{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc1f9a80",
   "metadata": {},
   "source": [
    "Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n",
    "SPDX-License-Identifier: Apache-2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ae5358",
   "metadata": {},
   "source": [
    "# Building a Knowledge Graph Application on Amazon Neptune\n",
    "\n",
    "This notebook shows how to use Amazon Neptune and Gremlin to construct a knowledge graph for a search solution based on semi-structured data from the [Amazon Web Services Database Blog for Amazon Neptune](https://aws.amazon.com/blogs/database/category/database/amazon-neptune/).\n",
    "  \n",
    "  - [Background](#Background)\n",
    "  - [Getting Started](#Getting-Started)\n",
    "  - [Stitching Entities Together](#Stitching-Entities-Together)\n",
    "  - [Finding Unknown Connections](#Finding-Unknown-Connections)\n",
    "  - [Providing Contextually Relevant Answers](#Providing-Contextually-Relevant-Answers)\n",
    "  - [Building a Knowledge Graph Solution](#Building-a-Knowledge-Graph-Solution)\n",
    "  - [Conclusion](#Conclusion)\n",
    "  - [What's Next?](#What's-Next?)\n",
    "  \n",
    "## Background\n",
    "\n",
    "Modern knowledge graphs are the result of connections of data from multiple different sources.  These sources can either be multiple different databases, different data silos, or data extracted from within entities stored in one or more of these options.  Knowledge graphs come in many different forms but the unifying aspect of them is that they organize data using the entities and connections (known as semantics) familiar to a particular domain.  It represents these semantics as definitions of concepts, their properties, relations between them, and the expected logical constraints. Logic built into such a model allows us to infer understanding and connections about the information contained within the model.\n",
    "\n",
    "Knowledge graphs consolidate and integrate an organization’s information assets and make them more readily available to all members of the organization. There are many applications and use cases that are enabled by knowledge graphs. Information from disparate data sources can be linked and made accessible to answer questions you may not even have thought of yet. Information and entities can be extracted not only from structured sources (e.g., relational databases) but also from semi-structured sources (e.g., media metadata, spreadsheets) and unstructured sources (e.g., text documents, email, news articles).\n",
    "\n",
    "The examples in this use case show how we can use our blog knowledge graph to demonstrate how we can use the connected nature of our knowledge graph to provide contextually relevant answers to search questions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3805a73",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "In this section we'll load the knowledge graph and set some visualization options. We'll then use some Gremlin queries to inspect the data model used throughout the solution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0fe846",
   "metadata": {},
   "source": [
    "### Load data\n",
    "\n",
    "The cell below loads the example knowledge graph into your Neptune cluster. When you run the cell it will automatically install the `knowledge-graph` dataset into your graph which takes a few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6daafac",
   "metadata": {},
   "outputs": [],
   "source": [
    "%seed --model Property_Graph --dataset knowledge-graph --run"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "9e78ccae",
   "metadata": {},
   "source": [
    "### Data model\n",
    "\n",
    "The knowledge graph used in our example is constructed from data extracted from the [Amazon Web Services Database Blog](https://aws.amazon.com/blogs/database/category/database/amazon-neptune/) for Amazon Neptune.  From this information, we'll create two categories of entities: structured entities and extracted entities.\n",
    "\n",
    "#### Structured Entities\n",
    "\n",
    "In many companies the a large proportion of the knowledge of the company is stored in semi-structured or unstructured documents such as emails, word documents, chat transcripts, or other web pages.  Knowledge graphs have the unique capability to connect this information together in a contextually relevant manner to provide critical insights into those connections.  Linking relevant pieces of data together enables systems that can recommend people to projects, connect related projects, or centralize access to avoid duplicate efforts. \n",
    "\n",
    "In this example the semi-structured data created from the Amazon Web Services Databases blog provides the backbone of the knowledge graph structure and consists of information such as the `post`, `author`(s), and associated `tag`(s).\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "96ac3fd0",
   "metadata": {},
   "source": [
    "#### Extracted Entities\n",
    "The second type of entity in this knowledge graph are extracted from the original entities and provide additional context and connections within our knowledge graph.  When building knowledge graphs a common practice is to augment the structured components of your data by extracting keywords and concepts from text-heavy content such as emails, word documents, PDF, and spreadsheets or meta-data from video, audio, and photos to build a knowledge graph. Augmenting a knowledge graph of structured data with additional relationships extracted from the data in those entities enables a comprehensive view of the domain being modeled.\n",
    "\n",
    "For this example the Natural Language Processing capabilities of [Amazon Comprehend](https://aws.amazon.com/comprehend/) were used to identify important entities within the blog text such as titles, dates, organizations, and items.  These entities and connections were then used to augment our graph as shown below.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "The following query shows a single post and its associated features. After running the query, click the `Graph` tab to see a visualization of the results. Note that the blog post below contains the following structural entities:\n",
    "\n",
    "* 1 `post` vertex\n",
    "* 3 `author` vertices\n",
    "* 1 `tag` vertex\n",
    "\n",
    "In addition to these structural entities this single post contains the following extracted entities:\n",
    "\n",
    "* 3 `title` vertices\n",
    "* 2 `organization` vertices\n",
    "* 2 `date` vertices\n",
    "* 1 `location` vertex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82eb8e48",
   "metadata": {},
   "source": [
    "### Checking the data\n",
    "\n",
    "The cell below provides a simple example of returning all the posts that were authored by `Dave Bechberger`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "548a15ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc\n",
    "\n",
    "MATCH p=(:author {name: 'Dave Bechberger'})<-[]-(:post)\n",
    "RETURN p \n",
    "LIMIT 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "818f57fa",
   "metadata": {},
   "source": [
    "### Set visualization options\n",
    "\n",
    "As we can see in the example above the `Graph` tab defaults to showing entites and connections using a variety of circle and lines.  The notebook allows for a rich customization of the visualization of the results.  The command below configures the visualization to use specific colours and icons for the different parts of the data model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59eb5f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%graph_notebook_vis_options\n",
    "\n",
    "{\n",
    "  \"groups\": {\n",
    "    \"post\": {\n",
    "      \"shape\": \"icon\",\n",
    "      \"icon\": {\n",
    "        \"face\": \"FontAwesome\",\n",
    "        \"code\": \"\\uf15b\",\n",
    "        \"color\": \"red\"\n",
    "      }\n",
    "    },\n",
    "    \"author\": {\n",
    "      \"shape\": \"icon\",\n",
    "      \"icon\": {\n",
    "        \"face\": \"FontAwesome\",\n",
    "        \"code\": \"\\uf007\",\n",
    "        \"color\": \"orange\"\n",
    "      }\n",
    "    },\n",
    "    \"tag\": {\n",
    "      \"shape\": \"icon\",\n",
    "      \"icon\": {\n",
    "        \"face\": \"FontAwesome\",\n",
    "        \"code\": \"\\uf02b\",\n",
    "        \"color\": \"green\"\n",
    "      }\n",
    "    },\n",
    "    \"date\": {\n",
    "      \"shape\": \"icon\",\n",
    "      \"icon\": {\n",
    "        \"face\": \"FontAwesome\",\n",
    "        \"code\": \"\\uf073\",\n",
    "        \"color\": \"blue\"\n",
    "      }\n",
    "    },\n",
    "    \"other\": {\n",
    "      \"shape\": \"icon\",\n",
    "      \"icon\": {\n",
    "        \"face\": \"FontAwesome\",\n",
    "        \"code\": \"\\uf074\",\n",
    "        \"color\": \"blue\"\n",
    "      }\n",
    "    },\n",
    "    \"organization\": {\n",
    "      \"shape\": \"icon\",\n",
    "      \"icon\": {\n",
    "        \"face\": \"FontAwesome\",\n",
    "        \"code\": \"\\uf1ad\",\n",
    "        \"color\": \"blue\"\n",
    "      }\n",
    "    },\n",
    "    \"location\": {\n",
    "      \"shape\": \"icon\",\n",
    "      \"icon\": {\n",
    "        \"face\": \"FontAwesome\",\n",
    "        \"code\": \"\\uf276\",\n",
    "        \"color\": \"blue\"\n",
    "      }\n",
    "    },\n",
    "    \"title\": {\n",
    "      \"shape\": \"icon\",\n",
    "      \"icon\": {\n",
    "        \"face\": \"FontAwesome\",\n",
    "        \"code\": \"\\uf1dc\",\n",
    "        \"color\": \"blue\"\n",
    "      }\n",
    "    },\n",
    "    \"commercial_item\": {\n",
    "      \"shape\": \"icon\",\n",
    "      \"icon\": {\n",
    "        \"face\": \"FontAwesome\",\n",
    "        \"code\": \"\\uf1b3\",\n",
    "        \"color\": \"blue\"\n",
    "      }\n",
    "    }\n",
    "  },\n",
    "  \"edges\": {\n",
    "    \"color\": {\n",
    "      \"inherit\": false\n",
    "    },\n",
    "    \"smooth\": {\n",
    "      \"enabled\": true,\n",
    "      \"type\": \"straightCross\"\n",
    "    },\n",
    "    \"arrows\": {\n",
    "      \"to\": {\n",
    "        \"enabled\": false,\n",
    "        \"type\": \"arrow\"\n",
    "      }\n",
    "    },\n",
    "    \"font\": {\n",
    "      \"face\": \"courier new\"\n",
    "    }\n",
    "  },\n",
    "  \"interaction\": {\n",
    "    \"hover\": true,\n",
    "    \"hoverConnectedEdges\": true,\n",
    "    \"selectConnectedEdges\": false\n",
    "  },\n",
    "  \"physics\": {\n",
    "    \"minVelocity\": 0.75,\n",
    "    \"barnesHut\": {\n",
    "      \"centralGravity\": 0.1,\n",
    "      \"gravitationalConstant\": -50450,\n",
    "      \"springLength\": 200,\n",
    "      \"springConstant\": 0.04,\n",
    "      \"damping\": 0.09,\n",
    "      \"avoidOverlap\": 0.1\n",
    "    },\n",
    "    \"solver\": \"barnesHut\",\n",
    "    \"enabled\": true,\n",
    "    \"adaptiveTimestep\": true,\n",
    "    \"stabilization\": {\n",
    "      \"enabled\": true,\n",
    "      \"iterations\": 1\n",
    "    }\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8301259d",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_node_labels = '{\"author\":\"name\",\"post\":\"title\",\"location\":\"text\",\"tag\":\"tag\",\"date\":\"text\"}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9eaf650",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc -d $my_node_labels\n",
    "\n",
    "MATCH path=(p:post)-[]->()\n",
    "WHERE id(p)='https://aws.amazon.com/blogs/database/how-to-get-started-with-neptune-ml/'\n",
    "RETURN path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "205b95b7",
   "metadata": {},
   "source": [
    "While seeing the connected nature of the structured and extracted entities within a single query is interesting the real power of a knowledge graph comes from seeing how these items are connected together.  \n",
    "\n",
    "#### Finding co-authors\n",
    "\n",
    "The next query shows all the posts that share the same author as the original post. In this case, the authors have written a total of 9 blog posts. After running the query, click the `Graph` tab to see a visualization of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eeec899",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc -d $my_node_labels -l 25\n",
    "\n",
    "MATCH path=(p:post)-[:written_by]->(:author)<-[:written_by]-()\n",
    "WHERE id(p)='https://aws.amazon.com/blogs/database/how-to-get-started-with-neptune-ml/'\n",
    "RETURN path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "831b147c",
   "metadata": {},
   "source": [
    "From this sort of query we can begin to see how knowledge graphs begin to enable seeing connections within our data.  \n",
    "\n",
    "### Interoperability with Gremlin\n",
    "\n",
    "Within Neptune property graph data is stored one time and can be queried using either openCypher or TinkerPop Gremlin.  In the cell below we have provided the equivalent Gremlin query. After running the query, click the Graph tab to compare the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e58db98c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%gremlin -d $my_node_labels\n",
    "g.V(\n",
    "  'https://aws.amazon.com/blogs/database/how-to-get-started-with-neptune-ml/').\n",
    "  outE('written_by').\n",
    "  inV().hasLabel('author').\n",
    "  inE('written_by').outV().\n",
    "  path().by(elementMap())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24bcc25b",
   "metadata": {},
   "source": [
    "## Stitching Entities Together\n",
    "\n",
    "Another powerful use case for a knowledge graph is to answer questions that require connecting entities together to provide meaning to the answer. Let's examine the following question:\n",
    "\n",
    "### What is the most common topic author X writes about?\n",
    "\n",
    "If we only used the structured data to answer this question, then we would only be able to determine the answer to this using the relatively limited set of tags provided on the blog. The knowledge graph we built allows us to leverage relationships to extracted entities as well to provide additional contextual information based on the text of the blog.  The following query shows a list of the most popular extracted phrases and the number of times each of them has appeared in relation to the author `Dave Bechberger`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f4e6113",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc\n",
    "\n",
    "MATCH (a:author)<-[:written_by]-()-[:found_in]->(co)\n",
    "WHERE id(a)='Dave Bechberger'\n",
    "RETURN co.text as topic, count(co) as count\n",
    "ORDER BY count DESC      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2b8c42",
   "metadata": {},
   "source": [
    "As we see, `AWS` and `Amazon` are the most commonly written about topics with `Neptune` coming in third place.  However this question really only scratches the surface of the types of contextual questions that a knowledge graph is able to answer.  Instead of just finding out information about a specific author what if we wanted to answer a question about Dave Bechberger's co-authors work?\n",
    "\n",
    "### What is the most common topic co-authors of author X write about?\n",
    "\n",
    "This question extends the scope of our previous work by stitching together not only the extracted entities (`organization`, `title`, `date`, `commercial_item`, `other`) of our data but also the structured entities (`author`, `post`, `tag`) as well.  Combining these two different categories of elements together shows how we can help stitch contextual answers to complex questions from our knowledge graph as seen here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f294b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc\n",
    "\n",
    "MATCH (a:author)<-[:written_by]-()-[:written_by]->(b)\n",
    "MATCH (b)<-[:written_by]-()-[:found_in]->(topic)\n",
    "WHERE id(a)='Dave Bechberger'\n",
    "RETURN topic.text as topic, count(topic) as count\n",
    "ORDER BY count DESC    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428edf24",
   "metadata": {},
   "source": [
    "As with the last query, `AWS`, `Amazon`, and `Neptune` also top the list here.  By further modifying the query above to remove the Amazon specific topics we can find the most commonly written about topics.\n",
    "\n",
    "### What is the most common (non-Amazon) topic?\n",
    "\n",
    "Run the following query and view the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b348f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc\n",
    "\n",
    "MATCH (:author)<-[:written_by]-()-[:found_in]-(p)\n",
    "WHERE NOT p.text IN ['AWS', 'Amazon', 'Neptune']\n",
    "RETURN p.text as topic, count(p) as count\n",
    "ORDER BY count DESC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a6f3e7",
   "metadata": {},
   "source": [
    "As we can see, `Comprehend` is by far the most commonly referenced topic in the blog with Python and Seattle in a close race for second. To take it a step further, what if you are also interested in how the above results relate to each other visually in a graph? Run the query below, and then click the Graph tab to see a visualization of how these topics are connected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84958496",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc -d $my_node_labels -l 25\n",
