2619 lines
1.5 MiB
2619 lines
1.5 MiB
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cda961ffb493d00c",
|
||
"metadata": {
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||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# CSC 3105 Project"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "73fe8802e95a784f",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Load and Clean the Data\n",
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||
"\n",
|
||
"This code block performs the following operations:\n",
|
||
"\n",
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||
"1. Imports necessary libraries for data handling and cleaning.\n",
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||
"2. Defines a function `load_data` to load the data from a given directory into a pandas dataframe.\n",
|
||
"3. Defines a function `clean_data` to clean the loaded data. The cleaning process includes:\n",
|
||
" - Handling missing values by dropping them.\n",
|
||
" - Removing duplicate rows.\n",
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||
" - Converting the 'NLOS' column to integer data type.\n",
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||
" - Normalizing the 'Measured range (time of flight)' column.\n",
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||
" - Creating new features 'FP_SUM' and 'SNR'.\n",
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||
" - One-hot encoding categorical features.\n",
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||
" - Performing feature extraction on 'CIR' columns.\n",
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||
" - Dropping the original 'CIR' columns.\n",
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||
" - Checking for columns with only one unique value and dropping them.\n",
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||
"4. Checks if a pickle file with the cleaned data exists. If it does, it loads the data from the file. If it doesn't, it loads and cleans the data using the defined functions.\n",
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||
"5. Prints the first few rows of the cleaned data and its column headers."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 70,
|
||
"id": "2aa3c6c09e8645d1",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:21.260710Z",
|
||
"start_time": "2024-03-20T11:36:21.256027Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import os\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.preprocessing import LabelEncoder\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"from sklearn.metrics import accuracy_score, classification_report\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import pickle\n",
|
||
"from sklearn.model_selection import cross_val_score\n",
|
||
"from sklearn.metrics import roc_curve, auc\n",
|
||
"import tensorflow as tf\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn.preprocessing import LabelEncoder\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"import pywt\n",
|
||
"from skimage import restoration\n",
|
||
"from tensorflow.keras.utils import to_categorical\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 71,
|
||
"id": "7bcd7cfc8dd11cbb",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:21.918674Z",
|
||
"start_time": "2024-03-20T11:36:21.913408Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Define the directory where the dataset is located\n",
|
||
"DATASET_DIR = './UWB-LOS-NLOS-Data-Set/dataset'\n",
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||
"\n",
|
||
"def load_data(dataset_dir):\n",
|
||
" # Load the data\n",
|
||
" # file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames if 'uwb_dataset_part7.csv' not in file]\n",
|
||
" file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames]\n",
|
||
" data = pd.concat((pd.read_csv(file_path) for file_path in file_paths))\n",
|
||
" print(f\"Original data shape: {data.shape}\")\n",
|
||
" return data\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 72,
|
||
"id": "9e0b1ed6f23a17cf",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:21.998580Z",
|
||
"start_time": "2024-03-20T11:36:21.993122Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
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||
"source": [
|
||
"def stat_analysis_and_plots(data):\n",
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||
" # Statistical Analysis\n",
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||
" print(\"Statistical Analysis:\")\n",
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||
" print(data.describe())\n",
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||
"\n",
|
||
" # Plot Boxplot to check for outliers for the first 15 columns\n",
|
||
" print(\"Boxplot of the first 15 columns:\")\n",
|
||
" fig, axs = plt.subplots(15,1,dpi=95, figsize=(7,17))\n",
|
||
" for i, col in enumerate(data.columns[:15]):\n",
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||
" axs[i].boxplot(data[col], vert=False)\n",
|
||
" axs[i].set_ylabel(col)\n",
|
||
" plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1dd92fe7b6881ea6",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Channel Impulse Response (CIR) Graphs\n",
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||
"\n",
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||
"This code block defines a function `cir_graphs` to plot the Channel Impulse Response (CIR) for Line of Sight (LOS) and Non-Line of Sight (NLOS) data. The CIR is a sequence of values representing the channel response to a single impulse. It is used to characterize the channel in wireless communication systems."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 73,
|
||
"id": "308d64639b199bc7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:22.090025Z",
|
||
"start_time": "2024-03-20T11:36:22.080262Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def cir_graphs(data):\n",
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||
" # Separate the data into LOS and NLOS\n",
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||
" data_los = data[data['NLOS'] == 0]\n",
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||
" data_nlos = data[data['NLOS'] == 1]\n",
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||
"\n",
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||
" # Extract the CIR columns\n",
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||
" cir_columns = [col for col in data.columns if 'CIR' in col]\n",
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||
" data_los_cir = data_los[cir_columns]\n",
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||
" data_nlos_cir = data_nlos[cir_columns]\n",
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||
"\n",
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||
" # Calculate the magnitude and time for each CIR column\n",
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||
" time_los = np.arange(len(data_los_cir.columns))\n",
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||
" magnitude_los = np.linalg.norm(data_los_cir.values, axis=0)\n",
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||
"\n",
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||
" time_nlos = np.arange(len(data_nlos_cir.columns))\n",
|
||
" magnitude_nlos = np.linalg.norm(data_nlos_cir.values, axis=0)\n",
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||
"\n",
|
||
" # Plot the magnitude vs time for LOS\n",
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||
" plt.figure(figsize=(20, 10), dpi=300) # Increase figure size and DPI\n",
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||
" plt.plot(time_los, magnitude_los, linewidth=2) # Increase line width\n",
|
||
" plt.title('Magnitude vs Time for LOS')\n",
|
||
" plt.xlabel('Time (ns)')\n",
|
||
" plt.ylabel('Magnitude')\n",
|
||
" plt.xlim([600, max(time_los)]) # Set x-axis limits\n",
|
||
" # plt.ylim([0, 2e6])\n",
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||
" plt.show()\n",
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||
"\n",
|
||
" # Plot the magnitude vs time for NLOS\n",
|
||
" plt.figure(figsize=(20, 10), dpi=300) # Increase figure size and DPI\n",
|
||
" plt.plot(time_nlos, magnitude_nlos, linewidth=2) # Increase line width\n",
|
||
" plt.title('Magnitude vs Time for NLOS')\n",
|
||
" plt.xlabel('Time (ns)')\n",
|
||
" plt.ylabel('Magnitude')\n",
|
||
" plt.xlim([600, max(time_los)]) # Set x-axis limits\n",
|
||
" # plt.ylim([0, 2e6])\n",
|
||
" plt.show()\n",
|
||
" "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 74,
|
||
"id": "80cfcfac265d9357",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:22.129821Z",
|
||
"start_time": "2024-03-20T11:36:22.125340Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def calculate_total_distance(data):\n",
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||
" # Speed of light in meters per nanosecond\n",
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||
" speed_of_light_ns = 0.299792458\n",
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||
"\n",
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||
" # Extract the CIR columns\n",
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||
" cir_columns = [col for col in data.columns if 'CIR' in col]\n",
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||
"\n",
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||
" # Calculate the total distance for each row\n",
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||
" data['Total_Distance'] = data[cir_columns].abs().sum(axis=1) * speed_of_light_ns\n",
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||
"\n",
|
||
" return data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bfd97fbe797a7067",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Signal to Noise Ratio (SNR) Graph\n",
|
||
"\n",
|
||
"This code block defines a function `snr_graph` to plot the Signal to Noise Ratio (SNR) for Line of Sight (LOS) and Non-Line of Sight (NLOS) data. The SNR is calculated as the ratio of the 'CIR_PWR' to the 'STDEV_NOISE' for each data point."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 75,
|
||
"id": "4afc8d71b3271351",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:22.213753Z",
|
||
"start_time": "2024-03-20T11:36:22.208758Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"def snr_graph(data):\n",
|
||
" # Separate the data into LOS and NLOS\n",
|
||
" data_los = data[data['NLOS'] == 0]\n",
|
||
" data_nlos = data[data['NLOS'] == 1]\n",
|
||
"\n",
|
||
" # Extract the SNR values\n",
|
||
" snr_los = data_los['SNR']\n",
|
||
" snr_nlos = data_nlos['SNR']\n",
|
||
"\n",
|
||
" # Create a new figure\n",
|
||
" plt.figure(figsize=(10, 5))\n",
|
||
"\n",
|
||
" # Plot SNR for LOS\n",
|
||
" plt.plot(snr_los, label='LOS')\n",
|
||
"\n",
|
||
" # Plot SNR for NLOS\n",
|
||
" plt.plot(snr_nlos, color='red', label='NLOS')\n",
|
||
"\n",
|
||
" # Set title and labels\n",
|
||
" plt.title('SNR for LOS and NLOS')\n",
|
||
" plt.xlabel('Index')\n",
|
||
" plt.ylabel('SNR')\n",
|
||
"\n",
|
||
" # Add a legend\n",
|
||
" plt.legend()\n",
|
||
"\n",
|
||
" # Show the plot\n",
|
||
" plt.show()\n",
|
||
" "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 76,
|
||
"id": "22025d6c8281fc09",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:22.268611Z",
|
||
"start_time": "2024-03-20T11:36:22.260730Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"from scipy.stats import norm\n",
|
||
"\n",
|
||
"def plot_histogram(data, feature):\n",
|
||
" \"\"\"\n",
|
||
" Function to plot a histogram of a given feature in the data for 'NLOS' and 'LOS'.\n",
|
||
"\n",
|
||
" Parameters:\n",
|
||
" data (pd.DataFrame): The data.\n",
|
||
" feature (str): The name of the feature to plot.\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" None\n",
|
||
" \"\"\"\n",
|
||
" # Check if the feature exists in the data\n",
|
||
" if feature not in data.columns:\n",
|
||
" print(f\"The feature '{feature}' does not exist in the data.\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" # Separate the data into 'NLOS' and 'LOS'\n",
|
||
" data_nlos = data[data['NLOS'] == 1]\n",
|
||
" data_los = data[data['NLOS'] == 0]\n",
|
||
"\n",
|
||
" # Create a figure with two subplots side by side\n",
|
||
" fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
|
||
"\n",
|
||
" # Plot the histogram for 'NLOS'\n",
|
||
" axs[0].hist(data_nlos[feature], bins=30, edgecolor='black')\n",
|
||
" axs[0].set_title(f'Histogram of {feature} for NLOS')\n",
|
||
" axs[0].set_xlabel(feature)\n",
|
||
" axs[0].set_ylabel('Frequency')\n",
|
||
"\n",
|
||
" # Plot the histogram for 'LOS'\n",
|
||
" axs[1].hist(data_los[feature], bins=30, edgecolor='black')\n",
|
||
" axs[1].set_title(f'Histogram of {feature} for LOS')\n",
|
||
" axs[1].set_xlabel(feature)\n",
|
||
" axs[1].set_ylabel('Frequency')\n",
|
||
"\n",
|
||
" # Display the plots\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"# Usage:\n",
|
||
"# plot_histogram(data, 'First_Path_Power_Level')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 77,
|
||
"id": "ac4db13fed3f9916",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:22.354769Z",
|
||
"start_time": "2024-03-20T11:36:22.349304Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_features(data, labels, feature1, feature2):\n",
|
||
" reds = labels == 1\n",
|
||
" blacks = labels == 0\n",
|
||
" plt.scatter(data[reds][feature1], data[reds][feature2], c=\"red\", s=20, edgecolor='k')\n",
|
||
" plt.scatter(data[blacks][feature1], data[blacks][feature2], c=\"yellow\", s=20, edgecolor='k')\n",
|
||
" plt.xlabel(feature1)\n",
|
||
" plt.ylabel(feature2)\n",
|
||
" plt.title(f\"Plot of data: {feature1} versus {feature2}\")\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "69413268ac5b549d",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"## denoise_cir Function\n",
|
||
"\n",
|
||
"The `denoise_cir` function uses the Discrete Wavelet Transform (DWT) to denoise the Channel Impulse Response (CIR) values. The DWT is a linear transformation that operates on a data vector whose length is an integer power of two, transforming it into a numerically different vector of the same length. The DWT of a signal `x` is calculated as follows:\n",
|
||
"\n",
|
||
"1. **Wavelet Decomposition:**\n",
|
||
"\n",
|
||
" The input signal `x` is passed through two complementary filters and emerges as two signals. The filter outputs are decimated by 2 (down-sampled) to get the approximation coefficients (cA) and detail coefficients (cD).\n",
|
||
"\n",
|
||
" The approximation coefficients represent the high-scale, low-frequency component of the signal, while the detail coefficients represent the low-scale, high-frequency component.\n",
|
||
"\n",
|
||
"2. **Thresholding:**\n",
|
||
"\n",
|
||
" The detail coefficients are thresholded to remove noise. The thresholding function `T` applied to the detail coefficients `x` is defined as:\n",
|
||
"$$\n",
|
||
"T(x) = x * I(|x| > \\text{{value}}) \\quad \\text{{for 'hard' thresholding}}\n",
|
||
"$$\n",
|
||
"\n",
|
||
"$$\n",
|
||
"T(x) = \\text{{sign}}(x)(|x| - \\text{{value}})_+ \\quad \\text{{for 'soft' thresholding}}\n",
|
||
"$$\n",
|
||
"\n",
|
||
"where $I$ is the indicator function that is one if the argument is true and zero otherwise, $\\text{{value}}$ is the threshold value, and $(x)_+$ equals $x$ if $x > 0$ and zero otherwise.\n",
|
||
"\n",
|
||
"3. **Wavelet Reconstruction:**\n",
|
||
"\n",
|
||
" The original signal is reconstructed from the approximation and detail coefficients."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 78,
|
||
"id": "fe3089568e99a58d",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:22.466282Z",
|
||
"start_time": "2024-03-20T11:36:22.462030Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def denoise_cir(cir_values, wavelet='db1', level=1):\n",
|
||
" # Perform wavelet decomposition\n",
|
||
" coeffs = pywt.wavedec(cir_values, wavelet, level=level)\n",
|
||
"\n",
|
||
" # Set the detail coefficients to zero\n",
|
||
" for i in range(1, len(coeffs)):\n",
|
||
" coeffs[i] = pywt.threshold(coeffs[i], value=0.5, mode='soft')\n",
|
||
"\n",
|
||
" # Perform wavelet reconstruction\n",
|
||
" denoised_cir = pywt.waverec(coeffs, wavelet)\n",
|
||
"\n",
|
||
" return denoised_cir\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e1edd5ef4f54e752",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"## deconvolve_cir Function\n",
|
||
"\n",
|
||
"The `deconvolve_cir` function applies the Richardson-Lucy deconvolution algorithm to deconvolve the Channel Impulse Response (CIR) values. \n",
|
||
"\n",
|
||
"In the context of signal processing, deconvolution is the process of reversing the effects of convolution on a signal. Convolution is a mathematical operation that blends two functions together and is often used to describe the effect of a linear time-invariant system on a signal. Deconvolution, therefore, attempts to recover the original signal that was convolved with the system's impulse response to produce the current signal.\n",
|
||
"\n",
|
||
"The Richardson-Lucy algorithm is an iterative method for deconvolution. It is particularly suitable for cases where the impulse response of the system (also known as the Point Spread Function, or PSF) is known, and the noise is Poissonian (such as in astronomical images). \n",
|
||
"\n",
|
||
"The algorithm works by iteratively refining an estimate of the original signal. In each iteration, it performs a prediction step where it convolves the current estimate with the PSF to predict the observed signal, and a correction step where it computes the ratio of the observed signal to the predicted signal, convolves this ratio with the PSF, and multiplies the result with the current estimate to get the next estimate.\n",
|
||
"\n",
|
||
"This process is repeated for a fixed number of iterations, or until the estimate converges to a stable solution. The result is a denoised estimate of the original signal.\n",
|
||
"\n",
|
||
"The mathematical formulas involved in the Richardson-Lucy deconvolution algorithm are as follows:\n",
|
||
"\n",
|
||
"1. **Prediction Step:**\n",
|
||
"\n",
|
||
" The current estimate of the latent image `x` is convolved with the PSF `h` to predict the observed image `y`. This can be represented as:\n",
|
||
"\n",
|
||
" $$\n",
|
||
" y = h \\ast x\n",
|
||
" $$\n",
|
||
"\n",
|
||
" where $\\ast$ denotes the convolution operation.\n",
|
||
"\n",
|
||
"2. **Correction Step:**\n",
|
||
"\n",
|
||
"The ratio of the observed image $y$ to the predicted image $y'$ is computed, then the PSF $h$ is convolved with this ratio and multiplied with the current estimate $x$ to get the next estimate $x'$. This can be represented as:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"x' = x \\cdot (h \\ast \\left(\\frac{y}{y'}\\right))\n",
|
||
"$$\n",
|
||
"\n",
|
||
"where $\\div$ denotes element-wise division and $\\ast$ denotes the convolution operation.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 79,
|
||
"id": "670e8c2cf19126ea",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:22.544990Z",
|
||
"start_time": "2024-03-20T11:36:22.541525Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"def deconvolve_cir(cir_values, psf=None, iterations=50):\n",
|
||
" # If no point spread function is provided, create a simple one\n",
|
||
" if psf is None:\n",
|
||
" psf = np.ones((5,)) / 5\n",
|
||
"\n",
|
||
" # Perform Richardson-Lucy deconvolution\n",
|
||
" deconvolved_cir = restoration.richardson_lucy(cir_values, psf, num_iter=iterations)\n",
|
||
"\n",
|
||
" return deconvolved_cir"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 80,
|
||
"id": "685463c2d6065b08",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:22.653819Z",
|
||
"start_time": "2024-03-20T11:36:22.643573Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"def clean_data(data):\n",
|
||
" print(\"Starting data cleaning process...\")\n",
|
||
" \n",
|
||
" # print(\"Before Cleaning\")\n",
|
||
" # stat_analysis_and_plots(data)\n",
|
||
"\n",
|
||
" # Calculate total number of missing values in the data\n",
|
||
" total_missing = data.isnull().sum().sum()\n",
|
||
" print(f\"Total number of missing values: {total_missing}\")\n",
|
||
"\n",
|
||
" # Data has no missing values\n",
|
||
" data = data.dropna()\n",
|
||
" print(\"Missing values dropped.\")\n",
|
||
"\n",
|
||
" # Data has no duplicate rows\n",
|
||
" data = data.drop_duplicates()\n",
|
||
" print(\"Duplicate rows dropped.\")\n",
|
||
"\n",
|
||
" # Convert 'NLOS' column to integer data type (0 for LOS, 1 for NLOS)\n",
|
||
" data['NLOS'] = data['NLOS'].astype(int)\n",
|
||
" print(\"'NLOS' column converted to integer data type.\")\n",
|
||
" \n",
|
||
" # Print line where CIR_PWR is 0\n",
|
||
" print(f\"Line where CIR_PWR is 0: {data[data['CIR_PWR'] == 0]}\")\n",
|
||
" \n",
|
||
" # Calculate the expression inside the log10 function\n",
|
||
" expression = (data['CIR_PWR'] * (2**17)) / (data['RXPACC']**2)\n",
|
||
"\n",
|
||
" # If the expression is 0, set 'RX_Level' to 0\n",
|
||
" zero_indices = expression == 0\n",
|
||
" data.loc[zero_indices, 'RX_Level'] = 0\n",
|
||
"\n",
|
||
" # For the rest of the data where the expression is not 0, calculate 'RX_Level'\n",
|
||
" # First, update the 'expression' and 'data' to exclude zero_indices\n",
|
||
" expression = expression.loc[~zero_indices]\n",
|
||
" data = data.loc[~zero_indices]\n",
|
||
"\n",
|
||
" # Now, calculate 'RX_Level' for the rest of the data\n",
|
||
" data['RX_Level'] = 10 * np.log10(expression) - data['PRFR']\n",
|
||
"\n",
|
||
" # Calculate the median of 'RX_Level'\n",
|
||
" median = data['RX_Level'].median()\n",
|
||
"\n",
|
||
" # Create the boolean mask on the same DataFrame 'data'\n",
|
||
" zero_indices = (data['RX_Level'] == 0)\n",
|
||
"\n",
|
||
" # Replace zero values in 'RX_Level' with the median\n",
|
||
" data.loc[zero_indices, 'RX_Level'] = median\n",
|
||
"\n",
|
||
" print(\"New feature 'RX_Level' created.\")\n",
|
||
"\n",
|
||
" # Calculate new feature 'First_Path_Power_Level'\n",
|
||
" data['First_Path_Power_Level'] = (10 * np.log10(\n",
|
||
" (data['FP_AMP1'] ** 2 + data['FP_AMP2'] ** 2 + data['FP_AMP3'] ** 2) / (data['RXPACC'] ** 2))) - 64\n",
|
||
" print(\"New feature 'First_Path_Power_Level' calculated.\")\n",
|
||
" data.drop(['FP_AMP1', 'FP_AMP2', 'FP_AMP3', 'RXPACC', 'PRFR'], axis=1, inplace=True)\n",
|
||
"\n",
|
||
" # Calculate SNR as the ratio of 'CIR_PWR' to 'STDEV_NOISE' for each data point\n",
|
||
" data['SNR'] = data['CIR_PWR'] / data['STDEV_NOISE']\n",
|
||
" print(\"New feature 'SNR' created.\")\n",
|
||
" data.drop(['CIR_PWR', 'STDEV_NOISE'], axis=1, inplace=True)\n",
|
||
" \n",
|
||
" plot_histogram(data, 'First_Path_Power_Level')\n",
|
||
" plot_histogram(data, 'RX_Level')\n",
|
||
"\n",
|
||
" # One-hot encode categorical features\n",
|
||
" categorical_features = ['CH', 'FRAME_LEN', 'PREAM_LEN', 'BITRATE']\n",
|
||
" encoder = LabelEncoder()\n",
|
||
" for feature in categorical_features:\n",
|
||
" data[feature] = encoder.fit_transform(data[feature])\n",
|
||
" print(\"Categorical features one-hot encoded.\")\n",
|
||
"\n",
|
||
" # Extract the 'CIR' columns\n",
|
||
" cir_columns = [col for col in data.columns if 'CIR' in col]\n",
|
||
" cir_data = data[cir_columns] \n",
|
||
" print(\"'CIR' columns extracted.\")\n",
|
||
" \n",
|
||
" # Convert 'CIR' columns to float\n",
|
||
" cir_data = cir_data.astype(float)\n",
|
||
" print(\"'CIR' columns converted to float.\")\n",
|
||
" \n",
|
||
" # Denoise 'CIR' columns\n",
|
||
" denoised_cir_data = cir_data.apply(denoise_cir)\n",
|
||
" # denoised_cir_data = cir_data.apply(deconvolve_cir)\n",
|
||
" print(\"'CIR' columns denoised.\")\n",
|
||
" \n",
|
||
" # Replace original 'CIR' columns with denoised data\n",
|
||
" data[cir_columns] = denoised_cir_data\n",
|
||
" print(\"Original 'CIR' columns replaced with denoised data.\")\n",
|
||
" \n",
|
||
" # List of columns to check for unique values\n",
|
||
" columns_to_check = ['CH', 'PREAM_LEN', 'BITRATE']\n",
|
||
"\n",
|
||
" # Iterate over the columns\n",
|
||
" for column in columns_to_check:\n",
|
||
" # If the column has only one unique value, drop it\n",
|
||
" if data[column].nunique() == 1:\n",
|
||
" data = data.drop(column, axis=1)\n",
|
||
" print(f\"Column '{column}' dropped due to having only one unique value.\")\n",
|
||
"\n",
|
||
" # Print the shape of the cleaned data\n",
|
||
" print(f\"Cleaned data shape: {data.shape}\")\n",
|
||
"\n",
|
||
" # print(\"After Cleaning\")\n",
|
||
" # stat_analysis_and_plots(data)\n",
|
||
" \n",
|
||
" print(\"Data cleaning process completed.\")\n",
|
||
" \n",
|
||
" # Return the cleaned data\n",
|
||
" return data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 81,
|
||
"id": "79c2c23691b26753",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.311103Z",
|
||
"start_time": "2024-03-20T11:36:22.707131Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loading data from pickle file...\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
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"image/png": 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||
"text/plain": [
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||
"<Figure size 6000x3000 with 1 Axes>"
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]
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},
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||
"metadata": {},
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||
"output_type": "display_data"
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||
},
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||
{
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"data": {
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9v1fPztTXrpql9HNc9x8MR9Xc41dzj19NXr8avQF1+oI6PUzrTIXvqtX0rvveQFh7GnvGHbY30UySLi5L1x1L8uWyW/RPfzym+mHOJ5+Lw2rWnUsL9PGVhUpNmH4BQBaLWenpLkWNwbCi0fZhIlFDO+o9ev5gq14/3jHm8LmxKE5P0JfWlenyysxJ2+Z0FIkaev14h362tU7H2vvHta7sJLv+/IoKXVGZOa7v+QuBYRja1eDVw9sbtKm6a8i2qU6rPnNJqT60JC8mE85dqI619el/NlZrS233ex7PTrLrS5eV67q5WXxux6mjP6gfvlWt5w60nrUfceZzvDiX8EVMOY7BTE+RqCGTSSMOiAbezTAMHWvvV5cvqFK3S3mM2Y0pwzD0/KFW/WhTrVp6A5KkzES7PraiUHcvK5CFPmpMHW7tVYPHrwSbWWtK0uk7QVL89V/6g2E9ubdZuxq8ihqGLqvI0I3zc5RgYwKXmYgAMGASEAA2vFAkqqf2Nevp/S2q7fIpLcGmq+dk6d6VRdN6lpV4EjUMvV3dpcd3NWpPY48C4ahykx26ek6WPraikPc5htp6A3rpSJv2NfXIYTVrcX6qbpiXzQwLMVTXPaD/2XhSb57o1OmJ2daUpOtP15epMitpaosD4lS87bgCAABI9GGAmegPh1v1dy8cHbJNTrJDT31qZUxnvDrQ3KP7H90z4vYLcpP17x9cwDE1AKNG/wUAAMQb+i8AACDe0H/BdHSys18/31qvl460KRrnoyeSHBatLknXxaXpWlPqVk6yY0K31xcI67FdjfrVjgb1X8DX4yc7rLpydqZumJetiwpSYz4gNBCO6pn9LfrR2zUErr1LqtOqy2dl6uo5mVpRnD7msJhwJKrXjnfosV2NOtDcO+rl0xNs+viqIvUHwmo6FfbV5PWrvS84Y8MFpcH3/5aFufrQkjwVpiWcedwfiuiHm2r02M7GMb3+RLtF96wo1N3LC5Ron9rr+7t9QW2p7dbm6m7tqPeo2zcYxlaS4VJxWoJK3Qkqy3CpzO1Sidt1zsGd1Z0+PX+oVX841DrmoLlYuWFetr521SwlOS6scRMeX0gbT3bq4e31qukaiOm6LylL119eOes9/wcwKBI1tOHE4Ps+2u/WsgyXvnp5udaUuieoupmppcev/9tUoxcOtQ35/bsoL1lfvaJCC/NSJq22mSIQjurxXY362da6Yfu+pe4EfXl9uS4tcxO4Nk5Rw1Cjx69G74DKMxKVPcH7ODPJaI7BRKKGGjwD8oUiqsxMnJEhLMFwVBazaUoCfNp6A3pmf4ter+pQbZdPSQ6r1pVn6N6VhSpxuya9nonS4w+pvntADptFFRkuvv9ibH9Tj/7ttSodbu0789jyolT96WXlmp+bPCk19AXC2t3g1Y56j6o7fYpEDS0vStMtC3OUmRTf38+hSFTfebVKT+9vOefzywpT9Q83zp3wY22nhSNRNfUE5B0IKS3BpqL0mbPP0eAZ0L+8fFzb6jxnHstMtOvzl5bqAwtz+O6IEV8wosd2NeiFQ23yDIQ0JztJNy/I0fXzsicthLI/GNbOeq+21XarrntAhjEYIn/zghylOM8d/h5P55B2NXj0/z13WF2+94b35yY79K2b5mpJQeoUVTYz9QXC8gyElGi3nHMyhslAABgwCQgAOz/DMLShqlPf23jynLOwJNjM+tjyQt2zsnDKT67Eq1AkqpeOtOnh7Q062ek7Zxu7xaRbF+Xp4ysLlUsi85jVdPn0yPYGPX+oVeH3XaHhsll066Jc3b28gNTrcQiEo/rFtjr9Ylu9gpGzuxAWs0mfWFWkT64ujtkscheySNTQ1tpu7W30qrM/pIsKU7SuPGNaznoV7+q7B3SwpVd2q1mritOm5MR3PO24jsWB5h69cKhN7X0B5SQ7dPOCHM3NmZyDjxcSwzBU1z0gfyiqWVmJzDwwQQzDUGtvQKkJNtLaAVzwZnofBrjQRA1Dd/18p6q7zn0M67S/vHKWPrI0P+bb/8tnDuqNqs5h2101O1N/f/0cOemLARgD+i8AACDe0H8BAADxhv4LprMmr19vV3fpcGuvfMGoAuGI/OGoAuGo/KHI4P37fh/PYAuTBidWyU91qiDVOXif5lR+ilMFaQlyu2wKntqmP/TeWgYfe6dGq9mkwrQEzc9NHnMI0nj0+EP61c5GPb6zUb5QfF2Xb5LG9O9ot5i0riJD18/N1iVl7km5LrbbF9T3Nlbr9wdbJ3xb01Vmol2Xz8rQlbMztbQwLeaf940nOvXPLx9XZ//UBjVNZwvzknXHknxdNTtzyHOyexu9+oeXjqmue2yBS2kJNn1iVZFuX5I37LnfcCSq2u4BnejoV3WnT4FwVO5Eu4rTE1ScnqDCVOeIQizCUUMHm3u0uaZbm2u6dbild1TfD3kpDpW6XSrLcCktwabXj3e8Z3B+LCQ7rIpEjTF/1+anOvWPN87V4vzpFfxjGIaOtfVrS223evwhZSU5VJbhUnmGS5mJ9lENAO/sD2p3g1e7Grza1eDRiY6hr3EYL4fVrPtXF+neFUWMkdDgmJIXDrXqkR0NY/7/f9racre+vL5cpTMoGGUi9AXC+vm2ej2+q1GB8Mj3rW6Yl60vrCubtBCLeGYYhl6v6tR/bzipJu/Z4yuHsrI4TX+2vlyzs5MmqLqZq7nHr98faNXvD7aoqSdw5vH5ucn65OpiXVZBuNpwRnIMprbLpyf3Nev5g63yngobTrCZdeP8HN2zonBGhFzubfTqVzsbtbm6SxHDUKnbpevnZuuWhblKc03cGLxI1NCW2m49tbdZb53s1DmGWspqNumuZQX61JriuA5p7fGH9IO3avT7g61n/hYVpyfok6uLdf287EkZuxSJGjre3qed9V4daO6Vxx9SRYZL18zJivsAFsMw9Ns9TfrPN06eNS76tJsX5OgLa0tjHsI1EIpoT6NXO+q82lnv0eHW3nOG58f7GHjPQEh/9ewh7WrwDtku1WnV3143W+tnZcZku5Ho4Piz+u4B1XYPqN4zoPruAdV1+9Tk9b/ne2N2VqI+taZYl1dmTlp400TYXNOlbzx/5LwB9+srMvQ311bKPcHhPoZh6ESnT9tqu3WopVdef1izs5J0w7xszcqaunCfWKnp9OnPnzl4zn2y2VmJ+rPLy7WyOD3m241EDR1p7dWW2m5trenWvuZeRc7xpZHitOq+lUX6yNL8s475xMM5JMMw9OvdTfqvDSfP+fokyWySHri4RJ9cXcwY3nHy+EL64aYaPX9osJ9hNknrZ2XqC2tLJz1IlQAwYBIQAHZuR1p79V8bTmpn/dAdVklyu2x64OIS3bYod0ame0+EvkBYT+9v0WM7G0Y8k4vFbNJN87N136piFc+gtN6JdrClV7/cVq/Xj3cMewLOYpKunpOle1YUEjwzSltquvSdV6vOGRb4fmVul75x3expd+IwXgx1UizRbtHHlg/OehXPB/6mi5oun/7rjZPaVN115jGH1azbl+Tp/lXFE3qg9/3iYcd1LKo7ffqfjSf15smus567anamvrC2bEYl1E8Vfyii3+5p0uO7Gs/0O1w2i26Yn637Vxdz4jZGGr0DenRHo1451q4uX0gWs0mL85J1z8oirSvnBGOshCNRbaru1s56j1p6A6rMTNQVlZkz4gDrdGIYhg619OpYe78GQhGtLklXObPwYAxmah8GuFC9dqxdf/Xc4SHbZCTa9fSnVk5I+FZ1p093/WLHOS8eOO2+VUX6k7WlcX1yG8DUov8CAADiDf0XAAAQb+i/YCYxDEPBiDEYwhU6Fc4VPhUUdtbvg0FdTptF+SkO5acmKC/FIdsMu+7a4wvp4R31+s3uJvlHEUAxmbKT7FpZkq6VRWlaXpSqzES7unwhtfcH1dEXVEd/QB19wXf9HlR7X0Bef1gZLpvKMxN17ZwsXVGZOWXXae5q8Ojbr1Sp+jyTT083dotJEUPnHRA3nLwUh66ozNSVlZlalJ8y4ecCPQMh/ftrVXrpSPuEbieeOK1mXTs3S3dclK95o7jG3h+K6Mdv1+pXOxuGPM87lKwkux5YU6xbFubKYjapsz+oqo5+HW/vV1VHv6ra+1Xd5VPoXIkKp1hMUkFawplAsHduLpkkbant1ubqbm2r6z7vIOSpNj83WbcvydO1c7LUH4zowc21enpf8zmDJIZjOTUI9f5pMAi1pcevFw+36YXDbef9TktyWFTmTlR55mAgWFmGS2Vul3KSHTKZTGrvC2hX/TuBXzVd4wudGqvi9AR97apZWl0S+0HUseLxhbS70atGr1+hSFT5KU6Vul0qdieMa8LbXn9YdZ4Bbavt1q93N8U0RNFiNukjF+XrgYuLleJkovR3C0WiemJvs36yufZMaM9oOaxm3beySPeuLIyrifb6AmE9f7BVW2u7NRCKqMTt0iVlbq0qTov56zja1qfvvn5i2ECQoZgk3bIwV5+7tCTmwSwTLRiO6nhHvxLtlhEHao6HPxTRG1Wdeu5Ai7bXeYYcBzgnO0kPrCnW+lkZcX19895Grzae6NTJTp/K3C6tKU3XyuK0mLym8x2DCUei2nCiU7/b26wddZ7zLm82SdfMydLHVxbFZYhdZ39Q//NmtZ4/T4CyzWLS1bOzdPuSPC3OT4nZ56ijL6BnD7Tq6f3Nan5XeN1Q3C6b/mRtqW5ekDvl/bPR2lDVoX95peq8f/8rsxL1p5eVaU2pO6bbjUQNVbX3a2eDRzvrvdrd4FVv4Nx/Dxfnp+jjKwu1riIj7q4tHQhF9M8vH9eLh9uGbeuyWXT/6iLdvbxQjjEG0/pDEe1v7tGOeq921nl0sKX3vKFj5xKPY+BPdvbrq08dVOMoQj7vWJKnL68vH3W/o6q9X68f79DRtj7VdQ+owTsw5L7suczOStRnLimNuyBMwzD08231+uFbNcOO809PsOnr11bGLGjttJYev7bVerStrlvb6zzq8oXO2W5lcZo+urxAl5S54+47Q5LeOtmpbzx/RP3DZKesK3frT2MQuNzk9Wtrbbe21g6+r6M5rpKdZNenLy7RzQtzz4T8T/dzSP5T38t/GMH3siQtLUjRP9w4N2bhiOFIVC29ATV5/Wo8dTvzs2dAfYGwMpMcunp2lu5eXhD344bfPNGpb/3x2Dn/vzqtZv35FRW6dVHupH0fEgAGTAICwN6rrTcwmIJ4sHXUsxkVpyfoC2tLdUVlZlx1HCdTR19Aj+9u0hN7m9QXGNvnzmySrp6dpftXFxN0cB6GYWhbrUc/314/5IGooawoTtM9Kwp1SWk6n+chtPcF9N3XT+qVY6M7yW2S9JGl+fqTtWVy2ePnRMFU6vWH9cTeJj0+gpNiqU6r7j2VgDyek3EXqr5AWD/dUqfHdjWe94KT02FrH11RoET7xF/EM913XEeroz+oB9+u1TP7h774wGI26Y4lefrUmmKlT3By+0wUjhp64WCrfvR2zXkDR20Wkz64KE+fWFWk7DjfoZ8qR1v79Mvt9XrlWPt5L1Kal5Okz15SqkvK4r9fYRiGmnr8SkuwTcr332nH2/v0+4Ot+sOhNnUPnH3Q5uLSdN27slArimJzwvNC1dkf1AuHWvX0/pazgkaXFqToT9eXa2EeIa6x1uMPyWWzzMhA7ZnWhwGmij8U0e8PtmpDVaeOd/SrINWplcVpumVhrvJTJ2emLMMwdM/Du3SsvX/Idl9eX657VhROWB2/2tGg/9pw8qzHLWaT/ubqSt2yKHfCtg3gwkD/BQAAxBv6LwAAIN7QfwEuDJ39Qf1ye72e2NuswDiDwJIdVq0uSZchQy09AbX0BkYVLJKWYNOKojStLE7ViuJ0FaU5x3RtiWEY0+qalFAkqkd3NurBzbXjfo8ngsNq1rpyt66ek6VLytyyW8xq7wu8Z2DamVuPX+3vu76tOD1BV1Zm6orKTM3LSZqS9/61Y+369itV57xW6ULgtJq1tjxDV8/J1CVl7nFdj3yguUf/8OIxVXeNPbQuK8muUMSQ5wL693BYzbpubpZuX5Kv+blnB6/VdPr0v29Wa8OJzjGtP9aDUEeqLxDWa8c69MLhVu2q94563NJpiXaLUpzWEQdrTJarZmfqz9aXT/r7ei6d/cHBYLR6j3Y1eHVyiODI3GSHSjNcKnW7VOpOOHXvkttlk8lkkj8UUYPHr7pun+q6B87c6j0D5x04H0upTqs+e2mpblucd2Zg+HQUCEe1paZLb53sUoNnQJlJDi0tSNGaUnfMrvExDEOvHuvQ99+qVoNn5EEVQ8lOsutLl5XrurlZ06q/83713QP69e5GPXegVb7Q2WPynFaz1pSma11FhtaWu+Uex/iDzv6gfripRs/ubxnz99T7JdjMum9VkT62fPoHrlW19+vRnQ3649H2M33NFKdV68rduqIyU6tL0mP2GgzD0KHWPj13oEUvHWkb9XjLyqxEPXBxiS6fFV/BPvuaevTDTTXnHPdYluHSRy7K143zc8Y15u79x2AO1nTqid2NeuZA66jDGi8pS9d9q4q0tCB1Wn9PSIPjaJ7c26QfbqoZ8eepMitRdyzJ0/XzxvaeRw1D22s9enJfszac6Bxz+PHc7CT9+RUVuqgwdUzLT6ZuX1D/9toJvXx0ZGNKV5ek6UvryjUnZ2xhclHD0PH2fu2s92hXvVe7G72jDu8tSU/QvSsLdcO8HNnHGJA1meq6B/S1Zw/qRMfo9qPyU536s/XlunwEAYlRw9Cxtj5tqenW1jqP9jV6FRxLyvD7nA4QvH91sSoyp+8Y+E3VXfr67w8PG5R0LhWZLv3TTfOGfX3+UER/PNqup/c1a39z71hLPcv83GR99pISXRwH49/7g2H9w4vH9NrxjlEtd8vCHH3l8ooxB/B7BkLaWe/R9jqPttV2q36Ufefi9ATdtaxANy/IiYsx2oZh6Bfb6vWDEYSsnWYxm3T74jx9+uISpblGFrjcFwhrZ71HW2s92lrbfdbYs7EodSfo82vLdMWsDFmtlml7DqnRO6CvPXNo2PEU75fssOob11bqytlZo1qupsunt6u7dLLTdybkq7XHP+IweKvZpBvmZevjK4tUmjG+oLfJ1h8M6z/fOKln9rcM2/aKykz9zTWVSkuY+NBwAsCASUAA2KCBUESPbG/QL7fXj3vGpUV5yfrSZeVaGgc7mpOlpsunR3Y06IVDraNO5B3K+ooM3b+mWAvOcULnQhSJGnr9eId+sa1eR9r6YrLOikyXPra8UNfPy55xM62NRzhq6Ld7mvSjTTVj2sE9LS/Fob+5pjLmKe4zSVtvQI/tatRT+5pH/V67XTZ9YnWxPrQ4b8zJ7ReSqGHohUOt+p+N1SM+EZmWYNP9q4t0+5L8CX2PZ8rFjwOhiB7Z0aCHt9drIDTy+hPtFt23qkh3LyuY9ie7zqWlx68tNd2Ds7JkuLS6JH1CgxoMw9CGqk794K2aEV8wY7eYdNviPN23qkhZcTazkHTqxF9LrzZVdylqSCXuBK0ty1Cyc2ICqgzD0LY6jx7eXq+ttZ4RL7cwL1mfuaREa0qm/wHW92vwDOjxXY166Uj7mYuoKjJdum5utq6dm6WC1NjPzuHxhfTSkTb9/mDriPt283KSdM+KQl05O2taX+QxlL5AWHsavWrw+FWW4dKS/JQJ/e6LRA1tqe3WM/tbtHEEJxyvmp2pP1lbFjczspyLYRg60NyrPY2DM7JVZCZqRVHapJ3IC0cNba/r1ktH2rXpZJc8AyEl2MxaXZKue1cWaXH+zAlZmw59GMMwVNs9oI6+oCoyXYSKToCWHr+eP9SqfU09clgtmpeTpOvmZk9aMNVECoSj6vWH1BMIqz8QkTvRptxk56TNsNbjD+l3e5r1+K7Gc15UbpK0ujRdty3K1WUVGRMWJBg1DP3grRr9Ylv9kO1SnVY9++nVExr0bRiGHtvVqEd2NJwZBEBIJYBYmg79FwAAgNGg/wIAAOIN/RfgwtLeF9DPttbr6f3No7p+Oj/FoctmZWp9RYYuKkg56zxYMBxVW19ALT0BtfYG1NLrPxMOFghH5XbZtDg/RSuL01SRmRhXQQCj1dzj17+/dkIbxxgAFEsOq1lry926anaW1paPLjAqEI6qucevUCSqjES70hNs0+L6qm5fUN9+pWrUg1TjVYLtVOjX7MHQr1hesxQIR/Xg5lo9vL3+vBNtYlBJeoJuvyhfN83PVopz+MGLuxu8+u8NJ3WwZfSDyZMcFv3NNbN1zZzRDUIdrXAkqi213XrhUJs2nuiclsGF0uCkurcszNUdF+Xr+YOtemxnw4gH1r6bw2rW/auLdM+Kokm9hr+tNzAY+NUwGIxRG4OB2MkOqxJs5vNORDzZyjNc+osrK7SyOH2qSzkjGI5qa223Xj7aro0nOs875qM4PUFrStK1pjRdy4vSRnV9TdQw1Nob0LG2Pv18W70OxDA84t0W5SXrK5dXaNE0uobRMAztrPfq0Z0Neutk14jDDEySFuen6LKKDF02K0Ol7vMPeI8agwGTHX1BdfqCOtTSq4e3N4xrrNRQspPs+sK6Ml0/L3ta9VMNw9Dmmm79akeDtp0jlOrdnFazLikbDANbW+4eUzBHty+oPxxu07MHWkYdbnMulVmJemBNsS6vzJxW7+v7HWnt1f9tqtWm6q5h2yY5LLplYa4+fFG+CtNGf922xWJWappLG4+165EttXr9aNu4+2GL8pJ136oirauYnoFrexu9+s6rVaMO5jgt0W7RjfNzdPuSvHOG+kSip74v+oPqPHVr8vr14pG2mIUySoPBSX96Wdm0CBR9P8Mw9Mcj7fq316rkHWUAl0nS9fOy9fm1pcob5rVFDUNV7f3a+a4w09EGfp1PRqJddy3N1+1L8idszNF4bajq0Df/cHRcf4tWFKXqq1dUqDLrvaFrbb0Bba3tPnXzTHjI8hWVmfrk6iLNzZk+Y+BPX4v83xtOjut70WE166uXl+u2xXlnHcOoau/Xk/ua9YfDraMOtxyNJfkp+tylpVpRnDZh2xiP2i6f/vLZQ6oeIox4KPkpDn3zhjlaVpg2bNtQJKp9TT16u7pb22q7dbStLyZBrilOqz64KE8fWZqvnOTpOebSH4roH146NuJQxvdLclj0ydXFunNpwVnjqsJRQ4dberWldvB93d/UM6b95JFYmJesP11frmsvemcy8ulyDmlzTZe+8fyRcf0t+uCiXP35FRVDHm9r8vr18tF2/fFI25j7M+9nknR5ZabuW1UUF3kkexq8+vsXj6rRO/K+VVaSXX9//RytKpnYYwUEgAGTIB4CwMLRwRRdk0kqTE2I6U7F6bCTH7xVc9bMOeN1WUWGvriuTGVxkAp5tK1PT+1rVk2XT06rRZVZiZqXm6z5OUnKSXaM+gRity+oA8292t/co31NPeOamWQkVpek6XOXll6wAx09AyG9dqxdj+xoGHUK70hlJdl159IC3bk0Py7DZ2LpQHOPvv1KlY7GKGRNkm5ekKM/W1+u1ElIWI2Fo219evVYu052+JSVZNeyojRdUpauRHvsvp+rO316eHu9/nC4TeFxHmHNTrLrk2uKdcvC3LgJsjMMQ7savNpe51F/MKKS9AStKU0f00HrkTjY0qt/f61qzCfDcpId+szFJbpxQc6EBM5M1MWPXb6gXj7Srj2NPUp2WjQ3J1lXzsoccWL3SEWihp470KIfvV2rjlHOFPJu2Ul2fe7SUt04P2fSQhfGo8EzoJ9trdPzh9reE6hjNklXVmbpnpWFMd9p3tXg0f9urNH+5p4xLe+wmvWhxXn6+KoiZSbGR0DKvqYe/e/Gk9rd+N7XbDWbdHFpuq6Zm6XLKjJi8h0djhp67Vi7frm9YVx/Bxfnp+gzl5RoVXHatLhQ7XwMw9DuRq8e29moDVWdQ/ZnF+Wl6Pp5WbpqdpYyxvHZCUeierumW78/2Ko3T3SO+W9gfqpTH1teoA8szI2LmRakwZkAHtvZqGf2t7wnlDnZYdVNC3L0ocV5Md23avL69dyBFj17oGXUF+dYzCZ9aHGeHri4eFwzpE22vkBYLxxq1e/2Np91AiHRbtHaUzOTXVzqjnl4TdQwtLexRy8dadOrxzqGPFG2oihVn1xTrBVF0/s7YjgNngFtr/OobSCsjCSHLqnIUKHLqugkXcXZFwjrqX3N+vXuJrX2vjPT59KCFN22JE9XVmYRkjsOhmFoX1OPHt/VqNePd5zzJM6ywlTdtCBHV83OjOm+Uix4BkLaVe/RiQ6fPAODAV99gbB6/GH1+sPqCYTV6w+dcxarZIdVV83O1PXzsrW0MHVCLmI5HQT95N7mc85WeS5ul003L8jRrYvyYhrS2BcI629fOKK3Tg5/wdHnLy3VJ9cUx2zbQwlHomrtC8hptZyZbRYAYoEBqAAAIN7QfwEAAPGG/gtwYWrp8euhrXV69kDreScmW5CbfCagoSLDxfmfUdpQ1aF/f+2EWt51fnykbBaTZmUmKjvJIc9ASN0DIXX7QuoNDD+gzXEq/OHq2ZlaW54xoZMFTRXDMPTy0XZ959XRD3CfSBmJdq0oStWyojStKEpTVpJdJzt9OtHer6qOwduJjv5hJ8RNsJm1rjxDV83J0iWl6RN+nfzh1l59b8NJ7aj3Tuh24o3NYtL6igx9aEnemK4ZOv05/f5bNWoaxQDJ0z6wIEd/ceWsc/4f9ociquro15HWPh1p69Oxtj7VdQ8oFIkqwWaR02ZRgs0sp/XUvc2iBNs7P4ejhjZWdZ5z4rPpwmk160NL8nTPisL3TKJ7vL1P336lSvuaxnZNbkGqU1+9okLryt1j+rtmGIb6g5HBa1v8YXn9IXkHwuo5de/1h+T1D/5e1z0Q0+CR6e7q2Vn68vqpC0YJRaLaVuvRy8fataGqY9ShDlazSUsKUrT6VCDYnOwkmU0mBcNR1XkGVNPpU03X6duAart877mudKJdNzdLX1hXNmw4y0QKhKN66UibHt/VqOMxGPhenJ6gdeUZctnN6ugPqqMv+E6Ajy807OS5E2FpQYr+9ro5KpriCXn9oYhePNymR3c1jimcw2o2aWVxmq6ozNT6WRlnrisOhqNq6Q2oucevlh6/mnsCaunxq+nUfWtvYEJCQSsyXXpgTYmunD29gsCqOvr147dr9foYgmVNki4td+vOpflaPcxE5B5fSDVdPtV2D35/vHGiUw0xCIR8vzK3Sx9fVajr52ZP2MSlo9HlC+p/Nlbr9wdbY7bOpQUpKkxLUKcvqM7+wdCvbl9w0sJsHVazPr6yUB9fWTRtxrK29Qb07VeO680RXE86FJvFpDuXFuj+1UVnAm+jhqGTHT7tqPdoZ71Huxu8E77/5bJZ9MHFubp7WcG0CVsLRw3936bhJ+wdKbNJ+uCiPF1a7taOOo+21HaPOYhpvC4pS9cnVxdrSUHqlGz/tFAkqn99tUrP7G+J2TqvqMzU16+plMNq1h+Ptuvpfc3aP0GBreezoihVn72kVBcVTu37+24bT3Tq7144Mu5QVZOkj60o1OcuLT1r/EeDZ0Cba7q1ubpLO+u9I77ufiwsZpOunp2pu5fHfjzoeLT0+PUXzxyKyXj//FSnvriuTPNykrSttltbaj3aXtc9oSF257J+dpa+dv0cLchPnfJzSIZh6Ofb6vXDt2pikhNS5nbpWzfN1ezsd8IZO/oCeuVYh/54pH3MY4JHakVxmj6xqmhajmkNhqP60duDkweM5b02SbpnRaE+v7Z0RDkKUcPQgeZebajqUFVHv0IRQyuK0nTXsoLzHuMmAAyYBNM5AKwvENajOxv0m91NZ3ZWTJJmZSVqcX6KLipI1UUFKaPeufAOhHS8vV/HO/r1wsFWHYlhiM/7mU3SLQtz9SdrS5U+DQeGd/uC+v6bNXrmwPk7626XTfNzkzUvJ+nUffJ7Qg3CkaiqOvq1r6lXB5p7dKC5Z8JCqIZikvSpNcX69CUl0+oA1bv1BcLaUedRTyCszES75uYkjSkwwDAMHWvv16aTXdpU3aUDzT2TdvCkINWpf71lvuZkJw3feApEooa21XVr76kQlMI0p8rcLpVmuMY14DoQjqrBM6Df7mnSk3ubJyTQzu2y6WtXzdKVlZnTruN42pHWXj24ue6cM6XZLCatKk7X5bMGL0AZy2fbOxDSgZZePbW3WRsmYDa2/BSHHri4RDfMn5iQqljwDIT0/MFWPbmvWXXnOMhc6k7QpWUZWlvuPufMfqPV5QvqB2/W6NkDLTH5XJe6E/T5S0t1RYw/x7G++LHLF9Qj2xv02z1NZ50MtJikVSXpunZuli6flTmm2VhOMwxDb1d363sbT+pkDA8QVmYl6kuXlWnNMCcvpkqDZ0APbanTC4dah000X1qYqntWFGptuXtcf7+Pt/fp+2/WjGgmmJFwWM26fUmePr6yaFxhThPpZGe/fvBmzYi+Lx1Wsy4tc+uaOYOzS472BIg/FNGzB1r1q50NY7o45nyWFqTos5eWanlRWszWGQuhSFQvH23XYzsbR72vYDFJK4vTdd284b9DTs/G2to7eDvS2qeXjrQNe9HZaKQ6rfrI0nx9+KL8abk/IunMTGGvHW8ftk+7tDBVty/O0xWVmWfNqjASwXBUG0906pn9Ldpa2z3uv30um0X3rCzUx5YXTusLOI+19emJvYOzpwyEhv/76bCataYk/czMZGMNqTUMQ0fa+vTS4Xa9fLRt1EFri/KSdf/qYq0d4wVgky0UiWpPo1dvnezSppNd55xBsjzDpZvm5+iG+dnvuVgulpq8fj2+azBMb6gTOKlOqz6wMFe3LY5tWNJk8fhCZy7YrenyySTJ7bJrWVGqFuenTFjwbzgS1SvHOvTYrkYdGuHMsU6rWVdUZuqmBTlaUZQ2JUGuA6GIdp8KGN5e59GxGM3ok5Ps0HVzs3XDvGzNyhr/gfyaTp8e3lGvFw6NLwh6eVGqbluUp8srM8cVdFfb5dNfPHNQNV3DXwCU5LDouU+vHtf+AwBMBwxABQAA8Yb+CwAAiDf0X4ALW6N3QL/Z3aTN1d2KGoaK0xO0tiJD68rdE3YO+UIyEIroJ5tr9audjecNsXBazZqdnaS52UmakzN4X57hOue1mKFIdDAQzPdOKNjgfVCJdquK0hO0piR9Wl8zEksd/UH98x+PjXuw+1ilJ9i0vChVy08FfpW4E0Z0PUmXL6iq9ncCwao7B+QPRzQ7K1HrZ2Xq4kkI/TqXbbXd+uGmmjFP3DtTLM5P0U3zs3X1nKwz4QfjEQxH9bu9TfrJ5roRhfi9W1GaU9+4brYMY3Cy7KNtfTrS2qeaLt+kjdWYbIl2i+5cmq+7lxWed/LkqDE4CfL/bKwecwjFpWVuffWKiiGvUTIMQ7XdA9rb6NWexh7tb+pRg9c/JaFEE81iNun6edm6KD9FP9tap6ae0YdXSoN/0z65plgfW144puspRysciWp7vUevHG3XG1Wd6olhKElagk3JDosavf5p8//NbjHprmUF+sSqYiU7J+96oM7+oJ7Y26Qn9jbH9Hri6cppNeuL68r04aX5kz4W8PR7/bs9zTELaTRJKs90yTMQVuc4Jo6PhfIMlz59cYmumj21Y9Tqugf047dr9Mcj7TG5ZrEkPUEfWZqvFcVpqu/2q/ZdYV+1Xb5JD6zNSXbok2uKdevC3Cm5NjQcNfTk3ib9cFPNpIeTTJacZIf+9LIyXTMna8o+y4Zh6NkDLfqvDSdj+j6nOK26dWGuGrx+7ar3TFngssVs0nVzs/TxlUWqyJy6UI8uX1Bff/6IdtR5pqyGyXBJWbq+sr5CpRmuSd+2xxfSXz13SLsaYh9InZlolz8cmfLvojUl6frcpSVakJcyZTVEDUM/2VyrBzfXxXS9FZku/c01s+UZCGlLTbc213RNWQjy4vwUfXJNsS4tc0/J9k/b1eDRXz97eFoHbo/HrRfl69Ori5QzRceN+wJh/b8Xj+qNqtiO9bdZTPriujI5bRa9fKRNO+u9E5LZMJR5OUm6b1WRLp+VOSV9uPc73t6nb/7haEzCn+dkJ+lbN84959+ZcCSqnQ1evX68QxuqOtVxjn2WojSnfnzXRco8x5hqAsCASfCzjSd0/ezMaZE0fVp/MKxf72rSIzsaRnTQPSfZoSX5KVpyKhCsIjNRFrNJkaihes/AYNhXe5+Ot/frWFvfqAcdx0Kq06qvXF6hG+dnT4uBy5Gooaf2NeuHm2rGdNA1J9mheTlJ8vrDOtTSq8AkzqQwnMtnZej/3TB3Wp1Mbenx61c7G/X0vuazgmaykuyak5105jY3J0m5yY6zPif9wbC213r0VnWX3q7uUvsUfI5Pc1jN+sa1s3X9vOwpq+H9PAMhPbO/Rb/b03Te2buyk+wqy3Cp1O1SWcapm9t1JgwjHImq0etXvWdAdd2Dt/ruAdV7BtTSE5i0DuTlszL0V1fN+v/Zu+/wts777OM3FjFIEAD33hKpvSXLe8h7xHGWkzR2mthpm9XMNk2bt0nb7NHs6SS2k2Y5sbO8hzxlLWtvieLeew8QOO8fHDEliiJBECCp7+e6cIEHBA4eQeA5v3PO89yPkuZQZ46jDV36ybaKKV+0N5ukVRnxunJkBotMz9kX67oHhnS8sVtH6rt0pL5bRxu6VBPGUJnJ5Pic+sCl4Q+pCpVhGDpY16WH99fq6eNNGjxfYtKI2BiLNuf5dElBgi7OT5hW6NpQIKjf7avVj7dVzDhFfCJLUuP0gUvztTE3PEnI4er8OFnw10RsFpMuzkvQdSXJuqwwUc4pdPLoGRxSZVufKlr79KdD9bN64nFjjlcfvrxAxalzI5Sxsq1PP9tRqSemEPx1prwEp965Lks3Lk2dckhD0DBU0dqnn++o1BNHG2dlO+2wmvXm1Rm6e0P2OTs3RFp9Z79+vK1Cjx5pCOkit9Nm1uWFibq2OFmb8xIUYzWrdzCgxq4BNXQPDN93Daixe0CNXYNq7B5QTXv/rM4CsG5kpoU1UZ5pob3Pr0cO1OmhfbVhqbViLCZdVpiozXk+dfYPjQV9jd4ieWHebjXr1mWpeuf6LGV5ox/0EzQMbStr1S92VYd08cDrtOnWZal648r0c878NRQIqrSlV8dHZl481tClE009s3LskuCy6R8uztVtK9LnTMjo4FBQz55s0u/31YU8A6M0fEFvfbZHVxYl6cqiRCXGxigQNOQPGhocCsofNOQPBMf93O8PamdFm5463jRhoOl0LUqO1Xs25eiqRXPjxOrrNfcMalvZcODXjoq2KddVZpN0UZ5PNy9N1RVFMwsoGnW4vkv/t7taz51omvZ+eEOOV3esTNcVRYmzFpwVqn5/QOWtvTr5us64p5p7J+0c47SZtS7bq4tyfdqU51Oub2qdficTrn1ESlyMblyaqluWps7qhduhQFCH6rpGAr/adLCua0aBWlOxKDlWN5Sk6LqS5GmH9R+u69T9O6v0wqmWsNZ0HodVNy1N1RtWpE27U8Irp1v1H48dnfKF6PdclKN/uiQvhFYCwNzCAFQAADDfUL8AAID5hvoFAGZfeWuv/nigXscau2Qzm1WQ5FJJapxKUtzK8TnnXN+D+cQwDD12pFFf23pqWoN6zabh8Qfp8Y5xffxHR2wZI1eKDUPjrhknuGxamurWuhyvChNdc6LPbzgZhqEXS1v1w1fKdap55gP85ov0eLtuWpqqm5amztqkdQ1dA/rPx4/ptarwD2xfCLxOm96xLlNvWZ0x5YnO2nv9+s5Lp/XnQw0hvafNYtI712XpPRflyGmzyB8I6lhDt/bVdGh/Taf213aqfYEO2h7lsll0+8o0vX1t5ljfmoGhoH71WrXu31EVcj/dbK9DH7+qSJcUTG/wfdAwVNPer6aeAXX2Damzf0gd/X519g+N3Pxq7x9SZ9/wY219/jk1dmwqrhwZy/K7fTXyT7dT3QiPw6p7NufqTavSQ+5b1zsYUFvfoDr6hj/jjr4hdfT51d7nV0f/8M+jj5e29ITc1mjIT3Tpw5fnq6ylVz/dXhny2JS1WR595vrFs9rHOWgY6ujzq7q9X386WK/HjzZMeezOfLYmy6N/ubooLBN8TkdtR79+ur1Cjx6e/tiS+ag4JU6fvLpQqzJnfzxEvz+gpu5Blbf26oevlOtEGIIi5oMV6fH66JUFWpER2VCfmo4+feGpk9q5wEOppOEQwxuWpOh9F+dGfMzJobpO/eufj0Rl7H80WMwmvW1Nhu65KDdiQaOnW3r0sUcOR2z8brRtWZysD12erwzP9Pq0z1T3wJA+89gxvRyl8PRI27I4Wf96TVFUxlz+YX+tvvpc6YIMjX49h9Ws912cq7evzYxoFkx5S68++eepTaQ+n+X4nLp7Y7ZuXpoalXPGgaCh/9tdrR9uKw/rcaDdatbHrizQG1ema2AoqB0Vbdp6slkvnW6dUq7MlUWJ+uoblp31OAFgQARc+40XZDVJ/3lDcVTTeaXhg8+H9tXqwV3VMzpxGxtjUabHoYq2vrCfXLSYNKOTDptyvfrUlkVRHXR/qK5TX37mlI41dketDZPxOKzK8bl0sC70QeqFSS597Q3Loh5uUNrco1/sqtITx5qmVUR6HFYtHgkE8zpt2lnRpj3VHbM+WHe63rEuUx+6vCCqIQfHG7v1u701evJYU8h/7x6HVXF2q+o7+8N+UtFhNU8pZOhMcXaLPnJFgW5bnhbVi9XTDf46l0XJsbqqKElxDquO1nfpSH2XKsIQBjFTVy1K0qe2FE0rOCucegaH9PiRRj18oG7GybwmSUvT3LqkIEHJsTHq9QfU5w+ozx9U32DgdcsB9Q0GVNc5cM6wvHC6ON+nT1xVdM5wlqmaaefH6QZ/TcRhNeuywkRdV5ysjbk+NfcMqqK1dzjsq61XFa3DwYETpR7PJpOkv1ufpX+8JC8iszdNpKK1dzj462jjjGddSnDZ9NY1GXrTqgx5ncMnf/r9AVW196m8tU/lrb0qb+lVeWvvrNR65+J12vTZG4qnfXE8nNr7/Lp/R5Ue2lcTtouNLptFZrOiPsPCqC2Lk/XJawojvl0ub+3Vb/bU6K+HG+Zd54TpsppNese6LN2zOWdKoYbhNjgU1BNHG/XL3dUqa+0Nyzo35nh1x6p0pcc7dKyhayTsq1unmiPfCSLX59QHLsvXlUWJUavhajr69PD+ev35UP2sdIgySRGfWWFUrs+pv9+Uo+tLkqMWYj4UCOp4Y7deKWvVy6dbdbRh5sfVbrtV1xYn65ZlqVqe7p7WdydoGHqptEX/t7tae2tCP4YeleCy6bblabp9ZdqEIb6zbSho6ERjt/ZWd+hQXadONvWoqr1vxvVFmtuuTXk+XZTr04YcrzzOqV9gKmsZ3kc8eiT8+4hlaW7dsixVNy9LDcs2ua13UE8ca9L28lbtre5Qnz86+zSTpLXZHl1XkqJEl234mGTkWGRgKPi34xR/QP3+gGo7BmZ0DmiqlqW5dduKNF1XnDxp51XDMPTAzip9/+XyKW/v4uwWPfKejXMmMBYAZoIBqAAAYL6hfgEAAPMN9QsAYCFo7BrQj7aV64mjjWN92ZJiY5ThcSg93q5Mj0MZr7ulxtnn1IT1c03QMPT0sSb9aFu5qtrn3mBsq9mk1VkeXbM0TTaLSacau3WstkNlLb3qmOJk9C6bRdcsTtLNy1K1JssjcwT6VgWChn6xq0o/3Fax4AcET4XFbNLSVLeuL0nWbSvSQu6rsr+mQ1985qRKm0Pr/5cSF6Msr1OH67sWfH/NUQkum+5cm6k3rUpXvGPiviXN3QP6wSvl+suhhpD7511WkKCPXVV4znFU/kBQRxu6tb+mQ3urO3SgtnPKf8PzzfJ0tz58ecHYxMTV7X369otl2nqyOeR1Znsd+uBl+VOahL6rf0i7qtq1o7xNOyraFmTQhsdh1fsuztMdq/42cW5b76B+8mqlHt5fG9LYLKfNrA9eVqA3r06f9n5icCio2o7hQLum7sGR24CaewbV2DWo5pHH59q4vEixmKS3rsnU+y7OnXLw43S19Q7qSEO3joyMGdte3nZBft43LU3Rhy7LV1KcPaTXDwWCqmjrU0PXgJpeN6H76OTuTd0D83rbbTZJmR7HjGre64qT9YHLZifUp6t/SBVtw+OVylv7VNHaqx0VbVHrjxstVrNJt69I03svygn5u3ymoGGoe2AkcLRvOGS0YyQIs76zX7/bWzutbYbdao5qLWk2Senxjhnv431Om/7p0jzdtjwt5OCZoUBQ7X1+tfb61dbrV1ufX629g8M/jyy39Q7qVHPPtL7LG3K82rI4Sd96oSzkoNpoi7GY9PZ1WXr3xuxZ2/8ZhqG2Pr/qOwdU09GvH75SrsppjJuO5viccPE5bfrUliJdvTg5Iu/nDwT1tedK9fCBuoi8XziYNDw2tW0GY7wWJcfq369dpGXp4QvC7B4YUmP3gBq6BtTYNVxrNIwsH6jpnNbfvtdpU5zdouo5eF5rKhYnx+pfrimalTDXoGGoZyCgroEhdQ0MqXtgSF39wz//+VC99oVhLNi5LE6OVWVbX0hj6n9917qzQoQJAAMi4NpvvKCTjd2yWUz6h4vz9M71WREP8xkYCurhA3W6f0elWnvn5owNFrNJb16VrvdelKPtFW36wcvlqusMLTjFbjXrHy7O1dvXRfazbusd1PdeKtefDtVH7D2nIyPerneuz9Kty4cvJuyv6dDPdlRqW1lbSOvzOKz60q1LtT7HG96GTsH+mg49sLNqxqFJs8Fskq5ZnKy7NmSp3x/UL3ZX68XSlpDXtyHHqy/cvCSig1yHAkFtPdWi3+2tmdXCZiZcNov+4ZJc3bEyXb96rUb3ba8IKYBifY5X/35t5EMDj9R36SevVlwQSdNep03/ek2RthRH5gBTGg6ue3h/nZ442jhvT4BMh81i0l0bsvXujdlyhHixONTOj+EI/povFiXH6r9uKlFRBANdy1t69dMdlXrq2MyDv85kt5q1MiNetR39qu3onzMns96zKVvvuzgvooneff6AfrOnRg/srAp5VqT5xOu06ZNXF+ra4uRZC1DqHhjSa1Xt2lXZrp2V7SprCU8Q1XyS6rbrY1cV6qoIBFWNzhL3zIkm/XZvrVoiHJQYDeuzPfqP6xfPaoDS8Cxkfapq71dVe5+q24eDKA/Xdc2ZbeZsyYi3666N2bp1Wdqsh192DwzpQO3wDJMHajp0qK5rVmuKXJ9T1y9JUWJsjCym4XMRZpNJFpNp+Gezaezx2o4B/XZvzbQuEE2VSdJFeT69c12WNuX5wr7+Uf3+gA7Xd2lvdYf21XToYG3XrNfHJklL0tzK9jrkDxjyB4IaChryBw0NBYLyB4zh5UBQg4FgRC5AJLhseteGbL15VXpI9XJzz6B+satKf9hfd8F0jJwJu9WsaxYn6bblaWd1Ku73B/RfT57Q08ebprXOb9y+TJcVJoa7qQAQFQxABQAA8w31CwAAmG+oXwAAC4k/EFTPQEAOmznk/qH4m6FAUH893KD7tleqIcRJfmNjLEqMjVF9Z/+MJhrNiLdrc36CNuclaH2OR/HOmLNqmKGhgNr6/Cpr6VXZyMSuo/eN3YOyW81ak+XRzUtTdWVRYtS+I4fru/SZR4/OyXC1iThtw2EGM+2Xa7OYtCzNrbVZHq3N8mplZnzYJg0dCgT16z01+smrFRdcIMZ0ZHsd+rv1Wbp5WZrsU+zndrShS9/YWhryOJmYkX77d2/M1lDQ0MG6Tu2r6dS+6o4LInQty+vQBy7N1zWLJw7peq2qXV/fWjqjydtXZsTrI1cUaEXG3wbeDwWCOjwSeLSjok2H67vC3rd+rrCYTXrL6gzdc1HOOSfiLGvp1bdfPB3yeKR12R595jx9cANBQ8cbu7Wjok3by9t0oLZzwYZN2SwmXVGYqPR4hx4+UDej8QSJsTH68OX5unFJyoz6j3cPDOnYaNhXw3DgV6jjbOeKGItJ1yxOVv9QUM+fbJ5RX+zYGIvee1GO7lybKdsUQnD7/QFtK2/TM8eb9PLpllndt+YlOFXeGv5+x+eTEhej21em6w3L05QcF6MnjzXpOy+eVmN3aOMKYiwm3bk2S3+/KbRQn8auAZW29IyFfI0GfoVjnEOc3aJgUFEdL5gUG6PugaEZ93l3WM26c22m7tqQLbdjap/zwFBQ+2s6xvaHzd2D6ugfUme/P2z7xmVpbn3p1iWqaO3T158vjdh4oEXJsVqf7dW6bK/WZnnkdlhnPAZ+VHFKnD5+VeFYeOlkegcD2l3Vru3lbdpZ0abKtr6wjx9586p0ffyqQlktZlW29ek/Hj0alonTJzMaqpbjcyrH51S216lsn1OH6jr1q9dqZrT/S3DZ9I+XhBa0ZhiGGroGVNXep/rOAdV3Daihc0B1nf3DP3cNhFxjJ8XG6Eu3LlFVe5++9lxp1MYMep029Q4OzegcgjQc0PjJq4tCyhoY/Zxbev3q6verayCgrn6/OvuHhn8e8Kurf/i+pqN/WmM8rGaTPnR5vkqbe2YUuDxdqW67LsrzadPIJPZxdqsePVyvH2+rCHn/Z5L0ltUZ+qdL86a1/wsahg7UdOrF0hadbOoZDvzqHgjbd25Japy+cttSuR1WffW5Uj16uCEs642Gm5em6IOXFygpNmbar+0eGNLjRxu1q7Jd1e19w0FfA0PqGQjMy3F+b1iepv+4fvG4xwgAAyJgNABs1PJ0t/7zhmLlJbhm/b0Hh4L606F6/XxHpZpC3FlFwuWFifrQ5fnjPpPBoaAe2lern+2oVGeIqdklKXH6j+sWqzg1LlxNnVAgaOiRA3X6wSvlIbd1NhWnxOmuDVm6enHyhIFoxxq69PMdVXouhJkWLCbpY1cV6S2r0yMSbPDK6VY9uKtqToZSxVhMumVZmv5ufZayfeNPgpa39upXr1Xr0cMNIRXp6fF2ffW2ZbP+XW7tHdQjB+r08P66kAvcSNiyOEkfvbJQKe6/JZyXt/Tqv586oQO10/9u2K1mvf/SPL1tTeasBs4EDUNH67t03/bKeRP8NXpRLM5u1U9erZjRScgti5P0L9cUyeeafmF+Pg1dA9pX3aG9I7PlnJ6HQTMr0t3qHwrO6GJXerxdH7+qUJcXTj9sZrqdH+dj8NfmPJ9uX5muh/bWaHdVR0jriLGY9IHL8nXn2sywz45mGIZqO/t1uK5Lh+u7dLiuSwfrOufdxcmUuBhlep3aWx3aZywNhzP+z00lSgzhQH4qDMNQa69fpc09OtrQrV/vqZl3gUnrsz06XN81o4tPVxQm6lNbisIyY0i/P6ADtZ3aVTkc+nW0YeFeWJ+uzXk+feLqIuX4whNU1e8P6FRzj0409ehkY7dONPXoVFPPBRF4eSaXzaJ/vrJAb1yRNqNjker2Ph2o7VRVW99I0Ndw4NdcPLaLtJS4GL17U47esDw8QWCGYaimo3848KumUwdqO1Xa3DMvT7SG00W5Pv3zFQVnzdwQis5+v/bXdGpfTYf2VnfqaEPXgu10E4oEl013bcjWm6YYBEbw18xlehy6ZVmqblmWKkPSJ/54WCemccxjt5r1H9ct1g1LUmavkQAQYQxABQAA8w31CwAAmG+oXwAAwPkMDAX18IE63b+jUq29/gmfYzFJOT6XipJjtSg5VoVJw/dpbrtMJpMCweHBsxVtvapsHZ5YcPjWq7rOgbP6w9itZq3N8oyEfvmU63OO6/M03RpmKGjIJEV0stXJ9A4G9LXnTukvc3QQqs1i0qUFibppSYouKUhQ0JAq23p1urlXp0eC1cpaelTV1qdzDfeIsZi0IiN+LPBrebp71kPXGrsG9O0XT+vJY9ObaC3aLGaTnDazugfC36/R47BqZUa8blqaqqsWJYX0N2AYhp490axvv3g65EAdt92qnsGhC6avrMdh1T2bc/WmVennDdsJBA395VC9fvBK+Tm3sVOxZXGS1mR5tLOiXbur2qMW2JASFxOx8VWX5CfoI1cUKC9xamNOd5S36ZsvnNap5umPQXHazPrw5QW6Y1X62LiI+s7+kcCvdu2qbFPHAu9Huyg5Vm9Ynqbrl6TIOxK21tnv169fq9Gv98wsCGVNZrz+5ZpFU+oXGjQMnW7u1b6aDh2o7dSR+i5VzMLEtdGS7XXojlUZumVZ6tjnXN7Sqwd3Venxo40z6uOal+DUJ64qmnAi3oGhoF4ta9UzJ5r0Yunshn5JUobHoU9cVajLChNV2danP+yv1V8PN8xqf3STpEsKEvTGlem6OD/hrHHDff6AHtxZpV/srg65D6zXadP7Ls7VG1emTzgueVRzz6Beq2zXrqp27a5sV03H7ATDXlGYqH/dUiSzyaT7Xq3QIwfqzlm7hVNibIzWZ3u0NturdVke5fic6ugf0h/21+q3e2rV1hf6/k4arivu2pClO9dmnlVfGoahstZebS8fDmTcU90xq32a37QqXR+7snBs7MBQ0NDD++v0423lYd8v5Ce4tC7bo/U5Xq3L8k4aanR0ZAz81hDGwL/etcXJ+vDl+UqLd4w9ZhiGTjT1jHzGrdpXM3uhlxaT9PGri/SW1RnjHvcHgvr+y+X65e7qsLyP02bW1YuStCg5TtkjgV+ZHsc567iOPr9+ubtav91bM6Pt5aLkWH3kigJtzJ18gvT2Pr92VbZrR8VwyNpshFyuzIjXl29dMjZmrq6zX5974rheC3FM6XQ4rGatzfZoQ45PG3O8KkqOVXufX3/YX6ff76udUY2c4LLpX7cs0tWLks773NFQqqePN+m5k81qnoWxkwkum75861KtHgnXO97YrW++cFq7K9vD/l4um0Xrsj26KM+njblnn1cZ1e8P6KF9tbp/Z1XI++HkuBh94uoiXVV07nHao9uOp4416qljTaoPMWj+fG5dlqp/3bJoXOj1E0cb9aVnTs76MZJJw0FrGR6HMj0OpXscOljbqVfLZxbKGBtj0fsuztVbV2fIOoUw11NNPXpoX60eP9owqzXdmsx4ffiKAt33aqVeKZv9DAi71ay/3rtp3P6PADAgAs4MAJOG/yD/6ZI83bk2fGEzQcNQW69fDSOJplVtfXpoX+2s7TDCYXFyrD56ZaHW53jP+Zyu/iHdv7NKv9lTHVJwksUkvXN9lu7dnDvlk+v+QHB4p2dIxusuvYz+9PotV2Vbn76xtVTHGmc3XTcUm3K9eteGbG3M8U5pQPzplh7dv6NKTx5rnPZJ6NtXpOlfrimaUmr6dAwMBdXR59fuqnY9uKtKpc1zL9QnNsaiN6/O0J1rM8+bONrSM6iH9tXq9/tqp33AO91Br4ZhqL5rQNXtfeodDKjXHxi+Hwyozx9Qz8j96GM9gwEdrOuUPxJnPUKU5XXok1cX6eL8hAl/HzQM/X5frb73UnlIIRjL0tz6j+sXqyhpesVRnz+gxq4BtfQOqqXHr5aewb/degfV2uMfuR+MyEmlcFiSGqe7N2bryqK/XRQLBA09eaxRP95WEfKJOJ/Tpk9tKdLVi5NDblvQMFTe2qt91R3Ds+XUdMzrmSySYmP0oZEZPgxJTx9r0g+3lU8rJftMl+Qn6ONXFZ4VRjiZc3UcGAoaqm7v0+mWXp1u7hm+b+lRRWvfvAmTWJQcq3++vGDspL5hGNpW1qZvv3g65LC4jTle/ecNxeOCCKervdevww1dOjIa+FXfpfYZnvSNJrfdqndvzNZb12TIYbPotap2/XhbhfaEGASWFBujz99SorVZ3hm1q73Pr9MtPSpt/tt3uLS5Z15