{ "cells": [ { "cell_type": "markdown", "id": "44bdc086", "metadata": { "toc": true }, "source": [ "

Table of Contents

\n", "
" ] }, { "cell_type": "markdown", "id": "f77accfd-4d93-4080-b73e-bfb23f3b89b2", "metadata": {}, "source": [ "# Customer Churn Analysis\n" ] }, { "cell_type": "markdown", "id": "002c3e9c-baa6-4804-86e6-1777aab23ae9", "metadata": {}, "source": [ "# The story Behind The Data\n", "Dollar Bank was concerned that more and more customers were leaving its credit card services. They asked me to use my Data Analytics skillsets to analyze the problem for them, in order to understand the main reasons for customers leaving the services. They also needed me to come up with recommendations for how the bank can mitigate further customer churns. Eventually, the bank wanted to proactively implement these recommendations in order to keep their customers happy." ] }, { "cell_type": "markdown", "id": "28a328f4-b93a-494a-b16a-4a616e416212", "metadata": {}, "source": [ "**A full ERD of Dollar Bank's datasets can be found [here](https://dbdiagram.io/d/638cdd8abae3ed7c45449eed)**\n", "\n", "# Data Description\n", "In this task, few datasets are provided:\n", "\n", "1. **`BankChurners.csv`** - this file contains basic information about each client (10 columns). The columns are:\n", " - `CLIENTNUM` - Client number. Unique identifier for the customer holding the account;\n", " - `Attrition Flag` - Internal event (customer activity) variable - if the client had churned (attrited) or not (existing).\n", " - `Dependent Count` - Demographic variable - Number of dependents\n", " - `Card_Category` - Product Variable - Type of Card (Blue, Silver, Gold, Platinum)\n", " - `Months_on_book` - Period of relationship with bank\n", " - `Months_Inactive_12_mon` - No. of months inactive in the last 12 months\n", " - `Contacts_Count_12_mon` - No. of Contacts in the last 12 months\n", " - `Credit_Limit` - Credit Limit on the Credit Card\n", " - `Avg_Open_To_Buy` - Open to Buy Credit Line (Average of last 12 months)\n", " - `Avg_Utilization_Ratio` - Average Card Utilization Ratio\n", "2. **`basic_client_info.csv`** - this file contains some basic client info per each client (6 columns) -\n", " - `CLIENTNUM` - Client number. Unique identifier for the customer holding the account\n", " - `Customer Age` - Demographic variable - Customer's Age in Years\n", " - `Gender` - Demographic variable - M=Male, F=Female\n", " - `Education_Level` - Demographic variable - Educational Qualification of the account holder (example: high school, college graduate, etc.`\n", " - `Marital_Status` - Demographic variable - Married, Single, Divorced, Unknown\n", " - `Income_Category` - Demographic variable - Annual Income Category of the account holder (< $40K, $40K - 60K, $60K - $80K, $80K-$120K, > $120K, Unknown)\n", "3. **`enriched_churn_data.csv`** - this file contains some enriched data about each client (7 columns) -\n", " - `CLIENTNUM` - Client number. Unique identifier for the customer holding the account\n", " - `Total_Relationship_Count` - Total no. of products held by the customer\n", " - `Total_Revolving_Bal` - Total Revolving Balance on the Credit Card\n", " - `Total_Amt_Chng_Q4_Q1` - Change in Transaction Amount (Q4 over Q1)\n", " - `Total_Trans_Amt` - Total Transaction Amount (Last 12 months)\n", " - `Total_Trans_Ct` - Total Transaction Count (Last 12 months)\n", " - `Total_Ct_Chng_Q4_Q1` - Change in Transaction Count (Q4 over Q1)" ] }, { "cell_type": "markdown", "id": "88c477bc-ca42-44df-8a56-eb3e1b6d6585", "metadata": {}, "source": [ "# SQL tasks\n", "\n", "After a thorough discussion with relevant business units and project stakeholders, business questions were translated into simple & advanced SQL queries to provide answers to frequently asked questions by the different business units and relevant project stakeholders.\n", "\n", "1. How many clients does the bank have and are above the age of 50?\n", "2. What’s the distribution (in %) between male and female clients?\n", "3. Let’s define a new variable called `age_group`:\n", " - 10 < x ≤ 30\n", " - 30 < x ≤ 40\n", " - 40 < x ≤ 50\n", " - 50 < x ≤ 60\n", " - 60 50\n;" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "clients_above_50": 3078, "index": 0 } ], "schema": { "fields": [ { "name": "index", "type": "integer" }, { "name": "clients_above_50", "type": "integer" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 1, "truncation_type": null }, "text/html": [ "
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clients_above_50
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" ], "text/plain": [ " clients_above_50\n", "0 3078" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-- How many clients does the bank have and are above the age of 50?\n", "\n", "SELECT COUNT(*) AS clients_above_50\n", "FROM basic_client_info\n", "WHERE customer_age > 50\n", ";" ] }, { "cell_type": "markdown", "id": "f3c0896e-ad31-449f-9407-77ae3ed2f6fe", "metadata": {}, "source": [ "**Question 2:**\n", "\n", "What’s the distribution (in %) between male and female clients?" ] }, { "cell_type": "code", "execution_count": 3, "id": "5fec3c74-f6a4-4392-8f13-bf37d7c26b6e", "metadata": { "customType": "sql", "dataFrameVariableName": "df", "executionTime": 1612, "initial": false, "integrationId": "340d6f6b-e6bc-4eae-a902-e9a40ad6dfb1", "lastSuccessfullyExecutedCode": "-- What’s the distribution (in %) between male and female clients?\nWITH total_count AS (\n SELECT \n COUNT(*) AS total \n FROM basic_client_info\n )\nSELECT \n gender,\n ROUND(COUNT(*) * 100 / total :: numeric, 1) as percent_distribution\nFROM basic_client_info, total_count\nGROUP BY gender, total\n;" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "gender": "M", "index": 0, "percent_distribution": 47.1 }, { "gender": "F", "index": 1, "percent_distribution": 52.9 } ], "schema": { "fields": [ { "name": "index", "type": "integer" }, { "name": "gender", "type": "string" }, { "name": "percent_distribution", "type": "number" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 2, "truncation_type": null }, "text/html": [ "
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genderpercent_distribution
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" ], "text/plain": [ " gender percent_distribution\n", "0 M 47.1\n", "1 F 52.9" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-- What’s the distribution (in %) between male and female clients?\n", "WITH total_count AS (\n", " SELECT \n", " COUNT(*) AS total \n", " FROM basic_client_info\n", " )\n", "SELECT \n", " gender,\n", " ROUND(COUNT(*) * 100 / total :: numeric, 1) as percent_distribution\n", "FROM basic_client_info, total_count\n", "GROUP BY gender, total\n", ";" ] }, { "cell_type": "markdown", "id": "d13f36ca-9aa3-49ef-bdbd-e709c6b9b675", "metadata": {}, "source": [ "**Question 3:**\n", "\n", "Per each age_group, marital_status and income_category, find out the following values:\n", "\n", "- a. Churn_rate (in %)\n", "- b. Average Total_Relationship_Count\n", "- c. Minimum value of Total_Amt_Chng_Q4_Q1\n", "- d. Count of customers \n", "\n", "(_Make sure to order the data by the number of customers in descending order_)\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "132a501d-8c52-47db-a384-31e4eac14bb6", "metadata": { "chartConfig": { "bar": { "hasRoundedCorners": true, "stacked": false }, "type": "bar", "version": "v1" }, "customType": "sql", "dataFrameVariableName": "df", "executionTime": 1774, "initial": false, "integrationId": "340d6f6b-e6bc-4eae-a902-e9a40ad6dfb1", "lastSuccessfullyExecutedCode": "-- Let’s define a new variable called age_group:\n\n--10 < x ≤ 30\n--30 < x ≤ 40\n--40 < x ≤ 50\n--50 < x ≤ 60\n--60 10 and customer_age <= 30 THEN '11 - 30' \n WHEN customer_age > 30 and customer_age <= 40 THEN '31 - 40'\n WHEN customer_age > 40 and customer_age <= 50 THEN '41 - 50'\n WHEN customer_age > 50 and customer_age <= 60 THEN '51 - 60'\n WHEN customer_age > 60 and customer_age <= 120 THEN '61 - 120'\n END AS age_group,\n marital_status,\n income_category\n FROM basic_client_info\n )\n;", "visualizeDataframe": false }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [], "schema": { "fields": [ { "name": "index", "type": "string" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 0, "truncation_type": null }, "text/html": [ "
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-- Let’s define a new variable called age_group:\n", "\n", "--10 < x ≤ 30\n", "--30 < x ≤ 40\n", "--40 < x ≤ 50\n", "--50 < x ≤ 60\n", "--60 10 and customer_age <= 30 THEN '11 - 30' \n", " WHEN customer_age > 30 and customer_age <= 40 THEN '31 - 40'\n", " WHEN customer_age > 40 and customer_age <= 50 THEN '41 - 50'\n", " WHEN customer_age > 50 and customer_age <= 60 THEN '51 - 60'\n", " WHEN customer_age > 60 and customer_age <= 120 THEN '61 - 120'\n", " END AS age_group,\n", " marital_status,\n", " income_category\n", " FROM basic_client_info\n", " )\n", ";" ] }, { "cell_type": "code", "execution_count": 9, "id": "90ac6142-ab8c-4732-9e4d-2e6dbea498c5", "metadata": { "customType": "sql", "dataFrameVariableName": "df", "executionTime": 1742, "initial": false, "integrationId": "340d6f6b-e6bc-4eae-a902-e9a40ad6dfb1", "lastSuccessfullyExecutedCode": "-- Solution approach: Created a Pivot Table that groups the dataset by demographic variables: age group, marital status and income category, while also aggregating individual values into a summary of the churn rate, avg total relationship count, minimum total amount change [Q1-Q4] and number of customers, per each demographic group.\n\nWITH churned AS (\n \n SELECT \n dem.clientnum AS clientnum,\n age_group,\n marital_status,\n income_category,\n total_relationship_count,\n total_amt_chng_q4_q1,\n CASE WHEN attrition_flag = 'Attrited Customer' THEN 1\n ELSE 0 END AS is_churned\n FROM demographics AS dem\n JOIN bankchurners AS bc\n ON dem.clientnum = bc.clientnum\n JOIN enriched_churn_data AS ecd\n ON dem.clientnum = ecd.clientnum\n )\n\nSELECT \n age_group, \n marital_status, \n income_category,\n ROUND(100 * SUM(is_churned) / (SELECT COUNT(*) FROM bankchurners)::numeric, 1) AS churn_rate_percent,\n ROUND(AVG(total_relationship_count)) as avg_total_product_count,\n MIN(total_amt_chng_q4_q1) as min_amt_chng_q4_q1,\n COUNT(clientnum) as client_count\nFROM churned\nGROUP BY 1, 2, 3\nORDER BY 1, 7 DESC\n;" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 69, "income_category": "Less than $40K", "index": 0, "marital_status": "Single", "min_amt_chng_q4_q1": 0.299 }, { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 36, "income_category": "Less than $40K", "index": 1, "marital_status": "Married", "min_amt_chng_q4_q1": 0.549 }, { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 29, "income_category": "$40K - $60K", "index": 2, "marital_status": "Single", "min_amt_chng_q4_q1": 0.331 }, { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 27, "income_category": "Unknown", "index": 3, "marital_status": "Single", "min_amt_chng_q4_q1": 0.391 }, { "age_group": "11 - 30", "avg_total_product_count": 5, "churn_rate_percent": 0, "client_count": 16, "income_category": "Less than $40K", "index": 4, "marital_status": "Divorced", "min_amt_chng_q4_q1": 0.632 }, { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 15, "income_category": "$40K - $60K", "index": 5, "marital_status": "Married", "min_amt_chng_q4_q1": 0.371 }, { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 15, "income_category": "$60K - $80K", "index": 6, "marital_status": "Single", "min_amt_chng_q4_q1": 0.502 }, { "age_group": "11 - 30", "avg_total_product_count": 3, "churn_rate_percent": 0, "client_count": 9, "income_category": "$60K - $80K", "index": 7, "marital_status": "Married", "min_amt_chng_q4_q1": 0.616 }, { "age_group": "11 - 30", "avg_total_product_count": 3, "churn_rate_percent": 0, "client_count": 9, "income_category": "Unknown", "index": 8, "marital_status": "Married", "min_amt_chng_q4_q1": 0.521 }, { "age_group": "11 - 30", "avg_total_product_count": 5, "churn_rate_percent": 0, "client_count": 7, "income_category": "Unknown", "index": 9, "marital_status": "Divorced", "min_amt_chng_q4_q1": 0.587 }, { "age_group": "11 - 30", "avg_total_product_count": 3, "churn_rate_percent": 0, "client_count": 6, "income_category": "Less than $40K", "index": 10, "marital_status": "Unknown", "min_amt_chng_q4_q1": 0.469 }, { "age_group": "11 - 30", "avg_total_product_count": 3, "churn_rate_percent": 0, "client_count": 4, "income_category": "Unknown", "index": 11, "marital_status": "Unknown", "min_amt_chng_q4_q1": 0.623 }, { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 4, "income_category": "$80K - $120K", "index": 12, "marital_status": "Married", "min_amt_chng_q4_q1": 0.778 }, { "age_group": "11 - 30", "avg_total_product_count": 2, "churn_rate_percent": 0, "client_count": 3, "income_category": "$60K - $80K", "index": 13, "marital_status": "Divorced", "min_amt_chng_q4_q1": 0.381 }, { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 3, "income_category": "$40K - $60K", "index": 14, "marital_status": "Divorced", "min_amt_chng_q4_q1": 0.643 }, { "age_group": "11 - 30", "avg_total_product_count": 5, "churn_rate_percent": 0, "client_count": 2, "income_category": "$40K - $60K", "index": 15, "marital_status": "Unknown", "min_amt_chng_q4_q1": 0.535 }, { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 2, "income_category": "$120K +", "index": 16, "marital_status": "Married", "min_amt_chng_q4_q1": 0.52 }, { "age_group": "11 - 30", "avg_total_product_count": 5, "churn_rate_percent": 0, "client_count": 2, "income_category": "$80K - $120K", "index": 17, "marital_status": "Single", "min_amt_chng_q4_q1": 0.786 }, { "age_group": "11 - 30", "avg_total_product_count": 6, "churn_rate_percent": 0, "client_count": 2, "income_category": "$80K - $120K", "index": 18, "marital_status": "Divorced", "min_amt_chng_q4_q1": 0.59 }, { "age_group": "11 - 30", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 2, "income_category": "$120K +", "index": 19, "marital_status": "Single", "min_amt_chng_q4_q1": 0.558 }, { "age_group": "11 - 30", "avg_total_product_count": 3, "churn_rate_percent": 0, "client_count": 1, "income_category": "$60K - $80K", "index": 20, "marital_status": "Unknown", "min_amt_chng_q4_q1": 0.524 }, { "age_group": "11 - 30", "avg_total_product_count": 1, "churn_rate_percent": 0, "client_count": 1, "income_category": "$80K - $120K", "index": 21, "marital_status": "Unknown", "min_amt_chng_q4_q1": 0.715 }, { "age_group": "11 - 30", "avg_total_product_count": 3, "churn_rate_percent": 0, "client_count": 1, "income_category": "$120K +", "index": 22, "marital_status": "Divorced", "min_amt_chng_q4_q1": 0.828 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 333, "income_category": "Less than $40K", "index": 23, "marital_status": "Single", "min_amt_chng_q4_q1": 0.262 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 330, "income_category": "Less than $40K", "index": 24, "marital_status": "Married", "min_amt_chng_q4_q1": 0.24 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 183, "income_category": "$40K - $60K", "index": 25, "marital_status": "Married", "min_amt_chng_q4_q1": 0 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 169, "income_category": "$40K - $60K", "index": 26, "marital_status": "Single", "min_amt_chng_q4_q1": 0.12 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 154, "income_category": "$60K - $80K", "index": 27, "marital_status": "Married", "min_amt_chng_q4_q1": 0.324 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 143, "income_category": "$80K - $120K", "index": 28, "marital_status": "Married", "min_amt_chng_q4_q1": 0.262 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 127, "income_category": "$80K - $120K", "index": 29, "marital_status": "Single", "min_amt_chng_q4_q1": 0.175 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 112, "income_category": "$60K - $80K", "index": 30, "marital_status": "Single", "min_amt_chng_q4_q1": 0.358 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 105, "income_category": "Unknown", "index": 31, "marital_status": "Single", "min_amt_chng_q4_q1": 0.359 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 87, "income_category": "Unknown", "index": 32, "marital_status": "Married", "min_amt_chng_q4_q1": 0.236 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 57, "income_category": "$120K +", "index": 33, "marital_status": "Married", "min_amt_chng_q4_q1": 0.153 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 49, "income_category": "$120K +", "index": 34, "marital_status": "Single", "min_amt_chng_q4_q1": 0.371 }, { "age_group": "31 - 40", "avg_total_product_count": 3, "churn_rate_percent": 0, "client_count": 46, "income_category": "Less than $40K", "index": 35, "marital_status": "Divorced", "min_amt_chng_q4_q1": 0.391 }, { "age_group": "31 - 40", "avg_total_product_count": 3, "churn_rate_percent": 0, "client_count": 45, "income_category": "Less than $40K", "index": 36, "marital_status": "Unknown", "min_amt_chng_q4_q1": 0.417 }, { "age_group": "31 - 40", "avg_total_product_count": 3, "churn_rate_percent": 0, "client_count": 32, "income_category": "$80K - $120K", "index": 37, "marital_status": "Unknown", "min_amt_chng_q4_q1": 0.399 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 29, "income_category": "$40K - $60K", "index": 38, "marital_status": "Divorced", "min_amt_chng_q4_q1": 0.461 }, { "age_group": "31 - 40", "avg_total_product_count": 4, "churn_rate_percent": 0, "client_count": 27, "income_category": "$40K - $60K", "index": 39, "marital_status": "Unknown", "min_amt_chng_q4_q1": 0.349 }, { "age_group": "31 - 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116 rows × 7 columns

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" ], "text/plain": [ " age_group marital_status ... min_amt_chng_q4_q1 client_count\n", "0 11 - 30 Single ... 0.299 69\n", "1 11 - 30 Married ... 0.549 36\n", "2 11 - 30 Single ... 0.331 29\n", "3 11 - 30 Single ... 0.391 27\n", "4 11 - 30 Divorced ... 0.632 16\n", ".. ... ... ... ... ...\n", "111 61 - 120 Single ... 0.564 2\n", "112 61 - 120 Married ... 0.424 2\n", "113 61 - 120 Divorced ... 0.808 2\n", "114 61 - 120 Unknown ... 0.722 2\n", "115 61 - 120 Unknown ... 0.628 2\n", "\n", "[116 rows x 7 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-- Solution approach: Created a Pivot Table that groups the dataset by demographic variables: age group, marital status and income category, while also aggregating individual values into a summary of the churn rate, avg total relationship count, minimum total amount change [Q1-Q4] and number of customers, per each demographic group.\n", "\n", "WITH churned AS (\n", " \n", " SELECT \n", " dem.clientnum AS clientnum,\n", " age_group,\n", " marital_status,\n", " income_category,\n", " total_relationship_count,\n", " total_amt_chng_q4_q1,\n", " CASE WHEN attrition_flag = 'Attrited Customer' THEN 1\n", " ELSE 0 END AS is_churned\n", " FROM demographics AS dem\n", " JOIN bankchurners AS bc\n", " ON dem.clientnum = bc.clientnum\n", " JOIN enriched_churn_data AS ecd\n", " ON dem.clientnum = ecd.clientnum\n", " )\n", "\n", "SELECT \n", " age_group, \n", " marital_status, \n", " income_category,\n", " ROUND(100 * SUM(is_churned) / (SELECT COUNT(*) FROM bankchurners)::numeric, 1) AS churn_rate_percent,\n", " ROUND(AVG(total_relationship_count)) as avg_total_product_count,\n", " MIN(total_amt_chng_q4_q1) as min_amt_chng_q4_q1,\n", " COUNT(clientnum) as client_count\n", "FROM churned\n", "GROUP BY 1, 2, 3\n", "ORDER BY 1, 7 DESC\n", ";" ] }, { "cell_type": "markdown", "id": "8b16ab4f-34cf-4abf-9287-ad840d4a9753", "metadata": {}, "source": [ "**Question 4:**\n", "\n", "Out of the male clients, who holds the “blue” card, how many (in %) hold the income category 40K - 60K?" ] }, { "cell_type": "code", "execution_count": 10, "id": "c51057cd-e96c-4acb-94d9-e8933505bb4f", "metadata": { "customType": "sql", "dataFrameVariableName": "df", "executionTime": 2360, "initial": false, "integrationId": "340d6f6b-e6bc-4eae-a902-e9a40ad6dfb1", "lastSuccessfullyExecutedCode": "WITH total_count AS (\n SELECT \n COUNT(*) AS total\n FROM basic_client_info AS bci\n JOIN bankchurners AS bc\n ON bci.clientnum = bc.clientnum\n WHERE gender = 'M'\n AND card_category = 'Blue'\n ),\n \n male_blue_card_holders AS (\n SELECT \n income_category\n FROM basic_client_info AS bci\n JOIN bankchurners AS bc\n ON bci.clientnum = bc.clientnum\n WHERE gender = 'M'\n AND card_category = 'Blue'\n )\n\nSELECT \n income_category,\n ROUND(100 * COUNT(income_category) / total :: numeric, 2) AS percent_of_male_blue_card_holders\nFROM male_blue_card_holders, total_count\nGROUP BY income_category, total\nLIMIT 1 OFFSET 3\n;" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "income_category": "$40K - $60K", "index": 0, "percent_of_male_blue_card_holders": 16.49 } ], "schema": { "fields": [ { "name": "index", "type": "integer" }, { "name": "income_category", "type": "string" }, { "name": "percent_of_male_blue_card_holders", "type": "number" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 1, "truncation_type": null }, "text/html": [ "
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income_categorypercent_of_male_blue_card_holders
0$40K - $60K16.49
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" ], "text/plain": [ " income_category percent_of_male_blue_card_holders\n", "0 $40K - $60K 16.49" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "WITH total_count AS (\n", " SELECT \n", " COUNT(*) AS total\n", " FROM basic_client_info AS bci\n", " JOIN bankchurners AS bc\n", " ON bci.clientnum = bc.clientnum\n", " WHERE gender = 'M'\n", " AND card_category = 'Blue'\n", " ),\n", " \n", " male_blue_card_holders AS (\n", " SELECT \n", " income_category\n", " FROM basic_client_info AS bci\n", " JOIN bankchurners AS bc\n", " ON bci.clientnum = bc.clientnum\n", " WHERE gender = 'M'\n", " AND card_category = 'Blue'\n", " )\n", "\n", "SELECT \n", " income_category,\n", " ROUND(100 * COUNT(income_category) / total :: numeric, 2) AS percent_of_male_blue_card_holders\n", "FROM male_blue_card_holders, total_count\n", "GROUP BY income_category, total\n", "LIMIT 1 OFFSET 3\n", ";" ] }, { "cell_type": "markdown", "id": "5dab908c-3c68-4076-8634-6c20bc975fe9", "metadata": {}, "source": [ "**Question 5:**\n", "\n", "Without the usage of group by at all, find the 3rd and 4th highest client IDs (CLIENTNUM’s) of Total_Amt_Chng_Q4_Q1?" ] }, { "cell_type": "code", "execution_count": 11, "id": "28b8f35d-b80c-4d7f-8ebf-3608eaaa7aeb", "metadata": { "customType": "sql", "dataFrameVariableName": "df", "executionTime": 2017, "initial": false, "integrationId": "340d6f6b-e6bc-4eae-a902-e9a40ad6dfb1", "lastSuccessfullyExecutedCode": "SELECT \n clientnum\nFROM enriched_churn_data\nORDER BY total_amt_chng_q4_q1 DESC\nLIMIT 2 OFFSET 2\n;" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "clientnum": 713989233, "index": 0 }, { "clientnum": 713982108, "index": 1 } ], "schema": { "fields": [ { "name": "index", "type": "integer" }, { "name": "clientnum", "type": "integer" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 2, "truncation_type": null }, "text/html": [ "
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clientnum
0713989233
1713982108
\n", "
" ], "text/plain": [ " clientnum\n", "0 713989233\n", "1 713982108" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SELECT \n", " clientnum\n", "FROM enriched_churn_data\n", "ORDER BY total_amt_chng_q4_q1 DESC\n", "LIMIT 2 OFFSET 2\n", ";" ] }, { "cell_type": "markdown", "id": "63c140b2-0f65-405f-84d2-7a3e897bbde3", "metadata": {}, "source": [ "**Question 6:**\n", "\n", "We’re interested in knowing which client (CLIENTNUM) has the 2nd highest Total_Trans_Amt, Per each Marital_Status." ] }, { "cell_type": "code", "execution_count": 13, "id": "161e23cd-f2e3-4ce0-b184-d0c7d029da76", "metadata": { "customType": "sql", "dataFrameVariableName": "df", "executionTime": 1789, "initial": false, "integrationId": "340d6f6b-e6bc-4eae-a902-e9a40ad6dfb1", "lastSuccessfullyExecutedCode": "-- Which client (CLIENTNUM) has the 2nd highest Total_Trans_Amt, Per each Marital_Status.\n\nWITH t1 AS (\n SELECT\n bci.clientnum,\n marital_status,\n total_trans_amt,\n DENSE_RANK() OVER (PARTITION BY marital_status ORDER BY total_trans_amt Desc) AS rnk\nFROM basic_client_info AS bci\nJOIN enriched_churn_data AS ecd\n ON bci.clientnum = ecd.clientnum\n)\nSELECT \n marital_status,\n clientnum AS client_with_2nd_highest_trans_amt\nFROM t1\nWHERE rnk=2\n;" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "client_with_2nd_highest_trans_amt": 716894658, "index": 0, "marital_status": "Divorced" }, { "client_with_2nd_highest_trans_amt": 717642633, "index": 1, "marital_status": "Married" }, { "client_with_2nd_highest_trans_amt": 716004258, "index": 2, "marital_status": "Single" }, { "client_with_2nd_highest_trans_amt": 719848008, "index": 3, "marital_status": "Unknown" } ], "schema": { "fields": [ { "name": "index", "type": "integer" }, { "name": "marital_status", "type": "string" }, { "name": "client_with_2nd_highest_trans_amt", "type": "integer" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 4, "truncation_type": null }, "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
marital_statusclient_with_2nd_highest_trans_amt
0Divorced716894658
1Married717642633
2Single716004258
3Unknown719848008
\n", "
" ], "text/plain": [ " marital_status client_with_2nd_highest_trans_amt\n", "0 Divorced 716894658\n", "1 Married 717642633\n", "2 Single 716004258\n", "3 Unknown 719848008" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-- Which client (CLIENTNUM) has the 2nd highest Total_Trans_Amt, Per each Marital_Status.\n", "\n", "WITH t1 AS (\n", " SELECT\n", " bci.clientnum,\n", " marital_status,\n", " total_trans_amt,\n", " DENSE_RANK() OVER (PARTITION BY marital_status ORDER BY total_trans_amt Desc) AS rnk\n", "FROM basic_client_info AS bci\n", "JOIN enriched_churn_data AS ecd\n", " ON bci.clientnum = ecd.clientnum\n", ")\n", "SELECT \n", " marital_status,\n", " clientnum AS client_with_2nd_highest_trans_amt\n", "FROM t1\n", "WHERE rnk=2\n", ";" ] }, { "cell_type": "markdown", "id": "2f5e2f65-9be1-4a89-965f-4d613b94ff72", "metadata": {}, "source": [ " -------------------------------------------------------------------------------------------------------------------- " ] }, { "cell_type": "markdown", "id": "c7be3f07-41b0-4be5-9d1a-8f90af29300c", "metadata": {}, "source": [ "# Python Deep Dive Analysis" ] }, { "cell_type": "markdown", "id": "cb1df8db", "metadata": {}, "source": [ "
\n", "\n", "## Database Integration" ] }, { "cell_type": "markdown", "id": "d159e112", "metadata": {}, "source": [ "**Import all the libraries required for this project, and connect to the postgres database hosted on AWS RDS**" ] }, { "cell_type": "code", "execution_count": 1, "id": "ec5f69f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully connected to the dollarbankdb database!\n" ] } ], "source": [ "# Import numpy library for performing scientific computing as well as various mathematical operations.\n", "import numpy as np\n", "\n", "# Import pandas library for loading tables from the DB into dataframes to perform data manipulation and analysis.\n", "import pandas as pd\n", "\n", "# Import the matplotlib.pyplot and seaborn libraries for creating data visualizations and plots.\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Import psycopg2 library that will connect to the database\n", "import psycopg2\n", "\n", "# Import the config.py file and alias as creds. The config.py file contains all credentials for the postgres DB hosted by AWS.\n", "import config as creds\n", "\n", "# Import the create_engine function from sqlalchemy \n", "from sqlalchemy import create_engine\n", "\n", "# Use sqlalchemy create_engine function to create a dialect object tailored towards postgre & assign the the conn string to engine\n", "# i.e engine = create_engine(dialect+driver://username:password@host:port/database)\n", "\n", "try: # Using a try-except block to handle any exceptions that might occur during the connection process\n", " engine = create_engine(f\"postgresql+psycopg2://{creds.PGUSER}:{creds.PGPASSWORD}@{creds.PGHOST}:{creds.PGPORT}/{creds.PGDATABASE}\")\n", " # Test the connection by executing a simple query. If the connection is successful, the first print statement will be executed\n", " with engine.connect() as connection:\n", " connection.execute(\"SELECT 1\")\n", " print(f\"Successfully connected to the {creds.PGDATABASE} database!\")\n", "except Exception as e: # If unsuccessful, an error message will be printed\n", " print(f\"Error: Failed to connect to the {creds.PGDATABASE} database!\")\n", " print(f\"Exception details: {e}\")\n" ] }, { "cell_type": "markdown", "id": "87f79dee", "metadata": {}, "source": [ "
\n", "\n", "**Load data into pandas dataframes**" ] }, { "cell_type": "code", "execution_count": 2, "id": "a99bf1a7-22e8-4d17-85fe-b49b32543a2f", "metadata": { "executionTime": 1787, "lastSuccessfullyExecutedCode": "# Loading + let's see the head of each file we have\nbank_churners_df = pd.read_csv('bankchurners.csv')\nbasic_client_info_df = pd.read_csv('basic_client_info.csv')\nenriched_churn_df = pd.read_csv('enriched_churn_data.csv')\n\nbank_churners_df.head(3)" }, "outputs": [], "source": [ "# Load all 3 tables from the dollarbank DB into 3 respective pandas dataframes, using engine (the connection string we created) \n", "# as an argument in the pd.read_sql_table() pandas method that reads data from a SQL DB\n", "\n", "table1 = 'bank_churners'\n", "table2 = 'basic_client_info'\n", "table3 = 'enriched_churn_data'\n", "\n", "bank_churners_df = pd.read_sql_table(table1, engine)\n", "basic_client_info_df = pd.read_sql_table(table2, engine)\n", "enriched_churn_df = pd.read_sql_table(table3, engine)" ] }, { "cell_type": "markdown", "id": "de58d07d", "metadata": {}, "source": [ "**Resources Used**\n", "\n", "- \"Using RDS on AWS with Jupyter Notebooks - _Creating, Connecting, and Querying a PostgreSQL Database on AWS\"_ by Samantha Jackson. [Link](https://medium.com/@sjacks/using-rds-on-aws-with-jupyter-notebooks-c2703299fcc8)\n", "\n", "\n", "- SQLAlchemy documentation:\n", "[Link](https://docs.sqlalchemy.org/en/20/core/engines.html#database-urls)\n", "\n", "
\n", "\n", "**Now let's see the head of each dataframe we have**" ] }, { "cell_type": "code", "execution_count": 5, "id": "0d83e33c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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clientnumattrition_flagdependent_countcard_categorymonths_on_bookmonths_inactive_12_moncontacts_count_12_moncredit_limitavg_open_to_buyavg_utilization_ratio
0806160108Existing Customer1Blue56233193.0676.00.788
1804424383Existing Customer1Blue563210215.09205.00.099
2708300483Attrited Customer0Blue56437882.07277.00.077
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" ], "text/plain": [ " clientnum attrition_flag dependent_count card_category \\\n", "0 806160108 Existing Customer 1 Blue \n", "1 804424383 Existing Customer 1 Blue \n", "2 708300483 Attrited Customer 0 Blue \n", "\n", " months_on_book months_inactive_12_mon contacts_count_12_mon \\\n", "0 56 2 3 \n", "1 56 3 2 \n", "2 56 4 3 \n", "\n", " credit_limit avg_open_to_buy avg_utilization_ratio \n", "0 3193.0 676.0 0.788 \n", "1 10215.0 9205.0 0.099 \n", "2 7882.0 7277.0 0.077 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bank_churners_df.head(3)" ] }, { "cell_type": "code", "execution_count": 36, "id": "c88fd744-6cf5-4b8a-af45-874c68aafa9a", "metadata": { "executionTime": 248, "lastSuccessfullyExecutedCode": "basic_client_info_df.head(3)" }, "outputs": [ { "data": { "text/html": [ "
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clientnumcustomer_agegendereducation_levelmarital_statusincome_category
070808208345FHigh SchoolMarriedLess than $40K
170808328358MUnknownSingle$40K - $60K
270808455846MDoctorateDivorced$80K - $120K
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" ], "text/plain": [ " clientnum customer_age gender education_level marital_status \\\n", "0 708082083 45 F High School Married \n", "1 708083283 58 M Unknown Single \n", "2 708084558 46 M Doctorate Divorced \n", "\n", " income_category \n", "0 Less than $40K \n", "1 $40K - $60K \n", "2 $80K - $120K " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "basic_client_info_df.head(3)" ] }, { "cell_type": "code", "execution_count": 37, "id": "bf6555e3-c9a0-4f88-9555-b9331fe8b383", "metadata": { "executionTime": 315, "lastSuccessfullyExecutedCode": "enriched_churn_df.head(3)" }, "outputs": [ { "data": { "text/html": [ "
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clientnumtotal_relationship_counttotal_revolving_baltotal_amt_chng_q4_q1total_trans_amttotal_ct_chng_q4_q1total_trans_ct
082834308331793.00.80336460.65968
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282829493332437.00.76525190.56536
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" ], "text/plain": [ " clientnum total_relationship_count total_revolving_bal \\\n", "0 828343083 3 1793.0 \n", "1 828298908 4 2035.0 \n", "2 828294933 3 2437.0 \n", "\n", " total_amt_chng_q4_q1 total_trans_amt total_ct_chng_q4_q1 total_trans_ct \n", "0 0.803 3646 0.659 68 \n", "1 0.613 1770 0.741 47 \n", "2 0.765 2519 0.565 36 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enriched_churn_df.head(3)" ] }, { "cell_type": "markdown", "id": "01cb95a3-d03d-4ea2-96d3-cd8594dc7932", "metadata": {}, "source": [ "
\n", "\n", "## Summary Statistics Short Analysis\n", "\n", "Here I want to understand the characteristics of the dataset including basic statistics, central tendency, count of unique values in each column, whether or not there are missing values and any outliers in numerical columns. \n", "\n", "I will then assess important information about the variability, skewness of the data, after dealing with outliers if any are present.\n", "\n", "This analysis will be done on all 3 datasets in the following order:\n", "- First, the **bank_churners** dataset\n", "- Next, the **basic_client_info** dataset\n", "- And finally, the **enriched_churn_df** dataset" ] }, { "cell_type": "markdown", "id": "298a8088-24dc-4571-80c7-db602e1cc20d", "metadata": {}, "source": [ "
\n", "\n", "### Summary Statistics of the bank churners dataset" ] }, { "cell_type": "code", "execution_count": 38, "id": "a563855e-593b-4cc0-97f0-e58cb3aacaca", "metadata": { "executionTime": 408, "lastSuccessfullyExecutedCode": "# Checking to see basic statistics of the numerical columns in the bank churners dataset\nbank_churners_df.describe()" }, "outputs": [ { "data": { "text/html": [ "
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clientnumdependent_countmonths_on_bookmonths_inactive_12_moncontacts_count_12_moncredit_limitavg_open_to_buyavg_utilization_ratio
count1.012700e+0410127.00000010127.00000010127.00000010127.00000010127.00000010127.00000010127.000000
mean7.391776e+082.34620335.9284092.3411672.4553178631.9536987469.1396370.274894
std3.690378e+071.2989087.9864161.0106221.1062259088.7766509090.6853240.275691
min7.080821e+080.00000013.0000000.0000000.0000001438.3000003.0000000.000000
25%7.130368e+081.00000031.0000002.0000002.0000002555.0000001324.5000000.023000
50%7.179264e+082.00000036.0000002.0000002.0000004549.0000003474.0000000.176000
75%7.731435e+083.00000040.0000003.0000003.00000011067.5000009859.0000000.503000
max8.283431e+085.00000056.0000006.0000006.00000034516.00000034516.0000000.999000
\n", "
" ], "text/plain": [ " clientnum dependent_count months_on_book months_inactive_12_mon \\\n", "count 1.012700e+04 10127.000000 10127.000000 10127.000000 \n", "mean 7.391776e+08 2.346203 35.928409 2.341167 \n", "std 3.690378e+07 1.298908 7.986416 1.010622 \n", "min 7.080821e+08 0.000000 13.000000 0.000000 \n", "25% 7.130368e+08 1.000000 31.000000 2.000000 \n", "50% 7.179264e+08 2.000000 36.000000 2.000000 \n", "75% 7.731435e+08 3.000000 40.000000 3.000000 \n", "max 8.283431e+08 5.000000 56.000000 6.000000 \n", "\n", " contacts_count_12_mon credit_limit avg_open_to_buy \\\n", "count 10127.000000 10127.000000 10127.000000 \n", "mean 2.455317 8631.953698 7469.139637 \n", "std 1.106225 9088.776650 9090.685324 \n", "min 0.000000 1438.300000 3.000000 \n", "25% 2.000000 2555.000000 1324.500000 \n", "50% 2.000000 4549.000000 3474.000000 \n", "75% 3.000000 11067.500000 9859.000000 \n", "max 6.000000 34516.000000 34516.000000 \n", "\n", " avg_utilization_ratio \n", "count 10127.000000 \n", "mean 0.274894 \n", "std 0.275691 \n", "min 0.000000 \n", "25% 0.023000 \n", "50% 0.176000 \n", "75% 0.503000 \n", "max 0.999000 " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking to see basic statistics of the numerical columns in the bank churners dataset\n", "bank_churners_df.describe()" ] }, { "cell_type": "markdown", "id": "420d305b", "metadata": {}, "source": [ "**Checking for outliers**" ] }, { "cell_type": "code", "execution_count": 186, "id": "7d11917c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "There are no outliers in the 'clientnum' column \n", "\n", "\n", "The 'attrition_flag' column is not a numerical column \n", "\n", "\n", "There are no outliers in the 'dependent_count' column \n", "\n", "\n", "The 'card_category' column is not a numerical column \n", "\n", "\n", "There are no outliers in the 'avg_utilization_ratio' column \n", "\n", "\n", " Detected too many potential outliers in the following columns: ['months_on_book', 'months_inactive_12_mon', 'contacts_count_12_mon', 'credit_limit', 'avg_open_to_buy']. Based on the high number of potential outliers detected, use the column definition and check the summary statistics (min & max values) above to find out if there are truly outliers in these columns.\n", "\n", "\n", "------------------------------end------------------------------\n" ] } ], "source": [ "# Create two empty lists, one to store names of categorical variables, and the other for numerical variables\n", "cat_vars = []\n", "num_vars = []\n", "\n", "# Iterate through the names of columns/variables in bank_churners_df, and add variables to the appropriate list \n", "# depending on whether the are categorical or numerical\n", "for column_name in bank_churners_df.columns:\n", " if bank_churners_df[column_name].dtype == 'object': \n", " cat_vars.append(column_name)\n", " else:\n", " num_vars.append(column_name)\n", "\n", "# Set the figure size\n", "fig, axs = plt.subplots(nrows=4, ncols=2, figsize=(15, 15))\n", "# Reshape the axs array into a one-dimensional array by flattening its elements\n", "axs = axs.flatten() \n", "\n", "# Iterate through the list for numerical variables and create box plots of data in each numerical variable\n", "for i, var in enumerate(num_vars):\n", " # Using Seaborn's box plot for outlier detection\n", " sns.boxplot(x=var, data=bank_churners_df, ax=axs[i])\n", "\n", "# Adjust the figure's subplot positions and margins, and then display the figure\n", "fig.tight_layout\n", "plt.show();\n", "\n", "# New line spacing\n", "print('\\n\\n')\n", "\n", "# Create new list to hold columns/variables that may have too many potential outliers\n", "outlier_vars = []\n", "\n", "# Iterate through the variables again and provide explanation as to whether or not outliers were detected and next steps\n", "for column_name in bank_churners_df.columns:\n", " if bank_churners_df[column_name].dtype == 'object':\n", " print(f\"\\nThe '{column_name}' column is not a numerical column \\n\")\n", " else:\n", " # Using the general rule for identifying potential outliers which is that if any data point in a dataset is more than \n", " # Q3 + 1.5xIQR or less than Q1 - 1.5xIQR, it's a high outlier. I would create a custom dictionary to capture potential outliers in each column.\n", " data = bank_churners_df[column_name]\n", " q1 = data.quantile(0.25)\n", " q3 = data.quantile(0.75)\n", " iqr = q3 - q1\n", " iqr_lower = q1 - 1.5 * iqr\n", " iqr_upper = q3 + 1.5 * iqr\n", " outliers = dict(data[(data < iqr_lower) | (data > iqr_upper)])\n", " \n", " list_of_outliers = list(outliers.values())\n", " rows_with_outliers = list(outliers.keys())\n", " \n", " # If no potential outliers detected, print message below\n", " if len(list_of_outliers) == 0:\n", " print(f\"\\nThere are no outliers in the '{column_name}' column \\n\")\n", " \n", " else:\n", " # If potential outliers detected were more than 10, add column name to the list of columns with possible outliers\n", " if len(list_of_outliers) > 10:\n", " outlier_vars.append(column_name)\n", " \n", " # If not, print message below\n", " else:\n", " print(f\"\\nThe potential ouliers in the '{column_name}' column are:\\n{list_of_outliers} \\nAnd the respective rows with the potential outlier are:\\n{rows_with_outliers}\")\n", "\n", "# Print message to explain next steps to validate outliers \n", "print(f\"\\n Detected too many potential outliers in the following columns: {outlier_vars}. Based on the high number of potential outliers detected, use the column definition and check the summary statistics (min & max values) above to find out if there are truly outliers in these columns.\\n\")\n", "\n", "# Print custom border at the end\n", "print(\"\\n\" + \"-\"*30 + \"end\" + \"-\"*30)" ] }, { "cell_type": "markdown", "id": "c071d41d-2355-4256-a76d-128c6c514a99", "metadata": {}, "source": [ "**Observation:** Based on the general rule for identifying potential outliers which is that if any data point in a dataset is more than Q3 + (1.5 x IQR) or less than Q1 - (1.5 x IQR), it's a high outlier, I want to get the lower and upper interquartile ranges for each column and use that to review the summary statistics to know if the min & max values of each column in the dataset can be considered as outliers based on the definition of the column/variable." ] }, { "cell_type": "code", "execution_count": 38, "id": "90c4c776-7105-41ea-a394-2b375d0da40d", "metadata": { "executionTime": 239, "lastSuccessfullyExecutedCode": "# Printing the Lower and upper interquartile ranges for all the columns in the dataset.\nvariables = pd.DataFrame(columns=['Variable','Lower Limit','Upper Limit'])\n\nnan_columns = []\nfor i, var in enumerate(bank_churners_df.columns):\n if var == 'clientnum':\n iqr_lower = np.NaN\n iqr_upper = np.NaN\n nan_columns.append(var)\n \n elif bank_churners_df[var].dtype == 'object':\n iqr_lower = np.NaN\n iqr_upper = np.NaN\n nan_columns.append(var)\n \n else:\n df = bank_churners_df[var]\n q1 = df.quantile(0.25)\n q3 = df.quantile(0.75)\n iqr = q3 - q1\n iqr_lower = q1 - 1.5 * iqr\n iqr_upper = q3 + 1.5 * iqr\n \n variables.loc[i] = [var, iqr_lower, iqr_upper]\n \nprint(f\"\\nFor the following variables with null values: {nan_columns}, clientnum is not applicable as it is only a unique id number for each client in the dataset. The others are of string/object datatypes. Hence the null values \\n\")\n\nvariables" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "For the following variables with null values: ['clientnum', 'attrition_flag', 'card_category'], clientnum is not applicable as it is only a unique id number for each client in the dataset. The others are of string/object datatypes. Hence the null values \n", "\n" ] }, { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "Lower Limit": null, "Upper Limit": null, "Variable": "clientnum", "index": 0 }, { "Lower Limit": null, "Upper Limit": null, "Variable": "attrition_flag", "index": 1 }, { "Lower Limit": -2, "Upper Limit": 6, "Variable": "dependent_count", "index": 2 }, { "Lower Limit": null, "Upper Limit": null, "Variable": "card_category", "index": 3 }, { "Lower Limit": 17.5, "Upper Limit": 53.5, "Variable": "months_on_book", "index": 4 }, { "Lower Limit": 0.5, "Upper Limit": 4.5, "Variable": "months_inactive_12_mon", "index": 5 }, { "Lower Limit": 0.5, "Upper Limit": 4.5, "Variable": "contacts_count_12_mon", "index": 6 }, { "Lower Limit": -10213.75, "Upper Limit": 23836.25, "Variable": "credit_limit", "index": 7 }, { "Lower Limit": -11477.25, "Upper Limit": 22660.75, "Variable": "avg_open_to_buy", "index": 8 }, { "Lower Limit": -0.697, "Upper Limit": 1.223, "Variable": "avg_utilization_ratio", "index": 9 } ], "schema": { "fields": [ { "name": "index", "type": "integer" }, { "name": "Variable", "type": "string" }, { "name": "Lower Limit", "type": "number" }, { "name": "Upper Limit", "type": "number" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 10, "truncation_type": null }, "text/html": [ "
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VariableLower LimitUpper Limit
0clientnumNaNNaN
1attrition_flagNaNNaN
2dependent_count-2.0006.000
3card_categoryNaNNaN
4months_on_book17.50053.500
5months_inactive_12_mon0.5004.500
6contacts_count_12_mon0.5004.500
7credit_limit-10213.75023836.250
8avg_open_to_buy-11477.25022660.750
9avg_utilization_ratio-0.6971.223
\n", "
" ], "text/plain": [ " Variable Lower Limit Upper Limit\n", "0 clientnum NaN NaN\n", "1 attrition_flag NaN NaN\n", "2 dependent_count -2.000 6.000\n", "3 card_category NaN NaN\n", "4 months_on_book 17.500 53.500\n", "5 months_inactive_12_mon 0.500 4.500\n", "6 contacts_count_12_mon 0.500 4.500\n", "7 credit_limit -10213.750 23836.250\n", "8 avg_open_to_buy -11477.250 22660.750\n", "9 avg_utilization_ratio -0.697 1.223" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Printing the Lower and upper interquartile ranges for all the columns in the dataset.\n", "variables = pd.DataFrame(columns=['Variable','Lower Limit','Upper Limit'])\n", "\n", "nan_columns = []\n", "for i, var in enumerate(bank_churners_df.columns):\n", " if var == 'clientnum':\n", " iqr_lower = np.NaN\n", " iqr_upper = np.NaN\n", " nan_columns.append(var)\n", " \n", " elif bank_churners_df[var].dtype == 'object':\n", " iqr_lower = np.NaN\n", " iqr_upper = np.NaN\n", " nan_columns.append(var)\n", " \n", " else:\n", " df = bank_churners_df[var]\n", " q1 = df.quantile(0.25)\n", " q3 = df.quantile(0.75)\n", " iqr = q3 - q1\n", " iqr_lower = q1 - 1.5 * iqr\n", " iqr_upper = q3 + 1.5 * iqr\n", " \n", " variables.loc[i] = [var, iqr_lower, iqr_upper]\n", " \n", "print(f\"\\nFor the following variables with null values: {nan_columns}, clientnum is not applicable as it is only a unique id number for each client in the dataset. The others are of string/object datatypes. Hence the null values \\n\")\n", "\n", "variables" ] }, { "cell_type": "markdown", "id": "9d38eb16-6a4e-435e-a878-98a52f39e5bf", "metadata": {}, "source": [ "
\n", "\n", "**So are there outliers or not?**\n", "\n", "After reading the column/variable definition and comparing the lower and upper Limits for each column to the min & max values, it is safe to assume that the potential outliers that were detected, are actually not outliers" ] }, { "cell_type": "code", "execution_count": 22, "id": "8f25868f-6da3-4d85-8cfa-7b8b797304f8", "metadata": { "executionTime": 164, "lastSuccessfullyExecutedCode": "# Checking to know the datatypes of each column and if there are any missing values\nbank_churners_df.info()" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 10127 entries, 0 to 10126\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 clientnum 10127 non-null int64 \n", " 1 attrition_flag 10127 non-null object \n", " 2 dependent_count 10127 non-null int64 \n", " 3 card_category 10127 non-null object \n", " 4 months_on_book 10127 non-null int64 \n", " 5 months_inactive_12_mon 10127 non-null int64 \n", " 6 contacts_count_12_mon 10127 non-null int64 \n", " 7 credit_limit 10127 non-null float64\n", " 8 avg_open_to_buy 10127 non-null float64\n", " 9 avg_utilization_ratio 10127 non-null float64\n", "dtypes: float64(3), int64(5), object(2)\n", "memory usage: 791.3+ KB\n" ] } ], "source": [ "# Checking to know the datatypes of each column and if there are any missing values\n", "bank_churners_df.info()" ] }, { "cell_type": "markdown", "id": "93b9df3b-a5db-4b38-ab12-59717a394bc9", "metadata": {}, "source": [ "There are 10 columns and 10127 rows in the dataset with no mising values in each of the columns. The datatypes for each of the columns are now known, as shown above" ] }, { "cell_type": "code", "execution_count": 23, "id": "e8730210-8508-43f4-b43d-616683c57511", "metadata": { "executionTime": 203, "lastSuccessfullyExecutedCode": "# Checking to see if there are any duplicates in the dataset. This is the count of unique entries (i.e rows) in the dataset.\nbank_churners_df[bank_churners_df.duplicated()].count()" }, "outputs": [ { "data": { "text/plain": [ "clientnum 0\n", "attrition_flag 0\n", "dependent_count 0\n", "card_category 0\n", "months_on_book 0\n", "months_inactive_12_mon 0\n", "contacts_count_12_mon 0\n", "credit_limit 0\n", "avg_open_to_buy 0\n", "avg_utilization_ratio 0\n", "dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking to see if there are any duplicates in the dataset. This is the count of unique entries (i.e rows) in the dataset.\n", "bank_churners_df[bank_churners_df.duplicated()].count()" ] }, { "cell_type": "markdown", "id": "7055bac9-04b0-477a-b7de-50faf25d730c", "metadata": {}, "source": [ "From above, no duplicated rows were found in the dataframe. So I assume that all rows are unique.\n", "\n", "Next I want to check for unique values in each column of the dataframe" ] }, { "cell_type": "code", "execution_count": 9, "id": "103ca5c9-8d00-4bf1-8fef-2c5cf1a67b57", "metadata": { "executionTime": 459, "lastSuccessfullyExecutedCode": "# Understand my variables\nvariables = pd.DataFrame(columns=['Variable','Number of unique values','Values'])\n\nfor i, var in enumerate(bank_churners_df.columns):\n variables.loc[i] = [var, bank_churners_df[var].nunique(), bank_churners_df[var].unique().tolist()]\n \nvariables" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "Number of unique values": 10127, "Values": [ 806160108, 804424383, 708300483, 808284783, 712720158, 717296808, 809164083, 787348608, 778286433, 822969858, 810449883, 717916758, 718715583, 794977533, 789467508, 812019933, 812420358, 802002858, 713650683, 737925558, 811721133, 798110358, 713757633, 807244983, 821872758, 714547458, 710523333, 822130683, 720770883, 713536158, 771234558, 802464033, 712004208, 816013383, 708295533, 780689733, 795052083, 718181433, 714460683, 813200058, 719729433, 714321408, 808909008, 716823408, 795810633, 713366583, 718476033, 718589133, 710687508, 822138333, 709468683, 770721858, 711659958, 767114958, 708563433, 806179683, 737952333, 766006758, 819189108, 817889583, 817977933, 712084383, 719357058, 822955008, 813380058, 816261858, 774760458, 708788583, 708887433, 804847308, 715548183, 778247358, 811527933, 822961533, 814219008, 823590483, 713168733, 814845933, 820633833, 818259558, 816528333, 789563658, 806995683, 790000608, 713246208, 783500283, 709310433, 768475383, 805546308, 808675083, 716924958, 716019333, 709597983, 719050458, 826808208, 813449508, 803115258, 709250508, 714418158, 816832383, 809169858, 819109308, 798553158, 812627208, 716284308, 755953308, 711840408, 713307708, 713216883, 718867683, 717551358, 813573333, 715612983, 719161683, 814497483, 718016808, 802884933, 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"Values", "type": "string" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 10, "truncation_type": null }, "text/html": [ "
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VariableNumber of unique valuesValues
0clientnum10127[806160108, 804424383, 708300483, 808284783, 7...
1attrition_flag2[Existing Customer, Attrited Customer]
2dependent_count6[1, 0, 2, 3, 4, 5]
3card_category4[Blue, Silver, Gold, Platinum]
4months_on_book44[56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 4...
5months_inactive_12_mon7[2, 3, 4, 0, 1, 6, 5]
6contacts_count_12_mon7[3, 2, 0, 1, 4, 5, 6]
7credit_limit6205[3193.0, 10215.0, 7882.0, 1438.3, 13860.0, 300...
8avg_open_to_buy6813[676.0, 9205.0, 7277.0, 1438.3, 12208.0, 489.0...
9avg_utilization_ratio964[0.788, 0.099, 0.077, 0.0, 0.119, 0.837, 0.679...
\n", "
" ], "text/plain": [ " Variable ... Values\n", "0 clientnum ... [806160108, 804424383, 708300483, 808284783, 7...\n", "1 attrition_flag ... [Existing Customer, Attrited Customer]\n", "2 dependent_count ... [1, 0, 2, 3, 4, 5]\n", "3 card_category ... [Blue, Silver, Gold, Platinum]\n", "4 months_on_book ... [56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 4...\n", "5 months_inactive_12_mon ... [2, 3, 4, 0, 1, 6, 5]\n", "6 contacts_count_12_mon ... [3, 2, 0, 1, 4, 5, 6]\n", "7 credit_limit ... [3193.0, 10215.0, 7882.0, 1438.3, 13860.0, 300...\n", "8 avg_open_to_buy ... [676.0, 9205.0, 7277.0, 1438.3, 12208.0, 489.0...\n", "9 avg_utilization_ratio ... [0.788, 0.099, 0.077, 0.0, 0.119, 0.837, 0.679...\n", "\n", "[10 rows x 3 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking for unique variables\n", "variables = pd.DataFrame(columns=['Variable','Number of unique values','Values'])\n", "\n", "for i, var in enumerate(bank_churners_df.columns):\n", " variables.loc[i] = [var, bank_churners_df[var].nunique(), bank_churners_df[var].unique().tolist()]\n", " \n", "variables" ] }, { "cell_type": "markdown", "id": "91552996-ff4d-455d-9d67-b1cef4c5c825", "metadata": {}, "source": [ "**Observation:**\n", "Looks like the bank_churners table has some interesting data with lots of unique values to explore. Only the 'clientnum' column has a unique value for all 10127 rows in the dataset, and this is because this variable contains all client id/number which are expected to be unique to each client.\n", "\n", "Interesting variables to explore based on their number of unique values include:\n", "`attrition_flag`, `dependent_count`, `card_category`, `months_inactive_12_mon`, `contacts_count_12_mon`\n", "\n", "For the `months_on_book` variable, it may be worth grouping them per number of years. E.g clients with months on book from 0 to 12 months could be grouped as \"New Clients\", then 13 to 24 months as \"Established Clients\" ...etc" ] }, { "cell_type": "markdown", "id": "f73259ee-84b0-464d-8f6f-9ddf2ca0de98", "metadata": {}, "source": [ "
\n", "\n", "### Summary Statistics of the basic client info dataset" ] }, { "cell_type": "code", "execution_count": 25, "id": "7c4c15cb-e613-4594-bfef-2819b5e1b879", "metadata": { "executionTime": 215, "lastSuccessfullyExecutedCode": "# Checking to see basic statistics of the numerical columns in the basic client info dataset\nbasic_client_info_df.describe()" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "clientnum": 10127, "customer_age": 10127, "index": "count" }, { "clientnum": 739177606.3336625, "customer_age": 46.3259603041, "index": "mean" }, { "clientnum": 36903783.45023115, "customer_age": 8.0168140325, "index": "std" }, { "clientnum": 708082083, "customer_age": 26, "index": "min" }, { "clientnum": 713036770.5, "customer_age": 41, "index": "25%" }, { "clientnum": 717926358, "customer_age": 46, "index": "50%" }, { "clientnum": 773143533, "customer_age": 52, "index": "75%" }, { "clientnum": 828343083, "customer_age": 73, "index": "max" } ], "schema": { "fields": [ { "name": "index", "type": "string" }, { "name": "clientnum", "type": "number" }, { "name": "customer_age", "type": "number" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 8, "truncation_type": null }, "text/html": [ "
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clientnumcustomer_age
count1.012700e+0410127.000000
mean7.391776e+0846.325960
std3.690378e+078.016814
min7.080821e+0826.000000
25%7.130368e+0841.000000
50%7.179264e+0846.000000
75%7.731435e+0852.000000
max8.283431e+0873.000000
\n", "
" ], "text/plain": [ " clientnum customer_age\n", "count 1.012700e+04 10127.000000\n", "mean 7.391776e+08 46.325960\n", "std 3.690378e+07 8.016814\n", "min 7.080821e+08 26.000000\n", "25% 7.130368e+08 41.000000\n", "50% 7.179264e+08 46.000000\n", "75% 7.731435e+08 52.000000\n", "max 8.283431e+08 73.000000" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking to see basic statistics of the numerical columns in the basic client info dataset\n", "basic_client_info_df.describe()" ] }, { "cell_type": "code", "execution_count": 28, "id": "df239c48-069a-41de-9695-a42d1d836a15", "metadata": { "executionTime": 110, "lastSuccessfullyExecutedCode": "# Checking for outliers \n\nfor column_name in basic_client_info_df.columns:\n if basic_client_info_df[column_name].dtype == 'object':\n print(f\"\\nThe '{column_name}' column is not a numerical column\")\n else:\n data = basic_client_info_df[column_name]\n q1 = data.quantile(0.25)\n q3 = data.quantile(0.75)\n iqr = q3 - q1\n iqr_lower = q1 - 1.5 * iqr\n iqr_upper = q3 + 1.5 * iqr\n outliers = dict(data[(data < iqr_lower) | (data > iqr_upper)])\n\n list_of_outliers = list(outliers.values())\n rows_with_outliers = list(outliers.keys())\n\n if len(list_of_outliers) == 0:\n print(f\"\\nThere are no outliers in the '{column_name}' column\")\n else:\n if len(list_of_outliers) > 10:\n # Using Seaborn's box plot for outlier detection\n sns.boxplot(x=column_name, data=basic_client_info_df)\n plt.title(f\"\\nChecking for Outliers in {column_name}\")\n plt.xlabel(f\"\\n{column_name}\")\n plt.show()\n print(f\"\\nDetected too many potential outliers in the '{column_name}' column to show. Based on the high number of potential outliers detected, use the column definition and check the summary statistics (min & max values) above to find out if there are truly outliers in these columns.\")\n else:\n print(f\"\\nThe potential ouliers in the '{column_name}' column are: {list_of_outliers} and the respective rows with the potential outliers are: {rows_with_outliers}\")\n \nprint(\"\\n\" + \"-\"*30 + \"end\" + \"-\"*30)" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "There are no outliers in the 'clientnum' column\n", "\n", "The potential ouliers in the 'customer_age' column are: [73, 70] and the respective rows with the potential outliers are: [4115, 8317]\n", "\n", "The 'gender' column is not a numerical column\n", "\n", "The 'education_level' column is not a numerical column\n", "\n", "The 'marital_status' column is not a numerical column\n", "\n", "The 'income_category' column is not a numerical column\n", "\n", "------------------------------end------------------------------\n" ] } ], "source": [ "# Checking for outliers \n", "\n", "for column_name in basic_client_info_df.columns:\n", " if basic_client_info_df[column_name].dtype == 'object':\n", " print(f\"\\nThe '{column_name}' column is not a numerical column\")\n", " else:\n", " data = basic_client_info_df[column_name]\n", " q1 = data.quantile(0.25)\n", " q3 = data.quantile(0.75)\n", " iqr = q3 - q1\n", " iqr_lower = q1 - 1.5 * iqr\n", " iqr_upper = q3 + 1.5 * iqr\n", " outliers = dict(data[(data < iqr_lower) | (data > iqr_upper)])\n", "\n", " list_of_outliers = list(outliers.values())\n", " rows_with_outliers = list(outliers.keys())\n", "\n", " if len(list_of_outliers) == 0:\n", " print(f\"\\nThere are no outliers in the '{column_name}' column\")\n", " else:\n", " if len(list_of_outliers) > 10:\n", " # Using Seaborn's box plot for outlier detection\n", " sns.boxplot(x=column_name, data=basic_client_info_df)\n", " plt.title(f\"\\nChecking for Outliers in {column_name}\")\n", " plt.xlabel(f\"\\n{column_name}\")\n", " plt.show()\n", " print(f\"\\nDetected too many potential outliers in the '{column_name}' column to show. Based on the high number of potential outliers detected, use the column definition and check the summary statistics (min & max values) above to find out if there are truly outliers in these columns.\")\n", " else:\n", " print(f\"\\nThe potential ouliers in the '{column_name}' column are: {list_of_outliers} and the respective rows with the potential outliers are: {rows_with_outliers}\")\n", " \n", "print(\"\\n\" + \"-\"*30 + \"end\" + \"-\"*30)" ] }, { "cell_type": "code", "execution_count": 198, "id": "2d3a0984", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "There are no outliers in the 'clientnum' column \n", "\n", "\n", "The potential ouliers in the 'customer_age' column are:\n", "[73, 70] \n", "And the respective rows with the potential outlier are:\n", "[4115, 8317]\n", "\n", "The 'gender' column is not a numerical column \n", "\n", "\n", "The 'education_level' column is not a numerical column \n", "\n", "\n", "The 'marital_status' column is not a numerical column \n", "\n", "\n", "The 'income_category' column is not a numerical column \n", "\n", "\n", " Detected too many potential outliers in the following columns: []. Based on the high number of potential outliers detected, use the column definition and check the summary statistics (min & max values) above to find out if there are truly outliers in these columns.\n", "\n", "\n", "------------------------------end------------------------------\n" ] } ], "source": [ "# Create two empty lists, one to store names of categorical variables, and the other for numerical variables\n", "cat_vars = []\n", "num_vars = []\n", "\n", "# Iterate through the names of columns/variables in bank_churners_df, and add variables to the appropriate list \n", "# depending on whether the are categorical or numerical\n", "for column_name in basic_client_info_df.columns:\n", " if basic_client_info_df[column_name].dtype == 'object': \n", " cat_vars.append(column_name)\n", " else:\n", " num_vars.append(column_name)\n", "\n", "# Set the figure size\n", "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 2))\n", "# Reshape the axs array into a one-dimensional array by flattening its elements\n", "axs = axs.flatten() \n", "\n", "# Iterate through the list for numerical variables and create box plots of data in each numerical variable\n", "for i, var in enumerate(num_vars):\n", " # Using Seaborn's box plot for outlier detection\n", " sns.boxplot(x=var, data=basic_client_info_df, ax=axs[i])\n", "\n", "# Adjust the figure's subplot positions and margins, and then display the figure\n", "fig.tight_layout\n", "plt.show();\n", "\n", "# New line spacing\n", "print('\\n\\n')\n", "\n", "# Create new list to hold columns/variables that may have too many potential outliers\n", "outlier_vars = []\n", "\n", "# Iterate through the variables again and provide explanation as to whether or not outliers were detected and next steps\n", "for column_name in basic_client_info_df.columns:\n", " if basic_client_info_df[column_name].dtype == 'object':\n", " print(f\"\\nThe '{column_name}' column is not a numerical column \\n\")\n", " else:\n", " # Using the general rule for identifying potential outliers which is that if any data point in a dataset is more than \n", " # Q3 + 1.5xIQR or less than Q1 - 1.5xIQR, it's a high outlier. I would create a custom dictionary to capture potential outliers in each column.\n", " data = basic_client_info_df[column_name]\n", " q1 = data.quantile(0.25)\n", " q3 = data.quantile(0.75)\n", " iqr = q3 - q1\n", " iqr_lower = q1 - 1.5 * iqr\n", " iqr_upper = q3 + 1.5 * iqr\n", " outliers = dict(data[(data < iqr_lower) | (data > iqr_upper)])\n", " \n", " list_of_outliers = list(outliers.values())\n", " rows_with_outliers = list(outliers.keys())\n", " \n", " # If no potential outliers detected, print message below\n", " if len(list_of_outliers) == 0:\n", " print(f\"\\nThere are no outliers in the '{column_name}' column \\n\")\n", " \n", " else:\n", " # If potential outliers detected were more than 10, add column name to the list of columns with possible outliers\n", " if len(list_of_outliers) > 10:\n", " outlier_vars.append(column_name)\n", " \n", " # If not, print message below\n", " else:\n", " print(f\"\\nThe potential ouliers in the '{column_name}' column are:\\n{list_of_outliers} \\nAnd the respective rows with the potential outlier are:\\n{rows_with_outliers}\")\n", "\n", "# Print message to explain next steps to validate outliers \n", "print(f\"\\n Detected too many potential outliers in the following columns: {outlier_vars}. Based on the high number of potential outliers detected, use the column definition and check the summary statistics (min & max values) above to find out if there are truly outliers in these columns.\\n\")\n", "\n", "# Print custom border at the end\n", "print(\"\\n\" + \"-\"*30 + \"end\" + \"-\"*30)" ] }, { "cell_type": "markdown", "id": "f20a4a96-cd43-4f2b-ba54-12e2f91b72bc", "metadata": {}, "source": [ "**Observation:** \n", "\n", "Detected 73 and 70 in the 'customer_age' column as potential outliers. After consulting with the data definition for that variable and comparing these potential outliers to the Q1 & Q3 values from the basic statistics above, my suggestion would be to keep these rows but exclude them when asking questions such as the average or mean age of clients of the bank." ] }, { "cell_type": "code", "execution_count": 27, "id": "d3abf555-17c5-4716-925e-9f100a642368", "metadata": { "executionTime": 175, "lastSuccessfullyExecutedCode": "# Checking to know the datatypes of each column and if there are any missing values\nbasic_client_info_df.info()" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 10127 entries, 0 to 10126\n", "Data columns (total 6 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 clientnum 10127 non-null int64 \n", " 1 customer_age 10127 non-null int64 \n", " 2 gender 10127 non-null object\n", " 3 education_level 10127 non-null object\n", " 4 marital_status 10127 non-null object\n", " 5 income_category 10127 non-null object\n", "dtypes: int64(2), object(4)\n", "memory usage: 474.8+ KB\n" ] } ], "source": [ "# Checking to know the datatypes of each column and if there are any missing values\n", "basic_client_info_df.info()" ] }, { "cell_type": "markdown", "id": "6096878e-25d4-4c6f-ad93-f771766bd5cb", "metadata": {}, "source": [ "There are 6 columns and 10127 rows in the dataset with no mising values in each of the columns. The datatypes for each of the columns are now known, as shown above" ] }, { "cell_type": "code", "execution_count": 28, "id": "7869edec-f8a9-4506-a5af-5b32222c71e4", "metadata": { "executionTime": 208, "lastSuccessfullyExecutedCode": "# Checking to see if there are any duplicates in the dataset. This is the count of unique entries (i.e rows) in the dataset.\nbasic_client_info_df[basic_client_info_df.duplicated()].count()" }, "outputs": [ { "data": { "text/plain": [ "clientnum 0\n", "customer_age 0\n", "gender 0\n", "education_level 0\n", "marital_status 0\n", "income_category 0\n", "dtype: int64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking to see if there are any duplicates in the dataset. This is the count of unique entries (i.e rows) in the dataset.\n", "basic_client_info_df[basic_client_info_df.duplicated()].count()" ] }, { "cell_type": "markdown", "id": "0166646d-359d-45a3-8e4a-ca927b132535", "metadata": {}, "source": [ "No duplicated rows were found in the dataframe. So I assume that all rows are unique.\n", "\n", "Next I want to check for unique values in each column of the dataframe" ] }, { "cell_type": "code", "execution_count": 22, "id": "625344f9-a199-4d14-b928-407079949bda", "metadata": { "executionTime": 444, "lastSuccessfullyExecutedCode": "# Just like before, I'm using a for loop for this step so that I don't have to repeat the process for each column in the basic_client_info_df dataframe\n\nvariables = pd.DataFrame(columns=['Variable','No of unique values','Values'])\n\nfor i, var in enumerate(basic_client_info_df.columns):\n variables.loc[i] = [var, basic_client_info_df[var].nunique(), basic_client_info_df[var].unique().tolist()]\n \nvariables" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "No of unique values": 10127, "Values": [ 708082083, 708083283, 708084558, 708085458, 708086958, 708095133, 708098133, 708099183, 708100533, 708103608, 708104658, 708108333, 708112008, 708113208, 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"index": 5 } ], "schema": { "fields": [ { "name": "index", "type": "integer" }, { "name": "Variable", "type": "string" }, { "name": "No of unique values", "type": "integer" }, { "name": "Values", "type": "string" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 6, "truncation_type": null }, "text/html": [ "
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VariableNo of unique valuesValues
0clientnum10127[708082083, 708083283, 708084558, 708085458, 7...
1customer_age45[45, 58, 46, 34, 49, 43, 32, 37, 55, 52, 47, 5...
2gender2[F, M]
3education_level7[High School, Unknown, Doctorate, Uneducated, ...
4marital_status4[Married, Single, Divorced, Unknown]
5income_category6[Less than $40K, $40K - $60K, $80K - $120K, Un...
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" ], "text/plain": [ " Variable ... Values\n", "0 clientnum ... [708082083, 708083283, 708084558, 708085458, 7...\n", "1 customer_age ... [45, 58, 46, 34, 49, 43, 32, 37, 55, 52, 47, 5...\n", "2 gender ... [F, M]\n", "3 education_level ... [High School, Unknown, Doctorate, Uneducated, ...\n", "4 marital_status ... [Married, Single, Divorced, Unknown]\n", "5 income_category ... [Less than $40K, $40K - $60K, $80K - $120K, Un...\n", "\n", "[6 rows x 3 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Just like before, I'm using a for loop for this step so that I don't have to repeat the process for each column in the basic_client_info_df dataframe\n", "\n", "variables = pd.DataFrame(columns=['Variable','No of unique values','Values'])\n", "\n", "for i, var in enumerate(basic_client_info_df.columns):\n", " variables.loc[i] = [var, basic_client_info_df[var].nunique(), basic_client_info_df[var].unique().tolist()]\n", " \n", "variables" ] }, { "cell_type": "markdown", "id": "46f189a7-36b6-4a17-9173-d137939c9794", "metadata": {}, "source": [ "**Observation:**\n", "\n", "Just like in the previous dataset, only the 'clientnum' column has a unique value for all 10127 rows in the dataset, and this is because this variable contains all client id/number which are expected to be unique to each client.\n", "\n", "The basic_client_info_df dataset contains demographic information about the bank's clients and would be very critical in understanding client behaviour and trends amongst the different client segments." ] }, { "cell_type": "markdown", "id": "b9fb6a59-bc19-4824-883a-796e33a3f673", "metadata": {}, "source": [ "
\n", "\n", "### Summary Statistics of the enriched churn dataset" ] }, { "cell_type": "code", "execution_count": 26, "id": "b5b984d1-d8c4-4035-ab38-e9a7e3f8021b", "metadata": { "executionTime": 361, "lastSuccessfullyExecutedCode": "# Checking to see basic statistics of the numerical columns in the enriched churn dataset\nenriched_churn_df.describe()" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "clientnum": 10127, "index": "count", "total_amt_chng_q4_q1": 10127, "total_ct_chng_q4_q1": 10127, "total_relationship_count": 10127, "total_revolving_bal": 10127, "total_trans_amt": 10127, "total_trans_ct": 10127 }, { "clientnum": 739177606.3336625, "index": "mean", "total_amt_chng_q4_q1": 0.7599406537, "total_ct_chng_q4_q1": 0.7122223758, "total_relationship_count": 3.8125802311, "total_revolving_bal": 1162.81406142, "total_trans_amt": 4404.08630394, "total_trans_ct": 64.8586945788 }, { "clientnum": 36903783.45023115, "index": "std", "total_amt_chng_q4_q1": 0.2192067692, "total_ct_chng_q4_q1": 0.2380860913, "total_relationship_count": 1.5544078653, "total_revolving_bal": 814.9873352358, "total_trans_amt": 3397.1292535571, "total_trans_ct": 23.4725704492 }, { "clientnum": 708082083, "index": "min", "total_amt_chng_q4_q1": 0, "total_ct_chng_q4_q1": 0, "total_relationship_count": 1, "total_revolving_bal": 0, "total_trans_amt": 510, "total_trans_ct": 10 }, { "clientnum": 713036770.5, "index": "25%", "total_amt_chng_q4_q1": 0.631, "total_ct_chng_q4_q1": 0.582, "total_relationship_count": 3, "total_revolving_bal": 359, "total_trans_amt": 2155.5, "total_trans_ct": 45 }, { "clientnum": 717926358, "index": "50%", "total_amt_chng_q4_q1": 0.736, "total_ct_chng_q4_q1": 0.702, "total_relationship_count": 4, "total_revolving_bal": 1276, "total_trans_amt": 3899, "total_trans_ct": 67 }, { "clientnum": 773143533, "index": "75%", "total_amt_chng_q4_q1": 0.859, "total_ct_chng_q4_q1": 0.818, "total_relationship_count": 5, "total_revolving_bal": 1784, "total_trans_amt": 4741, "total_trans_ct": 81 }, { "clientnum": 828343083, "index": "max", "total_amt_chng_q4_q1": 3.397, "total_ct_chng_q4_q1": 3.714, "total_relationship_count": 6, "total_revolving_bal": 2517, "total_trans_amt": 18484, "total_trans_ct": 139 } ], "schema": { "fields": [ { "name": "index", "type": "string" }, { "name": "clientnum", "type": "number" }, { "name": "total_relationship_count", "type": "number" }, { "name": "total_revolving_bal", "type": "number" }, { "name": "total_amt_chng_q4_q1", "type": "number" }, { "name": "total_trans_amt", "type": "number" }, { "name": "total_ct_chng_q4_q1", "type": "number" }, { "name": "total_trans_ct", "type": "number" } ], "pandas_version": "1.4.0", "primaryKey": [ "index" ] } }, "total_rows": 8, "truncation_type": null }, "text/html": [ "
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clientnumtotal_relationship_counttotal_revolving_baltotal_amt_chng_q4_q1total_trans_amttotal_ct_chng_q4_q1total_trans_ct
count1.012700e+0410127.00000010127.00000010127.00000010127.00000010127.00000010127.000000
mean7.391776e+083.8125801162.8140610.7599414404.0863040.71222264.858695
std3.690378e+071.554408814.9873350.2192073397.1292540.23808623.472570
min7.080821e+081.0000000.0000000.000000510.0000000.00000010.000000
25%7.130368e+083.000000359.0000000.6310002155.5000000.58200045.000000
50%7.179264e+084.0000001276.0000000.7360003899.0000000.70200067.000000
75%7.731435e+085.0000001784.0000000.8590004741.0000000.81800081.000000
max8.283431e+086.0000002517.0000003.39700018484.0000003.714000139.000000
\n", "
" ], "text/plain": [ " clientnum ... total_trans_ct\n", "count 1.012700e+04 ... 10127.000000\n", "mean 7.391776e+08 ... 64.858695\n", "std 3.690378e+07 ... 23.472570\n", "min 7.080821e+08 ... 10.000000\n", "25% 7.130368e+08 ... 45.000000\n", "50% 7.179264e+08 ... 67.000000\n", "75% 7.731435e+08 ... 81.000000\n", "max 8.283431e+08 ... 139.000000\n", "\n", "[8 rows x 7 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking to see basic statistics of the numerical columns in the enriched churn dataset\n", "enriched_churn_df.describe()" ] }, { "cell_type": "markdown", "id": "7c5f79b4", "metadata": {}, "source": [ "**Checking for Outliers**" ] }, { "cell_type": "code", "execution_count": 188, "id": "e07f876e", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "There are no outliers in the 'clientnum' column \n", "\n", "\n", "There are no outliers in the 'total_relationship_count' column \n", "\n", "\n", "There are no outliers in the 'total_revolving_bal' column \n", "\n", "\n", "The potential ouliers in the 'total_trans_ct' column are:\n", "[138, 139] \n", "And the respective rows with the potential outlier are:\n", "[1858, 10089]\n", "\n", " Detected too many potential outliers in the following columns: ['total_amt_chng_q4_q1', 'total_trans_amt', 'total_ct_chng_q4_q1']. Based on the high number of potential outliers detected, use the column definition and check the summary statistics (min & max values) above to find out if there are truly outliers in these columns.\n", "\n", "\n", "------------------------------end------------------------------\n" ] } ], "source": [ "# Create two empty lists, one to store names of categorical variables, and the other for numerical variables\n", "cat_vars = []\n", "num_vars = []\n", "\n", "# Iterate through the names of columns/variables in bank_churners_df, and add variables to the appropriate list \n", "# depending on whether the are categorical or numerical\n", "for column_name in enriched_churn_df.columns:\n", " if enriched_churn_df[column_name].dtype == 'object': \n", " cat_vars.append(column_name)\n", " else:\n", " num_vars.append(column_name)\n", "\n", "# Set the figure size\n", "fig, axs = plt.subplots(nrows=4, ncols=2, figsize=(15, 15))\n", "# Reshape the axs array into a one-dimensional array by flattening its elements\n", "axs = axs.flatten() \n", "\n", "# Iterate through the list for numerical variables and create box plots of data in each numerical variable\n", "for i, var in enumerate(num_vars):\n", " # Using Seaborn's box plot for outlier detection\n", " sns.boxplot(x=var, data=enriched_churn_df, ax=axs[i])\n", "\n", "# Remove the subplot at index 7 from the axs array within the current figure\n", "fig.delaxes(axs[7])\n", "\n", "# Adjust the figure's subplot positions and margins, and then display the figure\n", "fig.tight_layout\n", "plt.show();\n", "\n", "# New line spacing\n", "print('\\n\\n')\n", "\n", "# Create new list to hold columns/variables that may have too many potential outliers\n", "outlier_vars = []\n", "\n", "# Iterate through the variables again and provide explanation as to whether or not outliers were detected and next steps\n", "for column_name in enriched_churn_df.columns:\n", " if enriched_churn_df[column_name].dtype == 'object':\n", " print(f\"\\nThe '{column_name}' column is not a numerical column \\n\")\n", " else:\n", " # Using the general rule for identifying potential outliers which is that if any data point in a dataset is more than \n", " # Q3 + 1.5xIQR or less than Q1 - 1.5xIQR, it's a high outlier. I would create a custom dictionary to capture potential outliers in each column.\n", " data = enriched_churn_df[column_name]\n", " q1 = data.quantile(0.25)\n", " q3 = data.quantile(0.75)\n", " iqr = q3 - q1\n", " iqr_lower = q1 - 1.5 * iqr\n", " iqr_upper = q3 + 1.5 * iqr\n", " outliers = dict(data[(data < iqr_lower) | (data > iqr_upper)])\n", " \n", " list_of_outliers = list(outliers.values())\n", " rows_with_outliers = list(outliers.keys())\n", " \n", " # If no potential outliers detected, print message below\n", " if len(list_of_outliers) == 0:\n", " print(f\"\\nThere are no outliers in the '{column_name}' column \\n\")\n", " \n", " else:\n", " # If potential outliers detected were more than 10, add column name to the list of columns with possible outliers\n", " if len(list_of_outliers) > 10:\n", " outlier_vars.append(column_name)\n", " \n", " # If not, print message below\n", " else:\n", " print(f\"\\nThe potential ouliers in the '{column_name}' column are:\\n{list_of_outliers} \\nAnd the respective rows with the potential outlier are:\\n{rows_with_outliers}\")\n", "\n", "# Print message to explain next steps to validate outliers \n", "print(f\"\\n Detected too many potential outliers in the following columns: {outlier_vars}. Based on the high number of potential outliers detected, use the column definition and check the summary statistics (min & max values) above to find out if there are truly outliers in these columns.\\n\")\n", "\n", "# Print custom border at the end\n", "print(\"\\n\" + \"-\"*30 + \"end\" + \"-\"*30)" ] }, { "cell_type": "markdown", "id": "aaae239a-1b8a-496b-ac01-4a73b22f0c0d", "metadata": {}, "source": [ "**Observation:** \n", "\n", "Detected 138 and 139 in the 'total_trans_ct' column as potential outliers. After consulting with the data definition for that variable, 'total_trans_ct' represents the total transaction count a client made in the last 12 months. This data would be useful to identify active clients that may be interested in using other bank products and services, hence my suggestion would be to keep them." ] }, { "cell_type": "code", "execution_count": 32, "id": "8242dc23-8080-4a4f-8307-2575a5590596", "metadata": { "executionTime": 192, "lastSuccessfullyExecutedCode": "# Checking to know the datatypes of each column and if there are any missing values\nenriched_churn_df.info()" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 10127 entries, 0 to 10126\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 clientnum 10127 non-null int64 \n", " 1 total_relationship_count 10127 non-null int64 \n", " 2 total_revolving_bal 10127 non-null int64 \n", " 3 total_amt_chng_q4_q1 10127 non-null float64\n", " 4 total_trans_amt 10127 non-null int64 \n", " 5 total_ct_chng_q4_q1 10127 non-null float64\n", " 6 total_trans_ct 10127 non-null int64 \n", "dtypes: float64(2), int64(5)\n", "memory usage: 553.9 KB\n" ] } ], "source": [ "# Checking to know the datatypes of each column and if there are any missing values\n", "enriched_churn_df.info()" ] }, { "cell_type": "markdown", "id": "c9f5ccef-fe8c-4cb2-9dc7-8fabca0ec9e3", "metadata": {}, "source": [ "There are 7 columns and 10127 rows in the dataset with no mising values in each of the columns. The datatypes for each of the columns are now known, as shown above" ] }, { "cell_type": "code", "execution_count": 33, "id": "86bfa354-6370-4d78-a38e-aa8594132119", "metadata": { "executionTime": 209, "lastSuccessfullyExecutedCode": "# Checking to see if there are any duplicates in the dataset. This is the count of unique entries (i.e rows) in the dataset.\nenriched_churn_df[enriched_churn_df.duplicated()].count()" }, "outputs": [ { "data": { "text/plain": [ "clientnum 0\n", "total_relationship_count 0\n", "total_revolving_bal 0\n", "total_amt_chng_q4_q1 0\n", "total_trans_amt 0\n", "total_ct_chng_q4_q1 0\n", "total_trans_ct 0\n", "dtype: int64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking to see if there are any duplicates in the dataset. This is the count of unique entries (i.e rows) in the dataset.\n", "enriched_churn_df[enriched_churn_df.duplicated()].count()" ] }, { "cell_type": "markdown", "id": "eb11cef8-8611-4b38-9b02-87986f0471a4", "metadata": {}, "source": [ "No duplicated rows were found in the dataframe.\n", "\n", "Next I want to check for unique values in each column of the dataframe" ] }, { "cell_type": "code", "execution_count": 24, "id": "5a59b89c-f93a-4cf9-8781-b626f8b4528b", "metadata": { "executionTime": 216, "lastSuccessfullyExecutedCode": "# Again I'm using a for loop for this step so that I don't have to repeat the process for each column in the basic_client_info_df dataframe\n\nvariables = pd.DataFrame(columns=['Variable','No of unique values','Values'])\n\nfor i, var in enumerate(enriched_churn_df.columns):\n variables.loc[i] = [var, enriched_churn_df[var].nunique(), enriched_churn_df[var].unique().tolist()]\n \nvariables" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": 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4553, 3831, 14474, 4089, 4884, 4093, 4374, 4736, 1140, 14969, 5097, 4131, 3939, 4104, 3170, 2073, 2705, 4860, 4929, 1689, 3161, 7905, 15867, 14289, 1477, 3294, 4467, 16920, 3505, 4891, 3896, 5299, 1351, 1837, 2505, 1923, 1723, 4935, 4434, 1629, 2862, 4332, 1268, 1406, 1051, 14084, 5616, 15511, 2659, 1297, 2801, 3703, 4245, 1604, 911, 4440, 16500, 15180, 2022, 1616, 3914, 3502, 4828, 13820, 8055, 4656, 2558, 4302, 2123, 5371, 8773, 4777, 4839, 16236, 1060, 4956, 3585, 4826, 14054, 1765, 5034, 15683, 838, 7890, 1399, 2111, 1328, 2362, 1285, 13021, 3924, 2324, 8004, 15495, 7678, 4417, 8688, 3549, 3023, 3830, 14213, 1171, 4564, 2098, 2235, 1085, 4914, 3996, 5066, 5025, 15240, 3795, 4001, 4358, 2644, 1101, 3492, 933, 5107, 2086, 13986, 4626, 1279, 5043, 2688, 974, 1676, 3507, 3980, 3158, 2717, 1750, 3246, 7823, 8851, 14209, 8173, 3017, 4919, 13598, 2541, 4171, 1071, 3902, 4531, 4740, 961, 4007, 1551, 13355, 3365, 4309, 1505, 1848, 3094, 13179, 3993, 3536, 2356, 530, 2329, 1552, 3154, 4599, 4094, 2600, 3938, 4345, 15017, 2355, 5176, 15865, 1576, 654, 2973, 4852, 5938, 3250, 4485, 2052, 12847, 7976, 15817, 1800, 13432, 4597, 2183, 15055, 14428, 2578, 2033, 4425, 1645, 16423, 732, 5674, 2446, 7206, 7720, 1557, 10156, 3370, 14641, 1802, 7341, 4392, 5163, 14781, 2198, 3313, 1921, 8186, 4435, 1218, 15347, 8778, 3453, 5038, 4127, 4325, 4111, 2565, 7480, 2297, 4065, 16216, 4673, 4738, 4628, 8964, 2431, 15710, 9495, 3836, 4418, 4450, 4895, 17350, 15958, 5389, 4951, 2040, 3309, 4170, 15315, 2560, 1296, 1194, 2450, 3873, 2403, 5005, 1068, 4549, 15163, 2055, 1446, 3576, 5468, 1319, 4423, 1577, 3601, 9045, 3597, 1093, 3148, 3992, 1028, 3781, 1826, 8302, 3531, 1473, 5067, 1005, 4842, 761, 4923, 7345, 7242, 1720, 3317, 4770, 15005, 4636, 1679, 8220, 5320, 4262, 4932, 4780, 2968, 1483, 4555, 14571, 15930, 3104, 2753, 4080, 4233, 13816, 2477, 5290, 2905, 8526, 818, 2126, 13222, 2468, 1390, 1838, 1719, 3608, 3139, 4087, 2042, 4196, 6984, 3291, 15467, 1703, 4123, 4002, 1995, 4076, 15209, 3532, 2343, 13139, 13306, 2808, 3700, 4682, 8094, 14381, 7710, 1220, 3332, 1784, 7470, 2094, 14885, 14713, 4763, 2579, 8162, 4980, 3508, 15580, 2027, 2631, 1447, 2424, 4237, 709, 2901, 3887, 4547, 2479, 15615, 1564, 5062, 15063, 5035, 4844, 13677, 2718, 1289, 4341, 3702, 2095, 4390, 2510, 1569, 1456, 3427, 7544, 1612, 4411, 4086, 3780, 4488, 2609, 3440, 1855, 1580, 4168, 3975, 15143, 15554, 4863, 1636, 1840, 3843, 2408, 4504, 15578, 2429, 4365, 1266, 708, 3621, 1187, 4790, 1655, 2820, 1474, 2182, 7520, 7680, 4798, 3546, 17390, 1924, 7403, 13784, 14740, 7640, 16171, 1222, 4710, 1096, 4984, 15028, 16161, 3969, 13360, 4933, 3028, 1348, 16237, 2011, 1415, 3698, 14319, 4834, 4853, 4162, 14192, 5104, 3033, 2616, 4548, 1298, 13563, 2064, 1111, 7289, 1294, 1449, 8904, 1700, 2032, 2590, 8215, 3353, 2673, 2142, 3528, 7535, 14557, 14994, 856, 13324, 829, 15054, 2293, 2872, 14838, 3226, 4901, 3477, 4659, 16202, 2043, 4684, 7709, 2341, 4536, 1570, 3760, 1465, 2987, 4801, 13604, 2776, 4215, 4753, 2853, 8736, 2867, 2366, 2335, 8426, 2026, 4576, 16695, 7042, 1815, 2512, 14723, 3753, 4708, 8132, 2483, 4658, 8210, 4594, 1693, 7776, 1621, 3456, 5027, 15886, 1884, 1747, 4724, 2814, 2496, 4247, 7672, 1356, 14109, 8514, 5039, 3068, 5373, 1375, 3717, 2535, 1958, 3647, 7499, 5524, 741, 4279, 5657, 2751, 4487, 1145, 1672, 1771, 3496, 1910, 4600, 2152, 3837, 2902, 14786, 1768, 14326, 14829, 3879, 2697, 1550, 16706, 799, 3695, 8325, 5152, 2839, 3659, 4101, 1042, 2428, 6205, 2759, 4900, 4346, 1197, 14103, 13764, 2967, 2665, 4343, 8150, 1484, 2427, 2594, 5483, 16469, 4878, 7625, 2339, 967, 4031, 4781, 4184, 4882, 4379, 1002, 8427, 12595, 3072, 14524, 2840, 2681, 6950, 3596, 3471, 811, 8689, 7485, 14751, 4827, 15102, 7635, 1717, 4918, 1985, 4340, 1869, 4999, 14314, 3403, 2066, 4593, 14624, 804, 4314, 4112, 14812, 9177, 3060, 1681, 4034, 2998, 1609, 2048, 7636, 3761, 7916, 5136, 2637, 12576, 4585, 3000, 4380, 2554, 4635, 2873, 1000, 2375, 5577, 5421, 1651, 3396, 2313, 3787, 2784, 15004, 3031, 2605, 3325, 4713, 1811, 720, 8028, 3350, 3358, 1236, 2509, 4071, 1067, 850, 4128, 1179, 5537, 4055, 12871, 3707, 4876, 7664, 5357, 14521, 5175, 5063, 2932, 1956, 4114, 2360, 14119, 1753, 4204, 3422, 8943, 2239, 797, 3342, 4328, 15243, 4098, 14465, 3018, 5253, 1327, 7380, 854, 3904, 2236, 4491, 2720, 4211, 4258, 13545, 4326, 3806, 4978, 3526, 14224, 4019, 3265, 15763, 4121, 803, 4520, 5653, 13588, 2945, 2438, 2208, 5024, 3644, 2577, 4213, 3676, 3910, 14436, 1870, 4366, 12510, 1119, 16266, 4841, 3057, 13452, 5346, 7787, 1828, 1850, 13617, 1434, 5322, 2748, 1891, 2843, 1549, 6439, 3367, 1694, 4779, 16098, 8124, 4803, 4874, 1543, 5026, 2752, 1195, 5137, 8065, 1280, 886, 2686, 15352, 1933, 14330, 4922, 8332, 7409, 3981, 1916, 14270, 4517, 2851, 4917, 3257, 2567, 3465, 2372, 8998, 4021, 3771, 1192, 2228, 13109, 15584, 5272, 2202, 14304, 2747, 7154, 1997, 8814, 3145, 1243, 1117, 4194, 4092, 13534, 4875, 8472, 8000, 4406, 8127, 14727, 4671, 2927, 7653, 2196, 5052, 1255, 15212, 4672, 4825, 3120, 9102, 15349, 5440, 1668, 3850, 2671, 3972, 3789, 5170, 4105, 4944, 4129, 3886, 3310, 2419, 1292, 4276, 2295, 1181, 3434, 4529, 2910, 2962, 3354, 4319, 2778, 7205, 1831, 1102, 1361, 4227, 1751, 3742, 3734, 4454, 1528, 2333, 2386, 4214, 1149, 14145, 1274, 16605, 748, 14880, 7739, 1790, 1281, 2581, 3994, 8549, 4308, 15785, 15077, 5064, 4527, 1663, 3962, 3820, 3750, 4281, 2399, 1525, 3610, 2342, 1157, 9226, 14716, 1992, 862, 4244, 837, 3877, 2954, 7885, 1325, 3280, 5076, 7962, 15505, 7418, 4305, 3961, 7561, 14533, 4238, 3990, 1954, 13719, 3670, 744, 12779, 1418, 1657, 4948, 3793, 9105, 2120, 3066, 1607, 991, 4972, 2946, 2977, 1398, 4814, 2805, 3410, 3519, 4085, 2685, 9202, 2783, 16344, 4990, 3564, 1360, 13159, 2651, 4621, 2586, 3901, 1729, 1036, 4190, 3470, 4565, 14080, 4163, 5490, 5106, 4266, 4605, 8927, 1331, 4011, 7209, 3230, 3799, 3415, 15574, 3551, 694, 2172, 14973, 3704, 2148, 5014, 2116, 7509, 4771, 8256, 1600, 4476, 4964, 1613, 2463, 3900, 3327, 9610, 2394, 4977, 2506, 4575, 1986, 4361, 3420, 1052, 3705, 13937, 14455, 14336, 2422, 5337, 5121, 3290, 1969, 1374, 5153, 15053, 7548, 14223, 1058, 15172, 3875, 8963, 5082, 2877, 4202, 1799, 14887, 8092, 2680, 4312, 1733, 4073, 7523, 14402, 4571, 3779, 4817, 7437, 8177, 13140, 13083, 2037, 2461, 12956, 2992, 14844, 2802, 1535, 909, 1305, 9772, 15552, 4831, 3475, 2328, 4892, 4183, 13674, 14212, 809, 7332, 4845, 13810, 7249, 8879, 14432, 2128, 4045, 3445, 3944, 3289, 3720, 3340, 3423, 3792, 3270, 3723, 15998, 7642, 1142, 4668, 13930, 5210, 2444, 3092, 3193, 2261, 1007, 7801, 16328, 2569, 1364, 14450, 2983, 1309, 1601, 1064, 4207, 4152, 8677, 14397, 8453, 5806, 14501, 3504, 3602, 787, 3758, 15282, 2687, 14161, 3380, 9040, 12631, 4522, 8257, 3618, 14826, 2587, 1922, 7688, 2025, 8147, 14134, 7597, 2345, 8254, 14643, 3970, 2282, 8226, 4465, 2829, 5460, 7292, 8241, 1253, 3783, 15274, 3275, 1401, 3635, 1436, 5265, 2139, 7471, 4940, 13819, 3555, 4322, 15905, 5466, 4809, 1472, 14908, 3737, 5623, 5187, 18484, 5379, 4174, 16485, 660, 4704, 1627, 5603, 2628, 14071, 7395, 13836, 8798, 7494, 15842, 2155, 14037, 3334, 2028, 5120, 7342, 6639, 15111, 15108, 5363, 1521, 3449, 4252, 4688, 5168, 1428, 3689, 6993, 14625, 4393, 1591, 9081, 3667, 14666, 16461, 5100, 2712, 3058, 2822, 5783, 1277, 5093, 3554, 5262, 5095, 14490, 3228, 15772, 8328, 1365, 4044, 4859, 1310, 4689, 5838, 8001, 8362, 4350, 4772, 2338, 4061, 1382, 4791, 16464, 1229, 17038, 14082, 5189, 5464, 5298, 1469, 8938, 7761, 4096, 2252, 1414, 2258, 16732, 3274, 15277, 1675, 14511, 2184, 5425, 4760, 5002, 7906, 2523, 1737, 2354, 16428, 7199, 3571, 17995, 731, 3542, 8454, 1886, 1363, 5495, 2589, 2668, 4165, 7358, 5270, 1688, 2795, 7603, 3874, 4782, 4334, 4756, 13303, 8212, 14934, 3024, 7153, 7106, 2307, 4125, 7385, 2950, 1573, 1893, 15068, 4282, 2641, 2062, 3547, 14485, 15152, 4889, 14475, 7991, 1920, 2331, 5083, 4715, 8293, 2682, 1166, 2701, 1987, 738, 3731, 3303, 2824, 8395, 14218, 2774, 7570, 3638, 8780, 4513, 2161, 6453, 13759, 683, 13015, 7703, 2452, 14769, 14977, 2960, 2253, 5331, 1973, 4185, 15110, 15225, 2294, 1470, 3261, 1952, 1548, 7971, 14127, 7849, 4789, 14112, 4851, 2383, 5409, 15475, 3153, 2995, 4270, 2556, 1282, 8531, 4169, 3768, 1704, 15549, 7733, 1846, 2319, 3616, 2828, 3151, 9065, 13204, 4028, 1386, 2076, 1459, 4414, 4058, 2181, 4453, 3664, 2117, 3802, 16606, 14955, 2566, 2571, 2540, 8123, 1438, 8912, 4677, 1324, 4560, 3034, 2649, 4494, 7919, 3256, 2274, 1405, 4291, 15260, 3331, 5660, 7605, 1106, 4497, 4858, 15527, 1911, 5080, 5157, 3973, 3497, 4632, 3556, 4460, 4296, 3816, 3299, 1976, 16824, 6009, 4537, 5377, 5567, 2351, 2639, 7222, 1872, 8570, 2392, 15279, 8695, 14791, 7412, 3648, 1859, 9042, 4050, 8300, 5199, 4492, 3045, 2597, 3483, 16208, 5059, 7803, 7525, 3932, 7925, 14178, 7886, 13625, 8696, 8918, 3642, 3393, 2270, 2721, 8087, 4048, 4441, 2318, 7689, 2878, 5344, 3650, 4607, 3977, 15443, 14036, 2084, 4226, 1574, 9931, 2981, 3278, 3572, 1295, 4243, 1205, 1936, 1834, 2724, 1084, 5092, 4187, 2090, 3205, 7149, 1635, 5247, 15926, 1998, 1154, 4639, 3748, 3404, 10291, 3577, 5529, 5008, 14893, 13814, 1080, 13783, 14257, 2246, 1225, 1191, 4271, 13648, 2521, 2106, 4490, 13740, 3174, 3673, 13124, 4265, 2057, 2286, 1697, 1338, 1165, 1716, 14133, 4954, 5221, 4881, 3920, 5004, 8235, 2160, 1381, 14441, 7946, 5269, 13177, 7360, 5037, 2325, 5029, 1990, 5774, 2405, 14042, 7549, 7986, 1779, 2693, 3166, 8331, 7009, 1740, 14149, 9138, 4047, 3103, 791, 4619, 14696, 4907, 4120, 4722, 5225, 14238, 4229, 2023, 15372, 14805, 2552, 4640, 7486, 14873, 2544, 644, 3098, 13376, 3348, 7985, 8169, 4647, 3188, 4952, 3277, 7963, 4383, 3240, 938, 4091, 3755, 17634, 15691, 12841, 3081, 4590, 3448, 5105, 2016, 3588, 4694, 2433, 2397, 5031, 3800, 3046, 4580, 14999, 2882, 2201, 1151, 7566, 3433, 5458, 1517, 2350, 4870, 1369, 2150, 2793, 7376, 2462, 4004, 7111, 14414, 7454, 6564, 4083, 4591, 3565, 13614, 5248, 6574, 4016, 1513, 3643, 3797, 4147, 4217, 2525, 1199, 4066, 15237, 13212, 12489, 5293, 7936, 3157, 15853, 2112, 13958, 3955, 3418, 4629, 3369, 1204, 4557, 5016, 3777, 2612, 4420, 4906, 7786, 8480, 5212, 6317, 4603, 7730, 4405, 2336, 3632, 8193, 16692, 13722, 7686, 3132, 5085, 4861, 15226, 4573, 1632, 4167, 14949, 14603, 1312, 2707, 14268, 13703, 13234, 3934, 2353, 1466, 8751, 13980, 936, 4024, 1431, 4665, 1342, 2775, 4159, 7832, 3923, 9088, 3693, 2079, 2292, 14638, 7782, 3146, 7821, 5119, 2715, 3940, 7719, 4711, 3059, 3539, 3346, 1847, 3815, 2978, 2738, 7546, 4360, 8056, 5302, 2848, 5311, 3639, 3493, 4796, 15331, 1780, 3658, 8113, 3653, 14334, 3063, 2369, 7866, 13557, 2197, 3053, 8271, 1760, 2803, 15473, 14840, 2621, 7674, 1553, 5266, 13992, 7802, 4216, 1867, 2340, 2726, 1486, 13630, 1050, 5209, 15335, 7649, 1464, 7190, 2588, 7059, 1451, 2499, 1437, 5224, 9274, 8898, 15281, 4912, 15079, 1699, 5848, 5274, 8165, 5347, 2507, 2245, 8512, 14875, 5271, 3407, 1667, 2250, 8463, 2067, 2004, 14375, 9183, 1094, 1169, 1605, 8216, 7479, 4664, 13503, 9360, 2933, 1370, 5149, 2472, 9389, 5326, 1763, 1421, 5255, 7558, 1219, 13758, 2109, 1105, 2482, 2722, 8045, 3133, 16712, 3300, 3911, 7022, 3775, 4102, 13434, 1542, 8620, 3338, 14917, 5283, 2777, 1752, 8669, 7773, 3949, 3386, 15709, 3302, 2949, 3688, 4971, 7587, 10211, 1746, 5910, 3580, 3189, 1039, 7377, 15154, 3710, 1851, 4992, 13681, 2163, 8765, 8012, 2966, 978, 4033, 5428, 2257, 15123, 1303, 7836, 16557, 5446, 4103, 4942, 8565, 14948, 8757, 3069, 8178, 2178, 1556, 4119, 15445, 14881, 2102, 1587, 1796, 9330, 8392, 2007, 13138, 16377, 3567, 15999, 3852, 13803, 1953, 8979, 8024, 9441, 3360, 1982, 2212, 3243, 1565, 1488, 1174, 10468, 5115, 4556, 1467, 1332, 1664, 4888, 14703, 2156, 7119, 7041, 916, 2269, 4545, 1942, 4913, 8248, 16319, 4315, 2186, 602, 2256, 3790, 5843, 1326, 14132, 3950, 2979, 10294, 1429, 4126, 3321, 5429, 1509, 3706, 6800, 15039, 4584, 3337, 7037, 7495, 2242, 929, 3675, 4737, 2231, 2272, 3271, 2107, 1529, 4749, 1173, 2670, 3859, 6100, 2811, 5418, 1321, 1996, 1792, 1223, 4959, 3872, 8417, 12493, 2185, 2735, 2591, 1380, 15354, 4239, 14649, 1168, 5156, 1677, 8272, 4039, 5366, 1967, 4072, 7460, 4310, 7302, 2953, 2371, 4038, 3259, 5977, 1420, 2876, 1895, 2289, 15293, 13997, 2780, 15851, 14970, 16541, 4029, 3102, 14783, 17258, 1894, 1674, 4873, 864, 8935, 4118, 8930, 3392, 4461, 7758, 4819, 7584, 7791, 3892, 4381, 14324, 7889, 1984, 1890, 8883, 5036, 4767, 8416, 14901, 16373, 4135, 827, 3893, 1487, 15134, 7697, 808, 3002, 2147, 13152, 13794, 1900, 5116, 2547, 15380, 8450, 17628, 1264, 10310, 2190, 5232, 597, 1829, 2080, 10219, 2191, 1738, 12429, 13970, 7827, 5019, 3216, 15514, 8020, 3159, 13041, 5007, 15754, 9212, 7359, 1088, 3809, 14220, 5568, 5202, 1329, 1810, 2617, 1314, 2380, 3655, 2179, 3124, 4280, 13392, 2664, 3959, 14833, 13767, 15645, 7582, 3200, 596, 2020, 4299, 16023, 5065, 2934, 8291, 3459, 2308, 7663, 8838, 14204, 3479, 13289, 8987, 5480, 2794, 3381, 1245, 9038, 4198, 7613, 1841, 2674, 8636, 4581, 6782, 2162, 2698, 4304, 1868, 15925, 2464, 3786, 3320, 8042, 8108, 3345, 9442, 2285, 2263, 13299, 1845, 8376, 15802, 1993, 2177, 1139, 8160, 7422, 15313, 9456, 1816, 2168, 8847, 3088, 2221, 982, 7721, 5099, 8295, 2883, 3744, 1299, 7679, 1833, 7612, 2792, 1288, 5405, 3745, 1440, 13785, 3524, 5473, 3206, 994, 8872, 13117, 2092, 998, 8195, 5402, 8061, 1818, 12878, 8554, 4687, 2539, 2130, 853, 15751, 1964, 4543, 2634, 4075, 13653, 2388, 646, 4648, 5167, 14807, 1188, 5321, 739, 7396, 1653, 3730, 6826, 2969, 7136, 7026, 4743, 14222, 1100, 8659, 7939, 7693, 4407, 5413, 2845, 2965, 14709, 7155, 8871, 3671, 13866, 2045, 3052, 2219, 7265, 8629, 8603, 3179, 7921, 2304, 3999, 2059, 15019, 7950, 2387, 3919, 1871, 15982, 2406, 7234, 3498, 4886, 948, 3472, 2549, 3903, 1444, 3222, 8327, 3030, 8804, 1335, 3788, 4950, 5182, 9867, 7707, 3457, 2280, 3685, 2039, 1714, 7399, 2500, 1819, 4925, 3115, 2296, 2316, 15497, 847, 3333, 15508, 3713, 14830, 14822, 2834, 3073, 3150, 5471, 3178, 17498, 2997, 14779, 2601, 2091, 9071, 15203, 8100, 7737, 2281, 7998, 1994, 3464, 9865, 1164, 16730, 724, 5282, 4260, 2194, 14615, 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VariableNo of unique valuesValues
0clientnum10127[828343083, 828298908, 828294933, 828291858, 8...
1total_relationship_count6[3, 4, 6, 5, 1, 2]
2total_revolving_bal1974[1793, 2035, 2437, 1821, 659, 765, 848, 1387, ...
3total_amt_chng_q4_q11158[0.803, 0.613, 0.765, 0.63, 0.938, 0.644, 0.76...
4total_trans_amt5033[3646, 1770, 2519, 2381, 3756, 4053, 1408, 426...
5total_ct_chng_q4_q1830[0.659, 0.741, 0.565, 0.481, 0.842, 0.692, 1.0...
6total_trans_ct126[68, 47, 36, 40, 70, 66, 23, 92, 84, 33, 39, 4...