    "\n",
    "MATCH p=(a:author)<-[:written_by]-()-[:found_in]->(post)\n",
    "WHERE NOT post.text IN ['AWS', 'Amazon', 'Neptune']\n",
    "RETURN p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6c60be",
   "metadata": {},
   "source": [
    "From the visualization above, we can start to see that most of our data is highly connected with only a handful of outliers that have few to no connections. With this overview of our graph data, let's take a look at another common use for knowledge graphs: finding unknown connections between entities.\n",
    "## Finding Unknown Connections\n",
    "One of the advantages of using a knowledge graph for answering search questions is that the entities, in this case blog posts, share features with other posts. This allows for discovery of connections between data that are sometimes not obvious.\n",
    "\n",
    "### Find the connections between China and Ohio\n",
    "    \n",
    "Unlike the post-centric or author-centric graph local interactive queries in this notebook so far, this is more of an analytics query, which starts at a specific extracted entity. In this query, we start at the `location` of `China`, and continue moving through the connections in the graph until an end condition is met: the `location` of `Ohio`.\n",
    "\n",
    "The results here show how there can be multiple paths connecting these two seemingly unrelated entities together. Run the query below, and then click the Graph tab to see a visualization of how these topics are connected.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9e58a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc  -d $my_node_labels -l 25\n",
    "\n",
    "MATCH p=(l:location {text: 'China'})<-[:found_in]-()-[:found_in]->()\n",
    "    <-[:found_in]-()-[:found_in]->(:location {text: 'Ohio'})\n",
    "RETURN p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57eae84",
   "metadata": {},
   "source": [
    "We can add additional filtering properties into this sort of query to provide a more focused set of connections between `China` and `Ohio`.  In the query below we have modified our last query to remove any connections through `Wellington`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc651696",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc  -d $my_node_labels -l 25\n",
    "MATCH p=(:location {text: 'China'})<-[:found_in]-()-[:found_in]->(loc:location)\n",
    "<-[:found_in]-()-[:found_in]->(:location)\n",
    "WHERE NOT loc.text='Wellington'\n",
    "RETURN p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7716624",
   "metadata": {},
   "source": [
    "Examining the previous two results we see that the addition of the filtering criteria has narrowed our results by removing any connections through `Wellington`.\n",
    "\n",
    "\n",
    "## Providing Contextually Relevant Answers\n",
    "\n",
    "Combining all the concepts we have demonstrated so far, we begin to provide contextually relevant answers to questions about how the data elements relate to each other. These sorts of questions are common in many user interactions such as question and answer applications, chatbots, or search engines.\n",
    "\n",
    "Let's take a look at the following example:\n",
    "\n",
    "### Find me how Dave Bechberger is connected to posts that mention Seattle and Wellington \n",
    "\n",
    "\n",
    "This query might seem a bit complicated at first but this is exactly the type of question that a knowledge graph enables us to answer in a quick and efficent manner.  Run the query below, then click the `Graph` tab to see a visualization of the answer to our question.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb9d3778",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%oc\n",
    "\n",
    "MATCH p=(a:author)<-[:written_by]-()-[:written_by]->(b)<-[:written_by]-()-\n",
    "[:found_in]->(l:location {text: 'Seattle'})<-[:found_in]-()-[:found_in]\n",
    "->(:location {text: 'Wellington'})\n",
    "WHERE id(a)='Dave Bechberger'\n",
    "RETURN p"
   ]
  },
  {
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wTuo/h3eKxSM6J5ybglnb+dIhKFPlr4r0erPHe71UFeXbtIeC2esCfiABEiABEsiBAAVzDvCYlQRIgARIoPgEXBfMw9cvVT8Y1lHdMayj+sGwTvqzeb7hyD7dCYiD0mP5kuevivR6s8d5A6Mqyjfbn6x8cHM5uD7uXe47to0ESCBaBCiYo9Vf9JYESIAESMBHwHXhAOH3/aEdtGAOik3BbKZDtA5bt0Qhf1AMoQn79m++GpieLF+y61Ie4j+8OtbrJfO66V9VX4efLgfXx73Lfce2kQAJRItAUQRzgz5vKh5kUIpjIFp/HugtCUSDgOvCAWIXQvP7ZR0C4w2H4zPMh/clpA9fl1owSnmSP9felvIQm4LZvJ6qHfm2AzeXg+vj3uW+Y9tIgASiRaBogvmGRnMUDzIopTGAHwgYSIAE8k/AdeEA4fe9Ie3U98vaq+8NaR+PK85F8CI209stnqlwLdkh5U3avDqpTbK8QdelPMR1Z77odXTseoW/hTqnYPa6gB9IgARIgARyIEDBTOHOHy4KNAYomHP4S8WsJJCCgPOCeW1MMN8+uJ0WzhDPOOQc4hVhvRbMFTaS7rcvxLkpmAtRn8lD6hu21u0fKV0f9ylueSaRAAmQQEEJUDAXSCyV0kwq2xq8coCCuaB/21hZCRFwXThA+N0+qK0WyN+Nx+Y5hDIC4qB0CGfTvhDndWaM8UZgIeoLah8Fs9cF/EACJEACJJADAQpmCmbOMBdoDFAw5/CXillJIAUB1wXz0DVvaiH83UFtAuP1h/dqOoghmJPZFfK6KZiD6v3JiK6q8WsT1aztb6t1h2L+oxGHz5fr85c2rVKPTh6SU3vAzeXg+rh3ue/YNhIggWgRoGAukFjirGvwrGspcaFgjtYfR3obHQKuCwcIv/8Y2Fp9d2CbWDwoHsfP18cFJ2Jt50v38iXJXxXpv58+2htAZvl3D++shq5ZrM5fveKlp/qw7tAe9eikISnbb5Zvth/1uBxcH/cu9x3bRgIkEC0CFMwUzJxhLtAYoGCO1h9HehsdAq4Lh7I1i9V/DGitvjOgtY7x2TxPEMyGTY0Xe6unp43yjqeMz7ie73PTv99PqxDM4u8jkwarw+fKEwbWkfPl6tV339YiGu2EyJX2mIYT3n4rafulfLN+XCtbTcFsMuRnEiABEiCB7AhQMBdILJXSTCrbGjybTsGc3R8p5iKBdAScF8yrF6vv9G+lBWMsbhU/j8UiMBGb6RCghQwQrGb93xlQ4WfrN6YluAJfIdoT7Svy/3hoJwX/L1yrmIme9e5Gn31F+bFyKvLjnII5ATlPSIAESIAEsiRAwUzBzBnmAo0BCuYs/0oxGwmkIeC+YF6kbuv/grqtn3EY51i2jIDYs+n/gipbvSgNufwme3XDT8O/p6aO9Cq6cPWKenb2hIR0nc+wN8/vH91L7Th51Ms/c9uGWBuT2Hs+FKH9npMF+uD6uC8QRlZDAiRAAmkJUDAXSCxx1jV41rWUuFAwp/17RAMSyIqA68JhyKpF6tt9W2qhGBSbgtlMh9h86pURCoK1EDHEqlk/zn80pIP3vDLEcq0JA5O2Iyg/yrurrKNadzD2owAGSI+lr1Wqx18vzsHN5eD6uHe579g2EiCBaBEommCGeOBBBqU2BqL154HekkA0CLguHIasWqgF4rf7tIjFfVsmnIuYRAyh6B1J7AuZPmvbBm8Q1Ro/oMI3+JmBfxDeO04c0WVhwzCcp8sPbi4H18e9y33HtpEACUSLQFEEM//IR2uQ0Nv8EOC4zw9HlkICfgKu31sQft/q3UJ9q08L9a3ezeNxxbkpmIPSY/kq7At1/uvxA7yuGr9hZVL/bf353ZQRGZVHwezh4gcSIAESIIEcCFAw5wCPWUkgEwKuf6nPhAVtSSCfBFy/twa/tUB9s3fzpIcpmFPZFTpt/IYVXjffOahdUv8z8WvRrm26TMwyp8sHbi4H18e9y33HtpEACUSLAAVztPqL3kaYAL/cRLjz6HqoCbh+b0H43drrefXNXs0r4p4V56Zg9uyM9IR8Uk4B0g+dO6PHzaL3t1X4nWP9DWeO88Zi7ZeHVy7XKJ+C2UPFDyRAAiRAAjkQoGDOAR6zkkAmBFz/Up8JC9qSQD4JuH5vDVq5QN3a83l1a89mRozPsfO1B3ZrnIgr7JqplvOmKFzL5vjV2H4J5duUIf7E4mZeFw9O439iPmlnRfv86VJwcLkV+cHN5eD6uHe579g2EiCBaBGgYI5Wf9HbCBPgl5sIdx5dDzUB1++tQSvfUN/o0VQLWMTf6NlMn0sMMYuA2EzPRTA+OWmoV49t50PYSv0/6N/Gy9ZgxtiU/ks7zPxSjhlLumz+NW7Dcq8+2Em6lJdL+z3nQ/zB9XEfYvR0jQRIoMQIUDCXWIezucUjwC83xWPPmt0m4Pq9NWjFG+ob3Zuqr3dvWinGtQTBbNi1eO3lrGaXUd5DY/rq+qR8XEt3mP4hv4TfQnwbfpl2+Gye++2C0s32BqVLeeDmcnB93Lvcd2wbCZBAtAhQMEerv+hthAnwy02EO4+uh5qA6/cWhN+/d/uz+nr3PwfGpoAMtmsamK+ivKpJl0EDwRzsl7Qns/qlvWPXL4uXG5x/IAWzdAFjEiABEiCBHAhQMOcAj1lJIBMCrn+pz4QFbUkgnwRcv7cGLp+v/r3bc+rfuz4XGK89sEvjXLt/V2C6zpsif1WlSx8PXJHa/0zr98oVLmhbQPvAzeXg+rh3ue/YNhIggWgRoGCOVn/R2wgT4JebCHceXQ81AdfvLQi/W7o00YI5KIZQRkAclA6hXYzr4tfCnVsD628+Z7Jas39X0iPI7wdH9fbG4h+njQksV/JRMHuo+IEESIAESCAHAhTMOcBjVhLIhIDrX+ozYUFbEsgnAdfvrQHLXtfC8N+6NInHjRPOIToREEMY/1uXxPRinQ8wZni/3+cF5fffTA8aD357nE/fvFab4j3MQelm+8HN5eD6uHe579g2EiCBaBGgYI5Wf9HbCBPgl5sIdx5dDzUB1+8tCL9/7fSs+rfOjdW/dm4ci41zUzAHpet8hn2hzu8Z1NEbN9PeWVvJ/+dnT/Rml8Xw0Nkz3jV/ex+fMFjMFJj40/3nFMweLn4gARIgARLIgQAFcw7wmJUEMiHg+pf6TFjQlgTyScD1e6v/0nlaMEM0Bx0JgtmwyVUwotyg+jK5BqEs4ZlXRiUtT2zkxwF/HTVH9FTnrlzWZphdvr1Xi6RlSV5wczm4Pu5d7ju2jQRIIFoEKJij1V/0NsIE+OUmwp1H10NNwPV7C8Lvax3/pAWijjs+GzuPx2v2VSzJNtMHLM1tSTLKNcuDEE049/kRlP7dns3V9mOH9fiB4P3l8B7qX/35Oj7rjS/944AvvebwCrEMw2emjAr2w+cfBbOHlR9IgARIgARyIEDBnAM8ZiWBTAi4/qU+Exa0JYF8EnD93tKCucOf1M0dGqmvVYobqdX73tc4EZvp/zd2gELebI+ms15SX+vQKEm94k/69F8O764wK4wA0RwrNzG/jAf4arYTeWVmGTb+9FT+wdbl4Pq4d7nv2DYSIIFoEaBgjlZ/0dsIE+CXmwh3Hl0PNQHX763+S+apr7ZvqIXkzfHYPDcFc1A6BKhpX4zzB4Z1U+evxpZUYzD1WzI3oT0ywPrHr8Nfv1ietmmNyqR9KMvl4Pq4d7nv2DYSIIFoEaBgjlZ/0dsIE+CXmwh3Hl0PNQHX7y2Iy6+2axg72sdj49wUzBCani1sinT+nW7N1KNj+qtHX+zv+fPA0G7q0NnT3lhqOnOC559cNNt6sNxnm2F7UJbLwfVx73LfsW0kQALRIkDBHK3+orcRJsAvNxHuPLoeagKu31v93pyr/qVtA/XVdg0CY1Mwp7JLlr8qrkMsSzDLv61rU7msZ5nFX7mItoq9XBu9+s3AdotdshhluRxcH/cu9x3bRgIkEC0CFMzR6i96G2EC/HIT4c6j66Em4Pq91ffN19S/tP2jd3zF+Izrq/fu1P2DGOf+9GKcPzqmnzdm/PVLQr94u5AuAdfEXq6la7/YCyM5Rz6Xg+vj3uW+Y9tIgASiRYCCOVr9RW8jTIBfbiLceXQ91ARcv7cg/L7cur76Sps/qq+0icXmuSmYg9KRz7QvxPkjo/t6Y8ZfnySgXeJv4rWYv+a1bPynYBaCjEmABEiABHIhEGrBjC9BPMggCmPA5iZ0/Uu9DQPakEBVEHD93uq7eI4WvF9u/Uw8rp9wvio+w4wYwrLiEHt/LDb+63Kee7opmCv8jpUvY2DVnvdUn8Vz9FFxbWela+na7y9f2o+yXQ6uj3uX+45tIwESiBaB0AvmaOGkt6VIwPZLi61dKTJkm0kgFwKu31sQfv/cqp76cqtnAmMRzNuOHFS1RvZWj4zsU/S47WtTvC71++0lWH6AYE7Vfn/5ck7BbAmYZiRAAiRAAikJUDCnxMNEEkhPwPbLuq1d+hppQQIkYBJw/d7qs2i2+ucX6iU9Vu2JPcNsMgnTZ7/v89/dZO3e+SuX1f2DOiVtu79s8xzcXA6uj3uX+45tIwESiBYBCuZo9Re9DSEB2y8ttnYhbCJdIoFQE3D93oLw+38t/6BueuEP6iYd14vHsfM+i8K79BivhrrphXop/a+qdArmUN+2dI4ESIAEIkOAgjkyXUVHw0rA9su6rV1Y20m/SCCsBFy/t3ovnK3+X4u6saPlHyo+41r8/M4eLdSvR/RSvx7ROx7jc/HPxT/Tf/j68oaV6q3d76m39rwXi/E54PzpCWWB7TXL8z4bPHAN3FwOro97l/uObSMBEogWAScE8wdvtVcfrOqgXIijNXzoLQjYfmmxtSNVEiCBzAi4fm/1Xviq+qfmdbRwdCHOVMhC/GbTbnBzObg+7l3uO7aNBEggWgQcEcwd1OXef2Ucf218xvVonH/wVodojR56qwnYfmmxtSNWEiCBzAi4fm/1WhATzBCN+ng+Hkf0/NkpY7wOPlh+qmJmGbPN8RlnMcD5P2XZXnBzObg+7l3uO7aNBEggWgScEMzXV3ZQl3v9tbrU66988V/7zsOdjnYwRI+A7ZcWW7voEaDHJFBcAq7fWxB+/9jsafWPz/9e/WOz36t/0nHFeex6xXkU0g+eOaUHzcrdO+LtqvD/2SmjvQH1u7GDK6Xbto+C2cPIDyRAAiRAAjkQcEYwX+z51+qScUTxnII5h5FcxKy2X9Zt7YrYFFZNApEk4Pq91euNWeofmj2tDwhn+Yw4que9FszyxhpEs3kcOHNSp0FU59I+cHM5uD7uXe47to0ESCBaBJwQzNdWdFAXe/yNutDjr3V8MR5H7RztYIgeAdsvLbZ20SNAj0mguARcv7d6vjFLfanpU+ofmj7tTPz9Ls3SDprWr07Oqb3g5nJwfdy73HdsGwmQQLQIOCKYO6oL3f8mo+PqwiYKAjWT49KYOzKqI1OfKJijdfOIt7ZfWmztpFzGJEACdgRcv7d6zp+pvvTnp9SX/vw7p+LaLw5UELU935hZKW49a1LO7QU3l4Pr497lvmPbSIAEokXACcF8dXkHdaHbx9X5bn9jFV+aeF9WvfTh/iVW5dv64be7trxjVn4xU3EJ2H5psbUrbmtYOwlEj4Dr9xaE343P1VY3Pvc7HUM48zw9Dwrm6N3L9JgESIAEwkjAEcHcUZ3r+jfqfNePe4ecn4tfk3PYXHqpmu4LxEHpsBF7Sb8aF7PIE5Qu9uKDnEt+OU+VLnWEcaDQp+QEbL+s29olr4kpJEACQQRcv7d6zJ+hvtiktj4glOUzYp4n5wFuLgfXx73Lfce2kQAJRIuAG4J5WUd1rsvH1dkuH7eKL06ICWbEtvnO9/mS+svVs+qD/Uut67H1R+yuLuMMc7Run5i3tl9abO2iyIA+k0AxCbh+b/V4fYa6ofGT6otNnmScAQdwczm4Pu5d7ju2jQRIIFoEnBHMZzt/XJ3t/AllE1+c8HPdS4ht7L7CjMEAACAASURBVKXcq8s6ZZVP8qeLKZijdfOIt7ZfWmztpFzGJEACdgRcv7e6a8H8W3XDs79VNzRmbMsB3FwOro97l/uObSMBEogWAScE85WlHVV5p0+o8k4fV2fjcez8E77zWPqF8bEZ5gvjfx6YjnKC8p/tFZ9l3rc0MN22/mTlox0M0SNg+6XF1i56BOgxCRSXgOv3VvfXp6sv/OkJ9YVnf6tjCEaep+fRfd704g7MKq7d9XFfxfhYPAmQAAlYE3BEMHdSZzp+QpV3/ISO8TnV+flxsRnmC+N+bmVvlnd1zUAN9+zAf7euz8wvn4P8u7I0NoNt3Xs0DAUB2y8ttnahaBSdIIEIEXD93oLwg0D+/J8ej8f4nHjO9EQe4EPBHKGbmK6SAAmQQIgJOCGYLy/ppM50+Ft1usMnrOLzY2OCGXEm+VD+2QFf1915bdN46/ps/UI7GKJHwPbLuq1d9AjQYxIoLgHX7y0Iv881+o36fKPHK8cNk1wX+xJOp2Au7n3J2kmABEjAFQLOCObT7f9W2R7nx1bX/YfYNo9pd3XTeJ2/vP/Xs8pvlmV+pmCO5m1l+2Xd1i6aFOg1CRSPgOv3Vre50xSP7BgUb1RWfc2uj/uqJ8gaSIAESMCOgBOC+dKbndSpdp9Up9r9rTodj1Odn3sxJpg/OPqOur53aaXj/ORHUpZ3pl9slhnC2aY++JXKH0lHOxiiR8D2S4utXfQI0GMSKC4B3lvF5c/ai0OA47443FkrCZBA6RFwRjCfbPu36lTbT6qT3pH8/Ezfb6iPyvcn7e2/XDmrTnX9h5TlXX07NsuMsmJ1Jq/PNp2COWmXhDrB9kuLrV2oG0vnSCCEBHhvhbBT6FKVE+C4r3LErIAESIAENAE3BPPiTupkm0+qE20+mXN8dnRs9vnSm51Tlnemzzc0wMurBqW0y8SvS4s5wxzF+9L2S4utXRQZ0GcSKCYB3lvFpM+6i0WA475Y5FkvCZBAqRFwQjBfXNxZnWj9SXWi9d/lJf7g6Gb1Yfm+tOVd3T5bYTb6ZOd/yku9aAdD9AjYfmmxtYseAXpMAsUlwHuruPxZe3EIcNwXhztrJQESKD0CbgjmRZ3V8VZ/ZxyfND7/nTrWKvH8uO/cn35uWl09Es5N+4Mux58u+ctH3a/tIHTzUf/FRRTMUbwFbb+02NpFkQF9JoFiEuC9VUz6rLtYBDjui0We9ZIACZQaAWcE87EX/k7l8/iwfL/68My+tGVe27NczzIf7/hPaW3T+UfBHM3bz/ZLi61dNCnQaxIoHgHeW8Vjz5qLR4DjvnjsWTMJkEBpEXBCMF9Y2Fkda/n36mjLv8tbfG7qH/RIQJyq3DMjami7i4u6pLSz8Q/tYIgeAdsvLbZ20SNAj0mguAR4bxWXP2svDgGO++JwZ60kQAKlR8ARwdxFHW3x9/o4Eo8zPb+8YYK6tHKwMvPHZpn3e2WjTDNdzjHL/NGVsznVj7IuLOxiPQI/+uijBNu//OUvCkeqgHQz39WrV9WOHTsU4mTBtE9mk+31srIyNWbMGKvsU6ZMUb1797ayLbSR7ZcWW7tC+8/6SCDqBHhvRb0H6X82BDjus6HGPCRAAiSQOQEnBPP5BZ3VkeafUkea/72OD8dj2/MT/X/kkTve/ZtK8pe/EptlRpyq/NPjHtX5xU7y29Yv9ucX2AnmOXPmKPxH2b59e8/vGjVqKBwStm3bpqpVq6ZgK6F169bqpptu0qedO3fWZaAcHHXq1BEzLz5//rxOmz9/vnctnx8ee+wx9fTTT1sV2bx5c3Xvvfda2RbayPZLi61dof1nfSQQdQK8t6Leg/Q/GwIc99lQYx4SIAESyJyAI4K5izr8/KeyPi6tf8kjh89mWR+eOaCu7l6WcM1Ml8+w+/DM/rR2Yh8U2wrmBx98UAvfG2+8UX3wwQfa9yFDhmhxe+nSJX3er18/ff7UU095bbv99ttV48aN1TvvvKPThg4dqs6dO6fef/99tXr1as9OPlAwC4nUse2XFlu71LUxlQRIwE+A95afCM9LgQDHfSn0MttIAiQQBgJOCOZzb3RRh5t9Wh1q9il1uNmn4rHd+bGu39T9AKEswvlo12965YmIPTX0FynLL5/yjC6nfEq9jOo3/UU70oXDhw9rsbtq1Sodz5s3T2fZt2+fPl+4cKE+h6iGQIaoRjh+/LhOX7RokVqxYoX+jDhVSCWYMTOMsvEf9h133KE2b96si5o6dap66KGH9Oz3Lbfcou655x41ffp01aJFCy3yYbtlyxZtixnmO++8U9WuXVt96lOfUg0aNFBoB8KFCxcU0nH9gQceUHfffbc3w4yl3DfffLOuGzGWaxcz2H5psbUrZltYNwlEkQDvrSj2Gn3OlQDHfa4EmZ8ESIAE7Ag4I5gPNv2UOtT00yrT2BPJXb6pjnb5lqaGa1LO4TY3qY+unFNXdy1LWz5mmT84sz+tXTI/bQRz9+7dtRCGoxCatWrV8nr6tttuU1i6jFln/Ec6d+5cHWMGefLkyVp8Xrt2TT/H/Mgjj+i0hx9+WC/bDnr+OZVgXrp0qdqzZ486ceKEXgouS6uHDx+u65k1a5Y6ePCgXhaOZeDwBee33nqrnuWG0xDEENAQ7hDcEMVoE0LdunUVBPfatWt1Piw3lyXZmzZtUlhyXl5ertq1a6eFeJD/Hpgq/mD7pcXWrordZfEk4BwB3lvOdSkbZEGA494CEk1IgARIIA8E3BDM87uog3/+tDrw50/H409ZnR/pHBPIF9e95NlfWhdbnn2087e88kTIHi/7RcryT0+OzTKfnlzPKy/ml50/5+annmHGBlyYUe3QoYM6duyYmjhxoha9+IzQsWNHBdG8bt06fR3CGbPM2FgLgvaJJ55IGDKYpYYwxX+65vPPYpRKMJ85c0bPHGO2F8JXxCwEszwnjXLatGmjhbCUCUH8+OOP61PkE6GNC/ATM8rXr1/XZeA5awnmM8zYpOzNN99UgwYN0jPZ8F+Wpot9IWPbLy22doX0nXWRgAsEeG+50ItsQ6YEOO4zJUZ7EiABEsiOgBOC+ez8rurAc58OOD4TcK3C7uK6iZoahLPkN0X0gedi+Q+1+uf4LPNyzy5mX7n8D/DM8y6xq5wu9QTlPze/a8pehEjEf5AQzXLgXHaP3rhxo07Hc8pYFo2ApdAQylg+/corrwSWj029UI4sqxajZIL57NmzWthC8A4ePFhVr149I8Eswt0vmMeNG6fLhfiFv127VvCAYMYmZgiYHcdSbgjm+vXra98pmKXXGJNA6RGgcCi9PmeLlf6/jxxIgARIgASqnoAbgvn1rupAk8+o/U0+rfbHYzn3x5J+6IUvq48un1UX107U+cQO6RfXxmaZD3f6lleuiNmjvX+cYC/5JIZ4Rzg++IFAO6lf7M347OsVAjGo6yE0a9asmZCEna+xdBlLknHIc8UQkwgihvGFEkIX4ejRo2rlypXq1KlT6vTp06pZs2ZaqOLcDCKYIWT37t3rHcuWLdP/UW/fvl3hmWoI2ExmmE3BjCXZEPpY3o3nnevVq6ddgJjG8m0877xz5049A446IIzRlhEjRujnnLFEHecUzGbP8TMJlBYBCub89jc2g8QjNFX5WsH8epx9aQ0bNlR33XWXfsVi9qXkNydWXsGnI0eOpCyY4z4lHiaSAAmQQN4IOCOY9zX+jJJjv/EZ14LORZweg7D12R/t9WMNGDaS/0DLL+trFyCwffbmOezwzPOV95dn5A/qEZ+CehdLoPGf46RJkxKSt27dqq9DACNgdhl2Mlssotdccr1+/Xpv0yzYQrTOnDkzoVycYOMtpPsP5Ed5ch1CVwQzhKy5JLtt27aVlmSbghmz0/higLIg/CGOEbBJGfzCdSzTNuvo1KmTVzc2BIMNBXOl7uMFEigZAvgbYBvkbxf+7jBUEDh06JB+XEZ+dAVTHPg7jH0wXA34vwXtxA+3YQn4sRg+4YfqVAE2DCRAAiRAAlVPoCh/bW3/yNvalc/rqvY9+1m199nPWMX7W8Rml6/sWpHUHmmYgT7Q4iteuRDLCAc7fDtpPvhRHp8pPjrogZR2fn/RjkIGzC5jhjjbgA2/8LxxPgJ+EAgKuP7hhx9WSsIMyMWLFytdL8YF23Fqa1eMNrBOEogyAdt7S94yAHscLgvBTPoTmzLih0kwkR8oscoHPyrgHK8idDVQMLvas9FvFyYneJBBKY6BMN69zgjmvX/6rLI9yud1031xdOAvk+ZBGgJspdyD7W/T1y6smehdkzQz3t/8K94ss3k93edCC2bdGP6TMwHbL+u2djk7xAJIoMQI2N5bAwYM0KIQO/IjDzZKLPWAlUQyq/z888+H5ofIQvULBXOhSLOeTAmUolBim/kDAcZAGIMTgvnM3K5qT6PPqT2NPhsQ41rF9X3P/4ueOb7y/or49cR0s5xrh7ZoWzM/xDLCgXbfSZlfRPmh7ncn1G+WHyu3on60gyF6BGy/rNvaRY8APSaB4hKwvbfw1gDY7t69W8fYPNH/Sjo89oL32OM/7Z49e3qPhmBpMjZexB4QePQFbyTA7CveNID9HCTgM/ZiwLJambXF3hPYT0KC1IF6/Icp4rGKBntMoC74Df+xb8WVK1ekKP34DcqYNm2aWrhwoS4PAhhta9++vUq2ekcKkH0gsJGin4XYmDEY4FEatA314LGY1atXmyb60aFcGAofvMkBrPG4DurDxo941aAZWrZsqfB6RLDq0qWLtunWLfajOJ7BHjhwoN4kEvzApGnTpgk/CohgBjs8QiRjBBtnLliwwKxKP9OdrjzxHftv4K0RKB++g+/QoUMTysPJlClTdJ/BP/QzftSBnzjnkuxKuErqAsUjxWOpjoEw3uiOCOZuanfDz6rdDT+XNj4xob7uhyMDfpnSfm+zr3jC2ix3f9vYLPP5NRNT5re1M/0+Mzf2n3wYBwp9Sk4AX2xsgq2dTVm0IQESqCBgc2/h3e2wg5BDgIjFOUSZGSBYcV0OCCgRvnINseyxgM/yVgKU06tXLy+vLGmWfNhzAgGvzJNr/lj8gyiWZ1lFcIkfEF+yb8PSpUsTyoKIlXwoWzaANNtofq5Vq5bOP3LkSPNy4Gd5lSHKRR0QsuL/7NmzvTy5MgzKL7PgqG/Tpk1eXSJ4zTbj9YUIiMU/2SsD51huLkHyi53Z3+AtnG3L8/sORtjXQ8o3fZ8zZ453HWLZbAPsKZill0ozLlWxxHbzh4Iw3vF23/Tz7Dn+I7AJtnanX+umdjf4nNrV4HPx+LNJzz84vV99cPpA0vRYOZ9VIl6P9K+pdjVILO/86tgs856m/5KyPrHb1+a2tPWh3jOvUTDbjIuw2diOU1u7sLWP/pBA2AnY3Ft4JzzsJkyYoJszfvx4fd6oUaOE5onggZA8efKkTrt06ZInmp977jm9ISIS8Aw0ysQBGwTsbIwZbNlhGjFEMGyGDBmibfz/YEdqEcN4CwGCLB+H0JM3HMAfmW0eO3asthPBDFG2ePFir2i88g91Yvl5qiDl+WdT/XkwgyuiFbOiEiC0UQ9mRa9du6Yv58pQ8mPmWGbIsZcFdo9GXegbCSJ4IXQh2nft2qXfALFu3TrPL9mrQ16JiDLkmuR/9tlnvbrQl9JWzKgj2JYnvmMjTHlGHr7jbRKoF7P+CJcvX/b6/NVXX9XX8A827JSxQMHsYSnJDxSOFI6lOgbCeMPbKdc8e47/NGyCrR0E8/t//JzaZRyx889713B+fPwfdbWIg9Il/54/x5ZtX965IiG/pB/s+hNdzpnXugemww7l72vzH4HNPDm1ZWD9aAdD9AjYjlNbu+gRoMckUFwC6e4tiFaZ5RMBhhj5IE5E6KEVInggWM0gO/L7haUIq/3795vmCZ9FVHbo0CHhOk6uXr3qzVabs8HynLW5lBv2EKvwW2aiRTDLuVQgYt58a4GkmbGIs3S7REM4ol7MbpsBy7hlplnKyJVhsvz4YQE+4JAggnfDhg1ySccQprBDWWZ46qmn9PXly5fry5JffBdbmZ3GDysItuUl833MmDG6Xoh+BMw0wz/8IOIPMtNMwewnU1rnpSqW2G7+UBDGO73if50Cemf+Z5eqWlu703O6qffrf17trP85Hb+fJL5+Kja7nCxd8otwPdS3ZkJ5ko78ENPYRXv3c19VUp6ZDn9w/eQrLbUt7GN5zulY0s0Y7WCIHgHbcWprFz0C9JgEiksg3b0l747HM7Dl5eXeIWIJS2MlJBM8MkPoF8wiFg8cOCBF6JlevN8XS7Uxc4klwPAxSDDjeWekPfnkk15+fBAhLjOhkohnpGGPehGSCWa8mg926QSzzDDPmzdPqgiMR48erctr0KBBpXRpH56jRsiVYbL8KBttwiGz/9KHfsGL1xfCDuITNnLIDycyc5wsP/oN+UUw25aXzPfp06fr8kQwy/L2IJ4UzJWGWEleoHCkcCzVMRDGG94Rwdxd7Xzm8ymPY2Njs8uIU9nuavJVLYQhblPZHexbU/fn6Tnp6zbLEeFsXpPPKIshegTwpcom2NrZlEUbEiCBCgLp7i0RpbALOsxnWpMJHlvBLDPAqAfiTDZwwrlfMMusI5YTy5JuaZX4idf/mWHPnj26DSKEcxXM8gxzWVmZWU2lz5j9hk9Yku4PslRaxGWuDJPlR70yI37s2DHtRjLBix8r4C9mcNFG/yGzt8ny+wWzbXnJfPcL5hEjRmj/mjRp4sfpPcssPlYyiF9A+xjcJVCqYont5g8FYbyri/LX1vaPvK3dqdnd1M56X1Dv1ft80vjqwS3q+ukDSdMl/7GxsV/Pj5Q9kbI82MdmjM+q9xt/NW25Uv7lnSt1Pjk341OzKZjDeJOk88l2nNrapauP6SRAAokEUt1b5rOimPXFM8ty4Bx5cchzwskEj41gxgZRIpDNWWvsnIw6TMGMJcS4BgEYJIwgopHufweyzJbjGVmEXAVzp06ddD2YaZbnrhPpxs5kgyo8V+wP8AW+yvPXuTBE2cnynz9/XteDumRH72SCF8IedhCqqUKy/H7BbFteMt/9glmWuPuX0sNXzjCn6rHSSaNwpHAs1TEQxrvcEcHcXe34wxfUe8Zhnh/s/d+a/YkpL3g2ZjryyTmWbUNYy7mUGXQu5ULoBqVLXrP8y++tVDiC7CmYw3