvD0UqF3g+SszF0JT9khv9nkaCuRyI0cWNSF344N5uCmI0STuiY0zwaCZj/gkrxs9WqRn3KKnKZonOb4s70pgVhVZ0iu5+yT+hOPuClpd4u2TN5uKJlXdWLsvdZ4PBXD39EKnjX/G1mw2WRsd7h5eaDXxZVpNDB5ePhQtW4PHD8KYN64TuQuUJHtgYZRKVaL3/k8d3/kjD8PO8fzRTTLnKGZ9Xa+LsbkABHB190avi8HF1f1NuS9ms5kSVdtbl81VuCrP7l8jdxHLcVWn9VRi6g1k04/NaDN0J2o3T1Kap4cXWk38hYfZZLZpzJlMJtauWMCjB/cYMGwiCoUiqbdJUW7uXujf2dfKN5nc3L1tvgc6bYzNxeG7CpWqjbun75v/r8XutRPsktnT0xNNwnreZFvPm0wmli9fxoMH9xk+4gfrdoyOjub+vfsUL26/i3w3D9ttZjbFHztuHt7oEhw7Ok0M7p6+HNsThAIFIVeP8+juDTYvHspXLQfx/Mltfl05FqNBx9MHoWxfM4l6bYaneGZPT0+b86Y5ie25dPkKHjx4yMjhw1AoFHxepzZ3791j8LDhFC5UiLx58yQ616aUuhXdyJNNRdYMKu48tq0zNUnUmfkCVDSr4c7qPzQ8fWk5DyiAbyq7kSmtkqU7Ur4h4u7uneg4iq8zEx9HHp6+HN8bBCgIfbvflwylbb/5+KTNmOL5PjaJjiPzO8dRonrJUu+8evGIdT/1plyNlhSv+DUADb+dxK61k/h71zKy5S6KyiVlR5o0rOZJ3uxqsvu7EPYg/pra3VVBrC7xdUiBnGrafOXN0l9f2zQ6KxZ1Y99JTaLlxf+Wf32P6PLlyylRogQzZszgiy++wJyg/z9PnjycOWMZ4vbkyROioqJImzYtRqORGzcsQ8jOnj1L3rx5CQwMpG7dugQFBfHzzz/zxRdfkDFjRqKiooiMjARgwoQJ1oYqwMSJE+nTpw9Tp04lf/78Np+dUj4pVJCTZyzDLq7dCCYwZ/ykIzGxsfQdNgq9wYBSqcTDzQ2FQsGeA4dYu3kbAG5vXlPZeYKQTwoV4MTZ8wBcvZlEzuFjrDnd3d1RKJXsOXCYtVssOd3dXFEoFHZ74P6vCyZW7olj+iYj6XwUeLhann/I6a/k3rMP77eTN0ws3mlk5Z44/r4Sx+VwU4o3QgE2/fmK8Yuf0m3cA/wzuODloUSlgoK53bl1x7bF3KmBH4XzWIbranRmh9392n1Sz/xtGkYtjyFDGiWebpZtG5hVxe3HtietvNlUNKzsyuLtGu49tf8Q57dyFyjJtfNHALgdfJGsOfJZyzJnC+TZ47vERL/CaDQQev0sufIX527oFQILlqL36BUUK1uT9JkC3vf2/5GKdfvTuHcQnccfJfL5XbQxkcQZ9TwIPUOWXCVtls2SuxS3rx0C4M71w2QNLI1eG83aqV+j18VgNpu5d+skmbJ/wvXTv1qH6bq4eqBQKFCkYAM6oQKFi3L+zAkAgm9cIUeuQJvyn+dNx6DXM/CHydYhuvaWPW8pbl2ybKv7oRfwz5bfWpYhSyART+6gibZs67vBpwnIU/J9b8WaWZ15EGap88OuHydLriJ2yVy4cBFOn7H0cNy4cZ1cuXLZlM/7aS4GvZ6RI0dbh5QCXLlymRIlS9gl01u58pfi5oXDANwNuUDm7PHbM1PWQF48uUNsdCRGo57wm2fIkbcEXX9Yw3c/BPHdiNVkyVGQpl2nkLtgWfpP2cF3I1bTsucsMmXLY5dGKEDhwoU4fdpy7r9+4wa5ctlOMjZn3nz0BgOjR46wbs+bwbcoUqQw06dM5tNKFcmS2X4T5+08rmPulliGL3lNhjQKa52ZJ5sL4Y9se4/yBahoXNWdBdtiberMFjXdUasU/Lxdgz06+nLkL8XNi/H73T/Bfs/4zn6/ffMM2fOWoMuINXQZEUTn4W/2+3dTpBH6Ro68pbj1ZnveC7mAf0CC7ZnFdnveeXMcRb96zsoZnanT7HtKV2lsXT744iEafjuRtgMWExsdSd4ilVI067aDsUwPekX/WS/I5KfEy12BSgn5c6oJvW/7ZSuQU03Lz72Zve4Vdx7ZluXMrCbkvuN6oYVz+Nf3iFavXp0xY8awfft20qZNi0qlQq+3XLB37dqV4cOH8+eff6LVahk3bhwuLpZN8vPPP/Pw4UOyZs1K//79Afjhhx9o06YN0dHRtGrVCqVSyejRo+natStKpZLChQtTtGhR62fXr1+fHj16kD59ejJnzszLly9T/N9XuUI5zl64RK/BIzCbzQzp25N9h46g0Wip90VtalWtTN9ho3BRqQjMlZPa1SqjNxiYOmcBfYeOwhhnpGfnjnafXKlyhbKcuXCJnoNHYsbMkD7d2XfobzRaLfU+r0Wtqp/Rd9gYVC4q8uTKQe2qldEb9Eydu5A+w0ZjjIujV+f2uNk5p8kMf5yJo20tFQqFgvMhJl5rwMMV6ldUsfGQcwwhiTPBmu2RDOucEYVCwcHT0byMisPLQ8l3TdLxY9Bz/jj6mm8bpaNRLTCbYPm2CIdmNpksz4B2re+BQgEnrxt5FWPG0w2a13BnxW4tDSq7olIpaFXLcvH39KWJzQd1ds9WrGxNbl46zo8j24DZTKvu4znz90702lgq1WpKw3aDWDixK2aziQrVG5I2nT8uLq7s2jiPA9tX4uHlQ8uu4+ySTaVSU7nBUH5d9C1ms5nC5RvjndYfbUwk+zf+QN1O8yhXpzt71g7h6olNuHv58UXbmajdPKlYtz9b57WzPO+YvyK5ClfFoItl7/phbJnbGlOckcoNh9tMHJKSylaswqXzpxk5sBtms5nu/Ybz98E9aLUaAvMW5MDeHRQsUpzxw/sA8GX9ppSrVNUuWd4qVKo2YdeOsWxSCzCb+abTZC6f2I5eF0vpqs2p03wIa37sjNlsosRnjfH183/ve9VtO5pda8ejclHj7ZuReu3t8x2oWKkS58+f4/vv+4PZTL/+33PwwAE0Wg358uVjz54/KVLkE4YPGwJA/W8aUKnSpzy4f5/MmbN84N3/O4VL1+LWlWMsHNsSM2aadJnEhWM70GtjKVejGXVbDWX5tC6YzSbKVGlEmnTv356p5dOKFTl3/gL9vh8EmBnQry9/HTyIVqMlX768/LlnL58UKcyQ4SMAaFC/Pp8UKcyqoDX8snUbXl5eDOjbx+45TSbLM6A9GnqhUMCJq3prndmqtgdLd2hoXNUdFxW0/dwDgCcvTRy9pKfCJ2pCH8TRu4llpMPB83ouhabcRX/h0rUIuXKMxeNaYjabadxlEheP7UCni6Vc9WZ82WooK6db9ntpJ9nvzqxQ6VqEXj3GkgktwWym4beTuHh8B3pdLGWrNePLFkNZPbMLZpOJUpUb4evnz861k9DGRHHwt4Uc/G0hAO2+X0J6/5wEzeqK2s2d3AXLk7+4ferUOBNs3BtD/9ZpUCgss+ZGvjbh5a6gfT0fFmyOouXn3riooFN9y8iSxy/iCNoVjben7bDzfzuZNff9FGZ7dNN95GrUqMHu3btxc0v92dMe3rz04YWcgMKcer1V/41Fp+zTQ5HSgq88cnSEZMmcI52jIyTL55+lzqzV/63QRx9Hzk/zPvvwQk7g2hP7TbyUksplue3oCMly6YV9fo4opZVKF+LoCMkyc4f9f34sJVStmPKPEdmDx2eFHB0hWWIOX3d0hGT5c98LR0dIlmUjP75e9G5TU74jKiUsGpIKP/PwAf/6oblCCCGEEEIIIZzLv35o7n/ir7/+cnQEIYQQQgghxEdOBp++n/SICiGEEEIIIYRIVdIQFUIIIYQQQgiRqmRorhBCCCGEEELYgUlmzX0v6REVQgghhBBCCJGqpCEqhBBCCCGEECJVydBcIYQQQgghhLADmTX3/aRHVAghhBBCCCFEqpKGqBBCCCGEEEKIVCVDc4UQQgghhBDCDswya+57SY+oEEIIIYQQQohUJQ1RIYQQQgghhBCpSobmCiGEEEIIIYQdyNDc95MeUSGEEEIIIYQQqUoaokIIIYQQQgghUpUMzRVCCCGEEEIIOzCZZWju+0iPqBBCCCGEEEKIVCUNUSGEEEIIIYQQqUqG5jqZ8LhAR0dIltBn3o6OkCzVS2kcHSFZPi2e2dERkiWt28exPe9HfRxVW8a0JkdHSJbwl+kcHSFZMvnqHR0hWY4/yO3oCMmSPd3Hcbxvv5nP0RGSpXQJhaMjJEs23yhHR0iWe4evOzpCsnhVKeToCMnS8ORVR0f415JZc99PekSFEEIIIYQQQqQqaYgKIYQQQgghhEhVH8f4NSGEEEIIIYT4yJhl1tz3kh5RIYQQQgghhBCpShqiQgghhBBCCCFSlQzNFUIIIYQQQgg7MMmsue8lPaJCCCGEEEIIIVKVNESFEEIIIYQQQqQqGZorhBBCCCGEEHZglqG57yU9okIIIYQQQgghUpU0RIUQQgghhBBCpCoZmiuEEEIIIYQQdmA2y9Dc95EeUSGEEEIIIYQQqUoaokIIIYQQQgghUpUMzRVCCCGEEEIIOzCbTI6O4LSkR1QIIYQQQgghRKqShqgQQgghhBBCiFQlQ3PfaNu2LWPGjCFPnjz/0fpLliyhQoUKFChQgN9//52mTZumcMKkmUwm1iyewr3bwbioXenQcyT+WbLbLKPTaZg5picde44kS0BuAMYMaIWHpzcAGfyz8m3vMSmWKfjiX/y9Yz5KlQvFP21MycrNbMpjX0fw69KBGA1avNNmol77yajdPN673tLxDXDz8AEgbYYA6nWYbH2vKye3c+bAGjoM3Zgi2U0mE+t/nsS928Go1Wradh9Npiw5bJbR6zTMHtuNdj3GkDkgN6a4OIIWjePJg9solCo69BpLxszZ3/MJ/122TUsn8ODOTVzUrrTqNpaMmeOzXT5zkD9+WYRSqaJC9YZ8WqsJBoOetQt+4MXTB7h7eNH02xFkypKTe2HX2PDzeFzUrgTkKkDjDkNRKlP+vpTJZGLVoqncvX0LF7UrnXuNSOL7qWXqqF507v0DWQNycXj/Do78tQMAg17P3fBgflq5Gy9vnxTP9tuqcTy6ewMXF1cadR5PBv+c1vLr5w6w/9cFKFUqylRpRLnq8d/j6FcvmDeqCZ2GLCNT1kCiX71g6/JRaGJeYTaZaNp1Cun9cyT1sf915h2rx/H43g1ULq406DSe9Aky3zh/gIO/L0CpVFGqciPKVGuGyRTHbytG8fxROAqlkkadJ5Euk32yWbbnTVQurjTuPM5me147d4C/fl2YYHta6sg5PzTC/c3xnS5jNpp+N8m6zoVjOzi2dy09Rq9P8bxvM29cOpEHt98eU2OSPN5/Gt+V1t3Hkjmbpf58/SqCmT+0Y8TMX1C7uqVYHrPJxO61Y3hy37INv24/gXSZ4rdh8MW/OLI9vo4sVaXZB9fZs3ES6f1zU7paSwCO7V7C1VM7cfXwptLnnclXvHqK5Xfm+hMg/OpfnN6zAIVSReFyjSlS0fbcpIl+yZ4132M06PBKk4maLSahdvXgyd3L/P3bFMCMp08GareejovajTP7FhN+9S9McQaKVmpF4QpN/uNsty7+xd8756NUWvZtiXfPm9ER/PbmvOmTJhN1O0xG7eqR5HpGg56dq4YR+fweru7efN5yFOn8c1nf6+qp7Zz5aw3tU/C8uXrxVO7dvoWLi5pOvX5Isp6fPronnXqNJGuAJcuOLSs4f/oIRoOBGl82oWrtb1Ikz7vZ/r91ZpzRwLZlI4h88RCjQU/V+t0oVLIGD29f5fdVY3FRu5I5e0G+aj3cLufND0lbrhgFJw3kRK12qf7ZJpOJrcvH8/DuTVxcXGn23VgyZI7fnlfPHmDv1oUoVS6Uq9qQCjWbcurQNs4c+g0Ag0HHwzs3GLPwEBHPHrBt5SSUShUqtZpW3SfjkzZDqv+bUoPJJLPmvo80RFPId999B8D9+/fZvHlzqjVEz588iMGgY8TUlYTevMzGFT/SZ/gsa3l4yDVWL5rEyxdPra8Z9DoAhkxYkuJ54owG9m2aTMfhW3B182DV1JbkK1Yd7zQZrcsc2bmAIuW/pnilRhzbvYRzhzdSpnrrJNdz9/QFoO3AoESf9fjedS4e3ZKi02JfOHUAg17H0MmrCQu+xJZVs+gxdLa1/HbIVdYumUjkiyfW1y6dOQTA4EmruHnlNJtXzrRZJ6VcOv0XBoOO7yeuJTz4IttWT+e7wT8Blu2+ddU0Bk1ej6u7Jz+ObEvRMtU4f2IPbu6efD9xLU8ehrN5+SR6jljM+iVjadJxGIEFSrBjw1zO/r2TslXqpXjmsycPoTfoGT1tOSE3L7Nu+Rz6j5hhLQ+7dY2VC6cQkeD7WaXm11Sp+TUAKxdNo0qteineCAW4dnYfRr2OHqM3cDfkArvWTaNd//mAZXvuWDuFXuM2oXbzYNG41hQqWR2ftBktFykrRuOSoAGye8MMSlT6mmLlvyT02kmePQqzS0P0+rl9GA06vhu5gXshF/hjwzRa943PvHv9FLqNtmReOrE1BUpU517oRQC6/LCO8Oun2L1+qnWdlHTt7H4Mej09Rq/nbshFdq6bRvsE23Pn2in0HLcJVzcPFo1rQ6GS1azHd9cRqxK938M71zl96Be7Tnt/6fRfGPQ6Bk5aQ3jwRbaunkG3IXOt5XdCr7JhyXheJjjer104ym9r5/D61YsUz3Pzwj6MBj0dh23kfugF9m2aQrNeCwHLNty7cTKdRljqyJVTWpK/eHXuh55Pcp2Y1xH8vnwwL57cpmKdbwF4ev8mV07toNPwzQCsnNKCXAUroHbzSJH8zlx/xsUZ+PvXKTTtvxm1qwe//NSKXEWq4+Ubf246vWc++Ut9TaFyjTi7fwlXj2+keJX2HNg0ki/azyFtxpxcPbGZ1y8fEBP1jMe3z9Ok93oMBg3nDyz/r7Lt2zyZDsMs+3b1tJbkfee8+feOBRQp9zXFKjXi+B9LOH94I6Wrt05yvRvn/sTVzZP2Qzfx4nEYezaMp0XfZQA8uXedi39vAVLuuDp38iAGvY6RUy31/IYVs+k7fKa1PDzkGqsWTiEiwX6/fvkst25cYsTkpeh1Wnb/uibF8iT0n9SZty4fwdM7LU26TiM2+iULRjWmUMka/LZyNHVbjyBHvpLs+2U2l07soESl+nbJ/T6B33cmW5v6xMVoUvVz37pyZj8Gg44+49Zx59ZFfl8znU4D5wGW7flb0FT6TdiIq7sH80a3oXDpapSr2pByVRsC8Mvy8ZSr1hAPL19+ndGbhh2Gky1XIY7v28Rf25fxTdshDvl3Ccf5nxyaazAYGDRoEC1atKBp06bs2rULgLlz59KuXTs6d+5MREQEADNnzqRFixY0b96c3bt3A7B27VqaNm1K8+bNmTp1KgBDhw7l8OHDLFq0iJCQEObNm0eLFi24desWAIcOHWLs2LEp/m+5df0Cn5SsBECeAkW5HXrNptxo0NN76AyyZMtlfe3u7WB0Oi0zx/Rg2siuhN68nGJ5nj8OxS9TDjy80qBycSV73tLcu3XGZpl7t86Sp0hlS+ZPqhB+/dh713ty7wYGvYZ1P3Zizcx2PAi7AEBs9EsObJ1B7WbDUyw7QMj18xQp+SkAgfmLcSf0qk250ain++BZZE6wPUuUr0GbbiMBiHj2CJ806VI001thN85RuMRnAOTOX5y7Cfb14wdhZMycA0/vNLi4qAksUJLQ62d5fD+MwiUt29o/a26e3A8DIPLFEwILlLC8V4GShN44b5fMwdcuUKxkRQDyFihKeMh1m3Kj0UDfYdPJ8uYOeUJht67x4F4YNT5vaJdst4PPkb+YZXvmyFuCB+FXrGVPH1oakh5eaXBxcSVX/lLcvnkWgF3rp1O+Rgt802ayLn/n1jleRTxh6ZSOXDi2ncCC5eyS+e6tc+Qtasmc/Z3Mzx6FkS5TfOYc+UpxJ/gshUvXon4HS90T+eIh3r7p7ZLtdvA5Cli3Z3EehMcfO5btmRPPN9ly5i9F+M2zPLp7A4NOy7KpnVkyqSN3QyyN5pjXkfyx8UfqtRlml6xvhV4/T+E3x/u7xxRY6s/vBs229oQCKBRKeo9agqd3mhTPc+/WWfJ8YjleA/KU4NGd+P2bqI7MV5q7t868dx29NoYq9XpTtEJ8L9PzR6HkLFAOF7UbLmo30mXKyZP7N1MsvzPXny+fhJEmQw7cPS3bL0vu0jwKO2uzzKPwc+QoaNmWOQtW4V7wcSKfhePulZaLh1exdV4bdLGv8MsUyN0bf5M+S352rejFzqXdyVWk2n+c7cWjUPwyvnP+C7E9b94POUvgm/NmYJEq3L5+7L3rPX8UQuAnVQBInzmQF49CgfjzZq3mKXvevHX9IkVLWa5DkqznDQZ6D51mcx1y5cJxsufMy09TBjF74gBKlP0sRTO99Z/UmUXKfk7NRn2tyylVKgCiIp6QI19JAHLkK8Xd4HN2yfxPYsPucrZp71T/3LfCb56jYHHL9syZrzj3wuKP8ScPwsjg//Y6xJXcBUoRfiN+G90LvcKT+6FUrGnp7W/bZwbZchUCIM5kRK1OudEl4uPxP9kQ3bhxI35+fmzYsIEVK1Ywe/ZsXr58SZ06dVi9ejXVq1dn8eLFHDp0iPv377NhwwZWr17NokWLiIqKYuvWrYwYMYKNGzeSPXt2jEaj9b27detG3rx56dWrF02bNmXbtm0A/PLLLzRp8p8P23kfjSbaOsQWQKlUEhcXnydfoRKky5DZZh03N3e++KYNA0bPp1234Sz5cYTNOv8NvSbaOowWwNXdC60m2nYZbfwyru5e6DSv37ue2tWdCnW+pWW/ZXzZZuybIb16dq4aQe1mw3F190qR3G9pNTE221OhVNlsm7wFSybangAqlQsrfvqBDcumUrpirRTNlDCb+3v29btl7h5eaGKjCchVgCtnD2E2mwkPvkhkxFNMpjgy+Adw69ppAK6cPYReZ5+7q5rYGDy93v/9zF+oOOkz+ie57vYtK2nYvLNdcgHoNNG4e8Z/5xLua0tZfG43Dy+0mtecPbwNLx8/awP2rZfPH+Lh5UvnoStImz4Lh3YuTZXMyn/K7G7JDJbv5y8/D2XnmgkUKfu5XbJp3/l8RYJ9nfT2jMbVzYMqX3Wk0+CfadhxNBsWDsZo0PPL0h/4uvWQFD++k8r8T/VnnoIl8XvneC9UvCLePmntkkenjcbNw7b+MSXchu/UkTpN9HvX8cuYnWyBxW3eP1NAAe4Gn0GnjSY2+iX3Qs9j0Kfcse/M9adeG41rwu3n5oVO+zrRMm7ulmXU7l7ota/RxkTyKPw8RT9txTfdV3Dv1nHuBR9HGxPJ03tX+KL9bKo1HcPeNYP+4957nTbpffvuMu+eN9+3nn9AIUIuH8BsNvMg7AKvI59gMsWxa/UIajUbjqtbyh5XmtgYPD3j3zPxdUhx0me03e+voyIJD7lOz0FTaN99KItnjbTL6If/pM50c/fCzcMLnSaGDfP6WRulfpkCCL9xCoCbFw6g18WmeN4PebxtD2ZDylyv/Scs1xoJt2fC6xDbbe3m4YUmNv4Y2//bz9Rp3N36t6+fpcc/PPg8R/9cT5WvUn+ocWoxm81O+Z8z+J8cmhsaGkqlSpa7d97e3uTJk4ejR49SpkwZAEqVKsWhQ4fIkCEDV69epW3btgAYjUYePnzI5MmTWb58OTNmzKBEiRLv3ZlfffUVDRs25Ntvv+Xx48cUKVIkxf8tHh7eaLUx1r/NZjMq1T/vVv+sOcmUOTsKhYLM2XLi7ZOWVy+fJ3mBkFwHf/2ReyHneHr/JllzF7O+rtfaVloAru7e6LUxqF3d35T74uphee3d9dL558YvU04UCgXp/XPj4Z2WB2EXiHh6h91rx2A06Hj+KIQ9GydSp/mI/zj/W+4eXmg1CbanyfTB7flWx94TeNXmOVOGtmXMnK24uafMcLeE2XQJs5njs7l7eKFLsP20mhg8vHwoVrYGj++HMXdsJwILlCR7YGGUShWte4znlxVT2ffbCnLm+QQXF3WKZn3Lw9N2e5qS8f0EiIl+zcP7dyhcrIxdcgG4eXjbbLOE+9rNw9tmW+s0lu/psT1BKFAQcvU4j+7eYPPiobTrPx9P77QUKml51q5Qyer8uXl26mQ2v5M5QZnuzbH1VuMuU3jd9BmLxzenz6QduLp5pmg290Tb0/yP29PD04cMmXOR3j8HCoWCjFly4emdlrshF3n+5A7bVo7DaNDx9EEo29dMtkvvqLuHN1pN/MVkwu3pCG7utvWg2WRC+Z79+7aO/Kd13pUhSx7KVG/N+jldSJcxJ9lyF8fT2y/F8jtj/Xli12wehp/lxcNg/HMmODfpYmxufsKbc5MuBhdXdwzaGFw9fHD3TEuaDDlIlzkvADkLVubZ/au4e6XFzz83KhdX/DIFonJxQxMdgadP8kccHPr1R+6Hvue8+U42t3fOm26evri5e6PTxSRaL3+JWrx4HMq6We0IyFOKzDmL8PjOVV4+vcOf6+LPm3s3TqR2Cpw3LfV8wuPow/W8t08asmTLhYtaTZZsuVC7uvH61Ut806Zsj/h/Wme+evGIdT/1plyNlhSvaHlUpOG3k9i1dhJ/71pGttxFUbm4pmjWj0Hi6xBzguuQd7bnm+sQAE1MFE8fhpG3SHmb9zt/fDf7ty2h8+AFePvaZzSEcG7/kz2iefLk4cwZy7CX6OhogoODCQgI4PJlyxDVM2fOkC9fPgIDAylfvjxBQUGsWrWKL7/8koCAADZt2sTYsWNZs2YN169f5/z5+GGNSqUS05vfC/Lw8KB8+fJMnDiRb75J+YfwAfIWKs7ls0cBCL15mWw58n5wnb/3/8bGlT8C8DLiGRpNNGn8/rsHxKs16E/bgUH0m3GUl8/uoomJJM6o5+6tM2QLLGmzbPa8pQi5YnkuKPTKYbLnK02GzHmIeHon0XoXj25h3+YpALyOfIJeE032vKXoOnYnbQcG0bDLLDJkyZsijVCAvAVLcOXc3wCEBV8iW858H1znxMEd7N5qef7G1c0dhVJhlwkMAguU5Or5IwCEB18kS474bJmzBfLs0V1iol9hNBoIvX72zVDDK+QpWIq+Y1ZQvFxNMvgHAHD13GFadx9H92ELiImOpGCxiimeFyw9nhfOHgMg5OZlsudM3mRgN6+ep0jxsnbJ9Fau/KW4eeEwAHdDLpA5e35rWaasgbx4cofY6EiMRj3hN8+QI28Juv6whu9+COK7EavJkqMgTbtOwSdtRst7XbS8V/jNM/gHfPh785/IkbcUt958zr2QC/gHxGfOmMU28503mS8c/Y1DOyzPg6vdPFAolCgUqhTPljN/SW5csHw/74ZcJHP2+G2QKWsgzxNku/0m25lDW9m5bhoAUS+fotNEkzN/SQZM2U7XEato2XMmmbLlsdsQ3cCCJbh6Lv6YyprDPvstuQLyliLksmX/3g+9QKYE+zdRHRlsqSP/aZ13xbyOQBP9kg5D1lOnxQiiXj4iY7aU+zc7Y/1Z4at+NOoZRKdxf/Pq+V20b7bfw7DTZM5pe27KnLskd65bzk13bhwma+4y+KYPwKCPJfLZHQAehp0lXea8ZMldirs3/sZsNhP96glGvQZ3r7T/r2xVG/Sn9fdB9HnnvHkvifNmQJ5ShL45b4ZdPUz2vKVJnyUPLxN8J96u9/D2ZQLylqb190HkL1GLtBmykzV3MbqM2Unr74P4prPlvJkSjVCAvAWLc/HNdUjIzcsEJKOez1+oBFfOH8dsNvMy4hk6rQZvn5Qf7v6f1JnRr56zckZn6jT7ntJVGluXD754iIbfTqTtgMXERkeSt0ilFM/r7HLnL8n1N+fNO7cukiVBPe+fLZDnj+O3Z9iNs+TKVwKA0OtnyPeJ7XXG2SPbOfrnOnqMWkF6f/tMUCac3/9kj2izZs0YOXIkLVu2RKfT0atXL7Zu3cq+fftYtWoVXl5eTJ06FV9fX06dOkWrVq2IjY2lVq1aeHt7U6BAAZo0aYKfnx/+/v4UL16crVu3ApA+fXoMBgPTp09n0KBBNGvWjJYtWzJmzBi7/FtKla/OtQsnmTi0I5jNdOo9mhOHd6PVaqhWp1GS61Su2YBlP41m0rBOKBQKOvUanWK9ACoXNbWaDmX97G8xm80U/7Qxvn7+aGIi2bn6B5p0n8endbuzfcUQLhzZhIe3Hw06z3zveiU+a8L2lcNYNbUlCoWCrztMeu/d/pRQonwNrl86wdTh7TCboUPPsZw6sgutJpYqdZIeWl2yQk1WzRvF9B86ERdnpFnHQSk6k+ZbxcrV5Mal48z6oQ1ms5nWPcZz5u+d6LSxfFqrKQ3bDWLBxK6YTSYqVG9I2nT+uLi4smPjPPZvX4mHlw+tu40DIGPmnCyc3ANXN3fyFSlHkVJVUjwvQOkK1bhy4SRjB38LmOnSZxTHDv2BVqv5x2c/Hz24Q6bM2eyS6a3CpWtx68oxFo5tiRkzTbpM4sKxHei1sZSr0Yy6rYayfFoXzGYTZao0Ik26pIcQA3zVajBbl47k5P4NuHv60KLHdLtkLlS6FqFXj7FkQkswm2n47SQuHt+BXhdL2WrN+LLFUFbP7ILZZKJU5Ub4+vlTuExtti4dwdJJbTDFGfmq1TC7fD+LlK5FyJVjLBjbCjDTpMtELhzbgU4bS/kazajbagjLp31nsz3LVGvE5iUjWDi+DQqgSecJqdojWbxcTW5cOsGMEW3BbKZNz/GcPrITnVbDZ7VT/lGKDylYsjbh146yckoLzGYz9TpM4srJ7eh1sZSq0pzazYay7kdLHVniM0sd6ZPEOu/j6e3Hy+f3WTahMSoXNTWbDEapTLmbEs5cf6pUaj77Zgi/L+mM2WyiULnGeKf1RxsTyV+bRvJVx58oW7s7+9YN5eqJzXh4+VGnzQxULq7UbD6RPWsGAmYy5ypJrsLVAHgYdobNs5tiNpuo0njkf7wtVSo1NZsMZcOcb8Fsplilxvi8OW/uWv0DjROeN//ehKe3H/W/nfne9VRqNUd+n8PJPctx9/Thq3YTU25DJqF0hWpcvXiSCUM6YQa+7T2K44f+QKeNpdrnSV+HlChbmZtXzzNuUHtMJjNtuw62PouZkv6TOnPn2kloY6I4+NtCDv5mmSys3fdLSO+fk6BZXVG7uZO7YHnyF6+a4nmd3SdlaxF8+ThzR7UGzDTvOoFzRy31fMWazajfZjBLJn+H2WymbLWG1vPms0e3SZ8pwPo+JlMc21ZNwi9DFlbO6gdAYKEyfNG0lwP+VfZnlllz30thdpZBwv9Sly5dYs2aNUybNi1Zyx+9Fv3hhZxA6DPvDy/kBHKkd8zMcv9f+riUPwHbQ1q3j2N73o9K+Vl27cEY5+gEyaNWfRynCR93xz079f/xKPLjmJQje7qP43i/fMfd0RGSxcdL4egIyVLAP8rREZLlXuTHcR3iVaWQoyMki/nk1Q8v5AS+LvXx9aE17R/u6AhJ2vxj7g8vZGcf3978iKxZs4ZffvmFuXPnfnhhIYQQQgghhPgfIQ1RO2rTpg1t2rRxdAwhhBBCCCGEA8jQ3Pf7n5ysSAghhBBCCCGE40hDVAghhBBCCCFEqpKhuUIIIYQQQghhByazydERnJb0iAohhBBCCCGESFXSEBVCCCGEEEIIkapkaK4QQgghhBBC2IHMmvt+0iMqhBBCCCGEECJVSUNUCCGEEEIIIUSqkqG5QgghhBBCCGEHMjT3/aRHVAghhBBCCCFEqpKGqBBCCCGEEEKIVCVDc4UQQgghhBDCDsxmGZr7PtIjKoQQQgghhBAiVUmPqBBCCCGEEELYgclkcnQEpyU9okIIIYQQQgghUpU0RIUQQgghhBBCpCoZmutkNEa1oyMky5IZhxwdIVl219nh6AjJolB/JIdiyUqOTpAsvhlKOjpCsvjpnzg6QrJcNnzi6AjJkt3rqaMjJMu9F9kdHSFZAt1uOzpCskRkLODoCMlSm12OjpAsZ4w1HB0hWf7c98LREZKl4cmrjo6QLIryRRwdIXkMNx2d4P9Nfkf0/aRHVAghhBBCCCFEqpKGqBBCCCGEEEKIVPWRjAcUQgghhBBCiI+L2Syz5r6P9IgKIYQQQgghhEhV0hAVQgghhBBCCJGqZGiuEEIIIYQQQtiBzJr7ftIjKoQQQgghhBAiVUlDVAghhBBCCCFEqpKhuUIIIYQQQghhBzI09/2kR1QIIYQQQgghRKqShqgQQgghhBBCiFQlQ3OFEEIIIYQQwg5MZpOjIzgt6REVQgghhBBCCJGqpCEqhBBCCCGEECJVydBcIYQQQgghhLADmTX3/aRHVAghhBBCCCHEe2m1Wnr37k2rVq3o0qULERERSS5nMpno3Lkz69ev/+B7SkNUCCGEEEIIIcR7rV+/nvz587Nu3ToaNGjAggULklxu9uzZvHr1KlnvKUNzk2nr1q2EhYUxcOBAR0exYTKZ2Lh0Ig9u38RF7UqrbmPIlCWHzTJ6nYafxneldfexZM6WG4DXryKY+UM7Rsz8BbWrW6pk/bRsejq0zEFcHOzc+4jtex4nuVzxImkY9X1BGnc6CUDzbwL4uk5mIl8ZAJg2P5h7DzR2SqnArVpDVBmyYo4zov1rM+ZXL6ylykwBuFWuBygwx75Gu2c9LvlKoC5UxrK2iwvKDFmJXjYO9Fo7ZQST2cyU/RcIfv4KV5WSkbVLkT2tt7X86uMIZh26jBnI4OnG+C/L4uaiotWa/Xi7qQHI6uvJmM/L2C0jWL6fU1ZtJfjuI1zVKkZ+24zs/hkSLTdh+WZ8vTzp07yu9bWIqNe0GTWb+YO7kjtrJrvnXDR/DrfDQ1GrXenV93uyZM1mLT988C9+//UXlColuXIF0q1nX5RKy328yMiXDOjTnXETpxGQPcf7PiLFcs5avILQ23dQu6gZ3KsLAVkyW8sPHjvFuq2/gwLq16nJ17WrW8teRr6i8/cjmDV2GDkDsiX19ima82Oolyz7fS7h4aGo1Wp69f2erAn2+6GDf7H9160J9nsflEolfXt1xcvLCwB//yz0HTDILvluXfqLozvno1S6UKxSY0pUbmZTHhsdwe/LBmI0aPFOk4m67SejdvUAwKDXsGF2R75qN5H0mfNgMsWxe80PRDwOR6FUUbf9ZPwy2uf7ajKZmLtgMWHht1GrXRjQpxfZsmaxlh85eowNW7aiQMFXX9Thq89r2yXHu5l+WzWOR3dv4OLiSqPO48ngn9Nafv3cAfb/ugClSkWZKo0oVz1+W0e/esG8UU3oNGQZmbIGsn7eAF6/eg7Ay+cPyJGnOC17zbJL5imrfuHW3YeoXVwY2bkZ2f0zJlpu4rJN+Hp70rv518SZTExYtok7j56iUioZ3aUFAUnUuSmdc/3Pk7h/JxgXFzVtu49O8nifPa4b7XqMIXO23MQZDaxeMIYXTx9iMOr5qnEXipetZtecAMXzuVKviicmE/x9Qcvh87bn6XS+SjrW80GpBIUCVu2MRqM10bWRr3WZHJld2LI/hkPnUu4cbzKZ2Lp8PA/v3sTFxZVm340lQ+b47+fVswfYu3UhSpUL5ao2pELNppw6tI0zh34DwGDQ8fDODcYsPETEswdsWzkJpVKFSq2mVffJ+KS173cgKWnLFaPgpIGcqNUu1T/b2ZhN/45Zc8+ePUvnzp0BqFKlSpIN0T/++AOFQkGVKlWS9Z7SEP3IXTr9Fwa9joGT1hAefJGtq2fQbchca/md0KtsWDKely+eWF+7duEov62dw+sEDSx7U6kU9O6chy4DzqHRxbFwWgmOnnpBRKTBZrlMGdxo0SAAF5f4zvr8ebyZMOsGN0Oj7Z7TJU8RFC5qYrfMQ+mfA7fP6qHdudJa7l6jKZrdqzG/eoG6cDkUPn4Yb5zBeOMMAG5VG2K4dtqujVCAgyEP0cXFsbJFNS4/iuDHQ5eZ9U1FAMxmMxP2nWfa1+XJntabbZfDeRQVSxZfTwCWNE1e5ZAiOc9eRWcwsnJ0by6H3OHHdduZ1b+jzTK//HWckHuPKVUw0PqawRjHxBW/4KZWp0rOk8ePYjDomTZrHjdvXGP50kWMGDUeAJ1Ox9rVy5m7YClu7u7MmDqB06dOUL5CJYxGIwt++hE3V9dUyXnk5Bn0egMLp47j6s1bzF+xlsnDvwcgLs7E4qD1/DxjIh7u7rTrPYjPypcmra8vRqORGQuX4eaWOjk/lnrpxPGj6A16ps/6iRtv9vsPNvt9BT8t+Bk3d3emT53I6VMnKFnKcvNm0tSUb3gkFBdnYP/myXQYugW1mwdB01uSt1h1vNPEN0SO7lxA4bJfU6xSI47/sYTzhzdSrlYHHt25zJ9rR/M6Mn77hlw6AEDbwRu4c/Mk+zdPpkmPhXbJfvTESfQGPXNnTuXajZssXraCcSOHv/l3xbF0ZRALZs/A3d2dzj1682mF8qRJ4/uBd/3vXDu7D6NeR4/RG7gbcoFd66bRrv98SyajgR1rp9Br3CbUbh4sGteaQiWr45M2I3FGA9tWjMYlwY2Rt41OTcwrfp7Ugbpthtol88GzV9DrjawY3ZfLIbf5cd3vzOr/rc0yv/x1jJD7jyhVMA8AR85dBWD5qD6cuR7CrHW/JVonpV08dQCDQceQSasJC77EllWz6DF0trX8TshV1i6ZSGRE/Pfx5OGdePmkoWOfiUS/jmTioBZ2b4iqlNC8jjcTlr1EpzczrGNaLgTriIqJf36vQTUv/jqj4fxNPUUC1TSu4cWCzVFMD7L07uTJ5kLD6l6JGrD/rStn9mMw6Ogzbh13bl3k9zXT6TRwHmD5fv4WNJV+Ezbi6u7BvNFtKFy6GuWqNqRc1YYA/LJ8POWqNcTDy5dfZ/SmYYfhZMtViOP7NvHX9mV803ZIiub9kMDvO5OtTX3iYuzVaSBSwsaNG9m4caP17+bNm9O8eXMANm/ezKpVq2yWT58+PT4+PgB4eXnx+vVrm/Lg4GB27NjB3LlzmT9/frIyyNDc99BqtfTv35/mzZvTqFEjnj17Zi2bOXMmHTt2pFmzZgwbNgyw3CVo1qwZrVq1olu3bkRHRxMeHk6LFi1o06YN7du358mTJ+/7uP9Y6PXzFC75KQC58xfnbug1m3KjQc93g2ZbexwAFAolvUctwdM7TYrneZ9c2T158EjD6xgjRqOZS9eiKF7E9vNd1QoG9sjHzIW3bF4vkNebNk1zsGBqCdo0yW7XnKosuTHeuQGA6cldVJkCrGWKtBkxa2NwLVEZj0bdULh7Yo6M/14oMwWgTOeP4epJu2YEuPDwBZVy+QNQNEs6rj15aS278zKaNO6urDsXQpdNh4nSGsiVzofgZ6/QGuPo8cvfdN1yhMuPkh7bn6I5g8OpVKyAJWfenFy7fc+m/NKt21wOvUOjGhVsXp+9YTuNq1cko599L07funb1MiVLlwWgQMHChNy6aS1Tq9VMnTkXN3d3wHIh7fqm4bli6SK++Koe6dKnzt3my9dvUr5UMQCKFMjHzZAwa5lKpSRo3gy8vTyJev0aM2Y83mSev3It33xRkwx+fqmS82Opl65fvUKpN/u9YMHChNwKtpa9b7+Hh4Wi1+kYNWIII4YO5MaNa0m+93/rxaNQ/DLmwN0rDSoXVwLylOZ+yBmbZe6HnCWwSGUAAj+pwp0bxyxZDXoadZtPOv/4mzv5S9Tiy9aWRnZUxEO8fO33nb169TplS5UCoHDBAgTfCrGWqVQqli+ah5eXl+V7agYPD3e7ZXnrdvA58hf7DIAceUvwIPyKtezpwzDS++fAwysNLi6u5Mpfits3zwKwa/10ytdogW/axKMy9v4yj4q1WydZlhIuBIdTsVhBAIrmzcX18MT155WQOzSqXtH6WrUyRRnRqSkAj5+/JL2vj12yJRRy4zxFSliO98D8xbgTdtWm3GDU023wLPyz5rK+VqpiHeq36Gn9W6VU2T1nlgwqnkbEEas1E2eCW3cN5M9he7Nz095oLt3Sv8mkwGC0nWSm1RfeBO2OxpzCc8+E3zxHweKW72fOfMW5l2AbPnkQRgb/HHh6W76fuQuUIvzGOWv5vdArPLkfSsWall78tn1mkC1XIQDiTEbU6tQZ9ZZQbNhdzjbtneqfK/5/mjdvztatW63/vW2EAjRt2pQdO3bY/Ofj40NMTAwAMTEx+PraXqP9+uuvPHnyhPbt27Nt2zZWrlzJ4cOH/zGDNETfY8OGDWTLlo2NGzcyZcoU3NwsB3J0dDS+vr6sWLGCDRs2cOHCBZ48ecK+ffuoXbs2a9asoUmTJkRFRXHs2DGKFCnCihUr6NatW7LHS/9/aDXReHjGD8lUKpXExRmtf+cpWBK/DJlt1ilUvCLePmlTPMs/8fJUER0bnytWE4eXl22HfP9u+Vi/7T7PI/Q2r+8//IwZC4LpM+IixQqnoVLZdPYL6uqGOWFvptkECsthovTwQpUlF4ZLx9D8ugRVQF5UAXnjVy1TA/2pvfbLlkC03oC3a/wJVKlUYHwz9CNSo+fSwxc0LR7IgsafcereU07dfYq7WkWb0vmY3+hThtcswYjdp63r2C2nVot3gotMpUKJMS4OgGeRUSzetoch7RrZrPP7kdP4+XhbG7CpITY2Fi9Pr/icShVxb3IqlUrS+lm+czt+34ZWo6FEydLs3/sHadKktTZkUkNMrAYvT88EOeO3J4CLSsWh46fo2H8YxQsXxEXlwu79h0jr60u5ksVTLefHUi8l3u9Km/3u96bhnnC/u7m50aBxU8ZOmEKPXv2YNW2ydZ2UpNNG4+YR34hwdfdCp4l+7zJubl7oNJa70wF5S+ObLgvvUqpc2LFyCHs3jqdgqc9TPPNbMZpYvLwSfE9VSpttpFKpOHLsON1696foJ4VRqezfCNFponH3jN+eCqXK+p20lMV/X908vNBqXnP28Da8fPysDdiEol+9IPTacUpXaWi3zDEaLd6eCerPBMf788golmz7kyHtGydaz0WlYvTidUxfvZWa5ex/3Gs1Me8c7yqb4z1vwZKke+d4d/fwxN3DC60mhiUzBlK/ZU/szd1NgUYXf87T6s14uNleBkdrLI1U//Qqmtb24vfDsday4vldefAsjicvUv5412pibL6fCetM7TvfXTcPLzSx8T1R+3/7mTqNu1v/9vWzjJoIDz7P0T/XU+Wr1B8a+3jbHswG44cX/B9hNpmd8r//r1KlSnHo0CEADh8+TOnSpW3KBw8ezObNmwkKCqJhw4Z06NDhg0N0ZWjue4SFhVk3Xv78+bly5QrPnz/Hzc2NiIgIBgwYgKenJ7GxsRgMBrp168aiRYto3749/v7+FCtWjCZNmvDzzz/TuXNnfHx86N+/f4rndPfwRquJryjNZhMqlfPs1i5tclGscBry5PLiWnB8xenpoSI6Or6SSp/OleKF0xCQxQNa5sTX24UxgwoxZvp1Nv1+n5hYS8V//EwE+QK9OXbaTr15eh2KhHcPFQpLYxQwa2MwRT7H9PIpAMa7N1FlCiDufgi4uqP0y0Tcg1D75HqHt6uaGH389jObzbi8eWYxjYcrAWm9CUxvuVNVKZc/159E0rJkHrKn9UahUJDTz4e07q48j9GS2cczyc9IkZzu7sRodbY531x07jt1kcjoGPrOXMrzV6/R6gzkypqJ3w+fQoGCU1eDuXn3IaOXrGdWv45kSGu/3lFPT080mvghRGaTyebi2GQysWr5Eh48uM/QEWNQKBTs22N5DuLihXOEh4Uwe+YURoyagF86+90o8fL0IFYTf6Mk4fZ8q2rFclQuX4bJcxfx58HD7Np/GIUCzl68Qkj4HSbOWcjk4QNJ75fWbjmdvV56y7LfE+Q0mRPt95XLf+bhg/sMGzEahUJBtoAAsmTNZv1/H19fIiJekDFjyvSKHf7tR+6FnOPZg5tkzV3M+rpeG2PTMAVwc/dGr41B7eqOTheDm+eHj5GvO0wl+tVAVk9tRufRO3F1S/nj38vj3ePJnKixWblSRT6tUJ7pP85l718H+aJ2zRTPkZCbhzc6bUyCTPHfSTcPb3Sa+DKdJgZ3T1+O7QlCgYKQq8d5dPcGmxcPpV3/+fikzcjl039SouLXKO3Yk+fl4U5swvrTlKD+PHmByNcx9JnxMy9evUar15MrSybqVSkHwNiurXjePIoOY+awecpgPNzt1yvm7uGF9j3b9p9EPH/MomkDqPp5M8pV/spu+RpW8yRvdjXZ/V0IexB/3nR3VRCrS3wztkBONW2+8mbpr69tGp0Vi7qx76R9hpq6e3jZfAfNZrN1G7q/893VaWLw8LLUBZqYKJ4+DCNvkfI273f++G72b1tC58EL8Pa148178T+lZcuWDBkyhJYtW6JWq5k5cyYAK1asIEeOHNSs+f+vx53vysBJ5MmTh8uXL1OrVi3u3bvHrFmzaNCgAYcPH+bRo0fMnj2biIgI9u7di9lsZvv27TRs2JAhQ4awePFiNm3aRGBgIKVLl6ZXr17s2LGDpUuXMnny5BTNGViwBJfPHKJ0pc8JD75I1hz5UvT9/1s/r7kNWJ4RXbOgDD7eLmi0cZQokob1W+OHGb2I0NOq+2nr37+trsiY6dfx8lSxel4Z2vQ4jUZrolSxtOzcm/QkRykh7tFtXHIXxhhyCaV/Dkwv4j/L9CoChdoNRZr0mF+9QJU1N4Zrpyz/vmyBxN279b63TXHFs6bncNgj6hQI4PKjCPJmiB/OGJDGC43ByL3IaLKn9eb8gxd8UyQnv129Q8jzVwyrWZJn0Rqi9QYyeNl3SFzx/Lk4fP4adcqX4HLIHfJmj78r3rJOZVrWsQwr/P3IaW4/fEr9ymWpXzm+h/G7SQsY1qGJXRuhAIUKf8LpU8f5rEo1bt64Rs5cuW3KF/z0I2q1muEjx1knKZo8fba1fMSQAXTv1c+ujVCATwoW4Njpc9T4rAJXb94iMGf8UPWY2FiGTpzBzDHDcFWrcXd3Q6FQMm/SKOsyfUaM5/vunezaCAXnr5feKlS4CKdOneCzKtW48Z797qJ2ZfjIsdb9vnfPH9y5HU73nn158eI5sbGxpEuXPsUyVfnGcsMyLs7A0jF10cRE4urmyb2QM5SvbfusX7Y8pQi9cohilRoRduUw2fOWTuotAbhy4ldeRz6h4hddUbt6oFAo7NaIKlK4IMdPnaZq5c+4duMmuXPFT7oSExvLyHETmTJ+zJvvqTtKpcIuORLKlb8U188doFj5L7kbcoHM2fNbyzJlDeTFkzvERkfi6u5J+M0zVP6qE0XLxfcaL5nYjgYdx+CT1tLbFHr1ONW/6WbXzMXz5+LIuWvULl+CyyG3yZs9vpe7xedVaPG55Wb59sOnuP3oKfWqlGPn32d4GhFJx/q1cHdzRalQWL+79pKnYAkunTlEmUqfExZ8iWzJON6jIl8wd3x3Wnw7lILFyn9w+f/GtoOWm00qJYzv5oeXuwKt3kz+nGr+PGHbsCyQU03Lz72Zve4VL17ZNlJzZlYTct/2mbiUkjt/Sa6eO0iJil9w59ZFsmSP34b+2QJ5/jj++xl24yzVvrbMtRB6/Qz5Pqlo815nj2zn+P5N9Bi1Ak/vtHbJK/43eXh4MHfu3ESvd+zYMdFrvXsnb2i2NETfo0WLFgwfPpw2bdoQFxdHx44defnyJcWKFWPBggU0a9YMV1dXsmfPztOnTylatChDhw7F09MTtVrNuHHjMJvNDBo0iJ9++gmlUml9njQlFS9XkxuXTjBjRFswm2nTczynj+xEp9XwWe0mKf55/6m4ODPzloYxa1xRlAoFO/c+5nmEHh9vF4b2zs+IyUk/ZxUTG8eSoHDmTiqOwWDmzMWXnDhrv2cbjaFXUGXPh2eTnoAC7f6NuOQvgULthuHqSbR/bcLj81aAgrhHt4m7bXmeVJk2I6ZUnGSlet6snLzzlI4bDmIGRtcpze4b99DojTQqlpuRtUsxYtdpzJgpliU9lQOzYIgzMfrPM3TaeAjFm3Vc7HyBUr30J5y8EkzHcT9hNsPoLs3ZfewcGp2eRtUrfPgNUkmFSp9x4fxZBn/fG8xm+vQfzKED+9FqNeTNl599e3ZTuEhRRg6zzJr99TeNqFgp8XA9e6tSoQxnLl6m+5DRgJmhvbuy99BRNFot9T+vSe0qn9J7+DhcXFTkyZmDOlVTPyN8PPWSZb+fY/D3fTCbzfTtP4hDB/aj0WrIl68Ae/f8QeEiRfnhzX6v900jatf5kjmzpjFkYF8UCgV9+g20y9BSlUpNjaZD2Tj3W8xmM8UqNcbHzx9NTCS7g36gUbd5fPpVd3asHMLFvzfh4e1H/W9nvvf98pesw65Vw1gzozWmOCM1mw7HxU7Pjn1asQJnz1+k78AhmM0wsF9v/jp4CI1WS90vPqdmtSp8P2Q4KhcXAnPlpGa1qnbJkVDh0rW4deUYC8e2xIyZJl0mceHYDvTaWMrVaEbdVkNZPq0LZrOJMlUakSad/z++37NH4aTLaN85C6qXLsrJK8F0GjsXM2ZGd2nBH8fOEqvV06hGxSTXqVGmKGN/3kCXCfMwxsUxoE0D3FztO+lbiXI1uH7xBNOGt8MMtO85llNHdqHTxlL5Pcf77q1LiY2JYueWJezcsgSA3iPm4+pmv5ujcSbYuDeG/q3ToFBYZs2NfG3Cy11B+3o+LNgcRcvPvXFRQaf6lh7Hxy/iCNoVjbenpfFqL5+UrUXw5ePMHdUaMNO86wTOHd2BThtLxZrNqN9mMEsmf4fZbKZstYbW7+ezR7dJn2AuC5Mpjm2rJuGXIQsrZ/UDILBQGb5o2stu2cWHmc3/jllz7UFhNqf0I9fiv7Hvku7DCzmBMSNOODpCsuyus8PREZJFof5I7gmVrOToBMlyP0NJR0dIFj99yk9gZg+XDZ84OkKyZPd66ugIyXLsrn0bMCmlZsB1R0dIljMvU+958v9GbXY7OkKynPGo4egIybLmV/vPpJ8SGtZNnUni/luK8kUcHSFZ6hpufnghJ1OzxSlHR0jS/g3lHB1BJisSQgghhBBCCJG6PpJuGCGEEEIIIYT4uJj+gxlq/1dIj6gQQgghhBBCiFQlDVEhhBBCCCGEEKlKhuYKIYQQQgghhB2YTTJr7vtIj6gQQgghhBBCiFQlDVEhhBBCCCGEEKlKhuYKIYQQQgghhB2YZdbc95IeUSGEEEIIIYQQqUoaokIIIYQQQgghUpUMzRVCCCGEEEIIOzCbZdbc95EeUSGEEEIIIYQQqUoaokIIIYQQQgghUpUMzRVCCCGEEEIIO5BZc99PekSFEEIIIYQQQqQqaYgKIYQQQgghhEhVMjRXCCGEEEIIIezAbJJZc99HekSFEEIIIYQQQqQqaYgKIYQQQgghhEhVCrPZLFM5CSGEEEIIIYRINdIjKoQQQgghhBAiVUlDVAghhBBCCCFEqpKGqBBCCCGEEEKIVCUNUSGEEEIIIYQQqUoaokIIIYQQQgghUpU0RIUQQgghhBBCpCppiAohhBBCCCGESFUujg4g/reFhYURGBjo6BjCAUwmE2azmfPnz1OsWDFcXV0dHUkIAIYNG2bzt1qtJnPmzLRu3Zo0adI4KJWwt49pv38s9Wd0dDQKhYK9e/dSvXp1p9uOQgjHkobov9jt27e5c+cOBQoUwN/fH4VC4ehIiYwYMYL169c7OsYH3blzhz/++AODwQDA06dPGTdunINTfdjTp0/JlCmTo2MkMn36dLJnz87Dhw+5evUqGTJkYOrUqY6OlaSP5YLvY7Bp0yZWrVqFVqvFbDajUCjYv3+/o2MlotPpyJ49O2XKlOHixYtcvnyZdOnSMWTIEBYtWuToeFbz5s2z+fttw+mrr75CrVY7KFVi48ePp0mTJhQqVMjRUf7Rx7LfP5b6c/DgwXz66aecP38ek8nE3r17mT9/vqNjfbQ+luPo6dOnREVFoVKp+Pnnn2nbtq3TZxaOIw3Rf6k1a9awd+9eXr16RYMGDbh79y6jRo1ydKxEPD09mTRpErlz50aptIwUb968uYNTJTZkyBCqV6/OuXPnyJQpE7GxsY6OlKQ5c+awfv16DAYDWq2WXLlysXPnTkfHSuTs2bMMGjSItm3bEhQURPv27R0dKUkfywXf/v37Wbt2LUajEbPZTGRkJNu3b3d0rEQ2bNjAkiVLyJgxo6Oj/KOIiAhmzZoFQOXKlenUqRP9+vWjdevWDk5m6+bNm7i5uVkbTo8ePSJjxoz8/fffTJ8+3dHxrKpWrcqiRYt48uQJ9evXp379+nh7ezs6ViIfy37/WOrPBw8e8M0337BlyxanznnhwgW2bt1qc6N52bJlDk6V2MdyHA0ZMoSuXbuybt06Pv/8cyZNmkRQUJCjYwknJc+I/kvt3LmTlStX4uPjQ4cOHbh48aKjIyWpZMmS+Pr68uLFC549e8azZ88cHSlJ7u7udO3aFX9/f6ZMmcLz588dHSlJR44c4fDhw9SrV49du3bh7+/v6EhJMplMXLp0iYCAAPR6PREREY6OlKSzZ8/SokULzp8/z7Jly3j8+LGjIyVp/vz59O7dmyxZstCwYUMKFCjg6EhJ8vPzI1u2bLi6ulr/c0bR0dGEhoYCEBoaSkxMDC9fvnS6G1BRUVHMmDGDFi1aMHnyZJRKJdOnT+f+/fuOjmajSpUqzJkzhwULFnD27Fk+++wzhg4dyoMHDxwdzcbHst8/lvrTYDCwa9cu8ubNS0REBJGRkY6OlKQJEyZQrlw5oqOjyZo1K2nTpnV0pCR9LMeR0WikbNmyREVFUbduXUwmk6MjCScmPaL/UmazGcA6HNdZL/h69erFsWPHuH//PsWKFSN37tyOjpQks9nMs2fPiI2NJTY2llevXjk6UpLSpk2Lq6srMTEx5MyZE41G4+hISfrmm28YP348kyZNYvr06bRr187RkZL0sVzw+fn5UbJkSTZs2ECjRo3YunWroyPZeNvLpNfr+fbbbylcuLC1bhowYIAjoyVp1KhRDBo0iKdPn5IlSxZGjRrFrl276Natm6Oj2Xj9+jURERGkS5eOly9f8vr1a+toCGcSGhrK1q1bOXDgAOXLl2fdunUYjUZ69+7tVN/Vj2W/fyz1Z+fOndm5cyfDhg0jKCiIfv36OTpSknx9ffn66685evQovXv3pk2bNo6OlKSP5TgyGAxMnjyZMmXKcOLECeLi4hwdSTgxaYj+S9WtW5fWrVvz8OFDunTpQq1atRwdKUmzZs3i8ePHhIaGolarWbJkifWi1Zn06tWLvXv3Ur9+fWrWrEmDBg0cHSlJmTNnZsuWLXh4eDBz5kyio6MdHSlJrVu3tg5369SpE1myZHFwoqR9LBd8arWa06dPYzQaOXLkiNONLHh7g8lZbzS9q1ixYoku7IoWLeqgNO/Xu3dvmjVrhre3N7Gxsfzwww+sWLGCJk2aODqajREjRtC8eXN69+6Nu7u79fXGjRs7MFViH8t+T1h/jhgxwsFp3q9OnTrUrFkTgE8//ZRixYo5OFHSFAoFt27dQqPREBYW5nT151sfy3E0ZcoUjh49StOmTdm3b59TPSYgnI/C/LbrTPzrhIaGEhwcTGBgoNMO1WvdujVr1661PuvSrFkzNm3a5OhYHy2TycSjR49IkyYN27Zto1KlSuTJk8fRsRJZvXo17u7uREVFsXXrVipXrpxoxkpn8+jRI6dtMD958oSwsDAyZszInDlz+OKLL6hbt66jYyUSGxtrncRi06ZNNGjQgGzZsjk6ViK//vorS5YsQafTWV9zxkmVwHLMR0REkD59eqeckA5gwYIF9OjRw/r3zJkz+f777x2YKGnOvt/79OnD3Llz+eyzzxKV/f333w5I9M8+lmfsb926xa1bt/D392fixInUr1+fDh06ODpWIh/LcfS2nndxcWHjxo1OW88L5yAN0X+pj2Ua+hYtWrBq1Sq6dOnCihUraN26NRs2bHB0LKuP5cR/4MABqlevzsaNGxOVOePkT82bNycoKIjOnTuzatUq2rdvz+rVqx0dKxFnbzA/fvyYzJkzEx4enqjMGXsfe/ToQaNGjdizZw958+bl5MmTTjkpSN26dVmwYIHNjQdnfLzh6NGjrFy50qbh5EzH0ebNm9myZQuhoaHkzZsXgLi4OIxGI9u2bXNwusQ+lv3+sWjRogUbNmywmVRp1apVjo5lZTQacXFxQa/XJypzpv3+sR1HPXr0oHHjxvz5559OXc8L5yBDc/+lPpZp6Nu3b0+jRo2IiIigadOmTncXcu7cuYDlzm7FihUdnOb93k4C4axDit6lUCh49uwZGTJkQKFQOO0ztzt37rQ2mHfu3Ol0sz6uWLGCYcOGJZoRW6FQOFWD5K2oqChq1qzJ6tWrmTZtGkeOHHF0pCRlz56dnDlzOjrGB02ePJnhw4eTOXNmR0dJ0jfffEPFihVZvHix9TlLpVJJ+vTpHZwsaR/Lfj99+jQajQaz2cz48ePp27cv9erVc3SsRJz9GfshQ4Ywc+ZMvvjii0SjCZypJ/xjO46ioqKoUaOG09fzwjlIQ/Rf6mOZhv7LL7+kUqVK3Llzh4CAANKlS+foSEmaN2+eUzdEGzZsCEDPnj2tPyC+b98+qlev7uBkSStfvjxt2rRh5syZTJo0iTp16jg6UpKcvcH8tnf2Y5ka32AwsHz5cooUKUJISAgxMTGOjpQkd3d3OnfuTKFChZx6UqUsWbJQqVIlR8d4r5s3b1K0aFHq1Klj02sfGhqa5CgTR/tY9vv06dOZMWMGY8eOZf369fTr188pG6LO/oz9zJkzAUuj8/Hjx2TJkoVLly453bOsH9tx9LaeL1y4sFPX88I5SEP0X+rtNPR58uRxymno/2l44+TJk1MxSfIoFAp69uxp83unzniBMmTIkI/iB8T79+9Pv379ePnyJYMGDUKtVjs6UpI+lgZz5cqViYiIwM/Pj8jISFxdXcmQIQOjR4/m008/dXQ8qyFDhrBv3z66d+/O9u3bGTNmjKMjJalq1aqOjpAs6dOnZ9SoUTazEDvTUPzjx49TtGhRdu3alajMGS+gP5b97ubmRvr06XFxcSFjxoxJDi11Bq1bt6Z+/fo8fPiQ/v374+np6ehISRo9ejSZM2emR48e/P7772zfvt2pJoH62I6jwYMHs3//fqev54VzkGdE/6UuXbrEmDFjrNPQjxw5ksuXL5MhQwY+//xzR8ezDtVYv349JUuWpFSpUly+fJnLly9b71I6k6Sew3jbC+lM3p38ydmeyXnr5MmTDB8+HG9vb16/fs348eOdqsH0rlevXuHh4eFUzw0lNGDAAHr16kVgYCB3795l3rx59OzZk0GDBjnV5F8PHz5M9FrWrFkdkCRply9fpmjRokk+/+2MF3zz5s1L9FqvXr0ckCRp/9RAcqZj6WPb7927d+fFixe0atWKmJgYTp48aX2MxJn8+eefLFy4kLi4OOvw14ST7TiLpk2bsnnzZuvfb8+jziguLg6z2cyFCxcoVqyYUx1HCT19+hSj0YjZbObp06eULFnS0ZGEk5Ie0X+pq1evEhMTg6urKy9evGDgwIHs2bPH0bGsKleuDFiecevSpQsApUuXpmPHjo6M9V4BAQGOjpAsH8sPiM+ePZt169bh7+/PkydP6NWrl1M2RE+fPs3YsWOtF1JZs2aladOmjo6VyOPHjwkMDAQgR44cPHr0iJw5c6JSqRyczFb//v1RKBSYTCbu379Pzpw5Wb9+vaNjWb3tedi5c2eiMmdqkLydpMoZZ0ZOKKln78xmMwqFwqmewftY9vtbc+bM4e7du+TNm5fg4GCnrJPAcn7ftGkT3377rXUCG2dsiJrNZl6+fImfnx9RUVFO+7uX785CnDFjRqZMmeLoWIkMHz6cCxcuoNFo0Gg05MiRw6luiArnIg3Rf6nNmzcTFBTEwoUL+eKLL5yyVwws03y/vQg4f/48BoPB0ZGS9PZi2Ww2ExISQrZs2ShbtqyDUyXWuXNndu3axdChQ536B8RVKhX+/v4A+Pv74+bm5uBESZs9ezZr1qyhd+/edOvWjZYtWzrlRV/GjBmZMWMGJUuW5Pz582TIkIGjR4863ZDnhLM6R0VFJZpkydG+++47AHx8fBg+fLiD07zfxzJJ1V9//eXoCMnSoUMH9Ho9Y8eOdXSUZHn58iWLFi3i5cuXfP7552g0GooXL+7oWIkolUpcXV1RKBQoFAo8PDwcHSlJPXv2pHHjxqRJk4bXr18zevRoR0dK0tmzZxk0aJDNiCdnFBYWxs6dOxk1ahT9+/enb9++jo4knJg0RP+l/Pz8yJQpEzExMZQvX94ph+0ATJw4kTlz5jB+/Hjy5MnDjz/+6OhISXo78RNYhps5awOvTp06BAQEcO7cOb744gun/f1Yb29vgoKCKFu2LKdPn3aqnxRKSKlUkjZtWhQKBW5ubnh5eTk6UpKmTZvGxo0bOXz4MPnz56d3795cu3bN5nvrbHx8fLh7966jYyQpLCyMqKgofH19HR0lSR/LJFXjxo1j1KhRNG/ePFHPqDP9TNfH0nP71siRI+nYsSMLFiygTJkyDB061Cl7nMqUKcOAAQN48uQJo0aNomjRoo6OlKTq1atTpUoVnj9/TqZMmZz293idfRbit7y8vFAoFMTGxpIuXTqn7WAQzkEaov9SPj4+7Nu3D4VCwYYNG5y2wsqTJ4/TNpLfJy4ujnv37jk6RpJmz57NiRMnKFasGKtXr6ZWrVp07tzZ0bESmT59OgsWLODHH38kT548TJo0ydGRkpQjRw5mzpxJZGQkS5YscarnGRNSqVQULVqUQoUKYTab2bt3L19//bWjYyWSsEHy4sULp53