data-analysis-with-depression-dataset.ipynb

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   "source": [
    "# Data Analysis with Depression Data"
   ]
  },
  {
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   "source": [
    "## 1. Set up the environment"
   ]
  },
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     "text": [
      "Num GPUs Available:  2\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Check if GPU is available\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
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   "source": [
    "# Import necessary libraries\n",
    "\n",
    "# Data Exploration and Preparation\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Exploratory Data Analysis (EDA)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0cc207c",
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    "tags": []
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   "outputs": [],
   "source": [
    "# Load dataset\n",
    "df_ = pd.read_csv(\"/kaggle/input/depression-dataset/depression_data.csv\")\n",
    "df = df_.copy()"
   ]
  },
  {
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   "source": [
    "The dataset contains information about individuals' personal and lifestyle factors. More details about the columns can be found on the dataset's website: <br>\n",
    "https://www.kaggle.com/datasets/anthonytherrien/depression-dataset"
   ]
  },
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   "source": [
    "# Generate random NaN values\n",
    "import random\n",
    "\n",
    "def add_random_missing_values(dataframe: pd.DataFrame,\n",
    "                              missing_rate: float = 0.05,\n",
    "                              seed: random = 42) -> pd.DataFrame:\n",
    "    \"\"\"Turns random values to NaN in a DataFrame.\n",
    "    \n",
    "    To use this function, you need to import pandas, numpy and random libraries.\n",
    "\n",
    "    Args:\n",
    "        dataframe (pd.DataFrame): DataFrame to be processed.\n",
    "        missing_rate (float): Percentage of missing value rate in float format. Defaults 0.05\n",
    "\n",
    "    \n",
    "    \"\"\"\n",
    "    # Get copy of dataframe\n",
    "    df_missing = dataframe.copy()\n",
    "\n",
    "    # Obtain size of dataframe and number total number of missing values\n",
    "    df_size = dataframe.size\n",
    "    num_missing = int(df_size * missing_rate)\n",
    "    \n",
    "    # Set seed\n",
    "    if seed:\n",
    "        random.seed(seed)\n",
    "\n",
    "    # Get random row and column indexes to turn them NaN\n",
    "    for _ in range(num_missing):\n",
    "        row_idx = random.randint(0, dataframe.shape[0] - 1)\n",
    "        col_idx = random.randint(0, dataframe.shape[1] - 1)\n",
    "\n",
    "        df_missing.iat[row_idx, col_idx] = np.nan\n",
    "        \n",
    "    return df_missing\n",
    "\n",
    "df = add_random_missing_values(dataframe = df,\n",
    "                               missing_rate = 0.03)"
   ]
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   "source": [
    "## 2. Data Exploration"
   ]
  },
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Number of Children</th>\n",
       "      <th>Smoking Status</th>\n",
       "      <th>Physical Activity Level</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Alcohol Consumption</th>\n",
       "      <th>Dietary Habits</th>\n",
       "      <th>Sleep Patterns</th>\n",
       "      <th>History of Mental Illness</th>\n",
       "      <th>History of Substance Abuse</th>\n",
       "      <th>Family History of Depression</th>\n",
       "      <th>Chronic Medical Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Christine Barker</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Active</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>26265.67</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jacqueline Lewis</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>High School</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>42710.36</td>\n",
       "      <td>High</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shannon Church</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>125332.79</td>\n",
       "      <td>Low</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charles Jordan</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>9992.78</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michael Rich</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>8595.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "               Name   Age Marital Status    Education Level  \\\n",
       "0  Christine Barker  31.0        Married  Bachelor's Degree   \n",
       "1  Jacqueline Lewis  55.0        Married        High School   \n",
       "2    Shannon Church  78.0        Widowed    Master's Degree   \n",
       "3    Charles Jordan  58.0       Divorced    Master's Degree   \n",
       "4      Michael Rich  18.0         Single        High School   \n",
       "\n",
       "   Number of Children Smoking Status Physical Activity Level  \\\n",
       "0                 2.0     Non-smoker                  Active   \n",
       "1                 1.0     Non-smoker               Sedentary   \n",
       "2                 1.0     Non-smoker               Sedentary   \n",
       "3                 3.0     Non-smoker                Moderate   \n",
       "4                 0.0     Non-smoker               Sedentary   \n",
       "\n",
       "  Employment Status     Income Alcohol Consumption Dietary Habits  \\\n",
       "0        Unemployed   26265.67            Moderate       Moderate   \n",
       "1          Employed   42710.36                High      Unhealthy   \n",
       "2          Employed  125332.79                 Low      Unhealthy   \n",
       "3        Unemployed    9992.78            Moderate       Moderate   \n",
       "4        Unemployed    8595.08                 NaN       Moderate   \n",
       "\n",
       "  Sleep Patterns History of Mental Illness History of Substance Abuse  \\\n",
       "0           Fair                       Yes                         No   \n",
       "1           Fair                       Yes                         No   \n",
       "2           Good                        No                         No   \n",
       "3           Poor                       NaN                         No   \n",
       "4           Fair                       Yes                         No   \n",
       "\n",
       "  Family History of Depression Chronic Medical Conditions  \n",
       "0                          Yes                        Yes  \n",
       "1                           No                        Yes  \n",
       "2                          Yes                         No  \n",
       "3                           No                         No  \n",
       "4                          Yes                        Yes  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "# Show the first few rows to understand the data format\n",
    "df.head()"
   ]
  },
  {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 413768 entries, 0 to 413767\n",
      "Data columns (total 16 columns):\n",
      " #   Column                        Non-Null Count   Dtype  \n",
      "---  ------                        --------------   -----  \n",
      " 0   Name                          401567 non-null  object \n",
      " 1   Age                           401585 non-null  float64\n",
      " 2   Marital Status                401412 non-null  object \n",
      " 3   Education Level               401475 non-null  object \n",
      " 4   Number of Children            401473 non-null  float64\n",
      " 5   Smoking Status                401494 non-null  object \n",
      " 6   Physical Activity Level       401697 non-null  object \n",
      " 7   Employment Status             401546 non-null  object \n",
      " 8   Income                        401577 non-null  float64\n",
      " 9   Alcohol Consumption           401286 non-null  object \n",
      " 10  Dietary Habits                401666 non-null  object \n",
      " 11  Sleep Patterns                401569 non-null  object \n",
      " 12  History of Mental Illness     401754 non-null  object \n",
      " 13  History of Substance Abuse    401513 non-null  object \n",
      " 14  Family History of Depression  401636 non-null  object \n",
      " 15  Chronic Medical Conditions    401417 non-null  object \n",
      "dtypes: float64(3), object(13)\n",
      "memory usage: 50.5+ MB\n",
      "None\n",
      "(413768, 16)\n"
     ]
    }
   ],
   "source": [
    "# Display basic information about the dataset (columns, types, missing values, etc.)\n",
    "print(df.info())\n",
    "# Size of the dataframe (# of columns and rows)\n",
    "print(df.shape)"
   ]
  },
  {
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   "source": [
    "There are 16 columns in total."
   ]
  },
  {
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   "source": [
    "# Identify categorical and numerical features using list comprehension\n",
    "categorical_features = [col for col in df.columns if df[col].dtype in [\"object\", \"category\"]]\n",
    "numerical_features = [col for col in df.columns if df[col].dtype not in [\"object\", \"categorical\"]]"
   ]
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    {
     "data": {
      "text/plain": [
       "(['Name',\n",
       "  'Marital Status',\n",
       "  'Education Level',\n",
       "  'Smoking Status',\n",
       "  'Physical Activity Level',\n",
       "  'Employment Status',\n",
       "  'Alcohol Consumption',\n",
       "  'Dietary Habits',\n",
       "  'Sleep Patterns',\n",
       "  'History of Mental Illness',\n",
       "  'History of Substance Abuse',\n",
       "  'Family History of Depression',\n",
       "  'Chronic Medical Conditions'],\n",
       " ['Age', 'Number of Children', 'Income'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "categorical_features, numerical_features"
   ]
  },
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   },
   "source": [
    "Now, we change the data type of some features since these features have a limited set of distinct values and should be converted to categorical."
   ]
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     "start_time": "2024-11-23T16:09:15.531660",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                              object\n",
      "Age                              float64\n",
      "Marital Status                  category\n",
      "Education Level                 category\n",
      "Number of Children               float64\n",
      "Smoking Status                  category\n",
      "Physical Activity Level         category\n",
      "Employment Status               category\n",
      "Income                           float64\n",
      "Alcohol Consumption             category\n",
      "Dietary Habits                  category\n",
      "Sleep Patterns                  category\n",
      "History of Mental Illness       category\n",
      "History of Substance Abuse      category\n",
      "Family History of Depression    category\n",
      "Chronic Medical Conditions      category\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Create a list for columns to convert\n",
    "columns_to_convert = [col for col in categorical_features if col != \"Name\"]\n",
    "\n",
    "# Convert selected columns to 'category'\n",
    "df[columns_to_convert] = df[columns_to_convert].astype(\"category\")\n",
    "\n",
    "# Verify the changes\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "128cdb02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:15.969496Z",
     "iopub.status.busy": "2024-11-23T16:09:15.969223Z",
     "iopub.status.idle": "2024-11-23T16:09:16.049207Z",
     "shell.execute_reply": "2024-11-23T16:09:16.048254Z"
    },
    "papermill": {
     "duration": 0.093859,
     "end_time": "2024-11-23T16:09:16.051106",
     "exception": false,
     "start_time": "2024-11-23T16:09:15.957247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       count          mean           std    min       25%  \\\n",
      "Age                 401585.0     48.999933     18.160828  18.00     33.00   \n",
      "Number of Children  401473.0      1.298690      1.236892   0.00      0.00   \n",
      "Income              401577.0  50656.512581  40626.241790   0.41  20988.84   \n",
      "\n",
      "                         50%       75%        max  \n",
      "Age                    49.00     65.00      80.00  \n",
      "Number of Children      1.00      2.00       4.00  \n",
      "Income              37508.45  76614.36  209995.22  \n"
     ]
    }
   ],
   "source": [
    "# Summarize numerical features (mean, min, max, etc.)\n",
    "print(df.describe().T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a6ca5d",
   "metadata": {
    "papermill": {
     "duration": 0.011619,
     "end_time": "2024-11-23T16:09:16.075633",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.064014",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Data Cleaning and Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38640c1a",
   "metadata": {
    "papermill": {
     "duration": 0.011343,
     "end_time": "2024-11-23T16:09:16.098557",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.087214",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3. 1. Handle Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f2cc537",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:16.122958Z",
     "iopub.status.busy": "2024-11-23T16:09:16.122668Z",
     "iopub.status.idle": "2024-11-23T16:09:16.152112Z",
     "shell.execute_reply": "2024-11-23T16:09:16.151017Z"
    },
    "papermill": {
     "duration": 0.043781,
     "end_time": "2024-11-23T16:09:16.153890",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.110109",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                            12201\n",
      "Age                             12183\n",
      "Marital Status                  12356\n",
      "Education Level                 12293\n",
      "Number of Children              12295\n",
      "Smoking Status                  12274\n",
      "Physical Activity Level         12071\n",
      "Employment Status               12222\n",
      "Income                          12191\n",
      "Alcohol Consumption             12482\n",
      "Dietary Habits                  12102\n",
      "Sleep Patterns                  12199\n",
      "History of Mental Illness       12014\n",
      "History of Substance Abuse      12255\n",
      "Family History of Depression    12132\n",
      "Chronic Medical Conditions      12351\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check the missing values in each column\n",
    "missing_values = df.isnull().sum()\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9231256",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:16.177975Z",
     "iopub.status.busy": "2024-11-23T16:09:16.177699Z",
     "iopub.status.idle": "2024-11-23T16:09:16.183205Z",
     "shell.execute_reply": "2024-11-23T16:09:16.182014Z"
    },
    "papermill": {
     "duration": 0.01989,
     "end_time": "2024-11-23T16:09:16.185405",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.165515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                            2.948754\n",
      "Age                             2.944404\n",
      "Marital Status                  2.986214\n",
      "Education Level                 2.970989\n",
      "Number of Children              2.971472\n",
      "Smoking Status                  2.966397\n",
      "Physical Activity Level         2.917335\n",
      "Employment Status               2.953829\n",
      "Income                          2.946337\n",
      "Alcohol Consumption             3.016666\n",
      "Dietary Habits                  2.924827\n",
      "Sleep Patterns                  2.948271\n",
      "History of Mental Illness       2.903559\n",
      "History of Substance Abuse      2.961805\n",
      "Family History of Depression    2.932078\n",
      "Chronic Medical Conditions      2.985006\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the percentage of missing values in each column\n",
    "missing_percentage = (missing_values / len(df)) * 100\n",
    "print(missing_percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0cc4621a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:16.212059Z",
     "iopub.status.busy": "2024-11-23T16:09:16.211806Z",
     "iopub.status.idle": "2024-11-23T16:09:16.289042Z",
     "shell.execute_reply": "2024-11-23T16:09:16.288109Z"
    },
    "papermill": {
     "duration": 0.091769,
     "end_time": "2024-11-23T16:09:16.290811",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.199042",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                            401567\n",
      "Age                             401585\n",
      "Marital Status                  401412\n",
      "Education Level                 401475\n",
      "Number of Children              401473\n",
      "Smoking Status                  401494\n",
      "Physical Activity Level         401697\n",
      "Employment Status               401546\n",
      "Income                          401577\n",
      "Alcohol Consumption             401286\n",
      "Dietary Habits                  401666\n",
      "Sleep Patterns                  401569\n",
      "History of Mental Illness       401754\n",
      "History of Substance Abuse      401513\n",
      "Family History of Depression    401636\n",
      "Chronic Medical Conditions      401417\n",
      "dtype: int64\n",
      "The dataset contains a total of 6424667 non-missing and 195621 missing observations.\n"
     ]
    }
   ],
   "source": [
    "# Calculate non missing values\n",
    "non_missing_values = df.notnull().sum()\n",
    "print(non_missing_values)\n",
    "print(f\"The dataset contains a total of {df.notnull().sum().sum()} non-missing and {df.isnull().sum().sum()} missing observations.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "235feddf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:16.314901Z",
     "iopub.status.busy": "2024-11-23T16:09:16.314630Z",
     "iopub.status.idle": "2024-11-23T16:09:16.370182Z",
     "shell.execute_reply": "2024-11-23T16:09:16.369319Z"
    },
    "papermill": {
     "duration": 0.06923,
     "end_time": "2024-11-23T16:09:16.371893",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.302663",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Number of Children</th>\n",
       "      <th>Smoking Status</th>\n",
       "      <th>Physical Activity Level</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Alcohol Consumption</th>\n",
       "      <th>Dietary Habits</th>\n",
       "      <th>Sleep Patterns</th>\n",
       "      <th>History of Mental Illness</th>\n",
       "      <th>History of Substance Abuse</th>\n",
       "      <th>Family History of Depression</th>\n",
       "      <th>Chronic Medical Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Christine Barker</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Active</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>26265.67</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jacqueline Lewis</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>High School</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>42710.36</td>\n",
       "      <td>High</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shannon Church</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>125332.79</td>\n",
       "      <td>Low</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Charles Kaiser</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Active</td>\n",
       "      <td>Employed</td>\n",
       "      <td>39608.18</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Alexander Hernandez</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>77597.84</td>\n",
       "      <td>Low</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413759</th>\n",
       "      <td>Travis Brewer</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>54483.50</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413760</th>\n",
       "      <td>Sophia Allen</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>High School</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>26153.32</td>\n",
       "      <td>Low</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413762</th>\n",
       "      <td>Janice Robertson</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Active</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>5971.27</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413766</th>\n",
       "      <td>Gary Faulkner</td>\n",
       "      <td>71.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>24557.08</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Poor</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413767</th>\n",
       "      <td>Joseph Johnson</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Former</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Employed</td>\n",
       "      <td>107125.74</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Healthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>256186 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name   Age Marital Status    Education Level  \\\n",
       "0          Christine Barker  31.0        Married  Bachelor's Degree   \n",
       "1          Jacqueline Lewis  55.0        Married        High School   \n",
       "2            Shannon Church  78.0        Widowed    Master's Degree   \n",
       "7            Charles Kaiser  31.0         Single        High School   \n",
       "9       Alexander Hernandez  77.0        Married  Bachelor's Degree   \n",
       "...                     ...   ...            ...                ...   \n",
       "413759        Travis Brewer  20.0         Single  Bachelor's Degree   \n",
       "413760         Sophia Allen  46.0        Married        High School   \n",
       "413762     Janice Robertson  28.0         Single  Bachelor's Degree   \n",
       "413766        Gary Faulkner  71.0        Married   Associate Degree   \n",
       "413767       Joseph Johnson  62.0        Widowed    Master's Degree   \n",
       "\n",
       "        Number of Children Smoking Status Physical Activity Level  \\\n",
       "0                      2.0     Non-smoker                  Active   \n",
       "1                      1.0     Non-smoker               Sedentary   \n",
       "2                      1.0     Non-smoker               Sedentary   \n",
       "7                      0.0     Non-smoker                  Active   \n",
       "9                      2.0     Non-smoker               Sedentary   \n",
       "...                    ...            ...                     ...   \n",
       "413759                 0.0        Current               Sedentary   \n",
       "413760                 1.0     Non-smoker               Sedentary   \n",
       "413762                 0.0     Non-smoker                  Active   \n",
       "413766                 2.0     Non-smoker               Sedentary   \n",
       "413767                 0.0         Former                Moderate   \n",
       "\n",
       "       Employment Status     Income Alcohol Consumption Dietary Habits  \\\n",
       "0             Unemployed   26265.67            Moderate       Moderate   \n",
       "1               Employed   42710.36                High      Unhealthy   \n",
       "2               Employed  125332.79                 Low      Unhealthy   \n",
       "7               Employed   39608.18            Moderate      Unhealthy   \n",
       "9               Employed   77597.84                 Low      Unhealthy   \n",
       "...                  ...        ...                 ...            ...   \n",
       "413759          Employed   54483.50            Moderate      Unhealthy   \n",
       "413760          Employed   26153.32                 Low      Unhealthy   \n",
       "413762        Unemployed    5971.27            Moderate       Moderate   \n",
       "413766        Unemployed   24557.08            Moderate       Moderate   \n",
       "413767          Employed  107125.74            Moderate        Healthy   \n",
       "\n",
       "       Sleep Patterns History of Mental Illness History of Substance Abuse  \\\n",
       "0                Fair                       Yes                         No   \n",
       "1                Fair                       Yes                         No   \n",
       "2                Good                        No                         No   \n",
       "7                Good                        No                         No   \n",
       "9                Poor                       Yes                         No   \n",
       "...               ...                       ...                        ...   \n",
       "413759           Good                        No                         No   \n",
       "413760           Poor                       Yes                         No   \n",
       "413762           Fair                       Yes                        Yes   \n",
       "413766           Poor                        No                        Yes   \n",
       "413767           Good                        No                        Yes   \n",
       "\n",
       "       Family History of Depression Chronic Medical Conditions  \n",
       "0                               Yes                        Yes  \n",
       "1                                No                        Yes  \n",
       "2                               Yes                         No  \n",
       "7                                No                         No  \n",
       "9                                No                         No  \n",
       "...                             ...                        ...  \n",
       "413759                           No                         No  \n",
       "413760                           No                        Yes  \n",
       "413762                           No                         No  \n",
       "413766                           No                         No  \n",
       "413767                           No                         No  \n",
       "\n",
       "[256186 rows x 16 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at records that do not have any missing values.\n",
    "df[df.notnull().all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "029f0e51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:16.396384Z",
     "iopub.status.busy": "2024-11-23T16:09:16.396111Z",
     "iopub.status.idle": "2024-11-23T16:09:16.438871Z",
     "shell.execute_reply": "2024-11-23T16:09:16.437927Z"
    },
    "papermill": {
     "duration": 0.056668,
     "end_time": "2024-11-23T16:09:16.440601",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.383933",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of individuals with no missing values: 256186\n"
     ]
    }
   ],
   "source": [
    "# Count the number of individuals (rows) that do not have any NaN values\n",
    "non_missing_count = df[df.notnull().all(axis=1)].shape[0]\n",
    "print(f\"Number of individuals with no missing values: {non_missing_count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24c1a0d",
   "metadata": {
    "papermill": {
     "duration": 0.011474,
     "end_time": "2024-11-23T16:09:16.463984",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.452510",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**shape[0]** gives the number of rows in the filtered DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e0313e34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:16.488113Z",
     "iopub.status.busy": "2024-11-23T16:09:16.487859Z",
     "iopub.status.idle": "2024-11-23T16:09:16.542629Z",
     "shell.execute_reply": "2024-11-23T16:09:16.541633Z"
    },
    "papermill": {
     "duration": 0.069211,
     "end_time": "2024-11-23T16:09:16.544688",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.475477",
     "status": "completed"
    },
    "tags": []
   },
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Number of Children</th>\n",
       "      <th>Smoking Status</th>\n",
       "      <th>Physical Activity Level</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Alcohol Consumption</th>\n",
       "      <th>Dietary Habits</th>\n",
       "      <th>Sleep Patterns</th>\n",
       "      <th>History of Mental Illness</th>\n",
       "      <th>History of Substance Abuse</th>\n",
       "      <th>Family History of Depression</th>\n",
       "      <th>Chronic Medical Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Christine Barker</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Active</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>26265.67</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jacqueline Lewis</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>High School</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>42710.36</td>\n",
       "      <td>High</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shannon Church</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>125332.79</td>\n",
       "      <td>Low</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charles Jordan</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>9992.78</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michael Rich</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>8595.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413763</th>\n",
       "      <td>Sean Miller</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Former</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Employed</td>\n",
       "      <td>109233.43</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413764</th>\n",
       "      <td>Christina Brown</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "      <td>Active</td>\n",
       "      <td>Employed</td>\n",
       "      <td>96760.97</td>\n",
       "      <td>Low</td>\n",
       "      <td>Healthy</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413765</th>\n",
       "      <td>Matthew Jenkins</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>77353.26</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413766</th>\n",
       "      <td>Gary Faulkner</td>\n",
       "      <td>71.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>24557.08</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Poor</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413767</th>\n",
       "      <td>Joseph Johnson</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Former</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Employed</td>\n",
       "      <td>107125.74</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Healthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>413768 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name   Age Marital Status    Education Level  \\\n",
       "0       Christine Barker  31.0        Married  Bachelor's Degree   \n",
       "1       Jacqueline Lewis  55.0        Married        High School   \n",
       "2         Shannon Church  78.0        Widowed    Master's Degree   \n",
       "3         Charles Jordan  58.0       Divorced    Master's Degree   \n",
       "4           Michael Rich  18.0         Single        High School   \n",
       "...                  ...   ...            ...                ...   \n",
       "413763       Sean Miller  68.0        Married    Master's Degree   \n",
       "413764   Christina Brown  26.0         Single  Bachelor's Degree   \n",
       "413765   Matthew Jenkins  57.0            NaN  Bachelor's Degree   \n",
       "413766     Gary Faulkner  71.0        Married   Associate Degree   \n",
       "413767    Joseph Johnson  62.0        Widowed    Master's Degree   \n",
       "\n",
       "        Number of Children Smoking Status Physical Activity Level  \\\n",
       "0                      2.0     Non-smoker                  Active   \n",
       "1                      1.0     Non-smoker               Sedentary   \n",
       "2                      1.0     Non-smoker               Sedentary   \n",
       "3                      3.0     Non-smoker                Moderate   \n",
       "4                      0.0     Non-smoker               Sedentary   \n",
       "...                    ...            ...                     ...   \n",
       "413763                 0.0         Former                Moderate   \n",
       "413764                 NaN        Current                  Active   \n",
       "413765                 0.0     Non-smoker               Sedentary   \n",
       "413766                 2.0     Non-smoker               Sedentary   \n",
       "413767                 0.0         Former                Moderate   \n",
       "\n",
       "       Employment Status     Income Alcohol Consumption Dietary Habits  \\\n",
       "0             Unemployed   26265.67            Moderate       Moderate   \n",
       "1               Employed   42710.36                High      Unhealthy   \n",
       "2               Employed  125332.79                 Low      Unhealthy   \n",
       "3             Unemployed    9992.78            Moderate       Moderate   \n",
       "4             Unemployed    8595.08                 NaN       Moderate   \n",
       "...                  ...        ...                 ...            ...   \n",
       "413763          Employed  109233.43                 Low            NaN   \n",
       "413764          Employed   96760.97                 Low        Healthy   \n",
       "413765          Employed   77353.26            Moderate       Moderate   \n",
       "413766        Unemployed   24557.08            Moderate       Moderate   \n",
       "413767          Employed  107125.74            Moderate        Healthy   \n",
       "\n",
       "       Sleep Patterns History of Mental Illness History of Substance Abuse  \\\n",
       "0                Fair                       Yes                         No   \n",
       "1                Fair                       Yes                         No   \n",
       "2                Good                        No                         No   \n",
       "3                Poor                       NaN                         No   \n",
       "4                Fair                       Yes                         No   \n",
       "...               ...                       ...                        ...   \n",
       "413763           Good                        No                         No   \n",
       "413764           Poor                       Yes                        Yes   \n",
       "413765           Fair                        No                         No   \n",
       "413766           Poor                        No                        Yes   \n",
       "413767           Good                        No                        Yes   \n",
       "\n",
       "       Family History of Depression Chronic Medical Conditions  \n",
       "0                               Yes                        Yes  \n",
       "1                                No                        Yes  \n",
       "2                               Yes                         No  \n",
       "3                                No                         No  \n",
       "4                               Yes                        Yes  \n",
       "...                             ...                        ...  \n",
       "413763                           No                         No  \n",
       "413764                           No                        Yes  \n",
       "413765                          Yes                        Yes  \n",
       "413766                           No                         No  \n",
       "413767                           No                         No  \n",
       "\n",
       "[413768 rows x 16 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there is an observation where all values are missing\n",
    "df.dropna(how = 'all', inplace = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6076a2e7",
   "metadata": {
    "papermill": {
     "duration": 0.011936,
     "end_time": "2024-11-23T16:09:16.570365",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.558429",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We observed earlier that there are 413,768 individuals by using df.shape. After excluding the rows where all values are missing, we still have 413,768 rows, which means there are no observations with all values missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e5758c85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:16.595344Z",
     "iopub.status.busy": "2024-11-23T16:09:16.595065Z",
     "iopub.status.idle": "2024-11-23T16:09:16.811210Z",
     "shell.execute_reply": "2024-11-23T16:09:16.810412Z"
    },
    "papermill": {
     "duration": 0.230547,
     "end_time": "2024-11-23T16:09:16.812916",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.582369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                            0\n",
       "Age                             0\n",
       "Marital Status                  0\n",
       "Education Level                 0\n",
       "Number of Children              0\n",
       "Smoking Status                  0\n",
       "Physical Activity Level         0\n",
       "Employment Status               0\n",
       "Income                          0\n",
       "Alcohol Consumption             0\n",
       "Dietary Habits                  0\n",
       "Sleep Patterns                  0\n",
       "History of Mental Illness       0\n",
       "History of Substance Abuse      0\n",
       "Family History of Depression    0\n",
       "Chronic Medical Conditions      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "489f004d",
   "metadata": {
    "papermill": {
     "duration": 0.012317,
     "end_time": "2024-11-23T16:09:16.837747",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.825430",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There is no missing values in the original dataset. Therefore, dropping missing values is not evaluated in this project."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1474e75",
   "metadata": {
    "papermill": {
     "duration": 0.011859,
     "end_time": "2024-11-23T16:09:16.861415",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.849556",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### 3. 1. 1. Imputing Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ae90ad",
   "metadata": {
    "papermill": {
     "duration": 0.011733,
     "end_time": "2024-11-23T16:09:16.885048",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.873315",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Distribution of Numerical Features**\n",
    "\n",
    "In this section, we visualize the distributions of three key numerical features in the dataset: **Age**, **Income**, and **Number of Children**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dca05b56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:16.910354Z",
     "iopub.status.busy": "2024-11-23T16:09:16.910110Z",
     "iopub.status.idle": "2024-11-23T16:09:17.764013Z",
     "shell.execute_reply": "2024-11-23T16:09:17.763239Z"
    },
    "papermill": {
     "duration": 0.869081,
     "end_time": "2024-11-23T16:09:17.766013",
     "exception": false,
     "start_time": "2024-11-23T16:09:16.896932",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see the distributions of numerical features\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# Plot for \"Age\"\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.hist(df[\"Age\"], bins=20, color='skyblue', edgecolor='black')\n",
    "plt.title(\"Age Distribution\")\n",
    "\n",
    "# Plot for \"Income\" (or any other numerical column you have)\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.hist(df[\"Income\"], bins=20, color='lightgreen', edgecolor='black')\n",
    "plt.title(\"Income Distribution\")\n",
    "\n",
    "# Plot for \"Number of Children\" (or another numerical feature)\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.hist(df[\"Number of Children\"], bins=20, color='lightcoral', edgecolor='black')\n",
    "plt.title(\"Number of Children Distribution\")\n",
    "\n",
    "# Show the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c135c4f0",
   "metadata": {
    "papermill": {
     "duration": 0.01245,
     "end_time": "2024-11-23T16:09:17.791591",
     "exception": false,
     "start_time": "2024-11-23T16:09:17.779141",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Distribution Observations:\r\n",
    "\r\n",
    "1. **Age Distribution**:\r\n",
    "   - The age distribution appears balanced, with values spread uniformly from ages 18 to 80.\r\n",
    "   - Notable increases in the number of individuals are observed at the boundaries (ages 18 and 80), indicating slightly higher counts compared to other ages.\r\n",
    "\r\n",
    "2. **Income Distribution**:\r\n",
    "   - The income distribution shows a clear right-skewed pattern.\r\n",
    "   - Most individuals fall into the lower-income ranges, while only a few have significantly higher incomes.\r\n",
    "\r\n",
    "3. **Number of Children**:\r\n",
    "   - The distribution decreases from 0 to 4 children.\r\n",
    "   - A sharp peak is observed at 0 children, indicating that most individuals do not have children.\r\n",
    "   - The frequencies for individuals with 1, 2, and 3 children are nearly identical, while very few have 4 children.\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "552c7fcf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:17.817929Z",
     "iopub.status.busy": "2024-11-23T16:09:17.817652Z",
     "iopub.status.idle": "2024-11-23T16:09:17.847830Z",
     "shell.execute_reply": "2024-11-23T16:09:17.846915Z"
    },
    "papermill": {
     "duration": 0.045226,
     "end_time": "2024-11-23T16:09:17.849412",
     "exception": false,
     "start_time": "2024-11-23T16:09:17.804186",
     "status": "completed"
    },
    "tags": []
   },
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Number of Children</th>\n",
       "      <th>Smoking Status</th>\n",
       "      <th>Physical Activity Level</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Alcohol Consumption</th>\n",
       "      <th>Dietary Habits</th>\n",
       "      <th>Sleep Patterns</th>\n",
       "      <th>History of Mental Illness</th>\n",
       "      <th>History of Substance Abuse</th>\n",
       "      <th>Family History of Depression</th>\n",
       "      <th>Chronic Medical Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Christine Barker</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Active</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>26265.67</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jacqueline Lewis</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>High School</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>42710.36</td>\n",
       "      <td>High</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shannon Church</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Employed</td>\n",
       "      <td>125332.79</td>\n",
       "      <td>Low</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charles Jordan</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>9992.78</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michael Rich</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>8595.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name   Age Marital Status    Education Level  \\\n",
       "0  Christine Barker  31.0        Married  Bachelor's Degree   \n",
       "1  Jacqueline Lewis  55.0        Married        High School   \n",
       "2    Shannon Church  78.0        Widowed    Master's Degree   \n",
       "3    Charles Jordan  58.0       Divorced    Master's Degree   \n",
       "4      Michael Rich  18.0         Single        High School   \n",
       "\n",
       "   Number of Children Smoking Status Physical Activity Level  \\\n",
       "0                 2.0     Non-smoker                  Active   \n",
       "1                 1.0     Non-smoker               Sedentary   \n",
       "2                 1.0     Non-smoker               Sedentary   \n",
       "3                 3.0     Non-smoker                Moderate   \n",
       "4                 0.0     Non-smoker               Sedentary   \n",
       "\n",
       "  Employment Status     Income Alcohol Consumption Dietary Habits  \\\n",
       "0        Unemployed   26265.67            Moderate       Moderate   \n",
       "1          Employed   42710.36                High      Unhealthy   \n",
       "2          Employed  125332.79                 Low      Unhealthy   \n",
       "3        Unemployed    9992.78            Moderate       Moderate   \n",
       "4        Unemployed    8595.08                 NaN       Moderate   \n",
       "\n",
       "  Sleep Patterns History of Mental Illness History of Substance Abuse  \\\n",
       "0           Fair                       Yes                         No   \n",
       "1           Fair                       Yes                         No   \n",
       "2           Good                        No                         No   \n",
       "3           Poor                       NaN                         No   \n",
       "4           Fair                       Yes                         No   \n",
       "\n",
       "  Family History of Depression Chronic Medical Conditions  \n",
       "0                          Yes                        Yes  \n",
       "1                           No                        Yes  \n",
       "2                          Yes                         No  \n",
       "3                           No                         No  \n",