\n", "
" ], "text/plain": [ " Variable ... Values\n", "0 clientnum ... [828343083, 828298908, 828294933, 828291858, 8...\n", "1 total_relationship_count ... [3, 4, 6, 5, 1, 2]\n", "2 total_revolving_bal ... [1793, 2035, 2437, 1821, 659, 765, 848, 1387, ...\n", "3 total_amt_chng_q4_q1 ... [0.803, 0.613, 0.765, 0.63, 0.938, 0.644, 0.76...\n", "4 total_trans_amt ... [3646, 1770, 2519, 2381, 3756, 4053, 1408, 426...\n", "5 total_ct_chng_q4_q1 ... [0.659, 0.741, 0.565, 0.481, 0.842, 0.692, 1.0...\n", "6 total_trans_ct ... [68, 47, 36, 40, 70, 66, 23, 92, 84, 33, 39, 4...\n", "\n", "[7 rows x 3 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Again I'm using a for loop for this step so that I don't have to repeat the process for each column in the basic_client_info_df dataframe\n", "\n", "variables = pd.DataFrame(columns=['Variable','No of unique values','Values'])\n", "\n", "for i, var in enumerate(enriched_churn_df.columns):\n", " variables.loc[i] = [var, enriched_churn_df[var].nunique(), enriched_churn_df[var].unique().tolist()]\n", " \n", "variables" ] }, { "cell_type": "markdown", "id": "4374ed2c-a827-495f-93d7-eb3e87af7ab6", "metadata": {}, "source": [ "**Observation:**\n", "\n", "Again, just like in the previous dataset, only the 'clientnum' column has a unique value for all 10127 rows in the dataset, and this is because this variable contains all client id/number which are expected to be unique to each client.\n", "\n", "The enriched_churn dataset contains enriched data about each client's use of their credit cards including the total revolving balance on the credit card, total transaction amounts and number of transactions in the last 12 months. These data points can be used to understand client spending and repayment habits while using their credit cards. It will also be ued for in-depth analysis to help the bank understand the main reasons why clients are churning and leaving its credit card services." ] }, { "cell_type": "markdown", "id": "a2d7e7ee-d7e6-48a5-b6e2-b86f7cc3aab7", "metadata": {}, "source": [ "
\n", "\n", "## Data Cleaning\n", "\n", "In the summary statistics analysis, Pandas.DataFrame methods were used to assess dataset properties. 'df.info()' provided summary information, 'df.describe()' offered descriptive statistics, and 'df.value_counts()' counted unique values. I perfomed outlier detection and data was validated to check if identified values were actually outliers.\n", "Additionally, I needed to convert the 'Clientnum' variable from an integer datatype to a string/object datatype for numerical analysis tasks. I used the define-code-test framework to perform this cleaning task.\n", "\n", "_Note: Storing unique ids as integers in SQL databases is good practice due to performance and efficiency reasons (integers take up less space in memory than strings, leading to faster joins and more efficient processing). I needed to convert this variable to strings only for the sake of numerical analysis such as cross-correlation analysis, descriptive stats and answering data questions._\n", "\n", "
\n", "\n", "My approach to data cleaning was to make a copy of each dataset first, so that if there any issues I have my original dataset intact. I then used the define-code-test framework for data cleaning, which involves defining cleaning steps using verbs and action words that clearly describe the cleaning tasks, performing the cleaning tasks accordingly and testing programmatically to see if my desired results were achieved." ] }, { "cell_type": "code", "execution_count": 3, "id": "c7cb8727-6e96-440a-bc01-0de506123124", "metadata": { "executionTime": 272, "lastSuccessfullyExecutedCode": "# Making copies of all 3 datasets\ndf1 = bank_churners_df.copy()\ndf2 = basic_client_info_df.copy()\ndf3 = enriched_churn_df.copy()\n\n# And then joining the resulting dataframes into one master dataset for easier access and manipulation\ndf = df1.merge(df2, on='clientnum').merge(df3, on='clientnum')\n\n# Checking to make sure all 3 dataframes and their respective columns were joined correctly\ndf.info()" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 10127 entries, 0 to 10126\n", "Data columns (total 21 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 clientnum 10127 non-null int64 \n", " 1 attrition_flag 10127 non-null object \n", " 2 dependent_count 10127 non-null int64 \n", " 3 card_category 10127 non-null object \n", " 4 months_on_book 10127 non-null int64 \n", " 5 months_inactive_12_mon 10127 non-null int64 \n", " 6 contacts_count_12_mon 10127 non-null int64 \n", " 7 credit_limit 10127 non-null float64\n", " 8 avg_open_to_buy 10127 non-null float64\n", " 9 avg_utilization_ratio 10127 non-null float64\n", " 10 customer_age 10127 non-null int64 \n", " 11 gender 10127 non-null object \n", " 12 education_level 10127 non-null object \n", " 13 marital_status 10127 non-null object \n", " 14 income_category 10127 non-null object \n", " 15 total_relationship_count 10127 non-null int64 \n", " 16 total_revolving_bal 10127 non-null float64\n", " 17 total_amt_chng_q4_q1 10127 non-null float64\n", " 18 total_trans_amt 10127 non-null int64 \n", " 19 total_ct_chng_q4_q1 10127 non-null float64\n", " 20 total_trans_ct 10127 non-null int64 \n", "dtypes: float64(6), int64(9), object(6)\n", "memory usage: 1.7+ MB\n" ] } ], "source": [ "# Making copies of all 3 datasets\n", "df1 = bank_churners_df.copy()\n", "df2 = basic_client_info_df.copy()\n", "df3 = enriched_churn_df.copy()\n", "\n", "# And then joining the resulting dataframes into one master dataset for easier access and manipulation\n", "df = df1.merge(df2, on='clientnum').merge(df3, on='clientnum')\n", "\n", "# Checking to make sure all 3 dataframes and their respective columns were joined correctly\n", "df.info()" ] }, { "cell_type": "markdown", "id": "d6dbcebf-55b4-4ce9-a7b9-45eb3ce7203c", "metadata": {}, "source": [ "
\n", "\n", "### Quality Issue to be Cleaned\n", "\n", "#### Define\n", "Change datatypes of clientnum variable in all 3 datasets from integer to string/object datatype\n", "\n", "_Note: Even though storing unique ids as integers in SQL databases is good practice due to performance and efficiency reasons (integers take up less space in memory than strings, leading to faster joins and more efficient processing), I still needed to convert this variable to strings for the sake of numerical analysis that I want to perform such as cross-correlation analysis, descriptive stats and answering data questions._\n", "\n", "
\n", "\n", "#### Code" ] }, { "cell_type": "code", "execution_count": 5, "id": "e3acd942-1c47-4e46-b802-c5c633460749", "metadata": { "executionTime": 54, "lastSuccessfullyExecutedCode": "df = df.astype({'clientnum': 'object'})" }, "outputs": [], "source": [ "df = df.astype({'clientnum': 'object'})" ] }, { "cell_type": "markdown", "id": "edf2da87-c5ed-4f0d-88c9-df5e926ffc1a", "metadata": {}, "source": [ "
\n", "\n", "#### Test" ] }, { "cell_type": "code", "execution_count": 6, "id": "6fd03be3-f8c0-4f99-89a9-68b391f3b3f6", "metadata": { "executionTime": 248, "lastSuccessfullyExecutedCode": "df.clientnum.info()" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 10127 entries, 0 to 10126\n", "Series name: clientnum\n", "Non-Null Count Dtype \n", "-------------- ----- \n", "10127 non-null object\n", "dtypes: object(1)\n", "memory usage: 158.2+ KB\n" ] } ], "source": [ "df.clientnum.info()" ] }, { "cell_type": "markdown", "id": "2c16b62d-4a3f-450a-9aaa-82ec58600a85", "metadata": {}, "source": [ "
\n", "\n", "## Distribution Analysis for each of the columns in the dataset\n", "\n", "I took a close look at all the columns to know how values were distributed in each column using either a histogram plot to show the distribution in variables with non unique values, or a bar chart to show the distribution in variables where unique values were less than 10." ] }, { "cell_type": "code", "execution_count": 54, "id": "47152ca3", "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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cFJgNHF3yHw3MLulnlHxExBbAwcAgYA/g7Ijo3pUnIklSWwx+JUlawWXlpfJypfJI4CPAVSV9BLBveb5PeU3ZvktEREm/PDNfz8wnqG5huF3nn4EkSYtn8CtJkoiI7hExFpgO3AL8B5iTmfNKlilAv/K8HzAZoGx/HlinNr2VfVoe75iIGB0Ro2fMmNHBZyNJ0sIMfiVJEpk5PzOHAP2pems36+TjnZOZQzNzaJ8+fTrzUJIkAQa/kiSpRmbOAW4DdgB6RUSPsqk/MLU8nwpsAFC2rwk8V5veyj6SJNWVwa86xRlnnMGgQYMYPHgwhxxyCK+99hpHHHEEG220EUOGDGHIkCGMHTsWgMzkS1/6EptuuilbbbUV9913X3M5I0aMYODAgQwcOJARI0a0cTRJ0tsREX0iold5vgqwK/AwVRB8YMk2HLi2PL+uvKZsH5mZWdIPLqtBbwQMBEZ1yUm0MHnyZHbeeWe22GILBg0axFlnnQXApz71qeZ2aMCAAQwZMgSAUaNGNadvvfXWXHPNNYssR5K0/Omx+CzSkpk6dSq/+tWveOihh1hllVU46KCDuPzyywH42c9+xoEHHrhA/r/+9a88/vjjPP7449xzzz0ce+yx3HPPPcyaNYtTTz2V0aNHExFsu+22fOITn2Cttdaqx2lJUiPrC4woKzN3A67MzOsj4iHg8oj4IXA/cF7Jfx5wcURMAGZRrfBMZj4YEVcCDwHzgC9m5vwuPhcAevTowS9+8Qu22WYbXnzxRbbddlt23XVXrrjiiuY8X//611lzzTUBGDx4MKNHj6ZHjx5MmzaNrbfemo9//ONtlrPFFlvU47QkSW+Dwa86xbx583j11VdZaaWVeOWVV1h//fXbzHvttddy+OGHExFsv/32zJkzh2nTpnH77bez6667svbaawOw6667cuONN3LIIYd01WlI0gohMx8A3tdK+kRaWa05M18DPtlGWacBp3V0HZdU37596du3LwCrr746m2++OVOnTm0OWjOTK6+8kpEjRwKw6qqrNu/72muvUS1evfhyJEnLD4c9q8P169ePb3zjG2y44Yb07duXNddck9122w2A73znO2y11VZ89atf5fXXXweqnuINNnhrilj//v2ZOnVqm+mSJC2JSZMmcf/99zNs2LDmtDvvvJP11luPgQMHNqfdc889DBo0iC233JLf//739OjRY7HlSJKWHwa/6nCzZ8/m2muv5YknnuDpp5/m5Zdf5pJLLuHHP/4xjzzyCPfeey+zZs3iJz/5Sb2rKklqcC+99BIHHHAAZ555JmussUZz+mWXXbbQSKJhw4bx4IMPcu+99/LjH/+Y1157bbHlLC8WNXf517/+NZttthmDBg3im9/8JlAF+qusskrzPOjPf/7zzfl32mkn3vve9zZvmz59epefjyQtDYc9q8P97W9/Y6ONNqLp1hX7778///znPznssMMA6NmzJ0ceeSQ///nPgaqnePLkt24LOWXKFPr160e/fv24/fbbF0jfaaeduuw8JEnLt7lz53LAAQdw6KGHsv/++zenz5s3j6uvvpoxY8a0ut/mm2/Oaqutxvjx4xk6dGib5SxP2pq7/Oyzz3Lttdcybtw4evbsuUAgu8kmmzQvTtnSpZdeytChQ7uo9pLUMez5VYfbcMMNufvuu3nllVfITG699VY233xzpk2bBlTzrP70pz8xePBgAD7xiU9w0UUXkZncfffdrLnmmvTt25fdd9+dm2++mdmzZzN79mxuvvlmdt9993qemiRpOZGZHH300Wy++eZ87WtfW2Db3/72NzbbbDP69+/fnPbEE08wb948AJ588kkeeeQRBgwYsMhylid9+/Zlm222ARacu/y73/2OE088kZ49ewKw7rrr1rOaktSpDH7V4YYNG8aBBx7INttsw5Zbbsmbb77JMcccw6GHHsqWW27JlltuycyZM/nud78LwF577cXGG2/Mpptuymc/+1nOPvtsANZee21OOukk3v/+9/P+97+f733ve82LX0mStCj/+Mc/uPjiixk5cmTz8NwbbrgBgMsvv3yhIc933XUXW2+9NUOGDGG//fbj7LPPpnfv3ossZ3lVO3f5scce484772TYsGF8+MMf5t57723O98QTT/C+972PD3/4w9x5550LlHHkkUcyZMgQfvCDH1Dd5UqSln2xov3DGjp0aI4ePbre1ZAkNYCIGJOZjv18m2ybu85LL73Ehz/8Yb7zne+w//77M3jwYHbeeWd+9atfce+99/KpT32KiRMn8sYbb/DSSy+xzjrrMGbMGPbdd18efPBB1lhjDaZOnUq/fv148cUXOeCAAzjssMM4/PDD631qkgQsum2251eSJGkF0Nrc5f79+7P//vsTEWy33XZ069aNmTNn0rNnT9ZZZx0Att12WzbZZBMee+wxoFqrA6rh0//1X//FqFGj6nNCkrSEDH4lSZIaXFtzl/fdd19uu+02AB577DHeeOMNevfuzYwZM5g/fz4AEydO5PHHH2fjjTdm3rx5zJw5E6iC6euvv755DQ9JWta52rMkSVKDa5q7vOWWWzJkyBAAfvSjH3HUUUdx1FFHMXjwYN7xjncwYsQIIoI77riD733ve6y00kp069aN3//+96y99tq8/PLL7L777sydO5f58+fz0Y9+lM9+9rP1PTlJaifn/L5N2x5/UYeVJS2tMT9zrpVUD8757Rhttc2N2sb6P1uSOo9zfiVJkiRJKzSDX0mSJElSwzP4lSRJkiQ1PINfSZIkSVLDc7VnSZKk5YALgEnS21OXnt+I+GpEPBgR4yPisohYOSI2ioh7ImJCRFwREe8oeXuW1xPK9gE15XyrpD8aEbvX41wkSZIkScu+Lg9+I6If8CVgaGYOBroDBwM/Ac7IzE2B2cDRZZejgdkl/YySj4jYouw3CNgDODsiunfluUiSJEmSlg/1mvPbA1glInoAqwLTgI8AV5XtI4B9y/N9ymvK9l0iIkr65Zn5emY+AUwAtuua6kuSJEmSliddHvxm5lTg58BTVEHv88AYYE5mzivZpgD9yvN+wOSy77ySf53a9Fb2WUBEHBMRoyNi9IwZMzr2hCRJkiRJy7x6DHtei6rXdiNgfeCdVMOWO01mnpOZQzNzaJ8+fTrzUJIkLXciYoOIuC0iHiprcny5pJ8SEVMjYmx57FWzT6vrbkTEHiVtQkScWI/zkSSpNfVY7fmjwBOZOQMgIq4GPgD0iogepXe3PzC15J8KbABMKcOk1wSeq0lvUruPJElqv3nA1zPzvohYHRgTEbeUbWdk5s9rM7dYd2N94G8R8Z6y+bfArlQjsu6NiOsy86EuOQtJkhahHnN+nwK2j4hVy9zdXYCHgNuAA0ue4cC15fl15TVl+8jMzJJ+cFkNeiNgIDCqi85BkqSGkZnTMvO+8vxF4GHamEpUtLXuxnbAhMycmJlvAJeXvJIk1V095vzeQ7Vw1X3Av0sdzgFOAL4WEROo5vSeV3Y5D1inpH8NOLGU8yBwJVXgfCPwxcyc34WnIklSwym3FHwfcE9JOi4iHoiI88vUJWh73Q3X45AkLbPqMeyZzDwZOLlF8kRaWa05M18DPtlGOacBp3V4BSVJWgFFxGrAH4GvZOYLEfE74AdAlp+/AI7qiGNl5jlUF78ZOnRodkSZkiQtSl2CX0mStGyJiJWoAt9LM/NqgMx8tmb7H4Dry8tFrbvhehySpGVSve7zK0mSlhFlDY7zgIcz85c16X1rsu0HjC/P21p3415gYERsFBHvoFoU67quOAdJkhbHnl9JkvQB4NPAvyNibEn7NnBIRAyhGvY8CfgcVOtuRETTuhvzqFl3IyKOA24CugPnlzU6JEmqO4NfSZJWcJl5FxCtbLphEfu0uu5GZt6wqP0kSaoXhz1LkiRJkhqewa8kSZIkqeEZ/EqSJEmSGp7BryRJkiSp4Rn8SpIkSZIansGvJEmSJKnhGfxKkiRJkhqewa8kSZIkqeEZ/EqSJEmSGp7BryRJkiSp4Rn8SpIkSZIansGvJEmSJKnhGfxKkiRJkhqewa8kSZIkqeEZ/EqSJEmSGp7BryRJkiSp4Rn8SpIkSZIansGvJEmSJKnhGfxKkiRJkhqewa8kSSu4iNggIm6LiIci4sGI+HJJXzsibomIx8vPtUp6RMSvImJCRDwQEdvUlDW85H88IobX65ykRTnqqKNYd911GTx4cHPa2LFj2X777RkyZAhDhw5l1KhRAFx77bVstdVWzel33XVX8z7f/OY3GTRoEJtvvjlf+tKXyMwuPxdJ7WfwK0mS5gFfz8wtgO2BL0bEFsCJwK2ZORC4tbwG2BMYWB7HAL+DKlgGTgaGAdsBJzcFzNKy5IgjjuDGG29cIO2b3/wmJ598MmPHjuX73/8+3/zmNwHYZZddGDduHGPHjuX888/nM5/5DAD//Oc/+cc//sEDDzzA+PHjuffee/n73//e5eciqf0MfiVJWsFl5rTMvK88fxF4GOgH7AOMKNlGAPuW5/sAF2XlbqBXRPQFdgduycxZmTkbuAXYo+vORGqfHXfckbXXXnuBtIjghRdeAOD5559n/fXXB2C11VYjIgB4+eWXm59HBK+99hpvvPEGr7/+OnPnzmW99dbrwrOQtKR61LsCkiRp2RERA4D3AfcA62XmtLLpGaDpm30/YHLNblNKWlvprR3nGKpeYzbccMMOqr209M4880x23313vvGNb/Dmm2/yz3/+s3nbNddcw7e+9S2mT5/OX/7yFwB22GEHdt55Z/r27Utmctxxx7H55pvXq/qS2qEuPb8R0SsiroqIRyLi4YjYwXlFkiTVV0SsBvwR+EpmvlC7LavJjB02oTEzz8nMoZk5tE+fPh1VrLTUfve733HGGWcwefJkzjjjDI4++ujmbfvttx+PPPIIf/rTnzjppJMAmDBhAg8//DBTpkxh6tSpjBw5kjvvvLNe1ZfUDvUa9nwWcGNmbgZsTTW8ynlFkiTVSUSsRBX4XpqZV5fkZ8twZsrP6SV9KrBBze79S1pb6dIyb8SIEey///4AfPKTn2xe8KrWjjvuyMSJE5k5cybXXHMN22+/PautthqrrbYae+65J//617+6utqSlkCXB78RsSawI3AeQGa+kZlzcF6RJEl1EdUkxvOAhzPzlzWbrgOaRlYNB66tST+8jM7aHni+DI++CdgtItYqF6R3K2nSMm/99ddvXrBq5MiRDBw4EKh6eJtWcb7vvvt4/fXXWWedddhwww35+9//zrx585g7dy5///vfHfYsLePqMed3I2AGcEFEbA2MAb6M84okSaqXDwCfBv4dEWNL2reB04ErI+Jo4EngoLLtBmAvYALwCnAkQGbOiogfAPeWfN/PzFldcgbSEjjkkEO4/fbbmTlzJv379+fUU0/lD3/4A1/+8peZN28eK6+8Mueccw4Af/zjH7noootYaaWVWGWVVbjiiiuICA488EBGjhzJlltuSUSwxx578PGPf7zOZyZpUeoR/PYAtgH+OzPviYizeGuIM1DNK4qIDp1XBJwDMHToUG/AJklSjcy8C4g2Nu/SSv4EvthGWecD53dc7aSOd9lll7WaPmbMmIXSTjjhBE444YSF0rt3787//M//dHjdJHWeesz5nQJMycx7yuurqIJh5xVJkiRJkjpFlwe/mfkMMDki3luSdgEewnlFkiRJkqROUq/7/P43cGlEvAOYSDVXqBvOK5IkSZIkdYJ2Bb8RcWtm7rK4tPbKzLHA0FY2Oa9IkqS3oaPbbGlZte3xF9W7Cp1izM8Or3cVpIa1yOA3IlYGVgV6l6HFTYthrEEbKytLkqSuZ5stSdKiLW7O7+eobkW0WfnZ9LgW+E3nVk2SJC0B22xJS+Woo45i3XXXZfDgwc1pp5xyCv369WPIkCEMGTKEG264AYBLL720OW3IkCF069aNsWPHLlDeJz7xiQXKkpYViwx+M/OszNwI+EZmbpyZG5XH1plpQypJ0jLCNlvS0jriiCO48cYbF0r/6le/ytixYxk7dix77bUXAIceemhz2sUXX8xGG23EkCFDmve5+uqrWW211bqq6tISadec38z8dUT8P2BA7T6Z2ZiTLSRJWk7ZZktaUjvuuCOTJk1a4v0uu+wyDj744ObXL730Er/85S8555xzOOiggxaxp1Qf7brVUURcDPwc+CDw/vJobcEqSZJUR7bZkjrKb37zG7baaiuOOuooZs+evdD2K664gkMOOaT59UknncTXv/51Vl111a6sptRu7b3V0VBgi7LysiRJWnbZZkt624499lhOOukkIqI5qD3//LdusnLPPfew6qqrNs/tHTt2LP/5z38444wzlqoXWeoK7er5BcYD7+rMikiSpA5hmy3pbVtvvfXo3r073bp147Of/SyjRo1aYPvll1++QK/vv/71L0aPHs2AAQP44Ac/yGOPPcZOO+3UxbWWFq29Pb+9gYciYhTwelNiZn6iU2olSZKWlm22pLdt2rRp9O3bF4BrrrlmgdWb33zzTa688kruvPPO5rRjjz2WY489FoBJkyax9957c/vtt3dpnaXFaW/we0pnVkKSJHWYU+pdAUnLl0MOOYTbb7+dmTNn0r9/f0499VRuv/12xo4dS0QwYMAA/ud//qc5/x133MEGG2zAxhtvXMdaS0uuvas9/72zKyJJkt4+22xJS+qyyy5bKO3oo49uM/9OO+3E3Xff3eb2AQMGMH78+A6pm9SR2hX8RsSLQNPCGe8AVgJezsw1OqtikiRpydlmS5LUuvb2/K7e9DwiAtgH2L6zKiVJkpaObbYkSa1r72rPzbLyJ2D3jq+OJEnqKLbZkiS9pb3DnvevedmN6h6Cr3VKjSRJ0lKzzZYkqXXtXe354zXP5wGTqIZRSZKkZYtttrSC2fb4i+pdhU4x5meH17sKajDtnfN7ZGdXRJIkvX1L02ZHxPnA3sD0zBxc0k4BPgvMKNm+nZk3lG3fAo4G5gNfysybSvoewFlAd+DczDz97Z2NJEkdp11zfiOif0RcExHTy+OPEdG/sysnSZKWzFK22RcCe7SSfkZmDimPpsB3C+BgYFDZ5+yI6B4R3YHfAnsCWwCHlLySJC0T2rvg1QXAdcD65fHnkiZJkpYtS9xmZ+YdwKx2lr8PcHlmvp6ZTwATgO3KY0JmTszMN4DLcbi1JGkZ0t7gt09mXpCZ88rjQqBPJ9ZLkiQtnY5ss4+LiAci4vyIWKuk9QMm1+SZUtLaSm9VRBwTEaMjYvSMGTPayiZJUodpb/D7XEQc1jSsKSIOA57rzIpJkqSl0lFt9u+ATYAhwDTgFx1YRzLznMwcmplD+/TxerokqfO1N/g9CjgIeIaqATwQOKKT6iRJkpZeh7TZmflsZs7PzDeBP1ANawaYCmxQk7V/SWsrXZKkZUJ7g9/vA8Mzs09mrkvVsJ7aedWSJElLqUPa7IjoW/NyP2B8eX4dcHBE9IyIjYCBwCjgXmBgRGwUEe+gWhTrurdxHpIkdaj23ud3q8yc3fQiM2dFxPs6qU6SJGnpLXGbHRGXATsBvSNiCnAysFNEDAGS6l7BnyvlPRgRVwIPUd1H+IuZOb+UcxxwE9Wtjs7PzAc79tQkSVp67Q1+u0XEWk2NaUSsvQT7SpKkrrPEbXZmHtJK8nmLyH8acFor6TcANyxZdSVJ6hrtDWB/AfwrIv6vvP4krTR6kiSp7myzJUlqRbuC38y8KCJGAx8pSftn5kOdVy1JkrQ0bLMlSWpdu4cul4bTxlOSpGWcbbYkSQtr72rPHa7ce/D+iLi+vN4oIu6JiAkRcUVZKZKymuQVJf2eiBhQU8a3SvqjEbF7nU5FkiRJkrSMq1vwC3wZeLjm9U+AMzJzU2A2cHRJPxqYXdLPKPmIiC2obqMwCNgDODsiundR3SVJkiRJy5G6BL8R0R/4GHBueR1Uc5OuKllGAPuW5/uU15Ttu5T8+wCXZ+brmfkEMAHYrktOQJIkSZK0XKlXz++ZwDeBN8vrdYA5mTmvvJ4C9CvP+wGTAcr250v+5vRW9llARBwTEaMjYvSMGTM68DQkSZIkScuDLg9+I2JvYHpmjumqY2bmOZk5NDOH9unTp6sOK0mSJElaRrR7tecO9AHgExGxF7AysAZwFtArInqU3t3+wNSSfyqwATAlInoAawLP1aQ3qd1HkiRJkqRmXd7zm5nfysz+mTmAasGqkZl5KHAbcGDJNhy4tjy/rrymbB+ZmVnSDy6rQW8EDARGddFpSJIkSZKWI/Xo+W3LCcDlEfFD4H7gvJJ+HnBxREwAZlEFzGTmgxFxJdV9DOcBX8zM+V1fbUmSJEnSsq6uwW9m3g7cXp5PpJXVmjPzNeCTbex/GnBa59VQkiRJktQI6nmfX0mSJEmSuoTBryRJkiSp4Rn8SpIkSZIansGvJEmSJLXirLPOYvDgwQwaNIgzzzwTgFmzZrHrrrsycOBAdt11V2bPnr3APvfeey89evTgqquuqkONtSgGv5IkSZLUwvjx4/nDH/7AqFGjGDduHNdffz0TJkzg9NNPZ5ddduHxxx9nl1124fTTT2/eZ/78+Zxwwgnstttuday52mLwK0mSJEktPPzwwwwbNoxVV12VHj168OEPf5irr76aa6+9luHDhwMwfPhw/vSnPzXv8+tf/5oDDjiAddddt0611qIY/EqSJElSC4MHD+bOO+/kueee45VXXuGGG25g8uTJPPvss/Tt2xeAd73rXTz77LMATJ06lWuuuYZjjz22ntXWIhj8SpIkIuL8iJgeEeNr0taOiFsi4vHyc62SHhHxq4iYEBEPRMQ2NfsML/kfj4jh9TgXSeoIm2++efMQ5j322IMhQ4bQvXv3BfJEBBEBwFe+8hV+8pOf0K2bIdayyk9GkiQBXAjs0SLtRODWzBwI3FpeA+wJDCyPY4DfQRUsAycDw4DtgJObAmZJWh4dffTRjBkzhjvuuIO11lqL97znPay33npMmzYNgGnTpjUPcR49ejQHH3wwAwYM4KqrruILX/jCAkOiVX8Gv5Ikicy8A5jVInkfYER5PgLYtyb9oqzcDfSKiL7A7sAtmTkrM2cDt7BwQC1Jy43p06cD8NRTT3H11VfzX//1X3ziE59gxIjqX+OIESPYZ599AHjiiSeYNGkSkyZN4sADD+Tss89m3333rVfV1Yoe9a6AJElaZq2XmdPK82eA9crzfsDkmnxTSlpb6QuJiGOoeo3ZcMMNO7DKktRxDjjgAJ577jlWWmklfvvb39KrVy9OPPFEDjroIM477zze/e53c+WVV9a7mmong19JkrRYmZkRkR1Y3jnAOQBDhw7tsHIlqSPdeeedC6Wts8463HrrrYvc78ILL+ykGuntcNizJElqy7NlODPl5/SSPhXYoCZf/5LWVrokSXVn8CtJktpyHdC0YvNw4Nqa9MPLqs/bA8+X4dE3AbtFxFploavdSpokSXXnsGdJkkREXAbsBPSOiClUqzafDlwZEUcDTwIHlew3AHsBE4BXgCMBMnNWRPwAuLfk+35mtlxES5KkujD4lSRJZOYhbWzapZW8CXyxjXLOB87vwKpJktQhDH4lSZIkNYxtj7+o3lXoFGN+dni9q7Dcc86vJEmSJKnhGfxKkiRJkhqewa8kSZIkqeEZ/EqSJEmSGp7BryRJkiSp4Rn8SpIkSZIansGvJEmSJKnhGfxKUp0cddRRrLvuugwePHiB9F//+tdsttlmDBo0iG9+85sA3HLLLWy77bZsueWWbLvttowcORKAF198kSFDhjQ/evfuzVe+8pWuPhVJkqRlXo96V0CSVlRHHHEExx13HIcf/tZN62+77TauvfZaxo0bR8+ePZk+fToAvXv35s9//jPrr78+48ePZ/fdd2fq1KmsvvrqjB07tnn/bbfdlv3337+rT0WSJGmZZ/ArSXWy4447MmnSpAXSfve733HiiSfSs2dPANZdd10A3ve+9zXnGTRoEK+++iqvv/56cz6Axx57jOnTp/OhD32o8ysvSZK0nOnyYc8RsUFE3BYRD0XEgxHx5ZK+dkTcEhGPl59rlfSIiF9FxISIeCAitqkpa3jJ/3hEDO/qc5GkjvbYY49x5513MmzYMD784Q9z7733LpTnj3/8I9tss80CgS/A5Zdfzqc+9SkioquqK0mStNyoR8/vPODrmXlfRKwOjImIW4AjgFsz8/SIOBE4ETgB2BMYWB7DgN8BwyJibeBkYCiQpZzrMnN2l5+RJHWQefPmMWvWLO6++27uvfdeDjroICZOnNgc0D744IOccMIJ3HzzzQvte/nll3PxxRd3dZUlSZKWC13e85uZ0zLzvvL8ReBhoB+wDzCiZBsB7Fue7wNclJW7gV4R0RfYHbglM2eVgPcWYI+uOxNJ6nj9+/dn//33JyLYbrvt6NatGzNnzgRgypQp7Lffflx00UVssskmC+w3btw45s2bx7bbbluPakuSJC3z6rrac0QMAN4H3AOsl5nTyqZngPXK837A5JrdppS0ttJbO84xETE6IkbPmDGj405AkjrYvvvuy2233QZUQ6DfeOMNevfuzZw5c/jYxz7G6aefzgc+8IGF9rvssss45JBDurq6kiRpBTN//nze9773sffeewNw6KGH8t73vpfBgwdz1FFHMXfu3AXy33vvvfTo0YOrrrqqHtVdQN2C34hYDfgj8JXMfKF2W2Ym1VDmDpGZ52Tm0Mwc2qdPn44qVpLelkMOOYQddtiBRx99lP79+3Peeedx1FFHMXHiRAYPHszBBx/MiBEjiAh+85vfMGHCBL7//e8339aoaSVogCuvvNLgV5IkdbqzzjqLzTffvPn1oYceyiOPPMK///1vXn31Vc4999zmbfPnz+eEE05gt912q0dVF1KX1Z4jYiWqwPfSzLy6JD8bEX0zc1oZ1tz0rW4qsEHN7v1L2lRgpxbpt3dmvSWpI1122WWtpl9yySULpX33u9/lu9/9bptlTZw4scPqJUmS1JopU6bwl7/8he985zv88pe/BGCvvfZq3r7ddtsxZcqU5te//vWvOeCAA1pdwLMe6rHacwDnAQ9n5i9rNl0HNK3YPBy4tib98LLq8/bA82V49E3AbhGxVlkZereSJkmSJEnqYF/5ylf46U9/SrduC4eRc+fO5eKLL2aPPaplmKZOnco111zDscce29XVbFM9hj1/APg08JGIGFseewGnA7tGxOPAR8trgBuAicAE4A/AFwAycxbwA+De8vh+SZMkSZIkdaDrr7+eddddt83FNb/whS+w44478qEPfQioAuWf/OQnrQbK9dLlw54z8y6grZtQ7tJK/gS+2EZZ5wPnd1ztJElSrYiYBLwIzAfmZebQcrvBK4ABwCTgoMycXUZ3nQXsBbwCHNF0hwdJ0vLtH//4B9dddx033HADr732Gi+88AKHHXYYl1xyCaeeeiozZszgf/7nf5rzjx49moMPPhiAmTNncsMNN9CjRw/23XffOp1BnVd7liRJy4WdM3NIZg4tr08Ebs3MgcCt5TXAnsDA8jgG+F2X11SS1Cl+/OMfM2XKFCZNmsTll1/ORz7yES655BLOPfdcbrrpJi677LIFenmfeOIJJk2axKRJkzjwwAM5++yz6xr4Qp0WvJK04tn2+IvqXQWJMT87vN5VaBT78NaikyOoFpw8oaRfVEZt3R0RvZoWs6xLLSVJne7zn/887373u9lhhx0A2H///fne975X51q1zuBXkiQtSgI3R0QC/5OZ5wDr1QS0zwDrlef9gMk1+04paQsFvxFxDFXvMBtuuGEnVV2S1Bl22mkndtppJwDmzZu32PwXXnhh51aonQx+JUnSonwwM6dGxLrALRHxSO3GzMwSGC+REkSfAzB06NAl3l+SpCXlnF9JktSmzJxafk4HrgG2A56NiL4A5ef0kn0qsEHN7v1LmiRJdWfwK0mSWhUR74yI1ZueA7sB44HrgOEl23Dg2vL8OuDwqGwPPO98X0nSssJhz5IkqS3rAddUdzCiB/C/mXljRNwLXBkRRwNPAgeV/DdQ3eZoAtWtjo7s+ipLktQ6g19JktSqzJwIbN1K+nPALq2kJ/DFLqiaJKmdGvWOG0tzBweHPUuSJEmSGp7BryRJkiSp4Rn8SpIkSZIansGvJEmSJKnhGfxKkiRJkhqewa8kSZIkqeEZ/EqSJEmSGp7BryRJkiSp4Rn8SpIkSZIansGvJEmSJKnhGfxKkiRJkhqewa8kSZIkqeEZ/EqSJEmSGp7BryRJkiSp4Rn8SpIkSZIansGvJEmSJKnhGfxKkiRJkhqewa8kSZIkqeEZ/EqSJEmSGt5yH/xGxB4R8WhETIiIE+tdH0mSVnS2zZKkZdFyHfxGRHfgt8CewBbAIRGxRX1rJUnSisu2WZK0rFqug19gO2BCZk7MzDeAy4F96lwnSZJWZLbNkqRlUmRmveuw1CLiQGCPzPxMef1pYFhmHtci3zHAMeXle4FHu7SiWpzewMx6V0JaDvi3sux5d2b2qXclliXLcdvs39eCfD8W5PvxFt+LBfl+LGhZeD/abJt7dHVN6iEzzwHOqXc91LqIGJ2ZQ+tdD2lZ59+KGsmy1jb797Ug348F+X68xfdiQb4fC1rW34/lfdjzVGCDmtf9S5okSaoP22ZJ0jJpeQ9+7wUGRsRGEfEO4GDgujrXSZKkFZltsyRpmbRcD3vOzHkRcRxwE9AdOD8zH6xztbTklplhb9Iyzr8VLfOW47bZv68F+X4syPfjLb4XC/L9WNAy/X4s1wteSZIkSZLUHsv7sGdJkiRJkhZruR72LHWViAggADLzzTpXR5IkSdISctiz1EJEdKMKdN9M/0CkRYqIaPo7qX0uLS/KolxbA9tQjYi7OjOfrW+t6iMiVgK2ArYDXgL+nJlz6lqpOoqIVYAtgKczc5r/4yAi3gccBFyRmWNX5PckIjYBPgE8D1yXmfW+t23d1PzvGAx8GPjfzPxbfWvVOoc9a4UVle4t0zPzzcycX/vPPCIGRsQuEfGbiHhv19ZUWnbVBL7dMzPL39UmEbF9CSqkZd1w4GyqAHhb4FsR0bO+Vaqbz1G9F1sBuwE/qG916iciNgL+VR5fKMkr7Pfm0jEAsDtwDPCBOlan7iJiPeBkqv8ZHwa+V98a1d0XgTuBIcAjwMS61mYRHPasFUL5p51Uox3ehOYv7fNb5FsV2BnYEZgEXAjMpbpNx4PAXcCsrqq3VA9lmH+3zJzf2raagHcDYAAwKTMnR8R/A5+nutXNY8ADwBtdVnFp6ZyXmX8AiIh3ATcCawLT61qrLlb+7v8KXJSZL5QLvb+IiAGZOam+tauLacCngc2obtcF1feIFVJmvll6OrcATi8/oRoptyK+L9sBAzJzx4joD3w/Ig7KzCvrXbE6eRwYmZlfrXdFFsfgVw2nqTe39ot7zTzd2t7czYEDqYLamzPzJWAf4IPA34EdgKOproJPBh7JzDO74BSkLtE0xL9lkNvywlBErAG8m2ro33Pl9TeBjwBPAv+JiNOAR6mC4V0y85muOQvp7Slf6lcCDqHq1boxM1eowBea/+7/U5P0LFXgt0IOAc/M14B/R8RqwIYlbUVf82NTYDYwBvh/sEK/J92Ap8rz54C7qQLiFTX4fQAYEhGHUv2e3JiZ99S5Tq0y+NVyLSK2BWZnZvPwijZ6q3YCNqKa07U31VXLdcrjc8DawLnATlT/zGYB21PNXfgL8A9grVLWSpk5t5NOSepwEbEy1e/4C+UiD7Dgl5amHt1yBbsfcCgwFbgZ+F3Jdj9wLNXfxfqZ+f8i4gPAL6l6Sa6gCqaf8e9Ey5PMnFt+l2cAgyPifZl5f73rVQ81ozuOBa6nGv20InsC6BkRq2fmi/WuTJ0dQ9UL3hd4s0x3Weg71wpiPvBqRKycma9GxHRg2xX196SM/noU+CjVRbSfR8T3M/OWOldtIQa/Wm60sajCLsBzETEJWCUzX46I/aiGXr5ONZztWmBXqn/YnwP+DzgDuD4zvxIRXwR2jYjrqHq3NgFWB34D/D0zp0TEw1TDn6DFUGlpWdPKom3rAfsCl1AtYtO0OMUuwJbAjMy8MCLeTTXs8e9Uw/63AT6VmduUff5ThjpvBuwVEaOprnj/DbgrM2dGxBsRsXZmOj1Ay43SvnyuPP8CcGBEPFx6/1Yo5SLYplS94Mdn5rx616nOZgEvUP2v/OeKusBTGS33OnAEMIhqlNxtEXF0Zj5ez7rVyWRgJaA/MIFqis/LQC9ghQt+i70z81WAiHgZ+GhE3Lms/R81+FXdlWHKGwHPZ+aMiOjW2jCamnmG61IFqVPLft+iWmjgFyWAPYwquF0dODwi5gA3AHtk5t/KkM3rgFdK0WPKPvOo5izcm5kX1dRvTar5i1uUeqyoQ3y0jFnE38oCPbpAT6or9rtFxKtUi7cMA74MjKK6Wr1OZv4iIp6mGuJ/VkR8EPhJRLwzM1+mWsRiE6q243LghMx8veY4AFOoFgC5pa36ScuaFsHMy8D6lNvbrUhqArsvABdk5r31rlO9ZeYbEfEI8K7yeoULfIt3UI0g2oeqZ+9+qk6Ex1fQ//X/oWoLP0AV/G4KrJSZk+taqzpqCnyLOVSjxJY5Br+qm4jYGzgJeCfVULOLI+Li2qGSJfDcimq1xQ2A31N9KRkN/AG4A9gcOKz00O4L9MvMG0tQ/U6qHt/vUAXDUF25nEk1NxGqeQprU/Xo/h/w4zL0c2OqL/rHUfWC3RwRq2ZmU9Asdbrye3wYsAfQm2pY/vcyc2orQe47M/OliNgN+DrV7/VIquHI86iuVF+QmdMj4nTg8MwcExGDgT9ExB+o5vdNKcU+U15vQvV38izVBae/UN3q4uiIuItqZMXLVH+fj1H9zd7CirkIipYzEdGD6oLq1lTtzEeBq1p8kVshlF7fA4BPUv1b2Znqf8e3V8R50AARsQ7VAmg/i4j3A5dn5rg6V6vLlXPeo+l1RJxKNfwZVsD/9aWtvR3Yv4yI+n9Uo6tWyNv+le8gfammDG5A9b3g8mWt1xcMftXJmlZZbvlPICK2pFpM6luZObLkGwD0KY3tOzPzHGAg8EPgs1RfwJ/LzN1rynkQOIpqPk5QXWlqWmgnqL7s756ZsyLizYjol5lTI2ImMDQi1iirWq4FbJaZd0TE14CPUwXcZwMPlyDj2E54iyQAImJQZj7YyqY9qIYf3kB1pX1loF9EvELVuPyxzLn6OTA1Is6k+l09g+q2A5TpAH+iWpW5aQGKl6h6hMnM8WWExECq3t33lDxzqIb8vZcq+J0K7JCZF0TEl6hubfBfVAFv0yIfB5eekhWu8dfyKTPnRcRWVKMjxgLnUc11X1GtS/W3/gzVheZ7VuDAdxWq/23dqUaMjWMZvoVLZyvfs4IqfhgBvAordG/4hVQXpPelWjfmr7Bivh/lwtlWVN8/xlC9HzfVt1atM/hVp2rqmWrli/DWVLdSGflW1pxYrsD3o2p8oVo9dg7VHIrHgZ0i4uySfm9m/jMiEuifmf+JiPHAoIjYMDOfiojdqRorqOZmbEvVqM+kCoxXp5rL89FSJiU4WGiFurYCeWlptVgs5N9lkaiWc8pPoxpefFOLfQcBXwL+DTxM9Tu9KtUKlGtSrVzbkypofYLqti3vbfrboApYP0I1h20tqr+zbiXvHlHdo/dVqiC56d7W11MF3t0y8yGq4HcBmflG+enfiZYbmXkNcE2967EsyMzf8dYidyu00vu/S73rsawo/9eTan7rCnsRoEmZD38zK/bFsmaZeSPVreKWaQa/ardyBXTLzBxVAsFWV4utybsJ1ZfxD2TmGU3by0I776X0SpVysmyfFxFPAQOiWkHvxYh4jSq4HRUR21ANu9wTOKf0Ej8BbBIR/ylDny+gWmWuL9U/6C+Vw3wbeKgc71bg1lLX7pn5QItzbbqZe3OwuwLOZ1EHae3vpbyuDXSnUA3hH980fyoi3kN1kWZiKac2WJ5FFcBuQhX8TqFaoGpNqt/5Hajm9X6n/E38i+qiU9N9d68HPlVGOgwBZmbmveUC1FNA99JjfArwfKnvMnnbAkmSpPYw+FWbmoJdYHT50t4b+BgwqkXQuymwTtMX44h4L9Xc2elU9wDdOyLObloYJ6tbSnQveVfNzFda9Aw/R9UDtQFVb28A74lq+fhnyvDM56gWF+gF/A/wXaoFr47MzB9FxDCqq5PjMvP1EjRc0eL8otRnodWbDXS1JErP6WCq+/wNAsjMsU3bW/y9NN8CKCI+T7Xo1HVUIxO2BsZTDbFr+pt7imqhlcdLWpMXqf7GBpbXU6mGQQ/IzDGlHCLiMaqLRX+guj/jyIgYRTWX/XHgM1S38rq51PVfVIFyU93nLP07I0mStOww+F3BRcR7MvOx8nyBYb1Z3bfsB01zbLO6h9eIqO4Z2hM4n+rL9HuAXhHx1dKjejpwWmZeEREfpRpqPJCqR6up5+pRqknx6wMTanqMe1F9iV8TGBYR/6H64j8YWD0ivgwMp+r1Oj+rVQaDasGrV0oZ0bKHqinAjZoVCR2WqSVVM3qhaWRAU2DbF+hTLuxsC6wVES8AL2W1uNQBVEOE1wUujIgRwGpUvbPHUY1I+GB5fSlvrTL7LNU9NgdRjZToGRGvl9/doFptcm+q+b1vAB8CLomIjYAzgTWAVYAvl1EVZ1EtgDU+q/v93ke1qqskSVLD67b4LGo0tV/cgRMj4p1QfYkvX+xXjYj1So/udhHxz4gYXfYbCeyUmc9TrWw3PjN3pOp93bf0Fq/EWz1UI6kW0Nmm6fDl59VUy+Z/KyJ6lsdBwD5lLuHlwDfK/v+iCghmA7/KzIGZOSwz/6fUO2sC3241gfRCt6mwR1ftEZU1I2LV8nqTciGn9m/lzTI0+R3A08CYiNiE6mLQqVS/uweVkRCbAYcDQ6ku5hxPFTAPyMxbM3MaVQC7Q6lC0+/pJOBeYHhErJWZr5W/0Q+W4/wV6B3VipPf4a0Vlp8DLqYKrD/QdDEoM5/OzJsyc2pnvG+SJEnLMnt+G0xUtwYaRDWHbyuqOYFntdbzWfw31RBLIuJYqi/ofYCzqO7/+TDV3Ngfly/6d1P14t5Ytjfdluihkr4J1Yq076ca+vwOql7iTUu+LHV4MSJOAw6lCm5XoerFOq/k+zPw16bFc2o03Ty7GxAthyzXnps9u2qPcpHkQODVzLy+JK9HtQjaKKq/oV5UQ4fXKIuqrULVy7ol1S2xrqVarXwy8Ceqv4NTslpF+SNUtx3aEtiQqjf2EqohyxvVVGUSb902Yj40j1i4tAzj/3VErF3q9gzwxTLH/bNUIyXGZ+aLNeVd9TbfGkmSpIZi8Nt4HgWmUfUAjQNuycz5EdEbeDOrW/7sDQzNzFOolvC/KyJ+RrUI1Y8y889NhZUepW751r1tH6AKrKEKcjcrz5+h+sLem+reuydHxBCq3qnuvHVP3awZdjwxIn4E/CYzZ9eeRMnzRglMoiaNls+lt+ldVD21L0bEk5n5b6ohyftRjWb4B9Vw5WOAA6hubXAr1f2jH8nMXcsibl+lutgzmuoizbuo5t0+RTXK5gfAs5k5E5qD7hciYqfMvB3YDnhXlNtvNVWuDLX+UkTsSjWq4oHMbLoPL2WkhCRJkhbDYc+N52Gqe+d+MzN/l5kTSvopwG/KsMwdKL29VEMq+1IFrt2AUyLiM2UVZXirF7fJGN7qxR3PW4vtzCxlDs3MW4BfAL+n6h0bSysBbHk9vynwjYhuLYZkNw1pftNgV52oL9VqxlcAJ5e01XhrTvpEqmH3fwBOKheN7qZaZbxpYag3gRnABmUxq9eBdQDK3+BzwGqZOTMiVomI/an+3o4Dji9z23tRrcC8Vm3lyjDnyMxbMvOG2sBXktSxIuKUiPhGHY67U0Rcv/icbe7/7Y6sz1LW4StN04WkZZU9v41nDPDF0hO1N9UiOL/MzOPKsOaLqObgfqvkvwf4dvmC/WOqW60MBf4rIv4f1aJWp9SUPxbYMqrboTxBNeQTquD3z+UnVHMRb6JasflbVPMPF8kAV3Uyn6pH9Vzg8xHx7swcWxZX2yYzrwWIiJlU97htWqH8JWBu0+rNZXvPiOhJ1du7cfkbGgt8BTguIn5LFVjfANyVmbdHxETg+TKPvlUO4ZckLca3gR/VuQ5foZrW88pi8kl1Y89v47mV6nZEH6Kaq3gx1f0/oQp8VwI+mpnTS9oDQJ8SLD+XmSMz86dUc2/XLgvxjIyIcRFxV2Y+C1xAtfBPc69w6aG9NTPHlXLfT7Wo1W9LPf7ZqWctLb33AxeXocY/pwqAewKvAetGxAYl3xyqwLXJNKr5t+8sr1+g6rXdDDi7/LwE2KtMJfg28InM3Cwzv9b0N5iZTy0q8JUkda6I+E5EPBYRd1FNAWta6PDGiBgTEXdGxGYl/cKI+H1ZCPSxMpWMiOgeET+LiHsj4oGI+FxJ3ykibo+IqyLikYi4tGlBzojYo6TdB+xfU593RsT5ETEqIu6PiH1K+hERcXWp1+MR8dOSfjqwSkSMjYhLF3Geh5e6jYuIi0vagIgYWdJvjYgNa87zwJp9X1rU+UTEl6hGS90WEbd10EcjdTh7fhvPw8D0zPxmK9tWpeoJHhER1wAHZObUqO5RuiHV7Yp+TLXg1WvAkWW/71EN2ZwMkJlfrylzgS/t8daCWv/IzD068sSkTvIR4NWI+AGwC9W9dv9IdY9qeGuKwCSqkRGHR8SNVPN6N6JabGoO1UiIS4CZZV7v8NqDNP39SJKWHVHdnu5gqvVMelDdAm4McA7w+axuqTiM6qLmR8puA6jWadiEKtjblGrB0Ocz8/3lAuo/IuLmkv99VIuRPk11X/UPRMRoquk0HwEmUE29afIdYGRmHhXVLSBHRcTfyrYhpbzXgUcj4teZeWJEHJeZQxZxnoOA7wL/r0zBWbts+jUwIjNHRMRRwK+AfRfzti10Ppn5q4j4GrBz09oW0rLI4LfBZOak8k+3NV8Hrs/M0yPiHKp/5J+nWpW5J9ViWV8CnsjM12vKnE11myHgrVsItTYUs2YxK4cwa3kxHxhGtfjbd4BPUH0RupyqYb84Iv5KtXryOKoV0ucB11Ctdv4UVD24Tc8lScuNDwHX5Fu3TLwOWJnqdo7/F2/dNbH2u9WV5XvO42XqymbAbsBWNb2la1Kti/IGMKppvYaIGEsVPL9E9X3r8ZJ+CdXCipSyPlEz93hlqk4KgFubRgtFxEPAu6nuNLA4HwH+rykwzcxZJX0H3up1vhj4aTvKau187mrHflLdGfw2pucj4hSqXtkPUg3H/BHVAjxXljwnUG5TlJnH1ez7CCw2wHX+oRpCRKxB1YN7TGb+s6RNAr5GdUX/XKq5S/+k+pLyWEmTJDWubsCcRfSktvwelFQLe/53Zt5UuyEidqLqpW0yn8V//w6q0XmPtihr2FKUtbTmUaZHRrUY6TtqtnVVHaQO55zfxnQ1VdC7CtVVvO9m5uOZ+dnMvC8iemTm7Mx8qWmHaH2VZYNcrQjWBp6IarXxAJ7MzP/OzCvKfNzTM/OOsoqzJKmx3EF1W7tVImJ14ONUFz2fiIhPQtUhEBFb1+zzydJmbEK18OejVIt8HhvVGipExHsi4p207RFgQCkD4JCabTcB/10zN/h97TiPuU3HbsPIUu91SplNw57/STXaCeBQ4M7yfBKwbXn+Cao1YxbnRWD1duST6sYrNQ0oM7/W1rYyJ3deK/s4TFkrnLLI1YEtk+tRF0lS1yudAldQTWuZTnULSKgCwd9FxHepAr/LSx6opriMAtagmhf8WkScSzX8974StM5gEXNnyz7HAH+JiFeogs6mwPEHwJnAA6Vz4gmqO3gsyjkl/32ZeWgrx3swIk4D/h4R86kWLT2CairPBRFxfKlz03ovfwCujYhxwI3Ay4s5flMdboyIpzNz58Xmluog7NxrTFHdiiipOnENbCVJkt6miLiQav2Uq+pdF0lLzp7fBtVa764kSZIkrajs+ZUkSZIaQJnTe2srm3bJzOe6uj7SssbgV5IkSZLU8FztWZIkSZLU8Ax+JUmSJEkNz+BXWs5ExLdrnveKiC8sJv8/y88BEfFfNelDI+JXHVy3L0XEwxFxaUQcERG/6cjyJUmSpKVl8Cstf75d87wX0GrwW253RWb+v5I0AGgOfjNzdGZ+qYPr9gVg19buMShJkiTVk7c6kpZhEfEnYANgZeAsYGNglYgYCzwIdAc2Ka9vAf4C/ACYDWwGvCciXsrM1YDTgc1L3hFUN7j/RmbuHRFrA+eX8l8BjsnMByLiFGDDkr4hcGZmttpbHBG/L/n+GhHnlzo0bfs48F3gHcBzwKGZ+WxE9AH+F1gf+BewK7BtZs58e++cJEmStCBXe5aWYRGxdmbOiohVgHuBDwNPlmCWiBgAXJ+Zg8vrnagC4MGZ+URJeykzVyvbvpGZe9fkbQp+fw3MzMxTI+IjwC8zc0gJfncDdgZWBx4F3pWZc9uo7yRgaGbOjIgjyvPjImItYE5mZkR8Btg8M79ehkVPzcwfR8QewF+BPga/kiRJ6mj2/ErLti9FxH7l+QbAwHbsM6op8F0CHwQOAMjMkRGxTkSsUbb9JTNfB16PiOnAesCUJSy/P3BFRPSl6v1tqt8Hgf3KcW+MiNlt7C9JkiS9Lc75lZZRpWf2o8AOmbk11TDlldux68sdXJXXa57PZ+kumv0a+E1mbgl8jvadhyRJktRhDH6lZdeawOzMfCUiNgO2L+lzI2Kl8vxFquHI7bGovHcCh0Jz0D0zM19Ymkq3YU1gank+vCb9H8BB5bi7AWt14DElSZKkZga/0rLrRqBHRDxMtVjV3SX9HOCBiLg0M58D/hER4yPiZ4sp7wFgfkSMi4ivtth2CrBtRDxQjjW85c5v0ynA/0XEGKB2Pu+pwG4RMR74JPAMVZAuSZIkdSgXvJJUNxHRE5ifmfMiYgfgd5k5pM7VkiRJUgNywStJ9bQhcGVEdAPeAD5b5/pIkiSpQdnzK2mJRMQ6wK2tbNqlDMOWJEmSljkGv5IkSZKkhueCV5IkSZKkhmfwK0mSJElqeAa/kiRJkqSGZ/ArSZIkSWp4Br+SJEmSpIZn8CtJkiRJangGv5IkSZKkhmfwK0mSJElqeAa/kiRJkqSGZ/ArSZIkSWp4Br+SJEmSpIZn8CtJkiRJangGv5IkSZKkhmfwK0mSJElqeAa/kiRJkqSGZ/ArSZIkSWp4Br+SJEmSpIZn8CtJkiRJangGv5IkSZKkhmfwK0mSJElqeAa/kiRJkqSGZ/ArSZIkSWp4Br+SJEmSpIZn8CtJkiRJangGv5IkSZKkhmfwK0mSJElqeAa/kiRJkqSGZ/ArSZIkSWp4Br+SJEmSpIZn8CtJkiRJangGv5IkSZKkhmfwK0mSJElqeAa/kiRJkqSGZ/ArSZIkSWp4Br+SJEmSpIZn8CtJkiRJangGv5IkSZKkhmfwK0mSJElqeAa/kiRJkqSGZ/CrhhcRv4+IkzqorA0j4qWI6F5e3x4Rn+mIskt5f42I4R1V3hIc94cRMTMinuni4+4UEVO68piSJElaMRn8arkWEZMi4tWIeDEi5kTEPyPi8xHR/LudmZ/PzB+0s6yPLipPZj6Vmatl5vwOqPspEXFJi/L3zMwRb7fsJazHhsDXgS0y811deezO1p7PVJJWJF4Qbtdx63JBuD06ul2LiAcjYqeOKq+dx1zg90bqSga/agQfz8zVgXcDpwMnAOd19EEiokdHl7mM2BB4LjOnd+ZBGvj9W8iKdK6Slh1eEH77lqULwhFxYUT8sDOPkZmDMvP2zjxGy9+ljvy9aeN474iIq8pxs2VwHxHHR8T48nfyREQc3xn10LLJ4FcNIzOfz8zrgE8BwyNiMCzYeERE74i4vnwpmBURd0ZEt4i4mCoI/HO5GvnNiBhQ/mkeHRFPASNr0mqDm00iYlREvBAR10bE2uVYCw3pbWoAImIP4NvAp8rxxpXtzVfNS72+GxFPRsT0iLgoItYs25rqMTwinipXqL/T1nsTEWuW/WeU8r5byv8ocAuwfqnHhW3sv09EjC3n+J9SfyLiyIh4uDQgEyPiczX77BQRUyLihHL1/IKIWKV8HrMj4iHg/e35bCNig4i4utT/uYj4TUnfJCJGlrSZEXFpRPQq2xb6TEv69uUL4ZyIGFfbKEbERhFxRzmfv0XEb2u/jEXEJ6K6Sj6nfFabt/hsT4iIB4CXS+P6xxbn8auIOKs95yxJS8kLwm9Pl1wQVqe7CzgMaK33PoDDgbWAPYDjIuLgLqyb6ikzffhYbh/AJOCjraQ/BRxbnl8I/LA8/zHwe2Cl8vgQEK2VBQwAErgIeCewSk1aj5LndmAqMLjk+SNwSdm2EzClrfoCpzTlrdl+O/CZ8vwoYAKwMbAacDVwcYu6/aHUa2vgdWDzNt6ni4BrgdXLvo8BR7dVzxb7bgc8D+xKdcGsH7BZ2fYxYBOqhuTDwCvANjXlzgN+AvQs9TwduBNYG9gAGL+oY5dyugPjgDPKe7wy8MGybdNSr55AH+AO4My2fj9K3Z8D9irnsmt53ads/xfwc+AdwAeBF2o+z/cAL5d9VgK+WT6fd9Qca2w5r1WAviV/r7K9BzAd2Lbefzc+fPhozEfL/3klbTvgTWBweX0hb7WJvYHrgTnArPL/uRtwcdnnVeCl8v+uqd05mqqNvYPW28QfA6PK/89rgbXLtp1a/r9vqi9VAPIGMLccb1xNeU1tYjfgu8CT5X/pRcCaZVtTPYaXus0EvrOI92nNsv+MUt53S/kfLef8ZqnHha3suxMwpbwn04FpwL5U7cpj5X38dk3+nsCZwNPlcSbQs0VZX68p68iy7ZjyfrxR6vLnmvfsG8ADVG3zFcDKi/o82/s7Q/W95Mry3rwIPAgMrcl7IvCfsu0hYL8WZX0WeLhm+zYs+nepB1WHxegW5XwVuK7m/ft5+VyfpfoOt8oS/E1MAXZaTJ5fAb9uR1kJfAF4vJzjD6i+A/2T6vf9Ssp3gpr3Y0L5LK4D1m9R1udLWXOA31K+j/ro3Ic9v2pUT1MFWC3NpQpK3p2ZczPzziz/hRbhlMx8OTNfbWP7xZk5PjNfBk4CDoqOmcdyKPDLzJyYmS8B3wIObnG1/dTMfDUzx1EFiFu3LKTU5WDgW5n5YmZOAn4BfLqd9TgaOD8zb8nMNzNzamY+ApCZf8nM/2Tl78DNVBcUmrwJnJyZr5f37yDgtMyclZmTqRqcxdkOWB84vnwOr2XmXeX4E0q9Xs/MGcAvqYLwthwG3JCZN5RzuQUYDewV1VC39wPfy8w3yjGuq9n3U8BfyvHmUjXGqwD/rybPrzJzcvlMplF9Ofxk2bYHMDMzx7TjnCWpQ2TmKKoA4EOtbP562dYHWI9qRFJm5qepgo2PZzU89ac1+3wY2BzYvY1DHk518bYv1QXQxf6fz8wbgR8BV5TjLdSWAUeUx868dVH4Ny3yfBB4L7AL8L3a0Tkt/JoqAN64nM/hVEHn34A9gadLPY5oY/93UV2I7Qd8j+pC9GHAtlTv80kRsVHJ+x1ge2AIVRu9HVWwXVvWmqWso4HfRsRamXkOcCnw01KXj9fscxBVm7IRsFV5X6CNz7ONc2jLJ4DLgV5UbWDte/yfcn5rAqcCl0REX4CI+CRV8Hw4sEYp57nF/C4B/Bl4b0QMrEn7L+B/y/PTqS4+D6G64N30nneIiIhyTg+2c5fdqT7n7akC+XOoPvsNqDpCDinlfoTqQtBBVH8LT1K9r7X2pvresVXJ19bflDqQwa8aVT+qK20t/YzqKtzNZZjuie0oa/ISbH+Sqlewd7tquWjrl/Jqy+5B1aA1qR3O8wrVl4GWepc6tSyrXzvrsQFVg7eQiNgzIu4uQ8jnUF35rj33GZn5Ws3r9Vn4/WrP8Z/MzHmtHH+9iLg8IqZGxAvAJSz6vX838MkybHlOqfMHqRqm9YFZmflKTf7aui7weWTmm2V7vzbyA4ygahQpPy9eRN0kqbN4QbjogAvCUL1vp5ULoZdTtTtnlfIepOr1bDr2ocD3M3N6uUh7aotjzS3b52bmDVS9o+9dzPF/lZlPZ+YsquBxSE1ZS/p5tnRXuUA8n6rNan4PM/P/ynHfzMwrqHottyubP0MVqN9bLohPyMzFtvGlzb2Wt4LGgcBmwHUlMD0G+Gq5aP4i1UWSjhyifApVPHRBO/P/NDNfKJ/zeODm8jv5PPBX4H0l36FUHQf3ZebrVL+vO0TEgJqyTs/MOZn5FHAbb32O6kQGv2o4EfF+qoDkrpbbSsP09czcmOqq5NciYpemzW0UubiGY4Oa5xtSNT4zqYa8rlpTr+5UV2PbW+7TVMFabdnzqIb9LImZpU4ty5razv0nUw3rWUBE9KQa5v1zYL3M7AXcQDUEuknLc5zGwu9Xe46/YRvzy35UjrFlZq5BFWAu6viTqb6Y9ap5vDMzTy91WzsiVq3JX1vXBT6P0ihvwILvY8vj/QnYKqr553tTXcWXpK7mBeG3vN0LwlD1aDYt1tR0EaC2bX615tit1Xv9FmXVXtxtq9612jrPpfk8F1f2yk3tb0QcXtb/aLp4PJi3Pts2L5S3w/9Sgl+qXt8/laC4D9X3qDE1x7yRBb9LLbWIOI6qp/pjJUBtj5afc7s+93LB5jkW/D1rz++rOpjBrxpGRKwREXtTXYW9JDP/3UqevSNi0xK4PA/MpxqaC9U/sI2X4tCHRcQWJWj6PnBVaRQfo2o0PhYRK1ENc+pZs9+zwICoWYWzhcuAr5ZFmFbjrSFhC/WALkqpy5XAaRGxekS8G/gaVS9pe5wHHBkRu0S1SFa/iNiMal5sT6o5U/MiYk9gt8WUdSXwrYhYKyL6A//djuOPogpMT4+Id0bEyhHxgbJtdaqr5M9HRD+g5YqNLT/TS4CPR8TuEdG9lLVTRPQvV6hHA6dEtVLkDkDtMLMrgY+V92ElquFlr1PN9WlV6fW+iqphH1Wu7kpSl/GC8ELe7gXhJdVavZ9u575L1Gu7mM/zbSnfHf4AHAesUy54j+etC86tXihvqtpiir8F6BMRQ6iC4KYhzzOpAspBNRes18zMtx0kRsRRVHOYd8nMKYvLvxRaXjB/J7AOnfd7pnYy+FUj+HNEvEj1j/c7VPM+j2wj70Dgb1QB07+AszPztrLtx8B3y9XFbyzB8S+mWkDkGao5QF+CavVpqoURzqX6Z/cy1VycJv9Xfj4XEfe1Uu75pew7gCeA12hfsNia/y7Hn0j1Beh/S/mLVeaLHUm14NTzwN+phlS9SHWuVwKzqa7WXtdWOcWpVFdCn6CaH7zYYcAleP841Vyfp6jew0/VlLdNqddfqBYFq7XAZ5rVPON9qOZBzaD6nTmet/4XHgrsQHV19odUC4m8XurxKFXP8q+pGuSPU81hemMxpzAC2LI95ypJHcULwq3rgAvCS+oyqnaoT0T0ppqv2t5jLdFnsJjP8+16J1UQO6Mc60iqnt8m5wLfiIhto7JpeW9hMedRho//H1XP9dpUwXDT9KI/AGdExLrluP0iYrFzYyOiZ0SsXF6+o1zsjrLtUKrfn10zc2L7Tn+JXUbVcTCkjJT7EXBPGWavesplYNUtHz58+FgWH1TB76lvs4wNqYYzrVHv8/Hhw0djP6hW7n2VaiXa56ku8n4R6F6T50LeWu35q2WfpouzJ9Xk24fqguMcqtWFB1CzsnPJs0AaC6/2/Gegd03+I6hG8kwvZU7irZWG16G6ODsbuK+mvNrVnr9HddFyBlUAuVZr9Wi5byvv01pl/6aLoN+jrIrM4u+AsMB2qqHXCQyoSbsLOKw8X5lq0a9p5fEr3lqdeaFjtXhPBlLdRWAO1VDgBbaX16fw1l0J2vw8F/M70+pdKFr5fE+jGj4/k6qj4e+17zHV6sWPUnUwjAfetwS/Sx8qab9tUb+VqQLHiVS/Uw8DX2rneWWLx4Cy7QneWlm86fH7dpSZwKYtPucjal7/EDi3xfvxn/KeXQ/0X0RZF1L+Ln107qPpFi+StMIrwwNnUTWMu1HN2d0hM+9fyvK6UX1BWCMzj+qoekqSJGnJNeoNyiUtR6K6zdBDbWzeIrturuy7qIZOr0N11fzYtxH4vpNqqNeTVLekkCRJUh3Z8ytJkiQ1oGXo4nKHiohvU63f0dKdmbnnUpb5IarbFS0kO2CRLS0bDH4lSZIkSQ3P1Z4lSZIkSQ3P4FeSJEmS1PAMfiVJkiRJDc/gV5IkSZLU8Ax+JUmSJEkNz+BXkiRJktTwDH4lSZIkSQ2vR70r0NV69+6dAwYMqHc1JEkNYMyYMTMzs0+967G8s22WJHWURbXNK1zwO2DAAEaPHl3vakiSGkBEPFnvOjQC22ZJUkdZVNvssGdJkiRJUsMz+JUkSZIkNTyDX0mSJElSwzP4lSRJkiQ1PINfSZIkSVLDM/iVJEmSJDU8g19JkiRJUsMz+JUkSZIkNTyDX0mSJElSwzP4lSRJkiQ1PINfSZIkSVLDM/iVJEmSJDU8g19JkiRJUsMz+JUkSZIkNTyDX0mSJElSw+u04Dcizo+I6RExviZt7Yi4JSIeLz/XKukREb+KiAkR8UBEbFOzz/CS//GIGF6Tvm1E/Lvs86uIiM46l65y1llnMXjwYAYNGsSZZ565wLZf/OIXRAQzZ84E4Nprr2WrrbZiyJAhDB06lLvuuqs571NPPcVuu+3G5ptvzhZbbMGkSZO68CwkSep6r732Gttttx1bb701gwYN4uSTTwbg1ltvZZtttmHIkCF88IMfZMKECQvs98c//pGIYPTo0c1pDzzwADvssAODBg1iyy235LXXXuvSc5EkdY7O7Pm9ENijRdqJwK2ZORC4tbwG2BMYWB7HAL+DKlgGTgaGAdsBJzcFzCXPZ2v2a3ms5cr48eP5wx/+wKhRoxg3bhzXX399cwM9efJkbr75ZjbccMPm/Lvssgvjxo1j7NixnH/++XzmM59p3nb44Ydz/PHH8/DDDzNq1CjWXXfdLj8fSZK6Us+ePRk5cmRz23jjjTdy9913c+yxx3LppZcyduxY/uu//osf/vCHzfu8+OKLnHXWWQwbNqw5bd68eRx22GH8/ve/58EHH+T2229npZVWqscpSZI6WKcFv5l5BzCrRfI+wIjyfASwb036RVm5G+gVEX2B3YFbMnNWZs4GbgH2KNvWyMy7MzOBi2rKWi49/PDDDBs2jFVXXZUePXrw4Q9/mKuvvhqAr371q/z0pz+ltnN7tdVWa3798ssvNz9/6KGHmDdvHrvuumtzvlVXXbWLz0aSpK4VEay22moAzJ07l7lz5xIRRAQvvPACAM8//zzrr79+8z4nnXQSJ5xwAiuvvHJz2s0338xWW23F1ltvDcA666xD9+7du/BMJEmdpavn/K6XmdPK82eA9crzfsDkmnxTStqi0qe0kr7cGjx4MHfeeSfPPfccr7zyCjfccAOTJ0/m2muvpV+/fs2NcK1rrrmGzTbbjI997GOcf/75ADz22GP06tWL/fffn/e9730cf/zxzJ8/v6tPR5K0HIqI7hFxf0RcX15vFBH3lClGV0TEO0p6z/J6Qtk+oKaMb5X0RyNi966s//z58xkyZAjrrrsuu+66K8OGDePcc89lr732on///lx88cWceGI16Oy+++5j8uTJfOxjH1ugjMcee4yIYPfdd2ebbbbhpz/9aVeegiSpE9VtwavSY5tdcayIOCYiRkfE6BkzZnTFIZfY5ptvzgknnMBuu+3GHnvswZAhQ3j99df50Y9+xPe///1W99lvv/145JFH+NOf/sRJJ50EVMO17rzzTn7+859z7733MnHiRC688MIuPBNJ0nLsy8DDNa9/ApyRmZsCs4GjS/rRwOySfkbJR0RsARwMDKKajnR2RHRZt2n37t0ZO3YsU6ZMYdSoUYwfP54zzjiDG264gSlTpnDkkUfyta99jTfffJOvfe1r/OIXv1iojHnz5nHXXXdx6aWXctddd3HNNddw6623dtUpSJI6UVcHv8+WIcuUn9NL+lRgg5p8/UvaotL7t5Leqsw8JzOHZubQPn36vO2T6CxHH300Y8aM4Y477mCttdZi0KBBPPHEE2y99dYMGDCAKVOmsM022/DMM88ssN+OO+7IxIkTmTlzJv3792fIkCFsvPHG9OjRg3333Zf77ruvTmckSVpeRER/4GPAueV1AB8BripZWk5XaprGdBWwS8m/D3B5Zr6emU8AE6jW7OhSvXr1Yuedd+avf/0r48aNa57T+6lPfYp//vOfvPjii4wfP56ddtqJAQMGcPfdd/OJT3yC0aNH079/f3bccUd69+7Nqquuyl577WU7KkkNoquD3+uAphWbhwPX1qQfXlZ93h54vgyPvgnYLSLWKgtd7QbcVLa9EBHbl8b28JqyllvTp1fXAp566imuvvpqhg8fzvTp05k0aRKTJk2if//+3HfffbzrXe9iwoQJVJ3n1dCt119/nXXWWYf3v//9zJkzh6Ye7pEjR7LFFlvU7ZwkScuNM4FvAm+W1+sAczJzXnldO8WoeVpS2f58yd/WdKVON2PGDObMmQPAq6++yi233MLmm2/O888/z2OPPQbQnLbmmmsyc+bM5vZ1++2357rrrmPo0KHsvvvu/Pvf/+aVV15h3rx5/P3vf7cdlaQG0aOzCo6Iy4CdgN4RMYVq1ebTgSsj4mjgSeCgkv0GYC+qK8SvAEcCZOasiPgBcG/J9/3MbFpE6wtUK0qvAvy1PJZrBxxwAM899xwrrbQSv/3tb+nVq1ebef/4xz9y0UUXsdJKK7HKKqtwxRVXEBF0796dn//85+yyyy5kJttuuy2f/exnu+4kJEnLnYjYG5iemWMiYqcuOuYxVHd4WOBuBktr2rRpDB8+nPnz5/Pmm29y0EEHsffee/OHP/yBAw44gG7durHWWms1r5HRlrXWWouvfe1rvP/97yci2GuvvRaaFyxJWj5FU+/himLo0KFZey8/SZKWVkSMycyh9a7H2xURPwY+DcwDVgbWAK6huuvCuzJzXkTsAJySmbtHxE3l+b8iogfVIpZ9KLcwzMwfl3Kb8y3q+LbNkqSOsqi2uW4LXkmSpGVDZn4rM/tn5gCqBatGZuahwG3AgSVby+lKTdOYDiz5s6QfXFaD3ggYCIzqotOQJGmROm3YsyRJWu6dAFweET8E7gfOK+nnARdHxARgFlXATGY+GBFXAg9R9SJ/MTO9354kaZlg8CtJkppl5u3A7eX5RFpZrTkzXwM+2cb+pwGndV4NJUlaOg57liRJkiQ1PHt+27Dt8RfVuwrLvTE/O7zeVZAkLaeW53bY9k+Slk32/EqSJEmSGp7BryRJkiSp4Rn8SpIkSZIansGvJEmSJKnhGfxKkiRJkhqewa8kSZIkqeEZ/EqSJEmSGp7BryRJkiSp4Rn8SpIkSZIansGvJEnScuK1115ju+22Y+utt2bQoEGcfPLJABx66KG8973vZfDgwRx11FHMnTsXgOeff56Pf/zjzfkvuOACAMaOHcsOO+zAoEGD2Gqrrbjiiivqdk6S1FUMfiVJkpYTPXv2ZOTIkYwbN46xY8dy4403cvfdd3PooYfyyCOP8O9//5tXX32Vc889F4Df/va3bLHFFowbN47bb7+dr3/967zxxhusuuqqXHTRRTz44IPceOONfOUrX2HOnDn1PTlJ6mQ96l0BSZIktU9EsNpqqwEwd+5c5s6dS0Sw1157NefZbrvtmDJlSnP+F198kczkpZdeYu2116ZHjx685z3vac6//vrrs+666zJjxgx69erVpecjSV3Jnl9JkqTlyPz58xkyZAjrrrsuu+66K8OGDWveNnfuXC6++GL22GMPAI477jgefvhh1l9/fbbcckvOOussunVb8OvfqFGjeOONN9hkk0269DwkqasZ/EqSJC1HunfvztixY5kyZQqjRo1i/Pjxzdu+8IUvsOOOO/KhD30IgJtuuokhQ4bw9NNPM3bsWI477jheeOGF5vzTpk3j05/+NBdccMFCQbEkNRr/y0mSJC2HevXqxc4778yNN94IwKmnnsqMGTP45S9/2ZznggsuYP/99yci2HTTTdloo4145JFHAHjhhRf42Mc+xmmnncb2229fl3OQpK5k8CtJkrScmDFjRvPCVK+++iq33HILm222Geeeey433XQTl1122QI9uBtuuCG33norAM8++yyPPvooG2+8MW+88Qb77bcfhx9+OAceeGA9TkWSupwLXkmSJC0npk2bxvDhw5k/fz5vvvkmBx10EHvvvTc9evTg3e9+NzvssAMA+++/P9/73vc46aSTOOKII9hyyy3JTH7yk5/Qu3dvLrnkEu644w6ee+45LrzwQgAuvPBChgwZUr+Tk6ROZvArSZK0nNhqq624//77F0qfN29eq/nXX399br755oXSDzvsMA477LAOr58kLcsc9ixJkiRJanh1CX4j4qsR8WBEjI+IyyJi5YjYKCLuiYgJEXFFRLyj5O1ZXk8o2wfUlPOtkv5oROxej3ORJEmSJC37ujz4jYh+wJeAoZk5GOgOHAz8BDgjMzcFZgNHl12OBmaX9DNKPiJii7LfIGAP4OyI6N6V5yJJkiRJWj7Ua9hzD2CViOgBrApMAz4CXFW2jwD2Lc/3Ka8p23eJiCjpl2fm65n5BDAB2K5rqi9JkiRJWp50+YJXmTk1In4OPAW8CtwMjAHmZGbTag1TgH7leT9gctl3XkQ8D6xT0u+uKbp2nwVExDHAMVAt+S9JkrQs2Pb4i+pdhaU25meH17sKkrRE6jHseS2qXtuNgPWBd1INW+40mXlOZg7NzKF9+vTpzENJkiRJkpZB9Rj2/FHgicyckZlzgauBDwC9yjBogP7A1PJ8KrABQNm+JvBcbXor+0iSJEmS1Kwewe9TwPYRsWqZu7sL8BBwG3BgyTMcuLY8v668pmwfmZlZ0g8uq0FvBAwERnXROUiSJEmSliP1mPN7T0RcBdwHzAPuB84B/gJcHhE/LGnnlV3OAy6OiAnALKoVnsnMByPiSqrAeR7wxcyc36UnI0mSJElaLnR58AuQmScDJ7dInkgrqzVn5mvAJ9so5zTgtA6voCRJkiSpodTrVkeSJEmSJHUZg19JklZwEbFyRIyKiHER8WBEnFrSN4qIeyJiQkRcERHvKOk9y+sJZfuAmrK+VdIfjYjd63RKkiQtxOBXkiS9DnwkM7cGhgB7RMT2wE+AMzJzU2A2cHTJfzQwu6SfUfIREVtQrc0xiOo2hmdHRPeuPBFJktpi8CtJ0gouKy+VlyuVRwIfAa4q6SOAfcvzfcpryvZdyh0c9gEuz8zXM/MJYAKtrOchSVI9GPxKkiQiontEjAWmA7cA/wHmZOa8kmUK0K887wdMBijbnwfWqU1vZR9JkurK4FeSJJGZ8zNzCNCfqrd2s848XkQcExGjI2L0jBkzOvNQkiQBBr+SJKlGZs4BbgN2AHpFRNNtEfsDU8vzqcAGAGX7msBztemt7NPyOOdk5tDMHNqnT5+OPg1JkhZi8CtJ0gouIvpERK/yfBVgV+BhqiD4wJJtOHBteX5deU3ZPjIzs6QfXFaD3ggYCIzqkpOQJGkxeiw+iyRJanB9gRFlZeZuwJWZeX1EPARcHhE/BO4Hziv5zwMujogJwCyqFZ7JzAcj4krgIWAe8MXMnN/F5yJJUqsMfiVJWsFl5gPA+1pJn0grqzVn5mvAJ9so6zTgtI6uoyRJb5fDniVJkiRJDc/gV5IkSZLU8Ax+JUmSJEkNz+BXkiRJktTwDH4lSZIkSQ3P4FeSJEmS1PAMfiVJkiRJDc/gV5IkSZLU8Ax+JUmSJEkNz+BXkiRJktTwDH4lSZIkSQ2vXcFvRNzanjRJklRfttmSJLWux6I2RsTKwKpA74hYC4iyaQ2gXyfXTZIktZNttiRJi7a4nt/PAWOAzcrPpse1wG+W9qAR0SsiroqIRyLi4YjYISLWjohbIuLx8nOtkjci4lcRMSEiHoiIbWrKGV7yPx4Rw5e2PpIkNYBOabOlepg8eTI777wzW2yxBYMGDeKss84C4Pjjj2ezzTZjq622Yr/99mPOnDkAjBo1iiFDhjBkyBC23nprrrnmGgBee+01tttuO7beemsGDRrEySefXK9TkrQMWGTwm5lnZeZGwDcyc+PM3Kg8ts7Mt9OQngXcmJmbAVsDDwMnArdm5kDg1vIaYE9gYHkcA/wOICLWBk4GhgHbASc3BcySJK1oOrHNlrpcjx49+MUvfsFDDz3E3XffzW9/+1seeughdt11V8aPH88DDzzAe97zHn784x8DMHjwYEaPHs3YsWO58cYb+dznPse8efPo2bMnI0eOZNy4cc3b7r777jqfnaR6WeSw5yaZ+euI+H/AgNp9MvOiJT1gRKwJ7AgcUcp4A3gjIvYBdirZRgC3AycA+wAXZWYCd5de474l7y2ZOauUewuwB3DZktZJkqRG0ZFttlQvffv2pW/fvgCsvvrqbL755kydOpXddtutOc/222/PVVddBcCqq67anP7aa68RUY36jwhWW201AObOncvcuXObt0la8bR3wauLgZ8DHwTeXx5Dl/KYGwEzgAsi4v6IODci3gmsl5nTSp5ngPXK837A5Jr9p5S0ttIlSVphdXCbLdXdpEmTuP/++xk2bNgC6eeffz577rln8+t77rmHQYMGseWWW/L73/+eHj2qaz/z589nyJAhrLvuuuy6664LlSNpxdGunl+qRnOL0vvaEcfcBvjvzLwnIs7irSHOAGRmRkRHHAuAiDiGasg0G264YUcVK0nSsqgj22yprl566SUOOOAAzjzzTNZYY43m9NNOO40ePXpw6KGHNqcNGzaMBx98kIcffpjhw4ez5557svLKK9O9e3fGjh3LnDlz2G+//Rg/fjyDBw+ux+lIqrP23ud3PPCuDjrmFGBKZt5TXl9FFQw/W4YzU35OL9unAhvU7N+/pLWVvpDMPCczh2bm0D59+nTQaUiStEzqyDZbqpu5c+dywAEHcOihh7L//vs3p1944YVcf/31XHrppa0OYd58881ZbbXVGD9+/ALpvXr1Yuedd+bGG2/s9LpLWja1N/jtDTwUETdFxHVNj6U5YGY+A0yOiPeWpF2Ah4DrgKYVm4dTrU5JST+8rPq8PfB8GR59E7BbRKxVFrraraRJkrQi67A2W6qXzOToo49m880352tf+1pz+o033shPf/pTrrvuugXm+T7xxBPMmzcPgCeffJJHHnmEAQMGMGPGjOYVoV999VVuueUWNttssy49F0nLjvYOez6lg4/738ClEfEOYCJwJFUgfmVEHA08CRxU8t4A7AVMAF4pecnMWRHxA+Deku/7TYtfSZK0Ajul3hWQ3q5//OMfXHzxxWy55ZYMGTIEgB/96Ed86Utf4vXXX2fXXXcFqkWvfv/733PXXXdx+umns9JKK9GtWzfOPvtsevfuzQMPPMDw4cOZP38+b775JgcddBB77713Hc9MUj21d7Xnv3fkQTNzLK0vvrFLK3kT+GIb5ZwPnN+RdZMkaXnW0W22VA8f/OAHaW3a+l577dVq/k9/+tN8+tOfXih9q6224v777+/w+klaPrUr+I2IF4Gm/0DvAFYCXs7MNdreS5IkdTXbbEmSWtfent/Vm55HtbLAPsD2nVUpSZK0dGyzJUlqXXsXvGqWlT8Bu3d8dSRJUkexzZYk6S3tHfa8f83LblTzdV/rlBpJkqSlZputZdG2x19U7yostTE/O7zeVZDUQdq72vPHa57PAyZRDaOSJEnLFttsSZJa0d45v0d2dkUkSdLbZ5stSVLr2jXnNyL6R8Q1ETG9PP4YEf07u3KSJGnJ2GZLktS69i54dQFwHbB+efy5pEmSpGWLbbYkSa1ob/DbJzMvyMx55XEh0KcT6yVJkpaObbYkSa1ob/D7XEQcFhHdy+Mw4LnOrJgkSVoqS9xmR8QGEXFbRDwUEQ9GxJdL+toRcUtEPF5+rlXSIyJ+FRETIuKBiNimpqzhJf/jETG8U89UkqQl0N7g9yjgIOAZYBpwIHBEJ9VJkiQtvaVps+cBX8/MLYDtgS9GxBbAicCtmTkQuLW8BtgTGFgexwC/gypYBk4GhgHbASc3BcySJNVbe4Pf7wPDM7NPZq5L1bCe2nnVkiRJS2mJ2+zMnJaZ95XnLwIPA/2obpE0omQbAexbnu8DXJSVu4FeEdEX2B24JTNnZeZs4BZgjw49O0mSllJ7g9+tSiMGQGbOAt7XOVWSJElvw9tqsyNiQMl/D7BeZk4rm54B1ivP+wGTa3abUtLaSpckqe7aG/x2qx22VIY1tesewZIkqUstdZsdEasBfwS+kpkv1G7LzASyoyoZEcdExOiIGD1jxoyOKlaSpDa1N4D9BfCviPi/8vqTwGmdUyVJkvQ2LFWbHRErUQW+l2bm1SX52Yjom5nTyrDm6SV9KrBBze79S9pUYKcW6be3drzMPAc4B2Do0KEdFlRLktSWdvX8ZuZFwP7As+Wxf2Ze3JkVkyRJS25p2uyICOA84OHM/GXNpuuAphWbhwPX1qQfXlZ93h54vgyPvgnYLSLWKr3Pu5U0SZLqrt1DlzPzIeChTqyLJEnqAEvRZn8A+DTw74gYW9K+DZwOXBkRRwNPUq0iDXADsBcwAXgFOLIcd1ZE/AC4t+T7fplzLElS3TlvV5KkFVxm3gVEG5t3aSV/Al9so6zzgfM7rnaSJHWM9i54JUmSJEnScsvgV5IkSZLU8Ax+JUmSJEkNz+BXkiRJktTwDH4lSZIkSQ3P4FeSJEmS1PDqFvxGRPeIuD8iri+vN4qIeyJiQkRcERHvKOk9y+sJZfuAmjK+VdIfjYjd63QqkiRJkqRlXD17fr8MPFzz+ifAGZm5KTAbOLqkHw3MLulnlHxExBbAwcAgYA/g7Ijo3kV1lyRJkiQtR+oS/EZEf+BjwLnldQAfAa4qWUYA+5bn+5TXlO27lPz7AJdn5uuZ+QQwAdiuS05AkiRJkrRcqVfP75nAN4E3y+t1gDmZOa+8ngL0K8/7AZMByvbnS/7m9Fb2WUBEHBMRoyNi9IwZMzrwNCRJkiRJy4MuD34jYm9gemaO6apjZuY5mTk0M4f26dOnqw4rSZIkSVpG9KjDMT8AfCIi9gJWBtYAzgJ6RUSP0rvbH5ha8k8FNgCmREQPYE3guZr0JrX7SJIkSZLUrMt7fjPzW5nZPzMHUC1YNTIzDwVuAw4s2YYD15bn15XXlO0jMzNL+sFlNeiNgIHAqC46DUmSJEnScqQePb9tOQG4PCJ+CNwPnFfSzwMujogJwCyqgJnMfDAirgQeAuYBX8zM+V1fbUmSJEnSsq6uwW9m3g7cXp5PpJXVmjPzNeCTbex/GnBa59VQkiRJktQI6nmfX0mSJEmSuoTBryRJkiSp4Rn8SpIk6f+zd99xVlT3/8dfb4q9gFJEELHThKUEJVHsiGiC7atiQ8UYo+ZniyUxxpJYojGWaDQqiBgjGjWBGCwENIoNQQERoxBFARFQRBBEWfj8/piz62XZpe7u3b28n4/Hfey9Z2bOfM69u3vuZ+bMGTOzgufk18zMzMzMzAqek18zMzMzMzMreE5+zczMzMzMrOA5+TUzMzMzM7OC5+TXzMzMzMzMCp6TXzMzMzMzMyt4Tn7NzMzMzMys4Dn5NTMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzND0kBJcyRNyinbRtIISVPSz4apXJLukDRV0kRJnXO26ZfWnyKpXz7aYmZmVh4nv2ZmZgYwCOhVpuxyYGRE7AaMTK8BDgN2S4+zgLshS5aBq4C9gG7AVSUJs5mZWb45+TUzMzMi4kVgXpniPsCD6fmDwJE55YMj8xrQQFIz4FBgRETMi4gvgBGsnFCbmZnlhZNfMzMzq0jTiJiVnn8KNE3PmwPTc9abkcoqKl+JpLMkjZU0du7cuZUbtZmZWTmc/JqZmdlqRUQAUYn13RsRXSOia+PGjSurWjMzswo5+TUzM7OKzE7DmUk/56TymcAOOeu1SGUVlZuZmeWdk18zMzOryDCgZMbmfsDQnPJT06zPewNfpuHRzwI9JTVME131TGVmZmZ5V+3Jr6QdJD0vabKkdySdn8p9OwUzM7M8kfQI8Cqwh6QZkvoDNwKHSJoCHJxeAwwHPgCmAvcB5wBExDzgN8Ab6XFtKjMzM8u7ennYZzFwcUS8KWlLYJykEcBpZLdTuFHS5WS3U7iMFW+nsBfZ7RT2yrmdQleya5DGSRqWZpc0MzOztRARfStYdFA56wZwbgX1DAQGVmJoZmZmlaLaz/xGxKyIeDM9Xwi8SzYTpG+nYGZmZmZmZlUir9f8SmoFdAJepwpvp2BmZmZmZmYbtrwlv5K2AJ4ALoiIBbnLKvt2Cr6XoJmZmZmZ2YYtL8mvpPpkie/DEfFkKq6y2yn4XoJmZmZmZmYbtnzM9ixgAPBuRPwhZ5Fvp2BmZmZmG6xly5bRqVMnjjjiCAD69+9Px44d6dChA8ceeyxfffUVAIMGDaJx48YUFRVRVFTE/fffn8+wzWqNfJz5/QFwCnCgpPHp0RvfTsHMzMzMNmC33347bdq0KX196623MmHCBCZOnEjLli258847S5cdf/zxjB8/nvHjx3PmmWfmI1yzWqfab3UUEaMBVbDYt1MwMzMzsw3OjBkz+Ne//sUVV1zBH/6QDY7caqutAIgIvv76a7IBlGa2rvI627OZmZmZmcEFF1zATTfdRJ06K349P/3009luu+3473//y89+9rPS8ieeeKJ0OPT06dPLVmdm5XDya2ZmZmaWR0899RRNmjShS5cuKy174IEH+OSTT2jTpg2PPvooAD/84Q+ZNm0aEydO5JBDDqFfv34rbWdmK3Pya2ZmZmaWRy+//DLDhg2jVatWnHDCCYwaNYqTTz65dHndunU54YQTeOKJJwDYdttt2XjjjQE488wzGTduXF7iNqttnPyamZmZmeXRDTfcwIwZM5g2bRpDhgzhwAMP5KGHHmLq1KlAds3vsGHDaN26NQCzZs0q3XbYsGErTJJlZhWr9gmvzMzMzMxs1SKCfv36sWDBAiKCjh07cvfddwNwxx13MGzYMOrVq8c222zDoEGD8husWS3h5NfMzMzMrIbYf//92X///YFsOHR5brjhBm644YZqjMqsMHjYs9laaNWqFXvuuSdFRUV07doVgKuvvprmzZuX3mh++PDhAEybNo1NN920tPzss8/OZ+hmZmZmZhs0n/k1W0vPP/88jRo1WqHswgsv5Oc///lK6+6yyy6MHz++miIzMzMzq9nOOOOM0tmtJ02aBMDxxx/Pe++9B8D8+fNp0KAB48ePZ+nSpZx55pm8+eabFBcXc+qpp/KLX/win+FbLefk18zMzMzMqsVpp53Geeedx6mnnlpaVnILJ4CLL76YrbfeGoC//e1vfPPNN7z99tssXryYtm3b0rdvX1q1alXdYVuB8LBns7UgiZ49e9KlSxfuvffe0vI777yTDh06cMYZZ/DFF1+Uln/44Yd06tSJ/fbbj5deeikfIZuZmZnVGD169GCbbbYpd1lE8Nhjj9G3b18g+961aNEiiouL+frrr9loo43YaqutqjNcKzA+82u2FkaPHk3z5s2ZM2cOhxxyCK1bt+anP/0pV155JZK48sorufjiixk4cCDNmjXj448/Ztttt2XcuHEceeSRvPPOO/6nbWZmVqC6XDI43yGss3E3n7r6larYSy+9RNOmTdltt90AOPbYYxk6dCjNmjVj8eLF3HrrrRUmzjVNecO7L7nkEv75z3+y0UYbscsuu/DAAw/QoEGD0m0+/vhj2rZty9VXX13u5XS2/nzm12wtNG/eHIAmTZpw1FFHMWbMGJo2bUrdunWpU6cOP/7xjxkzZgwAG2+8Mdtuuy0AXbp0YZddduH999/PW+xmZmZmNdkjjzxSetYXYMyYMdStW5dPPvmEDz/8kFtuuYUPPvggjxGuudNOO41nnnlmhbJDDjmESZMmMXHiRHbfffeVZuy+6KKLOOyww6ozzA2Ok1+zNbRo0SIWLlxY+vy5556jffv2K9xo/u9//zvt27cHYO7cuSxbtgyADz74gClTprDzzjtXf+BmZmZmNVxxcTFPPvkkxx9/fGnZX//6V3r16kX9+vVp0qQJP/jBDxg7dmweo1xz5Q3v7tmzJ/XqZQNv9957b2bMmFG67B//+Ac77bQT7dq1q9Y4NzROfs3W0OzZs9lnn33o2LEj3bp14/DDD6dXr15ceuml7LnnnnTo0IHnn3+eW2+9FYAXX3yRDh06UFRUxLHHHss999xTa4bq1CZnnHEGTZo0KT3oANkEGe3ataNOnTordZITJ06ke/futGvXjj333JMlS5ZUd8hmZmZWxr///W9at25NixYtSstatmzJqFGjgOzEw2uvvUbr1q3zFWKlGjhwYOlZ3q+++orf/e53XHXVVXmOav1Mnz6dAw44gLZt29KuXTtuv/12ACZMmED37t3Zc889+eEPf8iCBQvyFqOv+TVbQzvvvDMTJkxYqfyhhx4qd/1jjjmGY445pqrD2uCVN2tk+/btefLJJ/nJT36ywrrFxcWcfPLJPPTQQ3Ts2JHPP/+c+vXrV3fIG5Tp06dz6qmnMnv2bCRx1llncf755zNv3jyOP/54pk2bRqtWrXjsscdo2LBhvsM1M7Mq1rdvX1544QU+++wzWrRowTXXXEP//v0ZMmTICkOeAc4991xOP/102rVrR0Rw+umn06FDhzxFXnmuu+466tWrx0knnQTA1VdfzYUXXsgWW2yR58jWT7169bjlllvo3LkzCxcupEuXLhxyyCGceeaZ/P73v2e//fZj4MCB3HzzzfzmN7/JT4x52auZWSXp0aMH06ZNW6GsTZs25a773HPP0aFDBzp27AhQek22VZ2KOsJBgwZx0EEHcfnll3PjjTdy44038rvf/S7f4ZqZWRV75JFHyi0fNGjQSmVbbLEFf/vb36o4ouo1aNAgnnrqKUaOHIkkAF5//XUef/xxLr30UubPn0+dOnXYZJNNOO+88/Ic7dpp1qwZzZo1A2DLLbekTZs2zJw5k/fff58ePXoA2XXPhx56aN6SXw97NrMNxvvvv48kDj30UDp37sxNN92U75AKXrNmzejcuTOwYkc4dOhQ+vXrB0C/fv34xz/+kccozczMqt4zzzzDTTfdxLBhw9hss81Ky1966SWmTZvGtGnTuOCCC/jlL39Z6xLfsqZNm8Zbb73FXnvtRbt27Rg6dCiQXZo2ffr0vMXl5NfMNhjFxcWMHj2ahx9+mNGjR/P3v/+dkSNH5jusDUZuRzh79uzSo8Pbbbcds2fPznN0ZmZmladv3750796d9957jxYtWjBgwADOO+88Fi5cyCGHHEJRURFnn312vsOsEl999RXHHHMMt912G1tttRUDBw7kT3/6E126dGHhwoVstNFGeYvNw56t1qjN986rKWrCPfzyqUWLFvTo0YNGjRoB0Lt3b958800OOuigPEdW+Mp2hLkklQ79MjMzKwTlDe/u37//are7+uqrqyCa6rN06VKOOeYYTjrpJI4++mgAWrduzXPPPQdko/D+9a9/5S0+n/k1sw3GoYceyttvv83ixYspLi7mP//5D23bts13WAWvvI6wadOmpbcJmzVrFk2aNMlniGZmZraeIoL+/fvTpk0bLrrootLyOXPmALB8+XJ++9vf5vWMt8/8mlmtVt6skdtssw0/+9nPmDt3LocffjhFRUU8++yzNGzYkIsuuojvfe97SKJ3794cfvjh+W5CQauoI/zRj37Egw8+yOWXX86DDz5Inz598hilmZmtjdo8Gm9DHwVXlV5++WUeeugh9txzT4qKigC4/vrrmTJlCnfddRcARx99NKeffnreYnTya2a1WkWzRh511FHllp988smcfPLJVRmS5aioI7z88ss57rjjGDBgADvuuCOPPfZYfgM1MzOz9bLPPvsQEeUuO//886s5mvI5+TUzsyqzqo7Qk42ZmVlN5jPchcfX/JqZmZmZmVnBq/XJr6Rekt6TNFXS5fmOx8zMbEPnvtnMzGqiWj3sWVJd4C7gEGAG8IakYRExOb+RmW04avOQoJrCQ5OskLhvNjOzmqpWJ79AN2BqRHwAIGkI0AdwB2tmZpYf7pvNzGqR2nwiY21PIKiiiUhqA0nHAr0i4sz0+hRgr4g4r8x6ZwFnpZd7AO9Va6BVoxHwWb6DsJX4c6l5/JnUTIXyuewYEY3zHURNUov65kL5HVydDaGdG0Ibwe0sNG5n1amwb67tZ37XSETcC9yb7zgqk6SxEdE133HYivy51Dz+TGomfy6W7755Q/kd3BDauSG0EdzOQuN25kdtn/BqJrBDzusWqczMzMzyw32zmZnVSLU9+X0D2E3STpI2Ak4AhuU5JjMzsw2Z+2YzM6uRavWw54golnQe8CxQFxgYEe/kOazqUlDDuAuIP5eax59JzeTPpUDVor55Q/kd3BDauSG0EdzOQuN25kGtnvDKzMzMzMzMbE3U9mHPZmZmZmZmZqtVq4c9m9VmkupExPJ8x7Ghk3QM8EpEzMp3LGZWtdI1yB2BzmQnAJ6MiNn5jarySaoPdCC75/JXwD8jYn5eg6oCkjYF2gKfRMQsSYoCHdIoqRNwHPBoRIwvxLbm/N62B/YD/hoR/85vVFVDUiPgcOAg4FWyy0O+yW9UlUvSLsCPgC+BYRFRI27r5DO/BUaS8h2DrUhSHUkr/a058a0+kupL2ltSw/S6Ts7fyi+Aw/IXna0pZeqk5+6/bF30A/5ElgB3AX4haeP8hlQlfkLWzg5AT+A3+Q2n8knaiSxpeBU4JxUX3P+FnP91h5LdF/sHeQynqp0LvAQUAf8FPshrNFUg57vH8UAfYALwFtn8CAVDUlPgKrL/s/sBv85vRN/xmd9aQlIDYHlELJBULyKKy1svIkJSPWAXYH4hHtGuyUr+qeUejS2b5EraBNiJ7J/BJxHhWVArmaSSTmR5+iyaAr8im3RhWEQsl7RJWm8y0DxtV3BH0muzktERJZ9L+mwCvvu7krRJRCzJa6BWmwyIiPsAJG0HPANsDczJa1SVKPVDTwOD03eGPYBbJLWKiGn5ja5SzQJOAVqTzSgO6f9DIUn/A3chO8N9Y/oJIAqvvVOAURFxYb4DqSrpe3onoGdE9Ml3PFWoG9AqInpIagFcK+m4iHgs34EV3BGy2k7SZpI6SfqxpP1yFv0ZOBuymTTLrN82JVRIugAYD/wR+En6h2mVJOd9PkbSPmWX53xBL1l/J0lnSjpX0o6p+Fqy237sTDYczdaBpG0knSHpTknDJV0rqTlARCxLj5LPYgEwieygA5IOBd4GHkhle+WhCVZG2bO5JQlu+rIgSbtKOk3SDunnm8CI9EXCbLVSIlFf0qnALcAzEVEwiS+U9kP/i4gFqWg2WYJYUAfDI2JJRLwNzABaprJCHVG1K/AFMA7YHgq2rROBIkknSbpKUqH2zUuBnSW1l3STpHNWu0XtUwf4OD3/HHiNLCHOOye/NYikH5AlQ1eQDVM6Q9KZafEE0j93ScdLOj6VdwHuBBpLag0URUR7siFAJT9tHaSDCj+WdJ+kYyWdD5yWFh9KdsS5ZN266WfjlOx2ldSMbJjHTsA2wPmSNgf+R3ZG8tKIGFWdbSoEOUOGTgN6k/1t3EjWabZL6xwv6SFJN0tqkr4EzgZKDkD8CPhNRBxHNjSwK6x4xt4ql6R2JcNLc87M5y5f4Rp4SVtL6iOpkaS+wGDgerK/uz8B7SOiMzAKOE3SFtXSEKv1ImIp2dDRuUD7Qj14kvO/8qfAU2RfuAvRh8DGkrbMdyBV6Czg58BUYHl5/0MLQURMB94DDgaWAb+XdEh+o6oSy4AlwHnAJ0D3lOxvlt+wKtUy4Os0OutrstE1W9aEv1MnvzXLdODViDg2Iv4PeJksCYZsaOZu6XkbsqQXsmsiFgLbAjsAx0gaCzwMfA2MrKbYC4qkbsAfyN7nD8j+aAcD96VVRgHbSGom6Q7g6FTenGz41XSyxGwx2dnFemmdA8iu7fg67ad+dbSnkKSzgPsBJwJnRMR9EfEi8PeIeE5Sd+AMsuGMM9JPgI+AZulLQzfg32k47SNQOvmEVYJ0lratcq6xJvu72A6yM/OpvIGkPSRtkc7I3Sbp56maFmmbTmR/T7sC90TEQWRJS9O03svARnx3YMNsldLf/U8i4gJgOHBsyaieQpL+V+5KdrD24YoulyoA88hG9+wJhTf3iaQ2wDdkB3wvAvYBnpe026q2q8WOiIjTI+K3wJPAwQX49/kp2QmtkRFxG3AD2e9v+3wGVcmmA/XJ+nKAb4FFQIN8BVTCyW/N8imwa0qozgSOBW5Oy94lfXEkO3O4aTpTMpfs6EpTsi+AzwCHRcReEdEvIoYXWkdQTXoAUyPi7Ii4ISVX2wEvSNqGbGj5HmRJ8SiyI+uQnbnfNl1rXUTWSd1Adm1O/7TudID0hb9Qj8RXteZkn88CZde4ExHL0lHTI4F/R8TDEXE7sFjS94CZwNYp8VoGtMg50yuyg0q2Gvpuwqn66Wfd9CjtT9L7ejzZ3wBk7+8NwLeStpR0jqS/kv2/eojvRqh8Aeybnn8OvE+W1E4nOwg1Py17DmiYnv8P2Jwa0KFa7VBmhMcismGkBdVP5vT75wAPRMQb+YynKkXEt2QnAkoOrhXaCJ6NyEaP9SH7PX0LeCwippS9VKQQpLOEJeYDBXdgOiK+AP7Dd/3YYrL/RYU0+d7/yE78lEzQtitQP53dzytPeFWDRMS36Qvl42TDeD4lGyp7HtlZq1A2QccnZEdTmpN9KRTZWeHBZEOme0t6Gvg+2RfCoWRfKm0NpC8NXwI7SvpRKh5N9kW8mOyL0jvAlsA2EfEPSVdL6gI0JruuAbKzU0Mi4oacurck+2e+lOwI32vpLEShddZVbQtgRhpOsyQNST8e2Izs/b1G0uYRsYjs72V7suHR36Th6E8Cp0sKsi8VDcjOML5U7S2p4dIBhTZkw/fHRsQ0SQ+R/Y/6dclZ3DLbbEOWxJ4iaTzZnAUdga4RcZyy6+UbRMTe6XKPP0i6mezg0EmpmsVkE9rsQpYgi+yzguxvsUl6Pp2sL2teuS23QpQOlu1I9vu4A9nwysfLfOGu9dJZ32OA/yPr1g4g679+WWjXOEvalmzSspvTgc4hETEhz2FVmtSWXiWvJV0DNCtZnJegqkj6/tUM2Jvs7/MQss+zECc1fBQ4VFIT4HvAOxHxUqF8J4yIryS9ABwtaQeynOQvkP/JRQvuiFEBGA2cFREnR8SpwO5A7/SHP5vsmsYJZF/mS85UNQW6pesaf0r2T2ME2QRZQXZdga2h9Af5IFmCegnZffVeIBu2vBTYMa3zBd99BncCPyS7fqMkgXoE2Dddx3GxpH8CR6YvWS+Tna0qxKPU1eEDskSo5Ijw82SfzxRgK7JhzSVHUBuksplkfw/7RcRNZEclHyD7UvHzVIfl0HfzEPyC7JqzK1Iy/DrprK6kNpLul/SypH+k6yeXk73f84DTIuJZsks3Sq71eZvs2jXIPrNPyYZ8TSAlten/WVOgUWT3PvwKaJq+HE0HWqbrub8lu0ZsfiGeBbHKlYb+diD7fW4GDAD+ltegqk4Tsr/DT8m+E9xYgInvpsBjZAeeh5H9DynI2+Mou0XfRmTfT+6Ewvv+kNrTgey7bDPgfuCJvAZVRSLiCbI7UGxNlhTelsoL6TMdBNxFlrPcD/wd8t9GFdZ7XPtJuhD4NiLuSq8fIxve+UtJA8lujfMrST8B/h/Zl8t3yK7t/VdELNYqboVka0fZBFU7kt2EfE+yiZHuiogBkh4BXoqIPymbyOe3wMVAv4h4KG2/K9l9JTcjm6VxZOTcfirfR79qK2W3/rqP7Br5P+SU/5HsoN4Msuu1NyU7aHQ+2YGLXsD0iBinMhMs2coktQQejYju6frd35BNLPYK8M+I2EnZvTa7kZ21bUl2lH43STun591SXbsDf4uIjpJOSdtcGBHFkh4nG5r5L0nv893ELleRfQHqBVxJNgLmgYj4RtKBZJ9/QZ2xMzMzs6rjYc81z2vAryWNIhuutBHZ0UzIEtyS634fAJ6IiM/KVuDEt3Kk5GgRMFnSV2Rn4Wfx3T32PgL2B/6UvowPJ/u8xpXUERFTyb60l8uJ77qJiPmSBgP9lN3OqwHZWcJZZIlTMXA42cGhVyNiYdr0Hzl1OPFdvU/JbslFRHyRholvRpaYbpvKP0yJ7V1kB4papIlYvgTqS9ouIj5NdS1Nw86nkw2LLnm+hOzv6l/A6WSzpG9JdqT4I7JrtK/M/d8WnindzMzM1pKT35rnv8B+wNXANODuiHgNICIehtKzhd8CKyW+VjnSmdzO6exVY7LZMkeSHYg4Jq32