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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A practical guide to time series - Store sales\n",
"Today we are going to talk about time series and forecasting! Forecasting is the use of a predictive model to predict future values based on previously observed values and meaningful characteristics of the time series data. It has application in various industries and use cases such as finance, retail, marketing and even anomaly detection for system outage."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Importing Libraries and dataset\n",
"We'll talk about individual libraries down the road as it's not obvious how they are helpful at this point of time."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda3/lib/python3.7/site-packages/statsmodels/compat/pandas.py:23: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
" data_klasses = (pandas.Series, pandas.DataFrame, pandas.Panel)\n"
]
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm\n",
"import itertools\n",
"import numpy as np\n",
"from pmdarima import auto_arima\n",
"from tsfresh.utilities.dataframe_functions import roll_time_series\n",
"from tsfresh.utilities.dataframe_functions import make_forecasting_frame\n",
"\n",
"from tsfresh.utilities.dataframe_functions import impute\n",
"from pylab import rcParams\n",
"\n",
"from statsmodels.tsa.stattools import adfuller\n",
"\n",
"from random import seed\n",
"from random import random\n",
"from sklearn.metrics import mean_squared_error\n",
"from statsmodels.graphics import tsaplots\n",
"from tbats import BATS, TBATS\n",
"from tsfresh import extract_features\n",
"\n",
"from pmdarima.arima import auto_arima"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"plt.style.use('fivethirtyeight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use a small dataset, the Superstore sales data that can be downloaded from here."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_excel('Sample - Superstore.xls')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each row represents a sales transaction with pretty rich information such as customer demographics and profit. These would be helpful for feature engineering if we are going down the machine learning approach. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Row ID</th>\n",
" <th>Order ID</th>\n",
" <th>Order Date</th>\n",
" <th>Ship Date</th>\n",
" <th>Ship Mode</th>\n",
" <th>Customer ID</th>\n",
" <th>Customer Name</th>\n",
" <th>Segment</th>\n",
" <th>Country</th>\n",
" <th>City</th>\n",
" <th>...</th>\n",
" <th>Postal Code</th>\n",
" <th>Region</th>\n",
" <th>Product ID</th>\n",
" <th>Category</th>\n",
" <th>Sub-Category</th>\n",
" <th>Product Name</th>\n",
" <th>Sales</th>\n",
" <th>Quantity</th>\n",
" <th>Discount</th>\n",
" <th>Profit</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>CA-2016-152156</td>\n",
" <td>2016-11-08</td>\n",
" <td>2016-11-11</td>\n",
" <td>Second Class</td>\n",
" <td>CG-12520</td>\n",
" <td>Claire Gute</td>\n",
" <td>Consumer</td>\n",
" <td>United States</td>\n",
" <td>Henderson</td>\n",
" <td>...</td>\n",
" <td>42420</td>\n",
" <td>South</td>\n",
" <td>FUR-BO-10001798</td>\n",
" <td>Furniture</td>\n",
" <td>Bookcases</td>\n",
" <td>Bush Somerset Collection Bookcase</td>\n",
" <td>261.9600</td>\n",
" <td>2</td>\n",
" <td>0.00</td>\n",
" <td>41.9136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>CA-2016-152156</td>\n",
" <td>2016-11-08</td>\n",
" <td>2016-11-11</td>\n",
" <td>Second Class</td>\n",
" <td>CG-12520</td>\n",
" <td>Claire Gute</td>\n",
" <td>Consumer</td>\n",
" <td>United States</td>\n",
" <td>Henderson</td>\n",
" <td>...</td>\n",
" <td>42420</td>\n",
" <td>South</td>\n",
" <td>FUR-CH-10000454</td>\n",
" <td>Furniture</td>\n",
" <td>Chairs</td>\n",
" <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
" <td>731.9400</td>\n",
" <td>3</td>\n",
" <td>0.00</td>\n",
" <td>219.5820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>CA-2016-138688</td>\n",
" <td>2016-06-12</td>\n",
" <td>2016-06-16</td>\n",
" <td>Second Class</td>\n",
" <td>DV-13045</td>\n",
" <td>Darrin Van Huff</td>\n",
" <td>Corporate</td>\n",
" <td>United States</td>\n",
" <td>Los Angeles</td>\n",
" <td>...</td>\n",
" <td>90036</td>\n",
" <td>West</td>\n",
" <td>OFF-LA-10000240</td>\n",
" <td>Office Supplies</td>\n",
" <td>Labels</td>\n",
" <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
" <td>14.6200</td>\n",
" <td>2</td>\n",
" <td>0.00</td>\n",
" <td>6.8714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>US-2015-108966</td>\n",
" <td>2015-10-11</td>\n",
" <td>2015-10-18</td>\n",
" <td>Standard Class</td>\n",
" <td>SO-20335</td>\n",
" <td>Sean O'Donnell</td>\n",
" <td>Consumer</td>\n",
" <td>United States</td>\n",
" <td>Fort Lauderdale</td>\n",
" <td>...</td>\n",
" <td>33311</td>\n",
" <td>South</td>\n",
" <td>FUR-TA-10000577</td>\n",
" <td>Furniture</td>\n",
" <td>Tables</td>\n",
" <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
" <td>957.5775</td>\n",
" <td>5</td>\n",
" <td>0.45</td>\n",
" <td>-383.0310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>US-2015-108966</td>\n",
" <td>2015-10-11</td>\n",
" <td>2015-10-18</td>\n",
" <td>Standard Class</td>\n",
" <td>SO-20335</td>\n",
" <td>Sean O'Donnell</td>\n",
" <td>Consumer</td>\n",
" <td>United States</td>\n",
" <td>Fort Lauderdale</td>\n",
" <td>...</td>\n",
" <td>33311</td>\n",
" <td>South</td>\n",
" <td>OFF-ST-10000760</td>\n",
" <td>Office Supplies</td>\n",
" <td>Storage</td>\n",
" <td>Eldon Fold 'N Roll Cart System</td>\n",
" <td>22.3680</td>\n",
" <td>2</td>\n",
" <td>0.20</td>\n",
" <td>2.5164</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Row ID Order ID Order Date Ship Date Ship Mode Customer ID \\\n",
"0 1 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 \n",
"1 2 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 \n",
"2 3 CA-2016-138688 2016-06-12 2016-06-16 Second Class DV-13045 \n",
"3 4 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 \n",
"4 5 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 \n",
"\n",
" Customer Name Segment Country City ... \\\n",
"0 Claire Gute Consumer United States Henderson ... \n",
"1 Claire Gute Consumer United States Henderson ... \n",
"2 Darrin Van Huff Corporate United States Los Angeles ... \n",
"3 Sean O'Donnell Consumer United States Fort Lauderdale ... \n",
"4 Sean O'Donnell Consumer United States Fort Lauderdale ... \n",
"\n",
" Postal Code Region Product ID Category Sub-Category \\\n",
"0 42420 South FUR-BO-10001798 Furniture Bookcases \n",
"1 42420 South FUR-CH-10000454 Furniture Chairs \n",
"2 90036 West OFF-LA-10000240 Office Supplies Labels \n",
"3 33311 South FUR-TA-10000577 Furniture Tables \n",
"4 33311 South OFF-ST-10000760 Office Supplies Storage \n",
"\n",
" Product Name Sales Quantity \\\n",
"0 Bush Somerset Collection Bookcase 261.9600 2 \n",
"1 Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3 \n",
"2 Self-Adhesive Address Labels for Typewriters b... 14.6200 2 \n",
"3 Bretford CR4500 Series Slim Rectangular Table 957.5775 5 \n",
"4 Eldon Fold 'N Roll Cart System 22.3680 2 \n",
"\n",
" Discount Profit \n",
"0 0.00 41.9136 \n",
"1 0.00 219.5820 \n",
"2 0.00 6.8714 \n",
"3 0.45 -383.0310 \n",
"4 0.20 2.5164 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are tons of products with different categories over here. For now, let's just look at \"Furniture\"."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Office Supplies 6026\n",
"Furniture 2121\n",
"Technology 1847\n",
"Name: Category, dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Category'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"sales_df = df.loc[df['Category'] == 'Furniture']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A quick look at the available period of the time series."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(Timestamp('2014-01-06 00:00:00'), Timestamp('2017-12-30 00:00:00'))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_df['Order Date'].min(), sales_df['Order Date'].max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have 4 years of data to play with!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data preprocessing\n",
"Let's transform the data to univariate time series data by using only the main variable that we are most interssted in: sales. When we model univariate time series, we are modeling time series changes in a single variable over time.\n",
"\n",
"As a side benefit, we don't need to check and clean the data for all features. So let's just check for empty values for this particular variable."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"sales_df = sales_df[['Order Date','Sales']]\n",
"sales_df = sales_df.sort_values('Order Date')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Order Date</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7474</th>\n",
" <td>2014-01-06</td>\n",
" <td>2573.820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7660</th>\n",
" <td>2014-01-07</td>\n",
" <td>76.728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>866</th>\n",
" <td>2014-01-10</td>\n",
" <td>51.940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>716</th>\n",
" <td>2014-01-11</td>\n",
" <td>9.940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2978</th>\n",
" <td>2014-01-13</td>\n",
" <td>545.940</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Order Date Sales\n",
"7474 2014-01-06 2573.820\n",
"7660 2014-01-07 76.728\n",
"866 2014-01-10 51.940\n",
"716 2014-01-11 9.940\n",
"2978 2014-01-13 545.940"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Order Date 0\n",
"Sales 0\n",
"dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are very lucky that this is a clean dataset; no empty value 😂\n",
"\n",
"Recall that each row of the dataset is a sales transaction so there will be multiple sales for each date(day). We want to summarize the data into a sales per day format. We are using summation to summarize, you could use other statistics as well."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#the reset_index is here is to regenerate the index on the grouped panda series\n",
"sales_df = sales_df.groupby('Order Date')['Sales'].sum().reset_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Data Wrangling\n",
"We are going to make the order date as the index so that moving forward, the data manipulation can be easier."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order Date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-01-06</th>\n",
" <td>2573.820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-07</th>\n",
" <td>76.728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-10</th>\n",
" <td>51.940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-11</th>\n",
" <td>9.940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-13</th>\n",
" <td>879.939</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Order Date \n",
"2014-01-06 2573.820\n",
"2014-01-07 76.728\n",
"2014-01-10 51.940\n",
"2014-01-11 9.940\n",
"2014-01-13 879.939"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_df = sales_df.set_index('Order Date')\n",
"sales_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Unevenly space time series\n",
"Notice that there are missing values for 8th, 9th and 12th in the time series data.\n",
"\n",
"We have met our first problem, the infamous \"unevenly space time series\" problem that is well studied in the field.\n",
"\n",
"1) If your eventual output allows you to forecast at monthly level, you are in luck: you could just resample at a higher level(roll up days to month and aggregate the values). There's a handly function to do it quickly: DataFrame.resample\n",
"\n",
"2) Interpolate the data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Naive way: Downsample to monthly frequency"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"monthly_sales_df = sales_df.resample('MS').mean()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order Date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-01-01</th>\n",
" <td>480.194231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-02-01</th>\n",
" <td>367.931600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-03-01</th>\n",
" <td>857.291529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01</th>\n",
" <td>567.488357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-01</th>\n",
" <td>432.049188</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Order Date \n",
"2014-01-01 480.194231\n",
"2014-02-01 367.931600\n",
"2014-03-01 857.291529\n",
"2014-04-01 567.488357\n",
"2014-05-01 432.049188"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"monthly_sales_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll talk about interpolation of time series later..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Visualizing the time series\n",
"Just like our usual data analysis work, we first plot the data before any modelling work(if any), in an attempt to identify some distinguishable patterns."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 936x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"monthly_sales_df.plot(figsize=(13, 5))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.1 Describing Patterns - Seasonality"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first obvious pattern we observe in the plot is the yearly seasonality(12 months=a cycle). Sales are relatively lower at the start of the year and end higher at the end of the year, and this pattern repeats itself every year. The sales are also trending slightly downwards over the years.\n",
"\n",
"A time series process could be decomposed into several components: \n",
"Level: The average value in the series.\n",
"Trend: The increasing or decreasing value in the series.\n",
"Seasonality: The repeating short-term cycle in the series.\n",
"Noise: The random variation in the series.\n",
"\n",
"Instead of guesstimating the components, We could use scipy's seasonal_decompose function automatically decompose a time series and plot it out to have a better inspection of each component.\n",
"\n",
"Note that there's a model parameter, which means whether the time series is an additive and multiplicative model. The difference is how the compoenents are being combined together.\n",
"\n",
"Additive Model = y(t) = Level + Trend + Seasonality + Noise\n",
"\n",
"Multiplicative Model = y(t) = Level * Trend * Seasonality * Noise\n",
"\n",
"A easy rule of thumb: If you observe increasing variance over time, it's likely to be Multiplicative."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ZsmVYvXo1rl+/DgDg8/l4+OGH8e2333r68oQQUics+VcDg61schop4eHlTiFee/1ICR/zXF5vVboelzXUmY6Qmvg+g9vcY2LLIAQJSqZFUgGDx1vLOI+vOc8N9AeqFWe0FT5mdQCvHldj+oFi6Kx3nkQSQoi/pBdb8ec1bhOOZsF8fNhbjnMTG+DN7nKvB/+AkjRgZztzTLA6Kq45SAghFTlww4z3TmnufGIt8jgAuGzZMuzcuRPLly/HiRMnwLJlA6NEIsFDDz2ExMREr1wkIYQEolSlBT9lclMIX+8aglAv14p5LiEYsU7d66wOYPHJwPowIaQuuaq1IekGdzX2dvrvbdPiuQHAEwVWpCi5DUMCzRWtDZsuGznHRsSK3c7beNmIYdsLkKEqv3smIYT426cuixldFEL8OyEGMxKCIRP6rgxKrygRIpzKrKgtLI7lBfbYTwgJPLkGO6YfKILz+kGEmIe9D0Rh3wNR2DhCgdWDwvFRHzkWdg3BrPYyPNY6CPc1kaBPtAjxcgFipDx4e7jzOEdtw4YNeOqpp/DMM8+gqKjI7fG4uDhs27atRhdHCCGBimVZvHlCA+c14Xi5AE+2kVX4HE9JBQwWdQ/FDKfOpFuuGHE8z4zeMe4394SQyv2QaeC8d7tGCtFJwa2N10ouwJBGYk6gcM15PT7rH7g19D4/o+NMNNuGCfDLcAX+vGbC7EPF0FjLHsxQ2zB0ewE+7x+GCS0pvY0QEjiuaG3YeIm7mDG/c0itdDPn8xiMiBXjl4tl3z8x24gBDWm+RUhtyzPYUWByoH24oE6VLrE5WDy9vwgFTqVmGACrBoWje1T15pEsy8JgY1FsdqDYcutvswMqswMqi6P069t/7sTjAGBBQQE6duxY4eNisRh6vb7CxwkhpC7bnWPGgVzuDqJ3esoh8NHk9OGWUqxM0+FUYdmOnTdOqLFrdFSd+kAkxN9sDhbrM7nzk4oC90+3lXECgBsuGfFOT3lAdoTMM9ixPov7c73UMQQ8hsGDzaRICBNiSpISacVl5QP0NhbPHCjGsXwL3usph4hPYwkhxP8+P6ODc+m/eLkAo5tWXtjem0Y1lXIDgNdMeK8nS/MtQmrR5ssGzD6sgsHGYnAjMX4cGoFgH+7+9aZ3T2pw1GXn8CtdQjCscfXHMYZhIBMykAl5iPXCtXn8G4yJicGVK1cqfPzkyZNo1qyZpy9PCCEBy+Zg8eYJNefYwIbiclPtvIXHMHi3p5xz7ESBFVuvmCp4BiGkPLtzTJwiyjIBgwktpeWeO6qJBI2CyqZKBhuLX106BweKlWk6mJ2ayjUJ5nN+rlZyAfY8EIVJrdx/1lXpeoxOLECOjmqLEkL866bBjh9dFmle7lSymFFbhjYWw3md57LWjgtqGh8JqS0Hc82YcbC4tM76/htmTNlXBHMFHcEDyZ9XjfjMpUHkkEZivNLZezXia8LjAOCYMWOwdu1aZGVllR67vSqSmJiIDRs2YPz48TW/QkIICTDrLhiQ4TQRZAAs6Rnq85XhexqIcb/LCvhb/6jrxIchIYHi+wvcAN74FlKEVLCiLOAxmOqyO3BNhp5T9zgQqMwOrD7PvWF+sX0whC47koMEPKwcEI4V/cLguonxRIEVg7YVIOk6LSoQQvznq3M6WJyy2FwXM2pDiJCHgS4pv4nXaGwkpDakF1vxxD4lXHuVJd0w47mDxbAHcFOeK1obnj9czDnWKIiHVYPCa6WEQVV4HAB89dVX0aRJEwwaNAjTp08HwzD45JNPMHz4cDz++OPo0qUL5s6d681rJYQQv9NYHFjq0s3psdZBbvXDfGVxj1A4Z+ld1dmxKr1udCclxN9u6O3YlcO9iXsyvvK6nVPbyDjvufMqG44EWEH4NRl6aK3cbuRPtCm/rh/DMHgqXoZdo6PQNJjbQVNpdmD8LiU+PK2BI8CCnISQ+q/Y7MAal8WMuR3cFzNqg2s34MRsCgAS4mu5Bjse2a2ExlL+HGTzFSP+c0wVcAuxAGCysZi6r4hz7QIGWDM4ApES73cs95THAcCQkBDs2rUL8+bNQ0FBASQSCY4dOwa9Xo/XX38d27dvh0RSe7UaCCGkNnx2RotCp4KuUj6DN7qF1tr3j5ML8bRLwGJ5irZKRV8Judutz9RzmmQkhAvQPVJY6XMayfhutadcb1D9yWhjsfIcdxHguYRgBAkqn+J1iRThwJhojHQpXcACWHpKi0d3K1Fkspf/ZEII8YFv0nTQ28oG6WgpD4/Heb+5WlWMjOWO+8n5FhQYaUwkxFe0Vgce3a1Ejp77PpOLuAsAazMMeO8Ut0t4IHjtuAqpRVbOscU95egTYA0ba1RFUSKRYP78+Th06BBu3LiBmzdv4ujRo1iwYAEF/wgh9U6OzoavXG60X+gQjEay2l3VebVrCEKFZR+GaguL5SmaSp5BCHGwLH7I5Kb/PtlGVqXU/Wfacm9At101Ij9AbgR/zNRzusyFCBlMb1u1G+ZwMQ8/D1fgzW6hcN1gs/u6GYO2F+BUYWDtdiSE3JnFzkJjcQTkLpmK6KwO/DeNO8ea3T4YUoF/0uZigwXoFFG2QMQC2JlDuwAJ8QWrg8W0pCK3ANrT8TIcHBPNqccMAB+laN0WP/3p5ywD/udSYubBZhLMSvDPAkZlPA4A/vjjj1Cr1Xc+kRBC6ol3/tXAeUNMjJSHuR2Da/06IiV8zOvELSS7Kl2PSxoqUE1IRfbfMOOaruwNLOEDj7YqP03W1cCGYrQOFZR+bXUAP1zwfzMQq4PF5y6FpqfFyxAmrvr0jscwmN85BJtHKBAp4T4vW2fHyD8LsOZ84NU9JISUL63Yir5b8tB0fS6e3l8MSx2pE/y/DD1UTqlzchGDaXco0eBrbmnAVAeQEK9jWRbzj6qw57qZc3xkrBgf9pGjWYgAm0ZGIlzMXQx4PVmNXy/6fy6WVmzFvCMqzrGWIXx8eU94QHYO9zgA+OKLL6JNmzZ47LHHsHHjRhgM/v/lE0KIr5wqtOC3i0bOsYXdQv3Wjv65hGDEOu08tDqAxSdpUYaQinx/gZu2O6a5tMqBMoZhMM1lV93aDL3fC1FvumxEtlNQU8wHZrX3bFFiUCMJDoyJRq8obj1TiwOYd1SFmYeKoXetyE0ICSgqswOT9ypxUVMyLmy+YsSXAbRLpiJmO4svXRYzZrQLRqhrt6JaNqoJNwCYdMMMk61uBFQJqSs+TtVhncuiaheFEKsHR0BwKz2hbZgQG+6NhMxlR/CsQ8XY6cf6nFqrA08mFcHotNAi4QPfD1VA7ufxqyIeX9W+ffvw7LPP4syZM5g+fTri4uLw9NNP448//oDFQukihJD6g2VZLEzmBtcSwgV4vHXVdg/5gkTA4K3u3NqDW6+YcDzPXMEzCLl7FRjt+Mtl58aTbaq3s2Ry6yA413DO0dux248dcx0sixWp3Bo4k1sHoUGQ5yUJGsv4+GNUJJ4rJ2Xlt4tG3PtHAbLU1nKeSQjxN5ZlMetwMa5oueUJlp/WIlsX2BkCP2cZcNNYtsAQJGDKHYdqW2eFkJN6aLCxOJhL8yxCvOXXiwYs+ZdbxqhpMB+/Dle4bbLoESXCD0Mj4HzYzgJPJilx5Gbtvy9ZlsWcwypkqrnj60d9w9AxovL60v7kcQCwa9euWLJkCc6ePYvExERMnjwZR44cwZQpU9C6dWs899xz2L17tzevlRBC/OKvaya3rp9Lesr93s59Qkspuro0MHjjhJpS9Qhx8XOWAc6b1+LkAvSLqV7n7nAxD+NbcIP+/mwGsjPbhHRV2aSTxwBzOoRU8oyqEfEZvN87DGsHhyPYZaU9TWXDkO0F2HrFWMGzCSH+8sVZndtCBwAY7Sz+LzlwMwRsDhYrznAXM56KD4IiALpmMgyDUU2lnGOJ2TT+EeINB26Y8cLhYs6xMBGDDfcqEFPBYubQxhJ8OzAczrMTkx2YtFeJM0W1u0C5Kl2PzS7zoSfigvCEnxoXVZVX9iX26dMHy5cvR3p6OrZs2YLx48fjzz//xKRJk7zx8oQQ4jdWB4u3/uGuTA1rLMbQxv5vdMRjGCzpKeccO1FgxRa6OSekFMuybqklU+OCPKrL4toMZHeOGVe0tb+zhmVZfJrKTZcb11yKFk51CmtqXIsg7HswCm3DuK+ptbJ4MqkIC5PVsPo5BZoQUuLvm2YsPllxM7DtV03YE6ANLDZfNnJ2LQp5wAvta76Y4S33uaQB78g2wUELrYTUSFqxFVP2KTmLsyIesH6YAvFhle+eG9ciCB/3DeMc01hYTNhViMu1VA/9nwILFp7gLqy0DxdgeZ+wCp4ROLyamHzz5k2cO3cO586dg06nC8iih4QQUh1rzuuRpeHusnnXJejmT/0biDHapUj12/9oYK4jRb8J8bW/8yyc97CQBzwW51n6frdIIboouF0hv8+o/V2AR/IsSC7g7kr2RUOiNmFC7HkgCo+0lLo99tU5HcbsKESuITC6IRNyt8oz2PH0/iI4f+yHiRh0cElBW3BMFXD16xwsi0/LKWXQSOb/3X+3DWgg5tQdyzU4kKKkUgiEeOqG3o5HdimhsXLHo5UDwtG/gbhKr/F0WxkWduUuFOQbHXhoZyFu+nheUmSy46mkIk7wMkTIYN0Qhd+6lldHjQOASqUSa9aswejRo9GhQwcsXLgQLMvivffew9mzZ71xjYQQ4hcqswMfnOZOTKfEBSEhPLDqOizuEQrnz5urOju+TQ/8ot+E1IZ1LgG60U2liPQwtYxhGDztsgtw3QVDrQfcXW+Y720sRidF9VKaqypYyMO3A8PxUR85XHseHc2zYODWfKqJRYif2BwsnjlQhDwjt0HPtwMj8GX/ME6a3GWtHV+c5Y4d/rYj24Q0l1IGczsGzu4/oKTm8tDG3KBEoh+bDhBSl2mtDkzco8R1lyDd4h6hmNCyeouz/+kc4lYr9KrOjvG7CqEy+6ZpmYNlMeNgMXL03Ov/8p5wtJJ7LwvDlzwOAP74448YP3482rZti/nz56O4uBgLFy7EqVOnsGfPHsyaNQsNGjTw5rUSQkit+iRViyKnDxCZgMH/dQ2t5Bn+0VoudOtQ+lGKFkUm2plD7m7FZge2XuWmxD/ZpmbNeya0kCJUVHZbrTQ7sK0W0+5TlRbsuc4NuL3cybc3zAzDYHq7YCTeH8XpPg4ABaaSFffV52nRgZDatvSUBodvcncD/6dTCEY0kaBLpMhtweLjVC2u+qFsQXlYlsXHKdyA5PgWUrT0YikDb3HtBpxYTq1FQkjlrA4WTyUV4axLrb5n2sowp0P1sxgYhsHSXnJMbMXNUkgrtmHSHiUMNu8HAT9O0brNwWa1l2Fsc/dMiUDlcQDwxRdfxNWrV/HSSy/h2LFjOHLkCObNm4fmzZt78fIIIcQ/rmht+G8a94Z2bsfgCovS+turXUIQKiwLSqgtLJanBNZKPyG17deLBpid4uDNgvkY1Khq6SUVkQl5eKyVSzOQWkwDXnGGOy71jhahbzUbmniqR5QIB8ZEYajL79DBAguOqd0m9YQQ39mRbcQnLrVABzYU43WntLg3uoVCIS673TPZgdeOB0ZDkIO5Fpws5I4ZLwXY7r/bRjSRwLnv25kia8B3ViYkkLAsi3lHVNjrEjy7r4kEH/SWe1w6jscw+OqecIyM5c5LjuVbbqXpei9D48ANE5ae4t5b9Y4WYXGPwCkNVRUeBwCTkpJw8uRJLFy4EPHx8TW+kL///huTJk1Cu3btEBYWhvXr13MeZ1kWy5YtQ9u2bdGgQQOMHj0a6enpnHNUKhVmzJiBpk2bomnTppgxYwZUKhXnnHPnzuH+++9HgwYN0K5dO3zwwQfUMZMQ4uadkxpYnBaOGgXx8IIHq1O1JVLCx/zO3Inzd+f1uFRLxXAJCTQsy7ql/05pIwPPC/WJXXfVHM2z4FwtBL8uaWxuTX5e7hRcqzWXFRI+NtyrwKtdQjjphQ4WWJhMXcgJqQ1XtDbMPMjtntlAysN3g8LBd4pUhYt5WNyTm7mQmG3CjgDoZPuJSymD+5pI3OoWBopICR+9o7kLLTsoDZiQKlueosUPmdyGbF0jhVg9KBwCXs3mMEIeg7VDItwWQ3flmDH7ULFXmvbc0Nsx/UAxnH+nI6oAACAASURBVF9JIeZhzeAICGt4/bXNowCg0WjE0KFD8fHHH3vtQvR6PRISEvD+++9DKnXfQvnZZ5/hq6++wgcffIB9+/YhKioK48aNg1Zb9uExffp0pKamYsOGDfj999+RmpqKmTNnlj6u0Wgwbtw4REdHY9++fXj//ffxxRdf4Msvv/Taz0EIqftO5Fuw6TJ3cvxGt1AECbzaN8nrZrYL5qTnWR3A2/8Exko/IbXtnwIrp7YUnwEe97D5h6v4MCHuacCdaK6thV2An5/RwnkxOyFMgBGxtd+RnM9j8HrXUKwaFM45fiDXTLWxCPExk62kE7faUjYY8Blg7ZAIREvdsxQmtw5CryjuePXqMTWMfmwI8k+BBQdcaofO93Epg5py7QZMacCEVM1PmXq3nXNNg/n4dbgCMtfiwh4KEvDw8zAF2odzSwj8dsmI147XbHHSeqvWaoGpbGcIA+C7QeFoHEANi6rKo9+4VCpFVFQUQkK8N1CPGDECixYtwtixY8HjcS+LZVmsXLkSL730EsaOHYuEhASsXLkSOp0Ov//+OwAgIyMDe/bswYoVK9C7d2/06tULn376KXbu3InMzEwAwIYNG2A0GrFy5UokJCRg7NixmDt3Lr7++mtasSakBqwOFudVVq9us/YXlmWxMJkbNOsUIcSk1t4JHPiSRMDgre7clf5tV01I0QR24JIQX/j+AjcgNyJWgoZeTOF/xmUX4K8XDdBZfVN0GgByDXb8lMVdPX+pU4hXdjR6akILKQa7pAO/kayGhbqQE+Iz/5esdutC+3aPUPSNKb+8AY9hsLyvnJPCelVnx2dn/FcmxHX334AGIvSMrp1SBp5yrQN46KYZGovvxnxC6oMDN0yY8zc3IzNMxOD3exXlLljURJiYh00jItEihPu636bra1QW6Z2TGhzN49Zafa1rCIY0rv0FWG/w+K5w3Lhx2Lx5MxwO3w98V69eRV5eHoYOHVp6TCqVol+/fjh+/DgAIDk5GcHBwejdu3fpOX369IFMJuOc07dvX84Ow2HDhiE3NxdXr171+c9BSH2kNNkxaGs++mzOR/vfbgZEWklNbLtqQnIBd5B/t6fcrzfZ1TGhpRTdIrkpNCsuC2mRg9xVNBaH2y7eJ+O9G8Qf3VSKGGnZNEprZbHhou/Gv5XndJyyBE2D+Rjfwr9FpxmGwXs9uYGFS1o7Vp2vvZqIhNxNfr1ocKs5+kBTCV5oX3mJks4KkduixadntLjih4YgacVW/OWye861hEkgipML0CqUm2WRdIM6oBNSkXNFVkzZVwTnzcZiPvDTMAXahPkm3T8miI/NIyM58zMAWHpKi+/Sq9+sbPtVI744y33esMZiLKgDY1ZFPG6zNHr0aBw8eBD33Xcfpk6diubNm5ebutu9e/caXSAA5OXlAQCioqI4x6OiopCbmwsAyM/Ph0Kh4NTBYRgGkZGRyM/PLz2nUaNGbq9x+7GKGpjc3kFICHH3bqYIaaqSoSTf6MCkPUV4tKEVL7awQlzHNp5l6hm8miaG89rIPeF2NNJfQ10aBmY25GFmYdmq1FktH18fvYwRUdQVmNwdNuUKYLCV7SaJFjnQzJjj9ffx6Egh1mSXTWK/TilCf94NeHu9QGMDvkuXAk5V9ybFGHH5YpZ3v5EHRADGxQix8WbZ72HZSRV6Mbnwxfye5mTkbnVRz2BuigTO40CsxIH5jYqQlVV0x+c/Jgc2CqUospY832wHXth7A58kmL0+ZlXmnQwRnG9BE4LtaKTLrhPzrD4hQlzUlA1sv57NR4LVUskzKkZjGanP8s0MpqWIobFybwbfam1GlPYaMn28AfnTtgxmpkqgtZcNbguOqWAuzq/y/VCOkcFzp7ljbrTIgddii3Exq7jiJ/pZXFxcpY97HAAcM2ZM6X+fOHHCrQA1y7JgGAZFRXf+QKqqir5HRY9X5Zzbu2IqK6B9p18iIXer04UWbM8rcDv+a64QZ01SrBkcgXgfrfB4k93B4ouzOryXooFzBh+fAT4Z3BBxdeBncBYHYJtaiT+dVtg/vybF5O4xCK9rUVlCPJCYng+gLEXuyXZytGvTxOvfZ15DG/6Xk1daly/TwEOxvCl6V5CK56nlpzUw2Mtmy1ESHl7u1xxSQWDsTP6giR27f8+Dxlryi9DZGfyqjsJHfcO8+n0yMzNpTkbuSlqrA5O3F8DkKNuxJ+EDP42IQSdF1VNn3xMa8PyhshvXw8V8ZElicX/T2tlNfEVrw66/8zjH/q9XFNo08+9u5qqaHGLG+uuFpV8f1QjRolXTajcxoLGM1GcaiwPTEguRb+GWKni3Ryhm1VKn7zgAGxqaMW6nEsZbZUlYMHjrghjxTRUYfof6yUYbi6f/LIDeXvYzCBjgx3uj0TPau3O82uZxAPCrr77y5nVUKiYmBkDJLr3Y2NjS44WFhaU7+KKjo1FYWMgJ+LEsC6VSyTnn9m5A59cA3HcXEkIqx7JsSVHVCh4/V2zD4G0FeL+3HFPbBNVql8rquKSx4flDxTie776C+0xbmc+2qPva4h6h2JltKt12n2d04JVjKqwaFOHfCyPEx1KUFpx2qo/FAJjSxjc1PGODBRgZK+E0vlidofdqANBgc+C/adyUv+fbBwdM8A8o6ZC5oHMI3vxHU3psbYYez7SVoV143RxDCQkULMtizmEVMtXcdN3lfcKqFfwDgEmtpFh3Qc+pZ/XqcTUGNxLXSqOzz1waGbUNE+D+pnWnjlbvaBHCRAxUtxqwFJtZHM+3oH+Duh0QIMRbrI6SJkVni7jBv2fbyvBCh8pLFXhbnxgxvh8Sgcl7laX3QzYWmJpUhC0jFehVSSDv1eMqnHH5Gd7tKa/0OXWFxyP95MmTq/THG5o1a4aYmBgkJSWVHjOZTDh69Ghpzb9evXpBp9MhOTm59Jzk5GTo9XrOOUePHoXJVDZRT0pKQsOGDdGsWTOvXCshd4vNl4045hI0i3apt2C0s5h7RIWn9hdBZQ6sQsksy2LNeT3u2ZpfbvBvYisp3u0p98OVeUdruRAvu3TU23DJiG1X6naNRkLuZN0FbqOMoY3FaBrs8XrnHbnW1dpy2QilyXvp9j9cMEDpNH6GChm37xkIZiQEcwpv21lgYXLNOu8RQkoK2G92+ex+PC4IU9pUfxxgGAbL+4SB77R+kK2z49PU6tfGqq5cgx3rM7nj88t+bmRUXQIegxHUDZiQcrEsi5eOqNxqY45qIsH7veV+2QwyookEKweEc44ZbCwm7lYivdha7nN+ytS7zSXHNpfguYTAm3t5witLPTk5OTh9+jR0Os8/PHQ6HVJTU5GamgqHw4GcnBykpqYiOzsbDMPg+eefx4oVK7Bt2zakpaVh1qxZkMlkePjhhwEA8fHxGD58OF5++WWcOHECycnJePnllzFy5MjSLdYPP/wwpFIpZs2ahbS0NGzbtg0rVqzArFmzAnZ3EiGByGBzYJHTTg8AGBkrxonxMZhQTlH6rVdMuGdrPo7lBUax5Bt6Ox7ercS8oyoYbNyb0wgxD98PicC3AyMg5tftcWFB5xB0iODuvpl3VIVCLwYnCAkkeqsDGy5yJ21TPbhJro6hjcVo7hT4sjjgdpPrKeut8gTOnmkrg1wUeKn8Yj6Dd1wWTfbdMGN3TmCM+4TURSfyLXjjhJpzrH24AB/18Ty9vkOEEDPaccfFz85ocVHt24YgX511b2RU3pwx0N3fhHvNiXW8+R0h3vJhitZt/tM9UojVg8PBr2aavDc90ioIH/Tmzk9UFhbjdxXiqksjpHNFVsw/yh1zW4Xy8UX/8HoTL6rRDPKPP/5At27d0KlTJwwdOhQnT54EACiVSvTr1w/bt2+v8mudOnUKAwcOxMCBA2E0GrFs2TIMHDgQS5cuBQDMnTsXs2bNwoIFCzBkyBDcvHkTmzZtQkhI2Q6XVatWoUOHDhg/fjwmTJiADh064Jtvvil9XC6XY/PmzcjNzcWQIUOwYMECzJ49Gy+88EJNfg2E3HW+OKtDjr4siCRggCW95JCLePhuUDi+uicMMpf0tBy9HfcnFuKD0xrYHf7ZEcKyLDZcNKDvljzsve5+UzqyiQRHH4rG2OZ1b0JaHhGfwcoB4RAwZb/vQpMD846oaFcOqZe2XDGW1qEDSmrljWri2/QyHsNgWjz3Znpthh4OL7zHfr9k5Iy1Yj7wXELtptBUxwNNJbinATcl8Y0Talj9NOYTUpcpTXZM21/EqU0cKmSwboiixiUAXusayumSaXEArxz33dygyGTHWpfuxXM7Ble7dl4gGNpYDKHTHfRFjR2Z6vJ3EhFyt/gpU49lp7idPZoF8/HLcEWtlBe4k5kJwXi1CzczKtfgwLidhcg3lsyzNBYHpiaV1QwEACm/ZMwNDcCFV08xKpXKo5F+586deOyxx9CjRw8MGzYM77//PrZs2YJBgwYBACZOnAg+n4+ff/7ZqxdMCPGvHJ0NPTflcwbH2e2D8V4v7spKltqKp/cXI7XIfVLUL0aEbweGI9aHaXmulCY75h9VY0s5KbDBAgbLesvxRFzg1iqsidf2XsZ/r3Fvyr8bFI6HW/qmLhoh/jLyzwJOSv/cDsFYXAup/EqTHQm/3YTZaXPtxhEKDGvsefDRwbLouzkfGU67cp5pK8PHXm6s4W2pSgsGbSvg1If9oLccM70QuKTC+eRuYXewmLhH6bZYuW5IBMZ4aZHyt4sGzDjI7WT5w9AIPOiDhhzLTmnwwemy4EC0lIfUhxtAEkC1TKtj/M5C7HNKc3ynRyjmVKO5AY1lpD7Zf8OEh3eV1dkDgHAxg12joxAnD5w6wCzL4pVjaqw6z12M6BQhxPZRkZj7t8rtPvHre8IwOa5+pP7e5nEo88MPP0Tv3r2xa9cuPPvss26P9+zZE2fOnKnRxRFCAs/ikxpO8E8h5mFBZ/dJT2u5ELsfiMLs9u43fUfyLLhna36t1aPbmW1Cvy355Qb/+jcQ4fBD0ZjSRlYvg38A8GQTG7pGcj+A/3NUhZsGSgUm9Ud6sdWtnqev039vU0j4bjuHv0vXV3B21SReM3GCfzwGeLGWC2h7opNChCfiuIsLy05pUBxgdWAJCWQfpWrdgn8vtA/2WvAPAB5pKUV/lx27rx9XQ2/17ntVa3XgmzRuKYMX2gfX2eAfAIxyaVzi3AiKkLvJ2SIrpuwr4gT/xHzgp2GKgAr+ASU1UD/oI3crPZBaZEX/cu4Tp7YJqnfBP6AGAcC0tDSMHz++wsdjYmJKO+wSQuqH5HwzNlziDo5vdg9FmLj8oUTMZ/BeLzl+v1eBKAn3HJWFxdSkIrx8pBgGm29uDLVWB+b8XYxH9yiRZ+R+DzEfeK+XHNvvi0TzkNrbiegPAgZYOSAc4rIyZVBZShq0UCowqS/WXeAG3O5pIEIree29t59xSQPemWNCts6zmlosy+LTM9xUmgktpHVmrHqjWyiCnW7uVRYWH5zWVPIMQshtSddNeN8lla5PtAhv9Qj16vdhGAYfuTQEydHb8XGqtuIneeB/5/WlXXMBQC5iMC0AGxlVx0iX0hLH8y1UX5ncda7r7Zi4uxBap9IrDIBvBkSgb0xgdsvlMSXlkYY15l6fc7kVAOgYIcQHvQM748JTHgcARSIRzOaKCztnZ2cjNNS7H1SEEP9xsCxeO84titohQogpcXdOIx0eK8HhsdEY2sj9w2BthgFDtxfgXDmpwjVx+KYZ/bfku3VxAoAuCiEOjInG7PbBdar7XE20DRNiYVfumLwz24SfsrzTrIAQfzLZWPzi0vzjyVra/Xdbr2gR2oeXBegcLPB9OeNPVRy+acE/Bdwx8aVqpJf5W0wQH/NddoZ/l67HBRXVySKkMtf1dkw/UMxJoY+U8LBmcASEPqiX1y5ciOdd0vO/OKvzWk07k43Fl+e4u/9mJgQjRFi362k1DRZwmqw5WGAX7QIkdxG1xYFHdhfihoG7weLdnqF4KMCb+4j4DNYNiUDPqPJ3KJbUWo2oca3VQOXx6NunTx9s3ry53Mc0Gg3Wr1+PAQMGeHxhxDv+LbCg56Y8xP+Si/9l1Cwdidzdfsky4N9C7oRwWS95lbs6xQTx8fsIBd7tEQrXed95lQ1D/8jHqnRdjXekmWwsFiar8WBiIa7puKs5fAZ4tUsIdj8QhbZhgbUtvTbMbh+M3tHu6T45Hu5SIiRQ/HHNiGJz2dgRLmZ8UseqMgzD4Jm23BvpHy7oPWqA8anLDpyRsWK0j6hbY9bzCcFoGly27djGAm+6dDMlhJSx2FlMSyqC0ildnscAqweFo5GMX8kza+bVriFoGFQ2MbM6gFeOqb2SIfBTloGTgSETMHiuXd3e/Xeba4OpHRQAJHcJo43Fk0lFSCvm3j/MaCcrt/RTIJIJefjt3ki0C3PPrPhqQDhahNaNjAtPeBwAfO2113Du3Dk89NBDSExMBACkpqZizZo1GDRoEDQaDV555RWvXSipPpXZgcf2KpGptiHP6MBLR1R4718NpfyRatNaHXjnJDd9a0wzCQY0rN72bh7D4MWOIdg1OgotQ7iTWbMdWHBMjcl7i1DkYRrF6UILBm/Px1fndHD9V95GLsDu0VF4vWuoT1bR6wI+j8HX94RD6pTvo7GymPM3pQKTuu17lwWuR1sF+aW+1COtpJzU1zyjA39erd5N4elCC6e4PAC83Knu7P67TSJg8E4PbgOWnTlm7LtON8mElOetf9RILuDWMf2/rqEY1Mi3ncxDhDy859IsKemGGduqOXa5sjlYfOZSyuCpeBkiJL4LZtam+13qAO69bobJRnMpUr/prA48ukeJ/S7zlPubSrCsl7xO1VMPF/OwaWQkWjjdk87vFFzrC8i1zeMAYNeuXfH777/j+vXreOGFFwAAixYtwvz588Hn8/H7778jPj7eaxdKqm/xSbVb3bPlKVq8dlwNB93sk2r4NFWLm07/lsR84J0adNbsGinCgbHRmNTKfYBNzDbhnq35OJRbcYkBV1ZHSX2p4X8U4LzKfTfb8wkyHBgTjW5RonKefXdpJRe41RHad8PscaoiIf52SWPDoZvcm+baTv+9LUTIw6OtuWURVp/XVXB2+Vac4Z7fN0aEPgFaS+dOxjaXoG8Md9xdmKyGzYNdkYTUZ1uvGLEyjbuQcW9jMeZ1qp3dNONaSDHQZVH39eMq6GrQEGTTZSOuOmViiHioM7uDqqKzQsjZOam3sTh8s+pzV3L3OVdkxboLelzR1s3MG7XFgQm7lDjoco/WPVKI7waFVzkrLJA0DOLj8NhofHlPGDaNUODN7p7f39YVNdrbeM899+DEiRM4c+YMLl68CIfDgRYtWqBLly51KvpbHx3NM2NtRvk39N+k66G1svi8fxgEdfCNSmrXFa0NX7nUb5ndPrjGxehDhDz8d2AEhjY2YP5RFaeA7A2DA2N2FGJ+pxC82jWk0h17F1RWzDxUjFOF7vVqYmV8fD0g3G1Se7eb0U6GP64acdgpaPJGshpDGonRrI40GSDkNtfmH72iRGgX7r902afjZVh9vuyaDt20IENlRXwVyg5kqa3Y6tKF7uU6VPvPFcMwWNpLjiHbC0qPpats+P6C3i1dmpDymGwsNl8xIjnfDAmfQXyYEHFyAdrIBYiU8OrF/UaW2ooXDhdzjsXK+PhmYHit1SlmGAbL+8jRf0t+aTfPGwYHlp/WYrEHC74OlnUrZTC5dZBPU5lrG49hMDJWgv85LaAmZpswPNa3OzZJ3XM0z4yPU7TYc6uzt4ABPukXhql+Wqz0RJHJjvG7lDit5N5vJYQJ8MtwBYIEdbeup0zIwxP1sNtvRbxyp9exY0d07NjRGy9FvMBiZ/HyEVWl5/yUZYDO6sCqQREQ8+v+5In4zqITapidMnIbSHleTUeb2CoIPaNEmH6gCCedgngsgI9StTiYa8aqQeFugSkHy+KbND0Wn1SjvIzhx+OCsKyXHKGiuvuB5Cs8hsGX94Sj/5Z86G/N9HU2FrMPF2PbfZH1ujGKycZCaXag0GRHkcmBQpMDSrMDStOtP2Y7zPaSXVfT28ogq+OFyus7q4N1a2QzNf7OjYl8qX2EEH1jRDiaVxZgX3Nejw/63Lmb3OdnueUL2ocLcG9s3V7A6BopwmOtg/Cz0/+npf9qMaFFUIUd5Am5prNhzXk91l0woMhc/i60MBGDNnIh4sJKAoK3A4PNQwR1ZoHbYHNg6r4iziKokAd8PySi1lNl48OEmN0+GJ+dLVv0/eqcDpPjgqq0gOEs8ZoJ6U4ZGTwGmFuHFzMqMqqplBsAvGbER33qVhok8Q2WZZF0w4yPUrQ4ksfNUrCxwJy/VbiksWFR99CAn3fnG+14aEch0lyyrLoohNg0QlFv0vrvFh4HAP/++2+cOXMGzz33XOmxDRs24IMPPoBKpcKECROwbNky8Hg0uattn53RuqVBLu8jx/IULfKd0ji3XTVBv1eJH4ZG1OmoPfGdQ7nuNWAWdQ/1eve2FqEC7BgdhaX/arDiDPcGOLnAggFb87GiXxjGtyy5sb+ms2H2oWK3tD8AiJLw8Fn/MNzftH7Xb6ip5iECvNtTjnlHyxYLDt+0YFW6HjMT6sbOHAfLQmV23AroOQfxSv4uDfLd+rrI5ICuivV5dmSb8E2aDu/2lGN8CylN5gNU4jUT53MtVMhgXHP/v/efjpdxAoA/XzRgUffQSgPKN/R2TpAMKKn9Vx/+7S3qHoqtV4ww3Hr/Kc0OfJSixZJe9T/VhlQdy7I4kGvGt+l67Mg24U6Z4ioLi+QCi1vdPCEPaBlyKyAYJkAbuRBt5AK0lgsCalGQZVnMO6Jyu6le1kuO7n4qWbKgSwh+v2TEdUPJyqqNLanPvHWkospjEcuy+MRl99+EFtJ6WVR/YEMxggRM6dh2w+BAitKKLpFUcuZu5WBZJF4z4eNUrVvzRFcrzuhwWWvDfwcEbsfZ63o7xu4oRJaGO071jhbht3sVkAfQmEqqhlGpVB4VYhkzZgwUCgXWrl0LALhw4QL69++PFi1aoFmzZti7dy+WLFmCWbNmefWCSeWy1Fb035rP2bE1uXUQvh4QjksaG8buLES2S2fUvjEi/DKc3sCEy+5gMWh7Ac4WlX14dYsUYs8DUT5dqdp/w4SZB4vd6lcCwBNxJbsF3zih5qyW3/ZgMwk+7ReGSFqJ4sjMzERcXJzbcZZlMX6XEklOhXylfAaHx0ajlTywJurXdDasPKdDitJaGuQrMjvueIPoDX1jRPiwTxg61rEurHeDh3cVlqbUACWBt0/63Xmnna+Z7Sza/3YThaaycezz/pWn+yxMVnPKLTQP4eOf8TF1ZifTnXx4WoOlp8qCAkIecOyhmGqNNRWNZaRu01gc+CXLgO/O63FB7dvaWA2DeIi7FRC8vWMwTi5AYxm/1oPt32foMdclY+fhllKsGhju18D/1itGPJlUxDm2ZlB46SLsnRy4YcLYnUrOsb/HRte5TuZV9fheJf68VrZY/lqXELzWNbSSZ9BYVh/ZHSXlCj5J0boF9Z3xGLjNXXtECfHTMAWipYF1/3JFa8PYHYWcWp5ASeD7p2ERCKYsmTrJ4wBgmzZtMGfOnNIGIEuWLMG3336Ls2fPIjQ0FM8//zxSUlJw5MgRr14wqRjLshizo5CzK0oh5uHE+OjSrbk5OhvG7SrpDOyss0KIjSMUFDghpf6XocdLLhPTXaMj0Sva9+lohSY7Zh8qxs6cqhVTDhUxWN4nDBNb0k6t8lQ20czR2dBvSz40TgHV3tEi/DUqMmCK+R6+acaUfUoUm/3XOIDHlASX/q9rCKU6BIhrOhs6b8jj7Bje/2BUwOy8ePsfNaehRxeFEPvHRJd7brHZgQ6/3SxNyQeAT/qG4em29acmjcHmQK9N+cjRl91IjG4qwfphiiq/Bt001y/nVVZ8l67HL1mGSndny0UMJrcOglzEQ6bahgtqG7LUNhjt3vtMkAkYtJYL0DpUgFgZH41lfDSS8Uv/29v1Bk8XWjDyrwLOgn28XIC9D0b5/aa6vMXBhkE8JI+PqVIGyJgdhZwmAaOaSPDz8Kq/z+uaHzP1eOFw2Xy5s0KIAxWM9bfRWFZ/WOwsfrlowIpULS5py6lJdMuQRmLM7xyCID6DSXuVnOwFAGgazMdv9yrQtprp9r6SqbZi7I5C3DBwr3NErBjfD1EE7I5Fcmceb/HQaDQICytbZd+7dy+GDBmC0NCSFY++ffti+/btNb9CUmU/ZxncUiKX9pZzblZjgwX4a1Qkxu9S4ozTzq4UpRX3/1WILSMj61WBXuIZldmBd09qOMceaSmtleAfAERK+PhluALfpOux6IQalkqa0A1uJMaX/cMQGxxYO9bqithgAZb2lnMmr8fzLfj6nA4vBkC9nh8u6DHvqAo1aETIIWAAhYQHhZhX8rek5MYuQsJD5K1jqUorvk7Tcb6ngwW+O6/HxssGvNEtFE+1kQVMgPRu9WOmgRP866wQBkzwDwCeipfhM6eSBqeVVvxbYCm3G/m36TpO8C9GysPk1v6tZehtQQIe3u4RiukHypod/HnNhAM3zBjUqG7XOSRVZ3OwSMw2YVW63q2TpKv24QLMaBeMh1tK3dLnHSyLHL29JCCost0KDFqRqbaVm0FwJ3obixSlFSnK8lP2RDyg0a1gYOOgsgBhY6c/CnHVgoQqswNPJhVxgn8yAYN1QwNjRw3DMPiwjxz9tuSXfg7mGhz44NSd0/ZP5Fvc/r/O82Ld6EA0MlYCBigd61OUVlzX29GY7qfqNaONxQ8X9Pj8rI6zsOVqVBMJ/tM5hJPWv+eBKEzareTsFLyms2PEHwVYNzQCgxv5t5HMuSIrHtpZiAITdyx9sJkEqwdFQET9A+o0j++YY2JikJGRAQDIzc1FamoqnnrqqdLHNRoN+Hwa+GqL0mTHGye4AZshjcSY2NK9FlKUlI/t90Xi0T1KHM8vCxheUNtwop/UsAAAIABJREFU318F2Doysl7W6SBVtzxFC6VT0e0gAYO3e9RurSaGYfBcQjD6xYgw/UCxW1qQlM/gnZ6heKatLOCL5wa6x1sHYftVE3Zml6WwLDmlwb1NJH5bibQ7WLx9UoMvzuoqPS9UyNwK5N0O6vGhkPBKgnrikr9LvuYjQsyDXMTc8QZtQktgahsZXk9WYZfLLtRiM4v5R9VYm2HAh73l6NeAAhf+YHewWH+BWy/vyQDrptc8RIDhjcXY7ZSivDpD7xYA1Fsd+CaN28l4VvtgSOrh6vqEFlJ8k6bDiYKyIMv/JatwcEw0BdTruQKjHesuGLA2Q1/pzbKAAcY0l+LZdjL0iRZVOF7zGAZNgwVoGizAsMbcx1RmB7I0NlxQWUt3DGaqbbiksaGKZWDdWBzAFa0dVyrZ4SPhA42CygKDsZwgoQCNg3gIE/Pw/KFit5S6Ff3Cqt1ow5fi5ELM6RCMj1PLPoNXppU0BEmopMu6a+2/gQ3F6BkdOAszvhAl5aNnlIhTi3JHtpE6nddTWqsDa87r8dU5ndsuvtt4DDCuuRTzOoWUm/reNFiAxNFRmJZUhH1OO201VhYP71L6tUPwqUILxu8qdMu6eaSlFCsHhNebsiR3M4+jPA8++CBWrVoFs9mMf//9F2KxGKNGjSp9/OzZs2jevLk3rpFUwcJkNadLmoRfkj5U0cQpTMzDphEKPLGviLPF/5rOjlF/FWDTyMhKP+BJ/ZWptuKbNG7QZW7HYL+tZHZSiJD0YBReT1Zj3a0b/p5RQqwcEI7Wcvo36g0Mw+CzfmHoszkPKkvJB77ZDsw6VIxdo6Nq/cNeZ3Xg2QPFSMw2uT32apcQPNhMWhrw89UqZCu5AL/dG4md2Sa8flzlltZxtsiK+xML8XBLKd7pIaed07Vsz3VzaZF6oGSR4uFyFrz87em2Mk4AcOMlA5b0lCPcqfuta5fTUBGDafGBFcz0FoZhsLRXGO79s6D02LliG9ZnGfx2s0N862SBBd+m67D5srHS3fwxUh6eipfhqXgZGgbVbDwNE/PQI0qEHi7BdquDxRWt847Bkl2DF9Q2aCw1Tyc22YFLWnulaYBiPjg7/wBgelsZHmkVeDt+53UKwa8XjaUBWzsLLDimwh/3RZZ7f3GuyOr2uT2/090RBBvVVMIJACZeM1EAsJ4pNjvwTZoO/03Tlc6VXQkYYFLrILzcMeSO9W3lIh5+vVeBV46psDajbEHTnx2Cj+eZ8chuJacsEABMbROET/uG0UJdPeFxAPD1119Hfn4+fvvtN4SEhODLL79EdHRJvQONRoPt27fj2Wef9dqFkooduGHCLxeNnGOvdAm94y4+mZCHX4Yr8Mz+IvzhVLz2ptGB0YkF2HhvZLmpSqR+eyNZzVkhj5Xx8WIH/05iZEIePu8fjgWdQ1BsdqBjhJBq/XlZgyA+lvcJw7MHy9Lz/i20YsUZHf7TufbSd7J1Nkzao8S5YvcdnysHhOOhFrUb5BnZRILBjWKw8pwOy1O0nDRNAPj9khGJ10yY3zkEs9sHQ3yXpUU4WBabLhuRobKhd7QIQxqJa2WC+P0F7o65cS2kAdXd87YRsRLEyvilN9AmO/BTlgGz25eMqRY7iy9ddrk+21YWkD+Lt/SMFmFiSyl+u1Q2b3n3pAYPNQ/M/4ek+kw2FpsuG7DqvB6n7tAFs2+MCM+2leGBZlKfp5UJeQzi5ELEuSwesiyLfKMDF9Q2XNXZcF1vx3W9HTdu/X3dYPdKgBBwD/51ixTivQDthi0T8rCstxxT9pU1BPn7pgUbLhkxsZyA5Yoz3N1/3SOFGNjw7tglP6qpBIudSucczDVDZ3UEREo3qZl8ox1fndVh9Xl9hbVKJXxgShsZ5nQIRpNqlCQS8hh80jcMLUMFWHRCwylrUtsdgg/cMGPyXqXbPHdGOxne7y2nbKt6xOMmIJVxOBzQarUICgqCUEg7dHzJaGPRf0seZ7UxIUyAA2OjIaziTZjNweKFw8VuQcRgAYOfhysw4C758CbAnhwTHt7N7dxWnc5vJDBVtdg0y7KYmlSE7VfLFgSEPGDfg9G10gH3nwILJpdTGLmBlIefhyvQ1c/13W7o7Xj7HzUncOGsRQgfy3rLS+oB3QUTJauDxaxDxdjg9PtoHMTHpNZSPB4nQ0sflZK4abCj/W834Vz/v7YaFHni4xQt3v237MawdagAJ8ZHg2EYrM/UY7ZT/U0JHzjzSANEBVgnQG/L0dnQc1M+p4nDSx2D71hqggrnB7ZrOhvWntfje5ddra6kfAYTW0kxvV1wnemurrU6SoOCOc7BQb0dNwwlf2ut1bulChMxODg2Gk0DuIYxy7KYuFvJ2ckcIy1pCCJ3Cthf1tjQfVMep7vp+qERGN0s8HZm+wLLsui2MQ+Xne7H1g2JwJjm5f/8NJYFvmydDZ+f1eGHC3qYKtjUGyxg8HRbGWa3D0ZMDXcub79qxIwDxW7NjbpHCvHzcN92CN6dY8KUfUq3n/PljsFY1D30rpjT3k28FgDUaktWfUJC6neh10Dz7kk1pz4HA2CnBzdCDpbFq8fVWJXO3VUh4QPfD1FgZBP/FiOtTXkGO369aECQgMHwWAmahwTuxMybrA4W/bfkc2rt9Y0p6QZLA3/dVp2JZoHRjj6b8zk1IDtECLHvgSif7s7YeMmAWYeL3XZHdIoomfgEUjHto3lmvHJMzWmk5OzexmIs6y2v1ynqJhuLafuLyk3Tvq1fjAiPxwVhbHOpV3dBuAbU2oUJcOSh6IAdp/KNJQFL56YyW0eWLK712cwdc59tK8PyvmHlvEr9s/SUBh+eLtsxJOIByeNjKv3MpZvmqrmht2PvdRPSiq3gMQzE/JKdJmI+AxEPt/5mIOIzEPMA0a2vxXzcOsZA6PTY7eeVPsZD6fuNZVkcyDXj23Q9dmSbOAEgV81D+JjeVoYn4mQIE9e/nVEai4MTFMxx3Umot5furgkVMfhhiKJONMC5pLGhz+Y8Tgr38wkyLPt/9u48LKp6/wP4+8y+si+CiKaiuFuaopX7ftXMTG2zbLktlm1m3Vu/bouVmZZWtlqa1U1bTKubWq65a5ZpooiYGyLIMsDs2/n9MQoMA4g4A8zwfj1Pj3HmzHBG4cz3fM5n6V1+rnp0WxGWVOjL2iFChm3j4ppU1tC/dxvw7sHy66ib22rw3nWRVe7Lc1njlVXsxJsHSrE8y1ztALoIhYD7Oupwf0edV0uPy/X7OXuVE4Jb6KT4OkATgr8/bsHdmwt93uszV+oxo5u+0a6tqO4uKwB47NgxzJ49G7/88guKi4sBAOHh4Rg6dChmzpyJtm3b+u1AyVd6kQP9VuV5lWvenarFvDpePIiiiJd/L8XcSg18ZQLwQb9I3NgEssB25dpwy/pCr+BHxwgZRiarMDJZjati5CG7mHk/3YindxWXfS0A2DgmtlFN1aS6udSF5qrjFtyxsdBr25Pd9HjmqjB/HxpEUcTsfaV4bV+pz2Ojk1X4oF+kz/THxsDlFrH0iBkv/V5SZaaLXAI82FGHGd310DfC478cRocbt64vxOaLTPC8QCcTMO4KNW5L0aB3DU39a8Mtirjym1yvBvqv9grHA50ad6+luzYVYsXf5ZmSY1uqcFMbjVdpnVQAfr8xHi2byE0nk8ONnitykWMu//25vpUKnw6MrvY5vGiumtUpYkeuDeuzbVifbcUhg/PiT7pMFwKJgoAay2MFAEOTlLi3gw6DmytDdg1VG6IootguIt/qQoJG2ig/26rz8u8leP3P8s9pqQBsHhuHzlFynDG50P2bs14Bwg/7RVZZJhzKtuTYMGZNftnXUUoJMic3q7ItBs9ljU96kQNv7C/Fir8t1d7IiFVJ8FBnHe5K1QZsbXfS6PSZEAx4ht75e0Lw11lm3L+lCJWSDjHr6jA81JlJXaGqzgHAvXv34oYbboDFYsGgQYPQtm1biKKIrKwsbNiwASqVCt999x169uzp72MmeC6CRv6U7zXFt5lagl2VUvLr4q0DpXjuN++JwgKAN/tG4M4QbUwOAN8cM2NaFRlIFcWpJRjRQoWRLVTon6iERhY8i7eaFFhduOrbXBRXWMTflqLBO9dWfeeSgktdFpr3bC7ENxVKO6UCsH60fwPCFqeIaVuLvAIjFzzeVYdnr6rf5sd1UWRz45XfS/BxhqnKBWO8WoIXeoZjYht1o38vtWGwuTHxlwKvZueA532WOkSYLzJis22YDLemaDC5raZOjf43nbFi3NryNgVKKXB4UoJf78AHwtazNoxeXX5hKBU8fxcZFbL/JrZR48N+UQ1xeA3my6NmPLClyGvb/0bG4JpqpmvzotlDFEVkFjuxPtuGDdlWbD1r9ykba2jhCgG3pWhxd2rg2gFQ/TE73Uj7Lg8nK9x8uVAl8uyeEiw8WF6N1FInxd4b45vctFCHW0TbL3O81tKrR8WgT7zv+YznssbD4hTxf3uKseiwqdp9LvRDn9JOWy/9+Irtbp8JwYAnKcdfE4KXHjHhkW0GVP7kmNcnnANsQlydA4BDhw5FdnY2fvjhB7Rp08brsaNHj2LMmDFo0aIFfv75Z78cKHn75LAJj+8weG37dGAUrq+m18SlWnz+9Sv/cLzUMwwPdwmtOwKiKOL1P0vxyh++GUg1UUsFDEhUYmSyCiNaqALamyHQZuwweH3w6eUCfhsff9n9LKhxqMtCs9DqQp+VecitUIbQIUKGTWPj/DLoItfswi3rC7C3UoN4uQRY0DcCt6QE182GvwodeGqXAdvO2qt8vFesAnPSwoM6o/acxYXxPxf4lD6nRsjw3fAY6OQCvvvbgi8yzV43p6oiEYAhzZW4NUWLES1Utf6ZmrqxEN8dLw8Y39RajY/6N/6gmSiK6LMyD4dryMzaMS4OHSJDt2y8Km5RxOAfz3kNiugWLcfGMbFVBsyb8kVzsd2NzWc8Ab912baywTKNTecoOf7ZQYsJrdUhc5OUPFaftODm9d7VAS/3CsfLv5d43fx5s08EpqYG12e4v9y7udCrL+70zjq8eLVvb9OmfC5rTA4bHLhrUyHSi6r+bG6tl+LRrnpMbqMJ+JCiypxuEU9WmhB8wSOddfhPz7rfJK9c9QV41mXvXBN862+6dHUOACYkJODJJ5/E448/XuXj8+bNw9y5c5GTk3NZB0i+zppd6PVdrlfJxfAWKiwbHOXXOv1vjplx36++acEzuunxzJWh0RPA5hIxfVsRlmf5ZiB1i5bjQKGjxn42FwgAesbKMSpZjZHJKrQPlwXN38/BQgeu+z7P632+0DMMj4RYoLcpq+tCs6rFfm0a9V/MgUIHbl5X4HMBG62U4PPBUVXeLQ8Goijiu78t+L89Jcg2+16cCwCmtNPg/3qEIUYVXMH1bJML49bmI7PYe5HcPVqOb4dFI7rS+8ksduCLTDOWHTXjrKX6YQCAp0zqptZq3NZOW+NAgHyrCx2We/fS+2FETNAMqvog3YinKi24LxjZQoUvh1Rf+hrKdubaMOKnfK9tC6+NwK1VXIQ0pYtmtyjizwIH1p22YsMZG3bn2X3WY9WRCMDVsQr0S1AiTC7A5vasdxxuETaXCPv5r+1uEXbXRR5zi7BX2rdyoq9cAoxpqca9HbRIu8wyf2rcJq0rwNoaer82U0uwb0IzqOohS6oxWnHMjLs2l2c1twuXYff4eJ/9mtK5rDESRU8bl6d3FVeZPd0xQobHu+kxrpW6QTNZRVHEOweNPhOCAU8rkQ/6XfqE4Df3l3pNrAY8mYUf9Y/EDVc0rbL9pqrOOflxcXGQSKq/syeVShEf73vCo8v39K5ir+CfViZgblq43xdcE1proJUJuHNToVdZ7Nw/S1Fidwf9SPBCqwu3bSjE9lzvTBW5BHj7mkhMbqtBodWFX7JtWH3SinWnrdWOfxcB7DnnwJ5zDrywtwRX6KWevoEt1OgTr2i0ZRCiKOLfu4u9gn9X6KW4vyNTvwkYmazGzW01+PJo+d3Ht/4y4h/JalwdV7dMtp9OWnDv5qKyRugXpEbIsGxIdFAP3REEAeNbazC8hQpvHjDi7b9Kvc6dIoBPj5ix8rgF/74yDHenahvtuaGiv0ucGLs2H6eM3kHNPvEKLBsSXWXbiZRwOZ7vGY5nrwrD+mwbvsg0YfUpa5UNtQttbnxwyIQPDpnQNUqO21I0uKmNxqes98uj3g2524RJcW2z4MmonNxWgxf2llRZJv1Y16Z7zk2LV+KGVmqvzM4X95b4fXhMMMg1u7DhjKeP38Zsm1c/4otprpFicJISg5ur0D9BGdAhG27ROzgYppD4JTOcGr/Xeodj0xlrte1ypnXSNdngHwAMTlJBJqAsSH6k2ImjxY6QHgoWbAw2Nx7dbsDK477JH1fopZh1dThGJqsaxTWuIAh4uLMerfQynwnB35+wInv1uVpPCBZFEa/8UerVyxPw9HNdMjAKo5KbxsRuAqRPP/3083V6olSK9957D2PHjkVYmHdj+JMnT+KJJ57Aww8/jB49evjjOOm8taesmPW7d9T+hZ7hGJIUmCm9KeFy9I5T4ofjFq/mvnvzHThldGF4i8ZxgrxUR4sdGLMmH/sLvbNZIpUCvhoaU3YSVMsk6Bwlx7gr1JjWWYe+8QqEySXIs7hR4qj+VrzBLmLPOQe+PGrGh4eMSC9ywCmKSNRKoWpEi+SfTlrxxgGj17aF10Y2uTK0UFdYWIjo6LplF13bTImvsswoPf/zLgLYkWvHbe00kF9C8EoURbz9lxEPbzN4nUsATyno18Ni0CxESs4VUgH9EpS4qbUGJ40un6w5mwtYl23DjycsSI2UI1nXeIOeh4o858ozZu9/tMHNlVg2JPqiTbAlgoC24TLccIUGd6dqkaCR4qzZhXPWqgMbuRY3fsm24b2DRqQXOaGTC2ipk0IA8NA2g9fAlUe76JEWRNmiKqmAk0Yn/izwLqG+ppkCM7v7f8BOMLkyRo7FGaayDDeTU4RE8PweVXQ557LGyO4SsS3Xjk8Om/B/v5Xgud9K8L+TVqQXOS/a008pBfonKHFPBx1e6RWO53qEYVSyGqkR8oAHYARBgEwiQCUVoJVLguJGBvlHhFIClxvYWkW7iwiFgA/7RzXpYLBKKmB7rh3HS8sjpC10UvSOC+1zWbDYnWfDDT8XVNmmZGJrNb4cEo0u0Y0vi7l9hByDEpVYc8rqdQM9x+zGyuMWDExUIraGIKAoinh2TwnmV7rmU0sFfDkkGsNaMPjXlNS6BHjBggU+27766itkZWVh1KhRaN26NQAgKysLa9asQZs2bTBx4kRMnz7dv0fchBkdnga8FcvmukfLsX50bJUTpvzpt3N2TPg5H4ZKk97GtlThoyD7sN921obbNhSgyOb9XtqESfHVkBi0Cb/4xbgoijhQ6MDqU1asPmnFvkoXdNWRSzwBlZEtVBiRrGrQC3+bS0Tad7n4u8IipX+CEiuHRze6Dz66PJdbarI+24obfy7w2vZARy1e7V27ieN2l4jHdxjweaZvH5P7Omjxcq/wkL6AXJ9txdO7in0CgRfc2U6D53uGBzRjpy7+yLdj/M/5PufKMS1VWHQZ533xfGnjF5lmfH3M7PO5UllzjRQDmivxRYWfH5kApE9qFnS9V/8ssKP/9+e8tn0zNDpgN/GCyUt7izFvf/nFiUoK7B4f7/U5GQplcxaniGVHzVh72ootOTafbOiatA+XYVBzJYYkqdA3XlkvzeiJKrM6RaStzPUKcgHAU931+NeVTftmBuDbX61vvAI/jYr12icUzmXBxC2KmH/AiJd/L/FppaCVCZjbJwI3t2385a81TQj+dGAUBjb3XUu4RRFP7izGx5WGnOhkApYPja526BaFrloHACMjL30aqCAIKCwsvPiOVCvP7C72mrIlEYANfp7KWZODhQ7c8HM+8ir1cxqUqMRng6KgDYJSnS+PmjF9W5FPGVrfeAU+HxSFqDr25co2ubD2lBWrT1qwOcfmk+FUnc5RctzcVoMp7TQBGydfnQUHSvGfCtOeJQKw9fo4dGT2X8jxx0Lz0W1FWHKkPAAjAPixhmmdFxRaXbh9Y6HPcAypAMxJazqTxuwuER8cMmLOvtKybMqKmqklmJMWgbF+GuR0ubaftWHSugKfY53cRo13ro30W8DW6hTx00kLvjhqxoZsm0+Pm+qMbanC0kHBmT0x6Zd8rD3tmex3bTMFfhgRw5suAEodbvT8Ntdr8NCNV6jx8YDyIS/BftGcVezEzesLcKSamwGVhSkEDEjwlPUOaq5Ei0acLUxNy9pTVkxaV35jUCsTcOCm+Dqvo0PJ8VInun+TW/a1RACOTm7m9XcT7OeyYHLW7MJ9vxZhc47N57GuUXJ8MiAyqEq0q5sQLBWAN/pE4I725f1zXW4RD28z4L9HvW/AhysEfDssBj1jg6eNCvlPrQOAJ0+erNM3SE5OrtPzyNu+fDsG/XjOq1fbtE46vNzr8prxX6pjJU5cX0UvqLQ4BZYPrboXVGNQXd8DwHNBu+CaSL9lMZY63NiYbcPqU1asPWX1KlmrToRCwD0ddLi/o7ZehgPkml3ouSLX6+L+nlQt5vapXUYXBRd/LDRLHW5cszIPJyv87rfUSbFtXFy1fbqOGByYtK7AK8sU8FzUfjqg6juVoS7X7MILe0t8FmMX/CNZhdfTIpCobbiLqHWnrbh9Q6FPCeK9qVq8lha43q+njU4sy7Lgi0yTz89MZd8Oi8bgIP35Kba78WmGCW4RuLO9ttFlfjakz46Y8PA2g9e2taNi0Pt8qXcwXzRvzLbizk2FKK4h41WApxx6cHMVBjdXomds4+0hTDRjhwGLzmcVvdEnAnc10cm/Vem7Mtdrsuz713l6i18QzOeyYPLLaSse2FKE/CrajtzfUYsXeoYHVRXbBbWZEOwSgft+LcKKv717HUYrJfhueDS6RjP411TVeQow1R+nW8TgH8959Q1K0kqx84bqL7wD6bTRiRt+LvApZ+saJceK4dGNbrql1Sli2tYifPu3b7PXZ68KwxNddQHLvnC5Rew+Z8fqk1b8dNKKoyU13/VXSYHbU7SY1lkX0GEID20t8irHDFcI+P3GeJ9JnhQa/LXQ/DXHhrFrvKd13p2qxbwqAscbs624Y1Oh18AiwNNgefmQaLSLCJ67rYGw/awNj2w3VFkWHCYX8J+eYZjaXlvvPVZXHbfgns2FPlnSj3fV4f+uCquXTDVRFLE9144vMj0DUyoPzUjWSbFvQnxQ9p+lmrncIgb+cA77C8vXOz1i5PhldCwkghCUF82iKOL9dBOe2eM9cOuCeLUEg5qrMKS5EgMSlfwcpqAhiiIyip1QSwW0DOIBXoEwa28J5u4vTzoY10qNJQNDJ5u5sbO7RLywt8Srcu6CKKUE714XgRFB3vfuYhOCHW5gdaWJ3c3UEqwcEYPUJr4Gb+ouKwB4/PhxZGRkoLS0FDqdDqmpqWjVqpUfD48AYOFBI57ZXey1bfmQaAxv0XDZD+csLoz/uQAHCr1737UJk+LZq8IwpmXDjk2/IN/qwq3rC32avSqlwLvXRuLG1vXb7yGz2IHVJ61YfcqKXXn2Ki8GAE8a9/gr1Hikix6do/x7kt6Xb8fAH855fVjM7h3Oyb8hzJ8LzZk7DfjwkHcfkZXDozEgsfx8tOiQEU/tKvbps3K5pfahxuoUMW9/KeYfKK1yOm5anALzr4mot4XafzNNeGibwee89FyPMDzeVV8vx1BZqcONlX9b8EWmGTvz7AhTCFgyIAqDgjT7jy5u61kbRq/2vtHwQb9ITGqjCbqLZptLxBPV9D+d0FqNR7vo0SlSxhJwohCz95wdg38s7/eqlws4enNCWbZZsJ3LgsmxEifu2lRYZX/2a5sp8GG/qAatsvC3H05YfCYEVyVJK8X3I2LQOozB+qauTgHAVatWYfbs2cjIyPB5LDU1FU899RSuv/56vxxgU3fK6ETad3leTaIr30VqKAabG5PWVT1JKUkrxT87aDGlXcOVN2UYHJj4SwFOVCpXjlZK8N/BUWUlRQ0lz+LCosMmfHTI6NNkv6KhzZV4pKse18Rf/lQqURQxanU+duSW/5u1D5dh67i4S5roSsHFnwtNk8ON61bl4ViFEs0kracUWCsT8O/dxT4BQgC4LUWDN/pEQBGEpRaBdqjIgenbirDnnO9iVS4Bnuiqx2Nd9QEtU/nokBFP7iz22f56Wjju7dA4bg6U2N2QSQCNjCWzoe72DQX44UR55kKiRoI94+Nx5nhW0Fw051lcuH2D7w1IAcDzPcMwvXPgqg+IqGG5RREdlp/16mm6Ylh02c2rUAgAiqIIi0uEWio0mnPZ8iwznthugLFS5YBUAP51ZRge66IL+ODMhvD7OTtuXl/g9fNW0RV6KVaNiGnQ4ZPUeFxyAHDWrFl44403oNfrMWrUKHTp0gU6nQ5GoxEHDhzA6tWrUVJSgscffxzPPvtsoI67SRBFEZPXF2JthfTdMIWA3TfEo5mmcdy5MDncuG1DITae8W2sCniaAt/SVoP7O+pqNV3XXzafseL2jb7lh+3CZfhqaHRAy2svldHhxtIjZrx70Og14bmyq2PleKSLHqOSVXUufVtxzIy7Nhd5beMEytDn74XmzlwbRv6U75VFelNrNQptbqzP9j4XCABe7BmGh3ixWyOXW8THh014cW+Jz8IV8ATqF1wTgbQA3Lh4Y38pXtxb4rVNIgDvXBOBW1LY04nq398lTvT+LtdroNbT3fW4UZcbFBfN+/LtuHV9IbLN3p/permARf2jGrSCg4jqx/RtRVhaYXjavalavH6+ZUpDBgBtLhGlDjdK7SJKHG4YHeVflzpEGB1ulDhElNrdMDo9f5ae38foEM/v59kmAohUChjaXIURLVQY1FzVIIkfRocbM3YYsCzLt91TklaKRf0jA7J+akxOGZ2YVMWE4PbhMqwcEYNQidPDAAAgAElEQVSERhI7oIZ3SQHA9evXY8KECRg7dizeeusthIf7DqAoKSnBI488glWrVuGbb77BoEGD/HKgr776Kl577TWvbXFxcThy5AgAT7Bs9uzZ+PTTT2EwGNCjRw/MnTsXHTp0KNvfYDBg5syZWLNmDQBgxIgRmDNnDiIiGufgg1XHLbhjo/cU5cbYZNfmEvHYdt8JQxUJAIa1UOHBjjr0S7j8TLaaLD1iwuPbDah8Dd0/QYlPB0Y12obrdpeIb46Z8dZfRhw2VN8rsF24DNO76DCxteaSsqksThFXr8j1CjIOT1Ji+dCYyzpuavwCsdB8dncx3qmit0pFWpmAj/pHYlRycPdZqU+njU7M2FmMNZX6tlxwd6oW/+kRhjA/DFwSRREv7i3Bmwe8/x3lEmBR/yhc30gmElPT9J89xVjwV/nPploq4OurzLi2c9sGPKqL++5vMx7cYvApx2qtl+LLIdFoz95LRE3C6pMW3Ly+/DouSSvFgZviIQS4n2lWsROfZ5pwsMiBUoeIEvuFIJ8ncFdVyxF/kQpAn3gFhrfwBATbhgW+xcG+fDvu3lyIrBLfJIoxLVV4+5rIRnvt528ldjembiosuxnfJUqO7xphf35qWJcUAJw4cSLOnDmDX3/9FRJJ9b9Ibrcb/fr1Q0JCAr7++mu/HOirr76KFStW4McffyzbJpVKERPjCV7Mnz8fc+fOxcKFC5GSkoI5c+Zg586d2LNnD/R6T++iCRMm4PTp01iwYAEEQcD06dPRsmVLLF++3C/H6E/Fdjd6r8jF2QqpvL3jFFg9KqbRNj4/bHDg/YNGLMsyw1rDAMdOkTI80EmHCVdooJL57724z1/Mzj/gG5S4PUWDN/pGBEWZq1sUseaUFQsOGKssr74gUSPBg510uKO9FvpaDIOZs68Er/xR3pBYJgA7bohDSjgvRkJdIBaaFqeI/t/n4UgVQywAz0L3yyHR6OLnHpZNgSiKWHXcipm7DMiropwjUSPB62kR+EfLugfo3KKImTuLyyY4XqCWCvh8cFTQTtil0FFid+Oqb3O9pjeOinXiv6NbNuBRVc8tinjlj1LM/bPU57EBiUosHhCFyCZyEUpEgNnpRpv/nvW6GbDl+jh0iZL7fV3mFkWsO23DR4eM+CW76qqshtBaL8Ww88HAvvFKv7aBEUUR76Wb8PxvxV7Z4oBnqOIrvSIwtb2myVWfXLiONDlEjG2lDsopxxRYlxQAbNOmDR555BFMnz79ovu+9dZbWLBgAbKysi7rAC949dVX8f3332PHjh0+j4miiNTUVNx7772YMWMGAMBisSAlJQUvvfQSpk6dioyMDPTu3Rtr1qxBWloaAGDHjh0YOXIk9uzZ0+hKSp7YYcDHFS7M5BLg17Fx6BDZ+C+mC6wuLMkw46NDRq8AZmWxKgnuTtXirlQt4tSXd2fC7HTj/l+L8P0J36yZF3uG4eEgLT/ckWvD/P2lWHu6+g/zcIWAezvocF8HLWKr+XvMNrlw9Ypcr2ma0zrp8HIv3yxeCj2ButO895wdQ/93zmdoRI8YOf47OBrxLDe4LAabG/+3pxifVTFAAPBMeZuTFnHJLSGcbs9k9OWVSmX0cgHLh0Sjb7PQLpOh4LEkw4RHtxu8ts1NC8eUdtpG1U+01OHGfb8W4aeTvmuQ+ztqMevq8EYxGI2I6tfN6wq8JrH++0o9ZnYP89u6zGBz44ujZiw6ZMTfpTVkXwSIVIDPwLfq6OUCBiYqMbyFCsOSVNVes9RGvtWFaVuKqrw+So2Q4ZMBUegYBNfMRA3hkhqhmUwmREXVbvhEZGQkTCbfRvCX4/jx4+jQoQPkcjl69uyJ5557Dq1atcKJEyeQm5vrVW6sVqvRt29f7Nq1C1OnTsXu3buh0+nQu3fvsn3S0tKg1Wqxa9euRhUA3J1nwyeVsjIe6awPiuAfAESrpHiimx4Pd9Zh5XEL3j1orHIS0zmrG7P3leKN/aW4qY0GD3TU1Wniba7ZhVvWF2Bvvvf3UEmBD/oFdxlbn3gl+gxV4mChAwv+KsW3xyw+H7TFdhFz/yzFO3+V4rYULR7qrPPpcfjCb8Vewb9opQRPdmuYqZ4UOnrEKvB4Fz3m7i/PeLnxCjXeuTYSaj9m9zZVEUoJ3r42Eje10eDRbUVeg1cA4PsTVmzKycVLPcNxeztNrbLDbS4R92wu9BqwAABRSgm+HRaNK2MUfn0PRJfj9hQNPjpkxMGi8kzjGTuLMf+AEY911eG2FG2DZzccL3XilnW+fZfkEmBenwhMade42rYQUf0ZmazyCgCuPmXFzO5hl/266UUOfHTIiOVZFq/1fW3IJYBeLoFeLkAnFxCmuPD/nj/1cgn0ivKvw+QS6OSC5zFF+T46uefcuzvPjrWnrFh72lpjC6NSh4jvT1jx/QkrBAA9YuUYnqTC8BYqdImS1zpR49ccG/65ubDKJJM722nwSu9wDgsjqsElBQATEhKQnp5eq33T09PRrFmzOh1UVXr27Il3330XKSkpyM/Px+uvv45hw4Zh586dyM3NBQDExsZ6PSc2NhY5OTkAgLy8PERHR3udXARBQExMDPLy8mr83i63WG8TgxxuEY9uM3g112+t9wTUgo1CKmBiGw1uaq3Gzjw73j1oxP9OWn2yhexu4ItMM77INKNfghIPdtJiWFLtBl2kFzkwaV0BTlWa9BunluDLwdHoERsaF7OdouT4sF8Unr3KiYV/GbH0iNmnv5DVBSw6bMLiDBNuuEKNR7ro0SVKjt15Nnx1zDvT59mrwppMPwwKrH9fpYdaJmB7rg1jWqpxR7umV24RaP0SlNg2Lh6v/1mCtw4YvfqblthFPLLdgOVZZiy4JqLGkn6z043bNxT6DGppppbgu+ExQXOTiZoOqUTAK73Ccf3aAq/tp00uPLGjGPP+LMX0Lnrc0U7bIDcdtuTYcMfGQhTavC9EY1USfDYoKuSbzhNRzYZXGrL3R74DZ2oY+FcTp1vETyet+PCQEVvPVt8iCPBUYtzZXovWYTJPkO98UE8vl/j9pknfZkr0babEC1eH43ip0xMMPGXF1rM2n9LcC0QAv51z4LdzDrz8Rymaa6QY1sKTHdgvQVllAM/pFjF7Xynm/VmKyiHPMIWAt/pGYtwVwZv0QVRfLqkE+Mknn8SyZcuwefNmtG7dutr9jh07hv79+2Py5Ml4/fXX/XKglRmNRnTv3h2PPvoorr76agwfPhx//fUXkpKSyvZ58MEHkZubi2+//Rbz5s3DZ599hn379nm9TteuXTF16lQ89thj1X6vlC9OYUSsC6PinGirvbS7LJdq8SkZ3j3hHbRa2NmKXhEB7Nhaj7KtAr46I8OqXBlMruo/gJJVbkxKdGJ0vBPVVbftKJLgX4eVPq/TRuPGmx1tSFAF9t+qIRkcwFdn5PgqR4ZiZ/V/j30iXci3C8g0lX+Qpmjc+OxKKxpR9RQR1dIRo4CXjyqQbvQ9MSoEEXcnO3B7cycqtwU1OoHH0pXYV+L9vASlG+92tiFJHbrnSwp+y87IsOBvOZxi1R9cUXIRU5IcGN/MicvsKFJr3+TIMPeYHK5Kx9RO68a8DjY0C+E1CBHV3tQ/lfirtPzE9K82doxPqD5TrrIiB7DyrAzfnpUh11b9zXu5IGJojAsTE53opG/460aTE9htkGJrkRRbC6UodNTuwkMpEXF1uBvXRrlwTZQLzZQicqwCns1QYH+p7wm+i96FWe3tSOQ5lwgALlrZekkBwJycHPTp0wcKhQIvvvgixo8fD4WiPFhlt9uxYsUKPP/887Bardi+fTsSExPrfvQXMXr0aLRr1w7Tp09H9+7dsWHDBlx11VVlj0+cOBFRUVF4//338dlnn+Ff//oXTp06VZadIooikpKS8Nprr+G2226r9vtELM4u+//OUXJMaqPGTa01l9x36WKOlTjRZ2UubBVuDE1uo8b7/WpXdh1MSuxufJFpxvvpRpwwVn8nLFwh4I52WtzbQYsWuvKE1U8Om/DkToNPOeyQ5kp8MiDKLxMyg4HJ4cbSI2YsPGj0mu5bkx9GxOC6BGYlNCWBnDZH9c/lFvHBIRNe/r0EpipKfzpGyPDWtZHoeT4DusDqwvifC/BnpVYMKeEyrBweg+Za9mqkxu+00YkXt57Gqjy51zqpohiVBA910uHuDrUbjlUXDreIp3YW45MM3zY341qpsfDaCGgD9L2JKPjM+7MUL/1eUvb1sCQlXm5VdNF12R/5dnx4yIQVf5urPecBnsFgd6XqcEc7zWX11QsktyhiX74Da057sgMrr0dq0jlKjlNGJ4rt3usdAcDjXXV4+sqwoBjySNRYXFIAEAD27NmDKVOmIDc3F0qlEm3btoVer0dpaSmOHj0Km82GuLg4LF26FL169QrUccNqtaJbt2646667MHPmTKSmpuKf//wnnnjiibLHU1JS8OKLL3oNAVm7dm1ZH8Bdu3Zh+PDhFx0CUjEAeIFEAAYmKjGpjQb/SFZd9mJPFEWMW1uAzTnlpVlRSgn2jI9DdAiP7na5Raw+ZcV76UZsqyGdXSoAY1uqcX9HLb4/YcXCg76Tfu9O1eK13k2z0bbDLeKbYxa8daAUh2rovzG2pQpLB0XX45FRY8AAYGg6aXTiie2GKif+CQDu7eC5eXL7hkKfvjxdouRYMSy60V4sEFUlMzMTuuat8daBUizJ8G2FcUGkUsC0Tnrc20GLcD/eECywujBlY2GV65VnrtRjRjc9WyAQkZf0Igf6rixvN6WUAr/0MqNrqu+6zO4SsfK4BR8dMmLPuZqDZH3jFbivow6jklVBFwDLMbvwy2krVp+0YnOO7ZL7GMarJfiwXyT6J6ouvjMRebnkACAAFBcXY/HixVizZg0OHz4Mo9EInU6H9u3bY8SIEZg6dSoiIiL8eqDPPvssRowYgaSkpLIegNu3b8e2bduQnJyM+fPnY968eVi4cCHatm2LuXPnYvv27dizZw/0ek//vAkTJuDMmTNYsGABRFHEo48+ihYtWmD58uU1fu+qAoAV6WQCxrRSY3IbDa5LUNSqd11ly46acf+WIq9t714bgVtSmk7z6H35dryXbsSKvy1wXELmugDg5V7heKCjtskvvN2iiLWnrFhwwIided4XKEopsOuGeJ8BIRT6GAAMXaLoCf4/vasYBbbanTh7xSrw1dBo9gGloFPxXJZnceGdv4xYdNhU7cVjuELAAx11uL+j7rJ/3v8qdOCW9QU4WalqQSsT8H6/SIxpyd5TRORLFEV0/ybXq+Lp9Q423JtW3k4rx+zCJ4dN+PSICXlVDLe4QC0VMLGNGvd2qNvgxMbI4hSx9awNP5+yYvUp60UrmoYlKfHudZGICeEEGaJAqlMAsCHcdddd2L59OwoKChATE4OePXvimWeeQWpqKgDPyXX27NlYsmQJDAYDevTogblz56Jjx45lr1FUVISnnnoKq1evBgCMHDkSc+bMuWiw8qssM5YdNWNTjs1ngEVlzTVS3NRGjUltNLVuqF5gdaHXijyvi7d+CUqsGh7dJANaZ80uzzCLw6aLXtBqZAIW9Y/EqGQuvCvbkWvD/ANGrD1lhUzwTCO8o33TCShTOQYAQ1+h1YVn9pTgy6PmGvfrn6DEF4OjoGOJIgWhqs5l+VYX3j1oxIfpJhirCQSGyQX8s4MOD3bSIqoOF40/nrDgvl+LfEruk3VSfDk4Gp1C5EKciALj6V0GvJ9e3jZgTJwTS0clY2eeHR+mm/DDCQtqSoJrqZPing5a3J6iDembd6Io4pChfJDI7nP2smtvuQR4vqcn4aMuyTZE5BE0AcDGIMfswjfHPMHAg0UXb97aLVqOSW00mNBajbgayqwe3FKE/1a4aFNKge3Xx6NNeNPO1LI4RXx9zIz3DhqrLGtN0Hgm/XaPCY1Jv4FiOB9EDeUFA9WMAcCmY2O2FY9uN1TZW3VkCxUWD4iCqgGmpRL5Q03nsiKbG++lG/F+uhEl9qqXtjqZgHs6aDGtk65W5e+iKGLun6V4+Y9Sn8euaabA0oFRId2mhYj8Y/MZq9c0c71URKtwBQ4U1lzmO7i5Evd20GJocxWkQVbm6w8FVhfWZdtwxuTCP5JVaBfBmy1El4sBwDr6q9CB5VlmfJ1lxtkaUrUBT/+6wc09/QJHJauhrnDx9WuODWPX5Hvt/8yVejzZPSwgxx2MRFHEpjM2vJduxM+nPb2uroyR4/NB0WxeT1QLDAA2LWanG7P/KMU7B41ld85vaq3Gu9dFBl2fIKKKanMuM9jc+PCQEe8eNMJQTSBQIxMwtb0W0zvrEF/NQDeTw41pWw1Yedzi89hd7bV4LS2cv09EVCsOt4g2X+ZUe3OiIr1cwC1tNbingxYp4Qx4EZF/MQB4mVxuEZtzbFiWZcaPJ6wXbWKqlwu4vpWnRLhnrALXrspFVkl5pkZqhAy/jo2DQspFZVWyTS7kml3oHiNn+jdRLTEA2DQdKnJg9SkrUiNkGNFCxXMmBb1LOZeV2N34+LAJb/9lRGE17URUUuCOdlo80kWPxAo3FE8Znbh1fSH2V8rOkQnAa2nhuDtVV/c3QURN0t2bCvHt3743FC5oFy7DvR20mNxWE7Ap5kREDAD6kdHhxg8nrFieZcbmMzZc7C82TC6gxOG915pRMUiLVwbuIImoyWEAkIhCQV3OZUaHG4sPm/DWX0acs1YdCFRIgCnttHikiw7ZJhdu31Dos2+UUoIlA6PQL4FrNCK6dN/9bcbUTd4DHyUCMKKFCv/soEX/BGWT7P1ORPWLAcAAyTZ5+gUuP2pGehX966oytb0Gb/aNDPCREVFTwwAgEYWCyzmXmZ1uLMkw460DpdW2brmQdOOo9HDHCBn+OyQarfRNuzczEdWdyy3itg2FWH3KinCZiDtT9bgrVYuWPK8QUT1iADDARFHEgUIHlmWZ8c0xS7Wj3ePVEuy6IZ6DGojI7xgAJKJQ4I9zmdUp4rNME+bvNyLb7Dssp7JRySp80C+SJXlE5Bf5VhfyTx5Dajuuy4io/nE1E2CCIKBrtAKv9IpA+sRm+GZoNCa0VkNdocefRABeT4tg8I+IiIgogFQyAfd20OH3CfF4s08EkmoYJjajmx6fD4pi8I+I/CZGJQVbvRNRQ2HOcT2SSQQMSVJhSJIKJXY3fjxhwXGjC9c2U7KnDBEREVE9UUoFTE3V4tYUDZZlmfHG/lIcL/VkBKqlAhZeG4HxrTUNfJRERERE/sMAYAMJU0hwS4q2oQ+DiIiIqMlSSAVMaafFLW01WH3Kir9LnBjbSs1+f0RERBRyuLohIiIioiZNJhEwpqW6oQ+DiIiIKGA4BISIiIiIiIiIiCiEsasxERERERERERFRCGMAkIiIiIiIiIiIKIQxAEhERERERERERBTCGAAkIiIiIiIiIiIKYQwAEhERERERERERhTAGAImIiIiIiIiIiEIYA4BEREREREREREQhjAFAIiIiIiIiIiKiEMYAIBERERERERERUQhjAJCIiIiIiIiIiCiEMQBIREREREREREQUwhgAJCIiIiIiIiIiCmEMABIREREREREREYUwBgCJiIiIiIiIiIhCGAOAREREREREREREIYwBQCIiIiIiIiIiohDGACAREREREREREVEIC5oAoMvlwqxZs9C1a1fEx8eja9eumDVrFpxOZ9k+DzzwACIiIrz+GzJkiNfr2Gw2PPnkk2jdujUSExMxefJkZGdn1/fbISIiIiIiIiIiqheyhj6A2po/fz4WLVqE9957Dx07dsTBgwfxwAMPQKFQYObMmWX7DRgwAB988EHZ1wqFwut1/vWvf+Gnn37Cxx9/jMjISDzzzDOYNGkSNm/eDKlUWm/vh4iIiIiIiIiIqD4ETQBw9+7dGDFiBEaOHAkAaNmyJUaOHIm9e/d67adUKhEfH1/laxQXF+Ozzz7DwoULMXDgQADABx98gC5dumDTpk0YPHhwYN8EERERERERERFRPQuaEuC0tDRs3boVR44cAQAcPnwYW7ZswdChQ73227FjB9q2bYsePXpg+vTpOHfuXNlj+/btg8PhwKBBg8q2JSUloX379ti1a1f9vBEiIiIiIiIiIqJ6FDQZgI8++iiMRiN69+4NqVQKp9OJGTNm4J577inbZ8iQIRgzZgxatmyJkydPYtasWRg7diw2bdoEpVKJvLw8SKVSREdHe712bGws8vLy6vstERERERERERERBVzQBABXrFiBZcuWYdGiRUhNTcWBAwfw9NNPIzk5GVOmTAEA3HjjjWX7d+rUCd27d0eXLl2wdu1ajB07ttrXFkURgiAE/D0QERERERERERHVt6ApAX7uuefw0EMP4cYbb0SnTp0wefJkTJs2DW+++Wa1z0lISEBiYiKOHTsGAIiLi4PL5UJBQYHXfvn5+YiNjQ3o8RMRNZTMzMyGPgQ6TxRFZBY7sPiwCe8eNGJ3ng0ut9jQh0UUFHguI6JQwHMZETWUoMkANJvNPlN6pVIp3G53tc8pKChATk5O2VCQ7t27Qy6XY+PGjbjpppsAANnZ2cjIyEDv3r0Dd/BERNRk5Vlc2HzGho1nbNh8xoZss8vr8XCFgAGJSgxursKgRCWSdEHz0UxEREREREEiaK4yRowYgfnz56Nly5ZITU3F/v37sXDhQkyePBkAYDQaMXv2bIwdOxbx8fE4efIkXnzxRcTGxmL06NEAgPDwcNx+++147rnnEBsbi8jISDzzzDPo1KkTBgwY0IDvjoiIQoXJ4caOXDs2nrFh0xkrDhY5a9y/2C5i1XErVh23AgDah8swqLkSg5qrcE0zBTSyoEnWJyIiIiKiRipoAoBz5szByy+/jCeeeAL5+fmIj4/HHXfcgZkzZwLwZAOmp6dj2bJlKC4uRnx8PK677josXrwYer2+7HVeeeUVSKVSTJ06FVarFf369cP777/vk11IRERUGy63iD8KHNh0PuC3O88Oe/XJ6ReVUexERrET76WboJQCfeKVGJzoCQh2jJSxZy0REREREV0ywWAwsPkQEVEIy8zMREpKSkMfRsgQRRHHSlzYlGPFxmwbtpy1odhe+49SlRToG69EuEKCTTlWFNlq/9xmagkGNldhcHMlBiYqEa3izStqOnguI6JQwHMZETWUoMkAJCIiaij5Vhd+Pd/Hb1OODaeMros/6TwBQPcYOQYkKDEgUYXecQqoZJ4sPpdbxL4CBzZkW7HhjA278+xw1RAPPGtx48ujZnx51Fz2uoMTVRjUXImr4xSQS5gdSEREREREvhgAJCIiqsTiFLEz93zA74wN+wsdl/T8VnopBiZ6An7XNVMgqppMPalEQI9YBXrEKvBkd6DY7savOTZsyLZifbYNJ2sINIoA/sh34I98B+buL4VeLuC6BCUGN/cMFGml50c8ERERERF58OqAiIgInuDbN8fM+P64FTvzbLDVPskPkUoB/RNUGJCoxIBEZZ2Db+EKCca0VGNMSzVEUURWiRPrsz0BwS1n7TA7q08PLHWI+OmkFT+dtAIoRmu9FIObqzDuCjWuaaas0/EQEREREVFoYACQiIiaLFEUsTffgSUZJqz421JjgK0ipRRIi/ME+wYmKtElSg6pn8tvBUFA23A52obLcV9HHWwuETtz7dh4xpMdeOAiWYnHSl04dtiEjw6b8NY1EZjSTuvX4yMiIiIiouDBACARETU5xXY3vs4yY8kRM/6qZXlv1yh5WcAvLV4Jtax+++0ppQL6JyrRP1GJ53sCuWYXNp6xlfUPzLdWP3r46V3F6JdQ98xEIiIiIiIKbrwSICKiJkEURfx2zoElR0z4rhbZfi105/v4JSjRL1GJmEY2cTdeI8XkthpMbquBWxSxv8CBDecDgrvy7HBUiAeanSKmbS3CDyNiIBE4KISIiIiIqKlhAJCIiEKawebG18fMWJJhwsEiZ437JmokuL2dFje1VqNNmAxCkATLJIKA7jEKdI9R4PGuepQ63PjokAkv7i0p22fbWTsWHTLhnx11DXikRERERETUEBgAJCKikCOKIvacs2NJhhnf/W2BxVV9tp9EAIYmqXBnOw2GJqkg83Mvv4agl0vwWBcdtubYsOGMrWz783tLMDRJhSvC+PFPRERERNSU8AqAiIhChsHmxldZZiw5YkL6RbL9mmukuL2dBrelaJCkC72PQ0EQsOCaCPRdmYdShycAeqEU+MeRLAUmIiIiImpKQu+Kh4iImpQL2X6LM8xYWYtsv+FJKtzZXoshzZV+n9zb2LTQyfByr3BM32Yo27Y9144PD5lwP0uBiYiIiIiaDAYAiYgoKBlsbizPMuPTDBPSDTVn+yVpL2T7adFc27iGeQTa7SkarDpuwfrs8lLgF34rwbAkFVqzFJiIiIiIqEngyp+IiIKGKIrYlWfHkgwTVh63wOqqfl+pAAxvocKd7bQY3ASy/aojCAIW9PWUApecLwW2uDylwP9jKTARERERUZPAACARETV6NpeITzNMWJxhwqFaZPtNOZ/tl9jEsv2qk3S+FPjhCqXAO3Lt+CDdhAc6sRSYiIiIiCjUMQBIRESNWo7ZhVvWF+CPfEe1+0gFYEQLT2+/QYlNN9uvJrelaPD9cQt+qVAK/OJeTylwm3AuB4iIiIiIQhlX/ERE1Gjty7fjlvUFOGN2V/l4C50Ud7TT4tYUDRI0zPariSAImH9NJPqszEWJ3bcUmEFTIiIiIqLQJWnoAyAiIqrK98ctGLU63yf4JxWA0ckqfDM0GvtujMeMbnoG/2qpuVaKV3qFe23bmWfH+4dMDXRERERERERUHxgAJCKiRkUURbyxvxRTNhbC7BS9HhuepMRfE5vh88HRGJKkYtZaHdzaVoNhSUqvbS/tLcbR4upLrImIiIiIKLgxAEhERI2GzSXigS1FeHFvic9jD3bS4r+Do5ntd5kEQcD8vpEIU5QHT60uYNpWA1xusYZnEhEREcwOOQgAACAASURBVBFRsGIAkIiIGoV8qwvXr8nHsiyL13aZACzoG4FXekUw489PErVSzK5UCrwrz4730o0NdERERERERBRIDAASEVGDO1TkwKAfzmFnnt1re4RCwIrhMbijvbaBjix03dxWg+GVSoFn/V6CTJYCExERERGFHAYAiYioQf1y2oph/zuHk0aX1/a2YTKsHx2HfgnKap5Jl+PCVODwyqXAW1gKTEREREQUahgAJCKiBiGKIt47aMSkdQUodXgHnPolKLFudCzahMsa6OiahgSNFK/1jvDatvucHe8eZCkwEREREVEoYQCQiIjqncMt4okdxfjX7mJUTjab2l6Db4dFI0LJj6j6MKmNGiNaqLy2zfqjBEcMLAUmIiIiIgoVvLoiIqJ6ZbC5MeHnAnySYfLaLhGAV3uF440+EZBz2Ee98UwFjkBEhVJgmwuYtrWIpcBERERERCHCb7VVCxYsuOTnCIKA6dOn++sQiIiokcsqdmLSugIcLXF6bdfLBXzcPwrDKmWiUf1oppHitbQI3PdrUdm2PeccWHjQiOld9A14ZERERERE5A+CwWDwy+39yMhI3xcXPNkEoij6bBdFEYIgoLCw0B/fnoiIqpGZmYmUlJSGPgz8mmPDlA0FMNi9PxOSdVIsGxKNjpHyBjoyAjyf1bduKMRPJ61l25RS4NexcWgfwX8baniN5VxGRHQ5eC4joobitxLgs2fPev134MABdOrUCaNHj8ZPP/2EzMxMHDlyBP/73/8wevRodOnSBfv37/fXtyciokZs6RETxq/N9wn+pcUpsH50LIN/jYAgCHizTwQild6lwA9uKYKTpcBEREREREHNbwFApVLp9d9zzz2HpKQkLF26FH369EFMTAxiY2PRt29fLF26FImJifjPf/7jr29PRESNkMst4pndxZi+zQBnpRjSpDZqrBoRg1i1tGEOjnzEa6SYU2kq8N58B975i1OBiYiIiIiCWcCGgKxbtw5Dhgyp9vGhQ4di3bp1gfr2RETUwEodbtyyoRALD/oGj/7vqjC8f10klFIO+2hsJrRW4x/J3r0YX/mjBIc5FZiIiIiIKGgFLAAokUhw6NChah9PT08P1LcmIqIGdtLoxPD/ncPaU1av7WqpgE8HRuGJbvqyPrHUuAiCgDf7RiBKWb5EsLtZCkxEREREFMwCFgAcNWoUFi9ejHfeeQcWi6Vsu8Viwdtvv40lS5bgH//4R6C+PRERNZDdeTYM/uEc0ou8J/0maCRYPSoG17dSN9CRUW3FqaV4PS3ca9vv+Q68xVJgIiIiIqKg5LcpwJUZDAZMnjwZu3btglKpREJCAgRBwJkzZ2Cz2XD11Vfjq6++QkRExMVfjIiI6qw+p819lWXGw9uKYHN5b+8eLcd/B0cjUct+f8FCFEVM2ViIH06UZ3EqJMCmsXEc2kINgpMziSgU8FxGRA1FFqgXjoiIwOrVq7Fq1SqsXbsWp06dgiiK6N27N4YPH46xY8dCIglYAiIREdUjtyjilT9KMffPUp/HxrZU4f1+kdDIeM4PJoIgYF6fCGw7m4dCmxuApxR42tYi/PKPWMgkLOEmIiIiIgoWAQsAAp6Lh3HjxmHcuHGB/DZERNSAzE43HthShFXHrT6Pzeiqx7+v0kPCfn9BKU4txdy0cNy1uahs2x/5Diw4YMQT3fQNeGRERERERHQpmI5BRER1YnWKWHbUjCE/nPMJ/ikkwAf9IvFsjzAG/4LcDVeocX0r76nAs/eVIL2IU4GJiIiIiIJFQDMAly9fjk8//RQnTpxAUVERRNG73eCFnoBERI2FzSViZ64dSimQpJWimUbKUsdKjpc6sfiwCZ9lmstKQyuKUUnwxaAo9I5XNsDRkb8JgoC5aRHYmpOHgvP/3o7zU4F/GR0LOX8/iIgaXLHdjQyDA4cNThw2OOB0A9c2U2J0SxVvxBEREYAABgBfffVVvP7660hJScGwYcM47IOIGr0tOTZM21qEk8byCRZSAUjQSNFcK0WSttKfOs//RyklEEJ8ce1yi1iXbcPHh4345bQN1U2P6hghw5dDotFSH9D7S1TPYtVSzOsTgTs3FZZt21fgwPz9pXiye1gDHhkRUdNSYncjw+DEIYMDhw0OZBicOFzkRLbZ5bPvh4dMaB8uw2Nd9ZjQWs0bmkRETVzApgC3b98eV155JZYtW+aX13O5XHj11Vfx1VdfITc3F/Hx8Zg4cSKefvppyGSeC01RFDF79mx8+umnMBgM6NGjB+bOnYsOHTqUvY7BYMDMmTOxZs0aAMCIESMwZ84cBiiJmjCz040XfivBB4dMdXq+WiogSecbJEw6HyRsrpU26ACMy5k2d87iwueZZnySYcIpo+/FRUXjr1Bjft8IhCnYXSJU3bmxECuPW8q+lkuAjWPi0DmKU4Ep8Dg5k5qSioG+jPNZfdUF+mqjpU6Kx7rqcXNbDZRSBgIbEs9ljYvLLeKvIgccbqBzpBwqGX8/KHQFLEWjtLQUI0aM8NvrzZ8/H4sWLcJ7772Hjh074uDBg3jggQegUCgwc+ZMAMCCBQuwcOFCLFy4ECkpKZgzZw5uuOEG7NmzB3q9p1n5Pffcg9OnT+Prr7+GIAiYPn067rvvPixfvtxvx0pEwWNXrg0Pbi1CVkndFtQAYHGJyCx2IrPYWe0+UUqJT2AwWSfFVTEKJOukjSqDUBRF7M6z4+PDJqw8boHdt8q3TLhCwG0pWtzVXos24cz6C3Vz+4Rj61kb8q3epcDrx7AUmIioLkrsbhwpduJQUXn5bobBidOmuq9LqnLC6MKj2w2Ys68ED3XW4872mga9OUnU0M6YXPgs04TPjpjLft/kEuDKaAV6xyuQFuf5M0YlbeAjJfKfgGUAjhs3DqmpqZg9e7ZfXm/SpEmIjIzE+++/X7bt/vvvR1FREZYvXw5RFJGamop7770XM2bMAABYLBakpKTgpZdewtSpU5GRkYHevXtjzZo1SEtLAwDs2LEDI0eOxJ49e3gnhqgJsTpFvPpHCd4+aIS7irNgp0gZci3uskBHICVoJOgdp0Ta+cVG5yi5X8t0anun2ehw4+ssCxYdNuJgUfXBTADoHi3HPR20GH+FmhcQTcyq4xbcsbHQa9u/r9RjJkuBKcCYNUPByOUWkW9146zFhTyLGzlmF45cyOjzY6BPKgBtw2RoHyFDaqQcx0ud+PaYBa5qrvRiVBI82EmHu1O1CGfmfr3iuazhuEURG7JtWJxhwppT1mp/PypqGyZDWrwCveMU6BOvQJswWaO6cU90KQKWrjFnzhyMHz8ePXv2xIQJEy779dLS0vDxxx/jyJEjaNeuHQ4fPowtW7bgscceAwCcOHECubm5GDRoUNlz1Go1+vbti127dmHq1KnYvXs3dDodevfu7fW6Wq0Wu3bt4omYqInYl2/H/VuKcNjgG+RK1kmx8NpIXJfgGWBhcYo4Y3LhtMmzSD9tciHb5MJp4/k/TS6YnJd3HyXH7MbK45ay0kqtTEDPWEVZQLBnnAJ6eeAW54cNDnx82IRlR80odVT/XlRSYPwVGtyTqsVVsYqAHQ81bte3UmP8FWqs+Lu8FPj1P0sxMlmNLiwFJqImwuhwI9fsRu75wJ4nwOfCWbMbeRYXci2ex/Kt7ipvNNaVVADahMmQej7Qlxru+bNtmAyKSqW9/77Sifn7S/HFUTMcle5n5lvdeHFvCeYfKMV9HXR4oKMWUcx0ohCVa/a0tPn0iMmr13dtHC1x4miJE59nmgF4gue94zxr9LR4JbpFy31+94gaq4BlAPbr1w+FhYU4c+YM9Ho9kpKSIJV6f6gIgoDNmzfX6vVEUcSsWbPwxhtvQCqVwul0YsaMGXj22WcBALt27cLw4cNx4MABtGjRoux506ZNQ05ODlasWIF58+Zh6dKl+PPPP71eu1u3brjjjjvw+OOPV/m9MzMzL+WtE1Ej5XADn5ySY/EpGVzw/aAe38yB6a0c0F7CrRFRBEpdwFmrgFy7gLM2CXJtAs7aBORV+LOq71dbEohI0YroFuZCtzA3uoW5Ea+8vFO3ww1sKpDimxwZfi+pecHfQuXGjQlOjI5zIpzxHQJgcACTflej0FH+c52iceOjrtZL+v0hImpMXCJQ5AAK7ALy7QIKHAIK7ILP1/l2ARZ3YC/4pRDRQi2itcaNKzSeP1tr3EhWi7jUhL1cm4DPs2X47qwMtmqOWy0RMT7BiduaOxDDe3wUAtwisKdYgu9yZNhUKIVLrP53VikRESUXkWO79BvuSomIjjr3+TW6C13D3AjjWogayMWS2gL2o6lQKJCQkICEhAS/vN6KFSuwbNkyLFq0CKmpqThw4ACefvppJCcnY8qUKWX7VU7HFUXRa1tV6bqV96mMmYFEwe9goQP3bynCgUKHz2PNNVK8fW0EBjVXBeR7u9wici1unDY5y7IHT5lcOFjkwO/nHLBcpP7ADQEZJgEZJgm+yvFsa6GTenqTnL/72CFCBmk1ZcMVS02yTS4syTBh6RETci3VlzdLBGBECxXuSdViQKISEpY6UCXzNRZMqVAKnGmW4KljEfhmWHRAM1ap6WLZHAVCgdWFL4+asSzLgvQih1+z9WrjQkZf+wgZUiPk6BAhQ/v/Z+++46Ou7z+Av763L3dJLmQxMlgJU2TJEsSFCAgqOFsVBz8XKmpbR7WttXVDHRWw2jorakUQQUFZIiJb9gwjgyRkX3J7fn9/XNaNLMjlRl7Px4MHyd3l7nuBfPL5vr/voZOjb7ys3YZ1ZAEYPxj4m8WFxYeNeO+IyS/j3+IW8GmhHEvPynF7lgaPXKBFhpZRjGDgWhZc5VYXPs0x48NjJpw2NJ/tN1Anw539NLipTwx0SgmKzS5sL7Fja4kN20vtOFDpaLFM2OYWsKdGij01UgCeK+UDdHVlw54WP5lh1u+bOq+grerr1q1r1+f785//jIceegizZs0CAAwaNAgFBQV4/fXXcccddyA1NRUAUFpairS0tPqvKy8vR3JyMgAgJSUF5eXlXgE/URRRUVFR/xgiii5Ot4i3Dhrx0p4av/IXALi1bwxeGhUPnTJ4AQupREB3jRTdNf6ZdnaXiP2VDmyr3WhsK7GjrBV9BwuMLhQYLfjylKcMM04uYFSjgOCIZHl9bz63CGwstOLfR01YXWBt9uQmRS3BHdkazM6OQTo3/tSMGT3VuKG3GktPNZQCby+144YfKvDlpEROgyaisOUWRWwutuGj42asymt+2FV7SFAK6KqWIkUtRWqMBBlaGQbUBvzaM9DXkmS1FH8eEY9HBsfi3SNGLD5sRJXNe1NgcwH/PmrCh8dMuKlPDB4fokVfpv9TmBNFET+ftePDYyZ8k2cJuOevo5QC1/VU4+5+GoxKUXgF5rrFSHFdLzWu66UG4Cn1311mx9YSO7aX2rGz1A5jK1r/HNE7cUTvxAfHPGXDXdUSjE5V4JJuStzTX3t+b5boPETM2Z3ZbPYrIZZKpXC7PT/dmZmZSE1NxcaNGzF8+HAAgNVqxdatW/H8888DAEaNGgWj0YgdO3bU9wHcsWMHTCaTV19AIooOx/UOPLC5CrvL/bP+UtQSvDFOh6kZ6hAcWQOF1NPvb2SyAg/Bs4E5bXDVX3ncXmLHsWamC9epcYhYV2jDukIbAANkAjAkUY6hiQqsy1Mh31rR7NePS1VgTn8NrslUs48Jtdo/xupwusbp9TNWFwRcehWDgEQUXs6aPZlBn+SYkNtCZlBLFBIgRS1F1xiJ52+1FClqCVJrg3ypailS1RIkq6UdFuBrLZ1SgieGxuHBQVp8cMyEtw8a/aoCnCKw5IQZn50w4/peajw+JBaD2eeVwkyVzY0lJzzZfjkt7Jez4z3Zfrf2jUFCKy/8a+USTOyuwsTuniohp1vEoSoHtpfYsa12n15obnktOWtxY0WuFcUmNwOAFFJB6wEIACaTCe+//z42b96MsrIyLFiwAMOHD6+f3Hv11VejZ8+erXquBx54AJs2bcLrr7+O/v37Y//+/Xj00Udxyy234IUXXgAAvPHGG1iwYAEWLlyIvn37Yv78+fjll1+wc+dOxMbGAgBuuOEGFBUV4c0334Qoinj00UeRnp6OL774IljfBiLqYC63iMWHjfj7rzWwBvidPKuXGq+NiY+YZteVVld9duD2Ujt+Lbe3S7ZCrFzALX1icFd/DQYmcFNP56ba7sasH8qxq8w70D4qWcEgILUrls3RuXC6RawrtOKjY2b8cKblqZ8JSqE2eOcJ4KV4BfQaPtYphKgp6bM6Rfw3x4Q3DhibnUp8dboKv78wFiM5COy8cC07P6IoYlupHR8cM2FFrgW2ZuJvComnYuGufhqMS1UE5We2wOis36NvLbHhcJUTTS0zjwzW4vmL4tv9GIhaK2gBwJKSEkybNg2nT59Geno68vPzsXz5ckycOBGiKGLYsGGYOnUqXnzxxVY9n8FgwAsvvIBVq1ahvLwcqampmDVrFp544gmoVJ6IvCiKePnll/Hhhx9Cr9djxIgRmD9/PgYOHFj/PFVVVXjyySexevVqAMCUKVPw6quvQqfTtf83gYg63OkaJx78uQpbS+x+93VRSvCPsbr6tP5IZXWK2FtRt9GwY3upza+EpzkDE2SY01+LG/uo2auN2kVTQcCLkuX46qokBgGpXfCkmdoiz+CZ2vlpjglF5uavmvWOleKObE9mUGpMZFwcDAaHW8QXJ814fb8BJ2uajqpc2l2J318Yi4uDFFCJdlzLzo3e5sYXJz3Zfkf0zWf79YmT4s5sDX6TFYPEDr7gX213Y2dpXYagDbsa9fteckWXkFcfUecWtADg/fffjzVr1uDrr79Geno6+vbti6+//hoTJ04EADz77LNYv349tm7dGoyXJ6JOxi2KeP+oCX/eVQNzgN4c0zJUeH2cDinq6NvYu0UROdXO+izBbSU2nPIpbZJLavud9NdgTAo37NT+qu1u3PBDOXYGCAIuvSoJ8QwC0nniSTO1xO4SsbrAio+OmbCxyNZkFg7g6QM2I1ONO7I1GN+Vvxcbc7lFrMi1YP5+Aw5XNR1oGZOiwONDYnFlGoeFtQXXstYTRRG7yhz44JgJy09bmh2cJxOAazI92X4TuinC5v+kwy3iQIUDW0vt+E0byo+JgiFoPQDXrl2L++67D0OHDkVlZaXf/b169UJhYWGwXp6IOpECoxMPb9HjxyKb333xCgGvjtHhpt7qqN3cSwQB/XRy9NPJcUe2BgBQanFhW4ndM9HQUIE5F2VEZfCTwke8QoKvrkrCLJ8g4M4yB2b9UI6vGAQkoiDJqXbg4+OefnXlLQzSGqiT4Y5+GtzchyfiTZFKBMzsHYPreqmxpsCKBfsMAfspbyu146Z1FcjUSnF7tga/zYpBt06cQUntRxRFrCmw4pW9Buyt8P+/11imVoo7+3n+/4XjXlcuETA8WYHhLJ2nMBC0AKDJZEL37t2bvN9qtcLlOr/mu0TUuYmiiP/mmPHMjmrUOPyvCF7ZQ4m3Lk4IOH032qWopZjRU40ZPdXIySkJyw0RRZ+4JoKAuxgEJKJ2ZnGK+CbPgo+OmfBLgLYfjWlkAmb28mT7jUyWR+0FwfYmEQRMzVBjSroKm4pteG2fAVvO+n+v84wu/P3XGry0pwZXpakwu18MruyhgkzC7zO1jSh6htq9uKcGewIEnetIBWBKugp399fg0u7MQCVqraAFAHv37o09e/Zg9uzZAe/fuHEjBgwYEKyXJ6IoV2x2Yd6WKvxwxj/rL1Yu4IVR8bg9K4abfKIOVhcEvOGHCuwoazhRZBCQwlWZxYX1hTZsKLSi2OxCZqwMgxLkGNRFjsEJsogZGNVZHKx04KPjJvzvpBnV9uY7GQ1P8mTGz+ylZi/S8yAIAi7trsKl3VXYVmLDgn0GrC3033+5RGB1gRWrC6zoHiPBb7M0uC0rBpmxQTvlpCghiiJ+LPIE/nxbiTSWppFidnYMbsvWMNuU6BwEbTW+7bbb8Nxzz+HSSy/FhAkTAHh+edjtdrzyyivYsGED3nrrrWC9PBFFKVEUsfSUBX/Ypoc+wMb/km5KvD1ehwwtN5tEoRKnkGDpVYm4cW0Ftpd6BwFnfu8JAupYekch4nKL2FVmx9pCG9YXWv2yTDb7ZDh1i5FgcG1AsC4wmBUvg5zZTR3G4HBj2SkLPjpuwq/NZAUBQJxCwM29Y3BHPw0u6MIJ9+1tTKoSX16lxN5yOxYdMmJFXuAprEVmN17bZ8D8fQZc1l2J2f00mJKugkLKnxvytrnYE/gLNMCvzuR0Fe7up8GVPZSQcu0lOmdBGwIiiiLmzp2Lzz77DAkJCaiqqkK3bt1QUVEBu92OO++8E6+//nowXpqIolSZxYXHt+qxMs/qd1+MTMBfR8bhnv4algH4YLNpCpUau9svCAgAI5LkDAJSm53PWlZidmFdoRXrztiwscga8AJSWygkQLbOkyHoyRT0BAbZbqF9lVtdmL/PgE+Om2EKMOCrsbGpCszO1uDanmqoZdwHdJSq2smsHx8z4XALk1mTVBL8pm8M7siOQd/4zhuc5b7MY2uJDS/+WuN30aWxazJUeGpYHAYzmE/ULoIWAKyzadMmrFixAidOnIDb7UavXr0wa9YsXHrppcF8WSKKAqIo4lSNC5vP2vBTsSdTI1C5z5gUBRZNSEDvOGb9BcKNJoWSweHGDT/4BwGHJ8mxjEFAaoO2rGVOt4gdpXasK7Ri7RkbDlQ2nzXWXpJVEgxulCk4KEGGfjo5lMx6ahOTw41Fh4x466ARhgA9fuskKiX4TVYMbs+KQbaOAYJQEkURu8sd+OiYCctOW1oM2I5LVWB2Pw1mZHa+gG1n35ftLLXjxT012BhgeF+dq9NVeGpoLIYmcXAGUXsKSgDQ4XBg//79SExMRM+ePdv76YkoiuUZnPip2IbNZ234udiGInPT0/yUUuDZ4XF4cKCW5QDN6OwbTQo9g8ONG3+owDYGAek8tLSWFZnqsvys+LHYhpo2ZPldmCjHpB4qDE2S43SNEwerHDhU5cQxvQOO5ofKtkgmAFnxMq/A4LAkOZLYW9CP0+0Z7vXynhqctQT+xgsALuuuxB3ZGkzNYElpOKqxu7HstKdku7lBDgAQrxBwc58YzM7WYFAnyfLqrPuyPeV2vLSnJmD/7jqTeijx9LA4TswlCpKgBABdLhe6du2Kv//977jvvvva++mJKIoUmlzYXBvw21xsQ76xddPBhyfJsXhCAvrxin+LOutGk8JLU0HAYUlyLGcQkFrBdy2zu0RsL7Vj3Rkr1hZacbiq+fLDxhKUAi7vrsKVaSpc3l2J1CaayTvcInKqnThU6cDBSgcOVXn+FDdzcao1JAJwVZoKd7GnFQBP9ti3+VY8v7sGx6sD/zt2azRUoieHSkSM/RV2fHLcjC9OmVsMyo9IkmN2P8/QFq08en8ndLZ92f4KO17aY8DqAv8WPnUu667E08NiMSpF2YFHRtT5BK0EePjw4Zg9ezbmzZsXjKcnoghVYnbh59pg30/FNpwytC7gVydOIWDe4FjMu0ALWSc/YWqtzrbRpPBlcLhx09oKv0bfDAJSa+Tk5EDVrRfWF9qw9owVPxXbmi0PbUyA5//ZlWkqTOqhwvAk+XkF3SqsLhyqqg0MVjlwqNKBo3oHrG37lQbAM9XytqwY3J6tQQ9N58sK3F5iw5931fi1CaiToBTw+wvjMKe/hqXUEczsdGNFrhUfHzc1O+wBALQyAbN6qzE7W4NhSXIIUdbbubPsyw5VOvDy3pqAvbvrXNxVgT8Oi8PFXRn4I+oIQQsAvvPOO3j33Xexfv16JCQkBOMliCgCVFhd+Pms3ZPlV2zDsSau7DdFLRUwOlWBCV2VmNBNgWFJCk5ebKPOstGkyNBUEHBoohxfT2YQkPzlGpz4z1ETVp2qwWlz6/9/JColuKKH0pPl10MZ9JJbp1vEqRqnJ0uw0lNGfLDSgTOm1kUFO1tW4HG9A3/dXYNv8wMHB1RS4IGBWsy7IJbrQpQ5pnfg4+NmfHbCjEpb89m0g7vIcXtWDPrGe2d9ij5nsL4ntC3e73cL0EUp6ZB9ZrTvy47pHXh5jwHLcy1NPmZMigJ/HB6HS7ox8EfUkYIaAPz4449RWFiI66+/Hj179oRKpfJ+cUFgiTBRlNHb3NhytqGk91AbSrIAz2TFi1IUuKSbEhO6KjEiWcEr/ucp2jeaFHmMDs90YAYBqTluUcR7R0x4blcNLK6Wt6sSARiZpMAVacr6fn7hMBVeb3PjcG3p8KFKB7aX2nGkhWmp0ZwVeNbswst7avBJjhmB/lklAvCbvjF4elhc1L138mZzifg2z4KPc8z4sZmBEB1JpxBwVZoKUzPUuCJNidgglCJH677sRLUDr+414MtTlgDhVY+RyXI8MywOl3ZXRl1mJ1EkCFoAsDVZf4IgoLKyMhgvT0RB5nCLKDS5kG90Id/oxDG9E5uLbdhX4Wjyl34gMgEYmazA+NqA36gURaebBhds0brRpMjGICA154zRibk/67GpuPmgQIpagit6qHBlDyUu665ElwgYrFE3LfWDYyYsO2VpNrgZTVmBNXY33jpoxKJDRpibmBA7OV2Fv4yIw8AE9vftbHINTnxy3IRPc8xNDoDpaAoJcEk3JaZmqDElQ4VuTfQKbato25edrnHi1X0GfHHSDHcTy9nQRDn+OCwOk9IY+CMKpaAFAHNyclr1uGha/IiiidUp4ozJiQKjJ8hXUBvoKzC5kG9wodjiavKXfHMkAjAsUY4J3ZSY0E2J0SmKqG70HA6ibaNJ0cNYWw78i08Q8MLaIGACg4CdjiiK+PykBU9u06MmQH8/qQCMSlHgyh4qXNFDiSGJ4ZHld66q7W7876QZHxwztTjEJFKzAu0uER8cM+G1fQaUWwMHdkYkyfHXi+Ixnn3AOj2nW8T3BZ5egWsLbee01wyW4UlyZZ3vdwAAIABJREFUTM1QY2qGCgN0snMOZEXLvizP4MSC/QZ82kQ2L+Ap4f7jsFhMSVcx8EcUBto1APjZZ59h3LhxyMzMbK+nJKIgMTncKDA1CuzVB/qcyDe6UNJOV18FABd08QT8LummxNhUBeIUPKnvSNGy0aToxCAg1Sm3uvDoFj1WBegJFycX8GCGDfePzozK7NBozAoURRHLT1vwt19rcLqJgV+9Y6X484h4XNuTwQHyV2hyYUmOCbvK7HD4bEt9/7sE+t/je1tzX+MSgV/LHS32JKzTM1aKqRmeUuExKYo2DaaL5H1ZqcWFNQVWfJtvxYZCq9+/S50BOhmeGhaH6ZmqiL5IQxRt2jUA2KVLF/zrX//CjTfe2F5PSUTnaU+5HTtK7X4ZfBWt3OCci4EJstqhHUpc3FXJE/gQi+SNJnUOTQUBh3SRY8XVDAJ2Bt/mWTDvF33ADLFLuimxcLwO1uLTnWIti4aswJ+KbfjLrmrsKXcEvD9JJcFTQ2Mxu5+Gg70obLjcIraX2vFdvhXf5luaDFz7SlAKmFzbN/DyHsoWK1sibV+WU+3Ad/lWfJdvxY5Se7OtfrLjZXhqaCyu66Vm4I8oDLVrADAhIQHvvvsuA4BEYcDhFjF3cxX+d6rpCVznq6tagnStFBlaGdK1UgxNVGB8N0XQJy1S20TaRpM6J5PDjZvWVWDLWQYBO5NquxtPb6/GkhNmv/tUUuC5kfG4d4AGEkHodGtZJGYFHqp04Lld1VhbGLh3o0Ym4KHBWjw0WBuU4QpE7UUURRzVO2sDXxbsbiKY7UspBS6t7Rt4dboKqQH6Bob7WuYWRewqqwuEWpFT3fJAvz5xUjw5NA6zeqnDNiuZiBgAJIpKTreIe3+qwrLT5x78kwhA9xgp0rWePxkaGTJipUjXeAJ+PTRSqDisIyKE+0aTqA6DgJ3LT8U2PLi5CmdM/lk2w5PkeGdCArJ1DcMgOvNaFu5ZgQVGJ17cY8DnJ8wBs4OkAnBnPw2euDA2YECEKNwVm11YUxsM3FRsg70VhTQCPFNv6/oGZsd7+gaG41pmcYrYVOzJ8ltTYEVpK1sB9YmT4vEhsbi5T0ybyqCJKDTaPQD43nvv4YYbbmivpySiNnK5RTzwcxX+d7L54J9MAHpopMjQSpFem8FX93GGVooeGinLcqJEOG40iZpicrhx87oK/OwTBLygixwrJidGxJRXap7FKeKvu6vxzmGT330yAXhiaCweHxLrdzLJtaztWYEJCgnUMgFqmQCVVEBM7d9qmQB13d+Bbmt0n1oqQNXEfU63iH8eNOJfR4ywNVEtOSNThT+NiENWPCf7UnQwONzYUGjDt/kW/FBghd7eutPpPnFSTElXI9NdgTHZ6cjUSkPaF7vS6sL3Z2z4Lt+C9YW2Jqdz+xrSRY5pmZ6S58EJ5z4MhYg6XrsHABUKBSSS1i1kgiCgqKiovV6eqNNziyIe3qLHpznepVSZWiluz9Y0BPk0UnSLkTJFv5PgSTNFGpPDjVvWVWBzgCDgkiu6IF0rC9GR0fn6tcyO+zdX4XiAkrL+OhnemZCAoUmKgF/LtcxbW7ICQ2FsqgJ/HRmHUSmc7EvRy+EWsbXEju/yLfgu34p8Y+v6BtZJUArI1MrQM1aGTK0UmbEyZMZKkVl7UV4pbd+9eq7BiW9rMxm3ldibnN7bmEwAxndTYmq6ClMyVPwdTBTB2j0AOHbsWPTs2bPVX7No0aL2enmiTk0URTz2ix4fHvcO/mVopfh2ShJ/WXdiPGmmSNRUEFAhAe7pr8HvLoxlv9EI4nCLeG2fAQv2GfxOOAUAcwdp8ezwuGZbS3AtC6wtWYEdoV+8DH8ZGYcp6ZzsS52LKIo4VOWsDwburWhd38CmCPC048moDQhm1gYJe8bKkBkrQ7cYSYuDNkRRxN4KR33Qr7UXC2LlAq7socLUDBUmpamicvo6UWfEHoBEUUAURTyxvRrvHfEup+oRI8W3U5PQM5bBv86MJ80UqcxON25e6x8EBACtTMDcwVrMHaQNaQkVteyo3oH7fqrCvgAnwxlaKRZNSMD4ri1niXEta1ldVuBHx804WHl+wYe26hYjwdPD4vCbvuwFRgQAhSYXVtcGAzeftcHRurZ6raaQAOlaaUMGYazn48xYKSptbnyXb8XqfAuKzK174W4xkvp+heO7Kts9+5CIQo8BQKIIJ4ointlZjUWHvIN/XdUSfDc1Gb3jGPzr7HjSTJHM7HTjt+srsbEo8FTRRKUEj18Yi3v6aTiYKMy4RRGLDhnxt19rAvaHuyM7Bi+Mim/1NFiuZW1jcrhhdoowO0VYXSIsThEWlwhro9vq/rY6RZh97vN6fO3fdbc1/rtbjBR3ZGvwwCANYmQMxhMFUm13Y/0ZK34+a8fhkhqUuRXIN7raPSjYVgN1svqg39AkeYsZhUQU2RgZIIpgoijir7tr/IJ/KWoJvrk6icE/Iop4MTIJlk5KxGcnzXh5j8FvYmyFzY1ndlRj8SEjnhwai1uZfRQW8gxOPPhzld9EZ8DzO+qti3W4Ol0dgiPrPDRyCTScu0EUFuIVEszsHYOZvWOQk1OGrKwMuNwiis0u5BldyDM4/f4uNrsDTtU+HxIBGJOiwLRMNaamq9CL5wpEnQp/4oki2Et7DXjjgNHrtkSlBCsmJyFbx10/EUUHqUTAbVka3NArBu8fM2HBPgMqbN5pE2dMLjy8RY9/HjTimeFxmJHJ/mOhIIoiPskx44/bq2EMMFHy2p4q/GOsDons30hEnZxUIiBNK0OaVoaLA7RBsLlEFBg9AcFcgxN5BhfyjA1/V9laFx6MkQm4vLsSUzNUmJyu4vpL1Im1awCwqqqqPZ+OiJrx2t4avLrX4HVbglLA11cnYUACg39EFH1UMgEPDtLitqwYLDpkxNsHjX5BpuPVTszeWIlhSXL8ZUQcLu2uCtHRdj4lZhce2VKF78/4l2vHKwTMH6PDDb3VDMwSEbWCUiqgb7wcfeMD7+ur7e6AmYN5BhdcomcS99QMFS7troKaLTKICO3cA5CIOsabBwz4y64ar9viFQJWTE7C0CRFiI6KwhX7ZlG0Kre68I/9Bvz7iAn2JvooXdJNib+MiMOIZK6NwbQi14LHftGj0ub/D3FZdyXeHp+AHprzyzrhWkZE0YBrGRGFCjv1EkWYhYeMfsG/WLmAZVcx+EdEnUuSSooXR+mwe1YqbsuKQaDWfz8V23DFqjLctr4CR/UdOxW1M9Db3Pi/TZWYvbHSL/gXIxOwYGw8ll2VeN7BPyIiIiI6PwwAEkWQ944Y8cyOaq/bNDIBSyclMruFiDqtdK0Mb49PwNbrUjAjM3DJ76p8K8Z9XYoHN1ch3+js4COMTj8WWTHu6xJ8ecrid9+oZAU2z0jBPf21LPklIiIiCgMMABJFiA+PmfCHbd7BP7VUwP8mJWJ0qn/jYCKizqafTo6PL0/EhmuScWl3/3XRLQJLTpgx8qsSPLVdjzKLK8CzUEusThF/3KHHdd9XoMjsnfUnlwB/GRGH1VOT0Cees+aIiIiIwgUDgEQR4L85Jjz6i97rNpUU+PzKLgGnhhERdWbDkxX4enISVkxOxPAk/+bpdjfwzmEThi0twYt7alDTVANB8nO4yoHLV5Vi0SGT330DE2TYMD0Fjw2JhTRQPTYRERERhQwDgERh7ouTZjz8s3fwTyEBPr0iERM53ZKIqEkTu6uw/ppkfHJ5F/QLkI1mdIp4da8BQ5eW4O2DBlidnIvWFLcoYvEhIy5bWYrDVd4l1AKAeYO12Dg9BRd04RR6IiIionDEACBRGFt+2owHNleh8SmpXAJ8fHkXXNGDwT8iopYIgoDpmWr8cl0KFo7XIS3AMIpKmxvP7qzBiK9K8PFxE5xuBgIbO2t24ca1FXh6RzVsPlXTaRopVk1Jwl8viodSyqw/IiIionDFACBRmFqZZ8GcTVVofB4qE4APLu2Cq9PVoTswIqIIJJUI+G2WBrtnpeKlUfFIUvlvgQrNLjyyRY+Ry0qw+JARBgdLg1flWTDu61KsL7T53XdjbzV+vjaFrSiIiIiIIgADgERhaHW+BXf/WAlXo+CfRAD+PbELrslk8I+I6FwppQIeGKTFnhtS8fSwWMTK/bPWcg0uPL2jGoO+OItnd1R3yqnBJocb87ZU4bYNlai0eQdC4+QC3r0kAe9N7AKdkltJIiIiokjAXRtRmFl3xorZGyvROPFEAPCvCQm4rheDf0RE7SFWLsGTQ+Ow94ZUPDRIC6V/ZTBqHCLePmTEsKUluGtjJXaW2jv+QEPg1zI7LvmmFB8dN/vdNzZVgc3XpuCmPjEhODIiIiIiOlcMABKFkR+LrPjthgrYfYJ/C8frcCNPtoiI2l2iSoq/j4rH7pmpmJ0dA0WAnZFLBJbnWjDp2zJMWlWKr09borJPoMstYv4+A676tgwna7yb/ckE4E/D47Dq6iRkxvoPVCEiIiKi8MYdHFGY+PmsDbeuq/RrsP7GOB1+k6UJzUEREXUSaVoZ3rw4Ac8Mj8N/jprwn6MmlFv9ewDuLHPgzh8rka6V4t4BGtyRrUF8oKhhhMkzOHH/5ipsLfHPcuwbJ8N7ExMwLEkRgiMjIiIiovYQMTvWCy64ADqdzu/PTTfdBAB46aWX/O7Lzs72eg5RFPHSSy+hf//+6Nq1K6ZNm4YjR46E4u0QedlWYsPNaytgcXlnlMwfE4/Z/Rj8IyLqKClqKZ4eFoeDN3bFWxfrMEAX+FppgdGFP+2swaAvzuKp7XrkGiKzT6AoivjipBkTVpQGDP7dmR2DTTOSGfwjIiIiinARkwG4ceNGuFwNqVFnz57FpZdeiuuuu67+tqysLKxatar+c6nUu6HPm2++iYULF2LhwoXIysrCq6++iuuvvx47d+5EbGxs8N8EUQC7yuy4cW0FTE7v4N+Lo+IxZ4A2REdFRNS5qWQC7sjW4PasGPxYZMPCQ0asCzAJ1+gU8c5hE949YsLUdBXmDtZiTIoCguA/XCTc6G1u/G6rHl+dtvjdl6iU4J/jdZiawd6zRERERNEgYgKASUlJXp9/8skniI2N9QoAymQypKamBvx6URSxePFiPProo7j22msBAIsXL0ZWVhaWLl2Ku+66K3gHT9SEveV2zPyhHAaHd/Dv+ZFxeHAQg39ERKEmCAIu66HCZT1UOKp34J1DRnx+0gyrT7sGtwisyrdiVb4Vw5LkmDtIi2t7qiGXhGcg8OezNtz/UxXOmFx+913ZQ4mF4xOQGhNgMgoRERERRaSIKQFuTBRFfPLJJ7j55psRE9MwGCE3NxcDBgzAkCFDcPfddyM3N7f+vry8PJSUlODyyy+vv02tVmPcuHHYvn17Rx4+EQDgQKUD131fjhq7d/Dv2eFxeOQCZqQSEYWb/jo53rg4AYdu6opnhsUiVR14G7Wn3IE5m6ow9MsSvLHfAL3Nv5dgqNhdIp7bVY3pq8v9gn8qKfDq6Hh8OSmRwT8iIiKiKCPo9fqIG2O3YcMGzJw5Ez/99BOGDBkCAFi7di2MRiOysrJQXl6O1157DTk5Odi2bRu6dOmC7du3Y/LkyThw4ADS09Prn2vu3LkoLi7GsmXLmny9nJycoL8n6lxEEbhnvxIHDN4nWHPSHbgv0xGioyIiorawu4EfyqRYUiRHjqnpa6oqiYjpqU7c0t2JDHXotl25ZgF/OqbE0QDHmqVx42/ZNvTRRNy2kIiIiIjgaYvXnIgpAW7so48+wvDhw+uDfwAwadIkr8eMHDkSQ4cOxZIlS/DQQw/V3+7bk0cUxRb79LT0TSQ6F1/0cGH6mjKcrPFkYDx2gRZ/HhEXEX2jKLLk5ORwHSMKkkH9gEdFEZvP2rHwkBHfF1j9HmN1C/iyWI6lxXJMTldh7iAtxnftuD6Boiji/WMmPLuvxm/YFAA8PFiLZ4fHQSkN798/XMuIKBpwLSOiUIm4AGBZWRm+++47zJ8/v9nHabVa9O/fH6dOnQKA+t6ApaWlSEtLq39ceXk5kpOTg3fARE3orpHi2ynJmLGmHJPSVAz+ERFFKEEQcEk3JS7ppkROtQPvHDZhSY7ZL9gmAlhTYMWaAiv6xEmRrpUhUSlBF6UECSpJ/cddaj9OqP1YKxPO+fdDmcWFh7boAwYmu8dIsHhCF0zsrjyn5yYiIiKiyBFxAcAlS5ZAqVRi5syZzT7OarUiJycHEyZMAABkZmYiNTUVGzduxPDhw+sfs3XrVjz//PNBP26iQLrGSLH2mmTEyc/95I6IiMJHVrwcC8bq8OzwOHx4zIR3jxhRbPbvAXiyxlWfAd4ShQSewGBtQLDu40RVbZBQKUGiSup1e5xCwLozNsz9uQplVv/Xv66nGq+P0yFBGZHtoImIiIiojSIqACiKIj7++GPMnDkTsbHeQxKeffZZXH311UhLS6vvAWg2m3HrrbcC8Fydf+CBB7BgwQJkZWWhb9++mD9/PjQaDW644YZQvB0iAEC8gidfRETRJkEpwWNDYjF3kBZf51qw6JAReyvOrcer3Q2ctbhx1tL6YSISwTOZ2JdWJuDVMfG4tW8MLzwRERERdSIRFQDcvHkzTp48iXfffdfvvqKiIsyZMwcVFRVISkrCyJEjsXbtWmRkZNQ/Zt68ebBYLPjDH/4AvV6PESNGYNmyZX7BRCIiIqL2oJAKuKlPDG7srcbWEk+fwO/yrQj2qI1Awb9RyQr865IE9IqLqO0fEREREbWDiJwCTERErcdm00ThpdLqQq7BhUqbGxU2Nyqtnr+rGn1caXOjyupGhc0Fa+sqhZskFYAnhsbid0NiIZNEbtYf1zIiigZcy4goVHgJmIiIiKgDdVFJ0UUlbfXjzc6mg4QV1trbaj+urH2MweG5vtsvXoa3xyfgohRFsN4OEREREUUABgCJiIiIwliMTIIYrQRp2tZ/jc0lwuRwQ6eUQMJef0RERESdHgOARERERFFGKRWglLY+y5CIiIiIoht7ABIREREREREREUUxSagPgIiIiIiIiIiIiIKHAUAiIiIiIiIiIqIoxgAgERERERERERFRFGMAkIiIiIiIiIiIKIoxAEhERERERERERBTFGAAkIiIiIiIiIiKKYgwAEhERERERERERRTEGAImIiIiIiIiIiKIYA4BERERERERERERRjAFAIiIiIiIiIiKiKMYAIBERERERERERURRjAJCIiIiIiIiIiCiKMQBIREREREREREQUxRgAJCIiIiIiIiIiimIMABIREREREREREUUxBgCJiIiIiIiIiIiiGAOAREREREREREREUYwBQCIiIiIiIiIioijGACAREREREREREVEUYwCQiIiIiIiIiIgoijEASEREREREREREFMUYACQiIiIiIiIiIopiDAASEUW5nJycUB8CEdF541pGRNGAaxkRhQoDgEREHWx3mR0zvy/HhV+exXtHjKE+HCKic7Kx0Iqp35VhxFdn8dUpc6gPh4iozURRxNenLbhiZSnGLS/BhkJrqA+JiKjN3KKIT46bWnycrAOOhYiIAJgcbvz91xq8c9gEsfa2J7ZVY1yqEoO6yEN6bERErVVpdeGZnTX47ERD0G/uz1W4uKsSXWOkITwyIqLWKzK58LuteqwuaAj6zdlUhYM3pSJGxjwZIooMJ6odeGSLHr+U2HF7tqbZx3JlIyLqAOsLrRj7dSkWNwr+AYAI4KvTzJwhovAniiKWnTJj9PJSr+AfAFhdwMo8S4iOjIio9dyiiPePmjBmeYlX8A8AKm1urC+0hejIiIhaz+EW8Y/9Bly8ohS/lNhb9TUMABIRBVGl1YX7fqrErB8qkG90BXzMN7lWiKIY8D4ionBQaHLhlvWVuHtTFcqs7oCPWZnH0jkiCm851Q5MW12Ox7fqUeMIvPfixQwiCnd7yu24bGUZnt9dA1vgU8yAIiYA+NJLL0Gn03n9yc7Orr9fFEW89NJL6N+/P7p27Ypp06bhyJEjXs+h1+tx7733IiMjAxkZGbj33nuh1+s7+q0QUScgiiK+OmXGqOWl+OJk8xvJEzVOHNU7O+jIiIhazy2K+PcRI8YsL8H3Bc0H+LactaHC2oZdKBFRB3G4RczfZ8D4FaXY2kKmzJoCK+wuXpglovBjdrrx7I5qXLGqDAcrHW3++ogJAAJAVlYWjh07Vv/nl19+qb/vzTffxMKFC/HKK69gw4YNSE5OxvXXXw+DwVD/mDlz5mD//v348ssvsXTpUuzfvx/33XdfKN4KEUWxM0YnbllfiXs2VaHcJ1NGIgAPDtJgQleF1+3f8GozEYWZ43oHpn5Xjt9vq4bBJ1NGIQH+OCwWA3QN7aRdIvBtPrMAiSi8/Fpmx8RvSvH3X/0zZbQyAS+PjkeyquG0uMYuYlMxy4CJKLz8WGTFuK9L8fYhI9w+1yi6KCV495KEFp8jooaAyGQypKam+t0uiiIWL16MRx99FNdeey0AYPHixcjKysLSpUtx11134dixY1i3bh3WrFmD0aNHAwBef/11TJkyBTk5OcjKyurQ90JE0cctivjPURP+uqsGRqf/leOBOhn+OT4BI5IV+Pi4CZvPNlyBXplnxZND4zrycImIArK7RLx5wIDX9hlgD1DtOzpFgTcv1qG/Tg63CBzZ23CxdVWeBXe00ICaiKgjmBxuvLjHgMWH/U+WAWBSDyX+MU6HdK0MR6sc+PB4Q2/TlXkWTEpTdeDREhEFVmVz49md1fg0J3Df+Jv6qPHiqHgkqVoexBZRGYC5ubkYMGAAhgwZgrvvvhu5ubkAgLy8PJSUlODyyy+vf6xarca4ceOwfft2AMCOHTug1Wrrg38AMGbMGGg0mvrHEBGdq2N6B6Z8V44/bKv2C/4pJMAzw2Lx44wUjEj2ZP5NzVBBIjQ85mClA6drWAZMRKG1u8yOS1eW4oU9/sE/rUzAa2PisXpqEvrrPJPLp2eqvR6zsciG6kBRQyKiDrSx0JMpszBApkyiUoL3LknA/yYlIl3ryYeZ3tN7Lfs2zwpnoKghEVEHEUURX5+2YPTykoDBvzSNFF9OSsS7l3RpVfAPiKAMwJEjR2LRokXIyspCeXk5XnvtNVx11VXYtm0bSkpKAADJycleX5OcnIzi4mIAQGlpKRITEyEIDWfcgiAgKSkJpaWlzb52Tk5OO78bIooWDjfw0RkZ3i+QwyEKfvcPjXPhmb529IwxI+9Uidd9w+KU2F3dsFi/v7sAd6QFJwjIdYyImmNxAe/kyfF5kQxu+K9l4xNceLKvHV1lJpw80XC7XAQyVCrkWz3XlB1u4KOduZiSEpxegFzLiKg51Q7g9dMKfFsa+DR3SrITj/e2Q+c24kSjtaybG4iVqmFweda/CpsbX+4+hZG64FzQ4FpGRM0ptQl49aQcmyr91zIBIm7q5sQDmWZoLAY0Xk5aqmyNmADgpEmTvD4fOXIkhg4diiVLluCiiy4CAK/gHuCJmPoG/Hz5PiYQlgcTUSC7yux45OcqHA4wwCNWLuC5kXG4q58GkibWmJsdRuzeXl3/+VaTBn/LSmn342SbAyJqzoZCKx79RR9wUnmSSoJXRsdjZi91k/ulWTXVeP2Asf7zHdZ4PJKV2O7HybWMiJoiiiKWn7bgyV3VASeVp2mkeGOcDlc2U9Y7raQSnzca3ParKxG3Zuna/Vi5lhFRU9yiiA+PmfHc3uqAk8r762R462IdRqUoz+n5I6oEuDGtVov+/fvj1KlT9X0BfTP5ysvL67MCU1JSUF5eDlFs+CaKooiKigq/zEEiouYYHW48tV2PSavKAgb/JqersPW6FNzTX9tk8A8ArvEpndtV5kChiRM0iahjVFpduP+nSsz8oSJg8O+WPmrsuD4Fs3rHNHux1LcMeH2hDSYHy4CJqGMUmly4dX0l7t5U5Rf8EwDcP1CDbdenNBv8A/zXslV5FrhFlgETUcfIqXZg2upyPL5V7xf8k0uAp4bG4qcZKecc/AMiOABotVqRk5OD1NRUZGZmIjU1FRs3bvS6f+vWrfU9/0aNGgWj0YgdO3bUP2bHjh0wmUxefQGJiJqzvtCKsV+X4p3DJvhuCZNUErw/MQGfX9EFadqWE6y7a6S4KFnuddtKTgMmoiATRRFfnTJj1PJSr2yXOulaKb66KhHvXNIFXVrRU2ZYkhxpmobHWVwi1hVygiYRBZdn+JoRY5aXYE2B/wTyAToZfpiWjJdH66CVt3zae3kPFTSyhosdxWY3dpXZm/kKIqLz53CLWLDPgPErSrG1xH/NGZWswOZrU/DUsDgopM1Xr7YkYkqAn332WVx99dVIS0ur7wFoNptx6623QhAEPPDAA1iwYAGysrLQt29fzJ8/HxqNBjfccAMAoF+/frjyyivx2GOP4c0334QoinjssccwefJkpmATUYsqrC78cUc1vghwsgwAt/aNwQsXxbXqZLmxGZlq7Cxz1H++Ms+C+wdqz+tYiYiacsboxO+2VeP7ACfLdZkyzwyPa9XJcv3XCQKmZ6qw+LCp/raVeRZc69NUn4iovRzXOzDvF33Ak2W5BPj9hbF47ILYNp0sq2UCrkpTYXluw15vZZ71vLJtiIias6fcjod+rsKhKv+qMo1MwJ9HxGFOfw2kkvML/NWJmABgUVER5syZg4qKCiQlJWHkyJFYu3YtMjIyAADz5s2DxWLBH/7wB+j1eowYMQLLli1DbGxs/XO89957ePLJJzFz5kwAwJQpU/Dqq6+G5P0QUWQQRRFfnbbgqe3VKA/QUyZDK8Wb43S4rEfzZSVNmd5TjT/tqqn/fGuJHWUWF5LVbQskEhE1xy2KeP+oCX/dXQNDgJ4yA3UyvDU+ASNrJ5W31fRMtVcA8PsCK2wuEcrzvFJNRNSY3SXizQMGvLbPf1I5AIxOUeDNi3X1k8rbanqmdwDwm1wLnh8Z12LPeCKitjA53HhpjwGLDvtPKgeAST2U+Mc4Xf2k8vYi6PV6NjYgIgop+IGtAAAgAElEQVSgwOjE77bq8cMZ/1I2iQA8MFCLPw6LhaYNmTKBTFhRigOVDVmAb4zT4c5+mvN6zsbYbJqoczumd+CRLXpsL/XPlFFIgD9cGIt5bcyU8eVyixjwv7MotTSckX9xZSImp5/bxZFAuJYRdW67y+x4eEsVDgfIlNHKBPxlZBzu6d/08LXWMDjc6PtZMWyN2qJumpGMCxPP7eJIIFzLiDq3H4usmLdFj7wA/ZcTlRK8PDoeN/Ruevja+YjYHoBERMHiFkW8e9iIsctLAwb/BibIsHZaMl4YFX/ewT8AmJHpfYLMPoBE1B7sLhGv7q3BhBWlAYN/Y1I8PWX+MPT8e8pIJQKuyfAu+f2GaxkRtQOTw40/7tBj0rdlAYN/V6UpsfX6FPzfgOaHr7VGrFyCy7v77sv8WyYQEbVVlc2NBzdX4brvKwIG/27qo8aOmSm4sU/zw9fOR8SUABMRdYQT1Q48uFmPHQGaPiskwBND4zDvAi3k7dSHAfCUAb+wx1D/+aYiG/Q2N3RKXqMhonOzt9yOBzdXBZxUrpUJeG5kHO4+z0wZX9MzVXj/WEMZ8Hf5FjjdOsjacb0kos5lU5END2+pCjipPFEpwStj4jGrV/tmykzPVGF1oz6pK3MteHZ4XLs9PxF1Pt/kWvC7rXq/SeUAkKaR4vVxOkxqYVJ5e2jXAOCWLVvO6esuvvji9jwMIgoBtyi264lkKJRbXZixphxFZv+FeWyqAm+O0yH7HHvKNKe/To7seBmOV3tO1J0isLrAilv7xrT7axFR86JhLTtV48SM78tRY/fv8jI5XYUFY+JbNam8rcZ3U0KnEKCvfd0qm4gtZ22Y2D34G1oi8hYNa9nuMjtuXFsesNffzX3UeHFUPBLbOHytNaZmqCET9HDWLqHHqp04pnegXxD2gETUvGhYy9YUWDB7YyV8d2UCgHsHaPCnEW0bvnY+2nX3d80117Tp6osoihAEAZWVle15GETUgZxuEQ9srsI3eRZM7KbEh5d1QYwsMjPX3jls8gv+xcoF/HVkPO7sFxPUXz4zMtWYv78hC3BlnoUBQKIOZHa6MXtDJTaftWFGphqLJiREbObagv0Gv+BfkkqCV0fH4/p2zpRpTC4RMDVDjSUnzPW3fZNnZQCQqANVWF34zfpK7K9wYHa/GLw0Kj5iB1i8uKfGL/iXrpXijXE6XHGOw9daQ6eU4JJuSmwoamgDszLPygAgUQfKNTjxm/UVKDC68NiQWDw+JLblLwpDoiji+V01fsG/AToZ3ro4AReltF9/0dZo1wDgypUr2/PpiCgCfHDMhC9Pefo8/XDGhpf3GPD8RfEhPqq2Mznc+M9Ro9dtk3oo8cbFCeihCf5E3msyVV4BwA2FVhgd7g67GkTU2S3YZ8DaQs/J3v9OWTA0SYEHB2lDfFRtd9bswv9Omr1uu6m3Gi+PjkeXIGTK+JqeqfIKAK7Ks+C1MfERf/WeKFI8u7OmvufnO4dNGJOixHW91C18Vfg5WOnA+kLvPsz3dWCmzPRMtVcA8JtcC35/YWQGIIgi0aO/6Ot7fj6/uwZjUxUYm6oM8VG13YYim1c7FokAPHGhJ6B5vv2Xz0W7BgDHjx/fnk9HRGFOFEV8cNTkddsHx0x4fEhsxPWvW3LCjCpbw7UZnULAB5d16bAA3IWJcmRopfU9bqwuYO0ZK67vxSxAomBzuEV8kuMdNFt40Ig5/TUh2Zydj3ePGOFolDHTM1aKxRMSIO2gbMbLuquglQkw1tbOlVjc2FFqx5gI3LQTRRq9zY3lp73XstcPGHBtT1XEZQEuPOR9UXZMigKvjNF12OtPy1Th8a2oz9rZX+lArsGJnrFsoU8UbKdqnPixyPsCwBsHjBEZAPznQe+1bEamGk8NC11P0cg6QyeisLKrzOHXYN7gEL2awEcCl1v022je01/Todl3giBgRqbPBM1cTp0j6gir860otXjXmRWaXVh6ytzEV4Qno8ON//hclJk7SNthwT8AUMkETE73Ls3jNGCijvHFSTOsPrMy9lU4sKnYFvgLwlSRyX/9fWhwx2Zkp6ilGJvqXZq3MpdrGVFH+Pi4/7nk9wVWHK5yhOBozt3+CrtfILOj1zJfQb+EYbPZsHLlSuzduxfV1dVwu7032IIg4O233w72YRBREHwUYHEGgHcOG/HgQC1Ussi42rwq34pcQ8OOWSEB/m9Axy/O0zNVeLtRIPKHM1ZYnWLEfB+JIlWgjSYAvHXQiFv6Brf/Z3v6b44Z1Y16/yUoBfwmBL1EZ/RU46vTDSfKK/OseOEiMeIykIgiiSiKTe7L3jhgxKUR1IvTN5O5T5wUU9I7/vinZ6rxS4m9/vOVeVY8fAHLgImCye4S8WlO4Auwbx0w4J1LunTwEZ27t30STMamKjAyuWN7/vkKagCwsLAQM2bMwKlTpxAfH4+amhokJCRAr9fD7XYjMTERGo0mmIdAREFSY3dj2enAV0JLLW58dsKMu/qH/8+3KIr450GD12039YlB15jg98rydVGKAt1iJCiuHURicorYUGTF1IzI691DFCnyDE6/PlN1juqd+L7AiikR8DPodItY5JfJrIUmBH1Er+ihhEqK+kykAqML+yocGJoU2k0vUTTbVeao75fl68ciG/aW2yPiZ9DgcPtVkswdFNuhmcx1pmeq8PSO6vrPd5TZUWRyoXsH9IYm6qxWF1hRZg0w+hvA0lMWPDPciXRt+JfinzE6seyU97nywyHO/gOCXAL8l7/8BWVlZVizZg12794NURTx/vvvo6ioCH/605+gVquxYsWKYB4CEQXJ0lMWmJ2+84wa/POgAS530/eHi+2lduwq804nnxuixv8SQcA1Gb5lwCw3IQqm/+aY/SazNfbmAWMz94aPlXmW+h6igCeT+d4BobkIo5VL/CZ0sgyYKLiayv6rEylr2SfHzV5TzBOVEtzSNzQXYdK0MoxI8p78u4prGVFQNVWVAQBOEX4XO8PVv46Y0PhUuW+cDFeHIJPZV1ADgD/++CPuuecejB49GhJJw0splUo8/vjjGDduHJ5++ulgHgIRBYnvRvP2rBg0TjQ5ZXBhZV7497Dzbcx6VZoSAxLkTTw6+K7x6QO4usAKRwQEUokikdMt4tMc77XsHp/M5W2ldmwrCe/+WZ5MZu+17Ja+MUhRhy5LZUZP/56mosi1jCgYAlVl+K5lK/IsOF0TOEMwXATKZJ4zQIMYWeja1k/32ZetZACQKGjyDE5s8KnK8F3LPj5uRpUtcIZguKi2u/GhTybzQ4O1YdFSJqirqdFoRK9evQAACoUn5dxgaCi1Gzt2LLZs2RLMQyCiINhbbse+ioasOQHAE0NjcVMf715TbxwwhPUJ34lqB77L9w5SPjQ4tL1dLu6qQJdGE5Sr7SI2R1jzbqJIsfaMFUXmhk2kRibguZFxuLy795S5N8I8c+aXEjt+LQ+PTOY6k9NUXheFTtQ4cVQf3sEHokjlW5XRI0aKV0bHY2BCQ5mcW/S/6BluVuRacMbUkMmskgJzQtxOxvdixpYSO8p9J60QUbv4xKcqY1iSHC9cFI9UdcOGwuQU8d6R8F7LPj5ugsHR8E6SVBLc3KfjezIHEtQAYLdu3VBUVAQA0Gg0SEhIwIEDB+rvLygogFweukwbIjo3vtl/V/ZQIl0rwyM+fQ32VjjwUxgHrxYdMnn9krkwUY4JXUPcmFUiYFqGT+kcy4CJguKj495Npmf1ViNWLsE8nybvawqsOBLGk+d8T+onp6vQTxfa/ZVOKcHEbt6BVJYBEwWH777st9kxkEkEv7Xs0xMmlFrCM3gliiLe8lnLbu0bg+QQZjIDQO84GQb5BFJ9Lx4T0fkLVJUxO1sDlUzAAwO9zzH/ddgEszM8swAdbhHvHPJ+H/83QAN1mAx1DGoAcOzYsdiwYUP95zNmzMA///lPvPbaa3jllVfwzjvvYOLEicE8BCJqZ0aHG0t9Gpre0c9zdbafTo6pPsGrcM2cKbe6sOSE9+L88GBtWEyp9C03+TbfGhH9FIkiSZHJhR/OeJ/Ezc72rGWXdFNgmE/fJ98T03BxXO/AmgLv9xEOTaaBQGXADAAStbdAVRm3ZXkyTWb2UiOt0cAKmwv41+HwXMt+Puv/Ph4McSZzHb8yYK5lRO1u7Rlr/SBEwFOVMau352fvrv4axMkbztEqbO4mJwWH2vLTFhSawyuTubGgBgDnzp2LGTNmwGr1bEyfe+45jBkzBi+++CJefvllDB8+HC+//HIwD4GI2tmy0xavlOZUtcSroemjF3hv1jbWTp4LN/8+YkLjCo40jRTX9gyPSZ8Tuyu9fsmVWd3YVhp+30OiSPbfHBMax9UHd5FjeG3QTxAEPOqTOfPlSTPOGMOvhHWhT7+sYUlyXJwaHpM+p2ao0Hhw56EqJ06FeQ8yokgTqCojo3ZCplwi4CGfCwL/PmqCwRF+mTNvHzR4fT4lQ4Ws+PCoFPO9mPFjsQ36MO9BRhRpPmyiKgMA4hUS3NXPO4j2z4NGOMMsQSJQT+bfZmmQqAqfyeFBDQAOGjQIDz30EFQqT3BAp9Nh2bJlyM3NRX5+PlauXImUlJRgHgIRtTPfyUy/zYqBvNEZ3qgUJcb6nHyGW+aMxSnivSPe7+OBQVqv9xFKSqngNyWKmTNE7cctivjE58rx7OwYrwzgazJU6BPXsGFzisCiMMucKbW48PlJ7/fx8KDwyGQGgCSVFON8fh+wgT5R+2muKqPO7Vkxfr2FfZvTh9pRvQPfn/FuGRMumcwAMEAnQ9+4hjJghxv4/gzLgInaS6HJhbVNVGXUeWCQFopG0at8owtfh9n50U/FNhyo9MlkHhg+axkQ5ABgU+Lj4xEbG9pG+0TUdgcrHdhV5t0H6/Ys/5Rm38yZr3PDa/Lc5yfMqGh05TZOIeCO7PBozFrHdxrwqjxO0CRqLxuLbCgwNqQAq6UCbuztvQZIJQIe9hkK9NGx8Jo8994RE2yNMpnTtVK/TJVQm5HJMmCiYFnuU5WR4lOVAQAauQT/N8B7r7bokBE2V/jsKRb6XCgemSzHmJTwyGQGPFnh0zO9v68sAyZqP5/6VGUMSpDVV2XU6RojxS19fQdOGsPq/Mg3+29ahgp94mVNPDo0gn40LpcLGzZsQG5uLqqqqvz+gQRBwBNPPBHswyCiduBbZnJpdyV6xfkvI1elKTFQJ8Ph2omPdZPn/jFO1yHH2Ry3KOLtQ95lJndma+pTzMPFlWlKxMiE+ql+hWYXfi13YERy+GyIiSKVb/bLdb3U0Cn914Bb+sTgxT01KLV4gn51k+eeGBrXIcfZHLPTjX8f9d5oPjhQC1mYZDLXuSZTjSe2V9d/vrvcgTNGJ9K04bUhJopEvvuy23yqMurcO0CDtw4YYakN+hWb3fjfSTNuzw59X6oSswtf+GYyD44Nm0zmOjN6qvF6o77W6wttMDnc0ITZ/pEo0rjcIj72Kf+9s58m4BrwyGAtPjneMCn4YKUDG4psuKKHyu+xHe1wlQPrCsM3k7lOUHdf+/fvx2233YYzZ840GZllAJAoMpidns1iY7ObyJoTBAGPXBCL+zdX1d/26QkTnhoWi5QQT3NbnW/FyZqGlBmZANwXZqnZABAjk+DKHkp8k9eQDv9NroUBQKLzVGJ2YXW+b5lJ4LWsbvLcX3fX1N/2r8MmPDRYixhZaE/6luSYUWVr2FvFKwTcFmaZzADQXSPFRcly7GyUPb4q34r7w3DdJYokra3KAIBElRS3Z8fg3UbtT946aMRvs2IgCXGg7b0jJtgbJVZnaqW4JiP0J/O+hibKkaaR4ozJs4e0uESsK7SFTf9ooki1schW/3MFBK7KqNM3Xo5rMlVY2ej86PX9hrAIAL7tk/03KlmB0anKEB1N04K6e/39738Po9GITz75BKdPn0ZVVZXfn8rKymAeAhG1kxW5VlTbG042E5USTM1oetMzq3d4Tp5726dh/qzeavTQhE9j1sb8ps7lWcIqzZ0oEi05YYaz0Y9Rv3gZRjdTanZ3GE6ec7lFLPJZy+7uF36ZzHVYBkzU/nyz/yZ2C1yVUeehwVpIG8X6cqqd+C4/tH3sTA7/TOa5g7SQhlkmM9BEGTB7mhKdN9+17NqeqoBVGXV8W039fNaO3WWhHZZYbHbhy1Pee0PfAUzhIqg7xf3792PevHmYNm0adLrQl/4R0bnzHf7xm6wYKKVNb9DCcfLczlI7tpZ4/4J4aHD49iOdnK7yanZ7yuDCoarw6aVIFGncoui3ls1uosykTjhOnvuuwIpThoar5XIJcG8YZ9RN98mQ2VpiR6nF1cSjiaglFqfoV5VxZ7/mM4AztDLM6u39s/jmAUNILyx+mmOGvtHFZZ1CwG+zwi+TuY5vj9XvC6xh1UuRKNIEqsq4s1/zrQlGJCswoav3hds3DhiaeHTHePewEY1PcXvFSjEtDDOZgSAHAFNSUiCTsccLUaQ7pnf4Bc5aMzQj3CbP+fb+u6y7Ehd0kTfx6NCLU0hwWXfv1PFveLWZ6JxtLrbjdKPAmUIC3NKn5fKtcJs851tmcmPvGHSLCc9MZgDoGSvzWmtFIOSZR0SRbEWupU1VGXXm+Vz03FnmwC8locmccblFLPTJZJ7TXxvWPfVGpyiQqm44PoNDxMYirmVE56qtVRl1Hh3ivZatyrMip9rRxKODy+Bw432f89twzWQGghwAvPfee/H555/D4QjNPwYRtQ/f1OxxqQpkxbccOAs0eW7xISPsIbhaerrG6dUvAgjPxqy+/KYBs3SO6Jz5rmUzeqrRRdVy4CycJs9tL7Fh+/+zd9/hUZXp38C/Z0qmppIChBRCD0XsghRFFBUpVlQUrCuIdV1Xd111V313XV33Zxe7ILqiiAUUK13AgqBCIAZCIIQS0jMzmZnMzHn/mExmzplJSJme7+e6uGRKkgOSO895nrtUyjOZoz+WTZeVzrEMmKjr5LHsqoHtV2V4DE9T47x+0oPFZyKUObPygBX7TdIDGfmaMdooBAFTc+XtWbgBSNQVgaoy5hynKsNjkiyJQ4T/BN5wWfK7RXIgk6ZR4OoozmQO6QZg3759oVKpMGbMGDz99NNYtmwZPvroI79fRBS9bE4R7+2R3qgdLzXb1x+GGaDzWZQesrjwfmn4+2e9WGSSjJcvTFX5ZddFo6m5WknPnqI6B/ZE6ISLKJZVW51YKcugnduJCZh3jDDCd0nqmTwXbvI+ppOzNShMjd5MZg956dz6wzbU2SLXEoIoVgWqyph7nPJfX3fK+md9ddCGnTXhXVeIoohnZRuPswbokRXFmcwe0/OlhxmfH2hCcwRbQhDFqg2HbX5VGVd1oCoDcPfkvHOk9PDzvT0WHLGEt72IwyXiRVmP+xuHGiI+KK49Ia3PvfHGG1t//49//CPgewRBwMUXXxzKyyCibli5vwk1PjdpKQmCX0P39gScPPebCVcPDN/kuRqr069p/+0jEjt0whRpaVolxvXWYN1h70bDp/ut+OOo6L/hJ4om/9tjkUyaHJCkxLjeHZ+qHWjy3NNhnjxX2uDAyhjMZAaAISlqDE5W4fd6dx9ThwisKrfiqoHRe0pOFI0W/y5dz3S0KsP3/fLJ3M/saMQrE9KCdo3Hs6XSjq1V0k3HBTESy87srUGqRmidwl5rE/HdERvO6hud/b6IotUiWSzraFWGx8x8HR7d2tCaSWx3uSvN/nFqclCvsz2fljWh3CeTWaOM/kzmkG4ArlixIpSfnojCQN6zb9YAPbSqzm2cLRhuxOu7zfBU/v5e78CqA1ZM7cRGYne8UWyBxafBRB+9Apf2D8/XDoZpeVrJBuCK/U3446joHV5CFG1EUfRbaM4Z3LEyE193jUyUbABuaJk8d3JGxzcSu+PFnSb45pmMTFNjQp/oz2T2mJ6nw39+9Wb9fFrWxA1Aok6wOUX8b480ls3tRFUG4MmcScQ1q2tan/uwtAl/O8mBXGN4erfLS/Wm9NNgaEpsHGyqFQIuyNHhXZ//Dyv2W7kBSNQJVQGqMuZ0oioDAFQtAyfv3VLf+tybxWb88YREJCeEPgNPFEU8K4tlVw7QI1MX3ZnMIf2bGTduXId+EVF0Km1wYMMReZlJ50818hJVfhtuT4dp8pzVIeKVXdLgPK/QiIQO9MqJFhfl6SSlh9uqmnHAxGnARB21+agdJfXe7xm1Ari6CxtPJ2ck+GUNhmvyXHWATObbRhhjIpPZY5qsdG71IWtEJ8MTxZruVmV4XJirxeBk72afU/QfLhQqJfXNflM/F4yIrUNNeRnwyv1NcEVwmjJRrJFXZRQkKv0m+3bE7EF69PIZONnQLOLN3eEZOPndUTu2V8symYdHfyZz2IqTd+7ciVWrVmHVqlXYuXNnuL4sEXWDvDHraRkJXe41dcfIyEyee7/Ugsom708Yo0roVN+vaNBbr/SbiMWm00QdJ2+Yf2GuFhldPKG9a2RkJs+9vtuMJp8BSn31ClwSQ5nMADAqTY1co/fv3eYEvjnIWEbUUfJM5lkD9NB1sioDcA+zkLcPePt3C6qtoe+fJc9kPqGXuks3/pF0Vh8tEtXev/ejTS6/4UxEFJgoin6tDOZ2cPiHnF6lwC2FsoGTRSZYHaHfkPfLZM7RYnAMZDKHfAPws88+w6hRozB+/HjMnj0bV199NcaPH48TTjgBn332Wai/PBF1kd0p+mWbdKbJtNyINDXOzQ7v5DmXKOIFWXCeM0SPFE30NmZty0V5/qfNRHR8dTYXPinr+vAPuXOyNRgR5slz7kxm6Sbm/EIj1IrYyf4D3KWH8mylT8u4AUjUEaUNDqw/LB081JWqDI8rBujRR+9dDzU5/eNMsFVZnX4lzLfHWCYzAGhVAs7rJ12XreC6jKhDglWV4XHzMCMMKumG/NK9oR04+XtdM74sj82ezCG9C/7mm28wZ84ciKKIBx98EEuWLMGSJUvw4IMPQhRFzJ07F99++20oL4GIumhVuRXHrN7MuSS1gJn53cs2uWtUeCfPfXPQhmKfHzBKwV3+G4umyW6atxy142iYJ10RxaKley3wTWrJNSpxVjcmgAuCgLvCPHlu6V4LqnzicaJawJxu3PhHkrx07quDVjSF4aSeKNYFsyoDADRKAbfKytVe2WWCOYRl+a/tMkvicT+DEjO6ubaMFPlk8xX7rWFpbUMU694KYlUGAKRqFH5JKs/uaIQzhNO5X9gpPfg9MV2NM7NiI5M5pBuATzzxBIYMGYJNmzbh7rvvxoUXXoipU6fi7rvvxnfffYfBgwfjySefDOUlEFEXyRealw/Qw6DuXsjwTJ7z9cyO0GUBPif73Bf314WtwXWw5SWqMLqXNOto5QGeNhO1xz38QxrL5gw2dHsC+cx8HfJ8Slk9k+dCwSWKeF72uecONoSlwXUonJKRIMk6MjtErDnELECi9gSqypjTjaoMj+uGGJCc4I2HtTYRb5eEJnOmySHiVXkm8/DYy2T2mJytge/A0nKT068fGBFJ1dlc+DSIVRketxYa4dsNYW+DEysPhGZtUdnkxHuyDMPbh8dOJnNIV487duzA7NmzkZjo39g1MTERs2fPxq+//hrKSyCiLtjf6MDqClmZyeDuLzQ9k+d8fVjaFJKBFtur7H4DTG6Lgcas7ZFnAbIPIFH7fjrWjKJaaRbw7EHdj2WeyXO+3iw2o94e/MyZL8utklIZlQDMK4zN7D/A3Xvsolx5GTAPM4jaE6gq4+IgZM4lqhW4aag0njy/w4TmEGTOvLfHgmqfASZJCQLmBGFtGSkGtQLnZLMMmKgzgl2V4dHPqMLlA6Tx5JkQDZx8dZcZNp8/Q45R6ZcRHM1CugGoVqthsbR9imQ2m6FWR3+jRKKe5u0Si6RB84npaozqFZy05kCT5+R9+oJBnjEzvncCRqfHRmp2W+SlcxsO21AThobdRLFKnv03JUeLPvqul5n4CtfkOXl/wUv669AvRjOZPabJFsqryq2wO1k6R9SWUFRleNxSaITGJyweNDuxfF9wN7LcmczSqozrBxuQGKQ/Q6TIb/o/LWMZMFFbRFHEouLgV2V43Clrz/JzVTPWHw7ucB6Lw4XXdkvXZbcWGqGKoUzmkEbdMWPG4NVXX8XevXv9XistLcVrr72GsWPHhvISiKiTHC4R75RIg3Mwp+YGnDxXYgnqRtYBkwMfyRavt4/wz0SONYOS1RiWIt08/bycWYBEgTTYXX43scGMZeGYPLf1mN1vWvqCGGky3Z6xWQlI89k8rbeL2HjE1s5HEPVcgaoygpk5l6lTYvZAaSwLdubMqgNW7G3wrvNUgnvjMdZN6aeF7x7mngYHdtUFv6qFKB78dKwZRXXBr8rwGJqixvk50mSJYA+cfLfEglqbNzYmJwi4JsYymUO6Afjwww/DarVizJgxmDt3Lh577DE89thjmDNnDsaMGQObzYaHH344lJdARJ301UErDlu8JRoGlYBLC4Kb1iyfPGcJMOGyOxYWmeCbTDI0RYXJ/bqfXh4NLmIZMFGHLCttgsVnMy5br8Tk7ODGgVBPnntelv03sY8GJwQpGzuSVAoBU3Oli3SWARMFFqgqI9hx4PYRRvgmsBTVOvD1weBtysurMi4r0KGvITjZ2JGUolFgYh/pzxWWARMFFsqqDA/5kLbVh2z4pTo4WYBOl+g3/OOGIbGXyRzSqx02bBjWrFmD888/H99++y2eeuopPPXUU1i9enXrc0OHDg3lJRBRJy36XXrzemmBLuiBTaMUcGuhfPKcOSiT5+psLiwulv4ZFgw3Bi29PNLk5SZrKqxoCEHfMaJYJ19oXjNYD2WQSzRSNQq/TJxgTZ4ra3Tgk/3yTObYz5jxkMeyzw5YQzqxjygWhboqw6N/kgozZAeMTwcpc+bHSjs2yzKZb4uDqgwP/zJgbgASyYW6KsPjjCwNzsiUHpA8+1twWk19dsCKfY3eTGa1AvhDDGHVu6kAACAASURBVGYyh3y7csCAAVi8eDEOHDiA4uJiFBcX48CBA1i0aBEGDhwY6i9PRJ1QYXbi64PSjLJQBGcAmDvEgCSfyXM1NheWBGHy3KLfzTD5ZP1k6hS4YkBspWa3Z0SqCv0TpdNHvzrILEAiX9ur7PjFZxqjAOCaIJaZ+FowPDST517aaYLvflhhigrnBDmDMZIm9NEgSe39iztmdWFLZXB79RDFuq/DUJXhIe+ftemoHT9Udj8LUN77b1JfDUakxU8P+AtztZLsyZ21DpQ2sAyYyFc4qjI85LHso7ImlDV2/3tSXpVxeYE+6BmM4RC2fEWFQoHMzExkZmaisrISu3fvDteXJqIOWlJiltxwjkhT46T00CzSkhL8J889183Jc3aniIVF0uB8yzAjNMr4yP4D3JOUp+fxtJmoPfLsv8nZGuSEaHBGP6MKlxX4Z850p39WbYADkQUjjBDiJJMZcGeCy3v1MJYRSb0VhqoMj9HpCX7TOJ/uZubMvgaHX6uSeMpkBoB0rRJnZkkzjhjLiKTCUZXhMSVHi6E+PdNdov/mXWd9f9SGH47JM5ljM5aFdAPwzTffxC233CJ57p577kFhYSHGjh2L8ePHo7q6OpSXQEQd5HSJeFu20Jw7WB/SG855QZ489+G+JslJuV4l4IahoclgjCT5BM1vKmywOFgGTAQApmYXlpXKykyGhDYO3DlSWs62rZuT594sNsPsc1LeW6fAZQXxk8nsIY9lK/db4eIETSIA4a3K8JD3z/r8gBXFdc1tvPv4XiySZjIPT1X5bTLGg2l+/Zm5AUjkEc6qDMA9cPIO2ebckhIzjjV1feDkc7INxMnZGhSmxmYmc0g3ABctWoTERO+ieP369XjjjTdw2WWX4aGHHsK+ffvwn//8J5SXQEQdtOaQDQfN3sCoUwq4PMQ3nJk6Ja4eKP0aXZ08J4ointshLTO5ZpAeqZrYaszaESelq5Htk3JucYj4toITNIkA4KN9TWhs9saQLJ0CU2SZZsE2LFXt9zW6OnnO5hTxijyTuTC+Mpk9zsnWQO9TP11hceLnqq5vNhDFk3dkVRnDU1Uhq8rwcA8akn6NZ7uYOVNjdeIdWSbz7SMS4yqT2UM+oG1rVTMOmlgGTASEtyrD47ICveReyeoEXu7iwMm99Q58diB+MplDeme8f/9+yZCPjz/+GNnZ2Vi4cCHuuusu3HzzzVi1alUoL4GIOuitYmlQnNlfh5QwbJ7dPiIxKJPn1hyyoajWu9hSCMCtw2M3OLdHIQi4KE+62bCC5SZEAPwXmrMH6aEOUZmJr7uDNHluWakFR5qkPb+uD3EGY6ToVQq/HkCMZUTuqozFsqqM64YYQr55JggC7pZlNL+/14IKc+czZ94otkh6fvXVK3BJ/9D0L4y0vgYlTsuQlgHLS5+JeqJIVGUAQIJSwALZJt1ru0wwdWHg5ItFJskk9pFpakzoE7uZzCG9u7fb7VCrvadIa9asweTJk6FQuL9sQUEBjhw5EspLIKIOOGJx4otyeZlJeMrNCoI0eU6emj0tT4v8xNCeLkWSvHTui3Ir7E6WzlHPtqOmGT8dk2aQzQlxyZxHMCbPiaLo16fm2sH6sBzGRIrfBM39Td3qn0gUDyJRleExLU+LAp9hY80u91CizrA6RLyyS/ox8wqNSIjDTGaPafKDWZYBE2F5BKoyPOYM1iPFZ+BknV3Eot87N3Cyyur0m8R+e4z3ZA7pijIvLw9r164FAPz8888oKyvDpEmTWl+vrKyUlAgTUWS8u8cCn0NaDElW4XTZjWwoBZo892MnpkH+VtOMNYekWYO3j4jv2DImMwEZWm8Ib2gWse4wy4CpZ5Nn/53VVxPWg4DuTp77tsKGXXXSTOb5hfGZyexxXj8tEnxWo/sandhRy9I56tnkVRkz8rVhOwhQKgS/NdRbxWbU2TqeOfN+qQWVPpnMiWohLFk/kSQ/mN181I7KbvQcI4oHiyNUlQEARrUCNw+TrqFe3GHqVMLE67vNsPp8G2frlbg4xjOZQ/qT5IYbbsDHH3+MsWPH4pJLLkF2djbOPffc1te3bNkiKREmovBziaJfcJ4bhjITX4Enz3U8C/B5We+/MVkJOCUjfBuYkaBUCJiaywmaRB5NDhHv7/UfZBRO3Z08J89knpmvQ14cZzID7onwZ8viPzNnqCc7GqAq47owb55dNVCPTJ33NtHkEPH67o71z3KJIl6QxbI5gw1ITojfTGYAyE9UYVSat/JNBPAZy4CpB4tkVYbHLYUG6JTSXsPLSjuWBdjkEPGqrG/gvOGGsG1ghkpII/FNN92EZ555BgUFBbjgggvw4YcfQqdz75jW1tbi2LFjuPzyy0N5CUR0HBsO21DW6D3aSFAAVw4I/8lGoMlzv3dg8lyF2YkPZb0lbovT3n9y8tPmzw9Y4XCxdI56pk/KmlBv9/77T9cqMDU3vLGsO5Pnfqm2+2XxxnKT6c6QxzL2AaSeLNJVGQCgVQmYJ8s+XlhkQpPj+GuMrw/aUFzvzeJVCsC8wvjO/vNgGTCRV6SrMgAgXav0mzj87A4TXB1oNbJ0rwVVVm8mc5JaCPkk9nAI+VHMnDlzsGTJErz00ksYMmRI6/OpqalYu3Yt5syZE+pLCOi1117DqFGjkJWVhYkTJ2LTpk0RuQ6iSHurWHoKMj1fhzStso13h4588pyIjk2ee7nIJFkoD0hS4oLc8PSWiLTxvTVI9ultUW1zYdPRzg8dIIoH8oXmVQP1Eek31dXJc/KMmTN7J+DE9PjOZPa4MEcL3/9Vu+ocKKnnNGDqeVyi6BfL5oS5KsPjhiEGJKq9X/eY1YX/7Tl+5sxzsqqMS/rrQj7xM1rIe5quP2zrVOk0UbyIhqoMjwUjjJI1xu46B74sbz871xWgJ/PcIQYkxUEmc+z/Cbpg+fLluP/++3HPPfdg/fr1OO2003D55ZejvLw80pdGFFZVVidWHpBNZorQyYYgCH5ZgEv3WnConclzDXaXX5+c24YnQhHDjVk7I0Ep4IIcTgMmKq5rxmbZ5vecCC00E5QCbu3k5LmDJgc+3Cf93u0p2X8AkKZVYlxveRkwS+eo5wlUlXFVBKoyACBFo/ArPX5uRyOc7VQabKuyY+MRaSxe0EOqMgBgSIoaQ5K9m50OEfj8ANdl1PNEQ1WGR36iyq9v39PHGdL2RbkVexq8mcwqAbhlWOxn/wFh2AA8duwY/vvf/2LOnDmYMWMGpk2bJvk1ffr0UF+CnxdeeAFXX3015s6diyFDhuDJJ59EVlYW3njjjbBfC1Ek/W+PBb73pAOSlBjXO3IZJ9PzdOgvnzxX1HaAXvy7GQ0+k6V6aRS4cmBkbvojZZpsgvLKA00dSmsniieLZVPdzuydgEHJ6jbeHXpzOzl5bmGRGb49qQcnq3Bev56RyewxPZ89TYnkcSJSVRke8wuNUMuG9HzaTlmrPGNmQh8NRveQTGYP+bqMhxnUE0VLVYaHvD3L95V2bD7a9vBEeU/mSwp06Bcnmcwh3QDcvXs3zjjjDDzxxBPYu3cvNmzYgOrqauzduxcbN25ERUUFxDDfqNrtdmzfvl0yjRgAJk2ahO+//z6s10IUSaIo+t00zxkcmTITj0CT597cHXjyXLNLxMIi6Q+Xm4cZoFP1jOw/j0nZWhh8/syHLS78dIxlwNRz2JyiX1lapHu0GNUK3CSbPPfCjsaAk+fq7S6/hfJtI4w9JpPZY2quDr5/4u3VzdjfiQnKRLGuyur06xkX7ob5cn0NSswaID1Y/b9fTQHv3/Y3OvBxWc/NZPaYJjvMWH3IisZ2MsCJ4k00VWV4jOqVgHOy5QMnAyeZ/HTM7nf98dRfPqTbmH//+9+hVquxZcsWJCYmYuDAgfjXv/6FiRMnYtmyZfjzn/8c9qy76upqOJ1OZGRkSJ7PyMhAZWVlwI8pKSkJx6URhdXP9QqU1HsXKSpBxBmKIygpORLBqwJOBZCm1qGm2X0raHKIeOK7/bg+R3oj+EWlEgfN3kCuUYiYpDmKkpKj4bzcqDAmJQHfVHnD+aLth5HaX9o/i3GM4tVXx5SosXljQZJKRKGjApH+J3+uBnhOoYPN5Y5lhywuPL95H6ZlSdsavH1QhcZmb4ZMmlrESeKhiF9/JIxK0uCXBm+201tby3F1tjT2M5ZRvFpyUIVmlzcW5Ghd6N14ACUdHyQeEjMSBbwDLcSWLfpfa5rxzo+lOD1Vuqn131I1nKI387q/3oU8S3mPi2U6Eeir0eKQzZ1nY3MCi38sw3kZ0tjPWEbx6plSNQBvLDgpyQlUlqEk8FZL2FyWqsC3Fd573y/LrVi1fQ8GGqQHGo/vToDvNtlpKU7oavajpCZcV9o9gwYNavf1kG4Abt68Gbfeeivy8/NRW1sLAK0nRpdddhm2bNmCBx98ECtWrAjlZQQkz3ISRbHNzKfj/SUSxaIn19cA8J7UTs3T4fTh/SJ3QT5uszfika0NrY8/OKrFgxN6t2b3iaKID4qOAfBucs0eZMRphdFx/eF2tdKCb9bVtj7eWK/F8wPzWmNaSUkJ4xjFrXv2VgHwlnFcPdiIEUOiIxbMaajDq7u92X1LKw2468zM1uw+u1PEsp+PAPDeSM8fkRQ11x9uV9hN+OWH+tbHm81GPDzIe2DLWEbxShRFfP5bJQDvhvdNI1IweHBi2x8UJoMATK2qxsoD3lLWD2qScc1p6a2P62wufLrlCNwj3NzuOSkNgwfFR8+szrqkrh7P7/Tu3P5kS8GCQWmtjxnLKF7ZnCK++FG6rpk3Oh2DBkS+RdNAUcRrR45ha5X3/vGThjQsHO393ixrdGD1d9JkkvtOy8Sg7PhpyxLSEuDm5mb06dMHAKDVuv/S6uu9C7uRI0di27ZtobwEP7169YJSqfTL9quqqvLLCiSKV7U2Fz6RlWlcF0VjzY83eW79YTt+rfEGbwHArcOj5/rD7dwcLTQ+LYL2m5ySvx+ieFXa4MD6w9IeLpEu//UlnzxXXO/AFz6T5z4qa8Ihi3eRrFMKuHFo9Fx/uE3Lky6wv6+044il7UFQRPFi01E7Suq9m39qBXB1FPU0vmuUdCNy3WEbtld5S+TeKjbD7PBu/mXpFLi8IHquP9zkPU2/OmhFk4P9mSn+rdzfhBqf1k0pCQKm50Vm+IecIAi4c6Q0li0rbUK5yRt7X9xpgu+co8IUFSb1lZYOx7qQbgD269cPBw4cAADodDr07t0bP/zwQ+vrRUVFMBjCu9BNSEjA6NGjsWbNGsnza9aswemnnx7WayGKlKV7LbD53FPlGZWYGEXB7XiT557f0Sh57cJcLQZGsOF/pCWqFZjUVz4NmE2nKf4tlvXOOy0jAcNSoycWBJo890xLzxlRFP2aTF8zSB/Rhv+RlmtUYXQv7/8/Ee6bCaJ4J+8DemGuFhm66IkFp2Qk4EzZkDhP/yy7U8RC2cC2WwqN0ESw4X+knZKRgD5672222SFidQXXZRT/5IOMrhyohzaK+rNPzdViYJJ0UveLLdm6tTYXlpRIr/+2EcaI9scPhZBuAI4fPx6ff/556+PLL78cL7/8Mm6//XYsWLAAr7/+Oi688MJQXkJACxYswLvvvovFixejuLgY9913H44cOYLrr78+7NdCFG6iKGJxsXShee1gQ9Q1nG9r8lxRbTO+rpBm/PTEJtNy8swZeSNxonhjd4p4R7ZQmzsk+jJO2po8t+6wDTv8MpkZy6bnc4Im9SyBqjKiKZPZ4y5Z5syn+5tQ2uDAslILjjR5M34MKgHXD4m+6w8nhSDgolx5LOO6jOJbtFdlAO6Bk3eMlK61Fv9uQa3NhTd2m2HxydTto1fgsjjMZA5pD8C77roLEyZMgNVqhVarxQMPPICGhgZ89NFHUCqVmDVrFh599NFQXkJAl1xyCWpqavDkk0/i6NGjGDZsGN5//33k5uaG/VqIwu3HY3YU1XlTnZUCMHtQ9AU3z+Q535OYp38zYbgsu+fUDDVOz0yQf3iPc2GuDiqhDp6fW8X1DhTXNWNISvRkQxEF06pyK45ZvTedSWoBM/Ojo8zEl2fy3Lc+BxdP/2aCwyUtB5uWp0X/pJAuy2LC9DytpAfsxiM21FidPTozkuKbvCoj16jEWVFUleExOVuD4akq7Kx1ryFdIvDsb434oVI6LfOaQXqkakKaYxITpuXrJD1gV5VbYXeKSOjBmZEU3+RVGadnRldVhsesAXr88+eG1oMLs0PECztMWFwivf5bhhnj8vs1pNE5JycHM2bMaO3/p9Fo8PTTT2P//v0oLS3FCy+8AKMxMqfdN910E3777TdUVlZi3bp1OPPMMyNyHUThJk/NnpKjRR99dN5Y3THCCN+w+0t1s6QXIADcNiIx7lKzuyJFo8CEPtIbhk/LeNpM8Uu+0Lx8gB4GdXTedMp7znxZbpVsCALA7SMi3+w/GgxMVmNYincj1CkCnx1gFiDFp0BVGXOisCoDCNw/a9HvFsmhskIA5jOTGQAwNisBvXw2QuvtIjYcsbXzEUSxK1BVxpzB0ZdgAgAapeAXp576tRGVPpnMRpXg144qXoTtqNnpdKK+vh5JSUlQqXjCTT2P0yXioNmJskYHShuc2NfogNUpYkxWAi7K00GtCP1ir97uwkf7or/MxGNwihoX5molN3++OTP5iUpclBs/U5m6a1qeDqsPeReXK/Zbce/opAheEcWjZpeIcpM7hu1rcGBfoxMiREzso8W5/TRhuXHd3+jA6gp5mUl0LjQBYHzvBJyUrsbPVYGH85yRmYBTmcncalq+Dru2e3u9rtzfhGuj+GcVxSabU8QBk3dNtq/BgQSlgPP6aTGud0JYDhd/OtYcE1UZHpf01+HRnxtQbnKnLMrHWszI0yE/kfd5AKBSCLgwV4u3fTZFVpQ14Zw4miZK0aHJIaKs0YF9jQ6UNjiwv9GJxAQB0/J0ODE9PGuLWKnK8LhuiAFP/dKIhmZ3FJPHsmsH65ESp5nMIY/QP//8Mx555BFs3rwZDocDy5cvx8SJE1FdXY358+djwYIFmDhxYqgvgygsrA4R+03u4Luv0b2gLGtwoLTRgQMmJ5pd/h/zyi4zsvUNuHmYAXOHGEJaNrGs1CLpbZCtV2JydvSVmfi6a2Rim9kfC4YboQzDxmmsmJqnxR83e3+I/VrTjLJGR7sfQxSIudmFspYY5r4x9v6+3OSEM8Awwxd3mjEgSYl5hUZcNVAPYwiz8d4usUgWayemqzGqV/RuoHkyZ+auqQn4+m3sYyoxPU+HJ3w2ANccsqHBHuAHKNFxNNhd7rVYo7PlwMK7RqswO/1u+gDguR0mjExTY36hAZcW6EM6zOItWSZzNFdlAO5NrduGG3Hf9/UBX2dPZqnp+TrJBuBnB6x4agynAVPn1dlckvjlPYR14LAl8M/H//5qwhmZCZg/3IipuVqoQnjPJK/KuCKKqzIAIDlBgRuGGlqHGflSCsC8wviNZSHdAPzhhx8wffp0ZGVl4corr8TixYtbX+vVqxdMJhPefvttbgBSTKmzuVpPWfa1LChLGx0oa3DikCXwYvJ4KixO/H1rA/69vRFXDdRjXqEBg0PQu21RsTQ1+5rB+qjfQDs1MwFjsxKw6ai0x0yqRsDVA6P3lDwSMnVKjJH9Xa0oa8L5PGwmGVEUUWtztS4iPQvKspYFpW9D987Y2+DEvVvq8ejPDZg72ICbhxmQawzuUsPhEvGOrE9LNGcye1yUq8WAJCX2Njglzw9IUuKCHH6T+hqeqkJBohKlje6/K7vLXTY9OsLXRdFHFEUcs7pas5F9b4r3NTpRZe1aLPutphm3bqzDwz814MahBtww1IDMIE/lbYixqgyPawbp8e/tjaixSf9ux2Yl4KSM6D2IiYSJfTRIUgutWUbHrC5srrQjK8LXRdFHFEUcaXK1rMfc95X7Gt33mPsaHKizd23jeEulHVsqa9DPoMQtwwy4drAh6JltgaoyorX819e8QiNe3GmC/HxxZr4OeXGcyRzSP9mjjz6KAQMG4Ntvv4XZbJZsAALuKcFLly4N5SUQdZpLFFHZ5JKUt5X53CDLFzzB1OQU8UaxGW8Um3Futgbzhxtxdl9NUMpQtlfZ8ats4uQ1UVxm4uuukYnYdLRa8tyNQ41RfbIUKdPydNINwP1WnD8kghdEEeN0uReTng2+Mp9MvtJGBxq6uJjsiAa7iOd2mPDCThOm5Wkxv9CI0zODU1L39UGr5LTboBJwaUH0lpl4KBUC7hiRiDs31UmeXzA8MeoPYsJNENylS8/s8J7Mr9jfhNE5EbwoihiHS0RFSwuVfY3O1hvkfY1OlDU4YHKELpYds7rw+PZG/PfXRlxW4D6gDVa28bLSppirygAAg1qBPwwz4HGfLF2A2X+BJCgFnJ+jxful3o3eFWVNuKlXBC+KIsbudLeD8r3H9KzPyhqdaApUXhEkB81OPPhTAx7f3oirB+pxS6EBA5ODk2wSa1UZHr31Slw1UO/XHz/eqzJCugH4888/429/+xu0Wi0sFovf69nZ2Th69GgoL4HIj8XhQoXZiYMmJ8rNTpSbnDhoduKgyYGDZndJSKgqjXppFOifpET/RBX6J6lQaXFi6d6mgAH/6wobvq6wYWiKCvMKjbhigA56Vdc3vN6SNZk+t58GOUHOzAmVc/tpMDJNjd9aNjB1SgE3D43+U/JImJanxV9+8Jbm/HDMjsp8AYMieE0UGqZmV0vs8sQxB8pbHh80O3HI7ESo7ouzdAr0T1QhP1GJ/kkq7K134KOyJr82By4R+KTMik/KrDgxXY35hUbMzNd1a6raW7KF2qUFOiTGyGHAlQP1+Pf2Bhxq2cDM0Cpw5cDo37yMhOn50g3AbypsuLdvBC+IQqbe7mqNW+Uta7GDvrHM4oQrRLGsr16B/EQVCpJUyE9UYXuVHZ8dsPpVc9hdwLt7LHh3jwXjeidgfqER5+dou7V5L1+XxUJVhscfhhnwwk4TGlsy24amqDCFmcwBTcvXSTYAV+634oa0CF4QhYQoiqiziyg3Obz3lq1xzP3c0SZXlyrFjkchANmGlvvLRCVyjSpsPGLDmkP+Q2fMDhGv7jbj1d1mTMnR4tZCAyb06XqySaCqjOtiIJPZ444RiXinxNK6Xp7QRxO2vomREtK7f4VCAYWi7UX50aNHodNx4UvB4ykF8W7uOSSLyHKTE9UhzOAT4A7A+YlKFCSpWgKx9yY5OcH/++HhU5KxqNiMV3aZWm8Kfe2uc+CuTXX4x9Z6XD/EgJuGGtHX0LkyFFOzC8tKpWUmc2IoOAuCgEVnp+H272pRZ3PhwZOTkBXFPXIiqZ9RhZPT1djqM2xgbbUSnHMeW1yiiKNNLncM88Qvn4OLg6aul4N0hEIAcgzuuNU/UYmCRBXyk7zxLFB/v3+c6sTru814c7c5YJzdVtWMP6yvxUM/1uOmYUZcP0SPXtrOfR9XmJ34+qC0J2gslMx5aJQClkzqhfu+r4NTBP51WnK3Dnbi2UnpamTrlaiwuMuALQ4Rm2qVGBnh66LOcbhEHLE4JYcTnkNXz02ypzwyFFQCkJeo9K7HPDEtSYU8owo6lf9Nb1mjAy8XmbCkxNK6weVr4xE7Nh6pQX6iErcMM2L2ID2SAqzv2hPLVRkAkKZVYtHZaXj4pwYY1QL+b2xKVE4ujgbnZGugVwmt2Z4VFieKTAqwOCO2NLdkIvveVx40SQ9ezSHMRtYogTyjqnVd5olp/ZPcG37yXqX3nJCIotpmvFxkwtK9Flid/p/zy3Irviy3ojBFhXnDjbi8QB8wJrYnUFXGJTFQleExIFmFlyek4slfGtFXr8Tz41IjfUkhJ9TV1YXsX+q0adOgUqnw0UcfoaamBgMGDMDHH3+MiRMnwm63Y9y4cSgoKMB7770XqkugOCCKIuwu97Q2m1N0nxT7Zu55fm9yoMLihC1AgAsmtQLIbzlhyW8JvgVJ3hMXbScDp0ezS8SKsia8WGTCT8cCT4oE3IvZmf11mF9oxMkd7LWy+Hcz7vjOW3aWpVNgxxW9wzJ5mMLv6V8b8fetDa2PT0l24ptLciN4RQS4Y5nNCdhcIuxOETU2nww+2YHFIUvgoUHBpFWiNYa1Zia3xLMco7LL8aHJIeKDUgsW7jRJplsG+vpXDNBjXqERhakdK0N5YnsD/rnNW3Y2PFWFjTMywzKtk8Lvvi11eHmXN7Pg/AwH3rsoL4JXRID7gMLmdK/L7C4RVVaXz+aed2Ov3OTEYUvgoUHBZFAJ7oPWluqKgpaYlp+oQj+DssuN7xvsLrxTYsHLu0woa2x7cZmoFnDNID1uKTR2eALu3Ztq8aZPX+ZzszX44Lz0Ll0nRb85q6vx6X7v4dWc7GY8e15+5C6IAEhjmc0potLqaq0I81aIuePaYUtosvd8JamFlg2+lk0+n9/3NSi7vMlebXXirWILXt1larfHcy+NAtcPNeCmoQb07mCixaxvqvFluc+/7cF6PHtm/G+ixbKQbgCuWbMGl112Ga666ipcfvnlmDlzJhYuXIhevXrhP//5D7Zu3YrPP/8cp556aqgugeC+GSttcMAFd08oEe6yLPcvES7JY0CEKHnsFFset7xPDPhxIpwi0OyzUee5yfXc8Hqet3fwsd0pwuoUQ1aO257kBAG5Ru8Ji6c0pH+SEtl6ZchLNH6stOOlIhM+KWtqd+F8WkYC5g83YFqert0F7jkrKiUZYfeMMuLBk5ODeckURUobHDjpQ297BQVE/H5VH6R3Mtsq2jQ2u7C/0emOPyI6FMucouw9HYxldpc7dnnjkPexzdXynM8NsPtxy3t84pnv40jEslSN4N3kazm08GQn99YrQpqxIYoi1h+24cUis2RxGMjZfTWYX2jE5H6aNq/J6RJxwrKjOGj23og/eUYybh4W371aerLvjtgwdVVV62ODUkTp7OyQTmUNh7qWzf+OxTJp3OpIuy2LvAAAIABJREFULHP6xjKnCJur5b+S9Rlaf98aq9p47I6D3sehPpwIJF2rcFdX+GQje9ZomTpFSA8BnC4RX5Rb8VKRCRuP2Nt8nwDgwlwt5g834systnuemppdGPreEUnvwiWT0nBRXuxkzVDnLCu14KZ1ta2P+2ld+O3KfjF/eHWsyV3W2lYsc0riVMdimfy+0zeWee4LbZ41l8vnMKJ1feaNd/YAj303/EKYsNem3jp32wFPdVhBojerL00T2lhmd4r4uKwJLxWZsK2q7WQTtQK4uL8OtxYaMbqdctgKsxMjPzgiadGw+qIMDgOKciHdAASAZcuW4d5770V9fT1EUYQgCBBFEcnJyXjmmWcwY8aMUH75Hs3pEvH0byb836+NIW2QHGuUAtBHr0SOUYkcgxL9jEr0M7hPiXOMSmQblJ0u5QiVgyYHXtttxlvF5nbL/foZlLh5mAFzA0x22lHTjHGfVEqe235ZVodPqSk2nfnxUeys9WZfPXtmSkyVffuyOUU8srUBr+82BSxh6KnUCnfLgX6eX0YVclriWD+DO5ZFy6CcPfXNeHmXGe+WWNotkRmYpMK8QgOuHKj3KzP+5qAVl33tHQakUwrYNat30KfZUfRwukQMXXoEx3wmub4/uRfOi9FeY6ZmF+7/vh7v7bFE5MYzWmmUaIljqpY1mftXbsv6rK9B2emytFD5tdqOhUVmLCu1tHuoMypNjfnDjbikv85vw5pVGT1Pg92Fgf87LPk3s2FGJkamBWcIQ7hVWZ24e1MdVu7375fZk+mUQusazBPLcozee8w+emVUHGCJoojvW5JNVuy3tttjdUxWAuYVGjE1V+uXbCKvyhiRpsaG6Rkxv7Ed70K+AQgAFosFq1evRmlpKVwuF/r3749zzjkHRmNsnNq/sduM64foY+of8xGLE7esr8W6w/7NP+NdklpAv9bNPW/Q9Swoe+u7Xg4SKeZmF5bubcLCIhN+r2+7pE6vElonOw1qmex075Y6vOpTQnVWXw0+nsIyk3j3+LYGyYS+5AQBz56Zihn5sZVhUNrgwA1ra7C9uu2TyniVqhFaDyf6GZXI9T2wMCqRqVXETMN4jzqbC2+XmPFykVmSySeXnCBg7mADbh5maB1WdO3qaqzwKaG6coAOCyewk3q8u+u7Wsngl946BRZOSMVZfWNrE/CXajtuWFuDvQ097xQjXavwOajwuSFueZyuDW02cihUNjnxxm4zXt9tlmxQy2XqFLhxqAE3DDEgQ+fOwp+8slLS6uWPo4x4iFUZcW/W11X48qD3vqwgUYnXz0qLuYEDGw7b8If1NZK+bz1Flk7hjWOyA4scY+gz+ELhgMmBV3eZseh3MxraSTbJMSrxh2EGXDvInWzCqozYFZYNQI8NGzbg/fffx5EjRzB48GDMmzcPOTk54fryXZbyZgWm5mrx3JkpSIuBErpvDloxb0MtqtpZkMQatQLQKAQkKAXoVYLsZEUpuUkONGgjXrhEEasrbHipyIRvK9rf3D2vnwY3DjXi5vU1koD+1llpmNk/tjaBqPN21TZjzMeVfs9fO0iPx09PjprMsPYsK7Xg7k11AZuwx6oEhXsQRIJCgEEt+Gzu+SwkWzKRY2WybVc4XCI+O2DFSztN2FLZdkmdUgCm5elwxQAd5qyukWRNfXFhOs7I0oThaimS1lRYcfFX1ZLnBAB3jDDigZOSujVROhxEUcQru8x48Mf6iLQBCJUEBaBVutdliS2xLMfojWM5PrEsngfd2JwiPiy14KUiM36rafugKkEBXD5Aj7P7aiSloACrMnqKd0rMWLCxTvKcSgAePDkJt48wRv0muMMl4olfGvHk9sa4yvrTKL33mCkJCr8Mvn4GFXKN7h580ZC9FyqmZhf+t8eChUWmdg+qDCoBVw/SY2iKCvdsrm99nlUZsSPoG4CPP/44nnrqKezYsQNZWVmtz7/zzju4/fbbIYreL5eeno5vv/0WubnR3Zw+5c0KAEBfvQILJ6RhQp/ovOFodol4bGsDntlh8nstKUFATkvzUIXgnvCogPu/SkGAIHnO/z2C5zkASgWggPc9gs/HJSiABKUAjeeX52ZXKUCjENxBVvLY5zmf172P3c9F+w/FSNhd557s9N6eJjR1sMN2ulaBoit6R/0NEwWHPPvTY2CSCq9NTG23r0ckmZtduO/7eiwpsfi9lqZRoE9L7zpPDFIK3pjUkVim9DwntB3LNEq0xqCElljmudl1xyf5YwFaWXxLUPq8p+UxY5m/n4/ZsbDIhOX7mjpcFjkkWYUtF3P4R08giiLmrqmRNND3ODFdjdcmpGFAcnRuntTaXFiwsRafH/C/9iydAulaBZS+caoDsUzpWZMBAT8uUCzTtMYg6drMN7619dgT7+SP+b0nJYoivjtqx0s7Tfj8QMfLIlmV0XM0u0RMW1UV8NDrrL4aLByf2uHBC+FWYXbi5nU12HTU/9r7GZRI0SjajEnyWOa7BjteLHP/cj9ua12lUQjQqrz3kQltPPbGwJbnlALUjGV+XKKIrw+6k03WHup4JeFVA/V4aTyHf8SCoG8ATp06FUajEUuXLm19zmazYdCgQVAoFFi8eDFOPvlkfPXVV7j11lsxa9YsPP3008G8hKDzbAAC7lPnu0cZ8ZcTk6KqV0dZowM3rasJOD12fO8EvDIxDX2i9IcKdU+N1YlFv7snOx06Tjr+7SOMePRUlpn0FC5RxEtFZjz8Yx0cojReqRXAQycnYcHw6Dp13lnTjBvW1qA4QKn7tDwtnjszlaeLceqwxYnXd5nxRrEZNbb2Y9n/Oy0ZC4azzKSnaHaJ+Pe2Rjz1awNESOOVQSXgyTOScdXA6GrVsuWoDTetqw1Y6n7dYD3+eXpyXGfG9WRljQ68XGTCkhLLcTPY3zwrFRf314fpyijSLA4X/vZDA94o9j+c7aVR4PlxKbggN7qqdL4ob8KtG+r8fi5H6z0xBU9RbTMWFpmwdK8FtuN0r2BVRuwI+gbgsGHDcMMNN+Dee+9tfe6rr77CrFmz8MADD+BPf/pT6/N33nkn1q1bh+3btwfzEoLOdwPQ4+R0NV6bmIb+SZE/df6krAm3f1frV7evEID7RyfinlGJMdcnijqv2SXi07ImvLjTJJn46+unSzIxMDk2Gw5T163ctgf/2JeIkgCbapP6avDS+FRkRfiAQBRFvFlswV9/qPMb9KFRAv/v1GTcONQQVTf4FBpNDhEflFrw0k4TdtX5/5tNUAC7Z/WOiZYcFFzv/bQXj+41oMLifydyaX8dnhqTEvEDAqdLxP/9ZsK/tjVAnpyfpBbw9NgUXFLADZ+eoMHuwpISC14uMmG/yf/fLKsyeq5XNpfiX/u0qLX534bfPNSAR05NjvjgG5tTxN9/qsdLRf6blRlaBV6ZkIqzs2OrFyt1TZXVibeKLXhtlwlHmvwPaIemqLB5JqsyYkXQV0m1tbXo3bu35LkNGzZAEARMmTJF8vzo0aNx5MiRYF9C0N090gj5P+etVc2Y8Gkllu71L1ELlyaHiD9uqsPcNTV+m3/ZeiVWnp+OP49O4uZfD6FWCLi0QI9vp2Xi66kZuKS/Dr5rypn5Om7+9VBDjCLWTsvAnMH+N52rD9lw5ieV+Krcv0QtXOpsLly3tgZ/3Oy/+TcoWYVvLsrETcOMXFj0EDqVgDmDDdg0MxMfT+mFKf2kJ8o3DDVw86+HOjnZhY0zM3FRrv9N54f7mjD+00p8fzRyw8+OWJy45KtqPPaz/+bfSelqrJ+Ryc2/HiQpQYFbhxvx86VZWDIpDWf2lrbduGOEkZt/PdTZ6U5snJGFcb39W7G8utuMc1ZUoqg2csPPShscmPLZsYCbf2f31WDjjExu/vUg6Vol/nRCIn69vDdemZCK0b2k95P3jErkGj2GBD0DcNSoUZg9ezbuu+++1ucmT56M33//Hfv375f841i0aBEefPBBHDhwIJiXEBLrDllxy/ragLveVxTo8J8xKUgK4/CJ4rpmXL+2BkW1/tkR5+do8eK42BhYQqF10OTAiv1WqBXu3gyxMPiBgq+kpASDBg0C4M4YvuO7WtQHmPQ1r9CAv5+cDG0YT51/OuaejHkgQHbE1QP1ePKM2BhYQqFVUt+MLw5YkaJRYNYAPW+aeyhPLBNFEYt+t+Av39f79cBVCsCfRyfiT2Gufvi2wr1ODDSA7fYRRjwYAwNLKPR+qbZjdYUNuUYlLu6vi6oWHBQ+nljmdIl4+jcT/hkgY1irBB6LQPVDWwPYlALwt5OScOfI6GodQ+EniiK+r7Rjw2EbRvVKwJQcbgbHkqBvAF533XXYtm0b1q1bh5SUFOzYsQMTJ07EBRdcgCVLlkje+9e//hVr1qzB5s2bg3kJIVNtdeK2jXVYFSBTJs+oxGsT03BqZmib6ouiiCUlFtz3fT0ssm7pagXwyCnJmFfIMjki8vLdAASAcpMDf1hfi80BmjmPSFPjtYmpGJoS2mxRlyjiuR0mPLq1wW/wg1El4KmxKZg1gJkyROQlj2W765px49oa7AxwGDomKwGvTEhFjjG0rVraG8DWS6PAS+NTcR5vjojIhzyW/XTMjhvX1gQsFb8gR4vnx6WgV4gTO9obwNbPoMQbZ6XitEz2eCOKdUHfANy5cycmTZoEg8GAoUOHYseOHbBYLFi1ahVOPfXU1veJoogTTjgBkyZNivohIL5EUcTru83424/1fqVqSgH464lJuGukMSSnzg12F+7ZXIcPSpv8XitIVOKNs9KidqonEUWOfKEJuPtUPfVrI/69vdHv1FmnFPDP05Jx3ZDQNNU/1uTEvA21+LbCv1RvVJoab5yVynJ1IvITKJZZHSL+sTVwn6rkBAHPnpmKGfmhaarPAWxE1BWBYlmD3YU/banD+3v97/P66BVYOD4NE/uGZgOuvQFsF+Vq8fw4DmAjihdB/04ePnw4PvnkE5xyyimoqqrCaaedhuXLl0s2/wB3X0Cj0Yjp06cH+xJCShAE3DTMiNXTMlGYIj1VdorAoz83YMaXVagIMPWtO7ZV2THx08qAm3+XF+iwbkYmN/+IqMOUCgF/Hp2Ezy9IR65ReoPa5BRx9+Y6XLu6BjXyk45uWnfIinGfVAbc/LtlmAFfX5TBzT8i6jCtSsC/Tk/B+5N7IV0rXdbW20XMXVODO76rhbm5/cnSnfVJWRMmfFrpt/mnEIC/npiIj6ekc/OPiDosKUGBVyak4eUJqUhUSw9fD1tcmPllFf7+Uz2aXcHL3RFFEW/sNuOclZV+m38aJfCfM5Lx9qQ0bv4RxZGgZwD2JE0OEQ/9VI9Xd/mfOqdqBDx3ZiouyuveqbMoinipyIyHf6qHfO2qVwl44oxkzB4YmiwdIooPgU6afdXbXfjjpjp8uM//gKGvXoGXJ6RhfJ/unTo7XCIe39aIp35thPyHTkqCgBfHp+LC3NBk6RBRfDheLDtqcWL+hlqsPuR/wDAoWYXXJqbihF7dOyxtcoh44Id6vFHsv/brq1fg1YlpOLM3y+SIqG3Hi2X7GtzZxVur/LOLT0pX47WJaShI6l57gzqbC3duqsUnZf6trQYlq/DGWWkYmcYDWaJ4o7z//vv/HumLiFVqhYDz+mlxQi811hyySRpRW53A8n1NONrkxIQ+Gqi7UBJcbXXi+nW1eGWXGfLDnsJUFT6ako5J2Vpu/hFRu2pqatCrV682X9cqBUzP0yLPqMTaQzbJYUNjs4j39ljQ7BIxtrcGyi7Em4MmB678pgbvB8hgHpOVgI+npOPkDN4wE1H7jhfLjGoFLh+gQ6JawIYjNsnaqcbmwjslFuhVAk7JSOjS2qm4rhmXfFWFrw76bzCen6PFh+f1wpAQ908loth3vFiWqlHg6kF6uEQRW2T9mg9b3LGst16BEWnqLsWyn47ZMfPLKvxQ6b/BePVAPd45Jy3k/VOJKDKYARgkh1tOndcGOHUekqzCa508RfnuiA03r6vBIYt/ycqNQw147NRk6MI4qZOIYtfxTpp97a134Kb1NdgW4NT5lAz3qXN+YscXhZ/tb8KCjbWok00dFgDcc0Ii7h+dCFUYJ3USUezqTCz7pdqOm9bVoiRAT6tzsjV4cVwqsjpYossBbEQUTJ2JZesP2zBvfeB7wssKdHhqTAqSEzpWotveADaDSsB/OYCNKO4xAzBIEtUKXDFAB4NKwIbDNviG6GqbC++UmJGYoMDJ6e2f1DhdIp78pRELNtahQTZ+PSlBwCsT0nDHyMQuZRQSUc90vJNmX2laBa4eqIfdKeL7Sump86GWU+dsgxIjjnOgYXOK+OsP9Xjgxwa/gUlZOgXeOacX5gw2QMEbZiLqoM7Est56JWYP1ONYkwu/1kgPNPY1OrF0rwXDUtUYcJwyuga7C7d/V4f//Grya8XSP1GJD89Lx7R8HTf/iKjDOhPL8hJVuGqgHnsaHH4HGkW1Dizf14STM9TINrQfy441OTF3TQ3eKLZAvpU4Kk2Nj6f0woQ+nFhOFO+4ARhEgiDg9CwNzu2nxYbDNtT6ZLw4ReCbChu2VdlxVl8NDGr/k5rDFieu/rYa7+5p8uuRdWqGGh9PScfpWSyTI6LO6cxCE3APCDk7W4szshKw9pANJp9jYrsLWLnfitJGByb20UCj9L/p3VPfjMu+qsbn5f59Zc7J1mD5eekoZF8ZIuqkzsayBKWAC3N1GJqixppDVth8DiMsDhEflDahzu7C+D6agJnI26vsuPjLKnwnK8ED3APY/je5F/I6kRFNRAR0PpbpVApc0l+H3jol1h+2SbL36u0i3t1jgQLAGZkJAQ9W1x2y4uKvqvFbjX9G9C3DDHjz7DRk6ji0iKgnYAlwiJiaXbjv+3q8U2Lxey1Tp8DC8amYlO09Zfmq3Ir5G2pRbfNP775rpBEPnJTErD8i6pLOlJrIVVudWLCxDl8E2MzLT1TitYlpOCXD21R/6V4L7tlUJ9k0BACVADx0chJuG2Fk1h8RdUl3Ylm5yYE/rK/F5gCbeSPS1Hh9Ympr/z4OYCOiUOpOLNtd14wb19ZgZ63/Zt7YrAS8MiEV/Vr69x1vANsL41IxtZsDK4kotjADMEQSlAKm5uowKFmFtYds8N3XMztELN3bBFOziNMyE/DI1gbc+329ZIgIAKRrFXh7UhpuGGrsUuN9IiKg8yfNvvQqBS7tr0O6VoH1h23wDVN1dhHvlligUggYnqrGXZvq8K9tjbDLbphzjUp8cF46Lu7PG2Yi6rruxLLkBAWuGqCHSgFsOmqX3AxXNrnbG6RrFcgxKjmAjYhCqjuxLF2rxOyBBjQ2u/ymBJebnXh3jwUFSSroVQKuOs4AtlMyWVlG1NMwAzAM9je6T53l/bQA90myvKE0AEzso8HLE1LRu4MNqomI2tKdk2ZfRbXuU+dddf6nzm3Fspn5Ojw9NgUpmo41qCYiakuwYtn3R224aX0tyk1Ov9faimUcwEZEwRKsWPZluRULNtaiyupfQRYolnEAGxHxjiwM8hJV+OyCdPx5dCLksVYemJUC8OBJSVh+Xi9u/hFRVClMVWP1tEzcPNTg95o8lmmVwNNjU/DmWanc/COiqHJ6lgYbpmfi0v7+pW/yWJaUIGDR2Wl4akwKN/+IKKpMydFi44xMnN3XP5NPHsuydAp8PCUdfzspiZt/RD0Y78rCRKUQ8NcTk7Dy/HT0MwTe2OtnUGLlBem454REKBmYiSgK6VQCnhyTgnfPSUNaGxt7Q5JV+PaiTFw3xMAyOSKKSikaBV6bmIoXxqXA0MbG3ikZaqyfnokZ+eyRRUTRqbdeiQ/P64VHT01CgBmTANwD2DbOyMTEABuFRNSzcAMwzMb2dgfgGfnSMetTc7XYMCMTYzjll4hiwIW5Onw3MxMT+0hj1rWD9Fg9LQPDOeWXiKKcIAiYPciA9dMzcWK6NGbdNdKIVRdmIJ9TfokoyikEAbePSMTXUzMwMMkbs1QC8MgpSfjg3F7I4JRfIgJ7AEaMKIpYVW7FmgobxvXRYHoeG0oTUWgEq9dMIC5RxPJ9Tfj+qB0X5Gol082JiIIplLHM7hTx3l4LdtQ049L+OpzOA1kiCpFQxjJTswtLSiwoNzlx5UA9RvJAloh8cAOQiCjOhXKhSUQULoxlRBQPGMuIKFJYAkxERERERERERBTHmAFIREREREREREQUx5gBSEREREREREREFMe4AUhERERERERERBTHuAFIREREREREREQUx7gBSEREREREREREFMe4AUhERERERERERBTHuAFIREREREREREQUx7gBSEREREREREREFMe4AUhERERERERERBTHuAFIREREREREREQUx7gBSEREREREREREFMe4AUhERERERERERBTHuAFIREREREREREQUx7gBSEREREREREREFMe4AUhERERERERERBTHuAFIREREREREREQUx7gBSEREREREREREFMe4AUhERERERERERBTHuAFIREREREREREQUx7gBSEREREREREREFMe4AUhERERERERERBTHuAFIREREREREREQUx7gBSEREREREREREFMe4AUhEFKccLhFfllvx8c97I30pRETdVlJSEulLICLqNsYyIooUVaQvgIiIgu+oxYnLvq7GbzXNALT4t9aEWwqNkb4sIiIiIiIiigBmABIRxZlykwMXrjrWsvnn9vBP9agwOyN4VURERERERBQp3AAkIooje+qbccHnVdjbIN3sszqBf25riNBVERERERERUSRxA5CIKE7sqHFv/h1sI9Pv3RILdvpkBRIREREREVHPwA1AIqI48GOlHVNXHcMxq0vyvErw/l4E8I+t9eG9MCIiIiIiIoo4bgASEcW4dYdsmPllFertouT5awbp8ebZaZLnvjpow7pDtnBeHhEREREREUUYNwCJiGLYqgNNuOKbKpgd0s2/+YUGPHtmCi7K1eLEJGlJ8MM/1cMlSt9PRERERERE8YsbgEREMWpZqQXXrq6BTdby777RifjnaclQCAIEQcAd/aV9/7ZXN2P5vqYwXikRERERERFFEjcAiYhi0FvFZty8rhayxD88emoS/nJiEgTB2/xvRKILM/N1kvc9srUBNiezAImIiIiIiHoCbgASEcWY53Y04q5NdfDdvhMAPD02BbePSAz4MQ+dnCQZCHLA5MRru80hvU4iIiIiIiKKDjGzAfjf//4XZ599NnJycjBgwADMmjULRUVFkvfMnz8fKSkpkl+TJ0+WvMdms+Hee+9FQUEB+vbtiyuvvBIVFRXh/KMQEXWJKIr457YGPPhjg+R5lQC8OjEV1w0xtPmxBUkqXD9U+vqT2xtQZ3O18RFEREREREQUL2JmA3Djxo248cYb8eWXX+LTTz+FSqXCzJkzUVtbK3nfWWedheLi4tZfH3zwgeT1v/zlL1ixYgVef/11fP7552hsbMSsWbPgdMqaaBERRRGXKOIvP9Tjie2Nkuc1SmDJOWm4rEB/3M/x5xMSkaj2pgHW2UX836+N7XwEERERERERxQNVpC+go5YvXy55/PLLLyM3NxdbtmzBBRdc0Pq8RqNBVlZWwM9RX1+Pt99+Gy+88ALOPvvs1s8zcuRIrF27Fuecc07o/gBERF3kdIm4c1MdlpRYJM8bVAL+N7kXJvTRdOjzZOiUuHNkIh772ZtBuHCXCTcNMyDHGDM/DoiIiIiIiKiTYiYDUM5kMsHlciElJUXy/ObNmzFw4ECcfPLJuOOOO3Ds2LHW17Zv347m5mZMmjSp9bl+/fphyJAh+P7778N27UREHWV3irhxXa3f5l9KgoBPzk/v8Oafx63DDeij94Z+mxP45zZmARIREREREcWzmN0AvP/++zFy5Eicdtpprc9NnjwZCxcuxCeffILHHnsMW7duxfTp02Gz2QAAlZWVUCqV6NWrl+RzZWRkoLKyMqzXT0R0PBaHC7O/rcbHZU2S5zN1Cqy8IAOnZCR0+nPqVQr85cQkyXPv7bHgt5rmbl0rERERERERRa+YrPn661//ii1btuCLL76AUqlsff7SSy9t/f3w4cMxevRojBw5El9++SWmT5/e5ucTRRGCILT5eklJSXAunIiog0wO4I9FGmxrUEqez9K48EJhEzTVJpRUd/zz+caxUwEU6LUotbjPgEQA9647jOdG2IJw5USxq9ImoMQsYHiiCynqSF8NBcI1GRHFA8YyIgqFQYMGtft6zG0A/uUvf8Hy5cuxYsUK5Ofnt/vePn36oG/fvigtLQUAZGZmwul0orq6Gunp6a3vq6qqwtixY9v8PMf7SyQiCqYaqxN/+Loa2xqkWXkDkpT4eEpWp/v1lZSU+MWxf+msmPWNdwdxS50SB/U5ODtb2/ULJ4phW47acPU31ai3i8jWK7HignQUJMXcMimuBYplRESxhrGMiCIlpkqA77vvPixbtgyffvopBg8efNz3V1dX4/Dhw61DQUaPHg21Wo01a9a0vqeiogLFxcU4/fTTQ3bdREQddcTixNRVVdhWJd38G56qwqoLM4I2rOO8fhqM6y0tIX7opwa4RDEon58olhxrcuL6tTWot7v//VdYnFiwsZbfD0REREQUN457J1leXt6lT5yTk9Olj2vLn/70JyxduhRLlixBSkoKjh49CgAwGAwwGo0wmUx4/PHHMX36dGRlZeHAgQN45JFHkJGRgYsuuggAkJycjGuvvRYPPfQQMjIykJqaigceeADDhw/HWWedFdTrJSLqrP2NDsz8sgr7Gp2S50/JUGPZuelI0QTvzEYQBDxySjImrfQOSvqtphkflDZh1gB90L4OUbRziSJuWV+Lw/+fvfsOb6ps/wD+PdlJkzRNJ5QNZa9CC7KhLAGRPRQFF6Ko+Kq/V0Vlq7iVoYAi+qIiWwTZe28oe5Qt0JWmzd7n/P4oNDlJS1d2n891cZWeJvQAzclz7uceRpp1/HC2FT9eMuCVptIAnRlBEATqRMPmAAAgAElEQVRBEARBeE+pAcCWLVs+sj9eSdRqdYVOqCSLFy8GAAwaNIh1/L333sPkyZPB5XJx8eJFLF++HBqNBvHx8ejSpQt++eUXyGSyosd/+umn4HK5eP7552E2m9G1a1csXLiQ1UuQIAjC364W2DB4qwr33YIQXRIEWNYrGjK+9xO228QKMLSuGGtvOoeMzDqpxaDaYoh45b/uE0Qo+uasHrvuF9//csYJLfrUEJFSYIIgCIIgCCLkUQUFBY+sb/njjz8qFAB8+umnK3xSBEEQVcmZPCuGbcuDyswO/j1eU4RfuysrHYx7VK+Zm1o72v2VDZvLt56VIscbLWTFPp4gwsn+TAsGbVWBfsRKqGO8AP/0iwGnAmshwrtI3yyCIMIBuZYRBBEopW5pjxkzxh/nQRAEUSUdzbZgxI48aK3sCMTwemIs6BIFPse3QYe6ch5ebByBhRcNRce+OqvDMw0jEOXFkmOCCDY5Jgde2qtmBf+ihRy82kyKj09pi44dyrbip0sGTCClwARBEARBEEQII3d3BEEQAbL7nhlDtnkG/8Y1lGCRH4J/D/23lQxyvvN7aawMvjmr88v3JohAcNAMXt6Xj2wTO+t2UdcovNNSil6JQtbxGSe1uKm1+/MUCYIgCIIgCMKrKtzU5tixY0hPT4dGowFNsxfQFEXh3XffrfTJEQRBhKt/bpvwwh41rOzLJ15vJsWsVHmFWi9UVLSIi7dayjDjpDPradFFPV5qHIHaMtL7jAg/X53VYY9b3793WkrRq4YIAPBdRwU6rsuB1lYYnDfaGbx+MB8bHielwARBEARBEERoKvednUajwejRo3H06FEwDAOKosAwhQvkh78nAUCCIIiS7b1vwbjdajjc+o59kCzDf1vJ/Br8e+iVplIsvmTAPWPhBGIrDXxyWosfuyr9fi4E4Ut771vw2Wl2hmvHeAEmJ8uLPq8h5eHjdpGYdLCg6NjBLCt+vmzA+CakFJggCIIgCIIIPeUuAZ4+fTpOnz6NhQsX4vTp02AYBmvXrsXJkycxduxYtGzZElevXvXFuRIEQYSFaSc0HsG/2e0i8W5r/2b+uRLzKHzQhj34Y+V1E87kWQNyPgThC9lGB8bvU8P15Rcj4uDn7krw3Erun02SIK06uxR4+gktbulIKTBBEARBEAQResodANyyZQvGjh2LkSNHQi4v3C3ncDioV68evvvuO1SrVg0ffPCB10+UIAgiHJxWWZGeZyv6nAIwr5MCrzYLfFbR6PoSNI1iJ4ZPPa4tyvImiFDmoBm8tFeNHJe+fxSAH7tGoZqE6/F4iqIwp5MCMpf+mAY7g9cP5IMmrwmCIAiCIAgixJQ7AKhWq9G8eXMAAJ/PBwAYDM7pkb1798aOHTu8dHoEQRDh5ZcrBtbnvRKFeLZhRIDOho3LoTAzJZJ1bG+mBbvceqURRCj64owO+7PYGa3vtJIhLVFU4nNqSnn4OJX9mjiQZfV4HRMEQRAEQRBEsCt3ADAuLg45OTkAAJlMBplMhoyMjKKv5+fnw+FweO8MCYIgwoTGSmPNDRPr2HONgiP491DPRCG6VmOXPU49roGDJhlPROjac9+ML9LZff86JwgwubWshGc4jW0oQY/q7q8JLW6TUmCCIAiCIAgihJQ7AJiSkoKDBw8Wfd6rVy/MmzcPK1aswJ9//okffvgB7dq18+pJEgRBhINV140w2J2BtOoSDvrWLDn7KBAoisLMFDnr2IV8O1ZcNwbojAiicrKMDozfm8/q+xcr4mBxNyW4nNJ7blIUhbnFlAK/cbCAlMcThI/ZaQbLMgx4aa8ac87poLPRpT+JIAgiyFgdDDI0ttIfSBA+Vu4A4Msvv4ykpCSYzWYAwKxZs6BUKvHKK69g4sSJUCqV+Oyzz7x+ogRBEKGMYRgscSsbHNswwmPwQDBoHSPAiHpi1rFPT+tgspNgBxFa7DSDF/eqkWtm9/1b3C0KCcX0/StJTSkPs9xKgfdlWvDrleANjB/LseCDYwXYcNtU+oMJIsgwDIP1t0zouC4HEw8UYPUNE6