xNSwsjAoVKuDn52fN60y/G/zWvHnzWLt2rc0QbGfK+XYYpjPfEIGPp+f2LZ1OR8WKFVm4cCGBgYFOO6JkwIABHD58mMKFC5MnTx6nncX91KlTjBs3zukfwXD2WYjfKlKkCMuWLSNTpkz0798fo9Ho6EjCiclkRf9S0dHR3L17lwwZMrB8+XKqV69O+fLlHR0rkUWLFrF06VLc3d2trznThdRbCZ8TMhqNtG/fnu7duzswUdIaNWrEli1bUCqVxMXF0bx5c7Zs2eLoWFYJp55/V+7cuVMxSfIYjUY2b95McHAwefLkoVmzZk45OUS3bt0wGAw8ffqUuLg4MmXKxMqVKx0dK5HQ0FDrse7m5kaGDBkcnChpx48fd+qfa3qrcePGrF271qb+dEb37t1j2rRp3L59m3z58jFo0CCyZMni6FiJ7N+/n3Xr1mEwGDCbzURGRrJ9+3ZHx0qkS5cutGvXjsWLFzNw4EB++uknli1b5uhYiURHR3P48GGbSasaNGjguEDv0bp1a+bPn0/v3r1ZunQpLVu2ZOvWrY6OlaTXr1/z8OFDsmfP7rSzEAPExMTg5ubG4cOHKV68uNP+5qlwPOkR/Zfy9vamcOHCAAwdOtTBad5v9+7dHDlyxGmfHXlr27ZtZMyY0dExPihz5szExMTg4+OD0Wh0ugv9pJ5pezsEzpmebXtLpVJRpEgR8ubNC8DFixed8tng6Oho1qxZw4gRI6zD9pzRDz/84FSTE72Ps/9u8Ftvf8LD2Q0fPpzOnTtTqlQpTp8+zfDhw1mxYoWjYyUyf/58Ro4cyYYNGyhfvjxHjx51dKQkjR8/nqlTp/Ly5UuWL1/utD+P0aNHDzJlymS96eCsQ14/lkcwnH0W4pkzZya5jy9cuOCUP3cnnIPzn8HEv1q2bNmc/m4+QJ8+fUiXLh1NmjShatWq1t8SdTZPnz7l888/p2DBgoSEhKBWq2nRogXgHM9kOfszbe/q1asXL1++JEuWLNYGszM2RN82RjQaDe7u7k77TI6npyeTJk2y+T1eZ/rdy7ec/XeDBwwYgEKh4Pnz5zRs2JB8+fIBltzO+PNXKpXK+hudNWrUcNrJ8/z8/ChZsiQbNmygUaNGTtsrtmLFCqedTyEhs9nMjBkzHB3jg94+gvHy5UunfgTD2Wchfjtz+7Nnz3Bzc8PX15dZs2bRqVMnBycTzkwaosKhDAYD9erVI3/+/NY7ac54IbV+/XpCQ0PZsmULCxcupGLFijRp0oTs2bM7OpqNOXPmODrCv8qLFy+cogH/IbVr12bevHkULFiQZs2aOe0d/be/JffixQsHJ/lnjRs3dnSEf/T25pKze/uYhYeHBz///ALH7FUAACDmSURBVDNly5bl0qVLTjdS4y21Ws3p06cxGo0cOXKEZ8+eOTpSkkJDQ516Mq23ChQowMWLFylUqJD1NWd8tGHs2LFs3ryZMmXK4Onpyfjx4x0dKUnOPgvx299Wb9KkCVOmTCFv3rzWybScdZSOcDxpiAqHevsboh+DTJkykT17dq5evUpwcDATJ06kUKFCTjE1+ebNm2natCkbNmxINDTGmXpyPja5c+fmyZMn1p+acVatW7e2/n/VqlXJlSuX48L8g+7du3Pt2jW0Wq2jo/wjZ//d4HLlygGWSXYuX75M3759+fbbb+nQoYNjg73j7e9ypk2blrCwMMLCwgDnbIyApUESFhZG9+7dmTNnDn369HF0pCSFhoZSvnx50qVL59STaZ06dcpmIihnnYXYaDSi0+msvx/qrEOIP5ZZiF1cXKyPs2TPnt1pR5AJ5yCTFQmHio6O5ueff+bZs2dUq1aNAgUKkDNnTkfHSqRv377cunWL+vXr07BhQ2vDxFmGbx05coTKlSuzbdu2RGVv71I6kwULFtgMKZo5cybff/+9AxMlrU6dOty/f5906dJZX3PGC76DBw+yfv16NBqN9TVnfOa2Z8+evH792toj5qxDSd/evEn4u8GLFi1ycKrEGjZsyNKlS0mfPj2vX7+mS5cuH0UP/tOnT8mUKZOjYyTp+PHj3Lt3j2LFipE7d26nnZHWmf3444/079+fffv2UatWLUfH+aD/a+/Ow6Iu1zeA3yObJbgxuIAgQmBqcNSGY+a+pJaJqIBbSUZxyBCMUhO3DAQXIDUjl9LcUVDLk5qlnhNJmnsaniM4LHpMZB0MUJZhfn9wzVyODNU5v5r3Re/PX8PMP/flMnyf932f550xYwbc3NzQs2dPnD9/HgUFBdIeKU5LSzMMz5N1CnFkZCQ6deqEnj174tKlS7hx44aU3/MkB+6IklBRUVEYOHAgzpw5A6VSifnz52P79u2iYzUQGBiIfv36NXhflsErzZs3x5kzZ6TfyUlJSUFqairUajXS0tIA1F+HU1tbK2Uh+vXXXxv9rNFoxAT5DatXr8a8efOkPfKoV1paip07d4qO8Zuayr3BlpaWhmmUdnZ20u48rFmzxjCN9t69e3B1dTXslsokMTER+fn5UKvVsLKywoYNG6S8eubixYvYt2+foRe8oKBAqqm5x44dQ7t27bBt27YGx/Bl7AnXaDR45513AADDhw/HlClTBCcyrbS0FN9//z1ycnJQVlYGlUoFOzs70bEaiIuLw65du/Dtt9/C3d1dqj5Wkg8LURJKo9HA398fBw4cQO/evSHbBr1+KAiABjufCQkJ0qyW6wvi69evo6amBl5eXrhy5QpatGgh1YCgsWPHom/fvli/fj1CQ0MB1Pe9yDraPTo6GgsXLgRQvxMaHR2NI0eOCE7VUKtWrQzHNWXm6OiIW7duSXl1R2NkvjfY29sbb7/9Nnr27InLly8bJqXLJi0tDWlpaYiNjcX06dOxZMkS0ZFMOnfuHHbs2IGXX34Z48aNk2ah8UExMTF45ZVXcOTIEXh6ehpdjyKD2NhYpKeno7q6Wto+2/s98cQTOHfuHJ5++mlcvXoVjo6Ohit8ZDpGPnfuXAwZMgR+fn44e/Ys5s6di6SkJNGxGrCxsZGuTYDkxUKUhFOr1QCA/Px86Vb0m8pQEP2qfUhICJKSkmBpaQmtVouQkBDByYxZW1ujU6dOmD17Nr7//nujXkEZ75eztbVFfHw8KisrkZWVhU8++UR0JCO7d+8GUD9kZeHChejRo4dh4USmnQf9PbzV1dX46quv0Lp1a8NnMh51fvDeYFkvjl+wYAGOHTuG7OxsjBw5EsOGDRMdyaTWrVvD2toaFRUV6Ny5s9ERcplotVpUVVVBoVBAq9VK9/tIr2XLlnjxxReRnp6OmTNn4qWXXhIdycjq1avx6aef4vz58wgLCxMd5zedO3cOJ06cgJWVlWGXeeTIkdL1tFZVVWHy5MkAgCeffFLKRVGi/xYLURJqwYIFmD9/PtRqNSIiIrB48WLRkYzod5nKy8vx0UcfQa1Ww9XVVdqjJvevPmu1WpSUlAhM07iwsDA4OTkZ9QrK6K233sLy5cuRl5cn1c6ynv7v+y9/+QsAoKioSGScRumLzQd3Q/WLULKRsTi+n1arhVarRWRkJBITEzFw4EDU1dVh2rRpUvYGd+jQAampqXjssceQkJCA8vJy0ZFMCgoKwvjx41FSUoKAgABpd3UUCgWysrJw9+5dZGdnS7frWFFRgfDwcFy5cqVBy4WMvYIHDx6ETqdDSUkJ2rRpI90CRE5ODoD664UOHz4MlUqFS5cuSd+KQ/R7cFgRCTVgwAAUFxejbdu2KC0thY2NDZRKJRYvXmyyJ1OU8PBw+Pj4QKVS4fTp0zh58qSUw0t27NiBrVu3wtPTE9euXUN4eDief/550bEaePnll6Us7PTu3xED6gs8fdEsY5Gi0+lw9OhR5OTkwMPDQ7ohFpmZmSgoKMDKlSsxZ84c6HQ61NXVISEhAV988YXoeAb3H8V/kEwP0Hv27MG6detQXFxs+HfZrFkzqFQqxMXFCU7XUF1dHW7duoVWrVph//79ePbZZ+Hu7i46lkllZWXIy8uDs7Mz2rRpIzqOSVlZWcjKykL79u2xdOlS+Pr6SlU0//LLL7h69SpiYmIQFRVl9JmMLQQ//PADoqKiYGdnhzt37iA6Olqq54+XX37Z8Fqj0eDGjRvo1KkT2rZtK+XCE9F/gzuiJJSPjw/CwsLg5uaG69evY+3atXjzzTcxe/ZsqX4RlJaWGn4ZdOvWTdojMSNHjsTYsWORnZ1t+EUlE30vk7OzMy5cuIAePXoYPpOpF0fGYvPXLFiwAJWVlejZsyc+//xznDp1CvPmzRMdy+DOnTs4ePAgiouL8eWXXwKo39WRbShIUzmKr1AocPz4cfTt2xcvvPCC0fsyuX/n9oMPPoBOp0NAQABCQkKkfIBuKtOnPTw8UFtbi5ycHMTFxaFr166iIxmpqKiASqXC8uXLpfpeb8yqVauwc+dOtG/fHrdv30ZYWJhUzx/6RdtDhw5h9erVePbZZ5GZmYnx48cLTkb0/8dClITKz8+Hm5sbAMDFxQW3bt1C586dYWFhITiZsaqqKhQWFsLBwQGFhYWoq6sTHcmkmTNnom3btvD398dTTz0lOk4Do0aNgkKhgE6nw6lTpwzvy9aLoyf7dEq9zMxMpKSkAKg/XhgYGCg4kTGVSgWVSoWMjAyjxQfZ6HdrSktLsX79euTm5sLDw0O6XusOHToAAObMmSM4ya/bu3cv1q1bh6KiIowaNQo6nQ4WFhZ4+umnRUczqalMn161ahVOnToFb29vbN26FcOHD8drr70mOpbB5s2bMW/ePMTExBi9r1AopCzsLSwsDFeytW/fXpohhA/asmUL9u3bhxYtWqC8vBxBQUFSzlYg+m+wECWhHBwcEB8fj169euHChQtQKpVIT0+HlZWV6GhGZs2ahcmTJxuGGURHR4uOZNKuXbugVquRmpqKjz/+GH379oW/vz+cnZ1FRwMAo8vNmwLZp1Pqubi44MaNG3B2dkZxcbG0U2llLkLvN3fuXAwePFja6ZQDBgwAIOcdwfcLDAxEYGAgUlNT4e/vLzrOb2oq06fT0tKQmpqKZs2aQavVYuLEiVIVovrTGNOnT8fQoUMN7x86dEhUpF9la2uLbdu2wcfHB2fOnEGrVq1ERzJJoVCgRYsWAOozy1owE/03WIiSUCtWrMDu3buRlpYGT09PzJw5E1euXJHu7rby8nLU1dXBwsIC1dXV0Gq1oiM1ql27dnB2dkZGRgYyMzOxdOlSdOvWDREREaKjGYwYMcLoz9DS0hIdO3bE7NmzpSpWZJ9OqXfx4kU8//zzcHR0xO3bt2FtbW3oc21qx4xlUFVVZTg2zOmU/7uUlBQEBAQgLy+vwXd6ZGSkoFQNNZXp03odOnRARUUF7OzsUFtbK90O7j/+8Q+cP38eBw8exMWLFwHU9wkfO3bM6Ci5LFauXImkpCR88MEHcHd3R2xsrOhIJrm4uGDZsmVQqVQ4e/YsXFxcREci+n9jIUpC2djYNLgaoVevXoLSNC4pKQkpKSmwt7dHUVERQkNDGwy0kUFERASysrLg6+uLlStXGo4bjR8/XqpC9JlnnsGoUaOgUqlw4cIFpKSkYMKECYiJiZHq7j7Zp1PqJScnw8HBQXSMRv3aTrJMPWScTvnH0h8hlrHd4n5NZfq0XkFBAUaOHIknn3wS165dg5WVlaG/OTk5WXC6+sUbjUYDGxsbdOnSBUD9d+no0aMFJzOm//8O1O/e63Q6KBQKlJSUSLkrGhsbi927d+P777+Hu7t7g4nERE0Rp+YS/Q6vvPIKPvvss0Z/lkV6errJIQtVVVVSHeN5cGpuUFAQtmzZgqlTp2LHjh0CkxmTfTql3uTJkw29wYMGDZLu+oGhQ4caeoPvJ1tv8P3TKe8na29bU/Hqq69i06ZNomM8NG7evNnoZ05OTmZM8uv0sxTq6upw8eJFeHt7S7XwpP///uCQr+rqaikKeqJHAQtRot8hLCwMd+/ehY+PDzIyMlBYWGjoJZLhiFlTuXZCLzg4GMOHDzf0Bh8/fhwRERGIiYmR7gEgNzcXeXl58PT0RIcOHaSbTKqn7w0+d+6cdL3B9GiLiIiAr68vXF1dDYsk+p0yenitXLkSzs7O+Pnnn5GRkQGlUonly5eLjtXArl278NlnnxmG0llaWuLrr78WnIro0cCjuUS/w7Bhwwyv9cddZdJUrp3Qi4+Px7p163Ds2DF4enpixYoVuHTpEpYuXSo6mpHt27fjm2++QVlZGcaNG4e8vDwsWrRIdCyTmkJv8LFjx7Bz507U1NRAp9NBo9Hg73//u+hY9CcrKSkxOkHCHeZHw7lz5zB79mzDCZigoCDRkUxKSUnBtm3b8PHHH2PUqFH8t0lkRixEiX4H2adT6ndny8vL8dFHH0GtVsPV1RUzZswQnMxYfn4+OnToAI1Gg0mTJhl6cjQaDQYNGiQ6XgMHDx7Ezp07MW3aNAQFBWHChAmiI5nUVHqDP/roIyxcuBDJycno06cP0tPTRUciMxg0aJBUU10f9Pnnnzf6Ga/H+N/V1dUZeqyrq6tRUlIiOpJJbdq0Qbt27VBRUYE+ffpgzZo1oiMRPTJYiBI9RKKiouDj4wNfX1+cPn0a7777LtatWyc6loH+frlFixYZjrjqi1EZV6H1nQv6rDL1N90vMDDQZG+wTIOfgPoHvl69eiE5ORnjx4/Hvn37REcycv/wkgfxKOn/Li0tDdOnT5d2YJFarQZQP336scceQ69evXD58mXU1tZKVYhOnDixQWuA/vtTtpYGABg7diyio6MRGxuLlStXNhhMKAs7OzscPXrU8Ocoa8FM9DBijyjRQ+TBIUBTpkzBzp07BSZq3C+//IKbN2/C2dnZcDeabLZu3YojR47g5s2b8PDwwDPPPIPg4GDRsQyaWm9waGgogoODkZycDD8/P8TExEh1NQqHFf05xowZg+LiYnTq1MnQIypj4RQcHIxPP/3U8LNsQ5aaypCipqa8vBzXr1+HUqnEpk2bMGTIEPTp00d0LKJHAndEiR4iVVVVKCwshIODAwoLCw1TC2Vz5MgRfPzxx9BqtRg1ahQUCoV0x4iB+iN7Li4umDp1Ktzd3dG1a1fRkYw0td7gJUuWIDs7G2+88QZWr14t1bFhAEaLOPf7tetnqHH6e0Q9PDzg4eFheF/WgV8lJSW4c+cOWrZsidLSUmg0GtGRjOiLzby8PHz11VeG4ToFBQV4//33RUZr0mxtbdG9e3cAwLvvvis4DdGjhYUo0UNk1qxZmDx5MqysrFBTU4Po6GjRkUzavHkz9uzZg+DgYMyYMQMTJkyQshDdt28f1Go1jh8/jq1bt0KpVGLt2rWiYxk0ld5gvb179xqyffjhh0hISJDygvvk5GRs3rwZtbW10Ol0sLKykmrntqnQ3yM6YMAAwUl+n9DQUEyYMAG2trYoLy9HbGys6EgmzZ07F0OGDMH58+fRrl07VFZWio5ERPQ/YSFK9BApLy9HXV0dLCwsUF1dDa1WKzqSSc2aNYO1tTUUCgUUCgUee+wx0ZFM+ve//4309HScOnUKAODm5iY4kWmy9wanpKQgNTUVarUaaWlpAOoHmdTU1Eh5KfuePXuMpmhu2bJFdKQmSV+Ayj7sTW/kyJEYOXIkiouL0bJlS1hZWYmOZFLz5s3xt7/9Dbm5uYiLi8OUKVNER2rUyZMncePGDXh7e6NLly5S3WdNROKxECV6iCQlJSElJQX29vYoKipCaGgo+vfvLzpWAyqVCpGRkbh9+zYWLVoELy8v0ZFMmjp1KpydnfHWW29JOdVXr7S01NDf2K1bN+l278aOHYu+ffti/fr1CA0NBVC/GGFvby84mWmcovloOnPmDJYsWWJoGXB0dERAQIDoWA3odDoUFhaisrISlZWVKCsrEx3JpMTEROTn50OtVsPKygobNmxAYmKi6FhEJJFmogMQ0R+ndevWhod7pVIJW1tbwYlMi4yMhJ+fHwICAjB48GBp+3J++OEHzJs3D2fPnkVQUBAiIyNFRzJJ3xsMQMre4KtXr6JTp04YMWIEcnJykJOTA7VajdOnT4uOZhKnaD6aVq1ahe3bt0OpVCI0NFS6qdN6YWFh+Oabb+Dr64thw4Zh4MCBoiOZdO7cOaxYsQKPP/44xo0bh//85z+iIxGRZLgjSvQQsbW1RXBwMHx8fJCRkYF79+4ZVqBlKqLGjx+P/v37Y8SIEXjqqadEx2nUnTt3cPv2bfz888+4d+8eHB0dRUcySfbe4JMnT8LLywuHDh1q8JmMO/YxMTG4fv063n77bWzatAnvvfee6EhkBs2aNUPr1q2hUChgY2Mj7TRvHx8f+Pj4AACGDRsmOE3jtFotqqqqoFAooNVqDROTiYj0WIgSPUTufyhp3769wCS/Ljk5GSdPnkRqaipiYmLg7e2NqKgo0bEaeO211zB8+HCEhoYaTf2Ujey9wSEhIQCAuLg4wUl+n3/9618AgIqKCqkf9OmP5eLigoSEBGg0GmzYsEG6hafw8HCsWbPG5OLNiRMnBCT6dUFBQRg/fjxKSkoQEBCAV155RXQkIpIM7xElIrMrKyvDyZMnkZ6ejoyMDHTp0kXKey+bCj8/P3z66adGvcGpqamiYzWwfv16bNy4Ec2bNze8J+MDtP70gE6nw7Vr1+Dk5CTV8Cf6c9TW1iIlJQWZmZlwc3PDxIkTYW1tLTpWA7du3ULHjh0NP6vVari7uwtM1LiysjLk5eWhU6dOaNu2reg4RCQZ7ogSkdn17dsXnp6eeOutt6Q7RtoUNZXe4EOHDuG7776Tdkqy3v0DVaqrqzFr1ixxYchs3nzzTQQEBCAwMBAWFhai4zSQmZmJ27dvIz4+HnPmzIFOp0NdXR0SEhLwxRdfiI5nEBkZ2ehdsVxwJKL7sRAlIrP75z//iRMnTuDAgQPYsmULevToIeU1Hk1FU+kNdnJyMtoNbQq0Wi1u3LghOgaZwZw5c7B3716sXbsW/fr1g7+/P7p06SI6lsGdO3dw6NAhFBcX48svvwQAKBQK6a5vmTRpkugIRNREsBAlIrNTKpXo3LkzcnNzcfPmTdy8eVN0pCatqfQG19TUYMyYMfD09DTsmMi4Q3J/D15tbS2mTZsmMA2Zi7u7O+bMmYOSkhIsXboUY8aMgY+PDyIjI6W4YkqlUkGlUmHt2rUICwsTHadRf/3rXwEAx48fx+XLlxEREYHg4GD2iBJRA+wRJSKzGzlyJFQqFUaMGIG+fftK2YdFfzxT17XoH1qJRPv222+xf/9+ZGdnw9fXF+PGjUNtbS1ef/11HDhwQHQ8g5deegnbt28XHeM3jRs3Dp988gns7e3xyy+/4PXXX0dycrLoWEQkEe6IEpHZHT58mKP8H0Hdu3fHxo0bUVhYiMGDB6Nr166iI5l05swZ3L17FzqdDtHR0YiIiMCYMWNEx6I/2YEDBzBlypQGiyOy7T5WV1fDz88PXbp0MXyPyniywNLS0tC7bmdnx+98ImqAO6JERGQW4eHhGDhwIPbt24d33nkHiYmJUu7sBAYGIj4+HkuWLMGyZcswa9Ys7NixQ3Qs+pPV1NTgp59+Qm1tLXQ6HQoKCvDiiy+KjtVAUzlZEB0dDY1Gg549e+