Z3ImYoiI5/HQ2WoTEf+UNIbsGtG5wBjg/ZwDCoPzFlyBSPMQSNKeQCuySd7uSddZf5PO+s4hS1iHRcRfJL0GdIyIxyUVkx2w+5RshvNFQGeyIedbkp0pnk52Dfa8dMDpZbK/NzMzM7NK5WHPZuVI1xUeSTZk+T2ys7mjyjvTblbI0qUXHcgOLvwL+Eca6fAG2bDkucClwNkR8bmkKcDQiPi5pKvJRrBMB35JNqnYB8CLZLOiz/HQfzMzM6suTn7NzKxCaR6CBRExoEz5/cC8iLhU0i1ks3K+R3ZGd1RE3JSuE24OfJQz9NzMzMwsL5z8mplZhSR1By6OiGMlbQIUp0mqTgd6RkRfSc3Jrtl9O13fa2ZmZlbjOPk1M7MKSWpENrnefuUNUfawZTMzM6stfE9EMzOrUER8FhH7pecrJblOfM2sEEhqIOmcnNf7S3pqPes8W9Kp6x/dCnWeJmn7nNf3S2q7qm3Wsv7rJE1Pd7nILb9I0mRJEyWNlLRjZe3TrDo5+TUzMzOzDV0D4JzVrbQ2IuKeiKjsOw+cBpQmvxFxZkRMrsT6/0l2H/ay3gK6RkQH4HHgpkrcp1m1cfJrZmZmZrWGpFaS/itpkKT3JT0s6WBJL0uaIqmbpG0k/SOdqXxNUoe07dWSBkp6QdIHkv5fqvZGYBdJ4yXdnMq2kPR42tfD6U4QSLox5yzo71cR59WSfp6evyDpd5LGpJj3zWnLS5LeTI/v52x/maS3JU1I+zwW6Ao8nOLcNNXbNZ1lvjln29Mk3Zmen5z2O17SnyXVrSjmiHgtImaVU/58RCxOL18DWqyi3ftL+o+koek9vlHSSSmGtyXtktP2UTlnk1um8kGS7pD0Str+2Ir2Zba2fJ9fMzMzM6ttdiW7ldoZwBvAicA+wI/Ibq02HXgrIo6UdCDZvd+L0ratgQPIZqd/T9LdwOVA+4gogiyBI7s9WzvgE+Bl4AeS3gWOAlpHREhqsBYx14uIbpJ6k90q7mCye6Ufku6fvhvwCNBV0mFAH2CviFgsaZuImCfpPODnETE2xVlS9xPAq8Al6fXxwHWS2qTnP4iIpZL+BJyU3o911R94ejXrdATaAPPIbnF3f2r7+cDPgAuAPwIPRsSDks4A7iC7zSRAM7LPszUwjOxss9l6c/JrZmZmZrXNhxHxNoCkd4CRKRl9G2gF7AgcAxARoyRtK2mrtO2/IuIb4BtJc4CmFexjTETMSPsYn+p9DVgCDEjXBK/NdcFPpp/jUl0A9YE7JRUBy4DdU/nBwAMlZ1sjYt6qKo6Iueks6d7AFLKk8WXgXKAL8EZKlDclS7jXiaSTyc4+77eaVd8oOYMs6X/Ac6n8bbIDDwDdgaPT84dYcSj1PyJiOTBZUkWfj9lac/JrZmZmZrXNNznPl+e8Xk72/XbpGm67jIq/D6+0XrrVWzfgIOBY4DzgwLWMOXefFwKzyc6U1iFLrNfVEOA44L/A39PBAJGdXf3FetQLgKSDgSuA/dLBg1VZ3eezOrnbq8K1zNaSr/k1MzMzs0LzEtnw3pIhzJ9FxIJVrL+QbBj0KknaAtg6IoaTJa4d1zPOrYFZ6SznKUDJ9bgjgNMlbZb2u80axPl3sqHSfckSYYCRwLGSmpTUsy4zNUvqBPwZ+FFErPOZ4zJeAU5Iz08i+8zMqpSTX7MCJGmasvuzVkZd++dOwGFmZlYLXA10kTSRbDKrfqtaOSI+B16WNCl34qhybAk8leodDVy0nnH+CegnaQLZUOVFKZ5nyK51HZuGXP88rT8IuKdkwqsybfgCeBfYMSLGpLLJwK+A51LMI8iupy2XpJskzQA2kzRD0tVp0c3AFsDf0r6HrWe7Ibv29/QU1ynA+ZVQp9kqybdoNKvdJNWLiOIyZdPIbknwWSXUfzXwVURUOKNlJexDZP+PllfVPszMzMxsw+Yzv2Y1iKRT05T/EyQ9JOmHkl6X9Jakf5dM+qDs9gkPSXoZeChN5PGcpHck3c9qro8pu59UttK+JLUCzgYuTEd695XUWNITkt5Ijx+k7RtLGlESg6SPSs4+S7ooHU2fJOmCVNZK0nuSBgOTgCsl3ZYT448l3VrJb7GZmZmZbaB85teshpDUjux6ne9HxGfp+p4A5qdJK84E2kTExels7A+BfSLia0l3kF3PdK2kw8lmn2xc3pnf8vaTbp/QcBX7Kj3zK+mvwJ8iYrSye/I9GxFtlN1PcGZE3CCpF9ltEBqTzbg5CNibLCl/HTgZ+ILs9gffj4jX0nVUE8huH7FU0ivAT0pm8zQzM6uJJF1BdtulXH+LiOvyEc+akPQ6sHGZ4lPWps+VtCfZLM25vomIvdY3PrOq4tmezWqOA8k6y88gu61B6lgeldQM2Aj4MGf9YRHxdXreg3S7gIj4l6Qv1mY/qbzFKvaV62Cgrb67t+BWKXHdh+zeh0TEMzkx7EM26+QiAElPAvuSXcv0UUS8lrb5StIo4Ahl91Gs78TXzMxqupTk1thEtzyVkaCmPrpo/aMxqz4e9mxWs/0RuDMi9gR+AmySs2xRNe4rVx1g74goSo/mEfHVOu6zbBvuB04DTgceWMc6zczMzMxW4uTXrOYYBfyfpG2h9LYGWwMz0/JVzVT5InBi2u4woOFa7odV7KvsbRWeI5uhkbR9UXr6Mtn9BZHUMyeGl4AjJW0maXOys8Pl3s4gIl4HdkhteWQVbTAzMzMzWytOfs1qiIh4h2zY1H/SLQ/+QHarhr9JGgesaubma4Aekt4hG/788Vruh1Xs65/AUSUTXgH/D+iaJsyaTDYhVkkMPSVNIrv26VNgYUS8SXbN7xiy633vj4i3VtGWx4CX0y0bzMzMzMwqhSe8MrNKIWljYFlEFEvqDtwdEUXrUM9TwK0RMbKyYzQzMzOzDZcnvDKzytISeExSHeBb4Mdrs7GkBmRnhyc48TUzMzOzyuYzv2YFKl3TW14SeVBEfF7d8ZiZmZmZ5ZOTXzMzMzMzMyt4nvDKzMzMzMzMCp6TXzMzMzMzMyt4Tn7NzMzMzMys4Dn5NTMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzMzMzMreE5+zczMzMzMrOA5+TUzMzMzM7OC5+TXzMzMzMzMCp6TXzMzMzMzMyt4Tn7NzMzMzMys4Dn5NTMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzMzMzMreE5+zczMzMzMrOA5+TUzMzMzM7OC5+TXzMzMzMzMCp6TXzMzMzMzMyt4Tn7NzMzMzMys4Dn5NTMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzMzMzMreE5+zczMzMzMrOA5+TUzMzMzM7OC5+TXzMzMzMzMCp6TXzMzMzMzMyt4Tn7NzMzMzMys4Dn5NTMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzMzMzMreE5+zczMzMzMrOA5+TUzMzOzWk/SPZKurKS6Wkr6SlLd9PoFSWdWRt2pvqcl9aus+tZiv7+V9JmkT6t732XiOE3S6HzGYBsmJ79WbdwprdF+a0SnZGZmVpNImibpa0kLJc2X9IqksyWVfpeNiLMj4jdrWNfBq1onIj6OiC0iYlklxH61pL+Uqf+wiHhwfeteyzhaAhcDbSNiu+rct1lN4eTXKoU7pfWXr05pTd7vNaynlaSQVK8y4qpOKe5d12C9jSQ9nt6zkLR/meWXSJqU/g4+lHRJVcVsZrYB+mFEbAnsCNwIXAYMqOyd1MZ+bA21BD6PiDn5DmR9lJz4MFsXTn6tMrlTWj8F0SltAEYDJwPlnZ0XcCrQEOgFnCfphGqMzcys4EXElxExDDge6CepPYCkQZJ+m543kvRUOiA/T9JLkupIeoisv/1nGkF2ac7B2/6SPgZGVXBAdxdJYyQtkDRU0jZpX/tLmpEbY8mBZUm9gF8Cx6f9TUjLS0espbh+JekjSXMkDZa0dVpWEkc/SR+n0WFXVPTeSNo6bT831ferVP/BwAhg+xTHoAq2v1TSLEmfSDoz9+CwpI0l/T7FMTuN6Ns09z2QdHFqwyxJp+fUu62kYem9GwPsUma/rSWNSJ/Ve5KOy1k2SNLdkoZLWgQcUFH7zVbHya9VOndKVdop9ZE0PrXxfyl+JG2fOpV5kqZK+nHONldLeiztd6GkdyR1TctWer9T+d8kfSrpS0kvSmqXU9+mkm5J8X8paXTq/F5Mq8xPdXWXtKuk/6T1PpP0aEXvTU797XI6wNmSfpnKN5Z0m7IO+ZP0fOO0bKVrh7Rihz1I0l2S/pXeg9cl7ZKWlcQ9IcV9fEWxRcS3EXFbRIwGVhp1EBE3RcSbEVEcEe8BQ4EfrEGbQ9I5kqak+H4jaRdlIygWpM9vo5z1f5w+53npc9++TF1np7rmp3ZrdTGYmdU2ETEGmAHsW87ii9OyxkBTsr4+IuIU4GOyA/ZbRMRNOdvsB7QBDq1gl6cCZwDNgGLgjjWI8RngeuDRtL+O5ax2WnocAOwMbAHcWWadfYA9gIOAX0tqU8Eu/whsnerZL8V8ekT8GzgM+CTFcVrZDdN3iouAg4Fdgf3LrHIjsDtQlJY3B36ds3y7tO/mQH/gLkkN07K7gCVk790Z6VGy383JvgP9FWgCnAD8SVLbnLpPBK4DtiQ7CG22Tpz8WpVxp1Su9emUugGDgUuABkAPYFpaPITs/dweOBa4XtKBOZv/KK3TABhWEv8q3u+ngd3IOqE3gYdz6vo90AX4PrANcCmwPMUD0CDV9SrwG+A5sjOhLVL7KyRpS+DfwDOpLbsCI9PiK4C9yTrdjkA34Ferqq+ME4BrUixTyTpRIqIk7o4p7tUm6GsiJZz7Au+s4SaHkr2ve5O9p/eSnWHeAWgP9E31HgjcABxH9rv+Edlnm+sI4HtAh7ReRX8zZma13SdkfVFZS8n+R+4YEUsj4qWIiNXUdXVELIqIrytY/lBETIqIRcCVwHGqnCG4JwF/iIgPIuIr4BfACVrxAP81EfF1REwAJpD1gytIsZwA/CIiFkbENOAW4JQ1jOM44IGIeCciFgNX59Qt4CzgwoiYFxELyb4/5Y5uWgpcm97v4cBXwB4prmOAX6f3dxKQe2nZEcC0iHggHTx+C3gC+L+cdYZGxMsRsTwilqxhe8xW4uTXqpo7paQSOqX+wMCIGJH++c+MiP9K2oHs7OJlEbEkIsYD95Ml1iVGR8TwdI30Q+XFlysiBqYYvyHr/Dqms9Z1yA4wnJ/2vywiXknrlWcp2TD47VNsqztaewTwaUTcktZfGBGvp2UnkXWqcyJiLlkiu6bvHcDfI2JMRBSTJfNFa7Hturia7H/sA2u4/k0RsSAi3gEmAc+l37kvyQ5GdErrnUT2e/Bmet9/AXSX1CqnrhsjYn5EfAw8T9W31cwsX5oD88opv5nsQOdzkj6QdPka1DV9LZZ/BNQHGq1RlKu2faovt+56ZCcHSuRearOY7EB8WY1STGXrar4WceS2Mfd5Y2AzYFwaVTSf7EB145x1Pk99bNk4G5O1p+z7V2JHYK+SelPdJ5GdSS4vFrN15uTXqpo7pe+sb6e0A/C/CuIrOQpbUb1l49tEFVw7LamupBvTsOoFfHd2uVF6bFJBHOW5lOw62DFpuPUZq1m/ojZC+Z/D9hWsW541+YwqhaTzyA4+HL6KAwNlzc55/nU5r0viXeF9SAdkPmfVn3eVtdXMLF8kfY/sf99KB1bTwdOLI2JnstFPF0k6qGRxBVWu7iD8DjnPW5Id4P0MWESWGJbEVZcVk8LV1fsJWQKYW3cxK/YDa+IzvjvonFvXzDXcfhbZKK0Sue39jKwvahcRDdJj64hYk/5lLll7yr5/JaYD/8mpt2QE2U9z1lnde2i2Rpz8WpVxp7SS9e2UplNmgoic+LZJQ4bXpd6y7T8R6EN2zc/WQKtULrI2LKkgjpXex4j4NCJ+HBHbAz8hu4ZnVbMqTycbEl6e8j6HT9Lzsp9x3m7hkBL8y4GDImLG6tZfByu8D+laqW1Z88/bzKxWk7SVpCPILvn4S0S8Xc46R6R5JwR8STZPw/K0eDYV9zWrcrKktpI2A64FHk8jqt4nO6h8uKT6ZJfkbJyz3WyglXLugFHGI8CFknaStAXfXY5VXMH65UqxPAZcJ2lLSTuSXcP7l1VvWeox4HRJbVIbS29PGRHLgfuAWyU1AZDUXNJqL6tJcT0JXC1ps3Qtb+7tJJ8Cdpd0iqT66fG9VVxCZrbOnPxapXOnVL5K6JQGkHVKBymbJKu5pNYRMR14BbhB0iaSOpANkV7Tesu+31sC35CdTdyMrL0lbVgODAT+oGySrbrKJrbamOzI7vLcuiT9n6SSo8hfkCXIJZ9zeZ4Cmkm6QNkEV1tK2istewT4laTGkhqRTbJR0sYJQDtJRZI2Iec6pXV8DyqU4tokvdwovedKy04ie78OiYgP1jKGNfUI2e9BUXrfrwdeT8PozcwK2T8lLSQ7UHoF8Afg9ArW3Y1sDomvgFeBP0XE82nZDWT9yXxJP1+L/T8EDCIbXbMJ8P8gm+gTOIfskqOZZAdkcw9+/i39/FzSm+XUOzDV/SLwIdlB5p+tRVy5fpb2/wHZyYe/pvpXKyKeJpsv5Xmy0XmvpUUlI5guKylPI8P+TTbfyZo4j2wU0qdk72HpJUFp5FpPskvDPknr/I4Vv6uZVY6I8MOP9X6QDY39GlhIlsy+CpwL1M1ZZxDw2/T8wrRNSQdxZc56fcgmYZoP/JzszGMA9XLWWaEMeIGsMxsDLAD+CTTKWf80suE8c1Kd04CD07JtyTqIL4A3c+o7Mz2vQ5ZoTSdL8P4CNCwvjrLblvM+NUzbz031/Rqok5btD8xYzft8FDAxvc9TgUNTeQuyxHEe2bDhs3O2uZrsIERF713Z93sLslmKF5INrz01rb9rWn9T4DayDv5Lss5607Ts2tS2+WQTN92U1vsqxXXWGvwutSeb5OoLsg7w8lS+CVmnPCs97gA2ydnuCrIz09PJJorKjXkQ6XevvPcaODvVOR84bg1+16PMo1Va9iHZ2f2vch73rEGbS2NNr0cDp+W8/i1wf5l4/5c+76eAFquoa4W2++GHH3744ceaPMgmGV1GznccP/yo7Q9FeAi9mZmZmdmGTtJRwHCykV8PAssj4si8BmVWiTzs2czMzMzMIJufYw7Z6KJlwE9XvbpZ7eIzv2ZWrSTtS3brnpXEms0aWaUk/ZLsvtNlvRQRh61jnTW6zWZmZmYbAie/ZmZmZmZmVvA87NnMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzApe3pJfSXUlvSXpqfR6J0mvS5oq6VFJG6XyjdPrqWl5q5w6fpHK35N0aJ6aYmZmZmZmZjVcvTzu+3zgXWCr9Pp3wK0RMUTSPUB/4O7084uI2FXSCWm94yW1BU4A2gHbA/+WtHtELFvVThs1ahStWrWqkgaZmdmGZdy4cZ9FRON8x1HbuW82M7PKsqq+OS/Jr6QWwOHAdcBFkgQcCJyYVnkQuJos+e2TngM8DtyZ1u8DDImIb4APJU0FugGvrmrfrVq1YuzYsZXaHjMz2zBJ+ijfMRQC981mZlZZVtU352vY823ApcDy9HpbYH5EFKfXM4Dm6XlzYDpAWv5lWr+0vJxtzMzMzMzMzEpVe/Ir6QhgTkSMq8Z9niVprKSxc+fOra7dmpmZmZmZWQ2RjzO/PwB+JGkaMIRsuPPtQANJJcOwWwAz0/OZwA4AafnWwOe55eVss4KIuDciukZE18aNfWmWmZmZmZnZhqbak9+I+EVEtIiIVmQTVo2KiJOA54Fj02r9gKHp+bD0mrR8VEREKj8hzQa9E7AbMKaammFmZmZmZma1SD5ney7rMmCIpN8CbwEDUvkA4KE0odU8soSZiHhH0mPAZKAYOHd1Mz2bmZmZmZnZhimvyW9EvAC8kJ5/QDZbc9l1lgD/V8H215HNGG1mZmZmZmZWoXzN9mxmZmZmZmZWbZz8mpmZGZIaSHpc0n8lvSupu6RtJI2QNCX9bJjWlaQ7JE2VNFFS55x6+qX1p0jqV/EezczMqpeTXzMzM4PszgvPRERroCPwLnA5MDIidgNGptcAh5FNNLkbcBZwN4CkbYCrgL3ILmW6qiRhNjMzyzcnv2ZmZhs4SVsDPUiTTUbEtxExH+gDPJhWexA4Mj3vAwyOzGtktytsBhwKjIiIeRHxBTAC6FVtDTEzM1sFJ79mZma2EzAXeEDSW5Lul7Q50DQiZqV1PgWapufNgek5289IZRWVm5mZ5Z2TXzMzM6sHdAbujohOwCK+G+IMQEQEEJW1Q0lnSRoraezcuXMrq1ozM7MKOfk1MzOzGcCMiHg9vX6cLBmenYYzk37OSctnAjvkbN8ilVVUvpKIuDciukZE18aNG1daQ8zMzCri5NfMzGwDFxGfAtMl7ZGKDgImA8OAkhmb+wFD0/NhwKlp1ue9gS/T8OhngZ6SGqaJrnqmMjMzs7yrl+8AzMzMrEb4GfCwpI2AD4DTyQ6SPyapP/ARcFxadzjQG5gKLE7rEhHzJP0GeCOtd21EzKu+JpiZmVXMya+ZmZkREeOBruUsOqicdQM4t4J6BgIDKzU4MzOzSuBhz+tgyZIldOvWjY4dO9KuXTuuuuoqAPr370/Hjh3p0KEDxx57LF999RUAH3/8MQcccACdOnWiQ4cODB8+vLSuG264gV133ZU99tiDZ5/1yDAzMzOrGq1atWLPPfekqKiIrl2z4xxXXnklHTp0oKioiJ49e/LJJ58A8MILL7D11ltTVFREUVER1157bWk9t956K+3ataN9+/b07duXJUuW5KU9ZmZrS9nB2w1H165dY+zYsetVR0SwaNEitthiC5YuXco+++zD7bffTtu2bdlqq60AuOiii2jSpAmXX345Z511Fp06deKnP/0pkydPpnfv3kybNo3JkyfTt29fxowZwyeffMLBBx/M+++/T926dSujqWZmVsUkjYuI8s6W2lqojL7ZVq9Vq1aMHTuWRo0alZYtWLCg9LvLHXfcweTJk7nnnnt44YUX+P3vf89TTz21Qh0zZ85kn332YfLkyWy66aYcd9xx9O7dm9NOO606m2JmVqFV9c0+87sOJLHFFlsAsHTpUpYuXYqk0s4jIvj666+RVLr+ggULAPjyyy/ZfvvtARg6dCgnnHACG2+8MTvttBO77rorY8aMyUOLzMzMbENU8t0FYNGiRaXfXValuLiYr7/+muLiYhYvXlz6vcbMrKZz8ruOli1bRlFREU2aNOGQQw5hr732AuD0009nu+2247///S8/+9nPALj66qv5y1/+QosWLejduzd//OMfgezo6Q47fHdHiBYtWjBzZrl3hDAzMzNbL5Lo2bMnXbp04d577y0tv+KKK9hhhx14+OGHVxje/Oqrr9KxY0cOO+ww3nnnHQCaN2/Oz3/+c1q2bEmzZs3Yeuut6dmzZ7W3xcxsXVR78itpE0ljJE2Q9I6ka1L5IEkfShqfHkWpXJLukDRV0kRJnXPq6idpSnr0q2CXVaJu3bqMHz+eGTNmMGbMGCZNmgTAAw88wCeffEKbNm149NFHAXjkkUc47bTTmDFjBsOHD+eUU05h+fLl1RmumZmZbeBGjx7Nm2++ydNPP81dd93Fiy++CMB1113H9OnTOemkk7jzzjsB6Ny5Mx999BETJkzgZz/7GUceeSQAX3zxBUOHDuXDDz/kk08+YdGiRfzlL3/JV5PMzNZKPs78fgMcGBEdgSKgV7pHIMAlEVGUHuNT2WHAbulxFnA3gKRtgKuAvYBuwFXpnoLVqkGDBhxwwAE888wzpWV169blhBNO4IknngBgwIABHHdcdneI7t27s2TJEj777DOaN2/O9OnTS7ebMWMGzZs3r94GmJmZ2Qah5DtGkyZNOOqoo1a61Oqkk04q/e6y1VZblV7i1bt3b5YuXcpnn33Gv//9b3baaScaN25M/fr1Ofroo3nllVeqtyFmZuuo2pPfyHyVXtZPj1XNutUHGJy2ew1oIKkZcCgwIiLmRcQXwAigV1XGXmLu3LnMnz8fgK+//poRI0awxx57MHXqVCC75nfYsGG0bt0agJYtWzJy5EgA3n33XZYsWULjxo350Y9+xJAhQ/jmm2/48MMPmTJlCt26dauOJpiZmdkGZNGiRSxcuLD0+XPPPUf79u2ZMmVK6TpDhw4t/e7y6aefUjIp6pgxY1i+fDnbbrstLVu25LXXXmPx4sVEBCNHjqRNmzbV3yAzs3WQl/v8SqoLjAN2Be6KiNcl/RS4TtKvgZHA5RHxDdAcmJ6z+YxUVlF5lZs1axb9+vVj2bJlLF++nOOOO47DDz+cfffdlwULFhARdOzYkbvvvhuAW265hR//+MfceuutSGLQoEFIol27dhx33HG0bduWevXqcdddd9WomZ6XLFlCjx49+OabbyguLubYY4/lmmuu4aSTTmLs2LHUr1+fbt268ec//5n69evz8MMP87vf/Y6IYMstt+Tuu++mY8eOvPfeexx//PGl9X7wwQdce+21XHDBBflrnJmZ2QZk9uzZHHXUUUA2YdWJJ55Ir169OOaYY3jvvfeoU6cOO+64I/fccw8Ajz/+OHfffTf16tVj0003ZciQIUhir7324thjj6Vz587Uq1ePTp06cdZZZ+WzaWZmayyvtzqS1AD4O/Az4HPgU2Aj4F7gfxFxraSngBsjYnTaZiRwGbA/sElE/DaVXwl8HRG/L2c/Z5ENmaZly5ZdPvrooypuWWGo6JZO8+bN47DDDgPgxBNPpEePHvz0pz/llVdeoU2bNjRs2JCnn36aq6++mtdff32FOpctW0bz5s15/fXX2XHHHfPRLDOzSuNbHVUO3+rIzMwqS4291VFEzAeeB3pFxKw0tPkb4AGy63gBZgI75GzWIpVVVF7efu6NiK4R0bVx48aV3IrCVdEtnXr37o0kJNGtWzdmzJgBwPe//30aNswuu957771Ly3ONHDmSXXbZxYmvmZmZmZlVq3zM9tw4nfFF0qbAIcB/03W8KLvB3JHApLTJMODUNOvz3sCXETELeBboKalhmuiqZyqzSlTRLZ0gS4gfeughevVa+VLrAQMGlJ4dzjVkyBD69u1bpTGbmZmZmZmVlY9rfpsBD6brfusAj0XEU5JGSWoMCBgPnJ3WHw70BqYCi4HTASJinqTfAG+k9a6NiHnV14wNQ8ktnebPn89RRx3FpEmTaN++PQDnnHMOPXr0YN99911hm+eff54BAwYwevToFcq//fZbhg0bxg033FBt8ZuZmZmZmUEekt+ImAh0Kqf8wArWD+DcCpYNBAZWaoBWrtxbOrVv355rrrmGuXPn8uc//3mF9SZOnMiZZ57J008/zbbbbrvCsqeffprOnTvTtGnT6gzdzMzMzMwsP7M91zRdLhmc7xDW2bibT62yuufOnUv9+vVp0KBB6S2dLrvsMu6//36effZZRo4cSZ06342c//jjjzn66KN56KGH2H333Veq75FHHvGQZzMzq3K1uV+3wlGV39HMbN04+bUKlXdLpyOOOIJ69eqx44470r17dwCOPvpofv3rX3Pttdfy+eefc8455wBQr149SmbvXLRoESNGjFjpTLGZmZmZmVl1cPJrFerQoQNvvfXWSuXFxcXlrn///fdz//33l7ts88035/PPP6/U+MzMzMzMzNZUXm91ZGZmZmZmZlYdnPyamZmZmZnlWLZsGZ06deKII44AYN9996WoqIiioiK23357jjzyyNJ1X3jhBYqKimjXrh377bdfafntt99O+/btadeuHbfddls1t8DK42HPZmZmZmZmOW6//XbatGnDggULAHjppZdKlx1zzDH06dMHgPnz53POOefwzDPP0LJlS+bMmQPApEmTuO+++xgzZgwbbbQRvXr14ogjjmDXXXet/sZYKZ/5NTMzMzMzS2bMmMG//vUvzjzzzJWWLViwgFGjRpWe+f3rX//K0UcfTcuWLQFo0qQJAO+++y577bUXm222GfXq1WO//fbjySefrLY2WPl85ncDUptv/eDbBZiZVS1J04CFwDKgOCK6StoGeBRoBUwDjouILyQJuB3oDSwGTouIN1M9/YBfpWp/GxEPVmc7zMzW1wUXXMBNN93EwoULV1r2j3/8g4MOOoitttoKgPfff5+lS5ey//77s3DhQs4//3xOPfVU2rdvzxVXXMHnn3/OpptuyvDhw+natWt1N8XKcPJrZmZmJQ6IiM9yXl8OjIyIGyVdnl5fBhwG7JYeewF3A3ulZPkqoCsQwDhJwyLii+pshJnZunrqqado0qQJXbp04YUXXlhp+SOPPLLCGeHi4mLGjRvHyJEj+frrr+nevTt77703bdq04bLLLqNnz55svvnmFBUVUbdu3WpsiZXHw57NzMysIn2AkjO3DwJH5pQPjsxrQANJzYBDgRERMS8lvCOAXtUcs5nZOnv55ZcZNmwYrVq14oQTTmDUqFGcfPLJAHz22WeMGTOGww8/vHT9Fi1acOihh7L55pvTqFEjevTowYQJEwDo378/48aN48UXX6Rhw4bsvvvueWmTfcfJr5mZmUF2pvY5SeMknZXKmkbErPT8U6Bpet4cmJ6z7YxUVlH5SiSdJWmspLFz586trDaYma2XG264gRkzZjBt2jSGDBnCgQceyF/+8hcAHn/8cY444gg22WST0vX79OnD6NGjKS4uZvHixbz++uu0adMGoHTyq48//pgnn3ySE088sfobZCvwsGczMzMD2CciZkpqAoyQ9N/chRERkqKydhYR9wL3AnTt2rXS6jUzqypDhgzh8ssvX6GsTZs29OrViw4dOlCnTh3OPPNM2rdvD2SzQn/++efUr1+fu+66iwYNGuQhasvl5NfMzMyIiJnp5xxJfwe6AbMlNYuIWWlY85y0+kxgh5zNW6SymcD+ZcpfqOLQzcyqxP7778/+++9f+rq8a4ABLrnkEi655JKVynNvj2Q1g4c9m5mZbeAkbS5py5LnQE9gEjAM6JdW6wcMTc+HAacqszfwZRoe/SzQU1JDSQ1TPc9WY1PMzMwqVO3Jr6RNJI2RNEHSO5KuSeU7SXpd0lRJj0raKJVvnF5PTctb5dT1i1T+nqRDq7stZmZmBaIpMFrSBGAM8K+IeAa4EThE0hTg4PQaYDjwATAVuA84ByAi5gG/Ad5Ij2tTmZmZWd7lY9jzN8CBEfGVpPpkne3TwEXArRExRNI9QH+yWyf0B76IiF0lnQD8DjheUlvgBKAdsD3wb0m7R8SyPLTJzMys1oqID4CO5ZR/DhxUTnkA51ZQ10BgYGXHaGZmtr6q/cxvui3CV+ll/fQI4EDg8VRe9nYKJbdZeBw4SJJS+ZCI+CYiPiQ7+tyt6ltgZmZmZmZmtU1eJrySVBcYB+wK3AX8D5gfEcVpldxbI5TeNiEiiiV9CWybyl/LqXaVt1MAzgJo2bJlpbbFzMzMzKw26nLJ4HyHYMa4m0+ttn3lZcKriFgWEUVks0B2A1pX8f7ujYiuEdG1cePGVbkrMzMzMzMzq4HyOttzRMwHnge6Aw0klZyJLrllAuTcTiEt3xr4nIpvs2BmZmZmZma2gnzM9txYUoP0fFPgEOBdsiT42LRa2dsplNxm4VhgVJpoYxhwQpoNeidgN7IZKs3MzMzMzMxWkI9rfpsBD6brfusAj0XEU5ImA0Mk/RZ4CxiQ1h8APCRpKjCPbIZnIuIdSY8Bk4Fi4FzP9GxmZmZmZmblqfbkNyImAp3KKf+AcmZrjoglwP9VUNd1wHWVHaOZmZmZmZkVlrxe82tmZmZmZmZWHZz8mpmZmZmZWcFz8mtmZmZmZmYFz8mvmZmZmZmZFTwnv2ZmZmZmZlbwnPyamZmZmZlZwXPya2ZmZmZmZgXPya+ZmZmZmZkVPCe/ZmZmZmZmVvCc/JqZmZmZmVnBc/JrZmZmZmZmBc/Jr5mZmZmZmRU8J79mZmYGgKS6kt6S9FR6vZOk1yVNlfSopI1S+cbp9dS0vFVOHb9I5e9JOjRPTTEzM1tJtSe/knaQ9LykyZLekXR+Kr9a0kxJ49Ojd8425XakknqlsqmSLq/utpiZmRWY84F3c17/Drg1InYFvgD6p/L+wBep/Na0HpLaAicA7YBewJ8k1a2m2M3MzFYpH2d+i4GLI6ItsDdwbuosIetgi9JjOFTckabO9C7gMKAt0DenHjMzM1sLkloAhwP3p9cCDgQeT6s8CByZnvdJr0nLD0rr9wGGRMQ3EfEhMBXoVi0NMDMzW4161b3DiJgFzErPF0p6F2i+ik1KO1LgQ0m5HenUiPgAQNKQtO7kKgvezMyscN0GXApsmV5vC8yPiOL0egbf9dfNgekAEVEs6cu0fnPgtZw6c7cxMzPLq7xe85uuEeoEvJ6KzpM0UdJASQ1TWWkHm5R0pBWVm5mZ2VqQdAQwJyLGVeM+z5I0VtLYuXPnVtduzcxsA5a35FfSFsATwAURsQC4G9gFKCI7M3xLJe7LHayZmVnFfgD8SNI0YAjZcOfbgQaSSkaJtQBmpuczgR0A0vKtgc9zy8vZZgURcW9EdI2Iro0bN67c1piZmZUjL8mvpPpkie/DEfEkQETMjohlEbEcuI/vhjZX1JG6gzUzM6sEEfGLiGgREa3I5tkYFREnAc8Dx6bV+gFD0/Nh6TVp+aiIiFR+QpoNeidgN2BMNTXDzMxslfIx27OAAcC7EfGHnPJmOasdBUxKzyvqSN8Adku3YdiIrLMeVh1tMDMz20BcBlyU5tvYlqz/Jv3cNpVfBFwOEBHvAI+Rzb/xDHBuRCyr9qjNzMzKUe0TXpENrToFeFvS+FT2S7LZmouAAKYBP4GsI5VU0pEWk9ORSjoPeBaoCwxMna6ZmZmto4h4AXghPf+AcmZrjoglwP9VsP11wHVVF6GZmdm6ycdsz6MBlbNo+Cq2KbcjTbdDqnA7MzMzMzMzM8jzbM9mZmZmZmZm1cHJr5mZmZmZmRU8J79mZmZmZmZW8Jz8mpmZmZmZWcFz8mtmZmZmZmYFb72SX0kj16TMzMzMqof7ZjMzs/Kt062OJG0CbAY0ktSQ725dtBXQvJJiMzMzszXkvtnMzGzV1vU+vz8BLgC2B8bxXQe7ALhz/cMyMzOzteS+2czMbBXWKfmNiNuB2yX9LCL+WMkxmZmZ2Vpy32xmZrZq63rmF4CI+KOk7wOtcuuKiMHrGZeZmZmtA/fNZmZm5Vuv5FfSQ8AuwHhgWSoOwB2smZlZHrhvNjMzK996Jb9AV6BtRERlBGNmZmbrzX2zmZlZOdb3Pr+TgO0qIxAzMzOrFO6bzczMyrG+Z34bAZMljQG+KSmMiB+tZ71mZma2btw3m5mZlWN9k9+r13YDSTuQXXfUlOwapHsj4nZJ2wCPkk3QMQ04LiK+kCTgdqA3sBg4LSLeTHX1A36Vqv5tRDy4Xq0xMzOr/a7OdwBmZmY10frO9vyfddisGLg4It6UtCUwTtII4DRgZETcKOly4HLgMuAwYLf02Au4G9grJctXkV3bFKmeYRHxxfq0yczMrDZbx77ZzMys4K3XNb+SFkpakB5LJC2TtGBV20TErJIztxGxEHgXaA70AUrO3D4IHJme9wEGR+Y1oIGkZsChwIiImJcS3hFAr/Vpj5mZWW23Ln2zmZnZhmB9z/xuWfI8DU/uA+y9pttLagV0Al4HmkbErLToU7Jh0ZAlxtNzNpuRyioqNzMz22Ctb99sZmZWqNZ3tudS6czsP8jOyK6WpC2AJ4ALImKFI9Lp9gyVdosGSWdJGitp7Ny5cyurWjMzsxptbftmMzOzQrZeZ34lHZ3zsg7Z9bdL1mC7+mSJ78MR8WQqni2pWUTMSsOa56TymcAOOZu3SGUzgf3LlL9Q3v4i4l7gXoCuXbv6vodmZlaw1qVvlrQJ8CKwMdl3g8cj4ipJOwFDgG2BccApEfGtpI3JJq/sAnwOHB8R01JdvwD6A8uA/xcRz1Zi88zMzNbZ+p75/WHO41BgIdnwqgqlIVgDgHcj4g85i4YB/dLzfsDQnPJTldkb+DINj34W6CmpoaSGQM9UZmZmtiFb676Z7JZIB0ZER6AI6JX63N8Bt0bErsAXZEkt6ecXqfzWtB6S2gInAO3I5uH4k6S6ldc0MzOzdbe+1/yevg6b/QA4BXhb0vhU9kvgRuAxSf2Bj4Dj0rLhZLc5mkp2q6PT077nSfoN8EZa79qImLcu7TAzMysU69I3p8uNvkov66dHAAcCJ6byB8luo3Q3WTJ9dSp/HLgz5/riIRHxDfChpKlAN+DVdWmLmZlZZVrfYc8tgD+SJbQALwHnR8SMiraJiNGAKlh8UDnrB3BuBXUNBAauTcxmZmaFbF365rRdXbKhzbsCdwH/A+ZHRHFaJXdiydJJJyOiWNKXZEOjmwOv5VRb4WSUks4CzgJo2bLlWrTQzMxs3azvsOcHyIYlb58e/0xlZmZmlh/r1DdHxLKIKCKbQ6Mb0LoKYyQi7o2IrhHRtXHjxlW5KzMzM2D9k9/GEfFARBSnxyDAPZiZmVn+rFffHBHzgeeB7kADSSWjxEomnIScySjT8q3JJr6qaJJKMzOzvFvf5PdzSSdLqpseJ5N1fmZmZpYfa903S2osqUF6vilwCPAuWRJ8bFqt7GSUJZNUHguMSpcpDQNOkLRxmil6N2BM5TXNzMxs3a3XNb/AGWTXFd1KNjHGK8Bp61mnmZmZrbt16ZubAQ+m637rAI9FxFOSJgNDJP0WeIvsbg2knw+lCa3mkc3wTES8I+kxYDJQDJwbEcsqs3FmZmbran3P/F4L9IuIxhHRhKzDvWb9wzKrPmeccQZNmjShffv2Ky275ZZbkMRnn30GwBdffMFRRx1Fhw4d6NatG5MmTSpd9/bbb6d9+/a0a9eO2267rbrCNzMra6375oiYGBGdIqJDRLSPiGtT+QcR0S0ido2I/0uzOBMRS9LrXdPyD3Lqui4idomIPSLi6Spsp5mZ2VpZ3+S3Q0R8UfIi3Wqo03rWaVatTjvtNJ555pmVyqdPn85zzz23wiyk119/PUVFRUycOJHBgwdz/vnnAzBp0iTuu+8+xowZw4QJE3jqqaeYOnVqtbXBzCyH+2YzM7NyrG/yW0dSw5IXkrZh/YdSm1WrHj16sM0226xUfuGFF3LTTTeR3boyM3nyZA488EAAWrduzbRp05g9ezbvvvsue+21F5ttthn16tVjv/3248knn6y2NpiZ5XDfbGZmVo71TX5vAV6V9BtJvyG7ruim9Q/LLL+GDh1K8+bN6dix4wrlHTt2LE1qx4wZw0cffcSMGTNo3749L730Ep9//jmLFy9m+PDhTJ8+PR+hm5m5bzYzMyvHeh0JjojBksYCB6aioyNi8vqHZZY/ixcv5vrrr+e5555badnll1/O+eefT1FREXvuuSedOnWibt26tGnThssuu4yePXuy+eabU1RURN26dfMQvZlt6Nw3m5mZlW99z/wSEZMj4s70cOdqtd7//vc/PvzwQzp27EirVq2YMWMGnTt35tNPP2WrrbbigQceYPz48QwePJi5c+ey8847A9C/f3/GjRvHiy++SMOGDdl9993z3JIVlTex15VXXkmHDh0oKiqiZ8+efPLJJ0DFE3tNnz6dAw44gLZt29KuXTtuv/32vLTFzFbNfbOZmdnK1jv5NSs0e+65J3PmzGHatGlMmzaNFi1a8Oabb7Lddtsxf/58vv32WwDuv/9+evTowVZbbQXAnDlzAPj444958sknOfHEE/PWhvKUN7HXJZdcwsSJExk/fjxHHHEE1157LVDxxF716tXjlltuYfLkybz22mvcddddTJ7s79VmZmZmVvM5+bUNXt++fenevTvvvfceLVq0YMCAARWu++6779K+fXv22GMPnn766RXOfB5zzDG0bduWH/7wh9x11100aNCgGqJfc+VN7FWSuAMsWrSodHKviib2atasGZ07dwZgyy23pE2bNsycObOaWmBmZmZmtu48+6Nt8B555JFVLp82bVrp8+7du/P++++Xu95LL71UmWFVmyuuuILBgwez9dZb8/zzzwPfTey17777rjCxV9OmTUu3mzZtGm+99RZ77bVXvkI3MzMzM1tj1X7mV9JASXMkTcopu1rSTEnj06N3zrJfSJoq6T1Jh+aU90plUyVdXt3tMCsU1113HdOnT+ekk07izjvvBLKJvebPn09RURF//OMfSyf2KvHVV19xzDHHcNttt61w9tjMzMzMrKbKx7DnQUCvcspvjYii9BgOIKktcALQLm3zJ0l1JdUF7gIOA9oCfdO6ZraOTjrpJJ544gmAVU7stXTpUo455hhOOukkjj766HyGbGZmZma2xqo9+Y2IF4F5a7h6H2BIRHwTER8CU4Fu6TE1Ij6IiG+BIWldM1sLU6ZMKX0+dOhQWrduDVDhxF4RQf/+/WnTpg0XXXRRXmI2MzMzM1sXNema3/MknQqMBS6OiC+A5sBrOevMSGUA08uU+8JDs1Xo27cvL7zwAp999hktWrTgmmuuYfjw4bz33nvUqVOHHXfckXvuuQfIJvbq168fkmjXrl3pJGAvv/wyDz30EHvuuSdFRUVANjN07969K9qtmZmZmVmNUFOS37uB3wCRft4CnFFZlUs6CzgLoGXLlpVVrdVQXS4ZnO8Q1tm4m0+tsrrLm9irf//+5a5b0cRe++yzDxFR6bGZmZmZmVW1GnGro4iYHRHLImI5cB/ZsGaAmcAOOau2SGUVlVdU/70R0TUiujZu3LhygzczMzMzM7Mar0Ykv5Ka5bw8CiiZCXoYcIKkjSXtBOwGjAHeAHaTtJOkjcgmxRpWnTGbmZmZmZlZ7VHtw54lPQLsDzSSNAO4CthfUhHZsOdpwE8AIuIdSY8Bk4Fi4NyIWJbqOQ94FqgLDIyId6q3JWZmZmZmZlZbVHvyGxF9yykesIr1rwOuK6d8ODC8EkMzMzMzMzOzAlVTJrwys7Xkib3MzMzMzNZcjbjm18zMzMzMzKwqOfk1MzPbwEnaQdLzkiZLekfS+al8G0kjJE1JPxumckm6Q9JUSRMldc6pq19af4qkfvlqk5mZWVlOfs3MzKwYuDgi2gJ7A+dKagtcDoyMiN2Akek1wGFkd2DYDTgLuBuyZJlsIsu9yG5beFVJwmxmZpZvTn7NzMw2cBExKyLeTM8XAu8CzYE+wINptQeBI9PzPsDgyLwGNEi3LTwUGBER8yLiC2AE0Kv6WmJmZlYxJ79mZmZWSlIroBPwOtA0ImalRZ8CTdPz5sD0nM1mpLKKys3MzPLOya+ZmZkBIGkL4AnggohYkLssIgKIStzXWZLGSho7d+7cyqrWzMysQk5+zczMDEn1yRLfhyPiyVQ8Ow1nJv2ck8pnAjvkbN4ilVVUvpKIuDciukZE18aNG1deQ8zMzCrg5NfMzGwDJ0nAAODdiPhDzqJhQMmMzf2AoTnlp6ZZn/cGvkzDo58FekpqmCa66pnKzMzM8q5evgMwMzOzvPsBcArwtqTxqeyXwI3AY5L6Ax8Bx6Vlw4HewFRgMXA6QETMk/Qb4I203rURMa9aWmBmZrYaTn7NzMw2cBExGlAFiw8qZ/0Azq2groHAwMqLzszMrHJ42LOZmZmZmZkVPCe/ZmZmZmZmVvCc/JqZmZmZmVnBy0vyK2mgpDmSJuWUbSNphKQp6WfDVC5Jd0iaKmmipM452/RL60+R1K+8fZmZmZmZmZnl68zvIKBXmbLLgZERsRswMr0GOAzYLT3OAu6GLFkGrgL2AroBV5UkzGZmZmZmZma58pL8RsSLQNlbH/QBHkzPHwSOzCkfHJnXgAaSmgGHAiMiYl5EfAGMYOWE2szMzMzMzKxGXfPbNCJmpeefAk3T8+bA9Jz1ZqSyisrNzMzMzMzMVlCTkt9S6f6BUVn1STpL0lhJY+fOnVtZ1ZqZmZmZmVktUZOS39lpODPp55xUPhPYIWe9FqmsovKVRMS9EdE1Iro2bty40gM3MzMzMzOzmq0mJb/DgJIZm/sBQ3PKT02zPu8NfJmGRz8L9JTUME101TOVmZmZmZmZma2gXj52KukRYH+gkaQZZLM23wg8Jqk/8BFwXFp9ONAbmAosBk4HiIh5kn4DvJHWuzYiyk6iZWZmZmZmZpaf5Dci+law6KBy1g3g3ArqGQgMrMTQzMzMzMzMrADVpGHPZmZmZmZmZlXCya+ZmZmZmZkVPCe/ZmZmZmZmVvCc/JqZmZmZmVnBc/JrZmZmZmZmBc/Jr5mZmZmZmRU8J79mZmZmZmZW8Jz8mpmZmZmZWcFz8mtmZmZIGihpjqRJOWXbSBohaUr62TCVS9IdkqZKmiipc842/dL6UyT1y0dbzMzMyuPk18wK1pIlS+jWrRsdO3akXbt2XHXVVQCMGjWKzp070759e/r160dxcTEAX3zxBUcddRQdOnSgW7duTJo0aVXVmxWaQUCvMmWXAyMjYjdgZHoNcBiwW3qcBdwNWbIMXAXsBXQDripJmM3MzPLNya+ZFayNN96YUaNGMWHCBMaPH88zzzzDK6+8Qr9+/RgyZAiTJk1ixx135MEHHwTg+uuvp6ioiIkTJzJ48GDOP//8PLfArPpExIvAvDLFfYAH0/MHgSNzygdH5jWggaRmwKHAiIiYFxFfACNYOaE2MzPLCye/ZlawJLHFFlsAsHTpUpYuXUrdunXZaKON2H333QE45JBDeOKJJwCYPHkyBx54IACtW7dm2rRpzJ49Oz/Bm9UMTSNiVnr+KdA0PW8OTM9Zb0Yqq6jczMws75z8mllBW7ZsGUVFRTRp0oRDDjmEbt26UVxczNixYwF4/PHHmT49+67esWNHnnzySQDGjBnDRx99xIwZM/IWu1lNEhEBRGXVJ+ksSWMljZ07d25lVWtmZlYhJ79mVtDq1q3L+PHjmTFjBmPGjOGdd95hyJAhXHjhhXTr1o0tt9ySunXrAnD55Zczf/58ioqK+OMf/0inTp1Kl5ltoGan4cykn3NS+Uxgh5z1WqSyispXEhH3RkTXiOjauHHjSg/czMysrBqX/EqaJultSeMljU1laz3bpJlZrgYNGnDAAQfwzDPP0L17d1566SXGjBlDjx49SodAb7XVVjzwwAOMHz+ewYMHM3fuXHbeeec8R26WV8OAkhmb+wFDc8pPTf3w3sCXaXj0s0BPSQ1TX90zlZmZmeVdjUt+kwMioigiuqbXazXbpJkZwNy5c5k/fz4AX3/9NSNGjKB169bMmZOdvPrmm2/43e9+x9lnnw3A/Pnz+fbbbwG4//776dGjB1tttVVeYjerbpIeAV4F9pA0Q1J/4EbgEElTgIPTa4DhwAfAVOA+4ByAiJgH/AZ4Iz2uTWVmZmZ5Vy/fAayhPsD+6fmDwAvAZeTMNgm8JqmBpGY5k3OY2QZs1qxZ9OvXj2XLlrF8+XKOO+44jjjiCC655BKeeuopli9fzk9/+tPSSa7effdd+vXrhyTatWvHgAED8twCs+oTEX0rWHRQOesGcG4F9QwEBlZiaGZmZpWiJia/ATwnKYA/R8S9rP1skyskv5LOIjszTMuWLaswdDOrSTp06MBbb721UvnNN9/MzTffvFJ59+7def/996sjNDMzMzOrZjUx+d0nImZKagKMkPTf3IURESkxXmMpgb4XoGvXrpU2U6WZmZmZmZnVDjXumt+ImJl+zgH+DnRj7WebNDPbYJxxxhk0adKE9u3bl5Ydf/zxFBUVUVRURKtWrSgqKipdNnHiRLp37067du3Yc889WbJkSR6iNjMzM6teNSr5lbS5pC1LnpPNEjmJtZ9t0sxsg3HaaafxzDPPrFD26KOPMn78eMaPH88xxxzD0UcfDUBxcTEnn3wy99xzD++88w4vvPAC9evXz0fYZmZmZtWqpg17bgr8XRJksf01Ip6R9AbwWJp58iPguLT+cKA32WyTi4HTqz9kM7P86tGjB9OmTSt3WUTw2GOPMWrUKACee+45OnToQMeOHQHYdtttqytMMzMzs7yqUclvRHwAdCyn/HPWcrZJMysMXS4ZnO8Q1tm4m0/Ndwi89NJLNG3alN122w2A999/H0kceuihzJ07lxNOOIFLL700z1GamZmZVb0alfyamVnleuSRR+jb97s72BQXFzN69GjeeOMNNttsMw466CC6dOnCQQetdHzRzMzMrKDUqGt+zcys8hQXF/Pkk09y/PHHl5a1aNGCHj160KhRIzbbbDN69+7Nm2++mccozczMzKqHk18zswL173//m9atW9OiRYvSskMPPZS3336bxYsXU1xczH/+8x/atm2bxyjXXatWrdhzzz0pKiqia9euKyy75ZZbkMRnn32Wp+jMzMyspnHya2ZWy/Xt25fu3bvz3nvv0aJFCwYMGADAkCFDVhjyDNCwYUMuuugivve971FUVETnzp05/PDD8xF2pXj++ecZP348Y8eOLS2bPn06zz33HC1btsxjZGZmZlbT+JpfM7Na7pFHHim3fNCgQeWWn3zyyZx88slVGFF+XXjhhdx000306dMn36GYmZlZDeIzv2ZmVitJomfPnnTp0oV7770XgKFDh9K8efPSWzmZmZmZlfCZXzMzq5VGjx5N8+bNmTNnDocccgitW7fm+uuv57nnnst3aGZmZlYD+cyvmZnVSs2bNwegSZMmHHXUUfznP//hww8/pGPHjrRq1YoZM2bQuXNnPv300zxHamZmZjWBk18zM6t1Fi1axMKFC0ufP/fcc3zve99jzpw5TJs2jWnTptGiRQvefPNNtttuuzxHa2ZmZjWBhz2bmdUAXS4ZnO8Q1tm4m0+t9n3Onj2bo446CsjuZ3ziiSfSq1evao/DzMzMag8nv2ZmVuvsvPPOTJgwYZXrTJs2rXqCMTMzs1rBw57NzMzMzMys4Dn5NTMzMzMzs4JX65NfSb0kvSdpqqTL8x2PmZnZhs59s5mZ1US1OvmVVBe4CzgMaAv0ldQ2v1GZmZltuNw3m5lZTVXbJ7zqBkyNiA8AJA0B+gCT8xqVmZmVy7NabxDcN5uZWY1Uq8/8As2B6TmvZ6QyMzMzyw/3zWZmViMpIvIdwzqTdCzQKyLOTK9PAfaKiPPKrHcWcFZ6uQfwXrUGCo2Az6p5n/ngdhaODaGN4HYWmny0c8eIaFzN+6zRalHfbKu2ofzfMFtf/lupeSrsm2v7sOeZwA45r1ukshVExL3AvdUVVFmSxkZE13ztv7q4nYVjQ2gjuJ2FZkNpZy1QK/pmWzX/PZmtGf+t1C61fdjzG8BuknaStBFwAjAszzGZmZltyNw3m5lZjVSrz/xGRLGk84BngbrAwIh4J89hmZmZbbDcN5uZWU1Vq5NfgIgYDgzPdxyrsaEM63I7C8eG0EZwOwvNhtLOGq+W9M22av57Mlsz/lupRWr1hFdmZmZmZmZma6K2X/NrZmZmZmZmtlq1fthzTZUm+egIdCY7yPBkRMzOb1SVT1J9oAPQDfgK+GdEzM9rUFVA0qZAW+CTiJglSVGgwyYkdQKOAx6NiPGF2lZJuwA/Ar4EhkVEwd2mIOfvsz2wH/DXiPh3fqOqGpIaAYcDBwGvkl1n+k1+ozKrfSRtRnbrqe7AlIgYkeeQzGqs3O9IkupExPJ8x2Sr5jO/Vacf8CeyBLgL8AtJG+c3pCrxE7J2dgB6Ar/JbziVT9JOZF+mXwXOScUF97cjqaRNh5Lde/MHeQynSklqClxF9re5H/Dr/EZUZc4FXgKKgP8CH+Q1miogSenp8UAfYALwFtlES2a2FiT9gOxA9hVAD0Cr3sJswyNp43SSi4gISRdJmkF28NVqOJ/5rToDIuI+AEnbAc8AWwNz8hpVJUpfOp8GBkfEAkl7ALdIahUR0/IbXaWaBZwCtCa7ZQdAwZ0JjYjl6WxoW+DG9BOyLz+F1t5uQKuI6CGpBXCtpOMi4rF8B1bJpgCjIuLCfAdSVdIXj05Az4jok+94zGq56cArEXFsvgMxq4kk7Qv8h+yA699ScQOynGpXwCMlariCO3tVU6REor6kU4FbgGciomASX8i+dEbE/yJiQSqaTZYgFtTw7ohYEhFvAzOAlqmsUIe17Ap8AYwDtoeCbWsd4OP0/HPgNbKEuNBMBIoknSTpKkl75TugKrIU2FlSe0k3STpntVuYWXk+JbtH8/aSTvbfktlKpqSfP1CmGbAN8Bey0Z5Wwzn5rUIRsZRs6OhcoH06O1FwcoYd/hR4iuyLaCH6ENhY0pb5DqQKnQX8HJgKLJdUqENHlwFfS9okIr4mG5GxZaF9thExHXgPOJiszb+XdEh+o6oSy4AlwHnAJ0D3lOxvlt+wzGqXiPgWqE92Rutgsn7A3xXNvvM58C1Z33p2et2d7NZuzfIYl60hD3uuQuki+J+k5+cAx0p6NyKW5Dm0SpWGHe5Kdq3oJRFRnO+Yqsg8YAGwJ/BKoU0EJakN8A1wGtAO2Ad4XlL/iJiyqm1roelkX/BakCX63wKLyIYuLcxfWFXiiJTgI2kRcLCklwrs/9CnZKMyboqIv0lqC1xLNtHXmLxGZlb7jAYui4h38x2IWU0TEUslfUk2vPnPwItkc00UA0hqUmgjPQuNj+ZVoTKJ0SKyYaQFNXlEzlnfc4AHIuKNfMZTldIR8f8C26XXBZP4JhuRDd3pQ/Z7+hbwWERMKcAj//8jO/hXMqnXrkD9dKa0oJQkvsl8oFGeQqkyEfEF2TVYDVPRYrL/uYU4yaBZVXue7GA2BTpRp9n6eoNsLpQxwGBgLNmlcUuAXfIYl60Bn/mtIpLqATuSjf/fgWz40ONlvojWeums7zHA/5HlwgeQHf36ZaEd+ZK0LdmkZTdL+h4wJCIm5DmsSpPa0qvktaRr+G4IT0El+hHxlaQXgKMl7QB8n+x6HQrpjH46ONUM2Jvs/9AhZL+3hXTWt8SjwKGSmgDfA96JiJcK6fM0qyavAhcBt5FdUmBmK3oeOBC4ju/muplONkKwHdnfkNVQTn6rSEQUS+pAdg3leGAA8Fxeg6o6TYCZZEMPxwKvF2DiuynwGNntU4aRDXEp1NvGiOx/w4PA11CQZ7kBBpFNenUkcD/ZzOUF1dZ0cKoD2fX448ja+Wx+o6oaEfGEpI/IZuD8C9mZ4IL6PM2qyVSgaXru5NdsZa8AF0bEfZLOJ0t665J9p2iQz8Bs9eTvBWZmZmZmZqsnqRHwRETsl+9YbO05+TUzMzMzM7OCV2iT2JiZmZmZmZmtxMmvmZmZmZmZFTwnv2ZmZmZmZlbwnPyamZmZmZlZwXPya2ZmZmZmZgXPya9ZNZLUStKJ67H9aZK2r8yYKoOkIkm9V7NOa0mvSvpG0s9zyneQ9LykyZLeSffMMzMzs9WQNEjSsfmOw6y2cPJrVr1aAeuc/AKnATUu+QWKgFUmv2Q3gf9/wO/LlBcDF0dEW2Bv4FxJbSs9QjMzsw2cpHr5jsEsn5z8mq0FSadKmihpgqSH0pncUalspKSWab1Bku6Q9IqkD3KOyt4I7CtpvKQL0/YvSXozPb6fs6/LJL2d9nVjqqMr8HDaftNUPjntv2xSmRt3U0l/T3VNKNmPpIskTUqPC1JZK0mTcrb9uaSr0/MXJP1O0hhJ70vaV9JGwLXA8Smu48uLISLmRMQbwNIy5bMi4s30fCHwLtB8FW15QdKtksZKelfS9yQ9KWmKpN/mrFdR296VdF86y/ycpE0r2peZmVllkXSlpPckjZb0SOpfd5H0jKRx6ftA67Ruud8jlLkz1fNvoElO/V0k/SfV9aykZqn8BUm3SRoLeHSVbdB89MdsDUlqB/wK+H5EfCZpG+BB4MGIeFDSGcAdwJFpk2bAPkBrYBjwOHA58POIOCLVuRlwSEQskbQb8AjQVdJhQB9gr4hYLGmbiJgn6by0/VhJ2wJHAa0jIiQ1WEX4dwD/iYijJNUFtpDUBTgd2AsQ8Lqk/wBfrOatqBcR3ZQNc74qIg6W9Guga0Sct2bvZvkktQI6Aa+vZtVvI6KrsiHSQ4EuZGeW/yfpVrIz7BW1bTegb0T8WNJjwDHAX9YnbjMzs1WR9D2y/qYjUB94ExgH3AucHRFTJO0F/Ak4MG1W3veIo4A9gLZAU2AyMFBSfeCPQJ+ImJsORF8HnJHq2igiulZ5Q81qOCe/ZmvuQOBvEfEZQEpGuwNHp+UPATflrP+PiFgOTJbUtII66wN3SioClgG7p/KDgQciYnHJvsrZ9ktgCTBA0lPAU6uJ/dRU1zLgS0n7AH+PiEUAkp4E9iXrYFflyfRzHFmSWSkkbQE8AVwQEQtWs3pJjG8D70TErFTHB8AOZF8WKmrbhxExviraYGZmVoEfAEMjYgmwRNI/gU2A7wN/k1Sy3sY525T3PaIH8Ejqyz+RNCqV7wG0B0akuuoCs3LqerQK2mRW6zj5Nas63+Q8VwXrXAjMJjsSXIcsmV0jEVEsqRtwEHAscB7fHS1eH8WseEnEJmWWl7RrGZX0PyQdsX4CeDginlzd+jkxLGfF93n5GsSUu/4ywMOezcwsH+oA8yOiqILla/I9Inf5OxHRvYLli9YyNrOC5Gt+zdbcKOD/0nBj0rDnV4AT0vKTgJdWU8dCYMuc11sDs9KR3VPIjtQCjABOT8OiS/a1wvbpTOnWETGcLInuuIr9jgR+mrarK2nrFOuRkjaTtDnZUKqXyJLxJpK2lbQxcMRq2lReu9aYskPUA4B3I+IP61JHOSpqm5mZWT68DPxQ0iap/z4CWAx8KOn/oPR63lX15QAvks2xUTdd03tAKn8PaJxGpCGpfrpcy8xyOPk1W0MR8Q7Z9TP/kTQB+APwM7IkdSJZ8rq6iSQmAsuUTTp1Idm1Pf1Sfa1JR2Yj4hmyIbpjJY0HSm4NNAi4J5VtCTyV9j0auGgV+z0fOEDS22RDfdumSaYGAWPIrrG9PyLeioilZBNYjSFLwv+7+neH54G2WsWEV5K2kzQjxfkrSTMkbUU2FOwU4MC0/Xit5rZJq1NR29anTjMzs3WVJnwcRvY94Gmyy3a+JDtw3j99D3iHbL6PVfk7MIXsWt/BwKup/m/JRoH9LtU1nmxItZnlUETkOwYzMzMzs4ImaYuI+CqN6noROKvkbgdmVj18za+ZmZmZWdW7V9l97Dchu1OEE1+zauYzv2YFRNIVwP+VKf5bRFxXjTGczsrDv1+OiHPXsp67yIZE57o9Ih5Yn/jMzMzMbMPk5NfMzMzMzMwKnie8MjMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzMzMzMreE5+zczMzMzMrOA5+TUzMzMzM7OC5+TXzMzMzMzMCp6TXzMzMzMzMyt4Tn7NzMzMzMys4Dn5NTMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzMzMzMreE5+zczMzMzMrOA5+TUzMzMzM7OC5+TXzMzMzMzMCp6TXzMzMzMzMyt4Tn7NzMzMzMys4Dn5NTMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzMzMzMreE5+zczMzMzMrOA5+TUzMzMzM7OC5+TXzMzMzMzMCp6TXzMzMzMzMyt4Tn7NzMzMzMys4Dn5NTMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzMzMzMreE5+zczMzMzMrOA5+TUzMzMzM7OC5+TXzMzMzMzMCp6TXzMzMzMzMyt4Tn7NzMzMzMys4Dn5NTMzMzMzs4Ln5NfMzMzMzMwKnpNfMzMzMzMzK3hOfs3MzMzMzKzgOfk1MzMzMzOzgufk18zMzMzMzAqek18zMzMzMzMreE5+reBIukfSlZVUV0tJX0mqm16/IOnMyqg71fe0pH6VVd9a7Pe3kj6T9Gkl1DVI0m8rI6613O87kvavwvqrvF2STpM0uir3YWZW07ifXqP9Vlo/vY77/0rSzuu4rfs2q7Hq5TsAs7UhaRrQFCgGlgGTgcHAvRGxHCAizl6Lus6MiH9XtE5EfAxssX5Rl+7vamDXiDg5p/7DKqPutYyjJXAxsGNEzKnu/a8LSYOAGRHxq5KyiGiXv4jMzKw87qcrJY6899MRUfqeltcHV6W13Z+k08h+T/apyrisMPjMr9VGP4yILYEdgRuBy4ABlb0TSYV6cKgl8HltSXzNzKzWcT+9fvLWTxfwe2oGOPm1WiwivoyIYcDxQD9J7WHF4aqSGkl6StJ8SfMkvSSpjqSHyDqXf6ahPZdKaiUpJPWX9DEwKqcstzPYRdIYSQskDZW0TdrX/pJm5MYoaZqkgyX1An4JHJ/2NyEtLx2eleL6laSPJM2RNFjS1mlZSRz9JH2chkJdUdF7I2nrtP3cVN+vUv0HAyOA7VMcgyrY/ghJ49P79oqkDjnLOkl6U9JCSY8Cm+QsW2moU4p71/R8U0m3pJi+lDRa0qZp2d8kfZrKX5TULpWfBZwEXJpi/mfue5uebyzpNkmfpMdtkjbO/VwkXZze11mSTq/ovVvFe1rueyLpMkmPl1n3dkl35HwWA9J+ZyobylZ3bfdvZlbbuJ+umn46p1+7NKdfO1JSb0nvp/fxlznrd5P0anqPZ0m6U9JGOctD0rmSpgBTcsp2XUUffLmk/yn7LjBZ0lGr/41YoQ2SdGuKf4GktyW1X9v9SWoD3AN0T+vPL/u5pdel308q2vfaxG+1l5Nfq/UiYgwwA9i3nMUXp2WNyYZh/TLbJE4BPiY7Or1FRNyUs81+QBvg0Ap2eSpwBtCMbFjXHWsQ4zPA9cCjaX8dy1nttPQ4ANiZbBjXnWXW2QfYAzgI+HX6p1+ePwJbp3r2SzGfnoaOHQZ8kuI4reyGkjoBA4GfANsCfwaGKUswNwL+ATwEbAP8DThmde3P8XugC/D9tP2lwPK07GlgN6AJ8CbwMEBE3Jue35Ri/mE59V4B7A0UAR2BbkDucKnt0vvRHOgP3CWp4ZoGvar3BBgC9Ja0ZVq3LnAc8Ne0+SCy35NdgU5AT6DSrkczM6vp3E+Xa5376WQ7soPPzYFfA/cBJ5P1sfsCV0raKa27DLgQaAR0T7GdU6a+I4G9gLa5havog/+X9rM1cA3wF0nNKoi1PD2BHsDuqY7jyM52r9X+IuJd4Gzg1bR+g3Xd91rEbrWYk18rFJ+QJVNlLSXr/HaMiKUR8VJExGrqujoiFkXE1xUsfygiJkXEIuBK4DhVzpm8k4A/RMQHEfEV8AvghDJHs6+JiK8jYgIwgSzRW0GK5QTgFxGxMCKmAbcAp6xhHGcBf46I1yNiWUQ8CHxDllzuDdQHbkvv5+PAG2tSqaQ6ZF9Gzo+ImanuVyLiG4CIGJji/Qa4GuhYckR9DZwEXBsRcyJiLlnHmNvepWn50ogYDnxF9uVkTVX4nkTER2TJeslR7wOBxRHxmqSmQG/ggvQ7NQe4lezzMTPbkLifTiqhn4bsfbsuIpaSHYRtBNye6nuH7FrrjgARMS4iXouI4rSvP5Ml3LluiIh5q3hPVxARf4uITyJieUQ8SnbGuNtaxr8l0BpQRLwbEbOqcH/rvG8rLE5+rVA0B+aVU34zMBV4TtIHki5fg7qmr8Xyj8iSwUZrFOWqbZ/qy627HtmR8BK5sz4upvxJPhqlmMrW1XwN49gRuDgNj5qfhhDtkOLbHphZ5ovJR+XUUZ5GZEep/1d2gaS6km5MQ5oWANNytlkT5b132+e8/jwiinNeV/TeVWRV7wlkZ3n7pucn8t1Z3x3JPotZOdv9mezstpnZhsT99HfWt5+GrF9blp6XJKyzc5Z/XbJvSbsrG1r+aepjr2fl92N17+kKJJ2q7y4Fmg+0L6fOCkXEKLKz5ncBcyTdK2mrqtrf+uzbCouTX6v1JH2PrMNYaVr9dAT04ojYGfgRcJGkg0oWV1Dl6o4475DzvCXZEcTPgEXAZjlx1SUbxrWm9X5Clizl1l3Mip3ZmvgsxVS2rplruP10sqPJDXIem0XEI8AsoLkklam7RNn3YLsycS0BdilnnycCfYCDyYYgtSqpIv1cl/fuk9VsszZW9Z5ANvx7f0ktyM4A/zVnu2+ARjnbbRWeqdrMNiDup1eyvv302rob+C+wW0RsRTa0XGXWWVXbV1gmaUeyYdbnAdumocaTyqlzlSLijojoQjbUenfgknXcX3mxr/BZkw0TX5N9W4Fz8mu1lqStJB1BNtznLxHxdjnrHJEmbBDwJdl1LyXXmM4mu9ZmbZ0sqa2kzYBrgcfT0df3gU0kHS6pPtk1pxvnbDcbaJWG/5bnEeBCSTtJ2oLvrj0qrmD9cqVYHgOuk7Rl6jQuAv6yhlXcB5wtaa80KcTmqU1bAq+SdfT/T1J9SUez4rCjCUA7SUWSNiEbvlwS13Ky62b/IGn7dLa3e7pudkuyJPFzss7q+v/P3n2HWVWdbx//3oBiQUUUlCqoKEVgRERNjDXYe4zRVyNYYor+okaNphp7jyUxGiMqaiIaG8bYCEgSK4KCBVFRMYBIR1AslOf9Y60ZDsMMdZgzc7g/1zXXnLN2OWufstd+VtuV8rS8z+o+4NeSmkvanDT+aUWPd0Us6z0hd7UeBtwJfJjHIJG7UT0DXJe/rw0kbSOpcnczM7OS43K6ajVQTq+sjYA5wGeSOgE/XsntK38OG5ICzmkASpNIrtSEUZJ2zmXqOqRA9Uuq/9yX93pTgDYqmMQLGAUcJWkDpUk3T1nB17YS5+DX6qN/SJpLalX7FfB7oLrZezsC/yKN8XwR+FNEPJuXXUEKmGZLOnclXv8e0iRGn5C68f4U0qyWpAkkbifV3n5OmsSj3N/z/xmSXq1iv3fkff8H+JB0Mv6/lchXof/Lr/8Bqab9b3n/yxURI4AfkLoEzSJ1R+uXl30NHJWfzyTN4Plwwbbvki40/kUaj1O5lv9c4A3SOOGZwFWk89DdpC5fk0jjlF6qtF1/oEv+rB6tItuXAiOA1/P+X81pNWJZ70mBv5Farv9WKf1EYF3Scc0CHiSNbzMzK1Uup5dvlcvpVXAuqYfVXFJl7v0ruf0SZXBEjCGNUX6RFHh2A55fyX1unPMyi1T+zyB1gV+V1xsKvAV8Iml6Trse+DqvP4A8ieYKvLaVOC1/TgEzMzMzMzOz+s0tv2ZmZmZmZlbyHPya2VpL0luSPqvi7/hi583MzKw+k/StasrYz4qdN1t7uduzmZmZmZmZlTy3/JqZmZmZmVnJc/BrZmZmZmZmJc/Br5mZmZmZmZU8B79mZmZmZmZW8hz8mpmZmZmZWclz8GtmZmZmZmYlr1GxM1DbNt9882jfvn2xs2FmZiVg5MiR0yOiebHzUd+5bDYzs5qyrLJ5rQt+27dvz4gRI4qdDTMzKwGSPip2HkqBy2YzM6spyyqb3e3ZzMzMzMzMSp6DXzMzMzMzMyt5Dn7NzMzMzMys5Dn4NTMzMzMzs5Ln4NfMzMzMzMxKnoNfMzMzMzMzK3kOfs3MzMzMzKzk1XrwK2k9ScMljZb0lqSLcvpdkj6UNCr/leV0SbpJ0jhJr0vqWbCvvpLey399a/tYzMzMzMzMrH5oVITX/ArYJyI+k7QO8JykJ/Oy8yLiwUrrHwh0zH+7ALcAu0hqBlwI9AICGCnpsYiYVStHYWZmZmZmZvVGrbf8RvJZfrpO/otlbHI4cHfe7iWgqaSWwP7A4IiYmQPewcABazLvZmZmZmZmVj8VZcyvpIaSRgFTSQHsy3nRZblr8/WSGue01sCEgs0n5rTq0s3MzMzMzMyWUJTgNyIWRkQZ0AboLWkH4BdAJ2BnoBlwfk29nqTTJI2QNGLatGk1tVszMzMzMzOrJ4o623NEzAaeBQ6IiMm5a/NXwJ1A77zaJKBtwWZtclp16VW9zm0R0SsiejVv3ryGj8LMzMzMzMzqumLM9txcUtP8eH2gDzA2j+NFkoAjgDfzJo8BJ+ZZn3cFPo2IycDTwH6SNpW0KbBfTjMzMzMzMzNbQjFme24JDJDUkBR8PxARj0saKqk5IGAU8KO8/hPAQcA4YB5wEkBEzJR0CfBKXu/iiJhZe4dhZmZmZmZm9UWtB78R8TqwYxXp+1SzfgCnV7PsDuCOGs2gmZnZWkjSeGAusBBYEBG98m0F7wfaA+OBYyJiVu6ldSOpcnoe0C8iXs376Qv8Ou/20ogYUJvHYWZmVp2ijvmtr7788kt69+5Njx496Nq1KxdeeCEAf/zjH9l2222RxPTp0yvW/+tf/0r37t3p1q0b3/jGNxg9enTFsuuvv56uXbuyww47cNxxx/Hll1/W+vGYmZlle0dEWUT0ys8vAIZEREdgSH4OcCDQMf+dBtwCkIPlC4FdSHN3XJiHJtVrCxcuZMcdd+SQQw4BICL41a9+xXbbbUfnzp256aabAPj000859NBDK64P7rzzTgCeffZZysrKKv7WW289Hn300WIdjpnZWqsY3Z7rvcaNGzN06FCaNGnC/Pnz2X333TnwwAP55je/ySGHHMJee+21xPodOnTg3//+N5tuuilPPvkkp512Gi+//DKTJk3ipptuYsyYMay//vocc8wxDBw4kH79+hXluMzMzCo5HNgrPx4ADCPdjeFw4O7cO+slSU3z3B17kW5hOBNA0mDgAOC+2s12zbrxxhvp3Lkzc+bMAeCuu+5iwoQJjB07lgYNGjB16lQAbr75Zrp06cI//vEPpk2bxvbbb8/xxx/P3nvvzahRowCYOXMm2267Lfvtt1+xDsfMbK3llt9VIIkmTZoAMH/+fObPn48kdtxxR9q3b7/U+t/4xjfYdNNU8b3rrrsyceLEimULFizgiy++YMGCBcybN49WrVrVyjGYmZlVEsAzkkZKOi2nbZEnmQT4BNgiP24NTCjYdmJOqy693po4cSL//Oc/OfXUUyvSbrnlFn7729/SoEG6jGrRogWQrg/mzp1LRPDZZ5/RrFkzGjVasp3hwQcf5MADD2SDDTaovYMwMzPAwe8qW7hwIWVlZbRo0YI+ffqwyy67rNB2/fv358ADDwSgdevWnHvuubRr146WLVuyySabuCbYzMyKZfeI6Enq0ny6pD0KF+ZW3qipF5N0mqQRkkZMmzatpnZb48466yyuvvrqikAX4P333+f++++nV69eHHjggbz33nsAnHHGGbz99tu0atWKbt26ceONNy6xHcDAgQM57rjjavUYzMwscfC7iho2bMioUaOYOHEiw4cP580331zuNs8++yz9+/fnqquuAmDWrFkMGjSIDz/8kI8//pjPP/+ce++9d01n3czMbCkRMSn/nwo8QhqzO6XgVoQtgal59UlA24LN2+S06tKrer3bIqJXRPRq3rx5TR5KjXn88cdp0aIFO+200xLpX331Feuttx4jRozgBz/4ASeffDIATz/9NGVlZXz88ceMGjWKM844o6KrNMDkyZN544032H///Wv1OMzMLHHwu5qaNm3K3nvvzVNPPbXM9V5//XVOPfVUBg0axGabbQbAv/71Lzp06EDz5s1ZZ511OOqoo3jhhRdqI9tmZmYVJG0oaaPyx8B+wJvAY0DfvFpfYFB+/BhwopJdgU9z9+ingf0kbZonutovp9VLzz//PI899hjt27fn2GOPZejQoZxwwgm0adOGo446CoAjjzyS119/HYA777yTo446Cklsu+22dOjQgbFjx1bs74EHHuDII49knXXWKcrxmJmt7Rz8roJp06Yxe/ZsAL744gsGDx5Mp06dql3/f//7H0cddRT33HMP2223XUV6u3bteOmll5g3bx4RwZAhQ+jcufOazr6ZmVllWwDPSRoNDAf+GRFPAVcCfSS9B3w7Pwd4AvgAGAf8BfgJQJ7o6hLglfx3cfnkV/XRFVdcwcSJExk/fjwDBw5kn3324d577+WII47g2WefBeDf//53Rdnerl07hgwZAsCUKVN455132HrrrSv2d99997nLs5lZEXm251UwefJk+vbty8KFC1m0aBHHHHMMhxxyCDfddBNXX301n3zyCd27d+eggw7i9ttv5+KLL2bGjBn85Cc/AaBRo0aMGDGCXXbZhaOPPpqePXvSqFEjdtxxR0477bTlvLqZmVnNiogPgB5VpM8A9q0iPYDTq9nXHcAdNZ3HuuSCCy7g+OOP5/rrr6dJkybcfvvtAPzmN7+hX79+dOvWjYjgqquuYvPNNwdg/PjxTJgwgT333LOYWTczW6splV9rj169esWIESOKnQ0zMysBkkYW3BPXVpHLZjMzqynLKpvd7dnMzMzMzMxKnoNfMzMzMzMzK3kOfs3MzMzMzKzkOfg1MzMzMzOzkufZnoGdzru72FlYZSOvObHYWTAzM6sV9bm8rit83WBmazO3/JqZmZmZmVnJq/XgV9J6koZLGi3pLUkX5fQOkl6WNE7S/ZLWzemN8/NxeXn7gn39Iqe/I2n/2j4WMzMzMzMzqx+K0fL7FbBPRPQAyoADJO0KXAVcHxHbArOAU/L6pwCzcvr1eT0kdQGOBboCBwB/ktSwNg/EzMzMzMzM6odaD34j+Sw/XSf/BbAP8GBOHwAckR8fnp+Tl+8rSTl9YER8FREfAuOA3mv+CMzMzMzMzKy+KcqYX0kNJY0CpgKDgfeB2RGxIK8yEWidH7cGJgDk5Z8CmxWmV7GNmZmZmZmZWYWiBL8RsTAiyoA2pNbaTmvy9SSdJmmEpBHTpk1bky9lZmZmZmZmdVBRZ3uOiNnAs8BuQFNJ5bdeagNMyo8nAW0B8vJNgBmF6VVsU/l1bouIXhHRq3nz5jV9GGZmZmZmZlbHFWO25+aSmubH6wN9gLdJQfDRebW+wKD8+LH8nLx8aERETj82zwbdAegIDK+VgzAzMzMzM7N6pdHyV6lxLYEBeWbmBsADEfG4pDHAQEmXAq8B/fP6/YF7JI0DZpJmeCYi3pL0ADAGWACcHhELa/lYzMzMzMzMrB6o9eA3Il4Hdqwi/QOqmK05Ir4EvlvNvi4DLqvpPJqZmZmZmVlpKeqYXzMzMzOzlfHll1/Su3dvevToQdeuXbnwwgsBGDp0KD179mSHHXagb9++LFiwYIntXnnlFRo1asSDD6Y7a3700Uf07NmTsrIyunbtyq233lrrx2JmtcvBr5mZmZnVG40bN2bo0KGMHj2aUaNG8dRTT/HCCy/Qt29fBg4cyJtvvslWW23FgAEDKrZZuHAh559/Pvvtt19FWsuWLXnxxRcZNWoUL7/8MldeeSUff/xxMQ7JzGqJg18zMzMzqzck0aRJEwDmz5/P/PnzadiwIeuuuy7bbbcdAH369OGhhx6q2OYPf/gD3/nOd2jRokVF2rrrrkvjxo0B+Oqrr1i0aFEtHoWZFYODXzMzMzOrVxYuXEhZWRktWrSgT58+9O7dmwULFjBixAgAHnzwQSZMmADApEmTeOSRR/jxj3+81H4mTJhA9+7dadu2Leeffz6tWrWq1eMws9rl4NfMzMzM6pWGDRsyatQoJk6cyPDhw3nrrbcYOHAgZ599Nr1792ajjTaiYcOGAJx11llcddVVNGiw9GVv27Ztef311xk3bhwDBgxgypQptX0oZlaLinGrIzMzMzOz1da0aVP23ntvnnrqKc4991z++9//AvDMM8/w7rvvAjBixAiOPfZYAKZPn84TTzxBo0aNOOKIIyr206pVK3bYYQf++9//cvTRR9f6cZhZ7XDLr5mZmZnVG9OmTWP27NkAfPHFFwwePJhOnToxdepUII3fveqqq/jRj34EwIcffsj48eMZP348Rx99NH/605844ogjmDhxIl988QUAs2bN4rnnnmP77bcvyjGZWe1wy6+ZmZmZ1RuTJ0+mb9++LFy4kEWLFnHMMcdwyCGHcN555/H444+zaNEifvzjH7PPPvsscz9vv/0255xzDpKICM4991y6detWS0dhZsXg4NfMzMwAkNQQGAFMiohDJHUABgKbASOB70fE15IaA3cDOwEzgO9FxPi8j18ApwALgZ9GxNO1fyRWyrp3785rr722VPo111zDNddcs8xt77rrrorHffr04fXXX6/p7JlZHeZuz2ZmZlbuTODtgudXAddHxLbALFJQS/4/K6dfn9dDUhfgWKArcADwpxxQm5mZFZ2DXzMzM0NSG+Bg4Pb8XMA+wIN5lQHAEfnx4fk5efm+ef3DgYER8VVEfAiMA3rXygGYmZkth4NfMzMzA7gB+DmwKD/fDJgdEQvy84lA6/y4NTABIC//NK9fkV7FNkuQdJqkEZJGTJs2rQYPw8zMrGoOfs3MzNZykg4BpkbEyNp6zYi4LSJ6RUSv5s2b19bLmpnZWswTXpmZmdk3gcMkHQSsB2wM3Ag0ldQot+62ASbl9ScBbYGJkhoBm5AmvipPL1e4jZWgnc67u9hZqPdGXnNisbNgttZwy6+ZmdlaLiJ+ERFtIqI9acKqoRFxPPAscHRerS8wKD9+LD8nLx8aEZHTj5XUOM8U3REYXkuHYWZmtkxu+TUzM7PqnA8MlHQp8BrQP6f3B+6RNA6YSQqYiYi3JD0AjAEWAKdHxMLaz7aZmdnSar3lV1JbSc9KGiPpLUln5vTfSZokaVT+O6hgm19IGifpHUn7F6QfkNPGSbqgto/FzMys1ETEsIg4JD/+ICJ6R8S2EfHdiPgqp3+Zn2+bl39QsP1lEbFNRGwfEU8W6zjMzMwqK0bL7wLgnIh4VdJGwEhJg/Oy6yPi2sKVK90zsBXwL0nb5cU3A31Is0m+IumxiBhTK0dhZmZmZmZm9UatB78RMRmYnB/PlfQ21dwGIau4ZyDwYe5iVX7PwHHltc2SBuZ1HfyamZmZmZnZEoo64ZWk9sCOwMs56QxJr0u6Q9KmOa26ewau8L0EzczMzMzMbO1WtOBXUhPgIeCsiJgD3AJsA5SRWoavq8HXOk3SCEkjpk2bVlO7NTMzMzMzs3qiKMGvpHVIge9fI+JhgIiYEhELI2IR8BcWd22u7p6BK3wvwYi4LSJ6RUSv5s2b1+zBmJmZmZmZWZ1XjNmeRbpFwtsR8fuC9JYFqx0JvJkfV3fPwFeAjpI6SFqXNCnWY7VxDGZmZmZmZla/FGO2528C3wfekDQqp/0SOE5SGRDAeOCHsOx7Bko6A3gaaAjcERFv1d5hmJmZmZmZWX1RjNmenwNUxaInlrHNZcBlVaQ/saztzMzMzMzMzKDIsz2bmZmZmZmZ1QYHv2ZmZmZmZlbyHPyamZmZmZlZyXPwa2ZmZmZmZiXPwa+ZmZmZmZmVPAe/ZmZmZmZmVvIc/Fq1JkyYwN57702XLl3o2rUrN954IwAzZ86kT58+dOzYkT59+jBr1iwAxo4dy2677Ubjxo259tprK/bz5Zdf0rt3b3r06EHXrl258MILi3I8ZmZmZma29nLwa9Vq1KgR1113HWPGjOGll17i5ptvZsyYMVx55ZXsu+++vPfee+y7775ceeWVADRr1oybbrqJc889d4n9NG7cmKFDhzJ69GhGjRrFU089xUsvvVSMQzIzMzMzs7WUg1+rVsuWLenZsycAG220EZ07d2bSpEkMGjSIvn37AtC3b18effRRAFq0aMHOO+/MOuuss8R+JNGkSRMA5s+fz/z585FUewdiZmZmZmZrPQe/tkLGjx/Pa6+9xi677MKUKVNo2bIlAFtuuSVTpkxZ7vYLFy6krKyMFi1a0KdPH3bZZZc1nWUzMzMzM7MKDn5tuT777DO+853vcMMNN7DxxhsvsUzSCrXiNmzYkFGjRjFx4kSGDx/Om2++uaaya2ZmZmZmthQHv7ZM8+fP5zvf+Q7HH388Rx11FABbbLEFkydPBmDy5Mm0aNFihffXtGlT9t57b5566qk1kl8zMzMzM7OqOPi1akUEp5xyCp07d+ZnP/tZUHq39wAAo89JREFURfphhx3GgAEDABgwYACHH374Mvczbdo0Zs+eDcAXX3zB4MGD6dSp0xrLt5mZmZmZWWWNVmdjSUMiYt/lpVn99Pzzz3PPPffQrVs3ysrKALj88su54IILOOaYY+jfvz9bbbUVDzzwAACffPIJvXr1Ys6cOTRo0IAbbriBMWPGMHnyZPr27cvChQtZtGgRxxxzDIccckgRj8zMrHS5bDYzM6vaKgW/ktYDNgA2l7QpUD7oc2OgdQ3lzYps9913JyKqXDZkyJCl0rbccksmTpy4VHr37t157bXXajx/Zma2mMtmMzOzZVvVbs8/BEYCnfL/8r9BwB+XtaGktpKelTRG0luSzszpzSQNlvRe/r9pTpekmySNk/S6pJ4F++qb139PUt9VPBYzM7NSsDpl83qShksancvmi3J6B0kv5zL4fknr5vTG+fm4vLx9wb5+kdPfkbT/mjlUMzOzlbdKwW9E3BgRHYBzI2LriOiQ/3pExDILWGABcE5EdAF2BU6X1AW4ABgSER2BIfk5wIFAx/x3GnALpGAZuBDYBegNXFgeMJuZma1tVrNs/grYJyJ6AGXAAZJ2Ba4Cro+IbYFZwCl5/VOAWTn9+rweuTw/FugKHAD8SVLDmj1SMzOzVbNaY34j4g+SvgG0L9xXRNy9jG0mA5Pz47mS3iZ1xzoc2CuvNgAYBpyf0++O1P/2JUlNJbXM6w6OiJkAkgaTCtr7VueYzMzM6rNVLJsD+Cw/XSf/BbAP8P9y+gDgd6RK6MPzY4AHgT8q3ffucGBgRHwFfChpHKmC+sUaODQzM7PVsroTXt0DbAOMAhbm5ACqLWArbd8e2BF4GdgiB8YAnwBb5MetgQkFm03MadWlWzV2Om+FPpY6aeQ1JxY7C2Zm9cKqls25hXYksC1wM/A+MDsiFuRVCsvZijI4IhZI+hTYLKe/VLBbl81mZlZnrFbwC/QCukR1syItg6QmwEPAWRExJ1UYJxERklZ6n8t4rdNIXaZp165dTe3WzMysLlqlsjkiFgJlkpoCj5DGDq8xLpvNzKy2re59ft8EtlzZjSStQwp8/xoRD+fkKbk7M/n/1Jw+CWhbsHmbnFZd+lIi4raI6BURvZo3b76y2TUzM6tPVqlsLhcRs4Fngd2AppLKK8oLy9mKMjgv3wSYgctmMzOrw1Y3+N0cGCPpaUmPlf8ta4M8Jqg/8HZE/L5g0WNA+YzNfUmzU5ann5hnfd4V+DR3j34a2E/Spnmiq/1ympmZ2dpsVcrm5rnFF0nrA32At0lB8NF5tcplc3mZfTQwNLc0PwYcm2eD7kCarHJ4zR2amZnZqlvdbs+/W4Vtvgl8H3hD0qic9kvgSuABSacAHwHH5GVPAAcB44B5wEkAETFT0iXAK3m9i8snvzIzM1uL/W4VtmkJDMjjfhsAD0TE45LGAAMlXQq8Rqq8Jv+/J09oNZM0wzMR8ZakB4AxpLs7nJ67U5uZmRXd6s72/O9V2OY5QNUs3reK9QM4vZp93QHcsbJ5MDMzK1WrWDa/TpqAsnL6B6TZmiunfwl8t5p9XQZctrJ5MDMzW9NWd7bnuaQZJAHWJd0a4fOI2Hh1M2ZmZmYrz2WzmZlZ1Va35Xej8scF9/fbdXUzZWZmZqvGZbOZmVnVVnfCqwqRPArsX1P7NDMzs1XnstnMzGyx1e32fFTB0wakewt+uVo5MjMzs1XmstnMzKxqqzvb86EFjxcA40ndq8zMzKw4XDabmZlVYXXH/J5UUxkxMzOz1eey2czMrGqrNeZXUhtJj0iamv8ektSmpjJnVhtOPvlkWrRowQ477FCR9rvf/Y7WrVtTVlZGWVkZTzzxBAAzZsxg7733pkmTJpxxxhlL7Of++++ne/fudO3alfPPP79Wj8HMrJzLZjMzs6qt7oRXdwKPAa3y3z9ymlm90a9fP5566qml0s8++2xGjRrFqFGjOOiggwBYb731uOSSS7j22muXWHfGjBmcd955DBkyhLfeeotPPvmEIUOG1Er+V1RVQX656667DklMnz4dgE8//ZRDDz2UHj160LVrV+68M/2sn3322YoKgbKyMtZbbz0effTR2jwMM1s+l81mZmZVWN3gt3lE3BkRC/LfXUDzGsiXWa3ZY489aNas2Qqtu+GGG7L77ruz3nrrLZH+wQcf0LFjR5o3T1//b3/72zz00EM1ntfVUV2QP2HCBJ555hnatWtXkXbzzTfTpUsXRo8ezbBhwzjnnHP4+uuv2XvvvSsqBIYOHcoGG2zAfvvtV5uHYWbL57LZzMysCqsb/M6QdIKkhvnvBGBGTWTMrNj++Mc/0r17d04++WRmzZq1zHW33XZb3nnnHcaPH8+CBQt49NFHmTBhQi3ldMVUF+SfffbZXH311aTbgSaSmDt3LhHBZ599RrNmzWjUaMkpAh588EEOPPBANthggzWedzNbKS6bzczMqrC6we/JwDHAJ8Bk4Gig32ru06zofvzjH/P+++8zatQoWrZsyTnnnLPM9TfddFNuueUWvve97/Gtb32L9u3b07Bhw1rK7aobNGgQrVu3pkePHkukn3HGGbz99tu0atWKbt26ceONN9KgwZKni4EDB3LcccfVZnbNbMW4bDYzM6vC6t7q6GKgb0TMApDUDLiWVPCa1VtbbLFFxeMf/OAHHHLIIcvd5tBDD+XQQ9MdRm677bY6H/zOmzePyy+/nGeeeWapZU8//TRlZWUMHTqU999/nz59+vCtb32LjTfeGIDJkyfzxhtvsP/++9d2ts1s+Vw2m5mZVWF1W367lxeuABExE9hxNfdpVnSTJ0+uePzII49UOUlUZVOnTgVg1qxZ/OlPf+LUU09dY/mrCe+//z4ffvghPXr0oH379kycOJGePXvyySefcOedd3LUUUchiW233ZYOHTowduzYim0feOABjjzySNZZZ50iHoGZVcNls5mZWRVWt+W3gaRNK9Uur+4+zWrVcccdx7Bhw5g+fTpt2rThoosuYtiwYYwaNQpJtG/fnj//+c8V67dv3545c+bw9ddf8+ijj/LMM8/QpUsXzjzzTEaPHg3Ab3/7W7bbbrtiHdIK6datW0XADum4RowYweabb067du0YMmQI3/rWt5gyZQrvvPMOW2+9dcW69913H1dccUUxsm1my+ey2czMrAqrWxheB7wo6e/5+XeBy1Zzn2a16r777lsq7ZRTTql2/fHjx6/wfuqSqoL86o7zN7/5Df369aNbt25EBFdddRWbb745kI5/woQJ7LnnnrWZfTNbcS6bzczMqrBawW9E3C1pBLBPTjoqIsasfrbMrKYtLzgvDOpbtWpV5VhgSC3EkyZNqsmsmVkNctlsZmZWtdXuBpUL1BUuVCXdARwCTI2IHXLa74AfANPyar+MiCfysl8ApwALgZ9GxNM5/QDgRqAhcHtEXLm6x2JmZlYKVrZsNjMzWxus7oRXq+Iu4IAq0q+PiLL8Vx74dgGOBbrmbf5Uft9C4GbgQKALcFxe18zMzMzMzGwptT4BRkT8R1L7FVz9cGBgRHwFfChpHNA7LxsXER8ASBqY13Utt5mZmZmZmS2lLs3+eIakE4ERwDl5lsrWwEsF60zMaQATKqXvUiu5tDpvp/PuLnYWVtnIa04sdhbMzMzMzEpSXQl+bwEuASL/vw44uaZ2Luk04DSAdu3a1dRuzYrKQb6ZmZmZ2YorxpjfpUTElIhYGBGLgL+wuGvzJKBtwaptclp16dXt/7aI6BURvZo3b16zmTczMzMzM7M6r04Ev5JaFjw9EngzP34MOFZSY0kdgI7AcOAVoKOkDpLWJU2K9Vht5tnMzMzMzMzqj1rv9izpPmAvYHNJE4ELgb0klZG6PY8HfggQEW9JeoA0kdUC4PSIWJj3cwbwNOlWR3dExFu1eyRmZmZmZmZWXxRjtufjqkjuv4z1LwMuqyL9CeCJGsyamZnZWklSW+BuYAtSRfRtEXGjpGbA/UB7UuX0MRExS5KAG4GDgHlAv4h4Ne+rL/DrvOtLI2JAbR6LmZlZdepEt2czMzMrqgWkOy10AXYFTpfUBbgAGBIRHYEh+TnAgaShSB1JE0reApCD5QtJd2DoDVwoadPaPBAzM7PqOPg1MzNby0XE5PKW24iYC7xNurXg4UB5y+0A4Ij8+HDg7kheAprm+Tv2BwZHxMx8y8LBwAG1dyRmZmbVc/BrZmZmFSS1B3YEXga2iIjJedEnpG7RkALjCQWbTcxp1aWbmZkVnYNfMzMzA0BSE+Ah4KyImFO4LCKCNB64pl7rNEkjJI2YNm1aTe3WzMysWg5+zczMDEnrkALfv0bEwzl5SvntCPP/qTl9EtC2YPM2Oa269KVExG0R0SsiejVv3rzmDsTMzKwaDn7NzMzWcnn25v7A2xHx+4JFjwF98+O+wKCC9BOV7Ap8mrtHPw3sJ2nTPNHVfjnNzMys6Bz8mllJOfnkk2nRogU77LBDRdrMmTPp06cPHTt2pE+fPsyaNQuAsWPHsttuu9G4cWOuvfbaivW//PJLevfuTY8ePejatSsXXnhhrR+HWS37JvB9YB9Jo/LfQcCVQB9J7wHfzs8h3WrwA2Ac8BfgJwARMRO4BHgl/12c08zMzIrOwa+ZlZR+/frx1FNPLZF25ZVXsu+++/Lee++x7777cuWV6fq9WbNm3HTTTZx77rlLrN+4cWOGDh3K6NGjGTVqFE899RQvvfRSrR2DWW2LiOciQhHRPSLK8t8TETEjIvaNiI4R8e3yQDbP8nx6RGwTEd0iYkTBvu6IiG3z353FOyozM7MlOfg1s5Kyxx570KxZsyXSBg0aRN++qedm3759efTRRwFo0aIFO++8M+uss84S60uiSZMmAMyfP5/58+eTeoWamZmZWX3l4NfMSt6UKVNo2bIlAFtuuSVTpkxZ7jYLFy6krKyMFi1a0KdPH3bZZZc1nU0zMzMzW4Mc/JrZWkXSCrXiNmzYkFGjRjFx4kSGDx/Om2++WQu5MzMzM7M1xcGvmZW8LbbYgsmTJwMwefJkWrRoscLbNm3alL333nupccRmZmZmVr84+DWzknfYYYcxYMAAAAYMGMDhhx++zPWnTZvG7NmzAfjiiy8YPHgwnTp1WtPZNDMzM7M1qFGxM2BmVpOOO+44hg0bxvTp02nTpg0XXXQRF1xwAccccwz9+/dnq6224oEHHgDgk08+oVevXsyZM4cGDRpwww03MGbMGCZPnkzfvn1ZuHAhixYt4phjjuGQQw4p8pGZmZmZ2epw8GtmJeW+++6rMn3IkCFLpW255ZZMnDhxqfTu3bvz2muv1XjezMzMzKx4itLtWdIdkqZKerMgrZmkwZLey/83zemSdJOkcZJel9SzYJu+ef33JPUtxrGYmZmZmZlZ3VesMb93AQdUSrsAGBIRHYEh+TnAgUDH/HcacAukYBm4ENgF6A1cWB4wm5mZmZmZmRUqSvAbEf8BZlZKPhwYkB8PAI4oSL87kpeAppJaAvsDgyNiZkTMAgazdEBtZmZmZmZmVqfG/G4REZPz40+ALfLj1sCEgvUm5rTq0s2shOx03t3FzsIqG3nNicXOgpmZmZlldfJWRxERQNTU/iSdJmmEpBHTpk2rqd2amZmZmZlZPVGXgt8puTsz+f/UnD4JaFuwXpucVl36UiLitojoFRG9mjdvXuMZNzMzMzMzs7qtLgW/jwHlMzb3BQYVpJ+YZ33eFfg0d49+GthP0qZ5oqv9cpqZmZmZmZnZEooy5lfSfcBewOaSJpJmbb4SeEDSKcBHwDF59SeAg4BxwDzgJICImCnpEuCVvN7FEVF5Ei0zMzMzMzOz4gS/EXFcNYv2rWLdAE6vZj93AHfUYNbMzMzMzMysBNWlbs9mZmZmZmZma4SDXzMzMzMzMyt5Dn7NzMzMzMys5Dn4NTMzMzMzs5Ln4NfMzMzMzMxKnoNfMzMzMzMzK3kOfs3MSkD79u3p1q0bZWVl9OrVC4C///3vdO3alQYNGjBixIgl1n/99dfZbbfd6Nq1K926dePLL78sRrbNzMzMao2DXzOzEvHss88yatSoikB3hx124OGHH2aPPfZYYr0FCxZwwgkncOutt/LWW28xbNgw1llnnWJk2czMStA777xDWVlZxd/GG2/MDTfcULH8uuuuQxLTp08HYNasWRx55JF0796d3r178+abbxYp56VrZT+TTz/9lEMPPZQePXrQtWtX7rzzziLlvGY1KnYGzMxszejcuXOV6c888wzdu3enR48eAGy22Wa1mS2royTdARwCTI2IHXJaM+B+oD0wHjgmImZJEnAjcBAwD+gXEa/mbfoCv867vTQiBtTmcZhZ8W2//faMGjUKgIULF9K6dWuOPPJIACZMmMAzzzxDu3btKta//PLLKSsr45FHHmHs2LGcfvrpDBkypBhZL1kr+5ncfPPNdOnShX/84x9MmzaN7bffnuOPP5511123GNmvMW75NTMrAZLYb7/92GmnnbjtttuWue67776LJPbff3969uzJ1VdfXUu5XH0LFy5kxx135JBDDgGgX79+dOjQoaImu7xgHzt2LLvtthuNGzfm2muvLWKO65W7gAMqpV0ADImIjsCQ/BzgQKBj/jsNuAUqguULgV2A3sCFkjZd4zk3szpryJAhbLPNNmy11VYAnH322Vx99dWkOrRkzJgx7LPPPgB06tSJ8ePHM2XKlKLkd22wIp+JJObOnUtE8Nlnn9GsWTMaNar/7ab1/wjMzIznnnuO1q1bM3XqVPr06UOnTp2W6u5cbsGCBTz33HO88sorbLDBBuy7777stNNO7LvvvrWc65V344030rlzZ+bMmVORds0113D00UcvsV6zZs246aabePTRR2s5h/VXRPxHUvtKyYcDe+XHA4BhwPk5/e6ICOAlSU0ltczrDo6ImQCSBpMC6vvWdP7NrG4aOHAgxx13HACDBg2idevWFT2PyvXo0YOHH36Yb33rWwwfPpyPPvqIiRMnssUWWxQjyyVvRT6TM844g8MOO4xWrVoxd+5c7r//fho0qP/tpvX/CMzMjNatWwPQokULjjzySIYPH17tum3atGGPPfZg8803Z4MNNuCggw7i1Vdfra2srrKJEyfyz3/+k1NPPXW567Zo0YKdd97ZY5lX3xYRMTk//gQovxJtDUwoWG9iTqsufSmSTpM0QtKIadOm1WyuzaxO+Prrr3nsscf47ne/y7x587j88su5+OKLl1rvggsuYPbs2ZSVlfGHP/yBHXfckYYNGxYhx6VvRT+Tp59+mrKyMj7++GNGjRrFGWecsUTFc33l4NfMrJ77/PPPmTt3bsXjZ555hh122KHa9ffff3/eeOMN5s2bx4IFC/j3v/9Nly5daiu7q+yss87i6quvXqrm+Ve/+hXdu3fn7LPP5quvvipS7kpfbuWNGtzfbRHRKyJ6NW/evKZ2a2Z1yJNPPknPnj3ZYosteP/99/nwww/p0aMH7du3Z+LEifTs2ZNPPvmEjTfemDvvvJNRo0Zx9913M23aNLbeeutiZ78krehncuedd3LUUUchiW233ZYOHTowduzYYmd/tTn4NTOr56ZMmcLuu+9Ojx496N27NwcffDAHHHAAjzzyCG3atOHFF1/k4IMPZv/99wdg00035Wc/+xk777wzZWVl9OzZk4MPPrjIR7Fsjz/+OC1atGCnnXZaIv2KK65g7NixvPLKK8ycOZOrrrqqSDksWVNyd2by/6k5fRLQtmC9NjmtunQzWwvdd999Fd1ru3XrxtSpUxk/fjzjx4+nTZs2vPrqq2y55ZbMnj2br7/+GoDbb7+dPfbYg4033riYWS9ZK/qZtGvXrmLSsSlTpvDOO++URIWEx/yamdVzW2+9NaNHj14q/cgjj6yYybGyE044gRNOOGFNZ63GPP/88zz22GM88cQTfPnll8yZM4cTTjiBe++9F4DGjRtz0kkneXKrmvcY0Be4Mv8fVJB+hqSBpMmtPo2IyZKeBi4vmORqP+AXtZxnM6sDPv/8cwYPHsyf//zn5a779ttv07dvXyTRtWtX+vfvXws5XPuszGfym9/8hn79+tGtWzcigquuuorNN9+8FnK5Zjn4NTOzOu+KK67giiuuAGDYsGFce+213HvvvUyePJmWLVsSETz66KPL7O5tyybpPtKEVZtLmkiatflK4AFJpwAfAcfk1Z8g3eZoHOlWRycBRMRMSZcAr+T1Li6f/MrM1i4bbrghM2bMqHb5+PHjKx7vtttuvPvuu7WQq7XbynwmrVq14plnnqmFXNWuOhf8ShoPzAUWAgsioteq3GfQzMxK3/HHH8+0adOICMrKyrj11lsB+OSTT+jVqxdz5syhQYMG3HDDDYwZM8bd6JYhIo6rZtFS04Dn8b+nV7OfO4A7ajBrZmZmNaLOBb/Z3hExveB5+X0Gr5R0QX5+PkveZ3AX0n0Gd6ntzJqZWe3Za6+92GuvvQAYOnRoletsueWWTJw4sRZzZWZmZnVdfZnw6nDS/QXJ/48oSL87kpeA8vsMmpmZmZmZmVWoiy2/ATwjKYA/R8RtrPx9BicXpCHpNOA0gHbt2q3BrJuZrZqdzru72FlYZSOvObHYWTAzswL1uUypK2q6bPNnsvpq4jOpi8Hv7hExSVILYLCkJW4oFRGRA+MVlgPo2wB69epVY/coNDMzMzMzs/qhznV7johJ+f9U4BGgNyt/n0EzMzMzMzOzCnWq5VfShkCDiJibH+8HXMxK3mew9nNuZmYroj53+3L3bjMzs/qtTgW/pLG8j6Q7GNEI+FtEPCXpFVbiPoNmZmZmZmZmhepU8BsRHwA9qkifwUreZ9DMzMzMzMysXJ0b82tmZmZmZmZW0xz8mpmZmZmZWclz8GtmZmZmZmYlz8GvmZmZmZmZlTwHv2ZmZmZmZlbyHPyamZmZmZlZyXPwa2ZmZmZmZiXPwa+ZmZmZmZmVPAe/ZmZmZmZmVvIc/JqZmZmZmVnJc/BrZmZmZmZmJc/Br5mZmZmZmZU8B79mZmZmZmZW8hz8mpmZmZmZWclz8GtmZmZmZmYlr94Hv5IOkPSOpHGSLih2fszMzNZ2LpvNzKwuqtfBr6SGwM3AgUAX4DhJXYqbKzMzs7WXy2YzM6ur6nXwC/QGxkXEBxHxNTAQOLzIeTIzM1ubuWw2M7M6qb4Hv62BCQXPJ+Y0MzMzKw6XzWZmVicpIoqdh1Um6WjggIg4NT//PrBLRJxRab3TgNPy0+2Bd2o1o7A5ML2WX7MYfJylY204RvBxlppiHOdWEdG8ll+zTqtHZfOasLb81uobfy51jz+TuqlUPpdqy+ZGtZ2TGjYJaFvwvE1OW0JE3AbcVluZqkzSiIjoVazXry0+ztKxNhwj+DhLzdpynPVAvSib1wR/B+smfy51jz+Tumlt+Fzqe7fnV4COkjpIWhc4FnisyHkyMzNbm7lsNjOzOqlet/xGxAJJZwBPAw2BOyLirSJny8zMbK3lstnMzOqqeh38AkTEE8ATxc7HcpRUt65l8HGWjrXhGMHHWWrWluOs8+pJ2bwm+DtYN/lzqXv8mdRNJf+51OsJr8zMzMzMzMxWRH0f82tmZmZmZma2XA5+6xEla91nlo9b+fEPJZ0jaZNi52t1FR7X2i5PilPn5Y+soaSGxc5LfeH3y2zFSVpH0obFzsfaqOD8vtZdZ9V3vp6qeyRtKGnzYuejKv6B10GS1pXUMj+u+DFHsqh4OSuOfNzl/fMXAq2BJkXM0iqpfGKudFxrFS22gaTzgIuLnadC5RVN+a/yb3BhRCwsZv7qolzQdZa0RWF6fX6/fCFstaHgXNMIuAM4vNh5KmUFlellkv6SHzcoOL+vdddZ9Y2vp+qm8vJS0gak67qTipujqrlQL7JqWpJaAj+A9IPO660raVdJZ0nap5RquJZ1LJLWl9RN0nY5aRywEdC0NvK2sgqPpaqTc8Gy9SXtJuno+tLquaqqavmLxeYBnwBbVL31mld+4VmYVl7RlP8KP7dNJV0s6SFJt0mqd5UwNaH8vFUpeX2gD7BtXqehpMaSfippoKS/SupY65ldAfn8uo+kTQvTfSFsNam6lsWCc80CYBqwkaT1ipPL0pTfd8ESZfHXwF45bZGkbSX9LJ+rvuuKr+Ly9VTdVXAuq/y5LMr/5wFTgUaSGhcjj8tS72d7ro9yDWP5FyRIrZmFtgO+J2kr4B3g90BfYG/gdeAEUstnvblvoqRjgWYR8aeCNBXW1klqDzSIiA/y8wNJxz4NmCDpWuAtYANgs1o+hKXkH30D0sdY+HlS/jgXnkHK7x7AS8AVwCbA50BnoLmkAflkUW/lmr7uEfFSYXpVrX5K3dZ/DbQH3mcNf56S2kfEeEk7AodFxEUF+VsquJHUBTgY2BDYHrg5Ip4DjgImA/cC3wZ+J+mKiJixJvNf28p/m5IaVvX5FZ63JG2Qv7ubAmcAiyRNBf6P9D1fj/Q7/hp4BNihlg5jCQXHVPkCmIj4WtIpwHWSFgHzImK+pIuAbwHrABcAL9XXVmyrPQVBU2OgP3BOREyuprxHUifS92tL0nXZXFJl0pe1k+P6rfw8lVvOF5Imc13ivF7+u83rdCVdW40H5kjaKiI+It2P+ivgBuA80jlrUG0dx9rM11N1V/n5rPA3VekaQAXX8dsDlwBtScHvB6TPZ2otZ3uZXKtVw8ovrCT1kXRqDgiWUPgFyq25v5H0qKTjc3IP0kW3gL8DOwKdgAdIJ+Idgf8nqdmaPZoVk2t/ti5vNVGlrqJZc2C/vLylpA4FP5adJb0B3AdcLam3pPWBXwJHRcQewLPApRExDZgP1No4glzD1aCa1suFlT7PbST1zo9PBf5D+uy6koKlIL0X0yLieFIB24MiBQSrqprW0nmkk17hehtIOlfSfZLu1OLxHz8iVeDcSAqamknasobytkSrpKQOwHP5O7kRsHd5q4qkrST1k3SrpL3zhRHAr0iVUG8CBwI9c3pfoBvQj9Q7Y0fqQEXM6ip/vyR1kPQUsBWkC8ZKte9b5P87S3pE0ivAIEk9gJnAaGAwcHhEjCa9j62Ao4GLgPZa3ItjTR2Lqjj/VFxIlVe4SWokqW1efQtgJ+AhYBSwraQDSAX2EcDZwJnkViKzcqp6eER5S+4XpMrqT/K67SVdJmmEpFsklZ/3jyEFWt8BxpIqut3yW4nSeOhvSDo9n7N7SLqVdE4mIhaU99rJ6zco2PbXksaSzkOvAfvmMmsWsLWknYAupDJ6N9JvfW9JG9fmMZY6X0/VbdWUnYsKflPr5/9bSbohX7v/VVL5NVI/YFJE7EbqqbkDUCdilUIOfldDVUFeQU1VC6B7/o+k1gWB8dWSfpTX6wl8BlwNlEk6JyKuBe4GXoiID0k1wPsAZwHnAwOAC0kn7aKRdKCkkaQL3ptJgUD5DyWUuqLsIGkjUg1dB0k3A/8C7soXAiIFTP+XfyyDgEtJJ7W2wMT8cncDbXJBNAXYQjXYlSLndcec1yUKzfLCtHKLj1IQf7GkVyVdp1QZ0QU4RVJb4NP8nnQhBXoREZOBkfn4IF0UfUpqAa2TqvmeF54M183/twV2lvSSpFckbUbqBtsAuAx4Afi1pKbAQcA/cmvqrcDHpPdpZfK1vdIwgD9JuldS15y3qPRZTQEmAR1IrcyzgW3yshNJ3XQHAacB+0raC1gA/DwiHgQGAi0kbQ1MIFW8vACcEBH7RsS7K5PvYiq/6NDSFRcL8/8PSRfd5RVZ55F7CEk6GPizUoXeXOB3QG/gFlKQ+xWpgm4mi3sVrUsao/8S6TuwaU29X5I2kbReFcdS2Juk/JzbTNK3JX1TqWLyEVIvmjuB/Unn4H8BD0dEh4h4mxSInExqBboF2Br3llorVKrwKR/D1rCa307F8Ii8fFNJ+0m6VtJBpB4QV+bVv0E6H+1CKgd+qTQUYEvg6Yj4nNSrZAJp+JMt6Yek64NtSRfWC0jv72gAScdIOlvSXZI+Ar6f03fJ2xwL/Jn02++W9zme1GLYkBRYtc/7PRg4LyLm1MaBlRJfT9VdVQW3hcsKys4NlSq5N87ns39Jeh84Ja/+TeDdiOhGapz6P6WedeuTKpAhXQ+MJVem1yUOfldQNTVVS4wHzOttI+kYUlfJ8i4BJwHXklpzIV0k7pwf3wO8TWpdOBrYPafPYXFr0yzgPeCMiOgXETdExDukk/UaURDQLNWnP6dvCvwMOC0idoiIA1lcAK0jaQApQLgR6EgKYjsAj0dEV2AE0De/f+1JhT2kgn9joB2pUOqR0zclBUibAR+SLgxWerxlwcVw5SCgJ6lioQwqxv9IUpN8AjhZ0n8k/TEHeZAmJdk2/59HujH4v0lds08jFc4j8nFvQrp4Bngjp0E6WX9Bqr2sdYWfczXLVXBh10i55VbSIZIelDQGuFOpy/pCUleyJ4HekboCn0q6sDiU1FLai/SZvsLi1vvJpIBpa1ZQDqwvIF3APE86ybaUtJnS2J/f5Dw2zrX700iVUVNI3aO2yEFu+dCCFqSeCbvlfMwseLnn83pf5nU/jIjHI+KtHFRtQx1ScLFeXYvUUuNYc1D4F6VWlA1YfPFwIbBrfjyTdC5qFxFjSb+ZZ0mTWnTOf9NIv9U2eZu3gQ8i4uGIGA5srNzyu6xCuJrjqhx8/BDoXKmlYENJe0o6WNJm+Xt7KzAUOI5UA/0hcFZEdCGdf3+Yj/kJYLP8m29A6q41l3TuPiQiekXE0yuTZ6s/lManrwsVXSx/JmkisG9OW1hFy1T59+0SSUNJ5d05pGD3A1K5/RWwZT7HHkaqEPwLcC7pQrEVqSyflnf7Punc2HRNH3N9kivdDgDOj4izI+LaiHiL9H7fmFc7GPgJqUL9JGAfpZbD7YENI2JURPyPVOm1V97mLVI5Mol0oX5HRPwpIl4hnQ9c4VUNX0/VTfnz6K7FPbWWqHwoTMufWydJ7fJ5b2+lRqq/AaeTflNHkRq4ukbEHyW1ITXIfV/S/cDPSRXDW5IqksuHa0yijp7LHPwWyBeyJ+cf5BO5FqoVLL5oLFi3naQjJX0nn5TLx+38idSS0JxUaDYjBYXr5z+Al0k1jJAuLP+PFAycDuyZ0z8gtxpHxBukk8A1ko5Xajl+hBQk1tSxl2ukNPbtqvzaCysH+Nk8YPeIGJm33yAWj3s8lXSRvEtuGXsVmE4KcGfndV4GNs3B1DgWB7lNSAFKY9KJ7lilWr/TgDdyy9TcfOzVdgtTmsBmk4Lnu0u6gtRdpqogYAopQC8P7i4htWx1JbX87Ez6gc8ErsnbdAeejYgJpJbpz0gBwGBSq1F74K/5vdqXxRc3b7H4s51BqrXcemWDgVVR/hnnx1cBP5K0fhW1sJtJ2iSfDA+QNBh4Fzgjbz8ZuCoHEO+Tasg/JAUQG+btWrJ4Mqv3SBcle0bEv0nvxcH55dYhff4rHPySLm7mRMQpEfHXiHgsIv5F+v1cQvqt9SMFyJCC1h0jTSjzCanldx7pt7obqZfB/4uI35EK0y1Z/BudR7pgnUMas9pZaQKn/5DG3RdtKv/8efaT9DdJQyU9DeyhPK9AQcXFukq18QdL+r2kf+SL9vILlh+QfgN/JP22vplfYgiLK+E+JRVqrSV1Jr3X55Nq418k/R4+JJUr5RNH9c/ZfCx/h54u33c155UqKxrz+pWDjx2AKyRdrtQLpRGpVeeifDy/zuu9DmyUvyv/IH1f95I0mnTh3JT0HXiPVKm2YX6NUUCTiBgTEVMktZBUXjlpJUTSt0gXzYWzLDclXdCVT+D2DaUWxf8o9TZpRvquH5u3+3+k30MjUre/P0XEe8AYYOt8jt2CVPbdDXwzIo7M58MJQPmwp3mk8qZDbZQJ9UgjUhl9lFJLVPk56h0Wn4OfBeZGxPuk8/h7pPPXWFIAXO4D8uea1+scEZNIZfflkq6X9Axp5u21Pvj19VTdpDSsrKfSLUD7KvWqg1SG9yQPyapUYbeVpCNyhcRhpPhkAGn+kh2B/5GGAI2MiH75ee+IeCQiyoPaL0kVxs+Qrre6RcT3I+JJ0jVA+QzPX5PK1vLKiTpjrf9RwxJN/f1I3ZKeJrUktQB6SJpHutA7kHQSfYZUcziaFOTtQRoP9g3gq4g4Rall6k7SCfcJUk3VJqQf7Fssrpk6EngyIv4uaTegqdJ4xBdJXaIeIAXG15FOFH1IweLNpIvxlT3WTsDGuRWmQsGF6AJJXwCNlfrw75Rf97GImF/wfn2lNOPtu6Qv+whJz+Uv/7eBJyJNIrN+RHwRafKY10knvxdJY+kakoLdF4Hv5GDiO6QW3vdJtecnAv/I79kfcx7/HhF/Xc6hXksaG3xOfv4Vabxna0mzSYFNv3xsvyV9Lp8BrSTtA+wUEQdJak06kb8WES9Jmg4My/tcl9Q6DeniZQOgUUS8Lel/pK6g3ye1iN4IXJkDjUn5fdwiIqaQAqoan9gkv5YKA9v8OS/IT6eSvpONJO0JbBARTyrNYPzHfJx/Jn1H74+I2/N+RQooTpJ0HalQGqtUG/g6KciFFCyNBRZF6j5c3lKyKXA/sJukQaTg9zXS+7cix7UO6btTXvGyXkR8mQvnb5O6rv5J0v7AyUrdlV8l1V5COplvR/pdvg/8prziRlKLnJcXgN8ojRHbkvT5bhIRb0g6Ob/OWODtiPhqRfK9hhxFGh/4DPAc6ffUBlhPUjdSpcJ+pIv06/KyV4DHSeMM1yWdwxoBt0TEJEl3sjjwf5XUtRlSi/ki0nemIemC/mWlltydgPkR0V/Sm8B9kl4mfRd+Rzp3Tib9jufnc8vOpO/9oxHxafkBRVQ5Adn6Ob/fJf1WnyD9rroDT5ECjGOAlhGxV654uULS0aSW+1/m/TQgVXScBXwjIj6X9Oec9nR+n7ZRus/qYFJFwhOki7gNSL/h56sL3K3eei///6akB0m/+Waknkjd87JGwIOkc9w3gT9FxLHl54iI+CR/v94jV8zmc+UHpHK9AemaoUFEDMvLdyQFCX8Brpf0AikIHwzMIJ0bv16TB15fRMQcSfeRJs/rBXwu6RzSkJWN8vl/JIvvHPAZqaKzE+nivpGksogYRao4K6+gG0cKltaJiDslTSb1VhsEvF5wwb82W+uvp+qaXPnzX+BRUhku4EBJN0XEC5IeBzZUmo/j9Ig4LW/aCzhbaW6PE4AfRcRz+brmhojYU2nIwKt5/RmkzxotngBzZl7eJSLezMt2yvkYCOwuaQTpO/Mw8IGkdSOizpzLHPxS0cVpT1LN7T5RMMZDqRvU70gBwKWkD/x9Us1TS1KN768k/YpUWP47/yA/JV1ktomIT3MAvQ35RAusrzR+9RXgAEk7k07Ui4BOETFKaabRJsDz+YLwvvy3TJI2rnQMB5Ba2c4mjb+bXRDwozTmaD1SN4aBpBPQwaSWk0mk8UkNSRUCkH5kQToRdiXVLu0P/D4X9qPJ4zcjTfhRrrx7EaSW4PVIF81XkS6Sh5EC6VsidVWdRyrofl9wLFrBH9ConKdyH5Nanb9JCqqfI3WF7Aj8MSJOkDST1K2qB6kyA9KJeg4wWak1bZzSGIh1SF06jwUG5+CrO4u7yw4k1Ro3zp/lPcB/8+c4OxfCkY/n9RU4nqWUf4ZVBblQbRDRjNTNcyfS+/8R6WJrd1KQ8yTpYmsMqWW+MylgOi9vXz6r5o7A90iVFRuQanPbkN738rFqX5OC3D8rdYNvRWpNuSAihkj6IVTMYP72SgQUDUmfSXnNbvn3YaOc9mZ+Ppr0O2xJah1ondPfJwU+k3Jgc5ukt0jf5fVIs0H/IRfMkfP3BLmnTERMJ32+a0T+ni1SGi+1OzAi0kRvheuU/35/AAyMiLsKFo/K62wH/BgYEBE35LSxpPHWfUnnsPGk1t0gXdBA6nFxYH78EimohFRpsiOpMLwceDNXfk0iBQXlrbV3kSrLPi7I00P59ZdXYG8AHELqJj8ZuDG3yHyDNCzkFlIw8Q7p8+1WcGx7kc4xkL4fz5AC7H+QC+/8vs4iVX7MV+pVsjvpguprUle7m0g9T/aLiF9I6gVMzvmw0jSD9Pm/Q5qMrz+pxeI8FpcFz5POedeTyvDycmAiqXxoEhGfSZpBqnzaLCJm5PPIPFKZeCnpov0pUgX4OqSLzxcknU+qcH8vPM60SrmVfKdc0bodqfw4ivR7bUc696+Xz6FfSJpGKtPnk4Zp/SFXcN4LfKo0y/N4SbvmCnpFxFOkCjVbbBQlfj1VD00AXoyIo6Cigvg8UkXFN0g9Sb5B6jXZVtIB+bu9IaknaZB6bx6TP4s7JJVfa88FmuT3c6xSD7G9I+JZpcaLRqRz5KVKwz02I5Whp0fEB5LOJcVDH1S+Lq0rHPwu1hoYl2sXG0XqHgnpgm4cqYtlRWujpJtI3WZeILWItCcVoL2AdfOPt4w8yyPpAqyvpENJJ411gZ65prEh6eLsMeCSXIA2iNRFb1V8JGmfiHit4BjWJ7Vu/IcUgDYFZkm6l9Q94hnSj+V/pIvTjkD/iPhzLpS/JemfEfF5eVCVa9mm5IDo77kW9kRSN4g/5Bq/N0njPkaQLrQPynkq72L0fkQskPSHiLipqoPJAXUDCqbAXwFjSUFeuWmkioU5pM/pzfxjbw68kE++H5MuSPYiTUBGzttMUuC2HukiZkZ+z/4A3JIDqPaki/4P8us9SqqFfjufQE4uzFx5oLciAV+ugFm/vHVMqevlwaQuQG9WE+Q2IV2oHZmP/ZZIrf375OM7j1SYnUG6IHuWxZ/Ngnwc3yZVUmxB+o4X3raoCbBFRExTmk15T9IEVi8pTYzwBim42Zs01vIwUtA5pvw48vdnVYLIr1ly0qpGOe2T/H8n4D+5JebbpImWviSN822S12srqVlEXC3pONLF7L2kQHNhrlD4iFTxcxTps62Yqj9/J1fo86tOYQVUoYLPcwGp1XYjpS54DfPFdHlwvBfp97rEraWUhhLMJbVgj8//y1vMy1uK7yBdsBxDauUvr/x6gFQo7pIDw+eB5yW9mvdZ3q3wS0m/JPVAGRtL9i74kvRbqkp1Bfb1pPe6jDRsYgCptXWQ0pi9Q4GhEfHPguP8iDQmbwvSRdKnpFnDFalFtxfwcaReKvMlbRMR7+dz/I2kiszPSV3s3iJdQD8IPFT+GeR9jajmWKxE5MDnU1KZ9GdSOTma3Esm/6bak86ZA0gVtc+TArCPSOegVqShIbNIF5gdgBn5t/I/UqX2m5J+TTrfTIjUxbP8ezaBxXNf2DJExERgolIPu/IhKh0i9czZmHQeeZUUbL1OmmjvKUkvRcRspcaOlqRrAsor6goaBMqvN9zDIymZ66kSUj58C6VW1S9yuXa2Ui+IN4A9IjW+3UgaVvkUqffoX3O52FBSm4iYmK9pJudz3QTS76oJqdw/FzhV0jWk2OHsiPiHpF+QKp0mxJK9t2aQPtc6y8HvYk1IJ9PyLpRnkoKHTUmtWi8pd6vIF53NgYMide09ivTjvZ90MflLSeWzwn4maduIuFXS16Qf9UOk7gXT84Xs7ZUzsxJBXlVeIxXK5cHv66TAZhPSF/VHwG+VWoQbRkSX/IVvSwqMB5G6Ii8o2F95N6HPy19EqWWqSURMVupmuCUwPNJEQJcBvyGNPRid8zCIdPFMRMxicUvyUoPwC48/L1vZ2qMPSK3rG+aA/WtJc0k1wA2AD5W6OU2T9CXpJDKRVMP5V+AmSQ/nz2YmqaKjKelk/TapxellSb8hfaYTI02kUZ7n2Swe37y6VrTL0XTgihxUdiddYJ1D+lwvVZp4rYwURLwuaQqp5rYj6fNpkQPtBXlfrfL7M540PucKpVrzzfP79EEOij4l9UiYkj+/3wJXRkR5xc8cUmBZI3LgNwy4UVL/iBiXF21K+tyPULpX6zakluwZpM/tGdJn+DpwXETMzAXpEr0pctoipdmjN815HxYF3ZtXtJBVasnsTLoQfjkiJmjxeNyl9iGpHelcMpX0OXyf1CPladJ5aAaLg/3Pc/7KJ67oSGqh/oL0efwhH3e5jUjntJ9HxKuSfg60zYXmXcBZkk4gfb+Gk+7LPSGvtx3wTqUCbjYr/x2vrsA+S6lXwhHAM+UVjUqTB/Yg/cbuzxdVQepK/5VSj5rN8nn5cdIEXafk829XUjAL6eKtA6kCBlKFz+UR8VkVeaz4XNayi6m13Sukz344qUKkP6lMWED6nR1I6gHwmNItijYllYvPk8rWjqTgdwppXG/Fby8ivgsVvTo+YXGlePlyf89WgNLt8LqQKmTbkyqB/4/Uk6VZXu175Mq3SPecfylvuxlpzorvkSpDLy2vfKhsNa+/SlEpXU+VhPwZNCiIWRrkQPcL0gSdH5N6QbTNFT9XKA032prF56bBwI8l9Sf1Yv1njkvmkWKc8gkAH1a6s8uCKOgBFamHypvUQw5+F/uANPnU5qQf7bOkC8i7SD/UOaTAcQrpC/Ep0E/S53mb7hFxt1Lr5ymkC94LSTVi0wEi4o7KL7qGTrKvku4DPJdU67YrqXD+B+mLOoNUUMxn8Sxss0nB+3H5wn8ai7uvvkOqHNiExbceglS73T+3EH2V931RPq4hkv4TeZxwgbnlD3JNU+UbmdfU+zGdNOaksBJgHimI/ph0ofJsTv+AFHD8h/R+jCB99rcqdfEYRurWMSfn8aCc/wa5xri6Vq6aMooV73J0HWkcx9GkoLcvqTtzO9JJbzPg5Rx4TJE0nxQA/Sd/X3aJiP8q3RqibQ52fwZcmE9+6wNXR8Rdkn6an79T6XObwyqMR18ZEfG8pPJx8bPy8bUhTTjzBovHmN5bEOAU1lx/lPezVKVLQS3yX1Ynj1rcxfdhUiXQPpJujIh3lCam2IE0/u8/+fV/TOrGPB74Jylwu4H0+VTkPRZ3+/+AdOHRntRN/SNSV83dgSMi9SCZR550JAf7rwHn5t/eJNKYoC0i4p/5XLY+aaKLqQWvN5sUEKy2ZRTYX5G+m1sAzypNoDePFCRsSqqw+C4wOiLm5ZrtT/P79EulMbq/I7UQ/5rU42EAedxSROwOi1vb877NCj1L6v1wGalCcQqpBWQGqTLxEeACpXtbjyaVEy1Iv6MbyOOGI82EPraqF3BQtdrmknokdSadEy4lTYR5FlT8vh8u3KC8spFUZt5Fanj4FFsZpXQ9VUpeYvGEk+uSKnVmkK7dnyO9xx1J57E/khpD1mVxI9bZpGuOv5N6kV2V02+pXDEcER+tyQOpbQ5+FxtBujg+Bvh95HEDkj4k/YBnkH74r0TEM7kW8SekoONU0gU3ETGGxS10xTKEFLC8R7ogHka6KO5Gyu98UiD0OiloKO+O0ozUHQVSy1P3vOyjHAS1YfGYOiJ1K72A1LVweuVMRB5DQ7rAX6rldk3WdufjmUTq4lF+st6adEHzAmks4F2k9+ID0nszkHSBMzPXfh0bi7u/V/UatXUhs7JdjhqQPsc5pJaIB4C38uexE6kL8pN5XzuQuvUDXEEKcluTgor/kFrV3s6tf19HREXlRUSMXyNHu4Ii4v8k9SF1l32B1GW5/AQ9tPL6+buoqj63NfRZTiC19h6dg91LSOP7PyV145pLGnqwG6nb7/7A9yPN7l6e5xdJ3bhRms34+Lze+hFxpNLszmdIGp1rZKfn1vvysfaLSBM4lQeTF5LuXfsZqZX457myq2ISnlpQVYE9k1Qh9wrpN/t8XndjUoBxLelC916lXir/U5pn4XJSa7FIXa9mknq2VMktbLYML5C68/1FqefXTNKQofHAVhHxkNIM+e2Bl2LJcfgv1nZm10aR7oP8y6qWlVdsVX4ci4dpLSRXetrKKbHrqVLyLKk334u5MvlA0jXhx6Syf15ePpTUAt+T1IBVXlE3TdKVEXF54U4rB76lyMFvFmkcyN2kcbnbkALelqRucn8mdZn7sGD9FZp8qkjeBqZGxLnlCUrdkrvklpdZpBaWMUADSUeQCvrtgQ1yK8onpIvmjXLAcwYpkFpCFEwuoCrGyeTHxRrwfj9wuKQrSce7BWkym+akLqDl+fpFLJ7R8f7yjZd1oq5lK9Pl6CtSi+9o0i1b7oLUXSw3tN9NChieVOqGPwn4WNKmkcZtjyGN6azocpQvJOrk+I2IGEzqurOE/F0UqXts4Xig2gx+PiHfvimfXxaRJgf7KfBARPxe6VZqI0ldlF8mTb71HKlm9i+k3+U6Sred+lTSHNJ5p7yy7Tal2RyvUOoSuD6pS/SVOQ83k8Ydzsvrz2PxrOkVavnCo6oCezqpZeZRUsXdX5TGZ/+PNAHKHEkXkyrxZpFmCy2vvb6rFvNupes98kzBhec/0vmSnP4G+bdnxaPFt0KLguDWwxXWrFK5niolL5HuSvEfUqPGLsDtkca+r08aavgZVMzFcXrlHaytlQ7yOWJJSpOnHE9qXRsOvFsfT6RKY0Hb5y6LKI1LPoj05T+dFMz/nBQoXU6qCPk3qWbv7Ih4p2BfDerrD0Rp3ObRpMB9eKT7/9UrucXvP6SZ9MonLzqdVNjsTmp1uC237A4itfT+ncW3ttmINKHblRExIAdcPUgtGmPr4/e7kKoYJ15XSJpKmlW5PSnoPRm4h/TbGxlpcq3XSDO+vpx7X7QmBb4P57+rSDecf4s0E+xSlUlK9yndGBgVdXxW4tzS/RvSRFflBXbF7ctyL5P9SUHui3W14sXMbG1SCtdTpSRfL0wk3brwI9J14uDw7bmWyy2/lUSaLOj3y12x7ptD6lpYPg5jNilYakbqArExqRXsfdK40HVJ09E/R0ELN9TNoGJFRcRbFHTVro9WssvRONINyf8q6XekwGI2aVzU3NyKW1Ljaur493MYqSJiOHBXpAmkpgNdY/G9tmeRJgh5mdTjpPweep9FuhXEs6TxrENI3aM/KuzWBxAR/62dw6kR75DmIriQVGD3p6D1PtJ443uKkjMzM6tSKVxPlZI8zGeDYuejPnLLb4mSdDvwROTJH7Tk7ZsK12tFmiRoN9J4u1sLxxxa3SDpaNJsfJNY3OXox6QuR/uS7of6paTGkWbBrbet9aVE0tmk26T1L0g7gVSR8QSp0unbwAWk8feXkLouDyf9Ft/zZ2lmZmZWMxz8lrjKLUQF6b6grmfc5aj+yV18z4k06dX6EfFFTv82cBZpQop7I90+pWFVXZrNzMzMrGY4+C1hvpg2K648M/FDEbFndRVRZmZmZlY7HPyamZmZmZlZyWtQ7AyYmZmZmZmZrWkOfs3MzMzMzKzkOfg1MzMzM6sFklpJejA/LpN00Apss5ekx1fyddpL+n81tZ5ZqXDwa2ZmZma2huXbTn4cEUfnpDJgucHvKmoPrEhQu6LrmZUEB79mZmZmZtXIraNjJd0l6V1Jf5X0bUnPS3pPUu/896Kk1yS9IGn7vG0/SY9JGgoMyft6U9K6wMXA9ySNkvS96vaxAvnbM+9jVN52I+BK4Fs57ez8uv+V9Gr++0bevPJ6/ST9sWDfj+eW54b5+N+U9Ea+j71ZvdOo2BkwMzMzM6vjtgW+C5wMvEJqLd0dOAz4JXAi8K2IWJDv5X458J28bU+ge0TMlNQeICK+lvRboFdEnAEgaeNl7GNZzgVOj4jnJTUBvgQuAM6NiEPyvjcA+kTEl5I6AvcBvapYr181r1EGtI6IHfJ6TVcgX2Z1jlt+zeq4yrWwNbTPIyR1KXh+cS5oa2r/ayLPNb5PMzOzFfRhRLwREYuAt4Ah+d7tb5C6Dm8C/F3Sm8D1QNeCbQdHxMwVeI1l7WNZngd+L+mnQNOIWFDFOusAf5H0BvB3oEsV6yzLB8DWkv4g6QBgzkpub1YnOPg1WzsdQUHBFxG/jYh/FS87ZmZmddpXBY8XFTxfROpJeQnwbG4ZPRRYr2D9z1fwNZa1j2pFxJXAqcD6wPOSOlWx2tnAFKAHqcV33Wp2t4Al44P18mvMytsOA34E3L4ieTOraxz8mhWZpBMkDc/jbf6cx9WclMcVDQe+WbDuXZKOLnj+WcHj8/M4nNGSrsxpP5D0Sk57SNIGeZzPYcA1+TW3KdyvpH3zmKE3JN0hqXFOHy/pojxW6I1qCteqjq95fu1X8t83JTXI+2tasN57kraoav3Ve4fNzMzWuE2ASflxvxXcZi6w0WruA0nb5Fbpq0hdsjtVs+/JueX6+0DDavIwHijL5XRboHd+jc2BBhHxEPBrUldus3rHwa9ZEUnqDHwP+GZElAELgROAi0hB7+6sQNckSQcChwO7REQP4Oq86OGI2DmnvQ2cEhEvAI8B50VEWUS8X7Cf9YC7gO9FRDdSbfaPC15qekT0BG4hjTFaETcC10fEzqSxS7fnwncQcGR+3V2AjyJiSlXrr+DrmJmZFcvVwBWSXmPF59R5FuhSPuHVKu4D4Kw8EdXrwHzgSeB1YGGu/D4b+BPQV9JoUnBc3hpdeb3ngQ+BMcBNwKt5vdbAMEmjgHuBX6xE/szqDE94ZVZc+wI7Aa9IgtRl6RvAsIiYBiDpfmC75ezn28CdETEPoGBs0Q6SLgWaAk2Ap5ezn+1J45rezc8HAKcDN+TnD+f/I4GjlrOvwrx1yccHsHGekON+4LfAncCx+fmy1jczM6t1ETEe2KHgeb9qlhWW1b/Oy+8iVSovtX4uq3eu9HJV7WMYqbtxdfn7v2oW7VPpefeCx+fnbedXsd7x1ezPrb1W7zn4NSsuAQMioqIGVdIRVB9YVozFkdSA6sfslLsLOCIiRucZHPdavexWjHFayIqfPxoAu0bEl4WJkl4EtpXUnDQG+dLlrL+qeTYzMzMzc7dnsyIbAhwtqQWApGbAa8CekjaTtA7p1grlxpNaiiGN210nPx4MnJRvZVC+H0jjeCbn/RTW5FYe41PuHaC9pG3z8+8D/171wwPgGaCiVlpSGUCeJfMR4PfA2xExY1nrm5mZrc3yfCCjKv3dXOx8mdUnbvk1K6KIGCPp18AzuSV3Pqmb8e+AF4HZwKiCTf4CDMpjdp4ij9mJiKdykDhC0tfAE6T7Dv4GeBmYlv+XB7wDSbc8+ClQMYFWvv/fSaRbLTQiTZxx62oe5k+Bm/NYpEbAf0gzRULq6vwKS07ssaz1zczM1koRcSdpqJCZrSKlxhczMzMzMzOz0uVuz2ZmZmZmZlby3O3ZzFZZ7iJ9ZqXk5yPi9GLkx8zMzMysOu72bGZmZmZmZiXP3Z7NzMzMzMys5Dn4NTMzMzMzs5Ln4NfMzMzMzMxKnoNfMzMzMzMzK3kOfs3MzMzMzKzkOfg1MzMzMzOzkufg18zMzMzMzEqeg18zMzMzMzMreQ5+zczMzMzMrOQ5+DUzMzMzM7OS5+DXzMzMzMzMSp6DXzMzMzMzMyt5Dn7NzMzMzMys5Dn4NTMzMzMzs5Ln4NfMzMzMzMxKnoNfMzMzMzMzK3kOfs3MzMzMzKzkOfg1MzMzMzOzkufg18zMzMzMzEqeg18zMzMzMzMreQ5+zczMzMzMrOQ5+DUzMzMzM7OS5+DXzMzMzMzMSp6DXzMzMzMzMyt5Dn7NzMzMzMys5Dn4NTMzMzMzs5Ln4NfMzMzMzMxKnoNfMzMzMzMzK3kOfs3MzMzMzKzkOfg1MzMzMzOzkufg18zMzMzMzEqeg18zMzMzMzMreQ5+zczMzMzMrOQ5+DUzMzMzM7OS5+DXzMzMzMzMSp6DXzMzMzMzMyt5Dn7NzMzMzMys5Dn4NTMzMzMzs5Ln4NfMzMzMzMxKnoNfMzMzMzMzK3kOfs3MzMzMzKzkOfg1MzMzMzOzkufg18zMzMzMzEqeg1+r1yTdKuk3NbSvdpI+k9QwPx8m6dSa2Hfe35OS+tbU/lbidS+VNF3SJ1Us+5akd2o7T2ZmVje4HF2h1622HK2l1w9J29bwPn8n6d7V2P4tSXvVXI6W2v9ekiYuY3mNfW9t7eLg1+osSeMlfSFprqTZkl6Q9CNJFd/biPhRRFyygvv69rLWiYj/RUSTiFhYA3lfqlCJiAMjYsDq7nsl89EOOAfoEhFbVl4eEf+NiO1rM091iaR+kp4rdj7MzNYEl6Orb3nl6PKCtNVdvy6QdJekSwvTIqJrRAwrUpZW+Htb11T1XlrtcvBrdd2hEbERsBVwJXA+0L+mX0RSo5reZx3RDpgREVOLnRFbUgl/58ysbnE5unrqZTla3vpuZkty8Gv1QkR8GhGPAd8D+kraAZasQZO0uaTHc+32TEn/ldRA0j2kwusfuTvWzyW1z92ITpH0P2BoQVphAb6NpOGS5kgaJKlZfq2lam7La8UlHQD8Evhefr3ReXlF96+cr19L+kjSVEl3S9okLyvPR19J/8tdrX5V3XsjaZO8/bS8v1/n/X8bGAy0yvm4q4ptlziOfAznSnpd0qeS7pe0XsHywyWNyu/H+/lYkdRK0mP5fR8n6QcF2/xO0t8l3ZtbH96QtJ2kX+RjnyBpv0rH01/SZEmTlLqbLbcQl/QDSW/n1xgjqWdOvyDntTz9yJzeGbgV2C2/P7NzemNJ1+b3fopS16r1C17n5zlvH0s6VQXd0ar7LPKyfpKel3S9pBnAxfn96law7xaS5klqvrzjNTNbGS5Ha74clbQh8GTB8s9yedhY0g25nPg4P268jPV7S3oxv++TJf1R0ror8/nmz/EWSU9I+hzYO+/7oXxcH0r66TK2/7ukT5TK/v9I6prTTwOOB36e8/uPws8qP67yePOyvSRNlHRO/pwmSzqp4HUPUiqb5yqV+edWyld12xV+b8tf45f5sx4v6fgVeM/Wl3Rd/sw/lfSccnkv6TClrt2z8/euc8F2S3RDryYvS+W7uvfSapeDX6tXImI4MBH4VhWLz8nLmgNbkArOiIjvA/8j1X43iYirC7bZE+gM7F/NS54InAy0BBYAN61AHp8CLgfuz6/Xo4rV+uW/vYGtgSbAHyutszuwPbAv8NvCE28lfwA2yfvZM+f5pIj4F3Ag8HHOR7/l5T07BjgA6AB0z/lEUm/gbuA8oCmwBzA+bzOQ9N63Ao4GLpe0T8E+DwXuATYFXgOeJp1/WgMXA38uWPcu0nu9LbAjsB+wzDFjkr4L/C4f+8bAYcCMvPh90vdlE+Ai4F5JLSPibeBHwIv5/Wma178S2A4oy3loDfw2v84BwM+Ab+dle1XKSpWfRcHyXYAPSN/PS0jv2wkFy48DhkTEtGUdr5nZqnI5WqVVKkcj4vNKy5tExMfAr4BdSeVID6A38OtlrL8QOBvYHNgt5/cny3ufqvD/gMuAjYAXgH8Ao0nl2L7AWZKq+5yeBDoCLYBXgb/mY7wtP7465/fQKrat8ngLlm9Jen9bA6cAN0vaNC/rD/ww907YARi6gttVtiXp/WsN9AVuk7S8YV3XAjsB3wCaAT8HFknaDrgPOIv0W3iCVPGzohUSVeZ7Bd9LW8Mc/Fp99DHpJFXZfFLhulVEzM/jWWM5+/pdRHweEV9Us/yeiHgzF1i/AY5RzXQlOh74fUR8EBGfAb8AjtWSteUXRcQXETGaVHgtVfjnvBwL/CIi5kbEeOA64PurkbebIuLjiJhJKjjLcvopwB0RMTgiFkXEpIgYK6kt8E3g/Ij4MiJGAbeTLh7K/Tcino6IBcDfSYXJlRExnxQAtpfUVNIWwEHAWflzmQpcn49xWU4lFSavRDIuIj4CiIi/5+NZFBH3A++RCualSBJwGnB2RMyMiLmkC7Dy1z8GuDMi3oqIeaSAu3zbFfksPo6IP0TEgvydGwAcl1+XvO49yzlWM7PV5XI0W0Pl6PHAxRExNVdmXrSs/UXEyIh4KZcN40kVwnuuwusOiojnI2IR0A1oHhEXR8TXEfEB8BeqKU8j4o58/F+RyrYeyi3pK2B5xzs/L58fEU8An5EqJcqXdZG0cUTMiohXV3C7qvwmIr6KiH8D/ySV2VVS6pV1MnBmvp5ZGBEv5OP/HvDPfL0znxQkr08KklfEyubbapGDX6uPWgMzq0i/BhgHPCPpA0kXrMC+JqzE8o+AdUg1i6urVd5f4b4bkWrayxXOKjmPVKtd2eY5T5X31Xo18lbd67YltaJW1gooDxSry8OUgsdfANNj8YQo5RdMTUhj0tYBJueuRrNJFwEtlpPn6vKGpBOVumqX728Hqv8MmwMbACML1n8qp5cfa+F3ovDxinwWS3zfIuJl0nu8l6ROpNbkx6rJm5lZTXE5utiaKEerylur6lZWGgr0eO52PIdU6boq71Hhe70VqXv17ILy7Jcs+f6Uv35DSVcqDRGaw+JeXSuah+Ud74xc+V2u8LP4DqnS+yNJ/5a02wpuV9msXMFSXR4q2xxYj+qvayqOJ1cmTGDFvxMrk2+rZQ5+rV6RtDPp5LPUDL25xvKciNia1O31Z5L2LV9czS6XV6PdtuBxO1Jt3nTgc1KQVJ6vhiwOkFZkvx+TCqbCfS9gySBxRUzPeaq8r0kruZ8VMQHYpor0j4FmkjaqgTxMAL4CNo+Ipvlv44jouip5k7QVqab7DGCzSF2b3wTKW1orf07TScF414LX3yQiygutyUCbgvXbVtp2eZ9FVd+LAaSuz98HHoyIL6s7SDOz1eVydCmrW45Wlc+q8vbxMta/BRgLdIyIjUlBqqpYb2XyMgH4sKAsaxoRG0XEQVVs9/+Aw0lDejYB2uf06srKypZ1vMvOcOqxdTipkvtR4IEV2a4KmyqNqV7RPEwHvqT665qK48m9s9qy+Dsxj4LvLqmb84pa3ntpa5iDX6sXJG0s6RBSF9l7I+KNKtY5RNK2+ST1KWkMzaK8eAppLM/KOkFSF0kbkMamPphbLN8F1pN0sKR1SGNbGhdsN4XUlbe639h9wNmSOkhqwuKxTQuqWb9KOS8PAJdJ2igHez8DVvnefcvQHzhJ0r5KE4G0ltQpIiaQxhZdIWk9Sd1JXaRXOg8RMRl4Brguf+YNJG0jaXndv24HzpW0k5Jt83uxIamgmQaQJ53YoWC7KUCb8nE8uXb3L8D1klrkbVoXjJF6IL8HnfN3ouIeg6vxWdwLHEkKgO9ezrpmZqvE5WjVaqAcnQJsVqmL8H3AryU1l7Q5ad6Ie5ex/kbAHOCz3AvoxytzDNUYDsyVdL7SxE4NJe2QKz8q24hU8TyDFNRdXmn58j77ZR1vtSStK+l4SZvk7sVzWPx9WxUX5X1+CziENMyqSrm8vwP4vdLEYA0l7aY0UdcDwMH5emcd0lj4r0jXOgCjgP+XtzmAleuivqq/I6shDn6trvuHpLmkGsxfAb9nyQmECnUE/kUaW/Ei8KeIeDYvu4J0Yp6tSjMJLsc9pAmYPiF1j/kppFkzSZNR3E6qCfycNElIufIT7gxJheNXyt2R9/0f4ENS7eP/rUS+Cv1ffv0PSDX5f8v7r1GRJkk5iTQG91Pg3yyuGT2OVFP8MfAIcGGkiUJWxYnAusAYYBbwIGkM2rLy9nfSJB9/A+aSao+bRcQY0titF0kFTjfg+YJNhwJvAZ9Imp7Tzid1+3spd//6F3msTkQ8SZqs5dnydfI2X+X/K/1Z5MqDV0lB+n+Xta6Z2SpwObp8q1yORsRYUvD3QX5vWgGXAiOA14E3SOf4S5ex/rmk1te5pArY+1fxOArztZAUAJaR3p/ppPe6qnG8d5O6+U4ilb0vVVrenzQud7akR6vYvtrjXQHfB8bn8vZHpPHDq+IT0jXDx6RJpX6U3+tlOZeU31dIwwCuAhpExDukCuk/kN63Q0mTvX2dtzszp83O+X10JfK5vPfS1jAtfx4DMzOritLMoW8CjVe2taHSfu4gTYb16+WubGZmZhUk7UXqzdBmOauaueXXzGxlSDpS6Z6Gm5Jqif+xmoFve+AoUm2wmZmZma0hDn7NrF6QdKvSTeEr/91ay1n5ITCVNEPkQlZjbJakS0gtx9dExIc1kz0zMytFkt6qphxc1a7CJc/vmVXmbs9mZmZmZmZW8tzya2ZmZmZmZiXPwa+ZmZmZmZmVPAe/ZmZmZmZmVvIc/JqZmZmZmVnJc/BrZmZmZmZmJc/Br5mZmZmZmZU8B79mZmZmZmZW8hoVOwO1bfPNN4/27dsXOxtmZlYCRo4cOT0imhc7H/Wdy2YzM6spyyqb17rgt3379owYMaLY2TAzsxIg6aNi56EUuGw2M7Oasqyy2d2ezczMzMzMrOQ5+DUzMzMzM7OS5+DXzMzMzMzMSp6DXzMzMzMzMyt5Dn7NzMzMzMys5Dn4NTMzMzMzs5JXtOBXUkNJr0l6PD/vIOllSeMk3S9p3ZzeOD8fl5e3L9jHL3L6O5L2L9KhmJmZmZmZWR1XzJbfM4G3C55fBVwfEdsCs4BTcvopwKycfn1eD0ldgGOBrsABwJ8kNaylvJuZmZmZmVk9UpTgV1Ib4GDg9vxcwD7Ag3mVAcAR+fHh+Tl5+b55/cOBgRHxVUR8CIwDetfKAZiZmZmZmVm9UqyW3xuAnwOL8vPNgNkRsSA/nwi0zo9bAxMA8vJP8/oV6VVsY2ZmZmZmZlah1oNfSYcAUyNiZC2+5mmSRkgaMW3atNp6WTMzMzMzM6sjitHy+03gMEnjgYGk7s43Ak0lNcrrtAEm5ceTgLYAefkmwIzC9Cq2WUJE3BYRvSKiV/PmzWv2aMzMzMzMzKzOq/XgNyJ+ERFtIqI9acKqoRFxPPAscHRerS8wKD9+LD8nLx8aEZHTj82zQXcAOgLDa+kwzMzMSoaktpKelTRG0luSzszpv5M0SdKo/HdQwTZV3nFB0gE5bZykC4pxPGZmZlVptPxVas35wEBJlwKvAf1zen/gHknjgJmkgJmIeEvSA8AYYAFwekQsrP1sm5mZ1XsLgHMi4lVJGwEjJQ3Oy66PiGsLV650x4VWwL8kbZcX3wz0Ic3F8YqkxyJiTK0chZmZ2TIUNfiNiGHAsPz4A6qYrTkivgS+W832lwGXrbkcmpmZlb6ImAxMzo/nSnqbZU8iWXHHBeDDXEFdXoaPy2U6kgbmdR38mplZ0RXzPr9mZmZWx0hqD+wIvJyTzpD0uqQ7JG2a06q744LvxGBmZnWWg98a8OWXX9K7d2969OhB165dufDCCwHo168fHTp0oKysjLKyMkaNGlWxzbBhwygrK6Nr167sueeeFeknn3wyLVq0YIcddqjtwzAzs7WcpCbAQ8BZETEHuAXYBigjtQxfV4OvtUbvxDBhwgT23ntvunTpQteuXbnxxhsB+M1vfkP37t0pKytjv/324+OPPwYgIvjpT3/KtttuS/fu3Xn11Vcr9tWwYcOKsvywww6r8byamVntqEtjfuutxo0bM3ToUJo0acL8+fPZfffdOfDAAwG45pprOProo5dYf/bs2fzkJz/hqaeeol27dkydOrViWb9+/TjjjDM48cQTa/UYzMxs7SZpHVLg+9eIeBggIqYULP8L8Hh+uqw7LqzwnRiA2wB69eoVNXAIS2jUqBHXXXcdPXv2ZO7cuey000706dOH8847j0suuQSAm266iYsvvphbb72VJ598kvfee4/33nuPl19+mR//+Me8/HJq/F5//fWXqMA2M7P6yS2/NUASTZo0AWD+/PnMnz8fSdWu/7e//Y2jjjqKdu3aAdCiRYuKZXvssQfNmjVbsxk2MzMroFRo9QfejojfF6S3LFjtSODN/Li6Oy68AnSU1EHSuqRJsR6rjWOorGXLlvTs2ROAjTbaiM6dOzNp0iQ23njjinU+//zzivJ60KBBnHjiiUhi1113Zfbs2UyePLkYWTczszXEwW8NWbhwIWVlZbRo0YI+ffqwyy67APCrX/2K7t27c/bZZ/PVV18B8O677zJr1iz22msvdtppJ+6+++5iZt3MzOybwPeBfSrd1uhqSW9Ieh3YGzgb0h0XgPI7LjxFvuNCRCwAzgCeBt4GHsjrFtX48eN57bXXliib27Zty1//+lcuvvhiACZNmkTbtosbrdu0acOkSanR+ssvv6RXr17suuuuPProo7WefzMzqxkOfmtIw4YNGTVqFBMnTmT48OG8+eabXHHFFYwdO5ZXXnmFmTNnctVVVwGwYMECRo4cyT//+U+efvppLrnkEt59990iH4GZma2tIuK5iFBEdI+Isvz3RER8PyK65fTD8qzQ5dtcFhHbRMT2EfFkQfoTEbFdXlb0OzJ89tlnfOc73+GGG26oaPW97LLLmDBhAscffzx//OMfl7uPjz76iBEjRvC3v/2Ns846i/fff39NZ9vMzNYAB781rGnTpuy999489dRTtGzZEkk0btyYk046ieHDhwOpNnn//fdnww03ZPPNN2ePPfZg9OjRRc65mZlZaZk/fz7f+c53OP744znqqKOWWn788cfz0EMPAdC6dWsmTFg8UfXEiRNp3bp1xTKArbfemr322ovXXnutFnJvZmY1zcFvDZg2bRqzZ88G4IsvvmDw4MF06tSpYqxQRPDoo49WzOB8+OGH89xzz7FgwQLmzZvHyy+/TOfOnYuVfTMzs5ITEZxyyil07tyZn/3sZxXp7733XsXjQYMG0alTJwAOO+ww7r77biKCl156iU022YSWLVsya9asimFL06dP5/nnn6dLly61ezBmZlYjPNtzDZg8eTJ9+/Zl4cKFLFq0iGOOOYZDDjmEffbZh2nTphERlJWVceuttwLQuXNnDjjgALp3706DBg049dRTKwLj4447jmHDhjF9+nTatGnDRRddxCmnnFLMwzMzM6t3nn/+ee655x66detGWVkZAJdffjn9+/fnnXfeoUGDBmy11VYVZfNBBx3EE088wbbbbssGG2zAnXfeCcDbb7/ND3/4Qxo0aMCiRYu44IILHPyamdVTiqjxuwvUab169YoRI0YUOxtmZlYCJI2MiF7Fzkd957LZzMxqyrLKZnd7NjMzMzMzs5Ln4NfMzMzMzMxKnoNfMzMzMzMzK3m1HvxKWk/ScEmjJb0l6aKcfpekDyWNyn9lOV2SbpI0TtLrknoW7KuvpPfyX9/aPhYzMzMzMzOrH4ox2/NXwD4R8ZmkdYDnJD2Zl50XEQ9WWv9AoGP+2wW4BdhFUjPgQqAXEMBISY9FxKxVydRO5929KpvVeSOvObHYWTAzM1slLpvNzKwm1XrLbySf5afr5L9lTTl9OHB33u4loKmklsD+wOCImJkD3sHAAWsy72ZmZmZmZlY/FWXMr6SGkkYBU0kB7Mt50WW5a/P1khrntNbAhILNJ+a06tKrer3TJI2QNGLatGk1eShmZmZmZmZWDxQl+I2IhRFRBrQBekvaAfgF0AnYGWgGnF+Dr3dbRPSKiF7Nmzevqd2amZmZmZlZPVHU2Z4jYjbwLHBAREzOXZu/Au4EeufVJgFtCzZrk9OqSzczMzMzMzNbQjFme24uqWl+vD7QBxibx/EiScARwJt5k8eAE/Osz7sCn0bEZOBpYD9Jm0raFNgvp5mZmZmZmZktoRizPbcEBkhqSAq+H4iIxyUNldQcEDAK+FFe/wngIGAcMA84CSAiZkq6BHglr3dxRMysvcMwMzMzMzOz+qLWg9+IeB3YsYr0fapZP4DTq1l2B3BHjWbQzMzMzMzMSk5Rx/yamZmZmZmZ1QYHv2ZmZmZmZlbyHPyamZmZmZlZyXPwa2ZmZmZmZiXPwa+ZmZmZmZmVPAe/ZmZmZiXu5JNPpkWLFuywww4VaaNHj2a33XajW7duHHroocyZMweAGTNmsPfee9OkSRPOOOOMivXnzZvHwQcfTKdOnejatSsXXHBBrR+HmdnqcPBrZmZmVuL69evHU089tUTaqaeeypVXXskbb7zBkUceyTXXXAPAeuutxyWXXMK111671H7OPfdcxo4dy2uvvcbzzz/Pk08+WSv5r2lVVQaMGjWKXXfdlbKyMnr16sXw4cMBmDVrFkceeSTdu3end+/evPnmm8vcj5nVXQ5+zczMzErcHnvsQbNmzZZIe/fdd9ljjz0A6NOnDw899BAAG264IbvvvjvrrbfeEutvsMEG7L333gCsu+669OzZk4kTJ9ZC7mteVZUBP//5z7nwwgsZNWoUF198MT//+c8BuPzyyykrK+P111/n7rvv5swzz1zmfsys7nLwa2ZmtpaT1FbSs5LGSHpL0pk5vZmkwZLey/83zemSdJOkcZJel9SzYF998/rvSepbrGOy5evatSuDBg0C4O9//zsTJkxY4W1nz57NP/7xD/bdd981lb01qqrKAEkVXb8//fRTWrVqBcCYMWPYZ599AOjUqRPjx49nypQp1e6nPlqZlnCAYcOGUVZWRteuXdlzzz0r0mfPns3RRx9Np06d6Ny5My+++GKtHofZ8jj4NTMzswXAORHRBdgVOF1SF+ACYEhEdASG5OcABwId899pwC2QgmXgQmAXoDdwYXnAbHXPHXfcwZ/+9Cd22mkn5s6dy7rrrrtC2y1YsIDjjjuOn/70p2y99dZrOJe154YbbuC8886jbdu2nHvuuVxxxRUA9OjRg4cffhiA4cOH89FHH9XbFu/qrExL+OzZs/nJT37CY489xltvvcXf//73im3OPPNMDjjgAMaOHcvo0aPp3LlzrR6H2fI4+DUzM1vLRcTkiHg1P54LvA20Bg4HBuTVBgBH5MeHA3dH8hLQVFJLYH9gcETMjIhZwGDggNo7ElsZnTp14plnnmHkyJEcd9xxbLPNNiu03WmnnUbHjh0566yz1mwGa9ktt9zC9ddfz4QJE7j++us55ZRTALjggguYPXs2ZWVl/OEPf2DHHXekYcOGRc5tzVqZlvC//e1vHHXUUbRr1w6AFi1aVKzzn//8p+J9W3fddWnatGktHYHZimlU7AyYmZlZ3SGpPbAj8DKwRURMzos+AbbIj1sDhX1kJ+a06tKtDpo6dSotWrRg0aJFXHrppfzoRz9a7ja//vWv+fTTT7n99ttrIYe1a8CAAdx4440AfPe73+XUU08FYOONN+bOO+8EICLo0KFDSbV4V+eGG25g//3359xzz2XRokW88MILQBorPn/+fPbaay/mzp3LmWeeyYknnsiHH35I8+bNOemkkxg9ejQ77bQTN954IxtuuGGRj8RssVpv+ZW0nqThkkbncUUX5fQOkl7O44ful7RuTm+cn4/Ly9sX7OsXOf0dSfvX9rGYmZmVEklNgIeAsyJiTuGyiAggavC1TpM0QtKIadOm1dRurRrHHXccu+22G++88w5t2rShf//+3HfffWy33XZ06tSJVq1acdJJJ1Ws3759e372s59x11130aZNG8aMGcPEiRO57LLLGDNmDD179qSsrKykguBWrVrx73//G4ChQ4fSsWNHIHXz/frrrwG4/fbb2WOPPdh4442Lls/aUl1L+IIFCxg5ciT//Oc/efrpp7nkkkt49913WbBgAa+++io//vGPee2119hwww258sori3wUZksqRsvvV8A+EfGZpHWA5yQ9CfwMuD4iBkq6FTiFNIboFGBWRGwr6VjgKuB7eSzSsUBXoBXwL0nbRcTCIhyTmZlZvZbL5IeAv0bEwzl5iqSWETE5d2uemtMnAW0LNm+T0yYBe1VKH1bV60XEbcBtAL169aqxoNqqdt9991WZXjhzcaHx48dXmZ7qQOq/4447jmHDhjF9+nTatGnDRRddxF/+8hfOPPNMFixYwHrrrcdtt90GwNtvv03fvn2RRNeuXenfv/8y91MeJNZ31bWEt2nThs0224wNN9yQDTfckD322IPRo0fzrW99izZt2rDLLrsAcPTRRzv4tTqn1oPfXHP8WX66Tv4LYB/g/+X0AcDvSMHv4fkxwIPAHyUppw+MiK+ADyWNI02u4WnlzMzMVkIuV/sDb0fE7wsWPQb0Ba7M/wcVpJ8haSBpcqtPc4D8NHB5wSRX+wG/qI1jMFsZ1VUGjBw5cqm03XbbjXfffXel9lMKylvC99prryVawg8//HDOOOMMFixYwNdff83LL7/M2WefzZZbbknbtm1555132H777RkyZAhdunQp8lGYLakoY34lNQRGAtsCNwPvA7MjYkFepXCMUMX4oYhYIOlTYLOc/lLBbj2uyMzMbNV8E/g+8IakUTntl6Sg9wFJpwAfAcfkZU8ABwHjgHnASQARMVPSJcAreb2LI2JmrRyBma2ylWkJ79y5MwcccADdu3enQYMGnHrqqRW3SPrDH/7A8ccfz9dff83WW29dMVbarK4oSvCbuyaXSWoKPAJ0WpOvJ+k00q0YKmamMzMzsyQingNUzeKlbuSae3GdXs2+7gDuqLncmdmatjIt4QDnnXce55133lLpZWVljBgxokbzZlaTinqro4iYDTwL7Ea6TUJ5MF4+dggKxhXl5ZsAM6h+vFFVr3NbRPSKiF7Nmzev6cMwMzMzMzOzOq7WW34lNQfmR8RsSesDfUiTWD0LHA0MZOlxRX1JY3mPBoZGREh6DPibpN+TJrzqCAyv1YMxMzMzqyU7nXd3sbOwRoy85sRiZ8HM1hLF6PbcEhiQx/02AB6IiMcljQEGSroUeI008Qb5/z15QquZpBmeiYi3JD0AjAEWAKd7pmczMzOztYMrA8xsZRVjtufXgR2rSP+ANFtz5fQvge9Ws6/LgMtqOo9mZmZmZmZWWooy4ZWZmZmZmdUMt4KbrZiiTnhlZmZmZmZmVhsc/JqZmZmZmVnJc/BrZmZmZmZmJc/Br5mZmZmZmZU8B79mZmZmZmZW8hz8mpmZmZmZWclz8GtmZmZmZmYlz8GvmZmZmZmZlTwHv2ZmZmZmZlbyHPyamZmZmZlZyXPwa2ZmZmZmZiXPwa+ZmZmZmZmVvFoPfiW1lfSspDGS3pJ0Zk7/naRJkkblv4MKtvmFpHGS3pG0f0H6ATltnKQLavtYzMzMzMzMrH5oVITXXACcExGvStoIGClpcF52fURcW7iypC7AsUBXoBXwL0nb5cU3A32AicArkh6LiDG1chRmZmZmZv+/vXuPs6qq/z/+egNeQlLwqxCCihcUBWFUEjW/5g0ULxllJl4QsTQTTVPT6ttXzUyzLDNvaZHSV8G7kj9ESSUpQy6GirdERYFQEEVUVAQ/vz/WOsOZYYbbXM6Zmffz8ZjHOWedtfdZe88+Z+/PXjczazIaPfiNiHnAvPz8fUkvAF1WsciRwOiI+AR4TdJMYI/83syIeBVA0uic18GvmZmZmZmZVVHSPr+SugG7Ak/mpOGSnpE0QlKHnNYFmF202JycVlt6TZ9ziqSpkqYuWLCgPjfBzMzMzMzMmoCSBb+S2gF3A2dFxGLgemA7oIJUM3xlfX1WRNwYEX0jou/mm29eX6s1MzMzMzOzJqIUfX6RtB4p8L01Iu4BiIi3it6/CXggv5wLbFm0eNecxirSzczMzMzMzCqVYrRnAX8EXoiIXxeldy7KNgiYkZ+PAY6RtIGkbYDuwGRgCtBd0jaS1icNijWmMbbBzMzMzMzMmpY6Bb+SHlmTtGq+BJwAHFBtWqMrJD0r6Rlgf+BsgIh4DriDNJDVOOD0iFgeEcuA4cBDwAvAHTmvmZlZi7Uu5+Y81sZ8STOK0jwFoZmZNSvr1OxZ0oZAW2CzPDCV8lsbs+qRm4mIvxflLzZ2FctcClxaQ/rYVS1nZmbWUtTl3AzcDFwDjKyW7ikIzcys2VjXPr+nAmeRTnrTWHGCXUw6eZqZmVnjWudzc0Q8nmdgWBOegtDMzJqkdQp+I+K3wG8lnRERv6vnMpmZmdlaaqBz83BJQ4CpwDkR8S6pFnlSUZ7iqQarT0HYr57KYWZmVmd1Gu05In4naW+gW/G6IqJ6sykzMzNrBPV4br4euASI/HglMKyeiomkU4BTALbaaqv6Wq2ZmVmt6hT8SvozaW7e6cDynBys3GfIzMzMGkF9nZsbegrCiLgRuBGgb9++sTZlMzMzWxd1nee3L7BzRPikZWZmVh7q5dwsqXNEzMsvq09BeJukX5P6FxemIBR5CkJS0HsMcGxdymBmZlaf6hr8zgC+AMxbXUYzMzNrFGt9bpY0CtiPNFL0HOBCYD9JFaRa41mkAbWIiOckFaYgXEaegjCvpzAFYWtghKcgNDOzclKneX6BzYDnJT0kaUzhrz4KZmZmZutkrc/NETE4IjpHxHoR0TUi/hgRJ0TELhHROyK+UlQLTERcGhHbRcSOEfFgUfrYiNghv7fSFIVmZuVu2LBhdOzYkV69elWmnXfeefTo0YPevXszaNAgFi1aVGWZN954g3bt2vGrX6WZ4T7++GP22GMP+vTpQ8+ePbnwwgsbcxNsFepa83tRfRTCzMzM6s1FpS6AmVlTNXToUIYPH86QIUMq0/r3789ll11GmzZtOP/887nsssv4xS9+Ufn+97//fQYOHFj5eoMNNuDRRx+lXbt2fPrpp+yzzz4MHDiQPffcs1G3xVZW19Ge/1ZfBTEzM7O687nZzGzd7bvvvsyaNatK2oABAyqf77nnntx1112Vr++77z622WYbNtpoo8o0SbRr1w6ATz/9lE8//RRJWOnVqdmzpPclLc5/H0taLmlxfRXOzMzM1o7PzWZmDWfEiBGVtbwffPABv/jFL2ps1rx8+XIqKiro2LEj/fv3p18/T3teDupa8/v5wnOl2xlHAq7PNzMzKxGfm83MGsall15KmzZtOO644wC46KKLOPvssytreYu1bt2a6dOns2jRIgYNGsSMGTOq9CO20qhrn99KeUqF+yRdCFxQX+s1MzOzdeNzs5lZ/bj55pt54IEHeOSRRyqbMD/55JPcdddd/OAHP2DRokW0atWKDTfckOHDh1cu1759e/bff3/GjRvn4LcM1LXZ89eK/o6SdDnw8WqW2VLSY5Kel/ScpO/l9E0ljZf0cn7skNMl6WpJMyU9I2m3onWdmPO/LOnEumyL1Z+aRsn75je/SUVFBRUVFXTr1o2KigoAli5dykknncQuu+xCnz59mDBhQuUy06ZNY5dddmH77bfnzDPPxNNJm5mt3rqcm83MrHbjxo3jiiuuYMyYMbRt27YyfeLEicyaNYtZs2Zx1lln8aMf/Yjhw4ezYMGCyhGhP/roI8aPH0+PHj1KVHorVtea3yOKni8jzQN45GqWWQacExFPSfo8ME3SeGAo8EhEXC7pAtId6vOBgUD3/NcPuB7oJ2lT0jyEfUlzEE6TNCYi3q3jNlkd1TRK3u233175/JxzzmGTTTYB4KabbgLg2WefZf78+QwcOJApU6bQqlUrTjvtNG666Sb69evHoYceyrhx46qMpGdmZjVal3OzmZkBgwcPZsKECbz99tt07dqViy++mMsuu4xPPvmE/v37A2nQqxtuuKHWdcybN48TTzyR5cuX89lnn3H00Udz+OGHN9Ym2CrUtc/vSeuwzDxgXn7+vqQXgC6kE/N+OdstwARS8HskMDI33Zokqb2kzjnv+Ih4ByAH0IcAo+qwSVYPaholryAiuOOOO3j00UcBeP755znggAMA6NixI+3bt2fq1KlsueWWLF68uHJI+CFDhnDfffc5+DUzW411OTebmVkyatTKocTJJ5+82uUuuuiiyue9e/fmX//6V30Wy+pJXZs9d5V0r6T5+e9uSV3XYvluwK7Ak0CnHBgDvAl0ys+7ALOLFpuT02pLtzI2ceJEOnXqRPfu3QHo06cPY8aMYdmyZbz22mtMmzaN2bNnM3fuXLp2XXEode3alblz55aq2GZmTUZdz81mZmbNVZ2CX+BPwBhgi/z3l5y2WpLaAXcDZ0VElSkYci1vvXXwlHSKpKmSpi5YsKC+VmvrYNSoUQwePLjy9bBhw+jatSt9+/blrLPOYu+996Z169YlLKGZWZO3zudmMzOz5qyufX43j4jiE+rNks5a3UKS1iMFvrdGxD05+S1JnSNiXm7WPD+nzwW2LFq8a06by4pm0oX0CTV9XkTcCNwI0LdvX4+aVCLLli3jnnvuYdq0aZVpbdq04Te/+U3l67333psddtiBDh06MGfOnMr0OXPm0KWLK/bNzNbAOp2bzczMmru61vwulHS8pNb573hg4aoWyHMO/hF4ISJ+XfTWGKAwYvOJwP1F6UPyqM97Au/l5tEPAQMkdcgjQw/IaVam/vrXv9KjR48qzZmXLFnChx9+CMD48eNp06YNO++8M507d2bjjTdm0qRJRAQjR47kyCM9XouZ2RpY63OzmZlZS1DXmt9hwO+A35CaKT9BGrV5Vb4EnAA8K2l6TvsRcDlwh6STgdeBo/N7Y4FDgZnAEuAkgIh4R9IlwJSc76eFwa+stGoaJe/kk09m9OjRVZo8A8yfP5+DDz6YVq1a0aVLF/785z9XvnfdddcxdOhQPvroIwYOHOjBrszM1sy6nJvNzJqN3c8bWeoiNIhpvxyy+ky2SnUNfn8KnFiYXihPP/Qr0om3RhHxd0C1vH1gDfkDOL2WdY0ARqxlma2B1TRKHqTJwavr1q0bL730Uo35+/bty4wZM+qzaGZmLcFan5vNzMxagro2e+5dPK9urnndtY7rNDMzs3Xnc7OZmVkN6hr8tsr9bYHKu8t1rU02MzOzdedzs5mZWQ3qGvxeCfxT0iW5/+0TwBV1L5ZZ8zFs2DA6duxIr169KtMuuugiunTpQkVFBRUVFYwdOxaAyZMnV6b16dOHe++9t3KZbt26scsuu1BRUUHfvn0bfTvMrMnwudnMzKwGdboTHBEjJU0FDshJX4uI5+teLLPmY+jQoQwfPpwhQ6oOUnD22Wdz7rnnVknr1asXU6dOpU2bNsybN48+ffpwxBFH0KZN+qo+9thjbLbZZo1WdjNrenxuNjMzq1mdm0HlE6pPqs2MR8mrP/vuuy+zZs1ao7xt27atfP7xxx+TZgYzM1s7PjebmZmtrK7Nns1sHV1zzTX07t2bYcOG8e67lWPT8OSTT9KzZ0922WUXbrjhhspaX0kMGDCA3XffnRtvvLFUxTYzMzMza5Ic/JqVwGmnncYrr7zC9OnT6dy5M+ecc07le/369eO5555jypQpXHbZZXz88ccA/P3vf+epp57iwQcf5Nprr+Xxxx8vVfHNzMzMzJocB79mJdCpUydat25Nq1at+Pa3v83kyZNXyrPTTjvRrl27yrmOu3TpAkDHjh0ZNGhQjcuYmZmZmVnNHPyalcC8efMqn997772VI0G/9tprLFu2DIDXX3+dF198kW7duvHhhx/y/vvvA/Dhhx/y8MMPVxk9uimpafTrgiuvvBJJvP322wBEBGeeeSbbb789vXv35qmnngJg+vTp7LXXXvTs2ZPevXtz++23N+o2mJmZmbVEv/3tb+nVqxc9e/bkqquuAuDOO++kZ8+etGrViqlTp1bJf9lll7H99tuz44478tBDD5WgxFV53j+zBjZ48GAmTJjA22+/TdeuXbn44ouZMGEC06dPRxLdunXj97//PZCaNl9++eWst956tGrViuuuu47NNtuMV199lUGDBgGwbNkyjj32WA455JBSbtY6q23069mzZ/Pwww+z1VZbVaY9+OCDvPzyy7z88ss8+eSTnHbaaTz55JO0bduWkSNH0r17d/7zn/+w++67c/DBB9O+fftG3hozMzOzlmHGjBncdNNNTJ48mfXXX59DDjmEww8/nF69enHPPfdw6qmnVsn//PPPM3r0aJ577jn+85//cNBBB/Hvf/+b1q1bl2gLHPyaNbhRo0atlHbyySfXmPeEE07ghBNOWCl922235emnn673spVCbaNfn3322VxxxRUceeSRlWn3338/Q4YMQRJ77rknixYtYt68eeywww6VebbYYgs6duzIggULHPyamZmZNZAXXniBfv36Vc5O8uUvf5l77rmHH/zgBzXmv//++znmmGPYYIMN2Gabbdh+++2ZPHkye+21V2MWuwo3ezazkrv//vvp0qULffr0qZI+d+5cttxyy8rXXbt2Ze7cuVXyTJ48maVLl7Lddts1SlnNmitJIyTNlzSjKG1TSeMlvZwfO+R0Sbpa0kxJz0jarWiZE3P+lyWdWIptMTOz+terVy8mTpzIwoULWbJkCWPHjmX27Nm15l+T67jG5ppfMyupJUuW8POf/5yHH354rZedN28eJ5xwArfccgutWvlenlkd3QxcAxRP9H4B8EhEXC7pgvz6fGAg0D3/9QOuB/pJ2hS4EOgLBDBN0piIeBczM2vSdtppJ84//3wGDBjARhttREVFRUmbMK+LRr9arOXO8kWS5kqanv8OLXrvh/nO8kuSDi5KPySnzcwnZDNrgl555RVee+01+vTpQ7du3ZgzZw677bYbb775Jl26dKlyR3HOnDmVo14vXryYww47jEsvvZQ999yzVMU3azYi4nHgnWrJRwK35Oe3AF8tSh8ZySSgvaTOwMHA+Ih4Jwe844GmOUCBmZmt5OSTT2batGk8/vjjdOjQoUpXtOpWdR1XKqWoKrmZmk+Ev4mIivw3FkDSzsAxQM+8zHWSWktqDVxLuvO8MzA45zWzJmaXXXZh/vz5zJo1i1mzZtG1a1eeeuopvvCFL/CVr3yFkSNHEhFMmjSJTTbZhM6dO7N06VIGDRrEkCFDOOqoo0q9CWbNWaeIKAxP/ybQKT/vAhS3dZuT02pLX4mkUyRNlTR1wYIF9VtqMzNrEPPnzwfgjTfe4J577uHYY4+tNe9XvvIVRo8ezSeffMJrr73Gyy+/zB577NFYRa1Rozd7jojHJXVbw+xHAqMj4hPgNUkzgcIemxkRrwJIGp3zPl/f5TUD2P28kavP1ARN++WQ1WeqZzWNfl3bAGCHHnooY8eOZfvtt6dt27b86U9/AuCOO+7g8ccfZ+HChdx8880A3HzzzVRUVDTSVpi1PBERkqIe13cjcCNA37596229ZmbWcL7+9a+zcOFC1ltvPa699lrat2/PvffeyxlnnMGCBQs47LDDqKio4KGHHqJnz54cffTR7LzzzrRp04Zrr7225M2ky6nP73BJQ4CpwDm5uVQXYFJRnuI7yNXvLPerbcWSTgFOAapMo2Jmja+m0a+LFY8ELYlrr712pTzHH388xx9/fH0XzcxW9pakzhExLzdrnp/T5wJbFuXrmtPmAvtVS5/QCOU0M7NGMHHixJXSBg0aVDklZ3U//vGP+fGPf9zQxVpj5TJCzPXAdkAFMA+4sj5XHhE3RkTfiOi7+eab1+eqzczMmrMxQGHE5hOB+4vSh+RRn/cE3svNox8CBkjqkEeGHpDTzMzMSq4san4j4q3Cc0k3AQ/kl7XdWWYV6WZmZraWJI0i1dpuJmkOadTmy4E7JJ0MvA4cnbOPBQ4FZgJLgJMAIuIdSZcAU3K+n0ZE9UG0zMzMSqIsgt9Ck6r8chBQGAl6DHCbpF8DW5CmVJgMCOguaRtS0HsMUHtvazMzM1uliBhcy1sH1pA3gNNrWc8IYEQ9Fs3MzKxeNHrwW8ud5f0kVZDmBJwFnAoQEc9JuoM0kNUy4PSIWJ7XM5zUlKo1MCIinmvcLTEzq5thw4bxwAMP0LFjR2bMSPf87rzzTi666CJeeOEFJk+eTN++fQFYuHAhRx11FFOmTGHo0KFcc801QJon+Rvf+AavvPIKrVu35ogjjuDyyy8v2TaZmZlZefHArSuUYrTnmu4s/3EV+S8FLq0hfSyp2ZWZNSL/gNafoUOHMnz4cIYMWfHZvXr14p577uHUU0+tknfDDTfkkksuYcaMGZWBcsG5557L/vvvz9KlSznwwAN58MEHGThwYKNsg5mZmVlTUS4DXpmZtTj77rsvm266aZW0nXbaiR133HGlvBtttBH77LMPG264YZX0tm3bsv/++wOw/vrrs9tuuzFnzpyGK7SZmZlZE+Xg18ysmVi0aBF/+ctfOPDAlbpompmZmbV4Dn7NzJqBZcuWMXjwYM4880y23XbbUhfHzMzMrOw4+DUzawZOOeUUunfvzllnnVXqopiZmZmVpbKY6sjMzNbd//zP//Dee+/xhz/8odRFMTMzMytbDn7NzEpk8ODBTJgwgbfffpuuXbty8cUXs+mmm3LGGWewYMECDjvsMCoqKnjooYcA6NatG4sXL2bp0qXcd999PPzww2y88cZceuml9OjRg9122w2A4cOH861vfauUm2ZmZmZWdhz8mpmVyKhRo2pMHzRoUI3ps2bNqjE9IuqrSGZmZmbNlvv8mpmZmZmZWbPn4NfMzMzMzMyaPQe/ZmZmZmZm1uw5+DUzMzMzM7NmzwNemZnVwe7njSx1ERrEtF8OKXURzMzMzOpVSWp+JY2QNF/SjKK0TSWNl/RyfuyQ0yXpakkzJT0jabeiZU7M+V+WdGIptsXMzMzMzMzKX6maPd8MHFIt7QLgkYjoDjySXwMMBLrnv1OA6yEFy8CFQD9gD+DCQsBsZmZmZmZmVqwkwW9EPA68Uy35SOCW/PwW4KtF6SMjmQS0l9QZOBgYHxHvRMS7wHhWDqjNzMzMzMzMymrAq04RMS8/fxPolJ93AWYX5ZuT02pLNzOzJu6ll16ioqKi8m/jjTfmqquu4rzzzqNHjx707t2bQYMGsWjRIgBuvfXWKvlbtWrF9OnTS7oNZmZmVl7KKfitFBEBRH2tT9IpkqZKmrpgwYL6Wq2ZmTWQHXfckenTpzN9+nSmTZtG27ZtGTRoEP3792fGjBk888wz7LDDDlx22WUAHHfccZX5//znP7PNNttQUVFR2o0wMzOzslJOwe9buTkz+XF+Tp8LbFmUr2tOqy19JRFxY0T0jYi+m2++eb0X3MzMGs4jjzzCdtttx9Zbb82AAQNo0yZNVLDnnnsyZ86clfKPGjWKY445prGLaWZmZmWunILfMUBhxOYTgfuL0ofkUZ/3BN7LzaMfAgZI6pAHuhqQ08zMrBkZPXo0gwcPXil9xIgRDBw4cKX022+/vcb8ZmZm1rKVaqqjUcA/gR0lzZF0MnA50F/Sy8BB+TXAWOBVYCZwE/BdgIh4B7gEmJL/fprTzMysmVi6dCljxozhG9/4RpX0Sy+9lDZt2nDcccdVSX/yySdp27YtvXr1asximpmZWRPQphQfGhG13ZI/sIa8AZxey3pGACPqsWhmZlZGHnzwQXbbbTc6depUmXbzzTfzwAMP8MgjjyCpSv7aaonNzMzMShL8mpmZrYlRo0ZVCWbHjRvHFVdcwd/+9jfatm1bJe9nn33GHXfcwcSJExu7mGZmZtYElFOfXzMzs0offvgh48eP52tf+1pl2vDhw3n//ffp378/FRUVfOc736l87/HHH2fLLbdk2223LUVxmy1JsyQ9K2m6pKk5bVNJ4yW9nB875HRJulrSTEnPSNqttKU3MzNbwTW/ZmZWljbaaCMWLlxYJW3mzJm15t9vv/2YNGlSQxerpdo/It4uen0B8EhEXC7pgvz6fGAg0D3/9QOuz49mZmYl55pfMzMzW1tHArfk57cAXy1KHxnJJKB9YRpDMzOzUnPwa2ZmZqsSwMOSpkk6Jad1ytMOArwJFEYk6wLMLlp2Tk4zMzMrOTd7NjMzs1XZJyLmSuoIjJf0YvGbERGSYm1XmgPpUwC22mqr+impmZnZKrjm18zMzGoVEXPz43zgXmAP4K1Cc+b8OD9nnwtsWbR415xW03pvjIi+EdF38803b6jim5mZVXLNr5mZ1ZvdzxtZ6iI0iGm/HFLqIpSEpI2AVhHxfn4+APgpMAY4Ebg8P96fFxkDDJc0mjTQ1XtFzaPNzMxKysGvmZmZ1aYTcK8kSNcMt0XEOElTgDsknQy8Dhyd848FDgVmAkuAkxq/yGZmZjVz8GtmZmY1iohXgT41pC8EDqwhPYDTG6FoZmZma819fs3MzMzMzKzZc/BrZmZmZmZmzZ6DXzMzMzMzM2v2yi74lTRL0rOSpkuamtM2lTRe0sv5sUNOl6SrJc2U9Iyk3UpbejMzMzMzMytHZRf8ZvtHREVE9M2vLwAeiYjuwCP5NcBAoHv+OwW4vtFLamZmZmZmZmWvXIPf6o4EbsnPbwG+WpQ+MpJJQHtJnUtQPjMzMzMzMytj5Rj8BvCwpGmSTslpnSJiXn7+JmneQYAuwOyiZefktCoknSJpqqSpCxYsaKhym5mZmZmZWZkqx3l+94mIuZI6AuMlvVj8ZkSEpFibFUbEjcCNAH379l2rZc3MzMzMzKzpK7ua34iYmx/nA/cCewBvFZoz58f5OftcYMuixbvmNDMzMzMzM7NKZRX8StpI0ucLz4EBwAxgDHBiznYicH9+PgYYkkd93hN4r6h5tJmZmZmZmRlQfs2eOwH3SoJUttsiYpykKcAdkk4GXgeOzvnHAocCM4ElwEmNX2QzMzMzMzMrd2UV/EbEq0CfGtIXAgfWkB7A6Y1QNDMzMzMzM2vCyqrZs5mZmZmZmVlDcPBrZmZmZmZmzZ6DXzMzMzMzM2v2HPyamZmZmZlZs+fg18zMzMzMzJo9B79mZmZmZmbW7Dn4NTMzMzMzs2bPwa+ZmZmZmZk1ew5+zczMzMzMrNlz8GtmZmZmZmbNnoNfMzMzMzMza/Yc/JqZmZmZmVmz1+SDX0mHSHpJ0kxJF5S6PGZmZi2dz81mZlaOmnTwK6k1cC0wENgZGCxp59KWyszMrOXyudnMzMpVkw5+gT2AmRHxakQsBUYDR5a4TGZmZi2Zz81mZlaWmnrw2wWYXfR6Tk4zMzOz0vC52czMypIiotRlWGeSjgIOiYhv5dcnAP0iYni1fKcAp+SXOwIvNWpBV7YZ8HaJy1BOvD+q8v5YwfuiKu+Pqsphf2wdEZuXuAxlxefmZsP7oyrvjxW8L6ry/qiqHPZHrefmNo1dkno2F9iy6HXXnFZFRNwI3NhYhVodSVMjom+py1EuvD+q8v5YwfuiKu+Pqrw/ypbPzc2A90dV3h8reF9U5f1RVbnvj6be7HkK0F3SNpLWB44BxpS4TGZmZi2Zz81mZlaWmnTNb0QskzQceAhoDYyIiOdKXCwzM7MWy+dmMzMrV006+AWIiLHA2FKXYy2VTTOvMuH9UZX3xwreF1V5f1Tl/VGmfG5uFrw/qvL+WMH7oirvj6rKen806QGvzMzMzMzMzNZEU+/za2ZmZmZmZrZaTb7Zs9UPSa0jYnmpy2FmDU+SAKIFN/3xPrCCPChXH2A3UqXAPRHxVmlLVVqSdgWOBm6PiOmS1BK/K5LWA3oDvYAvA7dFxF9LW6rSkbQZcBhwIPBPUn/+T0pbqtIoOjb2AD4A/hIRi0paqBKS9DlgZ+A/ETGvnH8zXPPbAknqI2nj/HxzSf8C9i9xscysgRQCvYLISlWecuB9YEVOBK4jBcC7Az+UtEFpi1QakgrXhQeT5mD+UgmLUw5OByYCFcCLwKslLU2JFJ1DvgkcCTwN/Is0oF1LdSrpd6M3MAC4pLTFKR1J25BuhvwT+G5OLtsY0zW/zZikDYH+pJPYfcDfI+Jj4B7Sl/Rm4Grg7qZ+J7OmO0ySzgXmRMToEhWr5Ar7RVKriPgsp/0S+FtEPFDi4jWKlnJsFG9n9W0ufp7vzlYAXYAxEbG0scvakIqO+W7A+hHx71rydQPOjIjvl/MdamsUf4yImwAkfQEYB2wCzC9pqUogIj6TtB2pBufy/AggoCV+R14GHo2Is0tdkFLKv6m7AgMi4shSl6fU8s2AB4GREbFY0o7AlZK6RcSs0pauJOYBJwA9SFPbQRn/XpRtVG6rVr0mR8lpko4tSv4K6Q7M/Pz4rZz+CLCtpIOBTYFfNUKR652kDpJaQ+UP85WSLsvvHUYK+p8tZRkbm6RWkiok9ZXUNu+Xu4Az8vsnATuS7tg2W8392Mjf99ZFtTRUD3bzsSBJm0n6mqQtJN0CjAKGAz8CTpbUtvG3oH5JaiNpf0n987Z3BO4g1eLVlL+w3/aRtJED35YtB3zrSRoCXAmMi4gWF/gW2R54F5gGbAFpH5W0RKXzDFAh6ThJF0rqV+oCldCnpGvHXpKukPTd1S7RTOWGQ69ExOKc9BYp8GuR3SUi4uOIeBaYA2yV08r2N8PBbxNTaIqVL/CKA+CdgZ8B/SVtlNP2Ah6JiJ8CdwP75vS/kpozHQn8JNcGl5XibcuBzPa5fwWSzpG0ELgLOK5osdmk5icApwG3NOe5JSW1Lw5+JLUDfg/cTqrZ/05+61Vgl3zsnAD8NiLmNnZ5G4Kkz+ftJgeD329Ox0YOXlsVAvmCfOJdXnxykbSdpD3y828Bj5NOxj2Bg0h3YTcHFkTEccB5pGaevRpna+pHDlIul/S4pBE5eF0GHMKK37irSf2vRtW0joj4LN+dnwV0yvt5O7XQpq4GEfEpqYnvAqBXruVqqU4BzgVmAp9V//1pSSJiNvAS6Td0OfArSf1LW6qSWQ58TLp5+h9gr3xDoMnfQF1XRdeqpwEPkG4QtGSvARtI+nypC7IqDn6bkBzUDpf0G0lHFGp38ts9gSdJP06tc9OtIPVRAXgCWCppd+AfQEfSgAUdG3Uj1lDetl6SJpBqKc8D1pO0C2mwhc1z2ol5mwD+TgryTgTejoj/K0HR652kNkXPD5H0mKR/A6OBwUVZBwBbRsSOwP8A/y2pJ2muzX1IwfDkiHik8Upf/yR9TtIFkl4C/gb8OO+jHsARNLFjI2/ProWTRfXa3ByoLa+2TGdJP5X0VK7V3pR0A+xkSVsC7wG75rR2eVXzSDU5hVrON3O+bg28iWtN0vqSjpD0C0n3SfpK0QX4/sAXSBfn75L+/xuQLlDfkfQzUhPNq/O6qreS2UzSNyRdAvQlNV17hvT9aN8Im2dlSJIi4tSIOIv0m3mUUtehFkXSTsAnwFDg+6Rzx2OSupeyXCV2eEScFBE/I3UbO6glHhukc8ZWpEqVq4DLgF1oYjdQ61O+Vt2e1Jrs1nwjtiV7B1hMOi5WOv+WCwe/ZaC4dqe2AyVfGF8KHE+6qO2k1Kz1M0lbkALCc4DupIv/xcBGwHp5FXOAj0jB0VzSyHT/A/xU0pmFWtVykQP9Y4GbI6JbRJxKKv/ngUL/1ReAMcDX8mJPk/oxXgf8pvFLXX8kbS3pVknzgStyWhtgCXBDROxAquk/Q2mgAUgDT7yTn79Cqvn7KjAF2IHUzPVPeV1N+bvfg1TLtwvphFN4bEsZHxuF73bRd73wP9gNOJ/UD7fQBFOS2kn6oqRhuZbzmnyShdRqY/v8uIQ0ofzfgOdItTYzganANqS+i9vm5Z7NaZAuZD4i/V40muLfOKXmyv8t6WeSDi4KcLsAh5N+p24ELiL9xkHavokRMZn0v9we6EcKfg8h3QC5NiLek7R+UT/owv7eI+d5D3gUGBsRu0TEedHCR/htyao1ff+Q1Ny3LC/cGtj6pO5QR5K2/1/AHRHxchM/b6yziPio6OUiYLMSFaWkIuJd0nmmQ05aQvqutMgWM0Xnsu8Cf4qIKaUsTznIY4i8SLpBXbazKXjAq0aWTx4CPiscFPlxdQfIf5FG2esF9I6Ix4ouFLcGZkfEC5I+ANaLiCWS3gM65iB5idJ0DuvnZd4FJgODSH0At5V0XW2Dw6zDdhYGnWkHdIyItR0hsRXwtYjokWt1OkXEG3l9r0vaICI+kjQL6CmpQ0S8K+lVYAZwqaQ/RsS99bE9JfAxqYb+caoOHjCl6ET8NOnk06bo/Q8AImKRpLnAARHxYQ6ixwDXSvpVRIxrpO1YST0cG0uBXSNiqaSPSM2Mnifd+JlV6mMjf88+FxHv5df7kAK3W4EZNfSDeYt0c2qznP8S4DNS7dOvSbWSPwAOBX5J+s72Bh6LiNmSRgI/BnYCxpO6ODyVP68NqYXHgvxZz5Fbe0TEQklB+u43yoBPkjpVCzC3JrVe+Hou62TSb9OHwA+K9mF/UoA7hnRjr9APcY6kOaTaqd+Tjo3OwPGSns3/9zYRsayw3yNiLGnfIukA0ki/SFovN321FibfWNya1A1gS1IT17uqBT0tQkQ8TbqJBICki0nfKSjjAWwaSg5wOgN7ko6N/sDocuwu1khuBw5WGlfhi8BzETGxsc4h5SRfx3wd+AbpUNkfWAb8KFromAGS/ot0w/2Xkr5I+q48XeJiraRF3sVraLkmttA3rco+LjRhLP6RkNRd0pdyzc5EpQF5qnuddJF/NHC0pG8UvXcR8Kqko0k1fI9JOoJU87MH0Cnn25IVw9K/CQyOiNdJF9UbA2epju30JfWQ9LX8o7A+cBup+VRNeYtrvKsfi62BtyRdBzwG/EHSgfm9j/O2QLoQ/pQVJ+cFpJqi64BzlZrHNvpNnqJavnaStl1d/upygPAH0qij/5XTlhcuxvL6NyX9yLydF3uP1DT8C/n1h6QgCtKgZ9cBNwAXSzqrsfdLPR4bzwOXSHoNmEtqDbAeqaneUkp/bPwK+N+i15/kMnaR1FXSQZL+T9LVktrncn0AbJGDsd0j4kJSQPwW8K+ImASMJF1sQLqJNSs/n02q9W4TES8Ab5CC4Ymk/v1DSDeMWpH2F5IKvwn3koLGBidpN+DXkm6TNDAnv0MajG14fv15gIiYn2tuCy1S3icd65BuBuxQtOpnSYH/+6Qa71+Q9t1fJfWMiGVFd+gLZWmV0xbnZXHg23Llpoq9Sa0KOgN/BO4saaFKqOj3d33gFuAaKN9anIaUt7k3qU9nZ9J5+e6SFqqEIuJu0nl0E+D/gKtyeos7NrKOpPPqm6Sbz5e34MD3c6SBJjcn3ah+mjKdGsw1v3WUL553jDx4Tr7AuxPYrvrFlKTNSXdU+5KCkp9ExAfAT0hfoNtJB8yvJf07NzMSVN5h+irpIrE3qZ/ejpKuIl3snk+62H2VdME8ISLel7Q3KVD4jDQU+RO5ODeSLhaJNCn3sLXc7s+Tmln3BO6PFX0ov06qlbmH1MT2+Yj435rWUVONd9Hdw/WADYHlEbF3vlgeCkwgBXkVpAvdjfOii/LjZOCoiDhZ0tukGrO/SPpWNMIgT5J6ADtHxD1FAd50qgZDhbwitQIQaXdUqRHMNZsLSX21t4uIV/Jy6+f3rgF+n5siQZqSYUNS7cWbpKauhWlsXgC+ERE/ljQPuIA00u13IuJt6lFDHRvF7+Wg6DcRcbWkM4CzgIdJx3QfSntsTCc1wy74Ty7Dl0hTh/yd1Ny2O3BNRBwv6R3Sb0Mf4Ht5ubdIwdk8pamqZkraOG/7C6QWAeMj4mNJvVnR5H00MALYICKmS/ozqZnwZ8AiSRV5HyoinqnH7a6VUt/zC0nH+tvAfKWa1nfz+3OBgawIcMnb/KmkHUj75qv5rcnAt3Oe9Uj7dmn+TnxI+n34NemC5I78PbmJdEceWDEKpaSni9ZrLVhuCdJUWwrVq6Lf36WU6cVrY8otpUrWWqrcRMRUUuVKixcR1wPXl7oc5SBXzhy42oxlwDW/a6hwJ7R6er5jfFVR0mzgXUkHSPqhpN9KKvQP+SLpgvcRYCHwvRwgTSZd5N4XEQ+TLmL3Knx04Y5aRDwVEcNIF733k5pp7RMR346IHSPiW6SL3jakga8g9ROeQKoduTjX9BIRf4yIO4prRKrXjlTb/gsl/V4rRkM9kTTwwW3A6ZIOzekdgH9KupJUm/XT4nUXPbaXtJek0yVdL2my0vyzhVrOBaR+qx/k9T5BqiH7lFTjVRjoqSewLCL+k19PzfuFSH0Cv00Kcgrv14tco3uRpDslHV/01teBA/Lz1QZ4RS0BahwSPt8cmUcKilBq0rtU0imki/wRRXlnkUbaOz7fhOnHiguXf7Giqeg/gJOBc+oa+Eo6TNIfJD2h1OQHUk1jvR8bhe+RUnPpzVnxPx1Hupn0IamFREmPDVJ/l25Frxfk8i0mfe9nRMQIUoDWLwdw/8nbNJgc9OfflndI/7fC4CoLSX2Efwcgaayk50lzdhf+1/eRjpcZOcAdln9XyOst7m7RWPqQ9snRwHURMY0Vv1GQ/m/rkb//uXyF78RFwP8V/R8nkQb165NvMB5E+k2FtJ+7kwL/P5IG7NmaNOjXSiLi06L1mpmZWTPnmt8a5CBXUTTCam21lKSLqn6SHiDVOl1IqnE4nXRB9kVSAHB1RIyV9AppOo7+pIu9O0jBzRukWo93SYHKnqRmjq1IUw20Io1Eun1e5wBS074nclkK/dX+Dvyb1DS4EET+obbtLA66arsYltQhl2cL0mA5L5KmkflJRPw1v3+wpCdItSvDSU0TL4vU17hV5EF8co3TMaSa0DdItZVTSIHi0xHxSVHt719IfSkg1XZvTqrdfp804NebpFq2C4qKe2tRTWNhgIZ3WUdKo+gOJtU0vw/8bw5Ih7IiwPuBpHci9SXsAEyqKcArqm0LSZuQRuLdlTRg026k/r2XR8TC4mVItZg98vZ8ImkrYD9SDeoWkvqQ+gK/SeoTehyp2d5fSLWAAL8o/vyoh2Y5kroAR5H+139gRTPcwcBFDXFs5PW3Id0EOZg0pVEPUsuJJ0nNbDZtjGNjFV4FPqc0Bc+H+WbF+7nMrYDX8vd1gaSPge1I3+XppH66V0u6JyL+QAp+tyd995eQanx3iYgnJf2EFFDOiYg3irZrEStqu8vFNFKt749Io27fEVWnIZpPClw7FR33SBoAvBcRhZGbC+MXXEDqu701qSb5Z3k9d5L201KAiHgIeKjhN8/MzMyaghYd/ObgVdVr3aq/Vmra3JPUf/bZiJhUdLG+K+mifxFwXkTMy7W5r0fEdblZ3WBSE+WXSM2T55H62f03KeiZQep3W2jyNwk4u3qZJHUj9WdbQrqQvjnyBNs58CVS880qTTgLNWrVg9vaahuLl8vLnEiqNWtPCjReJAUZxeXdNe+jZ0m1acUDZ1Wvabo7IkZTi6J89wHrS3qOVMt5d0S8lsv2s0j9ImtbtqbtWBenkvoEjiPdcDiPdIPjKFIA91AdArzZpP00lRTgPRNFg2gUlflZ4FhJj5KChC+Tav+7kQKKf5IGOCoEdNfkvyr7pY77oVLRes4gDXbxm6L32udtb5BjI+ddJGkEKfj5N6k2dET+LixuxGOjNm+TbnzsQLqRBek7u5xUw9ud1I8dUqBcuPHRnnQsjAJuyMfVBFJtaOF7fmgud6tcY9kkai0j4iVJQ0jfjdbA1/Pv2W8jYkmk5s0zgdaFYzUvegPwiaS/kG4WXSTptoi4T6nP9ycR8SJU7pNnq3929Zt8ZmZm1nI1++BXacqc5VHDyHy11OZ+AdibdME+NiL+SapN250U5M6W1DoiliuNIvq4pNuA7SPNownpQrYwncjbpAFvNiHVXu4TETvkmtzvkmq3HiLVXBUGoZlKnn4kiuYMi4ingP2VOpV/MdZwWPV1vbjPF6Eb5XJNyuXfLr/9LivmCH6HFID0JgXej5Mu2m+TdFhEvFMcZBQCda0Y+Tryx1UPzj9SGsl2dFGtHzWsY6Vl62P7syOAX0Xqw9se6JZrm55lRVPKeg/wACRtTOqbfQDpGPoRqSZ5HLBDRMxcxbK1jSpenz4DdpJ0Kqm/5h9Z0Ty9MOhWQx0bLyk1/V5S/bvdiMdGbetcptSHtT8rgt9tSX14nyC13LiZdAPrVVLN/2hSf8N3IuJtScfEKuYLbIrBXERMB76VbxjuRWpNsQUwU6nJ/HFAe0nXkppHP0W6KTSXVHM8MyI+LFrf09XWX1vXgSa3r8zMzKxhNPvglzT62q2kKV5aR27KnAPIfqSg9rmIGCepM2lAGpEuuBYp9Z1cDzg/B8LFChdVz5DmpC14ltQPDdIF7/ukWrrbgQ8lXUpqMjsH6J5rssaR+msSES+zIniuyWfA9Eaq0ehLmjv1fqWBjHbP6W+wYsTVj3OZPiPVXk2PiBG55upxSUcUamyLrUnZc3DyyZrW0jeAe4FvKo2uvR1pJN83STXRhZsVDRXgLc5NW4fWdPOmeB1R1EQ/L9tg+yXfFGlFOl6/Sgr+byPVhvck3dApjErckMfGO7DmLTga2e3AkZIuJx0nnYCrSTe1Ps+K/q4/LPrf3l5YeFWBb1OUj5cdSa1n9iL9rtwWaRCvz5G+O3eSasRfKLqReEkpymtmZmbNU0sIfp8gXZBD1QnrBwP7k5rw9pPUlVTT0I00aNHCfJHfnhTsXCXpMVJfsmsjTUVTCFamUXWAm5cLr3Ng+ymwVX5+KilguB94tHABHxHXrukG5VrQT1absY5yc+8KUh+9X5D6WG6Ta4OvIfVrLmzjkaQbAENJNw3Wj4grlQbzuULStyP1RVwnNdXSN5I/kZo9zyYFtN8k1di9Qh6EioYN8F6GygCvFUU1uWu6joYQqRn3XKBLRNyUy7iYNB3Ej8l9bZv5sVGriLhL0gukGwIPA5MjDTb3OkWjZNZ2U6O5ycdLD1L3jiWkeXavze99BPy2puVq67JhZmZmti5aQvA7idQMFXLwK2lH0sX490hB6qnA4RHxxdy/9I/AK0p9d79HGjH5V6S+eReTavquKqrJe0vSPyWNJ9XyngFMlLRZpNF0r2fFtEKTSaM7V1GOF3mxYn7Mt0gj6Y4ADgd+kIOayZJGAV1JI9i+JOkN0gBNn+V1XF6i4teXrYAOkUbZJv+b9idNnXIONGqAt3y1GRvXBNJczO0iDQK2BfBGRMxQGv15NNCF5ntsrFKk6c+eK3U5ykXkqWQkdQQ6R7WuDDU1Uy+n30MzK0+5kuLYiLhuFXm6AXtHxG2rWVc34IGI6FXHMu0HnBsRh68iTwWwRaTBMpH0FdJUifV2bpT0QUSsNNq9pO+Qug2NrK/Pqg9r+n8yq4uWEPxOZUWtbCF4WEgaNOhyUmA3FvgbQER8V2k6n61ITabvJtXkrp+Xf4ui/pxFzVlPIwWBL0YaSXdo0fuF+Tcrl8mfVfYXeRFxVeG50lQzA0jNF58k1fweTKr5/HvOv9J8Z8VNfpugranaYmAb4M2IeDbf8GiRAV5ucr9E0s1AYRqirUjz7UK6adTcjw1bS/m3bxE1jLJd4mbqZtZ0tSeNoVJr8Eu6DjyW1EWnXiiN+1KXLioVpC4gYwEiYgwwph6KtloRcUNjfM466EY9/5/MqlNLuO6U9B6p2fF7uUZuqdJIoV/MNbPkgHc5qV/nlqTBjL5DqsXdljTK70ekkXWvjzSq7qo+s+xqctdVbv68HNgAaFs9mK8hf7MZXTU3XR5JGsTpc6Rj4zu5dvNzFAV4ufazpnU06wBP0iHARqSmvbNXk7fZHBtmZlZ6+Sb0kcBLpEoLSIMwBvCziLhd0iRSF6bXgFtI43n8mXTuAhgeEU+sruZX0lDga6RrxNakrhy/A3qRxoe5KNIYKfuRa34l7UHq2rEh6TrypFyOmaTrirnAZfl534gYnssxAtiMdP1xUkS8kW84LyYFzV8gtcS7S2nMmtuBjUkVW6dFxERJH+TPPjx/9pG5teJFwAcR8StJE0gzeHw5Lzsst1Ksafvb5e3tm/fvxRFxt6TBpEE5Bfy/iDg/56+seZZ0FKmV5dBVbEeV/1MUzSZhVl9aQs0vpNraLUnzRS7NaQ8Cl+RmztsCmwLnkgZkOY7Ux/MG8g9UpPki11hzCnaK7mx+nP8q1TTYUHMJbnLQ+q6ks4EDSc3dn4g0nVShr+J9NSy3RvMnNxcRMa6m9OZ8bJiZWdm4AOgVERWSvk6quOhDChynSHo856lshiypLdA/Ij6W1J00xVzfNfy83YDekQaz/Dlp/JZhufn1ZEl/rZb/ReC/c1eyg4CfR8TXJf0vOdjNZRpatMzvSMHfLZKGkQZM/Gp+rzOwD2nqyTGk+e6PBR6KiEsltQba5rwbAZMi4seSrgC+zYp50Yu1zftvX1LQXVuz75+QrqV3yWXuIGkL0jScu5Na9Tws6asRcV/tu7DW7ajyfzJrCC0l+H0J+Kmkv5OmrplDavK8PfAVUrPmO4F3I+LPpLuBK8l90nwBXyQHds0yuCsErZGmFKpxWiEHeElNNbrN+dgwM7OytA8wKtIMCG9J+htp9oHF1fKtB1yT+90uZ8XsFWtifFELuAHAVySdm19vSOoCVGwT4JYcZEf+7NXZi1TDDOma9Iqi9+7L59vnJRVmnZgCjMjjjNwXaWo5SIO0PpCfTyNNwVeTUQCRpu/cWFL7qHmckoOAYwovcgXBvsCEiFgAIOlWYF9qqByopqbtMGtwLSX4vYvUB/EV0h2tf0TEq6TBaO6vnrkQ5LLy4CstLqixpLYbHw7wEn83zMysCTmb1CqwD2kmhbUZef/DoucCvh4RLxVnqBbMXQI8FhGDcnPmCetS4CLFgwUWutg9noPQw4CbJf06D2b1adF17HJqv+6vfh1TX9c1xevZsNp7K22HWWNotfosTV9E3BIRu0XEeRHx5xz4AimoKQp2C/k/y38tPqixpHBMlLocZmZmVsX7pPnTASYC35TUWtLmpBrIydXyQKqNnZfP6yeQ+u+ui4eAMwrjvEjatYY8m5D69UIeDLWGclf3BCtqWI8jbVetJG0NvBVp6sE/kJpmr41v5vXsQ2rW/F4t+caTp7nM+TuQ9u+XJW2Wm1wPJg8iS6p93ylfZw9ag3Ksap+Y1YsWEfwC5B/CVoUfqAIHNWZmZmZNU0QsBP4haQapufAzpAGcHiUNpPRmTlsu6ek8jsd1wImSnib1Of2w5rWv1iWkZszP5DFkLqkhzxXAZZL+RdWa18eAnSVNl/TNasucAZwk6RlScP691ZRjP+Dp/BnfpJa501fh47zsDcDJq8j3M6CDpBl53+0fEfNIfXUfI+33aRFRaFV5AanZ9RPAvDUoR/X/k1m9axGjPZuZmZmZWVV5tOdzI2Jqqcti1hhaTM2vmZmZmZmZtVyu+TUzMzMzyyQdTJq+p9hrEbEm/VabPEknsXJT639ExOk15TdrShz8mpmZmZmZWbPnZs9mjUzSE6UuQ32T9KNSl8HMzMzMbFVc82tmdSbpg4ho18Cf0SYiljXkZ5iZmZlZ8+WaX7NGJumD/LifpAmS7pL0oqRbi+YK/KKkJ/Jw/5MlfV7ShpL+JOlZSf+StH/OO1TSfZLGS5olabik7+c8kyRtmvNtJ2mcpGmSJkrqsYoydpJ0b/78pyXtndPvy8s/J+mUnHY58Lk8XcOtOe34XO7pkn6f5/5D0smS/p3fu0nSNTm9m6RHJT0j6RFJW+X0myXdIOlJ4ApJL+e5GwtzdM8svDYzMzMzWxUHv2altStwFrAzsC3wJUnrA7cD34uIPsBBwEekieUjInYhTSJ/i6QN83p6AV8DvghcCiyJiF2BfwJDcp4bgTMiYnfgXNI8h7W5Gvhb/vzdgOdy+rC8fF/gTEn/FREXAB9FREVEHCdpJ9I8g1+KiApgOXCcpC2AnwB7Al8iza1Y8DvglojoDdyaP7+gK7B3RHwf+D/guJx+EPB0RCxYxXaYmZmZmQFVJ9s2s8Y3OSLmAEiaDnQD3gPmRcQUgIhYnN/fhxQkEhEvSnod2CGv57GIeB94X9J7wF9y+rNAb0ntgL2BO3PlMsAGqyjXAeSgOSKW5zJBCngLo11uCXQHFlZb9kBgd2BK/qzPAfOBPUgB9Tt5e+4sKv9epOAd4M/AFUXruzOXAWAEcD9wFTAM+NMqtsHMzMzMrJKDX7PS+qTo+XLW/TtZvJ7Pil5/ltfZCliUa2LXiaT9SLWte0XEEkkTgA1rykqqxf1hteW/uo4f/WHhSUTMlvSWpANIwfRxtS9mZmZmZraCmz2blZ+XgM6SvgiQ+/u2ASaSgz1JOwBb5byrlWuPX5P0jby8JPVZxSKPAKflvK0lbQJsArybA98epObLBZ9KWq9o2aMkdczLbyppa2AK8GVJHfL2fL1o+SeAY/Lz4/K21uYPpObPxTXCZmZmZmar5ODXrMxExFJSn9nfSXoaGE+qYb0OaCXpWVKf4KER8Unta1rJccDJeZ3PAUeuIu/3gP3zZ00j9UkeB7SR9AJwOTCpKP+NwDOSbo2I54H/AR6W9Ewuf+eImAv8HJgM/AOYxYrm1GcAJ+X8J+TPr80YoB1u8mxmZmZma8FTHZlZo5HULiI+yDW/9wIjIuLetVxHX+A3EfHfDVJIMzMzM2uWXPNrZo3pojyw1wzgNeC+tVlY0gXA3cAPV5fXzMzMzKyYa37NWjBJPwa+US35zoi4tBTlMTMzMzNrKA5+zczMzMzMrNlzs2czMzMzMzNr9hz8mpmZmZmZWbPn4NfMzMzMzMyaPQe/ZmZmZmZm1uw5+DUzMzMzM7Nm7/8DfPynKCkv54cAAAAASUVORK5CYII=\n", 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G67opIklSOZXyzKakXmbJ8tUcfvZn25x3z7Vf7+ZoJPU29UuXc+pFl3e4zM2XfrSbopEklVu33PpEkiR1LLsd2OKImFFUdkNETM8ec1ovS4mIiRGxvmjeD4rWOTgiHo+IWRFxeTaGgiRJ3c4zm5Ik9QzXAFcA17YWpJROb52OiG8BK4uWfy6lNLmNeq4EPkTh3ta3AscDf8o/XEmSOuaZTUmSeoCU0t3AsrbmZWcnTwM6vNgxG/F9UErpvpRSopC4vj3nUCVJ6hSTTUmSer4jgEUppWeLynaLiEci4u8RcURWNhaYV7TMvKxMkqRuZzdaSZJ6vjPZ/KzmAmBCSmlpRBwM/C4i9utKhRFxHnAewIQJE3ILVJKkVp7ZlCSpB4uIKuBU4IbWspRSQ0ppaTb9EPAc8CpgPjCuaPVxWdkrpJSuSilNSSlNqaurK1X4kqSdmMmmJEk92zHAUymlTd1jI6IuIiqz6d2BScDslNICYFVEHJZd53k2cEs5gpYkyWRTkqQeICKuA+4F9oqIeRHxgWzWGbxyYKAjgceyW6H8GvhISql1cKHzgR8Dsyic8XQkWklSWXjNpiRJPUBK6cx2ys9to+w3wG/aWX4asH+uwUmStA08sylJkiRJyp3JpiRJkiQpdyabkiRJkqTcmWxKkiRJknJnsilJkiRJyp3JpiRJkiQpdyabkiRJkqTcmWxKkiRJknJnsilJkiRJyp3JpiRJkiQpdyabkiRJkqTcmWxKkiRJknJnsilJkiRJyp3JpiRJkiQpd1XlDkCSJO08nnrqSY44+vh259cNH8rNN17XjRFJkkrFZFOSJHWbxpbEqRdd3u78my/9aDdGI0kqJbvRSpIkSZJyZ7IpSZIkScqdyaYkSZIkKXcmm5IkSZKk3JlsSpIkSZJyZ7IpSZIkScqdyaYkSZIkKXcmm5Ik9QARcXVELI6IGUVlF0fE/IiYnj1OLJr3uYiYFRFPR8RxReXHZ2WzIuKi7t4PSZJamWxKktQzXAMc30b5ZSmlydnjVoCI2Bc4A9gvW+f7EVEZEZXA94ATgH2BM7NlJUnqdlXlDkCSJEFK6e6ImNjJxU8Brk8pNQDPR8Qs4JBs3qyU0myAiLg+W/aJvOOVJGlrPLMpSVLPdmFEPJZ1sx2alY0F5hYtMy8ra69ckqRuZ7IpSVLPdSWwBzAZWAB8K6+KI+K8iJgWEdPq6+vzqlaSpE1KlmxGxPiIuDMinoiImRHxsax8WETcFhHPZs9Ds/KIiMuzAQ0ei4iDiuo6J1v+2Yg4p1QxS5LUk6SUFqWUmlNKLcCPeLmr7HxgfNGi47Ky9srbqvuqlNKUlNKUurq6/IOXJO30Snlmswn4VEppX+Aw4IJskIKLgNtTSpOA27PXUBjMYFL2OI/Cr7lExDDgS8ChFBrZLxV1I5IkaYcVEaOLXr4DaB2pdipwRkTURsRuFNrOB4AHgUkRsVtE1FAYRGhqd8YsSVKrkg0QlFJaQKHLDyml1RHxJIXrRk4BjsoW+xlwF/AfWfm1KaUE3BcRQ7JG9ijgtpTSMoCIuI3CyHvXlSp2SZK6W0RcR6HNGxER8yj80HpUREwGEjAH+DBASmlmRNxIYeCfJuCClFJzVs+FwF+ASuDqlNLM7t0TSZIKumU02mx0vdcA9wOjskQUYCEwKpt2sANJ0k4rpXRmG8U/6WD5S4BL2ii/Fbg1x9AkSdomJR8gKCIGAL8BPp5SWlU8LzuLmXLcloMdSJIkSVIPUNJkMyKqKSSav0wp3ZwVL2q9BiV7XpyVO9iBJEmSJO0gSjkabVDo/vNkSunbRbOmAq0jyp4D3FJUfnY2Ku1hwMqsu+1fgGMjYmg2MNCxWZkkSZIkqYcq5TWbhwNnAY9HxPSs7PPApcCNEfEB4AXgtGzercCJwCxgHfA+gJTSsoj4KoUR9gC+0jpYkCRJkiSpZyrlaLT/BKKd2Ue3sXwCLminrquBq/OLTpIkSZJUSiUfIEiSJEmStPMx2ZQkSZIk5c5kU5IkSZKUO5NNSZIkSVLuTDYlSZIkSbkz2ZQkSZIk5c5kU5IkSZKUO5NNSZIkSVLuTDYlSZIkSbkz2ZQkSZIk5c5kU5IkSZKUO5NNSZIkSVLuTDYlSZIkSbkz2ZQkSZIk5c5kU5IkSZKUO5NNSZIkSVLuTDYlSZIkSbkz2ZQkSZIk5c5kU5KkHiAiro6IxRExo6jsGxHxVEQ8FhG/jYghWfnEiFgfEdOzxw+K1jk4Ih6PiFkRcXlERBl2R5Ikk01JknqIa4Djtyi7Ddg/pfRq4Bngc0XznkspTc4eHykqvxL4EDApe2xZpyRJ3cJkU5KkHiCldDewbIuyv6aUmrKX9wHjOqojIkYDg1JK96WUEnAt8PYShCtJ0laZbEqS1Du8H/hT0evdIuKRiPh7RByRlY0F5hUtMy8rkySp21WVOwBJktSxiPgC0AT8MitaAExIKS2NiIOB30XEfl2s8zzgPIAJEybkGa4kSYBnNiVJ6tEi4lzgbcB7sq6xpJQaUkpLs+mHgOeAVwHz2byr7bis7BVSSlellKaklKbU1dWVcA8kSTsrk01JknqoiDge+CxwckppXVF5XURUZtO7UxgIaHZKaQGwKiIOy0ahPRu4pQyhS5JkN1pJknqCiLgOOAoYERHzgC9RGH22Frgtu4PJfdnIs0cCX4mIRqAF+EhKqXVwofMpjGzbl8I1nsXXeUqS1G1MNiVJ6gFSSme2UfyTdpb9DfCbduZNA/bPMTRJkraJ3WglSZIkSbkz2ZQkSZIk5c5kU5IkSZKUO5NNSZIkSVLuTDYlSZIkSbkz2ZQkSZIk5c5kU5IkSZKUO5NNSZIkSVLuTDYlSZIkSbkz2ZQkSZIk5c5kU5IkSZKUu5IlmxFxdUQsjogZRWUXR8T8iJiePU4smve5iJgVEU9HxHFF5cdnZbMi4qJSxStJkiRJyk8pz2xeAxzfRvllKaXJ2eNWgIjYFzgD2C9b5/sRURkRlcD3gBOAfYEzs2UlSZIkST1YVakqTindHRETO7n4KcD1KaUG4PmImAUcks2blVKaDRAR12fLPpF3vJIkSZKk/JTjms0LI+KxrJvt0KxsLDC3aJl5WVl75ZIkSZKkHqy7k80rgT2AycAC4Ft5Vh4R50XEtIiYVl9fn2fVkiRJkqQu6NZkM6W0KKXUnFJqAX7Ey11l5wPjixYdl5W1V95e/VellKaklKbU1dXlG7wkSZIkqdO6NdmMiNFFL98BtI5UOxU4IyJqI2I3YBLwAPAgMCkidouIGgqDCE3tzpglSZIkSV1XsgGCIuI64ChgRETMA74EHBURk4EEzAE+DJBSmhkRN1IY+KcJuCCl1JzVcyHwF6ASuDqlNLNUMUuSJEmS8lHK0WjPbKP4Jx0sfwlwSRvltwK35hiaJEmSJKnEyjEarSRJkiRpB9epZDMiDu9MmSRJOzvbTEmSCjp7ZvP/OlkmSdLOzjZTkiS2cs1mRLwOeD1QFxGfLJo1iMKAPZIkie1vMyPiauBtwOKU0v5Z2TDgBmAihYH1TkspLY+IAL4LnAisA85NKT2crXMO8MWs2q+llH62/XvXfZ566kmOOPr4dufXDR/KzTde140RSZK21dYGCKoBBmTLDSwqXwW8s1RBSZLUC21vm3kNcAVwbVHZRcDtKaVLI+Ki7PV/ACdQuE3YJOBQ4Erg0Cw5/RIwhcLI7w9FxNSU0vLt2K9u1diSOPWiy9udf/OlH+3GaCRJ26PDZDOl9Hfg7xFxTUrphW6KSZKkXmd728yU0t0RMXGL4lMo3EYM4GfAXRSSzVOAa1NKCbgvIoZk97I+CrgtpbQMICJuA44HPBUoSep2nb31SW1EXEWhG8+mdVJKby5FUJIk9WJ5tpmjUkoLsumFwKhseiwwt2i5eVlZe+WSJHW7ziabNwE/AH4MNJcuHEmSer2StJkppRQRKa/6IuI84DyACRMm5FWtJEmbdDbZbEopXVnSSCRJ2jHk2WYuiojRKaUFWTfZxVn5fGB80XLjsrL5vNzttrX8rrYqTildBVwFMGXKlNySWEmSWnX21ie/j4jzI2J0RAxrfZQ0MkmSeqc828ypwDnZ9DnALUXlZ0fBYcDKrLvtX4BjI2JoRAwFjs3KJEnqdp09s9na0H2mqCwBu+cbjiRJvd42tZkRcR2Fs5IjImIehVFlLwVujIgPAC8Ap2WL30rhtiezKNz65H0AKaVlEfFV4MFsua+0DhbUE63f2MyCletZunYjw/rXsOuwfuUOSZKUo04lmyml3UodiCRJO4JtbTNTSme2M+voNpZNwAXt1HM1cPW2xNCdnlm0mtufWszGppZNZTWVFVTvcVgZo5Ik5alTyWZEnN1WeUrp2rbKJUnaWdlmbt0/Zy3hoReWM3pwHw7fYwTDB9SwaNUGps1ZzsY3nMPfn6nnyEkjiIhyhypJ2g6d7Ub72qLpPhR+ZX2YzW88LUmSbDM7VL37ITz0wnL2HzOIN+01koqKQkK56/D+jB/aj29+7yqm82Zqqyo4bPfhZY5WkrQ9OtuN9t+LX0fEEOD6UgQkSVJvZpvZvvrVDfR73XsYN6QvRxUlmq0qKoL1D97E5KNP4f7nl1E3sJY96gaUKVpJ0vbq7Gi0W1oLeB2nJElbZ5sJpJS446nFpMb1nHDALlRWtN9F9s17jWTUoFr+OnMRaxqaujFKSVKeOnvN5u8pjKQHUAnsA9xYqqAkSeqtbDPb9uziNSxctYEND0+l39umdLhsVWUFx++3C7+4/0XufqaeEw8Y3U1RSpLy1NlrNr9ZNN0EvJBSmleCeCRJ6u1sM7fQ1NLCPbOWMHxADSueu7dT6wzpV8NrJw7lvtnLmLN0LROH9y9xlJKkvHWqG21K6e/AU8BAYCiwsZRBSZLUW9lmvtKTC1azakMTR+w5AlLa+gqZg3cdytB+1fz9mXpaWjq/niSpZ+hUshkRpwEPAO+icEPp+yPinaUMTJKk3sg2c3MpJabPXcHIgbVMGNavS+tWVVRw+J4jWLGukScXripRhJKkUulsN9ovAK9NKS0GiIg64G/Ar0sVmCRJvZRtZpEXl61j2dqNHLvvqG26b+buI/ozcmAt9z+/jL13GVSCCCVJpdLZ0WgrWhvNzNIurCtJ0s7ENrPI9Lkr6FdTyaRR23YLk4jg9XsMZ/WGJma+tDLn6CRJpdTZM5t/joi/ANdlr08Hbi1NSJIk9Wq2mZmV6xuZs3Qdh+42jKqKbc+3Jwzrx+jBfXjoheUMoutnRyVJ5dFhshkRewKjUkqfiYhTgTdks+4Fflnq4CRJ6i1sM1/pyQWF6yz3G7N93V8jgoN3HcofHltA9bA98whNktQNtvYz43eAVQAppZtTSp9MKX0S+G02T5IkFXwH28xNUko8tXA144b2ZWCf6u2ub/cR/RnSr5pVuxxE6sKItpKk8tlasjkqpfT4loVZ2cSSRCRJUu9km1lk4aoNrFzfyN67DMylvojg4AlDaew/knufW5pLnZKk0tpasjmkg3l9c4xDkqTebkgH83a6NvPJBauprAj2HLltAwO1Ze9dBlLRuJ6f3TsntzolSaWztWRzWkR8aMvCiPgg8FBpQpIkqVeyzcy0tCSeXbyaPer6U1tVmVu9VZUV9F/yBH97cjELV27IrV5JUmlsbTTajwO/jYj38HJDOQWoAd5RwrgkSeptPo5tJgDzV6xnQ2MLk0bm04W22ID6GawZczDXPfAin3jLq3KvX5KUnw6TzZTSIuD1EfEmYP+s+I8ppTtKHpkkSb2IbebLnqtfQ2VFsOvwfrnXXdWwiiMn1XHdAy9y4Zv3pLpyp72FqST1eJ26z2ZK6U7gzhLHIklSr7ezt5kpJZ6rX8uuw/qVLBE867Bd+eC10/jbE4s44YDRJdmGJGn7+XOgJEnKzaLVDaxpaGKPHAcG2tKb9h7J2CF9+cX9L5RsG5Kk7WeyKUmScvPc4jVEFO6LWSqVFcGZh4znnllLmV2/pmTbkSRtH5NNSZKUm+eXrGXskL70qc5vFNq2nPba8VRXBr+8/8WSbkeStO1MNiVJUi6aagawdO1GdhteurOarUYO7MNx++3CTdPmsqGxueTbkyR1ncmmJEk9WETsFRHTix6rIuLjEXFxRMwvKj+xaJ3PRcSsiHg6Io7rrlg3DN4VoCSj0Lbl3YdOYNWGJm59fEG3bE+S1DUmm5Ik9WAppadTSpNTSpOBg4F1wG+z2Ze1zksp3QoQEfsCZwD7AccD34+I0vZpzWwYPJGBfaoY1r+mOzbH63YfzsTh/bj+wbndsj1JUteYbEqS1HscDTyXUupoGNZTgOtTSg0ppeeBWcAhpQ5sY1MLGwaNY9fh/YiIUm8OgIjg9NdO4IHnl/GcAwVJUo9T0mQzIq6OiMURMaOobFhE3BYRz2bPQ7PyiIjLs24/j0XEQUXrnJMt/2xEnFPKmCVJ6sHOAK4ren1h1mZe3dqeAmOB4lN987Kykpo2ZxmpsoaJ3XC9ZrF3HjyOqorgBs9uSlKPU+ozm9dQ6MJT7CLg9pTSJOD27DXACcCk7HEecCUUklPgS8ChFH6Z/VJRgypJ0k4hImqAk4GbsqIrgT2AycAC4FtdrO+8iJgWEdPq6+u3O767nqmHlmbGD+2e6zVb1Q2s5Zh9RvGbh+axsamlW7ctSepYSZPNlNLdwLItik8BfpZN/wx4e1H5tangPmBIRIwGjgNuSyktSyktB27jlQmsJEk7uhOAh1NKiwBSSotSSs0ppRbgR7zcVXY+ML5ovXFZ2WZSSlellKaklKbU1dVtd3CH7T6MwS89QE1V91+hc/oh41m6diO3PbGo27ctSWpfOa7ZHJVSah02biEwKptur9tPWboDSZLUw5xJURfa7AfZVu8AWi9ZmQqcERG1EbEbhR5DD5Q6uDfvPYpBC6aVejNtOnJSHWOH9OX6B73npiT1JGUdICillICUV315dwmSJKkniIj+wFuAm4uKvx4Rj0fEY8CbgE8ApJRmAjcCTwB/Bi5IKe3QN6KsrAjeNWUc/5y1hLnL1pU7HElSphzJ5qLWX2Oz58VZeXvdfjrVHQjy7xIkSVJPkFJam1IanlJaWVR2VkrpgJTSq1NKJxf1GiKldElKaY+U0l4ppT+VJ+ruddqU8QRw4zQHCpKknqIcyeZUoHVE2XOAW4rKz85GpT0MWJk1nH8Bjo2IodnAQMdmZZIkSQCMGdKXN76qjhunzaWp2YGCJKknKPWtT64D7gX2ioh5EfEB4FLgLRHxLHBM9hrgVmA2hfuB/Qg4HyCltAz4KvBg9vhKViZJkrTJ6a+dwKJVDdz1tJfSSFJPUFXKylNKZ7Yz6+g2lk3ABe3UczVwdY6hSZKkHczR+4xkxIBarn9wLsfsO2rrK0iSSqqsAwRJkiTlpbqygndNGcedTy9m0aoN5Q5HknZ6JpuSJGmHccZrx9PckrjJgYIkqexMNiVJ0g5j1+H9ef0ew7lh2lxaWnK7u5okaRuYbEqSpB3K6a8dz9xl67nnuSXlDkWSdmomm5IkaYdy3H67MKRfNdc/aFdaSSonk01JkrRD6VNdyamvGcdfZy5k6ZqGcocjSTstk01JkrTDOfOQ8TQ2J25woCBJKhuTTUmStMOZNGogr99jOL+49wWamlvKHY4k7ZRMNiVJ0g7p3NdP5KWVG7jtiUXlDkWSdkpV5Q5AUs/U1NzCQy8sZ3VDExURUNmv3CFJUpccvc8oxg/ry0//NYcTDhhd7nAkaadjsinpFdY2NPGHxxawcNUG+tdW0tDYQhr5Jv7xbD1HTKord3iSdmJPPfUkRxx9fIfL1A0fys03XkdlRXDO6ybytT8+ycyXVrLfmMHdFKUkCUw2JW2hqbmFmx+ez6oNjZx4wC5MGjmQlesbueHvj3LuTx/kt+e/nlePG1LuMCXtpBpbEqdedHmHy9x86Uc3Tb9ryni+9ddnuOaeOXzjXQeWOjxJUhGv2ZS0mQfnLGfZuo289dWjmTRyIACD+1azV/0/GDGghs/++jEaHWxDUi8xuG81/3bwWG559CVvgyJJ3cxkU9Im66sGMu2FZey1y0AmDu+/2byq1MhXT9mfpxau5qq7Z5cpQknqunNeN5GNTS1c/6C3QZGk7mSyKWmTuYP3p7qygiMnjWhz/rH77cIJ++/C/93xrGcIJPUak0YN5IhJI7j23jk0NDWXOxxJ2mmYbEoC4OmFq1nVZxcOmjCUfjXtX879qWNfxYbGFq6994VujE6Sts+HjtidRasauPnh+eUORZJ2GiabkgD48T9mU9HSxAHjOh6tcc+RAzlmn1Fce+8c1m/0DIGk3uGISSM4cNxgvn/XLJq87lySuoXJpiQWr9rA76bPZ/i6F+hbXbnV5T/8xt1Zvq6Rmx7y+idJvUNEcOGbJzF32Xpumf5SucORpJ2CyaYkfvXAizS1JEatea5Ty0/ZdSivmTCEa+6ZQ0qpxNFJUj6O2Wck+4wexPfumkVzi/+7JKnUTDalnVxKiVumv8Trdh9On+a1nVonInj3IROYvWQtD72wvMQRSlI+IoIL37Qns+vX8qcZC8odjiTt8NofBUTSTmHG/FU8v2QtHz5yd359d+fXO/GA0Xxp6kxunDaXKROHlS5ASUTEHGA10Aw0pZSmRMQw4AZgIjAHOC2ltDwiAvgucCKwDjg3pfRwOeLuiY7ffxf2qOvPFXfM4sT9R1NREeUOSZJyc+ppZ1K/tOMTAXXDh3Lzjdd1Szwmm9JObuqj86muDI7ffxd+3YX1+tdW8bZXj+aPjy3gSyftR/9a/51IJfamlNKSotcXAbenlC6NiIuy1/8BnABMyh6HAldmzwIqK4IL3rQnn7zxUW57chHH7bdLuUOSpNzUL13OqRdd3uEyN1/60W6Kxm600k6tpSXxh8cWcOSkOob0q+ny+qdNGc/ajc388XG7o0llcArws2z6Z8Dbi8qvTQX3AUMiYnQZ4uuxTj5wDLsO78dltz3jtZuSVEImm9JObNoLy1mwcgMnTx6zTesfvOtQJg7vx+8e8b51Uokl4K8R8VBEnJeVjUoptf7SsxAYlU2PBYqHip6XlSlTVVnBp47di6cWrvb/lySVkMmmtBOb+uh8+lRXcMw+o7a+cBsigpMPHMO9s5eyeNWGnKOTVOQNKaWDKHSRvSAijiyemQrDQnfpFF1EnBcR0yJiWn19fY6h9g5vO2A0B4wdzLdve4YNjd4zWJJKwWRT2kk1Nrdw6+MLOXqfUdt1veXJk8eQEnallUoopTQ/e14M/BY4BFjU2j02e16cLT4fGF+0+risbMs6r0opTUkpTamrqytl+D1SRUXwuRP2Zv6K9fz0njnlDkeSdkiO6CHtpP713FKWrd3IyQduWxfaVnuOHMg+owcx9dGXeN/hu+UUnaRWEdEfqEgprc6mjwW+AkwFzgEuzZ5vyVaZClwYEddTGBhoZVF3253CU089yRFHH9/u/NaRGF+/5wiO2WckV9zxLKceNJZRg/p0Y5SStOMz2ZR2UlOnv8TAPlUctdf2n9E46cDRfP3PTzN32TrGD+uXQ3SSiowCflu4owlVwK9SSn+OiAeBGyPiA8ALwGnZ8rdSuO3JLAq3Pnlf94dcXo0tqcPRGItHYvzPt+3LW759N//7p6f49umTuyE6Sdp52I1W2gltaGzmrzMXctx+u1BbVbnd9Z306sLZ0d8/9tJ21yVpcyml2SmlA7PHfimlS7LypSmlo1NKk1JKx6SUlmXlKaV0QUppj5TSASmlaeXdg55t1+H9+dCRu3HzI/N54Pll5Q5HknYoJpvSTuiupxezuqFpu7vQtho/rB8HTRjC1Okmm5J6nwvetCfjhvblczc/5mBBkpQjk01pJ/T7RxcwvH8Nr99jeG51nnTgGJ5auJpnF63OrU5J6g79aqr473ccwHP1a/n+nbPKHY4k7TBMNqWdzJqGJv725CJOPGA0VZX5/Qt466tHUxHw+0c9uymp9znyVXWc+pqxfP+u55gxf2W5w5GkHYLJprSTue2JhTQ0tXDy5Hy60LYaObAPr9tjOFMffYnCLf8kqXf5r5P2ZfiAGj5+w3S700pSDhyNVtrJTJ3+EmMG9+HgCUNzr/ukV4/hopsf5/H5K3n1uCG51y9Jeejo1igtg8Yza6+3c8RHv8ODV36qmyOTpB2Lyaa0E1m+diP/eHYJH3jDblRURO71n7D/aP7zlhn8/tGXTDYl9VhbuzXK35+pZzp78+cZCzh+/9HdGJkk7VjsRivtRP40YyFNLYmTchqFdkuD+1XzxlfV8YfHFtDSYldaSb3T4XsOp2bNQj5z02M8v2RtucORpF7LZFPaiUx9dD67j+jPfmMGlWwbJx04hgUrN/DgHO9XJ6l3qqqoYPhzf6ayMvjwz6exekNjuUOSpF6pbMlmRMyJiMcjYnpETMvKhkXEbRHxbPY8NCuPiLg8ImZFxGMRcVC54pZ6q4UrN3D/88s46cAxROTfhbbVMfuMok91Bb9/zFFpJfVeVRtX8713H8Rz9Wv52PXTaba3hiR1WbnPbL4ppTQ5pTQle30RcHtKaRJwe/Ya4ARgUvY4D7iy2yOVerk/Pr6AlChZF9pW/WurOGafUdz6+EIam1tKui1JKqXD9xzBl0/ejzueWsyXfz/TkbYlqYvKnWxu6RTgZ9n0z4C3F5VfmwruA4ZEhFfsS10wdfp89h09iD1HDij5tt7xmrEsW7uRO59aXPJtSVIpvfewXTnvyN259t4X+M7fni13OJLUq5Qz2UzAXyPioYg4LysblVJakE0vBEZl02OBuUXrzsvKJHXCrMWreXTeSk49qHv+bN74qjrqBtZy47R53bI9SSqlz52wN+86eBzfvf1Zrrr7uXKHI0m9RjlvffKGlNL8iBgJ3BYRTxXPTCmliOhSf5UsaT0PYMKECflFKvVyv3l4PpUVwcmTS9uFtlVVZQWnHjSWH//jeRav3sDIgX26ZbuSVAoRwf+cegDrNjbz37c+RUuCj7xxj3KHJUk9XtnObKaU5mfPi4HfAocAi1q7x2bPrX3w5gPji1Yfl5VtWedVKaUpKaUpdXV1pQxf6jWaWxK/fXg+R04a0a1J37sOHr9p25LU21VVVvDdMyZz0oFjuPRPT/E/f3rSWzxJ0laUJdmMiP4RMbB1GjgWmAFMBc7JFjsHuCWbngqcnY1Kexiwsqi7raQO/Ou5JSxctYF/O3hct253z5EDOHjXodwwba5fyCTtEKoqK7jstAN572ET+OHfZ/Pv1z3ChsbmcoclST1Wuc5sjgL+GRGPAg8Af0wp/Rm4FHhLRDwLHJO9BrgVmA3MAn4EnN/9IUu9043T5jGwT2GE2O72nkMnMLt+Lf+YtaTbty1JpVBVWcFXT9mfL5y4D7fOWMC7f3QfS9c0lDssSeqRypJsppRmp5QOzB77pZQuycqXppSOTilNSikdk1JalpWnlNIFKaU9UkoHpJSmlSNuqbepX93An2cs4J0Hj6NPdWW3b/+trx7NiAG1/PSe57t925JUKhHBh47cne+/+yBmvrSKk6+4h4deWFbusCSpx+lptz6RlKPrH3iRxubEew/btSzbr62q5L2HTeCup+uZXb+mLDFIUqmccMBobvzw66iogNN+eB9X3PEszV42IEmblHM0Wkkl1NTcwq8eeJE37DmCPepKf2/N9rz70Al8785Z/PSeOXz17fuXLQ5J6oqnnnqSI44+vt35dcOHcvON13Hg+CH88aNH8IXfzuCbf32Ge2Yt5VunHciYIX27MVpJ6plMNqUd1G1PLGLByg1cfPJ+ZY1j5MA+/NtB47jhwbmc/6Y9GD3YL2CSer7GlsSpF13e7vz/PvctmyWjCRg2fG/uaz6Kwy9ZwOD597Fb01x+e+OvuiFaSeqZTDalHVBKie/dNYtdh/fj6L1HljscLnjTnvzm4XlccccsLnnHAeUOR5K2W3vJ6Mr1jdz59GJeqDySGWsX89i8Fbx63JDuD1CSegCv2ZR2QHc9Xc+M+au44Kg9qaos/5/5+GH9OG3KeG6cNpe5y9aVOxxJKpnBfas55cAxnLj/LjRX9+eU793Dp296lJdWrC93aJLU7cr/LVRSrlJKfPf2Zxk7pC/vOGhsucPZ5MI370lE8L9/fqrcoUhSSUUEk0YNZPSMX3DeEbsz9dGXeNM37+LSPz3FyvWN5Q5PkrqNyaa0g7njqcVMn7uC89+0B9U94Kxmq9GD+3LBUXvyh8cW8Pdn6ssdjtRrRMT4iLgzIp6IiJkR8bGs/OKImB8R07PHiUXrfC4iZkXE0xFxXPmi37lVNG/kcyfuwx2feiNvPWA0P7z7Od7wv3fwzb88zbK1G8sdniSVXM/5Jippu21obObLv3+CPUcO4LQp48sdzit85Kjd2X1Ef/7zdzNYv7G53OFIvUUT8KmU0r7AYcAFEbFvNu+ylNLk7HErQDbvDGA/4Hjg+xHR/Tfa1Sbjhvbj26dP5o//fgRHTBrB9+6axeGX3sFX//AEi1ZtKHd4klQyJpvSDuRHd8/mxWXr+PLJ+/Wos5qtaqsq+do79i/E+PuZ5Q5H6hVSSgtSSg9n06uBJ4GO+sifAlyfUmpIKT0PzAIOKX2k2pp9xwzi++85mNs+cSQnHLAL1/xrDkf87518/reP85z3Ipa0A3I0WmkHMWvxar531yzeesBofn75JVy2fHWby40YOpAfXH5Zrtv+yEc/wZJObu/1e4zggjftwffufI7J44dwxiETco1F2pFFxETgNcD9wOHAhRFxNjCNwtnP5RQS0fuKVptHx8mpSmRr9+rcsGgZQw45lV/d28iv7n+RPiteYMCi6fRZ9SLBy/fylKTeymRT2gGs39jM+b98mAG1VXzppH05/8+rOfzsz7a57D3Xfj337S9Z3v72rvjEmbzzrA9uVpaAgcMP56JfP8JPrrqSwQ2vvIazFEmx1JtFxADgN8DHU0qrIuJK4KsU/qS+CnwLeH8X6jsPOA9gwgR/9CmFrd2r88tnH8OnzzyVdRubeHz+Sh6bV8mSIbsytF81B44bwrPXfbUbo5Wk/JlsSr1cSon/vGUGzy5ew7XvP4SRg/qUO6TNNLbQZiJ6cGMzP/zDv5g98ghOevVodh3ef7P5bSWprUxEtbOJiGoKieYvU0o3A6SUFhXN/xHwh+zlfKD4ou1xWdlmUkpXAVcBTJkyJZUmcnVGv5oqDt1tOFN2Hcazi1czfe4K7nqmnpj8fj538+O8+5AJHDBucLnDlKQuM9mUukFXupl2RUqJ/771SX790Dw+dvQkjphUt9V1HnnkkXaTuOmPPc7h2xRJ1/WtriT96xqGvvO/mProSxwxqY4Dxw0mIoD2k1QozdlZqaeKwh/FT4AnU0rfLiofnVJakL18BzAjm54K/Coivg2MASYBD3RjyNpGlRXB3rsMYu9dBrFw5Qb+8Ke/8NtH+nDdAy+y/9hBnHnIBE4+cAwD+1SXO1RJ6hSTTakbdNTNdFsTp+aWxP/c+iQ//ufznPv6iXz8mEmdWq+jJO6Bj525TbFss43r+LeDxnHbE4v4+zP1vLhsHW98VR2D+/pFSipyOHAW8HhETM/KPg+cGRGTKXSjnQN8GCClNDMibgSeoDCS7QUpJYd/7mV2GdyHYXNu5w9XfJyp0+fzy/tf5Au/ncElf3ySkw8cw+mvHc/k8UM2/UAnST2RyaaUk47OXuZ9xrB+dQPHf+UGllYOp27Nczx+0295102l214p9amu5G2vHs0jc1dw73NL+fl9L3DA2MHQd0i763R0dtYuttrRpJT+CbSVUdzawTqXAJeULCh1i6eeepK3ve0koPCLwsj+o1hbtx83rN/A9Q/OpWr9curWv8gN//NxJgzvV95gJakNJptSTjo6e9nRGcOuJE7rNzbz8/vm8H+3z2JNxRCO2Wck+42ZROFWep3bXk8UERw0YSivGjmQf81ewmPzVhDHfJybH5nHnnUD2KNuAP1rX/53ZRdbSTuD9gYYamhq5tnFa3h6QV/mrRjKkd+4kym7DuXtrxnLWw8YzdD+NWWIVpJeyWRT2kKprq9sz9YSp8bmFh56YTl/nrGQmx+ex6oNTbxprzoW3/0r9jvm/FxjKbcBfao4dt9deN3uw/nJT3/G6v7HcOfT9dz5dD2jB/dh3NC+jBncF6p61iBIktSdaqsq2X/MYPYfM5gbv/15zvqPS/ntw/P54u9m8OXfz+SovUby1gNG86a9R3pZgqSyMtmUtlCK6yu3prklsW5jE2s3NrOuoYmV6xtZsmYjc+reyAEX/4UNjS1UVwbH7z+asw7blUN2G8Y777iqJLH0BAP7VJOeup2zP/IBlq7dyKzFa5izdC0PvbCcB9Ny4oSL+OX9LzBmSF92GdSHUYP6MKRfNRVbuXapox8SnnxiBvvsu3+b8+yaK6mnqtq4hvOP2pP/98Y9eGLBKn778Hx+/9hL3PbEIqorg9fvMYLj9tuFt+w7irqBteUOV9JOxmRTKrGNTS0waBdmLV7D2oYm1m5sYk1DE+samlmzsYk47j+44s5Zr1ivb3UlVS1NnH7IBA7dbThvmDSCAbU7159sRDBiQC0jBtRy2O7DaWxuYeHKDfz6up/T7/Vv48kFq3hs3koAqiuDuoG1NA0+gN89Mp/9xw5m9xH9qah4OQHdWldnu+ZK6q0igv3GDGa/MYP5/In7MH3eCv4yYyF/mrGQz//2cb7wu8d5zfghHLXXSN74qjoOGDt4s/+PklQKO9c3V6mEGitqeWHpWpas2cjydRtZsa6RFes2snZjMxVHnc8fHy/coSAC+tdU0b+2ksF9qlmyYCaHvel4+tVW0b+mkn61VQzqU0W/miruufbXfOmk95V5z3qO6soKxg/rB8/cxTsu+DAtKbF87UYWrW5g8aoNLF7dwJJ+E/n4DdMB6F9TyX5jC13N9h0ziHVVg2huSVT6BUvSDuKpp57kiKOPb3f+iOFD+eF3f8ifZyzkrqcXc9nfnuHbtz3DsP41vGHPERz5qjoO230Y44Y6wJCk/JlsSl3wyCOPcOpZH2JD1UDWVw9iXfVg1lcPZl31YJpGn8ij018CCmclh/SrZsLwfgzpV8M913+P95z/GfrXVtG3pnKz7p6XXf0HDv3Ae8q1S71aRQTDB9QyfEAt+44eBMA/r/0Gl3zjWzw+byWPz1/JY/NW8qsHXmBDYwuMOpqn73qO4QNqGDGglrqBhceIAQ6mIal3am8QoVb/fe5bOO+9p216PbqqDxsGTWDD4F35w4oJTH200G6NHdKXQ3YbxmsnDuOQ3YaxR11/b6siabuZbEodaGlJLF27kUWrNrBo1Qaa3vARHh06luaUgMINuIf3r2HsgBpm3nYj7zzrg4wYWEvf6srN6rlnwROMHOSgNt0hSJtuiv6uKeOBwjWxzy9Zw4e++C2GTT6W+jUNPL9kLU8sWPXyem/+GH98fAF1RUlo/5pKIqLDEYPbu9bT6zwl9QQdJaMpJZas2citN/2cAw84nX88u4TfPjIfgOH9a3jtxGEctOsQXj1uCAeMHbzZqOCS1Bn+15CKzFu+jqV9x/H3Z+pZtGoD9asbaGopJJa1VRWwcR2TJwxhxIAa6gbUMqRfzaYumTN/eC/jh320nOGrHZUVwZ4jBzJ8/TwOnzQCKHzJWtvQTP2aBupXN/Cvf86kfnXh2tpWfasrC9eB7vUWRhz7LuoG1r5iIKL2rvX0Ok9JPV1E4Vr3+XffwOOLH6MPsEvtEBoGjmH9wDHctmwMf545GICKgD1HDuDV44Zw4PghHDhuMHvvMoiaqory7oSkHs1kUzusrd3C5MrvfpsXlq7j/ueXcv/zy7h/9jLmr1gPw15L1fyV1A2sZf+xg7PRTmsZ3Lea73z8i7zhzBO6eU9UChHBgD5VDOhTxW4j+nPPd27g3LPeTkNTM0tWb9yUhNavaYA9XsefZy4EoKoiXu6CO6AWhoylsbmF6kq/cEnqnTo6+7luYxP/d/GnGbn3FF5cPopZ80by64ey6ztbmqlZV0/1unpWvfgEo/sF1euXUtHS9Ip66oYP5eYbryvlbkjqgUw2tcPacuTRlBLL1m5k/or1PPDIwxz637ezeHUDUOgudMhuw/jgEbtx00+u4OjTz3OUvp1UbVUlY4f2ZezQvpvKLvv4e3nPV3/ycgK6uoGnF63m8fkrqTjyw1x513MM7V+zWRfch2c83W7XW7vYSuot+tVUsWHeDN73398BCm3p6g1N2eUlDSxaNYD6NWOoHHkAi4EAhvSr3vSDXOG6+Fr+ctkny7kbksrEZFO9WkdnLx95fAZ7rtrA/BXreWnFel5asYH1jc0AVNeO4ITdh3Po7sM4dLdh7FE3YNNACH/8wUoTTW0uNW9KIhmdFaXEqg1NXP2d/+Gw0y6gfk0D81es5+lF2efxuIt4qrZqsy9cdQNrGdSnin/9/Bvl2xdJ2g4RwaC+1QzqW82kUQOBwv/Dr51/Ou/+ytXUr25gyZoGFq7cwDOLXr4soeLA93PO1Q+w1y4DmTRyAJNGFZ69DlQqrZaU2NjUwsamFiIKI/snuu97rn/h6tWKz142NDVTv7qBl1Zu4KXl62katZzrH5wLwOC+1Uwc0Y+xQ/oydkhfZvz6O1x+5o/LGbp6uYhgcN9qWPgkr9tj+Kby9RsL14H+5hdXM/bod7JkdQNzlqwlZfNrKiuoGXEEF0+dyX5jBrHvmEFMGjlw03VPW+v+7RlRST1NRNCydhl71A1gj7oBm8o3NDazJOsRMu1fT1C/ejT3zl5auP90ZuyQvkwaNYBXjRrIniMLz7vX9WdQn+py7IrU6zXVDOTReSt4acV6lqzeyIr1G2lJmy8zYvCu3RaPyaZ6hI6+YLc12mdzVLKuegjPrurH6pkLWbxqA8vXNW6aP7x/Dcx9lOPfdjJjh/RlQJ/NP+qet1Sp9K2pZMKwfvDcPRz/0QsBaGpuYcnajZu64M6eXc8ND859+Ux7ZTBp5ED2HTOImRtHcuhJ5zJiYA21VZuPanzFJ860a66kXqNPdSXjhvZj3NB+PP+r27n1J5+hqbmFucvX88yi1cxavIZnFq3m2UVr+PuTC0kVL//Pq2hcR1XDSqo2rKBqwwqqG1ZSV9vCTT/+DgNNRKXNLFy5gT889hJ/eGwBCw48lwVP19O/ppJRg/qwW11/BtRWUV0ZtCRobG5h1qPLui02k031CFteX1nsgU+ezW4n/zuLVzewePUGFq9qYNnajYUzRXUwf/l6Rg6sZe9dBjFyYC2jBvehb3Ull113EXt90PtXqvyqKivYZVAfdsluf1P7wDXc8K0fMWfpWp54aRUzX1rFEwtWcdfTi1ky5NXMfXgeUDgjP2JAzaYuuI01g3j9WZ9p8953jn4rqTeoqqxgtxH92W1Ef47b7+XyNxx9Akd/9BssXbuRFesaWbFuIyvWDWfF+kZWNRQGHFoKHHDxX7NEdBWVG1cXPa+mrm9w408uNxnVTuOhF5bz43/M5i8zF9KSYL8xgxg89x5OOe3dDOlb3e69cudsXNVmeSmYbKpH2djUQn1rUrm6gcWrG4gTv8BNDxW+fPfLfqXZc+QARg6q5Zavf4IPfP1HZY5a6rrKitjU5eykA8cAheueTjn3QnY7/oMsXtPAkuxM6HP1awGoOPbT/Ogfz7PL4D6Myn5YGTWo8ONKR/cC9aynpJ4uSAzpV8OQfjWvmNfY3MLK9Y1cdennOfZDX2DFukGsWj+cVRuaWLOhadO9r5dQSEYH9ali7NDCpTPjhvYt/M8cVMuogX0YOagPIwfVMrC2qt0v4lJP1tyS+MvMhfzoH7N55MUVDOpTxYffuAfvOngcu9cN4IijL2Fov3PLHeYmJpsqm5XrG5n50kpmzl/F7KFTeO7eOZt1he1fW8nIgX1Y+vBfOOld72bkwD70r63cvHFoWNNGzVLvFBHUtDQwcUR/Jo7ov6l8Y1MLS9Y0cMNPf8DE485k0aoGnl+ydtP8wX2raXrNu+h71JsYPbgPdQNrqap4+VYsnvWUVG5PPfUkRxx9fPvzn3mm3XnVlRWMGFBL44uPMmXXYZvNSymxbmMzqzY0cvsNP+ZDF36c+cvXM3/FeuYuW8d9s5eypuGVt2LpW13JqEG1heRzYC2jstucjRhQy/ABtQzvX8PwATUM6//KSxqkcljT0MSND87lp/96nrnL1jNhWD++fPJ+vPPgcT16oK2eG5l2KCvWbeSDn/9fFjTUsK56COtqhtBQ9fIgAsQgdu9Xw167DGTkwMI//tY/nMuuvpPd684rU+RS+dVUVTBmSF+Y8wDH7vsJoDAg1uJVDSxavYFFKxtYMXQ8/3h2CVA4azpyYC2jB/dh9OC+bKyoLWf4ktThvTwBvnz2MdtUb0TQv7aK/rVV9Fv2LB954x6vWGZNQ+FWLYtXFXpOtU4vWt3AolUbmPnSKm5/cvGm6+hfsY2mBiqb1lPRuI7Kpg30q2zmzFNOYFiWkI4YULtpeli/Gqq877JytGDleq751xx+df+LrN7QxJRdh/KFE/flLfuOorIX3D3BZFNd1tFgPsOHDuRrl1zKEwtWMWP+SmbMX8nj81cyb/l66HMQ9IFBfaoYP7APdYNqGZVdi/bDz5zFSd/1Zs9SZ9VWVTJ+WD/GDyvcXP2yn3ySD3395yxYuYEFK9ezYOUGHp23kodfXAGjT+QN/3sHB00YykEThnDwrsPYe/RAqv1CJGkHsrWzpwB1w4dy842v/L6RUmJNQxNvOfW9HPG+z7O+sZn1G5tZtzF7bmzKnpupr6/nijuegWj7f2hFy0bGjhjM0H41DO5bzdB+NQztV83g7HlIv2qG9KthaL8ahmTzB/ap8rZr2iSlxP3PL+Pn976QXY+ZOOGA0XzwDbvxmglDyx1el5hsqstaB/NZt7GJpWs2smztRpau3cjStQ1MX7qSv1zyt03L7jq8HweOH8J7Dt2V3/7ixxx16tn0rbY7ilQK/Wur2HPkAPYcWeg10NRSuAb6pp/9iLW7v5o/LRnG1Ef7AlDR0kS/xuXUVazh8x94FwdNGMLwAT33DKi3hJG0NVs7ewrw3+e+pcOE9LlnnuH0IX07rOPLZ3+A//rZbWxoaskS0pcT0fUbm/nnrb9h6fLR1Ff2oaWq6FFZC+1cJ1oRMKC2ioF9qhnYp4q5c56jecM6onkjFdkjmjdS0VJ4HtK3mq984TP0q6mkb3UVfWsq6Vtduem59XZa6l3qVzfwx8de4lcPvMgzi9YwuG81575+Iue8fuKmH5d7G5NNtekjH/0E9ctX01jRh4aq/oVHZeF5Wf9DmHH3c2xofPk+WTVVFQzvX8OQDS9x/ulvZe9dBrLf2MGF+xBmbv9JvYmm1I2qKioYPbgvzc/dy9kf/SgAqzc0Zmc/N7BgZX/mrFzPh66dBsDE4f023etuz5GFwYv2GDmAAT3gWpCORqz2mlRJnZVXd96IKCR31ZUM67/5oEZ/eeBGPn3t316xTktKNDS1MPWKL/O9K69k5fqNLF/byIr1hdF3V29oYvWGJtY0NPL80w0MHr8XG5tb2NjUwvqmFpqKbpa4HDjrJw+0H2BLMxU0M3zwwCwhraRPdeWm6eLktF9NFf1qsnmtz9WFsq999ausXL6MaGmkoqWRaG4kWpqI7O7R7Z0pVuctXdPA7U8u5vePvcQ9s5bQkmD/sYP4+r+9mpMOHEPfmt793bn83yBUFus3NnPeZ/6TxWuaaazsw8bKvjRW9Nk0vSYdROX44TQX/WMLYGCfKtLcp9mzbgBD+9cULqDvX7tp4J57rr2O97/hwvLtmKQOFX41r+ZVowYC8H+fOotXvfYo1tQMY8X6ody9cCC3VQ0gFXUPGzGgcP3nLoP7MDobAXeXQX0Y1r+Gwf2qN3UFG9S3msqK8CykJLWhIktQqxtWcPCuHXeFPOKaL3LqqZsnxS0taVPy+d2LPsj7v/wDmppbaGxONLVkz0Wvn7zvDo457OSXuwNnZ15Xrm9kfWMzcxcspplKWiqqoaKdhGbscTD2lcWVFUFtVQULVtbzju/fw6A+hTZgcN+qTdOF5yoGb5quZlCfKgb1rd6pL+NYtnYjj81bwb3PLeUfzy7hiQWF25BMGNaP84/ak5Mnj9nURu8ITDa3ord9aWodtXLRqsIF8N+9+lcsbwgaK7KEsrKQUDZX1EDfw6Gop0hVReEi+8G1VayecT8H7rM7g/pWM6RvNYP7Fr6gVlYEl33s8xx9RsfXREjqHZqamjj+jA9sVtbckli5vpF//fEGTn33Ocxbvo4FKzcwd9k6Hnh+GSvXN7ZdWUpUpkaa00HU7T2amsoKqiuj8FxVQXVlBY/OvI8f/P05+tdUbhrUo09166/uFfSprqRPVSV9al6erq70OiZJO47OXFva1ui8FRVBn4rCGcqW5fMZu5Xuvgtv+if/c+oX251/xNHHbzrL29KSaMwS1sbmFpqy559e+llO/+T/0Njcsll5Y3OioamZ6c88wBMb15Aqa2mpqqGlsg8tlTXtJ6+Z2qoKBvYpJJ8D+7zcfXjz6WoG1hbK+tVWbTqT3DdrH1rPzPapquxR17u2tCRWNzSxdE0D85avZ+7ydcxdtp5nF61m5kurWLhqAwA1lRUcvOtQPnPcXhwxaQQHjB28Q96Ox2RzK3pC163Wi9YXZ/fcW7y6gcWrNlC/poH6VQ3Ur2ngkafnsCFV01S5xTVXNXtTUQv9aqoYUFtF/9rK7LmKf950Ff/2gY/Sv6ZQVlNVselDftm1n+GIc/6tW/ZPUs9SWREM61/D0A0LuOBNe75i/rqNTZx23qfY923vZ0NjCw2NzWxoamFDYzMbGpuZft+TDNh1PI1NLazb2MyK5sbCl5OmxMaBr+LSPz3VpXgqAhh9EjPvnk3f6pe7evWvrWJQnypW1o7Kac8lqfQ6c23pto7OW6wrt5upqAhqKyrZ8qqJppee3DQOQFv++T8/5lNbdBlOKdHUkmhobOEPP/waV1xxBavWN7JqQyMr1zUW7o/a0MTqDYXpf9z3EBtagpbKmkLSWllDqnzl/VY7UlNVsSkZra4KqisqqKoMqisrqKqsoLoiNr2urqygqqJ1XlBVUbHpUtrWVK8454usNAJSKtz3tfUMc+v0mg1NrFxf6BK9an0jRR0DgcIJnYkj+nPY7sPYb8xg9hsziMkThtCvZsdPxXrNHkbE8cB3gUrgxymlS7tz+yklmlsKfzxNLYmUEg2V/ZizZC3NKdHSkmjOlmlpIZtuobmlcJagJZvXumxTy8vrNDS2sGJ9IyvXbcz67Tduer18XSOLV2/Y7PrIVjWVFdRlo7lWNaxinz33oX9NJf1qX04sf/mVC/no13/U5i8l/5z3KBN66cXGksqnX00VfZrXMnpw27+qP/L933DyOe9sc95lHzuTC7/9y80a6abmwv/EW374dSbuOYmWqHzFY9GS5exx+LGbuoHVr25gztK1NDYn+g7at5S72yuVu82UVH6lut3M1kQE1VliN/uRe/jkB97d4fILnnmGz1/9583KWlJiY1Mhofv+5z/Erru/ilRRVXhUVtPSOl1RRaqoZvmq1dQMq2N1RRUpKguXglRkz1HBug0N9O3XnxQVpKiEqNg0r/WykaamZqqqql7OOAt7s2mqqampMD81Ey0tRGrOppupaGmkb2ULJ7/lKAZnPQKH9a9h3NB+jBval1GD+lBZEZx62pk8tHR5u8di9uxZ7L77K3/kLdbbrpPtFclmRFQC3wPeAswDHoyIqSmlJ0q1zZXrG3nr5f9g4S4n8uhds2hsTq9caJfjOOqbd+W30ZSgcT21FS1UtWyksmUjVS2NDG7ewIiWDQzrE/zHBR+gbmAtIwfWMrhv9aYk8p1n/ZTD937jK+vcuHaHPCUvqfdq/WV5S00Ln+akz13c5jqXfexMjv7gWZuVpZTY0NTCv278AfCeEkTaO5WjzZSktmzrWdyKiMKlFNWVNCydx5mXXbPVOj7ZxqBMxfM/0cH81mW+tJU6Opp/86Uf5StXdDxuSf3S5Vv9AWBrx+vmSz/a4fyeplckm8AhwKyU0myAiLgeOAUoWcPZp7qCQ3Ybxj/mzmTcbgdtdjq+siKIgOf+9Sc++uH3U1kRhUcEFdnzpd++nP2PeScRhZPvFVFYJwKu/9YXec9nLiEIKqLQZa1PdSW1VRV85+Pv5iPt3G/ynmu/zuF7jijVLktSr9I6GmSf5rXlDqWn6fY2U5J2dtt6LW7e28ljG3nqLcnmWGBu0et5wKGl3GBtVSXfPm0y7/z9FRw+6bg2l7nrnlv41boX25w357HHeevp57Rd+cqXGDmwT16hSpJUrNvbTEna2XXXtbjl6h69rSKlNrqH9jAR8U7g+JTSB7PXZwGHppQu3GK584Dzspd7AU93a6C93whgSbmD2IF4PPPl8cyfx3Rzu6aU6sodxPbazjZzR/5MuG+9k/vW++yo+wXu25a22m72ljOb84HxRa/HZWWbSSldBVzVXUHtaCJiWkppSrnj2FF4PPPl8cyfx3SHtc1t5o78mXDfeif3rffZUfcL3Ldt0VvuqPogMCkidouIGuAMYGqZY5IkqSeyzZQk9Qi94sxmSqkpIi4E/kJhGPerU0ozyxyWJEk9jm2mJKmn6BXJJkBK6Vbg1nLHsYOzC3K+PJ758njmz2O6g9qONnNH/ky4b72T+9b77Kj7Be5bl/WKAYIkSZIkSb1Lb7lmU5IkSZLUi5hs7oQiYnxE3BkRT0TEzIj4WFY+LCJui4hns+eh5Y61N4iIPhHxQEQ8mh3PL2flu0XE/RExKyJuyAbqUCdFRGVEPBIRf8heezy3Q0TMiYjHI2J6REzLyvyb1yYRcXxEPJ39jV1U7ng6oyuf6yi4PNu/xyLioKJ6zsmWfzYi2rlJdsn35eqIWBwRM4rKctuXiDg4O1azsnWjzPt2cUTMz9676RFxYtG8z2VxPh0RxxWVt/kZLWf70NXvVL3lvetgv3r9+xZd/N4WEbXZ61nZ/Inbus9l3LdrIuL5ovdtclZe+s9jSsnHTvYARgMHZdMDgWeAfYGvAxdl5RcB/1vuWHvDAwhgQDZdDdwPHAbcCJyRlf8A+H/ljrU3PYBPAr8C/pC99nhu3/GcA4zYosy/eR+tn4VK4Dlgd6AGeBTYt9xxdSLuTn+ugROBP2X/sw8D7s/KhwGzs+eh2fTQMuzLkcBBwIxS7AvwQLZsZOueUOZ9uxj4dBvL7pt9/mqB3bLPZWVHn9Fytg908TtVb3nvOtivXv++0cXvbcD5wA+y6TOAG7Z1n8u4b9cA72xj+ZJ/Hj2zuRNKKS1IKT2cTa8GngTGAqcAP8sW+xnw9rIE2MukgjXZy+rskYA3A7/Oyj2eXRAR44C3Aj/OXgcez1Lwb16tDgFmpZRmp5Q2AtdT+Hz0Ru19rk8Brs3+Z98HDImI0cBxwG0ppWUppeXAbcDx3RwzKaW7gWVbFOeyL9m8QSml+1Lh2+K1dOPfezv71p5TgOtTSg0ppeeBWRQ+n21+RsvdPmzDd6pe8d51sF/t6TXv2zZ8byt+L38NHJ3F36V9Lu1eFXSwb+0p+efRZHMnl3UFeA2FXz5GpZQWZLMWAqPKFVdvE4Uun9OBxRT+IJ8DVqSUmrJF5tHxP2lt7jvAZ4GW7PVwPJ7bKwF/jYiHIuK8rMy/ebUaC8wtet1b/sa68rlubx978r7ntS9js+kty8vtwqzr3tXxcjf+ru5bj2kfOvmdqte9d1vsF+wA71sXv7dt2ods/koK8ffI/ylb7ltKqfV9uyR73y6LiNqsrOSfR5PNnVhEDAB+A3w8pbSqeF72a4VDFXdSSqk5pTQZGEfhF629yxtR7xURbwMWp5QeKncsO5g3pJQOAk4ALoiII4tn+jevXmqn+VzvSPuSuRLYA5gMLAC+VdZottOO+p2qjf3aId63Hfl725b7FhH7A5+jsI+vpdA19j+6Kx6TzZ1URFRT+Ofxy5TSzVnxouz0ONnz4nLF11ullFYAdwKvo9AVofVetuOA+eWKq5c5HDg5IuZQ6HryZuC7eDy3S0ppfva8GPgthcbVv3m1mg+ML3rdK/7Guvi5bm8fe/K+57Uv87PpLcvLJqW0KPtS3AL8iMJ7B13ft6WUuX3o4neqXvPetbVfO9L7Bp3+3rZpH7L5gynE36P/pxTt2/FZt+iUUmoAfsq2v29d/jyabO6Esn7mPwGeTCl9u2jWVKB1tKlzgFu6O7beKCLqImJINt0XeAuFaxvuBN6ZLebx7KSU0udSSuNSShMpXIh/R0rpPXg8t1lE9I+Iga3TwLHADPyb18seBCZlozHWUPjbm1rmmDq0DZ/rqcDZ2eiLhwErs26OfwGOjYihWZfAY7OyniCXfcnmrYqIw7LvAGdT5r/31kQs8w4K7x0U9u2MKIwAuhswicKAJG1+RrOzhmVrH7bhO1WveO/a268d4X3bhu9txe/lOyl8L0l0cZ9LvmO0u29PFf3wERSusSx+30r7eUzdMDKSj571AN5AoTvHY8D07HEihf7ntwPPAn8DhpU71t7wAF4NPJIdzxnAf2Xlu1P4pzMLuAmoLXesve0BHMXLo9F6PLf9OO5OYTS8R4GZwBeycv/mfRR/Tk6kMOLkc62fkZ786OrnmsLIid/L9u9xYEpRXe/P/rfMAt5Xpv25jkK3xEYK10F9IM99AaZkbdRzwBVAlHnffp7F/hiFL7yji5b/Qhbn0xSNdNneZ7Sc7QNd/E7VW967Dvar179vdPF7G9Anez0rm7/7tu5zGfftjux9mwH8gpdHrC355zGylSRJkiRJyo3daCVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRT6uEiYkhEnF/0+qiI+EM5Y8o7johYk0c9kiR1RWv7ExFjIuLX2fTkiDhxK+udGxFXZNMfiYizu7jdf2XPEyPi3dsWvdTzmWxKPd8Q4PytLSRJkiAiqrq6TkrppZTSO7OXk4EOk80t1v1BSunaLm7v9dnkRMBkUzssk00pR9kvlE9FxDUR8UxE/DIijomIeyLi2Yg4JCKGRcTvIuKxiLgvIl6drXtxRFwdEXdFxOyI+GhW7aXAHhExPSK+kZUNiIhfZ9v6ZUREVselEfFEVvc3txLnHdlyt0fEhKz8moi4PCL+lcXwzvbqyAyKiD9GxNMR8YOIqMjqOTMiHo+IGRHxv0XbbbO8aP6IiLg3It7a2WMuSdr5RMTZWRv2aET8PGu/fhAR9wNfj4g9IuLPEfFQRPwjIvbO1tsta2cej4ivFdU3MWubaoCvAKdn7e7pnYjl4oj4dDZ9V0RcFhHTIuLJiHhtRNycfQco3l5rj55LgSOybX0ix0Mk9Qhd/uVH0lbtCbwLeD/wIIVfLN8AnAx8HpgLPJJSentEvBm4lsKvqAB7A28CBgJPR8SVwEXA/imlyVDovgq8BtgPeAm4Bzg8Ip4E3gHsnVJKETGkgxj/D/hZSulnEfF+4HLg7dm80Vm8ewNTgV93UM8hwL7AC8CfgVOzrkH/CxwMLAf+GhFvBx5oqzyl9Ltsv0Zl2/tiSum2DrYpSdqJRcR+wBeB16eUlkTEMODbwLisrDkibgc+klJ6NiIOBb4PvBn4LnBlSunaiLhgy7pTShsj4r+AKSmlC7cxxI0ppSkR8THgFgrt3jLguYi4LKW0tGjZi4BPp5Teto3bkno0z2xK+Xs+pfR4SqkFmAncnlJKwOMUusu8Afg5QErpDmB4RAzK1v1jSqkhpbQEWAyMamcbD6SU5mXbmJ7VuxLYAPwkIk4F1nUQ4+uAX2XTP89iavW7lFJLSumJDrZfHMfslFIzcF1Wz2uBu1JK9SmlJuCXwJEdlANUA7cDnzXRlCRtxZuBm7K2kpTSsqz8pizRHAC8HrgpIqYDP6TwQyrA4RTaK8ja4hKYmj0/DsxMKS1IKTUAs4HxJdqm1COZbEr5ayiabil63cLWexMUr9vcwfKvWC5L4A6hcCbybRTONG6L4rpjK8umrbzurCbgIeC4bVxfkqS12XMFsCKlNLnosU/RctvaVnVWcbu/5XcCexVqp2KyKXW/fwDvgU1dYpeklFZ1sPxqCt1qO5T9kjs4pXQr8AngwA4W/xdwRjb9niymbXFIdv1LBXA68E8K3WXfmF1/WQmcCfy9g3IoNPzvB/aOiP/YxlgkSTuHO4B3RcRwgKwb7SZZm/p8RLwrmx8R0dom3sPm7V9bOtXu5qQ7tyV1O5NNqftdDBwcEY9RGBjgnI4Wzq7tuCcbuOAbHSw6EPhDVu8/gU92sOy/A+/Llj0L+FgX4i/2IHAF8CTwPPDblNICCteg3Ak8CjyUUrqlvfKi/WymkIC+OYpu9SJJUrGU0kzgEuDvEfEohes1t/Qe4APZ/JnAKVn5x4ALIuJxYGw7m7gT2LezAwRtp8eA5mygIwcI0g4nCpeSSZIkSZKUH89sSpIkSZJy50XK0g4sIr5A4TYsxW5KKV3ShToO4JUj9jWklA7d3vgkSerJIuJ9vPJSk3tSSq+4bYqkV7IbrSRJkiQpd3ajlSRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k031SBHxg4j4z5zqmhARayKiMnt9V0R8MI+6s/r+FBHn5FVfF7b7tYhYEhELu3vbO4uImBgRKSKqyh2LpJ2XbWKnttur28Ti9zgijoqIeUXzZkbEUSXY5pqI2D3vevMUEZ+PiB+XOw5tu0gplTsG7WQiYg4wCmgCmoEngGuBq1JKLdtQ1wdTSn/rwjp3Ab9IKXX5n1dEXAzsmVJ6b1fXzVNETACeBnZNKS0uZyw9UURMBJ4HqlNKTeWuR5LaY5u4/XpbmxgR51J4n97QzvyjKLwn43Lc5l1s4/vcXUqx3yo/z2yqXE5KKQ0EdgUuBf4D+EneG9mBz0hNAJb2hkZVkrRVtonbxzaxh4sC846dkG+6yiqltDKlNBU4HTgnIvYHiIhrIuJr2fSIiPhDRKyIiGUR8Y+IqIiIn1NoYH6fdQX5bFG3xw9ExIvAHe10hdwjIh6IiFURcUtEDMu2tVnXlaxsTkQcExHHA58HTs+292g2f1MXpCyuL0bECxGxOCKujYjB2bzWOM6JiBez7j5faO/YRMTgbP36rL4vZvUfA9wGjMniuKaNdYdmx6w+IpZn0+OyeadHxLQtlv9EREzNpodHxO+zY/Ng1jXpn1t7LyPi9dnyK7Pn1xfNuysi/qetY57NPywi/pW9x48WdxfK1v1qRNwTEasj4q8RMWIr4dydPa/IjtHrOnpvOuH9EfFSRCyIiE8Xxbbpc5q93vT5iYjPRMRvtjhGl0fEdzu5TUk7GdvEHaNNbOsYtx6XiNgH+AHwuizeFVu+x23UNyfbT7L3fU32WJttZ+JW9vES4Ajgimy9K7LyFBF7dnR8s3nnRsQ/I+KbWd3PR8QJHR2Don2+JCLuAdYBu0fE+yLiySi057Mj4sPZsv2BP/Hy+7gmIsZExMUR8YuiOk+OQrfiFVn9+2wtDpWXyaZ6hJTSA8A8Cv8Mt/SpbF4dha5Gny+sks4CXqTwi/CAlNLXi9Z5I7APcFw7mzwbeD8wmkLXpcs7EeOfgf8Gbsi2d2Abi52bPd4E7A4MAK7YYpk3AHsBRwP/1cE/yv8DBmf1vDGL+X1Z96gTgJeyOM5tY90K4KcUfiWfAKwviuP3wF4RMalo+XcDv8qmvwesBXYBzskeHcq+mPyRwnEcDnwb+GNEDC9arM1jHhFjs3W/BgwDPg38JiLqtojvfcBIoCZbpiNHZs9DsmN0L517b9rzJmAScCzwH62N/lb8Ajg+IobApjMKZ1DoHidJ7bJNbFOvaRM7klJ6EvgIcG8W75Aurt/arg0Avgv8A5hPB/uYUvpCttyF2boXtlF1m8e3aP6hFLoqjwC+DvwkIqITIZ8FnAcMBF4AFgNvAwZl9V8WEQellNay+fs4IKX0UnFFEfEq4Drg4xQ+/7dS+HGlphNxqExMNtWTvEQh2dhSI4UGcNeUUmNK6R9p6xcbX5xSWptSWt/O/J+nlGZk/9z+EzgtssESttN7gG+nlGanlNYAnwPOiM1/Qf5ySml9SulR4FHgFQ10FssZwOdSSqtTSnOAb1H4p71VKaWlKaXfpJTWpZRWA5dQaDxIKa0DbgHOzLY1CdgbmJpt99+AL2XrPgH8rBObfCvwbErp5ymlppTSdcBTwElFy7R3zN8L3JpSujWl1JJSug2YBpxYtO5PU0rPZO/njcDkzhyHLXTmvWnPl7PP0+MUGvMzt7ZCSmkBhTOs78qKjgeWpJQe2obYJe18bBMzvbBNLLmIOJ1CUvxv2eeg3X3sRF2dOb4vpJR+lFJqpnAMRlP4sWNrrkkpzcy+GzSmlP6YUnouFfwd+Ctt/6jSltOBP6aUbkspNQLfBPoCr+94NZWTyaZ6krHAsjbKvwHMAv6adbm4qBN1ze3C/BeAagq/1m2vMVl9xXVXsfk/5OKR8tZR+KV3SyOymLasa2xngoiIfhHxw6wrzCoKSc+Qoi8Pv+LlhOndwO+yBrcui7f4+GztWMIr97uteNs75rsC78q6xKzIuhS9gUJD1qozx6yrMbb13rRny9jHdHKbP6OQTJM9/7yT60mSbeLLelubWFIR8RoKZy3fkVKqz8q2to8d6czx3fQ+ZccGOtcWb3a8IuKEiLgvCl3AV1D4Ybmzn7XNPk+pMIDWXDr5OVB5mGyqR4iI11L4Z/GK6yCyX9k+lVLaHTgZ+GREHN06u50qt/Yr7/ii6QkUfileQqGrTL+iuCopNDadrfclCslTcd1NwKKtrLelJVlMW9Y1v5Prf4pCt6RDU0qDeLlbaWuXl9uAuoiYTKGBbe0uVJ/FWzwSXPGxas+W+91WvO0d87kUflUfUvTon1K6tBPbbU9b79P2vDdbxt7atWezzwuFblbFfge8OgrXXb0N+GUntiVpJ2eb+Aq9rU1cmz231z5s860gImIkhbblgpTSI0WztraPHW1ze49vRzZtNyJqgd9QOCM5KutCfGsnY4QtPk9ZN97xOcWpEjHZVFlFxKCIeBtwPYXhrh9vY5m3RcSe2T+VlRSGhm8dDn4RhesLuuq9EbFvRPQDvgL8Ousa8gzQJyLeGhHVwBeB2qL1FgETo/0R1a4DPhERu0XEAF6+nqVLt83IYrkRuCQiBkbErsAnKVwH2BkDKVyvsSK7nvJLW9TfCNxE4RfyYRQa2tbt3gxcnP1KujeF6za25lbgVRHx7oioyrr37Av8oWiZ9o75L4CTIuK4iKiMiD5RGJRie4Y+r6fwGSn+bGzPe/Of2fHYj8I1Jjdk5dOBEyNiWETsQuE6kk1SShuAX1P44vJASunF7dgnSTs428S29bY2MTvbOJ/Cca2MiPcDexQtsggY19VrDbPux7+m8Nm4sSv7SAefjRyOb2fVUPj81ANNURhk6NgtYhwe7Q/edyPw1og4Ovs8fgpoAP6Vc5zKkcmmyuX3EbGawlmtL1AYUOZ97Sw7CfgbsAa4F/h+SunObN7/AF/Mul9ubdCYYj8HrqHQLaQP8FEojAQInA/8mEJDsZbCQAytbsqel0bEw23Ue3VW990U7s+4Afj3LsRV7N+z7c+m8Ov2r7L6O+M7FK5jWALcB/y5jWV+BRwD3LRFw38hhUECFlLYl+so/DNvV0ppKYUzd58ClgKfBd6WUlpStFh7x3wucAqFQS7qKXwmPsN2/H/KuvhcAtyTfTYOY/vem79T6LZ2O/DNlNJfi/bpUWAOhetObmhj3Z8BB2AXWknts03cul7TJmY+RKEtWwrsx+YJ0R3ATGBhRCxpY932jKNwfePH4+URW9dE4T6j36Hjffwu8M4ojCbb1gBQ23N8OyW7lvSjFJLG5RS6LE8tmv8UheM7O/sMj9li/acpXJLyfxT28yQKA2JtzDNO5Su2fk25pJ1ZRPwvsEtKaZtH4ItecDPpUsm+BDxF4RiuKnc8kqRtl0ebKO1MPLMpaTMRsXdEvDoKDgE+APy23HH1RlnXsk8C15toSlLvY5sobZ/ODPkvaecykEI3ljEUrp/4FnBLRBxB4YbLr5AK9/vqVhHxHuCHbcx6IaW0X3fX00a9/Skcvxco3PZEktT79Io2sVwiYk07s05IKf2jW4NRj2Q3WkmSJElS7uxGK0mSJEnKncmmJEmSJCl3JpuSJEmSpNyZbEqSJEmScmeyKUmSJEnKncmmJEmSJCl3JpuSJEmSpNyZbEqSJEmScmeyKUmSJEnKncmmJEmSJCl3JpuSJEmSpNyZbEqSJEmScmeyKUmSJEnKncmmJEmSJCl3JpuSJEmSpNyZbEqSJEmScmeyKUmSJEnKXVW5AyiVESNGpIkTJ5Y7DElSL/HQQw8tSSnVlTuOcrDNlCR1VWfazR022Zw4cSLTpk0rdxiSpF4iIl4odwzlYpspSeqqzrSbdqOVJEmSJOXOZFOSJEmSlDuTTUmSJElS7kw2JUmSJEm5M9mUJEmSJOXOZFOSJEmSlDuTTUmSJElS7kw2JUmSJEm5M9mUJEmSJOWuqtwB9HbvOvO91C9b0e78umFDuOm6X3RfQJIklcn4Cbsyb+6L27z+uPETmPviCzlGJEkqJ5PN7VS/bAWnfPrb7c6/5Zuf7MZoJEkqn3lzX+Tbf316m9f/5LF75RiNJKnc7EYrSZIkScqdyaYkSZIkKXcmm5IkSZKk3JlsSpIkSZJyZ7IpSZIkScqdyaYkSZIkKXcmm5IkSZKk3JUs2YyIqyNicUTMKCq7ISKmZ485ETE9K58YEeuL5v2gaJ2DI+LxiJgVEZdHRJQqZkmSJElSPqpKWPc1wBXAta0FKaXTW6cj4lvAyqLln0spTW6jniuBDwH3A7cCxwN/yj9cSZIkSVJeSnZmM6V0N7CsrXnZ2cnTgOs6qiMiRgODUkr3pZQShcT17TmHKkmSJEnKWbmu2TwCWJRSeraobLeIeCQi/h4RR2RlY4F5RcvMy8okSZIkST1YKbvRduRMNj+ruQCYkFJaGhEHA7+LiP26WmlEnAecBzBhwoRcApUkSZIkdV23n9mMiCrgVOCG1rKUUkNKaWk2/RDwHPAqYD4wrmj1cVlZm1JKV6WUpqSUptTV1ZUifEmSJElSJ5SjG+0xwFMppU3dYyOiLiIqs+ndgUnA7JTSAmBVRByWXed5NnBLGWKWJEmSJHVBKW99ch1wL7BXRMyLiA9ks87glQMDHQk8lt0K5dfAR1JKrYMLnQ/8GJhF4YynI9FKkiRJUg9Xsms2U0pntlN+bhtlvwF+087y04D9cw1OkiRJklRS5RqNVpIkSZK0AzPZlCRJkiTlzmRTkiRJkpQ7k01JkiRJUu5MNiVJkiRJuTPZlCRJkiTlzmRTkiRJkpQ7k01JknqAiBgfEXdGxBMRMTMiPpaVXxwR8yNievY4sWidz0XErIh4OiKOKyo/PiubFREXlWN/JEmqKncAkiQJgCbgUymlhyNiIPBQRNyWzbsspfTN4oUjYl/gDGA/YAzwt4h4VTb7e8BbgHnAgxExNaX0RLfshSRJGZNNSZJ6gJTSAmBBNr06Ip4ExnawyinA9SmlBuD5iJgFHJLNm5VSmg0QEddny5psSpK6ld1oJUnqYSJiIvAa4P6s6MKIeCwiro6IoVnZWGBu0WrzsrL2yiVJ6lYmm5Ik9SARMQD4DfDxlNIq4EpgD2AyhTOf38ppO+dFxLSImFZfX59HlZIkbcZkU5KkHiIiqikkmr9MKd0MkFJalFJqTim1AD/i5a6y84HxRauPy8raK99MSumqlNKUlNKUurq6/HdGkrTTM9mUJKkHiIgAfgI8mVL6dlH56KLF3gHMyKanAmdERG1E7AZMAh4AHgQmRcRuEVFDYRChqd2xD5IkFXOAIEmSeobDgbOAxyNielb2eeDMiJgMJGAO8GGAlNLMiLiRwsA/TcAFKaVmgIi4EPgLUAlcnVKa2X27IUlSgcmmJEk9QErpn0C0MevWDta5BLikjfJbO1pPkqTuYDdaSZIkSVLuTDYlSZIkSbkz2ZQkSZIk5c5kU5IkSZKUO5NNSZIkSVLuTDYlSZIkSbkrWbIZEVdHxOKImFFUdnFEzI+I6dnjxKJ5n4uIWRHxdEQcV1R+fFY2KyIuKlW8kiRJkqT8lPLM5jXA8W2UX5ZSmpw9bgWIiH2BM4D9snW+HxGVEVEJfA84AdiXwo2t9y1hzJIkSZKkHFSVquKU0t0RMbGTi58CXJ9SagCej4hZwCHZvFkppdkAEXF9tuwTeccrSZIkScpPOa7ZvDAiHsu62Q7NysYCc4uWmZeVtVcuSZIkSerBujvZvBLYA5gMLAC+lWflEXFeREyLiGn19fV5Vi1JkiRJ6oJuTTZTSotSSs0ppRbgR7zcVXY+ML5o0XFZWXvl7dV/VUppSkppSl1dXb7BS5IkSZI6rVuTzYgYXfTyHUDrSLVTgTMiojYidgMmAQ8ADwKTImK3iKihMIjQ1O6MWZIkSZLUdSUbICgirgOOAkZExDzgS8BRETEZSMAc4MMAKaWZEXEjhYF/moALUkrNWT0XAn8BKoGrU0ozSxWzJEmSJCkfpRyN9sw2in/SwfKXAJe0UX4rcGuOoUmSJEmSSqwco9FKkiRJknZwJpuSJEmSpNyZbEqSJEmScmeyKUmSJEnKncmmJEmSJCl3JpuSJEmSpNyZbEqSJEmScmeyKUmSJEnKncmmJEmSJCl3JpuSJEmSpNyZbEqSJEmScmeyKUmSJEnKncmmJEmSJCl3VeUOoKd715nvpX7ZinbnP/nU05zSfeFIkiRJUq9gsrkV9ctWcMqnv93u/MfOPa4bo5EkSZKk3sFutJIkSZKk3JlsSpLUA0TE+Ii4MyKeiIiZEfGxrHxYRNwWEc9mz0Oz8oiIyyNiVkQ8FhEHFdV1Trb8sxFxTrn2SZK0czPZlCSpZ2gCPpVS2hc4DLggIvYFLgJuTylNAm7PXgOcAEzKHucBV0IhOQW+BBwKHAJ8qTVBlSSpO5lsSpLUA6SUFqSUHs6mVwNPAmOBU4CfZYv9DHh7Nn0KcG0quA8YEhGjgeOA21JKy1JKy4HbgOO7b08kSSow2ZQkqYeJiInAa4D7gVEppQXZrIXAqGx6LDC3aLV5WVl75ZIkdSuTTUmSepCIGAD8Bvh4SmlV8byUUgJSTts5LyKmRcS0+vr6PKqUJGkzJpuSJPUQEVFNIdH8ZUrp5qx4UdY9lux5cVY+HxhftPq4rKy98s2klK5KKU1JKU2pq6vLd0ckSaKEyWZEXB0RiyNiRlHZNyLiqWzUvN9GxJCsfGJErI+I6dnjB0XrHBwRj2ej7V0eEVGqmCVJKpesffsJ8GRKqfgGz1OB1hFlzwFuKSo/OxuV9jBgZdbd9i/AsRExNBsY6NisTJKkblXKM5vX8MoBCW4D9k8pvRp4Bvhc0bznUkqTs8dHisqvBD7EyyPuOciBJGlHdDhwFvDmoh9fTwQuBd4SEc8Cx2SvAW4FZgOzgB8B5wOklJYBXwUezB5fycokSepWVaWqOKV0dzbAQXHZX4te3ge8s6M6su5Cg7JR9oiIaymMwvenXIOVJKnMUkr/BNrrvXN0G8sn4IJ26roauDq/6CRJ6rpyXrP5fjZPGneLiEci4u8RcURWNpbCKHqtHFFPkiRJknqBkp3Z7EhEfIHCzat/mRUtACaklJZGxMHA7yJiv22o9zwKN7ZmwoQJeYUrSZIkSeqibj+zGRHnAm8D3pN1ASKl1JBSWppNPwQ8B7yKwuh544pWb3NEvVaOrCdJkiRJPUO3JpsRcTzwWeDklNK6ovK6iKjMpnenMBDQ7GxUvVURcVg2St/ZvDwKnyRJkiSphypZN9qIuA44ChgREfOAL1EYfbYWuC27g8l92cizRwJfiYhGoAX4SNHIeedTGNm2L4VrPB0cSJIkSZJ6uFKORntmG8U/aWfZ31C4iXVb86YB++cYmiRJkiSpxMo5Gq0kSZIkaQdlsilJkiRJyp3JpiRJkiQpdyabkiRJkqTcmWxKkiRJknJnsilJkiRJyp3JpiRJkiQpdyabkiRJkqTcmWxKkiRJknJnsilJkiRJyp3JpiRJkiQpdyabkiRJkqTcmWxKkiRJknJnsilJkiRJyp3JpiRJkiQpdyabkiRJkqTcmWxKkiRJknLXqWQzIg7vTJkkSTs720xJkgo6e2bz/zpZJknSzs42U5IkoKqjmRHxOuD1QF1EfLJo1iCgspSBSZLUm9hmSpK0uQ6TTaAGGJAtN7CofBXwzlIFJUlSL2SbKUlSkQ6TzZTS34G/R8Q1KaUXuikmSZJ6HdtMSZI219lrNmsj4qqI+GtE3NH62NpKEXF1RCyOiBlFZcMi4raIeDZ7HpqVR0RcHhGzIuKxiDioaJ1zsuWfjYhzuryXkiR1n21qMyVJ2tFsrRttq5uAHwA/Bpq7UP81wBXAtUVlFwG3p5QujYiLstf/AZwATMoehwJXAodGxDDgS8AUIAEPRcTUlNLyLsQhSVJ32dY2U5KkHUpnk82mlNKVXa08pXR3REzcovgU4Khs+mfAXRSSzVOAa1NKCbgvIoZExOhs2dtSSssAIuI24Hjguq7GI0lSN9imNlOSpB1NZ7vR/j4izo+I0Vk32GHZGcdtMSqltCCbXgiMyqbHAnOLlpuXlbVXLklST5RnmylJUq/V2WTzHOAzwL+Ah7LHtO3deHYWM21vPa0i4ryImBYR0+rr6/OqVpKkrtimNrOdcQ4ujoj5ETE9e5xYNO9z2TgHT0fEcUXlx2dls7LLVSRJKotOdaNNKf1/9u47PI7q7Pv4915Jq94ty7IsW+4FAwaMgVBCCwYCMSGEkgQIIZACaSR5Q5InTxIITzqkE3oNJRAIpvcObjR3495t2ZKt3ve8f8zIrI2a7dWOyu9zXXvt7Jmy98yudPaec+bMyBi+51YzK3LObfa7yZb55RuBkqjlhvllG/mo221b+SsdxHkzcDPA1KlTY5bE7q2Ic1TUNtHQ3EpzcnZQYYiISAD2o868k4+PcwBwg3PuD9EFZjYJOB84ABgKvGBm4/zZfwc+hdcTaK4/zsHifYxJRERkn3Ur2TSzi9ord87tWSF2x0y8s76/8Z8fiyq/0swewBsgqNJPSJ8F/q9t1FrgFODH+/C+cbG9ppHnF2+lrLrRK5h4AVf8611+OH08pYPSgw1ORER63L7WmR2Mc9CRGcADzrlGYLWZrQCm+fNWOOdW+bE84C+rZFNEROKuuwMEHR41nQKcBLzLx8++7sbM7sdrlRxkZhvwRpX9DfBvM7sUWAuc6y/+FHA6sAKoAy4BcM5VmNm1wFx/uWvaBgvqbVZvr+WJ+ZtITkzgxPGDyU5L4t9338pTkZN4+t1VDFnxGOGG3QfRLcjL4aH77w0oYhER6QH7VGd24ko/gZ0HfN8fjb0YmBW1TPR4BnuOc3DEPr6viIjIfuluN9pvRb82sxzggW6sd0EHs05qZ1kHXNHBdm4Hbu8y0ADtrGvimUVbyE9P5rOHFJMaTgCg/v0nufTyr/Ofdzaw46Av8vnDhpGbHt613mN/uCqokEVEpAfsa53ZgRuBa/HGN7gW+CPwlf2JLyquy4HLAYYPHx6LTYqIiOymuwME7akWiOV1nH1aS2uEJxZsJgR8+qCiXYlmm9y0MJ87dBgATy/cQmsksMtJRUQk/va5znTObXXOtTrnIsAtfNRVtrNxDtorb2/bNzvnpjrnphYUFOxLeCIiIp3q7jWbj/PRqLEJwETg3z0VVF8zf2Ml5TVNnHlwEdmpSe0uk5se5qSJg3li/mbmrK7gqNH5cY5SRETiIZZ1ZtuAev7LzwJtI9XOBO4zs+vxBggaC8wBDBhrZiPxkszzgS/sy3uLiIjsr+5esxk9Cl4LsNY5t6EH4ul7klKYu7qC4XlpjBqU0emiowsymFiUydw1FYwuSGdwVkqcghQRkTjapzqzg3EOjjezKXjJ6xrgawDOuUVm9m+8gX9agCucc63+dq4EnsVLdG93zi2KzW6JiIjsne5es/mqmRXy0aAHy3supL4l+YCTaWiJcHQ3Wyo/Oa6ANdvreH3Fds4+pLjrFUREpE/Z1zqzg3EObutk+euA69opfwpv0D0REZFAdeuaTTM7F697zufxRo+dbWbn9GRgfUFjSyvhCScwdnBGt1spkxMTOGJkHht21LO2oq6HIxQRkXhTnSkiIuLpbjfanwKHO+fKAMysAHgBeLinAusLlm6uxpKSOXREbtcLR5lcnM1763fyxortZGA9FJ2IiAREdaaIiAjdH4021FZp+sr3Yt1+yTnHgo2VtJavY8heXnuZEDI+MTqf8pom6rI1qK+ISD+jOlNERITut2w+Y2bPAvf7r89jgF8PsmlnA+W1TTR9+Drt3Da0S2MGZ5CTmkRl4SE45zBTC6eISD+hOlNERIQukk0zGwMUOud+aGZnA8f4s94G/tXTwfVmCzZWEk4MUbXmnX1aP2TGYSNyebG+mbdWlnP0mEExjlBEROJJdaaIiMjuuurW8yegCsA594hz7irn3FXAo/68Aam5NcLKbTWML8yElqZ93s6EokwSmmv5xysrYhidiIgE5E+ozhQREdmlq2Sz0Dm3YM9Cv6y0RyLqA1Zvr6Ul4hhX2Pl9NbuSGAqRuW0Bb64o58Ot1TGKTkREAqI6U0REJEpXyWZOJ/NSYxhHn7J8aw1p4QSG5uz/IcgsX0I4IcS/Zq2NQWQiIhKgnE7mDdg6U0REBq6uks15ZnbZnoVm9lVg3y5W7OOaWiKsLq9l7OAMQjEY1CehpYFPH1TEf97dSG1jSwwiFBGRgKjOFBERidLVaLTfBR41sy/yUUU5FQgDn+3BuHqt1dtraY04xg7OjNk2v3TkcB59byOPvb+JLxwxPGbbFRGRuPouqjN345xjZ10z4cQQaeEEjbwuIjLAdJpsOue2Ap8wsxOAyX7xk865l3o8sl5qxbYa0sMJDM3Zu3trdubQ4blMLMrinllruWBaiSpjEZE+SHXmR1ojjjmrK1i8uYoav9dOSlKIw4bnMqUkh8QE3XZURGQg6NZ9Np1zLwMv93AsvV5rxLGuvI5xhRkxTQjNjC8dOZyfPrqQd9ft5LARuTHbtoiIxNdArzNrG1t4csFmNlc2UJqfxrTSPFqdY215LW+uLGfhpipmTBlKblo46FBFRKSH6dTiXti0s56m1gilg9Jjvu2zphSTkZzIvRooSERE+igLp/KfdzewrbqR0yYPYcaUYg4cls2UkhxmTCnmrClDaWqJ8NA8bxkREenflGzuhTXltSSYUZKbFvNtpycncvahxTw5fzPlNaqARUSkb3HOkX/qt9hZ18xnDh7KuMKPj20wIj+dcw4bRkLIePS9jVTVNwcQqYiIxIuSzb2wenstxbmphBN75rB96cgRNLVGeOidDT2yfRERkZ5y99trSZ94HJ8YnU9JXscnZfPSw5x9aDGtzvH4/E00t0biGKWIiMSTks1uqqxvZkddM6X5sW/VbDOuMJNppXk8MGcdzrkeex8REZFYCyeGqF36RrfGHchNC3Pa5CGU1zTx0tKyOEQnIiJBULLZTWu21wL0yPWa0c47vIQ15XXMWlXRo+8jIiISSxdMG872x37T7QH0SvPTmTYyj6VbqllRVtPD0YmISBCUbHbTuoo6slISe3z0vNMPLCIzJZEH567r0fcREREJ2uGleQzOTOalpWXUNbUEHY6IiMRY3JNNMxtvZu9HParM7Ltm9gsz2xhVfnrUOj82sxVmtszMpsc75kjEsWFnPcM7uQYlVlLDCZw1pZinFm5hZ11Tj7+fiIhIUBJCximTCmlqifD68u1BhyMiIjEW92TTObfMOTfFOTcFOAyoAx71Z9/QNs859xSAmU0CzgcOAE4F/mFmCfGMuay6kaaWSKcDHsTS+dNKaGqJ8N/3Nsbl/URERIKSn5HMoSNyWLqlmuTiCUGHIyIiMRR0N9qTgJXOuc5uLjkDeMA51+icWw2sAKbFJTrfuh11AAzLTY3L+x0wNJuDhmXzwNz1GihIRET6vakj8khPTiD3pK8RiajeExHpL4JONs8H7o96faWZzTez282sbTi7YmB91DIb/LK4WV9RR0FGMmnhxLi953mHl7B0SzUfbKiM23uKiIgEIZwY4pgxg0guGstD76zvegUREekTAks2zSwMfAZ4yC+6ERgNTAE2A3/ch21ebmbzzGzetm3bYhJnxBLZvLOBkrz4tGq2+czBQ0lNSuCBORooSERE+r/xhZk0bFjE755ZRmV9c9DhiIhIDATZsnka8K5zbiuAc26rc67VORcBbuGjrrIbgZKo9Yb5ZR/jnLvZOTfVOTe1oKAgJkE2ZhTS6hwlufG5XrNNZkoSZxxUxMwPNlHTqBH6RESkfzMzKp6/iYq6Jv78wvKgwxERkRgIMtm8gKgutGZWFDXvs8BCf3omcL6ZJZvZSGAsMCdeQTZkFGMGQ3Pi27IJcP604dQ1tfLEB5vi/t4iIiLx1ly2igumDeeut9fo3psiIv1AIMmmmaUDnwIeiSr+nZktMLP5wAnA9wCcc4uAfwOLgWeAK5xzrfGKtSFjKIMzkwknxv9QHTo8h7GDM3hgrq5fERGRgeH7nxpHalICv31madChiIjIfgok2XTO1Trn8p1zlVFlFzrnDnTOHeSc+4xzbnPUvOucc6Odc+Odc0/HK876plYa0wYzLCe+XWjbmBnnTxvO++t3snRLVSAxiIiIxFN+RjLfOH40zy/eypzVFUGHIyIi+yHo0Wh7tffW7YBQAsVxuuVJez57SDHhhBAPzFHrpoiIDAxfOXokQ7JSuO6pJboFmIhIH6ZksxOzVleAizA0JyWwGPLSw0yfPIRH39tIQ3Pceg+LiIgEJjWcwPdPGccH63fyxPzNXa8gIiK9kpLNTsxeVU64vpzkxIRA4zj/8BIq65t5dtGWQOMQERGJl7MPHcaEIZn87tmlNLboZKuISF+kZLMDDc2tvLd+Jyk17d5lJa6OGpXP8Lw07tc9N0VEZIBICBk/OX0i6yvquefttUGHIyIi+0DJZgfqm1q54PAS0irXBB0KoZBx3uElzFpVwerttUGHIyIiPcDMbjezMjNbGFWWZ2bPm9ly/znXLzcz+4uZrTCz+WZ2aNQ6F/vLLzezi4PYl1g5blwBx44dxF9fWkFlXXPQ4YiIyF5SstmB3PQwv5wxmZSa3nGtyDmHDSMhZDyo26CIiPRXdwKn7lF2NfCic24s8KL/GuA0vPtOjwUuB24ELzkFfg4cAUwDft6WoPZVPz5tIlUNzfz9lRVBhyIiIntJyWYfUZiVwgnjB/PwO+t17YqISD/knHsN2PNeHzOAu/zpu4Czosrvdp5ZQI6ZFQHTgeedcxXOuR3A83w8ge1TJg3N4uxDhnHnm2tYX1EXdDgiIrIXlGz2IRd/YgTba5p44oPe0doqIiI9rjDqvtNbgEJ/uhiI7uqywS/rqLxP+8H0cZjBH55bFnQoIiKyF5Rs9iHHjBnE2MEZ3PHWat13TERkgHHeP/6Y/fM3s8vNbJ6Zzdu2bVusNtsjirJTufSYkTz2/ibmb9gZdDgiItJNSjb7EDPjy0eXsnBjFfPW7gg6HBER6Xlb/e6x+M9lfvlGoCRquWF+WUflH+Ocu9k5N9U5N7WgoCDmgcfa148fTV56mP97aolOuIqI9BGJQQcw0C1evIjjp5/R6TIFeTk8dP+9AJx9yDB+98wy7nhzNYeX5sUjRBERCc5M4GLgN/7zY1HlV5rZA3iDAVU65zab2bPA/0UNCnQK8OM4x9wjslKS+M5JY/n5zEW8sKSMT00q7HolEREJlJLNgLVEYMYPru90mcf+cNWu6dRwAudPK+HW11ezcWc9xTmpPR2iiIjEgZndDxwPDDKzDXijyv4G+LeZXQqsBc71F38KOB1YAdQBlwA45yrM7Fpgrr/cNc65PQcd6r0shJl1PD+UQNElf+XLf9nC5tu+iWtp2m32sJLhrF+ne3KKiPQWSjb7oIuOKuXW11dz99tr+PFpE4MOR0REYsA5d0EHs05qZ1kHXNHBdm4Hbo9haPHjIlzfxSBA6yvqeOS9jZzzt1c4alT+bvOuOmV8T0YnIiJ7Sdds9kHFOalMP6CQB+asp66pJehwRERE4qYkL41xhRm8s3YHO+uaul5BREQCo2Szj7rk6JFU1jfzyLvtjvsgIiLSbx03toAEM175cJsGCxIR6cWUbPZRU0fkcnBJDje/toqW1kjQ4YiIiMRNenIiR47KY215HSu31QYdjoiIdEDJZh9lZlxx/GjWVdTx5ILNXa8gIiLSjxw8LIdBGWFe/XAbjS2tQYcjIiLtULLZh508sZBxhRn84+WVRCLqRiQiIgNHKGScOGEwtY0tvL58e9DhiIhIO5Rs9mGhkPHN48ewbGs1zy/ZGnQ4IiIicVWUncphI3JZtKmKVdtqgg5HRET2oFuf9AGLFy/i+OlntDvPYSRP/gI3PP8hn5pYSCjUyf3JRERE+pkjRuWxpryWF5eWEUrNCjocERGJomSzD2iJwIwfXN/h/H/ddiNLk07m6YVb+PRBRXGMTEREJFiJoRCnTBrCA3PXkXfKN3DOYaYTryIivYG60fYD6TtWMGZwBje88CGtunZTREQGmILMZI4clU/6hGOZ+cGmoMMRERGfks1+wHB87+RxrCir4ZF3NwQdjoiISNwdNjyXhg2L+ckjC1hRVh10OCIiQoDJppmtMbMFZva+mc3zy/LM7HkzW+4/5/rlZmZ/MbMVZjbfzA4NKu7e6vQDh3BwSQ5/fO5D6ps0BLyIiAwsoZCxfeZvSQ0n8LV73qG6oTnokEREBrygWzZPcM5Ncc5N9V9fDbzonBsLvOi/BjgNGOs/LgdujHukvZyZ8dPTJ7KlqoHb3lgVdDgiIiJx11pdzl8vOJQ15XX88KH5OKdLS0REghR0srmnGcBd/vRdwFlR5Xc7zywgx8w0Es4epo3M45RJhdz4ykq2VjUEHY6IiEjcHTU6nx+fNoFnFm3hn6/q5KuISJCCTDYd8JyZvWNml/tlhc65zf70FqDQny4G1ketu8Evkz389NMTaW51/PqpJUGHIiIiEohLjxnJGQcV8ftnl/LSUt2HWkQkKEEmm8c45w7F6yJ7hZkdFz3TeX1f9qr/i5ldbmbzzGzetm3bYhhq3zEiP52vfXIU/31/E7NXlQcdjoiISNyZGb875yAOGJrNN//1Lu+s3RF0SCIiA1JgyaZzbqP/XAY8CkwDtrZ1j/Wfy/zFNwIlUasP88v23ObNzrmpzrmpBQUFPRl+r/bN48dQnJPKzx5bSFNLJOhwRERE4i4tnMgdlxxOUXYqX7lzLh9u1Qi1IiLxFkiyaWbpZpbZNg2cAiwEZgIX+4tdDDzmT88ELvJHpT0SqIzqbit7SA0ncO1ZB/Dh1hr+8cqKoMMREREJxKCMZO7+yjSSE0NcdNscNu6sDzokEZEBJaiWzULgDTP7AJgDPOmcewb4DfApM1sOnOy/BngKWAWsAG4Bvhn/kPuWEycUMmPKUP7+8gqWbdHZXBERGZhK8tK46yvTqG1q4cJbZ7OlUgPoiYjESyDJpnNulXPuYP9xgHPuOr+83Dl3knNurHPuZOdchV/unHNXOOdGO+cOdM7NCyLuvuZ/z5hEZkoS33/ofXWnFRGRAWtiURZ3fPlwyqob+fxNb7GuvC7okEREBoTEoAOQ/bd48SKOn35Gu/MSsktZOOo0bnjhQ3506oQ4RyYiItI7TC3N419fPYKL75jD5296i3999QjGDM4MOiwRkX5NyWY/0BKBGT+4vsP5tz34GP98FY4bW8BRo/PjGJmIiEjvcXBJDg9efhRfvHU25940i7sumcaBw7KDDktEpN8K8tYnEid5G99kZH46337gPcqqda2KiIgMXOOHZPLQ148iNSmBc296myfna7xBEZGeomRzAAhFWvjHlw6luqGZb933Hi2tun5TRET6IQthZl0+RhVkMOvaz7Fj9QKuuO9dco79IiXDS4OOXkSk31E32gFiwpAs/u+zB3LVvz/gF48v4toZkzGzoMMSERGJHRfh+ueWdXvxlkiEl5duY/HRX6BuUClVDc1kpST1YIAiIgOLWjYHkLMPHcbXPjmKe2et49bXVwcdjoiISKASQyFOnjiYT44rIHXsEZz+59d5Z21F0GGJiPQbSjYHmB9Nn8CnDyziuqeW8NQCXaciIiIDm5kxpSSHLf/6EWZw7k2z+NMLH+qSExGRGFCyOcCEQsYfzz2Yw0bk8r0H3+fddTuCDklERCRwTZuW8tS3j+UzBw/lTy8s59yb3mbZluqgwxIR6dOUbA5AKUkJ3HLRVIZkp/CVO+eycGNl0CGJiIgELjMliRvOm8Kfz5/C6u21fPovr/O7Z5bS0NwadGgiIn2Sks0BKi89zD1fOYL0cCJfvHU2CzYo4RQREQGYMaWYF79/PGcdUsw/XlnJKTe8xotLtuKcCzo0EZE+RaPRDgCLFy/i+OlntD8znEn9+LP4wq2zuOfSI5hSkhPX2ERERHqjvPQwf/j8wZx9aDH/89+FXHrXPI4alc9PPz2RycXZQYcnItInKNkcAFoiMOMH13c4/z9//l9Cx32NC2+dze2XHM7hpXlxjE5ERKT3+sToQTz73eO4b/Y6/vzics746xt89pBivnPSWEoHpQcdnohIr6ZutEJicw0Pfu1ICjKT+eIts/nvexuDDklERKTXSEoIcfEnSnnlh8fzjeNH89SCzZz4x1e46sH3WbWtJujwRER6LbVsCgBF2an85xuf4Ov3vsN3/crzuyePIxSyoEMTERnwzGwNUA20Ai3Oualmlgc8CJQCa4BznXM7zMyAPwOnA3XAl51z7wYRd59iIbxD17VQeg5Z087m4abT+c8766hb9iYp695m/Xuv9XCQIiJ9i5JN2SU3Pcw9lx7B//x3AX95aQUrt9Xyu3MOIj1ZXxMRkV7gBOfc9qjXVwMvOud+Y2ZX+69/BJwGjPUfRwA3+s/SGRfh+ueW7dUqdU0tvLtuJwuSjqdp4nGc/Y83ufSYUUw/oJDEBHUeExHRf0LZTTgxxG8/dxA/Pm0CTy3czJl/fUO3RhER6Z1mAHf503cBZ0WV3+08s4AcMysKIL5+Ly2cyDFjBnHpMSOpeP6flNc2ccV973Lc717mpldXUlHbFHSIIiKBUpOVdDha7eCMoawdcRJn/PlVSna+z2s3/azbXYxERCSmHPCcmTngJufczUChc26zP38LUOhPFwPro9bd4JdtjirDzC4HLgcYPnx4D4be/4UTQ1S/+wQvzZ3JS0vLuP2N1fz66aX88bkPOeWAQi6YNpyjRuXr0hQRGXCUbEqno9XWN7Xy/JKtrA4dxiV3zuW6zx5IcU5qnCMUERnwjnHObTSzwcDzZrY0eqZzzvmJaLf5CevNAFOnTtUNJPeXhXbrOps0aAQZB5/CYzUn8sT8zTTv2EzN/OeoXfgSrTXlH1t9WMlw1q9bG8+IRUR6nJJN6VRqOIEzDyri3rvuYHbS8Zxy/av8cPp4LjyqlASdoRURiQvn3Eb/uczMHgWmAVvNrMg5t9nvJlvmL74RKIlafZhfJj2pg2s+W1ojrNhWw6KNqWzIvZjcT15MSW4qE4ZkMXpwOsmJCQBcdcr4eEcsItLjdM2mdMnMyNq+kOe+dxyHlebxi8cXc84/32LxpqqgQxMR6ffMLN3MMtumgVOAhcBM4GJ/sYuBx/zpmcBF5jkSqIzqbitxlpgQYsKQLD532DAuOmoE00bmUdXQwvNLtnLL66t5esFm7/YpIZ3/F5H+R//ZpNtK8tK465LD+e/7G7n2iSV8+q+vc97UEq46ZRyDM1OCDk9EpL8qBB71r5lPBO5zzj1jZnOBf5vZpcBa4Fx/+afwbnuyAu/WJ5fEP2RpT25amKNG5XPkyDy2VDWwdEs1H26t5sOyGoZdcRf/898FnH5gEdNK8zSarYj0C0o2Za+YGZ89ZBgnji/kLy8t56631vD4B5v45gljuOToUtLC+kqJiMSSc24VcHA75eXASe2UO+CKOIQm+8jMKMpOpSg7lePGFrC2opYH7n+Vh9/J5d5Z68hPDzN98hBOn1zEkaOUeIpI3xX3zMDMSoC78c7UOuBm59yfzewXwGXANn/RnzjnnvLX+TFwKd7NrL/tnHs23nHL7rLTkvjZGZP40pEj+L+nlvD7Z5fxxyfeI7vsfTK3LSLkWnZbviAvh4fuvzegaEVERHqnhJAxalAG2x//PWsf/j9eWbaNpxZs5r/vbeS+2evITUvilElDOO3AIRw1On/XNZ4iIn1BEM1QLcD3nXPv+tegvGNmz/vzbnDO/SF6YTObBJwPHAAMBV4ws3HOuda4Ri3tGjkonVsumsqRZ19K+MgvsS7pEzSUHsvU0lwOLM4myT8b+9gfrgo4UhERkd4tLZzI6QcWcfqBRTQ0t/LKsm08vXAzTy7YzIPz1pMeTuC4cQWcNLGQE8YXkJ+RHHTIIiKdinuy6Q9SsNmfrjazJXj3/+rIDOAB51wjsNrMVuCNwvd2jwcru3R0L842q5cu4+pv/IiNO+uZvaqc15dv5521Ozh0eC6Ti7PiGKmIiEjfl5KUwKmTh3Dq5CE0NLfyxvLtvLh0Ky8uKePphVswg0OH53LihMGcPLGQcYUZuhe2iPQ6gV5gZ2alwCHAbOBo4EozuwiYh9f6uQMvEZ0VtVrbzakljjq7FyfA/C9PB6A4J5WzDx3Gxh31zF5dzhsrtjN7dTmpxZ9gfUUdJXlp8QpZRESk77BQN5NFI1w4itQxR/DWmGm8s3YMv392Ga5mO5ecMpVPjivgiFF5GkNBRHqFwP4TmVkG8B/gu865KjO7EbgW7zrOa4E/Al/Zy21eDlwOMHz48NgGLHulODeVs3OHUVbVwLvrd7Ks5UCO/8MrnDZ5CJcdO4qDS3KCDlFERKT36OA+nV2paWhhdXktTzwxh/vnDObOt9aQlGAcNiKXY8cWcOzYQRwwNFv3xhaRQASSbJpZEl6i+S/n3CMAzrmtUfNvAZ7wX3b75tTOuZuBmwGmTp3qYh+57K3BWSmcesAQal/4O6d/8xfcP3sdT8zfzMHDsvnSkSM48+ChpCRpsAMREZF9kZGSyIHF2dzxn2tY19TC3DUVvL58O68v387vn13G759dRk5aEkePHsQnxuRzxMh8Rhekq8ttL1EyfAQb1q/b5/WHlQxn/bq1MYxIJLaCGI3WgNuAJc6566PKi6JuOv1ZvBtWg3dz6vvM7Hq8AYLGAnPiGLLEQGJzLT85fSLfOnEMj7y7kXtmreWHD8/nV08u4dypw/jiESMoHZQedJgiIiJ9k4VI3aPrbCgth5QRB1Mz8hAe23oITy7IB6C1dicNGxbRuG4BDesXUZgSYf26NQEELRvWr9unFu02V50yPobRiMReEC2bRwMXAgvM7H2/7CfABWY2Ba8b7RrgawDOuUVm9m9gMd5ItldoJNq+KzMliYs/UcpFR41g1qoK7p21ljveXMMtr6/mE6Pz+fzUYZx6QBGpYbV2ioiIdFsX3XCdc+ysb2bjzno27shkY/4gqscfDUBrQw1fuXMuh5TkMGV4DgcNyyE7NSlekQ9IDc2tbKtuJFw0jlXba6hvaqXOf9Q3tVLf3Epza8R/OJpbI7S0OiLuo457ZlDyvYc48OfPkpacQHpyIunhRNKTE8hITiQnLcygjGQGZbQ9JzM4K5mhOalkJOuaXomPIEajfQNor+/GU52scx1wXY8FJT2us9FshySm4UoOYcOOI/negx/wv8mLOOPgIs45rIRDh+eoq4+IiMh+MjNy08LkpoWZPDQbgKr6ZjbtrOfh+59l3fAhvLS0bNfyowvSmVKSy5SSbKaU5DJuSIbu8dmOppYIVQ3NVNZ7j6q254aWXdOVdc2U1zZRXttIeU0T5TWN1DZ57SZFF13P4x9s3rW9pAQjNSmB1HACSQkhUpOSSEwwkhJCJCWE2PPS25cffoSaUAhLSiEUTsXCqYTaHqlZJKTnYAkfP3HQWl9NqGEnn/rEVIpzUijOTaU4J42hOSkMy01jUEZYv78kJnRaQ+Kiq9FsH/vDVbz0j6uZvbqCh95Zz3/f28T9c9YzalA6Zx48lDMPLmLM4Mw4RiwiItK/ZaUmkZWaRMWzf+fFZ/6KhdNILhpLeOh4FhSN58Oh4/hPei4ArrWF5ooNNJetoalsNU3bVtNUtopI7c4+fd2gc47aptbdE8X6qOTRTxo/SiKjE8sW6ps772yXnBgiOzWJvHSvdXH48DTy05PJzwhTkJHMly/4HN/6za2khT9KMPfGoy/f1mWLdmNLZFeLaU1jC9WNzVTXZzPr1SVs2FHH7FXlVDe27LZeWjiBEfnpjMhLY8SgNEp3TadTlJVCSANOSTcp2ZReYfHiRZx42pm7XheEkqjNGc2m6nH8eVsNf35xOalNO7jyzCM586ChDM/XLVRERERiooMuuM45qhta2FLVwPaaRrYNzmL7sDHURCUmqUkJ7Fj5Pj9+ZD6l+emUDkpn1KB0SvLS4jYA4K5BdixEKCWDUFoWCSlZ3nNqJqHdpjP96SxvOjUTC3UeZ6ShhkhDDa0NNbjG2t2eM5MT+dX//pjs1CSyUrzkPTs10UvkU5K6PAbnrZzLkOyUWB6O3ZgZKUkJXhx7DI3x+Lev4ZmHfwlAVUMzG3fUs2lnPesr6lhbUcfa8jqWl1Xz0tIymloju9YLJ4YYnpfmJZ/56ZT6yeiYwRkUZaeoRVR2o2RTeoXOWj5rGltYUVbDrHe37BpZb1JRFidPKuRTEwuZXJylf2wifdjnL/gS2yp2dji/IC+Hh+6/N34BiQjgJSptrZ/jCj/qXVTf3Ep5TSPbqhvZXtPEjtUJPLtoKxW1Tbutn5uWRGFWCkOyUxiSlcLgrBRyUpPISE4kIyWR9OREMpITSUkKYXtcYeVwNDR/dB1jXVMLdU2tVDe0sKO2iYraJnbUec+R6T9hfOFwGloidCQhZKQkhUhJSiDVT75SkkK8+dAtfPqiK0hODJGcFCI5MYGUxBDJSQkkJ4YIJ4YIdfIb46rpE7n4mZv38QgHrLv3drUQCZn5JOUOJTFnCIm5Q9mZU8TivKEkZg8hFP4oWY401nkt4OUbaC5fv+u5ZedmiOzeCtyXW8Sl+5RsSq+XkZzIlJIcnv75rxh38BHU5o5mZc1IFm/ayV9eXE5CUw2pVWspimzn0Rv/Txe9i/Qx2yp2dtnNXkR6j9SkBIblpjEs1+tl9PR3/x9bnKOyrpnV5bWs2V7Luoo6tlQ1UFbVwJaqBhZtqmJ7TSMuBjemCyeGyE8Pk5MWJi89iaayVYw7+ADvWseoRLJtOjWcQGLI2k2snnz9Hg772f/sezD7eH/UNoGOJhuD2P/47FJqm1rZ6Sf+FbVNVBTls6N20m4t4CGDnNQwuelJ5KcnMygzzC3f+gyRiFOX3H5Ov8qlz2iJwOe+c82u13VNLawpr2P1tgzWpmayvNUx5ZfPcejwXI4ZO4ijxwzi4GHZJO7l9Q8iEjtdtVoCLFm6jBnxCUdEelB2WhJT0nKYUpLT7vyW1gg1jS27HrWNLVQ3tHDhJV+lfPv2jy3vWhpxTQ1EmutxzY245gYijXW45gaW77HsCVdeGvsdki6ZmddSnZy46+RDm8aWVnbUNlPhJ6I7apsor2li1bZaHFB8+c0c8PNnGT8kk4lFWUws8p7HD8kkK0WjIfcXSjalz0oLJzKpKItJRVm0tEZ4+JYb+OxXv8cbK7Zxwwsfcv3zH5KZksjUEblMLc3j8NI8DhqWHbdrSESk61ZLgPlfnh6naESkR3S3O2Yn+mzroHQoOTGBIdkJH7smtbk1QkVtE3/75Q/4wa9uYOmWKp5asJn756zbtUxJXioHFedw0LBsDhqWw+TiLDKVgPZJSjalX0hMCJFas5GrT5sATKCitom3Vm7nzRXbmbtmBy8v8yqxcEKIg4Zl+8lnLocMzyUvPRxs8CIiIn1ZX+5KKnGXlBCiMCuF2oUv8ssZk3eVJ2Tmk1QwkvDgkdQWjmL1kLE8mTMEAOcitFRspHHzcpq2eI+CpGbWr14R1G5INynZlH4pLz3MGQcN5YyDhgJQUdvEud/6GWUukwWVRcxbXcA//dHnEhurCNdtI49qfvf/vsbk4mx13xARERHpSd04SVHf1MrW6gbKqhrZWpDJ1qGl1Dae6K3e2sKMv73BYSPymFqay2EjcinM6rmRfWXfKNmUfmPx4kUcP/2MDucvWbqMq299EvCuG9lS1cDWqka2VmWwtSqPDQ0tfOGW2QCMKkjnwOJsJgzJYkJRJhOHZFGYlaxRb0UC0NXftkarFRHpn1LDCd4tdfI/um9LTWMLZVUN3HvTn0kZ83Xum7OW299cDcCw3FSmjsjlsNI8Dhuey/ghmSRoAKJAKdmUfqOz26fA7teFJSaEdhtJD+DX3zibkVOOpTGtgM07B7N24yAeC3801HtCayOHjy1iwhDv4vXRBRmMKkgnPz2sJFSkB3X1t63RakVEBo6M5EQyCjLY+fq9/Pu1uyGUSLhwFMnFE6kdNok16yfy3/fzAIg01tK4YQkNGxbRuH4RjVs+hFZvlFzdeiU+lGyK+Frqazn/a9/brayhuZXymia21zQy761XaGwp5N/z1lPX9NG9orJSEhlVkMGoQemMKkhnVEEGw/PSKMlNIys1UYmoiIiISKx10A3XOUdVQwubd9azsbKeTXm5VIyeCnj3Wx2SlUJxTiqP//YKahpbdMu8HqajK9KJlKQEinNTKc5NZc3613j01v9HJOLYsKOeVdtrWLWtdtfz26vKeeS9jbutb62NJDZVk9hYTWJTNTmJLfz025cxLDeVoTmp5KYlKRmVAcM5x466ZsprGqmsb6a2qZXGllZSjr6IZxdtIZzo3RcvOzWJvPQwBRnJuv+aiIjsFTMjOzWJ7NQkJhRlAd61n5sq69m4s56NO+qZu7aCwvOu5eBfPscBQ7M43L9rweGlueRnJAe8B/2Lkk2RvRQKGcPz0xien8bxewygd9xpZ3H0ZddQVd9CVUMz1fUtVDXkUdXQTFV9C+tbI3z93nd2LR9ODDEkK8V7ZPsPf7owK4Wi7BQKMpNJ0r1CpY9qSsljzuoKNlfWs6WygYaWyK554YQQ4cQQiYNGsmlnPY0tERqj5iclGEOyUwgfdBpbqhoozNR10yIisvdSwwmMLshgdEEGAE0tEX7xzQv41U0PMGd1BffOWsttb3jXfY4ZnMG0kXlMK83j8JF5FOekBhl6n6dkU6SbuhqkBGDZ0mV8NjOFwZkfn+ec4zffOJvRBx5OSziT1nAGLUnplJelszUpg3fD6bSGM3C2+31AzaAgI3m3BLTQT1AHZyUzODOFwZnJ5KiVVHqJdeV1PD5/EzPf38SmieexaVU5eelhRg/OYEi2/31NDRNO9E6iXPvlr/O9O58FoCUSobq+hW01jWza6Z2FTj7oNB6cu57s1CTGFWYwvjBTZ55FRGSfhRNDNKydzw+mT/AKEhJJHjKW5GEHsKBkMh+un8h9s71BiVoqt9KwfhGNGxbRsH4RLRUbdL3nXlCyKdJNXQ1SAp3fnN7MaGmo49wrftLhMr/+ynTGH3jorkS0NSmdlqQM6pLS+TCczgcuiYT0PCKJ7fzQjrSS0FJHimvk6EMO2JWIFvrPBZnJDM5KJj89WSOzScxtrWrgifmbmfnBJj5YvxOAqSNyyVv/GmdfcDHp3bwmJjEUIjc9TG56mHGF3lmbay//LJ/9xZ18uLWaeWt2MHfNDoqyUzh4WA5jBmf01C6JiEh/1smtVyLOUV7T5HW73ZnBpoKh1E32brmSmpTA9oWvcdsbq5lWmsfEokwS1QOtQ0o2RXqRlgic/b1fdzj/2i9P52d3Pktza4SaxhbqGlupbWqhtrGF2qZWahtbWPPhYtaU1zJnTQU765o/to2QwaCM5N1aRfPSw7s98tOTycsIk58eJiUpoZ1IRGBHbRNPL9zCzA82Mnt1Bc7BAUOz+PFpEzjj4KEU56Ry/H//r9uJZoea6pg8NJvJQ7OpbWxh2dZq5m+o5JlFW0hbnkDSkKlEIk7Xd4qISEyEzCjITKYgM5kpJTk459hZ38zGnfVs2lFPVeForn1iMeCNjnvoiFymleYybWQ+Bw3L1m+nKEo2RfqgpIQQuWlhou7cssuv/3gxQyYdQA6QbSFaE9P8VtI0WpPSSM7M47jDTqOsuoGtVQ0s2FjJjtomWiKu3fey1mZCLfUktDSQ0FJPWkKEs0870Wt9SguTmZJIZkoSmSmJZKUkkeW/TkkKqVtvP1TT2MLzi7cw8/1NvL58Oy0Rx6iCdL5z0ljOPHjoruthekp6ciKHDs/lkJIc1pbX8cGGnWxJH6JEU0REeoyZ+b+7wkwems0z3/skm3bWMWd1BXPXVDB39Q7+8NyHgDcewZSSHA4fmcvhpXkcNiKXzJSkgPcgOEo2RfqZrrr7/vor00le/fqu1+lAGhBJCBNJTGXVxjI+9/3fUt/USn2z/2j66HnLls3c+uqHuFDn/zgTQ0ZWapKfjCaSkZxIejiRtOREMpITSAsnkh5OID3ZK2ubTg8nct2vrqVy5w5CrU1YpJlQpBlzHw0cU5CXw0P337u/h0q6qaK2iReWbOW5RVt4ffl2GlsiFOekcumxI/nMwUOZVJQV9xMLZkbpoHRKB6Xz3xduAL4e1/cXEZEBzEIMzdn9jH8oJZPkYRNJLpnM6+sPYPaqMVgoARdppalstX/N50IaNyxmaH72gLnmU8mmyADTVTJ67Zend9o6de2XL9vVlbehuZUmfwTRxpaIP93K+y/N5IIvfskbkbehheqGFmoaW9hS1UBdUysbtmyjxRJxCeH236ToFCjavShkXotuUkKIjTvLmPH3N0kPe0lrRnLCbglrRnIiWalJ5PhDn+ekhf3nJHVt6YbGllbeW7eTt1aW89aK7by7bgcRB8U5qVwwbThnHFTEocNzCYWMz1/wJbZV7OxwW0uWLmNGD8drRLpeSEREJFY6ud6zTVNLhC1VDV7X2/wMNg8dS+tUr0ZsLl/Pjx+Zz9QReRxcks3IQRn9djwNJZsisk/aEr/2rC57n/936q86XPf46Wcw4wfX45yjJeJoaonQ3BqhudWbvvv3P+Gc71xDU1tZa4Rmf5mm1ggLlq9lWXMNLpREJCHJew4l4fzpzoQTQ+SkJlFVXoZrqiPU2kiopYGE1kZCLY3e69ZGclMT+d0vf7IrWc1KSeyXAwBUNTSzrryOxZurWLSxkoWbqli0qZKG5gghgwOLs/nm8WOYfsAQJhd/vAVzW8XOTk9edDZoloiISH8VTgwxPC+N4XleC2hrxFFW7SWfz6+YwxPzN3P/nPUApIUTOGBoFpOLsznQf4wq6B8JqJJNEYm5rm4T09baZWYkJdjHktaWTYsZW9jO/WN8s393G9/3b5Wxp4hzNLdEuOGqCxk1YTKRxGQiCSlEEpJpTUwmkpBMXUIKtfVNjDjwSBpaWmlsiVDf3Epz60fXrW4HPvuPt3bbdmZbi2ma12KanpxIalKC9wgnkLJrOkRq2JvXtn+JIf85wUgMhUhKMBL3KA/5iZzh3fLGmzai8zszdiV8bcs5x66EvbEtKW/xEvPG5giV9c3sqGuioraJHXVN7KhtZnNVA+vKa9kRNYhURnIik4Zm8cUjRnDkqHymjcwjO3XgXmciIiISKwkhoyg7laLsVO77zzVs+XeE5WU1LNhYycKNlSzYWMkDc9ZzR/MawEtAJxV5CeiEIZmMG5LJ2MEZfe76TyWbIhJzXXXV7cnWrpAZyUkJNFeXd3qbmWu/PJ3Pffmc3cpaI47GllYamyM8d9cN/PrXv2ZnfROVdc1U1rd40/XN/utmdtTV07DHNa1Nrb23S2diyMhJC5OXnsTgzBROO7CI4XlpjMhLY/yQTErz0zXQjoiISE+zEAnt9ZayEEn5wwgXjiE8ZAwVhaOZUziKUDh11yItlVtJqCnj61+YwfjCTEYXZFA6KL3XnhzuM8mmmZ0K/BlIAG51zv0m4JBEpJ9JCBlp4UTSwrBq3kv88jtf6XDZlSuWM3rMWABS/Ecu4DBcKJFlq9bwjd/fS2vEEXGOSMQRcdC6a9px3/U/49zvXkMk4mh1fquqg7b21cdv/T3FxcPain0fJYObt2zltIu/vSv2XQ/znu/77fcZOWI4oZZGEloasEgTBjQBG/zHPVH70R4NxtQ3qc4UEenFunHN565FnaOqoYXymkbKa5sor8lkwYI6bn9j9W49svLTw4wclM5If/C8UYPSGZ6fxrCcNLJSEwO7Q0CfSDbNLAH4O/ApvN9Hc81spnNucbCRiUh/1Z2BlLqa39VZxpaNizodjOnhlXP44s+u7fQ9Jhf/rMP5TVtXce5vb+w0hq7249dfmd6tLtHSe6jOFBHpP8yMbH/Aw1EFXtlz3z8BzEjKHUpiXjFJucVU5w1lY+5QZuUOJTEzf7dtRBrraKkqo6XSe6RveZ/1C96OS/x9ItkEpgErnHOrAMzsAWAGoIpTRKQHBdklWvaZ6kwRkf7MRbj+2Y5bRptaIrsu/aluaKGqvpnqhgKqGsZR1dDC+nve6nDdWOsryWYxsD7q9QbgiIBiERER6c1UZ4qIDGDhxBCDM1MYnJnS7vyrrpsft1jMOdf1UgEzs3OAU51zX/VfXwgc4Zy7co/lLgcu91+OB7rXGbpzg/AGpuwv+tv+QP/bp/62P9D/9qm/7Q/0v33al/0Z4Zwr6Ilg4kl1Zq+nY9Q5HZ+u6Rh1Tsena7E6Rl3Wm32lZXMjUBL1ephfthvn3M3AzbF8YzOb55ybGsttBqm/7Q/0v33qb/sD/W+f+tv+QP/bp/62P3tJdWYvpmPUOR2frukYdU7Hp2vxPEZ95Q7lc4GxZjbSzMLA+cDMgGMSERHpjVRniohIr9AnWjadcy1mdiXwLN4w7rc75xYFHJaIiEivozpTRER6iz6RbAI4554CngrgrWPaxagX6G/7A/1vn/rb/kD/26f+tj/Q//apv+3PXlGd2avpGHVOx6drOkad0/HpWtyOUZ8YIEhERERERET6lr5yzaaIiIiIiIj0IUo2O2Bmp5rZMjNbYWZXBx1PZ8xsjZktMLP3zWyeX5ZnZs+b2XL/OdcvNzP7i79f883s0KjtXOwvv9zMLo7zPtxuZmVmtjCqLGb7YGaH+cdohb+uBbRPvzCzjf5n9b6ZnR4178d+fMvMbHpUebvfRX/wj9l++YP+QCA9uT8lZvaymS02s0Vm9h2/vE9+Tp3sT1/+jFLMbI6ZfeDv0y87i8PMkv3XK/z5pfu6r3HenzvNbHXUZzTFL+/V37n+pKvvQWffrYGgG8fnKv9/z3wze9HMRgQRZ5C6+7/EzD5nZs7MBtToot05PmZ2blQddl+8YwxaN/7Ohvv1/Hv+39rp7W2nv7J2fofuMb/DOjOmnHN67PHAG1BhJTAKCAMfAJOCjquTeNcAg/Yo+x1wtT99NfBbf/p04GnAgCOB2X55HrDKf871p3PjuA/HAYcCC3tiH4A5/rLmr3taQPv0C+AH7Sw7yf+eJQMj/e9fQmffReDfwPn+9D+Bb/Tw/hQBh/rTmcCHftx98nPqZH/68mdkQIY/nQTM9o9nu3EA3wT+6U+fDzy4r/sa5/25EzinneV79Xeuvzy68z3o6Ls1EB7dPD4nAGn+9DcG0vHp7jHyl8sEXgNmAVODjrs3HR9gLPBe1P+ywUHH3QuP0c1R9d0kYE3Qccf5GH3sd+ge89utM2P9UMtm+6YBK5xzq5xzTcADwIyAY9pbM4C7/Om7gLOiyu92nllAjpkVAdOB551zFc65HcDzwKnxCtY59xpQsUdxTPbBn5flnJvlvL+uu6O21WM62KeOzAAecM41OudWAyvwvoftfhf91pcTgYf99aOPT49wzm12zr3rT1cDS4Bi+ujn1Mn+dKQvfEbOOVfjv0zyH66TOKI/u4eBk/y492pfA9ifjvTq71w/0p3vQUffrYGgy+PjnHvZOVfnv5yFdy/UgaS7/0uuBX4LNMQzuF6gO8fnMuDv/v80nHNlcY4xaN05Rg7I8qezgU1xjC9w3fgd2lGdGVNKNttXDKyPer2Bzn+EBs0Bz5nZO2Z2uV9W6Jzb7E9vAQr96Y72rTfuc6z2odif3rM8KFf63RVuN7/LKXu/T/nATudcyx7lceF3iTsEr6Wpz39Oe+wP9OHPyMwSzOx9oAwvqVrZSRy7YvfnV/px95r/E3vuj3Ou7TO6zv+MbjCzZL+sz3zn+rjufA86+m4NBHv7d3IpXuvCQNLlMfK79JU4556MZ2C9RHe+Q+OAcWb2ppnNMrO4NRD0Et05Rr8AvmRmG/BG5/5WfELrM+JSpyvZ7B+Occ4dCpwGXGFmx0XP9M/Y9+lhh/vDPvhuBEYDU4DNwB8DjWYfmFkG8B/gu865quh5ffFzamd/+vRn5Jxrdc5NwWspmQZMCDai/bPn/pjZZODHePt1OF7X2B8FF6HIvjOzLwFTgd8HHUtvYmYh4Hrg+0HH0osl4nWlPR64ALjFzHKCDKgXugC40zk3DK/L6D3+d0viSAe8fRuBkqjXw/yyXsk5t9F/LgMexfuBubWtKdx/bute0dG+9cZ9jtU+bGT3LkqB7Ztzbqv/4zkC3IL3WcHe71M5XneHxD3Ke5SZJeElZv9yzj3iF/fZz6m9/enrn1Eb59xO4GXgqE7i2BW7Pz8bL+5e938ian9O9btAO+dcI3AH+/4Z9Zr/DX1Md74HHX23BoJu/Z2Y2cnAT4HP+N/lgaSrY5QJTAZeMbM1eNeTzRxAgwR15zu0AZjpnGv2L3f4EC/5HCi6c4wuxRuzAOfc20AKMCgu0fUNcanTlWy2by4w1rwRHMN4gxvMDDimdplZuplltk0DpwAL8eJtG3HxYuAxf3omcJE/AtWRQKXfBfJZ4BQzy/W7DZ7ilwUpJvvgz6sysyP9a4YuitpWXO3RF/6zeJ8VePt0vnkjOI7EqzDm0MF30W9BfBk4x18/+vj0VOwG3AYscc5dHzWrT35OHe1PH/+MCtrObJtZKvApvGtRO4oj+rM7B3jJj3uv9jXO+7M06uSG4V1jGf0Z9drvXD/Sne9BR9+tgaDL42NmhwA34SWaA+1aO+jiGDnnKp1zg5xzpc65UrzrWj/jnJsXTLhx152/sf/itWpiZoPwutWuimOMQevOMVoHnARgZhPxks1tcY2yd+uozowt1wtGS+qND7zm9g/xrnf6adDxdBLnKLwRuD4AFrXFindtzIvAcuAFIM8vN+Dv/n4tIGp0N+AreAOBrAAuifN+3I/XZbEZ72zdpbHcB7xuSgv9df4GWED7dI8f83y8P/KiqOV/6se3jKgRMTv6Lvqf/Rx/Xx8Cknt4f47B6yI7H3jff5zeVz+nTvanL39GB+GNTjjfP47/21kceBXvQ375HGDUvu5rnPfnJf8zWgjcy0cj1vbq71x/erT3PQCuwUsIOv1uDYRHN47PC8DWqP89M4OOubcdoz2WfYUBNBptN79DhtfVeLH//+78oGPuhcdoEvAm3m/k94FTgo45zsenvd+hXwe+HvUdarfOjOXD/DcTERERERERiRl1oxUREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFpF1m9pN9XK/UzBbGOh4REZGumNkUMzs96vVnzOxqf/oXZvYDf/oaMzt5H7Z/lplNinq9T9vZX35d+4Wo11PN7C/xjkOkK7rPpoi0y8xqnHMZ+7BeKfCEc25y7KMSERHpmJl9Ge/m9Fe2M+8XQI1z7g/7sf078eq4h/d1G3vxXonOuZYO5h0P/MA5d0ZPxyGyP9SyKRIjZvZfM3vHzBaZ2eVm9nUz+33U/C+b2d/86Z+Z2TIze8PM7m8709rBdqeY2Swzm29mj5pZrl/+ipn92czeN7OFZjbNL083s9vNbI6ZvWdmM6Le/xEze8bMlpvZ7zp5z98Aqf62/+WXXeW/z0Iz+24XhyPRzP5lZkvM7GEzS/O3scbMBvnTU/19CPnxFPjlITNb0fZaRET6rh6sG18xs6n+9CC/fgkD1wDn+fXXedHb32P9O83sHL8uet9/LDAz58+/zMzmmtkHZvYfM0szs08AnwF+7y8/um07/jon+fXuAr8eTvbL15jZL83sXX/ehE726xdmdo+ZvQnc47dgvu6v+64fA8BvgGP9OL5nZseb2RP+NvL84z7f//1w0F58ZCIxpWRTJHa+4pw7DJgKfBt4FPhs1PzzgAfM7HDgc8DBwGn+8p25G/iRc+4gYAHw86h5ac65KcA3gdv9sp8CLznnpgEn4FWK6f68KX4cB+JVxiXtvaFz7mqg3jk3xTn3RTM7DLgEOAI4ErjMzA7pJObxwD+ccxOBKj++djnnIsC9wBf9opOBD5xz2zrZvoiI9A09VTd+jHOuCfhf4EG//nqwG+vM85edAjwDtLV6PuKcO9w5dzCwBLjUOfcWMBP4ob/OyrbtmFkKcCdwnnPuQCAR+EbUW213zh0K3Ah0mET7JgEnO+cuAMqAT/nrnge0dZW9Gnjdj+OGPdb/JfCe/7vhJ3i/I0QCoWRTJHa+bWYfALOAEmAksMrMjjSzfGAC8CZwNPCYc67BOVcNPN7RBs0sG8hxzr3qF90FHBe1yP0AzrnXgCwzywFOAa42s/eBV4AUYLi//IvOuUrnXAOwGBjRzX07BnjUOVfrnKsBHgGO7WT59c65N/3pe/31O3M7cJE//RXgjm7GJSIivVvM68aeYGbnAYfiJXEAk/0WxQV4J0MP6GIT44HVzrkP/dd71teP+M/vAKVdbGumc67en04CbvHjeAgvEe3KMcA9AM65l4B8M8vqxnoiMZcYdAAi/YF5106cDBzlnKszs1fwkrwHgHOBpXjJmjOzWL71nhddO8CAzznnlu0R4xFAY1RRKz33P6C9uABa+OgkV8qumc6tN7OtZnYiMI2PWjlFRKSP6uG6sd36ZB/jnAz8AjjOOdfqF98JnOWc+8C860CP35/34KP6tzt1b23U9PeArXgtviGgYT/jEIkrtWyKxEY2sMOvTCfgdTUFr7vQDOACvMoVvDO4Z5pZipllAB1e3O+cqwR2mFlbK+KFwKtRi5wHYGbHAJX+8s8C3zK/5u6iu2tnms0syZ9+HTjLv2YlHa8L1OudrDvczI7yp78AvOFPrwEO86c/t8c6t+K1gj4UVdmLiEjf1SN1o28NH9Un50SVVwOZ3Q3Q7xF0P3DRHpdvZAKb/Xow+gRoR9tfBpSa2Rj/9Z719b7KBjb7l5xcCCR0EQd49fMXYVfCv905VxWDWET2mpJNkdh4Bm9QnCV4F+3PAnDO7cC71mOEc26OXzYX75qP+cDTeNdhVnay7Yvxrrucj3fN5TVR8xrM7D3gn8Clftm1eN1u5pvZIv/1vrjZ38a/nHPv4p3lnQPMBm51zr3XybrLgCv845GLd40KeNeR/NnM5uGd3Y02E8hAXWhFRPqLnqwb/wB8w68DB0WVvwxMahsgqBsxzsC7pOSWtoGC/PKf4dV3b+K1wLZ5APihPxDQ6LZC//KUS4CH/C6vEby6eX/9A7jY74o8gY9aPecDrf4ARt/bY51fAIf5vxt+g/c7QiQQuvWJSADMLMM5V2PeKK2vAZf7Cd3ebOMVvGHP5/VEjPFm3qiCNzjnOrsWVERE+qlY1I0i0rvomk2RYNxs3k2hU4C7Bnplat4Nt7+BrtUUERnIVDeK9DNq2RTpJczs73ij8UX7s3OuR7uVmtlsIHmP4gudcwu6WC8feLGdWSc558pjFZ+IiAxcQdWNPc3MLgG+s0fxm865K4KIR6SnKNkUERERERGRmNMAQSIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyab0aWb2TzP7WYy2NdzMaswswX/9ipl9NRbb9rf3tJldHKvt7cX7/srMtpvZlni/t4iIxJ7qvm69b6B1n5k5MxsTxHvvycxK/XgSu7FszL5bnbzHPh8bM1tjZifHOibpOUo2pdfy/6HUm1m1me00s7fM7Otmtut765z7unPu2m5uq9N/Ts65dc65DOdcawxi/4WZ3bvH9k9zzt21v9veyziGA98HJjnnhsTpPWP6Q0VEZCBR3bf/uqr7zOx4M9uwF9vbq+X7su5+t0S6S8mm9HZnOucygRHAb4AfAbfF+k26c7avjxoOlDvnyoIOJAhtZ+pFRPoY1X37p0/WfaqzpD9Ssil9gnOu0jk3EzgPuNjMJgOY2Z1m9it/epCZPeGfCa4ws9fNLGRm9+BVPI/7XYX+X1SXkkvNbB3wUgfdTEab2RwzqzKzx8wsz3+vj53lbDuDbGanAj8BzvPf7wN//q4WPz+u/zGztWZWZmZ3m1m2P68tjovNbJ3fDeinHR0bM8v219/mb+9//O2fDDwPDPXjuLOD9WeY2fv+Pq704//YGfHoM9ZmlmJm95pZuX+855pZoZldBxwL/M1/z7/5y3/CX6bSf/5E1HZfMa+701v+Oo+bWb6Z/cuPaa6ZlUYtP8HMnvc/42Vmdm7UvDvN7EYze8rMaoETOjlunzaz9/z3WG9mv9hj/kX+8Sw3s59FHw//+F7tH69yM/t323dDRCRWVPfFvu4zs3Tg6aj5NWY21MySzexPZrbJf/zJL+to+Wlm9rZ/3Deb2d/MLLw3n297dZa/7f/4+7XazL7tLzvUvBbvvKj1D/GPU1Jnx3aP9zzPzObtUfY9M5sZFVPbd+t4M9tgZt/3t7nZzC6JWi/fvDq7ra7+lZm90c3dP93MVvnx/978lnszG21mL5lXt24377dAzt4cV+ldlGxKn+KcmwNswEto9vR9f14BUIhX6Tnn3IXAOrwzxRnOud9FrfNJYCIwvYO3vAj4ClAEtAB/6UaMzwD/Bzzov9/B7Sz2Zf9xAjAKyAD+tscyxwDjgZOA/zWziR285V+BbH87n/RjvsQ59wJwGrDJj+PLe65oZtOAu4EfAjnAccCarvYRuNh/zxIgH/g6UO+c+ynwOnCl/55X+hXjk3jHLh+4HnjSzPKjtnc+cCFQDIwG3gbuAPKAJcDP/XjT8X5E3AcM9tf7h5lNitrWF4DrgEygs0qvFu9Y5QCfBr5hZmf57zMJ+AfwRbzPPtuPrc23gLPwjvdQYAfw906PmIjIPlLd1659qvucc7V7zM9wzm0CfgocCUwBDgamAf/TyfKtwPeAQcBRfrzf7Oo4tSO6znoLeBz4AK/OOQn4rplN99/zbeBze6z7sHOume4dW/ztjzezsXts574O4hvCR3XgpcDfzSzXn/d3vLp0CN7vgr25NvezwFTgUGAG3vcNwIBf49WtE/F+Z/xiL7YrvYySTemLNuElIXtqxqsYRzjnmp1zrzvnXBfb+oVzrtY5V9/B/Huccwv9yuZnwLkWm24uXwSud86tcs7VAD8Gzt/jzPIvnXP1zrkP8Cqej1XcfiznAz92zlU759YAf8RL3LrjUuB259zzzrmIc26jc25pN9ZrxkscxzjnWp1z7zjnqjpY9tPAcufcPc65Fufc/cBS4MyoZe5wzq10zlXinUFe6Zx7wTnXAjwEHOIvdwawxjl3h7+t94D/AJ+P2tZjzrk3/f1p6GgHnHOvOOcW+MvNB+7H+8ECcA7wuHPuDedcE/C/QPR36evAT51zG5xzjXgV4TnWf7ukiUjwVPf5YlD3dRTbNc65MufcNuCXnW3Pr/dm+XXRGuAmPqpD9sauOgs4EChwzl3jnGtyzq0CbsHbV/ASwgsAzMz88rYksTvHFudcHfBY1HbGAhOAmR3E14x3XJqdc08BNXjJagJe4vtz51ydc24xsDfX5v7WOVfhnFsH/KktHufcCv83SaP/OVzPvh1X6SWUbEpfVAxUtFP+e2AF8JzfNePqbmxr/V7MXwsk4Z3F3F9D/e1FbzsR76x0m+gR9OrwzlLuaZAf057bKm5n2faUACu7uWy0e4BngQf87ka/M7OkDpbdc1/bi3Fr1HR9O6/b9n0EcITfbWmnme3Eq2CjB4Do6jMFwMyOMLOX/a5KlXgJZNtnOzR6O37lXB61+gjg0agYluCd5Y7+/EREYkl130f2t+7rbmxDO1rYzMaZ1315i5lV4bXq7ssxij7WI/C660bXcT/ho+PzH+AoMyvC64kUwetN1FH8ex7bNruSVrxWzf/69Vx7yv0Tv23aPpMCf/vR8Xer/m1n2V3H2rxLch4ws43+cb2X2Hz3JCBKNqVPMbPD8SqTj3WP9M9uft85Nwr4DHCVmZ3UNruDTXZ19rckano43hm+7XjdRtKi4krA+8fb3e1uwqtUorfdwu5JVnds92Pac1sbu7n+erxuq+3ZbR+JSuj8M5y/dM5NAj6B1+J4UdvsPbaz577ubYx7xvuqcy4n6pHhnPtG1DJdHfs29+GdyS1xzmUD/8TrvgOwGRjWtqCZpeK15EbHcdoecaQ45/Zln0REOqW672P2t+5rL872YtvUyfI34vXSGeucy8JLCq2d5fYmlvXA6j3qlkzn3OkAzrkdwHN41/B+AXggqhV7b47t80CBmU3BSzo76kLbmW3+9odFlZV0sGx79vyOtR3r/8M7Jgf6x/VL7NtxlV5Cyab0CWaWZWZnAA8A9zrnFrSzzBlmNsbvWlKJ19IU8WdvxbuGYW99ycwmmVkacA3etRGtwIdAinmDzCQB/wMkR623FSi1qKHq93A/8D0zG2lmGXx0nUtLB8u3y4/l38B1ZpZpZiOAq/DOBHbHbcAlZnaSP7hAsZlN8Oe9j9cFJ8nMpuJ1LQXAzE4wswP9HxpVeJV+R8f6KWCcmX3BzBLN7DxgEvDE3uyr7wl/Wxf6cSWZ2eGdXNPTmUygwjnX4F+7+oWoeQ8DZ5o3sFEYr5tsdGX3T7xjPgLAzArMbMY+xCAi0iHVfe2LQd23Fci33QfQuR/4H///+SC8yyfu7WT5TLz6r8avN6NPeu6rOUC1mf3IzFLNLMHMJvsnG9rch3dy9xx2TxK7fWydd43nQ3it4nl4yede8T+DR4BfmFmafwwu6mK1aD80s1wzKwG+Azzol2fiddWtNLNivDElpA9Tsim93eNmVo13tu+neH33L+lg2bHAC3j/pN4G/uGce9mf92u8SmSnmf1gL97/HuBOvG49KcC3wRshEG8ggFvxzqTW4g3Q0OYh/7nczN5tZ7u3+9t+DVgNNOANOrMvvuW//yq8s973+dvvkvMGnbgEuAHvR8qrfHRm9Gd4rZ478K5dia7UhuAlZFV4XUhf9fcH4M941y/uMLO/OOfK8Vo+v4/XFfX/AWc457bv7Y4656qBU/CuU9mE97n8lt1/7HTXN4Fr/O/X/+L9cGl7n0V4x/UBvFbOGqAMaIzax5l43daqgVnAEfsQg4hIe1T3dW1/6r6leMnZKv/YDAV+BcwD5gMLgHf9so6W/wHeScpqvOsqH/zYG+0lP4E7A2+QotV4Lbi34g3Q02Ym3me+xb+utc3eHtv7gJOBh/Y22Y9ypR/bFv+97+ejerIrjwHv4J3YfpKPbu3zS7xBgyr98kf2MTbpJazra8hFRAY2/yzxTrzuUqsDDkdERKTXMbPfAkOcc3szKq30c2rZFBFph5md6XcNSgf+gHeme02wUYmIiPQO5t33+iDzTMMb4f7RoOOS3kXJpoj0W2a2yD66CXf044vdWH0GXlfdTXhdls7vxu0EREREdtnPeqi3y8Tr5lqL1434j8BjZnZsB/tcE2i0Egh1oxUREREREZGYU8umiIiIiIiIxJySTREREREREYk5JZsiIiIiIiISc0o2RUREREREJOaUbIqIiIiIiEjMKdkUERERERGRmFOyKSIiIiIiIjGnZFNERERERERiTsmmiIiIiIiIxJySTREREREREYk5JZsiIiIiIiISc0o2RUREREREJOaUbIqIiIiIiEjMKdkUERERERGRmFOyKSIiIiIiIjGnZFNERERERERiTsmmiIiIiIiIxFxi0AH0lEGDBrnS0tKgwxARkT7inXfe2e6cKwg6jiCozhQRkb3VnXqz3yabpaWlzJs3L+gwRESkjzCztUHHEBTVmSIisre6U2+qG62IiIiIiIjEnJJNERERERERiTklmyIiIr2AmZWY2ctmttjMFpnZd/zyX5jZRjN733+cHrXOj81shZktM7PpUeWn+mUrzOzqIPZHRESk316zKSIi0se0AN93zr1rZpnAO2b2vD/vBufcH6IXNrNJwPnAAcBQ4AUzG+fP/jvwKWADMNfMZjrnFsdlL0RERHxKNkVERHoB59xmYLM/XW1mS4DiTlaZATzgnGsEVpvZCmCaP2+Fc24VgJk94C+rZFNEROIqsG60ZrbGzBb4XYLm+WV5Zva8mS33n3P9cjOzv/jdgeab2aFBxS0iItLTzKwUOASY7Rdd6dd/t7fVjXiJ6Pqo1Tb4ZR2Vi4iIxFXQ12ye4Jyb4pyb6r++GnjROTcWeNF/DXAaMNZ/XA7cGPdIRURE4sDMMoD/AN91zlXh1XmjgSl4LZ9/jNH7XG5m88xs3rZt22KxSRERkd0EnWzuaQZwlz99F3BWVPndzjMLyDGzogDiExER6TFmloSXaP7LOfcIgHNuq3Ou1TkXAW7ho66yG4GSqNWH+WUdle/GOXezc26qc25qQUGn9+QWERHZJ0Emmw54zszeMbPL/bJC/5oVgC1AoT+tLkEiItKvmZkBtwFLnHPXR5VHn1z9LLDQn54JnG9myWY2Eq/3zxxgLjDWzEaaWRhvEKGZ8dgHERGRaEEOEHSMc26jmQ0GnjezpdEznXPOzNzebNBPWi8HGD58eOwiFenChZdcRlnFzg7nD87L4Z47bolfQCLSFx0NXAgsMLP3/bKfABeY2RS8k7RrgK8BOOcWmdm/8Qb+aQGucM61ApjZlcCzQAJwu3NuUTx2oGT4CDasX7ff2xlWMpz169bGICIREQlSYMmmc26j/1xmZo/idQvaamZFzrnN/pncMn/xbncJAm4GmDp16l4lqiL7o6xiJ6deeV2H85/520/jGI2I9EXOuTcAa2fWU52scx3wsX8+zrmnOluvp2xYv47rn1u239u56pTxMYhGRESCFkiyaWbpQMgf2j0dOAW4Bq+bz8XAb/znx/xVZuKNxPcAcARQGdXdVkQ6oVZXEREREQlCUC2bhcCj3uUpJAL3OeeeMbO5wL/N7FJgLXCuv/xTwOnACqAOuCT+IYv0TWp1FREREZEgBJJs+jeaPrid8nLgpHbKHXBFHEITERERERGRGAhygCCRfq2huZUtVQ1sr2mkbPDhXP2f+YRCRmZKIsNyUhmak0pxbiqjBmVw6WVfU1dXEREREelXlGyKxFDEOZZvrWHx5irWVdTtKrfccby0tIyIc1TVt9DUGtk1L5wQIpR1NOMOG0NhVgqFmcnkpocJ2UfjhKirq4iIiIj0NUo2RWJkbXktry