iLpPcJX8psgq2dTVm0IQESqCCQ6t4SoVKjRo2KDMYneTZZNtFKJnhsBLMp6MwZY7xOCD6KYD516pT3TDVeZxQU6tevr/NAuJlBnq1t0aKFvpyrYMaz3DJri7pMv6VebFqFZ7TRBhyHDh2SJLVz507vOtqFkAtDMz/eC20G2QgNr7WSkEzwjhs3TvsFWxHXkseMk+X3C2bb8pK1XcahLMk+ceKExw0blUkAf1k2HvRDitghTjXuTTt+jiaBUhVLbDd/KAjjHWv3TT/Pntv+kbe1O/lqd7Wj7g1qe90vBMaX3sPzxufUzmdvDkyXfEdejM8uv9ggpZ3YI7703koFkZ2qfr898gTZox0M0SNgO05t7aJHgB6TQHEJpLq3ZCktnsENCrJbtszYJhM8NoIZ5cuO2HjmFctuReDCRxHMeHczziFU8QyrefTt21e7aQpUPN+MXa/FB+SF4ELIVTCjDFkajnLx7DR+XMAydrx6Cz5CLCLIjDwEXY8ePVTXrl29Z63xfmgJuTKU/PAHm59hwy3ZrAvXVqxYIVXpZ5Nxzf8MMzZyk9l+lIH3J+NAf2AGN90z0H7BbFue+O7fNM4vmNEALMeG72CM923jBwIRy7hOwex1c0l+oHCkcCzVMRDGG94Zwby9zhfU9jo3KH+8v1dsdhliNCjdtL9+6oDCkc7OTD8yJi6yxzSwygexjMOsV8qjYA7jLZLeJ3yxsQm2djZl0YYESKCCQLJ7C8uskYZDBGZFrtgn2S0bNngllLwj2dyxGpaysZV/RliWz8qmX2vWrNE7SUu92JxLdlgWwSzPDYuNGYtwR50oSzYVExssnTaXaWO3Z6ThhwEzyKZfEI02AWXK0mqpCzGWhssGWXg3NISdmY7PaN/169e9anJlKKITs/9m+yEsZ86c6dWDD9jIDT6Y7zcWA7zeS9LFZ5SBZeWyBF/S/flFzGI5vQSb8pK1fdasWdpP/BAhAUv4cQ6fxD/MQOMHC5zLmBJ7fwwbBncJsH/d7Vu2LDmBsI77ovy1tYVha3diVnf17u9v0Md2X3xpR2x2+b1GXwtMj9l/QR0eHRe+oxsE2H0hafnIL0IbPvjrl/LFP08wJ/gZKx/tYIgeAdtxamsXPQL0mASKSyCM9xZmB/07XGdLCeWsWrVKHT16NNsirPPhOWYINSy1hqALCnguHAJ7y5Yt+rVYQTa5XBPBLLO0EP/79u1LubQ6VX34UQT+QvDmI+S7PHDGa8DwirFMQhjHfSb+0zY1AfZvaj5MdZNAWMe9I4K5h9r29A3q3ae/GI9v0PGedv+lR9OJWcHpYo+8109idvlgQn4zPah8SRexfXh0w7T5L+1YqXCY5eEzDvjJED0Ctje3rV30CNBjEiguAd5bxeWf79r9gjnf5btSHse9Kz0Z3A72bzAXXnWbQFjHvRuCeWYPte2pL6qtT92QEJevrFhKZTO8jk56ISG/v7xU5xDbtuEiBHOAvydmUjDbMgyTne3NbWsXprbRFxKIAgHeW1HoJXsfKZjtWHHc23GKqhX7N6o9R79zIRDWce+MYN76uy8q/7Gr7X8piFCb4+jEVpXy+8tLdZ5JXbANKus4BXMu91jR8tre3LZ2RWsIKyaBiBLgvRXRjkvi9tSpU/Uz2earuZKYlvRljnu3u5/963b/snXBBMI67p0QzMdn9FBbat+ottT+otoaj6N4jnYwRI+A7c1taxc9AvSYBIpLgPdWcfmz9uIQ4LgvDvdC1cr+LRRp1hMmAmEd984I5s1P3qi2PHmjQixH1M6Pz+gZpjFLXywJ2N7ctnaW1dKMBEggToD3FodCKRLguHe719m/bvcvWxdMIKzj3gnBfGx6D7X5t19S7/z2xkjHaAdD9AjY3ty2dtEjQI9JoLgEcG9hd2d/CLrmt6mqc+xsjXcHnz59ulIVf/nLX7Le8blSYSG9APZyhNFFvOcaO1PnMxw8eFDhtV8oF30cNP7Ma+Zn0w/J60/fsWOHLv/QoUPanP+nmNTc+8z+da9P2aL0BMI67h0RzD3VO098KX7caHzGteicH5vOGeb0t1L4LGxvblu78LWQHpFAuAng3sLRo0fij4433XSTMt+jm6wVY8eOTfvO22R5k11/9913tU979uypZNKiRQv9ruZKCY5cOH/+vPdeYfQL3qXcvXv3hHc1F7upeG/13XffnVc3nn/+eXXXXXfpMtHHaDteByYBr8bCNby2SxjNnz9fkr34jjvu8PjhHc14J7S8ouzOO+9UzZs317Yoi8FdAuxfd/uWLUtOIKzjvih/bW1h2NodndZTQWy6ECcfQkwJKwHbcWprF9Z20i8SCCsB3FtyYBZOgq1ghih58803JVteYgrmj6kZM2ZooYfNu9A/06dPzwvbfBQCAQrhmq9w9uxZ3cZp06bpIkUw48eCK1eu6GuZCGaI7yNHjmjBffvtt6tnn31Wl4EN0cDy3LlzOs6X/ywnfATQzwwkUGoEwjrui3I32sKwtSu1wcT2houA7Ti1tQtX6+gNCYSfAO4tHDVr1tQzfB9++KF22hTMZWVl6uabb9Z2iKdMmaKOHTumqlev7l3DcloIH8zwLVq0yGs4BMuSJUv0+T333KN69+6tEDdu3FitWLFCL5NF/RDeEEoI2QjmkydP6nJRDsp78MEH1cWLFxPqrVOnjq4HfsMeYcuWLeqJJ57Q1yGsHnjgASU7TKdqy9NPP61uvPFGXRfsNm/erMs7ceKEbgf8eOSRRxTs2rdvr9PAp1evXnrWGLyGDRumr5v/yOzpsmXLvMs1atRQjz/+uNq9e7cCT/QH4smTJ2vx17dvX3XrrbdqlgMHDvTajfajDswGo9+GDh2qlzuPHz9e3Xvvvdr/Zs2aeaK0bdu22g7tEpEZdK1Dhw4K+RAwY9u0aVPVsmVLnRd+ycxwKhZe45TSP7igz44fP64vYxxgjMCPVq1a6WuZCOZOnTp5xWPlBPoCAWMW9WA8ImZwlwD7192+ZcuSEwjruC/KX1tbGLZ2ybEzhQSqnoDtOLW1q3qPWQMJuEUA9xYOzBpCWAwaNEg30BTMmzZtUtu2bVPl5eWqXbt2CmnXrl1TuI68EGB49hRiG+fmbCjOZ8+erctE+RCXo0ePVtu3b1cQQevWrVNnzpxRc+fO1XmxDDsbwXzp0iUFkXn06FGFMlAX/ELAZ4hXCDnMosOn4cOH6zQsA4ZgxozpmjVrvPYgMVVbli5dquuBKISghTBGgMCEGH7nnXfUrl27dHslDWIZ4hV1wReUDxFnBhHMEO14hnvBggXaDsvjhQvaghlozKJCLGMmFuUtX75c940IcYhn9BXK2rt3r/YXbQSPjRs36j6HkEZZ0nbUgX4Gp6Br8LVu3bqaJz4/9thjur2oA88hgyeuIaRioQ3i/+AHALCQAMH88MMPe2MCvmYimCHeMaaQD+OtXr16umg834x6wMesT+pl7A4B9q87fcmW2BMI67iv+Otu35acLW1h2Nrl7BALIIEcCNiOU1u7HFxhVhIoSQK4t+T+GjNmjP4MwWkK5qtXr+pZQIhpzJbC/oMPPlAQqfhsLsnGeSrBLCIWsCFgILqHDBmiJk6cqMuC6BVhmOkzzNiMCm2AIIdolZlGUzyjXsyCdu7cWQtG+Lt69Wqv703bVG2BIEM7IfYgEDFjiwDx2rNnxZ4amBkWwQyfMIONunGgLohVM4hgRt1Ih+DDrDx+oAjigrZ07NjRKwJiU54FRlqbNm28NHzAbDB8FB9gg9l+iGT0OfJOmDBB1xd0DWX4BbO0D2lYDm3DwnQKdbkGkAAAIABJREFUAhc+SRDBjPPatWvrvsQPMmBi+wwz2OHHAMyAyzPMKA/XMGuNshjcJcD+dbdv2bLkBMI67ovy19YWhq1dcuxMIYGqJ2A7Tm3tqt5j1kACbhHAvSX3FwQsZkurVauWIJgxo4kNkyCY69evr+3zIZghLLHsFstm+/Tpo8vNVjDLjG3r1q21nxBGyQQzRCgEI2Zd0faFCxd6nWojmLG0GnYQyoMHD9ZL00UkQnRiZlWCKZghCiEAIZLlSDbDbC7JlrKCBDPK7Natm5joutE+BKQJAzFo2LCh/sFA6ke8detWnYwfAdAeWd6NnaaDrqUSzBCoNizEH8Qy44vxh2AKZsyyY4xg13T0lY1g9rdZ6pIZ5hEjRuiy5Dpj9wjI3zT3WsYWkUByAmEd96EXzADHgwzCPgaS3/oVKWH9I1DhIT+RQDQJyN8H8R6ztHINy4AhjHEOkXHhwgW9YzPOcf369es6DYIHwgpLsiFuMMt4+fJlhV2MYWsuyTZnmCHOITpRFkQrbCEUZTYRIlhElPgHMYWZWohdOSA6IbpRN3zCcmhTLJoiGOWIYMZniEPMqs6cOVN16dJF+yA+JmuLiHMsK8fsJX5QEJHYqFEj7Qd2Dx85cqT+4UFmYPHsL354QPsgRrFkGxtQmUFmmG0FM3wGD5SFZdRod79+/XSRqA/na9eu1c81w1d5fhdtgA9Ywo7r6D+ZiUXd6AsIdP81nNsK5lQszDaLT/LjgSmYYYe+gT84TME8btw4bwxgLGAlBPo2mWDGEnYZY4gZ3CXA/nW3b9my5ATCOu6L8tc2rDCSdx9TSCB3Ahz3uTNkCSQQRAD3lv/+khk/ea0UBIjYYUkxPkPkIsjyVlyDIMFsp9hCEEN0yiZafuGK55ZxDfZ4thefIdYg5CBicV0Em/iO5btSvsTY4AtCCuIQ1zDLCyEswslfrymYsewboh32eAUR8mNTM4RkbZGZeKkfG1SJYMauzljijPIgpCFmMSuPgGd8sQRa8mEWHKLUDPhRAunYEM0fINCRZi5VB3O0R8oEN9nQDOyEI9LxQwb6De0Ve7BBP2NZPfoK13ENs+RB1+BTKsEMsWvDwmwbfiRAndjEDAF9jGeYzSA+g6EwkjZIvGHDBs0CPyIEBbQT9Uj+IBtec4MAxgQDCZQagbCO+6LcjWGFUWqDku0tLAGO+8LyZm2lQ8D23sJMqOw67aeD52vNmWAso8WMpU3AjLAIPL890jINmDE1fckkP2amwUN2a0beVG3Bhl+pfIQ4hUDDDKkZMJOKvPkMKNP02ywbfYfnzc2Aa1hN4Gd16NAh/fyyaRt0zUy3+ZyMheTFZnL4MaGqAtqJHy9kx3LbcV9V/rDcqiXA/q1aviw9nATCOu4pmMM5XuiVgwTC+kfAQdRsUokRKPV7C69PwpJpzApjhvWhhx7KaQRg0zHMLONZXghACGYsTy/FkAkL/ICAV5vh1VlVEbAMHuXLjzOlPu6rgnGYymT/hqk36EuhCIR13FMwF2oEsJ6SJxDWPwIl3zEEEHkCpX5v7dy5Uz9rjKXlQc9MZ9rBEH5Y0o2lya+++qp+rjbTMlyxDzOLUh/3royxZO1g/yYjw+suEwjruKdgdnnUsW2hIhDWPwKhgkRnSCALAry3soDGLJEnwHEf+S5M2QDb/oUdDzKIwhhIOeDjibbj3qasfNpQMOeTJssigRQEwvpHIIXLTCKBSBDgvRWJbqKTeSbAcZ9noCErzrZ/be1C1jy6U2IEbMeprV2h8VEwF5o46ytZAmH9I1CyHcKGO0OgEPfWxo0brTcBKyZY7CqN9/1ioy9/wCuzsHN1qk2+/HlSnWMHauxaXagQpj4Ay969e+sdvLEJF54rxoZrmQbkqVOnTqWNy2zKKcS4t/GDNlVDwLZ/be2qxkuWSgJ2BGzHqa2dXa35s6Jgzh9LlkQCKQmE9Y9ASqeZSAIRIFDV9xZ2J8bGV7I7cZiR4BVP4GG+tkn8lR20IXTzEYYPH65ff5WPstKVURV9gB3T8b5niN9Mw+rVqzXnPn366Oe88cMBxkimAa8lQ39l40NVj/tM20L7/BKw7V9bu/x6x9JIIDMCtuPU1i6z2nO3pmDOnSFLIAErAmH9I2DlPI1IIMQECnFv4f3BZ8+eDTGFmGupBDPeDY1ZWnn/dK6NKaRghq/57oNt27ZpsYpXimUaBgwYoN+ZLfnwCrIdO3bIqXVMwWyNquQMbf+u2dqVHEA2OFQEbMeprV2hGxdqwQxoPMggCmPA5sYN6x8BG99pQwJhJpDNvXXHHXeoRYsWec3C65OWLFmiRfETTzyhZwtvueUWhdcKITzwwAN652h8vueee/RyXCylxaxi9erVvVf9bNmyRUl+vO4J+ebMmePVIx/wyqaGDRuqBg0a6FdB4bVQM2bM0K+HQpl4fZC8dxhpeF0U2gm/N2/erIvBDOmwYcPU3XffrW6++WY1dOhQJYIZs+EoA2U988wz2v7IkSP6Pb54PRFCqnbApmnTprpetG/x4sU6j/mPXzAny1NWVqb9g//wEztwI0ydOlU9/vjjqlmzZurWW29VyJ/KJ7MPwA/+tWzZUpeJ/sMO4QjY2Ro7fKPteD0W+PlXB2CWHXngE95tDDsEvBIKs8XgbdanE+P/jBgxwmsP8srx6KOPagu0q1atWqpv377qzjvv1LPwI0eO9IqALzfddJPOhzzwgTPMHh5+iBPAuLAJtnYTdqxXL723QbkQ23ChTbgI2I5TW7tCt87ubsyzV7YwbO3y7B6LI4GMCNiOU1u7jCqnMQmQgBYcmWLA/Th9+nQvG85nz56tevXqpcUQZpMhMA8cOKBtIHAg/BBEiEGgYVYReSEeEe666y4tmJFvzZo1Om38+PE6zfznscce0+J36dKlevk0yqxRo4YWfRDEKHPmzJk6i9hACMIGAhBh4MCBWnhBkO/du1eXYwpmzMhOnjxZl4Xr+/fv158h6hFStQNiEe91xrPQEH4QtP7gF8zJ8mzatElhNre8vFy1a9dO+4wl1siPdkIwz58/X/9AkMonsw/AD0IVbT948KDmjmsIbdu21WlYgr5r1y7NWZhJG86dO6d/DEH9W7du1WyQhh8Z8B5rkx18N8Px48e1IIf4h8jH8dxzz+k6YSftGjdunB4/4Ih2oc0TJ07UbcbruvDcc+vWrfU5BbNJmJ9BAGPTJtjaQSjXmDlU1Zg1TN0fj6N4jnYwRI+A7Ti1tSs0Abu7Mc9e2cKwtcuzeyyOBDIiYDtObe0yqpzGJEAC1l8sTVS4H4MEM0Qw0iC+zFlVU6xB/JgiGDOVnTt3VthwC3nxfKsEv61cR/mmiIPQNmdBMcMps5JY7gtfIdiR795779XFoN42bdpIkToWwSzPMEP4w6fly5cHCuagdkDIIQ9mv9EuzOTiHILdDKZgTpXn6tWrCjO6gwYN0m1EWVgWbuaXcv28hC3SzT7w88OssHDByoCePXtKkXoW22QtCQsWLNDtkiXZ0gb8QCEBM/oQ+f6AHxFQjwTUBwGP4G+XuewafoifsDXTpCzbGBwZ3CVg27+2duO3r1P3zxiq7p9Rpo/qOpZzf1ymwpqOdjBEj4DtOLW1KzSBovy1tYVha1doaKyPBEwCtuPU1s4sm59JgATSE8jm3kKeIMGM2jBjiFlKiDfMDiKYYs0v6iCqICwxy4tyFy5c6Dntt5UEv+ALEsyjRo3SS8RRBuwHDx6sl3+L4IJg69SpkxSpY79gxkwqfLIRzNIOEf4QgVgmLof/2WdTGKbKg+XOWJoMwVy/fn3tj61gFp/QOLMP/PywRFu4wA79JwHLvm0EM1YUgJUs7UZ+9AvK9odsBTOW61erVs0rTgQzni/PNMBXBncJ2PavrR2E5s+nDVHVp5dVjqcnuS72IUqnYI7mmLcdp7Z2haZQlL+2tjBs7QoNjfWRgEnAdpza2pll8zMJkEB6AtncW5jBxazk5cuX9XJglIEl2VhKjRldBIhRmUU0xZpfBJuiDkuXIbKwnLpLly5agJmzuNIav+BLJphlxhtLhCFKIT5FGHbo0EH7t3btWoXnmZGeD8EMH/EsMZ59xqwrZmDluWnxHzGe7wYL2QwtKA+EMdjiud8LFy6o7t27V7lgbtSokX4GeezYsXqWHn0XJJhlp2u0TfocYrZ27dr6HLPi8H3Dhg1ms/XnbAUzfmRAmZjdPnbsmB6DOOeS7EqIS/4CxoVNsLUb9+5a9fNpg1W1qYMjHaMdDNEjYDtObe0KTcDubsyzV7YwbO3WTL+q1k6/plyI84yaxRWAgO04tbUrgMusggScIpDNvdWtWzctXJAXzwVDQON5WDzDjGsQgrgmzxFDdGFTLYRUghlLoSGGIZybNGmiy5JNrkzoNoJ59OjR+rlX+AefcECUimCGQIbQljT8AABhjXNZkn3+/Hl9bs4w45nddO2AWET7pWwsjfYH1AEb+VEhWR788CDl4DlnfIaQhogGVzOkYmv2gZ8fNvkSLleuXNErA9AH+IEBS6Uxs+0PEKnIA3+kfStXrkxod7169fzZ9Ln/uW6MG1mS7W/XvHnzdB2oD8eTTz7p8cAz06ifgjkQc0lfxLiwCbZ247atUfe9MlBVmzooFr8Si9u+NVeN3bZGIb0q4iGblqv/mTlcVYvX568/0/Nx2yiYbcZF2Gxsx6mtXaHbZ3c35tkrWxi2dhDKAx8/F/kD7WCIHgHbcWprFz0C9JgEiksg23sLG1rJzKLZAogXzDRjk6Zcgrz3GJtE5Rrw/PD169cDi8Gya9lRO9Agy4vggI3CMHudLMDGFHvJ8sDHVOUkKz/X6xDmEOHyw0dQeRDYZpA24MeGqgqYbffXm2ld2Y77TOuhfXEI2Pavrd3YrWvUz6YMVD+bMsA76r4xqSCNW3l4t/rZy6g3sf6Ec8t0tIMhegRsx6mtXaEJOCGYV0+7ogY8flb1/015pGO0gyF6BGxvblu76BGgxyRQXAJhurfwKiks/8Wzz5h9xY7LDIUlgKXimFnGs8eYOYZgxtJ710KYxr1rbMPQHtv+tbUbu3W1undyf3XvywO8uPHiabqpiM3rfrtczjedOKRw5Kt8tIMhegRsx6mtXaEJOCOY+z12RvV/7IwKjsuTXBf7cKRTMBd6+OenPtub29YuP16xFBIoHQJhurd27typn5tt1aqV3jwq11nq0unF/LUUs/FYBo9l2nh9E3bpdjGEady7yLfYbbLtX1u7F7esUv81qV/C0XjRVN1MxP60fJ1vOn5I4chXeWgHQ/QI2I5TW7tCE3BCMK+aeln1e+y06vt/p7UwjsXRO0c7GKJHwPbmtrWLHgF6TALFJcB7q7j8WXtxCHDcF4d7oWq17V9buxc3r1I/famPumdiXy/+08IpujmIcR3xmM2r1JjNb2UUo9xHZ43y8nVdNd+rZ9PxgwqHWa/fj0zOKZgLNQLzW4/tOLW1y6936UtzRDBfUn0ePaX6/N+pKokHP31avTn2onpr6iX11iuXdDz4qdN5r4+COf2ADaOF7c1taxfGNtInEggzAd5bYe4d+lZVBDjuq4psOMq17V9bO4jgn07oo346obc+fjKht/rTgrhgXjBF4RxiOZuAMhvFy0J+CGSUh/pEMMu5Wb/fH5tztIMhegRsx6mtXaEJOCGYIWJ7P3pS9f7fE1UST2lfXqlfIKDzXR/awRA9ArY3t61d9AjQYxIoLoFU9xY2blqzJrtNYrD504oVK5JutiWtxm7VTz31lMImYmEKWJbcu3fvnFwqKyvTr4/KtJBcuKeqKxVr2/5KVb6kYbMzvI87zCHVuA+z3/TNjoBt/9rajX7nLXX3uF7qJ+N7e3Gj+S9rZxCb1xPtenn2idelnNTpbx87qHDkq3y0gyF6BGzHqa1doQk4Ipgvql7/e1z1+t8TVRK/HBfMiFEPwluvoM781rfyleQ7kRZ6YLA+ewK2N7etnX3NtCQBEgCBVPfW4MGDdXo2mz7JLtd4XVKq4H/3sd92x44d+pla//WqPsemV/KqpWzr8r++KVk5/jbmwj1ZHbieirVtf6UqX9KwWdvdd98tp9Yx3v2MHdZzCbZlpBr3udTPvOEgYNu/tnajN61UPx7XU/14bE8vbjh/sm4sYvO63y6X87ePHVA48lU+2sEQPQK249TWrtAEnBDMK6dcUD0fOa56PHIsFteKx3k6n9TujO6XRS+eV5PjnyFu810f2sEQPQK2N7etXfQI0GMSKC6BVPcWXmW0efPmrBz86KOP1MaNG/U7g1MVkErEId+wYcOyEl+p6rRJK6Rg9rcxF+6p2paKtW1/pSpf0jCTnY3wxY7c6X5gkTqSxbZlpBr3ycrm9egQsO1fW7tRm1aou17srn48toeO73qxh2rweuy1UojlHHaj3jaO+HnnFXO9/EHp3rVNK1TnFa/p8lCfCGaU768/m/NRFMzRGcSGp7bj1NbOKLogH50QzCumXFDdax1VPYwjm/MXm55SKEuOuYPO6jIntau8zA7iOdf6/PlRL0P0CNje3LZ20SNAj0mguARS3Vtz5871xOrUqVNVrVq1VN++fdWdd96pbrrpJr2jNbw/e/aseuKJJ/QriG655Ra9DPnIkSPqtttu85bmbtmyxbPB66MeeOABNWfOHG/Ws3379qpmzZq6jGeeeUZDGThwoK4HPqIsLJHetGmTqlGjhp75vuOOO9T69esTAJ48eVLdc889uhzke/DBB713GEMEN23aVLVs2VLdfPPN+rVJq1bFnjvEu30xIwzBBd8wQxo0wwwOjz/+uGrWrJm69dZbdfuWLVumryHvk08+qXbv3q19MmeYsTwdbYBPsMMu1AhBbTS5w2bixImqevXq+lVb2EEcS54RUvVJECcRzEGszf6C/3ilFPoaPuPzhAkTdJ34p23btpofXv2FvvSHDh06aD64noq55Dt27JhuH9igX1AnxhSOXr16KYwpXMMPC3ifNvq0U6dOOvvSpUsVxgHaC0b+MqQOfww7BncJ2Pavrd3It1eoH43prn40plvsGN1N/XFeTDD/cd5E9aPR3bRQTkZ049EDXv5kNnJd245GPd0VPpvnZv1+f2zOIcwZokfAdpza2hWaQFH+2trCsLVb8fJ51f3XR1S3Xx/OKT6w7Vol/lLupLan1MS2p5TEcj2f8fKXz1eqnxfCT8B2nMJuw4YN4W8QPSSBiBFIdQ9CqEHcIQwfPlyLkXHjxunZQ7wrGWl49RNEDYQOBA6EF2YXIepQNoQywl133aUFM9LwXDTSxo8fnyCYt2/friZPnqzTIO6OHz+uIJ4hiPBMLF55BDGLZ54vXbqkdu3apc6cia1i0pUopa9DwB49elTt2bNH+4h6ECBgIbwg1A8ePKh9wjWEunXramG2du1anQZRHiSYhQME8/z58/Wz1+AAVvAF5YGN1If3SiPs27dPrVu3TttAEKP98C+ojSZ3/CAA2+nTp6tt27bp9j/66KO6TPElqE+COJmC2c/a7C+xww8Ue/fuVSNGjNA+QEhL38GmvLxcYTm5P4Al3uWMkIq55IMIhuCVMYG+xjPVGFf44QJjBj9sIB3ievXq1frztGnT9A8qQ4YM0UI6qAypwx+jLAZ3Cdj2r63dyI3L1Z2juqofju6qfqjjbuqPc1/SABHjuj89Zif22aVvPLpf4chX+WgHQ/QI2I5TW7tCEyjKX1tbGLZ2EJpdHz6kuj58OKd4/7Zrav+2q7ocEa/5KNfWL6mz0IOA9eVGwHacwg5fmhlIgATySyDVPWgKN4gzzCpLENEHYSNiBuJo8eLF2sQUYFiii3ogdCRAZJqCGeIRAaIbtsuXx77YtW7d2pvlRvqgQYN0OgQrRF9QQN1jxoxRo0eP1gJZZiPhnwhY5Hv++ec9UYy2de7c2Ssu2ZJsP4fZs2drf5AXB2bhMfOMYNaHHxYg6CDuwBVthLBH8LfR5I52YsZcwrx583Tec+fO6R8xkvVJECcRwkGszf7y26FuzCYPGDBAi2TUiR9AMOt87VrlH8v9gjkZc2kTYvwAAibmkmz8uAHhL2wxZmbMmKGz9ezZU9uDt4SgMiTNH6MuBncJ2Pavrd2IDcvUnSO7JBz1X4utukDsT8vXuQjmfJWHdjBEj4DtOLW1KzSBovy1tYVha7ds8jnV5VeHVJdfHdSCGXGXh33nFun7t15Vx/ZeVxNan1DvLI7tWA3BrMv71SHV54nD6qXWJ3X6hNYn1ajnjlXUZ1F+Ov/QDoboEbAdp7DDrFL9+vWj10h6TAIhJpDqHjSFm18omoIZzcOsIJbqQtQ0btw4YYYZs5SoZ+HChR6JZIIZQhC2yQQzCoDwrlOnjrbDLKMZRLxDhEI0YuY7mWA2RTEEYdeuXb2ikFatWjXvXD74OWBZNPyFHxBzOJYsWaLNTcEMgYc6evToofr06aPz2AjmRo0a6aXGUj8Yoj7MZvt98feJn5NfCJus0wlmiGRsRoaAuvEZPwxA1OL5ZzOkEswmczNPkNjFUuzatWt7XMEWM8wIHTt21Bwg3D/44AN9LagMsw7zMxgyuEvAtn9t7SA0fzC8k/rPEZ29uN7s2MoVxLjeYcmrasORfVbH5C1rdTmIJQ/ym+Wjvo1H9qsNR/ZXuu63sz2nYI7mmLcdp7Z2haZQlL+2tjBs7SA0Oz90oPLxq4Brpp0v/b01lxP4X730UUKZ/nQYB9YrdfjKr2TrS6dgTsAfmRPbcQo7LNv79re/ndVrWiIDhI6SQIEJpLoHbQUzlszK0miIUwgdU4ChSRBXEDczZ85UXbp00WInaIbZFHHIB6EJ0YvXTiENy7CxazdmtiFoGzRokEAMghTCFH8vsPMzfLERzBC38BFLyHfu3Kmfk062JNuc1cWzz2A4cuRI7Rd8lFcq4Xlm7Bh95coVXR7qgLgT0SuC2d9GkzuefUb5K1euVKdOnVJYjo1neBFSCeYgTpkKZvzggGfC0U/wQfoZKwYQ4D+uy7m+GF/ebi7JNmeYkwlm9BfKwnPKGEvoXzwLjeflwROiHG0CX7CALX5UQV/jmWyEoDLEJ3+M/AzuErDtX1u74euXqh8M66TuGNbROyoE8zh9bcR6+9lbiGSUh1gC8pvlSzpscN1fv5z/IO6TnEsZcm6mox0M0SNgO05t7QpNoCh/bW1h2NotnXxWdXpov+r4P/vi8f4kcer0nr85oMa3Pq7GtTqu44F1DyeUs2/rFd0/SH9ncewVULF6pb7U5afzb+mks4Xuf9aXBwK241Ts8EXpE5/4hNq6dWseamcRJEACcm8FkTCFG55jNYWiLA2GsMGzpigHs8YQMBDFIpjlXsUyYBGlTZo00fZ41zGWVSOvLBPGO4hxLjPMEEsQvbiGJdQQoPiMA7ObIk7Ff4g6sYe/EMHJBDM23hJRjGeJ8dgHykU7sAxa0qRsxH4OuNavXz/PJ+THDDsCOOC8YcOGCrO/KBfneC4Xn0Uw+9tocgdfzLIiHw7wxbPQCH5fzD4J4pSKtdlfIqyx3Fl8btOmja4TS6bhA3xBGlYV+EOqGWaTuT8fNjSTduJZeDxnjg3H5Bp+OMEz1OjX7t276+wLFizQ6bJ5m78Mfx1yjjIZ3CVg27+2dhCa3y/roO4Y2sGL/zBrrAaI2LzutwvTOQVzNMe87Ti1tSs0haL8tbWFYWu3dFK56vDgPi2YJe7oO5frEpvp4144pl4feVqhnE2LLqh9W654x9E91xQEOfKJYIbwFXGL68MbH9H5UU6Pxw5oP8zyYS/1ShyUjvoZokfAdpyadvhyms07PqNHhx6TQNUTMO+tXGqDsINYxbO66YK88xci1TZcvXrVM8UMMwRVqoBNv2x88Zchs5v+6+nO4R9EvywPFnvzHDOgmLFNFsw2+m0wswq+/uXPfjvz3IaTaS+fRTCjPSgDhz8cOnQo8Pllv12m53gm2t9vWIaNDd9sQ1AZ/rz5Gvf+cnkeDgK2/WtrN2zdEvW9svbqe0Pae3HdmTHBXHfW2ITrnh1sDXu5Dvtha5dUHOvin/2x2Pivy3mKdPim6/bVj3YwRI+A7Ti1tSs0AScE8xItmPeq9v+9R3V40C4e+8JRtX1V7DllP3QtmLfGRfPWK2pm/xO63B2rK9ujPtRvBojsl7scz8ifoHLMMvk5vARsb26/3W9+8xuFWSoGEiCB3Aj4763cSkueG68fwtJczL5ihhIzoAzhJGAK5nB6mLtXhRr3uXvKErIhYNu/tnbD1r6pbh/cVn1vcDsd3z64naozc4x2DTHO/elB5+0WzcymOVnl+b+XyxL8hT9oRzYBP9T5f8jKppxC5oG/UfM5GR/bcWprl6yeqrruhmCeeEa1q7lbtf/v3aqdHDWNz7hmpPf7fez9j2ePf6BWv3pWC+KhfzqYNn//3+9XSyadiR0Tz+h8Uh/yv9zlmE7Ds88IuCbpZv36WoB/SyYmvlqkqjqd5eaXgO3N7bfDc4NYdjlpUuw9iPn1iqWRQOkQ8N9bVdVyPBeM53yxZBbLZ135IlNVvIpZLmbZsZIHO5a7Ggo17l3lF/Z22favrd3QNW+q7w5qU3EMbKMenVwWiGHo2jfVdwfCtm2CPfK/uSe2s/8DY/sEpnt1JMlvk15nxmjtV50ZYyrVj3ZkGrC6A5yitrIPj39gDwQXgu04tbUrNBMnBPObE0+rdjV3qba/3KXaxuNU513/d48SUXt091W1auZZNW/4STWm+WE1psVhXU6q/KhH0sv+dFDnQ36Ug/IQUD7qsfFHykM7GKJHwPbmDrJbtGiR+sxnPqM3goley+kxCYSDQNC9FQ7P6AUJVB0BjvuqYxuGkm3719Zu6JrF6jsDWqv/GNg6IW6zcLoqW7NYIR3x+kN7dfNNu9YLpqtZ725UPx7WOTA9qFzJf/fwzuqpaaPU09NHJdQr6UHx09NighmxPx1+ZhrwOjnsGwBWQe9dz7S8QtlTMBeKdPrirssFAAAgAElEQVR6nBHMbX75vsrkmDci+TNYQdiuXEx85USQjXltYqcjGfkD3998iYLZZBiVz7b/WSWz69atm7rvvvui0lz6SQKhI5Ds3gqdo3SIBPJIgOM+jzBDWJRt/9rala1epL7Tv5W6rX8rHeNz0HnZ6pgglfRaE2OvYnvv5FH146Gd1Lq4oJZ02/KA+Olpo9LWj/Jgl8we7cgkYCUQVvNh93pshAgRKgE73mNDQ7ypAI/Z4JEbvPoNMTYErFmzpn6/OuxxTTYLxOaKmzdv1sVgg0Bs3igHNvnD6+F2796tN3lEHryLHRtESoAfvXv31q8WRD3Vq1cP3BsimWBGm7DzPzZ1RPl41z3eZNCuXTv18MMP630oMKuOtzrMnz9fqi1qbDtObe0K3RgnBPPil06p1g/sVK0eeC8hbu0796fv3XJZQQj3emqPGtxwvxrV/KCa3veYQnmLXzqt4+2rLirYyYFzM33u8BM63+jmB9XghrGt9d9ddUH7ka5+fzrKZYgeAdubO5Ud/sCZf8SjR4Eek0DxCKS6t4rnFWsmgaolwHFftXyLXbpt/9raDVm1SH27b0t1W78XUsYiSGFXa8JAdf7qFbXjxBF1V1lHnW/doT0aTapyZm7boPNtPxHb2HDdwT3qwtUrXlkooPUb05L68dQrI3QdiP31oB2ZBHmvPV7rN3bsWC2EsakeAt56APG7dOlSveEhxGuNGjX0IzcQxGCLNwUgiA2EKGwgoBHwSjq8JQAbQWIXfOxtgeelIbbxGTv7T548WZe1bds2nQf14LV18A0z3qgHr9jzh2SCGTvto4yNGzfq+lEvhD4eRcFMOl5Th1f3yavx/OUW49x2nNraFboNTgjmRS+dUi/8Yodq9Yv3VCsd77A67/W72E2/Z/MlK/t05R/ZfTUmwH+3O6vy0I58B/wK5d+R1H8eVKd/cwT8QfC/pzIoH67hD5F/51i8xgOvU3Ex2N7cqeyw6+yXv/xlNX36dBcRsU0kUKUEUt1bVVoxCyeBIhLguC8i/AJUbdu/tnZD3lqovt2nhfqWcQSdj9+4Urfu1+MHaIF7+Fy5+uHg9jof7CF+EVBOUP7fTYmJXeSDSB6yaqHOj+uztm1Qch1xUH6UK2U8NWVEJX9RXiahTp06egYXO9WLOBURDMEswhdlYkZW3omOc8zeYt8KBIhRfEcrKyvTQtt8ZR/esABxivyYXcZ3OvQLRLYECHPMACNA7GKGWAJmpTt37iynXpxMMDdt2lTPmiMPDuSXVwHKDwQQzmHaw8F2nNraeZAK9MENwTzhpBaoLWtsVy/8YruKxTuSxInpIlJfG3Y8bp+YDiGeWF5wOvIjVJTjzyfnwflRz6IJmS0Ttxkj8v5HvM4DYfXq1fomxg0lAUIWM5wSYIsBO27cOP1sLZaZ4BwH/nhs2rRJTBNi/BqHDRVM2y5dumgb/HKGm9fFgPbahHR2eMfpl770Jf3eTpvyaEMCJBAjkO7eIicScJEAx72LvVrRJtv+tbUb/NZC9c1ezdW3ejdPGYsgxswyBO+vx/VPsJf0VOWgFZhlRn0/HNROdV08WzWcOc4rB2VLelA5tV+OzbYi9qejHbZBvs/iuytmYXFArGKJNIKNYB41apQWnsgH+8GDB2sBbgpmiNVbb71Vi2qUi1cGol/M79oQ01gCjuAXzBDTmQhmLCPH93vMKsuxdetWXfaSJUt03agDr84LS7Adp7Z2hW6X3Tf9PHtlC8PWbuGEk6rF/e96R0vjs3ldPvvTj+y+oq5c/FC1f/g9XYY/XfJJ7E/v8eT7Oj9mqmHjT5d8EidLRzvyGbBUAwzxhwI3PALep4mbqFOnTgnnsMPzFghz5szR+TBLXKtWLf1cBpab4Nc13PxB75LE7DPKgD0ENX7JW7lypVqwYIEuk4JZaT4aRop/8MvmL3/5yxQWTCIBEvATsP2/wp+P5yQQZQIc91HuvfS+2/avrd3glQvUrT2fDz56VVwft2GFdg6i9ldj+8fsjfS1B2LfFRPKiqf/54B2auz65Tp/y3lTVLXh3dT5qxXvQEde5EN86OwZ9eTkYZXKRzquIwSlD1oZ+16pDdL8g++++M5rvh8e30vB7ODBg9aCWWZtsbwa33cxmyyCuVevXroOLMnG92QcWN1ZrVo1Vbt2bX2OVZaoc8OGDdrjTAQzJq327t3rHfh+LaIYfmFF6NGjR7Vfp06d8mbFsSQbPmL2OwzBdpza2hW6TU4I5gXjT6jm929TzatvVS3icSbn/evv0ty3rTyfVf7d71zUS7G7/xaCO+ZHJvXDb9ijHfkM+AXq0UcfVR06dNDPaEjZWJ6CX7oQcPNicGJDBDzbgYB3jUo6bnj8EifPe2iDgH9atmypy8EzIkHBL5jxaxp2iJaAX8rwBwAB4hzLTSD0sRHC4sWZ74go5RYitr25be0gmM0lQYVoA+sggSgTsL23otxG+k4CfgIc934ibp3b9q+t3aCVb6hv9GiqvtGjmfp6PPbOu8eu4/wH/dsq2D70Yr8KeyNdBDPK8edHPgTYIH1cXDz/z5i+SoTuk5OGqt9OGqoOn4u9ShX1fN0oH/mQjgBb7a+RLnVogzT/4JVM9erVS7DCxBG+X2LDVZsZ5tGjR2sBjOeWwRoHNu0SwSy7b0saYkwsYdII9ch10w9bwSzfraUMxBDCaAN8l+soD68oxTPT8BOCHSIa1yHowxDgq02wtbMpK582dt7ns0ZlN9OGKm2hLRh/XD3/8y3q+epbs45RBsK4dvszKmd2WWxDA8S51A//xYd84MYzFLhRsMx3165dmiV+/UKYPXu2Pi8vL1d9+/bVghg77OGXMAQsWenTp4/+jF/FcMPjD0LHjh31MhOd4PsHmxtA3CJAXCOfHLjmF8zoW/N5XZzDLwQIdCxvgfiGf1jmEuZgO05t7fCrJ5Zmo+8YSIAE0hOwvbfSl0QLEogOAY776PRVNp7a9q+t3cAV89XXu/+54uhmfMZ1y3MRzEH2g1bEBPOa/bt0eWPXL9MzzHf0a626LJylMfx2UpnCgRlmhP8Z0yfmk1E/0hEQez7H0wfG69AGBf4HQvj69evWtWJ2d//+/er8+fPWeTIxxJJzlA+BHPZgO05t7QrdXicE8xvjjqlmP9+imlbbHBg3S3Jd7CUdS7MvX/hQtXloW0I5ki72End5fIe2R7581I925CtMmDBBC2aILyzfwJIO2RAAYhoD8tVXX9W7+EGUQpxBGO/Zs0enme+pw6YBQ4YM0cIV+cyZYfEXv2jJMyGoDwIav+zBHstFbAWzbJSALf7xPAdmmlFG0DJwqbvYMfyzCbZ2KAs/JmATMPBgIAESSE0gk3srdUlMJYHoEOC4j05fZeOpbf/a2g1cPl/d0vU59e/dnsspXnsgtiozqJzv922lxq5bppvb/LXJ6sHRvROavv34YV0/BDVmmOtPGxPoz+MvDdH5EPvrQTsYokfAdpza2hWagN03/Tx7ZQvD1g5Cs2m1d3I++tbbqVu6deU5q7Jgh4B8+ag/n4IZm29hhlk2OYAYxvnly7FnSbBsA8tDwBjPOmO2GZ9l572gLscSECzVDtqmvkmTJjo/nt2QgKXUKDMTwSzPQvfs2dPbyAAbGqDusAbbcWprJ+3E7ojmZmxynTEJkEAigUzvrcTcPCOBaBLguI9mv9l6bdu/tnYDlr2ubunSRP1blyY5xRC7CKnKQfqCnVt0Pf89qreCyO28YKb6fp8XdD6kv7h2WVI/Hp8Qe/czYn89aAdD9AjYjlNbu0ITcEIwzx97VP35vk3quZ9tisdvx+L7fOcW6SJaX2y7Vz2XIj/SEWD/3M/i9VmUH/Mz2D+0Ix9Bts1fv369VxyELAYh3gWHgOeVcQ4RLRsCyIxwq1atvHx4BgPlYcMECGvY4xlnf5Bl33imAu+Zw6w0XtKOOiCYx4wZo/PKFvcQ8NidGwIeL1WHnSzJxrMhzzzzjJ5dxfJueTm8v86wnMN3m2BrZ5Z133336edszGv8TAIkkEggm3srsQSekUD0CHDcR6/PMvHYtn9t7SA0/7XTs+rfOjfOKfYEc5Jy/ntk7JnZd48dUqv3va86vTFD3d6rhfpe75aq/9J5+tq5K5cVyknmz2/GD9KoEPv9pWDOZBSFx9Z2nNraFbpldt/08+yVLQxbOwjNJj/bqIVrk5+9rWJHducvPLhZnTl2TS+1bvXg5nhZKLOiPNhg6fbhXZcD07OtP1+CuXXr1npm2d9tmBmW54yxbBp8sau1hDZt2uhr5jb4devW1ddgC7GM55yTbeyFfPIaK9jjwKZheLYCS70hkrG5GAI2WxAbLOdGGnbnRpDnpiUdZYY5wE+bYGtnloUfIj7zmc8ELoM37fiZBEqZQDb3VinzYtvdIMBx70Y/JmuFbf/a2kGsfq3jn3I+1ux7X7ucqqxp76zRNofOxjaCfWPHZnWwPPYZQhqh2asvJfXlsXEDtQ1ifz1oB0P0CNiOU1u7QhOw+6afZ69sYdjavf7iEdXk3o2q8b0b8hIPbhxbmr12/qnA8javKNdEetV9NzA9Wz/QjjAGLIfGztW2Gx1g1hi78/l31sZMtsxmo50Q3uYSbrPtslHCxYsXzcuh/Gw7Tm3t/I3Ezof4oeHChQv+JJ6TAAlksEEkYZGASwSy/T/FJQYut8W2f23tIDRvbt9I3dyhkRE39M6/lnBd7Cqn/3/23gTqiuJaG/6z1mfUda9JVnKvyf1/E7/PtWLucv3638+IMVHUqEQJ+nG9KBcVhIAicwRkDCqICAgIAjLKjIDMyCQyT6LM8/gCMs/zKIP7X0+97H7rnPcM+5zu06e7z661+lR31a5dez9V3aefrupqjBojFOspnc/6H+3Zlu5q15AmrC0mzyjzxucDnfpSlX9pWE9TB2LWx/YpYTbQhO5H2k+lcn4DEBnC/OafV5KX28Lxxatm9268LUbvoLeL391Avpf1QVdQCbPfnTJs9UlPbqlcIv/xjvgrr7ySKEvTFIGCR8DNuVXw4CkAoUVA+31om05kuLR9pXI9Fsyg/9W2YYKtQYI0Wy42nwlzia7Y/JL0Yh3/X6dmhLqbTR6ZpJ7S5V8aeoMwD+1J/6ttbD50aQgfAtJ+KpXzG4FIEOaZQw7Q3x9fUbz9eQU1eny56+OWz66mE4e/pxOHLsfowzHSkW/q9Kg+6IIfGsKHgPTklsolQwALufXo0SNZtqYrAgWLgNtzq2CBU8dDjYD2+1A3X1rjpe0rlesxfzr9z3fr0/98twHd+U5xnM3xV1uKP1EKPdmUl9T/34OL73WqDOlRyl74oSF8CEj7qVTObwQiQZhnDNlPjR5bboitiR9bTg3jjrPJ37H2LGFjnSDiCEjzQj/0ObofW07wQ0P4EJCe3FK5ZAhs3LiRbrnlFsJCbBoUAUWgBAG351aJJt1TBMKDgPb78LRVNpZK21cq133eNLrznXrO9pu3S/aRLj1uN3NcNu7QpkP76N4PmsbU//qo/oQFwJKF8n0+iJGHnd3nKWFOhleQ06X9VCrnt6+RIcwNH/uWGjz6DXkZ71hzxowwj+/5HX3caDMhRkC6l/Ww3UqY/e7+3tQnPbmlcqmswmrj9957r/h98lS6NE8RiAoCXpxbUcFC/SgcBLTfR7utpe0rlfto3jT6dZs69Ju369Kv29S9EWd33GTCMAIBh05JPOjreaaxNh7cR/9vhyZO/SiPkEhP5cHdE9rLZaLd+tHzTtpPpXJ+IxQJwjx98D6qX3YZNXh0mYnr34hBRJFecpxZ/oRexQTZbpSL56/RiI5FN+pzpz/ePvihIXwISE9uqVw6BOrVq0c1a9ZMJ6b5ikDBIODVuVUwgKmjkUBA+30kmjGpE9L2lcp9NHcq/fofdegOZ3vD1+PGE4YaX0Ga72nfmO74xxv00dxiwlxsk8we+KEhfAhI+6lUzm8EIkGYpw3eS/XLfk31HvnaipfFHWef373hRurRaCMhLqnHO/1sN/zQED4EpCe3VE6CQJkyZahv374SUZVRBCKPgJfnVuTBUgcjg4D2+8g0ZUJHpO0rles2Zyr9P61q0x2t38hb3Hgck+a9dM97jQk2IWRiF5dJCJomBhYBaT+VyvntaDQI86C9VPeRpTnZpg3aS8M67DC6EfN+LupDXRrCh4D05JbKSRBYtWqV+Y71ypUrJeIqowhEGgEvz61IA6XORQoB7feRas5SzkjbVyrXbfYX9H+3fD3v25vjhhhfNx7cSwOXzDH7mdjVdfYXpbDShOAjIO2nUjm/PY4EYZ46aA/VeXgJ1Xl4sYnf+FNx7MUxGmTb6tNG7/Y1pwmbl/phN+uDHxrCh4D05JbKSRHo168fYaRZgyJQ6Ah4fW4VOp7qfzgQ0H4fjnbK1kpp+0rlQDT/rflr9G8tXqN/a17rRpyf4zfHDo6BJRN7lDDHQBeaA2k/lcr57Xg0CPOne+iNPy0yxLM4xn72x60rLadu9ddRt/rrTXuAMEMf4r3bz9/IW0etK624UY+7+thmJcx+d39v6pOe3FK5TKyqVasW4Z1mDYpAISOQi3OrkPFU38OBgPb7cLRTtlZK21cq1+WrKfSrZrWsrSb9qnncsY/5f/98kIFm5sY1N2yqadkGuxLbBz80hA8BaT+VyvmNQCQI8xeffke1/7iIXv/jQtdx+1dXlWqDNYuOG70gzHa4cO6q6/psu+GHhvAhID25pXKZIHDlyhW67777CKtna1AEChWBXJxbhYql+h0eBLTfh6etsrFU2r5SuS5fTabbm/6NfvlWTbr9rSSxz/m/fbt+rD2C+uGHhvAhIO2nUjm/EYgMYX7toQX0+kMLyIv46+mHnXaYM2Y/tXz+G6MX8VIrb0j7rZ7Ux3YrYXZgD9WO9OSWymXqPL7LjO8z4zvNGhSBQkQgV+dWIWKpPocHAe334WmrbCyVtq9U7sNZk+j2pjXoX5vUCHUMPzSEDwFpP5XK+Y1AJAjzlIG76bU/zKdaf5hHrz2UJM4wf3D7LaYtjh+8RG2rLTf6EWNU+eK5q/RJ8w2x9WWov5Sdf5hP8END+BCQntxSuWwQ6NGjB5UtWzabolpGEQg9Ark8t0IPjjoQWQS030e2aY1j0vaVyn345ST6l8bV6V+bVKd/afwq/WvjG3HMcfDz4YeG8CEg7adSOb8RiAhh3kU1H5xLNR+cFxv/Ace8ZZ4PggxyfOHsVVoy9aBpm+OHLlHbqsuL63Gpv2Zc+SkDd/nd/lqfBwhIT26pXLYmvfLKK9SkSZNsi2s5RSC0COT63AotMGp4pBHQfh/p5jVfwpB4KO0HnWdOpH9581X6hbNVu7FfEochH35oCB8C0n4qlfMbgUgQ5skDdtLfHpxLfyszx/O4WcWltHf7OdMuiOs/uTAn9cB++KEhfAhIT26pXLYInDt3ju6++24aPXp0tiq0nCIQSgRyfW6FEhQ1OvIIaL+PdhNL21cqB6L580ZV6Rd/rxYT/zzuOOj5SpjD2e+l/VQq5zcKkSDMkwbspBoPzKYaZWbnJK73xHz6tO0mQpzLeuCHhvAhID25pXJuEJg3bx799Kc/paKiIjdqtKwiECoEcG7pphgUYh8I1YmqxmaEgPSeQSrXacYEisqWEZAqHAgEpP1UKue3U9EgzP2LqPoDX9Grv//qRjwrlMeT+ivJ8fsE8KI+6cktlXNrU+fOnempp55yq0bLKwKKgCKQEoGzZ89SpUqVCLEGRUAR8BYB6T2DVM5b61SbIpAZAtJ+KpXLrHb30pEgzBP7F1G1+7+kV38/i6r9flZxHMJj+KEhfAhIT26pnBcIvPDCC9SqVSsvVKkORUARUAQSItCyZUu66aabCLEGRUAR8BYB6T2DVM5b61SbIpAZAtJ+KpXLrHb30pEgzBP67TCEuer/nhnqGH5oCB8C0pNbKucFAseOHaM777yTJk7UxTG8wFN1KAKKQCwCGFW++eabzVR4xDrKHIuPHikCbhGQ3jNI5dzao+UVATcISPupVM6NLdmUjQxhfuV/z6Cwb0qYs+nC+S8jPbmlcl55NGPGDPrlL39J+/bt80ql6lEEFAFFwCCAUeUf//jHhjAj1lFm7RiKgLcISO8ZpHLeWqfaFIHMEJD2U6lcZrW7l44EYR7fdztN6LedohC7b1LV4DcC0pNbKuel/e3ataNnn33WS5WqSxFQBAocAXt0Gdc1bDrKXOCdQt33HAHpPQOfgxrrwodB7wOSk0Ta7yW6vJSJBGH2EhDVpQhkioD05JbKZVp/OnkQZhBnDYqAIqAIeIGAPbrMN2g6yuwFsqpDEShBIF/3DCUW6J4i4D8CQe33Spj97wtaY8QQkJ7cUjmv4dm/f7+Zmo0p2hoUAUVAEXCDAEaXsdDXLbfcQj//+c/N6PIvfvELuvXWW026vsvsBl0tqwiUIJCve4YSC3RPEfAfgaD2eyXM/vcFrTFiCEhPbqlcLuCZNGkS/eY3vyEsBqZBEVAEFIFsEejatashyz169DAq+LqGY5Bo5GtQBBQB9wjwueVek2pQBMKDQFD7vRLm8PQhtTSgCEhPbqlcrtxs3bq1+WZqrvSrXkVAESg8BPJ9XSs8xNXjQkFAz61CaWn100YgqP0+8IQZwOmmGAS9D9gne7L9IFwEypUrR506dUpmoqYrAoqAIpARAkG4rmVksAorAiFBQM+tkDSUmukpAkHt94EmzJ62gCpTBPKMQBAuAkVFRfTTn/6U5s6dm2c0tHpFQBGIAgJBuK5FAUf1QRGIR0DPrXhE9LgQEAhqv1fCXAi9T30MBAJBuQiMHj2a7r77btLFeQLRLdQIRSDUCATluhZqENV4RSABAnpuJQBFkyKPQFD7vRLmyHc9dTAoCATpItCkSRN65ZVXggKN2qEIKAIhRSBI17WQQqhmKwIJEdBzKyEsmhhxBILa75UwR7zjqXvBQSBoF4GyZcsSr3QbHJTUEkVAEQgTAkG7roUJO7VVEUiFgJ5bqdDRvKgiENR+r4Q5qj1O/QocAkG7CGzcuNF8Bmbp0qWBw0oNUgQUgXAgELTrWjhQUysVgfQISM8tyOmmGIShD6Tv9WT6skTObxklzH4jrvUVLALSPz8/ARo6dCjde++9dOXKFT+r1boUAUUgIggE8boWEWjVjQJHQHpuSeUKHE51P88ISPupVM5vd5Qw+4241lewCAT1IlC/fn2qWbNmwbaLOq4IKALZIxDU61r2HmlJRSAYCEjPLalcMLxSKwoVAWk/lcr5jaMSZr8R1/oKFoGgXgTQIGXKlKG+ffsWbNuo44qAIpAdAkG+rmXnkZZSBIKBgPTcksoFwyu1olARkPZTqZzfOCph9htxra9gEQjqRQANsmrVKvPeyMqVKwu2fdRxRUARyByBIF/XMvdGSygCwUFAem5J5YLjmVpSiAhI+6lUzm8MlTD7jbjW5yDw7bff0vz5853jqO8E9SLAuPfr18+MNPOxxoqAIqAIpEMg6Ne1dPZrviIQVASk55ZULqh+ql2FgYC0n0rl/EZNCTMRHTt2rNQKg7fddhs9/PDD1LhxYzpy5EjW7XLy5Elau3YtnT17NmsddsGrV686toZloaZNmzbR9u3bbTdiMMdqzYUQgnoRsLGvVasW1atXz07SfUVAEVAEkiIQhutaUuM1QxEIMALSc0sqF2BX1bQCQEDaT6VyfkOmhDmOMGPxoxo1atATTzxBIM1oOMTZjoT26dPH6Jg9e7YnbRtGwswPH2wA4MfTTz9tHkqcOXPGzorsflAvAjbgeAhz33330ZAhQ+xk3VcEFAFFwCDQtWtX8zk6/oY7X9dwfMsttxDyNSgCioB7BPjcSqdJKpdOj+YrArlEQNpPpXK5tDWRbiXMFmEGsbPDhQsXzAgzGu+OO+6gixcv2tmi/datWxc0YT537pzxH6P1hR6CehGIbxd8l/nmm2+mQhn5j/dfjxUBRSA5ApgtddNNN9Gtt95Kv/jFL8z1/ec//7k5RrpXs6mSW6A5ikBhICC9Z5DKFQZq6mVQEZD2U6mc334qYU5BmNEY169fNyNuaEB7FeFZs2bRiy++SHfddZe5YQDZrlOnDmH6McLWrVupRYsWTv5DDz1EFStWNGWQf+rUKXr77bfNO6O333670XH//fdT//796dq1a0ZHop9EI8zr1683uidMmEBz5swx+9AJ29q1a0eYFm6HPXv2UKVKlczIOeQwoj5t2jRTbubMmY4ofO/VqxfBdvgPfc2aNaPz5887Mrg56t27tzMiX65cOWrbtq2RwTuxVatWdfCB/9i++OILU75KlSrm+Pvvv3f0/fDDDwZnEGyuE/YdOHDAkQEG0IN6MNUbswLuvvtu40/dunVpy5YtjmyQdoJ6EUiEEUaLHnnkkURZmqYIKAIFjkDLli3pxz/+sblG47qGDcdI16AIKALeICC9Z5DKeWOValEEskNA2k+lctlZkX0pJcxpCDOgBSFEA9auXdtBukKFCiYNhJPJHWQwEn358mWaPHlyzM0E8niDEix4xccgyiB8fDxgwACnnvidRIR54cKFTlnogE333HOPkwb7ORw/ftyZag45JsNcNwgyBxBVTrd9fPnll1mEmjRpYmSgq3z58sZ/lAHBxdReLm/H3bp1M+XxkAHp9sg9HjqwLD51BL04hux3331nytkYcB5kudzzzz/v2BekHdgXpvDKK6+YGRZhslltVQQUgdwjgAelmIXC11zEONbR5dxjrzUUDgLSewapXOEgp54GEQFpP5XK+e1jXu7ggwYGL/oVPyWbGwOjr7AZ7zVzwAjy4cOH+dCQPiaAGPHlgFFolI1/hxnTvSFnj652797dyKJMsmCTRV70iwkzyPq8efOcop988onRV7ZsWScNo7KwB4QfxB4B7xBjxBbpTJhXrFhhjjGqzKO7p0+fdsg20i5dumRkUM4edbbfSYaPyE80JZvxYsK8evVqI4t0HiWGv9g4oAQAACAASURBVEzcESPYGOBdWxwjrFu3zrEHU8GDFoBDmAIwxIOcUaNGhclstVURUAR8QMAeZdbRZR8A1yoKDgHpPYNUruAAVIcDhYC0n0rl/HYuL3fwQQMjHWFetmyZIWIgD6kCTz9esGCBI5aMMDsC1g5IOLB5/PHHrdTYXZssxhNmkF477Nixw+gDkebAI7F4T9UOH3zwgZFlwoyp3LClffv2tpiZ/oz0xYsXE6ZP8wjw1KlTY+T4IBPCzHW2adOGi5sY5Bx1YkOdNgYxgkSOPbt3747Pyvsx7A9bwAOYn/70p1RUVBQ209VeRUARyCEC9iizji7nEGhVXbAISO8ZpHIFC6Q6HggEpP1UKue3U3m5gw8aGOkIM0+vxqrOHEDahg0bRvgMD0gxnrbz9GYpYQahRTm8T4zp3njnGdh4RZjxOSzoswkzj+oePXqUXTFxPGGuVq2aKYup3bCHN+iCTl41HNOrcYwNZHzEiBHmvW9Wnglh5jrHjh3LxZ2Y7caofirCzFPRlTA70Lne6dy5Mz311FOu9agCRUARiBYC+P/CQl+INSgCioC3COC+ShKkchJdKqMI5AoBaT+VyuXKzmR6ZWdjstJZpgcNjHSEGZ/JgM0NGjRwPMa3apGGDSPPPNKKYwlh5inPkAcZZKKHYz8I86FDhxxfsBNPmPEeMGzBVGo8EIjfbEKKxcbwHjbksWHqOi80lglhxoMDlOdFwWwDmajv2rVLCbMNjE/7L7zwArVq1cqn2rQaRUARCAMCGGXGdVvfXQ5Da6mNYUMA90OSIJWT6FIZRSBXCEj7qVQuV3Ym0ys7G5OVzjI9aGCkIsyY9swrYWP0FGHnzp0OOeT3e5HOC1ZJCDNP1QYJ4VWx9+3bZ/TmkjDzYmXx71THE+amTZsaWyZOnChuZUxdZ+LM3/HNhDA3b97c1GkvUobK8a41+gw2tIeOMIubxDNBnCN33nknZdIfPKtcFSkC6RDo/38R6aYYFGIfSHduaH5oEZDeK0vlQguEGh4JBKT9VCrnNyhKmFOsko3Fq3iaMEaRQdQQQDbRoPELWeEzSUi3CXP16tVN2siRI2PaFqQYsnPnznXS8d1bpOWSML///vumDkyfxorZCFh9mu3hd5iHDx9u5DANHe8NSwOIMnxo2LChKZLoPWrWxdOsedEvTMVGWZBufj8bsqwTNiMoYWYE/Y1nzJhhZlLgwY4GRSBQCBQiUVKf9QEB+oCGyCKA+yFJkMpJdKmMIpArBKT9VCqXKzuT6ZWdjclKZ5keNDB4hBl24Zu+WI0ZBJIJHaZb24tkYcST8959913q06ePQzihwybMo0ePNiQQU4obN25sdIMg4ju3kMV7z/iMFEaacYwtl4QZdfPCX6iLpzpz3UyYsXo3j6xjlW2kYwM2mD4OzLAQFHRhZB0Y9OzZ0/k8Flbu5sB6MKr+5ptvEjBBYAyZMIMI89R0rhPffWbbeFRcCTMj63+MhdkwS0GDIhAoBJQ8Knks1D4QqBNRjfESAdz7SIJUTqJLZRSBXCEg7adSuVzZmUyv7GxMVjrL9KCBgfdtYZO9gcyBtGGa8IkTJ0p5igW/bLKJkVgsAAYdNmHGp5h4lBp5II/4BBMIJ78njHTUxyRaSph5xBsrVkNH/PeHedEv1GkHpHfp0oXKlStnyA+mQGPRFujo16+fI4qp55BBOm+wE++swS8swAXyxMQXMhiJh247jB8/PuabzPATgcvx562QhinuPG3crnPcuHGOSglh3rt3ryMflB34E4Xw7LPPEj5PpkERCAwChUqW1G99UBCYk1AN8RoB6T2DVM5r+1SfIpAJAtJ+KpXLpG4vZPNyBx9UMDIFFFOVN23aRKdOnUpbFKOoib4NDLIOYsrvMadVlCOBl19+2ZDiWbNmlaoBNuIbx7AzUbh+/Trhk1iYfp0qYAo4ZCUBdX777beBwEZir0QmKv1+//799Mtf/pIwRVuDIhAIBJQ4KnEs1D4QiBNQjcgFAtJ7BqlcLmxUnYqAFAFpP5XKSev1Sk4Js1dIhkTPhQsXaPPmzc772BjdxarU6KDY4j83FRK3QmFmUC8C2YA3adIk+s1vfmNmSmRTXssoAp4iUKhkSf3WBwWenkiqLEgISO8ZpHJB8k1tKTwEpP1UKuc3gkqY/UY8z/XhXWx0RmxYXIunReO4b9++ebYu2tUH9SKQLeqtW7c20/OzLa/lFAHPEFDiqMSxUPuAZyeRKgoaAtJ7Bqlc0PxTewoLAWk/lcr5jZ4SZr8Rz3N9+F4mFt3CKtb33XefeecYi3HZ713n2cTIVh/Ui4AbwPGOe6dOndyo0LKKgHsECpUsqd/6oMD92aMaAoqA9J5BKhdQN9WsAkFA2k+lcn7DpoTZb8S1voJFIKgXATcNgpXSf/azn8V8Hs2NPi2rCGSFgBJHJY6F2geyOmG0UBgQkN4zSOXC4LPaGF0EpP1UKuc3UkqY/UZc6ytYBIJ6EXDbIJix8Nvf/pYwe0GDIpAXBAqVLKnf+qAgLyecVuoHAtJ7BqmcHzZ7WQfW2Pnuu+8Inzm1A2ZIPvzww2axWTs9F/t+1pUL+4OkU9pPpXJ++1YQhFm6MnOuwcc7wkOGDMlZNY0aNaJly5blTL8XivHZKHzrOtGnupLpBxHDitlhD0G9CHiBa5MmTQgrrWtQBPKCgBJHJY6F2gfycsJppX4gIL1nkMr5YbPbOrAwbZs2bczrgvCLN3yyFIuNIuDTq0hfvXq12+qc8vgU6dq1a0t9ySUXdaHSZPU5BkVwR9pPpXJ+QxR5wozPNQH8iRMn+oItvs+c7BvAIBT4VnOmYe7cuYTFutIFLOA1YsSIdGJ5zccK3WiPXbt2JbUj3t9PPvnElMGnucIcgnoR8ApTfLecv7HtlU7VowhIEIj6uSXBQGUKDwHt99Fuc2n7SuWCjhbuC++66y5zvwefypQpQ1WqVCGslXLHHXc4C9PmgsRiLR/UCcJuh1zUBf3J6rPrjtq+tJ9K5fzGRwmzx4iDtM6fPz+h1mwJMy4Y7dq1S6jTTowKYY739/z587R+/Xrb1VDuB/Ui4BWY+Cb5LbfcInq441WdqkcRAAJRP7e0lRWBRAhov0+ESnTSpO0rlQs6MhUrVjTX8vLlyycdeIIPuSCxIOfA0S/CnKy+oLeRG/uk/VQq58aWbMoWHGFu2bIlNWvWjPBJHDzJwqeVeBrz2LFjzRMtEE880QJR++GHH8yo7RNPPEG33347NW/enC5dukQ7d+40ZTHNGjr69+9PTz/9tDnhoBcrUJ8+fTqmTUCYH3roIapRo4b5nFODBg3M+xkQWrJkiSmDjoL6W7VqZcqyLOqGTqxmDbtQH0b0UFe/fv2MLMphBBtTnrEPe44dOxZjAw5ge4sWLYw/FSpUIPjNAReibt26mQtS48aNOdnE8T6PGTOGduzYYfCEfahv3rx5Rha48JNCxFxHuhHmRP7OmDHD+ArF48ePp+eff948QMAUHdiL2QPAC08gcRHasGGDsQH4d+3a1UztAXbALJ8hqBcBLzEZOnQo3XvvvXTlyhUv1aouRSAlAoVwbqUEQDMLEgHt99Fudmn7SuWCjBbuw+EHtiNHjqQ0lQnznDlz6N133zX34CiHe8PZs2fHlO3du7czQg0Z3CdiAOr48eNGDvJ4nZHrxj0xiHuHDh1MPteF+13cw+NeFxv2Dx8+HFMXDjBghnth3INDDvq++eYbRy5dfbt37zb23XPPPaZ8pUqV8n7v6hjvcgcYS4JUTqLLSxmZ9V7W6PNoQPyUbHRykKdp06bRvn37zMIBSLt69ao5YT799FPzhGnjxo2Esnh3Fh0f70rg/VuQP7xHwcSvcuXK5njPnj3m/Qc0NKZFg0iivB1QDwgdyDFGTEF4QRARsLDBihUr6OTJkwSCCD2YnoIN9mLhAegEWe7Vq5c56eEDTi6e3gw7YQ8uPFu3bjU6BgwYYJtg9p977jlzYdmyZQvhIoC6MDqIAB2wcfDgwYR8O8T7fPDgQXMxALHGO8ndu3cnnOQIeBcEOk+dOkVt27Y19uLhA+tgm2392E/k76hRo4xdyIc/sHHKlCmm/XiqDjBDe6J+Jvogy8AYU+T5YpzoAhdvQ66Og3oR8Nrf+vXrm4c2XutVfYpAMgQK5dxK5r+mFyYC2u+j3e7S9pXKBRmtPn36mHvRqlWrpjWTSSz8xoZBK9wXYh8x7uc5sAzu3XlUF2m4H0do2rSpKcdyHOM+GcGui++PuS6Qb3tEGveqXB73ohjU4eOpU6emrQ+cAb6gDMri/hZ1wfYoBPglCVI5iS4vZWTWe1ljAAiz/R4xRloxeoyAp0o4AT766CNnZBaj0ei4eNqEDZ0ZhCwR8cOJg4aWTsnGAmA4GTAaBzIJkomLBp90ixYtMnZhVNqekg0b3nnnnVKtAl32O8yQ46dkLIwRZ9i4cOFCTjIXEZBahHgdjhBRKZ9ZF0bKUQ+wgu6jR48SVjcEDni6B9uRjouYjRseErz99tvOxiP98f7GE2a0EQfgAFLMAQ8g+IKLBw14usdtB9940QiW9zMGBoUSHnzwQed9o0LxWf3MHwKFdG7lD2WtOWgIaL8PWot4a4+0faVy3lrnrTYmrrgnTBeYxL755ptmkAnyuP/GiC6wsO/BMdhlf8Fj5cqVRgb3g3bAMcraBBj5XBfuI5mIY2AL95eQxz0uAgazuH6eUYl0DMJBDqTXXu07UX2YQQpZEGU7nDlzxj4M7T58kwSpnESXlzIy672sMWCEGVO0mTDjZBg3bhzh/Ql0ZkxBxsguiCeIFm8YfbaJH8OTKWEePny4qQf1dunSxZxsH374oSHs6DDJCDMI/Pvvv8/VOjFstgkznqbFE2aMCkM3k1MUxvL8wAEhXoejPAFhxog7dMF2xgYx/MFIN4gvLib16tUzcki3cQNhZjKLGCPsCG4Jc7Vq1Ywe4AQCbdumI8x2i+Zuf9WqVfSjH/2I8OekQRHINQJB/YPNtd+qv7AR0H4f7faXtq9ULshoYcQXfuB+Ml1gEhu/Sjbu96DDvg9OpIuJrf3qWCICi7LJ6sLnNFEXBtoQQNJxjPtXO2AwDPeiyLPtTVQfZoxCDuQaPCNqAb5JglROostLGZn1XtYYUMIMMocpz4jPnTtnOi2eEvETHxBMfJ7q0KFDZmq2TfwYHpx8aGi8K4snUMmmZOOkwdRjnIh16tQxxUHSeWo43suAHibMyAMJxDu5WCn6vffeMyfg8uXLzVMtEFeEeLKbiDBDDk+vcGGBjXySg+Ak0mESb/wk8hk+1K1b14zI4+kZppoDQ9g/cOBAg2Xnzp3NMWOMPOCJC0miEO9vpiPMTJiBEy5eaFe0XVFREeXzSR38LqSAd+sfeOCBQnJZfc0TAoV2buUJZq02YAhovw9Yg3hsjrR9pXIem+epOh5h5vV7UilPRmIx4gwsbMKM1wJxD4r7a9wbYsYjk1U3hHndunWmLn4NEa8xom7MuIwP/DBgwoQJThbbED+izQufQRfeyWYe4BQM8Y60n0rl/IYiL3fwfoIB0or6+LNS6Lj2lGycnBhhxju4/BQI8nh/AQQQJI87O9LRyfFkCe/34jj+XVx8Pw7p2DCaawfowWIAGNFFPurbvn27EcE7uHwCYYox9vlEwQgp5+HdXRBk1gE9mFaOABn7QpGMMOMTVfyEDeWZtCfSYRTf+EnkMwi3rQuj8QgYAYdubJgWjRhYgriy7Uz07TqwH++vTZhBwu0p2VjwIX5KNhNmvNMMe9gOPLUD6c9XgB2FFnCu4YGKBkUglwgU4rmVSzxVdzgQ0H4fjnbK1kpp+0rlsrXDj3L8+VDMTkwXpIQZ95z2fT2mUfO9NDBzQ5h5AAn6ETCbEjpB/OMD7oOQZ9+fsx3xhBn3yNBl31fzujzxesN2LO2nUjm//c/LHXxQwQD4IM54IhUfMDKJhb2SjYra8iDa6eQwuhsfcPImWtUaciD+9smNNNgUf7LF60x2DH3wx363I5lsunTWhXc47AD74tM4P94XTuc4kb+cl2mMadh4rzrfIcj9PlfYoJ3xJ4X39TUoArlCoBDPrVxhqXrDg4D2+/C0VTaWSttXKpeNDX6VwQAR/MCWbDCFbZESZl4PCIMq9r0yBk9Qj30fmozAJqtr1qxZRgcvDoZFeKETq1rHBwzKIY8HwZCfrD4uC7KPWa4sh5mSYQ/AQBKkchJdXsrIrPeyRp+nZHtsuqpTBLJGIKgXgawdEhbEjIabb745ku/kCCFQsRwjUKjnVo5hVfUBR0D7fcAbyKV50vaVyrk0J+fFmZyCYGKGYHzAQAoCy9nvBCM9fko2FhADNryoLWTwWVgevbUJM6dhbR07cF0gxBwwCozp0tCN6d4IGIDCMbb9+/ezqJlFyun8KStkJqvPKXhjp3r16kYnZpeGPQAHSZDKSXR5KSOz3ssalTB7jKaqCwsCQb0I+IHfxx9/TI888ogfVWkdBYhAFM+tUV9+T6NmfU8ae4dD1E6NKPb7qLWRG3+k7SuVc2OLH2VBgHlEFT5hZBjTmatUqWIWwuLXKZnEpiPMWJsHekBOe/ToYRYU49FlpNuEGa8mIg3r3jRp0sR83xk+c13Iw2uV7du3N+sA4Ri2Ym0hDlgkGOl4bRAL+Hbs2NEhxvGrfyeqD595xcMCTOvGrDyswwN92EDIwx7ghyRI5SS6vJSRWe9ljUqYPUZT1YUFgaBeBPzCD5/7isq7OH5hpvXIEIjiuQWi/Fzjs7p5hAHwjFqIYr+PWhu58UfavlI5N7b4VRavz2FRWps4wz+8K9yzZ09jBhauRRo+xWoHEF2kY50hDrjnsHVhUS6QYsjZhBmLeGGNIaRje/HFF40KrgvvFdt68KpZ/Jo4GL1u3ry5o4N1YaExuy4oTlQfpmzzGj9cFse8BhP7FNYYPkmCVE6iy0sZmfVe1qiE2WM0VV1YEAjqRcAv/LD6/O9+9zvznXG/6tR6CgOBKJ5bn3152ZDlZ988o3Hjs+QWB+AZtRDFfh+1NnLjj7R9pXJubMlHWazps2nTppj3j7OxA0R269atZjp2uvJYwyie3HIZrE2EKdvp1sTB12xAiDds2ECXL6e+7iSqD2Uwep5sTSO2J2yxtJ9K5fz2XwmzC8TxQn63bt1caEheFItxffvtt8kFPMrBuxjxIVFavIweZ45AUC8CmXuSfYl58+bRT37yE/Opr+y1aElFIBaBKJ5bo2ZeNiQRRFE39xgAz6iFKPb7qLWRG3+k7SuVc2OLllUE3CIg7adSObf2ZFpeCXOmiFnyLVu2NO8bWEkJd4cNG0Z79+5NmJcskZfYx5OqdGHu3LmEhZUyDTNnzjRTR/CuhR3w/oU9pcXOk+5n47NUd1jlgnoR8BtPLJLx5JNP+l2t1hdhBKJ4bn028xI9+/czVOHvpzX2AAfgGbUQxX4ftTZy44+0faVybmzRsoqAWwSk/VQq59aeTMsrYc4UMUteSpjx3gO+V5xJwOeY1q9fLyqCBRHwjkSmgQkzOiemq3DwgjBn4zPXH9U4qBeBfOD9wgsvEL6BrkER8AKBKJ5bIHgVGp2iZxud1tgDHJQwe3GmqQ4/EZBe16RyftqudSkC8QhI+6lULl5/ro+VMGeAMN7BxCp5IIMVKlQwK/hhRTuEvn37mlX80NBYhQ/TtfH9X15EAGlYJAAr6iWSjTdjxowZRj/Sd+7cSffff79ZNa9ixYqm/rp165oivDgCVgGE/gULFpjvM3fv3p3uuecek9arV6+E30NmwozvyGFhAV6y3ybM+PZbs2bNzEp/8AVTahHwsAArAmLVQuCBfbzbkcznMmXKEEbCOcAf2IqAVQgxtf311183ulAPv7uRrH7WE6Y4qBeBfGCI9r3zzjsjs5hFPjDUOksQiOK59dmMYsJcoSFI8ymq0PCkIc56nB0ewDNqIYr9Pmpt5MYfaftK5dzYomUVAbcISPupVM6tPZmWV8KcAWK1a9c2K/UtX77cfCOufPnyzpRsrNaHxQnwAj+++QbS+f3335tV/ND4I0aMMO9tgpQmksViAnbAB9dBRBGwEh90YBR5y5YthKXncYz0Xbt2GVIMwgpyiZFpkGWsKLhs2TJavHixsaV///62erPPhBkfiUddWAUQwSbMeDCAVQZPnDhh9IKEI+DBAUjwkiVLzEg4lv+vWbOmWSwB/sX7jGN7pT8cT5061ehC3ZUrVzb2YqQbeQMGDDB5yeo3mSH7gV8aShDAQyE86En0vcUSKd1TBNIjEMVzyxBmkGTdPMFACXP680glgoWA9LomlQuWd2pNoSEg7adSOb/xy8sdfFDBSAc+iGSHDh0cMXtKNla1w7RrkE4QW/h49epVs7of9u0p2clkHcVEZiXheMIMcoyAUWroBBlGwBL59pRsjN7iW3EcMPUVI8jxgQkz0vHNN+hEHUyYMQqINCzDD78x0oxjrBAIwszfxOPysBerC164cMHI2T6jXCrCjAcKHGA/6ktVP8uGKQYGGmIRQL/FQxENioAbBKJ4bn02/aIhin9tcIIqNDhJCeOGSdJZXvMd3IBn1EIU+33U2siNP9L2lcq5sUXLKgJuEZD2U6mcW3syLZ+XO/iggpEOPIyG4UPkHECY8Y02BIyQgriCMNerV88QxmSEOZks60WcaISZCfOZM2eM/mSEGaPLnTp1ctS9++67ZjTYSbixYxNmjHBjxBz+MGHGyDPaqkuXLjRp0iRng1/xhHn48OFmlDqZz9AjJcwYuQZhTlV/vC9hOA5rv881tnglALMyNCgC2SIQxXMLBO+v9U+UbA2sfaRncfziWyfpvf7naOT0izRp3iVav/2K2SS4Hz5x3cjOXnbZlO824jw16HjaU/u89tfWp4RZ0soqEyQEpNc1qVyQfFNbCg8BaT+VyvmNoBLmDBAHScSUZHxbbfv27YZg4h1mkEQ08MCBAwnvOWMVYBwjHSOu2MeU6JMnT5pvsiWTtU3JhDCD6FarVs2MPGNV7Q8++MBM0y4qKjKLeYFA9+jRw1Zv9m3CjIQ9e/YYW2Efr5KN94vxvjRGezHFnBci4ynZ+FYciDzk6tSpY/TG+4xp6HjY0KJFC4J9s2bNMvXYU7LtEWYmzFCWrH5TUch+gKuG0gjs37+ffvWrX9H06dNLZ2qKIiBAIIrn1sjpF+iv9Y/TX+sdN6S0vIkzO37xrRPUbfg5+nrd93T4eOlPCAqgFYmAePcbd4NAu7DXrb+pygPPqIUo9