LSpUto1aoVFixYIDoWEUmEO6JERGQWGo0G/v7+OHDgAHr37g1Z10FtbGxgb28PS0tLODg4oLq6WnQkMoOZM2eipqYGBQUF0Gq1aNeunZSFqKenJ06cOGFUMMtYiC5YsADHjh1DdnY2nn/+eQwdOlR0JCKSDAtRIjK78vJypKWlGT3g+/n5iQtEZqNWqwEA+fn50h7Va9GiBaZPn44pU6Zgx44dRnc20sOrvLwc27dvx/z587Fw4UJMnz5ddCSTwsPD4erqiszMTNjY2Eh7HVJhYSFcXV3h5uaGTz75BB07dkS3bt1ExyIiicj5FEBED7UZM2bg+PHjUKvVUKvVyM7OFh2JzGD+/PmIiorClStXEB4ejnfffVd0JJPWrFmD2NhY+Pn5wcfHB/Hx8aIjkRlYWtavzd+9exfNmzdHTU2N4ESNe//999GlSxds3rwZZWVlouOYNHfuXBQVFWHVqlXo168fYmNjRUciIslwR5SIzE6n0/Hh/hHUtWtX7N69W3SMRqWkpCAgIAAffvih4XoZPZnuY6U/x3PPPYe1a9fiySefRGBgIFq0aCE6UqOqqqpw9+5dKBQKVFZWio5jUm1tLXx8fLBu3TqMHj0aO3fuFB2JiCTDQpSIzK5r16748ccfjY5p8QqXh9+AAQNQUlKCNm3aQKPRwNraGkqlEosXL0a/fv1Ex0OHDh0AAG5uboKTkAhTp041vB40aBBcXV3FhfkVU6dOxWeffYZ+/fph0KBBePrpp0VHMqmmpgZxcXFQqVQ4deoUtFqt6EhEJBlOzSUis/P19UV5ebnhZ4VCgWPHjglMROYQGRmJsLAwuLm54fr161i7di3efPNNzJ49G3v27BEdz2DGjBmYOHEiBg4c2GBnlB4+kZGRjf49y3gtCgDU1dWhpKQEzZs3h62treg4JuXm5iI9PR0BAQE4evQovLy84OzsLDoWEUmEO6JEZHb6y+E1Gg1atWrFh/1HRH5+vmG30cXFBbdu3ULnzp1hYWEhOJmxN954A/v370diYiKGDx8Of39/Dix6iE2aNEl0hP/K119/jWXLlqFly5aoqKjAe++9J8WJggcVFhbC09MTP/74IxwcHJCfn89ClIiMsBAlIrM7c+YMlixZAq1Wi1GjRsHR0REBAQGiY9GfzMHBAfHx8ejVqxcuXLgApVKJ9PR0WFlZiY5mxMvLC15eXigrK8N7772H5557Dj/99JPoWPQn0V99Ul5ejo0bN6KwsBCDBw9G165dBSczLSkpCSkpKbC3t0dRURFCQ0OlLER37doFoH4mwLVr1+Dk5AQfHx/BqYhIJpyaS0Rmt2rVKmzfvh1KpRKhoaGGBxZ6uK1YsQLt2rVDWloaOnbsiGXLluHxxx9HYmKi6GhGzp49i6ioKLz00kt44okncPToUdGRyAyioqLg7OyM3NxcKJVKzJ8/X3Qkk1q3bg17e3sAgFKplPZobmJiIhITE/HBBx9g79690l7XRETicEeUiMyuWbNmaN26NRQKBWxsbKSeTkl/HBsbG0ybNs3ovV69eglK07gtW7YgICAAS5cu5bHxR4hGo4G/vz8OHDiA3r17Q9YRGra2tggODoaPjw8yMjJw7949w2KOrNOdtVotbty4IToGEUmGhSgRmZ2LiwsSEhKg0WiwYcMGODo6io5EZPDhhx+KjkCCqNVqAPX9zLLu4A0bNszwun379gKT/Lr+/fsbXtfW1jZYhCIi4tRcIjK72tpapKSkIDMzE+7u7ggMDOT1LUQk1NWrV7Fo0SKo1Wq4ublh8eLF6NGjh+hYREQPLRaiRGQ2Z86cafQzDrEgInp4HD9+HPv27UNVVZXhvY0bNwpMRESy4dFcIjIb/VCi69evo6amBl5eXrhy5QpatGiBbdu2CU5HRI+ioUOHGvUCW1paora2FtbW1jh8+LDAZE3b8uXL8f7776NVq1aioxCRpFiIEpHZ6AdqhISEICkpCZaWltBqtQgJCRGcjIgeVV999RV0Oh2WLFmCSZMmwdvbG1euXMHOnTtFR2vSPDw80KdPH9ExiEhiLESJyOwKCwsNr7VaLUpKSgSmIaJHmb4//caNG/D29gYAdO/eHTk5OSJjNXnDhg3DxIkT4ebmZngvLi5OYCIikg0LUSIyO39/f4wePRqenp64du0aZs6cKToSET3i7OzssGrVKnh7e+PixYtwcnISHalJ27ZtG1577TXY2dmJjkJEkuKwIiISory8HNnZ2ejUqRPatm0rOg4RPeIqKyuxf/9+ZGVlwc3NDVOnToWFhYXoWE1WSEgINmzYIDoGEUmMhSgRmc28efMa/YxHtoiIHh7h4eGoqKhA9+7dDcOgIiMjBaciIpnwaC4Rmc0LL7wAoH56bq9evdC7d29cvnwZly9fFpyMiIj+SEOGDBEdgYgkxx1RIjK7V199FZs2bTL8PH36dGzevFlgIiIiIiIyp2aiAxDRo6eyshInT55EeXk5vvvuO9TU1IiORERERERmxB1RIjI7tVqN1atX49q1a3B3d8eiRYvg4OAgOhYRERERmQkLUSIiIiIiIjIrDisiIrPr37+/4bVGo4GzszMOHz4sMBERERERmRMLUSIyuxMnThhe37x5E2vXrhWYhoiIiIjMjcOKiEgoJycnZGdni45BRERERGbEHVEiMrvIyEjDBecFBQWwt7cXnIiIiIiIzInDiojI7E6fPm14bWNjg6eeegoWFhYCExERERGROfFoLhGZnaurK9q2bQulUondu3cjKytLdCQiIiIiMiMWokRkdnPnzkVRURFWrVqFfv36YenSpaIjEREREZEZsRAlIrOrra2Fj48P7ty5g9GjR6Ourk50JCIiIiIyIxaiRGR2NTU1iIuLg0qlwqlTp6DVakVHIiIiIiIz4rAiIjK73NxcpKenIyAgAEePHoWXlxecnZ1FxyIiIiIiM2EhSkRERERERGbFo7lERERERERkVixEiYiIiIiIyKxYiBIREREREZFZsRAlIiIiIiIis2IhSkRERERERGb1f9xutlBN1AhbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,14))\n",
    "sns.heatmap(diabetes_df.corr(), annot=True, fmt='.2g',cmap='coolwarm')\n",
    "plt.title('Correlation Between Variables', fontsize=18);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "be741312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.327473958333334,\n",
       " 0.12710799319896815,\n",
       " 1,\n",
       " array([[166.15384615, 265.84615385],\n",
       "        [ 33.84615385,  54.15384615]]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_obesity_crosstab = pd.crosstab(diabetes_df['obesity'], diabetes_df['class'])\n",
    "Chisquares_diabetes_obesity = stats.chi2_contingency(diabetes_obesity_crosstab)\n",
    "Chisquares_diabetes_obesity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d43cd7",
   "metadata": {},
   "source": [
    "* The chi-squared test between obesity & diabetes status showed that p-value = 0.13 > 0.05. \n",
    "* Hence, we cannot reject the null hypothesis and we cannot say that there is no significant relationship between obesity & diabetes. For this reason, I would exclude obesity from the model building."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dc9cd17e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103.03685927972559,\n",
       " 3.289703730553294e-24,\n",
       " 1,\n",
       " array([[ 73.84615385, 118.15384615],\n",
       "        [126.15384615, 201.84615385]]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_obesity_crosstab = pd.crosstab(diabetes_df['gender'], diabetes_df['class'])\n",
    "Chisquares_gender_obesity = stats.chi2_contingency(gender_obesity_crosstab)\n",
    "Chisquares_gender_obesity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48944909",
   "metadata": {},
   "source": [
    "* The chi-squared test between gender & diabetes status showed that p-value = 3.29e-24 < 0.05\n",
    "* Hence, we can reject the null hypothesis and we can say that there is a significant relationship between gender & diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aea6dee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqplot(diabetes_df['age'],line='s')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a40a5767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9785807132720947, pvalue=6.471266260632547e-07)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(diabetes_df['age']) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a456859",
   "metadata": {},
   "source": [
    "* Based on the qqplot (most points lied on the line) and the Shapiro result which showed that the p-value = 6.47e-07 < 0.05\n",
    "* We can say that the distribution of the age is normally distributed and we can conduct the z-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e1c83be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.4358875641213964, 0.014855301229383732)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_diabetes = diabetes_df[diabetes_df['class']==1]\n",
    "n_diabetes = diabetes_df[diabetes_df['class']==0] \n",
    "ztest(w_diabetes['age'], n_diabetes['age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a04016b",
   "metadata": {},
   "source": [
    "* With p-value = 0.014 < 0.05, we will reject the null hypothesis\n",
    "* It is said that there is significant difference in the mean of age between people with diabetes and people without diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "be590bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_df = pd.read_csv(\"https://raw.githubusercontent.com/jadanpl/Early-Diabetes-Detection-Using-Symptoms/main/diabetes_data.csv\")\n",
    "diabetes_df.columns = diabetes_df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "59560ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "a = sns.boxplot(y='age', x='class', hue='gender',data=diabetes_df, palette='spring')\n",
    "plt.title(\"Age & Gender of Diabetes & Non-Diabetes Patients\", fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34dbce2",
   "metadata": {},
   "source": [
    "# Model Building & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e5a93e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = diabetes_df.drop(['class','obesity'], axis=1)\n",
    "Y = diabetes_df['class'].map({'Negative':0,'Positive':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "58e9ce82",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = ['age']\n",
    "cat_cols = list(X.drop(['age'], axis=1))\n",
    "\n",
    "full_pipeline = ColumnTransformer([\n",
    "        (\"num\", MinMaxScaler(), num_cols),\n",
    "        (\"cat\", OneHotEncoder(sparse=False, dtype='int', drop=\"if_binary\"), cat_cols)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bbef45fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=42, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "88ea54a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = full_pipeline.fit_transform(X_train)\n",
    "X_test = full_pipeline.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3f76caa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LogisticRegression(max_iter=10000),\n",
       " KNeighborsClassifier(metric='euclidean'),\n",
       " DecisionTreeClassifier(random_state=42),\n",
       " RandomForestClassifier(random_state=42),\n",
       " AdaBoostClassifier(random_state=42),\n",
       " XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "               colsample_bylevel=None, colsample_bynode=None,\n",
       "               colsample_bytree=None, early_stopping_rounds=None,\n",
       "               enable_categorical=False, eval_metric=None, gamma=None,\n",
       "               gpu_id=None, grow_policy=None, importance_type=None,\n",
       "               interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "               max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "               max_leaves=None, min_child_weight=None, missing=nan,\n",
       "               monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
       "               num_parallel_tree=None, predictor=None, random_state=42,\n",
       "               reg_alpha=None, reg_lambda=None, ...),\n",
       " GaussianNB(),\n",
       " SVC(random_state=42)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifiers = []\n",
    "log_reg = LogisticRegression(max_iter=10000)\n",
    "classifiers.append(log_reg)\n",
    "knn_neg = KNeighborsClassifier(metric='euclidean') \n",
    "classifiers.append(knn_neg)\n",
    "dt_model = DT(random_state=42)\n",
    "classifiers.append(dt_model)\n",
