{
"cells": [
{
"cell_type": "markdown",
"id": "f86a6fd9",
"metadata": {},
"source": [
"# Merge Data & Dissolve Polygons\n",
"\n",
"By: Steven Chao\n",
"\n",
"---------------\n",
"```{admonition} Learning Objectives\n",
"* Import dataframes into Python for analysis\n",
"* Perform basic dataframe column operations\n",
"* Merge dataframes using a unique key\n",
"* Group attributes based on a similar attribute\n",
"* Dissolve vector geometries based on attribute values\n",
"```\n",
"```{admonition} Review\n",
"* [Data Structures](c_features.md)\n",
"* [Vector Data ](c_vectors.md)\n",
"```\n",
"--------------\n",
"\n",
"## Introduction\n",
"\n",
"Dataframes are widely used in Python for manipulating data. Recall that a dataframe is essentially an Excel spreadsheet (a 2-D table of rows and columns); in fact, many of the functions that you might use in Excel can often be replicated when working with dataframes in Python!\n",
"\n",
"This chapter will introduce you to some of the basic operations that you can perform on dataframes. We will use these basic operations in order to calculate and map poverty rates in the Commonwealth of Virginia. We will pull data from the US Census Bureau's [American Community Survey (ACS)](https://www.census.gov/programs-surveys/acs) 2019 (see [this page](https://www.census.gov/data/developers/data-sets/acs-5year.html) for the data)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b748b591",
"metadata": {},
"outputs": [],
"source": [
"# Import modules\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import geopandas as gpd\n",
"from census import Census\n",
"from us import states\n",
"import os"
]
},
{
"cell_type": "markdown",
"id": "09931157",
"metadata": {},
"source": [
"## Accessing Data\n",
"\n",
"### Import census data\n",
"\n",
"Let's begin by accessing and importing census data. Importing census data into Python requires a Census API key. A key can be obtained from [Census API Key](http://api.census.gov/data/key_signup.html). **It will provide you with a unique 40 digit text string. Please keep track of this number. Store it in a safe place.**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fae2d322",
"metadata": {
"tags": [
"remove-output"
]
},
"outputs": [],
"source": [
"# Set API key\n",
"c = Census(\"CENSUS API KEY HERE\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e5fb1755",
"metadata": {
"tags": [
"hide-cell"
]
},
"outputs": [],
"source": [
"#ignore this, I am just reading in my api key privately\n",
"c = Census(os.environ.get('census_api_key'))"
]
},
{
"cell_type": "markdown",
"id": "5cf8f92f",
"metadata": {},
"source": [
"With the Census API key set, we will access the census data at the tract level for the Commonwealth of Virginia from the 2019 ACS, specifically the `ratio of income to poverty in the past 12 months` (`C17002_001E`, total; `C17002_002E`, < 0.50; and `C17002_003E`, 0.50 - 0.99) variables and the `total population` (`B01003_001E`) variable. For more information on why these variables are used, refer to the US Census Bureau's [article on how the Census Bureau measures poverty](https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html) and the [list of variables found in ACS](https://api.census.gov/data/2019/acs/acs5/variables.html).\n",
"\n",
"The `census` package provides us with some easy convenience methods that allow us to obtain data for a wide variety of geographies. The FIPS code for Virginia is 51, but if needed, we can also use the `us` library to help us figure out the relevant FIPS code."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "51900d92",
"metadata": {},
"outputs": [],
"source": [
"# Obtain Census variables from the 2019 ACS at the tract level for the Commonwealth of Virginia (FIPS code: 51)\n",
"# C17002_001E: count of ratio of income to poverty in the past 12 months (total)\n",
"# C17002_002E: count of ratio of income to poverty in the past 12 months (< 0.50)\n",
"# C17002_003E: count of ratio of income to poverty in the past 12 months (0.50 - 0.99)\n",
"# B01003_001E: total population\n",
"# Sources: https://api.census.gov/data/2019/acs/acs5/variables.html; https://pypi.org/project/census/\n",
"va_census = c.acs5.state_county_tract(fields = ('NAME', 'C17002_001E', 'C17002_002E', 'C17002_003E', 'B01003_001E'),\n",
" state_fips = states.VA.fips,\n",
" county_fips = \"*\",\n",
" tract = \"*\",\n",
" year = 2017)"
]
},
{
"cell_type": "markdown",
"id": "4ba590c9",
"metadata": {},
"source": [
"Now that we have accessed the data and assigned it to a variable, we can read the data into a dataframe using the `pandas` library."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2b064255",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" NAME C17002_001E C17002_002E \\\n",
"0 Census Tract 60, Norfolk city, Virginia 3947.0 284.0 \n",
"1 Census Tract 65.02, Norfolk city, Virginia 3287.0 383.0 \n",
"\n",
" C17002_003E B01003_001E state county tract \n",
"0 507.0 3947.0 51 710 006000 \n",
"1 480.0 3302.0 51 710 006502 \n",
"Shape: (1907, 8)\n"
]
}
],
"source": [
"# Create a dataframe from the census data\n",
"va_df = pd.DataFrame(va_census)\n",
"\n",
"# Show the dataframe\n",
"print(va_df.head(2))\n",
"print('Shape: ', va_df.shape)"
]
},
{
"cell_type": "markdown",
"id": "c234b9bb",
"metadata": {},
"source": [
"By showing the dataframe, we can see that there are 1907 rows (therefore 1907 census tracts) and 8 columns.\n",
"\n",
"### Import Shapefile\n",
"\n",
"Let's also read into Python a shapefile of the Virginia census tracts and reproject it to the UTM Zone 17N projection. (This shapefile can be downloaded on the Census Bureau's website on the [Cartographic Boundary Files page](https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html) or the [TIGER/Line Shapefiles page](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html).)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e69308c7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC \\\n",
"0 51 700 032132 51700032132 321.32 Census Tract 321.32 G5020 \n",
"1 51 700 032226 51700032226 322.26 Census Tract 322.26 G5020 \n",
"\n",
" FUNCSTAT ALAND AWATER INTPTLAT INTPTLON \\\n",
"0 S 2552457 0 +37.1475176 -076.5212499 \n",
"1 S 3478916 165945 +37.1625163 -076.5527816 \n",
"\n",
" geometry \n",
"0 POLYGON ((897174.191 4119897.084, 897174.811 4... \n",
"1 POLYGON ((893470.562 4123469.385, 893542.722 4... \n",
"Shape: (1907, 13)\n",
"\n",
"The shapefile projection is: EPSG:32617\n"
]
}
],
"source": [
"# Access shapefile of Virginia census tracts\n",
"va_tract = gpd.read_file(\"https://www2.census.gov/geo/tiger/TIGER2019/TRACT/tl_2019_51_tract.zip\")\n",
"\n",
"# Reproject shapefile to UTM Zone 17N\n",
"# https://spatialreference.org/ref/epsg/wgs-84-utm-zone-17n/\n",
"va_tract = va_tract.to_crs(epsg = 32617)\n",
"\n",
"# Print GeoDataFrame of shapefile\n",
"print(va_tract.head(2))\n",
"print('Shape: ', va_tract.shape)\n",
"\n",
"# Check shapefile projection\n",
"print(\"\\nThe shapefile projection is: {}\".format(va_tract.crs))"
]
},
{
"cell_type": "markdown",
"id": "f595ddb1",
"metadata": {},
"source": [
"By printing the shapefile, we can see that the shapefile also has 1907 rows (1907 tracts). This number matches with the number of census records that we have on file. Perfect!\n",
"\n",
"Not so fast, though. We have a potential problem: We have the census data, and we have the shapefile of census tracts that correspond with that data, but they are stored in two separate variables (`va_df` and `va_tract` respectively)! That makes it a bit difficult to map since these two separate datasets aren't connected to each other.\n",
"\n",
"## Performing Dataframe Operations\n",
"\n",
"### Create new column from old columns to get combined FIPS code\n",
"\n",
"To solve this problem, we can join the two dataframes together via a field or column that is common to both dataframes, which is referred to as a key.\n",
"\n",
"Looking at the two datasets above, it appears that the `GEOID` column from `va_tract` and the `state`, `county`, and `tract` columns combined from `va_df` could serve as the unique key for joining these two dataframes together. In their current forms, this join will not be successful, as we'll need to merge the `state`, `county`, and `tract` columns from `va_df` together to make it parallel to the `GEOID` column from `va_tract`. We can simply add the columns together, much like math or the basic operators in Python, and assign the \"sum\" to a new column.\n",
"\n",
"To create a new column--or call an existing column in a dataframe--we can use indexing with `[]` and the column name (string). (There is a different way if you want to access columns using the index number; read more about indexing and selecting data [in the pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html).)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5f57355d",
"metadata": {},
"outputs": [],
"source": [
"# Combine state, county, and tract columns together to create a new string and assign to new column\n",
"va_df[\"GEOID\"] = va_df[\"state\"] + va_df[\"county\"] + va_df[\"tract\"]"
]
},
{
"cell_type": "markdown",
"id": "4e4ccee2",
"metadata": {},
"source": [
"Printing out the first rew rows of the dataframe, we can see that the new column `GEOID` has been created with the values from the three columns combined."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "af0b3c38",
"metadata": {},
"outputs": [
{
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" NAME C17002_001E C17002_002E \\\n",
"0 Census Tract 60, Norfolk city, Virginia 3947.0 284.0 \n",
"1 Census Tract 65.02, Norfolk city, Virginia 3287.0 383.0 \n",
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]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print head of dataframe\n",
"va_df.head(2)"
]
},
{
"cell_type": "markdown",
"id": "b0861d80",
"metadata": {},
"source": [
"### Remove dataframe columns that are no longer needed\n",
"\n",
"To reduce clutter, we can delete the `state`, `county`, and `tract` columns from `va_df` since we don't need them anymore. Remember that when we want to modify a dataframe, we must assign the modified dataframe back to the original variable (or a new one, if preferred). Otherwise, any modifications won't be saved. An alternative to assigning the dataframe back to the variable is to simply pass `inplace = True`. For more information, see the [pandas help documentation on `drop`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1c5150ae",
"metadata": {},
"outputs": [
{
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" NAME C17002_001E C17002_002E \\\n",
"0 Census Tract 60, Norfolk city, Virginia 3947.0 284.0 \n",
"1 Census Tract 65.02, Norfolk city, Virginia 3287.0 383.0 \n",
"\n",
" C17002_003E B01003_001E GEOID \n",
"0 507.0 3947.0 51710006000 \n",
"1 480.0 3302.0 51710006502 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove columns\n",
"va_df = va_df.drop(columns = [\"state\", \"county\", \"tract\"])\n",
"\n",
"# Show updated dataframe\n",
"va_df.head(2)"
]
},
{
"cell_type": "markdown",
"id": "33b6511b",
"metadata": {},
"source": [
"### Check column data types\n",
"\n",
"The key in both dataframe must be of the same data type. Let's check the data type of the `GEOID` columns in both dataframes. If they aren't the same, we will have to change the data type of columns to make them the same."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e56648c5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column data types for census data:\n",
"NAME object\n",
"C17002_001E float64\n",
"C17002_002E float64\n",
"C17002_003E float64\n",
"B01003_001E float64\n",
"GEOID object\n",
"dtype: object\n",
"\n",
"Column data types for census shapefile:\n",
"STATEFP object\n",
"COUNTYFP object\n",
"TRACTCE object\n",
"GEOID object\n",
"NAME object\n",
"NAMELSAD object\n",
"MTFCC object\n",
"FUNCSTAT object\n",
"ALAND int64\n",
"AWATER int64\n",
"INTPTLAT object\n",
"INTPTLON object\n",
"geometry geometry\n",
"dtype: object\n"
]
}
],
"source": [
"# Check column data types for census data\n",
"print(\"Column data types for census data:\\n{}\".format(va_df.dtypes))\n",
"\n",
"# Check column data types for census shapefile\n",
"print(\"\\nColumn data types for census shapefile:\\n{}\".format(va_tract.dtypes))\n",
"\n",
"# Source: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dtypes.html"
]
},
{
"cell_type": "markdown",
"id": "f2d743f0",
"metadata": {},
"source": [
"Looks like the `GEOID` columns are the same!\n",
"\n",
"### Merge dataframes\n",
"\n",
"Now, we are ready to merge the two dataframes together, using the `GEOID` columns as the primary key. We can use the `merge` method in `GeoPandas` called on the `va_tract` shapefile dataset."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "569ab656",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" STATEFP COUNTYFP TRACTCE GEOID NAME_x NAMELSAD MTFCC \\\n",
"0 51 700 032132 51700032132 321.32 Census Tract 321.32 G5020 \n",
"1 51 700 032226 51700032226 322.26 Census Tract 322.26 G5020 \n",
"\n",
" FUNCSTAT ALAND AWATER INTPTLAT INTPTLON \\\n",
"0 S 2552457 0 +37.1475176 -076.5212499 \n",
"1 S 3478916 165945 +37.1625163 -076.5527816 \n",
"\n",
" geometry \\\n",
"0 POLYGON ((897174.191 4119897.084, 897174.811 4... \n",
"1 POLYGON ((893470.562 4123469.385, 893542.722 4... \n",
"\n",
" NAME_y C17002_001E C17002_002E \\\n",
"0 Census Tract 321.32, Newport News city, Virginia 5025.0 161.0 \n",
"1 Census Tract 322.26, Newport News city, Virginia 4167.0 736.0 \n",
"\n",
" C17002_003E B01003_001E \n",
"0 342.0 5079.0 \n",
"1 559.0 4167.0 \n",
"Shape: (1907, 18)\n"
]
}
],
"source": [
"# Join the attributes of the dataframes together\n",
"# Source: https://geopandas.org/docs/user_guide/mergingdata.html\n",
"va_merge = va_tract.merge(va_df, on = \"GEOID\")\n",
"\n",
"# Show result\n",
"print(va_merge.head(2))\n",
"print('Shape: ', va_merge.shape)"
]
},
{
"cell_type": "markdown",
"id": "701398e7",
"metadata": {},
"source": [
"Success! We still have 1907 rows, which means that all rows (or most of them) were successfully matched! Notice how the census data has been added on after the shapefile data in the dataframe.\n",
"\n",
"Some additional notes about joining dataframes:\n",
"* the columns for the key do not need to have the same name.\n",
"* for this join, we had a one-to-one relationship, meaning one attribute in one dataframe matched to one (and only one) attribute in the other dataframe. Joins with a many-to-one, one-to-many, or many-to-many relationship are also possible, but in some cases, they require some special considerations. See this [Esri ArcGIS help documentation on joins and relates](https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/about-joining-and-relating-tables.htm) for more information.\n",
"\n",
"### Subset dataframe\n",
"\n",
"Now that we merged the dataframes together, we can further clean up the dataframe and remove columns that are not needed. Instead of using the `drop` method, we can simply select the columns we want to keep and create a new dataframe with those selected columns."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ce6f5166",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" STATEFP COUNTYFP TRACTCE GEOID \\\n",
"0 51 700 032132 51700032132 \n",
"1 51 700 032226 51700032226 \n",
"\n",
" geometry C17002_001E \\\n",
"0 POLYGON ((897174.191 4119897.084, 897174.811 4... 5025.0 \n",
"1 POLYGON ((893470.562 4123469.385, 893542.722 4... 4167.0 \n",
"\n",
" C17002_002E C17002_003E B01003_001E \n",
"0 161.0 342.0 5079.0 \n",
"1 736.0 559.0 4167.0 \n",
"Shape: (1907, 9)\n"
]
}
],
"source": [
"# Create new dataframe from select columns\n",
"va_poverty_tract = va_merge[[\"STATEFP\", \"COUNTYFP\", \"TRACTCE\", \"GEOID\", \"geometry\", \"C17002_001E\", \"C17002_002E\", \"C17002_003E\", \"B01003_001E\"]]\n",
"\n",
"# Show dataframe\n",
"print(va_poverty_tract.head(2))\n",
"print('Shape: ', va_poverty_tract.shape)"
]
},
{
"cell_type": "markdown",
"id": "ab5952cc",
"metadata": {},
"source": [
"Notice how the number of columns dropped from 13 to 9.\n",
"\n",
"### Dissolve geometries and get summarized statistics to get poverty statistics at the county level\n",
"\n",
"Next, we will group all the census tracts within the same county (`COUNTYFP`) and aggregate the poverty and population values for those tracts within the same county. We can use the `dissolve` function in `GeoPandas`, which is the spatial version of `groupby` in `pandas`. We use `dissolve` instead of `groupby` because the former also groups and merges all the geometries (in this case, census tracts) within a given group (in this case, counties)."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "0b0b5e8b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mmann1123/miniconda3/envs/pygis2/lib/python3.10/site-packages/geopandas/geodataframe.py:1676: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
" aggregated_data = data.groupby(**groupby_kwargs).agg(aggfunc)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" geometry C17002_001E \\\n",
"COUNTYFP \n",
"001 POLYGON ((971814.409 4160130.005, 971664.507 4... 32345.0 \n",
"003 POLYGON ((722871.236 4189239.708, 722813.526 4... 97587.0 \n",
"\n",
" C17002_002E C17002_003E B01003_001E \n",
"COUNTYFP \n",
"001 2423.0 3993.0 32840.0 \n",
"003 5276.0 4305.0 105105.0 \n",
"Shape: (133, 5)\n"
]
}
],
"source": [
"# Dissolve and group the census tracts within each county and aggregate all the values together\n",
"# Source: https://geopandas.org/docs/user_guide/aggregation_with_dissolve.html\n",
"va_poverty_county = va_poverty_tract.dissolve(by = 'COUNTYFP', aggfunc = 'sum')\n",
"\n",
"# Show dataframe\n",
"print(va_poverty_county.head(2))\n",
"print('Shape: ', va_poverty_county.shape)"
]
},
{
"cell_type": "markdown",
"id": "fb40ef74",
"metadata": {},
"source": [
"Notice that we got the number of rows down from 1907 to 133.\n",
"\n",
"### Perform column math to get poverty rates\n",
"\n",
"We can estimate the poverty rate by dividing the sum of `C17002_002E` (ratio of income to poverty in the past 12 months, < 0.50) and `C17002_003E` (ratio of income to poverty in the past 12 months, 0.50 - 0.99) by `B01003_001E` (total population).\n",