vuotZEbf6TtK5VzY4uWVQTcIiDtp1I5t/ZkWj4vd/BBBSMdeEeOHDEjtbAf049B5njRr/fff98hm5hiChkQZoQ2bdo4eQcPHqRUsmyDTZjx3jL08QgzvtGMYx5hxugv7EHalClTCHWAdOIYG6Zjg/DGByaudjqIPcowYca0apBd1oXp0gj88ACj6sgDKcdDBA7xPmPEm3WA4EPntGnTjDhsT0aYk9XP9YQphv8aEiOAPvyb3/wmYT9NXEJTFYESBKJ4bo2cdoHK1ztWTJhNfEx83G34WfHIMY8yI8boMUZiP5t+wWwgmXb++Yuxa22UtEDJHog5bH+x6XGxvX/N0L9s5GFT1EIU+33U2siNP9L2lcq5sUXLKgJuEZD2U6mcW3syLZ+XO/iggiEFDyPFvKK0XQZTpbHoVqKA0Vl7Ya9UsonKp0uDPRjZtQNWrAbJdxugG6PPtm/2lGzgkSjE+4yFw5LJJirPaYnq57wwxWHv97nGunXr1lSpUqVcV6P6I4hAFM8tEDwQw/J1j9Jf696I+Tg+vpEPonz4+LWELQziO3neRfpo2Flq8MGpYr2sR6if7fnb2yfpvX5nDKmG3mREevayS1SjzQmx/ay/VJyhfYnKK2FO2C00McAISK9rUrkAu6qmFQAC0n4qlfMbMiXMfiMekfpswhwRl3LuRlAvAjl3PIMK8A69/f59BkVVtIARiOK5BYJXvs7Rkq3ujf34uM5RqvGPE7RuW+wDU3SHZWsvG4L8QuNiwm30JSifMJ3l2AY+jo9v5INAT557kY6ciCXsINMg6SD+CesR6ndbXglzAV8gQuq69LomlQspDGp2RBCQ9lOpnN+wKGH2G/GI1IdPAmFKtwY5AkG9CMg9yL0k3rv/2c9+FvPN7tzXqjWEHYEonlsjp543BPOZN46kjOu9fyJmhBcEFeSwUuNigpqufC7yQZDjifNXX19K6Ucu7ABBZ73AM2ohiv0+am3kxh9p+0rl3NiiZRUBtwhI+6lUzq09mZZXwpwpYiqvCGSJQFAvAlm6k7NieH/+t7/9LeFdfQ2KgASBKJ5bIHjPvHGYyr9xxJA+EL/440qNj9D5CyXvFX+99jK90LiYJCaSjy+f6+N+Y8/GkHnzECCFP7m0Rwmz5ExSmSAhIL2uSeWC5JvaUngISPupVM5vBJUw+4241lewCAT1IhDEBmnSpIlZWC6ItqlNwUMgiufWiC/OGYL8TO3DSePZX5d8Wxj7IJyp5PORX6/98RjSXOnNYuLvt53AM2ohiv0+am3kxh9p+0rl3NiiZRUBtwhI+6lUzq09mZZXwpwpYiqvCGSJQFAvAlm6k/Nijz76KHXv3j3n9WgF4UcgiueWIcyvH6Jnah+iZ5LE5y8Ufypq576rKeWSlfcrvduQ004na9fnVFJ/cmmPEmanCXQnJAhIr2tSuZC4rWZGFAFpP5XK+Q2TEma/Edf6ChaBoF4EgtogmzZtoltuuYWWLl0aVBPVroAgEMVziwnz068dNAQzUczwT5pzgRLlg4AGIb151xNsKkn8yoXdSpidJtCdkCAgva5J5ULitpoZUQSk/VQq5zdMSpj9RlzrK1gEgnoRCHKDDB06lO69995Sn0wLss1qm/8IRPHcGjHlrCG7ILzJNkYan5Kq1OhwUrlk5f1Kt8kq9v2q165nxBfRWxMhiv2e+7TGRNL2lcoppopAPhGQ9lOpnN++zoOV/gAAIABJREFUKGH2G3Gtr2ARCOpFIOgNUr9+fapZs2bQzVT78ohAFM8tJsx/qXXAEMxEsQ35rCXFo8yJ5EAc85Vet+1ROndj6jjslfiVC3tRb9RCFPt91NrIjT/S9pXKubFFyyoCbhGQ9lOpnFt7Mi2vhDlTxFReEcgSgaBeBLJ0x9diDz74IPXt29fXOrWy8CAQxXMLBO8vNffT07UOJI3jW2jd1sv0aovDSeXT6fM6/63Ox2LIMuyV+OW1HdCnhDm+t+hx0BGQXtekckH3V+2LNgLSfiqV8xstJcx+I671FSwCQb0IhKFBVq1aRT/60Y9o5cqVYTBXbfQZgSieW8MnnzHE9y9/209P19xPf/nbvlLHiWDGaO6IyWfov+oXE+1U5UEkc5H/avNDNGtJ4u8ej5h8Nqk/ubIH+AHPqIUo9vuotZEbf6TtK5VzY4uWVQTcIiDtp1I5t/ZkWl4Jc6aIqbwikCUCQb0IZOmO78X69etHDzzwgO/1aoXBRyCK55YhzCDJKTZuma+WXCCMLscHkNZ3ex5PqSOV/kzyQNC7fHqClq4q+dQV2/P16pI0kPlM9Holq4SZW0PjsCAgva5J5cLit9oZTQSk/VQq5zdKSpj9RlzrK1gEgnoRCFOD1KpVi+rWrRsmk9VWHxCI4rk1fFIxsSxXY68hmIlihhYkFPltex2nw8evcnJMvHbLZTPK2rTjEarW7JCRBxlNpFeSXuedQ4YgT5h1lor2XImpiw9A4iH3VuejnGRskOjP1q5k5YBn1EIU+33U2siNP9L2hZxuikEY+oDkfJD2e4kuL2WUMHuJpupSBFIgENSLQAqTA5d15coVuu+++2jIkCGBs00Nyh8CUTy3hk86TeWq7zWENlnMiIMMgiiyXJeBJ2ipNarLcnaMqdsg0YZITzpNqA9bn89OOfuchhgjx5BNRo5t3V8tPk9vdTri2PNWxyNONkZ62U4/Y/gQtRDFfh+1NnLjj7avG/S0bFgRCGq/V8Ic1h6ldocOgaBeBMIG5Ndff00333wzbdy4MWymq705QiCK5xYIXrnqe0q2V6396nvpqVf3OGga2QT5VZseoL6fnTRk9/zF64681zvQDZLcZeBxer7uPkOIYR/bH0OYbzwIsPONnCUPIu11vhJmr1td9eUagShe13KNmeoPPwJB7fdKmMPft9SDkCAQ1ItASOCLMfPjjz+mRx55JCZNDwoXgSieWyB4T736HZUDkTTxd6WOucWLCXPp/Pjyb7Q5aEjtxFlnad2WS3T4WOLp26w3Pob8ui2XaemqC2YU+t2Pj1LVJgeS2sf1v/XBYUcV2wqfOD+Zf17mK2F2mkB3QoJAFK9rIYFezcwjAkHt90qY89gptOrCQiCoF4GwtkLVqlWpcePGYTVf7fYQgSieW8MmnjJE9Klqu5PGDKEh1ynkDDlNk9/0g0MEYhsfv/GPYkKcyo50+mMIs8CvdPqyyR8+8RTDFZk4iv0+Mo3jgSPavh6AqCpCh0BQ+33eCDMA0U0xKLQ+ELorV4ANPnfuHP3ud7+jUaNGBdhKNc0PBIL6B+vGdxC8p6ruJhBVJ8a+dcz6jSznWflOOdbDMnwcH+cov2mHQ2wqJfSL7chR/cBBCbPTBLoTEgSieF0LCfRqZh4RCGq/zwthzmM7aNWKgCIQIQTmzZtHP/nJT2jHjh0R8kpdyRSBoP7BZuqHLT9swklDlJ+suqs4fuVGbB2zPMjgkwnyQRRTlfcr3ybMGDk39fpsL/CMWohiv49aG7nxR9vXDXpaNqwIBLXfK2EOa49SuxUBRcAg0LlzZ3ryyScVjQJGIKh/sG6aBATvyVd20lOv7DJkGIQ4/pj1D5tQTJjj84Ny3OT9g2wqDXdsLe1PKntfeXMvAZO1my/RlwvPUed+R0vhkaq8EmanCXQnJAhE8boWEujVzDwiENR+r4Q5j51Cq1YEFAFvEHjhhReoVatW3ihTLaFDIKh/sG6ABMF76pWd9OTLOw0xLImLnGPWb8i1I1eSn668X/k2YTa23vBLWv+H/Uu+48w+I167+SJVfG23gwcI85MvJ/ZfCbONnO6HAYEoXtfCgLvamF8EgtrvlTDnt19o7YqAIuABAseOHaM777yTJk6c6IE2VRE2BIL6B+sGx2HjTxaTPxDAl4pK9q1j1h8ja+Ub8hiA4ybtD7CpZpQ4mT+J7LXLOkqsnSUrzifFx9YHjKIWotjvo9ZGbvzR9nWDnpYNKwJB7fdKmMPao9RuRUARiEFgxowZdPvtt9O+ffti0vUg+ggE9Q/WDfJDx58wRPCJl3YUx1VuxNYx6wcZfCJBPohpqvJ+5dukdxj7JbR3ycrz7CZ92PeI8eeVRt9R0XeXnXQcp/Mf9UYtRLHfR62N3PgjbV/I6aYYhKEPSM4Hab+X6PJSRgmzl2iqLkVAEcgrAu+99x5VqFAhrzZo5f4jENQ/WDdIgOCBBKbaWP+EGadSyqXS4Ude4/f2s6kk8cu2iQti+rWd/nbXkveiO4NIp8FKCTMjqXFYEJBe16RyYfFb7YwmAtJ+KpXzGyUlzH4jrvUpAopAThF47rnnqG3btjmtQ5UHC4Gg/sG6QWnouOOGBP75v7fTE1W20xMm3kHFx8Ux6z934Tr9n5pFlnysXLLyIJmp9HuV/3LD3TRs3AlDlhu325fAzsT+of7Dx64aN9duKibM7H8sYT6cEB/bfuAZtRDFfh+1NnLjj7R9pXJubNGyioBbBKT9VCrn1p5MyythzhQxlVcEFIFAI7B//3761a9+RdOnTw+0nWqcdwgE9Q/WjYcgeH/+722GCCaLbf07dl+mlxrsSimfTE8u0l9usJvebLvXtT14R5lD5z6HjL7nau4g+Mvh9Rbfpa1HCTOjpXFYEJBe16RyYfFb7YwmAtJ+KpXzGyUlzH4jrvUpAopAzhGYNGkS/frXvyYsBqYh+ggE9Q/WDfJDxx6nJypvoz9X3locv3gjto7j9Z87f516Dz2SVD6dPi/yn/vbdlq76YJjGmx6u8sB+nMC+yX1vd58t6Mr0Q7qMnrS6AeeUQtR7PdRayM3/kjbVyrnxhYtqwi4RUDaT6Vybu3JtLwS5kwRU3lFQBEIBQKtW7emSpUqhcJWNdIdAkH9g3XjFQgeSGaqjfWfv3Cdd0186OgV6tT7UMqyqfS6yVuzsYQss1EgzS/V25mVPSiH8okCFv56rsZ2kV4lzIkQ1LQgIyC9rknlguyr2hZ9BKT9VCrnN2JKmP1GXOtTBBQB3xAoV64cderUKaa+hQsXxhzrQfgRCOofrBtkh449Zojg4y9sSRqzfpDB3kOOUDxxBtGcOf80tflwP6XSA4LsRT7IKweM/I6fXvIpJ9iXTT02Af9ywWkzer1k+Tnq1PtgRvqAZ9RCFPt91NrIjT/S9pXKubFFyyoCbhGQ9lOpnFt7Mi2vhDlTxFReEVAEQoNAUVER/exnP6O5c+cam/v160f/43/8DzpwoOS7sKFxRg1NikBQ/2CTGizIGPr5MUNiQWSTbawGZBAyVeoWGZIaT5xZDuRzyOfH6B+d99Frb+1OqjdZfVzH39/ZY/Qs/vYsnTt/jZ6tvt3oQswBeR17l6xkzTam0h2fB6LPAQQ8Pj+TY+AZtRDFfh+1NnLjj7R9pXJubNGyioBbBKT9VCrn1p5MyythzhQxlVcEFIFQITB69Gj67W9/S2fPnqVf/vKX9M///M80a9asUPmgxqZGIKh/sKmtTp075POj9HilzSk31mDItSX77KvbqPeQw1S0+xKLpIwPHblCINPJNuSnCm0673PsTFYn7Ennj50PH0DGOVSpuyOj8rYu7APPqIUo9vuotZEbf6TtK5VzY4uWVQTcIiDtp1I5t/ZkWl4Jc6aIqbwioAiEDoEmTZrQf/zHf9Ctt95KN998M3Xr1i10PqjByREI6h9scovT54DgPfZfm+hxazPHIMY30ljLUJDr/9pULG/lQ75Kne3Ue/AhWvLt2VJTtrm8m3jtxgvUptNep/7Xmu6iw0cTE+yOvQ4Uk94b9qfyb/y0E45ZqfxjLJL5z/lKmB04dSckCEiva1K5kLitZkYUAWk/lcr5DZMSZr8R1/oUAUXAFwTwrvJLL71Ev//97+m2226jW265hXAhxvbiiy/6YoNW4g8CQf2DdeP9kDE3SPDzG4vJaIKY9YNQPpYg35DIuPRaTYoMwYX+L+eforUbzxNIryQGiUU5EGToSaQfdjxbbSv9o9MeQ9R7DTrEZpq4Y6/9Scuxvtea7nTKgHxDn9S/ZHKwO2ohiv0+am3kxh9p+0rl3NiiZRUBtwhI+6lUzq09mZZXwpwpYiqvCCgCoUCgV69eZkTZJsq4EGP793//91D4oEbKEAjqH6zM+sRSIHggf6k2LimRTaUn13kgyXbAcao6d+wqmUr+97d3p5RNpcfOU8Jst4DuhwEB6XVNKhcGn9XG6CIg7adSOb+RUsLsN+JanyKgCPiGwPXr1+nDDz+kn/zkJ/RP//RPzgjzTTfd5JsNWlHuEQjqH6wbz4eMPkKP/edGevQ/NxjCaOK4Y9YPMpgoP115P/M79owjzT33U8eP95NNjuGPfYxp5Kn8z8R+4Bm1EMV+H7U2cuOPtH2lcm5s0bKKgFsEpP1UKufWnkzLK2HOFDGVVwQUgdAhcOXKFfrggw/Mgl9Y9Avkedu2baHzQw1OjEBQ/2ATWytLBcF7tOJ6euw/N9CjFXmLPWZNxbKQic0P2nHHj/exyaK48utbU/qfiX9KmEWQq1CAEJBe16RyAXJNTSlABKT9VCrnN4RKmP1GXOtTBBSBvCFw8eJFat++vSHMEyZMyJsdWrG3CAT1D9aNl4NHH6bHKq6nR//POkOEEccer3fUG8KcID9WvnT5fOT3+jT2k27nL1yjNRvP05DRh8171I5TRLRmw/mU/mdiP/CMWohiv49aG7nxR9q+Ujk3tmhZRcAtAtJ+KpVza0+m5ZUwZ4qYyisCikDoETh37lzofVAHShAI6h9siYWZ7w0edbiYLIIwJ9lYK8hmMpmgpY/7ouR7yIu/OUN/fWljjO01/749Zlo2CLYXPgDPqIUo9vuotZEbf6TtK5VzY4uWVQTcIiDtp1I5t/ZkWl4Jc6aIqbwioAgoAopAoBAI6h+sG5AGjzpEjz63jso+t9YQRhPHHbN+EOZE+enK+53/1yobnW8rHzryPeE4kX+VX9tCGHlGgBwIs1v/lDBzb9E4LAhIr2tSubD4rXZGEwFpP5XK+Y2SEma/Edf6FAFFQBFQBDxFIKh/sG6cBGEu++xaQxRNjP24Y9YPMujIPHdDLoF8fHm/jxu1KmKTqefAA6X8se2ZOafkO8wv1tzs2j/gGbUQxX4ftTZy44+0faVybmzRsoqAWwSk/VQq59aeTMsrYc4UMZVXBBQBRUARCBQCQf2DdQPS4M9AmNfQIxXWmLisiddScbzGxKx/+86LN9Jj89OVd5tf/r/X04zZJ5xR43Pnr5ljpLOdtv32KG+jljtS+gf/OTRqtcO1f7Y+1hv2OIr9Puxt4qX90vaVynlpm+pSBDJFQNpPpXKZ1u9WXgmzWwS1vCKgCCgCikBeEQjqH6wbUEDwHqmwmspWWH0jBknGMUh0cWzrB3GNz09X3k1++crr6dDh720TnH0QeOQ/U3kdNWyxnQZ9dpBWrz/nEGsIftB9T0r/bILbsOX2YsIc538m9tv6HENDvhPFfh/yJvHUfGn7SuU8NU6VKQIZIiDtp1K5DKt3La6E2TWEqkARUAQUAUUgnwgE9Q/WDSaDRh6ksn9dTY/8dVUxaUYcdxyvH6T5mRcxynyjXJx8fHk3xzPmHHeqx3vGgz87aN43dhLT7KB8qvpBujl44Q/wjFqIYr+PWhu58UfavlI5N7ZoWUXALQLSfiqVc2tPpuWVMGeKmMorAoqAIqAIBAqBoP7BugFp8MiD9Ej5VYYwmxj7cceJ9GNadIdu3xXLxsnHl8/2+JkX1jpVr1l/rqSu8qtox64SousI3diBrJ3f4aPvEvrXs3/J95pBrJP5n4n9SpjjW0OPg46A9LomlQu6v2pftBGQ9lOpnN9oKWH2G3GtTxFQBBQBRcBTBIL6B+vGyUEjDxii+PAzK5PGrH/xstOlRncPHv6eQBJfqL4+aXkQzlT6k+VjmjUH1GHL2cQUdnXotpv+Vn+zU0+D5tu4qIk/n3SEkPZ0pTUmXvj1KSf//PlrntkPPKMWotjvo9ZGbvyRtq9Uzo0tWlYRcIuAtJ9K5dzak2l5JcyZIqbyioAioAgoAoFCIKh/sG5AAsEDmS3eVtAjzj7Sio9ZP2SfqbTGEGSMMMeH7Tsv0KARB6h+s20p9aWrj/NBwjnMmH3csQf5NuEFCU5kP0i0JLRsV5SwPPvP9kiOlTBLEFeZICEgva5J5YLkm9pSeAhI+6lUzm8ElTD7jbjWpwgoApFD4NqVa4RNQ34QCOofrBs0QHAffnpF8fbMjfjG8SPmeLmj3pDrG3lPV1pNg0ceiFlgyxG8sQMCvXrdWfp84mFDpFu220H139rqbA2aYX+Lc1y/2VYj16HrbmMP6sd7yxymf3XMyCLmsGb92ZT2N2i2JUYHl0O8Y+cFqlFvY8ryDz+9PKN84Bm1EMV+H7U2cuOPtH2lcm5s0bLeILBgwQJ6+OGHqWPHjt4oJKJTp07RpEmT6MiRI57pzIUiaT+VyuXCxlQ6lTCnQkfzFAFFQBFIgMD3F6/QlmW7adbAr2loiy/okzc+p96vj6HBTSfTjL5LaOOCHXT+VPJ3OROo1CQXCAT1D9aFS4ag/ukvIIXL6eGYeEXx8V8swmzINcuV5Ldsu4NmfHUsJXnOxMZz564Vk9S/LKcadTcmLYpRbuSnsx/5IM5r1p11dBlSDn//slxUPhU+dv1KmB2IdSckCEiva1K5GTNmEGTjt/vuu4+6detG339f8hAsJBCFzsyJEyca/GvVqpWx7SdPnqS1a9fS2bMl10soadWqldFZuXLljHX6WUDaT6VyftqOupQw+4241qcIKAKhRuBg0VGa1nsRdX1leMptUpd5tHtd9Ea1gth4Qf2DdYPVoOH7i0ljuW+TxqwfZPBPKeRAPjFi/HHfPbRo6UlDUBNN3WZ9qWK7ngZvbaFDhy/HiOPYkGXLHshxSOSXTWZhpyHLVnn7GPkHrTrHTDiUUp7tRb1RC1Hs91FrIzf+SNtXKjd58mRDrO644w6qVq2a2cqUKeMQaKRpyC0Cbghznz59TFvNnj07xsgpU6bQ3XffTZ9++mlMetAOpP1UKue3f0qY/UZc61MEFAHXCBzccYy+nbKRpvZcRCPaTKeRbabTjD5LaNWXW+j4vpJFg1xXFKdgx8q9NPKdGSmJsk2kMeK8cWFRnBY99BqBoP7BuvETBA9kL9XG+iWyyfQUT73G9OviDaQa+rC1fHe7k4786nU2JLQH6anykcchka1I4wDZZLZWqrqWMModHxLpjNdh1xFfPqzHUez3YW2LXNgtbV+pHBPmmjVrxpi7bt06hzRv2rQpJk8PvEXADWFu3bp1QsLsrYW50ybtp1K53FmaWLMS5sS4aKoioAgEFIFNi3fS5+9/Rf0bTki4Teo6j3auLrkB98qNY/tO0eh2X4rJMhPnIc2/oL2bD3tlhupJgEBQ/2ATmCpO+nTYPvpTuW/oT099YwjkH00ce8zKQAYT5acr71d+IsJs22uT2fpNNyf115bDSDmPkoNE2/pAluOPgWfUQhT7fdTayI0/0vaVyiUjzLDx/vvvN2Rs/Pjxjsk7duwgTPPFiPRtt91GZcuWpUWLFjn5vLN69WqqWrUq3XPPPWZr0KABbdiwgbNp/vz59PTTTxsdt99+O1WoUIG++eYbJx87IIPPP/887du3jxo1amRGTOEXyh04cMBMRX755ZeNLdDx7rvvEqYocxg9ejRVrFiRVq5cSV26dCEeOUeM+g8dOkSNGzcmTD+HL7Vr16YtW0oe5EEPpjpDBn6gbsgOGDCAfvjhB1PN1atXTR1t27al7du3Ex48YGQX+urWrVtKH+xu0aIF3XXXXUamUqVK1K5dO6PbnpI9a9YsevHFF40c6oW+OnXqED+82Lp1q6MH+Q899JCxA2UQpk2bZo5Hjhxpjvln9+7dxJhx+w0bNoyzTbx+/XpTdsKECTRnzhyzD3xhM2y1MYY+pAEfyMCf/v37x+hLdQDbJUEqJ9HlpYzMei9rVF2KgCKgCGSJwIb5O8w7w8nIMqePbjeTti/fk2UtiYvNHvxNxmSZSfMXPRYQ3nvWkBsEgvoH68ZbELw/PrnMEOZkMes35Pqpb9LKJ9OT6/T6TUpGrQy5j/PLJrMgzMns+XzCIXaZqr+xnhYuOeEc48FCsnJIt+twCoV8J4r9PuRN4qn50vaVyiUjzCCCIFTQg9FmBBApHGMDYQYx5GMmcpDDYlOczrKIQTQRRo0a5eSDaNl6pk6damTw8/jjjztysIUJL3SxbYiZ2CO9d+/eTvn27ds75ZEHOS6HY95svSDoHC5cuODYhnIgpVxm4MCBRgw4cRpiyCXTd+XKFUcH5LDQl22PTZjxAAH6QEIhx3UA98uXLxO3G6fbMQz75JNPTBk8ROCwefNmpz7ose0EieewcOFCpz62gR8Y4JgxvnbtmoM92rBcuXJGP4i1NECfJEjlJLq8lJFZ72WNqksRUAQUgSwQOHnoDI3tkHxkmckyx198vICuXL6aRU2li+zfcoR61BiZNWEGcd76zXelFWuKJwgE9Q/WjXOGBD+5jP74xNf0pyQx6x8Ecp1CLll5v9JtwpzIL9jPoX7jjQn97d57N507l/h8Xr3uTFr/lTAzwhqHBQHpdU0qx8TLnpKNEV0QLegAYePR1Oeee86kde/enUCWED788EOThhFkhIsXL5oyKItFw65fv27SQT6xnT9/3skfO3asycMP3rVFGZAtXmiMCTNWjwZJRMAoNOSwYQSZ7RgyZIhJQxkOTJgx6nrsWPFq/bCBSWrTpk3p3LlzRhwj56wXMggfffSRSatSpYqxG2nLli0zaSCICDZhhg04RrCntHMdwAN1YFSe0+AXRrCRbhNmjCAfPlwyCw24st14cMEBvqFs/DvMiQgzRtshi9F6bhfMBEAaNoyQIzBhBqmeN28eV+WQcNiPgNW9UQ5E2Q5nzpyxD1Puo7wkSOUkuryUkVnvZY2qSxFQBBSBLBD4dsqGhFOwmSAnijcs8Ob94a8nrHVFlkGYMUKtITcIBPUP1o23IHggwak21i+RTaUn13kgwRwS2Trty6OcTdh/tfY6x2/sr1qb/KYM07Jt+WS+KGF2INadkCAgva5J5ZgwQx6EjEkZjrGNGzfOIHPp0iWHWIH0cti5c6dJf+KJJ0wSky1MXU4UMBUaejFaaweQch5pBolDYMLMx0gDQWbbEtkBkseBCXPPnj05ycQ8ehtPMvFwALr37CmeiQZiiGP4ZAfUgXSMGNuE2ZbBPuvDtGUEjF6jXHy90neYMcUd5UFUOUgJM4g5ymKzp1RDDx52IJ1x4jYEwbYDP1RgjOEXyuEhx8aNJddzu0y6fZSXBKmcRJeXMjLrvaxRdSkCioAikAUCEzrPyZgwD2s/iabPmOF669d8jGvCPOIf07LwWotIEAjqH6zE9mQynw7da0jjQ39emjTmsiCDqeRAIvOZbxPm7r13Of6A6B48FLvKNvs0beYRAgZ2ADlG+fc77zB5PT7ZTU89962jL5Wf8bpsvWHdj2K/D2tb5MJuaftK5Zgwgyhjiq49vRlTeDlg9BE6sYHI2hvSeNSRR3rt0VLWgXjw4MFGB49I23l4txa68O4sQiLCjHQm9TwSjDS8j4yy9nTgZIQZ72BDNp64MmHfu7f4GsOEF77Z/nL9GAlPRZh5GjMTZibaeI/ZDokIM/Ti3WLgCFLcsmVLZzp3NoSZ249Hxu36Mb0ceGDkGSEZYcY3nSHHhBmyPGqNdDwQSPQ+u11X/D7KSYJUTqLLSxmZ9V7WqLoUAUVAEcgQgQtnLtGQZlMyJswDmo2locOGu9561E39CSl+VzlV3KduyZS0DN1X8TQIBPUPNo3ZKbNB8B56fIkhug89vtSJ//jnG/t/XuoserV9x/mE+enK+5U//csjjq+vvr7W2Pr8SyuTTrF2hK0dvK/8fJWVZPufif1KmC0wdTcUCEiva1I5Jsz2lGwmQVh0iwO+9QudIIsgcPEbFtVCwPutkEtEiO18TIeODyCHKDtixAiTlW/CDFuwxfuKY/7cViaEmYn28ePHY1xPRJjr1atn6kb9ILlM3nGcDWHmKeKJRv6BN/TyQ45MCDOmdqPNbfswxVwaUK8kSOUkuryUkVnvZY2qSxFQBBSBDBEAYU405Tpd2oCm3hDm7rXdE+aP/zYqQ69VXIpAUP9gpfYnkhs4ZE8xYX5sSdLYJqIYkTUEMoV8PvLbdyx+Vw4+4hvND92wb/rMEhINP56q8I2xv1qtNbS9qGQaKMo0b7PFKcflM42BZ9RCFPt91NrIjT/S9pXKJSLMRUVFDlnDu7QIJ06cMGkYNU0VvvjiCyOHac+JAlZvhm1YTTk+YFo38niUMt+EGeQS9mBkNVnIhDDzFG97ijn0xhNmnuaOuu3RaKySjbRsCDOmYaMsSDu/980+vf/++yYPMUImhJl1AAe8k84PBTB9WxJgkyRI5SS6vJSRWe9ljapLEVAEFIEMEcA7T0NbfJE5aW7xuevRZYxQezHC3K9+8fthGbqu4gIEgvoHKzA9qYghzI8tpoceW0x/MPESs49j3p6qsIzOnS9ZCGvh4uOENM7nOFn5XObDjjHjYqcj1vv7esc2XsAL5Djevmq1it9rBDjQEZ/PdnMsyVfCnLSraUZAEZBe16RyiQgzXH/nnXcMibIXdOIpxUuXLk2Kzq5du0w51B//iSYUwvvByMO2f3/Jpx55yjDSeQQ234REGqJ6AAAgAElEQVQZo+6wB4uOJQuZEGZe3Aufr7IDPsOEeniEF1PFcYzVse2AxceQbhPm6tWrm7T4z0clWvQL09VRHgSdA95N5/SZM2ea5GwIM+tje6ZMmcJJKWPYIwlSOYkuL2Vk1ntZo+pSBBQBRSALBL7svzRjwjy1/3zatm276+3zD2e6fod5wodzs/Bai0gQCOofrMT2ZDIDB+8x5PIPjy5KGVeruSqGNJ89d9WQzP+svDxluXR6s81/6q9fE8gp7OAAUv9ex20x9nDemHH7Y9K5Xs5fveZ0wnyWk8bAM2ohiv0+am3kxh9p+0rlkhFmezVrXvgL79RCLzZ8hmjQoEHmW8n4LjK+Q8yBR0Ihh6nZWHEb05hRHqFhw4ZGBwg4VtkGIeUpvW+//Taryfs7zDaJxxRsfBYLtmJ0vHz58sbOTAgzRo55BBYj8Ph+MS8EBqyYMGOBLpYDdn369HGwgJxNmLFSONKAJQh5jRo1zErliQgzvqcNWWyYbo9FvvjTUiDnvBq6lDCPGTOGMCsA0+vx7vp7773n6OeF05zGTLIDWyRBKifR5aWMzHova1RdioAioAhkgcDmJTszIsyDmkyivZtKvtuaRZVOkXVzt7kmzF+PX+vo0x1vEQjqH6wbL0Hw/lB2EYEQOluSY5BjvMccHxYsOk4f9dxJyDc6kpRPpz9dPvS3+2Abob74ALtA6uPrZzmMisfrB+nmMG3m4VL58fKS44GDo/dZtyj2e253jckQEgkO0n6QjDCjDkyxhR6QWZA4vK/at29fhxQhDxtIF793jHIYtQQh43zEIHRMvJHfvHnzmHzIgEBi5WkOGN1GOt6ftgOTSXxqiQMvSGUv+tWhQwdTnr8bzLK8uNicOXM4ycS8SBcv+oVErOrNI7DsD45hP4KEMNv6QEbthdWgiz83hQcNHPBwAZhxnXgowe9424T59OnT5n1qloM+POzo16+fKQtM7TB8+HCHjHMZPMzgz25BdvHixaas/U1qpMdjjKnz9jeioQ/H9gi2XXeifZSRBKmcRJeXMjLrvaxRdSkCioAikAUC31+6Ql99ukxMmj/7UDZNSGLKiQOnaXjrqVmT5v4NxxO+5awhNwgE9Q/WjbcgeCDMDz6ysDgueyNOcfzeB9vo0KFLCas9eOgSTZtxmAYM+o7qNFxHT5ZfmrF+tgflu31cZPRt2178bdP4SmFH9547k9q/as1pp0jz1pvoQcs/2MnhvQ5bxf6zfSa29OFYCTMjqnFYEJBe16Ry2fgNkoj3nEFk7ZWq43WBYGOkEStYJwogvFiMasOGDc53lhPJ5TsNI694l3jVqlUxxNKNXSCfBw8eTKkC9W7atIlOnTqVUg6ZwJK/7ZxOGO2Hd4xXrFhBmXwzOZlePEzBe9k26U4mG58u7adSuXj9uT5WwpxrhFW/IqAIeIbAwR3HaPJH89OS5gFtPqch/UfQ8uUrPKt71czNWRPmxWNK3sn0zCBV5CAQ1D9Yx8AsdkDwDFl+ZCE9+MgC+sONGGklxwuddDu/WauNhJFbSQDhXbX6lNlGf77PEOoBg3bTwEHf0dQZh528ZMQ4vg7Ua0iusTOxfbC/bsN1MUVhA+oHsecA0s1+cSzxPxE+SpgZVY3DgoD0uiaVC4vfamc0EZD2U6mc3ygpYfYbca1PEVAEXCFwbO8pWvjZyoSfmfr0rQk0sP1oGjJghLPY1/btshUc0xl19co1mj34m4xJ87Tei+jciQvp1Gu+CwSC+gfrwiUCaQXxe/BhkGXE8zM+fvKZJdSs5UYaM3Y/bd+ReCTYjY0oC1KLEeFmLTcQ6svEXhDrZAGLglX928qM9KXCC3hGLUSx30etjdz4I21fqZwbW7SsIuAWAWk/lcq5tSfT8kqYM0VM5RUBRSAQCIA4r52zjZaMW0NLx6+lDfN30P6dh2n0mNIrYx89dswTm0F8MyHN03otItipIbcIBPUP1o3XhjD/aX4xUQZZxubBcZ0Ga+i9Dlto4KDdxaPHa06l/R4yyOuqNcWj0CiHDXqe/Mti1/ZBz8JFJecn6po24xA9+fRiT/w1uD083zyAcNMeQSwbxX4fRJzzZZO0faVy+fJD61UEgIC0n0rl/EZVCbPfiGt9ioAikFMEsOgGPgVlb5OnTDELdnhR8fVr12n1rK00uu2XSUebh7eaSt98sYEunbvsRZWqIw0CQf2DTWN2yuwBn+42hLHMH+cVx39KEmu+CB/gGbUQxX4ftTZy44+0faVybmzRsoqAWwSk/VQq59aeTMsrYc4UMZVXBBSBwCOwfv2GGMIM8rxg4UJP7b525RptX76Hlo5bQzP7LTXbojGrafPSXXTp/Pee1qXKUiMQ1D/Y1Fanzh3w6S4CWdbNGwyUMKfub5obPASk1zWpXPA8VIsKCQFpP5XK+Y2dEma/Edf6FAFFwBcEFi5aXIo0r10Xu9CQL4ZoJTlHIKh/sG4cHzAQhHkulXloriHND5hYj7PFA3hGLUSx30etjdz4I21fqZwbW7SsIuAWAWk/lcq5tSfT8kqYM0VM5RUBRSAUCFy7do2mfDG1FGnGpy80RAuBoP7BukEZBO+BP8wpJswPIZ6jxy7wUMLspjdq2XwgIL2uSeXy4YPWqQgwAtJ+KpVjvX7FSpj9QlrrUQQUAd8ROHb8OA0bXrJiNqZmjxo9xpPvEfrujFaYFIGg/sEmNViQ0X/gTirzB5Dk2Rp7gAPwjFqIYr+PWhu58UfavlI5N7ZoWUXALQLSfiqVc2tPpuWVMGeKmMorAopAqBDYUVRUapR5xswvQ+WDGpsagaD+waa2OnXugIE76YEHZ+vmEQbAM2ohiv0+am3kxh9p+0rl3NiiZRUBtwhI+6lUzq09mZZXwpwpYiqvCCgCoUNgxYqVpUjzsmXfhM4PNTgxAkH9g01srSy1/4AiQ5Z/X+YrjR+cTW5xAJ5RC1Hs91FrIzf+SNtXKufGFi2rCLhFQNpPpXJu7cm0vBLmTBFTeUVAEQglArNnzylFmrds2RpKX9ToWASC+gcba2VmRyB4v39gFj1Q5itDFkEYf18m7ljzxfgoYc6s/6l0/hGQXtekcvn3SC0oZASk/VQq5zeWSpj9RlzrUwQUgbwgcOHCBRo7bnwp0nzo8GFjz+mj52j1l1voi48X0OC3JlPXl4ebbVDTyTSl+wJaNXMznTp8Ni+2a6WpEQjqH2xqq1Pn9u9fRCB5GnuHQ2rEw5cbxX4fvlbIncXS9pXK5c5S1awIpEdA2k+lculr9FZCCbO3eKo2RUARCDACBw4cKEWYRw8dT3OGfUO9a4+htuX7pdx6vTaa5g1fTsf3nQqwl4VnWlD/YAuvJdRjPxHQfu8n2v7XJW1fqZz/HmiNikAJAtJ+KpUr0ezPnhJmf3DWWhQBRSAgCGzatMkhzX0+GEKdqg9ISZITkWiMOm9eGr3vugakiTI2I6h/sBk7ogUUgQwQ0H6fAVghFJW2r1QuhBCoyRFCQNpPpXJ+Q6OE2W/EtT5FQBHIOwJLli6lT94bTB9UyZwsM4Hu/foYWjtnW959UQOIgvoHq22jCOQSAe33uUQ3/7ql7SuVy79HakEhIyDtp1I5v7FUwuw34lqfIqAI5B2BHSv2Uscqn2Y8ssxkmePu1UfS1mW78+5PoRsQ1D/YQm8X9T+3CGi/zy2++dYubV+pXL790foLGwFpP5XK+Y2mEma/Edf6FAFFIK8InD1+nka3m+maLDNpHvn2dF0MLK8tqiPMeYZfq88TAkG9scwTHJGrVtq+UrnIAaQOhQoBaT+VyvntvBJmvxHX+hQBRSCvCCwdv9YzssykedHoVXn1KeqVd+3alc6eTb5CeVD/YKPeLupffhFI1e9xvuC80RBcBLy6rqXqB8H1Xi0rNASk/VQq5zd+Spj9RlzrUwQUgbwhgE9HDfj7BM8Jc9964+jEwTN58yvqFd9yyy100003UcuWLRMS56D+wUa9XdS//CKQqN+DKOM8wfmC80ZDcBHw6rqWqB8E12u1rFARkPZTqZzfOCph9htxrU8RUATyhsDGhUWek2UeZdYFwHLXrD169DA3/z/+8Y/p5ptvLkWcg/oHmztEVLMiEPsqAhNlnB84T2699VbCeaMhuAh4dV3T619w21gtK0FA2k+lciWa/dlTwuwPzlqLIqAIBACBWQO+zhlhnv7J4gB4GF0Tfv7zn5vVsPFnGk+cg/oHG93WUM+CgAD6fTxRRhq2X/ziF0EwUW1Ig4AX1zW9/qUBWbMDgYC0n0rl/HZKCbPfiGt9ioAikDcEhreamjPCjG8zexnatm3rEET8gehWGgNMO61UqZLBxkvsVZciEAYEcE1A/8d5oNeH0teHsGKS6XUtrH6q3dHps9K2lFxXoSuIIZhWBREptUkRUARCj0Dv2p/njDD3qPFZ6PEJsgMYMeM/ZR1hDnJLqW1+IYDzIdkIM0YuNQQfAS9GmIPvpVqoCMgRUMIsx0olFQFFQBHICQJdXx6eM8Lc8YXBObFZlZJ5FxPvZMYTZcYmqH+wbJ/GikAuELD7fTxx1neYc4G4tzrxDrNe17zFVLWFHwH7uhYkb3SEOUitobYoAopAThHAKDAv0uV13OWlYTm1vZCVe7WabCFjqL5HD4FEN5ZMnHWV7OC3t17Xgt9GaqH/CCS6rvlvRekalTCXxkRTFAFFIKII5PId5sEev8Mc0SbIyi2vvleaVeVaSBEIKAKpbixBnPU7zAFtuBtm6XUt2O2j1uUHgVTXtfxYVFyrEuZ8oq91KwKKgK8IzOizOGcjzF/0WOCrL1pZCQJB/YMtsVD3FAHvEdB+7z2mQdKo7Ruk1lBb/EIgqP1eCbNfPUDrUQQUgbwjsHb2tpwR5pUzNufdv0I1IKh/sIXaHuq3Pwhov/cH53zVou2bL+S13nwiENR+r4Q5n71C61YEFAFfETh95Bz1en2M56S5R/XP6Pj+0776opWVIBDUP9gSC3VPEfAeAe333mMaJI3avkFqDbXFLwSC2u+VMPvVA7QeRUARCAQCCz5b6TlhnjtseSB8K1QjgvoHW6jtoX77g4D2e39wzlct2r75Ql7rzScCQe33Spjz2Su0bkVAEfAdgdNHztLQFl94RpoHNZmko8u+t2JshUH9g421Uo8UAW8R0H7vLZ5B06btG7QWUXv8QCCo/V4Jsx+tr3UoAopAoBDYuLCIPqo2wjVpxqek1s3bHijfCtEYv/9gly1bRqgT2+zZswsR8hifjx075uAxZMiQmDw+uO222whbLsPJkydp7dq1hBWiCyH43e8LAdMg+ShtX74WaVx8TVYcgouD5PyS9nuJLi9llDB7iabqUgQUgdAgsGrmZur68vCsSfOH/z2Ulk/dGBp/o2yo33+wjRo1cghi9erVowytyDebMIMUg7jGB7RRrglznz59Cuohht/9Pr5N9Ti3CEjbVyqXW2tVuyKQGgFpP5XKpa7N+1wlzN5jqhoVAUUgJAisnbON+jeckDFp7ltvHK2etSUkXkbfTD//YL///ntD/O644w7ChrrPnTsXfZBTeGgTZuDRoEGDUtJIzzVhbt26tRLmUshrQlgRkF7XpHJhxUHtjgYC0n4qlfMbFSXMfiOu9SkCikCgEDi86zjN7LeEOlYanJY4d/jPT2n6J4vp4I5jgfKh0I3x8w922rRphpS1adOGWrZsafZHjx4d0wRXr16lihUr0rvvvksrV66kypUr0+23324IY506dQike9KkSVS+fHmTdv/991Pfvn3phx9+cPT07t2bypUr55BykPN27drR8ePHjQzXgXoSbTt27HB0zZ8/n55++mlTF+yoUKECffPNN04+duAD9GzYsIEGDBhAjz/+uJF/6KGHqF+/fjGy8QdMmO+++27H3tWrV8eIoY3iCTOmTjdu3Jjuueceg+N9991n6mYc2Ef4PWvWLHriiSeMDtTTqlUrunLliqlj69at1KJFC7rrrruMHtgMX1588UWTP3DgQHM8d+7cGJswIg05TLFH4Pratm1L27dvp5o1axLqgt1169alLVtiH5Klsz+mshwc+Nnvc2C+qkyDgLR9pXJpqtNsRSCnCEj7qVQup8YmUK6EOQEomqQIKAKFh8D+bUdo8dg1NOa9L+mTNz6n9s8NMFvv2mNodLuZtGjMatq35XDhARMCj/38g61SpYohZatWraIVK1aYfRBfO4B4wSbeQFJBBu1j7IPggZBxOt6/5cBpkClTpowj8/LLLxsRkEWWSRQzYR01apQjB2Jq1zd16lSujtq3b+/IQR/keAQdx7ZtTqEbO0yYYeeUKVOMHpDW69evO6LQYRPmCxcuOLYgHfLsBwguQjyOsAcPF1iOsZg8ebKTxnkcQw9IOY5HjBjh2IOdevXqmfQxY8aY9Pj6YJeN/fPPP++Ul9jvCOdoBz5piC4C0vaVykUXKfUsDAhI+6lUzm+f9WrrN+JanyKgCCgCioCnCPj1B3vq1ClDsEDcMAqKjUnlwYMHHZ9s4mUvCtalSxdTHgR6+fKST5HVqFHDpIO0cgDhtRevwkg1/LRJJ8tyzFOSQeCvXbtG58+fNyPbKDd27FgWo08//dToAhnHaDcCE2aM4vLoNHRgdBzlMcqbLNiEGTKoH2XsBcDibf/oo4+MDB5AwE4EXkwNZB0hGY7r1683ZaFz3bp1RhY/GFFGmo050rMhzLAd9SOgDujFxtPvJfabwjn8gT0aoouAtH2lctFFSj0LAwLSfiqV89tnvdr6jbjWpwgoAoqAIuApAn79wYJEoS4QUw6YGoy0Xr16cVIM0XMSiQxJhixGU+0wfPhwo6N27dp2cql9EG2U56nItgCPsoIE86JbmIqdqD4QfR5p5pFoJsw9e/a01RrSCx21atWKSbcP4gkzpjOjDMg928LHXK5s2bJGZuHChZxkYn4AAR9twhwjRESY2g6dtr1eEub4+hj73bt3myyJ/fE6vD6G/xqii4C0faVy0UVKPQsDAtJ+KpXz22e92vqNuNanCCgCioAi4CkCfv3BYvQVdWEEE6PN2LCPNEwV5pCM6G3cuNHIxhPm8ePHm3SQQA7Q3blzZ8K042rVqpkRXhBQ1BVPmPEOL9Kx2SOugwcPNmmJFuGCXshPmDDBVJmMME+cONHIZUKYoRDveEM/1419e3ScCSiIJ96X5o19xMh3MhyhH+94QydGjznkkjDze9ZMmCX2s125iuG/hugiIG1fqVx0kVLPwoCAtJ9K5fz2Wa+2fiOu9SkCioAioAh4ioAff7B79+41BA11Jds2b95s/EpG9KSEGeV5BBh14f1nJpI4tgkzpm2zLL+Ly+AyqWzatCknOTEIMHTxe71eE2ZMXWZSiVFs1GUTZhxjA8mN3/CAACEZjsjDwmQoj098cfCTMEvsZ7tyFcMGDdFFQNq+UrnoIqWehQEBaT+Vyvnts15t/UZc61MEFAFFQBHwFAE//mC7du1qCNrDDz9sSBqIGm9Igw3vvPOO8SsZ0ZMSZl6oC6OvWFyKA6Zbox4mzJhaze8YN2/enMWcmFf0rlSpkpPGOzxavmjRIpPkNWGG0nHjxhl7eUEvmzDzImhHjhxhk0rFyXCEIL+vjXeJOaQjzPY71SiTatEv1slx/AizxH4um6vYj36fK9tVb3oEpO0rlUtfo0ooArlDQNpPpXK5szSxZiXMiXHRVEVAEVAEFIGQIODHHywTJv4EkQ3Nt99+a4gh3r/FytDJiJ6UML/99ttGHz5vxOHSpUvOiC0T5m7duhk5fH4KdcaHPXv2mHzgs3//fieb3zFGOn+mKheEGRUyMUddNmHGJ5uQ1rFjR8eu+B0bx6NHjzrZZ86ccbCw26N69epG58iRIx1Z7DCePD0caXjYwCue88i8XV+MAiLn01c8JVtif7wOr4+Bn4boIiBtX6lcdJFSz8KAgLSfSuX89lmvtn4jrvUpAoqAIqAIeIpArv9g8Ukl1IEpxvanktgJe7XsxYsXuybM+GQV19ejRw/C6to8uox0EOZdu3YZGRxjBBlk0N7wXjNCw4YNjRzI/IcffmgIKk+VBpHkkCvCvGnTJsdOmzDbpB1TsDHFGuQZvvBnumwCi7LwDw8ReAo6HhTYAd+SBh7wFe82Y/Xxixcv0tKlSx0bkN6pU6eYz0VlQ5gl9tu25WIfvmqILgLS9pXKRRcp9SwMCEj7qVTOb5/1aus34lqfIqAIKAKKgKcI5PoPlkcomzRpktRuniIMgmoTPbsA3nGGrZjCbQdeWMte9AvEDiQR8thAFnlqMwiz/WkllrFjEHcEjExjuradh318JopHqiHXoUMHI4P3nu3A31W2bbPzsY+VsKET3yxOFJo1a2bybcIMOazibT8IgA4c8/RyG0fgYfsAYo2F0exw+vRps0Aay0EXPlllfx6L8zC6zCucZ0KY8S47h3T2s1yuYviiIboISNtXKhddpNSzMCAg7adSOb991qut34hrfYqAIqAIKAKeIhDUP1i3ToLsYqQYsduAkVasoL1hwwa6fPmyW3Welcfo/IEDBwij6vg8lR1swox0rJyNkV3+brMta+/DV/5esp2O6efAIF15u0y6/VT2pyvrNj+q/d4tLlEpL21fqVxUcFE/womAtJ9K5fxGQQmz34hrfYqAIqAIKAKeIhDUP1hPnSxAZfGEuQAhSOmy9vuU8IQ+U9q+UrnQA6IOhBoBaT+VyvkNhhJmvxHX+hQBRUARUAQ8RSCof7CeOlmAypQwp2507fep8Ql7rrR9pXJhx0PtDzcC0n4qlfMbDSXMfiOu9SkCBYoAvheLqZf5Dvi0DN4hHTt2bL5N0fo9QiCof7AeuVewavDuMT4VVbVq1YLFIJXj2u9ToRP+PGn7SuXCj4h6EGYEpP1UKuc3FkqY/UZc61MECggBLJDz8ssvO5+AwYUQC/9UrFiR+vXrlxckkq0GLDEG71jCB3uDPyDgWJQo1Tdl0+nHwklYjRkPFjRkhkBQ/2Az80KlFYHMENB+nxleYZOWtq9ULmz+q73RQkDaT6VyfqOjhNlvxLU+RaBAEJg2bZpDLPEZm+eee45ef/11Qy5xQbzvvvvygoRXhBnfYcVna/CdWV7NGDFWzs0m9OnTx+A1e/bsbIoXdJmg/sEWdKOo8zlHQPt9ziHOawXS9pXK5dUZrbzgEZD2U6mc34AqYfYbca1PESgABDBKyt96BUGNX5UXUy3zNT3bC8IMYmyHCxcumBFmXOjxDVis0ptp4M8SKWHOFDkyDxoyLYXVjXfs2FHq00SZ6kFfXrJkScwnmiQ6cI58++23EtG0Mlj5GQ+j4FN8OHr0qPlME2ZBTJo0KT4778erV682n6XyypC+ffsSXrtIFGzM8WCrRYsWjlijRo1o2bJl5njPnj2mbziZAd0J6o1lQOEKnVnS9pXKhQ4ANThSCEj7qVTOb3CUMPuNuNanCBQAAp06dTIkBqOvkoBvymKaNm7ocdOKadxly5Y1pHrLli2Eb8Dec889zkguRqtnzZrlqMbiQCiPb8sinUd97777bvOtVft7s0yYu3btSoMHD6bHH3/c6MU3btNNE+cp2fGEGYZcv37djJrjYo+bdg6wB+9h8vdmURb+bNq0yYjgs0G4ced82AFfUAYB35rFd4DxjVt+CHH//fdT//79zfdluZ5CjjP5gwWpbNOmjYMlyqIPfPfddyII8UmiHj16ONiDrEJHpjMLPvnkE1Mum4cr8YbOmDHD6AJ5jw9dunQxD3EGDBhAK1asiM92fYz+m+16AGgLnA84b70KuHbUqlUroTobc+CBh1scYMeIESPM4fPPP2+uP5wX1DiTfh9UH9Su5AhI21cql7wmzVEEco+AtJ9K5XJvcWwNSphj8dAjRUAR8AABkD1c9EaPHi3StnDhQiMPgotyvBUVFRGILR9jlAw3tny8ceNGo99eTRd5uBEGqWQ53ERzYMLMeagT8nyM94iThVSEGWV69+5t9NSuXdtRUaFCBZMGsgv7uR7UiZH3yZMnO2mcxzGUYBSSj+GTjRFu+jVkNsL8zjvvmD4EkgnyiwcyIMx4YBE/EyIRtnjQgfbAN4ER8KAEo6Tog5kE1I0HRV6EVIQZ5K9BgwZeVJNQBx7c4OFWtgH4nz59OtvipcqlIsw25qkIM2a/YP2FoAf0Qw3RRUDavlK56CKlnoUBAWk/lcr57bNebf1GXOtTBAoAAbyfjIve0qVLHW9BRrCwlb0xyWDCjDIgNKtWraJdu3aZKaYHDx6knTt3GmICZSAoGIGFLN77RbAJsz2lGYQEctgwEojAhBmj0JiSi4CRucqVKxu5VKNd6Qgzv7dtj6xjBO7w4cOmHvxgRJFJv02Y+CHD/8/eu0BfVV33/nd03CTa0bQxucMx7HD8W21qG9rallwbKyExEkqJWutFrRq9WBWrBoyaCyoVUASUh4CiPARBHgHCQx4S0IjKQyPgC0QFUQQBQfEJqPhIXP/xWT/nYZ39O+f85jlnn3P23mfOMfZvn73XXGvN9V1r7d/+7rkeof3oM9wbPSFo3Bs1apS3VbzQucSb9Af1qxFpJ9dff32e+po1azyeM2fO9Pch0AMHDnRdu3b1H1MYMQHZwossH2Jo47QZ2ie/pS0RlzZGGPU8ZMgQ77lkVATXDL1HILlCNCdOnOjTIB056DsQST4Y8ZGE+5BTEdopH124f+6553r7ox7mK6+80udJvujxAWvfvn2+/TBig3tjxozxZSNdcME+bGekQ1QGDBjgPyzw8efqq6/2ceVjE2ndfvvtPgq2d+/e3eeNt1eGns+bN8+veN2nTx8/YgTM+KAkHmry7927t7eBDxhgLcOkGVp+ww03+DSxj3QL9VUIM7azvgDl5mOBjB4IMS9FmKl7bBShXXTp0sWPSmB0AkO2kVL28uGN9kPbZHTI008/LcnlzsVwyim08UPb7ttIxoITioC2frV6CS2mmdUkCGjbqVav3rDp3jLqbSaw2rQAACAASURBVJXlZwgYAqlGgBdVHnpbtmzJlYMXc+6Fx+rVq324EGaIsEYmTZrk0+HFFhEiVOhBy/Bn7t95551eVwizXEt+zHtEr9hwTvTaIsy83JMGBKeUsE0OeitWrMipFSPMOYXgBySc+JAzE72HWYZP095CgWiGZJbfEC68yY8++qgPmzZtmieb0k4Y3QBx4qAuNm7c6JMkLgvCkZd4fiGBeFLvvfder8tq6pAwdJF3333XE24IJB5hyCIEEbIMqcbbKW2Ljy/EJc/Fixf7NsmQfa6jhJl4tJELL7zQp8/HKj620D5Jj/4H4RUiDtmEDA8dOjTvYxc2ykiHl156yU8ToA1SjiuuuMITQmzHZj6MUS7ILPcglaRJH4WkYidklKkKfAwif5nCQP4Qbz487dy504/I4B4CWScMXBl5Agkt1FfRJ4x55XxoAj/qEgkxL0WYGSECKUcguth8//33+/YAwecDBVLKXvRoB5QRe8E+lFI4hXqlfmOXSXYR0NavVi+7SNWnZEwh4TnGYVI+Atp2qtUr34LqYtjTtjr8LLYhYAgUQIAXWx56QohRwXPKwkQcMl9XwtsizJCWXr16eTKBZ4sXVdLXEGYZJs22T0gxwswLMWkWegmXIrZFmGV4Nd4oEf65Tp061acLKYZA4AEjLy1hFuLRrVs3x8s8c56Jb4S5BWWw0IgQZml3EodRC5C8m266yd/it8xn5QaESz7m0I7JTzz+hQizxIUsoUv7RahHriGbIXnzgc455tiSN3oI/QjiNXjwYH8Qxjx/2mg4ikGIeZQwkwaebRa0EsFrSx8QwWvLVAGEflWs/TOXHnKL7owZM3Llh6yLp5w0ZJQFZUeIR5kfeeQRT5hJI5QoYQ7zp51LOSH5zMcW4aNTqCv3o2XgAwe4sY5BiLmWMEPuw362bNkyXx489dG8QnvluUN8PpZEpRROUd1i1+Bqkl0EtPWr1SuEFM8xRnPxYYj/b2mQbdu2+Y+FBw8erKu5PNfBWj6Y1TXzDGSmbadavXpDYk/beiNu+RkCTYAAxI6HHl65QiIeViEupQgzwzVJi4OXayHbXGsIs3i1hDTUkjDLfOtwzijDYsV+Xvrxtsm1hjCzUJPo8+LPUFq5Dl/kC+HcLPfAQyN8vEBXhkVLHMEYEoOAs5BervEUyvD3aggznkbyL0SY5YMN0xFEaC+QdUiyHHiY8Rh37txZ1HKebIh/VKKEmTQZYi6C5xaPLBIlgKIjZ7ykkHoZzk1+UcI8f/58X0aZD37gwAF/DamPklTSLUWY+bgkhBk9bBXREmaeQdQndV8JYea5EX4AW758uS8PWETxCu3FTob684GQOgeXUErhFOqV+q1t96XSsLDkIqCtX61eWFL6A+2VvkF8OWSECeGNFkb4hKPUxB6eV9jb1iKdoh/XmWcR+fIsC2XYsGE5/PhAaFIYAbDTiFZPk1acOjrr48zR0jIEDIHMIyCklH9shba60RJm/mkLQRYyA3jMxeShqiHMsl3TyJEjPe5iW3RIthCWQl4rqbBSHmY8WGKrkC3mXmMnR7iNlgwT1xBmGaqNJ1A8iAxXJU0jzC01AxZawQvISyFeX0gd3hUIEd5cyB3CSyTtAO8xQ5fRlwXsZL4zw30hTeV4mIsRZoYPU4bZs2fn5vjTnmjfjEbA4ww5JT6eTfkIBHmHQMuIA2kfIRZRwsycaspKWhB3CDSrfiNRAhimQ1mlDa9atSrXpvH60u4ZVo5t4lXHw8o2ToSDJ56saggzxJV6YLQGUzIg0IX6KmXgucNCbNQtfYT+hoSEWTzPsuAYafOM4JkTDsmWumG+8TvvvOO9SzLaIIpXSJjBl/UKqBM+boQf0bClFE4h7qV+l9PuS6VjYclEQFu/Wj0pJf+T6TvEo2/iMWVuvqy7wH36jYyikXj1PmObjH4J8+bDGc+tdevWhbdr+huvNrjwnIh+TMAWwjh4rpgURgB8NKLV06QVp47O+jhztLQMAUMg8wjwcs0/Ox58v/jFL/zLYVhoLWHmhZs0OGSIJ+nwZZl7hQgz8yhFeIHnHxy6EB+kFoSZfPD6kQ//POUfqngjo//0ZZGmkDCzSBLxGe4aCi8u3GdIqwjzZ7lnhLkFEbDQCsQTgintk7gszsTiXSKEQXIgZYQzLJpFvxAIkHgaGN4shFlWbCeufDCBMBFftpwSwozXJCRvEDT0wgOPMh9GZJExwiCmzCHGBmkv3IcUcy5GmGU6AvZTTvHQEIe2KUMxowRQ8OBMGaQvUUbx9kLm5YURXBHpn6TPIS+RLG4GpqFwLZ6iaP58JBIPM8MvKQfebV7sIf2M3ogKafABhHKRN7aJlyrEHDJNeQhHyAv9uXPn5hFmMMXLL2UhjmzPVcpe5qJLHGyVYfahvcVwCnVK/SZ9k+wioK1frZ4gxdQT4tAuZUE8CaNfyCgm+ZAmYfU8y8iU6P/OetoQ5sW0GDCTZ5yE8WGO+0xL4Wz/kwWZ1mfw0YhWT5NWnDo66+PM0dIyBAyBpkCAl2QefBy8ZPICyfxjXj7lxVszJFtWxIaQ8sIdvrwWIsy8zOPN4aVAXuTD4atxEGbKxDBdbOEfJHlKOfFEicjCPoRBMFjVWwgw90LCLF5zCATEgLQhXLy0oIunEQ+dvNhzz/45tyANFuUKXhZGABSaByekF3JdbIhdoXjl2qDVx4scfgiSeLxUVmoHabJoV7mya9eugp4nGYIt6XGNV6YWXio+SFFHrBlQSvhwV0ogw+FHhnC/9mg8PoqxiBptQiv03/BDTKF41eBUSbsvZIPdSyYC2vrV6kkp5f9v+P9HwjjLtAP6GG0Y4YMz/4ujI7MYbcJ9/jeHwqJ+/N8lDfJjpFRIzvkYzigUPoihgy7/s/kwyYck+ahOGOlzsMghcuutt/rrcAtInucsHigfyvi4yP9QGRUjtjHijHcRng141eXjIff4wF1ISFtGj4U7W6DLGgXgT3klLT6ihkIc7GcaBtjyG0xIk8URw+cUz0zu8dECHaa3sSgjz2rigWP4zMJm7rO7SCjUB/dlZB7PLRZelfVTyJvFKOVDMHHBhvy4x0gk6kSm71D/vFeJ3bz/UJZyRNtOtXrl5B2HbvlvGXHkamkYAoZAUyDAIkvijeMhKAf/BJnbt2fPHo8DxJkw/mlFhdV55SGPDiSYfyj8LkSYIZuSD2f+AYSkR74U8886lEWLFvl4MnwzDJPf/GML0+Y3ZeHrMv84GZYaFYaQiqcSfbxfMhwufGFhaKh4qdHjHxr/uPD+gYvkS35Coo0wt6ANNnEKGIuXOM50La3KEeADHJ5lhj3jdaeO5GW+8lTTHTPudp9uNLJnvbZ+tXogxP9c9KMjPaLoQYzQYx4xwogXrqPTIGSNEVbLFwk/luPFlrTIUz7wXXvttT49wmT7PtKH4BKH39FDtq2TaUohYZNpTsSBuEqePCdCoh5+sEZXniX8RldGh0lZOPMRXHTD++iSD/H4MMiUDvSY0xyKrNFCGAdxxIvPtbyLQIRlRBHvOfLBgXcBhDP6eLVFwtFJoe2SjuiGjgb5qEBajJIREWxC24jHR1J0OXgXk/iFRvhIWoXOxNeIVk+TVpw6OuvjzNHSMgQMgaZDgK+bDC9l2CrzAPliW67w5TX6tVjS4B+FPNC5xz8vhmGGX09FtxFnysuLR0jci9kBCZC5tKEOZB2PaPh1OQxv5t9x/4Pli768KDYzrkkqOx52Xs4ZYYGnKerRTpKt9bIl7nZfL7stHx0C2vrV6pGrTBOSqQ7FLJEhxuKh1BJm/ndBILGJ9R4Q/j/LtBH6MKSZcI7wfzSjOETwyhJeaEh2lDDLsGjylRXpyVNIImcRIYWMdhPPLtO9hGDL9BnR5yxkXIithAmWfPxHGM6OzRDOUIQw88FAdkwgnAUU0ZddBviAzjVEORTBRUaXyYcDdMKP8bLXO95o0iGMdw9Z1BLCLe9QfKCXepJ7gg1k+4EHHvDrXPC+hmOC9PA6i/AeEtad3C91Jg2NaPU0acWpo7M+zhwtLUPAEDAEYkYgSphjTt6SSzgCSf0Hm3DYzLyUI2DtPuUV2Ib52vrV6pHd5MmTPfmBdJYS2emCveMRLWGGEGIP5CsUVqvnPiO8IHFCUCFmhaQcwiwjzhhiHQpEkDw55CO9kELxvIq+kOvoyCJZnI80otNiJA7DsUVkWHaYvhBmhkiHwroGpAuxRWRhMYitrIkR6jMsGn1ZtV/05WPEqFGjvLp4/WW+teDDdLRQGFZNejI1TrAJd2pAX0YMkI+sdxGmo/1NXhrR6mnSilNHZ32cOVpahoAhYAjEjIAR5pgBTVlySf0HmzIYzdyUIWDtPmUVVqa52vrV6pE9HmP0xatZzCQZdiuEVkuYhZBDiCFgcsgwa8gbgpcUOzggmRDVcH2AcgizTGWCKEZFvKis2YAIKQwJLffxOGNLlDDL2iLR6WJ4V8V+PLqMHuNgrRLuC1kl7WKEOfQEe+Oc8/OOJV3yZI64CF5d+dDASDRsRVeGjENoEdnKThY6FXzwfEt9cBbvtHjVi2EDdjIcnPx69erlvc9il/ZMXI1o9TRpxamjsz7OHC0tQ8AQMARiRoB/JHwxZ6EQk+ZDoBH/YFlghi/v5QqL3TAvP27Bm4LHoNA8evLiBVGGIMaddyPSo8+z5VOphbpqYVcpHNuqg7jtaUS7j7sMll5xBLT1q9Ujp40bN3qSBfEKCWpoBX1LiKYQSy1hZtgy9jAHl//J0SPcw5hFsGSuLXEYJi7PqHIIs3jDZVGwsCxCChkujRQjhcUIM95cbGONk1CESBNW6ABfPuQj5RBm6gQMhRiTdrjLAfOGuYcnn/nk1BP5yDon/CZuWL8SxkeQaH1wjacaKYYNYXiWmacellW2WvSRFX+IqxGtniatOHV01seZo6VlCBgChoAhYAjEiEAj/sFGtxQqVhz2Og49HzJvLe4Fq9huChzkxTC0h+GIvFiJdycMS+tvFhSkvOIdqUc5ojjiZWIBPggGUqoOamFfI9p9LcphaRZGQFu/Wj1y4bkjZFjmJ0dzlwUwIV2yyr0QZrazC0WG/8qiX5BW7IGAagVPqBBn+QhZDmGWlaqjc4xZ5wBbOOTDWjFSWIgws+4KccFLcJAysdUgYSxEyB7x4SEkXVbdLocwS/oQX7CVupJt6Rj+Tb4MP6d+xPMt/1dk6Dv2iLC1J3Huv/9+uVXwXAybUJkPvqxSTnrkL0PdQ51iv4mjEa2eJq04dXTWx5mjpWUIGAKGgCFgCMSIQCP+wWoJMwuIhcMfIVm8DMYtbZE1FsNhoZesCJ4YvF/ixalXuUIcWRiOticv023VQdw2NqLdx10GS684Atr61epJTjIcGi+wEDEJY7FMGX4ru1AQJgtcEScU2UtcCLPsNQ/RE29xqF/st8yTZbgvEp3fG8bDK0qZZZVsIe2QbiHG6EuaDPkWKUYKCxFmVrsmn9DDSzqy0jhhsuq3pM95xIgRPp4sNlYJYZb0+EBBPuLhDhdM4758JGDOM9dCsMMPiUKi8ZaXIrjFsBFbwjNkmfxkp5MwrNhv9DWi1dOkFaeOzvo4c7S0DAFDwBAwBAyBGBGI6x/svHnz/LB+PBbM9+KljTlkDPXnRYSXF1YqR0LCzHA1XjbkZYXFXSDG7Hsp3gbm8PGiypDEkEDPnDnTL+LCCwheA9nDE1t4MWQhF7byIJ1Jkyb5vGULMvLjBZYXQyFreJGZy0aYvMQSCY+IeLqxlZdh2c6FvTaxF2EOHi/M2NOWlwhvAziQF2XGqyF5Mr8O+0WwEW8Mgv28VGI7uPBRAQFbXnoZ7s6Zfcc5h9uvsVcoW9KwvzFxqSOJRx7YES07njTKTLnAh3iyMqzP+Mu9XZnrJyL7wnLNwj+8dNMWBEdeSLGNtkd6lK2tOpC04zrH1e7jssfSiRcBbf1q9cQ6ac/Eo6/Q7lkQijPX3Ke/oCcSLn7FcxAyKUN90Zd+j74shsUzi2cL/ZhnA88xnn+QavoTq0+PHTvW7+3Ms4B0IJciQtx5jvAskiHAUcLMRzPZColnK89d9hgmPQ7x9JIu5eKeDDWXvKKEGWIpNkV1Zdh5+LyQdDjLatnkw3NVS5hnz57th6XjEeZZJqtTk478XyB9nrnc42CIPYK9QmKpw/BDIh/0BEvBB4yoJ3CThbyKYQOWPFfZJ5ttMmXV8LZWWveGBX+wVyNaPU1acerorI8zR0vLEDAEDAFDwBCIEYG4/sHyYkdaEGaGvjEfmJcPSC3eEl5UxNsQEmZeJiFTfG3nZYk4EEYWdeFFkpdDiB2rrJIW4QjbgJAfQ+XwVkLGzj33XB8mtuAd2LFjh8+XeLwYQTZ5AYJ4QhwJD8kaXlBevkib+wgvrxBRhHR4WSJP5sJxTT7MrZY4LGDDcPJSwuIyvFSuW7fObxsHAZeXKPYk5WVMhBdsiCWC/by4Ybes/MrCMlIGyCdDQCkb5B+sEV4+sY+5j7xA8psXRokHGY6WnS1Z0ONjBfjz0YFrmbfnE3bOe6u4zzYqkh4voOAtCyVRz4Ij6fJSSxy8O9gj8QrZIfnEeSZvk+wioK1frV6IFCSKPc3p+8SXg2uIkWxlFMahD4f6PNfkORUSZj6k4SmWNOXMRyieefR1nnVhWjxHhg8fHmbn+GjIM0PiM/0B4fnAvdCLygcw0hRdzqQ/d+7cvDSxgbD169fn3Zd9oYWUsyUWeuEzTCJA/AkTr6/cD89ggw5lYBVqfsvwadGTRb94liP8D5HF1tDn4Do6lJoyEUb5wnnoEHjuszdzVPioKGWXtInP/G8ZeSThUWz4vyEfJCQuZWnr/0PUBuJqRKunSStOHZ31ceZoaRkChoAhYAgYAjEiENc/WF7+IEQirBBL2myFwoFnQ16gQsKMPoQJAsUqsbzkDRo0yCfTr1+/Vh5lXlQQiDlf9UWWLVvm8+NlNWoLnhlsYb6skExskH09hazJHGZegtCXLUOE6JEX+YerwUKe8SBAktHjJW3GjBm5ocZiX/SMLriI8AKuIczgw8ut4Io9EORoGUhX5uzx8YKXWUgswy4LEeZCZZfhmjJUk5c8cIkSZjwy2MEHDV7M+RjANWQYjwoLqiEhjjJMtdiQ7GgdCE5xnSmHSXYR0NavVq8QUnwQgkzJvFTaPCNd8KqGHmaJy7xgCFV0iyUJD8/Ml+aDFh+xQo+n6ED26I98TCwlfMQKiWEpXT5s8uGPMsnaAqX0i4X17NnTPydkq6ZierW4D8bgL57fOPMAH9Z/AJ9yhecZezoXaheatLTtVKunyTNOHXvaxommpWUIGAKGgCFQdwTi+gcbJal4B0gbjyaEjkOGB4eEWQgs5JjhengMNISZYYqypyagMSeP/HipidoSEmZ0eclk+DQvuHi9o2RTPKsawgwZlOHT5M0CMnwYgNiWelGFvPKiLQJhxkuBlPIw402CpAumnEMPsxBf0iF/9PFy4MHGe4uUIsxh2WXrFbxeSDHCTBg44r2iDCxexNBTvF5gLMM6yyHMoR0+85j/xNXuYzbLkosJAW39avXaMouh0rIqNGlyhHsMtxU/K+HhwmiMcjGJBwFtO9XqxWOVPhUjzHqsTNMQMAQMAUMggQjE9Q82SlIPHDjgXxoZxstLFARIvCGQKYalsQgLQxXF88nXewieEGYIFwQaDynxwyHZbIuE7eyjiReF4dh4epGoLSFhZigzxBYhH/IrlzCzJQmkE7KP7XhvSVPm9jI8ENu4ZkhyOPzRZ/zlXDqINV4kFgsKh2TLfqwMO8SbARGFgCPMzWNYI1hCiHlRB5toGSQfCDxEFXsoO6IlzKIHQacclJt0oh5m0pQPH4RjD8PaIcvgIx6ykDDLsE0WcQO7qP2kQVry0ULKE9eZtE2yi4C2frV6GqTwOOPdZAoD5LkZCTPPKz6eNcK7rKmjtOpo26lWr9442NO23ohbfoaAIWAIGAKxIhDXP9iJEyfmDcnGSIbnkr4cMod54cKF/h5z9SBxkFZ0IFSQSCHMkEIJu+666/IIM0MGIXKSNsSMIW9I1BYZrk0cWYVVyBy2MOyRdMQ7i0eVayFr2MWKtgjx8KIKCWV4NPODIcXYQDx08GAjDNHGexwVyLDM1UOfcsqQbDzGkj5hzEVmkSyE7VpkwSzy4oMCZDNaBslPhjaHcwCFCDNkOhovWnY897z8k6csWCMfBiQPzpAFbJYyMCQS+1j0RiTEkbpAFx3SbssOSSOuM/maZBcBbf1q9bKLlJUsDQho26lWr95ltqdtvRG3/AwBQ8AQMARiRaDW/2CZUwYRFS+jGB+9ZtEvSFchIY1igicS0l1q+HM0LmSNOMXyi+qH1xBYhiqTH/OWo7Jr167c/GXyAV8W9iomeFfRC+cwo0v6ENsoTpIOpFozFxIbsSEOb5cMNxcb4jgX2lomjnTbSqPW7b6t/C28tgho61erV1trLXVDoDQC2naq1SudW/yhRpjjx9RSNAQMAUPAEKgjAkn9B1tHCMrKSgizJhLkOZxnXSpOlDCX0tWGMTQdbzhe7nI+KITps7Aa28aIN58tVbIg1u6zUIvFy6CtX61e8ZwsxBCoPQLadqrVq73F+TkYYc7Hw64MAUPAEDAEUoZAUv/BJhVG9j1mS6m4heHfLJQWp7CIGqtpFxpCrc2HOeLMyWRPWOZLZ0Ws3WelJguXQ1u/Wr3CudhdQ6A+CGjbqVavPlYfyqUhhLnnyMecHYZBM7aBQ13PfjUSAYaJmmQHgaT+g80OwlaSJCJg7T6JtRKfTdr61erFZ5mlZAiUj4C2nWr1yreguhgNI8z/66olzg7DoJnaAB8ITBqLwIMPPuj3fz3ssMNy+9c21iLLPQ4EkvoPNo6yWRqGQDEErN0XQyYb97X1q9XLBipWirQioG2nWr1642CE2Yi7fbioUxswwlzvx1tLfqywy/YQf/Znf+ZX/uVh/D//5/+saLGkxpTAcm0LgaT+g8VuFsNi+6jPPvvMDytmz2O2mELYW3jKlCn+d6jnbyTkD9tk9ejRo1V/YYh0WJaEmFsXM+bMmeOHeNclsxKZJLndlzDbgpQIaOtXq6fM1tQMgZogoG2nWr2aGFkiUSPMdSJLzeRJtbIWHjlghLnEk6gGQewDe95557k/+IM/cH/0R3/kV9nlQcxx0kkn1SBHS7JRCCT1Hyx4QDixjy2bovv0nn/++X5f4Kheo3AslG+4/3MYHi1LGBb9PXXq1Nz+ydGwNF7XYnGzSnBIcruvpDwWJx8Bbf1q9fJTtytDoL4IaNupVq++1jtnhNkIs3mY69QGjDDX9/HGyrpf+cpX3B//8R/nkeWvfe1r7tZbb62vMZZbTRFI6j9YCs3Kzs8++6zfWilKMkPCHOrVFKwyE4+DMLMqNx8MsiJGmLNSk8kuh/a5ptVLdmnNuqwjoG2nWr1642WEuU5kybyuhb2uzYSLEeZ6P96c27lzp99v9tvf/rb76le/6onzn/zJn7gnn3yy/sZYjjVDoBH/YNmq6JZbbnHnnHOOH+o/ZMgQ39ZOP/10f923b19f3t27d7vjjz/evfLKKyU9zKEe0wguvPBCn85xxx2XG7ZNGr1793ZHHnmk3+rp0Ucf9Xl8+OGHjpWvO3bs6I499lg3fvx4f3/mzJleD/0bb7zR74lMACtZn3322X6qwoknnuiOPvpoN2nSJB+HPzfffLO/h93nnnuu7zcMGQ8lSv4ZXk7e1AVnhi2zzzJbUsk90qNsHCNGjHCUjXvYjrRl14IFC7zdlOeyyy5zDz/8sGvfvr1jH2iEjw58KJs1a5a/jv55++23HfUGgcemM844w4EdAgkGW+oN+0lXnhMHDhxwfNwg3qmnnupxPuWUU6LJ1/26Ee2+7oVs4gy19avVa2IoregJQEDbTrV69S6SEWYjzOZhrlMbMMJc78dbS368yH/nO99x3/ve9/xL8uGHH94YQyzXmiHQiH+wkCfm8DLkWrywEK5Nmza5e++917e1t956y5NU7Nu4cWNJwszK7aIHmYS0QSwh0jt27PDYQdauueYaPweaefnt2rXz99lXGNK7ZMkSt23bNvfaa6+5p59+2qd3//33+y2kiAv5RdjbmLymTZvm0yZNyvPFF184SDZhixcvdhDMfv36+eu2CPP69et9Pu+//7676aabvD2ffvqp4z7pTZ8+3X80IB3KB7mnXJBSwiHXpezCS48eUy22bt3qXnjhBffxxx97u4mHMFccnWJbUH300Udu1apVbs+ePR4jyoxdCIQY8g6GfGhj32fuIZBzyP26det8WNeuXZ0RZg+N/akhArRljWj1NGmZjiFQKwS07VSrVys7i6Wr643FYld4H+LQTJ5FK6t5l2kDRpgrfGBUEW3hwoXuG9/4hic1c+fOdX/4h3/ofvSjH1WRokVNIgKN+Acbki2IGDaIxxdPMNebN2+uiDALiYSwSZqQV9Ls2bOn35cYcs713r17vTe0f//+eVXTp08f702Vm8uWLfP6+/bt88QUgi0ihB8ye8kll+SRwTBM9DlHPcyffPKJH3bNvsl4qLHt888/d4JNOCQbYgqBZ39lDrDEewzxLWbXdddd50lsaAO/8ZyfcMIJ/vbll1/uPf5RnfCaDxMstAbxxo5Bgwb5YLCm7CLkJ6QYm7BTxIZkCxJ2riUC9CGNaPU0aZmOIVArBLTtVKtXKzuLpavrjcViV3jfCLMRyGb8iGCEucIHRoXR8AbhTYYoiHD9i1/8Qi7tnBEEGvEPthRhfvXVVz1hrJQwUy2Q7gEDBngyiQcYrynlHD58uCeXEEwOSCneTyF+UqVXXXWVHw4t18uXL/fxGb5cipgyFJxhzSJCmBnuHEqUMDM0neHdEOYrr7yyJGHG3osuuiivHOJhLkaY+VAgBDa0Qzzza9eu9VjJB4ZQR37Lhwi85tiJF19wixLmkBQzBDxc94CwECNJv97nRrT7epexmfPTgyv+NQAAIABJREFU1q9Wr5mxtLI3HgFtO9Xq1btERpjrNBy3GQmilTn/w4gR5vo93hhSycswXqRQ8MbJ8Nbwvv1ONwKN+AdbCWEWzzPEjeHP3bt3d2eeeaY7ePBgnieaNirzciF0EEyE+bdXXHGFHyrNcOfnn3/e3x84cGBuyDBzciHXMjz5iSeecO+8844fjs2cXaQUYSYMPJkfDInF08p1dEh2WBa2zEJn4sSJjvm+Q4cOzRFmCWOeMmUiHeyFXJMGRJwPDG15vlesWOHTZMg0Zdy+fbsvC3+Yjw3RBidwZVj47bff7r3bOSXn3LBhw/z8b2xiKH34oaEUYSaM4e8Mq9+yZYuzIdkhqva7Vghon