       "4                          Yes                        Yes  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a copy to fill missing values\n",
    "df_fillna = df.copy()\n",
    "df_fillna.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd0e775",
   "metadata": {
    "papermill": {
     "duration": 0.012678,
     "end_time": "2024-11-23T16:09:17.875074",
     "exception": false,
     "start_time": "2024-11-23T16:09:17.862396",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Impute the missing values for Age\n",
    "To mimic the distribution, let's consider random imputation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "107a4518",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:17.901714Z",
     "iopub.status.busy": "2024-11-23T16:09:17.901413Z",
     "iopub.status.idle": "2024-11-23T16:09:18.117457Z",
     "shell.execute_reply": "2024-11-23T16:09:18.116724Z"
    },
    "papermill": {
     "duration": 0.231891,
     "end_time": "2024-11-23T16:09:18.119722",
     "exception": false,
     "start_time": "2024-11-23T16:09:17.887831",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import random\n",
    "df_fillna['Age'] = df_fillna['Age'].apply(lambda x: random.randint(18, 80) if pd.isnull(x) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e8ec4d4",
   "metadata": {
    "papermill": {
     "duration": 0.013777,
     "end_time": "2024-11-23T16:09:18.148418",
     "exception": false,
     "start_time": "2024-11-23T16:09:18.134641",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Impute the missing values for Income\n",
    "Firstly, we consider imputing missing values based on income groups, if it correlates with income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90dbbaac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:18.175453Z",
     "iopub.status.busy": "2024-11-23T16:09:18.175148Z",
     "iopub.status.idle": "2024-11-23T16:09:18.219729Z",
     "shell.execute_reply": "2024-11-23T16:09:18.218707Z"
    },
    "papermill": {
     "duration": 0.061067,
     "end_time": "2024-11-23T16:09:18.222434",
     "exception": false,
     "start_time": "2024-11-23T16:09:18.161367",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      count          mean           std       min         25%  \\\n",
      "Employment Status                                                               \n",
      "Employed           250062.0  70541.836662  37763.051923  22500.65  39056.0750   \n",
      "Unemployed         139630.0  15037.236879   8662.480999      0.41   7555.4375   \n",
      "\n",
      "                        50%         75%        max  \n",
      "Employment Status                                   \n",
      "Employed           61522.05  94869.7475  209995.22  \n",
      "Unemployed         15059.30  22549.9500   29999.88  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23/2730003720.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  income_by_employment = df_fillna.groupby(\"Employment Status\")[\"Income\"].describe()\n"
     ]
    }
   ],
   "source": [
    "# Group income statistics by Employment Status in df_fillna\n",
    "income_by_employment = df_fillna.groupby(\"Employment Status\")[\"Income\"].describe()\n",
    "print(income_by_employment)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72165624",
   "metadata": {
    "papermill": {
     "duration": 0.013384,
     "end_time": "2024-11-23T16:09:18.250765",
     "exception": false,
     "start_time": "2024-11-23T16:09:18.237381",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Based on the summary statistics, there appears to be a strong relationship between Employment Status and Income:\n",
    "\n",
    "- Employed individuals have a significantly higher mean income (\\~70,542) compared to Unemployed individuals (\\~15,037).\n",
    "- The income distribution of employed individuals has a wider spread, with a standard deviation of \\~37,763, compared to \\~8,662 for the unemployed group.\n",
    "- The median income for employed individuals (\\~61,522) is much higher than that for unemployed individuals (\\~15,059).\n",
    "\n",
    "Therefore, imputing missing Income values based on Employment Status is a logical choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1008bf1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:18.279643Z",
     "iopub.status.busy": "2024-11-23T16:09:18.278924Z",
     "iopub.status.idle": "2024-11-23T16:09:18.529738Z",
     "shell.execute_reply": "2024-11-23T16:09:18.528789Z"
    },
    "papermill": {
     "duration": 0.267251,
     "end_time": "2024-11-23T16:09:18.531404",
     "exception": false,
     "start_time": "2024-11-23T16:09:18.264153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see the correlation as boxplot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=\"Employment Status\", y=\"Income\", data=df_fillna)\n",
    "plt.title(\"Income Distribution by Employment Status\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3fcd246",
   "metadata": {
    "papermill": {
     "duration": 0.013034,
     "end_time": "2024-11-23T16:09:18.558774",
     "exception": false,
     "start_time": "2024-11-23T16:09:18.545740",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "HOWEVER, there are outliers in the income distribution of employed individuals. It could be a potential concern when imputing missing values by employment status. This could lead to imputations that are biased towards the higher end of the income scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a2938021",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:18.586411Z",
     "iopub.status.busy": "2024-11-23T16:09:18.586153Z",
     "iopub.status.idle": "2024-11-23T16:09:20.862974Z",
     "shell.execute_reply": "2024-11-23T16:09:20.862070Z"
    },
    "papermill": {
     "duration": 2.292717,
     "end_time": "2024-11-23T16:09:20.864893",
     "exception": false,
     "start_time": "2024-11-23T16:09:18.572176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1057: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_data = data.groupby(\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1057: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_data = data.groupby(\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see the distribution of income by Employment Status\n",
    "# Set the style for the plot\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot the distribution of income by employment status\n",
    "sns.histplot(data=df_fillna, x='Income', hue='Employment Status', multiple=\"stack\", kde=True)\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Income Distribution by Employment Status')\n",
    "plt.xlabel('Income')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed28ff1",
   "metadata": {
    "papermill": {
     "duration": 0.016421,
     "end_time": "2024-11-23T16:09:20.896685",
     "exception": false,
     "start_time": "2024-11-23T16:09:20.880264",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Based on the analysis of the income distribution for **Employed** and **Unemployed** individuals, it is evident that these two groups have distinct income patterns. To handle the missing income values, we will impute them using the **median income** for each group.\n",
    "\n",
    "**Why Use Median:**\n",
    "- The **median** is robust to outliers, meaning it won’t be skewed by extreme income values, making it a reliable statistic for imputing missing data.\n",
    "- By using different medians for the **Employed** and **Unemployed** groups, we ensure that each group's income distribution is respected, preserving the natural differences between the groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e5bd106",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:20.927617Z",
     "iopub.status.busy": "2024-11-23T16:09:20.927271Z",
     "iopub.status.idle": "2024-11-23T16:09:21.007171Z",
     "shell.execute_reply": "2024-11-23T16:09:21.006468Z"
    },
    "papermill": {
     "duration": 0.097646,
     "end_time": "2024-11-23T16:09:21.009128",
     "exception": false,
     "start_time": "2024-11-23T16:09:20.911482",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Imputing missing 'Income' values by median for 'Employed' and 'Unemployed' groups\n",
    "df_fillna['Income'] = df_fillna.groupby('Employment Status', observed=False)['Income'].transform(\n",
    "    lambda x: x.fillna(x.median())\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b717f5c",
   "metadata": {
    "papermill": {
     "duration": 0.01418,
     "end_time": "2024-11-23T16:09:21.038305",
     "exception": false,
     "start_time": "2024-11-23T16:09:21.024125",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Impute the missing values for Number of Children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "118c8677",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:21.069213Z",
     "iopub.status.busy": "2024-11-23T16:09:21.068454Z",
     "iopub.status.idle": "2024-11-23T16:09:21.641064Z",
     "shell.execute_reply": "2024-11-23T16:09:21.640360Z"
    },
    "papermill": {
     "duration": 0.590012,
     "end_time": "2024-11-23T16:09:21.643088",
     "exception": false,
     "start_time": "2024-11-23T16:09:21.053076",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Use random imputation proportional to the distribution\n",
    "\n",
    "# Define probabilities based on observed distribution\n",
    "prob_dist = df_fillna['Number of Children'].value_counts(normalize=True)\n",
    "df_fillna['Number of Children'] = df_fillna['Number of Children'].apply(\n",
    "    lambda x: np.random.choice(prob_dist.index, p=prob_dist.values) if pd.isnull(x) else x\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c852b437",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:21.679619Z",
     "iopub.status.busy": "2024-11-23T16:09:21.679302Z",
     "iopub.status.idle": "2024-11-23T16:09:21.708856Z",
     "shell.execute_reply": "2024-11-23T16:09:21.708136Z"
    },
    "papermill": {
     "duration": 0.046481,
     "end_time": "2024-11-23T16:09:21.710310",
     "exception": false,
     "start_time": "2024-11-23T16:09:21.663829",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                            12201\n",
       "Age                                 0\n",
       "Marital Status                  12356\n",
       "Education Level                 12293\n",
       "Number of Children                  0\n",
       "Smoking Status                  12274\n",
       "Physical Activity Level         12071\n",
       "Employment Status               12222\n",
       "Income                          12222\n",
       "Alcohol Consumption             12482\n",
       "Dietary Habits                  12102\n",
       "Sleep Patterns                  12199\n",
       "History of Mental Illness       12014\n",
       "History of Substance Abuse      12255\n",
       "Family History of Depression    12132\n",
       "Chronic Medical Conditions      12351\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the missing values of numerical features again!\n",
    "\n",
    "df_fillna.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "98defffc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:21.740008Z",
     "iopub.status.busy": "2024-11-23T16:09:21.739753Z",
     "iopub.status.idle": "2024-11-23T16:09:21.800030Z",
     "shell.execute_reply": "2024-11-23T16:09:21.799208Z"
    },
    "papermill": {
     "duration": 0.077085,
     "end_time": "2024-11-23T16:09:21.801791",
     "exception": false,
     "start_time": "2024-11-23T16:09:21.724706",
     "status": "completed"
    },
    "tags": []
   },
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Number of Children</th>\n",
       "      <th>Smoking Status</th>\n",
       "      <th>Physical Activity Level</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Alcohol Consumption</th>\n",
       "      <th>Dietary Habits</th>\n",
       "      <th>Sleep Patterns</th>\n",
       "      <th>History of Mental Illness</th>\n",
       "      <th>History of Substance Abuse</th>\n",
       "      <th>Family History of Depression</th>\n",
       "      <th>Chronic Medical Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kathryn Taylor</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Active</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Healthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Jason Lester</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Healthy</td>\n",
       "      <td>Poor</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Jeanette Rodriguez</td>\n",
       "      <td>71.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>High School</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>George Torres</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Former</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Ryan Gutierrez</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>Active</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Poor</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413594</th>\n",
       "      <td>Lorraine Stanley</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>High School</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Active</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Poor</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413601</th>\n",
       "      <td>Ian Murray</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-smoker</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Poor</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413620</th>\n",
       "      <td>David Garcia</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>PhD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Former</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Healthy</td>\n",
       "      <td>Fair</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413664</th>\n",
       "      <td>Sarah Guerrero</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Former</td>\n",
       "      <td>Sedentary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Good</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413751</th>\n",
       "      <td>Cynthia Murphy</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Former</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12222 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name   Age Marital Status    Education Level  \\\n",
       "8           Kathryn Taylor  50.0       Divorced  Bachelor's Degree   \n",
       "61            Jason Lester  56.0            NaN  Bachelor's Degree   \n",
       "122     Jeanette Rodriguez  71.0        Widowed        High School   \n",
       "137          George Torres  70.0        Widowed        High School   \n",
       "138         Ryan Gutierrez  28.0         Single  Bachelor's Degree   \n",
       "...                    ...   ...            ...                ...   \n",
       "413594    Lorraine Stanley  31.0        Married        High School   \n",
       "413601          Ian Murray  80.0        Widowed  Bachelor's Degree   \n",
       "413620        David Garcia  20.0        Married                PhD   \n",
       "413664      Sarah Guerrero  22.0        Married        High School   \n",
       "413751      Cynthia Murphy  34.0        Married    Master's Degree   \n",
       "\n",
       "        Number of Children Smoking Status Physical Activity Level  \\\n",
       "8                      0.0     Non-smoker                  Active   \n",
       "61                     2.0     Non-smoker                Moderate   \n",
       "122                    2.0     Non-smoker               Sedentary   \n",
       "137                    0.0         Former                Moderate   \n",
       "138                    0.0        Current                  Active   \n",
       "...                    ...            ...                     ...   \n",
       "413594                 2.0     Non-smoker                  Active   \n",
       "413601                 0.0     Non-smoker               Sedentary   \n",
       "413620                 0.0         Former                Moderate   \n",
       "413664                 0.0         Former               Sedentary   \n",
       "413751                 1.0         Former                Moderate   \n",
       "\n",
       "       Employment Status  Income Alcohol Consumption Dietary Habits  \\\n",
       "8                    NaN     NaN            Moderate        Healthy   \n",
       "61                   NaN     NaN                 NaN        Healthy   \n",
       "122                  NaN     NaN                High      Unhealthy   \n",
       "137                  NaN     NaN            Moderate       Moderate   \n",
       "138                  NaN     NaN                High      Unhealthy   \n",
       "...                  ...     ...                 ...            ...   \n",
       "413594               NaN     NaN            Moderate       Moderate   \n",
       "413601               NaN     NaN            Moderate       Moderate   \n",
       "413620               NaN     NaN            Moderate        Healthy   \n",
       "413664               NaN     NaN                High      Unhealthy   \n",
       "413751               NaN     NaN                 Low       Moderate   \n",
       "\n",
       "       Sleep Patterns History of Mental Illness History of Substance Abuse  \\\n",
       "8                Good                        No                        Yes   \n",
       "61               Poor                        No                         No   \n",
       "122              Fair                       Yes                         No   \n",
       "137              Fair                       Yes                         No   \n",
       "138              Poor                        No                        Yes   \n",
       "...               ...                       ...                        ...   \n",
       "413594           Poor                        No                         No   \n",
       "413601           Poor                        No                        Yes   \n",
       "413620           Fair                        No                        Yes   \n",
       "413664           Good                        No                         No   \n",
       "413751           Poor                       Yes                         No   \n",
       "\n",
       "       Family History of Depression Chronic Medical Conditions  \n",
       "8                                No                        Yes  \n",
       "61                               No                         No  \n",
       "122                             Yes                         No  \n",
       "137                              No                        Yes  \n",
       "138                              No                        Yes  \n",
       "...                             ...                        ...  \n",
       "413594                           No                         No  \n",
       "413601                          Yes                         No  \n",
       "413620                           No                        Yes  \n",
       "413664                           No                         No  \n",
       "413751                           No                         No  \n",
       "\n",
       "[12222 rows x 16 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for NaN or infinite values in 'Income'\n",
    "df_fillna[df_fillna['Income'].isnull() | df_fillna['Income'].isin([float('inf'), -float('inf')])]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa68e9b7",
   "metadata": {
    "papermill": {
     "duration": 0.014533,
     "end_time": "2024-11-23T16:09:21.831355",
     "exception": false,
     "start_time": "2024-11-23T16:09:21.816822",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "It looks like the issue may be that Income and Employment Status have missing values in the same rows, so when we're trying to impute missing Income values based on Employment Status, the Employment Status is also missing in those rows. Since Employment Status is required for grouping, rows with missing values in both Income and Employment Status would not be imputed properly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a604a3",
   "metadata": {
    "papermill": {
     "duration": 0.014432,
     "end_time": "2024-11-23T16:09:21.860533",
     "exception": false,
     "start_time": "2024-11-23T16:09:21.846101",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To adress this, let's first check how many rows have missing values in both Income and Employment Status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb360eb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:21.890831Z",
     "iopub.status.busy": "2024-11-23T16:09:21.890551Z",
     "iopub.status.idle": "2024-11-23T16:09:21.905523Z",
     "shell.execute_reply": "2024-11-23T16:09:21.904618Z"
    },
    "papermill": {
     "duration": 0.032017,
     "end_time": "2024-11-23T16:09:21.907229",
     "exception": false,
     "start_time": "2024-11-23T16:09:21.875212",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Name   Age Marital Status    Education Level  \\\n",
      "8           Kathryn Taylor  50.0       Divorced  Bachelor's Degree   \n",
      "61            Jason Lester  56.0            NaN  Bachelor's Degree   \n",
      "122     Jeanette Rodriguez  71.0        Widowed        High School   \n",
      "137          George Torres  70.0        Widowed        High School   \n",
      "138         Ryan Gutierrez  28.0         Single  Bachelor's Degree   \n",
      "...                    ...   ...            ...                ...   \n",
      "413594    Lorraine Stanley  31.0        Married        High School   \n",
      "413601          Ian Murray  80.0        Widowed  Bachelor's Degree   \n",
      "413620        David Garcia  20.0        Married                PhD   \n",
      "413664      Sarah Guerrero  22.0        Married        High School   \n",
      "413751      Cynthia Murphy  34.0        Married    Master's Degree   \n",
      "\n",
      "        Number of Children Smoking Status Physical Activity Level  \\\n",
      "8                      0.0     Non-smoker                  Active   \n",
      "61                     2.0     Non-smoker                Moderate   \n",
      "122                    2.0     Non-smoker               Sedentary   \n",
      "137                    0.0         Former                Moderate   \n",
      "138                    0.0        Current                  Active   \n",
      "...                    ...            ...                     ...   \n",
      "413594                 2.0     Non-smoker                  Active   \n",
      "413601                 0.0     Non-smoker               Sedentary   \n",
      "413620                 0.0         Former                Moderate   \n",
      "413664                 0.0         Former               Sedentary   \n",
      "413751                 1.0         Former                Moderate   \n",
      "\n",
      "       Employment Status  Income Alcohol Consumption Dietary Habits  \\\n",
      "8                    NaN     NaN            Moderate        Healthy   \n",
      "61                   NaN     NaN                 NaN        Healthy   \n",
      "122                  NaN     NaN                High      Unhealthy   \n",
      "137                  NaN     NaN            Moderate       Moderate   \n",
      "138                  NaN     NaN                High      Unhealthy   \n",
      "...                  ...     ...                 ...            ...   \n",
      "413594               NaN     NaN            Moderate       Moderate   \n",
      "413601               NaN     NaN            Moderate       Moderate   \n",
      "413620               NaN     NaN            Moderate        Healthy   \n",
      "413664               NaN     NaN                High      Unhealthy   \n",
      "413751               NaN     NaN                 Low       Moderate   \n",
      "\n",
      "       Sleep Patterns History of Mental Illness History of Substance Abuse  \\\n",
      "8                Good                        No                        Yes   \n",
      "61               Poor                        No                         No   \n",
      "122              Fair                       Yes                         No   \n",
      "137              Fair                       Yes                         No   \n",
      "138              Poor                        No                        Yes   \n",
      "...               ...                       ...                        ...   \n",
      "413594           Poor                        No                         No   \n",
      "413601           Poor                        No                        Yes   \n",
      "413620           Fair                        No                        Yes   \n",
      "413664           Good                        No                         No   \n",
      "413751           Poor                       Yes                         No   \n",
      "\n",
      "       Family History of Depression Chronic Medical Conditions  \n",
      "8                                No                        Yes  \n",
      "61                               No                         No  \n",
      "122                             Yes                         No  \n",
      "137                              No                        Yes  \n",
      "138                              No                        Yes  \n",
      "...                             ...                        ...  \n",
      "413594                           No                         No  \n",
      "413601                          Yes                         No  \n",
      "413620                           No                        Yes  \n",
      "413664                           No                         No  \n",
      "413751                           No                         No  \n",
      "\n",
      "[12222 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "# Check rows where both 'Income' and 'Employment Status' are missing\n",
    "missing_income_employment = df_fillna[df_fillna['Income'].isnull() & df_fillna['Employment Status'].isnull()]\n",
    "print(missing_income_employment)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a3acf2",
   "metadata": {
    "papermill": {
     "duration": 0.014899,
     "end_time": "2024-11-23T16:09:21.937493",
     "exception": false,
     "start_time": "2024-11-23T16:09:21.922594",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If the Employment Status is missing, it might be better to impute it first before imputing Income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2e7f330b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:22.003962Z",
     "iopub.status.busy": "2024-11-23T16:09:22.003303Z",
     "iopub.status.idle": "2024-11-23T16:09:22.013172Z",
     "shell.execute_reply": "2024-11-23T16:09:22.012417Z"
    },
    "papermill": {
     "duration": 0.062524,
     "end_time": "2024-11-23T16:09:22.014766",
     "exception": false,
     "start_time": "2024-11-23T16:09:21.952242",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Employed\n",
       "Name: Employment Status, dtype: category\n",
       "Categories (2, object): ['Employed', 'Unemployed']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fillna['Employment Status'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8e851364",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:22.046630Z",
     "iopub.status.busy": "2024-11-23T16:09:22.046343Z",
     "iopub.status.idle": "2024-11-23T16:09:22.053756Z",
     "shell.execute_reply": "2024-11-23T16:09:22.053120Z"
    },
    "papermill": {
     "duration": 0.025066,
     "end_time": "2024-11-23T16:09:22.055393",
     "exception": false,
     "start_time": "2024-11-23T16:09:22.030327",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Impute 'Employment Status' based on mode (most frequent value)\n",
    "df_fillna['Employment Status'] = df_fillna['Employment Status'].fillna(df_fillna['Employment Status'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "50906e34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:22.086359Z",
     "iopub.status.busy": "2024-11-23T16:09:22.086114Z",
     "iopub.status.idle": "2024-11-23T16:09:22.103258Z",
     "shell.execute_reply": "2024-11-23T16:09:22.102353Z"
    },
    "papermill": {
     "duration": 0.034503,
     "end_time": "2024-11-23T16:09:22.104997",
     "exception": false,
     "start_time": "2024-11-23T16:09:22.070494",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23/1194069477.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df_fillna['Income'] = df_fillna.groupby('Employment Status')['Income'].transform('median')\n"
     ]
    }
   ],
   "source": [
    "# Impute 'Income' based on the median income of 'Employment Status' groups\n",
    "df_fillna['Income'] = df_fillna.groupby('Employment Status')['Income'].transform('median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f0e9f0ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:22.136039Z",
     "iopub.status.busy": "2024-11-23T16:09:22.135801Z",
     "iopub.status.idle": "2024-11-23T16:09:22.140466Z",
     "shell.execute_reply": "2024-11-23T16:09:22.139772Z"
    },
    "papermill": {
     "duration": 0.02181,
     "end_time": "2024-11-23T16:09:22.142030",
     "exception": false,
     "start_time": "2024-11-23T16:09:22.120220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Check if there are still missing values for 'Income'\n",
    "print(df_fillna['Income'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4b2e707f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:22.172597Z",
     "iopub.status.busy": "2024-11-23T16:09:22.172335Z",
     "iopub.status.idle": "2024-11-23T16:09:22.202371Z",
     "shell.execute_reply": "2024-11-23T16:09:22.201617Z"
    },
    "papermill": {
     "duration": 0.047092,
     "end_time": "2024-11-23T16:09:22.204003",
     "exception": false,
     "start_time": "2024-11-23T16:09:22.156911",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                            12201\n",
       "Age                                 0\n",
       "Marital Status                  12356\n",
       "Education Level                 12293\n",
       "Number of Children                  0\n",
       "Smoking Status                  12274\n",
       "Physical Activity Level         12071\n",
       "Employment Status                   0\n",
       "Income                              0\n",
       "Alcohol Consumption             12482\n",
       "Dietary Habits                  12102\n",
       "Sleep Patterns                  12199\n",
       "History of Mental Illness       12014\n",
       "History of Substance Abuse      12255\n",
       "Family History of Depression    12132\n",
       "Chronic Medical Conditions      12351\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fillna.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7d1597",
   "metadata": {
    "papermill": {
     "duration": 0.015272,
     "end_time": "2024-11-23T16:09:22.235338",
     "exception": false,
     "start_time": "2024-11-23T16:09:22.220066",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Imputation for rest of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "27c1124b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:22.267825Z",
     "iopub.status.busy": "2024-11-23T16:09:22.267522Z",
     "iopub.status.idle": "2024-11-23T16:09:22.303261Z",
     "shell.execute_reply": "2024-11-23T16:09:22.302095Z"
    },
    "papermill": {
     "duration": 0.054169,
     "end_time": "2024-11-23T16:09:22.305138",
     "exception": false,
     "start_time": "2024-11-23T16:09:22.250969",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution for Marital Status:\n",
      "Marital Status\n",
      "Married     233247\n",
      "Single       69946\n",
      "Widowed      66446\n",
      "Divorced     31773\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for Education Level:\n",
      "Education Level\n",
      "Bachelor's Degree    120693\n",
      "High School          115364\n",
      "Associate Degree      77636\n",
      "Master's Degree       71541\n",
      "PhD                   16241\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for Smoking Status:\n",
      "Smoking Status\n",
      "Non-smoker    240153\n",
      "Former        112691\n",
      "Current        48650\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for Physical Activity Level:\n",
      "Physical Activity Level\n",
      "Sedentary    171762\n",
      "Moderate     153311\n",
      "Active        76624\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for Employment Status:\n",
      "Employment Status\n",
      "Employed      269967\n",
      "Unemployed    143801\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for Alcohol Consumption:\n",
      "Alcohol Consumption\n",
      "Moderate    168190\n",
      "Low         135007\n",
      "High         98089\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for Dietary Habits:\n",
      "Dietary Habits\n",
      "Unhealthy    165765\n",
      "Moderate     165550\n",
      "Healthy       70351\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for Sleep Patterns:\n",
      "Sleep Patterns\n",
      "Fair    191009\n",
      "Poor    125820\n",
      "Good     84740\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for History of Mental Illness:\n",
      "History of Mental Illness\n",
      "No     279478\n",
      "Yes    122276\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for History of Substance Abuse:\n",
      "History of Substance Abuse\n",
      "No     276399\n",
      "Yes    125114\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for Family History of Depression:\n",
      "Family History of Depression\n",
      "No     293716\n",
      "Yes    107920\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Distribution for Chronic Medical Conditions:\n",
      "Chronic Medical Conditions\n",
      "No     269318\n",
      "Yes    132099\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Loop through all columns and display value counts for categorical columns\n",
    "categorical_columns = df_fillna.select_dtypes(include=['category']).columns\n",
    "\n",
    "for column in categorical_columns:\n",
    "    print(f\"Distribution for {column}:\")\n",
    "    print(df_fillna[column].value_counts())\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c77d8bfd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:22.337622Z",
     "iopub.status.busy": "2024-11-23T16:09:22.337329Z",
     "iopub.status.idle": "2024-11-23T16:09:24.228972Z",
     "shell.execute_reply": "2024-11-23T16:09:24.228186Z"
    },
    "papermill": {
     "duration": 1.912773,
     "end_time": "2024-11-23T16:09:24.233977",
     "exception": false,
     "start_time": "2024-11-23T16:09:22.321204",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "image/png": 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VkclkuH79Om7cuFGr5RJR08B++b+aQr/co0cPbN26VednxIgRAMpvnHblyhW89tpraNWqlfg8X1/faq2N/ricnBx8//33eOONN8SRchqPzxari76vum0ZGBgIKysr8bHmu8qz3rOqSEpKQs+ePSGTyZCTkyP+9O3bF48ePRLr0qdPH7Ru3RqJiYnic5VKJb755htxVLmmPCcnJzg6OmqV16dPHwCo8ig8Impa2G//V2PutyUSCeRyOSIjIyGTyXD06FF89NFHGDhwICIjIytcs1yzRA1Qfg7+wgsvQBAEre0ymQwODg5ar6m69Ver1Zg2bRq+/vprbN68ucKlVStiYWGhdd8UqVQKNzc3rbqcPXsWdnZ2GDx4sLjNzMzsqTMlnsRzcePDZVjIKBQUFFQ6jSswMBAHDhzA3LlzsWLFCvj4+CAgIADDhg2DiUnVrinZ2dlV6+YjnTt31noskUjQuXNn3Lp1q8pl6OPWrVto27at1hcL4L9Te548frt27XTKsLKyeubNP27duoXOnTvrtJ/mOLdv36523Z/0j3/8Azt37sTVq1dx9OhRBAYG6iynopGbm4t169YhMTFR5yYceXl5OvE1vfN0UVER4uLi8Pnnn+PevXtaS7lUdLyqcHd3x7p161BSUoKrV6/i1KlT2LZtG6ZNm4b//Oc/z0w41EWdniYiIgJTpkzB0KFD4ezsDD8/P7zyyivo1q1brR6HiBon9sv/1RT65datW6Nv375P3a8p+8k2BgAHBwe9ThI1J8GVTYcG6qbvq25bPvmeaRLntXEjtZs3b+LatWvw8fGpcL9meZzmzZuL93spKSmBVCrFiRMnoFartZLlN2/exG+//fbU8ngjMyLjxH77vxp7vy2VSvHuu+/i3Xffxf379/H9999jx44dOHbsGJo3b47ly5drxT95QbpVq1YwMzPTWeqlVatWyM3N1bv+cXFxKCgoQHx8PLy9vav8ep577jmdHICVlRWuXbumVZdOnTrpxD1+EeNZeC5uXJgspybv7t27yMvLq/QPYYsWLbB7926cP38eX3/9Nc6ePYvExETs27cPW7ZsQbNmzZ55nOqsi1ZTjx49qlKdasPTjiM8ZR3v+tSjRw906tQJn3zyCf744w/84x//eGpsZGQk0tPTERISAldXV1hYWKCsrAwTJ06s8LXU9E7bH3/8MT7//HOMHz8eHh4eaNWqFSQSSaVroFeVVCqFu7s73N3d0aVLF8yZMwdJSUmYOnWqwer06NEjrce9evXCyZMnkZycjHPnzuGzzz7D9u3bsXDhQgQFBdXoWETUuLFfrpmG3C/Xhqdd9NZ3pGJd9n1VVZfvWVlZGXx9fTFx4sQK93fp0kX894gRI7Bv3z6kpKRgyJAhSEpKgqOjo9bJc1lZGZydnSu8GSiAGq0xT0SNE/vtmmnI/Xbbtm0xYsQIvPTSSxg5ciSSkpKwZMkSrbXMK7rYURevqV+/fjh79iwSEhLg7e1d5fPx+nofNXgubhyYLKcm79ChQwDwzGk8JiYm8PHxgY+PD+bMmYNNmzZh1apVOH/+PPr27fvUkzd93bx5U+uxIAi4efOm1l3Fn3bF+fbt27C3txcfV6duHTp0QFpaGvLz87WuhmdlZYn7a0OHDh1w7do1lJWVaXWwmuM8eYVaXyNGjMDGjRvh5OQEV1fXCmOUSiXS0tIQHh6u1YnV5bSk48eP49VXX9W6K3VxcXGtXzV+4YUXAJRPca+tOlX2earoM1lSUoI///xTJ9ba2hpvvPEG3njjDTx8+BD/+te/sHbtWnbQREaO/bK2ptYvV0RT9pNtDAAKhULrsWYq/5N905Mj9TTtnZGRUemxa6Pve5Ih2/JJnTp1QkFBQaUj+zV69eoFW1tbJCYmwsvLC99++y0mT56sU97Vq1fh4+NT6//HiKhxYr+trSn226ampnBxccGNGzfw4MED2Nra1rjM6ta/R48eGD16NMLCwjBt2jSsW7euSjcgrWpdMjMzIQiC1nv9+++/16hcnos3XVyznJq0tLQ0bNiwAR07dsTLL7/81LjHpwtpaBKvJSUlAABzc3MAtTNlFgD+85//ID8/X3yclJSEP//8E/7+/uI2e3t7/PTTT2IdAOD06dO4c+eOVlnVqZu/vz8ePXqE3bt3a23ftm0bJBKJ1vFrwt/fH3/++afW2pilpaXYuXMnLCwsxLtk11RQUBCmTp2KWbNmPTXmaVebt2/fXuPjW1hYVNjuFR1z586dOld9q+rbb7+t8IqzZk0+zZ2ya6NOms9TRYl9e3t7XLhwQWvb/v37dcp48OCB1uOWLVuiU6dOWp9lIjI+7Jd1NbV+uSJt27aFq6srvvjiC62+5dy5c8jMzNSK7dChA5o1a6azlunevXu1HtvY2KBXr144ePCgzlTux/vL2uj7nmTItnzS8OHDkZ6ejrNnz+rsU6lUKC0tFR+bmJhg2LBhOH36NA4fPozS0lKtJVg05d27dw/79+/XKa+oqAgFBQW1/yKIqMFiv62rMffbN27cqHD5FpVKhfT0dFhZWeksr6Ivferft29frFq1CmfPnsX777+v96yyJ/n5+eHevXta64sXFxdX2NdVhOfixocjy6nJSElJQVZWFh49eoS//voL58+fx7lz59C+fXts3Lix0mk869evx4ULF9C/f3906NABf//9N/bs2YPnnnsOPXv2BADx5guffvopWrZsCQsLC7i7u2tdka4OKysrvPXWW3j99dfx999/Y/v27ejcubPWTSaCgoJw/PhxTJw4EcOHD8fvv/+OI0eO6EyBq07dBg0aBG9vb6xatQq3bt2Ci4sLzp07h+TkZIwfP75a63ZVZtSoUdi3bx9mz56NX375BR06dMDx48dx8eJFREdH66zxpq8OHTogPDy80hhLS0v06tULCQkJUKvVsLOzw7lz5/DHH3/U+PjPP/880tLSsHXrVrRt2xYdO3ZEjx49MGDAABw6dAiWlpbo2rUrfvzxR3zzzTewtrbW6ziLFi1CYWEhAgIC4OjoCLVajYsXL+LYsWPo0KGD1s1walonV1dXNGvWDPHx8cjLy4NUKkWfPn3Qpk0bBAUF4cMPP0R4eDj69u2Lq1evIjU1Fa1bt9YqY8SIEejduzeef/55WFtb4+eff8bx48fxr3/9S6/XT0SND/tl4+mX7927J448fFzLli0xZMgQAMD06dMRFhaGt956C2+88QZyc3Oxa9cu/O///q9WArZVq1YYNmwYdu3aBYlEAnt7e3z99dcVrpU9d+5cjBkzBq+99hpGjRqFjh074tatW/j666/F+tRG31efbVmREydOiCPxHvfaa68hJCQEX331FSZPnozXXnsNzz//PAoLC5GRkYHjx48jOTlZK/ExfPhw7Ny5E7GxsXB2dhbXjtV45ZVXcOzYMXz44Yc4f/48vLy88OjRI2RlZSEpKQkJCQlwc3Or1ddHRA0D++2m329fvXoVM2fORL9+/fDiiy/CysoK9+7dw3/+8x/cv38f0dHRtbasib71HzJkCBYvXoxZs2bB0tISH330Ua3UZdeuXZgxYwbGjRsHW1tbHDlyRPxMP2tmAc/FjQ+T5dRkxMbGAiifQmRtbQ1nZ2dER0fj9ddff2ZHMmjQINy6dQsHDx7EgwcP0Lp1a/Tu3Rvh4eFo1aqVWO6SJUuwcuVKLFiwAKWlpYiJidG7c588eTKuXbuGzZs34+HDh/Dx8cGHH34oXkkEytftmj17NrZu3YrFixfjhRdewKZNm7B06VKtsqpTNxMTE2zcuBGxsbFITEzE559/jg4dOuD9999HcHCwXq+lIi1atMDOnTuxfPlyfPHFF8jPz4eDgwNiYmKqdZfz2rJixQp8/PHH2LNnDwRBgK+vL+Lj49GvX78alTt79mzMnz8fq1evRlFREV577TX06NEDH3zwAUxMTHDkyBEUFxfDy8sLW7dufeqaos/y/vvvIykpCWfOnMG+ffugVqvRvn17vPXWW3j33XfFaeu1USdbW1ssXLgQcXFx+OCDD/Do0SPs2LEDbdq0wZtvvok//vgDn332Gc6ePYuePXti69atmDBhglYZY8eOxVdffYVz586hpKQE7du3R2RkJEJCQvR6/UTU+LBfNp5++cqVK3j//fd1tnfo0EFMlvv7+2PNmjVYvXo1VqxYgU6dOiEmJgbJycn47rvvtJ43d+5clJaW4tNPP4VUKsWwYcPw/vvvY+TIkVpx3bp1w/79+7FmzRrs3bsXxcXFaN++PYYPHy7G1Ebf96T6/o7z5ZdfVri9d+/eaNeuHXbu3Im4uDgkJSXhP//5DywtLdGlSxet/y8aXl5eaNeuHe7cuaMzqhwo/zyuX78e27Ztw6FDh3Dy5EmYm5ujY8eOGDt2LBwcHGr99RFRw8B+u+n327169UJERATOnj2LrVu34sGDB2jZsiVcXV0xc+ZMDB06tEHU/5VXXsHDhw+xcOFCtGzZstJZ5FXRsmVLbN++HYsWLcKOHTtgYWGBV199FZ6enggPD3/m+ug8Fzc+EqEh3FWAiIiIiIiIiIiIqB5s27YNMTExSElJgZ2dnaGrQw0I1ywnIiIiIiIiIiKiJqmoqEjrcXFxMfbt24cuXbowUU46uAwLERERERERERERNUlTp05F+/bt0a1bN+Tn5+Pw4cPIysrC8uXLDV01aoC4DAsRERERERERERE1Sdu2bcNnn32GW7du4dGjR+jatSsmTpxY4f07iJgsJyIiIiIiImrijh07hsOHD+OXX36BSqVC586dMXbsWLzxxhuQSCQAym8M9+RNdwEgMTERTk5O4uO8vDzExMTg1KlTUKvV6NevH+bOnYu2bdtqPe/ixYtYunQprly5gjZt2mDMmDEIDQ0VjwcAgiAgPj4ee/bsQU5ODlxdXTFnzhx4eHholXXv3j0sWrQIqampMDU1RUBAAObMmfPMm08SERFVB5PlRERERERERE3cqFGj0KFDBwwZMgStW7fGN998g4SEBPz73//G1KlTAZQny0tLSzFr1iyt57q6usLMzEx8HBISgszMTMyaNQtmZmZYvXo1TExMcPDgQTRvXr7a682bN/Hqq6/C19cXb7/9Nq5du4bly5cjKioKISEhYlmbN29GbGwsZs6cCRcXF+zevRvffPMNDh06BHt7ewCAWq3G66+/DgCIiopCUVERli5dim7duiEuLq5O242IiIwL1ywnIiIiIiIiauI2btwIGxsb8bGPjw9yc3OxdetWTJkyBSYmJgAAmUymM6r7cenp6UhNTYVcLoefnx8AwMHBAYGBgThx4oS4rIFcLkfr1q2xcuVKSKVS+Pj4ICcnB5s2bcLYsWMhlUpRXFyMuLg4BAcHY8KECQCAnj17YtiwYZDL5ViwYAEA4Pjx47h+/ToSExPh6Ogo1jMkJASXLl2Cu7t7LbcWEREZKybLG7j09HQIggBTU1NDV4WIiOqYWq2GRCKBp6enoatCemK/TURkPBpbv/14olzD1dUV+/fvR0FBQZWXM0lJSYFMJoOvr6+4zdHREa6urkhJSRGT5SkpKQgICIBUKhXjAgMDERcXh/T0dHh7e+PixYvIz8/H8OHDxRipVIqAgACcPHlS65guLi5iohwAfH19YW1tjTNnzuidLGe/TURkPKrab5vUU31IT4IggCvlEBEZB/7Nb/z4HhIRGY+m8Df/hx9+gJ2dnVai/LvvvoOHhwfc3Nzwr3/9C99//73Wc7KysuDg4KC17jhQnjDPysoCABQUFODOnTtayW1NjEQiEeM0v5+Mc3Jywu3bt1FUVCTGPRkjkUjg4OAglqGPpvAeEhFR1VT1bz5Hljdwmivcbm5uBq4JERHVtZ9//tnQVaAaYr9NRGQ8Gnu/feHCBSQmJmqtT96rVy+88sor6NKlC+7fvw+5XI533nkHO3fuFEfiqVQqtGrVSqc8KysrXL58GUD5DUCB8qVSHieVSmFubg6lUimWJZVKtdZD1zxPEAQolUq0aNGi0mNqytKHqakpBEHQunkpERE1TZmZmToXeivCZDkRERERERGREbl79y6ioqLg7e2NcePGidsjIiK04gYMGICRI0diw4YNiI+Pr+9q1gu1Wo0rV64YuhpERFQPHl8a7GmYLCciIiIiIiIyEiqVCqGhobC2tsbatWvFG3tWxMLCAv3798fx48fFbTKZDHfv3tWJVSqVsLKyAgBxFLhmhLlGSUkJCgsLxTiZTIaSkhIUFxdrjS5XqVSQSCRacfn5+RUes127dlV96RUyNTVF165da1QGERE1fJmZmVWKY7KciIiIiIiIyAgUFRUhLCwMeXl52LdvX4VLmzyLo6Mj0tLSIAiC1nR2hUIBZ2dnAOVJ9nbt2umsJ65QKCAIgrj+uOa3QqFAt27dxLisrCy0b98eLVq0EOMyMjK0yhIEAQqFQutGo/qQSCSwsLCoURlERNTwVWUJFoA3+CQiIiIiIiJq8kpLSxEZGYmsrCwkJCTAzs7umc8pKCjA119/rXUvDn9/fyiVSqSlpYnbFAoFfv31V/j7+2vFJScnQ61Wi9sSExMhk8nE9c+9vLxgaWmJY8eOiTFqtRonTpzQKevq1au4ceOGuC0tLQ25ubno379/9RqCiIioEhxZTkRERHq5efMm5HI5fvrpJ1y/fh2Ojo44evSouD8/Px9bt27FmTNncOPGDUilUri7uyMqKgouLi5aZeXl5SEmJganTp2CWq1Gv379MHfuXLRt21Yr7uLFi1i6dCmuXLmCNm3aYMyYMQgNDdUaJSAIAuLj47Fnzx7k5OTA1dUVc+bMgYeHh1ZZ9+7dw6JFi5CamgpTU1MEBARgzpw5sLS0rP3GIiIiMrCFCxfi9OnTmD17NvLz8/Hjjz+K+7p3745Lly4hISEBAQEB6NChA+7fv4+tW7fizz//xJo1a8RYT09P+Pn5ITo6GrNmzYKZmRlWrVoFFxcXvPTSS2JcSEgIjhw5ghkzZmDMmDHIyMiAXC5HVFSUuGasmZkZwsLCsHbtWtjY2MDZ2Rl79+5Fbm4uQkJCxLKGDh2KuLg4hIeHY/r06SgsLMSyZcswYMAAuLu7133jERGR0WCynIiIiPRy/fp1nDlzBj169EBZWRkEQdDaf/v2bezbtw9vvPEGIiMjUVxcjC1btmDUqFE4ePAgnJycxNjIyEhkZmZiwYIFMDMzw+rVqxEaGoqDBw+iefPyrys3b95ESEgIfH19ERkZiWvXrmH58uVo1qyZ1gl1fHw8YmNjMXPmTLi4uGD37t0IDg7GoUOHYG9vD6B81NrEiRMBACtWrEBRURGWLl2KGTNmIC4urq6bjoiIqN6dO3cOALBkyRKdfcnJybC1tYVarcaqVauQm5sLc3NzeHp6YuHChToJ6dWrVyMmJgbz589HaWkp/Pz8MHfuXLHPBoDOnTtDLpdjyZIlmDRpEmxsbBAREYHg4GCtskJDQyEIArZs2SJe5JbL5WKfDZSvK56QkIBFixZh+vTpaN68OQICAhAdHV2bTURERASJ8OSZLTUoP//8MwBoTXsjIqKmqbH9zS8rKxNvCjZ79mxcvnxZa2R5QUEBJBIJzM3NxW0PHz7EoEGDMHLkSMybNw8AkJ6ejtGjR0Mul8PPzw9A+VqlgYGBWLlyJQIDAwEA8+fPR2pqKpKSksQRaStXrsTevXtx7tw5SKVSFBcXo2/fvnj77bcxffp0AOU3Exs2bBj8/f2xYMECAMDRo0cxc+ZMJCYmiuulpqamIiQkBAcOHNB7lFpjew+JiEh//Jvf+PE9JCIyHlX9m881y4mIiEgvmkT501hYWGglygGgZcuW6NSpE+7fvy9uS0lJgUwm07pBl6OjI1xdXZGSkqIVN3jwYDFRDgCBgYFQqVRIT08HUL5MS35+PoYPHy7GSKVSBAQE6JTl4uIiJsoBwNfXF9bW1jhz5kxVm4CIiIiIiIiaECbLiYiIqN6oVCpxfXONrKwsODg46Nyd3NHREVlZWQDKR6nfuXNH63maGIlEIsZpfj8Z5+TkhNu3b6OoqEiMezJGIpHAwcFBLIOIiIiIiIiMC9csJ6ohoawMkmeMriRtbDMi4/V///d/kEgkGDNmjLhNpVKhVatWOrFWVla4fPkygPIbgAKATCbTipFKpTA3N4dSqRTLkkqlMDMz04qTyWQQBAFKpRItWrSo9JiasvQlCAIKCgpqVAaVX7xoYWZmdP2FUFaGouJinXsAEFHDIwiCzoVeIiJqmsrKBJiY8G9+Q1JX7wmT5UQ1JDExgfLUXjx6cP/ZwYRmrdvCasiYZwcSUZNz8OBB7N+/H0uWLMFzzz1n6OrUGbVajStXrhi6Go2eubk5unfvblR9rKaPVCgUKCwsNHR1iKgKHl8ajIiImi4TEwlOpl7DAxUHxTQErWUWCPBzqZOymSwnqgWPHtxH6V+3DF0NIqIG68yZM5g/fz6mTJmC1157TWufTCbD3bt3dZ6jVCphZWUFAOIocM0Ic42SkhIUFhaKcTKZDCUlJSguLtYaXa5SqSCRSLTi8vPzKzxmu3btavBKAVNTU3Tt2rVGZRDE0ZrG2Mc6ODhwZDlRI5CZmWnoKhARUT16oCrAXzkPDV0NqmNMlhMREVGd+vHHHzFt2jS8+uqrmDZtms5+R0dHpKWl6UxnVygUcHZ2BlB+s9B27drprCeuUCggCIK4/rjmt0KhQLdu3cS4rKwstG/fHi1atBDjMjIytMoSBAEKhULrRqP6kEgksLCwqFEZZNyevDEuETVMXIKFiIio6TGuRSCJiIioXmVmZiIsLAx9+vTBwoULK4zx9/eHUqlEWlqauE2hUODXX3+Fv7+/VlxycjLUarW4LTExETKZDJ6engAALy8vWFpa4tixY2KMWq3GiRMndMq6evUqbty4IW5LS0tDbm4u+vfvX+PXTURERERERI0PR5YTERGRXgoLC3HmzBkAwK1bt5Cfn4+kpCQAQO/evSEIAkJCQmBmZobx48eLN+sEAEtLS3GpEk9PT/j5+SE6OhqzZs2CmZkZVq1aBRcXF7z00kvic0JCQnDkyBHMmDEDY8aMQUZGBuRyOaKiosQ1Y83MzBAWFoa1a9fCxsYGzs7O2Lt3L3JzcxESEiKWNXToUMTFxSE8PBzTp09HYWEhli1bhgEDBsDd3b3O246IiIiIiIgaHibLiYiISC9///23zrIqmsc7duwAAHEt8gkTJmjF9e7dGzt37hQfr169GjExMZg/fz5KS0vh5+eHuXPnonnz/35V6dy5M+RyOZYsWYJJkybBxsYGERERCA4O1io7NDQUgiBgy5YtyMnJgaurK+RyOezt7cUYU1NTJCQkYNGiRZg+fTqaN2+OgIAAREdH17xhiIiIiIiIqFFispyIiIj00rFjR1y7dq3SmGft12jVqhUWL16MxYsXVxrn5eWF/fv3VxojkUgQFhaGsLCwSuPs7Oywdu3aKtWPiIiIiIiImj6uWU5ERERERERERERERo/JciIiIiIiIiIiIiIyekyWExERERE1EWVlgqGrUO+M8TUTERERUd3gmuVERERERE2EiYkEJ1Ov4YGqwNBVqRetZRYI8HMxdDWIiIiIqIlgspyIiIiIqAl5oCrAXzkPDV0NIiIiIqJGh8uwEBEREREREREREZHRY7KciIiIiIiIiIiIiIwek+VEREREREREREREZPSYLCciIiIiIiIiIiIio8dkOREREREREREREREZPSbLiYiIiIiIiIiIiMjoMVlOREREREREREREREaPyXIiIiIiIiIiIiIiMnpMlhMRERERERERERGR0WOynIiIiIiIiIiIiIiMHpPlRERERERERERERGT0mCwnIiIiIiIiIiIiIqPXoJLlx44dw7vvvgt/f394eHjglVdewWeffQZBELTiDhw4gKFDh8LNzQ0vv/wyTp8+rVNWXl4eoqOj0bt3b3h6eiIiIgL379/Xibt48SJGjRoFd3d3DBw4EJs3b9Y5niAI2Lx5MwYMGAB3d3eMGjUKP/74o05Z9+7dQ3h4ODw9PdG7d2988MEHyM/Pr1mjEBEREREREREREVGda1DJ8m3btsHc3ByzZ8/Gxo0b4e/vj3nz5mH9+vVizJdffol58+Zh+PDhiI+Ph4eHB6ZOnaqTvI6MjMS5c+ewYMECLF++HAqFAqGhoSgtLRVjbt68iZCQENja2iIuLg7jx49HbGwstmzZolVWfHw8YmNjMWHCBMTFxcHW1hbBwcHIzs4WY9RqNSZOnIgbN25gxYoVWLBgAVJTUzFjxoy6aSwiIiIiIiIiIiIiqjXNDV2Bx23cuBE2NjbiYx8fH+Tm5mLr1q2YMmUKTExMEBsbixEjRiAyMhIA0KdPH2RkZGD9+vWIj48HAKSnpyM1NRVyuRx+fn4AAAcHBwQGBuLEiRMIDAwEAMjlcrRu3RorV66EVCqFj48PcnJysGnTJowdOxZSqRTFxcWIi4tDcHAwJkyYAADo2bMnhg0bBrlcjgULFgAAjh8/juvXryMxMRGOjo4AAJlMhpCQEFy6dAnu7u710IJEREREREREREREpI8GNbL88US5hqurK/Lz81FQUIDs7GzcuHEDw4cP14oJDAxEWloaSkpKAAApKSmQyWTw9fUVYxwdHeHq6oqUlBRxW0pKCgYPHgypVKpVlkqlQnp6OoDyZVry8/O1jimVShEQEKBTlouLi5goBwBfX19YW1vjzJkz+jYJEREREREREREREdWDBpUsr8gPP/wAOzs7WFpaIisrC0D5KPHHOTk5Qa1Wi8uiZGVlwcHBARKJRCvO0dFRLKOgoAB37tzRSm5rYiQSiRin+f1knJOTE27fvo2ioiIx7skYiUQCBwcHsQwiIiIiIiIiIiIiapga1DIsT7pw4QISExMxa9YsAIBSqQRQvrzJ4zSPNftVKhVatWqlU56VlRUuX74MoPwGoBWVJZVKYW5urlWWVCqFmZmZzjEFQYBSqUSLFi0qPaamLH0JgoCCgoIalUF1QyKRwNzc3NDVaJQKCwt1bqZLZOwEQdC50EtERERERERE9aPBJsvv3r2LqKgoeHt7Y9y4cYaujkGp1WpcuXLF0NWgCpibm6N79+6GrkajpFAoUFhYaOhqEDU4jy8NRkRERFRbjh07hsOHD+OXX36BSqVC586dMXbsWLzxxhtaF+sPHDiAhIQE3L59Gw4ODoiKisLAgQO1ysrLy0NMTAxOnToFtVqNfv36Ye7cuWjbtq1W3MWLF7F06VJcuXIFbdq0wZgxYxAaGqp1PEEQEB8fjz179iAnJweurq6YM2cOPDw8tMq6d+8eFi1ahNTUVJiamiIgIABz5syBpaVl7TcWEREZrQaZLFepVAgNDYW1tTXWrl0LE5Py1WKsrKwAlHfMtra2WvGP75fJZLh7965OuUqlUozRjALXjDDXKCkpQWFhoVZZJSUlKC4u1hpdrlKpIJFItOLy8/MrPGa7du30aIX/MjU1RdeuXWtUBtUNjgDVn4ODA0eWEz0hMzPT0FWolps3b0Iul+Onn37C9evX4ejoiKNHj+rE8aSbiIjI8LZt24YOHTpg9uzZaN26Nb755hvMmzcPd+/exdSpUwEAX375JebNm4fJkyejT58+SExMxNSpU7F7926tfjQyMhKZmZlYsGABzMzMsHr1aoSGhuLgwYNo3rw8zXDz5k2EhITA19cXkZGRuHbtGpYvX45mzZohJCRELCs+Ph6xsbGYOXMmXFxcsHv3bgQHB+PQoUOwt7cHUD6AbOLEiQCAFStWoKioCEuXLsWMGTMQFxdXTy1IRETGoMEly4uKihAWFoa8vDzs27dPa2kTzZrgT64PnpWVBVNTU7EjdXR0RFpams50doVCAWdnZwCAhYUF2rVrp7OeuEKhgCAIYvma3wqFAt26ddM6Zvv27dGiRQsxLiMjQ6ssQRCgUCi0bjSqD4lEAgsLixqVQdTQcPkaIl2N7QLc9evXcebMGfTo0QNlZWUVXgDjSTcREVHDsHHjRtjY2IiPfXx8kJubi61bt2LKlCkwMTFBbGwsRowYgcjISABAnz59kJGRgfXr1yM+Ph4AkJ6ejtTUVMjlcvj5+QEoHwgTGBiIEydOIDAwEAAgl8vRunVrrFy5ElKpFD4+PsjJycGmTZswduxYSKVSFBcXIy4uDsHBwZgwYQIAoGfPnhg2bBjkcjkWLFgAADh+/DiuX7+OxMRE8RxdJpMhJCQEly5dgru7ez20IBERGYMGdYPP0tJSREZGIisrCwkJCbCzs9Pab29vjy5duiApKUlre2JiInx8fMSp6/7+/lAqlUhLSxNjFAoFfv31V/j7+4vb/P39kZycDLVarVWWTCaDp6cnAMDLywuWlpY4duyYGKNWq3HixAmdsq5evYobN26I29LS0pCbm4v+/fvXoFWIiIgapkGDBuHMmTOIjY3F888/X2HM4yfdffr0wUcffQQ3NzesX79ejNGcdH/yyScIDAzE4MGDsWbNGly7dg0nTpwQ4x4/6fbx8cGECRMQHByMTZs2oaSkBAB0Trp9fHywcuVKWFtbQy6Xi2VpTrrXrFmDQYMGITAwEJ988gm+/vprXLp0qY5ajIiIyHAeT5RruLq6Ij8/HwUFBcjOzsaNGzcwfPhwrZjAwECkpaWJfW1KSgpkMpnWoDBHR0e4uroiJSVF3JaSkoLBgwdrLTEXGBgIlUqF9PR0AOUzxvLz87WOKZVKERAQoFOWi4uL1qA5X19fWFtb48yZM/o2CRERkY4GlSxfuHAhTp8+jcmTJyM/Px8//vij+KPpmMPDw3H06FHExsbi/Pnz+PDDD3Hp0iVMmTJFLMfT0xN+fn6Ijo7GsWPH8NVXXyEiIgIuLi546aWXxLiQkBDk5ORgxowZSEtLw/bt2yGXyzF58mSxQzczM0NYWBi2bNmC7du3Iy0tDTNmzEBubq7WKLahQ4fif//3fxEeHo7Tp08jMTER0dHRGDBgAK9yExFRk6RZJu1peNJNRETUsP3www+ws7ODpaWlOOvawcFBK8bJyQlqtRrZ2dkAymdZOzg46MyIc3R0FMsoKCjAnTt3tPpZTYxEIhHjNL+fjHNycsLt27dRVFQkxj0ZI5FI4ODgoDNbnIiIqCYa1DIs586dAwAsWbJEZ19ycjI6duyIkSNHorCwEPHx8di8eTMcHBywbt06cSS4xurVqxETE4P58+ejtLQUfn5+mDt3rjiVGwA6d+4MuVyOJUuWYNKkSbCxsUFERASCg4O1ygoNDYUgCNiyZYu49qlcLhencgPl64onJCRg0aJFmD59Opo3b46AgABER0fXZhMRERE1GlU56XZycqq1k25vb+9KT7q3b9+OoqIitGjRgifdRERk9C5cuIDExETMmjULQPn9toDy5U0ep3ms2a9SqbSWS9WwsrLC5cuXAfz33mBPliWVSmFubq5VllQq1bo/mOZ5giBAqVSiRYsWlR5TU5a+BEFAQUFBjcogoqZNIpFwKdkGqrCwsMr3w3tyue6naVDJ8q+++qpKcUFBQQgKCqo0plWrVli8eDEWL15caZyXlxf2799faYxEIkFYWBjCwsIqjbOzs8PatWsrjSEiIjIWPOmmmjDmk5LqfOl/HNuMNw6n+lXVk+6G6O7du4iKioK3tzfGjRtn6OoYlFqtxpUrVwxdDSJqwMzNzdG9e3dDV4MqoFAoUFhYWOX4x2cpP02DSpYTERERNXY86a4dxnxSUt0v/Rpss+q3GVFNVeWku6FRqVQIDQ2FtbU11q5dKy6rZmVlBaD8ArWtra1W/OP7ZTIZ7t69q1OuUqkUYzQXpDUXuzVKSkpQWFioVVZJSQmKi4u1LnSrVCpIJBKtuPz8/AqP2a5dOz1a4b9MTU3RtWvXGpVBRE1bY70wagwcHByqPGAiMzOzSnFMlhMREVGd4Ek31YQxn5RU50v/49hmHFlO9auqJ90NSVFREcLCwpCXl4d9+/ZpzbLSLE/25FJlWVlZMDU1FZchdXR0RFpams7IeoVCAWdnZwCAhYUF2rVrp7O0mUKhgCAIYvma3wqFAt26ddM6Zvv27dGiRQsxLiMjQ6ssQRCgUCi07nmiD4lEAgsLixqVQUREhlGdWZVV/a7MZDkRERHVCZ50E+nHWJdSqQm2GRlCY7tAVVpaisjISGRlZWH37t2ws7PT2m9vb48uXbogKSkJQ4YMEbcnJibCx8dHHEXv7++PDRs2IC0tDX379gVQ3u/++uuvmDhxovg8f39/JCcn47333oOpqalYlkwmE+855uXlBUtLSxw7dkzst9VqNU6cOAF/f3+tsg4fPowbN26gS5cuAIC0tDTk5uaif//+tdxSRERkzEwMXQEiIiJqmh4/6X5cRSfdSqUSaWlpYozmpPvJE+Xk5GSo1Wqtsp520q3xtJPuq1ev4saNG+I2nnQTEVFTtnDhQpw+fRqTJ09Gfn4+fvzxR/GnpKQEABAeHo6jR48iNjYW58+fx4cffohLly5hypQpYjmenp7w8/NDdHQ0jh07hq+++goRERFwcXHBSy+9JMaFhIQgJycHM2bMQFpaGrZv3w65XI7JkyeL3wHMzMwQFhaGLVu2YPv27UhLS8OMGTOQm5uLkJAQsayhQ4fif//3fxEeHo7Tp08jMTER0dHRGDBgANzd3eupBYmIyBhwZDkRERHppbCwEGfOnAEA3Lp1C/n5+WJivHfv3rCxsUF4eDhmzpyJTp06wdvbG4mJibh06RJ27dollvP4SfesWbNgZmaGVatWVXjSfeTIEcyYMQNjxoxBRkYG5HI5oqKidE66165dCxsbGzg7O2Pv3r0VnnTHxcUhPDwc06dPR2FhIZYtW8aTbiIiarLOnTsHAFiyZInOvuTkZHTs2BEjR45EYWEh4uPjsXnzZjg4OGDdunXiRWmN1atXIyYmBvPnz0dpaSn8/Pwwd+5cNG/+3xRD586dIZfLsWTJEkyaNAk2NjaIiIhAcHCwVlmhoaEQBAFbtmxBTk4OXF1dIZfLxRloQPkSZwkJCVi0aBGmT5+O5s2bIyAgANHR0bXZRERERJAIXNyvQfv5558BAG5ubgauCVUm58AalP51y9DVaBSa/08H2ARNM3Q1iBqkxvY3/48//sDgwYMr3Ldjxw54e3sDAA4cOID4+Hjcvn0bDg4OmD59OgYOHKgVn5eXh5iYGJw8eVLrpPvJKeIXL17EkiVLcOXKFdjY2ODtt99GaGio1lR4QRCwefNm7NmzRzzpnjNnjs6J/r1797Bo0SKkpqZqnXRbWlrq3SaN7T1sDIypj62tPnJ/Yjr+ynlYCzVq+P7HpiXeDPR8diBRHeDf/MaP7yERVYcxfcdq6PT5DljVv/kcWU5ERER66dixI65du/bMuKCgIAQFBVUa06pVKyxevBiLFy+uNM7Lywv79++vNEYikSAsLAxhYWGVxtnZ2WHt2rWVxhAREREREZHx4JrlRERERERERERERGT0mCwnIiIiIiIiIiIiIqPHZDkRERERERERERERGT0my4mIiIiIiIiIiIjI6DFZTkRERERERERERERGj8lyIiIiIiIiIiIiIjJ6TJYTERERERERERERkdFjspyIiIiIiIiIiIiIjB6T5URERERERERERERk9JgsJyIiIiIiIiIiIiKjx2Q5ERERERERERERERk9JsuJiIiIiIiIiIiIyOgxWU5ERERERERERERERo/JciIiIiIiIiIiIiIyekyWExEREREREREREZHRY7KciIiIiIiIiIiIiIwek+VEREREREREREREZPSYLCciIiIiIiIiIiIio8dkOREREREREREREREZPSbLiYiIiIiIiIiIiMjoMVlOREREREREREREREaPyXIiIiIiIiIiIiIiMnpMlhMRERERERERERGR0WOynIiIiOpUcnIygoKC4OnpCT8/P0ybNg3Z2dk6cQcOHMDQoUPh5uaGl19+GadPn9aJycvLQ3R0NHr37g1PT09ERETg/v37OnEXL17EqFGj4O7ujoEDB2Lz5s0QBEErRhAEbN68GQMGDIC7uztGjRqFH3/8sdZeNxERERERETUuTJYTERFRnTl//jymTp2Krl27Yv369YiOjsbVq1cRHByMoqIiMe7LL7/EvHnzMHz4cMTHx8PDwwNTp07VSV5HRkbi3LlzWLBgAZYvXw6FQoHQ0FCUlpaKMTdv3kRISAhsbW0RFxeH8ePHIzY2Flu2bNEqKz4+HrGxsZgwYQLi4uJga2uL4ODgChP5RERERERE1PQ1N3QFiIiIqOn68ssv0b59eyxevBgSiQQAYGNjg/Hjx+Py5ct48cUXAQCxsbEYMWIEIiMjAQB9+vRBRkYG1q9fj/j4eABAeno6UlNTIZfL4efnBwBwcHBAYGAgTpw4gcDAQACAXC5H69atsXLlSkilUvj4+CAnJwebNm3C2LFjIZVKUVxcjLi4OAQHB2PChAkAgJ49e2LYsGGQy+VYsGBB/TUSERERERERNQgcWU5ERER1prS0FC1bthQT5QDQqlUrABCXRcnOzsaNGzcwfPhwrecGBgYiLS0NJSUlAICUlBTIZDL4+vqKMY6OjnB1dUVKSoq4LSUlBYMHD4ZUKtUqS6VSIT09HUD5Mi35+flax5RKpQgICNAqi4iIiIiIiIxHg0qW37x5E/Pnz8crr7yC7t27Y+TIkToxY8eOhYuLi87Pb7/9phVniDVN7927h/DwcHh6eqJ379744IMPkJ+fX7NGISIiasRef/11/Pbbb9i9ezfy8vKQnZ2NlStXonv37vDy8gIAZGVlASgfJf44JycnqNVqcVmUrKwsODg4aCXegfKEuaaMgoIC3LlzB46OjjoxEolEjNP8fjLOyckJt2/f1loihoiIiIiIiIxDg1qG5fr16zhz5gx69OiBsrIynaS1hpeXF2bNmqW1rWPHjlqPIyMjkZmZiQULFsDMzAyrV69GaGgoDh48iObNy1+2Zk1TX19fREZG4tq1a1i+fDmaNWuGkJAQsSzNmqYzZ86Ei4sLdu/ejeDgYBw6dAj29vYAALVajYkTJwIAVqxYgaKiIixduhQzZsxAXFxcrbURERFRY/Liiy9i3bp1mDFjBj766CMAgKurKxISEtCsWTMAgFKpBADIZDKt52oea/arVCpxVPrjrKyscPnyZQDlF8srKksqlcLc3FyrLKlUCjMzM51jCoIApVKJFi1a6PWaBUFAQUGBXs+l/5JIJDA3Nzd0NQyisLDwqd+DK8M2q36bEdWEIAg6F3CJiIiocWtQyfJBgwZhyJAhAIDZs2eLJ75Pkslk8PDweGo5hljT9Pjx47h+/ToSExPFUWoymQwhISG4dOkS3N3da6GFiIiIGpeLFy/i/fffx5tvvokBAwYgNzcXGzZswKRJk7Bnzx69E9INmVqtxpUrVwxdjUbP3Nwc3bt3N3Q1DEKhUKCwsLDaz2ObVb/NiGrq8SW/iIiIqPFrUMlyE5PaWRXmWWuaapLlKSkpCAgI0FnTNC4uDunp6fD29q50TdOTJ09qHdPFxUVrOrevry+sra1x5swZJsuJiMgoLVq0CH369MHs2bPFbR4eHhgwYAAOHTqEUaNGwcrKCkD5qHBbW1sxTqVSAYC4XyaT4e7duzrHUCqVYoxm5LlmhLlGSUkJCgsLtcoqKSlBcXGx1uhylUoFiUQixunD1NQUXbt21fv5VM6YR2s6ODjoPbLcWOnbZkQ1kZmZaegqEBERUS1rUMnyqvruu+/g4eGBR48eoUePHpg2bRp69eol7q+tNU29vb0rXdN0+/btKCoqQosWLZCVlaUTI5FI4ODgIJZBRERkbH777TcMHjxYa9tzzz2H1q1b4/fffwfw3z72yb40KysLpqam4pJnjo6OSEtL05n2rlAo4OzsDACwsLBAu3btdPpehUIBQRDE8jW/FQoFunXrpnXM9u3b12jEu0QigYWFhd7PJzLWpVRqgm1GhtDYLlDdvHkTcrkcP/30E65fvw5HR0ccPXpUK2bs2LH47rvvdJ6bmJgIJycn8XFeXh5iYmJw6tQpqNVq9OvXD3PnzkXbtm21nnfx4kUsXboUV65cQZs2bTBmzBiEhoZqtZ0gCIiPj8eePXuQk5MDV1dXzJkzR2c2+b1797Bo0SKkpqbC1NQUAQEBmDNnDiwtLWuhdYiIiMo1umR5r1698Morr6BLly64f/8+5HI53nnnHezcuROenp4ADLOmaWXH1JSlL6592nAZ89qgNcW1RYl0NcW1T9u3b49ff/1Va9utW7fw4MEDdOjQAQBgb2+PLl26ICkpSVyODSg/Mffx8RFngPn7+2PDhg1IS0tD3759AZQnu3/99VfxviGauOTkZLz33nswNTUVy5LJZOJ3BS8vL1haWuLYsWNislytVuPEiRPw9/evo9YgIiIyHN4jjIiI6NkaXbI8IiJC6/GAAQMwcuRIbNiwAfHx8QaqVd3i2qcNlzGvDVpTXFuUqGJNbe3T0aNHY/HixVi0aBEGDRqE3NxcbNy4EW3atNFa4iw8PBwzZ85Ep06d4O3tjcTERFy6dAm7du0SYzw9PeHn54fo6GjMmjULZmZmWLVqFVxcXPDSSy+JcSEhIThy5AhmzJiBMWPGICMjA3K5HFFRUWL7mpmZISwsDGvXroWNjQ2cnZ2xd+9e5Obmap3A17YyoQwmktpZdq6xMMbXTETUEPEeYURERM/W6JLlT7KwsED//v1x/PhxcZsh1jSVyWTIz8+v8Jjt2rWr0Wvk2qcNV1MbAVqfuLYoka6muPbpuHHjIJVKsXfvXhw8eBAtW7aEh4cHVq9ejdatW4txI0eORGFhIeLj47F582Y4ODhg3bp14khwjdWrVyMmJgbz589HaWkp/Pz8MHfuXHEUGwB07twZcrkcS5YswaRJk2BjY4OIiAgEBwdrlRUaGgpBELBlyxZx2rdcLhdHsdUFE4kJdv90FvfyazbrrLGws7TC2z36GboaREQE3iOMiIioKhp9srwihljT1NHRERkZGVplCYIAhUKh9SVCH1z7lJoiLl9DpKspXoCTSCQYM2YMxowZ88zYoKAgBAUFVRrTqlUrLF68GIsXL640zsvLC/v3739m3cLCwhAWFvbMutWme/lK3FLl1OsxiYiIqor3CCMiImPW6JPlBQUF+Prrr+Hm5iZuM8Sapv7+/jh8+DBu3LiBLl26AADS0tKQm5uL/v3712kbEBEREREREdUU7xFGRKSL96truKpzP7yq3iNM72T5f/7zH7z44os6N/rQ+OOPP3DhwgW8+uqrVS6zsLAQZ86cAVB+86/8/HwkJSUBAHr37o2srCwkJCQgICAAHTp0wP3797F161b8+eefWLNmjViOIdY0HTp0KOLi4hAeHo7p06ejsLAQy5Ytw4ABAzgljIiIDK4u+m0iIiKqG4bqt3mPMCIiXbxfXcNV3fvhVeUeYXony+fMmYNly5Y9tfO+dOkS5syZU63O+++//8a0adO0tmke79ixA8899xzUajVWrVqF3NxcmJubw9PTEwsXLtRJSNf3mqampqZISEjAokWLMH36dDRv3hwBAQGIjo6u8usnIiKqK3XRbxMREVHdaCj9Nu8RRkTUNJfLbCqqcz+8qt4jTO9k+bMqUlBQgGbNmlWrzI4dO+LatWuVxsjl8iqVZYg1Te3s7LB27doq1Y+IiKg+1UW/TURERHWjIffbvEcYERE1FNVZHqeqFz2qlSy/evUqrl69Kj6+cOECHj16pBOnUqnw6aefwsHBoTrFExERUS1iv01ERNR4NMR+m/cIIyIiY1OtZPmpU6ewbt06AOXZ+H379mHfvn0VxspkMixdurTmNSQiIiK9sN8mIiJqPOq63+Y9woiIiJ6tWsnyN998EwMGDIAgCAgKCkJERITW1V7gv3eI7dSpk9b64ERERFS/2G8TERE1HnXdb/MeYURERM9Wrd61bdu2aNu2LYDyztTJyQlt2rSpk4oRERFRzbDfJiIiajzqut/mPcKIiIieTe8hZL17967NehAREVEdYr9NRKRLKBMgManazZ6aCmN8zY0R+20iIiLDqNF867Nnz+Kzzz5DdnY2VCqVzh27JRIJTp06VaMKEhERUe1gv01EpE1iIsHdU1egflBg6KrUC9PWFnhuiKuhq0FVxH6biIio/umdLE9ISMCKFSvQpk0buLu7w8XFpTbrRURERLWI/TYRUcXUDwpQ/Fe+oatBpIX9NhERkWHonSzfsWMH+vTpg82bN8PU1LQ260RERES1jP02ERFR48F+m4iIyDBM9H2iSqXC0KFD2XETERE1Auy3iYiIGg/220RERIahd7Lczc0NCoWiNutCREREdYT9NhERUePBfpuIiMgw9E6WL1iwACdPnsSRI0dqsz5ERERUB9hvExERNR7st4mIiAxD7zXLIyMjUVpaivfffx8LFizAc889BxMT7dy7RCLB4cOHa1xJIiIiqhn220RERI0H+20iIiLD0DtZbm1tDWtra3Tu3Lk260NERER1gP02ERFR48F+m4iIyDD0Tpbv3LmzNutBREREdYj9NhERUePBfpuIiMgw9F6znIiIiIiIiIiIiIioqdB7ZPn3339fpbhevXrpewgiIiKqJey3iYiIGg/220RERIahd7J87NixkEgkz4y7cuWKvocgIiKiWsJ+m4iIqPFgv01ERGQYeifLd+zYobPt0aNHuHXrFvbv34+ysjLMmDGjRpUjIiKi2sF+m4iIqPFgv01ERGQYeifLe/fu/dR9r7/+Ot566y1899138PHx0fcQREREVEvYbxMRETUe7LeJiIgMo05u8GliYoIRI0bgwIEDdVE8ERER1aL66Le/+OILvPrqq3Bzc4O3tzcmTpyIoqIicf9XX32Fl19+GW5ubhg6dCgOHjyoU0ZJSQmWLl0KX19feHh44J133kFWVpZO3G+//YZ33nkHHh4e8PX1xbJly1BSUqITd+DAAQwdOhRubm54+eWXcfr06dp90URERHWA59tERER1p06S5QCgVCqRl5dXV8UTERFRLarLfnvjxo34+OOPERgYCLlcjo8++ggdO3bEo0ePAAAXLlzA1KlT4eHhgfj4eAwfPhwffPABkpKStMpZtGgRDhw4gKioKKxduxYlJSWYMGGCVr2VSiXGjx8PtVqNtWvXIioqCvv378eSJUu0yvryyy8xb948DB8+HPHx8fDw8MDUqVPx448/1kkbEBER1SaebxMREdUNvZdhuX37doXbVSoVLly4ALlcjhdffFHvihEREVHtMVS/nZWVhXXr1mHDhg3o37+/uH3o0KHivzdu3Ah3d3d89NFHAIA+ffogOzsbsbGxGDZsGADg7t27+Oyzz/Dhhx/in//8JwDAzc0NAwcOxKefforQ0FAAwKeffoqHDx9i3bp1sLa2BlC+xuvChQsRFhYGOzs7AEBsbCxGjBiByMhI8ZgZGRlYv3494uPja70diIiIqoPn20RERIahd7J80KBBT707tyAI8PDwwMKFC/WuGBEREdUeQ/Xbn3/+OTp27KiVKH9cSUkJzp8/j5kzZ2ptDwwMxNGjR/HHH3+gY8eOSE1NRVlZmZg8BwBra2v4+voiJSVFTJanpKTAx8dHTJQDwPDhw/Hhhx/i3LlzeP3115GdnY0bN27gvffe0zmmZskWqVRaSy1ARERUfTzfJiIiMgy9k+WLFy/W6bwlEglkMhk6deqErl271rhyREREVDsM1W//9NNPcHZ2xoYNG7Bz507k5eXhhRdewJw5c9CjRw/8/vvvUKvVcHR01Hqek5MTgPKR6R07dkRWVhbatGkDKysrnbjPPvtMfJyVlYU33nhDK0Ymk8HW1lZc31zz28HBQacstVqN7Oxs8fhERESGwPNtIiIiw9A7Wf7666/XZj2IiIioDhmq3/7zzz9x+fJlZGRk4MMPP4S5uTk2bdqE4OBgnDhxAkqlEkB5Qvtxmsea/SqVCq1atdIpXyaTiTGauCfLAgArKysxrqrH1JcgCCgoKNDZLpFIYG5uXqOyG6vCwkIIglCt57C9qtdeANuMn7Gq0/czRtoEQXjq6O+a4vk2ERGRYeidLH9cZmYmbt26BQDo0KEDr3ITERE1YPXZb2sSx2vWrEG3bt0AAD169MCgQYOwa9cu+Pn51dmxDUWtVuPKlSs6283NzdG9e3cD1MjwFAoFCgsLq/Uctlf12gtgm/EzVnX6fsZIV30s28XzbSIiovpTo2T5qVOnsGTJErHj1ujYsSNmz56NwYMH16hyREREVHsM0W/LZDJYW1uLiXKgfK3x7t27IzMzEyNGjAAA5OXlaT1PpVIBgLjsikwmQ35+vk75KpVKa2kWmUymUxZQPlpcE6f5nZeXB1tb26ceU1+mpqYVJjLqavRhY+Dg4KDXqF9jpU97AWwzfsaqTt/PGGnLzMys0/J5vk1ERFT/9E6WnzlzBhEREWjfvj2ioqLEtT1/++037N+/H+Hh4di0aRP8/f1rrbJERESkH0P12127dsXvv/9e4b7i4mJ06tQJpqamyMrKQr9+/cR9mnXFNWuZOzo64q+//tJKemviHl/v3NHRUXyuRl5eHv7880+tsip6blZWFkxNTWFvb1+TlwyJRAILC4saldHUGOtSF/pie1Uf26x62F61oy4vuPB8m4iIyDBM9H3ihg0b4OLigsOHD2PSpEkYPHgwBg8ejEmTJuHw4cNwdnbG+vXra7OuREREpCdD9dsDBw5Ebm6u1rIkDx48wC+//ILnn38eUqkU3t7eOH78uNbzEhMT4eTkhI4dOwIA/Pz8YGJighMnTogxSqUSqampWokCf39/fPPNN+IocQBISkqCiYkJfH19AQD29vbo0qULkpKSdI7p4+NTL1PqiYiIKsPzbSIiIsPQO1l+7do1vPrqqxWOnLKwsMBrr72Ga9eu1ahyREREVDsM1W8PGTIEbm5uiIiIQGJiIpKTkzF58mRIpVK89dZbAIB3330XP/74IxYsWIDz588jNjYWR48eRXh4uFjOc889h3/+859YtmwZDh48iNTUVEydOhWtWrXC6NGjxbjRo0ejZcuW+Pe//43U1FQcPHgQy5Ytw+jRo2FnZyfGhYeH4+jRo4iNjcX58+fx4Ycf4tKlS5gyZUqttwEREVF18XybiIjIMPRehsXMzAxKpfKp+5VKJczMzPQtnoiIiGqRofptExMTbN68GTExMZg/fz7UajVefPFF7N69W1wv/MUXX8TatWuxevVqfPbZZ2jfvj0WLVqE4cOHa5U1d+5ctGzZEitWrMDDhw/h5eWFrVu3olWrVmKMlZUVtm/fjo8//hj//ve/0bJlS/zzn/9EVFSUVlkjR45EYWEh4uPjsXnzZjg4OGDdunXw9PSs9TYgIiKqLp5vExERGYbeyXJvb2/s2LED/fr10zmx/Omnn7Bz505xujMREREZliH7bRsbG/zf//1fpTGa6eWVkUqlmDVrFmbNmlVpnJOTE7Zt2/bMegUFBSEoKOiZcURERPWN59tEDY9QVgaJid4LNFAd4ftCtU3vZPl7772H0aNH46233oK7uzscHBwAAAqFApcuXUKbNm0wc+bMWqsoERER6Y/9NhERUePBfpuo4ZGYmEB5ai8ePbhv6KrQ/9esdVtYDRlj6GpQE6N3stze3h6HDx9GXFwcUlJSkJiYCABo3749xo0bh0mTJqFNmza1VlEiIiLSH/ttIiKixoP9NlHD9OjBfZT+dcvQ1SCiOqR3sry0tBRmZmaIjo5GdHS0zv78/HyUlpaiefOqH+LmzZuQy+X46aefcP36dTg6OuLo0aM6cQcOHEBCQgJu374NBwcHREVFYeDAgVoxeXl5iImJwalTp6BWq9GvXz/MnTsXbdu21Yq7ePEili5diitXrqBNmzYYM2YMQkNDIZFIxBhBEBAfH489e/YgJycHrq6umDNnDjw8PLTKunfvHhYtWoTU1FSYmpoiICAAc+bMgaWlZZXbgIiIqC7URb9NREREdYP9NhERkWHovajPokWLMHr06KfuHzNmDJYsWVKtMq9fv44zZ86gc+fOcHJyqjDmyy+/xLx58zB8+HDEx8fDw8MDU6dOxY8//qgVFxkZiXPnzmHBggVYvnw5FAoFQkNDUVpaKsbcvHkTISEhsLW1RVxcHMaPH4/Y2Fhs2bJFq6z4+HjExsZiwoQJiIuLg62tLYKDg5GdnS3GqNVqTJw4ETdu3MCKFSuwYMECpKamYsaMGdVqAyIiorpQF/02ERER1Q3220RERIahd7L87NmzGDp06FP3Dx06FCkpKdUqc9CgQThz5gxiY2Px/PPPVxgTGxuLESNGIDIyEn369MFHH30ENzc3rF+/XoxJT09HamoqPvnkEwQGBmLw4MFYs2YNrl27hhMnTohxcrkcrVu3xsqVK+Hj44MJEyYgODgYmzZtQklJCQCguLgYcXFxCA4OxoQJE+Dj44OVK1fC2toacrlcLOv48eO4fv061qxZg0GDBiEwMBCffPIJvv76a1y6dKla7UBERFTb6qLfJiIiorpRF/32zZs3MX/+fLzyyivo3r07Ro4cWWHcgQMHMHToULi5ueHll1/G6dOndWLy8vIQHR2N3r17w9PTExEREbh/X3cd54sXL2LUqFFwd3fHwIEDsXnzZgiCoBUjCAI2b96MAQMGwN3dHaNGjdIZDAeUz+QODw+Hp6cnevfujQ8++AD5+fnVagMiIqJn0TtZfv/+fdjZ2T11f9u2bXHv3r3qVeYZd6/Nzs7GjRs3MHz4cK3tgYGBSEtLExPcKSkpkMlkWncHd3R0hKurq9YXipSUFAwePBhSqVSrLJVKhfT0dADlnXt+fr7WMaVSKQICAnTKcnFxgaOjo7jN19cX1tbWOHPmTHWagYiIqNbVRb9NREREdaMu+m3O5CYiIno2vRc4s7a2hkKheOr+3377rdbX6s7KygIA8U7gGk5OTlCr1cjOzoaTkxOysrLg4OCgte44UJ4w15RRUFCAO3fuaCW3NTESiQRZWVnw9vYW45+Mc3Jywvbt21FUVIQWLVogKytLJ0YikcDBwUEsg4iIyFAM0W8TERGRfuqi3x40aBCGDBkCAJg9ezYuX76sE/P4TG4A6NOnDzIyMrB+/XrEx8cD+O9MbrlcDj8/PwDl5+iBgYE4ceIEAgMDAWjP5JZKpfDx8UFOTg42bdqEsWPHQiqV6szkBoCePXti2LBhkMvlWLBgAYD/zuROTEwUz7tlMhlCQkJw6dIluLu7V6stiIiInkbvZHm/fv3w6aef4h//+Ae6d++ute+XX37B/v37MWzYsBpX8HFKpRJAeaf4OM1jzX6VSoVWrVrpPN/Kykr8QpCXl1dhWVKpFObm5lplSaVSmJmZ6RxTEAQolUq0aNGi0mNqytKXIAgoKCioURlUNyQSCczNzQ1djUapsLBQZwomkbETBEHnQm9tMUS/TURERPqpi367qjO533vvPa3tgYGBWLZsGUpKSiCVSp85k1uTLE9JSUFAQIDOTO64uDikp6fD29u70pncJ0+eFLc9ayY3k+VERFRb9E6WT5s2DWfPnkVQUBAGDRqErl27Aiif2nX69GnY2Nhg2rRptVZRY6ZWq3HlyhVDV4MqYG5urvPllapGoVCgsLDQ0NUganAeP6GsTey3iYiIGg9D9NucyU1ERFSDZLmdnR0OHjyIFStWIDk5Wbzqa2lpiX/84x+IioqqdI01fVhZWQEoHxVua2srblepVFr7ZTIZ7t69q/N8pVIpxmhGgWtGmGuUlJSgsLBQq6ySkhIUFxdrjS5XqVSQSCRacRXdXESpVKJdu3b6veD/z9TUVPxyRA1LXY0ANQYODg4cWU70hMzMzDor2xD9NhEREenHEP02Z3ITPR1nlTdsdT1zne9/w1Wd976qM7n1TpYD5TcVWbp0KQRBQE5ODgDAxsamzhKImivJT15VzsrKgqmpKezt7cW4tLQ0nUZQKBRwdnYGAFhYWKBdu3Y6V6EVCgUEQRDL1/xWKBTo1q2b1jHbt2+PFi1aiHEZGRlaZQmCAIVCoTU9TR8SiQQWFhY1KoOooWFHQ6Srri/A1Xe/TURERPpjv10/OJObqoKzyhu2up65zve/4arue1+Vmdw1SpZrSCQStGnTpjaKqpS9vT26dOmCpKQk8cYkAJCYmAgfHx/xBfv7+2PDhg1IS0tD3759AZQ33q+//oqJEyeKz/P390dycjLee+89mJqaimXJZDJ4enoCALy8vGBpaYljx46JyXK1Wo0TJ07A399fq6zDhw/jxo0b6NKlCwAgLS0Nubm56N+/f901ChERUTXVV79NRERENVdf/TZnchM9HS9SNWx1PXOd73/DVZ33vqozuWslWV5bCgsLcebMGQDArVu3kJ+fj6SkJABA7969YWNjg/DwcMycOROdOnWCt7c3EhMTcenSJezatUssx9PTE35+foiOjsasWbNgZmaGVatWwcXFBS+99JIYFxISgiNHjmDGjBkYM2YMMjIyIJfLERUVJSbezczMEBYWhrVr18LGxgbOzs7Yu3cvcnNzERISIpY1dOhQxMXFITw8HNOnT0dhYSGWLVuGAQMG8GYjRERERERE1KBxJjcRNVacuW68qvPeV/WiR+W3w65nf//9N6ZNm4Zp06bhu+++w507d8TH169fBwCMHDkSH3/8MY4ePYqQkBBcvHgR69atE0eCa6xevRp9+/bF/PnzMWPGDHTp0gWbN29G8+b/vT7QuXNnyOVy3L17F5MmTcKWLVsQERGB4OBgrbJCQ0MxdepUbNmyBZMmTcLdu3chl8vFLwtA+dXohIQEdOnSBdOnT8eHH36Ivn37YsWKFXXYYkREREREREQ19/hM7sdVNJNbqVQiLS1NjNHM5H5y9nVycjLUarVWWU+bya3xtJncV69exY0bN8RtnMlNRER1oUGNLO/YsSOuXbv2zLigoCAEBQVVGtOqVSssXrwYixcvrjTOy8sL+/fvrzRGIpEgLCwMYWFhlcbZ2dlh7dq1lcYQERERERER1TfO5CYiInq2BpUsJyIiIiIiIqLap5nJ/TjN4x07dsDb2xsjR45EYWEh4uPjsXnzZjg4ODx1JndMTAzmz5+P0tJS+Pn5Ye7cuRXO5F6yZAkmTZoEGxubp87kFgQBW7ZsQU5ODlxdXZ86k3vRokWYPn06mjdvjoCAAERHR9d2MxERkZFjspyIiIiIiIioieNMbiIiomdrUGuWExEREREREREREREZApPlRERERERERERERGT0mCwnIiIiIiIiIiIiIqPHZDkRERERERERERERGT0my4mIiIiIiIiIiIjI6DFZTkRERERERERERERGj8lyIiIiqjcPHz6Ev78/XFxc8PPPP2vtO3DgAIYOHQo3Nze8/PLLOH36tM7z8/LyEB0djd69e8PT0xMRERG4f/++TtzFixcxatQouLu7Y+DAgdi8eTMEQdCKEQQBmzdvxoABA+Du7o5Ro0bhxx9/rNXXS0RERERERI0Hk+VERERUbzZs2IBHjx7pbP/yyy8xb948DB8+HPHx8fDw8MDUqVN1kteRkZE4d+4cFixYgOXLl0OhUCA0NBSlpaVizM2bNxESEgJbW1vExcVh/PjxiI2NxZYtW7TKio+PR2xsLCZMmIC4uDjY2toiODgY2dnZdfLaiYiIiIiIqGFjspyIiIjqxW+//YY9e/YgPDxcZ19sbCxGjBiByMhI9OnTBx999BHc3Nywfv16MSY9PR2pqan45JNPEBgYiMGDB2PNmjW4du0aTpw4IcbJ5XK0bt0aK1euhI+PDyZMmIDg4GBs2rQJJSUlAIDi4mLExcUhODgYEyZMgI+PD1auXAlra2vI5fK6bwwiIiIiIiJqcJgsJyIionqxaNEijB49Gg4ODlrbs7OzcePGDQwfPlxre2BgINLS0sQEd0pKCmQyGXx9fcUYR0dHuLq6IiUlRdyWkpKCwYMHQyqVapWlUqmQnp4OoHyZlvz8fK1jSqVSBAQEaJVFRERERERExoPJciIiIqpzSUlJyMjIwL///W+dfVlZWQCgk0R3cnKCWq0Wl0XJysqCg4MDJBKJVpyjo6NYRkFBAe7cuQNHR0edGIlEIsZpfj8Z5+TkhNu3b6OoqEjfl0pERERERESNVHNDV4CIiIiatsLCQixZsgRRUVGwtLTU2a9UKgEAMplMa7vmsWa/SqVCq1atdJ5vZWWFy5cvAyi/AWhFZUmlUpibm2uVJZVKYWZmpnNMQRCgVCrRokWLar9WoPzGoQUFBTrbJRIJzM3N9SqzsSssLNS5weqzsL2q114A24yfsarT9zNG2gRB0LmAS0RERI0bk+VERERUpzZu3Ig2bdrgjTfeMHRV6oVarcaVK1d0tpubm6N79+4GqJHhKRQKFBYWVus5bK/qtRfANuNnrOr0/YyRrseX/CIiIqLGj8lyIiIiqjO3bt3Cli1bsH79enHUt2bUdUFBAR4+fAgrKysA5aPCbW1txeeqVCoAEPfLZDLcvXtX5xhKpVKM0Yw81xxLo6SkBIWFhVpllZSUoLi4WGt0uUqlgkQiEeP0YWpqiq5du+psN+bRhw4ODnqN+jVW+rQXwDbjZ6zq9P2MkbbMzExDV4GIiIhqGZPlREREVGf++OMPqNVqTJo0SWffuHHj0KNHD6xYsQJA+Trij68hnpWVBVNTU9jb2wMoX188LS1NZ9q7QqGAs7MzAMDCwgLt2rUT1yR/PEYQBLF8zW+FQoFu3bppHbN9+/Z6L8EClCfgLCws9H5+U2SsS13oi+1VfWyz6mF71Q5jvuBCRETUVPEGn0RERFRnXF1dsWPHDq2fOXPmAAAWLlyIDz/8EPb29ujSpQuSkpK0npuYmAgfHx9xiru/vz+USiXS0tLEGIVCgV9//RX+/v7iNn9/fyQnJ0OtVmuVJZPJ4OnpCQDw8vKCpaUljh07Jsao1WqcOHFCqywiIiIiIiIyHhxZTkRERHVGJpPB29u7wn3PP/88nn/+eQBAeHg4Zs6ciU6dOsHb2xuJiYm4dOkSdu3aJcZ7enrCz88P0dHRmDVrFszMzLBq1Sq4uLjgpZdeEuNCQkJw5MgRzJgxA2PGjEFGRgbkcjmioqLExLuZmRnCwsKwdu1a2NjYwNnZGXv37kVubi5CQkLqsEWIiIiIiIiooWKynIiIiAxu5MiRKCwsRHx8PDZv3gwHBwesW7dOHAmusXr1asTExGD+/PkoLS2Fn58f5s6di+bN//uVpnPnzpDL5ViyZAkmTZoEGxsbREREIDg4WKus0NBQCIKALVu2ICcnB66urpDL5eKyL0RERERERGRcmCwnIiKieuXt7Y1r167pbA8KCkJQUFClz23VqhUWL16MxYsXVxrn5eWF/fv3VxojkUgQFhaGsLCwZ1eaiIiIiIiImjyuWU5EjVpZmWDoKjQqbC8iIiIiIiIioopxZDkRNWomJhKcTL2GB6oCQ1elwWsts0CAn4uhq0FERERERERE1CAxWU5Ejd4DVQH+ynlo6GoQEREREREREVEjxmVYiIhILwKXdKkWthcRERERERFRw8aR5UREpBeJiQR3T12B+gGXwHkW09YWeG6Iq6GrQURERERERESVYLKciIj0pn5QgOK/8g1dDSIiIiIiIiKiGuMyLERERERERERERERk9JgsJyIiIiIiIiIiIiKjx2Q5ERERERERERERERk9JsuJiIiIiIiIiIiIyOgxWU5ERERERERERERERo/JciIiIiIiIiIiIiIyeo0uWf7555/DxcVF52f58uVacQcOHMDQoUPh5uaGl19+GadPn9YpKy8vD9HR0ejduzc8PT0RERGB+/fv68RdvHgRo0aNgru7OwYOHIjNmzdDEAStGEEQsHnzZgwYMADu7u4YNWoUfvzxx1p97URERERERERERERUN5obugL6SkhIQKtWrcTHdnZ24r+//PJLzJs3D5MnT0afPn2QmJiIqVOnYvfu3fDw8BDjIiMjkZmZiQULFsDMzAyrV69GaGgoDh48iObNy5vm5s2bCAkJga+vLyIjI3Ht2jUsX74czZo1Q0hIiFhWfHw8YmNjMXPmTLi4uGD37t0IDg7GoUOHYG9vX/cNQkRERERERERERER6a3QjyzWef/55eHh4iD/t2rUT98XGxmLEiBGIjIxEnz598NFHH8HNzQ3r168XY9LT05GamopPPvkEgYGBGDx4MNasWYNr167hxIkTYpxcLkfr1q2xcuVK+Pj4YMKECQgODsamTZtQUlICACguLkZcXByCg4MxYcIE+Pj4YOXKlbC2toZcLq+/RiEiIiIiIiLSE2dyExGRsWu0yfKnyc7Oxo0bNzB8+HCt7YGBgUhLSxMT3CkpKZDJZPD19RVjHB0d4erqipSUFHFbSkoKBg8eDKlUqlWWSqVCeno6gPLOPT8/X+uYUqkUAQEBWmURERERERERNXQJCQnYt2+f+PP222+L+zQzuYcPH474+Hh4eHhg6tSpOsnryMhInDt3DgsWLMDy5cuhUCgQGhqK0tJSMUYzk9vW1hZxcXEYP348YmNjsWXLFq2yNDO5J0yYgLi4ONja2iI4OBjZ2dl12g5ERGR8Gu0yLCNHjsSDBw/Qvn17vPnmm5g4cSKaNWuGrKwsAICDg4NWvJOTE9RqNbKzs+Hk5ISsrCw4ODhAIpFoxTk6OoplFBQU4M6dO3B0dNSJkUgkyMrKgre3txj/ZJyTkxO2b9+OoqIitGjRolZfPxEREREREVFdeP7552FjY1PhvsdncgNAnz59kJGRgfXr1yM+Ph7Af2dyy+Vy+Pn5ASg/Rw8MDMSJEycQGBgIQHsmt1QqhY+PD3JycrBp0yaMHTsWUqlUZyY3APTs2RPDhg2DXC7HggUL6rQtiIjIuDS6ZLmtrS3Cw8PRo0cPSCQSfPXVV1i9ejXu3buH+fPnQ6lUAgBkMpnW8zSPNftVKpXWmucaVlZWuHz5MoDyaWMVlSWVSmFubq5VllQqhZmZmc4xBUGAUqmsUbJcEAQUFBTo/XyqOxKJBObm5oauRqNUWFioM72yutj++mHbG86z2l4QBJ2LuEREREQNhWYm93vvvae1PTAwEMuWLUNJSQmkUukzZ3JrkuUpKSkICAjQmckdFxeH9PR0eHt7VzqT++TJk3X8iomIyNg0umR5v3790K9fP/Gxn58fzMzMsH37dkyePNmANas7arUaV65cMXQ1qALm5ubo3r27oavRKCkUChQWFtaoDLa/ftj2hlOVtn/8ZJGIiIjIEDiTm4iIjFWjS5ZXZPjw4diyZQuuXLkCKysrAOWjwm1tbcUYlUoFAOJ+mUyGu3fv6pSlVCrFGM3Ic80Ic42SkhIUFhZqlVVSUoLi4mKt0eUqlQoSiUSM05epqSm6du1aozKobnAEqP4cHBxqZXQzVR/b3nCe1faZmZn1WBsiIiIibZzJTVQxzqxt2Gpj9nRl+P43XNV576s6k7tJJMsfp7nanJWVpXXlOSsrC6amprC3txfj0tLSdBpKoVDA2dkZAGBhYYF27dqJV7IfjxEEQSxf81uhUKBbt25ax2zfvn2Nr3JLJBJYWFjUqAyihoYdjeGw7Q3nWW3PixBERERkSJzJ/WympqZo3rzJpVIavdLSUqjV6jornzNrG7bamD1dGb7/DVd13/uqzORuEn/hExMT0axZM3Tv3h22trbo0qULkpKSMGTIEK0YHx8fsVH8/f2xYcMGpKWloW/fvgDKG/jXX3/FxIkTxef5+/sjOTkZ7733HkxNTcWyZDIZPD09AQBeXl6wtLTEsWPHxGS5Wq3GiRMn4O/vXy9tQERERERERFTbOJP7vyQSCczMzGBiYlKjY1LtKysrQ3FxcZ2NLuagloatNmZPV4bvf8NVnfe+qjO5G12yPCQkBN7e3nBxcQEAJCcnY//+/Rg3bpzYWYeHh2PmzJno1KkTvL29kZiYiEuXLmHXrl1iOZ6envDz80N0dDRmzZoFMzMzrFq1Ci4uLnjppZe0jnfkyBHMmDEDY8aMQUZGBuRyOaKiosTEu5mZGcLCwrB27VrY2NjA2dkZe/fuRW5uLkJCQuqxdYiIiIiIiIjqBmdyl9v901ncy1fW6LhUe+wsrfB2j36cQWvE+N4br+q891W96NHokuUODg44ePAg7t69i7KyMnTp0gXR0dEYO3asGDNy5EgUFhYiPj4emzdvhoODA9atWyeOBNdYvXo1YmJiMH/+fJSWlsLPzw9z587VmlLVuXNnyOVyLFmyBJMmTYKNjQ0iIiIQHBysVVZoaCgEQcCWLVuQk5MDV1dXyOVy8csCERGRMTp27BgOHz6MX375BSqVCp07d8bYsWPxxhtvaH1ZOXDgABISEnD79m04ODggKioKAwcO1CorLy8PMTExOHXqFNRqNfr164e5c+eibdu2WnEXL17E0qVLceXKFbRp0wZjxoxBaGio1vEEQUB8fDz27Nkj9ttz5syBh4dHnbYHERFRY8OZ3Lru5StxS5VjkGMTEVHdanTJ8rlz51YpLigoCEFBQZXGtGrVCosXL8bixYsrjfPy8sL+/fsrjZFIJAgLC0NYWFiV6kdERGQMtm3bhg4dOmD27Nlo3bo1vvnmG8ybNw93797F1KlTAQBffvkl5s2bh8mTJ6NPnz5ITEzE1KlTsXv3bq3kdWRkJDIzM7FgwQKYmZlh9erVCA0NxcGDB8UL3Tdv3kRISAh8fX0RGRmJa9euYfny5WjWrJnWbK/4+HjExsZi5syZcHFxwe7duxEcHIxDhw7xQjcRERktzuQmIiJj1+iS5URERNR4bNy4ETY2NuJjHx8f5ObmYuvWrZgyZQpMTEwQGxuLESNGIDIyEgDQp08fZGRkYP369YiPjwcApKenIzU1FXK5HH5+fgDKZ5sFBgbixIkTCAwMBADI5XK0bt0aK1euhFQqhY+PD3JycrBp0yaMHTsWUqkUxcXFiIuLQ3BwMCZMmAAA6NmzJ4YNGwa5XI4FCxbUW/sQERE1JJzJTURExo7JciIiIqozjyfKNVxdXbF//34UFBTgwYMHuHHjBt577z2tmMDAQCxbtgwlJSWQSqVISUmBTCaDr6+vGOPo6AhXV1ekpKSIyfKUlBQEBARo3eU8MDAQcXFxSE9Ph7e3Ny5evIj8/HwMHz5cjJFKpQgICMDJkydruwmIiIgaDc7kJiIiY8dbOBMREVG9+uGHH2BnZwdLS0vxpl4ODg5aMU5OTlCr1cjOzgZQfhMvBwcHnZuyODo6imUUFBTgzp07Wjcc08RIJBIxTvP7yTgnJyfcvn0bRUVFtfRKiYiIiIiIqDHhyHIiIiKqNxcuXEBiYiJmzZoFAFAqlQAAmUymFad5rNmvUqnQqlUrnfKsrKxw+fJlAOU3AK2oLKlUCnNzc62ypFIpzMzMdI4pCAKUSiVatGih92sUBAEFBQU62yUSSbXu1t6UFBYWQhCEaj2H7VW99gLYZvyMVZ2+nzHSJgiCzkVcIiIiatyYLCciIqJ6cffuXURFRcHb2xvjxo0zdHXqjFqtxpUrV3S2m5ubo3v37gaokeEpFAoUFhZW6zlsr+q1F8A242es6vT9jJGux5f9IiIiosaPyXIiIiKqcyqVCqGhobC2tsbatWthYlK+EpyVlRWA8lHhtra2WvGP75fJZLh7965OuUqlUozRjDzXjDDXKCkpQWFhoVZZJSUlKC4u1hpdrlKpIJFIxDh9mZqaomvXrjrbjXn0oYODg16jfo2VPu0FsM34Gas6fT9jpC0zM9PQVSAiIqJaxmQ5ERER1amioiKEhYUhLy8P+/bt01pORbNueFZWltYa4llZWTA1NYW9vb0Yl5aWpjPlXaFQwNnZGQBgYWGBdu3aiWuSPx4jCIJYvua3QqFAt27dtI7Zvn37Gi3BApQn4CwsLGpURlNjrEtd6IvtVX1ss+phe9UOY77gQkRE1FTxBp9ERERUZ0pLSxEZGYmsrCwkJCTAzs5Oa7+9vT26dOmCpKQkre2JiYnw8fERp7f7+/tDqVQiLS1NjFEoFPj111/h7+8vbvP390dycjLUarVWWTKZDJ6engAALy8vWFpa4tixY2KMWq3GiRMntMoiIiIiIiIi48KR5URERFRnFi5ciNOnT2P27NnIz8/Hjz/+KO7r3r07pFIpwsPDMXPmTHTq1Ane3t5ITEzEpUuXsGvXLjHW09MTfn5+iI6OxqxZs2BmZoZVq1bBxcUFL730khgXEhKCI0eOYMaMGRgzZgwyMjIgl8sRFRUlJt7NzMwQFhaGtWvXwsbGBs7Ozti7dy9yc3MREhJSb21DREREREREDQuT5URERFRnzp07BwBYsmSJzr7k5GR07NgRI0eORGFhIeLj47F582Y4ODhg3bp14khwjdWrVyMmJgbz589HaWkp/Pz8MHfuXDRv/t+vM507d4ZcLseSJUswadIk2NjYICIiAsHBwVplhYaGQhAEbNmyBTk5OXB1dYVcLheXfSEiIiIiIiLjw2Q5ERER1ZmvvvqqSnFBQUEICgqqNKZVq1ZYvHgxFi9eXGmcl5cX9u/fX2mMRCJBWFgYwsLCqlQ/IiIiIiIiavq4ZnkTUSaUGboKjQrbi4iIiIioeozxO7QxvmYiIiJjxpHlTYSJxAS7fzqLe/lKQ1elwbOztMLbPfoZuhpERERERI2KicQEqZkJUBbeNXRV6oWV+XPw6zrR0NUgIiKiesRkeRNyL1+JW6ocQ1eDiIiIiIiaKGXhXeQU/G7oahARERHVCS7DQkRERERERERERERGj8lyIiIiIiIiIiIiIjJ6TJYTERERERERERERkdFjspyIiIiIiIiIiIiIjB6T5URERERERERERERk9JgsJyIiIiIiIiIiIiKjx2Q5ERERERERERERERk9JsuJiIiIiIiIiIiIyOgxWU5ERERERERERERERo/JciIiIiIiIiIiIiIyekyWExEREREREREREZHRY7KciIiIiIiIiIiIiIwek+VEREREREREREREZPSYLCciIiIiIiIiIiIio8dkOREREREREREREREZPSbLiYiIiIiIiIiIiMjoMVlOREREREREREREREaPyXIiIiIiIiIiIiIiMnpMlhMRERERERERERGR0WOynIiIiIiIiIiIiIiMHpPltey3337DO++8Aw8PD/j6+mLZsmUoKSkxdLWIiIioAuy3iYiIGg/220REVNeaG7oCTYlSqcT48ePRpUsXrF27Fvfu3cOSJUtQVFSE+fPnG7p6RERE9Bj220RERI0H+20iIqoPTJbXok8//RQPHz7EunXrYG1tDQB49OgRFi5ciLCwMNjZ2Rm2gkRERCRiv01ERNR4sN8mIqL6wGVYalFKSgp8fHzEjhsAhg8fjrKyMpw7d85wFSMiIiId7LeJiIgaD/bbRERUH5gsr0VZWVlwdHTU2iaTyWBra4usrCwD1YqIiIgqwn6biIio8WC/TURE9YHLsNQilUoFmUyms93KygpKpVKvMtVqNQRBwKVLlyqNk0gk8Glhh0dSW72OY0yamZjg559/hiAItVKeRCJBWefegH1ZrZTX5JmY4FYtt38XWwGd2pjXSnlNmYmJUOuf/UedJRDsLWulvKYs30SC3Cq0vVqthkQiqadakSH6bWPrr2va5xpdH1sLfaSx9Ys17duMrS+ran/0NBKJBG3Vg/A/zR/Vcs0aJhN1s0o/X+y365chz7cfZ2x9eWNQ2+f4T2N030sag1rOL1TG2L5jNXT6fAesar/NZHkDp3kTq/JmWkpb1HV1mpTa/GJrYm4cJ1i1qTbb37yFaa2VZQxqs+2bmbPtq+NZbS+RSHjS3chVpd82xv66Jp9rY+xja/p3wBj7xZq0mTH2ZTVprxamrWqxJo3D09qL/XbjV53z7ccZY1/eGNTH/0dj/F7SGNTX32Jj/I7V0FXnva9qv81keS2SyWTIy8vT2a5UKmFlZaVXmZ6enjWtFhEREVWA/TYREVHjwX6biIjqA9csr0WOjo46a6Xl5eXhzz//1FlbjYiIiAyL/TYREVHjwX6biIjqA5Pltcjf3x/ffPMNVCqVuC0pKQkmJibw9fU1YM2IiIjoSey3iYiIGg/220REVB8kQn2sgm8klEolRowYAQcHB4SFheHevXtYsmQJ/vGPf2D+/PmGrh4RERE9hv02ERFR48F+m4iI6gOT5bXst99+w8cff4z09HS0bNkSr7zyCqKioiCVSg1dNSIiInoC+20iIqLGg/02ERHVNSbLiYiIiIiIiIiIiMjocc1yIiIiIiIiIiIiIjJ6TJYTERERERERERERkdFjspyIiIiIiIiIiIiIjB6T5URERERERERERERk9JgsJyIiIiIiIiIiIiKjx2Q5ERERERERERERERk9JsuJiIiIiIiIiIiIyOgxWU5ERA1WWVmZoatARGR0fvvtN1y4cMHQ1SAiIiIiqndMllOVPXr0yNBVMGqlpaWGrgJRvSosLMSCBQtw7tw5Q1eFiMgoCIKAkpISjB8/Hh988AG+//57Q1eJiIiIiKheMVlOVVJcXIyZM2fiyy+/NHRVjJJarUZQUBDWrl1r6KoQypMJVPdu3LiB/fv3Qy6XM2FD9ITH/w7xb9KzVXTBn+2mSyKRQCqVYsuWLVCpVFi6dCm+++47Q1er0REEgZ+vamBbEdUe/n8yXo9/17l3754Ba0L1TTOwUjPogWqOyXKqkqtXryIjIwNyuRynTp0ydHWMTlFREby8vBAXFwe5XG7o6hidJ5MsEokEAJcIqUuCIMDV1RV79uzBr7/+inXr1uHbb781dLWIGoTS0lJIJBI8evQIRUVFePjwoaGr1KCVlpaiWbNmKCoqwrFjx7Bjxw788MMPuH//vqGr1iCVlpbC2dkZu3btws2bN7Fs2TImzJ+hou8Jmu8K9GyatmKSj0g/jx49wjfffIOsrCzx/9O6deuQm5tr2IpRvWrWrBkAYMaMGdizZw/ffyPSvHlzAMCiRYuQmprKhHktkAj8VkJVlJaWhnXr1qGgoABTpkxBQECAoatkVJRKJeRyOeLj4/H+++/jnXfeMXSVjEJpaSmaN2+OgoIC7Nq1C3fv3oWdnR369++Pbt26Gbp6TZZarYapqSkAID09HZMmTULv3r0xbtw4eHt7G7h2RIbz6NEjNGvWDPn5+Zg7dy4UCgWaN2+O7t27Y/r06bC2tmaS7jFlZWUwMTFBfn4+Ro8ejYcPH0KtVkOpVGLQoEH4xz/+gSFDhhi6mg2O5nP222+/YfTo0ejcuTPef/999O7d29BVa3A0bfXw4UOsWrUK9+/fh1qtxujRo+Hm5gYbGxtDV7FBKSkpwfnz5/Htt98iJycHlpaWeO2119CpUydYWlqK/2eJqOoePHiA6dOnQ61WIyYmBosXL8a5c+dw+PBhdOnSxdDVozqm6YcAICkpCYsWLUJsbCzc3d3FJCo1TY+/9xs2bMDWrVuRkJCAF154QdxO+mGynJ6ppKQEUqkUAHDq1CkkJCSgsLAQ06dPR//+/Q1cu6bv8aTh5cuXsW3bNhw9ehTz58/HW2+9ZeDaNW2CIEAikSA/Px///Oc/0aJFCwiCICYQPvjgA4wcORIWFhaGrmqT8viJ8sKFCyGTybBjxw4UFxejZ8+emDZtGl588UUD15LIcAoKCvDPf/4TrVu3Rq9evWBqaoovvvgCFhYWmDVrFvr27cuE+WPUajXCwsJQVlaG2bNnw9nZGZcvX0ZkZCRatmyJ9evXo1OnToaupsE9LUmZmZmJ0aNHo0uXLkyYP8XDhw/x+uuvw9LSEh06dIBKpUJGRgYGDx6MCRMmwMnJydBVbBAePnyImTNnIjs7G0qlEhYWFrh9+zbMzc3x6quvYuLEiWjbti0T5kR6+PXXX/HOO++I542bNm3CCy+8YOBaUX3atm0bACAnJwfTp083bGWoXp09exY//PAD2rVrh1GjRhm6Ok0Cv4VQpcrKysRE+aJFi5CcnIzff/8d165dQ0xMDL766isD17BpEwRB/MIze/ZsLFmyBH/88QeaNWuGjz76CFu3bjVwDZs2zTIHH374Idq0aYOVK1fi888/x+effw53d3csXrwY2dnZnDZcyzQnyLNnz8bp06fh7OyMFStW4IMPPkB6ejrWrFmDCxcuGLiWRIaze/dutGjRAh999BEiIyPx73//G+PHj0dGRgYKCgoMXb0GJycnB7dv38brr78OJycnmJiY4MGDB7h9+zaCgoLQqVMno7+JeWlpKUxMTFBSUoLMzEykpaVBqVQiPz8fXbt2xZ49e3Djxg0uyVIBQRCwbNkytGrVCrGxsVi1ahW2bduGHj16ICkpCXfv3jV0FRuE/Px8vPHGGygsLERkZCS+/vprHDlyBImJiejRowd27tyJxYsX4969ezAxMeF3K6Jq6t69Ozp27Ii//voLzz33nHgOD3CJI2Pw7bffYsWKFViyZIm4TbOONTVte/bswdSpU7F9+3a0adMGAN/72sBkOVVKk7T64IMPcPLkSQwYMABxcXH46KOPIAgC1qxZg+TkZAPXsunSjAxctmwZUlJSEBYWhk2bNmHnzp148803sXTpUmzZssXAtWw6VCqVzrbCwkJkZmaiX79+cHR0RLNmzXD06FGkp6djypQpcHFxEdcu5xfR2nPr1i18//33GDt2LIYPH45Bgwbh7bffxrZt23Dp0iWsWbOGCRsyWtevX0eLFi3E0aqHDx9GTEwMpk+fjoCAACbMn3D37l3cuHEDXbt2hampKQ4fPoywsDBERUVh3LhxyM/Px6FDh4x2DfNHjx6hefPmyM/Px/jx4zFhwgS88847GDFiBNavX4+bN2/C2dlZTJj/3//9H2+6/BhBEHDjxg14eXmhQ4cOaNasGZKSkvD1119j0qRJ8PX1RUlJCQoLCw1dVYPJz8/Hyy+/jI4dO+Ljjz/G4MGD0axZM5iamsLe3h7x8fF49dVXcfz4cWzevBn5+fmcHUNUTYIg4OWXX8bHH3+M7OxsLFq0CNeuXQMA/n8yAi+88AJmz56Njh074vTp0ygpKUHz5s2ZNDUC7u7uGDx4sLjMGVC+hjnvr1YzTJbTM+Xk5OD8+fMICgrC0KFD4ebmhjfffBPR0dGQSCRYsWIFUlJSDF3NJqukpAQ//fQT/Pz80K9fP1hbW8PLywvh4eEYN24cli1bht27dxu6mo1ednY2XnrpJSQmJmptf/jwIZRKJaytrQEAhw4dwsyZMxEREYFJkybh4cOHWLhwIbKzs/lFtBaVlpbi1q1baN26NUxMTPDo0SM8evQIL774ImJiYnDhwgXs2rULZ8+eNXRVieqNIAjijC/NrKMjR47g/fffR2RkJCZNmgS1Wo1FixZh165dBq6t4WkuYNrb26NLly44c+YMPv/8c7z//vuIiopCWFgYACA5ORknTpxAXl6eIatrMM2aNUNxcTHGjRsHMzMzzJs3D4cOHUL//v1x8uRJLFmyBL///jucnZ3x6aef4vfff8esWbNw5coVQ1e9QXj06BHy8vKQn58PAEhMTERkZCSioqIQGhqKgoICbNy4EVevXjVwTQ2jpKQE4eHhuH//PmJiYmBvby/uk0gkYiInJiYG/fv3xxdffCG2FQchED1dRTcWHj9+PIKCghAX9//Yu+uAqLL+8ePvCToEBAEVW7E7V1cMLOxGRUFRwURBCUEMwkCxu7tz7V5jdVfXXhUTA1sscmBmfn/wm/uA7j7fZ3ddWZnz+kuHmeHcy41zP+dzPmcRcXFxREVF5bj2pKeniwH1POD3ZsOZm5vTvn17+vXrx7Nnz+jfvz+ACJjnMb8XAK9YsSI+Pj40bdqUzZs3S9UH5HK5CJj/DSJYLvxXWq2W9PR0nj9/jp2dHYC0sq6Liwv9+/fn/v37xMbGfhZkFL4MmUwmTY2G/1wg7ezs6NSpE7a2tkRERLB48eLcbOY3z9jYmEqVKjF+/HgOHz4svW5vb4+DgwOHDx/m0KFDBAcHS4FyyKojf/fuXeLj43Op5d++7A/Dun87ODhQo0YNDh48yMuXL3MsUFKlShWsrKw4dOgQGzduJC0t7au3WRC+hk87uLr7Qc2aNfn555+JjIwkODhYCpRDVn3pJ0+eSPcMfaJ7eNRdR3QDmNbW1lSsWJFFixYxduxYhg4dio+PD2q1mvj4eDZv3oy5uTklSpTItbbntitXrvDy5UsGDRpE8+bNcXZ2Jioqiu7du3P79m02btxIcnIypUqVYvXq1RQvXpwyZcrkdrO/uk+Dt7q1TcqUKcPt27dZvHgx/v7++Pv74+3tDcDt27c5ffo0z549y40m57r09HQKFy5MgQIFmD17NvCfMneQFcjR/TsiIgIDAwO2bdsmvU8QhM9pNBqpb7xw4UIiIiKIjo7m/fv3AFStWpUFCxYQFxfH5MmT+e2333j37h3jx49n9OjRel967FuWfUHHvXv3snTpUrZt28bdu3cxNzenQ4cOBAQEcPfuXby8vAARMM8r1Gq1VPnh2rVrnD59mrt376JSqXB2dmbw4ME0btyYVatWSfXrRcD8rxMLfAo56Dr9kHOhJy8vLz58+MDq1asxNzfPsehny5YtUalUFCpUiAULFmBubp5r7f/WZd//2UVFRbFr1y42bNhAyZIlc9wkfX19iY+P5+3btxw4cABra+uv3ew84+XLl0RFRXHy5EmmTp1K06ZNkcvl/PjjjwQGBvL+/XuCg4Oljsf9+/cZM2YMNjY2zJ07VyxG9RdkP5Y/NX/+fNasWUO/fv3o2LEjtra2QFbgYcmSJbRp04YiRYpQvHjxr9lkQfgqMjMzUSqVqFQqaTG88uXLY2hoiFwuZ9SoUezZs4c2bdowbdo0IGtxrwkTJmBoaMjKlSv/8NzKi3TXkuTkZObPn8+jR4+ws7OjRo0atG7dGo1GQ79+/bh48SLe3t60aNGCGzdusGnTJtLT09m6das0ZVUfr+U7d+4kODiY8+fPY2FhkaOfFxISwunTp9m/f/9nfbz/dg3Pa3TnZGZmJu/evcPQ0BClUompqSl37tyhZ8+efPz4ES8vL4KDgwG4d+8eYWFhmJqasnjxYr3ZVzq6fu27d+9YtGgR+/fvp379+kRFRQE5jx+1Wo1Go8HDw4N8+fKJJBBB+APZnxeHDx/OhQsXsLW15cWLF1hYWBATE0OVKlWQy+VS2UiZTIajoyNPnjxhyZIlVK5cOZe3QvgrPv3bX716FblcjoGBAS9fvmTs2LF06tSJtLQ0tm/fzty5cylXrhzLli3L5ZYLf1f2/qm/vz9Xr14lISEBJycnChQowNy5c7GysiIuLo4FCxZw8eJF+vfvT58+fXK55d8uESwXJJ8+8GQ/IXfu3Mm8efOoW7cuQUFB0sPSvXv3iIqKonXr1nz33Xc4OjrmStvzguz7X6VSkZ6ejoWFBZBVCsTd3R3Iyh4oVKgQAE+fPmXs2LG4u7tTp04dLC0tc6fx37jsx/rz58+ZNGkSJ0+eZMqUKTRv3pyPHz+yceNGli9fTunSpenZsycPHjzgyJEjZGZmsm3bNr0OsvxV2Y/5FStWcPv2bUxNTSlRogS9evUCIDAwkOPHj9OsWTN69OhBeno627Zt49q1a2zZsgUTE5Pc3ARB+Efozo2kpCT69evHmzdvSEhIoGzZsri4uDBs2DBevXrFrFmz2LlzJy1atCAxMZGkpCQUCgUbNmzAwMBAbwKZuofH5ORkOnfujJmZmTRwfO7cObp3705ISAhyuZygoCBu375NXFwc5cqVw8nJidjYWCm7VR/2F/xnn+nuW7dv36ZLly74+flJGdG6gPlvv/1G586dWbNmDbVq1crllueO7OfkiBEjeP78uZRpP2TIEKpWrcqFCxcYNGgQRYoUoVatWmg0Gn755RdkMhmbN2/Wq3MyO90xlj1g3qBBAyIjI4HPnz86depE4cKFpSx0QRD+I/uzxsOHD6WFvgsXLsybN28YO3YsT58+JSYmhpo1ayKXy3n8+DELFy7EwsKCrl27SuudCN+uOXPmsH37diZPnkyZMmUwNjZm7Nix7Nmzh1WrVlGnTh1SU1PZtWsXEydOpHHjxsybNy+3my18ARMnTuT48eOEhoZSuHBhXr58yfjx4wHYtGkTdnZ23L59m0WLFnHkyBGCgoLo2bNn7jb6G6XM7QYI/w7ZO6oLFizgzp07pKSk8P3339O1a1fatm1LXFwcR44cYejQoYwaNYo3b95w4MABXr9+TYsWLURG+d+Qff9HRkZy69Yt7t69S7NmzWjQoAEtWrRg3LhxhIeH4+7uTo8ePTAxMeHy5cvcv3+fihUrikD5X6TrdGZkZGBgYICDgwMhISEABAUFodVqadGiBd27d6dMmTLMnz+f2NhYbGxsqFixImPHjpUyzZRKcUn9M3TH/PDhw7l8+TJly5bl6dOn7Nq1i59++omIiAimTp1KbGwshw4dYvv27eTLlw8DAwOWLl0qAuVCnqWrIe3l5YWFhQWDBg2iUKFC7Nmzh40bN5KQkMC0adOIiIigbt26XL58GWtra8qWLUvXrl1RKBR6dU3SBX2nTJmCtbU10dHR0oyTwMBA1q1bR4sWLahduzYxMTG8ffuWhIQEChUqhJWVlVQ3WR/2l66/octM0wVdrKysqFWrFj/88ANOTk40b94cQ0NDafHKAgUKSLN79JFCoSA1NRV3d3csLCzo06cP79694/r167i7uzN//nyaNGnCxo0bWbJkCZcuXcLa2poGDRowYsQIvewn6AZksh9junJR+/fvJywsjMjISBQKhdQXu3DhAhkZGTRt2jTHdwiCkEV3Ps2ePZtbt26Rnp5O8eLFMTc3x9ramhkzZhAQEMCoUaOYPn06VatWxcnJiYkTJ+Y4H4Vvl1qt5urVqzRt2pTq1atjYGBAfHw8p0+fxs3NjSpVqgBgYmJCmzZtUCqV1KhRI5dbLXwJL168kDLGGzZsKK1h9OHDB1xcXKREyzJlytC3b18MDQ357rvvcrnV3y796bEJ/1X2oNW1a9eoUKECJiYmTJo0iR9//JGhQ4cSEBBAwYIF2bZtG126dMHMzAxzc3NReuUL0O1/f39/Ll68SIcOHahbty6//vorMTExJCQk0K9fP5YuXcq0adPYunUrqamp2NvbM3/+fJHR/xdlD5R37dqVTp060adPnxwBc91U6hYtWuDi4oKLiwuvXr3C0tISIyMjAL17AP6S1q9fz/Xr15k2bRrVq1dHqVSyadMmxo0bR6NGjejatSv+/v507dqVx48fA1CyZEns7e1zueWC8M/65ZdfePv2LSEhIVSuXBkDAwOKFCnCx48fqVixIgAGBgZ06NCBtm3b5sjMVKvVenFNyp5hp1aruX37NhUrVpQC5Xv37mXfvn34+/tTu3ZtUlNTMTExwdraOkfJMo1Goxf7S3evSk1NZcOGDTx79gxDQ0M6depEyZIlGTVqFCNGjGDBggU8fPiQzp07c/PmTdasWUOJEiUoWrRobm9CrtAdZ9u2bUOhUBAZGSllZq5cuZJDhw7x4sULNBoNJUuWlDKmsx9T+nJOQlaAO/v2fvjwAQsLC2QyGdbW1gwYMADICpiHhoYSFRWFXC7n7du3bNu2DSMjI+rWrQuImuWC8HsSExM5c+YMCQkJFClSRHoO12g0ODg4EBsbi7+/P0FBQURGRlK3bl29m9GSl6lUKl68eEHp0qUxMDDg3r179OjRg++++47IyEiMjY1ZsGABzZs3p2TJknTu3FlcS79Rn868Sk9P58mTJ9ja2mJoaMi9e/fo2bMn33//vfS337lzJ25ublSsWFEqzSj8NfrRaxP+J9u3b+fKlStMmzaNypUrY2RkxA8//MDo0aNxcXGhcuXKeHh44O7uzoULFzAzM8PBwYECBQrkdtO/WdkzZk6dOsXly5eZNGkS1atXx8jIiBMnTuDr60tCQgJqtZqCBQsSGxvL48ePMTQ0xMjICCsrq9zdiG9U9nrAFy9eRCaTER0djZWVFe3atfssYC6Xy6Ua5rrFbiHrb6gvD8D/hNu3b+Pk5ETFihVRKpU8evSIGTNm0Lp1a9q2bSu9z8nJCScnp1xsqSD8s3TXpPT0dIyMjHj27BkfPnygUqVKGBgYsGvXLsLCwvD398fLy4ukpCR+/vlnmjRp8tlDsD48FOseIBITE0lLS6NgwYIkJydLs6x0Nbj9/f0ZOHAgqampTJgwgS5dulCzZs0c36UPmXa6e1VSUhLdunVDJpOhUqnIzMxkzZo10nE1c+ZMZsyYwZw5c5g+fTq2trY4OTmxZMkSaZEofdhf8J9jTLe9jx49QqvVSgkK+/btY/LkyYwaNYoePXrw4cMHkpKSKFiw4GffpQ/nZHp6OoaGhshkMqlfNGnSJH777Tc0Gg3du3enfv362Nra5giYh4WFMWbMGObNm8fhw4dZt26dGBAXhGw+nWFhY2PDtGnTmDRpEsePH2fevHkMGTJEukbb29sTGxtL//79iY6OZuvWrRgbG+fiFgh/1e/NrjExMcHBwYFLly7x5MkTevbsSd26dYmMjMTU1JS7d+/y888/4+joSIkSJUSg/BuWfSHXJk2aYG5ujoGBAc+ePSM5OZkePXpQr149IiIiMDEx4cKFC+zdu5ciRYpQvXp1ESj/m0SER5Dcv38fR0dHqlSpIo1UTZkyhZYtW9K5c2cAMjIyMDQ0pF69ernc2m9XcnIyZ8+exdXVNcfN69WrV2RkZFC6dGmMjIx48OABQUFBtG7dmlGjRqFQKIiLi8PZ2VkEDf8mXRZhUlISAwcOxMTERCrpERgYSFpaGt26dZMC5jKZjJCQEMaNG5cjgAsi6+nP+HR0PCMjg/fv32NoaIipqWmOzIiIiAiMjY2ZO3cuhQsXpkOHDrnXcEH4h2UPZEZFReHp6UmxYsVIS0vj3r17PH36lKCgIEaOHMnAgQNRq9Xs2LGDBw8eUK1aNWxsbHJ7E74qrVaLQqEgLS2Ntm3bUq1aNebOnUv58uXZuXMnBQsWJDw8nOHDh0tlH27evMmTJ0949epVLrc+d8hkMtRqNWPGjMHCwoLo6GgKFSpEYmIiS5cuZfLkychkMjw9PYmKiuLDhw/ExcVhb29P1apV9a60j0ajQaFQ8PbtW5YtWyb1w1QqFaamphw6dAh/f3/8/f3p37+/NOiQkpKCn5+f3j2gqtVq+vbti0ajYf369cjlcoKDgzl9+jQuLi7cvHmTSZMm0a5dO/r27Yujo6MUMD98+LC0PsyGDRtwdnbO5a0RhH+P7H3n7IOVTk5OhISEoFar2b17N8bGxnh7eyOXy9Fqtdjb27Ns2TIyMjJEoPwblf1vr9VqpfsSwMCBA/H19cXV1ZX27dszZcoUIGvWwbJly3j//j1169YVz6nfGN3gSPa//erVq1m7di0tW7bExMSEbt26MWvWLKZNm0bbtm0ZN24cxsbGJCYmsnnzZtLS0ihWrFjubkgeoR+pIcJnNBrNZ6+pVCrS0tIwNDTk8ePH9OjRg1q1ahEdHY2xsTGLFy/m0KFDudDavEOj0RAREcEPP/xAZmZmjp9ptVrevn2LtbU1r1+/pnv37nz33XdMnDgRExMTdu/ezZ49e/jw4UMutT7v0JVeGTp0KFqtloCAANavX8/atWtp1aoV4eHhbN68GQAHBweCg4OpWLEi27Zty+WWf9t0N/3169cDWSUkKleuzE8//cTRo0fx8PCgbt26REVFYWpqyuPHj4mLi+PZs2efnS+CkFeo1WqpZraPjw/Pnz/H2NgYOzs7ypYtS0hICEOHDiUoKAgfHx8A4uPj2bdvHwqFQu8C5ZmZmVKN8h07dlC+fHmGDx8OQN++fTEyMiIsLIwhQ4YwePBgIGsx8qlTp2JiYkLz5s1zs/m56uPHjzx48ICGDRtSsmRJjI2NpYGFHj16EBsby4MHD7Czs6NkyZK4ublRo0YNFAqF3pURkcvlqFQqBg0axK+//sqLFy9wc3Pj1atX9OvXDz8/P0aPHi0FfG/fvs3Zs2extLTUu0A5ZPVv27dvz8OHDxk4cCBpaWlkZGQQExNDVFQU27dvp127dhw5coRFixbx7NkzbGxsGDBgAN999x0WFhZs2rSJ8uXL5/amCMK/RvaA2ezZs/H398fb25vFixfz+PFjnJycCA4OplixYmzevJlly5YBWYOjWq2WAgUKUKhQodzcBOEv0iUFAERHR+Pj40Pfvn05f/48Wq2WChUq4OPjg62tLS9evODq1avs2LGDyMhIDh8+zOTJk3FwcMjlrRD+jLS0NPr168etW7ekfhdAUlIS+fPnl46HFi1aUKdOHUxMTHB2dsbAwIDz588zZcoUfvzxR8LDw/Xu2eCfoh+9XiGH7Dde3Y0WoEiRImzbto1t27YxdepUKbvT1NSU58+fc/HiRZKTk6WFEIU/Ty6X07dvX4oUKYJSqeTKlSvSIhxly5alcOHCBAYGcvLkSRo2bMiECRMwMzPj1atXHDlyRFrcUPj7Pn78yMOHD+nevbv0cFazZk1sbGzQarWEh4djbGwslWSZPn16jhq3wl9z7tw55syZg1KppFu3brRv357Dhw8zZMgQvv/+e2bPng3AmzdvpMWGAwMD9SZII+gfXYb0q1evKFKkCJ07d5YyQnr06EF0dDSlSpWifPnyaLVazp07x6xZs8jMzCQoKAjQr0XwlEolKSkpTJkyhffv31OkSBHKlCkDQIkSJRg4cCBLly5l8+bNWFpa8vDhQy5duoRarWbt2rU5FhPUNxqNhpSUFJKSkqTXdMeOt7c3x44dY+fOnYwcOfKzfaQPZUTgP/sjMzOTK1euYGJiQnBwMAUKFMDc3JzOnTuza9cuqlSpgre3N+np6dy7d48JEyagUCjo379/bm/CV6VSqXjw4AEpKSnUq1cPIyMjYmJi6N69OwYGBjkCdWPGjEGhUHDgwAFkMhk+Pj44ODhIx1v+/PlzcUsE4d8n+5piFy9exMXFhY8fP3L48GG2bt3KvHnzKF26tLRY7o4dO0hLS2PIkCF60yfIq3R/v7CwME6fPk3lypV58+YN/fr1IzAwkB49etCjRw+cnJyYPXs2w4YNw9DQkGLFirFu3ToxQ+cbdOPGDR4+fIivry9Lly6lVKlSQFbsLnvwu3z58vj6+rJ9+3amTZvG8uXLMTQ0xMLCglWrVlG6dOnc2oQ8R0Qf9Ez2QHlERAR3796la9eutGnTBg8PD44cOUJoaKi0QIS5uTkvXrxg9uzZ3L17l9DQUBGs/Yu0Wi2AdPNavnw5sbGxTJo0ibZt21KhQgXq16/Phg0bKFmyJP7+/pibm5OQkMC8efO4dOkSq1evlsqFCH+dVqslPT1dKisEWQ98hoaGlChRgp49e3LgwAECAwPJyMigc+fO0kOcvgZZ/qpPS684OztTqVIl9u/fT4sWLbCxscHb2xuFQsH58+dZs2YNL1++5MGDB/z888+sWbNGlB0S8jSNRkNgYCCHDh2iQIECUvY4QKdOnZDJZKxfv57hw4ejVCqxsLDAwcGBJUuWoFQqPzvH9MGbN2/YtGkTgFQmDsDQ0JDWrVtTtmxZVqxYwc6dO7GysqJu3bqMHDkSpVKpN6VEfu9eZWFhQaFChTh58iQ9evSgSJEi0gO5tbV1jkwmfb3P6QLlXbt2RaPRYGVlJfXbzMzM8PT0BLJq4nfo0EEqT2NsbMyqVaukfagP52RSUhLDhw8nLi6ON2/eULJkSVxdXRk9ejTLly/n4cOHpKenA//pY+kG+I4cOUJycjIBAQGiPrkg/Bc7duzgt99+Y/bs2VSpUgWFQsGWLVsYO3asNLjp5OREWFgYQUFBHD9+nF69eok1rb5Rn967U1JSmDp1KtWqVSMzM5OYmBgmT55MRkYGPXv2pGXLljRr1ox79+6RP39+jI2NMTMzy8UtEP6q6tWrM2HCBGbNmoWXlxdLly6lbNmyvH37FpVKBWTdSw0MDKhatSqlSpWiV69e3Lt3j4IFC+Lk5CQGnb8wmVYXwRP0ip+fH9euXWPYsGHUqFGDIkWKAFlTlaOjo7l27Rrdu3cnNTWVhw8fcvXqVVatWkXZsmVzueXfruw3v+fPn/Px40emTJlCfHw8Q4cOlWoyh4eHc/bsWUxNTXF0dOTt27c8e/aMRYsWUa5cuVzcgm/XHwW4hw8fzpUrV9i3bx9mZmbSwxyAu7s7xsbG3Lx5k5kzZ4o6/X9B9mzXR48eYWtrK9Umb9++PQMGDMDPzw+AuLg4tm3bxs8//4xSqaRcuXL07duXkiVL5uYmCMI/InswTa1Wc/r0aVavXs0vv/zCnDlzaNSoUY6g7oMHD3j58iVPnz6laNGiVK1aFblcrjeBX93+0mq1UjmQR48e0bNnT1JTU4mNjcXFxeWzzyUlJWFubv7Z9+Rl2fdRRkYGz58/x9raGlNTU+RyOXFxcfTu3ZsaNWoQEhIi9f9u377N8OHD8fDwwMPDQ69mK8Dn/YTFixcTGxuLtbU1y5Yty1EeRFfOZt++fZiamlKkSBHatm2rV3Xdk5KSaN++PUWKFMHNzY38+fMzffp0EhMT8fHxwcrKiilTplC+fHlpcdjsM1PHjRvHxYsXWb58eY5F0wVB3316LZo5cyYnTpxg+fLl2NjY8OTJEzp16kSTJk0IDw/H1NSU9+/fky9fPp48eYJCoZAWIRa+Ldn7KE+ePEGj0TB06FBiYmJyZIpHRkayfv16AgMD6dixI/ny5cutJgtfSPY+18mTJ5k9ezbPnz9n586d/PDDDxw+fJj169d/dn0QSXz/LBEs10Nbt25l/vz5TJo0iVq1akkLgehOUN2DZ3x8PO/fv6dKlSq4u7uLoNUX0r9/fx49esT+/fu5cuUK8+fP5/79+wwfPlwKmO/bt4+bN2/y9OlTKlasiKurq8iu/Yt0D64ZGRm8e/dOquEHWQHawYMHky9fPlavXi0FVa5evUpMTAw9evRg7ty5NG3alICAgNzcjG/a+PHj2bdvHx4eHri5uVGqVClWrFjBkiVLiIyMpEmTJtJ7dR1+Ue5JyKt099vU1FRu375NlSpVyMjI4MKFC8TExPD27VtWr16Nk5PTfw286UsHWbe/0tPT8fPzo3///lSuXBlDQ0Pi4+Pp3r07hQoVIigoiDp16gD/ue5n79vk9eDvvXv3sLe3l+5jSUlJDB48mISEBFQqFR4eHrRq1YoiRYpIswjz588v1Ys+fvw4MpmMrVu35vkBhU/pjg2VSoVSqZTOq40bNzJ+/Hjc3d0ZMmTI/xnU1YfBGMjKdGzTpg3Ozs6MHz8eW1tbFAoFr169on379lSqVInJkydz8uRJIiIiqFmzJvPnz/8sYP7mzRuRBScI2WS/r+/fv19aR+nSpUv88MMPUqD8u+++Izo6GlNTU7Zs2UJiYiJeXl4YGRnl8hYIf1X2PkpISAg//fQTVlZWJCUlsWDBAsqUKZPj+hkZGcmWLVsYNGgQHh4eORIDhG9T9vP/xx9/ZObMmbx69YrixYvz5MkT8uXLh0ajwcDAAI1Gg0wmo3jx4lLZZOHLy/upD8JnD9R37tzB1NSUMmXKSK9nX3XXxMSE0NBQ0tLSMDAwQCaT6cUD+T8l+8PTmTNnSE5OZty4cWi1WqpXr86gQYNYsGCBVKu5Q4cOuLm54ebmlpvNzhO0Wi1KpZKkpCR8fHx49uwZ7969w9fXly5duuDs7ExAQADTp0+nVatW9OzZk8zMTI4dO4a1tTVubm6sWbOGO3fu5PamfLNSUlJITU3lw4cP3Lhxg127djF27Fjq1avHL7/8wuHDh6lQoYI0DdvS0hJALzLzBP2jux9oNBpmzpzJhg0bWL58OTVr1qRWrVoEBgYSFRWFl5cXq1atonDhwn8YFNeH+3L2wc4XL15w4sQJkpKSCAwMpFy5chQrVoz169fTo0cPpkyZIgXMddeP7MHxvBwo//jxI4MGDUImk7Ft2zbMzc2lhSd79+7N9evXWbFiBfHx8fj4+ODq6krRokWZNGkSp06dwszMjFKlSjF58mS9KiMC/+kjazQa+vbtS/78+Zk2bRqGhoa4u7uTnp7OpEmTMDY2xtvbWwqY6x5UdQvpyWQyvdlnS5cu5enTp/Tt21e6d6elpWFnZ0fjxo25cOECBgYGtGzZEsgq+zh48GDmz5+PgYGBFPARgXJB+A/dwsIAAQEB3L17l0qVKtG0aVMOHjzIjBkz2LhxIw0aNGDChAmYmpry+PFjfvzxR0qXLp2n73F5XfZ77sKFCzl79ix9+vTh7t27HD58GD8/PzZt2oSlpaXULwoLCyMlJYWVK1fi7u6ey1sg/FXZ//bZ+/UuLi5otVqWL1/OL7/8QseOHalVq5ZUkiUpKQkDAwNatWolAuX/IBGNyOOyn4AnTpygUaNGvHjxAkNDQ2mxQt2Dgu59586do27duhgbG+dau/OS7KuYv3v3jnz58lGnTh3p9Ro1auDr68vChQuZPXs2SqWSNm3aSJ/P69lw/xTdsa9Wqxk4cCAGBga4u7vz+vVrZsyYQXx8PCNGjMDNzY3SpUszb948Nm3ahFwup2zZssycOZOPHz+SnJxM1apV9SaL8+/6NMhiamqKr68vJ06coHDhwjRt2pQRI0bg5eWFUqnk9OnTtGzZEnt7+xyfFce8kNfoHnBSU1M5cuQIjx49AmD06NFMnTqVWrVqUbNmTUJDQ3MEzAsVKqSX9wGNRiMNdg4fPhxLS0ucnJy4cOGCtI/KlStHyZIl2bBhAz179mT69OkMGzaM77//Preb/1UZGxsTEBDA1KlT6du3LwsWLKBYsWJ4enpK5fMWLVrExo0byczMxNfXl9KlS7Nw4UJSUlKQy+XSQKW+lBGB/2xreno6586dw8rKikOHDjFlyhRCQkJQKpV4enqi1WqZPHmytBCqra1tjv6Avp2b3bt35+HDh8TExJAvXz7atWsnPTM8efJEqn9vZGRE8+bNAZg0aRK9e/dmzZo1YtaYIHwi+z0+Li6OR48eERISQsGCBTE0NKRGjRqsXLmSkiVLEhsbC8Dr169ZsGABcXFxBAYGSmUkhW+P7tnn559/5unTpwwZMoSuXbsCUKdOHebOnYuHhwdr167NETCPjo7m9evXoj79Nyr7c++WLVu4f/8+crmcChUq4ObmRqNGjaT3nTlzhgEDBlCiRIlcbLH+EWVY9ERAQADx8fEsWLCAc+fOERoayvjx43MsjAVZq/DGxMQwYsQIqlSpkkutzRs+rUnbsGFD3rx5Q61atVizZg2Q86H0woULLF26lF9++YXo6GgpI0f483SB7fT0dO7du8f69evp06cPZcqUAWDXrl0EBQXRvn17/Pz8KFiwIJBVS97CwgIzMzMSExOJiYnh2LFjbNy4keLFi+fmJn1zZs2aRa1atahQoQL58uVj586dzJo1i5UrV/L69WvWr19PUlISP/74I46OjuzatUsK1ghCXpWcnEyHDh0oVqwYjo6OyGQy9u3bh4WFBVFRUdSrV4/MzEx+/fVXJk2aRHx8PIcPH9bbmr668iEGBgaMHDkSe3t7rl27xrx581Cr1UydOpVy5cphYGDA/fv3cXNzo1OnTkRHR+d207+6jIwMTp06xbhx41Cr1dja2rJ8+XJsbW2l9yxevJgNGzZQq1YtfHx8Piuvp0+DMrptTUpKomfPnuTPn598+fJx79497ty5Q4cOHYiKipL6cStXriQmJoaOHTsyatQovQ9OvHr1iqioKI4ePUpUVBTt2rVj7ty5LFmyhA0bNlC+fHmpL5aamsqePXtYsGAB69atE/WUBeEPhIWFkZGRQWJiIrNmzZIyRm/cuMG0adO4f/8+9erVw8zMjPj4eK5duybWFPuGZY8VLF68mIULF2Jra8vUqVOpWrUqkBUr2L9/v3Q8fBowF75N2ftbw4cP5+LFi1hbW5OWlsbjx4/p1KkTISEhWFhY8OOPPzJr1iwSExNZsGCBWMPua9IKeZJarZb+ffXqVW3Hjh21P//8s1atVmsfPnyo9fT01LZt21a7e/du6X2vX7/WhoaGatu3b699+fJlbjQ7Txo8eLB28+bN2uTkZG3Hjh21zs7O2u3bt2szMjK0Wq1Wm5mZKb333Llz2mHDhmnj4+Nzq7nfrNu3b2svXbok/T8jI0M7YsQIba1atbTNmzfXfvz4UavV/md/7969W+vs7KwNDg7+bH+fPXtWO2jQIO3333+vvXnz5lfbhrzi7du32iZNmmhdXV21wcHB2kePHmlVKpU2ODhYGx0drdVoNNqEhATt4cOHta6urlpnZ2dtQkJCbjdbEP5x0dHR2mbNmuU43g8dOqR1d3fXNm7cWHv+/HmtVpt1/Tp58qR29OjROe4R+ub69eva+vXra/fu3Su9plartXfu3NG6ublp27Rpo71y5Yo2PT1dq9VqtQkJCdK9VV9k7++lp6drjxw5ou3YsaO2du3a0v1LpVJJ71m8eLG2adOm2oEDB+r9dTczM1Pr7++v7dixo/bevXtarVarffnypXbVqlXaSpUqaQMDA3McT/Pnz9e6u7trNRpNbjX5X+Xly5daPz8/bcWKFbW+vr7aSpUqSedq9uNSq9VqU1NTpX6YIAhZst/f79+/rw0PD9c6OztrW7RooX327FmO996/f1+7Zs0arbu7u9bT01MbGRmpvX///tdusvCFZL9G7tu3T5uSkqLt3bu31tnZWRsZGZnj3pORkaHdvXu3tkWLFloXFxfthw8fcqPJwj9gyZIl2u+//1579uxZrUql0r58+VK7adMmbZUqVbSjRo2S3nfixAlts2bNtC1bttSmp6eLfshXIoaj8ijd9NCJEycik8lwdHSkSpUqyOVyihQpgo+PD3PmzGHixImcOHECQ0NDnj9/zm+//cbq1av1NovtS8g+Srxy5Uru3btHt27dMDU1ZfXq1XTu3Jm5c+diaWlJo0aNctQHrVOnDlWqVBElcP4ErVbL27dv6devH0OGDJFG4pVKJdWqVSMhIYFbt27x6NEjypcvL32mbdu2yGQygoOD+fDhA+PGjZMW/rSzs8PV1ZXg4GCKFCmSW5v2zfi0RI2VlRVHjx5lwYIF/Pjjj3Tp0oWIiAhKly7N7t27uXz5MtWqVaNgwYJ8//33JCYmikwzQS+8e/cOa2trbG1tpfOmWbNmaLVagoODGT16NDExMdSsWZPvvvuOBg0a5FhTRN+YmJiQlpZGamqq9JpcLqdEiRL4+PgQGBjIpEmTGDduHGXLlsXBwQG5XK43GVe67VSpVNy/f5/SpUvj4uKCTCYjKiqKMWPGsGrVKiwsLKQ60QMGDCAlJYW7d+/i4OCQ25uQq2QyGY8ePaJUqVLS1GY7Ozs6d+6MRqNh8uTJmJmZERISgoGBAYMGDcLX1xeZTCZKs5G1r8LCwlAqlRw8eJDOnTtL6+18OkNB9GsF4XO6+3pgYCBWVlZ4enpibm7OsmXL2L9/P56entJ1pnjx4hQvXhwPDw9Av2YC5TXabPXpQ0JC2LVrF8ePH2fWrFkMHz6co0ePUrJkSbp164ZcLkepVOLm5kZGRgZr1qzh3bt3WFhY5PJWCF/CzZs3KV26NHXq1EEmk2FnZ0eXLl0wNjYmMDCQypUr07t3b1xcXDA0NKRw4cKi5NJXpN+9vDzu48ePPHr0iHXr1vH06VOSk5Oln9WrV4/w8HC8vb25ffs2d+7cwcHBgQ0bNoipXH+TruPz66+/8vTpU9q2bUuDBg3QaDSYm5uzdetWFAoF0dHRHD9+PEdtbRAPFH+WTCbDxsaG+fPn4+7ujkql4ubNmwD06dOHnj17Ym9vT1BQEHfv3pUW19NqtbRp04Zx48aRmJiYY6p6yZIl6dChgwiU/w/UarXU4Xvy5AkPHz6UajEPGjSIqKgo2rRpQ1BQEI8ePeLJkydMnjwZlUoFgJGRkQiUC3rD1NSUV69eYWBggFwul86D5s2b06pVK16/fk1YWBiXLl1CoVBID8L6ECjX/k5VQHNzc/Lnz8+pU6d4//699LpcLqdy5coULlyY+Ph4goKCpNdBPxYIVqvVUk13b29vJk2axC+//IJSqaRBgwaEhoaSmJiIl5cXHz9+lBZWBPDz82P27NnSwpb6SKPRoFKpMDU1JSMjA41GI/XDzMzMcHNzo3jx4qxfv56IiAjpc1qtNkegQ9/Z2toSGBhI8+bN2bZtG7t37waQFj4VBOFzumsNwPbt2/n5559xc3OjSJEi9O/fH3d3d6ZPn8727dtznEfZPyd8m7IPcly/fp1Xr16xePFiLC0tsba2ZtasWRQoUIAVK1awefNm6R6tUCho3749a9aswcnJKTc3QfgCtFotGo2G1NRUVCoVmZmZ0rkul8tp3Lgx9evX5/Tp06SkpABZ8Tvxt/+6RE8vD8l+M9VqtVhYWDBhwgS6du3KrVu3OHbsWI73ly1bFl9fX3bt2sXWrVuJiIj4rH6l8Nfs2bOHXr16sXnzZqysrFAoFMjlcjIyMrCwsGDbtm0oFApiYmI4dOiQ3mYN/l2pqanSw3+lSpVQq9X4+PgwYcIEfv31VwA6dOiAr68vcrmc4ODgHAFzjUZD165d2bBhw2dBA/Eg/H/TaDTScTt27FgGDRpE27Zt8fT0ZNasWahUKkqWLMnYsWOJiorixYsX5M+fnytXrrBy5crcbbwg/IP+6IG2S5cupKWlMXr0aAAMDQ2l645Go6Fx48bY2tqyfv160tLSvlp7c1tmZqaUrZueng5k9WMKFCiAn58fBw8eZOXKlTkC5s+fP8fZ2ZnY2FgSEhJYsmRJbjU/VygUClJSUujWrRsGBgb06dOH6tWrA1nH1ffff8+4ceN48+YNffv2lQLmmZmZwH+Cmfpyr/t0UEAul2NsbMz333/PgQMHOHv2rNQ3AChQoAClSpWiTZs27Nq1i2XLlkmfE9mcORUoUIDg4GCaNm1KaGhojoC5IAify76oX0pKCh06dKBq1aoolUqsra3x8/OjU6dOjB8/nq1bt0rP+NmfFcX59W3S/d1CQ0OZO3cuHz9+pGrVqpiYmKBSqbCxsWHevHlYW1uzYsUKtm7dmiNgbm5unpvNF/6iT/sgMpkMuVxO1apVuXDhApcuXZJmkgJYWFhgaWnJu3fvRCJlLtKPHrIeUKvVOW6auoehQoUKMWzYMJo3b05UVBSHDx/O8bns00hFsPbLadOmDcOGDSMtLY1Tp07x4sULACmzy8LCgu3bt/Pu3TsWLVqkV0GRL+X27dtMmDCBRYsWkZSUBGQdw61bt+bp06csXbqUCxcuANC5c2d69+5NRkYGISEh3Lt373ePd30JGnwpuv0VGBjIyZMn6d69O6GhoTRs2JAlS5YwZswY3r17B4CbmxtBQUH079+f0qVL4+rqmostF4R/TmZmJgqFgvT0dH788Uf27NnDrVu3UKlUlCtXDi8vL86dO0dAQABqtZr09HRp1kX79u2pXbs2J0+elK5reZ0uQzo5OZng4GD69u1L7969mTlzJq9fv8bNzY2hQ4eycOFCxo4dy4YNG9i1axdTp05FLpdToUIFrK2tefPmTW5vyle3du1aqZxYkyZNMDIykh7IDAwMqF+/vjR7qn379qSkpOTIuteXYEtmZqY0k+Px48fcuHFDGmjv378/Li4u+Pn5cfr0aSkodefOHV6/fk3z5s1xcXGRzkmRLf377OzsCA0NpXnz5gQGBrJv377cbpIg/Kv9+uuvjB07lsjISOl6pLt+W1tb4+/vT+fOnYmKimLdunXi2pPHWFhYcOLECeLi4khISACyBrozMzPJnz8/8+bNw87OjhkzZrBz587cbazwt2Sfhf3q1SspLgTg6elJzZo18ff359q1a1J84tWrV7x7944SJUqIGSW5KO/PU9UDn66kfPPmTRITE3FxcaFt27YUKFCAcePGMX78eAIDA5k6dSrNmjUDcgYH9eWh6Uv7o7qVgwYNQqVSsWjRInbt2oW7uzuWlpZSwNzc3JyjR4+SmJiImZlZLrT823Xx4kX8/f0pVqwY+fPnzzHK3qVLF0xMTIiOjmbp0qUA1KxZky5duiCTyVi3bh0DBgxg9erVFC5cOLc24ZuUfeqg7t/Xr1/n0qVLBAUF0bJlS+RyOampqdSuXZsxY8ZgZ2cnlUgoVqwYxYoVo3379qLempAnaTQaqTRGr169eP/+PampqSQlJdGtWze6deuGp6cnAKtXr6ZJkybkz5+fxMREzM3Nadq0KW/fvsXQ0FAq0ZLX6TKkO3fujJmZGXXr1iUhIYGzZ8+yf/9+1q1bx5AhQyhdujSxsbGcPXsWQ0NDSpYsyfTp00lOTkapVGJpaQnkzTquqampGBkZfdbXiIuLw8LCgjJlykiv6WZJyeVyDA0NadiwIYGBgezZswcjI6Ov3fRcl71cTf/+/Xnz5g2PHz+matWqNG7cGB8fH8aOHUt0dDQDBgygadOmGBgYEBcXh7m5Oc2bN+fMmTM8ePAAQ0PDPHdsfUl2dnYEBgZiaGiIs7NzbjdHEP5VPp1FXKJECaKjo5k7dy6//PIL79+/J1++fNL128rKipEjR5KSksK8efNo166ddJ8Tvi3Z+yW6tUaCg4OxtbVl2rRprFmzhiFDhuDo6IhSqZQC5jNmzCA4OJiaNWvm8hYIf4fuvA8KCuLy5cu8fPlSeiYoWbIkwcHBRERE0KdPHzp06ICxsTGPHz/m2rVrhIaGYmBgkMtboL9EsDwP0J2Aw4cP5+rVq5QsWRKlUsnUqVM5duwYQ4cOpW7duowbN44JEyYQGhqKSqWidevWudzyb1/2js+DBw+k/xcvXhy5XM7IkSNRqVTMnDkT4HcD5mI61Z/z22+/MWjQINq0aUPfvn2lgLdu1FYmk9G6dWvUajVTpkzJETDv3LkzqampXL9+XdTJ/pNSUlKYO3cuHTt2pHTp0lKnT6VS8fTpU8zNzaVAjomJCc2aNePBgwcsWbIENzc3KlWqJH2XCJQLeZUue9XHxwcrKysiIiIoUqQI169fZ+jQocTHxzNz5kz69OlDw4YN2bZtG6mpqdjZ2TF06FAADh06RKFChfTioVj3ALlw4UIsLCyIjY2V6jFOnjyZlStXcubMGTp06EDz5s2pVq2aVH6rZMmSqFQqJk2axPv372nbti2Q9wb+b9++zYoVK3BycsLT0xMzMzO0Wq2UaSSTyUhLS8PQ0FC6BsvlcpKTk7ly5Qp169bF1dWVli1bAp8HbPI63SyPPn36YGlpyejRo7Gzs2Pv3r0sW7aMZ8+eMX78eGbMmMH69es5deoUr1+/pnr16oSHh6NSqbh37x4lS5bU2/ruf4a9vT0RERF6sW6AIPwZuuvuokWL6NKlC/nz56dp06YAREdHM2bMGObNm5djwNPKyoqwsDBUKpVe9Anyouz3XK1WS0pKivS37N+/P8nJySxYsABLS0v69OmDg4MDSqWSjIwM7OzsWLJkiZj5nAfMmDGDS5cu0aVLF5KTk1mxYgV37twhICCAChUqMG/ePBYuXMiZM2fQaDQUK1aM9evXU6pUqdxuul4TPZk8Ytu2bVy4cIEZM2ZQo0YNlEolV65cwcfHh5kzZxIdHU2JEiUIDQ1lzJgxTJ06lUaNGomM5r8h+80vPDycq1ev8uzZM2k18549ewJIWbUzZ85EoVDQuXNnrKysxCjhX6BSqVi3bh116tRh0KBBORblzN4RkclktGvXDo1GQ0xMDMuWLUMmk1GjRg08PDyk9+hb0ODv+O2331i+fDnPnz9n6NChlChRAsja30qlkidPngDkyGhs0KABCxcu5PXr17nZdEH4qh4/fsybN28IDAykfPnyKJVKKaDZtGlT8uXLB2StGxIaGip97u7duyxYsIBLly6xdu3aPDeQ+nszU3T/j4+Px87ODgcHBwAOHDjAypUrCQwMpEOHDlJJGjs7O+n7Tp06xeLFi3nw4AHLli3Lk4se6WZRFS1aFGtra6nPJpPJUCqV1KxZkwkTJvDzzz/j4uIiXX+1Wi1nz55l/fr12Nvb51iPRh/veWfOnOHDhw+MHz+ecuXKSZnjSUlJFCtWDMgaxPXy8qJ79+6YmJgA8OjRIxYsWMCtW7fYuHGjqBv6PxKBckH4fb/88gvLli3j5MmTzJkzBxsbG6ksYWRkJEOHDmXu3LnI5XLpGUXXZxC+LboFHHX33JiYGK5evcq9e/do2LAhjRs3pkWLFvj5+aHRaFi0aBFarRYvLy/s7e2lOEFeSwDQF5/GGGQyGUOGDKF9+/YANGjQgH79+jF58mRGjx5N5cqVCQ0N5dWrV1haWqLRaKS+iJB7xDBVHhEfH4+NjQ21atWSpu9UqVKFlStXcv36dbZs2QJkZXxMnjyZzZs3i0D535D9Ajhq1ChOnjzJ4MGDWbp0KdbW1kycOJHFixdL7w8KCqJfv37ExMSwe/duUXfub7h27Rp2dnY5AuUvX75k3bp1+Pr64ufnx8KFC4GsxT0DAwO5fv06MTEx3Lp1C/jPwmb6GDT4q2rVqsXChQs5dOgQM2fO5N69ewDUqFEDFxcX5s2bR1xc3GelnWxtbfVy6r+gv54/f058fDzFihVDqVSye/duvL29GTlyJB4eHnz8+JEjR47w8eNH6TO3bt1iw4YNPHjwgDVr1uTJEgbZH/h0/9ZlR799+xbIqrO9b98+RowYwciRI+nXrx8qlYoVK1awb98+6d6p0WgoWLAg1atXZ/Xq1ZQrV+4rb80/TzeLqmnTpkRGRhIYGAjkXDy2ffv2NGrUiJEjR3L8+HFpcdSbN2+yYsUKaaabPtAdG1qtNsdxAllB79TUVCpUqICBgQG7d+9m/PjxjBgxAi8vL5KSkjh58iSAFBC/fv06sbGx/PLLL6xdu1ZkdwmC8LdVr16dgIAA3r17x9ChQ0lMTMTS0hJXV1fCwsL46aef8PPzA/RzYDMvyD7rS/c39PPzY+/evRQvXpxOnTpx9uxZZs6cyfz58wEYOXIkvr6+rFq1igULFuSoaS2C5d+e7HGin376icuXL3Pu3Dmpf5GZmUmtWrVYvXo1ly9fZtq0aVy+fBnISgoxMjISgfJ/CTH0/w3KXiM7IyMDAwMDDAwMePfuHW/evMHOzg6FQoFKpaJs2bL06dOHAwcO0K9fP+zs7HJkZgn/u9TUVDZt2kS7du2wsbEBslYxv3XrFtOnT6dGjRqsXLmSmzdv0qhRI2JjYwEYOHAgkBVU1y24JW58f41Go8HMzIz379/z9u1bLCwsuHTpEuHh4Tx48ABbW1syMzM5duwYDx8+ZNKkSbRv3x6VSsXRo0dz1HUVf4P/na6+XqNGjViyZAne3t7ky5cPDw8PnJ2dGTZsGEFBQQwYMICxY8dSvnx5aRaAXC4XQQYhz0lPTyclJYXbt29jb2+PpaWldF9wcHCgQIEC/Pbbb1y9epXg4GBGjhyJj48PAPv37+fYsWOUK1cOCwsLICvLvEePHgwZMkT6nrzk2bNnXLp0SVpk3M7Ojj59+lCgQAEUCgVubm7MnTuX8PBwtmzZgr+/P/379wfgxo0bnDt3jg4dOkjXbblcTsmSJRk+fHieDCioVCrWrl1L3bp1GTx4MPnz55d+ln17TU1NCQ0NJTIykkGDBlGlShU0Gg0fPnzA1NSUlStX5pjSn5fpZiu8fv0aU1NTzMzMpG0uXLgwHz584OnTp9y+fZvAwEBGjhzJwIEDUavVbNu2jadPn1KlShUpi9PJyQkPDw8KFixIwYIFc3PTBEHII5RKJV26dAFgxYoVUia5LsNcLpcTHBzM6NGjiYmJyeXWCn9WUlISffv2JSgoSKoz/uOPP3L+/HmmT58uJTV26dKF6dOns3v3bgoUKECXLl0YMWIE6enpbN68mWHDhuXylgh/R/ZBkpMnT2JiYkJaWhq3b9+mRYsW0syRatWqsXr1avr168fEiROZOHEiFStWzOXWC9mJYPk3SNf5nzx5Mo0aNaJu3boUL16cd+/esXfvXrp27YqZmZlUF1gmk2FgYCCmcf0NKpWKHj16cPfuXV68eMGAAQOwsbHBzMyMVq1aUaNGDdauXcu0adOIiYmhcuXKZGZmEhsbi7GxMX369AGQsgWEv8bY2JhRo0bRp08fXrx4gUKh4MKFCzg6OjJs2DC8vb15/fo1kZGRHDp0iO7du1O1alW6du1K165dgT9ekFX4fboFCwGmTp2KqakpBgYGbNmyhZSUFIYPH07p0qWJjIwkNjaWYcOGYWlpibW1NcnJySxdupQCBQrk8lYIwpcTFxfH2rVrOXv2LM+fP0epVFKuXDk8PDxo3bo1JUuWpFixYowbN4709HSGDx+Oj48PGo2Ghw8fsnv3bpycnKQAnC7Il1cHla5evUpkZCTJyckoFAoyMjI4evQoe/fuxdfXl/bt2+Pi4sL+/fvZvn07zZs3l4KY9+7dY/LkyRgbG9OpU6fPvjsvBsp1rl+/Tp06dXIEyl++fMmRI0c4efIkhoaGVK9eHS8vLxYtWsT69eu5cuUKAGXKlMHT01OaaZjXy2Lcv3+fH374gcOHD5OQkICDgwPfffeddD8qUaIExYsXx9/fn+vXrxMUFETfvn2BrJmZ+/fvp1q1ajn6yfny5ROLqgmC8KdkZGSQkJDAw4cPcXFxAWDx4sV89913UhBMoVBIAfPly5fj5+fHnDlzsLKyonHjxsTExFChQoVc2wbhr0lKSqJdu3YULlw4x4yuly9folarKVeunHRPLlasGKNHj2bIkCHs37+fDh06oFQqpcSjvJg0oQ+yZ5QfOXKE27dvM2PGDBITEzl+/DgLFiygaNGitGvXTlp7plq1aixevJgRI0ZgbW2dy1sgfCpv957zsMzMTHbs2MHz58+pW7cubdu25dSpU8yaNQtjY2OaN2+OjY0Nr1+/5vHjxzg5OZGZmSkW1vuLXr58ycOHD1Gr1Vy8eJFFixYxePBgXFxcqF27Nq9evWLTpk0MHz6cpk2bYmhoSIcOHfjpp5+Ijo4mLS1NyjAX/p6aNWuyevVqpk6dyps3b/D29qZFixaUL18eyMogGzBgACdOnOD9+/effV4Eyv83ugCebn8FBARw6dIlfHx8mDBhAnfu3GHlypVkZGQwatQoypcvz9KlS9m3bx+vX7/G3NycunXriow8IU+5ePEiI0eOpFy5crRv355y5cpx/fp1du7cSUBAAA8fPmTw4MHMnj2bAQMGEBcXh729PfHx8cTFxbFs2TIyMzOJiIhAJpPl+cG7CxcuMHDgQFq3bk2bNm2oU6cOKSkpXLlyhaVLlzJlyhTevXvH4MGDGTVqFNOnT+fXX38lICAAlUpFQkICAGvWrEGhUOjNOhPJycmYmJjw4cMH3r9/j7m5OZcuXWLs2LE8ePAAGxsb1Go1R44c4f79+0ycOJGePXvSvXv3HPtHrVbn+UD5pUuXCAoKomDBglSsWJEOHTpw6tQp1q9fz+XLlwkKCqJ27dq4u7szZcoUypUrR61atQCkbD+1Wk1AQACQs7a+IAjC/yo5OZmgoCASEhJISUnB2NiYQoUKERsby6lTpwgNDaVs2bJAVsC8Y8eOvHjxgvnz5xMQEEBMTAw2Nja0adNGXIO+MUlJSbRv3x4nJydiYmJyDHKbm5vz/v17njx5gpWVlZQ0UKRIEQYPHszIkSN5+PAhxYoVQ6FQiIDpN0zX/9q0aRN37tyhcePGNGrUCIBq1aphZmYmrWXXrl075HK5VJLl2LFjomzpv1De7kHnUboF9UaOHMmSJUv48ccfcXFxITo6GrVazcSJE9mxYweOjo68efOGmzdvsm7dOkxNTXO76d8krVZL4cKFGTNmDMuWLcPY2JgTJ04gl8vx9fXF1taWmzdvStlMugGJlJQUGjZsSO3ataXsAuHLqFmzJuvWrSMtLU0qY6CjVqu5c+cOTk5OFCpUKJda+O3SBaOyd9Tj4uL49ddfGTx4MF26dJGCe1WrVmXEiBEoFAoGDx5M6dKlcXNzy62mC8I/6sKFC3h5edGjRw88PT0pXLgwAK6urnz//fesWrWK2bNno1QqGThwIEuWLGHYsGEsXryYhw8fUq5cOezt7ZkzZw5KpTLPB34vX76Ml5cXXl5e9O/fHysrKyCrdEi9evWoUKECYWFhLFq0CAcHB7p27UpoaCiXL1/mwIED2Nvb07JlS/r166c3GdI61tbWNGrUiHnz5vH8+XMUCgXnz5/HwcGBoUOH4u3tzatXr4iMjGTfvn106dKFypUrfxZgycvHF2Sdk97e3nTp0oWePXtKC5l27dqV06dPM336dMaOHcvkyZPp2bMnKpWKnTt34u3tjYmJCcbGxhQoUIBly5bpxTkpCMI/Iykpic6dO+Po6Ei/fv1o3LgxhoaGGBoasnnzZgYPHkxkZCRhYWFSwNzQ0JCBAweyb98+Lly4wOjRo1myZIkIlH9jUlJSaNeuHc7OzowfPx47OzspEUKj0WBjY0OpUqVYvHgxo0aNokiRItLincnJydja2mJhYSHde8Tf/9t29OhRpk+fjrGxMf369ZNeL168OIMHDway1rKTy+W0adNG+ruLhNZ/J/146viGfbqSMvznIlqnTh0WL17MmTNncHFxQalUMn36dCpXrszly5d5/vw5JUuWZNy4cXl2evc/LfuDU7ly5ShUqBAdO3bk6tWr7N27F61Wi4+PD2ZmZpiamnLnzh2ePn2Kqakply5dolChQvTq1UuMFP4DdLX64T81tbVaLQkJCfzwww+UL1+eEiVK5HIrvy3JyclERETg4eGRo2aaVqvl+fPn5MuXD7lcLi2g1qxZMwICApg6dSqWlpb06NFDeggQhLzk6tWreHh44OXlxYgRI6RFenTrhtSoUQNzc3Op/FaxYsVo3rw5a9as4eHDhzx//hwnJyccHBykTJK8HPh99OgRvXr1onz58lKg/NOMXUtLS8aPH8/bt2+ZPXs2TZs2pUyZMpQpU4Zu3brl+D59yJDW0e2nwYMHI5fLOXLkCImJifTr148WLVpI0/OLFCnCwIED8fDw4N27d4B+zZz69ddfGTBgAD169MDf3z9HfyBfvny0aNECGxsbQkJCiI6OZsuWLXh5eVG3bl2ePn3Ky5cvKVq0KHXq1NGLc1IQhH+GSqXC39+fAgUKEB0djaOjIzKZTFrosXLlysyfPx8fHx8iIyMJDQ2VFqX+7bffKFSoEL1798bFxUWvruF5xdKlS3n69Cn9+vXD3t5eih2oVCopWaB169asWbOG+fPn069fP8qUKcOLFy84f/48BQoUEHGCb9inM0SbNm1Kv379WL58ORs3bqRRo0YUK1YMQJpNIJfLGTVqFAqFglatWgFikOTfSvQK/6UyMjKQy+UoFAopWHvv3j0pawayRqi8vb2ZNGkS7dq1k4Jbnp6e9O7dG7VajUwmE53/v0ClUmFoaJhjkKJixYo4ODiwdOlStm3bhkKhYP/+/cjlcgICAujTpw+zZ8/m4MGDmJiY8PTpUzZs2CBugF+BUqnk/fv3nD9/nmXLlpGSksKqVav0ZmGzL0GlUrF+/XpSUlJyXGcArKysKFWqFGfPnuW7777DwsICrVYLZA3amZiYsGnTJtLT04mIiJCCFoKQF2RmZnLgwAEAnJ2dpUA5ZA3a6YKbzs7OeHp68uuvv7Jnzx6+++47zM3NKVq0KEWLFpU+k30dgLzK3t6e6tWr8/z5c3bs2EGvXr0wNDT87HpsY2ODh4cHfn5+7Nq1i759+6JWq5HL5chkMmnf6lO2ry7IolAo8PX1xdPTk4yMDCwtLXO8T19nUWm1WlQqFX5+fqSmptKoUSMpIyv7uaVUKqlRowZDhw4lLCyMGTNmMHLkSMqWLfvZoK4+DcYIgvBl3b9/n4SEBAYNGoSDg4MU9Mp+36pcuTKLFi3C19eX8PBw+vXrR6FChdi1axfGxsZ06NABMzOz3NoE4W/o3r07Dx8+ZOrUqZibm9OhQwdUKhWdO3dGq9VSrVo1mjRpglqtZseOHRw6dIgSJUqgUql48eIFq1atEuvKfaOyJ1WePn0aa2trKlSogK+vLwYGBqxbt47Y2FhGjx6Nk5MT8J9EB0NDQ8qUKZObzRf+B6Jn+C+UmprKwIED6dOnD82aNQOyFgCZOnUqnTp1omHDhrRs2RKAxo0bs2XLFn744QfKlCkjBdflcrkIEP5FKSkpdOrUCXt7e2n019HREciq2+zl5cW2bdsICgoiOTmZvXv3olAoCAgIoFChQvzyyy/IZDJiY2M/CzoK/4ykpCQ6duxIvnz5cHJyIjY2Vu+m7f8dGo0GDw8PHBwcmDZtGoaGhixdupQyZcrQsGFDHBwcqFOnDtu3b6dWrVq4uLhgbm4OZAUSmzVrRsuWLSlatKgIlAt5jlKpxNPTk/fv3xMeHo5SqcxRUzR7NkidOnVwdXXl1KlTUlbZp/L6vVmtVmNkZMSSJUsYOnQoa9asQavVSrOsPg2Y16tXj3z58knZ0b83k07fKBQKaaDAxMQEExMT4PNZVHv27NG7WVQymQwjIyMWLVqEl5cXs2fPlhY71c180h03hoaGNGrUiNKlS3Pt2jXg92uS69NgjCAIX9atW7eIj4+nSpUqf3h/12q1VK5cmTVr1jB48GBGjx6NqakpxsbGLFq0SATKv2H29vYEBwejVqsZO3YsarWatWvXYmpqyowZM6QSdEOHDqVatWpcu3aNuLg4SpQoQdu2baWsY+Hbkj1QHhgYyN27d2nVqhVOTk5YWlri7e2NSqVi+/btxMTE5AiYFytWjNDQUPHM/A0QUaR/oczMTMqUKcN3330nvdapUyfMzc3ZvHkzx44dY/fu3fTq1Yv69evj5ubGihUr8PHxwcbGRixO9DctXbqU+Ph44uPjyZcvH0+ePGHgwIHUqVMHGxsbateuzcGDB+nWrRsTJ04kPDyc3bt3I5fLGTBgAK1btxbZzF+Zubk58+fP5/HjxzRt2lRMqf6T5HI5Xbp0YcKECezZs4dGjRoxbdo06tWrh4GBAfXq1WPs2LE8fPiQcePG4eXlRatWrVCpVGzcuJEHDx5Qv359MYtCyLPs7e3x9/dHrVYzZswYtFot7dq1y/EeXUmWIkWKkJ6ezocPH/QyW0i3EKexsTFz585l6NChrF27FuB3A+aZmZlS6QzhP36vH/dHs6j0YbHY7NRqNRUqVGDlypV4eHgQExPDqFGjqFGjRo4ZCZmZmdja2lKtWjXOnj1LWloaBgYGIjguCMIXk5mZiYGBgXT9/b1rsUajQavVYm5uzr59+zh9+jRpaWlUrFhRr2YG5VV2dnaEhYUhk8kYP348tra2HDlyRLrX6AKr9evXp379+rncWuFL0P1tAwICuHr1KiEhIVSpUgVLS0vpGjBo0CDUajW7du1ixowZ+Pn5STNNRaD826AfvepviEajwcLCgrCwMMzMzJg6dSrLli3DzMyMbt26MX36dCZMmEB8fDxjxoyhb9++lC1bFo1Gw+zZswH9zcT6Urp160aXLl2wsLDA3Nycjh07EhISQmhoKNu2bcPb25vz58+zZcsWACZOnEjTpk1ZvXo1S5culcrfCF9X2bJladasmVR6RQTK/5zGjRvTuHFjZs+ejUajYfXq1Tx48EBaFwGyBpJcXV3ZsGEDrVu3pn///hw7doyIiAgRKBfyvPz58zN69Ghat25NaGgou3fvzvFzXcf3xo0bVK5cWcog0UefBsxLlCjB2rVrWbduHenp6TmCCidPnqRw4cLUrFkzl1v99enKWf1fr+k8f/6cjh07Mm/ePGxtbdm2bZs0i0pfAuXwn+OrQoUKrF27llu3bjF9+nR+/fVXAGnwQKlUkpaWxq1bt6hcuTLGxsYiUC4IwhdVvnx51Go169evB5BmuGSnUChQKpUMHjyYw4cP4+LiQosWLUSgPA+xtbUlJCSEVq1a8fr1a/bu3Qtk3dP16f6sT06ePMkvv/zCuHHjaNKkCfnz5+f9+/fExcVx4cIFIGtGQadOnTh9+jQLFy4kMzMzl1st/BkimvQv8+LFC5RKJXZ2dqhUKs6cOUNSUhLGxsZ069ZNqn3apEkTdu3axZ49e6RRq5s3b5KUlCSVRxD+GgcHB4YPH05aWho//PADbm5u7N27l02bNjF16lTKlClD/vz5OXr0KI0aNcLOzo7w8HAMDQ3p2LGjeBD7FxCdkv9bZmYmSUlJ0vRAOzs7OnTowIULF1i4cCFhYWGMHz+e8PBwli5dikwm47vvvmPy5MlcvnyZp0+fSrX4ChYsmLsbIwhfiS5gDhAaGgpA27ZtpQHSq1ev8vjxY9zc3HKtjf8W/y3D3MPDA0NDQx4+fMjGjRspVapUjkWF9YFuJkJmZiZpaWkYGhpiaGj4X7PEHRwcGDp0KObm5ri6uur1LKpPA+YeHh5Mnz6dgIAAatSogVwuR61Wc+nSJdRqNd9//z3w+2VYBEEQ/qpChQpRrlw5du/eTbVq1XB1df3d6/jZs2fJzMwUAfI8rECBAowePRqVSkVoaCgajYYOHToA4t6TFyUmJpKZmUmFChVITk7m6tWrjB8/nqSkJN68eYOnpychISEMGjQIIyMjmjZtqpf9tW+ZTPvfUliEryItLY1Vq1Zx9uxZbty4AUCjRo3o3r07lSpVYvDgwdy/fx9vb2+6d+/+2Um2e/dufvzxRwYPHixqZH9Br169IjIykqNHjzJjxgyaNWvG27dvmTFjBrdu3cLGxoZJkyaRL18+EZwVvilpaWn069cPR0dHvL29KV++vPSz6OhoNm7cyPLly6lZsyZHjhxh4sSJlCpVCm9vbzF9UBCAN2/eEBMTw969e4mOjqZt27Y8ffqUgIAAlEolK1euFAOn/59u+nFaWhpDhw7l/v379O7dm0aNGhEWFkZycjJbt25FqVTm+VIiL168wNDQEGtrayBrvY3Q0FDu379PsWLFaNKkCR07dgRy1sOEP57an5f31/9Ct59+++03PDw8KFu2LKNHj5YWmB0xYgRmZmYsXrxYnJOCIPwjbt68Sc+ePSlevDjDhg2jcePGOX7+4cMHJk+ezP3795k/fz42Nja51FLha3j16hVRUVEcPXpU6iMKec+dO3fo1KkT1atXR6FQcP78edzc3GjSpAlxcXEsX76c1atXU7ly5dxuqvAXiWB5LktKSqJ///5otVocHR1xdnYmISGBvXv3olQqGTlyJF27dsXX15cHDx7kCJhnf5BSqVQYGhrm8tbkPdkD5hMnTqRTp06o1WqeP3+OQqHAwcEht5soCH/auXPn8PX1xdTUlMzMTEaMGEHLli2xsbFBpVLh7u5Oamoq27Ztw9TUlKNHjzJhwgScnZ3p06ePlKEnCPpMFzDfv38/AQEB7N+/nw8fPrBz504MDAw+C3bmVf9LwDZ7wHzYsGHcvXuXtLQ0bGxs9GZ/vXz5kubNm9OjRw8GDx6Mqakp3bt3B7LKiN28eZPXr1/TtWtXhg4dCnweMBd+36cB8/Lly+Pl5cWyZctITk5m+/btenGMCYKQe06ePImfnx/W1tZ06tSJPn36IJfLuX79Ojt37uTw4cOsX78eZ2fn3G6q8BW8evWKyZMns3fvXmJjY8WMwzxG1/c9ceIEixYtonTp0lSvXl2aSbB//35iY2NZsWIFhQsXzt3GCn+ZCJbnoqSkJDp06EDhwoUJCAigQoUK0gPnlStXiIyM5ObNm4wZM4YuXbrg4+PD48eP6du3L+7u7tIUVNHx/2dlHx2eOHGilPUlCN+q5ORkoqOjefToEc2aNWPu3Lk0btwYFxcX3NzcOHLkCOPGjaNdu3b4+/tjYGDAiRMnGD58OA0bNiQmJgYTE5Pc3gxB+OKyT5P9X4LAiYmJTJ8+nW3btlGqVCl27NghldbQh6mWuu1UqVTcuHGDokWLSlnTn8oeMPfy8iIjI4NNmzZJNbf1YX/FxsayfPlyfHx8qFWrFitXrmT06NGULFmSJ0+esGzZMg4cOIC7uzt+fn6AyB7/X/u5uvfduHGDPn36kJSURPny5dm0aZNenZOCIOSe69evExoayt27dzExMUEul2NtbY2pqSmTJk2ibNmyud1E4St68eIFM2fOpH///mL2fx7zadKqXC6X+hiJiYnExsZy9+5dFixY8If9YuHfTwTLc0lKSgpt2rTB2dmZ8ePHY2dnJy1MKJPJkMlkxMXFER4ezt27d5k7dy6VKlXC19eXxMREunTpgqenpwiUfyViOpWQ1zx//py2bdsSEBBAo0aNmDVrFrdv36ZKlSr4+/sTHBzMgwcPmDBhgrTw3unTpylcuDDFihXL3cYLwj9AF0zTdYA/XQPkj+pNvnjxgkOHDtGjRw+9Cvxm308DBw7E2tqaLl26fDb9/Pc+o1KpUCqVellze968ecybN4969ephZGTE/PnzpZ89fvyYFStWsH//fhEw5z/nZGpqKlu3bqVKlSqUL1/+D48X3fF1/fp1ZsyYwcKFC0WgXBCEryoxMZF79+5x5coVMjMzqVKlCqVLl8bW1ja3mybkAnH/yXt0fY1Hjx5x+/ZtXF1dpZ/t27ePU6dOcfToUdasWSNmknzjRLA8l8yePZv58+cTFhaGh4cHkPNhSPdQfu3aNfr27UvdunWZO3cub9++xdPTE6VSyYoVK8iXL19uboZeEdOphG9VSkoKe/fupUWLFlhaWkqvHzhwgKioKGbOnEmFChX46aefiIyMpHjx4nz//fdMnTqVrl27MmHChFxsvSD887IHfsePH8+TJ09QqVQ0adKEbt26UaBAgf/pe/TtoSglJYWuXbtib29P3759qVOnzv9ZEi57X0dfgsCfbueiRYuYMWMGxYoVY/HixRQpUkT62ePHj1m5ciUHDx6kVatW0kKy+ib7OdmnTx8APD09adOmzX9NFPk0E13fzklBEARBEP6eP1onRqvVSoHyVq1a0apVKyZPnoxSqeSnn35i3rx5ZGZmEhERQZkyZXKp9cKXInqPuaR79+48fPiQqVOnYmlpSbt27ZDL5VKQXLeKdqVKlejevTsrV64kPj6eYsWKsXbtWj5+/CgC5V+ZnZ0dgYGBGBoailFC4Zuydu1aYmNjOXDgAJ07d5YGeqpXr06jRo3YsmWLtLhcgwYNCAoK4vTp05iYmLBp0yaqV69O+/btc3krBOGfo1AopMCvjY0NFStWxMTEhGXLlvHzzz/j4+NDgwYN/s/vyctBuYyMDAwMDHK8NnfuXBQKBRERERQsWBCZTEZCQoLUlylUqNBnGfnZHz70IVCu1WqRy+U51pbx8fFBoVAwbdo0tm7dSt++faVpuk5OTvTt25f379/z6NGjP5zRkNfpSvb06tULW1tbRo8eTYkSJT4LlH/6QPvpz/PyOSkIwr9T9uu2vl7DBeFblX3Q/dWrVygUCmxsbKS+xqNHj6Tn6fDwcKmfUb58ecLCwrCzsxMzSfII0YPMJfb29gQHB6NWqwkNDUWr1dK+fXtkMtlnN9XixYsjl8tRq9VoNBosLS1zZIcKX4+9vT0RERHi4Uv4pnh6elK2bFnGjx/PpEmTOHbsGJGRkRQoUIBWrVoxbtw4fvrpJ9q2bYuhoSEzZsxg//79mJqacvz4cSpWrJjbmyAI/7gVK1ZgZGREREQExYsXRyaTYWJiwuzZs+nbt29uNy9X3bp1i9jYWMLDw6WFijIyMnjy5AnFihWjUKFC0gyWhQsXkp6ejoGBAVOmTKF27dq53PrcowvkajQaevfuTYkSJYiKikIul9O/f3/S09OZM2cOSqUSDw8PbGxsAChcuDCBgYHY2tr+br8wL3r16hV2dnY5Xjt27Bjp6emMGTOGYsWKoVAouHr1Kvfu3QOgUaNGWFtb68X+EQTh25H9eiSuTYLw7dBljgOEh4fz008/oVAoqFGjBtHR0QCcO3eORo0aMW7cuBzlGq2srLCyssqNZgv/EBHxy0V2dnbS9NqwsDAAKWCePVPm3r17ODk5UahQIb3Iwvq3E4Fy4VtjZGREw4YNWbt2LWvWrGH79u20b9+eoUOH0rZtW3r16kV4eDi1a9fG3t4egFatWlG/fn0yMzOlAI4g5CWfBthu375NgQIFKFGiBAC7du1i7ty5jBw5kiZNmpCWlgaAsbFxrrQ3Nzk4OHD+/HkmTpzI+PHjKViwIAYGBpQpU4YFCxYwbtw4Hj58yM8//0z//v2xtbXl8OHDLFmyhMqVK2NkZKR3AQNd+Y/09HQuXLiApaUlO3bswNbWloCAAACGDBmCVqtl7ty5APTu3VvKMNeV/tGHUjXv3r2jefPmeHl5SXXaIWsx6kePHmFjY8Pr16/Ztm0bixcvxtTUFLVazfHjx5k0aRJmZma52HpBEARBEL512ftbU6dO5dSpU3Ts2JHXr1+zb98+Hj9+zIIFC+jWrRsdOnT4P8sOCt8+EfXLZX8UMNeVZNEtHFC/fn0RpBUE4W8pWLAgw4cPp3nz5syYMYPQ0FAOHTrEgAEDaNGiBdHR0URHR0uBBzGDRchr0tPTMTIyArKyvTIyMlCr1RgbG2NkZERSUhIAe/fuJSgoCH9/fwYOHIhKpWLSpEnUqVNH79aryMzMxMrKimPHjtGuXTsGDx7M6tWrsbS0pHPnznz8+JGzZ89Srlw51qxZIy0IfP36dZKSkvRycEGr1aJUKklKSsLDwwNra2vMzMwoWrQoS5YsITk5mfDwcACGDh2KXC5n3rx5fPz4keHDh2NhYSF9V14PlENWNlZoaChRUVEYGRnh6+sLQLVq1ShTpgxNmjShcOHCPH/+nKCgIBo2bMi2bdvYsmULr1+/FsFyQRAEQRD+Fl1/6+rVq3z8+JHAwEBatWqFWq2mSZMmhIeHM2DAABYuXCjKIesJEX39F/g0YC6TyWjXrh3JycksXryYhw8fMm7cOBEsFwThbzMxMaFatWqsXr2auXPncvDgQTw8PKhQoQIqlYqTJ0/SsmVLvcsCFfK+5ORk9u7dS3p6Or179yYjIwNfX1969epFkyZNKFWqFEePHmX69OksX76ckSNHMnDgQADu379PXFycXi/WY21tzZ49e9i+fbs0kObo6EhISAjJycnI5XJMTEzIyMjg6dOnPHr0iKpVq+Zuo3OJTCZDrVYzZswY5HI5YWFhlCxZkoSEBPbs2cPs2bMBGDt2LDKZjMGDB5OcnMzFixdzTOnVF1qtli5dumBoaEhgYCDVq1endu3alChRgvDwcM6cOUO+fPmoXbs2ZcuWBaBs2bJYWFiIe5UgCIIgCF/ElClTOHbsGEqlEm9vbyBrLZT69esTFRVFaGgogwYNYsGCBSJgrgdE9PVfInvAPDQ0lPT0dB48eMCePXvYtGkTRYoUyeUWCoKQV+immQ0dOpT69etz4MABVq1aBcChQ4do2rSpmFom5ElXr17lp59+4u3bt+zcuRNHR0cqVKgAwMCBA7lw4QJLliyha9eu9O/fH8iq1z1+/HgMDAxwd3fPzeZ/ddkzpKdOnUrfvn2l+u3ZF0DSZfZ+/PiRM2fOsGrVKtLT0xk9erT0PXk5qHn//n0MDQ2leu6QdZ199OgRlSpVomTJkgAUKlSInj17otVqmTlzJqampvj7+yOXyxk9erS0n/L6/spOV64mKSmJli1bUr58eUqVKgVkZXlVr16d6tWrS8ebSqXiyZMnLF++nGLFiuHk5JTLWyAIgiAIQl7QoEEDDh48KJUWLFasGAAGBgbUq1ePqKgoxo0bh4eHB2vXrhUB8zxOBMv/RXQBc4VCwdixY1EqlWzatEnKohEEQfgSdGWeZDIZ1apVo1q1atStW5eFCxcyePBgESgX8iQzMzOCg4MZNWoUixcvplChQsyaNQtbW1spEKfLBN67dy8pKSkkJyfz7NkzlEola9asQaFQ5AgS52W67VSr1fTr1w8jI6McM9w+3QcqlYoNGzZw4MABChQowJo1a1AqlXl+fyUmJtKxY0fGjx+fI1ienp6OsbExmZmZwH/2p4WFBW3atGHz5s0sXboUtVpNUFCQ9Dl9CpQDOcrVeHl50aJFC4DPjhuFQsGHDx/Ytm0b+/btQ61WM2vWrM/W+REEQRAEQfi//F7foX79+kyfPp2AgAC2bNmCg4MDLi4uQFZ/pV69eoSFhTF9+nQ+fvwoguV5nEyr1WpzuxFCTi9evGDhwoV4eHhI2UiCIAj/NJVKJQLlQp6ly2Dt1asXDx48wNTUlK5du+Lj4wPkDFIuWrSIR48eoVarKVeuHL169UKpVErfoS/S0tJ49uwZK1asoHPnzlSpUuW/vv/WrVskJiZSt25d5HK53uyvGzduUL58eVQqFW/fvpUWSp49ezZLlixh1apVObKjAXx9fTE2NubIkSOEhYXp3ayF7Pti7ty5nDx5kunTp//XTPHdu3dz9OhRTE1NiYiI0MtzUhAEQRCEvyd7H+TVq1e8e/cOJycn5HI5hoaGnDt3jjFjxuDo6MjAgQOlgDlkPU+oVCpMTU1zq/nCVyKC5f9SovMvCMLXpm8ZjYJ+unDhAubm5syZM4e7d+/SoUMHBg0aBPz3e29ez5D+lFqtZuTIkRw6dAg7Ozs2btxIoUKF/ufP5/Vs3ydPnkiZ5Gq1GoDu3btjbW1NeHg4Tk5OpKen4+vrS1xcHHPmzKFGjRoAxMXFMX78eLy9vdmyZYtUlsXExESvrsGpqaksWbKEhw8fUqtWrf9zwECtVvPs2TMKFSok1YXXp3NSEARBEIS/J3v/dOzYsZw/f574+HiKFi2Ki4sLgwYNwtramrNnzxIaGoqjoyM+Pj40bNgwl1sufG159ynmGycC5YIgfG36FKQR9IMuiJldzZo1KVu2LGPHjqVkyZLs3LmTBQsWAFn33pcvX3LkyBHS0tJyfE7fgnIKhQJXV1dq167Nx48fefHiBfD7+/T35OVA+cuXL+nSpQt+fn5A1r5SKBR4eHhw/vx5Zs2axZMnTzAyMiIoKIiyZcvi6elJQEAAISEh+Pv7o9VqcXV1xdramtevX2NoaKh31+BNmzaxZs0ajh07Jg3EqFSq332vVqtFoVBQuHBhqa67vp2TgiAIgiD8NVqtFq1WK/VPg4KCOH36NO7u7ixatIjy5ctz/Phx/Pz8SExMpF69ekRHR/Pq1StiYmI4c+ZMLm+B8LXl3ScZQRAEQRD0VmZmJgqFgpSUFGJjYxk9ejQjR47kt99+Izk5GQcHB8LCwihVqhS7du1i2rRp3LhxgyFDhrB161aMjIxyexO+Ko1G89lr7dq1o1evXjg6OuLn58fTp0+lOub6TKlU0q1bN3766SdCQkKk1zt06MDkyZM5cOAAM2bM4Pnz55QtW5aFCxfi6+vLy5cviYuLo3LlyqxevRqVSsWjR48oUaLE7+7/vK5du3Z0794djUbDsmXLADA0NPzd4+vTgQR9G1gQBEEQBOHPS01N5fr161K/QavVcvv2bX7++Wf8/f3x9PTExcWFKVOm0Lt3b168eMHUqVNJTU2lbt26TJgwAZlMJi32KegPUYZFEARBEIQ8RTfFMikpiZ49e2JgYECFChWkQHm/fv1o1aoVFhYWPHv2TMoYUSgUFClShDVr1mBgYKA3pYl05WdUKhX3798nMzMTU1NTSpQoAcCRI0eYMWMG6enprF69moIFC+p9CYzExEQ2bNjA0qVLadGiBZMnT5Z+duDAAUaNGkWLFi0YOXKkVK4lJSVFqnH55MkT5s2bx+HDh9m4cSOlSpXKle34Wv7oeHn79i1Lly5l69atNGnShEmTJv3X9wuCIAiCIPwvNBoN48aNY/fu3SxdupRatWoB8Msvv9CnTx82b95M5cqVpXW7VCoVU6ZM4eDBg6xfv54iRYoAWWv4GBsb5+amCLlA1PoQBEEQBCFP0AW35XI5KpWK4cOHY21tzYwZM7CxscHPz4+DBw8yffp0tFotbm5uODo6EhYWxr1793j//j2NGzdGoVDozdoharUapVJJUlISPj4+vHjxgmfPnmFubk7v3r0ZNGgQrq6uAMyaNYs+ffpIAfO8Xpf8U9kDuDY2NnTr1g1zc3NiYmIwNTUlPDwcgJYtW6LVahk9ejQymQw/Pz+cnJykQPnVq1dZsmQJv/32G2vXrs3zgXLduZSamsqePXt4+vQppUuXxtnZmZIlS+Lt7Y1Wq2X//v2MGTOG6OhoaQaDCJgLgiAIgvBXyOVyGjduzJMnTwgKCmLy5MnUrl0bR0dHlEol586do3LlylKg3NDQkMGDB7Nu3TquX78uBcv1bbapkCXvPwUKgiAIgpCnXb58GVNTU8qUKSO99ssvv5CSkkJERAQ2NjYMGzaMa9eu8cMPPzBx4kRmzpwJQKtWrbCxscHGxkb6rC6ArA8UCgXp6el4eHhgbm5OYGAgkLX/Fi5cyLNnz4iIiMDV1RWNRsO8efNwc3PjyJEj2Nra5nLrv46HDx9StGjRHAHczMxM7OzspGNu/fr1pKamSpnRrVq1QiaTERQUxPv374mOjsbOzg6AwoUL06NHD4oWLfqnFk39Fmk0Gmkwplu3bshkMpKSkjAxMSEpKYmoqChcXFwYOHAgAPv37ycsLIzIyEgRKBcEQRAE4S/RDdQ3adIEQ0NDFi9eTHBwMFFRUdSrV48WLVqwceNGihQpQsuWLTE0NESr1fLgwQNsbW2xt7eXvksfZpkKn9OPJ0FBEARBEPKk5ORkpkyZQs2aNQkICJBeb9CgAU+ePKFEiRLMnTuXa9euERsbS+nSpQkJCcHT05PVq1eTmpqKu7t7jumVeTlIlz1jXpcZfurUKT5+/MiECROoUqUKAA0bNqRs2bKMHz+eQoUKMXjwYJo3b05GRgY//vgj1tbWubkZX82bN2/w8PCgWLFirFmzRhpcMDIy4vr16wwePJg2bdrg6OjIypUrAaSAecuWLUlLS2Pbtm3kz59f+k4bGxu+++673Nicr04ul5OZmUlgYCD58+eXFtZ9/fo1/fv3Z8SIEezatYsiRYrg4+ODTCZj1apVODk54ePjk9vNFwRBEAThG6Lr52ZPemnQoAEajYalS5cSGhrKnDlz8PPzY+TIkcyePZvExES6devGvXv32LVrF0ZGRlIJPUF/iZrlgiAIgiB80548eULhwoVJTU3l5cuXFC1aNMfPBwwYgKOjI+Hh4SiVSp48ecLAgQN5+fIlFStWZMWKFXqRNXL9+nV27dqFt7c3Dg4O0us7d+4kPDyc7du35ygJkpqaSkxMDAcPHmTdunWfLW6kD2UykpKSWL9+PatWraJSpUosXLgQyCql0qdPH9q2bUtERASJiYls3LiRJUuW0LJlSylgnp2+la3RefPmDb169aJ379706NEDuVzO3r17CQoKYvjw4QwcOJCMjAwMDAxITExk//79uLu75/ljSxAEQRCELyclJQUfHx9sbGxo164dZcqUwcnJSfr5yZMnWbRoEc+ePWPBggUYGBgwdepUTpw4gZmZGebm5mg0GhYvXky5cuVycUuEfwORWS4IgiAIwjdJV6O8cOHCaLVaxo8fz65du9i9e7dUHiM9PZ3nz58jl8ulLJNXr15Rrlw5du7ciVKpRCaT6cVinrdu3WLNmjVotVoGDBggTTE1NTUlMzOT+/fvU6pUKWlfmJiYULNmTTZv3kxaWtpn36cPwUxzc3N69uyJoaEhCxYsYOTIkfj6+uLp6UmbNm0YM2YMkJUt3r17dwCWL1/Ohw8fmDdvXo7v0pdA+aeDKG/fvuXVq1c4OTkhl8vZuXMnwcHBjBw5koEDB5KcnMzChQvp0aMHBQsWpFevXr/7PYIgCIIgCH9k8eLFnD9/HoBHjx6RkJBAq1atqFixIp06daJhw4ZYWVkxZcoUBg0axJw5c5g/fz4XL17k119/xcnJiSpVquT5EnnC/0Y/eu2CIHxxzs7OzJkz54t+Z+/evendu/cX/U5BEPIujUYj/Vsmk9G1a1eqVq1K3759iYuLA8DAwIBOnTpx8eJFQkND2bhxI1FRUbx8+RIDAwPkcjkajSbPB8oBunTpQkREBOvWrWPhwoW8ePECgObNm1O7dm0mT57Mw4cPP9sXBQsW1Jsa7r/H3NycLl26MHjwYM6dO0f79u1p3749kZGRGBoaAlkDN/nz58fd3Z3u3buTnJyc4/jM696+fcvZs2d5+fIlCoWCpKQkqSxNsWLFKFCgAPv37+fEiROEhITg5+cnlVk5f/48N27ckI5HHREoFwRBEAThf+Xu7k6XLl2wsLCgfPnyhIaGcunSJSIjI2nVqhWjR48GoEOHDjg7OzNs2DAuX75MzZo18fHxwc3NTQTKBYkIlgvCN2r79u04Ozvj7OzMhQsXPvu5VqvFxcUFZ2fnr1L38+LFi8yZM4cPHz78479LpVKxatUqOnToQPXq1alZsyatW7dm7Nix3Lt374u2ad26dWzfvv1LNFsQhC8oMzNTqh/9888/k5KSQs2aNQkJCaFw4cJSwFwul+Pq6kr37t358ccfWbRoERYWFixfvlzKKNeXjF+Arl27Mn78eDZu3Cgt4Ang7++Pqakpffv25cCBA9y6dYsLFy6wbNkyChUqRIkSJXK55bnL3Nyczp074+Pjg4ODA0+fPgWyAroZGRnSAIONjQ0+Pj6sWLFCGojRBwkJCYSGhrJy5Uri4+Nxc3Nj//79vH//HqVSiYeHB0ePHsXX15egoCAGDRoEwIMHD1i0aBFmZmZSvXxBEARBEIQ/y8HBgWHDhuHi4sKuXbvInz8/u3fvZuvWrXz33Xc8ePCA3r17s3r1au7fv09ycjLe3t5cvXo1t5su/Avpb5qQIOQRRkZG7Nmzh5o1a+Z4/ZdffuH58+dS1tuXdvXq1RxZX5cuXWLu3Ll07NgRS0vLf+R36gwfPpyTJ0/SunVrunbtKpUPOHHiBNWqVaNkyZJfrE0bNmzA2tqaTp06fclNEAThb1Cr1SiVSpKSkhgwYABqtZqBAwfi6upKlSpVGDNmDNHR0fTt25fly5dTtmxZBg0ahKenJ2/fvqVUqVLSwoP6kjGdvcyMrlzI+PHj0Wq1DBs2jMqVKzNp0iRmzJhBQEAAcrmcAgUKYGtry+LFi6XArz4NLHxKFzAHWLBgAYMGDZJqXmYvGaK73+jTQEzFihUZNGgQ4eHhbNu2jTJlyjBjxgzy5csHgKurK3fu3OHw4cNcu3aNq1evcuXKFXbv3k1GRgaxsbHiGBMEQRAE4W+xt7cnKCgIlUqFr68vkZGRdOjQQerznjhxghs3brBjxw4+fPiAVqv9x2MXwrdJP54QBSEPc3Fx4cCBA4SFheUI+uzZs4cKFSrw7t27L/a7NBoNGRkZGBkZYWRk9MW+98+4evUqx48fl+rGZqdWq79KZrsgCLlLoVCQmppKjx49sLOzY8CAAVSvXl36efaAube3N8uXL8fZ2RkzMzPs7OyArOuZPgTKdUHcT0urdO/eHY1Gw8SJE9FqtQwfPpxKlSqxfPlyjh49Snp6OmZmZjRo0ACFQqFXAwv/jYWFBV26dAFyBswVCsVngV59KO2TXadOnRg7diwfPnygePHiOX5mb2/PoEGDKF26NKtXr5Yy9MuUKUNUVBRKpVIcY4IgCIIg/G12dnaMHTsWuVzO2LFjgazSKzKZjMaNG9O4cWM8PT25du0axYoVw9HRMZdbLPwbiR6pIHzjWrduzeHDhzlz5gwuLi5AVpmSgwcPMmjQINasWfPZZ5YtW8bhw4d58OABqamplCpVioEDB9KyZcsc73N2dqZXr15UrVqVRYsWER8fz6xZs3B1dcXZ2ZmhQ4cybNgw5syZw9y5cwFo2rSp9PmjR49SuHBhtm3bxq5du7hz5w4fP36kSJEieHh40LNnzz+9vY8fPwbIERjTUSgUWFtbA3yRNjVp0oSEhARpXwDUrl2bNWvWSN+vq4uss337dkJCQqTfA3Dt2jVmzpzJ9evXSU1NxdbWljp16jBp0qQ/vf2CIGTZtWsXmZmZBAQEUL58eamkCmQFKXUB80mTJtG+fXsOHz6Mk5OT9Hl9yF7VBR9TUlJYu3Ytb9++xdzcHDc3NwoXLkyPHj3QarVEREQgk8nw8fHB0dExxzUT/pPJL2TR1TCXyWTSwpQbNmzQi2PqU9kz6lNSUhg6dCgajYb58+djampKv379KFCgAJAVMO/Vqxc9evTg4cOH2NraYm5ujkwmE4FyQRAEQRC+GDs7O0JDQwEYO3YsMpmM9u3bA5CRkYG5uTn16tXLzSYK/3KiVyoI37hChQpRtWpV9u7dKwXLT548ycePH3Fzc/vdYPnq1atp0qQJbdu2JSMjg7179+Ln58eiRYto1KhRjveeO3eO/fv306tXL6ytrX930YtmzZoRHx/Pnj17CAkJkQLWNjY2QFYpk9KlS9OkSROUSiXHjx9nwoQJaLVaevXq9ae2t2DBggD88MMPVK9e/Q8frr9Em8aMGUNERASmpqZSFrutre2fau+bN2/w9vbG2tqagQMHYmlpyZMnTzh8+PCf+h5BEHK6e/cuarWaChUqSK9lD5hDVob5qFGj2LVrl3Tt0BdarVYqVdO5c2cUCgWmpqY8e/aMgwcP0r59e3r06EHPnj1RKBRMmDABhUKBt7f3Z/tKLLT4OV1JlpSUFH799Ve9LB+iC3CnpqZy7Ngx6taty+DBg5HL5VhZWREdHY1Wq8Xb21sKmL9//558+fLlyDzXHauCIAiCIAhfSvaAeVhYGHK5nLZt22JgYJDLLRO+BaJnKgh5QNu2bZk+fTppaWkYGxvzww8/UKtWLezt7X/3/QcPHsTY2Fj6f69evejUqRMrVqz4LFj+4MEDfvjhB0qVKvWHv79s2bKUL1+ePXv24OrqKmVU66xduzbH7/Pw8MDb25sVK1b86WB51apVqV27Nps3b5YezqtXr07jxo1zBHi+RJtcXV2ZOXMm1tbW0kj0n3Xp0iXev3/PsmXLqFSpkvT6yJEj/9L3CYK+0wXDlUolMpmMN2/eYG1tLQUqZTIZycnJnDlzhu+//56aNWtKazpkz4LN62QyGWq1mpCQEGxsbJg6dSqOjo4olUpatWrFhg0bqFWrFpUrV6Z79+7IZDLCw8MpVKgQ/fr1y+3mfxPMzc3x8vLC19cXmUymVwHz7OsG9OvXj/T0dORyuTRDrU+fPmi1WiZNmoRCoaBHjx4YGRnh4+NDly5dctz79a1cjSAIgiAIX4cuYK5QKBg9ejQKhQI3N7fcbpbwDdCPHr0g5HGtWrUiPT2d48ePk5SUxIkTJ2jbtu0fvj97kPj9+/d8/PiRGjVqcOPGjc/eW6tWrf8aKP9fZP99Hz9+JDExkdq1a/P48WM+fvz4p75LJpOxbNkyRowYgaWlJXv27GHixIk0btyYESNG/M81y79km/4bCwsLAE6cOEFGRsYX+15B0BcajSbH/2UyGTKZjAYNGvDw4UP27NkjLQwIWcH0K1eusHz5cm7evJnjs/oSKNdJS0sjISGBFi1aULhwYZRKJSdOnODBgwf07NmTypUrk5mZiUajoVu3bsybN48+ffrkdrO/KSYmJtKMBn0JlMN/1g1wd3fHwsKCCRMm0Lhx4xyBb09PT4KDg1m+fDlDhgzB09OTtLQ0unXrlostFwRBEARBn9jZ2REYGEjHjh2l0qqC8H8RmeWCkAfY2NhQr1499uzZQ1paGmq1mhYtWvzh+48fP86CBQu4efMmKpVKev33srs+zcj+K3799VfmzJnD5cuXSU1NzfGzjx8/SgHl/5WhoSGDBg1i0KBBvHz5kvPnz7N69Wr279+PUqlk2rRpX71Nf6R27dq0aNGCuXPnsnLlSmrXro2rqytt27bF0NDwi/wOQcirdGUeVCoVDx484NWrVzg7O2Nubk6DBg3o1asXkyZNQqvV0rp1a6ytrfntt9+YO3cuxsbGVKlSJbc3IVelpKQQHx8vZeHv3buXgIAARo4cSb9+/UhKSmLLli24urri5OQk1SoX9aP/PH3Mjt60aRNyuZzx48dTsGBBFAoFt2/f5sWLFxgbG1O5cmW8vLxwdHTkxIkT5M+fnxEjRojFPAVBEARB+Krs7e2JiIgQfQ/hfyaOFEHII9q0acPYsWN5/fo1DRs2xNLS8nffd+HCBQYNGkStWrUYN24cdnZ2GBgYsG3bNvbs2fPZ+7NnYP8Vjx49wsvLixIlShAcHIyjoyMGBgb8+OOPrFy58rOs0T+rQIECtG7dmubNm9OmTRsOHDjA5MmT/+uN8Eu06Y8CI2q1+rP3zZ49m8uXL3P8+HFOnTrFmDFjWLFiBZs2bcLMzOzPbbAg6InsZR769+/P06dPefnyJba2tjRs2JDRo0czevRoZDIZkydPZv369VIZDAsLC1atWoVCodCb0hharTbHdUmr1WJiYkLZsmX57bffWLduHREREfj7+9O/f38gq0zUwYMHqVChQo7FT8WDhPC/eP/+PR8+fMDR0ZHk5GS2b9/OggULUKvVmJiY0L17d3x8fGjRogVNmjSRaoSKQLkgCIIgCF+b6HsIf4Y4WgQhj2jWrBnjxo3j8uXLzJgx4w/fd/DgQYyMjFi2bFmOzOZt27b9rd//R8HjY8eOoVKpWLBgQY6a4j///PPf+n2fMjAwwNnZmfj4eN6+fYudnd0XadMffYduMOLDhw85BiaePn36u++vWrUqVatWZeTIkfzwww+MGjWKffv20bVr1/95GwVBnygUCtLT0/H09MTCwoLw8HBKlSrFqlWr+Omnnxg+fDizZ88mLCyMpk2bcubMGdRqNSVKlKBTp04oFAq9CcplZGRgYGBAZmYmHz58IF++fCgUCszNzWnXrh3jx49nx44d+Pr6MnDgQADi4+NZsGAB+fPnl2q6C8L/Qlf739nZGVNTU9q1a4eVlRU3btxgyJAh1K1bl8WLF3Po0CF69+5Nvnz5ciympQ/npCAIgiAIgvDtEr1VQcgjzMzMGD9+PAkJCTRp0uQP36dQKKSF33SePHnC0aNH/9bvNzExAfis3reuRrBuUT7de/5qcD4+Ph5DQ8McQW7IClpfunSJfPnyYWNj88XaZGJi8rt10IsUKQLA+fPnpdIFKSkp7Ny5M8f73r9/j6WlZY6ge7ly5QBylMARBOFzV69e5cWLF4waNYq6desik8kIDQ1l8+bNrFq1iunTpxMaGkq9evWoV69ejs/qMtPzqmfPnmFlZYWRkREGBgYkJSUREhLCgwcPcHR0xNXVlW7duuHu7s7Hjx+ZPn06z549Y+vWrXz48IG9e/eiVqtZvXq1VPNdHzLwhT/v04VxdffOBg0a8P79ey5evIidnR0hISHSQtZ169bl6dOnn822EgRBEARBEIR/u7z7FCkIeqhjx47/53tcXFxYsWIF/fv3p02bNrx584b169dTpEgR4uLi/vLvrlChAgAzZszAzc0NAwMDGjduTP369TEwMMDX1xd3d3eSk5PZsmUL+fPn59WrV3/699y6dYtRo0bx/fffU7NmTfLly8eLFy/YuXMnL1++ZMyYMdJD/ZdoU4UKFdiwYQPz58+naNGiUn34+vXrU7BgQUJDQ7l//z4KhYJt27ZhbW2dI7t8x44dbNiwAVdXV4oUKUJycjKbN2/G3Nychg0b/tXdLQh64fnz57x584YSJUogk8lQqVQYGhrSrVs34uLiOHbsGP7+/piYmHxWhiQvL+aZmJhI69atcXNzIzw8HIB+/fqRmZlJ/fr1OXfuHEuXLiU+Pp7AwEAGDBiAubk5+/btIyYmhtKlS+Ps7MzEiRNF/Wjhv9IdGykpKaxcuZIHDx5gZmZG69atqVWrFl26dKF79+7SuZmRkUFCQgK7du2iRIkSWFtb5/YmCIIgCIIgCMKfIp6MBEHP1KtXj6ioKJYsWUJ0dDSFCxdm1KhRJCQk/K1geeXKlfHz82Pjxo2cOnUKjUbD0aNHKVGiBLNnz2bmzJlMmTIFW1tbevTogY2NDWPGjPnTv6dWrVoMHz6cU6dOsWLFCt6+fYuZmRnlypVj1KhRORY2/RJtGjJkCE+fPmXp0qUkJydTu3Zt6tWrh4GBAXPnzmXChAnMmjULOzs7PD09sbS0JCQkRPp87dq1uXbtGvv27eP169dYWFhQuXJlpk2blqNGsCDou0+zVwHKly+PQqFg165dDBw4EENDQyko17t3bzZt2sTt27elrHN9YW5uzpAhQ5g9ezZmZmZ07twZW1tbAgICKFmyJCkpKUyePJkjR46QmZnJmDFj6NGjB23atEGlUmFhYSGV4RKBcuGPaDQaad0Ad3d3zM3NKVCgAElJSfTt25eZM2fi6uoKZC28/f79e44ePcqmTZvIyMhg0qRJyGSyzwayBEEQBEEQBOHfTKbNXodAEARBEAThK9MFbFNTU9myZQstWrTA3t6ed+/eMXHiRH777TeGDRtGmzZtgKwyELrFfJcvX07JkiVzeQu+PpVKxebNm4mKiqJy5cqYm5uzcOFCqTb0u3fviI2N5ezZszRq1IgxY8Z8FrgUQUzhj+iODZVKha+vLxqNhmnTpmFra8uAAQM4deoUMpmMadOm0bp1azQaDUuXLuXMmTPY2NgQExMjZi0IgiAIgiAI3yRRnFIQBEEQhFyjqy2elJSEh4cHmzdv5vDhw2i1WqysrOjVqxfW1tbMnj2bxYsXk5CQwOnTp1m1ahVFixalePHiub0JX5WuBrSuHE1YWBi3b98mISGB58+fo9VqpX3n7+/Pd999x6lTpwgNDQVyLlosAuVCdg8ePOD8+fMA0sDKlStXkMvl+Pv7Y2try/Dhw7l9+zbz58+nadOmGITbjwABAABJREFUjBo1iiNHjiCXy+nZsydBQUFMnz5dBMoFQRAEQRCEb5YIlguCIAiCkGsUCgVpaWn07NkTCwsLYmNj6dKlixTIrVGjBmPGjKFSpUrMnj2bpk2bEhISglwuZ9myZdLilPogMzMThUKBSqXixYsXyOVyunXrRlBQEE+ePGHFihWkpaVJgU4rKytGjhxJuXLlSElJ0Zv9JPx5t27donPnzkyfPp2zZ88CWQHzggUL0r17dypVqsT8+fO5evUqM2fOpEmTJnTu3BmtVsuIESPYsmUL5ubmlC9fXjonRaBcEARBEP6c4OBgqlWr9n++r3fv3vTu3fsrtEgQ9JPoxQqCIAiCkKt27dpFZmYmEydOxMnJCZlMxs2bN4mLi8PGxoaaNWsyefJkhg8fTlxcHAUKFKBy5crI5XK9yV7VZeAnJyfj5+eHnZ0dXbt2pXr16nTo0IHMzEwmTZqEQqHIseiplZUVERERmJqaSkFMuVzkSgg5HTlyhJSUFN6+fcuqVavQaDTUr1+fQoUKUaBAAWQyGT///DNNmjSRHuLr1q1L+fLl0Wg07NixI8cglzjGBEEQhG9BXFwc8+bN49q1a7x+/RorKytKlSpFkyZNRDD6D6hUKjZs2MCOHTt49OgRcrkce3t7qlevjpeXl1Qe8eLFi5w5c0Za1+uvWLduHSYmJnTq1OlLboIg/J/y/tOlIAiCIAj/Kp/WytZoNLx7946MjAzevn3L5s2bWbRoEcbGxnz48AEvLy+GDBlC0aJFKVq0qPQ5XQBZHygUClJSUujSpQv29vY0aNCAChUqAGBsbEy3bt0ApEUVR44cKQXMzc3NAUSgXPhD3t7enD59muTkZJ4+fcry5cuRy+XSgtYfPnwgKSmJ9+/fS5+5cuUKlpaWjB49mvLly4uyPoIgCMI35eLFi/Tp04eCBQvStWtX7OzsePbsGVeuXGH16tX/6mD5smXLcu13Dx8+nP/H3p3H1ZT/fwB/3ZbbIjcy2SoU0xVKMVNSkkjKOmaMsTcl4WsJ2WJ8M2NsX9tUluRi7AzGNmWfEabZCLPYC8k2E1pI3er8/vC7Z7pupVWl1/Px6ME9930/53POvfW5530+531iY2PRs2dPDBgwADk5OUhISMAPP/wABwcHMVkeHx+PiIgIfPDBB6VOlm/fvh1169ZlspzeuJpxhElERERVQm5uLrS1tcV/AaBRo0Zo0KABxowZA0NDQyQlJWHixInw8fGBQqHAN998g+HDh8PQ0FCtLdXrawJBEBAeHg4dHR2EhoaiWbNmAP5NgEulUgwYMAAAsGjRIqSnp2Pu3LmQSqViG0yUk0r+E1a5ubnQ09ND586dcefOHbRt2xbr169HZGQkJBIJOnToAJlMBgcHBxw8eBCLFy+GhYUF9u/fjzp16oiJcp6MISKi6mTNmjWoXbs2du/erZHMTUlJqaReFU/+73dv0qVLl/D9999j0qRJGD16tNpzubm5SEtLq5R+EZU3fqMlIiKiN0JVc/vZs2eYNm0a1q5dCwBwd3eHv78/PD090b17d2zZsgW+vr6oX78+bG1tYWZmVuPrbefm5uLKlSt49913xUQ5ALWa7Xp6ehgyZAjGjRuHO3fu1JhZ91RySqVS/L+2tja0tLTg7u6OY8eOoWHDhli0aBHu3buHyMhI/PjjjwCA2bNno1OnTjhw4AAiIiKgr6+P8PBwJsqJiKhaunPnDlq0aFHgrOd69eqpPZbL5fj8888RExMDHx8f2NnZYeDAgbh69SoAYMeOHfD09IStrS2GDRuGu3fvarQZExOD/v37w87ODk5OTggODsbDhw9f28/Lly+jQ4cOGDZsGJ49ewZAs2b5zz//DLlcjujoaKxevRpubm6wtbXFiBEjcPv2bY02t27diq5du8LOzg4fffQRfvvtt2LVQU9KSgIAtGvXTuM5bW1t1K1bFwAQHh6OxYsXAwC6du0KuVwOuVwu7pc9e/Zg+PDhcHZ2Rps2beDj44Nt27aptefh4YHr16/jl19+EV+v6l94eDjkcrlGH/bu3au2HgD4/fff4e/vDycnJ9jZ2cHDwwMzZ84scjuJeBRFREREFU51w7+MjAwMHDgQJiYmsLa2RmZmJgwMDNCrVy/06tVLrEGuVCqRlJSELVu2wNzcHA0bNqzsTahUeXl5yM7ORnZ2tsZzWlpaePbsGS5fvgx7e3v4+/tj1KhRTGJSgS5evIhZs2Zh7NixsLGxgaWlJQDAxsYGvr6++Oqrr7Bt2zbMnDkTCxcuxLp165CXlwdXV1csWbIEN2/ehJaWFpo2bVqj7htARERvFzMzM8THx+PatWuwtrZ+bfxvv/2GkydPYvDgwQCAtWvXYvTo0Rg5ciS2bduGwYMHIzU1FevWrUNISAg2bdokvnbv3r2YOXMmbG1tMXnyZKSkpGDTpk04f/489u3bV2iZkkuXLmHkyJFo06YNVq1aBX19/SL7GBUVBYlEAj8/P2RkZGDdunUIDg7GN998I8Zs27YNn3/+Od577z34+voiOTkZ//nPfyCTyV77fbtx48YAgIMHD6Jdu3aFjv+enp64desWDh06hJkzZ4pJdBMTEwAvy6u8++678PDwgI6ODr7//nvMnTsXgiBgyJAhAICQkBDxvjuqWezvvPNOkf17VUpKCvz9/VG3bl2MGjUKMpkMd+/exbFjx0rUDtU8/GZLREREFU5LSwvZ2dkYP348TExMsGDBAjRs2BA6OjpiwlwQBOjo6CAlJQUnT57EN998g5ycHCxfvrxGJX7zl8gQBAGCIEAqlaJJkyY4deoUrly5gpYtW6q95uTJk4iLi0OTJk1Qv3598bU1YX9R8aWkpOCzzz7DjRs3sHTpUrRu3RoWFhaYMGECdHV14eXlhSNHjuDs2bPo1q0bsrKysHz5cmzYsAFaWlro2LGjWIsU+PckGBERUXXj5+eHgIAA9OvXD3Z2dmjfvj2cnZ3h5OQEXV1djfjExETExMTA3NwcAGBsbIw5c+Zg9erVOHz4sNo9YiIjI3H37l2Ym5tDqVRiyZIlsLa2xtatW6GnpwcAaN++PQIDA7Fx40ZMmDBBY33nzp3DqFGj8N577yE8PLxYpVeysrKwb98+MVYmk+HLL78UTwhkZ2fjq6++gq2tLb7++mtxDJfL5ZgxY8Zrk+X29vZwdHTErl27cPLkSXTo0AHt2rVDly5dxEQ6ALRs2RKtWrXCoUOH0K1bN3GfqWzZskUt8T906FD4+/tjw4YNYrK8W7duWLFiBerWrYu+ffu+dtsLEh8fj9TUVCgUCtja2orLJ02aVKr2qObgERQRERG9EUlJSbhz5w5GjBgBc3Nz6Ojo4MyZM5g7dy6mTp2KAwcOQKlU4tSpU9i7dy8sLS2xa9cu6OjoICcnp0YkfnNzcyGRSCAIAgD1hPeUKVOgr6+P0NBQ/PXXX+Jrbt68iW3btiE9PR2mpqbict5wkV4llUrh4+MDuVwOpVIJJycnfP/99xg4cCBWr16NZs2awdHREV999RXy8vLQo0cPBAcH4/79+/jf//6HP//8U629mvA7SUREbycXFxfs2LEDHh4euHLlCtatWwd/f3+4ubnhxIkTGvHOzs5qSd+2bdsCALp37y4mygHAzs4OwL8lS/744w+kpKRg0KBBYqIceFmG0MrKCj/88IPGun766SeMHDkSzs7OxU6UA0D//v3VYt977z2Nvjx9+hQff/yx2snu3r17w9jY+LXtSyQSKBQKBAUFQSaT4dChQ/j888/RpUsXBAUFFbtmef5EeXp6Oh4/fgxHR0ckJSUhPT29WG0UR+3atQEAP/zwg1oJOqLX4VQQIiIiqjD5Z0nr6upCIpHg4sWLqFWrFo4fP46tW7fi/fffR3JyMs6dO4eWLVuK9RybN28OiUSC3NzcGjF7VXXT0+fPn2PhwoVITk7Go0ePMGbMGLz33nuoX78+/ve//2Hq1Knw9/dH27ZtIZVKcfXqVRgYGIgz8PPvc6L8ateujSFDhkBfXx/r16/HTz/9hIMHD0KhUODkyZPYsWMHvLy8kJSUhEOHDqFPnz7w9PREdnY2jh8/Dhsbm8reBCIionJjZ2eHiIgIZGdn48qVKzh+/Dg2btyIiRMnYt++fWjRooUY26hRI7XXqhLkr87GViVoVYnje/fuAYBY9iw/KysrnDt3Tm1ZVlYWAgMD0bp1a6xYsaJE34Hzz+4GIJZ3ebUvTZo0UYvT0dGBmZlZsdYhlUoxZswYjBkzBo8ePcKvv/6KTZs2ISYmBjo6OliyZMlr2zh37hzCw8Nx4cIFZGZmqj2Xnp4u7sOycnR0hJeXFyIiIrBx40Y4OjqiW7du6N27d6XdJJWqB04HISIionJz5coVnDhxQpzBkj9p26BBA3Tq1AnfffcdJk+ejB9//BFhYWHYvHkzDhw4gEePHuHixYsAgBYtWoilV7S1tStlW94k1XY+e/YMH3/8MW7cuAEHBwc4ODjgs88+w7Zt2/Dw4UO0b98eu3fvRteuXZGZmYmsrCx0794du3fvFmfgM1FORalduzY+/PBD+Pn54ezZs5g2bRpGjRqFHTt2YMiQIbh06RKkUimePHmCnJwcAEDPnj2xfPlytRvKEhERvS2kUins7OwwefJkhIaGQqlU4vDhw2oxhX0fLWy56irB0vSlc+fOuHTpEk6fPl2i1xZ2xVdp+/I69evXR8+ePbFlyxY0a9YMhw8fFr87FObOnTvw9fXFkydPMGPGDKxduxYbNmyAr68vABTre0Zh33Vzc3M14sLCwrBz504MHToUDx8+REhICPr37y/eLJWoIG//NC0iIiJ6Ix48eIB+/foBAGxtbdGnTx/06tVLvKmPnp4epk6dCm9vb2hra6NBgwZiLcfbt2+jadOmamVEgJpT5kFV033q1KkwNTXFsmXLULduXQQHByMrKwtr1qxBVlYWhg4dCjMzM8ybNw/Z2dnQ0dER91FNmYFPZVe7dm189NFHAICIiAiMGTMGq1evxujRo+Hl5YWUlBRYW1tDR0dH40qFmvI7SURENVObNm0AAI8ePSqX9lSzvRMTE+Hs7Kz2XGJiosZscIlEgiVLlmDs2LGYOHEioqKi4OTkVK59uXPnDjp06CAuz8nJQXJyMuRyeana1dXVhVwux61bt/DkyROYmpoWmtA+efIksrOzsXr1arVt//nnnzViC2sj/4z5/DdHVc2cf5W9vT3s7e0xadIkHDx4EMHBwYiOjsaAAQOKvY1Us/DbLhEREZULU1NT2NvbAwAMDQ3xv//9D59++inCwsKQlZWFvLw8GBoawtHREe3bt4e5uTmysrJw+fJlzJ8/HzKZDG5ubpW7EZUoISEBaWlpGD9+POrWrYuJEyfil19+wbFjx/Dpp5/i66+/xs6dO8VZ+1KpVC1xWRNm4FP5MTIywkcffYRx48bh/PnzCAwMBPDyMnF7e3vIZDLk5eXxSgUiInor/fTTTwXOuD516hSAlyVSykObNm1Qr1497NixA9nZ2WrruXnzJtzd3TVeI5VKERERAVtbW4wePRqXLl0qt77UqVMHu3btUpsBfvDgQaSmpr729bdu3SowIZ2Wlob4+HgYGxvDxMQEAGBgYAAAGjXIVd9X8+/79PR07NmzR6NdAwODAuugq8rI/Prrr+Ky58+fY9++fWpxqampGu+xqqRc/veC6FWcfkRERERlpqq3PWrUKEybNk28lHX+/PlYv3499u3bhwEDBqBr166wtrYGAGRkZOB///sfLl++DC0tLWzevBna2tpiW2+r+/fv48aNG8jLy4OlpaX4hb9ly5YYOXIk2rRpg02bNiE+Ph7Lli1Do0aN4O/vj5MnT2LXrl1ITU3FxIkTxYMRotJSJcwBYPXq1eIMcx0dnbf+95CIiGq2efPmITMzE56enrCysoJSqcT58+cRExMDMzMz9O/fv1zWo6uri+DgYMycORNDhw5Fz549kZKSgk2bNsHMzEwsP/IqfX19REZGYvjw4QgICMDmzZvF79ClJZVKMX78eHzxxRcYMWIEvL29kZycjL1792rUMS/IlStXEBwcjE6dOuG9996DsbExHj58iH379uHRo0cICQkRvzu0bt0aALB8+XL4+PhAV1cXXbp0gYuLC3R1dTF69Gh88sknePbsGb755hvUq1cPf//9t9r6Wrduje3bt2PVqlVo2rQpTExM4OzsDBcXFzRu3BizZs1CQkICtLW1sWfPHtStW1ctmf/tt99i+/bt6NatG5o0aYJnz55h165dMDIyqtETdOj1mCwnIiKiMlN9MX733XfRqFEjXLhwAVOmTMHatWtx/vx5HDp0CCtXrsSGDRswcuRIdOjQAXZ2duJs8jFjxkBbWxs5OTlvdSmR8+fPIyQkBJmZmUhLS4OlpSVmzZqF9u3bA4A4u+iPP/5A69atxeX6+vp45513YGRkhMuXL4ulbYjKSpUwl0gkWLNmDQYNGoTt27czUU5ERG+1adOm4fDhwzh16hR27twJpVKJxo0bY/DgwRgzZoxaeY+y6t+/P/T19REVFYUlS5bA0NAQ3bp1w9SpU4tcj5GRERQKBYYOHQo/Pz9s3boVTZs2LVNfhg4dCkEQsGHDBixatAgtW7bE6tWrMW/ePOjp6RX52vfffx8TJkzA6dOnsWHDBjx58gS1atWCjY0NgoOD4eXlJcba2dlh4sSJ2LFjB06fPo28vDycOHECVlZWCAsLw4oVK7Bo0SK88847GDRoEExMTBASEqK2vv/85z+4d+8e1q1bh2fPnsHR0RHOzs7Q1dVFREQE5s6di6+++gqmpqYYMWIEZDIZZs6cKb7e0dERv//+O6Kjo/HPP/+gdu3asLOzw5IlS2BhYVGm/UhvN4lQUZX+iYiIqEbau3cvQkJCEBERgW7dugEAlEolXFxcIJPJ8ODBA9SrVw/vvfceZs2aJc6Qfttnsv7222/49NNP8eGHH+KDDz5AcnIytm7dCiMjIyxduhSGhoZiWZWJEyfir7/+wrFjxwC8nMmzZMkSrFixArVq1YJEItGoJU1UFhkZGdi8eTPOnTuHtWvXsjY5ERFRDZGXlwdnZ2d4enpi3rx5ld0dokr39k7dIiIiokrRqVMntGnTBqtXr4arqyuysrIwePBgmJmZISwsDP/88w/27NmDpKQkGBsbi697mxPl8fHx8PX1xaeffopRo0ahdu3aaNu2La5cuYJjx47BwMAASqVSnNHTq1cvnD9/HgMHDkSHDh3www8/wMDAAIaGhkyUU4UwMjKCr68vRo8eDYlEgry8PCbMiYiI3jJZWVmQSqVq3yP37duHp0+fwtHRsRJ7RlR1MFlORERE5crU1BTu7u5QKBQ4duwYVq1aBQMDA/zvf/+Dubk5LCws0LJlS/HGP297Ui4lJQVz5syBgYEBgoKCoK2tjczMTBgYGMDCwgK5ubnw8/NDeno6evXqhQ8//BDu7u5ISUnBvn37cOzYMVhZWWH58uXQ0tJ66/cXVR7V76QgCPyMERERvYUuXLiABQsWoEePHqhTpw7++usv7N69G9bW1ujRo0dld4+oSmAZFiIiIio3qhnP2dnZ+PDDD3H9+nU4ODhg8eLFMDc315gVXRNmSKenp2Pbtm3YvHkz2rRpgzVr1gAALl26hKFDh6JDhw5o2LAh0tLScPjwYQwYMACff/458vLyoFQqkZqaivr160Mikbz1Nd2JiIiIqOLcvXsX8+bNw++//47U1FQYGxvDzc0NwcHBqFevXmV3j6hK4NEWERERlRtVMlxbWxs+Pj5Yv349evbsqXYTnfzJ8bc9UQ4AtWvXxpAhQ6Cnp4fVq1cjKCgIY8aMwYgRI9CnTx9MmzYNMpkMqampMDMzg0KhgKenJ9zc3KCtrQ19fX0AL2fgM1FORERERKVlbm4uTtwgooLx+koiIiIqVxKJBNra2ujduze0tLRw6dIlAC9nkddURkZG+OijjzBmzBj8/PPP6Nu3L/r27Yt58+aJyXBjY2O4uroCAJKTkzXaYFkMIiIiIiKiisWjLiIiIqoQ5ubmCAwMxIEDB3DhwoUaMYu8KKqEeWBgIBo2bIh79+4BAKRSKZRKJQAgOzsb5ubmaNKkSWV2lYiIiIiIqEZispyIiIgqjKenJ3r06AFbW9vK7kqVYGRkhA8//BC+vr64ePEiRo8eDQDQ1dXFnTt3sGrVKjRt2hTOzs6V3FMiIiIiIqKahzf4JCIiojciNzcX2trald2NKiEjIwO7d+/G6tWr0b59e4SGhmLy5Ml48uQJ9u3bB11dXe4vIiIiIiKiN4zJciIiIqJKkJGRgT179iAqKgr//PMPmjdvLibKc3JyeDNPIiIiIiKiN4xHYURERESVQFWSJSMjAzdv3sTixYuho6PDRDkREREREVEl4cxyIiIiokr07NkzGBoaQiKRMFFORERERERUiZgsJyIiIqoCBEGARCKp7G4QERERERHVWFqV3QEiIiIiAhPlRERERERElYzJciIiIiIiIiIiIiKq8ZgsJyIiIiIiIiIiIqIaj8lyIiIiIiIiIiIiIqrxmCwnIiIiIiIiIiIiohqPyXIiIiIiIiIiIiIiqvGYLCciIiIiIiIiIiKiGo/JciIiIiKiSnT37l3I5XLs3bu3srtSqJ9//hlyuRw///xzZXelStm7dy/kcjnu3r1b2V0hIiIionKgU9kdICIiIiKqzvbu3YuZM2cW+vzOnTthb2//5jpUBlu3boWBgQH69+9f2V0RDRs2DE+ePMGhQ4cquytERERE9JZjspyIiIiIqBxMmDAB5ubmGsubNGlSCb0pne3bt6Nu3boayfL3338fly5dgq6ubiX1jIiIiIio4jFZTkRERERUDtzc3GBra1vZ3agQWlpa0NPTq+xuEBERERFVKNYsJyIiIiJ6Q9LS0jBjxgy0b98e7733HqZPn4709HSNuGHDhmHYsGEay2fMmAEPDw+1ZXl5efj666/Ru3dv2NraokOHDvD398fvv/8uxuzZswfDhw+Hs7Mz2rRpAx8fH2zbtk2tHQ8PD1y/fh2//PIL5HI55HK52IfCapbHxMSgf//+sLOzg5OTE4KDg/Hw4UONPjs4OODhw4cYO3YsHBwc0KFDByxatAi5ubkl24FFOHXqFAYPHgx7e3s4ODhg1KhRuH79uvi8QqGAXC5HcnKyxmuXLl2KNm3aIDU1VVx28eJF+Pv7o3379mjbti2GDh2Kc+fOlVt/iYiIiKjqYbKciIiIiKgcZGRk4PHjx2o/T548EZ8XBAFjx47F/v370adPHwQFBeHBgweYPn16mdY7a9YszJ8/Hw0bNkRwcDBGjRoFPT09XLx4UYzZvn07zMzMEBgYiBkzZqBRo0aYO3cutm7dKsaEhISgYcOGsLKywuLFi7F48WKMHj260PXu3bsXQUFB0NLSwuTJk/Hxxx/j2LFjGDRoENLS0tRic3Nz4e/vjzp16mDatGlwdHTE+vXrsXPnzjJtu8q+ffsQGBgIQ0NDBAcHY+zYsbhx4wYGDx4s3nzT29sbEokEMTExGq+PiYmBi4sLjI2NAQBxcXEYMmQInj17hnHjxmHSpElIS0vDiBEjcOnSpXLpMxERERFVPSzDQkRERERUDnx9fTWWSaVScYb3iRMn8Ouvv2Lq1KkYOXIkAGDQoEEYPnx4qdf5008/Ye/evRg2bBhmz54tLvfz84MgCOLjLVu2QF9fX3w8dOhQ+Pv7Y8OGDRgyZAgAoFu3blixYgXq1q2Lvn37FrlepVKJJUuWwNraGlu3bhVLtLRv3x6BgYHYuHEjJkyYIMZnZWXB29sb//nPf8Tt/uCDD7B7924MHjy41NsPAM+ePcOXX36JAQMG4IsvvhCXf/DBB+jRowciIyPxxRdfoHHjxrC3t0d0dLS4/wHg0qVLSEpKwrhx4wC8PKkRGhoKJycnrFu3DhKJBADwySefoGfPnlixYgXWr19fpj4TERERUdXEZDkRERERUTmYM2cOLC0t1ZZpaf17IWdsbCx0dHQwaNAgcZm2tjaGDh2K3377rVTrPHr0KCQSiZjozU+V5AWglihPT0+HUqmEo6Mjzpw5g/T0dNSuXbtE6/3jjz+QkpKCcePGqdUyd3d3h5WVFX744Qe1ZDkAte0GXibWDxw4UKL1FuTHH39EWloaevbsicePH4vLtbS00LZtW7XSMd7e3pg/fz7u3Lkj3ng1JiYGUqkU3bp1AwBcvnwZt27dwpgxY9SuDAAAZ2dn7N+/H3l5eWrvLRERERG9HZgsJyIiIiIqB3Z2dkXe4DM5ORmmpqaoVauW2vJXE+wlcefOHdSvXx916tQpMu7cuXMIDw/HhQsXkJmZqfZcaZLl9+7dA1Bw362srDRqe+vp6cHExERtmbGxsVqN8NK6desWAGDEiBEFPm9kZCT+v0ePHli4cCGio6MxevRoCIKAw4cPw83NTYxTtVdUeZz09HSxZAsRERERvT2YLCciIiIiqiZKc0PMO3fuwNfXF1ZWVmK9cl1dXZw6dQobN25EXl5eBfRUnba2doW1rSo3s3jxYpiamha57gYNGuC9995DTEwMRo8ejQsXLuDevXsIDg7WaG/atGmwsbEpcJ2GhobluQlEREREVEUwWU5ERERE9AaYmZnhp59+wrNnz9RmlycmJmrEGhsbIykpSWO5aka3SpMmTXDmzBk8ffq00NnlJ0+eRHZ2NlavXo3GjRuLy/OXJ1HJX7qlKKp2EhMT4ezsrPZcYmKi2noqmoWFBQCgXr166Nix42vjvb29MXfuXCQkJCA6OhoGBgbo0qWLRntGRkbFao+IiIiI3h4stEdERERE9Aa4ubkhJycH27dvF5fl5uZiy5YtGrEWFhZISEhQq8F95coVnD9/Xi2ue/fuEAQBERERGm2oZkirZlbnv+Fneno69uzZo/EaAwMDpKWlvXZb2rRpg3r16mHHjh3Izs4Wl586dQo3b96Eu7v7a9soL506dYKRkREiIyOhVCo1ns+/DwHAy8sL2tra+O6773D48GG4u7urzRRv06YNmjRpgvXr1+PZs2evbY+IiIiI3h6cWU5EREREVA5iY2ORkJCgsbxdu3awsLCAh4cH2rVrh6VLlyI5ORktWrTA0aNHkZ6ervGajz76CBs3boS/vz8++ugjpKSkYMeOHWjRooVaArdDhw7o27cvNm/ejNu3b6NTp07Iy8vDuXPn4OTkhKFDh8LFxQW6uroYPXo0PvnkEzx79gzffPMN6tWrh7///lttva1bt8b27duxatUqNG3aFCYmJhozxwFAV1cXwcHBmDlzJoYOHYqePXsiJSUFmzZtgpmZGXx9fcu+Q/N5/PgxVq1apbHc3Nwcffr0QWhoKKZNm4b+/fvDx8cHJiYmuHfvHk6dOoV27dphzpw54mvq1asHJycnbNiwAc+ePYOPj49am1paWpg3bx4CAgLQq1cv9O/fHw0aNMDDhw/x888/w8jICGvWrCnX7SMiIiKiqoHJciIiIiKichAWFlbg8gULFsDCwgJaWlpYvXo15s+fjwMHDkAikcDDwwMzZsxAv3791F7TvHlzLFq0CGFhYViwYAFatGiBxYsX49ChQ/jll1802pfL5di9ezcWL16M2rVro02bNnBwcADw8oabYWFhWLFiBRYtWoR33nkHgwYNgomJCUJCQtTa+s9//oN79+5h3bp1ePbsGRwdHQtMlgNA//79oa+vj6ioKCxZsgSGhobo1q0bpk6dCplMVsq9WLCUlBR89dVXGsudnZ3Rp08f9O7dG/Xr18fatWuhUCiQnZ0t1ifv37+/xut8fHzw448/olatWujcubPG805OTti5cydWrVqFLVu24Pnz5zA1NYWdnR0GDhxYrttGRERERFWHRMh/PSYRERERERERERERUQ3EmuVEREREREREREREVOMxWU5ERERERERERERENR6T5URERERERERERERU4zFZTkREREREREREREQ1HpPlRERERERERERERFTjMVlORERERERERERERDUek+VEREREREREREREVOMxWU5ERERERERERERENR6T5URERERERERERERU4zFZTkREREREREREREQ1HpPlRERERERERERERFTjMVlORERERERERERERDUek+VEREREREREREREVOMxWU5ERERERERERERENR6T5URERERERERERERU4zFZTkREREREREREREQ1HpPlRERERERERERERFTjMVlORERERERERERERDUek+VEREREREREREREVOMxWU5ERERERERERERENR6T5URERERERERERERU4zFZTm9MeHg45HL5G1nXsGHDMGzYMPHxzz//DLlcjsOHD7+R9c+YMQMeHh5vZF2l9ezZM8yaNQsuLi6Qy+X48ssvy9ymh4cHAgMDy6F3xVfR+/rVz1Jlu3v3LuRyOfbu3Vvi11aHz+WbIJfLER4eXtndIHqrcIyvWipijH+TqtrYS0RUXXF8rloqanyu6Pd52LBh6NWrV7m2yWOyyvEm/yZUZzqV3QGqnvbu3YuZM2eKj6VSKYyNjSGXy9G5c2f0798fRkZGZV7Pw4cPsWvXLnTr1g02NjZlbq88VeW+FUdkZCS+/fZbjB07FhYWFmjevHmhsR4eHkhOThYfm5iYwNLSEp9++ik8PT3fRHerjdzcXHTu3Bl///031q5di86dO5eqnYMHDyIlJQW+vr7l28F8MjMzsW7dOjg6OsLJyanc2lX9fdi9ezdsbW3LrV0iejM4xlftvhVHWcb4/FxdXaFQKCqqm5RPaT5zV69excqVK/H777/jn3/+QZ06ddCiRQt4eHioJazWrFmDFi1aoFu3bqXq240bNxATE4MPPvgA5ubmpWqDiMqO43PV7ltxlGR8Vimv48vqLCMjAxs3bsTRo0eRlJSE3NxcNGnSBJ07d8bw4cPRoEGDyu5ilVJRx/k1CZPlVCYTJkyAubk5cnJy8M8//+CXX37B/PnzsXHjRqxatQotW7YUY8eMGYNRo0aVqP1Hjx4hIiICZmZmJRoM38SBXVF9++KLLyAIQoX3oSx++ukntG3bFuPGjStWvI2NDT799FMAL7d9586dGDduHEJDQzFo0KCK7GqRqtq+/umnn/D333/DzMwMBw8eLPWXmUOHDuH69esayXIzMzNcunQJOjol//P96r7KzMxEREQExo0bx0GUiDRwjK+ZY3x+9evXL++uUSFK+vtw/vx5DB8+HI0bN8aAAQNgamqK+/fv4+LFi9i0aZNasjwyMhJeXl5lSpZHRETA0dGRyXKiKoDjc80Zn1WvKY/jy+oqKSkJvr6+uH//Pnr06IGBAwdCV1cXV69exe7du3H8+HEcOXKksrtZpRR1nF+avwk1EZPlVCZubm5qM0cDAwMRFxeH0aNHY+zYsYiOjoa+vj4AQEdHp1QJvpLIzMyEgYEBpFJpha7ndXR1dSt1/cWRkpKCFi1aFDu+QYMG6Nu3r/i4X79+6N69OzZu3FipyfKqtq8PHDiA1q1bo1+/fli+fDmeP38OQ0PDcmtfIpFAT0+vVK+tavuKiKo2jvEFqw5/S8s6xlPVt2bNGtSuXRu7d++GTCZTey4lJaWSekVEbwLH54K9jeMzUPHHl1VZTk4Oxo0bh5SUFGzatAnvvfee2vOTJk1CVFRUJfWuenoTfxPeBqxZTuXO2dkZY8eORXJyMg4cOCAuL6g20tmzZzFo0CC89957cHBwgJeXF5YtWwbgZY2zjz76CAAwc+ZMyOVytVrNqrpZf/zxB4YMGYK2bduKry2s1mVeXh6WLVsGFxcX2NvbY/To0bh//75ajIeHB2bMmKHx2vxtvq5vBdVLe/78ORYuXIjOnTujTZs28PLygkKh0Dj7LZfL8fnnn+P48ePo1asX2rRpg549eyI2Nrao3S5KSUlBSEgIOnbsCFtbW/Tp0wfffvut+Lyqdtzdu3fxww8/iH2/e/dusdpXMTU1hZWVVYGXbv/222/46KOPYGtri65du2Lfvn3ic0lJSZDL5di4caPG686fPw+5XI5Dhw4BeHm51ZdffgkPDw+0adMGzs7O+PTTT/Hnn3+KryloX+fl5eHrr79G7969YWtriw4dOsDf3x+///67GLNnzx4MHz4czs7OaNOmDXx8fLBt27YS7YNXvXjxAseOHYOPjw+8vb3x4sULnDhxosDYU6dOYejQoXBwcEC7du3w4Ycf4uDBgwBeftZ++OEHJCcni++PahtfrVmuUCggl8sLfB+WLl2KNm3aIDU1VWNf3b17F87OzgCAiIgIcT3h4eHYs2cP5HI5/vrrL40216xZAxsbGzx8+LBM+wp4eRnlzJkz0bFjR/Fzvnv3bvH5f/75B61atUJERITGaxMSEiCXy7FlyxZxWVpaGr788kvxd8zT0xNr165FXl5emftKRC9xjK8ZY3xBZsyYAQcHB9y7dw+BgYFwcHBAp06dsHXrVgAvS4IMHz4c9vb26NKlizimqezduxdyuRy//vor5syZAycnJ7Rr1w7Tpk0Tx6mybLsgCPDw8MCYMWM0XpuVlYX27dtjzpw5AP7dT9HR0YiIiECnTp3g4OCACRMmID09HdnZ2fjyyy/h7OwMBwcHzJw5E9nZ2Rrt7t+/H/3794ednR0cHR0xadIkjc+c6rN848YNDBs2DG3btkWnTp3UDu5f95kryJ07d9CiRQuNRDkA1KtXT/y/XC7H8+fP8e2334rtqn4HkpOTERoaCi8vL9jZ2cHJyQkTJkxQ+7zs3bsXEydOBAAMHz5cbOPnn38W2y+o5uyrv2tKpRIRERHo3r07bG1t4eTkhEGDBuHs2bOFbiMRFR/H57dzfC7J8WVB9u/fj48++ght27bF+++/jyFDhuDMmTNqMVu3bkXPnj3Rpk0buLq6Yu7cuUhLSyuwvaLGsuLui5I4evQorly5gtGjR2skygHAyMgIkyZNUlsWExMjjs1OTk4IDg7WOHZVfad5+PAhxo4dCwcHB3To0AGLFi1Cbm6uWux3332H/v37i8ftvXv3xtdffy0+X1gNcNX3nvzvseoebz///LPYx969e4tj6tGjR8UcRv/+/TWOx1X9TkpKgr+/P+zt7eHq6oqIiAjxM13UcX5h/c3JycHKlSvRrVs3tGnTBh4eHli2bJnGdx9V/4vK97wteDqBKkTfvn2xbNkynDlzBh9//HGBMdevX0dgYCDkcjkmTJgAqVSK27dv4/z58wCA5s2bY8KECQgLC8PAgQPRvn17AEC7du3ENp4+fYqAgAD07NkTffr0UTs4KMjq1ashkUgQEBCAlJQUfP311/D19cX+/fvFs+/FUZy+5ScIAsaMGSMO8DY2Njh9+jQWL16Mhw8fIiQkRC3+3LlzOHr0KAYPHoxatWph8+bNmDBhAr7//nvUrVu30H69ePECw4YNw507dzBkyBCYm5vj8OHDmDFjBtLS0jBixAg0b94cixcvxoIFC9CwYUPxsmsTE5Nibz/w8qDnwYMHqFOnjtry27dvY+LEifjoo4/wwQcfYM+ePZgxYwZat26Nd999FxYWFmjXrh0OHDigUWLk4MGDqFWrFrp27QoA+O9//4sjR45g6NChaN68OZ4+fYpz587h5s2baN26daF9mzVrFvbu3Qs3Nzd89NFHyM3NxW+//YaLFy+KszC2b9+Od999Fx4eHtDR0cH333+PuXPnQhAEDBkypET7QuXkyZN4/vw5evbsCVNTUzg6OuLgwYPo3bu3WtzevXsREhKCd999F4GBgahduzYuX76M06dPo3fv3hg9ejTS09Px4MEDsS5hrVq1Clynt7c3/ve//yEmJgYjR45Uey4mJgYuLi4wNjbWeJ2JiQlCQ0MRGhoKT09Psfa8XC6Hubk5Pv/8cxw8eBCtWrVSe93Bgwfh6OhY5rpw//zzDz7++GNIJBIMGTIEJiYmiI2NxaxZs5CRkQFfX1+88847eP/99xETE6NxqWJ0dDS0tbXRo0cPAC9ntAwdOhQPHz7EJ598gkaNGiE+Ph7Lli3D33//jVmzZpWpv0T0L47x6t6GMT4nJwePHz/WWG5oaKi273JzcxEQEID33nsPwcHBOHjwID7//HMYGBhg+fLl6N27N7p3744dO3Zg+vTpsLe3h4WFhVqbn3/+OWQyGcaNG4fExERs374d9+7dw+bNmyGRSEq97RKJBL1794ZCocDTp0/Vvp+cPHkSGRkZ6NOnj1q7a9euhb6+PkaNGoXbt29jy5Yt0NHRgUQiQVpaGsaNG4eLFy9i7969MDMzUxuLVq9eja+++gre3t746KOP8PjxY2zZsgVDhgzBvn371JLYqampGDlyJDw9PeHt7Y0jR45gyZIlsLa2RufOnUv8mQNelmWLj4/HtWvXYG1tXWjc4sWLMXv2bNjZ2Ym/r02aNAEA/P7774iPj0fPnj3RsGFDJCcnY/v27Rg+fDi+++47GBgY4P3338ewYcOwefNmjB49GlZWVgBQrDq7+UVERCAyMhIDBgyAnZ0dMjIy8Mcff+DPP/+Ei4tLidoiooJxfFb3NozPxT2+LEhERATCw8PFk8G6urq4ePEifvrpJ7i6ugJ4mTiNiIhAx44dMWjQIHFc/v3337F9+3a12fqvG8uKuy9KQnVioLhXv6nq+tva2mLy5MnijPTz589rjM25ubnw9/eHnZ0dpk2bhri4OKxfvx4WFhYYPHgwgJcnliZPngxnZ2cEBwcDeDlp6/z58yXeFpXbt29jypQp+OSTT9CnTx+sX78eo0ePxty5c7F8+XLxqv21a9ciKCgIhw8fhpbWv/Occ3NzMXLkSLRt2xZTp07F6dOnER4ejtzcXEycOLHI4/zCzJ49G99++y28vLzw6aef4tKlS4iMjMTNmzexcuVKjf4Xle95awhEpbBnzx7B2tpauHTpUqEx7du3F/r16yc+DgsLE6ytrcXHGzZsEKytrYWUlJRC27h06ZJgbW0t7NmzR+O5oUOHCtbW1sL27dsLfG7o0KHi459++kmwtrYWOnXqJKSnp4vLo6OjBWtra+Hrr78Wl3Xp0kWYPn36a9ssqm/Tp08XunTpIj4+duyYYG1tLaxatUotbvz48YJcLhdu374tLrO2thZat26ttuzy5cuCtbW1sHnzZo115bdx40bB2tpa2L9/v7gsOztbGDhwoGBvb6+27V26dBFGjRpVZHv5Y/38/ISUlBQhJSVFuHz5sjBp0iTB2tpa+OKLL9TirK2thV9//VVclpKSIrRp00ZYuHChuGzHjh2CtbW1cOPGDbV+Ojk5qe379u3bC3Pnzi2yb6/u67i4OI1+qeTl5Yn/z8zM1Hjez89P6Nq1q9qyV9/3ogQGBgqffPKJ+Hjnzp1Cq1at1D7jaWlpgoODgzBgwADhxYsXhfZv1KhRatulkpSUpPG5GzhwoPDBBx+oxV28eFGwtrYWvv32W3HZq/sqJSVFsLa2FsLCwjTWM3nyZMHV1VXIzc0Vl/3555+FfubzK87fh5CQEMHFxUV4/Pix2vJJkyYJ7du3F98f1Wfl6tWranE+Pj7C8OHDxccrV64U7O3thcTERLW4JUuWCDY2NsK9e/fEZYVtMxG9xDG+5o3x1tbWBf5ERkaqbbe1tbWwZs0acVlqaqpgZ2cnyOVy4bvvvhOX37x5U+Nvrepz9cEHHwjZ2dni8qioKMHa2lo4fvy4uOzV96O4256QkCBYW1sL27ZtU9vG0aNHC126dBHHWdVnplevXmp9mTx5siCXy4WRI0eqvX7gwIFq7/ndu3cFGxsbYfXq1WpxV69eFVq1aqW2XPVZzj8eZ2VlCS4uLsL48ePFZUV95gpy5swZwcbGRrCxsREGDhwoLF68WDh9+rTa9qjY29sX+Lkv6LtQfHy8Rn9jYmIEa2tr4aefftKIL2xMffV3rU+fPsX+TBJRwTg+16zxWRCKd3wpCJrv861bt4SWLVsK//nPf9SO5wTh32POlJQUoXXr1oKfn59azJYtWwRra2th9+7d4rLijmUl2RfFOSbr16+f0L59+yJj8q/H2dlZ6NWrl9px9vfffy9YW1sLX331lbhM9Z0mIiJCY335j6vnzZsntGvXTsjJySl0va/uexXV72tSUpK4TPWd6/z58+Ky06dPC9bW1oKdnZ2QnJwsLlcdB+cfe1X9zp/ryMvLE0aNGiW0bt1a/FwUdZz/an9Vn/NZs2apxS1cuFCwtrYW4uLiNPr/unzP24BlWKjCGBoa4tmzZ4U+rzqrd+LEiVKXSZBKpejfv3+x4/v166d2h/AePXrA1NQUp06dKtX6iys2Nhba2toal6X5+flBEASNy7s6duwozvoBgJYtW8LIyAhJSUmvXY+pqSl69eolLtPV1cWwYcPw/Plz/Prrr6XehjNnzsDZ2RnOzs7o27cvDh8+jL59+4pnWFVatGihdomUiYkJLC0t1fru7e0NPT09tUu0z5w5gydPnqjN+pLJZLh48WKJSn4cPXoUEomkwJum5J+xln8WQ3p6Oh4/fgxHR0ckJSUhPT292OtTefLkCc6cOaO277t37w6JRIKYmBhx2dmzZ/Hs2TOMGjVKo/Z4YTPqXsfb2xt//vkn7ty5Iy6LiYmBVCot9c28+vbti0ePHomXhAEvZ5Xr6+uje/fupWpTRRAEHD16FB4eHhAEAY8fPxZ/XF1dkZ6eLpba8fT0hI6ODqKjo8XXX7t2DTdu3ICPj4+47PDhw2jfvj1kMplaex07dkRubm6ZPvtEpIlj/L/ehjG+bdu22LBhg8ZPz549NWIHDBgg/l8mk8HS0hIGBgbw9vYWl1tZWUEmkxW4Taobc6kMGjQIOjo6Rb5Pxd12S0tLtG3bVu37xdOnT8Urt14dZ/v27avWFzs7OwiCgA8//FAtzs7ODvfv30dOTg4A4NixY8jLy4O3t7famPPOO++gadOmamMn8PL3Jf+sOKlUCltb29e+50VxcXHBjh074OHhgStXrmDdunXw9/eHm5tbsS/Rz/9dSKlU4smTJ2jSpAlkMlmBpdjKQiaT4fr167h161a5tktE6jg+/6u6j8/FPb4syPHjx5GXl4f//Oc/arOSgX+POX/88UcolUoMHz5cLWbAgAEwMjLSeH+KM5aV977IyMgo9ArrV/3xxx9ISUnBoEGD1I6z3d3dYWVlhR9++EHjNa/ee619+/ZqZVNkMhkyMzPLtWRYixYt4ODgID5u27YtAKBDhw5o3LixxvKCPn/5r4RXXamtVCoRFxdX4v6o3udXb/Tu5+en9nz+/r8u3/M2YBkWqjDPnz8v8pIsHx8ffPPNN5g9ezaWLl0KZ2dneHp6okePHhp/0AvToEGDEt1IpGnTpmqPJRIJmjZtWmC95/KUnJyM+vXrq31JAP69hPXV9Tdq1EijDWNj40Jrh+VfT9OmTTX2n2o99+7dK3HfVdq2bYugoCBIJBLo6+ujefPmBdbJLKzv+euRymQydOnSBYcOHUJQUBCAl4nYBg0aoEOHDmJccHAwZsyYAXd3d7Ru3RqdO3dGv379NC7pzu/OnTuoX7++RnmYV507dw7h4eG4cOECMjMz1Z5LT09H7dq1i3z9q6Kjo6FUKmFjY4Pbt2+Ly+3s7HDw4EFxQFMltMvzEqUePXpg4cKFiI6OxujRoyEIAg4fPgw3NzeNz1xxubi4wNTUFAcOHICzszPy8vJw6NAhdO3atdRtqjx+/BhpaWnYuXMndu7cWWgM8HLw7dChA2JiYsTPSnR0NHR0dMRLyoCXl4NdvXpVrM9WWHtEVD44xv/rbRjj69ati44dO742Tk9PT+OS8dq1a6Nhw4YaiejatWsXuE2vvk+1atWCqalpke9TSba9b9+++OKLL5CcnAwzMzMcPnwYSqWywEu48x+UqvoMaL5HtWvXRl5eHtLT01G3bl3cunULgiAUevL41RtnFbR/jI2NcfXq1UK3uTjs7OwQERGB7OxsXLlyBcePH8fGjRsxceJE7Nu377U3kXvx4gUiIyOxd+9ePHz4UK2Gb2kmDhRlwoQJGDt2LLy8vGBtbQ1XV1f07dsXLVu2LNf1ENV0HJ//Vd3H5+IeXxbkzp070NLSKrJklqpfqvJaKlKpFBYWFhr7pzhjWXnvi+KcrFBRtW1paanxnJWVFc6dO6e2rKDvNK/mLQYPHoyYmBgEBASgQYMGcHFxgbe3N9zc3Eq0HfkV9B0DeLl/81N9bl/9/GlpaWnkQ1TbXJrfqeTkZGhpaamdKAJe3qNOJpMV+/ekOPefqU6YLKcK8eDBA6Snp2v8wuWnr6+PrVu34ueff8YPP/yA06dPIzo6Gjt37sT69euhra392vWUpMZZWeXm5harT+WhsPUIr9yI5E0q7oF0cfdRv379cPjwYZw/fx7W1tY4efIkBg0apDaw+vj44L333sOxY8dw9uxZKBQKREVFITw8XKyLVhp37tyBr68vrKysMGPGDDRq1Ai6uro4deoUNm7cWKpZFqpZbK+enVZJSkoqMslfFg0aNMB7772HmJgYjB49GhcuXMC9e/c0Zv2XhLa2Nnr37o1du3YhNDQU58+fx6NHjzTqvZaGav/26dMHH3zwQYEx+Wuq9ezZEzNnzsTly5dhY2ODmJgYdOjQQe3LTV5eHlxcXDTqtqs0a9aszP0mopc4xpdNVRzji6uwvlelberZsycWLFiAgwcPYvTo0Thw4ADatGmjkQwAUGhiqLDlqu3Jy8uDRCJBVFRUgdtuaGio9riiP1tSqRR2dnaws7NDs2bNMHPmTBw+fLjAq+zy++KLL7B3716MGDEC9vb2qF27NiQSCSZNmlTm9+7VG6S9//77OHbsGE6cOIGzZ89i9+7d+PrrrzF37ly1qxWIqPQ4PpdNVRrLgMo9vizIm3of8rOyssJff/2F+/fvF5ikLYvibE+9evWwb98+nDlzBrGxsYiNjcXevXvRr18/LFq0CEDhV4e/Og6+br2V/fkr7lXulfE5qAxMllOF2L9/PwCIN44ojJaWlljaY+bMmVizZg2WL1+On