"\n",
"Side note: Notice that `C17002_001E` (ratio of income to poverty in the past 12 months, total), which theoretically should count everyone, does not exactly match up with `B01003_001E` (total population). We'll disregard this for now since the difference is not too significant."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "904d40e6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
geometry
\n",
"
C17002_001E
\n",
"
C17002_002E
\n",
"
C17002_003E
\n",
"
B01003_001E
\n",
"
Poverty_Rate
\n",
"
\n",
"
\n",
"
COUNTYFP
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
001
\n",
"
POLYGON ((971814.409 4160130.005, 971664.507 4...
\n",
"
32345.0
\n",
"
2423.0
\n",
"
3993.0
\n",
"
32840.0
\n",
"
19.537150
\n",
"
\n",
"
\n",
"
003
\n",
"
POLYGON ((722871.236 4189239.708, 722813.526 4...
\n",
"
97587.0
\n",
"
5276.0
\n",
"
4305.0
\n",
"
105105.0
\n",
"
9.115646
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" geometry C17002_001E \\\n",
"COUNTYFP \n",
"001 POLYGON ((971814.409 4160130.005, 971664.507 4... 32345.0 \n",
"003 POLYGON ((722871.236 4189239.708, 722813.526 4... 97587.0 \n",
"\n",
" C17002_002E C17002_003E B01003_001E Poverty_Rate \n",
"COUNTYFP \n",
"001 2423.0 3993.0 32840.0 19.537150 \n",
"003 5276.0 4305.0 105105.0 9.115646 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get poverty rate and store values in new column\n",
"va_poverty_county[\"Poverty_Rate\"] = (va_poverty_county[\"C17002_002E\"] + va_poverty_county[\"C17002_003E\"]) / va_poverty_county[\"B01003_001E\"] * 100\n",
"\n",
"# Show dataframe\n",
"va_poverty_county.head(2)"
]
},
{
"cell_type": "markdown",
"id": "f512dea3",
"metadata": {},
"source": [
"## Plotting Results\n",
"\n",
"Finally, since we have the spatial component connected to our census data, we can plot the results!"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b8eee60e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Poverty Rates (%) in Virginia')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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bf7/Ky7wLemRdpabXbVfYu7OkRKqpkylnwUQAAIYaGtIAAAAABsR2hbOKubAtrV4+dIbMFqO9XOU8T8Laxsa87r8/w2oRwxyzxniLZW4sFpNtXCvV1EnVNUxLAwCKzjh5PX8+IManawrTkAYAAACQNRvu7LlR2Nv2sbiUnG7NRGmpFHDy32h0nKJOR5vtt5epZMq3J1byFrrsa5vWZikakSqrpdJSmUCgILUBAICBoyENAAAAICu2s13aeBHCvra31ovqiEb73zippEQKhbIvLks2kVncSF5MmCBTU8V0dC9sLJ7ZRH1XOBXlYUtDMvUjZEpK81wdAAAYKBrSAAAAADLmNjZIrS3eNKqTWSvVNrdk14yWpI4O2YYGaeTIvE692va+J3DzxWy3nUxtNc3oXlgZqaMj+wdGI7ItzTKjRue+KAAA+uAYI8dnmR2OT3/ToCENAAAAICO2o132/XclayUnIFtfL1NVJQUDfUZrmIoK2YHENLe2SpVVsqWlMiV5+tOl0PnRjiNnt2m+y5j0Eysj29Iy4MebUqajAQDwM6fYBQAAAADwP5tIyP3gPa8ZLUluQmpskF26RHb5ctn2DtlEYtPHWSu7ZvXAj7t2jeynS2Xd3Edr2ERCqq7ycqQLxJm6A83oPlhJtqtrw+ssW6WlUnVtTmsCAAC5xYQ0AAAAgD5Z15X73ju9L/7X1SW7aqW3bU2tTE2NVFoik2z0hsqkSGRgB4/HJWNkm5qlutqcxXdYa2VbW6T2Nqms1KuxuWXgjdC+BIPSqFFyxoyWCTAT1Ccrqatr4I8Pled/IUwAAHriGP/9G2R9Vs96NKQBAAAA9Mmu+Exqbsps49YWr9EbCMrW10uBgJc5PagCrBRwcpslHU94kSCS1/SOt0sVZVJpqPfGdKhMisekHibBe1VaImeXnX2a4Ogv1hjZ5ubB7STSJWut/xoCAAAghYY0AAAAgD6ZcVt6U9ArV2T+oERcalibuyIGGKthrZVkpYSVrCslEl5UR0+LGcZi3qWiXCop8aa6y8q8j0UiXjO6LCRlseCemTBRNuF6jXlvqT70wEqy7QNYxHBj0YjU1iLV1A1+XwAAIC9oSAMAAADokwkEZLafIreiUnbRx/mJteivhpKSHu+31nr1uNbLtU4kZONxr7EcjXoXm2X+dCzqXSSpo33Tj0lSRUXvESbJmrfc0jt2a6uSz5gtK5dCpevjTGhQJ9l4wvua5WJfnR1SdS1T0gCAgnIcI8dn//Y4RHYAAAAAGKqstVK4syjNaEmyzS3eod2EF7ERi61vHOemiZl9QQmpqqrnSev6ETIj6r1aN9YVlrrCGxrU5RUyoVLJMZLdPBvUVqbn53Gg4nHZNSulqhopFJIJ9nwyAwAAFAcNaQAAAAD9smvXyK5YXvgDl5R4iwJGu6S1g1jsLtcSCcl1pdpaqaVbRrZjZOpre25G9yTcKRteP2ltjNegLi3x9lOk5n8hWRnZZJZ3LsVisk2NXu53VbUU9F5Hyalp67rS+utMUgMAUFg0pAEAAAD0yTY3yX74QXEOXhqSYpHiHLs/1krhDmlEvbTOW/TRTNp64FPk1kqdHbLr+9M2EJDKymVKg5IZng1qG4l4jf18iXTJRtafyAgEZI3jnSxwXS+X3FrZkhLJCcjUj5AJ8CcyAGBgjEmd6/QNn5WTwr+2AAAAAHpl43G5C98vWlSHOtqlurpNs5z9pL1Nqq+XQiFJOXyeEgmpo112/Vp/NhiUyspSedoml8cqAmslhcOFO2AiIanb5HqyER71csFtw1ppizHr870BAEC+8C8tAAAAgF7ZJYtSDbuiaW72Yhf8LBaVysvye4x4XGpvl21qkm1qktveITee8GIvfDsD1TNrjGz3qBM/iEVlm9cVuwoAAIY9JqQBAAAApDJ1u+fp2q6w7JrVxSsqEJAqyqVYvHg1ZCJUJo0cWfiWcCzmZSWvv2lDIW8Rv0BA8nGL2kqy7R3FLqNnnR2yNbUshAgAyJrjGDk+y+xwrL/qSWJCGgAAANjM2a6w3FdfkdY1ynaL5rDLPl0fc1AkiYQUCHoBiJ2dxaujP5WV/lgYLxKRWlvXT1A3yw13yU1Ybxq52LV1YxOu10z3Kbuu0TtBAwAA8oKGNAAAALCZs42NUjwu95OPZBd97N3X3ia7amWRK5OX8xuPewvR+ZExUlmeozoGqqtLam2RXbe+Qd0VkXVV1Aa1lZHa2op09AxFI7INa9NOzgAAgNwhsgMAAADYTNlwp9d4W7HcuyMSkV25Qm4wKPvp0uIWl1TIRe8GYuQof0xHZyIclk0+n8bIlldIJUGZRMLL0Qg6eV3Qz8rI+r0ZLUlOQFq7Siotlerqi10NAGCIMBtFn/mB8WmAFw1pAAAAYDNlly6RXbtmozutf5rRklReIYU7vElkazf83w9Gj5WCgWJXMTDWSp0dUjC4oUkdCkk11Xn7Y9pGosWNgMlEICita5Ak2RXLpGBApqqmyEUBADC8ENkBAAAAbIZsPCbbsLbYZfQv0iXFE97ChsGgVFlV7Io8xnjNSp9NQmWte4M4EpFd1yS3syvnh7FWUtjHOeCS9/pa34yW5J2cWbmC6A4AAHKMCWkAAABgM2RXrvDPpHFfIpEN18Nd8sXqfLV1UoVPFjIcrGg0/XYiIbW3yQYCMqGSnBzCGiPb3JyTfeVNMCg1Nmx6fzQiu3SRNGGSTJA/nwEAvXOMkeP463cDx/VXPUn8iwoAAABsZmxrq+zSJcUuY2DKy6X2IuUQO440ZpxkNDya0X3kRdtwOCcNaSvJdg6FyejG3j/e0S770Qey1dVSsESmqlqmqrpw9QEAMMwQ2QEAAABsZtxPlwyN6eiedJ+YLrSychnHfwsWDZjTR/51NCLbFRl0XIVNuFIk2v+GhVRRseF6ICA1NfX//eAmpJZmqXGt7MrPiPEAAGAQmJAGAAAAhhmbiEtNTTKjttjkY+7aNVLTuiJUlQPV1VJXuHjHr60t3rFzzVop2ndz37a2ytTXSyUD+7PRGiO1FWmavS/BoMyokZKVFyXiZrnQYjTq5WFXVOalPADA0GQcI+OzyA4jf9WTxIQ0AAAAMEzYeEzuRwvlznlFdqMIAhuNyF21QvbDD4pU3SDV1XkLHBbLiJEyfURcDDklpekLGvbCtrTIxuJZ797KyLa2D6SyvDPBEhl561Ka+jppxMis92HXrpZ13ZzXBgDA5mAY/UYFAAAAbN7s4kWyq1ZKriuZDb/qWzchd8Fbsh996H1sKIp0FTdmZKg+bz2xVoplGKPhurJNTV58h+tmHFVho1EpkX0juxBsLOplW6+fGjP1ddLo0dntpL1NdvHHstEo8R0AAGSJyA4AAABgGLCNDV4zOingNaTd1Stl166V/L6wXF/KK6R4kXOIm5tku7qkEfUyZgjP9RjjZUdnOW1uW1u9K6Ey2ZKgjOPIlIV63lbG36+3tjbZZJJIICBVVctUVsvWx7OLs+kKy366WKZuhNRDPA4AYPPiGO/iJ36rJ4mGNAAAADAMuKtXbXrfmtWyHy4sQjU5FiotfkNa2jCl7dM/7jJjBhd9EumSIpINBKRQ6SYLPFpjZJtbBlljhqz1mut2ENPrCW+xwtSM84iR0kZxN32KdMl2tsuIhjQAAJkawqf2AQAAAEiS7ezcpIlm16yW/WgYNKMrK6WOImcRGyNVVUvjxss4geLWMlj9LGSYuU278laS7QznL1rF2vXBz+sv0ajU2SG51jt4LibXEwlpxKjsHtPWKlvs1ygAAEMIE9IAAADAEGWt9RrPSxdv2gSMxYpTVK6VlEhuQooXKY947DjJcTaZBB6SAsHcvS7cxPom8Ia7bMJKkVw1vHvgBKS29dEhxtkwGd3Z4f2/rFwK5uCEQSIujRzlneTJNDO7tUWmsmrwxwYADFnGMTI+y8gwPn1bFw1pAAAAYAiy4U65iz7JLl5gKGprS+VhF4QxXmxDKJRquA6LZrTkfW65ZDd0pK2M1JbnqI7uJyV6iunI5ecXj3uvg6Z1PS5oaWVkxGKGAAAMBA1pAAAAYIixsZjct+d7kQXDXU2N1N7W/3a5UFYmjRixYdHCYdKHzgtjUs+PlWTbCxBZEQx6x01Gd2w8vRwIrK9m8Kxrpa6IpIAkV0aSdQJSe6c3ZR6Py9bVSsESmdoamZranBwXAIDNAQ1pAAAAYIixa9dsHs3oiorC5kdXVud+ithXcjjR67pSNCYbDMq6bmEiVazrZXlHurz4kXCnd39JiVRS2nOTeiCHsVZqbpGamrw7ystlq8q9652dGzZsapYkmXHjiesAAMgY47t3VfmtniQWNQQAAACGENvRLrtkUbHLyL/KCq8Bma8F8jZWUyt1dUrxRGGOVwxubp9L294uu26d1NGx4c58/+FrXak05B2nNOQ1qMvKJcf0HOOR7e6tlVrbNzSjJSkclprbeu3nuwve9h4HAAAyQkMaAAAAGELssk+lxDBumkpedEaiQAsZlpZKtXXetG0sJrW3FawHXjCBgBd3Ec3xgoPJbOVA0LtIklOIPzGt14AuKfGu56ARnRJLSA0NPdwfk9b2cL8kVVT4dgINAAA/IrIDAAAAGCJsV5dsw9pil5FfjuM1UHPdPO1NsMRrRicbirGYFOmSDYWGUZPReFO++dLVbd/lFfk7zsby8eUJOtlHf3R0yEajMqWleSgIADBUOI6R4/jrdwfHpwtiMCENAAAADBF21YrCRVgUS21t4ZrRjtPztHlbq9eYHi4K+ZrpCktOoHDHy7W2joE9X11dua8FAIBhalAN6VmzZskYo0svvbTXbV566SUdcMABGjlypMrLyzV16lT96le/2mS7hx9+WDvttJNCoZB22mknPfroo4MpDQAAABhWrLWyq1YWu4z8qqmV2tsKd7yq6t7jHlpbZHO5CGAxuQkvZ7kQrB3+J016UlpS7AoAABgyBhzZMXfuXP3ud7/TtGnT+tyusrJSF110kaZNm6bKykq99NJLuuCCC1RZWanzzz9fkvTf//5XJ598sq655hodf/zxevTRR3XSSSfppZde0r777jvQEgEAAICCsa0tsk2NMlttk5+oh6Z1w2tqd2PBoBQr0GS0JFXXpkdNbMxaqblFtrpacpyhH9/hJrKPohioRMLLd04UIAM816orpdZWL1u8slIqC0mB9XNcrrwTGImE1NYutbRseNxQngoHAOSEcYyMzyI7zHCK7Ghvb9fpp5+u3//+96qvr+9z2913312nnnqqdt55Z2299dY644wzdOSRR+rFF19MbXPzzTfr8MMP18yZMzV16lTNnDlThx12mG6++eaBlAcAAAAUnG1ukl2+TO7Cd2XzsOigO9yno6uqCrOIYVImf5/FotK6RqmlRbaQteWDtYWbko7HvFzuQA9NWmM25HX7kHEcacJ4mTFbyFRVyAQDMsZ4l4DxbodKpZH10oQtpVDIe+DmOBUOAMAADaghfeGFF+qYY47RjBkzsn7svHnz9Morr+iQQw5J3fff//5XRxxxRNp2Rx55pF555ZVe9xOJRNTa2pp2AQAAAIrBWiu7rsG7sa5R7jtvyeYwB9l2haXGhpztz3dq6qWO9sIdr7JKyiaOIxaVmtZ5i0oO5b5jLNpzkzgfgkHvBMPGzedkbrePn8hMpuGNMV5jevxYafx4IjsAAMhC1g3pBx54QG+++aZmzZqV1eMmTJigUCikvfbaSxdeeKHOO++81MdWrVqlMWPGpG0/ZswYrVq1qtf9zZo1S7W1tanLxIkTs/tEAAAAgFwJd0qdHRtud7TJfXuebI6arHbZpznZjx/ZeLliv31LiXfDklOgCd5gcGCL0LW1Si1Nsj5upvbJGC+KohDica/xHI1K8YR3PRaXwmHvY8Hh0cA1jiNTWS5jBrU8EwBgGDBGcnx28eubkrL6V3PZsmW65JJLdO+996qsrCyrA7344ot6/fXX9Zvf/EY333yz7r///rSPb3wW2lrb55npmTNnqqWlJXVZtmxZVvUAAAAAuWJbmje9MxqRu2C+bFPj4PYdjQ7bxQxtolzxhxZKktw3Vip217uK/XuNbFdICgx4uZv+ub0sZJiJWExqWOvnAd++xWKFa0q7rmStbDwmG+6SdbtF2eQh1qZo3KH6YgAAoDiy+i3vjTfe0Jo1a7Tnnnum7kskEpo9e7Zuu+02RSIRBXp5C9g222wjSdp11121evVqXXXVVTr11FMlSWPHjt1kGnrNmjWbTE13FwqFFErmdQEAAABFYF1X7vvvSC1NPW/gJuS+/47MNtvLGbflwI6xbOkgKvSxkgrFH/xg0/tXtCv+1/el0oAC+0+Us22FFB/ANHNfcpEH3dYiW1LqNVarqny6ZFAPjJEKOM1rrZU6OryFAkePkUqCsq7rTahXlMskEv4d38qY7XegCgAAbJDVbyKHHXaYFixYoPnz56cue+21l04//XTNnz+/12b0xqy1ikQ2ZOp9/vOf1zPPPJO2zdNPP639998/m/IAAACAwopGem9Gd2MXfyx30cdZRz3Y9jbZFcsHWp1/lYQUe/gTqa9B5WhCieeXKPbYEsnJYe5xSYmUyEFDOhKR2tukSFdWcdS+4CYKt8BhIOA93yNGSJ0d3vdAwpVWrpDCXevf3zwM4i6G7Mg8ACBXjGN8efGjrCakq6urtcsuu6TdV1lZqZEjR6bunzlzppYvX6577rlHknT77bdrq6220tSpUyVJL730km666SZdfPHFqX1ccsklOvjgg/Xzn/9cX/7yl/XYY4/p3//+t1566aVBfXIAAABAPtkVn2W+7arlsl1hOTvsKJNBHIW1Vu7HHw6mPH8yRvEX10odscy2b+xU/IW1Ch6ypeSGB3dsJyCVlw8sP7o3riuvI+3PP/h6ZV2vGZynRqo1RpKRWlu840QjUkWl1NbuNfElL/pk1CiZysrBxagAAIAhJefBbCtXrtSnn25YdMV1Xc2cOVOLFy9WMBjUdtttp+uvv14XXHBBapv9999fDzzwgH7yk5/opz/9qbbbbjs9+OCD2nfffXNdHgAAAJA7pVlGyDWvk7tgvpwdd5EJ9b0mi12zSmprG0Rx/uR2lsoubs7qMXZxs2KLm6X6MgUOnChnixLJxrOfdK6qkroG2dTuSXOzbF390EqecF1vSjrcmZ/9x+PeBHl3nR1STa13UmDN6g0LHg6L4eJh8UkAAFAQxg7ZJaLTtba2qra2Vi0tLaqpqSl2OQAAABimuv/67L77ljcBmq2SUq8pXVXd8zFcV+7cOV7DbjgpqVDsnh5yoweiplQlJ24rxTN8jsrKJBkvriLXyiukysqhmSHsujlfYNAaIzX3EmVjjFRVLcl4DeuaGplQyLtth+6UtBk3QSbDCEsA8DP6a9lLPmfPT7pOVU7fAweF1u52afrSH/nu65nHpasBAACA4cV2dcl9Z74XP1BSKsUG2DCOReW+M1/O5KkyI7fY9DhrVw/DZnRIsccW525/rRk+P7V13lR0PhfPG8pxE6WhnE5JW2vTvy9CIe95T8akOAGZ9ZnRtrraCzqJRKSKCi9T3HFys+gkAADwrWGwegQAAABQGHbVcq8ZLQ28GZ3kunIXvid3+bK0qWvrurJLlwxu335jjOKvNEgtkf63zW7HvX+ots5rcoY7vZzkfE4vV1QMzeloyXsdl5Tmbn+BgNTZrcEdiaRndndbwDD1nJn1TeiuLu9EzFB8LuMZZqIDAAAmpAEAAIBMmbFbZrWQYSbs0kVe03TbyTKOI7tmtdfEG0bc5qDsx71EOAxC4r02BfbawmtgRrok13oN0UDAyysuVGMzEpF1nNTk75BijPd85aCfao3jxXBUVEjhcM8LJoZ6yF237oZ3BMTj3uOCJUMrwmNYBGECAAbDOEbG8ddJVePTRZeH4G9MAAAAQHHY5nX52e+aVXLfXyA3FpVdtSIvxygap0KJf36Sl12b2jIvjiPc6cVmWNdb6DAaKeyUbWeH1NGuIbs8TyIu9bPIZn+stan9mJJSqabO+0BJyYaNKqukQAYzUYmEFBhif6oGyY8GACBTTEgDAAAAGbLrGvO385Zm2QXzpcgweut/Salif1+Ul10HT9lZptZ4TdCkYkY9JIbQNG+P1seaDKapHu6UZGQrK7391NZ5/69a36xNJGQyHSVO5nIbx2tOx2L+ifIwTvr0dnmFTLCk9+0BAEAaGtIAAABABmw8LnW25/cgXWEvqiBYlp67OxSVlCn25PLMFx/MxshymWpXSvhoIjkek+IJqWSI/onlulJZ+YAXODTGyFZWSS3NkjEysrLWejnRqeZyFg3l5LaOsyGTOljiPb+JxIBqzBUzcpR3xXW9+gY5XQ4AGB4ck7ZUgi84PvpVqbsh+tsSAAAAUDg2Hpf7zvwNObf5FI95f81UVUnteW6A50NJqWy7o/iji6SueP/bZ8FsP1LO9vVyJlRK1mc529ZK7W2ydfW+GeTNWjzm5UkPtOHrOFJZmdTWKltdM7jUyljci/voCqfXFwxITqA4k/HBoMzILbxIEgAAMGA0pAEAAIB+2KWLvZzgQnFdbxq7pkZqbS3ccTMVqPD+7260cJ0TUPzZNbKf5afm4EFbSoGoJJ81o5NCIfl07aDMlZZ6CxIOgHFd2dJQZjnR/YnHvMvGurq8RRNds74xnfAa4W6eI1Mqq2Rq6mQCZEUDADBYNKQBAACAPth4XHZdQ+EPbIzU3irV1kotLYU/fh8S7zbLnbdKqg0pcMAEOaMCUqxLboPJWzNaklRqpOKmNfSto11yHNmysqHbl47FvMnk2MCyzI0xssGgTD4XeIysX7Qy+Y6FmLy4EesOLgO7F6Z+hExldc73CwAYXowxXlSVj/itniQa0gAAAEAf7GdLpVgBojp6kmxK19R48R35ngLNREm53HmfetdbIkr88xMlJJkpI2U/zOOijyPKJbdIX4dstLVK8bhsZaVv/wjskzHehPMAG9JSAYbEe4oUSUZ7lFd4k9U5eu7NiFEyFZU52RcAAPD4LGobAAAA8A9rrWzD2mKXIXW0SeVl3uRqMRmj+Ks9T4vntRktKXDo1nmZfs2LcKe0rlE2GpW13utoSEnEh+5CfeFOyThenMdgVVTSjAYAIA+YkAYAAAB609ggRX2SV9wV9vJ9g+UDzvgdLLc5KLswv43n3jgjSiXXJ1+LTLiu1NLsXa+tk+2+EJ6RTHD9n2JWXqPd9VkWyVBroncXjXi50iUlA/88HEemtj63dQEAhjXHMXIcf707yrH+qieJhjQAAADQCxvpKnYJ6aJRL4qgusaLhiikkgol/vlBYY/ZjW1LyFQH/Ne4zUSyMV1aKsXjUkWFbFfXhgiWQFBmxAh/RLIkWVcqL97Jj0FzXXmdf+N9LtkqKWUBQwAA8oTIDgAAAKA3He3FrmBT1kod6xc7LBRjFHv6s8Idrwfx+99R4i1/Le6YtXjCi8Lo7ExvPifiKkDycvbicW/SeKiKdA38BAbNaAAA8mYI/3YBAAAA5I91Xdmm4sRT9Cu52GGBmtI2XCqtKn5z3n31MylQ5BztgSop8S69TN3btauloA/fwDpUs6STYjFJJvvojqHciAcAFIUxkjHGZ5diPys9419ZAAAAoCfxuJTweTxEe6tUXZXfac5gieL/Wpy//Wcr5tO/rPqSbOrGor1uYrYY473m/CYWLf5imoMVjXiLHGbRlDa5WBQRAAD0iIY0AAAA0ANTWiqVhopdRv86O6RQqRTKT63uZ3Gpyx+NUmfvLaWgP2rJWEWlFI/1f3IjuQiS3xqhxkgBH05uZyvSJQUzb6zbyBDNzgYAYAigIQ0AAAD0wtl5WrFLyEykS7IJqbIyt/sNBJR4eVlu9zkIZqsafy3815+qKincmdFkrm1skI1GZZvW+a8BnIgP/eiObEUisn6cWAcA+JbjGF9e/IiGNAAAANCbWKzYFWQukZC6OqWampzt0naVSFH/NIATD7/vv2Ztb6qqpY4OZRzeGI9Lba1SPCbb2ZHf2gYkywxmP8o0ssMYqbIqv7UAALAZGyK/zQEAAACFZ8OdxS4he8nFDltaBr2rxPyGHBSUQ2OqvGldPzPGi+nozKIZvbH2Nm/a3U8Tuq4rlZVLXUM0yqI0JLn9xKaEymQqq6TychnD7BYAAPlCQxoAAADogbVWdtWKYpeRPWM2LHbYEe6/CdebkjLZjz/NbW2DtbpdckKSGyl2JT1zHK9pOxRPZGQiEfc+x6EUm5IUCGx6MsMYqazca0KXlMrkc3FQAMCwZxzv4ifGp29woiENAAAA9MR1pY72YlcxcJ0dXu6v60qR7Bu4bqsP/4JxJBmfNkNLSrymZw4miM3IUf0vglgsobKh33AvK5eprpGCJTShAQAoAhrSAAAAgCQbj8l9/x2vSTVqC9nGtcUuafCiXV6TtLIq6+Z64rVVeSpq4Mw2I6WED3O9k43/aDQnu7Pt7TK1tf5sSseiUrBEivvw69CX9VPdpqZWqq6VGWicCgAAGDQa0gAAANjsWdeVO/e/qUXPbFNjkSvKoURCirdnlytdUiGt8FlchyRn1y2KXcKmKiq8CfRMF8zLRKRLttXIVFXldr+5YIy3sOQQbEibUVvIlFcUuxIAwDDlGCPHZyc8/VZPks+STQAAAIAiaGr0X+Mvl5K50jU1GS20l/igtQBFZSFgpDFVcsaUFruSdFXVUjicn9dOV1i2Ye3AF0bMp0RcCoWKXUXmSkplxoyjGQ0AgE8wIQ0AAIDNmrVW7sohuHjhQHS0SZUVUldEisd73iYYlDvvs8LW1Zego+DxU2RGlXhxEX5RVe3ldOerYVxe4eUcb7wQH7JTVi4zdpyMQ1Y0AAB+QUMaAAAAmzW7ZrXU2lzsMgon3CmVlHo5wD0swGfbA1LMPwsHOtuNkimPSjEjBYO9N9ILxRipvNJrRufzMFVV/m5Gu65UVp6TRRzzyVRV04wGABSEcYyM4693Nhnrr3qSaEgDAABgs2bXrCx2CYUXi0rGkaqqpPZuix0ao/i/Fhevrm7M+Fo5U0fIjF9/RzQiOeXeIo3FWuwvEJRKS6SuzrwfyjasldlitD8XNkxyE5LjpBYM7FFtXeqPc9vcItkCn+wIlRX2eAAAoF9kSAMAAGCz5Taskdp8lpdcKNaVOtcvdihJxiix1JW6/DGVGzhgCzkTNpo06gp7jXSnCH/GlJZKAcdbwLAQrPpu9PqBtX03fMvKZWS9z8N1ZZKvtUCwMNnYxnhfNwAA4CtMSAMAAGCzZdesKnYJxWWM1NYi1dbK/ahV7svLil2RJMlsO0oqS0jqoWnZ2SFVVkk2VriFKEtKJNd6E8EFYsaMKX48SSZi0Z6jVIyRKS9Lb6pbV6a+3rvPOLLNTfmtLVQm48dFIQEAw5IxRo7P/t3x67+DTEgDAABgs2RdV2ppLnYZxWeM3M/CSvxnabEr8ThGgT3q+v4DqqNdKg0Vph5jvIneAjajhxRjvIb9RveZuvqeJ7yT9yWb0xs/NpfKiOsAAMCPaEgDAABg8xTpKnYF/lBSpsQTHxe7Ck/QUeDYbWUqMoiqaGuVysvzX1NlpZdfXWB27Rpv8ngoiMXSThB4zegMGviu6y3eWF2d+5pKSmRqanO/XwAAMGhD5DccAAAAILdsY0PhIh98zF1W+GZrb8zWI+TUZfE1aWmR6uq9GI98qKqSOjoKk3e8MdeVbWyQqa0r/LGzZUzqknEzOsl1ZRxHtqZWam/LTW52WbnMFqNlgnmcvgYAYCPG2WjtCx8w1l/1JDEhDQAAgM2TP38/LyxjlHhjZbGrSLFr2mWz7Uc2N0kVlbkvpqxc6ugsTjM6KR6XAoHiHT8bbsI7OTDAaBNj5E00l1cMrg7HkRk7XqaExQwBAPArGtIAAACbKRuPy27GE8J2zepil1B8gTKpyT8T0uqKSRrAazLXTelkZrQPTlrYtjbJDIE/2yoqZAabs21dmdLSwUWxWMk4Q+D5AgBgM0ZkBwAAwGbIWlfu229LsnImTpRGjZIZCk2vHLGNDVK4s9hlFJ3bGC92CemiCdlGR2aLATalcxXfUVrqn4zxcKdsV1hm1Ba5ibPIh2Awh717KxMqkw2HB/bwyjxMywMAkAHj+O8csvHp7InPniYAAAAURFOztyhcW5vc996T+9prcj9bJhv3WYMyD6y1cj9dXOwyfMH9qKnYJaQLBWUGs75dLialq6r904xOstaL7/ApU1aW2x1aV6aubmC15CO+BQAA5BQT0gAAAJsh29aWfkdXl+wnn8guWSIzfrzMlhNkQqHiFJdv0aj/Go5FYj9aV+wSJK1fzHDHeplKyZQNcpSnuUmqrRvYBHxlldTRXtzc6F7YpnUyY8ZKrh1wTnM+mKrq/NRjrUxtnWxba+aT4cESqWKQGdQAACDvaEgDAABsZmwkIruklwnhREJ22TLZzz6TGT1aZsJEmaqqwhaYR9Z15b6/wL/RB4VUEpISxX8fZ+DgreVs7WhA2dG9aW2RqmukrixiH8rKvCa2D5vRSXb1Km/RvlGjpYQPJqbLK/LcHLcytbWycVeKRaSuvk8kmbp6GWeILAIJABh2jPHfrxF+qyeJhjQAAMDmpiToLRrWV0artbKrV8uuXi3V1snU18nU1km1tTJ+/c02E+sac5MxPAzYqA/S+ypDMlvl4fVkrTfpXFEhRSJeJnQg2PvUdDA4dE5SuK5sU6NUGsp9VEY2SksL80eu63p5nKGQd9LAGC9aaON3eVRWeSchAACA79GQBgAA2MzYjz7quxm9sZZm2ZZmb361vl7OlB2K2wgbIGut3NUri12Gb9hVxY8tcXYbI+PkqauZSHgTtdU1Gyafk/nQsVi3DY1U4qNFDDMRi3mXyqriTEoHgzIlJV7jv5Cs9aI8HEe2plbqXH+CIRSSqa4e2ifLAADYjNCQBgAA2IxYa2WbBrGQXVOT3Lmvyeywg5zRY3JXWAHYlZ9JLT5bxK9YjKPEy8uKc+y6cskYOduPkLNdnuMVYjGpvc3LFk42cUtDUqjMa1jHY971ITo1bxsbZEaO9D6XQnEcmVCZZIs7UW6MpMoKyQl4N8pZzBAAUFzGmPydaB8g4/qrniQfvE8PAAAABWNdL8JgMFxXdtEi723zQ4SNx2U/XVLsMnzDJkJSV3G+fqa8VMEZ4+XsUFqYidZo1Ms5dtb/6RONeA3oSJeUcIdsM1qSNx1dyCllY2QqKovejE5npdp6pqMBABikWbNmae+991Z1dbVGjx6tr3zlK1q4cGHaNtZaXXXVVRo/frzKy8s1ffp0vfvuu1kfi4Y0AADA5sR1vfzowYpE5H7wvqyvGlO9syuXD52M4HwzRonXVhft8HZNm2Tl5QIXSlfX+jUTN25aFn9Rx0ErYEPaVFbleRHDAQiW0IwGACAHXnjhBV144YWaM2eOnnnmGcXjcR1xxBHq6Nhw8v6GG27QL3/5S912222aO3euxo4dq8MPP1xtG6/t0A8iOwAAADYnVhvl5w5CY6PsokUy222fm/3liY3FZNesKnYZPlIu+/GSoh3d2Wm0TEURTg6EO73M5Xh80770EGYb1sqMHpv3LGlTVe2/ZrTkRa4AAOADxinwCfcMZFPPk08+mXb7zjvv1OjRo/XGG2/o4IMPlrVWN998s3784x/rhBNOkCTdfffdGjNmjO677z5dcMEFGR/LZ08TAAAA8irSldOJSrtihWw0mrP95YNtXDu0FqzLs/grRW7OlxVxJqajXSotLfxifHlm16ySAnl8Xqt8OBmdFCwpdgUAAAxLLS0tkqQRI0ZIkhYvXqxVq1bpiCOOSG0TCoV0yCGH6JVXXslq3zSkAQAANiOmqlpm8uTc7dB1ZT8r0uJ4mWptKXYFvhI8aJQCM7aWirTojrNVkSda21q9qdph2ZTOwyKRFZUyfo27CQRkHP6kBQCgP62trWmXSD9rylhrddlll+nAAw/ULrvsIklatcobahgzJn1h8zFjxqQ+lin+9QYAANjMmPKKnO7PLl8um6sYkDywba3FLsFf4lE5Y61KzpqswKGT0v8i2LI674e3jT5YDLO1RSrLQZa67+T4JEN5hcxwyNkGAKAAjPHnRZImTpyo2tra1GXWrFl9fi4XXXSR3n77bd1///09fJ7pv29Ya7Nez4EMaQAAgM2EbWiQbW2VXbE8tztePyVtttk2t/vNETNylOyKz4pdhv/EInLGS85ZU+SuTsgZWSoFYordld2iNFnzy7BtS7NUWzes4lzsukaZ2trc7Kw0JOOYYTdJDgDA5mjZsmWqqalJ3Q6FQr1ue/HFF+sf//iHZs+erQkTJqTuHzt2rCRvUnrcuHGp+9esWbPJ1HR/mJAGAADYTNh1jbLLPpUSuc+CtcuXy8Z9MPnaE59nXG/MjRe4YxuLyBkRl2ynZPIQ+dBdXbnMRB/9CdLSLIWG0aR0MIdfP5rRAAAMGzU1NWmXnhrS1lpddNFFeuSRR/Tss89qm222Sfv4Nttso7Fjx+qZZ55J3ReNRvXCCy9o//33z6oeH/02CAAAgHyw1spdtUo2y2y3rCQSssv9N4VsW1tk1zUUu4yMJaKu3vzL2uIVEOtS8OQdpEB+8qWdz43Ne887ay1NXqb0cNDVJdvZmZss6a4hMDlOvxwA4CeO8eclQxdeeKHuvfde3XfffaqurtaqVau0atUqhcNhSV5Ux6WXXqrrrrtOjz76qN555x2dffbZqqio0GmnnZbdU5XV1gAAABh61q2TXfhB3qcd7fLlsnmYvh4o2xWW++5bkl8XZOvBktfCWvnfVWpYUrxMblPSpZIzpuZ+x2UlcibnNr88Z1pbpNLe37o6pIQ7Zdeuyc2+HL+dPQAAAPlyxx13qKWlRdOnT9e4ceNSlwcffDC1zRVXXKFLL71U3/nOd7TXXntp+fLlevrpp1Vdnd06JGRIAwAADHO2o6MwB4rFZFetlNlyQv/bFoBtaR5ykQMfPbpUTe+s1T/PeEFTz9lZ+5w7rv8H5YHbmPuGuLP1CJl4RCovl9ZP2viGtVJbq1RbOzQmg/tjrbeK0WBe/8ESyfr8ZE4uJsEBAIAk712V/THG6KqrrtJVV101qGMxIQ0AADDMmZEjC3Ysu2yZbJEnkm08JnfZUtnVK6W6+qLWko32da7Wzl2Zuv3Bne/qs7eK0xx1RlkFT5mqwIytpRG5yVg2O61fSMdxpJKSnOwzp6yVmpuHT3zHYM/FxGOS4c9FAAAyZYw/L37EbxgAAADDnLt4UeEOFonkLi5ggNyli2WbGr0b8bhUN8LXcQzJaZRlr7XJuuldxE/+tbKnh+RfPC4TDMsZa1XypUkKHLGNAtMnSWUDfINlKCiT7PN2haU+VnYvuo4OqaS02FUMXk4WOPT5OwxisYymuQAAgL8Q2QEAADDMOZMmyW1sLNjx7LJlsqPHyBRrJCO2UdxEPOY1pMsrvAXsfObdfzRr1RsNijZHNvlY5+pOffB0i8rqSrT1PkXKX46H5Yz2rjqnbqfEwg65HzRK6zKM3agrV2DPsTKhbo1Dx8dzMfGYFJZUUSFFuvw7WtQP27ROpqau99gNY7zIi0Sij2gPv3/u1vt6DYcTCAAAbEZoSAMAAAxzprpGZsIE2c8+K8wBOzpk16yRRo8uaFPatrVKwWAvixhaKRGX6kdI7e1SLFqwuvqybllcb970Zq8NwbVzV2rt3JXa+suTtfU+kwpcXQ9iXQpsG1Bg+3GKPb1GWtXe70OcnUfL2WqjaV3X51Ot8ZjU0e7lXcfjxa5mYKJR2YY1UmmpTE2t9xpznPUNduN9v4Q7vYULy8pkqqvTm9PGyPcT0pL3OfikIW2tlWJRGR+/IwMAkD/G8V/ald/qSaIhDQAAsBkw9SMK15CWZD94X3bpEpnRo2W2GC1TWZnf40Wjcj/7tP9GcywmlZV509KtzXmtKRNLXliX0cJzLR83q2HJeI3a2ifZy25CgWlbKJFBQ9qMLNUmjc2uzvzUlUuVVVJ006n1ISca9ZrORrJrVm/6enMTUmeHbGeHZIxM/Qhvctpxejm54zOxqKx1ZYr8F7ftCsuua/Bu1NTKVNUUtR4AAPzMp31yAAAA5JKNFKGxFg7