ad0CJ5dTZ+vKiH1X2SFkEQRJC6q7ej/+ZcPLFZhWwjqZQkAosqKCio9DsowzA4f/48uFwuGjZsCB6PV/qTCIIgqpBjORb02agq+pxDAedGJCAxouxBiIrKyMhAUlJSuZ5zS2dHu7XZsLokW8xIkePNFqWXTBJEsPj4lBZfnWGX/r7XWobJyfISnlEyhmEwZFse9rj0xJTyKBwaEoda0uBa9yy8qMf7RzVFn/+vhxKD6ogf8YzQUJFrGRF69mVaMOOEBidVJWfL1JVxMbWtHIPriEFRwbeRRhCPQq5lVceue2a8tDcfakvhgrpTggB/940JygQAomoodwZgcSiKQosWLdC0aVMS/CMIgijGksvs7L/Ha4r8EvyrqDoyHsY3YU8m/vqsDmoz2bkkQsOue2Z87Rb861pNiHdbVSyITVEU5nRUQMpzLtr1dgaTgqgUmGEYzD6tZQX/AOCrM7qgOUeCKMmZPCuGbVPhyS2qRwb/AOCmzoHn9+Sj5z+52J9JBlURBBFcaIbB5+laDNuWVxT8A4CDWVbMPa8P4JkRVV25o3Wu/f8epVOnTuU+GYIgiHCUb6Gx7ha7DO+FIBv+UZz/ayXD7xkGaKyFgQOtlcFXZ3X4tJ0iwGfmGw6aQYGVhtkBVJNwwCFZJSHrvsGz71+cmIOfukaVqe9fSWrLeJiZGom3DxcUHdtz34KlV40YF+DXNM0weP+oBj9e8pxQfE5tw/FcK9rFCYt5JkEE1g2tHZ+c0mLNzeLL1TkU0LuGCAcyLaw+ugBwSmXDwC0q9KkhxLS2kWim5PvjlCssQ2PD4Wwrakt56FadvB4JIhzlmR14eV8+dt7z3JzoW1MUEvcARPgqdwDwiSeeKFOqvVqtrtAJEQRBhJs/rxnhmjhXS8pFWmLwL/yjhBy801KGqSe0Rcd+umTAy02kqCML3mxvhmGgtzNQm2nkWwp/qR/8Yv3ezD6msTJFAaOGkTys7B0d1H9PongP+/7luey4cyhgcTcl4svR968kzzWSYN0tE/a5ZB19dFyDtEQhagaoFNhGM3jtQD5WXi+539/Plw0kAEgElWyjA1+e0eHXKwaU1GJ2QC0RprSVo7GCj2yjA188eLx7C8Btdy3YfjcHTzWQ4INkGWoEUVm+xkpj3U0T/sgw4liutej4682kmJUqJyXMBBFGTuZaMW63GncN7IoZDgV81EaO/7SQkg1mIqDK3QPwwIEDHsccDgdu376NX375BRRFYdq0aejWrZvXTpIgCCJUMQyDdn/lIENjLzo2ta0cb7f0Xy+9yvSaMdsZpKzNZi1khtcTY3E3pbdOr0wYhsGFfDtu6uyFATszO5j3MJD38Jg3BkU2jORh24BYKIRe6ZZB+MnMkxp8c5ZdXjM5WYb3Wpe/719Jbuns6LQuh5WNlFZdiDV9ov1+M2+yM3h+jxpb/jWzjgu5gMXl/kPAAS6NSkC0KHhbD5SG9M0KDxorjXnn9Pjhoh7GEiJ/nRIEmN42EqlxAo+vXdPYMPOkFutvm4t5JiDiAhOaSPFWS1nArt80w2B/pgV/ZBix4bYZphKGlnyQLMO7Xrw2EaGBXMvCD8Mw+PmyAZOPaTzWoDEiDn7upiRZv0RQ8MoQkIccDgf69OmDXr16YfLkyd76YwmCIELWgSwLntjsHP7Bo4CLoxIQJ/bfTXhlF5rLrxnxyv581rHdA2ORHON5Y+ZtehuNVddNWHxZjwv59tKf4GXdqgmxuk80+KRZc0jYcdeM4dvzWMe6VxdiTe/oSpX+FmfxJT3+7wi7197cTgqMbei/0h6tlcZTO/NwMMvKOh4v5mB1nxg8szMPt/XOKODMFDkmhfAgH3LTHNrMdgaLL+vxzVk9qyeWq+ZKPqa1laNXorDUYPqxHAumndDicLa12K8rBBTeaSXD+MZSiHj+uYbf0tnxR4YRf14zemQAleTz9pGY0FRa+gOJsEGuZeHFYKPxn0MFWHXDMwv/sTgBlnRXonoQ9/0mqhavbotxuVwMHz4cv/32mzf/WIIgiJD1i9vwjydqi/0a/POGkfXFaO7WV2nKcY1PhwpkaGx470gBmq7IwluHC/wW/BNz2TeJezMteOdw8Ax5IEp2z1DYc8dVghf6/pXkhcYR6JzADoJ/dEyDu3r//KyqzA4M3KLyCP7VlnKxpX8sWij5eKExOxi55IoBNPlZJvzMQTP4PcOAlLXZ+Oi4ttjgX20pFz91jcK+J2PRu4aoTJm07eKE2NQvBst6KtEo0rPkt8DKYMpxLVLWZmP5NaPPfvb1NhrLMgwYsDkXrVdn48szukcG/4RuS4D3jmrw5zWjT86NIAjfulpgQ89/cosN/k1sFoEN/WJI8I8IKl5vkGE2m0n/P4IgCAC5JgfW32YvCJ4Pwca/HIrCzBQ5hm5zZlYdyLJi+10L+tQUee372GkGm/814+fLBuy5X7mpjiIuoBRyECXkQCnkQCniIErw4OODY0Vfe/B1hYADOw08uUXF6tO09KoRSXIe3gjhzKlwZ6cZvLhHzQoscChgcXclYn0UcOdQFOZ3jkLHdTlFZYxaG4M3DxVgdW/flgL/q7dj6LY8VmsBAGiq4GFN3xhUe9DrcEySBJ+c0sL64J/lls6BXfcs6FXDe69bgigJwzDYeMeMj09pcbmg+MB4rIiD/7aS4blGERBwy/+aoSgK/WuJ0aeGCMuuGTH7tBaZRnaA8a7BgVf252P+BT1mpMiRVr307MLSMAyDw9lW/HHNiL9vmqAvqYnhAzUiuHg6SYKnG0iQbXRgyLY8Vvnz6wfyIedTGFBbXKnzCkc775mx/a4ZHAqQcDmQ8CmIuRQiHnyU8Jy/xDwKETwOxC7HKvJzRRBl8ddNI944UODx+pfxC9cHg+qQ1zMRfLwWANRqtdi/fz/mzZuH5ORkb/2xBEEQAAonak0/oUWBlcY7LWVo7Yfy08pads3I6gNSX85F12rBf97FSUsUoUd1IXa7BOamn9CgZ6Kw0tlVOSYHll414tcrhkdmTXAooEuCEIkR3GKDd0qR85i4guVePA6wrJcSPTfkskonp57Qoo6ch4Hk5iwofXxKiyM57Ey4D5Ll6Jzg2347dWQ8TG8rx7tHnaXAO+9Z8HuGEc/6qBQ4Q2PDkK15Hq+V1Fg+VvaOQZRLz7MYEReD64pZw0F+vmwgAUDC5w5mWTDjhJa1meJKxqfwRnMpJjaTQsqvfEESj0NhbMMIDK8nxoILBsw5p4PWxr4pP6+2Ydi2PHSrJsSMFHmF1hF39XYsv27CsgwDbugeXeIr5lIYWEeEMQ0k6FJNWNT4v46Mh9/TlBi1I69ojeBggOf3qLGqdwzpE/YAwzD49pweM09qS3/wI/AosAKEzoAh50HA0Hm8royHpxpIwqL3r41mSPsSH7E6GEw5rsGiSwaPrzVR8LA0TYmkyOCeSE5UXeXuARgVFVXirhnDMKhVqxaWLVuGZs2aeeUECYIgHDSDvptycSLXBqAws+vksHjWjW6woRkGbdZk45bLDcKsVDneaO7/LDJv9Zo5k2dF9/W5cH3TmN9ZgWeSyh/oYBgGx3KsWHzZgHW3TI8c2hEj4mBcQwmeaxThtymrlwts6LMxF1qr828r5lLY3D8mJILPVcnWf80YtYPd9y+temHvRn9M2qMZBk9sVuGQSx8yOZ/C4SHxSPRy2U+6yoph2/JYE44BoEd1IX5LUxYbSDmabUHfTc4+pBwKODM8PmATiyuD9M0qO4ZhkGOiwecACiHHb1Mnz6ltmHlCg+33is/iFnCAFxtH4J1WMsT4cCBNnrlwwvDPlw0lvr8MryfGR23kpU57N9kZbLxTOMV3z30LSrtxahcrwJgkCQbXFSNSUPI65e9bJjy/Rw3a5Q+M4FH4+/EYpMRW7fcZhmEw/YQWc87rS3+wlz0WJ8DGfjE+aR3xkC+vZRfzbRi9Iw9ZRgdebiLFjBS5T/8uVc09gwPP71YXu7kxsr4Y33ZQIMILmxoE4SvlDgDOnj3bIwBIURQUCgXq1auHtLQ0cLmkzp0gCO9ZcEGPycfYzfYnNZdiZmpkgM6odLvvmTHEpWRWyAUujgzMBE5vLjQn7FNjhUs2UXUJByeGxUPCK9tix2CjsfqGCYsvG3BObXvkY9vHCfBS4wg8WUcMYQBKePbcN2PYtjy4Dm9MEHOw44lY1AjB4Ek4uqu3o8v6HORbnP9J1SQc7B8U59PggrubWjs6rsthTfrsnSjESi+WAh/IsuCpHXnQuWU1Daojwo9dlSW+RhiGQZf1uTjv8nr7v5YyfNQ29CaPkgBg6RiGwfrbZsw8qcF1beEGFJcq3DiLEXEQLeIgRsR1+f3D4w+OCQuP88oZMLils+PTU1qsumEqNkBGARjdQILJyTLU8uP185bOjo9PabG6mP5cAMB/EJD8bysZ6/2ZYRicVNnwR4YBa26aWJtBxakm4WB0fQmeTpKUK/Pnt6sGvHGwgHVMIaCwqX8smkZVzQwiB83gncMF+PVq4Poi/tBZgacrsLlZVr66ltlpBp3W5eCKS3uIoXXFWNQ1imQDesGe+2a8uCffYxNOwAE+a6/A840kPm3/QRDe4NUpwARBEN52R29Hh79yYHDrryHiAieHJXg9w8Zbnt2Vhw23zUWfj6wnxo/dlAE5F28uNO/o7Uhdmw2LS+XT1LZyvN3y0ZmN1zV2LL6sxx/XjI+8kRJzKYyoL8ZLjSPQMjrwGRD/u2LAm4fYN2fNlXxs7h8DGdnhDSgbzWDAJna/Ri4FbHg8Bh19XPpbnIUX9Xj/KHuj4vvOCozxwk3k5jsmPLdHzXrdAcDYhhJ820FRanbHL5cNeOuw8+c4TszB+REJIdcbiwQAHy1dZcXkY5oSp+KWh0JAPTJQ+PC4mEvhp8sG/Hql5Ey7fjVFmNJWHtCAVrrKiqkntNiXWXxmopxP4c0WMgyrJ8b6W4XZflc0jx7oI+AA/WuJMSZJgh7VheUOmj40/7wOHx1nl7kmiDnYMiC21OzEcGN1MHhlfz7W3mQHbGUP/n8cDAOTnYHBXvjR6PLLZKeLjj/8msHOsDbxyipRwsWJYfEVbidSGl9dy5ZcNuDtwwUex/vXEuGX7iVvFBGPRjMMvj6jw6endR4bHDWlXPyvuxJtqnjWLhE6SACQIIigxTAMRm7PK7GU6NkkCeZ1jvLzWZUu0+hA85VZrEXn5v4x6BAfmL4+3l5oTj2uwVyXshwZn8Lp4fEeGVcOmsHWu2YsvmTArlKGetSXc/FiYymeDsLeO1OOazDPrQypbw0hlvWMJmU1AVTc/8u0tnK8VUow2ldohsGAzSpW8EUuoHBkcHylJgCuuG7ExP35HjexbzaXYnqKvEzZBnobjSYrsljZg0u6RWFoPUmFzysQSACweFlGB2ad0mJZhrHU8lR/6hAvwLS2cjwWoPc+dwzDYOc9C6ad0FRqsnzraD7GJEkwvJ7Ea61IPj6lxVdndKxjtaVcbBkQWzTUJ9wZ7TTG7VJ7rPmihRys6RNd4fYbVodrkJCBwU57BA+1NhqTj2qKBiYBZdvcrChfXMu0Vhpt12Qj11x8JL5nYmGriLJWbBCF8i00JuxTY9tdz3Vs70QhFnWNgjIA1T0EUVGlBgBfe+218v+hFIX58+dX+KQIgiAAYNV1I8bvyy/x6xwKODQ4Do0VwVUm82W6Fp+cdi7kmyh4ODQ4LmBlAd5eaBZYaLRenYUCl0y+CU0i8PljCgCAylw41GPJ5dKHevStIcL4JhHoXl3otx5V5eWgGYzdrcbGO2bW8VeaRuCz9ooAnVXVtvmOCU/tVLOO9U4UYkVv//T9K8l1jR2d/2aXAvetIcTyXhUrBV50UY/33LIKAWB6Wzn+U84b0/8eLsBPl50NyzslCLCxX2y5zymQSACQzWxn8MNFPb45oyt2Ci2XKuwp5z4Mw9eaRvEwrW0k+tSo/LRdX3DQDFbeMOGTU9pHvke5ihFxMLK+GGMaRKCZ0vtrDoZh8O5RDX5yGyrQRMHDxn4xYR9g0FhpjN6R55G9Wl3Cwbq+MWjoh3XeR8c0mH+h9M1Nb/DFtezjk1p8dVb3yMd0ThBgea9orwzeqQpOq6wYu1uNf/Xs6wQF4INkGd5pJQvatStBlKTUAGCLFi083rxNJhNUqsKG0gqFAgzDQKMpXKDGxMRAIpHgzJkzPjplgiCqgjyzA+3W5rD6bHSIF0BlppHhUpYzoJYIf/SMDsQpFstBM2i1Opt1U/F5+0hMaCoN2Dn5YqHpXrLEo4Cfuyux8Y4J626aWLvo7qKFHIxtKMHzjSP82guqMgw2Gv03q3Amj9238MvHIjG+SeD+b6uiO3o7uv6dwwpAJ0q42DcoNiA9Nt39cEGPD9x6li7oEoWnGpQ9245hGHyersNn6eybOQrAdx0VGNeo/GXFl/Jt6LAuh3XsyJDg20B5FBIALMQwDP6+ZcaUExqPG9OH+tYQYlZqJBoq+LA4GOSZaajMjgcfC389PKYy08izPDzuYPXULI9aUi4+SJZjRD1xSGRHm+0Mfrqkx9dndazryUM8CuhTs3CKb5+aIp/3UKMZBq/uz2f12QWANjF8/P14+LadUJkdGLYtz+P9tZ6Mi7/6xqC2n8qgi9vcfLlJBL54zPsbfd6+lt3V25GyNhtml8vBC40icCDLgqtupeztYgVY2Ts66KotggnDMPjlihHvHy3wWM9GCzlY3C0KPRJFgTk5gqikcpcAZ2RkYMiQIRg1ahQmTpyI6OjCG++8vDx8//33WLlyJdatW4cGDRr45IQJgqga3IdNCLnAgUFxuJhvx7jd7MyfbQNi0C4uOEqMtvxrwugdzvMTcylcGpUQ0IWWL26aLQ4GKWuzS7z5LE5qLB8vNZFicICGelRWptGBnhtycN/oXA1yKGBFr2j0rkEWgv5gdTDov9k5ERwozHLa2C8maMoMHXRhKfCRHGcmS6SAwpEh8WUq5aMZBpOParDILROIzwF+7BqFIXUrXrbbb1MuK8NmfJMIfOmDm1tfIQHA0vv8NYrk4dP2kehZiZtTO80g31JCoNDsDBTmmWmoLDTixVyMSZLg+UYRIXltL7DQ+OasDj9e0sPsAJoqeHg6SYJR9SWIFft3U8FGMxi7S43N/7IzzrskCLCqdwxEPupJFyh39XYM3ZbnEaRqFsXD2j4xiPdz+XNxm5tHh8SjfqR3g5Devpa9sk+N5S5r5jgxB6eGxcNkZzB4q8qj5L1VNB9/9YkO+8zSijDYaLx1uAArr3sODkqN5eOX7koyCI4IaeUOAA4ZMgSJiYkllvi+9tpryMzMxNq1a71yggRBVD077xVOX3U1pY0c77SSgWEY9PonFydVzgBAh3gBNvWLCYpSo1E78rDVZeE+JkmC7wPcp9BXN80rrxvx8iNKtIHCYS3D60nwUuOICvfvCSZn86zot0nFGkoj5VHYMiAWzX1QFkawfXhMg+8vsPv+zUiR480Wgen7V5JrGhs6/53DysboW1OE5T2Vj7xO2WgGrx/wzACS8Cj8lqasVFAHANbcMOLFvc7XrJxP4eKohJApB6vKAcDS+vxFCSl8kCzH840iKjyMoqoz2GgUWBlUl3ACup4w2xmM2K7C/ix2kLd/LRGW9lCGzf/vdY0dg7aqPMqwA5mhZnEwaLc2G7ddNjefrC3C0jTvVpp481qWrrKix4Zc1nVhjkumeL6FxtBtKpxWsTMsmyp4WPd4DOL8HOQOZhkaG8btUuNigWeP0AlNIjArNTLkhmcRhLtyX1mPHTuGNm3alPj1Nm3a4NixY5U6KYIgqi6DjcZbblNXm0bxMKlFYZklRVGYnhLJ+vrhbGuxzXn97V+9HdvvsnftX6hAqV6oGF5PjJYlBL3qyrj4OFWOS6OqYX7nqLAI/gFAy2gBlnRXwvX+S29nMHpHHrKNZc+GJMpv422TR/Cvbw0h3mgefCXYDSL5+KiNnHVs679mrLzhmVHwkMnO4Nldao/gX6SAwrq+0ZUO/gHAwNpixIqcSz+tjcGaR5wTEXhme+H0ybZrsvFHMcE/HlXYj/T0sASMbyINm+BQIETwOUiM4AZ8M1HEo7CsVzTaxLDfXzfdMeO1A/mgmWAa9VIx59Q29Nuc6xH861FdiL/6Bq48VcilMLUt+9q9/rYZR7MDv8YsDsMwmHJcw7ouNFXw8EySM1M8SljYR/GxOPY67GKBHf03qXCvjH0ww93ft0xI25DrEfyL4FFY0i0Knz+mIME/IiyU++oqlUpx4MCBEr++f/9+SCTBP1Vu8eLFaNmyJeLj49GtWzccOnQo0KdEEASAT0/rcMdl55VDAfM6RbF673SpJkSvRHa534yTGjjowC6Kl141wvUUWir5Hgv4cMKhKHzdQYGHFSQUCrOcVveOxslh8Xi9ucxrExKDSd+aInzajh2Evmtw4KmdeTDaH9H8kCiV7UHp4R29HRfUNhzNtmDHXTNWXTdi4gF2tmmNCC4WdIkK2gbcrzaVol0s+4brvSMFyComUKy10hi+XYUtbmV/8WIONvWL9VqLAwGXwtiG7DXa4ssGMGEQUAg3DMNg3U0TUv/KxqxTWlbW8UN9awhxaHAcPmuvIP28woyMz8Hq3tForGCXGq64bsJ7RzUh/Zo9mm3BE5tzkWNiv18OrC3C8l7RiAhwRvKQumIku63dphzXBuW/+da7Zo9M0ZmpkR49OCMFHKzuE42u1djvJde0dvTflItbuopPxQ4HX6ZrMW63Gjq3gUmNInnYNTAWQ+sFf2yDIMqq3AXso0ePxty5cyGTyfDKK68U9fq7du0aFixYgPXr1+ONN97w+ol609q1a/H+++/j66+/xmOPPYbFixdjxIgROHLkCGrWrBno0yOIKutUrhULLrIzfCY0iUDbWM/ssWkpkdh5L6do1/Nivh0rb5jK1cYij2IAACAASURBVGjfm2w0g9+usnt2Pd8oIuCZBL6WGifAkSHxOK2yok2MwG/NugNtQpMIXNfYWVNVT6lseGVfPn7toQzaoJSvFFho6Gw0dDYGOisNvZ2BzsoUHdO7fSx6nK3wMQ8/msuYiMCjgCXdo4K6fxGXQ+H7Lgp0/jsHlgd/rwIrg7cOFWCZSylwSQ3wa0u5WNc3BnXl3n1NjWsUgW/O6ouunefUNpzItSE1LjyydMNBaX3+Git4+KRd5fr8EcFPKSocgvH4xlxWSepPlwxQCDj40C3LOBTsvmfGmF1qGN0C2k83kGBuJ0VQZLByKAqzUiPxxGZV0bFjuVasv23GoDriAJ4Zm51mMNWlXyFQmEHZM7H4DSMpn4MVvaIxbnceq2rmtt6BAZtU+PvxaDSIDN9N65L8ddOIT057Tk8eXk+M7zoqQqZFBkGUVbl7ANpsNrz55pv4888/QVFU0QKWYRgwDINRo0Zh/vz54PGC9yawZ8+eaNasGebOnVt0rE2bNhg0aBCmTZsWwDMjiKrLRjPovj6H1ai4ppSLw4PjSnzzfXmvmlVSV1PKxYmh8QFpQr7+lgljXYaTSHkULo1OCIqpfVW5b5Yv2enC0t8d99ilQW+1kGKaW5l6uLqjt+PpnWqcV9tKf7AXzUqV443mwdX3ryTzzukw5QT7Ju2nrlEYUV+Cu3o7hmzLY002BwpLuNb0jSnT0JCKcO9VOqq+GIu6Kn3yvbwp3K9lmUYHZp3U4s9rpM8f4XRTa8fjm3KR7ZYx90m7SLzWLPhaIJRk/S0TXtqr9piq+krTCHzaLjLoNs5G78hjZWXXk3FxZEi8V8pAvXEtW3LZgLcPO1vmUAD2DYpDi1L6EVsdDF7cq8aG2+yM8zhxYalw06iqEwS8oLah98ZcVkCazwE+bReJlxqH/yY+UTWV+86Uz+fjhx9+wMGDBzFlyhSMGzcOY8eOxZQpU3DgwAEsXLgwqIN/VqsV6enpSEtLYx1PS0vD0aNHA3RWBEHMO6/3mFL2bYdH77x90EYO1y//q3fg58uGEh/vS79eYX/fkfUlQRH8I3yHx6GwpLsSTd1KtL49p/fIBg1HDMNg4v58vwf/+tcS4fUQuumd2EyK1Fj2DdW7RwtwIMuCxzepPIJ/qbF8bOwf67PgHwC81Jjdm3TdLRPUZU2/JLzO9KDPX8qabCwrJvjHo4BXSZ+/KquunIe/+sZAIWD/v394TBMy7zV/ZBjw3B7P4N/7rWWYHYTBP6BwwJRrrO+GzoFfrgTHv7fWSmP2afbG0pgkSanBP6CwFcQv3ZUYUY+dzZhjovHEZhXSVcVnHoebAguNZ3bleQT//uobg/FNpCT4R4StcmcAhrrMzEw0adIEGzduRKdOnYqOf/7551i1ahVOnDjh8ZyMjAx/niJBVDm3TRSePiWClXG+2faLtWNmo9IXIV9d52NFpnPBE8ljsC7FBKkf9yHumigMOcleSP3e2oRG0ip1ea2yMs0Unjsjgtrm/PnlUgzmN7MgRRG+PQH35XHxziXv9KZ7iAKDCC4g4TKI4AERXOfnEi6QFEFjRDU7BCEWW79lpDDmNPsaV5z2Cge+aGKBD2N/AACaAYacEOG+xfkPOamOFc/WqNp9oPyNYYAdKi7m3eIj01L8D3XnKAferGtFHQl5P6nqzus4mHhOCBPtvI5wwGB2YyvSYoI3gP/nPR6+uenZYuDtulY8lRjc15zZ1/hYmxXYNWZxfrjFxy93necl4jBY09aMOGHZrxMOBph9TYC/s9l/GSmXwZxmFrSUh+/6xcEAb18U4lA++812cgMLhiYE72uJIMqitOzi4E3V8zH3qD7DMCVG+sO53IQgAo1mGPxniwpWxhnsixZyML9XDUSXob/XxzUc2Lg6G/oHO3gaO4WNpniPCZy+9McJDQBn78KUWD6eSE702/cvTbiXzQVaEoBVCVYM2Jxb1MPOwVB4/6oYO56IRVIY9tSx0wzGnMsB4Lx5kwsoVJdwIeVTkPE5rI/yh58LKEj5HMj4hR/lfApSvvOYhEeF5a57EoCPODpMdSsFdjWojgg/dlX6rYXBy2Ydpp90ns+GPDGmd48Pykych8LpWkb6/BHllQRAmWDGiO15RZl0NCh8dFWIFbWikRZkPysMw+CzdB2+ucnur1Y43E2BMUkRJTwzeMxOdGDrmuyiITwaO4UNxjhMbVu5Nh+VuZbd1dvx5+Fs1rFJLeXo1LxGuf+sX5IYvHdUg58uOTMb9Q4Kky6KsbxXNLpU8+4mX7D4+KQWh/LZP5fjGkrwXqfgWbsThK+UGgBs2bIlOBwOjh8/Dj6fj5YtW5a6OKcoCunp6V47SW+Kjo4Gl8tFTk4O67hKpUJsbGyAzoogqq7fM4w46DbBbHb7yDIF/wAgVszF682l+Czd+Ub+wwU9xjeOQLyv02gAWBwMfs8wso491yj4F7WEd7WNFWBRVyXGufSB1FgZjNyehx1PxJb55zlULL1qxFWX0lUKwKZ+sWhehvKjquq1ZlKsv23CiVzPkumxDSX4toPCY3KjLz3TUIJPT2uLAgk3dQ7svm8hAScfI33+iMroVl2EJd0L32scD36AbDTwzC41/uoTjfbxwRGwoRkGHxzTYOFFdsmsgAMs7qbEk0E0TONR4iVcTGohxezT7DXmC40iUCNAaYAfn9KyBmbFizmY1LxibTE4FIUv2kdCzKUw97xzI9tgZzBiuwp/9IwOu/eE9bdM+OosO/iXEsvHF48pAnRGBOFfpRbRdOrUCR07dgSHwyn6vLRfHTt29PmJV5RAIEDr1q2xe/du1vHdu3ejffv2ATorgqiasowOfHRcwzrWM1Ho0ZekNK81lyJG5LycGe0MvjzjOdHLF/65bYLK7CyTkAsoDK0bGgtbwrsG1RFjWlt25ulNnQPP7FLD4gif8j2drfjeQyT492hcDoXvO0dB6BYLfrO5FHM6+jf4BwAxIi4Gu92EB6qHalWgsdKYdVKDNqtJnz+icp6oLcb8zlGsY0Y7g5E78vzek7U4dprB6wcKPIJ/Eh6FFb2iQyb499DrzaSIFzvXmGYHip0a6w/pKitWXDexjn3YRl6pSbUURWFGihzvt2YP1jI7gKd25GHjbVMJzww9lwtsmLg/n3UsTszB0h7RARkgSBCBUOrWxYIFCx75eSh67bXXMGHCBLRt2xbt27fHkiVLkJWVheeffz7Qp0YQVcq7RwqgtTpvgyQ8Ct90UJS7BFDG5+DdVjK8e9QZTPz1igETm0lRT+7bHVr3htBP1ZdAwguxBmWE1/ynhRTXtXZWVujhbCveOJiPRV2iwqK8dc45PXJdgt5ibmHGElG6Rgo+FnaJwmsHCsClgMnJckwM4ECTFxtHsCapb/nXjH/1dtQMdIOrMGJ1MFhyxYAv03XIsxTfU6tvTRE+TpWHZbsAwjeeaiCBxkrjfZd1j8bKYOg2FTb3i0X9yMC8hi0OBi/uUeOfO+wJs5ECCqt6R6NdXHBkKJZHBJ+DD5LlePOQc+Lu8mtGTGwmLdPQDW9hGAZTjmtYmwdNFTyMaSCp9J9NURTeT5ZDzKMwzaVVhZUGxu1W48euURhar/LfJ5A0VhpjduYVtQwCCjde/tdDieoR4VWlQRCPUiXvUocOHYrZs2fjyy+/RJcuXXDkyBGsXLkStWrVCvSpEUSVseG2CetvsxeIH7WRo7asYovW5xpFoI7M+QZuZwrLJHzpaoENB9zKl59vTMp/qzKKKgxid0lgNzxfed3kt6xUX7pvcOB7lzIhAHi9uZQsnsthSF0J7j5TDRmjqwU0+AcA7eIEaBblvObSDPC/q8ZHPIMoK5phsOaGEe3+ysb7RzXFBv+aKHhY2ycaK3pFk+AfUW6vNJVicjI7ayvHRGPQVhXuGfw/yEBvozF6R55H8C9OzMHGfrEhGfx7aEySBI0VzmslA2CqWwWLr229a8Z+tzXnzNRIr2aPv9lChi/as/sb2hngpX35WJYRuhniNMPg5X35uK5lvy4+ax+JDkFSNk8Q/lLuAODBgwexcOFC1rFVq1YhJSUFDRo0wHvvvQeaDv6pQS+99BLOnTuHnJwc7N27lzURmCAI39JYafz3cAHrWJsYPiY0qXjwTMCl8KFbFtLamyakq0qfJFxR7tl/HeIFaKwgN3FVnYBL4be0aCS5ZWB8elqH1TdCO7jyyWktTC7lzLEiDia1CGwQKxRxKAoiXuCzQSmKwouN2f9/S68aYA2jkvVA2JdpQc9/cvHi3nzc0nkGYuLEHHzTQYH9g+KCbnADEVrebSXDq03Za6e7BgeGbFVBZfZfELDAQmPIVhV237ewjteUcrE5DPrD8jgUZqSwA2O771uw8565hGd4l51mMPU4e1M7rboQvWp4//rxclMp5nZSwPUdimaAiQcKsCRE20R8nq7D1n/Z/1djkiR4kWzaE1VQuQOAn3/+OY4ePVr0+dWrVzFx4kRwOBwkJyfjp59+8ggQEgRBuJpxQossk3OjgEcBcztFVXoXc1g9sUc5huuUS28y2Rn8eY0dzHmBDP8gHlAIOVjRKxpKIftt9rUD+TiabSnhWcHtvNqGZW4Dbz5IlkNWid5DROCNqC+GjO+89uaYaGy8Ez49n/zpgtqGEdtUeHKLCqdVnr3YIngUJifLcGpYPF5oTIZ8EJVHURQ+aReJp93KQK9q7Bi2LQ/ZRgdoxrcB/WyjAwM25+K424CjhpE8bOkfuHJkb+tTQ+iR3T/luAYO2vcbJv+7avAYvDUztXKTiB9lbMMI/Ng1Cu5t8d4+XIDvL+iLf1KQ2nTHhM/T2RUYyTF8fP1Y+VsOEUQ4KPcV+fLly5g0aVLR5ytXroRYLMaOHTsgl8vx6quv4vfff8fEiRO9eqIEEa7uGRxIrELlc4eyLFjiljn3nxYyr+wOcygK01PkGLYtr+jYnvsW7LlvRvfq3t0lXXfLhAKX/oVKISfkGlsTvlVPzsPvaUoM3qoqmrRqcQBP71Rj58BY1KlguXugTHXrPdQokodnG4Z2TyCisIfqqPoSLHbJ7Pj5sgFD6pL/27K6q7fjk9M6LC9hsi+PKmxT8W5rGeLEVef9nvAPDkVhbicFdDYaG1xaq5zJs6HRiiwAgIxPQc7nQCYo/CgXUJALOIXHBRzI+RRkDz4WHucg0u0xxQ1JuKO3Y/AWFW64Zbq2iuZjTZ9oxIjC5+edoijMSo1E9w25Rccu5tvx53Ujnkny3Qaw1kqzphAD/hm8NaK+BAIuhZf2qmFzKe778JgGJjuD/2slK/nJQSJDY8Mr+9hDP2JEHPzWQxkUWfgEEQjlvvvQarVQKJxjsnfu3IkePXpALi8svevQoQM2bNjgvTMkiDB2pcCGx/7KQVqiEK80laJnohCcMN6NMtsZVhNlAGgg53l1EZFWvXCH1rVPyvQTWuwa6N1/21/dgphjkiRkghjhoWOCEPM6R2GCywI0z0Jj5PY8bBsQC4UwNLLndt4zY5dbadeMVDnJYAoTLzSOYAUAD2RZcbnARloalKLAQuPbszosvKSHpYRqy0F1RJjaJjJssqCI4MTjUFjcTYlRO/Kw575nlrnOxkBncwCV6EIh5BZuGDiDhBSuauzINrFbP3WIF2B5r2hECkLj/a08WscIMLKemDU86ZNTWgytK/bZALg553RQuQzekvAofNjGP4O3BtURQ8yNxrO781jXuI9PaWGy0/iojTxos+i0VhpjdqqhtTm3ZbgU8GsPJWqQQVdEFVbuK1V8fDyuXLkCAMjMzMTZs2eRlpZW9HWtVgsuN3x2ewjCl366ZAADYOc9C0Zsz0Pq2mwsuqiH1hr8fTQr4quzOmS4lDAAwJxOCq/uwlGUZ5+W9Dwb1t30XknbebUNR3PYvQWfa0jKf4nijaovwbut2UHuqxo7xu1Ww+aH0qHKctCFkwdddU4QoK8Peg8RgdE0io8O8ezStlDt9eQPZjuDeed1aL06C3POFx/86xAvwPYBsfhfj2gS/CP8Qsil8HuaEqmxvgncWxyAykzjhs6B9Dwb9mdZPYJ/fWoIsaZPeAb/HvqorRxCl1vdTCONBRd8c728q7d7lNy+0VyKahL/3Wv3qSnCyl7RkLit1b8+q8cHxzRgfFxiXhE0w+DV/fmssmkA+KRdJDonkKEfRNVW7qvzwIED8dNPP+Hdd9/Fs88+C6FQiH79+hV9/fz586hTp443z5EgwlKBhfboIXdd68B7RzVotjIL7x0pwA2tvYRnh54Lahu+O8suYXiuoQSdfPBG3CZWgEF12MGJj09pvRZscc/+61ZNSG7wiEea3FqG4fXYJeJ7My0ew3CC0Z/XjbiYz74WfZwaGbS7/kTFuDdDX37NCIMtPDejKopmGKy4bkTqX9mYclzLagPxUGMFD3/2VGJTvxikxgmK+VMIwnekfA5W9Y7ByPpiVJdwIPVjmePQumL8nhbts0y4YFFLysOEJuzhSd+d0yHX5P2hK7NOaeE6yyVezMEbzf0/eKtbdRHW9Ilm9YsFgAUXDXj7cIFf+iCWxzdn9djoNo16ZH1xpYYNEkS4KPcd6+TJk5GTk4OVK1dCJpNh/vz5iIuLA1CY/bdhwwaMHz/e6ydKEOEmx+RAsyg+juV6TqnV2RgsumTAj5cM6FNDiAlNpehRXRiyN9wOmsGbh/Jhd1kfJIg5mJ7iuwbGU9rI8c9tMx4Os7yhc2DpVYPHxMvy0ttorLjuNvyDTBEjSkFRFOZ3isIdnYP1mv/1qhEpcQKf9g+qDIONxien2IN0RtYTo3UMCWyEm4G1xYgVaZD7oNRMa2Ow+oYJ48hwIwDA7ntmTD2hxTm153APAKgm4WByshxPN5CQ0ngioBRCDn7sqiz63EEz0NkYaG00tFYGWitd+LnLR62Nhs7KQPPgMTq3j1orzVrDuXuxcQS+aB9Z6WFuoeLtljIsvWoo2gTQ2Rh8ka7Dlx0UpTyz7NJVVqy4zq5e+bCNHNIADd7qEC/E331jMHSbirX58csVI86rbZjXOSoo2kZs+9fssW5pqeTju45k6AdBAABVUFDgtZA9TdPQ6XSQSCTg8wN/ASCIUHAq14qFl/T466YJj0q2aBTJw8tNIzCqviRgb/4VtfCiHu8fZZcQ/pamxMDavh2a8Z+D+fj1qjNYFyfm4NSw+Er9+y29asCkg86srTgxBxdGJoAfxIvejIwMJCUlBfo0CAAqswM9N+Titt65pS/iAtufiPOYYB0MvkzX4hOX5uNCLnB8aDxqkf45YWnmSQ2+OessN2uh5GPfk7FBc9MUiGvZmTwrpp/QYncxfdWAwuEK/2khw6vNIsI+84mouhiGgdkBVrDwYUCxppSL5Cq4KfT9BT0+POZc2/Io4MiQODSILP29vLRrGcMwGLhFhQMu/aybKnjYPygu4EHWc2obhmxVsfoSAgCfA7zTUoa3W8ogCFBP7BtaO7pvyIHWbUjf7oGxqB1ig9cIwlcqtVJxOBxQq9Ww2wtLgzgcDiIjI0nwjyDKoU2sAD92VeL8iAS831qGOHHxL8srGjveOaxB05VZ+PCYBrd0oVEefEdvx6yT7J24gbVFPg/+AcB7yXKIXRYhOSYaC9x6qZSXe1+sZ5MkQR38I4JLjIiLP9166ZgdwLhdedAEWe/PHJMDc86xXy+vNJGS4F8YG9cwAq5Xs3NqG07kFp/xFu5u6+x4ea8a3dbnFhv843OACU0icHp4PN5pJSPBPyKsURQFMY9CvISLpEg+2sQK0L26CE/WEVfJ4B8AvNQ4ArWlzl58dqZw6Jw3bL1rZgX/AGBWu+DIsGyh5GNjvxgkuN2v2Gjgs3Qduq3PwfEcz+omX9PbaIzZmccK/nEo4JfuUST4RxAuKrRaOXXqFAYPHozq1aujQYMGOHjwIAAgLy8PI0eOxN69e716kgRRFcRLuHg/WY5zIxKwqGsUkmOKD6RrrQy+v6BH8upsPL0zD3vvW4KyAS9QuIP5zqECGFzqRuQCCl885r0SiUepJuHi1Wbs8rW55/XIM1esT8tplRXpec6bYQrAWDL8gyinplF8fONWJnRD58DrB/KD6rX82Wkd9Hb2LvpbLb03sZsIPrVlPPSpye6f+vPlym2ahBq12YEPj2mQujabNenT1bC6YhwfGo/PH1MgRkQG3xFEVSTkUpjWlj2N9587ZhzOLj5buKxsNIOpx9mBxLTqQvRMDJ7BW40UfOx+Mg79anqe06UCO/pszMX7Rwug91MfWYZh8PqBAlwqYCdHzEiRo1v14Pl3I4hgUO5w+LFjx/Dkk08iPj4eo0ePxtKlS4u+Fh0dDb1ej99++w3dunXz6okSRFUh5FIYVV+CkfXEOJ5rxaKLBvx9y+TRe4UBsOmOGZvumNFEwcOEplKMrC8OqiyE1TdM2H6PvRCamRLp1+llk5rLsOQyu0/L12d1+LRd+YOQv7gN/+iVKCS7ikSFjG4gwdEcC3654ixR33DbjO8v6PF688AH2a4U2PC/q+yf93dby6AQBs/1hfCNFxtFYOu/zubpf90y4dN2DiiDMNDFMAysNGB2MLC4/DI78OCj63Hn48wOBtairzuPG+wM/rljYmWQuOqSIMDM1Mgqm/FEEATbkLpifH9Bj5Mq5+bw1OMabBtQ8dYJS68aWNNrKQAzU33XM7uiqkm4WNZTiXW3THj3iLN/LFB4j7LwogEb75gxp6MCaT4OXs49r8e6W+wNm2F1xXi9mf8HphBEsCt3D8CBAwdCrVZj586dMBgMaNCgAdatW1cU8Js9ezZWrFiB9PR0n5wwQVRF9w0OLLliwK9XDB49N1wpBBTGNYzAi00iAl6ml2d2oN3aHORZnOfbMV6Af/rFgOPnflLzzuswxWU3VcABTgwrXx8zjZVG0xVZrGzGZT2V6F/L96XMlUV6AAYns51B3025OOOSVcqlgH/6xaBDvPenY5fH6B152OISBKon4+LIkPiA9fUh/MdBM0hek407Ln0qZ6XI8UaLwAWm194w4qfLBtwqMMPB4bkE8Pzz/ZtG8TAjJRK9EkN3GBdBEL5xKMuC/ptVrGO/dldicN2S14clrcu0Vhpt1mSz1vrPJEkwv3OU907YB9RmBz48rsWf14zFfn10fTE+bRfpk42kXffMGL49D66DiJtF8bBtQCwiQqxnOkH4Q7lfFadOncIzzzwDkUhU7CIoMTER2dnZXjk5giAKVY/g4qM2cpwfkYDvOytKHBZQYGUw57werVdn49ldeTiYFbjy4A+PaVjBPyEXmNNJ4ffgHwCMbyxFjQjnosNKA5+eKl+fllXXjazgX3UJB31qkLICouJEPAr/66FEpMD5mnAwwAt71Mg1+SmyUYz9mRZW8A8ApqVEkuBfFcHlUHjBbfLvkisG0AF4L6EZBh8d0+CFvfk4nG1FpoWDHFPh4AF/BP8SJVz80FmB/U/GoXeN4te9BEFUbR0ThOhfi70enHFSA6uj/NfMOed0rOCfhEfhwzbyRzwjOChFXCzoEoW1faJRS+oZ5Ft+3YT2f+Vg7Q2jV+9LbunseGGPmhX8Uwgo/NEzmgT/CKIE5X5lcDgccDglPy07OxticfBnxBBEKBLxKIxJisC+J2OxuX8MBtcRo7h7cpopLCccsFmFLutz8b8rBtw3+C+gsOueGcuvs1Px320lR1IZJqP5gohH4f1kdvbKiusmXFCXrbk9wzBY4lb+O7ZhBHhB0IyZCG11ZDws7MLe2c800nhxbz4cdGACLlOOsyd2t48T4MnaJNhdlTzTUAKBy1Lvps6BPSVMwfUVk53B83vUmF/JwU0VIRdQmJEix4lh8Xg6KSIoGu8TBBG8preVs9bjN3UOj3Vjae7q7fje7Xr3RnOpX9vmVFZaogiHBsfh1absgVIAkGum8cLefDy1U417XrgnMTwY+lHgNvRjSXcl6pD2PARRonK/Olq3bo0tW7ZgwoQJHl+zWq1YtWoV2rVr55WTIwiieBRFoUO8EB3ihfhXb8eSywb8etWAfItnwOC82oY3DxUAAJIieehaTYiu1YTokiDwSSq+wUbjPw++30NNo3iY1CKwfTieqi/B9+f1RQ2CGQAzT2qwondMqc89nmvFxXxnPxYuRYZ/EN7Tr5YYb7WQ4luXibv7Mi2Yna7DR37e+V99w8QadAMAs1LlJPOpiokRcTGojhirXIZgLL5s8Hkfp4fyzA48vVONo2WYJMnnACIuBSGXgohLQfDwcx5VdFzIpSB0e1zhcbA+F3EpRAk56FpNSPpdEgRRZg0VfIxrGMEK+n2RrsPo+pIyX0tmndKyMpvjxRy80Tz0ethJ+RzMbq/AsHoSTDqQj4tugzm2/GvGwaxszEiJxHONJBWqDGIYBm8eKsCFfPafPbWN3G/vUwQRqsrdA3D37t0YPnw4nnrqKYwYMQKDBw/GwoULER0dja+++gonT57Epk2bkJqa6qtzJgiiGCY7g1U3jFh4Uc8KVj1KCyW/KCDYMUEAmRfS5T88pmHtYHIoYPuAWLSNDXzT9E13THh6p5p9rF8MOiY8ut/aK/vUrIzG/rVEWNYz2ifn6AukB2Dws9MMBm9V4UAWO+Cxsle0x1RWXzHbGaSszcZdl535wXXE+LWH0i/fnwguR7IteHyTs68VhwLODo9HDR/3l72ptWP4dhWua9kZIvFiDmY2MKJb0zqsAF4g2koQBEG4yzE50GZ1NvQu7WL+00KK6SmeAzzc12XpKiu6b8hlPWZuJ0XIbzZbHQy+PafDV2d0KG4gcMd4AeZ2UqBBOSuE5p/X4SO3ScmD6ojwa3cl2bAkiFKUOwAIAKtXr8Z///tfaDQaMAwDiqLAMAwiIyMxZ84cDBo0yBfnShBEGTAMgwNZViy6qMemf80oaxUhlwLaxggKswOrCdE+TgARr3xvoqdyrei1MZf1PSc2i6jQxF1fYBgG/TapcMQlq6RdrABbB8SUuGDIt9BosiKTtSu7unc0eoVQf179rQAAIABJREFU/z8SAAwN2UYHuq7PQbbJuUpWCCjsGxTnl6E+c87pMO2Ec0HN5wDHhsSjrpyU0lRFDMOg0985rA2l/7aS+bQf1fEcK0bvyGP1jwWAxgoeVvaOhiXzJrmWEQQRtL5I1+LT07qiz4Vc4MTQeNR0ew93XZcxDIOBW9gbgE0VPOwfFBc27QcuF9gw6UABjuV6ZnULucB7reV4o7kU/DL8fffet2DINhXrXqOJgoftT8RCSvr+EUSpKhQABACj0Yjdu3fj+vXroGkadevWRc+ePSGVSuFwOMDlhk6/AoIIV7d1diy7ZsSe+xaczLXCXo5Xu5ALtI8TPsgQFCA5RvDIN2YbzaD7+hxWOn5NKReHB8cF1Rvy4WwL+m1iT2v7PU2JJ2oX37v0hwt6fHDM2ROtlpSL9OHxIZV1QgKAoeNglgVPblHBtXd4mxg+NvePhdCHQzjyzA4kr8mG1qWXzitNI/BZ++AI3hOBseSyAW8fdrZ0iBdzcH5kQplu0sprw20Txu9Vewz36JIgwG9p0VAIOeRaRhBEUDPYaLRdk40sl428UfXFWNSVnUnvei3bfMeEp9yqU9b0iUbPMCtlddAMFl82YOZJLWuo3kMtlHzM66RA65iSK4bu6O3osT6XtUkUKaCwe2Ac6pHNSoIokwoHAItjsViwdOlSzJs3D2fPnvXWH0sQhBfobDSOZFuxL9OCvfctOKe2oTwvfhmfQsd4Abo8KBluruSzgmDfntVhxkl2On6wZsqN3pHHmnLaKJKHg4PjPIZ6MAyDdn/lIEPjDGpObSvH2y3ZA0WCHblpDi3umXgA8FLjCHzVwXfBuPeOFGDRJWfvIrmAQvqweJ/0CSVCh85Go8nyLFZJ26/dlRhc17vD3hY82Ghxf08aWV+M+Z2iiiZQk2sZQRDBbulVAyYddG6cUAD2PBmLVtHOwNbDa5mNZtBxHXudmVZdiLV9S+9PHaru6O14+1ABdtzzHCzFpYDXmknxfrIMEh47ecBkZ/D4plyccelTTAFY4cdWKQQRDsocKrdYLNiyZQtu3ryJqKgoPP7444iPjwdQmA24aNEiLFiwALm5uahXr57PTpggiIqR8TnoXUOE3g8CcmqzAweyrNifacHeTAuuah7dN1BnY7D1rgVb7xa+YSuFHHROEKBbdSHqyXj4LJ0dsBhZXxyUwT+gMIi39V9z0c3mFY0df14z4lm3XisHs62sRRmPAp5JkvjxTImqaFJzKY7mWLHpjjNIvfiyAe3jBBhR3/s/f9c1dvx8mT2t8P9aykjwj4CMz8GoBhLWz8fiy3qvBQAdNIOPjmuw4KLntMz/ayXDh8ky0s+JIIiQMqaBBAsv6IuGXzAAph7XYl3faI/r2dKrBtY6k0MBs1I9ewaGk1pSHlb1jsbKGyZMPqqB2iWbz8EAc8/rseG2CXM6RaFrtcIe3QzD4K1D+azgHwB8kCwjwT+CKKcyZQBmZmZiwIABuHXrFhim8OERERFYvnw5OBwOxo8fj/v376Ndu3Z4/fXX8cQTT5AFG0GEmEyjA/szLYUZgpkW/Kt3lP6kEkQLOTg2NA7RQRxAeHV/Pv68Ziz6vLqEg5PDEiB26Xv44h411tx0Dv8I1YEIJGsm9BRYaHRbn4PbLq/DCB6FnQNj0VhRvmbZpRm7Kw/rbzuDjTWlXBwfEl/uHqBEeLqgtqHT3zmsY0eHxKFRJX8OTXYGL+9TY4PLzx5QmAHybcfim9+TaxlBEKFg+10zRmzPYx1b1Tu6aBM+IyMD8bXro82abKjMzgDYs0kSzOsc5ddzDaRckwOTj2mw2mXivKuxDSWYmRKJFdeNeO+ohvW1AbVE+C1NGVIteQgiGJSpMdesWbNw584dTJo0CStWrMAXX3wBsViMN998E6NHj0bNmjWxefNmbN26FQMHDiTBP4IIQdUkXIysL8H8zlE4Ozwe6cPjMbeTAsPriREnLl8Pv9ntI4M6+AcU7hoKXU7xvpHGT5ec04tzTQ6sv81ekDzfKLSnsRGhQyHkYGmakvUzarAzGLdLDX1xo/Qq6Gi2hRX8A4CpbeQk+EcUaabko0M8uyfTksueGXvloTI78OSWXI/gn5RHYWXv6JCffEkQRNXWK1GIbg+y1x6adlwDh8vkiu/O6VjBPwmPwgc+HLIUjGLFXCzupsSKXtFIlHjeNyy9akT7v7JZvbgBoGEkDwu6RJHgH0FUQJnu6vfs2YMxY8Zg+vTp6NOnD8aPH4+vvvoKN27cQIcOHbBp0yY89thjvj5XgiD8hKIo1JHxMLZhBBZ3U+LKqAQcHhyHz9tHYkAtESIFJb/h9koUYkQ97/aH8oWaUh5eaixlHfvmrA4FD0oRll0zwjXO0kDOQ9dqJTcmJghvaxUtwJePsfv+XdHY8ebBgqJs/MpgGAZTjrNL95Nj+BgWAq9fwr9ecNv8+POaEYYKBqKva+zo/U8ujueyS7mqSTjY1D8m7BrfEwRR9VAUhRkp7GDexYLCwXwAkGWm8MMFPevrbzSXoloxQbCqoG9NEQ4PicNLjT03f7JNNGswmoxP4Y+eSsgFwTNgkCBCSZleOTk5OUhJSWEdS01NBQA888wz4HDIC5AgwhlFUWgSxceEplL80TMaN56qht0DYzEjRY6eiUJIHmQLNYviYW6nqJDJAn6npRRyvvNcC6wM5pzTgWYY/HKFneEyrpEkZP5eRPh4NkmCpxqw+/6tuWny6NlXEetvm3Es18o6Nis1kuyoEx6erCNGjMi51tPaGFZ7hLI6mm1B7425uKljt5hoquBh+4BYtIwmmywEQYSH1jECjKzP3lD79LQWBhuNBbf5rInnCWIOJjWXoiqTCzj4qoMCm/vHICmy5DEFi7pGISnSu61QCKIqKVPkzuFwQCRi78g+/Fwur1qpygRBAFwOheQYAd5sIcOaPjG49XQ1XBmV8P/t3XtUlNUe//HPwCh3HEXAe4ooommmpVZ61LRQymsalre0zOh0jnXynsegMLSDt8xf2TKzjnbCLPOS5KnURFPxaBcr7Wde0VQEBUEUgZnfH/4YHfGC6MzIzPu1Fmsxz+x5nr1Ze3155vvsi9b3DFEtv4rz9LKat6dGNbfd0fed3/L0nz/ydeCSL6hentKT4Wz+AcczGAyafl8VNa1qezM8IS1H2y9L3t2I88UWxf3PdkpN97real/D6yqfgDvz8jRo8GUbIM3fdeaGRqIuP3BWPddk2iz4Lkkda3op5ZFg1fEv8750AFAhTGoVaLOUx9F8s/62KVurT9jGu4mtAuVXiQE1knRfqJdSe4ZodIsAXb4aybiWAYquxywF4GaU+W7rwIED2r59u/X16dMXpg3t2bNH/v6ln1i0bt36FlQPQEVQ2dOg0Ao6beG5pn56b1eejp+98KX0XLE0alO2TZle9X1u+zUN4bp8jR76qHM1dV55QrmFFxIuhWZp6LqT2tAzuFy79b6/+4zNKCxPg0pNVwIu9VSEn2btzLPunv7zyUJtzyzUPcHXHrVnsVj0f347o0lpObo8XfhEuK9m329SZU9GnQJwPfX8jXou0l+zf7k43ffzy0ZPNzUZNZCHzDa8jQZNah2oXg18NHlbjnaeLNQT4b4a1zLg+h8GcE1l2gW4atUrT+mzWCyljpccO3ny5K2rJQDY0YLdZ/SPzdlXfT8lurruC624I6PYOdM1LD9wVkPX2f5vfai2l5IfCrqhabvZBWbd/dkxnSq4+O//6SZ+mn6f6RqfAqSYrzO15nCB9fUT4b56p8PVd6wsNls0IS1H7+0qPWV9XMsAjW8ZcENLKxDLAFQ02QVmtfrseKnRzyU+eziItU8BOEyZRgDOnTvX3vUAAKcZ3NhXc3/N1d7TxaXeizQZ1S6EdangfL3q+yi2qZ/e+e1iMuXrIwWa8XOeRt9V9qfiM37OtUn++RsNPFVHmQxv4m+TAPx8f76m3Bt4xVGo+UVmPfPdKa0+ZLvTr9EgzXrApEGN2OkXgOszeXlozF0BmnDZTraS1KW2F8k/AA5VpgTgk08+ae96XNOpU6f0xhtvaP369UpPT1dQUJCioqI0adIkVatWzVquefPmSk9Pt/nsiy++qLi4OOvr9PR0jR49WqmpqfL29la/fv2UkJCgypX5gg+4q0oeBv2zVRU9tb70yOWnIvzY/AO3jdfuraIdmYXamnFx/b83fjite4MrqWOt63+JOJhbpHd/s9158MUWAQrxYYo7rq9rbS/V8/fUobwLD0sKiqXFf+Trb3faJpBPnC3WgG+ytD3TdqffgEqGC9PZ+cILwI083cRP83bl2awv7WGQXrunihNrBcAdVYjVRo8ePaqjR48qPj5e33//vebNm6fvv/9eTz/9dKmyY8eO1e+//279GT16tPW94uJixcTEKC8vT6tXr9b777+vFStW6JVXXnFkcwDchnrV99bd1W13FfPxNCimIeuy4PZRycOgBZ2qKcjr4r9vs0V6+rtT+vNM6RGsl0vYcVrnL5m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"text/plain": [
"<Figure size 1296x576 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rcParams['figure.figsize'] = 18, 8\n",
"# note that the freq defaults to freq of dataframe's\n",
"decomposition = sm.tsa.seasonal_decompose(monthly_sales_df, model='additive')\n",
"fig = decomposition.plot()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could see the yearly seasonality more obviously in the seasonal component and the trend is heading downwards just like what we have observed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2 Describing Patterns - Stationary\n",
"We could describe the time series as a stationary or non-stationary process as well. This is an important concept in time series and is rooted in several time series models' assumptions so let's take a quick look at it.\n",
"\n",
"The definition can get a bit hairy and there are 2 forms of stationary: weak-form and strict stationary. \n",
"\n",
"#### 4.2.1 \"Weak-form\" or \"Covariance\" stationary:\n",
"<img src='ts_pics/cov_stat.png' width=500>\n",
"1) Constant mean\n",
"2) Constant variance \n",
"3) Same covariance between periods of the same length.\n",
"\n",
"#### 4.2.2 \"Strict\" stationary:\n",
"<img src='ts_pics/cov_stat2.png' width=500>\n",
"Strict stationary is more restrictive as it requires the same distribution between 2 periods of the same length. In practice, we often refer to the weak-form stationary as the strict variant is rarely observed.\n",
"\n",
"#### 4.2.3 Other observable traits of stationary:\n",
"1) The observations in a stationary time series are not dependent on time.\n",
"\n",
"2) They don't have trend or seasonal effect\n",
"\n",
"#### 4.2.4 Visual Example of stationary\n",
"The concept of stationary can be a little slippery, sometimes it's helpful to look at the plot visually. To nail it down, a stationary time series could look like this plot:\n",
"<img src='ts_pics/stat_eg.png' width=500>\n",
"Notice that the magnitude and mean values don't change over time. The values don't increase over time(trend), the values are not corelated with time, and there's no seasonality patterns like our store sales.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 4.2.5 Why do we care about stationary?\n",
"1) Stability; A stationary time series has stable statistical properties over time.\n",
"\n",
"2) Statistical modeling methods such as ARIMA(we will come to that later) assume or require the time series to be stationary to be effective.\n",
"\n",
"3) We are interested in modelling the relative difference between timesteps rather than the absolute values. For example: We want the model to learn the % of increase or loss following a pattern rather than learning the value at $1,200 at a certain time."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 4.2.6 How do we make our time series stationary?\n",
"1. A log transformation or square root transformation are two popular options, particularly in the case of changing variance over time.\n",
"\n",
"2. The most popular option when we have time series data exhihiting trend is differencing. We could difference(intergration) by subtracting $Y_{t-1}$ from $Y_t$ of our time series to make it stationary. Recall our time series plot from above:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"monthly_sales_df.plot(figsize=(10, 3))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could use Panda's diff() to quickly perform differencing."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"diff_monthly_sales_df = monthly_sales_df.diff()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff_monthly_sales_df.plot(figsize=(10, 3))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 4.2.7 Testing for stationality\n",
"To test for stationality, we could use a statistical test, the Augmented Dickey-Fuller(ADF) test where the null hypothesis of the test is that the time series is not stationary (has some time-dependent structure). The alternate hypothesis is that the time series is stationary. In other words, if your time series is truly stationary, you should get a p-value of <=0.05 (95% confidence interval, significance level(α) of 0.05) which suggets us to reject the null hypothesis.\n",
"\n",
"Here's an example using the statsmodels library."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first line is the test statistic which we could use to compare against certain critical value in the Dicker-Fuller table to determine statitical significant.\n",
"\n",
"Conveniently, it also provide us the critical values of several common levels of significance(1, 5 and 10%), from the DF table.\n",
"\n",
"In this case, we could see that the test statistic is smaller than not just 5% but also all levels of signifcant. Therefore, we can confidently reject the null hypothesis and <b>say that this time series is stationary.</b>\n",
"\n",
"Alternatively, we could simply use the p-value associated with the test statistic, on the second line. This can be pretty handy as you could just quickly compare against the 1%(0.01), 5%(0.05) or 10%(0.10) confidence level to determine statitical significant.\n",
"\n",
"The third line(10) refers to the number of lags and we could say that there are some auto-correlation going back for 10 periods.\n",
"\n",
"Finallt, the fourth line(37) simply expresses the number of observations.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-5.19107018733927,\n",
" 9.168756655665654e-06,\n",
" 10,\n",
" 37,\n",
" {'1%': -3.6209175221605827,\n",
" '5%': -2.9435394610388332,\n",
" '10%': -2.6104002410518627},\n",
" 521.9616303121272)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adfuller(monthly_sales_df.Sales.values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.3 Describing patterns - White noise"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A related topic of stationary is \"White noise\", which is a type of time series that the data doesn't follow a pattern. And if we cannot take advantage of the pattern in the past to infer the same for the future, then it cannot be predicted! Therefore, it's good to check for white noise pattern before we even consider modelling.\n",
"\n",
"How do we spot white noise? <b>These are the definition:</b>\n",
"\n",
"1) Constant mean($\\mu$) of 0\n",
"\n",
"2) Constant variance($\\sigma^2$)\n",
"\n",
"3) No(zero) autocorrelation (No relationship between past and present values)\n",
"\n",
"Have mean zero, and a finite variance: \n",
"$$E(X(t)) = 0, E(X(t)^2) = S^2\\text{, and } E(X(t)X(h)) = 0 \\text{ for } t\\neq h\\text{.}$$\n",
"\n",
"We will get to autocorrelation soon but it's basically the amount of correlation between a time series and a past version of itself. \n",
"\n",
"#### 4.3.1 Visual example of white noise\n",
"Again, to nail it down, a white noise looks like this in a plot."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rcParams['figure.figsize'] = 15, 5\n",
"random_white_noise = np.random.normal(loc=3, scale=2, size=1000)\n",
"plt.plot(random_white_noise)\n",
"plt.title('White noise time series')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could see the values are normally distribiuted around the mean of 3 (most values forming around 3) and the magnitude mostly staying within 2 units.\n",
"\n",
"Most importantly, it appears to behaves sporadically; there's zero correlation between past and future values, so there's no way that we could use the past patterns to project the future values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 4.3.2 Why do we care about white noise?\n",
"\n",
"1) If your time series is white noise, <b>it cannot be modelled.</b>\n",
"\n",
"2) The residuals of a time series model should be white noise. That means that all of the signals in the time series has been harnessed by the model in order to make predictions. All that is left is the random fluctuations that cannot be modeled.\n",
"\n",
"#### 4.3.3 White noise VS Stationary\n",
"\n",
"If you were thinking that white noise sounds like the stationary that just went through, you are right! Note that while a white noise time series is stationary, not every stationary time series is white noise.\n",
"\n",
"#### 4.3.4 Modelling white noise\n",
"One of the things that confused me when I started learning these concepts is that stationary is a prequistite for modelling a particular time series but if white noise is also stationary, why can't we model the white noise?\n",
"\n",
"To be precise, what I was thinking: White noise = Stationary = Okay to Model?\n",
"\n",
"Here's a way to think of it: Remember that we difference the orginal time series to make it stationary. This act of differncing is effectively a way to eliminating/reducing the trend and seasonality, in an attempt to stablize the mean of the time series. We might difference it multiple times(orders) until all temporal dependence has been removed. \n",
"\n",
"If there's no signal left, the number of orders could actually be used to model the time series with a linear trend and seasonality.\n",
"\n",
"If there's some signal left, it's probably still largely stationary and the remaining signals might have some autocorrelation. We could then move on to model this autocorrelation signals. \n",
"\n",
"We will see a clearer picture as we go through a end-to-end example later."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.4 Describing Pattern - Random Walk\n",
"The last important time series pattern we want to talk about is the \"Random Walk\", which is a special type of time series where values tend to presist over time and the differences between periods are simply white noise(random).\n",
"In other words, the prices of today is equals to prices of yesterday and some residuals of white noise\n",
"$$P_t = P_{t-1} + \\epsilon $$\n",
"\n",
"where $\\epsilon \\sim WN(\\mu,\\sigma^2)$\n",
" \n",
"#### 4.4.1 Visual example of random walk"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"rand_walk = pd.read_csv('RandWalk.csv')\n",
"rand_walk['date'] = pd.to_datetime(rand_walk['date'],dayfirst=True)\n",
"rand_walk.set_index('date',inplace=True)\n",
"rand_walk = rand_walk.asfreq('b') # convert/sample freq to business days(b)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rand_walk.plot(figsize=(10,3))\n",
"plt.title('Random walk')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could see that it's very different from the white noise we just talked about. In fact it looks like a series of stock or index prices where it exhibits some patterns. This is because the series itself is not random, although the differences are random.\n",
"\n",
"The random walk has it's root in finance, and is base on a financial theory that stock market prices are a random walk and cannot be predicted. However it's worth mentioning that this <a href='https://en.wikipedia.org/wiki/Random_walk_hypothesis'>claim is arguable.</a>\n",
"\n",
"Going back to the definition: we could definietly see small variations between consecutive time periods or in other words, values at $P_t$ depends on $P_{t-1}$\n",
"\n",
"To avoid some confusion that I had when I started learning the concepts we went through, here are some differences.\n",
"\n",
"#### 4.4.2 Random walk vs stationary\n",
"Random walk is a non-stationary process as we could see that it greatly violates the assumption of stationary. A stationary time series is one where the values are not a function of time. On the other hand, the observations in a random walk are dependent on time.\n",
"\n",
"#### 4.4.3 Random walk vs White noise\n",
"Not the same; white noise is like a sequence of random numbers. While the random walk values can appear random, the next value($P_{t+1}$) is always a modification of the previous value($P_t$) in the sequence. There is a underlying process that generates some consistency from step-to-step rather than spitting out random numbers. This is why random walk is also called a \"drunkard’s walk\".\n",
"\n",
"#### 4.4.4 Why do we care about Random walk?\n",
"If you have a random walk time series, <b>then it cannot be skillfully predicted.</b> This is simply because the next time step is a function of the prior time step and such model provide naive forecast. We call such models as \"persistence model\".\n",
"\n",
"Let's do a small experiment where we generate a time series of random walk and split the dataset into train and test sets."