3/+GR07dix1WYrC5D8jC7z8w3P79m21xFxhZ4/v3bunNhiVpG9mZmaIi4tDRkaG2pnthIQE8fnyYGZmhqtXryIvL0/tQE+1nldnUFWmTp06wcTEBAcPHkTbtm2RmZlZ4Kyv+vXrY8iQIRgyZAhSUlLwwQcfYM2aNYUmy5s0aYIzZ85o3Nwrv5MnTyI7OxurV69W2yevXjZdXElJSYiPj8fQoUPx/vvvqz2Xl5eHadOm4eDBgxg7dqz45fX69esasyzyK+ln39vbG3PnzkVCQgKio6NhYGCALl26FPma162jb9++WL9+PU6ePInY2FiYmJi89ne6OExMTFCrVi3k5eUV6wRMt27dMGfOHLEUy61btxAYGKgW06RJEzx//rxY7RFR2XCMV8cxvmRu376tdhXZs2fP8Pfffxc5S6sk216nTh24u7uLN0A7f/68xk3cyqpJkyYQBAHm5uYFzmArjfL6fWjTpg0A4NGjR6+NPXLkCPr164cZM2aIy7KysjRmlRfVt4J+p7Kzs/H3339rxNapUwcffvghPvzwQzx79gxDhw5FeHg4k+VE5YTjs7rqPD6X5PiyIE2aNEFeXh5u3rwJGxubAmNU/UpISFDbx9nZ2bh7926pjqvKe1+orkY/cOCAxvHfq1RtJyYmalxtnJiYWOrvSVKpFB4eHvDw8EBeXh5CQ0Oxc+dOjB07Fk2bNhWvtk9LS1O78r4sV/wVJS8vD0lJSWrfPxITEwH8+5ku6e9JXl4ebt++rXYlwj///IO0tLRy+z2pbliznMpdXFwcVq1aBXNz8yJnoT59+lRjmeoPeXZ2NgDAwMAAgOalJ6W1b98+ZGRkiI8PHz6scYBmYWGBixcvin0AgO+//x73799Xa6skfXNzc0Nubi62bt2qtnzjxo2QSCRluozn1fX8/fffavWdc3JysHnzZhgaGmoMtJVJR0cHPXv2RExMDPbu3Qtra2u1S3Fzc3M1Dtbq1auH+vXrq703r+revTsEQUBERITGc6qzsqqzoa9ebrxnz55SbYvqrP/IkSPRo0cPtR8fHx/xruXAyy+vtWrVQmRkJLKysgrsH/Dy81WSS6C9vLygra2N7777DocPH4a7u7vGzLZXve4z3LJlS8jlcuzevRtHjx5Fz549NS4tLw1tbW14eXnhyJEjuHbtmsbzr5ZMkclkcHV1RUxMDL777jvo6upq1GL39vZGfHw8Tp8+rdFeWlqaWGeWiMqGY7wmjvEls3PnTiiVSvHx9u3bkZOTU+R+Kum29+3bFzdu3MDixYuhra1dYO31sujevTu0tbURERGhMeNLEAQ8efKkxG2W9Pfhp59+KnC2maq2aP6Z9IaGhgW2W9DssM2bN2vMhlP1raDvJRYWFvjtt9/Ulu3atUujjVf3Sa1atdCkSZMiv9MRUfFxfNZUncfnkhxfFqRbt27Q0tLCypUrNa6aVo0dHTt2hK6uLjZv3qw2nuzevRvp6emlupK7vPeFqnTXmjVrEB8fr/F8RkYGli9fDuDlyeJ69ephx44dap+jU6dO4ebNm3B3dy/x9rw6dmlpaYkneVTrUE2Gy1+P/fnz59i3b1+J11dc+T/TgiBg69at0NXVFU8SlOT3RPU+f/3112rLN2zYoPZ8TcOZ5VQmsbGxSEhIQG5uLv755x/8/PPPOHv2LBo3bozVq1dr3MAwv5UrV+K3335D586dYWZmhpSUFGzbtg0NGzZE+/btAUC8ydCOHTtQq1YtGBoaws7OrtSXGxkbG2Pw4MHo378/UlJS8PXXX6Np06b4+OOPxZgBAwbgyJEjGDlyJLy9vXHnzh0cPHhQ43K2kvTNw8MDTk5OWL58OZKTkyGXy3H27FmcOHECI0aMKPJSuZIYOHAgdu7ciRkzZuDPP/+EmZkZjhw5Is6qKmut6fLWr18/bN68GT///LNGyZBnz56hc+fO8PLyQsuWLWFoaIgff/wRv//+u9oMqFd16NABffv2xebNm3H79m106tQJeXl5OHfuHJycnDB06FC4uLhAV1cXo0ePxieffIJnz57hm2++Qb169QqcCfU6Bw8ehI2NTaGXhnl4eOCLL77An3/+idatW2PmzJmYPXs2PvroI/Tq1QsymQxXrlzBixcvxMu5WrdujejoaCxYsAC2trYwNDSEh4dHoX2oV68enJycsGHDBjx79kzt5peF0dfXR4sWLRATE4NmzZqhTp06ePfdd2FtbS3G5L/ErKQlWPbs2VNg8nr48OGYMmUKfv75Z3z88ccYMGAAWrRogdTUVPz555+Ii4vDL7/8ovYaHx8fTJ06Fdu2bYOrq6tGvXx/f3+cPHkSo0ePxgcffIDWrVsjMzMT165dw5EjR3DixAmNmnREVDSO8TVnjH/48KE4IzG/WrVqlfpG0YVRKpXw9fWFt7c3EhMTsW3bNrRv3x5du3Yt9DUl3fbOnTujTp064v07iqrfWxpNmjRBUFAQli5diuTkZHTr1g21atXC3bt3cfz4cXz88cfw9/cvcZsl+X2YN28eMjMz4enpCSsrKyiVSpw/fx4xMTEwMzNTu/le69atERcXhw0bNqB+/fowNzdH27Zt4e7ujv3798PIyAgtWrTAhQsX8OOPP2pcmWdjYwNtbW1ERUUhPT0dUqkUHTp0QL169TBgwAD897//xfjx49GxY0dcuXIFZ86cQd26ddXa6NmzJxwdHdG6dWvUqVMHv//+O44cOYKhQ4eWaD8REcfnmjA+l/T48lVNmzbF6NGjsWrVKgwePBjdu3eHVCrF77//jvr162PKlCkwMTFBYGAgIiIiMHLkSHh4eIjjsq2tbanKb5b3vtDV1UVERAQ+/fRTDB06FD169EC7du2gq6uL69ev49ChQ5DJZJg0aRJ0dXURHByMmTNnYujQoejZsydSUlKwadMmmJmZwdfXt8TbM3v2bKSmpqJDhw5o0KAB7t27hy1btsDGxkache3i4oLGjRtj1qxZSEhIgLa2Nvbs2YO6detWyOxyPT09nD59GtOnT4ednR1Onz6NH374AaNHjxaPd4tznK/SsmVLfPDBB9i5cyfS0tLw/vvv4/fff8e3336Lbt26qV0NWJMwWU5lEhYWBuDlH7E6derA2toaISEh6N+//2v/EHp4eCA5ORl79uzBkydPULduXTg6OmL8+PHiTQ50dXWxcOFCLFu2DKGhocjJycGCBQtKPVCPHj0aV69exdq1a/Hs2TM4Ozvjv//9r3jmDXhZHmTGjBnYsGED5s+fjzZt2mDNmjViwlClJH3T0tLC6tWrERYWhujoaOzduxdmZmaYNm2aeJfh8qCvr4/NmzdjyZIl+Pbbb5GRkQFLS0ssWLCgRHcsf1PatGmDd999Fzdv3tQYjPX19TFo0CCcPXsWR48ehSAIaNKkCf773/9i8ODBRba7YMECcUb04sWLUbt2bbRp00a867SVlRXCwsKwYsUKLFq0CO+88w4GDRoEExOTEl+q/eeffyIhIaHQS+CAl5ePffHFFzhw4ABat26NAQMGoF69eli7di1WrVoFHR0dWFlZqQ3ggwcPxuXLl7F3715s3LgRZmZmRSbLgZcJ5R9//BG1atUq9hngefPm4YsvvsCCBQugVCoxbtw4tUG0d+/eWLJkCSwsLGBnZ1esNlW2b99e4PL+/fujYcOG+Oabb7By5UocO3YM27dvR506ddCiRYsCa617eHhAX1+/0BMBBgYG2Lx5MyIjI3H48GHs27cPRkZGaNasmdrfFCIqPo7xNWeMv3z5MqZNm6ax3MzMrNyT5XPmzMHBgwcRFhYGpVKJnj17Yvbs2UVeMlzSbZdKpfDx8cG2bdsKLPFWHkaNGoVmzZph48aNWLlyJYCXN+dycXF57XhdkJL+PkybNg2HDx/GqVOnxNn6jRs3xuDBgzFmzBi1k8ozZszAnDlzsGLFCrx48QIffPAB2rZti1mzZkFLSwsHDx5EVlYW2rVrhw0bNmjc/8PU1BRz585FZGQkZs2ahdzcXGzatAn16tXDxx9/jLt372L37t04ffo02rdvjw0bNmgkJYYNG4aTJ0/i7NmzyM7ORuPGjREUFFTikwpExPH5bR+fS3N8WZCJEyfC3NwcW7ZswfLly2FgYAC5XK42Lo4fPx4mJibYsmULFixYAGNjY3z88ceYPHkydHV1S9z3iviu0rRpU+zbtw8bN24U732Rl5eHpk2bYsCAARg2bJgY279/f+jr6yMqKgpLliyBoaEhunXrhqlTp2pMtiqOPn36YNeuXdi2bRvS0tJgamoKb29vjB8/Xiwzo0roz507F1999RVMTU0xYsQIyGQyzJw5s1TbXBRtbW2sW7cOoaGh+N///odatWph3Lhx+M9//qMW97rj/Fdjzc3N8e233+L48eN45513EBgY+Np7n7zNJEJ1uJsQEb21+vXrB2NjY43LfqhqePz4MTp16oSxY8dqDMBERETFtXfvXsycORO7d++Gra1tha9v/vz52L17N86ePauWkCEiIiKqjmbMmIEjR44UWJKGyhdrlhNRpfn9999x+fJl9OvXr7K7QoX49ttvkZubW2Ez84iIiMpbVlYWDhw4AC8vLybKiYiIiKhEWIaFiN64a9eu4c8//8T69ethamparPra9GbFxcXh5s2bWLNmDbp16wZzc/PK7hIREVGRUlJS8OOPP+LIkSN4+vQphg8fXtldIiIiIqJqhslyInrjjhw5gpUrV8LS0hLLli0r8iY0VDlWrVqF+Ph4ODg44LPPPqvs7hAREb3WjRs3EBwcjHr16mH27NmwsbGp7C4RERERUTXDmuVEREREREREREREVOOxZjkRERERERERERER1XhMlhMRERERERERERFRjcea5VVcfHw8BEGArq5uZXeFiIgqmFKphEQigYODQ2V3hUqJ4zYRUc3Bcbv647hNRFRzFHfcZrK8ihMEASwrT0RUM/DvffXHcZuIqObg3/vqj+M2EVHNUdy/90yWV3GqM9y2traV3BMiIqpov//+e2V3gcqI4zYRUc3Bcbv647hNRFRzFHfcZs1yIiIiIiIiIiIiIqrxmCwnIiIiIiIiIiIiohqPyXIiIiIiIiIiIiIiqvGYLCciIiIiIiIiIiKiGo/JciIiIiIiIiIiIiKq8ZgsJyIiIiIiIiIiIqIaj8lyIiIiIiIiIiIiIqrxmCwnIiIiIiIiIiIiohqPyXIiIiIiIiIiIiIiqvGYLCciegvk5QmV3QUqAN8XKqs8Ia+yu0BvMX6+iIiIiIqHx3ZVT0W9JzoV0ioREb1RWloSHDtzFU/Snld2V+j/1ZUZwtNVXtndoGpOS6KFrRdP42FGamV3hd4yDYyMMaRtp8ruBhEREVG1wGPuqqUij7eZLCcieks8SXuOfx4/q+xuEFE5e5iRiuS0x5XdDSIiIiKiGo3H3DUDy7AQERERERERERERUY3HZDkRERERERERERER1XhMlhMRERERERERERFRjcdkORERERERERERERHVeEyWExEREREREREREVGNx2Q5EREREREREREREdV4TJYTERERERERERERUY3HZDkRERGVSkxMDMaMGQM3NzfY29ujb9++2L17NwRBEGOGDRsGuVyu8XPz5k21ttLT0xESEgJHR0c4ODhgwoQJePTokcY6z58/j4EDB8LOzg5dunTB2rVr1dYHAIIgYO3atXB3d4ednR0GDhyICxcuaLT18OFDjB8/Hg4ODnB0dMSsWbOQkZFRPjuHiIiIiIiIqh2dyu4AERERVU8bN26EmZkZZsyYgbp16+LHH3/EZ599hgcPHmDcuHFiXLt27TB9+nS115qbm6s9DgoKwo0bNxAaGgo9PT2sWLECAQEB2LNnD3R0Xn5duX37Nvz9/eHi4oKgoCBcvXoVS5Ysgba2Nvz9/cW2oqKiEBYWhuDgYMjlcmzduhV+fn7Yv38/LCwsAABKpRIjR44EACxduhQvXrzAokWLMGXKFERGRlbI/iIiIiIiIqKqjclyIiIiKpXVq1fDxMREfOzs7IynT59iw4YNGDt2LLS0Xl7AJpPJYG9vX2g78fHxOHPmDBQKBVxdXQEAlpaW8PHxwdGjR+Hj4wMAUCgUqFu3LpYtWwapVApnZ2c8fvwYa9aswbBhwyCVSpGVlYXIyEj4+fnB19cXANC+fXv06NEDCoUCoaGhAIAjR47g+vXriI6OhpWVldhPf39/XLp0CXZ2duW8t4iIiIiIiKiqYxkWIiIiKpX8iXIVGxsbZGRk4Pnz58VuJzY2FjKZDC4uLuIyKysr2NjYIDY2Vi2ua9eukEql4jIfHx+kpaUhPj4ewMsyLRkZGfD29hZjpFIpPD09NdqSy+ViohwAXFxcUKdOHZw6darYfSciIiIiIqK3B5PlREREVG7OnTuHBg0awMjISFz2yy+/wN7eHra2thg6dCh+/fVXtdckJCTA0tISEolEbbmVlRUSEhIAAM+fP8f9+/fVktuqGIlEIsap/n01rnnz5rh37x5evHghxr0aI5FIYGlpKbZBRERERERENQvLsBAREVG5+O233xAdHa1Wn/z9999H37590axZMzx69AgKhQKffvopNm/eDAcHBwBAWloaateurdGesbEx/vjjDwAvbwAKvCyVkp9UKoWBgQFSU1PFtqRSKfT09NTiZDIZBEFAamoq9PX1i1ynqq3SEgShRDPrCyORSGBgYFDmdoiKkpmZqXGTXCIqHkEQNE70EhERUfXGZDkRERGV2YMHDzBp0iQ4OTlh+PDh4vIJEyaoxbm7u6NXr15YtWoVoqKi3nQ33wilUonLly+XuR0DAwO0atWqHHpEVLjExERkZmZWdjeIqq38pcGIiIio+mOynIiIiMokLS0NAQEBqFOnDsLDw8UbexbE0NAQnTt3xpEjR8RlMpkMDx480IhNTU2FsbExAIizwFUzzFWys7ORmZkpxslkMmRnZyMrK0ttdnlaWhokEolaXEZGRoHrbNSoUXE3vUC6urpo0aJFmdoAwNmK9EZYWlpyZjlRKd24caOyu0BERETljMlyIiIiKrUXL14gMDAQ6enp2LlzZ4GlTV7HysoKcXFxGpezJyYmwtraGsDLJHujRo006oknJiZCEASx/rjq38TERLRs2VKMS0hIQOPGjaGvry/GXbt2Ta0tQRCQmJiodqPR0pBIJDA0NCxTG0RvCkv9EJVedTupefv2bSgUCly8eBHXr1+HlZUVDh06pBYzbNgw/PLLLxqvjY6ORvPmzcXH6enpWLBgAY4fPw6lUolOnTph9uzZqF+/vtrrzp8/j0WLFuHy5cuoV68eBg0ahICAALV9JwgCoqKisG3bNjx+/Bg2NjaYOXMm7O3t1dp6+PAh5s2bhzNnzkBXVxeenp6YOXOm2n1SiIiIyoo3+CQiIqJSycnJQVBQEBISErBu3To0aNDgta95/vw5fvjhB9ja2orL3NzckJqairi4OHFZYmIi/vrrL7i5uanFnThxAkqlUlwWHR0NmUwm1j9v164djIyMEBMTI8YolUocPXpUo60rV67g1q1b4rK4uDg8ffoUnTt3LtmOICIiqgauX7+OU6dOoWnTpmqJ71e1a9cOO3fuVPsxNzdXiwkKCsLZs2cRGhqKJUuWIDExEQEBAcjJyRFjbt++DX9/f5iamiIyMhIjRoxAWFgY1q9fr9ZWVFQUwsLC4Ovri8jISJiamsLPzw9JSUlijFKpxMiRI3Hr1i0sXboUoaGhOHPmDKZMmVJOe4eIiOglziwnIiKiUpk7dy6+//57zJgxAxkZGbhw4YL4XKtWrXDp0iWsW7cOnp6eMDMzw6NHj7Bhwwb8/fff+Oqrr8RYBwcHuLq6IiQkBNOnT4eenh6WL18OuVyO7t27i3H+/v44ePAgpkyZgkGDBuHatWtQKBSYNGmSWDNWT08PgYGBCA8Ph4mJCaytrbF9+3Y8ffoU/v7+YlteXl6IjIzE+PHjMXnyZGRmZmLx4sVwd3eHnZ1dxe88IiKiN8zDwwPdunUDAMyYMUO8ifarZDKZxqzu/OLj43HmzBkoFAq4uroCeFnSycfHB0ePHoWPjw8AQKFQoG7duli2bBmkUimcnZ3x+PFjrFmzBsOGDYNUKkVWVhYiIyPh5+cHX19fAED79u3Ro0cPKBQKhIaGAgCOHDmC69evIzo6WryKTCaTwd/fH5cuXeLYTURE5YbJciIiIiqVs2fPAgAWLlyo8dyJEydgamoKpVKJ5cuX4+nTpzAwMICDgwPmzp2rcVC7YsUKLFiwAHPmzEFOTg5cXV0xe/Zs6Oj8+1WladOmUCgUWLhwIUaNGgUTExNMmDABfn5+am0FBARAEASsX79evJxboVDAwsJCjNHV1cW6deswb948TJ48GTo6OvD09ERISEh57iIiIqIqo6h7ipREbGwsZDKZWtkyKysr2NjYIDY2VkyWx8bGwtPTU+0mqD4+PoiMjER8fDycnJxw/vx5ZGRkwNvbW4yRSqXw9PTEsWPH1NYpl8vFRDkAuLi4oE6dOjh16hST5UREVG6qVLKcNdSIiIiqj5MnT742RqFQFKut2rVrY/78+Zg/f36Rce3atcOuXbuKjJFIJAgMDERgYGCRcQ0aNEB4eHix+kdERFRT/PLLL7C3t0dubi7atm2LiRMn4v333xefT0hIgKWlpUbNdisrK/HeIs+fP8f9+/fVktuqGIlEgoSEBDg5OYnxr8Y1b94cX3/9NV68eAF9fX0kJCRoxEgkElhaWmrcz4SIiKgsqlSyXFVDrW3btsjLy4MgCAXGtWvXDtOnT1dbVlANtRs3biA0NBR6enpYsWIFAgICsGfPHnGWmqqGmouLC4KCgnD16lUsWbIE2traapdqq2qoBQcHQy6XY+vWrfDz88P+/fvFWWqqGmoAsHTpUrx48QKLFi3ClClTEBkZWW77iIiIiIiIiKgivP/+++jbty+aNWuGR48eQaFQ4NNPP8XmzZvF+4OkpaUVeENvY2NjsbRLeno6gJelUvKTSqUwMDBAamqq2JZUKoWenp5anEwmgyAISE1Nhb6+fpHrVLVVWoIg4Pnz52Vqg4jebhKJhDdFr6IyMzMLzR+/ShCEYt2cu0oly1lDjahshLw8SMrp8koqP3xfiIiIiKg6mDBhgtpjd3d39OrVC6tWrUJUVFQl9apiKZVKXL58ubK7QURVmIGBAVq1alXZ3aACJCYmIjMzs9jx+UuDFaZKJctZQ42obCRaWkg9vh25Tx5Vdlfo/2nXrQ/jboMquxtERERERCVmaGiIzp0748iRI+IymUyGBw8eaMSmpqbC2NgYAMRZ4KoZ5irZ2dnIzMwU42QyGbKzs5GVlaU2uzwtLQ0SiUQtLiMjo8B1NmrUqEzbqKurixYtWpSpDSJ6uxVnNjJVDktLy2LPLL9x40ax4qpUsry4WEONqHC5Tx4h55/kyu4GERERERG9haysrBAXF6dxOXtiYiKsra0BvEyyN2rUSONYODExEYIgiMfOqn8TExPRsmVLMS4hIQGNGzeGvr6+GHft2jW1tgRBQGJiotokudKQSCQwNDQsUxtERFQ5SlIep7gnPapdspw11IgKxhpaVVtJ6miVFN/7qq0iaqgRERERvQnPnz/HDz/8AFtbW3GZm5sbVq1ahbi4OHTs2BHAy2T3X3/9Jd7HSxV34sQJTJ06Fbq6ugCA6OhoyGQy8di9Xbt2MDIyQkxMjJgsVyqVOHr0KNzc3NTaOnDgAG7duoVmzZoBAOLi4vD06VN07ty5QvcBERHVLNUuWc4aakQFYw2tqq2kdbRKgu991VYRNdSIiIiISiozMxOnTp0CACQnJyMjIwOHDx8GADg6OiIhIQHr1q2Dp6cnzMzM8OjRI2zYsAF///03vvrqK7EdBwcHuLq6IiQkBNOnT4eenh6WL18OuVyO7t27i3H+/v44ePAgpkyZgkGDBuHatWtQKBSYNGmS+H1HT08PgYGBCA8Ph4mJCaytrbF9+3Y8ffoU/v7+YlteXl6IjIzE+PHjMXnyZGRmZmLx4sVwd3dnyVMiIipX1S5Z/irWUCN6ibNRq7aS1NEqKb73VVtF1FAjIiIiKqmUlBRMnDhRbZnq8aZNm9CwYUMolUosX74cT58+hYGBARwcHDB37lyNhPSKFSuwYMECzJkzBzk5OXB1dcXs2bOho/NviqFp06ZQKBRYuHAhRo0aBRMTE0yYMAF+fn5qbQUEBEAQBKxfvx6PHz+GjY0NFAoFLCwsxBhdXV2sW7cO8+bNw+TJk6GjowNPT0+EhISU924iIqIartonywvCGmpEVNWwTErNVRE11IiIiIhKytzcHFevXi0yRqFQFKut2rVrY/78+Zg/f36Rce3atcOuXbuKjJFIJAgMDERgYGCRcQ0aNEB4eHix+kdERFRaWpXdgbIqrIZaamoq4uLixGWqGmqv1j07ceIElEqluKyoGmoqhdVQu3LlCm7duiUuYw01IiIiIiIiIiIiouqhSs0sZw01IiIiIiIiIiIiIqoMVSpZzhpqRERERERERERERFQZqlSynDXUiIiIiIiIiIiIiKgyVPua5UREREREREREREREZcVkORERERERERERERHVeEyWExEREREREREREVGNx2Q5EREREREREREREdV4TJYTERERERERERERUY3HZDkRERERERERERER1XhMlhMRERERERERERFRjcdkORERERERERERERHVeEyWExERERERERERFUHIy6vsLlAB+L5QedOp7A4QERERERERERFVZRItLaQe347cJ48quyv0/7Tr1odxt0GV3Q16yzBZTkRERERERERE9Bq5Tx4h55/kyu4GEVUglmEhIiIiIiIiIiIiohqPyXIiIiIiIiIiIiIiqvGYLCciIiIiIiIiIiKiGo/JciIiIiIiIiIiIiKq8ZgsJyIiIiIiIiIiIqIaj8lyIiIiKpWYmBiMGTMGbm5usLe3R9++fbF7924IgqAW980338DLywu2trbo06cPvv/+e4220tPTERISAkdHRzg4OGDChAl49OiRRtz58+cxcOBA2NnZoUuXLli7dq3G+gRBwNq1a+Hu7g47OzsMHDgQFy5c0Gjr4cOHGD9+PBwcHODo6IhZs2YhIyOjbDuFiIiIiIiIqi0my4mIiKhUNm7cCAMDA8yYMQOrV6+Gm5sbPvvsM6xcuVKM+e677/DZZ5/B29sbUVFRsLe3x7hx4zSS10FBQTh79ixCQ0OxZMkSJCYmIiAgADk5OWLM7du34e/vD1NTU0RGRmLEiBEICwvD+vXr1dqKiopCWFgYfH19ERkZCVNTU/j5+SEpKUmMUSqVGDlyJG7duoWlS5ciNDQUZ86cwZQpUypmZxEREREREVGVp1PZHSAiIqLqafXq1TAxMREfOzs74+nTp9iwYQPGjh0LLS0thIWFoWfPnggKCgIAdOjQAdeuXcPKlSsRFRUFAIiPj8eZM2egUCjg6uoKALC0tISPjw+OHj0KHx8fAIBCoUDdunWxbNkySKVSODs74/Hjx1izZg2GDRsGqVSKrKwsREZGws/PD76+vgCA9u3bo0ePHlAoFAgNDQUAHDlyBNevX0d0dDSsrKwAADKZDP7+/rh06RLs7OzewB4kIiIiIiKiqoQzy4mIiKhU8ifKVWxsbJCRkYHnz58jKSkJt27dgre3t1qMj48P4uLikJ2dDQCIjY2FTCaDi4uLGGNlZQUbGxvExsaKy2JjY9G1a1dIpVK1ttLS0hAfHw/gZZmWjIwMtXVKpVJ4enpqtCWXy8VEOQC4uLigTp06OHXqVGl3CREREREREVVjTJYTERFRuTl37hwaNGgAIyMjJCQkAHg5Szy/5s2bQ6lUimVREhISYGlpCYlEohZnZWUltvH8+XPcv39fLbmtipFIJGKc6t9X45o3b4579+7hxYsXYtyrMRKJBJaWlmIbREREREREVLOwDAsRERGVi99++w3R0dGYPn06ACA1NRXAy/Im+akeq55PS0tD7dq1NdozNjbGH3/8AeDlDUALaksqlcLAwECtLalUCj09PY11CoKA1NRU6OvrF7lOVVulJQgCnj9/XqY2gJfJewMDgzK3Q1SUzMxMjZvkElHxCIKgcaKXiIiIqjcmy4mIiKjMHjx4gEmTJsHJyQnDhw+v7O5UKqVSicuXL5e5HQMDA7Rq1aocekRUuMTERGRmZlZ2N4iqrfylwYiIiKj6Y7KciIiIyiQtLQ0BAQGoU6cOwsPDoaX1ssqbsbExgJezwk1NTdXi8z8vk8nw4MEDjXZTU1PFGNUscNUMc5Xs7GxkZmaqtZWdnY2srCy12eVpaWmQSCRqcRkZGQWus1GjRqXYC//S1dVFixYtytQGAM5WpDfC0tKSM8uJSunGjRuV3QUiIiIqZ0yWExERUam9ePECgYGBSE9Px86dO9VKm6hqgr9aHzwhIQG6urqwsLAQ4+Li4jQuZ09MTIS1tTUAwNDQEI0aNdKoJ56YmAhBEMT2Vf8mJiaiZcuWauts3Lgx9PX1xbhr166ptSUIAhITE9VuNFoaEokEhoaGZWqD6E1hqR+i0uNJTSIiorcPb/BJREREpZKTk4OgoCAkJCRg3bp1aNCggdrzFhYWaNasGQ4fPqy2PDo6Gs7OzuKl625ubkhNTUVcXJwYk5iYiL/++gtubm7iMjc3N5w4cQJKpVKtLZlMBgcHBwBAu3btYGRkhJiYGDFGqVTi6NGjGm1duXIFt27dEpfFxcXh6dOn6Ny5cxn2ChEREREREVVXnFlOREREpTJ37lx8//33mDFjBjIyMnDhwgXxuVatWkEqlWL8+PEIDg5GkyZN4OTkhOjoaFy6dAlbtmwRYx0cHODq6oqQkBBMnz4denp6WL58OeRyObp37y7G+fv74+DBg5gyZQoGDRqEa9euQaFQYNKkSWLiXU9PD4GBgQgPD4eJiQmsra2xfft2PH36FP7+/mJbXl5eiIyMxPjx4zF58mRkZmZi8eLFcHd3h52dXcXvPCIiIiIiIqpymCwnIiKiUjl79iwAYOHChRrPnThxAubm5ujVqxcyMzMRFRWFtWvXwtLSEhEREeJMcJUVK1ZgwYIFmDNnDnJycuDq6orZs2dDR+ffrypNmzaFQqHAwoULMWrUKJiYmGDChAnw8/NTaysgIACCIGD9+vV4/PgxbGxsoFAoxLIvwMu64uvWrcO8efMwefJk6OjowNPTEyEhIeW5i4iIiIiIiKgaYbKciIiISuXkyZPFihswYAAGDBhQZEzt2rUxf/58zJ8/v8i4du3aYdeuXUXGSCQSBAYGIjAwsMi4Bg0aIDw8vMgYIiIiIiIiqjlYs5yIiIiIiIiIiIiIajwmy4mIiIiIiIiIiIioxmOynIiIiIiIiIiIiIhqPCbLiYiIiIiIiIiIiKjGY7KciIiIiIiIiIiIiGo8JsuJiIiIiIiIiIiIqMZjspyIiIiIiIiIiIiIajwmy4mIiIiIiIiIiIioxmOynIiIiIiIiIiIiIhqPCbLiYiIiIiIiIiIiKjGY7KciIiIiIiIiIiIiGo8JsuJiIiIiIiIiIiIqMZjspyIiIiIiIiIiIiIajwmy4mIiIiIiIiIiIioxmOynIiIiIiIiIiIiIhqPCbLiYiIiIiIiIiIiKjGY7KciIiIiIiIiIiIiGo8JsuJiIiIiIiIiIiIqMYrdbJ83759uHv3bqHP3717F/v27Stt80RERFSOOG4TERFVHxy3iYiIKkepk+UzZ85EfHx8oc9funQJM2fOLG3zREREVI44bhMREVUfFTFu3759G3PmzEHfvn3RqlUr9OrVq8C4b775Bl5eXrC1tUWfPn3w/fffa8Skp6cjJCQEjo6OcHBwwIQJE/Do0SONuPPnz2PgwIGws7NDly5dsHbtWgiCoBYjCALWrl0Ld3d32NnZYeDAgbhw4YJGWw8fPsT48ePh4OAAR0dHzJo1CxkZGSXaB0RERK9T6mT5qwPcq54/fw5tbe3SNk9ERETliOM2ERFR9VER4/b169dx6tQpNG3aFM2bNy8w5rvvvsNnn30Gb29vREVFwd7eHuPGjdNIXgcFBeHs2bMIDQ3FkiVLkJiYiICAAOTk5Igxt2/fhr+/P0xNTREZGYkRI0YgLCwM69evV2srKioKYWFh8PX1RWRkJExNTeHn54ekpCQxRqlUYuTIkbh16xaWLl2K0NBQnDlzBlOmTCnRPiAiInodnZIEX7lyBVeuXBEf//bbb8jNzdWIS0tLw44dO2BpaVmizty+fRsKhQIXL17E9evXYWVlhUOHDmnEffPNN1i3bh3u3bsHS0tLTJo0CV26dFGLSU9Px4IFC3D8+HEolUp06tQJs2fPRv369dXizp8/j0WLFuHy5cuoV68eBg0ahICAAEgkEjFGEARERUVh27ZtePz4MWxsbDBz5kzY29urtfXw4UPMmzcPZ86cga6uLjw9PTFz5kwYGRmVaD8QERGVh4oet4mIiKj8VPS47eHhgW7dugEAZsyYgT/++EMjJiwsDD179kRQUBAAoEOHDrh27RpWrlyJqKgoAEB8fDzOnDkDhUIBV1dXAIClpSV8fHxw9OhR+Pj4AAAUCgXq1q2LZcuWQSqVwtnZGY8fP8aaNWswbNgwSKVSZGVlITIyEn5+fvD19QUAtG/fHj169IBCoUBoaCgA4MiRI7h+/Tqio6NhZWUFAJDJZPD398elS5dgZ2dXon1BRERUmBIly48fP46IiAgAgEQiwc6dO7Fz584CY2UyGRYtWlSizqjOdLdt2xZ5eXkFnk1XnekePXo0OnTogOjoaIwbNw5bt25VS14HBQXhxo0bCA0NhZ6eHlasWIGAgADs2bMHOjovN1t1ptvFxQVBQUG4evUqlixZAm1tbfj7+4ttqc50BwcHQy6XY+vWrfDz88P+/fthYWEB4N8z3QCwdOlSvHjxAosWLcKUKVMQGRlZov1ARERUHip63CYiIqLyU9HjtpZW0ReWJyUl4datW5g6darach8fHyxevBjZ2dmQSqWIjY2FTCaDi4uLGGNlZQUbGxvExsaKyfLY2Fh4enpCKpWqtRUZGYn4+Hg4OTnh/PnzyMjIgLe3txgjlUrh6emJY8eOictiY2Mhl8vFRDkAuLi4oE6dOjh16hST5UREVG5KlCz/+OOP4e7uDkEQMGDAAEyYMAFubm5qMRKJBAYGBmjSpImYlC4unukmIiIqPxU9bhMREVH5qexxOyEhAQA0Zqw3b94cSqUSSUlJaN68ORISEmBpaal2NTbwMmGuauP58+e4f/++WnJbFSORSJCQkAAnJycx/tW45s2b4+uvv8aLFy+gr6+PhIQEjRiJRAJLS0uxDSIiovJQotG1fv36YhmTTZs2oXnz5qhXr165dYZnuomIiMpPRY/bREREVH4qe9xOTU0F8HLSV36qx6rn09LSULt2bY3XGxsbixPe0tPTC2xLKpXCwMBArS2pVAo9PT2NdQqCgNTUVOjr6xe5TlVbpSUIAp4/f16mNujtpzpRRVVTZmbma+/1UBZ8/6uukrz3giBonOgtSKlPRTs6Opb2paXGM91ERESlUxnjNhEREZUOx+03R6lU4vLly5XdDariDAwM0KpVq8ruBhUiMTERmZmZFdY+3/+qq6Tvff4J04Up03Vbp0+fxu7du5GUlIS0tDSNTL5EIsHx48fLsgo1PNNNVDie6azaKvJMN9/7qq0iznSX1pset4mIiKj03vS4bWxsDODlsbKpqam4PC0tTe15mUyGBw8eaLw+NTVVjFEdG6uOu1Wys7ORmZmp1lZ2djaysrLUjrnT0tIgkUjU4jIyMgpcZ6NGjUq3wf9PV1cXLVq0KHZ8RX5Xo7Kp6JnFVHVZWlry/a+hSvLe37hxo1hxpU6Wr1u3DkuXLkW9evVgZ2cHuVxe2qboNXimm4qDZzqrtoo80833vmqriDPdpVER4/bt27ehUChw8eJFXL9+HVZWVjh06JBazLBhw/DLL79ovDY6OhrNmzcXH6enp2PBggU4fvw4lEolOnXqhNmzZ4uXo6ucP38eixYtwuXLl1GvXj0MGjQIAQEBal9gBUFAVFQUtm3bhsePH8PGxgYzZ85UuxE4ADx8+BDz5s3DmTNnoKurC09PT8ycORNGRkZl3jdERERlURnH26orpV+9ajohIQG6urqwsLAQ4+Li4jRO8icmJsLa2hoAYGhoiEaNGmlcZZ2YmAhBEMT2Vf8mJiaiZcuWauts3Lgx9PX1xbhr166ptSUIAhITE9XKr5aGRCKBoaFhsePzhDxoSYouIUtvHt+Xmo2Tx2qukrz3xT3pUepk+aZNm9ChQwesXbsWurq6pW2mRHimm6hwPNNZtVXkmW6+91VbRZzpLo2KGLevX7+OU6dOoW3btsjLyyt0O9u1a4fp06erLTM3N1d7HBQUhBs3biA0NBR6enpYsWIFAgICsGfPHvEGZrdv34a/vz9cXFwQFBSEq1evYsmSJdDW1oa/v7/YVlRUFMLCwhAcHAy5XI6tW7fCz88P+/fvFw/0lUolRo4cCQBYunQpXrx4gUWLFmHKlCmIjIwsl/1DRERUWpVxvG1hYYFmzZrh8OHD6Natm7g8Ojoazs7O4gl9Nzc3rFq1CnFxcejYsSOAl8nuv/76SxxbVXEnTpzA1KlTxW2Ijo6GTCaDg4MDgJffEYyMjBATEyMmy5VKJY4ePap2c1M3NzccOHAAt27dQrNmzQAAcXFxePr0KTp37lxxO6UAWhItbL14Gg8zynYFOZWfBkbGGNK2U2V3g4jeEqVOlqelpcHLy+uNDdwAz3QTUfXFM901V0Wc6S6Nihi3PTw8xIPpGTNmiKXOXiWTyTRmdecXHx+PM2fOQKFQwNXVFcDLkww+Pj44evSoeGNuhUKBunXrYtmyZZBKpXB2dsbjx4+xZs0aDBs2DFKpFFlZWYiMjISfnx98fX0BAO3bt0ePHj2gUCgQGhoKADhy5AiuX7+O6OhocayXyWTw9/fHpUuXeGNuIiKqVBUxbmdmZuLUqVMAgOTkZGRkZODw4cMAXtZINzExwfjx4xEcHIwmTZrAyckJ0dHRuHTpErZs2SK24+DgAFdXV4SEhGD69OnQ09PD8uXLIZfL0b17dzHO398fBw8exJQpUzBo0CBcu3YNCoUCkyZNEhPvenp6CAwMRHh4OExMTGBtbY3t27fj6dOnaifCvby8EBkZifHjx2Py5MnIzMzE4sWL4e7uXilj9sOMVCSnPX7j6yUioopX6mtUbG1tkZiYWJ59ea38Z7rzK+hMd2pqKuLi4sQY1ZnuV89OnzhxAkqlUq2tws50qxR2pvvKlSu4deuWuKyyznQTERG9qiLGbS2t8rnUNTY2FjKZTO3kspWVFWxsbBAbG6sW17VrV7VSNT4+PkhLS0N8fDyAl2VaMjIy4O3tLcZIpVJ4enpqtCWXy9VOvru4uKBOnTpiIoGIiKiyVMS4nZKSgokTJ2LixIn45ZdfcP/+ffHx9evXAQC9evXCF198gUOHDsHf3x/nz59HRESEeHyssmLFCnTs2BFz5szBlClT0KxZM6xdu1a8GgwAmjZtCoVCgQcPHmDUqFFYv349JkyYAD8/P7W2AgICMG7cOKxfvx6jRo3CgwcPoFAoxMlwwMurrdetW4dmzZph8uTJ+O9//4uOHTti6dKl5bqPiIiISj2zPDQ0FAEBAWjTpg169+5dLp3hmW4iIqKKURHjdnH98ssvsLe3R25uLtq2bYuJEyfi/fffF59PSEiApaWlxsx6Kysr8Qqw58+f4/79+2rJbVWMRCJBQkICnJycxPhX45o3b46vv/4aL168gL6+vsZVasDLmf2WlpYaV50RERG9aRUxbpubm+Pq1auvjRswYAAGDBhQZEzt2rUxf/58zJ8/v8i4du3aYdeuXUXGSCQSBAYGIjAwsMi4Bg0aIDw8vMgYIiKisip1sjwoKAg5OTmYNm0aQkND0bBhQ40ZZhKJBAcOHCh2m6oz3fmpHm/atAlOTk7o1asXMjMzERUVhbVr18LS0rLQM90LFizAnDlzkJOTA1dXV8yePbvAM90LFy7EqFGjYGJiUuiZbkEQsH79evFGYYWd6Z43bx4mT54MHR0deHp6IiQkpNjbT0REVFEqYtwujvfffx99+/ZFs2bN8OjRIygUCnz66afYvHmzOHanpaWJ9xLJz9jYWCztorrHiEwmU4uRSqUwMDBAamqq2JZUKlW7z4jqdYIgIDU1Ffr6+kWuU9VWaQmCgOfPn5epDeDl+8ESTlTRMjMzK+yeGkRvu1fLfpanyhq3iYiIarpSJ8vr1KmDOnXqoGnTpuXWGZ7pJiIiqhgVMW4Xx4QJE9Qeu7u7o1evXli1ahWioqLeaF/eFKVSicuXL5e5HQMDA7Rq1aocekRUuMTERGRmZlZ2N4iqrfylwcpTZY3bRERENV2pk+WbN28uz34QERFRBaoq47ahoSE6d+6MI0eOiMtkMhkePHigEZuamgpjY2MAEGeBq2aYq2RnZyMzM1OMk8lkyM7ORlZWltrs8rS0NEgkErW4jIyMAtfZqFGjMm2jrq4uWrRoUaY2gIq94SuRiqWlJWeWE5XSjRs3KqztqjJuExER1TSlTpYTERERlQcrKyvExcVpXM6emJgIa2trAC+T7I0aNdKoJ56YmAhBEMT646p/ExMT0bJlSzEuISEBjRs3hr6+vhh37do1tbYEQUBiYqLajUZLQyKRwNDQsExtEL0pLPVDVHo8qUlERPT2KXWy/Ndffy1WXP4beBEREVHlqCrj9vPnz/HDDz/A1tZWXObm5oZVq1YhLi4OHTt2BPAy2f3XX39h5MiRanEnTpzA1KlToaurCwCIjo6GTCYT65+3a9cORkZGiImJEZPlSqUSR48ehZubm1pbBw4cwK1bt9CsWTMAQFxcHJ4+fYrOnTtX6D4gIiJ6naoybhMREdU0pU6WDxs2rFhn0sujZicRERGVTUWM25mZmTh16hQAIDk5GRkZGTh8+DAAwNHREQkJCVi3bh08PT1hZmaGR48eYcOGDfj777/x1Vdfie04ODjA1dUVISEhmD59OvT09LB8+XLI5XJ0795djPP398fBgwcxZcoUDBo0CNeuXYNCocCkSZPEmrF6enoIDAxEeHg4TExMYG1tje3bt+Pp06fw9/cX2/Ly8kJkZCTGjx+PyZMnIzMzE4sXL4a7uzvs7OyKvQ+IiIgqAo+3iYiIKkepk+WbNm3SWJabm4vk5GTs2rULeXl5mDJlSpk6R0REROWjIsbtlJQUTJw4UW2Z6vGmTZvQsGFDKJVKLF++HE+fPoWBgQEcHBwwd+5cjYT0ihUrsGDBAsyZMwc5OTlwdXXF7NmzoaPz71eVpk2bQqFQYOHChRg1ahRMTEwwYcIE+Pn5qbUVEBAAQRCwfv16PH78GDY2NlAoFLCwsBBjdHV1sW7dOsybNw+TJ0+Gjo4OPD09ERISUqJ9QETlT8jLg0RLq7K7QW+x6vAZ4/E2ERFR5Sh1stzR0bHQ5/r374/Bgwfjl19+gbOzc2lXQUREROWkIsZtc3NzXL16tcgYhUJRrLZq166N+fPnY/78+UXGtWvXDrt27SoyRiKRIDAwEIGBgUXGNWjQAOHh4cXqHxG9ORItLaQe347cJ48quyv0FtKuWx/G3QZVdjdei8fbRERElaNCbvCppaWFnj17IjIyUmPGGREREVUtHLeJqKrJffIIOf8kV3Y3iKokjttEREQVp8KuPUtNTUV6enpFNU9ERETliOM2ERFR9cFxm4iIqGKUemb5vXv3ClyelpaG3377DQqFAu+9916pO0ZERETlh+M2ERFR9cFxm4iIqHKUOlnu4eFR6N25BUGAvb095s6dW+qOERERUfnhuE1ERFR9cNwmIiKqHKVOls+fP19j8JZIJJDJZGjSpAlatGhR5s4RERFR+eC4TUREVH1w3CYiIqocpU6W9+/fvzz7QURERBWI4zYREVH1wXGbiIiocpQ6WZ7fjRs3kJz88m71ZmZmPMtNRERUhXHcJiIiqj44bhMREb05ZUqWHz9+HAsXLhQHbhVzc3PMmDEDXbt2LVPniIiIqPxw3CYiIqo+OG4TERG9eaVOlp86dQoTJkxA48aNMWnSJDRv3hwAcPPmTezatQvjx4/HmjVr4ObmVm6dJSIiotLhuE1ERFR9cNwmIiKqHKVOlq9atQpyuRxbt26FoaGhuLxr164YOnQoBg8ejJUrV3LwJiIiqgI4bhMREVUfHLeJiIgqh1ZpX3j16lX069dPbeBWMTQ0xAcffICrV6+WqXNERERUPjhuExERVR8ct4mIiCpHqZPlenp6SE1NLfT51NRU6OnplbZ5IiIiKkcct4mIiKoPjttERESVo9TJcicnJ2zatAnx8fEaz128eBGbN2+Gs7NzmTpHRERE5YPjNhERUfXBcZuIiKhylLpm+dSpU/HJJ59g8ODBsLOzg6WlJQAgMTERly5dQr169RAcHFxuHSUiIqLS47hNRERUfXDcJiIiqhylnlluYWGBAwcOYNiwYUhNTUV0dDSio6ORmpqK4cOHY//+/TA3Ny/PvhIREVEpcdwmIiKqPjhuExERVY5SzyzPycmBnp4eQkJCEBISovF8RkYGcnJyoKNT6lUQERFROeG4TUREVH1w3CYiIqocpZ5ZPm/ePHzyySeFPj9o0CAsXLiwtM0TERFROeK4TUREVH1w3CYiIqocpU6Wnz59Gl5eXoU+7+XlhdjY2NI2T2WQJ+RVdheoAHxfiKgycdwmIiKqPjhuExERVY5SX7P16NEjNGjQoNDn69evj4cPH5a2eSoDLYkWtl48jYcZqZXdFfp/DYyMMaRtp8ruBhHVYBy3iYiIqg+O20RERJWj1MnyOnXqIDExsdDnb968CSMjo9I2T2X0MCMVyWmPK7sbRERURXDcJiIiqj44bhMREVWOUpdh6dSpE3bs2IG//vpL47k///wTu3btgpubW5k6R0REROWD4zYREVH1wXGbiIiocpR6ZvnEiRNx+vRpDBgwAB4eHmjRogUA4Pr16/j+++9hYmKCiRMnlltHiYiIqPQ4bhMREVUfHLeJiIgqR6mT5Q0aNMCePXuwdOlSnDhxAseOHQMAGBkZoXfv3pg0aVKRNdaIiIjozeG4TUREVH1w3CYiIqocpU6WAy9vKrJo0SIIgoDHj1/WxzYxMYFEIimXzhEREVH54bhNRERUfXDcJiIievPKlCxXkUgkqFevXnk0RURERBWM4zYREVH1wXGbiIjozSn1DT6JiIiIiIiIiIiIiN4WTJYTERERERERERERUY3HZDkRERGVyu3btzFnzhz07dsXrVq1Qq9evQqM++abb+Dl5QVbW1v06dMH33//vUZMeno6QkJC4OjoCAcHB0yYMAGPHj3SiDt//jwGDhwIOzs7dOnSBWvXroUgCGoxgiBg7dq1cHd3h52dHQYOHIgLFy5otPXw4UOMHz8eDg4OcHR0xKxZs5CRkVG6nUFERERERETVHpPlREREVCrXr1/HqVOn0LRpUzRv3rzAmO+++w6fffYZvL29ERUVBXt7e4wbN04jeR0UFISzZ88iNDQUS5YsQWJiIgICApCTkyPG3L59G/7+/jA1NUVkZCRGjBiBsLAwrF+/Xq2tqKgohIWFwdfXF5GRkTA1NYWfnx+SkpLEGKVSiZEjR+LWrVtYunQpQkNDcebMGUyZMqX8dhARERERERFVK+Vyg08iIiKqeTw8PNCtWzcAwIwZM/DHH39oxISFhaFnz54ICgoCAHTo0AHXrl3DypUrERUVBQCIj4/HmTNnoFAo4OrqCgCwtLSEj48Pjh49Ch8fHwCAQqFA3bp1sWzZMkilUjg7O+Px48dYs2YNhg0bBqlUiqysLERGRsLPzw++vr4AgPbt26NHjx5QKBQIDQ0FABw5cgTXr19HdHQ0rKysAAAymQz+/v64dOkS7OzsKmq3ERERERERURXFmeVERERUKlpaRX+NSEpKwq1bt+Dt7a223MfHB3FxccjOzgYAxMbGQiaTwcXFRYyxsrKCjY0NYmNjxWWxsbHo2rUrpFKpWltpaWmIj48H8LJMS0ZGhto6pVIpPD09NdqSy+ViohwAXFxcUKdOHZw6daoku4GIiIiIiIjeEkyWExERUYVISEgA8HKWeH7NmzeHUqkUy6IkJCTA0tISEolELc7Kykps4/nz57h//75aclsVI5FIxDjVv6/GNW/eHPfu3cOLFy/EuFdjJBIJLC0txTaIiIiIiIioZmEZFiIiIqoQqampAF6WN8lP9Vj1fFpaGmrXrq3xemNjY7G0S3p6eoFtSaVSGBgYqLUllUqhp6ensU5BEJCamgp9ff0i16lqq7QEQcDz58/L1AbwMnlvYGBQ5naIipKZmalxk9zKxM89vSnl8dkXBEHjRC8RERFVb0yWExEREZUjpVKJy5cvl7kdAwMDtGrVqhx6RFS4xMREZGZmVnY3RPzc05tSXp/9/KXBiIiIqPpjspyIiIgqhLGxMYCXs8JNTU3F5WlpaWrPy2QyPHjwQOP1qampYoxqFrhqhrlKdnY2MjMz1drKzs5GVlaW2uzytLQ0SCQStbiMjIwC19moUaPSbfD/09XVRYsWLcrUBgDOVqQ3wtLSssrNLCd6E8rjs3/jxo1y6g0RERFVFUyWExERUYVQ1QR/tT54QkICdHV1YWFhIcbFxcVpXM6emJgIa2trAIChoSEaNWqkUU88MTERgiCI7av+TUxMRMuWLdXW2bhxY+jr64tx165dU2tLEAQkJiaq3Wi0NCQSCQwNDcvUBtGbwpInVFOVx2efJ3eIiIjePrzBJxEREVUICwsLNGvWDIcPH1ZbHh0dDWdnZ/HSdTc3N6SmpiIuLk6MSUxMxF9//QU3NzdxmZubG06cOAGlUqnWlkwmg4ODAwCgXbt2MDIyQkxMjBijVCpx9OhRjbauXLmCW7duicvi4uLw9OlTdO7cuXx2ABEREREREVUrnFlOREREpZKZmYlTp04BAJKTk5GRkSEmxh0dHWFiYoLx48cjODgYTZo0gZOTE6Kjo3Hp0iVs2bJFbMfBwQGurq4ICQnB9OnToaenh+XLl0Mul6N79+5inL+/Pw4ePIgpU6Zg0KBBuHbtGhQKBSZNmiQm3vX09BAYGIjw8HCYmJjA2toa27dvx9OnT+Hv7y+25eXlhcjISIwfPx6TJ09GZmYmFi9eDHd3d9jZ2b2J3UdERERERERVDJPlREREVCopKSmYOHGi2jLV402bNsHJyQm9evVCZmYmoqKisHbtWlhaWiIiIkKcCa6yYsUKLFiwAHPmzEFOTg5cXV0xe/Zs6Oj8+1WladOmUCgUWLhwIUaNGgUTExNMmDABfn5+am0FBARAEASsX78ejx8/ho2NDRQKhVj2BXhZV3zdunWYN28eJk+eDB0dHXh6eiIkJKS8dxMRERERERFVE0yWExERUamYm5vj6tWrr40bMGAABgwYUGRM7dq1MX/+fMyfP7/IuHbt2mHXrl1FxkgkEgQGBiIwMLDIuAYNGiA8PLzIGCIiIiIiIqo5WLOciIiIiIiIiIiIiGo8JsuJiIiIiIiIiIiIqMZjspyIiIiIiIiIiIiIajwmy4mIiIiIiIgIe/fuhVwu1/hZsmSJWtw333wDLy8v2Nraok+fPvj+++812kpPT0dISAgcHR3h4OCACRMm4NGjRxpx58+fx8CBA2FnZ4cuXbpg7dq1EARBLUYQBKxduxbu7u6ws7PDwIEDceHChXLddiIiIoA3+CQiIiIiIiKifNatW4fatWuLjxs0aCD+/7vvvsNnn32G0aNHo0OHDoiOjsa4ceOwdetW2Nvbi3FBQUG4ceMGQkNDoaenhxUrViAgIAB79uyBjs7LVMTt27fh7+8PFxcXBAUF4erVq1iyZAm0tbXh7+8vthUVFYWwsDAEBwdDLpdj69at8PPzw/79+2FhYVHxO4SIiGqMajeznGe6iYiIiIiIiCpO69atYW9vL/40atRIfC4sLAw9e/ZEUFAQOnTogM8//xy2trZYuXKlGBMfH48zZ87gyy+/hI+PD7p27YqvvvoKV69exdGjR8U4hUKBunXrYtmyZXB2doavry/8/PywZs0aZGdnAwCysrIQGRkJPz8/+Pr6wtnZGcuWLUOdOnWgUCje3E4hIqIaodoly1XWrVuHnTt3ij9DhgwRn1Od6fb29kZUVBTs7e0xbtw4jeR1UFAQzp49i9DQUCxZsgSJiYkICAhATk6OGKM6021qaorIyEiMGDECYWFhWL9+vVpbqjPdvr6+iIyMhKmpKfz8/JCUlFSh+4GIiIiIiIjoTUhKSsKtW7fg7e2tttzHxwdxcXFigjs2Nhay/2PvrsOjuP63j783hiQECE4gkEBxghS3UtxdS4MFdy/Bi7s7BHeaIsEpEloKVIAvUErRIKFoIBC3ff7gyfxIAy0eQu7XdXHBzs6ePbO77Ozcc+Zz7O0pW7assY6Liwt58+blyJEjxrIjR45QuXJlbGxsYrX15MkTTp06BTwbvBYYGBjrOW1sbKhatWqstkRERN6FBFuGJX/+/Dg4OLzwvufPdAOUKlWKixcvMm/ePJYsWQL835luT09PypUrB4CzszO1atVi37591KpVC4h9ptvGxobSpUvj7+/PwoULcXNzw8bGJs6ZboDPP/+cGjVq4OnpyahRo97rayEiIiIiIiLyrtSpU4dHjx6ROXNmmjVrRocOHbC0tOTq1avAs2Pn5+XIkYOIiAhu3rxJjhw5uHr1Ks7OzphMpljrubi4GG0EBwfz999/4+LiEmcdk8nE1atXKVmypLH+P9fLkSMHK1euJDQ0lKRJk77T7RcRkcQrwYblLxNzpnvgwIGxlteqVYvJkycTHh6OjY3Nf57pjgnLjxw5QtWqVeOc6V60aBGnTp2iZMmS/3qme//+/e95i0VERERERETeXrp06ejZsyeFChXCZDJx8OBBZs6cyd27dxkxYgQBAQEA2Nvbx3pczO2Y+588eRKr5nmMlClTcu7cOeBZWdQXtWVjY0OyZMlitWVjY0OSJEniPKfZbCYgIOCtwnKz2UxwcPArrWsymUiWLNkbP5e8XyEhIXFK5r4reu8/bu/zvQe9/x+z13nvzWZznJO4L5Jgw3Kd6RYRERERERF5d8qXL0/58uWN2+XKlSNJkiSsXLmSLl26xGPP3p+IiAj+/PPPV1o3WbJk5MuX7z33SN7UtWvXCAkJeS9t673/uL3P9x70/n/MXve9f34w9MskuLBcZ7r/nc52fdx0pjvx0nufeL2PM90iIiIiH0rNmjVZtmwZf/75JylTpgSeHSunS5fOWOfJkycAxv329vbcuXMnTlsBAQHGOjHH4zHH3THCw8MJCQmJ1VZ4eDhhYWGxjrmfPHmCyWQy1ntT1tbW5MyZ85XW1e+0j5uzs/N7PeaSj9f7fO9B7//H7HXe+8uXL7/SegkuLNeZ7n+ns10fN53pTrz03ide7+NMt4iIiEh8iLma+urVq7GurL569SrW1tZkzZrVWO/YsWNxBgJcu3aNXLlyAZA8eXIyZcpkXKn9/Dpms9loP+bva9eukSdPnljPmTlz5re+ittkMpE8efK3akM+DhpAlHjpvU+8Xue9f9WTHgkuLH8Rnen+Pzrb9XHTme7ES+994vU+znSLiIiIfCi7du3C0tKSfPnykS5dOrJnz86ePXuoUqVKrHVKly5tnPSvUKEC8+fP59ixY5QpUwZ4FnafP3+eDh06GI+rUKECBw4cYODAgVhbWxtt2dvbU6RIEQCKFi2KnZ0du3fvNsLyiIgI9u3bR4UKFT7IayAiIonHJxGWP09nuuVjprOdiZfe+8TrfZzpFhEREXkf3N3dKVmyJLlz5wbgwIEDbNq0idatWxuD0Xr27MmAAQNwcnKiZMmS7Nq1izNnzrBmzRqjnSJFilCuXDmGDBnCN998Q5IkSZgxYwa5c+emWrVqsZ7P29ub/v3707JlSy5evIinpyd9+/Y1gvckSZLQuXNn5syZg4ODA7ly5WL9+vU8fvwYd3f3D/jqiIhIYvBJhOU60y0iIiIiIiLydpydnfHy8uLOnTtER0eTPXt2hgwZgpubm7FOnTp1CAkJYcmSJSxevBhnZ2fmzp1rHB/HmDlzJhMmTGDEiBFERkZSrlw5hg0bhpXV/8UQ2bJlw9PTk4kTJ9KpUyccHBzo1asX7du3j9VWx44dMZvNLFu2DH9/f/LmzYunp6cxGE5ERORdSXBhuc50i4iIiIiIiLx7w4YNe6X1mjZtStOmTf91nRQpUjB+/HjGjx//r+sVLVqUTZs2/es6JpOJzp0707lz51fqn4iIyJtKcGG5znSLiIiIiIiIiIiIyLuW4MJynekWERERERERERERkXfNIr47ICIiIiIiIiIiIiIS3xSWi4iIiIiIiIiIiEiip7BcRERERERERERERBI9heUiIiIiIiIiIiIikugpLBcRERERERERERGRRE9huYiIiIiIiIiIiIgkegrLRURERERERERERCTRU1guIiIiIiIiIiIiIomewnIRERERERERERERSfQUlouIiIiIiIiIiIhIoqewXERERN6b77//nty5c8f5M3Xq1Fjrbd68merVq1OwYEHq1avHoUOH4rT19OlThgwZQokSJShSpAi9evXi3r17cdY7efIkzZs3x9XVlS+//JLFixdjNptjrWM2m1m8eDEVK1bE1dWV5s2bc/r06Xe67SIiIiIiIpKwWMV3B0REROTTt3TpUlKkSGHczpAhg/HvnTt3Mnz4cLp06UKpUqXYtWsXPXr0YO3atRQuXNhYr0+fPly+fJlRo0aRJEkSZs6cSceOHfHy8sLK6tlPmuvXr+Pu7k7ZsmXp06cPf/31F1OnTsXS0hJ3d3ejrSVLljB79mwGDBhA7ty5Wbt2Le3bt2fbtm1kzZr1/b8gIiIiIiIi8tFRWC4iIiLvXf78+XFwcHjhfbNnz6Z27dr06dMHgFKlSnHx4kXmzZvHkiVLADh16hQ//fQTnp6elCtXDgBnZ2dq1arFvn37qFWrFgCenp6kTp2a6dOnY2NjQ+nSpfH392fhwoW4ublhY2NDWFgYixYton379rRt2xaAzz//nBo1auDp6cmoUaPe62shIiIiIiIiHyeVYREREZF4c/PmTXx9falZs2as5bVq1eLYsWOEh4cDcOTIEezt7SlbtqyxjouLC3nz5uXIkSPGsiNHjlC5cmVsbGxitfXkyRNOnToFPCvTEhgYGOs5bWxsqFq1aqy2REREREREJHFRWC4iIiLvXZ06dcibNy+VK1dm0aJFREVFAXD16lXg2Sjx5+XIkYOIiAhu3rxprOfs7IzJZIq1nouLi9FGcHAwf//9Ny4uLnHWMZlMxnoxf/9zvRw5cnD79m1CQ0PfxSaLiIiIiIhIAqMyLCIiIvLepEuXjp49e1KoUCFMJhMHDx5k5syZ3L17lxEjRhAQEACAvb19rMfF3I65/8mTJ7FqnsdImTIl586dA55NAPqitmxsbEiWLFmstmxsbEiSJEmc5zSbzQQEBJA0adI33maz2UxwcPAbPz6GyWQiWbJkb92OyL8JCQmJMwFufNLnXj6Ud/HZN5vNcU7iioiISMKmsFxERETem/Lly1O+fHnjdrly5UiSJAkrV66kS5cu8diz9yciIoI///zzrdtJliwZ+fLlewc9Enm5a9euERISEt/dMOhzLx/Ku/rsP1/2S0RERBI+heUiIiLyQdWsWZNly5bx559/kjJlSuDZqPB06dIZ6zx58gTAuN/e3p47d+7EaSsgIMBYJ2bkecwI8xjh4eGEhITEais8PJywsLBYo8ufPHmCyWQy1ntT1tbW5MyZ863aADRaUT4IZ2fnj25kuciH8C4++5cvX35HvREREZGPhcJyERERiTcxdcOvXr0aq4b41atXsba2JmvWrMZ6x44di3PJ+7Vr18iVKxcAyZMnJ1OmTEZN8ufXMZvNRvsxf1+7do08efLEes7MmTO/VQkWeBb2JU+e/K3aEPlQVPJEEqt38dnXyR0REZFPjyb4FBERkQ9q165dWFpaki9fPrJmzUr27NnZs2dPnHVKly5tXN5eoUIFAgICOHbsmLHOtWvXOH/+PBUqVDCWVahQgQMHDhARERGrLXt7e4oUKQJA0aJFsbOzY/fu3cY6ERER7Nu3L1ZbIiIiIiIikrhoZLmIiIi8N+7u7pQsWZLcuXMDcODAATZt2kTr1q2Nsis9e/ZkwIABODk5UbJkSXbt2sWZM2dYs2aN0U6RIkUoV64cQ4YM4ZtvviFJkiTMmDGD3LlzU61atVjP5+3tTf/+/WnZsiUXL17E09OTvn37GsF7kiRJ6Ny5M3PmzMHBwYFcuXKxfv16Hj9+jLu7+wd8dURERERERORjorBcRERE3htnZ2e8vLy4c+cO0dHRZM+enSFDhuDm5masU6dOHUJCQliyZAmLFy/G2dmZuXPnGiPBY8ycOZMJEyYwYsQIIiMjKVeuHMOGDcPK6v9+zmTLlg1PT08mTpxIp06dcHBwoFevXrRv3z5WWx07dsRsNrNs2TL8/f3Jmzcvnp6eRtkXERERERERSXwUlouIiMh7M2zYsFdar2nTpjRt2vRf10mRIgXjx49n/Pjx/7pe0aJF2bRp07+uYzKZ6Ny5M507d36l/omIiIiIiMinTzXLRURERERERERERCTRU1guIiIiIiIiIiIiIomewnIRERERERERERERSfQUlouIiIiIiIiIiIhIoqewXEREREREREREREQSPYXlIiIiIiIiIiIiIpLoKSwXERERERERERERkURPYbmIiIiIiIiIiIiIJHoKy0VEREREREREREQk0VNYLiIiIiIiIiIiIiKJnsJyEREREREREREREUn0FJaLiIiIiIiIiIiISKKnsFxEREREREREREREEj2F5SIiIiIiIiIiIiKS6CksFxEREREREREREZFET2G5iIiIiIiIiIiIiCR6CstFREREREREREREJNFTWC4iIiIiIiIiIiIiiZ7CchERERERERERERFJ9BSWi4iIiIiIiIiIiEiip7BcRERERERERERERBI9heUiIiIiIiIiIiIikugpLBcRERERERERERGRRE9huYiIiIiIiIiIiIgkegrLRURERERERERERCTRU1guIiIiIiIiIiIiIomewnIRERERERERERERSfQUlr9jV65coV27dhQuXJiyZcsyefJkwsPD47tbIiIi8gLab4uIiCQc2m+LiMj7ZhXfHfiUBAQE0KZNG7Jnz86cOXO4e/cuEydOJDQ0lBEjRsR390REROQ52m+LiIgkHNpvi4jIh6Cw/B3asGEDQUFBzJ07l1SpUgEQFRXFt99+S+fOncmQIUP8dlBEREQM2m+LiIgkHNpvi4jIh6AyLO/QkSNHKF26tLHjBqhZsybR0dEcPXo0/jomIiIicWi/LSIiknBovy0iIh+CwvJ36OrVq7i4uMRaZm9vT7p06bh69Wo89UpEREReRPttERGRhEP7bRER+RBUhuUdevLkCfb29nGWp0yZkoCAgDdqMyIiArPZzJkzZ175MSaTidJJMxBlk+6NnlPePUsLC86ePYvZbH6vz2MymYjOVgKyRr/X55HXYGGB3wd677OnM+OUJtl7fR55dRYW5tf+fx8REYHJZHqPvZLnfSz77X+jfbq8Lx/qt8mb0O8Zea/e4W8z7bc/rI9lv61988dHx9uJ2Ac63gYdc39s3ufxtsLyj1zMm/i6P8LsbJK+j+7IW/oQP6Ytktm99+eQ1/ch3vtkSa3f+3PI63ud995kMumgO4F70/32v9E+Xd6nj/U7R79n5H17F5997bcTPh1vf1p0vJ14fajvYh1zf3zex/G2wvJ3yN7enqdPn8ZZHhAQQMqUKd+ozSJFirxtt0REROQFtN8WERFJOLTfFhGRD0E1y98hFxeXOLXSnj59yv379+PUVhMREZH4pf22iIhIwqH9toiIfAgKy9+hChUq8PPPP/PkyRNj2Z49e7CwsKBs2bLx2DMRERH5J+23RUREEg7tt0VE5EMwmT/GWX0SqICAAGrXro2zszOdO3fm7t27TJw4kbp16zJixIj47p6IiIg8R/ttERGRhEP7bRER+RAUlr9jV65cYcyYMZw6dQpbW1vq169P3759sbGxie+uiYiIyD9ovy0iIpJwaL8tIiLvm8JyEREREREREREREUn0VLNcRERERERERERERBI9heUiIiIiIiIiIiIikugpLBcRERERERERERGRRE9huYiIiIiIiIiIiIgkegrLRURERERERERERCTRU1guIiIiIiIiIiIiIomewnIRERERERERERERSfQUlouIiIiIiIiIiIhIoqewXEREREQ+atHR0fHdBZEPws/Pj6tXr8Z3N0REREQSLYXlEi900CuSuERGRgJgNpvjuSciklBcvHiRefPmAWBhYaHvD/mkmc1m/P39adSoEbNnz1ZgLiIiryUqKiq+uyDyyVBYLh/EP7+4LSz00UtMdHJErKysCA4OZty4cYSHh8d3d0TkIxcREcGKFStYvXo1c+fOBcBkMikwl0+WyWTCwcGBrl278uOPP7JkyRKuXLkS390SEZEEwtLSEoDz589jNpv1mymRUNbyfljFdwfk0xcVFWV8cS9btgxfX1/CwsKoWrUq5cqVI2nSpPHcQ3mfnn//79y5Q2RkJPb29tjb2wPPRlKZTKb47KJ8IJcuXWLNmjUUL16c6tWrx3d3ROQjZm1tTffu3YmOjmb79u1ERkbSp08fIzDXfkM+NTEHu23btiVJkiRMnDgRKysr3NzcyJUrVzz3TkQSguePu56n/Wbi4enpycKFCzl69Cg2NjZ67z9xylreH5NZp5vkA+nVqxdnz57FyckJCwsLjh07RpMmTWjWrBmurq7x3T15D6Kjo42rCIYMGcIff/zBtWvXyJ8/P19++SWdOnWK5x7Kh/T06VP69OlDqlSpmDBhAtbW1tp5i8gLhYeHY2Njw99//82sWbP43//+R506dejevTugH//y6YmMjMTKyorQ0FDu37/PkCFD+Ouvv6hatSru7u64uLjEdxdF5CMUERGBtbW1sV8MCQlh06ZNWFhY4OLiQtmyZYHYx2Xy6fjn+/r48WMaNGhA2bJlGTduHKDfTJ8qZS3vl0aWywexdu1a/ve//zF9+nTy5MmDra0tnp6eTJkyhWLFilGgQAHtvD9BMe/pN998w/Hjx+nRowcmk4k7d+4wffp0/v77b4YPH673/hP0/FnumB15ihQpqFy5MhMmTKBr167kzJlTP95EJI6oqChsbGx4+vQp+/fv5/Hjx/j7+7Nw4UIsLCzo2rWrRpjLJ8VsNmNlZUVQUBCNGzfG0dGRZMmSkTt3bry8vIiKiqJjx47kyJEjvrsqIh+Rc+fOsWnTJjp27EjWrFkJDQ2lUaNGhIWFcf/+fdKnT88XX3zBiBEjsLCwUGD+iXn+/QwKCsLW1hZbW1s6duzIunXr2Lt3L9WrV9dvpU+Uspb3S6+afBCXL18md+7cuLq6Ymtry9WrV1m+fDk1a9akRo0aWFhYaEKKT9SZM2c4ffo0w4cPp379+jRp0oSKFSsaIx/0vn96zGYzlpaWhIaG8uTJk1g76EaNGpE3b14WL15MRESEfryJSByWlpaEhITQtGlTDh8+jKurKx4eHjg5ObFx40bmzJkDqIa5fDpiPsvffvstlpaWjBw5kvnz57N69Wq++eYbtm3bxpIlSzTpp4jEcv36dTZt2sSCBQvw8/PD09MTR0dHFi9ezPbt26lSpQoHDx7km2++ATACc/k0xBxj9evXj6+//pqLFy9ibW1N9erVsbe3x9vbm5CQkHjupbxPylreH4Xl8s696D/k06dPCQ4Oxtramps3b9KiRQuKFy/OuHHjSJo0KUuWLOHnn3+Oh97KuxYZGRnrdlBQEPfv3ydDhgzY2Njg6+tL+/btqVWrFiNHjsTa2ppTp07FU2/lXYuKisJkMhEeHs63335LiRIlmDp1Kr/88gsASZMmpUKFCvz2228EBAQAmpREROLasmULjx8/ZvDgwXTr1o0GDRqwaNEiChcuzHfffcfChQuBZyGjvkPkUxAeHo6fnx/58uXDyckJK6tnFwC3a9eOgQMHsnXrVpYtW8alS5fiuaci8rGoXbs2U6dOZdu2baxcuZL79+/zxRdfkDNnTpydnencuTNNmjTh559/VmD+iQoPD+fMmTP8+eeftG/fnrlz5xIYGMjYsWM5ePAgW7Zsie8uyjukrOXDUVgu71xM6YUVK1bw+PFjABwdHblx4wb79u2jSZMmlClThjFjxpA8eXL8/Pw4fvw4f/75p858JVDR0dEEBAQQERFhHNw9evTIuC88PJw0adLw9OlTmjVrRpkyZRg9ejTJkiXjwIEDLFu2jNu3b8fnJsg7EB0djaWlJQEBAfTv35/atWvTq1cvfv31Vzp16oSHhwc+Pj506tSJ8PBw1qxZA6BLw0QkjkePHmFhYUG2bNmAZzVZs2TJwoABA0iePDkLFixg/vz5gL5D5NOQJEkSUqVKha+vr7Es5qC4ffv2FC1alAMHDjB37lz8/PziqZci8rGpU6cO48aNY926dWzYsCHWPtHBwQE3NzeaN2/OsWPH8PDwALTfTMj+mZfY2NgwcOBAatSoQcmSJfnrr78YPHgwx48fp1u3bsyfP58//vgjnnor74Kylvihb0l5L65cucLEiRPZtWsXAF26dMHe3p5evXpRpEgRJkyYgJ2dHffv32fevHncunWLmjVrvnD2bvn4/fDDD0ycOJFr164B4ObmxogRI4iMjOTzzz/H1dWVdu3aUbFiRb788kvGjh2Lra0tDx48YPfu3ZhMJmPGZkmYYmrmhYeH06dPHwICAnB0dKRbt25Mnz6diRMncvHiRUaMGEHz5s1xcHDgp59+0gG/iLxQ3rx58ff35/fffwfAysrKCMzbt2+Pra0tK1euZNOmTfHcU5HX97JRnUWLFuXOnTt89913sQ6Kw8PDSZYsGdmyZePx48dkypTpQ3ZXRD4y/wxMGzRowLRp07CysmLv3r3cuHHDuC9lypRGYL5161bjRLMkTDF5iZeXlxGYFixYELPZTLZs2ejduzf16tVj+vTpeHl5ERkZyebNm3n69Gl8dlvegrKW+KGwXN4LJycn6tWrx+7du7l16xbJkiVj/Pjx5M2bl8uXL7N27Vrmzp3L8OHDOXDgALNmzSJr1qzx3W15Qzly5ODw4cMMHz4cNzc3bt26RadOnbCysiJp0qQ0a9YMKysrrKys6NixI3Z2dly5coVp06Zx7NgxevfujZ2dXXxvhryFmKD8zz//xMLCgr59++Lo6Ag8u7KkRo0aeHp6MmfOHFxcXHj8+DHnzp0zLgtT3WGRxOllV5TlypWLkiVLMmvWLM6dO4fJZMLa2hqA27dvU6hQIdq1a0fjxo0/ZHdF3lpkZKSxzzx//jwXL17k1q1bALRq1QonJyfmzZvH1q1bjcfcvHmTiIgIJk6cyMqVK1VGQSQRi5kbKCQkhFGjRhEYGAhA9erVmThxIr///jsLFiyINZI0ZcqUfPXVV4wZM4bOnTvHV9flHTlw4AAzZ86kcePG/PTTT2TOnBl3d3eWLFnClStX+Oqrr/D29qZgwYI8efKEo0ePxneX5S0oa4kfJrMSCnkLZrMZs9lsXMr1/IzMe/fu5ZtvvmHatGlUrlyZqKgoHj58yNixY7l37x4hISHkz58fd3d3cuTIEZ+bIe/ArVu3qF69OjY2NowaNYr69evHun/Dhg1s3ryZq1ev4uLiQmRkJEFBQcydO5c8efLEU6/lXYmOjqZVq1ZcvnyZTJkysW7dOuzs7IzvBLPZHGsyz7Nnz7JixQquXr2Kp6cnDg4O8dh7EYkPkZGRWFlZERISgpeXF8HBwSRPnpwGDRpgZ2fH/v37mTFjBnZ2dvTu3ZvChQvj6+vLuHHjqFmzJm5ubsCzwF1XpklCEPNZDQwMpEOHDjx48IBHjx5hZ2eHu7s7rVu3JiQkhE6dOuHr60vatGlxcnLi/Pnz2Nra4uXlhaWlZZx9qogkDjH7zaioKE6fPk2rVq2oW7cuI0eONMIwb29vBg8eTN26denVqxeZM2eO0472mwnLP7/zIyMjOXfuHCtXruTgwYPUq1ePtm3bcu7cOebPn8+sWbPIkycPgYGBHDt2jDx58mhgYgKnrOXDU1gub+T5UDxGeHg4NjY2sZb16tWLa9eusX79+lhns4KDg7G0tMTS0tK4xFQSNh8fHwYMGIClpSVZs2Zl1KhR5MuXL9aO/fbt2xw5coT79++TPXt2ihUrpkuJPyHHjh1j6NCh3L59m7lz51KpUqU43xPPf3ds376d8ePHs2LFCu3ERRKZmAO/wMBAmjVrhtlsJjo6msePH5M+fXp69uxJtWrV2LNnDxs3buTYsWOkTJkSs9lMpkyZ8PLywsrKSqGhJDhhYWG0bNkSW1tbOnbsiNls5sqVK0yePJnOnTvTt29fwsPDWb9+Pb///jtPnjwhS5YsjBo1ygjJFHKJJD7Pn2wbPXo0ERER/PLLLzx8+JDKlSszefJkbG1tgWeBuYeHB/Xq1aNr164KShOw57/zzWYzjx8/JnXq1Mb9q1evNq7mL1u2LPfv36dw4cK0b9+e5MmTx1e35R1T1vLhKSyX1xYYGMioUaPo2rWrMSJ80qRJeHl54eHhQaFChXBxcQFg//79jB07liFDhlC9enUiIiKMy6glYXtRQBEQEMDdu3fp2LEj6dOnZ8SIEeTPn1+TyHyCXhZQnTx5km7duvHZZ58xYsQIPvvss5e2cevWLZo2bcrIkSOpUaPG++yuiHyEIiMj6d69O0+fPmX8+PE4ODhgb29PmTJlSJMmDbNnz8bZ2Zm///6bv/76i4sXL5I6dWoaNWqEpaWlMcJOJCE5ePAgU6dOZeLEibi6ugLPTh4PGjQIDw8P3NzcYv1uen4wij7zIolbWFgYTZo0IXXq1Li5uZEhQwb27dvHhg0bKFKkCLNmzTIC0h07djBgwAB69+5N165d47nn8iaeD8qnTJnCmTNnuHLlCuXLl6dy5cpUq1YNgHPnznH06FGWLFlCYGAgadKkYfXq1UYmIwmPspb4p1dVXtvVq1e5d+9erEu6SpUqRfny5Zk6dSo9e/ZkxYoVPH78mKpVq5I9e3bWrFkDoKD8ExEVFWV8eQcHBxMcHEx0dDQpU6YkV65cLFq0iHv37jF69GguXLiA2WwmKiqKpUuXcvfu3XjuvbytyMhITCYTUVFRPH36FH9/f+O+okWLMmfOHC5cuMDEiRO5fPnyC9uIiopi+fLlREZGUrBgwQ/VdRH5iDx+/Ji///6bhg0bkj17duzt7Tl8+DD+/v40aNAAZ2dnADJlykTFihXp1KkTTZs2xdLSkqioKIWGkiD8s7b4gwcPePjwIenTpwdg586dDBo0iH79+tGmTRuePHnC6dOnjfVjgnKz2azPvEgiEhISEmfZ8ePHefDgAT169KBq1aq4urrSpUsXRo8ezcmTJxk4cKBRw7xOnTp4enrSsWPHD911eUdigvLevXuzc+dOnJ2dadSoEcePH2fGjBksWLAAgAIFCtC5c2c2btxIkSJFiIiIiHPFvyQcylo+DhpZLq8lpoRCzFnOJUuWUKpUKSPsOnjwIMeOHWP9+vXkypWLatWqkTdvXr755hs8PDzi1FaShOf5M9wzZszgzJkzBAQE4OTkxLhx44zL//766y86depEmjRpqFevHpcuXcLLy4s9e/aQPXv2eNwCeRsx739QUBAjR47k8uXLmEwmXFxcmDJlinFm+5dffqF79+64uroyZMiQF85LsHbtWooUKUK+fPk+9GaISDz456jYmzdvUq9ePaP24s6dO+nfvz99+/alc+fOPH36FC8vLxo1aoS9vX089lzkzcSMDHt+dLiXlxdjx47lxIkT/Pjjj3Tv3p1+/frRqVMnoqOjWbVqFb/99htjx44lVapU8bsBIhIvTp06xYwZM5gyZQoZMmQwlh89epTu3buzdOlSihUrFquk2bx581i+fDm1a9dm8uTJWFpaGsfuuiol4fLx8cHDw4Np06ZRvHhxrKys8PX1Zdq0aVy8eJEuXbrQsGFD470OCAggKipK80ElUMpaPh4aWS6vJSoqCnh2lvPGjRtMmzaNadOmGSNgKlWqxNChQ/Hy8iJPnjxs2LCBHj168PjxY37//XfCw8PjsffytmJmXwfo06cP3t7eFC9enPr16+Pj44Obmxt37twBIHfu3CxbtozAwEBWrFjB6dOn2bp1q768E7CY9z8oKIhmzZrx999/06pVK5o0acLOnTtp164dT58+BaBEiRIsWLCAP/74g4EDB3Lr1q1Y7QC0atVKQblIImJlZUVwcDA+Pj7AsxGzadKk4erVq2zZsoX+/fvTr18/YxTcyZMn2bVrF9euXYvPbou8sZirsNzc3Bg7dizwbLRn+vTpqVmzJt27d8fDw4NOnToBcOXKFQ4cOICjo6OCcpFEzGQyUaBAATJkyMDzYxtTp06NyWTi999/N5aZzWbs7OyoXLkyDg4O7N27l379+gEYg1gUlCdc9+7dIyoqirx582JlZUVkZCTZs2dn4MCB2NjYsGPHDuDZex0z+lhBecKkrOXjorBc/lNUVBRXrlwhICDAKKNy7NgxnJycWLNmDadOnWLWrFn873//Mx6TO3duhg8fzoYNG2jSpAk5cuSgTZs2uhwogYu5HGjevHlcvHiRGTNm0K1bN8LDw4mKisLPz482bdoYX+I5cuRgx44dLF26lDVr1mgSxwTOZDIRERHBiBEjSJ8+PXPmzKFp06YcP36cNGnScOHCBdq3b28E5sWKFWP69OmkTp06VtkmTcYnknjNmTOH3r17Ex0dTYYMGfjqq69YtGgRHh4e9O7dm06dOmFhYcG1a9dYvHgxmTJlUqkmSdDCwsLImjUrvr6++Pv7Y2VlRdeuXUmePDnp0qWjVq1a+Pv789tvvzFkyBDCwsIYOHAgALoAWCRxOX/+PD/99BOFCxdm0KBBBAcHM2zYMM6ePYvZbCZfvny0bt2aOXPm8MMPP2AymYzf1ffu3aNYsWKMHDmSn3/+mYMHD8bz1sjretF3vq2tLQEBAfj5+QHPBi1GRETg5OREt27dOHr0KFeuXAFQ7eoETlnLx0X/m+Q/nTp1iqFDh7Jt2zaio6Np3749Y8eO5eHDhxQrVgxPT09+/fVXZs6cyZkzZ4zHJU2alIwZMzJy5Eg2btz4wjIMkjDEXFEAz+rnhYSE0KhRIwoVKsSyZcuYNWsW48ePZ9KkSdy7d4+uXbsa9bJsbGzImTNnrFm7JWGJjIw0/v3kyRNSpkxJy5YtcXBwoG/fvpw5c4aFCxcydOhQzp49S+/evY3AvEyZMnh6ehqjHUQkcatZsyYODg5s3rwZgJYtWxoTjz19+pT9+/ezYcMG+vfvT1BQENOmTdP3hyRoyZMnp169evz888+cOnUKS0tLqlatiru7O7a2ttStW5f69eszevRokiZNytq1a7GysopVs1REPn3BwcHMmTOHDh068NNPPwFw48YNvLy8mDp1KhcuXACgdevWVKlShT59+jB//nyOHTvGzz//zMqVK0mTJg2lSpUiMjLSCNTk4/b8FXcmkylOYJ4zZ06yZ8/OokWLuHHjBiaTyRjAGBQURNq0aUmRIsUH77e8O8paPk6qWS6vpF+/fuzduxcXFxcCAwNZsGABefLkMWpj/fbbb7Rt25ZixYrRr18/XF1dgbj1SSXheX4m5vXr19O8eXPOnTtHlixZuHv3Ll26dKFjx458/fXXAHTv3p0DBw6QNm1avLy8YtXZk4Tj8ePHBAUF4ejoCEBgYCCnT5+mXLlynDlzhnz58rFnzx6mT5/OmDFjKFu2LJGRkTRr1ozz58+TJUsWtm/fTvLkyeN5S0QkvsT8xIw5+DOZTISGhtK2bVtSpkzJokWLgGcHips2bWL9+vU8evQIZ2dnsmTJwqRJk4xLjvVbQhKC52uN/vN2nz59ePjwIbNmzcLBwQGz2UxoaCj79u3DbDaTMWNGSpQoofrCIonYb7/9xtKlSzlx4gSzZs2iQoUKnD59mi5dupAzZ05GjBhBrly58Pf3Z9WqVaxatYqoqCiSJk2Kk5MTa9eu5e7du7Ru3ZpevXrRsGHDWMdy8vGZMmUKnp6eTJkyhbp16wLEec8WLVrEypUrqVChAu3btydXrlzcvXuX6dOnc+nSJZYvX07KlCnjaxPkLShr+XjpV5j8q5gwfPr06RQtWhRfX19atWplBGjw7ECgWLFirFixgrZt2zJr1iy6d+9O0aJF9UM/gYt5/wE8PDzYtm0bJUqUoECBAlhYWODj40N0dDRlypQxHpM5c2YqV65MUFAQoaGh8dV1eQtRUVH8/PPPzJs3j/Hjx1OoUCHq169PsWLFKFOmjHEy7Pz589ja2lKoUCHgWT3E9OnTkz9/fkJCQkiSJEl8boaIxKN/TioWcyCQNGlSevfuTbdu3di9ezc1a9YkefLktG3blnr16hEaGkqKFCmws7PDZDIpNJQExdLSkpCQEGbNmkXt2rXJnz+/cV/x4sWZO3cuDx8+xMHBgejoaJIlS0b9+vVjtREdHa3PvEgiExOYFStWzCiz0bt3b2bPnk358uVZuHAhnTt35ttvv2XkyJHkypWLPn36UKNGDR4/foyFhQXFixcHYObMmURHR1OqVClA5Q8/dq1bt+bOnTsMHTqU6Oho6tevbwwyMJvNWFhY0LlzZyIiIvj+++/Zt28fzs7OhIeHc+/ePVauXKmgPIFS1vJxUxkWeannD3QfPXpE9uzZKVy4MGvWrMHLy4uAgAAsLCyMyYuKFSvGqlWrOHr0KEuXLiUsLCy+N0HeQszOGeDs2bMEBQWxYMECsmXLZiyPqZ/1999/AxAQEMCdO3coXbo0ixcvJlu2bPHWf3lzlpaWZMmShQwZMtCuXTsqVKiAs7Mzffv2xcLCwvjxZjKZCA8Px9fXF4Br164RFBREpUqVmDp1KpaWlrEuKxORxMPCwoLw8HBatmzJ4MGDjUuMAT777DPy5s3L0aNHAYiIiADAwcGBzJkzkyJFCuNAUaGhJDQ7duzg0KFDxmf/+++/B55Nau3k5MSMGTMAYo1Af55qzookTjFXYxUpUoThw4fz2Wef0atXL44ePUrhwoVZtGgRly9fZvTo0Vy4cAGz2UyePHkoVaoUJUqU4Pjx43Tq1Imff/6ZhQsXkilTpnjeInkVGTJkYPDgwVSqVIlhw4axbds2AKMefcyxVI8ePRgzZgx9+vTBycmJmjVrsmHDBtWpTqCUtXz8VIZFXuj5y0aPHDnCZ599Zuxw+/fvz969exkwYAANGjQgVapUmM1mIiIisLGx4cyZM9ja2qpG+SdiwoQJnD9/nocPH7J69WrSpEljnEi5e/cuX331FcmTJydXrlyEhITw+++/891335E1a9b47rq8pWPHjtG+fXssLS2ZPHkytWrVAv7vRNrFixdxc3MjU6ZMZMqUidu3b2NlZcWmTZteGgKISOLh5+fH7t27+e677zCbzaROnZpevXpRuHBhjh49Sp8+fdi9ezdOTk7x3VWRN/b8yLAYISEh7N69m927d/Pbb7+RJ08eatSowaNHjzh+/DhDhgzB1dVV5RFEErE//viDa9euUadOHeDZd0lUVBTW1tZcvHiR5s2bkyxZMkJCQpg5cyZffPEFp0+fpmvXrnz22WcMHDjQmAA7ODiYX375hf3799OuXTty5swZn5smb+D+/fuMGzeOAwcOMHbsWOOqo+dzmevXr3P27FmyZ89OgQIF4rO78o4oa/l4aeiCxBEdHW18IQ8YMIAZM2awefNm/P39AZg2bRrVq1dn6tSpbN++ncePH2MymZg2bRpbt27F1dVVQfknJGnSpJw7d47r169z69YtAOOKgwwZMrBixQoyZsyIr68vT58+ZdWqVfryTuBiJvS8e/culSpVonDhwgwbNoxjx47FWidXrlwsW7aMjBkzEhgYSO7cudm4caNGlIskUv/8f+/o6EiHDh1Yu3YtAwcOJHny5Hh4eNC0aVMuXryIg4MDq1atMkaWiyQ0kZGRWFhYEBERwaNHj7h06RIAyZIlo1GjRkyaNIkNGzaQJk0adu3axcKFCzl9+jQnTpwAVB5BJLGKiIhgxYoVDBgwAG9vb+DZMbi1tTVnz56lWbNm1K9fn+nTp/P555/Tp08ffHx8KFy4MAsWLOCXX35hy5YtRnvJkyenXLlyjBw5UkF5ApUuXTqGDh1K5cqVY40wj8llbty4wYQJExg4cKDKrnxClLV8vDSyXF7Kw8ODEydOMGLECAoVKkTq1Klj1Q7t378/+/bto2LFigDs37+frVu36lKgBOz50VHP/3v16tVMnDiRL774gv79+xsnQyIiIrC2tiY0NBQrKyvCw8M1oWMC9s+JycLDw7GysuLkyZMsWLCAkydPMm/ePMqUKUN0dDTw4svFVWNYJPGJ+X8fGhrKiRMn8PPz47PPPsPR0ZHMmTMb6/n4+PD777+zdu1awsLCcHJyYtOmTdjZ2WmUrSQoMfvMwMBA+vTpw7Vr1/Dz8+Pzzz+ndu3atGjRwthHRkREcPv2bXbt2sX27dsJDg7G09NToZZIInbv3j0mTZrEvn37GD16NA0bNuTcuXN8/fXX1K1bl+HDh2NjY8Pp06eZO3cuJ0+eZObMmVSoUIFLly7h4uKiKzk/Qc+PMB8/fjx169blxo0bTJo0iePHj7N69Wry5csX392UN6CsJWFRWC4v9Oeff9KtWzd69+5NvXr1YgViMf9pAcaOHcuff/6JpaUlw4YNI1euXPHVZXlLzwelwcHBhISEkCZNGuP+RYsWsWzZMipVqkSHDh2ML/EXXX4sCU/M+x8aGsru3btJliwZWbNmNSYn++WXX1i0aBGnTp1i0aJFFC9enLt37zJ58mQaNmxIuXLlgLizt4vIpy9mPxAYGEirVq149OgRDx8+xMbGhqxZszJ27FhjYuAYly9f5ueff2bBggW0aNGC3r17x1PvRd5caGgoTZo0IXXq1FSpUoX06dPj6elJQEAAVapUYdCgQXH2iceOHWPAgAEMHz6cGjVqxFPPReRj8Hww2rlzZ5YvX06tWrUYPHgwyZMnN74/Tp8+zYIFC/Dx8WHDhg0ULlwY0ACVT9Xzn4t+/fpx4cIF9u3bx7p168ibN298d0/egLKWhEffrGJ4PuS6c+cOd+7coVixYlhYWBj/Sc1mM9bW1sbtYcOG8eTJE6ytrUmWLFk8b4G8qee/vEeNGsX58+e5dOkSFStWpEyZMjRt2pTOnTtjNptZsWIFAB07dsTFxUVf3p8IS0tLgoKCaNGiBY8fP+bBgwfkzJmTBg0a4O7uTokSJTCZTCxYsIDWrVvTqlUrTp48SVhYGKVKlTLaUVAukvjETObZrVs3UqVKxciRIylQoADe3t58//33tGnThpUrV+Lq6kp0dDQmk4mcOXOSNWtWLly4wO+//05wcLBGy0iC8Pxvpn379hEYGMjUqVPJnTs3JpOJkiVLMmXKFH744QecnZ1p1qwZ8H8HvKVLlyZr1qzs2rWL6tWra78pkoilS5eOYcOGYWFhwaJFiyhYsCBjxowx7o/53ihcuDAdOnTA2dk5Vq1qBeWfppiSLFZWVkyaNAkrKys2b96soDyBUtaSMOmVT8RCQkL47bffuHfvHhA75EqdOjVms5kLFy4AGIF5zDq7d+826i3a29srKE/gYr68+/fvj4+PD5UqVaJ///6EhYWxaNEiJkyYAECXLl1o3749P/74IzNnzsTX1zceey3vQkx98ujoaObMmUP69OlZsGABy5YtI1euXCxfvpy5c+cCULx4cXr37k3Tpk355ZdfyJYtG1u3bsXKyko1ykUSkdDQUODZSfYYf//9N76+vjRu3JiiRYtiY2ND48aN6devH/ny5WPEiBHcu3cPCwsL47dEkiRJKFy4MJcuXeLp06fxsi0ir+LChQtMmzYNINa8HDdv3iQ8PJw8efJgMpkIDw/HwcGBgQMHkiJFCnbs2GG0EfNbOjg4GIAUKVIY+2ARSbzSpk3L4MGDqVGjBmfPnjVqVccMZIvZ1xYvXpzBgwdjZWWl745EIF26dAwcOJCWLVuybds2BeUJmLKWhEmnIhOp8PBwGjduzNWrVylTpgy1atWiXr162NjYAM8C8GzZsrFlyxacnZ3JkSOHcVbLz8+PvXv3UrJkSYoVK6ZaaQnY85f1/PLLL5w6dYrRo0dTokQJkiRJQo4cOWjXrh3h4eGEhoaSNGlSOnXqRHBwMDt37tQowE+AlZUVwcHBHD58mMDAQOrWrWuMWMmYMSO2trasXbsWgB49elCoUCHy5ctHUFAQKVOmxGQy6RJQkUTk6tWrtG3blrlz5+Lq6moczPv7+3Pv3j0cHBwACAsLI0mSJHz++ec0aNCA8ePH4+fnR/r06Y22wsPDOXnyJKlSpTLKu4l8bCIjI/nuu+/47rvviI6OZuDAgcZvXxcXF/z9/Tl27BilS5fGxsbGCMzbtWvHwIEDuX79Ok5OTphMJiwsLDh37hynT59m1KhR+tyLCADp06dn0KBBREREMGzYMMxmMw0aNABin5iOod/diUOGDBmMEeaS8ChrSdg0sjyRsrGxMWokBQQEMGrUKNq0acOyZcuIiorCxcWF3r17c+DAAebPn8/x48eBZyNrFi5cyKlTpyhfvryC8gQoJCSErVu3Av83ygngwYMHBAcHkytXLpIkScLVq1fp06cPtWvXZtCgQSRNmpSzZ88C0KdPHzZt2hQr9JCE6/vvv6dfv354e3tjZ2dnLHd2dqZDhw5UrVqVdevWMW/ePACsra1JlSoVJpOJ6Oho/YATSUSCg4NJlSoV3bp14/z588Yo8bx585IlSxY2bdoEPBs1HhYWBkDt2rUJCwvjypUrsdoKDQ3l5s2bTJkyxQjZRT42VlZWuLu707hxY7y9vZk0aZJxn5OTEzlz5mTlypX8+eefwLPf2Gazmfv375MtWzZjfxmjRIkSHD16lDx58nzwbRGRj1dM6Y3KlSszYsQItm/fDqjEYWKn46yERVnLp0NheSIU8x+2Z8+e2NvbkydPHhYvXkxgYCAzZ86kRo0arF27li+//JI5c+Zw/PhxunXrRunSpenWrRs+Pj4sXrwYJyeneN4SeV1msxlPT08GDx7MsmXLAIyznTY2NgQEBGBjY8PDhw9p0aIFpUuXZvTo0SRLloydO3eyc+dOHjx4AECqVKniazPkHfv6668ZOHAgYWFh/PDDD8Z7DM+CgA4dOlC9enXmzJnD999/H+uxqqMmkrgUKFCA8ePH4+TkRLt27Th//jzw7BLTr776iqNHjzJ58mTgWWAOcO7cOdKmTUumTJmMdsxmM/b29qxcuTJW/VWRj43ZbCZTpkx06tSJSpUqsXPnTiZOnAhA/vz5admyJWfOnGHWrFn8/PPPREdHc+7cOfbu3UuOHDmwt7eP0+bzk3qJiMSIqWFetWpVBg0axI8//hjfXRKRV6Ss5dOi01SJUMx/2GzZsuHi4sKFCxcYNmwYq1ev5scff8Tb25uJEyeyaNEihg8fTp8+fUiVKhXnzp0je/bsFC9enCxZssTzVsibMJlMVK1alTt37rBkyRIiIyPp1KkT8OzzkDNnToYOHcqvv/5KhQoV+Pbbb7G1teXBgwfs27ePZMmSGZcDaZRDwvT8BCPPc3d3JygoiPnz55M1a1ZatWpl7KSdnJxwc3PD0dGR+vXrf+Aei8jHIqbkSoECBfDw8GDChAm0a9eOZcuWkT9/fmrUqMH169fZuHEjN27coEmTJvj7+7Np0yYyZsz4wsmAdYWafOxiagZnyJCBrl27ArBr1y6io6MZMmQIrVq1wmQysX79etq3b0/KlCmxsbEhffr0zJo1y7gKSyeXReRVpE2blkGDBuHk5ETp0qXjuzsi8oqUtXxaTOYXFcGSRGP37t307duXSZMmxQrBqlevzt27dwkNDeWzzz6jcOHCDBw48IWjYyThiKkt7evry+rVq9mzZw+dOnWiTZs2AMyYMQNPT0+yZs3KnDlzyJkzJzdu3GDRokX4+PiwatUqXFxc4nkr5E3FvP8hISFs2bKFgIAA0qZNS4UKFciQIQMA06dPZ/HixfTs2TNWYP6idkQk8YgJyp935swZJk6cyJUrV/D09KRAgQL8/fff7N27l3Xr1nHr1i0yZMhAjhw5WLBgAdbW1i89YSfysXnZZ/X27dssWrSIQ4cOUb16dYYOHQo8K1V469YtfH19yZw5M9WrV8fS0lL7TBF5K/oOEUkYlLV8WhSWJ3KBgYF069aNu3fvsm7dOtKkSUP//v05cuQI8+fPJzw8nI0bN3L69GnWrVunEeUJ2PMHffv37+enn37iwIEDPHjwAA8PD+NLfMqUKfzwww/As0lFgoKCePjwIQsWLNAs3AlYTNAVGBhI06ZNiYqKwsrKCj8/PwoWLEiDBg1o0qQJALNmzWLRokX07NmT5s2bq5awSCIX8+M/ZnRszOSd8CwwnzBhAlevXjUC88jISKKjo/nrr79IlSoVjo6OWFhY6IBfEoznTy7v2LGDgIAAcubMSb58+UifPj1+fn4sWbKEgwcPUqNGDYYMGfLCdnRySERE5NOnrOXTo7BcWLFiBdOmTWPGjBls376dEydOMH36dEqVKoWlpSWBgYEAsSb+k4SrR48eXLlyhaJFi5I2bVq2bNlCSEgIHTt2NC4TOnz4MJcuXeL69evkz5+fcuXKkTVr1njuubyJ5y/9joyMpHv37gQFBTFmzBgcHR0xmUxUrlyZlClTMmXKFGPCsVmzZrFgwQLGjx9Po0aN4nMTRCQexfz4DwwMZOzYsfj5+WFnZ8cXX3xBixYtAPjjjz8YO3YsV69eNUqy/JPKUEhCExQURJMmTQgMDCQwMNC4PHrWrFnkz58fPz8/Fi9ezOHDh6lVqxbffPNNfHdZRERE4pGylk+HwvJE7PlLqhs1asT58+dJmzYt06ZNo1ixYhoJ8wnat28fQ4YMYc6cORQrVgxra2v++OMPli9fzo8//kiXLl1o165dfHdT3oHHjx8bJVRiQqpHjx7Rpk0b3NzcaNSoEZaWlhw5coROnToxePBg2rZtG2vk56ZNm2jUqJFGgookUjG/E0JDQ2nSpAmWlpZkzZqViIgIfHx8qF27Nn369CFr1qycOXOGSZMmce3aNRYsWEChQoXiu/sir+35feDUqVM5c+YMgwcPJkeOHPz444+sWbOG//3vf6xdu5Z8+fJx+/Ztli5dyoYNGxgyZAhff/11PG+BiIiIxAdlLZ8WDfFJxGImLIJnYbm9vT2NGzemRIkSCso/UcHBwZjNZtKnT4+1tTVms5n8+fPToUMHHB0dmTRpEmvXro3vbspbunLlCl26dGHz5s3A/03qGxAQwKVLl7Czs8PS0pIdO3bQqVMn+vbtS9u2bQkMDGTz5s3cvXsXgGbNmmFlZUVkZGS8bYuIxI/o6GhjYkIfHx8cHByYNWsWc+fOZe7cucybN48ffviBWbNmAeDq6so333xDypQpmTt3bjz3XuTNWFlZERwczPz584mMjKR69erky5ePJEmSUKlSJYYPH46rqyt9+vTh/v37ZM6cmQ4dOtC/f39atmwZ390XERGReKKs5dOisDyRixlZXqVKFZImTcrFixc18+4nzM7OjuDgYKO0TmRkJGazmTx58tC+fXuSJEnCmDFjWLFiRfx2VN5KdHQ0jx8/ZvPmzWzbts1YniJFCnLlysW5c+fYtGkTAwYMoG/fvnTs2BGA48ePs2PHDiMsj6GR5SKJT0xt8nbt2uHt7U2aNGnInj27cV/lypWZNm0aO3bsME7Mubq6MnfuXBYuXBiPPRd5Oz4+PsyePZsVK1YQEREBPCtHZGFhgbOzM1999RWBgYH8+eefAGTOnBl3d3csLS2JioqKz66LiIhIPFHW8mlRWC4AZMyYkV69enHo0CGOHDkS392Rt/Syg7XSpUtTtGhRBg8ezJ07d7C2tjbue/LkCXnz5qVdu3ZUrFjxA/VU3jWz2cxnn33GnDlzMJvNrFq1ii1btgCQJk0aKlasiKenJyNGjKB79+507twZk8mEr68vy5cvJ23atBQoUCCet0JEPgYmkwkrKyt++OEHrly5gr+/P4ARClaoUIGyZcvi4+NDeHg4ADly5FBoKAlaqVKlGD16NGnSpOHAgQOEhIQYn2kLCwvKli1LUFAQV69ejfNYXZkpIiLyaVPWkjgoLBdDuXLlcHV1xcnJKb67Im/h+ZmYjx07xr59+zh06BCBgYHY2trSsWNHzGYzXbt25cqVK0RFRfHgwQPOnTtH0aJF6dmzpzF6UBKemJIpn332Gf379ycoKIj169ezfft2APr27Yubmxvw7FKxAwcOsGHDBgYMGEBQUBDTpk3DwsKC6OjoeNsGEYkfz09jYzabsbGxYc6cOTRs2JCLFy+yZ88egoKCgGehoI2NDdbW1gQGBmJjYxOrLYWGkhC86IA3derUVKlShR49enD27FmGDRtGUFCQ8Zm+cuUKtra2pEyZ8kN3V0REROKRspbEQ9fWiyFjxoysWbMmzgGvJBxms9n48u7Xrx+//fYbAQEBhIWFUbRoUdq1a0fVqlUJDQ1lyZIlNGzYkGzZshEREcHdu3fZuHEjyZMnj+etkDcVHR1tBFcdOnQgc+bMBAQEcPPmTebOnYvZbKZ+/foMHTqU5MmT89NPPxmTlGXLlo1JkyYZNcpVekUkcYn5fx8ZGUlUVBTh4eGkSJGC5MmTM2rUKIKCgpgyZQpms5maNWvi4ODApUuX8PX1pUSJEvHdfZHXFnPAGxwczNy5c4mOjiZbtmw0bNgQBwcHatSogdlsZvLkydy9e5cKFSpgY2PD7t27yZAhA/Xq1YvvTRAREZEPRFlL4mIyPz+MSEQSrOjoaGMix7Fjx7J//37Gjh1LunTpCAwMZNiwYZhMJnr06EHt2rW5ffs2+/fv56+//iJlypQ0bdoUFxeXeN4KeVvh4eG0bt0aKysrhgwZgoODA76+vgwfPpzkyZPTvn176tevD4C/vz9BQUE4ODiQPHlyTCaTgnKRRMhsNmMymQgMDKRXr148ePAACwsLunfvTtWqVYFn3y0DBgxg37595MiRAxcXFx48eEBYWBgbN240JjLSvCeSkISGhtK4cWPMZjMhISFER0fj5OTEwoULsbW15dGjR+zZs4c5c+bg7+9Px44dsbKyomvXrtjY2MQaYSYiIiKfJmUtiY/CcpEELCQkhI0bN1KvXj0cHByIjo4mODgYd3d3SpYsSe/evY2DOD8/P3r06IHZbGbu3LlkyZIlnnsv78Pt27dp3rw53bt3p0WLFsbyCxcu0KNHDywsLOjRo8cLR8Qp6BJJvCIiImjXrh0mk4k8efJw/fp1jhw5wqBBg3Bzc8Pa2pqwsDBGjRrFli1baNiwIV9++SXVqlUzHv98bUaRj9Xz+7pDhw6xZs0axo4di62tLTt27MDT0xN7e3tWr16NnZ0d/v7+7Nu3jxkzZlCtWjXGjBkDPDuBpKsxRUREPk3KWhI31SwXSaDCw8Np2bIlU6dOZcmSJfj7+2NhYUFQUBAXL14kefLkWFpaYjabiYyMxNHRkUmTJnHlyhV8fHziu/vynkRGRnL//n2SJUsGPLvMPCoqijx58jB27Fhu377NunXrWLduXZzHKigXSVyen5sgIiICOzs7Ro0axdChQ5k8eTIdO3ZkypQpLF++nIiICJIkScLIkSOpWbMmhw4dIjQ01GhDQbkkBJGRkZhMJuNza2VlRYYMGciUKRP29vY0adKEnj17EhAQgJubG4GBgTg4OFCtWjV69+7Ntm3bGDx4MICCchERkU+UshZRWC6SQN27d4/r168TFRXFyZMnWbhwIf7+/mTIkIEiRYpw+PBhbty4gclkMr7Is2fPTubMmbl27Vp8d1/egX9OxgeQJUsWypYty/r16/H19Y11ebijoyO2trZcu3aNP/74A11YJJJ4RUZGYmFhQXh4OOfOnePy5csEBASQIUMGAFKlSkXnzp3p1KkTM2bMYMWKFURERJA0aVImTZpEiRIlGDduHF5eXoSFhcXz1oj8t+joaKysrAgMDKRfv358/fXXTJgwgZCQEGMdGxsbateuTa9evXjy5Alt2rQxAvN69eoxZMgQtm7dysiRI+NxS0REROR9UtYiCstFEiCz2UyWLFkYMmQI2bJlI2nSpPj4+LBkyRJCQ0OpV68e586dY/Pmzdy+fRuTyYTJZMLf3x8rKysyZcoU35sgbylmdFzMRHzBwcEAWFhYUKNGDe7du8eyZcuMwNxsNvPo0SPKly/PkiVLGD16NCaTSYG5SCJkNpuN0LBFixZ06NABd3d3Ll68yI0bN4x17Ozs6NixI506dWLWrFnMmzePyMhIbGxsmDp1Kvnz52fevHlERETE8xaJ/LuYWqNhYWG0bduWS5cukSlTJp4+fcru3bvZuHGjsa61tTW1a9emd+/eXLp0iXHjxgFgZ2dH7dq1GTt2LK1bt46vTREREZH3SFmLAGgWN5EE5vnJpPLmzYujoyMNGzbkzJkzeHt7Y2lpyYABA7h16xbz5s3D19eXJk2aYDab2bVrFw8fPjQmbJOEKSoqygi6Ro4cybVr17CxsSF//vwMHDiQpk2bcufOHbZt28aff/5J8+bNCQ4OZvv27SRPnpyCBQsaQbsmJhNJXGJqi0dFRTFhwgRsbW0ZPXo0Z8+eZfPmzQwePJjp06eTM2fOWIF5YGAgv/zyizG5kY2NjTHKxs7OLp63SuTlzGYzFhYWREZGcv36ddKlS8egQYNwdnY2Ju1cuXIlFhYWNG3aFHgWmNeoUYPUqVNTpkwZo60UKVLQuHFjlS0TERH5BClrkRgKy0USiJiJpJ4PNwsUKEDGjBlZunQpXl5eWFpasm3bNkwmE/3798fBwYE1a9bQuXNn0qVLh729PStWrMDJySket0TelqWlJcHBwTRr1ozUqVNTunRprK2t+f777zl16hQjR46kZ8+eZM6cmb179zJs2DAyZMhA9uzZWbp0qTGiXEG5SOLw119/cefOHb744gusra0JDQ3l+PHjBAYG4u7uTsWKFalWrRrZs2dn1apVDB48mIkTJ8YKzAcMGEDSpEmNes8WFhbY2NiQMWPG+N48kX9lMpmIjIykWbNmhISE4OjoSNasWQFwcHBg/vz59OjRg+XLl2MymWjSpAnw7IRQ+fLlgdgHzwrKRUREPi3KWuSfFJaLJADBwcE0atSIDBky0L59e3LlymVc3tO/f3/atm2Ll5cX33zzDUFBQWzbtg2AXr160aBBAy5duoStrS0ODg44ODjE56bIO7Jq1SqSJk3K6NGjyZEjBwApU6Zk0qRJ3L17F4DGjRvTuHFjbt68ibW1NenTpzdG11lZ6etfJDH49ddfcXNzY8SIEUbgN3LkSLZt24ajoyN9+/Y11o0ZMbty5Uo8PDyYOHGi8f0SM2lwzChdkYQkOjqa2rVrs2jRIgD+/vtvsmTJgtlsJkOGDMydO5eePXuycuVKQkJCcHNzi/V4nVwWERH5NClrkRfR0Y5IArB06VJ8fX05ceIEXl5edO/enT179vDo0SMcHBwoUaIEe/fuBWD06NFUrFgRb29vZs2aRUhICIUKFSJnzpz68k6AQkJCiI6OjrP84sWL2NnZGUHW9u3bmTJlCv369aNatWoEBgYa62bNmpWMGTNiYWFhTHAmIp++3377jfbt2+Pm5kajRo2MwG/SpEl88cUX+Pn5sWXLlljfF40aNaJt27ZERUXRsWNHbt26FatNjaqVhOCf+00bGxuaN29O3759uXnzJsuXLyc0NNTYL2bIkIE5c+YQHBzM6dOnNZ+HiIhIIqGsRV5EYblIAtCsWTOaNGlCihQpsLOzo2HDhnh4eDB06FC8vLxwd3fn119/ZfPmzcCzL/FKlSqxbt06Vq9e/cKwVT5+Fy9e5Ntvv2X+/PlGmGU2m4mKisLa2hobGxsAvL29GTRoEH369KFTp06Eh4czZswY4/PwPI0IFUkcfvvtN9q2bUuLFi3o378/SZMmjbUvWLRoESVLlmTdunX88MMPhIaGGvc1bNiQxo0bU6JECU1SJAlOZGQkFhYWREREcP/+fW7evAk8m6CzadOmDBo0iA0bNjB9+nRCQkJiBeabN29m8uTJmgBbREQkkVDWIi+i4YUiCUDGjBnp1asXoaGheHt7U6tWLXbu3MnGjRuZPHkyuXLlIk2aNBw4cICKFSuSLl06RowYgY2NDQ0bNlRAmgCdPHmSfv36kT17dtKkSWNMoGcymbC0tKRYsWKMGDGCSZMmsWrVKiMoB7hy5Qp+fn7kzZs3PjdBROLJH3/8wddff02PHj1wd3cnSZIkRp3xs2fPsm3bNoYNG8bKlSv56quvmDBhAmazmZo1a5I0aVIAWrVqhdls1mTAkqA8PwF27969uX79OmFhYWTLlo3+/fuTL18+WrdujclkYuLEiZhMJvr27UuyZMkwm83GqDB95kVERBIHZS3yInpXRRKI9OnT880331CpUiW6dOnCH3/8Qd++fdmzZw/Ozs6kS5cOACsrK+Ps5uDBg8mWLVt8dlvewB9//EHXrl2pXLkyY8eOZeDAgcCzg/cY9erVo3r16ixfvpx69erRpUsXAP78809GjRqFyWSKU3NVRD59ERERbNiwAYBMmTKRLFkyIyg/f/48LVq0IDAwkJCQEADWrVtHjhw5mDRpEnv27DGWA5oMWBIcS0tLQkJCaNmyJREREXTs2JFhw4ZhaWlJ586d2bx5M1FRUTRv3hwPDw/Wr1/P6NGjCQ8Pj1ViSJ95ERGRxENZi/yTRpaLJCDp0qVj2LBhAPTt25fRo0fTqFEjRo4cyZ07d7C0tCR16tTx3Et5G+Hh4axdu5aSJUvStWtX0qZNa9z3/MF7kiRJ6NOnD2azmS1bthAREYG/vz+PHj3CwsKCNWvWYGlpqdFxIomMtbU1PXr0IDg4mFGjRmFpaUmDBg04d+4cX3/9NU2aNGHw4MEkS5bMmOx33bp1uLm5MXjwYFKnTs0XX3xhtKca5ZLQ7Nu3D7PZjIeHh3GFlclkolevXqRIkQJLS0ssLS1p1qwZwcHB+Pj4aC4PERGRRE5ZizxPvwxFEpiYL3GTycTIkSMxmUw0bNgQR0fH+O6avCNnz56lRIkSsYLye/fusX//fn788UesrKwoUqQI7u7uzJo1i7Vr13L+/HkyZ85M5cqVad68OVZWVkYQJiKJS4YMGRg8eDBRUVEMHz6cW7dusXz5curWrcugQYNIliwZ8Gx0TMwJtdWrVzN+/HjKli0bz70XeTtXr14lODjYCMq3bt2Kh4cHffv2pX79+gQGBhIcHEz69Olp27YtHTt2xGQyGVdgiIiISOKkrEViKEURSYDSpUvH0KFDARgxYgRWVlbUrVs3nnsl70J0dDS2trYEBATw6NEjUqRIwalTpxgxYgTXrl0jbdq0REZGcvDgQa5evcq4ceNo1apVnIP8mLqtIpI4xfzYt7CwYNGiRRQsWJAxY8YAxPq+uHPnDpMmTaJhw4YMGTIEQCfaJEGKqbGfOnVqrK2tCQ8PZ9++fQwePJh+/frRqVMnoqKimDdvHjY2NvTq1cuYKNtsNisoFxEREWUtAigsF0mwYr7ELS0tGThwIJaWltSqVSu+uyVvKWnSpAwYMIDWrVtz9+5dLC0t+e2338iUKRM9e/bE3d2dBw8eMHbsWHbv3k2zZs0oVKhQnHZUekVE0qZNy+DBg7G0tGTPnj1s27aN+vXrG6Hg7du36du3LwEBAZQvX954nIJySQj+WWYspmSQq6srkydPplevXvz000/07duXTp06YTabuXbtGufOnaN48eIvfKyIiIiIshbR0ZBIApYuXToGDRqEjY0NuXPnju/uyDtSrFgxVq1axeTJk3n48CHu7u5Ur16dfPnyAZAlSxY6duzI4cOHefz4MYBGxInIC6VPn55BgwYRERFh1GGsX78+fn5+9OvXj+DgYHbs2BGrJIvIxy7msxoaGsrBgwcJDQ2ldOnSpEmThqJFi9KnTx+mT59O8eLFadSoEdHR0fzvf/9j4sSJmEwmunXrFt+bICIiIh8xZS2Jm8lsNpvjuxMi8nZ0yfynKSIigtDQUFKkSBFreVRUFJs3b8bT05MFCxaQM2fOeOqhiCQU9+/fZ9y4cRw8eJD+/ftz+PBh7t27x9atW7G2ttZ+RBKcoKAgmjdvzqNHj3j48CFZs2alZcuWfPXVVyRJkoSlS5cyffp0ChQoQEBAAEmTJiV58uSsXr0aa2trnRwSERGR/6TfyImTwnIRkQQgZidtNpu5efMmHh4epE2blhkzZmhUuYi8kgcPHjBhwgR27tyJi4sL27ZtU1AuCYbZbCYiIsKoMz5x4kQuXbpEr169SJs2LWPGjOHq1avUq1cPd3d3kiVLxu+//86FCxd4/PgxuXPn5ssvv8TS0lKfeRERERF5KYXlIiIJREBAAL/++iuenp4EBwfj5eWFlZVVnMk9RURe5s6dO2zZsoWOHTtiZWWl0FA+en/99RdPnz6lWLFiAAQGBrJt2zbu3buHi4sL9evXByA0NJShQ4fyv//9j3r16tG+fXvs7OyMiT9jaES5iIiIiPwbheUiIglAYGAg9erVI2XKlGTNmpXp06cr6BKRt6LvD/nYBQYG0qdPH06fPs2CBQsoXrw43333nVF/f/bs2VSrVo3w8HBsbGwIDw9n8ODBnDt3jnr16tGuXTtsbW3jeStEREREJCFRWC4ikkBcuHCBmzdvUrlyZSwsLBR0iYjIJ+/w4cOsWrUKX19fpk6dStGiRVm+fDmzZ8+mUqVKTJs2DXg2z4e1tTXh4eEMGTKEAwcOMHToUJo0aRLPWyAiIiIiCYnCchGRBEilV0RE5FP2fPmUn376iUWLFuHn58e0adMoUqQIixcvZsaMGbRq1coYaR4TmIeFhbF48WK6deumkisiIiIi8loUlouIiIiIyEcl5hDlvwLz6dOn8/XXXxuBeUxJlhiqUS4iIiIir0PX74uIiIiISLy7ePEid+7coUKFCphMJsxmszHCvFy5cgAsWrSI/v37M2PGDDp16gTAzJkzsbCwYMiQIbGCckBBuYiIiIi8Fl3DLyIiIiIi8So8PJxJkyYxaNAgfHx8gP8bVR4zyrxcuXJ07twZR0dHRo0aha+vL23atKF///6sWrWKlStXxlv/RUQk/uXOnZs5c+a8l7ZPnDhB7ty52bNnzztrc86cOeTOnfudtSev7n1+ViThU1guIiIiIiLxysbGhmHDhpE3b17GjRvH4cOHgRcH5l999RWPHj1i7969JEmShPr16zN58mRatWoVX90XEZH3bO3ateTOnZumTZvGd1fizYkTJ+jRowdly5alQIEClC5dmi5durBv37747tpHycfHR4G4vBGF5SIiIiIiEu+cnZ359ttvyZAhA+PHj39pYF6zZk1cXV3Zu3cvZrOZtGnTUq9ePaysrIiMjIyv7ouIyHvk7e2No6MjZ86c4fr16/HdnQ9u9uzZtG7dmkuXLtG8eXNGjRqFu7s7QUFB9OzZE29v7/ju4kfHx8eHuXPnvvC+M2fO0LVr1w/cI0koFJaLiIiIiMhHwcnJiXHjxr0wMI+OjjbWs7S0JH369EaQHsPKSlMyiYh8am7evMmpU6fw8PDAwcEh0QXDe/bsYd68eVSvXp0dO3bQq1cvmjRpQocOHVi9ejVLly7Fzs4uvruZoCRJkkS/GeSlFJaLvAfff/89uXPn5uzZsx/sOW/dukXu3Ln5/vvv30v7MTXaTpw48V7afxODBw+mUqVKr/249/1aJRSqkSciIh+jfwbmBw8eBP5vss4rV65w5coVcubMGZ/dFBGRD8Tb25uUKVPyxRdfUL169dcKy+/evcuQIUMoV64cBQoUoFKlSowcOZLw8HBjnZs3b9KrVy9KlChBoUKFaNasmXGy9p+io6NZsGABFSpUoGDBgrRp0+aFI913795No0aNcHV1pWTJkgwYMIC7d+++9rYDzJo1i1SpUjF+/Hisra3j3F++fHm+/PJL4/bDhw8ZMmQIZcqUoWDBgtSrV48tW7bEekzMMbGnpycbN26kSpUqFChQgMaNG3PmzJlY696/fx8PDw8qVKhAgQIFKFeuHF27duXWrVvGOi+rAV6pUiUGDx5s3I7JSn777TfGjh1LqVKlKFasGCNGjCA8PJwnT54waNAgihcvTvHixZk8ebJxZdk/+71ixQq+/PJLXF1d+frrr7l48aKx3uDBg1m7dq3Rt5g//9bf8+fP06FDB4oWLUqRIkVo06YNp0+fjrVOTP9///13JkyYQKlSpShcuDDdu3fH398/zvZLwqTTKCKv4fvvv8fDw8O4bWNjQ+bMmSlbtizdunUjbdq08di7j8/atWsZPXo0rq6ubN68+Y3auHv3Lps2baJKlSrkzZv3Hffw//j4+HDmzBl69uz5TtutVKkSn332GYsWLXqn7YqIiHzKYgLzESNG4OHhQZ8+fShRogTXrl1j0aJFWFhY0KdPn/jupoiIfADe3t5UrVoVGxsb6tSpw/r16zlz5gyurq7/+ri7d+/SpEkTnj59SrNmzXBxceHu3bvs3buX0NBQbGxsePDgAS1atCAkJAQ3NzdSp07Nli1b6Nq1K7Nnz6Zq1aqx2lyyZAkmk4n27dsTGBjI0qVLGTBgQKzj3ZjcoGDBgvTr14+HDx+yatUqTp48ydatW7G3t3/lbff19eXq1as0btz4lUaPh4aG4ubmxo0bN2jVqhVZsmRhz549DB48mCdPntCmTZtY6+/YsYOgoCCaN2+OyWRi6dKl9OzZkx9++MEI5nv27Mnly5f5+uuvcXR0xN/fn6NHj/L333+TJUuWV96W540dO5a0adPSs2dP/ve//7Fx40ZSpEjBqVOnyJQpE3379uXIkSN4enqSK1cuGjRoEOvxW7duJSgoiK+++oqwsDBWr15NmzZt8Pb2Jm3atDRv3px79+5x9OhRJk+e/J/9uXTpEq1atcLW1pYOHTpgZWXFxo0bcXNzY82aNRQqVChO/+3t7enRowd+fn6sXLmS0aNHM3PmzDd6PeTjorBc5A306tWLLFmyEB4ezu+//8769evx8fFhx44dJEuWLF76FFO/7WO6lOifdeWyZcv22m3cu3ePuXPn4ujoGCcsHzNmTKyzzK/qRa+Vj48Pa9eufedhuYiIiLwZJycnJkyYwNy5cxk3bhwWFhakS5cOZ2dnFixYgJWVFVFRUcaIcxER+fScO3eOq1evMnz4cAA+//xzMmbMiLe393+G5dOnT+fBgwds2rSJggULGst79+5tHEcuXryYBw8esHbtWooVKwZA06ZNqVevHhMmTKBy5cpYWPxfUYawsDC2bt2KjY0NAPb29owbN46LFy+SK1cuIiIimDp1Krly5WLt2rUkSZLE6Hfnzp1ZsWIFvXr1euXtv3LlCgC5cuV6pfU3btzIlStXmDJlCvXq1QOgRYsWuLm5MXPmzDih++3bt9m3bx8pU6YEns0f0q1bN3766Se+/PJLnjx5wqlTpxg0aBDu7u7G4zp37vzK2/AiadKkMU48tGrVihs3buDp6Unz5s359ttvAWjevDmVKlXCy8srTlh+48YN9u3bR4YMGQCoUKECTZs2ZcmSJXh4eFCkSBGyZ8/O0aNHqV+//n/2Z+bMmURERLB+/XqyZs0KQIMGDahRowZTpkxhzZo1sdZPlSoVy5YtM8rBRUdHs3r1ap4+fUqKFCne6rWR+KcyLCJvoEKFCtSvX5+mTZsyceJE2rRpw61btzhw4EC89clkMpEkSZKP5oDxQ9SVs7a2Nn6kvI6P7bUSERGRF8uUKROjR49my5YtzJ49m1mzZrF48WKsra2JjIzUvlxE5BMXM1K4ZMmSwLNjuVq1arFr1y6ioqJe+rjo6Gh++OEHvvzyy1hBeYyYkNPHxwdXV1cjKAewtbWlefPm+Pn5cfny5ViPa9SoUaxj0JjH3bx5E3gW7j98+JCWLVsaQTlAxYoVcXFxeWl5l5cJDAw0+vQqjhw5Qrp06ahTp46xzNraGjc3N4KDg/n1119jrV+rVi0jKH/R9iRNmhRra2t++eUXAgICXqvv/6ZJkyax5h1xdXXFbDbTpEkTY5mlpSUFChQw+vK8KlWqGEF5zOMLFSqEj4/Pa/clKiqKo0ePUqVKFSMoB0ifPj116tTh999/N96HGM2aNYvV/2LFihEVFYWfn99rP798fBSWi7wDpUqVAohVswsgPDz8X+tYffPNN5QsWZKIiIg4bbZv357q1asbt48ePUrLli0pVqwYRYoUoXr16kyfPt24/2V1uK9cuULv3r0pVaoUrq6uVK9enRkzZhj3+/n5MWrUKKpXr27UU+vVq1ecbXldr1pX7smTJ4wfP55KlSpRoEABKlSowKBBg/D39+fEiRPGztLDw8OoMxazjc/XLI+IiKBEiRKxyuTECAwMpGDBgkyaNOmFr9XL6pmZzWYqVar0wlmyw8LC+PzzzxkxYsRbvU4xtm3bZtS0K1GiBH379uXvv/827h89ejRFihQhJCQkzmP79etH2bJlY/1Y9PHx4auvvqJw4cIUKVKETp06cenSpXfSVxERkQ/J0tKSzz77jIoVK1KgQAEsLCyIjo7+qK6mExGRdy8qKoqdO3dSsmRJbt26xfXr17l+/Tqurq48ePCAY8eOvfSx/v7+BAYG8tlnn/3rc9y+fRtnZ+c4y11cXIz7n5c5c+ZYt2NKqjx58iTW+i9r85/t/ZeYUeBBQUGvtL6fnx/ZsmWLNRoeIEeOHLH6FyNTpkyxbscE5zHbY2Njw4ABAzhy5Ahly5alVatWLFmyhPv377/WdvzTP1/HmNHY/+xPihQpXhjSv+iq9ezZs79RWO3v709ISMgL37McOXIQHR0d69j8Rf3/5+dAEjaF5SLvwI0bN4Bnl+I8b+zYsVy4cIEePXrQsmVLDh06xOjRo43769evz+PHj/npp59iPe7+/fscP37cuGzq0qVLdO7cmfDwcHr16sU333xDpUqVOHny5L/268KFCzRr1ozjx4/TrFkzhg4dSpUqVYyJsgDOnj3LqVOnqF27NsOGDaNFixYcP36c1q1bvzCYfVX/rCvn6+sbZ6KQoKAgWrVqxZo1ayhbtixDhw6lRYsWXL16lbt375IjRw7jErXmzZszefJkJk+eTPHixeM8n7W1NVWqVOGHH36INVkLYCyrVavWC/vavHlzypYtC2A8x+TJkzGZTNStW5cff/yRx48fx3rMwYMHCQwMNN6jt7FgwQK++eYbsmXLxuDBg2ndujXHjh2jVatWxs62Vq1aBAcHxxmJEBISwqFDh6hevboxum7r1q107tyZ5MmTM2DAALp168bly5f56quv3vokiIiIyMfgnyGAiIh8eo4fP879+/fZuXMn1apVM/7EzFnxPq5e/i8v2/+8SXnQVxET2j8/eeW79LIrtJ7fnrZt27J371769etHkiRJmDVrFrVq1eL8+fP/2f7LRv+/7HVMKPv3D/05kA9LwzFE3kBgYCD+/v6Eh4dz8uRJ5s2bR9KkSWPNQA3/XceqVKlSZMyYke3bt8d67M6dO4mOjjaC2KNHjxIREcGSJUtwcHB45X6OHTsWs9nMli1bYp35HDBggPHvihUrUqNGjViP+/LLL2nevDl79+6NUxvsVbxqXTlPT08uXrzI3LlzY02c0q1bN8xmMyaTiQoVKjB79mwKFy78n7XGatWqhZeXF0ePHo31eu7atYusWbO+8PI74F/rmTVo0ICFCxeye/duWrZsaSzfvn07jo6OfP7556/+wryAn58fc+bMoU+fPnTp0sVYXq1aNRo2bMi6devo0qULn3/+ORkyZGD37t3UrFnTWO/w4cMEBwcbJwKCgoIYN24cTZs2ZcyYMcZ6DRs2pEaNGixatCjWchERERERkY+Rt7c3adKkeeHVvPv372f//v18++23JE2aNM79Dg4O2NnZ/efVtZkzZ+batWtxll+9etW4/3XErH/t2jVKly4d675r1669dnvOzs44Oztz4MABgoKC/rMci6OjI3/99RfR0dGxAt033Z4YTk5OtG/fnvbt2+Pr60uDBg1YtmwZU6dOBZ6NSP/nqOrw8PC3HoH+MtevX4+zzNfXF0dHR+P282VS/o2DgwPJkiV76efAwsIizoh3+bQljFM2Ih+Ztm3bUrp0ab744gv69u2Lra0tc+fOjVUzC/67jpWFhQV169Y1RinH2L59O0WKFDHqZcVc0nPgwAGio6NfqY/+/v78+uuvNG7cOM4O8fk+Pf/DIiIigkePHuHk5IS9vf0rnSl+kVetK7dv3z7y5MkTZ4bxf/bxVZUqVYrUqVOza9cuY1lAQAA///zzS0eV/xdnZ2cKFSoUa9TC48eP+fHHH6lbt+4b9fN5+/fvJzo6mpo1a+Lv72/8SZs2LdmyZePEiRPAs9ejRo0a+Pj4xLoEb/fu3WTIkMEI7X/++WeePHlC7dq1Y7VnYWFBoUKFjPZEREREREQ+VqGhoezbt88Y3PXPP61atSIoKCjWVdPPs7CwoEqVKhw6dIizZ8/GuT9mBPAXX3zBmTNnOHXqlHFfcHAwmzZtwtHRkZw5c75WvwsUKECaNGnYsGFDrCuefXx8uHLlChUrVnyt9gB69erF48ePGTZsGJGRkXHu/+mnnzh06BDwbH61+/fvxzomjoyMZPXq1SRPnvyFV2n/m5CQEMLCwmItc3JywtbWNtb2Zc2ald9++y3Weps2bfrXuvJv44cffuDu3bvG7TNnzvC///2PChUqGMuSJUsG/HdpFEtLS8qWLcuBAwdiXYn94MEDduzYweeffx5rUlT59GlkucgbGDFiBM7OzlhaWpI2bVqcnZ1feBnOq9SxatCgAUuWLOGHH36gQYMGXL16lT/++MOYARqejZjevHkzw4YNY9q0aZQuXZqqVatSo0aNl17+EzMJxn/Nmh0aGsqiRYv4/vvvuXv3bqzLhp4+ffofr0Rc/6wrF8PV1ZVly5Zx7NgxypUrBzwrX1OtWrXXfo6XsbKyolq1auzYsYPw8HBsbGzYt28fERERbxyWw7NyOWPGjMHPzw9HR0f27NlDRETEK82q/V98fX0xm80vfR2er8daq1YtVq5cycGDB6lbty5BQUH4+PjQvHlzI7T39fUFoE2bNi9sTzt5ERERERH52B08eJCgoCBjjqp/Kly4MA4ODmzfvv2lx3r9+vXj6NGjuLm50axZM3LkyMH9+/fZs2cP69atw97enk6dOrFz5046duyIm5sbKVOmZOvWrdy6dYs5c+a8dlkQa2trBgwYgIeHB19//TW1a9fm4cOHrFq1CkdHR9q2bfu6LwW1atXir7/+YuHChZw/f546deqQOXNmYxDXsWPHmDZtGvCsxOjGjRsZPHgwf/zxB46Ojuzdu5eTJ08yZMiQ1z4e9PX1pW3bttSoUYOcOXNiaWnJDz/8wIMHD6hdu7axXtOmTRk5ciQ9e/akTJkyXLhwgZ9++onUqVO/9va+CicnJ1q2bEnLli0JDw9n1apVpEqVig4dOhjr5M+fH3h2xX25cuWwtLSM1efn9enTh59//pmvvvqKr776CktLSzZu3Eh4eDgDBw58L9sgHy+F5SJvwNXV9aUlPZ73KnWscubMSf78+dm+fTsNGjRg+/btWFtbxyq1kTRpUtauXcuJEyc4fPgwP/74I7t27WLjxo0sW7bspXXGXsWYMWP4/vvvadOmDYULFyZFihSYTCb69u37RvW2nq8rt3Pnzjj3e3t7G2H5+1C7dm02btzIkSNHqFKlCnv27MHFxYU8efK8VZsTJkzA29ubLl26sH37dgoUKGDUj3sb0dHRmEwmlixZ8sL3MXny5Ma/CxcujKOjI7t376Zu3bocOnSI0NDQWD8OY96zyZMnky5dujjtvc1nRURERERE5EPYvn07SZIkMeaW+icLCwsqVqyIt7c3jx49emEomyFDBjZt2sSsWbPw9vYmMDCQDBkyUKFCBeMK67Rp07JhwwamTJnCmjVrCAsLI3fu3CxcuPCNRoEDNGrUiKRJk7JkyRKmTp1K8uTJqVKlCgMHDjQG0L2uvn37UqpUKVavXs369esJCAjA3t6eQoUKMX/+fCpXrgw8yw5Wr17N1KlT2bJlC4GBgTg7OzNhwgQaNWr02s+bMWNGateuzbFjx9i+fTuWlpa4uLgwc+ZMqlevbqzXrFkzbt26xXfffcePP/7I559/zvLly9/o5MCraNCgARYWFqxcuZKHDx/i6urK8OHDSZ8+vbFOtWrVcHNzY+fOnWzfvh2z2fzSsPyzzz5j7dq1TJs2jUWLFmE2m3F1dWXKlCkUKlTovWyDfLwUlot8BBo0aMDEiRO5d+8eO3bsoGLFisYs1DEsLCwoXbo0pUuXxsPDg4ULFzJjxgxOnDhBmTJl4rQZU8LlvyYCialLPnjwYGNZWFjYG40qh9erK+fk5PSfNeRet8xJ8eLFSZcuHbt27aJo0aIcP348Vi3wN3meVKlSGT/E6tata5yVfxecnJwwm81kyZLlhbNv/1PNmjVZtWoVgYGB7Nq1C0dHRwoXLmzcH/O+p0mT5oWfCxERERERkY/dwoUL/3OdCRMmMGHCBOP2X3/9FWedzJkzM2nSpH9tJ2vWrMyePftf1ylZsuQL28+SJcsLl9eqVes/r27u2bMnPXv2/Nd1nheTB/yXNGnSxHpdXuRl/YbYr2Pq1KlfeGz/TxYWFgwYMCDW/GhAnDI5jRo1emFo/7LXYuLEiUycOPGFz9muXTvatWv30j5ZWloybNgwhg0bFue+F217vnz58PT0fGl78PL+v+zzIQmTapaLfATq1KmDyWRi3Lhx3Lx505jYM8bjx4/jPCZv3rwAseqEPc/BwYHixYvj5eXF7du3Y933/IjxF400Xr169RvVFnvdunLVqlXjwoUL7N+/P05bMX181TpjMSwsLKhRowaHDh1i+/btREZGvlIJlv96nvr163P58mUmT578r5dvva5q1aphaWnJ3Llz44zkN5vNPHr0KNayWrVqER4ezpYtW/jxxx9jXYEAUL58eezs7Fi0aBERERFxns/f3/+d9FtERERERERE5FOjkeUiHwEHBwfKly/Pnj17sLe3j3O517x58/jtt9/44osvcHR05OHDh6xbt46MGTMaEzu+yLBhw2jZsiUNGzakefPmZMmSBT8/Pw4fPsy2bdsAqFixItu2bcPOzo6cOXNy+vRpfv75Z1KlSvXa2/G6deXc3d3Zu3cvvXv3pnHjxuTPn5+AgAAOHjzIt99+S548eYzJRjds2ICtrS3JkyfH1dXVGEH9IjVr1mT16tXMnj2bXLlykSNHjv/s+3/VM/viiy9IlSoVe/bsoUKFCqRJk+aVX5fr168zf/78OMvz5ctHxYoV6dOnD9OmTcPPz48qVapga2vLrVu3+OGHH2jWrBnu7u6x+pktWzZmzJhBeHh4nBMBdnZ2jBo1ikGDBtGoUSNq1aqFg4MDt2/fxsfHh6JFi77SyAARERERERERkcRGYbnIR6J+/focOnSImjVrYmNjE+u+SpUq4efnh5eXl1GPrUSJEvTs2ZMUKVK8tM08efIYNdrWr19PWFgYmTNnjjUaeejQoVhYWODt7U1YWBhFixZl+fLlsSbGeFVvUldu7dq1zJkzh/3797NlyxbSpElD6dKlyZAhA/BsgpSJEycyffp0Ro0aRWRkJBMmTPjXsLxo0aJkypSJv//++5Un9vyvemY2NjbUqlWLdevWvfbEnteuXWPWrFlxljdp0oSKFSvSqVMnsmfPzooVK5g3bx7wrDZc2bJlX3jioWbNmixcuJBs2bIZIf/z6tatS/r06Vm8eDGenp6Eh4eTIUMGihUr9kZ16kREJH6cOHGC1q1bs2rVKkqWLBnf3RERERER+eSZzG8yg5+IvHM//PAD3bt3Z+3atRQrViy+uyMvMH78eL777juOHj1qlG0REZFPw/fff4+Hh8dL79+4cWOsOSI+hMQalnt7e/Pw4cNXnhQsPDyc9evXs2XLFm7cuIGFhQUZMmSgaNGitG3b1rjC7OTJkxw9epQ2bdq88QRra9euJVmyZDr5LCIiIvKJ0shykY/E5s2byZo167+WVZH4ExYWxvbt26levbqCchGRT1ivXr3IkiVLnOVOTk7x0JvEaceOHVy6dOmVw/JevXpx5MgRateuTdOmTYmMjOTq1ascPnyYIkWKGGH5qVOnmDt3Lg0bNnzjsHz9+vWkTp1aYbmIiIjIJ0phuUg827lzJ3/99ReHDx9m6NChmEym+O6SPOfhw4f8/PPP7N27l8ePH9O6dev47pKIiLxHFSpUoGDBgvHdDXlFZ86c4dChQ/Tt25cuXbrEui8qKuqVJwgXEREREQGwiO8OiCR2/fr1Y/Xq1TRp0oSvvvoqvrsj/3D58mUGDBjAyZMnGTZsGHnz5o3vLomISDy6desWuXPnxtPTk7Vr11K5cmUKFSpE+/bt+fvvvzGbzcybN48KFSrg6upK165defz4caw2KlWqROfOnfnpp5+oX78+BQsWpFatWuzbt++V+rB7924aNWqEq6srJUuWZMCAAdy9e9e438vLi9y5c3P+/Pk4j124cCF58+Y11ndzc6NOnTpcuHCBr7/+mkKFClG1alX27NkDwC+//ELTpk1xdXWlevXq/Pzzz3HavHv3Lh4eHpQpU4YCBQpQu3Ztvvvuu1jrnDhxgty5c7Nr1y4WLFhgnJRo06YN169fN9Zzc3Pj8OHD+Pn5kTt3bnLnzv3SicMBbt68CTybr+SfLC0tSZ06NQBz5sxh8uTJAFSuXNlo+9atW8Zr1rp1a0qXLk2BAgWMeUqeV6lSJS5dusQvv/xiPN7Nzc1oP3fu3HH68P3338d6HoCzZ8/i7u5OyZIlcXV1pVKlSv9aAkhEREREPhyNLBeJZ3/99Vd8d0H+RcmSJfUeiYgkIoGBgfj7+8daZjKZjNA1hre3NxEREbi5ufH48WOWLl1Knz59KFWqFCdOnKBjx45cv36dNWvWMGnSJCZMmBDr8b6+vvTt25cWLVrQsGFDvLy86N27N0uXLn3pRNnwf7XVCxYsSL9+/Xj48CGrVq3i5MmTbN26FXt7e6pXr87o0aPx9vYmX758cfpdokQJYyJtgICAALp06UKtWrWoUaMG69evp1+/fkRHRzN+/HhatGhBnTp18PT0pFevXhw+fBg7OzsAHjx4QLNmzTCZTLRq1QoHBweOHDnC0KFDCQwMjFNKZcmSJZhMJtq3b09gYCBLly5lwIABbN68GYAuXbrw9OlT7ty5YwTItra2L309MmfObGxX0aJFsbJ68eFN1apV8fX1ZceOHXh4eBjvp4ODA/CsvMpnn31GpUqVsLKy4tChQ3z77beYzWZatWoFwJAhQxgzZgzJkyc3RrGnTZv2pX17kYcPH+Lu7k7q1Knp1KkT9vb23Lp1i/37979WOyIiIiLyfigsFxERERH5/15UJ9vGxoazZ8/GWnb37l327dtHihQpAIiOjmbRokWEhobi5eVlhLaPHj3C29ubb7/9FhsbG+Pxvr6+zJkzh2rVqgHQpEkTatSowdSpU18alkdERDB16lRy5crF2rVrSZIkCQCff/45nTt3ZsWKFfTq1Qs7OzuqVKnCjh07GDhwIBYWzy4mPX/+PJcvX8bd3T1Wu/fu3WPatGnUqVMHgDJlylCzZk369+/Phg0bKFSoEAA5cuTA3d2dffv2GTW7Z8yYQVRUFN7e3kYA3bJlS/r168fcuXNp0aIFSZMmNZ4rLCyMrVu3Gq+Fvb0948aN4+LFi+TKlYuyZcuyatUqnjx5Qv369f/1vQIoXLgwJUqUYNOmTRw8eJBSpUpRtGhRvvzySyNIB8iTJw/58uVjx44dVKlSJU5d+jVr1sTq59dff427uzvLly83wvIqVaowc+ZMUqdO/Up9e5FTp04REBCAp6dnrHI/ffv2faP2REREROTdUhkWEREREZH/b8SIESxfvjzWnyVLlsRZr0aNGkZQDuDq6gpAvXr1Yo1udnV1JSIiIlaZFID06dNTtWpV47adnR0NGjTg/Pnz3L9//4V9O3fuHA8fPqRly5ZGUA5QsWJFXFxcOHz4sLGsfv363Lt3jxMnThjLvL29SZo0qRHQx0iePDm1a9c2bru4uGBvb0+OHDmMoBww/h1T+sRsNrNv3z4qVaqE2WzG39/f+FOuXDmePn3KH3/8Eeu5GjVqFOukQbFixWK1+bpMJhOenp706dMHe3t7duzYwejRo/nyyy/p06fPK9csfz4of/r0Kf7+/pQoUYKbN2/y9OnTN+rbi8R8Zg4fPkxERMQ7a1dERERE3g2NLBcRERER+f9cXV1faYLPTJkyxbodE4K+bHlAQABZs2Y1lmfLli3OpN7Zs2cHwM/Pj3Tp0sV5ztu3bwPg7Owc5z4XFxd+//1343bZsmVJly4d27dvp3Tp0kRHR7Njxw4qV65slFCJkTFjxjh9SZEiBRkzZnzhtsQE0P7+/jx58oSNGzeycePGOH2KWed5z4/2hmcjy59v803Y2NjQtWtXunbtyr179/j1119ZtWoVu3fvxsrKiqlTp/5nG7///jtz5szh9OnThISExLrv6dOnsU6MvI0SJUpQvXp15s6dy4oVKyhRogRVqlShbt26sU4iiIiIiEj8UFguIiIiIvKaLC0tX7g8puTJP5nN5vfZnTgsLS2pW7cumzZtYtSoUZw8eZJ79+5Rr169F677sjZeJGZboqOjgWej6Rs2bPjCdf856eX7fn3Sp09P7dq1qVatGnXq1GHPnj1MnDjxpbXMAW7cuEHbtm1xcXFh8ODBZMqUCWtra3x8fFixYoWxnf/mnycbYkRFRcVZb/bs2Zw+fZpDhw7x448/MmTIEJYvX87GjRv/tT67iIiIiLx/CstFRERERD6w69evYzabY4Wsvr6+ADg6Or7wMTGjsq9du0bp0qVj3Xft2rU4o7br16/PsmXLOHjwIEeOHMHBwYFy5cq9s21wcHDA1taW6OhoypQp887afVnw/Dqsra3JnTs3vr6+PHr0iHTp0r203YMHDxIeHs6CBQtivYbPl7D5r749P0I+5t/wf1cD/FPhwoUpXLgwffv2xdvbmwEDBrBr1y6aNm36ytsoIiIiIu+eapaLiIiIiHxg9+7dY//+/cbtwMBAtm7dSt68eV9YggWgQIECpEmThg0bNhAeHm4s9/Hx4cqVK1SsWDHW+nny5CF37tx899137Nu3j9q1a//rCOvXZWlpSfXq1dm7dy8XL16Mc/8/S7C8qmTJkr1ynXBfX98XBtJPnjzh1KlTpEyZEgcHB6NdIE7bMSPonx/d/vTpU7y8vF7YtxeVjHFycgLg119/NZYFBwezdevWWOsFBATEGUWfN29egFjvqYiIiIjED40sFxERERH5/44cOcLVq1fjLC9atGismuNvK3v27AwdOpSzZ8+SJk0avLy8ePjwIRMmTHjpY6ytrRkwYAAeHh58/fXX1K5dm4cPH7Jq1SocHR1p27ZtnMc0aNCASZMmAbywBMvb6t+/PydOnKBZs2Y0bdqUnDlzEhAQwB9//MGxY8f45ZdfXrvN/Pnzs2vXLiZMmEDBggVJnjw5lSpVeuG6Fy5cYMCAAZQvX55ixYqRMmVK7t69y9atW7l37x5DhgwxwvD8+fMDMGPGDGrVqoW1tTVffvklZcuWxdrami5dutCiRQuCgoLYvHkzadKkiTPZav78+Vm/fj3z588nW7ZsODg4ULp0acqWLUvmzJkZOnQoV69exdLSEi8vL1KnTh0rzN+yZQvr16+nSpUqODk5ERQUxKZNm7Czs6NChQqv/VqJiIiIyLulsFxERERE5P+bPXv2C5dPmDDhnYflw4cPZ/LkyVy7do0sWbIwY8YMypcv/6+Pa9SoEUmTJmXJkiVMnTqV5MmTU6VKFQYOHBir/EeMunXrMnXqVLJmzYqrq+s763+MtGnTsnnzZubNm8f+/ftZv349qVKlImfOnAwYMOCN2vzqq6/4888/+f7771mxYgWOjo4vDcuLFy9Or169+PHHH1m+fDmPHj3C1taWvHnzMmDAAKpXr26s6+rqSu/evdmwYQM//vgj0dHRHDhwABcXF2bPns3MmTOZNGkSadOmpWXLljg4ODBkyJBYz9e9e3du377N0qVLCQoKokSJEpQuXRpra2vmzp3Lt99+y6xZs0iXLh1t2rTB3t4eDw8P4/ElSpTg7Nmz7Nq1iwcPHpAiRQpcXV2N90hERERE4pfJ/KFnGxIRERERScQqVarEZ599xqJFi977c/n7+7hUJhIAAQAASURBVFO+fHm6detG9+7d3/vziYiIiIgkZKpZLiIiIiLyidqyZQtRUVHUr18/vrsiIiIiIvLRUxkWEREREZFPzLFjx7hy5QoLFy6kSpUqZMmSJb67JCIiIiLy0VNYLiIiIiLyiZk/fz6nTp2iSJEiDB8+PL67IyIiIiKSIKhmuYiIiIiIiIiIiIgkeqpZLiIiIiIiIiIiIiKJnsJyEREREREREREREUn0FJaLiIiIiIiIiIiISKKnsFxEREREREREREREEj2F5SIiIiIiIiIiIiKS6CksFxEREREREREREZFET2G5iIiIiIiIiIiIiCR6CstFREREREREREREJNFTWC4iIiIiIiIiIiIiiZ7CchERERERERERERFJ9BSWi4iIiIiIiIiIiEiip7BcRERERERERERERBI9heUiIiIiIiIiIiIikugpLBcRERERERERERGRRE9huYiIiIiIiIiIiIgkegrLRURERERERERERCTRU1guIiIiIiIiIiIiIomewnIRERERERERERERSfQUlouIiIiIiIiIiIhIoqewXEREREREREREREQSPYXlIiIiIiIiIiIiIpLoKSwXERERERERERERkURPYbmIiIiIiIiIiIiIJHoKy0VEREREREREREQk0VNYLiIiIiIiIiIiIiKJnsJyEREREREREREREUn0FJaLiIiIiIiIiIiISKKnsFxEREREREREREREEj2F5SIiIiIiIiIiIiKS6CksFxEREREREREREZFET2G5iIiIiIiIiIiIiCR6CstFREREREREREREJNFTWC4iIiIiIiIiIiIiiZ7CchERERERERERERFJ9BSWi4iIiIiIiIiIiEiip7BcRERERERERERERBI9heUiIiIiIiIiIiIikugpLBcRERERERERERGRRE9huYiIiIiIiIiIiIgkegrLRURERERERERERCTRU1guIv+PvXuPi6rO/wf+GpXhEg6Ii+YFccBlxBIBE0QQr3hBytZdY6koY0TaVgiUUkldal1vX2+BmlxGszRXzFq1UFEzSJfdLmKuaaLN4Jo3SoKBGGDQ8/uD35z1OIBcBeT1fDx84Jzzns/5zDkDn5n3+VyIiIiIiIiIiIg6PSbLiYiIiIiIiIiIiKjTY7KciIiIiIiIiIiIiDo9JsuJiIiIiIiIiIiIqNNjspyIiIiIiIiIiIiIOj0my4mIiIiIiIiIiIio02OynIiIiIiIiIiIiIg6PSbLiYiIiIiIiIiIiKjTY7KcOpXk5GSoVKoHcqzw8HCEh4eLj//9739DpVLh0KFDD+T4CxcuxPjx4x/IsZrq119/xRtvvAF/f3+oVCr87W9/a9HyVSoVkpOTW7TMh5lKpcJbb71137iPPvoIKpUKP/744wOoFRF1Vmyz25fWaLPZnhARNQ7bxvalNb/PduY2sqCgABERERg+fDhUKhWOHj3a1lV6IJpzzWv7fWE+pOmYLKcOy/SHxPRv6NChCAgIgFqtxnvvvYeysrIWOc7NmzeRnJyM8+fPt0h5Lak9160hUlJS8PHHHyMsLAyrV6/G9OnT64wdP368eK0HDx6MJ554Ak8++SSWLFmCb7/9tkXr1Z7Pq+kDclFRUa37x48fj6ioqAdcK2Dnzp346KOPHvhxiahjYJvdvuvWEI1ps6uqqrB9+3Y8/fTT8Pb2xhNPPIFp06ZhyZIl+OGHHx5grZvP1O6a/g0bNgzBwcFYv359o9+3BoMBycnJ+Pe//222Lzs7m19oiToZto3tu24N0djvs3V9T2upGxH1tTMdycKFC5Gfn4+4uDisXr0ajz/+eK1xP/74o/j7s3nz5lpj5s+fD5VKBS8vr9as8gM99/fLCVDzdWvrChA1V0xMDPr374/q6mr8/PPP+PLLL7F8+XK8++672Lx5MwYPHizG/ulPf8KcOXMaVX5hYSE2btyIfv36wd3dvcHP02g0jTpOU9RXt7/+9a8QBKHV69Ac//rXvzBs2DDMnTu3QfHu7u546aWXANTcxddqtTh06BAyMjIwa9YsLFq0SBJ/5swZdO3atdH1auo17yymT5+OadOmQS6Xi9t27dqFHj16YMaMGW1YMyJq79hmd442OyYmBjk5OZg2bRpmzpyJ6upqaLVafP755/Dy8oKrq+sDqHHLSkxMhI2NDcrLy3Hy5Els2bIF//73v7Fr1y7IZLIGlWEwGLBx40bMnTsXvr6+kn3Z2dnYuXMnoqOjW6P6RNSOsW3sHG1jY9X2net+6mtnOoqKigrk5eXh5ZdfxvPPP9+g51haWuLTTz/FK6+8ItleXl6Ozz77DJaWlq1RVYmH4dzT/zBZTh1eYGAghg4dKj6OiopCbm4uXn75ZbzyyivIzMyElZUVAKBbt27o1q113/YGgwHW1taNatRag4WFRZsevyFu3bqFQYMGNTi+d+/eZnfr4+PjMX/+fLz77rtwdnbGs88+K+57EI1iY5SXl8PGxqatq9FsXbt2bdJNCCIittm1e5ja7DNnzuD48eOIi4vDyy+/LNl3+/Zt6PX61qpiq5o8eTIcHBwAAGFhYYiOjkZWVhZOnz7d6r3VmkoQBFRWVoq/U0TUPrFtrN3D1DY2RXv6zvUgv8eaeksrFIoGP2fMmDHIysrC999/L7m5dOzYMRiNRgQEBHT43vb0YHEaFnoo+fn54ZVXXsHVq1exf/9+cXttc7ydPHkSYWFheOKJJ+Dl5YXJkydj3bp1AGqGQ/3hD38AACxatEgc4mOabiI8PBwhISE4e/YsnnvuOQwbNkx87r1zvJncuXMH69atg7+/Pzw9PfHyyy/j+vXrkpjx48dj4cKFZs+9u8z71a22OavKy8uxcuVKjBkzBo8//jgmT54MjUZjdsfeNHf10aNHERISgscffxzTpk1DTk5OfadddOvWLSQkJGDUqFEYOnQonnrqKXz88cfiftMwsx9//BGff/65WPemzM1lZWWF1atXw97eHlu2bJG8ltrm6Lp58yYWLVqEUaNGia/rww8/lNStvvP69ddfIyYmBmPHjsXjjz+OMWPGYPny5aioqJAcZ+HChfDy8sJ///tfREZGwsvLC/Hx8UhKSsJjjz1W65CpJUuW4IknnkBlZWWjz0N9NBoN/vjHP8LX1xceHh6YMWNGvUP89u/fj8mTJ2Po0KGYMWMGvvrqK8n+e+dSGz9+PC5evIgvv/xSPF+m96nRaMTGjRsxadIkDB06FL6+vggLC8PJkydb9DUSUcfFNvvharOvXLkCAPD29jbb17VrV/To0eO+dcrOzsazzz4LT09PeHl5Yc6cObh48aJZ3A8//ICYmBj4+PiIbdaxY8ckMaY266uvvsLSpUvh6+sLb29vvP766ygpKblvXeoycuRIADVDwKuqqvD2229jxowZGD58ODw9PfHss8/iX//6lxj/448/ws/PDwCwceNG8TwmJydj4cKF2LlzJwBIpmQwuXPnDt59911MmzYNQ4cOxahRo7B06VKz+puG+H/xxReYMWMGPDw88Pe//128hpmZmXjnnXfExNyLL76Iy5cvS8ooKChAdHQ0/P39MXToUAQGBiIuLg6lpaVNPldE1HhsGx+utrEpapu/+j//+Q/UarX4vW78+PHi6Or62hmT3NxcsX194okn8Kc//clsejTTe+zSpUuYP38+RowYgWeffRZ79+6FSqXCuXPnzOq6ZcsWuLu74+bNm/W+pnPnzmH27Nnw9vaGl5cXXnzxRZw+fVpy7HHjxgEAVq9eDZVK1aB56z09PdG/f38cOHBAsv3AgQMICAiAvb19rc9ryOcN0/f6mzdv4pVXXoGXlxdGjhyJVatW4fbt2wDuf+6///57LFy4EBMmTMDQoUPh7++PRYsW4Zdffrnva2tJpmt7+fJlLFy4EE888QSGDx+ORYsWwWAwmMXv27dP/Dzh4+ODuLg4s9/1hnxuqO9vVHvFnuX00Jo+fTrWrVuHEydO4Jlnnqk15uLFi4iKioJKpUJMTAzkcjkuX76MU6dOAQBcXV0RExODpKQkhIaGYvjw4QCkXwCLi4sRGRmJadOm4amnnkLPnj3rrdc777wDmUyGyMhI3Lp1C9u3b8esWbOwb9++RvX8aUjd7iYIAv70pz+JH0rc3d3xxRdfYPXq1bh58yYSEhIk8d988w2ysrLw7LPP4pFHHsH777+PmJgYHD9+vN4vuhUVFQgPD8d///tfPPfcc+jfvz8OHTqEhQsXQq/X48UXX4SrqytWr16NFStW4NFHHxWnVjH12GqsRx55BBMnTsSHH36IS5cu4be//W2tcT///DOeeeYZyGQyPPfcc3BwcEBOTg7eeOMNlJWVYdasWfc9r4cOHUJFRQXCwsJgb2+PM2fOYMeOHbhx4waSkpIkx6uuroZarcbw4cOxYMECWFlZwcvLC5s2bUJmZqZkWFlVVRUOHz6MSZMmNahHfF1f8O/cuWO27b333sP48ePx5JNPwmg04tNPP8Wrr76KlJQUjB07VhL71VdfITMzE+Hh4ZDL5di1axdmz56NPXv2wM3NrdZjJiQk4K9//StsbGzEXoS/+c1vANR8WEhJScHMmTPh4eGBsrIynD17Ft999x38/f3v+zqJqHNgmy3Vkdvsvn37Aqj5gurt7d3oHpD/+Mc/sHDhQgQEBCA+Ph4GgwG7du3Cs88+i48//hj9+/cHUPN+CAsLQ+/evREZGQkbGxscPHgQf/7zn5GcnIygoCBJuW+99RYUCgXmzp0LnU6HXbt24dq1a3j//fcbPI3K3f773/8CAOzt7VFWVoY9e/YgJCQEM2fOxK+//ooPP/xQbD/d3d3h4OCAxMREJCYmIigoSKyfSqWCwWBAYWEhTp48idWrV5sda+nSpfj4448xY8YMhIeH48cff8TOnTtx7tw57Nq1S9L7UqfTYf78+QgNDcUzzzwDpVIp7ktLS4NMJkNERATKysqQnp6O+Ph47NmzB0DNZxG1Wo2qqio8//zz+M1vfoObN2/i888/h16vR/fu3Rt9noio6dg2SnXkttGkurq61k5TDbkheevWLajVavTo0QNz5syBQqHAjz/+iCNHjojHrqudAYB//vOfiIyMRP/+/TF37lxUVFRgx44dCAsLw0cffSS2ryavvvoqnJ2dERcXB0EQMHnyZLz11ls4cOAAhgwZIok9cOAAfHx80Lt37zrrf/HiRTz33HN45JFHMHv2bHTr1g27d+9GeHg4duzYgWHDhiEoKAjdu3fHihUrEBISgsDAQDzyyCP3PTcAEBISgv379yM+Ph4ymQxFRUViu/rFF1+YxTf08wZQMzJOrVbDw8MDr7/+OnJzc7F161Y4OTnh2WefbdC5v3LlCmbMmAFHR0dcvHgRGRkZuHTpEjIyMpr0OaQ5YmNj0b9/f8ybNw/nzp3Dnj174ODggNdee02Meeedd/D2229j6tSp+MMf/oCioiLs2LEDzz33HP7xj39AoVA06HPD/f5GtVsCUQe1d+9ewc3NTThz5kydMcOHDxeefvpp8XFSUpLg5uYmPt62bZvg5uYm3Lp1q84yzpw5I7i5uQl79+412/f8888Lbm5uwq5du2rd9/zzz4uP//Wvfwlubm7C6NGjhdLSUnF7Zmam4ObmJmzfvl3cNm7cOGHBggX3LbO+ui1YsEAYN26c+PjIkSOCm5ubsHnzZklcdHS0oFKphMuXL4vb3NzchMcee0yy7fz584Kbm5vw/vvvmx3rbu+++67g5uYm7Nu3T9xWVVUlhIaGCp6enpLXPm7cOGHOnDn1ltfQWNO1PHr0qOR1JCUliY8TEhIEf39/oaioSPLcuLg4Yfjw4YLBYBAEof7zaoq5W0pKiqBSqYSrV6+K2xYsWCC4ubkJa9asMYsPDQ0VZs6cKdmWlZUluLm5Cf/617/qfI2C8L/3cH3/7j1P99a5qqpKCAkJEV544QXJdtPz//Of/4jbrl69KgwdOlT485//LG4z/e5duXJF3DZt2jTJe9PkqaeeavA1JqKHF9vsztNm37lzRzzXo0aNEubNmyfs2LFD0kaa3NuelJWVCU888YSwePFiSdxPP/0kDB8+XLL9xRdfFEJCQoTKykrJsUNDQ4VJkyaZHeN3v/udUFVVJW5PS0sz+9xQG9P7UKvVCrdu3RKuXLki/P3vfxcef/xxYdSoUUJ5eblQXV0tqYcgCEJJSYkwatQoYdGiReK2W7dumX02MXnzzTcl73eTr776SnBzcxP2798v2Z6Tk2O2fdy4cYKbm5uQk5MjiTW9n6dOnSqp5/bt2wU3NzfhwoULgiAIwrlz5wQ3Nzfh4MGD9Z4TImoZbBs7T9toir3f97i7//7e20aaXn9975f62pnp06cLfn5+wi+//CJuO3/+vDB48GDh9ddfF7eZ3mPz5s0zK2PevHlCQECAcPv2bXHbd999V+c1vNsrr7wiPPbYY8J///tfcdvNmzcFLy8v4bnnnhO3XblyRXBzcxPS09PrLe/e2Pz8fMHNzU346quvBEEQhB07dgienp5CeXm5sGDBAsHT01N8XmM+b5i+12/cuFES+/TTTwu/+93vxMf1nfvacgiffPKJpL6CUPv37NqYrtHdv/f3/r4Ignk+xPS8uz+bCIIg/PnPfxZ8fHzExz/++KPg7u4uvPPOO5K4CxcuCEOGDBG3N+RzQ0P+RrVHnIaFHmo2Njb49ddf69xvmgfr2LFjtfbIbQi5XN6oRQ2ffvpp2Nraio+nTJkCR0dHZGdnN+n4DZWTk4OuXbuaDaWLiIiAIAhmQ9JGjRqFAQMGiI8HDx4MW1tbcXh1fcdxdHRESEiIuM3CwgLh4eEoLy83m9KjpZjuONd1vQVBQFZWFsaPHw9BEFBUVCT+CwgIQGlpKb777rv7Hufu3hLl5eUoKiqCl5cXBEGodUhaWFiY2bbp06fj22+/FXulATV34/v06QMfH5/71gGoGUK1bds2s3+mHt111bmkpASlpaUYPnx4rfX18vKSrDbet29fTJgwASdOnBCHmTWGQqHAxYsXUVBQ0OjnElHnwjb7fzpymy2TyaDRaBAbGwuFQoFPPvkEb731FsaNG4fY2Nh65yz/5z//Cb1ej2nTpkna6S5dumDYsGHifKPFxcX417/+halTp6KsrEyM++WXXxAQEICCggKzoeChoaGSHthhYWHo1q1bg6/llClT4OfnhwkTJmDp0qVwdnZGSkoKrK2t0bVrV3Fu3zt37qC4uBjV1dV4/PHHa21rG+PQoUPo3r07/P39Jefkscceg42NjdkcrP3798fo0aNrLWvGjBmSOYifeOIJAP+bOsf0Xj9x4kStw7GJ6MFj2/g/HbltNBk2bFit3+EWLFhw3+eaRvd8/vnnMBqNjTpuYWEhzp8/j9/97neSKUkGDx6MUaNG1Xrt/vjHP5ptmz59OgoLCyVtz4EDB2BlZYVJkybVefzbt2/j5MmTmDhxIpycnMTtvXr1QkhICL755huUlZU16jXd67e//S1UKhU+/fRTAMAnn3yCCRMmwNra2iy2oZ837nbv9/rhw4c3eNqdu7+PV1ZWoqioCMOGDQOABuUgWtq91/aJJ55AcXGxeA2OHDmCO3fuYOrUqZLz85vf/AbOzs7i+WnI54aW+BvVFjgNCz3UysvL6x1GFhwcjD179mDx4sVYu3Yt/Pz8EBQUhClTpqBLl4bdS+rdu3ejFj9xdnaWPJbJZHB2dsbVq1cbXEZTXL16Fb169ZJ8sAFqhr+Z9t+tT58+ZmXY2dndd2Guq1evwtnZ2ez8mY5z7dq1Rte9IUwfIusaplVUVAS9Xo/du3dj9+7ddcbcz7Vr15CUlITPPvvMbCqUexv4bt264dFHHzUrIzg4GMuXL8f+/fsxd+5clJaW4vjx45g1a1aDh2A98cQTtQ7zq20Kl+PHj+Odd97B+fPnUVVVJW6v7Vj3vj8BYODAgTAYDCgqKoKjo2OD6mcSExODV155BZMnT4abmxsCAgIwffp0ycIrREQA2+y7dfQ2Wy6X409/+hP+9Kc/obCwEF999RXee+89HDx4EN26dcOaNWtqfZ7pxuqLL75Y637T+fjvf/8LQRDw9ttv4+2336419tatW5Lh4Pdey0ceeQSOjo4NvpbJycmwtbUV2/a7EzAA8PHHH2Pr1q3Q6XSSJMa9w9ob6/LlyygtLRXnQr3XrVu3JI/rO55pihwT0xdY0/vEyckJL730ErZt24YDBw7giSeewPjx4/HUU09xChaiNsK28X86etsIAD169MCoUaPMtjdkIU8fHx9MnjwZGzduxLvvvgsfHx9MnDgRTz755H2vn6nOd0/NZeLq6ooTJ06YLeJZW3vi7+8PR0dH7N+/H35+frhz546YlL73utytqKgIBoOhzuPfuXMH169fr3M61YYKCQnBtm3bMGvWLOTl5ZktNG7S0M8bJpaWlmbfve3s7Bq89klxcTE2btyIzMxMs3a7LdYEqevzQElJCWxtbVFQUABBEOq8AWKaYq8hnxta4m9UW2CynB5aN27cQGlpqdmXmbtZWVlh586d+Pe//43PP/8cX3zxBTIzM7F7925s3bq1QY1WY+Zla67bt28/sBWx6zqOcM/iKe2FaSGO2pK9wP/m8n7qqafwu9/9rtaYexfLudft27fx0ksvoaSkBLNnz4aLiwtsbGxw8+ZNLFy40OxOqVwur7UBsLOzw7hx43DgwAHMnTsXhw4dQlVVFZ566qn7vs7G+vrrr/GnP/0JI0aMwF/+8hc4OjrCwsICe/fuxSeffNLix7vXiBEjcOTIERw7dgwnT57Ehx9+iO3bt+PNN9/EzJkzW/34RNQxsM1unvbcZvfq1QvTpk3DpEmTEBISgkOHDmHlypW1zmVuqu/q1atrvTlrep2m9jYiIqLOXtT1vZeaoq6b1EDNAlgLFy7ExIkToVar0bNnT3Tt2hUpKSn37cF4P3fu3EHPnj3rvMFwb53qe4/X9aX07vfJwoUL8bvf/U5st5ctW4aUlBRkZGTU2gGAiFoP28bmac9tY1PIZDIkJSXh9OnTOH78OL744gskJCRg27Zt2L17d4Pn9m6o2jphde3aFU8++SQyMjKQmJiIU6dOobCwsFW+xzZFSEgI1q1bh8WLF8Pe3r7ONbIa+nmjrseNFRsbi7y8PKjVari7u8PGxgZ37tzB7Nmz2+T9eL/PA3fu3IFMJkNaWlqtr/3umyr3+9zQEn+j2gKT5fTQ2rdvHwAgICCg3rguXbrAz88Pfn5+WLRoEbZs2YL169fj3//+N0aNGtXiiy1cvnxZ8lgQBFy+fFmSqK3rjve1a9ckw5YaU7d+/fohNzcXZWVlkjulWq1W3N8S+vXrhwsXLuDOnTuSP8Km49x7F7Ml/Prrrzh69Cj69Okj3vG/l4ODAx555BHcuXOn1rv5d6vrvObn56OgoACrVq3C008/LW4/efJko+s8ffp0vPLKKzhz5oy4SEpz76TX5vDhw7C0tIRGo5H0ONi7d2+t8fe+P4GaO+/W1tb1LlhT33vR3t4ev//97/H73/8ev/76K55//nkkJyczWU5EIrbZUg9jm21hYQGVSoWCggL88ssvtX45NZ2vnj171ttWm+IsLCzu26abXL58GSNHjhQf//rrr/jpp58QGBjYmJdRq8OHD8PJyQkbN26UXOd7F/6u7z1Q174BAwYgNzcX3t7eDyyhpVKpoFKp8Morr+DUqVMICwvDrl27EBcX90COT0Q12DZKPYxtY1N4enrC09MTcXFxOHDgAOLj45GZmYmZM2fWeT5NddbpdGb7tFotevToIUmA1mf69OnYunUrPvvsM+Tk5MDBweG+71EHBwdYW1vXefwuXbrUOhKgsfr27Qtvb298+eWX4nRrtWno543GqOvcl5SUIDc3F9HR0Zg7d664vT1PUzpgwAAIgoD+/fvXOhrgXvf73HC/v1HtUfvt807UDLm5udi8eTP69+9f713O4uJis23u7u4AIE5XYZrj6n7DtRrqH//4h2S6jkOHDpl9WXNycsK3334rmTLj+PHjuH79uqSsxtQtMDAQt2/fxs6dOyXb3333Xchkshb5smg6zk8//YTMzExxW3V1Nd5//33Y2NhgxIgRLXIck4qKCrz++usoLi7Gyy+/XGcj1bVrV0yePBmHDx9Gfn6+2f67p2Cp67yaPizdffdXEAS89957ja53YGAgevTogfT0dHz11Vetdje+a9eukMlkkvnGf/zxRxw7dqzW+Ly8PMm8adevX8exY8fg7+9f711fa2vrWt+Hv/zyi+TxI488ggEDBkje20TUubHNNteR2+yCgoJah6jr9Xrk5eXBzs6uzpuvo0ePhq2tLVJSUmqdj9XUVvfs2RM+Pj7YvXs3CgsL64y72+7duyVl7tq1C9XV1S1yLk3t492fD7799lucPn1aElffe6CufVOnTsXt27exefNms+dUV1e32HsdqJlOrrq6WrLNzc0NXbp0YbtN9ICxbTTXkdvGllBSUmLWC7mh17pXr15wd3fHP/7xD8m+/Px8nDx5EmPGjGlwPQYPHgyVSoUPP/wQWVlZmDZtWp1JaZOuXbvC398fx44dk8zz/fPPP+OTTz7B8OHD653GpTFiY2Mxd+5cs7nt79bQzxuNUde5r+s79Pbt2xt9jAdl0qRJ6Nq1KzZu3Gj2nhMEQfyO35DPDQ35G9UesWc5dXg5OTnQarW4ffs2fv75Z/z73//GyZMn0bdvX7zzzju1Dh8y2bRpE77++muMGTMG/fr1w61bt/DBBx/g0UcfxfDhwwHU3FVTKBT4+9//jkceeQQ2Njbw8PCQ3BFvDDs7Ozz77LOYMWMGbt26he3bt8PZ2RnPPPOMGDNz5kwcPnwYs2fPxtSpU/Hf//4XBw4cMBuC15i6jR8/Hr6+vli/fj2uXr0KlUqFkydP4tixY3jxxRdbbLhyaGgodu/ejYULF+K7775Dv379cPjwYZw6dQoJCQnNagRv3rwp9rAoLy/HDz/8IH44i4iIqHURkrvNnz8f//73v/HMM89g5syZGDRoEEpKSvDdd98hNzcXX375JYC6z6uLiwsGDBiAVatW4ebNm7C1tcXhw4eb9MHTwsIC06ZNw44dO9C1a1dMmzat8SekAcaMGYNt27Zh9uzZCAkJEd/jAwYMwIULF8zi3dzcoFarER4eDrlcjl27dgEAoqOj6z3OY489hl27dmHz5s1wdnaGg4MD/Pz8MG3aNPj4+OCxxx6Dvb09/vOf/+Dw4cN4/vnnW+X1ElH7xjb74W+zv//+e8THx2P06NF44oknYGdnh5s3b+If//gHCgsLkZCQUOcXR1tbWyQmJuL111/HjBkzEBwcDAcHB1y7dg3Z2dnw9vbG0qVLAQB/+ctf8Oyzz+LJJ5/EM888AycnJ/z88884ffo0bty4gf3790vKNhqNmDVrFqZOnQqdTocPPvgAw4cPx4QJExp/4u4xduxYZGVl4c9//jPGjh2LH3/8EX//+98xaNAglJeXi3FWVlYYNGgQDh48iIEDB8Le3h6//e1v4ebmhsceewwAsGzZMgQEBIifDXx8fBAaGoqUlBScP38e/v7+sLCwQEFBAQ4dOoQ33ngDU6ZMafZrAIB//etfeOuttzBlyhQMHDgQt2/fxr59+8QOB0TUOtg2PvxtY0v4+OOPsWvXLkycOBEDBgzAr7/+ioyMDNja2oo3CuprZ15//XVERkYiNDQUf/jDH1BRUYEdO3age/fukh7PDfH0009j1apVANDgTl+xsbH45z//iWeffRbPPvssunbtit27d6OqqgqvvfZa405GPXx8fODj41NvTGM+bzRUfed+xIgRSE9Ph9FoRO/evXHy5MkGLw7aFgYMGIDY2FisXbsWV69excSJE/HII4/gxx9/xNGjR/HMM89ArVY36HNDQ/5GtUdMllOHZxriamFhAXt7e7i5uSEhIQEzZsy4b0M2fvx4XL16FXv37sUvv/yCHj16wMfHB9HR0eKCBBYWFli5ciXWrVuHxMREVFdXY8WKFU3+cPHyyy/jwoULSE1Nxa+//go/Pz/85S9/kazSPHr0aCxcuBDbtm3D8uXL8fjjj2PLli1ig2TSmLp16dIF77zzDpKSkpCZmYmPPvoI/fr1w+uvv46IiIgmvZbaWFlZ4f3338eaNWvw8ccfo6ysDEqlEitWrGjUKuu1OX/+PF5//XXIZDI88sgj6NOnD8aNG4eZM2fCw8Pjvs//zW9+gz179mDTpk04cuQIdu3aBXt7ewwaNAjx8fFiXF3ndcaMGdiyZYs4D5elpSWCgoLw3HPPYfr06Y1+PdOnT8eOHTvg5+eHXr16Nfr5DeHn54e//e1vSEtLw/Lly9G/f3/Ex8fj6tWrtSbLR4wYAU9PT2zatAnXrl3DoEGDsGLFivsuyPnnP/8Z165dQ3p6On799Vf4+PjAz88P4eHh+Oyzz3Dy5ElUVVWhb9++iI2NhVqtbpXXS0TtG9vsh7/NHjFiBGJiYvDFF19g27Zt+OWXX/DII4/A3d0d8fHx9026Pvnkk+jVqxdSU1Oh0WhQVVWF3r1744knnpDUadCgQdi7dy82btyIjz/+GMXFxXBwcMCQIUPw5z//2azcpUuX4sCBA0hKSoLRaMS0adOwePHiFpmeYMaMGfj555+xe/dunDhxAoMGDcL//d//4dChQ+KNeJNly5bhr3/9K1asWAGj0Yi5c+fCzc0NkyZNQnh4OD799FPs378fgiCIN9LfeustPP744/j73/+O9evXo2vXrujXrx+eeuopeHt7N7v+JiqVCgEBATh+/Dhu3rwJa2trqFQqpKWlwdPTs8WOQ0RSbBsf/raxJfj4+OA///kPMjMz8fPPP6N79+7w8PDAmjVrJOerrnZm1KhRSE9PR1JSEpKSktCtWzeMGDECr732WqPfC08++aR43IZ8DweA3/72t9i5cyfWrl2LlJQUCIIADw8P/N///R+GDRvWqOO3hIZ+3miMus792rVr8de//hUffPABBEGAv78/0tLS6lx3pT2YM2cOBg4ciHfffRebNm0CADz66KPw9/fH+PHjATTsc0ND/ka1RzKho65uQETUwX3//feYPn262RzoRERE1DI++ugjLFq0CB9++CGGDh3a1tUhIiLq8IqKijB69Gi88sortd6gJuroOGc5EVEbycjIgI2NDSZNmtTWVSEiIiIiIiK6r48//hi3b99u0uhqoo6A07AQET1gn332GS5duoSMjAw899xzDV55nIiIiIiIiKgt5Obm4ocffsCWLVswceJE9O/fv62rRNQq2LOciOgBW7ZsGTZu3IjAwMD7LpxJ1J4dPHgQf/rTnxAYGAhPT09Mnz4dH374odmq6Xv27MHkyZMxdOhQPPXUUzh+/LhZWaWlpUhISICPjw+8vLwQExODwsJCs7hTp04hNDQUHh4eGDduHFJTU2tdpT01NRVjx46Fh4cHQkNDcfr0abOybt68iejoaHh5ecHHxwdvvPEGysrKmndSiIiIiIgeQps3b8bKlSsxePBgLFmypK2rQ9RqOGc5ERERNUloaCj69euHiRMnokePHvjnP/+J9PR0/PnPf8bcuXMBAJ9++inmz5+Pl19+GSNHjkRmZib27t2LnTt3ShaMU6vVuHTpEhYsWABLS0ts2LABXbp0wd69e9GtW81AuMuXL+Ppp5+Gv78/nnvuOVy4cAFr1qxBXFycZNHY1NRUJCUlIT4+HiqVCjt37sQ///lP7Nu3T1zAyGg0iov3xMXFoaKiAqtWrcLgwYORkpLygM4gERERERERtSdMlhMREVGTFBUVwcHBQbJtyZIlyMzMxFdffYUuXbpg8uTJePzxx7F27Vox5o9//CO6d++OtLQ0AEBeXh7++Mc/QqPRICAgAACg1WoRHByMdevWITg4GACwdOlSnDhxAocOHYJcLgcArFu3Drt27cLJkychl8tRWVmJUaNG4bnnnsO8efMAAFVVVZgyZQoCAwORmJgIAPjkk08QHx+PzMxMuLi4AABOnDgBtVqNPXv2wMPDo/VOHBEREREREbVLnIaFiIiImuTeRDkAuLu7o6ysDOXl5bhy5QoKCgowdepUSUxwcDByc3NRVVUFAMjJyYFCoYC/v78Y4+LiAnd3d+Tk5IjbcnJyMGHCBDFRbipLr9cjLy8PQM00LWVlZZJjyuVyBAUFmZWlUqnERDkA+Pv7w97eHtnZ2U09JURERERERNSBcYHPdi4vLw+CIMDCwqKtq0JERK3MaDRCJpPBy8urravSZN988w169+4NW1tbfPPNNwAApVIpiXF1dYXRaMSVK1fg6uoKrVYLpVIJmUwmiXNxcYFWqwUAlJeX4/r165LktilGJpNBq9XC19dXjL83ztXVFdu3b0dFRQWsrKyg1WrNYmQyGZRKpVhGU7DdJiLqPB6GdruzY7tNRNR5NLTdZrK8nRMEwWzhMiIiejh19L/3X3/9NTIzM7FgwQIAQElJCQBAoVBI4kyPTfv1ej26d+9uVp6dnR3Onj0LoGYB0NrKksvlsLa2lpQll8thaWlpdkxBEFBSUgIrK6t6j2kqqylM7XZHv5ZERHR//Fvf8bHNJiLqPBr6957J8nbOdId76NChbVwTIiJqbf/5z3/augpNduPGDcTFxcHX1xcvvPBCW1enzVhYWKCqqgpGo7Gtq0JERA8AeyR3bPy+TUTUeTT0+zaT5URERNQser0ekZGRsLe3R3JyMrp0qVkSxc7ODkBNr3BHR0dJ/N37FQoFbty4YVZuSUmJGGPqBW7qYW5SVVUFg8EgKauqqgqVlZWS3uV6vR4ymUwSV1ZWVusx+/Tp04Sz8D8WFhYYNGhQs8ogIqL279KlS21dBSIiImphTJYTERFRk1VUVCAqKgqlpaXYvXu3ZGoT05zg984PrtVqYWFhAScnJzEuNzcXgiBI5i3X6XRwc3MDANjY2KBPnz5m84nrdDoIgiCWb/qp0+kwePBgyTH79u0LKysrMS4/P19SliAI0Ol0koVGm0Imk8HGxqZZZRARUft371obRERE1PF1aesKEBERUcdUXV2N2NhYaLVapKeno3fv3pL9Tk5OGDhwIA4dOiTZnpmZCT8/P8jlcgBAYGAgSkpKkJubK8bodDqcO3cOgYGB4rbAwEAcO3ZMMsVJZmYmFAqFuEiLt7c3bG1tcfDgQTHGaDQiKyvLrKzvv/8eBQUF4rbc3FwUFxdjzJgxzTgrRERERERE1FGxZzkRERE1yZtvvonjx49j4cKFKCsrw+nTp8V9Q4YMgVwuR3R0NOLj4zFgwAD4+voiMzMTZ86cwY4dO8RYLy8vBAQEICEhAQsWLIClpSXWr18PlUqFSZMmiXFqtRoHDhzA/PnzERYWhvz8fGg0GsTFxYmJd0tLS0RFRSE5ORkODg5wc3PDrl27UFxcDLVaLZY1efJkpKSkIDo6GvPmzYPBYMDq1asxduxYeHh4tP7JIyIiIiIionaHyXIiIiJqkpMnTwIAVq5cabbv2LFj6N+/P0JCQmAwGJCWlobU1FQolUps3LhR7AlusmHDBqxYsQJLly5FdXU1AgICsHjxYnTr9r+PKs7OztBoNFi5ciXmzJkDBwcHxMTEICIiQlJWZGQkBEHA1q1bUVRUBHd3d2g0GnHaF6BmXvH09HQsW7YM8+bNQ7du3RAUFISEhISWPEVERERERETUgcgEQRDauhJUN9NKrVydm4jo4ce/+R0fryERUefBv/kdH68hEVHn0dC/+ZyznIiIiIiIiIiIiIg6PSbLiYiIiIiIiIiIiKjTY7KciIiIiIiIiIiIiDo9JsuJHiLCnTttXQWqxYO4LnfucPmJ9ojXhRrijsC/3e0RrwsRETUW2w5qTXx/ET0Y3dq6AkTUcmRduqDk6C7c/qWwratC/1/XHr1gNzGs1Y/TpYsMR05cwC/68lY/FjVMD4UNggJUbV0N6gC6yLpg57df4GZZSVtXhf6/3rZ2eG7Y6LauBhERdTBs06m18LMJ0YPDZDnRQ+b2L4Wo/vlqW1eD2sAv+nL8XPRrW1eDiJrgZlkJruqL2roaRERE1Exs04mIOjZOw0JEREREREREREREnR6T5URERERERERERETU6TFZTkRERERERERERESdHpPlRERERERERERERNTpMVlORERERERERERERJ0ek+VERERERERERERE1OkxWU5EREREREREREREnR6T5URERERERERERETU6TFZTkRERERERERERESdHpPlRERERERERERERNTpMVlORERERERERERERJ0ek+VERERERERERERE1Om1q2T55cuXsXTpUkyfPh1DhgxBSEiIWUx4eDhUKpXZvx9++EESV1paioSEBPj4+MDLywsxMTEoLCw0K+/UqVMIDQ2Fh4cHxo0bh9TUVAiCIIkRBAGpqakYO3YsPDw8EBoaitOnT5uVdfPmTURHR8PLyws+Pj544403UFZW1ryTQkREREREREREREStrltbV+BuFy9eRHZ2NoYNG4Y7d+6YJa1NvL29sWDBAsm2/v37Sx7Hxsbi0qVLSExMhKWlJTZs2IDIyEjs3bsX3brVvOzLly9DrVbD398fsbGxuHDhAtasWYOuXbtCrVaLZaWlpSEpKQnx8fFQqVTYuXMnIiIisG/fPjg5OQEAjEYjZs+eDQBYu3YtKioqsGrVKsyfPx8pKSktdo6IiIiIiIiIiIiIqOW1q2T5+PHjMXHiRADAwoULcfbs2VrjFAoFPD096ywnLy8PJ06cgEajQUBAAABAqVQiODgYWVlZCA4OBgBoNBr06NED69atg1wuh5+fH4qKirBlyxaEh4dDLpejsrISKSkpiIiIwKxZswAAw4cPx5QpU6DRaJCYmAgAOHz4MC5evIjMzEy4uLiI9VSr1Thz5gw8PDxa4AwRERERERERNd7Bgwexf/9+fPfdd9Dr9XB2dkZ4eDh+//vfQyaTAagZyf3ll1+aPTczMxOurq7i49LSUqxYsQJHjx6F0WjE6NGjsXjxYvTq1UvyvFOnTmHVqlU4f/48evbsibCwMERGRorHA2pGcqelpeGDDz5AUVER3N3dsWjRIrPv/Ddv3sSyZctw4sQJWFhYICgoCIsWLYKtrW0LniUiIurs2tU0LF26tEx1cnJyoFAo4O/vL25zcXGBu7s7cnJyJHETJkyAXC4XtwUHB0Ov1yMvLw9ATeNeVlaGqVOnijFyuRxBQUFmZalUKjFRDgD+/v6wt7dHdnZ2i7wuIiIiIiIioqZ49913YW1tjYULF+Kdd95BYGAglixZgk2bNknivL29sXv3bsm/2kZynzx5EomJiVizZg10Oh0iIyNRXV0txphGcjs6OiIlJQUvvvgikpKSsHXrVklZppHcs2bNQkpKChwdHREREYErV66IMaaR3AUFBVi7di0SExNx4sQJzJ8/vxXOFBERdWbtqmd5Q3355Zfw9PTE7du3MWzYMLz66qsYMWKEuF+r1UKpVEruVgM1CXOtVgsAKC8vx/Xr1yXJbVOMTCaDVquFr6+vGH9vnKurK7Zv346KigpYWVlBq9WaxchkMiiVSrEMIiIiIiIiorbwzjvvwMHBQXzs5+eH4uJibNu2Da+88orYeY0juYmIqDPrcMnyESNGYPr06Rg4cCAKCwuh0Wjw0ksv4f3334eXlxcAQK/Xo3v37mbPtbOzE6d2KS0tBVDTwN5NLpfD2toaJSUlYllyuRyWlpaSOIVCAUEQUFJSAisrq3qPaSqrqQRBQHl5ebPKoIefTCaDtbV1W1eD6mAwGOpch6G5eO3bt8Zce0EQzG70EhEREbWEuxPlJu7u7sjIyEB5eXmDpzO530huU7I8JycHQUFBZiO5U1JSkJeXB19f33pHch85ckRyzPpGcjNZTkRELaXDJctjYmIkj8eOHYuQkBBs3rwZaWlpbVSr1mU0GnH+/Pm2rga1c9bW1hgyZEhbV4PqoNPpYDAYWqVsXvv2rbHX/u4vlERERESt6ZtvvkHv3r0liXKO5CYios6swyXL72VjY4MxY8bg8OHD4jaFQoEbN26YxZaUlMDOzg4AxF7gph7mJlVVVTAYDGKcQqFAVVUVKisrJb3L9Xo9ZDKZJK6srKzWY/bp06dZr9HCwgKDBg1qVhn08GNv1PZNqVS2as9yar8ac+0vXbrUyrUhIiIiqvH1118jMzMTCxYsELdxJHfTcbQnPQitOWKZ6GHX0JHcHT5ZXhsXFxfk5uaanQSdTgc3NzcANUn2Pn36mN2F1ul0EARBvGtt+qnT6TB48GAxTqvVom/fvrCyshLj8vPzJWUJggCdTicZntYUMpkMNjY2zSqDiNoWPzh3Xo259rzxQURERA/CjRs3EBcXB19fX7zwwgvido7kbjqO9qQHoTVHLBN1Bg0Zyd3hk+Xl5eX4/PPPMXToUHFbYGAgNm/ejNzcXIwaNQpAzR+Uc+fOYfbs2ZK4Y8eO4bXXXoOFhQUAIDMzEwqFQrxr7u3tDVtbWxw8eFBMlhuNRmRlZSEwMFBS1v79+1FQUICBAwcCAHJzc1FcXIwxY8a06jkgIiIiIiIiagi9Xo/IyEjY29sjOTlZXNizNhzJ3XDs9EAPQmuOWCZ62DV0JHe7SpYbDAZkZ2cDAK5evYqysjIcOnQIAODj4wOtVov09HQEBQWhX79+KCwsxLZt2/DTTz/h7bffFsvx8vJCQEAAEhISsGDBAlhaWmL9+vVQqVSYNGmSGKdWq3HgwAHMnz8fYWFhyM/Ph0ajQVxcnHinwdLSElFRUUhOToaDgwPc3Nywa9cuFBcXQ61Wi2VNnjwZKSkpiI6Oxrx582AwGLB69WqMHTuWi40QERERERFRm6uoqEBUVBRKS0uxe/fuWqc2uR+O5CZqOxyxTNR0Db2p2a6S5bdu3cKrr74q2WZ6/N577+HRRx+F0WjE+vXrUVxcDGtra3h5eeHNN980S0hv2LABK1aswNKlS1FdXY2AgAAsXrwY3br97yU7OztDo9Fg5cqVmDNnDhwcHBATE4OIiAhJWZGRkRAEAVu3bkVRURHc3d2h0Wjg5OQkxlhYWCA9PR3Lli3DvHnz0K1bNwQFBSEhIaGlTxMRERERERFRo1RXVyM2NhZarRY7d+5E79697/scjuQmIqLOpl0ly/v3748LFy7UG6PRaBpUVvfu3bF8+XIsX7683jhvb29kZGTUGyOTyRAVFYWoqKh643r37o3k5OQG1Y+IiIiIiIjoQXnzzTdx/PhxLFy4EGVlZTh9+rS4b8iQIThz5gxHchMRUafXrpLlRERERERERNTyTp48CQBYuXKl2b5jx47B0dGRI7mJiKjTY7KciIiIiIiI6CH32Wef3TeGI7mJiKizq3vZayIiIiIiIiIiIiKiToLJciIiIiIiIiIiIiLq9JgsJyIiIiIiIiIiIqJOj8lyIiIiIiIiIiIiIur0mCwnIiIiIiIiIiIiok6PyXIiIiIiIiIiIiIi6vSYLCciIiIiIiIiIiKiTo/JciIiIiIiIiIiIiLq9JgsJyIiIiIiIiIiIqJOj8lyIiIiIiIiIiIiIur0mCwnIiIiIiIiIiIiok6PyXIiIiJqksuXL2Pp0qWYPn06hgwZgpCQELOY8PBwqFQqs38//PCDJK60tBQJCQnw8fGBl5cXYmJiUFhYaFbeqVOnEBoaCg8PD4wbNw6pqakQBEESIwgCUlNTMXbsWHh4eCA0NBSnT582K+vmzZuIjo6Gl5cXfHx88MYbb6CsrKx5J4WIiIiIiIg6rG5tXQEiIiLqmC5evIjs7GwMGzYMd+7cMUtam3h7e2PBggWSbf3795c8jo2NxaVLl5CYmAhLS0ts2LABkZGR2Lt3L7p1q/m4cvnyZajVavj7+yM2NhYXLlzAmjVr0LVrV6jVarGstLQ0JCUlIT4+HiqVCjt37kRERAT27dsHJycnAIDRaMTs2bMBAGvXrkVFRQVWrVqF+fPnIyUlpcXOEREREREREXUcTJYTERFRk4wfPx4TJ04EACxcuBBnz56tNU6hUMDT07POcvLy8nDixAloNBoEBAQAAJRKJYKDg5GVlYXg4GAAgEajQY8ePbBu3TrI5XL4+fmhqKgIW7ZsQXh4OORyOSorK5GSkoKIiAjMmjULADB8+HBMmTIFGo0GiYmJAIDDhw/j4sWLyMzMhIuLi1hPtVqNM2fOwMPDowXOEBEREREREXUknIaFiIiImqRLl5b5GJGTkwOFQgF/f39xm4uLC9zd3ZGTkyOJmzBhAuRyubgtODgYer0eeXl5AGqmaSkrK8PUqVPFGLlcjqCgILOyVCqVmCgHAH9/f9jb2yM7O7tFXhcRERERERF1LEyWExERUav68ssv4enpiaFDh+L555/HV199Jdmv1WqhVCohk8kk211cXKDVagEA5eXluH79uiS5bYqRyWRinOnnvXGurq64du0aKioqxLh7Y2QyGZRKpVgGERERERERdS6choWIiIhazYgRIzB9+nQMHDgQhYWF0Gg0eOmll/D+++/Dy8sLAKDX69G9e3ez59rZ2YlTu5SWlgKomSrlbnK5HNbW1igpKRHLksvlsLS0lMQpFAoIgoCSkhJYWVnVe0xTWU0lCALKy8sbFCuTyWBtbd2s41HrMRgMdc7FT0QkCILZjV4iIiLq2JgsJyIiolYTExMjeTx27FiEhIRg8+bNSEtLa6NatS6j0Yjz5883KNba2hpDhgxp5RpRU+l0OhgMhrauBhG1Y3dPDUZEREQdX7tKll++fBkajQbffvstLl68CBcXF3zyySfi/rKyMmzbtg3Z2dkoKCiAXC6Hh4cH4uLioFKpxLgff/wREyZMMCt/2LBhyMjIkGw7deoUVq1ahfPnz6Nnz54ICwtDZGSkpIeAIAhIS0vDBx98gKKiIri7u2PRokVmi5XdvHkTy5Ytw4kTJ2BhYYGgoCAsWrQItra2LXSGiIiIOjYbGxuMGTMGhw8fFrcpFArcuHHDLLakpAR2dnYAIPYCN/UwN6mqqoLBYBDjFAoFqqqqUFlZKeldrtfrIZPJJHFlZWW1HrNPnz7Neo0WFhYYNGhQg2LZI7F9UyqV7FlORHW6dOlSW1eBiIiIWli7SpZfvHgR2dnZGDZsGO7cuWP25eTatWvYvXs3fv/73yM2NhaVlZXYunUrQkNDsXfvXri6ukri582bB19fX/HxI488Itl/+fJlqNVq+Pv7IzY2FhcuXMCaNWvQtWtXqNVqMS4tLQ1JSUmIj4+HSqXCzp07ERERgX379sHJyQlATS+y2bNnAwDWrl2LiooKrFq1CvPnz0dKSkqLniciIqKHiYuLC3Jzc82Gs+t0Ori5uQGoSbL36dPHbD5xnU4HQRDE+cdNP3U6HQYPHizGabVa9O3bF1ZWVmJcfn6+pCxBEKDT6SQLjTaFTCaDjY1Ns8qg9oFT5BBRfXjDk4iI6OHTrhb4HD9+PLKzs5GUlITHHnvMbH///v1x5MgRxMXFISAgABMmTEBqaiosLS3xwQcfmMU7OzvD09NT/Pfb3/5Wsl+j0aBHjx5Yt24d/Pz8MGvWLERERGDLli2oqqoCAFRWViIlJQURERGYNWsW/Pz8sG7dOtjb20Oj0YhlHT58GBcvXsTbb7+N8ePHIzg4GH/729/w+eef48yZMy18poiIiDqm8vJyfP755xg6dKi4LTAwECUlJcjNzRW36XQ6nDt3DoGBgZK4Y8eOwWg0itsyMzOhUCjE+c+9vb1ha2uLgwcPijFGoxFZWVlmZX3//fcoKCgQt+Xm5qK4uBhjxoxp0ddMREREREREHUO76lnepUv9ufvaemk98sgjGDBgAAoLCxt9vJycHAQFBUnmmQsODkZKSgry8vLg6+uLU6dOoaysDFOnThVj5HI5goKCcOTIEUlZKpVK7NEGAP7+/rC3t0d2djY8PDwaXT8iIqL2zGAwIDs7GwBw9epVlJWV4dChQwAAHx8faLVapKenIygoCP369UNhYSG2bduGn376CW+//bZYjpeXFwICApCQkIAFCxbA0tIS69evh0qlwqRJk8Q4tVqNAwcOYP78+QgLC0N+fj40Gg3i4uLEttzS0hJRUVFITk6Gg4MD3NzcsGvXLhQXF0tGjU2ePBkpKSmIjo7GvHnzYDAYsHr1aowdO5ZtNhERERERUSfVrpLlTaHX63Hx4kWMGjXKbF9iYiLi4uJgb2+PCRMmID4+Hvb29gBqerZdv35dktwGaoZly2QyaLVa+Pr6isO9741zdXXF9u3bUVFRASsrK2i1WrMYmUwGpVJpNmSciIjoYXDr1i28+uqrkm2mx++99x4effRRGI1GrF+/HsXFxbC2toaXlxfefPNNs4T0hg0bsGLFCixduhTV1dUICAjA4sWL0a3b/z6qODs7Q6PRYOXKlZgzZw4cHBwQExODiIgISVmRkZEQBAFbt24V1xrRaDTi1GlAzbzi6enpWLZsGebNm4du3bohKCgICQkJLX2aiIiIiIiIqIPo8Mny//u//4NMJkNYWJi4TS6XIywsDAEBAVAoFPj222+xZcsWnD17Fnv27IGFhYW4QJhCoZCUJ5fLYW1tjZKSEgA1yXi5XC5ZJMz0PEEQUFJSAisrK+j1enHxsbvZ2dmJZTWVIAgoLy9vVhn08JPJZJxbtR0zGAyttkgcr3371phrf++c3e1d//79ceHChXpj7p6yrD7du3fH8uXLsXz58nrjvL29zRbrvpdMJkNUVBSioqLqjevduzeSk5MbVD8iIiIiIiJ6+HXoZPnevXuRkZGBlStX4tFHHxW39+rVC4mJieJjHx8f/Pa3v0VUVBSOHDmC4ODgNqht0xmNRpw/f76tq0HtnLW1NYYMGdLW1aA66HQ6GAyGVimb1759a+y1v3tqMCIiIiIiIiJ6cDpssjw7OxtLly7FK6+8gt/97nf3jR8zZgxsbGzw3XffITg4WOwFbuphblJVVQWDwQA7OzsANT3Iq6qqUFlZKeldrtfrIZPJJHFlZWVmxy0pKUGfPn2a/DqBmqHigwYNalYZ9PDrSL1ROyOlUtmqPcup/WrMtb906VIr14aIiIiIiIiI6tIhk+WnT5/Gq6++iqefftpsrtSGsrGxQZ8+fczmE9fpdBAEQZx/3PRTp9Nh8ODBYpxWq0Xfvn1hZWUlxuXn50vKEgQBOp0O/v7+TaqjiUwmq3VxUyLqODhNSufVmGvPGx9ERETUWg4ePIj9+/fju+++g16vh7OzM8LDw/H73/9e8hlkz549SE9Px7Vr16BUKhEXF4dx48ZJyiotLcWKFStw9OhRGI1GjB49GosXL0avXr0kcadOncKqVatw/vx59OzZE2FhYYiMjJQcTxAEpKWl4YMPPhDXGlm0aBE8PT0lZd28eRPLli3DiRMnYGFhgaCgICxatAi2trYtf7KIiKjT6tLWFWisS5cuISoqCiNHjsSbb77Z4OcdP34c5eXlGDp0qLgtMDAQx44dg9FoFLdlZmZCoVDAy8sLQM3cqLa2tjh48KAYYzQakZWVhcDAQElZ33//PQoKCsRtubm5KC4uxpgxY5ryUomIiIiIiIhaxLvvvgtra2ssXLgQ77zzDgIDA7FkyRJs2rRJjPn000+xZMkSTJ06FWlpafD09MTcuXNx+vRpSVmxsbE4efIkEhMTsWbNGuh0OkRGRqK6ulqMuXz5MtRqNRwdHZGSkoIXX3wRSUlJ2Lp1q6SstLQ0JCUlYdasWUhJSYGjoyMiIiJw5coVMcZoNGL27NkoKCjA2rVrkZiYiBMnTmD+/Pmtc7KIiKjTalc9yw0GA7KzswEAV69eRVlZGQ4dOgSgZt5xQRCgVqthaWmJF198EWfPnhWfa2trK05VsnLlSshkMnh6ekKhUODMmTNISUnB448/jokTJ4rPUavVOHDgAObPn4+wsDDk5+dDo9EgLi5OnDPW0tISUVFRSE5OhoODA9zc3LBr1y4UFxdDrVaLZU2ePBkpKSmIjo7GvHnzYDAYsHr1aowdOxYeHh6tfu6IiIiIiIiI6vLOO+/AwcFBfOzn54fi4mJs27YNr7zyCrp06YKkpCRMmzYNsbGxAICRI0ciPz8fmzZtQlpaGgAgLy8PJ06cgEajQUBAAICaaeeCg4ORlZUlrhGm0WjQo0cPrFu3DnK5HH5+figqKsKWLVsQHh4OuVyOyspKpKSkICIiArNmzQIADB8+HFOmTIFGoxHXIjt8+DAuXryIzMxMcfS3QqGAWq3GmTNn+J2biIhaTLtKlt+6dctsWhXT4/feew8AcOPGDQAQG1ITHx8fvP/++wAAV1dX7Nq1CxkZGaioqEDv3r3xhz/8ATExMejW7X8v2dnZGRqNBitXrsScOXPg4OCAmJgYRERESMqOjIyEIAjYunWrOCxMo9HAyclJjLGwsEB6ejqWLVuGefPmoVu3bggKCkJCQkLLnBwiIiIiIiKiJro7UW7i7u6OjIwMlJeX45dffkFBQQFee+01SUxwcDBWr16NqqoqyOVy5OTkQKFQSKYbdXFxgbu7O3JycsRkeU5ODoKCgiSLlwcHByMlJQV5eXnw9fXFqVOnUFZWhqlTp4oxcrkcQUFBOHLkiLgtJycHKpVKTJQDgL+/P+zt7ZGdnc1kORERtZh2lSzv378/Lly4UG/M/fYDwMyZMzFz5swGHdPb2xsZGRn1xshkMkRFRSEqKqreuN69eyM5OblBxyUiIiIiIiJqS9988w169+4NW1tbfPPNNwBqeonfzdXVFUajEVeuXIGrqyu0Wi2USqXZWisuLi7immDl5eW4fv26JLltipHJZNBqtfD19RXj741zdXXF9u3bUVFRASsrK2i1WrMYmUwGpVJptg4ZERFRc7SrZDkRERERERERtb6vv/4amZmZWLBgAQCgpKQEQM30JnczPTbt1+v16N69u1l5dnZ24lSppaWltZYll8thbW0tKUsul8PS0tLsmIIgoKSkBFZWVvUe01RWUwmCgPLy8maVAdQk7xuzsDtRUxgMBgiC0NbVIOqQBEEwu9FbGybLiYiIiIiIiDqRGzduIC4uDr6+vnjhhRfaujptymg04vz5880ux9raGkOGDGmBGhHVTafTwWAwtHU1iDqsu6cGqwuT5URERERERESdhF6vR2RkJOzt7ZGcnIwuXboAqOmlDdT0Cnd0dJTE371foVCIa4ndraSkRIwx9QI39TA3qaqqgsFgkJRVVVWFyspKSe9yvV4PmUwmiSsrK6v1mH369GnCWfgfCwsLDBo0qFllAGhQb0Wi5lIqlexZTtREly5dalAck+VEREREREREnUBFRQWioqJQWlqK3bt3S6Y2Mc0Jfu/84FqtFhYWFnBychLjcnNzzYaz63Q6uLm5AQBsbGzQp08fs/nEdTodBEEQyzf91Ol0GDx4sOSYffv2hZWVlRiXn58vKUsQBOh0OslCo00hk8lgY2PTrDKIHhRO9UPUdA29qdmlletBRERERERERG2suroasbGx0Gq1SE9PR+/evSX7nZycMHDgQBw6dEiyPTMzE35+fuLQ9cDAQJSUlCA3N1eM0el0OHfuHAIDA8VtgYGBOHbsGIxGo6QshUIBLy8vAIC3tzdsbW1x8OBBMcZoNCIrK8usrO+//x4FBQXittzcXBQXF2PMmDHNOCtERERS7FlORERERERE9JB78803cfz4cSxcuBBlZWU4ffq0uG/IkCGQy+WIjo5GfHw8BgwYAF9fX2RmZuLMmTPYsWOHGOvl5YWAgAAkJCRgwYIFsLS0xPr166FSqTBp0iQxTq1W48CBA5g/fz7CwsKQn58PjUaDuLg4MfFuaWmJqKgoJCcnw8HBAW5ubti1axeKi4uhVqvFsiZPnoyUlBRER0dj3rx5MBgMWL16NcaOHQsPD4/WP3lERNRpMFlORERERERE9JA7efIkAGDlypVm+44dO4b+/fsjJCQEBoMBaWlpSE1NhVKpxMaNG8We4CYbNmzAihUrsHTpUlRXVyMgIACLFy9Gt27/SzE4OztDo9Fg5cqVmDNnDhwcHBATE4OIiAhJWZGRkRAEAVu3bkVRURHc3d2h0WjEaV+AmnnF09PTsWzZMsybNw/dunVDUFAQEhISWvIUERERMVlORERERERE9LD77LPPGhQ3c+ZMzJw5s96Y7t27Y/ny5Vi+fHm9cd7e3sjIyKg3RiaTISoqClFRUfXG9e7dG8nJyfXGEBERNRfnLCciIiIiIiIiIiKiTo/JciIiIiIiIiIiIiLq9JgsJyIiIiIiIiIiIqJOj8lyIiIiIiIiIiIiIur0mCwnIiIiIiIiIiIiok6PyXIiIiIiIiIiIiIi6vSYLCciIiIiInrA7twR2roKdA9eEyIiIurW1hUgIiIiIiLqbLp0keHIiQv4RV/e1lUhAD0UNggKULV1NYiIiKiNMVlORERERETUBn7Rl+Pnol/buhpERERE9P9xGhYiIiIiIiIiIiIi6vSYLCciIiIiIiIiIiKiTo/JciIiIiIiIiIiIiLq9NpVsvzy5ctYunQppk+fjiFDhiAkJKTWuD179mDy5MkYOnQonnrqKRw/ftwsprS0FAkJCfDx8YGXlxdiYmJQWFhoFnfq1CmEhobCw8MD48aNQ2pqKgRBugq6IAhITU3F2LFj4eHhgdDQUJw+fdqsrJs3byI6OhpeXl7w8fHBG2+8gbKysqadDCIiIiIiIiIiIiJ6YJqcLP/HP/6BH3/8sc79P/74I/7xj380qsyLFy8iOzsbzs7OcHV1rTXm008/xZIlSzB16lSkpaXB09MTc+fONUtex8bG4uTJk0hMTMSaNWug0+kQGRmJ6upqMeby5ctQq9VwdHRESkoKXnzxRSQlJWHr1q2SstLS0pCUlIRZs2YhJSUFjo6OiIiIwJUrV8QYo9GI2bNno6CgAGvXrkViYiJOnDiB+fPnN+ocEBERtYbWaLeJiIiodbDdJiIiahtNTpYvWrQIeXl5de4/c+YMFi1a1Kgyx48fj+zsbCQlJeGxxx6rNSYpKQnTpk1DbGwsRo4cibfeegtDhw7Fpk2bxJi8vDycOHECf/vb3xAcHIwJEybg7bffxoULF5CVlSXGaTQa9OjRA+vWrYOfnx9mzZqFiIgIbNmyBVVVVQCAyspKpKSkICIiArNmzYKfnx/WrVsHe3t7aDQasazDhw/j4sWLePvttzF+/HgEBwfjb3/7Gz7//HOcOXOmUeeBiIiopbVGu01EREStg+02ERFR22hysvzeqUruVV5ejq5duzauMl3qr86VK1dQUFCAqVOnSrYHBwcjNzdXTHDn5ORAoVDA399fjHFxcYG7uztycnLEbTk5OZgwYQLkcrmkLL1eL34wOXXqFMrKyiTHlMvlCAoKMitLpVLBxcVF3Obv7w97e3tkZ2c35jQQERG1uNZot4mIiKh1sN0mIiJqG90aE/z999/j+++/Fx9//fXXuH37tlmcXq/H3//+dyiVyubX8C5arRYAzMp1dXWF0WjElStX4OrqCq1WC6VSCZlMJolzcXERyygvL8f169clyW1TjEwmg1arha+vrxh/b5yrqyu2b9+OiooKWFlZQavVmsXIZDIolUqxDCIiogeprdttIiIiaji220RERG2vUcnyo0ePYuPGjQBqEsG7d+/G7t27a41VKBRYtWpV82t4l5KSErHse4919369Xo/u3bubPd/Ozg5nz54FULMAaG1lyeVyWFtbS8qSy+WwtLQ0O6YgCCgpKYGVlVW9xzSV1VSCIKC8vLxZZdDDTyaTwdrauq2rQXUwGAz37SHUVLz27Vtjrr0gCGY3epujrdttIiIiaji220RERG2vUcnyZ555BmPHjoUgCJg5cyZiYmIQGBgoiTElbQYMGIBu3RpVPNXBaDTi/PnzbV0Nauesra0xZMiQtq4G1UGn08FgMLRK2bz27Vtjr/3dU4M1F9ttIiKijoPtNhERUdtrVOvaq1cv9OrVCwDw3nvvwdXVFT179myVitXGzs4OQE2vcEdHR3G7Xq+X7FcoFLhx44bZ80tKSsQYUy9wUw9zk6qqKhgMBklZVVVVqKyslPQu1+v1kMlkkriysrJaj9mnT5+mveD/z8LCAoMGDWpWGfTwa8neqNTylEplq/Ysp/arMdf+0qVLLXrstm63iYiIqOHYbhMREbW9Jt+K9vHxacl6NIhpTvB75wfXarWwsLCAk5OTGJebm2s2nF2n08HNzQ0AYGNjgz59+pjNJ67T6SAIgli+6adOp8PgwYMlx+zbty+srKzEuPz8fElZgiBAp9NJFhptCplMBhsbm2aVQURti9OkdF6NufateeOjLdptIiIiahq220RERG2jWeO2vvjiC3z44Ye4cuUK9Hq9Wc85mUyGo0ePNquCd3NycsLAgQNx6NAhTJw4UdyemZkJPz8/ceh6YGAgNm/ejNzcXIwaNQpATbL73LlzmD17tvi8wMBAHDt2DK+99hosLCzEshQKBby8vAAA3t7esLW1xcGDB8VkudFoRFZWlmRIXGBgIPbv34+CggIMHDgQAJCbm4vi4mKMGTOmxc4BERFRUz3odpuIiIiaju02ERHRg9fkZHl6ejrWrl2Lnj17wsPDAyqVqtmVMRgMyM7OBgBcvXoVZWVlOHToEICaO+sODg6Ijo5GfHw8BgwYAF9fX2RmZuLMmTPYsWOHWI6XlxcCAgKQkJCABQsWwNLSEuvXr4dKpcKkSZPEOLVajQMHDmD+/PkICwtDfn4+NBoN4uLixMS7paUloqKikJycDAcHB7i5uWHXrl0oLi6GWq0Wy5o8eTJSUlIQHR2NefPmwWAwYPXq1Rg7diw8PDyafW6IiIiaozXabSIiImodbLeJiIjaRpOT5e+99x5GjhyJ1NRUsVd2c926dQuvvvqqZJvp8XvvvQdfX1+EhITAYDAgLS0NqampUCqV2Lhxo9gT3GTDhg1YsWIFli5diurqagQEBGDx4sWSRVCcnZ2h0WiwcuVKzJkzBw4ODoiJiUFERISkrMjISAiCgK1bt6KoqAju7u7QaDTitC9Azbzi6enpWLZsGebNm4du3bohKCgICQkJLXJuiIiImqM12m0iIiJqHWy3iYiI2kaTk+V6vR6TJ09u0Ya7f//+uHDhwn3jZs6ciZkzZ9Yb0717dyxfvhzLly+vN87b2xsZGRn1xshkMkRFRSEqKqreuN69eyM5ObneGCIiorbQGu02ERERtQ6220RERG2jS1OfOHToUOh0upasCxEREbUStttEREQdB9ttIiKittHkZHliYiKOHDmCAwcOtGR9iIiIqBWw3SYiIuo4WqPdvnz5MpYuXYrp06djyJAhCAkJMYsJDw+HSqUy+/fDDz9I4kpLS5GQkAAfHx94eXkhJiYGhYWFZuWdOnUKoaGh8PDwwLhx45Cammq2UKkgCEhNTRXX+woNDcXp06fNyrp58yaio6Ph5eUFHx8fvPHGGygrK2veSSEiIrpHk6dhiY2NRXV1NV5//XUkJibi0UcfRZcu0ty7TCbD/v37m11JIiIiah6220RERB1Ha7TbFy9eRHZ2NoYNG4Y7d+6YJa1NvL29sWDBAsm2/v37m9Xv0qVLSExMhKWlJTZs2IDIyEjs3btXXCfs8uXLUKvV8Pf3R2xsLC5cuIA1a9aga9euUKvVYllpaWlISkpCfHw8VCoVdu7ciYiICOzbt09cJ8xoNGL27NkAgLVr16KiogKrVq3C/PnzkZKS0uBzQEREdD9NTpbb29vD3t4ezs7OLVkfIiIiagVst4mIiDqO1mi3x48fj4kTJwIAFi5ciLNnz9Yap1Ao4OnpWWc5eXl5OHHiBDQaDQICAgAASqUSwcHByMrKQnBwMABAo9GgR48eWLduHeRyOfz8/FBUVIQtW7YgPDwccrkclZWVSElJQUREBGbNmgUAGD58OKZMmQKNRoPExEQAwOHDh3Hx4kVkZmbCxcVFrKdarcaZM2fg4eHRAmeIiIioGcny999/vyXrQURERK2oNdrty5cvQ6PR4Ntvv8XFixfh4uKCTz75xCxuz549SE9Px7Vr16BUKhEXF4dx48ZJYkpLS7FixQocPXoURqMRo0ePxuLFi9GrVy9J3KlTp7Bq1SqcP38ePXv2RFhYGCIjIyGTycQYQRCQlpaGDz74AEVFRXB3d8eiRYvMvvjfvHkTy5Ytw4kTJ2BhYYGgoCAsWrQItra2LXeSiIiImqA12u17e6Y3VU5ODhQKBfz9/cVtLi4ucHd3R05Ojpgsz8nJQVBQEORyuRgXHByMlJQU5OXlwdfXF6dOnUJZWRmmTp0qxsjlcgQFBeHIkSOSY6pUKjFRDgD+/v6wt7dHdnY2k+VERNRiWqa1JCIiok7HNJzb2dkZrq6utcZ8+umnWLJkCaZOnYq0tDR4enpi7ty5ZnORxsbG4uTJk0hMTMSaNWug0+kQGRmJ6upqMcY0nNvR0REpKSl48cUXkZSUhK1bt0rKMg3nnjVrFlJSUuDo6IiIiAhcuXJFjDEN5y4oKMDatWuRmJiIEydOYP78+S13goiIiDqgL7/8Ep6enhg6dCief/55fPXVV5L9Wq0WSqVScqMaqEmYa7VaAEB5eTmuX78uSW6bYmQymRhn+nlvnKurK65du4aKigox7t4YmUwGpVIplkFERNQSmtyz/N4Gsy4jRoxo6iGIiIiohbRGu92Q4dxJSUmYNm0aYmNjAQAjR45Efn4+Nm3ahLS0NAAczk1ERHSvtvq+PWLECEyfPh0DBw5EYWEhNBoNXnrpJbz//vvw8vICAOj1enTv3t3suXZ2duJngdLSUgA1bevd5HI5rK2tUVJSIpYll8thaWkpiVMoFBAEASUlJbCysqr3mKaymkoQBJSXlzerDKAmeW9tbd3scojqYzAY6lxvgIjqJwiC2Y3e2jQ5WR4eHt6gA5w/f76phyAiIqIW0hrt9v2Gc1+5cgUFBQV47bXXJNuDg4OxevVqVFVVQS6Xczg3ERHRPdrq+3ZMTIzk8dixYxESEoLNmzeLN7kfNkajsUXOo7W1NYYMGdICNSKqm06ng8FgaOtqEHVYd3+XrEuTk+Xvvfee2bbbt2/j6tWryMjIwJ07dziUmYiIqJ1oi3bbNCxaqVRKtru6usJoNOLKlStwdXVtseHcvr6+9Q7n3r59OyoqKmBlZcXh3ERE1K61l+/bNjY2GDNmDA4fPixuUygUuHHjhllsSUkJ7OzsAEDsBW7qYW5SVVUFg8EgxikUClRVVaGyslLSu1yv10Mmk0niysrKaj1mnz59mvUaLSwsMGjQoGaVAaBBNzeImkupVLJnOVETXbp0qUFxTU6W+/j41LlvxowZePbZZ/Hll1/Cz8+vqYcgIiKiFtIW7bZpWPS9Q7BNj+8egt1Zh3NzyHb7xqHO1Fr4u99+Neb3vqHDuZuiPX/fdnFxQW5urtnr1+l0cHNzA1CTZO/Tp4/ZDWidTgdBEMQb1qafOp0OgwcPFuO0Wi369u0LKysrMS4/P19SliAI0Ol0kpFpTSGTyWBjY9OsMogeFLYdRE3X0Da7ycny+nTp0gXTpk1DSkoKXn311dY4BBEREbUQttstqzHDuTlku33jUGdqLfzdb78a+3vfkOHcLe1Bttvl5eX4/PPPMXToUHFbYGAgNm/ejNzcXIwaNQpAzXk7d+4cZs+eLYk7duwYXnvtNVhYWAAAMjMzoVAoxPnPvb29YWtri4MHD4rJcqPRiKysLAQGBkrK2r9/PwoKCjBw4EAAQG5uLoqLizFmzJhWPQdERNS5tEqyHKjpLXbvkCsiIiJqn1qj3TYNnS4tLYWjo6O4Xa/XS/Z35uHcHLLdvnGoM7UW/u63X435vW/ocO7W0JR222AwIDs7GwBw9epVlJWV4dChQwBqerJrtVqkp6cjKCgI/fr1Q2FhIbZt24affvoJb7/9tliOl5cXAgICkJCQgAULFsDS0hLr16+HSqXCpEmTxDi1Wo0DBw5g/vz5CAsLQ35+PjQaDeLi4sSbDJaWloiKikJycjIcHBzg5uaGXbt2obi4GGq1Wixr8uTJSElJQXR0NObNmweDwYDVq1dj7NixXGeEiIhaVJOT5deuXat1u16vx9dffw2NRoMnnniiyRUjIiKiltMW7bZpaPW984NrtVpYWFjAyclJjONwbmqPONSZqPNpzO99a970aI12+9atW2Y90U2P33vvPTz66KMwGo1Yv349iouLYW1tDS8vL7z55ptmCekNGzZgxYoVWLp0KaqrqxEQEIDFixejW7f/pRicnZ2h0WiwcuVKzJkzBw4ODoiJiUFERISkrMjISAiCgK1bt6KoqAju7u7QaDTi5wSg5kZ0eno6li1bhnnz5qFbt24ICgpCQkJCo84BERHR/TQ5WT5+/Pg6PxwIggBPT0+8+eabTa4YERERtZy2aLednJwwcOBAHDp0CBMnThS3Z2Zmws/PT+xVxuHcREREUq3Rbvfv3x8XLlyoN0aj0TSorO7du2P58uVYvnx5vXHe3t7IyMioN0YmkyEqKgpRUVH1xvXu3RvJyckNqh8REVFTNTlZvnz5crPGWyaTQaFQYMCAAS2ymjQRERG1jNZot+83nNvBwQHR0dGIj4/HgAED4Ovri8zMTJw5cwY7duwQy+FwbiIiIil+3yYiImobTU6Wz5gxoyXrQURERK2oNdrt+w3n9vX1RUhICAwGA9LS0pCamgqlUomNGzeKPcFNOJybiIjof/h9m4iIqG20yAKfly5dwtWrVwEA/fr1411uIiKidqyl2u2GDOcGgJkzZ2LmzJn1xnA4NxERUe34fZuIiOjBaVay/OjRo1i5cqXYcJv0798fCxcuxIQJE5pVOSIiImo5bLeJiIg6DrbbRERED16Tk+XZ2dmIiYlB3759ERcXB1dXVwDADz/8gIyMDERHR2PLli2SxbSIiIiobbDdJiIi6jjYbhMREbWNJifLN2/eDJVKhZ07d8LGxkbcPmHCBDz//PN49tlnsWnTJjbeRERE7QDbbSIioo6D7TYREVHb6NLUJ164cAFPP/20pOE2sbGxwe9+97sGzWPaWOHh4VCpVLX++/TTT+uN+eGHHyRllZaWIiEhAT4+PvDy8kJMTAwKCwvNjnnq1CmEhobCw8MD48aNQ2pqKgRBkMQIgoDU1FSMHTsWHh4eCA0NxenTp1v89RMRETVFW7XbRERE1Hhst4mIiNpGk3uWW1paoqSkpM79JSUlsLS0bGrxdfrLX/6CsrIyybbt27cjKysLfn5+4jZvb28sWLBAEte/f3/J49jYWFy6dAmJiYmwtLTEhg0bEBkZib1796Jbt5pTc/nyZajVavj7+yM2NhYXLlzAmjVr0LVrV6jVarGstLQ0JCUlIT4+XuwBEBERgX379sHJyamlTwMREVGjtFW7TURERI3HdpuIiKhtNDlZ7uvri/feew+jR4+Gl5eXZN+3336L999/H/7+/s2u4L1qW/l7/vz58Pf3h4ODg7hNoVDA09OzznLy8vJw4sQJaDQaBAQEAACUSiWCg4ORlZWF4OBgAIBGo0GPHj2wbt06yOVy+Pn5oaioCFu2bEF4eDjkcjkqKyuRkpKCiIgIzJo1CwAwfPhwTJkyBRqNBomJiS32+omIiJqirdptIiIiajy220RERG2jycny1157DX/84x/x7LPPwsPDA0qlEgCg0+lw5swZ9OzZE/Hx8S1W0bqcOnUKP/74I2JjYxv1vJycHCgUCskHDBcXF7i7uyMnJ0dMlufk5CAoKAhyuVyMCw4ORkpKCvLy8uDr64tTp06hrKwMU6dOFWPkcjmCgoJw5MiR5r1AIiKiFtBe2m0iIiK6P7bbREREbaPJc5Y7OTlh//79CA8PR0lJCTIzM5GZmYmSkhK88MIL2Ldvn9m0J63hk08+gY2NDSZMmCDZ/uWXX8LT0xNDhw7F888/j6+++kqyX6vVQqlUQiaTSba7uLhAq9UCAMrLy3H9+nW4uLiYxchkMjHO9PPeOFdXV1y7dg0VFRXNf6FERETN0F7abSIiIro/tttERERto8k9y6urq2FpaYmEhAQkJCSY7S8rK0N1dbU493drqK6uxsGDBzF+/HjJwicjRozA9OnTMXDgQBQWFkKj0eCll17C+++/Lw5h0+v16N69u1mZdnZ2OHv2LICaBUCBmild7iaXy2FtbS3OIafX6yGXy83mjFMoFBAEASUlJbCysmry6xQEAeXl5U1+PnUOMpkM1tbWbV0NqoPBYDBbGLil8Nq3b4259oIgmN3EbSntod0mIiKihmG7TURE1Daa3LIuW7YMX3/9NT755JNa94eFhcHX1xeLFy9ucuXu5+TJkygqKkJISIhke0xMjOTx2LFjERISgs2bNyMtLa3V6tNajEYjzp8/39bVoHbO2toaQ4YMaetqUB10Oh0MBkOrlM1r37419trfPe1XS2oP7TYRERE1DNttIiKittHkZPkXX3yBp59+us79kydPxv79+5tafIN88sknsLe3FxforIuNjQ3GjBmDw4cPi9sUCgVu3LhhFltSUgI7OzsAEHuem3qYm1RVVcFgMIhxCoUCVVVVqKyslPQu1+v1kMlkYlxTWVhY1LqwKdHdWqs3KrUMpVLZqj3Lqf1qzLW/dOlSq9WjPbTbRCQl3LkDWZcmz4pIrYTXhdoDtttERERto8nJ8sLCQvTu3bvO/b169cLNmzebWvx9VVRU4OjRo3jqqadgYWHR6Oe7uLggNzfXbMi7TqeDm5sbgJoke58+fcQ5ye+OEQRBnKPc9FOn02Hw4MFinFarRd++fZs1BQtQkwi7e5oZIup4OE1K59WYa9+aNz7aut0mInOyLl1QcnQXbv9S2NZVof+va49esJsY1tbVIGK7TURE1EaanCy3t7eHTqerc/8PP/wAW1vbphZ/X5999hnKy8vx5JNP3je2vLwcn3/+OYYOHSpuCwwMxObNm5Gbm4tRo0YBqEl2nzt3DrNnz5bEHTt2DK+99pqYlM/MzIRCoRDnP/f29oatrS0OHjwoJsuNRiOysrIQGBjYYq+ZiIioqdq63Sai2t3+pRDVP19t62oQUTvDdpuIOgqOyKLW9qDfY01Olo8ePRp///vf8eSTT5rNlfvdd98hIyMDU6ZMaXYF63LgwAH07dsXw4cPl2z/+uuvkZ6ejqCgIPTr1w+FhYXYtm0bfvrpJ7z99ttinJeXFwICApCQkIAFCxbA0tIS69evh0qlwqRJk8Q4tVqNAwcOYP78+QgLC0N+fj40Gg3i4uLEeWUtLS0RFRWF5ORkODg4wM3NDbt27UJxcTHUanWrnQMiIqKGaut2m4iIiBqO7TYRdRQcKUetqS1G/TU5Wf7qq6/iiy++wMyZMzF+/HhxTu2LFy/i+PHjcHBwwKuvvtpiFb1bSUkJvvjiC7z44otmQ9YdHR1hNBqxfv16FBcXw9raGl5eXnjzzTfh4eEhid2wYQNWrFiBpUuXorq6GgEBAVi8eLFkRXFnZ2doNBqsXLkSc+bMgYODA2JiYhARESEpKzIyEoIgYOvWrSgqKoK7uzs0Gg2cnJxa5RwQERE1Rlu220RERNQ4bLeJqCPhSDl6mDQ5Wd67d2/s3bsXa9euxbFjx3DkyBEAgK2tLZ588knExcXVO8dac9jZ2eHs2bO17jMltxuie/fuWL58OZYvX15vnLe3NzIyMuqNkclkiIqKQlRUVIOO3ZruCHfQRcYhMO0NrwsRtaW2bLeJiIiocdhuExERtY0mJ8uBmkVFVq1aBUEQUFRUBABwcHBo1QXK6P66yLpg57df4GZZSVtXhf6/3rZ2eG7Y6LauBhF1cmy3iYiIOg6220RERA9es5LlJjKZDD179myJoqiF3CwrwVV9UVtXg4iI2iG220RERB0H220iIqIHh3NCEBEREREREREREVGnx2Q5EREREREREREREXV6TJYTERERERERERERUafHZDkRERERERERERERdXpMlhMRERERERERERFRp8dkOREREREREdFD7vLly1i6dCmmT5+OIUOGICQkpNa4PXv2YPLkyRg6dCieeuopHD9+3CymtLQUCQkJ8PHxgZeXF2JiYlBYWGgWd+rUKYSGhsLDwwPjxo1DamoqBEGQxAiCgNTUVIwdOxYeHh4IDQ3F6dOnzcq6efMmoqOj4eXlBR8fH7zxxhsoKytr2skgIiKqA5PlRERERERERA+5ixcvIjs7G87OznB1da015tNPP8WSJUswdepUpKWlwdPTE3PnzjVLXsfGxuLkyZNITEzEmjVroNPpEBkZierqajHm8uXLUKvVcHR0REpKCl588UUkJSVh69atkrLS0tKQlJSEWbNmISUlBY6OjoiIiMCVK1fEGKPRiNmzZ6OgoABr165FYmIiTpw4gfnz57fcCSIiIgLQra0rQERERERERESta/z48Zg4cSIAYOHChTh79qxZTFJSEqZNm4bY2FgAwMiRI5Gfn49NmzYhLS0NAJCXl4cTJ05Ao9EgICAAAKBUKhEcHIysrCwEBwcDADQaDXr06IF169ZBLpfDz88PRUVF2LJlC8LDwyGXy1FZWYmUlBRERERg1qxZAIDhw4djypQp0Gg0SExMBAAcPnwYFy9eRGZmJlxcXAAACoUCarUaZ86cgYeHR2udNiIi6mTYs5yIiIiIiIjoIdelS/1f/69cuYKCggJMnTpVsj04OBi5ubmoqqoCAOTk5EChUMDf31+McXFxgbu7O3JycsRtOTk5mDBhAuRyuaQsvV6PvLw8ADXTtJSVlUmOKZfLERQUZFaWSqUSE+UA4O/vD3t7e2RnZzfmNBAREdWLyXIiIiIiIiKiTk6r1QKo6SV+N1dXVxiNRnFaFK1WC6VSCZlMJolzcXERyygvL8f169clyW1TjEwmE+NMP++Nc3V1xbVr11BRUSHG3Rsjk8mgVCrFMoiIiFoCp2EhIiIiIiIi6uRKSkoA1ExvcjfTY9N+vV6P7t27mz3fzs5OnNqltLS01rLkcjmsra0lZcnlclhaWpodUxAElJSUwMrKqt5jmspqKkEQUF5e3qwygJrkvbW1dbPLIaqPwWAwWyS3LfF9Tw9KS7z3BUEwu9FbGybLiYiIiIiIiKhTMhqNOH/+fLPLsba2xpAhQ1qgRkR10+l0MBgMbV0NEd/39KC01Hv/7qnB6sJkOREREREREVEnZ2dnB6CmV7ijo6O4Xa/XS/YrFArcuHHD7PklJSVijKkXuKmHuUlVVRUMBoOkrKqqKlRWVkp6l+v1eshkMklcWVlZrcfs06dP017w/2dhYYFBgwY1qwwADeqtSNRcSqWy3fUsJ3oQWuK9f+nSpQbFMVlORERERERE1MmZ5gS/d35wrVYLCwsLODk5iXG5ublmw9l1Oh3c3NwAADY2NujTp4/ZfOI6nQ6CIIjlm37qdDoMHjxYcsy+ffvCyspKjMvPz5eUJQgCdDqdZKHRppDJZLCxsWlWGUQPCqc8oc6qJd77Db25wwU+iYiIiIiIiDo5JycnDBw4EIcOHZJsz8zMhJ+fnzh0PTAwECUlJcjNzRVjdDodzp07h8DAQHFbYGAgjh07BqPRKClLoVDAy8sLAODt7Q1bW1scPHhQjDEajcjKyjIr6/vvv0dBQYG4LTc3F8XFxRgzZkzLnAAiIiKwZzkRERERERHRQ89gMCA7OxsAcPXqVZSVlYmJcR8fHzg4OCA6Ohrx8fEYMGAAfH19kZmZiTNnzmDHjh1iOV5eXggICEBCQgIWLFgAS0tLrF+/HiqVCpMmTRLj1Go1Dhw4gPnz5yMsLAz5+fnQaDSIi4sTE++WlpaIiopCcnIyHBwc4Obmhl27dqG4uBhqtVosa/LkyUhJSUF0dDTmzZsHg8GA1atXY+zYsfDw8HgQp4+IiDoJJsuJiIiIiIiIHnK3bt3Cq6++Ktlmevzee+/B19cXISEhMBgMSEtLQ2pqKpRKJTZu3Cj2BDfZsGEDVqxYgaVLl6K6uhoBAQFYvHgxunX7X4rB2dkZGo0GK1euxJw5c+Dg4ICYmBhERERIyoqMjIQgCNi6dSuKiorg7u4OjUYjTvsC1Mwrnp6ejmXLlmHevHno1q0bgoKCkJCQ0NKniYiIOrkOlyz/6KOPsGjRIrPtkZGRiI+PFx/v2bMH6enpuHbtGpRKJeLi4jBu3DjJc0pLS7FixQocPXoURqMRo0ePxuLFi9GrVy9J3KlTp7Bq1SqcP38ePXv2RFhYGCIjIyVz3QiCgLS0NHzwwQdiA79o0SJ4enq27AkgIiIiIiIiaqT+/fvjwoUL942bOXMmZs6cWW9M9+7dsXz5cixfvrzeOG9vb2RkZNQbI5PJEBUVhaioqHrjevfujeTk5HpjiIiImqvDJctN0tPTxRW2gZqG0+TTTz/FkiVL8PLLL2PkyJHIzMzE3LlzsXPnTknyOjY2FpcuXUJiYiIsLS2xYcMGREZGYu/eveId8cuXL0OtVsPf3x+xsbG4cOEC1qxZg65du0qGhaWlpSEpKQnx8fFQqVTYuXMnIiIisG/fPskdcSIiIiIiIiIiIiJqfzpssvyxxx6Dg4NDrfuSkpIwbdo0xMbGAgBGjhyJ/Px8bNq0CWlpaQCAvLw8nDhxAhqNBgEBAQAApVKJ4OBgZGVlITg4GACg0WjQo0cPrFu3DnK5HH5+figqKsKWLVsQHh4OuVyOyspKpKSkICIiArNmzQIADB8+HFOmTIFGo0FiYmKrngsiIiIiIiIiIiIiap4ubV2BlnblyhUUFBRg6tSpku3BwcHIzc1FVVUVACAnJwcKhQL+/v5ijIuLC9zd3ZGTkyNuy8nJwYQJE8QFSExl6fV65OXlAaiZpqWsrExyTLlcjqCgIElZRERERERERERERNQ+ddhkeUhICNzd3TFhwgSkpKTg9u3bAACtVgugppf43VxdXWE0GnHlyhUxTqlUSuYdB2oS5qYyysvLcf36dbi4uJjFyGQyMc708944V1dXXLt2DRUVFS3xkomIiIiIiIiIiIiolXS4aVgcHR0RHR2NYcOGQSaT4bPPPsOGDRtw8+ZNLF26FCUlJQAAhUIheZ7psWm/Xq+XzHluYmdnh7NnzwKoWQC0trLkcjmsra0lZcnlclhaWpodUxAElJSUwMrKqsmvWRAElJeXNyhWJpPB2tq6ycei1mUwGCAIQquUzWvfvvHad16NufaCIJjdxCUiIiIiIiKiB6PDJctHjx6N0aNHi48DAgJgaWmJ7du34+WXX27DmrUeo9GI8+fPNyjW2toaQ4YMaeUaUVPpdDoYDIZWKZvXvn3jte+8Gnvt7572i4iIiIiIiIgenA6XLK/N1KlTsXXrVpw/fx52dnYAanqFOzo6ijF6vR4AxP0KhQI3btwwK6ukpESMMfU8N/UwN6mqqoLBYJCUVVVVhcrKSknvcr1eD5lMJsY1lYWFBQYNGtSgWPZIbN+USmWr9i6m9ovXvvNqzLW/dOlSK9eGiIiIiIiIiOryUCTL72aaN1yr1UrmENdqtbCwsICTk5MYl5ubazbkXafTwc3NDQBgY2ODPn36iHOS3x0jCIJYvumnTqfD4MGDJcfs27dvs6ZgAWoSYTY2Ns0qg9oHTpXRefHad16Nufa88UFERERERETUdjrsAp93y8zMRNeuXTFkyBA4OTlh4MCBOHTokFmMn5+fOLw9MDAQJSUlyM3NFWN0Oh3OnTuHwMBAcVtgYCCOHTsGo9EoKUuhUMDLywsA4O3tDVtbWxw8eFCMMRqNyMrKkpRFRETU2Xz00UdQqVRm/9asWSOJ27NnDyZPnoyhQ4fiqaeewvHjx83KKi0tRUJCAnx8fODl5YWYmBgUFhaaxZ06dQqhoaHw8PDAuHHjkJqaata7XxAEpKamYuzYsfDw8EBoaChOnz7doq+diIiIiIiIOpYO17NcrVbD19cXKpUKAHDs2DFkZGTghRdeEKddiY6ORnx8PAYMGABfX19kZmbizJkz2LFjh1iOl5cXAgICkJCQgAULFsDS0hLr16+HSqXCpEmTJMc7cOAA5s+fj7CwMOTn50Oj0SAuLk5MvFtaWiIqKgrJyclwcHCAm5sbdu3aheLiYqjV6gd4doiIiNqn9PR0ycLavXv3Fv//6aefYsmSJXj55ZcxcuRIZGZmYu7cudi5cyc8PT3FuNjYWFy6dAmJiYmwtLTEhg0bEBkZib1796Jbt5qPNJcvX4ZarYa/vz9iY2Nx4cIFrFmzBl27dpW0yWlpaUhKSkJ8fDxUKhV27tyJiIgI7Nu3TxyFRkRERERERJ1Lh0uWK5VK7N27Fzdu3MCdO3cwcOBAJCQkIDw8XIwJCQmBwWBAWloaUlNToVQqsXHjRrEnuMmGDRuwYsUKLF26FNXV1QgICMDixYvFL9wA4OzsDI1Gg5UrV2LOnDlwcHBATEwMIiIiJGVFRkZCEARs3boVRUVFcHd3h0aj4RduIiIiAI899hgcHBxq3ZeUlIRp06YhNjYWADBy5Ejk5+dj06ZNSEtLAwDk5eXhxIkT0Gg0CAgIAFDzmSA4OBhZWVkIDg4GAGg0GvTo0QPr1q2DXC6Hn58fioqKsGXLFoSHh0Mul6OyshIpKSmIiIjArFmzAADDhw/HlClToNFokJiY2KrngoiIiIiIiNqnDpcsX7x4cYPiZs6ciZkzZ9Yb0717dyxfvhzLly+vN87b2xsZGRn1xshkMkRFRSEqKqpB9SMiIiLgypUrKCgowGuvvSbZHhwcjNWrV6OqqgpyuRw5OTlQKBTw9/cXY1xcXODu7o6cnBwxWZ6Tk4OgoCBx9JeprJSUFOTl5cHX1xenTp1CWVkZpk6dKsbI5XIEBQXhyJEjrfyKiYiIiIiIqL16KOYsJyIiovYtJCQE7u7umDBhAlJSUnD79m0AEBfRViqVknhXV1cYjUZcuXJFjFMqlWaLoLq4uIhllJeX4/r165IFvk0xMplMjDP9vDfO1dUV165dQ0VFRUu8ZCIiIiIiIupgOlzPciIiIuo4HB0dER0djWHDhkEmk+Gzzz7Dhg0bcPPmTSxduhQlJSUAAIVCIXme6bFpv16vl8x5bmJnZ4ezZ88CqFkAtLay5HI5rK2tJWXJ5XJYWlqaHVMQBJSUlMDKyqrJr1kQBJSXlzcoViaTwdrausnHotZlMBjMFodtKbz27VtrXnuA1789a8y1FwTB7CYuERERdWxMlhMREVGrGT16NEaPHi0+DggIgKWlJbZv346XX365DWvWeoxGI86fP9+gWGtrawwZMqSVa0RNpdPpYDAYWqVsXvv2rTWvPcDr35419trfPe0XERERdXxMlhMREdEDNXXqVGzduhXnz5+HnZ0dgJpe4Y6OjmKMXq8HAHG/QqHAjRs3zMoqKSkRY0w9z009zE2qqqpgMBgkZVVVVaGyslLSu1yv10Mmk4lxTWVhYYFBgwY1KJY9Ets3pVLZqj3Lqf1qzWsP8Pq3Z4259pcuXWrl2hAREdGDxmQ5ERERtRnTvOFarVYyh7hWq4WFhQWcnJzEuNzcXLMh7zqdDm5ubgAAGxsb9OnTR5yT/O4YQRDE8k0/dTodBg8eLDlm3759mzUFC1CTBLOxsWlWGdQ+cJqMzovXvvNqzLXnTQ8iIqKHDxf4JCIiogcqMzMTXbt2xZAhQ+Dk5ISBAwfi0KFDZjF+fn7i8PbAwECUlJQgNzdXjNHpdDh37hwCAwPFbYGBgTh27BiMRqOkLIVCAS8vLwCAt7c3bG1tcfDgQTHGaDQiKytLUhYRERERERF1LuxZTkRERK1GrVbD19cXKpUKAHDs2DFkZGTghRdeEKddiY6ORnx8PAYMGABfX19kZmbizJkz2LFjh1iOl5cXAgICkJCQgAULFsDS0hLr16+HSqXCpEmTJMc7cOAA5s+fj7CwMOTn50Oj0SAuLk5MvFtaWiIqKgrJyclwcHCAm5sbdu3aheLiYqjV6gd4doiIiIiIiKg9YbKciIiIWo1SqcTevXtx48YN3LlzBwMHDkRCQgLCw8PFmJCQEBgMBqSlpSE1NRVKpRIbN24Ue4KbbNiwAStWrMDSpUtRXV2NgIAALF68GN26/e/jjLOzMzQaDVauXIk5c+bAwcEBMTExiIiIkJQVGRkJQRCwdetWFBUVwd3dHRqNRpz2hYiIiIiIiDofJsuJiIio1SxevLhBcTNnzsTMmTPrjenevTuWL1+O5cuX1xvn7e2NjIyMemNkMhmioqIQFRXVoPoRERERERHRw49zlhMRERERERERERFRp8dkORERERERERERERF1ekyWExEREREREREREVGnx2Q5EREREREREREREXV6TJYTERERERERERERUafHZDkRERERERER4aOPPoJKpTL7t2bNGkncnj17MHnyZAwdOhRPPfUUjh8/blZWaWkpEhIS4OPjAy8vL8TExKCwsNAs7tSpUwgNDYWHhwfGjRuH1NRUCIIgiREEAampqRg7diw8PDwQGhqK06dPt+hrJyIiAoBubV0BIiIiIiIiImo/0tPT0b17d/Fx7969xf9/+umnWLJkCV5++WWMHDkSmZmZmDt3Lnbu3AlPT08xLjY2FpcuXUJiYiIsLS2xYcMGREZGYu/evejWrSYVcfnyZajVavj7+yM2NhYXLlzAmjVr0LVrV6jVarGstLQ0JCUlIT4+HiqVCjt37kRERAT27dsHJyen1j8hRETUaTBZTkRERERERESixx57DA4ODrXuS0pKwrRp0xAbGwsAGDlyJPLz87Fp0yakpaUBAPLy8nDixAloNBoEBAQAAJRKJYKDg5GVlYXg4GAAgEajQY8ePbBu3TrI5XL4+fmhqKgIW7ZsQXh4OORyOSorK5GSkoKIiAjMmjULADB8+HBMmTIFGo0GiYmJrXouiIioc+E0LERERERERER0X1euXEFBQQGmTp0q2R4cHIzc3FxUVVUBAHJycqBQKODv7y/GuLi4wN3dHTk5OeK2nJwcTJgwAXK5XFKWXq9HXl4egJppWsrKyiTHlMvlCAoKkpRFRETUEpgsJyIiIiIiIiJRSEgI3N3dMWHCBKSkpOD27dsAAK1WC6Cml/jdXF1dYTQaceXKFTFOqVRCJpNJ4lxcXMQyysvLcf36dbi4uJjFyGQyMc708944V1dXXLt2DRUVFS3xkomIiABwGhYiIiIiIiIiAuDo6Ijo6GgMGzYMMpkMn332GTZs2ICbN29i6dKlKCkpAQAoFArJ80yPTfv1er1kznMTOzs7nD17FkDNAqC1lSWXy2FtbS0pSy6Xw9LS0uyYgiCgpKQEVlZWTX7NgiCgvLy8yc83kclksLa2bnY5RPUxGAxmC+C2Jb7v6UFpife+IAhmN3Fr0+GS5QcPHsT+/fvx3XffQa/Xw9nZGeHh4fj9738vvuDw8HB8+eWXZs/NzMyEq6ur+Li0tBQrVqzA0aNHYTQaMXr0aCxevBi9evWSPO/UqVNYtWoVzp8/j549eyIsLAyRkZGSEywIAtLS0vDBBx+gqKgI7u7uWLRokWSBEyIiIiIiIqL2avTo0Rg9erT4OCAgAJaWlti+fTtefvnlNqxZ6zEajTh//nyzy7G2tsaQIUNaoEZEddPpdDAYDG1dDRHf9/SgtNR7/+5pv+rS4ZLl7777Lvr164eFCxeiR48e+Oc//4klS5bgxo0bmDt3rhjn7e2NBQsWSJ7bv39/yWOuzk1ERERERERUt6lTp2Lr1q04f/487OzsANR0PHN0dBRj9Ho9AIj7FQoFbty4YVZWSUmJGGPqeW7qYW5SVVUFg8EgKauqqgqVlZWS3uV6vR4ymUyMayoLCwsMGjSoWWUAaFBvRaLmUiqV7a5nOdGD0BLv/UuXLjUorsMly9955x3Jqtx+fn4oLi7Gtm3b8Morr6BLl5pp2BUKRb29urk6NxEREREREVHDmeYN12q1kjnEtVotLCwsxI5iLi4uyM3NNRvyrtPp4ObmBgCwsbFBnz59xDnJ744RBEEs3/RTp9Nh8ODBkmP27du3WVOwADXJPhsbm2aVQfSgcMoT6qxa4r3f0Js7HW6Bz7sT5Sbu7u4oKytr1DxjXJ2biIiIiIiIqH6ZmZno2rUrhgwZAicnJwwcOBCHDh0yi/Hz8xO/NwcGBqKkpAS5ublijE6nw7lz5xAYGChuCwwMxLFjx2A0GiVlKRQKeHl5AagZNW5ra4uDBw+KMUajEVlZWZKyiIiIWkKH61lem2+++Qa9e/eGra2tuO3LL7+Ep6cnbt++jWHDhuHVV1/FiBEjxP0ttTq3r69vvatzb9++HRUVFc2+201ERERERETUmtRqNXx9faFSqQAAx44dQ0ZGBl544QVx2pXo6GjEx8djwIAB8PX1RWZmJs6cOYMdO3aI5Xh5eSEgIAAJCQlYsGABLC0tsX79eqhUKkyaNElyvAMHDmD+/PkICwtDfn4+NBoN4uLixMS7paUloqKikJycDAcHB7i5uWHXrl0oLi6WTI1KRETUEjp8svzrr79GZmamZH7yESNGYPr06Rg4cCAKCwuh0Wjw0ksv4f333xfvTj+sq3NzJeL2rTVXrua1b9947Tuvxlz7hq7OTURERNQalEol9u7dixs3buDOnTsYOHAgEhISEB4eLsaEhITAYDAgLS0NqampUCqV2Lhxo/hd22TDhg1YsWIFli5diurqagQEBGDx4sXi+mAA4OzsDI1Gg5UrV2LOnDlwcHBATEwMIiIiJGVFRkZCEARs3boVRUVFcHd3h0aj4fpgRETU4jp0svzGjRuIi4uDr68vXnjhBXF7TEyMJG7s2LEICQnB5s2bkZaW9qCr2WyNWZ2bKxG3b625cjWvffvGa995NfbaN2R1biIiIqLWsHjx4gbFzZw5EzNnzqw3pnv37li+fDmWL19eb5y3tzcyMjLqjZHJZIiKikJUVFSD6kdERNRUHTZZrtfrERkZCXt7eyQnJ4sLe9bGxsYGY8aMweHDh8VtD+vq3OyR2L615srVvPbtG69959WYa9/Q1bmJiIiIiIiIqOV1yGR5RUUFoqKiUFpait27d9c6ncr9cHVuagucKqPz4rXvvBpz7Xnjg4iIiIiIiKjt1N0du52qrq5GbGwstFot0tPT0bt37/s+p7y8HJ9//jmGDh0qbuPq3ERERERERERERERk0uF6lr/55ps4fvw4Fi5ciLKyMpw+fVrcN2TIEJw5cwbp6ekICgpCv379UFhYiG3btuGnn37C22+/LcZydW4iIiIiIiIiIiIiMulwyfKTJ08CAFauXGm279ixY3B0dITRaMT69etRXFwMa2treHl54c0334SHh4cknqtzExERERERERERERHQAZPln3322X1jNBpNg8ri6txEREREREREREREBHTAOcuJiIiIiIiIiIiIiFoak+VERERERERERERE1OkxWU5EREREREREREREnR6T5URERERERERERETU6TFZTkRERERERERERESdHpPlRERE9P/Yu+/4nM7/j+OvOwtJBCH2Hgk1qkY04oum9t4zofaeVWJUzVItirSaovbeojY1i2pVabU2FWoGmTLv3x8e9/3LLUFiReT9fDw82pxz3edc5z7n3Nc5n3OdzyUiIiIiIiKS5ilYLiIiIiIiIiIiIiJpnoLlIiIiIiIiIiIiIpLmKVguIiIiIiIiIiIiImmeguUiIiIiIiIiIiIikuYpWC4iIiIiIiIiIiIiaZ6C5SIiIiIiIiIiIiKS5ilYLiIiIiIiIiIiIiJpnoLlIiIiIiIiIiIiIpLmKVguIiIiIiIiIiIiImmeguUiIiIiIiIiIiIikuYpWC4iIiIiIiIiIiIiaZ6C5SIiIiIiIiIiIiKS5ilYLiIiIiIiIiIiIiJpnoLlIiIiIiIiIiIiIpLmKVj+kl24cIFOnTpRtmxZPD09mTJlClFRUSldLREREUmE2m0REZHUQ+22iIi8ajYpXYG3yYMHD+jYsSMFCxZk1qxZ3Lx5k8mTJ/Pw4UNGjx6d0tUTERGReNRui4iIpB5qt0VE5HVQsPwlWrFiBWFhYfj5+ZE5c2YAYmNjGTt2LD169CBHjhwpW0ERERExU7stIiKSeqjdFhGR10FpWF6i/fv34+HhYW64AerWrUtcXByHDh1KuYqJiIhIAmq3RUREUg+12yIi8jooWP4SXbx4kcKFC1tMc3JywsXFhYsXL6ZQrURERCQxardFRERSD7XbIiLyOigNy0sUHByMk5NTgumZMmXiwYMHz7XM6OhojEYjJ0+eTPJnDAYDHulzEGvn8lzrlJfP2sqKU6dOYTQaX+l6DAYDcQXcIV/cK12PJIOVFdde074v6GIkf9YMr3Q9knRWVsZkn/fR0dEYDIZXWCuJT+22PIna7TTsNbXboLb7TaN2+833prTbT6M2XV6V13Vt8jx0PSOv1Eu8Nktqu61g+RvOtBOTexHmaJf+VVRHXtDruJi2yuD4ytchyfc69n2G9LavfB2SfMnZ9waDQTfdqZza7beL2u2063X9FqvtfvOo3U5bnrfdfhq16fIqvam/ObqekVftZRz7SW23FSx/iZycnAgJCUkw/cGDB2TKlOm5lvnee++9aLVEREQkEWq3RUREUg+12yIi8jooZ/lLVLhw4QS50kJCQrh9+3aC3GoiIiKSstRui4iIpB5qt0VE5HVQsPwlqlq1Kj///DPBwcHmadu2bcPKygpPT88UrJmIiIg8Tu22iIhI6qF2W0REXgeD8U0cHSCVevDgAfXr16dQoUL06NGDmzdvMnnyZBo2bMjo0aNTunoiIiISj9ptERGR1EPttoiIvA4Klr9kFy5cYPz48fz+++84ODjQuHFjBg0ahJ2dXUpXTURERB6jdltERCT1ULstIiKvmoLlIiIiIiIiIiIiIpLmKWe5iIiIiIiIiIiIiKR5CpaLiIiIiIiIiIiISJqnYLmIiIiIiIiIiIiIpHkKlouIiIiIiIiIiIhImqdguYiIiIiIiIiIiIikeQqWi4iIiIiIiIiIiEiap2C5iIiIiIiIiIiIiKR5CpaLiIiIiIhZbGwsAEajMYVrIiIiIiLyeilYLiIiIiIiAERFRdG8eXO2bNmCwWBQwFxERETkKeLi4lK6CvKSKVgur138HxLdgIm8/Uw9FE103oukHjExMeb/j4yMTMGayOty48YNMmfOzCeffMLu3bsVME+jHm+7FQgQERFJKDY2FiurR6HVP/74g//++y+FayQvg4Ll8loZjUasrKyIjIzk9u3bGAyGlK6SvAa6wUrbrK2tiYiI4LvvvuPhw4c670VSERsbGwBGjx7Nnj17FDBPA/Lnz8+nn37Khx9+SJ8+fRQwT4Pi4uKwtrYGYPXq1QDmQICIiIg8Ehsba24vR4wYwaRJkzh48KCul98CuuqR18pgMBATE0P//v1ZunQpkLDnirxd4j9pvXHjBoGBgQQHB5vn6+Y7bVi8eDE//PADoaGhgB6giLzp4rfNc+fOZdu2beTKlct8QyBvp6ioKACKFCmCj48Pnp6e9OnTh0OHDilgnkaYOrYADB8+HD8/P06fPp3CtRKR1OBJ1/dqO+RtZbouHjRoEL/++is+Pj5Ur16ddOnSWZTTOZD6KFgur52pl9revXsBdOP9FovfM2nEiBH06NGDevXq0aNHD77//nsA9TJOI1q2bImtrS3z588H1ENN5E0W/7d79+7dhIWF0b9/f8qWLWtuw+XtExcXh52dHQBjx47l+++/586dOwB06dKFPXv2KGD+ljMajebrsn/++YfAwEA+//xzihYtmsI1E5E3XfwOUrdv3+bcuXNERUURExODwWBQRxl5a23ZsoVjx44xYcIE6tSpg4uLCyEhIVy7do2LFy8CinmkRopWyGtl6qk2cOBAbt++zYoVK1K4RvIqmS6Yhg0bxqFDh/D29mb06NF4enoybdo0xo4dqwunt9Dj+zQ6OposWbLQtGlTDh8+zLVr11KoZiLyJGFhYaxatYqoqCjzb/fq1av5+OOPWbBgAc7OzoBlDnN5u5j2+9ixY9m5cyetW7fmu+++Y9q0aVSqVInevXsrJctbznQzP3LkSGbMmEFcXBxlypTBzs5O+1xEnij+Q/ZPP/0UHx8fGjZsSMOGDfnyyy8JDg7GyspK933yVrp+/ToZMmTA3d2d2NhYjh8/jre3N+3bt8fb25slS5akdBXlOShYLq/U4zfV1tbWGI1GcubMSaFChThy5EgK1Uxel5MnT3LixAk+/fRTGjduTIsWLahevToGg4GIiAil4XnLmF7fjo6O5t69ewDY2toCUL16dc6ePcvvv/9uLisib4bx48dz8OBBi2nvvvsudevWJSYmhgMHDgCP3g7Tze7bKyQkhGPHjtGgQQNq1KhBrly5qFevHr6+vlSrVo0+ffpw8OBBBczfclmyZOGnn37i3Llz5gfc6hUnIo8zGo0WqZuGDRvGwYMHadOmDf7+/rzzzjvs2bOH/v378+DBA6ysrNR2yFvH1dWVq1evMmzYMIYOHUqHDh0oWLAgXbt2xdPTk8WLFxMYGJjS1ZRkUrBcXikbGxvCwsLw8/Mzp10xGAxkyZKFjz76iO3bt7N///6UraS8VI8/IAkLC+P27dvkyJEDOzs7Ll++TOfOnalXrx6fffYZtra25uCppH4Gg4Ho6GhatGjBoEGDWLRoEdHR0cTExFChQgXq1avHvHnzuHXrlm68Rd4g48aN44svvsDOzo79+/cTFhaGq6srPXv2pH79+mzdupVZs2YBqHfYW8poNBIXF0dkZKS5h6Aph3mJEiVo3749AD169GDHjh36DX9LxA9cmc7rIUOG4OvrS3BwMGvWrCEoKCilqicib6CIiAj+/PNPcztgNBo5e/YsR48eZfDgwXTs2JFq1arxxRdf4OPjw3///cfkyZOJjIxU2yGpktFofGInPw8PD4YNG8aJEycwGo189tlnzJgxA29vb8qUKYPBYMDR0fE111helILl8sodO3aMH374gbFjx9KlSxf27t1LUFAQNWrUwMPDg507d/Lw4UPdeKdicXFxPHjwgOjoaHM+W1Ov4ri4OKKiosiaNSshISG0atWKypUrM27cODJkyMDu3bv54YcfuH79ekpugrxEtra2tG3blly5cjF9+nS8vb2ZMWMGISEhfPDBB0RERHD+/HlAA/yKvAlMuaozZMjAggUL6NOnDytWrCAiIoICBQrQs2dP6tSpw5o1a/Dz8wMeBcx1/qZu8YOkplzVmTJlwtXVlZ07dxIdHY2dnZ05YF61alXKlSuHs7Mzo0aNIjQ0VD0EU7nY2FiLYFdYWJh53kcffUTfvn1ZsmQJS5YsUcBcRIBH1wyff/457du359ixYxgMBgwGA/fv3+fGjRsUKFAAg8FAVFQUdnZ2tGnThipVqnDgwAFu3bqV0tUXSRbTNZDBYDB3JJg9ezYjRoygV69ebNiwgaCgIDp16sSaNWuYOHEiLVu2BCAoKIi//vqLXLlyacyfVEjBcnnpHg96V69enYCAAMaMGUNoaCgTJ06kffv27N69m0yZMnHo0CFCQ0P1WlYqtmvXLiZPnsylS5cA8PHxYfTo0cTExFC+fHnKlClDp06dqF69Oh988AETJkzAwcGBO3fusHXrVgwGA05OTim8FfI8nvSUvU2bNowaNYoNGzZQuHBh9u7dS506dTh9+jSXL182j1egAX5FUl78AXfbtGnD+++/z/Lly1m6dCkREREULFiQ7t274+HhwapVq5g9ezag8zc1ix8khUdjS5j069eP0NBQevbsCWAe9PPSpUtYW1szcuRItm7diqOjo3oIpmKxsbHmc3jatGl06NCBRo0aMXz4cI4cOUJcXBx9+/alT58+fPvttyxZssTcEUJE0i4rKys++OADypUrx7Bhw/jll18AzAFBU5pV08NWOzs7evfuzZ07dzh16lRKVl0kWaKiomjTpg2LFy82T+vfvz9Lly4lLCyMe/fuMW/ePIYMGcLZs2fJmDGjuQf5/v37+fLLL9m9eze+vr7qWZ4K6fGGvFSmBjEqKoq///4be3t7cufOTZ48eciTJw/VqlXj4MGD7Ny5k7Fjx5I9e3auX7/O999/j6+vr8UNu6QeRYoU4bPPPuPixYvY2dkRGBjI0KFDsbGxwcbGhlatWjFnzhxsbGzo1q0bjo6OXLhwgblz53L48GEWLVqkBiSVCQsLw8HBwfyUPTw8nNmzZxMdHU3u3Llp3749Dg4OODg4MH78eB4+fMj8+fP566+/cHBwYNeuXRw5coT3338/pTdFJM2Ki4tL0O6mT58ePz8/+vTpw7JlywBo3749hQsXpnv37lhbWzN79mzz77mkPvGDpH5+fpw5c4bLly9Tu3ZtqlatSpkyZRg8eDBffvkljRs3pkePHsTExHDw4EHu3r1LxYoVyZo1awpvhbwo0zEwcOBA/vjjD6pUqcI777zDjz/+yG+//Ya3tzfe3t7069cPg8HAd999R3h4OD179iRz5swpW3kRSRExMTHY2Njg5eWFnZ2d+R5+4sSJeHh4ULt2bVasWEH+/PmpU6eOeXDgS5cukS1bNnLkyJHSmyCSZKGhody6dYuIiAgAli9fzp9//sk333xDiRIlzOfAtGnT2LNnD8WKFcNgMLB582ZWrFhBeHg4S5Yswc3NLYW3RJ6HwaiuvPKCzp49y3///UflypWxtbUlJCSEbt26mXsglShRgi+++IJs2bJZfO63337jn3/+YeHChdja2rJ06VIyZ85sfhVYUpfAwEBq166NnZ0dY8aMoXHjxhbzV6xYwerVq7l48SKFCxcmJibGnM++ePHiKVRreR6nT59m0qRJ9O7dGw8PD6Kjo2nQoAEGg4GHDx8SExNDwYIFWbBgATY2NuYLa4AbN25w5coV+vfvT5MmTRg+fHgKb41I2mPKTW0Klt28eZOHDx+SK1cucy/ihw8f0rdvXy5evEi7du1o3749GTJk4Pz58yxdupSOHTtSsGDBFNwKeVH9+/fnjz/+oFSpUtjY2HDw4EEKFixIr169qFGjBseOHWPq1KmcOXMGa2trnJ2dmTFjBiVKlEjpqstL8tNPPzFixAi+/PJLKleujJWVFUFBQQwaNIj//vuPTz75hJo1awKPep+vWLGCbdu24ezsnMI1F5HXKf61fHz79+9n7ty5BAYGMmvWLDJmzMigQYOIiIjA29ubVq1aceHCBZYsWcLPP//MsmXLFDCXVMEUkxoxYgRXr15l8eLFfP7555w6dYrvv/+ejBkzcv36dZo2bUr16tUZM2YMGTJkIDIykvv373P+/HmKFi2q4z0VU7BcnpvpZrtBgwbcv3+fyZMn4+HhwfDhw7lz5w5t2rThwoULbNmyhYcPH7JixQqyZ89u7n1ucuPGDerWrcuAAQP46KOPUm6D5IXs27ePIUOGYG1tTb58+RgzZgzvvPOOxYOP69evs3//fm7fvk3BggWpUKECuXLlSsFay/M4f/48DRo0oGzZsgwZMoTLly+zY8cOPv30UxwcHPjpp5+YOXMmzs7OrF69GhsbG/N5b+rJunjxYvz8/Fi9ejX58+dP6U0SSRNMPYrj9yifMGECR48eJSgoCHt7e8aOHUupUqVwcnKyCJj7+PjQunVr7O3tiY6OxtbWNoW3Rl7Epk2bmDx5MrNmzaJ06dLY2dlx6NAhvv32W0JDQxk5ciTu7u4AnDp1ivTp05MlS5YEHR8kdVu2bBkzZ85kz5492Nvbm9vqe/fu0bJlSwoVKsScOXPM5YOCghQoF0ljwsPD6dGjB87OzjRq1AhXV1fy5ctnnr9//378/f3577//mD17Nra2tkyZMoW9e/fi4OCAo6MjcXFxfP/993rYKqlC/Dfwvv/+e+bPn8+BAwcYPnw4Fy5cYN26dQQGBtKsWTMqV67M559/jr29PatXr8ZoNNKyZUt1/nwLKOeFPDdT+oWlS5fi5OTE559/zr59+4iOjqZHjx7UrVuXvn374uvri729PS1atODWrVvY2dmZc2PGxcXh4uJC+fLl+fvvv1N4iyQ5Hn/OVq1aNXbt2sWiRYu4desWY8aM4c8//7TIYZ87d27atGlDv379aNiwoQLlqVBsbCxFixYlICCAc+fO8e233/L777+bL5ydnZ1p0KCB+aFZy5YtiYmJMZ/3pgBdjhw5SJ8+vQYIFHlNQkNDadOmDceOHTOfh4MHD2bPnj107NiR+fPnYzQa+fTTT9mzZw8hISHmlCyurq7MmjWLtWvXAmiQorfAjRs3sLe3N79GbDQa8fT0pH///ty/f59NmzaZy5YuXZpixYopUP4Wypw5M8HBwZw7dw74/xzDWbJkYcCAARw+fJizZ8+ar/kUKBdJe77//nuOHTvG9u3b8fPzo3nz5nz22WesXr2a2NhYqlatyrBhw8iVKxe9evUiIiLCPM5B9+7dGTZsGCtWrFCgXN54phhV/DF5qlWrRlxcHH/++Seenp7cuXOHOXPm0Lx5czw9PRk3bhz29vb8+++/7N69m6CgIN3fviUULJcXEhkZibOzM8uXLycqKopRo0Zx4cIFi7Qa//vf/xg+fDhOTk60atWK27dvY2trS2xsLFZWVlhbWxMcHMzDhw/1w5JKxB8YLDw8nPDwcOLi4siUKROurq74+/tz69Ytxo0bxz///GMeBHLu3LncvHkzhWsvL8L08KNYsWKsWLGCU6dOsXbtWmJiYsxl0qVLh5eXFyNHjiQoKIjWrVsTExNj7okaFxfHiRMnCAsLU656kdcgNDSURo0akSFDBgoXLgzA4sWLuXDhAtOmTaNFixbs37+fmzdvkjlzZsaOHcuuXbt48OAB6dOnZ/r06VSpUoWqVasCqLfMW+LmzZtERUUBj9r1uLg4KlWqRNu2bfnxxx8JCgpK4RrKyxK/40J8efLkwdnZmRUrVhAYGAj8/4CuoaGhZM6cGWdnZ53zImlYmzZtaNGiBRkzZuSdd95h5MiR/P7770yYMIG6devyySefANCkSRPc3Nzo168fJ06coEKFCvTo0YN69eqRJ0+eFN4Kkad7+PAh3t7eNGrUiAkTJvDTTz9x8eJFMmfOTFxcHGfOnOGDDz4gR44cTJ06lSJFijB9+nScnJwICgrC39+fixcvUr9+fXUqeUsoWC7JFhERQWhoKPAoKBYaGoqzszPr168na9as5tQrpidzBoOBypUrM3z4cDJlykT16tW5d++e+Ynd3r17uXv3Lr169bJ4iidvpvivJU2fPp0+ffrg7e3N4MGDCQsLA6B48eJ8//333Lp1i1GjRrFw4UJGjx7NV199ZR4gQ1KPe/fucfz4ce7cuWMel2DdunUUK1aMVatWkTlzZjZv3szhw4fNn7Gzs+PDDz9k5MiR/P3333z66afmeffv38dgMLBkyRJcXFxSYpNE0ozQ0FAaN25Mvnz5+Oqrr8wDM1pbW+Pu7k7ZsmVZvHgxM2fO5IsvvmD+/PmUKFGCqVOnsm/fPu7fv0+GDBmYOXMmBQoUSOGtkeR6UpD03XffJVOmTMycOZPg4GCLGztra2uyZMlCunTpXlc15RUydU4B+Ouvvzh69CgnTpwAHh0HH330EevXr+eHH37gn3/+AR69eXDixAly5cqllEsiaVzOnDnp168f1apVY+PGjWTNmpVNmzaxZs0aKleuzKVLl/Dx8WHRokVcvHiRsLAwunTpwsmTJ1O66iJJ9uDBA6pVq0bx4sU5efIkffv2pWXLlvTt2xcrKyv27t1LpkyZGD9+PMWKFeP+/ftMmDCB6dOn4+vry65du5g5c6ZFiiJJ3ZSzXJIlMjKSBQsWEBkZSZcuXbC1taVOnTr06dOH5s2bc//+fVq1aoXRaGTEiBFUq1bNfIFuNBrZu3cvW7ZsYfLkyeaA6507dzAajQqapQLxB18dOHAgJ0+epEWLFjg4OPD1119TqFAhvv32W3LmzAnAhQsX6NWrF1FRUTg4ODB16lQN5pkKHTx4kNGjR9OmTRvq1KlD69atKVmyJNOnTydjxoxcuHCB1q1bU7hwYT755BMqVqxo/mxUVBS///47FSpUsHgY9vjYBSLy8oWHh9OgQQPc3NwYM2YMOXLkMD/wjIiI4Nq1azg7O9O2bVuaNWvGRx99RLp06fjhhx+YMmUKVlZWTJkyhfr16wPqUZ7axH+4ff78eWJiYrCyssLV1RWAzz77jN27d9OkSRO6d+9u7h01btw47t69y3fffYeDg0NKboK8oPjjE3zyySecOHGCq1ev4ujoSIkSJZg0aRJ58+bF39+fuXPnYmNjQ548eYiJieH69essWrRI120iAsDt27cZP348e/bsYcKECTRp0gT4/3v806dPs379eq5du4bRaGTbtm0aCFxSrQsXLnDr1i127NjBpUuX+PXXX/nkk0/o2LEjFy9eZOPGjRw4cAA7OztcXV3p2LEjRYoUSelqy0ukYLkk26JFi/j8889p06YN+/fvJ1euXEyfPp0sWbJga2tLUFAQLVq0wNbWFl9fX4uAeXxPGlVb3nzffPMNP/74I5MmTeLdd99lzpw5zJo1iwwZMpA5c2YWLlxoDphHRUXx77//kjVrVrJkyZLCNZfnNXHiRJYsWYKTk5M5f3HmzJnN5/G5c+do27ZtogFzk/iBGxF59WbOnMm3337LqFGj8Pb2Np+DUVFRdOjQgS5dupAxY0Z69uzJ3LlzqVChAgBLly7l1KlTZMiQAW9vb138p0Lxg6TDhw/n+PHjXLt2jfTp09O4cWP69OmDs7MzI0aMYP/+/djZ2VG8eHGCgoI4f/48S5cuxc3NLYW3Ql6W0aNHs2/fPoYOHUr27Nk5e/YsCxcuxGAwMHv2bAoXLsyePXv4559/OHv2LEWKFKFBgwYUKlQopasuIm+Q27dvM3HiRHbv3s348ePNAXOT0NBQTp06RcGCBTU2laQ6po6B8TsImkyYMIFly5aZA+bx41vxr7nk7aFguSTJf//9R6ZMmbC3twdgxowZzJkzh+zZs/P1119TpkwZ4P97i8YPmA8fPpyqVavqByQVix/kjIiI4JtvviFz5sx07dqVH374gWnTpjF58mQcHR0ZNGgQBQsW5LvvviNHjhwpXHN5Xn/++SdHjx7F29vb/Cp+8eLFsbGxoUOHDnTt2hVnZ2dzPnpTwLxdu3YUKVKEfv364enpmcJbIZK23bx5kylTprBz507GjRtHkyZNiIyMpEWLFgDMnz+foKAgunfvToMGDRgyZAhBQUFMnDiR7NmzM2zYsBTeAnlRw4cP5+eff2bgwIGkS5eOy5cvM2fOHN577z0mTZpEjhw52LBhA7/88gv//fcfhQoVon379npAkkpFRUVx+fJl7t27R+HChcmWLRv37t2jffv2tGvXDh8fH+DRdd2FCxcYNGgQ9vb2rF69OoVrLiKpRfyA+YQJE2jcuDHwaHBEpW2St8njQfPx48ezYsUKfH19adiwIZkzZ060nLwd1K1XnikoKIj69etTr149hg0bRsaMGbl8+TLp06fn+vXrBAQE4OLiQq5cubCzsyMqKgpnZ2fWrFlD69at+fjjj5kzZw7lypVL6U2R52A0Gs2B8uXLl9O6dWtq1apF3rx5+fvvv1m4cCG+vr40aNAAgMqVK7N7926aN2/O2rVrFTBPhUyvU/r5+WE0GunQoQPh4eE0b96cqKgofvjhBxwdHWnVqhXZsmXD2tqamJgYihUrxvLly2nQoAFbt25VsFwkheXIkQNfX19iY2P59NNPiY2NZcmSJdjb2zN9+nSyZcuGg4MD//vf//jxxx/ZtWsXWbJk4fz58yxfvjylqy8v6OLFi5w4cYK+ffvSuHFjc6eF8uXL06tXL6ZNm8YXX3xBkyZNaNKkCab+M7rhS51CQ0Pp378/Z8+e5c6dO+TPn582bdpQs2ZNHjx4YN6/puu6okWLMnDgQIYMGUJAQAANGzbUDb+IPJOLiwsjR44EYNSoUVhZWdGwYUMFyuWt83h7+Omnn2JlZcXEiROxsbGhTZs2GAwGtZtvKQXL5ZkcHR3p06cPM2fOJEOGDIwcOZKpU6diZWWFv78/06dPx2g00rlzZ3Lnzo2dnR3R0dE4OzuzcuVKxowZw7vvvpvSmyHP4fHXuDdu3Ii7uzulSpXCysqKffv2ERcXR+XKlc2fyZ07Nx9++CFhYWE8fPgwpaouL8BgMPDRRx9hNBqZOnUqkZGR9OnTh4kTJwKPfhNmzpyJ0WikdevWZMuWDRsbG0JCQihatCi7d+/WQxKRN4SLiwujRo3CYDAwZswYsmXLxq5du7C2tiY2NpYMGTIwePBgihQpwsmTJ0mXLh0TJkxQz+JUJCoqiv3793Py5Ek++OADSpUqha2trbmXsaOjo7ktj4uLo1KlSvj6+jJ69GiaNGmCh4cHoCB5amYayDd//vwMGDCAbNmyMXXqVL799lvCw8OxtrY2D95pesXcysqKcuXKYW1tzZ07d8zzRESexRQwt7a25pNPPsHa2pp69eqldLVEXrmRI0diZ2eHu7u72sy3nILl8kx2dnb4+PiQLl06Jk2aRGxsLEOGDMHe3p4ePXpgNBr5+uuvAejWrRs5cuQgKCiIffv20apVK2bOnAkoX3FqY7qRAjh16hRhYWHMnj2bAgUKmKdHRUURGxvLf//9R+HChXnw4AE3btzAw8ODVq1aaQDHVMzR0ZFOnToRGxuLn58fNjY2dOzYkfTp0/PZZ58BMGvWLGxsbGjUqBHp0qVjwIABtGzZkkaNGgEal0DkTZEtWzaGDx+Ora0tW7du5ccff6RRo0ZYWVkRHR1NlixZ6NixIwaDQa9RpzKhoaH07duXBw8eEBQURNmyZYmMjMTW1hZbW1syZ87MP//8g5eXF+nSpTPf2JUvX54MGTJw48aNFN4CeVHh4eE0atTIPJCv6Y2vUqVK0ahRIy5dusRHH33E1KlTKVu2LK1atTIfB8HBwWTPnl2vkotIsrm4uDB06FDs7Ow0xoWkKZ988klKV0FeA0UxJEns7Oxo1aoVAJMmTcLa2poBAwbg6OhIz549MRgMTJ8+nbCwMNzd3VmxYgUPHjygRYsW5sCqAuWpi+lmadKkSZw+fZq7d+9SqlQpbGxszD3Oq1evzvfff8/kyZNxdXUlIiKC3377zXzhJKmT6WbZ0dGRrl27AvD1119jMBjo0KGDRcD866+/5qeffiIsLIzw8HDq1q1rXo4C5SJvjuzZs/PJJ58QFRXFyJEjiYuLo0mTJtja2hIbG2tuq3Xeph5hYWE0b96cnDlzMmTIEMqXL0/69OnN84sUKUKdOnVYvHgxFSpUwMPDw7x/Y2JiyJIli3ksGkm95s6dy/Xr1+nUqZP5ra6HDx/i4uLCBx98wG+//Ub37t25cuUKY8aMITAw0DyW0KpVqwgJCcHd3R1Qz3IRSZ4cOXIwfvx4XTuIyFtHv2ryRI/3LrGzs6N58+bAowAqYA6Y9+jRAxsbG7777juOHDlCvnz5CAgIwMrKSr1UUrn06dPz559/EhUVRWBgIFmzZsXKyoqYmBhy5MjBggULGDduHJcvX8be3p5FixaRL1++lK62PAfTQ5D456ujoyOdO3cGYPr06QAWAfOcOXNy4cIF7O3tGTVqFDY2NupRLvKGip9n9NNPP8Xa2pqGDRtaPMxWe506REdHM2TIELJnz87nn39Onjx5zNNNbwb89ddf+Pj4cPfuXT7++GP69++Ph4cH0dHRzJ8/n7i4OKXJewu0bt2aK1eu8OWXX5IpUyYaNWpkfmgSGBiIs7MzhQsXpk+fPuTIkYPZs2ezcOFCnJycyJAhA3PnzjUfPyIiyaVrfhF5GxmMptFeROIxpUwxGo3ExsYSFxdn7ikcFhbG+vXrmTRpEu3atTMHzAHOnDmD0WjE1dXVHFBVA5p6xM9RHv//Fy9ezOTJk6lWrRoff/yxOZet6ab84cOH2NjYEBUVpV5qqZTpXI2KiuKff/4hJCSErFmzUrx4cQAePHjA/Pnz8ff3Z9CgQeaAOVgGZ3TOi7z5bt++zeTJk/nxxx+ZNm2a8oymQufOnWPQoEF07drVnFInfrs9Z84c/P39ad++PQ0aNGDx4sWsWbMGKysrcuTIQUxMDN999x0lSpRI4S2Rl+H27dtMnDiR3bt3M3HiRBo1aoSfnx9z5sxh6dKllCpVylz23LlzBAYGkj59eooUKUL27NlTsOYiIiIibx5FNCQBU6A8LCyMadOmcfbsWTJmzEjp0qXp1asXDg4ONG3aFHjUw9xgMNC/f38cHR0t8pXFxcUpaJaKxM8pHx4eTkREBFmzZgXAx8eH8PBwfvjhB+bOnUvXrl0pUqQItra2xMXFmYOm2t+pU2xsLDY2NoSGhtK1a1fCwsK4fv06efPmpU6dOvTq1YtMmTLRqVMn4P9TsnTs2BE7OztzoNxoNOoYEEkFlGc09Tt9+jSXLl2ifPny5gC56b/Tpk3jhx9+wNXVlYCAANKlS8e4ceNo3rw5ly5dwtHRkVKlSpEzZ86U3AR5ieK/NTJy5Ei2bt3KoUOHmDx5MqVKlbLoBFOsWDGKFSuWwjUWEREReXMpqiEW4uLizIHyVq1akTFjRipXrsy9e/f45ptvuH79OuPHj7cImH/xxReEhIQwZswY0qVLZ16W6aZN3nzxA+Vjxozh9OnTnDt3jurVq1O5cmVatmxpHsx1wYIFwKPBXAsXLqz9/BawtrYmIiKCDh064ODgwNSpU7G2tqZXr17MmDGD4OBghg0bZg6YW1lZMXXqVLJnz07jxo3Ny1H6BpHUQ3lGU7fY2Fjs7OzM+8/Uq/z69eucPHmS6dOnU7NmTYYNG8bSpUuJjY2lZ8+eSrvyFnNxcTGnQ9u+fTvNmzc3vzViulZTOy0iIiLybLpDEgtWVlZERUXxySefkD17dr766iuyZs1Kr169sLe3Z/369YSHhzN16lRzwDw8PJx9+/aZe5dK6mMKlH/88cccP36c1q1b06hRI37++Wf8/f05f/48w4cPp2fPnlhZWbFo0SLCwsIYPHgwBQsWTNnKywszGo3MmzcPBwcHvvrqK3LkyMGgQYN4+PAhjRo1YuHChdjZ2TFo0CAyZcqEj48POXPmpH79+ilddRF5AQqUp15ubm7ExsaybNkyPv74Y3MwNHv27EybNg0nJyfgUYeGWrVqceXKFV2npQHZsmVj6NChGI1G1q5dS7ly5WjUqBEGg0FjCImIiIgkke6SJIGrV6+SMWNGfHx8yJo1K/379+evv/7im2++4cCBA3z//fc4ODgwbtw4HBwc6NChA926dcNgMFjky5Q3X/z99csvv/D7778zbtw43N3dSZcuHUWKFKFTp05ERUXx8OFD0qdPT/fu3QkPD+fHH39UfvK3RHR0NLly5aJly5bkyJGDkSNH8uuvv7JgwQLs7Oy4ePEi/v7+WFlZMWDAALJkyUKrVq0A5SgXEUkJefLkoXjx4mzcuJF3332XGjVqAI8egDg7OwOPep8HBgZSsGBB3nvvPSDh4O3y9smePTu+vr7ExsaaU7OYAuYiIiIi8myKakoCLi4u1K1bl0qVKrF06VL+/PNPpkyZQsWKFenQoQNZsmRh1apVdOnSBYD06dObe6woUP7mi4iIYMOGDQDmAcEA7ty5Q3h4OK6urqRLl46LFy8ycOBA6tevz9ChQ0mfPj2nTp0CYODAgaxatUqDQqVSj4/rbGdnh6enJ3Xq1OH06dMcPXqUESNGULBgQfLly0fr1q1xdnZm9uzZLFy40OKzCpSLiLx+mTNnZuzYsYSEhPDtt9/y008/JSgTGhrKnDlzuH79Oh988AGgNBxphSmHea1atRg6dChbtmxJ6SqJiIiIpBqKcqRx8XNVmzg5OeHp6YmVlRV//PEHBQsWpGzZsgA4OzuTN29eypcvT0xMjEXPZN2AvflM6Tb8/PwICgqic+fO5v1nZ2fHgwcPsLOz4+7du7Rp04bKlSszbtw4MmTIwI8//sipU6fIlSsX2bJlI3PmzCm7MfJcTD3B4+LiiIqKws7ODisrK/NAb3fu3OHevXtkzpwZa2tr4uLiuHLlCpUqVaJq1ao0bNgwhbdAREQASpQowYwZMxgwYADjx4/n9OnTdOzYEYATJ06wceNG9uzZw9KlS8mdO3cK11ZeNw3kKyIiIvJ8FCxPw0xBs4cPH7Jv3z7u3LlD4cKFKV26NI6OjkRERPDgwQPCw8OJjIwkffr0XLx4kYwZM9KxY0cqVKig1CupjMFgoGbNmty4cYM5c+YQExND9+7dAShQoABFixZl5MiRHDt2jKpVqzJ27FgcHBy4c+cOO3bsIEOGDObUK3o4kvrExsZiY2NDaGgo48eP58qVKzg4OFCsWDGGDRuGwWDA3t6eyMhIfvvtN1xcXMwPzT788EPzoL5KvSIi8maoWrUqixYtYuTIkXz77bcsWLAAg8FAlixZcHJyYtmyZQqUpmEayFdEREQk+QzGx9/HlzTBFOAODQ2lTZs2xMbGcuPGDbJmzUqGDBnw8/OjQIEC7Nu3jx49evDBBx+QJ08eTpw4gY2NDcuWLcPKykq5L1MZU5Dz8uXLLF68mG3bttG9e3dzT7Tp06czb9488uXLx6xZsyhatCj//vsv/v7+7Nu3j0WLFlG4cOEU3gp5EeHh4TRv3hxHR0cqVKjA/fv3OXnyJLGxsfzwww/kzp2b77//nmnTppE1a1YMBgPZsmVjzZo12NjY6JwXEXkDBQUFcf78eU6dOkVsbCxly5alaNGi5vzlIiIiqZWXlxfu7u5Mnjw5pavy2s2dO5fly5dz/fp1XF1d2bhxY0pX6bV43n0eGBjIhx9+yKRJk2jWrBkAs2bNws/PjzNnzryKqspbSt0M0hhToMvKyoro6Gj69++Ps7Mzvr6+5M2bl2vXrtG5c2e6dOnCihUrqFatGtOmTePrr78mMDCQvHnzMnPmTHOua/UoTz1MvYoBzp07R0xMDAaDgUmTJgHQsWNHBg0aRExMDLt27aJPnz7kyJGDsLAw7t69y5w5cxQoT8VM576/vz8ZM2ZkypQpFCxYEICvv/6a7777jhMnTpA7d266d+9O/vz5uXLlCnZ2dvj4+GBjY5No2iYREUl5zs7OuLu74+7untJVEREReaJ169YxfPhw1qxZQ+nSpRPM9/Hx4d69e2zevPmF1rNv3z5OnjxJv379Xmg5KengwYN8+eWXNGrUiH79+pElS5YnlvX19WX9+vU4ODjw888/kz59eov5ly9fpnbt2gAMHTrUPP7cq/A6v3s3Nzfat2/P6NGjX/m6JG1RsDyNOH36NPfu3cPT09Mc5L5+/TpXr15l4MCBFC9eHCsrK/bt20dISAgfffQR2bJlIy4ujnr16vH++++TLl067O3tMRgMSsOQCpmCnH379uXChQuUK1eO5s2bs379evz8/IiMjKR79+588sknVKxYkXPnznHlyhVKlixJlSpVyJcvXwpvgbwIU2/wS5cukSVLFvLkyQPA1q1b8ff355NPPqFevXqEhobi4OBAnTp1LD6vQLmIyJst/ps/egtIRETeFtu2bUt2m7Zv3z6WLl2aqoPlR44cwcrKiokTJ2JnZ/fM8qYUu3v27KFevXoW8wICAkiXLh2RkZGvqrpmb8N3L6JoZxoQHh6On58fe/bsYe7cuVSpUgWAu3fvcufOHfLly4eVlRUbN25k2LBhDB48mO7duxMaGsqSJUvo0KGDxWu8cXFxCpSnUjt27ODIkSPMmjWLChUqYGtrS61atZg/fz7z5s3D1taWTp06Ub16dapXr57S1ZWXKC4uDoDg4GCcnJywtbUlICCATz75hMGDB9OlSxeioqL45ptvyJs3L+3bt7f4vALlIiJvtviBBAXKRUTkbZGUQPHrEBcXR3R0NOnSpXst67t79y7p06dP8vbb2dlRrlw5fvzxxwTB8s2bN1O9enW2b9/+Kqoq8tZRDo00wN7ens6dO1O9enX69evHgQMHAMiXLx+2trYcPXqUQ4cOMWzYMAYOHEj37t0xGo38/PPPHD9+nH///ddieUq9knqFh4djNBrJnj07tra2GI1GSpYsSdeuXcmTJw9ffPEFS5cuTelqyksQGxtr8beVlRVWVlbUr1+fPXv2MGHCBItzHh69gfLnn39ia2ubElUWERERERGx4OXlha+vr/nv6Oho/Pz8qFWrFqVLl6ZSpUq0bduWQ4cOAY9Skpjuad3c3Mz/TMLDw5k8eTLVqlWjVKlS1K5dm3nz5vH4cH5ubm6MGzeOTZs2Ub9+fUqXLs3+/fvx8vKiV69eCeoZGRlJ+fLln5kSJCYmhm+++YYaNWpQqlQpvLy8mDZtGlFRURbrXrduHeHh4eb6r1u37pnfVYMGDdi/fz/BwcHmaSdPnuTy5cs0aNAg0c8EBwczceJE8/dRs2ZNvv/+e3NnK3iUC9zNzY158+axcuVKc92bN2/OyZMnzeWe9d3PmzePNm3aUKlSJcqUKUOzZs3Ytm3bM7frZTPt2127dtGgQQNKlSpF/fr12b9/f4KyN2/eZPjw4VSuXNlcbs2aNQnKLV68mPr16/Puu+9SsWJFmjVrRkBAgHl+aGgoEydOxMvLi1KlSuHh4UGnTp3466+/Xum2yvNR9+C3nOk13AoVKmBtbW3OU/71119TrVo1WrduzTfffMPDhw/59NNPzb1JL126xPz588mTJ4/Fj5ukbo6OjoSHhxMaGgr8/4CfxYsXp3PnzowcOZLx48cTHR3NRx99lLKVlecWFRWFnZ0dkZGR5vz0ZcuWBaBSpUpUrlyZFStWULduXXr27El0dDSXL19m8uTJpEuXjubNm6fsBoiIiIiIyFsrNDSUoKCgBNOjo6Of+Vk/Pz/8/f1p2bIlZcqUITQ0lD///JO//voLT09PWrduza1btzh06BBTpkyx+KzRaKRXr14cPXqUFi1aUKJECQ4cOMCUKVO4efMmI0aMsCh/5MgRtm7dSvv27cmSJQt58+alYcOGzJs3j/v375M5c2Zz2T179hAaGkqjRo2eWv9Ro0axfv16ateuTadOnTh58iT+/v5cuHCBb775BoApU6awatUqTp48yYQJEwAoV67cM7+bmjVr8tlnn7Fjxw5atGgBPOpVXrhwYd55550E5SMiIvD29ubmzZu0adOGXLly8fvvvzNt2jRu377NyJEjLcpv3ryZsLAwWrdujcFgYO7cufTr149du3Zha2v71O8eYNGiRXh5edGwYUOio6P58ccfGTBgAP7+/q/9zfbffvuNHTt20K5dOxwcHFi8eDH9+/fnp59+MueHv3PnDq1atcJgMNC+fXucnZ3Zv38/I0eOJDQ01BwzWbVqFRMmTKB27dp06NCByMhIzpw5wx9//EHDhg0B+Oyzz9i+fTve3t4UKVKE+/fv89tvv3HhwgVKliz5Wrddnk3B8jTAFDB/7733+PTTT829SefOnUu3bt24fPkyR44cISgoiMuXL3PixAmWLVtGdHQ0kydPxmAwaDDPVOZJ+aU9PDwoV64cvr6+zJ8/n5w5c5qfoAcHB1OiRAnee+89pWBJhc6dO8eff/5J06ZNsbOzIzg4mE6dOnH16lXCw8OpWLEiEydOJH/+/HTt2hUrKyt27tzJiBEjuH//Pjdu3AAePRG3trZWjnIREREREXklntYxq1ixYk/97N69e6lWrRrjx49PdP57771HwYIFOXToEI0bN7aYt3v3bo4cOcLAgQPNvcPbt29P//79WbRoEd7e3uTPn99c/tKlSwQEBFC0aFHztPTp0/Pdd9+xdetW2rZta56+adMm8uTJQ/ny5Z9Y93/++Yf169fTsmVLcxDcFIT94YcfOHLkCO+//z6NGzfm8OHDnD59OsE2PI2joyPVq1dn8+bNtGjRgri4OLZs2UKbNm0SLT9//nyuXr3K+vXrKViwIABt2rQhe/bszJs3j86dO5MrVy5z+evXr7Njxw4yZcoEQKFChejduzcHDx7kgw8+eOp3D7B9+3aLwUfbt29Ps2bNmD9//muPQVy4cIEtW7aY93elSpVo3LgxP/74I97e3gBMnz6d2NhYAgICzAH0tm3bMnjwYPz8/GjTpg3p06dn7969FCtWjJkzZz5xffv27aNVq1YWb0l069btFW6hvAhFP99Cf/31l3n0aIPBgNFoND+hjYqK4uzZs6RPn56uXbty/vx5JkyYQJs2bVi4cCGtW7dm3rx55MyZk9WrV2NjY0NsbKwC5alI/CDn4cOH2bFjBz/99JN54MZu3bqZn6hfuHCB2NhY7ty5w59//km5cuXo16+fuaGUN5/RaOThw4dMnjyZGTNmsHbtWgAmTJhA5syZGTt2LJ999hlXr16le/fu/PPPP7i7uzNy5EhGjhzJzZs3cXJyol69eqxatQpbW1tiYmIUKBcRERERkVdi9OjRzJ8/P8G/pLzV7uTkxLlz57h8+XKy17t//36sra3x8fGxmN65c2eMRmOCNBwVK1a0CJTDowDxu+++a5Fi4/79+xw4cICGDRs+ddyQffv2AdCpU6cE648//0U0bNiQX375hdu3b3PkyBFu375t7t38uG3btlG+fHmcnJwICgoy/6tcuTKxsbEcO3bMony9evXMgXKAChUqAHD16tUk1S1+oPzBgweEhIRQvnx5Tp8+ndzNfGGVK1e2eDBSvHhxHB0dzdtiNBrZsWMHXl5eGI1Gi++nSpUqhISEmFOoODk5cePGDYuUNI9zcnLijz/+4ObNm692w+SlUM/yt0x0dDQLFiwgICAAo9FIw4YNiYuLw9bWllOnTuHj40OTJk2oU6cOc+bMoUuXLvj7+/Pxxx/TsWNH/vvvP1xcXMiRIwcGg8GcpkNSB6PRaA5yDh48mF9//ZUHDx4QGRlJuXLl6NSpEzVr1uThw4fMmTOHpk2bUqBAAaKjo7l58yYrV67E3t4+hbdCksNgMJA+fXoGDBjAtGnTWLBgAQ8fPsRoNNKlSxcqV64MPOphMXDgQAYPHsy0adMoXrw4rVu3pmXLlhYPw2JjY3XOi4iIiIjIK1OmTBlKly6dYHqmTJm4d+/eUz/bv39/evfuTe3atXF1daVKlSo0btyY4sWLP3O9165dI3v27Dg6OlpML1KkiHl+fHnz5k10OY0bN2b8+PFcu3aNPHnysG3bNqKjo5/ZC/zatWtYWVlZBGkBXFxccHJySrD+51GtWjUcHBzYsmUL//zzD6VLl6ZAgQIEBgYmKHvlyhXOnDmDh4dHost6PFVO/F7mgDlwHj9H+tP89NNPzJ49m7///tsiR3tKDEz++LbAo+0xbUtQUBDBwcGsXLmSlStXJroM0/fTrVs3fv75Z1q2bEmBAgXw9PSkQYMGFm8ZDBkyBF9fX6pXr07JkiWpVq0aTZo0IV++fK9g6+RFKSLylrG1teWTTz4hLi6OESNGEBMTQ9OmTfnzzz/x8fGhYcOGjBgxAjs7O9KnT8+sWbPo2bMn06dPp1q1amTLls28rLi4OAXNUpH4qXImTJjAb7/9xsSJE3FxcSE0NJRRo0aZBw6pX78+ZcqUYefOnZw5c4ZMmTLRsmVLChcunMJbIc/DaDRSpkwZhg4dyqRJk1i+fDnh4eHmnHuxsbEULVqUmTNn0r9/f4YMGcKUKVMoXry4+ZgxpWtSj3IREREREXlTVaxYkZ07d7J7924OHTrEmjVrWLhwIWPHjqVly5YvdV3xe0LHV79+fSZNmkRAQAA9e/Zk06ZNlCpVKsn3068yOGxnZ0fNmjXZsGEDV69epW/fvk8sGxcXh6enJ127dk10/uNvnD/pXvHxwVET8+uvv9KrVy8qVqzIZ599houLC7a2tqxdu9acGeF1eta2mAY4bdSoEU2bNk20rOlNiCJFirBt2zb27t3LgQMH2LFjB8uWLaNPnz70798feNQrv0KFCuzcuZNDhw4xb9485syZw6xZs6hWrdrL3jx5QYqEvoWyZ8+Or68vsbGxjB49mmvXrjF//nwaNmyIr68vtra2AJQtW5Z+/foxe/ZsevTowapVqyhTpox5OUq98uaLiIhg5cqVNGrUCGdnZ+Li4ggPD+fUqVM0btyYypUrmxuBefPm0bdvX+bMmcO7775L3rx56dixYwpvgbwsRqORd955B19fXyZPnsz58+dZtWoVPXr0wNraGqPRSOHChZk5cyYDBw6kc+fOLF++nEKFCgEp8zRfREREREQkuTJnzkzz5s1p3rw5YWFheHt7M2vWLHOw/En3Nnny5OHw4cOEhoZa9C6/ePGieX5S11+9enUCAgJo2LAhx48fTzA46JPWHxcXx5UrV8y92eHRQJLBwcFJXv+zNGzYkLVr12JlZUX9+vWfWC5//vyEh4eb30Z+GZ703W/fvp106dIxb9487OzszNNNaUTfNM7Ozjg4OBAXF5ek78fe3p569epRr149oqKi6NevH9999x09evQgXbp0wKNYXfv27Wnfvj13796ladOmfPfddwqWv4EUDX1Lubi4MGrUKGrWrIm/vz9ubm6MHz8eBwcH84Cd8Chg3rVrVz766KNER0eWN1dUVBRt27blq6++Ys6cOQQFBWFlZUVYWBhnz57F3t7eHCSNiYkhT548fPHFF1y4cOGl5EKTlBcbGws8uiAxXZSULFmSkSNHUrFiRdavX8/q1avNZUwB86lTp/K///0vwet/IiKScry8vCwGfRIREZGEHk/T4uDgQP78+S3SemTIkAFImB6katWqxMbGsnTpUovpCxYswGAwULVq1STXo3Hjxpw/f54pU6ZgbW391KC0iSkounDhQovp8+fPt5j/oipVqsSAAQP49NNPcXFxeWK5unXr8vvvv3PgwIEE84KDg4mJiUn2up/03VtbW2MwGMz3sACBgYHs3r072et4Haytralduzbbt2/n7NmzCebHT1Hz+DFpZ2dHkSJFzOMHxsbGEhISYlEma9asZM+e3eK4lTeHepa/xbJly4avry/W1tZs27aNjRs30rhxY3O6BdN/K1asSMWKFQGUozwVuXXrFleuXCE2Npbjx4/z3Xff0bNnT3LkyMF7773H3r17qVevHvnz5zcHzQsWLEju3Lm5dOlSSldfXpBpINeHDx+yZ88eHj58iIeHB1mzZqV48eIMHTqUKVOmsGjRIgBatmxpPu+LFSvGl19+abEcERF5Nc6cOcM333zDqVOnuHPnDpkzZ6Zo0aJ4eXklGGDsTRd/4DWDwUC2bNlwdXWlR48eVKpUKVnL2rdvHydPnqRfv34W0yMiIpg7dy7u7u7JXqaIiLz96tevj7u7OyVLliRz5sycOnWK7du34+3tbS5TsmRJ4FF60ipVqpiD2V5eXlSqVInp06dz7do13NzcOHToELt376Zjx47J6kxUrVo1MmfOzLZt26hatSpZs2Z95meKFy9O06ZNWblyJcHBwVSsWJFTp06xfv16atSowfvvv5/8LyQRVlZW9O7d+5nlunTpwp49e+jZsydNmzalZMmSREREcPbsWbZv387u3btxdnZO1rqf9N1Xq1aN+fPn07VrVxo0aMDdu3dZtmwZ+fPn58yZM8+1na/axx9/zNGjR2nVqhUtW7akaNGiPHjwgL/++ovDhw/zyy+/AI++x2zZslGuXDmyZs3KxYsXWbJkCdWqVcPR0ZHg4GCqVatG7dq1KV68OPb29vz888+cOnVKHSXeUIqKvuWyZ8/O0KFDiY6OZtSoURiNRpo0aQIknldKgfLUwWg0kjdvXkaMGMG8efNInz49+/btw9ramgEDBtCoUSNGjRrF6tWradu2Lblz5wYePf20sbFJdDALSV2sra0JCwujdevW3Lt3j7t375IvXz7atGlDmzZtKF26tEXA3GAw0KJFiwSvxSlQLiLy6hw/fpwOHTqQO3duWrZsiYuLC//99x9//PEHixYtSnXBcgBPT09z54vAwECWL19Ox44d8ff3T1aPuH379rF06dJEg+V+fn707dtXwXIREUnAx8eHPXv2cOjQIaKiosidOzcDBw6kS5cu5jK1atXCx8eHH3/8kU2bNmE0Gqlfvz5WVlbMnj2bmTNnsmXLFtatW0eePHkYOnQonTt3TlY97OzsqFevHsuWLXvmwJ7xTZgwgbx587J+/Xp27dpFtmzZ6NGjx1Nzi78qGTJkYPHixfj7+7Nt2zY2bNiAo6MjBQsWpF+/fmTMmDHZy3zSd+/h4cHEiROZM2cOn3/+OXnz5mXIkCFcu3btjQ2WZ8uWjdWrV/PNN9+wc+dOli9fbu70MGTIEHO51q1bExAQwPz58wkPDydnzpz4+PiYH1ikT5+etm3bcujQIXbs2IHRaCR//vx89tlntGvXLqU2T57CYExKJn5J9W7fvs3EiRPZs2cPEyZMoFGjRildJXlO8XsC//nnn0yfPp2mTZty8uRJtmzZQpMmTRgyZAh+fn5888031KhRgxYtWmA0GtmyZQv79+9n1apVSsGRCple4zLleJs8eTLnzp2jf//+ZMuWjfHjx3Pp0iXq1atH165dcXBw4NSpU3z11VecO3eOCRMm4OXllcJbISKSdnTv3t3c483Jycli3t27dy16oXl5eeHu7s7kyZNfdzWTzM3Njfbt2zN69GjztDNnztCoUSOqVKnCvHnzkryscePGsXTp0gQ3yEFBQXh4eNC3b98EgfQXER4ejr29/UtbnoiIyOeff86aNWs4dOiQOf2IiKR+6kacRphymFtbWzN06FCyZMnC//73v5SuliRDVFQUdnZ2Fj2BS5UqRc6cOZk7dy5r167F2tqajRs3YjAY+Pjjj3F2dmbJkiX06NEDFxcXnJycWLBggQLlqcyZM2cICQmhQoUK2NnZERoaysaNG0mXLh2NGjXi3XffBeDrr79m5MiRbN68GYPBQJcuXShdujQDBgxg8+bNGjhEROQ1+/fffylatGiCQDmQpNe1g4ODmTVrFjt27ODu3bvkypWLli1b0rVrV4uB2OPi4li0aBGrV6/m33//JWPGjNSoUYOPP/6YTJkymct5eXlRrFgxfHx8+PLLL7l48SL58uVj4MCB1KpV67m20c3NjSxZshAYGAjAr7/+yqJFizh58iR37twha9as1K5dm8GDB5M+fXoAfH19Wb9+vfnzJrt37+bDDz8EwM/PDz8/PwCLwPmFCxeYMWMGR44cISIigmLFitGnTx/z5wDWrVvH8OHDWbx4MVu2bGH79u3ExMRw7NgxfHx8uHfvHl9//TVjx47l5MmTODk50aFDB7p162axbYsXL2bFihUEBgZiZ2dHvnz56NSpEw0bNnyu70pERN4ekZGRbNq0idq1aytQLvKWUbA8DcmWLRtDhw4lf/78eHh4pHR1JBnCw8Np1qwZOXLkoHPnzri6uppTqXz88cd89NFHrF27lmHDhhEWFsbGjRsB6N+/P02aNOHcuXM4ODjg7Oyc7JxjkrJCQ0P58ssvOXHiBLNnz6ZixYps27aN8ePHAzBz5kzg0cOU9OnTM2nSJHx9fc0B806dOlGuXDnKlSsHKEe5iMjrlCdPHn7//XfOnj2Lq6trsj4bERGBt7c3N2/epE2bNuTKlYvff/+dadOmcfv2bUaOHGkuO3r0aNavX0+zZs3w8fEhMDCQpUuXcvr0aZYvX46tra257OXLlxk0aBBt2rShadOmrF27lgEDBjB37lw8PT2TvY0PHjwgODiYAgUKALBt2zYePnxI27ZtyZw5MydPnmTJkiXcuHHD3Ga1bt2aW7ducejQIaZMmWJelrOzM2PGjGHMmDHUrFmTmjVrAv8fUD937hxt27YlR44cdOvWDXt7e7Zu3UqfPn2YNWuWubzJ2LFjcXZ2pk+fPoSHh1vUuWvXrtSsWZO6deuyfft2vvrqK1xdXc0PlletWsWECROoXbs2HTp0IDIykjNnzvDHH38oWC4ikobdvXuXn3/+me3bt3P//n06dOiQ0lUSkZdMwfI0JkeOHAwYMADQYJ6pydy5c7l8+TKXL18mU6ZMBAYG0r17dypVqoSzszPu7u5s376dVq1aMW7cOEaPHk1AQABGo5FOnTqZex5L6uPo6Ii3tzdxcXEMGzaMr776ihYtWhASEsLMmTPZvn07tWrVws7OzpyiZfLkyYwYMYIffviBnDlz0rJlS/OAvgqUi4i8Pp07d6Zbt240adKEMmXKUL58eTw8PKhUqZJFADsx8+fP5+rVq6xfv56CBQsC0KZNG7Jnz868efPo3LkzuXLl4tdff2X16tV89dVXFkHcSpUq0bVrV7Zt22Yx/fLly8yaNcvck7xFixbUqVOHr776KknB8sjISIKCggAIDAxk2rRpxMbGUqdOHQCGDBli7kEOjwLjBQoUYNq0aVy/fp3cuXPz3nvvUbBgQQ4dOpQgz2vt2rUZM2YMbm5uCeZNnDiRXLlysXbtWnNKsnbt2tG2bVu++uqrBMHyTJkysWDBggRt361bt/jiiy/M4/i0aNECLy8v1q5daw6W7927l2LFipkD/CIiIgDnz59nyJAhZM2alVGjRlGiRImUrpKIvGRWzy4ibysFylOPVq1a0aJFCzJmzIijoyNNmzZl+PDhjBw5krVr19KlSxeOHTvG6tWrgUd5QL28vFi2bBmLFy8mLi4uhbdAnodpSInq1avTuXNn8uTJw5AhQ/j999/p1KkTvXr1YsuWLUyYMAEAW1tbc8B84sSJdO7cmWbNmgEkGNhTRERePU9PT1asWIGXlxf//PMPc+fOpUuXLlStWpXdu3c/9bPbtm2jfPnyODk5ERQUZP5XuXJlYmNjOXbsmLlcxowZ8fT0tChXsmRJ7O3tOXr0qMVys2fPbhFUdnR0pEmTJpw+fZrbt28/c5vWrFmDh4cHHh4etGzZkuPHj9OpUyc6duwIYBEoDw8PJygoiPfeew+j0cjp06eT/N097v79+xw5coS6desSGhpq3s579+5RpUoVLl++zM2bNy0+06pVq0QfEtvb21sE4u3s7ChdujRXr141T3NycuLGjRucPHnyuessIiJvn0qVKnHmzBl+/vlnvL29U7o6IvIKKFoqkgrkzJmT/v378/DhQwICAqhXrx4//vgjK1euZMqUKbi6upI1a1Z2795N9erVcXFxYfTo0djZ2dG0aVOLvKaSuph6hFepUgUAf39/Pv74Y6ZOnUr37t0BmDZtGgCjRo3C1taWqKgo0qVLZ87vqtQrIiIpp0yZMvj5+REVFcU///zDrl27WLBgAQMGDGDDhg0ULVo00c9duXKFM2fOPDF1nql395UrVwgJCXliubt371r8XaBAgQQPUE09169du4aLi8tTt+fDDz/E29sbg8GAg4MDRYsWtRg48/r168ycOZM9e/bw4MEDi8+GhoY+ddlP8++//2I0GpkxYwYzZsxItMzdu3fJkSOH+e+8efMmWi5nzpwJvoNMmTJZDDbarVs3fv75Z1q2bEmBAgXw9PSkQYMGlC9f/rm3QURERETefAqWi6QS2bNnZ9iwYURHR9OzZ0+mT5/OoEGD+Oijj5g+fTqRkZHAozcG4uLisLKywtfXN4VrLcl19uxZbty4QdWqVTEYDBiNxicGzKdPn24OmH/99ddYWVkxYsQI86vpJgqUi4ikPDs7O8qUKUOZMmUoWLAgw4cPZ9u2bfTt2zfR8nFxcXh6etK1a9dE55sC3HFxcWTNmpWvvvoq0XIve6ySnDlzUrly5UTnxcbG0qlTJ3NO8MKFC2Nvb8/Nmzfx9fV9oTfdTJ/t3LnzEwepf3wA83Tp0iVaLintYpEiRdi2bRt79+7lwIED7Nixg2XLltGnTx/69++fzNqLiIiISGqhYLlIKuLi4sKoUaMAGDRoEOPGjaNZs2Z89tln3LhxA2tra7JkyZLCtZTnFRUVxRdffMFff/3FF198QbVq1Z4aMB8zZgzTp0+nY8eOWFtb8+WXX5InTx7zq/AiIvJmKlWqFPAod/aT5M+fn/Dw8CcGpuOXO3z4MOXKlbNIgfIkV65cMbcpJpcvXwYeDUj6Is6ePcvly5ct8oEDHDp0KEHZJ6UHe9L0fPnyAY9Sjj3rO3lZ7O3tqVevHvXq1SMqKop+/frx3Xff0aNHjycG4kVEREQkdVNuBpFUxhQwr1GjBp999hnr16/H2tqaPHnykDNnzpSunrwAOzs78yAxEydOZO/evcD/Bw5MOcyrVKlCu3btuHfvHtu3byddunQ0btyYKVOm0L59+5SqvoiIPObIkSPm3+749u3bB0DhwoWf+Nm6devy+++/c+DAgQTzgoODiYmJMZeLjY3l22+/TVAuJiaG4OBgi2m3bt1i586d5r9DQ0PZsGEDJUqUeGYKlmcxpX2Lv81Go5FFixYlKJshQwbztiRletasWXF3d2flypWJPmQwpaV5We7du2fxt52dHUWKFMFoNBIdHf1S1yUiIiIibw71LBdJhVxcXBg5ciQAo0ePxsbGhoYNG6ZwreRlKFSoEGPHjmXkyJF8/vnnwKMBPh/vYV63bl22bt3K9u3b6d69O9myZaNRo0bAo+