    "rf_model = RF(random_state=42)\n",
    "classifiers.append(rf_model)\n",
    "ab_model=AdaBoostClassifier(random_state=42)\n",
    "classifiers.append(ab_model)\n",
    "xgb_model = XGBClassifier(random_state=42)\n",
    "classifiers.append(xgb_model)\n",
    "GNB_model = GB() \n",
    "classifiers.append(GNB_model)\n",
    "SVM_model = SVC(random_state=42)\n",
    "classifiers.append(SVM_model)\n",
    "classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cac4982d",
   "metadata": {},
   "outputs": [],
   "source": [
    "recall_train = []\n",
    "recall_test = []\n",
    "for clf in classifiers:\n",
    "    clf.fit(X_train, Y_train)\n",
    "    pred_train = clf.predict(X_train)\n",
    "    pred_test = clf.predict(X_test)\n",
    "    rn = round(recall_score(Y_train, pred_train, average='macro')*100,2)\n",
    "    rt = round(recall_score(Y_test, pred_test, average='macro')*100,2)\n",
    "    recall_train.append(rn)\n",
    "    recall_test.append(rt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5ef6d0e7",
   "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>Model</th>\n",
       "      <th>Recall-Macro_Training (%)</th>\n",
       "      <th>Recall-Macro_Test (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RF</td>\n",
       "      <td>100.00</td>\n",
       "      <td>98.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGB</td>\n",
       "      <td>100.00</td>\n",
       "      <td>97.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DT</td>\n",
       "      <td>100.00</td>\n",
       "      <td>96.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SVM</td>\n",
       "      <td>97.92</td>\n",
       "      <td>95.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>95.38</td>\n",
       "      <td>93.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>95.92</td>\n",
       "      <td>92.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>95.04</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gaussian Bayes</td>\n",
       "      <td>89.79</td>\n",
       "      <td>90.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model  Recall-Macro_Training (%)  Recall-Macro_Test (%)\n",
       "3              RF                     100.00                  98.00\n",
       "5             XGB                     100.00                  97.75\n",
       "2              DT                     100.00                  96.38\n",
       "7             SVM                      97.92                  95.50\n",
       "4        AdaBoost                      95.38                  93.62\n",
       "1             KNN                      95.92                  92.62\n",
       "0              LR                      95.04                  92.00\n",
       "6  Gaussian Bayes                      89.79                  90.62"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_result = pd.DataFrame(data={'Model':['LR','KNN','DT','RF','AdaBoost','XGB','Gaussian Bayes','SVM'],\n",
    "                                     'Recall-Macro_Training (%)':recall_train,\n",
    "                                     'Recall-Macro_Test (%)':recall_test})\n",
    "original_result.sort_values(by=['Recall-Macro_Test (%)'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f675c5eb",
   "metadata": {},
   "source": [
    "From the results above, it is clearly seen that the decision tree, random forest and xgb models have **overfitting** problems, as they are unable to generalize well and they only outperform when training data is used. To avoid overfitting problem, **cross-validation** is performed in the following step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "518d45da",
   "metadata": {},
   "outputs": [],
   "source": [
    "rc_mean_cv = []\n",
    "rc_std_cv = []\n",
    "kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "for i in classifiers:\n",
    "    score = cross_val_score(i,X_train,Y_train,scoring='recall_macro', cv=kf)\n",
    "    rc_mean_cv.append(round(score.mean()*100,2))\n",
    "    rc_std_cv.append(round(score.std()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bf808829",
   "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>Model</th>\n",
       "      <th>Recall-Macro_Mean(%)</th>\n",
       "      <th>Recall-Macro_SDev(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DT</td>\n",
       "      <td>97.00</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT</td>\n",
       "      <td>96.75</td>\n",
       "      <td>1.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SVM</td>\n",
       "      <td>96.00</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGB</td>\n",
       "      <td>95.67</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>94.75</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>93.50</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>92.33</td>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GNB</td>\n",
       "      <td>89.04</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Model  Recall-Macro_Mean(%)  Recall-Macro_SDev(%)\n",
       "2        DT                 97.00                  1.24\n",
       "3        RT                 96.75                  1.96\n",
       "7       SVM                 96.00                  2.61\n",
       "5       XGB                 95.67                  2.04\n",
       "1       KNN                 94.75                  2.68\n",
       "0        LR                 93.50                  1.97\n",
       "4  AdaBoost                 92.33                  1.94\n",
       "6       GNB                 89.04                  1.16"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_result = pd.DataFrame(data={'Model':['LR','KNN','DT','RT','AdaBoost','XGB','GNB','SVM'],\n",
    "                               'Recall-Macro_Mean(%)':rc_mean_cv,\n",
    "                               'Recall-Macro_SDev(%)':rc_std_cv})\n",
    "cv_result.sort_values(['Recall-Macro_Mean(%)','Recall-Macro_SDev(%)'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e98805",
   "metadata": {},
   "source": [
    "* Based on the cross-validation result above, only SVC, decision tree and random forest classifiers have recall of more than or equal to 96%. \n",
    "* However, the decision tree classifier is doing a better job than the SVC and random forest classifiers as it has lower standard deviation (how much the predicted value differ from the actual value). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba2c6614",
   "metadata": {},
   "source": [
    "### Handling imbalanced class with SMOTE-based techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3dfb5a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Positive    320\n",
       "Negative    200\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac2ee62",
   "metadata": {},
   "source": [
    "* There is imbalanced class in the dataset, where the majority class belongs to the “0” (we denoted it as negative) label and the minority class belongs to the “1” (we denoted it as positive) label. \n",
    "* Hence, SMOTE based techniques (`SMOTE`, `ADASYN`, `SMOTEENN`, `SMOTETomek`) would be used to overcome this issue on the random forest model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "021337dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Counter({1: 240, 0: 150})\n",
      "After Counter({1: 240, 0: 240})\n"
     ]
    }
   ],
   "source": [
    "# SMOTE\n",
    "counter = Counter(Y_train)\n",
    "print('Before',counter)\n",
    "smt=SMOTE(random_state=42)\n",
    "X_train_sm, Y_train_sm = smt.fit_resample(X_train, Y_train)\n",
    "counter = Counter(Y_train_sm)\n",
    "print('After',counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "424ec3ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Counter({1: 240, 0: 150})\n",
      "After Counter({1: 240, 0: 237})\n"
     ]
    }
   ],
   "source": [
    "# ADASYN\n",
    "counter = Counter(Y_train)\n",
    "print('Before',counter)\n",
    "ada=ADASYN(random_state=42)\n",
    "X_train_ada, Y_train_ada=ada.fit_resample(X_train, Y_train)\n",
    "counter = Counter(Y_train_ada)\n",
    "print('After',counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8077e265",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Counter({1: 240, 0: 150})\n",
      "After Counter({0: 232, 1: 215})\n"
     ]
    }
   ],
   "source": [
    "# SMOTE-ENN\n",
    "counter = Counter(Y_train)\n",
    "print('Before',counter)\n",
    "smenn=SMOTEENN(random_state=42)\n",
    "X_train_smenn, Y_train_smenn=smenn.fit_resample(X_train, Y_train)\n",
    "counter = Counter(Y_train_smenn)\n",
    "print('After',counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ca81df9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Counter({1: 240, 0: 150})\n",
      "After Counter({1: 239, 0: 239})\n"
     ]
    }
   ],
   "source": [
    "# SMOTE-TOMEK \n",
    "counter = Counter(Y_train)\n",
    "print('Before',counter)\n",
    "smtom=SMOTETomek(random_state=42)\n",
    "X_train_smtom, Y_train_smtom=smtom.fit_resample(X_train, Y_train)\n",
    "counter = Counter(Y_train_smtom)\n",
    "print('After',counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf1d038",
   "metadata": {},
   "source": [
    "* Above are the SMOTE-based techniques to be used to handle imbalanced data. \n",
    "* Below is the function which involved: \n",
    " 1. Pipeline to handle the inbalanced dataset, scale the data, and run the data with a set of classifiers one by one. \n",