LLl0q9/W5Srw+V+7SpbLhDGMesmRXfpb51LO1XhOufqQULF6D11qrlXNWZ7Rt07tr9epNH+S5ouw4470pULNVjbRFzycczKQRMj315VxXCvh8Nqa1ZdgscGib18k2NvR/8sNa2XWNsg1rve/VoRJXUuQTB9Zab+I8ebu1RbatlXxrAAB64fPfAgEAAJATxWhId9fRIduxWHbJYqmqav3k9BYyZeWD2q11Xa+B1tKc/YNjUS+SQRVSW8ug6hgIY4wO+/nO+vspLYo2dfW7fdXE6gJUlYV4WCVn7iiZiBf1EBgrmRLZiGSbY7KrO2VGlcuU9NKUcxOFrXcgolGptFSKxXvPYh4KollG1FjrNeRra/NTT67FE1IxUzK6wpucELNtLTKyslU1xcvTBwAUlHGMjOOvn/l+qyeJCWkAAIDNQZ4mkwekvV120SK5r76qxJtvyF22TLar/4bsxmwiIXfRR7Irlg28lu7T0kWY2C2rdLTrBbtktO12R47NczUD4HZ6zWjJy+iOh2UCYTkj4wrsVCpn614mjCurMooqKbpwp9Ta6jWlN0dDpZFaxEx467q9nhCzba1SZ/+xNgAAbG6YkAYAANgclPgke3hjbW2ybW2yiz6RqmtkRm8hM2pUZpPTjpObKVtjvIZWZaW32F57a/+PyaHmRX03rIKVJZp07HaqGjvEfnWvqZPCHb18cIg0OpNamqW6OmkAJ04KznG8KJocxFi4a9d63485KCuvEnFZ15VxCjtv5b1Do6Hvn0N+j6YBAKAI+NcRAABgmLMdHbLLC7eg4YC1tXq5q5984sV6jBolM2qUVFG5yVvebWuLbCy2yVp5g+K63tRu/UivAVmgSIlo24bpzprJI1S/Q71C9SFNOmik4hFXY6eGVFI+xN7YaBxvYrrXj/u+xbkpdwhMdDuOFAx6zejKKqljkNO54U7Z5maZurqclJdX4Q7ZiqqcxmNYa6VIl/d6dl3ZSNhbGDUYlAkGvYUgE/38nBgK0TQAgJwwxn+/4vitniQa0gAAAMOYjUTkvrOg2GVkr71dtr1ddskSqbw81Zy2ktS8Tra5KT/HTU5LV1V5jabBNvT6sfK9iJb9a1Hq9o6nba8dDu9pFcAhprrai7voTV/Nar+KRb0TFvn8yy4Y9BZS7Ozwmsvdm52BoPe8OQEpGOg5F7q8YsNrtqPd20f3+zJVWeXlIicSUmenNx3ud13rY4kqc5e1bttae37HRCya+bkwdwhnjwMAkCeDGrWYNWuWjDG69NJLe93mkUce0eGHH64ttthCNTU1+vznP6+nnnoqbZu77rpLxphNLl1D4S1xAAAAPuYu/GBoxAz0JRyWXbZM7rJPZZd8kr9mdHfJJlL9SK+plyfvP7Qh/3rcIVtpwl6VeTtWQZRXSDW1G5qDvekKe9sNBaUhqaraawTne8zIdb3msbVSWbnXGC4rW9+cjnu3Q6VeMzoQ2LBNIOA9vrfFSysq1y/g2TdTXSNTXi4Ti8pUVnl3JuL+Ha/aWFdYNkd50jYWy1F8zxB57gAAKKABT0jPnTtXv/vd7zRt2rQ+t5s9e7YOP/xwXXfddaqrq9Odd96p4447Tq+++qp233331HY1NTVauHBh2mPLynpZBAUAAAD9sh0dUktLscvIjVCo8G99T05LV9d6/+/sLQ95YDpbEvrsmSWp2yN3rldlfSCnxyi4kpK+J6O762jLTaxEvpWXe7EN+eYE0l/jPT0v3e9LJKTE+sZ/SYnXOI/HvOZz8rWabHAnlZZKJaWb7ru01Iu6SH6egaBsa4v37gRjZGMxmeAQeXNtV5f3OQ6CdV3Z5sbc1OPQkAaAzYajQY7+5oHf6llvQL9VtLe36/TTT9fvf/97XXvttX1ue/PNN6fdvu666/TYY4/p8ccfT2tIG2M0dqwPVw4HAAAYgqy1ct9/b/i8XTwS8bJbR9RLiVhhj52cEK0fITU3SzY3z2l5jaN9r9pHo6aUq6vVVdWoId6MNkaKZxnFUeBF6AakIPEixjvpkmkzf2OxmHcpr+h7u2jUu5SXe9vH41JJibeIaPepdut6Oe7G8ZrUjQ3S2PE5e+3n18Czvq3relPWHe3e85MTNKQBANjYgH4DvPDCC3XMMcdoxowZWT/WdV21tbVpxIgRafe3t7dr0qRJmjBhgo499ljNmzevz/1EIhG1tramXQAAALBeY6PUkduJ3qJzXSlRpIaYMV6DqqbGm0DNyS6NdphRo5FblWjLXUKqHTtEJlB7U7N+kjxT1vYf7VFs5eXZN9kz3neFNyFeUellQg+0Gd1duDOzSX7HkSkp9SI6jNn06+C6MtFo2mS4bW7yZ3SHE5A1jmwiIRuNegujZjDRbhMJ2c522bYWuS1NchvXyq5eIdu8LrvXcX/H6S1GBQCAzVjWv/U+8MADevPNNzV37twBHfAXv/iFOjo6dNJJJ6Xumzp1qu666y7tuuuuam1t1S233KIDDjhAb731liZPntzjfmbNmqWrr756QDUAAAAMZ9ZauatXFbuM/LC2uAOHicSGaemmdUUsxIdslpOpA5moLrRIxIu5yIWKSu9z7uz0cqBz0YAeiJJSGRnvnQbZTH93hSXVedeNIxv3JqxNKJSPKjNmEwkpmt70tY1rvRMkFVUy3abwrbVSNCrb2V6w598QQwkAmw3jGBmfRTX5rZ6krBrSy5Yt0yWXXKKnn356QPnO999/v6666io99thjGj16dOr+/fbbT/vtt1/q9gEHHKA99thDv/71r3Xrrbf2uK+ZM2fqsssuS91ubW3VxIkTs64JAABg2GlpkRoail1F7pSWSjXVXiM6XuC4jp4kp6Xr6r0m3VBfNHIwjPEaf4mEN5mb7QRtdY3U6uOcc9eVQmW5yZA2xstuNqa4r5lgYNATwLa1xft6h8q8qJEisY4jhXuesretLVJ7m2ywZEM0TDxe2J8hJaXewpMAACBNVg3pN954Q2vWrNGee+6Zui+RSGj27Nm67bbbFIlEFAj0nH334IMP6txzz9VDDz3Ub9SH4zjae++99dFHH/W6TSgUUqjIZ+MBAAD8yK4aRtPRxkjVVYXPjc5EPC4FS6S6Cql5M52WLindEBExkDiH9lYvBsXP8XsdHVJZaGDT3JVV3kR0WdmGJnC2k+Q5ZkpKNyyGmNUDjSQj232Bz0iXbDzu7bMI+dLGOH0nRrvuJtPThWRq67xIFAAAkCarhvRhhx2mBQsWpN13zjnnaOrUqfrhD3/YazP6/vvv1ze+8Q3df//9OuaYY/o9jrVW8+fP16677ppNeQAAAJCk6mppuER2VFdLrs9jHeIxqW6E16QrYvOrKMrLM8ss7k1yanjjRfX8JB6TVCaFyqWuzswb75VV3ucmFS+eY2OlpQN/jVoru2rFpvc3NnhN4VGjZQLpSxS56zpl13XK2aJSpjYPk8JFbu73yQl4J2wAAJsNY/y33ILf6knKqiFdXV2tXXbZJe2+yspKjRw5MnX/zJkztXz5ct1zzz2SvGb0mWeeqVtuuUX77befVq2f2CkvL1dtba0k6eqrr9Z+++2nyZMnq7W1Vbfeeqvmz5+v22+/fdCfIAAAwObGjBsn+8nH/m7WZCoSkSqGwLvi4jGpNOQtVNfSVOxqCqO8YnDN6O6CPl/Qsb3N+39NnRTNIG6jezPaR0w+G/8Na6Sx4yXryv20SYn5n0oN3vPmSjI7jFdgn0lyF6yUmTRCzsiKQR/SuolB7yNv3IQ3VV9SUuxKAADwHaf/TbKzcuVKffrpp6nbv/3tbxWPx3XhhRdq3Lhxqcsll1yS2qa5uVnnn3++dtxxRx1xxBFavny5Zs+erX322SfX5QEAAAw71nW9xbq0fkHD998b+s1ox5FGjpCqhlL+qvUWiasfsXlMRuZyQcJwhzdt7XetzX1/bcvKvcULfdiMHtR0dIZsdH0sScxNNaNTH1u4QvH7XpU7f4kSz3+Q+pk1cMY7YeVjtmG1XD9npAMAUCTGDv43AV9obW1VbW2tWlpaVFNTU+xyAAAACsK2t8ud96ZUWiqzxWjZcFhqWFvssgZvRL1kfTz92B9jJON4DczhqqzcmwzP1XtBjZHKK6U2n+ZJOwGvaZ7opRFvjDcNGx3cgoF54QRkysu8xTjzzFbXSqvDSrz6sdTZ+3MROGSqnO1GDfxATkDWL1Eo/TB1I2QqKotdBgBkhP5a9pLP2UeH36rqEn+dYG+LhTX5me/67uuZ8wlpAAAAFI5du9ZbuKurS3bZp8OjGV1WJvk07y5j1npv2a8f6S18OFyEQl4cRW2d97rLZTChtV7WcrV//lhKU13jNaON8ZrTSclGYyBQkIbvQJjKyoLVZtpaZCqiCkyfKNX2HsuReG2RbGzgJ51sYuicsLItzbJxf742AAAoBhrSAAAAQ9lwXESvqtJr5g4Hsag3VVtTW+xKBscYrwltrfea6+yQrJv741hX6miTqqq95rdf8neDJV5todD6vGvrTYiHyrznonz9dR+++dTU1kqRDHKvc82V5PRxwiIclftxw8D3P5ROWllXtmmd3CHURAcAIJ98vnoIAAAAemPDYdn1C0YPG6EhsIBhtqz1LvUjpdaW3iMf/Mo4XoM4VwsY9ns842VKS1JFlT+mjuMx72tXXr6h6Rzp2nA9nKeFAgfJVFUXrTYTMArsN1aJ2Z9JHT2fOHM/WClnh9EyfTWuhwHrutLK5dKKz+SO8N414YzbsthlAQByzBgjk8t3j+WA3+pJoiENAAAwxNhEQvaTj2UbBjFd6FfVlVLCBw3IfIhFpcpKb9K2ra3/7f2iutqL0iiGznYvCsMPk6WVVeknE3w4DZ2mNFT8Zr4rrxldXiqFN82TdsZWy0aiMhXl2U3cGyN1FWHqO0s2HvcyxVuaU68du3K5JMmNx+VMnFTE6gAAKB4a0gAAAEOIjcXkLnh7aDU0MzVy5PBtRie57oZp6ZZmf0eTGEeqqvJqLpbSkJfX3NFevBokqbZWigyheBzHkSkJFr8hLck5aFuZUqPE6yulFu/EhvO5ibIdUdmYK6e1SbYkKBMM9LOnDazxd/Kktdb7vlm1ovdtVn4mNxhkUhoAsFmiIQ0AADCE2BXLh2czWpI3TrkZMMablq6ukhKuN0GZXBivpckfk7e1dVJnp9RV5CiKkhKptbV4x3fWx5X4oLGbDVNVXfyvnSQTNDI13luFA7uPk+1KyDaGZcaWytjSDd/yTeukLUZLyuy1b2Qz3LJImpu8mJd+2GVLZOvqZcp7X/wRADB0GMe7+Inf6knyaVkAAADYmG1slF2ypNhl5MfIEf6eFs6HxPpuXGmp17yKx6S6EVJJaXHqKV1/3FRetA9afh3tUkVF4RaFDIXWL6hY5kV01NR6izgWc0o8S6am1hfN6I2ZSiNnZFCBKdUyAeM1q0vX51q6CdnVK72s5UxYSQGfz1ZlOFFv24frCUYAAHpHQxoAAGCIcBd9UuwS8meYL2qWsVjUa4QWWk2tN5ldVu4t1ucnkS6vKewU4E+XsjJvKrukxMv8HWILUPplMnrA/n/2/js+kvS+730/T1XnhIwBJs/szs7m5QaSyyQxSqJISrairWBLliVfm8cmLQcd2keW6KsrXt9zz1UwHUT5WLy0jkhaVqAoWWISc9rl7s7mnZ2dGUzEIAONzt31/M4fBcwAM0gNdHdVA7/369WvGQDdVQ8a3dXd3/o9v2dubmulXGIhGvEfr1thjH8JIZmaDHoISimlVMdpIK2UUkop1SXMocNBD6F9wtCmIiwadb/HdCzewX0uBa9hDWDF+tXLudzq70ci/vda0fLAcaDe8BdwXG630E2tOhJJ/4RGN2vUkWtXkcmJ1ceEm0JqOz6FfeEMRmTDSml7bRp7eQL7/MvIzDzg+G1CwjR/udZFvcmVUkptzAnpJYRCPs9JKaWUUkotM/v2IefP+T2Hd4tsFiKOHwCGtIIxEPWaX61raP9iepnsUhgb4sdVowE0/GrpTMYfr+eBu7TgYa7Xv78Ki82FyMkUxGP+9gT//i6V2vM7tFMkgjEG7C45sSPih9KZrH9iZnYaiSZw+vvwrkzAtQkAvOdfxvRkMUP9gPhBc60BUcf/m07P3tjk1Qnkqn87MzqMGegN4Be7lRkeCXoISimlVMdpIK2UUkop1SWMMTiPvg775BNQKAQ9nNaIx8DTMHpN1vphnBtZ6uncJtWqH8TGE34f6VLRD3vDyBi/gnmlRPLG9yIRv+XJ/NzWtlerQjLRnSH0MmMwieTurLQtLAKLCC5y/hLe+Uurf16pIJUKMjGFc88d2ItXYLGAc+8d/vPHcW70/3ZdzMgQpFKYZCw0fcG1h7RSSqm9KKSF20oppZRSai3GGJyjx4IeRmu4rh9Gq/WJ+CFsT2/79hFfag1Sr/nVxgJkclvvzxskY1a3bFi+v7bKcbq+qthkc7szjF4WjSOXr216Nfv8y5BfhKHBG+0+VrT9cO4+genLYeKR9ofRzTwG52eRxXz7xqKUUqpjlpcsCNsljDSQVkoppZTqNtmMXwna7ZZbLqiNGeO3rOgbaM/2C4uQTK/4hkCpsFQ9nPLbJoT100yud/thrOP4FeFdvAigyfV09fg35brYKxPN3aZUQip1ZGZhVSAtM1usmg+AvTSGaB99pZRSe4gG0koppZRS3caNhLelQrNE345uiTF+BXNff2vC4UgEevv8x1ImA9V1Qs1qxW/hEYlCNuf3aQ6LVHrtViblEvT0rX2bTBZyPf7vnkx29/MondndYTQgxRrUmpxFUSgir5xHxlcH2XJ1wq/+D6PCInL2NNLufvFKKaVUSOyC0hqllFJKqb3Dzk4j49dWVf51tUIBkrHwVuCGTb3ut+8oFppbvG9ZMukv/OY4fnCbSvmh82b3v9fw9wl+K49IFCqlpcUGA+C6G4eLtYofOt/cS9qY3dHeIh7HBHXfd0okjkxfbukmpVbHxKMt3WaryOwMMj/nnzRxI5hMBjM0gtkNs2GUUmqPMI7BOOF6Txu28SzTVzellFJKqS4gi3nstSt+ewVrob8XFovbCyXDJJUEwrG4WNdoNPxWGtFGc4sdptP+48VYWC4MrlWbPxlQq/oXEb9KF/xxdPIkSSa78UKEIn7QnsmCY/wQ3vP8YL3buS7GjUCjy5/7G4nGsGNXWrvNWBSTiIX7ZJ61kF8AQOZmkMkJnJP3YMI0M0EppZRqAZ0jqZRSSikVYiKCWM9f9Cq/cGMxrloFklHo7Ql2gDthDLj6dnRbrPWrnNdrTXGzVNoPZVtZiW6MX2VdLvkVy5mcH5S3Wya3cRi9TMQPbWs1P5xu1Nu/mF3bGUwqtbvDaMfBTsy0PDh2jh+5vk2ZnsdenfSfE2FWrWBffAaZmw16JEoppVRLaYW0UkoppVSI2Zdf8PvErtXr1lqwVRjog/w2WzgEJRqFnhx4XTTmMPKWFjucn10/wOvp9UPjdrZFsdZfCBEgFod43H88trrHcTS6u8PYTZhcbvf3ja40oFRp/YajEZClExIRF67NY0tlnDuOtf5ERSufa/U69syLmAOHcQ4cat12lVJKtZ4TwnOdYRvPkpAOSymllFJqb5P5OeyFc36P5UZj42rBahkSEejv86tmu4GG0a1Tr/mV0pE1euMmEn6A2cke3Y2632+6VoV4wl8MMRZrzbYTqe5eiHAH9kIYTTSOTM60ZdP2yjWkUgdPkMWlkyeVKvbZ0y3tGiQiUGyilc5Wt3vlInbsLFIpI7UqskGILiKI10BKRaRe82fZVKv+2JRSSqkQ0ApppZRSSqmQkUYDe2msucXXRKBWhkwSnCjMz7dreDuTSEAmDY2aLmTYSo06pFNQq/vV0HCjv3OQFcX1mn8R8dt5uJHtL4aY673xu+01qTRU2lA1HCaRCPbStfZtf3YemZ2/dS1MEewLL+PcfceOy7Wk0YCFebDtOWkik9eQyaX7KJ3B9A9i+gcw8YQfUC/mkZkpZGFu7RkzS611jOv6Jy8d12+bdP3/LiYS9Y/T8YR/PaWUUqoNNJBWSimllAoRqfg9Q7c9hdx6/qUvB3XrV1gHKRJZHT6m035ltIbRredZP3DK9dzoN96mYKxpxvh9nMEPp1NpPwQrFbf2WI8ndn918HpiMUxY/o5tZBe2eaKiVft/6RWck7eBu+LYtDzjZKvH42KhcydNigWkWEAujfnzw2ULY/Q8WJi7NZRfYdXPIlGIxzHxhN+GJxaHeg3TNwDJFKZbZuQopVSHGBO+t7hhG88yDaSVUkoppUJCvAZ27JXW9DNdrkrt7wUrUK35FZadmrIdj0Mu41fnOi6IgWoVQvqmeFfxPBgchsWFoEeyNmNWh8vpLGA3bnMQjfqPn73GcTHRaHf1h98GMS7MBfx4tRb74hno7cHZvw8pV5DzF8ExmMMHMdn0raGvcQC5cVyNJoB8p0e+tTB6Oxp1aNSR4uoTm3L1MgDm+AmcweH27FsppdSupoG0UkoppVQIiLXYsy/7FXatYgzUlqpSXSAVh2jMr6IVoOH5IV+7gr76UluO5erOmKt9ozvBGNiwBjJkyktB9PJiiJWK/9hZtodbdZh0+kZl+W4VjSPnLwc9ihvmF7DzK8JxK8jYJQRw7j3J8nNLZgrImUsw2Itz+ygYB3nmPOb2Yai0vod0KC3m/ZNfSimlVJM0kFZKKaWUCpiIIJcv+B/u28ng925eOSs+avyqvkhsqZIZvxqzXN7+4nGx2I3q6D1AxAE8TJjmRHZjRe1SNSYikEjeqIpuppf6LmJyPbu/TUk0hh27EvQotsyOT+Ic2IeMzyHnr/rfLC79jWSpZU6x7p8A3ANkdho5elu4jn1KKRUkx/iXMAnbeJZoIK2UUkopFSAp5LFnzwQf3jZqq79ORCCSBDe6VE3dWKpc3WScqZRfCR3079MBIgI1Fzk3gTkwCLmgR7QknoBCAG0DWsUYP4ReDqITSf938rzdXy28xGSyuz+Mdl3s1anOtRFqhZk5bLkOkysqqMvVpdYdgFjk2jzmUKY1rZfCzvNgfg76+oMeiVJKqS6jgbRSSimlVEBEBHttPJzhrTHgNfzLspgD2R5oCOTXCTytZbeVB4oYqDsQFb8XtgAesFhFJqf961yZhvgIJh6Cxed22wJ4lfKNcDYW98PpamX7FfxhF0+sblmyGxmDzZf93vbdZo2TcvbbLy61J1oK1x13bwTSgBQLGA2klVJKNUkDaaWUUkqpoCzM+ZdusVy5CtCbBVw/mF4OXnp6wOmiasctEHGQszNQb2x8RWOg1kBiDsYEeB8kkuFdzLAVrldOG0hn/DY05V1USey6GNfxZyTsYiIm+EUMt6tehf4eWFgEb+nYd3P43M6qb8cN1UknKbVw3QOllOpyxrkxaSYswjaeZRpIK6WUUkp1mMzNYmenYX426KFsX6MO1CGdgGjcD2YiKyoEd4v5+uZhNEAqAZmAw2hYXdG+qwkUF/3/RiKQTPtBdZcHuSalixh2hVrBf9x561R4RyLteS6aFN7LM7gnM+GZIbCYR2o1TCwW9EiUUkp1kZDm5EoppZRSu5PU69iL57o7jF5JLDSqfiC7y6aoixhkYosV7MUycnnRr/wMinGgVApu/0FpNPyq8GoFEglIptqzn0gCiEIs2ZbNm1zP7g+jI1HsxatBj6J56Zx/SeUgnQUb3bjdiGlD3ZeTwvvOZVgoI5Vo67e/XZ6HPf08UlgMeiRKKaW6iFZIK6WUUkp1kIlGIZP1F4LaDRJJP4heuQCb60I0CrX6TVPLDX4D5i5Rd5qbel8oA9m2DWdzQlfdv+1QXGofEIn6C2xWWtRrOp5Ezo7f+DqTxAxngRVtbFaKxfz9bnHfe2IRQ2OwMws32lx0k7oHs80sFLrO89AY//jYbCW/m8J77EZVub2axz0Soorkcgn74nM4d9yF6ekNejRKKRUYY/xLmIRtPMu0QloppZRSqtN2U1uLShncFTUOiQTUarCY93utRiJ+aB2Ngm34i9J1CZlrsto4mwq2ZUc0RFWTQWvUIb/gLw6YTPqPwa2KxpCS9atgY0kwMeTS1OrrFMrIuUnk3AQyngcT8xcjjCeh4SJTBeTCDHItv3lF9V5YxBCQBrBYDHoY29No7qSGjOchkb71ByaBjC1ApIkqfmd1GA3AdAGcNs0E2C6x2NPP451+HilqX2m1+9hKg8ZEgdqZaWx+l89mUaoDtEJaKaWUUqrTnJCWKjTN+NXey20G4nE/iF5mLZRvCnXbudhXC4kILDQXqphMok2j2SJX39rfQgSWWwnEE/4JkUp57fYy0SRy/kYV9JYfqZ5FLk2u/bOGRc6NY46PQm2NCug9soghkRhy+UrQo9i+ZleEKlaQVyqYw4Ngl/7uxiDX5v3/l2qwlQLnaArvO2u3OPEev4z7uoNQC1mbnoV57MI8pDM4x+/AJNvT4kapVrHlOo0Lc9QvzONNFrDzZex8BZuvYBcq2HwVb6ECNf/ElMnEGPm//nbAo1aq++m7VqWUUkqpTit1aZXgzdylkGa5wnOzHqLxhN/eIKxzB1cwxsBIP3Jleus3SsWAABca22U9vFuuWoFqBXHjmLoDiaj/GK55SLkKVyfatmu5PIU5NOg//qOx620+9sYihjHsWBf2jV4p6m7rZlKoYFL4gXYk6S+GCJCI+TNGNrqtl8A+sfHij94TV3EfHoFaCB9DxQL2uVOYAwcxQyN+uyqlAiSepXF5gfr5WRrnZqmfn6N+fhZvvLn+55kfvQ8nE6KWOSpUjDGYkBWemJC+79ZAWimllFKqg+zUhN/SYjfwPCg00Vc1Eu2u0DTr+v1et9IHOJ2EaIBhNDT3t9jLyh4yM9/ZfdYayLkJiLhQb0BPBhOPYq/MYDJJTDoGjovMlzDpFeGnMV0zq2BNrou9Nt3dv0M6B/PbbEExW0DI+re3N0IvuTiFOT4Mxvph8sr7J5bAXq0gF7YQ4tcs3lOTuA8OhzOUFotcvgiVCub4iaBHo/YIEcHOlamfm6V+bpbGef/f+oV5vx/8Dji9CTJ/897WDFSpPU4DaaWUUkqpDhGv4X8434uyOX+BuS5ijIUTAzBTQ6bm179iLIo52IMxAQbSydSe6EO8U+JEoNNh9PWdix9GAywUrrcEkXxxVXsQ6cngDPVi5wtQquAcHPIbMFsBWTqhs9UTJQGzhSqU11j0sVskUzBfhGp9+9uYXbv6Us4ttXlxHejPYjJxcAUE5MLs1rdfaeA9M4N7X7/ftz+EZLechFWh47fbmKd+fnX4bBfa834j9zOP4KS1OlqpVtBAWimllFKqU4oFsOEPkVoumYJyuStaddzMGEEGIuD0wUIJM9wDBuTStF/t3ZvBjCSDDaMBFheC3X+3qHXBY3ChgF3Rv9y+fOn6/53bD4IDdmIeZ18fNMIZQMJy+D+1+RXDSARSPTCbb3/w71mYWkCmlnctOEf7sWNNhNLFKt6L87h39YTzxFR+HlmYw/T0BT0S1aVssUbjwpzfcmNszg+hL87jXVvs2ELRkSO9pL7/ZEf2pbqXMeF7uxu28SzTQFoppZRSqs2kXsdePAeLzfUp3BVicT+4Deu74S0wxmD6I9Cf4/pSdyeGoCYQl3D8arH47u9FvENi3OCqo1vEvnIZXAOeYBcKmIPDmHgI23pE48j5jfsfh5oxUGsEUoVujIFyETOUQ6aaaMOzUMZ7yeCe7IdGyBY6BOy5V3DuuheT0EUO1fpsseYHzuf9FhuNsTnqF+awM8E/pnM/92p/EVqlVEtoIK2UUkop1W7G+Av+eRsvYrXrGOO3Fmjsvt/bOBYSQY9iBXd7i67tKZ7bsUq6tvJu/A5yeRJz4iB4IaqKjUSxl64FPYqdM/Vge3hnYtBsgfl8Ce+xEub2IWSygHv3ANSCD/IAqNewLzzrh9LJVNCjUQFbrni+Xu08Nkvj4jzeZDgXfY7eNUzi9UeCHoZSu4oG0koppZRSbWYiEb+CdRcGsxvKZLuub3TX8jxwnO5aNLKDBAPTu7OtiT13Bee2g+Fo3+E42OmF3XGsa9RhKAt1YC6ABUPLBYi6zS/CJiBn/CTbe3Ic94FR8EISSjfqSGFRA+k9xO/xPEf9/ByNi/N+AH1lAe9Kdy3C2/Pzr/FnLyi1CeP4lzAJ23iWaSCtlFJKKdUBJpVGSuGs/GmLeFzD6CaIJxh3Bx92YzFt2bERiYG3S1vmeII9dxX6stDwMP05jOxgEb4dkKqFwi46zi0fs3sysKKvdycYERjqQa420Uv6ZlUP77HLOPeOYJKNYE9YxVPQWHqMqF1Hqg0/bL64tMDg2ByNC3N41zr7vGmH+KsPEn9gNOhhKLXraCCtlFJKKdUBZvQARGPIxPjeWNgwEoNaCCo2u4A0hMafX/F71iajOHcN4ByLI+MNnIPRrW2kUvEr0gu7NHTdAcGB6fmgh9FeDQ+m5gGQuUXMyUNQ7/DzLxJDLl/p7D47JSLgOv4ChJ0Ua01Zm33uGgxmcI8lOh9KR2PYC0WYGwfAeSgLBzs7BNVeIsL0L/0ltWd3QaueNeT+3quDHoJSu5IG0koppZRSHWBicegfQCauBj2U9stkoaph9GbshRoyV8N79hrkb1Q3exfn8CJ+EBX9mbv8Reu2IoAF2LqBIYo09th90+m2x9EYdmwXH9uqFUg5kOyBybnO7bdebt22pgvYqINzuAcabaxidxxkxiL5MqQTMDMDjRshuH3yZWR6AeeuI5jRAW2DsAvM/srndm0YnXzzcWJ3DAY9DNVNHONfwiRs41migbRSSimlVAeItdhzZ/ZGj18R/6JBw7pEBO8bV5DZdYKhhvX7x26lQNIYSCS1OnoNu7l39Ebs+XGcw/ugE607HBd7bSa4xf86RQQqRUgloNSZ9jim0UB60zDfmgBZxvN4nuAe2uLMi+1wXGR2ESoNKKy92KZcnMC7OAGpBGa4D9OTBhGck4cxmWT7xqZarvrMOPEH9lP5+oWgh9J6jiH3s48EPQqldi0NpJVSSiml2kysxb7yEpRDsrBUuxULiJsGqWFCWpURNJny1g+jl9U9vKfyuI/kNr4fE0nIz7d0fK1UL3g0qpA/WyF7NE5quHMfQQwxpL4Hg/p6A3v2CubgMCYdAzFgBOprB4Q7IcUqlPdI/3LrQSbdsUAawMQiLS14d/bngBZWXq/auIM9X/DD6K0oVZCx8eu/nz17hci7X49JJdozPtVSIsLsr/01UgqmZ327pd55ksjBnqCHodSupYG0UkoppVSbyfhlWOyuFeV3QizYx66A4+A8NIiJBz2i8BAreE8uYr99cUvXt8+O4z6c2+RK4ay6tw3L+T+cpnA2z9y3x7HlOgd+/CQnf/5AR/YvThTGO9heIYTk8uT1sM+5o/X3u5gIMjPV8u2GlhvpbMsO8FtftJLb2s2t4sUhv4P7p1jB+8ITuO98FBNp50BVK1S+Poad2aUn2mMuuZ9+KOhRqC5kTPgmCIZtPMtas0qCUkoppZRak52ZRq7t4t6qa5ArHjKRR8bn8T43hsyEMzANREW2HEYDuG84gnHX+SSRXJraXgjfyQ4R4eqXFhn7yLNMf+ECXqGGeMLo9wxRL1pe/v+3t9+oYGC2HNqwPgji0dpPpdE4cmm8ddvrBvHOt5Mw/ZnWbq+VG4snIZoGm8BOCfaFnT+vZXoB7ytPI7u9BcwuUD83G/QQ2ibzN+7BHUoHPQyldjUNpJVSSiml2kREkJnJoIfRUdIw2OdWhBK1Bt6Xz+G9UPADsT3OpBzc7zq2teuO5HDvXCMAS6b8f/MLbWnB0ArVWcvpDz2x6nuZOwdIDUUY++NJLn30ear59jwgRARq7t5pI7FFxgUiMYi0YJJsJIq9uLdOtAFQLcO+3qVLPyQTMNQH2TYGV6YKiRb1fI65EF163hkD0RjEtt8ew55bwJ66in1xAiZbd2JMLlzDfvsFRE8ohcJ6Jwec3t3Z79ukY2T+1gNBD0OpXU9bdiillFJKtUu5tKdadQDI5Ro0bg0a5aVreNdSuI/ux6QCGFhIiAj2woIfBm1WAWjX+Hky5QfRLe0q21oiQr1k6X3NKPPfuhFaFl6a4Yl//iyFF6c5+a8fIZpsT22McRLIzExbtt3VjIM9exWzrx8T30GdrONgpxfA24NhodeA4oqe5C5Qrre1cto0GjCQRa60oBq15uF9ZxLnjiGkVEfG/JOHZn8PzpE01Jo4iRNPwXz7Fgy1L14A18V55CQmrPPNdykRoX56mtLnXqb6nSs0JhZxe5PE7h4mdu8I8VcfJHqoF7dnd/b6zv29R3bt76bazzj+JUzCNp5lGkgrpZRSSrXLHptyLEUH++wGU/jnS3ifPYvzyGHMgcieDBmMMUS+fxQpjiJnC3hfv7Dih6zKmaXhISIYY/wKtXIEU5/v9JCb5lWFx37mqxz7h/etCqQBCi9Og4H++9M40db//QUHpvfWSaCtsq9cAc8ilRqSr+MMba8VhFQ9KGyyIOdeUyn5VdMT8y3ftAAy2cLgt2GxL0ys3sfVBbzxBZx7RzFJz9+r54Hj+hX1jYa/oOPK21xrf+9g+9w5JF/EfdP9mFiLqsTVuhrXFil/4RVKn3+FxsX5VT/zpoqUv3ye8pfPAxB71Sje1d23YKxJRUl97x1BD0OpPUEDaaWUUkqpNpHpvdGuQ0RgwcF7emLzK1vBPnYBc3gA54FeTBtCybAzjsFkQe7PYF9KI4Uqkbcdh94I9rFJSERwbuuBqLkeRsvlGnL6Es4bRzFuuE90RBIOb/rT76Z4tc7V0QzV8cKqnx/9B/eRGm7TxxAvArV6e7bd7ZYrmmf8cFOSCUxfGpAtt34RJ4pM7MFWHZsxxu/lnk5CscWLEC5vv92E1ScUhzKwWIFKA4zBuWsYk1x6bsUSyHhnXt/k4oS/0OHbH8FEuze+8ObLeFNFvzK97iHlOlKuY0t1pOZhYi6RkSzuaJbISBYT78zvaherlJZC6PqLW/+b1k7tzv7xqXecwEnqyQ+lOqF7j+hKKaWUUiEmjQYyt0faBsw7eF+7sPn1VpCLM3jTiziPHsLp3XuhNCwF08f6ce/M4vT48ymd7x255XrySgk5N+X/f9LDjIZ07uWS8a8tcvmPLpA/tfYJiqkvXiExGOfA23paul9x4jC+R55zLSCTs8jkLLgG5+h+SCX8Kv1iYe3ZHbE4cu5yx8fZNVwXaq3vi24ARtNQd6FSQ2oNWL6009SKE0ki2BcmcF93EGolwO3oDCC5Nkvj01/HOXkY5/h+TDLesX3vVO3MNIsfe5LKN5p7jXQGUkRGsyTfejuZH7y7LWPzFipM/7M/p3F+ri3b7zqOIfMj9wU9CtXljGMwTrje14ZtPMs0kFZKKaWUagO5esmf8rwXxA1mXw6ZaLJVQqmG/euzcP8BzPFEaN8wt5P7YA4Tu/X3Fg+Yt2AFuXAjZJVXppDhkVBXSZ//L6cpX1i7xUBsOMXxnz9Jz22tDZQEB6Z23/TxjvAEe+4q5oHbMa4DvX0wd1O/4mgMe3F3VkS2jLWQy0Gl9QuNmspSb+comChI2kVqCZgrbHzDFrMX8jhHcsiCB70ZmO/g/heK2MdexD7+EubAIGZ0wO+H3pfFRNzOjWOLai9Psfjfnmo6iF5mZ0rUZkrE7htt8ciWtl+oMvNLf6lh9AqJNxwhsj8X9DCU2jM0kFZKKaWUagMptb+/ZliYlMW5tx+v2UB6iX3mCmY8i/PqYUxib4XSa4XRADJZR565cusP6h4ybTH7wns/3fbeu3juX34LgOSRHu54/z1c+fMr1Oer7H/3IYYfbu2qliICdQO11geBe0ZP1g+jAcRCX5/fJzoeh3oNe2lyzcVK1U0anXkMGoCeBNLhQFqu5vGuLh3nU3FMTwYWOjsGRJDLU8hlf9YIxkBvBufwPpyjI5j+YAPF2ukpvyL6Wxdbsr3IyPZ6vW/EluvMfOCvqJ+Zbvm2u1n63XcFPQSl9hQNpJVSSimlWky8hr/I1R4hHnjf2ln1pEwt4n22iPPaI5hhZ08ueLhMPJAX1r8/5eVJZGgE44SzSrr3RMLvE30gyfBr0rhxh/57bsOJGsS2fsymbwAZn2/5dvcS07Mi9Fp+7qVTYAxSKO35sF9sDMQsrTBowBFoGIhajOPfNyIGqlWM63ZmdowN+G9SqiLlKmakH1nIY9rw3N4SEZhbxM4tYp9+BTPch/Oq23EODHV0GLWXJsl/7Emq377U0u26I9mWbk+qDWb+9WeovbA31rjYKqc3QfzB/UEPQ+0CxnSm7X8zwjaeZRpIK6WUUkq1kIhgX35x77TrAOzZKhSrO99Qw2K/fh5z2xDOvT2Y8M3C7gwj0LDr/7zuwRww0LERNSXe63Lb3xpe9T1nafHKlrdliUYxySQcjUOpD7l4DSoteCzuNZnkrd9b/gQbVNAYAiIONKLY74yvfT9kEjj39oMF++IsNDyck1k68tm/UvH/boU2LKK4VQIyPosZzEHEBdcgcwGG04BMzuF99nHs/kHc19yF6WttoHuz2stT5H/vCaqPtTaIXhZpYSAtNY+ZX/kctae1/c7NEm84emOWiFKqIzSQVkoppZRqpcUFKBWDHkXHSMlBTq+9eN22t3l2Cm9yEfd1BzGtn60cflt4+NiXp3BeO4jZ45+fzfAIeJ5fUZ+Ow8nDMFtAxrXFxJZFXIivf/bHDPZBPIacb0/gFlYiUex3pjdePLBQwX7r6urb5V1MdoMTSi1ijIHBNBJkIL1Eple0axrIIcUCpjOx/Lrk6jSNT30V545DOK86gUklWrr9xtU8+f/6HcpfPNvS7d7MHV7/RdCW63hX83jTJWyhil28cZHF1V8vX6i3/7HZjVJvuS3oISi152ggrZRSSinVImItdrK14WzY2efbtCDSYgXvs6/gPHQIczi2pxY8lEtb6MlarsGCA317OFzoG7hlJoJxDAxmoT/jLy6XLyMzc1CrBzTILtCb27hFjlhMLs1eqpMWDPalxY3D6HWYXASkQ4+3yiJkU7AYohZRM3nIJCAZh6iLzMwH14JJwJ6+hH3lCs4dh3EevB0Tj+1ok958mcXff4rip1/ceCZLCzgDKWyhSmOpd3fj6qL//3H//3Y++JMRu4G7P0fsgfYsHqn2IGfpEiZhG88SDaSVUkoppVpErl6Chb2zYr0tOMhipb37ePISZrwX56FBTLytuwqclEDOLiDX5rd0ffvyFM6rB/ZmlbQb8Vt12LUDIeMYSMX9y74ev81JvoxcnQBvD4f4azC5LUxDEMG5/07wLPb5l9s/qIDJYhRmt7HgW08SHAsdKs43AvTH/CppCdEpg0IFKfivDWakB+Y7vPDhzTyLfXEMe/Yyzt3HcO46jEk094Jiy3UK/+NZCv/9GaTUmRMOdqbEtR/9vzqyr70s8zfu2VMnvZUKCw2klVJKKaVaQESQ4h5q1eGB/cYVqLb/g7mMz+N9ZhHnNYcw+9xdueCh1ME+1uT9WazCgoG+EAVRHWL2jYLdWupnjIFYxK+cziSQly9oKL1SZgvBnAggfpVVdinALpb8EwKxGM7tR7FnzkG9+YriMDI1u72K8IUy9skyZrQXMyQdOVaZShmODiDntxGgd0SIzpjVGthTZ7DPncO59zjOA7dhnI3HJw1L6S9Pk//YE9hZrUjebZz+JKl33Rn0MJTakzSQVkoppZRqhWIBCvnNr7dLyKVGR8Lo6xoe9htjmEP9OA/0Y3Y26zp8GmZb96c9PYXzmj3WS7qnb8th9M1MIgonjvih9DrV1XtKKomJNrF6qONg0inMUC84DjI971dYu2COHUZePte2oXaSVHcWrMv4PGZkCGxnjpGmtIg52o+dLEIpXIt6yrU5GMhgiiEKcxse9tQZZHIO9+2PrLmYnYhQ+doY+f/yOI3LCwEMUnVC7u8+jJPQWEy1kGP8S5iEbTxL9tJbV6WUUkqptrDXrmJfeSnoYXSMVB3s8+PB7PvSLN5nx7ATDSRMU9R3KiKY+w5g7j3Q3O1KNZgL5weNtnBdTDq9o02YZBRz8ghsUhm5F5i+XHM3sNYPo5f/35+DiH8/msTuOUtkhnbeH8iemkIaSYRUZ45V5SImB+boICTD9bdotj1Gp8jVaewTp2/5fvXZa0z/kz9j9lc/r2H0LuYMpEh938mgh6HUnqWngpRSSimldkBKReTqZZC9UW0pHnhPTIMNMAyuNbBfH8McGcC5r29XVEubKJj9UeyLi03f1r40ifO6YYyziwL6dfitOnb+XDOJKNx9FKbyyPTcLYsj7hnZVOu2JRZz+ABy6Wq4+hlvRyueS8L1E3fOQyPgtbffPiz1lC4vQo8Dw4PIpdlwzATo5GyaJtnnzyO1Ou6Dd9CYKpP/L49R+ebFoIelOiDzw/diInpiUqmgaCCtlFJKKbVNUshjT78Q9DA6RkSwpyswE/ACVUvkwgzetTzuaw7BoOn63tJSNcjFmeZvWK0j0xYz3N2//6ZyvS0N10w0Avv7YbTPP8HSsMjEHMzskYVJjYFktLWb7EljsiewL74SjiB0u2otfi6VgA4WCRsRqCzC/jRUDDIdbDspsUKYj05y5jK1qSLT//H5YE+2qo5x92VI/8DdQQ9D7UbG+JcwCdt4lujpIKWUUkqpbZCFOezLe6dNB4BMG+TsVNDDWK1ax/vqOewzeSS8RXhbU9x+la6cnkR2c5GvMZhMtk2bNhjXwcQjOIeHMHcdg94mW1l0o94spg19JaXegOzO2qoESUSQ6dZWM9uzU4jpfC2YqdfAqWAODXR836vMLiLpZLBj2IRTq2gYvVc4hr5/8V04LT4hp5Rqzo4C6Q996EMYY3j/+9+/7nX++I//mHe84x0MDQ2Ry+V43etex2c+85lbrvdHf/RH3H333cTjce6++27+5E/+ZCdDU0oppZRqGynksWdf3jNtOgCkYrCPXw56GOuSs1N4X7iIne3iv8lOKljqHjKze8MUM7J/2wsZNr2vRBTn2Ajm7uMw1N+RfQbB9LQp4I9EoFLFuf9OnPvvbMs+diyRgL7edZ9zZjgJqRaWNNc9CCCQBv+EC9VFzL7eQPZ/fRyxkId/pTLucLhDc9UaPe99HfEHm1yvQSnVctsOpB9//HE+8pGPcP/99294va985Su84x3v4H/+z//JE088wVve8hbe85738NRTT12/zje/+U1+/Md/nJ/+6Z/m6aef5qd/+qf5sR/7Mb797W9vd3hKKaWUUm0hxYJfGb2yR6oxeMUojbkI4uyChsY3ud432gt52FuqYb90Du+5xa6qFpZJDztex57eWfW5nJ7cnedIMrlAehKbeATn4CDm3tswI4OhnfK6bZlEe7brgnPHUf+EnVicO29rz352IpmAhQXo64HeHkjd1Eu77GFGW1clb4ay4JVbtr2m928MxAM8YWUMUqkGt/8tSr1mX9BDUO1koOd/eR2Zv3FP0CNRu5nBT1rDdAnp25dtnaYtFAr85E/+JL/7u7/Lr/3ar2143d/8zd9c9fWv//qv86lPfYpPf/rTPPjgg9ev8453vIMPfOADAHzgAx/gy1/+Mr/5m7/Jxz/+8e0MUSmllFKq5ezsLDJ2ZnU45riUvpGn/Jdj/teuIfHWQ8Tv6UXKFql5SN0iNUvkYIpITyOQsW+XeAbvm9MwVwx6KFsmL0/gXV3Aee0BnJ7wvQsXEZi20BCkWEPOTrZmw3UPmQYz3JrNhYIxmJ6eQBcdNFEXRvthqBfmCsi1KWh00RmPtUSjEHM7tC8Xc2g/cm0S6iE7/pXKUKtB8kZlrDEGyRnk3BzOw/uxT1zd+X4EiMTBq+18W9tkqhWkJw0LnT+Wm4EsFML/GhLfn6D5ZWVVNzBxl75fejPJ7z4e9FCUUku2FUi/973v5V3vehdvf/vbNw2kb2atZXFxkf7+G9PfvvnNb/JP/+k/XXW97/3e770lzF6pWq1Srd44y5rPB7tQg1JKKaV2L6k2KP73pyl96llS7zxK/IEeHFMFxyX/B5epvzh748qeUPncRSqfu3jrhlxD/wcfwHjd0exYagbv21MwXwp6KM0rVLBfOIvcPYpzIo3pUPa2FXK5jrzQgpBrrW2/PIkM7sM4u6N9h9k3GmgYvZKJODCUg4EM5MvI+DR0QdXnWkx/rqOLgJreDKYvB57FXrgSfDi5vOBibSkgjkT8SmkRcByMgPvG/VApYY71Ied3ttClTC+C68CICbRQzfQkkAACaVwXEQn9wrNOo7W9w1V79f6L78ZJRcl/9Ds0Lsyve73Yvfvofd8biR7fvS2YlOpGTQfSn/jEJ3jyySd5/PHHt7XD/+P/+D8oFov82I/92PXvXbt2jX37Vk+P2bdvH9euXVt3Ox/60If44Ac/uK0xKKWUUkptlXiW+X/7OepP+wFi6U/OUvoTcA9kcPoTq8PozXhC5bE8yYfD36dSyg7edya7M4xeQV4Yx7uSwnnNKE42+DBESrQtjAag1kBmBDPUvl10TLo9PY53yjgO9KahJwWFKnJtJviAtVm5ABYdFAsOOEcPYp87vfpnmRTO8cPYK9dgZr79Yyne9Pcqlfy+0jd/HzC9CTgxiFyah8o2KrxdBzIJzHAKQ3BtOwCwVUjGoNzZSm2ZmMOM9iOz8+EOpWt1Eg8OUXkqZIv3qlukvucE6e+7A4DEG45Q/c4VKo9fonF5AQATc4mdHCLx6GGitwW8qKfaWxygDQsG78iOVg9sn6YC6UuXLvG+972Pz372syQSzfcc+/jHP86v/uqv8qlPfYrh4dVzCW9+YdrsDOoHPvABfvEXf/H61/l8nkOHDjU9JqWUUkqp9UjDsvjhr10Po1fyrhTwrhSa3mbpz84Rf/ghHMJbWSkWvMcnYCHg8KRVFkrYz52F+w9gjiUwbnAfFOR8+2f1yelJpH8fxu3uKmnT2xua6ui1GGMgm4DMfkxdkNk8MjMHtS6YAZEKsNe9Y/wWHleu4dxzAozjN6oXi3NwBFupQbHNJ8KiUVgx2xbPWzOMBjCZOiYD3nx8W4G0c/c+jFOGoMNowNTrMJhAZh0odrYaWMZnoS8DqThSq0G+GMpwOnl3rwbSIWeycXL/4LU3vnYdEq89ROK1mgcp1U2aCqSfeOIJJicnefjhh69/z/M8vvKVr/DhD3+YarWK6649H/KTn/wkP/dzP8cf/uEf8va3v33Vz0ZGRm6php6cnLylanqleDxOPN7ClY+VUkoppW5S/G/fofL5My3fbuEPL5D70VH8xqLhI1Oye8LoFewzV+BSGvc1o5gdFohKFUyTb0WlZqDRgVUHaw2YFejiKmkzsj/UYfRKxo1gYg4mPYQcHIRKHckXkZmFcFZOZ1KYSIA9bMRierOY/pzfOmPlCqTW4hzaj33plfbtPxqB+tYrhKUcR+bKMLW9v6U9M41zdz/GC8dJSFOrQm8UIdHxUJq5AjLnn8g1+wdgdr6z+9+CSCZ8IblarefnX4PbG/6ZZkqpjTVVuP22t72NZ599llOnTl2/PPLII/zkT/4kp06dWjeM/vjHP87P/MzP8Ad/8Ae8613vuuXnr3vd6/jc5z636nuf/exnef3rX9/M8JRSSimlWqb23DilP3q2LduuPzdDIx+ipsYriAf2+RYtshdGc0W8z76CvdTclHVpGGTWYp+e9y9fGsOObS3MEQv2bBn75THk2vw2Bt08+/IUYrs0WEkF0E5iB0z8RrWxMQaTjOHs68O9+yjOPbcFOLK1md4wtEKRG32cbxaLQLpNYZPj+AsY2q2fDDQ9UZwDDu7bjuK+4zDmUE9z+6zUsU9PYa9axAmwMn0F06hjBpuf8dxKMlfwT8lmU5AIx/0CYEolnN7wjEetFrt7mNQ7TwY9DKXWZUw4L2HUVIV0Npvl3nvvXfW9dDrNwMDA9e9/4AMf4MqVK3zsYx8D/DD67/ydv8Nv/dZv8eijj16vhE4mk/T0+C/m73vf+/iu7/ou/t2/+3f84A/+IJ/61Kf4/Oc/z9e+9rUd/4JKKaWUUs0QESqfO0P5z59v637y/+Ul+v/FneBtoydpG8mEhWI4KvnaRsA+fhHMEZyD0c2vXjLYb1wAb3WAJqevIaPHMPH1wy2xgjwzj0ws7HjYTanWYQ7owtaZpq+/a6qjMWbDsZp0AjMyhFwLUQuATCroEWxMLM7RQ9gXzzQVHG9JOgWFJlst1Qo3/nXSyNVttN3xLDKVx4ymCXRVwxVMpQyHB5GL0+3bSTbpn3iIRsCzmEwCXIPkKzijaWy+Brml55BJQS0GE/PtG89WiJB67QiFz6yxMLEKlmPoff8bMWHrz6uU2paWt7YeHx/n4sUbB+/f+Z3fodFo8N73vpfR0dHrl/e9733Xr/P617+eT3ziE/ze7/0e999/Px/96Ef55Cc/yWtf+9q1dqGUUkop1TaVz59h8be/SuNcE4sVboMsVKk+H662GGLBPnkl6GF0jH3sAvba2icERAQ7VsH75iT2mxdvCaOvK2wSnM5J58PoJfb0JNKBDiGtZPaNdk8YDRBPgGwcmpr9A37P4jBwHEiGZCwbcR3MkYOt3WZvz7p9orfMAbzth+RSDtlju7qI6cu0fruugznYjxmJ4OyP4AyBM+JgMjVMsoqzz4AtYdJ1jK37MwuwkPD8RSBXcgzmyCD0dG7mRPxQyE/a7FGZH7pXFyhUahcxIpu8g+oS+Xyenp4eFhYWyOVyQQ9HKaWUUl2o+sRlFj742dZX5a3HQP+/fQhjw1GRbBcM9isXgh5Gx7nfdQwGHIwxiAgsglwtIhe2UDkYdXHeeBjqS48ZAbOU70gD7DfHoRTc39d51QFMf5e83U+mMD29QY+iKSYeX7/1xAp2voi8PNb+AW2mvxfn2Prr9IRGw2JfbGEfadeFiAv1Fiw46abxvrTN42RPEud4FmP9lkEiAo6LCfDMkcTiyHgRGq0Ly81tQxhnmwtTRjLIbAniUUwygpgGxqv67T0udajndSLG5H8535l9qS1xBtPs+70fwQlyQdY9RPO15i3fZ5P/y++Si4frpFa+WmL4wz8fur9nyyuklVJKKaW6kYhQ+UIbpohvuFMo/NmV0DR3k8shXICtA7yvjiEvlbAXqtjvzGC/eWFrYTRA3cN+8Tz2a2P+5cVpxC6F0Y9PBBpGw3Iv6e4IpE1/l1W+xRNbCqMBTE8K+prsPdwGpqcN1bDt4LTwY2o6BZl0a8JoAKeO8+Do9m67UMaemvQXSjRJZMpgX1xEnCZXSG0hU6tiDva1dptNNQa9SaOAyVlMvAq2eGMxSNPBdRcqNeJ3tvY+UTvT+48e1TBaqV1mJy8VSimllFK7gjdXIv/rX6Bxvr1tOtZSe3yCQswh+aYRnDQYqW/aAqAdpGKQ823sJRpmItjxBUy9uYUO1zRbwH6j7ldj5kPQkqVUgwUX+sLdu8MMj3RXqw6aawVsjME5MoJdLEIjwL7x6eCCz6a06iRKPA7lFj8P6zV/FkQ2DovbOOEkYF+eWPUt++Iszh2D4IFUPUzWwdgOVQMDlPKYgwPI5Zmdbcd1ML1taq0hnT0+JB4YoPrSXEf3qdYRdUm8/kjQo1BKtZgG0koppZTas6RhwVoK//Ux6i9OBjaO6tfHqX593P8iYojeM0DsZB+RAymcHhcnYsFbp7rPjSDiInWwJYvbY65PB98qEcGeKUJ3FNK2R7GKDKQxi9ucZn7TtsLEvjyF8+oBTFjnRiaSfkuFbuokGIuDbS4gM7EIzqtOQKWO5ItIuQqLRah06PESj0Gsg1WmO3FzH+HtSiVgoQUnmm6WSMNiC0/gVerYZ8avf2nuH+3o4ofGGKS2iDkyiFyaaf6EgOtgBrLQFwWvhHhFTItn/hiAwwPIxR2G5lsU6+uS58oeEDs5iInq30N1Ccf4lzAJ23iWaCCtlFJKqT2pMTZL/re+iok4gYbRt2gI9aenqT+9Ouxw+uLE7hvEHU7iTZVpXC7SuLhwo3fxsrhL7/vvw001MT294iJjnfmQH1p1D5NMQCsC6bApVqHoQjacFchmYLD7qqON2dYJHOM4kIpjUn6lsohAqYp9aazt94Hpy7U8JGybVoyzrxcW2rSgaK2MOTmInN49s0qMMVBZhANZaLjI+ObVwebEEMYRxFYBD2MbbW1BJdK5tlKmVMSkIkgpwBkNCoDU99wR9BCUUm0Q1joJpZRSSqm2aVyeZ/7ffIbGmelwhdEbsHNVKl+5QvF/vELly1donJ2/NYwGqHrM/7tTVJ6pbtqHVdwYOC7lU4vUUz1UGknswN7tmymlELTYaBN7bp4wrmVuhvZ1XRi9nero9RhjMOkE5uj+lmxvQ9k2tVJoBwPmyEHoyW7v9j259oXRANbiHF5xf8ZbW70pheBCUFOrYmwJcyiH2de7/hWjLjge2DIGi+nEFBsRGN5gTK1khdRrumAB0D0g/uqDQQ9BKdUGWiGtlFJKqT1FRFj88Nexs7uwEnaF4h+9Qu35PnI/dRi8W8MNcaPM//ZLULfYuRstA6pA8g0HiKUFZhc7OOIQ6LJctCkzi1DqhXSIfsl4AiKR7mrVARjHtPyxYvqzyHQOFvKt3fBK6S5aEEwsJpfC9GSwL74C9SYC2t4eyLfxfrzOYPbncO7pARG8L1xuWdsjOTeFvGoA02TfZMG0LBg2tSoigjk6iIzdVAnel8EMRzC2s+2JjDFIvII5NOC3Fmmz+PEMxS+1fTdqA85gGneoi06mKeUQvtLfsI1nSUiHpZRSSinVHuVPPU/9uWtBD6Mj6i/Nkf/9i+DeVIPgOCz+wSXsZHlVGL2s/PUrVK410fJjlzDZRNBDaCv7ylyoqqTN4FDXhdFEY22p6DbG4Bwb9Xtpt0M2jWlVX+ZO8cCevdhcGN3X26EwGqgUcO7MQKMOYlvfn1v8hUhFBHGiiEkiZu19iAhik9in57BXPIQkIimknsAWooizvTo0YwymvIjZf2PmjDkyiBkQjA3mNcIAYsqYY/1wrM//d7inLfuKRLRdR9CS332se1oNKaWaohXSSimllNozRITqty4EPYyOqr80R/4TDrkfP3C9zUDp2wXqL6xfXeaOZkj0A4UODTIM+jPI5CymkyuJddpMARZ7IWeDHglmcLj7WnUAxnXXnHHQkm3HIpjjB5EzrT9Gmd5ttr4IUtTBjAwhZy/4Vc/zm7TgaHebjrXUa0ghin3qamu3G4+AWMRJIddKyMSU//2oixnpwQzGMLYCgDgxZMEi5/xFEWV6EZlePbtFUnGcE6ntV0+7DUjFcQ7mQIKfXWTEQqN042gdE8z+PuTq5n2vm1KqED2WpX5+j80WCgmTiJD90fuDHoZSqk267DS5UkoppdT2SLVB4SPf2jPV0SvVn5sh/8fj4EaoPFOl/OfnN7x+6lUDUKh0aHThYHqSuzuMXmJfml4uvAxOPA7RaMCD2IZYrG1h9DLTm4ah/tZvOJNs/TbbzVpMKo5z/504h/ZB+qbfIeLeWEAvkYBKMMcsk2tDK5RqA0wKe2ocmVgRstc95NIs9nwecRN+VfSzM8i5TRZXLO2stYZJR3FeNQTR2o620y7GmNZXqC9JPTTclu2qzfX8kzdouw7VfYwJ5yWEtEJaKaWUUnuCN1Oi/OkXgh5GYOpPTTF7dh7Jbz7N2qnurTB6TylUIO9Ab3CpdNdWRzvtq46+vg9jcA4NY/MFqLYo/HMcSHTxxz6x/ofp4upFR527T4AB+8JZcA1EE1APoI2EW8O5bx/2uYmW9ZAGsE+Pr//D+RL2ySYrlZ042OaP7ebIINSLUFqEmIHePj8YXyiADfrs1g3SpnUhooNd/NzpYs5AitT3nAh6GEqpNtIKaaWUUkrtCe5IlsjxNlQedpGthNHxB4chH/yU7I4LvGy4c+zL04G1bu7WMNrvHd2ZfrIm4mIOjbZug705jNP9H/vMwRv3iTl6yG8mbC3mzuOYowcwBwKqZK3XMb0e7ptHcN9xGHO4Pf2Md0ouFxGnuZkJ5ugQ1Ao3er2LQHERaiVMOoIZ6sUM9kIi3voBN8kMtmcNAKdahmg4qwt3s+SbjmrvaKV2ue5/Z6KUUkoptYn66Ulm/8H/oHFuNuihhF5suAun9reA7KUQvlCBhQA+BsS6tFUHS72jO7m/XKp12+rNtGxbgTAG+8IryMKNPr4mmbhenWuQpcA94GnJjQZUSjgne4MbwwZkahH79DRSia+/uGks4ldE55KYQwNQ3aB3srVQKkC5gHE9zHCfX40fEGm0aWZPwyP16pH2bFutzTFkfujeoEeh1PY4JpyXENJAWimllFK7mje5yMKvfwFvPB/0UEIvfv8QzlyLF4XqAmZfD3h7p0IagqmSNkPDBFaavRPRaMeqo5eZiAP9va3ZWDr46tUdEcG5+wTO0QN+P+l77oDoGicIDNDf1/Hh3SKcbZZ9VrCnJ4DUraF0LILZl4RaAZMBGsXmtl1cxPRnAzspYMRCuj1V0okTubZsV60tenKIyIFwzjRQSrWOBtJKKaWU2tXy/98vY2f2UPXrDiRGY2C7MDBcjzGYE1uYxh8xmIaHOTwIR3ohtweqxJd7SXdK70B3tuoAjBtMD1kz2LvzjcRja4e33UYsIP6/Dmu32GlYTC7b6ZGtFk3jfflCsGPYAvvMOJjVi8WZg73QWGrrtN0TR+UiZqh3R2PbEbc9x7RIojuPXd0q+YYjQQ9BKdUBGkgrpZRSatdqXJij/sJER/cprll/OnSI9XzvAZjZRVXk2QTO3cPglTB3rz/d2hwagIEkHO6BWhFTrWIGkhCP+hWXu5g9M9ORx6qUXLxPPQe2C+/QAKqjl5lscsctEExfz57owyrWImcvIOcDDIONwXtyMrj9N8m+MIEY/2SLOTwA5UJrNlxcxAz0tmZbTRDjtm/9g2KZyP705tdTO2bSUdLvujPoYSi1fU5ILyEU0mEppZRSSu1M7akrzH/wsx3dp+lLwlAOZyTgKr0mxe8b3F1htGMwBzNQKflhXKWAOTZ46/USUchFMcUCprZinn25DL0u5shQ58YchHwZGu2v/pVFP9C1F7qvHYyJBFMdDWBcBzO0w4VYW9iLOsyM42D6ev2K8GQS4nHIdrh3tgjuI/twX38I9x1HcN9xGJLBPX42VffARqE3DY0WB7mNSsf7SRvb3tY0qVcHtGjmHpN62+04ufa0XlFKhYsG0koppZTadeqnJ5n/5b/CTrao4muLzHCOxvk5nN40zh1DOIdb2880/uh+iLT+7VviQJf3mF2pP41zch+mVr3+LWMMJmkgGfO/kU1i7h7FHM1iKmv3STXG7Il3ynKt1vYqaZP0W0bYU2PBLjrXrEjEX6guQGZfvx+ybldqB7ftNkO9mNsOY44dwNx2CDMSwAmlagHcMlSKUCnhvnF/58fQBCnUMT2R1vd2bzQwA53rASxOBLnS3hNesZFd9DoZYsnvOhb0EJRSHRLiU7ZKKaWUUs0TERb/8zcD2bcdm8FkYlROjQMQv3/9VhHN6vml1xAdcsj+6FG8IlRPTVP6zHko1ne03cRrRmC6A9XR2TjuwyMYx4BUIBLDznnYl2cgX2nJLsyxQTAVqK0RMlcrmGM9gAu2hqlufrJCqmXMwQGIOn41YTyCTBdgYRf1JBcBccC0MZReLtKtedipEs5gd/ToNtFo8IF0Iopz93FkegEpV6Fc8Sv4t9LrPZfFtKmnbhiZpYpcqdah1oBaCFYXrJQwJwaRM9NBj2RNUqxArU2RQLUMrtuZ3vFOBHNkAJksQKk1rye37KJWBteA130tubqFk4sTu3806GEotTOO8S9hErbxLNFAWimllFK7ip0p0Tg30/H9CoIk48j4IgAmEWnNB/FsjN5/+hCR3PKOLG4KUq/vJ/WGAWzFofrcLOW/PIedr264qbXEBmMwuXaVcCtFXjvqVw8u3yVuBJkpQWVngfpKJmGgtv6bblNtLqgwngeUYDmTbAAZF5MdQC53/jHWcqk4Jh31ezs7bQxZnBvPA/uNV3B+8P7WV2S2Q8Bh9DITdTGjN1p3iAg0PKg1kGoDyhXk6uQt96np7a7WQS2zUECmpiGZwBw/gpwLdpFBZ7+DZEaxT40HOo41TRbAZNvzfPQamP4eZGq29du+iWksHdsHwPT1I1fasM96g+RDw5Qf7+y6FHuJScf21Ek0pfY6DaSVUkoptau4g2mS776b8qee7+h+DQanJ4G3FEjH7hrCvrSzD66RE330/D/uwcg6wZgITtwj+XAPyYcfwjYcas/NU/rLc9iZ8ubbP5jFTM3vaIxb5b08h3skurTjCN4LeeRq6yqzzf4+v6K5zS0hjOchUoSeVPdXSpeq2KevQG8a51U9mG3mAGKBquufbEgIJmJXX6G84iNHuYb34iTunSHvzx2NBj2CdRljIBqBaASTBsggfTnsmYtQXXFSKrNH+7D2ZjEDPRjXQdY74WUMZt8wcq0D4aK1mIzFuXcfpicOIki5EZ6A2nHaV8VcLXauSpqldWidUtuOz4k7ezSQbiMpte4EtVIq/DSQVkoppdSuIp4l8dbbMRGH0h8929F9m7jfK5eoi0zsPGyNHMytH0bfQnAiHolXZUm86gGsF6H2cp7yZ87jXV5c8xbJe/pgpjMLzZl9GaAKsTjemUJLw2iM8fug1jpU0WoMxHbP22iTiSMvFOCuLMbdeqWkeA4sCPbsDJRW9Ow+0Ae5BCbqIAtV5MLq4E2evkhjtoj7yFFMyFocC4b6XB3jQmwgvKH0zUwqhnPPMezYNZidh4gLid3zGG2GWfncvDkIjUahXvf7g/emMemjyNmxzoyrb2nGBWD6Y/56AA278Y3aTUC8OIY2nVzzPEx/Dpnq7IKmpj/hzx6otLZtSyTVBTM7uphJh+wFQantMEuXMAnbeJbszXcpSimllNqVpNIg/+GvUX/6KibZ+TDJLvdzrns4w1m8uc2rlDeSeP32eyk6boPEXSkSd92DSIT6hSKlz12gcdqfyhw5ksMpdm7RR7lagLt7aXz9KrS4Csrctc+vxOuUWBQudqDvdoeY25OAAJa1PrVIw8U+NwuLZT+M70lhklHk2gJ4twZqcmUOrvhbXNelGbxLM5iHj+IeHwIn4GAOKL60yPlf+jI274dY/T90kpGfv49o1g14ZFtjIi7ObfuRbAopVdo+W6ArJOMQj0O1itk3BH055MoELBb8MDgWCWbxyloN9y2H8B6fhMUKxF3McAYZm+/sOADvs+eJvOsw1Jtv+bQl1bJfhW07+BxvlGA4gnFy/nGqRa2hTLGEO5TEm9rZa7taW/RIb9BDUEp1kAbSSimllNoVGpfmWfztr1J/cdL/xg7D4GY5h/qonr3Rt1IKO/tw7wyniPQ7S/0QdsaYBrGjcWI/fwdiIjSuVuDlSRjvXNWaXFmgcWVhW7f1EilqVZdo3BKprA6ezUAGKgU/KO0QU6sjQzmY2iWhtIBxYc0wuuxin52Ecu3GlWcLG4fNzez6iTEaT4zhvO4EzpHeljzet6NRFM7+w8+t+t7sH59m9o9P0/P2oxz5N49i2tlnu0WMMZh9ff6ah+J1R6/uNjKOgcP7od6ARBzjGhgZQhwH8kXMUC+SSkE+gOdypYh7XxrcnP93ikTwAgiksYJ3toR72AXTmtecVbwGpi+HzMy3drubMGLBK8Cgg3EHkQs7WFjSGEhEIRYl+WiMwqfPt26g6rrE644EPQSlVAdpIK2UUkqprieeZe6f/Vlg/QdFhPpsGbwb4Y+NRpCYi6lto3emA72/+KAfKLWYkQbR0Qj1Z1o7lbkdbCRK3SSpvJzHu5qH1x4gclNmakYyUOlgdfTyfnMJZDcE0q4DZu3QUsqu3+e23v7+r/abZ7DfNpjDA5jb9+EMppYG0f6AujJe45W//1fr/nzh82O88OwUd33i3Thd0sXDMSBOxK9MBb861WvsyYDaxCKrWuyYWARzcB8AUmsEE0avtNxWpFaDVHTNGSTm7mGYK19fNLfV5IUpbM9BnN4YtKN9xzrHmE4wxoAtYA4P+K20Iq5/3HNdiDj+Inqu8Z80jgEHMBZBwHpLzxtvacaBR+KeDIVPB/br7FpOT4Lk224PehhK7ZxZOpaESUhnTGkgrZRSSqmu1zgzDVEXCCaQNsYQyURo1OLYfBViLibqYvpTyLXmA4Sef/EanEj7QkBpCMx3PsRtVmnepfbc5etfS8PCikDQr44O/+8RapG1VzKUsot97PKaP2sbK8jYNDI2zfUY2gEsmKODmDtGcPriLd3l1KcuMv6b39n0eo2JImff/0Vu/w9vxbSsPry9jMiqHspiHP/vbZbaJzS2f7wUa6HhYWJdktCvQawgl68FPYxVnAdGsOfm/Mf8UAqTjWEyEagXECfVtkAawH7zMs67j0M7upeUi8G0RllJipjh5fY7dulykxXfXhXfrAhz3KguvNcOPf/k9Th7tO+9UnuVPuOVUkop1dVsqcbc//oXgS8OJRMFIj0JzLERZL6EnJnc1nbSP34n0aG1Q8JWkfnGJg1+gyMiFBcT1M/PI7XV4Us06yAVcz0QNMMZqAUUSHshvQObVW1gTy3g3NuPiXmI5yBjZUKzAs7S03o5qJb7DuHes2/Hlb4CXP1PLzDz31/a8m1KT0/y0o/9OT1vPcLI378Xp8s+SfmVogL4IbW4Eb9KVMS/rKymNsa/WOtfXAeMi1QrUCjC9DQ0GsjgIPTmNu1XLcv7aHj+dhseJGKB9rk2joHBPuTSGu2dlvpOd3xMkTLuHYmlryxQgaXJLKY/sd7NWkZqgmnTy4/pyXS8bUdbNOokHhmm8p3tvcarW0WO9ZF6821BD0Mp1WFd9jZKKaWUUmo1qTQCD6OXyUIFWahs67buwSyZn7qL6GCbw2gr2Gcn2rqPnTDGEB+OUXthdRWauz+L26jcqE7NJJBGObjYtBZgpV+rzRex367g3DcKDYtcmIGR3qBHtSZ59hKNl6/hvP4EznDaz8230dZj8uNnmwqjl9WuFpj6/ecpPjfF7R/unmrptRhYVUG96v8rA//rQXYDmZ2H2ZkbP5ue9sPb1PphqVgLpQrMzPhtKZYdP+a3SghSw4NcFrIZTDKBzM5jEnHozUCxglzo8CyBjTgVzG0DyNmZza+7Td7nzhN55/72LEDYBT3Ytyr58IAG0i0Uu2s46CEo1TpOCFt2hG08SzSQVkoppVRXqz12Megh7FjmZ+8jcU+6I/uyYyVkYnuLC3aKFYM7nMabvFH9HD2YBVlRyVhrgLS2fUNTEu09cdBxdQ/7pB++mYMDyNWZ0PYcpFrHfvGFGxPuD/bj3ncQklEwYKJmwwpqwTDxkad3NITSqUmm/scrDP/I3qnqExHIr3HsuHIFyWRgeAgcs6rqWURgsQiTa4R3pTJk04hnb7ldp5j+HKY/d/1rqdUg5lduSzrhV5B77e+hviWeh3M8i9fGQNo/v5KgLX2kS0VIJ6HY2QWH2yF6vfWHaoX4fSNBD0EpFQANpJVSSinV1aL3juDuz/mL3nWh6F0DJO7NdmTxNgCTjXVkPzsRq+bxDmavB9Kxu4dImkWMdyOwck4MBts/OqxhbQtIudpdv9/lWbzLsze+TsZw3343JrV2aGRrranUnPur83sqkKZUXr8HcKEApRJkMshgP2CgvrRg4MI6J8BmZpCIC5NTkMshPVmo1sCzmEyqbb/GRsyBkRuVZFZuPS735KBe9++LAMjE9mbgNKPx5CSR1wxBrfW/o0knkF0QSBuvgtMTwy6Ef3HgbhDTQFqpPWmXlXYopZRSaq+JHOyl55ffEfQwti3703d2LIwGMH2R0IeNjWSWypP+YmMmEyM5yOrqyd4U0uh8f9dVikXM7UPQnwl2HO3ghaMFzraVa3ifPoV3dm7NHxefbk2F6aF/9WhLtnOLaMyvzI2G6+SR5Dc56WetH0CfG4PzY3Dp0vphNPjB7uUrfhuP6WkYn/C/vnbNr6wOgIk4GMcgIsi16aVWJUAiAQP9UKlg9g37bT6WRTu0sGMkgn3eb7dkRnO4rzuEOdrX+v1cK9D4s/MQbcNJgeIipmcXHDNFSH3X/qBHsWtUnwnX4qJK7cTyEgxhu4SRBtJKKaWU6mrexCKL//5rQQ9jW9I/fBIn1tnp4CZiMAfaEGK0kOvd6B+dvH8It7Y6fDbDWcx6lZqdVChgYh6MpjG3DUHU9Rd/S4YrSNxQNOKPewWTaf/iaZ0g3zlH44tnsHNVpGzBMyw+u8DYv/zyjre9/5+9htTxFgdrjgNuBOo1v01EvRauT5HNrHa3nUC5VLpx24AC6VXqS8eYaBQzMowzMoA5fgiTimEODGOOH8EcPoC5/bB/AqHdGg3c7x7BfftBnNtjECnjHI1hjrXneC7tKv51pDP3V5sl78tufiW1JQu/9TUaE4ubX1Ep1RFf+cpXeM973sP+/fsxxvCnf/qnq37+Mz/zMxhjVl0efbT5k/TaskMppZRSXcsWqsz+4z9BSvXNrxxCidcNAZ0PVs3BHLKyxUHIlBf84MtkYkSjDaTuYuyK4L7SgA4VJW6FEQvFAhzI3WhpUI5jIg5EXWR8PpxVx6m4X6VqBXNoELk0DYBcnsUcGkCuhvcxsmWTeexnnwP8fsYXPnZ1x5s88L++jsF3H97xdlYxS32v7U3Hg0jUD6YDJiJQ6GBgNLeA9PcG0lcalmZkHNoHnmBiNz4yG8e58W8yBsT8Htid6jNdrwMrXu88D+doHK+UgYlCS3flffECzqv342Rb/Ppaq2IGssjUwq0nHpJxv13L9T+7/7yQag0WS+E4UbHEMRWIGqiHZ0zdSqoei//tKfr++XcFPRSlFFAsFnnggQf42Z/9WX74h394zet83/d9H7/3e793/etYrPliDA2klVJKKdWVpNJg7l/+edeG0akfugMTQBgN4AwlCWE8el0yW6eWiJB+ZBi3cmubAJONQiX4kO5mpnKjv6tEBTDQADPaG84TAJ71q3Kth1ydwRzoR67445TJvB8K7aKsxeJgyzt/zuUebUO/0/WC53oNHBdswAvrWbt+/+h2mJ2FTBriwc02MK4LWynknQ+4srPRwL07g+1PIi9OtW67NQ+5sAD3tqN1RwGTjUMiyfX02XpQLkLp1vvTAGRikExBrYEEfZ8DWEvy0RHKXx0PeiS7QvlL5+h9/xv9E7lKdTPH3FiLICyaHM873/lO3vnOd254nXg8zsjIzt4P6bNdKaWUUl1HRFj8T9/Auzgf9FC2zgGSfi1A/NH9JN84FNxYUgZz23Bw+9+E06jT80gPsTXCaJIxpA2LbbWaYcWb/1hIa0CqdUz/jWnnsiLkN/2ZUFUjtkLDtubvcPHffoP8E9PM/M9LlC+UWrJNGhucWBMLkYDbwJTbv5jeLYolxIb7MShWkMnpoIcBjQbOwWTLNyuX80i99dsFwGtAcRGKef9S3mSRWuv516+XMYM5SAez8OVKiXt7gx7CriHlOvXzITxxq9Quks/nV12q1e2vx/KlL32J4eFh7rjjDn7+53+eycnJprcR0nfHSimllFJrk4al+AdPUvnCmaCH0pTef/UokT6DPwXZBlrxaIwh8uoh6vMlmGntNO9WcdYK6HpTmH3xcPSPboLUyv70/nLIqrqTsdVVM/NFGM6BMcjEXLj6F7dAw2tNLU7xyQnOPzlx/ev9/+K1DP3g0e1v0DgbL2wqAo2afz3X3Ti8bhOpBzATZWbGXzAwm+78vrdAylUolCGZgGKLTkxsVyyGjLdnoVfvC+eJfP9xsAH/jiuVS/4pv6FepNqAfDCvY5FBjVNayZsqwonBoIeh1K516NChVV//yq/8Cr/6q7/a9Hbe+c538qM/+qMcOXKE8+fP88u//Mu89a1v5YknniAej295O3oEVUoppVRXmf/gZ6g/tfM+sJ2U/uGTRHpZqjgNR8WfeAL58Fcar2RGU5hae0KXdjKNBtIXxYxmkXMzQQ8H+rN+L9pSFbk2t/pnM0tT4XdZGA3QqLbnuTf5+8/R//3HcaPbbIQj1m/ZsVnQLBYaW7xuC4kILAbUIiGfD20gjesiU9PBzySIxvC+0N7XxMZnzuM8MIJzIA71AKrl11Mq+MH0cB8yveC3lukgxytDwoVKwC11dglt16F2hRC37Lh06RK5XO76t5sJj1f68R//8ev/v/fee3nkkUc4cuQIf/EXf8EP/dAPbXk7GkgrpZRSqqvEHzzYVYF06j23k3z9wMYVkEFoCNS750O0c/cIVDeZ0h1ixlowW2lI2wkCpe4L9neqXm5PcHjk377JD6Ndd/uL2zXqS/28t3CcaNT9yuFOVS2LQCmg6thSCanVMbEQrWIK4EZwsgnkgbuh1gDHYF8807nFDVeq13DfcRiqBinVMVnBjjeQl1rYT9oT7JPj2GcdIt97FBohqpYGKC5i+jNIqQalDgXmjgMRl+Rr91H+cve8JwkzdyD4NixK7Wa5XG5VIN0qo6OjHDlyhDNnmpu9qoG0UkoppbpK/M23Ufjo4xDy3qIkXPr+9WtxkzZ8YTQgxfCNaUMm5H/vrSguYo4PI+ea77PXMtEIzBd2ZQX0ZhrFNrR6MZC6vcf/v+eBG/HvW+v5Qa4xfqsNbwv7dt2tV3jW6/623aWTHO1sY2MMRCKdXdRwpQsXkaEh6MliwvK4XTr5YACi/t/AufdOpFJFTr/S+fFU/IDYJIEGOIfSeFMpTCqKTCxCrUXH+7ql8RfncN94FNMPVENULV0u+Ye1/oz/PBTxT94szyZwHP+5GIkutb6xSLUGjoNxnKWfL1/PLK2kuPS6I9Z/z2E9/+J512c8JR/q10C6BUw2TuRIX9DDUEptw8zMDJcuXWJ0dLSp22kgrZRSSqmu4vaniD1yiNpjF4Meyrrcg1l6/vEDOG44ex2LFexzE5tfMSxcBykXwxNG7YB4AfeRrjcgl4RiiIKkDmnkW3/fD//9V2FWFu/eHDyLAPZGeLxRqNvsOReRG9uLxqDenseWMQbJZmFubvMrt8vsLPRkN79ep6xx4sAgEI2EoylTpYh7dwriCazrIGMt/NsJSEMwjgu5HsgvtG7brbBeSO55gLeq3Y0BsEuXbYoOhWXmS3dLvv6ItuxQu4OzdAmTJsdTKBR45ZUbJ1fPnz/PqVOn6O/vp7+/n1/91V/lh3/4hxkdHWVsbIx/9a/+FYODg/zNv/k3m9qPBtJKKaWU6jrJ7zsZ2kA6/t2HyPzAEYyEM4wGsJfKyNX5oIexJaY/AyMZqIRz8cWmVSowlIOpfGBDMD1pZA8G0vXZ1rcp6XnjAbZ0mmQ5PF631YbZWhX1euq19vaWdgP+2JjNheuE1HrtVdwV4WR/r38CaDGgY1ckgveFy+1ZtiC5lG7UapDLgRMBxD++OY5/gmQhwBMYHWSkCqkIlIJ7zZeIv2CyaaydrJtcApNwsZMhbXtlIPPD9wY9CqXUku985zu85S1vuf71L/7iLwLwd//u3+U//af/xLPPPsvHPvYx5ufnGR0d5S1veQuf/OQnyWabO3GsgbRSSimluk7s4YM4g2nsdHAfrqL3DULDUn9xFgCnN07q3beTeDAHIQ6jAZyDCXjwMPapcIb61xkDwzFMtbhrWkwYY5CMCy1s70osghnMgQEZn9u8nU0X9Q5vlaoXpfxy6xeU9ApNBsD1pV7RjgsstfTA+GF1CFv7LDPxGLKTHtk7NT+H9Pdi3HCUnRnjsFZZrUFw7rsbe/kqzoFRjGMQDPbiFZib7+wgGw3ctx+BskDSABbv25OQ3/mJGRN3uP74rTeAhn/SJZGAWh1qVcj1Qn5+x/sKPRHSbxql+JlLHdule7AHpzcBnoDr0BhfxMRdsILNV4ge7fNfByIujfFFvOkizEP8vn3Uz81AOVzvUVLffyfR2waCHoZSasmb3/xmf0HjdXzmM59pyX40kFZKKaVU1zGuQ/qnHmLxN78ayP7dQ1lyP7ofwTD7/8kTv2eYzI8fwyz3jQ054zq4J7OYoZN4XxsL3wJ3roMZyMBQClMJaUXXDphyBUb7/PB4p9IJiBhkYmlbPSn//otH/bCoYZHJeT+cyCUxiRjSqcXwQmTy6VJbKkVj+7axCJe1W+8V3Yybz9k4LjhmxQ9k232gTTYDscPI+fM7GeGm6mWhPG/J7nMwzk2/0IWLyP5RTCLe1jHcwnEw0ThiPb+KfZO/nXHAPbz/xtcI7uEDeAjMbdLeotWhf6Xo//mXJkSYbAJpQSBN1HLLA84YqK7Ydr3mh9LVSrh6TbdB/N6etgTSIoJJRnFycZxUDJOKgWNojC9SH7u29o0cQ/WZtVtyVZ++htObIHJXP/UXJkIx6yB6YpCef/ho0MNQqnWMCV8RRdjGs0QDaaWUUkp1peTb78DOlCj+tyc6ut/Ue46RfHUGvDoG6Pulh3FTjr/QUZdx+iOY77sN74kJ5ELrq0e3JRbB3NaLqVX9MGW3Su680tMcGkAmF6Cy4rGX9xc