CIBvAVEUl5EyZMICIigpo1a1K4cGGio6M5fvw4W7duJU+ePOZBmBPTpUsX9uzZQ8+ePWnatCklS5YkIiKCs2fPsn37dnbv3o2zszPu7u60bt0af39//v77bzw9PbG1teXy5cts27aNkSNHUqdOHfNyCxYsyMiRIzl16hRZs2Zl7dq13L17l0mTJr3w9hYuXJj8+fPzxRdfcPPmTRwdHdm+fXuCwDdAyZIlzd9RlSpVsLa2pn79+qRPn56iRYuydetWChYsSObMmSlWrBiurq589tlntGvXjoYNG9KqVSvy5cvHnTt3OHHiBDdu3GDTpk0vvA0mXbp0IVu2bJQrV46sWbNy8eJFlixZQrVq1XB0dHxp6xERERGRN4uiKSKplClgbm1tzSeffIK1tTX16tVL6WrJS5A/f34mTpyYaMA8/mCd1tbWZM+ePcEr6wqUi4i8GYYOHcq2bdvYt28fK1euJDo6mty5c9OuXTt69eqFk5PTEz+bIUMGFi9ejL+/P9u2bWPDhg04OjpSsGBB+vXrR8aMGc1lx40bR6lSpVixYgXTp083v3HWqFEjypUrZ7HcggUL8umnnzJlyhQuXbpE3rx5mT59+hPzgCeHra0t3333HRMmTMDf35906dJRs2ZN2rdvT+PGjS3K1qpVCx8fH3788Uc2bdqE0Wikfv36wKMA+vjx45k0aRLR0dH07dsXV1dXihYtytq1a/Hz82P9+vXcv38fZ2dn3nnnHfr06fPC9Y+vdevWBAQEMH/+fMLDw8mZMyc+Pj707t37pa5HRERERN4sBmNi74aKSKpx8+ZNvv76a7p27UqRIkVSujryEv3777+MHDnSPDCal5eXed6FCxcYNGgQVatWZciQISlYSxERSS28vLwoVqwY/v7+KV0VEREREZE3koLlIm8Bpd14e/3777+MHj2av//+m4EDB+Lu7s6lS5fw9/cnOjqaNWvWaN+LiEiSKFguIiIiIvJ0irCIvAUULH175c+fn0mTJuHn58fEiROxsrLCxcWFQoUKMXv2bGxsbCxSs4iIiIiIiIiIyPNRz3IRkVQgNjaWixcvcu3aNbJly8Y777yDlZWV3ioQEZEkU89yEREREZGnU7BcRCSViouLw8rKKqWrISIiIiIiIiLyVlCwXERERERERERERETSPHVJFBEREREREREREZE0T8FyEREREREREREREUnzFCwXERERERERERERkTRPwXIRERERERERERERSfMULBcRERERERERERGRNE/BchERERERERERERFJ8xQsFxEREREREREREZE0T8FykTRg1qxZuLm5pXQ1Ug3T9xUUFPTMsl5eXvj6+r6GWomIiIiIiIiIyKukYLlIKrNu3Trc3NzM/0qXLk2VKlXo0qULixYtIjQ09KWuLyAggAULFrzUZb4sbm5ujBs3LtF5pu/p1KlTr7VO58+fZ9asWQQGBr7W9YqIiIiIiIiIyIuxSekKiMjz6d+/P3nz5iUmJoY7d+7wyy+/8Pnnn7NgwQK+/fZbihcvbi7bq1cvunfv/lzr2bx5M+fOneOjjz56STV/u2zbtg2DwWD++/z58/j5+eHu7k7evHlTsGYiIiIiIiIiIpIcCpaLpFJVq1aldOnS5r979OjB4cOH6dmzJ71792bLli2kT58eABsbG2xs3pzTPSYmhri4OOzs7FK6Ki/sbdgGERERERERERFRGhaRt4qHhwe9e/fm2rVrbNq0yTz9STnLN27cSLNmzShTpgzu7u4MGjSI//77zzzfx8eHvXv3cu3aNXPaFy8vLwCioqKYMWMGzZo1o3z58pQtW5Z27dpx5MgRi3UEBgbi5ubGvHnzWLBgATVq1KB06dKcPHmSsmXLMmHChAT1unHjBiVKlMDf3/9lfTUA/PPPP/j6+vLhhx9SunRpPD09GT58OPfu3Uu0/L179xgwYADlypWjUqVKTJgwgcjISIsy8XOWr1u3jgEDBgDQoUMH83d29OhRAE6dOkWXLl2oVKkSZcqUwcvLi+HDh7/UbRQRERERERERkefz5nQ1FZGXonHjxkybNo2DBw/SqlWrJ5abPXs2M2bMoG7durRo0YKgoCCWLFlC+/bt2bBhA05OTvTs2ZOQkBBu3LhhDuo6ODgAEBoayurVq2nQoAEtW7YkLCyMNWvW0LVrV1avXk2JEiUs1rdu3ToiIyNp1aoVdnZ25M6dmxo1arB161aGDx+OtbW1uezmzZsxGo00bNjwmdsbGRmZ6ECc4eHhCab9/PPPXL16lWbNmuHi4sK5c+dYtWoV58+fZ9WqVRbpVAAGDhxInjx5+Pjjjzlx4gSLFy8mODiYKVOmJFqXihUr4uPjw+LFi+nZsyeFCxcGoEiRIty9e5cuXbqQJUsWunfvjpOTE4GBgezcufOZ2ygiIiIiIiIiIq+eguUib5mcOXOSMWNGrl69+sQy165dY9asWQwcOJCePXuap9eqVYumTZuybNkyevbsiaenJ4sWLSI4OJjGjRtbLCNTpkzs2bPHIg1Jq1atqFu3LosXL+bzzz+3KH/jxg127tyJs7OzeVqTJk0ICAjg0KFDVK1a1Tx906ZNVKxYkdy5cz9ze9esWcOaNWueWQ6gXbt2dO7c2WJa2bJlGTx4ML/99hsVKlSwmJc3b15mz54NQPv27XF0dGTZsmV07tzZIie8Sb58+ahQoQKLFy+mcuXKVKpUyTxv165dPHjwgHnz5lmkzxk0aFCS6i4iIiIiIiIiIq+W0rCIvIXs7e0JCwt74vydO3cSFxdH3bp1CQoKMv/Lli0bBQoUMKcNeRpra2tzoDwuLo779+8TExNDqVKlOH36dILytWrVsgiUA1SuXJns2bMTEBBgnnb27FnOnDlDo0aNkrStH374IfPnz0/wr0uXLgnKmnK4w//3SH/33XcB+OuvvxKUb9++vcXf3t7eAOzfvz9JdYsvY8aMAOzdu5fo6Ohkf15ERERERERERF4t9SwXeQuFh4eTNWvWJ86/fPkyRqORWrVqJTo/qYOBrl+/nh9++IFLly5ZBIDz5s2boGxi06ysrGjYsCHLly8nIiKCDBkyEBAQQLp06ahTp06S6pAzZ04qV66cYPqNGzcSTLt//z5+fn5s2bKFu3fvWswLCQlJUL5AgQIWf+fPnx8rKysCAwOTVLf43N3dqV27Nn5+fixYsAB3d3dq1KhBw4YNNUioiIiIiIiIiMgbQMFykbfMjRs3CAkJIX/+/E8sExcXh8FgYM6cORa5wk3s7e2fuZ6NGzfi6+tLjRo16NKlC1mzZsXa2hp/f/9EU8DE79UdX5MmTZg3bx67du2iQYMGbN68merVq5t7Yr9MAwcO5Pfff6dLly6UKFECe3t74uLi6Nq1K0aj8ZmffzyneXIYDAZmzpzJiRMn+Omnnzhw4AAjRoxg/vz5rFy50pwLXkREREREREREUoaC5SJvmY0bNwJQpUqVJ5bJnz8/RqORvHnzUqhQoacu70kB4u3bt5MvXz78/PwsysycOTNZ9XV1deWdd94hICCAnDlzcv36dUaNGpWsZSTFgwcPOHz4MP369aNv377m6ZcvX37iZ65cuUK+fPks/o6Li0u0l7zJswLqZcuWpWzZsgwaNIiAgACGDBnCli1baNmyZdI3RkREREREREREXjrlLBd5ixw+fJhvv/2WvHnzPjXnd61atbC2tsbPzy9Bj2qj0ci9e/fMf2fIkCHRFCWmHunxP//HH39w4sSJZNe7cePGHDp0iIULF5I5c2aLwT5flsR60AMsXLjwiZ9ZunSpxd9LliwBeGr9MmTIACRM6/LgwYME33WJEiUAiIqKeuLyRERERERERETk9VDPcpFUav/+/Vy8eJHY2Fju3LnD0aNHOXToELlz52b27NmkS5fuiZ/Nnz8/AwcOZOrUqVy7do0aNWrg4OBAYGAgu3btolWrVuYBMkuWLMmWLVuYNGkSpUuXxt7eHi8vL6pXr86OHTvo06cP1atXJzAwkBUrVlC0aFHCw8OTtS0NGjTgyy+/ZOfOnbRt2xZbW9sX+m4S4+joSMWKFZk7dy7R0dHkyJGDQ4cOPTX/eGBgID179uR///sfJ06cYNOmTTRo0IDixYs/8TMlSpTA2tqaOXPmEBISgp2dHe+//z4BAQEsX76cGjVqkD9/fsLCwli1ahWOjo6v5OGAiIiIiIiIiIgkj4LlIqmUKd2Jra0tmTNnxtXVlREjRtCsWTMcHR2f+fnu3btTsGBBFixYwDfffAM8GizT09MTLy8vc7l27drx999/s27dOhYsWECePHnw8vKiWbNm3Llzh5UrV3Lw4EGKFi3Kl19+ybZt2/jll1+StS3ZsmXD09OTffv20bhx42R9NjmmTp3K+PHjWbZsGUajEU9PT+bMmcP//ve/RMt//fXXzJgxg6lTp2JjY4O3tzdDhw596jpcXFwYO3Ys/v7+jBw5ktjYWBYtWoS7uzunTp1iy5Yt3Llzh4wZM1KmTBm++uori1QvIiIiIiIiIiKSMgzGpIxqJyLyivXp04ezZ8+yc+fOlK6KiIiIiIiIiIikQcpZLiIp7tatW6+8V7mIiIiIiIiIiMjTKA2LiKSYq1evcvz4cdasWYONjQ2tW7dO6SqJiIiIiIiIiEgapZ7lIpJijh07xtChQwkMDGTy5Mm4uLikdJVERERERERERCSNUs5yEREREREREREREUnz1LNcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BctFREREREREREREJM1TsFxERERERERERERE0jwFy0VEREREREREREQkzVOwXERERERERERERETSPAXLRURERERERERERCTNU7BcRERERERERERERNI8BcslgVmzZuHm5vZa1uXj44OPj4/576NHj+Lm5sa2bdtey/p9fX3x8vJ6Let6XmFhYYwcORJPT0/c3NyYOHHiS1v2unXrcHNzIzAw8KUtM7W4fPkynTt3pnz58ri5ubFr167Xtu7XfZy/zby8vOjRo0dKV0PktVI7/WZ5le30q5LYMeTl5YWvr28K1Sh17OtXZf/+/TRu3JjSpUvj5uZGcHBwSlcpVXidv4Uir4La8zfLq2rPTfs5KCjopSzvZXn8mEiNTMfx0aNHzdNe9bGW2r+3x6/3EvsOnyQwMBA3NzfWrVv3Kqv4RrBJ6QrIq7Vu3TqGDx9u/tvOzo5MmTLh5uZGtWrVaNasGY6Oji+8nps3b7Jq1Spq1KhBiRIlXnh5L9ObXLek8Pf3Z/369fTu3Zt8+fJRpEiRJ5b18vKiWLFi+Pv7J5h39OhROnTowIwZM6hTp85z1yciIoK5c+fi7u5OpUqVnns5Kc3X15fAwEAGDRpExowZKVWq1BPLBgUF8e2333Lw4EGuX7+Og4MDefLkoVKlSvTu3RsHB4fXWPOEvvvuO4oWLUqNGjVStB4v24ULF6hXrx52dnYcOnQIJyenlK6SyEundvrNrltSJLedvnbtWqLzTp48Sbp06V5VNV+rwMBAPvzwQ4YOHUqXLl0SzJ81axZ+fn4cPnwYZ2fn517P+fPn2bp1K02bNiVv3rwvUuUUc+/ePQYOHEixYsUYPXo0dnZ2ZMiQIdGyr+v3QkSST+35m123pEhOew4QGxvLhg0b2LBhA2fOnCE8PJzs2bNTqVIl2rVrR+nSpV9TzVOej48Pv/zyCwUKFGDHjh0J5h86dIjOnTsDvHA8IjVJrcdIQEAAd+/e5aOPPkrpqqQYBcvTiP79+5M3b15iYmK4c+cOv/zyC59//jkLFizg22+/pXjx4uayvXr1onv37sla/q1bt/Dz8yNPnjzJahjnzZuXrPU8j6fVbfz48RiNxldehxdx5MgR3n33Xfr27fvSl924cWPq16+PnZ1dkj8TERGBn58fffv2TbXB8ocPH/L777/Ts2dPvL29n1r2/v37NG/enNDQUJo3b07hwoW5f/8+Z86cYfny5bRt2zbFg+X+/v7Url37rQuWb9q0CRcXFx48eMD27dtp2bJlSldJ5JVRO5122ukSJUrQqVOnBNNtbW1fdtWe6HmOoVftefb1+fPn8fPzw93dPdUGy0+dOkVYWBgDBgygcuXKSfpMcn4v3mZv4nEsovY8bbTnDx8+pG/fvhw4cICKFSvSo0cPMmXKxLVr19i6dSvr169n79695MyZ8zXU/Pm87GMiXbp0XLlyhZMnT1KmTBmLeQEBAaRLl47IyMiXus7EvCnHWmo5RipWrMjJkyctrkM3b97MuXPnEgTL8+TJw8mTJ7GxeftDyW//FgoAVatWtXhq1aNHDw4fPkzPnj3p3bs3W7ZsIX369ADY2Ni88oM/IiKCDBkyJCtI+yq8zhvT53X37l2KFi36SpZtbW2NtbX1K1l2coWHh2Nvb/9a1mV6BS4pPZXXrFnD9evXWb58OeXKlbOYFxoamiqOodTIaDQSEBBAgwYNCAwMZNOmTQqWy1tN7XTiUsNvbHLb6Rw5ctC4ceNXWKNnex3HUHK9SfvadPy/DqZrkowZMyb5M8n5vXgdjEYjkZGRr3Wd8GYexyJqzxP3Jv3GP0ly2vMpU6Zw4MABhg8fniCg2LdvXxYsWPBS6hQXF0d0dPQrefPsZR8T+fPnJyYmhs2bN1sEyyMjI9m5cyfVq1dn+/btL3WdiXlTjrXXdYy8KCsrqyQfXwaD4a15C/JZlLM8DfPw8KB3795cu3aNTZs2macnljvt0KFDtG3blgoVKvDee+9Ru3Ztpk2bBjxK79GiRQsAhg8fjpubm0UeIx8fHxo0aMCff/5J+/bteffdd82ffVK+p7i4OKZNm4anpydly5alZ8+e/PfffxZlnpRbM/4yn1W3xPJZhYeHM3nyZKpVq0apUqWoXbs28+bNS/B00s3NjXHjxrFr1y4aNGhAqVKlqF+/Pvv373/a12529+5dRowYQeXKlSldujSNGjVi/fr15vmm3FGBgYHs3bvXXPeXmV88sZzlp06dokuXLlSqVIkyZcrg5eVlfqUwMDAQDw8PAPz8/Mx1mjVrlvnzhw8fpl27dpQtW5YKFSrQq1cvLly4YLFe0zF2/vx5Pv74YypWrEi7du1Yu3Ytbm5unD59OkFdv/vuO0qUKMHNmzefuk2nT5+ma9eulCtXjvfee4+OHTty4sQJi3V/8MEHwKMGzM3N7ak5zf7991+sra0pW7ZsgnmOjo4WjUVSjsn4knKcX758mX79+uHp6Unp0qWpWrUqgwYNIiQkBHh0HIaHh7N+/Xrz/jDV4dq1a4wZM4batWtTpkwZKlWqRP/+/RMcQ6bj4LfffmPSpEm8//77lC1blj59+iSaW2/fvn14e3vz3nvvUa5cOZo3b05AQIBFmT/++IMuXbpQvnx53n33Xby9vfntt9+e8C0n9Ntvv3Ht2jXq1atHvXr1+PXXX7lx48YTyx88eNCc77VevXoJXv97Uk7I5J4DJnFxcSxYsID69etTunRpKleuzOjRo3nw4EGSt1HkWdROp712eu3atXTo0AEPDw9KlSpFvXr1WLZsWYJypvEajh49SrNmzShTpgwNGzY055vcsWMHDRs2pHTp0jRr1ixBu/qsPLlXr17Fzc0t0Ru548eP4+bmxubNm597OxOT2L7+8ccfadasmbm9adiwIQsXLgQe/X4PGDAAgA4dOpi///g5N5cuXUr9+vUpVaoUVapUYezYsQnygT/p+B82bBiVKlUiOjo6QV07d+5M7dq1n7lNW7duNe+fSpUqMWTIEIvrGB8fH4YNGwZAixYtLNrw5HrS7wU8SmvWv39/3N3dzcfE7t27LcqY2sNjx44xevRoKlWqRLly5Rg6dGiCts10/B04cMC8fStWrAAgODiYiRMnms/PmjVr8v333xMXF2exjKftW4Do6Gj8/PyoVasWpUuXplKlSrRt25ZDhw6ZyyR2HMfExPDNN99Qo0YNSpUqhZeXF9OmTSMqKirRbfj1119p0aIFpUuX5sMPP2TDhg3J++JFkkDt+dvVnt+4cYOVK1fi6emZaJoKa2trunTpkqDHcEhICL6+vlSoUIHy5cszfPhwIiIiEt3WTZs2me8zDhw4ADz7PheSd1+X2DERGRnJrFmzqF27NqVLl6ZKlSr07duXf//996nfsUmDBg3YsmWLxW/+nj17ePjw4RNTr9y8eZPhw4dTuXJl875ds2ZNgnI3btygd+/elC1bFg8PDz7//PMEv+2Q+LEWFxfHwoULzddG77//Pl26dOHUqVPmMkm9BkuK5zlGXvb+NRqNfPvtt1StWpV3330XHx8fzp07l6Auj+cs9/HxYe/evVy7ds18Lpi+zyflLE9ODOjKlSvPPA+e9jv4uuhRfBrXuHFjpk2bxsGDB2nVqlWiZc6dO0ePHj1wc3Ojf//+2NnZceXKFY4fPw5AkSJF6N+/PzNnzqR169aUL18ewKIX7v379+nWrRv169enUaNGZM2a9an1mj17NgaDgW7dunH37l0WLlzIRx99xMaNG5PVayUpdYvPaDTSq1cvc2NfokQJDhw4wJQpU7h58yYjRoywKP/bb7+xY8cO2rVrh4ODA4sXL6Z///789NNPZMmS5Yn1evjwIT4+Pvz777+0b9+evHnzsm3bNnx9fQkODqZjx44UKVKEKVOmMGnSJHLmzGl+ZftZuT1jYmISDXCagqtPc/fuXbp06UKWLFno3r07Tk5OBAYGsnPnTvO6x4wZw5gxY6hZsyY1a9YEMF/k/fzzz3Tr1o28efPSt29fHj58yJIlS2jbti3r1q1L8Ir0gAEDKFCgAIMGDcJoNFK7dm3GjRtHQEAA77zzjkXZgIAA3N3dyZEjxxPrf+7cOdq3b4+DgwNdu3bFxsaGlStX4uPjw5IlS3j33XepWbMmGTNmZNKkSTRo0ICqVas+NY1Knjx5iI2NZePGjTRt2vSZ32FyPOs4j4qKokuXLkRFReHt7U22bNm4efMme/fuJTg4mIwZMzJlyhRGjRpFmTJlzOdw/vz5gUdB399//5369euTM2dOrl27xvLly+nQoQM//vhjgl5zEyZMwMnJib59+3Lt2jUWLlzIuHHj+Prrr81l1q1bx4gRIyhWrBg9evQgY8aM/P333xw4cICGDRsCjxrLbt26UapUKfr27YvBYGDdunV07NiRZcuWJXgtLzEBAQHkz5+fMmXK4OrqSvr06dm8eTNdu3ZNUPby5csMGjSINm3a0LRpU9auXcuAAQOYO3cunp6eydonzzoHTEaPHs369etp1qwZPj4+BAYGsnTpUk6fPs3y5cvfmB4Nkvqpnbb0trbTGTJkIEOGDCxfvpxixYrh5eWFjY0NP/30E2PHjsVoNNK+fXuLz1y5coWPP/6YNm3a0KhRI3744Qd69uzJ2LFjmT59Om3btgXg+++/Z+DAgWzbtg0rq6T1k8mXLx/lypVj06ZNCW7yAgICcHBw4MMPP3zmciIiIhK9Jnn8pigxhw4dYvDgwXh4eDBkyBAALl68yPHjx+nYsSMVK1bEx8eHxYsX07NnTwoXLgxgzjFryoteuXJl2rZty6VLl1i+fDmnTp1K8Dud2PFvb2/Phg0bOHjwoPkhO8Dt27c5cuQIffr0eWr9TfmLS5cuzeDBg7l79y6LFi3i+PHjbNiwAScnJ3r27EmhQoVYuXKlOXWDqQ1/Hon9Xpw7d462bduSI0cOunXrhr29PVu3bqVPnz7MmjXLfC1nMm7cOPO1gOk7u379OosXL8ZgMJjLXbp0iY8//pjWrVvTqlUrChUqREREBN7e3ty8eZM2bdqQK1cufv/9d6ZNm8bt27cZOXIk8Ox9C486Zfj7+9OyZUvKlClDaGgof/75J3/99ddT2/ZRo0axfv16ateuTadOnTh58iT+/v5cuHCBb775xqLslStXGDBgAC1atDBfP/j6+lKyZEmKFSv23PtBJDFqzy2l5vZ8//79xMTE0KhRoyR/PwADBw4kb968DB48mNOnT7N69WqcnZ355JNPLModOXKErVu30r59e7JkyUKePHmSdJ8bX1Lu6x4XGxtrfhOifv36dOjQgbCwMA4dOsTZs2eT1D41aNCAWbNmcfToUXMnu82bN/P+++8neizeuXOHVq1aYTAYaN++Pc7Ozuzfv5+RI0cSGhpqvgZ5+PAhHTt25L///sPHx4fs2bOzceNGjhw58sw6AYwcOZJ169ZRtWpVWrRoQWxsLL/++it//PGH+U2Q5FyDPUtyj5FXsX9nzJjB7NmzqVatGtWqVeOvv/6ic+fOiXYCiK9nz56EhIRw48YNc4exp8VLkhsDetZ58KzfwdfGKG+1tWvXGl1dXY0nT558Ypny5csbmzRpYv575syZRldXV/Pf8+fPN7q6uhrv3r37xGWcPHnS6Orqaly7dm2Ced7e3kZXV1fj8uXLE53n7e1t/vvIkSNGV1dX4//+9z9jSEiIefqWLVuMrq6uxoULF5qnffDBB8Zhw4Y9c5lPq9uwYcOMH3zwgfnvnTt3Gl1dXY3ffvutRbl+/foZ3dzcjFeuXDFPc3V1NZYsWdJi2t9//210dXU1Ll68OMG64luwYIHR1dXVuHHjRvO0qKgoY+vWrY1ly5a12PYPPvjA2L1796cuL35ZV1fXp/7bunWrubzp+Lh69arF9j/teLl7967R1dXVOHPmzATzGjdubPTw8DDeu3fPPO3vv/82Fi9e3Dh06FDzNNMxNnjw4ATLGDx4sLFKlSrG2NhY87S//vrrifswvt69extLlixp/Pfff83Tbt68aXzvvfeM7du3N0+7evWq0dXV1Th37tynLs9oNBpv375tfP/9942urq7GOnXqGEePHm0MCAgwBgcHJyib1GMyqcf56dOnE+yzxJQtWzbR9UZERCSY9vvvvxtdXV2N69evN08zHQcfffSRMS4uzjz9888/N5YoUcK8rcHBwcb33nvP2LJlS+PDhw8tlmv6XFxcnLFWrVrGzp07WywrIiLC6OXlZezUqdNTt8VofHQuuLu7G6dNm2aeNnjwYGOjRo0SlDUd89u3bzdPCwkJMXp6ej71d+3xbU/OOXDs2DGjq6urcdOmTRbT9+/fn+h0kadRO612On6bmtjvdufOnY0ffvhhoss5fvy4edqBAweMrq6uxjJlyhivXbtmnr5ixQqjq6ur8ciRI+Zpif0mPr6/TJ87f/68xXdQqVKlRPdrfKZ29ln/4h+zj+/rCRMmGMuVK2eMiYl54nq2bt2aYNuMxkfXKiVLljR27tzZ4npiyZIlRldXV+OaNWvM0550/MfGxhqrVq1qHDhwoMX0+fPnG93c3CyuNR4XFRVl9PDwMDZo0MCivfzpp5+Mrq6uxhkzZpinJeU3IDllH/+96Nixo7FBgwbGyMhI87S4uDhj69atjbVq1Uqw7KZNmxqjoqLM0+fMmWN0dXU17tq1yzzNdPzt37/fYt3ffPONsWzZssZLly5ZTP/qq6+MJUqUMF6/ft1oNCZt3zZq1OiZ59Xjx7Hp/B45cqRFucmTJxtdXV2Nhw8fTrANx44dM0+7e/eusVSpUsbJkyc/db0iiVF7nnba888//9zo6upqPH369DPLGo3/v5+HDx9uMb1Pnz5Gd3d3i2murq7G4sWLG8+dO2cxPan3uUm9rzMaE+6/NWvWGF1dXY3z589PsA3xl5UYb29vY/369Y1Go9HYrFkz44gRI4xGo9H44MEDY8mSJY3r1683H3Px721HjBhh9PT0NAYFBVksb9CgQcby5cubr4tM+3HLli3mMuHh4caaNWsmuA54/Fg7fPiw0dXV1Th+/PinbldSr8Ee/94Sk9xj5GXvX9N1UPfu3S3KTZs2zejq6mpxPpv2S/zvsHv37hbfoYnp+i7+OZ7cGNCzzoOk/A6+DkrDItjb2xMWFvbE+aa8zrt3707wCmVS2dnZ0axZsySXb9KkicVo4XXq1MHFxYV9+/Y91/qTav/+/VhbWyd4Halz584YjcYEr3pVrlzZ4glr8eLFcXR05OrVq89cj4uLCw0aNDBPs7W1xcfHh/DwcI4dO/bc2/Duu+8yf/78BP9Mr/k+jSlf5t69e5/5xPFxt27d4u+//6Zp06ZkzpzZPL148eJUrlw50X3Xpk2bBNMaN27MrVu3LF6jDggIIH369NSqVeuJ64+NjeXQoUPUqFGDfPnymadnz56dBg0a8NtvvxEaGpqsbQLIli0bGzdupE2bNgQHB7NixQo+/vhjPDw8+Oabb15o8JBnHeemeQcPHkxSL7zHxe8NEh0dzb1798ifPz9OTk6JproxPdU3qVChArGxsVy7dg141BMsLCyM7t27J8hVZvrc33//zeXLl2nYsCH37t0jKCiIoKAgwsPD8fDw4NixY8/8Hdm/fz/379+3OD8aNGjAP//8k+irY9mzZ7foGefo6EiTJk04ffo0t2/ffuq6HpeUc2Dbtm1kzJgRT09P8/YFBQVRsmRJ7O3tLY5dkZdB7fT/e1vb6SZNmgCWv9shISEEBQXh7u7O1atXE7whVrRoUd577z2L5QK8//775M6dO8H0Z23z4+rWrUu6dOks0mwdPHiQe/fuJbmnVOvWrRO9JklKznYnJyciIiIs0m4k1c8//0x0dDQdOnSw6E3fsmVLHB0dExyniR3/VlZWNGzYkD179lhcP2zatIn33nvP4lrjcX/++Sd3796lbdu2Fu1l9erVKVy4MHv37k32NiVV/N+L+/fvc+TIEerWrUtoaKi5vbp37x5VqlTh8uXLCdLbtW7d2qLXfdu2bbGxsUnwneXNm5f//e9/FtO2bdtG+fLlcXJysmgfK1euTGxsrPm8Scq+dXJy4ty5c1y+fDnJ226q4+MD6Hbu3NlivknRokWpUKGC+W9nZ2cKFSqU7HNFJKnUnv+/1Nyem9qEp/W2Tczj974VKlTg/v37Ce5RK1asaJE7/Xnuc591X5eYHTt2kCVLFry9vRPMi7+sZ2nYsCE7d+4kKiqK7du3Y21tTY0aNRKUMxqN7NixAy8vL4xGo0W7UaVKFUJCQvjrr7+A/9+P8VO5ZMiQ4YlvaTy+XQaDIdGBW+NvV3KuwZ4lOcfIq9i/pusgb29vi3Kmt7delpcRA3r8PHgZv4Mvg9KwCOHh4U99PatevXqsXr2aUaNGMXXqVDw8PKhZsyZ16tRJ8uu8OXLkSNYAEgUKFLD422AwUKBAgaf+uL8M165dI3v27BYXDPD/r/Q+vv5cuXIlWEamTJkS5MNMbD0FChRI8P2Z1nP9+vVk190kS5YsVK5cOcH0pAzk6e7uTu3atfHz82PBggW4u7tTo0YNGjZs+Mz9Z6pzoUKFEswrUqQIBw8eTDCI5+Ov5AB4enri4uLCpk2b8PDwIC4ujs2bN/Phhx8m2C/xBQUFERER8cT1x8XF8d9//z3Xa7XZs2dn7NixjBkzhsuXL3Pw4EHmzJnDzJkzyZ49+3MPPPms4zxfvnx06tSJ+fPnExAQQIUKFfDy8qJRo0ZJGgjs4cOH+Pv7s27dOm7evGkR2E+swY8fXIH/b6hMx7MpV93TvkPTTe3THs6EhISQKVOmJ87ftGkTefPmNb9yBY9Sy2TIkIGAgAAGDx5sUb5AgQIJLuAKFiwIPDrXXFxcnriuxyXlHLhy5QohISHmVwsfd/fu3SSvTyQp1E7/v7e5nYZHr5nPmjWLEydOJHhIGhISYvHb//i2meY9niPV9F09a5sf5+TkxAcffMDmzZsZOHAg8OjhdY4cOXj//feTtIwCBQokuq1JGcOiXbt2bN26lW7dupEjRw48PT2pW7cuVatWfeZnTfvHlJrFxM7Ojnz58iU4Tp50/Ddp0oQ5c+awa9cumjRpwsWLF/nrr78YO3Zsktaf2DVJ4cKFkzWGR3LF/734999/MRqNzJgxgxkzZiRa/u7duxYp7h4/tx0cHHBxcUnwnSV2DXflyhXOnDnzxPbRlJInKfu2f//+9O7dm9q1a+Pq6kqVKlVo3LgxxYsXf+K2X7t2DSsrqwSpAlxcXHByckry74PGH5FXRe35/0vN7bmpzk978JGYJ91rPXjwwOJ7ePz39Xnuc591X5eYf//9l0KFCr3woLP16tXjiy++YP/+/WzatIlsuzMNAACcVElEQVTq1asneh8fFBREcHAwK1euZOXKlYkuy9RumPbj4/d8iX0nj/v333/Jnj27RTA3Mcm5BnuW5Bwjr2L/mo5r0z2xibOz81Pvw5PreWJAzzoPXsbv4MugYHkad+PGDUJCQp6afyp9+vQsXbqUo0ePsnfvXg4cOMCWLVtYuXIlP/zwQ5KCsMnJd/aiYmNjk1Snl+FJ63mR3sYpyWAwMHPmTE6cOMFPP/3EgQMHGDFiBPPnz2flypXJfnr+LImNpGxtbU3Dhg1ZtWoVY8aM4fjx49y6dSvZOeFeBYPBQKFChShUqBDVq1enVq1abNq06ZnB8hc5Jn19fWnatCm7d+/m0KFDTJgwAX9/f1atWpUgIPK48ePHm3OFly1blowZM2IwGMw54h/3pMYnOcezqezQoUMpUaJEomXiN5aPCw0N5aeffiIyMjLRNwk2b97MoEGDktW7AZ7cGyI2NjZBuWedA3FxcWTNmpWvvvoq0WU+K1+xSHKonX4xqamd/vfff/noo48oXLgwvr6+5MqVC1tbW/bt28eCBQsS9K550ra9zG1u0qQJ27Zt4/jx47i6urJnzx7atm37Wm5WsmbNas4Zvn//fvbv38+6deto0qQJX3zxxUtd15OO/6JFi1KyZEk2bdpEkyZN2LRpE7a2ttStW/elrv9lefz3wnTMdO7cOUEvcJPnzZGe2HcWFxeHp6dnouOLwP/ftCdl31asWJGdO3ear3/WrFnDwoULGTt27DOvu5J6jfC6fodEQO35i3qT2nPTg9gzZ8488X4nMUm913oZ+/Bl3Nc9r+zZs+Pu7s78+fM5fvw4s2bNSrScqY1q1KjRE8cGe9pg5C9Tcq/BnuV5j5GkSsn9+6KeVfeX8Tv4MihYnsZt3LgRgCpVqjy1nJWVFR4eHnh4eDB8+HC+++47pk+fztGjR6lcuXKyA1fPYupNamI0Grly5YrFj+WTniRfv37d4vWV5NQtT548HD58mNDQUIunnxcvXjTPfxny5MnDmTNniIuLs/ixMK3n8adtr1vZsmUpW7YsgwYNIiAggCFDhrBlyxZatmz5xO/TVOdLly4lmHfx4kWyZMny1CBpfI0bN+aHH35gz5497N+/H2dn52ceo87OzmTIkOGJ67eyskq0R8LzypcvH05OThZpPpJ6TJok5TgHzKNQ9+7dm+PHj9O2bVuWL1/OoEGDnlrH7du306RJE4vR6yMjI5P9GpmJ6eL+3LlzCXqhmJi209HR8Yk9J59mx44dREZGMmbMmASD9Vy6dImvv/6a3377zeK16StXrmA0Gi2OTVMPd9M5G/9pu+n/4cm9SZ52DuTPn5/Dhw9Trly513pDImmT2mlLb3M7vWfPHqKiopg9e7bF8lMytdP//vc/nJ2dCQgI4N133yUiIiJJKVReFjs7O7y8vPDy8iIuLo4xY8awcuVKevfunWgPMxPT93fx4kWLYy0qKorAwMBktU9NmjRh8uTJ3Lp1i82bN1O9evVn9sqKf030eC/rS5cuvbLrvMd/L0zbbmtrm+RtvnLlisWbA2FhYdy+fTtJPfrz589PeHh4ktb1rH0LkDlzZpo3b07z5s0JCwvD29ubWbNmPTFYnidPHuLi4rhy5Yq51yg8GkAuODj4pf0+iDwPteeWUnN7XrVqVaytrQkICDCnUXuVXtd9bv78+fnjjz+Ijo62SMf1PBo0aMCoUaNwcnJ6Yvvh7Oxs7oj0rHYjT548nD17NsE9X2LfyePy58/PwYMHuX///hN7l7/sa7DkHCOvYv+atuHy5csW52hQUFCS3p5K6rn8MmNA8T3rd/B1UM7yNOzw4cN8++235M2b96m9du/fv59gmunpWFRUFPAoXxQk/xXfJ9mwYYNFXqZt27YluFDPly8ff/zxh7kOAD/99BP//fefxbKSU7eqVasSGxvL0qVLLaYvWLAAg8GQpBuFpKhatSq3b99my5Yt5mkxMTEsXrwYe3t7Klas+FLWk1wPHjxI8DQyqfs6e/bslChRgg0bNljMO3v2LIcOHaJatWpJrkfx4sVxc3NjzZo17Nixg/r16z/zdTBra2s8PT3ZvXs3gYGB5ul37txh8+bNlC9f/qlpXJ7kjz/+IDw8PMH0kydPcv/+fYtXjpJ6TJo86zgPDQ0lJibG4jOurq5YWVlZrMPe3j7R4zuxp66LFy9O0Js6qapUqYKDgwP+/v5ERkZazDMdN6VKlSJ//vz88MMPib52ZnqV7kk2bdpEvnz5aNu2LXXq1LH416VLF+zt7S3y58KjXGk7d+40/x0aGsqGDRsoUaKEOQWLKdAfPy9heHg4GzZssFhWUs6BunXrEhsby7fffpug/jExMS/td1BE7XRCb3M7bfrNfjxl1tq1a1/6upLKxsaG+vXrs3XrVtatW4erq+tT02C8TPfu3bP428rKyhy8efy4fvwhcOXKlbG1tf0/9u48Lqp6/+P4e1QGMEXFzOuWgl4IUxQskSDcM9GyuplZmQYhLS64dFVcwjK3q2laFuJoZmZaVmbhUmqSRjtlpbmBZpbajQQJFNDz+8Mf5zqOC7LjvJ6Phw+cM5/5nu8chvnMfM75fr9atmyZ3fF8++23deLEiSv6TNK7d29ZLBY999xzOnToUKFGurVq1Up169bVm2++aff637p1q/bv369OnToVev+FdaH3i7p166p9+/ZauXKljh075vCYC+XklStX2q3ZsWLFCuXn5xfqb6tnz55KSUnRp59+6nBfZmam+ZmmML/b82OuueYaXX/99XbH83wFv9elS5fabV+yZInd/UBZI587qsz5vEGDBurbt6+2bdumZcuWOdx/5swZLV68WEeOHClW3wuU1vfc8912223666+/HH4n0pVfsXz77bdryJAhevrppy86LVDVqlXVo0cPbdiwQXv27HG4/9wcFRYWpmPHjmn9+vXmtpycHK1ateqyfbnttttkGIZefPFFh/sKnldJfwa7ktdIafx+Cz4Hvf7663bP6fz8eDHu7u6FusCuJGtABQrzPlgWuLLcSSQlJSk1NVWnT5/Wf//7X33xxRfavn27GjZsqJdffvmC02EUeOmll/T111+rY8eOatSokf7880+98cYb+sc//qF27dpJkrlo4JtvvqlrrrlG1atXl7+//yUXP7qUWrVq6YEHHtA999yjP//8U0uXLlXTpk3tFnDo27evNmzYoEcffVQ9e/bUL7/8orVr1zoMbbuSvnXp0kVBQUGaM2eODh8+LF9fX23fvl2bNm3SwIEDizxU9Xz9+vXTypUrNXbsWP30009q1KiRNmzYoG+//VaxsbElkuyK4t1339WKFSvUrVs3XX/99fr777+1atUq1ahRw/zA4ubmphYtWmjdunVq1qyZateurX/+85/y8fHRv//9b0VFRalfv3669957dfLkSb3++uuqWbPmBRfUuJRzh+IWdgqWmJgYffbZZ3rggQf0wAMPqGrVqlq5cqVyc3P11FNPXdnB+H9r1qzR2rVr1a1bN7Vq1UouLi7av3+/Vq9eLVdXVz322GNmbGFfkwUu9zr//PPP9cwzz+j2229Xs2bNdPr0aa1Zs8b8YFHgxhtvVHJyspYsWaLrrrtOjRs3Vps2bdSpUyetWbNGNWrUUIsWLfTdd9/ps88+u+x8bRdTo0YNjRs3ThMmTNC9996r3r17y8PDQz///LNOnjypGTNmqEqVKpoyZYqioqLUu3dv3XPPPapfv76OHj2qL774QjVq1NArr7xywfYLYs5f6KeA1WrVrbfeqvXr12vChAnmFQ/NmjXT+PHj9cMPP6hu3bpavXq1/vzzT02bNs18bEhIiBo2bKjx48crNTVVVatW1erVq1WnTh27q8sL8zfQvn179evXT/Hx8dq1a5dCQkLk4uKiAwcOaP369Ro/frzd4jNAYZCnydMF7yWPPfaY7r//fv3999966623VLdu3SterLgk3XXXXVq2bJm++OILjR49usz2O2HCBGVkZKhDhw6qX7++fvvtN73++uvy8/Mzrxr28/NT1apVlZCQoBMnTshqtapDhw6qW7euoqOj9eKLL+rRRx9Vly5dlJaWpjfeeEOtW7e+oqndPD09zdzj4eFRqEK3i4uLRo8erXHjxumhhx5Sr1699Oeff+q1115To0aNNGjQoCIelbOu5P3i6aef1gMPPKA77rhD9913n5o0aaL//ve/+u6773TkyBG9//77dm3n5eVp0KBB6tmzp3nM2rVrp65du162X5GRkdq8ebMee+wx3X333brxxhuVk5OjPXv2aMOGDdq0aZM8PT0L9bvt1auX2rdvrxtvvFG1a9fWDz/8oA0bNlxw4bkCN9xwg+6++26tXLlSmZmZuvnmm/XDDz/o3XffVbdu3Qo91z5QHORz58jnY8eO1aFDhzRlyhRt3LhRnTt3loeHh37//XetX79eqamp6tWrV4k8B6l0vuee76677tJ7772nadOmaceOHWrXrp1ycnKUnJys/v37X3CRzoupWbOmhg4detm4UaNG6YsvvtB9992nvn37qkWLFsrIyNBPP/2k5ORkffnll5LOLmi5fPlyjRkzRj/99JPq1aunNWvWFGqUb4cOHdSnTx8tW7ZMBw8e1K233qozZ87om2++UVBQkB566KFS+Qx2Ja+Rkv79enp6KiIiQvHx8YqOjlbHjh21c+dOJSUlOYzevpAbb7xRiYmJmjZtmlq3bq3q1aurS5cuF4wtyRqQVLj3wbJAsdxJzJs3T9LZD++1a9eWj4+PYmNjdc8991w2QXTp0kWHDx/W6tWr9ddff6lOnTpq3769hg4dai5y4OLiounTp+v5559XXFyc8vPzNW3atCIn7ccee0y7d+/WwoUL9ffffys4OFhPP/20ebZaOjs0eOzYsVqyZImmTp2qVq1a6ZVXXnGYx/JK+lalShW9/PLLmjdvnhITE/XOO++oUaNG+ve//62IiIgiPZcLcXNz07JlyzRr1iy9++67ysrKkpeXl6ZNm3ZFq5eXtPbt2+uHH35QYmKi/vvf/6pmzZry9/fXrFmz7I7XlClT9Oyzz2ratGnKy8vTkCFD5OPjo1tuuUWLFi3SvHnzNG/ePFWrVk0333yznnrqqSt+Ldxxxx3mfv39/Qv1mH/+859avny5Zs+erfj4eBmGIX9/f/3nP/9RmzZtrmj/Bfr16yc3Nzd9/vnn2rx5s7KyslSnTh2FhIQoOjpaLVu2NGML+5oscLnXua+vr0JDQ7VlyxYdPXpU7u7u8vX1VUJCgtq2bWu2M3bsWE2aNElz587VyZMndffdd6tNmzYaP368qlSporVr1+rUqVMKDAzUkiVLLjqXaGH07dtXdevW1cKFC7VgwQJVq1ZN3t7edl/8g4KCtHLlSi1YsECvv/66srOzVa9ePfn7+6tfv34XbTsxMVFnzpxR586dLxrTuXNnbdiwQUlJSeYX92bNmmnixImaOXOm0tLS1LhxY82ZM8duflYXFxe9+OKLmjx5sl544QXVq1dPAwcOlIeHh8aNG2fGFfZv4JlnnlGrVq305ptvas6cOapataoaNWqkO++8U4GBgUU5tHBy5GnytLe3t+bNm6e5c+dqxowZuvbaa9W/f395enoqNja2VPZZGK1atdI///lP7d+/v0zXD7nzzju1atUqvfHGG8rMzFS9evXUs2dPDR061BxKX69ePU2ePFnx8fEaP368Tp8+rddee01169bV0KFD5enpqddff13Tpk1TrVq1dN9992nkyJFXPLy8T58+2rJli3r27FnohfPuueceubm5KSEhQbNmzVL16tXVrVs3PfXUU3bTgRXFlbxftGjRQqtXr9aLL76od999V8ePH5enp6datmypJ5980qHtSZMmae3atZo3b57y8vLUq1cvTZgwoVBDst3d3bVs2TLFx8dr/fr1eu+991SjRg01a9bM7v2oML/bAQMGaPPmzdq+fbtyc3PVsGFDxcTEKDIy8pJ9mDJliho3bqx3331XH3/8sa699lpFR0cX6Qs7UBTkc+fI5+7u7kpISNA777yj9957TwsWLNDJkyd13XXXKSgoSLNmzbJbPLm4SuN77vkKTj6//PLL+uCDD7Rx40bVrl1bgYGBpTZ3+LXXXqu33npLL730kj766COtWLFCtWvXVosWLexO0Lu7u+vVV1/Vs88+q9dff11ubm664447FBYWVqjvttOmTTNHrs+cOVM1a9ZUq1atFBAQIKl0PoNdyWukNH6/MTExslqtevPNN/XFF1/I399fixcvVnR09GUf+8ADD2jXrl1655139Oqrr6pRo0YXLZaXZA1IKtz7YFmwGJVhBngATic9PV233nqrnnjiiQt+mQMAAGXjrrvuUq1atQo9fPdq8/HHH+vJJ5/U8uXL7dbMuJq88847GjdunN5++221bt26vLsDAABQbpizHECF9O677+r06dNlupAYAACw98MPP2jXrl1lsohZRfXWW2+pSZMmZTr8FwAAAOWDaVgAVCjJycnav3+/XnnlFXXr1k2NGzcu7y4BAOB09uzZo59++kmLFy9WvXr1FB4eXt5dKnMffvihdu/erU8++UTjx48v1FQkAAAAqNwolgOoUBYsWKCUlBQFBARo4sSJ5d0dAACc0oYNG/TSSy/Jy8tLzz///CUXpbtajRw5UtWrV9e9996rBx54oLy7AwAAgDLAnOUAAAAAAAAAAKfHnOUAAAAAAAAAAKdHsRwAAAAAAAAA4PSYs7yCS0lJkWEYcnFxKe+uAABKWV5eniwWiwICAsq7Kygi8jYAOA/yduVH3gYA51HYvE2xvIIzDENMKw8AzoH3+8qPvA0AzoP3+8qPvA0AzqOw7/cUyyu4gjPcrVu3LueeAABK2w8//FDeXbgiW7duVUJCgvbt26esrCzVr19f3bp105AhQ1SzZk0zbvPmzZo7d67S0tLUsGFDDR48WP/617/s2srNzdWcOXP0/vvv6++//1ZAQIAmTpwob29vu7j9+/drypQpSklJ0TXXXKM+ffooJiZGVqvVLu6tt97SokWL9Ntvv8nLy0sjRoxQ586d7WJOnDihadOm6eOPP1ZeXp5uvfVWTZgwQdddd12Rjwl5GwCcR2XL23BE3gYA51HYvM2c5QAAoEiOHz8uf39/TZ48WTabTY888ojee+89DR8+3Iz5+uuvNWTIELVt21YJCQnq2bOnxo8fr/Xr19u1NWXKFL311lsaMWKE5s+fr9zcXA0aNEgnTpwwYzIyMjRw4EDl5eVp/vz5GjFihFatWqXp06fbtfXhhx9q4sSJ6tmzpxISEtS2bVsNGTJE3333nV1cTEyMtm/frri4OM2aNUtpaWmKiopSfn5+yR8sAAAAAECFx5XlAACgSPr06WN3OygoSFarVRMnTtTRo0dVv359vfzyy/L399czzzwjSerQoYMOHTqkefPm6fbbb5ckHTlyRG+//baefvpp3XvvvZLOXuHVuXNnvfnmm4qKipIkvfnmm/r777/14osvqnbt2pKk06dPa/LkyYqOjlb9+vUlSfPmzVOvXr0UExNj7nPPnj166aWXlJCQIOnsHKXbtm2TzWZTaGioJMnLy0vh4eHauHGjwsPDS+/AAQAAAAAqJK4sBwAAJaagiJ2Xl6fc3Fx98cUXZlG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",
      "text/plain": [
       "<Figure size 1500x1600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Set up the plot size\n",
    "num_columns = len(categorical_columns)\n",
    "num_rows = math.ceil(num_columns / 3)  # Adjust number of rows to fit all plots\n",
    "\n",
    "plt.figure(figsize=(15, num_rows * 4))\n",
    "\n",
    "# Loop through all categorical columns and plot the distributions\n",
    "for i, column in enumerate(categorical_columns, 1):\n",
    "    plt.subplot(num_rows, 3, i)  # Adjust the subplot grid dynamically\n",
    "    sns.countplot(data=df_fillna, x=column, palette='Set2')\n",
    "    plt.title(f'Distribution of {column}')\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f08fe5d",
   "metadata": {
    "papermill": {
     "duration": 0.018756,
     "end_time": "2024-11-23T16:09:24.273376",
     "exception": false,
     "start_time": "2024-11-23T16:09:24.254620",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We use mode imputation for imbalanced features and random sampling for balanced features.\n",
    "\n",
    "**Imputation Logic**\n",
    "1. Mode Imputation\n",
    "   - For features with a dominant category\n",
    "2. Random Sampling Imputation\n",
    "   - For features with more balanced distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d0c8ecf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:24.314531Z",
     "iopub.status.busy": "2024-11-23T16:09:24.314211Z",
     "iopub.status.idle": "2024-11-23T16:09:24.404810Z",
     "shell.execute_reply": "2024-11-23T16:09:24.403847Z"
    },
    "papermill": {
     "duration": 0.113327,
     "end_time": "2024-11-23T16:09:24.407146",
     "exception": false,
     "start_time": "2024-11-23T16:09:24.293819",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                            12201\n",
      "Age                                 0\n",
      "Marital Status                      0\n",
      "Education Level                     0\n",
      "Number of Children                  0\n",
      "Smoking Status                      0\n",
      "Physical Activity Level             0\n",
      "Employment Status                   0\n",
      "Income                              0\n",
      "Alcohol Consumption                 0\n",
      "Dietary Habits                      0\n",
      "Sleep Patterns                      0\n",
      "History of Mental Illness           0\n",
      "History of Substance Abuse          0\n",
      "Family History of Depression        0\n",
      "Chronic Medical Conditions          0\n",
      "dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23/2817901283.py:16: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_fillna[column].fillna(mode_value, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# List of features for mode and random sampling imputations\n",
    "mode_imputation_features = [\n",
    "    \"Marital Status\", \"Smoking Status\", \n",
    "    \"History of Substance Abuse\", \"Family History of Depression\", \n",
    "    \"Chronic Medical Conditions\", \"History of Mental Illness\"\n",
    "]\n",
    "\n",
    "random_sampling_features = [\n",
    "    \"Physical Activity Level\", \"Education Level\", \"Alcohol Consumption\", \n",
    "    \"Dietary Habits\", \"Sleep Patterns\"\n",
    "]\n",
    "\n",
    "# Mode imputation\n",
    "for column in mode_imputation_features:\n",
    "    mode_value = df_fillna[column].mode()[0]  # Get the most frequent value\n",
    "    df_fillna[column].fillna(mode_value, inplace=True)\n",
    "\n",
    "# Random sampling imputation\n",
    "for column in random_sampling_features:\n",
    "    missing_indices = df_fillna[column].isnull()  # Identify missing rows\n",
    "    observed_values = df_fillna.loc[~missing_indices, column]  # Non-missing values\n",
    "    sampled_values = np.random.choice(observed_values, size=missing_indices.sum(), replace=True)\n",
    "    df_fillna.loc[missing_indices, column] = sampled_values\n",
    "\n",
    "# Verify if all missing values are filled\n",
    "print(df_fillna.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ba6bb69",
   "metadata": {
    "papermill": {
     "duration": 0.019111,
     "end_time": "2024-11-23T16:09:24.446032",
     "exception": false,
     "start_time": "2024-11-23T16:09:24.426921",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3. 2. Outlier Value Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b100b211",
   "metadata": {
    "papermill": {
     "duration": 0.018823,
     "end_time": "2024-11-23T16:09:24.485695",
     "exception": false,
     "start_time": "2024-11-23T16:09:24.466872",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Depression data includes 3 numerical features. Let's consider them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0cb31ee6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:24.526046Z",
     "iopub.status.busy": "2024-11-23T16:09:24.525743Z",
     "iopub.status.idle": "2024-11-23T16:09:25.149199Z",
     "shell.execute_reply": "2024-11-23T16:09:25.148301Z"
    },
    "papermill": {
     "duration": 0.64531,
     "end_time": "2024-11-23T16:09:25.150939",
     "exception": false,
     "start_time": "2024-11-23T16:09:24.505629",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of numerical columns to check for outliers\n",
    "numerical_columns = ['Income', 'Age', 'Number of Children']\n",
    "\n",
    "# Create a larger grid of boxplots for better comparison\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Loop through numerical columns and plot horizontally\n",
    "for i, col in enumerate(numerical_columns, 1):\n",
    "    plt.subplot(1, 3, i)  # Adjust grid to have 1 row and 3 columns\n",
    "    sns.boxplot(data=df_fillna, x=col, palette='Set2')\n",
    "    plt.title(f'Boxplot for {col}')\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "09d03408",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:25.192478Z",
     "iopub.status.busy": "2024-11-23T16:09:25.192172Z",
     "iopub.status.idle": "2024-11-23T16:09:25.237660Z",
     "shell.execute_reply": "2024-11-23T16:09:25.236599Z"
    },
    "papermill": {
     "duration": 0.067897,
     "end_time": "2024-11-23T16:09:25.239252",
     "exception": false,
     "start_time": "2024-11-23T16:09:25.171355",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers in Income:\n",
      "Series([], Name: Income, dtype: float64)\n",
      "Total number of outliers in Income: 0\n",
      "Outliers in Age:\n",
      "Series([], Name: Age, dtype: float64)\n",
      "Total number of outliers in Age: 0\n",
      "Outliers in Number of Children:\n",
      "Series([], Name: Number of Children, dtype: float64)\n",
      "Total number of outliers in Number of Children: 0\n"
     ]
    }
   ],
   "source": [
    "# Outlier detection using IQR for numerical columns\n",
    "numerical_columns = ['Income', 'Age', 'Number of Children']\n",
    "\n",
    "# Calculate IQR and define outliers for each column\n",
    "outliers = {}\n",
    "\n",
    "for col in numerical_columns:\n",
    "    Q1 = df_fillna[col].quantile(0.25)\n",
    "    Q3 = df_fillna[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "    # Calculate the bounds\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    \n",
    "    # Identify outliers\n",
    "    outliers[col] = df_fillna[(df_fillna[col] < lower_bound) | (df_fillna[col] > upper_bound)]\n",
    "    \n",
    "    print(f\"Outliers in {col}:\")\n",
    "    print(outliers[col][col])\n",
    "    print(f\"Total number of outliers in {col}: {outliers[col].shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06348b1",
   "metadata": {
    "papermill": {
     "duration": 0.026549,
     "end_time": "2024-11-23T16:09:25.285922",
     "exception": false,
     "start_time": "2024-11-23T16:09:25.259373",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The change in the maximum value for Income can be attributed to imputing missing values, which replaces NaN entries with reasonable values like the median. As a result, extreme outliers were effectively removed, leading to a decrease in the overall maximum."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e704e04",
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   },
   "source": [
    "## 4. Feature Engineering and Analysis"
   ]
  },
  {
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   "id": "8c341bbc",
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    "execution": {
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   "source": [
    "df_fe = df_fillna.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "502c4b57",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "1. We can create Depression Risk and Healthy Lifestyle Score columns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef3f8b3",
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     "start_time": "2024-11-23T16:09:25.504667",
     "status": "completed"
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   "source": [
    "**Healthy Lifestyle Score**: This column can be based on lifestyle habits such as Physical Activity Level, Dietary Habits, Smoking Status, and Alcohol Consumption. A scoring system can be created where:\n",
    "\n",
    "- Higher physical activity levels and better dietary habits increase the score.\n",
    "- Smoking and alcohol consumption would decrease the score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3bb15264",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-11-23T16:09:31.140198Z"
    },
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     "end_time": "2024-11-23T16:09:31.143229",
     "exception": false,
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     "status": "completed"
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   },
   "outputs": [],
   "source": [
    "def healthy_lifestyle_score(row):\n",
    "    score = 0\n",
    "    if row[\"Physical Activity Level\"] == \"Active\": score += 2\n",
    "    if row[\"Dietary Habits\"] == \"Healthy\": score += 2\n",
    "    if row[\"Smoking Status\"] == \"Non-smoker\": score += 2\n",
    "    if row[\"Alcohol Consumption\"] == \"Low\": score += 2\n",
    "    if row['Sleep Patterns'] == 'Good': score += 2\n",
    "    return score\n",
    "\n",
    "df_fe[\"Healthy Lifestyle Score\"] = df_fe.apply(healthy_lifestyle_score, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a4cbf97c",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-11-23T16:09:31.192952Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  2,  6,  8,  0, 10])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fe[\"Healthy Lifestyle Score\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc718023",
   "metadata": {
    "papermill": {
     "duration": 0.022094,
     "end_time": "2024-11-23T16:09:31.236966",
     "exception": false,
     "start_time": "2024-11-23T16:09:31.214872",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Depression Risk**: This column can represent the likelihood of depression based on factors like History of Mental Illness and Family History of Depression. \n",
    "We can also consider the Healty Lifestyle Score because lifestyle factors often influence mental health.\n",
    "- High Depression Risk:\n",
    "  * History of Mental Illness = Yes\n",
    "  * Family History of Depression = Yes\n",
    "  * If an individual has a low Healthy Lifestyle Score (i.e., poor physical activity, smoking, high alcohol consumption), they may be at a higher risk for depression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c71ff536",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:31.279409Z",
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     "shell.execute_reply": "2024-11-23T16:09:34.999074Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create Depression Risk based on History of Mental Illness, Family History of Depression, and Healthy Lifestyle Score\n",
    "def depression_risk(row):\n",
    "    # Start with a risk factor based on history of mental illness or family history of depression\n",
    "    risk = 0\n",
    "    if (row['History of Mental Illness'] == 'Yes') or (row['Family History of Depression'] == 'Yes'):\n",
    "        risk = 1\n",
    "    \n",
    "    # Now incorporate the Healthy Lifestyle Score: if score is low, increase depression risk\n",
    "    if row['Healthy Lifestyle Score'] <= 4:  # Let's assume 4 or below is considered unhealthy\n",
    "        risk = 1  # Higher risk\n",
    "    \n",
    "    return risk\n",
    "\n",
    "# Apply the function to create the Depression Risk column\n",
    "df_fe['Depression Risk'] = df_fe.apply(depression_risk, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f584226e",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fe[\"Depression Risk\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7339a91",
   "metadata": {
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     "duration": 0.019538,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2. Let's test if smoking status is associated with family history of depression. We use chi-square test. <br>\n",
    "**Chi-Square Test**: To check if there is a significant association between Smoking Status and Family History of Depression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6e968c7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:35.139432Z",
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     "shell.execute_reply": "2024-11-23T16:09:35.180756Z"
    },
    "papermill": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-Square Test p-value: 2.976627317364588e-126\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "contingency_table = pd.crosstab(df_fillna['Smoking Status'], df_fillna['Family History of Depression'])\n",
    "chi2, p_value, _, _ = chi2_contingency(contingency_table)\n",
    "print(f'Chi-Square Test p-value: {p_value}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b7f89d",
   "metadata": {
    "papermill": {
     "duration": 0.020237,
     "end_time": "2024-11-23T16:09:35.224344",
     "exception": false,
     "start_time": "2024-11-23T16:09:35.204107",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The p-value is extremely small, which indicates a very strong statistical significance in the relationship between Smoking Status and Family History of Depression. This suggests that people who have a family history of depression may be more likely to have a certain smoking status, or vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3dbd48",
   "metadata": {
    "papermill": {
     "duration": 0.01975,
     "end_time": "2024-11-23T16:09:35.265516",
     "exception": false,
     "start_time": "2024-11-23T16:09:35.245766",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's visualize the relationship using a stacked bar chart to show how the different smoking statuses are distributed across the categories of family history of depression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7b2e1d6e",
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a contingency table\n",
    "contingency_table = pd.crosstab(df_fe['Smoking Status'], df_fe['Family History of Depression'])\n",
    "\n",
    "# Plot the stacked bar chart\n",
    "ax = contingency_table.plot(kind='bar', stacked=True, figsize=(8, 6), colormap='Set2')\n",
    "ax.set_title('Smoking Status vs. Family History of Depression')\n",
    "ax.set_xlabel('Smoking Status')\n",
    "ax.set_ylabel('Count')\n",
    "ax.legend(title='Family History of Depression', labels=['No', 'Yes'])\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29937708",
   "metadata": {
    "papermill": {
     "duration": 0.020254,
     "end_time": "2024-11-23T16:09:35.764171",
     "exception": false,
     "start_time": "2024-11-23T16:09:35.743917",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "3. Let's create a new column as \"Family\" combining Marital Status and Number of Children."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c794386c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:35.805971Z",
     "iopub.status.busy": "2024-11-23T16:09:35.805375Z",
     "iopub.status.idle": "2024-11-23T16:09:39.054300Z",
     "shell.execute_reply": "2024-11-23T16:09:39.053629Z"
    },
    "papermill": {
     "duration": 3.272062,
     "end_time": "2024-11-23T16:09:39.056313",
     "exception": false,
     "start_time": "2024-11-23T16:09:35.784251",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def family_status(row):\n",
    "    if row['Marital Status'] == 'Married' and row['Number of Children'] > 0:\n",
    "        return 'Married with children'\n",
    "    elif row['Marital Status'] == 'Married' and row['Number of Children'] == 0:\n",
    "        return 'Married without children'\n",
    "    else:\n",
    "        return 'Single'\n",
    "\n",
    "df_fe['Family Status'] = df_fe.apply(family_status, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bd78d66c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:39.099399Z",
     "iopub.status.busy": "2024-11-23T16:09:39.098860Z",
     "iopub.status.idle": "2024-11-23T16:09:39.127457Z",
     "shell.execute_reply": "2024-11-23T16:09:39.126798Z"
    },
    "papermill": {
     "duration": 0.051335,
     "end_time": "2024-11-23T16:09:39.128994",
     "exception": false,
     "start_time": "2024-11-23T16:09:39.077659",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Married with children', 'Single', 'Married without children'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fe[\"Family Status\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e252139",
   "metadata": {
    "papermill": {
     "duration": 0.021566,
     "end_time": "2024-11-23T16:09:39.172783",
     "exception": false,
     "start_time": "2024-11-23T16:09:39.151217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's see the relationship between Family Status and Depression Risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "363acacd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T16:09:39.217260Z",
     "iopub.status.busy": "2024-11-23T16:09:39.216769Z",
     "iopub.status.idle": "2024-11-23T16:09:39.790602Z",
     "shell.execute_reply": "2024-11-23T16:09:39.789747Z"
    },
    "papermill": {
     "duration": 0.598558,
     "end_time": "2024-11-23T16:09:39.792778",
     "exception": false,
     "start_time": "2024-11-23T16:09:39.194220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df_fe, x='Family Status', hue='Depression Risk', palette='Set2')\n",
    "plt.title('Family Status vs. Depression Risk')\n",
    "plt.xlabel('Family Status')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d85cf246",
   "metadata": {
    "papermill": {
     "duration": 0.022581,
     "end_time": "2024-11-23T16:09:39.839517",
     "exception": false,
     "start_time": "2024-11-23T16:09:39.816936",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The analysis of the relationship between family status and depression risk revealed the following key observations:\n",
    "\n",
    "* **Married with children** individuals have the highest **depression risk** compared to other family statuses. This suggests that being married with children might correlate with higher levels of stress or responsibilities, which could contribute to an increased risk of depression.\n",
    "* **Single** individuals are the second highest in terms of **depression risk**. This could reflect social isolation or challenges related to being single, which may contribute to mental health struggles.\n",
    "* **Married without children** individuals have the lowest **depression risk**, which may indicate that the absence of children, along with the support system of a marital relationship, may provide a more stable and less stressful environment.\n",
    "\n",
    "These findings provide insight into how different family structures might be linked to varying levels of depression risk. Further analysis, including additional factors such as income, health conditions, and mental health history, would be beneficial to understand the underlying causes of these trends."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9752c7c",
   "metadata": {
    "papermill": {
     "duration": 0.020729,
     "end_time": "2024-11-23T16:09:39.881992",
     "exception": false,
     "start_time": "2024-11-23T16:09:39.861263",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 5. Results and Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8333be5f",
   "metadata": {
    "papermill": {
     "duration": 0.020555,
     "end_time": "2024-11-23T16:09:39.923295",
     "exception": false,
     "start_time": "2024-11-23T16:09:39.902740",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In this project, the analysis we conducted highlighted the relationship between depression risk and healthy lifestyle factors, providing significant insights for data-driven solutions in the healthcare sector. Specifically, the relationship between depression risk and demographic factors, such as family status, presents important findings for targeted group interventions and service design.\n",
    "\n",
    "#### **1. Potential Application Areas of the Data:**\n",
    "\n",
    "- **Healthcare and Psychological Support Services:**\n",
    "  Based on depression risk and factors like family status and lifestyle, individuals can be better targeted for interventions. For instance, the observation that individuals in the **\"married with children\"** category exhibit higher depression risk suggests that family counseling services might be more effective for this group. Additionally, these findings could inform the design of personalized mental health programs based on individuals' lifestyle and health habits.\n",
    "\n",
    "- **Strategic Decision Support in Companies:**\n",
    "  Depression risk data could be useful for companies aiming to make strategic decisions regarding employee health. The relationship between **family status** and **lifestyle** factors and depression risk could assist in targeting workplace health initiatives. For example, programs that promote a healthy lifestyle or provide family support could be implemented for employees.\n",
    "\n",
    "#### **2. Suggested ML Models:**\n",
    "\n",
    "For analyzing depression risk and healthy lifestyle data, we recommend using **classification algorithms** and **decision trees** in machine learning. Specifically, **Random Forest** or **Gradient Boosting** methods could be effective at modeling the complexity and heterogeneity of the data. These algorithms can be particularly useful in predicting depression risk based on various features.\n",
    "\n",
    "**Why These Models?**\n",
    "\n",
    "- **Random Forest and Gradient Boosting:**\n",
    "  These ensemble methods provide high accuracy and generalization, especially when working with complex and interacting data. These models would effectively capture the relationships between features like family status, lifestyle habits, and depression risk.\n",
    "\n",
    "- **Logistic Regression:**\n",
    "  Another option for classification is **logistic regression**, which could work well if the depression risk is represented as a binary classification (e.g., \"Risky\" vs. \"Not Risky\"). While logistic regression offers faster results and is more interpretable, it may have limitations in modeling complex interactions.\n",
    "\n",
    "#### **Conclusion:**\n",
    "\n",
    "This project has provided valuable insights into the relationship between depression risk and healthy lifestyle factors. The findings suggest that demographic factors, such as **family status** and **lifestyle habits**, play a significant role in depression risk, and this information can be used to inform health policies and interventions. \n",
    "\n",
    "**Recommendations:**\n",
    "1. Collect and analyze more data related to depression risk to improve prediction accuracy.\n",
    "2. Explore advanced machine learning techniques (e.g., deep learning) to uncover more complex interactions and factors.\n",
    "3. Design health programs or counseling services for high-risk groups based on the findings to offer tailored psychological support.\n",
    "\n",
    "These recommendations guide how the findings from this analysis can be applied both clinically and within corporate wellness programs."
   ]
  }
 ],
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