ekLRZTLppaar+bl2W8hJdausd1C93V+t3e0MP4nPbrfrYVQW6eWnqtAKUAkFDRxu6tKuyXTsr23WgpmPGMxHMFbExFl1XkqxblqUpzW3Xb/bUzHg2J9vIjG/v3pg9rQ4/o9uJE43dOtnUoxON3apq71sQHUZMkjbn+3TDkhS9crp1Rh2ULsrz6TPXLZ7WNtkwDG2vaNNPX63U/hACZC80KXExuntjtt6wIn3KMyWOau/168XTLdpWNnwxbL4FLkaK2SS9YUWa/uHivGkHYPb5A3rqWKP+dLBBh+o6L/hAtalIcNl098Zs3bFy4iCw5u4BPbirWg8fuDCDv9LcdjX3DIa13jdJctos0zpWT4mL0dduX6Ylqe6wtQMA5gIGoAIAgPmG+gUAAMw31C8AAGCqegcDeuJYo3ZWtCkQNJTpcaoo2aVFSXHKS3RNu5/MqH5/QNUd/aps65NhGPI6bVqWNnlY1EKpYZ4+3qQvPH1iVoKfQrE6M143LknRluJkxTvOHSgwyh8IqqKtbywQbDBgyG23akWGW8vS4kP+TszUa1Xt+tpzpSGF/ESCzWLS8jS31mR7tTbLo5UZ8XJYzWrt9au8tVcVrb0qb+1T2cjP5+v777SZleNzKdvrVE6CU7k+p7K9TmX7nGPhMeEwMBTUr16r1s+2V86bCbFfLy/BqcauwVmdQDbGYtKda7P07o3Zcjus03pt98CQfr6jSr/eUz2nxylNxGKSrlmcrDvXZmpFRrzqO/v1YmmrXipt0e6q9rCPo8hLcOqjVxaec6zUZGYauLYhx6uCRJd2VLSpvDXyoVM2i0k3LklRhseh50+2zPr4zHiHVTeUpOi25WkqTo075/M6+/36zZ7hILBQ92kWk/TWNZl638W5irP/7e+n3x/QkYauscleD9R2zup+02KSblmWpr/bkKUDtZ363d5aHZ/lz9likq4oStKbVqVrfY53LGTuTA1dA/rVa9V65EDdjMIcrixK1EevLFRibIy2l7fq6eNNeqm0NSITbMdYTHr3xhy9a0PWWbVevz+gp4836Q/763S4vits75kUG6PbVqTp9hVpSn9diNG51HX269svlOmZE6H3189PcOnDV+TrkvwEmUwmdfT5tae6Q7tHQr/KQhybNVVep02fvLpQ1xYnjxv3Ut7aq++/XD7jUKgzJbhsWp/t1bqR0K9zhcxIw//Pjx5p0P/trlbVDMYCSsNBY/dclKMrFyVpT1X7SChjW0SCKO1Wsz597SLdtDR1wt939Pn1k1cr9Pt9tSGPj02KjdGmXK825g6HIo0GM03HqeYe3b+jUk8fbwp5XI3datbdG7KV7XNqe3mrtle0R2SMg9tu1RdvXaJNk4RjvVreqs8+fjzkgKjilDjdsXI43DI2Znq1myS19g7qwZ3V+v3+2hn1ob+sIEH/fEWBchOGQ00HhoLaX9OhnZXt2lnRpmMN3bM65uHNq9L1sasKz8o7CBqGfrOnRt97qSysY+As5uHjoQ05w9/v5enuc2YtDA4F9eSxRv16T41OTmNS7TNdV5ysT15TdNbxkWEYOlLfpaeON+mZ402zuv0oSYnTV9+wdFyg3mgbXj7dqm+/eHpG9aVJ0tI0tzbl+XTReT7XiXT2+/Xgrmr9Zk9NyN/nywsT9cmrC8f9G6va+vTkSOhXWevs7f+sZpM+cXWh7liZPuE+qLq9T5957JgO1c28xoiNsWhDjlfZXqcyvY6RwC+n0tz2sUDI1zve2K0Hd1bpmROhb4slqTDJpU9eXaR12d6zfjcUCGrrqRY9tK9We0McnzxVNotJ/3RJnt6xLksWs0mGYeihfXX69ounZ3080fsvzdPfb8oZWyYADIiAiQLARq3OjNf/u754SgEdg0NBVXf0qaa9fyzk6/W3xu6BeXNCLCk2Ru+/NE83LU2dcgBafWe/frStQo8ebgipsMv0OPTpaxdpQ45X7X1+1XcNqL5zYOR++DMdXW7tGZy3A2YtJmlLcbLetT570hNRk6lu79MDO6v018MN0zopuTozXl++bakSXJMPVu7s96uspVfV7f1q6/Oro8+vzv4hdfQP/9zRPzR2P5cH2ibGxujtazP1plXp407ETUWfP6C/HGrQr16rnnaQ0jvWZepDlxeclZo+OBTUscZuHajtHLstlMH1NotJd2/I1t1TDM2o6+zXF58+GVKKrNVs0t9vyta7N+ZMWJgODgV1sqlbh+u7dGQksKeitW/ebjPOdFGeT3dvyNa6bM85T0wNBYL6y+EG3fdqRcgHgNeXJOsTV599gDmRzn6/TjX36HBdl/bVdGp/Tce8DO45k9Vs0jvWZek9F2WfdUJlKBDUnw/V677tlWoK8TOebtjMkGGoRyadauzRyYYuHa5uV2lzj8pbe+dNfXGmlLgY/dOlebpxycT1xlDQ0KOH6/XDVypCSjCPd1j1b1sWaUvx+QPtDMNQVXu/dla0aW91hw7Xd83ajBaRZrea9bY1Gbp7Y/aEnSN2V7brx9vKtbdm+gE7FpP0/kvz9a4NWVMKpzIMQ0cauvXCqWYdqutSaXPPjFLx54rYGIvetCpDd67NUPIEJ7jDcYL1kvwE/du1i5Q6SYBSc8+gtpe3altZm3ZUtM3abDRmk3TVoiSZTSa9WNoSkXrQJGlTrk+3LEvVFUWJZ203uweG9MiBOv16T03I22VJyoi36+NXF+nywsSzfjcUNFTa3KODtZ06WNepg7WdM74ANBelue26bUWabl2WOu4E6NGGLn3nxTLtCnGWB7fdqk9cXagbl6RMur0YDYG8b3tFWE5yzgc5PqdKUuL0/KnmGV+kSI6L0V0bsnX7irRJ64uGrgG9cKpZW082a091x4IIrYsUl82id2/K1tsnmMHpTKeaevTwgTo9dqQharMbRppJktViClt9mhg7HG73xpHvdHP3gB7YNdyZZC6fj5gty9Lcuntjtq4oSlR7n1+PHm7QHw/WqzIKswmuzozXl25dOu1APACYDxbK4A0AAHDhoH4BAADzDfULAACYjxZSDVPX2a//99gx7Quh32g45PicunFJim5YkqIs79QnU57rhoKG/rCvVj/cVh71gDW71awV6W6tzfJqbbbnvAF3Z+rzB1TZ2qfy1l5VtfcpEDSU4rYrx+dUjs+ppNiYaU1qO1P1nf361gyDUSKlKClWW4qTtGVxsnITXPIHgjpY16nt5cMBIeEIU7CYpByfS5cVJuotq9PPGtA/XdXtffrOi2V6LszhLLMh3mHV7SvSJ/13dw8M6dXyNr1wqlnbytrUNTD9/tRmk+RzxWhJapyuWZykG0pSZJ1GmMG52vXdl8r0h/11M1pPpHidNr15VbrevDpjXB+x2o5+PX+qWc+fbNa+mvBMiGqStCnPp9uWp+nywsRphTl29Q/pN3tr9OvXakL6v5b+NmFpc/eg9tV06mhDV9hD5CZiknTDkhTduzl33NhlwzB0oLZTv91bq+dONIUc5DORNLddt69M023L0yYc93AuHX1+PbSvVr/ZUxPyeDG71Syr2RTR/rSXFSToY1cVTqneONrQpT/sq9MTxxqn1T/VbJISXDFKcNm0KCVOlxcm6vKChJC2GXuq2/X150p1YgaBM6sz49XnD+pE4+yG97ze9SXJ+vhVhfJNMmZ5f02Hvv1imQ6EODF4gsumdSOBX+uyvMpNOHfg17kEgoZeONWsB3dVhzXwLRKyvA59+dalWpxy/vHopc09+ubzp7W94vzjdu1Ws9ZkeXRRrk+b8nwqTHSFrcarahsZA3+kQYF5MFghx+fUN25fNhaINZmWnkF97onjUx4b7bSZdX1Jiu5YlR62SZebuwd0/84qPXygLuR++xazSTctSVFT96D21nREpG9+jMWkf92ySLctT5v0eaXNPfrPx4/PKBAzx+fU5jyfNuX6tDbbM+3ANcMw9FpVh371WrVePt0a0jY1wWXTp7Ys0pVFiTrR1KOnjzfp6eNNqo3AmNXrS5L1H9ctnvQ4cCgQ1MMH6vWTVyvU3je18Z3p8XZtyvXpojyf1md75QlDAHRT94B+8mqF/nywPqS6x2kz630X58lskp481qQjEdjGJ8XG6Mu3LdXKjPhJnzcUCOrHr1bo/h1V0/4O2a1mXVaQoGtLUnRxnm9ax/Sjqtr69Mvd1frL4foZjfG5rjhZ/3xFgVLcdjV3D+iRA/V6+EBdSGO+p2tRcqz+68YSFSWfHbpV2tyjzzx2bEZhfeeTEhejP92zcay2IwAMiIDJAsCk4Q3khy7L11vWZMgwpPqu4Zk+Klv7VNXep4q2PlW29am+s3/eD5q1W826a0OW/m59tlwx098RSNLJpm59+8UybQ8h2Ge0DdEexJnjc6q+sz8sCbEWs0mLk2O1Ij1eyzPc2pTrO28A11QdruvUJ/98ZFohB6luu77+hmVanBKrpu5BlbX2qryld/i+tVdlLb3zOojDYjZpdWa8bihJ0Y1LU2c8g8tQ0NCPXinX/TurpvW69Tle/cvVRSpv7R0L+zra0DVvQ3omsyHHq3+9pmhKB7evZxiGHj/aqG9sLQ3p5F9+okv/fu0ixdmtY0FfR+q7dLKpJyInWkeZTZr1bf9ocOBdG7KndKJm1MBQUH88UKef7agM6e86wWXTp69dpCuKkiQN/z1UtfXpZFO3Tjb16FRzj0429aiha/IZfuajS/IT9NErC877ve73B/S7vbV6YFdVyEE76fF2ffyqQl1WmKjm7kHVdvSrpqNfNR19qunoH1ueSaDNXBMbY9HdG6cWnCENX0T+1WvVenBndUgzfNy8NEWfuLrorDDI5u4B7apq186Kdu2qbJ/17/KlBQl606p0vXy6VX893DDr9Y7ZJN26PE33bs6dNDRKGt4m76xs14+3VYR0Qv3ywkR99obiCWeOChqGDtR06rmTw0Ez9bP4OZsk3bQsVW9bk6H9NZ16+nhTyBcIpiI5bjhs9I0rpxY22twzqAd3VukP+2tDqjNjYyz65ysKdPuKNJlMJg0FgjpQ16ltZW16tax1Rhd6pvr+t69I19vWZozNRNMzOKQXS1v05NEmbS9vDesFRmm4Lr9lWapuXJIypQ4K/kBQTxxt1C93V+v0DGapuawgQf94SZ4augbGwr4O13fNaOai6TBJunJRkq4vSdYrp1v11PGmWd1mWM0mXVmUqDesSNOGHN85Q6ANw9D2ijZ958WykE+OXVmUqH+7dtFZx0SGYejF0lb9dHuFjjbM7qxVo1akx+vNq9NVkhqnl0tbtXUknDASUuJidF1Jiq4rSVZJSpxMJpOauwf0y901+sP+2hnPVpgUG6N3bcjSHSvTx/a1Fa29ev5Ui7aebJ53Fy3DYVmaW5cUJOiZ400z2j6MSnXb9cHL8nVdSfK4mc/6/QE9d7JZf9hfN6v7oLkmNsai25an6a1rMuRx2PT0iSY9erghbJ9BUmyMNuZ69czxprDO5iMNn0dYlBQrn8umeIdVbrtV7pH7eIdVbodN8fbh5SHD0HMnmvXE0YaIzNY16qI8n969MVtrs84OgjYMQ3trOvTHA/V69kT4P5+JvGlVuj4+waxLALBQLKTBGwAA4MJA/QIAAOYb6hcAADAfLbQaJhA0dP/OSv1kW8W0+v2lxMWoJNWtkpQ4pbrt6h8Kqt8fUJ8/oD5/UP1Df/u5zx8Y+V1QHodVRcmxurY4WcvS3BENj4q01t5Bff+lcv3pUP2svUec3SKPY7ivi8dpk8dhlcdhU6rbrpUZ8Vqa5p5wsvX5bndlu77y3CmVhaH/VzjlJ7h0bXGythQnKz9x8vEH7b1+7axs06vlw5P9TjZGwGWzKDfBqfxEl/ISXMpNcCk/waUsr2NW+u3sr+nQt144rYNzcPLWXJ9Tb1+XqZuWpso5jYHvQ4Gg9tZ06MXSVu2t7lBT94DsVrOSYmOUGBujpNgYJcWN3MfalRhrU1JsjHyumHP2pZ2pnRVt+u8nT8xqn/qZyE9w6e3rMnXjkpTzjvVo6RnUi6XD/VJ3VbZPOpbLpOG+9+nxDqXF25Ue71B6vF3pHoeWpLrlnWF4RPfAkH67t0a/eq1m1ibRDqdrFifp3s25KkyaPDygsWtAfzhQp0f216ltisEcr5cSF6OiVLcWp7p1xeJkLUtyyjSDLoZ9/oD+fLBeD+6qimgfyunK8Dj08asKJ5wU/Hw6+/169EijXiptUVuvX267RYkj24fR7Ubi67YhXqctrNuLQNDQnw/V6wcvl4f0fx5JSbEx+tSWRbqiaGqfs2EYeuFUi77zUtl5J75NcNm0Nmsk8Cvbq7wQAr8ma8femg79YtdwqM9cd1lBgj53Y8mE47XOxTAMvXy6Vd984fRZn/Xi5FhdNBKKtCrTM+Px1+dT19mvn26v1J8P1kcslG66NuZ49cVblyjeMfV9UdAw9OvXavTdl8rOuf8rTonTHSvTdP2SlGmHT01VfWe/fr6jSn86VD/ng9ZS3XZ9+balWpY2tRA0fyCo+16t0P07q6Y0fjs2xqINOV5tzvPporwEZXhmFpL7epVtffrNnhr95VB9SGN9UuJiIrbfNEn60OX5+rv1WVPebnb1D+nnOyr1m701Z2UgxMZYtC7bOxb6le11zNrxfHlrr374SrmePTG3g4lXZ8bri7cuVdI0JlLfXdmu//f4sfOOEbeaTdqc59N1JSm6rDAhbNuO5u4B/XpPjf6wvy7kMFanzay1WV5tr2iLyPbGbJLetSFb79ucO+n5jYGhoL7/cpl+9VrNrLXl8zeX6LqSFEkEgAERcb4AsFHJcTFq7/NHLcCnINGlt63NVGPXgPbXdOhgXdeMB4GbNJz+uyg5TmuyPLquJDks4VSGYejZE8366nOn5lWY1GgIy+WFiQoEDZU29+pIw3Co0NGGbp1q7jnvTikpNkYrMuK1It2tFenxKkmNCylVc6qauwf0yT8fmdZA9RiLSTaLOaKJ6bMpwWXTxfkJurQgQZtyfVMK4JiuZ0806XNPHI9Y6EO4zFagnt1q1oYcr96wPE1XFCXOqGBv7R3U154r1dPH5/5sLK9nMUk3LE3VezflyGkz64XSFj1/skW7qtrDUryaNBxydkl+gt68OmNGB5z9/oAe2ler+3eGFlK1Pser7v4hnW7pichg8nHvne3RHasydEl+gg7VdeqVsla9fLr1vCf6QpXtdehjVxXq0oLpneTt6h/SL3dX6Vev1YQc2GGzmKJWYyxPd+t9F+eqOCVOz51o1tPHm7S3uiPsJ9YsZpPetDJd92zOmXRmh3Np7R3Ufa9W6uEDddP+O0uPt+vT1y7SwJChXZVt2lnZHrGLv5cVJOiezbla+roTVG29g/r9/jo9tLd2Vk7KX1mUqPdfmn/ei8hnMgxDOyra9ONtFdO+eJvhcejLty5RSapbQ0FDe6vb9dyJZj1/qiUiad6XFSTo/Zflq+iMi2D1nf1jCfnhChXKT3TpXeuzdMOSlJAuoDd1D+iBGcy0sD7bozi7Vbsq2yNSz2V4HHr72kzdujx10hNHbb2DevZEs5461qi9Ic4M6HPalBZv14r0eF1XkqyVGfEh1RlBw9C2slb9Yle19lR3hNSWaLBbzbplWaresS5LOa+byamrf0iPHWnQHw7UhXXblZ/o0huWp+mmpSnT2i4HgoaeONqoH7xSHlJ4otdp079tKdLVi5MVHLmQd9+rFbMeYicNn3C8YUmK3rQqQ8UTBKs2dA3ohVPDgYV7qjvCGvTqc9p0zeIkXVeSolWZ8eNCo16vvdevX++t0e/21sx4JsgEl01XL0rSnuqOsIReTUeuz6lLChJ0eVGSLluappdONuv3r1Xr+eONEQvsNUm6oihR71yXpVWZw9sTwzC0v6ZTDx+oC0tY0tI0tz56RYG8LpseOVCnRw83hDy7WahMkjK9DpkkVbXP/mwwr5fhcejOtZm6dVnqhMfilW19evRIgx473DCnOiwtSo7VhhyvNub4tDorftoXRoKGob3VHXr8aKOePdE0K7O2mk3SlsXDQdDFqVMLgu7s9+vxI4165GCdSpvD/zdvNZv0yWuKdMfK9LCvGwDmkoU2eAMAACx81C8AAGC+oX4BAADz0UKtYQ7Uduq/nzyu8taz+2Znex0qTnGrOCVWJalxKk6JC6n/74XqcF2nvvJcqY6EMFljnN2iVRkercqMV2FS7HDA10jQl9thk3WWgonmg6FAUL/bV6sfb6uI6pioHJ9T1xYn69ri5PMG+JyLYRg63dKrnZXtauoakD9oKNvrGAv6So6LiXhYnmEYeu5ks77zYplqOiLbF20iF+X6dOe6TG3O852zz+V81D0wpG+/eFqPHJi9oMDp2pjj1TvWZ4X8WXf1D+mVslaVtfaqs8+vBFeM0uLtyvAMB36lxtlljcCEk90DQ/rRtgr9bm9NWPsAh8ulBQn6h4tzVZI6tfCTUYNDQT1zokm/2VNz1piIGItJOT6X8hKcykkYvs9LcCnH51S8M2ZW6pd+f0AP7KzSg7uqIj7ObDIxFpPevTFH79qQNavjeSOhe2A4jOXXe84OY5kLblueqo9cUTitUKpRQ4Gg/niwXr/bW6uy1uG+rj6nTeuyPVqb7dX6MAd+TeZUc49+vK1CW0/OvbAZs0n6x0vydPfG7JD3gYZh6FBdl8paeuVxWrU8PV6J0wisCafjDd362tZT2hfiWKPZ8tbVGfroVYUh19fHGrr07RfLtKuyXdJwWNK1xcl648r0ceMIZ1tNR5++/1K5npqjY7TXZXv0hVuWhJRfcbC2U5994viE44mXpMaNBX6tSHfPeq3R3D2gLz5zSi+Wtszq+4Qqzm7R529eoovzE0J6fXP3gJ472azq9n75XDatyfRoeQQ+1zPtrenQl545pdPNsz/GbLreujpDH7myIKTxpO19fn3h6ZNn7XPMJmlDjlfXFafoykWJ0wojnK6u/iH9fn+tfv1azZwOGs30OPS5G4u1KtMz5ddsL2/VZ584oZZpjGPO9TlVnBJ33m3ninS3fvaONZIIAAMiYqoBYNGS43PqfZtztaU4eVwa9FAgqOON3dpf26l9NZ3aX9MxadiWy2ZRUXKsFiXHanFyrIqS41SUFCtXzOwdzHb2+/XtF8pmdeaKcLBZTLprQ7bevTF70oP7fn9AJ5t6dHQkFKyxe1AWs0n5CS4tT3drZUa8Ut32iJ9gHRgK6ovPnNSjhxsi+r6huiQ/YexEyvGGLh1v7NGxxm6dauqe0kkfk6Rl6W5dnJ+gS/ITVJIaF5ETuaeae/TJPx1WdYQHVIdifbZHb12TqcsKE9U9MKTyll6VtfaqvLVXZS3Dt/MNvrZZTMryOJXtcyrb61SOzzH2c4rbHvbP/IVTzfrys6fOm2AbbRaTdOPSVL1nU46yXxfMMWr0pPXzp5q1rax1yqFxOT6nlqTGaWmaW0tS3SpOiQv79rmlZ1Bfeuaknj81Nw8wR8U7rLplWareuCJdeecIL6pq69PLZa165XSLXqvqmHGIhMtm0XsuytHb12bOaLaj5p5B/fDl2Z2xKZxGg78uyvWdte9q7BrQMyeGQ5OmEzJ5pniHVbk+pzbk+nTL0tQJ/26m61Rzj/7fY8d0MgJBMTNxRWGi7tmcM+nFmX5/QI8dbdT/7a6ecbCd3WrWxfkJetf6LK3IiJ/RugzD0J8O1uurz52a1gWZGItJlxcmaXdVu9ojdBJgVUa8PnhZvlZnnf+Avqqtb+x7Pdn3xyQpMTZGKW67Ut12pcTFjNzbVZwap1xfeC4m1Hf26+tbS+fsdnlNZrzevi5LlxcmTntWmvrOfj11rEnPnGhSRWufev0Bue1WpY58phPdUtz2WZk95GBtp76+tVSHQ+hEEylep01vXZ2hN69On7SD1Ghw0R9GgoumczHP67SpJDVOS1LjVJISp5JU94xntBgYCup3e2v08x1V6hqYfuDRlUWJqm7v16kInJTNT3TpzavSddPSiUOKJtLe6x+emexUs3ZUtIV08dTrtOmSggRdX5KsDTm+aV0Y6h4Y0kP7avWr12oitk2dCZvFpHVZXl1SMBzOnOUd3uefeXLzVFWbHj1cr0cPN8xa6Jvdataty1L19jPC9M7U3ufXo4cb9PCBulkLmA03r9OmoiSXCpNiVZQUq6LkWBUk/u3cTm1Hv3ZUDM8OubOiPaS/zalYm+XR29cOH3NOZR8xGpj118MNeu5Es3r9ke18l+V1aEOOVxtyfFqf7QlrZ9SBoaBeKWvVE0cb9fLplhl3tIixmHTr8jT93fqssb+j6Rq9YP+ng/V6+nhTWD7vBJdNX7516ZRqLgCY7xbq4A0AALBwUb8AAID5hvoFAADMRwu5hgkahl4ta1Nle58sJpMKk1wqTomblYnZLzRBw9BfDtXruy+VT9oHLD3erlWZHq3OjNeqTI8KEl0LKuxoNjT3DOq7L5WFdRyVzWKSx2FTvMMqj8Oq+JGf4x02eZxWxTus8jptKkyKDVtf4rnKHwjq9/vr9NNXK2ZtQky33SqP0yrPyOc7fG+T1znc33htlnfG/Vznuh3lbfrvp06ENBlvOPicNl1WmKA712ZqUfLUJqmcL042desrz86doJkNOV794yV5WhmGcR6VbX0qbemVw2pWboJTaW7HOftxznb9Ut3ep28+f1ovRDkIJT3erqsWJektqzNC7nc5V9V09Om7L5brmRNzI9SnINGlj11ZqE15vrCsr6l7QDazWR6nNar71cN1nfrey+VjIU7R5nPa9N83l2hTbng+57nCMAw9fbxJ336xLGr7vlFuu1UfuaJAt61IC8v6Ovv96uwfUobHEdU6/kBtp/73+dIZjQUNt7evzdSHryiYUYjxUNDQK6dbtbuqXU6bWQWJsdqU641KQLVhGHr8aKO+vrVUnRGeOH4yeQlOfe0Ny5SbMPF47PnEYjErNt6h728t1Q+eL9XgHDj34nXa9LGrCnTjktQZr6u0uUdPHG2U1WxSerxDlxYmhBSONxP9/oAe3FWlB3bOrTBXSXrjyjR95IrCkPIV2noH9T9PnZw0pK8kJU5XLUrSVYuSlJ/oUiBo6I6f7VLteQKwf/6O1VqeHk8AGBAJczUALNPj0L2bc3X9kpQpFTaGYai6vV/7ajp0qrlHA0NB+Zw2LUqJ0+Lk2KgWjq9VtesLT5+ckwNqL8736RNXFYUlkCSaDMPQr/fU6FsvnJ6TCfUWk3RdSYru2pCtouSJd2xDgaDKW/t0vLFbxxu7dayxWyebuhUMSrF2i1ZnenRJfoI25/siXsyM6uz3698fPabt5W1Ref/JOKxm3bQ0VW9Zk6GiKcwg0jsYUEXbcBhYVVufAoahpFj7WNDXZCcAZ0tX//AsFn88OPfCk84X/DWRfn9AOyvb9fzJZr10unXsol2mx6ElqW4tTYvTklS3SlIjd4HUMAw9eaxJX33u1Jw6wJSGU3DvWJWuLYuTpzXTQs/gkHZWtOuVslbtre5QQ9eALCaTHDazXDEWOW3DN5fNImeMRS6bWY7XLWd6HLqsIDGkWQfO5WBtp77y7Ckdm4P1hTR58NdEajr69PSxJj11jtAkm8WkLK9TuT6nckdmCsn1OZXrc8nrmp3E6cGhoH60rVy/2FWtubbbu7IoUfdclKvi1KlfBAsahl4qbdEvd1ef9yJTbIxFea+bmWX0lul1hJQePpnjDd361F+PzMnwy4JElz5wWb4uK0gI6UR/WUuvDtZ2qqF7QE6bZSzkK9VtV1JsTMTS6UdPYH/1udI5EfBjt5p11aIkvWNdppZMc2ahczEMQ/6AMaOAxZkKGob+eLBe33+pbNY6IoQi2+vQO9Zl6ZZlqdOeZai916+/HmnQIxMEFyXHxag45W9BXyWpcUqZxZngOvr8+s5LZfrTHKvhLGaTrl6UpDetStfaLM+M/v3dA0M63tithq4BWc0mWS1mxVhMslnMsllMirGYZTObZbMO/2w1m+SwWeRxzPxiZJ8/oEcO1OmXu6vnXFhuSlyMLs4fDvzakOOb8ATrZJ0HTjR269EjDXr8SGNYZo9IcNn01jUZetOqDHmdU69BDMPQa1Ud+sP+Oj1/qnnG4bLhkhIXo9WZHi1Nc6soKVaFybFKdNmm/J0KBA0dbejS9vI27axo04G6LgVm8G+zWUy6riRFb1+TOa0650x9/oCePt6k+3dUqmoWa4yVGfF6w/I0bcj1Kj0+Mh3BOvv9eu5Es5481qjD9V1jgdAmSU6bRQ6beez4xDFyXOK0Dj8WZ7dqWbpbl+QnhHV2rd7BgJ490aS/HKrX3hA7My1JjdNXbluqtAh9jgAQbQt58AYAAFiYqF8AAMB8Q/0CAADmI2oYzERnv1+/2VMzPJHbYEA+V4xWZcRr1UjgV6rbHu0mzlv7azr01edKdXya/fYz4u1aneXRqkyPVmXEK8PjkMNqXtChXqHo7Pfr5zuq9Nu9NSFNjGg2ScvS4rUp16u12R4lx9rlcVrldthmFAKxkHQPDOmbL5wOex9cn9OmpLgYJcfFKDnOruTY1/0cF6OkOLsSXLYFHTY4GtDxrRdOq7U3cv30LSapIClWS9PcWpoapw05vqiNW41U/fJqeau+9lxp2McOW0zDE7unuu1KjhuecDwlLkYpIz+PfqdnYxLyuWZ/TYe+9cJpHYxgqE9ibMzYuKXcBJdWpru1NM29oPeVOyva9L2Xy3UkQpPPm6Th4FGnTR6HVQmuGK3IiNcdK9PDOs5xrhkNnnlwV7UGhmbnmMrjsMrnssnnipHPaZPPZVOCyyavM0ZZXodWZcYrNmZhfsZBw9BTx5r03ZeiE7TmsJqVHu9QcWqc7liZrjULdPLp5u4BfeHpk3rpdGu0m6LrS5L1qS2LFkyI9+vrl1ON3frXh/bptaqOWX9fs0lKio1Rysj40tF6Iy/BqbVZ3pACqea66vY+/e/zpycNzJpNFrNJ8fbhsO1FybF6y5oMrc3yzmidxsg28Ld7a3WwrlOxMRYtTo7VlYuSdGVR0oQh0796rVr/+/zpSdd7fUmy/ufmJQSAAZHwv08e07e2lka7GWPS3Ha996Ic3bIsNWLBA5HQ7w/op9sr9Yvd1TMacBou6fF2ffyqQl1emLigDjq3l7fq0389pq6BuRFsYLeadfuKNL1jXdaCmXkhEDT0w1fKdf/Oqmg3RZKU4XHoLaszdNvyVMU7ZidoJ9J2V7br80+fmBOBMxaTdNPSVL3nopwZzQJgGIZaev1y2sxz4uRAc/eAvvjMqagV5qNcNotuXJqiN65MV3HKwpoxJBA09MiBOn3/5fI5s01enu7WvZtztTlvasFfE6ls61NFa6/a+vxKio1Rjs+p9PjIBwaOeq2qXZ99/Ljqo5z8bzZJVxQl6Z6LcrR4ht/lg7WdeuxIg0qbe2S3WpSbMByslpfgVH6CS4mxsxfiM5Gu/iH915PH9fyp6G4vRqW57fqHS3J145LUqH3vZkNb76C++lypnj4e2ZlZzCapJNWtDTlebcjxalVG/LSDqOaT9l6/vvtydIOqHFaz1mR5dPvKdF1RmDjj77FhGDrR1KOK1l65HVYtSo5TUhiDY6bjlbJWff6pE1EJqfI4rMr2OZXldSrb61BegksbcqIzo8dsGRwK6s+H6nX/zqqoXIgxm6TCpNiRjmcercqMV5rbft590lQ6DwwFgtpW3qanjjXqaEO3Ovr8ChrD9VTAMBQIGgoaxoRh1w6rWUtS43TLsjRdvyRlxhfxm3sGdd+rFXrkQF3Ew7VzfU6tzvJoTaZHq7PilRHvCOs+v3tgSK9VdWhPdbtqO/plGMOhXtaR0DqbxSSb2SyrxSSreTjcbvSx3ASnVmV4whruOhQ09OTRRt23vSKsx36rMuJ178W52pjjjer5HsMw1D0QUIx1ODBwLpx7qmzr018P1+uvhxumvK2+cUmKPn3togVdHwDAmRi8AQAA5hvqFwAAMN9QvwAAgPmIGgaYuwJBQ388ONxvf6JJ0k2SipKH+76tGQn9InRtemo7+vX9l8v05LHz93PO9Dh0UZ5PG3N92pDtXdABJuH0anmr/ufJE2oMoQ9uSlyMLsrzaVOuT8vS3UqOtUd14ua5pntgSD/aVqGH9tYohBy788pLcGpJ6nAw0tI0txYnx86Z/naRrF/8gaB+s6dG971aqV5/IOT1LEmN05bFybpyUZIyPdEbqzQXjU4+/92XylTXGZ6+5Bazaazv/ei4pbwEl3J9rgt2+20Yhp4/1aIfvFyustbeGa2rIHF4TEOGxyGPwyaP0zpyPxz4FWe3XtDf8frOfn3rhTI9cyL0cVRLUuN0UZ5PqzM9So6Lkc8VI6/DuqCyGULV7w/ol7ur9cDOKvWHMWgtMTZGaW670uPtSnU7lB5vV1q8XWluh9Li7Yp3WOdEv/lIMAxDjx1p1Ne3ls7K+GGnzSy33Sq3w6p4+3CIrttukdthU7zdqoRYm1ZlelSUFL0wotlwZv3S0tKtP+6v07dePD3h8d50pcTF6PLCRGV5ncNBXyMBo0lx9gs2pPiV06362tZTYR3XszIjXhfn+xTvsA1/j+1WxdktcjusY8v2WQ7hHgoaU/o/7R4Y0s0/2jFpDWsxm/TnezYq3eskAAyYbR0dvXq1tEX/9eQJ1XZEL2wmOS5Gf78pR29YnragT7KcaOzW558+GbEU5DPZLCbdtSFb796YPWdOpoRbZVufPv7HQypvDW9q+nR4HFa9ZXWG3rYmM6wDhOeSZ0806XNPHFefPzoX7jblevXWNZm6JD9hQR7o9/sD+tG2Cv3qteqID7yXpNgYi65ZnKS/3zSz4K+5bHQ2i689NzsHmBOxW81anu7W6kyPVmfGa3WmZ8Fui0e19Q7qey+V60+Hohc2szozXn+/KWdGwV9zWVf/kL7y3Ck9cbQxYu85GjayNC1ey9LdWpvlUWKUAnciwTAM/eq1Gn3nxdOzctFrMiZJWV6HipLjdEVhorYUJy/oGVq2nmzWl545OauzDOUnurQxx6v12V6tu0AvrB+s7dSXnz017RnfpislLkaLkuO0OCV2+D45Vlle54Ks3UZ19vv1tedK9fgsbJPtVrNKUuKU5RsO+cr2Dgd+ZXkdCyYIdyoGh4L66+F6/XxH1awGYLpsFi1Pdw/PMpnh0bJ0d0gzgoSz84BhGAoYUvB1wWAOm2VWTrKXNvfoWy+c1qvlbWFftzQcqLY4OW4k8Gs4VG0h1xKTGQoaevxIg366vVI1MzgvtyYzXvdsztWGKAd/zQdDQUM7Ktr0l0P1euFUi4YmOOg2m6QPXV6gd67L5PMEcMFh8AYAAJhvqF8AAMB8Q/0CAADmI2oYYO5r7/Pr8aON2lnRJqvZpNwEl1ZnxmtlRvwF1cdwNh2u79L3XirTrsr2scfi7BZtyPFpU65Xm3J9C3b8TSR0Dwzpf58v1Z8PNUz6PIfVrHXZXm3K8+miXJ/yEpz08ZqCk03d+sqzp7SvpjPkdficNq3KjNeK9HgtTXOrJDUupL61kRKN+qWpe0DffrFsWuN7ilPitGVxkrYUJ7MNmYKBoaB+u6dGP9tRqZ7B6YWtxVhMWpnp0YZsr9bneLUkNU42gpImFAgaevxog370SsWU++x7HFZtyvVp00goI4GjU7Onul1ff65UJ5p6zvvcBJdNF+X5xoIvExbQhPGzpal7QN9/uVyPHm7QdIcEWswmrUh3a2Pu8OddnBK3oMf0haqpe0BfePqkXj7dGvI6UuJitKU4WdcsTla21yG3/cINsjtX/dLaO6hvPn86pPFqHodV1yxO1nUlyVqT5ZGZ2vksA0NB/eq1av10e6UGQgwNtFvNumFJit6yOkPFKXFhbuHs+uqzp/S7fbWTPuc9m7L1wSsKCQADZltHR68GBwPqHQzoOy+e1u/310XkfR1Ws1LdduUnunRZQaKuK0le8CEoowJBQ7/dW6MfvFwe1uTYyZgkXVGUqA9fXqBs38I/EdA9MKTPPHZsRgVjKAoSXbp9ZbresDxNrpiF/30+1dyjT/7pcFhSTc0myWmzyBVjkWvkfqLl3ASnNuT4lHMBfI8l6Uh9l/77yRM61Xz+A/ipio2xKDE2ZvjmilFirO1vy7ExSoqNUWGi64I5QArHAea5eBxWrXpd2FfJBXxiMBJhM3arWQWJLuUnulSQGKuCRJdKUuOUHHdhnDB88mijvvTsSXUPhD5byEQsJqkgKVbL0tzDt3S38hNjL8hE733VHfr0o0fVFMLMQlOREW9XQVKsChJjVZjkUmFirHITnBdMjTyqo8+vbzxfqseOhCdAKdVt18YcrzbkerUh26ukC2SbcD6BoKE/7K/TD14pm/F2w2KS8hNjtSg5VotT4obvk2Plu4AvJjx3sllfevq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|
||
"text/plain": [
|
||
"<Figure size 6000x3000 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Data loaded successfully.\n",
|
||
"First few rows of the data:\n",
|
||
" NLOS RANGE FP_IDX MAX_NOISE FRAME_LEN PREAM_LEN CIR0 \\\n",
|
||
"0 1 6.18 749.0 3668.0 2 0 747.646447 \n",
|
||
"1 1 4.54 741.0 1031.0 2 0 123.353553 \n",
|
||
"2 1 4.39 744.0 796.0 0 0 439.646447 \n",
|
||
"3 1 1.27 748.0 1529.0 2 0 233.353553 \n",
|
||
"4 0 1.16 743.0 2022.0 0 0 82.353553 \n",
|
||
"\n",
|
||
" CIR1 CIR2 CIR3 ... CIR1010 CIR1011 \\\n",
|
||
"0 367.353553 744.646447 717.646447 ... 726.646447 367.353553 \n",
|
||
"1 470.646447 409.353553 332.353553 ... 491.353553 403.646447 \n",
|
||
"2 447.646447 130.353553 96.353553 ... 271.646447 73.353553 \n",
|
||
"3 239.353553 175.646447 532.646447 ... 225.353553 154.646447 \n",
|
||
"4 219.646447 110.646447 293.353553 ... 132.353553 131.353553 \n",
|
||
"\n",
|
||
" CIR1012 CIR1013 CIR1014 CIR1015 RX_Level \\\n",
|
||
"0 802.646447 818.646447 466.646447 767.646447 -33.465374 \n",
|
||
"1 334.353553 210.353553 102.353553 0.353553 -34.892422 \n",
|
||
"2 125.353553 169.353553 182.353553 0.000000 -38.436975 \n",
|
||
"3 171.646447 277.646447 317.646447 0.000000 -21.918230 \n",
|
||
"4 102.353553 126.353553 162.646447 0.353553 -21.603535 \n",
|
||
"\n",
|
||
" First_Path_Power_Level SNR Total_Distance \n",
|
||
"0 -39.074609 37.700000 234035.764530 \n",
|
||
"1 -53.179491 95.794118 127050.760471 \n",
|
||
"2 -49.812107 55.384615 115175.236492 \n",
|
||
"3 -25.863535 200.859375 101750.988607 \n",
|
||
"4 -23.232193 152.723684 119190.284232 \n",
|
||
"\n",
|
||
"[5 rows x 1026 columns]\n",
|
||
"Column headers:\n",
|
||
"Index(['NLOS', 'RANGE', 'FP_IDX', 'MAX_NOISE', 'FRAME_LEN', 'PREAM_LEN',\n",
|
||
" 'CIR0', 'CIR1', 'CIR2', 'CIR3',\n",
|
||
" ...\n",
|
||
" 'CIR1010', 'CIR1011', 'CIR1012', 'CIR1013', 'CIR1014', 'CIR1015',\n",
|
||
" 'RX_Level', 'First_Path_Power_Level', 'SNR', 'Total_Distance'],\n",
|
||
" dtype='object', length=1026)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pickle\n",
|
||
"\n",
|
||
"# File='data_original.pkl'\n",
|
||
"File = 'data.pkl'\n",
|
||
"\n",
|
||
"# Check if the file exists\n",
|
||
"if os.path.exists(File):\n",
|
||
" # If the file exists, load it\n",
|
||
" print(\"Loading data from pickle file...\")\n",
|
||
" with open(File, 'rb') as f:\n",
|
||
" data = pickle.load(f)\n",
|
||
" # plot_features(data, data['NLOS'], \"First_Path_Power_Level\", \"RX_Level\")\n",
|
||
" # plot_features(data, data['NLOS'], \"SNR\", \"RX_Level\")\n",
|
||
" # plot_features(data, data['NLOS'], \"SNR\", \"First_Path_Power_Level\")\n",
|
||
" snr_graph(data)\n",
|
||
" cir_graphs(data)\n",
|
||
" print(\"Data loaded successfully.\")\n",
|
||
"else:\n",
|
||
" # If the file doesn't exist, load and clean the data\n",
|
||
" print(\"Pickle file not found. Loading and cleaning data...\")\n",
|
||
" data = load_data(DATASET_DIR)\n",
|
||
" cir_graphs(data)\n",
|
||
" data = clean_data(data)\n",
|
||
" plot_features(data, data['NLOS'], \"First_Path_Power_Level\", \"RX_Level\")\n",
|
||
" snr_graph(data)\n",
|
||
" cir_graphs(data)\n",
|
||
" print(calculate_total_distance(data))\n",
|
||
" print(\"Data loaded and cleaned successfully.\")\n",
|
||
" print(\"Saving cleaned data to pickle file...\")\n",
|
||
" with open(File, 'wb') as f:\n",
|
||
" pickle.dump(data, f)\n",
|
||
" print(\"Cleaned data saved to pickle file successfully.\")\n",
|
||
"\n",
|
||
"print(\"First few rows of the data:\")\n",
|
||
"print(data.head())\n",
|
||
"\n",
|
||
"# Print Headers\n",
|
||
"print(\"Column headers:\")\n",
|
||
"print(data.columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 82,
|
||
"id": "12e16974341e6266",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.316699Z",
|
||
"start_time": "2024-03-20T11:36:26.312417Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"MODEL_DIR = './models'\n",
|
||
"\n",
|
||
"\n",
|
||
"def train_and_save_model(classifier, X_train, y_train, file_name):\n",
|
||
" if not os.path.exists(MODEL_DIR):\n",
|
||
" os.makedirs(MODEL_DIR)\n",
|
||
"\n",
|
||
" file_path = os.path.join(MODEL_DIR, file_name)\n",
|
||
"\n",
|
||
" # Check if the file exists\n",
|
||
" if not os.path.exists(file_path):\n",
|
||
" print(f\"Training the model and saving it to {file_path}\")\n",
|
||
" # Train the classifier\n",
|
||
" classifier.fit(X_train, y_train)\n",
|
||
"\n",
|
||
" # Save the trained model as a pickle string.\n",
|
||
" saved_model = pickle.dumps(classifier)\n",
|
||
"\n",
|
||
" # Save the pickled model to a file\n",
|
||
" with open(file_path, 'wb') as file:\n",
|
||
" file.write(saved_model)\n",
|
||
"\n",
|
||
" # Load the pickled model from the file\n",
|
||
" with open(file_path, 'rb') as file:\n",
|
||
" loaded_model = pickle.load(file)\n",
|
||
"\n",
|
||
" return loaded_model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b36814c942066d6",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"The selected code is performing data standardization, which is a common preprocessing step in many machine learning workflows. \n",
|
||
"\n",
|
||
"The purpose of standardization is to transform the data such that it has a mean of 0 and a standard deviation of 1. This is done to ensure that all features have the same scale, which is a requirement for many machine learning algorithms.\n",
|
||
"\n",
|
||
"The mathematical formulas used in this process are as follows:\n",
|
||
"\n",
|
||
"1. Calculate the mean (μ) of the data:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\mu = \\frac{1}{n} \\sum_{i=1}^{n} x_i\n",
|
||
"$$\n",
|
||
"Where:\n",
|
||
"- $n$ is the number of observations in the data\n",
|
||
"- $x_i$ is the value of the $i$-th observation\n",
|
||
"- $\\sum$ denotes the summation over all observations\n",
|
||
"\n",
|
||
"2. Standardize the data by subtracting the mean from each observation and dividing by the standard deviation:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\text{Data}_i = \\frac{x_i - \\mu}{\\sigma}\n",
|
||
"$$\n",
|
||
"Where:\n",
|
||
"- $\\text{Data}_i$ is the standardized value of the $i$-th observation\n",
|
||
"- $\\sigma$ is the standard deviation of the data\n",
|
||
"- $x_i$ is the value of the $i$-th observation\n",
|
||
"- $\\mu$ is the mean of the data\n",
|
||
"\n",
|
||
"The `StandardScaler` class from the `sklearn.preprocessing` module is used to perform this standardization. The `fit_transform` method is used to calculate the mean and standard deviation of the data and then perform the standardization.\n",
|
||
"\n",
|
||
"**Note:** By setting the explained variance to 0.95, we are saying that we want to choose the smallest number of principal components such that 95% of the variance in the original data is retained. This means that the transformed data will retain 95% of the information of the original data, while potentially having fewer dimensions.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8fefd253728ea2f0",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"## Data Mining / Machine Learning\n",
|
||
"\n",
|
||
"### I. Supervised Learning\n",
|
||
"- **Decision**: Supervised learning is used due to the labeled dataset.\n",
|
||
"- **Algorithm**: Random Forest Classifier is preferred for its performance in classification tasks.\n",
|
||
"\n",
|
||
"### II. Training/Test Split Ratio\n",
|
||
"- **Decision**: 80:20 split is chosen for training/test dataset.\n",
|
||
"- **Reasoning**: This split ensures sufficient data for training and testing.\n",
|
||
"\n",
|
||
"### III. Performance Metrics\n",
|
||
"- **Classification Accuracy**: Measures the proportion of correctly classified instances.\n",
|
||
"- **Confusion Matrix**: Provides a summary of predicted and actual classes.\n",
|
||
"- **Classification Report**: Provides detailed metrics such as precision, recall, F1-score, and support for each class.\n",
|
||
"\n",
|
||
"The Random Forest Classifier is trained on the training set and evaluated on the test set using accuracy and classification report metrics.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7d64d6490fa1c2c2",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Split the data into training and testing sets\n",
|
||
"\n",
|
||
"The next step is to split the data into training and testing sets. This is a common practice in machine learning, where the training set is used to train the model, and the testing set is used to evaluate its performance.\n",
|
||
"\n",
|
||
"We will use the `train_test_split` function from the `sklearn.model_selection` module to split the data into training and testing sets. We will use 80% of the data for training and 20% for testing, which is a common split ratio."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 83,
|
||
"id": "54d2a6506b584a03",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.322428Z",
|
||
"start_time": "2024-03-20T11:36:26.318009Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.model_selection import train_test_split\n",
|
||
"# from tensorflow.keras.utils import to_categorical\n",
|
||
"\n",
|
||
"# Assuming 'NLOS' is your target column\n",
|
||
"# y = data['NLOS']\n",
|
||
"\n",
|
||
"# Convert labels to categorical one-hot encoding\n",
|
||
"# y_categorical = to_categorical(y, num_classes=2)\n",
|
||
"\n",
|
||
"# Now split the data\n",
|
||
"# X_train, X_test, y_train, y_test = train_test_split(data, y_categorical, test_size=0.2)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ab55160e30fd6f99",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Train a Random Forest Classifier\n",
|
||
"\n",
|
||
"The next step is to train a machine learning model on the training data. We will use the `RandomForestClassifier` class from the `sklearn.ensemble` module to train a random forest classifier.\n",
|
||
"\n",
|
||
"The random forest classifier is an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.\n",
|
||
"\n",
|
||
"We will use the `fit` method of the `RandomForestClassifier` object to train the model on the training data."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 84,
|
||
"id": "dc485f3de9f8936f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.327125Z",
|
||
"start_time": "2024-03-20T11:36:26.324423Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.ensemble import RandomForestClassifier\n",
|
||
"# \n",
|
||
"# # Initialize the classifier with parameters to prevent overfitting\n",
|
||
"# classifier = RandomForestClassifier(n_estimators=200, max_depth=10, min_samples_split=10, min_samples_leaf=5, max_features='sqrt')\n",
|
||
"# \n",
|
||
"# loaded_model = train_and_save_model(classifier, X_train, y_train, 'random_forest_classifier.pkl')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "424cc5954c9e81cc",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Evaluate the Model\n",
|
||
"\n",
|
||
"To evaluate the performance of the trained model on the testing data, we will use the `predict` method of the `RandomForestClassifier` object to make predictions on the testing data. We will then use the `accuracy_score` and `classification_report` functions from the `sklearn.metrics` module to calculate the accuracy and generate a classification report.\n",
|
||
"\n",
|
||
"- **Accuracy:** The accuracy score function calculates the proportion of correctly classified instances.\n",
|
||
"\n",
|
||
"- **Precision:** The ratio of correctly predicted positive observations to the total predicted positive observations. It is calculated as:\n",
|
||
"\n",
|
||
" $$\n",
|
||
" \\text{Precision} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Positives}}\n",
|
||
" $$\n",
|
||
"\n",
|
||
"- **Recall:** The ratio of correctly predicted positive observations to all observations in the actual class. It is calculated as:\n",
|
||
"\n",
|
||
" $$\n",
|
||
" \\text{Recall} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Negatives}}\n",
|
||
" $$\n",
|
||
"\n",
|
||
"- **F1 Score:** The weighted average of precision and recall. It is calculated as:\n",
|
||
"\n",
|
||
" $$\n",
|
||
" \\text{F1 Score} = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}\n",
|
||
" $$\n",
|
||
"\n",
|
||
"- **Support:** The number of actual occurrences of the class in the dataset.\n",
|
||
"\n",
|
||
"The classification report provides a summary of the precision, recall, F1-score, and support for each class in the testing data, giving insight into how well the model is performing for each class.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 85,
|
||
"id": "702b4f40dda16736",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.331098Z",
|
||
"start_time": "2024-03-20T11:36:26.328328Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"# Make predictions on the test set using the loaded model\n",
|
||
"# y_pred = loaded_model.predict(X_test)\n",
|
||
"# \n",
|
||
"# # Evaluate the loaded model\n",
|
||
"# accuracy = accuracy_score(y_test, y_pred)\n",
|
||
"# classification_rep = classification_report(y_test, y_pred)\n",
|
||
"# cross_val_score = cross_val_score(loaded_model, X_test, y_test, cv=5)\n",
|
||
"# \n",
|
||
"# print(f\"Accuracy: {accuracy}\")\n",
|
||
"# print(f\"Classification Report:\\n{classification_rep}\")\n",
|
||
"# print(f\"Cross Validation Score: {cross_val_score}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "41957f9babb74a3",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Visualize a Decision Tree from the Random Forest\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 86,
|
||
"id": "1f6f826d6234591c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.334924Z",
|
||
"start_time": "2024-03-20T11:36:26.332269Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.tree import plot_tree\n",
|
||
"# import matplotlib.pyplot as plt\n",
|
||
"# \n",
|
||
"# # Select one tree from the forest\n",
|
||
"# estimator = loaded_model.estimators_[0]\n",
|
||
"# \n",
|
||
"# plt.figure(figsize=(100, 100))\n",
|
||
"# plot_tree(estimator,\n",
|
||
"# filled=True,\n",
|
||
"# rounded=True,\n",
|
||
"# class_names=['NLOS', 'LOS'],\n",
|
||
"# feature_names=data.columns,\n",
|
||
"# max_depth=5) # Limit the depth of the tree\n",
|
||
"# plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eef3be2c3026a909",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Support Vector Machine (SVM)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 87,
|
||
"id": "c970b0c1593d955c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.338799Z",
|
||
"start_time": "2024-03-20T11:36:26.336101Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# import os\n",
|
||
"# from sklearn.svm import SVC\n",
|
||
"# import pickle\n",
|
||
"# \n",
|
||
"# svm = SVC(kernel='linear', random_state=42, verbose=True)\n",
|
||
"# loaded_model = train_and_save_model(svm, X_train, y_train, 'svm_classifier.pkl')\n",
|
||
"# \n",
|
||
"# # Predict the labels for the test set with each model\n",
|
||
"# y_pred_svm = loaded_model.predict(X_test)\n",
|
||
"# \n",
|
||
"# # Calculate the accuracy of each model\n",
|
||
"# accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
|
||
"# \n",
|
||
"# # Print the accuracy of each model\n",
|
||
"# print(f\"Accuracy of SVM: {accuracy_svm}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cccaf1db0d5060a8",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Logistic Regression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 88,
|
||
"id": "ee7506f4aa805faf",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.342498Z",
|
||
"start_time": "2024-03-20T11:36:26.339857Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.linear_model import LogisticRegression\n",
|
||
"# from sklearn.model_selection import cross_val_score\n",
|
||
"# \n",
|
||
"# # Logistic Regression with L2 regularization\n",
|
||
"# log_reg = LogisticRegression(penalty='l2', C=0.1)\n",
|
||
"# \n",
|
||
"# # Use the train_and_save_model function to train and save the model\n",
|
||
"# loaded_model = train_and_save_model(log_reg, X_train, y_train, 'logistic_regression_model.pkl')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 89,
|
||
"id": "a44d38efa4b86d93",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.346181Z",
|
||
"start_time": "2024-03-20T11:36:26.343627Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"# # Predict on the test set\n",
|
||
"# y_pred_log_reg = loaded_model.predict(X_test)\n",
|
||
"# \n",
|
||
"# # Calculate accuracy\n",
|
||
"# accuracy_log_reg = accuracy_score(y_test, y_pred_log_reg)\n",
|
||
"# print(f\"Accuracy of Logistic Regression: {accuracy_log_reg}\")\n",
|
||
"# \n",
|
||
"# # Perform 5-fold cross validation\n",
|
||
"# scores = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
|
||
"# print(f\"Cross-validated scores: {scores}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 90,
|
||
"id": "a3646a4965b0707c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.352459Z",
|
||
"start_time": "2024-03-20T11:36:26.349609Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.metrics import roc_curve, auc\n",
|
||
"# import matplotlib.pyplot as plt\n",
|
||
"# \n",
|
||
"# # Compute ROC curve and ROC area for each class\n",
|
||
"# fpr, tpr, _ = roc_curve(y_test, y_pred_log_reg)\n",
|
||
"# roc_auc = auc(fpr, tpr)\n",
|
||
"# \n",
|
||
"# plt.figure()\n",
|
||
"# lw = 2\n",
|
||
"# plt.plot(fpr, tpr, color='darkorange',\n",
|
||
"# lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
|
||
"# plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
|
||
"# plt.xlim([0.0, 1.0])\n",
|
||
"# plt.ylim([0.0, 1.05])\n",
|
||
"# plt.xlabel('False Positive Rate')\n",
|
||
"# plt.ylabel('True Positive Rate')\n",
|
||
"# plt.title('Receiver Operating Characteristic')\n",
|
||
"# plt.legend(loc=\"lower right\")\n",
|
||
"# plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "aeaf5eeffa7ec104",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Gradient Boosting Classifier"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 91,
|
||
"id": "c7ecae5d021ad44f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.355930Z",
|
||
"start_time": "2024-03-20T11:36:26.353605Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.ensemble import GradientBoostingClassifier\n",
|
||
"# \n",
|
||
"# # Gradient Boosting Classifier\n",
|
||
"# gbc = GradientBoostingClassifier()\n",
|
||
"# \n",
|
||
"# # Use the train_and_save_model function to train and save the model\n",
|
||
"# loaded_model = train_and_save_model(gbc, X_train, y_train, 'gradient_boosting_classifier.pkl')\n",
|
||
"# "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 92,
|
||
"id": "4a8a1c3a7289ef7a",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.359480Z",
|
||
"start_time": "2024-03-20T11:36:26.357096Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# y_pred_gbc = loaded_model.predict(X_test)\n",
|
||
"# accuracy_gbc = accuracy_score(y_test, y_pred_gbc)\n",
|
||
"# print(f\"Accuracy of Gradient Boosting Classifier: {accuracy_gbc}\")\n",
|
||
"# "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "25102568a6e5c457",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# K-Nearest Neighbors (KNN, K=15)\n",
|
||
"\n",
|
||
"This code block is implementing the K-Nearest Neighbors (KNN) algorithm for classification. The KNN algorithm is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until function evaluation. \n",
|
||
"\n",
|
||
"The KNN algorithm works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression). \n",
|
||
"\n",
|
||
"The number of neighbors, K, is set to 15 in this case. This means that the algorithm looks at the 15 nearest neighbors to decide the class of the test instance. \n",
|
||
"\n",
|
||
"The mathematical concept behind KNN is the Euclidean distance. Given two points P1(x1, y1) and P2(x2, y2) in a 2D space, the Euclidean distance between P1 and P2 is calculated as:\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\text{Distance} = \\sqrt{(x2 - x1)^2 + (y2 - y1)^2}\n",
|
||
"$$\n",
|
||
"In higher dimensional space, the formula is generalized as:\n",
|
||
"$$\n",
|
||
"\\text{Distance} = \\sqrt{\\sum_{i=1}^{n} (x_i - y_i)^2}\n",
|
||
"$$\n",
|
||
"Where:\n",
|
||
"- $n$ is the number of dimensions\n",
|
||
"- $x_i$ and $y_i$ are the $i$-th dimensions of the two points\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 93,
|
||
"id": "705c62e64bf6d614",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.362915Z",
|
||
"start_time": "2024-03-20T11:36:26.360642Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.neighbors import KNeighborsClassifier\n",
|
||
"# \n",
|
||
"# # K-Nearest Neighbors\n",
|
||
"# knn = KNeighborsClassifier(n_neighbors=13)\n",
|
||
"# loaded_model = train_and_save_model(knn, X_train, y_train, 'knn_classifier.pkl')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 94,
|
||
"id": "cf4df4ef7bbfd74",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.366441Z",
|
||
"start_time": "2024-03-20T11:36:26.364032Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# y_pred_knn = loaded_model.predict(X_test)\n",
|
||
"# accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
|
||
"# print(f\"Accuracy of K-Nearest Neighbors: {accuracy_knn}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 95,
|
||
"id": "faabcf63e34005a9",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.370553Z",
|
||
"start_time": "2024-03-20T11:36:26.367583Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.model_selection import GridSearchCV\n",
|
||
"# \n",
|
||
"# # Define the parameter values that should be searched\n",
|
||
"# k_range = list(range(1, 31))\n",
|
||
"# \n",
|
||
"# # Create a parameter grid: map the parameter names to the values that should be searched\n",
|
||
"# param_grid = dict(n_neighbors=k_range)\n",
|
||
"# \n",
|
||
"# # Instantiate the grid\n",
|
||
"# grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')\n",
|
||
"# \n",
|
||
"# # Fit the grid with data\n",
|
||
"# grid.fit(X_train, y_train)\n",
|
||
"# \n",
|
||
"# # View the complete results\n",
|
||
"# grid.cv_results_\n",
|
||
"# \n",
|
||
"# # Examine the best model\n",
|
||
"# print(grid.best_score_)\n",
|
||
"# print(grid.best_params_)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 96,
|
||
"id": "2ed22b3fc59f74e6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.374844Z",
|
||
"start_time": "2024-03-20T11:36:26.371738Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# import matplotlib.pyplot as plt\n",
|
||
"# import numpy as np\n",
|
||
"# \n",
|
||
"# # Apply PCA to reduce dimensionality to 2D\n",
|
||
"# pca = PCA(n_components=2)\n",
|
||
"# X_test_2d = pca.fit_transform(X_test)\n",
|
||
"# \n",
|
||
"# # Print the number of features\n",
|
||
"# print(f\"Original number of features: {X_test.shape[1]}, reduced number of features: {X_test_2d.shape[1]}\")\n",
|
||
"# \n",
|
||
"# # Create a scatter plot\n",
|
||
"# plt.figure(figsize=(10, 7))\n",
|
||
"# \n",
|
||
"# # Create a color map\n",
|
||
"# cmap = plt.cm.viridis\n",
|
||
"# \n",
|
||
"# # Plot NLOS points\n",
|
||
"# nlos = plt.scatter(X_test_2d[y_pred_knn == 1, 0], X_test_2d[y_pred_knn == 1, 1], c='blue', label='NLOS')\n",
|
||
"# \n",
|
||
"# # Plot LOS points\n",
|
||
"# los = plt.scatter(X_test_2d[y_pred_knn == 0, 0], X_test_2d[y_pred_knn == 0, 1], c='red', label='LOS')\n",
|
||
"# \n",
|
||
"# # Add labels\n",
|
||
"# plt.xlabel('Principal Component 1')\n",
|
||
"# plt.ylabel('Principal Component 2')\n",
|
||
"# plt.title('2D Scatter Plot for LOS and NLOS')\n",
|
||
"# \n",
|
||
"# # Add a legend\n",
|
||
"# plt.legend(handles=[nlos, los])\n",
|
||
"# \n",
|
||
"# plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 97,
|
||
"id": "4ac86c268055c1b8",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.379861Z",
|
||
"start_time": "2024-03-20T11:36:26.375928Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.neighbors import KNeighborsClassifier\n",
|
||
"# from sklearn.metrics import accuracy_score\n",
|
||
"# import matplotlib.pyplot as plt\n",
|
||
"# \n",
|
||
"# # Define the list of numbers of neighbors (from 1-20)\n",
|
||
"# num_neighbors = np.arange(1, 100, 2)\n",
|
||
"# \n",
|
||
"# # Initialize the lists to store the accuracies\n",
|
||
"# train_acc = []\n",
|
||
"# test_acc = []\n",
|
||
"# \n",
|
||
"# # Loop over the different numbers of neighbors\n",
|
||
"# for k in num_neighbors:\n",
|
||
"# # Initialize the KNN classifier\n",
|
||
"# clf = KNeighborsClassifier(n_neighbors=k)\n",
|
||
"# \n",
|
||
"# # Fit the classifier on the training data\n",
|
||
"# clf.fit(X_train, y_train)\n",
|
||
"# \n",
|
||
"# # Make predictions on the training and test data\n",
|
||
"# y_pred_train = clf.predict(X_train)\n",
|
||
"# y_pred_test = clf.predict(X_test)\n",
|
||
"# \n",
|
||
"# # Calculate the accuracies\n",
|
||
"# train_acc.append(accuracy_score(y_train, y_pred_train))\n",
|
||
"# test_acc.append(accuracy_score(y_test, y_pred_test))\n",
|
||
"# \n",
|
||
"# # Plot the accuracies\n",
|
||
"# plt.figure(figsize=(10, 5))\n",
|
||
"# plt.plot(num_neighbors, train_acc, 'ro-', num_neighbors, test_acc, 'bv--')\n",
|
||
"# plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
|
||
"# plt.xlabel('Number of Neighbors')\n",
|
||
"# plt.ylabel('Accuracy')\n",
|
||
"# plt.title('Training and Test Accuracy for Different Numbers of Neighbors in KNN')\n",
|
||
"# plt.grid()\n",
|
||
"# plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5b9b66f92968957c",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Naive Bayes"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 98,
|
||
"id": "3d984228fb1d3026",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.383672Z",
|
||
"start_time": "2024-03-20T11:36:26.381305Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.naive_bayes import GaussianNB\n",
|
||
"# \n",
|
||
"# # Naive Bayes\n",
|
||
"# nb = GaussianNB()\n",
|
||
"# loaded_model = train_and_save_model(nb, X_train, y_train, 'naive_bayes_classifier.pkl')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 99,
|
||
"id": "98cd350871bc3201",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.387312Z",
|
||
"start_time": "2024-03-20T11:36:26.384890Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# y_pred_nb = loaded_model.predict(X_test)\n",
|
||
"# accuracy_nb = accuracy_score(y_test, y_pred_nb)\n",
|
||
"# print(f\"Accuracy of Naive Bayes: {accuracy_nb}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "92c8498137a5e32e",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# K-Means Clustering"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 100,
|
||
"id": "305a796294814705",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.390727Z",
|
||
"start_time": "2024-03-20T11:36:26.388474Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.cluster import KMeans\n",
|
||
"# \n",
|
||
"# # K-Means Clustering\n",
|
||
"# kmeans = KMeans(n_clusters=2, max_iter=600)\n",
|
||
"# loaded_model = train_and_save_model(kmeans, X_train, y_train, 'kmeans_clustering.pkl')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 101,
|
||
"id": "494bb537046bf5a7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.394465Z",
|
||
"start_time": "2024-03-20T11:36:26.392061Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# y_pred_kmeans = loaded_model.predict(X_test)\n",
|
||
"# accuracy_kmeans = accuracy_score(y_test, y_pred_kmeans)\n",
|
||
"# print(f\"Accuracy of K-Means Clustering: {accuracy_kmeans}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 102,
|
||
"id": "62401c8d1a4d61cc",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.398192Z",
|
||
"start_time": "2024-03-20T11:36:26.395730Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# labels = loaded_model.labels_\n",
|
||
"# # Print the data table with the cluster labels\n",
|
||
"# print(f\"Data table with cluster labels:\\n{pd.concat([X_test, pd.DataFrame({'Cluster': labels})], axis=1)}\")\n",
|
||
"# \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 103,
|
||
"id": "f0f5284581e70e6e",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.403315Z",
|
||
"start_time": "2024-03-20T11:36:26.399350Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.cluster import KMeans\n",
|
||
"# from sklearn.metrics import accuracy_score\n",
|
||
"# from sklearn.decomposition import PCA\n",
|
||
"# from mpl_toolkits.mplot3d import Axes3D\n",
|
||
"# import matplotlib.pyplot as plt\n",
|
||
"# \n",
|
||
"# # Define the range of cluster numbers\n",
|
||
"# cluster_range = range(1, 15)\n",
|
||
"# \n",
|
||
"# # For each number of clusters\n",
|
||
"# for n_clusters in cluster_range:\n",
|
||
"# # Create a KMeans model\n",
|
||
"# kmeans = KMeans(n_clusters=n_clusters, max_iter=600)\n",
|
||
"# \n",
|
||
"# # Fit the model to the training data\n",
|
||
"# kmeans.fit(X_train)\n",
|
||
"# \n",
|
||
"# # Make predictions on the test data\n",
|
||
"# y_pred_kmeans = kmeans.predict(X_test)\n",
|
||
"# \n",
|
||
"# # Calculate the accuracy of the model\n",
|
||
"# accuracy_kmeans = accuracy_score(y_test, y_pred_kmeans)\n",
|
||
"# \n",
|
||
"# # Print the number of clusters and the corresponding accuracy\n",
|
||
"# print(f\"Number of clusters: {n_clusters}, Accuracy: {accuracy_kmeans}\")\n",
|
||
"# \n",
|
||
"# # Apply PCA to reduce dimensionality to 3D\n",
|
||
"# pca = PCA(n_components=3)\n",
|
||
"# X_test_3d = pca.fit_transform(X_test)\n",
|
||
"# \n",
|
||
"# # Create a 3D scatter plot\n",
|
||
"# fig = plt.figure(figsize=(10, 7))\n",
|
||
"# ax = fig.add_subplot(111, projection='3d')\n",
|
||
"# \n",
|
||
"# # Create a color map\n",
|
||
"# cmap = plt.cm.get_cmap('viridis', n_clusters) # We use 'viridis' colormap and we specify that we have n_clusters\n",
|
||
"# \n",
|
||
"# # Plot the points with colors according to their cluster assignment\n",
|
||
"# scatter = ax.scatter(X_test_3d[:, 0], X_test_3d[:, 1], X_test_3d[:, 2], c=y_pred_kmeans, cmap=cmap)\n",
|
||
"# \n",
|
||
"# # Add labels\n",
|
||
"# ax.set_xlabel('Principal Component 1')\n",
|
||
"# ax.set_ylabel('Principal Component 2')\n",
|
||
"# ax.set_zlabel('Principal Component 3')\n",
|
||
"# plt.title(f'3D Visualization of {n_clusters} Clusters')\n",
|
||
"# \n",
|
||
"# # Display the plot\n",
|
||
"# plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 104,
|
||
"id": "82c7ba8cbb2aa17a",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:36:26.407446Z",
|
||
"start_time": "2024-03-20T11:36:26.404679Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# from sklearn.decomposition import PCA\n",
|
||
"# import matplotlib.pyplot as plt\n",
|
||
"# \n",
|
||
"# # Apply PCA to reduce dimensionality to 2D\n",
|
||
"# pca = PCA(n_components=2)\n",
|
||
"# X_test_2d = pca.fit_transform(X_test)\n",
|
||
"# \n",
|
||
"# # Predict the cluster labels for the data points you're plotting\n",
|
||
"# labels = loaded_model.predict(X_test)\n",
|
||
"# \n",
|
||
"# # Create a scatter plot\n",
|
||
"# plt.figure(figsize=(10, 7))\n",
|
||
"# \n",
|
||
"# # Create a color map\n",
|
||
"# cmap = plt.cm.get_cmap('viridis', 2) # We use 'viridis' colormap and we specify that we have 2 clusters\n",
|
||
"# \n",
|
||
"# # Plot the points with colors according to their cluster assignment\n",
|
||
"# plt.scatter(X_test_2d[:, 0], X_test_2d[:, 1], c=labels, cmap=cmap)\n",
|
||
"# \n",
|
||
"# # Add labels\n",
|
||
"# plt.xlabel('Principal Component 1')\n",
|
||
"# plt.ylabel('Principal Component 2')\n",
|
||
"# plt.title('2D Visualization of Clusters')\n",
|
||
"# \n",
|
||
"# # Display the plot\n",
|
||
"# plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "862a9b7ee430a667",
|
||
"metadata": {
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"collapsed": false
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"source": [
|
||
"# Convolution Neural Network\n",
|
||
"\n",
|
||
"This code block is implementing a Convolutional Neural Network (CNN) for a classification task using TensorFlow. The CNN is a class of deep learning neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. Here's a step-by-step breakdown of what the code does: \n",
|
||
"1. Data Preparation: The target column 'NLOS' is separated from the rest of the dataset. The target values are then encoded from categorical to numerical values using LabelEncoder. These numerical values are then one-hot encoded to create binary variables for each class. \n",
|
||
"2. Data Reshaping: The input data is reshaped to fit the model. Each data instance is reshaped to a 3D array where the third dimension represents the number of input channels, which is 1 in this case. \n",
|
||
"3. Data Splitting: The data is split into training and testing sets using a 80:20 ratio. \n",
|
||
"4. Model Creation: A Sequential model is created using Keras. This model is composed of the following layers: \n",
|
||
"5. Conv1D layers: These are convolutional layers that will convolve the input data with a set of learnable filters, each producing one feature map in the output. The kernel size is set to 3, and the activation function used is ReLU (Rectified Linear Unit). \n",
|
||
"6. MaxPooling1D layers: These layers are used to down-sample the input along its spatial dimensions (height and width). The pool size is set to 2. \n",
|
||