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(660, 340)"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate the random walk\n",
"seed(1)\n",
"random_walk = list()\n",
"random_walk.append(-1 if random() < 0.5 else 1)\n",
"for i in range(1, 1000):\n",
" #add -1 or 1 to prev value\n",
" movement = -1 if random() < 0.5 else 1\n",
" value = random_walk[i-1] + movement\n",
" random_walk.append(value)\n",
"# prepare dataset\n",
"train_size = int(len(random_walk) * 0.66)\n",
"train, test = random_walk[0:train_size], random_walk[train_size:]\n",
"len(train), len(test)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"random_walk_series = pd.Series(train+test, index = range(1000))#index=list(range(len(train)))+list(range(len(test))))"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(random_walk_series[:660],label='train')\n",
"plt.plot(random_walk_series[660:],label='test')\n",
"plt.title('Generated Random Walk')\n",
"ax.set_xlabel('Date')\n",
"ax.set_ylabel('Sales')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use the persistence model to predict the outcome using a rolling forecast method and we calculate the MSE for all predictions are collected from the test set."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Persistence MSE: 1.000\n"
]
}
],
"source": [
"# persistence\n",
"predictions = list()\n",
"history = train[-1]\n",
"#loop thru each step and take last observation as prediction for current step..\n",
"for i in range(len(test)):\n",
" yhat = history\n",
" predictions.append(yhat)\n",
" history = test[i]\n",
"error = mean_squared_error(test, predictions)\n",
"print('Persistence MSE: %.3f' % error)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obviously, since the observations are constructed by generating either -1 or +1 of the previous values, calculating the MSE of \"predictions\" would also be 1. \n",
"\n",
"But what happens if we say that we know the variance of the process, then we can sprinkle a little variance when generating the values?"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random MSE: 1.976\n"
]
}
],
"source": [
"seed(1)\n",
"predictions = list()\n",
"history = train[-1]\n",
"\n",
"for i in range(len(test)):\n",
" yhat = history + (-1 if random() < 0.5 else 1)\n",
" predictions.append(yhat)\n",
" history = test[i]\n",
"error = mean_squared_error(test, predictions)\n",
"print('Random MSE: %.3f' % error)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It ended up with even worse performance. We can conduct more experiments later, with more complex model if that doesn't convince you. So where does it go from here? There are 2 takeaways:\n",
"\n",
"1) The best model/predictor for a random walk is at most a persistence model. \n",
"\n",
"2) A persistence model could be a baseline model before you employ fancy time series modelling techniques. If the performance(skill) of your final model cannot beat a persistence model, it also means we are better off with just taking the previous value as a prediction. Ouch!\n",
"\n",
"#### 4.4.5 How to tackle a suspected random walk time series?\n",
"Granted that most time series out there are random walks, we could <b>try</b> to model the first order differences instead of the raw values. Remember in the stationary section where we talk about differencing the time series to make it stationary before we begin modelling. However, if making it stationary still shows no obviously learnable structure in the data, then it cannot be forecasted."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Modelling - ARIMA model\n",
"Finally, with some of the important concepts out of the way, we can start to look at modelling time series!\n",
"\n",
"ARIMA and it's variants(ARMA, ARMAX, SARIMA) is the classic model for almost all time series literature. ARIMA is a traditional time series model that models the Autoregressive(AR) and Moving Average (MA) nature of the time series. For time series with seasonality, like the one we talked about above, we could use the Seasonal ARIMA (SARIMA) to model such process. Let's look at the building blocks and assumptions of the simpliest ARMA model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.1 Autoregressive(AR)\n",
"\n",
"In time series, there's often a relationship between past and present values. For example, we could do a reasonable guess of the sales today given yesterday's sales, or maybe the sales 7 days ago. Therefore, we use <b>Autocorrelation</b> which is the correlation between a sequence of itself.\n",
"<img src='ts_pics/autocorr1.jpg' width=500>\n",
"<img src='ts_pics/autocorr2.jpg' width=500>\n",
"For example, the above picture illustrates the corelation between the current series and a lagged version of itself (t-1, lag of 1). The frequency of lagged values could be in days, weeks or months."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ARIMA takes advantage and model this AR nature of a time series. We could picture this as a linear model that relies on a sum of past period values multiplied by a constant(coefficient) to predict the current period values.\n",
"<img src='ts_pics/ar_model.png' width=500>\n",
"\n",
"It's likely that the more lagged values(lags) we use in a ARIMA model, it could model more complex relationships and interactions. Hence, we often not only use lagged values of 1: AR(1), but also further lagged values: AR(2), AR(3), etc.\n",
"<img src='ts_pics/ar_model2.png' width=500>\n",
"\n",
"However, when we include more coefficients, it's more likely that some of them are not significant(different from zero) and just like any machine learning or Neural network models, more parameters might lead to overfitting. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Auto-correlation Functions (ACF)\n",
"We could imagine that it's extremely helpful to calculate the auto correlation of different lags simultaneously:\n",
"\n",
"$$\\rho(k) = \\frac{\\frac{1}{n-k}\\sum_{t=k+1}^n (y_t - \\bar{y})(y_{t-k} - \\bar{y})}{\n",
"\\sqrt{\\frac{1}{n}\\sum_{t=1}^n (y_t - \\bar{y})^2}\\sqrt{\\frac{1}{n-k}\\sum_{t=k+1}^n (y_{t-k} - \\bar{y})^2}} \\,,$$\n",
"\n",
"The top portion is essentially the covariance between the original data and the k-unit lagged data. The bottom is sum of the squared deviations of the original data set. \n",
"\n",
"And this is the main idea behind the ACF. We could calculate it manually or we could simply just use the plot_acf() function from statsmodels library."
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rcParams['figure.figsize'] = 8, 3\n",
"tsaplots.plot_acf(monthly_sales_df.Sales.values, zero=True)\n",
"plt.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obviously, we could see that the time series has the highest correlation with its lagged value at 0(lag=0, basically itself). What is more interesting is that we could see a significant correlation at a lag of 12. This makes perfect sense and it's a good indication of seasonality; this month's sales is highly correlated with 12 months ago.\n",
"\n",
"ACF and the closely related PACF plots are deeply rooted concepts in traditional time series analysis and they could taught for half day in a formal class so we'll skip this for now..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2 Moving Average (MA)\n",
"\n",
"There's a problem with a AR model; Since present values relies on past values, an unpredictable sudden increase in value(shocks) couldn't be reliably predicted. One idea is to introduce an additional component(MA) which takes into account past residuals to auto-correct and make adjustments to the predictions. Mathematically, it looks like this:\n",
"\n",
"<img src='ts_pics/ar_model3.png' width=500>\n",
"\n",
"\n",
"\n",
"The same set of components(AR and MA) could be applied to the seasonality too, with an additional term.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.3 Integration (I)\n",
"This refer to the number of times we need to integrate(difference) the time series to ensure stationarity which ARIMA assumes.\n",
"\n",
"Recall that if you have a non-stationary time series at hand, we could transform the time series into a stationary process by differencing so that it fits the assumption.\n",
"\n",
"In finance, one common approach is to simply use returns, which is the % change between the values of 2 consecutive periods, instead of prices. In pandas, we could simply use the \"pct_change()\" function to accomplish that.\n",
"\n",
"#### Notation\n",
"We often express ARIMA model with the p,d,q parameters which refers to the order of AR, I, and MA respectively. Moving forward, we will use this notation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All these concepts might sound abstract but it will make sense after we start to fit a time series to a ARIMA model. The very last thing that we have to touch on before we begin modelling, is how we should go about evaluating the model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Model selection and data leakage\n",
"While the model selection strategy is similar to general modelling where you split the dataset into train and validation/test, and fit model on train while selecting the model based on its performance of validation split, it's more tricky with time series.\n",
"\n",
"This is because we need to make sure we are not leaking information from the future into the past. This could sneakily happen in common preprocessing techniques such as expoenential smoothing. When dealing with time series, it would be a mistake to randomly split the data into train and test sets using the usual(train_test_split from Scikit-Learn). That would lead to, for example, making predictions about 2014 using data from 2015.\n",
"\n",
"A simple solution is to split the time series data into 2 windows of date range. For example, we could use the time series from 1/2014 to 12/2016 for training and 2017 for validation.\n",
"\n",
"If you are a big fan of cross-validation (CV) methods, you could roll forward training, validation and testing windows that makes use of all data at once:\n",
"<img src='ts_pics/roll1.png' width=500>\n",
"\n",
"Alternatively, you could also move the training window rather than expanding it which would look like this:\n",
"<img src='ts_pics/train_split.png' width=500>\n",
"\n",
"For simplicity, we'll just split the data using the simple solution in this notebook.\n",
"\n",
"TODO: Talk about the rolling CV..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Fitting a ARIMA model efficiently\n",
"The traditional way is to increase the coefficient sequentially, look at the statistical signifacnt of the coefficients, compare the log likelihood and stop increasing when the model fails to improve. A more efficient way is to use <b>AutoARIMA</b> or a couple of nested loops(<b>Grid Search</b>) to find the optimal set of parameters that yields the best Akaike's Information Criterion(AIC) for our model.\n",
"\n",
"The AIC of a model is equal to AIC = 2k – 2lnL where k is the number of parameters of the model and L is the maximum likelihood value for that function. In general, we want to lessen the complexity of the model (i.e., lessen k) while increasing the likelihood/goodness-of-fit of the model (i.e., L). So we will favor models with smaller AIC values over those with greater AIC values."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"p = d = q = range(0, 3)\n",
"pdq = list(itertools.product(p, d, q))\n",
"seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember that we had the luxury of 4 years of data? We could seperate first 3 years worth of them to be the \"training data\" and the last 1 year to be the \"validation set\"."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"train_monthly_sales_df = monthly_sales_df[:-12]\n",
"valid_monthly_sales_df = monthly_sales_df[-12:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just to make sure there's no data leakage..."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order Date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-10-01</th>\n",
" <td>624.872474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-01</th>\n",
" <td>1271.345152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>1410.719808</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Order Date \n",
"2016-10-01 624.872474\n",
"2016-11-01 1271.345152\n",
"2016-12-01 1410.719808"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_monthly_sales_df.tail(3)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order Date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-01-01</th>\n",
" <td>397.602133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-02-01</th>\n",
" <td>528.179800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-01</th>\n",
" <td>544.672240</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Order Date \n",
"2017-01-01 397.602133\n",
"2017-02-01 528.179800\n",
"2017-03-01 544.672240"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"valid_monthly_sales_df.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 7.1 GridSearch \n",
"Running the GridSearch using the good ol' loops."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"param: (0, 0, 0)\n",
"ARIMA(0, 0, 0) - AIC:503.4335944371523\n",
"****BEST****\n",
"param: (0, 0, 1)\n",
"ARIMA(0, 0, 1) - AIC:488.8972633551307\n",
"****BEST****\n",
"param: (0, 0, 2)\n",
"ARIMA(0, 0, 2) - AIC:477.4721439070175\n",
"****BEST****\n",
"param: (0, 1, 0)\n",
"ARIMA(0, 1, 0) - AIC:503.4250970460873\n",
"param: (0, 1, 1)\n",
"ARIMA(0, 1, 1) - AIC:477.74578597733233\n",
"param: (0, 1, 2)\n",
"ARIMA(0, 1, 2) - AIC:465.12985166740384\n",
"****BEST****\n",
"param: (0, 2, 0)\n",
"ARIMA(0, 2, 0) - AIC:524.1351010222072\n",
"param: (0, 2, 1)\n",
"ARIMA(0, 2, 1) - AIC:478.17920833300695\n",
"param: (0, 2, 2)\n",
"ARIMA(0, 2, 2) - AIC:457.24955345064654\n",
"****BEST****\n",
"param: (1, 0, 0)\n",
"ARIMA(1, 0, 0) - AIC:503.6859140970196\n",
"param: (1, 0, 1)\n",
"ARIMA(1, 0, 1) - AIC:490.9888150754283\n",
"param: (1, 0, 2)\n",
"ARIMA(1, 0, 2) - AIC:479.4712148668157\n",
"param: (1, 1, 0)\n",
"ARIMA(1, 1, 0) - AIC:497.5089291893447\n",
"param: (1, 1, 1)\n",
"ARIMA(1, 1, 1) - AIC:478.63613140108936\n",
"param: (1, 1, 2)\n",
"ARIMA(1, 1, 2) - AIC:467.1294818542604\n",
"param: (1, 2, 0)\n",
"ARIMA(1, 2, 0) - AIC:506.6294753582548\n",
"param: (1, 2, 1)\n",
"ARIMA(1, 2, 1) - AIC:473.8250072112295\n",
"param: (1, 2, 2)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" \"Check mle_retvals\", ConvergenceWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ARIMA(1, 2, 2) - AIC:459.213961399202\n",
"param: (2, 0, 0)\n",
"ARIMA(2, 0, 0) - AIC:490.77583016301736\n",
"param: (2, 0, 1)\n",
"ARIMA(2, 0, 1) - AIC:492.8970451694744\n",
"param: (2, 0, 2)\n",
"ARIMA(2, 0, 2) - AIC:483.38750079889166\n",
"param: (2, 1, 0)\n",
"ARIMA(2, 1, 0) - AIC:481.4665623346183\n",
"param: (2, 1, 1)\n",
"ARIMA(2, 1, 1) - AIC:483.4594258013412\n",
"param: (2, 1, 2)\n",
"ARIMA(2, 1, 2) - AIC:472.0229894684058\n",
"param: (2, 2, 0)\n",
"ARIMA(2, 2, 0) - AIC:482.2859114270986\n",
"param: (2, 2, 1)\n",
"ARIMA(2, 2, 1) - AIC:473.1825692321964\n",
"param: (2, 2, 2)\n",
"ARIMA(2, 2, 2) - AIC:460.7219115408243\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" \"Check mle_retvals\", ConvergenceWarning)\n"
]
}
],
"source": [
"bestAIC = np.inf\n",
"bestModel = None\n",
"for param in pdq:\n",
" print('param:',param)\n",
" try:\n",
" mod = sm.tsa.statespace.SARIMAX(train_monthly_sales_df,\n",
" order=param,\n",
" enforce_stationarity=False,\n",
" enforce_invertibility=False,\n",
" trend='c')\n",
" results = mod.fit()\n",
" print('ARIMA{} - AIC:{}'.format(param, results.aic))\n",
" if results.aic < bestAIC:\n",
" bestAIC = results.aic\n",
" bestModel = results\n",
" print('****BEST****')\n",
" except:\n",
" continue\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Statespace Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Sales</td> <th> No. Observations: </th> <td>36</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>SARIMAX(0, 2, 2)</td> <th> Log Likelihood </th> <td>-224.625</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Thu, 02 Jan 2020</td> <th> AIC </th> <td>457.250</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>12:21:25</td> <th> BIC </th> <td>462.986</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>01-01-2014</td> <th> HQIC </th> <td>459.119</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td>- 12-01-2016</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>opg</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>intercept</th> <td> 0.9168</td> <td> 2.503</td> <td> 0.366</td> <td> 0.714</td> <td> -3.989</td> <td> 5.822</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ma.L1</th> <td> -1.6232</td> <td> 0.315</td> <td> -5.152</td> <td> 0.000</td> <td> -2.241</td> <td> -1.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ma.L2</th> <td> 0.6232</td> <td> 0.243</td> <td> 2.570</td> <td> 0.010</td> <td> 0.148</td> <td> 1.098</td>\n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 1.029e+05</td> <td> 4.48e-06</td> <td> 2.3e+10</td> <td> 0.000</td> <td> 1.03e+05</td> <td> 1.03e+05</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Ljung-Box (Q):</th> <td>nan</td> <th> Jarque-Bera (JB): </th> <td>0.28</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Q):</th> <td>nan</td> <th> Prob(JB): </th> <td>0.87</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Heteroskedasticity (H):</th> <td>0.51</td> <th> Skew: </th> <td>0.14</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(H) (two-sided):</th> <td>0.31</td> <th> Kurtosis: </th> <td>2.62</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 1.64e+27. Standard errors may be unstable."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Statespace Model Results \n",
"==============================================================================\n",
"Dep. Variable: Sales No. Observations: 36\n",
"Model: SARIMAX(0, 2, 2) Log Likelihood -224.625\n",
"Date: Thu, 02 Jan 2020 AIC 457.250\n",
"Time: 12:21:25 BIC 462.986\n",
"Sample: 01-01-2014 HQIC 459.119\n",
" - 12-01-2016 \n",
"Covariance Type: opg \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"intercept 0.9168 2.503 0.366 0.714 -3.989 5.822\n",
"ma.L1 -1.6232 0.315 -5.152 0.000 -2.241 -1.006\n",
"ma.L2 0.6232 0.243 2.570 0.010 0.148 1.098\n",
"sigma2 1.029e+05 4.48e-06 2.3e+10 0.000 1.03e+05 1.03e+05\n",
"===================================================================================\n",
"Ljung-Box (Q): nan Jarque-Bera (JB): 0.28\n",
"Prob(Q): nan Prob(JB): 0.87\n",
"Heteroskedasticity (H): 0.51 Skew: 0.14\n",
"Prob(H) (two-sided): 0.31 Kurtosis: 2.62\n",
"===================================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
"[2] Covariance matrix is singular or near-singular, with condition number 1.64e+27. Standard errors may be unstable.\n",
"\"\"\""
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bestModel.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The GridSearch method reveals that <b>ARIMA(0,2,2)</b> has the lowest AIC and hence the best model!\n",
"\n",
"### 7.2 AutoARIMA\n",
"AutoARIMA is a stepwise approach to search multiple combinations of p,d,q parameters and chooses the best model that has the least AIC. The orginal package is in R but fortunatly it's now availble in Python, in the pmdarima library."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The auto.arima() function will do the differencing before estimation to ensure consistent estimators. The estimation of the regression coefficients and ARIMA model is done simultaneously using MLE.\n",
"\n",
"\n",
"Recall that ARIMA assumes stationality, so we will use that time series that we have differenced our time series by a order of 1. The first value is NA so we should start from the second index."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit ARIMA: order=(1, 2, 1) seasonal_order=(0, 0, 0, 0); AIC=503.640, BIC=509.746, Fit time=0.070 seconds\n",
"Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 0); AIC=539.784, BIC=542.836, Fit time=0.004 seconds\n",
"Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 0, 0); AIC=521.895, BIC=526.474, Fit time=0.008 seconds\n",
"Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 0, 0); AIC=508.765, BIC=513.344, Fit time=0.028 seconds\n",
"Near non-invertible roots for order (0, 2, 1)(0, 0, 0, 0); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.997)\n",
"Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 0); AIC=537.788, BIC=539.315, Fit time=0.005 seconds\n",
"Fit ARIMA: order=(2, 2, 1) seasonal_order=(0, 0, 0, 0); AIC=503.211, BIC=510.843, Fit time=0.097 seconds\n",
"Near non-invertible roots for order (2, 2, 1)(0, 0, 0, 0); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.999)\n",
"Fit ARIMA: order=(1, 2, 2) seasonal_order=(0, 0, 0, 0); AIC=503.360, BIC=510.991, Fit time=0.091 seconds\n",
"Near non-invertible roots for order (1, 2, 2)(0, 0, 0, 0); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.998)\n",
"Fit ARIMA: order=(0, 2, 2) seasonal_order=(0, 0, 0, 0); AIC=501.408, BIC=507.513, Fit time=0.047 seconds\n",
"Near non-invertible roots for order (0, 2, 2)(0, 0, 0, 0); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.998)\n",
"Fit ARIMA: order=(2, 2, 0) seasonal_order=(0, 0, 0, 0); AIC=512.450, BIC=518.556, Fit time=0.012 seconds\n",
"Fit ARIMA: order=(2, 2, 2) seasonal_order=(0, 0, 0, 0); AIC=505.208, BIC=514.366, Fit time=0.110 seconds\n",
"Near non-invertible roots for order (2, 2, 2)(0, 0, 0, 0); setting score to inf (at least one inverse root too close to the border of the unit circle: 1.000)\n",
"Total fit time: 0.490 seconds\n"
]
}
],
"source": [
"auto_model = auto_arima(train_monthly_sales_df, start_p=1, start_q=1,\n",
" test='adf', # use adftest to find optimal 'd'\n",
" max_p=3, max_q=3, # maximum p and q\n",
" m=1, # frequency of series\n",
" d=None, # let model determine 'd'\n",
" seasonal=False, # No Seasonality\n",
" start_P=0, \n",
" D=0, \n",
" trace=True,\n",
" error_action='ignore', \n",
" suppress_warnings=True, \n",
" stepwise=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The AutoARIMA determines that a ARIMA(0,2,2) model has the best results which collides with our Grid search method! Notice that the coefficients are extremely close, albeit not exact. "
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Statespace Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>36</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>SARIMAX(0, 2, 2)</td> <th> Log Likelihood </th> <td>-246.704</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Thu, 02 Jan 2020</td> <th> AIC </th> <td>501.408</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>12:27:30</td> <th> BIC </th> <td>507.513</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>0</td> <th> HQIC </th> <td>503.490</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td> - 36</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>opg</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>intercept</th> <td> 0.6247</td> <td> 5.088</td> <td> 0.123</td> <td> 0.902</td> <td> -9.348</td> <td> 10.597</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ma.L1</th> <td> -1.6170</td> <td> 15.326</td> <td> -0.106</td> <td> 0.916</td> <td> -31.656</td> <td> 28.422</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ma.L2</th> <td> 0.6178</td> <td> 9.379</td> <td> 0.066</td> <td> 0.947</td> <td> -17.764</td> <td> 19.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 1.008e+05</td> <td> 1.56e+06</td> <td> 0.065</td> <td> 0.948</td> <td>-2.95e+06</td> <td> 3.16e+06</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Ljung-Box (Q):</th> <td>nan</td> <th> Jarque-Bera (JB): </th> <td>0.46</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Q):</th> <td>nan</td> <th> Prob(JB): </th> <td>0.80</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Heteroskedasticity (H):</th> <td>1.02</td> <th> Skew: </th> <td>0.20</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(H) (two-sided):</th> <td>0.98</td> <th> Kurtosis: </th> <td>2.60</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Statespace Model Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 36\n",
"Model: SARIMAX(0, 2, 2) Log Likelihood -246.704\n",
"Date: Thu, 02 Jan 2020 AIC 501.408\n",
"Time: 12:27:30 BIC 507.513\n",
"Sample: 0 HQIC 503.490\n",
" - 36 \n",
"Covariance Type: opg \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"intercept 0.6247 5.088 0.123 0.902 -9.348 10.597\n",
"ma.L1 -1.6170 15.326 -0.106 0.916 -31.656 28.422\n",
"ma.L2 0.6178 9.379 0.066 0.947 -17.764 19.000\n",
"sigma2 1.008e+05 1.56e+06 0.065 0.948 -2.95e+06 3.16e+06\n",
"===================================================================================\n",
"Ljung-Box (Q): nan Jarque-Bera (JB): 0.46\n",
"Prob(Q): nan Prob(JB): 0.80\n",
"Heteroskedasticity (H): 1.02 Skew: 0.20\n",
"Prob(H) (two-sided): 0.98 Kurtosis: 2.60\n",
"===================================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
"\"\"\""
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"auto_model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Validating model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We should plot out the model diagnostics to check if the model is valid."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x576 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"auto_model.plot_diagnostics(figsize=(14, 8))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's a good sign that the residuals are near normally distributed(bottom left). Not perfect though; standardized residuals show that there's some signal that has yet to be modelled. The correlogram looks fine; the auto correlation is within bounds.\n",
"## 9. Forecasting with ARIMA\n",
"With the best model, let's try to predict the values for 1 year period and see how accurate it is."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some technical note: We could use the get_prediction() and get_forecast() functions for prediction. One thing that intially confused me was the difference between them. Note that get_prediction() and get_forecast() are actually the same. The latter is designed to do 1-step out-of-sample forecast from the last available data, or perform multi-step out-of-sample forecast which is parameterized by \"step\" argument.\n",
"\n",
"For example:\n",
"ts_model.forecast(steps=12)\n",
"\n",
"It's more flexible with predict() where you just need to supply start and end date which could span across in-sample to out-sample. If you don't supply the end date, it will just do a in-sample forecast.\n",
"\n",
"For example:\n",
"ts_model.get_prediction(start=pd.to_datetime('2014-01-01'), end=pd.to_datetime('2017-12-01'),dynamic=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 9.1 Generating forecast"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"#autoarima\n",
"auto_oos_forecasts, auto_oos_forecasts_ci = auto_model.predict(n_periods = 12, return_conf_int=True)\n",
"#turn it into series so that we could plot easily...\n",
"auto_oos_forecasts = pd.Series(auto_oos_forecasts, index=valid_monthly_sales_df.index)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"#gridsearch\n",
"#in-sample forecasts\n",
"#results = bestModel.fit()\n",
"is_forecasts = bestModel.get_prediction(start=pd.to_datetime('2014-01-01'),dynamic=False)\n",
"#out-of-sample forecasts\n",
"oos_forecasts = bestModel.get_prediction(start=pd.to_datetime('2017-01-01'), end=pd.to_datetime('2017-12-01'),dynamic=False)\n",
"\n",
"#forecasted values stored in is_forecasts.predicted_mean\n",
"#conf_int contains the confidence intervals\n",
"is_forecasts_ci = is_forecasts.conf_int()\n",
"oos_forecasts_ci = oos_forecasts.conf_int()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 9.2 Visualizing forecast\n",
"Finally, we could see how the models' forecast fare against actual observed values. Since the model also provide 95% prediction interval, we also include them in the plot to have a better idea of the upper and lower bound values. For the sake of rigor, we'll show the forecast for both models selected via GridSearch method and AutoARIMA."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = monthly_sales_df.plot(label='observed')\n",
"is_forecasts.predicted_mean.plot(ax=ax, label='In-sample forecast(GridSearch)', alpha=.7, figsize=(14, 7))\n",
"oos_forecasts.predicted_mean.plot(ax=ax, label='Out-of-sample forecast(GridSearch)', alpha=.7, figsize=(14, 7))\n",
"auto_oos_forecasts.plot(ax=ax, label='Out-of-sample forecast(AutoARIMA)', alpha=.7, figsize=(14, 7))\n",
"ax.fill_between(is_forecasts_ci.index,\n",
" is_forecasts_ci.iloc[:, 0],\n",
" is_forecasts_ci.iloc[:, 1], color='k', alpha=.2)\n",
"ax.fill_between(oos_forecasts_ci.index,\n",
" oos_forecasts_ci.iloc[:, 0],\n",
" oos_forecasts_ci.iloc[:, 1], color='k', alpha=.2)\n",
"ax.set_xlabel('Date')\n",
"ax.set_ylabel('Sales')\n",
"plt.title('Sales Predictions vs Actual')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have a couple of observations:\n",
"\n",
"1) The models produced pretty decent in-sample forecasts(obviously) but the out-of-sample forecast appears like a constant line, although it's generally in the right direction.\n",
"\n",
"2) As the forecast goes further, the prediction interval increases, demonstrating uncertainity. The prediction at each time steps exhibits some variation, and that variation accumlates as a function of the number of steps."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 9.3 Quantitative evaluation\n",
"A quantitative way to assess the accuracy and compare models is to use metrics such as RMSE and MSE that basically measures the difference between the actual values and the predicted values."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Mean Squared Error of our forecasts is 454280.48\n",
"The Root Mean Squared Error of our forecasts is 674.0\n"
]
}
],
"source": [
"y_forecasted = oos_forecasts.predicted_mean\n",
"y_truth = monthly_sales_df['2017-01-01':]\n",
"mse = ((y_forecasted - y_truth.iloc[:,0]) ** 2).mean()\n",
"print('The Mean Squared Error of our forecasts is {}'.format(round(mse, 2)))\n",
"print('The Root Mean Squared Error of our forecasts is {}'.format(round(np.sqrt(mse), 2)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It tells us that our model was able to forecast the average daily sales in the test set within 660.91 of the real sales. Since our daily sales range from around 400 to over 1200, the variance of error is considerable high and we might be even do a better job without the model. \n",
"\n",
"## 10. Seasonality - SARIMA\n",
"Notice that our plot above didn't take advantage of the seasonal pattern that we spotted earlier. It turns out that we could include a seasonal(S) component to the ARIMA model, by repeating the same AR, MA, I component for a seasonal cycle and we call it <b>SARIMA</b>.\n",
"\n",
"For example, we want to capture the previous festive period(12 months ago), we could include add a phi(𝜙) parameter and a seasonal term that hold that value, or even 2 periods(24) back. This is actually very similar to the original AR term which holds value a month back.\n",
"<img src='ts_pics/seasonal.png' width=500>\n",
"\n",
"At the same time, we also add an additional parentheses containing the seasonal orders to the expressed in the notation:\n",
"SARIMA(p,d,q)(P,D,Q,s)\n",
"\n",
"Seasonal AR, I, MA order is denoted by the uppercase variants and \"s\" refers to the length of cycle. If s=1, it means there's no seasonality. Assuming the frequency of time series is monthly, we could use s=12 for yearly seasonaality.\n",
"\n",
"For example, if we have:\n",
"SARIMA(1,0,2)(2,0,1,12)\n",
"\n",
"What it means: Ignoring the first parentheses, the first 3 orders of the 2nd parentheses are simply the seasonal variations of the ARIMA orders. We are including the lagged values from 12 and 24 periods ago. Intuitively, we are interested in every \"s\"-th value.\n",
"\n",
"Formally, it looks like this:\n",
"<img src='ts_pics/sarima.png' width=500>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 10.1 AutoARIMA-Seasonal\n",
"We use the same method as above except for a few parameter changes to activate the search in the seasonal parameter space."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit ARIMA: order=(1, 2, 1) seasonal_order=(0, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 1, 0, 12); AIC=344.604, BIC=346.786, Fit time=0.006 seconds\n",
"Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 1, 0, 12); AIC=329.207, BIC=333.571, Fit time=0.113 seconds\n",
"Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 1, 0, 12); AIC=342.680, BIC=343.771, Fit time=0.008 seconds\n",
"Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 1, 0, 12); AIC=331.805, BIC=335.078, Fit time=0.026 seconds\n",
"Fit ARIMA: order=(1, 2, 0) seasonal_order=(2, 1, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(1, 2, 0) seasonal_order=(2, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 1, 0, 12); AIC=340.150, BIC=343.423, Fit time=0.108 seconds\n",
"Fit ARIMA: order=(2, 2, 0) seasonal_order=(1, 1, 0, 12); AIC=326.957, BIC=332.412, Fit time=0.119 seconds\n",
"Fit ARIMA: order=(2, 2, 0) seasonal_order=(0, 1, 0, 12); AIC=331.612, BIC=335.976, Fit time=0.065 seconds\n",
"Fit ARIMA: order=(2, 2, 0) seasonal_order=(2, 1, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(2, 2, 0) seasonal_order=(1, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(2, 2, 0) seasonal_order=(0, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(2, 2, 0) seasonal_order=(2, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(3, 2, 0) seasonal_order=(1, 1, 0, 12); AIC=324.808, BIC=331.355, Fit time=0.309 seconds\n",
"Fit ARIMA: order=(3, 2, 0) seasonal_order=(0, 1, 0, 12); AIC=329.456, BIC=334.911, Fit time=0.068 seconds\n",
"Fit ARIMA: order=(3, 2, 0) seasonal_order=(2, 1, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(3, 2, 0) seasonal_order=(1, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(3, 2, 0) seasonal_order=(0, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(3, 2, 0) seasonal_order=(2, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(3, 2, 1) seasonal_order=(1, 1, 0, 12); AIC=323.696, BIC=331.334, Fit time=0.464 seconds\n",
"Fit ARIMA: order=(3, 2, 1) seasonal_order=(0, 1, 0, 12); AIC=327.300, BIC=333.846, Fit time=0.180 seconds\n",
"Near non-invertible roots for order (3, 2, 1)(0, 1, 0, 12); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.999)\n",
"Fit ARIMA: order=(3, 2, 1) seasonal_order=(2, 1, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(3, 2, 1) seasonal_order=(1, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(3, 2, 1) seasonal_order=(0, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(3, 2, 1) seasonal_order=(2, 1, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
"Fit ARIMA: order=(2, 2, 1) seasonal_order=(1, 1, 0, 12); AIC=321.940, BIC=328.486, Fit time=0.399 seconds\n",
"Near non-invertible roots for order (2, 2, 1)(1, 1, 0, 12); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.996)\n",
"Fit ARIMA: order=(3, 2, 2) seasonal_order=(1, 1, 0, 12); AIC=325.973, BIC=334.702, Fit time=0.488 seconds\n",
"Near non-invertible roots for order (3, 2, 2)(1, 1, 0, 12); setting score to inf (at least one inverse root too close to the border of the unit circle: 1.000)\n",
"Fit ARIMA: order=(2, 2, 2) seasonal_order=(1, 1, 0, 12); AIC=324.518, BIC=332.156, Fit time=0.262 seconds\n",
"Near non-invertible roots for order (2, 2, 2)(1, 1, 0, 12); setting score to inf (at least one inverse root too close to the border of the unit circle: 1.000)\n",
"Total fit time: 2.664 seconds\n"
]
}
],
"source": [
"auto_sea_model = auto_arima(train_monthly_sales_df, start_p=1, start_q=1,\n",
" test='adf', # use adftest to find optimal 'd'\n",
" max_p=3, max_q=3, # maximum p and q\n",
" m=12, # frequency of series\n",
" d=None, # let model determine 'd'\n",
" seasonal=True, # No Seasonality\n",
" start_P=0, \n",
" D=None, \n",
" trace=True,\n",
" error_action='ignore', \n",
" suppress_warnings=True, \n",
" stepwise=True)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Statespace Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>36</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>SARIMAX(2, 2, 1)x(1, 1, 0, 12)</td> <th> Log Likelihood </th> <td>-154.970</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Thu, 02 Jan 2020</td> <th> AIC </th> <td>321.940</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>12:31:40</td> <th> BIC </th> <td>328.486</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>0</td> <th> HQIC </th> <td>323.482</td>\n",
"</tr>\n",
"<tr>\n",