    " 2. Hyperparameter tuning process with 5-fold cross validation for each classifier to select the best model and its best parameters with the `PipelineHelper`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6a63c67b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def diabetes_prediction(X,Y,smote=False,adasyn=False,smote_enn=False, smote_tomek=False):\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=42, stratify=Y)\n",
    "    \n",
    "    trf = ColumnTransformer(transformers =[('cat',OneHotEncoder(sparse = False, drop ='first'),\n",
    "                                               ['gender','polyuria','polydipsia','sudden weight loss',\n",
    "                                                'weakness','polyphagia','genital thrush','visual blurring',\n",
    "                                                'itching','irritability','delayed healing','partial paresis',\n",
    "                                                'muscle stiffness','alopecia']),\n",
    "                                           ('num',MinMaxScaler(), ['age'])],\n",
    "                            remainder='passthrough')\n",
    "    \n",
    "    clf = [('log_reg', LogisticRegression(max_iter=10000)),\n",
    "           ('knn_neg', KNeighborsClassifier(metric='euclidean')),\n",
    "           ('dt_model', DT(random_state=42)),\n",
    "           ('rf_model', RF(random_state=42)),\n",
    "           ('ab_model', AdaBoostClassifier(random_state=42)),\n",
    "           ('xgb_model', XGBClassifier(random_state=42)),\n",
    "           ('GNB_model', GB()),\n",
    "           ('SVM_model', SVC(random_state=42))]\n",
    "    \n",
    "    if (smote==True) & (adasyn==False) & (smote_enn==False) & (smote_tomek==False):\n",
    "        pipeline = imbpipeline(steps = [('preprocessing',trf),\n",
    "                                        ('smote', SMOTE(random_state=42)),\n",
    "                                        ('classifier',PipelineHelper(clf))])\n",
    "        \n",
    "    elif (smote==False) & (adasyn==True) & (smote_enn==False) & (smote_tomek==False):\n",
    "        pipeline = imbpipeline(steps = [('preprocessing',trf),\n",
    "                                        ('adasyn', ADASYN(random_state=42)),\n",
    "                                        ('classifier',PipelineHelper(clf))]) \n",
    "                               \n",
    "    elif (smote==False) & (adasyn==False) & (smote_enn==True) & (smote_tomek==False):\n",
    "        pipeline = imbpipeline(steps = [('preprocessing',trf),\n",
    "                                        ('smote_enn', SMOTEENN(random_state=42)),\n",
    "                                        ('classifier',PipelineHelper(clf))]) \n",
    "                                                               \n",
    "    elif (smote==False) & (adasyn==False) & (smote_enn==False) & (smote_tomek==True):\n",
    "        pipeline = imbpipeline(steps = [('preprocessing',trf),\n",
    "                                        ('smote_tomek', SMOTETomek(random_state=42)),\n",
    "                                        ('classifier',PipelineHelper(clf))])\n",
    "                                         \n",
    "    else:\n",
    "        pipeline = imbpipeline(steps =[('preprocessing',trf),\n",
    "                                       ('classifier',PipelineHelper(clf))])\n",
    "    \n",
    "    kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
    "    \n",
    "    param_grid={'classifier__selected_model': pipeline.named_steps['classifier'].generate(\n",
    "                    {'log_reg__C':np.logspace(-10, 1, 15),\n",
    "                     'log_reg__penalty':['l1','l2'],\n",
    "                     'knn_neg__n_neighbors': [2, 3, 5, 7, 10],\n",
    "                     'knn_neg__leaf_size': [1, 2, 3, 5],\n",
    "#                      'knn_neg__weights': ['uniform','distance'],\n",
    "                     'dt_model__max_depth':[i for i in range(5,16,2)],\n",
    "                     'dt_model__min_samples_split':[2, 5, 10, 15, 20, 50],\n",
    "                     'dt_model__min_samples_leaf':[1, 2, 5],\n",
    "                     'dt_model__max_features': [1,3,6],\n",
    "                     'rf_model__max_depth': [3, 10, 30],\n",
    "                     'rf_model__n_estimators': [10, 20, 50, 100, 150],\n",
    "                     'rf_model__max_features':  [i for i in range(5,20,2)],\n",
    "                     'ab_model__n_estimators': [10, 20, 40, 100],\n",
    "                     'ab_model__algorithm': ['SAMME', 'SAMME.R'],\n",
    "                     'xgb_model__n_estimators': [10, 20, 50, 100],\n",
    "                     'xgb_model__max_depth': [3, 10, 30],\n",
    "                     'xgb_model__gamma': [3,5,7,9],\n",
    "                     'xgb_model__min_child_weight' : [3,7,10],\n",
    "                     'SVM_model__C': [0.1, 1.0],\n",
    "                     'SVM_model__kernel': ['linear', 'rbf'],\n",
    "                     'SVM_model__gamma': [1, 0.1, 0.01, 0.001, 0.0001]})\n",
    "               }\n",
    "\n",
    "    grid_search = GridSearchCV(pipeline, param_grid, cv=kf, \n",
    "                               scoring='recall_macro',\n",
    "                               verbose=1, n_jobs=-1)\n",
    "    grid_search.fit(X_train, Y_train)\n",
    "    print(grid_search.best_params_)\n",
    "    cv_score = grid_search.best_score_\n",
    "    test_score = grid_search.score(X_test, Y_test)\n",
    "    print(f'Cross-validation score: {round(cv_score*100,2)}% \\nTest score: {round(test_score*100,2)}%')                                                           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "751436b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 667 candidates, totalling 6670 fits\n",
      "{'classifier__selected_model': ('rf_model', {'max_depth': 10, 'max_features': 5, 'n_estimators': 150})}\n",
      "Cross-validation score: 96.54% \n",
      "Test score: 97.38%\n",
      "CPU times: total: 13.3 s\n",
      "Wall time: 1min 14s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "diabetes_prediction(X,Y,smote=True,adasyn=False,smote_enn=False,smote_tomek=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cd33b4ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 667 candidates, totalling 6670 fits\n",
      "{'classifier__selected_model': ('rf_model', {'max_depth': 10, 'max_features': 5, 'n_estimators': 20})}\n",
      "Cross-validation score: 97.54% \n",
      "Test score: 99.0%\n",
      "CPU times: total: 20.3 s\n",
      "Wall time: 1min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "diabetes_prediction(X,Y,smote=False,adasyn=True,smote_enn=False,smote_tomek=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "84a9999b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 667 candidates, totalling 6670 fits\n",
      "{'classifier__selected_model': ('SVM_model', {'C': 1.0, 'gamma': 1, 'kernel': 'rbf'})}\n",
      "Cross-validation score: 94.96% \n",
      "Test score: 95.12%\n",
      "CPU times: total: 19.7 s\n",
      "Wall time: 1min 34s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "diabetes_prediction(X, Y, smote=False,adasyn=False,smote_enn=True,smote_tomek=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "34dca5cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 667 candidates, totalling 6670 fits\n",
      "{'classifier__selected_model': ('rf_model', {'max_depth': 30, 'max_features': 5, 'n_estimators': 50})}\n",
      "Cross-validation score: 96.54% \n",
      "Test score: 97.38%\n",
      "CPU times: total: 19.4 s\n",
      "Wall time: 1min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "diabetes_prediction(X, Y, smote=False,adasyn=False,smote_enn=False,smote_tomek=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b290e27c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 667 candidates, totalling 6670 fits\n",
      "{'classifier__selected_model': ('rf_model', {'max_depth': 10, 'max_features': 5, 'n_estimators': 100})}\n",
      "Cross-validation score: 96.75% \n",
      "Test score: 97.38%\n",
      "CPU times: total: 21.1 s\n",
      "Wall time: 1min 28s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "diabetes_prediction(X, Y, smote=False,adasyn=False,smote_enn=False,smote_tomek=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358f66f1",
   "metadata": {},
   "source": [
    "It is always recommended for us to treat the imbalanced data beforehand, to avoid degradation of model performance; hence, the <b> SVM model with SMOTE-ENN technique </b> is chosen as the <b>final model</b> as the difference between its cross-validation score and testing score is the smallest. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2b8ac6",
   "metadata": {},
   "source": [
    "# Final Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0b23c46a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 50 candidates, totalling 500 fits\n",
      "Cross-validation score: 95.38% \n",
      "Test score: 95.75%\n",
      "CPU times: total: 1.39 s\n",
      "Wall time: 5.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=42, stratify=Y)    \n",
    "cat_cols = list(X.drop(['age'], axis=1))\n",
    "preprocess = ColumnTransformer(transformers =[('cat',OneHotEncoder(sparse = False, dtype='int', drop=\"if_binary\"),cat_cols),\n",
    "                                              ('num',MinMaxScaler(), ['age'])],\n",
    "                              remainder='passthrough')\n",
    "\n",
    "final_pipeline = imbpipeline(steps=[('preprocessing',preprocess),\n",
    "                                    ('smote_enn', SMOTEENN(random_state=42)),\n",