"7. Dense layers: These are fully connected layers. The first Dense layer has 64 units and uses the ReLU activation function. The second Dense layer has a number of units equal to the number of classes and uses the softmax activation function to output a probability distribution over the classes. \n",
|
||
"9. Model Compilation: The model is compiled with the Adam optimizer, categorical cross-entropy loss function, and accuracy as the evaluation metric. \n",
|
||
"10. Model Training: The model is trained on the training data for 10 epochs with a batch size of 32. The validation data is set to the testing set. \n",
|
||
"11. Model Evaluation: The model's performance is evaluated on the testing set and the accuracy is printed. \n",
|
||
"\n",
|
||
"12. The mathematical concept behind the Convolutional layer (Conv1D) is the convolution operation, which is a mathematical operation on two functions that produces a third function. In the context of a CNN, the two functions are the input data and the kernel or filter. The convolution operation involves sliding the kernel across the input data and computing the dot product at each position.\n",
|
||
"\n",
|
||
"The mathematical formula for the convolution operation is: $$ (f * g)(t) = \\int_{-\\infty}^{\\infty} f(\\tau)g(t - \\tau) d\\tau $$ Where: \n",
|
||
"$f$ and $g$ are the input data and kernel respectively\n",
|
||
"$t$ is the position of the kernel\n",
|
||
"$\\tau$ is a dummy integration variable\n",
|
||
"In the context of a CNN, the integral is replaced by a sum over the discrete spatial dimensions (height and width) of the input data and kernel."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 105,
|
||
"id": "1c1dd203ad7db076",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:48:39.730855Z",
|
||
"start_time": "2024-03-20T11:36:26.408455Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/30\n",
|
||
"1050/1050 [==============================] - 26s 23ms/step - loss: 0.8091 - accuracy: 0.6635 - val_loss: 0.5219 - val_accuracy: 0.8042\n",
|
||
"Epoch 2/30\n",
|
||
"1050/1050 [==============================] - 26s 24ms/step - loss: 0.5925 - accuracy: 0.7790 - val_loss: 0.4711 - val_accuracy: 0.8326\n",
|
||
"Epoch 3/30\n",
|
||
"1050/1050 [==============================] - 24s 23ms/step - loss: 0.5297 - accuracy: 0.8096 - val_loss: 0.4609 - val_accuracy: 0.8440\n",
|
||
"Epoch 4/30\n",
|
||
"1050/1050 [==============================] - 26s 25ms/step - loss: 0.5034 - accuracy: 0.8216 - val_loss: 0.4540 - val_accuracy: 0.8443\n",
|
||
"Epoch 5/30\n",
|
||
"1050/1050 [==============================] - 25s 24ms/step - loss: 0.4806 - accuracy: 0.8330 - val_loss: 0.4468 - val_accuracy: 0.8487\n",
|
||
"Epoch 6/30\n",
|
||
"1050/1050 [==============================] - 27s 25ms/step - loss: 0.4720 - accuracy: 0.8358 - val_loss: 0.4471 - val_accuracy: 0.8476\n",
|
||
"Epoch 7/30\n",
|
||
"1050/1050 [==============================] - 25s 24ms/step - loss: 0.4606 - accuracy: 0.8416 - val_loss: 0.4418 - val_accuracy: 0.8496\n",
|
||
"Epoch 8/30\n",
|
||
"1050/1050 [==============================] - 24s 23ms/step - loss: 0.4517 - accuracy: 0.8451 - val_loss: 0.4389 - val_accuracy: 0.8519\n",
|
||
"Epoch 9/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.4418 - accuracy: 0.8500 - val_loss: 0.4360 - val_accuracy: 0.8527\n",
|
||
"Epoch 10/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.4380 - accuracy: 0.8526 - val_loss: 0.4345 - val_accuracy: 0.8535\n",
|
||
"Epoch 11/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.4379 - accuracy: 0.8535 - val_loss: 0.4312 - val_accuracy: 0.8540\n",
|
||
"Epoch 12/30\n",
|
||
"1050/1050 [==============================] - 24s 23ms/step - loss: 0.4325 - accuracy: 0.8527 - val_loss: 0.4287 - val_accuracy: 0.8564\n",
|
||
"Epoch 13/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.4247 - accuracy: 0.8553 - val_loss: 0.4308 - val_accuracy: 0.8533\n",
|
||
"Epoch 14/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.4212 - accuracy: 0.8583 - val_loss: 0.4250 - val_accuracy: 0.8557\n",
|
||
"Epoch 15/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.4187 - accuracy: 0.8575 - val_loss: 0.4292 - val_accuracy: 0.8537\n",
|
||
"Epoch 16/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.4173 - accuracy: 0.8586 - val_loss: 0.4223 - val_accuracy: 0.8575\n",
|
||
"Epoch 17/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.4112 - accuracy: 0.8608 - val_loss: 0.4208 - val_accuracy: 0.8555\n",
|
||
"Epoch 18/30\n",
|
||
"1050/1050 [==============================] - 24s 23ms/step - loss: 0.4089 - accuracy: 0.8591 - val_loss: 0.4201 - val_accuracy: 0.8545\n",
|
||
"Epoch 19/30\n",
|
||
"1050/1050 [==============================] - 25s 24ms/step - loss: 0.4078 - accuracy: 0.8604 - val_loss: 0.4178 - val_accuracy: 0.8556\n",
|
||
"Epoch 20/30\n",
|
||
"1050/1050 [==============================] - 25s 24ms/step - loss: 0.4061 - accuracy: 0.8635 - val_loss: 0.4183 - val_accuracy: 0.8581\n",
|
||
"Epoch 21/30\n",
|
||
"1050/1050 [==============================] - 24s 23ms/step - loss: 0.3997 - accuracy: 0.8644 - val_loss: 0.4131 - val_accuracy: 0.8568\n",
|
||
"Epoch 22/30\n",
|
||
"1050/1050 [==============================] - 24s 23ms/step - loss: 0.3959 - accuracy: 0.8646 - val_loss: 0.4125 - val_accuracy: 0.8582\n",
|
||
"Epoch 23/30\n",
|
||
"1050/1050 [==============================] - 24s 23ms/step - loss: 0.3980 - accuracy: 0.8621 - val_loss: 0.4088 - val_accuracy: 0.8598\n",
|
||
"Epoch 24/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.3930 - accuracy: 0.8647 - val_loss: 0.4090 - val_accuracy: 0.8589\n",
|
||
"Epoch 25/30\n",
|
||
"1050/1050 [==============================] - 25s 24ms/step - loss: 0.3930 - accuracy: 0.8651 - val_loss: 0.4078 - val_accuracy: 0.8592\n",
|
||
"Epoch 26/30\n",
|
||
"1050/1050 [==============================] - 25s 24ms/step - loss: 0.3900 - accuracy: 0.8664 - val_loss: 0.4076 - val_accuracy: 0.8586\n",
|
||
"Epoch 27/30\n",
|
||
"1050/1050 [==============================] - 24s 23ms/step - loss: 0.3877 - accuracy: 0.8665 - val_loss: 0.4098 - val_accuracy: 0.8582\n",
|
||
"Epoch 28/30\n",
|
||
"1050/1050 [==============================] - 24s 23ms/step - loss: 0.3849 - accuracy: 0.8691 - val_loss: 0.4071 - val_accuracy: 0.8607\n",
|
||
"Epoch 29/30\n",
|
||
"1050/1050 [==============================] - 23s 22ms/step - loss: 0.3857 - accuracy: 0.8682 - val_loss: 0.4058 - val_accuracy: 0.8601\n",
|
||
"Epoch 30/30\n",
|
||
"1050/1050 [==============================] - 24s 22ms/step - loss: 0.3871 - accuracy: 0.8673 - val_loss: 0.4040 - val_accuracy: 0.8630\n",
|
||
"263/263 [==============================] - 1s 4ms/step\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from tensorflow.keras.models import Sequential\n",
|
||
"from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, BatchNormalization\n",
|
||
"from tensorflow.keras.callbacks import EarlyStopping\n",
|
||
"from tensorflow.keras import regularizers\n",
|
||
"from tensorflow.keras.optimizers import Adam\n",
|
||
"from sklearn.metrics import classification_report\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Set random seed for reproducibility\n",
|
||
"tf.random.set_seed(42)\n",
|
||
"\n",
|
||
"# Drop the target column 'NLOS' from the data and assign the remaining data to X\n",
|
||
"X = data.drop('NLOS', axis=1)\n",
|
||
"# Assign the target column 'NLOS' to y\n",
|
||
"y = data['NLOS']\n",
|
||
"\n",
|
||
"# Split the data into training and testing sets with a 80:20 ratio\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
|
||
"\n",
|
||
"# Initialize a StandardScaler object\n",
|
||
"scaler = StandardScaler()\n",
|
||
"# Fit the scaler to the training data and transform it\n",
|
||
"X_train = scaler.fit_transform(X_train)\n",
|
||
"# Transform the testing data using the fitted scaler\n",
|
||
"X_test = scaler.transform(X_test)\n",
|
||
"\n",
|
||
"# Initialize a Sequential model\n",
|
||
"model = Sequential()\n",
|
||
"\n",
|
||
"# Add a Conv1D layer\n",
|
||
"model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1), kernel_regularizer=regularizers.l2(0.001)))\n",
|
||
"model.add(BatchNormalization())\n",
|
||
"model.add(Dropout(0.5))\n",
|
||
"\n",
|
||
"# Add another Conv1D layer\n",
|
||
"model.add(Conv1D(filters=32, kernel_size=3, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
|
||
"model.add(BatchNormalization())\n",
|
||
"model.add(Dropout(0.5))\n",
|
||
"\n",
|
||
"# Add a Flatten layer\n",
|
||
"model.add(Flatten())\n",
|
||
"\n",
|
||
"# Add a Dense layer\n",
|
||
"model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
|
||
"model.add(BatchNormalization())\n",
|
||
"model.add(Dropout(0.5))\n",
|
||
"\n",
|
||
"# Add the output Dense layer\n",
|
||
"model.add(Dense(1, activation='sigmoid'))\n",
|
||
"\n",
|
||
"# Define early stopping\n",
|
||
"early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
|
||
"\n",
|
||
"# Compile the model\n",
|
||
"model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])\n",
|
||
"\n",
|
||
"# Train the model\n",
|
||
"history = model.fit(X_train, y_train, epochs=30, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
|
||
"\n",
|
||
"# Evaluate the model\n",
|
||
"scores = model.evaluate(X_test, y_test, verbose=0)\n",
|
||
"\n",
|
||
"# Make predictions\n",
|
||
"y_pred = model.predict(X_test)\n",
|
||
"y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
|
||
"\n",
|
||
"# Generate a classification report\n",
|
||
"report = classification_report(y_test, y_pred_classes)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 106,
|
||
"id": "89aa08d7d1866179",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:48:40.119743Z",
|
||
"start_time": "2024-03-20T11:48:39.731977Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Test loss: 0.4039548933506012\n",
|
||
"Test accuracy: 0.8629761934280396\n",
|
||
"Classification Report: \n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" 0 0.83 0.91 0.87 4192\n",
|
||
" 1 0.90 0.82 0.86 4208\n",
|
||
"\n",
|
||
" accuracy 0.86 8400\n",
|
||
" macro avg 0.87 0.86 0.86 8400\n",
|
||
"weighted avg 0.87 0.86 0.86 8400\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# Plot the training and validation accuracy over epochs\n",
|
||
"plt.plot(history.history['accuracy'], 'ro-', history.history['val_accuracy'], 'bv--')\n",
|
||
"plt.title('Training and Test Accuracy')\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Accuracy')\n",
|
||
"plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Plot the training and validation loss over epochs\n",
|
||
"plt.figure(figsize=(12, 6))\n",
|
||
"plt.plot(history.history['loss'], label='Training Loss')\n",
|
||
"plt.plot(history.history['val_loss'], label='Validation Loss')\n",
|
||
"plt.title('Training and Validation Loss Over Time')\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Loss')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Print the testing loss and accuracy\n",
|
||
"print('Test loss:', scores[0])\n",
|
||
"print('Test accuracy:', scores[1])\n",
|
||
"\n",
|
||
"# Print the classification report\n",
|
||
"print('Classification Report: \\n', report)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 107,
|
||
"id": "dd49203934ca9cf6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:48:41.104783Z",
|
||
"start_time": "2024-03-20T11:48:40.121419Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 500x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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dVY9EAC+fp0KuXr168PLywsaNG5Geng6FQoG6deti+/btePnyZZb6/z7N1Lp1a9y7dw9Hjx7NUu/1r/N27dohMjISO3bsyFInLS1Ne/XT27i6usLNzQ2HDh3CoUOH4OTkpJOUyGQytGnTBocPH872g/pDpmOoVasWGjZsiN27d+tcqv/a4sWLERoais8//zzLL/Vjx47pjM+4desWbt68iaZNmwKAXsf5XbK7id7mzZuhVqt1yiwsLAB8+Omyf6tWrRrs7OywY8cOqFQqbfmBAweQkJDw3vVLliyJ7t27448//sDmzZuzPK/RaPC///0PL1680Cuu18fkv6cJd+3alefbaNy4MaysrLBmzRqkp6frPPfvdS0sLLI99h/6/siOWq3Osi+FQoHixYvrXJL/tpj+y8HBAXXq1MGuXbu0p0Nfy6veQSrc2CNEhd7gwYPx1VdfYffu3ejVqxemTZuG3r17o2PHjujRowfKlCmD6Oho3LhxAy9evMD+/fu16x0+fBhfffUVunXrBk9PTyQkJODEiROYMWMGqlSpgo8//hhBQUGYNm0aLl68iFq1akGtVuPRo0cIDg7GunXr3juFgp+fHwIDA2FmZoZPPvkky6mRcePG4eLFi+jRowe6d++OSpUqISEhAbdv38b58+dx6dKlXB+b+fPnY+DAgRg2bBg6dOiA2rVrQ6lU4siRI7h06RL8/PwwePDgLOuVLVsWvXr1Qq9evaBUKrFp0ybY2dnh888/19bJ6XF+l+bNm2Pfvn2wtrZGpUqVcOPGDZw7dw52dnY69Tw8PCCTyfDjjz8iMTERpqamqF+/PhQKRa6PjampKUaOHIlZs2ZhwIABaNeuHSIiIrB79+4cnz6dNGkSwsPDMXv2bBw5cgQ+Pj4oVqwYnj9/juDgYDx69EinBzAnGjVqBLlcjqFDh6Jnz55ITk7Gzp07oVAoEBUVlafbsLa2RkBAAL755ht88skn6NChA4oVK4Z79+4hLS0N8+fPBwB4enri0KFDmDt3LqpXrw5LS0v4+vrmyfvjv5KTk9GsWTO0adMGVapUgaWlJc6dO4c///xTp+fwbTFl55tvvkGvXr3QpUsXfPrppyhdujQiIiJw6tQp7Nu3T6/4qOhhIkSFXuvWrVG2bFn873//Q48ePVCpUiXs2rULy5cvx549exAfHw8HBwdUrVoVw4cP165nZWWFLVu2YNmyZTh69Cj27NkDhUKBBg0aaAf5SqVSrFixAj/99BP27duHo0ePwsLCAqVLl0a/fv2092x5Fz8/PyxZsgSpqalo165dlucdHR2xc+dOrFixAkePHsXWrVthZ2eHSpUqYfz48R90bIoXL46dO3dq5xo7cuQIZDIZ3N3dMW/ePHTu3DnbUwydO3eGVCrFxo0bERMTAy8vL3z77bcoXry4tk5Oj/O7TJkyBVKpFAcOHEB6ejpq1aqFDRs26CRcAODk5IQZM2ZgzZo1mDJlCtRqNTZt2vRBiRAA9O3bF4IgYMOGDZg/fz6qVKmCVatWYfbs2Tm6O7GFhQV+/PFH7N69G3v37sXKlSuRlpamnWts4cKFes+DV7FiRQQGBmLJkiWYP38+HB0d0atXLzg4OGDy5Ml5vo3u3btDoVBg7dq1WLlyJUxMTFCxYkWd+1f17t0bd+/exe7du/HTTz/BxcUFvr6+efL++C9zc3P06tULZ8+exZEjRyAIAsqWLatNvN8XU3aqVKmCHTt2YOnSpdi6dSvS09NRqlSpbN+PZHwkAvsGiegfT58+RYsWLfD1119n21NkDDQaDRo0aIBWrVph9uzZYodDRPmMY4SIyGilp6dnGSeyd+9exMfHo27duiJFRUQFiafGiMho3bhxA3PnzkXbtm1hZ2eHO3fu4Ndff4Wbmxvatm0rdnhEVACYCBGR0XJxcUGJEiWwefNmJCQkwNbWFh9//DHGjx8v6qz2RFRwOEaIiIiIjBbHCBEREZHRYiJERERERouJEBERERktJkJERERktIz2qrHY2ETk0QTMlEsSCaBQ2CAmJhEcsi8+tofhYFsYDraF4ZBKAQcHmzzfrtEmQoIA/lEbCLaFYWF7GA62heFgW4gvv44/T40RERGR0WIiREREREaLiRAREREZLSZCREREZLSYCBEREZHRYiJERERERouJEBERERktJkJERERktJgIERERkdFiIkRERERGS9RE6PLlyxg6dCgaN24Md3d3HDt27L3rXLx4EV26dEG1atXQqlUr7N69uwAiJSIioqJI1EQoJSUF7u7umDZtWo7qh4eHY8iQIahXrx727duHAQMG4JtvvsGZM2fyOVIiIiIqikSddLVZs2Zo1qxZjutv27YNpUuXxqRJkwAArq6uuHr1Kn766Sc0adIkv8IkIiKiIqpQzT5/48YNNGjQQKescePGmDNnjkgRERERGSlBgDTxESSajGyflqa9hDTlBSCoIdEoYRJ1BRoLJ0g0KkCTAdOnQVDbVwckkvfuSqMB7kXaQzHyf3n9KgpXIhQdHQ1HR0edMkdHRyQlJSEtLQ3m5uY53pZEkqNjT/no9fFnOxgGtofhYFsYjkLXFoIACBpAUAOCChJBDQhqSFNfQpryHNBkAJoMSDNeQZr8FILMIjMxETJgdW0GMorXz1xfkwF5zHUAgMbMPjMTEdSQQANoMrcpEVQfHK5J4qP31nn+yhqDtnfG9agKiBz5wbvMGkPeb7JwcHCwETsE+odCwbYwJGwPw8G2MBx6t4XwOmF4nZT8879GDSRFAAmPgecXAJkpoFFl/hPUQPJzIOYOYFvhTXn0n0DKS8C24r+29e9/6n/WffHBr1P+8kKWMml63AdvN7f2/eWOz3d2QnSyFWxs8idlKVSJkKOjI6Kjo3XKoqOjYW1trVdvEADExiZCo8nL6EhfEknmh0tMTCIEQexoiO1hONgW4pAoEyBNCocs8TGgToNJzA3A1BqW5nKkpKS+6W1BZgIiEdSQpEXBJPZPmMTfhcbUDlJlfN4E8/J61rLYu3mz7RwQJFJAItf2KqmKVQYkMkAqAyAFJDIIEilkyU+htnKB2r5attuRpkQgo6QPNKa2gFQOCALUNuUzH0tNIJhYQZBnn2RGx6Shz9SDSE7J7Hkq7qjf93xOFapEqGbNmvj99991ys6dO4eaNWvqvS1BAD9gDATbwrCwPQwH20JPGhUkynhIUyMhe/Xwn2QlczyKND0O0qRQCKb2mV/uGjUgZMDyznKo7DxhEn/7nZu2zMHu8ywJegeN3BaQSLX/BIks8zGkgFQGWdITqC2coS5WKTNx+ed5QSKDScLfSKvYA5CaQpCYQKJOg2BmD7VFCW1iorZ0gdre859tisveEpg12wdjxx5Fu3auWLq0Tb7sR9REKDk5GWFhYdrlp0+f4u7du7C1tUWpUqXwww8/IDIyEgsWLAAA9OzZE1u2bMGCBQvQrVs3XLhwAUFBQVizZo1YL4GIiD6UIECSHgtpWvQ/SUoGIKggSwyFJCMRslcPIJhYQ6LJgDTpCWTJT6GxLPHPqaMMmEUc/qDdvy8Jyo0MhTcgM/+nZ+WfhAQSCFIZpGkx0JgroLEoCbVdFajsq/3Tw2LyT4+LBBqLEhBk5pnLEhMIUjlgYpHncRoStVoDlUoDM7M3qUmfPtVQqpQNfHzKQSbLn4FaoiZCf/31F/r3769dnjt3LgCgS5cumDdvHqKiovD8+XPt82XKlMGaNWswd+5cbNq0CSVKlMDs2bN56TwRkZjUaZAlh2eOf9FkQCKoIFG+ykxsAEDIgEncbQgyM5hGHIXaugIk6hSYPQ0WN+7/0Jg7QZoWhRQPf0hTo5BRogkkmjRYl/ZCQmI6BMje9MRA+q+eGQnUVmUgmDsZRE9KYRQRkYgRI4JQpYoj5s711ZZLJBL4+pbP131LBME4O15jYjhGSGwSCeDoaIPoaI6DMARsD8ORJ23xz5U/kowkSNNjgX9OEUk0KkiTwwBIINEoIVEmQPbqIQRT28xLml/8AY2FMwSJ9J/TSiqYPQ2Cyq4qAEE7qFciaCBLCs27F51HlCV9IEu4D2XptpAoX0HlVOefnhYTQKOCYOYAjblCpwdGY1UGGsuS2W6P74v8t2/ffYwffwwJCekAgF9+6YyWLStmqSeV5s8FBIVqjBARkdHKSIIs5TmgUUKaFg35y/OARAaTqEuAzCzzFNHTIADIHP+RB5c2/5tJ/J083d5/CTJzCDJzpJf9OHMQrdQEkMggVSYgw6EGBHMFNObFAakcgkQKjWUpCDIz4J8kRzCx/ue0EhUWiYnpmDz5JLZvf/O35eJiA2tr0wKNg4kQEZEYBA2kKc8gyUiENPkppOmxMIn7C7LEUEDIAKIvwV5mnVlHo9Rr03mdBP2bdrCuNLNHRaLJgDQ9BmrL0lDZe0BjWTrzOakc0tRIqItVgsbcCZCaAup0qO09oTG1g8aieOagXXkxJjBG6PLlZxg2LAhPniRoyzp3dseCBS1gZ5c/V4e9DRMhIqIPkZGUeXO6pDBINBkwibmuHScDTQZkSeEwf7QVKjsPAIBJfM4vgZYh+v2V3kJtVQZq67KARA6TmGtIL/dx5pVB/wzalaTHZJ42kpoCggaCaTFo/rl6SJCa/utxZs+MILfJPL1E9AFUKg0WL76IRYsuQK3OPNdobW2KefN80b27ByQi3LmSf9VERG+jUUGSHgvzxzsgUaVC/uJM5vgSQQPzJ3v02pQ+CVCWMP4ZxCuYWCGtfFdAZg6JKhlqqzLIcG4ESOWZl0AD0FiXY8JCBik2NhV9++7FlStvLoKqU6cUVq5sh3LlbEWLi+8WIiIAsvj7sL44FrKk0MwroPKJxtQekJpAmhaFDHsvSKBBRvH6kKa+REbxBtCY2UFjXR525aojOkmaOfaFqAiwtTWDiUnmVXUymQTjxtXH6NH1tGViYSJEREWboAFUKZnjbDQZ2gHH0GTOlSRJj4Pt6b4ftAuNeXGobSpCbV0WapsKkKhSkFG8IQQTc+3ddFX21SCYK3K0PYkEgI0NkJ4I8EolKiJkMilWrGiHQYP2Y948X9SuXUrskAAwESKiwkTQAKpU7WXdr2+69/qKJlliKMzC9kJt4wrTZ8c+eHeqYpVh8uoB0ir0AACkufaBYGIBjaULBJkZBDP7zESHiLI4dy4c5uYmqFXrza0JypQphqNH+4gyFuhtmAgRkcGRpL6ESfwdyOLvwexpUOaUCYmPIFGn5Wh9WeLjXO/7VZMNSC/XmVcyEeWSUqnGggXnsGzZZZQta4uTJ/vpXBJvSEkQwESIiMQmCJCkvYTVzXkw//t/kOTTuaB0l1aA1AwSdRokGUnIcKqbef8ZiQyQSJDh3BgZJZvny76JjMXDh7EYOvQQbt16CQB48iQBGzbcxMiRdUSO7O2YCBFRwRA0kCWGQJocAVniY8hf/A7z0F16bUIjt4U0IwHKkj6Zcy9J5QAkkL84jZTqE6CxLAFBaga1Q3Worcr+c7m4Yf36JCqKBEHA5s1/4ttvTyE1NfM+VnK5FAEBjTBsWG2Ro3s3JkJElGckynhY3FsLs9BdUFuXh0SjhEnUJUgzEvXelsq2CtS2lZGh+Agqp7qZl4kzqSEyONHRKRg79iiCg0O0ZZUq2WP1aj94eTmLGFnOMBEiIv1oVJC/vABZ4mOYRF+BILeG5Z1lWarpe9+cDHsvaIpVRNJHszLvhUNEBu/EiVCMGnUYL18ma8sGDPDCjBnNYGlZOC4kYCJERNkTNIA6HRJNOsxC90D+8gLMH2394M1mKLyhcqzzz6Xmrshwbph59RURFSovXyZj4MB9SEtTAwAUCgssWdIabdq4ihyZfpgIERk7QQDUaZCmvwSSnsPmyiqY6XnX5H9TW7pA5VgbybWmQWPulDmFg1TOux0TFTHFi1vhm2+a4JtvTsHHpxwCA9vC2dlK7LD0xk8mIiMgUb6CLP4u5NFXYBL3JwSJCWSJj2EaeSZLXbMcbjNDUQtpFXtCY+EMzT83EhTMHPI2cCIyGBqNALVaA7n8za0lPv/cGyVLWqN9+8qQSgvnGD4mQkRFgSYDsoT7kCWFQ/7iNORRF2ESdwfQKCER1B+0aWWpFpk9RsoEJDZYBpWtOyDntA9ExiQyMgkjRx5GtWpOmDq1qbZcKpWgY0c3ESP7cEyEiAycNOkJTMODII+9Cfmz41AXc4VEkwFoMiCPuZ5n+1G6tIJpyhOklmyFDLtqmTcVlBe+bm4iyltBQQ8xZswRxMam4fTpJ/DxKY8mTcqKHVaeYSJEJDJp8lOYhe6G7FUITGJvQqLJgGBiBXnUhWzry1Jf6L0PtVVZSDJeQeVQExnODaCy94LatjIEE0toLF0AiQQSCeDoaIPk6EQInN+KyOglJ2dg2rTT2LTplrbMyano/ThiIkRUwKRJYTCJvQnTp4dh8XBTrrcjSKSARA6JJh1A5s0GlaXbQKJOR4ZjLShLt4PauhxgYpFXoRORkbh5MxJDhx5CSEictqxtW1csXtwaCkXR+kxhIkSUxyQZiZBkJEOS9hKmL36HSdQVSNOiIE19DpNXD3O8nX8nOoKJFVKqDkeGUz2oHD+CYGoHSKT59yKIyCip1RqsWHEF8+adg0qlAQBYWppg1qzm6Nu3usHNE5YXmAgR5YY6DbLEJ5AlP4EkIwkmURdh+vyU3jcR/LdU175Qlu0AlV0VaKzK8nJzIipQMTGp+PzzAzh79qm2rEYNZ6xe7QdX16J7ry9+0hK9gyQjEWaPd8E0/CAk6nSYvjiVZ9tO9voaGksXpJfpAMHCKc+2S0SUG8WKmSI5OQNA5mw2o0bVxYQJDWBqKnvPmoUbEyEiQcgcsBx+ANLEUEjUaQAAiwc/fdBm08p/AmlqJDTWZZBe9mNoLEtAXawSBJklIC3aHyxEVPjI5TKsWuWHAQP2YcGCFmjYsIzYIRUIJkJkfNRKyKMuwfzBBpg/3pnrzWjMHaEs0QwSQQ2lc2MIJpaZU0fYVub4HSIyeJcvP4OFhRzVqr3pkXZ1tcfvvw8otDdHzA0mQmQc1GmwPfEpTJ+fzNXqqe6fI61CD6iLVc68e3IRHDBIRMZBpdJg8eKLWLToAlxd7XHkSB+dCVKNKQkCmAhRUaZOg+Wt72H15/c5qq4xd0J66bbIKNk88xSWiRXUVqUBE8t8DpSIqGCEhsZj2LAgXLnyHADw99+x2LDhJoYPry1yZOJhIkRFiyoV1lcmw+Lv9e+tml66HdLLd0F6mY68gzIRFWmCIGDHjrsICDiBpCQlAEAmk2D8+AYYMqSWyNGJi4kQFV6CAGnSE5g92QeLuyshS33+3lWUpVoiwecXQGZeAAESEYkvPj4NEyYcw759f2vLype3xcqV7VC7dikRIzMMTISo0JDF3Yb1lSkwfX4CaksXyFIicrReqttgJNWewzssE5HROXs2HMOHB+HZsyRtWa9envjuOx9YW5uKGJnhYCJEBkuaFAbrKwEwCzuQ5bl3JUGC1BRplfoh2fvbzIHNRERGKDIyCZ9+uhtKpRoAYGdnhoULW6FTp8I9W3xeYyJEBkWSHguLO8th9efC99YVJCYQ5NZQ27pDZe+JlGpjoLEuVwBREhEZPmdna4wfXx9z5pxF48ZlsHx5W5QqZSN2WAaHiRCJR5MBPA6C424/ZCi8IY+5nqPVEusvQZprH0Bmls8BEhEVHoIgQKMRIJO9uY/ZyJF1UKqUDT75xMPoLovPKSZCVKBksbdgd7QTpOmxOuXvSoIS6y1GWuX+gFT+1jpERMYsOjoFY8ceRfXqxTFhQgNtuUwmRY8eVUWMzPAxEaL8pcmA+f31MH1xGmbhB99bXTCxhESVghQPf6RWGQKNTcUCCJKIqPA6cSIUo0YdxsuXyTh69BGaNy+HOnV4NVhOMRGivCNoIIu/A4u7a2D+aCskGuX717EuDaVNZaR6+ENZum3+x0hEVESkpanw3Xd/YM2aa9oyOztz7X2CKGeYCNEHkShfQZr0BDbnR+R4jA8AxLc+BFXJxnB0tMGr6EQIQj4GSURUxNy5EwV//yDcvRutLfPxKYfAwLZwduYNYvXBRIj0Jou/C7vDfpCmx+R4HWWJpkiqMx9qW3dAmvlnx2F7RET60WgErFt3HbNmnUF6euZl8WZmMkyd2gSDB3tzQHQuMBGinFOnw2mL03urqew8kF6uM9Jc+0JjXaYAAiMiKvpiY1Ph738IJ08+0ZZ5eDhi9Wo/eHg4ihhZ4cZEiHLE6tLXsLy3OtvnBKkplKXbIaN4faS6f87L2omI8oGlpRwvXry5Q/SQIbUwZUpjmJvzq/xD8OjRW8mfn4b5g40wD/012+eTvSYipcZkQMKuWCKi/GZuboJVq/zQv/9+fP99C/j4lBc7pCKBiRBly2GnG2SpL7J9TpCZI7p3JBMgIqJ8dPNmJCwt5ahc+c1UQVWrOuHChUEwMZG+Y03SBxMh0mEScxP2B5u89fmoPi85czsRUT5SqzVYseIK5s07hypVFAgK6gUzszdf10yC8hYTIQIAyCOOwu54t2yfe9VwJdIrdOfYHyKifBYRkYjhw4Nw7txTAMBff0Vhw4abGDr0I5EjK7qYCBkrdRpszo2A+eMd76wW1ScakJkWUFBERMZr3777GD/+GBIS0gFkjj4YNaouPvuspriBFXFMhIyQ1dWpsLy95J11Epr9DGW5TgUTEBGREUtMTMfkySexffsdbZmLiw1WrGiLhg15C5L8xkTIiMhfnIHdkfbvrBPb8RzU9tUKKCIiIuN2+fIzDBsWhCdPErRlnTu7Y8GCFrCz43jMgsBEyAiY31sLm0vjs30u3aUNXjXbBJhYFHBURETG7fnzRHTpshNKZeYdoq2tTTFvni+6d/eAhFflFhgmQkWYNPERFHtqvvX5qN6RTICIiERSsqQNhg37CEuWXEKdOqWwcmU7lCtnK3ZYRoeJUBFkGnEEtsc/eevzsR0vQG1ftQAjIiIi4Z/Zpf/d2zNhQgO4uBRDnz7VeFm8SJgIFSEm0Vdhf8jnrc9H93gMwVxRgBEREREAxMenYcKEY6hZswSGD6+tLZfLZRgwwEvEyIiJUFEgCFBsLw+pMi7bpxOabYay3McFHBQREQHA2bPhGD48CM+eJeHQoYdo2rQsqlcvLnZY9A8mQoWYNCkcxU73hTzmerbPx7U9AlXx+gUcFRERAYBSqcb8+eewfPll/HNWDFZWcrx8mSxuYKSDiVAhJH9+GnZHO771+QTfHVCWbluAERER0b89fBiLoUMP4datl9qyxo3LYPnytihVykbEyOi/mAgVNhr1W5Mgjak9Yj4N5WSoREQiEQQBmzb9ialTTyE1VQUAkMulCAhohGHDakMq5eezoWEiVIhIk59CsSvr1V7J3lORUj37+wQREVHBiItLxVdfHUFwcIi2rFIle6xe7QcvL2cRI6N3YSJUSNj8/hnMQ3/VKVPZV0Ncx3MiRURERP9mamqCBw9itcsDB9bA9OlNYWkpFzEqeh/etKAQMH2yP0sSBABxHc6KEA0REWXHykqOVavaoUQJK2ze/DEWLGjBJKgQYI+QgZMoX8H2dF+dsgSf7VCWaSdSREREBAB37kTB0lKO8uXttGU1a5bA5cuDYWbGr9fCgj1CBs5xW2md5dhOl5kEERGJSKMRsHbtNbRp8wv8/YOgUml0nmcSVLgwETJgdgebZSlT27mLEAkREQFAZGQSevbcjW++OYX0dDWuXn2On366KXZY9AFET4S2bNkCX19fVK9eHd27d8etW7feWf+nn35CmzZt4OXlhWbNmmHOnDlIT08voGgLjiQjMcuNEqP6vxIpGiIiCgp6iGbNNuHUqSfasiFDaqFv3+oiRkUfStT+u0OHDmHu3LmYMWMGatSogY0bN2Lw4MEIDg6GQpF1TqwDBw7ghx9+wJw5c+Dt7Y3Q0FBMmjQJEokEAQEBIryC/OO41UVnOapPtEiREBEZt+RkJcaPP4aNG9/8UHd2tkJgYBv4+JQXLzDKE6L2CG3YsAE9evRAt27dUKlSJcyYMQPm5ubYtWtXtvWvX7+OWrVqoWPHjihdujQaN26MDh06vLcXqbBx2lRMZ1ll5wnITEWKhojIeN28GYlatdbqJEHt2rni1Kn+TIKKCNF6hJRKJW7fvo0hQ4Zoy6RSKRo2bIjr17OfO8vb2xv79+/HrVu34OXlhfDwcJw+fRoff6z/hKISiQHegFmdDsefnbIUx398HoYWal54ffwNrh2MFNvDcLAtDENERCL8/LZBqVQDACwtTTB7tg/69q0GCRunwOXXIRctEYqLi4Narc5yCkyhUODRo0fZrtOxY0fExcWhd+/eEAQBKpUKPXv2xNChQ/Xev4ODgc31kpEKBBbLWj5WDUeJ6EO58pVCYWBtYeTYHoaDbSEuR0cbDBtWG0uWXMRHH5XEL790g5tb1mEbVLgVqmv8Ll68iDVr1mDatGnw8vJCWFgYvvvuO6xYsQLDhw/Xa1uxsYnQaN5fr6A4bsyaBMX0joAQU3RnKZZIMj/oY2IStTMzk3jYHoaDbSEeQRB0envGj6+HsmVt0bOnB+RyGaKjE0WMzrhJpfnTiSFaImRvbw+ZTIaYmBid8piYGDg6Oma7ztKlS9GpUyd0794dAODu7o6UlBRMnToV/v7+kEpz3nMiCDCMDxiNGk4/22cpjuqXkPlpaAgx5jODaQsCwPYwJGyLgpOYmI7Jk0/C27sEPvusprbczMwEY8Y0QHQ0k1Kx5dfxF+2ci6mpKTw9PXH+/HltmUajwfnz5+Ht7Z3tOmlpaVmSHZlMBiAziy+Msk2C+r/i4AAiogJy+fIz+Pj8jO3b72D69NP4+++Y969ERYaop8YGDRqEiRMnolq1avDy8sLGjRuRmpqKrl27AgC+/vprODs7Y9y4cQAAHx8fbNiwAVWrVtWeGlu6dCl8fHy0CVFhIou7naUsuudTESIhIjI+KpUGixZdwOLFF6FWZ/6YNjGRITQ0gWOBjIioiZCfnx9iY2MRGBiIqKgoeHh4YN26ddpTY8+fP9fpAfL394dEIsGSJUsQGRkJBwcH+Pj4YMyYMWK9hFyTxf4Jh98a6ZTxholERAUjNDQew4YF4cqV59qyOnVKYeXKdihXzlbEyKigSYTCek7pA8XEiDhYWp0Gpy3FdYoSmm6EsnwXkQISh0SSeVUGz70bBraH4WBb5B9BELB9+x0EBJxAcnIGAEAmk2DcuPoYPboeTEx0h1+wLQyHVJo/V1IWqqvGigrbE5/qLCfVnmt0SRARUUFLSEjD+PHHsG/f39qycuVssWpVO9SuXUrEyEhMTIQKmGnYQZg+P6lTllpVv0v/iYhIfxKJBNeuvdAu9+zpiTlzfGBtzTv3G7Oifac+QyNoYHuql05RVO8Xb6lMRER5qVgxM6xY0RYKhQXWreuAwMA2TIKIPUIFyS6opc5yXLujgImlSNEQERVtDx/GwtJSjlKl3owrqV+/NK5c+RxWVnIRIyNDwh6hAiSPvqKzrHKqJ1IkRERFlyAI2LjxFlq0+BkjRgRDo9Ed5cwkiP6NiVABMf/7J53lqL6x4gRCRFSERUenYMCA/Zgw4RhSU1X4449wbNp06/0rktHiqbECIEmLhs2FUbqFUh56IqK8dOJEKEaNOoyXL9/M0ThggBd69KgqYlRk6PhtnN80ajjuqKhTFNvx/FsqExGRvtLSVPjuuz+wZs01bZlCYYElS1qjTRtXESOjwoCJUD6zOTdUZzmp1kyo7T1FioaIqGi5cycK/v5BuHs3Wlvm41MOgYFt4exsJWJkVFgwEcpP6nSYP9quU5RabbQ4sRARFTHh4a/Qps0vSE9XAwDMzGSYOrUJBg/2hlTKiaspZzhYOh85bXHSWY7qyxmNiYjySpkyxbTjfzw8HHHkSB988UUtJkGkF/YIFSQpL9kkIspLM2c2R+nSxeDv/xHMzfmVRvpjj1A+cfjVQ2c5ql+CSJEQERV+yckZGD/+GLZtu61TbmUlx5gx9ZgEUa7xLyc/aFSQpUTolknYVUtElBs3b0bC3/8QHj6Mw65dd1GvngsqVLATOywqItgjlA+cfnbQWY7q81KkSIiICi+1WoPAwEto124rHj6MA5B51+h796LfsyZRzrFHKI9J0nQHRKvsPAGZuUjREBEVThERiRg+PAjnzj3VltWo4YzVq/3g6movYmRU1DARymPmDzfpLMd1PCtSJEREhdO+ffcxfvwxJCSkA8gcWTBqVF1MmNAApqYykaOjooaJUF4SBFhfm6ZdTC/7MSDh2UciopxISlIiIOAEtm+/oy1zcbHBihVt0bBhGREjo6KMiVAeMo04orOcXHOKSJEQERU+6elqnDr1RLvcubM7FixoATs7Di+g/MPuijxke6K79rEgkUJtV0XEaIiICheFwgLLlrWFjY0pli9vizVr/JgEUb5jj1Aekb84o7P8qtkWkSIhIiocQkPjYWkpR/Hib+YEa968HK5d+xy2tkyAqGCwRyiP2B1pr7OsLNv+LTWJiIybIAjYtu02fHw2Y/ToIxAEQed5JkFUkJgI5YWMZJ3FuHZHRQqEiMiwxcen4csvD2LUqMNITs7AsWOPsXXr7fevSJRPeGosDzhtLamzrHKqJ1IkRESG6+zZcAwfHoRnz5K0ZT17eqJTJzcRoyJjx0ToA1n8tURnOb3sx+IEQkRkoJRKNebPP4flyy/j9VkwOzszLFzYikkQiY6J0AeyvjZVZ/lV880iRUJEZHgePIiFv/8h3Lr1Zqqhxo3LYPnytihVykbEyIgyMRH6ALKEv3WWoz8NFScQIiIDFBoaj5Ytf0ZqqgoAIJdLERDQCMOG1YZUyomoyTBwsPQHsLgdqLMsmDm8pSYRkfEpX94Ofn6VAACVKtkjKKgXRoyowySIDAp7hD6A2ZM92sfJNb8VMRIiIsM0f34LlClTDKNH14OlpVzscIiy+KAeofT09LyKo1CSZiRqH6dW+ULESIiIxJWWpsK3357C/v26QwaKFTPD5MmNmQSRwdI7EdJoNFixYgWaNGkCb29vhIeHAwCWLFmCnTt35nmAhkr6KkRnWTC1EycQIiKR3bkThTZtfsGaNdcwbtxRREQkvn8lIgOhdyK0cuVK7NmzBxMmTIBc/ibDd3Nzw6+//pqnwRkyy/+MDyIiMjYajYC1a6+hTZtfcPduNIDMnqEbN16IHBlRzumdCO3btw+zZs1Cp06dIJW+Wd3d3R2PHj3K0+AMmcWDDdrHqW6DRYyEiKjgRUYmoVev3fjmm1NIT1cDADw8HHHkSB+0b19Z5OiIck7vwdKRkZEoW7ZslnJBEKBSqfIkKIMnaHQWk2sEiBQIEVHBCwp6iLFjjyImJlVbNmRILUyZ0hjm5rwGhwoXvf9iK1WqhCtXrsDFxUWnPDg4GB4eHnkWmCEzf6g7s7xgUVykSIiICk5ycgamTTuNTZtuacucna0QGNgGPj7lxQuM6APonQgNGzYMkyZNQmRkJARBwJEjR/D48WPs3bsXa9asyY8YDYsgwOb8cO2i2rq8eLEQERWgpKR0HDz4QLvcrp0rFi1qDYXCQsSoiD6M3mOEWrZsidWrV+P8+fOwsLBAYGAgQkJCsHr1ajRq1Cg/YjQoip2VdJZfNVkvUiRERAXL2dkaixa1gqWlCRYtaoWffurEJIgKPYkgvJ4Cz7jExCRCo3l/vf9y2lRMZzmq/6s8isj4SCSAo6MNoqMTYZx/hYaF7WE4DKUtIiISYWlpAnt73WQnKioFTk6WIkVVsAylLQiQSgGFIu/np9O7R6hFixaIi4vLUv7q1Su0aNEiT4IyVJJ03dcd1S9BpEiIiPLXvn330bz5Jowffwz//b1sLEkQGQe9E6GIiAhosulKUSqViIyMzJOgDJX5g026BRLOl0NERUtiYjpGjgzGF18cREJCOg4ceIBdu+6JHRZRvsnxYOnjx49rH585cwY2Nm+6pzQaDc6fP5/lSrKixvram/nE0kv7iRgJEVHeu3z5Gfz9gxAW9qa3u3Nnd7RsWUHEqIjyV44ToeHDM6+UkkgkmDRpku5GTEzg4uKSpbwoMQ0P0llOrTpMpEiIiPKWSqXB4sUXsWjRBajVmafBrK1NMW+eL7p394CEvd9UhOU4Ebp3L7Nr1NfXF7/++iscHBzyLShDZHvyU53ljBJNRYqEiCjvhIbGY9iwIFy58lxbVqdOKaxc2Q7lytmKGBlRwdD7PkInTpzIjzgKlTg/HgMiKvwePYpDy5ZbkJSkBADIZBKMG1cfo0fXg4mJ3kNIiQqlXN0LPSUlBZcvX8azZ8+QkZGh81z//v3zJDBDIkmN0llWOdYWKRIiorxToYIdmjQpg6CgEJQrZ4tVq9qhdu1SYodFVKD0ToTu3LmDL7/8EqmpqUhNTYWtrS3i4uJgYWEBBweHIpkISTPixQ6BiCjPSSQSLFrUGmXKXMCkSY1gbW0qdkhEBU7vvs+5c+fCx8cHly9fhpmZGXbs2IGTJ0/C09MTEydOzI8YDUpaxV5ih0BEpDelUo1Zs87g6NFHOuUKhQVmz/ZhEkRGS+9E6O7duxg0aBCkUilkMhmUSiVKliyJCRMmYNGiRfkRo+gkSt49mogKr4cPY+HntxXLll3GV18dwcuXyWKHRGQw9E6ETExMIJVmrqZQKPDs2TMAgLW1NV68eJG30RkCdTrsD/m8WdZkvL0uEZEBEQQBGzfeQosWP+PWrZcAgISENFy69EzkyIgMh95jhKpWrYo///wT5cuXR506dRAYGIi4uDjs27cPlStXzo8YRWV3SHfaEF42T0SFQXR0CsaOPYrg4BBtWaVK9li92g9eXs4iRkZkWPTuERozZgycnJy0j4sVK4bp06cjLi4OM2fOzPMAxSaPu6WznOY2UJxAiIhy6MSJUDRvvlknCRo4sAaOHevLJIjoP/TuEapevbr2sUKhwPr16/M0IEPy37nFOMkqERmytDQVvvvuD6xZc01bplBYYMmS1mjTxlXEyIgMV57dMev27dsYMmRIXm3OINicH6FbwNvME5EBi45Owdatt7XLvr7lcepUfyZBRO+gVyJ05swZzJ8/H4sWLUJ4eDgAICQkBMOGDcMnn3yS7az0RUVc+9/FDoGI6J1Kly6GBQtawMxMhjlzfLB1axc4O1uJHRaRQcvxqbGdO3fi22+/ha2tLV69eoWdO3di0qRJmD17Ntq1a4fffvsNrq5F6FeHOk1nUaWoKU4cRERvERmZBEtLOWxszLRlXbtWQb16LnBxsRExMqLCI8c9Qps2bcL48eNx8eJFLFmyBHFxcdi6dSsOHDiAmTNnFq0kCIDtiU/fX4mISCRBQQ/RvPlmTJ58MstzTIKIci7HiVB4eDjatm0LAGjdujVMTEwwYcIElChRIt+CE5Pp8zcfLmrr8uIFQkT0L8nJGRg//hgGDNiPmJhUbN9+BwcO/C12WESFVo5PjaWlpcHCwgJA5vw0crkcxYsXz7fAxGRxd6XOclwHjg8iIvHdvBmJoUMPISQkTlvWrp0rGjYsI2JURIWbXpfP79y5E5aWlgAAtVqN3bt3w97eXqdOUZh01Sxkm86yYGonTiBERADUag1WrLiCefPOQaXKvCjF0tIEs2f7oE+fapDwilaiXMtxIlSqVCns2LFDu+zo6Ih9+/bp1JFIJHonQlu2bMH69esRFRWFKlWq4Ntvv4WXl9db67969QqLFy/G0aNHER8fDxcXF0yePBnNmjXTa7/vIo+9oX0c1/Zonm2XiEhfERGJGD48COfOPdWW1ajhjNWr/eDqav+ONYkoJ3KcCJ04cSLPd37o0CHMnTsXM2bMQI0aNbBx40YMHjwYwcHBUCgUWeorlUoMGjQICoUCS5cuhbOzM549e4ZixYrlWUyS1CidZZXjR3m2bSIifYSExKFNm1+QkJAOIPNWZqNG1cWECQ1gaioTOTqiokHvO0vnpQ0bNqBHjx7o1q0bAGDGjBk4deoUdu3ahS+//DJL/V27diEhIQHbtm2DXC4HAJQuXTpPY5LHXNMtkIp6iIjIiFWoYIdatUrg5MkncHGxwYoVbTkeiCiP5dmdpfWlVCpx+/ZtNGzY8E0wUikaNmyI69evZ7vOiRMnULNmTcycORMNGzZEhw4dsHr1aqjV6jyLyyTqovZxetmOebZdIiJ9SaUSBAa2Qb9+1XHyZD8mQUT5QLTujri4OKjV6iynwBQKBR49epTtOuHh4bhw4QI6duyItWvXIiwsDDNmzIBKpcKIESOyXedtJJLsZ8yw+Pt/2sfqYpU5q0Y+en1seYwNA9tDXCqVBosXX0T9+i5o2rQsgMy2KFHCGosWtRI5OuPF94XhyK82KFTnfQRBgEKhwKxZsyCTyVCtWjVERkZi/fr1eidCDg5vueHYv+4obenVA5aOvDFZflMoeIwNCduj4D16FIe+fXfj/PmncHGxwa1b/gDYFoaEbVF0iZYI2dvbQyaTISYmRqc8JiYGjo6O2a7j5OQEExMTyGRvBglWrFgRUVFRUCqVMDU1zfH+Y2MTkd3UaA5SU0iRAgCIRlkgOjHH2yT9SCSZHy4xMYkQBLGjIbZHwRMEATt23MWkSSeQlKQEALx4kYT9++9g4MCP2BYGgO8LwyGVvqMT4wPkKhEKCwvDrl27EB4ejilTpkChUOD06dMoVaoUKleunKNtmJqawtPTE+fPn0fLli0BABqNBufPn0ffvn2zXadWrVr47bffoNFoIJVmDm8KDQ2Fk5OTXkkQAAgCsv2jlirj39QxsQb4h5/v3tYWJA62R8GIj0/DhAnHsG/fm7tClytni1Wr2qFOnVIA2BaGhG0hvvw6/noPlr506RI6duyIW7du4ciRI0hJyew9uX//PpYtW6bXtgYNGoQdO3Zgz549CAkJwfTp05GamoquXbsCAL7++mv88MMP2vq9evVCfHw8vvvuOzx+/BinTp3CmjVr0KdPH31fRrYkadF5sh0ionc5ezYczZtv0kmCevb0xMmT/VC7dikRIyMyPnr3CP3www8YPXo0Bg0aBG9vb215/fr18fPPP+u1LT8/P8TGxiIwMBBRUVHw8PDAunXrtKfGnj9/ru35AYCSJUti/fr1mDt3Ljp16gRnZ2f0798fX3zxhb4vI1sW99bmyXaIiLKjVKqxYME5LFt2Wfvr1tbWDD/80AqdOrmJGxyRkdI7Efr777+xcOHCLOUODg6Ii4vLZo1369u371tPhW3evDlLmbe3t84drvOS1a152scZTnXzZR9EZLyePUvEunU3tElQo0alsXx5O84WTyQivU+N2djYICoqKkv53bt34ezsnCdBGYLUKkPFDoGIipjy5e3w3XfNIZdLMXVqE+za1Z1JEJHI9E6E2rdvj4ULFyIqKgoSiQQajQZXr17F/Pnz0blz53wIsWBIlAk6y+nlu4kUCREVFTExqUhJydAp6927Gs6cGYARI+pAKuXNaYjEpnciNGbMGFSsWBHNmzdHSkoK2rdvj759+8Lb2xv+/v75EWOBkL0K0S3g3bOI6AOcOBGKZs02YcaM33XKJRIJKlbkZKlEhkLvMUKmpqaYPXs2hg0bhgcPHiA5ORlVq1ZF+fLl8yG8giNLuK99rLKtImIkRFSYpaWp8N13f2DNmsx5CzdsuImWLSugVauKIkdGRNnROxG6cuUKateujVKlSqFUqSJ0mafw5u6KKofqIgZCRIXVnTtR8PcPwt27b27F4etbHl5eRWf8JFFRo3ciNHDgQBQvXhwdOnRAp06dUKlSpfyIq+D961SYyrG2iIEQUWGj0QhYt+46Zs06g/T0zEmgzcxkmDatKQYPrgkJT7UTGSy9E6Hff/8dhw4dwm+//Ya1a9fC3d0dHTt2RIcOHVCiRIn8iLHACVK52CEQUSERGZmEUaMO4+TJJ9oyDw9HrF7tBw+P7KcLIiLDofdgaQcHB/Tt2xfbtm3D0aNH0bZtW+zduxe+vr7o379/fsRYIORRl8UOgYgKmYcPY9G8+WadJGjIkFo4fLg3kyCiQuKDJl0tU6YMvvzyS1SpUgVLly7F5cuFN5mQxd/VPpamx7yjJhFRpgoV7ODm5oDz5yPg7GyFwMA28PEpL3ZYRKSHXCdCV69exYEDB3D48GGkp6ejRYsWGDt2bF7GVqDUtm7Ay3MAAKVzU5GjIaLCQCaTYsWKdpg79yxmzmwOhcJC7JCISE+5mmvs4MGDePnyJRo1aoQpU6agRYsWsLAoOh8AgmkxsUMgIgOjVmuwYsUV1K9fGnXrvrlitnTpYlixop2IkRHRh9A7Ebp8+TIGDx6Mdu3awcHBIT9iEoU86pLYIRCRgYqISMTw4UE4d+4pypa1xcmTfWFjYyZ2WESUB/ROhLZt25YfcYjOJP7OmwWpqXiBEJFB2bfvPsaPP4aEhHQAQHh4Ak6deoKOHTlbPFFRkKNE6Pjx42jatCnkcjmOHz/+zrotWrTIk8DEpLapIHYIRCSyxMR0TJ58Etu3v/mR5OJigxUr2qJhwzIiRkZEeSlHidDw4cNx9uxZKBQKDB8+/K31JBIJ7t69+9bnDZUkLVq3QPpBF9MRUSF3+fIzDBsWhCdP3kzG3LmzOxYsaAE7O3MRIyOivJajb/x79+5l+7iosL4SIHYIRGQAVCoNFi++iEWLLkCtFgAA1tammDfPF927e/AO0URFkN43VNy7dy+USmWWcqVSib179+ZFTAVO/vKi9nGq+xciRkJEYgoNjUdg4CVtElSnTimcPNkPPXpUZRJEVETpnQgFBAQgMTExS3lycjICAgpnz4rKoYb2car75yJGQkRiqlTJAVOnNoFMJsHXXzfAvn09UK6crdhhEVE+0nswjCAI2f4yioyMhI2NTZ4EVdDMwvZpHwty3kOIyFjEx6fBwsIEZmZvPgo//9wbjRuX5RQZREYix4lQ586dIZFIIJFIMGDAAJiYvFlVrVbj6dOnaNKkSb4EWZAEuZXYIRBRATh7NhzDhwehc2d3TJ/eTFsukUiYBBEZkRwnQi1btgQA3L17F40bN4aV1ZuEQS6Xw8XFBa1bt877CPObOk1nUTC1EycOIioQSqUaCxacw7JllyEIwMqVV+HrWwFNm5YVOzQiEkGOE6ERI0YAAFxcXODn5wczs6JxV1VZcrjYIRBRAXn4MBZDhx7CrVsvtWWNG5dBpUr2IkZFRGLSe4xQly5d8iMO0UgTn2gfK50biRgJEeUXQRCwadOfmDr1FFJTVQAAuVyKgIBGGDasNqRSXhFGZKxylAjVrVsXwcHBcHBwQJ06dd55GemlS4Vszi6JTPtQMHcSMRAiyg/R0SkYO/YogoNDtGWVKtlj9Wo/eHk5ixgZERmCHCVCAQEBsLa21j4uSvfTkEdf1j5WW5cXLxAiynMPH8aic+edePkyWVs2cGANTJ/eFJaWchEjIyJDkaNE6N+nw7p27ZpvwYhBmvLszePUSBEjIaK8Vq6cLVxcrPHyZTIUCgssWdIabdq4ih0WERkQvW+oePv2bdy/f1+7fOzYMQwbNgyLFi3K9o7Ths4k9k/tY2UpXxEjIaK8JpfLsGqVH9q3r4RTp/ozCSKiLPROhKZOnYrQ0FAAQHh4OMaMGQMLCwsEBwfj+++/z+v48p3Kror2scaqtIiRENGH0GgE/PjjNfz550ud8ooV7bFhQyc4O/MeYUSUld6JUGhoKDw8PAAAQUFBqFu3Ln744QfMnTsXR44cyfMAC5LGTCF2CESUC5GRSejVazemTDmFoUMPISUlQ+yQiKiQ0DsREgQBGo0GAHD+/Hk0bdoUAFCyZEnExcXlbXQFQP7yvNghENEHCAp6iObNN+PkycxbYTx4EIvjxx+LHBURFRZ6J0LVqlXDqlWrsHfvXly+fBnNmzcHADx9+hSOjoXvtvTStGjtY0FWNG4SSWQMkpMzMH78MQwYsB8xMakAAGdnK2zf3hUdO7qJHB0RFRZ631Bx8uTJmDBhAo4dO4ahQ4eiXLlyAIDDhw/D29s7zwPMV4IAqTJeu6gxLy5eLESUYzdvRmLo0EMICXnTC92unSsWLWoNhcJCxMiIqLDROxGqUqUKDhw4kKX866+/hlSqdweTqCT/6g0CAHDCVSKDplZrsGLFFcybdw4qVeYpektLE8ya1Rx9+1YvUvc4I6KCoXci9Npff/2FkJDMO7VWqlQJnp6eeRZUwRG0jzLsvUSMg4hy4sGDWJ0kqEYNZ6xe7QdXV84VRkS5o3ciFBMTg9GjR+Py5csoVqwYAODVq1eoV68eFi9eDAcHhzwPsiDw0nkiw1eliiMmTWqI7777A6NG1cWECQ1gaip7/4pERG+h97msWbNmISUlBQcPHsSlS5dw6dIl/Pbbb0hKSsLs2bPzI0YiMlJJSUpt789rw4fXxuHDvTFlSmMmQUT0wfROhM6cOYNp06bB1fXNHVorVaqEadOm4ffff8/T4PKbNPXFmwWB9x0hMiSXLz+Dj89mLFp0QadcJpOiZs0SIkVFREWN3omQRqOBXJ51skITExPt/YUKC5P4u9rH8qgrIkZCRK+pVBp8//15dOq0HU+eJGDRoou4dOnZ+1ckIsoFvROh+vXr47vvvkNk5JsJSiMjIzF37lw0aNAgT4PLb4LMXPs4vXzRmkyWqDAKDY1Hp07b8f3356FWZ17M8NFHJTk9BhHlG70HS0+dOhX+/v5o0aIFSpTI7J5+8eIFKleuXCjnGntNbVNB7BCIjJYgCNix4y4CAk4gKSlz8maZTIJx4+pj9Oh6MDEpXLfmIKLCQ+9EqGTJktizZw/Onz+vvXze1dUVDRs2zPPgiKjoi49Pw9dfH8fevfe1ZeXK2WLVqnaoXbuUiJERkTHQKxE6dOgQjh8/joyMDDRo0AD9+vXLr7iIyAg8fBiL7t13ISIiUVvWs6cn5szxgbW1qYiREZGxyHEi9Msvv2DmzJkoV64czM3NcfToUYSFhWHixIn5GR8RFWGlSxdDsWJmiIhIhJ2dGRYubIVOnThPGBEVnByfeN+yZQtGjBiBw4cPY9++fZg3bx62bt2an7ERURFnbm6C1av90LJlBZw61Z9JEBEVuBwnQuHh4ejcubN2uWPHjlCpVHj58mV+xFUgZElhYodAZDQEQcCmTbdw/36MTrmHhyN++aULSpWyESkyIjJmOT41plQqYWlpqV2WSqWQy+VIT0/Pl8AKgmnEEe1j6X8nYCWiPBMdnYKxY48iODgEnp5OCA7uBTOzXE91SESUZ/T6JFqyZAksLCy0yxkZGVi1ahVsbN78kgsICMi76PKZRJ2qfaxyqC5iJERF14kToRg16jBevkwGANy+HYUjRx6hY0eeBiMi8eU4EapTpw4eP36sU+bt7Y3w8HDtskQiybvICoDkX71AahvXd9QkIn2lpakwe/YZrF17XVumUFhgyZLWaNOG7zciMgw5ToQ2b96cn3GI4t9jhNScfZ4oz9y5EwV//0O4e/fNeCAfn3IIDGzLu0QTkUEx6pP0EkGlfSyYO4kYCVHRoNEIWLfuOmbNOoP0dDUAwMxMhqlTm2DwYG9IpYWr15iIij7jTYQEQXe5kJ3WIzJEd+5EY+rU09BoMt9fHh6OWL3aDx4ejiJHRkSUPeOdwEddeK92IzJU1ao5YfTougCAIUNq4fDh3kyCiMigGW+P0L9kONUTOwSiQiklJQPm5iY6p7zGjauP5s3LoX59jrsjIsNnvD1C/yLIzMQOgajQuXkzEi1a/IyVK6/olMvlMiZBRFRo5CoRunLlCsaPH49PP/0UkZGRAIC9e/fiypUr71mTiAo7tVqDwMBLaNduK0JC4jB37lncuhUpdlhERLmidyJ0+PBhDB48GObm5rhz5w6USiUAICkpCWvWrMnzAInIcEREJKJbt18xe/YfUKk0AICqVZ1gZcWZ4omocNI7EVq1ahVmzJiB2bNnw8TkzRCjWrVq4c6dO3kaXH6SpMeKHQJRobJv3300b74J5849BZB5oeVXX9XFwYM94epqL3J0RES5o/dg6cePH6N27dpZym1sbPDq1as8CaogyF491D6WpjwTMRIiw5aYmI7Jk09i+/Y3P3RcXGywYkVbNGxYRsTIiIg+nN6JkKOjI8LCwlC6tO5gyKtXr6JMmUL0oShotA+VpduKGAiR4Xr4MBa9eu3BkycJ2rLOnd3x/fctYGtrLmJkRER5Q+9TYz169MB3332HmzdvQiKRIDIyEvv378f8+fPRq1ev/IgxX8gS/n6zIOX4BqLslCxpAxOTzI8Ja2tTLF/eFmvW+DEJIqIiQ+9E6Msvv0SHDh0wcOBApKSkoG/fvvjmm2/w6aefol+/frkKYsuWLfD19UX16tXRvXt33Lp1K0frHTx4EO7u7hg2bJje+xTk1m8WVCl6r09kDKys5Fi92g+NGpXGyZP90KNH1UI3uTIR0bvofWpMIpHA398fgwcPRlhYGFJSUuDq6gorq9xNpHjo0CHMnTsXM2bMQI0aNbBx40YMHjwYwcHBUCgUb13v6dOnmD9/frbjlXJClhSufay2r5arbRAVJYIgYNOmm/DwcED58nba8ho1nLF7d3cmQERUJOX6hoqmpqaoVKkSvLy8cp0EAcCGDRvQo0cPdOvWDZUqVcKMGTNgbm6OXbt2vXUdtVqN8ePHY+TIkbkelyRRJ795nJGYq20QFRXx8Wn44ouDGDBgL/z9DyEjQ63zPJMgIiqq9O4R6tev3zs/FDdt2pTjbSmVSty+fRtDhgzRlkmlUjRs2BDXr19/63orVqyAQqFA9+7dcfXq1Rzv79/k0W/WUxeryDlXRfD6mPPYi+vs2XAMGxaEZ8+SAABXr77A0aOP0L59ZZEjM158bxgOtoXhyK820DsR8vDw0FlWqVS4e/cuHjx4gM6dO+u1rbi4OKjV6iynwBQKBR49epTtOleuXMGvv/6KvXv36rWv/zKxKQE8z3xs61QCcLT5oO1R7ikUPPZiUCrVmDr1JBYsOAshc7J42NubY+3ajvjkk6riBkcA+N4wJGyLokvvRGjy5MnZli9btgwpKfk76DgpKQlff/01Zs2aBQcHhw/alhB+Gq+Ty7h0a6ijeXqsoEkkmR8uMTGJ2i9iKhgPH8ZiyJBDuHXrpbasceMy2Lr1E1hYSBDN94Oo+N4wHGwLwyGVAg4OeZ+Q5tns8506dUL37t0xceLEHK9jb28PmUyGmJgYnfKYmBg4OjpmqR8eHo6IiAj4+/tryzSa17f5r4rg4GCULVs2R/uWqFO1j1UWpQD+gYtGEMAPmAKSOSD6T0ydegqpqSoAgFwuRUBAIwwfXhvFixdDdDQ/8A0F3xuGg20hvvw6/nmWCF2/fh2mpvrdj8fU1BSenp44f/48WrZsCSAzsTl//jz69u2bpX7FihVx4MABnbIlS5YgOTkZU6ZMQYkSJXIXvIll7tYjKmT+/PMlJkw4pl2uVMkeq1f7wcvLmWMgiMgo6Z0IjRgxQmdZEARERUXhr7/+ytX9fAYNGoSJEyeiWrVq8PLywsaNG5GamoquXbsCAL7++ms4Oztj3LhxMDMzg5ubm876xYoVA4As5XrhNwAZCS8vZwwd+hFWr76KgQNrYPr0prC0lIsdFhGRaPROhGxsdM/PSSQSVKhQAaNGjULjxo31DsDPzw+xsbEIDAxEVFQUPDw8sG7dOu2psefPn0MqzfVV/kRGLT1dBVNTmc6VnlOmNIKvb3k0b15OxMiIiAyDRBByftZNrVbj2rVrcHNzg62tbX7Glf+WFQOUidCYOyGmR4jY0RgliQRwdLThmJR8cudOFPz9D2HAgBr47LOa763P9jAcbAvDwbYwHFJp/ly9p1dXi0wmw2effVaoZpl/H2UpX7FDIMpTGo2AtWuvoU2bX3D3bgymTz+N+/dj3r8iEZER0vvUWOXKlfH06dPCNdP8O2jM3j6NB1FhExmZhFGjDuPkySfasgoV7EWMiIjIsOk9+Gb06NGYP38+Tp48iZcvXyIpKUnnHxGJIyjoIZo336yTBA0ZUguHD/eGuzsTfiKi7OS4R2j58uX47LPP8OWXXwIA/P39dQZgCoIAiUSCu3fv5n2U+UiWHP7+SkQGLDk5A9OmncamTbe0Zc7OVggMbAMfn/LiBUZEVAjkOBFasWIFevXqpddcYoWBysFL7BCIci0kJA59++5FSEictqxdO1csWtQaCoWFiJERERUOOU6EXl9cVrdu3XwLRgyCiZXYIRDlmpOTpXameEtLE8ye7YM+fapxtngiohzSa4xQUfxwlaTHih0CUa4VK2aGFSva4aOPSuD48X7o27d6kXyfEhHlF72uGmvTps17P2QvXbr0QQEVNHWxymKHQJRj+/f/jY8+KgkXlzf30qhXzwWHDvViAkRElAt6JUIjR47Mcmfpwk5dzFXsEIjeKzExHZMnn8T27XfQqFFp/PrrJ5DJ3nToMgkiIsodvRKh9u3bQ6EoYpfhSvJs3lmifHH58jMMGxaEJ08SAABnzz7FkSOP0K5dJZEjIyIq/HKcBfAXJ1HBUqk0WLz4IhYtugC1OvNiBWtrU8yb54u2bdmTSUSUF/S+aoyI8l9oaDyGDQvClSvPtWV16pTCypXtUK5cIZ/nj4jIgOQ4Ebp3715+xkFEyPzBsWPHXQQEnEBSkhIAIJNJMG5cfYweXQ8mJnrfDJ6IiN6BA2SIDMiNG5EYOTJYu1yunC1WrWqH2rVLiRgVEVHRZfQ/LwW5tdghEGl5e5dA//6Zdzvv2dMTJ0/2YxJERJSPjL5HSG3pInYIZMQyMtQwMZHqXIwwY0YztGxZgQOiiYgKgNH3CEHOKTZIHA8fxqJdu63Yvv2OTrmVlZxJEBFRATHqREiQmokdAhkhQRCwceMttGjxM27deomAgBN49Cju/SsSEVGeM/pTY0QFKTo6BWPHHkVwcIi2rGRJa6SlqUSMiojIeBl1IiTRpIsdAhmREydCMWrUYbx8mawtGzDACzNmNIOlpVzEyIiIjJdRJ0IaUzuxQyAjkJamwnff/YE1a65pyxQKCyxZ0hpt2nAsEBGRmIw7ETIrYvOmkcF59CgOgwYdwN270doyX9/yWLq0DZydOVCfiEhsRp0IEeU3OztzxMWlAgDMzGSYNq0pBg+uybn7iIgMhFFfNUaU3xwcLBAY2Baenk44cqQPPv/cm0kQEZEBYSJElIcOHw5BZGSyTlnz5uVw7FgfeHg4ihQVERG9DRMhojyQnJyB8eOPoV+/fRg9+jAEQdB5XibjW42IyBDx05noA928GYmWLX/Gpk23AADHj4fiyJFHIkdFREQ5wcHSRLmkVmuwYsUVzJt3DiqVBgBgaWmC2bN90Lp1RZGjIyKinGAiRJQLERGJGD48COfOPdWW1ajhjNWr/eDqai9iZEREpA8mQkR62rv3PiZMOIaEhMw7k0skwKhRdTFhQgOYmspEjo6IiPTBRIhID1euPMOXXx7ULru42GDFirZo2LCMiFEREVFuGfVgaYk6TewQqJCpXbsUunf3AAB07uyOkyf7MQkiIirEjLpHSJYSIXYIZOA0GgFSqe4NEOfN80WrVhXx8cduvDkiEVEhZ9Q9QsqSPmKHQAYsNDQeHTpsw75993XKbWzM0LmzO5MgIqIiwKh7hIiyIwgCduy4i4CAE0hKUuLBg2OoXbsUXFxsxA6NiIjyGBMhon+Jj0/DhAnHsG/f39oyOztzxMamMhEiIiqCjDsREjRiR0AG5OzZcAwfHoRnz5K0ZT17emLOHB9YW5uKGBkREeUXo06ETGKuix0CGQClUo35889h+fLLeD1FmK2tGX74oRU6dXITNzgiIspXRp0IpZf7WOwQSGShofH4/PPfcOvWS21Zo0alsXx5O54KIyIyAkadCBFZWJggIiIRACCXSxEQ0AjDhtXOcsk8EREVTUZ9+bzsVYjYIZDInJ2tsXhxa1Su7ICgoF4YMaIOkyAiIiNi1D1CKsdaYodABez06SeoXr04HBwstGVt27qiRYvykMs5TxgRkbEx6h4hQc4xIMYiLU2Fb789he7dd2H8+GMQXo+K/geTICIi42TUiRAZhzt3otCmzS9Ys+YaAOC33x7gxIlQcYMiIiKDYNSnxqho02gErFt3HbNmnUF6uhoAYGYmw7RpTeHrW17c4IiIyCAwEaIiKTIyCaNGHcbJk0+0ZR4ejli92g8eHo4iRkZERIaEiRAVOcHBIRgz5ghiYlK1ZUOG1MKUKY1hbs4/eSIieoPfClSkXLwYgf7992mXixe3wrJlbeDjU168oIiIyGBxsDQVKXXrloKfXyUAmZfFnz7dn0kQERG9lVH3CElTXogdAn0gQRAgkby5AaJEIsGiRa3Qtq0rPv20qs5zRERE/2XUPUIqx9pih0AfICIiEV277sSRI490yh0cLNCzpyeTICIiei+j7hGCoBY7AsqlffvuY/z4Y0hISMf9+zE4ebI/nJ2txA6LiIgKGaNOhNQ2FcQOgfSUmJiOyZNPYvv2O9oyMzMTREYmMREiIiK9GXUiRIXL5cvP4O8fhLCwBG1Z587uWLCgBezszEWMjIiICismQmTwVCoNFi26gMWLL0KtzpwjzNraFPPm+aJ7dw+OBSIiolxjIkQGLSwsAUOHHsKVK8+1ZXXqlMLKle1QrpytiJEREVFRwESIDJpUKsHff8cCAGQyCcaNq4/Ro+vBxMSoL3gkIqI8wm8TMmilSxfD99+3QLlytjhw4FOMH9+ASRAREeUZ9giRQblw4Sk8PZ1gY2OmLevSpQratavEecKIiCjPGcRP6y1btsDX1xfVq1dH9+7dcevWrbfW3bFjB3r37o06deqgTp06GDhw4DvrU+GgVKoxa9YZfPzxDgQEnMzyPJMgIiLKD6InQocOHcLcuXMxfPhw7NmzB1WqVMHgwYMRExOTbf2LFy+iffv22LRpE7Zt24aSJUvis88+Q2RkpN77FkwsPzR8ygMPH8bCz28rli27DEEAduy4g5MnQ8UOi4iIjIDoidCGDRvQo0cPdOvWDZUqVcKMGTNgbm6OXbt2ZVv/hx9+QJ8+feDh4QFXV1fMnj0bGo0G58+f13vfGjOHDw2fPoAgCFiz5gp8fX/GrVsvAQByuRRTpzZBs2blRI6OiIiMgajnG5RKJW7fvo0hQ4Zoy6RSKRo2bIjr16/naBupqalQqVSwtdX/UmqJJPMfFbzo6BSMGXMUwcEh2rJKleyxerUfatRwFjEy4/X6vcD3hPjYFoaDbWE48qsNRE2E4uLioFaroVAodMoVCgUePXr0lrV0LVy4EMWLF0fDhg313r+DvTVgb6P3evRhDh9+iIED9+HFiyRtmb9/bSxc2BqWlnIRIyMAUCj4njAUbAvDwbYougr1CNS1a9fi0KFD2LRpE8zMzN6/wn/ExiVBo07Mh8jobc6ff4pOnXZolx0dLbFkSSu0aeOKlJQ0pKSkiRidcZNIMj/sY2ISIQhiR2Pc2BaGg21hOKRSwMEh7xNSURMhe3t7yGSyLAOjY2Ji4Ojo+M51169fj7Vr12LDhg2oUqVKrvYvCOAfdgGrV88Fvr7lceJEKHx9y2PLlm4wMRHYDgaE7wvDwbYwHGwL8eXX8Rd1sLSpqSk8PT11Bjq/Hvjs7e391vV+/PFHrFy5EuvWrUP16tULIlTKIxKJBEuXtsH8+S2wbVsXlChhLXZIRERkxES/amzQoEHYsWMH9uzZg5CQEEyfPh2pqano2rUrAODrr7/GDz/8oK2/du1aLF26FHPmzIGLiwuioqIQFRWF5ORksV4CvUVkZDJ6996D338P0yl3drbCoEE1OFkqERGJTvQxQn5+foiNjUVgYCCioqLg4eGBdevWaU+NPX/+HFLpm3xt27ZtyMjIwKhRo3S2M2LECIwcObJAY6e3Cw4OwZgxRxATk4rbt6Nw8mQ/ODhYiB0WERGRDtETIQDo27cv+vbtm+1zmzdv1lk+ceJEQYREuZScnIFp005j06Y3d/vWaASEh79iIkRERAbHIBIhKhpu3oyEv/8hPHwYpy1r184Vixa1hkLBJIiIiAwPEyH6YGq1BitWXMG8eeegUmkAAJaWJpg92wd9+lTjWCAiIjJYTITogzx7lojhw4Nw9uxTbVmNGs5YvdoPrq72IkZGRET0fkyE6IOkpalw/XrmhLcSCTBqVF1MmNAApqYykSMjIiJ6P9Evn6fCrWJFe8yZ4wMXFxvs2dMdU6Y0ZhJERESFBnuESC/Xrj1HlSqOOnOC9erliU6d3GBtbSpiZERERPpjjxDliEqlwfffn0f79tswffrvOs9JJBImQUREVCgxEaL3Cg2NR6dO2/H99+ehVgv46aeb+OOPsPevSEREZOB4aozeShAE7NhxFwEBJ5CUpAQAyGQSjBtXH/XrlxY5OiIiog/HRIiyFR+fhq+/Po69e+9ry8qVs8WqVe1Qu3YpESMjIiLKO0yEKItz58IxfHgwIiIStWU9e3pizhwfjgUiIqIihYkQ6Th3LhxduuyEIGQu29mZYeHCVujUyU3cwIiIiPIBB0uTjnr1XNCgQeb4n8aNy+DUqf5MgoiIqMhijxDpkMmkWLGiHfbv/xtDhtSCVMp5woiIqOgy6h4hCQSxQxBVdHQKBg3aj4sXI3TKXVxs4O//EZMgIiIq8oy6R0htUVLsEERz4kQoRo06jJcvk/Hnny9x8mQ/2NiYiR0WERFRgTLqHiFI5e+vU8SkpanwzTcn0bPnbrx8mQwASE7OQEhInMiRERERFTyj7hEyNnfuRMHf/xDu3o3Rlvn6lsfSpW3g7GwlYmRERETiYCJkBDQaAevWXcesWWeQnq4GAJiZyTBtWlMMHlwTEgnHAhERkXFiIlTERUYmYdSowzh58om2zMPDEatX+8HDw1HEyIiIiMTHRKiIi4tLw7lzT7XLQ4bUwpQpjWFuzqYnIiIy7sHSRqBKFUdMm9YUxYtbYfv2rpg1qzmTICIion/wG7GI+euvKFSubA8zszdNO3hwTXzyiQfs7MxFjIyIiMjwsEeoiFCrNQgMvITWrbdgzpyzOs9JJBImQURERNlgIlQEREQkolu3XzF79h9QqTRYteoqLlyIeP+KRERERo6nxgq5ffvuY/z4Y0hISAcASCTAqFF1UatWCZEjIyIiMnxMhAqpxMR0TJ58Etu339GWubjYYMWKtmjYsIyIkRERERUexp0IFdIbCV6+/AzDhgXhyZMEbVnnzu5YsKAFxwIRERHpwbgToUI419jZs+H45JNfoVYLAABra1PMm+eL7t09eIdoIiIiPRntYGmhECZBAFC3binUqOEMAKhTpxROnuyHHj2qMgkiIiLKBaPtEZJoMsQOIVfkchlWrmyHffv+xsiRdWBiYrS5LBER0Qcz2kQoQ+EtdgjvFR+fhkmTTsDf/yNtLxAAVKxojzFj6okYGZFxEQQBGo0aGo2mQPYnkQBpaWnIyFBCEApkl/QWbIuCJZOZQCot2B/4RpsIGbqzZ8MxfHgQnj1Lwq1bkTh2rC8sLQvn6TyiwkylykBCQiwyMtIKdL+xsdICS7zo3dgWBUkCe3snmJlZFNgemQgZGKVSjfnzz2H58svaXx/R0Sm4fz8G3t68NxBRQRIEATExLyCVSmFr6wiZzKTAxuPJZBLtRREkLrZFwRAEAUlJCYiLi0Lx4qULrGeIiZABefgwFkOHHsKtWy+1ZY0bl8Hy5W1RqpSNiJERGSeVKgOCoIGtrRNMTQv21hQmJlKoVOyFMARsi4JjbW2L2NhUqNUqSKWmBbJPJkIGQBAEbNr0J6ZOPYXUVBUAQC6XIiCgEYYNqw2plFeEEYlJIuFFCUQFQYwroJkIiSw6OgVjxx5FcHCItqxSJXusXu0HLy/nd6xJREREH4o/c0T27Fkijh17rF0eOLAGjh3ryySIiIioADAREpmXlzMmTWoIhcICmzd/jAULWvDqMCIiEYWFhaJTpzZISUkWO5QiJSMjA5980hH37t15f+UCxFNjBezBg1iUL28LuVymLRs+vDZ69aoGJydLESMjoqLku++mIyjoNwCATCZD8eLO8PFpgcGDh8LMzEyn7tmzZ7B162bcv38PGo0aFSq4omvX7vDz65hlu6dOHcevv27Hgwf3odFoUKqUC5o3b4Fu3XqgWDHbAnlt+W316hXo1q0HLC2txA4l3+zatQNbt25GbGwMXF0rY8yYCahatdpb66tUKmzevAFBQb8hOjoKZcqUg7//SNSv31BbZ8+eX7F37694/vw5AKBChYoYOPBzNGjQCAAgl8vRq1dfrFq1DEuXrsrfF6gH9ggVEI1GwNq11+DruxmLFl3UeU4mkzIJIqI8V69eQ+zbF4wdO/Zh5Mix2LdvN9avX6NT59dftyEgYByqV6+BtWt/wsaN29CiRWssXDgXy5cv0am7Zs0KTJs2GR4enli4MBCbNm3HiBGj8fDh3wgOPlRgrysjI/9mBnjx4gXOnTuTbRKoj/yM8UMdP34Ey5cvxqBBX2D9+p9RqZIbxo4dibi42Leus3btSuzbtxtjxkzA5s070LlzN0yePAF//31PW8fJqTiGDh2B9es3Y926TahVqzYCAsbh0aM3Y2BbtWqHW7du6JSJjT1CBSAyMgmjRh3GyZNPAACLF19Eq1YVUKtWSZEjI6KizNRUDoXCEQDg7FwChw/XxZUrb36IRUa+wPLlS9C9ey8MGTJcW96rV1/I5SZYsmQhfHxawtOzGu7c+QubN2/AqFHj0KNHL23dkiVLoU6d+khMTHxrHC9fRmLFiqW4dOkCMjKUKFeuAsaOnQhPz2r47rvpSEpKxNy5P2jrL136Ax48uI/ly9cCAEaM+BIVK7pCJjPBkSOHULFiJSgUjtBoNJg5c652PZVKhY8/boMRI8agXbsO0Gg02LJlI/bv34OYmBiUKVMWAwcOho9Py7fGeuLEUVSq5AYnp+LasoSEeCxYMA83b15HYuIruLiURr9+g9CqVVttnexiXLZsDR49eogVKwJx69Z1mJtboG7dehg5chzs7OwAABcunMPGjevx+HEIpFIZqlWrjq++Gg8Xl9JvjfFDbdu2BR07dkb79p0AABMmBOD8+T/w22/70a/fwGzXOXz4EPr3/wwNGjQGAHTp8gmuXLmIbdu2YOrUWQCAxo2b6qwzZMhw7N27C3fu/ImKFV0BAMWKFUP16jVw/PgRVKzon0+vUD9MhPJZUNBDjB17FDExqdqyL77wRtWqTiJGRUQfwjR0D6xufgdJRlK+7UMC4N+38BPk1kiu+Q2U5TrnanuPHj3EX3/dgrPzmx9gp04dh0qlQq9e/bLU//jjblizZiWOHTsMT89qOHIkGBYWlujatXu227exyf5eZykpKRgx4ks4ORXHvHmLoFAocP/+PQiCfvflCQo6iC5dumHVqvUAgKdPn+LbbyciJSUFlpaZPeoXL55HWloamjXzAQBs3rwBR44EYfz4AJQuXQY3b17HrFlTYWdnD2/vj7Ldz61b11GliodOWXq6Eu7uHujbdwAsLa1w/vwfmD17GlxcSuucTvpvjImJiRg1yh8dO3bGqFFjkZ6ehlWrlmHq1EkIDFwNAEhLS0XPnn3g6loZqakpWLduNSZPHo8NG3556w0FN236HzZv3vDO47V5806UKJH1JrwZGRn4++976NdvkLZMKpWidu26uH371lu3l5GRATMz3fv6mJmZ49atG9nWV6vVOHnyGNLSUuHp6aXznIeHJ27evP7O+AsSE6F8kpycgWnTTmPTpjd/WMWLW2HZsjbw8SkvXmBE9MEsby+FScLfouxXn0To3Lk/0KpVE6jVaiiVSkilUowZ87X2+fDwMFhbW8PR0THLunK5HKVKuSA8PLMn++nTMJQq5QITE/2+No4eDUZ8fDzWrdukHUNUunQZvbYBAGXKlMGwYV9pl0uVKg0LCwv8/vtJtG3bXruvxo2bwtLSCkqlEps3b8CSJStRrVrmF7GLS2ncunUD+/btfmsi9OLFC1SpUlWnrHjx4ujd+02y+MknPXHp0gWcOHFMJxH6b4w//bQObm7uOr1tAQFT0bVre4SFPUHZsuXQvHkLnX0FBExDhw4tERr6CBUrVso2xs6du8HXt9U7j1d2bQpk9m6p1Wo4ODjolDs4OODJk9C3bq9u3frYtu0X1KhRCy4upXH16iWcPn0iy9QjISEPMXToICiVSlhYWGDOnO9RoULF/8TmhMjIF++MvyAxEcoHN29GYujQQwgJidOWtW3risWLW0OhKLj5U4gof6RUGw2rG7MLvEcoxfOrt1XPlrf3Rxg/PgCpqanYseMXyGSyLF+8OZXbCUcfPPgbbm7uHzyQ2t1dt5fGxMQEPj6tcORIMNq2bY/U1FT88cdpTJ8+BwDw9Gk40tLSMGbMcJ31MjIyULmy+1v3k56eBlNT3Z4PtVqNn35ajxMnjiIqKgoqVQaUSiXMzHTvNv7fGB8+fIBr166gVasmWfYTEfEUZcuWQ3h4GNatW407d24jISFe21MWGfnirYlQsWK2BT4w/auvxmPBgtno0+cTSCQSlCrlAj+/Tjh4cL9OvbJly2HDhl+QlJSEU6eO47vvpmPZsrU6yZCZmRnS0gp27r53YSKUx86cCcOnn+7W3o7d0tIEs2Y1R9++1UW5YyYR5T1luc65PkWVU3kxrYOFhYW29yUgYCoGDuyF337biw4dOgMAypQpi6SkJERHR8HRUfd0fUZGBp49e4patWpr6966dQMqlUqvXqH/XqH2XxKJBMJ/siyVSpWlnrl51h+RrVu3xYgRXyIuLhaXL1+EmZmZ9iqm1NTM4QgLFizRGe8DZPZ2vY2dnV2W8U5btmzCzp1bMWrUOFSsWAkWFhYIDPwBKpXugOj/xpiamopGjZrA339Ulv28Hrs1ceIYlChREhMnToGjoxM0Gg369/8UGRlZj8FrH3JqzNbWDjKZDLGxugOjY2NjoVAo3ro9e3t7zJ37A9LT0/HqVQIcHZ2watUylCrlolNPLpdr/+aqVPHA3bt3sHPnVnz99RRtnVevErRjpAwBE6E8VrduKbi5OeDOnWjUqOGM1av94OpqL3ZYRGTkpFIp+vUbhOXLF6NVq7YwMzNHs2YtsGrVMmzd+jNGjhyjU3/v3l1ITU1Fy5ZtAACtWrXFr79uw+7dO3UGS7+WmJiY7TihSpUq47ff9uLVq4RsezHs7Ozx+LHuFUQPH96HTPb+r6fq1WugePESOH78CC5cOAcfn5baJK1ChQowNTVFZOSLt54Gy07lyu4IDX2kU3bz5g00btwMbdr4AQA0Gg3CwsJQoUKFd27Lzc0dp0+fQIkSJbNNHhMS4hEW9gQTJ36DGjW8tft6nw85NSaXy+HmVgVXr15C06bNta/n6tXL6Nq1x3v3bWZmBien4lCpVDh9+sR74xAETZYr6B4/DoGb29t75QoaL5/PY2ZmJli92g9jxtTDwYM9mQQRkcHw8WkJqVSGXbt2AgBKlCiBYcNGYefOrVizZgWePAlFRMRTbNv2M1atCkTPnn3h6Zk5BsbTsxp69+6PFSuWYOXKpfjrr1t48eI5rly5hG++mai9Z9F/tWzZBg4OCgQEjMetWzcQEfEUp04dx19/ZY6f/OijOrh37y6Cgn5DeHgY1q9fo9el1a1atcHevbtx+fJFtGrVTltuaWmFnj37YtmyRQgK+g0REU9x//49/PrrtrfGCgB16zbAX3/9CbVarS0rU6YsLl++iD//vInQ0Mf4/vs5iIuLeW9s3br1wKtXrzB9+hTcvXsbERFPcfHiecyZMwNqtRo2NsVga2uL/ft34+nTcFy9ehnLly9673aLFbNF6dJl3vnvXb12PXv2wYEDexEU9BtCQx9j4cK5SE1NRfv2b24ZMGvWVKxevVy7fPv2Xzh9+gQiIp7i5s3rGDduJDQaAb1799fWWb16OW7cuIbnz58hJOQhVq9ejuvXr6J167Y6+7958wbq1Kn/3tdZUNgj9AESE9MxbdppfPllLVSp8ib7rlLFEQEB2WfjRERiMTExQdeuPfDLL5vQpcsnsLCwQI8evVGqlAu2bv0Zv/66DWq1BhUqVMS4cZO0l1e/NmzYKLi7e2DPnp3Yu3c3BEGDUqVKw8enBdq165DtPuVyORYvXoHlyxdjwoSvoFarUb58RYwdmzlou169Bhg48HOsWrUMSmU62rfvhLZt2yMk5GGOXlPr1u2wadP/UKJESXh51dB57osv/GFnZ4/Nmzfg2bMIWFvbwM2tCvr3H/SWrQH16zeETCbDlSuXUK9eAwDAoEGfIyLiKcaOHQlzc3N06tQFTZo0R3Lyu8eIZZ4+Wo9Vq5ZhzJgRyMhQokSJkqhXrwGkUikkEgmmT5+DpUsXon//T1GmTDmMHj0eI0cOydFrz60WLVojPj4O69atRmxsDCpVcsMPPyyDg8ObU2ORkS90rlpTKtPx44+r8OxZBCwsLFC/fiN8++1MnV7AuLhYzJ49DTEx0bCysoara2UsWrRMJ+n5669bSEpKgo9P7saq5QeJ8N+Ts8ZiWTFE9YzI9eqXLz/DsGFBePIkAVWrOuLw4d4wM2NeqQ+JBHB0tEF0dGKuB2JS3mF7ZJWRoURMzHMoFCUhl5u+f4U8lBdjhCh3du3agbNnf8eiRZk9ImyLvDN1agAqVaqM/v0/y/b5d73npFJAocj+Ng0fwmhPjSmdm76/UjZUKg2+//48OnXajidPEgAAYWGvcOdOdF6GR0REIvn4466oUcObc43lsYyMDFSs6IpPP+0tdig6jLYLQzAtpvc6oaHxGDYsCFeuPNeW1alTCitXtkO5ckVjjh0iImNnYmKCAQMGix1GkSOXyzFw4Odih5GF0SZC+hAEATt23EVAwAkkJSkBADKZBOPG1cfo0fVgYmK0HWtERESFGhOh94iPT8PXXx/H3r33tWXlytli1ap2qF27lIiRERER0YdiIvQef/8di/3739xKv2dPT8yZ4wNr64IdOElE4jHWa0qICpoY7zWe03mPunVLYfTourC1NcO6dR0QGNiGSRCRkZDJZAAyLx0movynVmfeUfttE87mB/YI/ceTJwkoXdoGMtmbRhg3rj4GDPBCyZJ5f9keERkuqVQGCwtrJCVlzhtoampWYFPlaDQSqNXsiTIEbIuCIQgaJCbGw9TUHFKprMD2y0ToH4IgYNOmPzF16imMG1cfo0bV1T4nl8uYBBEZqWLFMmfpfp0MFRSpVJplZm8SB9ui4EgkUhQr5lCgc3MyEQIQHZ2CsWOPIjg487bu8+adg49PeVSvXvw9axJRUSeRSGBrq4CNjb222z7/9wnY21shLi6ZN7cUGduiYJmYyAt8gnKDSIS2bNmC9evXIyoqClWqVMG3334LLy+vt9YPCgrC0qVLERERgfLly2P8+PFo1qxZrvZ94kQoRo06jJcv39w4q0+fapwjjIh0SKVSSKUFMz5QIgHMzc0hl2fwy1dkbIuiT/TB0ocOHcLcuXMxfPhw7NmzB1WqVMHgwYMRE5P9hHbXrl3DuHHj8Mknn2Dv3r1o0aIFhg8fjr///jvb+m+TppTgm29OomfP3dokSKGwwObNH+P771vC0lL+wa+NiIiIDJvoidCGDRvQo0cPdOvWDZUqVcKMGTNgbm6OXbt2ZVt/06ZNaNKkCT7//HO4urpi9OjRqFq1Kn7++We99tt6eiWsXXtdu+zrWx6nTvVHmzauH/R6iIiIqPAQNRFSKpW4ffs2GjZsqC2TSqVo2LAhrl+/nu06N27cQIMGDXTKGjdujBs3bui177tPzQEAZmYyzJnjg61bu8DZ2Uq/F0BERESFmqhjhOLi4qBWq6FQKHTKFQoFHj16lO060dHRcHR0zFI/Olq/SU9tbExRpYoCS5a0hru74v0rUJ57PR5OKgXPvRsAtofhYFsYDraF4civMdQGMVhaDBER48QOgf7h4MBbExgStofhYFsYDrZF0SXqqTF7e3vIZLIsA6NjYmKy9Pq85ujomKX35131iYiIiN5G1ETI1NQUnp6eOH/+vLZMo9Hg/Pnz8Pb2znadmjVr4sKFCzpl586dQ82aNfMzVCIiIiqCRL9qbNCgQdixYwf27NmDkJAQTJ8+HampqejatSsA4Ouvv8YPP/ygrd+/f3+cOXMG//vf/xASEoJly5bhr7/+Qt++fcV6CURERFRIiT5GyM/PD7GxsQgMDERUVBQ8PDywbt067amu58+f60y+VqtWLSxcuBBLlizBokWLUL58eaxYsQJubm5ivQQiIiIqpCSCGHPeExERERkA0U+NEREREYmFiRAREREZLSZCREREZLSYCBEREZHRYiJERERERqtIJkJbtmyBr68vqlevju7du+PWrVvvrB8UFIS2bduievXq6NixI06fPl1AkRZ9+rTFjh070Lt3b9SpUwd16tTBwIED39t2pB993xuvHTx4EO7u7hg2bFg+R2g89G2LV69eYcaMGWjcuDGqVauGNm3a8LMqj+jbFj/99BPatGkDLy8vNGvWDHPmzEF6enoBRVt0Xb58GUOHDkXjxo3h7u6OY8eOvXedixcvokuXLqhWrRpatWqF3bt3679joYg5ePCg4OnpKfz666/CgwcPhG+++UaoXbu2EB0dnW39q1evCh4eHsKPP/4oPHz4UFi8eLHg6ekp3L9/v4AjL3r0bYuxY8cKP//8s3Dnzh3h4cOHwqRJk4SPPvpIePHiRQFHXjTp2x6vhYeHC02aNBF69+4t+Pv7F1C0RZu+bZGeni507dpV+OKLL4QrV64I4eHhwsWLF4W7d+8WcORFj75tsX//fqFatWrC/v37hfDwcOHMmTNCo0aNhDlz5hRw5EXPqVOnhEWLFglHjhwR3NzchKNHj76zflhYmFCjRg1h7ty5wsOHD4XNmzcLHh4ewu+//67XfotcIvTJJ58IM2bM0C6r1WqhcePGwpo1a7Kt/9VXXwlffvmlTln37t2Fb7/9Nl/jNAb6tsV/qVQqwdvbW9izZ08+RWhcctMeKpVK+PTTT4UdO3YIEydOZCKUR/Rti19++UVo0aKFoFQqCypEo6FvW8yYMUPo37+/TtncuXOFnj175mucxiYnidCCBQuE9u3b65SNHj1a+Oyzz/TaV5E6NaZUKnH79m00bNhQWyaVStGwYUNcv34923Vu3LiBBg0a6JQ1btwYN27cyM9Qi7zctMV/paamQqVSwdbWNr/CNBq5bY8VK1ZAoVCge/fuBRGmUchNW5w4cQI1a9bEzJkz0bBhQ3To0AGrV6+GWq0uqLCLpNy0hbe3N27fvq09fRYeHo7Tp0+jWbNmBRIzvZFX39+iT7GRl+Li4qBWq6FQKHTKFQoFHj16lO060dHRWWauVygUWWa4J/3kpi3+a+HChShevLjOhxTlTm7a48qVK/j111+xd+/eAojQeOSmLcLDw3HhwgV07NgRa9euRVhYGGbMmAGVSoURI0YURNhFUm7aomPHjoiLi0Pv3r0hCAJUKhV69uyJoUOHFkTI9C/ZfX87OjoiKSkJaWlpMDc3z9F2ilSPEBUda9euxaFDh7B8+XKYmZmJHY7RSUpKwtdff41Zs2bBwcFB7HCMniAIUCgUmDVrFqpVqwY/Pz8MHToU27ZtEzs0o3Px4kWsWbMG06ZNw+7du7F8+XKcPn0aK1asEDs0yqUi1SNkb28PmUyGmJgYnfKYmJgsWeNrjo6OWXp/3lWfciY3bfHa+vXrsXbtWmzYsAFVqlTJzzCNhr7tER4ejoiICPj7+2vLNBoNAKBq1aoIDg5G2bJl8zfoIio37w0nJyeYmJhAJpNpyypWrIioqCgolUqYmprma8xFVW7aYunSpejUqZP2dLG7uztSUlIwdepU+Pv760wSTvkru+/v6OhoWFtb57g3CChiPUKmpqbw9PTE+fPntWUajQbnz5+Ht7d3tuvUrFkTFy5c0Ck7d+4catasmZ+hFnm5aQsA+PHHH7Fy5UqsW7cO1atXL4hQjYK+7VGxYkUcOHAAe/fu1f7z9fVFvXr1sHfvXpQoUaIgwy9ScvPeqFWrFsLCwrTJKACEhobCycmJSdAHyE1bpKWlZUl2XieoAucwL1B59v2t3zhuw3fw4EGhWrVqwu7du4WHDx8K3377rVC7dm0hKipKEARBmDBhgrBw4UJt/atXrwpVq1YV1q9fLzx8+FAIDAzk5fN5RN+2WLNmjeDp6SkEBwcLL1++1P5LSkoS6yUUKfq2x3/xqrG8o29bPHv2TPD29hZmzpwpPHr0SDh58qTQoEEDYeXKlWK9hCJD37YIDAwUvL29hd9++00ICwsT/vjjD6Fly5bCV199JdIrKDqSkpKEO3fuCHfu3BHc3NyEDRs2CHfu3BEiIiIEQRCEhQsXChMmTNDWf335/Pz584WHDx8KP//8c64uny9Sp8YAwM/PD7GxsQgMDERUVBQ8PDywbt06bTfn8+fPdbL5WrVqYeHChViyZAkWLVqE8uXLY8WKFXBzcxPrJRQZ+rbFtm3bkJGRgVGjRulsZ8SIERg5cmSBxl4U6dselH/0bYuSJUti/fr1mDt3Ljp16gRnZ2f0798fX3zxhVgvocjQty38/f0hkUiwZMkSREZGwsHBAT4+PhgzZoxYL6HI+Ouvv9C/f3/t8ty5cwEAXbp0wbx58xAVFYXnz59rny9TpgzWrFmDuXPnYtOmTShRogRmz56NJk2a6LVfiSCwL4+IiIiME3/+ERERkdFiIkRERERGi4kQERERGS0mQkRERGS0mAgRERGR0WIiREREREaLiRAREREZLSZCRKRj9+7dqF27tthh5Jq7uzuOHTv2zjqTJk3CsGHDCigiIjJkRe7O0kSU+UW/Z8+eLOVHjhxBuXLlRIjojd27dyMgIAAAIJFIULx4cTRq1Ajjx4+HQqH44O3/8ccfsLW1BQA8ffoULVq0wN69e+Hh4aGtM2XKlHyfF2rZsmVYvnw5AEAqlaJ48eJo2rQpxo0bBzs7uxxvZ9KkSXj16hVWrlyZT5ESGTcmQkRFVJMmTbS3qH/NwcFBpGh0WVtbIzg4GBqNBvfu3cPkyZPx8uVLrF+//oO37eTk9N46NjY2H7yfnKhcuTI2bNgAjUaDkJAQTJ48GYmJiViyZEmB7J+I3o+nxoiKKFNTUzg5Oen8k8lk2LBhAzp27IiaNWuiWbNmmD59OpKTk9+6nXv37qFfv37w9vZGrVq10LVrV/z555/a569cuYLevXvDy8sLzZo1w+zZs5GSkvLO2CQSCZycnODs7IxmzZqhX79+OHfuHNLS0qDRaLB8+XI0bdoU1apVw8cff4zff/9du65SqcTMmTPRuHFjVK9eHT4+PlizZo32+X+fGmvRogUAoHPnznB3d0e/fv0A6J4a2759Oxo3bqwzszuQOafU654rADh27Bi6dOmC6tWro0WLFli+fDlUKtU7X6dMJtO+zoYNG6Jt27Y4d+6c9nm1Wo3JkyfD19cXXl5eaNOmDTZu3Kh9ftmyZdizZw+OHz8Od3d3uLu74+LFiwAy58D66quvULt2bdStWxf+/v54+vTpO+MhoqyYCBEZGYlEgilTpuC3337DvHnzcOHCBXz//fdvrT9+/HiUKFECv/76K3bv3o0vvvgCcrkcABAWFoYvvvgCrVu3xv79+7F48WJcvXoVs2bN0ismc3NzaDQaqFQqbNq0CRs2bMDEiROxf/9+NG7cGMOGDUNoaCgAYPPmzThx4gSWLFmC4OBgfP/993Bxccl2uzt37gQA/PTTT/jjjz+wbNmyLHXatm2L+Ph4bYIBAPHx8Thz5gw6deoEIDPZmzhxIvr3749Dhw5h5syZ2L17N1avXp3j1/j06VP88ccf2mMHABqNBiVKlMDSpUtx8OBBDB8+HIsXL8ahQ4cAAJ999hnatWuHJk2a4I8//sAff/wBb29vZGRkYPDgwbCyssKWLVuwdetWWFpa4vPPP4dSqcxxTETEU2NERdapU6fg7e2tXW7SpAkCAwMxcOBAbVnp0qUxevRoTJs2DdOnT892O8+ePcPgwYPh6uoKAChfvrz2uTVr1qBjx47abZYvXx5TpkxBv379MH36dJiZmb03ztDQUGzduhXVqlWDtbU11q9fjy+++ALt27cHAEyYMAEXL17Exo0bMW3aNDx//hzlypXDRx99BIlE8tYkCHhzKtDOzu6tp8xsbW3RtGlTHDhwAA0aNAAAHD58GPb29qhXrx4AYPny5fjyyy/RpUsXAJmzXn/11Vf4/vvvMWLEiLfu/++//4a3tzfUajXS09MBQKeXSS6XY9SoUdrlMmXK4MaNGwgODoafnx+srKxgbm4OpVKpE/++ffug0Wjw3XffQSKRAMicqbtOnTq4dOkSGjdu/NaYiEgXEyGiIqpevXo6yY2FhQUA4Ny5c1izZg0ePXqEpKQk7Zd0amqqts6/DRo0CN988w327dunPb1TtmxZAJmnze7fv48DBw5o6wuCAI1Gg6dPn2qTp/9KTEyEt7c3NBoN0tPT8dFHH2H27NlISkrCy5cvUatWLZ36tWrVwr179wAAXbp0wWeffYa2bduiSZMmaN68+Qd/8Xfs2BHffvstpk+fDlNTUxw4cADt27eHVCrVvs5r167p9AC977gBQIUKFbBq1Sqkp6dj//79uHv3Lvr27atTZ8uWLdi1axeePXuG9PR0ZGRkoEqVKu+M9969ewgLC8tynNLT0xEWFpabQ0BktJgIERVRFhYWWa4Qe/r0KYYMGYJevXphzJgxsLW1xdWrVzFlyhRkZGRk+4U+cuRIdOjQAadPn8bvv/+OwMBALF68GK1atUJKSgp69uypHXvzbyVLlnxrbFZWVtizZw+kUimcnJxgbm4OAEhKSnrv6/L09MTx48fx+++/49y5cxg9ejQaNmyIwMDA9677Nr6+vvjmm29w6tQpVK9eHVeuXNHpuUlJScHIkSPRunXrLOu+q9dLLpdr22D8+PH48ssvsXz5cowePRoAcPDgQcyfPx8TJ06Et7c3rKyssH79ety8efOd8aakpMDT0xMLFy7M8pyhDIgnKiyYCBEZkdu3b0MQBEyaNEnb2xEUFPTe9SpUqIAKFSpg4MCBGDt2LHbt2oVWrVqhatWqePjwod6X5Eul0mzXsba2RvHixXHt2jXUrVtXW37t2jV4eXnp1PPz84Ofnx/atGmDzz//HPHx8VkuS389HketVr8zHjMzM7Ru3RoHDhzAkydPUKFCBXh6emqfr1q1Kh4/fvzBtx7w9/fHgAED0KtXLzg7O+PatWvw9vZGnz59tHX+26Mjl8uzDOT29PREUFAQFAoFrK2tPygmImPHwdJERqRcuXLIyMjA5s2bER4ejr1792Lbtm1vrZ+WloaZM2fi4sWLiIiIwNWrV/Hnn39qT3l98cUXuH79OmbOnIm7d+8iNDQUx44dw8yZM3Md4+DBg/Hjjz/i0KFDePToERYuXIh79+6hf//+AIANGzbgt99+Q0hICB4/fozg4GA4OTmhWLFiWbalUChgbm6OM2fOIDo6GomJiW/db8eOHXHq1Cns2rULHTt21Hlu+PDh2LdvH5YvX44HDx4gJCQEBw8exOLFi/V6bd7e3nB3d9de5VauXDn89ddfOHPmDB4/fowlS5boXJEHAC4uLrh//z4ePXqE2NhYZGRkoGPHjrC3t4e/vz+uXLmC8PBwXLx4EbNnz8aLFy/0ionI2LFHiMiIVKlSBQEBAfjxxx+xaNEi1K5dG2PH/r+dO2RRGIzjOP7bWDOIaV2LxSbogtWgUSayIBabxSsz+AIWFkSYiJrcXo1vwCh2EYNa78KB3HEWDw4Onu8nP/Bs7cvz/HneFIbh0/W2betyuSgMQ51OJxUKBTWbzceAb7lcVpqmms1mCoJA0ufAb6vV+vU39vt9Xa9XRVGk8/msUqmkxWLxGNLO5XLabDY6Ho+ybVuVSkWr1epxwvWV4ziaTqdKkkTz+VzValVpmj7dt16vK5/P63A4/AihRqOh5XKpJEm0Xq/lOI6KxaJ833/5/waDgSaTiYbDoXq9nvb7vcbjsSzLUrvdVhAE354L6Ha72u126nQ6ut/v2m63qtVqyrJMcRxrNBrpdrvJdV15nscJEfAi6/2vn1cFAAD4p7gaAwAAxiKEAACAsQghAABgLEIIAAAYixACAADGIoQAAICxCCEAAGAsQggAABiLEAIAAMYihAAAgLEIIQAAYCxCCAAAGOsDAtUuDmcvKbsAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from tensorflow.keras.models import Sequential\n",