/fTnltE1kpiUwrzaMkL5WCzGT+esVnyT3wQADygNbEVJoTM2gOZ9CQkk9FJJUlm6uYv6ES8BLQIdkpDM1OoSgnlYjpT1VERERE+hb9ghXZT00tEcoGH86y9zeRnZrE6ZOHMGZwBhbVMtkccZ0O0vPry87k2zfPZEdtE1urG9lS2cCmynpmra7wFhj/Bc746+tMHZHHYSNymVqaS1F2ak/vmoiIiIjIPlOyKbIfqhuaufTOeezIP4CDh2VzzNhBJIZC+7StkBn5GcnkZyQzqSgLgMbmVjZXNfDGC0+TOfpEHpy7njvfWgNASV4qJ44fzKmTizhiZB6hUHu35xMRERERCYaSTZF9tKO2iYvvmMPiTVUUbXyV40/6aszfIzkpgdL8dB5+6W4GbXuPEozGlDzqUwdTUV3E3duruevttZTkpXLRkaV88cjhpIX1Zy0iIiIiwdOvUpF90NDcylfvnsfSLdXcdOFh/O7qO3r0/TrqhtvcGuHRf93B0JFncd1TS/jnqyv54fTxnHd4yW7deEVERERE4m3f+vuJDGCRiOOHD8/nnbU7+NN5UzhpYmFgsSQlhMiqWsWDXzuK/3zjKEYXZHD1Iwv44q2z2VLZEFhcIiIiIiJq2RTppgsvuYyyip1U5B3AtsLDGVQ2jz//z538GVi0eAmnBhTXwgULmD7j8wA4oDBnHG9HDufoax6neMNLrAowNhEREREZuJRsinRTWcVODrnoZ/x73npGD0rn0yeev6ur6nuXnRlYXO11sd1e08jjH2xi4+gzaVmzPqDIRERERGQgUzdakW6KWCJPL9xCWjiRkycW9uprIgdlJHPe4SXkpCWRfOylbNhRF3RIIiIiIjLAKNkU6abtBVOorG9m+gGFpCQlBB1Ol9LCiZx9SDGutoLHP9hMRW1T0CGJiIiIyACiZFOkGxZurGRH3iQmD81iWG5a0OF0W1o4kcZXbiIxwZj5wSYamluDDklEREREBgglmyJdiEQcP3l0AQmtjRw9ZlDQ4ew1V1/Jpw8sorqhmWcXbcE5F3RIIiIiIjIAKNkU6cJ/3t3A/A2VDN46p090n23P0JxUjh1bwJryOuZvrAw6HBEREREZAJRsinSitrGF3z+7jEOG55BZtTrocPbLwcOyGZGfxuvLt+v6TRERERHpcUo2RTpx06srKatu5GdnTKL3jj3bPWbGpyYWkpRgvLBkq7rTioiIiEiP0n02RTqwvaaRW99YzRkHFXHo8Nygw4mJ9OREjhtbwHOLt7JgYyUHDcth4YIFTJ/x+XaXH5yXwz133BLnKEVERESkP1CyKdKBm19bRUNzK989eVzQocTUhCGZLNlcxZsryhlVkEFzxHHqlde1u+wzf/tpnKMTERERkf5C3WhF2lFW3cDdb6/hrCnFjBmcEXQ4MWVmnDhhMK0Rx1srtwcdjoiIiIj0U0o2Rdpx06uraG51fOuksUGH0iNy0sJMGZ7Dks3VWO6woMMRERERkX5IyabIHrZWNXDvrLWcfUgxIwelBx1Ojzm8NJfUpATCh8zQYEEiIiIiEnNKNkX2cOMrK2mNOL51Yv9s1WyTnJjAkaPySBg8mpXbaoMOR0RERET6GSWbIlE2V9Zz3+x1nHPYMIbnpwUdTo+bPDSbyM7NvLFiOy2RSNDhiIiIiEg/omRTJMpNr64i4hxXnDAm6FDiIhQymt5/jMr6Zuavrww6HBERERHpR5RsivjKaxp5YO46zjqkmJK8/t+q2Say5UNG5Kcxe00F9c2tQYcjIiIiIv2Ekk0R351vraGxJcLXPzkq6FDi7tgxg2huiTB3TUXQoYiIiIhIP6FkUwSoaWzhrrfWcMqkQsYMzgw6nLjLz0hmYlEW89dXUlXfHHQ4IiIiItIPKNkUAe6bvZaqhha+cfzAuFazPUeOygODt1eVBx2KiIiIiPQDiUEHIBK0xpZWbn19NdmN2/jRlZd1uNyixUs4NY5xxVtmShJTSnJ4Z+0ODh2eS0FmctAhiYiIiEgfpmRTBrxH3t1IWXUjw7a8y6lXXtfhcu9ddmYcowrG4SNyWbSxkjdXbOesQ4qDDkdERERE+jB1o5UBrTXiuOnVlRxYnE1a3eagwwlcclICh4/MY21FHesq6oIOR0RERET6MCWbMqA9tWAza8rr+Obxo7Ggg+klDirOJjMlkTdXbMcFHYyIiIiI9FlKNmXAcs7xj1dWMrognekHDAk6nF4jMSHEUaPyKatupDprZNDhiIiIiEgfpWRTBqxXlm1jyeYqvv7J0YRCateMNmFIJoMywmwvOISmlkjQ4YiIiIhIH6RkUwasf7yyguKcVA2E0w4z4+gxg2gOZ3HPrLVBhyMyIJhZiZm9bGaLzWyRmX3HL88zs+fNbLn/nOuXm5n9xcxWmNl8Mzs0alsX+8svN7OLg9onEREZ2JRsyoA0Z3UFc9fs4LJjR5KUoD+D9ozISyOtZiN/fuFDKmqbgg5HZCBoAb7vnJsEHAlcYWaTgKuBF51zY4EX/dcApwFj/cflwI3gJafAz4EjgGnAz9sSVBERkXgK7Fe2mSWY2Xtm9oT/eqSZzfbP0D5oZmG/PNl/vcKfXxpUzNJ//OOVFeSnhznv8OFBh9JrmRmDt86htqmV659fFnQ4Iv2ec26zc+5df7oaWAIUAzOAu/zF7gLO8qdnAHc7zywgx8yKgOnA8865CufcDuB56Ne3CRYRkV4qyCad7+BVpG1+C9zgnBsD7AAu9csvBXb45Tf4y4nss4UbK3ll2Ta+csxIUsMJQYfTqyU3VXLhkSO4b/Y6lmyuCjockQHDP7F6CDAbKHTOtd2baQtQ6E8XA+ujVtvgl3VULiIiEleBJJtmNgz4NHCr/9qAE4GH/UX2PHPbdkb3YeAkf3mRfXLjqyvJTE7kwqNGBB1Kn/C9k8eRnZrENY8vxjndDEWkp5lZBvAf4LvOud3O8jjvjzAmf4hmdrmZzTOzedu2bYvFJkVERHYTVMvmn4D/B7QNc5kP7HTOtfivo8/C7jpD68+v9JcX2WurttXw1ILNXHjUCLJSkoIOp0/ITkviqlPG8/aqcp5dtCXocET6NTNLwks0/+Wce8Qv3up3j8V/LvPLNwIlUasP88s6Kt+Nc+5m59xU59zUgoKC2O6IiIgIASSbZnYGUOace6cHtq2ztNKpm15dRTghxFeO0f0j98YFh5cwvjCTXz25hIbm1qDDEemX/F47twFLnHPXR82aCbSNKHsx8FhU+UX+qLRHApV+d9tngVPMLNcfGOgUv0xERCSuEgN4z6OBz5jZ6UAKkAX8GW9gg0S/9TL6LGzbGdoNZpYIZAPl7W3YOXczcDPA1KlT1d9PdrO5sp5H3tvAF6YNZ1BGctDh9AkLFyxg+ozPA1CXNoQNI07l6K//hkHb3wdgcF4O99xxS4ARivQrRwMXAgvM7H2/7CfAb4B/m9mlwFrgXH/eU8DpwAqgDrgEwDlXYWbXAnP95a5xzlXEZQ9ERESixD3ZdM79GPgxgJkdD/zAOfdFM3sIOAd4gI+fub0YeNuf/5LThWOyD256dRXOwWXHjQo6lD6jOeI49crrdr1+euFmVoYO4YzPzCAnLcwzf/tpgNGJ9C/OuTeAjsYkOKmd5R1wRQfbuh24PXbRiYiI7L3edIPBHwFXmdkKvGsyb/PLbwPy/fKr+Oj+YiLdtrmynvvmrOOcw4YxLDct6HD6rOPGFpBgxisfbtNgQSIiIiLSqSC60e7inHsFeMWfXoV38+k9l2kAPh/XwKTf+cfLK3HOccUJY4IOpU9LT07kyFF5vLZ8Oyu31QYdjoiIiIj0Yr2pZVOkR2zYUccDc9dx7tQSSvLUqrm/Dh6Ww6CMMK9+uI2IBXq+SkRERER6Mf1SlH7v7y+vwDC1asZIKGScMH4wD72zgfJBB3e67IWXXEZZxc4O52uAIREREZH+S8mm9Gvryut4aN4GvnjEcIbmpAYdTr8xNCeVSUVZLHYHsHxrNWMLM9tdrqxi524DDO1JAwyJiIiI9F/qRiv92l9fWk4oZHxTrZoxd/SYfEKRZn722EINFiQiIiIiH6NkU/qtldtqeOS9jXzpiBEUZqUEHU6/kxZOpKDsHWatqmDmB5uCDkdEREREehl1o5V+6zdPLyU1KYFvnjAa6Pr6wUWLl3BqnGLrL7J3LmfI4adx7RNLOGHCYLJSkoIOSURERER6CSWb0i/NWlXO84u38sPp4xmUkQx0ff3ge5edGa/w+o1FC+YzJuVetpeewXHf/hOFW+fsPl8JvIiIiMiApWRT+p1IxHHdk0soyk7h0mNGBh1Ov9YccZz11at4eWkZC2wSp596KgWZybvmK4EXERERGbh0zab0OzM/2MSCjZX8cPp4UpISgg5nQDhqdD4pSQm8tLRMgwWJiIiICKBkU/qZhuZWfvfMUiYXZ3HWlOKgwxkwUpISOGbsILZUNbBoc1XQ4YiIiIhIL6BkU/qV299czabKBn5y+kRCIQs6nAFl4pBMhuak8OaK7TQ0twYdjoiIiIgETMmm9BvlNY384+WVnDxxMJ8YPSjocAYcM+OE8YNpaI4wd01F0OGIiIiISMCUbEq/8acXllPf3MrVp00MOpQBa1BGMhOLMvlgQyVVDc1BhyMiIiIiAdJotNIvrCir4b456xhcs5IrLruj3WV0G474OHJUPh9urWH2KrVuioiIiAxkSjalX/jN00tJTUogbcOcDu+lqdtwxEdWShIHDcvm/XU7sazCoMMRERERkYCoG630eW+vLOeFJVv55gmjSWxtCDocAQ4vzSMpIUTSQZ8OOhQRERERCYiSTenTIhHHdU8tZmh2Cl85emTQ4YgvNSmBw0pzSRw2mY0764MOR0REREQCoGRT+rTHPtjIwo1V/PDU8aQkJQQdjkQ5pCSHSH0lb63YjnMu6HBEREREJM6UbEqf1dDcyu+fWcaBxdnMOLg46HBkD0kJIVoWvcCmygY27FDrpoiIiMhAo2RT+qzb3ljNpsoGfnL6REIhCzocaUfLqtmkhROYo/tuioiIiAw4SjalT9pe08iNr6zk5ImFHDU6P+hwpCORFg4bkcuGHfVs0rWbIiIiIgOKkk3pk/70wofUN7fy49MnBB2KdOHA4mxSk9S6KSIiIjLQKNmUPmdFWTX3z1nPF48YzuiCjKDDkS4kJYQ4ZHgOa8vr2FKlW9OIiIiIDBRKNqXP+fVTS0lLSuA7J40NOhTppoOGZZOcGGLuarVuioiIiAwUSjalT3l7ZTkvLi3jGyeMJj8jOehwpJuSExOYUpLDqu21bKtuDDocEREREYkDJZvSZzjn+M3TSyjKTuErR48MOhzZS1NKcggnhJirazdFREREBoTEoAMQ6a6nFmzhgw2VDNn0BjPO+We7yyxavIRT4xyXdE9KUgIHDsvm3bU72FnXRE5aOOiQRERERKQHKdmUPqG5NcLvn11KuGEHn//SlwlZ+/fVfO+yM+McmeyNQ0pyeH/dTt5Zt4OTJhQGHY6IiIiI9CB1o5U+4f4561hTXkfBtnc6TDSl90tPTmRiUSZLNlVT29gSdDgiIiIi0oOUbEqvV9PYwp9fWM4RI/NIr9kQdDiynw4bkUvEOd5bvzPoUERERESkBynZlF7vltdWUV7bxI9Pn4jaNPu+nLQwYwdnsGBDJa0hXbcpIiIi0l8p2ZReraK2iVtfX8Vpk4cwpSQn6HAkRg4rzaWpNcLO3PFBhyIiIiIiPUTJpvRqN722krrmVr73qXFBhyIxNDgzhRF5aezIm0RDc2vQ4YiIiIhID1CyKb3WtupG7n5rLZ85eCjjCjODDkdibGppLq2JqTw0b33QoYiIiIhID1CyKb3Wja+spLGlle+cNDboUKQHFOekklJXxk2vraKlNRJ0OCIiIiISY0o2pVfaUtnAvbPXcvahwxhVkBF0ONIDzIy88gVs2FHPkws2Bx2OiIiIiMSYkk3plf7+8goiEadWzX4uo2Y9YwdncOMrK3HOBR2OSKDM7HYzKzOzhVFlvzCzjWb2vv84PWrej81shZktM7PpUeWn+mUrzOzqeO+HiIhIm0CSTTNLMbM5ZvaBmS0ys1/65SPNbLZfQT5oZmG/PNl/vcKfXxpE3BIfG3fW88DcdXx+agkleWlBhyM9yICvf3I0S7dU8/KysqDDEQnancCp7ZTf4Jyb4j+eAjCzScD5wAH+Ov8wswQzSwD+DpwGTAIu8JcVERGJu8SA3rcRONE5V2NmScAbZvY0cBVepfqAmf0TuBS40X/e4ZwbY2bnA78FzgsodulhN76yAoBvnTgm4Eikpy1csICbrrmKxDGf44p/PMnwtU/vmjc4L4d77rglwOhE4ss599penEydATzgnGsEVpvZCmCaP2+Fc24VgJk94C+7ONbxioiIdCWQlk3nqfFfJvkPB5wIPOyX3wWc5U/P8F/jzz/JzCw+0Uo8ba1q4N/zNnDOYcMYmpMadDjSw5ojjtOu/BVHHzCS+rRCDvzS/3Dqlddx6pXXUVaxM+jwRHqLK81svt/NNtcvKwaih3Le4Jd1VC4iIhJ3QbVs4nf1eQcYg9flZyWw0znX4i8SXUHuqjydcy1mVgnkA9vjGrT0uLN/ehNNaaOYdd8NTL+r5mPzFy1e0m4fM+nbDhiaxezV5byzdgfFOskgEu1G4Fq8E7LXAn8EvhKLDZvZ5cDlAMOHD4/FJkVERHYTWLLpnGsFpphZDvAoMGF/t6mKs2+rqG1iU+oIJhRlM/3kH7e7zHuXnRnnqCQekhJCTCnJYdaqCrbXNDIoIznokER6Befc1rZpM7sFeMJ/uREoiVp0mF9GJ+V7bvtm4GaAqVOnaoQuERGJucBHo3XO7QReBo4CcsysLQGOriB3Var+/GygvJ1t3eycm+qcm1pQUNDToUuM3f7GapwlMnVEbtcLS79z8LAckhKMeWt3BB2KSK9hZkVRLz8LtI1UOxM43x9AbyQwFpgDzAXG+gPuhfEGEZoZz5hFRETaBDUabYHfoomZpQKfApbgJZ3n+ItdDDzmT8/0X+PPf8npPgn9SmV9M3e9tYaM6rXkq1VrQEpJSmBycTYfbq2msr456HBE4s7M7gfeBsab2QYzuxT4nZktMLP5wAnA9wCcc4uAf+MN/PMMcIVzrtW/FOVK4Fm8evXf/rIiIiJxF1Q32iLgLv+6zRBeZfiEmS0GHjCzXwHvAbf5y98G3OOPtleBd6ZW+pF73l5DdWMLI7bPxzv3IAPRISU5fLB+J++uU+umDDzOuQvaKb6tnbK25a8Drmun/CngqRiGJiIisk/2K9k0s6Odc292VbYn59x84JB2ylfx0dDt0eUNwOf3J1bpveqbWrntjdWcML6ATUsqgg5HApSZksSEIVks2lRFaUJK0OGI7JN9rRtFRET6m/3tRvvXbpaJdOjhdzewo66Zr39ydNChSC8wdUQurRHHjryJQYcisq9UN4qIiLCPLZtmdhTwCaDAzK6KmpUFJMQiMBkYIhHH7W+s5qBh2UwbmRd0ONIL5KaHGVOQwarWiVQ3NJOZkhR0SCLdorpRRERkd/vashkGMvCS1cyoRxUfDfAj0qWXlpaxenstXz12FGYWdDjSS0wtzSWSEObeWeuCDkVkb6huFBERibJPLZvOuVeBV83sTufc2hjHJAPILa+vYmh2CqdNHhJ0KNKLFGalkFazkVtfD3PxJ0aQFg7slsAi3aa6UUREZHf7+wsu2cxuBkqjt+WcO3E/tysDwIINlcxeXcFPTp9AUkLgt3yVXiZ/+weszyjmvtnr+Oqxo4IOR2RvqG4UERFh/5PNh4B/ArcCrfsfjgwkt76xivRwAudPGx50KNILrZrzItkFh/Drx+p48I8/IeR2/xczOC+He+64JaDoRDqlulFERIT9TzZbnHM3xiQSGVA27aznyfmbueioUrI0AIy0ozniOPWYqTzy3kaKP3MVB5fk7Db/mb/9NJjARLqmulFERIT9v/XJ42b2TTMrMrO8tkdMIpN+7a631xBxjkuOLg06FOnFhuWmMjQ7hXlrd9ASiQQdjkh3qW4UERFh/1s2L/affxhV5gBdYCUdqmls4b7Z6zhtchEleWlBhyO9mJkxbWQe/31/E0s2VXPgsOygQxLpDtWNIiIi7Gey6ZwbGatAZOB4aN56qhta+Oqx+vpI14bnpTEkK4W5ayuYNDSLhJBukSO9m+pGERERz34lm2Z2UXvlzrm792e70n+1Rhy3v7maw0bkcsjw3KDDkT6grXVz5gebWLqligOGqnVTejfVjSIiIp797UZ7eNR0CnAS8C6gClXa9dyiLayvqOenp08MOhTpQ0rz0xicmczcNTuYOCSLkFo3pXdT3SgiIsL+d6P9VvRrM8sBHtifbUr/dsvrqxiel8anJg0JOhTpQ9paN5+Yv5llW6uZWJQVdEgiHVLdKCIi4tnfls091QK6VkXa9c7aHby7bie/OHOSrruTvTZqUDr5GWHmrqlg/JBMFi5YwPQZn+9wed2HU3oR1Y0iIjIg7e81m4/jjbAHkABMBP69v0FJ/3T7G6vJTEnkudt+y/2/K293mUWLl3BqnOOSvsHMOKI0j6cWbmFFWY13H84rr+twed2HU4KiulFERMSzvy2bf4iabgHWOuc27Oc2pR9aX1HH0ws3c9lxo3jlvfIOk4T3LjszzpFJXzJmcAZ5aWHmrK4A1DouvZbqRhERESC0Pys7514FlgKZQC7QFIugpP+54801hMz48idKgw5F+jAz4/CRuZTXNpEwbHLQ4Yi0S3WjiIiIZ7+STTM7F5gDfB44F5htZufEIjDpPyrrm3lw7jrOOKiIouzUoMORPm5cYSY5qUkkHfApnHNdryASZ6obRUREPPvbjfanwOHOuTIAMysAXgAe3t/ApP94cO46apta+eqxo4IORfqBkBmHl+bxfH0zq7fXMqogI+iQRPakulFERIT9bNkEQm2Vqa88BtuUfqS5NcIdb67hyFF5TC7ODjoc6SfGD8kkUlPOnDUVat2U3kh1o4iICPvfsvmMmT0L3O+/Pg94aj+3Kf3IUws2s7mygV+dpevrJHYSQkbz4hfZmnEuayvqKM1PDzokkWiqG0VERNjHM61mNsbMjnbO/RC4CTjIf7wN3BzD+KQPc85x2xurGVWQzgnjBwcdjvQzrWvmkpGcyLw1O4IORQRQ3SgiIrKnfe3W8yegCsA594hz7irn3FXAo/48EeasrmD+hkouPWYkoZBuUyExFmnlkOE5bNxZz5bKhqCjEQHVjSIiIrvZ12Sz0Dm3YM9Cv6x0vyKSfuPWN1aTm5bE2YcMCzoU6acmD80mnBjinXVq3ZReQXWjiIhIlH1NNnM6mad7Wwirt9fywpKtfOnIEaSGE4IOR/qpcGKIg4qzWVFWw8463cpQApfTyTzVjSIiMuDsa7I5z8wu27PQzL4KvLN/IUl/cPsbq0kKhbjwqBFBhyL93JSSHBLMeHfdzqBDEVHdKCIiEmVfR6P9LvComX2RjyrQqUAY+GwM4pI+bEdtEw+9s54ZU4YyODMl6HCkn0tPTmRCUSaLN1dx5Kg80sL7O8i2yD77LqobRUREdtmnX2XOua3AJ8zsBKDtnhZPOudeillk0mfdN2cdDc0RvnrsqKBDkQHisOG5LNpUxQfrKzlqdH7Q4cgApbpRRERkd/vVBOCcexl4OUaxSD/Q2NLKnW+t4dixgxg/JDPocGSAyE0PM2pQOvM37GRqaS5JCft6hYDI/lPdKCIi4tEvMompxz/YzLbqRi5Tq6bE2WEjcmloibBoU1XQoYiIiIgISjYlhpxz3Pr6KsYXZnLs2EFBhyMDzNCcVIqyU3h33Q4iERd0OCIiIiIDnkbSkJh5c0U5S7dU87tzDsLMgg5HBqDDRuTyxPzNLC+rYeGCBUyf8fl2lxucl8M9d9wS5+hEREREBhYlmxIzN7++ikEZyTz2919xW0VFh8stWryEU+MYlwwcowalk5uWxDvrdtAccZx65XXtLvfM334a58hEREREBh4lmxITSzZX8dqH2/jh9PHMnFvR4Y98gPcuOzOOkclAYmYcOiKXF5eUESocG3Q4IiIiIgOartmUmLj5tVWkhRP40hEjgg5FBrgJQzJJCyeQNPHEoEMRERERGdCUbMp+27CjjpkfbOKCacPJTksKOhwZ4BJDIQ4pySFhyHjKqhuCDkdERERkwFKyKfvt9jfWYMBXjhkZdCgiABxYnI1rbuCdtTuCDkWk28zsdjMrM7OFUWV5Zva8mS33n3P9cjOzv5jZCjObb2aHRq1zsb/8cjO7OIh9ERERgQCSTTMrMbOXzWyxmS0ys+/45XtdoUrwKuuaeWDuOs48eCjFOalBhyMCQHJSAi0r3mZ5WQ2V9c1BhyPSXXfCx8ZPuxp40Tk3FnjRfw1wGjDWf1wO3AheXQr8HDgCmAb8vK0+FRERibcgWjZbgO875yYBRwJXmNkk9rJCld7h3tlrqWtq5fLjRgUdishuWj58DQPeW6fWTekbnHOvAXsO5T0DuMufvgs4K6r8bueZBeSYWREwHXjeOVfhnNsBPM/HE1gREZG4iHuy6Zzb7Jx715+uBpYAxex9hSoBa2hu5Y43V/PJcQVMLMoKOhyR3bj6SsYPyWTRpirqm1qDDkdkXxU65zb701uAQn+6GFgftdwGv6yjchERkbgL9JpNMysFDgFms/cVqgTskXc3sr2mia99Uq2a0jsdNjyXlohj/oadQYcist+ccw5wsdqemV1uZvPMbN62bdtitVkREZFdAks2zSwD+A/wXedcVfS8fa1QVXHGT2vEccvrqzhoWDZHjcoPOhyRduVnJDNyUDofbKikuTUSdDgi+2JrW28e/7nML98IlEQtN8wv66j8Y5xzNzvnpjrnphYUFMQ8cBERkUCSTTNLwks0/+Wce8Qv3tsK9WNUccbP84u3snp7LZcfNwozCzockQ4dNjyX+uZWFm+u6nphkd5nJtA2ouzFwGNR5Rf5g+gdCVT6vYOeBU4xs1x/YKBT/LIe9+qH28g89Aw27aynRSd3RESEYEajNeA2YIlz7vqoWXtboUpAnHPc9NpKhuelceoBQ4IOR6RTQ3NSGJKVwnvrdhKJxKwHokjMmdn9wNvAeDPbYGaXAr8BPmVmy4GT/dcATwGrgBXALcA3AZxzFcC1wFz/cY1f1uOeWbiFvE99nYfe2cDds9aytrw2Hm8rIiK9WBAtm0cDFwInmtn7/uN09rJCleC8vbKc99bt5LJjR5KYoFu1Su9mZhw2IpfK+mZWbKsJOhyRDjnnLnDOFTnnkpxzw5xztznnyp1zJznnxjrnTm5LHP1B865wzo12zh3onJsXtZ3bnXNj/Mcd8Yr//z47mQ1/v4jTDxxCYsj47/ubmLWqPF5vLyIivVBivN/QOfcG0FG/y5PaWd4BV/RoULJX/vLScgZnJvP5qSVdLyzSC4wuSCcvLcyc1RWMHZwRdDgi/ZKZ0VpTwdjBmYzMT+elZWXMXl1BSlICU0pygg5PREQCoGYp2StzVlcwa1UFX//kaFKSEoIOR6RbzIxpI/Mor21iRZlaN0V6WmJCiJMnFDK6IJ1XP9zG6u3qUisiMhAp2ZS98teXljMoI8wF04YHHYrIXhlbmEFeWpjZqytid+8IEelQKGScesAQ8jPCvLhkKw3Nut+tiMhAo2RTuu2dtTt4ffl2Lj9uFKlhtWpK3xKKat2sziwNOhyRASExIcQpEwupa27lteW6JZmIyEAT92s2pe/660vLyUsP88qdv+c/f+x40IdFi5dwahzjEumusYUZzFkdprzgYCIRRyik2/aI9LTBWSlMHZHL3DU7mFSUxbDctKBDEhGROFGyKd0yf8NOXlm2jR9OH8/MeeWceuV1HS773mVnxjEyke5ra918pq6JpxZu5oyDhgYdksiAMK00jyWbq3lzRTnnTk3V/ZlFRAYIdaOVbvnzC8vJTk3ioqNGBB2KyH4ZW5hBuHEHf35hOa2676ZIXCQmhDhiZB5bqhpYpcGCREQGDCWb0qV5ayp4cWkZlx83isyUpKDDEdkvITPyt33A8rIaHnl3Q9DhiAwYk4qyyE1L4q2V5Xh3NRMRkf5OyaZ0yjnH755ZRkFmMpccXRp0OCIxkVm9hoNLcvjDc8uob9IImSLxEAoZR4zMp6K2Sa2bIiIDhJJN6dQrH25jzpoKvn3iGNLCusRX+gcD/ufTE9la1cgtr68KOhyRAWPs4AyyUhJ5Z+2OoEMREZE4ULIpHYpEvFbN4XlpnHe47qsp/cvhpXmcesAQ/vnqSsqqG4IOR2RACIWMQ4bnsrmygc2V9UGHIyIiPUzJpnToiQWbWbK5iqs+NY5wor4q0v/86LQJNLVEuOH55UGHIjJgTCrKIjkxpNZNEZEBQBmEtKu5NcIfn1vGhCGZfOZg3R5C+qeRg9K58KgRPDh3Hcu2VAcdjsiAEE4McWBxNqu21VLd0Bx0OCIi0oOUbEq77pu9jrXldfxw+njd+F76tW+fOJb05ER+9eRijZApEieTi7NxwMJNVUGHIiIiPUjJpnzMjtomrn/+Q44ek8+JEwYHHY5Ij8pND3PVp8bx+vLtPLlgc9DhiAwI2alJjMhPY9HGSt3vVkSkH1OyKR/zx+eXUdPYws/PPAAztWpK/3fhkSOYXJzFNY8vVrc+kTg5qDib2qZWVus2KCIi/ZbuZSG7Wbypivtmr+Oio0oZV5gZdDgiPWLhggVMn/H53crqU/IpKz2DY7/9J6pfv4cx48Z3uP7gvBzuueOWng5TpF8rHZRORnIiCzdVMmZwRtDhiIhID1CyKbs49//Zu+84uep6/+Ovz8xs7y2bzW42nYSEUELoCAhKURQLdgER5RasqPdiu/Zrx/LTq2IDUZqIgooo0osQQgJskiWkJ9t77zvf3x/nbNiETbKbzM6Z3X0/H4957Mypnz07O2c+3+r40p83kpOWxMdfc1TQ4YhMmsGo48IPfe0Vyx/a3ECFLacntWjM9SPu+9FnJzM8kRkhZMbRJVms3dlKd/8QGSn6SiIiMt2oGa3sdW9FHU/vaOET5y8lJz0p6HBE4u70RQWkJYdJXn0pUQ0WJDLpjp6djQNe1GjQIiLTkpJNAaC7f4j/vbeSo0uyedfJ5UGHIxKIlEiYs5YUES4o5/k9bUGHIzLt5WUkMzs7lcraDo0GLSIyDSnZFAC+/ffN1LT38pVLVhDWVCcygx1VnMlwzSae3NZMW89A0OGITHtHl2TR3D1AY1d/0KGIiEiMqYOE8OyuVm76104uP3Ueq+fnc9mVH6Shpe2A22/cVMmF8QtPJK7MjIFnfk9a+TH8s7KBt64q1ajMIpPoqOIsHn2picraTmZlpQYdjoiIxJCSzRmuf2iY//7DC5Rkp/KpC5cB0NDSdtDBUdZ/8A3xCk8kEK63nbOWFPLPygZeqGrnuLm5QYckMm2lJoVZUJjB5rpOzlxcqNY1IiLTiJrRznA/fnArWxu6+NpbVpKpkQBF9lpeks28gnQe39pEe6/m3hSZTEeXZNE7OMyuFs25KSIylrnl8zCzmDzmls+LW9zKLmawytoO/u/hbbz5hFJevXRW0OGIJBQz47xls/jtU7u5f1O9mtOKTKJ5BRmkJYV5sbaThYWac1NEZH9Ve3Zz/T82x+RY155/4LnEY001mzNU/9Aw197xPDlpSXz+4uVBhyOSkLJSkzjrqEKq23pZt7st6HBEpq1wyFhanMX2xm76BoeDDkdERGJEyeYM9a37NlNZ28G3Lj2W/IzkoMMRSVjLS7JZVJTBk9uaaOjsCzockWlrWUkWw86xpaEr6FBERCRGlGzOQA9vbuCXj+/gitPmcd7RxUGHI5LQzIzzji4mLTnMfRvqGByOBh2SyLQ0KyuFvPQkNtd1Bh2KiIjEiPpszjCNnf188vfPs7Q4i0+/7uigwxGZEtKSwpy/fDZ/XF/N41uagg5HZFoyM5bNzuZf25sJZxUFHY5ITM0tn0fVnt1jrrOkVCI5xURyZhHOnkU4I5dwWjah9BzC6TmE0rIJpaRjkWQskoJFksAMnAMXxUWHcQO9RPt7iQ70EO3tYLirleGuFoa7mhlsq2WopYah9npwLxeYls0tZ8/uXfG6BDJDKdmcQZxzfOrO5+noG+J3HziV1KRw0CGJTBnl+emsKs9l3e42SjPLgg5HZFpaOjuLf21vJmP52UGHIhJTVVVVfP5PFbR0D9DcNUBL9wCtPQN09g3RO0Y/5dSkEGlJYdKSw6QlhUmJhImEjUjIeOi2n/Lad/8nDodzEHWOgeEoA0Peo28wSlf/ED0DQ0Tdy8cMGeSmJVOUlcKsrBR+/5UP0tU/pNkIZFLp3TWD/OzR7Ty8uZEvvXEFX/30tTS0tI253cZNlVwY39BEpoTTFhWwu6WHqqJTOO8tlxEZHrsP56z8XG7+9c/jHJ3I1JeTlkRJTioDK84JOhSRw9bSPUBFdTsbqtvZXNfJS/WdlF97J7/518u1iFmpEfLSk5mVnUJ2ahLZqUnkpCWRlRohLTlM6CCjn//xsd9y2uc/f8g4nHP0DAzT3jtIa88AbT2DNHcPUN3Wy+b6Tma/6+sc+8W/c0xpDqcsyOeUBQWctCCfnLSkmFwHEVCyOWM8sbWJb933Iq8/toTLT5vHLd9s48IPfW3Mbdd/8A1xjk5kaoiEQly4YjY3P9HN0Gnv5/XHzRlzOpT7fvTZAKITmR6Wzs6itn0+lbUdHF2SHXQ4Igc1OrGsqGqnorqd6rbevevL8tJYMiuTp+/+M5e+7z/Jz0wmPz2Z5MjkD5tiZmSkRMhIiTAnN22fdT0DQ3z1w5eTUrqMNXOP4fmdy/j5Y0k4F2Wgfjt929fSs3UNA7VbADf2CXxqjisHo2RzBqhq7eFDt6xjUVEm33rrsZorUOQIFGSmMPjcPexa/Vae29PGCeV5QYckMq0cNSuLhzbV8qfnqpVsSkKJRh3bm7pYu7OVtbtaeXZXKzuauveun1+QzgnluVxx+jyOKc1hxZycvbWEN77/FJZ/5rqgQn+F9OQIfTue5X9/dgsAQ8NR6jr6qG7tZXdeOrWzF5Nz+jtJSwozvzCdBYUZzMvPGDNJjuecjTL1KNmc5voGh/mP365jaNjxs8tOJEPt8kWO2NDWJ1h24eU8vrWJ2TmplOSkHXonERmXtOQwvTvWcc9zmfz3BcsIhVRAKhN3sAF5xs1CpJUuI6lsBSmlR5NSuoxwmlcAMtzTTn91pfeofYmB+u3s6u/mkRjEHoRIOERZXjpleemcQgF9g8PsbO5mR1M32xu7qaztJBwyFhRksKQ4kwWFGSSFNamFHJoyj2nMOcf/3L2Biup2brjsRBYWZQYdksi08drlxdy6Zjf3VtTx7lPKSdOAWyIx073pYWoXn8zTO1o4bVFB0OHIFFS1ZzfX/2PzhPfr6h9iV3M3u5t72N3SQ9+QN3prXnoSc3LTKMlJZU5OGrnpSZitHvdxp1rtX2pSmGWzs1k2O5to1FHT3svWhi62NHSxtbGLSMhYUOglnhZJCTpcSWBKNqexGx7dzh1rq/jwuYs5f8XsoMMRmVZSk8K8bmUJv19bxd831nHJAfpvisjE9W55mozkMHc/V61kUybVUDRKTVsfu5q72dXSQ3PXAADpyWEWFGXw6M8+z2e+/RPSk2fuV+ZQyPbWep51VBE1bb28VN+1N/ks+/Bv+cit63nzCaW8akkhEdV4yigz9z9nmrtvQy3fuO9FLj62hI+/5qigwxGZloqzUznrqEIe2tzIMztbOXlBftAhiUwLbqifC1bM5q8VtXzxjSs0VZfEjHOOtp5BdrX0sKu5m6rWXoaijrAZJbmpnLG4gHn5GRRmJmNm3Ff56IxONPcXspcTz3OOKqKqrZff/PqXPJKTzT3P11CYmczFx87hzSeUcmxZjgphRcnmdPT8njY+dvtzHD83l++87Tj1dxGZRCtLc6hp6+Op7c3MykphfmFG0CGJTAuXnFDKXeureXhzAxceUxJ0ODKF9Q8Ns6ell10tXvPYjr4hAHLTklgxJ5vygnTKctPjMkLsdBIKGeX56bT8/UfU/OX7PLy5kT+tr+aWp3dz45M7WViUwZuPL+VNJ5QyNz896HAlIIEkm2b2K+BioME5d4y/LB+4HZgP7ATe7pxrNa9I5AfA64Ae4H3OuXVBxD0VVLX2cNVNaynKSuHnl69WabDIJDMzzjt6Fs3d/fxtQx3vOGlu0CHJNGRmO4FOYBgYcs6tnu73zTMWFVCYmcyf1tco2ZQJiUYdybMXs2ZHC7tauqlt78M5SAobc/PSOXFeHvMKMjSfZAylRMJcsGI2F6yYTXvvIH+rqOWu9dV89/6X+O79L7F6Xh5vXlXK61eWkJueHHS4EkdB1WzeCPwI+M2oZdcBDzjnvmFm1/mv/xu4CFjiP04BfuL/lP109A3y/hufoX9omNuuPoXCTHXYFomHpHCINxw7h9ue2cM9z9dQFNKNVCbFq51zTaNeT+v7ZiQc4g3HzeF3T+2mvXdQiYEcVENnH4+91MSjWxp5bEsTJVd8n3/5LU5OLM9jXkE6JTlphNXaK/YsdMDmsuHsIjKWn8OTK17N2l2tfObO9fRuW0vXxgfp3fYMDA/t3VbzdU5PgSSbzrlHzWz+fosvAc7xn98EPIx307wE+I1zzgFPmVmumZU452rjFO6UMDgc5ZrfrWN7Yzc3vf9kvvTf19LQ0nbA7TduquTC+IUnMu1lpyVx8bEl/GFdFYOl5zA0HNUgCTLZpv19803Hl/LrJ3Zy34Za3nFSedDhSALpGxxm3e5WHn2piUdfamRTbQcAhZnJnH1UET//8se57ps/1pRv8eCihxz51zlHY1c/L9Z1sjntTNKPOo2USIglszJZNjubObmpfOKCZXEKWOIpkf4Di0fdCOuAYv95KbBn1HZV/rIpd9OcLM45PvvHCh7b0sQ337qSMxYX8uWWNi780NcOuM/6D74hjhGKzAxzctM4b1kx91fCl/68iS9fskKDI0isOOAfZuaAnznnbmAG3DePLcthQWEGf1pfo2RzhhsajvJCdTv/2tbME1ubWLurlYGhKJGQceK8PD51wVLOPqqI5SXZhELG99/5sBLNBGJmzMpKZVZWKmcuKmRPa4+XeNZ3sqGmg6zUCLmvei/bG7s0Vd80k5D/hc45599QJ8TMrgauBigvnzk3pW/9fTN3rK3iI+ct0c1YJGDL52Sz9vF/cvNTMDsnlWtevThu577syg8etEXDrPxcbv71z+MWj8TUmc65ajObBdxvZi+OXnk4982pcM80My45fg4/eGALte29lOSkBR2SxEk06qis6+Bf25p5clsza3a00NXvNblcNjuLy06dx2kLCzhlYT5ZqWpiPZWEQsa8ggzmFWQwOBxlW2MXL9Z20nHq2zj3u49wQnkub11VxsXHqn/ndJBIyWb9SDMfMysBGvzl1cDoETfK/GWv4Jf03gCwevXqCSerU9EvH9/BTx7exrtPKefjr1kSdDgiAhQ1PMvZ513At/++maKsFN6+Oj6DBjUcokXDfT/6bFzikNhzzlX7PxvM7I/AyRzhfXOq3DPfdHwp3//nFu55roZ/O3tR0OHIJHHOsb2pmye3NvHktmae2t5Ma88gAAsLM7jk+DmcvqiQUxfmU6AxKaaNpHCIZbOzWTY7m0+95TR+/Jc1/GFdFZ/70wa+/OdNnHf0LN66qoyzlxaRpK4pU1IiJZv3AFcA3/B/3j1q+YfM7Da8AQ7ap2K/k8nwp/XVfOUvm7jomNl85ZJj1FxPJEEY8K1Lj6O5e4BP31VBQUYy5x1dfMj9RMZiZhlAyDnX6T8/H/gyM+S+Ob8wg+Pn5vInJZvTTlVrD09ua/ZrL5uo7+gHYE5OKucdXczpiwo4bVGBarRnLXqxtAABAABJREFUiOGuFv7t7EVcfdZCNtZ08Id1VdzzXA1/21BHQUYybzx+Dm9dVcaKOdn6zjuFBDX1ya14gxoUmlkV8AW8m+UdZnYVsAt4u7/5vXjDt2/FG8L9yrgHnIAeeamRT/7+eU5dmM/33nG8RlcTSTDJkRA/ee+JvOuGp7jmlnXcfNUpnDQ/P+iwZGoqBv7of7mKALc45+4zs2eYIffNNx0/hy/+eROb6zpZOjsr6HBkDHPL51G1Z/dBtwml55I671hS5x1HavmxJOV5U9oMd7fRt/sF+nY9T+5AE09sfEbJxEw01qi2oTBpC06ke+V5/Kr9ZH79xE4GGnfSveFBujc9zHBXyysOo1FtE0tQo9G+6wCrzhtjWwdcM7kRTS3P7WnjP377LEuKs7hBc2mKJJwNFRVccMnbABgKpzI87yLe/uNHKdtzPzXPP8bio5YecF/1q5T9Oee2A8eNsbyZGXLfvPi4OXzlr5X8cX01112kESsTUdWe3a8YkXRoOEp1Wy+7W3rY3dJDU9cAAMnhEGV5aczNT6csL42CjMWYnQTAtRccTSik5pIz0iFGte0bHOal+k4qs1OpK5pP/qvfT3l+OkeXZLOoKGPvCPDXnn/ge6zEXyI1o5Vx2NrQxZW/XkO0p4OBDbfwtgd/OOZ2mtpEJDiDUbdP38mu/iH+sK6KukWvp3vLVvWrFJmgwswUXr10Fnc+W8Unzj9KfbcSlHOOpq4BdrV0s7ulh5q2PoajjrAZJbmpnL6ogLn56czKTCF0oBZZ45hGYzyUcEw/qUlhji3L5diyXFp7Bqis7eDFuk7u21hHaiTEspJsjpmTHXSYsh8lm1NIbXsvV/xqDeGQUbLzPi7+j88dcFtNbSKSODJTIrx1VRl/eLaKgXOu1qiaIofh3afM5Z+V9fxzUz0XrSwJOhzxDQ5HeWZHC3mv+Td+9cTOvSPGFmQkc2xpDuX56ZTmpamAQGIqLz2Z0xcVctrCAna39LCxpoMXqtp4bk8bs9/7He54Zg+vP7ZE098kAP0FpoiW7gEu/+Ua2nsHue3qU/nEf94QdEgiMgEjCefP//Y0f1xfzcXHzqE8Pz3osESmjLOPmsWcnFRuWbNbyWbABoaiPPJSI3+rqOWBFxto7x0k89jzmZWVwqkL85lXkEGmvuRLHJi9PI1Kz8AQL9Z18kBTOv/1hxf40p838sbj5/DOk8o5tixH/YADok+CKaCjb5ArfrWG3S093HjlyRxTmhN0SCJyGDJTI/Q/9BOK3vM17n6umtceXcyyEjX5ERmPcMh4x0nlfO+fL7G7uYfyAhXWxMJ4BvYZkVxyFBkrziXj6FcRTs9huLeT3q1r6NnyFH071/Hxv74wydGKHFh6coRV5Xn89gP/ydqdzdy6Zg9/Wl/DrWv2sGx2Fu86uZw3HV9KTrrmZY0nJZsJrndgmA/cuJbK2g5uuPxETltUEHRIInIEXG87bzuxjL+8UMvfN9XT1T/EifPyVOIqMg5vP6mMHzzwErc9s5v/ulADBcXCWAP7jNbdP8TG2g5erO2gtWeQcMhYWJjB0SXZlOenEw6tAv5dfSQloZw4L58T5+XzP29Yzp+fr+G2NXv4wj0b+dq9lbzumNm846RyTl2Yr3tvHCjZTGADQ1H+/bfP8syuFn74zhM4d5nm6ROZDlIiYS45fg73b6rniW3NtPcOcvZRRXtH0hORsZXkpHHusmLuWFvFx1+rgYImi3OO2vY+nq9qY2tDF1EHpblprJqXx5JZmaRENAq+JLCxplABkmYtJPPY87mr6xz+9FwNg601dFX8k+6KBxjuan7F9ppCJTaUbB7CZVd+kIaWtjHXTeYUBUPDUT52+3oeeamRb7xlJW84bs6knEdEghEJhbhwxWyyU5tZu6uVuo4+Xqd+aCKHpIGCJs/QcJTN9Z28UNVOQ2c/yZGQP/pnDnnpyUGHJzI+hxjReGg4ytaGLjbWpFGVN4f8sy5nXkE6K+bksKAwY+/c9aqtjw0lm4fQ0NJ2wGkKJmuKgmjU8em7Kri3oo7Pvf5o3nly+aScR0SCZWacsbiQOblp/GNTHbeu2U1h9oKgwxJJaBooKPb6h4apqGpn3e42egeHKchI5tyls1g6O4vkiGqPZXqJhL1pUpaVZNPWM8Cm2g421Xbw14pa0pLCHF2SxYo5Gh8lVvQJkmCcc3zlr5v4/bNVfOS8JXzgVQuDDklEJtmCwgzefXI5RZkp1Jaezb/f/CzVbb1BhyWSkEYGCnpsSxO7mruDDmdKa+0eIOfMd/PrJ3byxLZmirJSeMsJpbznlHJWluUo0ZRpL9efQuX9py/gjcfNYU5uKs/taePmp3Yx+7Lr+cVj26lr7ws6zClNNZsJ5nv3v8Svn9jJ+89YwLpbvs0FP2obc7uNmyq5ML6hicgkykpN4q2ryrj19tt5OOkkHvluIx8+bzEfOHOhvvCJ7OedJ8/lRw9t4ddP7OSLb1wRdDhTTnNXPz97dDu/fWoXuWe8m7K8NFbPz2d2dmrQoYkEIhQyFhRmsKAwg+7+ITbXdfLPuq189a+VfO3eSk6an88bjpvD646ZTUFmStDhTilKNhPI9+5/iR8+uJW3ry7j8xcfzYW/OHAT3vUffEOcoxORyRYKGQXNFfzpu5/kK3/ZxLfu28yda6v4rwuXccGKYo2aJ+Irzk7lDcfN4Y61e/j4a47SVAbj1Nk3yM8f28EvH9tO7+AwbzxuDv/34Tfx0VvuDzo0kYSRkRJh1bw8fvvBj7GtoZO/vFDLPc/X8Pk/beALd29gVXker142i/OOnsXS4qy43pujztHRO0hL9wCdfUP0Dg7TNzhM32AU5xwWMlIjIdJTIuSlJVGYlUJuWlKg3x+UbCYA59zeRPNtJ5bx9bccqy+VIjNYWV46P7tsNQ9tbuBrf63k33/7LCfOy+PTFy1j9fz8oMMTSQgfOHMhd62r5pY1u/mPcxYFHU5C6xsc5rdP7eLHD22ltWeQ162czbWvXcriWZn84F3jm2NTZCZaWJTJR85bwofPXcyLdZ38raKWBzc38O2/b+bbf9/MrKwUTlqQz8nz8zlpfj5LijNjMkr20HCUXS09bKnvYmtDJy/Vd1Hyvh/yfw9vYzjq9tk2JRIiNSmMmTfuS/9QlP6h6N71aUlh5hWks3hWJvMK0omE4ttaSslmwJxzfPcfL/Gjh7byjtVz+fpbVhIKKdEUmak2VFRwwSVv2/s6hFGcu5jnBk/g0p+2UtBXw+2feReLZ2UGGKVI8JbPyebMxYXc+OQO3n/mfE3HMYZo1HHnuiq+d/9L1Lb38aolhXzqgqUcW5YbdGgiU4qZcXRJNkeXZHPt+Uup7+jjoRcb+Nf2Zp7Z0cJfX6gFIDkcYmFRBkcVZ7GoKJNZ2SkUZaZQlJVCRkqEkEHIr1Dq6h+irWeQtt4BGjv72d3Sw56WHnY1e4+B4ZcTxtLcNIa7WjiubBX5GckUZKSQlRohNSm8d/Tc0QaHo7T2DNDQ0U91Wy87m7p5sa6TtKQwx5RmE86MX8G1ks0ARaOO/723kl88voN3nTyXr71JiabITDcYdWM2nx8cjrJ+dxtPbxnggu8/ypWnz+cT5y8lLVlfsGXm+vezF/HeXz7Nnc9W8Z5T5gUdTlzNLZ9H1Z4D10qmzD2GvHM/QMrsxfTXbKb1kRv57e4KfhvHGEWmtAPM1zmWcHYRKaXLSZ41n7bCeWwomkckp3hCp0tPDlOen878wgzOPXoWR83KYklxJouKMslIiWB2Hq+6+l3jOlZSOMSsrFRmZaVyTGkOw1HHnpYeKqrbWbuzlaSi+ROK7Ugo2QxI3+Awn7jjef5aUcv7Tp/P/1y8XImmiBxQUjjEyQvyabjvR5z83v/mF4/v4P7Ken7wzhM4fm5u0OGJBOKMxQWsKs/l/x7axttOnDujBtOq2rN7zLkE23oGeHxrE9sau8lMiXDm4kKOOncxdtnFYx5HcwmKHMAh5us8lKFolN6BYb529Zv56wOP0Ts4TNQ57xGFrNQIuenJ5KYn+bWVyZPWjS4cMuYXZjC/MIOO3kG++M31k3KesSjZDEBr9wAf/M1a1u5q5XOvP5qrzlygPpoiMi6R4X6+/paVXHL8HD5xx/O87adP8rnXL+fy02ZWrY4IeE3bPnLeEt7362f4w7oq3jWD56XuHxpmzY4Wnt/TTigEpy30EvFIDPqPicjERUIhslJDDNRt4TXLJ1bLOZmy05IAd8jtYkXJZpxtqe/k325+lqq2Xpa1reH337mR339n7G01vYmIHMipCwv460fO5BN3PM8X7tnIjqbuON46RBLH2UcVcdzcXH704FbefEIpqUkzq2l5NOrYUNPOU9tb6B0cZnlJNqctKiAzRV/xRCR4+iSKE+ccd6zdwxfu2UhmSoRbPnAKn/3orw44tQloehMRObjc9GR+fvlqvvrXSn71xA6y5pxFNOrUJF9mFDPjvy5Yynt+8TS/+ddOrj5r5oxMu6u5m8e2NNHcPUBpbhpnLSlklubKFJEEomQzDnY2dfP5uzfw2JYmTl9UwPffeTyzsnQzEJEjFwoZn7/4aAqzkvnWfXB/ZT3nL9ecnDKznLG4kFcvLeL/PbiVt504l7yM5KBDmlTbG7soesvn+dNzNeSkJfH6lSUsKsrQ/72IJBw15J9Ee1p6+MwfK3jN9Y+wfncbX7lkBTdfdYoSTRGJKTPjP89ZTGHDOl6s6+ThzY04p0a1MrN8+nVH090/xA8e2BJ0KJOmvXeQr/xlE+d/71FSy1dy5uJC3ntqOYtnZSrRFJGEpJrNGHLOUdXay6NbGvlbRR1PbGsiEjLedXI5Hzp3McVq2iIik6ig+QXKV5/Lut1t5Gckc5xGqZUZ5KjiLN5zyjx+86+dvHVVGSvLcoIOKWaGhqPc+swerv/HZtp6B3nnSXP51mVnc+Kfng46NBE5lAlMoTIdKdk8DFHn6OwboiujlF88tp1tjV1sqe9iS0MX7b2DAJTlpfHR85bw9tVzmZObFnDEIjJTnLm4kNaeQR7Z0kheRjLl+elBhyQSN5+8YCn3bazjurte4O5rzpgWI7E+vqWJr/xlE5vrOzl1YT6fv3g5K+bk8I23tgUdmoiMxxFOoTJiqk5TpGRznFq7B3ipvpPdLT3Ud/YzHHVQ/lq++tdK8tKTWFKcxcXHlrB0dhanLypgUZGatIhI7G2oqOCCS9425rqNmyq50IwLV8zmjrV7uLeilnecNJe89Ondf01kRE5aEl98wwquuWUdv3h8B/9+9tQdLGh7Yxf/e28l/6xsoDw/nZ++90QuWKH+2CIytSjZPITu9BLufLaK6rZeAGZlpXBsaQ75Gcm8dN+N/OkX11OQmRJwlCIyUwxG3QFHsR4ZwTo5EuINx83h9mf28Ofna3jH6rmkzLDpIGTmet3K2Vywopjv/mMzZywqnHLNaRs6+vjBA1u4/Zk9pCaFue6iZVx5xnxSIvofFpGpR8nmATR09PGJ3z9P1bwLyOwd5IxFBRxdkk3GqHmrqnoblGiKSEIaGaHyrvVV3LexjjceNyfokETiwsz45luP5aIfPMaHb13HXz7yqikx52RH3yA/e2Qbv3p8J4PDUd59SjkfPncJRVn6niEiU9fU78wwSXLSk2jtGaCofg1XnDaP1fPz90k0RUQSXWleGmcfVcTO5h6e2t4SdDgicZObnsz33nE8u1t6+Nhtz3ldXxJU3+Awv3hsO2d96yF+/NA2Xru8mAc+cTZfvuQYJZoiMuUp2TyAlEiYP3/oTPJbNk2LAQZEZGZaWZrD8pJs1uxsoTOzPOhwROLm1IUFfOENK/hnZT1fv7cy6HBeobt/iJ89so0zv/kQX/1rJStLc/jLh8/kh+86gXkFGUGHJyISE6qqO4hDdcI/2EAdALPyc7n51z+PdVgiIuNmZrx6aRHN3f3UzTmTrQ2dLJ6VFXRYInFxxenz2dHUzS8e30F2WhIfOW9J0CExd9EyOouPJ+ukSwinZdO7Yx3tT97Ob6s28tuggxMRiTElm0fgYAN1AHzn39540GR046ZKLpyMwERERomEQ7x+ZQk3PdLG1Tc/y5+uOYPs1KSgwxKJi89fvJyOvkGuv/8lhoajfPy1RwUyomtNWy83P7ULe+OXyU3JYEFhBifNz6PkvCXwgXdM+HhTdRoEEZlZlGxOokMloyMjR4qITLas1CTmVD3M7uTXce3tz3PDZScSCmkKBZn+wiHj25ceR9iMHz64lZ3NPXzzrceSljz5o7s653hmZys3PbmT+zbW4Zyjd8d6PnD5u5iVlTrp5xcRCZqSTRGRGSK9t57PX7ycL9yzkR88sIWPv/aooEMSiYtwyPjWpccyvzCD7/xjM5vrOvnWpcdy3NzcSTlfXXsfd62v4g/PVrGtsZvs1AgfOHMB7z11HuUFFzPrmisn5bwiIolGyaaIyAxy+WnzqKhu5wcPbKEkJ5V3nqxBg2RmMDOuefViVszJ5ro/VPDm/3uCd55czodevZg5uWlHfPyatl4eqKzn7xvreXJbE1EHq+fl8Y23LOSNx88hPVlfuURk5tEnn4jIDGJm/O+bV9LY2c9n/lhBbnoSFx5TEnRYInFzztJZ/OPas/ju3zdzy5rd3Lm2iguOmc1bTijltEUFpCaNr3ltU1c/63a1sm53G49taWRjTQcA8wvS+dCrF/OWVWXML9SosiIysynZFBGZYZIjIX7y3lW89xdPc80t6/n+OxxvOG5O0GGJxE12ahJfuuQYPnjWQn7x2A7+9Fw1f36+huRwiGPLclhSnElZXjrZqREi4RBDw1Gaugaobe+lpq2Pnc3dVLX2AhAJGcfPzeW6i5bxmqOLWVSUEcgARCIiiUjJpojIDJSeHOGm95/MVTeu5aO3raetZ4D3njpPX5JlRinLS+eLb1zBp1+3jCe3NvPktibW727j7xvraeke2GdbM5iVlUJJThrHz83l8tPmsao8j2NKc8ZdGyoiMtMo2RQRmaGyUpO48f0n8aFb1vP5uzeyqbaDL7xhhb44y4yTEgnz6mWzePWyWXuX9Q0O09E3yHDUEQ4ZJ65czppdOwKMUkRk6lGyKSIyg6UnR/j55au5/v7N/PihbazZ0cK3Lj2OE+flBR2aSKBSk8L7FLxU7drB9f/YfMTH1fyYIjKThIIOQEREghUOGZ+6YBm/ef/J9A1GeetPnuRDt6xja0NX0KGJiIjIFDZlkk0zu9DMNpvZVjO7Luh4RESmm7OOKuK+j72Ka169iAdfbOA11z/Ce3/xNH9cX0Vbz8ChDyAJQ/dMz9xyrx9yLB4iIjJxU6IZrZmFgR8DrwWqgGfM7B7n3KZgIxMRmV6yUpP41AXLuPKMBdz69G5ue2YPH7/9ecIhY2lxFivmZHNMaQ5Hl2QzJzeVWVmpJEemTLnljDAt7pkWilmCF4umr6DmryIih2NKJJvAycBW59x2ADO7DbgEmDo3ThGRBHfZlR+koaVtn2UZQHlqId2ZZdT0llDfMZffP1u1zzaFmcnkZySTkRIhMyVCenKYjJQIGckRksIhksJGJGxEQiEiISPiLwv7z0Ojcgrj5Re2z3JesXxk2+y0JC48ZnYMrsC0MfXvmS6q/pEiItPAVEk2S4E9o15XAacEFIuIyJS0oaKCCy552wHXb9xUySf+764Drv/Ov72RFStXkhNJZyAlj8GkdIYi6Qy2ptOclkXxymPp6h+ivqOP7v5hugeGGBp2DA5H6R8cApucGtD0wXYu/O67J+XYU5TumSIikhDMORd0DIdkZpcCFzrnPuC/vgw4xTn3of22uxq42n+5FIhN25ngFAJNQQcxxeiaTZyu2cTpmk3cVLhm85xzRUEHcaQCvmdOhb9zkHR9Dk7X5+B0fQ5O1+fgJuP6HPK+OVVqNquBuaNel/nL9uGcuwG4IV5BTTYzW+ucWx10HFOJrtnE6ZpNnK7ZxOmaxVVg90z9nQ9O1+fgdH0OTtfn4HR9Di6o6zNVRnV4BlhiZgvMLBl4J3BPwDGJiIgkIt0zRUQkIUyJmk3n3JCZfQj4OxAGfuWc2xhwWCIiIglH90wREUkUUyLZBHDO3QvcG3QccTZtmgTHka7ZxOmaTZyu2cTpmsVRgPdM/Z0PTtfn4HR9Dk7X5+B0fQ4ukOszJQYIEhERERERkallqvTZFBERERERkSlEyWYCMLNUM1tjZs+b2UYz+5K/fIGZPW1mW83sdn+gBxnFzMJmtt7M/uK/1jU7CDPbaWYVZvacma31l+Wb2f1mtsX/mRd0nInEzHLN7E4ze9HMKs3sNF2zAzOzpf77a+TRYWYf0zWb/szsQjPb7H/+Xhd0PEGZyOeseX7oX7MXzGxVsNHHnpn9yswazGzDqGUTvh5mdoW//RYzuyKI32UyHOD6fNHMqkd9jr5u1LpP+9dns5ldMGr5tPv/M7O5ZvaQmW3yvx9/1F+u9w8HvT6J9f5xzukR8AMwINN/ngQ8DZwK3AG801/+U+A/go410R7AtcAtwF/817pmB79eO4HC/ZZ9C7jOf34d8M2g40ykB3AT8AH/eTKQq2s27msXBuqAebpm0/vh/623AQv9/5PngeVBxxXQtRj35yzwOuBv/veAU4Gng45/Eq7HWcAqYMPhXg8gH9ju/8zzn+cF/btN4vX5IvDJMbZd7v9vpQAL/P+58HT9/wNKgFX+8yzgJf8a6P1z8OuTUO8f1WwmAOfp8l8m+Q8HnAvc6S+/CXhT/KNLXGZWBrwe+IX/2tA1OxyX4F0r0DXbh5nl4H0R+CWAc27AOdeGrtl4nQdsc87tQtdsujsZ2Oqc2+6cGwBuw/ubi+dA7/9LgN/43wOeAnLNrCSA+CaNc+5RoGW/xRO9HhcA9zvnWpxzrcD9wIWTHnwcHOD6HMglwG3OuX7n3A5gK97/3rT8/3PO1Trn1vnPO4FKoBS9f4CDXp8DCeT9o2QzQZjXHPQ5oAHvn2Ab0OacG/I3qeLgb6CZ6PvAfwFR/3UBumaH4oB/mNmzZna1v6zYOVfrP68DioMJLSEtABqBX5vXXPsXZpaBrtl4vRO41X+uaza9lQJ7Rr2eyZ+/E/mcnanXbaLXYyZepw/5TUF/NarbwYy9PmY2HzgBr/Wf3j/72e/6QAK9f5RsJgjn3LBz7nigDK+EYVmwESU2M7sYaHDOPRt0LFPMmc65VcBFwDVmdtbolc5rZ6Ehql8WwWve9BPn3AlAN16Tnb10zcZmXn/pNwK/33+drplMc/qcnQBdjzH9BFgEHA/UAt8NNJqAmVkm8AfgY865jtHr9P4Z8/ok1PtHyWaC8ZvoPQSchlf9PzIXahlQHVRcCegM4I1mthOvuv9c4Afomh2Uc67a/9kA/BGvYKN+pNmW/7MhuAgTThVQ5ZwbKSm8Ey/51DU7tIuAdc65ev+1rtn0Vg3MHfV6xn7+TvBzdqZet4lejxl1nZxz9X4lRBT4Od57CGbg9TGzJLxE6nfOubv8xXr/+Ma6Pon2/lGymQDMrMjMcv3nacBr8dpdPwRc6m92BXB3IAEmIOfcp51zZc65+XhN9R50zr0HXbMDMrMMM8saeQ6cD2wA7sG7VqBrtg/nXB2wx8yW+ovOAzahazYe7+LlJrSgazbdPQMsMW9E8GS8z+V7Ao4p7g7jc/Ye4HJ/FM1TgfZRzQOns4lej78D55tZnt8k8Hx/2bS0X7/dN+O9h8C7Pu80sxQzWwAsAdYwTf///LE4fglUOueuH7VK7x8OfH0S7v0Tq5GG9Dii0aSOBdYDL/hviP/xly/03wRb8ZqipQQdayI+gHN4eTRaXbMDX6eFeCOMPQ9sBD7rLy8AHgC2AP8E8oOONZEeeM1Q1vr/n3/CG8lO1+zg1ywDaAZyRi3TNZvmD7yRIF/CG3Pgs0HHE9A1mNDnLN6omT/2r1kFsDro32ESrsmteE35BvFai1x1ONcDeL9/b98KXBn07zXJ1+dm//d/Ae9Lf8mo7T/rX5/NwEWjlk+7/z/gTLwmsi8Az/mP1+n9c8jrk1DvH/NPICIiIiIiIhIzakYrIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyJThJl9JugYRERERETGS8mmyNQR12TTzCLxPJ+IiExPZpZrZv95iG3mm9m7x3Gs+Wa24VDbxZqZvc/MfnSIbb5sZq+J4Tkn/Lua2Y1mdumhtxSJDyWbInFiZpeb2Qtm9ryZ3bz/DcHMuvyfJWb2qJk9Z2YbzOxVZvYNIM1f9jt/u2v99RvM7GP+svlm9qJ/7JfM7Hdm9hoze8LMtpjZyf52GWb2KzNbY2brzewSf/n7zOweM3sQbw6rsX6PTDN7wMzWmVnFyL7+us+b2WYze9zMbjWzT/rLF5nZfWb2rJk9ZmbLJuUii4hIIsoFDppsAvOBQyabExHvQlPn3P845/4Zz3OKJDolmyJxYGYrgM8B5zrnjgM+epDN3w383Tl3PHAc8Jxz7jqg1zl3vHPuPWZ2InAlcApwKvBBMzvB338x8F1gmf94N97Ev5/k5drRzwIPOudOBl4NfNvMMvx1q4BLnXNnHyC+PuDNzrlV/r7fNc9JwFv9mC8CVo/a5wbgw865E/04/u8gv7+IiEwv3wAW+QWm3/YfG/wCy3eM2uZV/jYf9wtPH/MLNteZ2enjOdH+haYHKVx9yr83j+z3sJmtNrN8M/uTXzj8lJkdu9/xc8xsl5mF/NcZZrbHzJJGFyKb2U4z+9Kogtll/vIiM7vfzDaa2S/8YxUe5FeK+AXHlWZ2p5ml+8f5HzN7xr+ON5iZjesvIRJnSjZF4uNc4PfOuSYA51zLQbZ9BrjSzL4IrHTOdY6xzZnAH51z3c65LuAu4FX+uh3OuQrnXBTYCDzgnHNABV7JMcD5wHVm9hzwMJAKlPvr7j9EfAb8r5m9APwTKAWKgTOAu51zfX7MfwavJhQ4Hfi9f76fASUHOb6IiEwv1wHb/ELUp4Dj8QomX4NX2Fnib/OYX6j6PaABeK1fsPkO4IcTON/oQtMDFa7eDrwdvBZFQIlzbi3wJWC9c+5YvALa34w+sHOuHXgOGCmQvRivgHhwjDia/Ph/glfQCvAFP54VwJ28fO89kKXA/znnjgY6eLmG+EfOuZOcc8cAaX4cIglHyaZIcIbw/wf9EtJkAOfco8BZQDVwo5ldPsHj9o96Hh31OgqMNCky4K3+Tf1451y5c67SX9d9iOO/BygCTvS/ONTjJasHEgLaRp3reP+mKSIiM8+ZwK3OuWHnXD3wCHDSGNslAT83swrg98DyCZxjdKHpgQpX7wBGurK8HS/xG4nvZgDn3INAgZll73f82/ESYIB3+q/Hcpf/81leLuw9E7jNP/59QOshfpc9zrkn/Oe/9fcHeLWZPe1fn3OBFWPuLRIwJZsi8fEg8DYzKwAws3xgJ3Civ/6NeDdWzGweUO+c+znwC7wSWoBBM0vynz8GvMnM0v0S2jf7y8br78CHR5rdjGqCOx45QINzbtDMXg3M85c/AbzBzFL92syLAZxzHcAOM3ubfy4zs+MmcD4REZl5Po5XmHkcXreM5AnsO7rQdMzCVedcNdDsN5N9BwdOGMdyD3Chfy8/Ee8eP5aRwt5hXi7snSi3/2szS8XrjnKpc24l8HMOXugrEhglmyJx4JzbCHwNeMTMngeux7s5nO2/Po2Xb47nAM+b2Xq8G+AP/OU3AC+Y2e+cc+uAG4E1wNPAL5xz6ycQ0lfwktsXzGyj/3q8fges9ktTLwde9H/HZ/BuwC8Af8Nrttvu7/Me4Cr/d90IXLL/QUVEZNrqBLL8548B7zCzsJkV4bXkWbPfNuAVbNb6XUIuA8KHee6DFa7eDvwXkOOce2FUfO/xtz0Hrylsx+gD+t1XnsG7P//FOTc8gXie4OXmu+cDeYfYvtzMTvOfvxt4nJcTyya/cFejz0rCMq8rl4jIkTOzTOdclz+AwaPA1X5iLCIiM5iZ3QIci1cYCd5Acg74qnPudr/lzt+BArzC1L8Af/C3uQ+4xjmXaWbz8RK8Yw5wnvcBq51zH/JfpwHfxxs7IIQ3rsHF/rpivC4rX3HOfclflg/8ClgI9ODdx14Y47iX4jXvPcc594i/7EY/tjvNbKe/fZOZrQa+45w7x8xmAbfijXXwL7xWQPOdc6O7wIz8LvP9330tXg3qJuAy51yPmX0VeBdQB7wE7HLOfXF0DAf9g4jEiZJNEYkZ/8vEcrxS15ucc18POCQREZGEYWYpwLBzbsivsfyJP/6ByLSkZFNExmRmK/EHSRil3zl3ShDxiIiITHVmtgRvcKIQMAD8p98NRWRaUrIpIiIiIlOKmV0AfHO/xTucc28OIp4j4Q8e+MAYq85zzjXHOx6RWFKyKSIiIiIiIjGn0WhFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNmRRm9lMz+3yMjlVuZl1mFvZfP2xmH4jFsf3j/c3MrojV8SZw3q+aWZOZ1cX73P75nZktDuLch8vM5vtxR4KORUREREQOTsmmTJiZ7TSzXjPrNLM2M3vSzP7dzPa+n5xz/+6c+8o4j/Wag23jnNvtnMt0zg3HIPYvmtlv9zv+Rc65m4702BOMoxz4BLDcOTd7jPXnmFnVBI43oe2DYGbvM7PHg45jsphZsplVxuLvYGbHmNnf/cIIF4v4RGRmUaHvuM6rQl+RSaZkUw7XG5xzWcA84BvAfwO/jPVJpnENVjnQ7JxrCDoQiZlPAY0xOtYgcAdwVYyOJyLTiAp9j9xUKfQ1sxvN7KuxPm6iUIul6U/JphwR51y7c+4e4B3AFWZ2DOz74WhmhWb2F/+G2GJmj5lZyMxuxku6/uyXmP7XqA+dq8xsN/DgAT6IFpnZGjPrMLO7zSzfP9crPuxHbqRmdiHwGeAd/vme99fvLaH14/qcme0yswYz+42Z5fjrRuK4wsx2+6Whnz3QtTGzHH//Rv94n/OP/xrgfmCOH8eN++2XAfxt1PouM5tjZilm9n0zq/Ef3/eXHWj7k83sX/51rzWzH5lZ8kT+vmb2ejNb71/nPWb2xVHrRq7Hlf66Vv/Lzklm9oJ/3h/52x4N/BQ4zY+v7RDnTTOz7/rXrd3MHjeztFGbvGesv4H/JeYO/7p3mtlGM1s9av0q//fpNLPfm9nt47mJm9mn/GtYY2bvt/1Ko81sAfBe4OuHvKgv73OZ//s1m9lnbdQXPufcZufcL4GN4z2eiMw4KvQ9MtOi0Hca/31kunDO6aHHhB7ATuA1YyzfDfyH//xG4Kv+86/jJRpJ/uNVgI11LGA+4IDfABlA2qhlEX+bh4Fq4Bh/mz8Av/XXnQNUHShe4Isj245a/zDwAf/5+4GtwEIgE7gLuHm/2H7ux3Uc0A8cfYDr9BvgbiDL3/cl4KoDxbnfvmP9Hl8GngJmAUXAk8BXDrL9icCpQMQ/fyXwsVHrHbD4EH/rc4CVeAVTxwL1wJv2ux4/BVKB84E+4E9+jKVAA3C2v/37gMfH+R77sf93KQXCwOlAyqH+Bv7ftw94nb/f14Gn/HXJwC7go3jvw7cAA/jv04PEcqH/e4+8327Z/9oBfwHefKi/66jtlwNdwFn+73U9MMR+/1fAYsAF/T+vhx56JNaDMe7DwMlAFDjGf30jL9+HC/3PqTagBXjM/1y/2d+n1/9M+q9Rn7NX4d3XH2Xs+/DXgTVAB969Lt9f94rPwZF4/c/TAbzWG13A86OON3IfDgGf8z+vG/DupTn+upE4rvBjawI+e5DrlOPv3+gf73P+8V/j/85RP44b99svY7/1XcAc//P6+0CN//i+v+xA258M/Mu/7rXAj4DkUec56H0YuNq/VgP+Mf886nr+N/AC3j0wAlwHbAM6gU3Am0cd533A48B3gFZgB3DRfuu3+/vuAN5ziPffIuBBoNn/G/wOyN3v7/0pP75uvEKQYryC8U7gn0Cev+1u/zqMXLfTgv7/0iO2D9VsSizVAPljLB8ESoB5zrlB59xjzv+EOYgvOue6nXO9B1h/s3Nug3OuG/g88Hbz+5IcofcA1zvntjvnuoBPA+/cr+TwS865Xufc88DzeAnPPvxY3gl82jnX6ZzbCXwXuOwIY/uyc67BOdcIfOlgx3POPeuce8o5N+Sf/2fA2RM5oXPuYedchXMu6px7Abh1jGN8xTnX55z7B95N5VY/xmq8LzQnTOSc5jUDez/wUedctXNu2Dn3pHOuf9RmB/sbPO6cu9d5zb1uHrVuJPH+of8+vAvvi9KhvB349aj32xf3i/fNQNg598cJ/JqXAn9xzj3q/16fx/uSIiJyWJxza4AqvALd/X3CX1eE96X/M94u7jK8L/tvcF4z2W+N2uds4GjgggOc8nK8z+oSvMKyH44jxvuA/wVu98/3ivsnXuLzPuDVvFzw+6P9tjkTWAqcB/yP33pmLP8PL+Fc6P8+lwNXOuf+CVwE1PhxvG+/OLv3W5/pnKsBPot3Lzke795yMvC5g2w/DHwcL9k/zY/3Pw91nUbFcQNeIvct/5hvGLX6XcDr8ZK8IbxE81X+7/sl4LdmVjJq+1OAzX4s3wJ+aZ4MvL/dRc6rKT8deO4QoRleYcMcvPfIXPa7NwJvBV4LHAW8AS/R/AzeezAEfMTf7iz/Z67/O/7rEOeWKUbJpsRSKV6J6f6+jVdb+A8z225m143jWHsmsH4XXk1V4biiPLg5/vFGHzuCd3MeMXoggR68G+H+Cv2Y9j9WaYxjm3Ogjc3sKPOaL9eZWQfeDX5C18jMTjGzh/ymwO3Av49xjPpRz3vHeD3W9TmYQrya0m0H2eZgf4P916X6hQVzgOr9CjoO9T7D32//9xuwt8nzt3j5pjle+xzT/6LSPMFjiIjsT4W+vulS6HsQP3TO7Rn5+zjnfu+cq/ELh28HtuAlwyN2Oed+7hfE3oT3fhj5bhMFjjGzNOdcrXPuoF04nHNbnXP3O+f6/etw/Ri/1/9zztWPKnh+2jm33jnXB/yRCRZEy9SlZFNiwsxOwkukXjHaqP8h/wnn3ELgjcC1ZnbeyOoDHPJQN8G5o56X491Im/Bq1tJHxRXGK0Ub73Fr8Pq/jD72EPsmUOPR5Me0/7Gqx7n/WHGOFVvNQbb/CfAisMQ5l41XomjjPP+IW4B7gLnOuRy8JrMTPcaI8Y6q2oTXFHbRYZ7nQGqBUjMbHf/cA2283377v99GLMFr1vWYeaMZ3gWU+An+/PEe08zSgYJxxCIicjAq9H3ZtCj0PYh9/j5mdrmZPeePl9CG1/Vj9Ln2XjPnXI//NNMvLHgHXmFyrZn91cyWHezEZlZsZreZWbX/e/2WyS+IlilKyaYcETPLNrOLgdvw+kJWjLHNxWa22P+S347XrGSkyWA9XvOWiXqvmS33v6R/GbjTL617Ca8m6/VmloTXPyNl1H71wHwbNWLffm4FPm5mC8wsk5eb+wxNJDg/ljuAr5lZlpnNA67F+0Aej3qgwPzBiUbF9jkzKzKzQuB/Rh1vrO2z8PrSdPk3jv+YyO8w6hgtzrk+MzsZePdhHGNEPVBmhxikyDkXBX4FXG/eQEdhMzvNzFIOtt84/AvvvfchM4uY2SXsW+p7IHcA7xv1fvvCqHUb8JLG4/3HB/B+z+M5+Be1O4GLzexM/3p8mVGfx37TplS8fqaYWWoMfn8RmcZU6PsK06XQ95B/H/87xs+BDwEFzrlcvPvTuM7lnPu7c+61eLWdL/rHOpj/9c+/0v+93jvec411+sPcT6YIJZtyuP5sZp14X6g/i9eE4soDbLsErzN4F94X/v9zzj3kr/s6XgLVZmafnMD5b8Yb/KAOr8nlR8AbHRevP8Qv8G4o3Xj9VEb83v/ZbGbrxjjur/xjP4rXSb4P+PAE4hrtw/75t+Pd/G/xj39IzrkX8ZLL7f61mQN8FViL1+G+AljnLzvQ9p/ESw478W4ctx/G7/CfwJf9v/X/4CVeh+tBvNFV68ys6RDbfhLvd3wGr5T+mxzh55VzbgBvUKCr8AZreC/egBn9B9kN59zf8AaBeBCvZuDBUeuGnHN1Iw8/1qj/+oBTBPhNlK7Be0/U4g3YMPp9Og+v5HekKVMvXl8bEZF9qNB3bNOo0Hc8f58MvKStEcDMrsSr2Twkv5byEr9bSD/ed7VDjSGQ5W/XbmaleIMBHa5G/3yH8x6UqcAlwChFeuihhx5BPICn8QaLmOh+hxzJ9zCOuZMxRnnWQw899Nj/4X9e9OIVJrbjFeRegzdY2cg2N/LyaLQf9/cZKYD9/KjtLsEbJKgNr6BvPqNGnvW32WcZrxyN9s9A4ajt34dXkNbgH3Pv5xtel4HH8QrZ1o063ujRaP8HrzC7ES+Zyxsrjv33HeM65fn7N/rH+x8g5K87h0OMHo5XQNzsX5s5eIXbP/R/t1r/eepBtj8Lr6awC6/f4pcZNSr7eO4leAX2z/nH/NOov//+oxF/Da/AswmvAuCRUdf0few3GvzIufFqMx/Bex+1+ddz+SFiWgE86/9ez+EPQLXf+3P0TAO/xesDPPL6A8A/R73+sv83agNODfr/S4/YPkamnxARmfbM7Gy8GsImvIEefgosdM7VTvA4Dq9Z1NYYxrYT74vBP2N1TBEREZEgqRmtyAxnZhvNrGuMx3um4XmX4o1c2IZXEnupc67WzD5zgFj+drgnMrP3HOCYBx3lT0RERGS6UM2miIiIiEhA/ELIeWOs+jfn3O/iHQ+Amf0Ub2yD/f3WOffv8Y5Hpi4lmyIiIiIiIhJzakYrIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYU7IpIiIiIiIiMadkU0RERERERGJOyaaIiIiIiIjEnJJNERERERERiTklmyIiIiIiIhJzSjZFREREREQk5pRsioiIiIiISMwp2RQREREREZGYiwQdwGQpLCx08+fPDzoMERGZIp599tkm51xR0HEEQfdMERGZqPHcN6dtsjl//nzWrl0bdBgiIjJFmNmuoGMIiu6ZIiIyUeO5b6oZrYiIiIiIiMSckk0RERERERGJOSWbIiIiCc7MPm5mG81sg5ndamapZrbAzJ42s61mdruZJfvbpvivt/rr5wccvoiIzFBKNkVERBKYmZUCHwFWO+eOAcLAO4FvAt9zzi0GWoGr/F2uAlr95d/ztxMREYk7JZsiIiKJLwKkmVkESAdqgXOBO/31NwFv8p9f4r/GX3+emVn8QhUREfEo2RQREUlgzrlq4DvAbrwksx14Fmhzzg35m1UBpf7zUmCPv++Qv31BPGMWEREBJZsiIiIJzczy8GorFwBzgAzgwhgc92ozW2tmaxsbG4/0cCIiIq+gZFNERCSxvQbY4ZxrdM4NAncBZwC5frNagDKg2n9eDcwF8NfnAM37H9Q5d4NzbrVzbnVR0UHn5BYRETksSjZFREQS227gVDNL9/tengdsAh4CLvW3uQK4239+j/8af/2DzjkXx3hFREQAb8ABkSMSjUZpaGggGo0CEAqFmDVrFqGQyjJERI6Uc+5pM7sTWAcMAeuBG4C/AreZ2Vf9Zb/0d/klcLOZbQVa8EaulQQzODhIRUXFK5avXLmSpKSkACISEYk9JZtyxBoaGvju3Wvobm/FklJJT0/jE5eczOzZs4MOTURkWnDOfQH4wn6LtwMnj7FtH/C2eMQlh6+iooLr73iQ4vJFe5fV797GtcCqVauCC0xEJIaUbEpMZOYW4IBQUioZGRlBhyMiIpLwissXUbZkRdBhiIhMGrVzFBERERERkZhTsikiIiIiIiIxp2RTREREREREYk7JpoiIiIiIiMSckk0RERERERGJOSWbIiIiIiIiEnNKNkVERERERCTmNM+mxEzUwXDUBR2GiIiIiIgkACWbcsScc2xvHWBtVYhhN8Sbjx6moaEBgFmzZhEKqQJdRERERGSmCSzZNLOdQCcwDAw551abWT5wOzAf2Am83TnXamYG/AB4HdADvM85ty6IuOWV/rqpmUd29pCVBN1DsHZ3Ox2NtaSn7+QTl5zM7Nmzgw5RRERERETiLOgqp1c75453zq32X18HPOCcWwI84L8GuAhY4j+uBn4S90jlgP66qZmc1BDnl0ZZkG1s7TCiablk5hYEHZqIiIiIiAQk6GRzf5cAN/nPbwLeNGr5b5znKSDXzEoCiE/2U9PWy3PVXSzMS8YMVhaGCRtUNA0HHZqIiIiIiAQoyGTTAf8ws2fN7Gp/WbFzrtZ/XgcU+89LgT2j9q3yl0nA/vJCDQ5YmJcEQGrEmJfpqO12DGmwIBERERGRGSvIAYLOdM5Vm9ks4H4ze3H0SuecM7MJZSt+0no1QHl5eewilQO65/kaji5OJzslTGevt2x2umNbJ9R3DQUbnIiIiIiIBCawZNM5V+3/bDCzPwInA/VmVuKcq/WbyTb4m1cDc0ftXuYv2/+YNwA3AKxevVrVapNsV3M3G6o7+MhZZXR2du1dPisVwgZ7OgYDjE5ERCRxDA4OUlFRsfd1ZWUl0WhqgBGJiEy+QJrRmlmGmWWNPAfOBzYA9wBX+JtdAdztP78HuNw8pwLto5rbSkCe2dkKwGnzs/dZHg7BrHSjqn0I55Tzi4iIVFRUcP0dD/K7p3Z5j/ufoaWlJeiwREQmVVA1m8XAH70ZTYgAtzjn7jOzZ4A7zOwqYBfwdn/7e/GmPdmKN/XJlfEPWfb3QlUbmSkR5uW9smS2JMNY1xBlT1s/JRrKSUREhOLyRZQtWQFA/e5tr1g/PDxEZWXlK5avXLmSpKSkSY9PRCTWAkk2nXPbgePGWN4MnDfGcgdcE4fQZAKer2rnmNJsQl6hwT5KMkJAlCd2tHPy0fGPTUREZKppqt7FrRvbWNqevndZ/e5tXAusWrUquMBERA5TkAMEyRQ2MBSlsqaDK8+YP+b6jCQjOyXEuqrO+AYmIiIyhRXMmbe39lNEZKpLtHk2JcFFo1Hq6up4YuMOBoajHFuWe8BtizMiVNR2q9+miMgRMLOlZvbcqEeHmX3MzPLN7H4z2+L/zPO3NzP7oZltNbMXzExVYiIiEgglmzIhDQ0NfPfuNfy/f2wC4NiynANuOyszTFvvENubuuMVnojItOOc2+ycO945dzxwIt7YBX8ErgMecM4tAR7wXwNcBCzxH1cDP4l70CIiIijZlMOQmVtARzSF7JQQkf52Ghoaxqy9nJXhtdJeu1Oj7YmIxMh5wDbn3C7gEuAmf/lNwJv855cAv3Gep4BcfzoxERGRuFKyKYeloWuAFDfADY9u56f3raenp+cV2+SkhMhJDbPWnyJFRESO2DuBW/3nxaOmAavDG+kdoBTYM2qfKn+ZiIhIXCnZlAkbijo6BqEoM5ns/CLSc/LG3M7MWDknk2d3KdkUETlSZpYMvBH4/f7r/FHbJ9RB3syuNrO1Zra2sbExRlGKiIi8TMmmTFh73zBg5KS8csqT/R1bksn2pm6au/onPzARkentImCdc67ef10/0jzW/9ngL68G5o7ar8xftg/n3A3OudXOudVFRUWTGLaIiMxUSjZlwtr7ogBkJ48j2ZyTCaDaTRGRI/cuXm5CC3APcIX//Arg7lHLL/dHpT0VaB/V3FZERCRulGzKhLX1DwOOzKRDb3t0cTqRkLF+Txvw8tQpdXV1RKPRSY1TRGS6MLMM4LXAXaMWfwN4rZltAV7jvwa4F9gObAV+DvxnHEMVERHZKxJ0ADL1tPdFyYxAOHToms2USIijS7J53k82R6ZOAfjEJScze/bsyQxVRGRacM51AwX7LWvGG512/20dcE2cQhMRETkg1WzKhLX1DZOVPP7tj5ubwwtV7USj3tgVmbkFZOYWHGIvERERERGZypRsyoQMRR0d/VGyksY/6OHxc/Po6h9ie1PXJEYmIiIiIiKJRMmmTEhNez9RB9nj6K8ZjUZpaGigNG0QgPW72yY3OBERERERSRjqsykTsrOlD2BcNZvdHa387IEa0tLSSE9O4vmqNl5VpuH1RURERERmAtVsyoSMJJvZ4+yzmZGTR1ZeAcuLM3h+T/skRiYiIiIiIolEyaZMyK7WPtIiRtIE3zkrZmdQWdtB/5CmOxERERERmQmUbMqE7GzpIyc1POH9lhdnMBR1bGnsmYSoREREREQk0SjZlHGJRqPU1dWxq6WXnJSJv22WFacD8GKDkk0RERERkZlAyaaMS0NDA1+/aw1dA1FSbGhC+0ajUehpJTctwnO7WvDmGxcRERERkelMyaaMWzQtB4DMJJvQft0drdzw4ItkRODpXe309Kh2U0RERERkulOyKePW0e8N7pOZPLFkE7xRaUvys+gaDjMcVc2miIiIiMh0p2RTxq3TTzYzkg5v/1lZKTiM9gElmyIiIiIi052STRm3zoEoKWFHUmjiNZsAs7JTAWjtU7IpIiIiIjLdKdmUcevsj5IZOfz9s1MjJIUcrf2xi0lERERERBKTkk0Zt87+YTKSDr9W0szITVbNpoiIiIjITKBkU8ZlYChK96A7oppNgLwUR/uA0yBBIiIiIiLTnJJNGZeajgEAMo4w2cxNhqiDdn+wIRERERERmZ6UbMq4VLd7HS2PpBktQE6yt39r7/ARxyQiIiIiIolLyaaMS1Wbl2xmHua0JyOykrw3XYuSTRERERGRaU3JpoxLdXs/kRCkHOE7JmSQnQKtfUo2RURERESmMyWbMi7V7f1kJYeww5ticx85yaZmtCIiE2BmuWZ2p5m9aGaVZnaameWb2f1mtsX/medva2b2QzPbamYvmNmqoOMXEZGZScmmjEtdRz9ZR1qt6ctJMXoGHe29QzE5nojIDPAD4D7n3DLgOKASuA54wDm3BHjAfw1wEbDEf1wN/CT+4YqIiCjZlHFwzlHbMUBGcmzeLrkpXvXo1qbemBxPRGQ6M7Mc4CzglwDOuQHnXBtwCXCTv9lNwJv855cAv3Gep4BcMyuJa9AiIiIo2ZRDiEajvLSzmp7BKJlJsavZBCWbIiLjtABoBH5tZuvN7BdmlgEUO+dq/W3qgGL/eSmwZ9T+Vf4yERGRuAos2TSzsH/T/Iv/eoGZPe33MbndzJL95Sn+663++vlBxTwTNTQ08O0/rwMgyQ3G5JipYUiNGFubemJyPBGRaS4CrAJ+4pw7Aejm5SazADjnHDChuanM7GozW2tmaxsbG2MWrIiIyIggazY/itfnZMQ3ge855xYDrcBV/vKrgFZ/+ff87SSOhlOyAMhIisHoQICZkZ8WVs2miMj4VAFVzrmn/dd34iWf9SPNY/2fDf76amDuqP3L/GX7cM7d4Jxb7ZxbXVRUNGnBi4jIzBVIsmlmZcDrgV/4rw04F+8GCq/sezLSJ+VO4Dx/e4mTroEoABlHOMfmaLmpIXa29BGNTqggXkRkxnHO1QF7zGypv+g8YBNwD3CFv+wK4G7/+T3A5f6otKcC7aOa24qIiMRNJKDzfh/4LyDLf10AtDnnRoYnHd2/ZG/fE+fckJm1+9s3xS3aGa5rIErEHEkhcNHYHDMnNUzv4AC1HX2U5qbF5qAiItPXh4Hf+V1MtgNX4hUY32FmVwG7gLf7294LvA7YCvT424qIiMRd3JNNM7sYaHDOPWtm58T42FfjDfNOeXl5LA89o3X1R8lI8pq/xqoeMjc1DMCW+k4lmyIih+Ccew5YPcaq88bY1gHXTHZMIiIihxJEzeYZwBvN7HVAKpCNN39YrplF/NrN0f1LRvqeVJlZBMgBmsc6sHPuBuAGgNWrV6t9Zox0DURJj/E7JTfVa8G9taGLc5bOiu3BRURkShscHKSiouIVy1euXElSUgz7dIiIyKSKe7LpnPs08GkAv2bzk86595jZ74FLgdt4Zd+TK4B/+esf9EttJQ6cc3QNRCnIjO0lT42EyE2LsK2xK6bHFRGRqa+iooLr73iQ4vJFe5fV797GtcCqVauCCyzGhh30Dw4TCYcIhzQchYhMP0H12RzLfwO3mdlXgfX4k1f7P282s61AC/DOgOKbkTr6hxmMEvOaTYD5+alsqVeyKSIir1RcvoiyJSv2vh4eHqKysvIV203V2s420tnUO5vHH91OJGRcfGzJmNtNt99bRGaWQJNN59zDwMP+8+3AyWNs0we8La6ByV61HQMAZERiX5m8ID+Vh7a145xDAwyLiMjBNFXv4taNbSxtT9+7bKrWdtZ39FFJGak2zImLZ7GptoN7K+o4huS9IyeOmE6/t4jMPIlUsykJqLajH5i8ms22iiaauwcozEyJ/QlERGRaKZgzb5/azqloaDjKn1+oIcIwx6a0sKp8OYtnZXLbmj1sGp7LSe6Vg+1Ph99bRGamQObZlKnj5ZrN2B97fr43Cu3WBjWlFRGRmWFDTQfd/cMcRS0pIW8+sezUJF6zfBZ9JNM4rBHaRWT6ULIpB1Xb0U9SCJIm4Z2yID8VgC1KNkVEZAaIOnh2VytzclLJoWefdQsKMkhlgJqhjICiExGJPSWbclC1HQNkJoeYjC6VRZlJZKZE2KZkU0REZoBd3SG6+oc4aUH+K9aZGSW00hFNpqGzL4DoRERiT8mmHNRIsjkZzIyFRRlsrGqmrq6OaDQ6KecRERFJBFs7QxRlpTAvP33M9cW0ESLKC1XtcY5MRGRyKNmUA3LOUdvRT2bK5L1NyrIiVFR38N2719DQ0DBp5xEREQnS7vZBOgZDLC/JPuAI7BGiFIX72FLfxXBUU4qLyNSnZFMOqL13kJ6B6KTVbII3Im3fsJGc9comRSIiItPFE3v6AMeSWZkH3a4g3MfAcJS6djWlFZGpT8mmHFBVay/ApCebAO19w5N2DhERkSA553hiTy9FKY6MlIMP754X7idksLO5O07RiYhMHs2zKQe0T7I5MDnnGEk225RsiojINDE4OEhFRcXe19tbB6npHOb43EPf6yLmKMlJY1dzD2csnswoRUQmn5JNOaCqVm9Y9qzkEAOTlGzOyUkhbNDWp8GBRERkeqioqOD6Ox6kuHwRABvawuCM9P6mce0/ryCdJ7c1090/NJlhiohMOjWjlQOqau0lPTlEcngS5j3xRUJGdkpIzWhFRGRaKS5fRNmSFZQtWUFzNI0c6yXJxjfoz/wCb67NXc09h9hSRCSxKdmUA6pq7aUkO+WAo+bFSk5qWDWbIiIyLXX1D9HUNUAe459TujAzmYyUMLta1G9TRKY2JZtyQFWtPZRkJ0/6eXJTw3QOROkbUsIpIiLTyy5/oJ88xp84mhmlOWnUakRaEZnilGzKmJxzVLf2TlqyGY1GaWhooKGhgZwUr+Z0T6tuqiIiMr3sau4hIzlMOv0T2m92TiqdfUP0a3gNEZnC9AkmY+roHaKzf4iS7BQ6OwdjfvzujlZ+9kANbrCPlKzZAOxs6eNVMT+TiIgksv1HbgWorKwkGk0NKKLYiUYdu1t6WFSUidVObN/ZOd7v30ka6ZMQm4hIPCjZlDHt8UeiLclOprNzcs6RkZNHdLAPFwbDSzZFRGRm2X/kVoDKNc9QtHAF5QHGFQv1nX30D0WZV5BO5wSTzaKsFMJmdLpUiidr/jERkUmmZFPGNDLHZkl2Ci9VT+65wiEjIznEnjYlmyIiM9HIyK0j6ndvCzCa2NnT4t1L5+ans2mC+0ZCIYqyUujsSGPSJrsWEZlk6rMpY6oaVbMZDzkpIXa3Tqw/i4jITGFmO82swsyeM7O1/rJ8M7vfzLb4P/P85WZmPzSzrWb2gpmtCjb6mau6rZfCzGTSksKHtf/snFS6SMONb8YUEZGEo2RTxlTV2ktmSoTslMO7QU5UdmqI3W19ON1RRUQO5NXOueOdc6v919cBDzjnlgAP+K8BLgKW+I+rgZ/EPVIh6qC2vZfS3LTDPkZJTipRQnQ5NUQTkalJyaaMqarVu0FO9hybI7JTwvQMRGnqUlMhEZFxugS4yX9+E/CmUct/4zxPAblmVhJAfDNa24AxOOyOKNmcne0NEtQxHJ9WRiIisaZkU8ZU1dpDWd7h3yAnKifFeyvuaNIE1iIiY3DAP8zsWTO72l9W7JwbGXamDij2n5cCe0btW+Uvkzhq7PcKa+ccQbKZlRohwhBd0aRYhSUiEldKNmVM1W29cU02s/1kc6eSTRGRsZzpnFuF10T2GjM7a/RK5/VBmFA/BDO72szWmtnaxsbGGIYqAE19IfLSk8hIOfwmsGZGBv10OyWbIjI1qROAvEJ77yCdfUOU5cVvZq+M5BCRkLFdyaaIyCs456r9nw1m9kfgZKDezEqcc7V+M9kGf/NqYO6o3cv8Zfsf8wbgBoDVq1dPyQ7zw8NDVFZWvmL5ypUrSUoKLkEbjjqa+42lJUdeaJtBH3XRdKJRRygUn64tIiKxomRTXmFkJNp41myGzCjNSWFHU1fczikiMhWYWQYQcs51+s/PB74M3ANcAXzD/3m3v8s9wIfM7DbgFKB9VHPbaaWpehe3bmxjafvLhaP1u7dxLbBqlTcI7+DgIBUVFa/YdzIT0t3tQww6ozQG99EM+olitPUOkp+hvpsiMrUo2ZRXGJlj06vZ7I3becvzUtjZ1BO384mITBHFwB/9AdsiwC3OufvM7BngDjO7CtgFvN3f/l7gdcBWoAe4Mv4hx0/BnHn7zNG5v4qKCq6/40GKyxftXbZ/QhprGxu9we6OZHCgERl4c1A3dvYr2RSRKUfJprzCy8lmGv2d8Uk2o9EohSmONbu6qKmtZXZxMaGQuhSLiDjntgPHjbG8GThvjOUOuCYOoU0ZxeWLDpqQxtqmxgHSw46s1COvOU2nH8PR2NXPUrJiEJ2ISPwo2ZRXqGrtISM5TG56EvWd8Tlnd0crW5sG6R9O5Wt3PcPn33oys2fPjs/JRURk2ti/H2dlZSXRaGrczu+cY1PTAEWp0ZgcLwSk2xBNXf0xOZ6ISDwp2ZS9otEoDQ0NbKttpTQvfnNsjijIyYDOYYaSs+N6XhERmT7278dZueYZihauoDxO59/a0EVHf5QlGbEbcykzNEhTp5JNEZl6lGzKXg0NDXz37jU8XxXiqKI06urqaGhowGuRNfkyk7zktqM/NqXBIiIyM43ux1m/e1tcz/30jhYAClNidy/LCA1SPzBMz8AQ6cn66iYiU4c+sWQfmbkF9OxqpaG1g589so36XVvJLCyJy7nTIhAJGe1KNkVE5CAGh6NU1nawjWJsIIX5PQPkpifG4DlrdrSQnxYiI4bfsDJDgwA0dQ1Qnq+vbiIydWgEFtlH/1CUwaiRm5FKdn4R6Tl5cTu3mZGbnkRH/3DczikiIlNLz8AQd62r5qHNjTSQQ91QOjc/tYuntjfHrSXOgTjneHpHM8sLk4llT5SM0BAAzeq3KSJTjIrHZB9dA16tYkZSMBNH56UnU9+u6U9EROSVhpzx+2er6Owb4nXHzKZ9w0MMp+bRkrOYp3e0kBwJtgx9d0sP9R39XLI4m+am2B03iSipkRAt3QOxO6iISByoZlP2MZJspgeUbOamJ9HZH2VoONjSaRERSTw7B7No6xnkkuPmsKQ4CwNSQlEuXDGbxUWZPLaliWYyA4tvpL/miqLYNuk1g7yMZFp6lGyKyNRyRMmmmZ0xnmUydbxcsxnM+XPTk3FATYeaConI9KP75uHrJoXqoQxWluYwNz99n3VmxvkripmVlcJWShhywRSYrtnRQn5GMmXZsW84VpCRTEvXQOBNhUVEJuJIazb/3ziX7cPMUs1sjZk9b2YbzexL/vIFZva0mW01s9vNLNlfnuK/3uqvn3+EccsBdA1ECZsjOaA677x0L8vd06ZkU0SmpcO6bwpso5gkopy+qGDM9UnhEOctm8UgYXYOZsU5Os+aHS2cND9vUqYOy89Ipm8oSu+gxjUQkanjsIrezOw04HSgyMyuHbUqGwiP4xD9wLnOuS4zSwIeN7O/AdcC33PO3WZmPwWuAn7i/2x1zi02s3cC3wTecTixy8F1DUTJiBD3OTZH5KZ5TY92t/YFcn4RkckQg/vmjFbf0UcHGSxKaic16cCXa1Z2KrNpo3ool+aufgoyU+IWY217L7tberji9PlAa8yPn5/h3R/Vb1NEppLDrb9KBjLxktWsUY8O4NJD7ew8Xf7LJP/hgHOBO/3lNwFv8p9f4r/GX3+eBZUNTXNdA470AIeNSk0KkRw21WyKyHRzRPfNma6iup0QUWZHDj2A3DwaCeP41/bmOET2sjV+f81TFuRPyvGVbIrIVHRYaYVz7hHgETO70Tm363COYWZh4FlgMfBjYBvQ5pwb8jepAkr956XAHv/cQ2bWDhQAMRzrTcCr2ZybHlx/EDMjOyWkmk0RmVZicd+cqfoHh9lc10kR7UTs0PenJIYpjXSzrTG+o7c+vaOFzJQIR5dk83x97I+fmRIhOez9Tvka3lFEpogjrcNKMbMbgPmjj+WcO/dQOzrnhoHjzSwX+COw7AhjwcyuBq4GKC8vP9LDzTidfUMMDDvSAxocaEROSogq1WyKyPR02PfNmWpTbQdDUUcJbcD4msWWJnVTE81m7a4Wxu7hGXtrdrSwen4e4dDkNLwyM/IykrwEOpguqSIiE3akyebvgZ8CvwAOq8e6c67NzB4CTgNyzSzi126WAdX+ZtXAXKDKzCJADvCK9jHOuRuAGwBWr16t4domqK7TKwHOiAR76bKSQ2xr7WdnVQ3lc2YTCqkIV0SmjSO+b840lbWdzMpKIbOzj/Emm8kW5ZjSHF6oamMVETImN0SauvrZ2tDFW1eVTep58jOS2d3co2RTRKaMI/0WP+Sc+4lzbo1z7tmRx6F2MrMiv0YTM0sDXgtUAg/xct+VK4C7/ef3+K/x1z/oNPZ3zNV2eMlmkH02AVKivQD87x/X0tDQEGwwIiKxdVj3zZmqo3eQxq5+jiqeeHa1qjwXB9SRF/vA9rN2p9df8+RJ6q85oiAjhe6BYfxZykREEt6RJpt/NrP/NLMSM8sfeYxjvxLgITN7AXgGuN859xfgv4FrzWwrXp/MX/rb/xIo8JdfC1x3hHHLGGr9uS0zAk42M5O8coTBZBXdisi0c7j3zRlpW6M3luDCoonXTWalJrGgIIN6colOcvH00ztaSE0KsbI0Z1LPMzI9WNegxkgUkanhSNOKkdrGT41a5oCFB9vJOfcCcMIYy7cDJ4+xvA942+GHKeNR2zFAJERgc2yOyPT7jHb0q+hWRKadw7pvzlTbGrspyEgmLz35sPZfWZbD9qZumoZTYxzZvtbsaGFVeR7Jkcm9gY5ch84hJZsiMjUcUbLpnFsQq0AkeLUdA2QmhzALthtRUghSwko2RWT60X1z/AZdiJq2XlbPP/xmsPPy00lhgNqhyeu12dE3yKbaDj563pJJO8eI7LQkzFSzKSJTxxElm2Z2+VjLnXO/OZLjSjBqO/rJDLpa05eVZHT0a+wMEZledN8cv+bhFBywqCjzsI9hZsymjV3RWbR2D5CXcXg1pAezdmcLzk1+f02AcMjITk2ia0j3RxGZGo60Ge1Jo56nAucB6wDdNKeg2o4BSrPCQYcBQGYy1PeoZlNEph3dN8epZTiVjJQws7LGNwLtgRTTxm6KqKhp56wlRTGK7mVP72ghKWycMHfyByICr99ma4emBxORqeFIm9F+ePRrf4TZ247kmBKM9t5BOvuHySwIeJJNX2aSsXMoSs+ASm9FZPo4kvummYWBtUC1c+5iM1vg71sAPAtc5pwbMLMUvOT1RLxpwt7hnNsZs18iDhzQNpzMoqJ0zI6syWgywxSG+6is6eD0hQVEwrFtwfOvbc0cV5ZLWnJ8Cmtz05PZ02JoUH4RmQpi3WayG1B/lCloT0sPAFkpCdKMNtn7clHVptJbEZnWJnLf/CjeNGEjvgl8zzm3GGgFrvKXXwW0+su/5283pfSQwiBh5ualx+R4JZFu+oaibG3oisnxRrR0D1BR3c5ZR8W+xvRA8tKTGHZGS69a/4hI4juizMLM/mxm9/iPvwKbgT/GJjSJp6pWL9lMlD6bmUlesrlHyaaITCOHe980szLg9cAv/NcGnAvc6W9yE/Am//kl/mv89efZkVYPxlkbXpJZlp8Wk+PlhgbITUviher2mBxvxBNbm3AOXrWkMKbHPZhcf0Tamq6huJ1TRORwHWmfze+Mej4E7HLOVR3hMSUAe1p6AchKDjEwEHAweH02Afa09QUbiIhIbB3uffP7wH8BIxMQFwBtzrmRjKMKKPWflwJ7AJxzQ2bW7m/fdGShx087GaTZENmpsenaYQbHzMnh8a1NNHfFrhDz0ZcayU6NcGxZbsyOeSgjc23WdCrZFJHEd0TVWM65R4AX8W5+eUACpCkyUdFolM3VTWQkGcnhxCj8TgoZaRFTzaaITCuHc980s4uBBufcs7GMxcyuNrO1Zra2sbExloc+ItGoo510csOx/fxfXpJN2IwN1R0xOZ5zjse2NHHmkkLCofjdOzNTIoTNUdOpMQ1EJPEdaTPatwNrgLcBbweeNrNLYxGYxE9DQwOPv9RAODpIT09P0OHslZ0aUs2miEwrh3nfPAN4o5ntxBsQ6FzgB0CumY20UCoDqv3n1cBc/3wRIAdvoKB9OOducM6tds6tLiqKX5/DQ2no7GeYMLmh2CabaclhFs3KoLKug+EYdHfc2tBFXUffpIxwezBmRmbEUauaTRGZAo60Ge1ngZOccw0AZlYE/JOX+5DIFNHrwmQe2ejyMZedElbNpohMNxO+bzrnPg182t/+HOCTzrn3mNnvgUvxEtArgLv9Xe7xX//LX/+gm0JDl1a3ed06csOxbyy1sjSHl+q7qOo58vEJHnnJqw0+M479NUdkRtRnU0SmhiP9tA2N3DB9zTE4psSZc46u/igZkcT6LpKdEqK1Z4jOvsGgQxERiZVY3jf/G7jWzLbi9cn8pb/8l0CBv/xa4LrDDTYIte29pDJAssV+tNXS3DTy0pPY3nXkX1X+samepcVZlMVoxNyJyExy1HcNMxiLKloRkUl0pDWb95nZ34Fb/dfvAO49wmNKnLX0DDHsICMxptjcK9ufhmVnUw8ry3ICjkZEJCaO6L7pnHsYeNh/vh04eYxt+vCa6U45zjlq2/vIYnK6dJgZx5Tm8NiWJna1DbLqMI/T2NnPMztb+Mi5S2Ia33hlRRzDDqpae1lQmBFIDCIi43FYyaaZLQaKnXOfMrO3AGf6q/4F/C5WwUl81LR7TVUTr2bTmyB7R3O3kk0RmdJ03xyfjr4hegaGKaF30s5xdEk2T2xp5B/be3jzuYd3jH9W1uMcXHjMbAYHB6moqNhnfWVlJdFoagyiHVtmkne/3tHUpWRTRBLa4dZsfh+//4hz7i7gLgAzW+mve0MMYpM4qenw+sVkHGk9d4y9XLPZHXAkIiJH7PvovnlIte3+NFz0ApOTrKUlhSlNj/LIrl56B4ZJSw5P+Bh/21DHvIJ0ls3OYv369Vx/x4MUly/au75yzTMULVxBeSwDHyXTLxze3tjNucsm6SQiIjFwuJ0Wip1zFfsv9JfNP6KIJO5qOryazfQESzYjIWNWZpKSTRGZDnTfHIfa9j6SwkYGkzs43ILMKD2Djj+/UDPhfdt7B3lyaxMXHjMbM2/Kk+LyRZQtWbH3kT+7LNYh7yMlDJnJxg7dH0UkwR1uspl7kHVph3lMCUhNez9pESOSgEM7zc1NZUezbqYiMuXlHmSd7pu+2vY+ZmenMtmzVhamOOZmR/j1EzuZ6EC9922oZSjquHDF7EmKbnzmZEWUbIpIwjvc9GKtmX1w/4Vm9gEgppNOy+Sr7RggMzkBM01gbm6KajZFZDrQffMQBoaiNHX1U5Iz+bm3GVyyNIPK2o69U5iM1y1P72bJrEyOn5s7OcGN05xMJZsikvgOt+Hkx4A/mtl7ePkmuRpIBt4cg7gkjmra+8lKSdBkMy+F1p5B2nsGyUlPsOFyRUTG72PovnlQDZ19OAezc1JpicP5XlWexh9e6ucnD2/jnKWzxrXPhup2nq9q54tvWL63CW1Q5mSFeXhXFz0DQ6QnJ1g/GBER32FlGM65eufc6cCXgJ3+40vOudOcc3WxC08m29BwlPrORK7Z9AaIUFNaEZnKdN88tAZ//IDi7JS4nC8pbFx15gKe3tHCs7vGl97+7undpCaFePOqye2TOR5zsrwEc2fT5EwTIyISC0eUYTjnHnLO/T//8WCsgpL4qW3vY9iRuDWbud6XDjWlFZHpQPfNA6vv7CMrNRLXWrp3nVxOYWYy/3vvi4fsu9neM8jdz1XzxuPmkJMWfEubEj/ZVFNaEUlkiZlhSNzsafFKRBO1ZnNOTgpmsF03UxGRaa2+o5/irMmbm3IsGSkR/uuCZTy7q5W7nzv4yLQ/fHALvYPDvO/0BXGK7uBKMv25qJu6Ao5EROTAEjPDkLjZ0+olm1kJmGxGo1HaW5oozkxmR6NupiIi09VA1JtSZFacmtCOdumJZRxblsPX/1ZJV//QmNtsa+zipid38o7Vc1k+JzvOEY4tNRKiJCdVhbEiktASL8OQuNrT0kvYICMBk83ujlZ+9kAlbrCfLXXtQYcjIiKTpG3An68yO741mwChkPHFN66gsbOfj922nuHovs1po1HHl/+8idSkMJ84f2nc4zuYBYUZbG9UsikiiSvxMgyJqz2tPczKSiYU8Kh6B5KRk0d+Vgq7WvuIRic2F5qIiEwNrf3ePWhWVvxrNgFWlefxpTeu4J+VDXzpzxsZGo4CMBx1fPLO53nkpUY+ef5RFAUU34EsKMxgpwbQE5EEprGyZ7g9LT3MCaDZ0kTkpobpHRygpr2Xsrz0oMMREZEYax0IkZOWRGpSOLAYLjttPjube/jl4zt4bEsT5y2bxTM7W3i+qp1rX3sU7zsjMfpqjragMIO2nkFauwfIy0gOOhwRkVdQzeYMt6e1lzk5iX2Dyk31vnxsaVC/TRGR6ah1wChOgFrDz73+aG647ERSIiFufHInUQdfedMxfOS8JUGHNqYFhRmApgcTkcSlms0ZrG9wmMbOfkqyCxjsS9x5unJTvTKRrfVdvHqcE2+LiMjU0NkfpWfYKEqAVjZmxvkrZvPa5cUMRx2RcGKXyc8fSTYbu1lVnhdwNCIir5TYn6Iyqar8kWgTvWYzNRIiLz3ClobOoEMREZEY29k2CEBRZvDJ5ggzS/hEE2BuXjrhkGmuTRFJWIn/SSqTZk9LL0DC99kEWJCfylY1oxURmXZ2jCSbCdCMdqpJjoSYm5emZrQikrCUbM5ge/bWbCb+DX5BfhpbGrpwTiPSiohMJzvahkgNO9KT1bPncMwvzGCHpj8RkQSlZHMG29PSQ0okREF64t/gFxSk0tk3RENnf9ChiIhIDO1sGyQ3SQWJh2tk+hMVxopIIkr8LEMmza7mHkqyk2lsbEzom1Q0GiU31AfA5rqOQCb9FhGR2OsbHKaqY4jFWYl7DxptcHCQioqKfZZVVlYSjQZ3X1pQmEHPwDANnf26P4pIwlGyOYNtq29noK+Xn963nszCkqDDOaDujlYeru4DMli/rY6zjtKItCIyc5hZKvAokIJ3377TOfcFM1sA3AYUAM8ClznnBswsBfgNcCLQDLzDObczkOAPYWtDF8MOcpOjQYcyLhUVFVx/x4MUly/au6xyzTMULVxBeUAxjUx/sr2xW8mmiCQcNaOdoaJRR1VbP3mZqaTnJP5w6Xl5OaSEje3NvUGHIiISb/3Auc6544DjgQvN7FTgm8D3nHOLgVbgKn/7q4BWf/n3/O0S0qaaDgByplAz2uLyRZQtWbH3kT+7LNB45hd4yeZODRIkIgko7smmmc01s4fMbJOZbTSzj/rL883sfjPb4v/M85ebmf3QzLaa2QtmtireMU9HdR199A87slOmRnmDmZGfFmZLY+LOByoiMhmcZ2Q47iT/4YBzgTv95TcBb/KfX+K/xl9/nplZfKKdmE21HaRGjEy1szpsc3LTSI6ENP2JiCSkIDKNIeATzrnlwKnANWa2HLgOeMA5twR4wH8NcBGwxH9cDfwk/iFPPyMloFMl2QTITw+ztamXweGp0dxKRCRWzCxsZs8BDcD9wDagzTk35G9SBZT6z0uBPQD++na8prYJZ1NNB/NyIiRmKjw1hEPG/IJ0JZsikpDinmk452qdc+v8551AJd6NcXRJ7P4ltL/xS3afAnLNLHE7GE4RO5u8GsLslHDAkYxfQVqYgWHHtkbNtykiM4tzbtg5dzxQBpwMLDvSY5rZ1Wa21szWNjY2HunhJiwadWyq7WBBblLczz3dzC/IULIpIgkp0GotM5sPnAA8DRQ752r9VXVAsf98bwmtb3TprRymnc3dJIeNjKSpU5xckO4lxhuqOwKOREQkGM65NuAh4DS8wteRBqhlQLX/vBqYC+Cvz8EbKGj/Y93gnFvtnFtdVFQ02aG/QlVrL139Q0o2Y2BBUQa7m3sYjk6dvq8iMjMElmyaWSbwB+Bjzrl9sgfnzcMx4U/MoEtpp5IdTd2U5qSQoN14xpSdEiI1EmJjTXvQoYiIxI2ZFZlZrv88DXgtXqugh4BL/c2uAO72n9/jv8Zf/6BLwPmtNtV6n+Xz89Rh80gtKMhgYDhKTZsG0RORxBJIsmlmSXiJ5u+cc3f5i+tHmsf6Pxv85XtLaH2jS2/3EXQp7VQQjUapq6tjW307s9ItoefX3F/IjCVFaWxUzaaIzCwlwENm9gLwDHC/c+4vwH8D15rZVrw+mb/0t/8lUOAvv5aXx0BIKJtqOggZlGerZvNI7Z3+RE1pRSTBxL040R8R75dApXPu+lGrRkpiv8ErS2g/ZGa3AacA7aOa28oENTQ08J0/rWFXSxiX0sO89JygQ5qQpbPSue/FVqJRRyg0dWplRUQOl3PuBbwuJ/sv347Xf3P/5X3A2+IQ2hHZVNvBoqJMUiL6LJ+o4eEhKisr977u6h0GYFt9B2cfpcJ2EUkcQbRdOQO4DKjwR9YD+AxeknmHmV0F7ALe7q+7F3gdsBXoAa6Ma7TTkGXkMew6yMuYepM/H1WUzp3PN7K7pYf5fkmuiIhMPZtqOjhpQT6H0Wtmxmuq3sWtG9tY2p4OgHMQJszazbt5/6sWBRydiMjL4p5sOuceBw5UjHneGNs74JpJDWqG6ej3SkAzptAk2iOWzvJurBXV7Uo2RUSmqNbuAWra+1heko03M0t87V8zOGLlypUkJU2NZr0Fc+ZRtmTF3tdZdZup6RoOMCIRkVdSr/wZqKPfm6cya2rcT/exsCCVlEiI9bvbeMNxc4IOR0REDkNlrdf3fvmcbOiMf7K5f80gQP3ubVwLrFq1Ku7xxEJmkqO2c+jQG4qIxJGSzRmovW+YSMhImzpTbO6VFA5x/Nxc1u5qCToUERE5TJv8ZPPokmx2dwYTw/41g1NdZgRe6hxmYChKciTQme1ERPbSp9EM1NYXJT8jmSk068k+Tl6Qz8aaDrr7VYIrIjIVbarpoDg7hcLMlKBDmTaykhxRB7tbeoIORURkLyWbM1B73zB5GclBh3HYVs/PZzjqWL+7LehQRETkMGyq7fD7a0qsZEa8cRh2aPoTEUkgSjZnmO6BYboHHflTMNmMRqM0NDRQljJAyODpHc1BhyQiIhPUNzjM1oYur7+mxEymP+jf9saugCMREXmZks0ZZldLHwD56VMv2ezuaOVnD1Ty26d2kp3k+NeW+qBDEhGRCdra0MVQ1LG8ZGrN85zokkOQkxJie6NqNkUkcSjZnGF2+slmwRSs2QTIyMkjO7+IkpwUNtR2MzgcDTokERGZgE01o0ailZgqzY6wVTWbIpJAlGzOMDtaegkZ5KRNwXlPRinOiNA3FKWiOv5D5ouIyOHbVNtBenKYefnph95YJqQsK8LWhi68KcpFRIKnZHOG2dnSR3ZKiFBoig5F6yvOCGHAvet2EI2qdlNEZKrYVNvB0SXZU/4+lIhKsyO09w7S0j0QdCgiIoCSzRlnZ0sfualTcILN/Qz3tJMTGeau9TU0NDQEHY6IiIyDc47KGo1EO1lKs7z7+zb12xSRBKFkcwbpGxymur2fnNTp8Wefk51ES7/R3qv5NkVEpoKq1l46+4fUX3OSlGVHANimfpsikiAiQQcg8bOzuZuoY1rUbAKUZBibWuDpXR0sXRB0NCIicigb/cGBQh01rFvXBEBlZSXRaGqQYU0bhelhUpNCbG1QsikiiUHJ5gyyua4TmD7JZl6qkRI2/rWrncuDDkZERA5pU007IYM//fMJ5pQvBKByzTMULVxBecCxTQchMxYWZqpmU0QShpLNGWRTTQfJYSN3mjSjDZlRmh3hqZ0dRKNOg02IiCS4DTUdlGVHmJOzkLIlKwCo370t4Kiml0WzMnluT2vQYYiIAOqzOaNsqu1gQUEaIZs+SVlpVoTW3iEefH6bRqUVEUlwG6rbWZg7tafeSnSLijKoau2lb3A46FBERJRszhTOOTbVdHBUUVrQocRUHl0Yjm/fV6lRaUVEElhDRx8Nnf0szJs6yebg4CDr1q3b+/D6lyZ2weaiokycg+0akVZEEoCa0c4QDZ39NHcPsKQondb2zqDDiZnkMMxKD1HbZ5rEWkQkgY0MDrQwL4n1LQEHM04VFRVcf8eDFJcvAqZG/9IlxZkAbGno1Ki/IhI41WzOEJv8m/x0q9kEKM00Ovqj7GztCzoUERE5gA3V7QAsyJ1a5dzF5YsoW7KCsiUryJ9dFnQ4h7SgMINwyNhSr0GCRCR4SjZniE21XrK5uDA94Ehib06G9zZ+ZGtbsIGIiMgBbahpZ2FhBmlJ+uoxmVIiYRYUZrC5fvq0YhKRqUuf+DPExup2SnOS6WlvnnbNTdOTjML0MI9uaws6FBGRmDOzuWb2kJltMrONZvZRf3m+md1vZlv8n3n+cjOzH5rZVjN7wcxWBfsbeDZUd7CiNCfoMGaEo4oz2aJkU0QSgJLNGaKiqhUG+/npfevp6ekJOpyYm5eTxKb6Hmrbe4MORUQk1oaATzjnlgOnAteY2XLgOuAB59wS4AH/NcBFwBL/cTXwk/iHvK/W7gGq23o5Rn0I4+Ko4ix2tfTQO6ARaUUkWEo2Z4Cu/iGq2vqZlZ1Gek5e0OFMinJ/KP37N9UHHImISGw552qdc+v8551AJVAKXALc5G92E/Am//klwG+c5ykg18xK4hv1vkYGBzpGNZtxcVRxFs7Btkb12xSRYCnZnAGe39OGA4oywkGHMmlyU8PMy0vl7xvrgg5FRGTSmNl84ATgaaDYOVfrr6oDiv3npcCeUbtV+csCs6HGGxxohWo24+Ko4iwANtepKa2IBEvJ5jQXjUZ5ZKP3naMgbfommwBnL8rlqe0tvLSzirq6Ourq6hJ+PjQRkfEys0zgD8DHnHMdo9c5rzP+hDrkm9nVZrbWzNY2NjbGMNJX2lDdTmluGrnpyZN6HvHML0gnORzipQYlmyISLCWb01xDQwP3PFdNZniI4YHp258xGo2yMj/KcNTx2TvX87NHtvHdu9fQ0NAQdGgiIkfMzJLwEs3fOefu8hfXjzSP9X+OfOBVA3NH7V7mL9uHc+4G59xq59zqoqKiyQserxntMaWq1YyXSDjEwqIMXlLNpogEbGpNdiUT5pyjdSBESdr0GoF2f90drTxU20uKpVHTF+H0/Mn94iQiEi9mZsAvgUrn3PWjVt0DXAF8w/9596jlHzKz24BTgPZRzW3jrrNvkB1N3bzlhEBb8h7S8PAQlZWV+yyrrKwkGk0NKKIjs3R2Fmt3tgYdhojMcEo2p7ndrf30DzsKpua9ckIyc/Mo6emnuscRjU7v5FpEZpQzgMuACjN7zl/2Gbwk8w4zuwrYBbzdX3cv8DpgK9ADXBnXaPezaYoMDtRUvYtbN7axtP3l+agr1zxD0cIVlAcY1+E6qjiLu5+robNvkKzUpKDDEZEZSsnmNFdR641EV5AyM5Kv4jTY2QX1nX1kBB2MiEgMOOceB+wAq88bY3sHXDOpQU3ABj/ZXDEFmtEWzJlH2ZIVe1/X794WYDRH5ugSb5CgF+s6OWl+fsDRiMhMpT6b09yG2m6Sw0bWDCnUnOU3F97dMv3mEhURmYo2VLczKyuFWVkzoIlNAlle4tUkj9Qsi4gEQcnmNFdR20VRehg7UJn4NJMShrwUJZsiIoliQ3V7wjehnY6Ks1MoyEhWsikigVKyOY01dfWzrbmP4syZ1Vq6OD1EXXsfg8Mzo+mwiEii6h0YZltjF8dofs24MzOWz8lmY2170KGIyAymZHMae3JbMwBzsmZYsplhRB3UdQ0FHYqIyIy2oaadqEv8wYGmq+Ul2bxU18XgsOacFpFgKNmcxp7Y0kRWSpiC9HDQocRVYaoRDhm1nUo2RUSC9NzuNgBOKM8LNpAZavmcbAaGo2xr7Ao6FBGZoZRsTlPOOR7f2sSJc7MIzZQOm75wyCjOTqG+W8mmiEiQ1u9ppSwvjaKslKBDmZFW+M2XN1ar36aIBCOQZNPMfmVmDWa2YdSyfDO738y2+D/z/OVmZj80s61m9oKZrQoi5qlmV3MP1W29nDR3ZvaTmZOTRnPPMH1DajokIhKU9bvbVKsZoAWFmaQmhdhUq2RTRIIRVM3mjcCF+y27DnjAObcEeMB/DXARsMR/XA38JE4xTmmPbW0C4KTyrIAjCUZJTioOePLFKurq6ohGlXSKiMRTXXsfte19HD83N+hQZqxwyFg6O1sj0opIYAJJNp1zjwIt+y2+BLjJf34T8KZRy3/jPE8BuWZWEpdAp7DHtzRSmpvG3NyZ2XSpJCcNgJuequK7d6+hoaEh4IhERGaW5/a0AnBCeW6wgcxwK+ZkewM1RTVCu4jEXyL12Sx2ztX6z+uAYv95KbBn1HZV/jI5gL7BYR7b0sQ5S4uwGdZfc0RacpisJEeHSyUztyDocEREZpz1e9pIDof29huUYBxflktn3xDbm7qDDkVEZqBESjb3cs45YMJFcGZ2tZmtNbO1jY2NkxDZ1PCv7f+fvfsOj+ss8z7+vWfUe5eb3B3baXYSJ3EqIY00CCWhk4QNBBbYl7qUhaUs7AK7S8uyGzYQNgmEJBBIIwVCeo9L3Etsy7IsWVbvXZrn/WOOHFmWbJXRnBnp97muuXTmzDPPuc/MaM7c5ymnno6efi4+vvjYhaew/GRHfacj/HESEZFoer28ieWzskhOmF4zosealV7L8ob9Tb7GISLTUywlm9UD3WO9vwP9HiuBkkHl5njrjuCcu9U5t8o5t6qwsHBSg41lf9tWTVpSkIXpfdTU1EzbZCs/BXpC0Nyt8ZoiItHU2x9ic0Uzp2i8pu8WFWaQkZxwqFuziEg0xVKy+RBwvbd8PfDgoPXXebPSrgaaB3W3lSGcc/xtezVnzs3k54+s5RePv05HR4ffYfkiPzmcZNfoEigiIlG1pbKZzt5+zliQ53co014wYJw8J1stmyLiC78ufXI38DKw1MwqzOxG4AfAJWa2C7jYuw/wKFAK7AZ+CXzKh5DjxpbKFqpbujlvYQ4ZOfmkZU/fKeczEyEpADVt/X6HIiIyrawpC88BePp8JZuxYGVJDjuqWunq1fFQRKIrwY+NOuc+MMJDFw1T1gGfntyIpoZQKMT9a/YQMDguo4fymunZfXaAGeSnmlo2RUSi7LW9DSwsSKcwc3rOiB5rVpbk0BdybD3QzGnzdAJARKInlrrRygTV1NRw3/oDFKUH+d0zm6Zt99nBClKN5u4QzZ1KOEVEoiEUcqwpa1SrZgwZmCTo9fImX+MQkelHyeYUEAqFOHjwIOt3V9LSayydlTetu88OVpASvvTL5qo2nyMREZke3qhppbmzV+M1Y0hRZgqzc1J5XeM2RSTKlGxOATU1Nfzowdf46ZN7AFhcmOFzRLEjN8UwYNMBXV9MRCQaXtsbHq+pZDO2rJqfy2t7G6btDPUi4g8lm1NERk4+Nf1p5KcYGSm+DMWNSQkBIz8tqJZNEZEoeW1vAzOzU5iTm+p3KDLI6oX51LZ2U1qnk68iEj1KNqeI1u5+mnqM2Rnmdygxpyg9gW0H2+np0/U2RUQmUyjkeHlPPWcuyMNMx6NYsnphPgCvlNb7HImITCdKNqeIsqZeAOZk6i0dqjgjSHe/Y3Nlk9+hiIiMmZn92sxqzGzLoHV5ZvaEme3y/uZ6683Mbjaz3Wa2ycxOjWas26paqG/v4bwlhdHcrIzC/Pw0irOSeaW0we9QjtDb28v69euPuPX29vodmohMkDKTKaK0sZfcZEdGos4kDzUjI9yt+OU9OpsrInHpduCyIeu+CjzpnFsCPOndB7gcWOLdbgJuiVKMALywuw6A85YURHOzMgpmxuqF+bxSWh9z4zY3b97Mj3//FHe9su/Q7ce/f4rNmzf7HZqITJAG900B+xq6aOjsZ0VebB08YkVKQoAlBam8tKeez1y4xO9wRETGxDn3nJnNH7L6auACb/kO4BngK976O71rVL9iZjlmNtM5VxWNWJ/fVcuyGZkUZaUA4RaroQnD9u3bCYVSohGODLF6YT4PbjhAaV07i2JsMsHiuYuYs+QEv8MQkQhTsjkF/O2NcJeYOelKNkeyam4mf9xUR1dvPymJQb/DERGZqOJBCeRBoNhbng3sH1Suwls36clmZ08/a/Y2ct1Z8w6tG2ixKp676NC67a+toXDhCcyd7IDkCAPjNl/eUx9zyeZQ/f19bN++/Yj1J510EomJiT5EJCLjoWQzzjnn+OvORmZkBElN6Pc7nJi1qiSLu9fXsH5fI2cvVvcuEZk6nHPOzMZ8ttHMbiLc1Za5cyee+r26t56e/hDnHXf4eM2hLVbV5XsmvC050nDJ2dDEbH5+GrOyU3jujVo+vHre0CpiSl3lPu7e2sTS5rRD66rL9/AF4NRTozoUWUQmQMlmnNte1cq+xi7OKkkFevwOJ2atnJVBMGC8tKdeyaaITAXVA91jzWwmUOOtrwRKBpWb4607gnPuVuBWgFWrVk24a8wLu+pISghwxnxdX9MPQ5Oz4RIzM+PC5UX8cV1lXPT0yZ81T11rReKcks049/CmAwQN5uck0qtLSY4oPTnIijnZvLinji+x1O9wREQm6iHgeuAH3t8HB63/jJndA5wJNEdrvOZblhYyIzuF1KTYTmCmstEkZxctL+a3r5TzSmk9FywtilJkb9I4XpHpRclmHHPO8fDGA5w+N4uUhACaIPzozllcwH8/vZu6tm4KMpL9DkdEZFTM7G7CkwEVmFkF8C3CSebvzexGYB/wXq/4o8AVwG6gA/hotOI8b0mhLnkSB85amE9qYpAnt9f4kmxOZByvxnGKxB8lm3Fsw/4mKho7+ejpxVTUNvkdTsy76uRZ/NdTu/nzxgPccM4Cv8MRERkV59wHRnjoomHKOuDTkxuRxLOUxCDnLingye3V/MvVJ2AW/UumjXccr8ZxisQfXWczjj208QBJQeP4rN6Yu2ZWLFo6I5PlM7O4f8MBv0MRERHxzcXLizjQ3MX2qla/QxlRL0Fa+hMpq2unorGD+rZuHG92FR64DW4hFZHYo5bNONXXH+LhDZUUJof4zVMbySiY6XdIMSsUClFTE547410rZ/Fvj+2gtLaNhTE+7buIiMhkuHBZMQHbzCObD3D8rCy/wzmksb2HzQeaKatrp5HjoBte3/jmCeIAS8ns6oX9TSwpyiA9WT9jRWKd/kvj1DM7a6lr7+XCBemkEfI7nJjW3tLI/z55gLS0Mq67aCVm8MCGA3zhkuP8Dk1ERCTqCjOTOW9JIfevr+SLlywlEIh+V9rBOkiirCuPZ1/ZR8CgJC+N7I4K0pMSOOHkFfT1O9p7+ti8dRvNLotn36jl+V21HFecSQn+xi6xZ7hJqEBje/2iZDMOhUIhfv3cG+SlBpmTlUBHi98Rxb707FzS09MpzEji3MUF3PNaOZ84f6HOioqIyLT07lNn89l7NvDK3nrOXuTPJcH6+kO8VFrP6ywkGHKsXpjHibOySU9OYN2T6wgmZDMzO/VQ+fatNQRTu5l78llsqmhiW1ULO/oT6HutiR8s7qI4SzPaTiXDJY2jSRiHm4RKY3v9o1/acWjDrv28VNbM4pROujo17HasPnfxcbznlpf4xbN7+OKlugyKiIhMP5ceP4OM5AT+tL7Sl2SztrWbx7ccpKGjhxk0sTC1m9MXjO6YnJeexAVLizhzYT5Pv76LF8o7ufhHz/KPly3lQ2fOI+hzS61ExtCkcbiEcaRL6RTOWaBrtMYIJZtx6MEtdRjGcYW6fMd4nFKSzaVL87j12T2897Q5lOSn+x2SiIhIVKUmBbnipBk8sqmKf7n6BNKSoveTsLw9wOtr95OcEOCdK2dRt2E7Qcsecz2piUFOyOrm7TP7eLK5gG8+uJU7n9vJTadksrjg8FZOdaGMT0NnLgYIhRytXX109vaza/sWfvHAM8wYx6V0JDqUbMaZ9u4+Hthcy5ysBNIS+v0OJy7V1NSQ2FFLXyjA5+5ex+8+eQ7JCboIuYiITC/vXVXC79dWcN+6Cq47a/6kb885x12bW1hTn8DsnBQuP3EG6ckJ1E2gzrrKfexuauK4k04jKT/A+ppuvvxkD8uzHcuy+wmYulDGmtGOqWzpDlHVaRwoa6CutZuDDQk89sdKuv5QReiwizAsJ6HCyEhJID89iZ4ZK3F9Qdq7+zRcKgboHYgzv3llH81d/Zw7NwN6u/0OJ64MnpW2MD+PcwK9PLevlc/fu4GfvW8l9XW1ABQVFREIqHuyiIhMbafNy+WUuTn88vlSPnjGXBKCk3fs6+kL8eX7NvLA9nbmp/dz1SmzI9bdNX/WPEqOO4ESIPDkn9kbLGF7Sxp1Lo1Ljy+mOCJbkUgZbkxl1d43uGLbdpJnLmbdgS7WH+xmV30PjkSorSc7NZHEvnYSu9uZW1hIUsARDMCBsj2k5M8kb+ZcWjp7qW/voYkC6DG2v7CXgowk5ualkd5l9Id0mUA/KNmMIx09ffzyuVLOnJtFUXqA1ia/I4ovA7PSut4uMgpmsigvnZXz8rj5uQo6Ol8ht7eehAB88eozmDFjht/hioiITCoz45NvWcQnfrOOR7cc5B0rZk3Kdpo7e/nkb9bxcmk9Hzwxk67m+kkbV5lIiOXJTZy+aBFP7ajh7tfKWZ4VUKIRYwZ3j23p7GVNeTP/sjGZni11gCM3yVHQupdZBdmctfoMkhOCrHtyE8HcbFaevuxQPesadhFMbGPlksJD69Y8+TCdyfmkzllGeUMHG/c30+8S2fLnGt59cBtXr5zFSbOzMdPY3mhQshlHfvvKPurbe/j+lQt4eVe13+HEpfTsXEK9XYfuf/DUYrIyM/m3R7eTnZzIBQs0flNEZDxGmqgjFNIMobHskuXFLCpM5xfP7OGqk2ZG/DIo+2pb+PAvX6aqrY/PnplNUcc+1rrJ/0wsLspgVk4Kz+ysZWtNG//0VD23zG1lcVHmpG9bjs052FPbxob9TVQ0dgKFZCd2c/7SIhYUpJOWlMC6J3cSDPaMeahTAEdWsJeV8/M4fX4evf0h1mzaRlufccdLe7nthb3MzU7g4vnJnD83hazUpEPP1djeyFOyGSdqWrr4ryd385bjCjl5VoaSzQj62HkLKUzq48sP7eahHa2UFFbz/95WNKndiUREpprhusZpoo7YFwgYn7lwMZ+/dyP3ravgvaeXRKzurQea+fCtL9HS1cvZRSEqqmp5IoqfibSkBK44aSYvb9jOtlbjiptf4B8vXcrfnbtAM9b6pK27j0feaOcvVYm0768iMyWB1Qvz6Ct9jfSUNE6YdWLEt5kYDJDcUEpnUxOXn3galR0B9raF+PXGPm7f0MK8TFiYEaL74G6N7Z0ESjZj3MA4w39+tJTu/hDffscJ0Nfqd1hTwuAxnAtSu7h6WQYv7+/k5ucqeGR7I1+6dCmXHF98KOkcXF7jOkVEjjR05sjq8j0+RiOj9c6Vs7n71f18/7HtXHJ8MbnpScd+0jE8vqWKz9+7kfREeOuMECeccDzgz2eiJD3EJ84u5O7dxr8+up2/bD3If167gvkF6s002QZ6PNS09/Horg7+treDjl5HXpLjLSfOZFFhBoGAsa60d9JjyZ81j4VLT2AhcB7wzJN/pTqhiP0dGextc+QlLeXpsg6OP6mflERNHBkpSjZjXE1NDf9492s8Xx3kxjNnsqAgnYMHlWxGwsAYzqJZrVTv201GwUzeOj+Vkpxkfru5lb+/az1FmclccdJMzliQx8zkHu56aiNmh4/rHJyEghJRERGJL2bGv7zzBK68+QX+7dHt/Me1K8Zdl3OO/3pqNz9+4g1OmZvDZ1Yk8ejGighGOz65qUF+ed0p/Gl9Jd9+eCuX/+x5vnr5Mj6yel7Euw7Lm+57eh0/emov9YS7L89OC1F0cC3zSkpYUuzvtc4z6SInuZmrV53E9qoW1u+t5b9ea+Y3W57k2tPm8KEz5+mERAQo2YxxjR29rK1PIDvZuO50TVoTaenZuWTlFdLaVA9AR2sTG6o7OS0zmQ+fNJNnK3q5Z005t79UBkBSMEhOSpD2R3ZyUkktq5fOpjCxm589vIaMnHzamuo1wZCIiPiuv7+P7du3H7F+pDFpy2Zk8cm3LOS/n97DipIcPrx63pi32dHTx5fv28SfN1Xx7lNm82/vPoltmzeOK/5IGvxaLDD40cV5/M/aZr710FYe33KQf7/mZEry0nyOcuro6w/x+NaD3PbCXl4vbyLRMjm1JI+TS7LJSklkXccbfod4mJTEIKfMzaWg6wArFs3g1fok/u/FMn75/F7OW1LAR1bP48JlGl41Xko2Y1QoFOJgdTX//Oc36O53XLwwjeaGOg4mBKipqcE5zao2WQYmEdqws4wZKcnc9e4FNJLB+tJqntrbTm1rN3/d3cqju9rgqXLSkwIUpKVwQkYK+Vl5focvIiJCXeU+7t7axNLmN5OoY11v8guXLGXbgRa+9dBW5ualcf5xhcOWG87r5Y18/t4N7Gvo4GuXL+Om8xfGzGyfw70W6Qf38PenrebOzU1c9tPn+PJly/jQmZN7+ZepZuikYK3dIZ4o7eDJ8l6qmruYl5/GjadkUVdbx/wlBT5GOjqhUB8J9aV8bPly3j2/kL+VdvDX0gZu+k0dM7NTeM+pc3jXqbNZVJjhd6hxRclmjKqpqeHjd6xla1OAUwoDJPe28L9PVh/W5VMm10DS+etndlA0aw7V+3ZzcsFMQpkhLCGZUEIKi/KS2XCwi+f2dbBv80FSEozUtCo+dUkeOWkTH/MiIiIyXvmz5h02hvZYggHj5g+cwrW/eJm/u30N337HCcds4axv6+bHT7zB3a+VMzM7lbs/vprVC/MnGnrEDX0t+vv7mNOzjx9dsoT/WRNu5fzlMzv51tUncfHxM2MmUY5lA5OCpcxYzO7WAOUdAULOWJjey1fPKeK0mcm8sXMHDcTHjNTDnZRY3rmHc885m1dqE/ifZ3bz86d3s6Ikh/ecOpu3nzwrIuObpzolmzHqiZ0NbG0KMC8jxOKcBFzfkV0+JTqGe93NDOtqZuvuThJ6u7hqwQz60otYs6eGW18+wJ1rDvK+00v42HkLj9k1RxMPiYhIrMhMSeTeT5zFZ+95nW88sIW/bqvmM29dzKp5uYfGNvaHHFsPNHPfugruW1dBd1+I686azxcuPY6slPi4bMShxOLkNBYnQ1qBsaGum4//5nXOW7KfL1xyHKfMzfU7zJjV1dvPC+Wd7ExeSu3BAAkB4/iZmSQe2EBvZTW78k9j1/74m5F6uJMSWS17+ezK5XxkaRHPl3fyTFkr33xwK//y8FZWzkjmrDkprJqZwtmrVuiyKcNQshmDntpRzXf/WkZxepBTC/oxM9RpNjYNtH4GzJiXn05Cczc1Sd3s607hrlfL+c0r+7j8xJm8fcVMzj+ukLSkhCOSy5qaGn704GsAGu8pIiK+y05N5LbrT+e2F0r532dLee//vkxGcgILCtLp7Q9R2dhJa3cfScEAb18xi0++ZSFLijPp7e1l/fr1h9UVy9daHZxYlABF2zfS7zp4dl897/qflzixKIkvXn4SFywrVksn4cmftlS28Id1+3lwwwGaO3tJCxrnLM7nhFnZpCYGWXegm+Cg1zXeZ6Qetgv2zmdZlFZEYPaJbK3uYu2Bbsw1sejJCi5ams+Zs1PISQnqmp0eJZsx5qGNB/jCvRtYXJDKKcUJ9Lb1+B2SjNGM/GwWpKbylgXpPFnez8PbanhkcxVBgwX5qcxMg6qmdlKD8O4zFpKZHKQtIZukoFHV0k1qdi+ZyQmaHU9ERHwTDBg3nb+Ij6yez2Nbqti4v4my+g6SEwKcNi+XMxbkcd6SQvIGdSOM92utNhzYR1NTExedeBqlbQF21vTy0TvWccKsLN5/egnvWDGb7LTplzxUNHbw+JaD3Leugh0HW0lKCHDZCTM4JaeLzaUHKZk3teerGNraWV2+h2BaNivPWIZzjurWbl5c8zrlzen877oW/nddMxl0ccWWVt73lpNZWZIzra/rqmQzRjS29/DdR7bxp/WVnDE/j+9fPpffvlLG5F91SCZDe0sjf3z5AK63i3OyUqhP7Kc+lEpndwLrGrrpcUH6HGz+S9lhz3twxxZgC0lBY25uMvNyU5iXm8KC/FROXTyLBQXptDSGu/Oqy62IiEy21KQg7z51Du8+dc6oysf7tVbzZ81j/tITmA8semMrM4uzeboixD8/uJXvPrKdt50wg6tXzOLsxfmkJU3Nn9HOOXbVtPG37dU8vuUgmyqaAVicl8gnTs3i3LmppCc5tm8vxbnYbLWOFjNjRlYK86llYUYPc05aSmltG29U1vKHba38fttLZCQZK4qTObk4mXeefSLHzcyeVi3lcfNfYmaXAT8DgsCvnHM/mOxtTuZYOucc5fXtvLh9Py+UNvPkrgb6Q/APb13EtSdk0dRQpxln49yhLraJKaSndzE/MYWZJfOpLN1BIDGRhKQUzp+fTltPiL/uaaOltZX2rl5SMnOoq2+kpaWXlxvaeTIUAAzYiwGpCY7UIKycm0dJYTbFWSkUZSZTmJlEYm87+emJLCqZSTB45AWJNT5UZHrw45gpsW+sl0OZ7oIGlyxM48vvOYWtB1r4w9r9PLDhAA9vPEBSQoCzFubz1qWFnL4gj6XFmXE7k61zjgPNXawta+D5XXU8/0Yt1a3dACzJS+QjJ2dS2FXJqzv3U5exhAfqws+Lp1braDCDwsxkCjOTSdj7InXNbWQtPIXqLmNtZYgX93dxy9oXyUtPYtW8XE6fn8ep83JZNiOT9OS4ScnGLC72zMyCwH8DlwAVwBoze8g5t20ytxupsXT9/f1s2F3B9oNt7KjpYGdNJztrO2jp6gcgMQAlaf18/vxZzMvp4eY/r6WjpVEzzk5xPe1NPLymCtfbRU7BTLLSIZCTycySEipL2wkkphDq7cIFE+kJJLO8MIX9Lb28WtFJR2+IHdWtvLKvmfae0BF1JwU3UZCeSEF6IoUZSeSnJzK/OIdgfzdPbdxLkBBXnjyThbMLOWHBbLJSNZuayFTh1zFTYt94LocCR17iYsBUT1KHJudXl8AVs/LpypzNs7saeHpnDd9+OPxvlZoY5OQ52Zw8J5slRZksKspgcVEG2amx9fqEQo7Kpk7eqG5lZ3Urm/Y3s768kRovucxIMhakdpPpalkyu5C0hB46mtp5/LVXKVx4Qly3Wkdb8czZrFy5HAgn9Fu3bCYr0dGUmMrm8jr+uq0aCCepJblpLJ2RybIZmSydkcmCgnTm5KSRlZoQ962gcZFsAmcAu51zpQBmdg9wNTBpB86evhB/2dFAdSgTgMe215Nd2Ut3ZxspCQFmFuaTlpxAamKQFO/WH3K09/RR29rNvvp21u+pZlddBzur22nvDbdSGo7MYD/ZSY6StG5mFmSzdNF8qsve4PF1e3C9XWQUzCQtWzOgTQcDrZ9HEwwYyb0tbN1djevtYlXhTEK9PXR2dFK0aA6VZbvpCaTS1dML6fl09PTS1tVHV1cnlZ0J7GtIor2nnz5X49UYAAK89GQtUAtsIy0pQGF6IoUZiRSmJ1GYkUhRZhKLZxeSlhSko7WZxGCApKAxo7CApIQATY0NBAPGjKJCEhOCJASMhKCREAgQMEb15ThwPVmAGcXFo2ppHUvr7OCyI5VXa69MQVE/Zkr8GG62zaGtnb29vZgZCQnhn4nbt2/n0U2VzJi35FCZqr1vcOX27SxfvvzQulieDGg8hkvOq/a+wZUr53DV8uVcNTuLg21pbK/pZFdjP7sbW/m/fQ30DToHnJFk5KcGyUsNsmR2AbNy0yjKTCEnLZHs1DdvWamJpCUFSRxn62jI+w3a1NFLQ3sPDR09NLb30NDeQ2VTJxWN4Vt5fTvtPf2Hnjc3L41luUZG60Hmz8gnJ9GxY82zzF54AsctV2IZKWZGd00Zu5qaWHryaZydB13ZsLu8ijkzi+hMSmHb/jqe3F5NaFDHxpQEoyA1QGFakIL0BLKTA2QmGZnJAU5aupCCzFQyU8KfnfSkBFKTgiQlxNbvmHhJNmcD+wfdrwDOnMwNdvX1863H9x66/9y+siEldh+zjiCO7GTI6mthQWYyWdZFVkoCgb4uLDEF19uFdUNHUz0dzY1YYvgLur25MfzYQJlBf1vS0w+VjZcyiityZYZ+PgASDBITHGmuD3PN5NGFZb75nKJZBbQ11XPNqrm094a4d00Fra2t9AeT6erpo7W7j0ByNk1NLTQ2JbLZBeghAYdx+L/d2CQEjGAgnCwneIlnv3OEXHja/H7nCIU4NNNywCDRS1gTg+Fp1AfnqwOLoVCIts7wxFmZqUmHugsHDAIBI2jh0iHn6O3rp7mjGyxAKBQiNTkRs3AyHAxYeMB+KERLRxcGFGankpyY6D0GQQuXSfDKBg9tY2C9V493PxgwAgYhBw6Hc+AchLwu8c5BiPAZTufC++6cw+E9x0F4bXjfAxbebzPz/oaXA4OXbeD1G1wmfH9gmwMc7oh1g++7Y5QbtuyQQkfWNXy54et6M9Kh6wdeq4HXqd97DUPOsbA4h+9cfeKRwU5fUT9mDhj6g7ThYAWBlCYqdm0dcd1oykxk3XSoayL1v7HuRV5rbKBkUdmhdfu2byCQlErJoqWH7hfMW8rg/l1NtVX89++3HvG8gnlLDztpF6v7Pfq6MhhspP0eeL3ygG6S2Fd5gFBaAakFs2npSuJgrWNXXSftfYGjXmEgYJAUtEO3xEF5g9mbx8LeEPT0O3r6Hb39jt4jOzkdkpJgFKUHKUoL8pa5yczNTmRudgIlWQmkJwXYvn07j9FIbtKbE/3UH9gXo+9HPH12hivz5ucpJQjB2jfY/sYrlCxayhxgFkZp2T5CaflkzphPT38StfUdVCdn4ZIz6COBQz+OXnud4QQt/J4nJxjJwfBvmMRg+HdZovfb5dvvWRW1S/tYPIwLNLNrgMuccx/z7n8EONM595kh5W4CbvLuLgV2RmDzBUBdBOqJJdqn+DEV90v7FD+m4n4dbZ/mOecKoxnMZJikY+ZU+SxMlf2AqbMv2o/YM1X2ZarsB8TuvhzzuBkvLZuVhC+BNGCOt+4wzrlbgVsjuWEzW+ucWxXJOv2mfYofU3G/tE/xYyru11Tcp2FE/Jg5VV63qbIfMHX2RfsRe6bKvkyV/YD43pfY6tQ7sjXAEjNbYGZJwPuBh3yOSUREJBbpmCkiIjEhLlo2nXN9ZvYZ4C+Ep3H/tXNu6zGeJiIiMu3omCkiIrEiLpJNAOfco8CjPmw6ot1yY4T2KX5Mxf3SPsWPqbhfU3GfjjAJx8yp8rpNlf2AqbMv2o/YM1X2ZarsB8TxvsTFBEEiIiIiIiISX+JlzKaIiIiIiIjEESWbgJldZmY7zWy3mX11mMeTzexe7/FXzWy+D2GO2Sj26wYzqzWzDd7tY37EORZm9mszqzGzLSM8bmZ2s7fPm8zs1GjHOFaj2KcLzKx50Pv0zWjHOFZmVmJmT5vZNjPbamafHaZMXL1Xo9yneHyvUszsNTPb6O3Xd4YpE1ffgaPcp7j7/vPDsY4jfhvp/9LMvm1mlYPe3ysGPedr3v7sNLO3DVrv+76aWZmZbfZiXuutyzOzJ8xsl/c311s/4neomV3vld9lZtdHeR+WDnrdN5hZi5l9Ll7ek+GOyZF8D8zsNO893u0915gEI+zHf5jZDi/W+80sx1s/38w6B703vzhWvCO9JlHcl4h9niw8mdqr3vp7LTyxWrT2495B+1BmZhu89TH9noxJ+OLi0/dGePKEPcBCIAnYCBw/pMyngF94y+8H7vU77gjt1w3Az/2OdYz7dT5wKrBlhMevAB4jfN3j1cCrfsccgX26APiz33GOcZ9mAqd6y5nAG8N8/uLqvRrlPsXje2VAhrecCLwKrB5SJq6+A0e5T3H3/efD63jM44jft5H+L4FvA18apvzx3n4kAwu8/QvGyr4CZUDBkHX/DnzVW/4q8ENvedjvUCAPKPX+5nrLuT5+hg4C8+LlPWGYY3Ik3wPgNa+sec+9PIr7cSmQ4C3/cNB+zGfk3yDDxjvSaxLFfYnY5wn4PfB+b/kXwN9Haz+GPP4j4Jvx8J6M5aaWTTgD2O2cK3XO9QD3AFcPKXM1cIe3fB9w0WSdiYqg0exX3HHOPQc0HKXI1cCdLuwVIMfMZkYnuvEZxT7FHedclXNuvbfcCmwHZg8pFlfv1Sj3Ke54r3+bdzfRuw0dzB9X34Gj3Cc5tpg/jozj//Jq4B7nXLdzbi+wm/B+xvK+Dv7/uwN456D1w32Hvg14wjnX4JxrBJ4ALotyzAMuAvY45/YdpUxMvScjHJMj8h54j2U5515x4YzgzkF1Tfp+OOf+6pzr8+6+QvgavCM6RrwjvSYRN8bfSWP6PHnHsgsJH9tgEvflaPvhxfFe4O6j1REr78lYKNkMH5T2D7pfwZEHqkNlvH/SZiA/KtGN32j2C+A9XneK+8ysZJjH481o9zvenGXhLoGPmdkJfgczFhbucnkK4dalweL2vTrKPkEcvldmFvS67tQQ/oE04nsVL9+Bo9gnmHrff5EWV/+jw/xffsZ7f389qDvZSPsUK/vqgL+a2Tozu8lbV+ycq/KWDwLF3nKs7wuEe0IM/vEcj+8JRO49mO0tD13vh78j3Co2YIGZvW5mz5rZed66o8U70msSTZH4POUDTYOScL/ek/OAaufcrkHr4vE9OYKSzentYWC+c+5kwmfd7jhGefHHemCec24F8F/AA/6GM3pmlgH8Eficc67F73gi4Rj7FJfvlXOu3zm3kvBZ7jPM7ESfQ5qwUeyTvv+mkGH+L28BFgErgSrC3dPiwbnOuVOBy4FPm9n5gx/0WjLiopXeG/f2DuAP3qp4fU8OE0/vwUjM7OtAH3CXt6oKmOucOwX4AvA7M8sabX0+vSZT4vM0yAc4/MRMPL4nw1KyCZXA4DPac7x1w5YxswQgG6iPSnTjd8z9cs7VO+e6vbu/Ak6LUmyTaTTvZ1xxzrUMdAl04WvnJZpZgc9hHZOZJRL+8XeXc+5PwxSJu/fqWPsUr+/VAOdcE/A0R3a5i8fvQGDkfZqi33+RFhf/o8P9Xzrnqr0TDiHgl4S70MHI+xQT++qcq/T+1gD3E467emCIgfe3xise0/tCOGFe75yrhvh9TzyReg8qObzratT3ycxuAK4CPuQlJHhdTuu95XWExzYed4x4R3pNoiKCn6d6wt2fE4asjxpv2+8G7h1YF4/vyUiUbMIaYIk3E1US4S4fDw0p8xAwMJPYNcBTA/+gMeyY+zVkfNw7CI91iXcPAddZ2GqgeVCXgrhkZjMGzTR2BuH/25j+oe/Fexuw3Tn34xGKxdV7NZp9itP3qtDenJEwFbgE2DGkWFx9B45mn6bo91+kjeb46KuR/i+HvL/vAgZmf3wIeL+FZ1heACwhPNmG7/tqZulmljmwTHgyly0c/v93PfCgtzzSd+hfgEvNLNfrWnipty7aDmupicf3ZJCIvAfeYy1mttr77F43qK5JZ2aXAV8G3uGc6xi0vtDMgt7yQsLvQekx4h3pNYmKSH2evGPZ04SPbeDDvgAXAzucc4e6x8bjezIiFwOzFPl9Izyb2BuEzxp83Vv3L4T/GQFSCHcD2U34A7vQ75gjtF/fB7YSnpHraWCZ3zGPYp/uJty1oJdwP/UbgU8Cn/QeN+C/vX3eDKzyO+YI7NNnBr1PrwBn+x3zKPbpXMLdNzYBG7zbFfH8Xo1yn+LxvToZeN3bry28ORNe3H4HjnKf4u77z6fX8ojjSCzdjvJ/+Rvve2UT4R9gMwc95+ve/uxk0Eygfu8r4VkyN3q3rbx53M4HngR2AX8D8rz1I36HEh6Pt9u7fdSHfUknfKIte9C6uHhPGP6YHLH3AFjlfS/tAX4OWBT3YzfhcYsD/ysDs4y/x/vMbSA8HOTtx4p3pNckivsSsc+T97/3mvf6/AFIjtZ+eOtvx/sdMahsTL8nY7kNBCciIiIiIiISMepGKyIiIiIiIhGnZFNEREREREQiTsmmiIiIiIiIRJySTREREREREYk4JZsiIiIiIiIScUo2RUREREREJOKUbIoMYWY5ZvapY5SZb2YfHEVd881sy7HKjZeZvdPMjh/H8y4wsz9PRkxjiOGLZubMrGCcz/+1mdVM5usrIiLREc1jr5mtNLMrxhOn38zsc2aW5nccIqOlZFPkSDnAUQ94wHzgmAe8KHgnMOZk029mVgJcCpRPoJrbgcsiEpCIiPgth+gde1cCwyabZpYQgfon0+cAJZsSN5RsihzpB8AiM9tgZv/h3baY2WYze9+gMud5ZT7vnUV93szWe7ezR7OhkZ7ntTw+a2YPmlmpmf3AzD5kZq95cSzyyr4D+A8vjkUjbGOxmf3NzDZ62xgol2Fm95nZDjO7y8zMK19mZt/xym42s2Xe+kIze8LMtprZr8xs39FaJc3s62b2hpm9YGZ3m9mXBj38E+DLgDvG65NvZn8dbpvOueeAhtG8ziIiEvOicuw1syTgX4D3efW8z8y+bWa/MbMXgd8c49j8zAjHzh+Y2TYz22Rm/3mU7b/dzF41s9e9Y3Oxt/7bZnaHt919ZvZuM/t3b/8fN7NEM/t/wCzgaTN7eiIvtkjUOOd00023QTfCZ063eMvvAZ4AgkAx4Za4mcAFwJ8HPScNSPGWlwBrh9Y1wrZGet4FQJO3rWSgEviO99hngZ96y7cD1xxjf14F3uUtp3jbvABoBuYQPun0MnCuV6YM+Adv+VPAr7zlnwNf85YvI5woFoywzdOAzd62soDdwJe8x64GfjZoW8PW4T1+M/BNb/nKods81uurm2666aZbfNyifOy9Afj5oPvfBtYBqceod9hjJ5AP7ATMK5dzlG3nDir3MeBHg2J4AUgEVgAdwOXeY/cD7/SWj3rc1E23WLvFelcBEb+dC9ztnOsHqs3sWeB0oGVIuUTg52a2EugHjhtl/Ud73hrnXBWAme0B/uqt3wy8dTSVm1kmMNs5dz+Ac67LWw/wmnOuwru/gfDB+QXvqX/y/q4D3u0tnwu8y6vncTNrPMqmzwPud851ePU/5P1NA/6JcBfa0Th/YPvOuUeOsU0REZkaJvvYO5yHnHOdo6h3uGPnK0AXcJuF50M42pwIc4B7zWwmkATsHfTYY865XjPbTDjRftxbv9nbjkjcUTdakcj4PFBN+GzkKsIHkIk+r3vQcmjQ/RBE5ETR4Pr7h9TZPcL6iVoELAA2mlkZ4YPuejObEcFtiIjI9DDeY+9w2kdZ7xHHTudcH3AGcB9wFW8micP5L8KtqicBnyDc4+iwup1zIaDXOTcw1CRSx32RqFOyKXKkViDTW36e8LiOoJkVEm5pe21IGYBsoMo7QHyE8BnJ0Rjv84aL9QjOuVagwszeCWBmyTb+WexeBN7r1XMp4a5AI3kOeKeZpXqtq2/34tnsnCtyzs13zs0HKoBTnXMHj1LPB71tXn6MbYqISPyK5rH3qMfOsdZrZhlAtnPuUcKJ6opj1F3pLV8/yngHO1bsIjFFyabIEM65euBFC0+bfhawCdgIPAV82UuMNgH93qQ7nwf+B7jezDYCyzj8DOnRjPd5A+4B/tGbaGDYCYIIHyj/n5ltAl4CxtuK+B3gUu91uRY4SPigdwTn3HrgXsKv22PAmgls83wz20q4O+2h2WvN7G7C42WWmlmFmd04zm2IiIjPonzsfRo4fmCCoGEeH2u9mcCfvePsC8AXjlL228AfzGwdUDfKeAe7FXhcEwRJvLA3W+hFREZmZslAv3Ouz8zOAm5xzq0c5XO/DbQ550acoW+U9ZQBq5xz4zlAi4iIiEgUqf+3iIzWXOD3ZhYAeoCP+xyPiIiIiMQwtWyKRIGZvQ344ZDVe51z74rgNv4bOGfI6p855/4vUtsYZpv5wJPDPHSR1yVqtPV8lPAlXQZ70Tn36YnEJyIi01c0jr1H2fbXCQ85GewPzrl/nexti8QSJZsiIiIiIiIScZogSERERERERCJOyaaIiIiIiIhEnJJNERERERERiTglmyIiIiIiIhJxSjZFREREREQk4pRsioiIiIiISMQp2RQREREREZGIU7IpIiIiIiIiEadkU0RERERERCJOyaaIiIiIiIhEnJJNERERERERiTglmyIiIiIiIhJxSjZFREREREQk4pRsioiIiIiISMQp2RQREREREZGIU7IpIiIiIiIiEadkU0RERERERCJOyaaIiIiIiIhEnJJNERERERERiTglmyIiIiIiIhJxSjZFREREREQk4pRsioiIiIiISMQp2RQREREREZGIU7IpIiIiIiIiEadkU0RERERERCJOyaaIiIiIiIhEnJJNERERERERiTglmyIiIiIiIhJxSjZFREREREQk4pRsioiIiIiISMQp2RQREREREZGIU7IpIiIiIiIiEadkU0RERERERCJOyaaIiIiIiIhEnJJNERERERERiTglmyIiIiIiIhJxSjZFREREREQk4pRsioiIiIiISMQp2RQREREREZGIU7IpIiIiIiIiEadkU0RERERERCJOyaaIiIiIiIhEnJJNERERERERiTglmyIiIiIiIhJxSjZFREREREQk4pRsioiIiIiISMQp2RQREREREZGIU7IpIiIiIiIiEadkU0RERERERCJOyaaIiIiIiIhEnJJNERERERERiTglmyIiIiIiIhJxSjZFREREREQk4pRsioiIiIiISMQp2ZSoM7NfmNk/R6iuuWbWZmZB7/4zZvaxSNTt1feYmV0fqfrGsN3vmVmdmR2M9ra97TszW+zHtgfFUGZmF/sZg4iIiIiMn5JNiSgvQeg0s1YzazKzl8zsk2Z26LPmnPukc+67o6zrqMmGc67cOZfhnOuPQOzfNrPfDqn/cufcHROte4xxzAW+CBzvnJsxzOMXmFnFGOobU/mJMrP5XrKaEK1tRpuZ/ToSCbmZJZnZfd5n3ZnZBZGJUERkeDrhO6rtTvsTviKRomRTJsPbnXOZwDzgB8BXgNsivZEpnMzMBeqdczV+ByJHMrNzgUURrPIF4MOALz9qRGTq0AnfiYuXE75mdruZfS/S9U4W9VaavpRsyqRxzjU75x4C3gdcb2YnwuFfkGZWYGZ/9g6KDWb2vJkFzOw3hJOuh72zpl8e1GJ2o5mVA0+N0Iq2yMxeM7MWM3vQzPK8bR3xhT/w5WdmlwH/BLzP295G7/FDZ2m9uL5hZvvMrMbM7jSzbO+xgTiuN7Ny74zo10d6bcws23t+rVffN7z6LwaeAGZ5cdw+5HnpwGODHm8zs1lmlmxmPzWzA97tp966kcqfYWYve697lZn93MySxvL+mlmqmf3Ii7/ZzF4ws1TgOa9Ik7e9s45Rz8fNbLv342ibmZ066OGVZrbJq/9eM0vxnnOBmVWY2Re996LKzD46qM58M3vY+wyssfBZ6hdGsU+XmNkOb3s/N7NnbdBZeu9z9l/AP4zhdTrFzNZ7+3evmd0z8Pl3zvU4537qnHsBmPCPNRERdMJ3oqbECd8p/P5InFGyKZPOOfcaUAGcN8zDX/QeKwSKCSd8zjn3EaCc8EEzwzn374Oe8xZgOfC2ETZ5HfB3wEygD7h5FDE+DvwbcK+3vRXDFLvBu70VWAhkAD8fUuZcYClwEfBNM1s+wib/C8j26nmLF/NHnXN/Ay4HDnhx3DAkzvYhj2c45w4AXwdWAyuBFcAZwDeOUr4f+DxQAJzlxfupY71OQ/wncBpwNpAHfBkIAed7j+d423t5pArM7Frg297+ZwHvAOoHFXkvcBmwADiZ8Os/YAbh13A2cCPw32aW6z3230C7V+Z673ZUZlYA/An4BuHXZQ9wzpBinweec85tOlZ9Xp1JwAPAbwi/Rn8A3jOa54qITIRO+E7dE75mdhPwIeDLXp0PD3o9v2Jmm4B2M0sws6+a2Z5BJ3TfNaieGyx8ovg/zazRzPaa2eVDHi/1nrvXzD40itiOOIE83OdptPsq8U/JpkTLAcI/tofqJZwUznPO9TrnnnfOuWPU9W3nXLtzrnOEx3/jnNviJVr/DLzXvPEkE/Qh4MfOuVLnXBvwNeD9Qw6y33HOdTrnNgIbCSd+h/FieT/wNedcq3OuDPgR8JEJxvYvzrka51wt8J2j1eecW+ece8U51+dt/38JJ72jYuEuWX8HfNY5V+mc63fOveSc6x5j3B8D/t05t8aF7XbO7Rv0+M3OuQPOuQbgYcLJ9IBewvvc65x7FGgDlnqv73uAbznnOpxz24DRdMO6AtjqnLvPOdcL/JRBXVvNrAT4BPDNMezfaiAR+KkX533AmjE8X0RkQnTCd1hxfcLXOXcrcBfh42eGc+7tgx7+AHAl4RO+fYRPnJ7n7e93gN+a2cxB5c8Ednqx/Dtwm4WlE37vLvdays8GNhwtrpFOIB/j8yRTnJJNiZbZQMMw6/8D2A381Tt79tVR1LV/DI/vI/xjv2BUUR7dLK++wXUnED5ADxg87q6D8MFwqAIvpqF1zY5wbLNGKmxmx3lnsw+aWQvhg/xYXqMCIIXwQWwiSo5Rx9Fez3rvQDr08ULC78vgz8GxPjMQfr0OlfNOegx+3k8JJ7fNo6hrcJ2VQ06g7BupsIjIJNEJX89UOOF7DDc75/YPvD/OuT94J21Dzrl7gV2Ek+EB+5xzv3Thcbd3EP48DPyuCQEnmlmqc67KObf1GNs+1glkmYaUbMqkM7PTCSdSR4yZ877ov+icW0j4DNgXzOyigYdHqPJYB8KSQctzCR9M6wh3q0wbFFeQcGIy2noPEB4DM7juPqD6GM8bqs6LaWhdlaN8/nBxDhfbgaOUvwXYASxxzmURPptto9w+hPehi+EnyjnW6zjY/hHqmIhawu/LnEHrSkYoO1jV4HJmZkOedxHwH16CPpAEv2xmHzxGnbO9ugbMHUUsIiKRpBO+b5oKJ3yP5rD3x8yuM7MNXpfdJuDEIds69Jo55zq8xQzvZMH7gE8CVWb2iJktO8a2j3UCWaYhJZsyacwsy8yuAu4Bfuuc2zxMmavMbLH3Y7yZcNeSkPdwNeEuLmP1YTM73szSgH8B7vPO2L0BpJjZlWaWSHhsXvKg51UD823QrH1D3A183swWmFkGb3b56Ruh/LC8WH4P/KuZZZrZPOALwG+P/szD4sw3b6zKoNi+YWaFFh57+M1B9Q1XPhNoAdq8g8ffj3EfQsCvgR9740+CZnaWmSUTTvZCjO69+xXwJTM7zeu2s9h7PcbNe33/BHzbzNK8/btuFE99BDjBzN7tnSn/f4THfA44jvBZ8pW82Z337cD9R6nzZcKJ7/8zs0QzezeHn1HGG9eT4t1NMrOUIcmpiMi46YTvEabCCd+R6j1svXc8/SXwGSDfOZcDbBnttpxzf3HOXUK4tXOHV9fRHO0E8lhORMsUomRTJsPDZtZK+Evn68CPgY+OUHYJ8DfC4+1eBv7HOfe099j3CSdQTWb2pTFs/zfA7YTP1qUQThrwuj9+inCCU0n4wDd4soI/eH/rzWz9MPX+2qv7OWAv4Za9Uc9KOsQ/eNsvJfwD4Hde/cfknNtBOLks9V6bWcD3gLXAJmAzsN5bN1L5LwEfBFoJHzzuHcc+fMnb1hrCZ8x/CAS8M6P/CrzobW/1UfblD17Z33mxPMDwXb3G6jOEx6ccJPye3Q0cdTypc64OuJbw7I31hD+bLw56vMY5d3Dg5q2uO0pXMpxzPcC7CY8zaiB8lvhPQ4rtBDoJ/xj8i7c8oYRbREQnfIc3FU74Dqr3WO9POuEkrxbAwrO2nziays2s2Myu9sZudhP+nRY6xtOOdgJ5vJ8niXfOOd100023KX0jnAjfMY7nPQN8LMKx3A58z+/XRDfddJt6N6CM8AmrVsLJ48vAp4HgoDKHvoMIT1JTxpsnX/95ULmrCU/q0kT45OJ8wolLwqAyh63zvjO/D7xGOJl6GCgYVP4GwsMLarw6y4CLvcfyCZ98bQTWD6rvY95ygHASt59w8vRbIHe4OIY+d5jXKdd7fq1X3zcJnywFuACoOMbr/GvCJyWbCHeXTSE8mU6Vd7sZSDlK+fMJtxS2Ac8TTspfGFTeAYuPEcMSwhP2NAEPDHr/Lx5S7l8Jn+ysI3zy/9lBr+kNg7c7eNuEWzOfJfw5avJez+NH8Rn8JOGTqG2EW1FPGe7z5Pf/im7Ru5n3ARARmTK8M8VJhFteTwceJXxwfWCM9TxDuEXgVxGM7XbCP2S+Eak6RURERGKRutGKyIjMbKu9eW2wwbdjXmtrUB2/GKGOX0xi6JmEu6u2E+4i/CPgQTM7b4RY2sa7ITObO1KdZqbJgERERGTaUsumiIiIiEiMMbOtDD+G/xPOubuiHQ+ETyADHx7mod865z4Z7Xgk9inZFBERERERkYhTN1oRERERERGJOCWbIiIiIiIiEnFKNkVERERERCTilGyKiIiIiIhIxCnZFBERERERkYhTsikiIiIiIiIRp2RTREREREREIk7JpoiIiIiIiESckk0RERERERGJOCWbIiIiIiIiEnFKNkVERERERCTilGyKiIiIiIhIxCnZFBERERERkYhTsikiIiIiIiIRp2RTREREREREIk7JpoiIiIiIiERcgt8BTJaCggI3f/58v8MQEZE4sW7dujrnXKHfcQzHzD4PfAxwwGbgo8BM4B4gH1gHfMQ512NmycCdwGlAPfA+51zZ0erXMVNERMZqNMfNKZtszp8/n7Vr1/odhoiIxAkz2+d3DMMxs9nA/wOOd851mtnvgfcDVwA/cc7dY2a/AG4EbvH+NjrnFpvZ+4EfAu872jZ0zBQRkbEazXFT3WhFRERiXwKQamYJQBpQBVwI3Oc9fgfwTm/5au8+3uMXmZlFL1QREZEwJZsiIiIxzDlXCfwnUE44yWwm3G22yTnX5xWrAGZ7y7OB/d5z+7zy+dGMWUREBJRsioiIxDQzyyXcWrkAmAWkA5dFoN6bzGytma2tra2daHUiIiJHULIpIiIS2y4G9jrnap1zvcCfgHOAHK9bLcAcoNJbrgRKALzHswlPFHQY59ytzrlVzrlVhYUxOS+SiIjEOSWbIiIisa0cWG1mad7Yy4uAbcDTwDVemeuBB73lh7z7eI8/5ZxzUYxXREQEULIpIiIS05xzrxKe6Gc94cueBIBbga8AXzCz3YTHZN7mPeU2IN9b/wXgq1EPWkREhCl86RMREZGpwjn3LeBbQ1aXAmcMU7YLuDYacYmIiByNWjZFREREREQk4tSyKWPmnGNg5sLCwkJ0+TYRERERERlKyaaMWW1tLT96cA0AX7z6dIqKinyOSERERGTiLrn8Smpqj5i8+TBFhfk88dgjUYpIJL4p2ZRxSc/O8zsEERERkYiqqa3nhu/fedQyt3/tuihFIxL/NGZTREREREREIk7JpoiIiIiIiESckk0RERERERGJOI3ZlKMaPPMshGefFRERERERORYlm3JUAzPPpmfn0d7cwBevPt3vkEREREREJA4o2ZRjSs/OIzM33+8wREREREQkjmjMpoiIiIiIiESckk0RERERERGJON+STTMrM7PNZrbBzNZ66/LM7Akz2+X9zfXWm5ndbGa7zWyTmZ3qV9wiIiIiIiJybH63bL7VObfSObfKu/9V4Enn3BLgSe8+wOXAEu92E3BL1CMVERERERGRUfM72RzqauAOb/kO4J2D1t/pwl4Bcsxspg/xiYiIiIiIyCj4mWw64K9mts7MbvLWFTvnqrzlg0Cxtzwb2D/ouRXeOhEREREREYlBfl765FznXKWZFQFPmNmOwQ8655yZubFU6CWtNwHMnTs3cpGKiIiIiIjImPjWsumcq/T+1gD3A2cA1QPdY72/NV7xSqBk0NPneOuG1nmrc26Vc25VYWHhZIY/LYWc4841VTy9q9HvUEREREREJMb5kmyaWbqZZQ4sA5cCW4CHgOu9YtcDD3rLDwHXebPSrgaaB3W3lSho6ezlzztb+PnzFXzl4d1sOtiJc2NqeBYRERERkWnEr260xcD9ZjYQw++cc4+b2Rrg92Z2I7APeK9X/lHgCmA30AF8NPohTw/OOWprawEYaB12zvHYloM0d4X4l8sX8nxpE0/sbCAlIdbmlxIRERERkVjhS7LpnCsFVgyzvh64aJj1Dvh0FEKb9mpra/nRg2sA+OLVpwNQ3dbHwZYuzipJ47Ll+Vy6LI915c3sqOvyM1QRkWnBzJYC9w5atRD4JnCnt34+UAa81znXaOEzuT8jfJK2A7jBObc+mjGLiIhA7F36RGJAenYe6dl5h+5vru4iNTHIkvxkAAJmLMlPpq6jn9L6Tr/CFBGZFpxzO71rUq8ETiOcQN6Prk0tIiIxTsmmHNWeug72t/SyoiSbhIAdWr8wNwkDHt1W519wIiLTz0XAHufcPnRtahERiXFKNuWoHtxSR9Dg5Dk5h61PTQwwJyuRx7fX0x/SREEiIlHyfuBub1nXphYRkZimZFOO6pWyZmZkJpKaGDw0eVB4AiHH4vxkatp6WVPW4HeYIiJTnpklAe8A/jD0MW9ugzFfm9rM1prZ2oGJ4URERCJJyaaM6EBzN2UNXczJSgSgvaWRXzy5nVse30BHRyczM4IAPLt1vy6DIiIy+S4H1jvnqr37uja1iIjENCWbMqJX9jUDMDsz8dC69Kxc0rJyAOjraCY9IcSDr1ewbds2ampqlHSKiEyeD/BmF1rQtalFRCTGKdmUYTnneHZnLYXpQbJTbMRyhWkJNHQbt/xtOz96cA3qiiUiEnlmlg5cAvxp0OofAJeY2S7gYu8+hK9NXUr42tS/BD4VxVBFREQO8eU6mxL7WpsbWVPhmJXcQ2dnIlkjlMtPDVDWEsLSckjPTo9qjCIi04Vzrh3IH7JO16YWEZGYppZNGVZ9F/Q5Y0528lHL5aeGP0L1naFohCUiIiIiInFCyaYMq74r/Lcw9ejlcpKNgCnZFBERERGRwynZlGE1dkNaAiQHj14uYEZuEtR3KdkUEREREZE3KdmUYTV2Q17K6D4eeSnQ2OUIaSZaERERERHxKNmUI3T3hWjrg9zRJpvJ0O+guat/kiMTEREREZF4oWRTjlDfGU4a845yyZPBMpPCf5vVlVZERERERDxKNuUI9R19wOi70WYmhv82d6tlU0REREREwpRsyhHqOvrDkwMljK5lMzEAqQnqRisiIiIiIm9SsilHqO/oI/fol9c8QmZSQMmmiIiIiIgcomRTDtPW3UdLd2jMyWZWktHcHcJpRloREREREUHJpgxRWt8JQE7S2J6XlWT09DsavPGeIiIiIiIyvSnZlMOUNXQBkDXGZDMzOTy+c19DZ6RDEhERERGROKRkUw5T1tBFwCAtYWzPy0oKf5T2NXZNQlQiIiIiIhJvlGzKYfY1dJGdHCQwuoloD0lLgKAp2RQRERERkbAxtl/JVFfW0El2SgAY28yyZkZ2SpB9DUo2RUREJDIuufxKamrrj1qmqDCfJx57JEoRichYKNmUQ7r7+jnQ3M2JxSlA75ifn50SPDTmU0RERGSiamrrueH7dx61zO1fuy5K0YjIWCnZlEPK6zvod5CTEhzX87OSA2yp7qKvP0RCUD20RUREJL64QAKldW0caOqipqWLlq4+Onr6cA4SgwEyUxLoWH4lD288wIXLikhP1k9pkaPRf4gcsqe2DQi3UI6jYZPMpCD9DqqauyjJS4twdCIiIiKTY315I3e+VEbL+V/g4Y1VBAwKMpIpzkomLSmdgEFPX4iWrj56i47nH+5+nayUBD5wxlw++ZZF5KaPcRp/kWlCyaYcsqe2HYDs5CDd40g2M7wZaSsaO5VsioiISMxbX97IDx7dwWtlDWQmJ5B4cCtXXnUVs3NSR+yl9X//9E/88t4/c+cr+/jVC3v5w7oKvnnV8bzzlNlRjl4k9qmvoxyyp6aNooxEEoNjnIrWk5Ec/jjtb+yIZFgiIiIiEdXc2csXf7+Rd//PS5TWtfOttx/Py/90EWk7HmFefvpRhwOZc5y5MJ///uCp/PkfzmVuXhqfu3cD33xwC339oSjuhUjsU8vmNOeco7a2FoDdtW3Mz0sdd13piQECFm7ZFBEREYlFa8oa+H93v05NazeffusiPnXB4nGPvVw+M4s//v3Z/PDxHdz6XCn7Gzr4xUdOIzlhfPNfiEw1atmc5mpra/nRg2v4zwfWsKemjXm5KeOuKxgwCjOSqFDLpohIRJlZjpndZ2Y7zGy7mZ1lZnlm9oSZ7fL+5nplzcxuNrPdZrbJzE71O36RWPH7Nfv54C9fISUxyJ/+/mz+8W3LJjzJTzBg/NMVy/neO0/k6Z21fP7eDfSHXIQiFolvSjaF9Ow8EjJyaO/pZ3ZO8rjrcc5RmGqUVjfjnL5kRUQi6GfA4865ZcAKYDvwVeBJ59wS4EnvPsDlwBLvdhNwS/TDFYk9tzyzhy//cROrF+bzwKfOYUVJTkTr//DqeXzjyuU8uvkg3/3ztojWLRKvlGwKAK3d4TEGs7PHn2y2tzTS1NrOG9Vth7rmiojIxJhZNnA+cBuAc67HOdcEXA3c4RW7A3int3w1cKcLewXIMbOZUQ1aJMbc8swefvj4Dq5eOYv/u+F0stMSJ2U7HztvITeeu4DbXyrjkU1Vk7INkXiiZFMAaO3uB2DWBJJNgOy0ZDr6oFcD5EVEImUBUAv8n5m9bma/MrN0oNg5N/Br9iBQ7C3PBvYPen6Ft05kWvrD2v388PEdvGPFLH507YpJvxb4Vy9fxilzc/jqHzdRXq+hRTK9KdkUAFp7wsnhRJPN9MTwTLbVrT0TjklERIDwZH6nArc4504B2nmzyywALjx2YUzjF8zsJjNba2Zr1RtFpqpXS+v5p/s3c+7iAn703slPNAESgwFufv8pYPDlP27U0CKZ1jQbrQDhls2s5AAdzQ2M8ffKYQaSzW3lNczJSaGwsBCz8V1KRUREgHDLZIVz7lXv/n2Ek81qM5vpnKvyusnWeI9XAiWDnj/HW3cY59ytwK0Aq1at0q9hmXJqWrr41F3rmZuXxn9/6FQSo5BoDijJS+Orly/j6/dv4cENB0a8Bucll19JTW39UesqKszniccemYwwRSadb8mmmQWBtUClc+4qM1sA3APkA+uAjzjneswsGbgTOA2oB97nnCvzKewpq7mjh0AoxC2PbyAjf8a468lICn+R/37NfjbvreaLV59OUVFRpMIUEZl2nHMHzWy/mS11zu0ELgK2ebfrgR94fx/0nvIQ8Bkzuwc4E2ge1N1WZFoIhRxf+P1G2nv6uPcTq8lOnZwxmkfzgdPn8vu1FXzvkW28dVnRsDHU1NZzw/fvPGo9t3/tuskKUWTS+dmN9rOEZ9Mb8EPgJ865xUAjcKO3/kag0Vv/E6+cRFh7L2SlJJCWlTOhelITwIC+hFTSs/MiEpuIiPAPwF1mtglYCfwb4STzEjPbBVzs3Qd4FCgFdgO/BD4V9WhFfHbbC3t5YXcd33r7CSwuyvQlhkDA+Nd3nkh9ew//8/RuX2IQ8ZsvyaaZzQGuBH7l3TfgQsJdg+DIWfUGZtu7D7jI1C8zokLO0d73ZhfYiQiYkZYA7b3qkSUiEinOuQ3OuVXOuZOdc+90zjU65+qdcxc555Y45y52zjV4ZZ1z7tPOuUXOuZOcc2v9jl8kmsrrO/jPv+7kkuOLef/pJcd+wiQ6cXY271w5mzteLqOmpcvXWET84Fc32p8CXwYGTjXlA03OuT7v/uCZ8w7Nquec6zOzZq98XdSineI6ekI4ID0pMjl8upJNERER8YFzjm88uIXEYIDvXn2ib/NGDB6L2Z+aS9dZn+LcT36f1Df+cli5vWVlPkQnEj1RTzbN7Cqgxjm3zswuiHDdNxG+gDVz586NZNVT2sBMtBkRaNkESEuEmk4lmyIiIhJdf95UxXNv1PLttx/PjOwU3+IYOhbzye3VbA+eyXs+8F4yU94cu/n1a8/xIzyRqPGjG+05wDvMrIzwhEAXAj8jfNHpgeR38Mx5h2bV8x7PJjxR0BGcc7d63YxWFRYWTt4eTDEDyWYkutECpCVAZ1+4e66IiIhINHT19vODx3ZwwqwsPnLWfL/DOczp8/MI4diwv8nvUESiKurJpnPua865Oc65+cD7gaeccx8Cngau8YoNnVXvem/5Gq+8spgIau3ux4hssumAjt5QROoTEREROZY7XiqjsqmTr1+xnGAgtqb3yEpNZElhBlsqW+ju6/c7HJGoiaXrbH4FuMfMvge8Dtzmrb8N+I2Z7QYaCCeoEkFt3SFSE8KT+0QiPUzzPlVtPUo2RUREZPI1tvfw86d3c8HSQs5eXDCp29pbWsqKVauPXmaYsZinzMvljZo2th5o4dS5uZMUnUhs8TXZdM49AzzjLZcCZwxTpgu4NqqBTTOtPSHSI/hJGEg225VsioiISBT88vlS2rr7+OrlyyZ9W30hd8xrYw43FnNGVgqzclLYsL+JlSU5BHRxBZkGYqllU3zS2t1PcWrk6lPLpoiIiERLKCGFO14q48qTZrJsRtawZQbPDns0kz077CkluTyyuYp99R0sKEif1G2JxAIlm9NcV2+Izj5HRuKxy45WQgCSAmrZFBERkcnXM/dMunv6+YcLl4xYZujssCOZ7NlhFxSkk5YUZEtls5JNmRaUbE5zVS3dABHtRguQlmhKNkVERGRSdff20zlnFUnV23nvVd8dsVysXM8yGDCWz8xifXkj7d19x36CSJxTsjnNHWj2ks0ItmyG6zN1oxUREZmA0XT9LCrM54nHHolSRLFn84FmAklpXPv2Syn64DtGLBdL17M8YVYW6/Y1srWqxe9QRCadks1prrJ5klo2E4zaTiWbIiIi4zWarp+3f+26KEUTe/pDjo37m+mp2EbRRSN3oY01uWlJzMlJZdsBJZsy9UX9OpsSWw60dBM0SAlGtt60RKOn39GmLiIiIiIyCXZVt9LW3UfHpsf9DmXMls/Mormzl4SiRX6HIjKplGxOcweau8lMDhDp2bfTE8MVVrf2RLZiERERmfacc6zf30ReWhI95Zv9DmfMFhWlEwwYKUvO8jsUkUmlZHOaq2zuJjMpws2ahFs2AQ62KNkUERGRyDrY0kVtazcrS3IA53c4Y5acEGRBQTopi8+gPxR/8YuMlpLNacw5x4HmHjKSI/8xSEvwWjbblGyKiIhIZG2ubCYxaCydkel3KOO2bEYmgdQs9jd2+B2KyKRRsjmNNXf20t7TT2ZS5D8GKQlgqGVTREREIqurt583qttYOiOTpIT4/Sk7Lz+NUHc7Ow+2+h2KyKSJ3/9QmbDyhvCZtMzkyHejDZiRnhTQmE0RERGJqO1VLfSHHCfNzvY7lAlJCATo3rue0rp2daWVKUvJ5jS2v6ETgIxJaNkcqFctmyIiIhIpzjm2VLZQnJVMUWaK3+FMWHfpWnr6QuxvUFdamZqUbE5jA2MEJqNlEyA9KcDB1u5JqVtERESmnwNNXTR09HBinLdqDuip2EpSMMDu2ja/QxGZFEo2p7Hyhg6yUxJICkb4uiee9MQANW296hoiIiIiEbH5QDNJwQBLi+N3YqDDhPqYX5DGnto2Qvq9JFOQks1pbH9DB7Oykyat/oykAP0hR61aN0VERGSCOnv72V3TxrKZmSQGp85P2MVFGXT1hqhs6vQ7FJGImzr/qTJmFY2dzMpOnrT6072xoPryFBGZGDMrM7PNZrbBzNZ66/LM7Akz2+X9zfXWm5ndbGa7zWyTmZ3qb/QikTFVJgYaan5+OgkBY4+60soUpGRzmuoPOSobO5k9icnmwMRDB5RsiohEwludcyudc6u8+18FnnTOLQGe9O4DXA4s8W43AbdEPVKRSbCtKjwxUEHG5P128UNiMMCc3FTK6jtwTl1pZWpRsjlNVbd00dMfmuSWzfDEQ0o2RUQmxdXAHd7yHcA7B62/04W9AuSY2Uwf4hOJmNrWburbelg+I8vvUCbFgoJ0mjt7aero9TsUkYhSsjlNDUyxPStr8pLNpKCRkRxUsikiMnEO+KuZrTOzm7x1xc65Km/5IFDsLc8G9g96boW3TiRu7TjYQsDguKkyMdAQ8/PTAdhb3+5zJCKRpWRzmtrfGE4AJ7MbLcCMzCQqm7omdRsiItPAuc65Uwl3kf20mZ0/+EEX7ns3pv53ZnaTma01s7W1tbURDFUkskLOsbO6lXn56aQmTc7l2vyWlZpIfnoSZXVKNmVqUbI5TZU3dGAGM7ImbzZagOLMJLVsiohMkHOu0vtbA9wPnAFUD3SP9f7WeMUrgZJBT5/jrRta563OuVXOuVWFhYWTGb7IhFQ0dtLe3c/yGVOzVXPA/IJ0Kps66ekL+R2KSMQo2ZymKho6mJWdOulTh8/ISqKqWcmmiMh4mVm6mWUOLAOXAluAh4DrvWLXAw96yw8B13mz0q4Gmgd1txWJOzuqWkgKBlhQkO53KJNqfn4aIRduEBCZKhL8DkD8sb+xgzm5qZO+nRmZyTR29NLR00dakj5uIiLjUAzcb2YQPm7/zjn3uJmtAX5vZjcC+4D3euUfBa4AdgMdwEejH7JIZLhAIrtr2ziuOJOEKXRtzeHMzE4lKSFAWX07i4sy/A5HJCL063+aKm/o4Lwlk99tqjgz3E33QFOXvjhFRMbBOVcKrBhmfT1w0TDrHfDpKIQmMul6C5fS2+9YNsW70AIEA8a8vDTK6tpxzuGdYBKJa1P7FJEMq6u3n+qWbubmpU36tgbGhGrcpoiIiIxV78wTyUxJYHbO5PfGigULCtJp7+mntq3b71BEIkItm1OYc47BMwwWFhZiZlR6iV9JXjS60SrZFBERkbGrbe2mL28RS4szp00r37z8cENAWV0HRZkpPkcjMnFKNqew2tpafvTgGtKz82hvbuCLV59OUVHRoYHnGXRTW9vMGGfLH5OCjCQCpmRTRERExubhjQcgEGD5zCy/Q4matKQEirOS2VvXzhkL8vwOR2TClGxOcenZeWTm5h+2rsJLNh9+ZQehjiYy8mdM2vYTAkZxVoqutSkiIiJjcv/rlQRbDpCXvsTvUKJqQX46r+xtoLOnf8peV1SmD43ZnIb21XeQnBAgPy+XtKycSd/erJxUtWyKiIjIqO2uaWVzZTOJVZv9DiXq5uWHL/Gyv1GXQJH4p2RzGiqr72BOdnLUxj/MyknlgK61KSIiIqN0/+uVBANGYvVWv0OJuqKsZJISArrepkwJSjanofKGdubkJEdte7NyUqhq6iIUmryxoSIiIjI1hEKOB14/wLmLCwj0tPsdTtQFzCjJTaW8oYPwlYxE4peSzWmmvz/Evrp28pJDTObEQIPNzkmlpz9EXbum8RYREZGjW1PWQGVTJ+8+dbbfofhmbl4arV19NHf2+h2KyIQo2ZxmtpcdoLvfsbv8IB0d0enaOis7fImVA5okSERERI7h/tcrSU8KcunxkzeBYawr8a6Frq60Eu+UbE4zFV7Cl5uZNunbGrjOZ4oLJ7VVmiRIREREjqKrt59HNlfxthNnTOuZWHNSE8lMSWB/g347SXybULJpZueMZt0wZVLM7DUz22hmW83sO976BWb2qpntNrN7zSzJW5/s3d/tPT5/InFPZxXN4a6sGYmTv632lkZ+8eR2/vh8eHB/pZJNEZnmxnvcFJkuntpRQ2tXH+8+ZY7fofjKzCjJTWN/YweO6EzoKDIZJtqy+V+jXDdUN3Chc24FsBK4zMxWAz8EfuKcWww0Ajd65W8EGr31P/HKyThUNHVjQFqUrrCanpVLTm4uaYkBdaMVERn/cVNkWvjT+kqKs5I5a1H+sQtPcXPz0ujuC9GfNX27E0v8G1fKYWZnAWcDhWb2hUEPZQHH7PPgwlNrtXl3E72bAy4EPuitvwP4NnALcLW3DHAf8HMzM6cpusZsf1MXmckBAhaK2jbNjOKsJF1rU0SmrYkeN0Wmg4b2Hp7ZWcPfnbuAYECteSV54Tkv+vIW+hyJyPiNt2UzCcggnKxmDrq1ANeMpgIzC5rZBqAGeALYAzQ55/q8IhXAwDRks4H9AN7jzYBOeY1DZVM3mcnR/V3jnCMv2dhX16IpvEVkuprwcVOmF+dclOaMjx0PbzxAX8jxnlOndxfaAWlJCRRkJNGXt8DvUETGbVwtm865Z4Fnzex259y+cdbRD6w0sxzgfmDZeOoZzMxuAm4CmDt37kSrm3Kcc+xv6mZudpT60HraWxqpbeqjujuB2tpaioqKorp9ERG/ReK4KVNbR08fj20+yF+2HmRLZTN1bT30XPCP/O7VcubkprJ0RiZFmcmYTd0Wvz+ur+CEWVksnZHpdygxY25eGnUtJXT29E/rCZMkfk0060g2s1uB+YPrcs5dONoKnHNNZvY0cBaQY2YJXuvlHKDSK1YJlAAVZpYAZAP1w9R1K3ArwKpVq6bbCcFjau7qo72nn6zk5KhvOzsthfKOPrp6o9d9V0QkBk34uClTSyjk+MO6/fzor29Q09rNjKwUzlqUT1FmMv931z2kFl3ApopmXt/fREleKuctLqQwM/rH8cm2q7qVTRXN/PNVx/sdSkwpyUtjfXkCa8oaOP+4Qr/DERmziSabfwB+AfwK6B/tk8ysEOj1Es1U4BLCk/48Tbg70T3A9cCD3lMe8u6/7D3+lMZrjt3+xvBMtJlJ0b/iTVpi+ExsdVsPanMWkWlsXMdNmZqaO3r5/O838NSOGk6dm8PP3n8KZy7II+CNV7znm3/hXTd8iO7efrZWtbBmbwN3v1bO6kX5nD4vd0q1cv7p9UqCAeMdK2b5HUpMmZ2TCqE+Xtxdp2RT4tJEk80+59wt43jeTOAOMwsSHjf6e+fcn81sG3CPmX0PeB24zSt/G/AbM9sNNADvn2Dc09J+bzbY7JRgeD7gKEr3ks2DLVHesIhIbBnvcVOmmP0NHXzoV69S1dzJd68+gQ+vnjdi8picGOTUubkcPzOLp3fW8PKeeqqaOrnipJlRjnpy9Icc96+v5ILjpmar7UQkBgMEmyp4YXee36GIjMtEk82HzexThMdcHsoinHMNR3uSc24TcMow60uBM4ZZ3wVcO8FYp73yxi6CBhlJATp9SjYPNCvZFJFpbVzHTZla9jd08P5bX6Gtu497P3EWp87NHdXzUhKDXHbCDGblNPPszlruf70SlxD/ydnLe+o52NKlLrQjSGjcy7aq+TS295CbnuR3OCJjMtFk83rv7z8OWucAzdEcg/Y1djErO9mX6cRTEyBgUKlkU0SmNx03p7mG9h4+9KtXae3q5XcfX82Js7PH9HwzY8WcHNKSgjy+5SB2yodp7+4jPTm6k/9F0p/WV5CZksBFyzWB4HASGvbS7d7Ky6X1U6Y1W6aPCQ3ec84tGOamA2aMKm/sYm5uii/bDpiRkRRQy6aITGs6bk5vff0hPvO79Rxs6eL2vztjzInmYEuKMrnypJn0Z87g7+9aT29/fE7A19bdx2NbDnLVybNISdRsq8MJtlSRnhTkxd11fociMmYTOg1mZtcNt945d+dE6pXIc86xv7GbVSVZQK8vMWQmBdSyKSLT2kSOm948B2uBSufcVWa2gPCEevnAOuAjzrkeM0sG7gROIzxz+/ucc2UR2gWZgB88toOX9tTzn9euGHXX2aNZWJhB6o5HeC7wdv75gS384D0nRyDK6Hp8y0E6e/t5z6mzj114mjIX4syF+by054gLMYjEvIlOS3r6oNt5wLeBd0ywTpkEHb2Orr6Qby2bABnJQQ409/i2fRGRGDCR4+Znge2D7v8Q+IlzbjHQCNzorb8RaPTW/8QrJz57YVcdv3phLx9ZPY9rTpsTsXqTDmzg029dxD1r9vO7V8sjVm+03LumnAUF6Zw2b+LJ91R29qJ89ta1c6Cp0+9QRMZkQi2bzrl/GHzfzHIIn2WVGNPcHZ5hf25uCrWNrb7EkJkUYGdXN61dvWSmJPoSg4iIn8Z73DSzOcCVwL8CX7DwtKUXAh/0itxBOHG9BbjaWwa4D/i5mZkuGeaf5s5e/vG+jSwsTOefrljOJZdfSU3tsVup9paVjar+L1yylM2VLXzroS0cPyuLlSU5Ews4St6obmVNWSP/dMWyKXUZl8lwzuICAF7aUx/RkxUiky3So8nbgQURrlMioKXrzWRznU8xZCaHx2Lsb+jk+FlKNkVEGP1x86fAl4FM734+0OSc6/PuVwAD/RBnA/sBnHN9ZtbsldeArwkaTZJYVJjPE489cti6f3tkOzWt3fzp788mNSlITW09N3z/2COOvn7tOaOKKxgwbn7/Sq68+QU+d8/rPPrZ80hLiv0Jg373ajlJwQDXnFbidygxb2lxJvnpSby0u07JpsSViY7ZfJjwLHoAQWA58PuJBiWR19zdT0pCgMIM/5K8zORwr+39jR0cPyvLtzhERPwynuOmmV0F1Djn1pnZBRGM5SbgJoC5c+dGqtopbTRJ4u1fO3xY7rp9Ddy7dj+fOH8hKyaxxTEnLYkfvXcFH/jlK3zvke3827tOmrRtRUJHTx9/XF/B5SfNIE+X8zimQMA4a1E+L+6pwzmnlmCJGxM97fWfg5b7gH3OuYoJ1imToLmrn1mZCdTX1fHm75zoykzyks2GDl+2LyISA8Zz3DwHeIeZXQGkAFnAz4AcM0vwWjfnAJVe+UqgBKgwswQgm/BEQYdxzt0K3AqwatUqdbGdBH39Ib7xwFZmZqfw/y5aMunbW70wn5vOW8j/PlfKRcuKuGh58aRvc7z+vLGK1q4+PnTmPL9DiRtnLyrgz5uq2FPbzuKiDL/DERmViV765FlgB+FuPbmAZn+JUU2dfXR1dnDL4xvo6PBncHlS0MhIDirZFJFpazzHTefc15xzc5xz84H3A0855z4EPA1c4xW7HnjQW36IN6/neY1XXsmkD+5es5/tVS3881XHR+06mF+49DiWzcjkK3/cRF1b7M4Af9dr5SwpyuD0+ZoYaLTOWZwPwEt71CNe4seEkk0zey/wGnAt8F7gVTO75ujPkmjrDznaeyEnPZm0rBzf4jAzZmUls79RM6mJyPQU4ePmVwhPFrSb8JjM27z1twH53vovAF+dWNQyHm3dffzsb29w5oI8Lj9xRtS2m5wQ5KfvX0lLZx9f/eNmYvE8w5bKZjbub+KDZ85Vd9AxmJuXxuycVF1vU+LKRE+zfR043TlXA2BmhcDfCM9+JzGiubMXx0A31n5fY5mVnUS5WjZFZPqa0HHTOfcM8Iy3XAqcMUyZLsLJrPjo1udKqWvr4VfXL496QrVsRhb/+Lal/Ouj23lgQyXvOiW2JpS569VyUhIDvDvG4op1ZsY5i/P5y9Zq+kOOYECJusS+iV5nMzBwwPTUR6BOibCG9nAvrewk/7+UZmcns7+hIybPtIqIRIGOm9NAbWs3v3q+lCtPnunbZUj+7twFnDo3h28/tI2a1i5fYhhOW3cfD22o5O0nzyI7TTPTj9U5iwto7uxl24EWv0MRGZWJtmw+bmZ/Ae727r8PeHSCdUqENXSEk83MZPO7YZPZOSl094Woae2mOCvF32BERKJPx80pbm9pKed8/F/ombeaZ2/5Bit+3HBkmVFeP3MiggHj369ZwRU3P8+3HtzKLR8+bdK3ORr3rtlPe08/H16tiYHG46xF4XGbL+6p46Q52T5HI3Js40o2zWwxUOyc+0czezdwrvfQy8BdkQpOIqOxvYfUICQGjJCPyaZzjizCZ1df313J205ZqLEaIjIt6Lg5ffQnpeMWncPSwgwu++ZPhy0z2utnTtTiogw+f/Fx/PDxHTyyqYorT54Zle2OpK8/xK9f2MsZ8/Mm9TIwU1lRZgrHFWfw4u46PvmWRX6HI3JM4+2681OgBcA59yfn3Becc18A7vcekxjS0N5DVgxcwqq9pZGnN+8D4Land1BbW+tzRCIiUfNTdNycFtJOupTefsfp82JjltWPn7eAk2Zn880HtxwaVuOXx7YcpLKpk4+fv9DXOOLd2YsKWFPWQHefz93VREZhvMlmsXNu89CV3rr5E4pIIso5R2NHD5kxkGwC5OdmEzDoDqgLrYhMKzpuTgPdvf2knngxi4syyM9I9jscABKCAf7j2pNp6erlOw9v9S0O5xy/fL6UhQXpXLSsyLc4poKzF+XT1Rvi9fImv0MROabxJps5R3ksdZx1yiTo6HX09juyYmQMfsCMrNREWrpDfociIhJNOUd5TMfNKWJjRTOB5DTOmJ/ndyiHWTYji0+/dTEPbjjAE9uqfYnh+V11bKpo5mPnLSSgWVQn5MyF+QQMXtpT73coIsc03gmC1prZx51zvxy80sw+BqybeFgSKU1d4S4WsdCNdkBOaiItHbF7oWkRkUmg4+YU19MX4vXyRrrLNlB40RK/wznCpy5YzONbDvL1+zdzxoI8slOjdxbaOcdP//YGs7JTuOa0Ny93csnlV1JTe/SEKRqTKcWb7NRETpqTw0u76/jCJcf5HY7IUY032fwccL+ZfYg3D5KrgCTgXRGISyKk2Us2M2OkZRMgJzWJikZd/kREppXPoePmlLa5spmuvhDt6x+CG2PvMqdJCQH+45oVvPN/XuRfH9nGv1+zYlz1jCZBLCrM54nHHjl0/4Xddawvb+J77zyRpIQ3O9XV1NZzw/fvPGpd0ZpMKd6csyifW58rpb27j/TkiV5cQmTyjOvT6ZyrBs42s7cCJ3qrH3HOPRWxyCQimrr6SUoIkBKMnW6r2WmJ9IWgoaOPYr+DERGJAh03p7b+kGN9eSMleanU1JRGddt7S0tZsWr1UcsMJH8nzcnmE+cv5H+e2cNVJ8/i/OMKx7y90SSIt3/tukPLzjl+8sQbzMxO4dpVc47yLBmLcxYX8D/P7OG1vQ28VWNgJYZN6FSIc+5p4OkIxSKToLmrn7y0JMxi54LOOV7XnYqmLpb7HIuISDTpuDk17a5po6Onn0tKcqPeJ7ov5MaU/P2/i5bwl60H+dqfNvOXz59PxiS3ij225SDry5v4/rtPIjkhOKnbmk5Om5dLUkKAF3fXKdmUmDbeCYIkTjR19ZObHkN9aAm3bALsb9K4TRERiX8bK5rITk1kXn6a36EcU0pikH+/ZgUHmjv5wWPbJ3VbPX0hfvDYDpYWZ/LeVSWTuq3pJiUxyGlzc3lRkwRJjFMn7ymsqbOPzj5HfnoyuFa/wzkkKyURI9yyKSIiEs+qW7qoau7i/CUFmMXHLKunzcvlxnMW8KsX9nLlSbM4a1H+pGznzpfLKG/o4I6/O4OgZqAdt5G6SnfNP5fuxW/lpLPeyoyctMPGyYrECiWbU1hpfScA+elJ0OZzMIMEA0ZGUoAKtWyKiEic21jRRGLQOH5Wlt+hjMkXL13KE9ur+cofN/HYZ8+L+CQzlU2d/OSJN7hgaSFvGcfYUHnTSF2lq5o7+f3aCs779H/y4k8/7UNkIsembrRT2KFkMyOGrnviyUoOUK6WTRERiWMdPX28Ud3GshlZcTceMTUpyL+/52QqGjv4+v2bIzpDvAO+cf9mQg6+e/WJxywv41OcmUJSMEB5Y4ffoYiMSMnmFLa3rpPEgE364P/xyEoJsr+xW5c/ERGRuLX1QAv9IceKOdl+hzIuZy7M53MXH8cDGw7wu9fKI1Zv74wTeXpnLV9621JK8mJ/HGu8CgSM2bmp7G/o9DsUkREp2ZzCSus7yUkNxOQYkuzkIO09/dS2qSutiIjEn1DIsamimTm5qeRnJPsdzrh95q2LOf+4Qr7z0DZe29sw4foa23voXHYFq+blcsPZ8yceoBxVSW4qzZ29hJLjqxu3TB9KNqewPfWd5KbEXqsmQHZKuLtRaW27z5GIiIiMXWldO23dfayYk+N3KBMSCBg3v38lc3JTuek3aymtHf8kD739IR7ZUoWF+vivD56iSYGiYKDluC9vgc+RiAwvNjMRmbC6tm6aOvtYmh974zUhPGYTYG9dO6sXTs4seCIiIpNlY0UTmSkJLCxI9zuUYxppNtPB+lNz6TzzRq779Wvc/fHVY+7+2h9yPLK5ivq2HtK2PsjM7PdOJGQZpfz0JFITg0o2JWYp2Zyi3qgOX+okJyU2JyzISAqQHLQJnUEVERHxQ11bNxWNnZyzKJ9AHLTejTSb6VC/+rev0PKWT/K+/32Zuz6+mgWjTKT7Q46/bjvIvvoOLlxWxLq/7ZloyDJKZkZJXiq72ubjnIvJoVMyvakb7RS1qzqcxOXGaLJpZpTkprC3Tt1oRUQkvmyqaCYYME6YFZ8TA40kobWKu29aTWdvP1f//AX+tq36mM/p7OnngdcreaO6jXMW53PS7Kn1msSDktw0XHIme3QCX2KQks0pamd1K1nJQVITY/cM19zcFI3ZFBGRuNLd28/2qhaWFmeSmhSbJ3Qn4oRZ2Tzw6XMoyUvjY3eu5Yu/38j+hiMvrRFyjm0HWvjda+VUtXRx6fHFrJqX50PEMtDl+cXd9T5HInIkdaOdot442MqCgtSY7k4xNzeF5/Y00dsfIjGo8x4iIsMxsxTgOSCZ8HH7Pufct8xsAXAPkA+sAz7inOsxs2TgTuA0oB54n3OuzJfgp6BtVS30xfHlTkZjXn46f/z7s/nJ397g/14s48ENlZw6N5fT5ufStfB8nthWTXlDB23dfRRnJXPVyTMpzkrxO+xpKzs1Eets5MXddVyvGYAlxugX/hQUCjl2HGzluILYvrbVvLwU+kKODbsrqKmp0TU3RUSG1w1c6JxbAawELjOz1cAPgZ845xYDjcCNXvkbgUZv/U+8chIBDthY0czM7BSKpnhylZIY5GuXL+fZf7yAm85fSEdvH794dg/dC9/Cnto2ZmSncMWJM3jfqhIlmjEgoWEvL5fW09sf8jsUkcNEPdk0sxIze9rMtpnZVjP7rLc+z8yeMLNd3t9cb72Z2c1mttvMNpnZqdGOOd5UNHbS1t3HksLYTjbn5oQPTj97bDM/enANtbW1PkckIhJ7XNjAYKxE7+aAC4H7vPV3AO/0lq/27uM9fpHFcjeXONKXv4jmzt64v9zJWMzMTuXLly3jz/9wHnu/fyXZf/sunzh/IVeeNJMlxZkx3YNqOkms20NrVx/r9zX6HYrIYfxo2ewDvuicOx5YDXzazI4Hvgo86ZxbAjzp3Qe4HFji3W4Cbol+yPFlW1ULAEsKU32OZGTOOdJD4d9O3cFU0rM1zkNEZCRmFjSzDUAN8ASwB2hyzvV5RSqA2d7ybGA/gPd4M+GutjJBPSWnk5YUZHFRht+h+EoJZuxJaCglIWA8vVMn7iW2RD3ZdM5VOefWe8utwHbCB8bBZ2KHnqG90zuz+wqQY2Yzoxt1fNlxsAUzWFgQu8lme0sjd73wBkkWor6tx+9wRERimnOu3zm3EpgDnAEsm2idZnaTma01s7XqWXJse+va6StYwkmzswnGweVOZHqx/h5On5/H0ztq/A5F5DC+ThBkZvOBU4BXgWLnXJX30EGg2Fs+dIbWM3D2tgoZ1vaqFhbkp5OaGNuz5KVn5ZLV0EVrj8YXiIiMhnOuycyeBs4ifPI1wWu9nANUesUqgRKgwswSgGzCEwUNretW4FaAVatWadD8Mfzm5X0Q6telPUZhb2kpK1atPna5srLJD2YauXBZEf/66HYqmzqZnRO7DQ4yvfiWbJpZBvBH4HPOuZbBXTKcc87MxnzgM7ObCHe1Ze7cuZEKNe5sr2qNm4NhViJUdOg3jojISMysEOj1Es1U4BLCk/48DVxDeEba64EHvac85N1/2Xv8KacZ2CakvbuPP6zdT2LNdtKTJ9yoPOX1hRw3fP/OY5b7+rXnRCGa6eOtywr510e388zOGj505jy/wxEBfJqN1swSCSeadznn/uStrh7oHuv9HegHMHCGdsDgs7eHcc7d6pxb5ZxbVVhYODnBx7jWrl7KGzpYPjPT71BGJSsJevqhs1etmyIiI5gJPG1mm4A1wBPOuT8DXwG+YGa7CY/JvM0rfxuQ763/Am/OgSDjdP/rlbR295G0/zW/QxEZ0aLCDObkpvL0DnWLl9gR9ZZNb0a824DtzrkfD3po4EzsDzjyDO1nzOwe4EygeVB3Wxli58FWAJbPzPI5ktHJSgr/be7q9zcQEZEY5ZzbRHjIydD1pYTHbw5d3wVcG4XQpgXnHHe+XMaJs7Mo/9uw57pFYoKZ8dalRdy3roKu3n5SYnw4lUwPfrRsngN8BLjQzDZ4tysIJ5mXmNku4GLvPsCjQCmwG/gl8CkfYo4b271kc1mcJJuZieG/TUo2RUQkBr28p543qtu4/qz5aFogiXUXLiuis7ef1/Y2+B2KCOBDy6Zz7gUY8fv6omHKO+DTkxrUFLLtQDPZqYnMyk6htrbV73COKS0BEkzJpoiIxKY7Xi4jNy2Rt6+Yxff8DkbkGFYvzCc5IcDTO2s4/7jpOaRMYosvYzZl8myqaObkOdlxcw0sM8hKNiWbIiIScyoaO3hiWzXvP2OuuiRKXEhNCnLWonxdAkVihpLNKaSrt5+dB+NnJtoBWUlKNkVEJPbc9Wo5AB9erZk9JX68dWkRZfUd7K1r9zsUESWbU8mOg630hRwnz4mzZDM5QEevo61bCaeIiMSGrt5+7nmtnEuOL9Y1CyWuvHVpEQBPqXVTYoCSzSlk0/4mAGal9BFPl1TLSgp3+S1r6PQ5EhERkbCHNh6gsaOX68+e73coImMyNz+NJUUZPLHtoN+hiCjZnErW7KkmOQC/fWoTtbXxc42lrOSBZLPL50hERETClzu546UyjivO4KyF+X6HIzJml504g9f2NlDf1u13KDLNKdmcQrZXt1OQkUhGTp7foYxJRqIRMCitU8umiIj4b315I1sPtHDdWfPjZsI9kcHedsIMQg6e3K6utOIvJZtTRGdPP6X1nRSkxd9seQEzclKC7K7r8DsUERER7nhpH5kpCbzrlNl+hyIyLifMymJ2TiqPb1VXWvGXks0pYltVMyEHBWlRv3RqROSmBtmjlk0REfFZTUsXj26u4trTSkhPjs9jqoiZcdmJM3hhVx1t3X1+hyPTmL5Fp4jXy5sAKIzTZDMvNciehk4a23vITU/yOxwREZmm7nq1nL6Q47qzpt/lTvaWlrJi1eqjlykri04wMmGXnTiD217Yy1M7anjHill+hyPTVHxmJnKEdfsamZmVRFpSfDZW56aGu//uONjKWYvenIzBOXdosqPCwkKNnRERkUnT0xfid6+Vc8HSQuYXpPsdTtT1hRw3fP/Oo5b5+rXnRCkamahT5+ZSnJXMnzceULIpvonPzEQO45xj7b5GVszK8DuUcctLDZ/32HGw5bD1tbW1/OjBNfzowTVxNcOuiIjEn8e2VFHb2q3LnciUEAwYV508i2d21tLc2et3ODJNKdmcAvY3dFLb2s3JszP9DmXcUhOMnNQEdh5sPeKx9Ow80rPja4ZdERGJP3e+vI/5+Wm8ZUmh36GIRMTbV8yipz/EXzVRkPhEyeYUsHZfAwAnx3HLppmxqCCV7cMkmyIiIpNtS2Uz6/Y18pGz5hMIaMiGTA0r5mQzNy+NhzdV+R2KTFMaszkFrC1rID0pSFaoDXBAfB4klxSk8eCWOkIhpwO9iIhE1Qe+80vIWsTPvngDN/d3D1tGk+NIvDEz3r5iJr94tpS6tm4KMpL9DkmmGSWbU8Cre+rIDPZz6183kpE/g7S0NL9DGpdFBal09vZT3tAxLSdmEBERfzS099CavYST5uZz4dt+OWI5TY4j8ejqlbP576f38OCGA9x47gK/w5FpRt1o41xzZy+l9Z3MzEklLSvH73AmZElhKgDbq1qOUVJERCRy7n6tHIIJrJiT7XcoIhF3XHEmK+Zkc9+6Cr9DkWlIyWacW7O3AQfMyIjvRmrnHNm0EwwYmyub/Q5HRESmid7+EL95eR8J9aXkq4uhTFHXnDaH7VUtbD2g31gSXUo249wLu+tITghQmB7fyWZ7SyO/fmYnmQmOtaW6xImIiETH41sOcrCli6T9r/kdisikefuKWSQFA/xhrVo3JbqUbMa5l/bUsXJ2BglTYEKd9KzwxYe3V7fjnPM7HBGRmGBmJWb2tJltM7OtZvZZb32emT1hZru8v7neejOzm81st5ltMrNT/d2D2PZ/L+5lfn4aCXW7/A5FZNLkpCVxyQnFPLihku6+fr/DkWkkvpvDprnq5k7eqG7j707NJTwLbZhzjtragdbB+EraCtKC7KzrZl+9JgkSEfH0AV90zq03s0xgnZk9AdwAPOmc+4GZfRX4KvAV4HJgiXc7E7jF+ytDbNzfxPryJr719uP56b1+RyMyfntLS1mxavVRy6QvWEHjonfw2OaDvPOU2VGKTKY7JZtx7PHX9wKwq7Sc2TPevAB1e0sjv3jyAK63m4z8GWTm+hXh2BWkhT+SmyqblWyKiADOuSqgyltuNbPtwGzgauACr9gdwDOEk82rgTtduIvIK2aWY2YzvXpkkNtfKiMjOYFrTpvDT/0ORmQC+kKOG75/51HL/N/XrmPhmR/gzpfLlGxK1KgbbRx7rbyF5KAxIy/ziMfSs3Ljcnba3NQgSUFjc0WT36GIiMQcM5sPnAK8ChQPSiAPAsXe8mxg/6CnVXjrZJCali7+vOkA15w2h8yURL/DEZl0Bnxo9TzWlzexRZMxSpQo2YxDzjmqq6t5tayJmZkJWPwP1zzEgAW5iawtrdO4TRGRQcwsA/gj8Dnn3GHXiPJaMcf0pWlmN5nZWjNb++bQi+njt6+Wh1uDzp7vdygiUXPNqXNISQzw21f2+R2KTBNKNuNQbW0t3/zDWuo6+ilMmVoJWXtLI92dHWw+0MrB6hq/wxERiQlmlkg40bzLOfcnb3W1mc30Hp8JDHxpVgIlg54+x1t3GOfcrc65Vc65VYWFhUMfntK6+/r53av7uHBpkYZsyLSSnZbIu06Zzf2vV1LX1u13ODINKNmMU7X9qQDMygj6HEnkFWel0udgb32n36GIiPjOzAy4DdjunPvxoIceAq73lq8HHhy0/jpvVtrVQLPGax7u4Y1V1LX18NFzFvgdikjUfey8hfT0h7jzpTK/Q5FpQMlmnNrf3ENeMqQmTKE+tJ7C1PA+bahs8zkSEZGYcA7wEeBCM9vg3a4AfgBcYma7gIu9+wCPAqXAbuCXwKd8iDlmOef4vxf3clxxBucszvc7HJGoW1SYwSXLi7nj5X20d/f5HY5McUo241B9ey+1Hf3MTPM7ksmRnmikJRobKlv9DkVExHfOuRecc+acO9k5t9K7Peqcq3fOXeScW+Kcu9g51+CVd865TzvnFjnnTnLOrfV7H2LJ2n2NbD3Qwg1nL8Cm0qQHImPwibcsormzl3vX7D92YZEJULIZh17c2wTArCk6zMTMmJGRyOsVrZokSEREIurXL+wlxxu3JjJdnTYvlzMW5PGLZ/fQ2dPvdzgyhSnZjEPP72kiPTFATpLfkUye4owEatt7qWzW4HUREYmMffXtPL71IB88Yy6pSVNvzgORsfjiJcdR09qtmWllUinZjDMdPX28sq+FkuzEKXXJk6FmZCQA8HqFutKKiEhk/Or5vSQGArrciQhw5sJ8zltSwC3P7qFNYzdlkijZjDNP76iluy/Egtwp3KwJ5KQEyU5J0CRBIiISEQ3tPfxh3X7edcpsirJS/A5HJCZ86dKlNLT3cOtzpX6HIlOUks048+jmKvLSEij2Wv6mKjNj5ewM1qtlU0REIuA3L++jqzfEx8/X5U5EBqwoyeHtK2bxv8/uoaKxw+9wZApSshlHOnv6eWpHDRcsziUwlfvQek4ryaSyuZvWbg1cFxGR8evq7efOl8u4aFkRi4sy/Q5HJKZ87fJlmMG/Pbrd71BkCprazWNTzDM7a+js7efC4/JYX1rrdziTbvX8bGA/lS29LCvURA4iIjJ2l1x+JfuT5tG1/Epe+e1/sOLn5cOW21tWFt3ARGLErJxUPnXBYn78xBs8v6uW85YU+h2STCFKNuPII5uryEtP4tQ5mdMi2ZyXm0JxZhKVrb0sK9T4GhERGbvq2gZS3v5ZshMDvO/L3x3x2ppfv/acKEcmEjtuOn8hD2yo5Kt/3MxfPn8+GclKESQyfOlGa2a/NrMaM9syaF2emT1hZru8v7neejOzm81st5ltMrNT/YjZT845yisP8uT2at52QjEJganfhRbC4zZXz8uiqrWPkK63KSIi49BXuJSmzl5Om5s7YqIpMt2lJAb5j2tO5kBzJz94TN1pJXL8Om1xO/Bz4M5B674KPOmc+4GZfdW7/xXgcmCJdzsTuMX7O23U1tbytd+vo7MXzi5J9TucqDpzfjYPbqmjtl1TcouIyNg45+hacC45qYksKszwOxyRmHbavDw+evYCfv3iXv50yw9JrN89YtmiwnyeeOyRKEYn8cqXZNM595yZzR+y+mrgAm/5DuAZwsnm1cCdzjkHvGJmOWY20zlXFaVwY0JVTxLJwV5OmzO9JjY4vSQLAypbev0ORURE4sxTO2oIZc3k9Pl5BKZJryCRifjyZUu5/fGXCZ3xId5zxlwyUxKHLXf7166LcmQSr2KpQ3bxoATyIFDsLc8G9g8qV+GtmzbJZndfiPLmHhbkJtHYUO+tnR7dSrNTEyhIC1KhZFNERMbAOcfNT+3GOhtZOmOx3+GI+G5vaSkrVq0+ZrmGhk4KP/B9HttykHefOpuEgC5eIeMXS8nmIc45Z2ZjzqbM7CbgJoC5c+dGPC6/vFLWTF8IZib18Isnt+N6u8nIn+F3WFFTkp3E+qpOatt6KCryOxoREYkHz++qY+P+JlLLXiQYOMPvcER81xdy3PD9O49Z7uvXnsMly4t5dMtBntxew6XHF2u8s4xbLJ2qqDazmQDe3xpvfSVQMqjcHG/dEZxztzrnVjnnVhUWTp1pm/+6s4HkoFGUCulZuaRl5fgdUlTNzQl34XihtMnfQEREJC4457j5yV3Myk4h8cAmv8MRiTtLijM5a2E+Ow628ureBr/DkTgWS8nmQ8D13vL1wIOD1l/nzUq7GmieTuM1W7t6eW53Iwtyk5iuw01yU4JkJAV4bk+T36GIiEgceLm0nrX7GvnkBYsw1+93OCJx6fT5uSyfmcmrextYX97odzgSp/y69MndwMvAUjOrMLMbgR8Al5jZLuBi7z7Ao0ApsBv4JfApH0L2zV+2VtPd71iUl+R3KL4xM+ZmJ/LavmbKKqpwugyKiIgcxX89uZuizGTeu6rk2IVFZFhmxsXLillSlMHzu+p4XQmnjINfs9F+YISHLhqmrAM+PbkRxa4HXq9kVnYyRekJtLf4HY1/ihK72RaCf/rDen76odMp0uBNEREZxvO7anm5tJ5/vup4UhKDfocjEtcCAeNtJ8wg5Kp4blcdXb0hVi/M8zssiSMxOUGQhNW0dPHSnjo+esYsXG+X3+H4qiAFkgJwsGf6tvCKiMjRhUKOHzy2gzm5qXx49dSZKFDET8GAccWJM3lqZw2vlTXQ3NWLCyiFkNGJpTGbMsRDGw8QcnDZcp1BChjMyQxS3txDZ6/G34iIyJEe2niArQda+NKlS0lOUKumSKQEAsZFy4o4a2E+Ow+20r7qesrq2v0OS+KATkvEsAc2VHLynGzm5aX6HUpMmJ8dpLS5n+f2NPGR2TP9DkdERHx2yeVXUlMbvv60CwRpPetTBHo7+ebHv8u3vDJ7y8p8i09kKjEzzliQR0FGEg+v6+KKm5/nn65YzofOnKtLo8iIlGzGqN01rWypbOGbVx3vdygxozDVSE8M8Pj2ej5yvt/RiIiI32pq6w9dN3B9eSPP76rjnWefwNx3vXmQ+Pq15/gVnsiUtLAwg8xXfsGKj36PbzywhSe2VfPD95zMjOwUv0OTGKRutDHGOUdNTQ13vbiL/9/encfJVVYJH/+dWntf093ZOgskEpIQEpawY1gF5CMiMAYdBUQZnUGRcYZXxNdXHUdREAdRQAQSQIddENk0hIRAQkgCZCN7ujt70kvSe3d1ddV5/6ibUGl6TbrrVnWf7+dTn666dZfz9FN3Ofc+97kegctPtCt4B4kIx+QHWLqtnv1NbW6HY4wxCSEij4pIpYisjRtWICLzRGSz8zffGS4i8lsR2SIiq0XkJPciT5zWcIRl5fsZU5DBmIIMt8MxZtDzhBp4/Gsz+ekVU3ivvIYL73mLB9/aSqjdbnUyh7NkM8lUVVVx94vLeeb9Pcwck0Nxtp0lindMQYBIVHlp5S63QzHGmESZC1zSYdj3gfmqOhGY73wGuBSY6LxuAh5IUIyuWl6xn1B7lLMnDHM7FGOGDI9H+OoZ43j9lnM5bXwBd762gYvuWcTra/faY+rMIZZsJqEGbzZN7XDW6ACVlZVUVVUBttICFGb4mFySyRNLt9mGzBgzJKjqImB/h8FXAI857x8DPh83/HGNWQrkicigbiKzv6mNlTtqOX5ENkXZQbfDMWbIGTcsk0euP5XHvzaTNL+Hb/7pfb7wwBLe2lRlx2rGks1ktKk6hN8D68t28ODCrTzw+kqam1vcDitpXDOjmK1VTSzeUuN2KMYY45YSVd3jvN8LlDjvRwE74sbb6QwblBRYsLESv9djVzWNcdm5nyri1e+cw8+vPIF9da1c9+gyrrx/CQs2VlrSOYRZsplk6lraqahtY2wW5OTmk51fSEZOntthJZULP1VAYWaAx96tcDsUY4xxncaO4vp8JCciN4nIChFZEWtBk3rCJVPZeaCFM48tJCNgfR4a4zaf18OXThvDwv88j59feQJVDSFumLOcz/zPIp5atp1We3zdkGPJZpJ5fUMNEYVjctyOJHkFfR5mzyxl/vp9fLhpO5WVdsbMGDPk7DvYPNb5W+kM3wWUxo032hn2Car6kKqeoqqnFBUVDWiwA6GmMUTrcRdTkhNk6qhct8MxxsQJ+GJJ54L/mMVdV0/D6/Hw/b+s4cw73+TX/9hIZX2r2yGaBLHTgEniYC+0z3+4h2EZXvKCduanM6pKVVUVl03M5aG34N+fXc3MYuF7V5xKcXGx2+EZY0yivARcB9zp/P1r3PCbReQp4DSgLq657aDy47+tQ31pXHR8CR57xp8xCVVeVsaJp5ze7Ti7du5g1OjYuS8FMvPHUj/mNO5rDHHfGxvxV27Av2cVIz11vPHaywmI2rjBks0kUVVVxe1Pr6CiFk4dbtXSlab6Azw4fzdFw0cxOhiioiHIyaV2RtsYM3iJyJPALGCYiOwE/h+xJPMZEbkR2Ab8kzP6q8BlwBagGbgh4QEnwOtr9/C3VbsJlr9N4UWT3A7HmCGnPaqHnnHblTuuOavTcWqb21i1o44NgRNoHj6FraEGfvHqei6eMpzppXl4PQN78uiiSz9LZVX3/X4UFxUy77VXBjSOocKymiRS3hwg4GljbI4XIu1uh5O0MnNi97JOLd7P9u3w4V7rPMkYM3ip6rVdfHVBJ+Mq8G8DG5G79tS18H+eX8O00blUzF8MfMPtkIwxfZCXEeDTxxVx1sRCKqqbeeHlD/nDwkz+sKgMaWvCV70F3/4yvPV78DTXIPRv8ldZVdNjojz39q/2y7KMJZtJo6qxjYraNibmgs8jRK0VbY/SfTAx38uG/W1s2NeEtaI1xpjBLRJVvvfMKtrao9w7ewafnxt1OyRjzBHyeTxMKM6i9rV7+b//u4htNc2U1zSxLSOblpEnAhDweijKDlLx0RLumbeJ4TlpFGUHyQh4SfN7CPq8+L0ePAIigkfAI4JHBBEQ53PA5yE33Y/fa93VJJolm0nihdVVKDDBWoT2yeRCHxX1UX41fxtnTxmHZ4CbXhhjjHHPvfM3s2RrDb+86gTGD8t0OxxjTD9J83s5bng2xw3PJhpVapraqGxopbI+RGVDiPZhE7jvzc0cbX+QWUEfTWd9m2dW7CA33X/oVZwdJD8zYPd/DwBLNpNAazjC86sqKc3xk+UPux1OSgl4hVNHZfD2tiaeWbGD2TPHuB2SMcaYATB//T5+O38zV588mn86pbTnCYwxKcnjEYqygxRlB5kyMjZs7u13sOK9JVQ1hKhqCNEajtDaHiUUjhCOKIoS1VhHklFVolGIqqIKitIajlLXEqa2OcwTz32Ad8QIdtW2sGFvw6Hl+jxCcXaQEbnptOePI9QeIejzuvRfGDws2UwCz72/kwMt7Zw+Ohuilmz21YSCAA3twi9e28CnjytiRG662yEZY4zpR1sqG/ju0yuZPCKHn31+KmJXH4wZcvxeDyPz0hmZd3THec//5Gtc9c9XAdAejVLXHKayIURlfYh9Da18uOMA0ZO/wvSfzOP0Ywq4dOoILjlhODlp/v4oxpBjyabL2iNR/rBwM58qDDA8y0tTvdsRpR4R4YcXj+erf17Ht/+0nN9dfRwlxcV2MGKMMYNAdWOIG+YuJ+jz8tBXTybNb1cajBlq+vqolW7nVVFx6L3P46EwK0hhVpDjR8SGtbVH+dXt3yYw5UzeqpvAgo1V3PbMB/iqNxHYswZf9WYEtR5re8mSTZc9v3QTO2pDnJTTREuL3bR8pMbkp3Hrp0v573kV3PTYCh6+3p67aYwxqa4x1M6Nj62gqiHEUzedwej8DLdDMsa44GgetdLZeN0J+Dy0VKzkW3f9HlVlX32IDXvr2ZR2As0lk8lJ8zGtNI9VD/1nn8owVFl246JoVHlk6R6ygx6OsY4Ojtrnpg5jXF6A1fvhnbJat8MxxhhzFFrDEW56fAVrd9Xx29kzmF6a53ZIxpghRkQYnpvGrOOKufHs8Vx2wnCygj7e3lxN/Tnf5eevrqemMeR2mEnNkk0X/f2jvWyqambG8HSsE9Ujp6pUVVVRXV3NOWMzKEz38sNXtrJut7VJNsaYVNQajvCtP73Pkq013HX1NC6eMtztkIwxQ5zXI0wszuaaU0qZfWop/qpNPPx2Gef8agF3/30jdc3W70pnLNl0STSq/OaNTYzNT+OYgoDb4aS0pvoDPDh/PQ+8vpJwqJULjski3S986Y/vsm53ndvhGWOM6YOWtgjfeHwFCzdV8YsvnMAXThrtdkjGGHOYkpw0Mj56kX/c+mkuOL6E3y3Ywtm/epOHFm2lrd2e/xvP7tl0yYsrd7FpXyO3nT2M6uaI2+GkvMycfKLh1tiH1jqmZbWytCaN2Q+9y5M3ncGUkfYAU2OMSXZVDSG+/vgKVu+s5VdXTeMae8SJMSZJlZeVcdVlFwKQlVVMy7Hn8fNX27nz2cWkbfoH/pot1okQlmy6ojHUzi9eWUd+ED7asIXsYdY8qL+VFORx2bAAiyqauPYP73LfVZ/i+OFZFBUVWS+1xhiThLZUNnD9nOVUN4Z48J9P5jPWdNYYk8Q667SooqaJRZv8HMi8lrGFGez5290uRZc8LNl0wX1vbqaqKczlx+WQ0e52NIOXN1TPtKxWllQH+PqT6zhnhHDnbOul1hhjks27W2v4lydWEPB5ePqmMzixNI+LLv0slVU13U4X/wgDY4xx27jCTEpPy2DVzlreK9tP2+n/ws9eXsctF04ke4g+p9OSzQTbtK+BR98p5/IpwygORmm0WwoHVFFBHuentbJoj/DWHmXR1gNcbcmmMcYkhWhUefidMn71+kbGDctkzvWnUloQe7xJZVVNrx51YIwxycTrEU4ak8+k4dnMfep5Hlns5a+rdvODyybx+emjhlwLO+sgKIFC7RG++9RKctL83HyOdXiQKJl+uGBsgPx0L7e9tIWH3vgIVXU7LGOMGdL2N7Xx9cdX8PNXN3Dh8SU8/60zDyWaxhiT6jICPjLWv8IL/3oWI3PTuPXpVXzxD0tZv2doPS3BrmwmiKrysxdXsm5PPXd9bgKRplrAEp5ESfMJ55REWLw7ws/fqGBvQ4g7rpiB1545Y4wxCXXRpZ9ldziT5qlXooEM0jbNY8kbKzj33sPHsyayxphUV15WxnVXXoIC6SOns7ztAi79TTWBnctJK3sLaY89o3MwdyRkyWaCPLV4I0+s2MuYtBCLVqzhrXCIrELr/CCR/B6YNS6DD6uVR9/bw/qqNu754omMyE0/NM7BZ3YC1pmQMcb0s/rWMNsKT6Nt9Mnkpvu5bOpwii/6TqfjWhNZY0yq69iJUGs4wpKtNazxnIb32DM5e8Iwjh+RzWM/uM7FKAeWJZsJ8MH2A/z4tTKKM32cWgz+YNrHj+kwCeUR4azSdD77qWweXH6AT9+1kC/NHMMXThrF5BE57K+p5td/XY6qct1Z4ykqKrKk0xhj+sEb6/bxwxfX0jZqBjNK8zjj2EL8XrubxxgzdKT5vZw/qZipI3NYuKmKeev38eGOA4QLjkFVB+XxpiWbA2xZeQ03zl1OQYaX88Zn0t5kPQK5rbmhlprKFs4fkUY4kM0T71Ywd0kFaX4P+ek+QmEvPo2w+LktDMuo4NbPTObcqWOprq4G7IqnMcb0xca9DfzitfUs3FjFcSXZNLwxh3MvvNPtsIwxxjXFOWlcc/JoNu5r4N2tNTSf9GX++ZH3uO0zkzixNM/t8PqVJZsDRFV5adVu/vPZVQQ9yvH+eqJt9u9OFpk5+UTDrbS0VHHN1BFsq6xl3KgialvaWburnpa2CPvDPnbVwPX/u44x+WXkSSvjs+GOq+zxKcYY05O9da3cM28jz72/k6ygjx9cNonrzxzPqX++ze3QjDHGdSLCpOE5TCjO4pEH72ddxuVc8fvFnDNxGP86awKnH1MwKC5uWPYzAHbsb+Znr6zj7x/tY9rILKYO89He5HZUpjOZOfmUFBeR7hOunhK7d/P59CiNdQfw+NPAF6Q4y8eC8iZW74O1B6D11a3c+GkfJ4/NxyMcusfzYA+3ImJXP40xQ9bmfQ08/HY5L6zchapyw1njufm8CeRnBtwOzRhjko7P4yG4YxmLnvw1f1q6nUfeKefaPy5lxpg8bjhrPBdPLiHN73U7zCNmyeZRiO9MJiMnn3e2VPPMe+Us3HIAn0e4+exRXDzWx4vr62l0OdZUE/+/TUSvvU31B3hw/m60Q8dN0ZY6yve3MDYaonR8MeWNwttbD/D3De+Sl+5n2shM9lbXkpeVTmtdDX6/l6xggNmnjmLyuOGMGRmbV6p3OmQdJxljerK0rIav3f00zTljIRImsHsVadvf5bm/1/Lcjz8ez3qZNcaYT8pO8/OtWcdyw1njePb9nfxxURnfefJD8jL8XDljFNecXMrxI7JT7hgsZZJNEbkEuBfwAg+r6oDf8NHdAfa++lbeXF3BnLe3Ut2q1IeF9igEPMqU4nSKw/vYVtbMQxut19kj0VXyN5AONq3tarjH7+EETyvjvC0ciKZTVJTL2j2NbK+HSF0L8PHz4d76225gN6V5QSaVZFJZtZ/8IPzwyhlMGDMS6Pz3laxJXVVVFb/+63IAvneFNSM2Jtm5sc/8x0f7aAkWcsYxhZwwKpf0wGTg2k+MZ73MGmPM4crLyjjxlNMPG6ZAZsF4mkbOYE5jC3MWV1BakM4Fk0q48PgSTh2fT9CX/Fc8UyLZFBEv8HvgImAnsFxEXlLVdQO53MrKSv77LytoCMPpk8dSHfKwcfcBNlc1s6e+DQCvQH5QGJsRpsgbYszwQkaMLmVPRSsev/U6ezS6Sv7clpuXz4iMDL45azwADyzYQjA7nx0VZUQ8AUJtbbSonzZ85GQGWbO7kb0NsWkX3v8hYwo2csKoXLJ8EVaVV+IBzj5+JJmZWTQ0NrJk424E5dyJhYwuymfs8EKKstMoyQmSm+4/6gT0SBPazNyCo1quMSYx3Npn3nLhRJ77r28w87I5A7kYY4wZdDo+IqWjlrYIjz90HxOvvIEnl21n7pIKAj4PU0bmML00j+mleUwszmZsYQaZweRK75Irmq7NBLaoahmAiDwFXAEM2I6zpjHErN99QEs49nnB7m34PZDuVTI9YabkQJ4nxMiSYUh7CI8/m2jYjydJrkSZgRWfsIlA0O8l0w8ev4eoFzx+L+npaVw9NRfI48+rDlDdFGFEto8dTcLK7fupbW6jtR2iCuve3d1hCcJHy/YD+4Gth4YGvEJ+hp/MgId0v5cMv4eAz4PPI/g8gtf56/PG3mdnZuD3CKFQKz4PqEJDUzNrdtWhClNG5pCTlYnPKwS8HvJysvCIEI5EaWuP0hZR2tqj1DU2smZnE4oSXrSD0uIGCjODFGYGkHATQWd5Xo9QNKwQr8eDCLRHlKrqaiJRpT2qRA6+VIlGYxvXqEJObi4eEerr6xAUrwgej1CQn4/XI9TV1nJw1crPzz+UIKsqBw4cIBp3v2x+fj7C4euhiFJXWxv7/wgMGzYMv9eD3yv4vULdgf34vUJJcfERJfN9TeAPbybe92n6+6r3kcRztMuJX8aRlC1ZWwEkiZa9TgYAAA/USURBVITvM4HYybBoZCAXYYwxQ1J6wEtw1wc8ev39tLRFeGdLNcsr9rNyey1PLtvOnMUVh8Ytzg4ypiCDgszAJ15ZQR+ZQR+TR+Qk7D76VEk2RwE74j7vBE4byAUWZAa4cloRG7btwx9uxBtuJhBpJruwBA2HEH8QDUdobaiN+xz725CRQXN97WHDjvRvf8wrWeYxmOJprNnLPZtbiLaHyCooAeQT8+g4TiAcYue+FrQ9xCRfkKg3RFZR7PfU3NxCYfFwqnZvI7uwhEhbiLA3SFtbmIbWMKH2CG0SJBSBcIufhialVrxEohAVL1FVFEEBRRCPl0hUCfh9RFQJhaOH7nwVFI8IglK+oQ6ljigCfPJgPeAkY16BcHsUgCc/2Et7dO9Arn6u8QgfJ6GeWNmdPNb53zrvnYGqsWHRaJSWtnYAAn4fHpG4cQ/O/eN7j6NRpS0S+6+rgt/niUu8Do/p48mVcCRWBz6v51BteZwk3yPETjiI89kTS7k7xhw3u0PfRSIRGlvDh5pyZwb9eDyej6ftYvr4+GLzO3zg4dNDVKO0hT8uw8Hvoho7CQHg9QgiH58uEODgmYZDw5x/XFskCgrHlWTx6q2zMIckfJ9pjDEmMdIDXi6aXMJFk0sACEeibN7XSHl1Ez/69f0c2J1GTVou6s9AAxmoPwM8hze3HVn2CkueuT8h8UrHA4hkJCJXA5eo6tedz18BTlPVmzuMdxNwk/PxOGBjPyx+GFDdD/NxS6rHD1aGZGFlcF+qxw/JXYaxqlrkdhBHy+V9ZqIl8++pPwz28sHgL6OVL7VZ+brX434zVa5s7gJK4z6PdoYdRlUfAh7qzwWLyApVPaU/55lIqR4/WBmShZXBfakePwyOMqQA1/aZiTbYf0+DvXww+Mto5UttVr6j5xnImfej5cBEERkvIgFgNvCSyzEZY4wxycj2mcYYY5JCSlzZVNV2EbkZ+DuxbtwfVdWPXA7LGGOMSTq2zzTGGJMsUiLZBFDVV4FXXVh0SjcxIvXjBytDsrAyuC/V44fBUYak5+I+M9EG++9psJcPBn8ZrXypzcp3lFKigyBjjDHGGGOMMaklVe7ZNMYYY4wxxhiTQizZBETkEhHZKCJbROT7nXwfFJGnne/fE5FxLoTZrV6U4XoRqRKRlc7r627E2RUReVREKkVkbRffi4j81infahE5KdEx9qQXZZglInVxdfCjRMfYExEpFZEFIrJORD4SkVs6GSdp66KX8Sd1PYhImogsE5FVThl+0sk4Sb1N6mUZknqbZJJPV+u3iBSIyDwR2ez8zXc71qMhIl4R+VBEXnY+j3fW8y3Oep+YJ7EPABHJE5HnRGSDiKwXkTMGU/2JyK3Ob3OtiDzpbAtTuv46O7bpqs6S+figK12U7y7nN7paRF4Qkby47253yrdRRD7jStB90N2xqYh8T0RURIY5nwek/oZ8sikiXuD3wKXAZOBaEZncYbQbgQOqOgH4DfDLxEbZvV6WAeBpVZ3uvB5OaJA9mwtc0s33lwITnddNwAMJiKmv5tJ9GQDejquDnyYgpr5qB76nqpOB04F/6+S3lMx10Zv4IbnrIQScr6onAtOBS0Tk9A7jJPU2id6VAZJ7m2SST1fr9/eB+ao6EZjvfE5ltwDr4z7/EviNs74fILb+p6p7gddVdRJwIrFyDor6E5FRwHeAU1R1KrHOuWaT+vU3l08e23RVZ8l8fNCVuXyyfPOAqao6DdgE3A7gbG9mA1Ocae53jsGT2Vw6OTYVkVLgYmB73OABqb8hn2wCM4Etqlqmqm3AU8AVHca5AnjMef8ccIGISAJj7ElvypDUVHURsL+bUa4AHteYpUCeiIxITHS904syJD1V3aOqHzjvG4gdCIzqMFrS1kUv409qzv+10fnod14db65P6m1SL8tgTJ90s37Hrw+PAZ93JcB+ICKjgc8CDzufBTif2HoOKVw+EckFzgUeAVDVNlWtZRDVH7GON9NFxAdkAHtI8frr4timqzpL2uODrnRWPlX9h6q2Ox+XEntWMcTK95SqhlS1HNhC7Bg8aXVzbPob4DYO3zcPSP1ZshnbUe2I+7yTTx6cHhrH+fHVAYUJia53elMGgKucy+LPOWc0Uklvy5jsznCaFr4mIlPcDqY7TtPMGcB7Hb5KibroJn5I8npwmtGtBCqBearaZR0k6TapN2WA1N4mGRd1WL9LVHWP89VeoMStuPrB/xA7AIw6nwuB2rgD36Tc3vbSeKAKmOM0E35YRDIZJPWnqruAu4ldKdpDbLv8PoOn/uJ1VWcpcXzQR18DXnPeD4ryicgVwC5VXdXhqwEpnyWbQ8ffgHFOk4B5fHxGyiTOB8BYp2nhfcCL7obTNRHJAp4Hvquq9W7H01c9xJ/09aCqEVWdTuxs6kwRmepySH3WizLYNskcke7Wb411sZ+SV9FF5HKgUlXfdzuWAeIDTgIeUNUZQBMdmsymeP3lE7syNB4YCWTS8601KS+V66wnInIHseb7f3Y7lv4iIhnAD4CE9VdhySbsAuLPqI92hnU6jtM0IheoSUh0vdNjGVS1RlVDzseHgZMTFFt/6U09JTVVrT/YtNB5Bp7/4E3ZyURE/MQO5P6sqn/pZJSkroue4k+VegBwmpgt4JMHLMm+TTqkqzIMgm2ScUEX6/e+g029nL+VbsV3lM4CPiciFcRuhzmf2D2Oec56Dkm2ve2jncDOuFYOzxFLPgdL/V0IlKtqlaqGgb8Qq9PBUn/xuqqzpD4+6AsRuR64HPiyk1DD4CjfscROiKxytjWjgQ9EZDgDVD5LNmE5MNHpLSxA7MbflzqM8xJwnfP+auDNuB9eMuixDB3aXH+OwzsfSAUvAV91eso6HaiLa8KREkRk+MH76kRkJrH1L6kSBCe+R4D1qnpPF6MlbV30Jv5krwcRKRKn5zsRSQcuAjZ0GC2pt0m9KcMg2CaZBOtm/Y5fH64D/pro2PqDqt6uqqNVdRyx/fibqvplYidrrnZGS+Xy7QV2iMhxzqALgHUMkvoj1nz2dBHJcH6rB8s3KOqvg67qLGmPD/pCRC4h1pz9c6raHPfVS8BsifUIP55YRzrL3IjxSKnqGlUtVtVxzrZmJ3CSs34OTP2p6pB/AZcR621qK3CHM+ynxH5kAGnAs8RuBF4GHON2zEdQhl8AHwGriG34Jrkdc4f4nyR2j0PY+eHfCHwT+KbzvRDrcXcrsIZYb2+ux93HMtwcVwdLgTPdjrmTMpxNrDnMamCl87osVeqil/EndT0A04APnTKsBX7kDE+ZbVIvy5DU2yR7Jd+rm/W7kFiPmJuBN4ACt2Pth7LOAl523h/jrOdbnPU+6HZ8R1Gu6cAKpw5fBPIHU/0BPyF2Ym0t8AQQTPX66+LYptM6S+bjgz6WbwuxexcPbmcejBv/Dqd8G4FL3Y7/SMrX4fsKYNhA1p84MzfGGGOMMcYYY/qNNaM1xhhjjDHGGNPvLNk0xhhjjDHGGNPvLNk0xhhjjDHGGNPvLNk0xhhjjDHGGNPvLNk0xhhjjDHGGNPvLNk0xhhjjDHGGNPvLNk0phdEJE9E/rWHccaJyJd6Ma9xIrL2CGL4rohkHMF0PxaR/+jrdP1JRF46kjI70xaKyAIRaRSR3/V3bMYYY5JTIve9IjJdRC47kjgHQm/KbkwqsGTTmN7JA3ra6I8DetzhHYXvAn1ONt0mIl8AGo9iFq3A/wVcTZiNMcYkXB6J2/dOBzpNNkXE1w/z76s8ei67MUnPkk1jeudO4FgRWSkidzmvtSKyRkS+GDfOOc44tzpnUd8WkQ+c15m9WZCIeEXkbmf+q0Xk2yLyHWAksEBEFnQz7SXOslaJyPy4ryaLyEIRKXPmdfAs73oR+aOIfCQi/xCRdOe7U51lHyxvd2eD00XkKWdeL4jIeyJyivNdFvDvwM96Ue6TnbhXxS9TVZtU9R1iSacxxpihIyH7XhEJAD8FvujM54tOq6AnRGQx8ERX8xWRWc7+9TkR2SAifxYRcb67U0TWOfvTu7tZfomz/zy4DzyzY9mP7t9ojHvcOFNjTCr6PjBVVaeLyFXAN4ETgWHAchFZ5IzzH6p6OYDT5PUiVW0VkYnAk8ApvVjWTcTO1E5X1XYRKVDV/SLy78B5qlrd2UQiUgT8EThXVctFpCDu60nAeUA2sFFEHnCGTwSuVdVviMgzwFXAn4A5wDdU9V0RubOHeL8FNKvq8SIyDfgg7rv/An4NNPei3HOAm1V1ke1YjTHGkKB9r6q2iciPgFNU9WZnPj8GJgNnq2pLD/OdAUwBdgOLgbNEZD1wJTBJVVVE8roJ4bfAW6p6pYh4gaz4svf6v2VMErIrm8b03dnAk6oaUdV9wFvAqZ2M5wf+KCJrgGeJ7bR640LgD6raDqCq+3s53enAIlUt72S6V1Q15CSqlUCJM7xcVVc6798Hxjk7xGxVfdcZ/r89LPdcYgkqqroaWA2x+1+AY1X1hZ4Cd5aZp6qLnEFP9DSNMcaYIWWg972deUlVW3ox32WqulNVo8BKYieM64i1yHlEYreTdHfS9XzgAQCnfHVHEbMxScWubBozcG4F9hE7C+vB3Wagobj3ET5e9zsOT+/HZZ4BnCIiFc7yikVkoarO6sdlGGOMMfH6c9/b1Mv5fmIf67RMmglcAFwN3EwsqTRmSLErm8b0TgOxJqgAbxO7r8PrNF09F1jWYRyAXGCPc6bzK4C3l8uaB/yLOB0SxDWH7Tj/jpYC54rI+A7T9Ymq1gINInKaM2h2D5MswumcQUSmAtOc+TygqiNVdRyxM9Kbuko0nWXWisjZzqAvH0nsxhhjBpVE7nt72sf2ab5OnwW5qvoqsUT1xG5Gn0/slpSD/Tbk9iIeY1KCJZvG9IKq1gCLnU5rziDWVHQV8CZwm6rudYZFnJv7bwXuB64TkVXE7pls6nzun/AwsB1Y7Ux7sJe9h4DXpYsOglS1itj9nn9xpnv6CIp60I3EmgutBDKJNQfqygNAlnN/yk+JNcc9EjcAv3eWKfFfOFdH7wGuF5GdInI0zaKMMcakgATvexcQ60xvZVznQ/H6Ot9s4GURWQ28Q6yzvK7cApznNNF9H5gcX3brx8CkMlFVt2MwxiQZEclS1Ubn/feBEap6Sy+nXUiss4YVR7H8ccDLqjr1SOdhjDHGGGPcZfdsGmM681kRuZ3YNmIbcL274RhjjDHGmFRjVzaNcYmIfAb4ZYfB5ap6ZS+mfQ8Idhj8FVVd01/xdbLMI463w3x+D5zVYfC9qjrnaOIzxhhjetJf+7IjXPYdwDUdBj+rqv890Ms2xi2WbBpjjDHGGGOM6XfWQZAxxhhjjDHGmH5nyaYxxhhjjDHGmH5nyaYxxhhjjDHGmH5nyaYxxhhjjDHGmH5nyaYxxhhjjDHGmH73/wHUvweNgBTtUgAAAABJRU5ErkJggg==\n", 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months_on_bookcredit_limitavg_open_to_buyavg_utilization_ratiocustomer_agetotal_revolving_baltotal_amt_chng_q4_q1total_trans_amttotal_ct_chng_q4_q1total_trans_ct