    "                                    ('SVM_model', SVC(random_state=42))])\n",
    "\n",
    "param_grid= [{'SVM_model__C': [0.2, 0.4, 0.6, 0.8, 1.0],\n",
    "              'SVM_model__kernel': ['linear', 'rbf'],\n",
    "              'SVM_model__gamma': [1, 0.1, 0.01, 0.001, 0.0001]}]\n",
    "\n",
    "kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
    "grid_search = GridSearchCV(final_pipeline, param_grid, cv=kf, scoring='recall_macro',verbose=1, n_jobs=-1)\n",
    "grid_search.fit(X_train, Y_train)\n",
    "final_model = grid_search.best_estimator_\n",
    "cv_score = grid_search.best_score_\n",
    "test_score = grid_search.score(X_test, Y_test)\n",
    "print(f'Cross-validation score: {round(cv_score*100,2)}% \\nTest score: {round(test_score*100,2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "50e41e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessing',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('cat',\n",
       "                                                  OneHotEncoder(drop='if_binary',\n",
       "                                                                dtype='int',\n",
       "                                                                sparse=False),\n",
       "                                                  ['gender', 'polyuria',\n",
       "                                                   'polydipsia',\n",
       "                                                   'sudden weight loss',\n",
       "                                                   'weakness', 'polyphagia',\n",
       "                                                   'genital thrush',\n",
       "                                                   'visual blurring', 'itching',\n",
       "                                                   'irritability',\n",
       "                                                   'delayed healing',\n",
       "                                                   'partial paresis',\n",
       "                                                   'muscle stiffness',\n",
       "                                                   'alopecia']),\n",
       "                                                 ('num', MinMaxScaler(),\n",
       "                                                  ['age'])])),\n",
       "                ('smote_enn', SMOTEENN(random_state=42)),\n",
       "                ('SVM_model', SVC(C=0.4, gamma=1, random_state=42))])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9bdd4f65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall score from the final model (training data):  97.79 %\n",
      "Recall score from the final model (testing data):  95.75 %\n"
     ]
    }
   ],
   "source": [
    "pred_train = final_model.predict(X_train)\n",
    "pred_test = final_model.predict(X_test)\n",
    "recall_train = round(recall_score(Y_train, pred_train, average='macro')*100,2)\n",
    "recall_test = round(recall_score(Y_test, pred_test, average='macro')*100,2)\n",
    "print(\"Recall score from the final model (training data): \",recall_train,\"%\")\n",
    "print(\"Recall score from the final model (testing data): \",recall_test,\"%\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8fbf46ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_names = ['True Neg', 'False Pos', 'False Neg', 'True Pos']\n",
    "train_cnf_matrix = confusion_matrix(Y_train,pred_train)\n",
    "train_counts = [\"{0:0.0f}\".format(value) for value in train_cnf_matrix.flatten()]\n",
    "train_percentage = [\"{0:.2%}\".format(value) for value in train_cnf_matrix .flatten()/np.sum(train_cnf_matrix)]\n",
    "train_labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,train_counts,train_percentage)]\n",
    "train_labels = np.asarray(train_labels).reshape(2,2)\n",
    "plt.figure(figsize = (16,5))\n",
    "sns.heatmap(train_cnf_matrix, annot=train_labels, fmt='', cmap='coolwarm')\n",
    "plt.title('Confusion Matrix for Train Data (Random Forest Model)',fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ac98c3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_names = ['True Neg', 'False Pos', 'False Neg', 'True Pos']\n",
    "test_cnf_matrix = confusion_matrix(Y_test,pred_test)\n",
    "test_counts = [\"{0:0.0f}\".format(value) for value in test_cnf_matrix.flatten()]\n",
    "test_percentage = [\"{0:.2%}\".format(value) for value in test_cnf_matrix .flatten()/np.sum(test_cnf_matrix)]\n",
    "test_labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,test_counts,test_percentage)]\n",
    "test_labels = np.asarray(test_labels).reshape(2,2)\n",
    "plt.figure(figsize = (16,5))\n",
    "sns.heatmap(test_cnf_matrix, annot=test_labels, fmt='', cmap='icefire')\n",
    "plt.title('Confusion Matrix for Test Data (Random Forest Model)',fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821b66ac",
   "metadata": {},
   "source": [
    "* To predict who will likely to have diabetes, we prefer algorithms that can score a high probability of finding out people who\n",
    "have the same characteristics and actually have diabetes later (TRUE POSITIVE). \n",
    "* Based on the severity of the illness, we may also prefer that the algorithm wrongly chooses some people who don’t get ill after all rather than miss the people who actually do get ill (FALSE POSITIVE). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f8939c6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.98       150\n",
      "           1       0.98      1.00      0.99       240\n",
      "\n",
      "    accuracy                           0.98       390\n",
      "   macro avg       0.98      0.98      0.98       390\n",
      "weighted avg       0.98      0.98      0.98       390\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"{classification_report(Y_train,pred_train)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0328e0ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.94      0.95        50\n",
      "           1       0.96      0.97      0.97        80\n",
      "\n",
      "    accuracy                           0.96       130\n",
      "   macro avg       0.96      0.96      0.96       130\n",
      "weighted avg       0.96      0.96      0.96       130\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"{classification_report(Y_test,pred_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "550efd55",
   "metadata": {},
   "source": [
    "# Making Prediction With New Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "017741aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(model, inputs):\n",
    "    input_df = pd.DataFrame([inputs])\n",
    "    pred = model.predict(input_df)[0]\n",
    "    if pred == 0:\n",
    "        print('It is predicted that this patient does not have diabetes.')\n",
    "    else: \n",
    "        print('It is predicted that this patient would have diabetes.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9423a68b",
   "metadata": {},
   "outputs": [],
   "source": [
    "male_input = {'age':25, \n",
    "               'gender':'Male', \n",
    "               'polyuria':'No', \n",
    "               'polydipsia':'No', \n",
    "               'sudden weight loss':'No',   \n",
    "               'weakness':'No', \n",
    "               'polyphagia':'No', \n",
    "               'genital thrush':'No', \n",
    "               'visual blurring':'Yes',     \n",
    "               'itching':'No', \n",
    "               'irritability':'No', \n",
    "               'delayed healing':'No', \n",
    "               'partial paresis':'No',\n",
    "               'muscle stiffness':'Yes', \n",
    "               'alopecia':'No'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8c32efc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It is predicted that this patient does not have diabetes.\n"
     ]
    }
   ],
   "source": [
    "predict(final_model, male_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "98e6f363",
   "metadata": {},
   "outputs": [],
   "source": [
    "female_input = {'age':25, \n",
    "               'gender':'Female', \n",
    "               'polyuria':'No', \n",
    "               'polydipsia':'No', \n",
    "               'sudden weight loss':'No',   \n",
    "               'weakness':'No', \n",
    "               'polyphagia':'No', \n",
    "               'genital thrush':'No', \n",
    "               'visual blurring':'Yes',     \n",
    "               'itching':'No', \n",
    "               'irritability':'No', \n",
    "               'delayed healing':'No', \n",
    "               'partial paresis':'No',\n",
    "               'muscle stiffness':'Yes', \n",
    "               'alopecia':'No'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cd95de23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It is predicted that this patient would have diabetes.\n"
     ]
    }
   ],
   "source": [
    "predict(final_model, female_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcfbbe3c",
   "metadata": {},
   "source": [
    "# Recommendation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9215451",
   "metadata": {},
   "source": [
    "* We could implement ensemble learning, such as by using voting classifier or stacking classifier.\n",
    "* Randomized Search could be used to tune the hyperparameters to see if the hyperparameter combination is the same.\n",
    "* If it is possible, we could increase the size of dataset by collecting more data. "
   ]
  }
 ],
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