" <th></th> <td> - 36</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>opg</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>intercept</th> <td> 4.1748</td> <td> 22.137</td> <td> 0.189</td> <td> 0.850</td> <td> -39.213</td> <td> 47.562</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ar.L1</th> <td> -0.5462</td> <td> 0.369</td> <td> -1.481</td> <td> 0.139</td> <td> -1.269</td> <td> 0.177</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ar.L2</th> <td> -0.1845</td> <td> 0.335</td> <td> -0.552</td> <td> 0.581</td> <td> -0.840</td> <td> 0.471</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ma.L1</th> <td> -0.9959</td> <td> 16.629</td> <td> -0.060</td> <td> 0.952</td> <td> -33.587</td> <td> 31.596</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ar.S.L12</th> <td> -0.7072</td> <td> 0.204</td> <td> -3.460</td> <td> 0.001</td> <td> -1.108</td> <td> -0.307</td>\n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 4.381e+04</td> <td> 7.28e+05</td> <td> 0.060</td> <td> 0.952</td> <td>-1.38e+06</td> <td> 1.47e+06</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Ljung-Box (Q):</th> <td>nan</td> <th> Jarque-Bera (JB): </th> <td>1.31</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Q):</th> <td>nan</td> <th> Prob(JB): </th> <td>0.52</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Heteroskedasticity (H):</th> <td>0.42</td> <th> Skew: </th> <td>-0.44</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(H) (two-sided):</th> <td>0.27</td> <th> Kurtosis: </th> <td>2.20</td> \n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Statespace Model Results \n",
"==========================================================================================\n",
"Dep. Variable: y No. Observations: 36\n",
"Model: SARIMAX(2, 2, 1)x(1, 1, 0, 12) Log Likelihood -154.970\n",
"Date: Thu, 02 Jan 2020 AIC 321.940\n",
"Time: 12:31:40 BIC 328.486\n",
"Sample: 0 HQIC 323.482\n",
" - 36 \n",
"Covariance Type: opg \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"intercept 4.1748 22.137 0.189 0.850 -39.213 47.562\n",
"ar.L1 -0.5462 0.369 -1.481 0.139 -1.269 0.177\n",
"ar.L2 -0.1845 0.335 -0.552 0.581 -0.840 0.471\n",
"ma.L1 -0.9959 16.629 -0.060 0.952 -33.587 31.596\n",
"ar.S.L12 -0.7072 0.204 -3.460 0.001 -1.108 -0.307\n",
"sigma2 4.381e+04 7.28e+05 0.060 0.952 -1.38e+06 1.47e+06\n",
"===================================================================================\n",
"Ljung-Box (Q): nan Jarque-Bera (JB): 1.31\n",
"Prob(Q): nan Prob(JB): 0.52\n",
"Heteroskedasticity (H): 0.42 Skew: -0.44\n",
"Prob(H) (two-sided): 0.27 Kurtosis: 2.20\n",
"===================================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
"\"\"\""
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"auto_sea_model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"#autoarima\n",
"#technical note: the predict() for autoarima lib is different from statsmodel's arima\n",
"auto_sea_is_forecasts, auto_sea_is_forecasts_ci = auto_sea_model.predict_in_sample(return_conf_int=True)\n",
"auto_sea_is_forecasts = pd.Series(auto_sea_is_forecasts, index=train_monthly_sales_df.index)\n",
"auto_sea_is_forecasts_ci = pd.DataFrame(auto_sea_is_forecasts_ci,index=train_monthly_sales_df.index)\n",
"\n",
"\n",
"auto_sea_oos_forecasts, auto_sea_oos_forecasts_ci = auto_sea_model.predict(n_periods = 12, return_conf_int=True)\n",
"#turn it into series so that we could plot easily...\n",
"auto_sea_oos_forecasts = pd.Series(auto_sea_oos_forecasts, index=valid_monthly_sales_df.index)\n",
"auto_sea_oos_forecasts_ci = pd.DataFrame(auto_sea_oos_forecasts_ci,index=valid_monthly_sales_df.index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 10.2 Visualizing forecast"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
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HOhs3biQ9PZ369eszcODAfH9sbGy4cOGCOsmsVKkSzs7OPHr0iJMnTxZ5jKLeM4VCAcDt27fzbMtdVut58fHxPH78GDc3tzyJcHJysrpbvBBCvE0kGRZCiLfU1q1bOXjwIDk5OXm23bt3T70u6/PjW+3s7EhISOD8+fMa5YODg9m/f3+xjz1mzBgyMzMZPXo0CQkJebY/efJEI2E4duxYvq1dua2ThoaGxT52aXrZ8+jSpQsKhYKffvopT0I0b968AtclfpGOjg4jRowgPT2djz/+mNTUVI3t2dnZ3L17V/177mRN+SVOBfH390dfX581a9Zw+fJljW3ffPMNsbGxdOzYESsrq2LX+aI///xT/R4+ryTva5cuXTAzM2Pr1q3qLsz9+vXLU+7QoUOkpKT8o2Pm1h8UFMTixYvz/cntrv38RFq5PSw++eSTfD8vz09GVtR7ljvueP369RqTXsXHxzN9+vQ85atXr46xsTGnTp0iOTlZ/XpmZiafffZZiR5MCSHE6066SQshxFvq5MmTrFixAqVSScuWLdVjaGNiYvj1119JTU3Fzc1N3c0TIDAwkP379+Pt7U337t0xMTHh1KlTHD9+HD8/P3XX0aIMGDCAM2fO8N1339GoUSO8vLyws7Pj8ePH3Lx5k2PHjuHh4aHu6rpkyRIOHDhAmzZtcHBwoEqVKvz999+EhYVhZGREYGBg6V+gMjiPypUrs2jRIoYMGYKPjw89evSgRo0ahIeHc+HCBdzd3Yvd5Xry5MmcOnWK/fv306RJEzp37oyZmRmxsbEcOXKE999/X70kT/v27dHW1mbFihUkJCSoxwGPHDmywLWG7ezsCAoKYsKECXh4eNC9e3eUSiUREREcPXoUa2trFixY8I+u35YtW1i9ejWtWrXC0dERc3Nzbt26xZ49e9DW1uajjz4qdl0GBgb06tWL1atXs3TpUo21hZ83bdo0bt68SevWrbGzs8PQ0JCoqCj279+Pubm5etbrghw9epTLly/j5OSEu7t7geX69u3LzJkz2bFjB3PmzMHU1JQPPviAiIgINmzYQOPGjfHx8cHS0pJ79+5x/PhxmjdvzvLlywFwc3OjcuXK/PTTT+jr62NjY4OWlhYBAQHY2dnRpEkT2rdvz++//867777Lu+++S0JCAr/++ivt27fP88BKW1ubUaNG8e233+Lu7k6XLl3IzMzkyJEjJCQk0LZt23x7AQghxJtMkmEhhHhLjRs3jjp16nDw4EEuXLjAwYMHSUlJwczMDDc3N7p3787777+vMbvye++9x//+9z/mz5/P9u3b0dbWpmnTpuzevZsbN24UOxmGZy2hHTt2ZM2aNfzxxx8kJCRgampKjRo1GDZsGH369FGXHT58OGZmZvz5559ERkaSmZmJlZUVffv2ZezYsSWeVbo0vMx5APj5+bFt2zaCgoLYuXMn+vr6uLu789tvv/Htt98WOxnW19dn8+bNrFu3jh9//JEtW7aQlZWFUqmkdevWeHt7q8vWrl2bNWvWsGjRItavX69uSfb39y8wGQYYMmQItWrVYvHixYSGhvL06VOsrKwYOXIkn376abEn/CpI7969yczMJCIigp07d5KSkoJSqaRTp06MGTOGpk2bvlR9AwYMYPXq1WRmZtKjR498lxGaOHEioaGhnDp1Sp381ahRg8DAQEaPHo2NjU2hx/jhhx8A+OCDDwotp1Ao8PX1ZfPmzWzatImRI0cCsHTpUjw9Pfnhhx/4+eefSU1NxcLCgkaNGmmMezc1NWXDhg3MmTOHn376Sd2a27JlS+zs7AAICQlh1qxZ/Pzzz6xatQo7OzvGjRvH2LFj810v+PPPP6dq1aqEhITwww8/YGJiwrvvvsu0adOYM2dOoecjhBBvIq3ExERV0cWEEEIIIYQQQog3h4wZFkIIIYQQQgjx1pFkWAghhBBCCCHEW0eSYSGEEEIIIYQQbx1JhoUQQgghhBBCvHUkGRZCCCGEEEII8daRZFgIIYQQQgghxFtHkmEhhBBCCCGEEG8dSYaFEK+lK1eulHcIQry15P4TovzI/SdE6ZFkWAghhBBCCCHEW0eSYSGEEEIIIYQQbx1JhoUQQgghhBBCvHUkGRZCCCGEEEII8dbRLe8AhBBCCCEqiqdPn5KVlVXeYQhRIENDQx4/flzeYQhRoVSqVAld3ZdPbV/bZHjBggXMnj2bESNG8J///AcAlUrF3LlzWbduHYmJiTRt2pT58+fzzjvvqPdLTExk8uTJ/PLLLwB07tyZefPmoVAo1GWioqKYNGkSf/31F2ZmZgwePJjJkyejpaX1ak9SCCGEEK9Meno6AKampuUciRAFMzAwwNDQsLzDEKLCUKlUJCYmUqVKlZdOiF/LbtInTpxg3bp1uLi4aLy+aNEili5dSlBQEAcOHKB69er06NGDJ0+eqMsMHz6cs2fPsmXLFrZu3crZs2cZNWqUentSUhI9evTAwsKCAwcOMHfuXBYvXsySJUte2fkJIYQQ4tVLS0vD2Ni4vMMQQgjxErS0tFAoFDx9+vSl933tkuHHjx8zYsQIFi9erNGaq1KpWL58OR9//DF+fn44OzuzfPlykpOT2bp1KwDR0dHs27ePhQsX0qJFC9zc3Pj2228JCwtTr9m2ZcsWUlNTWb58Oc7Ozvj5+TF+/HiWLVuGSqUql3MWQgghxKshvcCEEOL1U9K/3a9dMpyb7LZv317j9ZiYGO7du4enp6f6NSMjI9zd3YmIiAAgMjKSypUr06JFC3WZli1bUqlSJY0yrVq1wsjISF3Gy8uLuLg4YmJiyvLUhBBCCCGEEEK8Iq/VmOF169Zx7do1Vq5cmWfbvXv3AKhevbrG69WrVycuLg6A+/fvU7VqVY0nB1paWlSrVo379++ry9SoUSNPHbnbHBwc8o0tt2VZCPHqyH0nRPl5E+8/Q0NDDAwMyjsMIYqUlpZW3iEIUeEkJSWpc7rn1alTp8B9Xptk+MqVK3z55Zfs3bsXfX39Asu92ESuUqnyJL8vKqpMbvfowprfC7vIQojSd+XKFbnvhCgnb+r99/jx47dyYqKYmBgaNmzIwYMHady4cXmHI4qQlpb2Vn5OhSiKiYkJtra2L7XPa9NNOjIykvj4eFq1akXVqlWpWrUqR48eZfXq1VStWhVzc3OAPE8DHj58qG7ZtbCw4OHDhxpjf1UqFfHx8Rpl8qsD8rY6CyGEEEKUt4cPHzJx4kTq16+PhYUFderUwdfXl4MHD5Z3aEIIUaG9Ni3DPj4+eZ5WjhkzBkdHRyZMmEDt2rVRKpUcPHiQJk2aAM+enIWHh/Pll18C4ObmRnJyMpGRkepxw5GRkTx9+lT9u5ubGzNnztR46nbw4EGsrKywt7d/VacrhBBCCFEsAwcOJDU1lSVLllCzZk0ePnzI0aNHefToUXmHJoQQFdpr0zKsUChwdnbW+DE2NsbMzAxnZ2e0tLQIDAxk4cKF7Nq1iwsXLjB69GgqVapE7969Aahbty7vvfcen3zyCSdOnCAyMpJPPvmETp06qbt79e7dGyMjI0aPHs2FCxfYtWsXCxcuZPTo0TLDpBBCCCEqlMTERMLDw5k5cybt27fHzs6OJk2aMG7cOHr16gXApk2b8PDwwMbGhtq1azNo0CBiY2MLrffSpUv4+/ur9xk2bJh6fhaAqKgofH19sbW1xcbGhtatW3P48OEyPVchhChtr03LcHGMHz+e1NRUJk2aRGJiIk2bNuWnn36iSpUq6jKrVq1iypQp9OzZEwBvb2/mzZun3m5qasr27dv59NNP8fDwQKFQMGbMGMaOHfvKz0cIIYQQ5Uux9s4rPV7iEOuXKl+5cmUqV67Mnj17aNmyZb5jSTMyMpg6dSpOTk7Ex8czY8YMhg0bxt69e/Ot8+7du3Tp0oWBAwcye/ZsMjMzmT17Nv369WPfvn1oa2szYsQIXF1d2b9/P7q6ukRFRck4ViHEa0crMTFRFs8VQrx2Ll68iJOTEzo6OuUdihBvnTd5Ai1TU1ON1yp6Mgywc+dOdYNAgwYNaNGiBd27d6dZs2b5lr98+TJubm5ERUVhbW2dZwKtr776ioiICHbt2vV/cSUm4uDgwP79+2natCm2trYEBQXRv3//Ep+rKBmZQEuI/OX3N7wor003aSGEyKVSqYiOjiY+Pr68QxFCiHLn5+fHpUuX+N///sd7771HZGQk7733HgsWLADg9OnT9OvXD1dXV2xsbPDw8ADg9u3b+dZ35swZjh07hrW1tfrHxcUFgOvXrwMwevRoPvroI7p168b8+fO5fPnyKzhTIYQoXZIMCyFeOzdv3iQ7O5vExMTyDkUIISoEQ0NDPDw8mDJlCr/++isDBw5k7ty5PH78mF69emFsbMzKlSs5cOAAW7duBZ51n85PTk4OHTt25MiRIxo/f/31F506dQJg6tSpRERE4OPjQ2RkJK1btyYkJOSVna8QQpSGN2rMsBDizZeUlMSdO3fQ09MjNTW1vMMRQrzhStJtuSKoW7cuWVlZnDt3jvj4eKZPn46DgwOARvfn/DRs2JDt27dja2uLnp5egeUcHR1xdHTkww8/ZMKECYSEhDBw4MDSPA0hhChT0jIshHhtZGdnc/HiRYyNjYFn46aeXzdcCCHeNo8ePaJbt25s2rSJ8+fPc+PGDXbs2MF///tf2rdvT7169TAwMGDVqlXcuHGDsLAwvv7660LrHD58OElJSQwZMoSTJ09y48YNDh06xPjx43ny5Ampqal8+umnHDlyhJiYGE6ePMnx48epW7fuKzprIYQoHdIyLIR4bURHR2tMmJWdnU16erpMJCKEeGtVqlSJ5s2bs2LFCq5du0ZGRgZWVlb07t2bSZMmYWZmxvLly/nyyy9ZvXo1Li4ufPXVV+pll/JjZWVFWFgYs2bNolevXqSnp6vHGhsYGADPJtQKDAzk/v37mJub06lTJ2bPnv2qTlsIIUqFzCYthHgtxMbGcvPmTXXie/PmTSwtLbGzs8PS0rKcoxPi7fI2zSYtREUjs0kLkT+ZTVoI8UZKSUnhxo0beb789fX1efz4cTlFJYQQQgghXmeSDAshKrScnByioqIwMjLKd3taWtorjkgIIYQQQrwJJBkWQlRof//9NwBaWlr5bk9NTZVJtIQQQgghxEuTZFgIUWE9fPiQhw8fFrq0R05OjrQOCyGEEEKIlybJsBCiQsrIyODvv/9WL6NUEF1dXRk3LIQQQgghXpokw0K8oVJTU0lJSSnvMEpEpVIRFRWFvr5+kWVlEi0hhBBCCFESkgwL8Ya6desWV65cKe8wSuTGjRtkZGSgrV28P1GpqallHJEQQgghhHjTSDIsxBtIpVKRmJjI06dPSUhIKO9wXkpiYiJxcXEYGBgUe5+0tDSZREsIIYQQQrwUSYZLSUxMjLROiQojKSmJ7OxsjI2NuX79enmHU2xZWVlER0cXOU74RdnZ2XL/CSGEEEKIlyLJcCl58OABp06dIjIykkuXLhEfH092dnZ5hyXeUnfv3sXQ0BCA9PR0Hj58WM4RFc+lS5fQ1dV96f309fVJTEwsg4iEEEKUpyNHjqBQKIiPj/9H9Rw/fhx3d3eqV6+Oj49PKUVX8cyZM4cxY8aUdxjiNbdhwwasra3Vv3/33Xf07du3HCMqO5IMlxJdXV2MjY3R19cnLS2NK1euEBERwZkzZ7hx48ZrO5GReP3kdpHOXZfXyMiIGzduVPhuxHfu3OHJkyfo6Oi89L56eno8efKkDKISQoiKLTAwkICAgPIOo8L77LPPcHV15dSpU6xfv768w/lHevTowaRJk/K8/uDBA5YtW5bvtjNnzmBubk6nTp1KdMyYmBgUCgWnTp0q0f4AkydPxtzcnHXr1uXZtmHDBhQKhfqnTp06BAQEcPHiRY1yL37e58yZg0KhwM/PL0+dq1evRqFQ0KpVqzzbMjIycHR0xMbGptiTcJ47d45+/frh5OSEUqnE1dWVgQMHcvPmzWLt/zobNGgQp0+f5tixY+UdSqmTZLiMGBoaYmxsjEqlIj4+ntOnTxMZGcmFCxd4+PAhWVlZ5R2ieEMlJiaSk5Oj8VpWVhZ3794tp4iKlpKSQkxMDEZGRv+oDiGEECI/165do127dkUikw8AACAASURBVNjY2GBmZlaiOjIyMko5qtIVHBxMkyZNcHBwyHfbsGHDuHjxItHR0a88tvT0dLZs2cInn3xCcHBwvmWMjY2Jjo7m0qVLbN68mZSUFPz9/Yu87kqlkvDwcGJiYjReX79+PTY2NvnuExoair29Pc2aNWPr1q1Fxv/w4UP8/PyoXLkymzdvJjIykhUrVlCzZs234mG8gYEBvXv3ZuXKleUdSqmTZPgV0NHRUbcaZ2RkcPXqVSIjIzl9+jTXrl0jOTm5vEMUb5C4uLg8SaWhoSG3bt3KkyRXBDk5OURFRf2jRBiefdFW9NZvIYQoa7ktZ8uXL+edd97B3t6e0aNHF/nAcNeuXbi7u2NpaYmDgwNdunTh/v37AFy/fl3dIlajRg3atWvHL7/8orF//fr1CQoKIjAwEBsbG1xcXPjpp59ITExk6NChWFtb06RJEw4cOKDeJ7cL9C+//EKbNm1QKpW0b9+e06dPFxprREQEXbp0wcrKinfeeYcJEyaQlJSUb9ncFs2kpCTGjh2LQqFgw4YNABw9ehQvLy+USiV16tRh6tSpGomXj48PEyZMYNq0aTg6OqpbVR8/fsz48eOpXbs2NjY2dOnSJU+L6YkTJ+jWrRs1atTAzs4OX19f4uLiANi3bx/e3t7Y29vj4OBAz5498ySoQUFBuLq6YmFhgZOTE6NGjQKevb/h4eGsWrVK3YqamwRu3boVb2/vPNcgNTWVLVu2MGjQIHx9fQkJCcn3Gr14DgqFgp07dwLQsGFDADw8PFAoFOqu5jk5OcybNw8XFxcsLCxwd3cnNDQ0Twy7d+/Gzs6OiRMncvnyZS5cuJCnjJaWFkqlEktLSxo3bszo0aOLtTKGubk5HTt2VL+vAOfPn+fKlSv5thgDhISEEBAQQN++ffNcj/wcP36cxMREli5dSqNGjbC3t6dNmzZ8+eWXuLi4qMvFxsYydOhQ7O3tsbe3x9/fn6tXr6q3F+deKuxeBFi7di2NGzemevXqNG7cOE9Lu0Kh4IcffmDQoEHUqFGDhg0bsmnTJo0yM2fOpFmzZlhaWlK/fn2++OIL0tLSCr0G3t7e7N27941rfHj5wXniH3t+ltyEhATi4uLQ0dGhWbNmJRovWVaSk5PJyMjA3Ny8vEMRxZSTk0NSUpJ6vPCL7ty5g62t7SuOqnBXrlxBS0tL3a27pHIn0XrZybeEEKIwht9OfaXHS/tkzj+uIzw8HKVSyY4dO7hz5w6DBw+mdu3aTJgwId/y9+7dY9iwYXzxxRf4+vry9OlTTp48qd6enJxMhw4dmDZtGkZGRvz0008MHDiQo0eP4uTkpC63fPlypk2bxqeffsr3339PYGAg7dq1o2fPnkybNo1vvvmGkSNHcv78eY3vqenTpzN37lysrKwICgrC39+f06dP5/v3PCoqip49e/LZZ5+xePFiEhISmDp1KmPHjs23xdHGxobo6GgaN27M9OnT6dmzJyYmJsTGxtKnTx8CAgJYtmwZ169f56OPPkJbW5uvvvpKvf/mzZsZNGgQe/fuRaVSoVKpCAgIwMTEhE2bNmFmZsbGjRvx9fXlxIkTWFpacu7cObp160ZAQABfffUVBgYGHDt2TN0r8OnTp3z44Ye4urqSmprK/Pnz6du3LxEREejr67Nz506WLFnC6tWrcXZ25uHDh5w4cQKAuXPncuXKFerWrcsXX3wBQLVq1UhISODSpUs0btw4zzXYuXMntra2uLq6EhAQwJAhQ5gxYwZ6enqFfo6ed+DAATw9Pdm2bRuurq7o6+ur3/PFixfzzTff0LhxYzZt2sTAgQM5dOgQDRo0UO8fHByMv78/xsbGdO3aleDgYObOnVvg8RITE9UttsWJc+DAgXz66ad89tlnaGtrExISQvfu3alcuXKesjdv3uSPP/7gu+++w8jIiIkTJ3L27FmNeF+kVCrJyclh586d9O7dO9//WVJSUujWrRtubm6Ehoair6/P4sWL8fPzIzIyEmNj4yLvpaLuxd27dzNp0iS+/vprPD092b9/PxMnTsTCwkLjQci8efOYMWMGM2bMICQkhLFjx9KqVSvs7OyAZ63wS5YswcrKiujoaCZMmIC+vj7Tpk0r8Bo0btyYrKwsTpw4Qfv27Yt8T14X0jJcznR0dKhUqRI6OjrqJ4YVxc2bN/N0OREV26NHjwrcpq+vT2xsbIWa2O3Bgwc8evSoVB4CGRgYFHr+QgjxtqhSpQrffPMNdevWxdPTk+7du/P7778XWD4uLo7MzEz8/Pywt7fH2dmZDz74AAsLC+BZq+/QoUNxcXGhVq1afPrppzRs2FDdapjLy8uL4cOH4+joyNSpU0lPT6dmzZr069ePWrVqMWnSJB4+fJhnHOikSZPw8vLC2dmZpUuXkpaWVmDX1f/+97/06NGDcePG4ejoSLNmzViwYAG7du3iwYMHecrr6OigVCrR0tLCxMQEpVKJkZERa9asQalUsmDBAurWrUvnzp2ZMWMGq1at0mj5srOz46uvvsLJyYm6dety+PBhzp07x7p162jatCm1atVi2rRp2Nvbq1vf/vvf/+Lq6sqiRYto0KABdevWZciQIeqH0X5+fvj5+eHo6IirqytLly4lJiaGP//8E4Bbt26hVCrx9PTE1taWxo0bM3LkSABMTU3R09PD2NgYpVKJUqlER0eHW7duoVKpUCqVea5BcHCwevKjNm3aYGRkxJ49ewr8POSnatWqwLNWWKVSqe5qvmTJEsaOHUufPn2oXbs2n3/+Oa1atWLJkiXqfW/cuEF4eDi9e/cGoG/fvmzevJn09HSNYzx9+hRra2tq1KiBg4MD27Ztw9vbW+OBS0Hee+89srKy+P3339Vdst9///18y65fvx4PDw+qVatGpUqV6Nq1a5Gtw82bN2fixIkEBgbi4OBAjx49WLBggcZ44W3btqFSqVi2bBmurq44OTmxcOFCnj59SlhYGFD0vVTUvbhkyRICAgIYOXIktWvXZtSoUfTp04dFixZpxBsQEEBAQAC1atXi888/R1dXl/DwcPX2yZMn07JlS+zt7enYsSMTJkxg27ZthV4DY2NjTExM3rjcQJLhCkJPT4979+6VdxhqWVlZJCYmkpqaWmDXI1HxPD+LdH50dHQqzB+xjIwM/v7773/cPTqXrq6uDDkQQgigbt26Gg8ZLS0t1Yni5s2bsba2Vv8cO3aM+vXr8+677+Lu7s7AgQNZs2aNxioET58+5YsvvqBFixbY29tjbW3NqVOnuH37tsZxn+8uWrlyZYyNjTVey/2H/sWk1c3NTWM/FxcXLl26lO+5nTlzJs85dO7cGeCllhKMjo6mefPmaGv/37/CrVq1IiMjg2vXrqlfa9SoUZ7jp6SkULt2bY0YLl68qD7+2bNnC205u379OsOHD6dRo0bY2tri5ORETk6O+np2796dtLQ0GjZsyNixY9mxY0eexPFFuV1cX/wf4Nq1a0RERKgTUS0tLfz9/YvVNbgoSUlJxMXF0bJlS43XW7VqpfH+rV+/nvbt26sT9bZt22JsbJynO7WxsTFHjhzh0KFDLFy4EEdHRxYuXFisWHR0dOjXrx/r168nNDQUc3PzfCfOysnJYePGjRqTcPXt25ctW7aor+GCBQs03ttbt24Bz3owXL58mUWLFuHs7ExISAgtW7ZUP2g6c+YMMTEx2NjYqPe1s7MjMTFR/dko6l4q6l6Mjo6mRYsWhV5v0LwXdXV1qVq1qsZ9t3PnTjp37oyTkxPW1tb861//ynM/58fIyKjI7tSvm4rTJ1eQmZlJYmIiCoWivEMhNjYWfX19dHV1iYmJoX79+uUdkihCdnY2T548KTS51NXV5d69e9ja2r5U96jSplKpiIqKKjRxLwlZa1gIIfJ2K9XS0lLPqeDt7U2zZs3U26ysrNDR0WH79u2cOHGCAwcOEBISwqxZswgNDaV+/fpMnz6dffv2MXv2bBwdHTE2NubDDz/MM7FRfsd9PinP7Vr6T+avyMnJ4YMPPmD06NF5tllZWRW7HpVKVeDwnOdfr1SpUp7jW1hYsHfv3jz7ValSRV13Yfr27YuVlRULFy7EysoKXV1dWrRoob6eNjY2nDx5kt9//51Dhw4xbdo0goKC2LdvX554cuW23CYmJmJpaal+PTg4mOzsbFxdXTXOHeD27dvY2NioHwg8H3dmZmah51CU3GuYnZ3Nxo0biYuLU8cIz65jcHAwPXv21NinVq1aADg5OXH37l2GDRvGzz//XKxjvv/++7Ru3ZqbN28W2Cp84MABbt++zYgRIxgxYoT69ezsbHbt2oW/vz9Dhw6lR48e6m3Pf67Mzc3p3r073bt3Z8aMGbRr14558+bRvn17cnJyqF+/Pt9//32e4+a2pBd1LxV1Lz5/bZ/34muF/Q04ceIEQ4cOZcqUKXz99deYmpqyZ88epk+fXvDF/f8SEhKoVq1akeVeJ5IMVyBGRkbcunWrQiTD9+/fV3+BPXnyhLS0tFJPXETpevDggcYT7oLo6+tz/fr1YnU7Kis3btwgIyNDY/x8aUhLSyMnJ6dY10EIIYqjNMbwViRVqlRRJ23P09LSws3NDTc3N6ZMmULLli3Zvn079evX5/jx4/Tt21c9GVFaWhrXr1/H0dGxVGI6ceKEegbkp0+fcuHChQLXNG3YsCEXL15UJ00lVa9ePbZv367xnREeHo6+vj41a9YscL+GDRty//59tLW18521ObfM4cOH89326NEjoqOj+c9//kO7du0AOH36dJ5VRgwNDenUqROdOnXik08+wcnJiYiICDw9PdHX188z5KlmzZqYmJgQHR1NvXr1gGe9/H788UdmzJiRZ0mlUaNGsWHDBqZMmaJObp5fdeLcuXMa5XPHCD9/XBMTE6ysrDh+/LhGS3h4eDh169YFnk0W9ujRIw4ePKiuA54l4gEBAcTExGBvb5/vtRo9ejTLli1j165d+Pr65lvmebVq1aJx48ZERkYW2PIdEhJCt27dmDpVcy6AlStXEhISgr+/P2ZmZsWacVxfXx8HBwf1dWvYsCFbt27F3Ny8wP/li3MvFXYv1q1bl+PHjzNw4EB1+fDwcPV7XhzHjx/HysqKyZMnq1/Lbf0uzPXr19U9Ft4kkgxXME+ePCEzM7NcW+0SEhLIyMhQJ8OGhobExMSo/7CJiun+/fvFSi51dHR4+PAh9vb2pZ6MFkdiYiJxcXFlMtFVTk4OqampBT45F0IIkdeJEyc4dOgQXl5eVK9enbNnz3Lnzh31976joyM///wzXbp0QU9Pj6CgoCK77b6M+fPnU61aNSwtLZk3bx76+vrqbr0vGj9+PB06dOCTTz5h8ODBVKlShcuXL/PLL78Uu0stwLBhw1i+fDkTJ07kww8/5MaNG8yaNYsRI0YU+v307rvv0rJlS/r378+sWbOoU6cO9+/fZ9++ferurePGjaNDhw6MHz+e4cOHY2hoSHh4OB4eHlhbW1O1alWCg4OxsbEhNjaWL774QqMFfcOGDWRnZ9O0aVMqVarE9u3b0dPTUz8AsLW15c8//yQmJobKlStjZmaGtrY27du3Jzw8XJ1ohYWFER8fz6BBg/JMhtqrVy/WrFnDpEmTMDIyonnz5ixatIiaNWuSlJTErFmzNMpXr14dIyMj9u/fj52dHQYGBpiamjJu3DjmzJmDo6MjjRo1YtOmTYSHh3Po0CHgWcv0e++9l6e7ubOzM3Xq1GH9+vV8/vnn+V5rExMTBg4cyNy5c+natWuxHnRv3bqVjIyMfJPR+Ph49u7dyw8//ICzs7PGtoEDB9KhQweuX7+e78OQX375hZ9++omePXtSu3ZtVCoVv/zyC7/99ps6se7Tpw+LFy+mf//+/Otf/8LGxoY7d+6wZ88ehg4diqOjY5H3UlH34rhx4xg8eDCNGjXC09OTffv2sWXLlpfq9l67dm3i4uLYvHkzbm5u7N+/v8jxwgDHjh3DwcGh1B6CVRTSfFLB6OvrF6vPflm6c+eOxheBtrY28fHxsjZyBZaZmflS42UNDQ01xkS9KpmZmURHR5fZjM8GBgYkJCSUSd1CCPGmMjExISIigoCAAJo2bcq0adOYNGmSelzlV199RfXq1enSpQt9+vShefPm+Y7HLKkZM2bw+eef0759e65evcqmTZsKfKjp6urKnj17uHnzJl27dlUvb1O9evWXOmaNGjXYsmULZ8+epW3btowdO5ZevXqpZ2guiJaWFps3b6Zt27aMHz+e5s2bM2TIEP7++291d9oGDRqwY8cOLl++TIcOHfDy8mLbtm3o6emhra3N999/T1RUFK1atWLSpEl8/vnnGg+nTU1NCQkJwdvbG3d3d3bt2kVISIi6JTowMBB9fX1atmyJo6OjulVv8ODBbN++Xd16GxISQtu2bfNdFaR79+7cunVLnbTmTnjl6enJJ598kmdWYV1dXYKCgggJCaFevXr0798fgA8//JBx48YxY8YMWrVqRWhoKMHBwTRo0ID79+8TFhZW4PJGfn5+bNy4sdBu8x9++CGXL18uVrIGz8YdF9Qq++OPP2JgYICXl1eebc2aNcPa2rrApLJevXpUqlSJ6dOn065dO7y8vNi0aROzZ89m4sSJ6mPv2bMHBwcHBg8ejJubG4GBgRpDIIu6l4q6F7t27cq8efNYtmwZLVq0YMWKFSxYsCDfJbUK4u3tzUcffcTUqVNp3bo1Bw8e5F//+leR+23bto1BgwYV+zivC63ExERZmLMUFLUm3svIyMigefPm/3ipmZLIzMzkxIkTeZKVrKwszM3N/3G3JFE2bt++TVxc3Ev1KEhJSaFRo0avbCmi7OxsTp06hba2dql0Y75586Z6iYDnGRgY8M477/zj+oUQBbty5Qp16tQp7zBK3ePHjzE1NS3vMN4aR44coVu3bly9elVjPKkoXGFD1zp06MCwYcMK7GYuRElcuHABPz8/Tp48WaH/Rpbkb7i0DFdA2dnZJCYmlsuxb926pTGmI5euri4PHjz4R5NeiLLz8OHDl+5ab2RkpLEQfFnKnTALKPPxvDKJlhBCiLfVt99+K/+riVJ39+5dVqxYUaET4ZKSMcMVUO5EWsUZvF+aVCoVDx8+zDcZzhUXF4e1tfUrjEoUJSMjg5SUlJdu4dXS0uLJkyckJSVhYmJSRtE9c/nyZdLS0gr9bJWW9PR0mURLCCHEW8nV1VVj5mghSoOnp2d5h1Bm5L/FCio5OblUJ6cojkePHuWZnfB5BgYGxMbGFrlkgHi14uLiSpxkGhsbl3nr8PXr10lISHgliTA8e6jz9OnTV3IsIYQQJde2bVsSExOli7QQotxIMlxBGRgYFGua89J0586dQteohWdjhx89evSKIhLFER8frzEL5ctKS0srs/c0Li6Ou3fvvtJlufT19cttmIEQQgghhHh9SDJcQeno6BAfH//KWmHT09N58uRJkeVyu3CLiiEtLY20tLR/VIeRkRHXrl0r9c9aQkICN27cKPIBS2nT1dV9qZm1hRBCCCHE20mS4Qosdwzvq3Dz5s1it96lpKQUK3EWZS82NrZU1grOysriwYMHpRDRM8nJyVy6dOmVJ8K5UlJSyuW4QgghhBDi9SHJcAVmaGhIbGxsmR9HpVLx6NEjdHR0ilXeyMiIGzdulG1Qolhe5n0rjKGhITdu3CiV1uGMjAyioqLKLRGGZz0dChv/LoQQQgghhCTDFVxycvI/7gZblAcPHrxUEpQ7C/GrnuBLaHr69CkZGRmlVp9KpeLOnTv/qI7s7GzOnDnzyibLKoy0DgshhBBCiMJIMlzBGRkZcfPmzTI9Rmxs7EtPcJTbkijKT0net8IYGBhw586dEq9PqFKpOH/+PNra2mhpaZVaXCWhr68vE70JIYQQQohCSTJcwWlra5OQkFBmC6inpKSUqAVNW1ubR48ekZWVVQZRieJISEgo9bV0tbW1SzxB2qVLl8jIyCiVbtv/lK6uriyvJIQQ5SwlJYUPPvgAOzs7FAoFMTEx5R3SS9mwYQPW1tb/uJ7Q0FCaNGlC1apVCQwMLIXIKqbAwECCgoLKOwzxmpszZw6tWrVS/z5t2jQmT55cZseTZPg1cf/+/TKp99atWyVuXdTT0+P27dulHNGrk5qaSkJCQnmHUSJJSUll8iBCT0+PuLi4l6772rVrJCUloaenV+oxlVRqamp5hyCEEK9EbGws48ePx9nZmerVq/POO+/w0UcflWjoi4+PD5MmTSqVuNavX8+xY8fYu3cv0dHR2NjYlEq9r5uPPvoIX19fzp07x9y5c8s7nH+kfv36LF68OM/rUVFR7NmzJ99kf9euXZibmzNixIgSHfPIkSMoFAri4+NLtD9A3759MTc35+DBg3m2zZkzB4VCgUKhwMzMjHr16jFixIg8/+O+eG8EBgaiUCgYN25cnjq/+OILFAoFAQEBebY9ePAApVKJq6trsRu7/vjjD3x9falVqxZWVlY0atSIESNGkJSUVKz9X2cff/wxP/74Y5n1SH1tkuFVq1bh7u6Ora0ttra2dOjQgbCwMPV2lUrFnDlzqFevHpaWlvj4+HDx4kWNOhITExk5ciR2dnbY2dkxcuTIPOuRRkVF0aVLFywtLXnnnXcICgp6ZcsbFcTAwIC4uLhSrzcnJ+cftS7q6upy7969Mmu1LmuXLl3i4sWLr2V377i4uDKboEpXV/elrsmdO3e4f/9+qcxqXZpkEi0hxNvgxo0beHh4cPHiRZYvX85ff/3FypUruXTpEp6enuXaGnvt2jWcnJxwcXFBqVRWiJ5Dr1piYiLx8fF4enpSo0YNTE1NS1RPac4RUha+++47fH19MTExybMtJCSE8ePHExoamuf/7lfh7t27HD58mNGjRxMcHJxvmTp16hAdHc2FCxf4/vvviYqKYsiQIUXWbWNjw/bt2zV6o2VlZbFp06YCH/5s3LiRzp07Y2BgwP79+4s8xqVLl+jduzcuLi7s3r2b8PBwvvnmG0xMTCr856I0VKtWDQ8PD9asWVMm9b82yXCNGjWYNWsWv//+OwcPHqRdu3YMGDCA8+fPA7Bo0SKWLl1KUFAQBw4coHr16vTo0UNjCaDhw4dz9uxZtmzZwtatWzl79iyjRo1Sb09KSqJHjx5YWFhw4MAB5s6dy+LFi1myZMkrP98Xpaamlnq3z7t375bK2M67d++WQjSvVmxsLOnp6RgbG3Pv3j3Onj372nT5VqlUPH78uMzG5erq6vLgwYNi/YF99OjRSy3L9apJV2khxJtu0qRJaGtrs2PHDtq3b4+trS3t2rVjx44daGtra7Rk5dfqGxgYqG69CgwM5OjRo6xatUrdUlZYMr1r1y7c3d2xsLDAxcWF+fPnqxsQfHx8WLFiBceOHUOhUODj41NgPWvXrqVp06YolUocHR3p2bOn+jv5r7/+okePHtSqVQtbW1s6d+5MZGSkxv4KhYI1a9bQr18/rKysaNq0KYcPH+bOnTv07NmTGjVq0KZNG06fPq3eJ7cL9N69e9XH7tq1a5EPg/fu3Uv79u1RKpU0aNCA2bNnF/h9eeTIERwcHADw9fVFoVBw5MiRIq8dPGuBnTNnDmPGjMHOzk7dqhobG8uoUaOwt7fH3t4ef39/rl69qnHcsLAwvLy8sLS0pGbNmgQEBKgnY920aRMeHh7Y2NhQu3ZtBg0apLFySWZmJpMnT6ZevXrq2GbOnAk8e09v3brF9OnT1Z8PeDZ55vbt2/H29s5zDe7cucORI0cYN24czZo1Y/PmzXmu0YutvjExMSgUCk6dOkVMTAzdunUDwNHREYVCoW59Tk9P57PPPqNOnToolUree+89wsPD88SwceNGvLy8GDVqFHv37s13ThFdXV2USiVWVla4u7szaNAgTpw4UWTLq4uLC7Vq1WL79u0a19/AwIA2bdrku8/69evp27cvAQEBhISEFFo/wIEDBzA3N2fOnDm4uLjg4OCAp6cnCxYsoFq1aupyly5dwt/fX/3eDhs2jHv37qm3F+deKuxezMnJYd68ebi4uGBhYYG7uzuhoaHqfXPft507d9K9e3esrKxo0aKFRmt8dnY2Y8eOpUGDBlhaWtKkSRMWLVpUZMOat7c327ZtK/JalcRrkwz7+PjQoUMHatWqRe3atZk+fTqVK1fmxIkTqFQqli9fzscff4yfnx/Ozs4sX76c5ORktm7dCkB0dDT79u1j4cKFtGjRAjc3N7799lvCwsK4cuUKAFu2bCE1NZXly5fj7OyMn58f48ePZ9myZeXeOmxkZFTqXZLv3r37j1vzDAwMXsnyT6UpKytLo3u4gYEBmZmZ/PnnnyQnJ5dzdEVLTEws8xZPAwMDrl27VmiZJ0+elOtawkUxMDB4bbvBCyEqjrQzM17pz8tISEhg3759DB8+HGNjY41txsbGDBs2jN9++63YrXFz587Fzc2NAQMGEB0dXWjX5tOnTzN48GC6du3KsWPHmDFjBt9++y3fffcd8Owf/gEDBuDm5kZ0dDTr16/Pt55Tp07x6aefMmXKFE6cOMGOHTvw8vJSb3/y5AkBAQHs3buX/fv3U79+ffr06ZOny+z8+fPp1asXf/zxB40bN2b48OGMGzeOYcOGcfjwYaysrBg9erTGPunp6QQFBbF06VJ+/fVXsrOzGTBgQIH/8+3fv5+RI0cyYsQIjh8/zpIlS9i5cydffvllvuVbtGjB8ePHAQgODiY6OpoWLVoUee1yLVu2DCcnJw4dOsQXX3xBSkoK3bp1w8DAgNDQUH777TeUSiV+fn7q+V/27dtH//798fDw4NChQ+zevZs2bdqok42MjAymTp3KH3/8waZNm4iPj2fYsGHqY65YsYLQ0FDWrFnDn3/+yffff0/t2rXV76m1tTWTJ09Wfz4Azp8/T1JSEo0bN85zDTZs2ICHhwfm5uYEBAQU2DJbEBsbG/U+x48fJzo6Wt3V/IsvanZMAwAAIABJREFUvmD79u0sWbKEw4cP4+zsTO/evTUaaVQqFevXr8ff3x9bW1uaNm3K//73v0KPee/ePXbv3o2Ojk6xejMMHDiQDRs2qH/P/ezn12hx7NgxHj16xHvvvYe/vz+//PILDx8+LLR+pVLJw4cPOXz4cIFl7t69S5cuXXjnnXfYv38/O3bsIDk5mX79+qnf+6LupaLuxeXLl7N48WJmzpzJsWPH8PHxYeDAgZw9e1Yjln//+9+MGjVKfS8OHTpU/f91Tk4OVlZW/PDDD0RERDB9+nQWLFhQ4N+HXE2bNiU2Npbr168XWq4kXptk+HnZ2dls27aNp0+f4ubmRkxMDPfu3cPT01NdxsjICHd3dyIiIgCIjIykcuXKtGjRQl2mZcuWVKpUSaNMq1atNP659/LyIi4urtwnfdDS0iIhIaHUkqDSXLIpMzPztUo6/v77b3R1dTVe09HRwcDAgLNnz5ZJl/TSVJZdpHPlTpBW0LjbtLQ0Lly4kOefr4pER0fntXi4IYQQJXX16lVUKhVOTk75bq9bty4qlSpPy2FBTE1N0dPTw9jYGKVSWWjX5qVLl9K6dWv+9a9/Ubt2bfz9/Rk7diyLFi0CwMzMDGNjY/T09FAqlZiZmeVbz61bt6hUqRLe3t7Y2dlRv359xowZo/6ebt++PX379qVu3bo4OTkxb948DA0N2bdvn0Y9ffv2pXfv3jg6OjJhwgTu37+Pp6cnPj4+1K5dm48++ogLFy5oJNFZWVnMnTuXli1b0rBhQ1auXMnFixf5/fff8411/vz5jBs3jvfff5+aNWvSrl07Zs6cydq1a/NNoPX19alevbr6eiiVSvT19Yu8drnc3d0ZP348tWrVwtHRkW3btqFSqVi0aBGurq44OTmxcOFCnj59qh46+J///Ac/Pz+mTZtGvXr1cHV1Zdy4cerv64EDB9KxY0ccHBxo2rQp33zzDeHh4erx5bdu3cLR0VE9NLFFixa8//776nPQ1tamSpUq6s9H7j5aWlrq33OpVKr/x96dx7dR3vkD/4w00mhk+YrtxE5iJ+QgJyQQSAltKZRQll5JaIBtt79tYYFtwtECadn0oGW3LZR2C0s52i0tLUehQFs2LZRAG0oTCM1hO/ERX7EtyZZlW/d9zPH7w9HY40s+dDrf9+uV1ysajUaPLI30fOf5Pt8Hzz//PP75n/8ZALBt2zZ0dnaqRuiT0Wq1ymenoqICCxYsQHFxMYLBIH75y1/iO9/5Dq6++mqsWrUKDz/8MCoqKvDUU08pjz948CDcbjeuvvpqAEOfk/ECr9bWVixatAhVVVVYtWoVDh06hJtvvhkFBQVJ23jdddehrq4Op0+fRn9/P/7617/ic5/73Lj7PvPMM7j22muh0+mU9+CFF16Y9Pjbt2/Hzp078elPfxorV67EDTfcgMcee0wVRP/iF7/A+vXrcf/992PVqlVYv349fvazn6G2thZ1dXUAkp9Lyc7Fxx57DLfffjuuu+46rFixAt/4xjewZcuWMRm0u3fvxjXXXIPly5fjvvvug9vtRkNDA4Ch2jTf+MY3cOGFF2LJkiXYsWMHbrrppqSjvpWVlQCQlhV28ioYbmpqwqJFizB//nzcddddeO6557Bu3TolBSDxhZNQUVGhFJ4aGBhAWVmZ6ioNwzAoLy9X7TPeMRL3ZZtGo0lZSrLVak1ZQJWJ5Z9Sxefzwe12T/jjbjQaYTab0drampNzoSVJUqX+pxPP8+N2oARBQENDQ06sJZwMFdEihJwNJpo2kwjQZjut5pJLLsGiRYuwaNEi7Ny5EwCUUc6RtmzZApvNNmFq6X//938rx1m0aBGsVquSsrthwwbccsst+M1vfqP6nRscHMRXvvIVbNq0CTU1NVi8eDEGBwfHZMutW7dO+f/8+fMn3DY4OKhs02g02LRpk3K7pqYGVVVVaGlpGbf9J06cGPMabrnlFgSDQVU6ajJT/duNHmk9ceIEzGYzli9frjx/TU0NPB6PMmJ28uRJfOQjH5nwuevr6/HZz34W69evx+LFi3HFFVcAgPL3/NznPoeGhgZs2rQJe/bswf79+5P2hyKRCHQ63ZgaNO+88w48Hg/+6Z/+CQBgMpnwiU98Ytqjw+Pp6upCPB7HJZdcomzTarXYvHmz6v177rnnsGPHDqXPsm3bNnR1deHYsWOq451zzjk4ePAgDhw4gG9961vYsGED7rvvvim1paSkBJ/85Cfx3HPP4YUXXsCHPvQhVFdXj9nP5/Nh3759qqJao4Pzu+66S/X5SryuJ554As3Nzfiv//ovVFdX4yc/+QkuvvhipT7SiRMn8N5776kem/j8Jz4byc6lyc5Fn8+Hvr4+1d8bGPrcjj5fRp53VVVVynMn/PKXv8Tll1+ufI6feOKJpNmviZglHf06NvkuuWPlypU4ePAgvF4v9u3bh127duFPf/qTcv/oL3tZlscEv6Ml22eqPySZCgZtNtuMlkIaSRRFNDQ0pHSeZ2KUOZdHCmVZRlNT05RSXmw2G5qbm7Fy5cqcKgyVmKObqTZFIhEIgqBcGZUkCS0tLZBlOeXLOs1EsvMuEomgoKBgTCYAIWT2ElOM5hKDwTDm+1WUMluIbzpZW4sWLQLDMGhsbMTWrVvH3N/c3AyGYVBVVaUcNx6Pq54jUWwwsU2SJAiCoNrn2WefVeYNGgwGRCIRSJKkehwwXOQpFospvx+SJCn7fO5zn8PHP/5xZf/S0lKwLIs333wT77//Pt555x38+Mc/xn/+53/ijTfeQGVlJW699VY4HA585zvfQXV1NTiOw86dOxEKhVTPLcuycjsajSqvJbEt0bZwOIxIJIJ4PK78vUdm3cmyrLz+eDyuOq4kSbjnnnuUOawjmUymcd+7kc8/8jjJ/nayLEOv14/ZZ926dfjZz3425nlKSkomfI8TgsEgrr32Wlx22WX4yU9+gvLycrhcLmzbtk3JGFy9ejWOHj2KAwcO4NChQ9i1axfWrl2Ll156CRqNBrIsjzl+YWEhYrEYXC6Xqh/49NNPw+v1YuHChaq/r8lkwje/+U0YjUblc5V4XxLtBIbex0gkovxtIpHImPd45N8VGOrjJt6zRLwQi8Xw61//WrXP008/jfXr1wMYusjPsqzSzttuuw1tbW2466678OijjyqPG31uiKKovI/XX3897rzzThQUFOCrX/2q8rka+T6/8MILCIVCyij1yPb8/e9/x+bNm3H33Xfj1ltvVe4b+drmzZuH7du3Y/v27dizZw8uvfRSPPzww3j00UcRj8exdetWfPvbY6daVFRUIBKJJD2XdDrdhOdioh84+r1PvH+RSGTc8y4h8V6++uqr2Lt3L+677z5cfPHFKCwsxNNPP43XX39deczo7w1gOJguKiqa9DvS5/ONO4C5cuXKCR+TVz1EvV6PZcuWARi6WlZbW4snnngCe/bsATA0ejtybovD4VBGdufPnw+Hw6EKfmVZhtPpVO0z+g+YSEEYPWI8Wk1NTQpeYXKhUAgLFiwYt1rfVFmtVpxzzjkpXQZHlmWwLDvphy3brFYrFi5cOOVAUpZl+P1+LFy4EPPmzUtz66amsbExo3/jxPmSeM7m5mYsXLgwJ5ZQslgsSc+7UCiEqqoqpcgHISQ12tvbc/r7fqa8Xu/YC8UXfDc7jZmCqqoqXHnllfj1r3+NO++8UxWIhEIh/OpXv8JVV12ljM4k+kIjX+OpU6dQU1OjbDMYDGAYRrXPeO/1mjVrcOzYMdV+x48fx6JFi5SiPizLQqPRKPtUVVUpbRlt69at2Lp1K771rW9hxYoV+Nvf/oYvfvGLOHr0KB588EElAB0YGMDAwABYllU9t16vV72G0dsSv/0cx8FgMECn00GSJDQ3NyujtFarFXa7HevWrVP2Gfm32LBhAzo7O7FmzZrJ3haV8doylb8dwzDQ6XSqfTZt2oRXX30V8+bNG5OSnHD++efjvffew8033zzmvpaWFrhcLnznO99RCnvt27dv3L/f9ddfj+uvvx7/+q//iq1bt8Jms2HFihXgOE71nibaBQwVUEqMZrvdbrzxxht48sknsWHDBlU7tm3bhv379+Ozn/2sEoB6PB6lD9/W1gZg+L0ymUwAoPp7rF69Gnq9HrW1tVi1ahWAoaDy+PHj2LlzJwwGA5599lmUl5ePKdp19OhRfPOb38RDDz2kXDAf/Zr27t2Liy66CLt378bGjRsBDGUSjPzcJeYUGwwGXHXVVdDr9XC5XNixYwc4jlPdDwAvvvgibrnlljFVqu+//3789re/xWWXXTbuiPJ4KisrUVlZiUgkAoPBgAsvvBB/+MMfsGLFign7aFM9lyY6F6uqqnD8+HHVhbejR49i9erVqguJifdtpMTn6/jx49i0aRNuu+025T6LxaI6z8Z7Pzo7O6HT6bBx48ZJB/OKioqm/DdMyP7QzixIkoRYLIYlS5ZgwYIFqmplkUgEhw8fVr7gNm/ejEAgoKqaduTIEQSDQdU+hw8fVl1xePvtt1FVVYUlS5Zk6FVNLhWFtPr7+1MezDAMA4/Hk7Ml3mOxGKxW67RGVBmGAc/zaG1tRVdXV9aLqImimLEU6QSGYRAKheB2u9HR0QG/358TgfBUUREtQshc98Mf/hCCIGD79u1455130NPTg4MHD2LHjh2QZRkPPfSQsu9ll12Gv/zlL3j99dfR3t6Or3/962PWIq6pqcHx48dhNpvhdDonTJG97bbb8O677+KBBx5AR0cHXnrpJTz++OO48847p9X+RMB04sQJWCwWvPzyywgEAso86OXLl+Oll15CS0sLamtrcdNNN6Vsmg7Lsti7dy+OHDmCkydPYteuXVi9ejUuv/zycff/2te+hldeeQXf+9730NzcjLa2Nvzf//3flNNpE2b6t7vuuuswf/58fOELX8ChQ4fQ3d2Nd999F9/4xjeUaU333HMPXn31VXz3u99VlpB8/PHHEQqFsHjxYnAch5///Ofo7u7G/v378f3vf1/1HI899hheeeUVtLa2orOzEy+//DKKioqUoLWmpgaHDx+GzWZT5l+Xl5djw4YNqkrOL774IkwmE66//nqsXbtW9e9Tn/qUkiq9bNkyLF68GA8++CA6Ojpw4MAB/PCHP1S1qbq6GgzDYP/+/XA4HAgEAigoKMBNN92E+++/H2+++SZaW1tx9913Y3BwULkQ8Oyzz+LTn/70mOf/7Gc/C4Zh8Pvf/37Cv/XSpUtxzTXX4Hvf+95U3lIwDIN3330XJ06cGLev2djYiLq6OnzhC18Y054bbrgBr7766oR9vKeffhp33303Dhw4gK6uLpw6dQrf/va30dzcrFRpv/nmm+Hz+XDjjTfi2LFj6O7uxt/+9jd8+ctfVo6b7FxKdi7ecccdyuejo6MD3/ve93D48GHcfvvtU/obAcCKFStw8uRJvPXWWzh9+jQeeughvPfee0kf995772HLli1pyUDNm2A4UbnMbDajqakJ999/Pw4dOoTrrrsODMNg165deOSRR7Bv3z40Nzdj9+7dKCgoUOa2rFq1Clu3bsVdd92Fo0eP4siRI7jrrrtw9dVXK1c8d+7cCZ7nsXv3bjQ3N2Pfvn145JFHsHv37rQtYzNdDMPA6/XOeBkgr9ebtoDVYDDk7Jq9bW1tM04L53keAwMDaGhoyOryS4ODg1lJTeZ5Hk1NTXA4HDmVMj4VWq121tMKCCEkl51zzjl4++23sXr1anzpS1/Cxo0bccstt+Dcc8/FgQMHlBFAAPj85z+Pz3/+87j99ttx9dVXo6CgYMySR3fccQf0ej0uueQSLF++HFarddzn3bhxI371q1/hj3/8I7Zs2YL7778fX/nKV1QpnlNRXFyM1157Ddu3b8fmzZvx2GOP4dFHH8Wll14KYCg4CwaDuPzyy3HTTTfh85//fMqy8TiOwz333IMvfelL2Lp1KyRJwnPPPTdhn+/KK6/ESy+9hEOHDuHKK6/ElVdeiYcffnjCitsTmenfzmg04vXXX8eSJUvwxS9+EZs3b8auXbvg8XiUDKiPfexjeO655/DWW2/hsssuwyc+8QkcPHgQGo0G5eXlePLJJ/Haa6/hAx/4AH7wgx+MCfYKCwvx6KOP4sorr8RHPvIRNDQ04OWXX1aCkK9//evo6enBBRdcgOXLlyuP++IXv4iXX35Zuf3ss8/ik5/85LhT07Zt24bDhw+jo6MDOp0Ov/jFL9Dd3Y0PfehDeOCBB8ZcXFi4cCH27t2L7373u1i5cqWyPNj999+P7du347bbbsOHP/xhNDU14ZVXXkFlZSXq6+tx8uRJbNu2bczz6/V6XHPNNUmXNbr99tvx1ltvKYV2kyksLJwwc/OZZ57B8uXLldTska6++mpIkjRhEakLL7wQoVAId999N7Zs2YKPf/zjePfdd/HTn/5UmX9cVVWF/fv3Q6PR4DOf+QwuueQS7NmzB3q9Xum7JTuXkp2LX/rSl3DHHXfg29/+NrZs2YLXXnsNzzzzDM4///wp/X0A4MYbb8T27dtx880344orroDFYlGNEk/kd7/7Hb7whS9M+Xmmg/F4PNkd7pqiXbt24eDBgxgYGEBRURHWrVuHO++8Uyn5LcsyHnzwQfzqV7+Cx+PBpk2b8KMf/Qhr165VjuF2u3Hvvffiz3/+M4ChNaseeughVQplU1MT9uzZg9raWpSUlODGG2/EvffemzQYnk5lvNmKx+OorKycdhoAMPT6YrFY2oL7SCSCiy++eErzcjPF6XSitbV11leTRFFEPB7HunXrUFhYmKLWTd3JkydzsqhXtkwlTRoYOl8uvvjiDLSIkLPHXE6TLi4uznYzSAY8//zz+NrXvjZmZDwfJFJjc0k0GsXFF1+Mn/70p0rwREgq7N+/H/fddx/efffdpDVgZvIdnjdzhp988slJ72cYBnv37sXevXsn3Ke0tHTMGm6jrVu3TgmWc5VOp4Pdbp92MByPx+H1etNa5IplWfT09ORMWrkkSTh9+nRKXnNi7kdDQwOWLl2qKgiRboIgIBAI5HSBslwVj8eV4hiEEEIIST2O4/Dkk0/S1CSScqFQCI8//nja+nHUO8xTgiCo0mKmwmazpX05HJZlYbfbUVNTkxOp5V1dXSlPLTYajbBYLPD5fDj33HMzkrqcjnneZ5NAIEBFtAghhJA0+uAHP5jtJpA5aMeOHWk9ft7MGSZq0y2kJcuyUjEuE6az3l66hEIh2O32tLxmg8GAQCCA2traaS2DMVMOh4OC4RkyGAxwuVzZbgYhhJAc8i//8i95mSJNCEktCobzmM/nU9bJS8bj8WSs+BPHcTnxA9PW1pb2lHCtVou6urq0BluxWExZc49Mn0ajoSJahBBCCCFkDAqG85her5/y6LDVagXP82lu0bBYLJbVeSN2ux2RSCTtqdqJ5ZdaWlrQ3t6elgJXdrs97entc104HM52EwghhBBCSI6hYDiPsSyLwcHBpOvfxmKxjK9Py/P8hMsxpJsgCDCbzRmttGg0GuHxeFBbW5vyUUin00nFn2YpHo9POYuCEHJ2y/aa8oQQQqZvpt/dFAznOVEUk47AWq3WrJTgDwQCWUnv7ejoyMrSTjqdDlqtFvX19Sm7EBCJRCjFNwUYhqFUc0JIUgaDgb5zCSEkB8iyjFAopPwLh8MIh8OIRCLKv1gspvxLDBCGQiHlPlEUkwbJNNyU5xKFtObNmzfu/bIsw+FwKAtuZxLP8zCbzaq1ntPN5/PB5XJlbQkihmFgNBphs9ngdruxZs2aWRW+stlsObeWYD7iOA5Op5MqShNCJsVxHARBgNfrzXZTCJmQz+dDUVFRtptBSFolguCJBrgSQa4kScoqO4ngd+Q0SYZhsGXLlgmfh4LhOSAQCCAajY4b8DqdzrTMY50KhmHgdrsRi8UyMudVluW0F82aqkSH6vjx41ixYgXKy8tndByXy0Up0ilARbQIIVNVUFCQ7SYQMqmBgQFUV1dnuxmEpI0kSWhpaZnWYN5MB/4oTXoO4DhuwrTc3t7ejBbOGs1gMMBsNmfkuXp6eiCKYkaeayo0Gg0MBgPa29vR0tIy7YsSwWAQsVgsTa07+1ARLUIIIYSQ3NfX15ex56JgeA7QarVwOp1jcuIjkUjW50km2pbuIDUWi6Gnpycr6eDJ8DyPQCCAY8eOTauQGaVIp5YgCFREixBCCCEkh8myjN7e3oz16SkYniNkWYbT6VRts1gsORFMabVa2Gy2tD5HW1tbTgbCCSzLQq/Xo7GxEWazeUoV79xuNzQaOkVTRaPRZLyqOiGEEEIImbq+vr6MVvWnnvYcYTAY0Nvbq9yWJClngimdTqcUlErHh9vlcsHv9+fEa02G53nY7XbU19cjGo1OuJ/f74cgCBls2dzHcRxcLle2m0EIIYQQQsYhy3LGMz1zP3ogUxYIBBCJRAAMFVfIJRzHoaWlBceOHUNXV1fK5sJKkoSOjo6szouersQJXltbC7vdPu4+Npstr15TPmAYhuYNE0IIIYTkqP7+/oyv9U7B8BzC8zwsFguAoRSDXEsb5nkeOp0OTqcTx44dQ0NDA1wu16w+9N3d3ary6fmCYRjwPI/u7m40NzerRoFlWYbH48nL15XrKBgmhBBCCMk9sizDYrFkPH6hYHgO0Wg0cLvd8Pv9Od3pZ1kWRqMRoiiitbUVR44cQWdn56Rpw+MJh8Ow2+2zWsc32wwGA8LhMI4fPw6PxwMA8Hg8WVsOa64TBIEqdBNCCCGEjBCNRuHz+bLahv7+/qz0f2kB0zmooaEhJwpnTUUiFdjtdsNut8NkMmHhwoUoKytLOjLa1tY2J1KJtVottFotTp06hfnz5yMajebN+5dvEkW0ysrKst0UQgghhJCc0NnZCb/fj4suuigrNXhkWYbVas1K/5dGhucYjuPAMExeFJMaSavVwmg0QpIktLe34+jRo2hvb1fmQI/W39+PUCg0p1KJeZ6H0+mkFOk0oiJahBBCCCHDotGoUnS3s7MzK20YHBxM+zKsE6GR4Tko30dLE+33+Xyora2F0WjEwoULUV5eDo1GA1EU0d3dnfevczx6vR56vT7bzZizqIgWIYQQQsiw7u5uGAwGaDQaDAwMYMGCBSgsLMzY8yfmCmcrK5KCYZKzEqPFANDV1YWuri6UlpZCEARotdost47kq4myDQghhBBCziaxWAxOp1PpbxuNRrS2tmLTpk0Zy1J0OBwQBAEsm52wNL9yaclZi+M4cByHQCCAQCCQtROG5D9BEKZdrI0QQgghZK7p7u4eU71ZkiR0d3dnrA3ZHBUGKBgmeUaj0VAaMZkVrVYLv9+f7WYQQgghhGRNPB6H0+kck22p1+ths9kQCoXS3gaHw5H1VT4oGCaEnFX0ej0V0SKEEELIWc1sNk+4PKnRaERLSwtkWU57G7JdA4iCYULIWYWKaBFCCCHkbCYIAhwOx4TTDhmGQTweR09PT9ra4HQ6sz4qDFAwTAg5C1ERLUIIIYScraxWa9L6OxzHwWq1pm0AIRdGhQEKhgkhZyFBECggJoQQQshZRxRFDAwMTKkYLc/zaG1tTXkbXC5XTowKAxQME0LOQizLwufzZbsZhBBCCCEZ1dPTA41maiEgwzCIRqMpT5dOrG2cCygYJoScdfR6PTweT7abQQghhBCSMZIkwW63T1g4azyJdOlUjeS63e6cWuKSgmFCyFmHimgRQggh5GzT29sLhmGm/TiO49DS0pKSNnR3d+fEXOEECoYJIWelSCSS9iUDCCGEEEJygSzLsNls0Ov1036sRqNBMBiE3W6fVRvcbnfODUZQMEwIOSuJophTaTqEEEIIIenS19c3q8fzPI/u7m7E4/EZH6O7uxtGo3FW7Ug1CoYJIWclrVYLr9eb7WYQQgghhKSVLMvo6ekBx3GzOo5er0dbW9uMHuv1enNyJQ8KhgkhZyWO46iIFiGEEELmvP7+fkiSNOvjaDQa+Hw+DA4OTvuxuTZXOIGCYULIWSsXr1ASQgghhKSKLMuwWq0pW8qI53l0dnZCEIQpP8br9SIYDKbk+VONgmFCSN5hRAGFfhcYUZzVcaiIFjmb+P1+dHR0ZLsZhBBCMsjhcECcZX9pNJZlp/V7Yjabc26ucAIFw4SQvMJGgljx1m9x4cm/Y8VffgtNbOZFsERRpNFhclaQZRltbW1wOp0pSZUjhBCSHywWS8pGhRO0Wi1cLhdcLlfSfX0+HwKBQEqfP5UoGCaE5JXy1nroIiEAgC4cxLyuphkfi2VZKqJFzgq9vb0QBAGyLE+p80IIIST/OZ1OxGKxtBzbaDSivb096ahzLlaQHomCYUJI/pBEFPWeVm0qGOyd8eH0ej0Fw2TOi8VisFqt4DgOBoMBNpst200ihBCSARaLJa1Fq7RaLU6fPj3h/X6/P6dHhQEKhgkhecTUbwU7Ki3a6BoAI069iMNoubb4OyGp1t7erlpOIxAIpG2kgBBCSG5wu91pnwrGsiycTueEq3N0dXXlZAXpkSgYJoTkjRJr+5htjCTB6Oqf8TGpiBaZy1wuF3w+HzSa4Z97vV6P3t6ZZ1QQQgjJfWazOSOBKM/zaG9vH1OPIhgMIhAIgGGYtLdhNvImGP7xj3+MK664AtXV1Vi+fDluuOEGNDc3q/aRZRkPPPAAVq9ejcrKSnziE5/AqVOnVPt4PB7ceuutqKmpQU1NDW699dYxVzOamprw8Y9/HJWVlVizZg1+8IMfUGeZkCzTxKIotFvGvc84OPO0T0mSaHSYzEmSJKGjo2NMZyhxJZ8QQsjc5PP5EAqFMvqcnZ2dY27n+qgwkEfB8KFDh/Bv//Zv2L9/P/bt2weWZbF9+3a43W5ln//5n//B448/jh/84Ac4cOAAKioqsGPHDvj9fmWfm2++GSdPnsTLL7+MV155BSdPnsS///u/K/f7fD7s2LED8+fPx4EDB/Dggw/iJz/5CR577LGmTEIeAAAgAElEQVSMvl5CiFqRrRPMBFVwCxx9Mz6uTqebML2HkHzW1dU14RX5WCwGn8+X4RYRQgjJhEwXrdLpdBgYGFBirmAwCL/fn/OjwgDAZrsBU/X73/9edftnP/sZampq8P777+Oaa66BLMt48skn8ZWvfAXbtm0DADz55JNYuXIlXnnlFdx4441obW3FX/7yF7zxxhv4wAc+AAB4+OGHcc0116C9vR0rV67Eyy+/jHA4jCeffBI8z2Pt2rVoa2vDE088gdtvvz0v3lRC5qISy9gU6QTeMwBGiENmddM+rk6nU10wI2QuCIVC6O/vn7AzxPM8ent7UVRUlOGWEUIISadE0apMV3A2Go1obW3Fpk2b8mKucELejAyPFggEIEkSSkpKAAzlxff39+OjH/2osg/P87j00kvxj3/8AwBw5MgRmEwmJRAGgEsuuQQFBQWqfbZs2aJ6A6+88kr09fXBbDZn4qURQkbRBX1j5gXHdXrl/4wkw+i0z/j4lCZN5pq2trZJOyIMw8Dr9SZdEoMQQkh+6e7uzlogKkkSWltb4fP58mYAMW9Ghkf7j//4D5x33nnYvHkzAKC/f6ijXFFRodqvoqICfX1DKZQDAwMoKytTvTkMw6C8vBwDAwPKPgsXLhxzjMR9S5cuHbc9Fsv4cxkJIbO3xNqiCli9RfMQLC6Czt6tbIu2NsASndnc/kgkAlEUaZSMzAmDg4Ow2WzQ6/WT7heLxSBJ0pjfzalqb584W4MQkl50/pHxhMNhtLS0wGAwZK0N3d3d4Dgup4LhjRs3TnhfXgbDX//61/H+++/jjTfegFarVd03+g8vy/KY4He0ZPskimdN9qbW1NRM/QUQQqZOlrG89TD0I65yutdtgsflxnLv8GhxtRyFMIvzMBaLYdmyZWO+UwjJJ4IgwOVyYcWKFVN+zMqVK6f9PImpRYSQzKPzj0yksbERK1euzKlANNflXZr03r178bvf/Q779u1TjdIuWLAAAJQR3gSHw6Fc9Z4/fz4cDoeqMrQsy3A6nap9xjsGMHbUmRCSfrx7APrgcKEfWaOBb+EyeIrKVfsZPA5o4tHRD58yjUYz6cLxhOSDjo4OsOzUr3MHg8G0r0NJCCEk/cLhMLxeLwXC05RXwfC9996LV155Bfv27cO5556rum/JkiVYsGAB3n77bWVbJBLB4cOHlTnCmzdvRiAQwJEjR5R9jhw5gmAwqNrn8OHDqs7B22+/jaqqKixZsiSdL48QMo7iUWsL+ytrIOk5xPUGRIpKle2MDBhnUVWaZVk4HA6qsEvylsfjgcvlmlZ2g8FggNVqTWOrCCGEZEI25wrns7wJhvfs2YPf/OY3eOqpp1BSUoL+/n709/cjEAgAGEph3rVrFx555BHs27cPzc3N2L17NwoKCrBz504AwKpVq7B161bcddddOHr0KI4cOYK77roLV199tZJusnPnTvA8j927d6O5uRn79u3DI488gt27d9OVFkIyTRJR1Ktet85TPZwaFixXz++fzRJLwHAlxNELxxOS62RZRnt7+7Srh2q1WrhcLlXGFCGEkPwSjUbhdruh0eRNaJcz8mbO8FNPPQUAyrJJCffeey/27t0LAPjyl7+McDiMr371q/B4PNi0aRN+//vfo7CwUNn/5z//Oe69915ce+21AIBrrrkGDz30kHJ/cXEx/vCHP2DPnj244oorUFJSgttuuw233357ul8iIWSUwn4r2Nhw6rOg5xBYUK3cDpVXoayzSbld4LCl5Hm7urqwfPnylByLkEywWCwzDmglSYLH40FpaWnynQkhMyLLMiRJoroUJC26u7uzWjQrn+VNMOzxeJLuwzAM9u7dqwTH4yktLcX//u//TnqcdevW4c9//vO020gISa3RKdK+RcsAzXBHIlReBZkZSpEGAIPXBW00DJGbeZqQTqdDf38/FixYAJPJNOPjEJIp0WgUNpttxulxiTWHKRgmJH2am5tRVlaGysrKbDeFzDGxWAxOpzPj6wrPFTSWTgjJSdpYBIV29ZJl3mp19UxRb0C0qEy1bTbzhpVjnEmXptRRkg9aW1tnPSLg8/kgCEKKWkQIGamtrQ1ut5t+U0haJJYyIjNDwTAhJCcV9XaCGTF3N2oqQrh0/pj9ghWj5w2nJlVaEARaP5zkvMHBQYRCoVnXtNDpdOjrm/2FJEKI2unTp+F2uylYIWkRj8fhcDgo/X4WKBgmhOSkYmuH6ra3eiUwTod/bBGt1ATDHMeht7cX4XA4JccjJNVEUURnZ2dK5onpdLoxywoSQmanq6sLg4ODFAiTtJBlGWazGXq9PttNyWt5M2eYEHL20AW8MLr6Vdu8i1eOu2+orBIyw4A5k37G+b1gI0EIhoJZt4PnebS0tGDjxo1UTZ7knNOnT6d0NCAajSIQCNBceUJSwGq1or+/n4oakTGsViv8fj9kWVZS5yVJUm5P9g+A6v8AaDmlWaKRYUJIzinpUY8Kh8oWIF5QOO6+kk6PcGmFalsq5g0DQ0X5YrEYent7U3I8QlIlEAjA4XCAZVN3TTtRSIsQMju9vb3o6emhQJioiKKIhoYG2Gw2xGIxxONxCIIAQRCUYBgY6ntoNBpotVqwLAudTge9Xg+O48BxHAwGA3ieV/6R2aFgmBCSW2R5TIq0p3r8UeGEUFmV6nbBYGpSpYGhdGmLxYJoNJp8Z0IyQJZltLW1pbxyKMMw8Hg8tM42yYpwOIyenp5sN2PW+vv7YbFYKEghKoFAAMePH0csFqO0+RxDwTAhJKfwrn7ogz7ltqTVDi2pNIl0FdFS2nQmXZqQXNDb24t4PJ624zscjrQdm5CJtLe3w2KxwOVyZbspM+ZwONDZ2UmBMFHp7+/HyZMnodfrqdBVDqJgmBCSU4pHpUgHFtRA0k1+FTU0rxKyZvjrTB/0Qxfyp6xNDMMgHA7DZkttkE3IdMXjcfT09KRtZIHjONjt9rQcm5CJOJ1OBINBZVm7SCSS7SZNm9vtRnt7OwXCRJHI4unq6qI1gHMYBcOEkJzBiCKKe0+rtnmqVyR9nMyyCI1adilV84YTDAYDLBYLYrFYSo9LyHS0tbWlvXJoIBCgzznJGEmScPr0aSWINBgMaGxshCiKWW7Z1Hk8Hpw6dYoCYaKIxWKoq6uDx+OhueM5joJhQkjOMPVboB3RCRf0HAILqqf02FD5qHnDKU6VBgC9Xo/W1taUH5eQqXC5XPB6vdBo0vvTrdfr58TcTZIfzGazqlp/4v/58l3r9/tx6tQpGvkjCo/Hg9raWgCgZY/yAAXDhJCcUWxtV932LV4OaKY2vyZYsUh1u2DQBoxYeiAVNBoNgsEgrcdKkvL5fHA6nQiHwykpSCVJEjo6Ombc4S4xt2LZ27/DoqN/gSY+eTE4lmXhdDpn9DyETEcsFkNfXx90Op1qO8uy8Pv9MJvNWWrZ1IRCITQ1NdGIMFFYrVY0NzfDYDCk/cLlXKaRgiiMHkdR5AgM8W5opFDanovWGSaE5ARtLILCfqtqW7Iq0iOFS+dD0mqhOZNapwsHoQv6EDcVp7SdBoMBXV1dmDdvXkqXtSH5T5ZlOJ1OWK1WhMNhaLVaCIIAhmHAsiw4joNer4der0dhYSEKCgrAcdyUPkfd3d0zXuu6YKAHC+v+DgAweF2QtSxsF14+6WMEQYDX60VxcWrPH0JGamtrmzCFlOM42Gw2FBYWYt68eRluWXLhcBgnT56kFFgCYGjZpNbWVvh8PsoSmCVGjqM8/GdopQAAoCDeDAAQNSbENAsQ085HTLsAgqYYmOHv4kjUkyOE5ISi3k4wI0bQoqZiREoqJnmEmqzVIjRvAUwjllUqcNjgSXEwDAyNWrS1tWHt2rUpPzbJP5IkwW63o7e3F4IggOd5pTM0stCVLMuIRqMIh8NwOBxKoKzValWBsslkgslkAsdx0Ol0CIVCsNvtM+pgMaKAqhPvqrYV9Xaif/0lEPUTd+INBgN6e3spGCZp43a7kwYOPM+jra0NGzZsyKnR11gshhMnTtASOQTA0IWRxsZGMAyTlxdHWNEJg9CLmHYBYuyCbDcHBbFmJRAeSSsFwEsB8MJQbRmZ4RA9ExjHtPMR15QBzPSrdVMwTAjJCaNTpL3VK6Z9xS9UvnBUMNwHz9I1KWnfSFqtFl6vF06nE2VlZSk/PskPgiDAarUqafOJ4DUZjUYDjuPGdKRjsRii0ShcLhfi8bgSKMuyPONAoLy1TrVUGQBoRBHF1g64lq+f8HEMw8Dr9UIURVoKhKScLMtTTvvnOA6NjY248MILc+KzGI/HUV9fD47jZpytQeaOwcFBdHR0wGAw5OXngRWdqAi9DkAEwMDFX4kouzhr7dFIEZhijVPal5GjMAhWGIREVqEWMW3FmZHjoX8yk3zONgXDhJCM6YtqwGtllLDqubz6gBdGl3oerncaKdIJwYqFwKnh28q84TT8QPE8j46ODpSUlOREB41kTjQahdlshtPphE6nS+noEMMwygjxbHE+N8o7Tox7X2n3KbiWrZv03NBqtbDb7Vi0aNGE+xAyEz09PVOeS88wDBiGwalTp7B+/cQXcDJBEATU19eDZdm8DHxI6siyjM7OTgwMDORU1sK0yDKKo8cwFAgDgIzi6PsY0G4HmOyEiIWxejCIDzeR4SBoiqETHQCSfWeI0It26MXE8oAM4ppSxLQLAGyc8FEUDBNC0k6SgR9ZTHjdxcOgkfGtpT58sHi4avToUeFgeSXixsJpP0+4pBwSy0IjCAAANhqGPuBBrLB0di9gAlqtFu3t7Vi9enVajk9ySzAYhNlshtvthsFgyO0OkCyjqv4gGGn8InKc3wPe1Y9wWeWEh9Dr9RgcHKRgmKRUPB5Hb2/vtNJJWZZVzr8lS5aksXUTE0URJ06cgEajocJIZ7l4PI7m5mZEIpHc/h1IQi/2QS+ql6HUSgGYYs0IcOdnvD1ayQdjvE21za/fgKB+LSAL0IsO6MUB6MV+6KVBMHKyJQBl6CQXdJJr0r0oGCaEpN2rDgNedw39YEQkBt/rLsTTa9xYoJeGrkz2dKj2n8moMABAo0WorBKm/uFlYQoGbWkLhlmWhcvlgsfjQUlJSVqeg2Sf2+2GxWJBMBgEz/MoKCjIdpOSKjG3wOjqV22LGU3Qh4bnYZWaWyYNhoGharnhcDivO3wkt5w+fXpK0wlGSxTUMplMGZ+eIkkSTp48CQCUCZRGgiCgr68PAwMDkGUZOp0OLMuCZVnodDoYDAYYjUbo9XrlvkyP0Pv9fjQ3N6c8KyjjZBlF0ePj3mWKNSCkWw5Jk9nfuqJoLUaO/oqaQgR1q4ZuMCxibCVi7JnfLFkCK3mU4JgT+6GRZ1ZxmoJhQkha9US0+FmvSbUtJGnwkKUQP1ruhdHVD33Qr9wnabXwLTxnxs8XLF+oDoYdNriXrZvx8ZIxGo1oa2vDRRddRKMFc4gsyxgYGEBPTw9isZiqKFau00ZCWNB0RLXNX1kD99I1qHl/v7KtqLcT9vO2QNJN3KEzGAzo6enBypUzvEBFyAg+nw8ul2vG51KioNbGjRszdoFGkiQ0NjZCFEVaQSBNAoEArFYrPB6PEvgCQ9/D8Xgc8XgcwWAQoihCEARIkgSGYZRR+kRgrNPpoNFowPM8eJ6HwWBQAubx1rKe7v9tNhvMZnPe/BZMxiB0QyeNv4QegziKorXw8B/OWHt04iAMQrdqm09/wcQFsRgNBO08CNp5CGE1IMvQysGhUeMzATIreab03HRWE0LSRpSBByyFiMpjr9we9+uxz2HAl3rVKdL+yppJO+fJBMsXqm4bHX1pmzecoNFo0NHRgXPPPTdtz0EyQxRF2Gw29PX1QZKk3E+HHkdl4/vQxofTxySWRd/5H4TAGxHnC6ALBwEMF9Ka7GKRRqOB2+2GLMs0R5LMiizLaG9vn3UgYTAYMlZQKxaLoaWlBdFodEaj2QCgEWLgfG7EC3NveahsEkURdrsddrsd0WhUCWAnMjLwneh44pmlFX0+nxI4p+p7KxFQz4VAGLKIolidapPEGFUjq7xwGkFxFeLa+Rloz9hR6rimHBF2GgMjDAORMSGsMSGsWz60SY4qgfFkNbIpGCaEpM3LAzyaghN3IJ7qMeCOni7VNm/17ALKSEkZRJ1eCQbYWBSc14loSfmsjjsZlmXhcDhQWVmJoqKitD0PSa9AIICGhgawLJuSAlbZUNBvRXHPadW2gTUXQTAOZWe4l6zC/JZa5b5Scwvc56yd9GKRKIpwuVxUOZ3MSl9fH+Lx+KwD2EwU1BIEAd3d3RgYGIDBYJhxIMwIAqrf3w/ePYiuC68AFmevSm+uCAaDyiiwRqOBXq9PeYCZSK3O6zTmNDLGO6CVRq4ywMDJfwylkXfASm5la3HkCBzGT6R1MAEAOLFnRNGrIT5u06yfV2Y4RNlqRNnqSfejnD5CSFp0hbX4RZ96vskGUwy8Zng+yHneTnT7RCRK/AicAYH5syzWw2gQKq9SbSpw9k2wc+oYjUa0trZOuUIqyS2CIKCpqQk8z8+445ttjCCg6sQh1bZwSRlc5wyP/HqWrMLIRA2D1wWDZ3DS4/I8D5vNNuk+hEwmsQxZqtZgTRTU6urqSr7zNEiSBIvFgmPHjsHj8cBoNM58+oskovroWyhw2MGIIqqO/BVs56nkj5uDEmux19XV4cSJEwiHwzAYDHl70TGfMbKAwli9altItxKCtgRe7gOq7TrJoazpmzayNGZUOKpdhBhbNcEDUo+CYULmoIKBHixofB8FAz3Jd04DQQYeMBciPqLXXaSVcN9SP25bFFS2Xe5uhk/UoC829FXkXbQc0Mw+7W10qnTBYGY68rIsp7xzRtJPlmU0Nzfn/XzAitbjqgJZMgP0bfwwMKIzL/AmBBaor5KXdrckPbbf70c8Hk+6HyHj6ezsTHlKM8dx6Ovrg8PhmPWxZFmG3W7H0aNHYbfblbmmMyZJWHzsbWj7emGOanHcr0O9X4c33joOs+/sOY8ikQja2tpw9OhRmM1mJc2YplxkT0G8GRo5PGKLFn79BgBAjK1EhF2q2r8oenwKVZtnziicHjW3lxkaFc4gCoYJmWN4px01h/+Mso4G1Bz+c1YC4uftRrSF1aNrX64OoEwn4RNlEWwujKFQCGGTfyhwtERYhCVm5lWkRwmOGhk2OvuADIzY6vV69Pf3p6RzRjKns7MThwYlfKO7FI/1FCCah4P7nNeJso4G1TbXsvWIlFSM2de9RL0UWHHvaWiEyTs7er2eRofJjIRCIQwODqblYpPRaER7ezvC4XDynSfgdDpx7NgxdHd3g+O4WY9W+uOA893D6G63oi6gQ29Ui5jMoNtQgb2LduC7df7kB8ljieKD9fX1qK2tRSAQAMdxlLKcAxg5ClOsUbUtoF+rqhrt4y4CMHzhSiOHYYqpf1tS1x4BhVH13OWwbjkEbWbn11MwTMgcM6+zEcyZvGNGBhY0vg/Imevdt4dYPGNXz/+5vCSCj5ZEh9rEAHtq/Njqb4H2TAl9CcC7cgWCxamZ1xstmgeBG07H08bjMHjHr5qYaqnonJHMGRwcRH2PC9+xluN9H4dXBo34nx5T8gfmEllC1YlDYOThNYXjfAEG14x/dT2woAZxw/A5qhEEFPVMngqXmBdPyHS1tbWltehQoqBWonjSVPl8PtTX16OtrU1ZtmemBBl436vHdzpN+NNbJ6Ht6kBAHO5i2/Sl+PbSnQiwPP5kjsAXy8MrbkmIooiOjg4cPXoUnZ2dADC7NHOScoWxBtUor8zoEdCr592LGtOYbaZY06g5xqlREGsetRySdqiCdIbRJ5SQOYQR4ii0W1TbDD73mII66RKTgO+bCyFiOAWqlJXwleqAqg7CfL2Er8TVc1Z+b1qP3w6mqMPEMAiVjZo3PNibmmNPwUw7ZySzQqEQ2tvb8TtvKWIjUvrfcBrQE82fn8fS7hYYXQOqbX3nXwqJnWCES6OBZ8kq9THMyVOlo9EoAoFA0v0ISRgYGEA4HE6aFqsRYjAO2sCGp//5ShTUam5uhjzigtBEwuEwGhsb0dg4NEI2m2rxnWEtnugtwPWN8/AfncVY1HoM1zjUI12DuiLcd8518OqGRt/Cooz/655bF0tjsRhqa2vh8Xig1+tTNjecpI5GCqIgpp6zHtCfB5kZO2If0K+HxIzsj0koih5NbXvkCEzx0aPUazK+tjFAwTAhc0pRXzc04wRgFaeOgclAYPZrewG6IupUuLur/Shh1R0UfcCD1WE75rHDV8ffKVmDp/sK0BlOzbyyYMWoecOO9BfRSkh0zlpakgcYJDsEQUBjYyMCrAlvutQdNwkMnrVn/gd5JthIEPOb1WsK+xYuRaBq6aSPG11Ii3c7wHkmH/nleR69vZm7qETymyRJ6OrqShpsamMRLD34Ryx99zWs+OvLMI26oDsVLMsiFAqhu7t7wn3i8ThaW1tRV1eHeDw+49Fqj8DgdwM8bm0pwU0t8/DSgBEuQYtrB/6B6wb/odrXzRbgxfXbsbJcHXC80BHCXBEOh1FbWwutVpv3dRfmsqGiWcP9QIkxIqhbM+6+MqM7ky49zCBYwQmp+/43RU+OGqXmENCfl7LjTwcFw4TMIUW9448A60MBlHant4plU5DFC/3qTs/HSiP4cMnYuYjF1g4wAJbxAlhGRmNBNQb1xYjLDB40F0JIfnE/qTHrDTvtgJS5kVqWZREIBGCxTL9jR9IrUTBLq9XixQEewjjrYL/l4vJidLjy5GFoRxS2Elkd7OddmvRxcWMhghXqZV6SjQ4zDAO3200V08mUdHV1TaloVsWpYzB4XQCGUvYXH/sr9H53kkeNNVFBLVEU0dnZiWPHjiEQCMyogFNcAg569PhmZxF2NpbhJ70mVV2Ma5z1+Nf+g8ptAyOjokCHeR/bim+dx2D3iMKRAPBefwzdfmHarzHB6XSiv79/xo9PFb/fj/r6enAcR+nQOYyVvDDGO1Tb/PoNkJmJL16E2XMQG7XGcFH0KCDPvh+llfwoiKt/b/wTjFJnAn1yCZkjtLEITJMUyypvq0taJGemIhLwoLkQ0oj06HKdiDsWj5PyJssotrYDAPQMsMwg4m8la5W728I6PG+ffbp0zFQMwTAcnGtEAbx78iVkUo3jOPT29sLtnn7HLtfIsgyz2Yz6+noIwsw7cbmgu7sb4XAYXonFnxzjj1rlwuhwU5DF/2suxQ2N8/BiPz/mIpHJbkGRTV29fHDNRRD4qbXbvXRUIS1rBxhh8kq3DMNgYGBg0n0ICYfD6O/vTzpSyHkcYy7UagQBNe+/CU08Ou3nNRqNaGtrQygUgizL6OnpwbFjx+ByucDz/LQrWreHtPhJTwF2NpXhW13FOOTlxlw8u8LdhH+3/QVayJivE7G+II4NpTK0H70KxeUlAIBqg4g1vPr397enZz46HA6Hcfr0abS1tU0pNTwdXC4XGhsbwfM8VYfOcYXRWgDDnxNRU4yQLknBUoaBj9sMjOjXsZIHBfHWFLVn+KKqyJgQ1Kl/jzwCg+ftPH5uK8Dzdh7/N2jAX1wcDnv1OBnQ4XRYC3tMA7/AQJzlKUD5DITMEYW2LjDS8DdCzGgCG4tAcyZwYaMRzDvdCMeqC1P+3E/ZCmCNqr9OvlrjRyE79hvK6LSrln+ZZ2CgrakBRsTNz9iNuLQ4hpXGWQRdDINg+ULVfOkChw3hssqZH3MGeJ5Ha2srLrjggrytphmJRNDc3Ix4PA6dToe6ujqcf/75efl6HA4H7HY7eJ7Hr21G1VxhHSOrlgN7y8Xh/1UGsZjL/EhoSGTwrc4iuIShzvtPbSb8xc1hT00Aq40CGCGOqpOj1hQuLYdr2drxDjcuf2UNBI4HGx2av6gV4ijq7YR31HzikTiOQ39/f1oLIpH8197ennwuriyjsuGwUvBxJH3Qh8XHDsByydUAM71xG57n0dDQAIZhIMvyjL+nftlnxDNJLohd6m3FffbXMZ8XMU8nQQtA0mphueSfEClVV3K/qjSEU+HhefwvdoTwtQ2FMw4kDQYDvF4v6urqsH79+oyu2Wu326eUAk+yTycOwiCYVdt8+gumdF7FteUI6VbAGG9XthXG6hHWLYPEzGxeuE50gBfUF3H93AXAiFHqwZgGd7SXwB6b+sUrXiOhQCvDpJVh1Mhn/i/BqB36//9unPixNDJMxlXSfQo1772OsvYTQJauOpLpGV0ky1NzLpzL1fMvytpPQhtNbeGOer8OvxtU/yB+qiyMDxSNP8KUGBVO8Fcuwa5z4igdMX9YBIMHzIWYbcHNbM4bHonjODQ0NORleqnNZkNdXR0YhlFS4ViWRX19PUKh/Jr3liiYxfM8/AKDVwfVP+a3Lgyihhu+ACOBwXNZGh1+vt+oBMIJHWEddreW4PGeApQ0H4cuNJx6KTMMbBs+PL3AQaOFp+Zc1aapFNIKBoOIxdK37iRJr0gkgo6ODthstrQU+XO5XAgEAkmDvKLeThQ47RPeb+rvwfzmY9N+/sR3lV6vn3Eg3BZi8ewkGUrVnID/5JrxoudVrDPGUXEmEJY1Glg3X4XQqOX9AOAjRWHoRvxJuvwi/jEwu/NIp9OBYRjU1tZmLAPJarVSIJwvZBlF0VrVprimHBF2yZQP4ddfCJkZnhLAyLExyyFNrz3HR7VnHsLsMuW2R2Cw53TxtAJhAAhLGjjiWnRHWDSHdDjq1+NtjwGvOXm8NDD5xVsKhskYBf1WLKw/BNNALxY0HUHJFDpHJLvYcHBoLd0RfIuXw7nifAj64c6AVoijvK1+9MNnLCQCP7AUQh6RRlOpF7Fr1PyoBEYUxqR1eqtXooSVcXe1eu3FzsjYJZqma/S8Yd7VD0bMfIpvolPY2jr79KJMicfjaGhogNlsHpMGl+hsnjx5El6vN4utnDpRFNHU1KRUOf2Dg0dIGv4JLGElfKo8jH+tVAf4b2Zh7nBfVCXoffgAACAASURBVIOXBiZO3z5mCaD3ZCvcwvB74lq+HtGS6S9N5h41Cmx0DYBLsgxZYnSY5A9RFNHb24u6ujrU1tbC5/PBarXi6NGjaG1tRTA4/nf2dEmShI6OjqSZA4wQx4Km91XbAvMXITRPPUexvP1E0mW/Uk2WgUd7TKrfNQAwaSV8ujyMx89145X5zbj19B9hGFGQSGaAnk1XILigetzjFrEyPqp+eXgxBYW0GIaBwWBAS0sLurq60po23dHRgd7eXgqE8wQn2qAXR/UNuQuBUReqZBlwxcdPN5Y0PPz6DaptxngbWNE1g/b0jmmPn9uktCckMviP08UwRzKbuEzBMBmj7LR6ce3ytnogD0e0ziZFtk5Vqlm4pAwxUwkknR6OVeo12+Z1NUMX8iMVfmozoW/U1bt7a/wwasf/MS60W6CND18JFzgDAvOHivh8uCSGq0ojqv1/02/EqeDMvxTjxkLEjcMjexpRBO/KznxHlmXh9XrR0zPxvO5c4XQ6UVtbi1gsNmmnx2AwoLm5OS/Wn21ublaqfIdE4JVRweZ1FSEYNMAVpVFUZ3l0+EmbSZWurR9xcjOyhNt630RMAk6FdGgLsfAbTBhYPf6awsnETcVjMihKzJNftNFqtfB4PFmbq0imRpZluN1uNDU14ciRI+jt7QXDMMrarxzHwWAwIBgM4sSJE6irq0NfX9+sMlisVuuU9itvq4cuPBwIyhoN7Odfip7NW1W1HgBgYd07SS/QpNIBD4fGoE617cuL/fjdeifurg5gU6wPNe/vH7Nyg+2Cj8C/aBkms22ROgj5fXcY4VRUjMRQevjAwAAaGhpSXtdBlmWcOnUKTqeTlk3KF7KMwlGjsFHtQsRY9fe9I67B7e0luLaxHP/WUoqucVb0COrWQNQUjTw4iqNHppc5KktjRoWj2oWIsouG/i8B3+wsQktIfe5daIrhhvkhfKosjCtKIthcGMO6gjiWGgRU6EQYNbOPT2jOMFHRBbwwDahLp+tDART1dcOX5EueZE9xj7pKoG/xCuX/7qVrUNbRAF146Mo/I0moaDkO24WXz+o5j/h02Deq+NBnKkK4oHDiAjzF1jbVbe/iFcCICpR3Lg6gLqCDIz70ZSydSZf++Wo3uJlcujszb7jEMpyaXeCwITSq858pBoMBFosFhYWFKC4uzkobJiOKIjo6OuB0Oqc8J5TnebS3tyMej6OqamxqYC7o6upCKBRSUib/6ODhE4c/UAVaCdsqhi7EaBngC5UhfNc8/MP/5pm5w4syMHe43q/D3z3q1M57zixP9uMeEy62ncCK8HBqqUPQYK/p49jiLcQ18yKjL/hPiXvJahQM2pTbJdZ2DKzbDFk7cRdBlmX09vZi8eLFE+5DsiMSicBqtcLlckGSJPA8P+lFLY1Go5zvFosFFosFJSUlqK6untbc8Fgsht7e3qSP0QW8KO84qdrmXL4eMdNQsSnr5quw9NCfwJwJyjWiiOp/vImuj+yAyKU3EItIwE971Re/LimKYseZ7wfO50bN4T9DO6rQnP28LfCOmnIwng+VA+UGDRyRodfmi8l4wxrGjnNSMwef4zjE43EcP34ca9euRWFh4ayPmciqiUQieVkn4mxlELqgk9Sjt35OXTOmL6rBPR0lsJ0Z1OiOsLijvQTfX+bD+aYRn3FGCy93MeaF/6ps0ot2GAQzIrqlU2oPL3SCldSp/D5u6CKuIAP/1V2E2oB63vuFphgeWO5N2v+TZCAkMQiKI/9pEBQZBCQGITHJOudTegXkrFE6wYhAWQfNHc5VuoAXvFs9MucdceFC1rJjRo2Kre3gfDOfX+QXGDxkUf/ILuYE3LJw4lQ7bTQ8ptq1t3qF6nYhK+Or1eoK1JYoi1/0zXxkbnSq9MhOfzYYjUa0tLQgHp+8am+m+Xw+HD9+HH6/f9rFkXieh9lshtlsTr5zhiUKZiU6cVEJY1KQry0PwzQim2H80eH0F4wSZeCxUR3x1cY4rpoXxQeKY3h+iQV7PX9TJW++W7wKBwtW4CFLIe7qKIY1Mv11uv1VS9XTKeKxMdMZRtPpdLBYLHk3b3yuEkURNpsN9fX1Sho0x3HTTmflOA4cxyEYDKK+vh51dXWw2+1TGi1ub2+f0qhhZdM/lEAXAOIGoyqDKTxvAfo2fFD1GH0ogMXH/pr2LLUX+o0YjA+fQywj47Yz0350AS+WvPca2Ji6yvXAmovgWr5+SsfXaRhct0z9nqR6zWGtVguO49DY2DjrTCRBEFBfX49YLJbRAl1klmQRRTH1lLgIuxRx7fBUmu6wFne0DwfCCQFRg3s6ivF3j/r9jmoXI6pdpNpWFD0GyFPIQpAFFI2aZxxml0HQlkGSgR9ZCnHIq77QstoYx3eX+aY0EKJhAJNWxgK9hGW8iPNMAi4pjuHKeVFsK4/gswsmr5VDwTAZJokosYwfDPNux9A6rSTnFI9aWzhYVgmBN6m2eatXIlpYotxmZKDi1NEZP+djvSZl9BYANJCxd4kfhkm+UYp7TquqXUcLSxApHjvH8QPFMXy8TP3F9fIAj4bAzBJZRqeA8p6BpMvHpJtOp0NjY2NOpJnKsozOzk40NjZCr9cnXQplIgaDAX19fWhvb0++c4aEw+ExVW3fcBngHFGYyqCR8Zn56s9bYnR4pP0uA3rTPHf4DZcBHWF1itjtiwLQnIl+z2l6Dyt1EZxXEEeBRkJIo8dTVVco+9YH9LippRTP2o2ITyNmkLXaMaNaJd3Ja0XwPI/m5ua8LAw3F8iyDI/Ho6RBJwIfo9E47SWERkuMFjMMA7PZjKNHj6KtrQ2RSGTc/T0eD7xeb9K1Zgv6rSjsU180G1i3GU6Zwx8dBjSfmRbjWbIarnPWqB87aMOCpn/M4lVNrj+mwQv96oten6kIo9oggg0HsOS918FG1N8VjpUb4Dh3kjK14/jnFern+GtvFP2h1Bcy43kePT09OHXq1IzO0Wg0itraoeJLM/1dINlhjLdDK/lGbGGG5gqf0RJicWd7iaofN1JcZvDtriL8YWSRSYaBz7AZI0NHrRyAKdaUtD0F8RZo5JGDJRr4uQshy8ATvQV4w6W+iLbUIOAHy70TTrlLNQqGiaKozww2Ov4PHTB2LjHJAbI8poq0b/HysftpNBhYe7FqU1GfGbxr+kVw3vXqsX/UF9cN88NYVzDJ1UFZRnGPOkjyVK8cU8Qh4bZFQSzQjShMAgYPWgoRnkF/QeBNiBUMp7wykpz1CzsajQbxeDzrgWM4HMbx48fhcDhSslSOwWCAy+XCqVOnsh7oi6KIxsZG1UiVIGNMZ/dTZWGUjLMEWKZHh4Mig6ds6lHhK0sjWG8aakNhX7cSRJi0Ms4zCXCsvRghvfoxcZnBL/oKcGtrKZqmMd/evUS9xmOB0w69//+zd95hcp3l2f+dNnWnbC9abVXvtmzJ3SAXwBAc0ujw5QvYudJoCSFxYogDwQ4hGAwYQsB8gAPBphts4wpGlq2uVd3Vane1vU7vp31/jHZmzs5WaVcFdF+XLknnnDlzZuac932f57mf+56dPTJpXdPV1TXv97mMc8ekGvSePXs4fvw4qqridDqXrHI3WS2OxWLs37+fgwcPMjo6mguwTNOcl2gWhk7N4V2WTYmyKvaUreHdx8v4bJ+Hv+rw8/REtkI0vPFa4hVWK7zyU0fw9VrbbRYLXxlwW6zWSmWDd9ckkNJJGnf+wmIJCBBoXpudVxfYm7CpTGFdaf7Z1E14rGtpGBaTPeH79u0jmZy/k0QikeDgwYMoinLOiZXLOL8QTBVP5pBlW0JZlev5PRRT+PBJn6VVCGCV01okMBH4fL+Hrw26c8RQTfQV+QF7Mm2IxsysQMFM48lY2yLiyhp0sYRvj7h4fMw6blTbdD7TGsY3zby8VLgcDF9GDlON75Ol1qqdZ+g0tljofF7SZcwBeySAPZr/TUxBIFLXPO2x0ZrGIqXOqmMLE0AIaQL/MYUe3eTQ+NPa2ZVIy08dtlC5TaGYIl0It2Ty0UaryNdAWuZrZ0mXvlgslgphs9kIBAIMD1+YwLy/v58DBw4gSdKiLqInF82HDx9eEtuW+eL48eM5waxJPBewW+waFMHkrTPQpySBImXppawOf2fYRVDLn9sumNx1pu1AVDPUtO20HJ8qq2LTFa18c22Aqz3F9izdKZm/6vDzYF8J8Tn6pQAyHn9R4DFT20whFEVhbGzsvNm6/C5jMkDZt28f4XAYm812XlV9C3uLu7q6ctXinp6eeT3r5V1Hscfy6vOmAPtX38hHu/zEzizMDQT+s89DT1ICUaL/6ltRndZxv/bQb3AEF1cI8VBM4YWQNcn7vro4Xj1F48u/sFw3QGj5CoY3Xb/gQBiySaS3t1oDgMWmShdCluWcHd7Y2Nicx4dCIdra2rDb7WftgXwZFw7uzHFEs3Bek3Jq0K+Ebfxdp8/ipADw5ookX1kd4u8bIohY14SPjri4v9fDpM5b1L5lisewXiSMVQhPpg3BzM9RpmAjat/MD8ccfGPKmq5UNvjsihCVtvPLNrocDF8GALZYuKiXcnDLTSSnWHWUd16uDl9MmFoVjlctQ7fPsDgShKLqsHt8GPfo/HuKPt9XYlmwS5j8Y2MU2ywjiXegi+ojVmpbvHJZEZV7KrZ6VH6/whqo/HDMxYGoMsMrZkZR3/D4wAxHnl84HA66urqIxWJzH7xIyGQytLW15YRulmKxoygKmUyGgwcPLrqq6Xxw+vRp4vG4hdpnmNlJvRCvL0tRocw86e6Ypjr86FzVYcPAM9hN5fG9lHW24Rnsxh4aR1TTM75kIC3y+BSv7rdVJ6g+syCoPL53ivKuwNDmG0AQqLUb/HtrmHsaI/hk62cxEfjxuJP3Hi/lN6G5Ex6hKdVhX28HwjyCHKfTSUdHxwX5rX9XEAgEOHQoW+1xu90XvFrncDhyia/CnvyZIKfiVJ6wLph769fy52MrCGnWCSRtCtzX4yVtgG530rf9NoyCzyvqOst3P4OUWpwAUjfhoX7ronyVU+UN3igNrzyFI2wVIYrUNTF4xc1nFQhP4o9bXbn2B4CjQY3DgaVr3xEEAafTSWdnJydPnpyRuTM+Ps6xY8cuK0ZfohDMNCXqEcu2mG0dhuji+aCde7q8FvYDwDuqE3yoPtuO84byNJ9uieAQrffH0wEH/3DKR0IXssGszSrE5dS6UPTiBJVkxHBnrIW2mG0jvwx6+UK/tbDilgw+0xqi/jwIVU7F5WD4MoDiqnCirIq0r5yJFZss2319J5HS86faXMYSwjTxTukXDi+bhiJdgERFXc7KaBLVx/bMqzr8fNBelDl/d02CVa6ZF8DOiWHq9r9o2aYrCsMbr53z/QDuqotRZ7Muxh/o9cypDDgV8QqryrFjjuDkfMLlcnHkyJHzEkiMjY2xf/9+NE1bclVQWZYRBIEDBw6QyRRXLpcKgUCAwcHBos/3UthGbzofHIuYvL169sX0dNXhpwIOBqepDgu6jr/nOCuee4zlu5+lsv0ANUdeZfnuZ2l98Ues+fm3WPWLb9H84o+o3/0sVUdfpbT7GO6RPh7vVDELgs4KRedtVdn3dQTHKOu29mRNtG4i7SvPv7cAt5Wl+dbaAK8vK251GVcl/qnbx71dXsbVmaf9SF0zegFLQM6k8Qz1zPod5Y6VZU6cuOxJvxQYGBjgxIkTF6W3qyiK87quqmN7EAvGuKRs473yLYzO0LPYlZL58kA2YZryVzK05UbLfiWZYPnuZ8E4d/bJkxNTevVNk0+L+1n1wuO4pljxxaqWMbB1h8UF4WxQ45K4pc46Rn23c3G8nmeD0+kkGAzmRLEKMTg4yMmTJxelZeYyLgyKq7B2YraNPDHu4F97POhTvLPfXxvjrrq4Ja+z3ZfhwRUh/FOSq3uiNj540kdAFUgoK1DFMst+X+rVorWkJ30AyJ/HENw8m9jMp09bA2G7YHJ/S5gVrgvDJrukguGdO3fytre9jbVr1+L3+3n00Uct+03T5NOf/jRr1qyhpqaGN77xjRw/bg3yQqEQd911Fw0NDTQ0NHDXXXcRClmpv0ePHuWOO+6gpqaGtWvX8sADD1zw/relhKDr+PqsvYvBpqxwRaSuucintaxr7mb5y1h6OIOjlh4mQ5KI1jbN+bqp1WFHeALvwOw9fxOqwIN91kruKqfKu2pmDiaUWJjlu5+xeDGaokD/ttvIeErnvE4AlwQfa4wiFNB2hjMSDw8sjC6tO1xFAmIXum+4EIqicOzYsSUbZ3Rd5/jx43R2duJwOOYUuVksiKKILMscOHDgvKgOp1IpOjo6ihbnpklRv+8tpWnq5pGBnq46XHguQVMpO3WYFc98j7qDv8EWj0x3GiAbXDpD43gHu6k42UbtoZ34X3qa9+77Do8d/TxfP/4VPn3qu3wz+BMaTu7D19tB3cGXLB7iGbeHsdVXTnt+n2zyscYon10RKkoiAfw6bOf/HC+d0bvblGRC9Sst20pPzy/AlSSJWCzG4OCFU2vXdZ1odHE81C8WnDp1ir6+vks6QHEGRiz2dpoJn/TcxDHDazmuTLbesz8Zd+YUbcPLVzKxYqNlvyswQm3by+d0bVFN4L8LqJqbYqd5rP8Rbjz2XFGPcKK8mr5tt2MuUlV+qpDWY6eSaMbSrzVtNhumabJ///5ce0NPTw+nT5++KBMulzE/iEYcd8Y6XkdtG/nfMR//0efBLAiEBUw+VB/lnTXTF7fWuDW+uCpYNI90JBX+sqOU/rRCxL7dsk8xJnBpeZtPWZ/AqVnXlgf0q7i3pwyj4FpkweS+ljAbSy4cs+iSCobj8Tjr1q3j/vvvn/aB/fznP8+XvvQlHnjgAZ5//nkqKyt5y1veYpkc3/e+99HW1sZjjz3G448/TltbG3fffXdufyQS4S1veQtVVVU8//zz3H///Tz00EN88YtfPC+f8ULAM9RjEc7SFVveU1gUmWi1TkCl3ccQLtPhLji8UyjSserlGMrcVMiUv8JivQRQdXzvjBl204T/7PNYxBYUIbvolmco0EqZFA2vPFUkyDa45Sbilcumf9EM2FSi8keV1gH7ZxNO2jLeGV4xPS42i6VCSJJEMplcVCGiSa/Rw4cPs2fPHuLx+AVZ6AiCgN1u59ChQ4TD4blfcJbQdZ3Dhw9PW/HeHbFxcopK8zvnqApPQhKyDIhCPBVwMBJTqWjfz8pffpeaw6+gnAVl0yTb2wvZxUm5FuPqdB9XT5yg8sR+lu3/FY7whOU1Q5uux5xD2XWrR+WRtQHeWZ1AmtL/FdNFPtHjnbGPONQ0RUhrbBBbbH6/m8PhoKenZ0FCPYsFXddpa2ujra2Nzs7OS17h2jAMjh49yvj4+KVNWTVNagoCVh14wazm656rLIfd7E/z/9YWL77/vdfDcCY794ys20asyjp/lPacwD+F2bYQfGvYRUgTaUyOcW/3D/hk92NcoxcnSpP+Cnqved2cz95CcEeDE6+Sfw7HUgbPDZwfxpIoijgcDk6cOJGzz7ocCF/a8GQOkn3CstAFF1+euDLHsJiEeKa97c7KmQVzAertBl9aFWS1y0rfH8pI/OVJP4fSy0jJVo0aT3pfrjKd7SPOzz/jRhnv77nSQtMWzlzLdu+Fdfi4pILh22+/nXvvvZc777yzqLJhmiYPP/wwH/zgB7nzzjtZt24dDz/8MLFYjMcffxyA9vZ2nn32WR588EG2b9/Otm3b+NznPsfTTz+dU3V97LHHSCaTPPzww6xbt44777yTD3zgA3z5y1/+ra0OT6VIh5avxJTyA36ocTW6YqXO+fuWRs3xdxmaCYdjMr3z8Qk1DHxTqrnh+pkFqaZibO1VmAW8GFs8MqNYztMBOzun+L/9aW2cFuf0wbOgayx/9ZfYY9YK2djqK4rsW+aL99XFLdU5gK/HG4lq86dLF4toXTzBMGSFp0ZHR+clcDIdVFVldHSUY8eOsWfPHvbv38/IyAi6ruNwOC64NYbT6eTo0aNMTEzMffBZoL29vUgwC7LJnG9P6RW+yZemaYb7dzoUVod9apx3Dr9E9S/+l6rj+4o8RwFMUSTUsJJA81piVfWkS7yY01TjRzMiCcN6vc2OqUS2PML1rcSrl8/rmu0ivL8uzn+tCbJ2ymJmJCPxxf7p2RVpb1mR0J5/ntVhyNstnc/5cjIQ1nUdt9tNMBhk3759l6wHsqqqHDx4kGQyueTtDEsNf28HzlBWPNEAOhIy91fehiHkn4erPRnuaYzgkU3ubY4gF1AhYrrIJ3u8WfEeUWTgqh1k3FaKZW3bTpxnwfQ5nZL41YDG3/Q9yYOd3+LKWDfL7Dr2ggfQFEUmWtZz+oY3Yijn9ltommbp9XbKAr/fbA1Av7eEQlrTwel0IgjCpZ1wuQxkPYRL7bRsezRyDY8M+yzbFMHkX1si3FY2v6RLqWLyuRUhtk0RaQxrIh/q9PNc+lqgoJ/fTOHJHMKmDWLX82uspC7wd0OvJW5Y1yEfWh5jR+mFb1m7pILh2XD69GlGRkbYsWNHbpvT6eS6667j1Vez4j27d++mpKSE7dvzpf1rrrkGt9ttOebaa6+1ZMhuueUWhoaGOH3a6o332wBbLFSkrBuc4u1nyLYcbXoS5Z2Hwby0M+8XE3QT/rnLy1+fLOU9x8v4RLeH0czMj6d7Ygi5oHdblxVi81wkA2RKfIQaV1u2VbbvL6r496dFHpqSVVznUnlr1QyVH9Ok7sCvcU1YLZtCy1cwtmbrvK9vKuwi/ENj1KJyGDRsfHFgdhGuQiQqainUjXCEA0iZ2TOj5xtOp5OTJ0/OaxGv6zrBYJD29nb27dvH7t276e7uJpPJoCgKLpfrggfAU+Fyuejo6GBo6OzVvA3DIJPJkEgkCIfDjI6O0t7eTjQanfbztsUVjsSnVIVnofdPB1mAu/0jvH/wOb7W/jX+cGw34ZROasoQaEgSE60bOHnbWxm88jUMb76B3uvewKlb38rx3/u/dLzu7fTc8CYGrryZvlVX8qhrI8dc9YwrWQpbhWLgmcFXUVdsDG+4ZkHXDdDq1PniqhBvniJG92TAOaOo1tTx3t/bMe/eTEEQ0HWd7u7uBV/r2aAwEJ78/RVFyannTnrvXipIJpO/Nd6uoprOOhaQrQ91JmWecK/lcElD7pj1bpX7msM5EcY1Lo33T3EnOBJX+OZQNqGl2xz0bbsNo+C7EQyT5bufQU7OX4hQVNN07zrIFzu+wY7QUQRM7IJJnT1/n4eXtdC5448Y2XQdhnxuivuqquL1eqmsrLRsf/sUqvQv+pKE0pfXVpexMHgyB5iswprA3ngFnx65wnKMQzR5oDXM9b6FaXi4JPi31nCRHkXKEPjb7jpeyljbdtyZ4/jTedHUjCHw03ATLyetVeT318Z4c8XFsQa7tEfaAoyMZBffUweaysrK3MJrdHSU8vJyS+VAEAQqKioYHR3NHVNXV1d0jsl9TU1N075/b2/vonyO842W7sMWSlvYW0ZnMApBa9/VsM3H9nQKcbKfJZkkcXA34+XW7+oyzg4/S9awK5HPOr8YcvBySOFO1zCvd4ygCNYF8qrO/ZbfbbiqgtMDC6t0DpdUsS19CGmSTphMYux+kb76VaRMkSeSNTyZrEYtyJnZMHivrZ2Bvukzec2nj2Hr76Bw2R3yldNW3oTZ17eg65uKEuAORx1PpPJiWE8HHDRnBrnOPj9blzLBRkkiT/mMHj140d3Dpmny1FNPsW7dOksVwTRNEokEgUCAWCxGKpVCEARsNtslZ3/R399PdXU11dXV6LqOruuoqkomkyGTyaCqam574R/DMDAMI1d1FAQh15c8Ux/01yJWxsRGJYxzvIv5jtjOZJSG/pO8c6yPE5kS0meeBxM4nYAGKYkmKwzUNDNQ14qq2GEsAARmOauD7wmt/GJZ3srIZWR40LmHoBrGkU7gSCVwpOM4Uwk0WaGrcR2x0fFZzjk73pDR2K9soV/NjzMP9Ljw+3vwitYkmKjLeFUVeTI5NjneV1gpqrPNez09PYRCITwez4zHnCt0Xae9vR3DMGZUVx4cHMThcNDa2nrRB5eRSIRTp0791ljatHa3oYaCZIB+3cGgaeObNTfl9i+XEvyVrYOxAWuiZbsJO5UVtKn5qtajIy7qkwOsU7Jrk+Cytaw/sTv/omQS/7M/5ODGGzHEmdlVgqFTN9yNu7eLLSlrkqxWTJBJaoR85XQ1biDqKYVAOPvnHKBpGjabjVWrVtHZaa3elZuwzOFgIJUdV9I6fPXV0/xB7dytaL29vedN/+EyLl64hCB++wmSZB0EejQnnwvdiFGwdnMLGn9bcpKKYILes3TBeztgc9bx02R+DWYg8Be91/PdqiO0KoVzXjahpZkCJ7USHoncCAWcpzscw9yQHuB8hk5btmyZcd/FPTOcBYopcmZR8DsVcx1TuPCaCQ0NDTPuu1gh6BotR15ELqiCBzZtp2H59J9FjQ5ZRDDWRUfpuWLhlYrLsKI9IfPDdn/R9gwSjyWWsUuv5m/qY2w701Mh6DqNh19AKvjdxA1X0bCAyvAkUukwFSfz5uyrgwM823IrD4+VEdCKFxR3L0uwvap62nP5e45TN9EHBdeV9vgI33gny22LQ/X7GwOOtmu5PkuAb8Sb2VJXyhr33IsHMbwK56m87UCLqOO6CJ9dTdPQNI3GxkbGx8cJBoMkk0l0Xcfv91NWVjb3SS5ypFKpHGV6cgyWJAmXy4UkSYsSDJyIyxyZsNLE3t9k0lAy929uD41TcfIg3sHurICVw0GDZHAymV9gdAs+2LQNc9UaDMVO7cyns6A/LfLL49bf8I/rVFbV5tsIdLLLickaWdmZP2cLVVX5TvMybn1iPO8XaSp811jDJxsjRS4xamQTnu58C82aVJDehrwKfG9v75zzXjqdpqWlZUlsgCYrwsuWLZszyDUMg0gkQmtrKxUVFbMee6EwNDTEWnVZHQAAIABJREFU2NgYq1adXSvJxQZ7JEhLaBjB6aQ3LTGuSjxetZ0xW/Z5rLPpPLgqQbkyvYbEv6gqf3ZCz81DJgJfS7Ty9TVB/IoJDQ3EHDKV7Qdyr3HqabYFTk9ve2SaeAe6qGrfgxyPcihjDYS9kkFZTSWj67cTq15OqSAwP5nH2aHrOqIosnnz5hkD13fHI9x/MF+AeD5awt/fVDntsZPo7++noaHhcjB8iUE04ngyh5GMCLroRRX9aKIfVSrFFM5inWSalCePYdOdGCa0JxQOZGrZm847i5TKBv+xIkKr89zHvg8DrWNRHuwvyQlyZVB4IHw7/1Lxc5qdWi7k1U2Bk3GZl1Kr6Nby68Y7ypP83XIJQbh41l6/NU9RdXX2i56s8E5ifHw8V9mtqqpifHzc0stkmiYTExOWY6Y7BxRXnS91eAe7Lf1uus1GpK55xuOnCmm5AiM4AyMzHH0Z80HKgE9NI3dfiP60zEdP+fmnLi9DaRH3aB+Smqe5aHZHUT/sfDG+cnOuHzysCRwJmvTtPzZtIHyTL81bKqenR7tH+qg9tNOyTbM76L3mDRiLFAgD2M7QpQsr5RlT4J+6vUzMYhkzianiXRdb3/AkZFkmHo9z4MABRkZGMAwDu92Oy+X6rVn8OBwOXC4XLpcLt9uNy+XCbrfnLJkWA9+Z0iu8yZ1hU8nsQh3OiWGW73qK1hd/hG+g26LkXK4YOESTccXDf9fu4H1r7uJ+z/UL7iV8eKAErYCzX6noc9o8nQuSySSrV69mS4Wdv99irdTuDNt5MlDcLxic4jlcMjqAEl+YUrMkSbS3T69FcC6Yjho9G0RRxG6309HRkaskX0zo7u6mp6fnklaMtsA0qTn8MoJpMpAW6U9LjCpeflSZdTKoUHQ+uyJEuTJzX3mpYnJPk9VJYEKTuL/XwyRBbWzNVqI11iSwv/ckZV1Wn1XX2CDNv/ox9XufxxaPMpQWSRb06gdlN8mrrqfrtX9IrKbhnPyDCzHJYtm0adOs4/ZUVelXRzOcCs8juWtcmj3xc8LUEcwLK6i06DA1StKHqIr/CJd6Ars+iEs9gS/9CuXJp6iJfZfq2P9Slvgl3tRuXGoHij5msUmaDnZ9AJs+jG4KHEsohDSRx2LXMVmFrbbpPLQqSOsCNDLmwp2VKe5rjmArmBx3p1fy6/hyOhIyhgmGCScSMmFd5vH4dbnjbvan+cjy2GI9YouG345VFdDY2Eh1dTUvvPBCblsqlWLXrl25HuFt27YRi8XYvTtPrdm9ezfxeNxyzK5du0il8jz2F154gdraWhobG8/Tpzk/8PdYRVFCy1dZhLOmIu0rL/KoLe9sW5Jr+13BVwdKLN6nAG+pSOKVihdrvwnbee/xMjpP9FE4rEXqmmEWWthsMGx22puu4ERC5mhCIWEIvGniAGVqftFbKhv8XUOUjzdHEKcZwOzhCer3PIdQkGQyJInea16H6l58iuQql8ZHllsX5eOqxD91eZmr1SpRXm3pG7ZHQ0hnoQJ8PjAZ/F7s1M6LFd1Jid9MEX6bzQrMHp6g8Tc/o/mln+EZmZ7Sr5Z4GdhyE3eveh9PVFxJRlR4asLB0DS+wzNhX1QpEqS7qy6OY4lmY03TKC8vx+vNqq9/aJOHreXWe+qhfnfRZ0j7K0iWWisJCxHSgmxSJxKJ5NqYFgO6rnPo0KF5B8KFcLlcRKNR9u3bd1FYMJmmyfHjxxkdHf2tUvL1DPXgHhtkRBU5fWZ++0bta1FFGa9k8B+tYWrnYWu21aMWqb6/ErHzg7Ez35UgMLD1taRLrOyP6iOv4h4bwB4JsHzXUzTt/HlOxCtjQn86O18mRRuPVl/Pz659J+7Vq87ZO7gQhmGgaRpbtmyZkxnR5JG5ttrak/y9U7PPSzYzTHXyJ3jSe4u8XS8liEYCuzaAO3MEf/LXVMZ/Qm3sUWpij1IZ/zHe9B5s2jCYF8Z/9pxhmji0XqoSP8aTOYDAzEkO0Uxi1wdxq8fwpV6mIvFzamL/Q1XsMcoSz+BN78WpdqLo4wimBqaJJ70f1RA4GpeJaiJHMg0cV7MJouV2jYdWhqifx7O2UNzoz/DZFSE8ubWqwKOxm5lQJY4lFDoSMhFN5IXkRkb1LPPxqjNCedJFFggDSB/72Mc+caEvYr6IxWKcOHGCkZERvv3tb7Nu3Tq8Xi+ZTAafz4eu63zuc59jxYoV6LrOPffcw8jICA8++CB2u52Kigr27t3L448/zqZNmxgYGOBDH/oQV155Zc5eqbW1lUceeYTDhw+zcuVKdu3axb333ssHP/hBi/DWVAwPXzyepfOBLRqk5uhuy7bBK29Ct88+IWt2J/6+fM+LLRYiXL8Cw3ZpKRG6xoco7T4Gpok6ZSI9X9gdUXhowBos3lGe5CMNMe4oT5EwBDoSMoV9FrKu8gddzxLKgF0ycYowsuEaVNfCg86IJvBfg24+EW7m2oljOI1sBlLCwGGotPlaeHt1gk80R1nv1qYNhOVknKadT1gYBqYAA1fdQmJK4mQxscKlMxKK0qnlBbTGVYnRjMQNvsyMWUdTkvGM9FlscFL+StLeS592fBlWfGmghK4COv0qp8rddYlp7w17JEDzSz8tUkCfRMpXxvDGaxnefD3eqjKeDTmI6pO9wwIpQ5iXKIlmwr1dPoJaftG9zqXy1/XxJcuUq6rKhg0bcpUpURC4ocbBt9pjueq0ZgqcTMjcXpYues49w/mmLls8QqBlAwgC4XAYn2/usVOWZcbGxqiqqjrnxM5kIGwYxlmfSxRFJEliYGAAwzDw+XwXpD9X0zQOHz5MKpXCZjs3caaLCYKm0fDq0wQTGqfO2JkdKmnkO9U34JRMPrsizArX/AObTSUq+6M2RtV8QHkgprDdm6FCMTAlmXjVMnx9JxHPVPwFwDvUTVnX0aJnujslETZknizfwv2Nd9Lhb+K+1hiORWTym6aJqqpcccUVKIoy9wvIVtKe7MsXYfriOn++zj3tvWmkA9D3HRRRw2aMIpkJ0lL9olW0lwSmjmyEsOsDuLRTuDNH8ab34Mkcwql1YdcHUYwgopliUghKNFPY9DFcWicl6olsEIiGITgxhfl9rxcSsh7Cn3qJkkwb4hwV3tkgoiKbUWz6KA6tF5faQUnmMG71JIYe5VhcJmFkx/cvRu4gbLhZ6VT53Iow5balS5RU2wyu92XYFbYRN0TChhuPmKReGiVliEQMJw9HXk8GhXUulU+3hhf1OVsoampqZtx3SQXDr776KrfffjuPPPIImqbxm9/8hkceeYRQKMSb3vQmtm/fTjqd5jOf+Qxf/epX8Xq9fP3rX6e+Pr8ov+2222hra+NTn/oU3//+97n++uv5whe+kJOVdzgc7Nixgx/+8Ifcf//9vPTSS9x11118+MMfnnXCvBiC4ZAm8P1RF6eSEk0ODWWWJGdFx0FcwTwdPF5RQ2Dl5jnfQ3V58Az35JSMBUAwzSy16BKBe7SfxpefxD0xgr+/EzkZI15Vv6hZ4bkQ1gQ+2ukjaeTfs86m88mWCIoIDhGu9WW41pfhVFJm/MxC4LpIBzeG29EQGFclBiQPwxuuwbOAdaFqwA/HnHy828vBmA1NkEiJCldH81ZN27Vhfm9rDddViTPeR6KWofHlXxQtNkY2XkN4ilL1UqA+0c+gXMFAJj+6nkrJuESTDbOYt9viEVwF9H7dZidW89vF+vhdx0Ba5D/7PLmeJoAPLI9Na6ckqmmadv7Cos4+iURZFUObb2BkwzWkfWUgCIgClIimperclZS5vSyFR5594fHEhINfBKwJx39pjlBlWxrabjKZZMWKFZSUWFXXyxwiNi3JC6P59x1RpaJnJ1Pio6zrKMKZIEPSVJL+CjIe/7yDYcgHxDU1NWcdeC5GIFwIRVEIh8OMjY1RXl6+JH3NMyGVSnHoUFar4beN+VHRcQCjr4/2pIIJ6Ih8qvEtJG1O7m+NsHGWsXlSSK/wOxEFuNKT4amAI+dPaiCwP2rj9eUpbGJWYTrtLcM7cCr3xAuGUdR8FNMFvmNby/2Nv8+vSteRFhXuqouzdZE9TtPpNFu2bFmQLVaTR+Yrx2K5fv5IxuTGWjsNJdb7wzRN1K7/RzIyiCRKIIBiBFCMICl5OQgXnvApGolc4ObOtOPJHMSX3oNbPYFD68WmjyGb0VmrpFMhoCMbYRxaHyXqURxaH5KZwBRkDMF5USUCBDODN7Mff2onslmcYDUFO1H7FWSkGgzBgYBwJlheWOCa1jWOJhRSZ9aRr6RX8VxyMxvcKp9pDeObpQ1hseCXTV7jT7M/aiN4pjIdN+0M6mU8GnsNE4aXZofGZ1eEKbnAQ91swbAQCoUuXX7FRYSDBw9e0PePagJ3t5cyeCYwaHJo3NccocFRvPgTdI1VTz1q6Tvtv+q1RObpU+vrO8myfS/m/m9IEidf9w70S6A6LOgarc//AFvcOkClfOX0bbsV1e1d8mswTfh4j5dfh/ITpYjJQ6tCrJ9GBMow4amAg68Ouvmrzp9wdfRUbt+PK67i0bqbeUd1gndUJ7DPMg+aJuwM2/jKoJv+KdRsydB56OQ3Wa1P0OTQ8Ugm4WXNDFx96/QnMwwaXn2akhGrbclEy3pGNl57Xiam3t5eSusa+YsOP30Fn0fE5NMtEbbPUKlzj/TRuOup3P8zbi+dt711ya/3Ms4f/qO3hCcm8kFnk0PjG2uCxewGM2vJ4hmy2ubFqpYxvmoLifLaae9lzYT3Hi9loOC+e1N5kr9tmNnaJaoJvOt4GeGCqvBtpSnuaVoauq5hGNhsNjZs2DDj/tsf72ZvPD9uK4LJV1Zb+8tqD75EaUFLTbR6OX3Xvn5eAlqFSKfT1NTUnFW70WIHwoUwTZN0Ok1zc3NOe2QpEQ6HOXbsGA6H4/xXpE2DkpF+/L0d2OJh4hV1hBrXkPYuhlQUKIkolU8/zomoyGSa5aflW/lm3Wu4rznCDf7Zq2OpVAq/308kEimqlv8qZOPj3dbky62lKe5pjOYe0Yr2A1Qd3zvtueNl1fx1yW08USDa02DX+MbaIPIi/gypVIpNmzadVf/3+34V4PGufFLunStdfOmG4t/GSAzQ98pncchGIXGMjFRDwLkDUzjPTAPTxKH14FI7UYyJM9Xdc4XAfINDQ3CQlpaRkpeRlpednRDVfN7HZFqGXA6miVPrxJveN8N3IBBX1hC1bym+RlNHNqLIRgjFCCEbIWQjiGxEmO57CKkiHUkZ/UyCSEfgHwLvptHl4l9bwkvWdjMTYrrAP3d5ORCz3nt1Np2HVoUoVy68TsNsatKXVGX4YsaFrAwbJtzX4+VYIk8bCWkiTwXs1Nv1omqIb+AUvv58QKXZ7AxtuWneldG0x4+/tx1JO6NubJoYskKiYr5aqhcOle378U5Z+ALI6ST+vpOkvWVklpg2/XTAzqMjbsu2d1UneF359HZFggArXRp3ekJsOv4SiYKf87/qbmFCLuFQzMazQQfVNp0Gu160fj+ZkPjkaS//M+omohf/zlV2k9vrBG6MdOQCakc0RLS6Ac1pvVZMk9q2nfgGuiybozUNDF5583nLTIfDYapKfVzlUfll0I56ZlIwEdgVtnGDP4N/mkqd5nBR0XkoJ4wkqWmCjasxlIuLqmiaF1Wy+5LBaEbkgV4PhXWhv1gWm5aaWdFxkLICxWSAYONqBrbdmm09mOEHmK46fGqO6vDXh9zsj+bvMYdo8smWCO4ZfIXPFel0mg0bNsxY8RQEgdViiJ8MiZaK29GYwhvKU7m+Ls3uorSgV9iWiBBqWE0wkZx3ZRiyFdBgMIjf719QxWwpA2HIfg+yLDM+Pk4kEqGsrGzJROomPbFdLteCAmHNhD0RG08F7ByJK4xkRBK6gCRk76O5TqUkopR3HmbZ/hcp6zmBPRpCTidxBUcp6z6Ge7QfELK9t2epPwFg3/0b+kYiOUHIsOTigcY385GmJDvKZg+E0+k0y5Yto6WlhYmJCQzDsHxHTQ6dgCbSXrDO6UrJ1Nr03LOdKK/BHg1ij4by5y3xMXjFTXyv7kb+O2xNdvxjU4QGx+It0pPJJBs2bChiYswXbkXg+6fywXB3ROPudW5sU5osBcXLke40fnkUkXxVWzJj2LUBUnLDeaMRi0YKf/olPJm2BVd7s5BQxXLS8jKSygpito1EHFcTs21EFctBkJHMxKznFdBQjCBO7TQlmaPY9SEkM4mBgiE4zmkiTerwbNDOg/0e/rOvhD1RG2tcKqVTqq6KPkZZ8gXcavu015qRagk4X0vSthKEacYxQcQQHWiSn4xcQ0ppImFbS8y2kZTchCpVo4ledOycSkr0pXQL8+nH8WsocdXzL82RWYsiSwWbCDtK0/SnJXrOtCdVKDqfWxmieomYTwvFbJXh3y5+zu8o/nfUycuR4sVF0hD5RI+PP0kkuKsunst+lk5Z/IUbVmEuhCImSgRaNlBd0HNc1nWUiRWbZhXgutCwxUIWG6GpkNQMDa88zdjqKxhbc+WSBHVDaZEv9FsnytUulffWzi3iVDfWTZ09Q40s0J2SOCGV0+Woyu0fzkj8c7ePbZ4Mf1Mfo96hM6GKfH3QxZMBh2XgnIRLNHhXTYI/rExiF+pJDZfjDE3k9lcd30PvdXdYXlPe2WapFAEk/RX0X7XjglC0Ghw6H2+K8LFTvlwAFDdE7uny8vCqUFFwYsoKSX+VhSrtHhsk3LC4liamCUkDYrpIVBeIaiIxXSCqC7ltsTPbLds0gaguYgKbS1TeUZ3gihL1cmA8T3x/1GlRaq616ewoLU40uUf7qTxhrSIlSysY3nRd0bHT4ZayNN8a0XLVYR2BR0dc01aH+1MSPxyz0qPfXpVYMnp0Op2mvr5+zl7UK1cs5y9PH+X+obxTwqmUzCNDbu5eljV1SvkrSPnKcYSz44JgQmlvO7gW7q7gdDo5fvw4W7dunRcteakD4UI4HA4SiQR79+5l9erV+P3FdnfngtOnTzMwMDDviqFhwqGYwvNBO78K2adNYkK2ml9j06m1GdTadWptOjU2nTpFZU3gFPX9J3CPDSDMknNxBUZxBUapOfwy4foVBBvXkPJXLCiIiA0MEe/qszx73665kT9r0GZM9BZCFEXq6+sRBIH169ezb9++oqTJXy6LcTimWKz1Huz3sM6tZVlwgsDA1teQcXuxRwNEaxoJNawmhchXjlnn3Wu9abYvIj06mUyydu3ac/LVfk2tnRqnyHAyOy7ENJOf96b4k9bie0YVPIw5Xk9F+jlkIx/8K0aAiuSTTDhvQxeXzuMbwK714U+9jGhO7zIxFYbgRpVKUcUyVLEUTSpDEzwzrhtSShMppQlME8WYwK7149D6UYzZ/NZNbPoINn0ED/sxBBdpuY6MWIkqVaCKfhDmHns6EjJPTDh4LmAnXtDOdiSucHd7KXfVxfnDyiSymcCb2Y9T7Zz2PLrgJmK/mpTceHZBuSBlvyepjJgu8OkBDzvDduyo1MkBqqQQE7qHld5S7m2ILCrLYaGwiXBvU5RbI2lGMiK3lKbxzdE6dLHg4o1cfkcgp+LU734OWyLC+MotBFqnp7TNhEMxha8Numc95vujLk7EZT7eHKUuOWEJAgCCTWtmeOXMCDatoaL9QK46LKdT+Po7CTUu/FznBaZJTdvLud43yIqBJcqqiirFle0HcAZHGbhqx6JSv3UTPnXamxM6ALALJvc0Ruc1gE1W80skkw1ujdFljZQqJkHN+uLdURt/eqKUm/xpdobtpIzik4uYvLE8xZ/WxinLZTgFRtdto/HlJ3PHlYwO4B4byFkSeQe6LEkQgIyrhN5rXocpXzhBi21elT9fFufLA/kFT39a5l96vNzfGi76fuOVddZgePzcg+GXQjZ+Ou5kKCMS1UVimjCrZdZ8sDdqY2/Uxnq3yruqE1zjnVkc7DIgpAr8bHxK0FmdKPr9lUSUZXuftwQIms1O/9W3zjuhJwvw7uoE9/fmWyuenHDwzupEkVLulwfcliChStF56xJZKZmmic1ms2hlzARFUXhTvczeVIpng/mx7nujTq71pdlUooEgEGxaY7FO859uhzVn51kpiiInT55kzZrZ54rJQNg0zfPWVyvLMrIsc+zYMaqrq6mursY0zZxFzuTfk/8u/DN12+RxkzTseDw+ZyBsmlnf+eeCdl4I2XNaEbNBNQX60jJ9aSAK9akJbg0epiV4lICeJIyJXVRwiCZ20cz9bRfBJpjIQp5pK2oapT0nKO05QcpXRqhhNeHlK+acB8dTJqFde6kouMc7nTXUr23mD6vmDpQSiQRr167NVYJlWWb9+vW0tbVZvjO7CPc2Rfjz9lLSZ94rZQjc1+Phy6tC2MSsSOLo+m2W8393yMVYwXcpCyZ/sSzOYmHSuuxcEyiSKPAnrS6+cCSfUPtuZ2LaYBhAF92Mu95AefJZFH0sfx4jQkUiGxBr0uJQ4AshmCre9F5c6ky2aRKq6EeVytDEM8Hv2froAghCNpCVKojZtyAaSez6AA6tH7s+OKv9kGgmcKqdOJkMVqUzAXkFGakCVaxAE70giMR1geeCdp4Yd9CRnHk9o5oCXxlwQvIYd5XuwiFM9/4SMdtGYrYNmNNVgheI7qTEP3d7cy1uaRS6tWpOa1XcVRfnrVUXh12RIDAvMcmLDZdp0ouEs6VJL3/1GdwTw4iaRsloP6rDRco/v4z7hCrwkU6fJbjySNlK3+G4YqEJjqgSzwQc3DnyCjXRfBAQr6wjsGLTgq/blGTkTApXIC/CZYuHCTavuyi5nd6BLipOWm2gBq+4ibG1V2HIMu7xAUvYYotH8Q2cIlFWU0wTPkt8d8TJU1PEc/66PsZ239zZaTkVz/o2nvm/ABjbruMNdZAxsn5uhZVfA4HulGxZgE/iKk+Gf22J8MaKNM4pay3V5cEVGMaWyPcx2mNBQo1rcAZGWL77GYuFkq7Y6L3+jeel13oqpgr4rHNpjGQkOpP5iWcwI5HUBbZNUwHw953M/VtS0zmV3IUiqgl8ptfD14dKGMxIRHSRtCFMW4k/W4ypEs8FHbwctuGTjWmp8JeR9RUu7FmqUHT+viFqsXIQdI2GXU9iL/DMNQXo3377vMfeSTQ5dZ4NWJWl04bAdQWLgb0Rha8PW6tSH14eY9UCFHUXgkmq5nxVbO12OzWx07wQLSmYSwQOxmzcUZ5CESFT4s0KaZl5Ia2AowRbzbKZTzwDRFEkGo3icDhwu6cfWwsD4fMpbDUJRVGIx+MMDg4yOjrK2NgY4+PjBAIBgsEgwWCQcDhMOBwmGo0Sj8eJx+Mkk0lSqRSZTAZVVXN/5grou5MSPxhz8tk+D98ddXEsoVjm9blgM1RuDh3n7sFnec/IS6xJDGI3s1RNEwHVFEgaWfbJiG7j8ZKNfKvkSiZSJmosxkhGZEwVCWoiYU0gaohkEimU4QE8nUcxwmFU2YbpKimid4c1gZ/v6uaKcStbaP8Vt/P2ZnHOccowDFwuV1Evuc1mw263Mzo6armXSxWTUtmwsOECmkTCEKat9I5kRO7r8VoSk39cleSWsrmr1fNBMpmktbWVioqzSw5NRbVL4usn8oH66ajOe1a58dis90NPT0+271xUSMrNKHoA2cyPaQIqLq2btFSNIS7OGgaydODy5DPY9cGifapYQdC5g7B9OwnbGtJyA6pUiS6WTE8NPkuYgoImlZFSmogp68hIdRiiE9FU59GvbCKZCRRjPCvwpZ7ASB7nWGiUJ0bTHIiYDGXsJEw7zDCHb7T18EHfE6yWOhnPgFM0cRa0u6TkRgLOW0gpDYvClnsxaOMfuvwENOtY6JMN/q0lwm1l6cvrgXngMk36IoVzYhj3mHVAqW17mbS3jGTZ7EIemgmf7PEWPRz3NEa5xpfhKk+Gj3d7LdnQeEZnrL2bAUWkzp4NlYPnUMmdaNlA2akjueDIHg1TMtJ30SlLi2qamiO7LNtilXVElrWAIDCxcjPJ0krq9zyHnM4PpEoiTvNLP2Vo03XZivc5jDYdCZlvDFknpO3eNHdWzE9owjvQbalipXzlZDx+SjD5q/o4d5Sn+Hx/tnd4JjTYNf5iWZzts1UXBYHRtVfRPPbT3CZncJzyzjbKTx5C1PMLeFMU6Nt226KJr5wrBAE+vDxKX1riaDy/eHpszEWLU+MNBVS9ZFkVhiTlPo+SiKMkogsO6vdGFO7v9cyrgrMYOJlU+Hi3j0aHxjurE+woTV9QWtTFhKgm8KMpVOS3ViWxrCFNk9pDOy2tAACja6/OKsovELIA76mJF1WH31WToMZmoJnwxQFrILzerU5L214MZDIZamtrFyTe4/P5KLVLfKwhyt+eyle2hjISXxpw83cNMQzFTmRZC/7ejtz+2pEeomyb7pRzwul0curUKXw+XxGV+0IHwpNQFGXeCYWzwWBa5Pmgg+eCdgvtdyZ4JIOb/GnckslwWmIoI2IPjXP9+FFuDh3DZcxejel01vBM6UZ+7V9DUsoGkk+Xb6YmHeTW4BFuCR6hVJuhWtpxGjpOM2Lz82rleo5UrUFyuShTDPrDKh/te9X62Zat5g/WeOY1ZaZSKdavXz/tvqqqKqLRKOPj4xbK9BvLU+yLKrwQylesfzDm4kqPWlSV+sqAO9cTD1AqG7x7Fr/xhSCZTNLU1ERVVdXcB88T60oVNpcrHJrIBvYm8P1TCT64aWbKsykoBJw78Kd+g1Przm0XzAwViacJOF9LWj5Hq0NTx5NpoyTTRrGgk0DUtpmYbeO8KMiLCkEiI9eQkWuI2rciGdm+aYfej00bmrHXWDcFxlQx13+vMMqtjlFuPXNLxUwHPWoV3VoVY0YVK70+6m1pEtH9rFd6cufRTIH2hEKlTafe5SHuuJqMXLcoH00z4b8H3XxvtHg8X+1Sua85ctH0417quBwMX0BUnCxWoBYMg/o9z9J181vQHTMvaL455CpSbXtXdZxrzkwYpQluAAAgAElEQVQE69waX1sd5JOnvew9I9pyQ7gdl5HmdFomphs0+mWitU1nff2aq4TIshaLGFd556GLLhiuOr4XOZWnapmiyPDm6y3BbaKijq7X/AH1e56z0GcFw6Du4G9wBUYY2nzDWfVEpw341GmPJTPtkw0+2hCdd3ztHThl+X+4vtXy/xanzoMrwjwftPPlATcTBUkSr2Twp7Vxfq8iNa/AKVlWTaSuCe9gT27bVGo0wOCWm0hULs6gv1iwiXBfc5g/by+1JIL+s8/Dcrues40xJZlkWbUlGeUeHyQ0z2A4ZcB/Dbr54djsQYdNMPFIBh7ZpEQyKZEMPJKJR87+bd2WPbZEyv7dlZL59rCLV6bRAzidkvm3016+OaTzjuoEt5elsF0A0YyzgWbCj8acHI4rtDo0bvCnaXGce6X7J+NOS2+XVzJ4U7mVouk/fcIS0AFEahuZmIet3Ey4tSzNt4b1nJK/jsCjwy4+0hDj5+OOnJjIJP562dLR2QRBOCu15pqaGhge5g8qbZZ7+ucTTq73ZbjOlyHYtMby3ZUHR0im4miOs6s62Ww2jh8/zubN+e9e1/WcM8OFDISXCuOqyAtBO88H7RxPzB1oO0STG3xpbilNc5UngyJmk7u+/lP4h0/gDE2gmZByCqRNSOkCaTPLTkgZAgHBwfP+dTxTupEe5/QB27C9lO/U3Mh3q65ja6yb2wNtbI12I0yjYFudCfHmgZ28aeBl9npaeKZsEzdFTuI28skdt11i3XWbZiqqWaCqKjU1NbMKqrW0tJBIJEin07nquiDARxpiHE8oDBdY6z1w2sN/rwnmevEPxawBM8D76+KULIJoXSqVor6+ntraxRcOfVuri0MT4dz/v3cqwQc2FlflLRAkQo6bMNIO3GqhLoxOWfJ5Qo7rSSqtM758NkhGmNLkS9P26uqil6DjRlRp4RoCSwFdLCFhW02C1WBq2PRRbPo4ijGGok+QUJOMqBITqogxDXNuEiVCkhtcPfyh7RSlyiTPUiCjwMmkTKTAFSBh2ngwcA0nxtbx943xWa0d54uQKnBfj5f90xQ47ihL8oHlsQsilPXbisvWSouEhVor2UPjtL74oxn3xytqOH3dG6dVeN4VtvEPXVYVzytKMnxmRXFvpG5mA+dvj7j5985HWZUcyu37Ve2VbLxhCy3TeG/OF47QOC1TPkfXzb9PqnTxB8aIJvBC0I4kwKYSleXzoIo6gmM0//rHlqrq2OorGFt71fQvMHSqj7xKedfRol1na7/0hf7ioOlfm8PcOIfNxCSUeISVz/yvZVvH7W9Hc02vWJnQBf531MmhmMJGt8qfVCXn9D+dCls0SOvzj88oujK25krG1mxd0DkXG7NZu7QnZP66w19UEfjq6vxCqaJ9P1XH9+X2h+tbGbhqx5zveyIh8289HnrTxYmRVofGB5ZHWWY3KJGMRZmsTiZkvjPi4tch24zU60pF5+3VCd5YnlqyCdLbf4qy7qOoLg8j67edVRA0cWaCn8pgqLPp3OBLc4M/w3q3irTAYDGpw9uOlVtsi/5vbZz3FFSAHMFRml/6mUU3IF3io/vm3z9nJfEnJ+w8UFAdlshaFH2k028RPnpdWYp/aFwaK6VkMsm6desWpPA8CV3X2b17N4LdyftPlFqsykplg0fWBPDLBi0v/ABHJJh7v+iVNzK++oqzvuZ0Ok1dXR3Lly+/KAJh04SBtMSRuMxgRkIkK1AlC1kWgCxm/60IJpJQuM9EEUA683fhNgSTA1EbzwftHIwpc7ZPKILJNd4Mt5SmuMaXyVmk2KJBKk4ewjvQZWHoTId4RQ2hxjWEa5uZMLMB41BaYiiTrSgPpyVGVZGgKloSSJMoU6PsCB7ltsBhqtXwNO8wPXySgXfbVqIr59d6lU6nufrqq+dU79Z1nf379yPLsiUgPBqX+ZsOvyXRvLkkw3+uyF7z3e1+Ogt6P1c5Vb6yOjS7Pc48kEwmqampobm5+dxONAPGUzprvjec8xwGeOH3KrmiIj9OvfDCC/h8vuLvzjQpybThyRwoOm/Evo24bd38L8Q0cant+NJ7gOJ7LqGsIWLfet6Uq88WYU3glwEHT0w4CGdSNMujNCmjNMsjtCgjlAh5hp5NNKlSdKpsBnZx5nXTYFridErmxdR6fhC7lqiZXeOJmLy7JsG7a4q1KuaLEwmZe7u8jE5hnMmCyQfqY7ypPHWZFn0WmM1a6XIwvEhYaDC8bM9zFmuaQsrmJCZWbGRkwzWWbUNpkbvaS3M9agDlss5/rQlSPovB9tH+KI0v/MDSQ/rnq/6MkNPPR5ZHue0c+mcad/7cUmELL2th4Opbzvp80+GVsI0Hej0ECxa7ZbLOlhKVKzwqW0pU6qcGx6ZB869+gjOUz2Zm3B5O7fijOSu83v5T1B38NaJmzfDpio2Bra+dd/V7d0Tho6esohp3lCX5aOPMfqRTMdU7MVFWTc9Nb573688WtQd+RenpjqLtoeUrGLzyNRe8N3wun9Pngnb+tceauFjlVPnCqhAOMdum0PzSz3L7dMXG0OYbcvT5qdBMeHTYxbeGXUXCWAImb6tK8qe18SWr0PYkJR4dcfFc0G7RAyhEqWzwJ1UJ7qxI4Vokyx5B16k5/LJFQVx1uui95vWkfeXzPs/hmMwnur0W1sJ08MsG13mzgfFWT2Zewf1jo06+VEBHdosG31sfyCWBpHSSlhd/hJLM00ANWab7pt9fFJq/ZsJ7jpXlqsMAJZJBrGCcdogm314boHIJaG2aplFSUjKnKNVsOHHiBMlkkvaEwl90+C332I2+NPc1RyjvOkLN4WzLSTKZRCqvpPO2t52bdcmZIL6zMytwcz4D4YyRbWE5Elc4HFc4GlcIaee/3CJistWjcktpihv8maLKZclQD/V7n581CNbsDkINqwg1riZTMn8hp7QBAVUkoGWD44AmEjjTPxzICFQGB9g8fJRNwc5cz/h0KJEMWqtLOL3jD+ZlzzTZaztfinEymeTgwYM4ndZWiP8ZcfJfg9bE8P+piVOhGPxHn5Va/NDKIBvPsWqXTqcpKytj5cqV53SeufD2Zyd4si8fpN211s2/X5P/XWcMhs/AlTmBL/0qUynNMdtmorYtcz6zopHAn9qJXR8o2mcITkKO68+der3E6ElKfHvExa9DeevFYphUiWHe5Bvg9b5BVtuHsRsTCObsWi6qVMUh41ru6WspYv8ArHWp3NMUod6+sPH+FxMOPtdXUnS9FYrOvzRHWO8+96rz7you+wyfByxEQMsWC1HbttOynO3fdityOoktkQ+SXIFR0iV+0t4yIDtxf6zLx2Am/+CJmHy6NULzHNXdTd17qI+NENGyYhpt7gZ+VnkVminwUthOSBO50pNZcEUGsqrM/v68rLw9FiTcsApDOXfj85QBX+wv4YsDJUWqyElDpDslsyti50fjzjMKgFn6ils0aeg7Stlpq9ph/1U7yHjmXvymvWVEa5pwjw0gZ/KJAtHQs7Rww8j6Ks8yoYQ1gY92+kgWZN7rbDqfbImgLGC9VdO209LLPL5yM6nSxetRmgkpXwWlPcctglnxyjr6r75l3p7US4mpAlpT0eLU0Uxoi+ez6ROaxEBa4mZ/Bs3hpPzU4VylUDR0vIPdeIZ7UJ0lZNze3O/bl5L4xy4fzwSLLapqbTqfagnzxor0WT0/84VfMbnJn+HWshQZQ6ArJRcFxSlDYF/Uxk/HHaiGQKtTO6dKsZyM0/DKU0WK65Km4u8/ScpbPqcvt2nCD8acfLLHO20VaipShsDJpMJzQQePj7noSMqoJlQp01faM0bWZ71QcOhPqpJcO9k7aBg07H4GRzhged3A1tcuGs1fFMAlGews8B3OTFnMvKcmYRHWWkyoqsrGjRvPyR/X6XTS399PrVvCBA4WVO9701k/14ZK9xkhLRNN03Bgottsc2pczAZFUejr60NRlCUPhEOqwN6ojV9MOPjmsJsv9JfwxISTvVEbfWl5WuX9pcQmd4Z3VCf4aEOUN1emWOHSi5Jppd3HWLb/RUSjOLllChCvrmdk/TaGttxAvHr5gh0QZAE8skmVzaDBobPapbHFo3KtL8OOsgzb6+w0rVpOydrVlHkc1OkRas0EpYqBTzJxSyblikGzU2f4qteiziMQN00TRVFobZ0/bVdRFNxuN8PDw5Ze7vVujaNxxZKIaospHIwrloDiltIUf1w1P42OmZBOp/8/e+8dJtdd3u3f50zvszO7s71rd6Vd9S4b23LBNhjbYEwNoaSBMZA3lLxASEILhEDe5EdCCPwceAMJBBxjh2LcsCwXuUiWJUtWW7XtvcxOOTNz2vvHaMtoZvvs7qw893XpsvdM2e/szJzzfZ7n83wevF4vjY3ZHcOXCZMo8NDFKTOHQyr3NDsxXCprTxhoTbMHkQ2FKKIHm9LB1IDYrPZh0GPEDeXT7l+schv+2BMYtZG022LGaobtN6EYfIt4dUtLVBW4r8fB37a5OBczTZs8LjarvCsg8Ykqmav8dhy2EmKmNYRNG4iZ6pLmX4IDQRAx6DFARxPsBK27GbPsxG2x8SZ/jKgmpLU9DMoGHh6y4TVqNNiUWfOFCQ3+scPJD3sdaevd5Ezw92uCVGdxJvbrkZkMtPLBcJaYTzAceO3FFAOXmMdH3/o9hEuq8HSdmxhXBODs6yBUUoVqtfNPnU6eDaYGmB8ui3DTLFVdQZEpf2U/Zl2jyKwha/Ddor10WCfdD09FTRwKmdnhTs9Kz4bscOPuPo8xkbzQjH+NI8WV83qeyzkdNfKZsx5eCs0tqJ4aHD/Zo3HNq78lrugoOhgEiJbXMjQPSZ9qsRGsbMAcCWIJjabc5hjqxTbSTzhQiZ7BJVTX4Wvtbk5NOUGK6HytLkj5PE5olrFhAqcOTz6vINC99bplGWOkmczooohzIJkZjnl8tO++dUVHKE1ltmAYYLNT5qxkTJF9XowZMQmw0aViDgfTgiRjXMLTeQ7HQBdxu5v7o0X81QUPvRlMst7sl/ibujEqlvEi5TbqXOVJcKsvhgqck4xpleqEnnQD/p9BKxFNpM6qpDmHz4Z9sIfqAw9jCWeWSQqahrvrHKrFNm1bRFSFv21z8bMBe1oSYZMzQZ016QA+3WZF0QXaYkaeCVr4eb+NVyMmoqqA36ThuHSeenjIyu+mjAWyCDp/VTM28XoDJw7i7UidATm0ZgPDazbM6e8wV2ovc5aeSrFJ5S9rlmYG5HiFzenM3DYxV8xmM4ODgwiCwHqnzEtj5hRjuMMhEzcUqfikEaxjwyiKkgxQBjqRfMWLcpS3WCyLCuQzoenQFjPwbNDCLwZs/Gu3g+/3OHly1MrxiIl+efrP3VLSaJN5R0Diz6tC3B2IsdYxzXdT1yk6eYjiEwfTVinbHQzVb6B7615G6lqSCd4lnvGuG4zI/gCRunXIJRXYRPDHRvGKKi6DzlhVw5wnU4yrAWabg305NpsNTdMIBoMp/cPbXAkeH7ZOJDPGHbTHsYo6f1M3NnHOWAiyLE+oL2bs3c0StS4j950ME7tU55BUnU1+E43e5PV3tmAYQDEUIBsKsSodCExeo0zaECYtSMxYmfK5EfQE3vgBXInDCJfJonXBRNB6FSHz1pyVRes6PDlq4S/OuzkYsmRsSTCgc40nwb0VYT5eEWGLS07/XAgCmmBFMfiIGyuImhoIm9cjmRoJmTehGPwTiQSjALvcMuscModDppTih6ILHBizcE4ystU12fJwOQMJkc+e8/BcBn+Qu4ui/EVNCMeVZ6Gw7OSD4WVgrsGwMRqm/MjTKb2YvRv2EPf40I0mov4SvB1nJqpxgq7hHOjiIWcL3+1Lzbhe7YnziYrIrBknb8dZPF1Jh0ERcDst9Gy+hpdCqXLLQdnAY8NWGmwKZfORdggCusGIq3eyemQNDTNc07wgwylVT44h+upFN6Nq6hlARGeDQyaoihnHBo1zb9dj1Ej9RDWBEUXkomLlj4rv5tW4gzFFxH7JtGi2v51uMDBWVodmMuEY6M48fslfnDZ+6dFhC//Zl3rsfcVRbvXPT47uO3ccx9DkZysSKGe0dh49P4tE8pcQKSwlHKikd8PunAmEYW7BsCDAHneC54LmFPnj4bCZNTYFX2UJBjmONTiUdtnUohH6T5+np3uEixY/I6bJYKPAqPGXNWO8p1iaV5U/mzgMOrvcMrf5JUTgnGRI+07IusCxiImHBmyEVYFGuzLtBXkCXcd3/jXKD+9LScwBqGZzilRTAFx9HYhygkggtdLQHjPw6XPeNKM/gPcWR/lsdYibfXHuLpJosMkYBeiTxWnlbDoCPQkDL45ZuH/AzvNjyff0wQFbSgD61iKJ6wqSFVhX9wVKj6U6yUcKS+jaen3WZf6ZqsPjfKoqTMMSjFIaH0uTrd5FQRAYGhrCbDSy0Snz8JB1ItEi6wJnJSPX1djxtp9BTcQxmUzJz0BvO6Gy2qzOZZ8vmg7HIiYeH7bykz4b3+508vMBOwfGLJyVjCm92zNhEzU2OWWuL4izxZWgxaGwzi7TaFdosCnU2xRqrSrVVoVKi0qZRaXUrBEwqxSaNHwmDa9Rw23UcRg07KJOuUXljsIYn6oM874SifVOZebATFMpO/I0/vMnUg7rAvRuvOqSqqF80b3uC0IQUGxOwqXVDNe1IHmLCFbUM7Rmw5wCclVV8Xg8lJUtTJXh9XoZGxsjHo9PJFDsBqi3KTw+kvnzt1hVhqIoWK1WWlpaliUQBjCKAh0RlVcGJ8/BCVXnrrpkb+pcgmFIGlzFjaXYlPaUANeojWJRB4gZq0EwYFZ68UuPY1b70p4jYShm2HYzCWPJirdHTcdFycCXL7r5Wb8941iyMnPSV+Nz1SFuK4xTbtHm1zsuiOiCedrXX2HRuMUXoyNuSEm+Q1JZ89iwhRqbkiabPhIy8elz3rTHWASdz1WHeHextOge9zxJZgqG8z3DWWKuPcPFrx5IMWdKONycvekdKRcRb9tpyl55euLnqCbw72Ijf11118QXsdSs8v2mkTkZI9XufwjbyORA9sGGTfS37KQ1auSvLrjpSaQGnAI6HyqN8r7i6Jy/hIKq0vDYTzHGJ2U9fc07GGqcXqOfiZ64yNfa3ByLpAddZWaVv6hJ9kwoerJyfCRk4pWwmeMR00RWeFPoIl+6+N8pj/230r38qjDVNKvIpLLHk+DNvhhN9tllLPbBnkvjl1IdanVRpHfDHkZq1oEg0BMX+cNTBSkn5Ca7zHcaR+dXGdJ11jzxM8xT5qF2bb2OYNXSS7RWA7P1DE+lOy7ykdMFKRtim6jxncZR6mwqpnCQwKmXJ5zRB2SRC7HU4PI5TxP/WXw1dQEHn64M4Z2hR38lCCoCvxiw8cCALaVXdSoOUeNdxRJ3F0kZe4oFRaH06DNplVSAcHEFXduux9NxluLjz6eZq4VKq+ncdj260cT+UTPfaHOlbUocosbnqkO8YRrzOFlLOsA+G7TwbNA875FVRkHnJ83DBMwa5tAodfsfTOn7l612zu+d2al/MWTqHV7vkPmnhtEl2UNKksS2bdvmXWGbDk3TOHjw4IS774MDVv6/ztS+y4+Wh/kD7TSFTz2E3TrZvxl3ebhw7Z1ZaY+ZL61RI08dbmdj72t0WHz8Z/EbUpJXM1FsUlnvlFnvkFnvUKi1KSs6rkyUE1QcfAJnf2qvpmYw0LnjRsIl83cLzyVisRjbt2+fce7ybGiaxuHDhzEYDCnB4Pe6HPz0sjE0xSaVHzUPL7hdRJIknE4nLS0tWVcvzMahgQQ3/Xpy72YS4fS7SvBZDbP2DF+OUR3FLz2OqKeOz5LFQhLGYhyJE6SPTBIJWbYQNrUsufJgoURV+FGvg/v7bWkKKYCASeWj5WGu9SaWJajUdfjNkDVjax/AXUVRPlwWwSzAfw/Y+G5Xuiy6zKzylbog9Yswt82TTr5neBmYS2XYEJcoP/xUSg9m3/qdxLypEsOYtxBjLIptdBBVh5NRE/5YUqZ73FmFSdD55prgnKq3ltFBAqdeTjnWtW0vmtmC35TMZF2IGehMyUoJvBI2czpqZJd7bgY2iCKCpuIYnDTSsoRGGa6b20lU1+HxEQufP++hK5F+kXyzX+KrdWOUX3rNogABs8ZGp8LNvjjvCkTZ5U5QYUrwwTO/wpSIT5zWz1sDfKfiFvTLdqNRTeR01MSvh2w8M2ohoQuUm1Ws0+y/ZbuLYGU9tpH+FCMeQddx9XVgjgQZKyzj823+lNdgufR+FcwzeLKODlDY+urEz5rBQM+WaxZUbb8SmUtleByXUWetXeHx4UnplKILvDRm5iZfDLPVQqislu6iWg72yUjBUNoFqjY+yMejL3OXfQB8/pWpysyAVYQtLpk7C2M4xORopssvxrKe/G7/esiKUYA1Uzb+pmiI6gO/TduEQ9KBvWfzNehGE5IvQMxbhKu3LcWZ2RIO4ujr4Nv6Ov6h15dW4a2zKvyfNcEZx04YBCizaOz2JLi7SGKPO4HXqBFUxRSn6Ol4kz/Gzb44opKg+sDDmGKTbtK6KNK+59Y5eQYsFFEAv0nlqUvjXMyCzpdqxyhcAtOsWCxGZWUlBQXZez2CICBJEvF4HEEQWGtXOHFZP+aRsIld5VaIjRGITLaPGBNxLGPDjFVkNqBbChQdftRr5+TBk7yncx+Fcoi6WD83jhyn3+RJaQWCpLKowaawtyDOOwMS95aH+WBplOu8CdY5FPymeVaLsowxFqH6wG9TxvtB0hyr7ao3Ey0qX6GVZYd4PE5FRQVe79wNvjIhCAKFhYV0dnamBNWbXTIHL5P3f6YqxJoFqDLi8Ti6rtPQ0EBNTc2yVYSnUmoXeeC8xHA8ef7QdKhwGthWZJ5zZXgcTbQSM1ZjVbsQ9UmFmkGPYlYH0u6viAUM2d5IzFSTk9VgXYenRpN7xpcySKKNgs57iiX+umaMBvviR/fNFUGARrvCXm+ck1FjWkL3ZDSZ7D0SNnN/hvahXe4436wPUjJP4608s5OXSS8DcwmGi868gmNwcrSRbHPQveXajMFipKgCx0AnbSMJgpeqPOsjnZy1FfOuNaY5S36KTh9OcVMOB8oZqV8/8bNFhBsK4pgE0sY+dMaNPDxsZVAW8Rh1/EZtxhNK3F2A7/yJCcdJgyKTcLhndZwdUwS+0e7ix32OtA2026DxhZox3juLHNVwKTi+vvsgLUNnKbOoeI0aFgP8R+NbOC94M2YNxxlRRA6GzPz3gI2zkhGbqFNqUdM2RprRzGjlGkRFxj7Sn3KbdWyEobNt/EqvYMA82T/38YowuzwzOxNmwn/21ZTfESqtzleFpzCfYBig1KLhMWopc3vDqsipqJGbfHFeDpn4s65S/su+gSPOakoTowTkMSD5OWx2KHiNOrbgEL4LJzAk4sS8hTklHYfkrOWNToW3Fkl4jTrnJENKHxNAXBM4GDLz6LAVu6izMXSRmud/izmaOvZHNZro2nEjI7XNKRuihNNDKFCJq7d9Qkqd0OHssIyz+wJHHdUETZNtAm8siPHVuiB+89wTQoIAhWaNrS6ZtxXFuKEgTsCsIqlCygzpcUR0/qp2DLdBo/zlp1LaCyApLw2VLc0olKnU2JImRCVmjQ+XRVi7BO6fuq5jNBppbGzM+ibd6XROGFoJAmxxyjwybJ0wBNMQeC1iZEOBQrFJwDo22XNviYwhaCqRwNK7zJ6XDHzunAfPmaP8ft8zKbdZdIWrx85QIw9hKC7lxiKF95dE+URFmLsCMXa5ZWpt6qJ6SLONOTRCzXO/TuvRTzjcXLz6NhLzcG7PVXRdz1rPrcFgwOl00tPTM2GoJQqwx5PggmRE0ZPtGLcXxucVCCmKgizLVFZW0tTUhN2+NCqSuSAIAiFZ5+meyeB1KKbxgSbHvINhAF0wI5lqsSi9GPToNPcSiJhbGLFeiyYubIb4UtMWS0qi/2saSfR2V4Kv1Y0l97YrVNB2G3Vu9Sd9dI5dtrceVUTaMjhQf6Akwqcqw9MWZPIsjrxMehmYTSYtynEaHvspBnkyKOrdsIfhKYHp5TzRqdCw/0E86uRJy2U1Yn3TW5BdswcBgiLT+Mh/pvT9dey8adoN4aExE19pc09bgamyKLzRF+eNvhgl01Q6LpeBx9wFnL/+7dNmFl8Omfh6myujHHKnK8H/rg7hN80tQ2YKB6nf90BKT+NIzVp6Nl9DQkvObjsSNnMkZOJ4xJTm9no5hSaVW30xbvXHMtrju7vOU/bK/gkZZlhN9mdqCPx30U7+K3AV270Kf1s3Nv+spK7R8OhPUypbnTtuTI79yQPMTyY9jq7D/+lw8quh1PEca2xyyjzK8TvviFzga6F9bEz0ZUylaEYjQ/UbGFqzYUXkoXMhpsFDAzZ+0mdP75vUde4aeIk/HniaaouK3zRZD4+7PHTsvJmEa/oqjlEKU/XCoySGRzgTNU58pyTRzDerbudVdw33lod5a2F25yIOySLPBc08O2rhcDjZt/onZRHeEZDwn32V4uMvptw/V8aBZYtoNMrmzZuXbKP+6quvok2p+mcaU3a7rYdPN0DNs7/CNjKYclvXtr0EK5dm9Iyiw8/6bPzfXgdv6T/IB3qfTrndZ0z27LqMOnZRR7HZ6d5y3bIE6AvFNtRL1YuPYkikJrmlgkLad9+KarFN88jVQzQaZd26dVlVMgB0dnbS2dmJ1bq4fnVVVYnH45SUlFBdXb1is64vpyOssPH+vhQB84tvC9D9yrPzkklPRdBlfNKTmNUeEppAQgdNF4ji4BV9L916OZIqENNA0gQkTSA25Z+kTh6beltCg3KLyianzEZncuRlIIuqmKgKP+51cP+ALaNnTJFJ5d7yMNd5Ezl1qj8eNvI3bektieM4RI2/qAkt2bSBPEnyc4aXgdmC4ctnxSoWK61vfPe0VaXTUSMfO+OlMdTJVy78HJHkhX2DQ0b2FHDh2jtnrWth9nAAACAASURBVEh5L56k7Mizk7/TauPMze+ZcQZgf0LkixfcnIjO/NybnAneWBDnOm88pW/ZFBljzRM/S+knbNtza5qzdFyD+7od3D+QvpkzCzr3zHcDretUPf8Izv7OiUOKxcrZG9+JZk4PUhIavDiWHLHx4ph5VlfRTc5kb/G13niK86c5NELFoScxBYd5NWxCmiJLbbcXU3/jVTgK5u+yah/opua530z8rBmNnH7T7+cl0lNYSDAMyb7UT5/zcDSDsdNU6m0Kf1E9Rp1VwdV9gcDJQ9M6KytmC0ONmxmuXZhp3HIQVgXu77fx834bkiZiVRP8aedv2TPWOnEfu6hTZVUwVFbTs+06NOPMfyNdh1/1GHC8+BTbQudTbjOKoG3bjXtd05K8nnFkLRkg2QzJ7031gd+knH9iHh8Xrrkzo+v7akSWZfx+P3V1S5cYGxoa4syZMykzXb98wcWTo5MBh4DOPzWMstkYpPaph1ISd5rBQNvVty1q5FIm2mMGvt7m4mTUxFsHDvLB3v0Tt5kEnWoHFBnVFPn+OMN1zfQ178q5z4Gr+wLlL+9LmyEcKqmkc/uNOac8WQiapmE2m1m/fvrk/2I4ffo0Y2NjKSOX5oqu60iShM/no76+fkHPsdTc8chgSnX4zzY4uTZ0eMHB8EBCZP+oEU/sAA1i8vz/fKyJH4X3IunZTeqWmJPBcfJfgjLzzArDTOg67B81850uZ0ZVkAGddwYkfr8kgj03chhpRFSBf+p08shwatKmxqrw1doxKqwr3x8sSRK6rq+oGmIpyfcMLwMzyaQFRaHi5ScR1Um53GDjZqKBzP0/IUXgk2e9jKkiA2YPUYOZ7eGLNDuSM0ON8RjmSDBZ4Z3hrFJ65NmUDcpQ3Xqis2THHQadm30xNATOxwzTOrv2JQwcGLPw3wM2zklGzKJOqVkFiwVLaARraGovmZRSJTgnGfjzc96MNvINNplvrQmy2yPP64Tp7r5AYevRlGM9m99AzJd5Hq9BgGqryk2+OLcVxvAaNfoT4rSOo32J5IiOBwds9CQMeIwaRSYNzWJjtKqRpwYNabLpnaZR6rpPoVisxDyF86pKFbYeSZG3B8vrCJXPfSbj64H5yqTHMVxymH5q1JLRaEpE573FEl+oHqPQrIMgkHAXMFKzDtnuwhocTHNZFlUVZ38X3vYzaEbTvN/v5cB8qaf4Lf4Y7ugI7z/xIM3R1P7ghC7yj77r+E7JjZTZhWkVIJDM0n+j3cVPBl0862nCqcZolJLnQY9Bo8UhUz3UhqjIRIrKluzvYRCSxjJGKUz1gYcxTDHMUk1m2q5685IZZq0EmqbR3Ny8pD2MNpuNnp6elOrYFpfM4yOWKbJEgefHLMQMZgrLiijpbp0yBUHH2dfBWHl9VnrrNT1pNvPFC276ZAN3DhzkQ1MCYb9Ro8kNI2+4leG6FuwjfWlGh7aRAdzdF5AKitLc/1cK37njlB15Om2G8EjN2qTjeY4m1uaLJEmsX79+UaZZM+H3++nv70fX9Xl9L6LRKFarlebmZkpLS3OmGpyJ37RPzkjuCKvcYh/ENg+ZdFAReHzYyr90OfjnLicvhaw8IzVwIN7Er6PbeS6+DoXsvz9hVeScZOS5oIVfDNj5zZCV01Ejo4qITdTxzDLVoz1m4Cttbn7a78goid7qTPD1+jFu9K2cJHoumEV4gzdBjVXhdNREXBO4xRfjy7Vj82ofWirGXd4dDgexWGzZzeKWg3zP8DJwsbt3WgdK34UTuLsvTvysmkx0bb8hYwVJ0+HLF90pA7zP2Ep5v6OH6tjkbGJraBTNZJo2824dHbhsPi10b9s7JymnQYBtLpm7iyTW2BRkXaA7bsg4s00jOQf0yREr/zNooy9hwOO2Udd9cuLe5kiIsZJqZIud+wdsfPmimyEl3cE6GYCE8M3TaEqUE1S+8GhKgBIpLKVv/e45bcDtBp0NToW3FcbY5krKVDrjxowyHFkXOCOZeHjIxv5RC3FNoFc28VmphZP2MjaF27FpCQImlQqLhqBruHrbsY4NESkqm1uWX1Mpf2V/SqWgv3kHCef8A78rmYUGwwBWA2xxJXhs2JryPpeZVf6mLsib/HEMl7/9gkDMW8hI7TpUiw1rcDAlwQXJPnlXbzu2kT7CgYqcrOoU9l/kjuO/ooExVJ2JDUbYYOXr1W9ln289/bKBR4atnIiYqLaqaa0KHZeNTdIFgcOuOsIGK2+Rz6UYc9mH+7GMDRMqqZpRlbIYBFWl6oVHUyr3ugCdO24iluXq5EoSjUZZu3ZtSsV2KRAEgUQiQSQSmdgUWUSotaaOr4lpSUO2n4YKiVudbBw9i0VMjtwyKDL24Z5kInQRG6uuuMhfnnfz66GkW+zUQNgo6KyxqVQ4BLquuoVoYRmq1c5oVRMCGrbh1PYGYyKOt/0Mgq4R9RevnEOurlP82osETr2cdlXtX7eN/pZdi/qb5RKyLBMIBCgqyjyLPBuMG2p1dXXNKeCOxWIIgkBjYyM1NTU5WQ2eSq3LwPdORJAvnYZDsk6zVaLaaZgxGI6qsH/Uwve7HfxDh4vnxiz0JQww5VMX0a3E9eUzg4xqIhdiRl4Ys/DQoI3/GbRxImpiWBYxCzpeYzI4llT4Ya+Dr7W56Iqnv6eFJpXPVIX5cFkk5yY7zESNTeXtRRLvLI6ytyCRMwG8LMu0tLTg9XonPCOuNLLWM/zcc89x7NgxPvKRj0wcu//++/nGN77B6Ogob3/72/n6179+RWYUZiPwfzvZ5kqwx5NgjztB0XhFRVNpePxnKe7Dg42b6G/emfF5ftpn43vdqSMh3lYo8b9KR6h95pdYg5NmJboAbVfdRrQofV5f6ZFnKLh4auLncHEF7XvetODXN6oI7Bux8PiwdVYJNcC3237C1fEOCk0qNhE6yxr4o4I7OZxBmlpsUvl8TYhNzvmbTAEUH3se/7njEz/rosi56+9alGtsVBXYN2rht0NWjmcY8zQdTkXic72P8gfKibRgSrHa6NpyXZpkPO05etupeuHRyceZLZy59feWLJBYrSxUJj2Vg2MmvnLRjaQJvMkf4yNl4TnLrEQlge/ccfxnX03xAhhHttrp2n4D0cLSRa0xa+g6Radepuj0KymHJU3gJUMx95a8nX5z5uTCdd44f1Aaodqq8vSomb+dZmzSZ6tDvEk6Q8WhfWmJAqmgkI5dN6NYs1+VKzn6LL4LJ1OO9a/dyuDabVn/XXNFURRUVcVkMmXlmqgoCk6nk7Vr12ZhdbMjyzIHDx5Mk8z9Y4eThwYzB+Pv79nPe4deotisUmTWMAsQrFxD1wL6tTUdfjlo5V+7J0eU3DFwiD/ofQpI9gbX2RSMRiPte5KB8OXYhnopP/xUyni6caSCQrq2Xj9jT/xSIKgqZa/snxjjNo4uCHRvvoZg9dK2FSw38XicHTt2LMu+MBQKcezYsWllnrIso2ka1dXVFBcXr4hD9EL5yNPD/Ne5SbXDdY4x/qo+mlbNTmhwMGTmiWFLUrmRYbzPVAR06qwqTqOGTdSxijpWEayijk3UsRn0if+3inra8am36QiciBg5GjZxNGzmVNQ4rbpwOpwGjQ0OmXOSkf5pJNHvCEi8P4cl0auNWCxGVVXVxOzvc+fOMTIysmRKjpUiaz3Dd9xxB36/nx/+8IcAnDlzhquvvpra2lqqq6v53e9+x1e/+lU++tGPLn7VqwzvD1Plho02mT2eBHeHjrLzxFMTeTjNYKD15vdkNMQ4EjLxybOelP7VtXaZbzeMYhaTBlF1+x/CIE822SsWK+f3vg3FNhlAi3KCxkf/M2W+ZseuNxIqrcnKa+2MGXj8UmDcPY0hwM6xs3y+7SEAXAaNsGbgDxr/hCFz6szKmwtifKIyjHOBrp6W0UHq9j+Y0iM4U7JhIbTFDDwyZOWRYSsjs4x3EdH59poR3jB8gpJjB1Leg3GG6lrob9k5bW9p+aEnUzZK4yZgeVLJRjAMoOog68nRRAvBkIjhbz2K7/xraX1/ugADa7cx2LhlRWXTohyn/NA+XH0dabcFK9fQvekaziQs/FuPI8VtO+U50NnklCeqwVOptSp8ZUrfk3V0gMoXHktp04Ckg3777ltmdZifD572M5Qf3p9yLFRcScfuW1bkb64oCoqiUFxcjNPpJBwOk0gkSCQSyLKMLMuolz4ngiBgMpnmtOmIx+Ns27ZtWTcor732GolEIiVoUPSk38Mv+q0kSP3SCLrGF9oeZFvoAgLJgLXErBLbuH1eM+d7EyJ/1+ZKSZ7ePniIP+x5CqOgU2tVKTRp6AYD7XtuzRgIT6xJkSk5/kJKcngczWCgv3nnpRGAS/9ZEeU4lS8+njJVApKeEB07bpo1UbrakCSJ+vp6AoHM7UpLQV9fH+fPn09RT6iqiizLlJaWUllZuSoLNvu7Y9z56BR1oKjzkzVdFNiTnidHwyaeGLHw9DTtP5ez1i5zY0Gcvd74ZPEmy8Q1OBExcTRs4tWwidciJuLzDI6nssWZ4E8rwtTk5+9mFVVV2bp168R5XlEUDh06tGhTulwja8FwY2Mjn/jEJ/jYxz4GwFe/+lW+//3vc/z4cdxuN/fccw9Hjx7lwIEDi1/1KuPyYBiSG4N/OfMDquURCowaBSYNuaGZoU1Xpd13SBb441MFDE+RD7sMGv//2pGUvj1nbzuVLz6aEvxJBYVcfMPtE8FVwYUTlB59buJ22Wqn9eb3ZF12pevwWsTI4yNW9o1YUvttdZ3vnPkB5YmRiUO/9m/lvtLrQRBwGjQ+WRnmhoJ4hmee+wJqnvkl9uHJXt2E3cm5G96xJCYpij5puvV8MLPp1vuKI/xRWTIAMIWDlB9+KmV948TcBXRtuz4tKBAUhaZHfpwSRF98w1typ7qYQ2QrGM4WRilM+cv7cAym+weEA+V0bbt+RVxhLcEhKl96AnNkLOW4Lgj0rd+dFggcCxu5r8cxq8HYODcVxPhUZSjFWA4mnaanqlkgOa6pc8eN89r4C6qCUYpgikUwSZGJ/zdKEZz9nSlJiITDxfnr3pbROG8pGa8EBwIBqqqqZuw/VFWVRCJBPB4nFAohSdJEoJxIJFBVFf1S/63RaERVVdasWbOkUtNMBINBXnvttYyVtpMXuzjpqOeXgzYuThkTYldj/N25n1ARn3zfraLO0W1vYl1DCW7j9FsOXYffDCX7GqcqD8YDYa9Ro96mYBGSgWz77lszKqMy4extp+yVp9N6iQHCRWV0b70uJamcbYxSmKrnH8E6NpJyXLHYaN9zCzHv8r63S814/+5MG9Cl4uzZswwNDWE2m4lGoxQVFVFXV7eqK12arrPx/j46I5Pnuj9tttHW088zIXta61kmqiwKN/ni3OCNr4hhk6wlzWHHK8fHIsa0sX+ZKDSpfLQ8wvXe+Y3JyjM7kiTR0tKC251q9NrW1kZ/f/+Ky6VlWcZgMCBfUt9ZLAu/rmctGC4pKeFb3/oW73vf+wC4/vrrqaqq4t///d8B+NGPfsTnP/95Ojs7Z3qaK5JMwfAbRk/x6Y5fT/ysIvLxtX9Ild/KVVPk1IoOnz7r4chlm8+/rQuyO4PVeuHpwwROvpxybKS6iZ4t14KuU/fUg1iDkxnEgaYtDKzbvtiXOCOyBi+MmXl8JBkoyrrALUNHuaf78ZT7dVp8nC9dxxu2VOJzLW6zerlbNkD77lsIlyx9gDQkJ80oHh6y0n6pn2W3O85X68ZSe8c1laLTr1B45pWUBAYk5dz9zTsYqt8wEYy4O89RcejJifvINjutN78358yYcoGzZ89SUVGRW9lLTaPo9OGM73dSNn39jFWsbDIu4y48czRNsqxYrHTuuGnaJIuuw8GQifu6HZy5fOTUJYyCPuvYJFFOUHHodzj7Uq8JuiDQs+lqRqvXIioJTFIUoxTGFItizBDwGhNzS5ppBgMXrr0zq5Xn2ZhPEDxXdF2fCJaj0SiqqlJentlwcak5dOhQxiBiPBml63AsYuSXg0kfBVkXKI2P8K2z/4FDm3zfJNHM59e8l4YSJ7cXSqx3KCmfm4GEyLc6nLx4mTLh9sFD/EnPPmpsKoFLo7/mGwiPY4jHKD36LO7uC2m3qSYzvRuvIlixJuvnW0twiKoXHsEkpSol4k4P7XvehOxwTfPI1Us0GmXTpk04HMtvVqbrOsePH0cQBOrr65e8x365+MrLQf7+1fC8HlNsUrmhIM4NBTHW2NSc2kooOpyVjBwNmXg1kqweh6YUVQzo3B2Q+EBJFHsOzQS/UlBVFYfDkbH1RlVVDh06tKjgMxskEgm2b0/GL729vfT09BCPxxfkeD1TMDyvNFlxcTGnT58GoKenh1dffZUPfvCDE7ePjY3ltBvfUvJva4c5ELRwIJjsk9B1uHsgddblvoJmuk0euseYkCI22mR8Ji0tEH5fcSRjIAww2LgF28gArt72iWMFbaeRCoqIeQpTAmFdgJHqpe8xM4lwjTfBNd4EIUVg/6iFJ20NjPU9i1tNZuIF4A1CP+8Y7oV9ImOl1YxWryVSVD7vzYchFqX4tZdSjo2V1SxLIAzgN+m8u1jiXQGJCzEDUVWg2aEgXv4yRAMD67YTDlSk9a4Jmkbx8Rdx9HXQvXUvis2Bpyu1j2ysvD4fCM9AzsndRJGBdduJ+ksof3kfxvikA6gpFqX6ud9ckk1vXjLzHkGR8V04gb/1aMYgMuoL0LnjxhmrYIIAO90y212jPD1q5t96HHRMMTEpNKl8qXaMFkd6G8BUNJOZ9l23UHLsQEpPr6DrlB15lpLjL2RsJVgoPZuvWbZAeLz/sKSkhIqKiqxe+wRBwGKxYLFY0jL2y01JSQk9PT3TVggEATY6FTY6Q3xMCfPokJVfDbn4u6rb+euLDyBempBq0xL8+cWH+Izh93hspIBaq8LthRI3++IcCJr5dqczTd55++AhPtn/JPUuBeul06BmMNCx+5Z5B8IAqsVK544b8XSepeTVAyktRwY5QfnLT+HquUjPpmtQLdlJstkHuql86bE0X4GoL0DHrluy9ntyCVVV8fv9KxIIQ/L7s2HDhhX53UvJu9fY5xQMe4wa13uTAfD6TPuSHMEowFq7wlq7wruQ0HS4EDNwNGwipIjsLYhTnQMjh65UZFmmvj7zlBKDwUBZWRk9PT2YzctnsDYVSZJoamqa2OeVlZVRVlZGMBiko6ODsbExLBZLVq6986oMf+ELX+C+++7j/e9/P4cPH+bEiRMcOXJkoh/kIx/5CCdPnmT//v2zPNOVx9Q5w8OyQOf5bja//AijiogG6Ajc2/ghui2+WZ9rizPBt9YE091spyDKcWr3P4QlPCl91EURqaAI+1DfxLFQSSUdu29d0GvKBvqZk3hfOYCCQJFJwy6mf9wSDhejVU2MVjfO2Vyn7OV9eDvOTvysGY2cvfEdSypzWyyinKDk2AG87a1pt6lmM30tuyk9+mzKjMzz172VWMGVJZ/LFp2dnTQ0NBCNRnPSCMUYi1B+6Mllk00LqkLBxZMUnjmSEoRPZaRmLb0brkKf58VD0eHxYQvPBi2UmFV+rzg6P9d3Xcd3/jjFx19Iq5hnA11IJgmXWgEDSxsE5yKqqnLw4ME0BcZMbQq6Dq+ETQwcO8Xus88y9S1/1VHFF2vvRruUDDIKekbn/ruGDvHF4ScoNmspnhsdu29JJlAXiVEKU354P46B7rTbFKuN7s3XEg6UY5BlRDmOqMgYlASinMCgyMn/yglERUZUxv8/gShP3k9UZAxyPO0zP1ZaPe1EieVAURTi8TiCIGC1WrOeVIzFYmzfvn1Vy5JzlZt+3c+hgXTDRocRdtvD3FyksM0lTzvdJE8eSHpQlJeXU1Ex/bhVTdM4dOjQigTDc5lNnkgkaG9vZ2goWQCcrYqdNZl0JBLhz/7sz3jsscdwuVx86Utf4q677gKSVeF169bxx3/8x3zxi1+c61NeMUwNhqf2sqrAmCLwvKeJ/116J30Z3PGm4jeqfH/tCP45bDQtY8PUPv0/M1ZW2nffTLikeq4vY0mwD3Tju/Aart42BG2GfjFBIFxSxUj1WsLFFdNWzuwD3dQ895uUY33rdzG0ZmNW171UuLvOU3r0GQyJzJX/cRION2dveme+MpwBTdMYGRlhx44dHD9+PHeHxC+DbFpQVbxtpyg8cyTNsGqc8WTL6Ao71bp6LlJ+6Mk0o7HZ0AUBxWZHtjqQbQ6Uy/6bcHqWvB/79RYET+XUqVNIkpSSdJpTz76u43n5GfRzrfQlDBPmOb/2b+W+shumfdjHwi/xmYEnsE25BGgGAx27biYSmH7zNm90Hd/51wiceGnen8mFMlzbTO/GPSsy1kmSJAwGA0VFRVRUVJBIJOjq6mJkZARN07IiJ57LJjvPwtnfHePtjw2h6GAxwM0VVu6us3NzhZXI6BCtra1XjCw8z9KhKArbtm2btZDQ3d1NR0fHssulo9Eo27dvn1Mgrmka/f39dHd3E4vFsNlsGV9X1oLh2RYTCoWw2+0r3nC9EkwNhu2D3dQ8mxqsndv7NmKeQs7HDBwIWng+aOZk1Jgyu1dE5x8agvMaMeTuOk/Fwd9lvE222Wl9Y/aNsxaKIRbF29FKwcVTaWY+lyPb7JeqxU3I9in9VJpK/b4HsIQm54nGPD7OX/fWVTV6aKaqxDjL0eu9WolGo1itVrZu3cpLL720YjKeueLo70yTTcNUt+kFyKY1FW/7GYpOv5Iyum0qqsnEUP0GhuvXz2nG+HJgHRmg4uATmKNJuZ9mMFwKau0oNueUgNeObHOi2OwoFtuKzYR9PQfB40QiEY4ePZqSdJqrgZ2gqlQ/9xtsw32MKgJ9CQMjisg/l9/M477UBKZZ0PlH7QBvb9+fYk+4JIHw1N8bGqH85aewjQ4uyfOP09e8g6GGTcua4FRVlXg8jtPppKKigoKCgrSNoq7rDA0N0dPTQygUwmw2L7iqO9dNdp6F0xFWePZkG2/eVIvHnHpezOSmnSfPVKLRKM3NzXi9s4+V03WdQ4cOLWtcl0gkKC4uprp6/oW8cDhMe3s7o6OjaeexJQmGVVUlGAzidrvzUhhSg+GqAw/j7J801AoVV9KxJ12qPCwLvDhm5qUxM5Im8I6AxDbX/GftFh9/Af/ZY2nHB9ZuZWAF52xOi65jH+qh4OIpXD0XZ8zI6wJEiioYqVlLqKSKwtZXCZw8lHKfC9fcjuSffph2zqLr+M8dI3DiYIo0epyzN969qFnJVzKSJOHz+WhqauLMmTOEw+Hc6x++jFll01v3olrnUOHWNDydZyk6fTjj/FRItg0M1a1neM0GVHMO9iRqKqZoGM1sQTVZclL9MB4El5aWUlFRkfOfr6XmlVdemX9l+BKGWJS6/Q9NJG3iOvTKJv5X1Tt5zpLc8Kyzy/yT/jwbT6WaIi51IDz5i8bNDo8g6NnV8sddXgbWbmOsvC6rzzsTsVgMURTx+XxUVlbOubIjyzLd3d0MDg4Sj8enrbJkIhqNsm7dOgoK8tetpaa1tZWGhoaMt3V2dtLR0ZEPiPOkoWkaVquV5ubmOT+mv7+f8+fPL5tZqSzLbN++fVEJNVmW6ezspL+/f0L1ktVg+PDhw3z5y1/m+eefR5ZlHnzwQa677jqGhoa45557uPfee7nuuusW/AJWK+PBsHVkgLr9D6XctuTBmqZR/fxvU6qMugCtN78np3toITmj1dNxFm/bqbSRE5ejWKzJ/qwpwfNIdSM9W1b3580yOkjFy/uwhEYnjklePxf23rWCq8ptBEHAbrfT0NCQsWqVs8wgm1asNjq33zC9bFrXcHedp+jUYSzhYMa7aAYDI7XNDDZsWpExTlcCiUQCXdcpKyujvLz8dR8Ej9Pb20tbW9tEUDXf0WaW0UFqn/llyvlbtlh5ePvdRKxurhs4QvmrqWMZly0QnoJ1pJ+S4y9gG+4DBFSTGc1oQjOZUY1mNJMJzWi+dDx5m2oyX7p9yv2MJjSTBdVkWjbVkqZpxGIx7HY7ZWVlFBUVLWpDGQ6H6erqYnQ0eW2aKaCeS49fnuwxUzAMydE4PT09uTVtIc+KI0kS27Ztm7eabrqpAtlmPlXruaDrOoODg3R1dWXPTfqll17ijjvuoLi4mHe/+9386Ec/mrjN7/cTDof58Y9//LoMhscpPPNKys+RwpKlr1qKIp3bb6B2/0MT0sNgZUPOB8IAqtnKcP16hutasI304207hafzfNooGCBNYqqYLfS37FqupS4ZcW8h5/e+jcCJg3jbT6OarXRvvnall5Wz6Lqe4lLqcDhyXiY9wQxu08aYlNltWtdxdV8gcOrllITJVHRRZLhmHYONm+dWXc6TkVgsRiAQoKamJh8EX0YgEKCtrW3Bj497C+neujelrccUj3HL8YcJVjZQfDx1+sJKBMIAsYIAF6+5AzQ1+R3MQdXC5cTjSdf4goIC1q1bl7WKoNPppKmpCU3TGBwcpLe3l3A4nFFGHYvFaGlpycrvzbN4qqurURSFwcHBFR+Pkyc3iMfjVFRULGi/VFtbS2tr65ImVzRNw+v1Zi0QhmThpKioiKKimY1o5xUMf+UrX6G+vp7f/e53RCKRlGAY4JprruFnP/vZ/Fd7hWAZG8Hdk7pZGGzcsiy/W7XYuHDtnfgunEAzmRmuWbcsvzdrCAKSrxjJV0zf+j24u85RcPHUjD1cfS27clMCugB0g5G+DXvoa9m5ajZgK0UsFqO2tpaBgYGJYwUFBYyOjq6afs5IoILz19+VJpsWdAicfBn7UC9dW/diG+kncOplrMHhjM+jiyIj1U0MNm5eFcmvXEZRFDweD3V1yydlXU2MS24XM0JxrLyOgbFhik5PJo2twWGswQyB8M43LnsgnEKOe1Douo4kSVitVqqqqiguLl6yBI4oigQCAQKBAIlEgs7OToaGhpBlGZvNhqIolJaW5oOuHKO+vh5VVRkZGVmW9yYajWIwda8MuQAAIABJREFUGNA0Df2yVgNd1xEEAUEQEEUx43/zLC2iKC7Y2M7v9y8qGToXEonEio1Em1cwfPjwYb7whS9gtVqJRtNdS8vLy+nr68vwyNcH/tYjKT9L3sKsjICYK6rVfkUYLmkmM6M16xitWYdldJCCtlN4Os+mzGmMFJYQrGpcwVUuETm+AcsVXC5XSjBcXl5Ob2/vis21XAiK1UHbVbdllE07+7tofOynGXvJIemuPFrVwGDT1lSDuTwLQtd1dF2nqWll3bZznaqqKg4dOrSo79nA2m1YxobTEsfjTATCxZUL/h1XMrIso6oqXq+XxsbGZT/nmc1m6urqqKurIxgM0tXVhaqqCzK7ybP0NDQ0cOrUqQljtKUgGo3icDhoaWnB4/EAySpfpn+KoqT8U1V14r+6rk/cT1VVQqEQVqt11SS5c5nxfv7FJB1qa2s5derUkvSij1etV8qAeV7BsCiKM2Ye+/r6XrcN+6bIGJ7OcynHklLHfLZrMcS9hfR630Bfyy7c3Rdw9bahmiz0N+/M/21fp2S6OFqt1tV57plBNp0pENYFCFasYaBpK7LTs5wrvaKJxWJs3rw5L42eBYvFgsvlQpsmSTMnBIGubddjfuaXaYqHfCA8M9FolIqKCsrLy3MiQPB4PBPBT57cRBAE1q5dy/Hjx4nH41nr+xxXJjidTtavX4/b7U65fbZ4YS4kEgkuXLjA0NAQFoslJz7zqxFN03C73Ys2tisoKMBut6dV/bOBwWCgvHz5ioeXM69P6ubNm3nkkUcy3pZIJLj//vvZuXNnVha22vCffTXFgTLu8hIqrVm5BV1h6EYTwapGOne+kZ4t16Jargx5dJ75oev6tEZZPp8PZYaZ27nMuGw6Uji9v0CwvI5zN9xN97br84FwFpEkiTVr1qzOZMoKUF5ejiRJi3oO3WiiY9fNKFPO47oo5gPhGRiv7FRVVeWDgjzzQhAEWlpaMBqNi75G6rpONBrFaDSyceNGNm7cmBYIZwuz2UxTUxM7duzA7XYjSdKqvcavJLFYjMbG7Cgp6+rqMiqDF4MkSTQ2Nq6oVH5ewfAnP/lJnn76aT72sY9x7FhylE9vby9PPPEEd9xxBxcuXOBTn/rUkiw01yloO53yc74qnCdP9onH49MaIZSWlk4YyaxGxmXTA01b0KecOsbKajh3/V107bgxP2oryyQSiTmZa+SZxOfzZSUYk+0u2q56M+GiMiL+EtquenM+EM6AruvEYjE2bNiQH1mUZ8GIosiGDRsQBAF1hnGW0zEeBJtMJjZv3syGDRtwOpfHp8JkMtHQ0MCOHTvwer3EYrF8UDxHEokE5eXlWZPIu1wuXC5X1qrDqqpSUFCwZAmVuTIvvcT111/P9773PT7zmc/wk5/8BIB77rkHXdfxeDzcd9997NixY0kWmutMlTQm7E6CyzhPME+e1wvjcp9MWCyWVdUznJFLsumx8nrsQz1EfcXEPf6VXtUViaZpmEwm6uvrV3opqwpBEAgEAnR2di76ueIeP+1X35aFVV2ZaJqGLMts2rQpr1zIs2gMBgObNm2amBk+Fxnz+Lgur9fL2rVrV/RzaDQaWbNmDTU1NXR0dNDf348oiivWZ7paqKzMbpKxvr6eI0eOZGWcpSzLrFmzJgurWhzzbh64++67efOb38y+ffs4d+4cmqZRW1vLjTfeuGxZolxnqGFT3ggpT54lwGq1ztjz5Pf76evrW5Z5eEtJ3F1A3J2vAi0lsiyzdevWvIvpAigvL+ell15a6WVc0aiqiqZpbN26Nb/Zz5M1jEYjmzdv5vDhw5jN5mnPf+NBsM/no6WlJaecwo1GI7W1tVRVVdHZ2Ulvby+CIKyeEYvLxLg8OtteGHa7HY/HQzweX9Rzx2IxqqqqcmK/tqAV2O12brstn83NhGK1MXoluhznyZMDzFb5LSkpoaOjIydOrnlyl2g0SnNzcz7IWCAmk2lCKpdPJmQfRVEwGAxs3rw53x+cJ+uYTCY2bdrEkSNH0ubGqqpKPB7H7/ezfv36nA4wDQYD1dXVVFZW0tXVRXd3N0BOBe4rha7rOBwOfD7fkjx/fX09hw8fXlR12Gw2U1pamsVVLZy8dWaWGVqzEd2Q34jnyZNt4vH4rD1zJpMpr1DJMyPxeJzKykq8Xu9KL2VVU15eTiwWm/2Oy0gsFkOSpAX1ROYK8Xgcq9XKpk2b8oFwniXDarWycePGie+woihEo1FcLhfbt2+nqakppwPhqYiiSGVlJTt27KCiooJEIrGq/UOywbgp1VJhtVrx+XwLniwQjUZX3DRrKjNGbQUFBfNeqCAIDA0NLWpRqxXVbGakZu1KLyNPniuS8dmas1FYWEhnZ+equZDnWT4URcHhcGS9h+r1iM1mo7a2luPHj+dEP2ssFqOoqIjq6mrOnDlDMBjMiXXNh3g8PjE/OFc2iXmuXOx2Oy0tLZw4cQK/3091dfWqVsuIokh5eTllZWX09fXR0dGBoiir7jywWGRZpqSkZMkr5HV1dRw6dGje1WFFUSgsLMypwsWMwfCf//mfv25PyPfddx/f/va36evrY+3atXz961/nqquumvExw3Xr0Yz5DXiePEuB2WyeU4AbCAS4ePFiPhheIhRFWbUydE3TWLdu3Uov44rB5XLR3NzMa6+9lhUzlYUSi8UoLCycMENrbm5mYGCA8+fPYzQaV0WFVZIkAoFA3tAtz7LicrnYtWvXSi8jqwiCQElJCcXFxQwMDNDR0UE8Hsdms+VETDPej20wGJYkYNV1nZqamqw/7+WYzWYKCwsJhULzOseqqppz57kZdzSf+9znlmsdOcUvfvELPvvZz/L3f//37N69m/vuu493vOMdvPDCC9NWFDSjkeG6lmVeaZ48rx/mmkU0Go24XK5VLZXMRXRdn5CqDw8Pr7psezQaZePGjasiMFpNeDweWlpaViwgHu9vvNyRtKioCK/Xy+nTpwmHw2m9kblENBqlsrIyr1jIkyeLjDvfBwIBxsbG6OzsJBgMYjQaV6QCPi7dHld/hMNh+vv7CYfDmM3mrCSZY7EY9fX1WTfNmo7a2loOHTo05/1ALBajpqYm567D+Z7hDHznO9/hve99Lx/4wAdoamrim9/8JsXFxfzgBz+Y9jEjNetQzbl7sc2TZzWTSCTm1eNZXFz8uu8ZyibjI142b95MU1MTdXV1RKPRlV7WnJEkidra2pySZV1JeDwempubkSRpWX9vPB7H5/PR0NCQ8XaTycT69eupra0lFostuL9tKZEkifr6+nwgnCfPEuJ2u2lubmbHjh2UlJQgyzLRaDRr83KnQ1VVotEogiBQW1vLjh07aGpqwul0UlJSwsaNG9m+fTtFRUUTfdsLPU/puo7dbqeoqCjLr2J6TCYTgUBgTnOfdV3HarVSUlKyDCubHwtKQ/T09HD06FGCwWDGN+0973nPohe2UiQSCY4cOcLHP/7xlOM33HADL7744rSPG1qzYamXlifP6xZFUWY1z5pKYWEh58+fX8IVvX5QFAVRFNm6detE5rq4uBiHw8Frr72W8zJUWZbx+Xw541p5pTI+h/TUqVPLohoYVylMFwhPpbi4GJ/Px8mTJ4lEIjmjaohGo6xbt25e57Y8efIsHKPROKHCCAaDE9XibFVmx5EkCVEU8fv9VFZWziiHNpvNVFdXU11dTSQSoauri5GRETRNm9e5SpIktmzZko3lz4uamhoGBgZm/ftJksTmzZuXaVXzY17vfCKR4GMf+xgPPPAAmqYhCMJEVmWqDn81B8NDQ0OoqpqWWSkqKqK/v3/ax53vHwJen8ZhefIsNYqi0N7enna8tbV12seMjIwgy/JSLuuKR5ZlrFYra9as4cKFC2m3u91uWltbc7aPWNd1NE2jubl5xs9KnoWR6W9qNBo5c+bMksqSZVnG7Xbj8/nm9b7abDbC4TCtra2YTKZlkxJeznjLQWNjI4ODgwwODq7IOvKsbvLntMVjNpspKCigv7+f4eFhFEVZcB+vqqrIsozD4SAQCODxeNB1PePeZSYEQaCgoIBQKERPTw/hcHjWOcqKouDxeOjs7FzQ2hdLPB6nu7t72n3A+Pq6urqWeWWTzJQ4ndfu5Wtf+xoPPPAAn/vc59izZw9vectb+O53v0tJSQn//M//zMDAAP/6r/+66AXnApc32c82T7Gqqmqpl5Qnz+sWs9mcdiJrbW2d8eTm8/k4c+ZMzlSBVhuxWIyCgoJZnW3Xrl1La2srw8PDOdeXOZ4pz7V1XQnM9P2rra2ltbV1Sb574xXhhY4NaWhoIJFIcOrUKaLR6LJ/NjRNI5FIsGnTpvy5Kc+Cme36l2d+rFu3Dl3XGR0dpaOjY859vLquI0kSZrOZoqIiysrKst6PrKoqAwMD9Pf3EwqFsFgsaeuKx+Ns3759xVRa9fX1HDx4cNpEwkqvbzbmlRZ94IEHeNe73sWnP/3pCUfO0tJS9u7dy/3334/dbp+xr3Y14Pf7MRgMaVXgwcHBZdXh58mTJ8l4FWi+FBQUrFjlZ7UjSRKlpaU0NTXN6r4pCAKNjY1UV1fnVB9xNBqlqakpHwivAOPOztnuIZ46emgxmM1mNm7cSFVVFZIkLXnf4DiqqqKqKlu3bs0Hwnny5BjjFdnxPl6fz0c8Hs94XZNlGUmSsFgstLS0sH379iUbTWUwGCb6i3fs2EFRUdFEL7KmaROeGCsZaIqiSFlZWUavllgsRl1dXc4GwjDPYLi/v3/Cgn08KzE+sFsQBO68805++ctfZnmJy4vZbGbz5s3s27cv5fi+ffuuOPv5PHlWA7IsU1hYOO/HiaKI2+1eto3ulUI0GqWmpobq6up5Pa60tJQNGzbkhFFRPB6npKQEn8+3out4PRMIBKitrc1aQDweCDc1NWXl+QDKysrYunUroiguueHeeO/9li1b8mPf8uTJccxmM3V1dezcuZOmpiZEUSQSiRCNRlFVldLSUnbu3ElzczMej2dZ11VdXc22bdvYtGkTTqcTr9dLIBBYtjVMR3l5eUZVrc1my/li4rxk0n6/n9HRUSA5m8xms3Hx4sWJ22VZJhKJZHWBK8G9997Lhz/8YbZt28auXbv4wQ9+QG9vLx/60IdWeml58rzuMBqNC67ulZaWcvLkyXwVZo5Eo1HWrl274CDS5XKxbds2jh07hizLKzK+Ytx0pLa2dtl/d55USkpK0HWdixcvLuo7uBSB8DgWi4VNmzbR2dlJe3v7kswiTSQS2Gw2Wlpa8mqVPHlWEYIg4Pf78fv9xONxFEXB4XCs9LIAcDgci1bJZBNRFKmoqKCjo2NCLr1Spl7zZV7B8IYNGzh48CCQ/IBcffXV/Mu//AsbN25E0zS+//3vs2HD6ndVvuuuuxgeHuab3/wmfX19rFu3jp///Of5vuA8eVaAxVx4PB5PTktzcoVxQ5+NGzcuevyQyWRi8+bNtLa2MjIysuwyZVmW2bBhQ9YDmjwLo7S0FE3T6OjoWNBnIR6P43a7l3zTV1FRQWFhIadOnSIej8/JREfXdRRFQVEUVFVFFEUEQcBgMGA0GifmmRYUFFBVVZX/TObJs4qxWCwLNtd6vVBaWjphkiXLMiUlJauiGDGvYPiDH/wg//Ef/0EsFsNqtfLlL3+Z22+/ndtuuw1d1/H9v/buPTrq+s7/+GsuSWaSTJLJ5DKB3ENCwq1cJIn1UgHPobC1FipQj93VImLBS4+trNju2bW7uoil1Farta1Ze7bUonYt0N3arTXVuEVDj3WpokBLBbGYABKBzP3y+8PN/AgECMnMfDMzz8c5nENmPvl+31++fkxe8/l8P5/iYt13332JqjWpVqxYoRUrVhhdBpDRQqGQXC7XiL/fZDKpsLAwts8fzhQOhxWJRDR9+vS4BVez2ayJEyfq4MGDevfdd5P2w9Dj8WjKlCmGjEjj7MaPH69IJKL33nvvgv4bGwjCzc3NSem/NpstNkr87rvvDnrPYrHIbDYrKysrFnKtVqvsdrtyc3OVnZ0de43/1wDIRCaTSTU1Ndq3b5/MZrNqa2uNLmlYLigML1iwQAsWLIh93dLSotdee01dXV2yWCxqb29XUVFR3IsEkJkCgcCowrD00SeVb7zxhnJzc+NUVfo49TnGRGyNVFlZqfz8fL399tvKyclJaEjw+Xyqrq4e0WJrSLyqqipFo1G99957w/pwJNlBeIDJZFJVVZXKysrk9/tjIZcZJgBwfmVlZdq/f7/q6+tT5rGQUf3209XVpaeeekrvv/++mpqaNHnyZMIwgLixWCyjHlUsKCgYk3vgGs3v9ysvL0+TJk1K6A+soqIizZw5Uzt37pSkhNyLUCikgoICVVZWxv3YiJ/q6mpFo1EdOnTonCPERgXhUzElEgBGZvr06Sk1Q+u8vwHdf//9Ki0tVU9Pz6DXN23apKuvvlo//vGP9fzzz+uRRx7R3LlzL3hzaQA4m9zc3Lj8Mux0Og1f4XgsGdhDOFkL+mRnZ2vmzJnKy8uL7UAwUuFwWD6fTx6PRx6PR4FAQDk5OQlZXAnxV1NTo/Ly8rOuMh0IBAwPwgCAkUulICwNIwx3dXVp7ty5Ki8vj73m9/t19913q6CgQFu2bNHBgwfV0dGhkydPauPGjQktGEBmCIfDcZvyOm7cuFGHsHQxsIdwU1NTUsOG2WxWS0uLxo8ff97tdkKhkLxerzwej7xer4LBoEwmk+x2u0pKStTU1KQZM2aovb1dra2tmjp1KtNYU0hdXZ3KysrO6JOBQED5+fkEYQBA0px3vtq+ffu0fPnyQa+9+OKLOnHihL72ta/p8ssvlyQtWrRIv/3tb/Xb3/42IYUCyCx+v3/UzwsPyMvLY29PfRSE6+rq5Ha7Dauhqqoq9hzxwB7QZrNZVqtV2dnZysnJUW5urhwOh2w2W8KfNYYxGhoaFIlE9MEHHygnJ0eBQEB5eXlqaWnhfgMAkua8YfjYsWNn/OLU1dUlk8mk+fPnD3p9+vTp2rx5c3wrBJCRzGZzXPfzKy4u1rFjxzJ2BNHr9aq5uVlOp9PoUuR0OjVjxgz5/X7ZbDZlZ2cTgDJQY2Oj9uzZo8OHD6uoqEiTJk3ivwMAQFKdNwyXlZXpr3/966DXtm/frvz8fE2ZMmXQ62azmdEXAHERr+eFB4wbN07vv/9+xq0qHYlEYnvvjnYP4Xiy2WxJ34MYY09TU5MKCgpUXl5OEAYAJN15nxmeOXOmfvKTn6ivr0+S9MYbb+gPf/iDLr/88jN+cO3evVvjx49PTKUAMkYkEol7cMvE8OX3+2W1WjVz5swxFYSBU7ndboIwAMAQ5x0ZXrNmjebOnauZM2equblZb7zxhkwmk770pS8NaheNRvWLX/xCc+fOTVixADKDz+dTSUlJ3I/rcrl0+PDhjNhqyev1yu12q7a2lqABAAAwhPOODE+ePFlbtmzRRRddpCNHjqi1tVX/8R//odmzZw9q19XVpfz8fH36059OWLEAMoPZbE7ISGZFRYX8fn/cjzuWRCIR+Xw+NTc3q66ujiAMAABwFsMaHmlvb9dTTz11zjaXX365fve738WlKACZzW63J2T/2+zs7LguyjXWBAIBZWVlaebMmazfAAAAcB7x/20TAEYhGo0mNLC6XC4Fg8GEHd8oHo9HLpdL06dPJwgDAAAMA2EYwJji9XoT8rzwALfbnVZhOBqNxqZFNzQ0MC0aAABgmNJ/FRkAKcfhcCTs2FlZWcrPz1ckEknYOZIlGAzKYrEwLRoAAGAEGBkGMKbk5ubKYrEk9BwlJSUKBAIJPUeieb1eFRYWasaMGQRhAACAESAMAxgzotGocnNzE36esrIyhUKhhJ8nEaLRqLxeryZMmKCmpiamRQMAAIwQYRjAmJGo/YVPZ7Va4z4VOxQKyefzyWKxyO/3q7+/P+7bOAWDQYXDYc2YMUOlpaVxPTYAAECm4ZlhAGNGNBpVQUFBUs5VXl6uv/zlL8rJyRnVcYLBoCKRiMrKylRVVSWr9aP/rfr9fh07dkx9fX3q7++Xz+eT2WyWzWYb0Wiuz+eT0+lUY2NjQradAgAAyDSEYQBjhs1mi4XJRCspKdG+fftG/P2BQEDRaFRut1uVlZVnPOeck5Mjt9stt9st6aOR4+PHj+vIkSPyer3yer2KRCLKyck55zPSA9OiGxoaVF5ePuJ6AQAAMBhhGMCYkcj9hU9nNpvlcDgueJslv98vk8mkcePGady4ccMepbVarSouLlZxcbEkKRKJqL+/X0eOHNHJkyfl8XgUDoeVnZ0d+0AgFAopGo1qxowZstvtF3aBAAAAOCfCMIAxwe/3q7KyMqnndLvd2rNnz7CC5sA056qqKlVUVIx64aqBMH7qs8ter1dHjx7V8ePH1d/fL4fDoaamJqZFAwAAJABhGMCYEAqFVFhYmNRzFhcXnzdoer1eZWVlqb6+XqWlpQldvdlutyf9AwEAAIBMRRgGMCbYbDZlZWUl9Zwmk0kFBQXy+XxnhFyv16ucnBw1NjYmZYVrAAAAJBdhGMCYkMznhU9VUVGht956KzZV2uv1ym63q7m5WU6n05CaAAAAkHiEYQCG8/v9qqioMOTchYWFMpvN8ng8ys/P16RJk5I+XRsAAADJRxgGYLhwOGzYKKzJZFJtba1yc3MHLWYFAACA9EYYBmC4rKwsZWdnG3Z+9u8FAADIPOzXAcBw+fn5RpcAAACADEMYBmCoYDDIM7oAAABIOsIwAEMFg0EVFxcbXQYAAAAyDGEYgKGsVqtsNpvRZQAAACDDEIYBGMqo/YUBAACQ2QjDAAwTCoVUUFBgdBkAAADIQIRhAIYJBAIqKSkxugwAAABkIMIwkKai0aii0ajRZZyTxWKR3W43ugwAAABkIMIwkKY8Ho+8Xq/RZZwTzwsDAADAKIRhIE3l5eWpvLxcoVDI6FKGFAqF5HA4jC4DAAAAGYowDKShYDAol8ul2traMRuG/X4/zwsDAADAMIRhIA0FAgG53W5ZLBZVVFQoEAgYXdIZrFarcnNzjS4DAAAAGSplwvATTzyhT33qU6qurlZRUZH2799/Rpu+vj6tXLlS1dXVqq6u1sqVK9XX1zeozZtvvqmFCxfK7XarpaVF69evP2ORoS1btqitrU1lZWVqa2vTtm3bEnptQLw5HA5lZWVJkqqqqsbkQlp2u10mk8noMgAAAJChUiYMezwezZ07V2vXrj1rmxUrVmjnzp16+umn9cwzz2jnzp26+eabY+8fP35cixYtUllZmV544QXdf//9euihh/Twww/H2nR3d2v58uVasmSJurq6tGTJEt1www36/e9/n9DrA+IlEAiotLQ09rXZbFZVVZX8fr+BVQ0WDod5XhgAAACGshpdwHCtXr1akvSHP/xhyPd3796t559/Xs8995za2tokSd/61re0YMEC7d27V42NjXr66afl9Xr16KOPym63a9KkSdqzZ48eeeQR3XrrrTKZTHr00Ud12WWX6c4775QkTZw4UV1dXXr00Uf1+OOPJ+digVEIhUIqKysb9FpFRYXee+89gyo6E88LAwAAwGgpMzJ8Pt3d3crPz48FYUlqb29XXl6eXn311Vibiy++eNC+pvPmzdOhQ4di06537NihuXPnDjr2vHnzYsfIJJFIZMwuvoSzczgcslgsg14zmUyqra0dM1stmc1m5efnG10GAAAAMljKjAyfT29vr1wu16BnEE0mk0pKStTb2xtrM27cuEHfNzCdtLe3V7W1terp6Rk0xXSgzcAxzubAgQPxuIwxxefzyWw2Kzs72+hSMEyBQECVlZXau3fvkO/39PTIbDb+MzCz2aw//elPoz7O2a4TQOLR/wDj0P+A4WtsbDzre4aG4XvvvVcbNmw4Z5tt27bpsssuG9bxhlqMJxqNnhGQT3//9NeHanO+hX6qq6uHVWMqsVgsysvL0wcffCCrNW0+N0lrPp9Pra2tZw28JSUlevvttwfNjki2SCSigoICTZgwYVTHGXj8AUDy0f8A49D/gPgxNOGsWrVKS5cuPWebysrKYR2rrKxMR44cGRRco9Gojh49GhvpLSsrO2OE98iRI5L+/whxeXn5kG1OHy1Od16vVxMnTpTD4VBPTw9hOEU4HI5zjvw6nU7l5uYqEokYtpKzz+dTfX29IecGAAAABhg6X9Llcqmpqemcf4a7D2lra6tOnjyp7u7u2Gvd3d3q7++PPUfc2tqq7du3y+fzxdp0dnaqoqJCNTU1kqTZs2ers7Nz0LE7OzsHPYucCcxms5xOp7KysuR0OhWJRIwuCefh8XjOeAxgKPX19YP6QLKZTCYVFBQYdn4AAABASqEFtHp6erRz587Yc4a7d+/Wzp07dezYMUkfrfp85ZVX6o477tCOHTvU3d2tO+64Q/Pnz49NJbnmmmtkt9u1evVq7dq1S1u3btWDDz6o1atXx0bJvvjFL+qll17Sxo0btWfPHm3cuFFdXV1atWqVMRdugHA4rNLS0ti/SU1NzZhZeAlnZ7FYVFRUdN52DodDDofDsL2H7Xb7mHhuGQAAAJktZX4j7ejo0OWXX66bbrpJkrR06VJdfvnl+q//+q9Ymx/84AeaMmWKFi9erM9+9rOaMmWKHnvssdj7hYWFevbZZ3Xo0CHNmTNHa9as0S233KJbb7011qatrU0dHR168skndckll+inP/2pOjo6dNFFFyXvYg3m9/sHTU+32+3sCTvGRaNRFRYWDnvqc0NDgyEfcPj9frlcrqSfFwAAADidqa+vz5jhoTTz+uuvG11C3JjNZk2bNm3Qax9++KHefPPNYU9bR3J5PB5NnjxZhYWFw/6et99+W/39/Wdsw5QowWBQ+fn5amlpicvxWEAEMA79DzAO/Q+In5QZGUZyeL1ejR8//ozXCwsLZbPZDKgIw2GxWC74Odz6+noFAoEEVTRYOByW1WrVxIkTk3I+AAAA4HwIwxjEbDa1/S75AAAgAElEQVSruLh4yPcqKysNXXgJQ4tEInI6nRe8OnR2drZKS0sVCoUSVNlHotGowuGwpk6dyrPCAAAAGDP4zRQx4XBYLpfrrKGqtLSUMDMG+Xy+Ya0iPZTa2tqEh2Gfz6epU6eyPRcAAADGFJINYnw+n6qqqs76vslkUkVFhYLBYBKrwvlkZWUpPz9/RN9rtVoTek89Ho9aWlpkt9sTcnwAAABgpAjDiHE4HMrJyTlnm3HjxikcDiepIpxPOByW0+kc1TGqqqoSso+01+tVfX39sLZ7AgAAAJKNMAxJH40KV1RUnLedxWJRSUkJgXiM8Pv9Qy54diHMZrMqKyvl9/vjVNVHdbndbrnd7rgdEwAAAIgnwjAkfTQFuqSkZFhta2pq4hqcMHI5OTlxmYI8bty4C16A62yCwaAKCgpUV1cXl+MBAAAAiUAYhiKRiIqLi4cdhrKysuR0OhWNskW1kUKhkFwuV1yOZTKZVFtbO+rVwiORCFsoAQAAICUQhiGv13vOhbOGUlNTI6/Xm6CKMByBQGBYU9uHq6ysTFlZWSP+/mg0qmAwyBZKAAAASAn8xgrl5eXJZrNd0Pfk5uaOeAVjxIfdbj/vgmcXqr6+fsQfcvh8Pk2bNo0tlAAAAJASCMMZzu/3j3iP2qqqqpQeHY5Goyk71TsUCg37Ge8L4XQ6R/QMMlsoAQAAINUQhjHiUOV0OpWdnR3napJnIMgHAgGDK7lwgUAgYSs1NzQ0yOPxDLu91+tVXV0dWygBAAAgpRCGM1gkEpHT6RzV853jx48f9aJLRvD7/aqqqtL06dPldDpTboQ7Pz9/VM/3novD4ZDD4RjWqLnf71d5eXlcn10GAAAAkoEwnMF8Pp8qKytHdYzy8vK4bcmTTBaLRZWVlTKZTGpsbFR9fb08Hk9KTJsOBAIJmSJ9quGMDodCIRUUFKi+vj6htQAAAACJQBjOYHa7Xbm5uaM6hslkUkVFhYLBYJyqSjyv16umpqZBIb68vFzTp09XKBRSKBQysLrzC4VCKisrS+g5cnNz5XK5FIlEhnw/EonIbDazhRIAAABSFmE4Q/n9/rhNbR0/frzC4XBcjpVo4XBYTqdTBQUFZ7yXm5urmTNnKjc3d0xP/XY4HElZsbm+vn7If4eBLZSmTZvGFkoAAABIWfwmm6Gi0WjcRhctFotcLldKBOJQKKQJEyac9X2LxaJJkyZp/PjxF7SIVLIMPKObDNnZ2SopKTljpNzv97OFEgAAAFIeYTgDxWPhrNPV1NTI7/fH7XiJ4PP5VFVVNawQV1VVpUmTJsnv9591qrARotFowp8XPlV9ff2gMOz1etXc3MwWSgAAAEh5hOEMFI+Fs06XnZ2toqKiMbsAVTQaVU5OzgXtqVxUVKSZM2fKZDKNme2XHA5HUqcmW61WlZeXKxgMyuv1qra2li2UAAAAkBYIwxnIZrMpLy8v7setqakZs1sUeb3eES32lJWVpenTp6u4uNjwadNer9eQLYyqq6sVCATYQgkAAABphYf+MkwgEIj7qPCAvLw85eXljbnR4YHVl0e6crbJZNKECRNUWFiovXv3ym63G7KdlNlsltPpTPp5LRaLZsyYMeqVxwEAAICxhJHhDBOJRBK6AFN1dfWYGx0Oh8Oqq6sb9XFKS0s1Y8YMQ7ZfikajKigoMGxPZ4IwAAAA0g1hOINEo1EVFRXJYrEk7BxOp1NZWVkJO/6F8nq9mjBhQtyu2W63G7L9ks/nY4oyAAAAEEeE4Qzi9XoTNkX6VJWVlWNiZelIJKK8vDy5XK64HvfU7ZeSNQpusVhUWFiYlHMBAAAAmYAwnEFycnKUn5+f8PMkax/c8/H7/SNaNGu4BrZf8vl8Cd1+KRqNqrCw0LAp0gAAAEA6IgxniIHVgJPBZDLJ7XYn/bnaUwUCAY0bN045OTkJPU9hYaFmzZoli8WSsNFwj8dzQVtCAQAAADg/wnCGiEQiSX3mdPz48YaGYZPJpOrq6qScKysrS9OmTZPb7U7I9kvZ2dlyOBxxPy4AAACQyQjDGWBgmm0iF846ndVqVXFxcUKnD5+Nx+NRY2NjUqcVm0wm1dTUaOrUqQoGg3H7ICASiRiynRIAAACQ7gjDGSBZC2edrqamJqkrLksfbaNUXFysoqKipJ53gMPh0KxZs+RwOOIySuzz+TR+/Pg4VAYAAADgVIThDGDUNNucnBwVFBQoGo0m7ZzBYFANDQ1JO99QzGazJk6cqKamplEvrpWTkyO73R7H6gAAAABIhOG0FwwGDV3duba2NmnbD/n9flVXV4+ZfY5LSko0a9YsWa3WEY2Qh0IhFRcXJ6AyAAAAAIThNBcKhZK6cNbp8vPzlZubm/DzRKNRZWdnj7lVlwcW16qsrJTH47mgUfKBFbEBAAAAxB9hOI0NLJxltVoNraOqqirho8Ner1dNTU0JPcdoVFZWavr06YpEIgoGg8P6HrvdnvCtoQAAAIBMRRhOY16vd0wsvuRyuRIayEOhkEpKSpSXl5ewc8RDbm6uZs6cKafTed4PBwauCQAAAEBiEIbTWHZ2tgoLC40uQ5LU1NQks9ms/v7+uO8/HA6HDV80a7hMJpMmTJig5uZm+f1+hcPhIdv5/X5Dn/UGAAAA0h1hOE2FQiGVlZUZXUZMYWGhpk2bptmzZ6u4uFiBQCAuU6d9Pp/q6+uTuodyPDidTs2aNUs2m23IxbXy8/OVnZ1tQGUAAABAZiAMp6lgMDgmF1/Kzs5WfX29Zs+erebmZlmtVnk8nhGNFkejUeXm5qq0tDQBlSae1WrV5MmTYytuDyyuFQgEmCINAAAAJJixKyshYQoKCgxfOOtcTCaTnE6nnE6ngsGg/vrXv+rw4cMKBoOy2WwymUznPYbX69XMmTOTUG1iud1uFRUVadeuXQoEAopEIkyRBgAAABJs7KalDOP1emWxWBSJRBSJRCR9NPJpNptlsVhif4bD4/Fo0qRJiSw3rrKyslRTU6Oamhp9+OGHOnjwoD788ENlZWWddc/gQCCg8ePHy2azJbnaxLDZbJoxY4beeecdeTyeMf1BBgAAAJAOUmKa9LFjx7RmzRrNnj1bbrdbkydP1pe//GV98MEHg9r19fVp5cqVqq6uVnV1tVauXKm+vr5Bbd58800tXLhQbrdbLS0tWr9+/Rl7v27ZskVtbW0qKytTW1ubtm3blvBrtNvtam1tVXt7u9rb23XRRRdpxowZamlpUU1NjUpLS5WbmyubzSar1SqTyaRwOBx79ra/v1/9/f06efKkLBaLioqKEl5zIhQWFmry5MlqbW2V2+1WOBwecn9ek8mk6upqg6pMDJPJpLq6Ok2ePNnoUgAAAIC0lxLDT4cOHdKhQ4f09a9/Xc3NzfrrX/+qO++8UzfeeKOeffbZWLsVK1bo4MGDevrpp2UymXT77bfr5ptv1ubNmyVJx48f16JFi/Txj39cL7zwgvbu3atbbrlFubm5uu222yRJ3d3dWr58ue6++25dddVV2rZtm2644Qb96le/0kUXXZSQ64tGo4O2BTKbzcrOzlZ2drZyc3OHdYxwOKxQKKRQKJRyi0kNxWq1qqqqSlVVVTp+/Ljee+89ffjhhzKbzQqFQmppaRnWVGoAAAAAGIqpr68vev5mY89///d/a9myZdq/f78KCgq0e/dutbW16bnnnlN7e7skafv27VqwYIF27NihxsZGPf7447rnnnu0Z88e2e12SdI3vvENdXR0aNeuXTKZTPrCF76gY8eO6ec//3nsXFdffbVKSkr0+OOPn7We119/fcTX4vf71dDQwKJJ5xEOh/X+++8rEAiorq7O6HJgsL1796qxsdHoMoCMRP8DjEP/A+InJaZJD+XEiRPKycmJjZx2d3crPz9fbW1tsTbt7e3Ky8vTq6++Gmtz8cUXx4KwJM2bN0+HDh3S/v37JUk7duzQ3LlzB51r3rx5sWMkQigUUkFBQcKOny4sFovGjx9PEAYAAAAwaikxTfp0fX19uu+++/R3f/d3sYWGent75XK5Bk2dNZlMKikpUW9vb6zN6dsNDWzL09vbq9raWvX09JyxVU9paWnsGGdz4MCBEV9PKBSKhXEAw7d3716jSwAyFv0PMA79Dxi+c82kMDQM33vvvdqwYcM522zbtk2XXXZZ7Ov+/n5de+21qqio0D//8z8PajvUM6TRaPSMgHz6+6e/PlSb8z2fOprFnLKyspjuAlwgpokBxqH/Acah/wHxY2gYXrVqlZYuXXrONpWVlbG/nzx5UkuWLJEkbd68edC2OmVlZTpy5Mig4BqNRnX06NHYSG9ZWdkZI7xHjhyR9P9HiMvLy4dsc/pocbyEQiG5XK6EHBsAAAAAMDRDw7DL5Rp2EDxx4oSWLFmiaDSqZ555Rvn5+YPeb21t1cmTJ9Xd3R17bri7u1v9/f2xr1tbW3XPPffI5/PFgnRnZ6cqKipUU1MjSZo9e7Y6Ozt1++23x47d2dk56FnkePL7/XI6nQk5NgAAAABgaCmxgNaJEye0ePFi9fX16ZFHHpHH41FPT496enoUCAQkSRMnTtSVV16pO+64Qzt27FB3d7fuuOMOzZ8/PzaV5JprrpHdbtfq1au1a9cubd26VQ8++KBWr14dG03+4he/qJdeekkbN27Unj17tHHjRnV1dWnVqlUJuTaLxTJoWyUAAAAAQOKlxAJar7/+unbs2CFJmjVr1qD3Tn2m+Ac/+IHuuusuLV68WJK0YMECPfDAA7G2hYWFevbZZ3XnnXdqzpw5Kioq0i233KJbb7011qatrU0dHR269957tW7dOtXV1amjoyNhewzb7Xb2ywUAAACAJEvZfYbHmpHsMxyNRpWXl6empqYEVASkNxYQAYxD/wOMQ/8D4iclpkmnK7/fr+LiYqPLAAAAAICMQxg2UCQSUWFhodFlAAAAAEDGIQwbKCsrS1lZWUaXAQAAAAAZhzBsIFaRBgAAAABjEIYNEgqF5HA4jC4DAAAAADISYdggLJ4FAAAAAMYhDBvEYrEoNzfX6DIAAAAAICMRhg1it9tlMpmMLgMAAAAAMhJh2ADRaJRRYQAAAAAwEGHYAH6/Xy6Xy+gyAAAAACBjEYYNEA6HWUkaAAAAAAxEGDZATk6OsrKyjC4DAAAAADIWYdgAPC8MAAAAAMYiDCdZKBRSQUGB0WUAAAAAQEYjDCeZ3+9XcXGx0WUAAAAAQEYjDCeZ1WqV3W43ugwAAAAAyGiE4SSz2WwymUxGlwEAAAAAGY0wnETRaJTFswAAAABgDCAMJ5HP51NJSYnRZQAAAABAxiMMJ1E0GpXD4TC6DAAAAADIeIThJMrJyZHVajW6DAAAAADIeIThJOJ5YQAAAAAYGwjDSRIMBpkiDQAAAABjBGE4SYLBoFwul9FlAAAAAABEGE4ai8Uim81mdBkAAAAAABGGk8Zut8tkMhldBgAAAABAhOGkiEQiLJ4FAAAAAGMIYTgJfD6fSkpKjC4DAAAAAPB/CMNJkp+fb3QJAAAAAID/QxhOgpycHFmtVqPLAAAAAAD8H8JwEuTl5RldAgAAAADgFIThBAsGgyooKDC6DAAAAADAKQjDCRYIBOR0Oo0uAwAAAABwCsJwglmtVtntdqPLAAAAAACcgjCcYARhAAAAABh7CMMJFIlEWDwLAAAAAMYgwnAC+Xw+uVwuo8sAAAAAAJyGMJxgDofD6BIAAAAAAKchDCeQzWaTxWIxugwAAAAAwGlSJgzffvvtmj59utxutxoaGnTttddq9+7dg9r09fVp5cqVqq6uVnV1tVauXKm+vr5Bbd58800tXLhQbrdbLS0tWr9+vaLR6KA2W7ZsUVtbm8rKytTW1qZt27aNqObc3NwRfR8AAAAAILFSJgzPmDFDjzzyiF599VX97Gc/UzQa1Wc+8xkFg8FYmxUrVmjnzp16+umn9cwzz2jnzp26+eabY+8fP35cixYtUllZmV544QXdf//9euihh/Twww/H2nR3d2v58uVasmSJurq6tGTJEt1www36/e9/f0H1BgIBFRYWjv7CAQAAAABxZ+rr64uev9nY88Ybb+jSSy/Vjh071NjYqN27d6utrU3PPfec2tvbJUnbt2/XggULYm0ef/xx3XPPPdqzZ09sy6NvfOMb6ujo0K5du2QymfSFL3xBx44d089//vPYua6++mqVlJTo8ccfP2s9r7/++qCvPR6PZs6cKZvNloCrB7B37141NjYaXQaQkeh/gHHof0D8pMzI8Kn6+/u1adMmVVZWqrq6WtJHI7r5+flqa2uLtWtvb1deXp5effXVWJuLL7540N6/8+bN06FDh7R//35J0o4dOzR37txB55s3b17sGMNlsVgIwgAAAAAwRlmNLuBC/PCHP9Q//dM/qb+/X42Njdq6datycnIkSb29vXK5XDKZTLH2JpNJJSUl6u3tjbUZN27coGOWlpbG3qutrVVPT0/stVPbDBzjbA4cODDoa4vFor17947sQgEMC30MMA79DzAO/Q8YvnPNpDA0DN97773asGHDOdts27ZNl112mSRpyZIlmjNnjt5//3099NBDuv766/WrX/0qtlDVqUF4QDQaPSMgn/7+6a8P1WaoY59qYIRakiKRiIqKilRfX3/O7wEwckwTA4xD/wOMQ/8D4sfQMLxq1SotXbr0nG0qKytjfy8sLFRhYaEaGho0e/Zs1dbWauvWrfrc5z6nsrIyHTlyZFBwjUajOnr0aGykt6ys7IwR3iNHjkj6/yPE5eXlQ7Y5fbT4XHw+n1wu17DbAwAAAACSy9Aw7HK5Rhwao9GootGoAoGAJKm1tVUnT55Ud3d37Lnh7u5u9ff3x75ubW3VPffcI5/PF3uet7OzUxUVFaqpqZEkzZ49W52dnbr99ttj5+rs7Bz0LPJw5Ofnj+i6AAAAAACJlxILaO3bt08PPvigXn/9db377rt69dVXdf311ys7O1vz58+XJE2cOFFXXnml7rjjDu3YsUPd3d264447NH/+/NhUkmuuuUZ2u12rV6/Wrl27tHXrVj344INavXp1bDT5i1/8ol566SVt3LhRe/bs0caNG9XV1aVVq1YNu16bzSaLxRL/fwgAAAAAQFykRBjOzs7Wyy+/rGuuuUYzZ87U8uXL5XA49Otf/1rl5eWxdj/4wQ80ZcoULV68WJ/97Gc1ZcoUPfbYY7H3CwsL9eyzz+rQoUOaM2eO1qxZo1tuuUW33nprrE1bW5s6Ojr05JNP6pJLLtFPf/pTdXR06KKLLhp2vXl5efG5cAAAAABAQqTsPsNjzcA+w4FAQNXV1XK73QZXBKQ3FhABjEP/A4xD/wPiJyVGhlNJKBRSUVGR0WUAAAAAAM6BMBxnVqs1tjgXAAAAAGBsIgzHmd1uN7oEAAAAAMB5EIbjKBwOs6USAAAAAKQAwnAc+f3+Ee+bDAAAAABIHsJwHJlMJkaGAQAAACAFEIbjyGazyWzmnxQAAAAAxjqSWxzl5uYaXQIAAAAAYBgIw3Hi9/vldDqNLgMAAAAAMAyE4TgJhUIqLCw0ugwAAAAAwDAQhuMkKytLOTk5RpcBAAAAABgGwnCc8LwwAAAAAKQOwnCcOBwOo0sAAAAAAAwTYThOiouLjS4BAAAAADBMhOE4ycvLM7oEAAAAAMAwEYbjxGzmnxIAAAAAUgUJDgAAAACQcQjDAAAAAICMQxgGAAAAAGQcwjAAAAAAIOMQhgEAAAAAGYcwDAAAAADIOIRhAAAAAEDGIQwDAAAAADIOYRgAAAAAkHEIwwAAAACAjEMYBgAAAABkHMIwAAAAACDjEIYBAAAAABmHMAwAAAAAyDiEYQAAAABAxiEMAwAAAAAyDmEYAAAAAJBxCMMAAAAAgIxDGAYAAAAAZBzCMAAAAAAg4xCGAQAAAAAZhzAMAAAAAMg4hGEAAAAAQMYx9fX1RY0uAgAAAACAZGJkGAAAAACQcQjDAAAAAICMQxgGAAAAAGQcwjAAAAAAIOMQhoFTrFu3ThdffLHRZQAZif4HGIf+BxiH/mccwvBZrFq1SsuWLTO6DIwS9zE1cd/SA/cxNXHf0gP3MTVx39ID9zF1EIYBAAAAABmHMDwMr732mhYtWqT6+npVVVXpk5/8pLq7uwe1KSoq0hNPPKHrr79e48aN08c+9jFt3rzZoIoxlKE+pWNaythH/0sP9L/URP9LD/S/1ET/Sw/0v7GNMDwMJ06c0LJly/TLX/5Sv/nNbzR16lQtWbJER48eHdTugQce0MKFC/Xyyy9r8eLFuvXWW3XgwAGDqgbSA/0PMA79DzAO/Q9IPMLwMHziE5/Q5z73OU2cOFFNTU164IEHZLPZ9Pzzzw9qt2zZMi1btkz19fX62te+JqvVqu3btxtUNZAe6H+Aceh/gHHof0DiWY0uIBUcPnxY9913n7q6unT48GGFw2F5vV4dPHhwULvJkyfH/m61WuVyuXT48OFklwukFfofYBz6H2Ac+h+QeIThYVi1apV6e3v1r//6r6qurlZOTo4+/elPKxAIDGqXlZU16GuTyaRoNJrMUnEOZrP5jPsRCoUMqgbDRf9LD/S/1ET/Sw/0v9RE/0sP9L+xjWnSw/DKK69o5cqVmj9/vlpaWpSfn6+enh6jy8IFKikp0fvvvz/otT/+8Y8GVYPhov+lB/pfaqL/pQf6X2qi/6UH+t/YRhgehoaGBj311FN6++239dprr2n58uXKzs42uixcoMsvv1w7d+7Uv//7v2vfvn369re/rVdeecXosnAe9L/0QP9LTfS/9ED/S030v/RA/xvbCMNnEYlEZLFYJEkPP/yw+vv7dcUVV2j58uX6/Oc/r+rqaoMrxHCceh/nzZunu+66S/fee6+uuOIKHThwQCtWrDC4QgyF/pce6H+pif6XHuh/qYn+lx7of6nD1NfXx0MFQ1i0aJHq6uq0ceNGo0vBKHAfUxP3LT1wH1MT9y09cB9TE/ctPXAfUwcjw6c5evSo/vM//1P/8z//oyuuuMLocjBC3MfUxH1LD9zH1MR9Sw/cx9TEfUsP3MfUw2rSp7nhhhu0b98+3X777brqqquMLgcjxH1MTdy39MB9TE3ct/TAfUxN3Lf0wH1MPUyTBgAAAABkHKZJAwAAAAAyDmEYAAAAAJBxMjoMb9y4UXPmzFFVVZUaGhq0bNky7dq1a1CbaDSqdevWqbm5WW63W3/zN3+jt956a1CbDRs2aP78+Ro3bpyKiorOec6jR4+qpaVFRUVFOnr0aNyvCUgVyex/RUVFZ/zp6OhI2LUBY12yf/5t3rxZl156qcrLy1VfX6+bb745IdcFpIJk9b9NmzYN+fOvqKhIr732WkKvEUgVGR2GX375Zd1444361a9+pa1bt8pqteozn/mMjh07Fmvz7W9/W9/97ne1fv16vfDCCyotLdWiRYt04sSJWBu/369PfepTWrVq1XnPuXr1ak2dOjUh1wOkkmT3v+985zvavXt37M+1116bsGsDxrpk9r/vfe97+sd//Efddttt2r59u7Zt26aFCxcm9PqAsSxZ/W/x4sWDfu7t3r1bS5cuVU1NjWbMmJHw6wRSAQtoneLkyZOqrq7Wpk2btGDBAkWjUTU3N+umm27SnXfeKUnyer1qbGzUv/zLv+gLX/jCoO/fsmWLrr/+evX19Q15/EcffVS//OUv9ZWvfEVXX321/vznP8vlciX8uoBUkMj+V1RUpB/96Ee6+uqrk3ItQKpJVP/r6+vTpEmTtGnTJs2ZMydp1wOkkkT//jnA4/GoublZX/rSl/SVr3wlYdcDpJKMHhk+3cmTJxWJRGJTTfbv36+enh7NnTs31sZut+vjH/+4Xn311Qs69v/+7//q29/+tr73ve/JbOafHThdIvufJK1du1b19fWaM2eOOjo6FIlE4lY7kOoS1f86OzsVDofV29urtrY2tbS06LrrrtM777wT70sAUlaif/4NePbZZ+XxeHTdddeNumYgXZDKTrF27VpNnTpVra2tkqSenh5JUmlp6aB2paWl6u3tHfZx+/v7tWLFCq1fv17jxo2LX8FAGklU/5Okr371q+ro6NDPf/5zLV68WP/wD/+gb37zm/EpHEgDiep/77zzjiKRiDZs2KD77rtPP/7xjxUKhfSpT31KHo8nfhcApLBE/vw71Y9+9CPNnz9fbrd75MUCacZqdAFjxVe/+lW98soreu6552SxWAa9ZzKZBn0djUbPeO1c7rrrLrW1tTFFEziLRPY/Sfr7v//72N+nTZumSCSib37zm1qzZs3IiwbSRCL7XyQSUTAY1Pr162OjXN///vc1ceJEPffcc1q8ePHoLwBIYYn++TfgrbfeUnd3t5566qkR1wqkI0aGJd1999362c9+pq1bt6q2tjb2enl5uSSd8SnckSNHzvi07lxefPFF/eQnP5HL5ZLL5YqF4qamJv3Lv/zL6C8ASGGJ7n9DmTVrlo4fPz6qT9iBdJDo/jdwnIkTJ8ZeKywslNvt1sGDB0dROZD6kvnz74knnlBlZaWuvPLKEdcLpKOMD8N33XWXnnnmGW3dulVNTU2D3qupqVF5ebk6Oztjr/l8Pm3fvl1tbW3DPsezzz6rl19+WV1dXerq6tJ3vvMdSdIvfvELtpdARktG/xvKH//4R9lsNhUWFo7qOEAqS0b/a29vlyT96U9/ir128uRJ9fT0qKqqapRXAKSuZP788/l82rx5s6677jrWrQFOk9HTpO+8805t3rxZP/7xj1VUVBR7RiMvL0/5+fkymUxatWqVvvnNb6qxsVETJkzQhg0blJeXp2uuuSZ2nHfffVfHjh3TgQMHJEk7d+6UJNXX1ys/P18TJkwYdN6B/YWbmppYTRoZK1n975e//KV6e3s1e/Zs2e12dXV1ad26dbr++uuVk5OT/AsHxoBk/vxbuHCh1q5dq29961sqKirSunXrVFJSovnz5yf/woExIFn9b8CWLQ1OG9UAAAW8SURBVFt0/Phxff7zn0/iVQKpIaO3Vhpqg3Lpo0/r7r77bkkfPZ9x//3364knnlBfX59mzZqlDRs2aNKkSbH2q1at0pNPPnnGcbZt26bLLrvsjNe7urp01VVXsbUSMlqy+t/zzz+vr3/96/rLX/6iSCSi2tpa/e3f/q1uuukmWa0Z/XkgMlgyf/6dOHFCX/3qV7Vt2zZFo1G1t7fr/vvvV11dXQKuDBj7kv3758KFC5WXl6enn346zlcCpL6MDsMAAAAAgMzEgwMAAAAAgIxDGAYAAAAAZBzCMAAAAAAg4xCGAQAAAAAZhzAMAAAAAMg4hGEAAAAAQMYhDAMAAAAAMg5hGACANLBp0yYVFRXF/pSXl6u5uVmLFy/W9773PZ04cWJEx921a5fWrVun/fv3x7liAACMZTW6AAAAED9r165VXV2dgsGgent79fLLL+vuu+/Wd7/7XT355JOaMmXKBR3vrbfe0vr163XppZeqpqYmQVUDAJB8hGEAANLIvHnzNHv27NjXX/7yl/Xiiy/qc5/7nK699lp1d3fLbrcbWCEAAGMD06QBAEhzn/jEJ7RmzRq9++67euqppyRJb7zxhlatWqXp06ervLxcDQ0NuvHGG3Xw4MHY923atEk33nijJOmqq66KTcHetGlTrM0f/vAHLVu2TNXV1XK73Zo7d66ee+655F4gAAAjQBgGACADLFu2TJL0wgsvSJI6Ozu1d+9eLV26VA888IA+//nP69e//rWuuuoqeb1eSdIll1yim266SZL0la98RY899pgee+wxXXLJJZKkl19+WZ/85CfV29urNWvW6Otf/7qys7N17bXXauvWrQZcJQAAw2fq6+uLGl0EAAAYnU2bNumWW27Rr3/960HTpE9VXV2t2tpavfTSS/J4PMrNzR30/vbt27VgwQJ9//vf19KlSyVJP/vZz3TjjTdq27Ztuuyyy2Jto9GoWltb5Xa7tWXLFpnNH32+HolENH/+fB0+fFivv/56gq4WAIDRY2QYAIAMkZ+fr5MnT0rSoCB88uRJffDBB2pqalJhYeGwQuwf//jH2MjysWPHdPToUR09elTHjh3TlVdeqXfeeUcHDhxI2LUAADBaLKAFAECGOHnypEpKSiRJfX19uueee7RlyxYdO3ZsULsPP/zwvMf685//LEm67bbbdNtttw3Z5siRI6qurh5l1QAAJAZhGACADPDee+/p+PHjqq+vlyQtX75cv/vd73Trrbdq2rRpcjgcMplMWr58uSKRyHmPN9Dmnnvu0fTp04dsM2HChPhdAAAAcUYYBgAgA2zevFmSNHfuXPX19emFF17Q2rVrtXbt2lgbn8+nvr6+Qd9nMpmGPF5dXZ2kj6ZeX3HFFYkpGgCABOKZYQAA0tyLL76ob3zjG6qpqdHSpUtji11Fo4PX0HzkkUfOGBUeeLb49JA8ffp0NTQ06KGHHhpyWvWRI0fieQkAAMQdI8MAAKSR3/zmN9q3b59CoZAOHz6sl156SZ2dnaqqqtKTTz4pm80mm82mSy+9VN/5zncUDAZVVVWl7du363e/+52Ki4sHHe9jH/uYzGazvvWtb+nDDz+U3W7XrFmzVFtbq4cfflif/exn1d7eruuuu07V1dV6//33tWPHDr377rt65ZVXDPpXAADg/AjDAACkkfvvv1+SlJ2dLafTqUmTJmndunW67rrr5HA4Yu1++MMfau3atfq3f/s3hUIhffzjH9fWrVt19dVXDzpeRUWFHnzwQT344IP60pe+pHA4rO9+97uqra3VxRdfrN/85jd64IEH9MQTT+j48eMqLS3VlClTdPfddyf1ugEAuFDsMwwAAAAAyDg8MwwAAAAAyDiEYQAAAABAxiEMAwAAAAAyDmEYAAAAAJBxCMMAAAAAgIxDGAYAAAAAZBzCMAAAAAAg4xCGAQAAAAAZhzAMAAAAAMg4hGEAAAAAQMb5f3ZvuJ+0Yd1BAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1008x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = monthly_sales_df.plot(label='observed')\n",
"auto_sea_is_forecasts.plot(ax=ax, label='In-sample forecast(AutoARIMA-Seasonal)', alpha=.7, figsize=(14, 7))\n",
"auto_sea_oos_forecasts.plot(ax=ax, label='Out-of-sample forecast(AutoARIMA-Seasonal)', alpha=.7, figsize=(14, 7))\n",
"ax.fill_between(auto_sea_is_forecasts_ci.index,\n",
" auto_sea_is_forecasts_ci.iloc[:, 0],\n",
" auto_sea_is_forecasts_ci.iloc[:, 1], color='k', alpha=.2)\n",
"ax.fill_between(auto_sea_oos_forecasts_ci.index,\n",
" auto_sea_oos_forecasts_ci.iloc[:, 0],\n",
" auto_sea_oos_forecasts_ci.iloc[:, 1], color='k', alpha=.2)\n",
"ax.set_xlabel('Date')\n",
"ax.set_ylabel('Sales')\n",
"plt.title('Sales Predictions vs Actual')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compared to the previous attempt, a seasonal model has captured the seasonaliy really well, both for in-sample and out-of-sample forecasts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 10.3 Quantitative evaluation"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Mean Squared Error of our forecasts is 89577.16\n",
"The Root Mean Squared Error of our forecasts is 299.29\n"
]
}
],
"source": [
"y_forecasted = auto_sea_oos_forecasts.values\n",
"y_truth = monthly_sales_df['2017-01-01':]\n",
"mse = ((y_forecasted - y_truth.iloc[:,0]) ** 2).mean()\n",
"print('The Mean Squared Error of our forecasts is {}'.format(round(mse, 2)))\n",
"print('The Root Mean Squared Error of our forecasts is {}'.format(round(np.sqrt(mse), 2)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The RMSE has decresed to 303.21 compared to 674.0 above!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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