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mean35.9284098631.9536987469.1396370.27489446.3259601162.8140610.7599414404.0863040.71222264.858695
std7.9864169088.7766509090.6853240.2756918.016814814.9873350.2192073397.1292540.23808623.472570
min13.0000001438.3000003.0000000.00000026.0000000.0000000.000000510.0000000.00000010.000000
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50%36.0000004549.0000003474.0000000.17600046.0000001276.0000000.7360003899.0000000.70200067.000000
75%40.00000011067.5000009859.0000000.50300052.0000001784.0000000.8590004741.0000000.81800081.000000
max56.00000034516.00000034516.0000000.99900073.0000002517.0000003.39700018484.0000003.714000139.000000
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" ], "text/plain": [ " months_on_book credit_limit avg_open_to_buy avg_utilization_ratio \\\n", "count 10127.000000 10127.000000 10127.000000 10127.000000 \n", "mean 35.928409 8631.953698 7469.139637 0.274894 \n", "std 7.986416 9088.776650 9090.685324 0.275691 \n", "min 13.000000 1438.300000 3.000000 0.000000 \n", "25% 31.000000 2555.000000 1324.500000 0.023000 \n", "50% 36.000000 4549.000000 3474.000000 0.176000 \n", "75% 40.000000 11067.500000 9859.000000 0.503000 \n", "max 56.000000 34516.000000 34516.000000 0.999000 \n", "\n", " customer_age total_revolving_bal total_amt_chng_q4_q1 \\\n", "count 10127.000000 10127.000000 10127.000000 \n", "mean 46.325960 1162.814061 0.759941 \n", "std 8.016814 814.987335 0.219207 \n", "min 26.000000 0.000000 0.000000 \n", "25% 41.000000 359.000000 0.631000 \n", "50% 46.000000 1276.000000 0.736000 \n", "75% 52.000000 1784.000000 0.859000 \n", "max 73.000000 2517.000000 3.397000 \n", "\n", " total_trans_amt total_ct_chng_q4_q1 total_trans_ct \n", "count 10127.000000 10127.000000 10127.000000 \n", "mean 4404.086304 0.712222 64.858695 \n", "std 3397.129254 0.238086 23.472570 \n", "min 510.000000 0.000000 10.000000 \n", "25% 2155.500000 0.582000 45.000000 \n", "50% 3899.000000 0.702000 67.000000 \n", "75% 4741.000000 0.818000 81.000000 \n", "max 18484.000000 3.714000 139.000000 " ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creating an empty list that will be used to store columns with non unique values, \n", "# distribution in these columns will be explained using histogram plots and descriptive stats\n", "column_list = []\n", "\n", "# Create two empty lists, one to store names of variables with unique vals < 10, and the other for variables with non-unique vals\n", "unique_val_vars = []\n", "non_unique_vars = []\n", "\n", "# Iterate through the names of columns/variables in bank_churners_df, and add variables to the appropriate list \n", "# depending on whether the are categorical or numerical\n", "for column_name in df.columns:\n", " unique_vals = df[column_name].value_counts().sort_values(ascending=False).index\n", " unique_count = len(unique_vals)\n", " \n", " if unique_count < 10: \n", " unique_val_vars.append(column_name)\n", " else:\n", " non_unique_vars.append(column_name)\n", "\n", "# Set the figure size\n", "fig, axs = plt.subplots(nrows=5, ncols=2, figsize=(16, 25))\n", "\n", "# Reshape the axs array into a one-dimensional array by flattening its elements\n", "axs = axs.flatten()\n", "\n", "# Set the color of the bars to default blue color\n", "plot_color = sns.color_palette()[0]\n", "\n", "# Iterate through the list of variables with unique vals < 10 and count plots of data in each variable\n", "for i, var in enumerate(unique_val_vars):\n", " # Sort the bars in the count plot in descending order using the sorted unique values in the variable\n", " sorted_order = df[var].value_counts().sort_values(ascending=False).index\n", " # Create the plot using seaborn's countplot and pass the sorted values in the order parameter,use preset color palette\n", " plot = sns.countplot(x=var, data=df, ax=axs[i], order=sorted_order, color=plot_color) \n", " # Rotate the x-axis tick labels to make then readable\n", " axs[i].set_xticklabels(axs[i].get_xticklabels(), rotation=10)\n", " # Set the plot title\n", " axs[i].set_title(f\"\\n\\nDistribution of {var}\\n\", y=0.95)\n", " \n", " # Add bar labels containing the count\n", " for p in plot.patches:\n", " plot.annotate(f'{p.get_height():.0f}', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='baseline', fontsize=10, color='black', xytext=(0, 5), textcoords='offset points')\n", " \n", " # Set the top margin to to 110% of the highest bar's count (i.e 1.1 times the maximum count)\n", " axs[i].set_ylim(0, max(df[var].value_counts()) * 1.2)\n", " \n", "\n", "# Adjust the figure's subplot positions and margins, and then display the figure\n", "plt.subplots_adjust(hspace=0.6)\n", "plt.tight_layout\n", "plt.show();\n", "\n", "\n", "\n", "# Now let's do the same for the variables with non-unique values\n", "# First, calculate the number of rows and columns needed for the subplots\n", "num_rows = (len(non_unique_vars) - 1) // 2 + 1\n", "num_cols = 2\n", "\n", "# Set the figure size\n", "fig, axs = plt.subplots(nrows=num_rows, ncols=num_cols, figsize=(13, 5 * num_rows))\n", "\n", "# Reshape the axs array into a one-dimensional array by flattening its elements\n", "axs = axs.flatten()\n", "\n", "# Filter out the 'clientnum' variable from the non_unique_vars list\n", "filtered_non_unique_vars = [var for var in non_unique_vars if var != 'clientnum']\n", "\n", "# New loop for filtered_non_unique_vars\n", "for i, var in enumerate(filtered_non_unique_vars):\n", " # add to list that would be explained also using descriptive stats\n", " column_list.append(var)\n", " \n", " # Plot the distribution histograms using the corresponding axes\n", " sns.histplot(x=var, data=df, color=plot_color, kde=True, ax=axs[i])\n", " # Set the plot title\n", " axs[i].set_title(f'\\n\\n \\nDistribution of {var}\\n')\n", " \n", " \n", "# Adjust the figure's subplot positions and margins, delete empty plots in the figure, and then display the figure\n", "plt.subplots_adjust(hspace=0.4, wspace=0.4)\n", "plt.tight_layout()\n", "fig.delaxes(axs[10])\n", "fig.delaxes(axs[11])\n", "plt.show();\n", "\n", "# Leave space between last histogram plot and the descriptive stats table\n", "print(\"\\n\")\n", "\n", "# Finally, use descriptive statistics to show how values are distributed in numerical columns\n", "df[column_list].describe()" ] }, { "cell_type": "markdown", "id": "213c2d74-a851-458c-8854-7cbbbf1c8ff6", "metadata": {}, "source": [ "**Observation**\n", "\n", "The attrition_flag column has categorical data and 2 unique values, hence a bar chart is satisfactory to show the distribution of customers who have churned (attrited) to those who are still existing customers. \n", "\n", "Most of the bank's credit card customers have 3 or 2 dependants, while only few have 5 dependants in their care.\n", "\n", "The blue credit card category is the most popular with approx. 93% of customers belonging to this card category. In second place is the Silver credit card, and then the Gold credit card. Customers who own a Platinum credit card are very few, accounting for a meager 0.2% of all customers.\n", "\n", "In the last 12 months, the majority of customers are inactive for 3 months or less; while only a select few customers have been inactive for longer periods. \n", "\n", "The same trend is also noticed in the number of contacts made by customers, as majority of customers have made contact 3 or 2 times in the last 12 months.\n", "\n", "There are more female customers than male customers, with a difference of 589 more female customers than male.\n", "\n", "Majority of the credit card customers are graduates accounting for alsmot 31% of all customers, while minority of the customers have doctorate or post-graduate degrees.\n", "\n", "More customers earn less than 40k while less customers earn 120k or higher.\n", "\n", "Most customers use 3 of the bank's products. Almost the same number of customers use 4, 5 or 6 of the bank's products. Fewer customers only have 1 product from the bank.\n", "\n", "For numerical columns, the table shows the distribution of the variables. \n", "The shortest recorded month on book for a customer is 13 months while some customers have been around for up to 56 months. 36 months is the median number of months on book for the bank's customers.\n", "There are records of extremely high and extremely low card utilization ratios; however, the median average utilization ratio is 0.18.\n", "The youngest credit card customer is aged 26, while the oldest is aged 73. There are more customers aged 46 as this is the mdeian age.\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "8044aede-c31e-48d1-bace-96b13b7155c6", "metadata": {}, "source": [ "
\n", "\n", "## Cross-Correlation Analysis\n", "By cross-examining columns against each other, more insights and broader questions can be asked and answered. So let's see how variables are correlated to each other.\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 10, "id": "b665605c-1686-462c-bd79-6276a91afa7e", "metadata": { "executionTime": 180, "lastSuccessfullyExecutedCode": "# Checking correlations between all numeric variable in the dataset\ndf.corr()" }, "outputs": [ { "data": { "application/com.datacamp.data-table.v1+json": { "table": { "data": [ { "avg_open_to_buy": 0.0682913039, "avg_utilization_ratio": -0.0371351585, "contacts_count_12_mon": -0.0405049598, "credit_limit": 0.0680646002, "customer_age": -0.122253752, "dependent_count": 1, "index": "dependent_count", "months_inactive_12_mon": -0.0107679185, "months_on_book": -0.1030622815, "total_amt_chng_q4_q1": -0.035439429, "total_ct_chng_q4_q1": 0.0110871809, "total_relationship_count": -0.039076389, "total_revolving_bal": -0.0026881459, "total_trans_amt": 0.0250462376, "total_trans_ct": 0.0499124766 }, { "avg_open_to_buy": 0.0067323916, "avg_utilization_ratio": -0.007540837, "contacts_count_12_mon": -0.0107744785, "credit_limit": 0.0075070092, "customer_age": 0.788912359, "dependent_count": -0.1030622815, "index": "months_on_book", "months_inactive_12_mon": 0.0741635143, "months_on_book": 1, "total_amt_chng_q4_q1": -0.0489593201, "total_ct_chng_q4_q1": -0.0140716709, "total_relationship_count": -0.0092030802, "total_revolving_bal": 0.0086228045, "total_trans_amt": -0.0385906295, "total_trans_ct": -0.0498190835 }, { "avg_open_to_buy": -0.0166053838, "avg_utilization_ratio": -0.0075026328, "contacts_count_12_mon": 0.0294929101, "credit_limit": -0.0203937914, "customer_age": 0.0543609988, "dependent_count": -0.0107679185, "index": "months_inactive_12_mon", "months_inactive_12_mon": 1, "months_on_book": 0.0741635143, "total_amt_chng_q4_q1": -0.0322467124, "total_ct_chng_q4_q1": -0.038989338, "total_relationship_count": -0.0036753769, "total_revolving_bal": -0.0422096088, "total_trans_amt": -0.0369824251, "total_trans_ct": -0.0427870393 }, { 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0.1717301505, "total_trans_ct": 0.075926912 }, { "avg_open_to_buy": 1, "avg_utilization_ratio": -0.5388077476, "contacts_count_12_mon": 0.0256459612, "credit_limit": 0.9959805439, "customer_age": 0.0011506779, "dependent_count": 0.0682913039, "index": "avg_open_to_buy", "months_inactive_12_mon": -0.0166053838, "months_on_book": 0.0067323916, "total_amt_chng_q4_q1": 0.0075945292, "total_ct_chng_q4_q1": -0.0100755384, "total_relationship_count": -0.0726013628, "total_revolving_bal": -0.0471671279, "total_trans_amt": 0.1659232285, "total_trans_ct": 0.0708851016 }, { "avg_open_to_buy": -0.5388077476, "avg_utilization_ratio": 1, "contacts_count_12_mon": -0.0554712847, "credit_limit": -0.4829650714, "customer_age": 0.0071142222, "dependent_count": -0.0371351585, "index": "avg_utilization_ratio", "months_inactive_12_mon": -0.0075026328, "months_on_book": -0.007540837, "total_amt_chng_q4_q1": 0.0352348347, "total_ct_chng_q4_q1": 0.0741432099, "total_relationship_count": 0.067662878, 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dependent_count1.000000-0.103062-0.010768-0.0405050.0680650.068291-0.037135-0.122254-0.039076-0.002688-0.0354390.0250460.0110870.049912
months_on_book-0.1030621.0000000.074164-0.0107740.0075070.006732-0.0075410.788912-0.0092030.008623-0.048959-0.038591-0.014072-0.049819
months_inactive_12_mon-0.0107680.0741641.0000000.029493-0.020394-0.016605-0.0075030.054361-0.003675-0.042210-0.032247-0.036982-0.038989-0.042787
contacts_count_12_mon-0.040505-0.0107740.0294931.0000000.0208170.025646-0.055471-0.0184520.055203-0.053913-0.024445-0.112774-0.094997-0.152213
credit_limit0.0680650.007507-0.0203940.0208171.0000000.995981-0.4829650.002476-0.0713860.0424930.0128130.171730-0.0020200.075927
avg_open_to_buy0.0682910.006732-0.0166050.0256460.9959811.000000-0.5388080.001151-0.072601-0.0471670.0075950.165923-0.0100760.070885
avg_utilization_ratio-0.037135-0.007541-0.007503-0.055471-0.482965-0.5388081.0000000.0071140.0676630.6240220.035235-0.0830340.0741430.002838
customer_age-0.1222540.7889120.054361-0.0184520.0024760.0011510.0071141.000000-0.0109310.014780-0.062042-0.046446-0.012143-0.067097
total_relationship_count-0.039076-0.009203-0.0036750.055203-0.071386-0.0726010.067663-0.0109311.0000000.0137260.050119-0.3472290.040831-0.241891
total_revolving_bal-0.0026880.008623-0.042210-0.0539130.042493-0.0471670.6240220.0147800.0137261.0000000.0581740.0643700.0898610.056060
total_amt_chng_q4_q1-0.035439-0.048959-0.032247-0.0244450.0128130.0075950.035235-0.0620420.0501190.0581741.0000000.0396780.3841890.005469
total_trans_amt0.025046-0.038591-0.036982-0.1127740.1717300.165923-0.083034-0.046446-0.3472290.0643700.0396781.0000000.0855810.807192
total_ct_chng_q4_q10.011087-0.014072-0.038989-0.094997-0.002020-0.0100760.074143-0.0121430.0408310.0898610.3841890.0855811.0000000.112324
total_trans_ct0.049912-0.049819-0.042787-0.1522130.0759270.0708850.002838-0.067097-0.2418910.0560600.0054690.8071920.1123241.000000
\n", "
" ], "text/plain": [ " dependent_count ... total_trans_ct\n", "dependent_count 1.000000 ... 0.049912\n", "months_on_book -0.103062 ... -0.049819\n", "months_inactive_12_mon -0.010768 ... -0.042787\n", "contacts_count_12_mon -0.040505 ... -0.152213\n", "credit_limit 0.068065 ... 0.075927\n", "avg_open_to_buy 0.068291 ... 0.070885\n", "avg_utilization_ratio -0.037135 ... 0.002838\n", "customer_age -0.122254 ... -0.067097\n", "total_relationship_count -0.039076 ... -0.241891\n", "total_revolving_bal -0.002688 ... 0.056060\n", "total_amt_chng_q4_q1 -0.035439 ... 0.005469\n", "total_trans_amt 0.025046 ... 0.807192\n", "total_ct_chng_q4_q1 0.011087 ... 0.112324\n", "total_trans_ct 0.049912 ... 1.000000\n", "\n", "[14 rows x 14 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking correlations between all numeric variable in the dataset\n", "df.corr()" ] }, { "cell_type": "code", "execution_count": 11, "id": "6f384edd-08a7-4d05-9e3f-e677950ed645", "metadata": { "executionTime": 781, "lastSuccessfullyExecutedCode": "# Let's use correlation heatmaps to display the same information but in a visually appealing way.\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfig, ax = plt.subplots(figsize=(14, 6))\n\nsns.heatmap(df.corr(), vmin=-1, vmax=1, annot=True,cmap=\"rocket_r\")\n\nplt.xticks(rotation=75)\nplt.show()" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's use correlation heatmaps to display the same information but in a visually appealing way.\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "fig, ax = plt.subplots(figsize=(14, 6))\n", "\n", "sns.heatmap(df.corr(), vmin=-1, vmax=1, annot=True,cmap=\"rocket_r\")\n", "\n", "plt.xticks(rotation=75)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1de5a835-a042-4526-9c81-2660032fe72a", "metadata": {}, "source": [ "\n", "The correlation heatmap above helped me to start asking the right questions that can generate useful insights for the bank. Some of them include:\n", "- How long have clients of different ages been customers of the bank's credit card service, and what is the relationship between age and months on book? \n", "- What is the relationship between credit limit of clients or average open to buy credit, and the avg utilization ratio in the last 12 months? \n", "- How does transaction amount increase or decrease based on the number of products that clients use? \n", "\n", "To answer these questions, numerical columns can be plotted against each other using scatter plots to better understand the correlation trends between them. For example, making a scatter plot of total transaction count vs total transaction amount, or avg utilization ratio vs total revolving balance. Also numerical columns like months_on_book, months_inactive_12_mon, contacts_count_12_mon and Avg_Utilization_Ratio can be plotted against categorial columns with unique values like attrition_flag, gender, education_level and/or income_category, using preferably a bar chart or other charts like a pie chart or tree map, depending on the number of unique values in the categorical column. \n" ] }, { "cell_type": "markdown", "id": "9c33344d-e7d8-4f37-a55d-322ea00357b4", "metadata": {}, "source": [ "
\n", "\n", "## Raising Data Questions\n", "After the distribution and cross-correlation analyses performed above, I now have a better understanding of the bank's dataset, and can raise data questions to try and find clues and answer to help in my investigation.\n", "Grouping by the attrition flag or categorical demographic columns would help in plotting bar charts, pie charts, creating text tables and even tree maps using columns that hold numerical data about credit card usage patterns and repayment habits of all clients of the bank, in order to understand the reasons for leaving and recommend ways the bank can mitigate existing customers from churning.\n", "\n", "Some possible data questions that can be raised include:\n", "\n", "- How do the total relationship count (i.e number of products customers have) differ between age groups, or gender, or income category or any other demographic? A bar chart of total relationship count (numerical datatype) vs demographic categorical column can be used to answer this question.\n", "- On average, how many of the bank's products do customers typically have? Who are the top or bottom customers by number of the bank's products they have? A histogram plot of the total_relationship_count and a text table showing top and bottom customers of the bank by their total product count, can be used to answer such questions.\n", "- Which is the most popular credit card category? What type of credit card holders have churned the most? What is the credit limit according to the different credit card categories? How much credit is currently available for a customer to use based on the type of credit card they have? A histogram of the card_category, bar chart of card_category vs attrition_flag, text table of average credit_limit grouped by card_category, and a text table of avg_open_to_buy grouped by card_category can answer such questions.\n", "- Does the number of dependents per customer affect their credit card needs or usage? Is there a relationship between dependent count and the type of credit card/credit limit a customer has? Plotting a graph of dependent count vs avg utilization ratio or total_trans_ct can answer the first question. A bar chart of dependent count vs credit limit or card category vs dependent count can answer the second question.\n", "- Who are the top (10 or 20) and bottom (10 or 20) clients based on % change in transaction count or % change in transaction amount from Q1 to Q4? Are there any significant differences in % change for churned customers compared to existing customers? Are there any observed patterns when plotting these % change columns grouped by demographic columns? The total_amt_chng_q4_q1 and total_ct_chng_q4_q1 columns which are both numerical columns can be used to create a text table of top or bottom (10 or 20) clients based on their unique client numbers (clientnum column). Bar charts can also be used to answer these questions by grouping demographic columns like gender, marital status, education or income category.\n", "\n", "But for now, I would select the most investigative questions out of the list of so many questions that can reveal the possible reasons why customers are churning and other insights that might have a direct or indirect connection to this problem." ] }, { "cell_type": "markdown", "id": "50345a86-8c33-4bb5-901d-0ecdc7e7882d", "metadata": {}, "source": [ "### Question 1: What type of credit card holders have churned the most? How about Customer Retention?" ] }, { "cell_type": "code", "execution_count": 12, "id": "06f2f86f-dea2-4ac6-8d10-63e57365af35", "metadata": { "executionTime": 252, "lastSuccessfullyExecutedCode": "import numpy as np\n\nresult1 = df.groupby('card_category')['clientnum'].count().reset_index(name='total_customer_count')\nresult2 = df.groupby('card_category')['attrition_flag'].apply(lambda x: (x == 'Attrited Customer').sum()).reset_index(name='churned_count')\n\ntext_table = result1.merge(result2, on='card_category')\n\ntext_table['overall_churn_rate_pct'] = np.round(text_table['churned_count'] / len(df) * 100, 1)\ntext_table['churn_ratio'] = np.round(text_table['total_customer_count'] /text_table['churned_count'], 1)\n\ntext_table.sort_values(by='overall_churn_rate_pct', ascending=False)\n\n", "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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card_categorytotal_customer_countchurned_countoverall_churn_rate_pctcategory_churn_rate_pctcategory_retention_ratio
0Blue9436151915.0016.16.2
3Silver555820.8114.86.8
1Gold116210.2118.15.5
2Platinum2050.0525.04.0
\n", "
" ], "text/plain": [ " card_category total_customer_count churned_count overall_churn_rate_pct \\\n", "0 Blue 9436 1519 15.00 \n", "3 Silver 555 82 0.81 \n", "1 Gold 116 21 0.21 \n", "2 Platinum 20 5 0.05 \n", "\n", " category_churn_rate_pct category_retention_ratio \n", "0 16.1 6.2 \n", "3 14.8 6.8 \n", "1 18.1 5.5 \n", "2 25.0 4.0 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "result1 = df.groupby('card_category')['clientnum'].count().reset_index(name='total_customer_count')\n", "result2 = df.groupby('card_category')['attrition_flag'].apply(lambda x: (x == 'Attrited Customer').sum()).reset_index(name='churned_count')\n", "\n", "text_table = result1.merge(result2, on='card_category')\n", "\n", "text_table['overall_churn_rate_pct'] = np.round(text_table['churned_count'] / len(df) * 100, 2)\n", "text_table['category_churn_rate_pct'] = np.round(text_table['churned_count'] / text_table['total_customer_count'] * 100, 1)\n", "text_table['category_retention_ratio'] = np.round(text_table['total_customer_count'] / text_table['churned_count'], 1)\n", "\n", "text_table.sort_values(by='overall_churn_rate_pct', ascending=False)\n", "\n" ] }, { "cell_type": "markdown", "id": "dac66556-e2b1-41da-8850-0c2ebe41b402", "metadata": {}, "source": [ "#### Explanation:\n", "\n", "The overall churn rate % shows that the bank's blue credit card holders have churned the most with a 15% churn rate. For Silver, Gold and Platinum credit card customers, the churn rate is less than 1% which is below the generally acceptable churn rate levels of around 5-8% for most banks and credit card companies. From this perspective, it can be seen that the customers with blue credit cards tend to churn the most compared to other credit card categories that the bank offers.\n", "\n", "The category churn rate % and category retention ratio variables are being used here to highlight the likelihood of churn and provide a comparison of the initial customer base to the churned customers within each category. This is because the number of customers with blue credit cards are relatively larger than all the other three card categories combined. A higher category retention ratio indicates a lower proportion of churned customers to the initial customer base, which is generally a positive sign for customer retention within that category. Conversely, a lower ratio could indicate a potential issue with customer retention that might need to be addressed for that specific category.\n", "\n", "Now all of a sudden we see that 1 in 4 platinum credit card holders churned, 1 in approx 5 Gold credit card customers churned, 1 in 6 Blue credit card customers churned, and finally 1 in approx 7 Silver credit card customers churned. From this perspective, it appears that there is a potential issue with customer retention of Platinum credit card customers. This is because out of a small number of only 20 total customers with a platinum credit card, 4 of them have churned.\n", "\n", "Could this be related to the income category of customers in certain card categories? Or could there be something else happening? Let us investigate further. \n" ] }, { "cell_type": "markdown", "id": "a03f15b2-b9b3-481e-8f7e-69fa3d0affec", "metadata": {}, "source": [ "### Question 2: Is there a relationship between credit card category, income category and the usage of credit cards by customers?" ] }, { "cell_type": "code", "execution_count": 45, "id": "1c380792-dc2e-4b2c-bf8d-165d5593f5bd", "metadata": { "executionTime": 501, "lastSuccessfullyExecutedCode": "# Grouping by card category & income category to see card utilization trends\nview = df.groupby(['card_category', 'income_category'])['avg_utilization_ratio'].mean().sort_values(ascending=False)\n\n# Using a suitable figure/chart size for the plot\nplt.figure(figsize = [10, 5])\nax = view.plot(kind='bar', xlabel=\"\\nCard Category , Income Category\" , ylabel= \"Avg Utilization Ratio\", legend=False)\n\nax.bar_label(ax.containers[0], fmt='%.2f', label_type= 'edge')\n\nplt.show();" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Grouping by card category & income category to see card utilization trends\n", "view = df.groupby(['card_category', 'income_category'])['avg_utilization_ratio'].mean().sort_values(ascending=False)\n", "\n", "# Using a suitable figure/chart size for the plot\n", "plt.figure(figsize = [10, 5])\n", "ax = view.plot(kind='bar', xlabel=\"\\nCard Category , Income Category\" , ylabel= \"Avg Utilization Ratio\", legend=False)\n", "\n", "ax.bar_label(ax.containers[0], fmt='%.2f', label_type= 'edge')\n", "\n", "plt.show();" ] }, { "cell_type": "markdown", "id": "50995a04-11e9-417b-856f-a2b9804dec49", "metadata": {}, "source": [ "#### Solution:\n", "\n", "The plot above shows that on average, Blue credit card customers who earn less than $40k and those who earn between 40k-60k tend to use their credit cards the most; while Platinum credit card customers who earn between 40k-60k and those whose income category is unknown, tend to use their credit card the least.\n", "\n", "On average, Blue credit card customers of all income categories tend to use their cards way more than Silver, Gold and Platinum credit card customers. And average usage trends amongst Silver, Gold and Platinum credit card customers who earn higher than 60k does not differ much, indicating that these customers are not heavily dependent on their credit cards and might also suggest that they are less likely to be interested in trying out other credit products/services the bank offers. \n", "\n", "It is worth investigating further to see how factors such as dependent count affect the credit card needs of customers." ] }, { "cell_type": "markdown", "id": "98901a2e-5297-4c3c-878b-6876a075846d", "metadata": {}, "source": [ "### Question 3: How does income category & number of dependents per customer affect their credit card needs or usage?" ] }, { "cell_type": "code", "execution_count": 46, "id": "429d7a90-8864-425e-94b5-90cd322572f8", "metadata": { "executionTime": 581, "lastSuccessfullyExecutedCode": "# Grouping by dependent count & income category to see card utilization trends\nview = df.groupby(['dependent_count', 'income_category'])['avg_utilization_ratio'].mean().sort_values(ascending=False)\n\n# Using a suitable figure/chart size for the plot\nplt.figure(figsize = [12, 5])\nax = view.plot(kind='bar', xlabel=\"\\nDependent Count , Income Category\" , ylabel= \"Avg Utilization Ratio\", legend=False)\n\n#ax.bar_label(ax.containers[0], fmt='%.2f', label_type= 'edge')\n\nplt.show();" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Grouping by dependent count & income category to see card utilization trends\n", "view = df.groupby(['dependent_count', 'income_category'])['avg_utilization_ratio'].mean().sort_values(ascending=False)\n", "\n", "# Using a suitable figure/chart size for the plot\n", "plt.figure(figsize = [12, 5])\n", "ax = view.plot(kind='bar', xlabel=\"\\nDependent Count , Income Category\" , ylabel= \"Avg Utilization Ratio\", legend=False)\n", "\n", "#ax.bar_label(ax.containers[0], fmt='%.2f', label_type= 'edge')\n", "\n", "plt.show();" ] }, { "cell_type": "markdown", "id": "cf31d228-7d12-430a-b51c-63bee0ee802d", "metadata": {}, "source": [ "#### Solution:\n", "There is no clear correlation between credit card utilization and number of dependants, but when income category is grouped with dependent count, some ineteresting insights can be obtained.\n", "\n", "We can see that on average, customers who earn less than 40k use their credit cards the most regardless of their number of dependants.\n", "Customers who earn between 40k-60k and those who earn between 60k-80k are not significantly different in their card usage rates regardless of their dependent count.\n", "But as earnings further increased, we observed a trend where credit card utilization decreased as number of dependants increased, especially for customers earning 120k or higher.\n", "\n", "In general, Card Utilization decreased as income increased suggesting that customers who earn higher tend to use their credit cards less. " ] }, { "cell_type": "markdown", "id": "43b0c357-1871-4330-80c2-e110579b3323", "metadata": {}, "source": [ "### Question 4: What age groups utilize their credit cards the most and the least? And What age groups have the most churned customers?" ] }, { "cell_type": "code", "execution_count": 47, "id": "d6c671e1-e1af-449b-b82e-c76e531c0916", "metadata": { "executionTime": 454, "lastSuccessfullyExecutedCode": "# Creating a new column to group customers according to their age groups\nimport numpy as np\n\nconditions = [\n (df['customer_age'] > 10) & (df['customer_age'] <= 30),\n (df['customer_age'] > 30) & (df['customer_age'] <= 40),\n (df['customer_age'] > 40) & (df['customer_age'] <= 50),\n (df['customer_age'] > 50) & (df['customer_age'] <= 60),\n (df['customer_age'] > 60) & (df['customer_age'] <= 120)\n ]\n\nvalues = ['11 - 30', '31 - 40', '41 - 50', '51 - 60', '61 - 120']\n \ndf['age_group'] = np.select(conditions, values)\n\n\n\nfirst_view = df.groupby(['age_group'])[('avg_utilization_ratio')].apply(lambda x: np.round(x.mean(), 2)).sort_values(ascending=False)\n\nsecond_view = df.groupby(['age_group'])['attrition_flag'].apply(lambda x: (x == 'Attrited Customer').sum()).sort_values(ascending=False)\n\n# Resize the chart, and have two plots side-by-side\n# Set a larger figure size for subplots\nplt.figure(figsize = [12, 5]) \n\n# 1 row, 2 cols, subplot 1\nplt.subplot(1, 2, 1)\n# Plot a bar chart with the data\nax = first_view.plot(kind='bar', xlabel=\"\\nAge Group\" , ylabel= \"Avg Utilization Ratio\", legend=False)\n# Include the bar labels\nax.bar_label(ax.containers[0], label_type= 'edge')\n# Rotate the x ticks\nplt.xticks(rotation=10)\n\n# 1 row, 2 cols, subplot 2\nplt.subplot(1, 2, 2)\n# Plot a bar chart with the data\nax = second_view.plot(kind='bar', xlabel=\"\\nAge Group\" , ylabel= \"Count of Churned Customers\", legend=False)\n# Include the bar labels\nax.bar_label(ax.containers[0], label_type= 'edge')\n# Rotate the x ticks\nplt.xticks(rotation=10)\n\n# Now show the figure\nplt.show();" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Creating a new column to group customers according to their age groups\n", "import numpy as np\n", "\n", "conditions = [\n", " (df['customer_age'] > 10) & (df['customer_age'] <= 30),\n", " (df['customer_age'] > 30) & (df['customer_age'] <= 40),\n", " (df['customer_age'] > 40) & (df['customer_age'] <= 50),\n", " (df['customer_age'] > 50) & (df['customer_age'] <= 60),\n", " (df['customer_age'] > 60) & (df['customer_age'] <= 120)\n", " ]\n", "\n", "values = ['11 - 30', '31 - 40', '41 - 50', '51 - 60', '61 - 120']\n", " \n", "df['age_group'] = np.select(conditions, values)\n", "\n", "\n", "\n", "first_view = df.groupby(['age_group'])[('avg_utilization_ratio')].apply(lambda x: np.round(x.mean(), 2)).sort_values(ascending=False)\n", "\n", "second_view = df.groupby(['age_group'])['attrition_flag'].apply(lambda x: (x == 'Attrited Customer').sum()).sort_values(ascending=False)\n", "\n", "# Resize the chart, and have two plots side-by-side\n", "# Set a larger figure size for subplots\n", "plt.figure(figsize = [12, 5]) \n", "\n", "# 1 row, 2 cols, subplot 1\n", "plt.subplot(1, 2, 1)\n", "# Plot a bar chart with the data\n", "ax = first_view.plot(kind='bar', xlabel=\"\\nAge Group\" , ylabel= \"Avg Utilization Ratio\", legend=False)\n", "# Include the bar labels\n", "ax.bar_label(ax.containers[0], label_type= 'edge')\n", "# Rotate the x ticks\n", "plt.xticks(rotation=10)\n", "\n", "# 1 row, 2 cols, subplot 2\n", "plt.subplot(1, 2, 2)\n", "# Plot a bar chart with the data\n", "ax = second_view.plot(kind='bar', xlabel=\"\\nAge Group\" , ylabel= \"Count of Churned Customers\", legend=False)\n", "# Include the bar labels\n", "ax.bar_label(ax.containers[0], label_type= 'edge')\n", "# Rotate the x ticks\n", "plt.xticks(rotation=10)\n", "\n", "# Now show the figure\n", "plt.show();" ] }, { "cell_type": "markdown", "id": "185e1264-6178-48f3-af58-a9b4c04b0afd", "metadata": {}, "source": [ "#### Solution:\n", "Here we see that on average, the difference in credit card utilization amongst age groups is not so much; with customers in the 11-30 age group using their credit cards the most while customers in thee 41-50 age groups have the lowest utilization ratio.\n", "However, the count of churned customers per age group adds context to this analysis. Now we see that the customers aged between 41 and 50 have churned the most, while customers between 11 and 30 churned the least.\n", "From this perspective, a big focus should be made on customers in the 41-50 age group, and more data should be collected to enrich the dataset and provide deeper insights about this group of customers in particular and all age groups in general." ] }, { "cell_type": "markdown", "id": "ab047696-0f11-4573-afa4-07cc0c209e1e", "metadata": {}, "source": [ "
\n", "\n", "# Data Enrichment\n", "\n", "When enriching a customer churn dataset for a bank's credit card service, the following additional variables may be useful to collect:\n", "\n", "Internal data points such as customer complaints history, complaints resolution times, customer satisfaction ratings/comments/suggestions, can help perform better customer churn and retention analyses.\n", "\n", "External data points like industry trends and economic indicators such as inflation rate, interest rates, unemployment rate, consumer sentiments and changes in GDP. \n", "This is because high inflation can reduce the purchasing power of consumers and make it more difficult for them to manage credit card debt. Changes in interest rates can affect the affordability of credit card debt and the attractiveness of credit card rewards programs; by benchmarking with the average industry interest rate, the bank can offer more competitive interest rates to retain existing clients and mitigate churn. High unemployment rates can affect consumer confidence and spending habits, which can impact credit card usage. And finally, consumer sentiments as well as changes in GDP greatly impact spending habits and credit card usage.\n", "Thus these economic indicators can provide valuable context for understanding customer behavior and predicting churn, while also identifying opportunities to improve customer retention and maximize profitability.\n", "\n", "These external data points are readily available through public/private, free and paid data sources online, some of which are:\n", "\n", "- World Bank Open Data: https://data.worldbank.org/\n", "- US Government's Open Data Portal: https://www.data.gov/ \n", "- AWS Public Datasets: https://aws.amazon.com/public-datasets/\n", "- Google's Dataset Search: https://datasetsearch.research.google.com/\n", "- UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/index.php\n", "- Data World: https://data.world/\n", "- Statista for unemployment rates, economic indicators and customer sentiments data: https://www.statista.com/\n", "- Bloomberg Datasets for economic indicators: https://data.bloomberg.com/\n", "- Alpha Vantage API for Economic Indicators : https://www.alphavantage.co/documentation/\n", "- Bright Data for Customer Sentiments Data: https://brightdata.com/\n", "- US Government Bureau of Labor Statistics for U.S unemployment rates: https://www.bls.gov/\n", "- Statistics Canada for unemployment, economic indicators & customer sentiments: https://www.statcan.gc.ca/\n", "- Kaggle Datasets: https://www.kaggle.com/datasets.\n" ] }, { "cell_type": "markdown", "id": "cc788082-49a6-40d7-b66b-0132ef752a69", "metadata": {}, "source": [ " -------------------------------------------------------------------------------------------------------------------- " ] }, { "cell_type": "markdown", "id": "1e6ae324-d94c-4cef-b772-65ad3ed10e50", "metadata": {}, "source": [ "# Tableau Tasks\n", "\n", "The task was to tell the story visually by building an interactive Tableau dashboard that contained:\n", "\n", "1. **KPIs in Bold at the Top**. Metrics like:\n", " - Total number of clients\n", " - Churn rate %\n", " - Average transaction amount per customer\n", " - Average transaction amount before churn\n", " \n", "
\n", "\n", "2. **Churn rate %** among different **age groups**. This will be done by:\n", " - Using the Bins function to split client age into age ranges\n", " - Defining Churn rate % as a calculated field\n", " - Choosing the most suitable way of visualizing the comparison\n", " \n", "
\n", " \n", "3. **A Scatterplot** visualizing certain customer spending behaviors and their connection to churn if such exists. For example, comparing total transaction amount and total transaction count of each client and using color to visually identify churned and existing customers.\n", "\n", "
\n", "\n", "4. **A Highlight Table** comparing Churn rate % among two demographic dimensions, where one is placed on Rows and another on Columns, forming a matrix.\n", "\n", "
\n", "\n", "5. **A Bar Chart** with multiple metrics by `Clientnum`, where each row represents one Client and has multiple metrics as columns. Also using filters or parameters to let user drill down into the set of clients they are interested in.\n", "\n", "
\n", "\n", "6. **A Pareto Analysis** based on custom defined **customer segment**. \n", " - Segment can include values from the demographic information of the client. Such as,\n", " - Gender | Income\n", " - Gender | Income | Marital status\n", "\n", " - Then allowing the dashboard user to choose a metric(s) to analyze. Such as,\n", " - Total number of clients (People from which customer segments most often become clients of the bank?)\n", " - Churn rate absolute (How many churned customers are in each segment?)\n", " - Churn rate % (What is the relative churn rate in each customer segment?)\n", " \n", "
\n", "\n", "7. **Parameters** that allows dashboard users to interact with the dashboard. Such as,\n", " - to change the metric used in the chart(s)\n", " - to change the value of a benchmark\n" ] }, { "cell_type": "markdown", "id": "bc9f51c6-4d20-48c6-9490-36a8e105eb2e", "metadata": {}, "source": [ "![Full Customer Churn Dashboard](Full%20Customer%20Churn%20Dashboard.png)\n" ] }, { "cell_type": "markdown", "id": "b9595fee-30cf-498b-b646-8ea79450c423", "metadata": {}, "source": [ "[Link to Dollar Bank Customer Churn Tableau Dashboard](https://public.tableau.com/app/profile/nsikan.udoma/viz/DollarBankCustomerChurnDashboard/CustomerChurnDashboard)" ] }, { "cell_type": "markdown", "id": "b348f0d9-7129-4899-a550-cc5dcce75b85", "metadata": {}, "source": [ "
\n", "\n", "## Insights\n", "\n", "
\n", "\n", "![KPIs Only - Customer Churn Dashboard](KPIs%20Only%20-%20Customer%20Churn%20Dashboard.png)\n", "\n", "**Overall Churn Rate:** The overall churn rate is 16\\%, which indicates that around one in six customers were churning. This is a key metric for the bank to continuously monitor in its effort to mitigate churn and increase customer retention.\n", "\n", "**Transaction Amounts:** The dashboard also shows the average transaction amount for all customers is \\\\$4,404, which suggests that the bank's customer base includes customers with a range of spending levels. However, the average transaction amount for churned customers is \\$3,095, which is significantly lower than the average for all customers. This may indicate that customers who spend less money on transactions are more likely to churn, perhaps because they are not as invested in the bank's services or do not find them as useful. The bank may want to consider strategies for encouraging customers to increase their transaction amounts in order to improve retention rates. For example, they could offer rewards programs or incentives for customers who maintain high transaction volumes or balances. Alternatively, they could explore ways to improve their services or product offerings to make them more attractive to customers with lower transaction amounts.\n", "\n", "
\n", "\n", "![Customer Churn Demographics](Customer%20Churn%20Demographics.png)\n", "\n", "**Customer Demographics:** Overall, the average customer age is 46 yrs, and there are more female customers than male customers, with the majority of customers in the 41-50 age range. The churn summary indicates that female clients also aged between 41-50 have the highest churn rate, with 4.4\\% of the 2461 female clients in this age group having churned, compared to 3.3\\% of the 2191 male clients in the same age group. Overall, the churn rate is higher for females at 9.2\\% across all age groups, compared to 6.9\\% for males across all age groups. \n", "\n", "According to the analysis, established customers (those who have been with the bank for 25 to 36 months) have the highest churn rate at 5\\% for females and 3.6\\% for males. The second-highest churn rate is seen in long-term customers (those with the bank for 37-48 months), with rates of 2.8\\% for females and 2.3\\% for males. \n", "\n", "The analysis also shows that the largest number of churned customers were female blue credit card holders earning less than \\\\$40k, with a total count of 559. The second highest count, at 215, were male blue credit card holders earning between \\\\$80k -\\\\$120k. However, a majority of customers across all income categories owned blue credit cards. Both male and female customers who churned were established and long-term customers of the bank. Male blue cardholders had churn counts ranging from 97 to 215 across income categories of \\\\$40k -\\\\$120k+, with all churned male blue cardholders being established and long-term customers of the bank.\n", "\n", "This information can be used to tailor marketing and communication efforts to specific demographic groups to mitigate customer churn and increase customer retention.\n", "\n", "
\n", "\n", "![Visualizing customer spending behaviours](Visualizing%20customer%20spending%20behaviours.png)\n", "\n", "**Customer Behavior:** Visualizing customer spending behaviors and connection to churn reveals that customers with higher number of transactions and higher transaction amounts are less likely to churn. Additionally, the scatter plot shows that customers who churned made less than 100 transactions and typically spent less than 5K before churning, regardless of income category. This suggests that offering incentives for customers to maintain high levels of activity and transaction amounts may be an effective retention strategy.\n", "\n", "
\n", "\n", "![Paretor Analysis of Customers](Paretor%20Analysis%20of%20Customers.png)\n", "\n", "**Short-Term vs Long-Term Focus:**\n", "Vital few - The Pareto analysis reveals that around 20\\% of the total churned customers are responsible for approximately 80\\% of the total churn. This means that a small subset of customers is responsible for the majority of customer churn.\n", "Customer segments: The dashboard identifies two customer segments that constitute the vital few: female graduates who are married or single, and male graduates who are single. This suggests that Dollar Bank may need to pay special attention to these customer segments to reduce churn.\n", "\n", "
\n", "\n", "# Recommendations\n", "The analysis highlights the need for the bank to focus on retaining its established and long-term customers in all income categories, especially female blue cardholders earning less than \\\\$40k.\n", "By focusing on the vital few customer segments, Dollar Bank can reduce the overall churn rate to less than 7\\%, which is a generally acceptable churn rate level for most banks and credit card companies. The vital few of around 20\\% can be increased to 30\\% of the total churned customers to capture more customers segments and further reduce churn.\n", "\n", "Targeted marketing ad campaigns should be used to effectively reach the customer segments contributing the most to churn. Examples of such campaigns include: \n", " \n", "- Providing financial education and resources to both male and female graduates, such as webinars, podcasts, or blog posts, to help them manage their finances more effectively, improve their financial literacy, and pay back student loans and other expenses without running into debt.\n", "- Partnering with brands that appeal to the target audience (e.g partnering with fitness brands and offering discounts on gym memberships or workout gears to male and female graduates who are married or single)\n", "- Creating a loyalty program specifically for established and long-term customers, which could include perks such as waived fees, free financial planning sessions, or personalized investment advice.\n", "- Providing personalized investment advice and retirement planning services to established and long-term customers, which could help them maximize their savings and achieve their long-term financial goals.\n", "- Offering credit counseling services to blue credit card holders who earn less than \\\\$40k per year, which could help them improve their credit scores and reduce their financial stress.\n", "- Rewards and incentives e.g sign-up bonuses, discounted interest rates on loans or cash back rewards on purchases, to increase credit card usage and attract new customers to the bank" ] }, { "cell_type": "markdown", "id": "bd3fe7f4", "metadata": {}, "source": [ "---\n", "\n", "
\n", "\n", "

Stay tuned for more insightful data analytics projects from me 📈 🚀

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