|
||
"from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, BatchNormalization\n",
|
||
"from tensorflow.keras.callbacks import EarlyStopping\n",
|
||
"from tensorflow.keras import regularizers\n",
|
||
"from tensorflow.keras.optimizers import Adam\n",
|
||
"from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# Visualize Weights and Biases\n",
|
||
"for layer in model.layers:\n",
|
||
" if 'dense' in layer.name:\n",
|
||
" weights, biases = layer.get_weights()\n",
|
||
" plt.figure(figsize=(10, 5))\n",
|
||
" plt.subplot(1, 2, 1)\n",
|
||
" plt.hist(weights.flatten())\n",
|
||
" plt.title(f'{layer.name} weights')\n",
|
||
" plt.subplot(1, 2, 2)\n",
|
||
" plt.hist(biases.flatten())\n",
|
||
" plt.title(f'{layer.name} biases')\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"# Confusion Matrix\n",
|
||
"cm = confusion_matrix(y_test, y_pred_classes)\n",
|
||
"plt.figure(figsize=(5, 5))\n",
|
||
"sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
|
||
"plt.title('Confusion matrix')\n",
|
||
"plt.xlabel('Predicted class')\n",
|
||
"plt.ylabel('True class')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# ROC Curve\n",
|
||
"fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
|
||
"roc_auc = auc(fpr, tpr)\n",
|
||
"plt.figure()\n",
|
||
"lw = 2\n",
|
||
"plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
|
||
"plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
|
||
"plt.xlim([0.0, 1.0])\n",
|
||
"plt.ylim([0.0, 1.05])\n",
|
||
"plt.xlabel('False Positive Rate')\n",
|
||
"plt.ylabel('True Positive Rate')\n",
|
||
"plt.title('Receiver Operating Characteristic')\n",
|
||
"plt.legend(loc=\"lower right\")\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 108,
|
||
"id": "81f2d793ada5c410",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:48:41.231645Z",
|
||
"start_time": "2024-03-20T11:48:41.109033Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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66y9Jnry8PBw+fBgGBgZK3QflUadOHUyePBkPHjzAkiVL8Ouvv1bIq2NCl43wFEGQnp6OuLg4tcrs1KkTAKgcgSy83vXiWwSakJubqzQp1uXLl3H37l24u7tLvtANGzYU74YE9+7dU3qULDA0NJRcQJ2dnbF27doKrL3mnDt3DtnZ2ZI0ocvuxe4/Yf+9PK1xSSPjDQ0NAfxf4JGamgqZTFajJm1q1aoVAOU2l0Xr1q2xatUqPHv2DJs3b1Za5ujoiMuXLyt95mJiYpCSkoJ27dopdXuUdbtNmzbFpUuXVAZAPj4+GD58eLnLVdcHH3wAPT09bNmyRZKenJyM48eP47333pNc6AsKCjBo0CB0795dfKOtNCNHjlQ5qVVqair+/fdfWFhYqJxIrLIJ5xfhM/QmqtWBxPz58+Hg4IApU6bgr7/+QlZWFlJSUjBx4kSkpqaKgYSQLyAgAHv37kVWVhZu3LiB4cOHIzU1FcuXLxfvLNT10UcfwczMDF9//TV8fX3F16Fexw8//AALCwsEBATg0KFDePr0Ka5du4aRI0eq7O4oi4kTJ6JZs2ZYvXo1fvrpJzx48AAPHz5EaGgoFixYABsbG43/hoCZmRlmzpyJqKgoPHv2DGfPnsXIkSOhq6uL5cuXS/L26dMHd+/exYoVK/D06VMkJCRg8uTJJd65tGvXDjdu3MCdO3cQFRWFxMRE9OjRoyqaVenMzc0xZswYxMfHIysrC7t378bs2bNhZ2eHr776SszXoEEDuLm5Ye/evTh27Biys7Nx8uRJbNq0SWW5Tk5OMDU1xalTp5CVlYWtW7fCxsYGTk5OAIovzo0bN4aLi0u57/iryltvvQUAuHTpklrrjx07VmVXpZaWFkJDQ1GnTh0MHjwY58+fx7NnzxAVFYXhw4fDxMQE69atU2ubMpkMGzZsQH5+Pvz8/HDhwgXk5OQgOTkZkyZNQnR0NGbNmiVZx9bWVmk8RUVp3LgxFi5ciN9//x3BwcF49uwZ4uPjMXToUNSrVw9Lly6V5P/www9x6NAhrFy5UqlrRhgj9bKVK1di5cqVuHfvHrKzsxEVFYV33nkHubm5WL16tRjUVkV7BRcvXoS2trY4LuWNpLmBnuWj7kjW9PR0CggIIAcHB9LR0aGGDRvS0KFD6caNG6XmMzMzo759+9Lhw4fFPFFRUUrzuwsj1V9OV/W7El988QUBoEuXLpVY3z179pQ4l/zLr6ESEcXFxZGvry+ZmpqKr0zu3buXvL29xfXGjRtXrn326NEj+uKLL6hFixakp6dHurq65OTkRJ999pnk7ZWyEEaPv/hvxIgRau9Ld3d3srGxoWvXrlHfvn3JxMSEDAwMSC6X04kTJ5S2n5GRQePHj6eGDRuSgYEBde/enaKjo8nDw0Ms/8UR1devX6cePXqQkZER2dnZ0cqVK8vVXlTQWxuTJ09Wefznz5+vlH7o0CE6evSoUvrixYuJ6P/e2hgxYgT98ccf1LZtW9LX1ydzc3MaNmwYJScnK20/NjaW+vbtS+bm5mRqakr+/v6UkJAgKX///v1i/j179lCLFi3IwMCA3Nzc6NixY+KypKQksrGxqdZvbRARde3alVq2bElFRUViWkREhNJ+LenNg5ycHGrbtq3K39q4fPky+fv7U/369alOnTrUoEEDGj16tOS1cHWOLRHR1atXadiwYVS/fn3S0dEhOzs7Gj16NMXFxUnqUFBQQHp6euTj41PmfRYUFFTi+Sg+Pl7lOjt27KD27duTvr4+WVpa0ogRI1R+xoyMjEosG4DSW2fp6em0YsUK8vLyIhsbG9LR0aEGDRrQoEGD6PTp00rlV0V78/LyyMbGhoYMGaKyvDflrY0aU9sauHNZJRACieqqogKJivRiIFFbVEYgERUVRdra2qX+KFdNduDAAQJAW7Zs0XRVqkRVtHf58uVkaGiocp4gojcnkKjVXRuMMVZWnTt3xtq1a/HZZ59p5DXCypSZmYmgoCB4eXlh2LBhmq5OpauK9u7btw9z5sxBeHi42I33puJAgjH2RnFxcYFMJsOAAQOUln3wwQfYuXMnfv75Zw3UrPJER0ejSZMm+P3338W3cmqzqmjvunXr8M8//6j8HHXu3BkymazWjK96JU0/EymrGvi4p1pBKX2Rwr/Zs2drvMySLF68uMQxFdUJqlnXRv/+/VX2u9d06nRtMFYT1MBr3Q7+GfE3BKmYd6E6llmSqVOnavxtkZpo7969mq4CY6yWq/3PuBhjjDFWaTiQYIwxxpjaOJBgjDHGmNo4kGCMMcaY2jiQYIwxxpjaatxbG7XhlwZZ7ebv7w9/f39NV+ONwOcDxjSvxgUS4eHhmq4CYyXy9/dHQEAAunTpoumq1GrLli0DAPGH9xirLaKiovDjjz9quhrlUuMCiSFDhmi6CoyVyN/fH126dOHPaSWLiIgAwOcDVjvVtECCx0gwxhhjTG0cSDDGGGNMbRxIMMYYY0xtHEgwxhhjTG0cSDDGGGNMbRxIMMYYY0xtNe71T8YYU0dcXBxSU1PFvy0tLeHm5qYyb2ZmJszMzJCWloarV6+K6a1atYKVlZVS/pfzAYCdnR2cnJwqqPaad/HiRchkMri7u78yb0ZGBm7cuAEjIyO4uLhAS6vke9bMzEzcuHEDWlpaaNy4scr9CwBEhGPHjpVYTknHRl2vaq/wGVHl/PnzePLkifi3vb09HB0dK6xu1Q7VEOHh4VRdqiuXy8nS0pIAkIeHB/Xr10/TVVLLr7/+WmvaUl0AoPDwcE1Xo9bz8/MjPz+/cq0zbtw4AkAdOnQguVxO06dPV8pTVFREc+fOJR8fHyIiUigUJJfLydHRkQBQt27dqKCgQGk9IZ+DgwNZWVmRXC6nVatWqde4aiYxMZF8fX0JAHl7e5eaNzc3lyZOnEj6+vrk4eFBtra2ZG9vT/v371fKu23bNvL09CQDAwNyd3cnd3d30tXVpQ4dOtDBgweV8hcUFBCAEv/t2rWrStvbsWNHWrlypcplEyZMILlcTm3atCEA9OWXX5Z5+9XpWldGO2pMbavbzn333XcJAF24cEHTVSlVcnIyHT16lB49elRinprSlpqgugUSMTExdPToUSoqKqpV23+dQCI2NrbEPOPHj6dWrVrR/fv3JemLFy8WL1ilXRTmz59PgwYNKle9qrNvv/2W6tWrR0FBQWUKJMaMGUP6+vp08uRJIiIqLCyk8ePHk66uLkVGRkryOjk5UdeuXSktLU1MS0xMpObNm5O2tjYdPXpUkr+goIC0tLRILper/Hf8+PEqbe+tW7fIwcGBZs2aVWKe48ePvxGBBI+RqOXCw8Ph5eWFc+fOaboqTANmzJgBLy8vPH/+/I3cfnls3boV69evx/r161G/fn2l5a1bt4aJiQkWLVqEffv2aaCGVS8hIQHnz5/HJ5988sq8p0+fxqZNm/Dhhx+KU8Rra2tj2bJl0NPTU5rOvFOnTli8eLGkO8LBwQHffvstnj9/jlWrViltw8DAAAqFQuW/7t27v2Zry9feJk2aICQkBN999x0UCsVrb7sm40CCMfbGKyoqwqxZs+Dt7Y1OnTqpzOPg4IDQ0FAQEUaPHo07d+5UcS2r3qZNm2Bra1umvGFhYQAAX19fSbqxsTG8vb1x5swZXL9+XUzfunUrunbtqlSOMK4kIyND3WqrrTztBYD+/fvD3d0dX3/9dSXWqvrjwZYV6OzZs3j69CmA/xtck5qaiuTkZNja2sLGxkaS//jx4+KdmrOzMxo2bIjbt2/j7t27aNCggdLgnGPHjoGIAADu7u4wNzcHIB3Y06xZM3E7x48fR0JCAgDg0qVLqFOn+HA7ODigcePGr9XWtLQ0JCYmwtjYGC1atIC2tra47ObNm0hJSRH/lslkkMvlAIBbt24hKSlJXNahQwcYGRmJf+fk5CA2NhYFBQVwdHREvXr1JNtVtY/v37+PxMRE5OXlwcbGBs2aNXuttmnK48ePER8fD5lMBmdnZ5iamorLMjMzceHCBfFvuVwOmUyGoqIi/Pvvv2K6sD+zsrJw7tw5PHz4EEDxZ0c4Rm3btoWWlpbkKVWnTp2gp6eH2NhYPHnyBE2bNpXs+4refkmD1DQlMjISiYmJ+Pjjj0vNN3jwYAQEBODHH3/E0KFDcezYMfF79SpEhLi4ODx69Aj16tVT+py++L0xNTVFu3btkJWVhdjYWFhYWMDBwUHyPXtRUVERYmNjkZmZiYYNG8LBwaFMdapIJ0+eBFA86PFlrVq1wu7du3Hq1Cm0aNGi1HJu3rwJAPDy8ioxz507d5CSkgIzMzM4OzuXuF+qwltvvYXg4GAkJibW7gGVpdFw30qZVbd+I1XjCsaNG0ceHh4EgIKCgmjcuHHUqlUrcnFxIQDk6+tL+fn5Yv5+/fpRy5YtCQB999131KNHD3J2diYXFxfS0tKizp07U2Jiopi/V69e4qCvQ4cOiekTJkygVq1aEQAKCQmRlC/kb926tdiXuHnz5le2pSTh4eHUpk0bsrKyoo4dO5KVlRVZW1vT9u3bxTwhISHk7OxMAMjR0ZF69eolLlu/fj116dKFAFDPnj3F9uXk5FBAQADp6+uTo6MjtW7dmnR1dalv376UkpKich9PnjyZhg4dSs7OztSuXTuSyWT06aefvrINlQlqjJH477//yNfXl3R0dMjV1ZWaNWtGOjo6NHz4cEpPTyei4rEGcrmcLCwsCIA44C8vL4/kcjnZ2toSALp8+TIREV2/fl2Sv2fPnuLxj4mJEZfb2NgQANq4cSM1a9aM2rRpQ3Z2diSTyWj06NGUnZ1dKdsX5OTk0JkzZ1QOYCxNRY+R+O677wgA7dmzR+W6ixcvpnfffZeIiPLz86lr164EgKZOnSrJV9IYidDQUGrUqBFZWFiQh4cHmZqaUpMmTWjHjh1inpCQEJLL5aStrU2dOnWiFStWiJ9tXV1datWqleR8IFizZg1ZW1tTvXr1yMPDg4yNjcnV1ZX+/fffcu2f0sTHx79yzEDdunVJS0tL5XiYkJAQAkDffPPNK7cll8upSZMm9OTJE0l6QUEB6ejoUOfOnalhw4bUvn17MjU1JUtLSwoODq7QcThlaa9gw4YNBIDWr1+vtOxNGSNRY2pb3XZuSRffCxcuEACytLSUXOznzZtHAOjnn3+W5N+1axcBIBMTE8kXPzIykoyNjcnJyYmePn0qpguDvl4sm4hoz549SoFEafnL0hZVPDw8yM/Pj3Jzc4mo+EIyfvx40tLSolOnTon5kpKSSFtbm7y8vJTKWLdunVL6wIEDCQCFhoaKaVevXqX69euTs7OzeEEj+r99bGBgIDnxr1y5kn788cdXtqEylTeQyMjIICcnJ7K0tKSzZ8+K6QqFggwNDcnd3Z1ycnLE9P79+0su5AJhcJhwIX9VfsHkyZPFQDM5OVlMX758OQEgf3//MpWn7vYHDx5MAOjzzz9XubwkFR1IjBgxggBQdHS0ynVfDCSIiFJSUqhevXokk8kkn0FVgYRwER0/fry4H7Kzs8nPz48A0LZt2yT5zczMyMzMjGbPni2mRUZGEgAaOnSoJK9wnMaNGyfepDx69Ih69OhB+vr6dPXq1TLsmVcry4VVW1ubDAwMVC5bv349AaCAgIBSt7NkyRIyMTGhc+fOKS0rLCykt956SxzISVT8lsiHH35IAGjGjBllbM2rlSeQ2L9/v8qgkujNCSR4jEQl6dChA3r37i3+PWLECABAdHS0yvzDhg1Djx49xL+7du2KDz74AAkJCWLfY3Xg4eGB77//Hnp6egAAXV1dBAcHo6ioCOvXrxfz2dvbY9CgQTh69CjOnz8vphMRli5disDAQDHt5MmT2LVrFzw9PfHBBx+I6a6urggMDERcXBy2bNmiVJdevXphwIAB4t8TJ07E5MmTK7S9lW3lypVISEhAQEAAPDw8xHS5XI7x48fj0qVL+OWXXyq9HtOnT4ednZ3496RJk9C6dWuEh4cjNja20rY7ZMgQ+JjBPa0AACAASURBVPj44H//+1+lbaMs0tPTART355eFjY0Ntm3bBplMhjFjxiA5OVllvtzcXHz11VcwMzPDsmXLxG4QAwMDrFixAjo6OggKCkJRUZHSujNnzhT/37VrVzRp0kRy/sjNzcU333wDY2NjLF++HDo6OgAAc3NzLF68GLm5uZg3b17ZdkAF0NHRKXFQbWFhIYDi80VJ1q9fj7lz5+Lvv/9Gu3btlJZra2vj4MGD4kBOANDT00NISAhsbGwQHByMBw8evGYryk/omhW68d5EHEhUEmdnZ8nfwsjkkj5sqvoVhQtLZGRkBddOfWvWrIGjoyNu3ryJkydPQqFQ4MKFCzAyMkJiYqIkb1BQEAAgODhYTNu7dy+eP3+O/v37i2nCiGdbW1ulkdgymQwA8M8//yjV5eV9XBMJbe/YsaPSMiHt6NGjlV6P0j5/Qt93ZfDz88O+ffskQbcmCBe60iZOelnv3r0xZ84cPHr0CP7+/igoKFDKI4xfcnV1VQpSrK2t0bhxY6SmpiIuLk6yrEmTJkoXXSsrK8n5QyhbCDBe/N5kZmYCUP29qSz16tVDfn4+cnNzlZYJY7hKmjDqt99+Q1BQEA4cOCAJFMpCT08PnTp1QkFBAU6dOlX+ir8mYXyGquP/puDBlpVEX19f8rfwYaP/P1jyZcId/otMTEwAANnZ2a/cXknlViRhZPuaNWtARGjatKnYztzcXOTl5Unyd+zYEV27dkVERAQWLFgAe3t7BAcHIyAgQAwQAODZs2cAgKioKJUj4eVyOaytrZXShTuwmkxou3CsXyQMthTylOZ1j39N+PxVJuGuMj8/v1zrff3114iKisL+/fsxffp0pcHBpR1foORj/PL5Ayg+h7y4n4V1UlNTMWfOHKX8woDY58+fV8lgRDc3N9y5cwe3b99WGlB5+/ZtAKoD1sjISIwfPx67du0S35jJy8vD6dOn0bNnzzJtWxi8W5bvSkUTzntlfZpVG3EgUU3cvXtXKU24qL44Alu4S3n55H7//n2V5b58h/Xw4UNcu3ZN0o3yKklJSXj27BlOnz6NefPmYdiwYdi8ebNktHrdunVVrhsYGAg/Pz/8+OOPGDZsGK5evYoxY8ZI8jRt2hQAMGjQICxcuFCpjPPnz0veYKhNnJyccPLkSSQnJyvdiQlvtwj7B5Ae/xf3SVmPf0JCArKzs5Wmhr579y6aN28uSXvV568it69pwltMjx49Ktd6MpkMW7ZsQbt27bB06VJ0795dEvQKrzKW1PWRnJwMLS0ttd6yED4XTk5OKucxSExMxKNHj6rsjYa3334b+/btQ2RkpFIgERkZCSMjI3h6ekrSr1+/jvfeew+bN29Gr169xPS0tDT06dNH8nQjNjYWJiYmKl/PFN700MRbE8Jn5nXfhKvJuGujmvjtt98k/aREhC1btkAmk2Ho0KFiur29PQBI3scGgL/++ktlucIFXojUd+/ejffff79cdQsLC8N3330n/k5Bly5dJEHE06dPS7xrHThwIBwdHbF+/XrMmjULH3/8MQwNDZXyWFhYYPfu3Up3hAkJCejSpQtu3LhRrjrXFMKYkA0bNkjSi4qKsGnTJmhpaWHs2LFiuqrjn5ubiyNHjqgs/+XjP2fOHCxfvlwpX0REhOTvtLQ0/P3336hfv77kBF/R28/NzUV0dLTYtaApbdu2BVD8enJ5WVhYICIiArq6ujhx4oRkmaOjIzw9PXH9+nVERUVJlv35559IT0+Hr68vLC0ty71dBwcH9OrVCxcvXlT6fuTm5uKdd97Bjh07JOmnTp2qtK7SMWPGwNraGqtXr5acyyIjI3Hp0iV89tlnMDAwENPv3bsHHx8fDBw4ECYmJpKumZf3FQDMmzdPaVIroPiV8BMnTsDFxQUdOnSQLKvM9gqEpy3CZ+hNxE8k1KBQKMTBWWfPnkVWVhZ69OiBmJgYnD17FkDxnYZCoYBcLkdSUpL4RU9PT4dCoYCHh4fkcaeDgwPefvttjB07Ftra2vjll18QHR2NOXPmoH379mK+t956C40aNUJwcDDMzc3RoEED7N27V7zriI+Ph0KhQM+ePaGlpQVPT0/UqVMHoaGh0NbWRkhICN577z0AxXecCQkJkraomgRGOLn6+flh8eLFmD17NvLz89GqVSukp6fjl19+AREhMzMTCoUCbm5u4olRS0sLkyZNQkBAAI4cOaJ0wQSKLzYRERF477330L17d0yYMAENGzZEXFwcFi9ejDFjxsDHxwcAVO7jJk2aoEmTJmoeTc3y9PTE/PnzMXPmTAwcOBDDhw9HYWEhNmzYgIsXL2L16tVo06aNmH/UqFEICQnBxIkT8eWXXwIonkSndevWSE1NRXR0NPLz88XBar1790ZYWBgWLVqEVq1a4c8//8T27duV6pGamoqJEyeib9++ePToERYuXIjnz59j8+bNksCvorc/evRoRERE4PPPP8dPP/1U8Tu4jPr16wdtbW1ER0dLnpgJP8YlfE8UCoXKH+Pq0KEDli1bhk8//VSp7M2bN6N3794YMGAAvvrqKzg7OyMmJgbz5s1D27ZtsWbNGgDFn+fExEQUFhbiyZMnUCgUcHd3h0wmw8WLF/HkyRMUFhZCoVDAyckJdnZ2YtleXl6YOnUqWrRogbS0NKxcuRKGhoaSAZtA8WRR5ubmZR5Am5CQgDt37uC///4DUDzXifD0o1OnTpLAwNjYGOHh4ejfvz98fX3x8ccf4+7du/j666/h6emp1P3y7rvv4vbt21izZo24D170cndb/fr1sWzZMgwePBiDBg2CqakpLl26hIULF6JRo0bYsWOH0hOwymyvIDo6GqampuJcOW8iGdWQzs0dO3bA39+/WvTFvvx4zsDAAPv378e0adNw5swZybLDhw8jLCxMaeT9mjVr4OzsjN27d2PgwIFYt24dXFxcsGHDBty5cwcNGjTA8OHD0a9fP6Xtp6SkYOnSpYiLi4OZmRnGjh0LExMTzJgxQ8xz8OBB8TH0sWPHsG7dOmRkZKBbt24IDAyEnp4ewsPDsXr16jK1uVevXpg1a5b4xb927Rry8/Ph6uqKCRMmICAgQJwoav78+ZLH9E+fPoWtrS3effddbNq0qcRtpKamIjQ0VLwYCW9+9OnTR8yjah+PHTtWcteuSTKZDOHh4RgyZEi51jt//jw2b96MuLg4yGQytGzZEu+//z5cXV2V8kZFRSE0NBQpKSlwcHDA5MmTcfToUYSHhwMofttFmF6YiBAaGop9+/ZBS0sLgwYNwrBhw8SyAgICsHz5csTHx+PUqVP4888/kZmZiebNm2PChAmVvv2IiAhs3LgRgYGB5RpwOXjwYHH9sho/fjxCQ0MRGxurclKk4cOH48iRI0hMTBSDp2PHjmH27NmSfP7+/pgwYYLKbQQGBsLExATffvutJD07OxubN2/G0aNH8ejRI1hZWaFPnz4YMWKE+D3dtm0b1q5dK1lvyZIl0NbWRkBAgCR9woQJ8Pf3B1A8iVtYWBgOHz6Mx48fo0GDBvDy8sLw4cMlF+OsrCyYm5tj6NChKt+CUmX16tXicX3Z1q1blSbZA4pvPFatWoUrV67AyMgI/fr1w9ixY5Um7vLx8UFOTk6J29bV1cXBgwclaVevXsVvv/2GGzdu4MGDB7C0tES3bt0wevRope7Pqmhveno6HBwcMGHCBCxatEhpnRMnTqBHjx748ssvsWDBgjLVoTpd68ooosa8rFoD360tE2EeiXXr1mm6KpUmJyeHrKys3ogfBUM1+9GuVxHmkYiPj9d0VcqlMn60KyUlhaysrCTzN9Qma9euJQB07NgxTVelSlRFeydOnEiOjo5Kk2cJ3pR5JLhrg1WKU6dOoVOnTpDJZNi0aRPc3Nwkj+gZ05QzZ87g3r17sLS0lAz6tLGxwYEDB/DOO+/AysoKn332mQZrWbFiYmIwffp0TJkypcxvQtRkVdHeWbNm4e+//8bff/+t9FaO8Gru5cuXK2Xb1Q0HEhp0/PhxXLlyBQAQFxcnjql48dXImsrPzw8BAQFo0KABZs2ahd9//13TVWIvEH4LQ/hth9OnTyMrK6tWDxhzdnaGXC4Xx+l06dIF8+fPl+Tx8PDAxYsXVY4jqckMDAwwY8YMTJ06VdNVqRJV0V5jY2OcO3dO5e/GrF+/HteuXQNQ/Bpubf8NDh4joUGq+ggPHz6s0R+gqSiDBw9Genq6OIbj5V8ErK3UHSNR1eLi4pR+oMrNzQ0hISEaqlH5qDNGgrGaoAZe6yL4iYQG7d+/X9NVqDR8gq/enJ2dVc49wBhj5cXzSDDGGGNMbRxIMMYYY0xtHEgwxhhjTG0cSDDGGGNMbTVusOXLc8czVt2o+p0AVrGE11b5fMBqm5p4/qhxr38yxhhjtV0NuTQDQESNCSQYY1WvZcuWGDx4sNIPLjHG2P8XwWMkGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2DiQYY4wxpjYOJBhjjDGmNg4kGGOMMaY2GRGRpivBGNO8SZMm4ZdffsGLp4ScnBzUqVMHOjo6Ypq2tjZ+++03eHt7a6KajLHqJYIDCcYYAODgwYPo27fvK/OZmZkhLS1NElwwxt5YEdy1wRgDAHh7e8PCwqLUPDo6Ohg+fDgHEYwxEQcSjDEAxV0WI0aMgK6ubol5CgoKMGzYsCqsFWOsuuNAgjEmGjZsGPLz80tc3qBBA3Tr1q0Ka8QYq+44kGCMiTp37gxbW1uVy3R1dTF69GhoafFpgzH2f/iMwBgTyWQyjBo1SuUYiPz8fO7WYIwp4UCCMSYxcuRIFBQUKKU7OjqiTZs2GqgRY6w640CCMSbh6uoKZ2dnSZqOjg7Gjh2rmQoxxqo1DiQYY0pGjx4t6d4oKCjA0KFDNVgjxlh1xYEEY0zJiBEjUFhYCKB43ESbNm3QrFkzDdeKMVYdcSDBGFPSuHFjeHh4QCaTQVtbG2PGjNF0lRhj1RQHEowxlUaPHg0iQlFREYYMGaLp6jDGqikOJBhjKg0ZMgRaWlro3r07GjVqpOnqMMaqqTqargBjrHqytrZG7969MXjwYE1XhTFWjXEgwRgr0ZgxY9CvXz9NV4MxVo1xIFEGrq6uiI2N1XQ1GGOMVaHZs2djzpw5mq5GtceBRBn5+fnxI94aJiIiAlFRUVi6dKmmq1Kr3blzB1OnTkVwcDDs7Ow0XR3GKkRgYKCmq1BjcCBRRi1btuSR6zXMtWvXcPXqVT5ulezq1auYOnUq+vXrh5YtW2q6OoxVCH4SUXb81gZjjDHG1MaBBGOMMcbUxoEEY4wxxtTGgQRjjDHG1MaBBGOMMcbUxoEEY4wxxtTGr38yxqotIsLTp09hYmKC6OhoPHv2DABgbGyM9u3bq1znxXyCHj16QFtbu9LrWxUyMjJw8eJFtG3bFmZmZq/MHxcXh4yMDDg5OcHKyqrEfESEmzdvIi0tDdbW1rCzs4Ourq7KvLdu3UJSUpLKZaUdG3W8qr1PnjyBqalphW2PlR8/kWASx44dQ+vWrSGTyfD999+Xa93Ro0ejbdu2kMlkWLBggVrb37ZtGxwcHGBhYYGOHTvCysoKNjY2WL9+vVrlVaRHjx7h5s2bmq7GG+P69eto3749zp8/DwBYtWoV5syZA29vb3Ts2BH79+9XuZ6Qr1evXvjiiy8wZ84c5OfnV2XVK0VRURHWr1+P5s2bw8vLC5cvXy41/4kTJ9C8eXN4enri888/h52dHcaMGYOnT59K8qWkpCAgIAANGjSAl5cXAgMD0aNHD1hYWCAoKAhPnjxRKjs0NBReXl4q/40fP75K23vgwAF4enoiJSWlQrbLyo+fSDAAxVH9xx9/jDNnzqB79+6vPEmpsnnzZpw4cQI9evRQqw67d+/GiBEj0KdPH/z+++8wMjJCXl4eRo0ahQ8//BDa2tp4//331Sq7IvTu3RsxMTFITEyEvb29xurxKpcvX8bDhw8hl8shk8k0XR21XLlyBT179sS8efMgl8sBABs3bgRQfMf77NkzjBo1ChcvXoStra1kXSGfvr4+Nm7ciFatWlVt5SvBtWvXMGbMGJiYmMDFxQVpaWml5r9y5Qr69esHHx8fbNu2DTo6Orh8+TJ69OiBR48eYc+ePWJehUKBkJAQbNq0CSNHjgRQ/HRi4cKFmDFjBi5evIjDhw8rbcPR0VHlTKZNmzZ9zdaWr71DhgxBbGwsunTpgpMnT/LsqhrATyQYgOJpju3t7XHlyhW1A4HXJVwAZs2aBSMjIwCAnp4e5s6dCwDYsGGDRuolGDlyJIYPHw5ra2uN1uNVZsyYAS8vLzx//lzTVVFLfn4+/P390blzZ0yYMEFlnm7duuHhw4cYOnQoCgsLq7iGVe/cuXOYOHEijhw5UqYL5bRp05Cfn48VK1ZAR0cHAODm5oYpU6Zg7969kqc51tbWGDdunBhEAIBMJsP06dPRokULHDlyBNeuXVPaxoQJE6BQKJT+VcTTw/K2d/bs2WjYsCHGjBnz2ttm5ceBBANQPAX4woULYWBgoLE65OTkAAAsLS0l6cLfL/d7V7XAwEBs3rwZenp6Gq1Hbbd161Zcu3YNM2fOLDHPkiVL4OHhgcjIyFLz1RajRo0q89O4+/fv4++//0bnzp2Vgl5fX18AwKZNm8S0t956C2vXrlVZlqOjI4DicQpVqTztFcycORNHjx7FP//8U0m1YiXhro1KlpeXh9jYWBQWFsLBwUHpIvlyvpycHDRu3BiNGjWSLD979qzYt2lvbw9HR0ekpqYiOTkZtra2sLGxEfPevHlT0l8ok8nEx8MvD5Lq0KGDePevjrt37yIpKQn16tV77Uea3t7eOHToEBQKBVq0aCGmHz16FEBx14ImFBYW4sSJE+LfrVu3hoWFRYnpN27cwJMnT9C0aVPUrVtXXJ6VlYVz586Jf3fq1Al6enqIjY0V89erV09cnpmZiQsXLoh/C10VRUVF+Pfff8V04RgK5T98+BBA8XgXYYBhWQfmVQdbtmyBsbExunbtWmIePT097Ny5E+3atUNwcDDkcjn69+9f5m08fvwY8fHxkMlkcHZ2lgzWK+9xfVl6ejoSEhJQp04duLi4wNDQsMz1qginT59GUVGRyi4dV1dXyGQynDp1qkxl3bx5E+bm5nB3d1e5PDs7GwkJCcjJyYGjo2OpgzkrW+/evSGTybB582aNnSveWMReycXFhWbPnl2udXJzc2nq1KlkaGhI9vb21KFDB6pbty61atWKVq1aVWI+d3d30tPToy5dutClS5fEfOPGjSMPDw8CQEFBQTRu3Dhq1aoVubi4EADy9fWl/Px8IiIKCQkhZ2dnAkCOjo7Uq1cvsZz169dTly5dCAD17NmTEhMTleq+bt06AkBz584tsX0PHz6kd955h2QyGTk4OJCbmxt17dqVtmzZQgBo/vz55dpfRER5eXk0fvx4MjMzo3nz5tFff/1FwcHBZGFhQf7+/vT06dNylTd79mxycXEpdz1elpWVRXK5nJycnAgA7d+/X2X6hg0byMvLi9q1a0c2NjZUp04d+uGHH8Ryrl+/TnK5nGxsbAgAbdy4kZo1a0Zt2rQhOzs7kslkNHr0aMrOziYiopiYGJLL5WRhYUEAqKCgQNxPcrmcbG1tCQBdvnxZUr6Qv2fPniSXy0kul1NMTIxYj5ycHDpz5oxY3uu6cuUKAaArV668dlkFBQWkq6tLHh4eJeYxMjKiCxcuEBHRnj17SCaTkYWFBSUnJ0vy6enpiftG8N9//5Gvry/p6OiQq6srNWvWjHR0dGj48OGUnp5OROU/roI7d+5Q//79SUdHh9zc3Khp06akp6dHn332GeXm5r7urhGNGDGCANDx48dVLv/xxx8JAH377bcql1taWpJMJqPCwsJSt3P06FECQD/99JPSsq+++oratm1LlpaW1Lp1a3JyciItLS3y9vamuLi48jeqFK9q74vs7e3J1ta2Qrarznn/DbWDA4kyUOcDNWjQIAJAmzZtEtOys7NpyJAhZGZmppRv48aNYlpSUhK5uLiQqakpXb9+XUy/cOECASBLS0s6dOiQmD5v3jwCQD///LOkjDp16pCXl5dS3datW0fe3t4l1v1VgURRURF1796dAFB4eLiYfvnyZWrWrJnagQQR0enTp2no0KFkaWlJ7du3JysrK3rnnXfo2LFj5S6rogIJwbJlyySBxMvprVu3plu3bhFR8QXRx8eHZDIZXbt2TZJ/8uTJYv4XL37Lly8nAOTv7y/J379/f0kgIQgKCpIEEq/KLxg8eDABoM8//7xc7S9JRQYS8fHxBID69+9fYp4XAwkiohkzZhAA6tKlixhMEykHEhkZGeTk5ESWlpZ09uxZMV2hUJChoSG5u7tTTk6OmF6e4/r48WNycHAgMzMzOn/+vJgeERFBAGjUqFHq7RAVXnVhnTt3LgGghQsXqlwuBKAZGRklbiM9PZ0cHBxoxIgRKpevX7+epk2bRs+ePRPToqKiqH79+lSvXj1KSUkpR4tKV55AolOnTgSAsrKyXnu7HEiU2Q4eI1EJTp48id9++w29e/fG6NGjxXQDAwMsWLAA3bt3l+Tz9vbG2LFjxXz29vaYN28enjx5gtmzZyuV36FDB8mjuxEjRgAofn/+xTIGDx6Mo0ePiq/PAcWjsZcuXYqpU6eq3b6jR4/ixIkT6NOnj+Qnulu1avVab1Vs3LgRXbt2hYmJCe7evYvo6Gjcu3cPLVq0gKenJ5YvX6522VVh+PDhaNKkCQCgTp068Pf3BxFJujNeNH36dMlAskmTJqF169YIDw9HbGxspdVzyJAh8PHxwf/+979K24a60tPTARS/mVFWc+fOhZeXF6KiojBjxowS861cuRIJCQkICAiAh4eHmC6XyzF+/HhcunQJv/zyi9J6ZTmuq1atwq1btzBlyhS0bdtWTPfz80O/fv2wZcsW3Lhxo8xteh3C4MqSBtsKg1NLmiPi4cOH6N27Nzp16iQZS/GicePGYeHChZJum86dO2PWrFlIS0vDokWLXqcJahO6aR89eqSR7b+pOJCoBAqFAgDQsWNHpWUODg7Yu3fvK/MJacL4gBc5OztL/hb6JYW+ccEXX3wBAAgODhbT9u7dC21tbfTr168sTVHp5MmTAKBy0pk2bdqoVWZubi4CAgJgamqK5cuXiyc5bW1tzJ8/H/b29vjyyy/FC011VNbjIlDVhy1c4IR9XBn8/Pywb9++atmPLFzktLTKfmrS1tbG9u3b0ahRIyxZsgR//vmnynyV+X0TyjY2NlZ6i8Hc3BxEpPIVysogjLPJzMxUufzJkycwNDRUObA6JycHAwYMgLOzM7Zu3VruSbx69uwJADh+/Hg5a10xhPoWFBRoZPtvKh5sWQlenH2vLPlMTEyUlgmDv1S9qaCvry/5W/jyEJEkvW3btvDy8kJERAQWLFgAe3t7BAcHIygoqIwtUS07O7vEepfnTvJFcXFxePLkCTp37qx0gtPW1oabmxuSkpIQExODXr16qbWNylbW4yJQ9faHsE+FfVyaksqtyYQ7yvJOIGVtbY3w8HB4eXlh7NixkkGqgsr8vgnrbd++XTJHg0Aul1fZ2z5ubm4AgNu3bystS0tLQ3Z2tspgqqioCMOGDYOlpSXCwsLEYC4uLg4GBgZlmjtFGNCrqTes8vLyAKh/HmLq4ScSlcDJyQkAkJycrLQsOzsbCoUCWVlZpeYT3qx43Tchpk6disLCQvz444+Ijo5GfHw8hg8f/lplCo9579y5o7QsNTVVrTKFx7ElvWb2+PFjACU/jq2J7t69q5Qm7FMHBwcxTWjzy8HF/fv3VZb78t18QkKCWhOMaULjxo0BqPdounv37liwYAEeP34sdj+8qDK/b8J6c+fOVXoiceTIEXz77bdV1pXk4eEBa2trnDx5UmkfREZGAoDKN1w+//xzZGRkYOfOneL3EQAWL16Mbdu2iX/n5ORI3mp5kTDzq/DaaFV79OgRDAwMUL9+fY1s/03FgUQlGDhwIMzNzbFz506l6WjXrl2LQYMGQUdHR8wXERGBrKwsST5h8qVx48a9Vl18fHzQsmVLrF+/HrNmzcKkSZNe+2Ls6+sLQ0ND7Ny5U+ni9vvvv6tVpouLC5o0aYK4uDicPXtWsiw+Ph7R0dGwsrKq0Dn8NS0iIkLyd1paGv7++2/Ur19f8tRFuBO8fv26mJabm4sjR46oLFd4NVG4K5wzZ45kfElubi6io6Or5UROFhYWsLe3x61bt9RaPygoCAMHDsTp06eVnmp88MEHAJQnNisqKsKmTZugpaUlGatUHsL3NDw8XGnZ1q1b4ePjI0m7d+8eFAoF/vvvP7W2VxotLS0EBQXhzp07Sk9HVq1aBRMTE6WJvubPn48dO3YgKCgIp06dkgRCL98c/Pfff+jRoweuXLmitO0ff/wRACRjw4DKba+AiJCUlAR3d/caO6NrTcWBRCUwNzfHjh07kJ+fj27dumHjxo3Yt28fvvrqK8yYMQNr1qyBvr6+mK+goEDM98cff2DixIlYtmwZxowZg08//RQAEBMTI15gk5OToVAoQES4ffu2OKdAenq6+LRDIJPJEBgYiKysLJw4cQKffPJJifU+d+4cFAoF4uLiABTPOaFQKBAVFSXJV79+faxdu1achjksLAy7du3CmDFjxPkrEhMToVAoxEeNcG20gwAAIABJREFUryKTybBp0yaYmprCx8cHS5Yswb59+xASEgJPT0/UqVMHGzduVHrMXBUKCwuhUCjEu62YmBhxn/z777+SdOGR+sWLFxETEwOg+C5NVZ9xamoqJk6ciD/++AMbN25Ejx498Pz5c2zevFkyiG3UqFHQ0tLCxIkTERERgYiICPj5+aF169YAigfZvjigVhj7sGjRImzfvh1//vkn3nvvPXH56NGj0bFjRwQGBlbYPqpIAwYMQFJSktK0yNHR0VAoFHj+/DnOnj0r/v9lGzduVPlkwdPTE/Pnz8ehQ4cwcOBAREREYPv27ejbty8uXryI1atXi2N8yntce/bsicWLFyMsLAzDhg1DeHg4/vrrL8ycOROffPIJ1qxZI5ljYffu3fDy8irxzv5lz58/Fy/swpOoCxcuQKFQqByYGxgYCF9fX4wePRorV67Evn37MHz4cPz777/YsmWLZL4S4dyUnp6O//3vf0q/nbFv3z5J2QYGBjAxMUHfvn2xaNEi7Nu3TwyW9uzZg0mTJmHYsGGSdSq7vUBxF0xWVla1HERc28moNna0VjBXV1cMGTIEc+bMKdd6d+/exfr163Hu3DkUFhaiadOm+PDDD5UG2b2YT5iQys/PD3379hXzTJs2DWfOnJGsd/jwYYSFhSmNNF+zZo1kgFh+fj58fX0hl8vx5Zdflljfjz/+WAwiXlSvXj2lu2egeJKsdevW4datW7C0tISvry9cXV3x+eefi3kiIiIkJ61XuXfvHjZt2oTo6GhkZGTAxMQEbdu2xdixY8v9+xZz5szBjh07VE7vWx5Pnz7FgAEDJGnCPunTp4/kztfNzQ0hISEICAjAxYsXxXQDAwNxWuKAgAAsX74c8fHxOHXqFP78809kZmaiefPmmDBhAlxdXZXqEBUVhdDQUKSkpMDBwQGTJ0/G0aNHxTtgV1dXrFq1CkDxnVloaCj27dsHLS0tDBo0SHJij4iIwMaNGxEYGFghAy6vXr2KVq1a4cqVK2jZsuVrlxcbGws3NzcsXLhQMp7n/fffV3pSsX//fpWDBi9duoSAgABs2LBB0k0EAOfPn8fmzZsRFxcHmUyGli1b4v3335fsd3WOK1AcaGzatAnXr1+Hrq4umjVrhnHjxsHFxUVSh2nTpmHx4sW4ceMGmjVr9sp9kpOTo/RUQ/DWW2/hq6++UkonImzbtg179uxBRkYGnJ2d8cknnyjVJTw8HKtXry51+x999JGkSzQnJwe7d+9GZGQkkpKSUFRUhGbNmmHIkCEqJxKrivZOnz4dK1aswK1bt8p1zimJuuf9N1AEzyNRBvw+cc1U0fNIVBRhHon4+HhNV6VCVOQ8EoLp06eTpaUlpaWlVViZ1UVeXh45ODhIJoqrzaqivbdv3yZDQ0NasWJFhZXJ5/0y43kkGGPVzw8//AA/Pz94enriwYMHmq5Ohfrss8+QmZlZIT9uVRNUdnsTExPh6emJoKAgsSuYVS1+/ZNVqtu3b6t8De1lnp6elV4XTRN+C0MYR3L69GlkZWVJJjBixWQyGX7++Wfs3r0bSUlJtWoUftOmTXH69GmlLpfaqrLbe/v2bYSGhlbb18LfBBxIsEqlUChUzhaoKl9td/fuXbG/VS6XY926dWLfO1NN+LXK2mTatGmarkKVquz2cgCheRxIsEo1duxYtV+pq22cnZ3fiICJMfZm4TESjDHGGFMbBxKMMcYYUxsHEowxxhhTGwcSjDHGGFMbD7Ysg6KiIly9ehU7duzQdFVYOVy9ehVZWVl83CqZ8ENjBw4cwNWrVzVcG8YqhqZ+wbQm4imyy8DJyQmJiYmargZTg66ubrl/kpoxxvT09DB9+nSeIvvVIviJRBno6elh9uzZ/IGqYSrqtzZY6Sr6tzYYqw5U/eYNU43HSDDGGGNMbRxIMMYYY0xtHEgwxhhjTG0cSDDGGGNMbRxIMMYYY0xtHEgwxhhjTG0cSDDGGGNMbTyPhIbl5eXh+PHjiI2NxbNnz2BtbQ1bW1t07NgRZmZmmq7eKyUlJSEsLAw9e/ZEz549lZafPHkSBw8eVErX19eHpaUl2rRpg7Zt26JOHf4ovsnu3r2LvLw8ODg4YNWqVXjw4AEAwNTUFFOmTIFMJlNa58V8gpkzZ0JXV7dK6lzZTpw4gX/++QcffPAB7O3tS82blJSEAwcOICMjA87OzvDx8YGenp7KvFeuXEFUVBTS0tJgbW2NFi1aoFu3biWWnZ2djSNHjiA2Nhba2tpo0qQJ+vTpA2Nj49dq34uKiooQFRWF8+fPo6CgAK6urvD29oaOjk6p6yUkJCAsLAyOjo4YPXq0ZNn169dRr149WFpaVlg9mWr8REKDVq1aBVtbW3zxxRe4efMmHj9+jEOHDsHf3x+Wlpbo2rUr/vrrL01XU6Xc3Fx89913cHFxwTff/D/2zjMqiuRr48+QYRAUREFAxYABwTWuOQvGxQAGxLBrdk2Ydo0DZtfAYo64uq4K5pwVFfMqCqiACQVMYAAkh/t+8J3+03QPzAxRt37neI7culV1q7qm+3bVreq5uHLlSp76e/fuhZeXF96+fQsAiIuLw6lTp9C5c2dUrlwZq1atQnZ2dnGYzihl7NmzB40aNUJycjJPvnjxYkydOhXLli3LM//ChQsFDsW3zOvXr+Hu7o42bdrAy8sLr169ylPf29sbNWvWxNmzZ/HhwwdMnz4d9vb2iIiI4OndvXsXP/zwA5o1a4aAgAAkJCTg3LlzaN++PRo3bozQ0FBB2WPGjEGFChWwZMkSvHv3Dk+fPsWECRNQuXJl7Nq1q1DaGxYWhsaNG2PEiBF4/vw5Xr16hREjRsDGxgYBAQEK82VnZ2PIkCHw8vLCzp07BelxcXGwt7fHmTNnCsVORh4QI1/q1KlDMpmsUMscO3YsaWho0NatWwVpSUlJNGDAAAJAs2fPLtR6C4OYmBiqWrUq9e/fnxYuXEgAaMGCBXnmcXZ2JgAUFBTEk8fHx9O4ceMIADk5OVFaWlqh2SmTyahOnTqFVl5BiYmJIZlMRhcuXPiu6g8NDSUAFBoaqnLeffv2ka6uLt26dUuQJpVKCQBpaWnR1atXFZahq6tLISEhKtddGjlz5gyZmpqSp6cnubq6EoA823706FECQAsXLuRkCQkJVLNmTbK1teX9nv7++2/S19ensLAwXhmXL18mDQ0Nqlq1KmVkZPDSKlasSJMmTeLJPn36RNbW1qSjo0NRUVEFaS69ffuWLCwsyM3NjVf3+/fvqXz58rRmzRqFeZcvX04WFhYEgDp27Ciqc/r0adLT06PLly+rbFtR3Pe/U/zZjEQJsGPHDmzYsAGTJ0/G8OHDBekGBgbYuXMnLCwsSsC6/MnIyMD69euxd+9eVKxYsUBlGRkZYd26dRg5ciTOnDmD6dOnF5KVpY/Xr1/Dy8sLFy9e/E/Wn5sPHz5g9OjRGDFiBJo2bSqqM3r0aGRmZmLAgAGIi4srZguLH21tbVy7dg0ymUypJZrZs2fD2NgYU6dO5WRlypTBtGnTEBERgR07dnByBwcHrFu3DrVq1eKV0aZNGzRp0gSRkZG4e/cuL23atGmC32TZsmXRs2dPpKen49atW+o0k2d/QkIC1q5dy1veNDMzw+rVqxWOi7CwMHh5eWHDhg15lu/k5ISffvoJw4YNQ1paWoFsZSiGORIlgKenJyQSCSZNmqRQR1tbG+vXr0eHDh2K0TLlqFKlCrp27VqoZS5evBh6enrYsGEDYmJiCrVsRulk48aN+PjxIyZOnKhQZ8yYMXB3d0dMTAzc3d1B3/k3Btu3by940CsiIiICISEhaNmyJfT09HhpnTp1AgDs37+fkzk4OODnn38WLats2bIAIIhJmDZtGiwtLQX68i9jFuRlJykpCXv27EG7du1Qrlw5QfrAgQNFHYmsrCwMGzYMkydPRqNGjfKtZ+LEiXjx4gX27t2rtq2MvGERbiVAZGQkqlWrlm8AVa9evUTlV69exd27d5GSkoKqVavCyckJJiYmXPrGjRu5WIRWrVqhZcuWOHr0KCIjI2FlZQVnZ2cuUOr06dO4efMml1dDQwPz5s0DAFy8eJEX+zBu3DhUqFBBvUbnQ/ny5dGqVSucP38eJ0+exMiRI4ukHmUJCwtDQEAAPn78CDMzM3To0AHVq1fn0nfu3Ml9EbZNmzacw3f+/HkEBgYC+PrRn379+gEAdu/eza33XrlyhfsAnDxIbPfu3dyatoODA7p3746TJ08iIiIC+vr6cHR0RO3atYus/pJg3759MDMzg62tbZ56mzZtwv3793HmzBksWbIEs2bNUqr81NRUnDlzBuHh4ZBIJLCzs0Pnzp25h2VqaiqWLl3K6bu7u6NMmTI4cuQIEhISUKdOHXTr1k000DM5ORmnT5/GkydPoKWlhfr166NDhw7Q0Ci+d7N79+4BgGj/2djYQFtbm9PJi8zMTNy/fx+2traoX79+vvrPnj3DkSNH0KZNGzRv3lx1w/+fO3fuIDk5GfXq1UNycjJOnDiBp0+fwsjICG3atIG9vb1ovuXLlyMlJQVz585VKjbmxx9/hI6ODvz9/TF06FC17WUohs1IlBA2NjYq53n16hWaNm2Kbt26ISgoCO/evcOSJUtQuXJlbN++naf79u1beHl5Ye/evejevTuuXLmCly9fYuzYsahbty4+fPjA6T5+/FjhlHdcXByWLFlSLG+C8q/tleTXOtPS0jBkyBDUrVsXJ0+exMePH7Fv3z7Y2tpi/PjxyMrK4nQVLRWkp6fDy8sL/v7+KtWdmpoKLy8vbNq0Cc2bN8f+/fvx7t077NixA3Z2doKvzxZ2/USE7du3w8/PT6V86pCcnIzg4GDUrFkzX10DAwPs378fZcqUwbx583D58uV881y8eBE2NjaYNGkSXr58iSdPnmD48OGwtbXFnTt3eLo3b97kAvZcXV0RHh6Of//9Fz179kSPHj0EZZ87dw42NjaYOnUqYmJiEBYWBhcXFzRs2DDfwMjCRD5zV758eUGaRCKBiYkJ4uLikJ6enmc5vr6+iIuLw/r166GpqSmq8+rVK/z+++/o378/nJycMG7cOJw+fVrUyVKW8PBwAEB0dDTq1KmDv/76Cx8+fMCBAwfg4OCAoUOHIjMzk5fn0aNHWLRoEXbs2KH07hwtLS1UrVoV169fV9tWRj6UcJDGN0FhB90AoB49eqiUJyMjg+zs7EhHR4cXWJaenk7dunUjiURCp0+f5uRBQUEEgIyNjSkmJoaT79+/XxAcmZiYSGXLliVbW1vKzs7m1Ttv3jwaNmyYQru2bNlSoGDLnMyePZsA0M8//5xnWcqiTrDlqFGjCABt2rSJJ1+0aBEBoFmzZnGyO3fuiAbEJiYmEgDq27cvT65IX86nT58IAEkkEl6AXWZmJvXs2ZMA0KFDh4qs/ps3bxIAAkCRkZGiOmKoE2wpz9OrVy+FOlKplDde/P39CQBZWFjQu3fvOHnuYMsnT56QVCqlOnXq0OfPnzn5u3fvyNLSkkxMTOjNmzec3NvbmwBQixYtKD09nZOPHz9eEOz45MkTMjAwoJo1a1JCQgJPrq+vT02bNlW6D/Jj0KBBeQZbzp8/nwDQH3/8IZpubW1NAOjTp08K6/j3339JKpXS2rVr87Tl5cuXNHfuXPr555/JxsaGOnbsKBogqwrLli3jxtuff/7JS5sxYwYBoLlz53KyzMxMaty4MXl6enKyqKioPIMt5bRs2ZIA0IcPH5S2jwVbKg0LtiwpUlJSVNI/fvw4Hj58iD59+qBevXqcXFtbG7NmzQIR8aZp5XTp0gWVKlXi/m7RogWAr1P3cgwNDTFq1ChERETg6NGjPBs3bNjAC+QqShITEwH8b722uImLi8O2bdtgaWmJESNG8NKmTJkCQ0NDeHt7q3ztVOXHH39Eq1atuL81NTW5gLc1a9YUWb1NmzbF1q1bsXv3blSpUqXI6gGA+Ph4ABCs7eeFq6srJk2ahDdv3sDd3V3hduG1a9ciKSkJkyZN4p3FUqFCBYwdOxYfP37Epk2bBPkGDx7MixEQ+62sWbMGycnJ8PDwQJkyZTh5jRo10KdPH9y+fRvXrl1Tuk0FwcDAAAAUzjjI5XK93Ny7dw+Ojo74448/8Ouvv+ZZV+XKlTF//nz4+voiJCQEWVlZaNmyJW9ZVFXky0DlypUT1D979mxoaWlh/fr13Gzo0qVLkZWVpfTSVk709fUB/G/cMQoXFiNRQrx48UIl/aCgIABAnTp1BGnytXOx9VBra2ve3/Kgpi9fvvDkkyZNgre3N1asWAFnZ2cAwPbt29GoUSOe41KUPHz4EABgZ2dXLPXlRn6DtLW1Fax16+npoWrVqggNDUVYWBgaNGhQZHaIPcTlAXghISFFVq9EIhHdRVQUyB8Oqk6NL1++HLdv38a5c+ewaNEizJ07V6BTlL+V+/fvAwBu376Nd+/e8fSjoqIAfF0qyeuAp8JCHgQptpuFiPDx40eUL19edAngxYsX6NatG2QyGcaNG6dSvVKpFIsXL0aLFi3g5eWFU6dOqWW//KCoatWqCQ6kMzIygpWVFSIjI/H27VsQEebPn49evXph0aJFnF5CQgIA4Pnz59zS308//YSGDRvyyqPvPEi3pGEzEiWAjY0NXrx4wd14FLF8+XL4+voCUO6HIHZTVrTmmZtKlSph4MCBCAwMxK1bt5CdnQ1vb29MmzZNqfwFJTY2FoGBgdDR0Sn0HSHKouzNJr+HX+51XVURe9NW5cFb0PqLA/lMgaqzO9ra2vD394eZmRm8vLxEDywq6t8KANGTWNu3bw+ZTIYffvhB6XIKgvxh+eTJE0FaZGQkMjIyBA9U4Ou22y5duuDXX3/l7Zg5evQoF6ibH/IAz4LEM8mDKTMyMkTTc455LS0tzJw5U9Q5VIbU1FQA+CZOC/4WYTMSJcDixYsxcOBArF69GsuXLxfVOXfuHGbMmIF169YB+N9N4/HjxwJduaygb8nTpk3Dzp07sWLFCgwYMACGhobo2LFjgcpUllmzZiEtLQ1TpkzhLcUUJ/Xr14empibCw8ORnZ3Nm5VITU1FZGQk9PX1udkBqVQKQDhdqshBlD+o5DfIlJQULFu2DGPGjIG5uTmnJ9+NkRP5jo6ckexFVX9xUK1aNUgkErVOpLSyssLu3bvh5OSEgQMH8gJgga+/Ffmx87mPbS/ob6Vhw4a4cuUKnJyc4OLiwkv78uULVq5ciWrVqqlVtqrY2trC3t4egYGBSEtL4x2Jff78eQAQ2JiSkoKffvoJPXv2FMzmHD16FDVq1OCW1SIiInDp0iWMHj1aULc8qDTnbjFVadiwIaysrPD8+XMkJyfzlmASEhIQExMDc3NzVKxYERKJRBBsDHwN1PT29ka1atVE0+XExsaibNmyBbKXoRg2I1ECDBgwAB4eHvD29sZff/0lSH/06BGGDh2K1q1bc1PNPXr0gJ2dHQ4ePMg7yjYjIwNLliyBRCLB77//XiC77O3t4ejoiEOHDmHOnDnFMhuRkJCA8ePHY+vWrejWrZtonEdxYWpqiuHDh+P169fYunUrL23lypX48uULPDw8uPXWqlWrQkdHBzdu3ODpbt68WbR8+Z57+Y6ZsLAwzJ8/X/BNhKCgIN6bdlZWFudwjh8/npMXdv1UjLs2DAwM4ODgIPo2rQydOnWCTCbD27dvBTMwEyZMgIGBAXx8fHhO1vv377Fx40aYmJiIPhyVYcKECZBKpfD29ubecuV4eXlh8+bNvHMX7t+/D09PT8FOkcJi4cKFiI+Px6pVqzhZYmIiVqxYgZo1a/K2O2ZnZ8PNzQ0REREwNDSEp6cn71/u5Z6IiAhMmDBBIE9LS8OcOXMAfI0ryYkq7dXQ0ICnpye+fPmCFStW8NIWLVqEzMxMzJgxo0A7Q4CvM3SRkZFczAujCCiJEM9vjaKK3t20aROZmZlRw4YNadKkSTRt2jTq0aMHaWlpkaurKyUmJvL0IyMjqXHjxmRoaEhDhgyhyZMnk729PUmlUtq2bRun9/fff9Po0aMJALVs2ZJkMhllZWXRpUuXuJ0RtWrVIplMxtvRQUR09uxZAkDW1taC43Jz2y6TybjdBO3btyeZTEYrV67k6V27do1kMhnVqlWLANDo0aNJJpPR9OnTqU+fPmRsbEyVKlWiVatWUVZWViH06v9QZ9dGamoqubu7k0QioZ49e9K0adPI0dGRNDQ0aNy4cZSZmcnTnzdvHgGgdu3a0fTp08nJyYlWrFhBALhx8+TJE07fycmJjIyMaMqUKWRvb09Dhgzh0uS7Nnr37k1t27YlNzc3mjJlCjVs2JA0NDR40epFUX9x7togIlqwYAEBoPDwcJ583bp1JJPJSFtbmxsvYkenZ2dnU5cuXQiA4IjsCxcukLm5OVWpUoXGjRtHo0aN4v7OudtgwYIF5OTkRABo0KBB3O9o+/bt3K4JJycnWrRoEZfn0qVLZGFhQVWrVqUxY8bQtGnTqHnz5mRtbU3379/n2eHj40MA6MiRI0r1SVpaGslkMpLJZGRvb8/tYpLJZHTgwAHRPCtXriRtbW1ycXGh6dOnU82aNalmzZqCft28eTN3fRX9W7JkCaf/8OFDaty4Menq6lKvXr1o+vTpNHbsWKpWrRppaWnR9OnTBb9ZVdtL9HUMa2pqkqOjI02dOpU6dOhAmpqa9Ntvvwl2kckJCQkhmUxGHh4eBIBsbGxIJpOJ7h67du0aASBfX1+lbSJiuzZUwF9CxKJQ8kN+sE9eU2eM0oenpyf8/f3VWsd9/PgxdyBV+fLl0alTJ96BVDm5efMmbt++jaysLDg5OaFGjRpYvHgxl+7u7o4aNWoA+DqDdPDgQbx8+RI1atRAr169uCWUz58/o1y5chg0aBC2b9+OU6dO4fHjx6IHUhVF/dnZ2fD19YVUKsXAgQOV7quHDx+iXr16CA0NVSlQNi4uDjVr1sSgQYOwdu1aTq7KVz0/fPiAtWvXYuzYsYLD0lJSUkQPpMpZzsKFC3kzGpUrV8Yvv/yCv/76C5GRkZxcR0eHt1tAfthVWFgYdHR0ULNmTXTp0kUQOzFq1Cjs2LED0dHRMDMzy7dP0tPTedcuJw4ODujTp49o2suXL3Hq1Cnu65/dunUTzHQp+hJvTjp16sTbMQR8nZkICgpCVFQUMjMzUaVKFXTo0EH0eHxV2yvnxYsXOHPmDD5+/IiKFStyH/NTRGhoKO/UTjlaWlrcbImc/v3749atWwgPD1f4RVQx2H1fafaxGQklYJ7pt0lp+2hXfshnJAYNGlTSpqhEQT7a5efnp/CjXd86b9++JWNjYxo1alRJm1IslMb2nj59mnR1denSpUsq52X3faXxZ8GWDAajxOjXrx8yMzPh7OyMCxcucKebfusQEfr27YtatWrx4he+V0pjewMDA/Hzzz/j8OHDaNeuXUmb813DHAkGoxSwe/duBAcHAwCCg4Ph6emJIUOGFNsOgJLEzc0Nbdu2zfco528JiUSCpUuXonHjxioduvWtUhrbW758eYSEhHDnVTCKDuZIMBilBD09PchkspI2o0QQ+8Lkt07uWIPvndLWXkUxRYzChzkSDEYpwM3NraRNYDAYDLVg50gwGAwGg8FQG+ZIMBgMBoPBUBvmSDAYDAaDwVAb5kgwGAwGg8FQGxZsqST79u3jPnPN+DZ49OgRYmJi4OrqWtKmfNfIP+U8ZcoUGBkZlbA1DEbhEB0dXdImfDOwI7KVYMqUKfl+8pvB+B559+4d9PX1mYPA+E/Sr18/9iKSP/uYI8FgMBRiZ2cHV1dX9r0BBoOhiH0sRoLBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtpolbQBDAajdBAUFIQPHz7wZElJSXj+/DnOnz/Pk9epUweWlpbFaR6DwSilSIiIStoIBoNR8shkMsyfP18p3ZCQENSrV6+ILWIwGN8A+9jSBoPBAAAMHDhQKb1atWoxJ4LBYHAwR4LBYAAAateujXr16kEikSjU0dbWxrBhw4rPKAaDUephjgSDweAYMmQINDU1FaZnZmaif//+xWgRg8Eo7TBHgsFgcAwYMABZWVmiaRKJBE2aNIGNjU0xW8VgMEozzJFgMBgc1tbWaN68OTQ0hLcGTU1NDBkypASsYjAYpRnmSDAYDB6DBw8WjZMgIri4uJSARQwGozTDHAkGg8GjX79+AkdCQ0MD7dq1Q8WKFUvIKgaDUVphjgSDweBhYmKCjh07QkuLf17d4MGDS8giBoNRmmGOBIPBEODu7o7s7Gzub01NTfTq1asELWIwGKUV5kgwGAwBvXv3ho6ODgBAS0sLPXr0gLGxcQlbxWAwSiPMkWAwGAKkUil69uwJTU1NZGVlYdCgQSVtEoPBKKUwR4LBYIgyaNAgZGVlQV9fH926dStpcxgMRimFff2TwWCI0qVLFxgZGaF3797Q19cvaXMYDEYphTkSDAZDFF1dXbi6urIjsRkMRp4IPiMeHR2N69evl5Q9DAajFBEZGQlra+s8v7/BYDD+O/Tr1y+3aJ/AkfD392dvIAwGg8FgMATkchkAYJ/CpQ0RZQaD8Y0jkUjg5+cMBdTbAAAgAElEQVQn9lbBKERcXV0BAPv27SthSxiMwiGvSQa2a4PBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaMEeCwWAwGAyG2jBHgsFgMBgMhtowR4LBYDAYDIbaFPiI7OvXr+Ps2bMAACMjI0yZMqXARhU2YWFh2Lt3L/f3nDlzoKXFTgdXlW/hWn8rEBH27NmDp0+fYt68eQr1fH198erVK9G0Zs2aoUuXLkVlIqMEISLcuHEDLVq0wKNHj+Dv78+l9ejRA40bNxbkya0HAC1atICjo2OR21scJCUlYcuWLTAxMcGQIUPy1M3MzMTZs2cRGhoKqVQKR0dH1KxZU2G5Fy9eRFhYGDQ0NFClShW0bdsWZmZmCst/9OgRrl27hri4OFSsWBENGjRAgwYNCtS+3MTGxuLs2bOIjo6Gubk5WrZsiRo1auSbb9u2bYiKisIvv/yCypUr89KuXbuGli1bFqqdQCHOSGzcuBGrVq0qrOKKhL1798LLywuZmZklbco3jaJr/c8//+DixYslYJGQ0mRLbu7cuYPmzZtj0KBBmD9/fp66vr6+8PLyEv13+vTpYrJYnJSUFPj4+ODp06clasf3xsePH+Ho6Iht27bx5NevX4eXlxd69+6NuLg4hfkDAwMFDsW3zt69e1G7dm14eHhg586deepGR0ejcePGGDt2LN6/f49r167Bzs5O1GGfPHkyKlasCE9PT8TExCAqKgoymQxVq1bFvHnzBAczXrt2DfXr10eLFi1w48YNfP78GQcOHEDDhg3h5OSET58+FUp7Fy5ciOrVq+Po0aNcHbVr14abm1ue+U6fPo0RI0bAy8tL9AVk8eLF6NevH5KTkwvFTg7KhZ+fH4mI86V+/fpkaWmpcj5lWLRoEW3btq3A5Tg7OxMASklJKQSr/ruIXevXr18TAKpUqVKx2JDXmChuW1Rh4cKFVKlSJdq7dy9ZWlqSpqZmnvotW7akkSNHkkwmE/w7deqUyvUDID8/P3XN57F7924CQG5uboVSXlERExNDMpmMLly4UGx1uri4kIuLi8r5UlNTqUmTJtSnTx/KzMzkpS1fvpwAEADq2rUrZWdni5axZMkS6tu3r1p2l0Y6d+5MjRo1In9/fwJAHTt2VKiblZVFTZo0IQsLC4qNjeXkGzZsIAC0fft2nn716tXpl19+4ckyMjKoS5cuBIC2bt3KS9uwYQMZGxtTZGQkT75y5UoCQOPGjVOzlf/Dy8uLpFIp3blzhyeXyWRkamqqMN/nz5/JysqKLCwsCABdvXpVoJOamkrt27enbt26KRw/isjDN/D/JhwJqVRKLVu2LHA5zJEoHBRd6507d9L58+eLxYb8xkRx2qIKe/bsoU+fPhERKe1I5L6hFITCdCSSkpLI29ubwsPDC6W8ouLOnTsEgGbPnl1sdarrSMybN4/09PQoKipKkLZ8+XJq37491axZkwDQ4sWLRcv43hyJzZs3U2ZmJj158iRfR+LAgQOifZOVlUWWlpZkZWVFWVlZnNzHx4eeP38uKOfUqVMEgLp06cKT37lzh3bt2iXQf//+PQGgevXqqdo8Hs+ePSNtbW2aOXOmIO3169e0bNkyhXmHDh1KvXr1ouHDhyt0JIiIQkJCSENDgzZv3qySbXk5EizYklFoDB48GB07dixpMwCULltyMmDAAJQtW7akzSgUDAwMMHnyZNja2pa0Kd8FSUlJ8PHxQe/evWFlZSWqY2RkhAMHDsDAwABz587F1atXi9nK4mfkyJFKf312//79AIBOnTrx5BoaGujQoQOio6Nx69YtTj5x4kTY2NgIypH/RrW1tXnyxo0bY9CgQQL9pKQkAICFhYVSdipix44dyMjIQJ8+fQRpFhYWmDFjhmi+EydO4MSJE9i4cWO+ddSrVw8dOnTA0qVLC+2bWkUScXjnzh0EBgYiIyMDP/74I9q2bSvQISJcv34dISEh+Pz5M2rWrIlu3bpBX1+f05EH96Wnp+PVq1fw9PQE8HVQ5F7vev/+Pc6cOYPo6GiYmZmhdu3aaNmyJSQSiaiNDx8+xLlz56CpqYlmzZqhSZMmKrVRLIAzPDxcqTLltkZFRUEqlaJZs2b48ccf8yw7LCwMly5dwsePH0FE+Omnn3D06FGBzpkzZ6CjowMnJyfuBn/37l0EBATA0NAQnTp1QvXq1QU2KXM9VOkLeTDr0qVLkZqaqjDvxIkTYWJiorQN+Y0JZQJr1el/Za9tUZCeno7Dhw/jyZMn0NLSgoODA9q3bw8NjZJ7Dzh9+jRu3rwJALCyssKIESMUys+fP4979+7BxMQEnTt3RpUqVbhydu/ejYiICACAg4MDunfvjpMnTyIiIgL6+vpwdHRE7dq1Of2dO3fi+fPnAIA2bdqgQ4cOAIDz588jMDAQAFC3bl3uo2S7d+9GQEAAAODKlSvceKlWrVq+AXvFzblz5xAfH49WrVrlqWdvb48NGzZg6NChGDBgAO7fv59nYGBOwsLCEBAQgI8fP8LMzAwdOnTg3Q9UvX45CQ0NxeXLlxEfHw8LCwt07txZoUNUVNy7dw8ARJ1buezevXto3rx5nuXcuXMHgOgns0Xx9vaGhoYGpk+froq5Ai5fvgwAsLOzw8OHD3HhwgUkJyejRo0a6Nq1K6RSqSDPp0+fMGrUKKxduxYVK1ZUqp7WrVvj/Pnz+PfffwvlXlbodyIPDw/MmjULMTExuHLlCtq1a4cePXogJSWF03n06BHs7OzQv39/PH78GNHR0ZgyZQpq1KiB+/fvq1ynt7c3qlSpgjVr1iAuLg53796Fq6srqlevjkuXLgn0165di+nTpyMmJgb79+9H06ZN8ccff6jVXnkA559//smVuW/fPoVl5rb11q1baNu2Ldq3b4/379+Llj1v3jxMnDgRUVFRuH37Nry8vLhgGbmOj48PZs+ejbdv32Lr1q2oW7cu/Pz84OnpiYULF+LNmzfw8fFBvXr1cOHCBV49hXU9xIJZly5divT0dJ7es2fP4OXlBX9/f+jp6RWqDXnZAqjX/8pe26KiV69e8PX1xfv373H79m307NkTdnZ2CA0NLTYbFLF06VJs3bpVVL5lyxYMGTIEO3fuxLt377Bs2TLUqVOHu1nKSU1NhZeXFzZt2oTmzZtj//79ePfuHXbs2AE7Ozvu4S/n9evX8PLyEgTTpqenc+NKFYgI27dvh5+fn0r5Chv5A1zR7oKcDBkyBCNHjsTr16/h7u6O7OzsPPXT0tIwZMgQ1K1bFydPnsTHjx+xb98+2NraYvz48cjKyuLpq3r93Nzc4ODggPPnz+Pjx4/w9fVF9erVsXz5chV7oWDExMRAS0sLxsbGgrTy5ctzOnmRnJyM1atXo0OHDnkGN+7cuRO//vorfvjhB4SHh+PSpUvo3LlzgewPDw+Hnp4eZs6ciW7duiEsLAzPnj3DuHHjUL16ddEZqAkTJqBFixYKv8wphnyMXb9+vUD2cqiwDpIn9evXJ4lEQnPnzuXJly1bRgBo0qRJnOzYsWNUoUIFXjDMly9fyNbWlurUqSMIAslrPVwe8DVkyBBevtjYWLKwsCBvb29OJo+RmDZtGq+MJk2akL6+PqWlpancbrEys7Ozyd7eXlCm3NZBgwbx1umuXr1KEomEWrRowWuDvOycfZednU0LFiygpKQkns7vv//O6Xz58oXKli1LxsbGNH/+fE7+6dMnkkql1LZtW14bVL0eimIkxGJQjI2NKTExkWdDrVq1qHz58vTs2TO1bcgvRkLMFnX7X5lrqw7KxEhs27aNHj16xJOFhISQgYEBVapUiRISElSqE4UYI0H09fr++OOPonINDQ06cuQIJ3vz5g1pa2sL1rg/ffpEAEgikfDWdTMzM6lnz54EgA4dOsTJFcU8JCYmEgBBfEB+MRI3b97kghhzB9GpizoxEi4uLgSA7t+/L5q+fPlycnZ25v5OTU2lhg0bEgBasGABJxeLkRg1ahQBoE2bNvHkixYtIgA0a9YsnlyV6zdy5EgCQBs2bBCVnzhxQonW548yMRIaGhpkYGAgmrZt2zbB/TQ3WVlZNHDgQKpbty69f/8+T3t27NhBU6ZMIUdHR6pSpQrNnj2bUlNTlWuMAvT19QkAVatWjT5+/MjJo6KiyMTEhMqVK0dxcXGc/MiRI2RmZsazNb8YCSKic+fOEQCaMGGC0rYVW7AlAIqPj+fJU1JSSCqVkr6+PtfJ4eHh5O/vLyhjzpw5BIAeP37Mk+f10JDXKxbwdfDgQbpx4wb3t/zBkLv88ePHEwB68uSJco3NgaIy5T/cnGXKbQ0LCxOU06lTJwJAAQEBgrLzskuuk7vMVq1aEQCKjo7myRs1akQVK1bkyVS9Hqo4EkuWLOEeuBkZGdSxY0fS1dWlwMDAAtmgjiOhbv8rc23VQRlHQhHyMbtx40aV8hWnI2Fubi6Q29raCsaO3JFo1qyZQP/KlSsEgDp06MDJCtuRyM7Opq1bt9Lu3bsVN1RF1HEkOnfurHB8EgkdCSKi58+fU7ly5UhTU5MuXbpEREJHIjY2ljQ1NcnS0pLnQBN9vT8bGhqSvr4+JScnc3Jlr5+87IoVKwqcffmDv1OnTsp1QD4o40gYGhqSlpaWaJp854ZYICPR13EwdOhQsre3p3fv3qlk2/Hjx0kikVD37t1VypcbqVRKAGjFihWCtBkzZhAA8vHxISKiDx8+kLm5Oe3fv5+np4wjERgYyL2AK0tejkShxkiYmZnByMiIJ9PT00PlypXx+PFjPHv2DHXr1oWtrS0sLCxw4MABREdH4/PnzyAibl3qzZs3vHVRRWRlZSE4OBhaWlqiB3X07t1bNJ+1tTXvb/m605cvX5RqpzJllitXjldmTlvFpi5r167NrUXmjimxtLTMt/7ca5FSqRQaGhqoVKkST25oaChoZ2FdDzF+//137v/jxo3DhQsX8M8//wgORSlKG4CC9X9+17YkqF+/PgAgODi4xGzID7H18XLlyuHdu3ei+mJr77Vq1QIAhISEFK5xOZBIJBg+fHiRla8s9P+Bb4riusSwsbHBjh074OzsDDc3N9FlwJCQEGRlZcHW1lYQV6Onp4eqVasiNDQUYWFhvEOVlLl+8rL19fXh5eXF05Uvt8iXbIoDS0tLhIeHIz4+XrC8IT97Q9H99LfffsPNmzdx5coVVKhQQaV6u3fvjs6dO+PEiRO4desWL+ZKFUxNTZGUlMSN+5zUrVsXwNf4PgCQyWTIzs5GSEgI7/chjxPx9fXF+fPnRQ8PpEIKspRTqDESioK/cv9Adu7cCXNzc0ydOhURERGC9b3c63WFjbIRwCVRpthNRJkbi1j9EolEqbzFcT1WrFiBLVu2QCaTia47lvSYkCPWX0UxXgqKvH9UeegUN6r2m9g6vyoP12/9oDn5gy9nPJky9OzZE7/99hvevHmDQYMGCfpR2YdG7j5W5fqJnRSsoaEBmUyGqVOnFtvvt2HDhgCAJ0+eCNLkMrlOTtauXQt/f3+cO3eOcyISEhKwcOFCpeuWB3M+evRIZbvl2NvbAwAyMjIEabl/C61bt8bYsWPVqkceAC8WS6IOhTojERsbi5SUFF6UfWpqKl69egUDAwNUr14d2dnZ+PXXX6GpqYng4GDeDMaff/6JM2fOCMrV1NTk/Rg2bNiABg0aoFmzZnBwcMCDBw/w9OlTQaTu7t27oaOjAxcXl8Jsplpoampytj558kTgcT5+/BgACv2Y1fxQ53qoypEjR/Dbb79h4MCBvMA5f39/ODg4wNbWtlDHhBiltf/z4t69e3j48CEGDx4sSHvw4AGArzsdvhfkuzFyIt/RIb/BAv+bQYyPj+fpRkVFiZYrfyDKx0tKSgqWLVuGMWPGwNzcvOCGFxLy3RO5g36VYeHChbh58ybOnz+PsLAw3htx/fr1oampifDwcGRnZ/Ne+FJTUxEZGQl9fX3Rt+D8cHBwgKamJnR1dQVBsQBw/PhxxMbGFpsz3rdvX+zZswfnz5/nHSNORLh48SKsrKwEswWHDh3CkiVLcPnyZd7so9yRmDNnDifbvHkz2rZtK9pX8pMk5bvQ1MHZ2RknTpxAaGioYEZdfo/64YcfACjeURIdHY2goCD88ssvCncAxcbGAoDoDj51KNQZiezsbPj4+PBkq1evRnJyMkaNGgUdHR1IJBLo6OiAiARvEHfv3hUt18LCAh8+fADw9a1jzpw5ePv2LYCv01EAsGjRIt6D5cmTJxg1alSpekuR27pgwQLeW0NgYCAuXryIFi1aoE2bNsVqkzrXQxWCgoIwaNAgNGvWDNu3b+el+fv7IywsrNDHhCJKY//nxb179zBhwgTBAzY4OBjbt2+HhYUFBg4cWELWFT5BQUHcVk3g6yyUPOp//PjxnLxq1arQ0dHBjRs3ePk3b94sWq58b798vISFhWH+/PnQ1dUFUHp2bci3JIq9TeeHpqYm9u7dCwsLC0RHR/PSTE1NMXz4cLx+/Vqww2blypX48uULPDw8lNrqnRtTU1OMGjUKDx8+5G1HB4DIyEj88ssvguOY//zzzyLbzdG7d280btwYa9as4a43AGzZsgXR0dFYsGABz5G6fv063Nzc0Lx5c+zatQuenp7cP7HPAGzevBlz5swRzPoEBATgxIkTsLCwEJxhoUp7hwwZgho1amDDhg28+1l0dDS2bdsGS0tL0XMsVEU+xlq0aFHgsgBA0zOXG/nw4UPs379f1LsU4/r169i2bRsCAgJgZGQEPT09bNu2DQ8ePMDq1auxfv16dO/eHZs2bYK2tjYkEgnMzMxw+PBh7Nq1C8+fP8fly5exZMkSREVFcZ0XGxuLRo0aAfi6Fu3n54eoqChs3boVqampWLVqFbS0tGBvbw8jIyOsXr0ax48fR1hYGPbt24cpU6ZgxIgRmDp1KqKjo7Fq1SoEBATwBpeNjQ08PT0REBDAvc0kJCTw3n4UoU6ZclvXrFmDY8eOISwsDDt27MDMmTPRsmVLHDhwAIaGhqJly7fS5le/rq4uVq5cye0VJyJu2tHb2xsBAQH4/PkzN9VoY2Oj9PVIS0vjrvWXL1+QlpaG7OxspKWlYe3atTxbPn/+jPLly6Nt27b48OEDevbsiXv37iEgIID3r2PHjqhTp06hjYmnT5+K2lKnTp0C9X9Bx4uciIgIrFmzhmt/WloaiIj7/eQ80CYtLQ1XrlzB0qVL8ejRI9y+fRvbtm3DtGnTYGNjgyNHjgjiN/LDy8sLrq6usLOzUylfbk6fPo1du3YhICAAmZmZSEhIgJGREZ49ewZfX1+evHLlyoiPj8eff/6JgIAAJCQkIDMzEzo6OrC2tkZqaiqWLVuGXr164dSpUzh37hyuX7+O2bNn4+rVq5DJZBgzZgxXt7a2NjIzM7F3715cvnwZjx49wsqVK9GoUSPuo3KxsbGwsrKCiYkJDA0NcfPmTZw6dQpxcXFYuXIlnJycOCdMvqV2//79+PnnnwvlwLB9+/YBUP4cAuBrLM7atWuhp6eHvn37cvJHjx5h3bp1CAgIwLNnzxAXF4f09HTB26ShoSGaNm2Kv//+G7Vr1+bV7ejoiMjISCxZsgR3797FvXv3sHLlSmzbtg1jx47FH3/8AQ0NDQQGBqp8/Tp37oyoqCjMnj0bd+/exf3797Fnzx54eHjAzc0N8+bN4y2bdOvWDdHR0Rg9erRS/XL27Fn8/fffCAgIwO3btwF8PTshICAADg4OPAdIIpGgW7duOHz4MHx8fPD69Wvs2LEDK1aswKxZswSxAs2aNcOnT5/w+PFjXL58mffv5s2b0NLS4s1IREVFYc+ePfjrr7/w+PFjXL16FevWrcPs2bNha2uLw4cPC36TqrRXS0sLXbp0wcGDB7nv2Bw9ehQeHh4wMTHBsWPHFP7m165di5MnTyIgIIC7ZwYFBUFLS0vw8a5FixYhOzsbS5cuVXp5NA/f4FGhLW2MGTOGC+q4cuUK7ty5AzMzM0ybNo33AASAX375BY6Ojjh79izevn0LExMTjB49Gu/fvxf9ENHvv/+Opk2b4t69e2jZsiVcXFy4twkAmDJlCtzd3XH69GlER0ejVq1amDp1KurUqcMrZ8CAAaK2d+rUSeBFKouqZea21draGhMnThSdkldUtjI67u7uovJhw4YJZKpej5w39bxsSUlJ4Q4qEqNfv35cAGVhjwlF/VIY/V+Q8ZLblrxo3rw5QkJCEBwcjODgYO4rgGPGjEHr1q1L9EAqOTmDaZWR5zUeDAwM4Ofnh1OnTuHx48cYOnQo/vnnH9EgWy8vL3Tt2hW3b99GVlYWhg0bhho1aiAxMVG07GPHjuHgwYN4+fIlPD090atXLy6tSZMm2LJlC6RSqcLDlooDqVSKiRMnYsWKFYiOjhYEO7Zo0SLfN8jWrVvDz89PMLOnq6uLv//+G7NmzeJeMvr06YP169eLTm+rcv10dHTw119/YebMmbh48SI+ffqEWrVqYfbs2YKyY2Ji8OnTJ9H7UH6YmJhAJpPlq2dlZYW7d+/izJkzCA0NRZUqVSCTyUQPqfr1118F59zkJHfsx4IFCzgH98WLF4iNjUXHjh0xdepUtGjRQvCbVKe9tra2CA4Oxrlz5/Dw4UNoaGhg165d6Nixo1JLRD/99BN++uknhemhoaG4cOEC1q9fX3j3EBW2eDAYjG8cFPL2z8JAvv1z0KBBJW1KoaHutzZSUlKocePGoh/t+h74/fffycDAoMBbp78VSlt75R/t6tKlS6F+tKvkX2kYDAaDAeDrdszTp08jPj4eo0aNKmlzCpXTp0/D29sbW7duFd2u/71RGtvbp08fmJqaYv/+/YW646tIvrXxraNMfEi7du0ESzaM/yZsvKjP7t27ubMwgoOD4enpiSFDhqBatWolbFnJYWpqysWJfE+0bt0aAQEBCndWfW+UxvbOnDkz32+5qANzJERQNtCUwQDYeCkoenp6Sq19/5eQSCSCQ9u+deQfyPuvUBrbWxROBMAcCQaDUYLk9VEkBoPxbcBiJBgMBoPBYKgNcyQYDAaDwWCoDXMkGAwGg8FgqA1zJBgMBoPBYKiNwmBLV1fX4rSDwWAUE97e3twRzoyiQf7pbHYfZXwv5P6GS07YjASDwWAwGAy1UTgjwd5YGIzvD4lEAg8PD5U+JsVQHflMBLuPMr4X/P390b9/f9E0NiPBYDAYDAZDbZgjwWAwGAwGQ22YI8FgMBgMBkNtmCPBYDAYDAZDbZgjwWAwGAwGQ22YI8FgMBgMBkNtmCPBYDAYDAZDbUqVI2FoaAiJRCL6z8DAAPXr18eqVauQlZVVonYePnyYZ1tqamqe+nFxcTz9Bg0aiObJrSeRSNC4ceOiakaxsmLFCq5NVlZWJW0Og1GquXz5MoYOHQoA2L9/P++eMHbsWNE8ufUkEgmmTZtWnGYXGZmZmfDx8UHZsmXRqVOnfPX37duHpk2bwsDAAGZmZhg8eLDoyYzPnz+Hl5cXGjVqBKlUijJlyqBevXqYPn063r17J9BPS0vDoUOH0K9fP1haWkJXVxeVK1eGs7Mzrl27VihtBb4+C6ZMmYKaNWtCX18fNjY2GDp0KO7fv59nvvj4eFhbW0MikSAwMFCQ3rdvXwQFBRWanRyUCz8/PxIRFxtBQUEEgJydnTlZQkICXb58mRwcHAgAeXh4lJh9OXF2diYAlJKSopT+nTt3CAABoNGjRyvUu3HjBpmamhaWmaWK+vXrk6WlJU+WmJhINWrUoO7du5eQVaXTlqIAAPn5+ZW0Gd89Li4u5OLiolbe7du3k6mpKV27do0nX758OXf/2LNnj8L8S5Ysob59+6pVd2nkwoULZGdnR61atSIA1LFjxzz1fXx8CAAtW7aMEhMTKTw8nJo1a0YWFhYUFRXF061evTpZWFjQ0aNH6dOnT/Tp0yf6559/yNjYmMzMzOjZs2c8/Q0bNhAAmjx5Mr18+ZKSk5Pp9u3b1KBBA9LQ0KAjR44UuL1hYWFkbm5OLi4u9OjRI0pNTaW7d+9SvXr18n0u/Pzzz9wYuXr1qiD9xIkTVK5cOTp+/LjKduXhG/iXqhkJRZQpUwZt2rTBxo0bAQCbNm1CRkaG2uUZGhqiVatWhWWeSujq6sLU1BSbNm3Cnj17SsSG0gYRITs7G9nZ2cVSX17Xv7ht+a/To0cPSCQSZGZm/ifrz82dO3cwYsQIrF27Fi1atBCkV6pUCRKJBKNGjUJEREQJWFj8TJo0CYsWLcL27dvz1X316hVmzJiBXr16YcaMGTA0NIStrS327t2L9+/fY8qUKYI8K1euRM+ePVG2bFmULVsWbm5u8PLyQmxsLJYuXSrQb9SoEby9vVG5cmXo6+ujSZMm2LJlC7Kzs+Ht7V2gtmZmZqJfv36oVq0a/Pz8UKdOHejq6qJhw4b5ln3y5EkcPXoU3bt3V6jTrVs3eHp6on///njx4kWBbM3JN+FIyKlVqxYAIDk5GfHx8SVsjXro6enhn3/+gYaGBkaPHv2fuRnkRZkyZfDs2TOcPHmypE0pVbYw/nt4eHigdu3aCo8ibtKkCWbOnInExET069cv32XV74Hbt2/D2dlZKV1fX1+kpaVh8ODBPHmVKlXQunVrHDx4EO/fv+fkT58+xcCBAwXlNGvWjEvPyZgxY/Dvv/8K9GvUqAEABX4uHTlyBMHBwZg0aRI0NPiP506dOiEuLk403+fPnzFy5EisWbMG5ubmedYxZswYlC1bFjNnziyQrTn5phyJ8PBwAICZmRnKly9fwtaoj5OTE+bMmYPExES4urr+J24GDAYjbx49eoRr166hV69ekEgkCvXmz5+PDh064MGDB5g4cWIxWlgy6OvrK6178eJFABCNLWvcuDGysrIQEBCQbzkfPnwAANjb2ytV7+3btwEA7dq1U85QBRw8eBAAVJ4xnzhxIpo1aybqFOVGR0cH3dH68qsAACAASURBVLt3x4EDB7h2FpRvwpH48uULrl69ijFjxsDAwIBb4pCTmZkJPz8/dO7cGebm5tDX14e9vT18fHx4U9TygL+kpCRcu3aNC0jS0uJ/u+zDhw+YMmUKqlevDl1dXVhZWaFTp07466+/kJKSIrDv7du36N+/P8qWLQtTU1P06NEDz549y7NNMpkMjo6OCA4OxoQJE5Tqh5x26ejooFy5cujatSsuXbrE6eQOBA0PD0e/fv1gamrKybZu3crTefnyJfr3748yZcrA1NQUgwcPxqdPnxAZGYmePXuiTJkysLCwwMiRI5GYmKhW3ysir8DVsmXLKgy+1dDQ4IKnCuv65xdEq07/R0ZGqjw2ShtZWVnw8fFBgwYNYGBgAKlUiqZNm2LLli0gIgBAYGAgr93ypYLU1FSePDQ0FADw77//QiKR4MSJEwAAbW1tTicwMJBLzylzd3eHmZkZdHV1YW9vz5vqLuz6S4KzZ88CAOrXr5+nnqamJvbs2YNKlSphy5Yt2L17t1LlP3z4EAMHDoS5uTl0dHRgaWmJYcOG8cbj5MmTuX5o1qwZbt26hVatWsHAwABWVlYYN24ckpKSBGUHBwfD1dUVZmZm0NHRQZUqVfDrr7/y3v6Lg7CwMEgkElSqVEmQZmlpCQBKzQL7+/vDwMAAkydPzlMvNjYWBw4cwPDhw9G5c2d4enqqZbece/fuQUtLC8nJyejXrx9MTEygp6eHevXqYeXKlaIbDY4dO4ZTp05hw4YNStfzww8/IDMzExcuXCiQvRwqBFQUC/JgS7F/tWrVogMHDgjyHDt2jADQ4sWL6ePHjxQbG0urV68mDQ0NmjZtmkBfKpVSy5YtRet/8+YN2djYkLm5OR07dowSEhLo7du3tGDBAgJA3t7enK482NLZ2ZmuX79OX758oQsXLpCRkRE1adJEUPadO3fI2NiY+zs2Npasra0JAO3atYuTiwVbyu2qWLEiHTt2jOLj4yk8PJz69OlDEomEtmzZwtOX29a2bVu6dOkSJSUl0c2bN0lTU5NiY2N5On369KF///2Xvnz5Qjt37iQA1LVrV3J2dqagoCBKTEykjRs3iga6qtr3YsGWOW3JGbhqbGxMiYmJPL358+dz9alrQ17XX5Et6vZ/zrFx7tw50tfXFx0bxQVUDLbMzMykHj16kJaWFnl7e9P79+8pJiaG5s+fTxKJhAYPHszT7969OwGgjIwMnnzq1KkEgEJCQpTSlzNp0iQCQHXq1CF/f3/6/PkzPXv2jPr37y8YB0VR/9y5c8nAwIDWrFmjuJNEUCfYcvjw4QSArl+/Lpq+fPlyXhD61atXSUtLiwwNDSksLIyTiwVbBgQEkL6+PjVt2pTu3r1LiYmJdO3aNbKzsyMjIyO6d+8eT9/Y2Jisra1pwIAB9OzZM0pISKAVK1YQAMFv6uLFi6Snp0dNmzal+/fvU2JiIp07d46sra3JxsaGPnz4oFI/KOLJkyf5Bltqa2uTnp6eaNqWLVsIAE2ZMiXPei5evEiampr0999/56nXsWNHAkASiYRGjRpF7969y78R+WBsbEza2tpUoUIF+uOPP+jdu3cUHR1NM2fOJADUv39/nv6HDx/I3Nyc/P39OZl8HIkFW8o5cuQIAaC5c+cqbVtewZal1pHI+YPJyMig58+fk0wmI4lEQn369KH09HQu/dixY9SuXTtBWe7u7qStrU3x8fE8eV4PkmHDhim82Xbp0kXUkTh27BhPz83NjQBwD2w5uR0Joq9Og7a2NkmlUnr8+DEny+1IyO3KHa2dmppKlSpVIn19fXr79q3AtpMnT4q2M6fOiRMneHI7OzsCQJcvX+bJbWxsqFatWjyZqn1fEEfCz8+PJBIJDRs2rEA2qONIqNv/uceGi4uL6NgoLlR1JP766y+Fu4z69u1LAOjo0aOcrKgciZy/OyKitLQ0qlSpEuno6PD6vbDrnz17Nunr65OPj49ouiLUcSS6detGAOjhw4ei6bkdCSLiHu729vaUnJxMREJHIisri6pVq0ba2tr08uVLXv4HDx6QRCKhhg0b8uTGxsakq6tLcXFxnCw7O5sqVKhAdnZ2vLJtbGxIS0uLIiMjeWUcOnSIANDUqVNV6AXFFNSR2Lx5c772/Pvvv1SuXDmlHceEhAS6fv06tWvXTu3dEDnR1dUlAAIHnYioU6dOBIAuXLjAydzc3Khfv348PWUciYCAAAJAI0aMUNq2b37XhpaWFmxsbODp6Qk3NzccPHgQq1ev5tJ79OjBm16WU79+fWRkZODhw4dK13Xo0CEAQNeuXQVpp06dEp3qatKkCe9v+RTa69ev862vWbNmWLFiBZKSkuDq6iq6dJLTrtwRubq6uujYsSNSUlJw5swZQb6mTZvma0Pu9UT5tGBuuaWlpaBNhdn3ufn8+TMMDQ0BALdu3cLQoUPRpk0bbNq0qdhskKNu/+ceG9bW1gCUGxulAUXtBr5GgOfUKUpyrxnr6OigXbt2SE9P59bFi4KFCxciOTm5WGIR5Etp2traSueZOnUq+vTpg5CQEIwfP15UJzg4GM+fP4e9vT0qV67MS3NwcICVlRXu3buHV69e8dJsbGxgamrK/S2RSAT3gODgYLx48QL169dHlSpVePk7dOgA4OtyX3FRtmxZpKWlie7CkS/JlC1bVjTv48eP4ejoiEWLFinsy9yUKVMGzZs3x/Hjx2FoaAh3d3eFAZHKIJVKAQBdunQRpPXs2RMAuPvM4cOHcf78eaxbt07leuRjTNHzRlW+CUciJ23atAEA3tpOfHw85s2bB3t7e5QrV45b45s+fTqAr7s8lCEtLQ3x8fHQ09NDmTJllLbJ2NiY97c82lbZLYQTJ05E//79ERoaKjqA87OrYsWKAL7GauRGPjDzwsjISGC/pqYmDAwMeHJNTU1Bmwqr7/Pi1atXcHZ2hrW1NQ4ePAgdHZ1itaEg/Z97bMht/1a2l8oP5alQoYIgLa92FzZiN3+5TcW9Dl9U6OnpAYDKW9u3b9+OmjVrwtfXF3///bcgPa9rCCi+jrnHLvB1/OYcu/Ky7969K4hjkud/8eJFsR0iWLt2bRCRqKMeExMDALC1tRWkvXnzBl26dMFvv/2m8MCvvJBKpWjfvj0+f/5coBibqlWrAgDPgZMjv36xsbEAgH/++Qfv37+HmZkZr9+3bdsGAGjdurXCAwDlY0yVQNa8+OYcCfr/4K6cD4eePXtiwYIFGDlyJCIiIpCdnQ0i4vbdyvPIURQRraurC2NjY6SmpgqCCouarVu3olatWqI3g/zskv+Y89v2UxSo2veqkpiYiB49eiAjIwPHjx+HiYlJgW3IKyJejNLc/0WN/CEj9rAWa7fciU5PT+fpfv78WbR8Za+F2Fue3KacD8iiqr84sLCwAKD6FkIjIyPs378f+vr6GDt2LB4/fsxLz+saAgUbv/I8bdu2BRGJ/svKyoKmpqbKZatD+/btAXx1bHJz9+5daGpqCnZWJCYmomvXrhg8eDBmzJjByaOjoznnThl0dXUBAB8/flTD8q/IZ97evHkjSJNfP/n13Ldvn2h/Dx8+HABw9epVEJHoiZ7yMSYfcwXlm3Mkrl69CuB/U8ZZWVm4du0azM3NMXHiRM47AxRP2xgYGPBuNLVq1cLmzZsBAL179wYA0XMEGjRoAA8Pj8JrTA4MDQ1x4MABSKVSrF+/XpAut0seZS4nLS0NFy5cgL6+PpycnIrENkWo0/eqlj9gwACEhYXhwIEDvDcJFxcXHD58uNCvvyJKY/8XB4ranVMm1wH+d2PKffOSb4/LjXzWS349Ro8eDTc3N4HelStXeH+np6cjICAAurq63BR6UdZfHNSrVw+A0HZlcHBwwPr165GUlISdO3cK0qpVq4aQkBDB8kVwcDCio6PRsGFDwbKHsvXWqFEDDx48EHWAunbtWqz9+csvv0BXVxe7du3iyV+9eoWrV6+iT58+PMczIyMDffv2RatWrbBw4cJ8y2/cuDG2bt0qkKelpXHLq7mXM1Vh+PDh0NDQEF0OOnbsGAAofaZGXshnZ+RjrqB8E45EZmYmIiMj4enpid27d8PS0pI7oUzuYb59+xbLly9HXFwcUlJScOnSJcE2UTkNGzZEREQEoqKicOPGDTx//hytW7cGACxZsgQ2Njbw8PDAiRMnkJiYiOjoaIwbNw5v3rwpMkcCAOzs7ATr/3Lkdk2ePBnHjx9HYmIiIiIi4Obmhjdv3sDHx4fzVIsLdfpeFTw8PHDy5Els3rxZ4f7swr7+iiiN/V8cuLu7o0ePHvD19cWff/6J2NhYvHnzBgsWLMChQ4cwePBgbu0WABwdHQF8jS2IjY1FbGwsZs6cqXBqu2HDhgC+7v9/8+YNzp49i7Zt2wr0Dh06BH9/f8THx+PFixcYOnQoXr9+DU9PT16/F3b98+bNg1Qqxdq1a1XpNrXo3LkzAODBgwdq5R82bBhGjBghkGtoaGDbtm3Q0tKCq6sr7t27h6SkJNy4cQNubm4oU6YMtmzZoladEokEvr6+SE9Ph4uLC4KCgpCSkoJXr15h4sSJuHPnDubNm8fLY2VlJYinKCyqVKmCZcuW4eDBg1zs2ZMnTzBgwACYmZlh1apVPP2RI0fi3LlzWLdunWBpRh7PlJsZM2Zwywqpqal48OAB+vTpg6dPn2LcuHGCsydUaa+DgwNmzZqFo0ePwsvLC2/fvsXr168xa9YsnD9/njsvoqDcv38fmpqaPCe8QKgQmVnkSKVS0W2fEomEypQpQ/Xr16cZM2YIttnExsbS6NGjydramrS1talixYo0bNgw+v3337kyGjVqxOmHhYVR69atSSqVkrW1Na1bt45XXlxcHE2ePJlsbGxIW1ubLCwsaMCAARQREUFEX3dV5LZx9uzZREQCeffu3Sk2NlYgz2lPbsaOHSt6pnpuu4yNjcnJyYkXxStmW+7rqcj+/2PvvuOiuNb/gX8WWJaOgIBKUcQuRQQUUbMiJErUaKKAgqKJ3uSary1iEs1NrqQYTSzRGM2NNdZcwMSoxJLiogYsiAVFQVBDMTZEitLh+f3hb+c67gLLiizq8369fL3kzJkzz8zs7jw755zZh38LRPlv4cKFdOTIEZXy+fPnN+rYP/w7AQ9vUzmy++F/ERERdPLkSbX78fC/nTt3Nun5ryuWxz3+9b02mhsaOWuD6MEU0K+++oo8PT3JyMiIjI2NycfHh7777juqra1Vqb9o0SJydXUlmUxG3bp1oy1btgizJgBQ3759hbolJSUUERFB1tbWZGNjQ2+++aZoRpZy1kZycjJNnjyZbG1tydDQkNzc3Gj9+vVq423K7c+bN6/ZZm0QEfn7+1PPnj1FxzUuLk7ltVPXzIOysjLy8vJS+1sb586do7CwMLKzsyMDAwNq06YNRUZGUlZWllBn4cKFKtv67bffSKFQqJQvXrxYWC8tLY3GjRtHdnZ2JJVKycnJiSIjIykjI0MUQ1VVFclkMgoODtb4mDx87h79l5mZqXad2NhY8vHxISMjI7KxsaGIiAjKyclRqVfXNUf5TyaTiepnZ2fTggULyN/fn9q2bUsGBgZkY2NDgYGBtG3bNpX2tdlfIqJNmzaRj48PGRsbk6mpKfXr1482b95c7zpdu3ZVuw8LFy4U1auoqCAHBweV2R4NeaqmfzLGnhxtEgldUiYSdV0wWiptE4mjR4+Svr5+vT/K9TTbv38/AeLn5jzLWuL+rlixgkxMTEQJpCae+umfjDH2PPDz88OaNWswbdo0JCUl6TqcJlVUVISoqCgEBARo9Cjnp11L3N+9e/ciOjoaMTExcHV1bbJ2OZFgjLEW5I033sCOHTuaZJxRS5KcnIwOHTrgp59+UvlBqmdRS9zftWvX4vfff8fw4cObtF2DhqswxljzOnnypGj0e+fOndG/f3+d/Q5Gcxs0aNBj/wBUSxMUFISgoCBdh9FsWuL+PqmHx3EiwRhrcXx8fB77GSSMsebRMu63MMYYY+ypxIkEY4wxxrTGiQRjjDHGtMaJBGOMMca0xokEY4wxxrRW56yNlvSreIyxphMWFoawsDBdh/Fc4M9R9jxQSST8/f0RExOji1gYYy3M5s2b0aVLlyb5oSDG2LNJQjxZmzFWh549eyIkJATR0dG6DoUx1jLF8RgJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpzUDXATDGWoY1a9YgJSVFVHb9+nXEx8fj+vXrovI333wT3t7ezRkeY6yFkhAR6ToIxpjurVu3Dv/4xz9gYGAAiUQCAKitrYVEIhH+rqmpgUQiwY0bN9C6dWtdhssYaxniuGuDMQYAGDNmDKRSKaqrq1FVVYWqqirU1NSI/pZIJHjxxRc5iWCMCTiRYIwBAFq1aoWhQ4fCwKDuHk8iwoQJE5oxKsZYS8eJBGNMEBERgZqamjqXS6VSvPLKK80YEWOspeNEgjEmGDFiBIyMjNQuMzAwwMiRI2FmZtbMUTHGWjJOJBhjAhMTE7z22muQSqUqy2pqahAREaGDqBhjLRknEowxkfDwcFRVVamUm5mZYciQITqIiDHWknEiwRgTeemll2BlZSUqk0qlCAsLg0wm01FUjLGWihMJxpiIgYEBwsLCYGhoKJRVVVUhPDxch1ExxloqTiQYYyrGjRuHyspK4e/WrVvjhRde0GFEjLGWihMJxpiKgQMHom3btgAedGtERkZCX19fx1ExxloiTiQYYyokEgnGjx8PPT09VFVVYezYsboOiTHWQnEiwRhTa9y4caitrYWzszN8fX11HQ5jrIXiX/9kjKnl5eWFLl268N0Ixli9OJFgjNUpIiICY8aM0XUYjLEW7Ln+GfGjR49i2bJlug6DsRarsrJSNA2UMaYqLi5O1yHo0vP9M+K5ubnYsWOHrsNgOrZjxw7k5eXpOowWqSmTiGPHjuHYsWNN1h5jupaXl8fXEHDXBoDnPpt87kkkErzzzjsIDQ3VdSjPtJCQEAD8fmPPjtjYWISFhek6DJ17ru9IMMYYY+zxcCLBGGOMMa1xIsEYY4wxrXEiwRhjjDGtcSLBGGOMMa1xIsEYY4wxrXEiwRhjjDGtcSLRBP773/9CIpFAIpHAyMhI1+E8tfbu3YsuXbrAwKD+x5sMGDBAON6P/ps1a1YzRcua26FDhzBx4kQADx4i9vB5nzp1qtp1Hq0nkUgwZ86c5gz7iamursaKFSvQqlUrBAUFNVg/Li4Offr0gYmJCWxtbTFhwgS1D2K7cuUKPv74Y3h7e8PU1BTm5uZwc3PDu+++i5s3b6rUr6iowM6dOxEaGgoHBwfIZDI4Oztj5MiRSExMbJJ9BYD8/HzMnj0bnTt3hrGxMVxcXDBx4kScOXOm3vWKiorg5OQEiUSCP//8U2X56NGjcfr06SaL87lEz7GYmBhqykMQGBhIMpmsydp7XmRlZdGIESPIw8ODLCwsSF9fv976/fv3JwBq/82cObPR2wdAMTEx2oYvcuLECWrVqhUtWrSoSdp7lowZM4bGjBmj1bobN24kGxsbSkxMFJUvXrxYOPc//PBDnesvXLiQRo8erdW2W6I//viDevbsSQMGDCAAFBgYWG/9FStWEAD64osvqKSkhDIyMsjPz4/atm1Lubm5orqurq7Utm1b2r17N929e5fu3r1L27ZtI0tLS7K1taXLly+L6n/77bcEgGbNmkXZ2dlUWlpKJ06cIC8vL9LT06Ndu3Y99v6mp6dTmzZtaMyYMXThwgUqLy+nlJQUcnNzIxsbm3rXff3114XXyJEjR1SW//LLL2RlZUXx8fGNjqupryFPqVi+I8Eei5mZGQYMGPBYbXz00Ufw9/dHSkoKzM3NNVonOTkZRKTyb/ny5Y8Vy+Oqra0FEaG2tlancTTk5MmTkEgk+PDDD3UdSoOSk5MxZcoUfPPNN/D391dZ3q5dO0gkErz55pu4dOmSDiJsfjNnzsSCBQuwcePGBuvm5OTgvffew6hRo/Dee+/BzMwMXbp0wX//+1/cunULs2fPVlln6dKlGDFiBFq1aoVWrVohPDwcH3/8MW7fvo1Fixap1Pf29sZXX30FZ2dnGBsbw9fXF2vXrkVtbS2++uqrx9rX6upqhIaGomPHjoiJiUH37t0hk8nQu3fvBtveu3cvdu/ejWHDhtVZ5+WXX0Z0dDTCwsJw9erVx4r1ecWJBNO59evXY+7cuQ12aTwN+vbti8LCQsybN0/XoTwz3nnnHXTr1q3ORxH7+vpi3rx5KCkpQWhoKMrLy5s5wuZ34sQJjBw5UqO6GzZsQEVFBSZMmCAqb9++PQYOHIiffvoJt27dEsqzsrIwbtw4lXb8/PyE5Q/75z//iZMnT6rU79SpE4AHXQuPY9euXUhNTcXMmTOhpye+ZAUFBSE/P1/teoWFhfjHP/6BlStXok2bNvVu45///CdatWrF71stcSLBdM7Y2FjXIbAW6sKFC0hMTMSoUaMgkUjqrPfJJ59g8ODBOHv2LGbMmNGMEepGY94zBw8eBAD4+PioLPPx8UFNTQ0SEhIabOfOnTsAAHd3d422e+LECQDAoEGDNAu0Dj/99BMANPrO54wZM+Dn56c2KXqUoaEhhg0bhh9//FHYT6Y5TiS0kJ6ejlGjRsHS0hKmpqYYOHCg2kE8P//8s2iQV0ZGBkJDQ2FjYyOUKbPpO3fuYPbs2XB1dYWhoSGsrKwQHBwMhUIhtLdkyRJhPUdHRyQnJyMwMBDm5uYwMTFBQECA2sFNmrT92WefCW0//Ibdv3+/UN66dWuVWO7fv4/ExEShTnPdVdiyZQt69eoFU1NTWFpaYuDAgdi+fXuzbLsus2bNEo6D8tubuvLjx49jwIABMDExgaOjI95++23cv39fqD98+HCh/vjx47FlyxZ4enrC2NgY1tbWiIiIEA2SCwoKEuo/3FUxd+5coXzMmDGi9n19fQEACxYsEOpoMmCvuf36668AAE9Pz3rr6evr44cffkC7du2wdu1ajV8LaWlpGDduHNq0aQNDQ0M4ODhg0qRJuHz5slCnsedPKTU1FSEhIbC1tYWhoSHat2+P//u//xN9+28O6enpkEgkaNeuncoyBwcHANCoSyg2NhYmJiYNDmi+ffs2fvzxR0yePBkvvvgioqOjtYpb6dSpUzAwMEBpaSlCQ0NhbW0NIyMjuLm5YenSpaipqVFZZ8+ePdi3bx++/fZbjbfTq1cvVFdX448//niseJ9LuhyhoWvaDJTJzMykVq1akYODA/36669UUlJCqamp9NJLL1GHDh3UDrYcOXIkASC5XE4KhYLu379Px44dI319fbp9+zZdv36dXFxcyN7envbs2UNFRUWUkZFBr732GkkkElq7dq2oPU9PTzI1NaV+/fpRUlIS3bt3j5KTk8nDw4MMDQ0pISFBqNvYtk1NTal///4q++Dt7a12UFNd9bXl4OCg0WDLCRMmUEpKCt27d4/S09NpwoQJBICmT5/e6G2iCQdbEhFZWlpS37591ZY7OTnR2LFj6fLly1RcXExLliwhADRnzhxR3bt37xIAYYDZpUuXqKioiH766SeytrYmJycnunnzplA/OTmZANC//vUvUTslJSUEQGWgYV31lXJzc8nZ2Zm6detGhYWF2h4KEW0GW06ePJkAUFJSktrlixcvppEjRwp/HzlyhAwMDMjMzIzS09OFcnWDLRMSEsjY2Jj69OlDKSkpVFJSQomJidSzZ0+ysLCgU6dOieo35vwdPHiQjIyMqE+fPnTmzBkqKSmh3377jZycnMjFxYXu3LnTqONQl8zMzAYHW0qlUjIyMlK7bO3atQSAZs+eXe92Dh48SPr6+rRly5Z66wUGBhIAkkgk9Oabb4peo9qytLQkqVRKdnZ29OWXX9LNmzcpLy+P5s2bRwAoLCxMVP/OnTvUpk0bio2NFcqUryN1gy2Vdu3aRQDoo48+0jg2HmxJRESxz/UR0OZFEBISQgBox44dovJr166RTCarN5HYu3ev2jYnTZqkdtR5eXk5tWvXjoyNjenGjRtCuaenJwGg06dPi+qnpqYSAPL09NS67achkahLnz59CAAdO3asUes1ZyIhk8koPz9fKKutrSU7Ozvq2bOnqK4ykbC3t6eysjLRspUrV6rMUGnqRCInJ4ccHR2pS5cuOk0kXn75ZQJAaWlpapc/mkgQkXBxd3d3p9LSUiJSTSRqamqoY8eOJJVKKTs7W7T+2bNnSSKRUO/evUXlmp6/mpoacnFxIQMDA/rrr79EbezcuZMAUFRUVCOOQt0eN5FYs2ZNg/GcPHmSrKysaOXKlRrFVFxcTElJSTRo0CCtZ0M8TCaTEQCaMGGCyrKgoCACQH/88YdQFh4eTqGhoaJ6miQSCQkJBICmTJmicWycSBARz9povP379wMAhgwZIipv164dunTpUu+6ffr0UVu+c+dOAFAZWSyTyRAYGIiysjIcOHBAtMzU1BS9evUSlbm7u6Ndu3Y4e/Ysrl+/rnXbTyvl7fs9e/boOJK6ubi4wMbGRvhbIpHAwcEBf//9t9r6Pj4+Ks8mUb729u3b98TidHJyQm5uLjIyMmBpafnEttMQ5cBJqVSq8TpRUVF47bXXcO7cOUybNk1tndTUVFy5cgXu7u5wdnYWLfPw8ICjoyNOnTqFnJwc0TJNzl9qaiquXr0KY9aYAgAAIABJREFUT09PtG/fXrT+4MGDATzo9mwurVq1QkVFBaqrq1WWKbtkWrVqpXbdixcv4qWXXsKCBQvqPJaPMjc3R79+/RAfHw8zMzOMHz++zgGRmjA1NQUADB06VGXZiBEjAED4DPv555/x+++/Y9WqVY3ejvI1VlZWpm2ozy1OJBqhoqICJSUlMDIygpmZmcpyOzu7etdXviEebbOoqAhGRkZqpz7a29sDAG7cuCEqr+uNr4zh1q1bWrf9tGrbti0ANHsfdGOouygbGhrWOV1U3Xl++Bw/65RJVFVVVaPW27hxIzp37owNGzZgy5YtKsuVD1aq6z1b13tDk/OnbDslJUXlYVjK9a9evaq2b/9J6NatG4hIbbJ67do1AFD7Jej69esYOnQo3n///Tof+FUfU1NTBAQEoLCwUO0YMk116NABAEQJnJLy/N2+fRsAsG3bNty6dQu2trai475+/XoAwMCBA4UxZo9SvsZ48HfjcSLRCDKZDObm5igvL8e9e/dUlhcUFGjVpqWlJcrLy1FSUqKyXPmh9Oj0pTt37oCIVOorLy52dnZata2np4fKykqVuoWFhWrjr28kfXNTflA2lNA9TdR9k3v4HCspp8U9eu6ehvNWH2Vy2NgphBYWFtixYweMjY0xdepUXLx4UbRcmSjUlYzV9b7ThHIduVyu9lknRISamhro6+s3um1tBAQEAHiQ2DwqJSUF+vr6KjMrSkpKEBwcjAkTJuC9994TyvPy8hr19F6ZTAZAu89GJeXgb+Vd1ocpz5/yfMbFxak93pMnTwYAHDlyBESk9omeyteY8jXHNMeJRCMFBwcD+F8Xh1J+fj4yMjK0avPVV18FAPzyyy+i8oqKCvzxxx8wNjZW6UopLy9HcnKyqOzcuXP4+++/4enpKbwZGtt227ZthW8pSjdu3FC5xatkYmIiunh17doVa9asaXCftbVu3Tp4e3urlBMRYmNjAfzvduezICUlBaWlpaIy5W1c5WsR+N+H36MfkMePH1fbromJCYD/JR7Xr1+HRCJpcQ90cnNzA6C6X5rw8PDA6tWrcf/+fWzevFllWceOHXHu3DmV13Zqairy8vLQu3dvlW4PTbfbqVMnnD17Vm0CFBwcjPDw8Ea3q6033ngDMpkMW7duFZXn5OTgyJEjeO2110RJaVVVFUaPHo0BAwbgs88+a7B9Hx8frFu3TqW8oqJCmBmmnCWkjcmTJ0NPT09td5CyG1PTZ2rUR/m5p3zNsUbQwcCMFkObgTJZWVlkbW0tmrWRlpZGQ4YMITs7u3oHWz46aE7p0ZkVxcXFopkVa9asEdX39PQkS0tLCgwMbPSsjYbanjZtGgGglStXUklJCWVlZVFoaCg5ODioHWw5dOhQsrS0pJycHEpKSiIDAwO6cOFCo47pwxoabKkcZf72229TZmYmlZWVUXp6OkVERDwVszbUlfft25csLS1FZcrBlp07dxZmbRQXF9POnTuFWRu3bt0S6tfW1pK7uzuZm5tTQkIC3b9/nxITE2nEiBFqB1tWVFSQhYUFDRw4kIqLi2nx4sXk4OBA1dXVRNRyZm2cP3++3kGh6gZbPmrKlClqj4FCoRDN2rh37x4lJSUJszZSUlJE9Rtz/g4fPkwmJiYUFBREp06dotLSUsrOzqbp06eTjY0NXbx4UVTfwcGBnJ2d690PdTQZbElEtHz5cgJAixcvpnv37tGlS5eoX79+ah+RPXHixDofQQ9A5TPO29ubrKysaOvWrXTz5k0qKyujM2fOCANl3377bZV4Gru/H374IUkkEoqOjqbr16/TtWvXhFkbM2bMaHB9TQZbvvnmm8JMOk3xYEsi4lkb2r0IMjIyaNSoUWRhYUHGxsbk6+tL8fHxwtQnADR58mQ6evSo2jeiOvn5+TRr1ixycXEhqVRKlpaWNGTIENFoZCVPT09ycHCgCxcu0JAhQ8jc3JyMjY1JLpfTn3/++VhtFxYW0pQpU6ht27ZkbGxMAwYMoOTkZPL29hbif//994X66enpNHDgQDI1NSUnJydatWpVo4/nnj176vzQenR6anl5OcXFxdGrr75Krq6uJJPJyNLSkgYNGkTbt29v9LaJmi6RmDlzptr4Fy5cqFL+22+/kUKhUClfvHgxEf0vkYiIiKBdu3aRl5cXGRkZkZWVFY0bN45ycnJUtn/x4kUaMmQIWVlZkYWFBYWFhdHly5dF7e/bt0+ov2fPHurWrRsZGxuTu7s7HTp0SFiWnZ1NDg4OOp+1QUTk7+9PPXv2pNraWqEsLi5O5djVNfOgrKyMvLy81P7Wxrlz5ygsLIzs7OzIwMCA2rRpQ5GRkZSVlSXU0eb8ERGlpaXRuHHjyM7OjqRSKTk5OVFkZCRlZGSIYqiqqiKZTEbBwcEaH5OoqKg63zOZmZlq14mNjSUfHx8yMjIiGxsbioiIUPs6MjU1bVQikZ2dTQsWLCB/f39q27YtGRgYkI2NDQUGBtK2bdtU2tdmf4mINm3aRD4+PmRsbCxMf9+8eXO963Tt2lXtPixcuFBUr6KighwcHFRmezSEEwki4kTi6XwRKBMJ1jSa+o5EU3g4kXhWaJtIHD16lPT19ev9Ua6n2f79+wkAbd26VdehNIuWuL8rVqwgExMTUQKpiaf1GtLEePonY6xl8/Pzw5o1azBt2jQkJSXpOpwmVVRUhKioKAQEBGj0KOenXUvc37179yI6OhoxMTFwdXXVdThPJU4kGGMt3htvvIEdO3bgP//5j65DaVLJycno0KEDfvrpJ5UfpHoWtcT9Xbt2LX7//XcMHz5c16E8tVrGmWQaUf6+xdmzZ3Ht2rUW/TPQj86fV/fvcZ/B/6waPnw4rKysADyYFy+RSPD777/rOCrdGzRokMrsi6ddUFAQ4uPj63wuzLOmJe7vzp070bt3b12H8VR7+n+3+TkyZ84czJkzR9dhaITUPOOCaSY+Pl7XITDGmMb4jgRjjDHGtMaJBGOMMca0xokEY4wxxrTGiQRjjDHGtMaJBGOMMca0xrM28PT8EiJ7csLCwhAWFqbrMJ4L/H5j7NnCiQSAmJgYXYfAdCgsLAyzZs1Cv379dB3KM+2rr74CALzzzjs6joSxpnH06FEsX75c12HoHCcSAEJDQ3UdAtOhsLAw9OvXj18HT1hcXBwAfr+xZwsnEjxGgjHGGGOPgRMJxhhjjGmNEwnGGGOMaY0TCcYYY4xpjRMJxhhjjGmNEwnGGGOMaY2nfzLGnhpFRUWwtLTE7du3kZaWJpS7ubmhdevWKvUfrQcATk5OcHV1feKxNpczZ85AIpHA09OzwbqFhYW4dOkSTE1N0b17d+jp1f1dsqioCJcuXYKenh7at2+v9vg+jIiQlZWF/Px82Nvbw8nJCVKptNH705Br164hLy8Pbdq0gbOzs0YPODt//jzy8/Ph5eUFS0tL0TLla4o9BnqOxcTEUHMegqFDh5K3tzcBIBsbG5LL5TRgwADq1q0b6evrU/fu3WnJkiVUWlrabDExIgAUExOj6zCeeWPGjKExY8ZotW5tbS19+umnFBwcTERECQkJJJfLqWPHjgSA+vfvT1VVVSrrKeu5uLhQ69atSS6X0+rVqx9rP1qKK1eu0KhRowgABQYG1lu3vLyc3n77bTIyMiJvb29ydHQkZ2dn2rdvn0rd7du306BBg8jY2Jg8PT3J09OTDA0NydfXl3799Ve1ccyYMYPs7e3JxcWF+vTpQ9bW1mRjY0Off/451dbWNsn+KhQK8vLyojZt2pCfnx/Z2tpS+/bt6Ztvvql3vYyMDDI2NiYAdOTIEZXlffr0oVWrVmkVU3NfQ1qo2Of6COjiRXD69GkCQCNHjhSV37lzh6Kjo8nAwIB69OhBly9fbta4dO3w4cN06tQpnWy7pSUSqamppFAomuwDuKVs/3ESiSlTppCbmxvdvHlTVL548WICQADo/fffr3P9hQsX0ujRo7Xadkv08ccfk62tLUVFRWmUSEycOJGMjIwoKSmJiIiqq6tpypQpZGhoSImJiaK6rq6u5O/vT7dv3xbKrly5Ql26dCF9fX1SKBSi+t9++y0ZGBjQzp07hbKysjIKDw8nAPTFF1885t4S7d69m6RSKa1YsUJ4XZaXl9Po0aPJxsamzvVqamrI39+fZDJZnYnE1atXycXFhf797383Oi5OJIiIE4mWk0go7dy5kyQSCXXp0oXu3bvXrLHpkqmpKfXv318n225picSwYcMIgNpv2E/z9rVNJLZu3UoA6NixYyrLFi9eTB4eHmRubk4SiYR++eUXtW08a4lEZGQk5ebmUmZmZoOJxLFjxwgATZ8+XVReUlJC5ubm1KdPH1F5eHi4SnJBRPTDDz8QAAoJCRGV79q1i2bMmKFS/+rVqwSAvL29G7NrKu7evUu2trY0fvx4lWXnz5+nV155pc51v/zyS3Jzc6MJEybUmUgQEcXHxxMAlSSpIZxIEBFRLA+2bGFGjRqF0NBQXLp0CStWrNB1OIzpVG1tLf79738jMDAQffv2VVvHxcUF69evBxEhMjISubm5zRxl89u0aRMcHR01qrtlyxYADz5bHmZmZobAwECcOHEC6enpQvm2bdvg7++v0o5yXElhYaGo/JVXXlH7WWVqagoAkMlkGsVZl23btuH27duYOHGiyrKePXti165datdLT0/HJ598gu+//x6Ghob1bmPYsGHw9PTEhx9++FixPq94sGULFBkZiZiYGGzfvh0ffPAB8vPzcf78eWH5Cy+8ACLChQsXUFBQACJC//79RQObcnNzkZubC1NTU/To0UNl2eXLlwE8eJP369cPZWVluHDhAmpqauDm5gYTE5M646uv7ezsbFy9ehUAYGlpCS8vLwBAQUEBUlNTAQBSqRT9+/cXxVJTU4OioiIkJCQAePALkXK5/HEOY7O4e/cuMjMzIZFI0LVrV1hYWAjLioqKcPr0aeFvuVwOiUSC2tpaHD58WCj39fWFqakpSkpKkJKSgjt37gAADh06BH19fQCAl5cX9PT0kJKSIqzXt29fyGQyXLx4EcXFxejUqRNsbW2f2PZ1MSAtMTERV65cwVtvvVVvvZCQEMyaNQvLly/H2LFjcejQIRgYaPbxRkTIyMhAQUEBbG1t0blzZ9HyrKws5OXlAQAsLCzQu3dvlJSU4OLFi7C2toaLi4twnB5VW1uLixcvoqioCG3btoWLi4tGMTWlpKQkAA8GpD7Kzc0NP//8M44dO4Zu3brV205WVhYAICAgQKPt7ty5EwAwduzYxoSrYt++fQAAb29v1NTUIC0tDaWlpXB1dRW93h9WU1ODSZMm4Z133oG3t7dG23nxxRexZMkSXLlyBR07dnysmJ87ur0jolstsWuDiOjGjRtCv29paSkdOXKE5HI52djYEADauXMndenShXx9fcne3p4ACP2ZSUlJ5OXlRcbGxuTl5UVt27Ylc3Nzio6OpurqaiIi+u9//0tyuZxMTU3JwcGBVq9eTQ4ODuTj40NWVlZkYmJCixcvVolLk7Y3b95Mcrmc9PT0RF0Vx48fJ7lcTmZmZqI+TWUsenp6ZGFhQXK5nORyOQ0ePLhJjrcmoEXXxrVr12jUqFEklUqpR48e1LlzZ5JKpRQeHk75+flE9GCsgVwuJ2tra1FXQUVFBcnlcnJ0dCQAdO7cOSIiSk9PF9V/4YUXhOORmpoqLHdwcCAAtHHjRurcuTP16tWLnJycSCKRUGRkpDBYt6m3r1RWVkYnTpxodNeHNl0bn3zyCQGgPXv2qF2+ePFi4b1UWVlJ/v7+BIDmzJkjqldX18b69eupXbt2ZG1tTd7e3mRhYUEdOnSg2NhYoc7KlStJLpeTvr4+9e3bl7755hvq2rUr9e7dmwwNDcnNzY2uXLmi0vZ3331H9vb2ZGtrS97e3mRmZkY9evSgw4cPN+oY1EeTro1WrVqRnp6e2jEvK1euJAD00UcfNbgtuVxOHTp0oOLi4jrrnDlzhmJjY2natGlkZ2dHn332GdXU1Gi2M3Vo3749yWQy2rFjBzk4OFD37t2pU6dOpKenR8OHD6fr16+rrPP555+Tp6cnVVZWEhHR5MmT6+3aICLasGEDAaB169ZpHBt3bRARj5FomYlEZWWlkEjk5OQI5SNHjiQANHz4cCoqKiIioqKiIgoMDKTCwkJKTk4mIyMj8vX1FQ1KUw5Ie+utt0Tb8fT0JAMDAwoJCREuPhUVFTR27FgCQN9++61Qt7Ft1zXmwdvbW+3gqKdpjERhYSG5urqSjY0NnTx5UihPSEggExMT8vT0pLKyMqG8rjEHyoFyygt5Q/WVZs6cSQDIw8ND9PpYsWIFAaCwsDCN2tN2+yEhIWr73BuiTSIRERFBACg5OVnt8ocTCSKivLw8srW1JYlEIko+1CUSyovolClThH0tLS2lMWPGEADavn27qL6lpSVZWlrS/PnzhbLExEQCQGPHjhXVVZ6LyZMnCxezgoICGjhwIBkZGVFaWlqjjkNdNEkk9PX1ydjYWO2ydevWEQCaNWtWvdtZunQpmZubU0pKSr31Zs+eTb179yZLS0sKCwuj3377reGdaICZmRnp6emRlZWVaJzMvn37SCqVkpubm3CMiR6MmzAzM6PTp08LZZokEvv27VObhNaHEwki4jESLVN5ebnwf2NjY5Xl8+fPF26hW1hY4Pfff4elpSU++OADlJeXY9myZbCzsxPqz5kzB56enlizZg0uXrwoaqu6uhorVqwQtmNoaIgVK1ZAKpXi448/BhEBgFZtP6tWrVqFy5cvY9asWaLbpnK5HFOmTMHZs2fx/fffP/E45s6dCycnJ+HvGTNmwMPDAzExMU/0XISGhiI4OBivvPLKE9uGUn5+PoAH/fmacHBwwPbt2yGRSDBx4kTk5OSorVdeXo5//etfsLS0xFdffSV0gxgbG+Obb76BVCpFVFQUamtrVdb94IMPhP/7+/ujQ4cOSE5OFrX90UcfwczMTHgvAYCVlRUWL16M8vJyLFiwQLMD0ASkUilqamrULquurgaAescQrFu3Dp9++ikOHDiA3r1717utpUuXIiUlBTdv3kT79u3x4osv4pNPPtE++P8fY21tLd566y3ROJmhQ4ciIiIC58+fF36ivrq6GpMmTcK7776LXr16NWo7yjEdyq49pjlOJFog5RgDCwsLWFtbqyzv2rWrShkR4dChQ5BIJPD19VVZ7uvrCyISxiAoWVtbo23btqIyOzs7ODo64saNG7hy5YrWbT+rlPvZp08flWXKMoVC8cTjUNfnrUxslP3iT8KYMWOwd+9eBAUFPbFtKCkvdPU9OOlRQUFBiI6ORkFBAcLCwlBVVaVS59SpUyguLkaPHj1UkhR7e3u0b98e169fR0ZGhmhZhw4dVC66rVu3Fl18lG0rE4yEhAThX1FREQDg999/13h/HpetrS0qKytFX1CUiouLAaDOh039+OOPiIqKwv79+9GvXz+NtymTybBo0SJ4eXnhk08+QWZmpnbBA8LYHB8fH5Vlfn5+AIBjx44BeJDI5OTkwN/fX3Tcr1+/DgA4ffo0EhIScPToUZW2lONc1L1eWP14sGUL9OuvvwJ4MPhH3QeouqfFVVdXo7KyEjKZTO0oaeUdjPv374vK6xpRbW5uDgAoLS3Vum11lHc4nmbK/VQeo4c157FQdy4ePm9PevvNQfktsbKyslHrffjhhzh69Cj27duHuXPnqgzKq+8cAnWfRyMjI5W6+vr6omOpXOf69euIjo5Wqa8c9FpTU1PnIM2m5O7ujtzcXPz1118qAyr/+usvAOqT0sTEREyZMgU7d+4U7gRUVFTg+PHjeOGFFxrcrkQiQd++fXH69GmcOHFCZRCrprp27YqbN2+qPfbK86R8vZeWlqJ79+747LPPRPWUCeGGDRtgaWkJW1tb4S6GUkVFBQDN736x/+FEooUpLi7G119/DYlEgvfee0/j9aRSKZycnJCbm4ubN2/C3t5etDw7OxsA0KlTJ1H5rVu3UF1dLRrhTkTIy8uDnp4enJ2dtWrb0NBQ7cXs5s2bauN/NGFKSUmBnZ2d6NZ9S+Hq6oqkpCTk5OSofEur61gADz7kHp7VoemxuHz5MkpLS+Hu7i4q//vvv9GlSxdRmXLq48OzA57U9ptD+/btATyY9dMYEokEW7duRe/evbFs2TIMGDBA9LpVTmWsq+sjJycHenp6Ws2yUJ57V1dXtXfprly5goKCgmZJIgDg5Zdfxt69e5GYmKiSSCQmJsLU1BSDBg0Slaenp+O1117D5s2bMXjwYKH89u3beOmll0R3N1JSUtCxY0dYWVmpbFt5x+NxvPjiizh8+LBwp/Zhyveb8nx+/PHHatuYMmUK1q9fj5UrV2LAgAFq6yhfY8rXHNMcd220IIWFhXjttdeQm5uLzz//XO2t8/pMnjwZwIOs+2E3b97E3r174eDggCFDhoiW1dTUCNO0lH755RcUFBTg5ZdfFm4rNrZtZ2dnXL16VfRN8uzZs7h27Zra2Fu1aiX69jdq1CgcOnRIo/1ubm+88QYA1WNRW1uLTZs2QU9PD5MmTRLKnZ2dAUA0V7+8vBwHDx5U236rVq0A/O+bbXR0tNp5+o9+o7p9+zYOHDgAOzs70Yd/U2+/vLwcycnJQrfDk6ScPqzuItIQa2trxMXFwdDQEH/++adoWceOHTFo0CCkp6er3ObevXs38vPzMWrUKNjY2DR6uy4uLhg8eDDOnDmDS5cuiZaVl5djxIgRiI2NFZUfO3YMiYmJjd6WJiZOnAh7e3t8++23ojEfiYmJOHv2LKZNmyYai3Xjxg0EBwfj1Vdfhbm5uaiLQF2XwFtvvYVly5aplF+/fh379u2DkZGRypTRxuzvlClTYG5ujg0bNojGelRXV2PTpk2QyWQYN26cRm3VR3l3RvmaY42gq2GeLUFzj7g9fPgwrV27VvhtAIVCQX/88QfFxMTQrFmzyNrammxtbWnz5s2i9YqKikihUFD//v0JAB04cEDtE9gqKytp5MiRpK+vT1FRUbR792767rvvyNXVlVq3bq3yZEBPT0+ys7OjF198kT7//HOKj4+nxYsXk4WFBTk7O1N2drbWbX/11VcEgMaNG0d79uyhdevWUXBwMHXr1o0sLCxIoVCIHgP++uuvk6GhIW3evJk+//xzMjMzU3kc8pMCLaZ/Lly4kCQSCY0aNYpiY2Np+/btFBQURAYGBvTdd9+J6p48eZL09PTI29ubYmNjKTY2loYNG0ZDhgwhALRhwwbRaPhNmzYRAPrggw9o+/btZGFhIXpio3LWxquvvkpTp06ln3/+mTZs2EBdu3YlmUxG+/fvf6Lbb85ZG3///Tfp6+vT//3f/4nKb926RQqFgv75z38K76WsrCy1baxatYoAqMzayMnJoS5dupC1tTUtXbqU4uPj6fPPPydTU1Py8vISplRnZ2eTQqEgU1NT6t69OykUCiooKKC7d++SQqGg7t27k6mpKSkUCmEWTV5eHnXr1o3atWtHy5Yto71799KmTZuoT58+5OPjQ3fv3hXFYm9vT926ddP4uGRlZZFCoRCe+tm7d29SKBSkUCjU/lZPQkICmZqa0ogRIyg+Pp7WrFlDdnZ2NGjQINEMI6IHvz2B/z9rTN0/mUwmqh8cHEwSiYSmTJlCMTExtG/fPlq+fDk5OjqSTCajTZs2qcTT2P39+eefSSaTUWBgIP3www+0bds2GjhwIMlkMvrhhx/qXO/48eOkUCjo5ZdfJgD09ddfk0KhEH22KYWFhZGFhYXK8agPz9ogIqJYCdFT0FH6hMTGxiIsLKzZ+oqDg4NRVlYmKtPT04OFhQWcnZ0xYMAAvPLKKyp9gRcuXMDbb7+t0p6626ZEhN27d2PXrl3Iy8uDiYkJ/Pz8MGXKFJUBVb169UJ+fj4uXbqEr7/+GklJSaiurkafPn0wffp0lW9jjWkbePBEul9++QWFhYXw9vZGVFQU3n//faG/Mjw8HG+++SaAB99+v/jiC5w6dQrW1taYMWOG2sFVT4JEIkFMTAxCQ0Mbtd6pU6ewefNmZGRkQCKRoGfPnnj99dfRo0cPlbpHjx7F+vXrkZeXBxcXF8ycORMKhQIxMTEAgB49emD16tUAHhzn9evXY+/evdDT08Po0aNF37hmzZqFFStWIDMzE8eOHcPu3btRVFSELl26YOrUqU98+3Fxcdi4cSNmz57dqAGXISEhwvqNER4ejoMHD+LKlSvCg9IOHTqE+fPni+qFhYVh6tSpatuYPXs2zM3NVW59l5aWYvPmzVAoFCgoKEDr1q3x0ksvISIiQugS2r59O9asWSNab+nSpdDX18esWbNE5VOnTkVYWBgAoKysDFu2bMEff/yBu3fvok2bNggICEB4eLhofEtJSQmsrKwwduxYbN26VaNj8u233wrn7lHbtm2Dg4ODSvnVq1exevVqnD9/Hqamphg6dCgmTZqk8uAudZ9TDzM0NBTGcSklJSXhl19+wdWrV3H79m1YWlqiV69eiIiIUOke0mZ/ASAzMxNr1qxBWloa9PT04OHhgTfeeEOlu/ZhkZGRaruv3nzzTYSHhwt/5+fnw8XFBVOnTsWXX36pcUzNfQ1poeKe61Tqec8mPT09ycHBQddh6Bxa2G9tNER5RyIzM1PXoTSKtr+1kZeXR61btxY9v+FZsmbNGgJAhw4d0nUozaIl7u/bb79NHTt2rPdhW+o879eQ/y+WB1syxlo0BwcH7N+/HyNGjEDr1q0xbdo0XYfUZFJTUzF37ly88847Gs2EeNq1xP3997//jQMHDuDAgQN1zuJh9ePBls+h3NxcJCQk4N69e6ioqEBCQoJWg9lY8yopKUFCQoLwuw/Hjx8X/ZbGs8zb2xtnzpyp88FKTytjY2PMmzdP7WDFZ1FL3F8zMzOkpKTU20XC6sdjJJ7D/q2YmBh8++23orIJEyYIMzPot00pAAAgAElEQVSeN9qOkWhuGRkZKj9e5e7ujpUrV+ooosbRdowEYy3V83oNeUQcd208h8LCwoQBYezp0bVr1+fm6aGMsacHd20wxhhjTGucSDDGGGNMa5xIMMYYY0xrnEgwxhhjTGs82BJQee49e/6o+w0B1rSU01b5/caeFfy58QBP/+TZC4wxxh7Dc3wZBYC45zqRYIzVr2fPnggJCUF0dLSuQ2GMtUxxPEaCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1rjRIIxxhhjWuNEgjHGGGNa40SCMcYYY1qTEBHpOgjGmO7NmDED33//PR7+SCgrK4OBgQGkUqlQpq+vjx9//BGBgYG6CJMx1rLEcSLBGAMA/PrrrxgyZEiD9SwtLXH79m1RcsEYe27FcdcGYwwAEBgYCGtr63rrSKVShIeHcxLBGBNwIsEYA/CgyyIiIgKGhoZ11qmqqsK4ceOaMSrGWEvHiQRjTDBu3DhUVlbWubxNmzbo379/M0bEGGvpOJFgjAn8/Pzg6OiodpmhoSEiIyOhp8cfG4yx/+FPBMaYQCKRYMKECWrHQFRWVnK3BmNMBScSjDGR8ePHo6qqSqW8Y8eO6NWrlw4iYoy1ZJxIMMZEevToga5du4rKpFIpJk2apJuAGGMtGicSjDEVkZGRou6NqqoqjB07VocRMcZaKk4kGGMqIiIiUF1dDeDBuIlevXqhc+fOOo6KMdYScSLBGFPRvn17eHt7QyKRQF9fHxMnTtR1SIyxFooTCcaYWpGRkSAi1NbWIjQ0VNfhMMZaKE4kGGNqhYaGQk9PDwMGDEC7du10HQ5jrIUy0HUAjLGWyd7eHkFBQQgJCdF1KIyxFowTCcZYnSZOnIihQ4fqOgzGWAvGiYQGevTogYsXL+o6DMYYY81o/vz5iI6O1nUYLR4nEhoaM2YM3+J9ysTFxeHo0aNYtmyZrkN5puXm5mLOnDlYsmQJnJycdB0OY01i9uzZug7hqcGJhIZ69uzJI9efMhcuXEBaWhqftycsLS0Nc+bMwdChQ9GzZ09dh8NYk+A7EZrjWRuMMcYY0xonEowxxhjTGicSjDHGGNMaJxKMMcYY0xonEowxxhjTGicSjDHGGNMaT/9kjOkcEeHevXswNzdHcnIy7t+/DwAwMzODj4+P2nUerqc0cOBA6OvrP/F4m0NhYSHOnDkDLy8vWFpaNlg/IyMDhYWFcHV1RevWreusR0TIysrC7du3YW9vDycnJxgaGtbbdnFxMTIyMqCvr4/27dvDxsam0fvTkPLycmRmZqKqqgpdu3aFqalpg+vcu3cPJ0+ehJWVFTw9PVVitrCwaPI4mSq+I8FEDh06BA8PD0gkEnz22WeNWjcyMhJeXl6QSCRYtGiRVttfv3492rVrB1tbW3h7e8PExAS+vr5ITU3Vqr2mVFBQgKysLF2H8cxJT0+Hj48PTp06BQBYvXo1oqOjERgYiD59+mDfvn1q11PWGzx4MN59911ER0ejsrKyOUN/Impra7Fu3Tp06dIFAQEBOHfuXL31//zzT3Tp0gWDBg3C9OnT4eTkhIkTJ+LevXuienl5eZg1axbatGmDgIAAzJ49GwMHDoS1tTWioqJQXFys0vbmzZsxePBgODo6YurUqRg/fjzs7Ozw0ksvNRiXpu7fv49Zs2ahbdu2iIyMxBtvvAErKyuEhoYiMzOz3nVnzpyJgIAAREVFqSzbv38/Bg0ahLy8vCaJk9WDWIO6d+9O8+fP13UYT1RRURGNHTuWOnbsSJGRkQSAPv3000a3c+TIEQJACxcubPS6K1euJAA0depUqq6uJiKiW7duka+vL9nY2FBubm6j2ps/fz5179690XHUxcvLi/T19Sk7O7vJ2nwSUlNTSaFQUG1tbbNs7/z58wSAzp8/3+h1z507R1ZWVrR69WqVZaampgSgwXMvk8no3Llzjd52S5SWlkY+Pj4UEBBAL7zwAgGgI0eO1Fn/3LlzZGpqSmPGjKHKykoienD+LS0tafjw4aK6W7ZsIT09PdqyZYtQVltbSwsXLiQANHjwYJX27e3tKSgoiO7evSuUHTt2jExMTMje3p6Ki4sfa38rKipo4MCB1KtXL/rrr7+E8j///JNkMhmtXLmyznX37t1LRkZGBIACAwPV1omOjiZHR0fKyclpdGzPw+d+E4nlOxIMwIPHHDs7O+P8+fMYOHBgs2+/vLwc//rXv2BjY4OlS5cKt6dtbW2xbNky3LlzB/Pnz2/2uB42fvx4hIeHw97eXqdxNGTevHkICAhATU2NrkOpV2VlJcLCwuDn54epU6eqrdO/f3/cuXMHY8eORXV1dTNH2PxSUlLw9ttv4+DBgxo9bvy9995DZWUlvvnmG0ilUgCAu7s73nnnHcTHx4vu5tjb22Py5MkYP368UCaRSDB37lx069YNBw8exIULF0Tt+/v748svv0SrVq2Esr59+yIkJAQ3b97EoUOHHmt/Fy9ejKSkJMTExKB9+/ZCef/+/TF9+nQ4ODioXa+wsBD/+Mc/8PHHH9fb/vz589G2bVtMnDjxseJk9eNEggF48AjwL774AsbGxjrZ/qlTp1BcXAwPDw+VGPz8/KCvr4+4uDidXkxmz56NzZs3QyaT6SyGZ8m2bdtw4cIFfPDBB3XWWbp0Kby9vZGYmFhvvWfFhAkT8Prrr2tU9+bNmzhw4AD8/PxUkttRo0YBADZt2iSUvfjii1izZo3atjp27AjgwQX6YT/99BO8vLxU6ivHLzzOe6GmpgbffPMN/Pz80KVLF5Xlixcvxquvvqp23ZkzZ6JPnz4IDw9vcDsffPABFAoFfv/9d61jZfXjwZZPWEVFBS5evIjq6mq4uLjUOUhJWa+srAzt27dHu3btRMtPnjwp9Hk6OzujY8eOuH79OnJycuDo6CjK3LOyskT9ghKJBHK5HABw9epVZGdnC8t8fX01GtRUl7///hvZ2dmwtbVFp06dtG5HuW/m5uYqywwMDGBmZoaioiJcunQJPXr00Ho72qiursaff/4p/O3h4QFra+s6yy9duoTi4mJ06tRJ9E2upKQEKSkpwt99+/aFTCbDxYsXhfq2trbC8qKiIpw+fVr4Wy6XQyKRoLa2FocPHxbKledQ2f6dO3cAPBjvoryzo+mAvea0detWmJmZwd/fv846MpkMO3bsQO/evbFkyRLI5XIMGzZM423cvXsXmZmZkEgk6Nq1q2jwXWPP36Py8/Nx+fJlGBgYoHv37jAxMdE4rqZw/Phx1NbWws3NTWVZjx49IJFIcOzYMY3aysrKUjtgUZ3S0lL8+uuvcHZ2rvfcNeT06dO4ceMGwsLCAAC3bt3C5cuXYWFhgW7dutU5aDY+Ph579+5FWlqaRmNigoKCIJFIsHnzZgQFBWkdL6uHrjtXngba9JWVl5fTnDlzyMTEhJydncnX15datWpFbm5uov7gR+t5enqSTCajfv360dmzZ4V6kydPJm9vbwJAUVFRNHnyZHJzc6Pu3bsTABo1apTQR7py5Urq2rUrAaCOHTuK+j7XrVtH/fr1IwD0wgsv0JUrV1RiX7t2bYNjJO7cuUMjRowgiURCLi4u5O7uTv7+/rR161atxkikp6cTAPL09FRZVlxcTAAIAP32228at9lUYyRKSkpILpeTq6srAaB9+/apLd+wYQMFBARQ7969ycHBgQwMDOjzzz8X7aNcLicHBwcCQBs3bqTOnTtTr169yMnJiSQSCUVGRlJpaSkRPejrlsvlZG1tTQCoqqqKiB70K8vlcnJ0dCQAwvgAZfvK+i+88ALJ5XKSy+WUmpoqxFFWVkYnTpwQ2ntc2oyRqKqqIkNDQ/L29q6zjqmpKZ0+fZqIiPbs2UMSiYSsra1V+rvVjZG4du0ajRo1iqRSKfXo0YM6d+5MUqmUwsPDKT8/n4gaf/6UcnNzadiwYSSVSsnd3Z06depEMpmMpk2bRuXl5Rofg4ZERETUO0Zi+fLlBIA+/vhjtcttbGxIIpEI443qolAoCAB9/fXXddYpLCykffv20erVq8nT05P8/f3pwoULmu+MGhs3biQA9P7779OYMWPIxsaGvL29ydzcnOzs7Gj9+vUq6xQUFFDbtm0pJiaGiB6cC9QzRkLJ2dmZHB0dGxUfj5HQWCwnEhrQ5gU1evRoAkCbNm0SykpLSyk0NJQsLS1V6m3cuFEoy87Opu7du5OFhQWlp6cL5adPnxYGnz18QV2wYAEBoP/85z+iNgwMDCggIEAltrVr19b7xmsokaitraUBAwYQAOENTfRg4Ffnzp21HmzZq1cvAkDJycmi8hUrVgiJRHx8vMbtNfVgy6+++kqUSDxa7uHhQVevXiWiBxfK4OBgkkgkKh+4M2fOFOo/fFFU7mdYWJio/rBhw0SJhFJUVJQokWiovlJISAgBoOnTpzdq/+uiTSKRmZlJAGjYsGF11nk4kSAimjdvHgGgfv36CUkzkWoiUVhYSK6urmRjY0MnT54UyhMSEsjExIQ8PT2prKxMKG/M+bt79y65uLiQpaUlnTp1SiiPi4sjADRhwgSNj0FDGkokPv30UwJAX3zxhdrlykSzsLCwzm3k5+eTi4sLRURE1BtLamoqDRw4kFxcXMjV1ZU+/fTTetvVxJIlSwgAGRgY0IQJE6iiooKIHiR4wcHBBIC2b98uWiciIoJCQkKEvzVNJPr27UsAqKSkROP4OJHQGA+2fBKSkpLw448/IigoCJGRkUK5sbExFi1ahAEDBojqBQYGYtKkSUI9Z2dnLFiwAMXFxWoHGPr6+opu0UVERAB4MK/+4TZCQkKgUCiEaXXAgznky5Ytw5w5c7TeP4VCgT///BMvvfSS6Ce63dzcNO7fVWfTpk1o3bo1goOD8fXXXyM+Ph4ffPABPvnkE3h4eABQ3/XRUoSHh6NDhw4AHnTHhIWFgYhE3RkPmzt3rmhA3YwZM+Dh4YGYmBhcvHjxicUZGhqK4OBgvPLKK09sGw3Jz88H8OA5EZr69NNPERAQgKNHj2LevHl11lu1ahUuX76MWbNmwdvbWyiXy+WYMmUKzp49i++//15lPU3O3+rVq3H16lW88847orEDY8aMwdChQ7F161ZcunRJ4316HMrBlXUNqlWOJ6rrGRF37txBUFAQ+vbtKxpLoY67uzsOHz6MK1euIDY2FuvXr4e7uzuuXbumdfzK+KRSKVasWCHEaWZmhlWrVkEikYg+/3bt2oXffvsNq1evbvS2lN23BQUFWsfL6saJxBOQkJAAAOjTp4/KMhcXF8THxzdYT1mmUChUlnXt2lX0t/LhM8q+caV3330XALBkyRKhLD4+Hvr6+hg6dKgmu6JWUlISAKh9UFCvXr20btfDwwNpaWmYMWMGfvvtN6xatQq1tbU4c+YMXF1dAUD4oG+JND0vSur6tpUXPuUxfhLGjBmDvXv36rS/WHkR0dPT/CNIX18fP/zwA9q1a4elS5di9+7daus9yfeVsm0zMzMkJCSI/llZWYGI8Mcff2i8T49DOZ6mqKhI7fLi4mKYmJioHUBdVlaG4cOHo2vXrti2bVujHuLVu3dvLF++HLm5uY81AFY5ZsfV1RVWVlaiZS4uLrC3t0dmZiYKCgpQVFSEt956CxERETh//rxwzI8ePQrgwVgYZdn169dVtqXcv6qqKq3jZXXjwZZPwMNP5dOknrpv2cpBYY8+uQ8AjIyMRH8r3yREJCr38vJCQEAA4uLisGjRIjg7O2PJkiVqH97SGKWlpXXG3ZhvmOrY2dnho48+UinPzMxEmzZt4Ozs/FjtP0manhcldSPelcdUeYzrU1e7TwPlN8TGPkDK3t4eMTExCAgIwKRJk0SDUZWe5PtKud4PP/yAPXv2qLQhl8ubbVaPu7s7AOCvv/5SWXb79m2UlpaqTaZqa2sxbtw42NjYYMuWLUIyl5GRAWNjY43eY35+fgAeL+FVJm6PHnclCwsL3LhxA6WlpaiqqkK3bt1w6tQp0R3WiooKAMCVK1cQHR0N4MEXqEcH5CrrPe7nE1OPE4knQPntOScnR2VZaWkpTpw4AW9v73rrKWdWPM5MCACYM2cOFAoFli9fjnHjxiEzM1OjKVP1Ud4VyM3NVVmm7tuApjIzM2FoaCiaTw4A165dQ1paGmbPnq112y3R33//rTLtTXlMXVxchDLlLd/S0lLRrIObN2+qbffRb/mXL19GaWmpcOFpCZTnWJtbzQMGDMCiRYswZ84cofvhYa6urkhKSkJOTg769esnWva476tOnTohKSkJn376KYKDg0XLamtrceTIEfTs2VOrthvL29sb9vb2SEpKAhFBIpEIyxITEwFA7QyX6dOno7CwEPv37xe6R4AH0y07deqEuXPnAnhwJyYnJ0ft9E91T8FsLD8/P5iZmSE7O1sl/pqaGuTl5cHU1BT29vaQSqXC3aCH5eXlwcnJCd7e3vVO7ywoKICxsTHs7OweO26mirs2noBXX30VVlZW2LFjh8pjatesWYPRo0dDKpUK9eLi4lBSUiKqt2HDBgDA5MmTHyuW4OBg9OzZE+vWrcO///1vzJgxo8Hn6jdk1KhRMDExwY4dO1S+Of/0009at7tixQpMmzZNpVz5oCrlB9yzIi4uTvT37du3ceDAAdjZ2WHw4MFCufIbYnp6ulBWXl6OgwcPqm1XOWVR+e05OjoaK1asEK2bnJys02dyWFtbw9nZGVevXtVq/aioKLz66qs4fvy4yl2NN954A8D/3kNKtbW12LRpE/T09ERjkhpD+X6MiYlRWbZt2zaV5OLGjRv/j737jori7P4A/l16U1BERGyogA2MNSpGsIEFY8OGiCbG3rHEqHHBhokmvERfjT1qbKAx0dgbCtgVIyrNAgoWwIKIgAL394e/nZdhF9hdlqK5n3NyTnzmmZk7hd27M3eeQUhISIlqCQqjpaWFmTNn4tGjR3JXR9asWYNKlSrJDfTl7++PoKAgzJw5ExcvXhTdmin4I+DChQtwcXFRmOxt2rQJANCrVy9Ruyrba2hoiAkTJiAlJQX79+8XTdu1axfevn0LLy8vUbKjDiJCQkICmjdvLkpWmObwFYlSUKVKFQQFBWHgwIFwcnLC9OnTYWlpifDwcPz888/Yvn07DAwMYGBgIOo3Y8YMVK1aFceOHcPatWsxcuRITJo0CQBw8+ZNXL16FcCHKxghISFwdnZGQkKCUNyVmpqKkJAQtGrVSrisK5FI4OPjg9GjRyMsLAy7du0qNO5r164hPT0dMTExAD6MORESEgJ9fX3RL7vq1atj/fr1GDlyJJydnTF16lSYmJjgzz//FMavuH//PkJCQtC+fXulL/VWrVoV//3vfzFu3Dh8+eWXePv2LbZv344rV67gyJEjRb6IqDTJxhuQvWfj5s2bMDU1Rfv27XHu3DlRu6WlJVq0aIEbN24I7we5e/cuQkND5UYMffLkCSZOnAg3Nze8ePECP/zwA3Jzc7Ft2zbRmAQjRozAqlWrMHHiRHz77bcAPhSmOjo64smTJ7hy5QrevXuHli1bAvjw3Pz27dvx448/olmzZjhw4IDouHt7eyM4OBhTpkzBL7/8Uno7rhju7u5Yu3YtUlJSRONnyF7GlZubi6tXr+LVq1cKX8a1ZcsWREZGyr3/xMXFBf7+/pg3bx769+8PT09P5OTkYPPmzbhx4wbWrl0r1PKoevw6deqEFStW4Ntvv0V2djb69esHExMThIeHIzAwEOvWrROdp3/++ScmTJiA3bt3C+MlFCU3NxehoaEA/nfFKSIiAjk5ObC0tETjxo1F/X18fHD+/Hl4e3tj6dKlsLGxwe+//45z584hKChItF8PHz6M+fPng4gKLbTNf46ampoiKysL7du3x8SJE2Fra4s3b97g4MGD2LFjB9q1ayc3sqSq2+vn54erV69ixIgRiImJgYODAyIiIrBs2TI4OTlhxYoVCudLTk7GnTt3kJKSAuB/NRJaWlro1KmTqG9MTAzS09PLtbj4Uyehj/lGaxlp0qQJBg8eLNyDU9bjx4+xceNGXLt2DTk5OWjYsCHGjBkjV2SXv59sQCoPDw+4ubkJfebMmYPLly+L5jt16hS2b98uV4G+bt06UeHYu3fv0K9fPzg7OwtfRIqMGzdOSCLys7CwkPv1DHwYJGvDhg148OABzM3N0a9fPzRp0gRTpkwR+gQHB4s+zIpz7tw57N27F3fv3oWJiQmcnJwwatQotQZT8vX1RVBQkNywv6p68+YN3N3dRW2yfeLq6ir6Rezg4IBVq1Zh+vTpuHHjhtBuaGgoDFc8ffp0BAYGIi4uDhcvXsSBAweQlpYGOzs7TJgwQeGAWxcuXMCmTZuQmJgIGxsbTJs2DWfOnBF+GTdp0kSoZicibNq0CYcPH4aWlhYGDhyIYcOGCcsKDg7Gli1b4OPjo5GCy9u3b6NZs2a4deuWSpf1o6Ki4ODggB9++EFUt/PVV1/JXak4cuSIwqLBf/75B9OnT8fmzZtFt4OAD6Olbtu2DTExMZBIJGjatCm++uor0f5V5/gBwI0bN7B161ZER0dDT08Ptra2GD16tNwX/Zw5c7BixQrExsbC1ta22H2SmZkpd1VDpnv37pg/f75cOxFh586dOHjwIF69egV7e3uMHz9eLpY9e/Zg7dq1Ra5/7NixolufL1++RHBwMCIiIvDo0SPk5OSgbt26cHV1Rb9+/eSSO1W3F/iQPO3YsQOHDx/GixcvYGlpiZ49e2Lw4MHQ0VH8W/f06dNYtGiRXLuenh6OHz8uaps7dy5Wr16NBw8eqPRZpO7n/r9QMI8joQR+nvjjpOlxJDRFNo5EXFxceYeiESV5adfcuXPJ3NycUlJSSiGy8pWdnU02NjYKX4b1KaqI2xsfH09GRka0evVqleflz32l8TgSjLHys2zZMnh4eMDFxQXJycnlHY5GTZ48GWlpadi4cWN5h1ImKtr23r9/Hy4uLpg5c6Zwi5iVDq6RYKUqPj5e4eNpBbm4uJR6LOVN9i4MWR3JpUuXkJ6errAq/t9CIpHg119/xZ9//omEhIRPqqq+YcOGuHTpktwtl09VRdve+Ph4bNq0SVS4zEoHJxKsVIWEhCgcRVBRv0/d48ePhfutzs7O2LBhg3BP/t9O9rbKT8mcOXPKO4QyVdG2lxOIssOJBCtVo0aNUvtRu0+Nvb39vyJhYoz9u3CNBGOMMcbUxokEY4wxxtTGiQRjjDHG1MaJBGOMMcbUxsWWSsjLy8Pt27cRFBRU3qEwFdy+fRvp6el83EqZ7EVjR48exe3bt8s5GsY0Q9EbYpliPES2Eho0aID79++XdxhMDXp6eiq/qpoxxvT19TF37lweIrt4wXxFQgn6+vqQSqV8Qn1kNPWuDVY0dd+1wVhFpuidN0wxrpFgjDHGmNo4kWCMMcaY2jiRYIwxxpjaOJFgjDHGmNo4kWCMMcaY2jiRYIwxxpjaOJFgjDHGmNp4HIlylp2djdDQUERFRSEjIwOWlpaoVasW2rZtC1NT0/IOr1gJCQnYvn07OnXqhE6dOslNP3/+PI4fPy7XbmBgAHNzc3z22Wdo0aIFdHT4VPw3e/z4MbKzs2FjY4M1a9YgOTkZAFC5cmXMmDEDEolEbp78/WTmzZsHPT29Mom5tIWFheHkyZP4+uuvUadOnSL7JiQk4OjRo3j16hXs7e3Rs2dP6OvrK+x769YtXLhwASkpKbC0tESjRo3g5OSk0XhU8fbtW5w+fRpRUVHQ1tZGvXr14OrqChMTkxLFHx0dDQsLC5ibm2ssVqYYX5EoR2vWrEGtWrUwe/Zs3L17Fy9fvsSJEycwZMgQmJubo0OHDjh06FB5h6lQVlYWFi1ahMaNG+P777/HuXPniuy/e/du+Pn54enTpwCA1NRUHDlyBN27d0edOnXw888/Iy8vryxCZxXMrl270KpVK7x9+1bUvmzZMsycORM//PBDkfMvWbJELqH4mD1+/BheXl7o1KkT/Pz88PDhwyL7BwQEwNbWFsePH8fz588xe/ZsODg4IDY2VtTv2rVr+Oyzz9CuXTuEhITg9evXOHHiBDp37ozWrVvj1q1bGolHFePHj0f16tXh7++PZ8+e4e7du5gyZQrq1KmD33//vUTxp6amwsHBAceOHdNYvKwQxIrVuHFjkkqlGl3mhAkTSEtLizZu3Cg3LSMjg4YOHUoAaP78+RpdryYkJSVRvXr1aMiQIbRkyRICQIsXLy5ynr59+xIAioiIELWnpaXRxIkTCQC5ublRdna2xuKUSqXUuHFjjS2vpJKSkkgqldKpU6c+qfXfunWLANCtW7dUnjc4OJj09fXp0qVLctOMjY0JAOno6FBoaGihy9DX16fIyEiV110RHTt2jMzNzcnX15cGDRpEAIrc9gMHDhAAWrJkidD2+vVrsrW1JTs7O9Hf0/bt28nQ0JCio6NFyzh79ixpaWlRvXr16P379yWKR1WWlpY0bdo0UdvLly+pdu3apKenR48ePSpR/EePHiUDAwM6e/asyrGVxuf+JyqIr0iUg61bt2Lt2rWYPn06Ro8eLTfdyMgI27Ztg5WVVTlEV7z3799jzZo12L17NywtLUu0rMqVK+O///0vxowZg2PHjmH27NkairLiefz4Mfz8/HD69Ol/5foLev78OcaNG4dvvvkGbdu2Vdhn3LhxyMnJwdChQ5GamlrGEZY9XV1dhHhT4KUAACAASURBVIeHQyqVKnWLZv78+TA1NcXMmTOFtkqVKmHWrFmIjY3F1q1bhXZHR0f897//hb29vWgZnTp1Qps2bRAfH49r166VKB5VzZo1S+5v3szMDH369MG7d+9w6dKlEsXv5uaGL7/8EqNGjUJ2drbG42cfcCJRDnx9fSGRSDBt2rRC++jq6mLNmjXo0qVLGUamnLp166Jnz54aXeayZctgYGCAtWvXIikpSaPLZhXTr7/+ihcvXmDq1KmF9hk/fjy8vLyQlJQELy8v0Cf+jsHOnTvLfVEWJjY2FpGRkXBycoKBgYFoWrdu3QAAe/fuFdocHR3x1VdfKVyWmZkZgA+fO+rGo45Zs2bB2tparl325s38P6bUiR8Apk6digcPHmD37t2aCJkpwBVu5SA+Ph7169cvtmCpX79+CttDQ0Nx7do1ZGZmol69enBzc0PVqlWF6b/++qtQi9CxY0c4OTnhwIEDiI+PR61atdC3b1+hkOno0aO4ePGiMK+WlhYWLlwIADh9+rSo9mHixImoXr26ehtdjGrVqqFjx444efIkDh8+jDFjxpTKepQVHR2NkJAQvHjxAhYWFujSpQsaNGggTN+2bZvwRthOnToJCd/JkycRFhYG4MNLfwYPHgwA2LlzJ0JCQgAA586dE14AV79+fXh7e2Pnzp3CPW1HR0f07t0bhw8fRmxsLAwNDeHq6opGjRqV2vrLQ3BwMCwsLGBnZ1dkv3Xr1uHGjRs4duwY/P39MW/ePKWWn5WVhWPHjiEmJgYSiQRNmzZF9+7dhS+brKwsLF++XOjv5eWFSpUq4a+//sLr16/RuHFj9OrVS2Gh59u3b3H06FHExcVBR0cHzZs3R5cuXaClVXa/za5fvw4ACvefjY0NdHV1hT5FycnJwY0bN2BnZ4fmzZtrPE5V3bt3D3/99Rc6deqE9u3bF9u/uPg///xz6OnpISgoCCNHjiyNkP/1+IpEObGxsVF5nocPH6Jt27bo1asXIiIi8OzZM/j7+6NOnTrYsmWLqO/Tp0/h5+eH3bt3o3fv3jh37hwSEhIwYcIENGnSBM+fPxf6RkVFFXrJOzU1Ff7+/mXyS1D2tr3yfFtndnY2vL290aRJExw+fBgvXrxAcHAw7OzsMHnyZOTm5gp9C7tV8O7dO/j5+SEoKEildWdlZcHPzw/r1q1D+/btsXfvXjx79gxbt25F06ZN5d4+q+n1ExG2bNmCPXv2qDSfOt6+fYubN2/C1ta22L5GRkbYu3cvKlWqhIULF+Ls2bPFznP69GnY2Nhg2rRpSEhIQFxcHEaPHg07OztcuXJF1PfixYvw8/PDtm3bMGjQIMTExODq1avo06cP3N3d5ZZ94sQJ2NjYYObMmUhKSkJ0dDQ8PDzQsmVLjRYiFkd25a5atWpy0yQSCapWrYrU1FS8e/euyOVs3rwZqampWLNmDbS1tUsl1uI8fPgQc+fOxZAhQ+Dm5oaJEyfi6NGjCpO4goqLX0dHB/Xq1cP58+dLI3QGcLGlMjRddAOA3N3dVZrn/fv31LRpU9LT0xMVlr1794569epFEomEjh49KrRHREQQADI1NaWkpCShfe/evXLFkenp6WRmZkZ2dnaUl5cnWu/ChQtp1KhRhca1YcOGEhVb5jd//nwCQF999VWRy1KWOsWWY8eOJQC0bt06UfvSpUsJAM2bN09ou3LlisKC2PT0dAJAAwcOFLUX1l/m5cuXBIAkEomooC0nJ4f69OlDAGj//v2ltv6LFy8SAAJA8fHxCvsook6xpWyefv36FdrH2NhYdL4EBQURALKysqJnz54J7QWLLePi4sjY2JgaN25Mr169EtqfPXtG1tbWVLVqVXry5InQHhAQQACoQ4cO9O7dO6F98uTJcsWFcXFxZGRkRLa2tvT69WtRu6GhIbVt21bpfVCc4cOHF1ncuGjRIgJAP/74o8LptWvXJgD08uXLQtdx9epVMjY2ptWrV5c4npJISEig77//nr766iuysbGhrl27KizALUjZ+J2cnAgAPX/+XOmYuNhSaVxsWV4yMzNV6v/333/j9u3bGDBgAJo1aya06+rqYt68eSAi0WVamR49eqBmzZrCvzt06ADgw6V7GRMTE4wdOxaxsbE4cOCAKMa1a9eKCrlKU3p6OoD/3e8sa6mpqdi0aROsra3xzTffiKb5+PjAxMQEAQEBKh87VX3++efo2LGj8G9tbW2hIG3VqlWltt62bdti48aN2LlzJ+rWrVtq6wGAtLQ0AJC7t1+UQYMGYdq0aXjy5Am8vLwKfVx49erVyMjIwLRp00RjsVSvXh0TJkzAixcvsG7dOrn5RowYIbrHruhvZdWqVXj79i1mzJiBSpUqCe0NGzbEgAEDcPnyZYSHhyu9TSVhZGQEAIVecZC1y/oVdP36dbi6uuLHH3/EpEmTSidIJdWpUweLFi3C5s2bERkZidzcXDg5OYluuxakSvyGhoYA/nfeMc3iRKKcPHjwQKX+ERERAIDGjRvLTZPdO1d0P7R27dqif1epUgUA8ObNG1H7tGnToKuri5UrVwptW7ZsQatWrUSJS2m6ffs2AKBp06Zlsr6CZB9gdnZ2cve6DQwMUK9ePWRmZoq+WEqDoi9xWcFbZGRkqa1XIpFg9OjRGDZsWKmtQ4b+/1aZMpeu81uxYgXat2+PEydOYOnSpQr7lObfyo0bNwAAly9fhq+vr+i/R48eAUCRX36aJCtSVPQ0CxHhxYsXqFatmsKnLR48eIBevXpBKpVi4sSJpR6rKoyNjbFs2TLk5OTAz89PYR9V46dPvEi3vHEiUQ5sbGzw4MED4YOnMCtWrMDmzZsBKPeHoOhDWdl7njVr1sSwYcMQFhaGS5cuIS8vDwEBAZg1a5ZS85dUSkoKwsLCoKenp/EnQpSl7IdNcV9+OTk5JYpD0S9tVb54S7r+siC7UqDq1R1dXV0EBQXBwsICfn5+QgFpfqX9twJA4UisnTt3hlQqxWeffab0ckqiZcuWAIC4uDi5afHx8Xj//r3QJ7/nz5+jR48emDRpkuiJmQMHDgiFuuVNVkCqqF5KnfizsrIA4KMYLfhjxIlEOVi2bBmICL/88kuhfU6cOIE5c+YIfwCyD4SoqCi5vrK2Fi1alCguWdKwcuVK7N+/HyYmJujatWuJlqmsefPmITs7G5MnTxbdiilLzZs3h7a2NmJiYuS+zLOyshAfHw9DQ0Ph6oCxsTEA+culhSWIsi8q2RddZmYmfH19hSdsZGRPY+Qne6LDwcFBaCut9ZeF+vXrQyKRqDUiZa1atbBz504QEYYNGyYqgAVK929Ftmw3Nze5KxKzZs2CRCJB/fr11Vq2quzs7ODg4ICwsDC5MRJOnjwJAPDw8BC1Z2Zm4ssvv0SfPn3w/fffi6aVdSIRGxur8BYTAKFoNf/TaID68aekpMDMzExueUwzOJEoB0OHDsWMGTMQEBCA3377TW76nTt3MHLkSHzxxRfCgFXu7u5o2rQp/vjjD9FQsO/fv4e/vz8kEgnmzp1borgcHBzg6uqK/fv3Y8GCBWVyNeL169eYPHkyNm7ciF69eims8ygr5ubmGD16NB4/foyNGzeKpv3000948+YNZsyYIdxvrVevHvT09HDhwgVR3/Xr1ytcvuyZeNkTM9HR0Vi0aJHcOxEiIiJEv7Rzc3OxYsUKAMDkyZOFdk2vn8rwqQ0jIyM4Ojoq/DWtjG7dukEqleLp06dyV2CmTJkCIyMjBAYGipKs5ORk/Prrr6hatSrGjRun1nqnTJkCY2NjBAQECEm+jJ+fH9avXy8aF+HGjRvw9fWVe1JEU5YsWYK0tDT8/PPPQlt6ejpWrlwJW1tb0eOOeXl58PT0RGxsLExMTOQSIWUeFS2OKtsbGxuLKVOmyK03OzsbCxYsAPChbqWk8efk5CA+Pl6oeWGloFxqPD8ypVW9u27dOrKwsKCWLVvStGnTaNasWeTu7k46Ojo0aNAgSk9PF/WPj4+n1q1bk4mJCXl7e9P06dPJwcGBjI2NadOmTUK/7du307hx4wgAOTk5kVQqpdzcXDpz5ozwZIS9vT1JpVLREx1ERMePHycAVLt2bbnhZgvGLpVKhacJOnfuTFKplH766SdRv/DwcJJKpWRvb08AaNy4cSSVSmn27Nk0YMAAMjU1pZo1a9LPP/9Mubm5Gtir/6POUxtZWVnk5eVFEomE+vTpQ7NmzSJXV1fS0tKiiRMnUk5Ojqj/woULCQC5uLjQ7Nmzyc3NjVauXEkAhPMmLi5O6O/m5kaVK1cmHx8fcnBwIG9vb2Ga7KmN/v37k7OzM3l6epKPjw+1bNmStLS0yNfXVy5eTa6/LJ/aICJavHgxAaCYmBhR+3//+1+SSqWkq6srnC+Khk7Py8ujHj16EAC5IbJPnTpFNWrUoLp169LEiRNp7Nixwr/zPw2wePFicnNzIwA0fPhw4e9oy5YtwlMKbm5utHTpUmGeM2fOkJWVFdWrV4/Gjx9Ps2bNovbt21Pt2rXpxo0bojgCAwMJAP31119K7ZPs7GySSqUklUrJwcFBeIpJKpXSvn37FM7z008/ka6uLnl4eNDs2bPJ1taWbG1t5fbr+vXrheNb2H/+/v4likeV7b19+za1bt2a9PX1qV+/fjR79myaMGEC1a9fn3R0dGj27NmizwR14if68BkEgDZv3lxsTPnxUxtKC5IQcRVKcWQD+xR8jp9VbL6+vggKClJrXIqoqChhQKpq1aqhW7duogGp8rt48SIuX76M3NxcuLm5oWHDhli2bJkw3cvLCw0bNgTw4QrSH3/8gYSEBDRs2BD9+vUTCjtfvXqFKlWqYPjw4diyZQuOHDmCqKgohQNSlcb68/LysHnzZhgbG6tUcHn79m00a9YMt27dUqlQNjU1Fba2thg+fDhWr14ttKvyVs/nz59j9erVmDBhgtxgaZmZmQoHpMq/nCVLloiuaNSpUwdff/01fvvtN8THxwvtenp6ooGwZINdRUdHQ09PD7a2tujRo4dc7cTYsWOxdetWJCYmwsLCoth98u7dO9Gxy8/R0REDBgxQOC0hIQFHjhwR3v7Zq1cvuStdhb2JN79u3bqJnhhSNR5Vtxf4cGUiIiICjx49Qk5ODurWrYsuXbrIDb+vTvwAMGTIEFy6dAkxMTGFvhFVEf7cV1owX5FQAmemH6eK9tKu4siuSAwfPry8Q1FJSV7atWfPnkJf2vWxe/r0KZmamtLYsWPLO5QyURG39+jRo6Svr09nzpxReV7+3FdaEA+RzRgrN4MHD0ZOTg769u2LU6dOCaObfuyICAMHDoS9vb2ofuFTVRG3NywsDF999RX+/PNPuLi4lHc4nzROJBirAHbu3ImbN28CAG7evAlfX194e3uX2RMA5cnT0xPOzs7FDuX8MZFIJFi+fDlat26t0qBbH6uKuL3VqlVDZGQkzM3NyzuUTx4nEoxVEAYGBpBKpeUdRrlQ9AbIj13Be/Wfuoq2vYXVFDHN40SCsQrA09OzvENgjDG18DgSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbF1sqKTg4WHjNNfs43LlzB0lJSRg0aFB5h/JJe/36NQDAx8cHlStXLudoGNOMxMTE8g7ho8FDZCvBx8en2Fd+M/YpevbsGQwNDTlBYP9KgwcP5h8ixQvmRIIxVqimTZti0KBB/L4BxlhhgrlGgjHGGGNq40SCMcYYY2rjRIIxxhhjauNEgjHGGGNq40SCMcYYY2rjRIIxxhhjauNEgjHGGGNq40SCMcYYY2rjRIIxxhhjauNEgjHGGGNq40SCMcYYY2rjRIIxxhhjauNEgjHGGGNq40SCMcYYY2rjRIIxxhhjauNEgjHGGGNq40SCMcYYY2rjRIIxxhhjauNEgjHGGGNq40SCMcYYY2rjRIIxxhhjauNEgjHGGGNq40SCMcYYY2rjRIIxxhhjauNEgjHGGGNq40SCMcYYY2rjRIIxxhhjauNEgjHGGGNq40SCMcYYY2rjRIIxxhhjatMp7wAYYxVDREQEnj9/LmrLyMjA/fv3cfLkSVF748aNYW1tXZbhMcYqKAkRUXkHwRgrf1KpFIsWLVKqb2RkJJo1a1bKETHGPgLBfGuDMQYAGDZsmFL97O3tOYlgjAk4kWCMAQAaNWqEZs2aQSKRFNpHV1cXo0aNKrugGGMVHicSjDGBt7c3tLW1C52ek5ODIUOGlGFEjLGKjhMJxphg6NChyM3NVThNIpGgTZs2sLGxKeOoGGMVGScSjDFB7dq10b59e2hpyX80aGtrw9vbuxyiYoxVZJxIMMZERowYobBOgojg4eFRDhExxioyTiQYYyKDBw+WSyS0tLTg4uICS0vLcoqKMVZRcSLBGBOpWrUqunbtCh0d8Xh1I0aMKKeIGGMVGScSjDE5Xl5eyMvLE/6tra2Nfv36lWNEjLGKihMJxpic/v37Q09PDwCgo6MDd3d3mJqalnNUjLGKiBMJxpgcY2Nj9OnTB9ra2sjNzcXw4cPLOyTGWAXFiQRjTKHhw4cjNzcXhoaG6NWrV3mHwxiroPjtn4wxhXr06IHKlSujf//+MDQ0LO9wGGMVFCcSjDGF9PX1MWjQIB4SmzFWJLnXiCcmJuL8+fPlFQ9jrAKJj49H7dq1i3z/BmPs32Pw4MEFm4LlEomgoCD+BcIYY4wxOQVSBgAILvTWhoLOjLGPnEQiwZ49exT9qmAaNGjQIABAcHBwOUfCmGYUdZGBn9pgjDHGmNo4kWCMMcaY2jiRYIwxxpjaOJFgjDHGmNo4kWCMMcaY2jiRYIwxxpjaOJFgjDHGmNpKPET2+fPncfz4cQBA5cqV4ePjU+KgNC06Ohq7d+8W/r1gwQLo6PDo4Kr6GI71x4KIsGvXLty9excLFy4stn9mZiZOnjyJ2NhYGBoaolWrVvj888/LIFJWHogIFy5cQIcOHXDnzh0EBQUJ09zd3dG6dWu5eQr2A4AOHTrA1dW11OMtCxkZGdiwYQOqVq0Kb2/vIvvm5OTg+PHjuHXrFoyNjeHq6gpbW9tCl3v69GlER0dDS0sLdevWhbOzMywsLDQWj6ru3LmD8PBwpKamwtLSEi1atECLFi1KHH94eDicnJw0GiugwSsSv/76K37++WdNLa5U7N69G35+fsjJySnvUD5qhR3rHTt24PTp0+UQkbyKFEtBV65cQfv27TF8+HAsWrSo2P4HDx6EjY0NAgICkJycjKtXr6Jz585o1aoVnj17VgYRK5aZmYnAwEDcvXu33GL4FL148QKurq7YtGmTqP38+fPw8/ND//79kZqaWuj8YWFhcgnFx2737t1o1KgRZsyYgW3bthXZNzExEa1bt8aECROQnJyM8PBwNG3aVGHCPn36dFhaWsLX1xdJSUl49OgRpFIp6tWrh4ULFxY6MKMq8agiPDwczZs3R4cOHXDhwgW8evUK+/btQ8uWLeHm5oaXL1+WKP5ly5Zh8ODBePv2rcZiBgBQAXv27CEFzcVq3rw5WVtbqzyfMpYuXUqbNm0q8XL69u1LACgzM1MDUf17KTrWjx8/JgBUs2bNMomhqHOirGNRxZIlS6hmzZq0e/dusra2Jm1t7SL7nzp1inR0dGjNmjWi9jNnzhAAioyMVGn9AGjPnj0qx63Izp07CQB5enpqZHmlJSkpiaRSKZ06darM1unh4UEeHh4qz5eVlUVt2rShAQMGUE5OjmjaihUrCAABoJ49e1JeXp7CZfj7+9PAgQPVirsi6t69O7Vq1YqCgoIIAHXt2rXQvrm5udSmTRuysrKilJQUoX3t2rUEgLZs2SLq36BBA/r6669Fbe/fv6cePXoQANq4cWOJ4lHV2rVrydTUlOLj40XtP/30EwGgiRMnlij+rKws6ty5M/Xq1avQ86cwReQGQR9FImFsbExOTk4lXg4nEppR2LHetm0bnTx5skxiKO6cKMtYVLFr1y56+fIlEVGxiURubi7Z2tpS+/btFU739/enZ8+eqbR+TSYSGRkZFBAQQDExMRpZXmm5cuUKAaD58+eX2TrVTSQWLlxIBgYG9OjRI7lpK1asoM6dO5OtrS0BoGXLlilcxqeWSKxfv55ycnIoLi6u2C/uffv2Kdw3ubm5ZG1tTbVq1aLc3FyhPTAwkO7fvy+3nCNHjhAA6tGjR4niUdWVK1fo999/l2tPTk4mANSsWTNRuzrxR0ZGkpaWFq1fv16l2IpKJLjYkmnMiBEj0LVr1/IOA0DFiiW/oUOHwszMTKm+Z8+eRVxcHAYMGKBw+ty5c1G9enVNhqcSIyMjTJ8+HXZ2duUWw6ckIyMDgYGB6N+/P2rVqqWwT+XKlbFv3z4YGRnh+++/R2hoaBlHWfbGjBmj9Ntn9+7dCwDo1q2bqF1LSwtdunRBYmIiLl26JLRPnToVNjY2csuR/Y3q6uqWKB5VtW7dGsOHD5drz8jIAABYWVmJ2tWJv1mzZujSpQuWL1+usXdqlUrF4ZUrVxAWFob379/j888/h7Ozs1wfIsL58+cRGRmJV69ewdbWFr169YKhoaHQR1bc9+7dOzx8+BC+vr4APpwUBe93JScn49ixY0hMTISFhQUaNWoEJycnSCQShTHevn0bJ06cgLa2Ntq1a4c2bdqotI2KCjhjYmKUWqYs1kePHsHY2Bjt2rUTFc4pWnZ0dDTOnDmDFy9egIjw5Zdf4sCBA3J9jh07Bj09Pbi5uQkf8NeuXUNISAhMTEzQrVs3NGjQQC4mZY6HKvtCVsy6fPlyZGVlFTrv1KlTUbVqVaVjKO6cUKawVp39r+yx1aSzZ88C+PCH//TpUxw6dAjPnj2DlZUV3NzcULNmzVKPoTBHjx7FxYsXAQC1atXCN998U2j7yZMncf36dVStWhXdu3dH3bp1heXs3LkTsbGxAABHR0f07t0bhw8fFopKXV1d0ahRI6H/tm3bcP/+fQBAp06d0KVLFwDAyZMnERYWBgBo0qSJ8FKynTt3IiQkBABw7tw54XypX7++xgvkSurEiRNIS0tDx44di+zn4OCAtWvXYuTIkRg6dChu3LhRbGGgTHR0NEJCQvDixQtYWFigS5cuos8DVY9ffrdu3cLZs2eRlpYGKysrdO/evdCEqLRcv34dABQmt7K269evo3379kUu58qVKwAUvjK7XAQEBEBLSwuzZ89Wqn9x8X/xxRc4efIkrl69qpHPMo1fkZgxYwbmzZuHpKQknDt3Di4uLnB3d0dmZqbQ586dO2jatCmGDBmCqKgoJCYmwsfHBw0bNsSNGzdUXmdAQADq1q2LVatWITU1FdeuXcOgQYPQoEEDnDlzRq7/6tWrMXv2bCQlJWHv3r1o27YtfvzxR7W2V1bA+Z///EdYZnBwcKHLLBjrpUuX4OzsjM6dOyM5OVnhshcuXIipU6fi0aNHuHz5Mvz8/IRiGVmfwMBAzJ8/H0+fPsXGjRvRpEkT7NmzB76+vliyZAmePHmCwMBANGvWDKdOnRKtR1PHQ1Ex6/Lly/Hu3TtRv3v37sHPzw9BQUEwMDDQaAxFxQKot/+VPbaaFhMTAwA4cuQIHBwccPbsWSQnJ2PFihWoX78+Vq1aVeoxFGf58uXYuHGjwvYNGzbA29sb27Ztw7Nnz/DDDz+gcePGQoIkk5WVBT8/P6xbtw7t27fH3r178ezZM2zduhVNmzYVvvxlHj9+DD8/P7li2nfv3gnnlSqICFu2bMGePXtUmk/TZF/ghT1dkJ+3tzfGjBmDx48fw8vLC3l5eUX2z87Ohre3N5o0aYLDhw/jxYsXCA4Ohp2dHSZPnozc3FxRf1WPn6enJxwdHXHy5Em8ePECmzdvRoMGDbBixQoV90LJJCUlQUdHB6ampnLTqlWrJvQpytu3b/HLL7+gS5cu8PT0LJU4lbFt2zZMmjQJn332GWJiYnDmzBl079692PmUiV92jp0/f14zwapwH6RIzZs3J4lEQt9//72o/YcffiAANG3aNKHt4MGDVL16dVExzJs3b8jOzo4aN24sVwRS1P1wWcGXt7e3aL6UlBSysrKigIAAoU1WIzFr1izRMtq0aUOGhoaUnZ2t8nYrWmZeXh45ODjILVMW6/Dhw0X36UJDQ0kikVCHDh1E2yBbdv59l5eXR4sXL6aMjAxRn7lz5wp93rx5Q2ZmZmRqakqLFi0S2l++fEnGxsbk7Ows2gZVj0dhNRKKalBMTU0pPT1dFIO9vT1Vq1aN7t27p3YMxdVIKIpF3f2vzLFVR3E1Ej179iQApK2tTdeuXRPas7KyqFOnTgRA5ToQaLBGgujD8f38888VtmtpadFff/0ltD158oR0dXXl7im/fPmSAJBEIqHQ0FChPScnh/r06UMAaP/+/UJ7YTUP6enpBECuPqC4GomLFy8KRYwFi9zUpU6NhIeHBwGgGzduKJy+YsUK6tu3r/DvrKwsatmyJQGgxYsXC+2KaiTGjh1LAGjdunWi9qVLlxIAmjdvnqhdleM3ZswYAkBr165V2H7o0CEltr54ytQkaGlpkZGRkcJpmzZtkvs8LSg3N5eGDRtGTZo0oeTk5BLHUxJbt24lHx8fcnV1pbp169L8+fMpKyuryHmUjf/EiRMEgKZMmaJ0PGVWbAmA0tLSRO2ZmZlkbGxMhoaGwk6IiYmhoKAguWUsWLCAAFBUVJSovagvDdl6FRV8/fHHH3ThwgXh37IvhoLLnzx5MgGguLg45TY2n8KWKfvDzb9MWazR0dFyy+nWrRsBoJCQELllFxWXrE/BZXbs2JEAUGJioqi9VatWZGlpKWpT9Xiokkj4+/sLX7jv37+nrl27kr6+PoWFhZUoBnUSCXX3vzLHVh3FJRK9e/cmAOTu7i437fDhwwSA+vfvr9I6yzKRqFGjhly7nZ2d3LkjSyTatWsn1//cuXMEgLp06SK0aTqRyMvLo40bN9LOnTsL31AVqZNIdO/evdDzgBceQgAAIABJREFUk0g+kSAiun//PlWpUoW0tbXpzJkzRCSfSKSkpJC2tjZZW1uLEmiiD5/PJiYmZGhoSG/fvhXalT1+smVbWlrKJfuyL9pu3boptwOKocwXt4mJCeno6CicJnty47vvvlM4PS8vj0aOHEkODg5KFTGXdiKR399//00SiYR69+5daB9V4g8LCxN+gCurqERCozUSFhYWqFy5sqjNwMAAderUQVRUFO7du4cmTZrAzs4OVlZW2LdvHxITE/Hq1SsQkXBf58mTJ6L7ooXJzc3FzZs3oaOjg4YNG8pN79+/v8L5ateuLfq3sbExAODNmzdKbacyy6xSpYpomfljVXTpslGjRsK9yII1JdbW1sWuv+C9SGNjY2hpacndRzcxMZHbTk0dD0Xmzp0r/P/EiRNx6tQp7NixQ25QlNKMASjZ/i/u2JYWc3NzAIC9vb3ctCZNmgD4UOtTUSm6P16lSpVCx75QdO9dtu2RkZGaDS4fiUSC0aNHl9rylUX/X/hWWF2XIjY2Nti6dSv69u0LT09PhbcBIyMjkZubCzs7O2hpie9mGxgYoF69erh16xaio6NFgx4pc/xkyzY0NISfn5+or+x2i+yWTVmwtrZGTEwM0tLS5G5vyMbeKOzz9Ntvv8XFixdx7ty5ci1iVqR3797o3r07Dh06hEuXLikcjE6V+ElDRZYyGq2RKHiSyhT8A9m2bRtq1KiBmTNnIjY2Vu7+XsH7dZpWGhW3mlqmog8RZT5YFK1fIpEoNW9ZHI+VK1diw4YNkEqlCu/blfc5IaNof5VWhXZxHBwcAADv37+Xm6bOl05ZU3W/KbrPr8p2fuwDzcm++PLXkymjT58++Pbbb/HkyRMMHz5cbj8q+6VRcB+rcvwUjRSspaUFqVSKmTNnltnfb8uWLQEAcXFxctNkbbI++a1evRpBQUE4ceKE8CX8+vVrLFmypBSjVY2sWPTOnTty01SNX1YAr6iWRB0avSKRkpKCzMxMUZV9VlYWHj58CCMjIzRo0AB5eXmYNGkStLW1cfPmTdEVjP/85z84duyY3HK1tbVFfwxr165FixYt0K5dOzg6OuKff/7B3bt35Sp1d+7cCT09PXh4eGhyM9Wira0txBoXFyf3KzMqKgoACh0GtbSoczxU9ddff+Hbb7/FsGHDRIVzQUFBcHR0hJ2dnUbPCUUq6v4vypdffonZs2fj1q1bctNk8X722WdlHVapkT2NkZ/siQ5ZUgX87wpiWlqaqO+jR48ULlf2hSg7XzIzM/HDDz9g/PjxqFGjRskD1xDZ0xMFi36VsWTJEly8eBEnT55EdHS06Bdr8+bNoa2tjZiYGOTl5Yl+8GVlZSE+Ph6GhoYKr3wVx9HREdra2tDX15crigWAv//+GykpKWWWjA8cOBC7du3CyZMnRcOIExFOnz6NWrVqyf2a379/P/z9/XH27FnR1UfZF/GCBQvKJHYAWL9+PZydnRUei4cPHwKA8JSbjDrxp6SkAIDCJ/jUodErEnl5eQgMDBS1/fLLL3j79i3Gjh0LPT09SCQS6OnpgYjkfkFcu3ZN4XKtrKzw/PlzAB9+dSxYsABPnz4F8OFyDgAsXbpU9MUSFxeHsWPHVqhfKbJYFy9eLPrVEBYWhtOnT6NDhw7o1KlTmcakzvFQRUREBIYPH4527dphy5YtomlBQUGIjo7W+DlRmIq4/4tiZ2cHLy8vnD59WlQpn52djeXLl0NHR+eTet9JRESE8Kgm8OEqlKzqf/LkyUJ7vXr1oKenhwsXLojmX79+vcLlyp69l50v0dHRWLRoEfT19QFUnKc2ZI8kKvo1XRxtbW3s3r0bVlZWSExMFE0zNzfH6NGj8fjxY7knbH766Se8efMGM2bMUOpR74LMzc0xduxY3L59W/Q4OgDEx8fj66+/lhuO+T//+U+pPc3Rv39/tG7dGqtWrRKONwBs2LABiYmJWLx4sSiROn/+PDw9PdG+fXv8/vvv8PX1Ff7T1CsfVNne9evXY8GCBXJXlUJCQnDo0CFYWVmJxshQN37ZOdahQwc1tkgBFQoqFAoPDyepVEqWlpZkZWVFQ4cOpR49etCsWbOEYrHevXuLCnk2bdpEOjo6VKtWLZo0aRLNmTOHOnfuTC1atCAANGLECFF1sb+/PwGgr7/+mnr16kV2dnai6tWffvqJ9PX1qW3btuTj40OjR48mMzMzmjFjBhERPXr0iKRSKdnb2wtFV7LhcqVSKTk5OREAGjduHO3YsUOp7VZ3mbJY27RpQz4+PjRs2DDS19cnZ2dnoUBG0bKlUqlS65cNB9ygQQOSSCQklUopNDSUEhISSCqVUt26dUlXV5ekUqlQnKXs8ch/rCtVqiQMOxwVFSUXy759+yg5OZmsra2FoV2lUqnov8aNGwvV+Jo6JwqLpaT7v6Tni0xMTIyw/ZUqVRKOkVQqFT2ZIZORkUHu7u6kr69Pnp6eNG3aNGrSpAmZmpoqLE4tDjRUbHnkyBGSSqWkr69P1tbWQvyhoaFy7ffu3aMHDx6QVCola2tr0tfXJ6lUSuHh4UT0v2LL/v37k7OzM3l6epKPjw+1bNmStLS0yNfXV279CxcuJADk4uJCs2fPJjc3N1q5ciUBoMaNG5NUKhUVw7q5uVHlypXJx8eHHBwcREVmFeWpjTdv3lDlypVp2LBhovbbt2+TVCql7t27k729PUmlUjp27JjCZZw7d450dHTkCk6zsrLIy8uLJBIJ9enTh2bNmkWurq6kpaVFEydOFIbjVuf4ZWdn08iRI0lbW5u+/PJLmjNnDn311VdUpUoVmjJlilwRZpUqVahFixZK75djx46RVCqlKVOmEACysbER/maeP38u1//Ro0fk6OhIderUoVmzZpGnpyfp6urSggUL5PrWrFlTOPaK/tPX1y9xPKps74IFC8jU1JTs7OxowoQJ9O2331K/fv1IS0uLGjVqJPdEjzrxE30o7K1Xr55c8W1RyqTYcvz48cIbIc+dO4crV67AwsICs2bNgouLi6jv119/DVdXVxw/fhxPnz5F1apVMW7cOCQnJ+Po0aNyy547dy7atm2L69evw8nJCR4eHsKvCQDw8fGBl5cXjh49isTERNjb22PmzJlo3LixaDlDhw5VGHu3bt3kRkJTlqrLLBhr7dq1MXXqVIWX5AtbtjJ9vLy8FLaPGjVKrk3V4zF+/HilYsnMzBQGKlJk8ODBQgGlps+JwvaLJvZ/Sc6XgrEUx8jICAcPHsTFixdx8eJFZGZmYuHChejZs6dcYXN5yF9Mq0x7UeeDkZER9uzZgyNHjiAqKgojR47Ejh07FBbZ+vn5oWfPnrh8+TJyc3MxatQoNGzYEOnp6QqXffDgQfzxxx9ISEiAr68v+vXrJ0xr06YNNmzYAGNj40IHWyoLxsbGmDp1KlauXInExES5YscOHToU+wvyiy++wJ49e+Su7Onr62P79u2YN2+eMCDVgAEDsGbNGoWXt1U5fnp6evjtt9/w3Xff4fTp03j58iXs7e0xf/58uWUnJSXh5cuXCj+HilO1alVIpdJi+9WqVQvXrl3DsWPHcOvWLdStWxdSqVThIFWTJk2SG+cmv6LeEq1MPKpu7+LFizF//nyEhobiwYMHSElJQdeuXTFz5kx06NBBrg5Rnfhv3bqFU6dOYc2aNYXWNapMhayDMfaRg4Yf/9QE2RWJ4cOHl3coGqPuuzYyMzOpdevWCl/a9SmYO3cuGRkZlfjR6Y9FRdte2Uu7evToodGXdvG7NhhjrIIwMDDA0aNHkZaWhrFjx5Z3OBp19OhRBAQEYOPGjQof1//UVMTtHTBgAMzNzbF3716NPvFVKu/a+Ngpqj4uyMXFRe6WDft34vNFfTt37sTNmzcBADdv3oSvry+8vb1Rv379co6s/Jibm+PEiROaG764gvjiiy8QEhJS6JNVn5qKuL3fffddse9yUQcnEgoo88XAmAyfLyVjYGCg1L3vfxOJRCI3aNvHTvaCvH+Liri9pZFEAJxIMMbKUXm+FIkxphlcI8EYY4wxtXEiwRhjjDG1cSLBGGOMMbVxIsEYY4wxtRVabDlo0KCyjIMxVkYCAgIQHBxc3mF80mSvzubPUfapKPgOl/z4igRjjDHG1FboFQn+xcLYp0cikWDGjBkYPHhweYfySZNdieDPUfapCAoKwpAhQxRO4ysSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFNbhUokTExMIJFIFP5nZGSE5s2b4+eff0Zubm65xvnnn3+KYsvKyiqyf2pqqqh/ixYtFM5TsJ9EIkHr1q1LazPK1MqVK4VtqlWrVnmHw1iFdvbsWYwcORIAsHfvXtFnwoQJExTOU7CfRCLBrFmzyjLsUpOTk4PAwECYmZmhW7duxfYPDg5G27ZtYWRkBAsLC4wYMULhyIz379+Hn58fWrVqBWNjY1SqVAnNmjXD7Nmz8ezZM43Fo6zs7Gzs378fgwcPhrW1NfT19VGnTh307dsX4eHhJY5/4MCBiIiI0Fi8Aipgz549pKC5zERERBAA6tu3r9D2+vVrOnv2LDk6OhIAmjFjRrnFl1/fvn0JAGVmZirV/8qVKwSAANC4ceMK7XfhwgUyNzfXVJgVSvPmzcna2lrUlp6eTg0bNqTevXuXU1QVM5bSAID27NlT3mF88jw8PMjDw0Otebds2ULm5uYUHh4ual+xYoXw+bFr165C5/f396eBAweqte6K6NSpU9S0aVPq2LEjAaCuXbsW2T8wMJAA0A8//EDp6ekUExND7dq1IysrK3r06JGob4MGDcjKyooOHDhAL1++pJcvX9KOHTvI1NSULCws6N69eyWORxVr164lADR9+nRKSEigt2/f0uXLl6lFixakpaVFf/31V4niP3ToEFWpUoX+/vtvlWMrIjcIqlBXJApTqVIldOrUCb/++isAYN26dXj//r3ayzMxMUHHjh01FZ5K9PX1YW5ujnXr1mHXrl3lEkNFQ0TIy8tDXl5emayvqONf1rH827m7u0MikSAnJ+dfuf6Crly5gm+++QarV69Ghw4d5KbXrFkTEokEY8eORWxsbDlEWPamTZuGpUuXYsuWLcX2ffjwIebMmYN+/fphzpw5MDExgZ2dHXbv3o3k5GT4+PjIzfPTTz+hT58+MDMzg5mZGTw9PeHn54eUlBQsX768RPGoo1WrVggICECdOnVgaGiINm3aYMOGDcjLy0NAQECJ4u/Vqxd8fX0xZMgQPHjwQGMxfxSJhIy9vT0A4O3bt0hLSyvnaNRjYGCAHTt2QEtLC+PGjfvXfBgUpVKlSrh37x4OHz5c3qFUqFjYv8+MGTPQqFGjQocibtOmDb777jukp6dj8ODBxd5W/RRcvnwZffv2Varv5s2bkZ2djREjRoja69atiy+++AJ//PEHkpOThfa7d+9i2LBhcstp166dML0k8ahq/PjxuHr1qlx7w4YNAUDue0+d+MePHw8zMzN89913mggZwEeWSMTExAAALCwsUK1atXKORn1ubm5YsGAB0tPTMWjQoH/FhwFjrGh37txBeHg4+vXrB4lEUmi/RYsWoUuXLvjnn38wderUMoywfBgaGird9/Tp0wCgsLasdevWyM3NRUhISLHLef78OQDAwcGhRPFoyuXLlwEALi4uSvUvKn49PT307t0b+/btE/qV1EeRSLx58wahoaEYP348jIyMhFscMjk5OdizZw+6d++OGjVqwNDQEA4ODggMDBRdopYV/GVkZCA8PFwoSNLREb+77Pnz5/Dx8UGDBg2gr6+PWrVqoVu3bvjtt9+QmZkpF9/Tp08xZMgQmJmZwdzcHO7u7rh3716R2ySVSuHq6oqbN29iypQpSu2H/HHp6emhSpUq6NmzJ86cOSP0KVgIGhMTg8GDB8Pc3Fxo27hxo6hPQkIChgwZgkqVKsHc3BwjRozAy5cvER8fjz59+qBSpUqwsrLCmDFjkJ6erta+L0xRhatmZmaFFt9qaWkJxVOaOv7FFdGqs//j4+NVPjcqmtzcXAQGBqJFixYwMjKCsbEx2rZtiw0bNoCIAABhYWGi7ZbdKsjKyhK137p1CwBw9epVSCQSHDp0CACgq6sr9AkLCxOm52/z8vKChYUF9PX14eDgILq0rOn1l4fjx48DAJo3b15kP21tbezatQs1a9bEhg0bsHPnTqWWf/v2bQwbNgw1atSAnp4erK2tMWrUKNH5OH36dGE/tGvXDpcuXULHjh1hZGSEWrVqYeLEicjIyJBb9s2bNzFo0CBYWFhAT08PdevWxaRJk0S//stCdHQ0JBIJatasKTfN2toaAJS6ChwUFAQjIyNMnz5d4zGqIiUlBfv27cPo0aPRvXt3+Pr6KjVfcfF/9tlnyMnJwalTpzQTqAoFFWVCVmyp6D97e3vat2+f3DwHDx4kALRs2TJ68eIFpaSk0C+//EJaWlo0a9Ysuf7Gxsbk5OSkcP1PnjwhGxsbqlGjBh08eJBev35NT58+pcWLFxMACggIEPrKii379u1L58+fpzdv3tCpU6eocuXK1KZNG7llX7lyhUxNTYV/p6SkUO3atQkA/f7770K7omJLWVyWlpZ08OBBSktLo5iYGBowYABJJBLasGGDqL8sNmdnZzpz5gxlZGTQxYsXSVtbm1JSUkR9BgwYQFevXqU3b97Qtm3bCAD17NmT+vbtSxEREZSenk6//vqrwkJXVfe9omLL/LHkL1w1NTWl9PR0Ub9FixYJ61M3hqKOf2GxqLv/858bJ06cIENDQ4XnRlmBisWWOTk55O7uTjo6OhQQEEDJycmUlJREixYtIolEQiNGjBD17927NwGg9+/fi9pnzpxJACgyMlKp/jLTpk0jANS4cWMKCgqiV69e0b1792jIkCFy50FprP/7778nIyMjWrVqVeE7SQF1ii1Hjx5NAOj8+fMKp69YsUJUhB4aGko6OjpkYmJC0dHRQruiYsuQkBAyNDSktm3b0rVr1yg9PZ3Cw8OpadOmVLlyZbp+/bqov6mpKdWuXZuGDh1K9+7do9evX9PKlSsJgNzf1OnTp8nAwIDatm1LN27coPT0dDpx4gTVrl2bbGxs6Pnz5yrth8LExcUVW9yoq6tLBgYGCqdt2LCBAJCPj0+R6zl9+jRpa2vT9u3bSxxPSXTt2pUAkEQiobFjx9KzZ8+Umk+Z+P/66y8CQN9//73S8RRVbFlhE4n8fzDv37+n+/fvk1QqJYlEQgMGDKB3794J0w8ePEguLi5yy/Ly8iJdXV1KS0sTtRf1RTJq1KhCP2x79OihMJE4ePCgqJ+npycBEL6wZQomEkQfkgZdXV0yNjamqKgooa1gIiGLq2C1dlZWFtWsWZMMDQ3p6dOncrEdPnxY4Xbm73Po0CFRe9OmTQkAnT17VtRuY2ND9vb2ojZV931JEok9e/aQRCKhUaNGlSgGdRIJdfd/wXPDw8ND4blRVlRNJH777bdCnzIaOHAgAaADBw4IbaWVSOT/uyMiys7Oppo1a5Kenp5ov2t6/fPnzydDQ0MKDAxUOL0w6iQSvXr1IgB0+/ZthdMLJhJEJHy5Ozg40Nu3b4lIPpHIzc2l+vXrk66uLiUkJIjm/+eff0gikVDLli1F7aampqSvr0+pqalCW15eHlWvXp2aNm0qWraNjQ3p6OhQfHy8aBn79+8nADRz5kwV9kLhSppIrF+/vth4rl69SlWqVFEqcSztRILowxOL58+fJxcXF6WetlA2/pCQEAJA33zzjdKxfPRPbejo6MDGxga+vr7w9PTEH3/8gV9++UWY7u7uLrq8LNO8eXO8f/8et2/fVnpd+/fvBwD07NlTbtqRI0cUXipq06aN6N+yS2iPHz8udn3t2rXDypUrkZGRgUGDBim8dZI/rt69e4va9fX10bVrV2RmZuLYsWNy87Vt27bYGAreT5RdFizYbm1tLbdNmtz3Bb169QomJiYAgEuXLmHkyJHo1KkT1q1bV2YxyKi7/wueG7Vr1wag3LlRERS23cCHCvD8fUpTwads9PT04OLignfv3gn3xUvDkiVL8Pbt2zKpRZDdStPV1VV6npkzZ2LAgAGIjIzE5MmTFfa5efMm7t+/DwcHB9SpU0c0zdHREbVq1cL169fx8OFD0TQbGxuYm5sL/5ZIJHKfATdv3sSDBw/QvHlz1K1bVzR/ly5dAHy43VdWzMzMkJ2drfApHNktGTMzM4XzRkVFwdXVFUuXLi10X5a1SpUqoX379vj7779hYmICLy8vpKamKuyrSvyyc6yw7xtVfRSJRH6dOnUCANG9nbS0NCxcuBAODg6oUqWKcI9v9uzZAD485aGM7OxspKWlwcDAAJUqVVI6JlNTU9G/tbQ+7FZlHyGcOnUqhgwZglu3bik8AYqLy9LSEsCHWo2CjI2Ni11/5cqV5eLX1taGkZGRqF1bW1tumzS174vy8OFD9O3bF7Vr18Yff/wBPT29Mo2hJPu/4Lkhi/1jebxUNqhN9erV5aYVtd2apujDXxZTWd+HLy0GBgYAoPKj7Vu2bIGtrS02b96M7du3y00v6hgChR/Hgucu8OH8zX/uypZ97do1uTom2fwPHjwos0EEGzVqBCJSmKgnJSUBAOzs7OSmPXnyBD169MC3335b6IBf5cnY2BidO3fGq1evFNbwqBq/7BzTVOHoR5dI0P8Xd+X/cujTpw8WL16MMWPGIDY2Fnl5eSAi4Zlb2TwyhVVE6+vrw9TUFFlZWXJFhaVt48aNsLe3V/hhUFxcsj/mGjVqlEms+am671WVnp4Od3d3vH//Hn///TeqVq1a4hiKqohXpCLv/9Im+5JR9GWtaLtlSfS7d+9EfV+9eqVw+coeC0W/wmQx5f+CLK31lwUrKysA8o/4Fady5crYu3cvDA0NMWHCBERFRYmmF3UMgZKdv7J5nJ2dQUQK/8vNzYW2trbKy1ZH586dAXxIbAq6du0atLW15Z58SE9PR8+ePTFixAjMmTNHaE9MTBSSu4pAX18fAPDixQtRuzrxy84x2TlXUh9dIhEaGgrgf5eMc3NzER4ejho1amDq1KmwsLAQPhwKu2xjZGQk+qCxt7fH+vXrAQD9+/cHAIXjCLRo0QIzZszQ3MbkY2Jign379sHY2Bhr1qyRmy6LS1ZlLpOdnY1Tp07B0NAQbm5upRJbYdTZ96ouf+jQoYiOjsa+fftEvyQ8PDzw559/avz4F6Yi7v+yUNh252+T9QH+98FUcDhi2eNrBcmuesmOx7hx4+Dp6SnX79y5c6J/v3v3DiEhIdDX1xcuoZfm+stCs2bNAMjHrgxHR0esWbMGGRkZ2LZtm9y0+vXrIzIyUu72xc2bN5GYmIiWLVvK3fZQdr0NGzbEP//8ozAB6tmzZ5nuz6+//hr6+vr4/fffRe0PHz5EaGgoBgwYIEo8379/j4EDB6Jjx45YsmRJmcVZmNatW2Pjxo1y7dnZ2cLt2/y3S9WNX3Z1RnbOldRHkUjk5OQgPj4evr6+2LlzJ6ytrYURymQZ5tOnT7FixQqkpqYiMzMTZ86ckXtMVKZly5aIjY3Fo0ePcOHCBdy/fx9ffPEFAMDf3x82NjaYMWMGDh06hPT0dCQmJmLixIl48uRJqSUSANC0aVO5+/8ysrimT5+Ov//+G+np6YiNjYWnpyeePHmCwMBA4ZfH/7F353FRle3/wD/DsA8wsosIiKSSoogo5taouJGZZCAKuTxJiz5mpD5lWWqLmpFbpT6a2je3AivKPU1BCyQJRc0UBRQxN1BZVHau3x/+5jweZsCZwzKjXu/Xi9dL73Of+1znnJk515z7vs80FynHXh9vvvkmdu3ahTVr1tQ5f7qxz39djPH4N4cXX3wRzz77LNavX49ly5YhPz8fV65cwUcffYSEhASMGzcOI0aMEOoPGTIEwL2xBfn5+cjPz8c777xT563tbt26Abg3///KlSvYu3cvVCqVRr2EhATEx8ejqKgI58+fx4QJE3D58mXMmzdPdNwbe/tz5syBQqHAl19+qc9hk2Tw4MEAgOPHj0taf+LEiYiOjtYoNzExwbp162Bqaorw8HAcPXoUd+7cweHDhxEZGQlbW1t89dVXkrYpk8mwfv16VFRUICwsDMeOHUNpaSkuXryIadOmIS0tDXPmzBGt07p1a43xFI3Fy8sLixYtwo8//iiMPTt37hzGjBkDZ2dnLFmyRFT/5Zdfxr59+7BixQqNrhn1eKaG0nd/33rrLWzevBnXr19HWVkZjh8/jlGjRiErKwtTpkwRPRtCavwZGRmQy+WiJLxB9BiZ2eQUCoXWaZ8ymYxsbW3J39+f3nrrLY1pMPn5+fTqq6+Sh4cHmZmZkaurK02cOJFmzZoltBEYGCjUP3PmDPXr148UCgV5eHjQihUrRO0VFBRQTEwMeXt7k5mZGbm5udGYMWPo7NmzRHRvVkXtGGfPnk1EpFE+fPhwys/P1yi/P57aJk+erPW3NmrHpVQqaejQobR//36hjrbYap/PuuK//7dA1H8LFy6k3377TaN87ty5eh37+38n4P5tqkd23/8XFRVFf/75p9b9uP8vISGhUc9/XbE09PjX99pobtBz1gbRvSmgS5cuJX9/f7K0tCQrKyvq3r07rV69mmpqajTqf/LJJ+Tj40MWFhbk6+tLGzduFGZNAKCePXsKdUtKSigqKoocHBzI0dGRXnnlFdGMLPWsjbS0NJo0aRI5OzuTubk5+fn50bp167TG25jbf+edd5pt1gYRUe/evalTp06i47p161aN105dMw9KS0spICBA629tnDx5kiIiIsjFxYVMTU2pZcuWNH78eMrKyhLqLFy4UGNb+/bto8TERI3y2NhYYb1Tp07R2LFjycXFhczMzMjDw4PGjx9PmZmZohgqKyvJwsKCQkJCdD4m95+72n/nzp3Tuk6p64cZAAAgAElEQVR8fDx1796dLC0tydHRkaKioujixYsa9eq65qj/LCwsGhSPvvubm5tL8+fPp969e5ObmxuZmpqSo6MjBQcH0+bNmxsl/vLycnJ3d6fRo0frFJPaQzX9kzHWdKQkEoakTiTqumAYK6mJxOHDh0kul9f7o1wPsz179hAgfm7Oo8wY93f58uVkbW0tSiB18dBP/2SMscfBU089hTVr1mDq1KlISUkxdDiNqqioCDNmzMCAAQO0/j7Eo8YY93fXrl2YN28e4uLi4OPj02jtciLBGGNG5KWXXsL333/fKOOMjElaWhratGmDH3/8UZhd8ygzxv396quv8Ouvv+LZZ59t1HZNH1yFMcaa159//ikand6uXTv06dPHYL+D0dz69++v8w80PSwGDRqEQYMGGTqMZmOM+9tUD4/jRIIxZnS6d+/e4GeQMMaah3Hcb2GMMcbYQ4kTCcYYY4xJxokEY4wxxiTjRIIxxhhjknEiwRhjjDHJ6py1YUy/iscYazwRERGIiIgwdBiPBf4cZY8DjUSid+/eiIuLM0QsjDEjs2HDBrRv3x5PPfWUoUNhjBkpGfFkbcZYHTp16oTw8HDMmzfP0KEwxozTVh4jwRhjjDHJOJFgjDHGmGScSDDGGGNMMk4kGGOMMSYZJxKMMcYYk4wTCcYYY4xJxokEY4wxxiTjRIIxxhhjknEiwRhjjDHJOJFgjDHGmGScSDDGGGNMMk4kGGOMMSYZJxKMMcYYk4wTCcYYY4xJxokEY4wxxiTjRIIxxhhjknEiwRhjjDHJOJFgjDHGmGScSDDGGGNMMk4kGGOMMSYZJxKMMcYYk4wTCcYYY4xJxokEY4wxxiTjRIIxxhhjknEiwRhjjDHJOJFgjDHGmGScSDDGGGNMMk4kGGOMMSYZJxKMMcYYk4wTCcYYY4xJZmroABhjxmHNmjVIT08XlV25cgU7duzAlStXROWvvPIKAgMDmzM8xpiRkhERGToIxpjhrV27Fi+//DJMTU0hk8kAADU1NZDJZML/q6urIZPJcPXqVTg5ORkyXMaYcdjKXRuMMQBAWFgYzMzMUFVVhcrKSlRWVqK6ulr0f5lMhsGDB3MSwRgTcCLBGAMAtGjRAsOGDYOpad09nkSEcePGNWNUjDFjx4kEY0wQFRWF6urqOpebmZnhueeea8aIGGPGjhMJxphgxIgRsLS01LrM1NQUI0eOhI2NTTNHxRgzZpxIMMYE1tbWGDVqFMzMzDSWVVdXIyoqygBRMcaMGScSjDGRyMhIVFZWapTb2Nhg6NChBoiIMWbMOJFgjIkMGTIE9vb2ojIzMzNERETAwsLCQFExxowVJxKMMRFTU1NERETA3NxcKKusrERkZKQBo2KMGStOJBhjGsaOHYuKigrh/05OTnj66acNGBFjzFhxIsEY09CvXz+4ubkBuNetMX78eMjlcgNHxRgzRpxIMMY0yGQyvPjiizAxMUFlZSXGjBlj6JAYY0aKEwnGmFZjx45FTU0NPD090aNHD0OHwxgzUvzrn4wxrQICAtC+fXu+G8EYqxcnEoyxOkVFRSEsLMzQYTDGjNhj/TPihw8fxpIlSwwdBmNGq6KiQjQNlDGmaevWrYYOwZAe758Rz8vLw/fff2/oMJiBff/997h06ZKhwzBKjZlEpKamIjU1tdHaY8zQLl26xNcQcNcGgMc+m3zsyWQyvPnmmxg9erShQ3mkhYeHA+D3G3t0xMfHIyIiwtBhGNxjfUeCMcYYYw3DiQRjjDHGJONEgjHGGGOScSLBGGOMMck4kWCMMcaYZJxIMMYYY0wyTiQYY4wxJhknEo3gu+++g0wmg0wmg6WlpaHDeWjt2rUL7du3h6npgx9vUllZiaVLlyIwMBC2trZwcXFBSEgItm/fjsf4Ya2PtIMHD2LChAkA7j1ETP2ek8lkmDx5stZ1ateTyWSYOXNmc4bdZKqqqrB8+XK0aNECgwYNemD9rVu3IigoCNbW1nB2dsa4ceO0PogtJycHH3zwAQIDA6FQKGBraws/Pz/85z//wbVr1xotHl2Vl5cjISEBo0ePhru7OywsLODp6YmRI0ciOTm5wfG/8MILOHbsWKPF+1iix1hcXBw15iEIDg4mCwuLRmvvcZGVlUUjRoygLl26kJ2dHcnl8nrr3759m/r27UtdunShgwcP0t27dyk3N5fCwsIIAJ08eVKv7QOguLi4huyC4MiRI9SiRQv65JNPGqW9R0lYWBiFhYVJWvfrr78mR0dHSk5OFpXHxsYSAAJA3377bZ3rL1y4kF544QVJ2zZG+/fvp06dOlHfvn0JAAUHB9dbf/ny5QSAFi1aRCUlJZSZmUlPPfUUubm5UV5enqiuj48Pubm50bZt2+jWrVt069Yt2rx5MymVSnJ2dqbs7OwGx6OPVatWEQCKiYmh3Nxcunv3Lh05coQCAgLIxMSEfv755wbFv3PnTrK3t6cdO3boHVtjX0MeUvGP9RHgRKLhFAoF9enTp0FtjB07lhYuXEiVlZXk7u7+wERi8uTJZGdnR1evXhWV3759mywsLAyaSKSmppJSqaQFCxY0SntNJS0tjQDQ7Nmzm22bUhOJI0eOkFwu15ooxMbGUqtWrUgmk5GtrS1lZmZqbeNRSyT8/Pzop59+onPnzj3wwp2bm0sWFhYUGhoqKr9w4QLJ5XIKDw8Xlfv4+NCWLVs02lm2bBkBoJdffrlB8ehr1apVFBgYqFH+559/EgDq379/g+Nfvnw5KRQKysnJ0Ss2TiSIiCieH5HNDG7dunWwsrLSqe61a9ewZs0avPLKK3B1dRUtUygUKCsra4oQddazZ08UFhYaNIZHzZtvvglfX986H0Xco0cPdOrUCQsWLMDo0aORmpr6yHcxHjlyBFZWVsjKynpg3fXr16O8vBzjxo0TlXt5eaFfv3748ccfcf36dbi4uABAnW0+9dRTdS7XJx59vfbaa3jttdc0yp944gkAQFFRkahcSvyvvfYaPv30U7zzzjv47rvvGhryY4fHSDCD0zWJAIBt27ahuroaffv2bcKImLH4+++/kZycjNDQUMhksjrrffjhhxg4cCCOHz+OadOmNWOEhqHPe+bAgQMAgO7du2ss6969O6qrq5GUlPTAdm7cuAEA6Ny5c4PiaSxHjhwBAPTv31+n+vXFb25ujuHDh+OHH34Q6jHdcSIhwZkzZxAaGgqlUgmFQoF+/frh999/16j3008/iQZ5ZWZmYvTo0XB0dBTKCgoKANx7kU+fPh0+Pj4wNzeHvb09QkJCkJiYKLT32WefCeu1bt0aaWlpCA4Ohq2tLaytrTFgwACtg490afvjjz8W2r7/Ir1nzx6h3MnJSSOWO3fuIDk5Waijy0DJhjh69CgAwN7eHjNmzICHhwfMzc3h5eWFadOm4ebNm026/frExMQIx0H97Udb+R9//IG+ffvC2toarVu3xpQpU3Dnzh2h/rPPPivUf/HFF7Fx40b4+/vDysoKDg4OiIqKEg2SGzRokFD/vffeE8pnzZollIeFhYna79GjBwBg/vz5Qp3GHCDXWPbu3QsA8Pf3r7eeXC7Ht99+i1atWuGrr77Cli1bdGr/1KlTGDt2LFq2bAlzc3O4u7tj4sSJyM7OFuroe/7UTpw4gfDwcDg7Owuv0X//+9+4fv26Hkeg4c6cOQOZTIZWrVppLHN3dwcAnD179oHtxMfHw9raGjExMY0eoz7y8/Pxww8/YNKkSRg8eDDmzZun03oPir9r166oqqrC/v37GzHax4ShO1cMSUr/1rlz56hFixbk7u5Oe/fupZKSEjpx4gQNGTKE2rRpo3WMxMiRIwkAqVQqSkxMpDt37lBqairJ5XLKz8+nK1eukLe3N7m6utL27dupqKiIMjMzadSoUSSTyeirr74Stefv708KhYJ69epFKSkpdPv2bUpLS6MuXbqQubk5JSUlCXX1bbuuMQ+BgYHk6OioUd4YYyTu96AxEupj2bJlS4qKiqLs7Gy6desWffPNN6RQKKh9+/ZUWFio1zbRiGMkiIiUSiX17NlTa7mHhweNGTOGsrOzqbi4mD777DMCQDNnzhTVvXXrlrCfYWFhdPbsWSoqKqIff/yRHBwcyMPDg65duybUr2vMQ0lJCQHQGB/woDESeXl55OnpSb6+vnofz7pIGSMxadIkAkApKSlal8fGxtLIkSOF///2229kampKNjY2dObMGaFc2xiJpKQksrKyoqCgIEpPT6eSkhJKTk6mTp06kZ2dHR09elRUX5/zd+DAAbK0tKSgoCDKyMigkpIS2rdvH3l4eJC3tzfduHFDr+NQF13GJJiZmZGlpaXWZV999RUBoOnTp9e7nQMHDpBcLqeNGzc2OJ6GCA4OJgAkk8nolVdeEb0H6qNL/D///DMBoPfff1/neHiMBBHxYEv9XwTh4eEEgL7//ntR+T///EMWFhb1JhK7du3S2ubEiRO1jjovKyujVq1akZWVlWhgob+/PwGgY8eOieqfOHGCAJC/v7/kto09kRg6dCgBIG9vb6qsrBQt+/jjj/X+ICBq3kTCwsKCCgoKhLKamhpycXGhTp06ieqqEwlXV1cqLS0VLfviiy8IAL3xxhtCWWMnEhcvXqTWrVtLSszqIiWReOaZZwgAnTp1Suvy2okEEQkX986dO9Pdu3eJSDORqK6uprZt25KZmRnl5uaK1j9+/DjJZDLq1q2bqFzX81ddXU3e3t5kampKFy5cELWRkJBAAGjGjBl6HIW6NTSRWLNmzQPj+fPPP8ne3p6++OKLRomnoYqLiyklJYX69++v02wLXeNPSkoiABQdHa1zLJxIEBFRPHdt6GnPnj0AgKFDh4rKW7Vqhfbt29e7blBQkNbyhIQEAMDw4cNF5RYWFggODkZpaSl++eUX0TKFQoGuXbuKyjp37oxWrVrh+PHjuHLliuS2jZlCoQBw73Z+7W6UESNGAIBR74+3tzccHR2F/8tkMri7u+Py5cta63fv3l1j4KD6tbd79+4mi9PDwwN5eXnIzMyEUqlssu08iHrwrJmZmc7rzJgxA6NGjcLJkycxdepUrXVOnDiBnJwcdO7cGZ6enqJlXbp0QevWrXH06FFcvHhRtEyX83fixAmcP38e/v7+8PLyEq0/cOBAAPe6PZtLixYtUF5ejqqqKo1l6i6ZFi1aaF339OnTGDJkCObPn1/nsWxutra26NWrF3bs2AEbGxu8+OKLQhdxbfrEr36NlZaWNnrMjzpOJPRQXl6OkpISWFpawsbGRmO5etRzXdQXwdptFhUVwdLSEra2thrL1TMTrl69Kiqv642vjuH69euS2zZmbdq0AQDRh7maet/z8/ObMyS9aLsom5ubo6amRmt9bef5/nP8qFMnUZWVlXqt9/XXX6Ndu3ZYv349Nm7cqLFc/WCiut6zdb03dDl/6rbT09M1HoalXv/8+fOorq7Wa5+k8vX1BRFpTVb/+ecfAND6JejKlSsYNmwY3n777Tof+GVICoUCAwYMQGFhodYxavrGr36NGWLg6MOOEwk9WFhYwNbWFmVlZbh9+7bGcikD/SwsLKBUKlFWVoaSkhKN5eoPpZYtW4rKb9y4ofUJjuqLi4uLi6S2TUxMUFFRoVG3rimN9Y2kbwrqgaDqOy73U+977WmhDzNt37TuP8dqJib33sq1z52xnDep3NzcAGhO8XsQOzs7fP/997CyssLkyZNx+vRp0XL1a6SuZKyu950u1OuoVCoQkda/6upqyOVyvduWYsCAAQDuJTa1paenQy6Xa8x8KCkpQUhICMaNG4e33npLKL906ZJRTa21sLAAoPnZKyV+9WtM/ZpjuuNEQk8hISEA/tfFoVZQUIDMzExJbT7//PMAgJ07d4rKy8vLsX//flhZWWl0pZSVlSEtLU1UdvLkSVy+fBn+/v7Cm0Hftt3c3IRvKWpXr17VuMWrZm1tLbp4dejQAWvWrHngPkv1zDPPwN3dHXv27NF4ZsT27dsBAKGhoU22/eaWnp6Ou3fvisrUXTfq1yLwvw+/2o88/uOPP7S2a21tDeB/iceVK1cgk8l0Gr3fnPz8/ABo7pcuunTpgpUrV+LOnTvYsGGDxrK2bdvi5MmTGq/tEydO4NKlS+jWrZtGt4eu233iiSdw/PhxrQlQSEgIIiMj9W5XqpdeegkWFhbYtGmTqPzixYv47bffMGrUKFFSWllZiRdeeAF9+/bFxx9/3Gxx1qV79+5Yu3atRnl5ebkw80w9CwmQHr/6c0/9mmN6MNz4DMOTMlAmKyuLHBwcRLM2Tp06RUOHDiUXF5d6B1vWHjSnVntmRXFxsWhmxZo1a0T1/f39SalUUnBwsN6zNh7U9tSpUwkAffHFF1RSUkJZWVk0evRocnd31zrYctiwYaRUKunixYuUkpJCpqam9Pfff+t1TO+ny5Mtd+/eTaampjRy5Eg6e/Ys3bp1izZs2EAKhYJ69uwpDLDTFZpxsKW28p49e5JSqRSVqQdbtmvXTpi1UVxcTAkJCcKsjevXrwv1a2pqqHPnzmRra0tJSUl0584dSk5OphEjRmgdbFleXk52dnbUr18/Ki4uptjYWHJ3d6eqqioiMp5ZG3/99Ve9g0K1DbasLTo6WusxSExMFM3auH37NqWkpAizNtLT00X19Tl/hw4dImtraxo0aBAdPXpUeIz766+/To6OjnT69GlRfXd3d/L09Kx3P7TRdXCj+qmOsbGxdPv2bTp79iz16tVL6yOyJ0yYIDx2XNtffU/v1TUeffY3MDCQ7O3tadOmTXTt2jUqLS2ljIwMYSDulClTGiX+V155RZhJpysebElEPGtD2osgMzOTQkNDyc7OjqysrKhHjx60Y8cOYWoSAJo0aRIdPnxY6wtZm4KCAoqJiSFvb28yMzMjpVJJQ4cOpf3792vU9ff3J3d3d/r7779p6NChZGtrS1ZWVqRSqej3339vUNuFhYUUHR1Nbm5uZGVlRX379qW0tDQKDAwU4n/77beF+mfOnKF+/fqRQqEgDw8PWrFihd7Hc/v27XW+6WtPT1VLSUmhoUOHklKpJHNzc/L19aV58+bpnUQQNV4i8cYbb2iNf+HChRrl+/bto8TERI3y2NhYIvpfIhEVFUU///wzBQQEkKWlJdnb29PYsWPp4sWLGts/ffo0DR06lOzt7cnOzo4iIiIoOztb1P7u3buF+tu3bydfX1+ysrKizp0708GDB4Vlubm55O7ubvBZG0REvXv3pk6dOlFNTY1QtnXrVo1jV9fMg9LSUgoICND6iOyTJ09SREQEubi4kKmpKbVs2ZLGjx9PWVlZQh0p54+I6NSpUzR27FhycXEhMzMz8vDwoPHjx2s8xruyspIsLCwoJCRE52MyY8aMOt8z586d07pOfHw8de/enSwtLcnR0ZGioqK0vo4UCoXeF2J94tF3f3Nzc2n+/PnUu3dvcnNzI1NTU3J0dKTg4GDavHlzo8RfXl5O7u7uNHr0aJ1iUuNEgog4kXg4XwTqRII1jsa+I9EY7k8kHhVSE4nDhw/X+Vsbj4I9e/YQANq0aZOhQ2kWxri/y5cvJ2tra1ECqYuH9RrSyHj6J2PMuD311FNYs2YNpk6dipSUFEOH06iKioowY8YMDBgwAGPHjjV0OE3OGPd3165dmDdvHuLi4uDj42PocB5KnEgwxozeSy+9hO+//x7//e9/DR1Ko0pLS0ObNm3w448/CjNvHmXGuL9fffUVfv31Vzz77LOGDuWhZRxnkulE/fsWx48fxz///KPx2wrGpPb8eW1/uj4j/3Hz7LPPwt7eHgCwefNmyGQy/PrrrwaOyvD69++vMfviYTdo0CDs2LGjzufCPGqMcX8TEhLQrVs3Q4fxUOOfEX+IzJw5EzNnzjR0GDohLc+4YLrZsWOHoUNgjDGd8R0JxhhjjEnGiQRjjDHGJONEgjHGGGOScSLBGGOMMck4kWCMMcaYZDxrAw/PLyGyphMREYGIiAhDh/FY4PcbY48WTiQAxMXFGToEZkARERGIiYlBr169DB3KI23p0qUAgDfffNPAkTDWOA4fPoxly5YZOgyD40QCwOjRow0dAjOgiIgI9OrVi18HTWzr1q0A+P3GHi2cSPAYCcYYY4w1ACcSjDHGGJOMEwnGGGOMScaJBGOMMcYk40SCMcYYY5JxIsEYY4wxyXj6J2PsoVFUVASlUon8/HycOnVKKPfz84OTk5NG/dr1AMDDwwM+Pj5NHmtzycjIgEwmg7+//wPrFhYW4uzZs1AoFHjyySdhYlL3d8mioiKcPXsWJiYm8PLy0np8GxqPPogIWVlZKCgogKurKzw8PGBmZlZnfV3jV7+mWAPQYywuLo6a8xAMGzaMAgMDCQA5OjqSSqWivn37kq+vL8nlcnryySfps88+o7t37zZbTIwIAMXFxRk6jEdeWFgYhYWFSVq3pqaGPvroIwoJCSEioqSkJFKpVNS2bVsCQH369KHKykqN9dT1vL29ycnJiVQqFa1cubJB+2EscnJyKDQ0lABQcHBwvXXLyspoypQpZGlpSYGBgdS6dWvy9PSk3bt3a9TdsmUL9e/fn6ysrMjf35/8/f3J3NycevToQXv37m2UePSRk5ND06ZNI1dXV/L29qagoCBycHAgR0dHWrBgAdXU1DQo/qCgIFqxYoWk2Jr7GmKk4h/rI2CIF8GxY8cIAI0cOVJUfuPGDZo3bx6ZmppSx44dKTs7u1njMrRDhw7R0aNHDbJtY0skTpw4QYmJiRofkA/79huSSERHR5Ofnx9du3ZNVB4bG0sACAC9/fbbda6/cOFCeuGFFyRt2xh98MEH5OzsTDNmzNDpwj1hwgSytLSklJQUIiKqqqqi6OhoMjc3p+TkZFFdHx8f6t27N+Xn5wtlOTk51L59e5LL5ZSYmNjgePSxatUqMjU1pYSEBKGstLSUIiMjCQAtWrSoQfGfP3+evL29ac6cOXrHxokEEXEiYTyJhFpCQgLJZDJq37493b59u1ljMySFQkF9+vQxyLaNLZEYPnw4AdD6Dfth3r7URGLTpk0EgFJTUzWWxcbGUpcuXcjW1pZkMhnt3LlTaxuPWiIxfvx4ysvLo3Pnzj3wwp2amkoA6PXXXxeVl5SUkK2tLQUFBYnKIyMjNZILIqJvv/2WAFB4eHiD4tHXzz//TNOmTdMoP3/+PAGgwMDABse/Y8cOAqA1SaoPJxJERBTPgy2NTGhoKEaPHo2zZ89i+fLlhg6HMYOqqanBnDlzEBwcjJ49e2qt4+3tjXXr1oGIMH78eOTl5TVzlM3vm2++QevWrXWqu3HjRgD3PlvuZ2Njg+DgYBw5cgRnzpwRyjdv3ozevXtrtKMeV1JYWNigePT13HPPaf0sVCgUAAALCwtRuZT4hw8fDn9/f7z33nuNEfJjhwdbGqHx48cjLi4OW7ZswbvvvouCggL89ddfwvKnn34aRIS///4bN2/eBBGhT58+ooFHeXl5yMvLg0KhQMeOHTWWZWdnA7j3JuzVqxdKS0vx999/o7q6Gn5+frC2tq4zvvrazs3Nxfnz5wEASqUSAQEBAICbN2/ixIkTAAAzMzP06dNHFEt1dTWKioqQlJQE4N4vRKpUqoYcxmZx69YtnDt3DjKZDB06dICdnZ2wrKioCMeOHRP+r1KpIJPJUFNTg0OHDgnlPXr0gEKhQElJCdLT03Hjxg0AwMGDByGXywEAAQEBMDExQXp6urBez549YWFhgdOnT6O4uBhPPPEEnJ2dm2z7hhiQlpycjJycHLz66qv11gsPD0dMTAyWLVuGMWPG4ODBgzA11e3jjYiQmZmJmzdvwtnZGe3atRMtz8rKwqVLlwAAdnZ26NatG0pKSnD69Gk4ODjA29tbOE611dTU4PTp0ygqKoKbmxu8vb11iqkxpaSkALg3ILU2Pz8//PTTT0hNTYWvr2+97WRlZQEABgwY0PhBSpCQkAAAGDNmjE71HxT/4MGD8dlnnyEnJwdt27ZtnCAfF4a9I2JYxti1QUR09epVod/37t279Ntvv5FKpSJHR0cCQAkJCdS+fXvq0aMHubq6EgChPzAlJYUCAgLIysqKAgICyM3NjWxtbWnevHlUVVVFRETfffcdqVQqUigU5O7uTitXriR3d3fq3r072dvbk7W1NcXGxmrEpUvbGzZsIJVKRSYmJqKuij/++INUKhXZ2NiQo6OjUK6OxcTEhOzs7EilUpFKpaKBAwc2yvHWBSR0bfzzzz8UGhpKZmZm1LFjR2rXrh2ZmZlRZGQkFRQUENG9sQYqlYocHBxEXQXl5eWkUqmodevWBIBOnjxJRERnzpwR1X/66aeF43HixAlhubu7OwGgr7/+mtq1a0ddu3YlDw8PkslkNH78eGGwbmNvX620tJSOHDmid9eHlK6NDz/8kADQ9u3btS6PjY0V3ksVFRXUu3dvAkAzZ84U1aura2PdunXUqlUrcnBwoMDAQLKzs6M2bdpQfHy8UOeLL74glUpFcrmcevbsSV9++SV16NCBunXrRubm5uTn50c5OTkaba9evZpcXV3J2dmZAgMDycbGhjp27EiHDh3S6xjUR5euhBYtWpCJiYnWMS9ffPEFAaD333//gdtSqVTUpk0bKi4ublA8DZGRkUHx8fE0depUcnFxoY8//piqq6t1WvdB8a9fv54A0Nq1a3WOh7s2iIjHSBhnIlFRUSEkEhcvXhTKR44cSQDo2WefpaKiIiIiKioqouDgYCosLKS0tDSytLSkHj16iAalqQekvfrqq6Lt+Pv7k6mpKYWHhwsXn/LychozZgwBoFWrVgl19W27rjEPgYGBokTiQfWbg76JRGFhIfn4+JCjoyP9+eefQnlSUhJZW1uTv78/lZaWCuV1jTlQD0xTX8gfVF/tjTfeIADUpUsX0etj+fLlBIAiIiJ0ak/q9sPDw7X2uT+IlEQiKiqKAFBaWprW5fcnEqxSnDYAACAASURBVEREly5dImdnZ5LJZKLkQ1siob6IRkdHC/t69+5dCgsLIwC0ZcsWUX2lUklKpZLmzp0rlCUnJxMAGjNmjKiu+lxMmjSJKioqiIjo5s2b1K9fP7K0tKRTp07pdRzqosuFWy6Xk5WVldZla9euJQAUExNT73YWL15Mtra2lJ6e3uB4GmL69OnUrVs3UiqVFBERQfv27dNpPV3i3717t9YktD6cSBARj5EwTmVlZcK/raysNJbPnTtXuIVuZ2eHX3/9FUqlEu+++y7KysqwZMkSuLi4CPVnzpwJf39/rFmzBqdPnxa1VVVVheXLlwvbMTc3x/Lly2FmZoYPPvgARAQAktp+VK1YsQLZ2dmIiYlBYGCgUK5SqRAdHY3jx4/j//7v/5o8jlmzZsHDw0P4/7Rp09ClSxfExcU16bkYPXo0QkJC8NxzzzXZNtQKCgoA3OvP14W7uzu2bNkCmUyGCRMm4OLFi1rrlZWVYfbs2VAqlVi6dKnQDWJlZYUvv/wSZmZmmDFjBmpqajTWfffdd4V/9+7dG23atEFaWpqo7ffffx82NjbCewkA7O3tERsbi7KyMsyfP1+3A9AIzMzMUF1drXVZVVUVgHvv+7qsXbsWH330EX755Rd069atSWLU1eLFi5Geno5r167By8sLgwcPxocffljvOrrGrx5zoe7aY7rjRMIIqccY2NnZwcHBQWN5hw4dNMqICAcPHoRMJkOPHj00lvfo0QNEJIxBUHNwcICbm5uozMXFBa1bt8bVq1eRk5Mjue1HlXo/g4KCNJapyxITE5s8Dm193urERt0v3hTCwsKwa9cuDBo0qMm2oaa+0NX34KTaBg0ahHnz5uHmzZuIiIhAZWWlRp2jR4+iuLgYHTt21EhSXF1d4eXlhStXriAzM1O0rE2bNhoXXScnJ9HFR922OsFISkoS/oqKigAAv/76q87701DOzs6oqKgQfUFRKy4uBoA6H9b0ww8/YMaMGdizZw969erVpHHqw8LCAp988gkCAgLw4Ycf4ty5c1rr6RO/epyLttcLqx8PtjRCe/fuBXBv8I+2D1BtT3OrqqpCRUUFLCwsNEYxAxDuYNy5c0dUrq0uANja2gIA7t69K7ltbdR3OB5m6v1UH6P7Neex0HYu7j9vTb395qD+llhRUaHXeu+99x4OHz6M3bt3Y9asWaJBqED95xCo+zxaWlpq1JXL5aJjqV7nypUrmDdvnkZ99aDX6urqOgdpNqbOnTsjLy8PFy5c0BhQeeHCBQDak9Lk5GRER0cjISFBmDFTXl6OP/74A08//XSTx/0gMpkMPXv2xLFjx3DkyBGNQbL6xl9eXg5A97tf7H/4joSRKS4uxueffw6ZTIa33npL5/XMzMzg4eGB8vJyXLt2TWN5bm4uAOCJJ54QlV+/fl341qdGRLh06RJMTEzg6ekpqW1zc3OtFzNt6wOa3zjT09ONdhqfehqZttvmdR0LQPPiruuxyM7OxsmTJzXqXb58WaNMfczunx3QVNtvDl5eXgDuzfrRh0wmw6ZNm+Dl5YUlS5Zg586douX1nUN1uYmJiaRZFupz7+PjI7obof5bv349YmNjmyWJAIBnnnkGwL0La23JyclQKBTo37+/qPzMmTMYNWoUNmzYgIEDBwrl+fn5GDJkSJPGW1t6ejpu3bqldZn6jkptUuJXv8bUrzmmO04kjEhhYSFGjRqFvLw8LFiwQOut8/pMmjQJALB+/XpR+bVr17Br1y64u7tj6NChomXV1dXCNCq1nTt34ubNm3jmmWeEKX/6tu3p6Ynz58+LvkkeP34c//zzj9bYW7RoIfr2FxoaioMHD+q0383tpZdeAqB5LGpqavDNN9/AxMQEEydOFMo9PT0BQDRXv6ysDAcOHNDafosWLQD875vtvHnztM6j37p1q+j/+fn5+OWXX+Di4iL68Gzs7ZeVlSEtLU0jAW0K6unD6u4+fTg4OGDr1q0wNzfH77//LlrWtm1b9O/fH2fOnMHhw4dFy7Zt24aCggKEhobC0dFR7+16e3tj4MCByMjIwNmzZ0XLysrKMGLECMTHx4vKU1NTtV7oG8OECRPg6uqKVatWicZ8JCcn4/jx45g6dapoLNbVq1cREhKC559/Hra2tqIkqPaxkkqf/X311VexZMkSjfIrV65g9+7dsLS0FE3plBq/+u6M+jXH9GCoYZ7GoLlH3B46dIi++uor4bcBEhMTaf/+/RQXF0cxMTHk4OBAzs7OtGHDBtF6RUVFlJiYSH369CEA9Msvv2h9AltFRQWNHDmS5HI5zZgxg7Zt20arV68mHx8fcnJy0ngyoL+/P7m4uNDgwYNpwYIFtGPHDoqNjSU7Ozvy9PSk3NxcyW0vXbqUANDYsWNp+/bttHbtWgoJCSFfX1+ys7OjxMRE0WPA//Wvf5G5uTlt2LCBFixYQDY2NhqPQ24qkDD9c+HChSSTySg0NJTi4+Npy5YtNGjQIDI1NaXVq1eL6v75559kYmJCgYGBFB8fT/Hx8TR8+HAaOnQoAaD169eLRpN/8803BIDeffdd2rJlC9nZ2Yme2KietfH888/T5MmT6aeffqL169dThw4dyMLCgvbs2dOk22/OWRuXL18muVxO//73v0Xl169fp8TERHrttdeE91JWVpbWNlasWEEANGZtXLx4kdq3b08ODg60ePFi2rFjBy1YsIAUCgUFBAQIU6pzc3MpMTGRFAoFPfnkk5SYmEg3b96kW7duUWJiIj355JOkUCgoMTFRmEVz6dIl8vX1pVatWtGSJUto165d9M0331BQUBB1796dbt26JYrF1dWVfH19dT4uWVlZlJiYKDz1s1u3bpSYmEiJiYlaf6snKSmJFAoFjRgxgnbs2EFr1qwhFxcX6t+/v2iGEdG9357A/581pu3PwsKiwfHos78hISEkk8koOjqa4uLiaPfu3bRs2TJq3bo1WVhY0DfffNPg+ImIIiIiyM7OTuN41IdnbRARUbyM6CHoKG0i8fHxiIiIaLa+4pCQEJSWlorKTExMYGdnB09PT/Tt2xfPPfecRj/s33//jSlTpmi0p21wIxFh27Zt+Pnnn3Hp0iVYW1vjqaeeQnR0tMaAqq5du6KgoABnz57F559/jpSUFFRVVSEoKAivv/66xrcxfdoG7j1hbufOnSgsLERgYCBmzJiBt99+WxjAFhkZiVdeeQXAvW+/ixYtwtGjR+Hg4IBp06ahe/fuDz6ojUAmkyEuLg6jR4/Wa72jR49iw4YNyMzMhEwmQ6dOnfCvf/0LHTt21Kh7+PBhrFu3DpcuXYK3tzfeeOMNJCYmIi4uDgDQsWNHrFy5EsC947xu3Trs2rULJiYmeOGFFzB27FihrZiYGCxfvhznzp1Damoqtm3bhqKiIrRv3x6TJ09u8u1v3boVX3/9NaZPn67XgMvw8HBhfX1ERkbiwIEDyMnJER6UdvDgQcydO1dULyIiApMnT9baxvTp02Fra4sPPvhAVH737l1s2LABiYmJuHnzJpycnDBkyBBERUUJXUJbtmzBmjVrROstXrwYcrkcMTExovLJkycjIiICAFBaWoqNGzdi//79uHXrFlq2bIkBAwYgMjJSNL6lpKQE9vb2GDNmDDZt2qTTMVm1apVw7mrbvHkz3N3dNcrPnz+PlStX4q+//oJCocCwYcMwceJEjQd3afucup+5ubkwjktKPFL2NyUlBTt37sT58+eRn58PpVKJrl27IioqSqP7SUr8BQUF8Pb2xuTJk/Hpp5/qFBPQ/NcQI7X1sU6lHvds0t/fn9zd3Q0dhsHByH5r40HUdyTOnTtn6FD0IvW3Ni5dukROTk6i5zc8StasWUMA6ODBg4YOpVkY4/5OmTKF2rZtW+/DtrR53K8h/188z9pgjBk1d3d37NmzByNGjICTkxOmTp1q6JAazYkTJzBr1iy8+eabRjEToqkZ4/7OmTMHv/zyC3755Zc6Z/Gw+vFgy8dQXl4ekpKScPv2bZSXlyMpKUnSYDbWvEpKSpCUlCT87sMff/wh+i2NR1lgYCAyMjLqfLDSw8rKygrvvPOO1sGEjyJj3F8bGxukp6drzGhjuuMxEo9h/1ZcXBxWrVolKhs3bpwwM+NxI3WMRHPLzMzU+PGqzp0744svvjBQRPqROkaCMWP1uF5DatnKXRuPoYiICGFAGHt4dOjQ4bF5eihj7OHBXRuMMcYYk4wTCcYYY4xJxokEY4wxxiTjRIIxxhhjkvFgS0Djuffs8dNYvyHA6qaetsrvN/ao4M+Ne3j6J89eYIwx1gCP8WUUALY+1okEY6x+nTp1Qnh4OObNm2foUBhjxmkrj5FgjDHGmGScSDDGGGNMMk4kGGOMMSYZJxKMMcYYk4wTCcYYY4xJxokEY4wxxiTjRIIxxhhjknEiwRhjjDHJOJFgjDHGmGScSDDGGGNMMk4kGGOMMSYZJxKMMcYYk4wTCcYYY4xJxokEY4wxxiTjRIIxxhhjknEiwRhjjDHJOJFgjDHGmGScSDDGGGNMMk4kGGOMMSYZJxKMMcYYk4wTCcYYY4xJxokEY4wxxiTjRIIxxhhjknEiwRhjjDHJOJFgjDHGmGScSDDGGGNMMk4kGGOMMSYZJxKMMcYYk4wTCcYYY4xJxokEY4wxxiTjRIIxxhhjksmIiAwdBGPM8KZNm4b/+7//w/0fCaWlpTA1NYWZmZlQJpfL8cMPPyA4ONgQYTLGjMtWTiQYYwCAvXv3YujQoQ+sp1QqkZ+fL0ouGGOPra3ctcEYAwAEBwfDwcGh3jpmZmaIjIzkJIIxJuBEgjEG4F6XRVRUFMzNzeusU1lZibFjxzZjVIwxY8eJBGNMMHbsWFRUVNS5vGXLlujTp08zRsQYM3acSDDGBE899RRat26tdZm5uTnGjx8PExP+2GCM/Q9/IjDGBDKZDOPGjdM6BqKiooK7NRhjGjiRYIyJvPjii6isrNQob9u2Lbp27WqAiBhjxowTCcaYSMeOHdGhQwdRmZmZGSZOnGiYgBhjRo0TCcaYhvHjx4u6NyorKzFmzBgDRsQYM1acSDDGNERFRaGqqgrAvXETXbt2Rbt27QwcFWPMGHEiwRjT4OXlhcDAQMhkMsjlckyYMMHQITHGjBQnEowxrcaPHw8iQk1NDUaPHm3ocBhjRooTCcaYVqNHj4aJiQn69u2LVq1aGTocxpiRMjV0AIwx4+Tq6opBgwYhPDzc0KEwxowYJxKMsTpNmDABw4YNM3QYjDEjxomEHuLj4xEREWHoMBhjjDUhIjJ0CA8VTiQkiIuLM3QIrB4RERGIiYlBr169DB3KI23p0qUAgDfffNPAkTDWOA4fPoxly5YZOoyHDicSEvAIduMWERGBXr168XlqYlu3bgXA7wf2aOFEQn88a4MxxhhjknEiwRhjjDHJOJFgjDHGmGScSDDGGGNMMk4kGGOMMSYZJxKMMcYYk4ynfzLGDKaoqAhKpRL5+fk4deqUUO7n5wcnJyeN+rXrAYCHhwd8fHyaPNbmkpGRAZlMBn9//wfWLSwsxNmzZ6FQKPDkk0/CxKTu74ZFRUU4e/YsTExM4OXlpfX4NjQefRARsrKyUFBQAFdXV3h4eMDMzKzO+rrGr35NsWZETGdxcXEk5ZDV1NTQlClTyNzcnAIDA8nLy4sAkKOjIykUCnJ3d2+CaB9OZWVl1LFjRwJAH330kaQ2AFBcXFyjxZSRkUGlpaWN1t6jIiwsjMLCwiStW1NTQx999BGFhIQQEVFSUhKpVCpq27YtAaA+ffpQZWWlxnrqet7e3uTk5EQqlYpWrlzZoP0wFjk5ORQaGkoAKDg4uN66ZWVlNGXKFLK0tKTAwEBq3bo1eXp60u7duzXqbtmyhfr3709WVlbk7+9P/v7+ZG5uTj169KC9e/c2Sjz6yMnJoWnTppGrqyt5e3tTUFAQOTg4kKOjIy1YsIBqamoaFH9QUBCtWLFCUmxSP+Mfc/F8xPQg9UW2adMmAkBbtmwhIqLq6mp68cUXKScnh/z9/RucSBw6dIiOHj0qebkxeeutt8jCwsJoEomff/6ZANDLL7/cKO01leLiYkpMTKScnJxm22ZDEono6Gjy8/Oja9euicpjY2MJAAGgt99+u871Fy5cSC+88IKkbRujDz74gJydnWnGjBk6XbgnTJhAlpaWlJKSQkREVVVVFB0dTebm5pScnCyq6+PjQ71796b8/HyhLCcnh9q3b09yuZwSExMbHI8+Vq1aRaamppSQkCCUlZaWUmRkJAGgRYsWNSj+8+fPk7e3N82ZM0fv2DiRkIQTCX1IfZG9/PLLBED0RlBrjERCoVBQnz59JC83FqmpqWRra0tz5841mkQiNzeXRo4cSdu2bWuU9ppKWloaAaDZs2c32zalJhLqxDo1NVVjWWxsLHXp0oVsbW1JJpPRzp07tbbxqCUS48ePp7y8PDp37twDL9ypqakEgF5//XVReUlJCdna2lJQUJCoPDIyUiO5ICL69ttvCQCFh4c3KB59/fzzzzRt2jSN8vPnzxMACgwMbHD8O3bsIABak6T6cCIhSTwPtmwG+fn5AAAbGxsDR2K8ysrKMHHiRHz88cd44oknDB2OwNPTEz/99BNGjBhh6FAeCTU1NZgzZw6Cg4PRs2dPrXW8vb2xbt06EBHGjx+PvLy8Zo6y+X3zzTdo3bq1TnU3btwIAAgNDRWV29jYIDg4GEeOHMGZM2eE8s2bN6N3794a7ajHlRQWFjYoHn0999xzWL58uUa5QqEAAFhYWIjKpcQ/fPhw+Pv747333muMkNkD8GDLJlRQUIC//vpLSCQOHToEc3Nz2NnZoVu3bg9cv7KyEtnZ2SgsLMQTTzyhMbgoLy8P2dnZqK6uRlFREZKSkgAAMpkMKpXqgctru3DhAi5fvgylUglfX1/I5XKNfVF7+umnUVNTg7/++gtyuRzt2rWDpaWlvodIMHv2bLi6uuL111/H5s2bJbfTmLKysnDp0iUAEJ0zbeUlJSU4ffo0HBwc4O3tLTp2J0+exI0bNwAArq6uePLJJ1FQUIDs7GxYWVnBz89PNEju+PHjuHXrFgDAy8sL3t7eAICcnBxcvHgRAODs7IxOnToJ7aenpwMAcnNzhfNsb2/f6APkGio5ORk5OTl49dVX660XHh6OmJgYLFu2DGPGjMHBgwdhaqrbxxURITMzEzdv3oSzszPatWsnWq7v+btfTU0NTp8+jaKiIri5uQnnpjmlpKQAuDcgtTY/Pz/89NNPSE1Nha+vb73tZGVlAQAGDBjQ+EFKkJCQAAAYM2aMTvUfFP/gwYPx2WefIScnB23btm2cIJl2hr4n8jDR97bXb7/9RiqVihwdHQkA9evXj1QqFU2ePFmoo61r4+rVq/Taa6+RjY0NdenShTp16kSmpqYUGhpKN27cEOp99913pFKpyMTEhOzs7EilUpFKpaKBAwfqtFzt0KFD1LlzZ1IoFBQYGEiurq7k5ORE//3vf+vcl3379pGfnx91796dlEolOTo6ah3opYvk5GSys7Oj7OxsIiLauHGjUXRtfPHFF6RSqUgul1PPnj3rLP/yyy+pQ4cO1K1bNzI3Nyc/Pz/RWIVZs2ZRnz59CABFRETQpEmTyNPTkwICAsjS0pI8PDxoz549Qv3p06dTt27dNLoqVq9eTX379iUAotv6s2bNEup7enoK53n69Omi/Tl37hzl5uY2+LioSena+PDDDwkAbd++Xevy2NhYGjlyJBERVVRUUO/evQkAzZw5U1Svrq6NdevWUatWrcjBwYECAwPJzs6O2rRpQ/Hx8UIdfc+f2urVq8nV1ZWcnZ0pMDCQbGxsqGPHjnTo0CG9jkF9dOlKaNGiBZmYmGgMSlTvGwB6//33H7gtlUpFbdq0oeLi4gbF0xAZGRkUHx9PU6dOJRcXF/r444+purpap3UfFP/69esJAK1du1bneLhrQxIeI6EPqS+ykSNHEgCtI/+1JRLbt28nS0tL+v3334WyY8eOkVKppOHDh2u00ZAxEkeOHCELCwsKDAyk69evE9G9gVtvvPEGAaB169Zp3ZcxY8bQnTt3iIiooKCAWrZsSa1atdL5Q0Dt7t271K5dO9Eoa2NJJNSUSqUokbi/XKlU0ty5c4Wy5ORk4fjc79atWwSAzMzMKDY2Vii/fPkyderUiSwsLCgjI0Mor2vMQ0lJiUYiUV99tRMnTpCJiQlZWFjQ5cuXdd73+khJJKKioggApaWlaV1+fyJBRHTp0iVydnYmmUwmSj60JRLqi2h0dLQw4+Pu3bsUFhYmGuysps/5W758OQGgSZMmUUVFBRER3bx5k/r160eWlpZ06tQpvY5DXXS5cMvlcrKystK6bO3atQSAYmJi6t3O4sWLydbWltLT0xscT0Ook2alUkkRERG0b98+ndbTJf7du3drTULrw4mEJDxGwhg5OjoiJiYGffr0Ecq6du2Kf/3rX9i5cyf++eefRtvW7NmzUV5ejsWLF8PZ2RkAIJfLsWjRIrRo0QJz5swBEWmsFxMTA2trayHeYcOG4fLly7h8+bJe23/nnXfg4eGByZMnN3xnDOTdd98V/t27d2+0adMGaWlpWuu2bdsWM2fOFP7v5uaGjz76COXl5Vi4cGGTxdimTRtERkZiwoQJOj8/oCkUFBQA0H28kLu7O7Zs2QKZTIYJEyYIXTu1lZWVYfbs2VAqlVi6dKnQDWJlZYUvv/wSZmZmmDFjBmpqajTWfdD5Kysrw/vvvw8bGxssX75ceNaBvb09YmNjUVZWhvnz5+t2ABqBmZkZqqurtS6rqqoCAJibm9e5/tq1a/HRRx/hl19+0amLtSktXrwY6enpuHbtGry8vDB48GB8+OGH9a6ja/zqMRfqbkXWdHiMhBHq1asXevXqhYKCAvzzzz8oLCwEEaGyshLAvb5yd3f3Bm+HiHDw4EGYmJigvLxc6FtXc3Nzw+nTp3HmzBk8+eSTomUdOnQQ/V99cbpx44bOg7QOHz6ML7/8EuvWrcPBgweF8tOnTwMAzp8/L8TUs2dPWFlZ6bN7zaJNmzYaH9pOTk5C/21t6nEN9wsMDARwb/xAU7G1tRUG6RmS+kJX34OTahs0aBDmzZuHOXPmICIiAocOHdKoc/ToURQXF6NXr14aSYqrqyu8vLyQlZWFzMxM0WtZl/OnbtvPz08jQayoqAAA/PrrrzrvT0M5OzsjLy8PZWVlGuOSiouLAaDOZPGHH37AjBkzsHfv3joHuxqChYUFPvnkE+zbtw8ffvghxo4dqzG2BdAvfvU4F/XnJms6nEgYoWPHjuGNN95AcnIyfH194eTkBJlMJgwQKy8vb5TtVFVVoaKiAnK5HAsWLNBY7uLiAhcXF5SWlmosq/0Bpn7Tart7UZerV6+ib9+++Prrr0Xl165dAwAcOHAA2dnZAO6N3G6M5KmxaRtgKpfL6zwOtUekA/cu8gBw9+7dB25Pn+NrjNTfEtUXYF299957OHz4MHbv3o1Zs2YJd8/U7ty5A+B/x7I2Ozs7UT01Xc6fep0rV65g3rx5GvVVKhVkMhmqq6vrHKTZmDp37oy8vDxcuHBBY0DlhQsXAGgfiJmcnIzo6GgkJCQIF+Hy8nL88ccfePrpp5s87geRyWTo2bMnjh07hiNHjmgkEvrGr/6c5NlyTY8TCSP0/PPP4/Lly0hPT0fXrl2F8mXLluHNN9/UqF/72116ejpcXFzg4eHxwOWenp7Iy8vDrl27hK4KtZs3b+LEiRPo0qVLY+2ayPPPP4/nn39eo3zTpk0YN24cJk2a9MhN39LW9aOe3nj/DAD1t+TayYU6yaqt9jmurq7Gb7/9hsDAwDovrobg5eUF4N5rSx8ymQybNm1Ct27dsGTJEvTt2xeurq7CcvVUwLq6Pi5evAgTExNJsyzU05F9fHw07toB9+4Q3rx5s1mSCAB45plnsGvXLuGLxv2Sk5OhUCjQv39/UfmZM2cwatQobNiwAQMHDhTK8/PzMWTIEJSVlTVH6ADuff60bdsW9vb2GsvUd1RqkxK/+jWmfs2xpsNjJIxMdXU1rl27BqVSqXEBv379utZ1WrRoIfqmFRoaKuoqqG95dHQ0iAjx8fEa7f7nP//BW2+9pfO0O/Zgqampwp0ltQ0bNgAAxo4dK5Spk8D7nwcAADt37tTabosWLQD879vzhQsXMGDAABQVFQl1srKy6rzQNpeAgAAA97qt9OXg4ICtW7fC3Nwcv//+u2hZ27Zt0b9/f5w5cwaHDx8WLdu2bRsKCgoQGhoKR0dHvbfr7e2NgQMHIiMjA2fPnhUtKysrw4gRIzTeP6mpqU3WVTVhwgS4urpi1apVojEfycnJOH78OKZOnSrqBrx69SpCQkLw/PPPw9bWFklJScJf7WMllT77++qrr2LJkiUa5VeuXMHu3bthaWkpmtIpNX713Rn1a441Hb5CNCH1sxfUA8zUz5Ho1q0bioqKkJ2djdu3bwvjE9TPDHjppZewcuVKjBw5EhMmTIBCoUBqairWr18P4N5zBuzt7YW+9UGDBmHz5s3YuHEjLl26hMLCQgwZMkSIo77l77zzDjIyMjB58mScPn0avXv3RnV1NbZu3Yp9+/bhwIEDAO59Uzh69KhoX9q1awdvb28kJSUJF6g///wTcrkcnTt31vt4nT17FpcvX9YYI+Hg4NBkd0Xqo37eQFVVFYqLi5GUlIQOHTqgsrISOTk5onJ/f3/IZDJkZGSguLgYVVVVSEpKgo+Pj5AUAECPHj3wwgsvIDo6Gi1btsT+/fvx+eefY/DgwXjjjTeEevb29hg5ciR27NiBDz74AD169EBGRoaQWOTn5yMpKQldunQRnn3g4+ODHTt2YODAgdiyZQuCgoKE8SonT55E165dYWZmhvPnz8PNza15D+b/N2zYMMjlcqSlpWHChAlCufrHuLKzs1FQUICkpCStP8bVo0cPey5riQAAIABJREFULF26FP/+97812t6wYQMGDRqEZ599FrNnz0aHDh1w4sQJzJ8/HwEBAVi9ejWAe3cn9D1/6rYHDBiAmTNnwtfXF/n5+VixYgWsra1FAzaBe8m6vb298Fp+kOzsbOTl5QkDqW/dulXn+CAbGxvExcVh+PDhCA0NxauvvorLly/jvffeQ//+/TW6X0aOHIkLFy5g9erVwjG4n7buNn3i0Xd/XVxcMH/+fFy9ehWDBw+GnZ0dMjMz8dlnn+Hu3btYs2YNWrVq1aD4ASAtLQ12dnZan5nDGpeMHvZO12YUHx+PiIgInfupf//9d6235leuXImTJ09i1apVonL17Xz1hXzXrl24evUqHBwcEBISAisrK6xcuRLAvcGO6jfVnTt3sGjRIhw9ehQODg6YNm0aunfvLrT7oOVEhB07duCnn35CXl4eWrRogYCAAERHRwt90X///TemTJmiNd7at1F79eolaQbCkiVLsG3bNo3yoKAgfPrppzq3I5PJEBcXh9GjR+sdw/2+/PJLfP/996Ky//znPygqKsKaNWtE5YsXL4ZcLkdMTIyofPLkyYiIiEBhYSHs7e0RFRWF+fPnY/ny5Th9+jSsrKwwbNgwvPTSSxp3fu7evYvPP/8cR44cQXV1NUJCQjBu3DgMHz5cqPPpp58iKCgIwL1vYIsWLUJubi6eeOIJvPPOO0LCUFJSgsmTJ0OhUAizGBoqPDwcALB161a91ouMjMSBAweQk5MjdKcdPHgQc+fOFdWLiIioczbP9OnTYWtriw8++EBUfvfuXWzYsAGJiYm4efMmnJycMGTIEERFRQndRVu2bNH7/AFAaWkpNm7ciP379+PWrVto+f/Yu/O4GtP/f+Cv034qpRIlIZQW2c0Y61gilOxRxFhC9mXGNIZTthljTMMHQwxjmyHNjGWMfQkRZoRKJUspIYmihZb37w+/c75u57Sd6XQO3s/Hoz+67uu+7/e5zzn3/T7XfV3XbWWFbt26wcfHR3Axe/78OczMzDB8+HDs2LGjQsfkp59+wu7duxUuK61/0N27d7Fu3TrExsbCyMgI7u7uGDNmjNznqE+fPgr7OUnp6enh6NGjSsejzOs9f/48Dh48iLt37+Lx48cwNTVFy5Yt4evrK3f7SZn4MzMzYWdnh8mTJ1fq3FHZczwDAOzhAbOVwGOM3w2o4nkkqoJ0HglfX191h1JllH3WRlpaGtWqVUswf8P7JDQ0lABQRESEukOpFpr4egMCAqhRo0ZlTralCJ/jlRLGtzYYY9XKxsYGhw8fhqenJ2rVqoWpU6eqO6Qqc/36dXz55ZeYNWuWRoyEUDVNfL0LFy7EkSNHcOTIEY3qaPw+40SCqYSi3u1va9iwIRo2bKjyWNQtJiYGKSkpAF6PupDek1fUa/1D0aZNG1y9ehW//fabukOpUmKxGIGBgYJJx95nmvh6jY2N8e+//8LU1FTdoXwwuI9EJfD9s4p7u9+EImPGjMGYMWOqfN9V1UeiqgQGBsr1Ll+5cqWss+y7Stk+EoxpKj7HK2UPt0gwlahIi8SHQpVTXzPGmLrxPBKMMcYYUxonEowxxhhTGicSjDHGGFMaJxKMMcYYUxp3tlSCoudSMM1SVc8QYKWTPjOEvw/sfcHnDeXw8M9KkA4NYowx9v7iy2Kl8PBPZfCHTLNp2jwS7yueR4K9b/jHonK4jwRjjDHGlMaJBGOMMcaUxokEY4wxxpTGiQRjjDHGlMaJBGOMMcaUxokEY4wxxpTGiQRjjDHGlMbzSKjR+fPncfTo0QrVNTExwezZs7Fs2TK8evUKANC/f3+0bt1alSEypjGICBcuXECHDh1w48YNwYyaHh4eaNu2rdw6b9cDgA4dOqBXr14qj1fVbty4gcjISGRmZqJOnTpo1aoVWrVqpbBubm4uTp48iYSEBGhpaaFBgwbo2rUrLC0ty9zH48ePcfToUaSlpcHKygodO3ZEkyZNBHXCw8MRGxtb5nZ69uyJTp06KRVPZGQkOnbsWOb2mXpxIqEBdu3ahcTEREycOBFWVlZyy5OTk3H8+HHMnj0bAJCQkIDdu3ejXr16nEiwD0JWVha8vb1Rv359dOjQQVZ+/vx5HDt2DD///DOio6NRq1YtheufO3cO6enp78UkZZGRkQgICEBKSgoGDRoES0tLnDt3DuPGjUOvXr2wa9cumJmZyerPnDkTmzZtQtOmTdG5c2cAwObNm5GcnIw5c+YgODgYIpFIbj9LlizBd999hz59+qBRo0aIjIzEuHHjMGzYMPz666+yeuHh4di9e3eZMVtbW8sSicrGs2zZMhgZGeGXX36BoaGh8geOqQ6xCtu9ezep4pB5eXkRAIqOjla4/NSpU2RjYyP7/88//yQAtHHjRqX3GRkZSRKJhG7fvq3Uck0GgHbv3q3uMGR27txJEomEiouL36v9DxkyhIYMGVKl21SkoKCA2rVrR4MGDaKioiLBshUrVhAAAkB9+vShkpIShdv45ptvaPDgwSqPtTr89NNPZGpqSsnJyYLylStXEgAKCAgQlDdu3JjGjh0rKCssLCR3d3cCQJs2bZLbR3BwMBkZGdHly5cF5RKJhCwsLARl3t7e1Lt3b5JIJHJ/vr6+JBaLKSsrS+l4CgoKqFu3btS3b99S39+qoqpz/HsujI9YJagrkbh79y6tXLlS9n9VJBLSE/CxY8eUWq7JNC2R6NevHwGgwsLC92r/1ZVILFy4kAwMDCg1NVVu2YoVK6hbt25kb29PAGjZsmUKt/E+JRKXL1+mHTt2yJVnZGQQAGrWrJmgfNWqVXTnzh25+ocOHSIA5O7uLii/ffs26erqUmBgoNw66enptHz5ckGZt7c3hYSEKIx1/Pjx9Nlnn/2neIiIYmJiSEtLi0JDQxXup6pwIqGUMO5sqcHy8vIQFBQEKysr2W0Nxj4kubm5WLVqFQYOHIh69eoprGNiYoLff/8dhoaGWLBgAc6ePVvNUVavtm3bwtfXV648NzcXwOvbCG+aPn067Ozs5OrXrFkTAKCrqyso37p1KwoLCzFo0CC5daytrfHFF18Iyn744QeMHj1arm5WVhZ27tyJKVOm/Kd4AKBZs2bo3r07vv32W37WkQbiPhIaLC8vD8HBwZg5cyYMDAwqtE5GRgYiIiJw584dGBsbo2vXrmjWrJmgzrJly3D69GkAwLZt23Du3DkAQPfu3dGlS5dyl0sRESIiInDlyhUUFhaicePGcHd3h7GxsazO+vXr8fDhQwBAp06d0LFjR+zfvx/JycmoV68evLy8BPXVqaCgAEeOHEFiYiJEIhFcXFzg5uYmO7Hdu3cPmzdvltVfuHAhtLS0UFRUhCVLlsjKAwICULt2baSnpyM0NBQ3b94EACxatAhaWq9z97Fjx0JHRwehoaGy9aZPn47Hjx/j+PHjyMnJgYODAzw8PKCvr6+S/devX79Kj58qHDt2DNnZ2YKOeoq4urrip59+wujRozF8+HBcvXq13I6EUgkJCTh9+jSysrJgaWmJ7t27o3HjxrLlhw8fRlRUFACgXr16GD9+PI4fP44rV67A3Nwcbm5uaNCggcJtx8bGIiIiAtnZ2bC2toabm1upCdF/FRISAi0tLXz++ecVqn/58mUAkOs3EhERAQBwcXFBXFwcTpw4gby8PDRp0gR9+vSBkZGRoH7dunUVbn/jxo1wdXVFmzZt/lM8Up07d8bx48fxzz//oF27dhXaJqse3CKhQdavX4+goCDZ33fffVep9efNm4cGDRpg27ZtyMrKwtGjR9G8eXO5XwRVITk5GW3btoWnpyfi4uKQnp6OwMBA2NnZ4cSJE4K6Dx8+RHBwMHbt2oV+/frhzJkzSElJweTJk+Hs7IwnT55UeXyVdfLkSdjZ2WHGjBlISUlBUlISxo0bBwcHB9kJTurXX39FcHAwSkpKBOVHjx5FcHAwMjIyKrXvw4cPIzg4GEFBQRgxYgRu3bqF+Ph4jBo1Cq6urrhz545K95+YmIgVK1YgNTW1UutVB+kF3N7evty6fn5+mDBhAtLT0zFy5Ei54/O2ly9fws/PD87Ozvj777+RlZWFPXv2wMHBAVOnTkVxcbGg/rfffouNGzfCz88P27Ztw6NHj7B8+XI4OTnJLr5SBQUF8PHxQfPmzXH8+HFkZWVh8+bNaNy4MVasWFHJo1C6bdu2YcqUKWjZsiUSExNx6tQpuLm5lbteXl4eVq9eje7du8PHx0ewLDExEQYGBggMDETfvn2RkJCA27dvIyAgAI0bN65Qi09xcTF++umnCp97yopHSvoZOH/+fIW2yaqRum+uvEtU3Udi4sSJgo5Kn3/+OQGgp0+fCuqX1keiTZs2FBwcLCgLDQ0lAPTbb78Jyv9LH4nCwkJydnYmXV1dunbtmqw8Ly+PWrRoQSYmJpSeni4rj46OJgBkampK9+/fl5WHh4cTAFq8eHE5R6hyUMk+EklJSWRkZEROTk707NkzWfmjR4/IxsaGzM3N6cGDB7Ly0voczJkzhwBQTEyMoLy8PgozZsyQdRZ8s86FCxdIJBJRy5YtBZ3Mqnr/0k5uY8aMUbi8NNXRR2LIkCEEgK5evapw+YoVK8jLy0v2f0FBAbVu3Vruc6Woj4S/vz8BoA0bNgjKly5dSgDoq6++EpSbmpqSlpYW7du3T1b24MED0tXVpR49egjqTpgwgQDQTz/9pLD84MGDFXj15du6dSvNnj2bevXqRQ0aNKD58+dTQUFBmesUFxfTiBEjyNnZmTIyMuSWi8ViAkCNGjUSdJJMTU0lc3NzMjMzo8zMzDL3ER4eThYWFpSfn1/uaygvHqljx44RAJo2bVq521QW95FQCveR0CSTJk0StEi8fS+yPP7+/pg8ebKgzM/PD9ra2ti7d2+VxXngwAHcuHEDAwcORPPmzWXlYrEYU6dORU5OjqAJXsrd3V3QDCodxpeQkFBlsSljzZo1yM3NxYwZM2Bqaiorr127NiZPnoysrCxs2LBB5XHMmDEDOjr/d7exffv26NatG65evSq7vaQKISEhWL58OYKDg1W2D2VlZ2cDQIVv7enr6yM8PBxmZmYICgqS3aJ7W2ZmJn7++WfY2Nhg/PjxgmWzZ8+GsbExQkJCkJ+fL1hWu3Zt9O/fX/a/lZUV7OzsBJ/hzMxMbN68GXXq1MHEiRMF60u/0yEhIRV6PeXx8/PDypUrceTIEaxduxbLli3D4MGDS61PRBg7dixiY2Nx6tQphbd/pLe/AgICBMNIpbd1nj59ip07d5YZ1+rVqzF27Nhy37eKxCMlFosB/N9ngmkO7iOhwQwNDSGRSCp8EvX390d0dDT27duHjIwM2cRVOjo6ePDgQZXFdfXqVQCv+2MEBQUJlt29exfA/zVJv8nW1lbwv/Qk9eLFiyqLTRnR0dEAACcnJ7lljo6OAIArV66oPA5F99mbNm2KkydPIiYmRjbmvqo5OjrKXqemof/fsU7RPAelsbOzw9atW+Hl5QUfHx/Z5/VNMTExKC4uhoODg+zCKWVgYICGDRsiNjYWCQkJgkmeFPVvMDMzw6NHj+S2LRaL5ZIz6e0WRd+P/6pfv35wc3PDwYMHcfHiRXz88cdydebNm4eoqCicOXMGtWvXVrgdCwsL5ObmomnTpnLLnJ2dAQBxcXGlxnH9+nWcO3cOW7ZsKTfmisQjRdzJUmNxi4QGMzQ0RFBQUIUSCWmHtHbt2uHAgQPIyckRLH/7fm9VePPXs5SdnR0kEgkGDBggt0xbW7vKY6gKFTlBVeRCVlRU9J/iUHRPvzIX0v+6f00kbSF6u2WgPJ6enpg3bx4ePHgAX19fuWNb0YvS28e9Mp9hRd8PLS0tSCQSzJkzRyXfSQcHBwCvZ71825o1axAWFoZjx47JLto5OTmCjrrA646rAFBYWCi3jYp8HletWgV3d3c0atSozFgrGo9UQUEBAAhaDZlm4BaJ90RoaCgiIyOxcOFCuV9BP/74o1x96QlRemKIj4/Hvn378OWXX5a7XDqbZtOmTeVaJIDXJ4jyTiKapHXr1jh79izi4+MFo1KA168bgOBXqbTXenZ2NiwsLGTlpXVWfPtYHjp0CNnZ2Rg+fLig3p07d2S/+KSkIy6kJ3dV7l8TSUdPVLYDKfB6ZsaoqCgcP34cCQkJgl/oLVq0gLa2NhITE1FSUiJolSgoKEBycjLEYrHCX+Xlad68ObS1taGvr6/w+/HXX3/h8ePHSifWoaGh6Nq1q8LY7t27BwAwNzcXlP/555/45ptvEBERIWgZlF64v/76a1mZl5cXDh48iNjYWAwcOFCwHen3oWXLlgpje/LkCX777TeEh4eX+RoqE4/U48ePAUAwooZpBm6ReE/o6ekBkP9VeuvWLbnWCeD/xppLR0wcPnwYu3btqtDyfv36wdXVFTt37pSduKQOHjyIGTNmCC5wmm7atGkwNDTEqlWrBPdfMzIysH79epibmwvudUt/9V24cEFWlpKSUupzU94+luvXr1fYtL1mzRrB+xcVFYVTp06hVatWgmcNVPX+NXnUxieffAIASEpKqvS62tra2LVrF6ytrZGWliZYZmFhgXHjxiE9PR2bNm0SLFu5ciVevHiBWbNmye7LV4aFhQX8/f0RFxeH/fv3C5YlJydj7NixyMvLE5T/+OOPFR7NERoaiq+//lquleX06dM4ePAgrK2t0bNnT1n5+fPn4ePjg08++QQ7duwQ9MP64Ycf5Lbv5+eHJk2a4KeffpIN3QaAtLQ0Wb8SRfNYSGOzsrKCu7t7qfFXNh4p6WfgzSnSmYZQTyfPd1NV9+iVTkPdtGlTwaiNt6elfdPSpUvJ29ubAJCnp6ds6uNnz55Rq1atSCQSkYeHB3355Zc0fvx46ty5MxkYGJCtrS1JJBJKTEwkIqKsrCyysrKipk2b0pw5c8jCwoJ+/vln2X7KW37v3j366KOPqEaNGuTn50fz5s2jAQMGkKmpKW3btk1Wb/v27TRx4kQCQB07dpTFe+rUKZo/fz4BoKZNm5JEIhGM6PgvoMTMlidOnCArKytq0KABBQQEkL+/v+z/ixcvCuqmp6eTlZUV1axZkwICAiggIIC6d+9OY8eOJQA0efJkwYiaiIgI0tXVpZ49e5K/vz8ZGxtTXFycbLl01MaSJUuoTZs2NHPmTPLz8yOxWEz29vZy05RX9f41edTGixcvyMTEhEaMGCEoj4uLI4lEQm5ubrLPz5EjRxRu48yZM6SjoyM3aqOgoIBGjhxJIpGIPD09ae7cudSrVy/S0tKigIAA2XTcZ8+eJYlEQvr6+mRjYyObOv7u3bskkUjIxsaG9PX1SSKRUGRkJBERvXz5kkaPHk3a2trUv39/+uKLL+izzz4jMzMzmjZtmtxUz2ZmZtSqVasKHZOvv/6aTE1NycHBgSZPniz77mlpaZGjo6PcCJe6devKphFX9Kevry+3j8TERGratClZWVnRxIkTacKECWRpaUkODg5yo4KkioqKyNbWVm7my7cpEw8RkZubGzVs2FClU83zqA2lhPGtDQ1Q2SZmR0dHSCQSQZmpqSn++ecfHDt2DDdu3MCrV6/QoUMH9OnTB99//73s/qKUmZkZYmJisHfvXjx79gz79+8XZPrlLbe1tcXFixdx7tw5XL58Ga9evcKwYcOwefNmQU9v4HXP9rfjBV7fQ1ZUrg7du3fHnTt3BBNSbdq0CW5ubrLWHilra2skJCTgwIEDSEtLg52dHVauXIkzZ87IdSgFgC5duuDq1as4fvw4tLS0cO3aNYW3fry9vTFixAgcPnwY2dnZ2L59u2BCKlXtf+XKlejWrZtG3uowMjLC9OnT8f333yMtLU2us2OHDh3K/YXauXNn7N69W661Tl9fH9u3b8dXX30lm5Bq0KBBWLduncLmc+ltv7e9PeoDeN1C+MsvvyAwMBAnT57E06dP0bRpU8yfP19u2/fv38fTp08xZsyYMl+H1OLFizF//nycPXsWd+/exePHj9GjRw/MmTMHHTp0kOs8OmXKFFnHa0UU9eVwcHDA9evXcezYMcTFxUFLSws7duxAjx49Sr0l8+DBA4wdOxbjxo0rM35l4omNjcWJEyewbt06udfH1E9ExF1hKyosLAze3t7ce1jDiUQi7N69+5150uPMmTOxatUqJCUlyT2iWZMNHToUALBnzx6V7qegoACdO3dG/fr1ERYWprGddpUVGBiI1atX49q1a+/U+19dXr58iT59+kBfXx9///13pUbwVBaf45Wyh1M7xphGMzAwkLXS+Pv7qzucKnX48GGEhIRg06ZNnESUYtCgQbCwsEB4eLhKkwimPL61wZiaSJ+FIe34uHr1arRs2RJjx45Vc2Sax8LCAseOHXvvpkfu3LkzTp8+jfbt26s7FI0VGBhY7rNWmHpxIsGYmrm7u5fZy529JhKJBKNX3gdGRkacRJSDkwjNx4kEY2pSt25dhfMMMMbYu4T7SDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMadzZUgnSiXiY5goJCVH5REkfOumwVf4+sPfF289kYRXDM1tWwoULF8p8qAxj75tHjx5BLBbDxMRE3aEwVm34R0il7OFEgjFWKhcXFwwdOpSHqTLGSsNTZDPGGGNMeZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGk66g6AMaYZoqOj8eTJE0FZbm4u7ty5g+PHjwvKnZycYGNjU53hMcY0lIiISN1BMMbUTyKRYNGiRRWqGxMTg2bNmqk4IsbYO2AP39pgjAEARowYUaF6TZs25SSCMSbDiQRjDADg6OiIZs2aQSQSlVpHV1cXY8aMqb6gGGMajxMJxpiMn58ftLW1S11eVFQEb2/vaoyIMabpOJFgjMkMHz4cxcXFCpeJRCK0a9cOdnZ21RwVY0yTcSLBGJOxtbXFJ598Ai0t+VODtrY2/Pz81BAVY0yTcSLBGBMYNWqUwn4SRIQhQ4aoISLGmCbjRIIxJjBs2DC5REJLSwuffvop6tSpo6aoGGOaihMJxpiAubk5evToAR0d4Xx1o0aNUlNEjDFNxokEY0zOyJEjUVJSIvtfW1sbAwYMUGNEjDFNxYkEY0zOwIEDoaenBwDQ0dGBh4cHTE1N1RwVY0wTcSLBGJNjZGQET09PaGtro7i4GL6+vuoOiTGmoTiRYIwp5Ovri+LiYojFYvTt21fd4TDGNBQ//ZMxppC7uztMTEwwcOBAiMVidYfDGNNQnEgwxhTS19fH0KFDeUpsxliZ+DHiKnDkyBFkZ2erOwzG/rPk5GTY2tqW+fwNxt4VLi4ucHFxUXcY75s93CKhArNmzUJ8fLy6w2CMMfYGiUTCiYQKcCKhIhKJBEFBQeoOg1VCUFAQwsLCcOPGDXWH8l6Li4tDs2bNEBsbyyd1Vm2cnZ3VHcJ7i0dtMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpnEgwxhhjTGmcSDDGGGNMaZxIMMYYY0xpPI+EBluzZg0yMzMBAD179kSnTp3UHFH1SUlJwfbt29GlSxd06dKl1Hrp6ek4ffo0UlJSYGJiAjs7O/To0QP6+vrVGC2rbunp6Xj58iXs7Oywbt06ZGRkAABMTEwwa9YsiEQiuXXerCf11VdfyR6X/q7Ky8vDyZMnER8fD21tbTRs2BC9evWCsbGxwvqxsbG4cOECHj9+jDp16sDR0REdO3Yscx8lJSW4cOECrly5gsLCQjg7O6NHjx7Q1dWV1cnMzMSaNWvK3I6Ojg6+/vprpeJJSEiApaUlLCwsytwHq37cIqHh7t27h+DgYJw7d07doVSLgoICLFq0CE5OTliwYAHOnDmjsF5OTg569+6Nhg0bYseOHXj69CmuXbuG0aNHo3HjxggPD6/myOWdO3cOW7ZsUXcY753ffvsNbdq0QV5enqB82bJlmDNnDpYvX17m+kuWLJFLKN5VkyZNQu3atfHNN9/g0aNHuHXrFqZNm4b69etjx44dgrr//vsvWrZsifbt2+P06dPIycnBsWPH0K1bN7Rt2xaxsbEK95GQkIC2bdti/PjxuHPnDu7du4fx48fDzs4Op0+fltXLzMxEcHBwmX9r165VOp7MzEy4urriyJEjVXcAWdUgVuWcnJxIIpFUybbOnj1LAOibb76pku1psvv371PDhg3J29ublixZQgBo8eLFCuumpqYSANq2bZugPCMjg6ytrUlHR4fi4+MrtX+JREJOTk5Kx/82BwcHAkA3b96ssm2qws6dO0kikVBxcXG17C82NpYAUGxsbKXX3bNnD+nr69PFixfllhkZGREA0tHRobNnz5a6DX19fYqJian0vjVRnTp1aMaMGYKyp0+fkq2tLenp6VFqaqqsfPv27SQWiykhIUFQPyIigrS0tKhhw4ZUWFgoWPbw4UOytrYmHx8fwbKMjAyqVasW/e9//5OVxcfHk76+PkkkEoV/NjY2NG/evP8Uz+HDh8nAwIAiIiIqeaSq9rzMBMI4kVABTiSUk5ycTH///TcREW3cuLHMRCI7O5uCg4OpqKhIbtm8efMIAH377beV2n9VJxIRERH0888/V9n2VKVfv34EQO6krSrKJhKZmZlkbm5OU6ZMUbjcyMiIJk6cSADIxsaGHj9+rLDe+5RIrFixgtLS0uTKAwICCACFh4fLyq5du0YaBeAQAAAgAElEQVSbN29WuJ2PP/6YAFBUVJSgfNy4cWRkZERZWVly6/z666+ChC4+Pp5MTU0Vbj8lJYX09PTo7t27/ykeIqJhw4aRnZ0dFRQUKFy3NJxIqEwY95FgGqNBgwZo0KBBheqamJhg4cKFCpfVrFkTAAT3b9WhvP4drHLWr1+PrKwsTJ8+vdQ6kyZNQm5uLnbs2IGRI0fi0KFDCvtLvC/mzp2rsDw3NxcAYG1tLStr3rw5mjdvrrC+ou9Mbm4ufvvtN3Tr1g1mZmZy64wYMULwf6NGjXD58mWF21+7di169eqFhg0bKh2P1PTp09GpUyfs2rULo0ePVrg+q16cSGiQFy9e4MCBA0hOToalpSU8PT3LrE9EiIiIkHWAaty4Mdzd3QWdrNavX4+HDx8CADp16oSOHTti//79SE5ORr169eDl5aWwU9alS5fw77//4tmzZ7Czs0Pfvn1hYmKiVAzV7fLly9DR0cHAgQPVsv+CggJ8++23sv9HjhyJJk2aKCyvUaMG9u3bh5ycHDg5OaFv376yC196ejpCQ0Nl9adPn47Hjx/j+PHjyMnJgYODAzw8PGQdS+/du4fNmzfL6i9cuBBaWlooKirCkiVLZOUBAQGoXbu2bPs3b94EACxatAhaWq+7TY0dOxb169dXwdFR3p49e2BpaQkHB4cy623YsAFXr17FkSNH8M033+Crr76q0PYLCgpw5MgRJCYmQiQSwcXFBW5ubrKLWWXfvzfl5eXh8OHDSEpKgo6ODlq0aIHu3bvLjndVun37Nvbt24cuXbrgk08+Kbd+UVERrl69CgcHB7Ro0UJWfvnyZeTl5aFZs2bIy8vDwYMHcevWLZiYmKBLly5wdXUVbEdPTw/29vZy28/Pz8emTZvk+mxUNh6pjz/+GHp6eggLC+NEQkNwZ0sNceXKFTg4OCAgIABJSUm4fv063N3dceLECYX1k5OT0bZtW3h6eiIuLg7p6ekIDAyEnZ2d3DoPHz5EcHAwdu3ahX79+uHMmTNISUnB5MmT4ezsjCdPnsjqFhYWon///ujduzeuXLmC7OxsbNy4EXXr1sXixYuVjqG6xMXFYf/+/fj6669hZ2enlhikoqKiEBwcjFu3biks37ZtG4YOHYrExET8888/8PT0hIeHh9x2Dh8+jODgYAQFBWHEiBG4desW4uPjMWrUKLi6uuLOnTuC+r/++iuCg4NRUlIiKD969CiCg4Mr3dEwMTERK1asQGpqaqXWq0p5eXm4fv26wgvV2wwNDREeHo4aNWpg4cKFiIiIKHedkydPws7ODjNmzEBKSgqSkpIwbtw4ODg4yP3Kruz7d+zYMdjZ2WHOnDm4f/8+EhISMGTIELRu3Rr37t2r+EEow7179/Dll1/C29sbvXv3RkBAAA4fPlyh1pjNmzcjMzMT69atg7a2tqw8MTERAJCWlgYnJyf88ssvePLkCX7//Xc0b94co0ePRlFRUbnb37FjB8zMzODu7l6h11JaPFI6Ojpo2LAhzp8/X6HtsWqg7psr76PK3ovLz88nW1tbMjAwoMTERFn5ixcvqEWLFnJ9JAoLC8nZ2Zl0dXXp2rVrsvK8vDxq0aIFmZiYUHp6uqw8OjqaAJCpqSndv39fVh4eHi7XD2H//v0EgPbu3SuIcerUqdS1a1elY6is8vpIKPLkyRNydnam4cOHK9VxsKr7SISEhBAAOnTokMLyDh060KtXr2TlU6dOJQByHQVnzJhBAKhPnz6CfgwXLlwgkUhELVu2pJKSEll5aX0e5syZQwDk+geU10fC3d2dANCYMWMqdwBKoUwfCek6AwYMKLWOkZERRUdHy/4PCwsjAGRtbU2PHj2Slb/dRyIpKYmMjIzIycmJnj17Jit/9OgR2djYkLm5OT148EBWXpn3LykpiQwNDcne3p5ycnIE5WKxmD766KMKH4OypKSk0IIFC+izzz4jOzs76tGjh8IOqW/7559/yMjIiNasWSO3bPny5QSAANCPP/4oWPbFF18QAFqwYEG5+2jevDmtWLGiQq+jrHje1LFjRwJAT548qdB2ibiPhAqFcYuEBti/fz9SU1MxePBgQbOtkZERAgIC5OofOHAAN27cwMCBAwX3GMViMaZOnYqcnBxBE7eUu7s76tatK/u/Q4cOAF4P75IqLi4G8LpZk4hk5dOnT8eYMWP+cwyq8vTpU3Tv3h0tW7bEjh07VNJkXNVGjRoluAes6P1404wZM6Cj8393I9u3b49u3brh6tWrKh0eHBISguXLlyM4OFhl+yhPdnY2AMDAwKDC6wwdOhQzZszAgwcPMHLkSLkWGqk1a9YgNzcXM2bMgKmpqay8du3amDx5MrKysrBhwwa59Sry/v3vf/9DXl4eZs2ahRo1asjKmzRpgkGDBuHSpUuIjIys8GsqTf369bFo0SJs3rwZMTExKC4uRseOHREVFVXqOleuXEGvXr3w3XffYcqUKXLLpd8hMzMzueXz58+Hjo4O1q1bJzhPvO3UqVO4efMmxo4dW+5rKC+eN4nFYgD/97lg6sV9JDSAdLy0onu/jRs3liu7evUqACAjIwNBQUGCZXfv3gUAhScQW1tbwf/SDlQvXryQlfXv3x8BAQFYvnw5duzYgX79+uGTTz5B3759BYmEsjGowsuXL+Hl5QV7e3ts27ZNYXOoJqrI+/EmRR1RmzZtipMnTyImJgadO3eu+iABODo6wtHRUSXbrijpxaqyHSdXrFiBS5cu4dixY1i6dCkWLFggVyc6OhoA4OTkJLdM+rqvXLkit6wi75/0e3Lp0iU8evRIUF96qygqKqrcCaEqw8jICMuWLUOHDh0QHByMQ4cOydW5e/cu+vbtC4lEovDHCgDZxE+NGjUSJLDA687O9erVQ3JyMh4+fCjo1Pmm1atXY/jw4TA3Ny8z5orE86aykhdW/TT/Z9sHRNFJsqyL4ttfbgCws7ODRCLBgAEDKrUtKS0tLaxduxb379/H4sWLUVBQgFmzZsHa2hrLli37zzFUNSLCqFGjYGRkhJ07d8pe4/Xr1yvcuUtdKpvwKPpFXZkLbEXuZ2sqaUtBfn5+pdbT1dVFWFgYLC0tERwcLJhASaoiF6XKfjffpuh70q1bN0gkErRs2bLC26ko6Y+SGzduyC178uQJ3N3dMWXKFMEImP379wtatqSdKQsLCxXuo7zPXkpKCg4cOFBuYlDReN5UUFAAAIIWJKY+3CKhAaRfWEVN2snJyXJlrVu3BvD61+jbrQHA66baRo0aKRVLVFQUMjMz4eHhgVGjRmHUqFHIz8/HwIEDMX/+fPTv3x/NmjVTaQyVMXv2bDx8+BBHjhwRTHUsTSRGjhyp8hiqy507d+Ds7Cwok464eLMHvZGREYDXzb5vTidcWmdJ6QVRemE4dOgQsrOzMXz48KoL/j9q1KgRRCKRUjNS1qtXD7/++it69+6NESNGyG7fSbVu3Rpnz55FfHy83HDd+Ph4AECrVq2Uirt169Y4c+YMevfujSFDhgiWvXjxAitXrlT6e3Lz5k2cOnUKEydOlFsm7cT5dktAfn4++vfvD09PT7nWmf3796NJkyayqfhbt26NevXq4c6dO8jLy4OhoaGsbk5ODu7fvw8rKyvUqVNHYXxr1qxB69at0a5du1JfQ2XiedPjx49Rs2bNcls6WPXgFgkN0L9/f9SvXx9//PGH7MIAvM6635xSVqpfv35wdXXFzp075Xp9Hzx4EDNmzFB6PvqoqCh8/vnnsowfeH0/UtqMKz0JqzKGivrhhx+wevVqtG3bFsuXL0dQUJDs748//lDpvtVhzZo1glaFqKgonDp1Cq1atRI0jUt/jV64cEFWlpKSgqNHjyrcrrRZWjp6Z/369YLbUpowasPQ0BDNmzdHUlKSUuv37NkTEokEDx8+lGuZmTZtGgwNDbFq1SrBPfeMjAysX78e5ubmCi/WFTFt2jQYGRkhJCRE8J0CgODgYISGhsLGxkZWdvXqVQQFBZU6H8Obbt68iWnTpsnddnn58qXseRajRo2SlZeUlMDHxwc3b96EsbGx4PsSFBQktx0tLS0EBQXhxYsX+P777wXLli5diqKiInzxxRelDnf9+eefy2yNqGw8UkVFRUhOTpb1SWEaQG39PN9jyvQOjo6OJhsbG6pZsyaNHTuWpk+fTq6urrKe4D169CCJRELZ2dlERHTv3j366KOPqEaNGuTn50fz5s2jAQMGkKmpqWDa6O3bt8tm++vYsaNsKuRTp07R/PnzCQA1bdqUJBIJ3b9/n06cOEENGjSgxo0b07Rp0+jLL7+kPn36kLa2Ns2cOVMQc0VjqIwNGzaQRCIhT09PAkDdunUjiURCK1euFNRLSEggkUgk61Wu6K93796V2ndVjdrIz88niURCvXv3JgDk6+sri3/x4sWCcunMl1u2bCFfX19Z3EuXLpVtTzpqY8mSJdSmTRuaOXMm+fn5kVgsJnt7e7p9+7Zg/+np6WRlZUU1a9akgIAACggIoO7du9PYsWMJAE2ePJk2btwoqx8REUG6urrUs2dP8vf3J2NjY4qLi5Mt14RRG0Svjx0AwcgmIqK1a9eSRCIhXV1dmjhxIkkkEnr58qXc+iUlJbLX8vbIlRMnTpCVlRU1aNCAAgICyN/fX/b/m6MflHn/Tp06RdbW1tSwYUOaNGkSzZ07lz755BOytbWlq1evCuJYtWoVAaB9+/aVezzi4uKobdu2pK+vTwMGDKDPP/+cJk+eTI0aNSIdHR36/PPPBaOXQkNDy/y+oJQZdBcuXEja2trUq1cvmjNnDnXv3p20tbVp3rx5gtFCb1q/fj2Zm5tTfn5+qfErG09kZCQBKHVWzNLwqA2V4ZktNUXLli2RmJiI/fv34+7du7CwsMCcOXMAQOEve1tbW1y8eBHnzp3D5cuX8erVKwwbNgybN2+Wm4XOysoKEolEbhs6Ojpy5d27d8edO3dw9uxZJCQk4NmzZxg2bBjWr18vN0FRZWKorNatW8tunyginSOgLE2aNPlPMfxX7du3R/v27Stc3qRJE4Xvk5S3tzdGjBiBw4cPIzs7G9u3bxdMSCVlbW2NhIQEHDhwAGlpabCzs8PKlStx5swZuQ6CwOsZOK9evYrjx49DS0sL165dEzS3r1y5Et26dVP7rY5JkyZh5cqVWL16tcKnTJY38ZRIJMKOHTuwZs0a1K5dW7BM+rl/c0KqTZs2wc3NTe7poJV9/z799FPZthMSEqCnp4evvvoK7u7ucn0nYmNjoaenV6GJpJydnXH58mXcvHkT0dHRSE1Nhbm5OZYsWYLu3bvL3XJwcXEp8/MFQOFthODgYIwZMwZHjhxBVlYWfHx8sGXLljInLDMyMkJoaGiZo2yUjWfVqlVo0KABfHx8ylyXVSN1pzLvI858301VPY9EVZG2SCQlJak7lCrxXx7atXv37lIf2vWue/jwIZmampK/v7+6Q9FYhw8fJn19fTp16lSl1+XzsspwiwRj7N0xbNgwFBUVwcvLCydOnJDrfPquIiIMHjwYTZs2xQ8//KDucDTSuXPn8Nlnn2Hv3r349NNP1R0OewMnEkylTp8+rXDI3dsUjfz40EmfhSHt+Lh69Wq0bNmyQpP7vM98fHzQtWtXvHr1St2hVBmRSIRvv/0Wbdu2rdSkWx+SWrVqISYmRuWduFnlcSLBmIZzd3ev8HMKPhRvjnR4XyjqD8D+j7onRWOl40SCqdSnn37KzZBKqlu3LrfUMMY0Hs8jwRhjjDGlcSLBGGOMMaVxIsEYY4wxpXEiwRhjjDGlcWdLFSgsLMSePXsQFxen7lBYJdy4cQMPHjzA0KFD1R3Key0nJwfA6weumZiYqDka9qF4+zHurOpwiwRjjDHGlMYtEiqgq6uLoUOH8tC9d0xQUBDCwsKwZ88edYfyXouLi0OzZs3www8/wMXFRd3hsA/E+zILqibiFgnGGGOMKY0TCcYYY4wpjRMJxhhjjCmNEwnGGGOMKY0TCcYYY4wpjRMJxhhjjCmNEwnGGGOMKY0TiXfc999/D5FIJPcnFothY2ODfv36Yd26dcjNzVV3qIxplPz8fMyaNQtHjhwBADRr1kz2/TExMUFSUpLC9d6sJ/178eJFdYauMvHx8XBzc4NIJMK5c+fKrb937164ubnBwsICJiYmaN++PdatW4e8vDxZnV9//RVBQUEoKipSZehMjTiReMfNnTsXRAQvLy8AQHR0NIgIjx8/xqFDh/DRRx/hq6++gr29PU6ePKnmaBnTDNnZ2ejUqRMyMjLQo0cPAEBsbCyICEZGRnj+/DmGDh2KgoICuXWl9fT19RETEwMigrGxcXW/hCqVk5ODOXPmoEePHkhNTa3QOrNnz8a4ceMwevRo3LlzBykpKfDy8sKUKVOwefNmWb0BAwbg4sWLcHd3x8uXL1X1EpgacSLxnjI2Nkbz5s0hkUhw+fJliEQi9O3bF5GRkeoOjVXQP//8A5FIhK+//vqD3L8q+fn5oaCgANu2bYOOjvwEvzY2Nrh27RpmzJihhuiq35YtW5CZmYmYmBi0bdu23PphYWEICQnBzp07MXLkSJiamsLMzAyBgYHo2LGjoK6hoSH++OMPJCQkYNasWap6CUyNOJH4ANjb22PTpk14+fIlJkyYgOLiYnWHxJjaHD9+HPv370dQUBC0tbUV1tmwYQMsLS0RGhqK3377rZojrH7+/v7YunUrLCwsKlR/8eLFcHJygru7u9yyc+fOYerUqYIysViMr776CuvXr0d8fHyVxMw0BycSH4g+ffqgSZMmiI+Px+nTp9UdDmNqExoaCl1dXfTr16/UOjY2Nvjtt9+gpaUFf39/JCYmVmOE1U8sFle4blJSEmJjY9GpU6dK7WPgwIEgIoSGhlY2PKbhOJH4gEibHM+ePSso37dvHz799FOYmJhALBajWbNm+PbbbwWdo1q2bCnrWPbll19i69atcHJyglgshr29PVatWiW3v8jISPTt2xd16tSBvr4+HB0dsWDBAoX3YCsSg6aKi4vDiBEjYGVlBT09PdjY2GDMmDG4ffu2rE7Pnj1lx+/NWwVffvmlrHzIkCGycg8PD7Rr1w4AsHTpUlmdnj17ypZLy0aOHInt27ejRYsWEIvFMDc3h6+vL9LS0lS2/3fZsWPHYG9vD0NDwzLr9ejRA8HBwXjx4gWGDh2K/Pz8crddXFyMVatWoVWrVjA0NISRkRE++ugjbNy4EUQEAHj27Jmgo+aBAwcQGBiIunXrwtjYGO3atcPx48fltl1UVITvv/8ezZs3h1gsRo0aNdCpUyf8/vvvyh0IJV25cgUAYGtri7Vr18LFxQX6+vqwtLTEkCFDEBsbq3A9a2tr1K5dW9a5lb1HiFU5Jycnkkgk1bpPLy8vAkDR0dGl1pk3bx4BIH9/f1nZsmXLCAB99tlndO/ePcrKyqLQ0FDS09OjAQMGUElJiaxudHQ0ASBnZ2dasGABZWRk0KNHj2jYsGEEgP766y9Z3aSkJBKLxeTn50dpaWmUn59PZ86cITs7O+ratasgrsrEoEoSiYScnJwqtc7p06dJLBbTRx99RP/++y89f/6cIiMjycXFhUxMTOjKlSuyupcvXyYANH/+fME2nj9/TgBo8ODBgvLS6ks9ffqUAJCVlRUNGTKEbt68SdnZ2fTHH3+Qubk52dra0qNHj1S2/9TUVKpfvz45OjrSs2fPyj9Y/19sbCwBoNjY2AqvU1VSU1MJAPXq1avUOkZGRrLvUUlJCfXt25cA0Pjx4wX19PX1KSYmRvZ/UVEReXh4kI6ODoWEhFBGRgbdv3+fFi1aRCKRiEaNGiVYPyQkhABQu3btaNeuXZSTk0Px8fHk7OxMxsbG9PDhQ8G2+/TpQ9ra2rR69Wp68uQJpaWl0dSpUwkAhYSEVMXhISIiX19fAkBnz55VuFwat5WVFbVq1YouXbpEz58/p4iICGrcuDEZGhrSP//8o3DdVq1akba2Nr18+bLK4q0odZyXPxBh3CLxAaH//4tI6vbt21iwYAEcHR2xceNG2NrawszMDBMmTMDkyZOxd+9eHDx4UG47YrEYixYtgqWlJWrXro2FCxcCAA4dOiSrc/78eeTn52PkyJGwsbGBgYEBOnfujCVLllRJDJqgpKQEY8eORVFREfbs2YPWrVvD2NgYHTp0wK+//ornz59j/PjxKo+DiLB9+3bY29vDxMQEAwcORHBwMFJTU7Fs2TKV7rekpAQlJSUq20dVe/DgAQDA1NS0QvVFIhG2b9+OBg0aYNOmTdixY0epdXfs2IG//voL48aNw8yZM2FpaYm6detiwYIFGDRoELZv344DBw7IrdelSxd4e3ujRo0acHR0REBAAF68eCFoOdy+fTsOHTqEsWPHYtq0aTA3N4eNjQ1Wr16NVq1aITAwEI8fP67k0VCOdCRLRkYGdu/ejXbt2sHY2BhdunTB1q1bkZeXV2qnSlNTUxQXF1dbrKx6cCLxAZGeROvWrQsAOHDgAIqLi9GvXz+5TmfSIXF79+6V2460yVvK1tYWAJCeni4r69SpE4yNjTFp0iRs3rxZduLw8fER9NFQNgZNcP36ddy5cweurq6oX7++YFnz5s1Rr149XLlyBffu3VNpHG3btoWBgYGgrHfv3gCEyV1Vs7W1RWpqKhITEyt8YVY36UVQV1e3wuuYm5tjz5490NPTw6RJk5CQkKCw3p9//gkACvte9O3bV1DnTRX5Pkm/A/379xfUFYlE6NatGwoKCqrtloGRkREAoHHjxrC3txcs69ixI8zMzBAZGalw7hrpca/IbSL27uBE4gMiHfrZpUsXAMCjR48AACtXrpSbYEd6wlI0Kc/bFw09PT0AEPwybdSoEa5fv45+/fph6dKlqF27Npo0aYK5c+ciIyNDVk/ZGDSBNPbatWsrXF6nTh0AwMOHD1UaR82aNeXKpDG9eawZZAlXYWFhpdZr164dfvzxR+Tm5pbaX6Ksz0NZn4WKfJ+k2/b09JT7nvzwww8Aqu970rBhQwAodYRH7dq1UVJSgidPnsgtkx73ynTuZJqPE4kPxMGDB3Hnzh24uLiga9euAAArKysAgEQiAREp/IuIiFB6n3Z2dli9ejVu376NlJQUTJo0CRs3bkSbNm2Qk5NTLTGokvTiUNrFWnryl75GLa3XX7dXr14J6j179kzh+iKRqEJxZGZmypVJY3rzoqaq/b9LrK2tAbyekKqyJk+eDF9fX8TGxmLKlClyy8v6PLz9Wags6XqnTp0q9XsSHBys1LYrq3379tDW1pa1cL4tIyMD2traChON7OxsaGtrw9LSUtVhsmrEicQH4NatW/D394eBgQE2bdoku6B4enpCR0dH4YU6NzcXtWrVUnqo1o8//ojhw4fL/q9fvz7mzp2LyZMnIy0tDdeuXVN5DKrWvHlzNGrUCDExMXK3L65fv460tDS0bt1adttDehF7czQFAFy8eFHh9qWjCqQX/gcPHkAkEuHmzZuCev/++69gSmIAsmbuPn36yMpUtf93iY2NDWrWrCl3DCpqw4YNcHFxwZYtW+RmaRw4cCAAKOzTIy2T1qmsQYMGAYDC78nFixehpaVV6i2XqmZpaYkBAwYgJSUF0dHRgmWRkZF4+vQp3NzcZLdA3nT//n04ODjIWl3Y+4ETifdUXl4eYmJisGjRIrRr1w5EhEOHDqF9+/ayOo0aNcK3336L06dPY+bMmbh79y4KCgpw7do19O/fH3Z2dhg1apTSMfz+++/YuHEjsrKy8PLlS1y6dAl//vknatWqBVdX12qJQZW0tLTw888/Q0dHB0OHDsWVK1eQm5uLCxcuwMfHBzVq1MDGjRtl9a2srODq6oq//voLERERyMvLw/nz57F161aF22/cuDFMTEwQFRWF58+fY+fOnbCxsUHjxo0F9czMzDB69GgkJSXh+fPn2Lt3LyQSCWxtbTF//nyV7T8tLQ0NGjSAk5OTUr/w1UE6hDUpKUmp+/RGRkYIDw9XOCX2yJEj4eHhgc2bN+PHH3/E48eP8eDBAyxevBh//vknRo0aBU9PT6Xi9vX1hZeXF7777jts2LABjx49Qm5uLo4ePQpvb29MmTIFjo6OsvqbNm2CSCTCli1blNpfeUJCQlC3bl34+PjgwoULss6hY8aMQa1atRQOB3/w4AEyMjLQq1cvlcTE1Kg6x4h8KKpzmNGKFSsIgNyfvr4+WVtbU58+fWjt2rX04sWLUrdx8OBB6tGjB5mYmJCBgQE5ODhQYGCgYEhf79695fZRWFhIEolErvzy5cuUm5tLGzdupE8//ZSsrKzIwMCAGjRoQOPGjaOkpCSlYlA1ZYZ/EhHFxMSQt7c31a5dm3R0dMjKyor8/Pzo1q1bcnXj4+Opd+/eZGZmRiYmJuTt7U23b98WHL9Dhw7J6h84cIAcHR1JLBaTq6srRUREyJZJh3/6+vrSvn37qFWrVmRgYEBmZmY0YsQIunfvnkr3n5KSQjY2NuTg4PDODP8kIjp69CgBoLCwMEG5i4uL3Gf5+fPnCrexa9cuAiAY/kn0ephmSEgItWjRggwMDEgsFlPbtm1pw4YNgmHM+vr6gv107NiRiIi6du0qKDcyMhJse9WqVdSyZUsyMDAgExMTatOmDa1fv15uiPSiRYsIAF28eLFCx0Q6BFjRn6+vr8J1UlNT6bPPPqM6deqQrq4u1atXjyZMmKDwc0dEtHbtWhKJRBQXF1ehmKoaD/9UmTAR0VtjAtl/5uzsjGHDhiEoKEjdobBKCAoKQlhYGG7cuKHuUCrk2bNnMDMzg6+vb5nDEjVNXFwcmjVrhtjYWLi4uKglBg8PD9y9exfXr18vdZrsd9knn3yCzMxM3Lx5UyP6uuTn58Pe3h4eHh5Yv369WmLg87LK7OFbG4yxD8727duhr68PPz+/d2L21MrYtGkTLl26hJ9++kkjkoi8vDwMHjwYTWwopHoAACAASURBVJs2xY8//qjucJgKcCLBGPvgmJmZ4dy5c7C0tMSJEyfUHU6VISLs27cPu3fv1pjpzP/880+0a9cOhw8flpvvhL0f5J+fyxjTeB4eHrKRADt37sTOnTtx7Ngxjbl4vAsMDQ3fu1/I0md3aBJfX191h8BUjBMJxt5Bf/31l7pDYIwxAHxrgzHGGGP/AScSjDHGGFMaJxKMMcYYUxonEowxxhhTGicSjDHGGFMaj9pQkeDg4Gp7Gh+rWpowic+HoFmzZuoOgTFWBTiRUIGQkJB35iFGjJVl27ZtcHBwEDzsjbF3lbqmZH/f8bM2GGOlcnFxwdChQ/n5BIyx0vCzNhhjjDGmPE4kGGOMMaY0TiQYY4wxpjROJBhjjDGmNE4kGGOMMaY0TiQYY4wxpjROJBhjjDGmNE4kGGOMMaY0TiQYY4wxpjROJBhjjDGmNE4kGGOMMaY0TiQYY4wxpjROJBhjjDGmNE4kGGOMMaY0TiQYY4wxpjROJBhjjDGmNE4kGGOMMaY0TiQYY4wxpjROJBhjjDGmNE4kGGOMMaY0TiQYY4wxpjROJBhjjDGmNE4kGGOMMaY0TiQYY4wxpjROJBhjjDGmNE4kGGOMMaY0TiQYY4wxpjROJBhjjDGmNE4kGGOMMaY0TiQYY4wxpjQddQfAGNMMoaGh+PfffwVlDx48wF9//YUHDx4Iyv39/dGmTZvqDI8xpqFERETqDoIxpn6bNm3ChAkToKOjA5FIBAAoKSmBSCSS/V9cXAyRSISHDx+iVq1a6gyXMaYZ9vCtDcYYAGDIkCHQ1dVFUVERCgsLUVhYiOLiYsH/IpEIbm5unEQwxmQ4kWCMAQBq1qwJd3d36OiUfseTiDBq1KhqjIoxpuk4kWCMyfj6+qK4uLjU5bq6uujfv381RsQY03ScSDDGZDw9PWFgYKBwmY6ODry8vGBsbFzNUTHGNBknEowxGUNDQwwaNAi6urpyy4qLi+Hr66uGqBhjmowTCcaYgI+PDwoLC+XKjY2N0bt3bzVExBjTZJxIMMYEevXqBTMzM0GZrq4uvL29oa+vr6aoGGOaihMJxpiAjo4OvL29oaenJysrLCyEj4+PGqNijGkqTiQYY3JGjBiBV69eyf6vVasWunTposaIGGOaihMJxpiczp07w9raGsDr2xp+fn7Q1tZWc1SMMU3EiQRjTI5IJMLIkSOhpaWFwsJCDB8+XN0hMcY0FCcSjDGFRowYgZKSEtSvXx/t2rVTdziMMQ3FT/9kjCnUqlUrODg4cGsEY6xMnEgwxkrl6+uLIUOGqDsMxpgG05jHiF+4cAE//PCDusNgjL3h1atXgmGgjDHNsGfPHnWHIKU5jxFPTU1FeHi4usNgjL2hqpKI8PBwpKWlVcm2WOmioqIQFRWl7jCYCqWlpWnctVLjbm1oUJbFGKsiIpEIs2bNwrBhw9Qdyntt6NChAPg8+j4LCwuDt7e3usMQ0JgWCcYYY4y9eziRYIwxxpjSOJFgjDHGmNI4kWCMMcaY0jiRYIwxxpjSOJFgjDHGmNI4kWCMMcaY0t7ZROL777+HSCSCSCRCvXr11B2OQnv37pXFKBKJUFBQoO6Q3knvwnv9rvj777/h4OAAHZ3Sp5BZv3694HOr6K9Pnz7VGDWrLhERERg9ejSA15OIvfmeT548WeE6b9cTiUSYO3dudYatMkVFRVi1ahVq1qyJnj17lls/MzMTs2fPhr29PcRiMezs7DB69GhcvXpVUG/w4MGIjo5WVdjV7p1NJObOnQsiQosWLdQdSqkGDBgAIoKXl5e6Q3mnlfVev3jxAvb29vDw8FBDZJoby9tu376N/v37IzAwEI8ePfrP2+vQoUMVRKWcy5cvw8zMDMuXL1dbDO+jX375BYMHD8bEiRMBAEOGDAERYcWKFQBeJ5i7du2SW09a75tvvsHgwYNBRPj++++rNXZVOHnyJFq2bInw8HBkZ2eXWz8xMRGurq5ITU3F/v378ezZM/z++++4cuWKXBIybtw49OjRAwcPHlRV+NXqnU0kqoOxsTE6deqk7jBYGYgIJSUlKCkpqZb9lfWZqO5YKmPBggXo0KED/v33X9SoUaPc+l5eXiAiub+bN29CX18fEyZMqIaoFSspKZEda032zz//QCQS4euvv1Z3KOW6fPkyxo8fjzVr1ihMEuvWrQuRSAR/f3/cvHlTDRFWvxkzZmDp0qXYsmVLuXWLioowbNgwNGrUCLt374aTkxP09fXRunVrhISEyNXv27cvgoKC4O3tjbt376oi/GqlcVNkM1YZNWrUwO3bt9UdBgDNiuVtP//8M8RicYXqNmnSBJ07d1a47H//+x8GDBgAKyurqgyvUj7++GM8e/ZMbft/H82aNQuOjo6lTr3crl07uLi4YNmyZRg2bBiioqJgYGBQzVFWr0uXLkEsFuPWrVvl1t23bx+uX7+O3bt3Q0tL+Pu8Z8+eyMzMlFtn0qRJ+O677xAYGKiwpeddwi0SjH0AKppEAK9PfHPmzJErf/78ObZu3YqAgICqDI2p2Y0bNxAZGYkBAwZAJBKVWm/RokXo3r07rl27hunTp1djhOpRme/MH3/8AQCVasHW09NDv3798Pvvv+PJkyeVjk+TvDeJREJCAvr16wdTU1MYGhqiW7duiIyMFNQpKirC7t274ebmBisrK4jFYri6umLVqlWCZlJp577c3FxERkbKOhC93UHtyZMnmD17Nho3bgx9fX3Uq1cPPXv2xC+//IL8/Hy5GB8+fAhvb2/UrFkTFhYW8PDwqPQv2Lc7cCYnJ1dom2/GqqenBzMzM/Tp0wenTp0qdduJiYkYNmwYLCwsZGWbNm0S1ElJSYG3tzdq1KgBCwsLjBo1Ck+fPkVycjI8PT1Ro0YNWFtbY8KECXj+/LlS70dFj8WbnVlr1qxZakdBLS0t2ZMoq+oz8f/Yu/O4qMr2f+CfGWRHEGQV2URFEUFA3DABd1MfNFEUS3nU8tHMJf2WWimY5qNUppYmLiWmheRjivs6uABGIqK4BqYQSCAKKKAs1+8Pf3PyMIMMIzBo1/v18vWS+9znPtfc58yZa8657zO1DaxVp/9V3beN5bvvvoO9vT369OmjsRhmz54t9E+PHj1qLD937hx69+4NAwMDtG7dGtOnT8ejR4+E+sOGDRPqv/nmm9i2bRs8PDygr68PMzMzjB8/XvRrpf379xfqP3urYv78+UJ5UFCQqH0fHx8AwLJly4Q6qgzYa2xHjhwBgFrHm2lpaeHHH39Eq1atsHHjRuzYsUOl9tPS0jBu3DhYW1tDR0cHtra2CA0NFR3Ldd1/cqmpqRg9ejQsLCygo6MDBwcHvPvuu/jrr7/q0AMvLjk5Gc2aNUNJSQnGjBkDMzMz6Onpwc3NDV988QUqKyuVrtelSxdUVFTg+PHjjRpvvaMmIjo6mtQJx8PDg0xMTCggIIDOnDlDxcXFlJSURO7u7qSjo0MymUyoGxsbSwDos88+o4KCAsrLy6M1a9aQVCqlefPmKbRtaGhIvr6+Srebk5NDTk5OZG1tTbGxsVRUVER3796lTz/9lADQqlWrhLqBgYEEgAIDAyk+Pp4ePnxIx48fJ2NjY/Lx8anza66pzaNHj5K+vr5Cm/JYraysKDY2lgoLC+n69ev0xhtvkEQioY0bNypt28/Pj06ePEmPHj2ixMRE0tLSory8PFGdN954g3777Td6+PAhRUVFEQAaMmQIBQYG0oULF6i4uJi+/fZbAkBz5swRbaeu+8PDw4NsbW1r7IvS0lKhzMTEhIqLi0X1lixZImxP3Ried0zUFIu6/a/KvlWHra0taWlp1Wmdqqoqat++Pa1bt06tbQKg6OhotdZVxsTEhLp376603M7OjsaOHUvp6elUVFREn3/+OQFQ2J/3798nAGRtbU1BQUF048YNKiwspP/9739kZmZGdnZ2lJubK9RPSkoiAPTRRx+J2ikuLiYANGrUKFF5TfXlMjMzyd7enjp06EAPHjxQtytEgoKCKCgoqE7rTJ48mQBQfHy80uUREREUGBgo/H369Glq1qwZGRkZ0bVr14Ty5cuXK/SBTCYjfX196tatG50/f56Ki4vp7Nmz1KlTJzI2Nqbk5GRR/brsvxMnTpCenh5169aNUlJSqLi4mI4ePUp2dnbk5ORE9+7dq1M/1OTmzZsEgPr161djHRMTE9LW1iZLS0tauXIl5ebmUlZWFi1YsIAAUHBwsNL19uzZQwDok08+UTkedT8rG9DOJhPNiyQSACghIUFUnpqaSgDIw8NDKIuNjSV/f3+FNt58803S1tamwsJCUfnzPjRCQ0NrPDkOHjxYaSIRGxsrqhcSEkIAhA/nuqipzaCgIIU25bH++OOPorplZWXUqlUr0tfXp7t37yq0feDAgVq3v3//flF5p06dCADFxcWJyp2cnMjFxUVUVtf98SKJRHR0NEkkEgoNDX2hGNRJJNTtf1X2rTrUSST2799PzZs3V0jOVNWYiYSuri7l5+cLZVVVVWRpaUmdOnUS1ZUnElZWVqL9RUS0du1aAkCzZs0Syuo7kbhz5w61bt2a2rdvr9FE4vXXXycAlJaWpnR59USCiIQP986dO1NJSQkRKSYSlZWV1KZNG9LW1qbbt2+L1r948SJJJBLy8vISlau6/yorK8nJyYmaNWtGf/zxh6iN3bt3EwCaO3duHXqhZqokErq6ugSA3nrrLYVl/fv3JwB0/PhxhWUymYwA0JQpU1SOpykmEq/ErQ09PT10795dVNa5c2e0atUKFy9eRE5ODoCnlxufvZQs5+HhgfLycqSlpam8zd27dwOA0vn0Bw8exOzZsxXK5Zc65WxtbQEA2dnZKm+3tjbt7OwU2pTHOnToUFFdXV1d9OvXD6WlpTh8+LBC2926dat1+127dhX93apVK6Xltra2Cq+zPvdHdQ8ePICRkREA4Ny5c5g4cSL69OmDDRs2NFoMcur2vyr7trGsWbMGEyZMEPq0KXNyckLLli2FvyUSidLjT65r164KAwcHDRoE4Ol7uaHY2dkhMzMT169fh4mJSYNtpzby23Da2toqrzN37ly88cYbuHTpEmbMmKG0TmpqKjIyMtC5c2fY29uLlrm7u6N169ZITk7GnTt3RMtU2X+pqam4desWPDw84ODgIFq/b9++AJ7eKmwshoaGAIDBgwcrLBs+fDgAKH2Py/tc2a3wl8krMWtDfg+/OktLS2RnZ+Ovv/6CjY0NCgsL8cUXX2D37t3IyspSGPldUlKi0vYeP36MwsJC6OnpqTSVTq76yUI+uvdFprFVb1NHR0fUZm2xWllZAXg6fqM6+ZvjeYyNjUV/S6VSaGlpwcDAQFSupaWl8Drra388z507dxAYGAg7Ozv873//E/qnsWJ4kf6vbd82lhs3buDIkSP48ssvG3W76lL2oayjo1Njv7Vo0UKhzNLSEgAa/V67JsiTqPLy8jqt99133+HSpUvYsmUL/P39FZbLn1ci78vqrKyskJmZibt374oSDVX2n7zt8+fP1zhA9NatW6isrISWlpbKr0ldjo6OKCgoECVAcvLXn5eXp7BM3ud1GdjZFL0SVyRqeliI/CQg35HDhw/Hp59+irfffhs3btwQ5qPL5/kSkWj9mg5QXV1dmJiYoKysTGEAYVNTW6zyN6QmpvPVdX/UVXFxMYYNG4by8nLs27cPZmZmLxzD80a1K9OU+19Va9asQZ8+feDq6qrpUBqEsql51c8dwN+J/5MnT0R1a5qKWtdjRVNsbGwA1HwerYmxsTF+/vln6OvrY9q0abh69apouTxJrikZe5FjX76On5+f0uedEFGjJRHA37M15Fe/nyV//fL+eJa8z+X74GX1SiQSDx8+xMWLF0Vlly5dQnZ2Njw8PGBjY4PKykqcPXsW1tbWmDlzJiwsLIQ3ek2XlQwMDEQnDRcXF0RGRgIARo4cCeDpI4er8/T0xJw5c+rltdUHeazVn6L2+PFjHD9+HPr6+sKl3Maizv6oa/tjx47FtWvXsGvXLrRv315YFhQUhF9++aXej4maNMX+V1VRURGioqLw7rvvajqUBnP+/HmFK0/yy9DP3rqUn+yfnc0BPL11poz8qpz8eMnJyYFEImlyD3Ryc3MDoPi6VOHu7o5169bh0aNHiIqKUljWpk0bXLp0SeH2RWpqKrKysuDl5aVw20PV7bZt2xYXL15UmgANGTIEISEhdW5XXZMnT4ZUKlV6OyU2NhYAlD7h+M8//wTw9z54Wb0SiYShoSFmzJiBc+fO4dGjR/jtt9/w5ptvQkdHB6tXrwbw9NK6v78/7t69i4iICOTn56O0tBQnT57Et99+q7RdLy8v3LhxA5mZmUhISEBGRobwoJ7ly5fDyckJc+bMwf79+1FcXIysrCxMnz4dOTk5TSqRkMc6e/Zs7Nu3D8XFxbhx4wZCQkKQk5OD1atXK82WG5I6+6Mu5syZgwMHDiAyMlLpZVd1Y3jeMVGTptj/qtqyZQuMjIyEZOhVZGpqiokTJ+LmzZsoLi7GL7/8gsWLF8POzg4fffSRUM/a2hqdO3fGvn37EBcXh5KSEsTHx2Pr1q1K23V2doaxsTESExNRXFyM7du3w9bWFs7OzgCefnA7ODigY8eOdb4aUJ8GDBgAAApfxlQVGhqKKVOmKJRLpVJs3rwZzZo1w+jRo5GcnIxHjx4hISEBISEhaN68OTZu3KjWNiUSCbZs2YInT54gKCgIFy5cQGlpKe7cuYOZM2ciKSkJixYtEq3TunVrhfEU9cXd3R0LFy7E3r17ER4ejrt37yI7OxsLFy7EsWPHMHPmTNF0ZbmUlBRoaWkJ4zpeWpoZ5KmoriNRIyIiCAABIFtbW/r1118pICCAjIyMSF9fn/z8/OjMmTOidfLy8mjq1KlkZ2dH2traZGVlRaGhoTR//nyhLW9vb6H+tWvX6LXXXiNDQ0Oys7Ojb775RtRefn4+zZ49m5ycnEhbW5tsbGxo7NixdOPGDSIiSkhIENqV/5OP4K5ePnToUJVet7ptVo/VxMSEBg0aJBpJrKzt6vukpu3LR6g/+2/58uV0+vRphfLFixfXaX88u6+f3aZ8dPaz/8aPH0+//fab0tfx7L/du3fX6zFRUywv2v8verzIyae5KvtXffqpXFVVFbVt25YWLVpUp20pg3qatTFr1iyl8S9fvlyh/OjRo3Ty5EmF8oiICCL6e9bG+PHjac+ePeTp6Ul6enpkampK48aNozt37ihs/+rVqzRo0CAyNTUlY2NjCg4OpvT0dFH7Bw8eFOrHxsZShw4dSF9fnzp37iyazXT79m2ytbXV+KwNIqJevXpRp06dqKqqSiiLiYlR6LuaZkKUlpaSp6enwswVIqJLly5RcHAwWVpaUrNmzcja2pomTJhAv//+u1BHnf1HRJSWlkbjxo0jS0tL0tbWJjs7O5owYQJdv35dFEN5eTnp6urSkCFDVO6TuXPn1vieuXnzptJ1tm7dSl27diV9fX0yNDSknj17UlRUlNK6jx8/JltbWxozZozKMRE1zVkbTSaaJtg5jLF6Ul+JRH16NpF4VaibSCQkJJCWlpbCFOVXxaFDhwgA/fDDD5oORbB69WoyMDAQJVSqaIKfla/G9E/GGGPq69GjByIjIzFjxgzEx8drOpx6VVhYiLlz5yIgIADjxo3TdDgAno6tCwsLQ3R0tHCr62X2Skz/ZIwx9mImTZqENm3a4Ntvv9Xoz8TXt6SkJDg6OuKHH35Q+EEtTdm4cSOOHTsGLy8vTYdSL5pGrzIAqPG3IZ79FxYWpukwWRPBx4v6hg0bBlNTUwDA9u3bIZFIcOzYMQ1HpXn+/v4Ksy9edv3798e+ffuUPi9EU3bv3v3KJBEAX5FoUugFn5vA/ln4eFHfvn37NB0CY68MviLBGGOMMbVxIsEYY4wxtXEiwRhjjDG1cSLBGGOMMbVxIsEYY4wxtTW5WRsvyy/mMcbqJjg4GMHBwZoO4x+Bz6OsMTW5RCI6OlrTITDG6llwcDBmz56Nnj17ajqUV9qqVasAoEn9aCCrXwkJCfjqq680HYZIk0skxowZo+kQGGP1LDg4GD179uT3dwOLiYkBwOfRV11TSyR4jARjjDHG1MaJBGOMMcbUxokEY4wxxtTGiQRjjDHG1MaJBGOMMcbUxokEY4wxxtTW5KZ/MsYY05zCwkKYmJggLy8PaWlpQrmbmxvMzc0V6levBwB2dnZwdnZu8FgbS0pKCiQSCTw8PFSq/+effyIrKwvW1tawt7dXeECYvI9fFXxFAsCQIUPQtWtXSCQSmJubw9/fH/7+/vD29oaxsTGkUin+9a9/IS8vT6NxnjlzBv7+/jA3N4dEIkFZWdlz6xcWFgqvQyKRwMLCAllZWTXW8/Lygra2Nvz9/TF16tSGehmNKjo6Gv7+/jAyMkLr1q0VlmdkZGh8v8o1pVjYPw8RYenSpRg3bhwA4MqVKwgLC8PkyZMREBCAESNGoKKiQmE9eb1JkyZh9OjRCAsLw5EjRxo7/AZx69YtjBw5Ep6enpg7d26t9WUyGby8vNC1a1fMnj0bPj4+cHJywjfffCOqN3DgQKxbt66hwm581ERER0eTJsO5cOECAaDAwEBReXFxMY0ZM4YA0Ouvv/5C2zh16hQlJye/UBtERIGBgQSASktLVaqflJREAAgA+fr6Unl5udJ6CQkJ1LJlyxeOryny8PAgW1tbUVleXh7p6OhQu3btGiWG5+3/xo6lsQGg6OhoTYchSE1NpZMnT1JVVdUrtf2goCAKCgpSa90pU6aQm5sb5ebmisojIiKE88eHH35Y4/rLly+nUaNGqbXtpig8PJwsLCxo7ty5BID69ev33Pp79+4lbW1tWr16tbBfy8rKaNSoUQrn1Vu3bpGTkxMtWrSoznFp+rNSiZ18RaIWRkZG+PTTTwEAhw4dQklJidptDRkyBO+99159hVYnhoaG8PDwwNmzZ/HRRx9pJIampkWLFnjzzTcxceLERtne8/Z/Y8fyT7dgwQIEBASgsrLyH7n96rZv345NmzZh06ZNsLS0VFju7u6O5s2bY+XKlThw4IAGImx86enpSE5Oxn/+859a6z548ACTJ09GcHAwZs6cKdzK0NXVRXh4OHx9fUX1HR0dsXbtWixZsgQymawhwm9UnEiowMzMDABQVVWF8vJyDUejnmbNmuHnn3+GiYkJIiIisH//fk2HpHHNmjXD5s2bm0Ri1ZRiYf8sVVVVWLRoEfr164fu3bsrrePk5ITNmzeDiDBhwgRkZmY2cpSNb+vWrUpvhyqzfft25OXlKf0i0KlTJ+zZs0ehfOjQofDw8MDHH3/8wrFqGg+2VIE8Y3R3d1c6QKa8vBzp6el48OAB2rZtqzAgKTMzE+np6aisrERhYaHQnkQigZ+fn0J7mZmZyMrKgoWFBRwcHKCtrV1jbBUVFbh8+TK0tLTQrl076Onp1Vi3bdu2+P777zFy5EhMnDgRFy5cgJ2dnQo98HdcmZmZMDQ0hKurqyiu/Px8XL58Wfi7T58+ICJcuXIFBQUFICK4uLjg+vXrojqVlZVITU2Fjo4OXF1doaWlBQB4/PgxLl++DCMjIzg7O6NZM+WHam19XxNl8UqlT/PqM2fOKL0XLNe9e3fo6+urHENt+/95sVRvpy79X1VVpfKx0ZTdv38fN2/ehEQigYuLC4yNjYVlhYWFuHDhgvC3n58fJBIJqqqqcOrUKaHcx8cHhoaGKC4uxvnz53Hv3j0AQFxcnHDMeXp6QiqV4vz588J63bt3h66uLq5evYqioiK0bdsWFhYWDbZ9TQzAO3v2LDIyMmodFzV69GjMnj0bX331FcaOHYu4uLga35fVERGuX7+OgoICWFhYoF27dqLlv//+uzB+y9jYGF5eXiguLsbVq1dhZmYGJycnoZ+qq6qqwtWrV1FYWAgbGxs4OTmpFFN9OnjwIADA29sblZWVSEtLQ0lJCZydnUXHS3UDBgzA559/joyMDLRp06axwq1/mr218jdN3/eRj5Hw9fWlkydP0smTJyk2NpaWLFlCpqam5OnpSZcvXxatc/fuXfrPf/5DRkZG5O7uTp06daJmzZrRiBEj6N69e0K9n376ifz8/EgqlZKxsTH5+fmRn58f9e3bV9RefHw8eXp6kr6+Pnl5eVGHDh1IX1+fJk6cSDdu3BDqycdIHD16lNzc3Khr165kYmJCLVu2pIMHDyq8tqSkJDIxMRH+njdvHgGgnj17isZL1DRG4tm4PD09ycbGhpo3b05hYWFUUVFBRESnT58mPz8/atmyJQGg3bt3U/v27cnHx4esrKyEsmfrHDt2jDw8PKhr166kr69Pjo6OdOHCBdq/fz+5urqSt7c36enpUYcOHej3339Xq+/lqo+RqB7vs+NNTExMqHfv3sJ+8vPzIx8fHwJAxsbGlJ+fX6/7/3mxqNv/qh4bjQVqjJH4888/acSIEaStrU2urq7Url070tbWppCQEGEfpKamkp+fH5mZmREA4Xh+/Pgx+fn5UevWrQkAXbp0iYiIrl27Jqrfp08fYX+kpqYKy21tbQkAfffdd9SuXTvq0qUL2dnZkUQioQkTJlBJSUmDbF+utLSUfv311xrHM9VEnTESS5YsIQAUGxurdHlERIQwduzJkyfUq1cvAkDz5s0T1atpjMTmzZupVatWZGZmRt7e3mRsbEyOjo60c+dOoc7atWvJz8+PtLS0qHv37vT111+Ti4sLeXl5kY6ODrm5uVFGRoZC2xs2bCArKyuysLAgb29vMjIyIldXVzp16lSd+uB5bt68WesYCQcHB9LV1aWff/6ZbG1tqWPHjtS2bVuSSqU0bNgwysnJUbreli1bCABt2rRJ5Xg0/VmpxM4mE42mO0eeSLRs2VJ4Y/v6+pK9vT25uLjQZ599RkVFRaJ1YmNjSU9Pj86cOSNqx8TEhIYOZD0grAAAIABJREFUHaqwDUNDQ/L19VW6/aSkJNLT06Pu3bvTX3/9JZSfOnWK9PX1adWqVUKZPJEYO3YsPXr0iIiI7t27Rw4ODtSqVSuqrKxUaPvZRKK8vJxee+01hZOBskRCHpePj49oEJZ8ANbUqVNF9eWxDRs2jAoLC4mIqLCwkPr160cPHjwQ1Rk/fjyVlZUR0dMTrLa2NnXo0IEmTpxIT548ISKilJQUkkqlCifHuva9ssGWz8ZSPZEoLi4W/q6srKTAwEDS0tKiQ4cOqR3D8/Z/TbGo2//PHhv5+flkbW2t9NhoLHVNJB48eEDOzs7UsmVL+u2334RymUxGBgYG5OHhIeqnoUOHij7I5eQD5eQf5LXVl5s1axYBIHd3d7pz545Qvnr1agJAwcHBKrWn7vZHjx5NAOi9995Turwm6iQS48ePJwCUlJSkdPmziQQRUVZWFllYWJBEIhElH8oSibVr1xIAmjJlivBaS0pKKCgoiADQjh07RPVNTEzIxMSEFi9eLJSdPXtWOKafJd8XkydPFs4XBQUF9Nprr5Genh6lpaXVqR9qokoiYWRkRFKplExNTSkxMVEoP3jwIGlra5Obm5sQ47MOHjyoNCl7Hk1/VirBiYRcTbM2iIhOnjxJhoaG5OjoSHl5eUJ5fHw8zZ8/X6H+7NmzCQBlZWWJyp/3QTJgwAACIPpQklu0aBHFxMQIf8s/LJ49YImI3n77bQJAmZmZovLqiQQRUXZ2NllZWZFEIqG9e/cSkfJEQh7X6dOnFeLy8PAgiURCV65cUYitppPSs3XOnTsnKu/WrRsBUDgBdO7cmRwcHERlde37uiQSQ4cOFb5xEv39YbBu3boXikGdRELd/q9+bISGhio9NhpLXROJZcuWEQD69NNPFZbNnDmTAND69euFsoZKJKp/0BERubu7EwBRv9f39mNiYmjIkCF09OhRpctrok4iMWjQIAJAV69eVbq8eiJBRHT06FGSSqVkZmZGt2/fJiLFRKK0tJSMjY0VEnOip1fztLW1ycbGRpTcyhOJx48fi+o7OjqSs7OzQttGRkb08OFDUd3ExEQCQCEhIXXohZqpkkjo6ekRAKXnA/l7b/v27QrLTp06RQDo3//+t8rxaPqzUgmetaEKf39/TJ8+HX/88QeWL18ulPfs2RPLly9Hfn4+Ll68iLi4OMhkMmFAZkZGhkrtExHi4uIgkUjQtWtXheXh4eEICgpSKHdxcRH9bWpqCgDC/dfnsbGxwU8//QSpVIqJEyfizp07z43Lx8dHYbmPjw+ISOmo4+qxKdO+fXuF+CUSicL9UzMzM4XXVF99r8y+ffuEMRCbNm3CF198gVmzZmHatGmNFgNQv/0vH7ehyrHRFMhfU7du3RSWyctOnjzZ4HG4ubkplHl7ewMA4uPjG2y7QUFBOHDgAPr3799g25CTjwdSNi6nJv3790dYWBgKCgoQHBysdBB6cnIyioqK4OrqCiMjI9EyKysrODg4ICcnRzRuCng6o0FHR0dUZm5uLjp25W07OjoiKSkJMplM+FdYWAgAOHbsmMqv50XJx7YoO3/36NEDAJCYmKiwTD7u42UdxC/Hgy1V1KlTJwBPD2C5CxcuYNasWTh79iw6dOggPChKPmjo8ePHKrVdUVGBJ0+eQFdXF7q6uirHVH3wnHzKERGptL6/vz+WLl2KBQsWYMyYMYiIiKhTXPJBb48ePVJY9rwBojXFDzw9mSlbt/prqq++f54TJ05g+vTpGDp0KL788kuF5Q0dw4v0f/W+lZ+wVD02NE3+mpo3b66w7Hmvu7oXfb3K+l0ekypTwV+G/jY0NAQAPHnypE7rffzxx0hISMDBgwcxf/58hUGFz9uHQM37Udl5QUtLS9SX8nVycnIQFhamUF8+6LWysrLGQZr1ycXFBbm5uUpjl79OZceL/BxRPdF62XAioaL79+8DEJ9YRo4ciezsbJw/fx5dunQRyr/66ivMmTNHoY3qGf/58+dhaWkJOzs72NnZITMzE7m5ubCyshLVu3LlCiorK9G5c+f6fEkAgA8//BDx8fGIjY1VmHqora393Lhu374N4OlskMZW176vq+vXr2PUqFFwdXUVrtwAQFpaGqysrGBubl6v+1+Zptz/Dc3Z2Rnx8fG4c+cOevbsKVqm7HXLv8GWlJSIZnXk5uYqbb/6vkhPT0dJSYnCeyw7O1vhypl86uOzswMaavuNwcHBAQBQUFBQp/UkEgl++OEHeHl54csvv0Tv3r1Fx6j8EdnKrnbKy6VSqVqzLOT73tnZWekVuYyMDBQUFDRKEgE8nX1x6tQp3Lp1S2GZ/HhV9shweZ/L98HLim9tqICIsGvXLgBPH20KAJWVlcjNzYWJiQnc3d1F9f/66y+l7bRo0UKUfY8YMQJxcXEAgMmTJwMAvv/+e9E6Dx8+xMCBA3Ho0KF6eS3VSSQSREVFwcnJCadPn1ZYLo9ry5YtovLc3FwcOHAAtra2GDRoUIPEVhN1+r4u7t27h6FDh0JfXx+xsbGibwuLFy/GmTNn6n3/16Qp9n9jmDRpEgDF111VVYWtW7dCKpUiNDRUKLe3twcAXLt2TSgrKyvDiRMnlLbfokULAH9/sw0LC8Pq1asV6sXExIj+zsvLw+HDh2FpaYm+ffs22PbLysqQlJT03GnI9cXT0xMAlH4I1sbMzAwxMTHQ0dHBmTNnRMvatGkDf39/XLt2DQkJCaJle/fuRX5+PkaMGIGWLVvWebtOTk7o27cvUlJScOPGDdGysrIyDB8+HDt37hSVJyYm4uzZs3XeliqmTJmC5s2bY8uWLaKHjFVUVGDr1q3Q1dUVHj3+rD/++APA3/vgZcVXJACcPn1auE+Xn58vZLgVFRXIysrC1q1bcebMGQwcOBDTp08H8PRS26RJk7Bu3ToEBgZi4sSJMDQ0RGJionDyu3jxIkxNTYV7qv3798f27duxbds2ZGVl4cGDB0JisnDhQly4cAEfffQR8vPz0adPHxQWFmL16tWws7PDu+++i6KiIiQnJyM/Px8AcOrUKbRr1w5OTk6QyWRC5v/bb79BS0sLHTp0wNmzZ3H9+nVUVFRAJpOhefPmQjxyLVq0wM8//4xevXop9I08rk8++QT37t2Dn58fcnJysHLlShgaGmLXrl3Q19dXGpuOjg78/f2FtmqK39zcHOfPnxeeNyGTydCmTRuYmJjgwoULePDgASorKyGTyeDo6AhHR0eV+97S0hLp6el4+PAhHj9+DJlMBgcHBzRv3hyXL18WxSL/oaGRI0ciPT0dERERSE9PR3p6uvAa5L+FUZ/7X/4MiOqxdOzY8YX6/3nHhia++daFv78/li9fjoULF2LkyJEICQlBRUUFtmzZgpSUFKxfv150Feitt97C2rVrMX36dHz44YcAnj5QyN3dHTk5OUhKSsKTJ0/g5eUF4Om+2LZtG1auXAk3Nzfs3bsXP/74o0IcOTk5mD59OgYNGoSCggKsWLEClZWViIqKgoGBQYNtf8KECYiJicF7772HNWvW1H8HP2Pw4MHQ0tJCUlKS6IFK8h/jSk9PF86Lyn6My8fHB6tWrcK7776r0HZUVBT69++PYcOG4aOPPoKLiwtSU1OxbNkyeHp6YsOGDQCeXp3IyMhARUUFioqKIJPJ4OHhAYlEgpSUFBQVFQnnMGdnZ9jZ2QltBwQEYN68eejQoQPy8vLwzTffwMDAAAsXLhTFMmLECJiamuLq1asq9Ut6ejoyMzPx559/Anh6VVr+2VD9WTLW1tbYtm0bgoODMWjQIEyZMgVVVVX49ttvcevWLXz//fdwdHRU2EZSUhKMjY2VPk/oZSKhJnITb+fOnQgODtbIPcUhQ4agtLRUoVwqlcLIyAht2rTBoEGDMHjwYNGvuFVWViImJgYHDhzA3bt3YWZmhiFDhkBfX1/4QRYXFxfhzfLo0SOsWLECycnJMDMzw8yZM0WDc4gIe/fuxZ49e5CVlQVzc3P4+fkhNDQUurq6uHLlipDIyL311luYPHmy6AMbeDoQcP78+QgMDBSVPxtPdVFRUdi1a5fCU9iqx2VgYIAePXpgypQpwiA+ZbEBEF12rCn+3r17KzwM55133kHnzp0VHikdGhqK0NBQlfu+b9++WL9+vcI2XVxcFJ4oN2DAAAQGBmLGjBlK+0du6dKl6N27d73t/zNnziiNRX6rSd3+f96x8eyg4cYgkUgQHR2NMWPG1Gm95ORkREVF4fr165BIJOjUqRP+/e9/w9XVVaFuQkICNm/ejKysLDg5OWHWrFk4efIkoqOjAQCurq7CfiEibN68GQcOHIBUKsWoUaNE3xhnz56N1atX4+bNm0hMTMTevXtRWFiI9u3bY9q0aQ2+/ZiYGHz33Xd4//336zTgcvTo0cL6dRESEoITJ04gIyNDSJDi4uKwePFiUb3g4GCFQcdy77//Ppo3b47w8HBReUlJCaKionDy5EkUFBTA3NwcAwcOxPjx44VbQjt27EBkZKRovS+++AJaWlqYPXu2qHzatGkIDg4GAJSWlmLbtm04fvw47t+/D2trawQEBCAkJER0G7q4uBimpqYYO3YsfvjhB5X6ZP369cK+q2779u2wtbVVKL958yYiIyORlpYGqVQKd3d3TJo0Sentx/z8fDg5OWHatGlYuXKlSjEBmv2srEFMk5lD0gSntDDG6gma2I921UY+/fPmzZuaDqVO1P3RrqysLDI3Nxc9v+FVEhkZSQAoLi5O06EIpk+fTm3atFF4PlFtmuBn5U6+tcEYY/9wtra2OHToEIYPHw5zc/Nar8q9TFJTUzF//nzMmTMHffr00XQ4AIBFixbh8OHDOHz4cI2zWl4mPNiSMcb+v+LiYshkMmEK77lz50S/pfEq8/b2RkpKSpP5RdL6oq+vjwULFiidwq0pRkZGOH/+/Csz44oTCcYY+/+ys7MRFhaG/Px8+Pn5YePGjQozR15llpaWmDVrlqbDqFft2rXDvHnzNB2GyAcffKCRH2hrKHxrgzHG/j8XFxelzyVgjNWMr0gwxhhjTG2cSDDGGGNMbZxIMMYYY0xtnEgwxhhjTG1NbrBl9eejM8ZeDdV/b4HVP/m0VT6Pvrqa4vuoyT0imzHGGGPP10Q+ugEgpskkEoyxpqdTp04YPXo0wsLCNB0KY6xpiuExEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTGycSjDHGGFMbJxKMMcYYUxsnEowxxhhTm4SISNNBMMY0b+bMmfj+++/x7CmhtLQUzZo1g7a2tlCmpaWFXbt2oV+/fpoIkzHWtMRwIsEYAwAcOXIEgwYNqrWeiYkJ8vLyRMkFY+wfK4ZvbTDGAAD9+vWDmZnZc+toa2sjJCSEkwjGmIATCcYYgKe3LMaPHw8dHZ0a65SXl2PcuHGNGBVjrKnjRIIxJhg3bhyePHlS43Jra2v4+vo2YkSMsaaOEwnGmKBHjx5o3bq10mU6OjqYMGECpFI+bTDG/sZnBMaYQCKR4K233lI6BuLJkyd8W4MxpoATCcaYyJtvvony8nKF8jZt2qBLly4aiIgx1pRxIsEYE3F1dYWLi4uoTFtbG6GhoZoJiDHWpHEiwRhTMGHCBNHtjfLycowdO1aDETHGmipOJBhjCsaPH4+KigoAT8dNdOnSBe3atdNwVIyxpogTCcaYAgcHB3h7e0MikUBLSwsTJ07UdEiMsSaKEwnGmFITJkwAEaGqqgpjxozRdDiMsSaKEwnGmFJjxoyBVCpF79690apVK02HwxhropppOgDGWNNkZWWF/v37Y/To0ZoOhTHWhHEiwRir0cSJEzF48GBNh8EYa8I4kdCAnTt3Ijg4WNNhMMbYK4WINB3CPxInEhoUHR2t6RCYBgUHB2P27Nno2bOnpkN5pa1atQoAMGfOHA1HwhpKQkICvvrqK02H8Y/FiYQG8Uj4f7bg4GD07NmTj4MGFhMTA4Dfb686TiQ0h2dtMMYYY0xtnEgwxhhjTG2cSDDGGGNMbZxIMMYYY0xtnEgwxhhjTG2cSDDGGGNMbTz9kzHGalFYWAgTExPk5eUhLS1NKHdzc4O5ublC/er1AMDOzg7Ozs4NHmtjSUlJgUQigYeHh0r1//zzT2RlZcHa2hr29vaQSCSi5fI+Zi8fviLxEjtx4gT8/f1hamoKPT09TYfzUnr48CE+/PBD6OjooFmzmvPqb7/9FhKJ5Ln/hg8f3oiRK7p48SLKyso0GsOrhoiwdOlSjBs3DgBw5coVhIWFYfLkyQgICMCIESNQUVGhsJ683qRJkzB69GiEhYXhyJEjjR1+g7h16xZGjhwJT09PzJ07t9b6MpkMXl5e6Nq1K2bPng0fHx84OTnhm2++EdUbOHAg1q1b11BhswbEicRLrG/fvpDJZPD29tZ0KI3u9OnTuHDhwgu1sWfPHnTo0AFFRUVo0aJFrfVbtmwJPz8/pf8kEglGjBjxQvG8iL1796JLly6YOXOmxmJQRXFxMWQyGW7duqXpUFTyzjvvIDo6Gt9//z0AwM/PDzKZDNOmTQMAnD17Fh9//LHCevJ677zzjsI6L7MlS5age/fuKl9ZiY2NxcCBAxEaGors7GwkJCQgMzMTXbt2xeLFi0V1o6Oj8fnnnyuUs6aPEwn2UhoyZAjee++9F2ojKSkJMTExWL9+PXR0dGqt37t3b8hkMoV/4eHhaNGiBUJCQl4onhfRpUsXBAYGavyqSG2uX7+OgIAAbN68WdOh1Gr79u3YtGkTNm3aBEtLS4Xl7u7uaN68OVauXIkDBw5oIMLGl56ejuTkZPznP/+pte6DBw8wefJkBAcHY+bMmcKtDF1dXYSHh8PX11dU39HREWvXrsWSJUsgk8kaInzWQDiRYP9YS5cuVfl3Lvr06VPjN8o1a9YgNDQU+vr69Rlendjb2+OXX35p8onEy6KqqgqLFi1Cv3790L17d6V1nJycsHnzZhARJkyYgMzMzEaOsvFt3boVrVu3Vqnu9u3bkZeXh4kTJyos69SpE/bs2aNQPnToUHh4eCi9ysOaLh5s+ZIpLy/H5cuXUVFRgY4dO8LIyEihTn5+Pi5fviz83adPHxARrly5goKCAhARfH19oa2tLdTJzMxEZmYmDA0N4erqqrAsPT0dwNNvEz179kRpaSmuXLmCyspKuLm5wcDAoMaYn9f27du3hcvcJiYm8PT0BAAUFBQgNTUVAKCtrS18e5HHUllZicLCQuGbi0QigZ+fX536si5cXV3h6uqqUH7nzh3ExsYqDKxrTL///juysrIAAMbGxvDy8qqxvLi4GFevXoWZmRmcnJygpaUltHPp0iXcu3cPAGBlZYWOHTsiPz8f6enp0NfXh5ubG6TSv797XLx4Effv3wcAODg4wMnJCQCQkZGBO3fuAAAsLCzQqVMnof3z588DeLrf5fvO1NRU5QF7jeXs2bPIyMjA1KlTn1tv9OjRmD17Nr766iuMHTsWcXFxzx1r8ywiwvXr11FQUAALCwu0a9dOtLyu++9ZVVVVuHr1KgoLC2FjYyPsm8Z08OBBAIC3tzcqKyuRlpaGkpISODs7w8LCosb1BgwYgM8//xwZGRlo06ZNY4XLXgSxRhcdHU3qdP2OHTvI3NycDA0NydvbmxwcHOjzzz+nfv36ka6urlDv9OnT5OfnRy1btiQAtHv3bmrfvj35+PiQlZUVAaC8vDwiIoqPjydPT0/S19cnT09PsrGxoebNm1NYWBhVVFQQEdFPP/1Efn5+ZGhoSLa2trRu3TqytbWlrl27kqmpKRkYGFBERIRCvKq0HRUVRX5+fiSVSsnX11dY99y5c+Tn50dGRkbUsmVLoVwei1QqJWNjY/Lz8yM/Pz/q27dvnfvzWba2tqSlpVXn9T744AMaOHCgWtsEQNHR0Wqt+6y1a9eSn58faWlpUffu3Wss//rrr8nFxYW8vLxIR0eH3NzcKCMjQ6g/f/588vX1JQAUHBxMkydPJnt7e/L09CQ9PT2ys7OjQ4cOCfXff/998vLyIgD00UcfCeUbNmyg3r17EwAaNWqUqH15fXt7e2Hfvf/++6LXc/PmTbp9+/YL94tcUFAQBQUF1WmdJUuWEACKjY1VujwiIoICAwOJiOjJkyfUq1cvAkDz5s0T1Vu+fLmoD+Q2b95MrVq1IjMzM/L29iZjY2NydHSknTt3CnXquv/kNmzYQFZWVmRhYUHe3t5kZGRErq6udOrUqTr1wfPcvHmTAFC/fv1qrOPg4EC6urr0888/k62tLXXs2JHatm1LUqmUhg0bRjk5OUrX27JlCwGgTZs2qRyPuudUVi92cs9rgDoH/ZEjR0gikZCvry8VFBQQEVFFRQXNmDGDTExMRImEXGBgIAGgYcOGUWFhIRERFRYWUr9+/ejBgweUlJREenp65OPjQ7m5ucJ6ERERBICmTp0qas/Dw4OaNWtGo0ePppKSEiIievz4MY0dO5YA0Pr164W6dW3b0NBQlEjIeXt7ixKJ2uqrS51EoqSkhMzMzOiXX35Ra5v1lUjImZiYiBKJZ8tNTExo8eLFQtnZs2cJAI0dO1ZU9/79+wSAtLW1RclhdnY2derUiXR1dSklJUUoT0pKUkgkiIiKi4sVEonn1ZdLTU0lqVRKurq6lJ2drfJrfx51Eonx48cTAEpKSlK6/NlEgogoKyuLLCwsSCKRiJIPZYnE2rVrCQBNmTKFysvLiejpsRQUFEQAaMeOHaL6ddl/q1evJgA0efJkevLkCRERFRQU0GuvvUZ6enqUlpZWp36oiSqJhJGREUmlUjI1NaXExESh/ODBg6StrU1ubm5CjM86ePCg0qTseTiR0ChOJDRBnYP+tddeIwAUHx8vKi8pKak1kajpZDhgwAACQKdPn1ZY5uHhQRKJhK5cuSIqA6Bwgs/NzSVtbW2ytramqqoqtdp+GROJyMhIsre3F66u1FVjJxKPHz8WlTs6OpKzs7OoTJ5IuLi4KLTzv//9T7haIVffiURRURG9+eab9M477yj9kFGHOonEoEGDCABdvXpV6fLqiQQR0dGjR0kqlZKZmZlwRaV6IlFaWkrGxsZkYmJCxcXFovXv3r1L2traZGNjQ5WVlUK5qvtP3raRkRE9fPhQVDcxMZEAUEhISB16oWaqJBJ6enoEgObPn6+wLDQ0lADQ9u3bFZadOnWKANC///1vlePhREKjdvJgy5cAESExMRESiURhqqe+vj5cXFyeu76y5USEuLg4SCQS+Pj4KCz38fEBESmMnjYzM4ONjY2ozNLSEq1bt8bdu3eRkZGhdtsvm7Vr12Lq1Kk13qduShwdHRVmppibmwtjIqqTj2t4lvzYO3v2bP0H+P81b94c27Ztw4YNG0RjaRqb/NkQz44JqU3//v0RFhaGgoICBAcHo7y8XKFOcnIyioqK4OrqqjC+ycrKCg4ODsjJycH169dFy1TZf/K2HR0dkZSUJJpZVFhYCAA4duyYyq/nRckfLtW1a1eFZT169AAAJCYmKiyTv5+U9R9rmniw5UugvLwc5eXl0NXVVTpNUdmAy2cpOyFXVFTgyZMn0NXVha6ursJyY2NjAMCjR49E5crqAk8/AACgpKRE7baVIaJa62jCyZMncf36dUyZMkXToahE2QPLtLS0auxfZfvt2X1cm6a631RlaGgIAHjy5Emd1vv444+RkJCAgwcPYv78+QqDCuXHvLwvq6vpvaHK/pOvk5OTg7CwMIX68uedVFZWNkry6+LigtzcXKWxy1+nsmPp8ePHAGo/r7GmgxOJl4COjg5sbGyQk5ODvLw8hZNTTk5OndvU1taGnZ0dMjMzkZubCysrK9Hy27dvAwDatm0rKv/rr79QUVEhGplORMjKyoJUKoW9vb1abevo6Cg9qeTm5iqNv/o3xfPnz8PS0hJ2dna1vfR6sWbNGgQFBSl9vsCrIDs7W6FMPr3x2RkA8sS2+r5Tdb9VVlbi9OnT8Pb2rvHDVRMcHBwAPJ09VBcSiQQ//PADvLy88OWXX6J3796i41/+ICf5rJbq7ty5A6lUqtYsC/n7ydnZWenVvoyMDBQUFDTaFbQBAwbg1KlTSh8+Jj8HKHuwlbzP5fuANX18a+MlIX/YUVRUlKg8LS0NN27cUKvNyZMnAwC2bNkiKs/NzcWBAwdga2uLQYMGiZZVVlZi9+7dorL9+/ejoKAAr7/+unA5s65t29vb49atW6JvgBcvXsSff/6pNPYWLVqIvrWNGDECcXFxKr3uF3X79m3ExsZi+vTpjbI9TUhMTBSmHsrJjz3546IBCInbtWvXRHX379+vtF35E0Tl++6PP/5AQECAcOkdeDrtsaYP2sYin4aszhM4zczMEBMTAx0dHZw5c0a0rE2bNvD398e1a9eQkJAgWrZ3717k5+djxIgRaNmyZZ236+TkhL59+yIlJUXhnFBWVobhw4dj586dovLExMQGu1U1ZcoUNG/eHFu2bEFlZaVQXlFRga1bt0JXV1d0LMn98ccfAP7eB6zp4ysSL4mwsDDExcVh/vz5KCgoQM+ePXHnzh1ERkaibdu2yMjIgEwmg42NDWxsbJCcnIz8/HwAwKlTp6CjowN/f39RmwsXLsSFCxfwySef4N69e/Dz80NOTg5WrlwJQ0ND7Nq1S+EhS5aWlti4cSN+//13uLu74+rVq/j0009hb28venZ+XdsODQ3FnDlzEBoaipAu6cbSAAAgAElEQVSQEOTm5mLXrl3o0KEDsrOzIZPJYG9vL8wr79+/P7Zv345t27YhKysLDx48wMCBA+vUp/fu3cOlS5cAPL2c+uy4DRcXF4WxIHJff/013NzcFJ7Mpyny5w1UVFSgqKgIMpkMLi4uKC8vR0ZGhqjcw8MDEokEKSkpKCoqQkVFBWQyGZydnUVXc3x8fDBq1ChMmTIF1tbWOH78ONasWYMBAwZg1qxZQj1TU1MEBgZi3759CA8Ph4+PD1JSUoTEIi8vDzKZDO7u7sKzD5ydnbFv3z707dsXO3bsQLdu3YSHHF26dAldunSBtrY2bt26VeM+aGiDBw+GlpYWkpKSRA9Ukv8YV3p6OvLz8yGTyZT+GJePjw9WrVqFd999V6HtqKgo9O/fH8OGDcNHH30EFxcXpKamYtmyZfD09MSGDRsAPL06Udf9J287ICAA8+bNQ4cOHZCXl4dvvvkGBgYGWLhwoSiWESNGwNTUFFevXlWpX9LT05GZmSkk+Pfv3xfeM927dxe9p62trbFt2zYEBwdj0KBBmDJlCqqqqvDtt9/i1q1b+P777+Ho6KiwjaSkJBgbGzfoc2FY/ZLQy34z8yW0c+dOBAcH1/k+cllZGTZs2IATJ06gvLwcbm5umDlzJlatWiU86Gfo0KEYOnSo0m/Lyi53EhH27t2LPXv2ICsrCwYGBujRowemTJmi8KuGXbp0QX5+Pm7cuIE1a9YgPj4eFRUV6NatG9577z2Fb1F1aRt4+iS8/fv348GDB/D29sbcuXPx4YcfCgPPQkJC8M477wB4+o12xYoVSE5OhpmZGWbOnKl0UNfzJCQkYMGCBUqX/d///R+GDh2qtL9GjRqFsWPHYsyYMXXaXnUSiQTR0dEv3M7XX3+Nn3/+WVT2f//3fygsLERkZKSo/IsvvoCWlhZmz54tKp82bRqCg4Px4MEDmJqaYvz48Vi2bBlWr16Nq1evQl9fH4MHD8akSZMUHrhUUlKCNWvW4Ndff0VlZSWGDBmCt956S9R/K1euRLdu3QA8/ca5YsUK3L59G23btsWCBQuEhKG4uBjTpk2DoaEhvv7663oZcDl69GgAQExMTJ3WCwkJwYkTJ5CRkSE8cC0uLk7htyCCg4NrfOrp+++/j+bNmyM8PFxUXlJSgqioKJw8eRIFBQUwNzfHwIEDMX78eOF20Y4dO+q8/wCgtLQU27Ztw/Hjx3H//n1YW1sjICAAISEhorEvxcXFMDU1xdixY/HDDz+o1Cfr169HdHS00mXbt2+Hra2tQvnNmzcRGRmJtLQ0SKVSuLu7Y9KkSQq3TYGnD9NzcnLCtGnTsHLlSpViAtQ/p7J6EcPzZTTgZZ2q5OHhQba2tpoO45WBep7+WR/k0z/Hjx+v6VDqjTrTP4mePhvC3Nxc9PyGV0lkZCQBoLi4OE2HIpg+fTq1adOGioqK6rTey3pOfUXs5FsbjDGmhK2tLQ4dOoThw4fD3NwcM2bM0HRI9SY1NRXz58/HnDlz0KdPH02HAwBYtGgRDh8+jMOHDzepgbesdpxIsFrJf9/i4cOHePz4MWQymei3FZoSVZ5N4ejoqPTe7D/dpUuXhNH0ubm5wj15U1NTDUemOd7e3khJScGPP/6o6VDqlb6+PhYsWIB58+ZpOhSBkZERzp8/LwzYZi8PHiOhAS/b/bzo6GisX79eVPbWW28JMzOakuoDSpUJDQ1FaGhog8dSm/oaI1FfFixYoDCT4IsvvlB4CNrLRt0xEuzl8bKdU18xMXxFgtUqODhYGMjV1L3sT8vUpOXLl2s6BMbYS4ifI8EYY4wxtXEiwRhjjDG1cSLBGGOMMbVxIsEYY4wxtfFgSw2q/tx79s9TfZYEq3/y3wzh99uri99HmsXTPzVAPlWJMcZY/eGPM43g6Z+axAf9P1tTe47Eq4qfI/Hq4y9nmsVjJBhjjDGmNk4kGGOMMaY2TiQYY4wxpjZOJBhjjDGmNk4kGGOMMaY2TiQYY4wxpjZOJBhjjDGmNn6OxD/AZ599hidPnojKpFIpTExMYG9vj169esHKykpD0THWtBEREhIS0KtXL1y5ckX0hMxhw4aha9euCutUrwcAvXr1wsCBAxs83sbw6NEjbNy4EWZmZpgwYUKt9fPy8nDkyBFkZWXB2toavr6+aNu2rajO2bNn4evr21AhswbEVyT+Ie7evYvw8HD89NNPAIDy8nJcu3YNS5YsgY2NDYYPH46rV69qOErGmpaCggIMHDgQmzdvFpXHx8cjPDwcI0eORH5+fo3rnzlz5pV7NPdPP/2EDh06YM6cOYiKiqq1/tKlS+Hs7Iy9e/fiwYMH2LVrFzp06ICQkBBRvc8++wxjxoxBSUlJQ4XOGgqxRhcdHU2N3fUXLlwgABQYGKiwTCaTka2tLRkYGNDBgwcbNS5NW7ZsGW3evFkj2wZA0dHRGtm2Mtu3b6fFixdTZWXlK7X9oKAgCgoKqvN6ZWVl5OPjQ2+88QZVVFSIlkVERBAAAkBDhgyhqqoqpW0sX76cRo0apVbcTdGAAQPI29ubdu7cSQCoX79+z60fHh5OhoaGlJSUJCpfvHgxtWzZUlRWVlZGAQEB9Prrr9fYnzXRxDmVCXbyFQkGPz8/nDhxAhKJBGPGjMGdO3c0HVKj+eyzz7BlyxZNh9Ek7NixA+Hh4aiqqvpHbr+6zz77DJcuXcLq1auhpaWlsDwgIADt2rXDwYMH8d///lcDETa+0aNH49y5c/D09Ky1bkZGBpYuXYqZM2cq3P6ZOnUqPvjgA1GZrq4u1qxZg0OHDmHTpk31GjdrWJxIMABA+/btMW3aNBQXF/9jToqM1eTRo0dYvXo1Ro4cidatWyutY2xsjF27dsHAwACffPIJTp8+3chRNr63335baVKlzNatW1FeXo433nhDYZmNjY1CIgEAbm5u6Nu3L/773//ybxG9RHiwJROMGDECn3/+Ofbs2YN169bh2rVrwpgKAPj4449x7do1nDx5EgUFBSAifPDBBzAwMAAA/PXXXzh8+DAyMzNhaGiIHj16oHv37sL68fHxOHLkCICnJ+H3338fp0+fxq+//orKykp0794dfn5+SmOrre1Tp07hxIkTAAB7e3tMmjQJAPD777/jhx9+AAAYGBgIJy95LE+ePMGdO3cQFhYG4Okg1EWLFtVHdzaYsrIyHD58GNevX4dEIkGnTp0wYMAAaGtrAwDu3LkjusqyaNEiSKVSVFRUYOnSpUL59OnTYWlpiezsbERGRuLGjRsAgCVLlkAqffodY9KkSWjWrBkiIyOF9WbOnIm8vDwcO3YMRUVFaN++PYYNGwZdXd0G2b69vX299p8qjh49isLCQvTu3fu59Tp37oz169dj4sSJGDt2LFJSUmBhYaHSNq5duwaZTIaCggJYWFigb9++cHZ2FpYfOnQIiYmJAIDWrVtjypQpOHbsGJKTk2FmZoYBAwbAwcFBaduXL19GXFwcCgsLYWNjgwEDBtSYEDWUuLg4AECnTp2QlpaG48ePo6SkBG3btsWQIUNgaGiodL3XXnsNx44dw2+//QYfH5/GDJmpia9IMIGrqysAIDs7G4WFhUL5Tz/9hPDwcCxatAgzZ85EZmYmfv31V4SHhwsDo1atWgUHBwesXbsW+fn5OHfuHPz8/BAQEIC//vpLtJ1vv/0Wn3/+OcaOHYulS5fi7t27OHXqFPz9/TFs2DCUlpaK6tel7ZpuVURGRmLlypX10k+adOLECTg5OWHWrFm4ffs2bt68icmTJ6N9+/ZISkoS1a3pVsGRI0cQHh6u0He1OXToEMLDwxEWFoZx48bh999/x9WrV/HWW2+hc+fOyMjIaNDtX79+HREREcjMzKzTeuqQf4C3a9eu1roTJkzA22+/jezsbLz55pu13pp5/PgxJkyYAFdXVxw4cAAFBQWIiYlB+/btMWPGDFRWVorq//e//8XGjRsxYcIEREVFITc3FytWrEDHjh2FD2u5srIyhISEwN3dHceOHUNBQQG2bNkCZ2dnRERE1LEXXsz169ehp6eHBQsW4PXXX8e1a9eQnp6O6dOnw9nZucYrOPI+j4+Pb8xw2YvQ9CiNf6KmNthSrry8XBhAdvv2baE8MDCQANCsWbOEsqqqKvr000/p0aNHtGPHDgJA48ePFw2UO336NEkkEurVq5do8JSHhwcBoGXLlom2v2LFCoXt1LVtQ0ND8vX1VXht3t7eCoO7nle/MaCOgy1v3rxJhoaG1LFjR3rw4IFQnpubS7a2tmRmZkY5OTlC+dChQwkAlZeXi9qZO3cuAaBLly6JymuqLzdr1ixhcOGzdRISEkgikVCXLl1E+6K+tz948GACQKGhoUqX10SdwZZBQUEEgFJSUpQuj4iIEL2XysrKyMvLiwDQp59+KpQrG2z5zjvvEADasGGDqHzZsmUEgBYuXCgqNzExIalUSnv27BHKcnJySFtbW2Gw49tvv00AaP369UrL9+/fr8Krr93NmzdrHWypr69PAKhNmzZUUFAglGdmZpKZmRmZmppSfn6+wnpHjx4lAPTee++pHA8PttQoHmzJ/lZcXCz8v0WLFgrLZ8yYIfxfIpHg448/hoGBAVasWAEA+OSTT4RL0gDQu3dv9OvXD/Hx8Th16pSoLYlEgjlz5ojKZs6cCUNDQ0RGRuLx48cAoFbbr6qvv/4ajx49wqxZs2BiYiKUW1paYtq0aSgoKMCGDRsaPI5Zs2ahWbO/74r26NEDAQEBSElJwZkzZxpsu6tWrcKKFSsQHh7eYNuQk1+R09PTU6m+rq4ufv75Z5iamiIsLAwymUxpvfz8fGzevBm2traYMmWKaNn7778PIyMjrFq1SuGqnKWlJf71r38Jf1tbW8PJyQnXrl0Ttb1lyxZYWVlh6tSpovXlt/RWrVql0uupD/L36/Tp02FqaiqUy2/T3L9/H9u3b1dYT19fHwBEV0VZ08aJBBOkpaUBePpGNzY2Vlhua2urUFZZWYnU1FQ0a9ZM6WXgDh06AACSk5NF5ZaWlsIJQ05PTw/29vYoLf1/7d13XBTX2gfw39KWBV0DiAERFQtFioglVuyKFFtERECNGlQSu29iCS7YktwEUa9RBKIxaGLAJNYLKjYsWBJQARFRREVELFGkqcB5//DuXNddcHdli/h8Px/+4MyZmWdnd2eePXPOmQpcv35d6W03VOnp6QAABwcHqWXqPBay7svb2dkBADIyMlS2X3t7e3zxxRdq6TPB/tvRj8fjyb2OjY0Ntm7dipqaGowfP17mrZuMjAxUV1fD1tZWIjEGXn7+W7dujYqKCokEAYDM/g0mJiYoLS2V2rZAIOBuQYn/xPM9iG/ZqIOZmRmA/302XiW+jSo+57yKUSfLdw4lEoTzxx9/AHjZ6VIWRU6qb1q3tvvIypzA5albVVUl9/a0lTwnWHUcC1nvnSLv27vwXohbfF5vGXgTHx8ffPnll7h79y4CAgKkjpW8F8nXj6O8IyUASLQWieno6EAkEmH+/PlSfTBUxdnZGcDLye9eV9fnpbKyEgAkWt2IdqNEggB42TEqOjoaQqEQCxculHs9XV1duLi4oKqqCrm5uVLLxbNlvj7u/P79+xK3UoCXJ5Bbt27ByMgIbdu2VWrbxsbGMptECwoKao3/1ZP7xo0b1fqrTRFubm4AIHMG0tqOBSDdRFxbZ0XxxUp8PBITEyVG7Yi93qkSADfiQnzxUOX+1UE8ekLRDqHAy5kc+/Xrh+TkZGzcuFFiWceOHaGrq4ucnBypJKOyshL5+fkQCAQyf8W/iYuLC3R1dcHn8yVaI8R/Xbp0QatWrRRKSt7GiBEjALwcQfI68efV1dVVatn9+/cBQGIEC9FulEgQpKSkYODAgQCAnTt3yryFUZcvv/wSALB8+XKJk+PJkydx5MgR9OzZE+7u7hLr8Hg8rF69WqJs3bp1KC8vR3BwMAwMDJTatq2tLXJzc/Hw4UOuLD4+Hv/884/M2C0tLbm6VVVV+Oqrr1BUVKTQ61eXmTNnwsjICGvXrpW4OBcXFyMqKgqmpqYS98ZtbW0BAKmpqVzZzZs3uSG4r7O0tAQA7nhERUXJTKrWr18v0apw5swZHD16FJ06dZJ4VkJ971+dozZ69OgBADIT2DfR1dXFjh07YGlpKZXAmpmZYcqUKSgsLJSadCkiIgKlpaWYO3eu1G0/eZiZmSE4OBhZWVnYs2ePxLL8/HxMnjxZavrpNWvWqGw0x4QJE9CuXTts3LhR4jtVUFDA9RMJCAiQWk98zHv27KmSuIgKaKqb5/tM3T2MV65cyaZNm8YAMDs7OyYSiVhoaCibMWMGc3V1ZTwej3l7e7Ps7GyJ9W7fvs1EIhGzs7NjANiSJUuYSCSSuY+IiAjG5/NZ165d2bx585i/vz/j8/msb9++7N69exJ1O3bsyKysrNisWbPYkCFD2IIFC7ge+15eXqy8vFzpbSclJTE+n8/s7OzY/PnzWWBgIJs0aRLr3LkzEwgETCQSsUOHDnH1v/76awaATZ48mXl6ejJbW1tWWVn5FkdbflBiiuzDhw8zCwsL1qpVKxYSEsKCg4O5/8+ePStRt7CwkFlYWLAPPviAhYSEsJCQEDZgwAA2efJkBoDNmDGDxcTEcPWPHz/O9PX12aBBg1hwcDBr1KgRy8rK4paLR22sWLGCde7cmc2ZM4dNmDCBCQQC1r59e3b9+nWV7l+dozZKS0uZUChk/v7+EuVZWVlMJBKxwYMHc9+lAwcOyNxGSkoK09PTkxq1UVlZyQIDAxmPx2M+Pj5swYIFbMiQIUxHR4eFhIRw03GfOHGCiUQixufzmZWVFROJROz69evsxo0bTCQSMSsrK8bn85lIJGKnTp1ijDH27NkzNnHiRKarq8uGDx/OvvjiC/bJJ58wExMTNnPmTKmpp01MTFinTp3kPi4HDhxgIpGIzZw5kwFgNjY2TCQSMZFIxB4+fChVPycnh9nZ2TELCws2bdo09umnnzJzc3Nma2srNWpHbPDgwax169YKTZVOozY0Kp4mpHpPWFhYQCQScf/r6enB1tYWgwYNQq9evep8+ue4cePeuP158+YhMDAQSUlJKCgogLW1NWbNmoXu3bvXus7atWtx5swZnD59Gubm5liwYAH69ev3VtseOnQoLl++jOTkZDx+/BjDhg3DwIEDER0djcLCQqn6CxcuRLdu3ZCWloZevXphzJgx3MRK2mjAgAHIy8uTmJAqNjYWgwcP5lpxxCwtLXHlyhXs3bsXBQUFsLGxQUREBFJSUmBtbS21bXd3d1y4cAHJycnQ0dHBxYsX0aZNG6l6fn5+8Pf3R1JSEp48eYK4uDiJCalUtf+IiAj0799frs/j2zI2NsasWbPw/fffo6CgQKqzY8+ePd/4i7lPnz747bffpPqE8Pl8xMXFYfHixdyEVKNHj8aGDRtkNufXdqvx9VEfAGBgYICffvoJixYtwpEjR/DPP//Azs4OS5Yskdr2nTt38M8//2DSpEl1vg5ZTE1NJc4ntbG1tcWlS5dw6NAhZGVlQUdHB9u2bcPAgQNl3mLJzMzE4cOHsWHDBqnOqER78RijLrLqFh8fDz8/v/e2d7KrqysePHhQa7+F9wWPx8Nvv/2GsWPHajoUucyZMwdr165Fbm6u1COgtZmvry8AICEhQaH1Kisr0adPH7Rs2RLx8fFq61ugLosWLcK6detw8eJFrXg/nz17hmHDhoHP5+M///mPQh2u3/dzqoYlUMpHCCEyGBoacq0uwcHBmg6nXiUlJSEyMhKxsbFakUQAwOjRo2FmZoadO3e+1Qgxon50a4Oojfj5FkVFRSgvL0dYWBjc3d0xYMAATYdG6iB+Foa44+O6devg6urKPc+kITMzM8OhQ4ca3HTNffr0wbFjx+q89ahuixYteuOzTYh2okSCqI34vrL4AVnk3eLh4QEPDw9Nh6F2PB5PYjRKQyB+8J02oSTi3UWJBCGkTs2bN6fkjxBSK+ojQQghhBClUSJBCCGEEKVRIkEIIYQQpVEiQQghhBClUWdLDRJPlEPeX5GRkQpPlEQUIx62St+3hut9n9xO02hmSw1ITU2VemAVIdro3r17EAgEEAqFmg6FkDeipFwjEiiRIITUytHREb6+vjT8kxBSG5oimxBCCCHKo0SCEEIIIUqjRIIQQgghSqNEghBCCCFKo0SCEEIIIUqjRIIQQgghSqNEghBCCCFKo0SCEEIIIUqjRIIQQgghSqNEghBCCCFKo0SCEEIIIUqjRIIQQgghSqNEghBCCCFKo0SCEEIIIUqjRIIQQgghSqNEghBCCCFKo0SCEEIIIUqjRIIQQgghSqNEghBCCCFKo0SCEEIIIUqjRIIQQgghSqNEghBCCCFKo0SCEEIIIUqjRIIQQgghSqNEghBCCCFKo0SCEEIIIUqjRIIQQgghSqNEghBCCCFKo0SCEEIIIUqjRIIQQgghStPTdACEEO2Qnp6Ohw8fSpSVlZUhLy8PycnJEuUODg6wsrJSZ3iEEC3FY4wxTQdBCNE8kUiEZcuWyVU3IyMDTk5OKo6IEPIOSKBbG4QQAIC/v79c9ezs7CiJIIRwKJEghAAA7O3t4eTkBB6PV2sdfX19TJo0SX1BEUK0HiUShBDOhAkToKurW+vyqqoq+Pn5qTEiQoi2o0SCEMIZN24cqqurZS7j8Xjo2rUrbGxs1BwVIUSbUSJBCOFYW1ujR48e0NGRPjXo6upiwoQJGoiKEKLNKJEghEgICgqS2U+CMYYxY8ZoICJCiDajRIIQImHs2LFSiYSOjg769euHDz/8UENREUK0FSUShBAJpqamGDhwIPT0JOerCwoK0lBEhBBtRokEIURKYGAgampquP91dXUxcuRIDUZECNFWlEgQQqSMGjUKBgYGAAA9PT14e3ujSZMmGo6KEKKNKJEghEgxNjaGj48PdHV1UV1djYCAAE2HRAjRUpRIEEJkCggIQHV1NQQCATw9PTUdDiFES9HTPwkhMnl4eEAoFGLUqFEQCASaDocQoqUokSCEyMTn8+Hr60tTYhNC6kSPEddiBw4cwJMnTzQdBnmP5efnw9raus7nbxCiao6OjnB0dNR0GES2BGqR0GJz585Fdna2psMghBCNEolElEhoMUoktJxIJEJYWJimwyAKCAsLQ3x8PC5fvqzpUBq0rKwsODk5ITMzky4yDViHDh00HQJ5Axq1QQghhBClUSJBCCGEEKVRIkEIIYQQpVEiQQghhBClUSJBCCGEEKVRIkEIIYQQpVEiQQghhBCl0TwS74H169fjwYMHAIBBgwahd+/eGo5IfW7evIm4uDi4u7vD3d291no1NTU4ffo00tLSUFFRgRYtWsDd3R3W1tZqjJaoW2FhIZ49ewYbGxts2LABxcXFAAChUIi5c+eCx+NJrfNqPbHFixdzj11/1508eRLJycmYPHkyWrZsWWfdmpoapKamIi0tDS9evECHDh0wcOBA6Ovrc3WuXLkCc3NzmJmZqTp0oiHUIvGeuHXrFsLDw3Hy5ElNh6IWlZWVWLZsGRwcHBAaGoqUlJRa627atAnW1taYOHEicnNzUVxcjIiICLRt2xbz5s2DpmeRP3nyJLZs2aLRGBqiX3/9FZ07d0Z5eblE+apVqzB//nx8++23da6/YsUKqYTiXVZYWIjAwEC4u7sjPDwct27dqrP+lStX0KVLF0ydOhV5eXm4desWpk6dChsbGxw7doyr9+DBAzg7O+PAgQMqfgVEYxjRWg4ODkwkEtXLtk6cOMEAsK+//rpetqfN7ty5w1q3bs38/PzYihUrGAC2fPnyWuv7+fmxbt26sWfPnnFlNTU1bMSIEQwAi4+PV2j/IpGIOTg4KB3/62xtbRkAdvXq1Xrbpips376diUQiVl1drZb9ZWZmMgAsMzNT4XUTEhIYn89nZ8+elVpmbGzMADA9PT124sSJWrfB5/NZRkaGwvvWRgcOHGBmZmYsLCyM+fr6MgB1vvaioiJmaWnJxo8fz168eMGVFxcXs6ZNm7J///vfEvWTkpKYoaEhO378uMKx1ed5kKhEPLVIkAbnxYsX2LBhA3bs2IEPP/zwjfXHjBmD1atXSzRN83g8BAQEAECdrRnqEBMTgx9//BHt27fXaBxv8ssvvyA8PBw1NTWaDqVODx8+xLRp0zB16lR069ZNZp1p06ahqqoK48aN424LNmT6+vo4deoURCKRXLdolixZgpKSEqxfvx56ev+7Q25ubo5169ZJHdehQ4di+PDhmDRpEp49e1bv8RPNoj4SpMFp1aoVWrVqJXf9MWPGyCwvKysDAFhaWtZLXMp6U/8OopioqCg8evQIs2bNqrXO9OnTUVZWhm3btiEwMBCJiYky+0s0FP3795e7bllZGX799Vf0798fJiYmUsv9/f1lrjdr1iz07t0bO3bswMSJE5WOlWgfSiQaoNLSUuzduxf5+fkwNzeHj49PnfUZYzh+/DjXYapt27bw8PBAo0aNuDpRUVEoKioCAPTu3Ru9evXCnj17kJ+fjxYtWmDEiBES9cXOnTuHv//+G48fP4aNjQ08PT0hFAqVikGdKioqsHHjRjRt2hRTpkzRSAyVlZX45ptvuP8DAwPRrl07meWNGzfG7t27UVJSAgcHB3h6enIXvsLCQkRHR3P1Z82ahfv37yM5ORklJSWwtbWFt7c3+Hw+gJf9aTZv3szVX7p0KXR0dFBVVYUVK1Zw5SEhIWjWrBm3/atXrwIAli1bBh2dl42d8nTYU7eEhASYm5vD1ta2znqbNm3ChQsXcODAAXz99ddYvHixXNuvrKzEgQMHkJOTAx6PB0dHRwwePJjrgKjo+/eq8vJyJCUlITc3F3p6eujYsSMGDBjAHW91OH/+PMrLy+Hk5ITy8nLs378f165dg1AohLu7O5ydnWWu99FHH8HAwADx8fGUSDQwdGujgUlLS4OtrS1CQiwecwcAABuCSURBVEKQm5uLS5cuwcPDA4cPH5ZZPz8/H126dIGPjw+ysrJQWFiIRYsWwcbGRmqdoqIihIeHY8eOHfDy8kJKSgpu3ryJGTNmoEOHDnj48CFX98WLFxg+fDiGDh2KtLQ0PHnyBDExMWjevDmWL1+udAyqJhKJMGXKFDg7O8Pe3h5///23XLdHVOnMmTMIDw/HtWvXZJb//PPP8PX1RU5ODv766y/4+PjA29tbajtJSUkIDw9HWFgY/P39ce3aNWRnZyMoKAjOzs7Iy8uTqF/brYqDBw8iPDxc4Y6GOTk5+O6773D79m2F1qtP5eXluHTpkly3iYyMjLBz5040btwYS5cuxfHjx9+4zpEjR2BjY4PZs2fj5s2byM3NxZQpU2Bra4vz589L1FX0/Tt06BBsbGwwf/583LlzB1euXMGYMWPg5ub2xo6R9SknJwcAUFBQAAcHB/z00094+PAhfv/9d7i4uGDixImoqqqSWk9PTw+tW7fG6dOn1RYrURNN99IgtVO0k1FFRQWztrZmhoaGLCcnhysvLS1lHTt2lOps+eLFC9ahQwemr6/PLl68yJWXl5ezjh07MqFQyAoLC7ny9PR0BoA1adKE3blzhyvfuXOnVIfGPXv2MABs165dEjF+/vnnrG/fvkrHoKiYmJg3drZ8VXh4OJsxYwZzdXVlrq6u7Oeff1Z4n/Xd2TIyMpIBYImJiTLLe/bsyZ4/f86Vf/755zI7y82ePZsBYMOGDZPoIJeamsp4PB5zdXVlNTU1XLmXlxcDIFGXMcbmz5/PAEh1NKytvpiHhwcDwCZNmqTYAaiFMp0txeuMHDmy1jrGxsYsPT2d+z8+Pp4BYJaWluzevXtc+eudLXNzc5mxsTFzcHBgjx8/5srv3bvHrKysmKmpKbt79y5Xrsj7l5uby4yMjFj79u1ZSUmJRLlAIGDdunWT+xi8SUBAQJ2dLb/99lsGgAFga9askVj2xRdfMAAsNDRU5rq9evViANjDhw/ljoc6W2o96mzZkOzZswe3b9/Gxx9/LNFsa2xsjJCQEKn6e/fuxeXLlzFq1Ci4uLhw5QKBAJ9//jlKSkokmrjFPDw80Lx5c+7/nj17Ang5HEysuroawMtmUPbK8MlZs2Zh0qRJbx2DqixduhQbNmxAWloahgwZggkTJuD7779X2/6VERQUJDFuX9b78arZs2dLdJDr3r07+vfvjwsXLqh0eHBkZCS+/fZbhIeHq2wfb/LkyRMAgKGhodzr+Pr6Yvbs2bh79y4CAwNr7Uy6fv16lJWVYfbs2WjSpAlX3qxZM8yYMQOPHj3Cpk2bpNaT5/3797//jfLycsydOxeNGzfmytu1a4fRo0fj3LlzOHXqlNyv6W2Ib6OYmJjgs88+k1i2ZMkS6OnpYcOGDTKHTQsEAgD/ex9Iw0B9JBqQzMxMAJB577dt27ZSZRcuXAAAFBcXIywsTGLZjRs3ALxsfn3d65M0iTtclZaWcmXDhw9HSEgIvv32W2zbtg1eXl7o0aMHPD09JRIJZWNQNR6PhxUrVmDDhg0ICwvD3Llzoaurq/Y45CHP+/EqWR1R7ezscOTIEWRkZKBPnz71HyQAe3t72Nvbq2Tb8hJf3BTtOPndd9/h3LlzOHToEFauXInQ0FCpOunp6QAABwcHqWXi152Wlia1TJ73T/w9OXfuHO7duydRX3yr6MyZM+jVq5fcr0lZ4oml2rRpI5GQAi8n8mrRogXy8/NRVFQk1VFZVnJB3n3UItEAyTpJ1nURfP1kAAA2NjYQiUQYOXKkQtsS09HRwQ8//IA7d+5g+fLlqKysxNy5c2FpaYlVq1a9dQzqoK+vj9atW6OsrEyt96AVpWiCI+sXtSIXWFn3v98V4paCiooKhdbT19dHfHw8zM3NER4eLjHhkpg8F0lFv5uvk/U96d+/P0QiEVxdXeXeztsQd6Z88eKFzOV1fZYqKysBQKLFhrz7qEWiARF/wWU1aefn50uVubm5AXj5a/T11gDgZVNtmzZtlIrlzJkzePDgAby9vREUFISgoCBUVFRg1KhRWLJkCYYPHw4nJyeVxiCP58+f4+uvv8bChQu5UQtiNTU1KCgoAI/Ha1Anvry8PHTo0EGiTDzi4tUe98bGxgBeNkO/Or1xbZ0lxRdE8YUkMTERT548wbhx4+ov+LfUpk0b8Hg8pWakbNGiBX755RcMHToU/v7+3O07MTc3N5w4cQLZ2dlSw3Wzs7MBAJ06dVIqbjc3N6SkpGDo0KFSw5VLS0sRERGh0u/J67G0aNECeXl5KC8vh5GREbespKQEd+7cgYWFhcxOyvfv38cHH3wAU1NTtcRK1INaJBqQ4cOHo2XLlvjjjz+4CwPw8lfADz/8IFXfy8sLzs7O2L59u9Qv7v3792P27NlKz49/5swZ/N///R/3CwR4eX9U3IwrPgmrMgZ5PH/+HGFhYRLDI8VWrVqFx48fw9vbu0Gd+NavXy/RqnDmzBkcPXoUnTp1kmgaF98iS01N5cpu3ryJgwcPytyuuBlbPHonKipK4raUNozaMDIygouLC3Jzc5Vaf9CgQRCJRCgqKpJqmZk5cyaMjIywdu1aiT4AxcXFiIqKgqmpKaZNm6bUfmfOnAljY2NERkZKfKcAIDw8HNHR0bCysuLKLly4gLCwMKmRIvVBR0cHYWFhKC0tleo/tHLlSlRVVeGLL76QapGoqqpCfn4+1weENBzUItGA8Pl87N69G97e3vjoo48wevRoNGrUCEePHkXfvn3x119/ITk5GZWVlZg3bx6EQiH279+PMWPGwMnJCaNGjYKlpSVycnJw9OhR/PTTT1wHyG3btnEd8U6ePImwsDAsXboUKSkpSE5OBvCyJSQsLAzBwcFwcXFBRUUFnJyc4OnpCWNjY1y8eBEHDx7EnDlz0LFjRwAvm2rljUER0dHRKCws5O5JHzlyBFVVVRAKhZg3bx5Xz8DAAF5eXpg/fz7i4+PRpUsX6OjoIDU1FampqRg2bBi2bt36Vu+LssTzDYgvxtu2bcPly5cxb948rFixgns/tm3bhsLCQkyePBk//fQT934kJSWhvLxcav6DPn36oHv37ujTpw8ePXqEhIQEtGvXDjt37pQ4+YeEhCA2NhZBQUEYP348gJfv8dixY7F582Zs2LABbm5umDp1KgBg/Pjx2Lx5M4KCgtCmTRscOXIEZ8+e5bY3Z84cJCUl4fLlyxp9dsiYMWMQGhqKq1evSvQnEj+M6/nz54iKioKFhYXMh3GFhoYiNTUVSUlJEuVt27bF3r17ERAQgI4dO8LLywtVVVXYs2cP+Hw+EhMTYWFhAQAKv39t2rTBvn37MH78eDg4OHBzrJw6dQoFBQX4z3/+IxFnSkoKwsPDuRa/N3n+/Dl3y/HSpUsAgM2bNyM5ORkuLi4YPXq0RP0pU6bg1q1bWLZsGU6dOgVnZ2ekp6fj+PHj+PLLLzFnzhypfZw7dw7Pnz+vdQI48u6iRKKBcXV1RU5ODvbs2YMbN27AzMwM8+fPBwCZv+ytra1x9uxZnDx5EufPn8fz58+5C8Xrs9ZZWFhAJBJJbUNPT0+qfMCAAcjLy8OJEydw5coVPH78GGPHjkVUVJTUBEWKxKAoNze3Ok+mBgYG2LdvH4qLi5GamoqbN2+itLQUkydPxo8//iiz45y6de/eHd27d5e7vF27djLfJzE/Pz/4+/sjKSkJT548QVxcnMSEVGKWlpa4cuUK9u7di4KCAtjY2CAiIgIpKSkyn4rq7u6OCxcuIDk5GTo6Orh48aJEc3tERAT69++v8Vsd06dPR0REBNatW4f169dLLX/TxFM8Hg/btm3D+vXr0axZM4ll4s/9qxNSxcbGYvDgwVIJiaLvX79+/bhtX7lyBQYGBli8eDE8PDyk+k5kZmbCwMAAPXr0qPO1yDJ69GipxEGW8PBwTJo0CQcOHMCjR48wfvx4bNmypdYJyNauXYtWrVpxSSlpQDQ39JS8CY2ffjfV9zwS9UU8j0Rubq6mQ6kXb/PQrt9++63Wh3a964qKiliTJk1YcHCwpkPhJCUlMT6fz44eParwunQe1Hrx1CJBCHnvjB07FlVVVRgxYgQOHz4s1fn0XcUYw8cffww7OzusXr1a0+EAeHkr9JNPPsGuXbvQr18/TYdDVIASCfJOOHbsmMwhd6+TNfLjfSd+Foa4r8W6devg6uqKyZMnazgyzRo/fjz69u2L58+fazqUesPj8fDNN9+gS5cuCk26pUpNmzZFRkaGSjtNE82iRIKQ94SHhwc8PDw0HYZWeXWkQ0PRu3dvTYcgQdOTkBHVo0SCvBP69etHzaJKat68ObXUEEJUhuaRIIQQQojSKJEghBBCiNIokSCEEEKI0iiRIIQQQojSqLOlFnvx4gUSEhKQlZWl6VCIAi5fvoy7d+/C19dX06E0aCUlJQDATfdOGqbXH5tOtA+1SBBCCCFEadQiocX09fXh6+tLQ/feMWFhYYiPj0dCQoKmQ2nQsrKy4OTkhNWrV8PR0VHT4RAVaSizjjZk1CJBCCGEEKVRIkEIIYQQpVEiQQghhBClUSJBCCGEEKVRIkEIIYQQpVEiQQghhBClUSJBCCGEEKVRIkEAAN9//z14PJ7Un0AggJWVFby8vLBhwwaUlZVpOlRCVKqiogJz587FgQMHAABOTk7c90EoFCI3N1fmeq/WE/+VlpaqM3SVyc7OxuDBg8Hj8XDy5EmZdX755ReEhYWhqqpKzdERTaNEggAAFixYAMYYRowYAQBIT08HYwz3799HYmIiunXrhsWLF6N9+/Y4cuSIhqMlRDWePHmC3r17o7i4GAMHDgQAZGZmgjEGY2NjPH36FL6+vqisrJRaV1yPz+cjIyMDjDE0atRI3S+hXpWUlGD+/PkYOHAgbt++XWfdkSNH4uzZs/Dw8MCzZ8/UFCHRBpRIkDo1atQILi4uEIlEOH/+PHg8Hjw9PXHq1ClNh0bk9Ndff4HH4+Grr756L/eviAkTJqCyshI///wz9PSkJ/61srLCxYsXMXv2bA1Ep35btmzBgwcPkJGRgS5dutRZ18jICH/88QeuXLmCuXPnqilCog0okSBya9++PWJjY/Hs2TN8+umnqK6u1nRIhNSb5ORk7NmzB2FhYdDV1ZVZZ9OmTTA3N0d0dDR+/fVXNUeofsHBwdi6dSvMzMzkqi8QCLB48WJERUUhOztbxdERbUGJBFHIsGHD0K5dO2RnZ+PYsWOaDoeQehMdHQ19fX14eXnVWsfKygq//vordHR0EBwcjJycHDVGqH4CgUDhdUaNGgXGGKKjo1UQEdFGlEgQhfXq1QsAcOLECYny3bt3o1+/fhAKhRAIBHBycsI333wj0fnK1dWV64i2cOFCbN26FQ4ODhAIBGjfvj3Wrl0rtb9Tp07B09MTH374Ifh8Puzt7REaGirznq08MWirrKws+Pv7w8LCAgYGBrCyssKkSZNw/fp1rs6gQYO44/fqrYKFCxdy5WPGjOHKvb290bVrVwDAypUruTqDBg3ilovLAgMDERcXh44dO0IgEMDU1BQBAQEoKChQ2f61yaFDh9C+fXsYGRnVWW/gwIEIDw9HaWkpfH19UVFR8cZtV1dXY+3atejUqROMjIxgbGyMbt26ISYmBowxAMDjx48lOmru3bsXixYtQvPmzdGoUSN07doVycnJUtuuqqrC999/DxcXFwgEAjRu3Bi9e/fG77//rtyBeEuWlpZo1qwZ11mVvAcY0VoODg5MJBKpdZ8jRoxgAFh6enqtdb788ksGgAUHB3Nlq1atYgDYJ598wm7dusUePXrEoqOjmYGBARs5ciSrqanh6qanpzMArEOHDiw0NJQVFxeze/fusbFjxzIAbN++fVzd3NxcJhAI2IQJE1hBQQGrqKhgKSkpzMbGhvXt21ciLkViUCWRSMQcHBwUWufYsWNMIBCwbt26sb///ps9ffqUnTp1ijk6OjKhUMjS0tK4uufPn2cA2JIlSyS28fTpUwaAffzxxxLltdUX++effxgAZmFhwcaMGcOuXr3Knjx5wv744w9mamrKrK2t2b1791S2/9u3b7OWLVsye3t79vjx4zcfrP/KzMxkAFhmZqbc69Tm9u3bDAAbMmRIrXWMjY2570VNTQ3z9PRkANjUqVMl6vH5fJaRkcH9X1VVxby9vZmenh6LjIxkxcXF7M6dO2zZsmWMx+OxoKAgifUjIyMZANa1a1e2Y8cOVlJSwrKzs1mHDh1Yo0aNWFFRkcS2hw0bxnR1ddm6devYw4cPWUFBAfv8888ZABYZGfnWx0YsICCAAWAnTpx4Y91OnToxXV1d9uzZs7ferybOg0Qh8dQiQRTG/vsLSuz69esIDQ2Fvb09YmJiYG1tDRMTE3z66aeYMWMGdu3ahf3790ttRyAQYNmyZTA3N0ezZs2wdOlSAEBiYiJX5/Tp06ioqEBgYCCsrKxgaGiIPn36YMWKFfUSgzaoqanB5MmTUVVVhYSEBLi5uaFRo0bo2bMnfvnlFzx9+hRTp05VeRyMMcTFxaF9+/YQCoUYNWoUwsPDcfv2baxatUql+62pqUFNTY3K9vEmd+/eBQA0adJErvo8Hg9xcXFo1aoVYmNjsW3btlrrbtu2Dfv27cOUKVMwZ84cmJubo3nz5ggNDcXo0aMRFxeHvXv3Sq3n7u4OPz8/NG7cGPb29ggJCUFpaalES2BcXBwSExMxefJkzJw5E6amprCyssK6devQqVMnLFq0CPfv31fwaLy9Jk2aoLq6WiP7JupHiQRRmPik27x5cwDA3r17UV1dDS8vL6lOauIhdLt27ZLajrjJW8za2hoAUFhYyJX17t0bjRo1wvTp07F582buxDR+/HiJPhrKxqANLl26hLy8PDg7O6Nly5YSy1xcXNCiRQukpaXh1q1bKo2jS5cuMDQ0lCgbOnQoAMnkrr5ZW1vj9u3byMnJkftCXt/Ewzn19fXlXsfU1BQJCQkwMDDA9OnTceXKFZn1/vzzTwCQ2ffC09NTos6r5Pl+iD/Tw4cPl6jL4/HQv39/VFZWauQWg/g4ynPbh7z7KJEgChMP/XR3dwcA3Lt3DwAQEREhNSGP+AQnaxKf1y8aBgYGACDxy7RNmza4dOkSvLy8sHLlSjRr1gzt2rXDggULUFxczNVTNgZtII69WbNmMpd/+OGHAICioiKVxvHBBx9IlYljevVYN0TiBOrFixcKrde1a1esWbMGZWVltfaXqOv9reu9lef7Id62j4+P1Od+9erVADTzuRcfR2U6a5J3DyUSRCH79+9HXl4eHB0d0bdvXwCAhYUFAEAkEoExJvPv+PHjSu/TxsYG69atw/Xr13Hz5k1Mnz4dMTEx6Ny5M0pKStQSgyqJLya1XazFFwvxa9TRefm1ff78uUS9x48fy1yfx+PJFceDBw+kysQxvXoRVNX+NcnS0hLAywmpFDVjxgwEBAQgMzMTn332mdTyut7f199bRYnXO3r0aK2f+/DwcKW2/TaePHkCXV1dmJubq33fRP0okSByu3btGoKDg2FoaIjY2FjuguLj4wM9PT2ZF+qysjI0bdpU6aFga9aswbhx47j/W7ZsiQULFmDGjBkoKCjAxYsXVR6Dqrm4uKBNmzbIyMiQun1x6dIlFBQUwM3NjbvtIb7ovTqaAgDOnj0rc/viUQjiC//du3fB4/Fw9epViXp///03ysvLJcrEzeLDhg3jylS1f02ysrLCBx98IPWa5LVp0yY4Ojpiy5YtUrM6jho1CgBk9tERl4nrKGr06NEAIPNzf/bsWejo6NR6y0WV7ty5A1tbW64VhTRslEiQOpWXlyMjIwPLli1D165dwRhDYmIiunfvztVp06YNvvnmGxw7dgxz5szBjRs3UFlZiYsXL2L48OGwsbFBUFCQ0jH8/vvviImJwaNHj/Ds2TOcO3cOf/75J5o2bQpnZ2e1xKBKOjo6+PHHH6GnpwdfX1+kpaWhrKwMqampGD9+PBo3boyYmBiuvoWFBZydnbFv3z4cP34c5eXlOH36NLZu3Spz+23btoVQKMSZM2fw9OlTbN++HVZWVmjbtq1EPRMTE0ycOBG5ubl4+vQpdu3aBZFIBGtrayxZskRl+y8oKECrVq3g4OCgVItAfRAPSc3NzVXqvr6xsTF27twpc0rswMBAeHt7Y/PmzVizZg3u37+Pu3fvYvny5fjzzz8RFBQEHx8fpeIOCAjAiBEj8K9//QubNm3CvXv3UFZWhoMHD8LPzw+fffYZ7O3tufqxsbHg8XjYsmWLUvuTx927d1FcXIwhQ4aobB9Ey6hzjAhRjDqHPX333XcMgNQfn89nlpaWbNiwYeyHH35gpaWltW5j//79bODAgUwoFDJDQ0Nma2vLFi1aJDGkb+jQoVL7ePHiBROJRFLl58+fZ2VlZSwmJob169ePWVhYMENDQ9aqVSs2ZcoUlpubq1QMqqbM8E/GGMvIyGB+fn6sWbNmTE9Pj1lYWLAJEyawa9euSdXNzs5mQ4cOZSYmJkwoFDI/Pz92/fp1ieOXmJjI1d+7dy+zt7dnAoGAOTs7s+PHj3PLxMM/AwIC2O7du1mnTp2YoaEhMzExYf7+/uzWrVsq3f/NmzeZlZUVs7W11djwT8YYO3jwIAPA4uPjJcodHR2lPptPnz6VuY0dO3YwABLDPxl7OUwzMjKSdezYkRkaGjKBQMC6dOnCNm3aJDEsmc/nS+ynV69ejDHG+vbtK1FubGwsse21a9cyV1dXZmhoyIRCIevcuTOLioqSGvK8bNkyBoCdPXtWrmMiHtIr6y8gIEDmOj/88APj8XgsKytLrn28CQ3/1HrxPMZeG8tHtEaHDh0wduxYhIWFaToUooCwsDDEx8fj8uXLmg5FLo8fP4aJiQkCAgLqHMaobbKysuDk5ITMzEw4OjrWyza9vb1x48YNXLp0qdZpst9lPXr0wIMHD3D16lWV9F2pqKhA+/bt4e3tjaioqHrZJp0HtV4C3doghJD/iouLA5/Px4QJE96J2VAVERsbi3PnzmHjxo0qSSLKy8vx8ccfw87ODmvWrKn37RPtRYkEIYT8l4mJCU6ePAlzc3McPnxY0+HUG8YYdu/ejd9++01l05P/+eef6Nq1K5KSkqTmIyENm/Rzcgkh7w1vb29u5MD27duxfft2HDp0SCufhaEuRkZGDe4XtfjZHaoUEBCg0u0T7UWJBCHvsX379mk6BELIO45ubRBCCCFEaZRIEEIIIURplEgQQgghRGmUSBBCCCFEaZRIEEIIIURpNGpDy4WHh2vk6X3k7b0LT71sCJycnDQdAiHvNUoktFhkZKTGHmJECCHaor6mQCeqQc/aIIQQQoiy6FkbhBBCCFEeJRKEEEIIURolEoQQQghRmh6ABE0HQQghhJB30pn/BzPPxli8OH4TAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<IPython.core.display.Image object>"
|
||
]
|
||
},
|
||
"execution_count": 108,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Plot the model\n",
|
||
"from tensorflow.keras.utils import plot_model\n",
|
||
"\n",
|
||
"# Generate the plot\n",
|
||
"plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 109,
|
||
"id": "6b7329b28452b82a",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:48:41.298377Z",
|
||
"start_time": "2024-03-20T11:48:41.233101Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Save the model\n",
|
||
"model.save('CNN.keras')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "42eff9445377f73c",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Multi-Layer Perceptron (MLP)\n",
|
||
"\n",
|
||
"This code block is implementing a Multi-Layer Perceptron (MLP) for a binary classification task using TensorFlow. The MLP is a class of feedforward artificial neural network that consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training.\n",
|
||
"\n",
|
||
"Here's a step-by-step breakdown of what the code does:\n",
|
||
"\n",
|
||
"1. **Data Preparation**: The target column 'NLOS' is separated from the rest of the dataset. The remaining data is assigned to X and the target column to y.\n",
|
||
"\n",
|
||
"2. **Data Splitting**: The data is split into training and testing sets using an 80:20 ratio.\n",
|
||
"\n",
|
||
"3. **Data Scaling**: A StandardScaler object is initialized and fitted to the training data. The training and testing data are then transformed using the fitted scaler.\n",
|
||
"\n",
|
||
"4. **Model Creation**: A Sequential model is created using Keras. This model is composed of the following layers:\n",
|
||
" - Dense layers: These are fully connected layers. The first Dense layer has 64 units and uses the ReLU activation function. The second and third Dense layers have 32 and 16 units respectively, and also use the ReLU activation function. The final Dense layer has 1 unit and uses the sigmoid activation function for binary classification.\n",
|
||
" - BatchNormalization layers: These layers are used to normalize the activations of the previous layer, which speeds up learning and provides some regularization, reducing generalization error.\n",
|
||
" - Dropout layers: These layers are used to prevent overfitting. They randomly set a fraction of input units to 0 at each update during training time.\n",
|
||
"\n",
|
||
"5. **Model Compilation**: The model is compiled with the Adam optimizer, binary cross-entropy loss function, and accuracy as the evaluation metric.\n",
|
||
"\n",
|
||
"6. **Model Training**: The model is trained on the training data for 20 epochs with a batch size of 32. The validation data is set to the testing set. Early stopping is used to stop training when the validation loss has not improved for 10 epochs.\n",
|
||
"\n",
|
||
"7. **Model Evaluation**: The model's performance is evaluated on the testing data and the loss and accuracy are printed. The model also makes predictions on the testing data, converts the predicted probabilities to binary outputs, and generates a classification report.\n",
|
||
"\n",
|
||
"8. **Visualization**: The training and validation accuracy and loss over epochs are plotted.\n",
|
||
"\n",
|
||
"The mathematical concept behind the Dense layer is the dot product operation, which is a mathematical operation that takes two equal-length sequences of numbers and returns a single number. In the context of a MLP, the two sequences are the input data and the weights of the neurons. The dot product operation involves multiplying each pair of input and weight and summing the result.\n",
|
||
"\n",
|
||
"The mathematical formula for the dot product operation is: $$ a \\cdot b = \\sum_{i=1}^{n} a_i b_i $$ Where:\n",
|
||
"- $a$ and $b$ are the input data and weights respectively\n",
|
||
"- $n$ is the number of dimensions (length of the sequences)\n",
|
||
"- $a_i$ and $b_i$ are the $i$-th elements of the input data and weights respectively."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 110,
|
||
"id": "c8745832a585d5ec",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:49:34.073929Z",
|
||
"start_time": "2024-03-20T11:48:41.299882Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/20\n",
|
||
"1050/1050 [==============================] - 4s 3ms/step - loss: 1.0607 - accuracy: 0.5246 - val_loss: 0.8445 - val_accuracy: 0.6040\n",
|
||
"Epoch 2/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.9423 - accuracy: 0.5647 - val_loss: 0.7906 - val_accuracy: 0.6873\n",
|
||
"Epoch 3/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.8554 - accuracy: 0.6142 - val_loss: 0.7314 - val_accuracy: 0.7438\n",
|
||
"Epoch 4/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.7918 - accuracy: 0.6648 - val_loss: 0.6835 - val_accuracy: 0.7739\n",
|
||
"Epoch 5/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.7319 - accuracy: 0.7110 - val_loss: 0.6470 - val_accuracy: 0.7875\n",
|
||
"Epoch 6/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.6907 - accuracy: 0.7468 - val_loss: 0.6125 - val_accuracy: 0.8017\n",
|
||
"Epoch 7/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.6502 - accuracy: 0.7732 - val_loss: 0.5862 - val_accuracy: 0.8086\n",
|
||
"Epoch 8/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.6240 - accuracy: 0.7870 - val_loss: 0.5660 - val_accuracy: 0.8151\n",
|
||
"Epoch 9/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.5995 - accuracy: 0.7994 - val_loss: 0.5473 - val_accuracy: 0.8213\n",
|
||
"Epoch 10/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.5735 - accuracy: 0.8137 - val_loss: 0.5342 - val_accuracy: 0.8252\n",
|
||
"Epoch 11/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.5655 - accuracy: 0.8177 - val_loss: 0.5221 - val_accuracy: 0.8264\n",
|
||
"Epoch 12/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.5485 - accuracy: 0.8227 - val_loss: 0.5098 - val_accuracy: 0.8298\n",
|
||
"Epoch 13/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.5358 - accuracy: 0.8277 - val_loss: 0.5021 - val_accuracy: 0.8342\n",
|
||
"Epoch 14/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.5218 - accuracy: 0.8307 - val_loss: 0.4914 - val_accuracy: 0.8355\n",
|
||
"Epoch 15/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.5076 - accuracy: 0.8371 - val_loss: 0.4864 - val_accuracy: 0.8352\n",
|
||
"Epoch 16/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.5023 - accuracy: 0.8363 - val_loss: 0.4793 - val_accuracy: 0.8363\n",
|
||
"Epoch 17/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.4901 - accuracy: 0.8388 - val_loss: 0.4747 - val_accuracy: 0.8361\n",
|
||
"Epoch 18/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.4824 - accuracy: 0.8429 - val_loss: 0.4691 - val_accuracy: 0.8385\n",
|
||
"Epoch 19/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.4719 - accuracy: 0.8455 - val_loss: 0.4645 - val_accuracy: 0.8365\n",
|
||
"Epoch 20/20\n",
|
||
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.4657 - accuracy: 0.8467 - val_loss: 0.4613 - val_accuracy: 0.8371\n",
|
||
"263/263 [==============================] - 0s 1ms/step\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 0 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
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"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 0 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Test loss: 0.4613083004951477\n",
|
||
"Test accuracy: 0.8371428847312927\n",
|
||
"Classification Report: \n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" 0 0.80 0.90 0.85 4192\n",
|
||
" 1 0.89 0.77 0.83 4208\n",
|
||
"\n",
|
||
" accuracy 0.84 8400\n",
|
||
" macro avg 0.84 0.84 0.84 8400\n",
|
||
"weighted avg 0.84 0.84 0.84 8400\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Import necessary libraries\n",
|
||
"from tensorflow.keras.models import Sequential\n",
|
||
"from tensorflow.keras.layers import Dense, Dropout\n",
|
||
"from tensorflow.keras.callbacks import EarlyStopping\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"from tensorflow.keras.layers import BatchNormalization\n",
|
||
"from tensorflow.keras import regularizers\n",
|
||
"from tensorflow.keras.optimizers import Adam\n",
|
||
"from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# Set random seed for reproducibility\n",
|
||
"tf.random.set_seed(42)\n",
|
||
"\n",
|
||
"# Drop the target column 'NLOS' from the data and assign the remaining data to X\n",
|
||
"X = data.drop('NLOS', axis=1)\n",
|
||
"# Assign the target column 'NLOS' to y\n",
|
||
"y = data['NLOS']\n",
|
||
"\n",
|
||
"# Split the data into training and testing sets with a 80:20 ratio\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
|
||
"\n",
|
||
"# Initialize a StandardScaler object\n",
|
||
"scaler = StandardScaler()\n",
|
||
"# Fit the scaler to the training data and transform it\n",
|
||
"X_train = scaler.fit_transform(X_train)\n",
|
||
"# Transform the testing data using the fitted scaler\n",
|
||
"X_test = scaler.transform(X_test)\n",
|
||
"\n",
|
||
"# Initialize a Sequential model\n",
|
||
"model = Sequential()\n",
|
||
"# Add a Dense layer with 64 units, ReLU activation function and L2 regularization\n",
|
||
"model.add(Dense(64, input_dim=X_train.shape[1], activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
|
||
"# Add a BatchNormalization layer to normalize the activations of the previous layer\n",
|
||
"model.add(BatchNormalization())\n",
|
||
"# Add a Dropout layer to prevent overfitting\n",
|
||
"model.add(Dropout(0.5))\n",
|
||
"# Add another Dense layer with 32 units, ReLU activation function and L2 regularization\n",
|
||
"model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
|
||
"# Add another BatchNormalization layer\n",
|
||
"model.add(BatchNormalization())\n",
|
||
"# Add another Dropout layer\n",
|
||
"model.add(Dropout(0.5))\n",
|
||
"# Add another Dense layer with 16 units, ReLU activation function and L2 regularization\n",
|
||
"model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
|
||
"# Add another BatchNormalization layer\n",
|
||
"model.add(BatchNormalization())\n",
|
||
"# Add another Dropout layer\n",
|
||
"model.add(Dropout(0.5))\n",
|
||
"# Add the output Dense layer with 1 unit and sigmoid activation function\n",
|
||
"model.add(Dense(1, activation='sigmoid'))\n",
|
||
"\n",
|
||
"# Define early stopping to stop training when the validation loss has not improved for 10 epochs\n",
|
||
"early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
|
||
"\n",
|
||
"# Compile the model with Adam optimizer, binary cross-entropy loss function and accuracy as the evaluation metric\n",
|
||
"model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])\n",
|
||
"\n",
|
||
"# Train the model on the training data for 20 epochs with a batch size of 32 and validate on the testing data\n",
|
||
"history = model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
|
||
"\n",
|
||
"# Evaluate the model on the testing data and store the loss and accuracy in 'scores'\n",
|
||
"scores = model.evaluate(X_test, y_test, verbose=0)\n",
|
||
"\n",
|
||
"# Make predictions on the testing data\n",
|
||
"y_pred = model.predict(X_test)\n",
|
||
"# Convert the predicted probabilities to binary outputs\n",
|
||
"y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
|
||
"# Generate a classification report\n",
|
||
"report = classification_report(y_test, y_pred_classes)\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(figsize=(12, 6))\n",
|
||
"\n",
|
||
"# Plot training & validation accuracy values\n",
|
||
"ax.plot(history.history['accuracy'], 'ro-', label='Training Accuracy')\n",
|
||
"ax.plot(history.history['val_accuracy'], 'bv--', label='Test Accuracy')\n",
|
||
"\n",
|
||
"# Plot training & validation loss values\n",
|
||
"ax.plot(history.history['loss'], 'go-', label='Training Loss')\n",
|
||
"ax.plot(history.history['val_loss'], 'yv--', label='Validation Loss')\n",
|
||
"\n",
|
||
"ax.set_title('Combined Graph')\n",
|
||
"ax.set_ylabel('Values')\n",
|
||
"ax.set_xlabel('Epoch')\n",
|
||
"ax.legend()\n",
|
||
"\n",
|
||
"# Show the figure\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Create a figure for the accuracy plot\n",
|
||
"fig1, ax1 = plt.subplots(figsize=(12, 6))\n",
|
||
"\n",
|
||
"# Plot training & validation accuracy values\n",
|
||
"ax1.plot(history.history['accuracy'], 'ro-', label='Training Accuracy')\n",
|
||
"ax1.plot(history.history['val_accuracy'], 'bv--', label='Test Accuracy')\n",
|
||
"ax1.set_title('Model Accuracy')\n",
|
||
"ax1.set_ylabel('Accuracy')\n",
|
||
"ax1.set_xlabel('Epoch')\n",
|
||
"ax1.legend()\n",
|
||
"\n",
|
||
"# Show the figure\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Create a figure for the loss plot\n",
|
||
"fig2, ax2 = plt.subplots(figsize=(12, 6))\n",
|
||
"\n",
|
||
"# Plot training & validation loss values\n",
|
||
"ax2.plot(history.history['loss'], 'ro-', label='Training Loss')\n",
|
||
"ax2.plot(history.history['val_loss'], 'bv--', label='Validation Loss')\n",
|
||
"ax2.set_title('Model Loss')\n",
|
||
"ax2.set_ylabel('Loss')\n",
|
||
"ax2.set_xlabel('Epoch')\n",
|
||
"ax2.legend()\n",
|
||
"\n",
|
||
"# Show the figure\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Show the figure\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Print the testing loss and accuracy\n",
|
||
"print('Test loss:', scores[0])\n",
|
||
"print('Test accuracy:', scores[1])\n",
|
||
"# Print the classification report\n",
|
||
"print('Classification Report: \\n', report)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4114f5c851874555",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"# Multi-Layer Perceptron (MLP) visualization\n",
|
||
"This code block is used to visualize the performance of a trained Multi-Layer Perceptron (MLP) model. It generates three types of visualizations:\n",
|
||
"\n",
|
||