3W5W9ZuJ+0JUv7clWHaVajMXHLrRl25G+WFu2uy2e5y8+KOKnoo36rUW8O2nrEXEhlYZSa5+XC5cbTD9foud4kktfmOLiJ05z5/texcm/edMCZ54Hly4jPb3Q1wMRt/2BmjGYaAy8hh+/Oq7fvqTp9iqCe+QgcmA/Uiohs/MwfyOcNkcP4fTkAPErqp9+fmubdV2cE8cxiTjiediXXlmnJQyQSCNXWvA8SEYx7hbv93oN4vEbgXQ647f5aFO/86BEB5rrIy0OuL1JTCaGiUcxsaWTR1aQuodUGthCDVmoYgt1bDMzMTaZIWPnK9TmK0SP9yENi70aXPsopzfJwP/re3GS0c2vrJTadTSQVkoppVTXSv3YA8Qe2E/lr89Q+ep5ZLF9lb7RewfI/PBhHCo3woh0FseYrqiKXo+JGdxH92H357DfHtu83UM7RRycEwNQKQU3hg6RYhEGszC9uP1t5EvQ2MKJkGLIKuA7SESY+MZs27bvxENUdbSq8nqdx0WjvkEf640Z18UcPohUqsjYWFO3FU/WDTHH/nKKsf/rpVXfmzk1AzcH0ssW5v1LNIoMDkI62bZg2sQS6yxUuQ3i3wcml8G7uqKCNZ3E6c1d365BIJeF/ObHBnPkoD8bQizGMZiDo8j5dVoxSQP3TYex40Xkle2fgDSZRJM3MNDT6z82Xdc/KVKSbT0Gw8pIFZOJIOLgZOM46ah/ctV1/EUgPYtUG0ixjrdYQRZrNCZLMBnca1393Bw4hvh9+6g9fw2ztW74LZX9iQdwB7Yxy0QptStoIK2UUkqprmWMIXrnMNE7h8n8/KNUv3mBwv/5bexMaz/kuQcz5H7sAHg3VZfGYphtToEPE2MM7pEUpv8k3jcuwVwHK5MdA9GIH0Yf7d0TYTT49zk9CWQHgbTJJpHC3qt4bkahlKD8Unt6pSfvHgwgwmmBen2HldIRyGZhceuP3Wd+b4JDbx+k/+itHz8zR9K3fK9wdgGvZnFjG8wkqNdhfBwGB5GeLMZpbX9pE0/ubKHJdYjgL/63rFjGO/U85uTtOAm/4t49fhjv1Ioq6WgUc+wwJhZFFgvIhcv+tq5OwMnjN7Zd2uB4sNROwzkUwztv/P7D2xn/fJPH6NpSNbTr9zi+vgBnOgPzXbLw4fJCja7r/x+/mhlr/cr9eh1nIEP97Bx2oYtOAFrBLtYCCaOdgRTp99zV8f0q1XbO0iVMwjaeJRpIK6WUUmpXMFGXxHcdJ/bwQQr/57epfPbllmw39w/uIXrAAW+N8KYdfWAD5GRdzNuPYJ+exb68Tn/KVnIdzG09N4KxPRJGL5PCIuRS19tsNH378Xnoy8Lc9kPt3UwEJv56vG3bz752tG3bbrvGysUVV7De5j2SIy6MjiBbCKTFEy4/Vmb62xNc+ORpbv979zD6ul76jka4+lSFkftiHHtrhvOf7KN49kY4Wb6c5ws/+yRezcMY+O7feYRk3zptEaanYXYWyWahtweikR1XTJtEctv9tjfdNoJz/93+QofzC37YfGBkqTf2Uki8ogrbnDiOk07eqKDuzeEtd96oVLDlKk4y7lciT0xuPoBGHef+EexT23xuJLbZXsHzVvfHrlSgt+/GzxYDah0RifonWRz3xiKjIktBc8MP1L06lDc+geOkui9acXJxpB7MSfXsTz6IiXXffaaUah09AiillFJqV3HSMbL/6FGSr0kz978/A9XmeztH7+on9bb9uMMOxquDt05AUykh0XgoFgZqFeManAf7YTiF/ebY+r/7dvWlcW4fQGbKmLgLtWJoF1tpN2MMDGX81hvb4VlYLOOHWHvzPtzI3GyU2pX2hfWpe4fatu2OWG9xxU2qp0WWHm/JJJTLG+5i5lydJ//Vt6/nrGd+51nOfAQOvOc2rnz6LKPfe4zeu3oonp+/5bblFX+7sS8scNeP9G/8uyws+JdcDzLY5y9GuJ1jS7x9YfQyY8A9cgCOHPSfuiKs6vy+NG5z+CBOKrG6TchNv5NcvgonjiH1NV7r+vv80Pumv7MZ2P7CkKa3RS0WVi6CKNKW/uRrchyIJaFU8gPnbbxHWHOz6S7rgxx1cAeSeJc2WWizDdzhDOl3nuz4fpVS4RLSwm2llFJKqe0REeTiGI5bof8D9+P0r+53efPXN4veN0jub+0nMuD5YfRGqlW/smqXMcbgHkwS+f47YCDTug0nY7iv3oez38U5lPLD6D1OSgVI7GBhvEziphUMFYAnDlOfudTWfSSO5Nq6/cAsV09Hbg3YxPOQs+eQV17ZNIwGcGMOD/36a4kNJHFiSxXOAlf+7CwIjP/VeV78jVOb9q4vXWnipE1+Ac6NwdQM0rD+a8JWe+M7LqYNbTrWJ7f2pDYGOzF9/f+33GL56q6LOXEc947bALCT06uvmEjgHhrFuXuN4K+ygxON2e2H2esyxq9Q7ulbuvRufhvHAVzI5CCVgXTW/38ytf7rsjEQicNC3g+jW8h0W4V03eJkEzgj2Y7vuucfPeov5KjUbuSYcF5CqMuOmkoppZRSG5PLF5GZKQCMVOj7x8fAOOA4iLgYW6XwmTmq37q1JUX81fvIvHvIn7a+Vfl5JJvDBLkYYJuYtEvkrYexLy5gn7uy4+25r96PSa6+n5x79kPEghvBnroS7KKKATAisL8XObeFqfYrbzfcgxhgemHPVphvZOYSePn29nKN7qDKNPSWq6ddFzBIve4H1bV6U9XDfUci9B2JcOiPHuWv/8EzLJ7e3mJ6+797sPkbLSxAPr/UjsFBRkYg6m5YNW2isbb0jd4qsWBfeOlGa4s17muD4D5wz43KarGIBaZWB9LmoN9SxrgG+nthdt7/QSKD97mxbY/R5HZwAm0jXmP1fd/T6/9++XUqeJMpmF2Aam3tn6dTYOzqxRNTWZhtT99qJ919AWv1mWvE7h/BXutc2yd3KE3ijUc7tj+lVHhpIK2UUkqpXUFEkKlryLU1glOx4FkM/ofd5Bv33RJIZ37qJPE7thFGXJ/2vjtDQeMa3Ht7MftSeF8fg8o2F0IDpC7XF08yvQ5mcBQaNfAAr4FzcgT7Yvt6/oaVVEsQcaGx9RMhUihDtc5ufdztRM1GmPnc+fbuxIDpshn627IcjBYKyLVrG578WLzmUZyskxqKkh1xyF/xOPfpSbyKRyQV2XYYDXDm988z+qptLIAmciOQvHABolEknYZcFhNfEaxGohg3EmgYDYBjMEcPIZeu+r2LY+s9yOTGzAhjsM+/uOqn5sCI33d6ebMH92OXAmkZ32Gv/kSHJlnXav7fL9cL+flbf242GUexBIkERKP+YyAa9U9QtImT6L5AGuj4SeDEdx3bVW3OlFLbp4G0UkoppbqeeB723BmYn93S9d10jf5/+yrK356n/vI8uZ86irHV7YcRi3mktx/jtaYXZRg5QzF42214f/HStrchr8wigwOY5amDN/epdes49w8jcx5yafvhVbcxngcH+5Gxqa3fKJNYCqTVzaZeqCL19i44mn5oZM+cChDPghHM6D7/65X5lRUw8OQvP8ml/376+rfjI2mqk6WWhV1zT1zjy++zjLxlPyf/Rt/2N1Svw/w8LCwgo6OQSmBc1/9bBh1G41c/m0wK7rr9Rvh/c0uPm0i1vrpH9PAQzuDqftvGAPuGYWISM7yzHtCmk2v5GgP1GmR7/P9bD8qlpQUStzCQSsUPrlMJKFdXL6rYYk6yS48InZzK7xgyP3Jf5/anVBDC2CIjbONZoj2klVJKKdXVpFTEnn5+y2H0MuNVST2SpOenDvph9E6Viru/lW9jZ7+hTC0ihU3eflqL6UtufJ1dSGytuWLnSgPSG/dD34vK9RgLX7vc9v1kX7u/7fsIA/E8KBVWfc+YFRfXgIH8S6vbIFSvFVtbeSkw//Qk5z9+ZmlRxZ1uT+DqVbgyjszOI4sh7Gcva/SXXoOJRXDuuxv6+zCH9uOODq95PXd0CFLJHU+qsGMLrfkbNKNRh1oVSkszatyYv1jjVoj1q6XbfMLYJMIZ+GwkevsA9edvbV/WLvFX7Scy3MJ1KZRSXU0DaaWUUkp1JanXsK+cxj7/NBQLm99gPc30i95IrQru7n5rJYstqMid2UL4vwfn8JlaDXNgYOs3yJegXIfh3raNqduICNe+0Z7+sDdL3dXE36pL+WH05kHt9BPzLDxzaw/029/7ENGe1vbZrk4Uefp3r1GvtCgQLZdhagq5fAl79lxrthkA4+AvYNjfy7qrnIrg3HEc7/GdtUWSi7PIpQCqyR0HqhW/Z3Rp8wU1O82Jd1cgHbt/BG8i39H2Gal33N6xfSmlwm93f2pSSiml1K4jIthrV7HPnULmQtbWYWEBCem0uFZwRhKY40M72oY9N4P35AJS2eB+qlVwTo7uaD/dSCJNzoWvB99iIEwWi3HKL05vfsUWiB/exVV+xiCNxpbCaIDBh3vJ3HGjRUTuvmFe/ZG3c+/77+T4z7V+en5hbBGv1oYK3VoNu9Wq2y5lRCC+80UJ7ZOXkWqHX+vc6OrWJCHjtGmtx3YxBijv/DUkcvsApjdJ5LYBoveO4B7uRdy1HxuRw7073p9SXcGE7BJSe7D+RCmllFLdSvLz2CuXoNC5FeGbItafFrzZYktdysQM7quHsCNZ7LfOb29KfqHiT/eO9Gx8vYT1e951eMGlIJlKBUZ6kWvzW7v+oQFkvLlWNbuVtTDx2asd21+kt8vSp60yBqnVoLL1ClRjDG/7s+/BNqCWr5MculEVffxvHQPHcOAd+3nqlx9n7rGd/43u+YXjJHJtOsaOjyPxOCa5e9vhRN50gsZnnoNibfsbEcG+uIDzQLYzFbbGgXxIX/eXONHueq1qjC/ijmQRB9z+NN5METu+iHsgh0nG/HYxDQ9xDFJuYK+tff/bSgNvuoQ3vWKxzKhL5HAvTi6OVBs0rixAqY60IABXSu0eGkgrpZRSKvSkUkGuXERmO1P9uCOFxV29wKExBvdwEqf/JI2vX4K5Jnqvug70JHHvGsJs9i7U8/yF+/Lhm5rdVumtvz2XamPd2fl7zex0lPpkZ/oAm4SLcTuyq84yBqlW/bYIzd7UdXBdVoXRAPG+KHf9gxMARHPbD/EzJ/opnPFPvjz3H1/hjf/uZNuCUBkbgxMnMJHd+EcGZ3+G2M8+il2o0fiLZ2C2tPmN1iDnp5Hbcphsiwe4FjcCtR0E6B3gxLrrYOxNFll+l9K4mMfpT+Ie7sWbLWMv3DpTIHbfPhpnp6FmkYgheqwfIi61Zydu3Xjdo35u9cnSyIEckSO9rf9FlFJda3eW7yillFJq17BXL2GfP9UdYfSycqnziz51mMm4RN52BOfOrbfWcN58lMibhjGDWwuSzMgmVdS7kBQK0JeGXBJzbAhzcAB602te16Ri0LP2z/YSTxymP3exY/vLvu5AmGfAbs8OwuitSh3YfpuTR37lHobedBCAe997e9urcuXMGex0yFpCtZjTEyP2E6/GfcMO+vrmteJ1mXGBWPeexLCzZern5rDzax8Das9O4Axmid6zD4xL7cXptcPo9RiD259q0WiVCjHHhPMSQlohrZRSSqlwc9xQ941cU7UCyZRf5buLmYjBfVU/ZiSN9/XzUF/n93UMZn8vJm1pppmdibh7rgDYGIP0xACBcgERwezrRbwGpi+LXFxxYsYTaDSgPwOzO1jYs8vNXAG72LnqyczDu6y/eQfCaIBIOrrt27oxw6O/chvXnjlEpFOLx01NIQMDdHDNtwAI7oP7IRPH+8zzzd3UdTCxDtW3Ner+ooYhfy/gZGLY2d07q6dxcZ7GNs/9xe4Zbu1glFJdTyuklVJKKRVqZt8o9PQGPYzmLS4guzvJuM4ZiRN55x2Y4dzqH0RdnLcfx/3+ozgP9TRf1RizfpuPPcZUKlCt+v/v6UEuTEG9gUzOQX8Kc3gQc3gQWSxArQ7lKgzmNtnq7lS3LjMdrI4GSN3Zv/mVuoVxkEql7WE0wPCbRra1uFLvA8OkhxyciMP+hxJkRzpXhSovvYQ0dveJRQD3xAAc6mvuRvEI9HfqNU4gEv5aOie7S3vLt0D02C46biqlWmLvvcNXSimlVFcxxuAcu92vjuomntd9Y94Bk3Jw33wQ5wF/Wj29Kdy3HMFJC8Zle1PsG3Wc+w60dqDdIJeBhTrixldXRAOUqsjknB9OV+r+9xoeLBYx+3o7PtSgTb/SQKqdDQxjo7tk2rnjIOUS1Kod2d3wI30kDzV/4mT4DSOdWThvHXLxEnYi7/9/9zVruS765pNNXd+5ax/G7eD9kUx2bl/b5OxgFsCutwdO7CgFBN+ao4taduydT0lKKdUGXlV75ynVbnZmCnv6hdBP1b1FNue3U9hDjGNw7+rBfccduG/Yh0m2oOFGo4w5Orjz7XQRyRcgGYOZxa3fyAoyl8eMNlnl2MWqXpS5L3a2Ohogkt0FoZPjIKUi1Du7UNzBHzzR9G0WxwJuR2Mj2KfO4l2YgWJnwvtA9CYgscXH9kAGc6DDFcvR8PdndppYlHavcUf25iwepdT6NJBWSqltmj41zud+5ON86Wf/iPmXpqhMF4MeklK7jliLTE1AuXT9e2ZwGLNvP6Fu7JnJgmyzKngXMENxTAs/l5v+ROs21gUMYPqzzd9QQKbmMQcGWj6msBERJr6zSKebjDu9cYyzk52G4JhwPYyud3zXh37gUNO3qec7G5qvYgz2yjwA8uJFZLFC47lLeBdngxtTG0i1CrMzOPfu3/B6zoMHcd90DOfEQGero6ErKmydpAbSa4kc6iH1luNBD0MpFTJ6xFRKqW24+D9P84W/9UnqBf9DUmW2TOZAjjf+xx8IeGRK7S7GcXBO3gML89jJcZx9+zE9vUhhEZm4GvTw1haNguNg9tpqfCu1upq9VsF54CD26cut3W5YZdLIVH57tzUGmZjFHBxELk9vfv0uVawmKDzV+ero3Gs3Duw2IlUP75sv4NxzFGdoGyccWsFxkcU82GDCvdzRFMNvP8rk58e2fBsT5FTjZAbKk9e/tKfOAiD9VTzPQ86O4779AUw3L7/qOFD2F+JzjmWwT7urF6iNRaDWwNw+jDkSxRiDIYBqZTf8FdImFf4xBiH3dx7CRLQWUu0NxoSvZiZs41mmRwWllGrScx/+Fp/5gd+/HkYDXPz0S4x/7QKgbTyUajVjDKa3D/eOu/0wWgR7+ULQw1qb40A6s3fDaNdFHHdVRXvLeBWcBw9gToxAXzoUhaatJiKQyyKX56Cyg6rQ5VD60O5sdSIWJr86Fci+Mw+NbO+GxuB97TnwLPaZc9jZElKziO1gX2LHRRYXAgujlx1419Gmrt8odL6Se5ksrPM8nF1ETl+Ghod96UpnB9UMY8DZOCQVa0H8Fy0Tc3Bec2TVz923HMH93ttwbs8EPOsn/Ad9J6mB9M2ix/tJvfW2oIehlAqhHQXSH/rQhzDG8P73v3/d64yPj/MTP/ETnDx5Esdx1rzuRz/6Uf/D5k2XSqX9qz0rpdRWWc/yjff9Bd/4J3+O2FvTJsc1lK4t8mdv+l2KV7ZZ2aaU2tzCPCyG8DnmOJDrxXhd1uu6ldwIFJvoe9ysehUTb+AcSOLcP4Jz5+iNjCIVw9w2jPPgQcyJff73jMG5cxTn/oM49x2EWHjCAnFdJJKAnhwigjgORJPIxZnW7ePaLObg7mvfMT8fo3J2LpB9J09ur0e33PS2wT51Bu+rz+J98Wm8L5yi8c3T2Mn89XDazpaw+RZ+FroeRgd/fDr8/aMc/4VXrdnywURv/Xg6/8wkjWoAZ/miMeTq/KZXk7EJJPi71ec4SK2GVKvI/DwyO4vMTCOFApi1P/qbm77vHEndWJwWwAomJZig1xTsgmzASWi930omFWXw179Hq6OVUmvadsuOxx9/nI985CPcf//9G16vWq0yNDTEv/7X/5rf+I3fWPd6uVyO06dPr/peIrG3+gUqpcKrXqjyhZ/8Qy5++qV1rzP73CTP/4dv88b/+AMkh9MdHJ1Se0eoq6N7+jB7bBHD6+IJpLDYnsro9XgNiIDzqkPITBnTH4VGHeoVTCyGuMYPVWplsHWIJSAsoRFgPA9xBJmqgjUgHlRbf6JFJuYwB/qRq3O3pqJdyFrD1F8HV5EaG0lt41ZbqOwsVbDPnvf/n0pAyQ/f5Mg+/2QX4B4f3sa+8cPo/Hxo/v7GMdz/L+7mrn90B3/9A39F+kgPqUM5Tv7CHWAMtXydia9NUji7wOU/eglpWOYu1Bm6I9bZgTrxLV/V++xTmKP7cO86EMz97LiI58HMOie06nVkdgZyPZhoBDD+VAMMsrj6JKIxBueeXnAdZKoAaSEU1clhnXO+ggbSq+V+4gEio7qYodpjHONfwiRs41myrUC6UCjwkz/5k/zu7/4uv/Zrv7bhdY8ePcpv/dZvAfBf/+t/Xfd6xhhGRrY5BU4ppdqoeDXPZ37g95l+cv1+tbHeBPvfcpxX/z/f3sGRKbW3SKWMHTvb2dBzq4wBL/wLLrVULA61qh+EzAe4wFe9jMnhh9HLYgbngQN+GH39eoRuUSxjLfRlkCvtvf9kch5G++DaHKwxw6ebzE66NKaDOwa46QhbWUnRTuaxL13E7B9EZvIQi259J6UblaByYeLG92/b13zY6brIwnxowuiVoukIj/7Om0kfSBJZ0eogvT9B351+j+3Df/Mo3/knX6Z4tdbxQNqONzfbQ8Ym8ByDe8eKPuOGtt/3AjCzxX7x+YUbj15j1h2bMQbnzhzcMwCFhRaMsgUScX+8lUpoX29NPJyhTxCcgRSZH7kv6GEopUJsW6fw3vve9/Kud72Lt7+9dcFLoVDgyJEjHDx4kHe/+9089dRTG16/Wq2Sz+dXXZRSqtWmT43zp4/+5w3DaIA7/u5DfM8f/USHRqXU3iKNOvbiGPb5p8PZqgMglcaEMPBpm1gMWZjzK6PDWHVRr0LtpundUsHcvi+Y8axDohFkobz5FVthah5GekNbJbMVDesw/ZngZkjE9mcwW2gQL1XPr3aue36gXCjD7A6PXSP9N4WHW/k7mtCG0ct6bs+sCqNvNvyafr77T7+P3ts73C8imYbF5p+bcu4ajb968sblC89iZ4ptGODS/qpVmNtm+5pNHhfGGIxtYHq216am5Ro1iDqQSUJ/LwwO+JcQaaKoftfr+XuP4CSbOBGnlNpzmq6Q/sQnPsGTTz7J448/3rJB3HnnnXz0ox/lvvvuI5/P81u/9Vu84Q1v4Omnn+bEiRNr3uZDH/oQH/zgB1s2BqWUutnYp17kr3/qD2kU119Y6sDbjnPv+15PbT78fe2U6ibSqPuVXOUSMjUB9eAWtdqSaCy0FVstZwxS8gOWQCujt8FkDJKKQ6ka2BhEBNOTg5qFqQXoZM/xqQXY1wsT4egl3KyZS2DLwbTFiR3Ikn3DgS1dV+YKLd+/iSyFtsbQeHoMrCXyqmObjSTUYfRWpYbipLzO9n+Xcovut1ode+oc3HkIOz6Dc3QfJpcC18G04lfaZMHClqjX/AWFF+bbv6+tMMZfmHN5cc5EIjT9pZ0Od5UJK3dfhvT33RH0MJQKhrbs2LKmAulLly7xvve9j89+9rMt7e/86KOP8uijj17/+g1veAMPPfQQ//7f/3t++7d/e83bfOADH+AXf/EXr3+dz+c5dOhQy8aklNq7RISn//ev8dgHPrvmB7mBB0c59H13kD3ay50/9zDG0X5xSrWKFBaRqQlkZqp7gpRcL6bbwmhj/AW7GnVoNDCJpN9+YyuiUSi1r+KvrRoNnJMDIC54gp3Iw2Tnqu7FcTDRREsXLmza9AIM93Q+DN+hmkSY/fz5ju83friH+OEs+9+SwRscurXNgXFofPFpnLuOYLJJpN7AvjjW8nHI5SnsYA/20hTMLODcfWTzG+2W9ydzHW4Z4brIhRYumllvXO8PbqeXjjfDPUQeOn7LVUXMUqvkLb7+udteEqo59Xq4QumV4vHQBNIm2j3H1Hby5spItYGJaEKvlFpfU69gTzzxBJOTkzz88MPXv+d5Hl/5ylf48Ic/TLVaxXV3fpbWcRxe/epXc+bMmXWvE4/Hicd1ToxSqrXEWr72jz7Nix9ZexaIcR3e8NvvZuQNW/ggqNQeINUaxKKYFi02ZK9egjB+4F2P4yz1CA16IE2IxZByeVWoLNUKJtezeSV6PI7MtzCoGt69EAAA0jJJREFUCYBdrOIVBFu0xI7nkA4F0mIAE0Um5juyvw3NLMBQDqbyXRNKT71YQ+qdHWvq3iGO/kj/9eObOz+FVPZh4i7SEKg1IBoBz2Kfa39Ybk+9cuP/V6ZxRns3vL50yd92U50u7IqlwGt9lfsqs4tIzfo93aMu3guXcA4PYb99GgxE3vEqtvTC0lh/Fl/L1euYnj6/XVOYRDsUym+BE+mmNwNtVPOoX5wnftc2F2JVSu0JTR293/a2t/Hss8+u+t7P/uzPcuedd/JLv/RLLQmjwa9OPHXqFPfdp03wlVKdYxseX/mFP+Xlj67fw964hn2v09kYam8SESiX/fDl4kUoV5HFPM6J2zEHtzaVfaNty+S17gqjAdJZTDctEue4SLG4egHAJZJfwPT2Q3X9SjNpBNMuoVXqU5a5j5xG8n41eOzeYXoebH8FlzgOJplGrgRYGX2zmTwM5mA6/KF0pRFl4SuvbH7FFsg8MkL27h68kmXwHueWk23e154D1wHPYo7sC26RyIUidrGKk92gQKfY3KJ8odXfB/lFiET81kjFYltbztipDiya2bB4f/306v1eWVqYMJda/f3Lc5j9fZhVBe9LZ0I7PTsnbO07Qsa4BpOKIqWQtxnrALevw33flQoLh22u1tdGYRvPkqYC6Ww2y7333rvqe+l0moGBgevf/8AHPsCVK1f42Mc+dv06p06dAvyFC6empjh16hSxWIy7774bgA9+8IM8+uijnDhxgnw+z2//9m9z6tQp/sN/+A87+d2UUmrLavkKn//xT3L5M+vPzACwNY/xr4yx/823TrNUarcSa5ELF7FjF9esoLUvv4IZHMQkmp+5JCIwP4sdvwLFNlektYPrdk/vaGMQZM0wepl4jY2LETcIq8Os8kqd2pkC5a9fghVVto2LC3gPjOA67fsbiggmngxXGL1sNo/Z14tcDXcv8MknO3dsiI+m6L/NABsU2iwF+HJhojODWod97CXM216FWauS1nW7p+3RJowBerL+6wXG/93m59uzs3gCWtmuYzvyJRp/9ST05yDmwrU5nEwC07scVAuNZy8Suf9wMK8/9TqkM+F5zW7U/bYd1eDWBljJycTwNJDWQFoptamWz28ZHx/n4sWLq7734IMPXv//E088wR/8wR9w5MgRxsbGAJifn+cXfuEXuHbtGj09PTz44IN85Stf4TWveU2rh6eUUrcoXJznr97z35h9dmsfLM9+8jlGv/tYy1oUKBVmsriI9+JpmN+gh2ejgT39Mu4Dzc1skkoZe+Ec5DvcH7RVumwhQ3EcKGxSMbmYh94+P3BzHX/GeKNxY/GoWDz8C0wC4gnGNYgI5VMVCp86h5TXOJmSr7LwhRn63tZ7U/Vhi8aRTmGMi1wOYRi9RIrl6xW/YVSoxCk8eaFj+5NaOO+H9ax6J+K6iBW/H/xi53qjd8ry+y6JNTmrIbZ0rN7K8doLT/sHZv2/oTk2gtObxE4vYl+8hBnu9Re5NCaw47FxHMRxb7w2BEogk/RPVJQ6UN2+CScVJQz3SpCcbBwTD9FzSSkVSjs+SnzpS19a9fVHP/rRW64jm5yd/43f+A1+4zd+Y6dDUUqppk09cYW/es9/o3xt61UeL/7OY6QPZJn45iW+509+Ajemb7jU7iPWIpevYE+f2VKVnUxMIvk8Jpfb2rYnryFXLoXkw+w2pdKYNk4bb6l4HLbY+1nm51Yv3BaLY5Ip/3v5+faNsUXECtP/vzPE7xnElhtUH7u64fW9awXmv+qSe1MPrtO6v6ekk3BlPvztxUtVzGg/cnk66JHcQkSY/OpUR/bV812HyN2bJZoECPnzOhbBufMg9uostlDB6Un5feG7dAZD09wmzx4NDvitVa5d2/y6JhxVtis5+/sBkNlFKFZwDg1i0jGwFtPXj8wFMMPB8zBDw/6+t7ogbpvHQ8wFk/ZbugTIpPRzQepttwU9BKWCY8zS1J4QCdt4lujRUim1Z1349Et84W9/ksY2ptV95998AYAX//PjDD68n54TAySHM60eolKBEBHsi6eRKxsHebfYwmLDsjCHvTgGlfL2BhcWxgmud2wzjAMRt/mFCFeehKhVkTAEDlskDUCg/OWLm153WePiAvN/WSPz5lHi6dYsEmYi0fCH0Uvk2gzmwEDo2ooslhJUzm7977gT8X0JcvtCHkSvYBIG9/gASBWZ757nZysYxyD7R2FqemsVwsLGH8aTaSgvhZj1Os6dw9iXJlsy1lawl6bBMciYP5NPSlVMKuZXw3sB9fVPp/1+yUOD2JkZqITkZEgyHngg7ST3eMTiGLI/dn/Qo1BKdYE9frRUSu1Vz334W3zz/X/hv5nfgW+8/y8AiKRjvON//G0Ofe+JVgxPqcBIoYh96WVktsmKq1QKs0EgLSLI1cvI1Us7HGFI9PRiwt6uIxpFKhUohaTPZ4fUxurYmeanbdvZMvk/PkfqLYdxcxESueaCaTPSC64HTgzJV5Bia4LtjjAGmZyDoRzGOGBAJuYDHZIITH5xCxWtLdJzW5TQV0YvqzXAjYIX/vY57WKMnzMTjUJvL8zMrL3QYSQCRvz7bC2xGPaVOZzjA1BdamkUsoeBXFwdjtvvnMGm4jh37MfEW3zaK57ApP1e1VKrQWHp9SMSgWQSE4ngN4qx/pNUBGdgEHvlcmvHsV31mt+ipRbc8dckNug/vwckv/sYkQObz5ZTSikNpJVSe4r1LN/653/Fc7/1jZZut1Gs8fkf/TiDD+3nPV/6+y3dtlKdILUaduwCMrbNasRG3V+8bY0qNLEWGTuLzHRm6n1bGeP3WK43Qjv9DYB4HFnMd1WP61aJ3xZl8H97ABOF+d+/TP1Mc1W/pS9exN2fJf7W3OZrBRhYLoOWiXnMvhSU85h4BBkPvpdp0+YL16u6zYEBcAx4NpBFD+fno9Qud6YPspOO4ka7pZ7dJ1UPs4c/yYkIRKIQj2FiEWTfMIyvcQKj3293wXonWRsNzLEh7MvXMAd6MakY9uXwVEevq1RdCuD9RR6lYTBm59XSJp263krLRFzo6/Vf65bDflkjrRcLuZ5wrAdhDOSyMB3cbA8nuXcD6dT33UHf+14f9DCUCpazdAmTsI1nyR5+G6OU2mvqxRp//VN/yIVPvdie7RdqDDww2pZtK9UuYi2Uy3jPPAeLO6ikrdWRySnMvuEb2xZBZqf9XtG7pbdpTz+m0QVhdLMtOnYR4xrcrP//5BuGqY/NQ725YN67uohIH8ZsXCpphpOQTCKTRX9RwOWFARsNnBOD2Oc7V+HbajK59BhKJ/xguoMtaqwYpj5/pSP7cpJRDv3cCdxIyMpiN2EvTOLeNhj0MAJjjEH6VlRhWoFs1q+OXarcJR5bOqkit1ZPGwOjI4DguDG8M9eQbuj5vsR5+DgmYsE42POzyPgc7utP7KxqPpu9dV2H5ftys/HksthiIRwnQetVSAfXS3ovVkibTIz+f/YmUm/V3tFKqa3TQFoptScUr+b5zA/+PtNPNNkTt0m9dw+1dftq95J83p8OG412bp8i2CeeQubmW7I9+/SzyLGjOLcfBxHk4nlkaqIl2w6FbA4a9fCG0a7rt16Ynw96JKGRuCNGaX+WxoX5pm/r1SM48U2mfSdTkM9jEviPi5VhTK0EySiUu7utgsmmkPnOVnvPjDs0ZjrTZ77vbQfJDHRXGI1jcO8YCa5/cEismsEQcTC5tdfykNk1qmXj8aVDuVlqPN9FHIOJGyCK953zfjuSntSOHw8mGlm77clWWIszMhqO1h3GQMwBggmlnXhISxHbxTUM/b+/j/h9I0GPRCnVZfbY0VIptRdNnxrnTx/9z20PowFO/9cnsE1W4iklInhPP4f3xa/gPfMsja9/Cym1N4yR+XnsU0+3LIy+vt3zY3jffhzvuad3TxgdiWAGhiAa37yFQ1DiCaRa9dt0dE2NX/sVv7a4rTAaYPErk1jPf6ss1tCo31rHIWMz4C69nb65itDzcHZBBavki36V6UrxCAz2tGV/DXGZ/syFtmx7LamDyY7tq1XMkeE9H0bfbMMjcyx26/dqNb/tB4DpsorWvgx4HvbqwvXe2M7xfez82L/D1zexmIGQHPNEIOpAT3uOUxsxeyyQ7vnZRzSMVmolY8J5CaG9dbRUSu05Y3/6An/2pt+l2KE+lFOPX+GvfuD3KV7pzP7ULtFoQNkPoOXaJBSLeI893pZKV6nV8V48jffYE0i7eizmFzG75bxMrgeTSkO1grl5KnPQHBciUcR1kflZfzEndZ2tCZXvbP+kiHetQP47RawYauUoc58YozBmbm2hGtlgVkOlAKk1wrBuYm606zBDPZgD/RCPYGfqWJtAIq2dcDl9zkOq7X+uRQaSHPj7d5HdF7Ln9RbIXDCtCLpWT85f/HClFV/bC53vkb4jM4t4T19Bxm70urbPXfIXugyYicf9ftKh4UF/X0f36MTDGfy0Q/yBUbI/8UDQw1BKdSlt2aGU2pVEhFMf+jKP/2+f7/i+Z06NU7i0QFpXmFZbFYncuip8rY73+JM4996NM7rzyhOxFrl0GXv2vB+At5kUyhBfe5HDbmEGhlb3vm7UIZH02zJ04D5cVzT2f7P352GSpFd9L/55IyL3rMzal96r15menRlpNCOhXUJCEiC0AJJBxva1MdgG87vXWMZcyzaGC7ZBGC7XBoMlxBVwkRBCgKSRGGkkzabZ1963qq6uvSr3NeI9vz+iuruqu5asqlwis+LzPPV0V2ZkxKnMyIj3/b7nfA8iGrL+wtd6GEFF388epnSyQu5vL6HnN287UT09TypVQgXcDMrit8ep7kmS/L4BjMrSuREKQ7m8+g60xjjUh35pcqt/RuvJl1B7+gCQig0zKffxsgVdQVQd/eHLjsXCw+frtr+1MHvC7PrRUboGl5rCtRvl9raBaTZKgQz2u17S5TIYBoSDbk/SoiDjc60OcfOUbjgHqva2E6RlYQFicVQoeLOXdO17wYjH0eWyN3pHKOX6avf0wGJzeiuoNl+DrBUVCdD78TejTD/H0cfHZ2v4grSPj0/HYZeqfOsf/yVn//iFlhx/77uOMPS6vS05tk97IlcmV4rR154Q9EuvILk85pGtN4qRVArn5BnINFHALBYhngS7DbN2412ul/dqk+nSkpVKJOoK09UWCEOiW3PcNkRZisjtIXR+N7m/OLOlfTgzK7NRrV0xjKSgQr3IxCIbqkDFHMRDkFtDtG4DZGrhpnJPKVUxEwZC/STd2VdLrmjYYKxkiFCija1tHAeMwJJNjFNT07mdjgIwFUTD1x80LPRzp1sVUv1RdTgP8jmkoFDJ7SRVCEZ/vzf8pGGpyqMK3UlIpRt/uGAbLnJtge5//gDWSFerw/Dx8R4K7611ey2eJfzlLB8fn46iMJ3jb97+v1omRgNUsu0rOvg0H7Ft9Jmz628zO4fkN5/dKeUyzvMv4nz3meaK0dcCaDNfTsNA9Q24Y7aNBN9iASplCIVW9ydtJLaNCoaae8w2RleF/NfH6rIva0+C+GtDKEdDIYvqD2783dIaY7S3LsdvGatUOkimjP3cDHoRpA6VECU7QObRiW3vpxbKF9PYlTbOy6nYOE+cxnnyDM7FBde+x2fTOOdmWx1CnamT4lCPyibRqCEP+Qor5S7mxldvfFlPjNY7pzSc8IP7iH3/sVaH4ePj0+a08UjMx8fHZyXzL0zy1R/8Y3Jjjc9+WI97f+ktLT2+T3uhL1yEygbiZy6H89gTEAqhenswbz++7ubiOK49x4VLrc2kzWSRqNketh2JpBvnZkuMr2ZMh8LuhLdUak7jkGoF1d3rekf7rEvxhSKS2f5CYejeYWL3xTHMZaXszo2G0mtQyEIiDBkPlLDXAQHILf0tSkE4gogJTgm1xWvOzHO5usW3Fv1v2EVppsjwvb0Egh2SlzOdgtGBVkfRfigFk82xcGga9aou0BqUwc2G+ZtDmQYMDCKzMxtv3AwUEDBvtkir92GCnV+x0P0zD7TH2M7Hx8fT+IK0j49PR3Dxiyd4+O/9OXa+tfYA8X1JwgOxlsbg0z5IvoBcrDFzUwRKJWRyCj00iDFwcyd7qVaRiSvoS+Nre9o2k0oVumNQ9UAsq6EUBAKoruT2vS6vvj4YBNNyLUsaPVkrl1DdS5m3jo34ntI3IVWh8NDWsqO7PnCIQL8i/3QOqzdM9FZQW+3WKYJxoAf9Yht7SS/HClxzKpFsCWexgApbGId7trQIli8HyT1zqc5B3kzvgAEmcGUB9kQbfrxmodMljETAt+7YDKoDp8Gmgjr06BQt6IvzmPu33wxQBSwY2YVMXtl+YPVANCTiMNe4xVzD6vDvoakwEn6Vlo/PmviWHTXTgXdiHx+fnYSI8MJ//g7f/fhDnpiIiRbKC0Viu/yGhj41ILJURrqJc1cE/epJ1Pc+iDLcDD9ZWERPTSOTU663qJdoYe+/5aiePreRFbjZX9q5/l7Vs/FSpQJU3EaVgdD1DOpGUV7WWM/nJoovl9DpTX6+AZPkh0YJDbjnR+LBCKg6NOjMZ6E7CqnN2+94DbXcImLJG1tKVZzTYO4NoXTtmZUiwsxjjROHzHiA2MEkmRfnKBAkypIneLX1Y4Z6ISfGcRIRjGO73YUCLaigAcg2mtN1LiLiNgFcjXAAYiFYyHtiXFkzyajbvK8O6EsF9FMXMX7sPlTX9i2plAK1Zw96dtYbi+V2Ffr7YG5+e/sJRyDs2napUMBdqLMsTMMA4wxsL8Hcs0TfdBAz4Y85fHx8to8vSPv4+LQtTsXm2z/1RU5/6rlWh3KN/OUMQT9rwKdGVDyGcesx9CsnNvfCchkZv4yEQq4tRzbbmADrQSaLdAVbujCvunuh2uTqCdt2fwwTwmEolbdd/rwmwRCSaa1VkReRqpD/yuayblU8SPJD+wgmrot4yoC6pJaIYOxLojtAkF7z7ciW0KkQxnAMHAWl7IZCfq4QpniqPh7fq9H7miF6dJ6hHxlleQNKqXSYUJspop+6uR+BOjSC0eeLR1epzFSgVKJ4KkPX7f3IhbnrT8bDqB7LzfLfFVlqXqsgW0IWcqBrEKijIdRIApwKOBYyXoPoudmF6dVIF8AM1EWUNg5Ekek+JFW6SZAW2eLinNYYAwPoiQlvCP3VsusnnVuyCgoGl36WhOWAhbICrsWHaYFpLPu7ZcO/weiPomc64Fq/CvEP3dHqEHx8fDoEX5D28fFpS8qLRR76wGeZ/OaFVodyE9/5mS/xfV/86LXsVR+f9VC7RlDzC8jU9KZep0+daVBEdcZ2IBCB6tYyhUWE2b/LE78tSnRkE98pw4B4FyoQrG8G9GbRDhTyruAQibrZYZvIHt3UcXxWUF1QBEa7KT83VdsLAibdP7KPQLyB72UuA70xN/uynSmXMO/ejfP8DU0IRZAraZwraRBBjSQxdsVQhZX+0KKU+z2IxJj+68Y1Mux5zRCR/jDM5FFTqRv+Bocd0d/da1UzDUREoOwgqQwLjxdJvmkQq8eieKZA7rtz2KkKuUcvE717kOKJeaL/+hDmoV6oKAgaUClet5xxHMgtLfZaoAaXBMtA0F2lcoByFcmVoWxDOIAaiLmVEPkl+6T4xtVyam8vSNmtqrFN5PLWqwWcpy9g3rNny6+/FpNSqJEE9rfPEvjRe1DGdQHa/vIpzDt3Y+zp2vyOtcbYvdsbmdLJblRXF0qE5QtVa7OxCL0cqy9KpQMF6eDxQULHB1sdho+Pt1GA4TGPDI+FcxVfkPbx8Wk7MhcW+Mp7PkPqpDe7o8+/OEVxtkB0qPGdvH3aH6UUxvFbcNIZ13e4E6luXRCZf7TI5B+8igoY7Ponx+l7XQRlbjCqCodRkZibFd1KMXo5IkvCtAGRiNv8sE5ZYmK3sHGlRxGBgFkkdCSGs9CNM51DSuv7xyTef6CxYvQSxp4udBsL0iICkTjOU2sIycvOa5lMIz0RV5xeyi4UEURHUHED55lJ9nzvIIVUD/mxHNlXtllCv4ye+4cZCJdhZvV9SqkKdHhFUySEMZQE8Yh3UsNRyJVpRMP8Z15l4U9OYsSCOKmV94H8E66f8cyX0gy+F8wQUMtltFK5qRmeigJRA3DcBaflrLdQGA2hRuKQX1qssZc+o20sWBm37aU2cXVjVDyAioWQyxnY3QX5KvaTY8jYPHJ4G4Kk1hh9roWWXliAQpNF23AYo6/frVhqVNUSYPZ2ZlVC8h+9ptUh+Pj4dBA7IC3Ax8enk5h+Ypy/fN3/8KwYDZAfT/OlN/1P7JIvEvnUhrIszDtva3wTvFaRziJbWJpffLbMxH9/BQCpaiZ+52VO/eJpFp8tI87qk27V1+9mRTfboqNWRLvCNOIK09tFqcb7VLchyghCpUJ4RNP7Qz10f/TAiuwQoy9K1w8dIvLGvYRfu4vYuw8QHmmS4Wcui+pv4wXLq2J0jbqXfnUKMWM4eQtnTkMkgT41jfOM2+DRujJPopBh5FAIKxEkUAd7CSsZXJHVuSobLFB0AuZd+3eEGC3XFkHcf5UBvR84hGi5SYxeTu6xCSb+cBK7UZroWtfm3hiqL3BdjF5OcYtjx/4EKlinRU4tyGIJYkHsR87gPHOF6mefRs7NAKBP1lh1sh5aY3R3owaHtr+vWjBN1MgujN7ehgrRVzG6O1OQrrw60+oQfHx8OghfkPbx8Wkbzv/5y/z1W/+A0qz3M8vSp+f40pv/gMUT/sDNpzZUMolx+FCrw2gMWqMCtU/OqlnNpT+cZuw/vwA3CM/lS2nG/vMLnPo3p1h4prRCmFa9/W4ZsBf8KTdCLwnThnI9prcScyiMIDuqJL8WRARZWOmrHog79PzkYay9ScyBGD0fHiGyX9N1j0XigRCxo81dDFK72lOQllAYfT696SRM/coUMraAzOZwnlsjszpTYvT7dzN6e4SjH9jLgR89jBGsYaoSWLnNwFv2cPBN/fQH16+OkIJHF63qSMf5ZK+BjGeX7gXXv8d9D4QI7ktu+NrKWAan3KDvv9aQWLnwqPq7UFG5bg1yI+WtLSCo6PabD15FMg766UvI2RnIl9HPrvTiN+7aXbdjKctsuCitBgYxhoebuuZvdndm9UXpuSutDsHHx/soj/54EF+Q9vHx8TwiwnO/+ghf/5E/xWmjjKbZ717m4b/351z665OtDsWnDRDbRpz2Ob83i9RQMWAXNVNfzXDyZ58n9bX1m9GVxzKM/5cXOfmvTzHzcA56+qHSYk/KreA4UCy4/qGbEabDYSTVgnLndsAMQfFmMTLQ5dDz/h66PziMGWxSNvRa5LKowY39Zb2EEMV5agqZWyWrsy4HENRkyv3/XI7g5DyH3r+f2OHuNV/S+8AIR985SPLuAQCMqEVPrwHpGqoGitVlmbWdiT5zBYxA7dU3ykCK2m3i1ga49i+CTMwji5Wbmg5aNYiCg3//CKGexp0HapkgrYaTELTd3gprbT+ysYjecDZo3ihTWVCm27S3DijLhK4GXA+T3Rh79qACVtMXqo1ObXBut/je6ePj01G0x2jDx8dnx+JUbL79U3/F6U892+pQtsT8c5M8+QtfZf97b2l1KJ5ETyyghpLuZGQHoyeuoE+fgWrnCtKs0+RTV2Hm6xlmP38Ond9cuXLlcobJ33+VgR/Y67nFf6ekmX9oDhQMvm8Dz0276v4Egq4YVCquLiIpBcEQklpsTNBtjohAam2RXinVejF6CTUcRWYyG2/oASQUwXmq+ZlxairFyGv6OHs2BUCgNwRKMfzm3Ux86SL9uyyYy9F/ax/dR5OE7DLMZdff6VVEXFENb5wPDSFbxHniNADq2G6M7tC6wpzYCv3SJejrwjzUv/SoiZQdVNSCFi6aSlWWMrzcTGgpOMjpSVRP3G2mOZtBJXpXvEYF1x5bJN62j/63xxufNRt2731qTy84hQ3FXgLa/Ts3qZ/K2Bzs6a7LZ6TCJgTMNfs/6AuzON1B1EgEI7GF5oarYCS60I5dn0XWnl6MWNTNUG9EE+EaMOL1y1j3EuJ08PXSx8en6fiCtI+Pj2cpLxb52of+hCsPn291KNsic3aBL3//p4nv72Hgvl3c8g/va3VInkC0YH/pKYzhbswffO21hlc7DX1lEv3KiVaH0VjiMaDMjfViooXMKZupP75A6XxqW4eQKhDY1i7qSu5EgSv/7RXsmTwqaNL/ff0oU11ryCgiFM6XiR26wcqkWnF/giE3i26512UwiJSKkPK+bVHLMEOQ826PgRXkstAVhqxHGm+ugxRal0lsXFngyI+OorIlyC29V3MLHPzh/ZBxvwvmlXm2tqxp0NGC9DLk1AR6Vx/Gvp6bm+0t3X/VVfuT+SwcHQbHxnn+IlRsV9BOtk5kk6kcMj4L8QhqXx/y6mX38elF6E+iDvQiCysX6sKjMfJPrtxP5I4But80QHy0SYHnsqi9cajWeN0ulVB7+5CxzTf41OMpjDrYAamogXHvPvQTF1bfIFXA+cYZjNeNYtyTrI8nswhGbx/atm9qHFkT4QgqmURZlhtPi4ToqxhxDw1I6kjHZn77+NQTL1pkeC2eJXxB2sfHx5Okz8zxlR/4Y9Kn5lodyrbRVYfxr5wB4OQfGOx911Fiu9urVLshOA7YDvrEZUhGsd5yR6sjajr68gT6xKlWh9F4LBOlrk8OS7Oahe+kST86RWWixmzGDXCKDmaz53+y1JiwVFr2kDD3t3PM/uEr1x+zNdkX88x//gK9P3QAqztA7tlFyhczBP/pYaqLNtHRG4TpShnCS/tWyvWLTi006y9rS0QE0u1lYaK6I4jXBelQGP18a0X+a1YeyzAmt18lULygUTGLQI9gWnbHL4zKlXm0qTBGVma1OufnIVuAwPWpofPqFdduouJm3MrUIiSb1IDuBqQiyOKSVUyuCFUNluku2gUs1KFemJgBe2V2cKB35VR35J8db54QvZy1/KLXpOL2F9gom7qBGPujqMgRZLaAFKvgaFRP1L1eOYIKW6iuIKJ1/bLMtYPRP4AAUixCJrN2xncw5ArQoeBK8bkJDQtrQUU6U2aJvf1wq0Pw8fHpIDrzSunj49PWTH7rAg/98GcpL9TgAdlmiK358zv+G2/9zIfY955jrQ6nJegL06jBJM7jpyDrfsb6idM43THMew62OLrmICLos+eRCxdbHUpzyOSQrjhiGSw+mmLi/3kFqdZ30mhnbYKJJlq/hCOuyFAsgmWhC1Uyz2Upnc+x8PkzK7fVwsSvPgPAlV9fKSqf+YdToGDk5+6m+8Ekylg2sy8VIRoDLb4YXQtGELLttYipooHNVuY3FUGhz2fXLN1vd8qnF6hccm1TzP4owWO9BHeFCCQcTNMbwla9kXQelgvSSkE67wrPy73+03l322u/FxBtooyV54KIQFVQtTSg3CIqqFC7epGJeciXkDPX7WNUXwJVKCP2zcJldcFm6B/fihU3yDyXaY0YvRUqFdT+PuRC7dczNTqEMRhl014fa+1PKdRQCIZuzIi9IaEinYbeJauU1TLvldpktrK4yYWRMETCYBiI1tctzQIWyjCu77PFmdBrYXSgIK3iQcJvONDqMHx8fDqIzrtS+vj4tDVnP/sCj/7MlyinPZ4xtg0qqRLV/BbKETsA58WLOF9+dtWsH+erz6MSUYxDwy2IrHlIuYw+cQqZaRNbgXqgNZWJDON/PE3+xZmGHKK6WIbd0Ybs+xqmBcEglMtQWCbUBENM/f4FUn+zRXshgcnffJ78S/vZ9Y8OYASWidKFPITCa7/WB1gSxbJtuIi5jsdtqxERJGci851rESPLGnQ5cwWKcwWunkXW3iTBw0mCwwECURvD9PLSwSbIFJGiRsUCroBomNeyoDdCv3gR43sOgG0j5zJIuojqiaJPXsG4ay/GnljDwla9QVTvCPrJiyt9sLVGsqs32gzvDRIdFkCI7q6P13EzEBFURUNfEuZSNTWlVH0xWmU/IwtLC6ZKuc0JDQPyOXfRNhhExWJbz1zW2q00X5a971URejkq5N1r+1aJvvkgRsiXj3x8NuTqYpyX8Fo8S/hXFB8fH08gWnPxd55k7vef4bbX7OXEcxMU59ur9HozzD49Qag7zJ53Hml1KE3FeezU2iWoIthfeALrx9+MMdTd1LiagWiNXBpHnzvfFpOpelJcUFz4zTPY840TDBe/Nk7y9gZWHVwVQPKrCB/5HMravliV+folKuM5Rv/T7SutA642O6zuzIWsmjDbLzsaALuMcWwQfaoxCzXbIhRHvzTR6igaynJB+kbs8TT2eJoCgKEIHO4leCBOaNDCilS9OrerCf3SJfc/htqcr2TFRr80jYwvXMualyXrFP3COGrXsZVVHnVEGQqx5eamjAKUV782umJ0+yBLH4ZywsjlpetZLIxKRJGF9LoWHlKooLpaPLUXgUx65WOVCsTj9UrcbhsaWTHQKmLvPtrqEHx8fDqMzrtS+vj4tB3iaE7//FeY+NVvY88VKL00zS3Hhxo2qfECL/6X73DlkYutDqOp6MvzUNnAR7HqYP9/jyJt5gMrto2s0YRHSiWcM2dxvvM4+szZHSdGp84K5371REPFaIDUw5dJv9yg80Yp1yt6HUE4dltfXQ5VvpS+OcnNtNhxs/nNkt+sR6tHKJfBsCEZaXUkKxArgHOi86s4arYO0kL19Dz5hy6x8MfnmPnDcRa+kSN3QVHJBdC6TccrWsCp/doiIu6laDULFy3IXLl+sa1GdbVYBd30BgL1RUTc93ZRICXXRH7AtSiZXAArAIm1M9Bl0cOVDDcsIuiZ9hrjbYVrDUI7CHNw+w0zfXx8fJbjZ0j7+Pi0nOyzk8z+2csrHiufnufu1+zlzMUFctOrl2K2O+UOzgBfDf3cecjXMFnNlaj+ybcIfOj1qD5vltiK47hCUrGA5DLu/5M9qCHXbkREoFhCT0wgl8Z3nAgNoG2Y+Nssi1+52LRjXvg3j3P759+JFaiD320geL3krlSEwvrf18zjU9s/pmWQ+N496JLGjC2V+yqF3Jhx5rMCMSxYnG91GFunXMboN2FoEH269ZnSIoKec2AHWEtt2cveEapnFqieWSAPbgb1aA/BA10EBq3OsvhYTiiIXFh7oUI/eQE5NIRxsBsVboAgF1zK6BYgYCHdXYzN2HzuX3ydO95/lDf98BChcPstDqhAHJnMQGmd71y+BIUS9HWDqaBQhGLZvUeJQCqPjCTAdhcZpKrdhYOq41qy2Np977rCqL4wymre+6SLJSgJVByc5yaQc9NYP3w3xnDnCpzKbL/zcD3MwRhmb4Nt0Xx8OgTfsaN2fEHax8enJTiFCk6uQvrRMS7+8iOrblO+mGL37cOcmj7b5OiaQ+bCAnahQiVdJjIUc5u04IoBTtnGCrd3xs9ypFhBvzpe+wsW81T/6BtYH3gAY99A4wKrAREB23azY0tFJJeFUumGbYDJaZwZNwtcspmNs8E7GKciXPzDmYb5Ra+JwLl/8zRH/8u9qK36VYLbsLBUrH0hQSlyT09v+XBGNIARD9L3gYOILdfF6KV9+2xAqQMWfETAKUAs2HohONSFjF1ubQxNQurVrFEL1XMLVM8teekaisAhV6AODlhYURvDaF+BWhQgglLBDWs15Nw0zoVZzHccAVXnTFHDvR0/8VSOE98Yo7hQpDBfRBzN059+iaGDce56XRuKnLasL0ZfRXA9pa+iWGoaKJApoZ+8sPE+pkDGAhh37UEFN39/kaq48SogpFbaS61FPo/9Z6+sKPSxv/wqgQ/fjYoFNx3Dtrgq4G93H9d+DNwTnWX/ApUOuC8tI/HRe1BW52V9+/j4tBZfkPbx8WkJY//lUSY//Ty6sL5oF5rIcM/3H+Pk42MUF9uwYdU6THztHP8r8R8RLbzuP7+LIz9+N6X5As/9p29y/nOvsPtthwj3R7n/176P6LA3M4VrRb90aV3vw1UpVbH/5NuY77kP8/Z9jQnsBsS2XSGyWnUtOCol15vyxs7xy18jIJk8LPpZrKKF9Dlh+i8vU77QmvejeHKB2a/OMPjO/q3vpFJxM6RtG0Ih99/KOtn9Ioz+lwcZ/0/PUBnbREWHoej/6C30vaN/pQi9HK0hHL5pEcTHRZQJ84sbb9gOiKB6Y0gLBWkxTZyX65Dt3yZIo0QjfUMGtakIHOheyqBuryaJIoKKJJCxeYQav2tao59zzyPz/l11iWP6bJZHf/9VXv2LE2tu8/XfeI4Dv/cgye72aSgnIpDb4vVd2FqzwHIV/fIVjLt21ZzJKxVBn1+AuWX39lAAdXAI1RtYV5hWpoHxwCj6sWWCeaGC/dAZrB+4FWU2SegMBN33S2RJSOa6kAw3LADLdQFdZOn3GzzMRUDWGB92kO2gNdpD7L23tDoMHx+fDsQXpH18fJpO+rEx5v7m9IZiNIC9WMJ+ZpJb7t/Hc1851YTomossibRP/B9f4Yn/4ysrnhv/8mkAdNXh9b/zPlKvzhDdlSBxsLfpcW4HPZXCeeL0Fl8sOF96ClnIYX7vrbVl4mwSKZfdjOZ81rXeqPV1AtgamU/5YiFQSimufGGW3FOtF7Ou/PYLJF/zNkI9W9yBdtwfEXeBIrBxBleoV4je2b85QVoLc585gRG+nf53rSGgB4KQzdS+z52GXYdsNw+hukKtdQsPhKBktzKC5nE1s7QZOEL13CLVc0uCrsIVqA8mCQ55N4NagkGYyiMzm/cTl5k0KIU+14U6EHezagEsdZMIKlqg7FpKSEmDoVBxE0HIzJQ58+1pvv6Jb+NU1s9oL6dLvPJEhgfftdWLf/NRSiE9FqSafOBCGX1mAeNo74Y9W6Qi6OfGoXrDtaFcRU5chsFuONSLWmcdwNgfQ78cgcz15BK5sojzxGWs1zc46cAw3F4MzrL41xKS64RCrtvLtDk9/+wBPzvax2czKDbXMLgZeC2eJXxB2sfHpymIo0k/Pk72uUlm/uwlymOby550LqYaE1gbcO5PX+Lcn70MIrzzCx/1vCCtz08hM2kklUft7kOfvOx6H25nn4+egIUs5nvuQwW2n/kkjgPplOvNu17m63pUNXKl9eJrKxERspdg7ptz5L7rrffizL/4Nrd95i0otiGuXV0AqVYgEoXi2j7SGovMw5uwpVlG5tuT9H1f36oLLlLqrMqQeiIomOuQ7OirtFi8aNvmfFuhlR6vAtULKaoXUtczqA/1Lll8mAQi1WsJnC3FcVD9iZVN9jaDCPrkBJyzoLyUhBAKYNyzF2MghGRt9KUUciV1/fllFLTB7/zvj2/qkI/+3ovsv+1Bdu9tE9szKwYzLeqVMpdGYiHYE1t3wV/GUjeL0cufn0kh5SrGbYNritvKUJiv24fz0MrkEv38GM5gHPNIg8a2gYBbaeQ0f6HN6ImgF9r7Hh5+/X7C9+1pdRg+Pj4dii9I+/j4NIzci1PMf/UsxXMLZJ+dpDy+9RJ+e7FItD9KYW5nNQK8xlIG4Dd+4nN873//AQ7/2F0tDuhmpFzF+fKz6BPLvEefq8HPsEb0ictIuoD1wQdQsfDm47NtyOeQfA7yuW1nVaqgAXtHkMtTHZWhWQvlDGTP2sx/7QrlS960KrFTFS791gkO/OyR+uxwtcm6ZeHYJtWpItN/9Aq6sLWMq/DBJE5esOI3H0PF4kiqw0TXOqHEQpzO8ulckcHSE8PY1YVkysh0BjbIDt0OJdvk7Ocn2f/GISINO4rHaJZNQC04QvX0PNXT865AHTAIHu4leCBOsN/ECjVXoJZwGFUUlDK3LkZfRctKsblcRT87hvTGkanUui+N4PD9P3cff/vJp2s+XDVf4Rv/8yzv/udH6ev3qHVHIIpkbVQkgKTK21603w5yaQbCu1EDodWftwWZquEcSOehoCG+znu+RhNR5+9Oonrvweir49VHqSXrrdb18zD721uQViGTnn/+QKvD8PHx6WB8QdrHx6duiBYWvn6Oxa+dY/GRi9sSoFejf7SXsZ0qSC9RzZZ59J/9Nft/4FYCzW4EswyxHZR1fdIhxQrOE6dWitGNOO6VBaqf/gbWhx7EGEjW9ppqFZmbaYjtgTKVK0pPTIPT2PLPVlLOQn5ckz+bJ//iPJUr2VaHVBOpr4+RfstuknfWoTN8qQjRmJtpZdvuJNcwmPnjCyx+cXuNV7NPTNL33hGs+CqKUwuyutoBEbXSy7RTqOQx7hgBNBTyUMyiAqAOJcAKI5NZZK4+2ZRVx6BYNMhcrjD5xTOgBeMtw3XZd1tgeVSsBKhqKifmqJyYA0CFLYLH+gjtjxHoBTNQbYyFVTCI0hZcWmhssn7F3lCMBjertlDY/DVw7InL/EW6zE/8X3cRCnsv6190FWYzrS6IuIacmkAH9mJ0r5JVvommsVLVKG7+XunZCvqlaWRmjXGYo7G//CrGrh7U/h7MQ9u0XLECrld0C8VoALM3Qju3t0587F6skUSrw/DxaT8M5T0fea/Fs4QvSPv4+GyL6kKRqc88T3WhyPzfnqZ8uXFep129dRCVOoDyYpELn3uZox/7npbF4HzlOcw33w4BE/3SJZyHX4JmZSqmC9if+SbW+1+HMTq05mYiAtkMMj3Z0AxmZQB7hpArs1Bt56nHSkQLuQnF3N/Nkf3uZKvD2TIX/u3j3P65d2IF67BgcNU+Q7jW9LDw8vy1p82eMIHBGFZfmNxjEzXvNnrHAMHBNYZk1SrEuyDXHosAzUKpANJB37draA35Ve6j5QqUK6iBEDK3/cNUHIOXfusMurzyexFssYDTVFpp2bFJpGRTfmGa8gvu70ZfhNCxXgJDIawuhRWsbOgDvOExRFAVA5leqEPE9ePSq1uLZ/bELE8+tMAbf6CvzhFtDQGUiiG5MqS8dz2Xl8fRd+7DSKy8F8lcvvad2DePA3XGxvna6Y3HYekiOl1EZYoYo91bP5+DoZYL0VcxujdfzecVrAM9dH34jlaH4ePj0+H4grSPj8+WKJ5bYPrPXubybz/RtGMGPbqy1wpG3jTasmNLKo9+6RL65UvNbQq1nLKN/WePYr7jLsx7D90co227QnS+Ob6MSgG7BpHJGah4YyK0VewizD5eJP3ENJXL3ps0bxqBM/+/J7j1d15bv4UJxbXM5X2/eBupby/S+9ZejIiBnXIoXCjWLEhHbuun+61D62Y7KsvyTCadFxARWNyhjR7LZdT+XuTSxiLd/KxJ+nweI2iy964g5rKmedlFdZMY3Xv3AGquA77ztWJ4yLJjk+j5IsXHJrhqBqDiQULH+wntjxBMOBjm5haIRQQVTyDnN9+8sJGIyLXmz5vl2LsO8cC7vNFzQ0QgZyKpOqwmNRB5cQx9YAjVEwYBWSwilzcRs73ymqJTVZxvXdzUvVcmFtGn5jFvXaPR71oYBpimZ8RoALN7dRsUr2Mkw/T/h3fUpWeLj4+Pz3r4grSPj0/NlMZSTH3mBdKPjZE/MYcuNnfQp7KVph7Py1z5xnmOvPsQMpPCuGUPKti8y/k1L0Ghtd7JIjgPPY+kC5hvud3tVH81K3p2pulWB0oJjAy0baa0BCJQhMyJLLOf254FhdcoX8yQv1ghtr/+Ta4CSYuB9w5c/73HIhGP4fzc3Sx+8aLrsb2GoNL9noPs+gf7Nj6I7jCf5G2ijBBS2qGCNKC6QhsuUCwuWJz/n69e+704MUDylm4Wnp6l/4Ehrvz1+ZteM3BXH8x5Kzu2oXjJQ3qbSK5C6btXKH0XUBA82kf41iShPn1NnBYRsCyw7RULYGJZUMBzYjTAuXl3mBFKhChnNteA+A0/uo9A0COJDFYQUu1xzZKL08jFLb646grSUtHoMxn0C5e3NE50nrq4eUHatDxncWUk2k+QVpEAA7/+bgL7u1sdio9P+6JY2RPEC3gtniV8QdrHx2ddrvpCh/clefmDf0Z1vnUezpUr7TGYbwb9qVns/3URAJlYwHp38+w7pOQtsVU/eRpZzGG+9RZYnG9pdoybKT2ATMy4PsPtQCCKFAUmUkg0wtzXxlsdUUM488++xaHffANdhxs/QVQBg97v7UapA5QvF1j489Orbmd21egDXym7J9cqE3tJKfTYIsbRPlS4c33Mr+JmR++gLN5VkA2EuXzJ5NwfnlzxWO6lWXIvuYLj+IXUqq+rFtvkmlUvOrXqSqByap7KqXkwFKHbB4ne00OglIVSHkwDiYVQPTH3PZjPQcabjdcO9ysOf2gX8oER5koGf/npM8yfmb9pu+S+JOmx657yR95xkMFhb2R3ihWEzM6ocZFyFedMFv38Zahs43pStpFMGbUZQbcBnurbxaj1Hu8hev/NmwneMrDxhj4+Pj51wBekfXx81uXUT3+JuS+e3HjDJqDzVZJ7E6THfWG6Wrk+udHnppD5LKqvq6HHlFwJ5+mz6Kc9lj0bDaH2xmF2qtWRAMvsOzzc6FCUibIiSKoMqcVrj098dZbSpebYnLSCzJMzdB3e27Tj9byhG9FJso9OUl1qBGnEAvT96DEW/+o8yQdrKCdXCsECKSG2AVWg4EAvblVAqgTpAlR6oH3tKmvHCEFxZ98DVHcIzD5kPg+Z0orntIYLfzWFVDefVR+MWrAJu9i2p4MypNdEC+UXp4kcCINaWqx1NGSKiEdF6NVQhmIgKtz/nlH+9pPXBelANMB9Hz3OnW/u4/SzGR79vZfQVYeho90Nafq4WSQQgYupHVHlooZ7EEuhH724/Z1VbOwvn8R6/x2oYK3fU++J/kas/lVZDUNB/IO3E31j6ywBfXx8dh6+IO3j43MT2WevoEyDsd98jIWvekt87NnbvaMF6XBPmAf/8R30WBW46mCSLVL940ewvv9ejCMjdT2eiCATCzhPnELOeKyxnWWgRgcwjvWgQt4SFpQB7B5CLk95ayIajCJZB+aziNxc7RDeE2tBUM2jdC4NNE+QBldI6f2hUaZ/90UAhv7J7fS8PknfW3swwhtk8CkDDAP9wjTqkGupop+/7D51fAQSBpJ1BUn90iTG60ZQpvcm5XUlV9p4m04nl0EpUP0m7O4lNymUz2eJJTQzZ22K5xY33ofPjiHywG6Cqn3E5/WYmXTvW3tfu5v73jfK7mNBEkn3OvrAO3u488E3EAoprIAHxGgzCJez3hoDNILeLtRAEKVLSKF+GcEyl8X5zkXMt4zWuLjQ+s/8RoxoewjS0e87QuKj9/g2HT4+9cK37KgZX5D28fG5hq46VGcLnPynX6K8rPTRS3QlI60OoaW85efuZsgqXxejr1IoY3/uMawf+16MA4N1O57zV0+hX/WIhUPQQnVHIRlFDURRfcFNZM40H2UAe4Y9IUoLoIghF9b3h7UinT0syJ9ojT9u39t6Qe5g4QsXCA65JcjritFmAPvxMTBNjAO9yGQKmUqtSACTk1OIocB2zy11aACUxrMjzjogKgBpbzcFazrlMlMPLzL/1Yvb3tXJT53k2MeOUZgrY5ccuvfHCImNKIUxk2ltz4BG0GF/zqooCPZbuKUV7c8tt3Rx6ydex2BME9wbRSVXfoixuDfGBGIYMFdp+ybHG6EO9INVQGl3oVA59f1S6RNXUENdmLfVYiEha1pbNR/lhiHeX4wI3j5E78ff7ImKAh8fn51HZ888fXx8akLnK0z830+w+NhlMk9ebnU466IupbDCFnZph3ldLpHoDkBubf9Q+y8eR/V0Yf34m1DW9vwTnROXvSNGA8b9+zEG26tBzDVRenyyJZMkAVQgDrN5JL+xGBsabD+/w82gc1XXrkSab6XS9/Y++t7et+F2ogI4Xzl17Xe9sGShcuPpo2Vls8SqgzK84ZnaMAqdLe5sBW0Lqceu1G1/pz59/dxbXhNz9MePEVvosMxrTwhX9cXsj2KNxNFFG3s8Q9dbdxMyOiM7GmBPEq4uscpCHtUTbXFENyMAORNyO8D/RqmVDTLL9f9OOY+cRvVHMYY2qOCqVCEY3FYfEamCTKcx9nS7v19t3u2Iu/hbdZCqA+UqlKpIqYrkK0ihBPmyW8FjLxOhE4ktx9Isuv/p/b4Y7ePj0zJ8QdrHZ4dTeX6C/O9+h1Jae16MBrDnixx94yivPnSm1aE0jXAyxIHv3U1+tki4ukGX+bKNTC0iY3Oog0NuAzBHu81lKjZSdf+l6ri/V2x3EH/t/0vbpfLIpLfEBzm/AIP1tSRpBq4oPYJcbp4oLShUIAYzOaRwcwOotYgYRQ7+/J2c/40X19xGBQ0Gvm8fidu7MYOKy396gfypVB2ibg7iKPcz8SKmhUxtTcSQ8QUkMYIEFCridNwEUwzLbVrqs4LMBDjZG0tm6o9d9qYf/rZwvJ+9WCsqHiRy5wDhQYUlFURMON6L6hCrjlUp2WBZnmogLCIoHUXmW1ON01SiITDzLK/KkWIDrhMi2F9+lcCH70ata4EhUKmAaW4pM1nPF3G+6o596vVXeP02HHvPMUK3D7c6DB+fzkMp710AvBbPEr4g7eOzQ9HZEoXPPkv5IbdhoZWItzii2glM5jADJk61AyfIN7D73iHe+LGjhLI5CPS5WRk1YP/5oxC0XIFZd0YWmEwsIqUhVNirauLaKJPrmdINQkQgGIWFCuRKiN6a124sUqL3rXtYeHjlAtXwBw7Re08Cs1xcsiApgA27fvgAZ371+e3/AU3CKWoMj1plO5fzyMsTW3yxRr/ovla9ftcK5w7R7i/KaONrQblzxMN6svhCtinHKS2USbZXgcrGdMi9EQXxN4wQCRWvVVJ02oLUmpimZwRpEUEZceTyDrEVMoybzjM1aCGn1th+O+TL2A+dxnrfrah1m5HK0jmh0QslVCKM8+I4xkg3xkiCtX16FM53GhC4hy07QvfsoudfvqHVYfj4+OxwfEHax2eHIY6m8CfPUvryq252yRJWqH0uB9WZPEfeNMrJr3ur4WK9CcYC3Pfhw64YDVDdxKRLC5Q6r7xdJvKoQ12tDmNLKFNdz5Re8YQC0wB78wssYpgoMwIVgfkslFN1ibXvwf4VgvSx//Megk4eKkXsQITSgk10wEJpm8VH2ysTrJqqEoh509pCJUJ1sbWVU1kk6tqvqJ4wejyNigRRB8N12HvzEQyYS7U6DM8hjrDwaP3sOtZj/sV5ht7aD9kOairZIc3m4m/b74rROxEPlbsoI46M7RAxGqBYRuhCcX1sqoIV1Eg3Mpmq++FkYhHnictYr9+37nbOS5eRJasrueR+Hs7JKziGgfWjr0Ut3WWlKsh8ARTos1Ou5UadUR4VpK393fT/x3egAt4cC/n4+Owc2keB8vHx2TZSqlL8q5cpfeHmcnwz4J1BfS2E5goo00A6qOQWID4cZc99wwwe7mZ0t4mqFFodkqfQZ2ZQo3GU0Z7ZX8pSsHsYmZhyHwgGUMMD4Ojrj22AiKCCMaQoMJNGpP7nSNgsE7ulh/zJRY594h6CVddG4vI3Myx+62Tdj9dMKjMloru9mSItc/XxHJW561mzMrb0bzKKSKg9MycdoyP9frdLdhrsheYIkeWZAq/+3RzHfnAvpsfsnLZMu55SQROlIHCol+jhKAEKdHIz03XxiiBtxK6JnzsGR4MKgCwTpJVCHYo1RJAG0M+P4SQjGLf1r30vCwdW/yy0xnl2DKM3jvPCpYYI0DexhUSDhmIZxN97C4mPfQ9GV6eVvPj4eAiF927LXotnCV+Q9vHZAYgIlScukf+fjyOp1SevhuXRq9QaVCdzHHnzKKf/7lyrQ9k2g7f385ofPUp6psjo4ShmtoDrheeNMlRPkS8jKRvVu56PoLdRAcMVpbN5VE+X2xReKYhFIb+2uHzNFzpdRq40WBCyHUY/shtHHcAquSJpZiHA4reak43ZSMoTObjHW4K0nq2ipzMwmW7cQdIFyPVCV3st4gnKz45eg8WXmts0rTxb4NzXJjn6xn642myznWnTDOne7x/GFHsp07OIZ2eZzcALC2xmDLm4w8RoQO3uZTW3ZRWqoIYSyHSmIcd1HjmF8/IE5l17IOj6RRsDMSRfAUfQz19a87Vy8krd/KFrYjOVjU1g8Lfe63tG+/j4eApfkPbx6XB0ukj2N7+J/dL63rXtmHAaXSyjDIW0oQ/k3teNcNcPHKScr7I7boMuMjAEZP2M6I2QiynoHWh1GNtCBQzoiS+bS6t1M0DFCsNMESk2r6mbKlewWGqWZhpc+h+vNO3YjaR4Ng0M1W+HZgC9UMboDSJVhbI06E1MeZVCPz9ev3jWQZ+fx7yrpynHqhvaAsdjWWYeQERY+E7jPOnXIn8pw3OfyXDghw8RjFnEpAKLy4TxcADCAXQ4iDGVanp8m6INxw5wNfGqPWOvO61+G6wocmHnidEACCi5uaGqUgp1pHGCNADzOZyHT6IO9kM6i2eXlmwHLAV2q09UiH/oDl+M9vHx8Ry+IO3j08FI1SH7nx/GPjG94bYeKXrcFJWJDEffcohTf9c+XtJGwOBdv3Q/A12gCgWIgndH0h4jGsIY7Uft8lZ261ZRSrmNCItVZH7BLX9dBVEmzFegePPEr2k4mlv+3T2c/a8vY2fa25u8cLIOntemBSh0qop++izYzrWvsToyhDkar9liQnTzhmIqHEAcBVWFCnv/wiMomE+1OgxPkp9TVKZal6V88S/c6qRjHztGVLlVPXMSZfwvLl4TCe/54G4oevh60aaCtLTliK1BSIuyKUwTlegCW7dcE/ciKlRGHRlAzsw27hijA0gm6/n6ALM7jDPXWo/38Gv30P1T97c0Bh+fHYWB98QVr8WzhC9I+/h0GFK2kUKF8rfPU3l2vCYxGkC1aelqtFBl/+v2Mvbdy22RKW0FTfq6DZSfCV0biQjGnh7YFUclzPb0v10HpRQSCa6Z5SWBKMyXINf6hlWBSp59/+hWzv/GzR707URlIo8ohdqMJ7EZRGxBmQr7hSswtXbml4oFahOjTQu9WEE/fbr2OLaJzGRQBxPIfAVGrg8BvWLDeiMKC/FYybNXWHzFG/eQU58+Rc+d/QR7Qkw/cnHFcxfPlNFVTd8dfSQLGe8JwG067tGtEmG9SAvOKdWThFwO0ilQCuPoIJgKKdjI2DxqTw9oQSZSTY+taXTHIC5rjl2UUhj7BG0OISdrm4dsilgIyWZQHrukrIbZHWqpIG10h+n7P9+Gsjx6o/fx8dnR+IK0j08HISIUP/8CxS+8uOlBumrT5oDl84vEAwa3vv0wrz50ptXhbEjvoW43689nfQYSGHcOoZJWx4nQN6IUrqf0+HV/ZkGhjBhcWvBUM7dorxDaE6fn9UPkT6XJPt+epcoiJorahU7n3AJybhaiQcit3whJT6QxhoYAtbZ1hxnAfuwSZEqbiLo+yEIVFbWQKRsqDtgaDkY8J0qLAAsNLPluY0SEhccaIPJskcUXV78OLL7gPp5+dYHkbX0cvCMG2eaf82tit+e4R2zlz+Cu0kwrhEAAFQ5CepnXvwiU3SayyjRRh5NQKUEogFhG255jG6EGwyhZ/7uslMLYrZHQCPqFOtsL5cuoIdeuw+uYXa3teRK8ddBvYOjj4+NZ/OGMj0+HUH1litzvfge9TubeurRxAz2paoIeXvl/zy8/iGFATwyMUhmyzW1E1W6oW0Ywbu1GtaOx+RZRJq4oPTGFBMIwW0byzfOLrhVVLHP0Y7sA6L9jF8+9Mg9KEai016RblwWjhvmZzoFMZ5DzS4LbBmI0ALNZ7K9mMd92DGXcLEg7k2Xk5CXXW7IFyOnpm5PagiZqX7AV4ayJUkGk3MAmj21MMaUoXUi1OoxNkX5lnnOO5tCxkHdEumQUo0ehF1tfgbIZdBV/BncVWzenDDkWQzlVyK8zfnOc63731SrGaD/6wpxrx+WddeU6Udv4TCmFGnDgvt3opycaHJM3MeOtFaQDh3pbenwfnx2JUt5ourscr8WzhHcVHB8fnw1xrqTJ/68nyf3f3ybzH76ydTEaoNy+gjRA5ZlJ7vz+Y60O4yaGjvfRH7bpkyJGruidibhXSUYxju8sMfoaSgFxuJiGvIeyCJc4s1DlDx6e5OnLbmyqWOKb+yw+a+XI9LVX9o2dreF6pwz04+evi9GbRJ+adgXt0rKhlgoiL0+0TIxeCxmbR7R3vnMigiyukflmRRAdqX1fZshLRQZ1YfFV710faiFzcpFyX3erw4BwAOPIEDgOsVt7UKH2Und11R9HXKPRlh3KtehQxTxUNtnHwSlgjHZh3DKAcUsfeDhxYjOowSRKNreIY3RXMR/YA5ZZtzgk0zoP/c2goq29vgQP9bX0+D4+Pj7r0V4jMB8fHwB0qkjx8y9Q+uoJcOozGBcvNx6qEXO+SPe+JKkxb2TV3fOBY9yaFJTZGZOQppAuIBMlJB5AdZko0zsiWaMQESTrICeueE6oBBjPOnzqi2d5+mG3eehfG4q/91Ov44fv7WFyOsOZlybIvG2AxHwN2cMeoTJXJtwfXn8jw3CX7beo/cjlFHI55f4SDaBGul3bDy8igpzJwZGYJ6w7lBFCiqsvsIpTRhY09G+coydEkZMzqP39EGxPEXc15h+danUIW+byo1McOtyibPyAibGrG315ATk3gwICuRJd9w6QeazOlgINRBc1JFodhTeQRpqgRcOuZVh6i2PKqxnTV4XsZBTm20NEXZctJgyoWAXz9UM4zy9CevtVCSoeAY/3Y9HKJHTnAESCFB6/gjPd/HgDviDt4+PjYXxB2senjXBmcpQeOkn5KyfqLyDbmv7XjTD3RPtMym6kfDHFwXuGebbBgrQZNHBusCgIxgIkd8eZPb3I+3/hNYQcBxCYyyHhCKrQPmJdq9FPXnD/M5jAGOpCHehCBT2gkjUAEUFmy8gZb37vHj6T57//1mOUC9czw7QW/uh3Hyf3sdcwdnaGPYcGiLSZZYe9UAbWFqSlYuI8fn7LYvRNFKreFaOXkJk0hCzUgdZmu4sIpK+LNoIrPAsKZUaQmQIs5hCJoUaiYK8ubIgZQE6777lcmoNbelFOZekY7p7b0Z++lFEUzyy2OowtowL1y5A0RvvBNNCTKVQogDgaFbSQctW119ECsSCYBqor7D53aX6FgKlQbdfXQYrtXdFWVxp061E9Schk6trDQUWDSM66VpGo9vSgEgH0eBay7WEbowaTEA8AW5uDqICNeW8CfSaMjG/vOialime/uVqZVHIByi9M0/fOUUKHgiTeNYAoCyerqVzIUnp6luIzUw31QVdBE2u3v3rl49N0FLU6GzUPr8WzhC9I+/i0CeJoMv/xq+grjRNbB3ssFmMBnHz7ZktLgwWGez98C0N74jzxF2dYuJjmB//Va4iWy2AYqMU89g8dxJpJr5jEVF+eIbg/AlXvZb96mpkMeiYDJwyIhVGRIGpvN6o/jIq2v0AtWpCpInLBO83JlvM//26SL/2/z675/F98+ikMQ/He0d30XegcX3RRQZxHTrY6jJYgsxlkf39rhVozBHk3O1pMN8NZYmGwbaS8zMYjlUen8qjDg6AKN4+zJbiynL9sguUK3HKlihpOIEah7UTpxZPtnemdfnme/D3HiC1sT4wyjg4hZ2eApTlWsXrtHFCA2tsDjqCn065VVq68po1vuzm66KIN1E/Yb2vq7ccTDqICga1nRa+DssqokSCEk6AscCqQz2L0Anv6kYUKMu3NRq6qPwG9JkoqsImGwKvuyxCMIxbSPYJ+aeuL8SoagrS3FmeuCtGlp6egbBN51yjo66smSmysOFh3xIjeEYN/MIrWJk7KpnopR+nFeYrPTkOpPvMFa7QX1SFWMT4+Pp2JL0j7+PhcQ9JFRt97kLN/dqrVoWwZZyyNsgykzl7NQ7f2cff3HaCvUoKFNO/6/j1I6ABqJrViO2tmlclEvgLxXljsgFLNVmBr18ojXUCmUtAVwXrnaKuj2jIiAnmNPj/r2ayoxZJeV4wG6Bnq4iPHDzJ0IYdnl93XYjUrGCuEZEvoK+1rh7BtSlXIGxBvjUQnIpC5/p1QTgGOxGFBIfOrC7FydgbVn0B69LWzUMwIMrXyeitzWdTw0rC3VEHOz6JuGQTH2yXfN7Lw5NY8zb3E6c+c4s5/dAxzcmuitBpKoKcz6151rmZfKtiwkU9wdp7wnUOUXvTm4uCN6EIVX5CuP6onCdns5r2ia+WqMFlc5ZpTyKPiFqq3H31i6TtuGRAPo/piSKrYGruPrghqJAa6gJL6JVUoQ6GGHFR8D85Tk1uzK1PekTGuCdHPTEHpukgefu0g6y55iWAoG6MHAj1xonfH4Sf2I1g4GYfKeIHyK/OUnppCZzefLBT0Gxr6+Ph4HO9cyX18fNZFmQbBe/dQamCGNEBgcpH4wW5y51MNPU6jsOcK3D7ahz7YwytfPV23/b72vQfpmk1df6BQQRU2MWkJtLbLdkeRLSIljQq3T9aHVAQpaUgVkNkMlBo04a0Ta/WJ+sC77mJ/TngmUMFUBiOXCrSdGG0ojNAyMUcZ6IUizCwJ0aYJpgFOe9mQ1At9Zh7j7p7WZA4bQcjcILiKIBtUl8hcBqUSSDwEVQeZmLs5czJbQnYlwbEhGoJcCQoOtFE/zkoeci/OtDqMunDizy9w2/v3ombXaF65nIAJIQtjVzdoQV9eQFXqKI4pRayUJvjAMFqZ5F+Ydc+5Oh6jnuh8lfUsh3YU9bhOhcOulUwDsqI3hW2DbWMc6wFzqZyjVAA7B8kY0kxBOmCh9vWAUXAbGDbofrAdX2k5Pwt7E6gWNoFeS4gGwDIwk2pLjTcVNlYCrNsiRG/bAx/egygLe8Gm9MoihYfHsa9sXJkW8AVpH5/W4Ft21IwvSPv4tBEq1oSZs63Z+9pBTrSpIA3gZMoUc/XzbB65fYCYbK8ssPrKLIF9USh6W4hsG9osOUxyVeTERKvDqJm+iMHobcNceGVltvBUscg9k8JbTEW5q70+BBUw6Hn7fgbujxGKZNFzhpuVlSusFJ+Fhk2+24JcCSomhFogyN+wyCdKgROBzMb+2zKbgQ02k6kqKmhBzp3IS6qIGmqfha3UOW+Vp2+HarrCq1+aIDIUJXkoSW48x677+ghMLrgbmAYqGUH1x9HnZ1GFykqLjgYQmE+5+7+rH7ukKT47hdEdBkO5i1YeQWfLiMTbzm6mIWxB7FuO6umGTBoqHjJuKd0gsBqGWynWDBSoPX0QrqBoTvXIdV/pCDK+UPsLRVDBEASCkK8iuoxq0kLyukL0EpG3719h17FdlNgEeiDwhh663tDjCtTzNqWXF8k/PIYzdfPn5Tc09PHx8Tq+IO3j00bYZ5rUFGtsgeE372Xqm+PNOV4DUFvsAr4aPXu7MOa2l5kiqRJ2XxTL2lq2hM911EgPKtA+IhKASgTazqf09W8cXSFIB8MB3lAMAmUsR7BS7bG4osIWfe/aT/+9EYIR4Vr57Pzq2XBKO6gDPeiz7W+NsFX0C9MYdw+hgs0TpcWwYHEeUQYYbvanTOYgXcf7Xrqw4nuowgFgg+xrEbBirgjaYnuP+Q6w61hOZb5EZb5E+lVXhFp4fpY97x1loMuBbMmtKEmt4g/eYIJzC1iWSfh4DCyT7KK3BGlsjRgmSnZmFccK8pWtJYtfy4pO1Tui+iMamWq8t/R1n+jmZxy7vtIm0jOCfrF2X2m5krq2kKkOD8JiY7PcaxGir7KhXcc2UWIT6IXAG3voemMPgoU9W6X4wjz5r4+h81WCtw427Pg+Pj4+9cAXpH182gijq3m1xb1dBjMhE132ZsnqRiTr2LV6eDQJhe2XSupzC8j9e1ALqe0HtYNRtw60OoRNoywFA0mYbXFJ8CZ4z909fOf4MBdfnUIpxQMPHCJypT1EaAxF4v4Rkvd0k9hjYAaXCdG1UCmghrtdz/KdSLmK/u4EajABiTDGcBOy4YtL9xrDQk42yZbCNNhIkIaY21gRULf2oez6Vd9shmoJMs90vr+5rjioXLnlVQrGVU9b2yG0pxd73FvXbo2FQZtcjxuI5Muovs2NjVVPEjIZb2VFr4fQ2FLrSBC1JwFSX5/ozaIMhRp04PW70U9NQ6WGipBlVTVyaR4GIqhy/Ruzb0aIBsBSW7br2CoKm8CAIvD2fhJv70cbMYywL/X4+LQEpVo+jrkJr8WzhH+V8vFpIyIfupvyExdrGwxtE5nPc+B9hzj/ufr5MDcTJ1UiuTdJepuTyHAiyHClfllx1ScvE3xgL8xtrZnTTkeNdKMS7ZUdfRU12IW0kSAdDSh+5ifv5Om/G2L/gkNirDVC3GYIDMfpe+suum8NLmVDw1YylEQEybXOl9ITiCCLeZjLIn27UIHGZWOKYcL8Uqm2bqItRVcQZG3xQgwTmV12/a+aLfPgWzxrg9MmAto2iA9HYdFj1xrx3iROS3tZJjWMbBnMKDg1CKnhICoYbL1X9FaIhaBc52ujUqh9fRAso2icT/RmMSJV1IOD6JczyGaqE6sOKhiGOgvStoqQ//YUUqh9v+E37aurXcdWMHqiLT2+j4+PTy34grSPT5ugs2Xyn/puU5vshObSRPd2URivoemQx7AXihy6Z5hntylIa1uYi8boz9TvPag8PUHwnkFYbEHH9HYlFMC4Yxdqb7SudizNRMUDrifujc3WPMzRvgDBeJTsGQ9bBRiKxAO76HtdN/EhWTo/tukrqhQMx5GzO1uUVokI6lgXKIeGKrGt0h8NuZYgLYbpfje1A2YM0pWbM+QXi9CiHlELT3j4O1hHwh5sMmkaDsFjfVROzbc6lGuIrfxZ3FUMY0NBWnUnIZeFSntmlatInW2/+hOoXtNtWOhBlGVj3BlFj0WQs7XbNsnFOdT+HlAgmfy2fdYrdpTCN8c3nekcvn9oW8etByriC9I+Pj7epz3TzHx8diKOpvrUWHP9hysO+96wq3nHqzOl56Y4/KbRbe1DayERqfOlsqqpnk5BV6S+++1EuiIYrz2A+f2HMfbH2laMBlAWqMFkq8PYNLvetxcrEWDkI0daHcpN9L7rALf+h+MceH83XSP19Y7HaU/hop7IXBa5UGykDabrGb28YkQ7TctClgVXCRcRZE4j0zYUgsipmVXtWmQ+i6RNxIwiTUyVrhYh/XTn23UAWHnvCWSBuRTRsE3o9kFCxwfAAz0MdLV9FjYbznqZqKEgKhF3Gxe2OGN1W4TqtPoQCaKODKC6bZR4+x6nDDD2g3HX5uYhki4j80VUMrmtW1cpH6bw8NbmXWafByoYor4g7ePTMhSu0uqlH49OoVs/ovLx8akJlQhj7u1u/nEvzdP/2pGmH7dedMWC29uBCGYDMlolVaJ6uQiRbcbXwRh378N8236MNs6KXo5oga5Yq8PYNEEnz7FfuI2+W7fSOapx9H7fAXa/LUqgUWGZHphQegC5sohcKrOd/mniKCSjkOoqw07buFY1IICyog0VwFcwk3bL/c0AzGfdpodX1rdTkpk0cnIGZz6AnbLcpocNZvHMzrDrCA1F3WaGHsQslolXMsTtLL2Hw8Qf3IXa7vhiG+hKG4ur9WYNoVl1J1HagVz7V6Mpa/tTdrW3D7UnhKKw7czhZqGUwhiwMR/YA7W+B6kCZEvIhVlUKIqYm3/vHCNE+eWtVaWEXrfLPe9aSTCEMv0SCh8fH++zrbvbr/7qr6KU4ud+7ufW3GZycpKPfOQjHDt2DMMw1tz285//PMePHycUCnH8+HG+8IUvbCc0H5+OQxkKo7c1q90Do/GWHLcuzBU58MC+Lb30Tf/4Lj78v92KOdOY7uYyk8Ne1BD0B43XCAdQR4cx330LxqE4agsTCS8hIkhJo8cLOF85h37qkhftSDfEKBRQpTKJ+7zRsT3x4C52vzXasEm1WEFIN9HL2OPI5QXkYmnL4quMFdHPXUY/No7zcgZZBNGgUchMCX3FRl8qIZdK6JO1l2fXAz2WhsLmFx+qF9IU/vI8hYezOLnNXcNFBKdc+zHnn9wZdh29t/e1OoSaUI4mNLdA94EgkXtHahfK6ogu+4I04Poe33hdCgZQyS43K7oWb+l2wNzGvS4aQt0yiAqXULTneaNiFczXj0DX5lag5coiiIXUOM4WEaoSJfvwFJLe2uJY2AuVpX52tI+PT5uw5RHUU089xe/93u9x5513rrtduVxmYGCAX/zFX+Suu+5adZvHH3+cH/mRH+HHf/zHeeGFF/jxH/9xPvzhD/Pkk09uNTwfn45E7NYMJFXR22V961G+lKKY2fyg0rAMdoUFco01N9XjKeyKCW0uvG4LQ6FGBzAePIj57sOYd/Siou39fkhV0JeLOI+M4/zNSfR3L0KxAuUqKta+Czz739VDzxtbO9mK3j7Avh/oQW1ngr4ReZDZxixEtSsysYCsYaErZQNx1v481Ej0esOs+Sz6xQn0tyeQRy4jp6chX4KK7f40m0IZGd+8N7B9ye1P4IynyH/uPIXHCth5C6dsYmetdd+PyrhB/nNjVKZM9DrbAVRLkNkhdh1du9pLRDGqNpFcisRrB1GhLVRUBEyCR7Ymwuu8v2AGwA3vu+pOoERDtv16n6yLbO3zVrt6UQMWSnmsUegWUIEq5n3d0LvJSrPZLBQEiaxtUC+AbUTIX4T818a21a/HGm595aOKtF81no9PR6E8+uNBtpSWl8vl+OhHP8rv//7v88u//MvrbnvgwAF+67d+C4A//MM/XHWbT37yk7zjHe/g4x//OAAf//jHeeSRR/jkJz/Jn/zJn2wlRB+fjsGZSFN58iLWsUH0fKE1QbS5WBqOrxwcxvojHH3TPp77/Kk1X3Pb9x9CZZrjZanPzOPcOYxZbP+y0jUJBTBu3wWmQhaLkC2j9nVDVxAVM1EBj94lN4kUNfrkPHJxbm3fwXYutbZMqoutm9hGjvUx+pEhDKvB9gWdcTrWFbW7d+2MdEuQ8RLsC6FWuV2osMa4czf6hcuNDbJZRENIdmHFQ/bpOezTyzKZQxaB2wcxB0IEhmyUUjglk+q4TeXRMQBKX7kApkHwe4ax9oUxY9Wb7IkWTlWb2zuihYTD7ffFUyIYhSLJO7rRgQCVnKb00sya25sDMQK7uwgHbIygQelKnq0s+etcBQhsOe6OIbAkSBvGkld0hy4klopu88ZafbCDAdTeBMou1NT0sV1Qpsa8K4nzmLvAXzPlKiq5ci5QlShOXuOkylTPL0Jp+5Uogdv6UVtcPKgrfoa0j49Pm7AlQfpnfuZneM973sPb3/72DQXpWnj88cf5l//yX6547Pu+7/v45Cc/ueZryuUy5fL1SXGmUwcgPjue4l+/Qvmhky2NQaazHPuxY5z9q/M4+U0MAFuNAuOeYRaev7Li4Td97Da6F9L0//Pv4Wu//SwAQ8f7uP0t++iKWZgGhGYz4DRPOHRenMJ8/R6YSTXtmE3BNDDu3I3aH7+e0bq3MQNlcXRTLD5ECxQ1UnQgX0XyFchVkGzR9S7c6PVTaegPotpMZMoXwwQCJns/tI8TL20+o3S7hEe7Gf2JEcxgE963KDCYdD2GfQCQmQz0RkDLNdFZNMjZPCTCyEwG1T8E8TWum+29rrmS0NqZdtco21SfuUIVMH7oIGaXQ+HLkzeXgTuaylNXqDwF5p4k4Tf2Ygavi0cLTzb/u9YqrGJ7ZnGahTJQxmRJIr5zCHuhiJRsjESIWEKjlIETCWMWSxj5FACqPw6lKpF7Ryg+M7mpY/qC9BKGgepOQLHYuWI0gAhqsMsdP2yAGuqGiO2K0QDhENjt+d1aDWXaGHcNor87UdsLLBM10gWZ/IqHnaxD6Yka91Ejkbfurev+toRpQqD1Wdo+Pj4+tbBpQfpP//RPefbZZ3nqqafqFsTU1BRDQ0MrHhsaGmJqau0SxV/91V/l3//7f1+3GHx8vIp9arrVIQBgXJrn6PtGOfFnp5vXcGoLHPzwEWJdJvmcpissOAruPLifclcUJYKDIpLJg8BAPscP/cJrmJ8rszdoQz4H+Y2P0Siqr84TGLQ6IxvOUKgjwxgHkw233xAtyKk0+sQV1GASdagPNRSqeyNEqWj0iUXk/Mz2PqOKjYr2tF2jpYUnZ0k/Nc1tv3QbBBRUm3eeBvd0MfoP92CFmnNMZVfACHr5Utd8qjbMFtCvplH7+1FhE8KmK1RPpgDQz4yjRgdQPSGIgTKWvYPVNq4MuAGtN3dtKXzpIioWQDLri0LO5TSFvyoRev0IyjLQYU3mmZ1h1xHsDUO6RVVgdSZCERUpQ1SBFFAF93wx8jdUXVkmytYwNrtpUVqnS8AOL8vvCmEe6+5sIXoZKhFC1rscBEzU3h6wc6jlzSrszsiOXo7qqkIyAukNKhkNhdrXA+mbLVwa0RjUGokAHsiQdlsEtzoIH5+di1LXreq8gtfiWWJTgvT4+Dg/+7M/y0MPPUQ4XN+29jeWgYrIus2KPv7xj/PzP//z137PZDLs3euBVUkfnzpgX1qg8MdPE7x3L86lxVaHc505b3ryGUEDXdEc/8lbCU4tQBYSAJnrF7lQfpWiWC1Ep1NEAZrjzrEusliE43thxkOf+SZQ+/pQB3tRcRNMhbIaf+OTkka/MINcdsvnZTqNTKehL475mt2o2Oq+nuIIknMwkmvfBkUE8hq5nENPpmGhjgJyGzak6nltP9X5Euc+Pd5UMTowGOPg/7afQLjJ8nDczzC6EcmVoOogZ6fXFOvlwixyAQhYGPeOoELuua76FMb9e9HPTbbGK7qO6MwmK4UcvaEYfRXJlSl99aL7y77ezligrIGe23qBzhDOjFLl+sRvvQngUkWPAtTYLIFDvVTPLay9/TKk7OAoC3M1e4BkHCoVt29Bh6IO92N0aSi2MIug2ZhrXwtUfwISgnLyN59z0QgUvDl+3ypKgXm8D+fxDWyg+nqQsvu+3fhNlFL970MqSOuTdhwHCgVo434lPj4+O4dNCdLPPPMMMzMz3HvvvdcecxyHb33rW/zO7/wO5XIZ09x8U4/h4eGbsqFnZmZuyppeTigUIlRLyaSPT5uhsyWyv/wQeqFA9Vlv+W2qUIDhN+0ldWqB0qQ3JgH733+Yni4omwGCmfbKOF0NeyqP1aal7VKsYnRbjW02x1JG9EQRmUgvdVBfZaP5HM53JzDfuHdFPCKCTJfRz4xDqQqvG8XYHVn5/HwVObeATGfcrNBG/A2TKRgIoaTVM5faiUfLZPfFmXtovGnHtHojjP7TUYKxVrxPNiSjHZO1uW0sEzKbeC+qNvrJcdTRYdSQgVLK9ZJ+zTByJuNmVrcp9nRzzolm9THwAl1745DdWRY5coPFVCDgYEcCSLEKAWPDqgKtghjdbpa0UiCOu9ha/NoYKhkidNcgRshApTvofbUMzLtHoJz1RiJqM6ms0qDbNFD7esHJodZqkOrNpLjtEy2jdncjE6lVn5ZoCHl1yrXf6wpjDHWBZYCtIRFEinX+XihQ4o1FNcnn2rqBto+Pz85hU4L02972Nl566aUVj/3kT/4kt9xyC7/wC7+wJTEa4IEHHuBrX/vaCh/phx56iAcffHBL+/PxaVcqL0yQ+2/fQlLenITKYoEeoOd4gvTtA1z52sWWxnP0x28hOrcIJeiU5Sl9bgFeOwyLbSiuz2bQr0Qw7lin+dk2EUejX1pAzq3dNOoaCzn0d6cwbh8A00DyNvrkDCwTwvSTF+GtR1ARA5kuoS8uwGwThDLbcScLuc7KWqo3Iz86Srir+WK0iLhVE5tpmtTpdMc276ktIKemUNE9kBD3fRWFGoy3ryCtwL6YasqhJFXk2EeOcu5LF7Cz2z8Xb/mxoxjpPNOzDvNPecsKJBIzYaddDm+wlVLzeSKHkyjDgMU8MpgAFNW5ItUrWaz+GGZvGNMCFnOUn52iUlhdlZX5IqWHLwEQ+J5hrIEIUtWYYYFUG44vADXYhbE/AqWddqIsYdvQHb3eq6Injuo2UXqVrOjlaAERz5Zrb5lgGOP2GNoEGUvd9LSyQoizdM/KltDZ64K+2tONFOu8oiFQPlMkdKS+VeRbIt+e33Efn45B4b3FQK/Fs8SmBOmuri5uv/32FY/FYjH6+vquPf7xj3+ciYkJ/uiP/ujaNs8//zwAuVyO2dlZnn/+eYLBIMePHwfgZ3/2Z3njG9/Ir/3ar/GDP/iDfPGLX+TrX/863/nOd7bzt/n4eBKpOlSfn0DP55GyjXMljdEXw5nMYL865VkxegXFKkmqxH/sGGe/cBZdanJGgKW47SduITBZW2lru6Fto736f8XCUKq4ZelnppGeKGpvZOPXbQKpaORKEX1qGnKrZAmt9borizhX1rFAEUE/cq4lHotydgZ1eKCtvKRlixndo//77Vz4ry8v7QRUyGT0X9zKxf/nNEbQ5MA/PURpXuMUNYlRi2oOgkkIhFpobeJoN4veBwAVsLZciaxfnsR43TCkFfpFb1X+bJp4BKrNu/cYl+bZ9ZZ9jP3Vue3vq2oj6RJ9h/s8J0hb1c61l1iTVQRCtaxpnbo0B0AQCCQAXUJtITu/+uwUy69k5r4E4du6XUF8sT3EXeOOYZQqQqn2+38nonoiSKaI2tcHOofSNYxd0hlIxKDUBvOLGhDDgFAMFtMggrHXhCO7AQvKghRspKpxvn5pzX2oWBCdr++iaOzHbkWFN92eqzGUiohtoyyPxOPj4+OzBnW/Sk1OTjI2NrbisXvuuefa/5955hk++9nPsn//fi5evAjAgw8+yJ/+6Z/yb//tv+WXfumXOHToEH/2Z3/G/fffX+/wfHxajnM5Rfb/+nqrw6gL5qV5bnnXPi6/miZzujkTdCse4PiHD2F0qBgNYJ+eI7gr5Ga0eJX+hDvwz5SRU4sY9+6CaVf41U9dhMSRdf2ZN4MUHJzvjEG2QZOpFjb8kQvzqAO9kPeGBc5GBBI31yJYPWHsxdVFAhW2iB7pIRBVGGGLA//iFuy8Zvx/nEQF4Mi/OooVEMyAEO1RgAkIoWuVpi1cznfaz+e7oWzne1J10CcykGqP83w9xGqyt3jAZOHlubrs6uRfX+LI+w95L1HSVKiUb42zHkqpul2TnLEM+TFXjIu+ZxS16GFLj3gQ83g/FHOt9+b1ACoWhAPJ1b2i16Oql3xd2vNNlEAQAmE3/lwe8qmVGyxbqFARUN1RnHX+1vKig2Rr8/WvhdADu4l8T1fd9lcX8jlIdrc6Ch8fH5912bZa8M1vfnPF75/61Kdu2qaWjKoPfvCDfPCDH9xuOD4+nseZbo9slFqRmSy7dwVIHj3E+F9vP4PrKt2391GeK1Gcui5g7H3vKH1xgcn2bPpXM7kK9A3AnEcniX3d6FdnoOjmXKldyWtiNADJKM43x1Bv2+82ONwGkndwvnm+czNVHY1cWkQd6GsL+47o/tiK363eMAd+6jBjnxmjMn5ztlFoTxeHPjYIaPb8w2PE+jT0w+WASfrVIrve4IHS1uUEo4gYyGIBFtrUUqJByHYXhDzaFHezrNZDrpHkYjFy52uwKFqH8FCUwdeOMPHwGKWCQ8yunxBTD5LHenboAlDrVwZ0WdjeXbpxqEP9GElxxWgf6O6GVGprZ02x2LZZ0iICVSCzibG/bPAuVeubiFB+apLYe/djBL1jbC75HMoXpH18WoJS3nNJ8lo8V/HrOHx8msDVRZnqC1fI/ddvtDiaBlBxiC+kOfZjxzj/1+epbsPrMn6wm8Ov74e5LPb9+3j5UyfBEbpv76cv7EDaWxPpRqEreMu2oyuCioaRskY/O7HyueWz2aEe9AtTULaxizbWBw+jttilUS9W0d+5ABXvDPAbgu249h2HBiHvbdEu2rNygdleKFGcdRh5/14u/bdXAFABA6lq+t57EGVdH/10HwBQiAi7//4RIoMGXkt5E62QbYp/HUk4AIWdce3dCJ1r7uLY4ilXhAkPR9nzlr0E0zlAQcjCDodQyv0OGbaDNk0uPTJB6crKTPTRN4/A5RRH703CXAoBYqNJ8he8seiZOJQEdl6GtOjWX//KT00SfcMIgvJOpnTAxLx72PWK7tC16E0RCoFlQSq1vf2IAbEE5NLeVSYAsQIoe9kHH+2ChU2emxusb5ldgc0Hth62JvunZ0j+xGh997sd8llEpGE9XXx8fHzqgS9I+/g0GF2okP/d76Dn8thnZlsdTkMxLs1z5M27mJmuMPfU5Ka1poEHdrF7f/BaJp11ZYE7/v4xHFEEJxcgv3MEEfvMHMHhQGv1ur4uMEyoaPRLU2uWeqr+OCykYKAH/d3r/rByJY3zrWnMtwxvekCsUzb64TPbib7tkHMzqMNDkPNuZq4qlel/9z7mvuxac/W9d5SewwqnqlAhEyk7JN+wm/DuCE5BM/LgzRYfSil6DoLXxGgAqjnUaAKZqzbOIqYdiUegsAM9flfBnmvuebH3vgEyh7pJmjYyNrfiW2Pe0AXQAA7e2c3lZJjMiflrj6t8xX2dc/3Vod6wZwTpWF8I5n1BuiUxpMvk/+Yi1qEegrd0t1yUVruTGCPBndu48EaSSchmoVyH8W82C91JT4vRRLsgnUMsgas2HektnAsbJEAbUmerNgWJv3cE8FAChW27503YY5VoPj4+PsvwBWkfnwZS+rvT5P/wCag4bpfrHYDM5xmwYODDR5i9mKc0WyB3PrX+ixQc+egxYgtpSK+c7JtXFj1bTtpQMmW4rQ/mmihOxsIQiUDVQSYzyKWNm14ZdwyDoRHbQp6+uVmZfv4yajiGeTxRcxiSddCPXthU6J2CnJ32fKPDvtf2UZouk7ynh95jBspQGKZw4J/dyuU/voCuCoN3B7bcALGVqHAMSVV8MfoGttPQsNOwLzVZsBtfIEHtyzeSLbHnli5eXSZIr1Zloiut886/kWCdkxXbBg+NC+1zi0ixSuiuAVQ+C3aTLVSUwrh7BGXn6iO+tjuRiCscp+t8vSlXwApDKOhaoWgPWeVEE8j8ki1HPAn5AmRTW9qVbNRsfWKR6P0jFJ6c3NL+byT+929HGR4So6+Sz/mCtI9PKzCU++MlvBbPEp6qCPfx6TTss3NQsj016Wga44sMmBX23dFN/2t3rblZZHecO//+MWLzqbZtttIoxG7ijWOoBz1RQD9zGf3iJDJbW/Mx/fIU+plJZGLtSZPz0Gn0VG0TTClpnEc62DO6BuTCPMRiG2/YIoI9Afb+cD99t5qoZYObrhFh148c4MAPJQHar0w0GEFfWERmvZuh3jL8a7NLLAxFD4oONyAzWXa9c//1B+I3CxLJw93NC2gDrNLOFCDFY77ZzpUchS9fQGJNbs7WH8O8b8gVo31cr+hiEQoNqBq4ut/FFIQ8JFRGu66L0QCpNFS3Pg7U4xucS1owL88RvX/t+clmCG0i6aKZiMdt4Hx8fHx8QdrHp4GYI94coDQTmc0xYFUY/cCRm54bfMNujj3YizGVan5gbYB9vsYGLvEw9G/jXLtqtZHeQlZoLTqVFuy/OoUU1s9YES04370C5Z0rRgNuo8PxFOLRrBYVUgTWCC25r7mx1BPBANs7WaNeQoq+XQcAwZstaLxKOHk9VgneXBBpbtHbv96YUQvSO8+uA4CqtwTpq5RPLoLVnNo04/ZhzN0BKO3Qc2A5waC7GL1dr+haUAovNNUElsToVH33Gazt+ubUqS9NddyjVVWFPOKlLHgfHx+fG/DGaNTHp0MJve0I1h0jrQ4DAEeBtLDoOqKWDYgsxeEfOUYfFU/ayHoFmS9A7xqZSpYJQz0QiaHPptFnU5svxQkFoKsL/czNVht1J1fG/psLiLP6By6ORn93CvzsVJeKDQtlxPSQYU3Agv4kkuvQjJtybtVM0h2PCGR8sQjAqbTPDSs0n+bYjx3l+A/sg/GFm54PeMQmI3lr344dB0jVmwtgzoUU5ekGi1ixIOZrd6FUwVu2Ea0ikQDHgXxt1WnbRgRPfPEaIUYDqj+y8UbhAOXTN18bt0LmD15CvOiEKgKFJp1TPj4+11Ee/fEgviDt49NAjFiI+E9/LyrSmpmfiDATi3BaBRmzwrxYNElHWyO4yGyO+MFuBl6/m1vff4jA+DxoQbaT2bsDELVMkLRMGExCogt9uYD+7mX0yRko225280B37Tse7EZPl9EvTzVtTiLjiziPzVz7Xc9X0Wey6MUq+sV5ZKLGjPCdQqaA0lZLF5KuEbCgJ4RyCu1nxVErSqEiwVZH4T0SUfCYtUCr0AttZC1RtjHG5pH51cUIY2ye/gfqU66+HRL7460OoWV4VZAGsC9nGuY3qQ71Yx7rcj2MdzpKuY0LMxlXkG4WXfHWC5XReEPEaAIB9IkahOZktH6WinnbE/r+akje/575+Ph4Fw8u5fn4dBblb55Bis23ILgQDHPmu5NkLlxg9wN7yc3kSZ9boPLmA7ympwVrUVWHvcMWSBkmrk/qncWSfyFai6AJBY2ooJstbVfg3NoZxDKWRtVSUT7Yg36qCVnRq6CfGsMZjmGMxtCPXVi12ZbPdWQyhTo4AIUWTiiSMVRXCDrQi/Bq40WlFFgxZHa2xRF5kEho51oq3IA93kEVHAK9B7qYe7y1YUR7gjCz8XadiN6o8VoLUQED6UpQnSwQ7FVQqoNtT8DEvHsYSlnY4c5cgNu4EOrfuLAWqqUl244WEY0j8w34u5XCfiaDXExtvO10mviDu8g9fmX7YrICe65KYNDwXs8FX5D28fHxMH6GtI9Pg9GLzZ/Ip6JhXvrKOTIX3IzTicfHSZ9bILY7wf7d3mqW5oynkEQNpXU7BJUIY906hDXai2WBnJxELi5CtgwbLGzIVBYGu9feIGhBvKtlYvRVnC+fQp/J+mJ0jcj52ZY1OVS7ekGVO1OMVlFksgKBEBKMQaaNsl+biekPFQEIWDjTnVH6rBJh2NPN+LcnWh0KQbWDs+893C9BUmUKX75A9flp7JIF4eC2MqbVrgTmXb2uGO3jZkWXSm6TwVYgBpitSQeRaBxZaNDiXlcSY38S692HMG4Z3HBzdWmWrnv6sfZts1pTIP3rT7H42ycQw0NWawDlErKNBpE+Pj5bQClv/ngQPzHRx6fBBI4PU37oVFOP+cqZNJUbGnWoJVGhsIaHb0vpiUPGow1Bmoh5qB91ZRHObT1dTGYKq1tE9SeQ6QJyZmrL+64btsZ5chxjIAglf5BcE4ZCaK79lxrqRgqZjrToECuInJ9z/38h5dlSWy+gHO2/PQCxzvAX1/v6OPUnp1sdxjWM3M6994uHM6SXU37sMmWlIGwSes0uLL0J32cFxj27UNUclP1FP0zTXWBuRVb0VaIRMBWUS00/tERisJBpTBZxJAxmGfOAKwhLPgLnTNjIGmc6TcRQ6NftIv/ElW2FIEUbpT34vc7noLun1VH4+Pj43IQvSPv4NBgj2bzsX63glZLJ5FPXB1TxPQle/8bdxIpltySiBQPQjbDnCnikv1LrCJoYto1s06tVLi2i7h2BuQyYBqo/iWQr6Gcm6xRonVgoQG8UVNUXA2uhpFGBKKLLqEb6TPZ0oWJBEAcp5DtTjAZkrrzyAZ81kYL37hmtYIWff5tjhEx0ufWiSWQ4BvkdKlIaqr282UWgaFP+1hhy3wiB3WGM/hAyV0Rm18h67otiHkr4XtFXicehUnH9oluFZUEo2JKqJ4nEYDHbOEuLrgiqev1+ZR4NoIYPYv/FObA3+K5pwRifpeuBYbKPbzFxQ0Hyn98FtP7aeiOSz6J8QdrHx8eD+HWYPj4NxJnLkf2tR5p2vFQ0zKm/PbNisHf7G/fSdVWM9ij6Sgbp8ZaVSLMJ7O1GJuuUMZOuooZ6kKLC+e5l9AlvGnTqs3PQ5w+Qa0HG55HxebfJYQMmc2p3H2p3N6gS5DNQyHu1GfP2CQShWAc/1J2AofzqlSV0oY3Ew3Uwxua55R276LtvqNWhkLxlB1//Q+2bE1R5epLyyTSks6juCIQD0LtyDGfcNoS5JwhF338epaC7G3I5V5BuJZYFxeZbD0k4Bqlc48To3u4VYvRVVFxvbuFnbJ7oa0bW3URFAwTuHYbgyplV979+LWbYe2I0APlcQ8aOPj4+a6A8+uNB2nc05OPjcaTqkPvkI0iqOZP5qmUwmbYJ9UYoL1w/5vximT3tUGmciMFiZ/hzbgbjQA/WaDf60Yt126c+O1e3fTUa/cxljNfshplUq0NpC2QyhTo0WN/spngEpALFklfHKvWlZGxcwuvjkojCgp/dCODMdY4wL4sFBkPQ/5GjnPmLcy1rrhcfiUKuhdYFrSTQ3hn3spRhL/kyxr5ecMroUhAMMI/3u1nRnbGGsz3CYVeQTqVaHYlLJOwuOjcRCUchnavd5mUrrFFtpwyFcc9u9LO1++UbgbVHQrEPHSF0i4nq7gZ9kMI3prAvZ6m+OIvVp7zX0PAqjuN6lkf8nj0+Pj7ewstJkz4+bU3+9x/HPjHdtOMFbM1tls2b3j16zS8aYOzJCVJR7yvS9lR2x63eq4Eo1p4gFLKog72tDqdl6JemIRltdRhtg5yfgVgd3q/eBGIEkKkskt8h371gFJltYbl0m6HCwVaH4BmqY50nnBqX5jn2QwfBas1SVDje3qLstgi0d06QLPV/UMkw+twsWBZGbwDz1kHXP87wp5h0J13f7FY1LlyOUpCMN1+MDkUgswnP8a3Qk0StsX/RQHpztkDKXtZwe9ml0TrUS+iYiTIUOA4Km9hb+kn++EF6f+VB74rRV+nA5tQ+Pj7tjz9a8PFpEKG3H4Vw8yccSgRxNH3HB9jz+n0M3zlEqQ2+6nomhxrYZpfrdsIyCNzZB+UqaI0aCkJXqNVRtYaSjUwXIdjeE/SmISDTecTchpgTjyCX5t2qBEcjYwvQ1fnfP71Qgoq98YY+Lh3oIb4lwkEk1aFex+MLHPvQ0ZYcOrCDU2iV5f1x2XrozFJD4nQGYygE2TxojUzMI5OLiA5BrPPvKasSCkE8Bqm0t0TKRorCqyDBEORKbnZuI7HWfo+d5wroc5usGpxYxBqJYx3sofvnbyX2oSNYB7qJ/9AQyly6J+ZzYF3tfiMYAe+PKyTvVzv5+DSNVltz+JYdPj4+kipi9ETRk83NRogXStz3o8fZUy1j6aVBWsG7Hn7W0X6cmTxGPLijBMngW/a7k5WrFMoY3zOE/tbYjmyyJtM5VM8g4P1BvSfIlVDdvYgUNtd40DTcLKmSA3rliaYvLLjCQqMnj62knZqIeQHf2sQl3NmLhcbYHAOv383so7WXtW+XvT8wihkUdCwIhcqOu++J2d6CNIXq2s+JwHwGmQe1rxvyW7BjUwpCcXcBMRJoembvlkkmIZt1M6O9RE8Scs17DyUYcs8Ru8FjumQCZa9+LkpBoZ+7supzGxH/4F7MpcJF87hF6Nbhm9dn260KoFBAtIMydnBlio+Pj+dosyupj097UM2Wmf/sc00XowGUUhwol66L0V5kaVRnHerDmcoRjCrMbBauLLY4sCZgKILvGF0pRl8llcV4w/7mx+QR9MkZGNjBTa42iVxeQCU2kYEWjyB5B7kwh0yu8l3LlSHY4dYpviC9KSTngVJzDyA7YLg8kIDuOweaciwzZjE4ZMB8DsPQmAd6IOF9a7G60u6CNNT0N0imCpEt3FdUGJlYcC2W2qFSIxSCWAzS6aZnItdEqXnXcgkEoWhDdZ1Fi3qxTnNQfe7mJoe1om7oPbnawr9kUlvef2uQrS0O+fj4+DSQDhgN+fh4B6dU5eRvPsbnen6Fb/y3Z9G7u1sdkiewjg0QvGPY/blzmNCuCIRMjFgAoyuE5Esor9aR1JOIRfBt+1YXo6+SyWDctX6H705GP30ZBpKtDqNtkPNzSKRWIUe5FjHroE9O1cef2oNIIOaWD/vURsiCgsey/FqEk+38yg3JlBjpge47+ht+rKMfPYLKLglkWpCZDEZ3CONgH8ahfoiHIBpADXeu5YPqBEG6lkzLVB6ZL0I4DvGlzzOeQDKCpBzEDrjWHqGlKoRAAMJdN3j9e1DgXU53EioV74p9gQCEm9PMTqwAlB33/Wg0SkFl7QrQq403t0S5hnPOtiHUXgtpvm2Hj0+TMJQ3fzbBt771Ld73vvexa9culFL85V/+5YrnRYRPfOIT7Nq1i0gkwpvf/GZeeeWVTb9VO6c+3sengYjWPPYTf8GVvzlNNX1d8Hj5sUnu3N/ijsZKQcAAQ7kr/IZyHzOWnlNXH1/6/eoFS+GKxEv/d7dlaXuQpX+XNry+DSD5Cs6VDNbRAQzloKYW3Me7o6iUO3gM7e2CS7NYgwnIdb4YrXZ1EbglCakamopYVdRIFzK5QxuQ5H2bgJpxNCyUkZixZlMfwkEIh90M6I0QkPkyqr3mWBsigRhybrbVYbQX8SgUO6+R31awpz0qNNUbW9N/Wx+plzbpuboJEsd7ia3WXGv+ulBiRAJQBZUM4UzdsJ2hbrIbakekExbha7UsKFaQCVegVANJt2fBVW/ldAFJF9wmj5EgpErg3CAyrmHJ0HLCYTdLfL0kAy8QizbFrkOsAFQEyk0QowEsa13LMvN4DNV/EOebF8GucVHj6pwpWtu5LXa1vb7JfmNDHx+fGsnn89x111385E/+JB/4wAduev7Xf/3X+Y3f+A0+9alPcfToUX75l3+Zd7zjHZw6dYqurq6aj+ML0j4+W6SSKjL72Djjn3+V8nyBiS+duql5yeSz0wzdcpyhcrHpE6jQ3cOYuQJkCqhrhxY2NGoUYBt6oCiwdw9i9kdhfA617O++KkYDMLs0KJppE1/ALWLeM4zZH3KF6GyNXt62gzqUQOYLUNl54qw+M4sx2uU3n6uVdAHV3Q/lZaKZAnoSUNXIxAJI7VkxMpVB3TEC2c74bkogjpybaXUYbYcKWjvN1nd1FNgXPS441ZHgQpbInjjFy43JpDv01iGY2+D9LLoCpFxewDw+jPPqlLv23RfD6IsijkZZprswDuh0CbnSZp9RW6lYa7AFD12ZXeNzqtruz2p4zY8ZoLvbtefwUtPCtWiC5YkYJtgKyk2sQrLWz9BXUcE8YKDecgD7a+fX3/ZgH8b+BGrIQsV07W9ZJu02hPaiTctqVCpIpYIKBlsdiY+Pj8d597vfzbvf/e5VnxMRPvnJT/KLv/iL/PAP/zAAn/70pxkaGuKzn/0s/+Sf/JOaj+ML0j4+m6CyWOT8Hz1PYTzN2f/xNHZ+4yyA5z/7Kt/7M/cQbaI/cuD2IayJxmU4rYsWjEIBmerMVXhjXxLraC+V74y5jeHWwLxzCLM/4IrQqS1ki2QLmA/udTM7dhq2hp4umN4BnuJ1Qi7NoQ4PguFAMIhMZ+DC1q8B+vQsxv44VDyamVYjvhi9DdpBaGkG8QiUF1odRdOQfIUDhyKo1w0ydTHPwtPTddv3gQ8ewtxIjL4xnkwB8/gwcnkeHBu5vFRxdfV5QPXEkJAJVQ1BE2N3N/pci8ZANdMBinQzvJ0jQW9di8JhV4hPpVodSe04jb2Pi2EAFhSb3EDdNKnFzkXtVRhH+tFnlq4JhkLtTmLs7kINhlDdoK7ps8KmvpsiEAg2V4jfLvkcBHtbHYWPT2dztXrcS9QxngsXLjA1NcU73/nOa4+FQiHe9KY38dhjj/mCtI/PdhERMifnSL86Q2W+SPddQ5z67ScZ/9wr6C1krE6cXOTI8FIn+QZjHeghMD1Hq66CSims7gjVThGklcK8awhzIOIOOItlSKcxbxlAT+WQmfz17HdLYd09gtETWMqI3t4kQBbTGPfvRT85Xoc/pL2QuaLn7uOeJRZC9XchqTzk6zQpKtugA0B7CtLaAU0UdWl63ZJen7WRUpPKrj2OWDswk8zWyGSaocO9dROkgz0h+hMaNqtZLeaRxaXqj7Waki7mMQ4NQLYEhRLMpjF2dUFXBH1m1pMWH+LBmDaLNKP7RzQEeEDsU0Ay6X17jtUwA0BjmhqKUq6am2uBrZFhUJMgbSjM13ejdsdRvQFUUlA12J/XipRLbTVelXwW1eML0j4+O5VMZmUFbCgUInS1j0ONTE25fmpDQ0MrHh8aGuLSpUub2pcvSPvsKMY+9woLz1zBLlTpOtKHnStz+Ysn0RWH1/zue+m/fy8AT/yDL3Dh08/X7bhzp+Y5MrqvKYJ0oMtE5Vs7NJLxBYg2R4BvBKonjHXXECoAZPPgVG7KcjYDFcy9QbhjAJ2tIvkqZr8FuaLrgVgvKnnUwV7k/M7J0AOQ8/OouwYhtUO8WzdLKIAaTCBaw3wamal/Nrk+O4tx+xDk2qcJTiVvkvrSNGJrnJk8Zl+U4GiSwGCIYNTBlLIvUNeCCKSbnO3mUXSl/YXDLXMlRag/Qnlu+2LW0R87DAsNrHq5NAtB67rVU7oA6QLmLUOu5YdPW6LCASi1WJCORNxs8HYUowGKDRKjAcwwZFs0RtiEZYyywDxssaFt4VbIZSHZA06b2Mzlc4iIPxby8dmh7N27d8Xv/+7f/Ts+8YlPbGlfN15HtnJt8QVpnx1FZbHIq7/27VWf+/ob/5DdP3CM8GC8rmI0QKQnDPkydEcg1ZiBIYBKhlFeEBGqDtbhPuxX61fq22jUSBxztAcjqlwRutbGH6k0BkAYyDUgm1QLajCEzIYg60EfxYbi36JuRO3qQUzlitBzqaUHGzep0JM5jITyVsn0DZQzFjhC4VSR8ivz6GWNZZ35AsX5wrXcMBWxCB7oJrgrSiChsIwKhmoT78cmIskuVC0NWHcAenGnXXeX4Whi+xPbFqT7XjtEeLEJFkyr9B2Qy/MYexLoK1lPZUp3QoY00gRBy9y8T3XdUGopKzrVuhjqQSQC+fqOT0UEQnFIt7DXhKEaoi9vBamUUdGY24DT8XjvF61de5VorNWR+Pj4tIDx8XESicS13zebHQ0wPDwMuJnSIyMj1x6fmZm5KWt6I/zZvs+OoJorM/65V7nwmefX3EZXHcY//2pDjj/90ixffXmOUCLImz56C2qyMVkWgf3dqEmP+CbOZMBU4HhktLgalsK6fy+GVYVCGap58GICTLGM8T1D6G+Ne1oYrDf6xDTGgS4ot6dtRF0JB6C/C5lv8gk6n4fhEch7r8GhU1XM/fkMztQmGjYWbcon5iifWHpAgbU3SXBvnGBvACvsYOqdnUUtorBzEGh1IB7BntiZwrzqiVIIhFj4wtnt7ciA/a/phvkWvY+OhsU85pF+nEuLUPJIFqOXx0Y1IroJ5nCtWjCMRt3xVruL0YkuqNQ3w1xEINIFiy0eMBtqW03Y60qxgFz10A6FIRRCWRYowz2PHNtbQnU+5wvSPj6NRKnm9FnYDEvxJBKJFYL0VhgdHWV4eJivfe1r3HPPPQBUKhUeeeQRfu3Xfm1T+/IFaZ+OZ+Y7l3jpEw8z/fCF1gYiQjld5tt/eoo3/shRmKqfwKOiAQKH+rCmPCJGA2RKWAf7sc/MtjqSVVHdYQL3DUI6B+3gLJLKYnzvfvS3LrY6kuZRcSAZhwbYUXgeBQwkUdEAoJBMHpotRi+hT01jHO6GkrcyRYuX2ZQYvSoC9lgaeyx9zdrWiAcJHuomMBQhEIeAKgOaUjmKFdJYlDpWsBYRChOK8GEFHtHtWopl4lxpH8uaemHv6+PMn5yuy74OfeQoRqvE6GXI5QWMoS6wLAiaSKaMTKRaF4/tIXFqqzRDK262DYJhQCLR/kK0UtCdhGLOzYitE54Ro7m5VNwzlEtQLq1M3jYMVHcPVLwx4ZBsGvoHvfse+vj4tJxcLsfZs9eTEi5cuMDzzz9Pb28v+/bt4+d+7uf4lV/5FY4cOcKRI0f4lV/5FaLRKB/5yEc2dRxfkPbpaCqpIg+/7VPoqncG/sWFEt/8k9O86SPHNsyUVl0hZMmmweiLoudvtuOwRnsJFnMoL4nRS6iKNzNbVW+EwB09rhjdTqTTGHcOo1/cOX6Y+sw8RrfZ2ZnhQQvV3wUBEymUIGBBvgjZHNJ6HcdtcFZwE31aiYhQXgggVaEyVSH/jbGGHEfnKpRemLneRssyUKZCyjMAxN64h65Bb17btktVolReGSN6PL755nOdSCzS6ghaQr1EisiuGD1myTuLG3PXL6gKMEZ7IBJqjcd0tf2tgqQZWd7lJi6EJhKuX3W7i9EA8VjdK5tEBKIJWEjVdb9bpp20VK2RVArV3e0NUbpchlIRItFWR+Lj4+NRnn76ad7ylrdc+/3nf/7nAfjYxz7Gpz71Kf7Vv/pXFItFfvqnf5rFxUXuv/9+HnroIbq6ujZ1HF+Q9ulogt0Rel+zi7nHxlsdygrKqRLf/MwJ3vSx4xhrZOiE7h7BXEijR4dQlTI6HKF8gyBtHewjmE01o8/5lpDJNMZgHD3jHeFXDccJ3NYDmTZtlhd0UMNdyJQXlMomkCqiju5GplOtjqS+9HWh4iGkWoXFLLLoPUuM5cjYAurOEci0Ls7SrMXiH59p/oFtjSwJapHvGfZeCV6dcIwwpVdSAKiqBybMHkAMs9UhtATz0hxHfuwY4383Tmlm6ysTR374AMx5t8JFZjJgKIzjw664ZWsQ0Bfn3QqdRh67EzKkbYFGLlRGAnXN7l2TUAiCwZbe3+qOiPtTp/uV58TodkQ7SDqFSnSDB+6xsriA8gVpH58GofDeqtnm4nnzm9/sXvvX2ptSfOITn9hyQ8Sr+IK0T+fj0cTKSrbCN/7gZd78j+7AvLxywmYd6sO8PINSCrM4D4BSOcJ3DoJSlE7NowImAV3yrBh9FaMv5ilBOnDHQPt2Sgeo2qjDCWSh0PAJs1fQMwWPn+U1EAmi+uJgKiSdh0IBKVwVetrjr9MXFjEGg031QawWTEpjNoH+AIuf3aaXbR0QLYT7N/68HCOAUwlgBm0Mx3ue1IKiaodxCg7KMqicTyO5rNsQMmJBvr6eo+2KLrZ/FutWscbmGL0lxlgsQP7C5u+Zw2/dQ9DDYvQ1tMClldZiRgBIRKA7CvkyKrzkqB4J4oynIXvz98PY242KB3FOz7m+1RsdthMypG0NwQYeIBoGGngtutq0MJ1ubiZ2o4lG3JImw6hbdZmKJZF5b32fBWmT0dMyHAfJeESUzqSRoRGUuTMXXn18fLyBL0j7dDQTf3OK+acmWh3GmthFm4f/+wu8+afuInBVlA4YBHTlJgFDCZiTCwBEjvSg5jOofBsMoKfTbuMRD3SUV0Ox9hajr5ItYDy4F/3Ni62OpCnIxUXU3UOw6J2FjQ2xTNRAF4QCrg1HJt/8hoT1JluCPd1QbF52vrIg++WLnllYLD0/Tfzwvg23c6oB8n93CQAVCxLYn8QciGBFBFPKqBb9QRoDR4UoPD2PnptZdZvgoe7mBuVhnPmdK8yreIh8OEL+2+c2/VojZLD7SBjSbez7Uqy4P4BkitceNgwF+7tRsRA6XUIm3Ou6nkhjxEyMniCqN444oM+tbaUm5fZfUJZqYwVpFbYap0fH42DbnWHPcSOGglKuftU80S7PidEuHhkYbBaviNKiIZOGnt7WxeDj47Pj8QVpn47Fzle4+NkX3QwOD6Ormm/+7vO88afvITSZInKsF2MDewJjyosDwzXIlTEP9OKcn291JKjuCM3pwtMEFtMYr92D/u7lVkfSHKQNMjh6YqhEBHEc14YjtUy49ViG7FbRJ6cwjg9AvjlCkxmwMZJhdMo7wmDuTJmuIyYmawtK9uL1SabkK1ReXZaBaSisfd1YIzGsLgPTqGJIY012tbIojjtUXpnecNvASBTfQNrFHu+gEv5NkibIxBc3L0YDHPl7x1DpNl+AWwstMJdF5rJuQe7BXlQ4CAgyNgfFKjLhjtHMW4fQU1lk0f0+qYE4Mru0sFpufx96qTgQa+ABGpG5aVkQi7lZ0Z1KIACVOt0zo13IfKo++6o37dxbxHGQTBqVSLZUlJaFOeju8VwVl49P26OU9+Z+XotnCV+Q9ulIRIRHfvD/ZfrhC60OpSa0I3zzt5/le//lfUQznScEGKZaR7ppHvrMPLx+CEqt926rC9UiarQXubDQ6kgajj41g7EnCq1uUKoUBE0o224zwoEEWAaSLUCxhJS8I5w2BAGZL6PCzTlcZdHylBgNUHphGnu2i74HomuO7ezL62SRa8G+uIh98frCotEfJbA3gdkTxAppDKdUtwmiY4TJfLn2PgpmVwB2iEX9uoQC6PnOv7auRfbS1k6C+OFuusptVM2yXabTa+ZpytgcyjJRRwfRYwuoQhF1qBcckBdm13hVG1Fp8AK/qvP+k0nI5TpbjIb6JQ5H494Vo8HN8G1nHNvNlO7udcX1SguqXitlyGYgkWz+sX18fHzwBWmfDuXcHzzbNmL0cpTYzWng0mRkfAFiQci3WAjWAqFI5wjSWqOGQ8hsEHId8jetRcmG3gRMt7A6IGTBQALyReiLu1nQCx0+sV0Fmcqg7hhxJzGNOoYIlYzF/Kdb7xu9GvaVLKmTYRK3BjFZmd2slYkzvTlBTs8VKM9dX4xUYQtrfzfWYAQrppZsPjZ/bxBRZL+1cVb0cgyz8+5BWyIaaXUELSV5OEnm5OYrmw6/exfMpuofULtiOzAxjxEPuhYgU2l3YdMDNmbbxbUdaWBXQ6dOlSORiPued7oQHQ67vtvZ9PYz4bycGX2VTpgvOQ4y7y5Oqb4BKDd/AV7mZqAr4WdJ+/j4tARfkPbpKGa+dZEz//0pxv6/l1sdyqa57x/cTr9dgkpjS7dbghasAz3YJzcnjDSC6kszBG5Ndk7TrkIZ474R9COX2tZOr1bkShbVCueOSJCKE0I7EL7qA90pixpbRJ+exdgfh0r9ys5FhMxzDghUJwtUTrfe5mc9yq/MMn85TN87+laI0o5s31RVSjbVU3NUTy09oMDckySwK46VNDFMG8O53mugqiOUzucI7IoRiAtaDAJSpJQNIptcCFSdnuVfI1rv7Ml5l+WApcCu/cay9wdGsXwxenWKy76HwTawoKoBKdk01ES6Uof7bHd3Z/pEr4ZlQC6zM8RoAO14tgR9K0ip2BpRuFxyz5suP0vax6duKLzXs95r8SzhC9I+HYURMrn0py+1OoxNM3T3APusEnSyxpUtbrxNA7FesxsjaUI61zli9FUWMxiv34/+zqVWR9JQ5HK6+c0Nu+PkL5YpPuO+t+b79hGQ1p7LnqBsgxMA6iNIixbm/jpN9Wx7+OOb/VF0oYo5EMUmhImNbUQQB5x8A7K2BJzxNM749Qw/oyeCtS+JChiUnx4DwD63QNFUhL5nBElGqZxdu6naahh9ESi1v7dtPdCZnf0+yGyOA+87xMUv1FalYHUFGRpUsIPcOrZMoDOmX7rYQEE6HABnGxZdoZDrF71TxGiAQHD73tHtIkabRudl9OZzboPBavPvPTI3A3E/S9rHx6f5dMaIyMdnCWU2sHSwAVghk9s/dJThfTGYS7U6nMYym8UYSaAnm9ckyjjSh7WvC5yKa7PQydWa2QzGbYPoV2ZaHUljaVaKdDhI1YyQ/vxK65/cUwt03xdFdXo6eg3oc7MYdwxBdvsKVCUd8LwYreJBwkf7oFpFMiV0xUFNpyi/pAneEQYRSqeyOHPN6QOgF4tUFldZHHGE8rOTlJ3Nn6PB0STQgVU6W8C+km91CC0nMp+i+45+Ui9tvLBx7KOHd5b4tx2s9hqrroXkKkC0MTuPhYAteuomE+59qdwCT95WIlsX8EUEogloBzEawOxMCUMWF1pj3VEquYJ4vKu5x/Xx8dnxdObV3GfHUvVY86v1GLitj9e/by/GQq7zxegljO5IYwXpiIWxrxtrdxywIVuATPME8JbTH2p1BA1HX1jESDZ2Mm8nukl94fyqzzlTOYrZHqJd7XOtaST6chajx9y2l2Owu0rgcI9nRWlzMEawN4Q+c9126GoekXMlQz4WIHRIEb4lQf4RD6SIbkGMBggMhqHqgfhbjWFgX/K2ZUxTcISeo70bCtI99wwQyXTyim+dabPkibWQYuMWr1QosHlB2bIgGoX0Dhr3XaU7CYWtNSIVgHAcFlL1jKixmJ1he7M6rUl4kMUFlC9I+/jUCeVBSyGvxePiC9I+HUXf6/aQuKWfzMnNlSk3G8NUPPiD+zDmtjZ4bFsyDbA6UGA9sBcjZkE2C45uaLM1T5PKoYa7kKkOPq9SRTjQWNsO2aDZVOHblwm9Zzem7LDsq9VYLMCuYchv75zLn1OeFaMBgr1B9ERqzeerZ+apnml/AdOImp1dSVIr8ciWRf1OQfVEWcgrZr96ccNtRx/sg/kdet/dCkaHCNKFBloLWMbmEqTjcddzeockIYgAokAM91/H2ZLUIKYJBCDVZu+bacAWGv16GZXs/v+3d+dhcpVl/v8/zzm19r5k6c6+SBKEABFki6jINqLM15+4+xVFZYZrcI24oI6Ceg0jgw4iiBczQYaRL8ylKCOKMzCjEjaBIGGHhBDIvnR3uquql1rOeX5/VPWWdJLurq61369cdaX71NJ3J6erqz7nPvcjOW72fUwpJGKymYxMgHgIQPHwjIOqEqgNKf5KV6nLOKLVnzlB7nQLoyVpX1ymISIby7+71MyIKnj8bCnZLyV7pe78y6sGZnlrdQfSkhQMFvThbfoIQZRvFX82ocZjAszbk+S/uEfO8hapf/I/15EFrmKOkY5wMKBkaqr/7ANJcpTHzNYqYqv0dPCJ2D/gaPcfj7wuwZIPL5NDGD0xVdIhrZSf7QCz+T9v29xjDP1OtZJq66T+vsOfgWNMdkRH9/Q5kubvceVv2CVlss/XZla9nNMbJtz8ZiXJhKZk7FbRua6qJZA2rTOzc6M9L7+56VOhp1tqnVHaGgBMK7ziRlXZeMNjMk55T3edd9oczUxW4Iu/KeK21SuTRyDtLGxSYHlTdnHCadIJMyGJhBQNSAU8lbbUbGf/5DqBWhrkpY0CiZ7sO7GG7OxLPxCStbn3ckbq//O+Iz5W5rVuDbxhgaIRFjiUb2XjnswkX1H4nhR7JF6+YbQkr9+TrK3+AxC97M+S5KfKd18sBtPeqF2/2nLE20XaatQSTkpMMJoYp4qeR1xnKBidKDviOfU3D3YpHkvq/757Tva6bbkzToxkFrRmP04nR4/xiESy/5bTKYzeYeRv2D5qm+3qk7+xRsr4co8Z3yKT1lopXFe5400q/SyDaI1MTU32YEux50Ufhu3ZL7W0Vv9rHaDQTBmO7Ci3enIIpFFRMn0pmYAjJ+BoYG+vArUh7frv7Arwqa5+bfrJ4/JT5d3hteqsdmnv9HnxfKDJvsgxLVEFT5ot9cSl/RX6AroYMp6clW3yH99+5NtWGmNk5zYr3WvleWF53UnVrWyUlVH8iQ7Vv3mGnOSA1DcgKdu11dvlynpSaG6NEv+9U34sJacxpMixM5R8plPevskHcL0P7lDovNly/eKviF5u7PZumePaJ3WQKB0LaOAve458wxJKvbRPoXqnVKMdiyIwv37SwVK18btTpS6hpPbvz4zrANGy9y2WOsp31E7ZqqZA2nGlCZ5Z8duHuxQKOnp83Valkp4aWqLa+MR2feorpx18YyvZkfPcI0Ep4Mi0NWYX0ZyC7uxKYurGeOue8WRf2CPnrIUaz6K01lopUl/ZQb5jKrpB2kSi5bnoZnJAGuiXogVarBQADkAgjbIX29SpHf/5orbe9YI6H8uFbLlTBJ2gKz9dWW+gNz69X8e0V/iR/TzYfRMLrJzFzQosbZJ6egiix6uapgsEXakurExNjXqf65S3adeoq5MvDY/o6XptxP4RcRV5Q5MGXuqQMlYDzwxf5fek1PfwzvxrS3rq3ZhS/dLJH2ipJv4rnXLmRqX0BLvz3fIPFJzGiIxX3SFlaEG9pOr+Hscrs316/65pjlr1Ht2q2IuHnos+++1zFSKMnqTq+H0RXDkzO7d5ArbsS+v3P39Gia6+UduNY/TGRdEjP4BnZWbXSvun575nD9UAHQ3K1B75/VBVhNFS5f8IlfFrRtvVKTOXQBpAcRBIo+xY31d8835t/9UL2nXfK9rzxzFOG811RFRaGC1JAz0pqT1S6jJKJ5GUM7tB/p7Dv+F3FjYpsKwpu9BKT4W/cC62WK/MgibZrd2lrmRiIkGppVa+G1S6N6PUjoTSryQk9Uma4JvPAU8DzxV+kbnUSx1KLVqocKDvyDeudn0pSY06VIdWut9VIOTJuKO3h+o8uTNq5HWU779haEmz7Kby7uLOl9sakfoJpBVw5e2cvmO1JMnu79PcuUH5yUYlXj34968TdDRvRY3U3VuC6iqfrfg0LSt8QovUP7GfFd/XQWG0JEXrw7rmmqf03W+deOg7t9bLhHwpUeXrZBzOIbqCTV1ENunK1Bz6gLCVpEhd5YfRogmgoGLdsrNmywTHN/4FwBgY2TFuBNIoC/274trzxy3afs9L2nnvJmXiZXga0xQINYR07FvbpL3dpS6lpJzm6CED6WwQ3Sh1xytv1e8yYpaUeSCdC589N6jMqPC58oKgxAM7FTh7hlxbvXO7x8vftE/OsbOzs8xHSLwixX67WSbkquaUdpmgkdsYlBfPyHpW3v7ynl2c2tSp8MIW+a+X/6K5k+WGjVTe/w3FUTeOLs3poD+tBcc1aeO+PmXio8cSHfWx5TJVEGph8txlzTITDKMlaeGMgOpn1CreMfpgRl/PgPZZyfN9uQfOBzZGZn6L1Bub9idxOLVpmfPnSFby/5KQzb2Wtvvi0pao7IrgQQd9pVwYHaqtntfV5ZmrjF+ZBkODbGeHTNucUpcBYBogkEZJ+BlPfVt7tP0/X9KO372sfQ9urchu54k4/iMrtKTNlZnmYbQkmTFWkTZtdQoeNyMXRE/j7pepEk9INUGpr8zmG7c3qXdvSsnn9qsSw+ex2P60el/NqH7RNFj0bhz8nQk5TWZoBm26z1Xs3lezix8OZNT7wLYSVzhxfjwlm6jOA6WDjFdmzxUlYsdKc6Ypuy+hxqNnqPPx4VFJDcubVJ+qjuduTF705FnSwMT3g4BrdPzbFuuhu54btT1cG1J/bEAdCavZDSOuaKyRqQ9kw2hk+Y78jf1DYfQguysmHT1DBy52YCUpWFO5Cxii+Lq7ZGfMkgkQFQEoLJ5lUDSxlzu0ee2T6nxihzof2y5voPq7CUMNIS07b5GWLKtTYF9MSlT/9zwedu+IwNlIwbctkuljscIplfHkHNcm/89lEv7NadZAzFffg9U59iD1/D6l5jO6Q5LU1Su1t0m92Z/zQNhT+JgZSj69t8SFTZ47q1Z+V2/FN2UdUtCRErRHS5LfW8ErZRVAy5L6UYH00nPbpX10R+ej0tfhcxc1ykwijB7U3DJ6bJ0bcPR333yLlrSHVB/JPcsOdkX3xaV+DpaN4mTkzKuVb4xsV5+0P/u6w1naIuMcEEYbIwWiUqy6Gj2sKr9JuqxZKyViUlNLqSsBKhMjO8aNQBoF4yUz6lq/Qzt/v0nb7npBsZc7Sl1ScThGR523SEtWtao2npCSGWmCC/lVvf6UnNn18jsTCp25sHpOISw3bhmcddAYVW/cKLlud6krKThGdwzzX9wjZ3mL1D+g2LNeRYfRkhSc3yC7sXoD29DixspPyaaI11G9/8+TEdjWqaM/dJT2bo4r2haRSxidvwr/WYusbpOSkw+kl72hQYGQqxnzm3TCWxaqu6tfJywZEVLX18g0BemKPgRjjFSflrsiKGsbZPtnyH+2S/6GnTJzF8iEsq/9rDGSG6m6MDqrsn+GKoGNx2QIpAEUGIE0plymN6U9f9qiJ/7ut+rbNn3euDiu0Qn/92gtnOnKxPqlju5Sl1TWnFl1CqxsJowupES/nBUz5b+0rzhfr7lWcozUlZCaa+XV1ij+5D75seoedTCI0R0j+FY27kuO1PdY5R+MyOxMqNiDHPpa6yUrRbviBe8EC86tEwOks9Kvd5e6hPKzrUuzahwF59VKGUeKD5S6ospWwU347vx6Oan8FrM8blFE19x0joKu1BB1Dr5ByJXt82XqGiTZ6b2I4REYY2Rq0jJvrpMSLbJeQMZmZB0nG0bHq3S8ToUf1KmI+nsTsr4vc+BMdwCYQgTSmBLW9/X6nc9p+3++qO3/+ZL8VBl0ZhbRvFPbddLbZ8vpSkjkq+PiRIzUU6UvlMtJW4300hQ/Zjgg1YSlgCM/GpGX9JXc1av0sz2yA54UcqVNXZKqdwG4Q0k9v0+peQsVDjK6w27fL3/hLNneyl+FKrMrrsBRTbK7i/ME39vSoM3/uUmSFGyOqPVN7WqsMQp1JQpysMNtCjKyQ5KiIdlY5e+vUyboyp0RldMYkVsfUmrzfqVm1yokAul8WL8CwqhDiLx1Tl7d0YNa6w4TcnVmA2jbKZl5rVIgIGU48+hwjGOkhrSM0pINSNF6qbOaX4NV8FEdSRXR4W2t1JuQ6huOfFsABzAqv8FC5VZPFoE08rb7fzbr4Y/+Usl9+XVMVKJoa0RvueRY1Xf3ZLtCMW7+q51yltSWuozq1x2XmqPS/jzCJteRN6dVstnxU/07epV84jBvdKbZAakDJdbtVODsmXItcy+1tVP1Zy9Q/I/bJK8C3oAdhpeSnJBb8P3bCwe09eHtQ5+n9w9o9/9u0W5JLSfP1bzQ1AczDq8GsyKRI99mOjBG0ZPnyNm3X0qnpd601CtFZwSVkqTZjdKe6XMG3JSrhO7IMbjz8u+Onii7vVNqiMo0RCXfl5JJKRyWAsHheZgD/QTWI2Uyklfl/x4VfFBHUsXUb+MxGQJpAAXEWxDkZf8zu/WnC26Xn6zyFz5jePOnV2p+rSd1dZe6lMoUT0n1M6U4naQFZa2cY2bJf+j1Sd09PbtVib/slX15+5FvDEm50R2b06pfzOgOx3qKNvjqa62Rt7eyD1qmX+9WZHmz7M7CBnF7U67SnWMfQHIjruRP/e9bk5oeY3WOxPen98+rJLmz6xRZ0iBtH2Pdj/60QhEpFY8oZEzFBqulZiu0uTPy1jlSqgTNF7F+2URSigSlSFjqSkojF1VsrpNpqJEG+gimB8Xj1d1Z7lfoD1GO9b0y7VU8QCIma3ktC6BwCKSnMev76npyl7qf2yOvP6P+XXEtu+xkRdvqD3s/P+Np572btPHGx7T7/s1FqrZ8zDp2hk776/lyO+KM3MxXIFjqCqaHTDLbSTSJ8CCojGqPn6nEn3cVoLDqlXphn1ILFiocGH3AJeWHFTBJ+SYoz3PlOL4CSlb1i/1kf0Te3j2lLmNKmNqIrAoXSPe11Gvfb1455PXhupA0xTPZnYaQ1EcgLUl+vErDmwmILKqXtnce+gYDaYXm1Mmra5bb1SNlpvcZMZNSId2RIzlz6uSkS3hQ0fezz1NjPVftT8juT0gtdTL10Wk/c9paK9XVV+lihjnWG+6Qr0SVEqh7ntTfJ9VwRiswIUbl9xxVZuUMIpCehqy1eumfH9GzV/5RmcToWYnP/8M6NR03W41vnKnaBU3y056cgKN9j2xVeEatkvt61fPCPqXyOf2/gjUvbdJb3tYqdVTxi7xi6ueNbFH0J+Uc1yb/6UmEynt6FJrLKtuTkXhgpwLnzJTrD4/u6HkqrsiieqX2Dqh/wx7JSjPev0iRYPXOZE11Vc/oEi+eKtjrOWutdjx1+AVIQ8Gp/+rBpU2q/HmcUyOzp7K7+KdCJj2ONwc798ud26J0TZ0CDUGZsbqpcWgVGEhH3zZXSpf5aLquhKypl6nmzuAjsNGoJCMNVPFBRtet/IP4mXTFNOXYeEyGQBpAgRBITyPWWqW7B7Tha/frlX9Zf6gbqfvp3ep+endxi6sQKy9YLPURRk8V29knU4TXOL4vTftFopvyeOE7Td/Y5cv2p9X7SnZ0h6xkjavUtriSLw/P3w4ta1E40K+yPWydJ2ulgecOH7JWkvTWHoVmRqSeqT+AYIzR7FUz9dq2Q3dghwrwsxicXSN5ZR40FUlmK3OR3cg4f1nu6FJQkhdtUdoPKTKnRtrdXcjSUCLOrBo5XoWMV+tPyqZ9mTmNUmIarjKeTmePL6ar50DwQVy31BXkz6mg7yERk53VVvkHAQCUJQLpaeKFf3pIG3/8Z/XvjFf06t4l5Ri1BgjmppLd2i3zxoaCLTbt+0a+jAbiroI1UjhYxS/Qj6Q7LtNWL7t74gdU/HCoAAVND+nOAQ0saFVyd1LpjoGDxqY0valexlRvJ1NGEdne6vm5s8mM/HCDHBWmo72+M6b2c5eov7Nf3U8efEaD258a4175cetcFXAKSeWojUgDh1msdRoILG6W6ZlYl7i7p0vOghlKx1JyZrUwxmMcbIXN3o6eOU/yKuTsgb6pf46sKJ4nqco7MNwq+P4q6TkglZJSSSnMor/AuBlThiM7yqyenCp4RsdY0vGkel7Yq32PbNW6C+/Qhq/ep77tMcLoPKy4YIlMbHqOKimYlCfVRgv28Mb4MsZqYF9a2//1FcW6KuP0uEIxy1ondb/+rRXyRrQMmfqw+l8fkNebUfKFDtn+3EEtx6j1wsUKOdUbRvtyFX+u+jpv05u75MxvKshjG2M0s69X8yOe5r5zqSLzRq9uv/GpTmVqpvYAkSPCQ0lSiANvobYaaRKvc8z2DgVjMVnPVzITlGY1HPlO05lXOa/FnRlRORW4YIrt7J2WP9Omrr76z2qrhlMevQr7vdtbfa/lAJQHOqSrSDo2oFf+5Un1PL9Xr/2/Z+SnKuyXXZlbcmyztK+71GVUHRMMyRbozY4xRq6smpY46ptbLzdUnkcGi6a/Vwo6UnoC82Lbm5R8sDoWpCs26xolt8TkjTFzv+Gs+YqGy/tN/sguvomcqunLUWKPq77Hd01sX6sg/S92KbKoQbajMG/SjDFqjSfUsjCi9PEteuXBXcrEksokUnp9S58WLK1TMDFFXdq95b0fFovvTfPfD0FXJplfd2kgkZAfCqtvY0yho1oUCEnaMb27zsdi05Xz+jzyjvmV0x09Un9ailZO8D9V7ED1rkcxzKhgp1YWi5eRVAEHTFxXcl3ZTKZKB8sBKDUC6SrhpTL671NuVuxlFpYphPq5dYp0ck5zIdhk4QMr13iKLGqQN2CluoJ/ufKVysg5vl3++h2Hvo2R7LwZkpHMQEo9z3UXrbxq47Y1KPniwWFM+I0zVD8jqXKfGz2QqlHP716X0xRR85mzFNDAYc/2Ggyw+3vD6nv49SJVWSJWUrTwZ1wYYxToTcnrzQaF0fmNSnf3y9ipeSILzK9nvEKOH5vmp/pbm/++kMwopIxCTZL2dUnNtdLMBmnfNJzlexg2VRkH6kxTWK6p0IDT8yTVSaav4rPLCUlV71lXQ8r7pdP4FLJD2pjsjGrXzXaTO07286GPHZlDbB8MoOW6knGYGw1MFiM7xo1AukJl+lJ6/c7nVLuoSb1b9uuVf32SMLqAjvv/lkqpCuwQqQC2q1+mwGPJMn5A4Xlh7f/jTsXqQmo/p1GuM53eoYxQc/hTHZONTer942ECa4ybTR582qxTH1LzCREZlfcptRmFldye7Zz1uwfU+eutqn3rPIUaAwoF+w56P+gpoMRWX7JSYOb0+NmyXcVZ5MtkPC1591LFezLyM55mNDTKelMTaIUW1EsqzyC2PxWR40phtziBWHrH9P4db0Lu1M813d8rtTdP7WNWg1R5P/8Pip69QKqUxQzHYPf1yLQ1Senp87NtjJF1THY172rlOMqu3FjhHGf4/+nAgDgXCB8cIo8RIA9uz31MiAygkhBIV6gdv92oxz59d6nLmDZm1qpc37NXPLutW+aowrYtB11PtY1W3UaqXdks15nGHYE9CZn5TbLbuoe3OUZ2bosGOtPqX894jqni70sosnK2Bp4d/jetPbldrh0o26PUg7qf6FFm2+iuxt5129Urqen/LFTE7Ze1kucE1b/HqO8ve2T7qmfxwvFwZtTK7y18N5qRVNsZV42R0g1RhaZwLQO3NSIVYKHEfHnW0b5fvq5ge51qj2tWuMmRMbZwM9ddR97r+wvz2BXC9mcKs9DWrun97zqWShipZxrCcgNJlfmx0yOyaS8b0FXSInKTZMNhmVBYilf5vN9yWNTQmOFOYmf4bzOyw9hxR3UcG2f0dTYQGOpUJkQGMF0RSFegV2/boPWX/bbUZUwbS89ZKGf/9OmuKLq+tFQbkXqntgvO+lKiJ6hgjRQOpuRbR8HZtWpoq4KuijyZpblAOuQqGa1X7zP7pI07S11W1THGSK93KNwSlNMclR8Myduf1EB9jaI15T23120MK7Nt7OsSf9mv9KIGpbYllH5tb3ELKyPFXiTYWE1pGC1JbtiobNcrM1J6W0zduQMj4RWtmn1ygWZu1kWnRWB1WGWQ8UwXtgIC6ei5C6RM5XZHD+npk5oDVf/zbR1HCodlu2NV/71mO4snePuhbuLBoNgZDpBHXueODJedUWGzRnw+FQEyETRQxRjZMW4E0hWk4/Hteu32Z7Txx38udSnTylFvnil1dJe6jOoWCk1pIJ3KBLT7t3uU3pWQAo6UGX7lGlu8RM1zyv/NYEHFE3LevkjKZNR76+ZSV1P1TMaXPKvk612qWdWm3ge2KXT+XLm2PGc9pmxEqU2HHtuS2RY7qHt6WopWwIJER2C88uxq73lNBy2IWcgQzzqFnwde9jwr9ZVft3xVGijztuP6UFV0R0vKzkUPRKRUle/b0ai0v8LXuhnZdew6khvIhr+BEUFxwJWpiWZf248YVzEqPD4gQKb7GADKF4F0BUls7lLN3Hq98StvUap7QAN7e9X72n4ltnQr3VOhi46UuWhrRDU98VKXUf3yzEQyvquu55KKP7ZLxnXk1ATk7R8YvHLodnUnt6upLaNp35eQ8aT9PbIN9aWuZPpoqpXZOyD1JSXPKr4hrsbjQmV3sNrKqPt/98gmp/lBm3HI7OmVW+oi8hFypERx2qOtb2Wc8e3sKS+sxBiLYhZyn/T72N9NXeUfYKkIjpGKfHbFRNWcs7A6uqMlmbkt0sA0eB1viniKw6jF74Y/NiNC4+y2wOgRFiOC5VHbB4Nmh9M0AGC6IZCuIIs+fNwhr0vHBpTpS8vrSyuTu3i9KWX6M0PbvP60vIGM/GQm97cnbyAjL5n7OJWRn/KGL2lPfnKMz9OevJHbkxl5Sa8qTxE77n1HSelyPZ+5etitPTItE0/mrC/17A2o63evDXXP2Ywvb4zF5OrfMk8zj3GL+pq93KW7CWGKxSTTisyKyM/N7s5s61H/4vmqaSifg4nWWvX2hOXHyrNzu9xk9vQqMCsiGyuf/8OJCC1uKvjvbd8adb3gK7WrV/UnzVCqMykTcBSocRVqdhRwMwqY0c/X6YGxA7v0roQytlEBM/XPW15nZf4fTqXIG2dIuzpLXUb1C5b5Yay6oNxwKu9GgbIxRQvAljuTiMvW1cqEQrKZzIhF8A5/yXYWH7BAXi5oNs6IwNnJdSwH6DgGgCNiZMe45RVIX3311fr617+uz3/+87ruuusOebsHHnhAa9as0fPPP685c+boK1/5ii699NKh62+99VZdfPHFB92vv79fkUgknxKnjWBDRMGG0v5b+RlP1rOynp/9O+PLz/jZzwc/HtqWvT7Tl9KO37ys1/7fM+rfFZfNlNcLx/YWV+L9WcHZbd0ySxZI3ePvYukfCKrjjx1KvdZ9xNvWv3WeZh7tFC2Mds85USYSzHZne55sxsu+KUp72RW1PV/WG7HN82VTaSmdyXYvZ3LX5z7W4P2natX0aFh+IKzee7dPzePhiPwd3Qdt61u3TcF3L1CwTIb4ZpyIEn84xOBoHCzjyR9IV+z5FsG5dZIK1wVprVUyFVHfk1skSft/M/bze+O5CxVudRQJDshaqe+VQ6zZ4Fvtvb9bJuSo7W01UxqKpLdV+KnuU8Dr9xgjXQyB8g6ko+csrKpGDNuZkGkOSF71H4A3/X0ySxbJbWsrdSkAAIzLpAPpJ554QjfffLOOO+7QXbuStGXLFp1//vm65JJL9POf/1wPP/yw/u7v/k4zZ87UhRdeOHS7hoYGvfzyy6PuSxhdWZyAO6k9auZpC3TC1edIkvxceD3Y5ZrtwM51Z6cGO7Mz8tO5cDvt5f4e/nxkl7eXGt3JPbQtOaLre1Tnd0b+QLaDvHlBg9xIQGprzAWI/uhwMONPm86Loug+cjuO7xulUkF1P5dQ7xMHn9I9lvDSZs14o1u0g4JmTqvcN8wpyGNba7Odg4NhtlW2w3Fouy87GFqP2O7tS6j3V89K1sqmffU/sTU7LxQlF3+0Q02n1cuZ0Ao9heHatEzYZVzHeFnJXdgqf1NlLuzoNgalROEeP6OQOn699Yi367kv+1w+4/1LJEn9T7966MfcnS043tEsK8nr9dSyKM/9NRKS7erK7zGqQKA+IHWXuoppIFDGsX9NUIFIunq6o6Xsgf5Io9Qby3aH1eZGlSXKYB0EYw7uTHacEYvnDXY5j1hQ74DrR89PdqSamlJ/VwAAjNukAulEIqGPfvSj+pd/+Rd973vfO+xtf/rTn2rBggVDHdRHH3201q9fr2uvvXZUIG2MURtHdKc9x8290ApLgdryn2dorR0OptPZjlbr+cNhn58LDX0/GxjmbmszXvbzXIg4fLHZQDHtDT+ul72v9XOPO/hYI+471Fnu564fvJ21w6dnWI0enTy03WZf4A5eN5hTDgaaUvb/JJWR7UjIxgeytx08FWVkIDpY0+DHE+A/vUvO6jmHnGnqWaPO5zOKP/TauB/TqQ1q1jta5JjirczjHre4YI9tjJFck/3/ONRtDvg8vbVbnd97UH7HIboOUVJ+Z5/69jSpbnbpA+nEHsLoCSvT09/GwwkU9qBU0KTV9oF56no8ruTGIwe+Hb84dBB9oO7fZ0NspymilkXNk65RkhQN53f/asExyuI4zO/vUoueW13d0YPs1k6pLiITDcm+3pkdVTG7UQrlXsP2JUbv/4OL6x1q1EXAlamtk+rrZCKR7MLcjiv19comEtnXv0NhsSsFA1IwKAUCo8JnRl8AAKa7SQXSl112md71rnfp7LPPPmIg/eijj+rcc88dte28887T2rVrlU6nFQxmVzZPJBJauHChPM/TCSecoO9+97tatWrVIR83mUwqmRyecxmLlcGRbkw7xpjsPMCgK0Wy+zIvL7PsYFDt2cPPKbV2qNM3m5kfEHAPBusJTzPOzmjGF7P/wjbtyfal5fen5fflLomU/LQ39MYiMrdG0RlurjPY03AqP+Lxh75WbnTG4LbB7mN/RNgvDR8QGBHED3Yum0hIZmH5HFhLb+5U51fvld/NfNRyNvDEToXetUAhU9ogwLg8e01YBa+dYJKFnxUedFKadUpIPQsXKHb/kbulJ8qmPKVtUEEz+ZZO65dvQFgswRUzSl3C9FGugXTEVSCaKd/u6MHO4KGF9EbMOj5gcb2hjwOuTCC3mF5uQT2z3JUCzohF9RzZwdsNzlWebFAcDsk053mADACAaWTCgfSdd96pv/zlL3riiSfGdfvdu3dr9uzZo7bNnj1bmUxGHR0dam9v14oVK3Trrbdq5cqVisVi+tGPfqTVq1fr6aef1lFHHTXm41599dW66qqrJlo+gCIxgx3UE3jvdbi3ACFe409I6sW96rzi97KJVKlLwTgkHt2nptMbSjq6I9QYUF/IHVogFONQoXm00xiS+ouzeKUxRg3tnvrn1iuyrEmSlHh455TsZ7YvrZ6NaTlhV27UVajJUTQ8sQNwXqJ4Z9CUI3d2nUL1rrS9o9SlTA9OeQbS0XMXSekj/OwYjRgVcUBAPLhA3hiB8VBH8MhtAffgQDkw3D0sx4xYbC+PkHgcOBQLAEBpTCiQ3rZtmz7/+c/rvvvum9B85wNfRNhcR9Hg9lNPPVWnnnrq0PWrV6/Wm970Jv34xz/W9ddfP+ZjXnHFFVqzZs3Q57FYTPPnzx93TQBQzZyGsFr/8fxsZ7jnZ8Mfz892lqezY2Fs2pMdyMjr6pff0Suvs1deR5+8jl6JULKo/K5+9e1tVt2s0gXS4WCfGs+bp+57xjefHVKlJtLBJc2Sivcz7sjX7HfUD41Pavhgu/oTQXXfv0N+ngfN+h7bNfRxeEWroidPbNxXZm/hFnasBOEljYTRxeQUIP50TbYbOBSQqQnKhANSOJD9fOhjN/txbpsJB6TI8MfuvFqZSGA4HHbGCJaNYcwEAABHMtiYV07KrZ6cCQXSTz75pPbu3asTTzxxaJvneVq3bp1uuOEGJZNJue7o1aPb2tq0e/fuUdv27t2rQCCg1tbWMb+O4zh685vfrE2bNh2ylnA4rHCYuX8AMJbA3MZJ39daKxtLjgqo/c4+efv7ZftS8uNJ+T3J7Mf9adnchYUS8zPw+E6F3r1AIZVudIcb5P9wQko/+ntSgrOjklfAFQ3H4Jjhfcs1nmrqrLqS+XUnR1bOVHRxnSINvuKvZxSZHZI0sQ7pzGvdedVQ0SIBmXS5zmiocEa5sRG5zt+gIxNwZZvr5B7lZ8PfaEimJiQTDWb/DgekUDZYHvzbhNxsoJwLmBVwstsGrwu4MuW8UCIAAMAhTCiQPuuss/Tss8+O2nbxxRdrxYoV+upXv3pQGC1Jp512mu65555R2+677z6ddNJJQ/OjD2St1YYNG7Ry5cqJlAcAmALGGJnGiJzGiIJLxj5wOBabzMjr7pff1S+/LyU7kMle+tOyA2nZ/szQduU6tW3ay76ZdnJHkgcXzByU9mRTg5dMttM77WcfJ56U35+W0hWaCo5haHSHLc335Co1NGYd41Ci/6d8uXWu1FO6r2+tUW9PMK+f3dD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