"1. Weights and Biases Visualization: This visualization is used to understand the distribution of weights and biases in the model's layers. For each layer in the model, if the layer is a dense layer, it retrieves the weights and biases, and plots histograms of their values. The x-axis of the histogram represents the value of the weights/biases and the y-axis represents the frequency of these values.\n",
|
||
"\n",
|
||
"2. Confusion Matrix: A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. It gives a more detailed breakdown of correct and incorrect classifications for each class.\n",
|
||
"\n",
|
||
"3. ROC Curve: The Receiver Operating Characteristic (ROC) curve is a plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. It is created by plotting the true positive rate (TPR) against the false positive rate (FPR).\n",
|
||
"\n",
|
||
"## Weights and Biases Evaluation\n",
|
||
"\n",
|
||
"The weights and biases of the MLP model layers were visualized to understand their distributions. The weights in all layers (dense, dense_1, dense_2, and dense_3) are not close to zero, indicating they are likely being updated during training and contributing to the model's learning. The weight distributions show a spread around zero, suggesting the model is capturing complex relationships in the data.\n",
|
||
"\n",
|
||
"The biases in dense and dense_2 introduce a slight positive bias to the activations in subsequent layers, potentially affecting the model's predictions. The biases in dense_1 and dense_3 are centered around zero, with a slight spread towards positive values, introducing a small positive shift in the activations of the next layer.\n",
|
||
"\n",
|
||
"The impact of these biases would depend on the network architecture and data. Overall, the model's weights and biases suggest that it is learning effectively from the training data."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 111,
|
||
"id": "41091791008ff727",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:49:35.361384Z",
|
||
"start_time": "2024-03-20T11:49:34.075287Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 1. Visualize Weights and Biases\n",
|
||
"for layer in model.layers:\n",
|
||
" if 'dense' in layer.name:\n",
|
||
" weights, biases = layer.get_weights()\n",
|
||
" plt.figure(figsize=(10, 5))\n",
|
||
" plt.subplot(1, 2, 1)\n",
|
||
" plt.hist(weights.flatten())\n",
|
||
" plt.title(f'{layer.name} weights')\n",
|
||
" plt.subplot(1, 2, 2)\n",
|
||
" plt.hist(biases.flatten())\n",
|
||
" plt.title(f'{layer.name} biases')\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "618bc6deb5ea296b",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"## Confusion Matrix Evaluation\n",
|
||
"\n",
|
||
"The confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. It gives a more detailed breakdown of correct and incorrect classifications for each class.\n",
|
||
"\n",
|
||
"The confusion matrix shows that the model has a good performance in distinguishing between positive and negative instances. The majority of the instances are correctly classified, with a small number of false positives and false negatives."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 112,
|
||
"id": "6b7d586ea49a858a",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:49:35.531355Z",
|
||
"start_time": "2024-03-20T11:49:35.362848Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 500x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 2. Confusion Matrix\n",
|
||
"# Convert the predicted probabilities to binary outputs\n",
|
||
"y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
|
||
"# Generate the confusion matrix\n",
|
||
"cm = confusion_matrix(y_test, y_pred_classes)\n",
|
||
"# Plot the confusion matrix\n",
|
||
"plt.figure(figsize=(5, 5))\n",
|
||
"sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
|
||
"plt.title('Confusion matrix')\n",
|
||
"plt.xlabel('Predicted class')\n",
|
||
"plt.ylabel('True class')\n",
|
||
"plt.show()\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8645f7e159d38f0a",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"## ROC Curve Evaluation\n",
|
||
"\n",
|
||
"The Receiver Operating Characteristic (ROC) curve is a graphical representation that illustrates the performance of a binary classification model at all classification thresholds. It is commonly used in machine learning to evaluate the performance of a classifier system for two-class problems.\n",
|
||
"\n",
|
||
"The ROC curve has two axes:\n",
|
||
"- The X-axis represents the False Positive Rate (FPR), which is the proportion of negative instances that are incorrectly classified as positive.\n",
|
||
"- The Y-axis represents the True Positive Rate (TPR), which is the proportion of positive instances that are correctly classified as positive.\n",
|
||
"\n",
|
||
"A perfect classifier would classify all positive instances correctly (TPR = 1) and all negative instances correctly (FPR = 0). This would be represented by a curve that goes straight up the left side of the ROC graph and then along the top to the right corner.\n",
|
||
"\n",
|
||
"The Area Under the Curve (AUC) is a numerical measure of the ROC curve’s performance. A larger AUC indicates a better performance. In our case, the AUC is 0.91, which is considered to be very good.\n",
|
||
"\n",
|
||
"In summary, the ROC curve shows that our binary classification model has a good performance in distinguishing between positive and negative instances."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 113,
|
||
"id": "4d080bef0cf9bec4",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:49:35.715900Z",
|
||
"start_time": "2024-03-20T11:49:35.532577Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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665EI4O3zVMDVrl0b3t7e2LRpE9LS0qBQKFCrVi3s2LEDr1+/zrT9vy8ztWzZEg8fPsSxY8cybff213mbNm0QERGBnTt3ZtomNTVVe/dTdtzc3ODu7o7Dhw/j8OHDcHJy0klKZDIZWrVqhSNHjmT5Qf0p0zFUr14d9erVw+7du3Vu1X9ryZIlCAkJwddff53pl/rx48d1+mfcvXsXd+7cQaNGjQBAr9f5fbIaRG/Lli1Qq9U6ZRYWFgA+/XLZv1WuXBl2dnbYuXMnVCqVtvzAgQOIj4//4P7FixdH165dcf78eWzZsiXTeo1Gg//7v//Dq1ev9Irr7Wvy38uEu3btyvVjNGjQAFZWVli7di3S0tJ01v17XwsLiyxf+099f2RFrVZnOpdCoUDRokV1bsnPLqb/cnBwQM2aNbFr1y7t5dC3cqt1kAo2tghRgTdw4EB8++232L17N3r06IHp06ejZ8+eaN++Pbp164aSJUsiKioKt2/fxqtXr7B//37tfkeOHMG3336LLl26wMvLC/Hx8Th58iRmzpyJihUr4vPPP0dAQACmT5+OK1euoHr16lCr1Xj69CkCAwOxfv36D06h4OfnB39/f5iZmeGLL77IdGlk3LhxuHLlCrp164auXbuifPnyiI+Px71793Dp0iVcvXr1o1+bBQsWoH///hg2bBjatWuHGjVqQKlU4ujRo7h69Sr8/PwwcODATPuVKlUKPXr0QI8ePaBUKrF582bY2dnh66+/1m6T09f5fZo0aYJ9+/bB2toa5cuXx+3bt3Hx4kXY2dnpbOfp6QmZTIZffvkFCQkJMDU1RZ06daBQKD76tTE1NcXIkSMxe/Zs9OvXD23atEF4eDh2796d48unkyZNQlhYGObMmYOjR4/C19cXRYoUwcuXLxEYGIinT5/qtADmRP369SGXyzFkyBB0794dSUlJ+OOPP6BQKBAZGZmrx7C2tsbkyZPx/fff44svvkC7du1QpEgRPHz4EKmpqViwYAEAwMvLC4cPH8a8efNQpUoVWFpaomnTprny/vivpKQkNG7cGK1atULFihVhaWmJixcv4q+//tJpOcwupqx8//336NGjBzp16oQvv/wSJUqUQHh4OE6fPo19+/bpFR8VPkyEqMBr2bIlSpUqhf/7v/9Dt27dUL58eezatQsrVqzAnj17EBcXBwcHB1SqVAnDhw/X7mdlZYWtW7di+fLlOHbsGPbs2QOFQoG6detqO/lKpVKsXLkSv/76K/bt24djx47BwsICJUqUQJ8+fbRjtryPn58fli5dipSUFLRp0ybTekdHR/zxxx9YuXIljh07hm3btsHOzg7ly5fH+PHjP+m1KVq0KP744w/tXGNHjx6FTCaDh4cH5s+fj44dO2Z5iaFjx46QSqXYtGkToqOj4e3tjR9++AFFixbVbpPT1/l9pk6dCqlUigMHDiAtLQ3Vq1fHxo0bdRIuAHBycsLMmTOxdu1aTJ06FWq1Gps3b/6kRAgAevfuDUEQsHHjRixYsAAVK1bE6tWrMWfOnByNTmxhYYFffvkFu3fvxt69e7Fq1SqkpqZq5xpbtGiR3vPglStXDv7+/li6dCkWLFgAR0dH9OjRAw4ODpgyZUquH6Nr165QKBRYt24dVq1aBRMTE5QrV05n/KqePXviwYMH2L17N3799Ve4urqiadOmufL++C9zc3P06NEDFy5cwNGjRyEIAkqVKqVNvD8UU1YqVqyInTt3YtmyZdi2bRvS0tLg4uKS5fuRjI9EYNsgEf3P8+fP0axZM3z33XdZthQZA41Gg7p166JFixaYM2eO2OEQUR5jHyEiMlppaWmZ+ons3bsXcXFxqFWrlkhREVF+4qUxIjJat2/fxrx589C6dWvY2dnh/v37+PPPP+Hu7o7WrVuLHR4R5QMmQkRktFxdXVGsWDFs2bIF8fHxsLW1xeeff47x48eLOqs9EeUf9hEiIiIio8U+QkRERGS0mAgRERGR0WIiREREREaLiRAREREZLaO9aywmJgG5NAEzfSSJBFAobBAdnQB22Rcf68NwsC4MB+vCcEilgIODTa4f12gTIUEA/6gNBOvCsLA+DAfrwnCwLsSXV68/L40RERGR0WIiREREREaLiRAREREZLSZCREREZLSYCBEREZHRYiJERERERouJEBERERktJkJERERktJgIERERkdFiIkRERERGS9RE6Nq1axgyZAgaNGgADw8PHD9+/IP7XLlyBZ06dULlypXRokUL7N69Ox8iJSIiosJI1EQoOTkZHh4emD59eo62DwsLw+DBg1G7dm3s27cP/fr1w/fff49z587lcaRERERUGIk66Wrjxo3RuHHjHG+/fft2lChRApMmTQIAuLm54caNG/j111/RsGHDvAqTiIiICqkCNfv87du3UbduXZ2yBg0aYO7cuSJFRERERAAypofXKCFRp0GSFgOJKgkQ1JBoVICgBgQNJIIa0sRngFSuLXtbDkENWUIIoFECMnNAkw5ZYgikyS+gsiiBhxH2UIz8v1wPu0AlQlFRUXB0dNQpc3R0RGJiIlJTU2Fubp7jY0kkGf9IPG9ff9aDYWB9GA7WheEodHUhaAB1KiTpCTCJugmJKgkSTTqgUcEk6gYECydA8zZBUWsTFJPXlyGYO0KQmECW8BQm8Y+gKlIhY19BDVlSWJ6F/PKNNQbsqIhbkWURMTL3j1+gEqHc5OBgI3YI9D8KBevCkLA+DAfrwnCIXhdqJRD7GFClAvH/ZCQq/0tgEH0PkJkCEllGK4tG/b/WFjXw+jYAAQg7neshmbx5kuvH/K99f3vg6z86ICrJCjY2eZOyFKhEyNHREVFRUTplUVFRsLa21qs1CABiYhKg0eRmdKQviSTjwyU6OgGCIHY0xPowHKwLw5ErdaFWQpoUColaCQgq7eUiifINJMpYSNITYHlnAVSK6jALOwh1ETfI3gQDAASJFBLBcL+sNGYOEKSmgNQEEnUqpKlRUBb3BSQSyOKDoHRtnpGgSWWARAZBIgMggTTlFVSOnwES6btyiQyQSCFRpUBtUxaRb0zQ64dbSErJeP5FHfX7ns+pApUIVatWDWfPntUpu3jxIqpVq6b3sQQB/IAxEKwLw8L6MBysC8ORbV1o1JCkx8Ps2V5I02IAVTKkabEwC9kNqTJWr3PIkl9k/P+/JAhAniRBqiIVoLEuDZOo61ApfJBWsi0glUOQyiHRKKG2LqubuEikgEQKQWoGjUUxQCbPSH5MLHM9tn+zdQFm/+iAsWOPoU0bNyxb1ipPziNqIpSUlITQ0FDt8vPnz/HgwQPY2trCxcUFP//8MyIiIrBw4UIAQPfu3bF161YsXLgQXbp0weXLlxEQEIC1a9eK9RSIiKggEzQZHXyV8ZCmRkOSngBZ4jNAkw6TmNuAmR2QFgbbmIxOu5L0N9CYO0Ka8hpSZVzehycxgcrBGwBgEvcAaaU7QGPuBGnSC6Q71wUkJhkdj1XJUBdxy3gs+U8SIzWB2qoUBNMiGesNlFqtgUqlgZnZu9SkV6/KcHGxga9vachkedNRS9RE6O+//0bfvn21y/PmzQMAdOrUCfPnz0dkZCRevnypXV+yZEmsXbsW8+bNw+bNm1GsWDHMmTOHt84TEVEm0sRnkKQnQaJJgyzuPkxfnYNJ5FVI1ErIkkI/fIB/+Xf6IE2L0TsWlV0lCHJrqOwrZyQpUhNAIoM0+QXUNuUAmRlU9t5IL1oHgtzaoBOWvBAenoARIwJQsaIj5s1rqi2XSCRo2rRMnp5bIgjG2fAaHc0+QmKTSABHRxtERbEfhCFgfRgO1sV7CAKgSYdEnQqLh2sBaGD6PBCCqT1MXxyHys4TJnEP8jwMjbkjBKkpZMkvkO5UGxJVEtQ25ZDiPgCQmUOQyCCYK6C2KV+IbjnLG/v2PcL48ccRH58GAPj9945o3rxcpu2k0rzptF6g+ggREVEhpEqBRFBBmhQOk5i7kKgSAY0K0tRIyOIfwyTuHkziH0OQmEAiqN57KH2SII3cBtL0BCidGwBSOWRx96Es1QFQpUAwd4TapiwgNYFNiUqIS0iH2qoUNPIigNz6U58xAUhISMOUKaewY8d9bZmrqw2srU3zNQ4mQkRElLsEAZL0eECjgkSjhDT5BWRvgiBJT4T81XnIksJgEnP3f+PUvD+x+becbitI5RBMbSFNjUK6vTdURWtlJFGadChdmkKlqA6NlWvOzikBbBxtoGLrXK66du0Fhg0LwLNn8dqyjh09sHBhM9jZ5c3dYdlhIkRERPpTp0Ga9Bymr87C4tEv/xtoLxjS9IS8Pa11GagtXTJacN4EQW3rjuQq46ExtYXG0hWCmQMvRRkwlUqDJUuuYPHiy1CrMzJLa2tTzJ/fFF27ekIiQt0xESIiogyCAGnS84yRg2P/gkSVAqhTYB68HbLk51BbFM9o4UmLztXTauRFAIkMKkU1SFMjoXSuD7V9ZQhyGwgSKTTmztBYuUBjXTpXz0v5KyYmBb1778X16+9ugqpZ0wWrVrVB6dK2osXFRIiIyBioUiBVxkOaGAJpWgykqZGQvzoH0xcnoLYqCXnM7Q8eQpby8oPb/JuyWJOMW7YlUkjSYqCy94JK4QPB1B4q2wrQWJfJuL2bjIKtrRlMTDLqWyaTYNy4Ohg9ura2TCxMhIiIChhp8iuYRN/MSGZeX4LGzAHyiAsQTKwzplrQqABBBdOICzk7nh4tPGqL4hBMbTLG3FElQW1dBsqSbZHu4A1lyTaA1IyXpihLMpkUK1e2wYAB+zF/flPUqOEidkgAmAgRERkWQci4ayouEiavg2Easg+mL0/CJO4B1JYlIEt+nmen1pg7QZoaiXSnWtBYFIPKwRsq+yoQ5NbQWBaD2saNLTiUYxcvhsHc3ATVqxfXlpUsWQTHjvUSpS9QdpgIERHlF0EA1CmQJYZBlvgU0Khh+vI0TKJvQR51DRq5LaTp7+6isfvP7p+aBKnsq0CSGok0t17QWDhBY+YIwcQqYxA/c8UnHZvoLaVSjYULL2L58msoVcoWp0710bkl3pCSIICJEBHRp1MrIVGnAuo0WP69COZPtkBtWwHQqCGPvQsAEKRmkGjS3nuYfydB2RGkZhktNykvkVq+L1SKqhDktlAXcYNgYgWNRVEIEhNA+r+pF9iCQ/koKCgGQ4Ycxt27rwEAz57FY+PGOxg5sqbIkWWPiRARUU6oUiCPOA+T+EeARg1pyiuYB2/Ndr4pafQtneUPJUGZlGoGVcIrKF1aQjAxR6pbL2isSrL/DRkkQRCwZctf+OGH00hJyRjvSS6XYvLk+hg2rIbI0b0fEyEiov+QJr+E+aNfIEt8BvN//vikY2nkNpCoUyHRpCNdUR2QmkIW/xDpRetAY1UKapuyEGRmSC/eJGPOKYlUO8VGHAfxowIgKioZY8ceQ2BgsLasfHl7rFnjB29vZxEjyxkmQkRkXAQB0qTQjIkzNemQqNMgf30R8teXIX95ChLh4yYh1MhtkO5cH4LMArKEf/DGd1uORy8mKqhOngzBqFFH8Pp1krasXz9vzJzZGJaWBWPiWCZCRFToSFIiYRL/EPJXZyGPugFJWgykylho5EUgj7nzScdOLdsN6iJuGXdTmVhCbVMWGpuyuRQ5UcHx+nUS+vffh9RUNQBAobDA0qUt0aqVm8iR6YeJEBEVHIIAacorQJ0CiyebAXUKJMoEmIUfgdrS9YODAsr0OZXUDKluPZDiOSxj8k2Z2SeFTlTYFC1qhe+/b4jvvz8NX9/S8PdvDWdnK7HD0hsTISISnyAAmnRAk3H3lenzQJiGH8+YqBMCIGhgEnfvvYeQpkbm7FQmlpCokiHILJBSoV/GnVVSOQQTC6Q71Ua6Yw3OLk6UBY1GgFqtgVz+7ifF11/7oHhxa7RtWwFSacHsyM9EiIjyjiYdsvgnkCU/hzQxFKavzkKSGglp8ivIUl5CbVEMJglPc/20gkQGiaBGSoX+AIC0cl9CbVMOGotivOuK6CNERCRi5MgjqFzZCdOmNdKWS6UStG/vLmJkn46JEBHlCklqNGRJYTB/tAGm4YGQpUR8cB99kiBBZgGJOgUAoHRpDrWlCyTqFCR7fZsxOaepHQQz+4+On4iyFhAQhDFjjiImJhVnzjyDr28ZNGxYSuywcg0TISLKOUEDk6hrMIm+DUACiUYJqxvTIBFUH31ItaULpCmvAakc6U61IcjkkKQnQbAoipTyfaG2rwSNRXG25BDls6SkdEyffgabN9/Vljk5Fbw+QB/CRIiIsicIMIm6CosHq2EesvujD5NaunPGHVYOlaG2KQuVgw80pkUAE8tcDJaIcsudOxEYMuQwgoNjtWWtW7thyZKWUCgsRIws9zERIqJ3NGqYvL4MaWI4ipwb8FGHSK40EpCaIrnyGAimRXI5QCLKS2q1BitXXsf8+RehUmWMqWVpaYLZs5ugd+8qBjdPWG5gIkRkrDRqyBKfQhb3CKYvTsDi8QYAmSf6zEpaidZQ23pApfCBIDWD2rok1A7eeRouEeWt6OgUfP31AVy48G5y36pVnbFmjR/c3Apv/zsmQkRGQBb3ELKEf2DxaB1kb4IhSwzRa/90hQ9S3b9GarluHE+HqJAqUsQUSUnpADK65I0aVQsTJtSFqak+I3AVPEyEiAohWfxjmD3dDst7/pBolHrvr7KvgrTSn0NlVwnKUu3yIEIiMjRyuQyrV/uhX799WLiwGerVKyl2SPmCiRBRISBRxsPi3jKYvjoLeeTVHO+nMVNAZe8FjVVJpBdrABvvLxCVbMaJPomMwLVrL2BhIUflyk7aMjc3e5w926/ADo74MZgIERUQspi/II++DVnCU8jePAYEAWZhB/U6RrLXaGgsnJFWpgs0lsV01kkkgI2lDZCckJthE5GBUak0WLLkChYvvgw3N3scPdpLZ4JUY0qCACZCRAZHmvwCVjd+gESTDrNne6Gy8/rg9BLZSai1CMpS7aCxdMnlKImoIAoJicOwYQG4fv0lAODx4xhs3HgHw4fXEDky8TARIspv6UmQqFNgEncfsoR/AI0K5k82ZTthaE6TILVVKaSVaosUj0HQ2JTjAIREpCUIAnbufIDJk08iMTGj36BMJsH48XUxeHB1kaMTFxMhonxgEnkN9gHNPukYagtnyFIikFhjLjSWLtBYOENtUxYaU3vApHANcEZEuScuLhUTJhzHvn2PtWVlythi1ao2qFGDrcVMhIjygloJafJzmL46B9MXJ2D2bG+OdxUkMkAiQ2LNBUgv1ghq69KAzDTvYiWiQuvChTAMHx6AFy8StWU9enjhxx99YW3NzxWAiRBRrpEmv4Tt0bYweRP0wW1VRcpDbecFaXI40sp0zphLCxqklerAcXqIKFdERCTiyy93Q6lUAwDs7MywaFELdOhQsGeLz21MhIg+kiz+McyDtsLy3pIc75NU7Qcke0/Iw6iIiDI4O1tj/Pg6mDv3Aho0KIkVK1rDxcVG7LAMDhMhIn2o02Ae9BtsrozJ0eap5XpAkFsjtWw3qBTV2NpDRHlGEARoNAJkMqm2bOTImnBxscEXX3ga3W3xOcVEiOg95K/OwezpdlgEbcnR9oLUFEnVpiLFazTv2iKifBMVlYyxY4+hSpWimDChrrZcJpOiW7dKIkZm+JgIEQkCZPGPIE0Oh0SdBvmrc7B8sDLHuyfU+gnKUh2gsSyeh0ESEWXt5MkQjBp1BK9fJ+HYsado0qQ0atbk3WA5xUSIjJI84gLsjrT56P1TKvRHcpVx0FiXzsWoiIhyLjVVhR9/PI+1a29qy+zszLXjBFHOMBGiwk+VAlniM1jcX57jS1z/pjFTILHWIiiLNYBg4ZwHARIR6ef+/UgMHRqABw+itGW+vqXh798azs5WIkZW8DARokJJkhIBi4drYfXXohzvo7Z0QWr5PhDkRQBBg7SyXaCxKpGHURIR6UejEbB+/S3Mnn0OaWkZt8WbmckwbVpDDBzoww7RH4GJEBV8ggCoU2ES/xA2Z7+CSUJwjnZTWxTHmya/QeVUM48DJCL6dDExKRg69DBOnXqmLfP0dMSaNX7w9HQUMbKCjYkQFTiy2PswD9oM05enYRJ3P8f7pZVoDUFmgcQ6SyGY2edhhEREuc/SUo5Xr96NED14cHVMndoA5ub8Kv8UfPWowDB/tCHH4/cAgAAJUsv3QZLPD+zbQ0QFnrm5CVav9kPfvvvx00/N4OtbRuyQCgUmQlQgWF8eA4vHG967jbJYY0hTIpBavg9SPIcBUlk+RUdElPvu3ImApaUcFSo4aMsqVXLC5csDYGIifc+epA8mQmS4BAHWl0fD4snGLFcnVp+JtNKdoLEqyaSHiAoNtVqDlSuvY/78i6hYUYGAgB4wM3v3dc0kKHcxESLDIQgwfR4IeeQVmD3dDlnyiyw3i+78F8fvIaJCKTw8AcOHB+DixecAgL//jsTGjXcwZMhnIkdWeDERInGlJ8H64giYP9mco81j2xxjEkREhdK+fY8wfvxxxMenAciYpWfUqFr46qtq4gZWyDERovwlCJAm/gOLxxtheW8ZAMD8A7ukK6ojrs0xQCrP+/iIiPJZQkIapkw5hR073t0F6+pqg5UrW6NevZIiRmYcmAhRnrO8/SNMYu7ANPwoJILmg9ur7DyRVH0G1DbloS5SnpOXElGhde3aCwwbFoBnz+K1ZR07emDhwmaws/vQz0TKDUyEKG8IAkyib8H+cJMcbZ7kPQnJ3hPY6kNERuPlywR06vQHlMqMEaKtrU0xf35TdO3qCQl/AOYbJkKUq0xDD8H2dI9s1wsSE0gEFTSmdkip/C2sPvsKUUp7CEI+BklEZACKF7fBsGGfYenSq6hZ0wWrVrVB6dK2YodldJgIUe7QqOC41fG9l74ie0UBMlPtskQCWBWxAaIS8iNCIiJRCf/7xffv1p4JE+rC1bUIevWqzNviRcJEiD6J7E0QHPZWz3a9yq4SEmvOR3rxJvkXFBGRgYmLS8WECcdRrVoxDB9eQ1sul8vQr5+3iJEREyH6KOaP1sPmyths18e2PQeVomo+RkREZJguXAjD8OEBePEiEYcPB6FRo1KoUqWo2GHR/zARIr1IUqPhuLNstuuVxZogvtlOQMa7HYjIuCmVaixYcBErVlzT9oO0spLj9eskcQMjHUyEKEc+dAnsTaNfkVamcz5GRERkuIKCYjBkyGHcvftaW9agQUmsWNEaLi42IkZG/8VEiN5LmvQcil2Vsl0f0/Fmxlg/REQEQRCwefNfmDbtNFJSVAAAuVyKyZPrY9iwGpBKeVu8oWEiRNmyOfsVzEP+zHJdQq2fkVrxm3yOiIjIcMXGpuDbb48iMDBYW1a+vD3WrPGDt7eziJHR+zARokxkcQ/gsL92luvimu9FukvTfI6IiMjwmZqa4MmTGO1y//5VMWNGI1hacqBYQ8ZEiN4RBNge7wTTlyczrUqsMRcplUaIEBQRUcFgZSXH6tVt0LfvPvz0U3O0auUmdkiUA0yECAAgSXkNxz+y7usT/cVjaCyL5XNERESG7f79SFhaylGmjJ22rFq1Yrh2bSDMzPj1WlBwGEuC5e0fs0yC3jRYh8i+b5gEERH9i0YjYN26m2jV6ncMHRoAlUp3RH0mQQULa8vIZdchOurLZxDM7EWIiIjIcEVEJGLkyCM4ffoZAODGjZf49dc7+PprH5Ejo48leovQ1q1b0bRpU1SpUgVdu3bF3bt337v9r7/+ilatWsHb2xuNGzfG3LlzkZaWlk/RFh6SlAg4bS6SKQl60+AXRPZ9wySIiOg/AgKC0LjxZm0SBACDB1dH795VRIyKPpWoLUKHDx/GvHnzMHPmTFStWhWbNm3CwIEDERgYCIVCkWn7AwcO4Oeff8bcuXPh4+ODkJAQTJo0CRKJBJMnTxbhGRRAGhWcfnPIclVciwNIL944nwMiIjJsSUlKjB9/HJs2vfuh7uxsBX//VvD1LSNeYJQrRE2ENm7ciG7duqFLly4AgJkzZ+L06dPYtWsXBg0alGn7W7duoXr16mjfvj0AoESJEmjXrh3u3LmTr3EXVDZn+8M8ZHeW6yJ7xwBSXiklIvq3O3ciMGxYIB4/jtaWtWnjhsWLW0KhsBAxMsoton3zKZVK3Lt3D4MHD9aWSaVS1KtXD7du3cpyHx8fH+zfvx93796Ft7c3wsLCcObMGXz++ed6n18iyfhnDExenYfdEb8s1yVVn4mUKmMgxkvx9vU3lnowdKwPw8G6MAzh4Qnw89sOpVINALC0NMGcOb7o3bsyJKycfJdXL7loiVBsbCzUanWmS2AKhQJPnz7Ncp/27dsjNjYWPXv2hCAIUKlU6N69O4YMGaL3+R0cjGCul/RkwN8q+/XjBFgBeM8W+UKhMIK6KEBYH4aDdSEuR0cbDBtWA0uXXsFnnxXH7793gbt75m4bVLAVqGshV65cwdq1azF9+nR4e3sjNDQUP/74I1auXInhw4frdayYmARoNB/erqCSxT6AfTajQ0f1fAnIrYCohHyOSpdEkvFBHx2doJ2ZmcTD+jAcrAvxCIKg09ozfnxtlCpli+7dPSGXyxAl8uemMZNK86YRQ7REyN7eHjKZDNHR0Trl0dHRcHR0zHKfZcuWoUOHDujatSsAwMPDA8nJyZg2bRqGDh0KqTTnN8EJAgrlB4zFX4thfWtGluve1FuNtHLdAKkcMKDnXljroqBifRgO1kX+SUhIw5Qpp+DjUwxffVVNW25mZoIxY+oiKopJqdjy6vUXLREyNTWFl5cXLl26hObNmwMANBoNLl26hN69e2e5T2pqaqZkRyaTAcjI4o2aJh1Ov2XfZBvZ900+BkNEVHBcu/YCQ4cGIDQ0Hvv2PUKDBiV5CcyIiHppbMCAAZg4cSIqV64Mb29vbNq0CSkpKejcuTMA4LvvvoOzszPGjRsHAPD19cXGjRtRqVIl7aWxZcuWwdfXV5sQGR1BgO2JzjB9cSLTKo2pHRJrL0Za2S9ECIyIyLCpVBosXnwZS5ZcgVqd8WPaxESGkJB4JkJGRNREyM/PDzExMfD390dkZCQ8PT2xfv167aWxly9f6rQADR06FBKJBEuXLkVERAQcHBzg6+uLMWPGiPUUxCUIcNpim+WqmA5XobarmM8BEREVDCEhcRg2LADXr7/UltWs6YJVq9qgdOmsP1epcJIIRnpNKTq6YHeWtvh7Gaxv/pCpPM21Bd402yVCRPqTSDLuyuC1d8PA+jAcrIu8IwgCduy4j8mTTyIpKR0AIJNJMG5cHYweXRsmJrrdL1gXhkMqzZs7KQvUXWOUwfZIG5hGXMhUHtknngOPEBFlIz4+FePHH8e+fY+1ZaVL22L16jaoUcNFxMhITEyEChJBgNnT3zMlQRpTe0R3fcwkiIjoPSQSCW7efKVd7t7dC3Pn+sLa2lTEqEhsTIQKAkFAkdO9YBZ2MNOq6E53obEpk/8xEREVMEWKmGHlytYYMOAAFixohg4d3MUOiQwAE6ECILsO0Ql1VzAJIiLKRlBQDCwt5XBxedevpE6dErh+/WtYWclFjIwMSc5HIKR8J0l5DafNRTKVa0ztEN3pLlIr9BUhKiIiwyYIAjZtuotmzX7DiBGB0Gh0ezkzCaJ/Y4uQgZKkxcLxj/KZyiN7R2eMDE1ERJlERSVj7NhjCAwMBgCcPx+GzZvvon//qiJHRoaKiZCBsrqR+db4qO6hTIKIiLJx8mQIRo06gtevk7Rl/fp5o1u3SiJGRYaOiZABMn/8KyyCNmuX1ValENPxBiAzEzEqIiLDlJqqwo8/nsfatTe1ZQqFBZYubYlWrdxEjIwKAiZCBsjm8iid5di2Z5gEERFl4f79SAwdGoAHD6K0Zb6+peHv3xrOzlYiRkYFBRMhQ5LFlBlJ3pMgmHPOGyKi/woLe4NWrX5HWpoaAGBmJsO0aQ0xcKAPpFKOq0Y5w7vGDEU284YlV5siQjBERIavZMki2v4/np6OOHq0F775pjqTINILW4QMRFZJUFTXIBEiISIqOGbNaoISJYpg6NDPYG7OrzTSH1uERCZJT8hyrKDIPvEQLIqKEBERkeFJSkrH+PHHsX37PZ1yKys5xoypzSSIPhr/csSkToPjNtdMxZw8lYjonTt3IjB06GEEBcVi164HqF3bFWXL2okdFhUSbBESiTThKZy2OmUqZxJERJRBrdbA3/8q2rTZhqCgWAAZo0Y/fBj1gT2Jco4tQiKwO+wLedQNnTK1pStivnggUkRERIYlPDwBw4cH4OLF59qyqlWdsWaNH9zc7EWMjAobJkL5TJr8MlMSBIBJEBHR/+zb9wjjxx9HfHwagIxG8lGjamHChLowNZWJHB0VNkyE8pNaCcWfHjpF8Y1/g7J0B5ECIiIyHImJSkyefBI7dtzXlrm62mDlytaoV6+kiJFRYcZEKB/ZBTTTWX5TfzWTICKi/0lLU+P06Wfa5Y4dPbBwYTPY2ZmLGBUVduwsnU+sbkyDPOaOTlmaWy+RoiEiMjwKhQWWL28NGxtTrFjRGmvX+jEJojzHFqF8IE1+Act7S3XKIvvEixMMEZGBCAmJg6WlHEWLvpsTrEmT0rh582vY2jIBovzBFqG8lp4ExZ8VdYpiPr/BW+SJyGgJgoDt2+/B13cLRo8+CkEQdNYzCaL8xEQoD0mTnsNpW3GdsiTv76C2rSBSRERE4oqLS8WgQYcwatQRJCWl4/jxf7Bt270P70iUR3hpLI/I4h7AYX/tTOXJ1b4XIRoiIvFduBCG4cMD8OJForase3cvdOjgLmJUZOyYCOWR/yZBSuf6iG8VIFI0RETiUSrVWLDgIlasuIa3V8Hs7MywaFELJkEkOiZC+SDFfSAS6ywROwwionz35EkMhg49jLt3X2vLGjQoiRUrWsPFxUbEyIgyMBHKAybRt3WWmQQRkTEKCYlD8+a/ISVFBQCQy6WYPLk+hg2rAamUN4yQYWBn6dwmCLA/1EjsKIiIRFemjB38/MoDAMqXt0dAQA+MGFGTSRAZFLYI5SZBgNMWW52iZK9vRQqGiEh8CxY0Q8mSRTB6dG1YWsrFDocok09qEUpLS8utOAoF+32fZSpL+my2CJEQEeWv1FQVfvjhNPbvf6xTXqSIGaZMacAkiAyW3omQRqPBypUr0bBhQ/j4+CAsLAwAsHTpUvzxxx+5HmBBYvImSGc5sk+cOIEQEeWj+/cj0arV71i79ibGjTuG8PAEsUMiyjG9E6FVq1Zhz549mDBhAuTydxm+u7s7/vzzz1wNriAxfbZfZzmydwwgYRcsIiq8NBoB69bdRKtWv+PBgygAGS1Dt2+/EjkyopzT+5t63759mD17Njp06ACp9N3uHh4eePr0aa4GV2CoU2F7prdumZTdr4io8IqISESPHrvx/fenkZamBgB4ejri6NFeaNuWo+dTwaH3t3VERARKlSqVqVwQBKhUqlwJqqCxPdldZzm+qXFfIiSiwi0gIAhjxx5DdHSKtmzw4OqYOrUBzM35I5AKFr3/YsuXL4/r16/D1dVVpzwwMBCenp65FlhBYvrypM6yskQrkSIhIso7SUnpmD79DDZvvqstc3a2gr9/K/j6lhEvMKJPoHciNGzYMEyaNAkREREQBAFHjx7FP//8g71792Lt2rV5EaNBs7o+RWc5qnuoSJEQEeWtxMQ0HDr0RLvcpo0bFi9uCYXCQsSoiD6N3n2EmjdvjjVr1uDSpUuwsLCAv78/goODsWbNGtSvXz8vYjRYsjdBsLy/QqdMMLUTJxgiojzm7GyNxYtbwNLSBIsXt8Cvv3ZgEkQFnkQQ3k6BZ1yioxOg0XzCAbIYPDH6i8fQWBb7tMCMiEQCODraICoqAcb5V2hYWB+Gw1DqIjw8AZaWJrC31012IiOT4eRkKVJU+ctQ6oIAqRRQKHJ/fjq9W4SaNWuG2NjYTOVv3rxBs2bNciWogqDIqS91lpOqfc8kiIgKjX37HqFJk80YP/44/vt72ViSIDIOeidC4eHh0GTRlKJUKhEREZErQRUEZs8DdZaTvb8TKRIiotyTkJCGkSMD8c03hxAfn4YDB55g166HYodFlGdy3Fn6xIkT2sfnzp2Djc275imNRoNLly5lupOssJLF6X4oRPZ4IVIkRES559q1Fxg6NAChofHaso4dPdC8eVkRoyLKWzlOhIYPHw4AkEgkmDRpku5BTEzg6uqaqbwwksX+DYcD9XQL5dbiBENElAtUKg2WLLmCxYsvQ63OuAxmbW2K+fObomtXT0gknC2eCq8cJ0IPH2a0gjRt2hR//vknHBwc8iwogyUImZKg5EqjRAqGiOjThYTEYdiwAFy//lJbVrOmC1ataoPSpW3fsydR4aD3OEInT5788EaFlGJHGZ3lFI9vkFRjjjjBEBF9oqdPY9G8+VYkJioBADKZBOPG1cHo0bVhYsK5Esk4fNRY6MnJybh27RpevHiB9PR0nXV9+/bNlcAMjUQZD6ny3d1yGlN7JNb+WcSIiIg+TdmydmjYsCQCAoJRurQtVq9ugxo1XMQOiyhf6Z0I3b9/H4MGDUJKSgpSUlJga2uL2NhYWFhYwMHBodAmQpZ/LdJZju4WLFIkRES5QyKRYPHilihZ8jImTaoPa2tTsUMiynd6t33OmzcPvr6+uHbtGszMzLBz506cOnUKXl5emDhxYl7EaBDMg7dpH6c71ebs8kRUoCiVasyefQ7Hjj3VKVcoLDBnji+TIDJaeidCDx48wIABAyCVSiGTyaBUKlG8eHFMmDABixcvzosYDYI09bX2cYrnUBEjISLST1BQDPz8tmH58mv49tujeP06SeyQiAyG3omQiYkJpNKM3RQKBV68yBhDx9raGq9evcrd6AyU0sV4RtAmooJLEARs2nQXzZr9hrt3M37Mxcen4upVjn1G9Jbe13cqVaqEv/76C2XKlEHNmjXh7++P2NhY7Nu3DxUqVMiLGEUnTQzTWRZMeUspERm2qKhkjB17DIGB7/ozli9vjzVr/ODt7SxiZESGRe8WoTFjxsDJyUn7uEiRIpgxYwZiY2Mxa9asXA/QEFjemad9rDF3EjESIqIPO3kyBE2abNFJgvr3r4rjx3szCSL6D71bhKpUqaJ9rFAosGHDhlwNyBCZhe7TPla6NBcxEiKi7KWmqvDjj+exdu1NbZlCYYGlS1uiVSs3ESMjMly5NmLWvXv3MHjw4Nw6nEGRpidoHyf5/CBiJERE2YuKSsa2bfe0y02blsHp032ZBBG9h16J0Llz57BgwQIsXrwYYWEZ/WaCg4MxbNgwfPHFF1nOSl/YaKxKiB0CEVGWSpQogoULm8HMTIa5c32xbVsnODtbiR0WkUHL8aWxP/74Az/88ANsbW3x5s0b/PHHH5g0aRLmzJmDNm3a4ODBg3BzK3y/Okyf7RU7BCKiLEVEJMLSUg4bGzNtWefOFVG7titcXW1EjIyo4Mhxi9DmzZsxfvx4XLlyBUuXLkVsbCy2bduGAwcOYNasWYUyCQIAywerxQ6BiCiTgIAgNGmyBVOmnMq0jkkQUc7lOBEKCwtD69atAQAtW7aEiYkJJkyYgGLFiuVZcIZAonyjfZxQd7mIkRARAUlJ6Rg//jj69duP6OgU7NhxHwcOPBY7LKICK8eXxlJTU2FhYQEgY34auVyOokWL5llghkCa9Bwmce86HqYrfESMhoiM3Z07ERgy5DCCg99NAN2mjRvq1SspYlREBZtet8//8ccfsLS0BACo1Wrs3r0b9vb2OtsUpklXzYO26Cyr7SqJFAkRGTO1WoOVK69j/vyLUKkybkqxtDTBnDm+6NWrMiQSicgREhVcOU6EXFxcsHPnTu2yo6Mj9u3bp7ONRCLROxHaunUrNmzYgMjISFSsWBE//PADvL29s93+zZs3WLJkCY4dO4a4uDi4urpiypQpaNy4sV7nzQmrfw2kmOz1LSdaJaJ8Fx6egOHDA3Dx4nNtWdWqzlizxg9ubvbv2ZOIciLH3+wnT57M9ZMfPnwY8+bNw8yZM1G1alVs2rQJAwcORGBgIBQKRabtlUolBgwYAIVCgWXLlsHZ2RkvXrxAkSJFcj02SWq0znKK+4BcPwcR0fsEB8eiVavfER+fBgCQSIBRo2phwoS6MDWViRwdUeEgahPHxo0b0a1bN3Tp0gUAMHPmTJw+fRq7du3CoEGDMm2/a9cuxMfHY/v27ZDL5QCAEiXyZlwfs7CDOssam3J5ch4iouyULWuH6tWL4dSpZ3B1tcHKla3ZH4gol+XayNL6UiqVuHfvHurVq/cuGKkU9erVw61bt7Lc5+TJk6hWrRpmzZqFevXqoV27dlizZg3UanWuxyePuKB9nOLWO9ePT0T0IVKpBP7+rdCnTxWcOtWHSRBRHhCtRSg2NhZqtTrTJTCFQoGnT59muU9YWBguX76M9u3bY926dQgNDcXMmTOhUqkwYsQIvc4vkWT8y4750+3ax6qitd67LX2ct68pX1vDwPoQl0qlwZIlV1CnjisaNSoFIKMuihWzxuLFLUSOznjxfWE48qoOClTvX0EQoFAoMHv2bMhkMlSuXBkRERHYsGGD3omQg0POBxyzqfw5bIpwgLK8olDwtTUkrI/89/RpLHr33o1Ll57D1dUGd+8OBcC6MCSsi8JLtETI3t4eMpkM0dG6nZKjo6Ph6OiY5T5OTk4wMTGBTPauk2C5cuUQGRkJpVIJU1PTHJ8/JiYB2U2NZhJ5FXb/Wo5S2gNRCVlvTB9NIsn4cImOToAgiB0NsT7ynyAI2LnzASZNOonERCUA4NWrROzffx/9+3/GujAAfF8YDqlUv0aMnPqoRCg0NBS7du1CWFgYpk6dCoVCgTNnzsDFxQUVKlTI0TFMTU3h5eWFS5cuoXnz5gAAjUaDS5cuoXfvrPvkVK9eHQcPHoRGo4FUmtG9KSQkBE5OTnolQQAgCMj2j9r26OeZtqW88766oPzH+sgfcXGpmDDhOPbtezcqdOnStli9ug1q1nQBwLowJKwL8eXV6693Z+mrV6+iffv2uHv3Lo4ePYrk5GQAwKNHj7B8uX5TUAwYMAA7d+7Enj17EBwcjBkzZiAlJQWdO3cGAHz33Xf4+eeftdv36NEDcXFx+PHHH/HPP//g9OnTWLt2LXr16qXv03gviSpJ+/hNvVW5emwiogsXwtCkyWadJKh7dy+cOtUHNWq4iBgZkfHRu0Xo559/xujRozFgwAD4+LybcqJOnTr47bff9DqWn58fYmJi4O/vj8jISHh6emL9+vXaS2MvX77UtvwAQPHixbFhwwbMmzcPHTp0gLOzM/r27YtvvvlG36eRY2luuZtkEZHxUirVWLjwIpYvv6b9dWtra4aff26BDh3cxQ2OyEjpnQg9fvwYixYtylTu4OCA2NjYLPZ4v969e2d7KWzLli2Zynx8fHRGuM5tsriHugW8VYCIcsmLFwlYv/62NgmqX78EVqxow9niiUSk96UxGxsbREZGZip/8OABnJ2dcyUoMUnTYsQOgYgKqTJl7PDjj00gl0sxbVpD7NrVlUkQkcj0ToTatm2LRYsWITIyEhKJBBqNBjdu3MCCBQvQsWPHPAgxf9kdaa19nFxppIiREFFBFx2dguTkdJ2ynj0r49y5fhgxoiakUrY4E4lN70RozJgxKFeuHJo0aYLk5GS0bdsWvXv3ho+PD4YOHZoXMeYbSbruLfKCiYVIkRBRQXfyZAgaN96MmTPP6pRLJBKUK8fJUokMhUQQPu6GtBcvXuDJkydISkpCpUqVUKZMmVwOLW9FR2ceR8g0ZA9sz/bTLkf2iQMkos1CUuhJJICjow2iojg+hyFgfeSO1FQVfvzxPNauvakt27q1I1q0yPl8hawLw8G6MBxSad4MbKl3Z+nr16+jRo0acHFxgYtL4brN0+TNE+3jdMeaTIKISC/370di6NAAPHgQpS1r2rQMvL0Lfv9JosJK70Sof//+KFq0KNq1a4cOHTqgfPnyeRGXKGSx97SPlSVaiRgJERUkGo2A9etvYfbsc0hLy5gE2sxMhunTG2HgwGqQ8O5TIoOldyJ09uxZHD58GAcPHsS6devg4eGB9u3bo127dihWrFhexJhvNBZFtY/VVq4iRkJEBUVERCJGjTqCU6eeacs8PR2xZo0fPD2zni6IiAzHR/cRAjJmgz948CAOHTqEp0+fokaNGti8eXNuxpdnsuoj5LS5iPZxXKtApDvXy+eojAuvvRsW1of+goJi0L79DkRHp2jLBg+ujqlTG8Dc/OOncmRdGA7WheHIqz5Cn9QJpmTJkhg0aBDGjRsHd3d3XLt2LbfiEoXG9N2dHCo7TxEjIaKCoGxZO7i7OwAAnJ2tsGNHZ8ye3eSTkiAiyl8f/W69ceMGDhw4gCNHjiAtLQ3NmjXD2LFjczO2fCfIzN89NuPtrUT0fjKZFCtXtsG8eRcwa1YTKBQccoOooPmoucYOHTqE169fo379+pg6dSqaNWsGC4uC/wEgS3kJAFBblhA5EiIyNGq1BitXXkedOiVQq9a7O2ZLlCiClSvbiBgZEX0KvROha9euYeDAgWjTpg0cHBzyIiZxaNTah7Lk5yIGQkSGJjw8AcOHB+DixecoVcoWp071ho2NmdhhEVEu0DsR2r59e17EITrpv5IfQSITMRIiMiT79j3C+PHHER+fBgAIC4vH6dPP0L49Z4snKgxylAidOHECjRo1glwux4kTJ967bbNmzXIlsPxmEn1b+1giqLPfkIiMQkJCGqZMOYUdO+5ry1xdbbByZWvUq1dSxMiIKDflKBEaPnw4Lly4AIVCgeHDh2e7nUQiwYMHD3ItuPxke6aP9nFaqQ4iRkJEYrt27QWGDQvAs2fx2rKOHT2wcGEz2NmZv2dPIipocpQIPXz4MMvHhZWyWEOxQyAiEahUGixZcgWLF1+GWp0xaIy1tSnmz2+Krl09OUI0USGk9zhCe/fuhVKpzFSuVCqxd+/e3Igp/6nTdBZTPQaJFAgRiSkkJA7+/le1SVDNmi44daoPunWrxCSIqJDSOxGaPHkyEhISMpUnJSVh8uTJuRJUfpMo3/yngB94RMaofHkHTJvWEDKZBN99Vxf79nVD6dK2YodFRHlI77vGBEHI8pdRREQEbGxyf+jr/GD+zw7tY6UzL4sRGYu4uFRYWJjAzOzdR+HXX/ugQYNSnCeMyEjkOBHq2LEjJBIJJBIJ+vXrBxOTd7uq1Wo8f/4cDRsWzCRC/vqS9rFEGf+eLYmosLhwIQzDhwegY0cPzJjRWFsukUiYBBEZkRwnQs2bNwcAPHjwAA0aNICVlZV2nVwuh6urK1q2bJn7EeYDk5i72scplYaJGAkR5TWlUo2FCy9i+fJrEARg1aobaNq0LBo1KiV2aEQkghwnQiNGjAAAuLq6ws/PD2ZmhWdUVZVDNcgSn2U8VviIHA0R5ZWgoBgMGXIYd+++1pY1aFAS5ctzbkEiY6V3H6FOnTrlRRzi+lefJ0FeRMRAiCgvCIKAzZv/wrRpp5GSogIAyOVSTJ5cH8OG1YBUyhskiIxVjhKhWrVqITAwEA4ODqhZs+Z7byO9evVqrgWXXySp0WKHQER5JCoqGWPHHkNgYLC2rHx5e6xZ4wdvb2cRIyMiQ5CjRGjy5MmwtrbWPi5s42nIX18QOwQiygNBQTHo2PEPvH6dpC3r378qZsxoBEtLuYiREZGhyFEi9O/LYZ07d86zYMQiETTaxxpLFxEjIaLcVLq0LVxdrfH6dRIUCgssXdoSrVq5iR0WERkQvQdUvHfvHh49eqRdPn78OIYNG4bFixdnOeK0wRME3eVC1tpFZMzkchlWr/ZD27blcfp0XyZBRJSJ3onQtGnTEBISAgAICwvDmDFjYGFhgcDAQPz000+5HV+eM/vXYIpEVHBpNAJ++eUm/vrrtU55uXL22LixA5ydrbLZk4iMmd6JUEhICDw9PQEAAQEBqFWrFn7++WfMmzcPR48ezfUA85rN+cHaxyrbiiJGQkQfKyIiET167MbUqacxZMhhJCenix0SERUQeidCgiBAo8noU3Pp0iU0atQIAFC8eHHExsbmbnT5QOVQVfs4sfZiESMhoo8REBCEJk224NSpjLHAnjyJwYkT/4gcFREVFHonQpUrV8bq1auxd+9eXLt2DU2aNAEAPH/+HI6OBW9YesHUTvs4nYMpEhUYSUnpGD/+OPr124/o6BQAgLOzFXbs6Iz27d1Fjo6ICgq9B1ScMmUKJkyYgOPHj2PIkCEoXbo0AODIkSPw8Sl4iYTpq9Nih0BEerpzJwJDhhxGcPC7Vug2bdyweHFLKBQWIkZGRAWN3olQxYoVceDAgUzl3333HaRSvRuYDIus8EwbQlQYqdUarFx5HfPnX4RKlXGJ3tLSBLNnN0Hv3lUK3RhnRJT39E6E3vr7778RHJwxUmv58uXh5eWVa0Hll0wzzUs/+uUgonzw5EmMThJUtaoz1qzxg5sb5wojoo+j9zd/dHQ0Ro8ejWvXrqFIkYx5ud68eYPatWtjyZIlcHBwyPUg84os/rHYIRCRHipWdMSkSfXw44/nMWpULUyYUBempjKxwyKiAkzva1mzZ89GcnIyDh06hKtXr+Lq1as4ePAgEhMTMWfOnLyIMe9I3j391HLdRQyEiLKSmKjUtv68NXx4DRw50hNTpzZgEkREn0zvROjcuXOYPn063NzejdBavnx5TJ8+HWfPns3V4PKTxtRW7BCI6F+uXXsBX98tWLz4sk65TCZFtWrFRIqKiAobvRMhjUYDuTzzZIUmJiba8YUKDuHDmxBRvlKpNPjpp0vo0GEHnj2Lx+LFV3D16guxwyKiQkrvRKhOnTr48ccfERERoS2LiIjAvHnzULdu3VwNLq/J3jzVPpao00SMhIgAICQkDh067MBPP12CWp3xQ+Wzz4pzegwiyjN6d5aeNm0ahg4dimbNmqFYsYzm6VevXqFChQoFbq4xafJL7WNJeqKIkRAZN0EQsHPnA0yefBKJiRmTN8tkEowbVwejR9eGiUkBH5qDiAyW3olQ8eLFsWfPHly6dEl7+7ybmxvq1auX68HlvXeX8jRWJUSMg8h4xcWl4rvvTmDv3kfastKlbbF6dRvUqOEiYmREZAz0SoQOHz6MEydOID09HXXr1kWfPn3yKq78IX3X10ll5yliIETGKSgoBl277kJ4eIK2rHt3L8yd6wtra1MRIyMiY5HjROj333/HrFmzULp0aZibm+PYsWMIDQ3FxIkT8zK+PGX64uS7BY4qTZTvSpQogiJFzBAengA7OzMsWtQCHTpwnjAiyj85vvC+detWjBgxAkeOHMG+ffswf/58bNu2LS9jy3M6AyoKBe2ON6KCz9zcBGvW+KF587I4fbovkyAiync5ToTCwsLQsWNH7XL79u2hUqnw+vXrvIgrX8iSQrWPVYpq4gVCZAQEQcDmzXfx6FG0TrmnpyN+/70TXFxsRIqMiIxZji+NKZVKWFpaapelUinkcjnS0grHbedqq1Jih0BUaEVFJWPs2GMIDAyGl5cTAgN7wMyMc/sRkfj0+iRaunQpLCwstMvp6elYvXo1bGze/ZKbPHly7kWXlzRq3WUZO2YS5YWTJ0MwatQRvH6dBAC4dy8SR48+Rfv2vAxGROLLcSJUs2ZN/PPPPzplPj4+CAsL0y5LJJLciyyvadLFjoCoUEtNVWHOnHNYt+6WtkyhsMDSpS3RqpXbe/YkIso/OU6EtmzZkpdxiEpZrJHYIRAVKvfvR2Lo0MN48OBdfyBf39Lw92/NUaKJyKAY7UV6Seq7D2iJMl7ESIgKD41GwPr1tzB79jmkpWVcfjYzk2HatIYYONAHUmkBajUmIqNgvImQMlb7WKqMEy8QokLk/v0oTJt2BhpNxjxhnp6OWLPGD56ejiJHRkSUNaOdwMck/oH2scaiuIiREBUelSs7YfToWgCAwYOr48iRnkyCiMigGW+LUHqS9rHakokQ0cdITk6HubmJziWvcePqoEmT0qhTh/P3EZHhM9oWIUH67nZ5lVMtESMhKpju3IlAs2a/YdWq6zrlcrmMSRARFRgflQhdv34d48ePx5dffomIiAgAwN69e3H9+vUP7Gk4pOnvJnkU5BzRliin1GoN/P2vok2bbQgOjsW8eRdw926E2GEREX0UvROhI0eOYODAgTA3N8f9+/ehVCoBAImJiVi7dm2uB5hXZG+C3y2oU8QLhKgACQ9PQJcuf2LOnPNQqTLm56tUyQlWVhyQlIgKJr0TodWrV2PmzJmYM2cOTEzedTGqXr067t+/n6vB5SVB+i52wUwhYiREBcO+fY/QpMlmXLz4HAAgkQDfflsLhw51h5ubvcjRERF9HL07S//zzz+oUaNGpnIbGxu8efMmV4LKDyZx7+4aE0yLiBgJkWFLSEjDlCmnsGPHux86rq42WLmyNerVKyliZEREn07vRMjR0RGhoaEoUUK3M+SNGzdQsmTB+VBUW7pA/vaxRTFRYyEyVEFBMejRYw+ePXs36GjHjh746admsLU1FzEyIqLcofelsW7duuHHH3/EnTt3IJFIEBERgf3792PBggXo0aNHXsSYJ8xC979bkFlkvyGRESte3AYmJhkfE9bWplixojXWrvVjEkREhYbeidCgQYPQrl079O/fH8nJyejduze+//57fPnll+jTp89HBbF161Y0bdoUVapUQdeuXXH37t0c7Xfo0CF4eHhg2LBhep9TY/mu9Upt5ar3/kTGwMpKjjVr/FC/fgmcOtUH3bpVKliTKxMRfYBEEAThY3ZUKpUIDQ1FcnIy3NzcYGX1cRMpHj58GN999x1mzpyJqlWrYtOmTQgMDERgYCAUiuw7MT9//hw9e/ZEyZIlYWtri1WrVul34uVFAGXGLfSRfQtO36bCRCIBHB1tEBWVgI/7K6TcJSAg4B94ejqgTBk73TWCwAQoH/G9YThYF4ZDKgUUitwf7uajB1Q0NTVF+fLl4e3t/dFJEABs3LgR3bp1Q5cuXVC+fHnMnDkT5ubm2LVrV7b7qNVqjB8/HiNHjixQ/ZKIDFVcXCq++eYQ+vXbi6FDDyM9Xa2znkkQERVWeneW7tOnz3s/FDdv3pzjYymVSty7dw+DBw/WlkmlUtSrVw+3bt3Kdr+VK1dCoVCga9euuHHjRo7PlxWNvAj4GS+Ot687X39xXbgQhmHDAvDiRSIA4MaNVzh27Cnatq0gcmTGi+8Nw8G6MBx5VQd6J0Kenp46yyqVCg8ePMCTJ0/QsWNHvY4VGxsLtVqd6RKYQqHA06dPs9zn+vXr+PPPP7F37169zpUdafobODpyZGkx5UVTJ32YUqnGtGmnsHDhBW2Tv729Odata48vvqgkbnAEgO8NQ8K6KLz0ToSmTJmSZfny5cuRnJz8yQG9T2JiIr777jvMnj0bDg4OuXLM9KL1EB+V8OENKddJJBkfLtHRvPae34KCYjB48GHcvftaW9agQUls2/YFLCwkiOJ7QlR8bxgO1oXhkEoBB4fcT0hzbfb5Dh06oGvXrpg4cWKO97G3t4dMJkN0dLROeXR0NBwdHTNtHxYWhvDwcAwdOlRbptG8Hea/EgIDA1GqVCn9Alen8I9bZIIA1kE+EQQBmzf/hWnTTiMlRQUAkMulmDy5PoYPr4GiRYuwU6gB4XvDcLAuxJdXr3+uJUK3bt2Cqal+8w2ZmprCy8sLly5dQvPmzQFkJDaXLl1C7969M21frlw5HDhwQKds6dKlSEpKwtSpU1GsmP4DI6YXrav3PkQF1V9/vcaECce1y+XL22PNGj94ezuzDwQRGSW9E6ERI0boLAuCgMjISPz9998fNZ7PgAEDMHHiRFSuXBne3t7YtGkTUlJS0LlzZwDAd999B2dnZ4wbNw5mZmZwd3fX2b9IkYzpMf5bTkSZeXs7Y8iQz7BmzQ30718VM2Y0gqWl/MM7EhEVUnonQjY2utfnJBIJypYti1GjRqFBgwZ6B+Dn54eYmBj4+/sjMjISnp6eWL9+vfbS2MuXLyGVfvRd/kRGLS1NBVNTmc6dnlOn1kfTpmXQpElpESMjIjIMeg2oqFarcfPmTbi7u8PW1jYv48p7/xtQMdlzGJJqzhc7GqPEgcry1v37kRg69DD69auKr76q9sHtWR+Gg3VhOFgXhsMgBlSUyWT46quvCtQs8x8kqD+8DVEBotEIWLfuJlq1+h0PHkRjxowzePQo+sM7EhEZIb0vjVWoUAHPnz8vNCM6S1Nef3gjogIiIiIRo0YdwalTz7RlZcvaixgREZFh07vzzejRo7FgwQKcOnUKr1+/RmJios6/gkblVFPsEIhyRUBAEJo02aKTBA0eXB1HjvSEh0f28/YRERmzHLcIrVixAl999RUGDRoEABg6dKhOB8y3kzI+ePAg96PMQ5K0WLFDIPokSUnpmD79DDZvvqstc3a2gr9/K/j6lhEvMCKiAiDHidDKlSvRo0cPveYSKwhUDlXEDoHoowUHx6J3770IDn6X0Ldp44bFi1tCobAQMTIiooIhx4nQ25vLatWqlWfBiIOjyFHB5eRkqZ0p3tLSBHPm+KJXr8qcLZ6IKIf06iPED1ciw1KkiBlWrmyDzz4rhhMn+qB37yp8nxIR6UGvu8ZatWr1wQ/Zq1evflJARJS9/fsf47PPisPV9d1YGrVru+Lw4R5MgIiIPoJeidDIkSMzjSxNRHkvISENU6acwo4d91G/fgn8+ecXkMneNegyCSIi+jh6JUJt27aFQsHbcIny07VrLzBsWACePYsHAFy48BxHjz5FmzblRY6MiKjgy3EixF+cRPlLpdJgyZIrWLz4MtTqjJsVrK1NMX9+U7Ru7SZydEREhYPed40RUd4LCYnDsGEBuH79pbasZk0XrFrVBqVLF/B5/oiIDEiOE6GHDx/mZRyikWhUYodApCUIAnbufIDJk08iMVEJAJDJJBg3rg5Gj64NExO9B4MnIqL30HuuscJGY+YgdghEWrdvR2DkyEDtcunStli9ug1q1HARMSoiosLL6H9eCqa8zECGw8enGPr29QYAdO/uhVOn+jAJIiLKQ0bfIiSYFhE7BDJi6elqmJhIdW5GmDmzMZo3L8sO0URE+cDoW4Q0crYIkTiCgmLQps027NhxX6fcykrOJIiIKJ8YfSJElN8EQcCmTXfRrNlvuHv3NSZPPomnT2M/vCMREeU6o780RpSfoqKSMXbsMQQGBmvLihe3Rmoq714kIhIDEyGifHLyZAhGjTqC16+TtGX9+nlj5szGsLSUixgZEZHxMvpESDB3FDsEKuRSU1X48cfzWLv2prZMobDA0qUt0aoV+wIREYnJqBMhARKAU4dQHnr6NBYDBhzAgwdR2rKmTctg2bJWcHa2EjEyIiICjDwRkoDThlDesrMzR2xsCgDAzEyG6dMbYeDAapy7j4jIQPCuMaI85OBgAX//1vDycsLRo73w9dc+TIKIiAyIUSdCSucGYodAhcyRI8GIiEjSKWvSpDSOH+8FT0/2RyMiMjRGnQiBl8YolyQlpWP8+OPo02cfRo8+AkHQ/duSyYz8rUZEZKCM+tNZ5VBV7BCoELhzJwLNm/+GzZvvAgBOnAjB0aNPRY6KiIhywqg7S3PCVfoUarUGK1dex/z5F6FSaQAAlpYmmDPHFy1blhM5OiIiygmjToSIPlZ4eAKGDw/AxYvPtWVVqzpjzRo/uLnZixgZERHpg4kQkZ727n2ECROOIz4+DUDGUFSjRtXChAl1YWoqEzk6IiLSBxMhIj1cv/4CgwYd0i67utpg5crWqFevpIhRERHRxzLqztJE+qpRwwVdu3oCADp29MCpU32YBBERFWDG3SIkaMSOgAycRiNAKtUdAHH+/KZo0aIcPv/cnYMjEhEVcEbdIiRNfiF2CGTAQkLi0K7dduzb90in3MbGDB07ejAJIiIqBIy6RSitdEexQyADJAgCdu58gMmTTyIxUYknT46jRg0XuLraiB0aERHlMqNOhCA1FTsCMjBxcamYMOE49u17rC2zszNHTEwKEyEiokLIuBMhon+5cCEMw4cH4MWLRG1Z9+5emDvXF9bWTJqJiAojJkJk9JRKNRYsuIgVK67h7RRhtrZm+PnnFujQwV3c4IiIKE8xESKjFhISh6+/Poi7d19ry+rXL4EVK9rwUhgRkRFgIkRGzcLCBOHhCQAAuVyKyZPrY9iwGplumSciosLJqG+fJ3J2tsaSJS1RoYIDAgJ6YMSImkyCiIiMCFuEyKicOfMMVaoUhYODhbasdWs3NGtWBnI55wkjIjI2bBEio5CaqsIPP5xG1667MH78cQhve0X/D5MgIiLjxESICr379yPRqtXvWLv2JgDg4MEnOHkyRNygiIjIIPDSGBVaGo2A9etvYfbsc0hLUwMAzMxkmD69EZo2LSNucEREZBCYCFGhFBGRiFGjjuDUqWfaMk9PR6xZ4wdPT0cRIyMiIkPCRIgKncDAYIwZcxTR0SnassGDq2Pq1AYwN+efPBERvcNvBSpUrlwJR9+++7TLRYtaYfnyVvD1LSNeUEREZLDYWZoKlVq1XODnVx5Axm3xZ870ZRJERETZYosQFWiCIEAieTcAokQiweLFLdC6tRu+/LKSzjoiIqL/YosQFVjh4Qno3PkPHD36VKfcwcEC3bt7MQkiIqIPYosQFUj79j3C+PHHER+fhkePonHqVF84O1uJHRYRERUwTISoQElISMOUKaewY8d9bZmZmQkiIhKZCBERkd6YCFGBce3aCwwdGoDQ0HhtWceOHli4sBns7MxFjIyIiAoqJkJk8FQqDRYvvowlS65Arc6YI8za2hTz5zdF166e7AtEREQfzbgTIUEtdgT0AaGh8Rgy5DCuX3+pLatZ0wWrVrVB6dK2IkZGRESFgVEnQhrrUmKHQB8glUrw+HEMAEAmk2DcuDoYPbo2TEx4wyMREX06o/42EcBLKoauRIki+OmnZihd2hYHDnyJ8ePrMgkiIqJcY9QtQmR4Ll9+Di8vJ9jYmGnLOnWqiDZtynOeMCIiynUG8dN669ataNq0KapUqYKuXbvi7t272W67c+dO9OzZEzVr1kTNmjXRv3//925PBYNSqcbs2efw+ec7MXnyqUzrmQQREVFeED0ROnz4MObNm4fhw4djz549qFixIgYOHIjo6Ogst79y5Qratm2LzZs3Y/v27ShevDi++uorRERE5HPklFuCgmLg57cNy5dfgyAAO3fex6lTIWKHRURERkD0RGjjxo3o1q0bunTpgvLly2PmzJkwNzfHrl27stz+559/Rq9eveDp6Qk3NzfMmTMHGo0Gly5dyufI6VMJgoC1a6+jadPfcPfuawCAXC7FtGkN0bhxaZGjIyIiYyDq9QalUol79+5h8ODB2jKpVIp69erh1q1bOTpGSkoKVCoVbG31v5VaIsn4R/kvKioZY8YcQ2BgsLasfHl7rFnjh6pVnUWMzHi9fS/wPSE+1oXhYF0YjryqA1ETodjYWKjVaigUCp1yhUKBp0+fZrOXrkWLFqFo0aKoV6+e3ud3sLcG7G303o8+zZEjQejffx9evUrUlg0dWgOLFrWEpaVcxMgIABQKvicMBevCcLAuCq8C3QN13bp1OHz4MDZv3gwzM7MP7/AfMbGJ0KgT8iAyys6lS8/RocNO7bKjoyWWLm2BVq3ckJyciuTkVBGjM24SScaHfXR0AgRB7GiMG+vCcLAuDIdUCjg45H5CKmoiZG9vD5lMlqljdHR0NBwdHd+774YNG7Bu3Tps3LgRFStW/KjzCwL4h53Patd2RdOmZXDyZAiaNi2DrVu7wMREYD0YEL4vDAfrwnCwLsSXV6+/qJ2lTU1N4eXlpdPR+W3HZx8fn2z3++WXX7Bq1SqsX78eVapUyY9QKZdIJBIsW9YKCxY0w/btnVCsmLXYIRERkRET/a6xAQMGYOfOndizZw+Cg4MxY8YMpKSkoHPnzgCA7777Dj///LN2+3Xr1mHZsmWYO3cuXF1dERkZicjISCQlJYn1FCgbERFJ6NlzD86eDdUpd3a2woABVTlZKhERiU70PkJ+fn6IiYmBv78/IiMj4enpifXr12svjb18+RJS6bt8bfv27UhPT8eoUaN0jjNixAiMHDkyX2On7AUGBmPMmKOIjk7BvXuROHWqDxwcLMQOi4iISIfoiRAA9O7dG717985y3ZYtW3SWT548mR8h0UdKSkrH9OlnsHnzu9G+NRoBYWFvmAgREZHBMYhEiAqHO3ciMHToYQQFxWrL2rRxw+LFLaFQMAkiIiLDw0SIPplarcHKldcxf/5FqFQaAIClpQnmzPFFr16V2ReIiIgMFhMh+iQvXiRg+PAAXLjwXFtWtaoz1qzxg5ubvYiRERERfRgTIfokqakq3LqVMeGtRAKMGlULEybUhampTOTIiIiIPkz02+epYCtXzh5z5/rC1dUGe/Z0xdSpDZgEERFRgWHULUIScJhQfd28+RIVKzrqzAnWo4cXOnRwh7W1qYiRERER6c+oW4TUli5ih1BgqFQa/PTTJbRtux0zZpzVWSeRSJgEERFRgWTUiRBk5mJHUCCEhMShQ4cd+OmnS1CrBfz66x2cPx/64R2JiIgMnFFfGoPEuPPADxEEATt3PsDkySeRmKgEAMhkEowbVwd16pQQOToiIqJPZ7SJkMa8qNghGLS4uFR8990J7N37SFtWurQtVq9ugxo1eEmRiIgKB6NNhCh7Fy+GYfjwQISHJ2jLunf3wty5vuwLREREhQoTIdJx8WIYOnX6A8L/bqizszPDokUt0KGDu7iBERER5QF2kiEdtWu7om7djP4/DRqUxOnTfZkEERFRocUWIdIhk0mxcmUb7N//GIMHV4dUynnCiIio8GKLkBGLikrGgAH7ceVKuE65q6sNhg79jEkQEREVemwRMlInT4Zg1KgjeP06CX/99RqnTvWBjY2Z2GERERHlK7YIGZnUVBW+//4UunffjdevkwAASUnpCA6OFTkyIiKi/McWISNy/34khg49jAcPorVlTZuWwbJlreDsbCViZEREROJgImQENBoB69ffwuzZ55CWpgYAmJnJMH16IwwcWA0SCfsCERGRcWIiVMhFRCRi1KgjOHXqmbbM09MRa9b4wdPTUcTIiIiIxMdEqJCLjU3FxYvPtcuDB1fH1KkNYG7OqiciImJn6UKuYkVHTJ/eCEWLWmHHjs6YPbsJkyAiIqL/4TdiIfP335GoUMEeZmbvqnbgwGr44gtP2NmZixgZERGR4WGLUCGhVmvg738VLVtuxdy5F3TWSSQSJkFERERZYCJUCISHJ6BLlz8xZ855qFQarF59A5cvh394RyIiIiPHS2MF3L59jzB+/HHEx6cBACQSYNSoWqhevZjIkRERERk+JkIFVEJCGqZMOYUdO+5ry1xdbbByZWvUq1dSxMiIiIgKDiZCBdC1ay8wbFgAnj2L15Z17OiBhQubsS8QERGRHpgIFTAXLoThiy/+hFotAACsrU0xf35TdO3qyRGiiYiI9GS0naWlqa/FDuGj1KrlgqpVnQEANWu64NSpPujWrRKTICIioo/AFqECRi6XYdWqNti37zFGjqwJExOjzWWJiIg+mdEmQmkl24kdwgfFxaVi0qSTGDr0M20rEACUK2ePMWNqixgZkXERBAEajRoajSZfzieRAKmpqUhPV0IQ8uWUlA3WRf6SyUwglebvD3yjTYQM3YULYRg+PAAvXiTi7t0IHD/eG5aWcrHDIjI6KlU64uNjkJ6emq/njYmR5lviRe/HushPEtjbO8HMzCLfzshEyMAolWosWHARK1Zc0/76iIpKxqNH0fDx4dhARPlJEARER7+CVCqFra0jZDKTfOuPJ5NJtDdFkLhYF/lDEAQkJsYjNjYSRYuWyLeWISZCBiQoKAZDhhzG3bvvOnI3aFASK1a0houLjYiRERknlSodgqCBra0TTE3zd2gKExMpVCq2QhgC1kX+sba2RUxMCtRqFaRS03w5JxMhAyAIAjZv/gvTpp1GSooKACCXSzF5cn0MG1YDUinvCCMSk0TCmxKI8oMYd0AzERJZVFQyxo49hsDAYG1Z+fL2WLPGD97ezu/Zk4iIiD4Vf+aI7MWLBBw//o92uX//qjh+vDeTICIionzAREhk3t7OmDSpHhQKC2zZ8jkWLmzGu8OIiEQUGhqCDh1aITk5SexQCp1Bg/rj9OkTYoehg5fG8tmTJzEoU8YWcrlMWzZ8eA306FEZTk6WIkZGRIXJjz/OQEDAQQCATCZD0aLO8PVthoEDh8DMzExn2wsXzmHbti149OghNBo1ypZ1Q+fOXeHn1z7TcU+fPoE//9yBJ08eQaPRwMXFFU2aNEOXLt1QpIhtvjy3vLZmzUp06dINlpZWYoeSZ3bt2olt27YgJiYabm4VMGbMBFSqVDnb7VUqFbZs2YiAgIOIiopEyZKlMXToSNSpU0+7ze3bN/H771vw6NEDREdHYe7cRWjUqInOcfr1G4jlyxejUSPffB8vKDuGEYUR0GgErFt3E02bbsHixVd01slkUiZBRJTrateuh337ArFz5z6MHDkW+/btxoYNa3W2+fPP7Zg8eRyqVKmKdet+xaZN29GsWUssWjQPK1Ys1dl27dqVmD59Cjw9vbBokT82b96BESNGIyjoMQIDD+fb80pPT8+zY7969QoXL57LMgnUR17G+KlOnDiKFSuWYMCAb7Bhw28oX94dY8eORGxsTLb7rFu3Cvv27caYMROwZctOdOzYBVOmTMDjxw+126SkpKB8+QoYO3ZitsepU6cekpOTcfnyxVx9Tp+CLUL5ICIiEaNGHcGpU88AAEuWXEGLFmVRvXpxkSMjosLM1FQOhcIRAODsXAxHjtTC9evvfohFRLzCihVL0bVrDwwePFxb3qNHb8jlJli6dBF8fZvDy6sy7t//G1u2bMSoUePQrVsP7bbFi7ugZs06SEhIyDaO168jsHLlMly9ehnp6UqULl0WY8dOhJdXZfz44wwkJiZg3ryftdsvW/Yznjx5hBUr1gEARowYhHLl3CCTmeDo0cMoV648FApHaDQazJo1T7ufSqXC55+3wogRY9CmTTtoNBps3boJ+/fvQXR0NEqWLIX+/QfC17d5trGePHkM5cu7w8mpqLYsPj4OCxfOx507t5CQ8AauriXQp88AtGjRWrtNVjEuX74WT58GYeVKf9y9ewvm5haoVas2Ro4cBzs7OwDA5csXsWnTBvzzTzCkUhkqV66Cb78dD1fXEtnG+Km2b9+K9u07om3bDgCACRMm49Kl8zh4cD/69Omf5T5HjhxG375foW7dBgCATp2+wPXrV7B9+1ZMmzYbAFC3bn3UrVv/veeWyWSoU6ceTpw4gnr1GuTek/oETITyWEBAEMaOPYbo6BRt2Tff+KBSJScRoyKiT2EasgdWd36EJD0xz84hAfB2CD9Bbo2kat9DWbrjRx/v6dMg/P33XTg7v/sBdvr0CahUKvTo0SfT9p9/3gVr167C8eNH4OVVGUePBsLCwhKdO3fN8vg2NlmPdZacnIwRIwbByako5s9fDIVCgUePHkIQ9BuXJyDgEDp16oLVqzcAAJ4/f44ffpiI5ORkWFpmtKhfuXIJqampaNzYFwCwZctGHD0agPHjJ6NEiZK4c+cWZs+eBjs7e/j4fJblee7evYWKFT11ytLSlPDw8ETv3v1gaWmFS5fOY86c6XB1LaFzOem/MSYkJGDUqKFo374jRo0ai7S0VKxevRzTpk2Cv/8aAEBqagq6d+8FN7cKSElJxvr1azBlynhs3Ph7tpeONm/+P2zZsvG9r9eWLX+gWLHMg/Cmp6fj8eOH6NNngLZMKpWiRo1auHfvbrbHS09Ph5mZ7rg+ZmbmuHv39nvjyEqlSl747bdNeu+XV5gI5ZGkpHRMn34Gmze/+8MqWtQKy5e3gq9vGfECI6JPZnlvGUziH+f7OfVNhC5ePI8WLRpCrVZDqVRCKpVizJjvtOvDwkJhbW0NR0fHTPvK5XK4uLgiLCyjJfv581C4uLjCxES/r41jxwIRFxeH9es3a/sQlShRUq9jAEDJkiUxbNi32mUXlxKwsLDA2bOn0Lp1W+25GjRoBEtLKyiVSmzZshFLl65C5creAABX1xK4e/c29u3bnW0i9OrVK1SsWEmnrGjRoujZ812y+MUX3XH16mWcPHlcJxH6b4y//roe7u4eOq1tkydPQ+fObREa+gylSpVGkybNdM41efJ0tGvXHCEhT1GuXPksY+zYsQuaNm3x3tcrqzoFMlq31Go1HBwcdModHBzw7FlItserVasOtm//HVWrVoerawncuHEVZ86c/KipRxwdnfD6dQQ0Go1B9BNiIpQH7tyJwJAhhxEcHKsta93aDUuWtIRCkX/zpxBR3kiuPBpWt+fka4tQste379s8Sz4+n2H8+MlISUnBzp2/QyaTZfrizamPnXD0yZPHcHf3+OSO1B4euq00JiYm8PVtgaNHA9G6dVukpKTg/PkzmDFjLgDg+fMwpKamYsyY4Tr7paeno0IFj2zPk5aWClNT3ZYPtVqNX3/dgJMnjyEyMhIqVTqUSiXMzHRHG/9vjEFBT3Dz5nW0aNEw03nCw5+jVKnSCAsLxfr1a3D//j3Ex8dpW8oiIl5lmwgVKWKb7x3Tv/12PBYunINevb6ARCKBi4sr/Pw64NCh/Xofy8zMDBqNBunpmV9DMTARymXnzoXiyy93a4djt7Q0wezZTdC7dxVRRswkotynLN3xky5T5URuTOtgYWGhbX2ZPHka+vfvgYMH96Jdu44AgJIlSyExMRFRUZFwdNS9XJ+eno4XL56jevUa2m3v3r0NlUqlV6vQf+9Q+y+JRALhP1mWSqXKtJ25eeYfkS1btsaIEYMQGxuDa9euwMzMTHsXU0pKRneEhQuX6vT3ATJau7JjZ2eXqb/T1q2b8ccf2zBq1DiUK1ceFhYW8Pf/GSqVbofo/8aYkpKC+vUbYujQUZnO87bv1sSJY1CsWHFMnDgVjo5O0Gg06Nv3S6SnZ34N3vqUS2O2tnaQyWSIidHtGB0TEwOFQpHt8ezt7TFv3s9IS0vDmzfxcHR0wurVy+Hi4vreOLLy5s0bWFhYGEQSBDARynW1arnA3d0B9+9HoWpVZ6xZ4wc3N3uxwyIiIyeVStGnzwCsWLEELVq0hpmZORo3bobVq5dj27bfMHLkGJ3t9+7dhZSUFDRv3goA0KJFa/z553bs3v2HTmfptxISErLsJ1S+fAUcPLgXb97EZ9mKYWdnj3/+CdYpCwp6BJnsw19PVapURdGixXDixFFcvnwRvr7NtUla2bJlYWpqioiIV9leBstKhQoeCAl5qlN2585tNGjQGK1a+QEANBoNQkNDUbZs2fcey93dA2fOnESxYsWzTB7j4+MQGvoMEyd+j6pVfbTn+pBPuTQml8vh7l4RN25c1d7artFocOPGNXTu3O2D5zYzM4OTU1GoVCqcOXPyg3Fk5enT4Pe2yuU3JkK5zMzMBGvW+GHPnkcYN64OTE1lH96JiCgf+Po2x6pV/ti16w/07NkHxYoVw7Bho7BixVKYmpqideu2MDExwblzp7Fu3Sp0794bXl4ZfWC8vCqjZ8++WLlyKaKiXqNRI184Ojrh+fMw7N27C97e1bJMkJo3b4XNm/8PkyePx+DBw6FQOOLJk0dwdHRC5cre+Oyzmti2bQsCAg6icmVvHD0aoNcXZYsWrbB3726EhT2Dv/+7oQEsLa3QvXtvLF++GIIgwNu7GhITE/HXX7dhZWWNNm3aZXm8WrXqYsGCOVCr1ZDJMj6/S5YshZMnj+Ovv+7AxqYIduzYitjY6A8mQl26dMOBA3sxY8ZU9OrVF0WK2OL58zCcOHEUEyd+DxubIrC1tcX+/buhUDgiIuIV1qxZ/sHn/KmXxrp374Uff5yBihUrwdPTCzt3/o6UlBS0bftuyIDZs6fByakohgwZAQC4d+9vREW9Rvny7oiKisT//d86aDQCevbsq90nOTkZ4eFh2uWXL8Px5Mkj2NjY6rRO3blzC7Vq1fno+HMbE6FPkJCQhunTz2DQoOqoWPFd9l2xoiMmT846GyciEouJiQk6d+6G33/fjE6dvoCFhQW6desJFxdXbNv2G/78czvUag3Kli2HceMmaW+vfmvYsFHw8PDEnj1/YO/e3RAEDVxcSsDXt1m2iYVcLseSJSuxYsUSTJjwLdRqNcqUKYexYzM6bdeuXRf9+3+N1auXQ6lMQ9u2HdC6dVsEBwfl6Dm1bNkGmzf/H4oVKw5v76o66775Zijs7OyxZctGvHgRDmtrG7i7V0TfvgOyOVrGODcymQzXr19F7dp1AQADBnyN8PDnGDt2JMzNzdGhQyc0bNgESUnv7yOWcfloA1avXo4xY0YgPV2JYsWKo3btupBKpZBIJJgxYy6WLVuEvn2/RMmSpTF69HiMHDk4R8/9YzVr1hJxcbFYv34NYmKiUb68O37+eTkcHN5dGouIeKXTkVmpTMMvv6zGixfhsLCwQJ069fHDD7N0WgEfPryPUaOGaJeXL18CAGjTph2mTp0BAIiMfI2//76rveXeEEiE/16cNRJpe3viTf21H94wG9euvcCwYQF49iwelSo54siRnjAzY16pD4kEcHS0QVRUwkd3xKTcw/rILD1diejol1AoikMuN/3wDrkoN/oI0cfZtWsnLlw4i8WLVwBgXeSmVav8kZCQgIkTp2a5/n3vOakUUCiyHqbhU/CbW08qlQZLllzB4sWXoVZnfFuEhr7B/ftR8PHJ3DGNiIgKls8/74zExAQkJycV6mk2xGBv74Du3XuJHYYOJkJ6CAmJw7BhAbh+/aW2rGZNF6xa1QalSxeOOXaIiIydiYkJ+vUbKHYYhVKPHr3FDiETJkI5IAgCdu58gMmTTyIxUQkAkMkkGDeuDkaPrg0TE/EHhCIiIiL9MRH6gLi4VHz33Qns3ftIW1a6tC1Wr26DGjVcRIyMiIiIPhUToQ94/DgG+/e/G0q/e3cvzJ3rC2vr/O04SUTiMdJ7SojynRjvNV7T+YBatVwwenQt2NqaYf36dvD3b8UkiMhIvB1HRqlMEzkSIuOgVmeMqJ2fc5CxReg/nj2LR4kSNpDJ3lXCuHF10K+fN4oXz/3b9ojIcEmlMlhYWCMxMWPeQFNTs3ybKkejkWjvTCVxsS7yhyBokJAQB1NTc0il+TcYMROh/xEEAZs3/4Vp005j3Lg6GDWqlnadXC5jEkRkpIoUyZil+20ylF+kUulHzexNuY91kX8kEimKFHHI17k5mQgBiIpKxtixxxAYmDHfzfz5F+HrWwZVqhT9wJ5EVNhJJBLY2ipgY2OvbbbP+3MC9vZWiI1N4uCWImNd5C8TE3m+T1BuEInQ1q1bsWHDBkRGRqJixYr44Ycf4O3tne32AQEBWLZsGcLDw1GmTBmMHz8ejRs3/qhznzwZglGjjuD16yRtWa9elTlRKhHpkEqlkErzp3+gRAKYm5tDLk/nl6/IWBeFn+idpQ8fPox58+Zh+PDh2LNnDypWrIiBAwciOjo6y+1v3ryJcePG4YsvvsDevXvRrFkzDB8+HI8fP85y++ykKiX4/vtT6N59tzYJUigssGXL5/jpp+awtJR/8nMjIiIiwyZ6IrRx40Z069YNXbp0Qfny5TFz5kyYm5tj165dWW6/efNmNGzYEF9//TXc3NwwevRoVKpUCb/99pte5205ozzWrbulXW7atAxOn+6LVq3cPun5EBERUcEhaiKkVCpx79491KtXT1smlUpRr1493Lp1K8t9bt++jbp16+qUNWjQALdv39br3A+emwMAzMxkmDvXF9u2dYKzM+eUISIiMiai9hGKjY2FWq2GQqHQKVcoFHj69GmW+0RFRcHR0THT9lFRUXqd28bGFBUrKrB0aUt4eCg+vAPlurf94aRS8Nq7AWB9GA7WheFgXRiOvOpDbRCdpcUQHj5O7BDofxwcODSBIWF9GA7WheFgXRReol4as7e3h0wmy9QxOjo6OlOrz1uOjo6ZWn/etz0RERFRdkRNhExNTeHl5YVLly5pyzQaDS5dugQfH58s96lWrRouX76sU3bx4kVUq1YtL0MlIiKiQkj0u8YGDBiAnTt3Ys+ePQgODsaMGTOQkpKCzp07AwC+++47/Pzzz9rt+/bti3PnzuH//u//EBwcjOXLl+Pvv/9G7969xXoKREREVECJ3kfIz88PMTEx8Pf3R2RkJDw9PbF+/Xrtpa6XL1/qTL5WvXp1LFq0CEuXLsXixYtRpkwZrFy5Eu7u7mI9BSIiIiqgJIIYc94TERERGQDRL40RERERiYWJEBERERktJkJERERktJgIERERkdFiIkRERERGq1AmQlu3bkXTpk1RpUoVdO3aFXfv3n3v9gEBAWjdujWqVKmC9u3b48yZM/kUaeGnT13s3LkTPXv2RM2aNVGzZk3079//g3VH+tH3vfHWoUOH4OHhgWHDhuVxhMZD37p48+YNZs6ciQYNGqBy5cpo1aoVP6tyib518euvv6JVq1bw9vZG48aNMXfuXKSlpeVTtIXXtWvXMGTIEDRo0AAeHh44fvz4B/e5cuUKOnXqhMqVK6NFixbYvXu3/icWCplDhw4JXl5ewp9//ik8efJE+P7774UaNWoIUVFRWW5/48YNwdPTU/jll1+EoKAgYcmSJYKXl5fw6NGjfI688NG3LsaOHSv89ttvwv3794WgoCBh0qRJwmeffSa8evUqnyMvnPStj7fCwsKEhg0bCj179hSGDh2aT9EWbvrWRVpamtC5c2fhm2++Ea5fvy6EhYUJV65cER48eJDPkRc++tbF/v37hcqVKwv79+8XwsLChHPnzgn169cX5s6dm8+RFz6nT58WFi9eLBw9elRwd3cXjh079t7tQ0NDhapVqwrz5s0TgoKChC1btgienp7C2bNn9TpvoUuEvvjiC2HmzJnaZbVaLTRo0EBYu3Ztltt/++23wqBBg3TKunbtKvzwww95Gqcx0Lcu/kulUgk+Pj7Cnj178ihC4/Ix9aFSqYQvv/xS2LlzpzBx4kQmQrlE37r4/fffhWbNmglKpTK/QjQa+tbFzJkzhb59++qUzZs3T+jevXuexmlscpIILVy4UGjbtq1O2ejRo4WvvvpKr3MVqktjSqUS9+7dQ7169bRlUqkU9erVw61bt7Lc5/bt26hbt65OWYMGDXD79u28DLXQ+5i6+K+UlBSoVCrY2trmVZhG42PrY+XKlVAoFOjatWt+hGkUPqYuTp48iWrVqmHWrFmoV68e2rVrhzVr1kCtVudX2IXSx9SFj48P7t27p718FhYWhjNnzqBx48b5EjO9k1vf36JPsZGbYmNjoVaroVAodMoVCgWePn2a5T5RUVGZZq5XKBSZZrgn/XxMXfzXokWLULRoUZ0PKfo4H1Mf169fx59//om9e/fmQ4TG42PqIiwsDJcvX0b79u2xbt06hIaGYubMmVCpVBgxYkR+hF0ofUxdtG/fHrGxsejZsycEQYBKpUL37t0xZMiQ/AiZ/iWr729HR0ckJiYiNTUV5ubmOTpOoWoRosJj3bp1OHz4MFasWAEzMzOxwzE6iYmJ+O677zB79mw4ODiIHY7REwQBCoUCs2fPRuXKleHn54chQ4Zg+/btYodmdK5cuYK1a9di+vTp2L17N1asWIEzZ85g5cqVYodGH6lQtQjZ29tDJpMhOjpapzw6OjpT1viWo6Njptaf921POfMxdfHWhg0bsG7dOmzcuBEVK1bMyzCNhr71ERYWhvDwcAwdOlRbptFoAACVKlVCYGAgSpUqlbdBF1If895wcnKCiYkJZDKZtqxcuXKIjIyEUqmEqalpnsZcWH1MXSxbtgwdOnTQXi728PBAcnIypk2bhqFDh+pMEk55K6vv76ioKFhbW+e4NQgoZC1Cpqam8PLywqVLl7RlGo0Gly5dgo+PT5b7VKtWDZcvX9Ypu3jxIqpVq5aXoRZ6H1MXAPDLL79g1apVWL9+PapUqZIfoRoFfeujXLlyOHDgAPbu3av917RpU9SuXRt79+5FsWLF8jP8QuVj3hvVq1dHaGioNhkFgJCQEDg5OTEJ+gQfUxepqamZkp23CarAOczzVa59f+vXj9vwHTp0SKhcubKwe/duISgoSPjhhx+EGjVqCJGRkYIgCMKECROERYsWabe/ceOGUKlSJWHDhg1CUFCQ4O/vz9vnc4m+dbF27VrBy8tLCAwMFF6/fq39l5iYKNZTKFT0rY//4l1juUffunjx4oXg4+MjzJo1S3j69Klw6tQpoW7dusKqVavEegqFhr514e/vL/j4+AgHDx4UQkNDhfPnzwvNmzcXvv32W5GeQeGRmJgo3L9/X7h//77g7u4ubNy4Ubh//74QHh4uCIIgLFq0SJgwYYJ2+7e3zy9YsEAICgoSfvvtt4+6fb5QXRoDAD8/P8TExMDf3x+RkZHw9PTE+vXrtc2cL1++1Mnmq1evjkWLFmHp0qVYvHgxypQpg5UrV8Ld3V2sp1Bo6FsX27dvR3p6OkaNGqVznBEjRmDkyJH5GnthpG99UN7Rty6KFy+ODRs2YN68eejQoQOcnZ3Rt29ffPPNN2I9hUJD37oYOnQoJBIJli5dioiICDg4OMDX1xdjxowR6ykUGn///Tf69u2rXZ43bx4AoFOnTpg/fz4iIyPx8uVL7fqSJUti7dq1mDdvHjZv3oxixYphzpw5aNiwoV7nlQgC2/KIiIjIOPHnHxERERktJkJERERktJgIERERkdFiIkRERERGi4kQERERGS0mQkRERGS0mAgRERGR0WIiREQ6du/ejRo1aogdxkfz8PDA8ePH37vNpEmTMGzYsHyKiIgMWaEbWZqIMr7o9+zZk6n86NGjKF26tAgRvbN7925MnjwZACCRSFC0aFHUr18f48ePh0Kh+OTjnz9/Hra2tgCA58+fo1mzZti7dy88PT2120ydOjXP54Vavnw5VqxYAQCQSqUoWrQoGjVqhHHjxsHOzi7Hx5k0aRLevHmDVatW5VGkRMaNiRBRIdWwYUPtEPVvOTg4iBSNLmtrawQGBkKj0eDhw4eYMmUKXr9+jQ0bNnzysZ2cnD64jY2NzSefJycqVKiAjRs3QqPRIDg4GFOmTEFCQgKWLl2aL+cnog/jpTGiQsrU1BROTk46/2QyGTZu3Ij27dujWrVqaNy4MWbMmIGkpKRsj/Pw4UP06dMHPj4+qF69Ojp37oy//vpLu/769evo2bMnvL290bhxY8yZMwfJycnvjU0ikcDJyQnOzs5o3Lgx+vTpg4sXLyI1NRUajQYrVqxAo0aNULlyZXz++ec4e/asdl+lUolZs2ahQYMGqFKlCnx9fbF27Vrt+n9fGmvWrBkAoGPHjvDw8ECfPn0A6F4a27FjBxo0aKAzszuQMafU25YrADh+/Dg6deqEKlWqoFmzZlixYgVUKtV7n6dMJtM+z3r16qF169a4ePGidr1arcaUKVPQtGlTeHt7o1WrVti0aZN2/fLly7Fnzx6cOHECHh4e8PDwwJUrVwBkzIH17bffokaNGqhVqxaGDh2K58+fvzceIsqMiRCRkZFIJJg6dSoOHjyI+fPn4/Lly/jpp5+y3X78+PEoVqwY/vzzT+zevRvffPMN5HI5ACA0NBTffPMNWrZsif3792PJkiW4ceMGZs+erVdM5ubm0Gg0UKlU2Lx5MzZu3IiJEydi//79aNCgAYYNG4aQkBAAwJYtW3Dy5EksXboUgYGB+Omnn+Dq6prlcf/44w8AwK+//orz589j+fLlmbZp3bo14uLitAkGAMTFxeHcuXPo0KEDgIxkb+LEiejbty8OHz6MWbNmYffu3VizZk2On+Pz589x/vx57WsHABqNBsWKFcOyZctw6NAhDB8+HEuWLMHhw4cBAF999RXatGmDhg0b4vz58zh//jx8fHyQnp6OgQMHwsrKClu3bsW2bdtgaWmJr7/+GkqlMscxEREvjREVWqdPn4aPj492uWHDhvD390f//v21ZSVKlMDo0aMxffp0zJgxI8vjvHjxAgMHDoSbmxsAoEyZMtp1a9euRfv27bXHLFOmDKZOnYo+ffpgxowZMDMz+2CcISEh2LZtGypXrgxra2ts2LAB33zzDdq2bQsAmDBhAq5cuYJNmzZh+vTpePnyJUqXLo3PPvsMEokk2yQIeHcp0M7OLttLZra2tmjUqBEOHDiAunXrAgCOHDkCe3t71K5dGwCwYsUKDBo0CJ06dQKQMev1t99+i59++gkjRozI9vyPHz+Gj48P1Go10tLSAECnlUkul2PUqFHa5ZIlS+L27dsIDAyEn58frKysYG5uDqVSqRP/vn37oNFo8OOPP0IikQDImKm7Zs2auHr1Kho0aJBtTESki4kQUSFVu3ZtneTGwsICAHDx4kWsXbsWT58+RWJiovZLOiUlRbvNvw0YMADff/899u3bp728U6pUKQAZl80ePXqEAwcOaLcXBAEajQbPnz/XJk//lZCQAB8fH2g0GqSlpeGzzz7DnDlzkJiYiNevX6N69eo621evXh0PHz4EAHTq1AlfffUVWrdujYYNG6JJkyaf/MXfvn17/PDDD5gxYwZMTU1x4MABtG3bFlKpVPs8b968qdMC9KHXDQDKli2L1atXIy0tDfv378eDBw/Qu3dvnW22bt2KXbt24cWLF0hLS0N6ejoqVqz43ngfPnyI0NDQTK9TWloaQkNDP+YlIDJaTISICikLC4tMd4g9f/4cgwcPRo8ePTBmzBjY2trixo0bmDp1KtLT07P8Qh85ciTatWuHM2fO4OzZs/D398eSJUvQokULJCcno3v37tq+N/9WvHjxbGOzsrLCnj17IJVK4eTkBHNzcwBAYmLiB5+Xl5cXTpw4gbNnz+LixYsYPXo06tWrB39//w/um52mTZvi+++/x+nTp1GlShVcv35dp+UmOTkZI0eORMuWLTPt+75WL7lcrq2D8ePHY9CgQVixYgVGjx4NADh06BAWLFiAiRMnwsfHB1ZWVtiwYQPu3Lnz3niTk5Ph5eWFRYsWZVpnKB3iiQoKJkJERuTevXsQBAGTJk3StnYEBAR8cL+yZcuibNmy6N+/P8aOHYtdu3ahRYsWqFSpEoKCgvS+JV8qlWa5j7W1NYoWLYqbN2+iVq1a2vKbN2/C29tbZzs/Pz/4+fmhVatW+PrrrxEXF5fptvS3/XHUavV74zEzM0PLli1x4MABPHv2DGXLloWXl5d2faVKlfDPP/988tADQ4cORb9+/dCjRw84Ozvj5s2b8PHxQa9evbTb/LdFRy6XZ+rI7eXlhYCAACgUClhbW39STETGjp2liYxI6dKlkZ6eji1btiAsLAx79+7F9u3bs90+NTUVs2bNwpUrVxAeHo4bN27gr7/+0l7y+uabb3Dr1i3MmjULDx48QEhICI4fP45Zs2Z9dIwDBw7EL7/8gsOHD+Pp06dYtGgRHj58iL59+wIANm7ciIMHDyI4OBj//PMPAgMD4eTkhCJFimQ6lkKhgLm5Oc6dO4eoqCgkJCRke9727dvj9OnT2LVrF9q3b6+zbvjw4di3bx9WrFiBJ0+eIDg4+P/buV/W1MIAjuM/xWYQ04JhMMNOWRgc0IXVBYdpTMQwLIaBZZajsLiwIHoYHBGPace34CswKYuDseJBVhbkYJgrhnvDBbnjrnjhwoXn+8kHzvO0L88/jcdjdbvdneZ2fHysw8PD7S23/f19PT8/azKZKAxDua775UaeJGUyGb2+vmo+nyuKIm02GxWLRaXTaV1fX+vp6Ulvb2+aTqe6u7vT+/v7TmMCTMeKEGAQy7LUarXk+746nY5s21aj0ZDjON9+H4/HtVqt5DiOlsul0um0zs7Otgd8LctSEARyXVeVSkXSrwO/hULhr8d4dXWlj48P3d/fK4oiZbNZ9Xq97SHtZDKp4XCoxWKheDyuo6MjDQaD7QrX7xKJhG5vb+V5nh4eHmTbtoIg+Pa/+XxeqVRKYRj+EUKnp6fq9/vyPE++7yuRSOjg4ECXl5c7z69ararZbKpWq6lcLuvl5UU3NzeKxWI6Pz9XpVL58lxAqVTSbDbTxcWFPj8/9fj4qFwup9FopHa7rXq9rvV6rb29PZ2cnLBCBOwo9uNfP68KAADwn2JrDAAAGIsQAgAAxiKEAACAsQghAABgLEIIAAAYixACAADGIoQAAICxCCEAAGAsQggAABiLEAIAAMYihAAAgLEIIQAAYKyfKC2N2I//WqMAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 3. ROC Curve\n",
|
||
"# Compute ROC curve and ROC area for each class\n",
|
||
"fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
|
||
"roc_auc = auc(fpr, tpr)\n",
|
||
"# Plot the ROC curve\n",
|
||
"plt.figure()\n",
|
||
"lw = 2\n",
|
||
"plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
|
||
"plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
|
||
"plt.xlim([0.0, 1.0])\n",
|
||
"plt.ylim([0.0, 1.05])\n",
|
||
"plt.xlabel('False Positive Rate')\n",
|
||
"plt.ylabel('True Positive Rate')\n",
|
||
"plt.title('Receiver Operating Characteristic')\n",
|
||
"plt.legend(loc=\"lower right\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5d1867cb3af9788d",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": [
|
||
"## Learning curve"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 114,
|
||
"id": "c67bb53e5a864293",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-20T11:49:35.981276Z",
|
||
"start_time": "2024-03-20T11:49:35.717252Z"
|
||
},
|
||
"collapsed": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_learning_curve(history):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
" plt.plot(history.history['loss'], label='Training Loss')\n",
|
||
" plt.plot(history.history['val_loss'], label='Validation Loss')\n",
|
||
" plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
|
||
" plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
|
||
" plt.xlabel('Epoch')\n",
|
||
" plt.ylabel('Loss / Accuracy')\n",
|
||
" plt.title('Learning Curve')\n",
|
||
" plt.legend()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"plot_learning_curve(history)\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|