2tavVrZaekaAhoEtO1Uq6fJM06dhhFmyIMdhkGztYE4O6+lVRgBXsiZfxgOoSysaXezgkAj/sGGhJm5tNggw47Fwwy5Eg8oc24hh7RNdCG1TBngd69evfL0mOPLffLAu4keQvpcE8bBwlQIaUm63Ifk0g/w4oou8Z566imvD7ENPbkyXJs4HBB5icciZvzmfijRskDsJQ7DrfmN9xth2LSEMSebD1rYLvf4uIXHupRdpMWQaYkjw7BJH1y4z1xuJPSm+xtf/uFDBCQZXcoPCS7mYb7hhhtyHm3mopMf8agTPlwU8naHedXjN/aYZBcBbf1q9bKLlJUsDQho26lWr95lbsjTNqlg1Bt8y6+5ELB2X5/6Puuss/w8z/rkZrkkAYG09S08nCJCKuVazhBUCB4e01C4DwmX1Z3DMLy0zBkOhXukEx1SHeoU+o2nGM93WxKWhbwK2UUaeMSjZcGDzaiPcoS50uJ9l3hCmN99911/C+8ypL2YsOhX1JZiuuF98ZCH9xr5O23tvpFYpTFvbf1q9dKIgdmcHQS07VSrV29kjDDXG3HLr2kRSOpDIEsVgqeuW7duWSqSlUWBgPUtBUgZVIGcs7UXHmrmJYsw95tVvrMu1u6zXcPa+tXqZRstK13SEdC2U61evctrhLneiFt+TYtAUh8CWakQFuU56aSTXOjxykrZrBylEbC+VRqfrIZCmNk3mX2nGRbfbGLtPts1rq1frV620bLSJR0BbTvV6tW7vEaY64245de0CCT1IZCFCrnvvvvcMccc44eqZqE8VobyELC+VR5epp0NBKzdZ6Mei5VCW79avWL52H1DoB4IaNupVq8eNod5GGEO0bDfhkANEUjqQ6CGRa5L0g899JA77LDD3Jo1a+qSn2WSPASsbyWvTsyi2iNg7b72GDcyB239omeHYZCGNqDpT9p2r0krTh0jzHGiaWkZAiUQSOpDoITJiQ9iW50jjjjC7+eaeGPNwJohkKS+9eyzz7ZalKragrOoFStfi7SVBwuDsa1ULacnRG0S25JyZh9qtu+qVFhYjW2vkixJavdJximttln9prXmzO5qEEhquzfCXE2tWlxDoAwEkvoQKKMIiVJ9++23/bYwY8eOTZRdZkz9EUhK32LlZbYduvnmm2MFIdw3WZMHewyDCaQ2Ltm8ebObM2dOLrnQptzNBP0It/1irvPo0aNbbY1Vylz2x+7YsWMplYaHJaXdNxyIjBpg9ZvRirVilUQgqe3eCHPJarNAQyA+BJL6EIivhPVN6cc//nHeyrj1zd1ySxICSepbmzZtch988EGs8ETJaVt5sIUUXuhiW1ZVYtyECRPyCGTUpkrSrGWckDC/+OKL/gMCW1tphb2t2Y4ryZKkdp9knNJqm9VvWmvO7K4GgaS2eyPM1dSqxTUEykAgqQ+BMoqQGNULL7zQXXrppYmxxwxpLAL17ltDhw51Z599dq7Q27Ztc8cff7zDC8v+v+KJ5XzCCSd4r3Pnzp39HsUMFR42bFgu7pVXXunYNxgZN26c3yKJ8rBVkqQTJaeSxzPPPOPzJW857rrrLrd7925/zZDirVu3uvbt27spU6a4M844w9tyxRVX5PJfsmSJO/nkk31+eMZJB7IYypgxY9zRRx/tSSfh2ItNkNLhw4f7MhI+adIkHw0v+PTp090pp5zijjzySNenT5+CezqTL2lxvuaaa/zeyIXi8QGCPk9+xx13nC8LGYHtI488kjOVcq5YscJfC2HGy859MMX2c845x4ez9RQYY9/VV1+dS0N+DBw40NvN9fXXX+969+7tV+QmDulVM9xb8qj2XO92X629Fr88BKx+y8PLtLOBQFLbvRHmbLQvK0UKEEjqQyAF0OWZyMs3hMHEEBAE6t23IKrk+dJLL3kTBg8e7KcHcAFxhPji3UUHEvnRRx+5F154wQ8JPv300/MIWteuXT0ZI+769esd3tD333/f3XTTTT4tyGeUMEsepAsp5hgxYoTP74knnvCrxZP3xo0bvY38hgzjmZ49e3bO9n379vnfkNa9e/d6W9HlA0Aob731loNkQ1DJC11sQnfatGneEwvhhaRi79q1a/1vvNyQb0jmggULwiT9b/RJc/Lkyd62YvEoG2lAnPkYIJ5f8r///vtz6XL9wAMP5NKGfFNGPhYQRh0wN5l8uKb+wJoPHVG57LLLcuT6/PPP92Sbjws7d+50HTp0cNxrtFAGk+wiYPWb3bq1khVHIKntviFP26SCUbz6LMQQqB4Ba/fVYzhq1Cjv3dm/f3/1iVkKmUGgEX3rxBNPdDfeeKMniJC5iRMnejyFzHKBR5frkSNHOubcI6UI8yeffOLnHeMlhuBSLoh3McLsE3QuRwAXLVrkb0EKiRsS5tdee82HQToJW716tfdg81sWB4M4ch0lzETs169fySHZS5cu9XFZcAxvLJ5gPiRw4JGFUEdFvMByv1g8vLnYBUl99NFHRd3fa4swo/zwww97XRmSDUmmXiC+M2bMcHI/l7BzLkqYL7nkklwwC7DhPW+0gIlJdhGw+s1u3VrJiiOQ1HbfkKdtUsEoXn0WYghUj4C1++owxDN21FFHuZdffrm6hCx25hBoRN+aOXOmH867cuVK7009cOCAxzUkzJDduXPnOrzIkEOGR5cizAwXhohDmBmqTbnaIsxbtmzxaUOqRUoRZvEqQ5jxwJKHfICKizD36tXLk2S8ynLg3Y1KlDCXiodnm2HUxBHyje2VEGbseO+999zdd9/tRwYwVJt536GUIswM0TbCHKJlv2uBQCOea7Uoh6VpCJSDQFLbvRHmcmrRdA2BKhBI6kOgiiLVLSqkBPxkfmLdMraMUoFAI/rWwYMHPXnDuywEDrCEMEN0IXmcIdPYyJxk5uLi2fz444/dU0895T2xeFbRQwdPNfrMk+aa+wwphijKYmKSx5tvvumHKjMvGgLIwTBtLWEWvYsuusgx3BgvKnkW8jAzV5myvvvuu36Yc9TrHXqY6aekg2cYIrpnz55W86LBKkqYi8VjCDZlQwYNGuQx4zfzj/H2giX7sZOnDMkmDM8++LFHO2FsQyc4yTztVatW+TC59pm04WE2wiwo2bmWCNBmTQyBZkMgqe2+Ib0xqWA0W6O08tYXAWv3leGNB+1P//RP3axZsypLwGJlHoFG9a3+/ft7ssXcYBHI7Pjx4z2xZFgytnHgWWbo7/Llyz1R5B7hzOFlXj4CGRR95unzG8LHcGoIIPqI5MFwYtGXM+RdiDBeXWwjTIZk403mGg8zgj1dunTxHuHLL7/ch0XJI3qQfykPJBVijx0iy5Yt83EZko3NDJ8WmyDGhfpvlDAXiyfzs9EHh4ULF/psb7vttlweePEJg/gjN9xwgw/Dw49NeISxh+HhLASGLtekiec6KqU8zKRtHuYoYnYdNwK0TxNDoNkQSGq7b0hvTCoYzdYorbz1RcDaffl442n77ne/6z1F5ce2GM2CQJL7Fh5Z5syGgne6EClFhyHT7BscFUgfRy1FhiiXyoN51lqhLJB3FgIrRwrFo+x4mqNpga94n6N5yNxsuQ/uoezatavg/OVQJ8m/k9zuk4xbWmyz+k1LTZmdcSKQ1HZvhDnOWra0DIESCCT1IVDC5IYHnXbaabkVhBtujBmQWASsb1VeNWzpxLZKDMsGR7aQMkkHAtbu01FPlVpp9VspchYvzQgktd0bYU5zqzLbU4VAUh8CSQWxR48e7oILLkiqeWZXghCwvlV5ZbANFdtKMbz51VdfrTwhi1l3BKzd1x3yumZo9VtXuC2zhCCQ1HZvhDkhDcTMyD4CSX0IJBF55oYmYY5gErExm1ojYH2rNSZ2J/sIWLvPdh1b/Wa7fq10hRFIars3wly4vuyuIRA7Akl9CMRe0CoTHDdunPvOd77j9u7dW2VKFr1ZELC+1Sw1beUMEbB2H6KRvd9Wv9mrUytR2wgktd0nmjADmh2GQRraQNuPAOfbskavmXVY/fYb3/iG27BhQzPDYGUvE4Gk/oMtsxiJU+fjFVtaRYVVplkpu9bCwmgXX3yxX3G81nmlMX1r92msNb3NVr96rEwzOwgktd0nnjBnpwlYSbKKgLZza/WyilNb5Vq7dq07/PDD3YMPPtiWqoUbAnkIWN/KgyO2C7aGYm/mqET3YI6Gx3X90ksv+Q+NsiVWXOlmJR1r91mpycLlsPotjIvdzTYCSW33Rpiz3e6sdHVAQNu5tXp1MDlxWezcudMde+yxbvLkyYmzzQxKPgLWt2pTR0aYa4NrXKlau48LyWSmY/WbzHoxq2qLQFLbvRHm2ta7pd4ECGg7t1avCSDLKyL7q3bo0MENHjw4775dGAJaBKxvOXfmmWe6u+66y0O2fft2d/zxx7sHHnjAX69Zs8a1b9/e7/HMqtjdu3d3X//61733mJEdyNatW70Ow7DRnT17tgsJ88MPP+zvL1++3OFhJv7111/vjjzySHfGGWc40hVZtWqVX+EeHfIibYQtrFiRmxXwCevSpYt7++23fRh7T0+YMMF17NjRfzwbP368Ew/zzTff7E4//XQf54orrpBsmv5s7T7bTcDqN9v1a6UrjEBS270R5sL1ZXcNATUC2s6t1VNnnBHFs846y/Xs2TMjpbFiNAIB61vODRgwwJNk8IfQggl7KyPMN+7cubP75JNPPOmEgL7yyis5wvv555/nyOk555zjFixY4Hbv3p0jzIsXL/bpTZ8+3acnhHnRokWO4dJXXnmlO/roo90XX3zhDh486POYOXOme++993wa11xzjY8HSSb9J5980m3evNmnSVoI+z+TxpIlS9y2bdt8uiFh3rRpkyfxlIv7JrYuRtbbgD3Xsl7DVr5CCCS13RthLlRbds8QKAMBbefW6pWRdepVe/Xq5bp165b6clgBGouA9S3nNm7c6Ano66+/7j2+kGQIKiM4mO4wbdo0T0bB6qOPPvIV9v777/s4jzzySI4wh/OF8TDjQSaOeKuJCMmF3IpAvtF59tlnvR6/GTHCcfbZZ7t27dp5VewR0s0NPNkysoTfbCcXihBmsemDDz7w+axevTpUa9rf4GySXQS09YueHYZBGtqAprdq270mrTh1GvK01YKh1YsTEEvLECgXAW071eqVm39a9YcMGeJOOukkh3fLxBCoBgHrWy3oQUxHjhzpX57Zlg2yyyrX4APZnD9/vv+Npxk5cOCAv166dGlRwnzcccd5nTvvvDNXRVHCzJBr8mB1+3nz5vnf5IWnmmPFihU+bpQwn3DCCTnCTD6DBg3K5cGPKGHet2+fT9sIcwtM1u7zmkvmLrT1q9XLHEBWoFQhoG2nWr16F94Ic70Rt/wyh4C2c2v1MgdQgQLdd9997phjjnF4w0wMgWoRsL7VguCwYcM8oTzxxBP9Deb7QlLPPfdcf41nGayY67x//343fPhwH/7pp5+2IqdEkDnMeKCJN2LECJ+ODMl+6KGH/H7pDLnGi40ICZ80aZL7+OOP/bxpPNBIKcI8cOBAB2let26dYz4zW0oZYfawFf1j7b4oNJkI0NavVi8ToFghUouAtp1q9eoNhBHmeiNu+WUOAW3n1uplDqBIgXjJPuywwxwLEZkYAnEgYH2rBUWGLoOFDHOWucfhcGoW00JHDtlnmTnC3JPhz6QohJnfLPpFOKR54sSJntwyH5l7EOEwj9GjR+fSJzycwxwOyQ49zBBkFv9Dn4Mh5VGbIPmEmYe5pb7BwiS7CGjrV6uXXaSsZGlAQNtOtXr1LnNDnrZaMLR6gHbHuCnuL/7+B6k9sN8knQho26lWL50o6Kx+/vnn3RFHHOGHaepimJYh0DYC1rfaxijUYEg2C2vhWa5WGOrNPOmokAfku9wpFwy7ljnW0TTtOh8Ba/f5eGTtSlu/Wr2s4WPlSRcC2naq1at36TNEmCenlixD9O8YZ/vP1rvxx5WftnNr9eKyK2npsH0McyzHjh2bNNPMnpQj0Ox9K+XVZ+ZXiIC1+wqBS0k0bf1q9VJSbDMzowho26lWr94wZYgwm4e53o3H8mtBQNu5tXpZxbVTp06uX79+WS2elauBCDR732og9JZ1AxGwdt9A8OuQtbZ+tXp1MNmyMASKIqBtp1q9ohnVKCBDhNk8zDVqI5ZsGwhoO7dWr43sUhl84YUXuksvvTSVtpvRyUegmftW8mvHLKwVAtbua4VsMtLV1q9WLxmlMiuaFQFtO9Xq1RvHDBFm8zDXu/FYfi0IaDu3Vi9ruPbp08edeuqpWSuWlSdBCDRr30pQFZgpDUDA2n0DQK9jltr61erV0XTLyhBohYC2nWr1WmVQ4xsZIszmYa5xW7HkiyCg7dxavSLZpPL2qFGjXPv27f0WNqksgBmdCgSasW+lomLMyJoiYO2+pvA2PHFt/Wr1Gl4gM6CpEdC2U61evcHMEGE2D3O9G4/l14KAtnNr9bKC6+zZs91RRx3lXn755awUycqRUASarW8ltBrMrDojYO2+zoDXOTtt/Wr16my+ZWcI5CGgbadavbzE63CRIcIcv4f5/Euucv/1875+BWtWsebg+ryLr4p9RW5bJbsOrb1GWWg7t1avRmbWNdmVK1f6/VJXrFhR13wts+ZEoJn6VnPWsJW6EALW7guhkp172vrV6mUHGStJGhHQtlOtXr0xyBBhrt7D/MOu57hbht3pXtr8Sl49vPTyK27t08/5Y/+BD3Nha556zusTr9o9oG0f5hysqfuh7dxavdQBEDF4y5Yt3rM8a9asSIhdGgK1QaBZ+lZt0LNU04qAtfu01pzObm39avV0uZqWIVAbBLTtVKtXGyuLp5ohwlydh1kI667db7opM+Y6vMv/+P2fFCXChKOHPoKHuBrSbB7m4o006SHazq3VS3p5S9l34MAB993vfteNHDmylJqFGQKxItAMfStWwCyxTCBg7T4T1Vi0ENr61eoVzcgCDIE6IKBtp1q9Opicl0WGCHPlHuZBw8Y4PMd9+t1aEeklHvFJp1LSLIQ9r3bsIhUIaDu3Vi8VhS5i5GmnneZ69+5dJNRuGwK1QaAZ+lZtkLNU04yAtfs0117btmvrV6vXdo6mYQjUDgFtO9Xq1c7SwilniDBX7uFluPX8xcsqJruQ5IcfW+2HbFdOmCcXriG7m3gEtJ1bq5f4AhcxsEePHu6CCy4oEmq3DYHaIZD1vlU75CzlNCNg7T7Ntde27dr61eq1naNpGAK1Q0DbTrV6tbO0cMoZIsyVe5ghzAjzlyshvHiWEdKpJD5xzMPsIUzlH23n1uqlEYT+/fu7Tp06pdF0szkDCGS5b2WgeqwINULA2n2NgE1Istr61eolpFhmRpMioG2nWr16w5ghwlydh5mFvZCdb+xxk2fM8SthtzWHGT30GY4tC4NVTpjNw1zvxh9XftrOrdWLy656pTNu3DjXrl07t3fv3nplafkYAnkIZLVv5RXSLgyBCALW7iOAZOxSW79avYzBY8VJGQLadqrVq3fxM0SYq/Mw4+FltWu8xUKepTJYNZsVsTlCwaOMPvGIbx7mEJ3m+a3t3Fq9NCG3cOFC941vfMNt2LAhTWabrRlDIIt9K2NVZMWpAQLW7msAaoKS1NavVi9BRTNTmhABbTvV6tUbwgwR5uo8zBDeqHeYlbAvv/q/PRkmnIN7HFHd6gmzeZjr3fjjyk/bubV6cdlV63TWrl3rDj/8cPfggw/WOitL3xAoiUDW+lbJwlqgIfAlAtbus90UtPWr1cs2Wla6pCOgbadavXqXN0OEuTXhjZLaYtd4hgsR5mL6he5XT5in1LvuLb+YENB2bq1eTGbVNJkdO3a4Y4891k2ZYu22pkBb4ioEstS3VAU2JUPAOWftPtvNQFu/Wr1so2WlSzoC2naq1at3eTNEmOP3MBcixsXuVU+Y4/cw//73v3dffPFFrk3xm3ttSVTnjTfecBzFRNKNxgv1i4VJ3FD3gw8+cK+88orK1jBeo35rO7dWr1Hl0Ob7u9/9znXo0MENHjxYG8X0DIGaIpCVvlVTkCzxzCFg7T5zVZpXIG39avXyErcLQ6DOCGjbqVavzua7DBHm6jzMzE8uRoY19/FSJ2kOMwsw0eg6duyYa1PDhw/39z755BN/7/PPP3ddu3Z1gwYNyuk8/PDDXmfr1q3u1Vdfdccff7y/Jq0jjzzSrV+/PqcrP2644YacDnonnHCCmz9/vgS7ZcuW+fCRI0fm7vGD/Fks6uijj/b3P/zwQ3f22Wfn0vr617/uJk+O/0NCnhExXGg7t1YvBpNqmsRZZ53levbsWdM8LHFDoBwEstK3yimz6RoC1u6z3Qa09avVyzZaVrqkI6Btp1q9epc3Q4S5Og8zwEOaC81PLkWY0ZfFwKojzPESwzvvvNMTURre5s2bfbt6/vnnPRldtWqVv37mmWf8NURY5LrrrvMklmvIK4QZb+97773nnnzyyYIrIUOY27dv79566y332GOPuUsuucSnO3v2bJ+sEGYI8DvvvCNZeTKMfUKY7777bh+PfA4cOOAXkhLbc5ES+EPbubV6CSxizqRevXq5bt265a7thyGQBASy0LeSgKPZkC4ErN2nq77KtVZbv1q9cvM3fUMgTgS07VSrF6dtmrQyRJir8zDPX7zMe4gBja2ifvPoKnfHuMl+e6nzLr4q78x+zfMWLfV66EOUH35sdWI8zAxzPu6449yECRPcySef7CC0IpDTAQMG+MvRo0fnPMh4kxE8vjfddJP/3blzZ3fqqae6Tz/91F8X+0P6J554Yl4wnm1sQIQw43m+5ppr/D0IMUQdPSHMQ4YMcVFSnZdoQi+0nVurl9BiOurnpJNO8iMDkmqj2dWcCKS9bzVnrVmpq0XA2n21CCY7vrZ+tXpxlfajjz7yow1LTdWLK69K03n22Wf91LHrr7++0iQaFm/btm1u8eLF7uDBgw2zoRYZa9upVq8WNpZKM0OEuToPsyz6JVtLQYB37X6zIHbch2DLllJ4oJM0hxkPLQ3u3XffdVOnTvUkVEhv7969/ZBpCnb66ae722+/3XuHWbxp165dPt5TTz3ly423GFILob3lllvc7t27C+JRiDCPGTPGp/XZZ5/lCLPYtWXLFjdw4ECf74wZM3KEmfzxVGM7nsxCw78LGtDgm9rOrdVrcHEKZk/7OOaYYxyLfZkYAklDgL5lh2HQjG0gaX3R7IkPAe07g1avWsu2b9/uSWjYz3ByMLWPEYZJkpUrV/r/CWeccUZFZjHaMixn9LeMoKwo8TYi4Vwiv/Hjx7ehma5gyqQRrZ4mrTh1dNbHmWMZKzuWA5oQ3lLDp4uF4SGuJn48hDm+1YZ79OjhunTp4t58800/HBsc2S8XkYcI5Jf7Tz/9tOPBcNFFF7np06d7ghwu0MUCXGPHjvWeZ/QfeeSRVq2hEGGW4dUQdfEwE/HCCy/0Xm/Sevzxx92sWbNyhJlw5jUz/5kHMDpDhw5tlV/SbmCnRrR6mrTqqfPQQw+5ww47zK1Zs6ae2VpehoAhkHAE9u/f76docDYxBAyBeBHQvjNo9aqxDgcMDhTyYocM1jHhYAFQSDNe0SSJvOtWSpgZDUlZGXV57rnntjrYVrNWwihQRmiuW7euVlk0JF1tO9Xq1bsQujf9mK3SgqHVwzyGTxcjxG3dTwZhjmcO8759+3wn58HGQ42DhxlDqxEIKdfXXnutP3MNoUX/ggsuKLqYE3o8GM8555xWraEQYT7llFNaDckm4s6dO32+EGckSpjDxPv27et1GWKeZNG2U61eksrKvPcjjjjCLViwIElmmS2GgCGQAAQY7viVr3zFpXHYYwLgMxMMgZIIaN8ZtHolM2sjkHc18oHIsVNGKEl8R4uLMOP8MYkHAW071erFY5U+lQwR5urmMGfFw3zvvfd6kikrYdMUZOVryCqCN5kGKYs3CcnmHt5EkSeeeMJ7qEmL+SAQ7auvvlqCc2cIsyz6xUPqiiuu8OnPnTvX64Qe5lykL3+EhPnFF1/0+TA/5rXXXnOQ7nCV72jcpFxrO7dWLynlevvtt/3XVUYYmBgChoAhECKAV/lrX/uaf9ZzNi9ziI79NgSqR0D7zqDVq8Yi2WUFb6tGeJdkHRzeG3HIsIgsQ7pFNm3a5C6//HL/joEOZWCaYPgOiqMGD/HNN9/sp4NdddVVfoSi6DCC8bbbbvP5MHUQx9CoUaN8FiFhZnoh6YgjifRYyLaUiIe5LcKMY+fMM8/06d14441+yiNl4R7v3qF8/PHHfh0fcWQxknL16tXeNvAVufXWW/09mZaI4wL7GX25fPnyNsvCsxj78Y5jC4v33nPPPXnbzDJFkhECgsnFF1/s0xYbanHWtlOtXi1sLJVmhghzdR7mffsPlL1CtniuWSmb+ElYJZvFt3gIhcJDh07BgwXBW0iDDIkQ3mPuhYsMXHbZZf4e93mgQbQZlhMVHhjoyEFaDzzwQE6NhxthhWTOnDm5IdmTJk3y+Ug6PPxeeOGFQtESda9Y2aJGavWi8Rp13alTJ9evX79GZW/5GgKGQIIRwKv81a9+1T/bOZuXOcGVZaalEgHtO4NWrxoQNm7cmHvHg0SyPk0xYc0TbOKArPH+yW9IrbyZVsikAAAgAElEQVRjjhgxIqcjw7oljrz38e7KPSHdEs7aPOTP+67cI230OBAhzBKODUIguXfXXXcVM9/f1xJmFtaVPDjjPMIGfnOmDCJ8QBBdyiy4cA+CLSLbq0KOkXLKgsOJUQCSf4jRxIkTfXqyXhE6hMv7/5VXXikm1ORMfhrR6mnSilNHZ32cOSZ0DvP+Ax/6UrJFVO8bh7h//P5PSg7xJvy/ft7Xr6ZNROJXR5jjm8McZ3XR2dlWqtTDMc78SIuFpfB6p0W0nVurl4RyMzz/0ksvTYIpZoMhYAgkDIHQu8xzjcO8zAmrJDMn9Qho3xm0etUCwto40t/xkuIVjS72xQ4oQhhl3RPeI/EeExcnCcI6Olu3bnWyZg5nvKjoiDNHCDP3mA7IlqjMlYYUjhs3zutCghmRiDA0XJw6QjIh0o8++qgP54+sr9PW6EUhzCzA1b1797yD0ZciQpgZfSlea+wTMox3G2H9H8qBPbK6OEPbBw0a5O9rCLOmLCNHjvTpMRLgww9beI0suCs717DoLraw84kItoi+3Iv7TJ4a0epp0opTR2d9nDnWjDBX52FmSDaeYla/FvLM9lIQaLaYmjxjjv/NNfdFWE2beMSvjjDHM4dZ7LJz/RDQdm6tXv0sL5xTnz59cnPeC2vYXUPAEGhmBELvMs81DvMyN3OLsLLXAgHtO4NWr1obmZ7HzipCiMmX30ytE4Gcch8iGcq0adP8/cGDB4e3834zypC4EDokJMx5is7lFo9dtGhRNMhfC2GGhIeCA4g8IJ+lRAgzutGDqY8iQpiZthiKTH1kMV2EqYukIyM9RZfFb7mvIcyasvAhgPQofyiUl/s4v2QEAB8xmHpXLyF/jWj1NGnFqaOzPs4ca0aY453DfNo5F/ttoyDCU2bM9WSY3xxsJ0W4DMnmXD1hTqaHOeaqz2Ry2s6t1WskSMz/YUiRzUdsZC1Y3oZAchHg2cBCX6yc/81vftO/hH3rW99yhx9+uL9vz47k1p1Zli4EtO8MWr24Sg9xxlMcDnEWz/HkyZP9MwEPK2RSDoZmYyfzh0Ug12whClnEQ3v++ed7HQ1hFg+ueJclTTkXI8x4xLFDS5ixd8+ePXkHHmSRYoSZ8pCPEGY8vlxHF1CthjAXKovgAnEW7DnLRw7mfbODDiMEsIeDOnj11VelSDU7k5dGtHqatOLU0VkfZ441I8zVe5hDAlzu7+oJs3mYY25mdUtO27m1enUzPJIR+woeddRR7uWXX46E2KUhYAgYAi0IMPcQsjx69Gh/Q55rXHOfcBNDwBCoHgHpW22lpNVrK51yw/FWslgU+csOKswN5prhv8zFjR5Lly712UCw0eOAvIYETkOYJS5DuwtJXIS5rUW/tISZaW7YPG/evDxz4ybMgksUd65ldxoMwLMsC/RKnHCkQJ6RMV2Qj0a0epq04tTRWR9njjUjzJV7mBmGzVEuSQ71xQsd3ivnN4TbJJ0IaDu3Vq8RKMg/lxUrVjQie8vTEDAEUopAkp9rKYXUzDYEPALavqXVqwWssqireGwXL17siWGhHVUkf4ZaC0FesmSJ3PZDuymLhjCLx7rYfsjyThMdxlzIK5szIPghQ7LjIsyyyFl0IdW4CbPgEp1bHhQt7ycjgliVG9zxTtdyizBtO9Xq5RWkDhcZIsyVe5iZg4z06XdrRaT58qv/26+SzVDtckhyqMs+0ibpREDbubV69UZhy5Yt3rNc66+L9S6X5WcIGAK1RyCpz7Xal9xyMARqi4C2b2n1qrEWb7Is0BWmg9eS/NnVBGFoL9cMAZZFsEJ9fkPS0OEIhzePHz/e39MQ5h49enhd5goXInlJI8wsWCa4yHZRLJDGcGjuVzKHuRD5F48/JLgckaHcDD+vlVBOjWj1NGnFqaOzPs4cE+hhhrhCdpGXNr/iIK/nXXxVSfILyb5l2J25BcDwEIcEuNzf5mGOuZHVMTlt59bq1dF0xwObOcusrGhiCBgChkC5CCTxuVZuGUzfEEgiAtq+pdWrpoysXE0+zI3Fe3zdddflzWGWvZHJQxa8wuvMIlfsAcweymxhJEOy8fySHoSb7Y4kDvc0hJkVttEVm2666Sa/rzBDpJG4CDMeW+YfRw9ZKVs7JBubZF4zNoON2M85LsKMA0TSBVuwhzx369bNse8z0rt3b78iOZixRRdb0RLnlFNO8eG1+kMeGtHqadKKU0dnfZw51owwV+5hFnL7w67n+AW+du1+M6/ErIwtB/sti7z08iten3iSRqVn8zALquk7azu3Vq+eCJx22mn+4VnPPC0vQ8AQyA4CSXyuZQddK0kzI6DtW1q9arBkeyQIFXmFB0OrFy5cmJc0HmTxnIa67EP89NNPe12GUod7BDPnmQW20NcQZhJZt26d/+Af5iGEefXq1T6tkIgSR7yy2F1KZEh2mHb4WxY5k72VxWssaV577bU+/3DkHkPRWWGb+cR8eOjbt29uGLrMASc+v8lLtqQqtyzEkyHvYjPX7ICCsCVXuGAbOuC0efNmMb8mZ/LRiFZPk1acOjrr48yxZoS5Og9vlOiyzzJeZIZp4/2Vg+HX3G9rn+Zoem1dm4c55kZWx+S0nVurVy/TGdLEQhQmhoAhYAhUikDSnmuVlsPiGQJJQ0Dbt7R6cZSP/XohVqx3gpcXElhMPv74Y7dx40a3adOmonrsqyz7EhdLp637jJTDs8qw8bQJ3nXq78Ybb4zVdIapg+szzzxTdOuoDz74wD311FN5w+JjNSKSmLadavUiydf8MkOEuToPM3soR7eKaovkSjjx8EDLdSVn8zDXvK3XLANt59bq1czQIOH+/fu7Tp06BXfspyFgCBgC5SOQpOda+dZbDEMguQho+5ZWL7klbQ7LNmzYkCOnEFrIKp516i8c0p5VNLTtVKtXb5wyRJir8zADPJ7jkOziXY7eE69zqMc9JLxX7m/zMNe76ceXn7Zza/Xis6xwSjIcZ+/evYUV7K4hYAgYAkoEkvJcU5praoZAahDQ9i2tXmoKnlFDqScOSHI4hzkcjp3RovtiadupVq/eWGWIMFfnYQb4KDnG6wyRDckv19wP78VDmG2V7Ho3/rjy03ZurV5cdhVKh7lGRxxxhONLp4khYAgYAtUikITnWrVlsPiGQBIR0PYtrV4Sy9hMNrHgGUOvmb8MYWYRrkmTJhVcfTyLuGjbqVav3hhliDDnE9uQ0Gp+A3xjCbPtw1zvxh9XftrOrdWLy65oOiyycdhhh7kHH3wwGmTXhoAhYAhUhECjn2sVGW2RDIEUIKDtW1q9FBTZTMwwAtp2qtWrN1QZIszVeZghy9GFvLQeZlkgTEPMi+nYHOZ6N/348tN2bq1efJYdSmnHjh1+1cQpU+zDzCFU7JchYAhUi0Ajn2vV2m7xDYEkI6DtW1q9JJfVbMs+Atp2qtWrN2IZIszVeZgLEVntHOZCccu9Z3OY693048tP27m1evFZ1pISq1p26NDBDRkyJO6kLT1DwBBocgQa9Vxrctit+E2AgLZvafWaADIrYoIR0LZTrV69i5ohwlydh7lcghu3vnmY693048tP27m1evFZ1pLSWWed5fdEjDtdS88QMAQMgUY91wx5QyDrCGj7llYv63hZ+ZKNgLadavXqXdoMEeb4Pcxxk+JS6ZmHud5NP778tJ1bqxefZc4TZQiziSFgCBgCtUCgEc+1WpTD0jQEkoaAtm9p9ZJWPrOnuRDQtlOtXr3RyxBhNg9zvRuP5deCgLZza/XiwpUh2CeddJL7/PPP40rS0jEEDIEmR2DEiBF+8cDRo0d7JOS5xjWLChJuYggYAtUjIH2rrZS0em2lY+GGQC0R0LZTrV4tbS2UdoYIs3mYC1Ww3as9AtrOrdWLw2IW9zrmmGMci32ZGAKGgCEQFwL79+93X/nKV9zhhx/uvvWtb/l9Rb/5zW/6a+4TbmIIGALVI6B9Z9DqVW+RpWAIVI6Atp1q9Sq3pLKYGSLM5mGurAlYrGoR0HZurV619jz00EPe08M2UiaGgCFgCMSNwPXXX++++tWverLMc42Da+6bGAKGQDwIaN8ZtHrxWGWpGAKVIaBtp1q9yqyoPFaGCLN5mCtvBhazGgS0nVurV40tzz//vDviiCPcggULqknG4hoChoAhUBQBvMhf+9rX8ggz1+ZdLgqZBRgCZSOgfWfQ6pVtgEUwBGJEQNtOtXoxmqZKKkOE2TzMqho3pdgR0HZurV6lBr799tuuXbt2buzYsZUmYfEMAUPAEFAhEHqZzbusgsyUDIGyENC+M2j1ysrclA2BmBHQtlOtXszmtZlcZghzmyU1BUOgRghoO7dWr1IzO3Xq5Pr161dpdItnCBgChoAagdDLbN5lNWymaAioEdC+M2j11BmboiFQAwS07VSrVwMTSyZphLkkPBZoCLSNgLZza/XazrG1xgUXXOAuvfTS1gF2xxAwBAyBGiGAl5mFvmzuco0AtmSbGgHtO4NWr6nBtMI3HAFtO9Xq1btARpjrjbjllzkEtJ1bq1cuQL1793annnpqudFM3xAwBAyBqhDAy9ytWzebu1wVihbZECiMgPadQatXOBe7awjUBwFtO9Xq1cfqQ7kknjADnB2GQdLbwKEuVfxXLR4Co0aNcu3bt7cX1uKwW0gzIDDhfzhnh2HQjG2gGfp3k5ZR+86g1WtSGK3YCUFA2061evUuVqIJc73BsPwMgVoiEPdDYPbs2e6oo45yL7/8ci3NtrQNgeQj0IxEycpsHwhoAyaZRUD7zqDVyyxQVrBUIKBtp1q9ehe6IU/bpIJRb/Atv+ZCIM52v3LlSj/yYsWKFc0FopXWECiEgJFHI4/N2gYK9Qe7lwkEtO8MWr1MgGKFSC0C2naq1as3EEaY64245de0CMT1ENiyZYv3LM+aNatpsbSCGwJ5CDQrWbJy24eCvI5gF1lCQPvOoNXLEjZWlvQhoG2nWr16I2CEud6IW35Ni0AcD4EDBw74OcsjR45sWhyt4IZAKwSMOBpxbNY20Koz2I2sIKB9Z9DqZQUXK0c6EdC2U61evVEwwlxvxC2/pkUgjocAq2GzKraJIWAIBAg0K1myctuHgqAb2M9sIaB9Z0DPDsMgDW1A00O17V6TVpw6RpjjRNPSMgRKIFDtQ6BHjx6O/ZZNDAFDIIKAEUcjjs3aBiJdwS6zg0C17wzZQcJK0kwIJLXdG2FuplZoZW0oAtU8BPr37+86derUUPstc0MgsQg0K1myctuHgsR2SjOsWgSqeWeoNm+Lbwg0CoGktnsjzI1qEZZv0yFQ6UNg3Lhxrl27dm7v3r1Nh5kV2BBQIWDE0Yhjs7YBVQcxpTQiUOk7QxrLajYbAoJAUtu9EWapITsbAjVGoJKHwMKFC90RRxzhNmzYUGPrLHlDIMUIQJYm/5EdhkFztQHavUlmEajknSGzYFjBmgaBpLb7hjxttWCgZ4dhkIY2oHmSadu9pLV27Vp32GGHuQcffFBu2dkQMAQKIHDM//ofzu1/zQ7DoKnagG/3BfqD3coGAuW+M2Sj1FaKZkcgqe0+8YS52RuOlT/5CGg7t1aPEu/YscMdc8wxbsqUKckHwCw0BBqMQDl9q8GmWvaGQGwIWLuPDcpEJmT1m8hqMaNqjEBS270R5hpXvCWffQS0nVur9/nnn7sOHTq4IUOGZB88K6EhEAMC2r4VQ1aWhCGQGASs3SemKmpiiNVvTWC1RBOOQFLbvRHmhDccMy/5CGg7t1bvrLPOcr169Up+wc1CQyAhCGj7VkLMNTMMgVgQsHYfC4yJTcTqN7FVY4bVEIGktnsjzDWsdEu6ORDQdm6NHkQZwmxiCBgCegQ0fUufmmkaAulAwNp9OuqpUiutfitFzuKlGYGktvtMEOYFGya6hRsmuiyc09zIm9V2beduS48h2AzFZki2iSFgCOgRaKtv6VMyTUMgPQhYu09PXVViqdVvJahZnLQjkNR2nxnC3H3aP7nu077nWs78Tt81hN8kfQhoO3cpPRb3OvbYY/1iX+lDwCw2BBqLQKm+1VjLLHdDoHYIWLuvHbZJSNnqNwm1YDbUG4GktvtMEOb719/j/u/Uf3IXTj0h1WfKYZI+BLSdu5ge20Ydfvjhjm2kTAwBQ6B8BIr1rfJTshiGQHoQsHafnrqqxFKr30pQszhpRyCp7T4zhPmC+05wF953gkvz2QhzOru5tnMX0tuwYYM74ogj3MKFC9NZeLPaEEgAAoX6VgLMMhMMgZoiYO2+pvA2PHGr34ZXgRnQAASS2u4zQZjnPzfBXTDlBPfTKf871ef5z5mHuQF9s+ostZ07qvf222+7du3auXHjxlVtgyVgCDQzAtG+1cxYWNmbBwFr99mua6vfbNevla4wAklt95khzOdP/q47f/L/zjt+mnetC79l6WWOY9qaEW7+sxPcvMhx+/Jf+PAbFp7vKkk/tDEaH+Jvkj4EtJ07qtepUyfXv3//9BXYLDYEEoZAtG8lzDwzxxCoCQLW7msCa2IStfpNTFWYIXVEIKntPhOEGVJ7/r3fdecFh1xHz6Jz6fST3YiHr3XLXvile3H30+6jT/dX1Bz2HnjDPbX9MU+sr19wXs6GaL5yLfnLdXimHCbpQ0DbuUO9Cy64wF166aXpK6xZbAgkEIGwbyXQPDPJEKgJAtbuawJrYhK1+k1MVZghdUQgqe0+M4T53Ent3aHju8Fv7rdcXzzth27syv6eIId1v/3dl3Okd9zKAW7gry9zA3/dw1234Lwv02mJz72W4zI39ckRniS/tAeyfSCXHMR7xZbFnoy3ZU803AhzDsZU/dB2btHr3bu3O/XUU1NVRjPWEEgyAtK3kmyj2WYIxI2Atfu4EU1Wela/yaoPs6Y+CCS13WeCMM99Zrz7j4nt3X9M/Ed/PufLs1z/59QfOHQ+/NKLvPfAbrd040w34jfXuoun/tBF9Su57jn7NDd25QC3YssDuRa1d/8bbuyK/ur0sdEkfQhoOzd6I0eOdO3bt3f791c2oiF96JjFhkDtEdD2wdpbYjkYAvVDwNp9/bBuRE5Wv41A3fJsNAJJbfeZIMxznhnvzrnnHwsefeafmyPKDL0e/ptrC+oVi1/J/f+87wdu7IoBDmKObHtns8OOttKiHCbpQ0DbudE76qij3Msvv5y+QprFhkCCEdD2wQQXwUwzBMpGwNp92ZClKoLVb6qqy4yNCYGktvtsEOanx7uzJ/yjO2vCP+Sd/9+8//BkmSHTNz3Qo1V4VL8W1/f9drgfso23ufuUjnn2RfOb87StlhxTf6trMtrOjd6KFSvqaptlZgg0AwLaPtgMWFgZmwcBa/fZrmur38rq99lnn3UdOnRw119/fV4Ca9eudY899ljevVpfbNu2zS1evNgdPHiw1lllJv2ktvtMEOZfPT3OdRv/D+6s8f+Qd37s5cW+AV3xy5/k3Y/q1fr6rsdaVkKe8sTwknZQDpP0IaDt3Fq99CFgFhsCjUXA+lZj8bfcG4OAtfvG4F6vXLNQv127dnWUAwJbL1m5cqXP84wzzshlyTae2MHxwgsv5O7H9ePFF190W7ZsaZXcCSec4PMcP95GkLYCp8iNpLb7TBDm2U+Nc93G/b37P+P+3nUb9w9fnv/eLXl+hq+O2x68umB4VL9W149uXuTtGPNovzz7ovn96ikjzEX6T6Jvazu3Vi/RhTXjDIEEItAMfevtgx86O8rDIIFNNVaTmqHdxwpYyhJLe/2+8cYbOZJKWV555ZW61EAhwvz555+7Ll26eOK+b9++2O34+te/XvCjwIABA9xxxx3n1q1bF3ueWU0wqe0+M4T5zLHHuzPH/r1rOfP7eAeRFnlgw3T3X9O75oVH9eO+7rfwErfxjafEBMd1aF80v9DeXCT7kXgEtJ1bq5f4ApuBhkDCEGiGvvXknu2u66IJ7ieL73E/4bxoQu7a3190T+7awie4NW9uT1grjd+cZmj38aOWnhTTXr933nmnJ8wdO3b051tuuaUu4BcizLXM+MCBA7589fSi17I8jU47qe0+E4R51rqx7t/vPt79+91/l3e+denPfb2/EJDWNa894iatvs1dNu1fW+lH41dy/d8LLvbpv/b2Zp8386e3ffn7p5NOyrMvmj7lMEkfAtrOrdVLHwJmsSHQWASaoW89uWebJ8T/unC8nRdNcG3hsGaPEebG9krLvVoE0v5cY0cQyrB161Z/PvbYY90XX3yRB8usWbMcQ6effPJJd80113hvLN7azp07t/LK9u3b15155plu8+bN7pxzznFHHnmkP84//3z35ptv5tItRpjPPfdcn9enn36a0+X3bbfd5vM7+uij/Zafo0aN8uHvv/++69evn2NYNXlRFso0YcIE97vf/c7rMNT6ggsu8GHYTVk4mLeM3Hrrrf56/fr1/po/YDBu3DjvkSZNcLnoooscHvlQpLzvvfeeu/HGG70d6IPBww8/HKq6OXPmeEywAXvBstAQ8bxICb2gjEmUhlilBUOrB9E8466/a3VAXpE7lt/oekzt4h7ZtMix+JbIh5/sdxt3rXOz1o71R9/7/9NxoFsoPbl39eyzvR66xH1k00KfjqTLGZI8afVQd/7Ek3y+3JP4xc5GmEME0/Nb2061eukpuVlqCCQDgWboWxBmSKI/FhQ5W3gOHyPMyeibZkXlCKT5ucacXuyXecSnn366v4YYh4LXGT05IKRCTrkXEs2TTz45pyfEkDN6kN2PPvrIJ12MMIvuxx9/7PU+++wzd+KJJ+bSJA10OBAWCQvtYmi1XN9zzz1e5/jjj8/dkzDOt99+uw8/++yzffjy5cv9NX8uv/zyXJyQjJPv9u2HPvSF5SVNsJEycGaYObJo0aJcenxoEJsg0WkUyppEaYhVWjC0ejPX3u3+bczfudPH/G2rM6DPXDs27/7PZ53lie6a1x51bwUEupoKwqMMcYacXzL1X/Ly27jrKcdRyL7Qbuw0SR8C2naq1UsfAmaxIdBYBJqhb/129zb3rwvGuS73j7OzAgcjzI3tk5Z79Qik+bnWv39/T+JmzGhZS2j69On++qqrrsoDRghzt27dHJ5UBO/tJZdc4vUhnCJCIAcPHpwji8QRgnj33Xd7VS1hxssLxu3atXOvvfaaj4v399133/W/IeDPP/+8Cz3SeJ+JE9qFDvcKDcmOEmZW8EYXwrtp0yafD8QXDzP3OYtIea+++uocNtgkHxRkxW/Re/zxxyWq++STT/LszgWk4Ac4JFEaYpUWDK0ehPm0O//WnR4ccs2Q6DVbHy0aLnGumnWW6zv/P93gJVd5gg15LXXcMP8/vf4l9/2Lz1fyk/TCazzZ2CBhnMNwuaYcJulDQNtOtXrpQ8AsNgQai0Az9C0I87/cP9Z1uX9sqzP3Ct0X/WYMX7NnW2MbZR1yb4Z2XwcYE5tFWuv397//vff4Yr+QYM5cQxRDAiqEmfnOoezcudPrhxgIMYR0hsKwbvTEm60lzJIeHlqtMBycvIgrUg5hvvnmm318hliHEi6QJsPWxb5oeYVc8xEC6d69u0+TBcZCbMP00/Q7rPMk2Z0JwvzLNXe70+74W3fqHX/jTrvjb748t1yLd7dYeFS/FtdU+Mw1YwvaF+ZHOUzSh4C2c2v10oeAWWwINBaBZuhbv939mvuX+WNd53l3t5znf3m264J4PLnbCHNje6XlXi0CaX2urVq1yhM4hgczD1gOIYBLlizJQVOMMKNA+TnYEgqR+FECuWHDBq+HpxjREmbx1Ip32UeO/GFlb/ZzxgN+2WWXueuuu87nVSlhvvDCC338QsOlZbi1zMcuVl48zuAihHn16tX+mnsMK2feNAuRpVUoRxKlIVZpwdDqQTR/Mrqd+8nov2l1fnLrIx73YuHcP7VAvFC/mvDr5nX3+U9YeVtB+8L8jTAnsYu0bZO2nWr12s7RNAwBQyBEoBn61m/feM11nneXJ8x2bhsHI8xhD7HfaUQgrc+1cI4uZYgeLNIlUoowawnkSy+95PNgjjGiJcxi1+7du8WcvPNTTz2Vsx1bIOQSp1LCDPEmDVkULMwQskuYEHgtYSYN5nrLPHHSYCExPiSkUbA/idIQq7RgaPVmPHmX6zqqnes66jvuJ3nndu6XT7Z4bYuFi36x8AXPTnUbdq51Ix/qWzD9tuJfN7eFMHMuZF8Yn3KYpA8BbTvV6qUPAbPYEGgsAs3Qt56AMM+9y/147hhPnP1ZrqPneV/qRe/LdRnhw9Y97P7figUt+ZYR/98X3uPGrl/tiJ+zu4z41ZbPCHNj+6TlXj0CaXyusaCWEN1evXo55izLwTVl4vjggw88QMUI8/79+3O6bQ1Rfuihh7wuhBHREmaZ+8ziXoVE5h/fcMMNuVWxZah4pYS5T58+3ta77sp/32fOsWDDYmRIOYRZ7H/99ddzq3aH86ElPA3npLb7zBDmfx35HVfouP2hvr599JnTvWB4oThyT+IyBxnpdvc/lZ2GkODuk37cZlzRrbRB81Bh7kgahYcnQ19C+6U83JMjiWXTdm6tXhLLaDYZAklGoBn6FoS505wx7sfBIdfRs+hE78u1Jvy/fjPLrd+7K1ft/P7pr6eq8h/wxK/dmx/uaxW3nPyxUfSjZ439T77RsohPzogM/miGdp/BalMXKY31e//993vi17Vr14LlPPXUU3341KlTfbgQZoY6hyJ7OHfp0iV3WwhkOKSbd0O2WQKroUOHel0hzFEbhMjLKtk9evTw8SCWQspzmQWE9ZFHWkaqEvbCCy/4OCFh5t2V/PEQR0VIt6ySzVBsdFnxWogxcaZMmeLvs2q2iJQ3OgQ9OiRb9OWMh5o8xOMu99NyxvYkSkOs0oKh1Zv+27tcl9u/4/7l9r/25y7BufecFg/vzYt6FgwnnuhH49+86Ge+zg58SZjf3LfLjVh2Q05f4sk5Gp/7C56Z5tMoZl+YP+WoRuiAYLZv36EXlWrSq8H32xEAACAASURBVEfcDz/80K82iN0cPNAmT57ss2afPbkv5wceeKAeZpWVB7ZpRKunSct0DAFD4BACzdC3nti11XWac6c75Vd31vT8b/dPcPO2PHcI3MiveS8/585YMKGgHT/99X1u/VuHSHYkqpv6wpqC8WpRLoawZ12aod1nvQ5LlS+N9SvkVd7jouWT1bJPOeUUHySEmbJ27NjRsSjWxRdfnHv3C1d+FgKJLnsqE5d50lzz7ihea+YAc4+jZ8+ebs+ePT6vKGGW/aHRI++bbrrJ6wsZHj16tE+DrafYRgpPs6QrOlI+hkATBkGG0LIQGRIlzKyILUO7yXPMmDGud+/euXTD/ZWlvG0RZtJhL+hhw4a5e++91/GRAVvAJ42C7UmUhlilBUOrN+23Y9y/jPjrgsf/GdPytQYyWkyn1H3ibdi5znF+a1/LHs4Q6GlPjHGkXSouYcTdundTm3roUo5KhQ4FXixiQIdJi7ANAHazNx+LFDDnglUIEVZV3LVrl2OICQ8APgjIcv9JKp+2nWr1klQ2s8UQSAMCzdC3IMynzL6jpke/1UvcnsAz/OaH+x33hq79jfvws09zTeHAZ5+429b8JmfLv80f7+7buCYXzo9X33/bXfPofB8/jEv63K91WYww51WHXaQQgbQ91yCs2Myxd+/egojLatnoMHdYCDOe53CfY8jtwoUL89IQAgmhFvJLOgytZh5zKLI4F+HyTilxGP4ssm7dOv9uiZ4cQoZZbEw+ABBGfCHRoiPpzJs3L7e9FbroIeL4kS2guMeK2OJplzxJe+7cuZKcP8vHgHAvagKuvfZab6uQchYjkwXMxE7uyb7UeYmm4IIyJFEaYpUWDK0e5LXz8L9yPx7+V67z8L/+8nzoGuB/88KCouEt8Q7pl7oevuwGt2HHulxdPrTxfvdfU/+9aP5vfrDLHfhknyfYxeyT/ChHpcLcEL64DRw40IVDOujEdHi+2vEwopMzZIYvZQwfQXfjxo0+W/akk69knGUVv4kTJ/oHAQ8lOZ544gnvyWZPOr6WcZ8vZXiMEVYV5KtZ3759fZqQ3eiG9egNGTLEP4TeeecdHy/6h2EyV155pX+gyfYEUZ1GX2vbqVav0eWx/A2BtCHQDH3r8S8J849mjfZkM87z+Q9Mcc+9tTOv2iHAEGHJ5/T549z8l/M9z+vf2unuemZFHsn+8LNPPMGWeJxJB890KKt3vpqXfqgPma722ghziLb9TiMCzfBcE8Is20q99dZbbvv27SWHSOMgYij2tm3bihJz6vvgwYO5/Zrbqn8cNlu2bMkbJi1xePfEG83+0G0J77LYphHSZf60Nu220oSIQ641draVViPDk9ruM0GYpz4xxnUadpz78bC/KniG4G7YsbZoeLF4pe5fO+sC99DGBbk2tX7HWjds6XWt8kdB5kC/+tZL7rL7zihqB+WoRPiKxNeppUuXuldffdV/eZLV8RhGQhj7zLFYAV+sIMrocg3Zveaaa3y2dLQXX3zRbwHA0BT0IKx4dZmjwQH5hkzz9RCyDAmHCLOsPfoTJkzwabEKIiSauSbkw4bu4cqIUk48yDKUHNIf/ZIGoabzQPDlK6HETcpZ27m1ekkpl9lhCKQFgWboWxDmk2eO8kQyzvN9G590Bz495HFhSPWly2YUzee8xZMdRLmQzNv8rINYF7Pv6uVzvedZ4pIvhLuYfjX3f7trq2ST2XMztPvMVp6iYM1Qv1HCXAoW8TBHhyiXimNh6UMgqe0+G4T58Ttdp6HHuVOG/mXBM2R2zwe7ioafMvS4gvEOpVc8/Kfjf+SmPj7GvZkbrr3PPbhxvrtsyhnusin/5lvq3Y8MdmMfGeyJM97m88efHMmvJf37Hs/fuF3bzGfMmOFJMcSUuRsQVSHBEGaIrEj//v39XA25ZrED5j4gDFNh2Air98nm6sy3EGH4NOQb4oxAdMM5EpBaiDECOb7kkkv8b/4wPEbmrORufvmDPObPn+9YoIGOIgs3EMx+fiwOIdsURAl1NK1GXGs7t1avEWWwPA2BNCPQDH3r8Z2vemL5w1+OjOX88+Vz8jzD1D/kWZv+f69c5PAmIyzwdcnS6Wq7yEfiEh/vdjnxIdJt2ckQ9qxLM7T7rNdhqfI1Q/0aYS7VApozLKntPhOEGaL5o9v+sughRLSUThxh/eZf4R7f8nCrFn7f6jvdeeNOdj0mtxDoeU/dV9BWsbNVAm3cYMI/RBbPLwdzGbhmJUANYWYjdYS5FixuAGFmGDSNVgizrHz4zDPP5KzBu3zbbbflrgcMGJAbDh4lzAzRLkaYcwk454dwY3uhFQv5EMD2BEkTbefW6iWtfGaPIZB0BJqhb0GYIYnVHqfOvdst2/pCwSqFRJeTvniaOZcTD93/WDjJUaZQpjz/W4d95aZVSN8Ic4is/U4jAs3wXJNpg+HK18XqipGPjHJkKLZJdhFIarvPBGGesvpOd/Ktf+lOvvXb7kdfnsPru5YP8i3r0smnFwwnXqhf7fVpo9q725Zc5159a1PBFg2BLpQf5ShXGKZM43r66adzUWVRhdmzZ6sJM8SYdJivzFwOvLxcc59VCvlNeqTNwXL4DJeGxDIMHDsg0LLQgZYwMwSc4TUMK2cpfEg1HwAQhnnjzSaMlQOxgRUWkybYpRGtniYt0zEEDIFDCDRD32LO7w9n3O5+MP32L88j3A/yruW+nFuHj3n6sbzh16+8t9dd9Zs5OSB//vCcstKXec8Q5kN2Fc+/kP3/vWJR3hZUew7sc3ivfzC9tf2F4rfOtyX/J3aahzlXsfYjlQg0w3MtlRVjRtcUgaS2e92bfszQaMHQ6k1ZfYf74ZBvFz1+PuOnvgScS+nFHTZ33X25fMc8PMhBiG9d0qeoDZUQ5n79+nmvcrSK8BazsjQEOBySjRdYCClxGJItHuZBgwZ5UgrusoIfhJnV9rgXHgsWLPArHLJomNxnODarCiJRwsxw7UIe5kmTJnlvuKRBvuxzhzCkW+5TBtLQLqbgE6jTH2zUiFZPk5bpGAKGwCEEmqFvQZg7QiKnj/BnfofXcl/O0fBX3nsrBxjDoSdv+G0uvgRc9ZtflZX+c2+2zGXmLPnKOZq/3JdzGN71V2Pc3E2HRi9hz9JXX8jZV6icYfxC4U9EvNdSxiydm6HdZ6m+yi2L1W+5iJl+FhBIarvXvenHXANaMLR6k1fd4X4w+Nuu4+C/KHg++64f+hK0pVcsfqX3n3t9rXvlrU1F7Yqmi32NFvZwlpWutbYwb5qVDauRHTt2FNw/GsJOWJJF2061ekkuq9lmCCQRgWboW6t3vOI6Thvhvj9teP55auS6QPjFS6blqo10zr7/nkPpTB2eC7vqoV8dui/plEg/JMyt7FLE9+UJ0sdOvN4i0fByyh8d7i1pZuncDO0+S/VVblkqrd+oY4EpbtF75dqi0WfKXKHdUErFZeog20TJ2jildJMSxo4yU6ZMqbs5rIINVrK9KluuxoUbK2szmpTRo42WStt9re3ODGHuOOhY13HQX7jvf3mWazkD5Ny1U4qGi16x+JWE7/lgp3vu9TXeLk38JBDmWje4LKav7dxavSxiZGUyBGqJQDP0LYju96cOc9+fOrzsc6+HZuXg7/XQ7FbxJRC9ctJ/7s2Wj5mcy4nX88FZrueDs91Z8ye0iod9IpWWl3hGmAVFO6cVgUqea8uWLfMj88TxwZQ31oVhNGKthXzKnTaHk4ZyMgUvLRIdQVkvu9lrGqyYvogwnzscMVqOHbQPplDKFlTsrEPa4X7R5aQXp24l7T7O/IullQnCfO/K0e77txzrOtxy7JfnY1pds0r2c9vXFA1vK34l4YA+Z+0U1+GW1vYUSo9ymKQPAW3n1uqlDwGz2BBoLALN0LdW7XjFdbhvmOtw31BPeP05cl0sPCTMkFUIZRhfao+waPoXLZ7iuH/WvPGt8g8JM/GK5S/5dZl5h3t2z+uSnT8PfvzXOXuIT14i31eWT9IP8398R/6CYpJmls7N0O6zVF/llqWS+g0JM9PbILF9+vQpN+uK9I0wVwSbOlKUMONxrnQEJusH0b4+/fRTnz8jEPi4wqjORksl7b4eNmeGMJ808BjXYeAxjrMc4fWz29e4LXtezIW1pV9tePd7TvX1BwkuZE+h9I0w16PJx5+HtnNr9eK30FI0BLKNQDP0rVWvv+JOmjLUdZgytOCZsGLhP1t2iIT2XDarlZ60DvSi6T+3p8WLfO9zj7eK9+yXYZxL5S9hS1/ZKFnlnW9Z/Wv3s2Uz/TF67fJcWFgeSSNqn1xHwx/f0bL9YS6xDP5ohnafwWpTF6mS+hXCDPlhxxTZYpRMt27d6rcDZTjxGWec4cn0FVdckbOHcLYAJR7rycyZ07IgIJ5Mdk9Btm/f7hd7feCBB/z1mjVrfJp4ikPCzDBwvM2sXUN6kPaDBw/6OGwjyoKx3O/Zs2eeh5nVstlvmR1f2N4UPYghwjajbINKPt27d/fl8QHBn7179/r1btBhLR/WwiEdhN1a+vbtm9sRhjV3yAtdsAYT8cxzf+DAgX67U9bQYUcYCcPDjGcXrAhji9VCQ9EZuk05SJuz4Cn1MGrUKF8+4rM9LFIqLEqYsU8+hmDbhAkTvF3kNX78eL+mUKHy4UUmT+wCX3DavXu3/y1DvJ944gmPMdiA4dq1a/PsK9SGPvjgA78mEnFYBLjSYeuVtHtvXI3/ZIIwT1ox2p108zHun2/+86LnX62Z7KFsSy+u8J9NPc/nx7mUXWF+lMMkfQhoO7dWL30ImMWGQGMRaIa+ter1Le6kKbe5f558W9lnyKjIz5bObBU/F7ZsZqv0hRRP8oQ5P38J46yxS/K5Y+1y13nGKMeK2KWk0vISzwhzKWQtLA0IVPJcE8IMaenRo0feFp1CuCCQmzZt8jufkAf3kdNPP90P8w3D8ESyWCzECmGrUuKwYCwCaezcubP/TZ4yJBuCxTXEHcILiWOxWHZVIT77L7P+DcSba4Zky/Ds22+/3UF8WRSWMLaRgmyT3syZM/1OLZDW8GOAN8C5nK0MMSYvFqaF8CHEgaSzCwyEkB1YIOF79uzxw5xD+/lNGSn/o48+6vOeNq1lLQjSAQ/I/c6dOx0L3nIvKuvXr/fx33//fceWWJBrPiRIPVBOysbivJQTsqwJkyHZLMgL2UXGjBnj08cm0kSnWPnAGTJLnoxCYC40B9cbN250n3zyiS8v7QQCzYcGcMP7LPYVakMjRozw9QxxhoBX6v3GjiRKQ6zSgqHVm/TYKHfiTX9e8hAyeubo75fUaysdbbjkd+H4n6jzkzhJbChmU3EEtO1Uq1c8JwsxBAyBQgg0Q9+CMP/zvbe6f558a8uZ38rrny39ZQ42fvt4QXzucXSePqpV+jKEetKzq1vlJ2Gcc2mWsE+MGL1muc9r94EP5FbBs7Z8ubwDPJjznXVphnaf9TosVb5K6lcIM4SO45133sllIWRHCBfEhjwgq3hb+b1y5cqcPmQTogeJIgxShRcWkgyhZP4rRFiIZEg4e/fu7b2MgwcPdhx4NCG4EGrSYrEvREgyNuCBJUwWnpJtUyGAQqwlvbPPPtu1a9cuZ6v8wLM5fPihhQzxSIeEWX6LPmWCPE6ePNnjxW4xSFgWriHPlB2BHIfpgEehXWAgnnhz8c5DMClbSDqlHkgTQnrnnXfmCKkmLCTM4Nu/f39vX/inWPlkq1YZkh0SZkg3tkK4EQg/14888kgr+8I2hJcdPfDhI0OlQhpJlIZYpQVDqzfxsVHuewP+3H1vwJ8VPV8x5VyPP+dSem2low2f+FiLt1irjx7lMEkfAtp2qtVLHwJmsSHQWASaoW+t2t5CmE+cNMST03LOIWG+8tczyoovpFgIc5ivhHEO70NgC13/esvzBRvKoFVLHHZh5y0rl+R0iqWjub/6dSPMOSDtRyoRqOS5JoQZry6eT0izrKocJcwhWcUjSH7h0GLi411EIKcjR470Onh/IXjipYQ0ISHJ7NWrlyfJeJXlwJsJMSUfGZ4tNrBCs5Dp/fv3+/RCwjxv3jwfj+Hckt6KFSu8XvgHLy4ecZFShFkIHguiQWoh/8UIM6tTQ9KRKGEGo0KEGe/viSee6NO+8sorvf3FCDN233333a0IKfkVCwsJMx8KxHYpe6nylSLMYEwdQfiRAwcO+OulS5e2sk/qTxZtwyMN/rSFQiMAxLZS50rafan04grLBGG+57FR7p/6/3/un/r/WXDm96Hryye3DFsYuezmL+/nh7cVv9zwZ7atcXve3xXYU9o+0qccJulDQNu5tXrpQ8AsNgQai0Az9K2V27e4700c7IlouecrlrTMj6OWIKblxBdSDGGOxnt2d8sCXpwhyNHw6PWPp97eatEvCHKoh30i4X1N+qH+6te3SDKZPTdDu89s5SkKVkn9CmFmTitEFi8x3sf/v703/7eqyu+8/4L80umQSkKlO6ZDdZEKqVBlwlO23T70Q/PwENumfWGIDYGCYNCABVrIIPOgzKiMAkIJFFjMU1BkFEFAwEIEQWYVcQKZRMWh1vN6L/hu9t13n3PPufeee/bZ+7Ner8Me1vRd77X34X7Odw2fffZZjWKHodV4UkmLZ5T69++/sT/6uHHj/DUCkMDcZ0RRhw43nFHcQ0QjqhGFiFnyI9pYUIphz4h4W2wKLzDeb/MqI7jMy4kNeDnx4lIGHmYTbQzTxjuNULP5tmGUbG2FHc8//7wf0o3YNG9wVOjSJtLi0WYId1h00jbyYRNtIN3ixTfWgoiWEyeYYYDtDLfGdoaBcx0WzIh0PPv2QwFDmO1Hjbg42mtMGdodFszMZ8b+119/3c+1hnW+9jH3nLIOHjzo+9vYM5oAzzJx2MCPF/QVPPBGm33mAQ8LZuzn2SEg3rGnNoG6kxjKYlWhMApNN2vLJHf74D91Px38pzeP34+9pgNmbZnsfjo4Pr6m/MXEv/PhYbf/9K689kTLox0KlUeg0Oe00HSVR0AWi0B5CWTh3XrlzDteWP7trFFFH8OCucfaBUXl339TFM/ev71avrBgLsauTivmOOz4Xy9MddF83LeAAI7GF3otwWwUdaxUArX5XtuwYYMXOzacFgGDYEY4I34p08QOYohr8w4yrxdhyD0+PXr0CNCRh3sMiSasWbPGX9viX9wbMGCAv7d06VIvDBGWVhaCywQnQoxr4lq3bu2PZsOmTZv8PWymftIg/ghsg2TlcYzzYOK55j4ecTy8eNjx7hKiQheBh6ijLIQ1ecxLi338gMB94lkELbzol4lwyqXdcR5myjJ7yc95WDDjsTYONpzaBGlcHD884PWnHJiEBTPXFkc8w8TztY/h9NhMWlibYGYUAIFFw4izjy3gxfx27sU9Q8xhJo428RytWrXKl1XsP5SRxFAWqwqFUWi6Z7dMcj8Z9H3309An7vrql1fctrc3+HRx8TXlLyaezl782nOBTYXURzsUKo9Aoc9poekqj4AsFoHyEsjCu4Vg/ttnR3kBiWj0nwKuh29b465cv7E6Lb3E+cTXXq4x/z2Lp7j9H5wJOhbBHK3fxPQ75z+qsbxC7Q0L5nNXLrm+Ly+91eYC2mtsJJiDrtNJhRIox/caQgrxZMOii0Vn848tHx5IysMjGg6kY25svsAQZURsODBMGLGG8KwpkAbxVpNww/sdtY98eH4RqTXZmc8O2m9C29KZKKYdeMttPjfx+eIsf5Sx3edIffZjid2Pa5/F2dB4uw4fYY133+Y5h+NynfP8INSjPHOlj7tfjuc+zo7ovXQI5s2T3E8e/75r/njjvMd9p3a5/ad21ZiupnJqiv/7CTeGrDxboF1WHukVKo9AoS93oekqj4AsFoHyEsjCu/XKaQTzSHf7zJEFHXusme8QsrnCuSsXXY+186uV13LueLf2nQNVsh07/5H7n7+eUq3ede+8GaTLVV6h9obTLX7rxhYmVjjC/f8snVWt/nw8mPOd9pCF5z7tfZivfVnsX7ZB6t27tx8aTvtZ/bmYgCcUzzLDpPGcInzDgrTQskwwF5q+mHRhURzNly8umjat10l97lMhmGdunuT+ZmDjGj9r9t/YU27osj41pi2kvLg0/3VEU7f6Zj3dZ7cvqh7aoVB5BAp9uQtNV3kEZLEIlJdAFt4tBPNPZ45wt88ckffYcu44t/ZoVcG7/9wZ9z9//Yy7+9fPuG2nj1bprH0fnPH3KffZvduqeKOvXv/KDd+6Omd9//fccW5dRFxTN/drsrOm+B5rnnfYHQ6LDu52tK8QDtvPvBPOmsrzLDz3qey4AhuVxf5lWDjbLbHvMdtCFRtYkIx50QyTZti4LVxVbDnsacx861IEhskzvNwWSwvXkS8unC7N50l97lMhmGdsmuh+PKCx+/GAP3F/c/MYd33n8B+6d87deAH2nnzNDV7a2/3X4U19vrj0NZUXjv+HZ1o77Pjgs/f9c4w4D8cXUj75FSqPQKEvd6HpKo+ALBaB8hLIwruF0P3pjBHuJzOG3zhOv3m06xnD3YQdL7krX90afs2Q5kdf+k3VfDOGu39Z/Xw17/MHV24Nj0QoI54RvkF9oXq8HaH6KY+6LGDDoy9Wr7cm++Pih21ZVa1s2vmTUP1V8t20U4LZekPHSiWQhe+1Su0b2V06Akl97tMhmDdOdH/d/4/dX/f/k9An/vq/DPuhF7bnLt4QtnT50XOH3Op9v3EzNk50v5jf1XV99l7/Ie2tMv84uE/8uLVDfHqE9wefvRc8OQjywUv7FGxPuHwJ5gBjRZ0U+nIXmq6iGi9jRSABBLLwbiGYfzJ9mBeKN46c37p+YNXzQU+Y4A3HIzCrXg9ziFHShgMe4rsXPHUz7a3y4/JXLW+4F9nhsvZ9cNoN3bzKceSz5shv3d0Lng6VXVj5dz03xpcdtvWBVb+KtKdq+7afloc53Bc6rzwCWfheq7xekcWlJpDU5z4Vgnn6xgmuWb8/dn/V74/9kfPwtd23o8W3f7pVIHpr+wCc++x9t+XwS27c2qGO8qzsQuo3e+xIOxQqj0ChL3eh6SqPgCwWgfISyMK7te3UDcHcfNpQLxTDx+bThrnuq+YFnYDgjcaHrxG6dv3f5jwZ5ENA2/3wkfLD1+H83A/H//38yVUWCwsKv3mC9xkBHC4vnD983+qxeNplgfZG4+2aI0PY0x6y8NynvQ/ztU/9m49OZcSx4Fnc9leFWD9jxgy/13UhadOUJqnPfWoE81899keurp+fDf2B6zrzXv9BvMZ9fvH8z4M0da0vml+CuTJf+UJf7kLTVSYFWS0C5SOQhXdr26kj7m+mDnXNpw6tcuQen+4rbwnmcDrO2z4/2f3985Or5LNyiLdAGXbfjsTzyZefeEtvxwmvvmjFunc+/dA9+/pWd/Xmat2/PrArSG/lW77oMRpvhWKrxcXV/0pkrrblS9MxC899mvqr2Laof4slVv70mzdvdszDttCuXTt355132mVRx+g2WEVlruDESX3uUyGYp708wf2o7x+5v+z7vYo+0g6FyiNQ6MtdaLrKIyCLRaC8BLLwbm31gnmI+/GUIe5vplY//vOKuUEnhONn7Nni5zVvOYngrp6P8ix0Xzk3tvyhm1b6JL8+8Jq7c9YTOcuJlh8tF6FMYHh2rnbUdN/KpL3R+sLXr5yquriZ5UvTMQvPfZr6q9i2qH+LJVb+9B06dHDDhw8PDGF/ZLZZqk2QYK4NtdLlSY1gbvrL77m//OX3HEf7VNq1BHPpHvRSllzof2qFpiulrSpbBNJIIAvv1taTR9yPnxnsfjxl8I0j56Hrf15+SzBzn+ujn3wYdDfe3f/vVxNj81si8sSVj4fYAkOqB29cfsuGHPZgg3mUt5x82/Ve92v3weUbC4shvL3tIfsLvTY7vK158kswGykdK5VAWr/XPv30U8f2UWzdRBvvueeeYK9ixOXIkSNdkyZN/LZQeGuXLVvmOnbs6B577DG/LzNDnE+ePOn69evnGjVq5Nq2betXxrZ+Hjp0qLvtttt8HFtUEeLuWfrz58/7urZs2WK33MCBA90jjzzi9xNmP+aWLVv68rCBvYupn22rGDbN8YUXXvBbYdEmbGrWrJnbtm2bGzFihLebgtmPmdW38Thj38yZM10+FmHBfODAAdemTRvP6/bbb3f79u0LbE3bSVKf+1QI5qkbxrumj37P/fDRP6zoI+1QqDwChb7chaarPAKyWATKSyAL75YJ5r9+epD762cGO46ITDt2W/5c0Amr334jOOeEvP9l5qggfTS/JUaExpV/57Oj3Yzdt/6YJP3e90+5f1g8Lajf7AjnH799vRUdHBHRbX41MTaftSdqn93naIH2hu9H62fOd9pDFp77tPdhvvaltX+vXbvmtm/f7j788EN36tQpL5wRpQSGMLdu3drt2rXLHTt2zCFmZ82a5YUiYnXDhg2O/HfffbdPe+TIES9WYcU2UHv27PFp2c/44sWL7ujRo7H3otwR7QhUAsKW8latWuXzIoLfeOMNh7cYobty5Upn+yWz5zPX586d821BKPfq1cvPW6acBx54wO8LTbnsKd24cWO3bt06d/r0aZ8+H4uwYOZHga5du/q2s90W20+lNST1uU+NYP7Pj/yhi/80ynHf0icnXoK5Ml//Ql/uQtNVJgVZLQLlI5CFd2vrybdds6ced82eHuSPCMRmTz/uhSf3w4LZeuLclYv+PvE+fY78lr7rsjk5yyf//zt3gtt79rQl98eFv93p7pgxMmf5415Z77CDgKe5/aJpsfbP2L3Zi3CE+PRdm2+UF2qf2W+V095w+y3e+DDnO+0hC8992vswX/vS3L8shjVv3jw3d+5c740dNWqUu3DhgheqGzdurIIFwYzQtIBXFjavvPKK3XJ4XYcNG+ZFMmnvuOMOt3DhQnf9+vXYe0HGmycIccpEiC5atMh7ib/55hvXt29f7+0ePXq044M3OIOg/QAAIABJREFUuU+fPoFgRvCHQ4sWLaoMyQ4LZvIOGXJrCozli2NBXFgwT5061dvHjwb8SJDmkNTnPhWCecpL49yUl8a7NBzT/BKktW2FvtyFpksrJ7VLBEpFIAvvFmIT4ftXkwfGHrsunR3gvfrVl1505ktPOQjk19+/9QffB5c/c5RTU77eaxcGw6updOFvX8tpVy57w/cRydGw6vD+WDssXU12bjuZ7j8q4ZCF5976O4vHtPYv3mPaNnjwYIcQxGuLYMaDy328z+EQFcx4c0lHORYQyP379/eXiN5p06b54dt4fL/77jsvhKP3LC9H0mDHnDlzXKtWrdwTTzzho/EWI3TxItvn0KFDtRLMDDOnneGQiwVpwoKZ6927d7vu3bv7ti9fvjxcTKrOk/rcp0Iwp+pJUWMqjkChL3eh6SoOgAwWgTITyMK7teXE216UIjTjPmHB3Pq58bFpLB/xCNJcYcEbO93Ppo/IWwbxRz8554tA8FrZxR5/sWZhYMbCN3Y62mmh/cIp1cq1ONqbry4JZiOlY6USSOv32rhx47wH9+uvv3Zvvvmm9+CakGRuc/v27f0cYTzOiN+oYKY/EbVdunTx8Vu3bvUicv/+/f4a4U1AeMOQ4dPRe3YdfjYYMo1oJg/DxQnMQ+YaYYuo5j55bUh21MPMPONOnTq5S5cuuS+++KLKkGzmMyOaX3/9dT/sm3LysejcubMfds6caYZhU963337r296zZ8+w6ak6T+pzL8GcqsdMjSkHgUJf7kLTlaMNqlMEKplAFt4tL5gnDXA/mjjA/VXk+KNJA9zPl9zyMMfFk+Zn04a7+ft3VOlqFuJ6fMNSn98W5SIBi3v9YvUCXx95+UTLNc8wx7h4S58rP/HTX9vk7UF8W3ozcOy2ddXqtzjaa+njymfedtpDFp77tPdhvvaltX9Z2AvhSPsYPt20adPA88oiX9wjjg9e3dmzZ1cZkg0z0rG4lqXr0aOHR4l4tvvMPWaxr7h7cdwR55Rnc5lJw7Bsrq0eyly8eLEfFs29qGDGXtIQt3r16iqCGYGMJ9zKYtGyfCyYQ01avNzM7bZ8eM1ru7dzXLuTdo92JjGUxapCYRSaLolgZVN2CBT6nBaaLjvk1FIRqB8CWXi3tpw47P5yYn/3o4n9qxy5x+fnS2YFMMPpwvGIYAsM2x6zda3PG05vAtbSbTp+2LWYOqxavdhRRTBH7DI7rX67jh4ff2mpVeV6rZrvbbIbRz455/7H7LFV2mtxtNfKDttv5TPnO+0hC8992vswX/vS3r94a3/3u9/FImCeMmI1X8DbyvzfK1euVEt29uxZP385HBF3Lxyf7/zy5cu+rlz2hvNiF97zXIGyWOwrHHKxCDPAw8xw9LSHpD73Esxpf/LUvpITKPTlLjRdyQ1WBSKQMgJZeLc2Hz/s/nJCf9d0Qr/qx4n9XeffPBv0arV0E/u7v5syzCGSEc3TXtvkr4N0E6uWe+/8p4Ph1hRKnv89/+lq9YYFc6xdZm+k/KDeCTfsCnu2g0aETvCKYz/5LNDeoJyY8reGhnZbnrQds/Dcp63PimmP+rcYWkqbFgJJfe4TL5gBp48YJP0ZKOSLKqlfAoXYrjQikGQCWXi3EMxNxz/mmo7vF3vs/MItwZwrXa+Vz7tWs8bE5o8rd8yWNV4s0/fTdm6slu/19076x4JjXP5cdkTvt5r1pLOyKJDzgS8uCerm3pWvvnBPbl7j6+Mf2hstJ3wtwRyg0kmFEsjC91qFdo3MLiGBpD73iRbMJewPFS0CDU4gqV8CDQ5CFYpAPRPIwrtlgvmH4/q6H45/zHFEINp158UzA6px8dH0hV6bkJ268+Uq9f3t00PckY9vLK5DmkLLM3sLSf93zwx11BsXaG+4/dHyJJjjqOleJRHIwvdaJfWHbG0YAkl97iWYG6b/VYsI+JESwiACIlD/BJL6H2x9tnTz8UPuP4/tG3x+GDrnflgw73n3hGs3b3KQlvhoeq7/afFMR9qpO16OjSdfIJh3vByU98Sm1d7ja+3zgjliT1x9+ezPl/6/z3zCbT52yKrzR9qbrzzmfKc9ZOG5T3sf5muf+jcfHcWllUBSn3sJ5rQ+cWpX4ggk9UsgcaBkkAgUSSAL79amYwjmX7ofjPll7PH2yYMDcWv4Vry113E/mu+/zxztVhzca8m8+I1LRz4TzFN2vOz+afEM9/ZNr3KQ+eYQ6lx21cf9KTs2uMtffRFUyX7Ruey1+rYcl2AOgOmkIglk4XutIjtGRpeUQFKfewnmkna7CheBWwSS+iVwy0KdiUBlEsjCu4Vg/sGTj7ofjHn0xpHz8PXN+/3/7QX3waXPgo5EaE55dUOQj/PLX1YVn/+6fF4QHy3/9fdO+LLOXroQlMkJ9gRDst89kTN/UJ7Zbccc9gfpn3zUYVe4XhYfQzzfPnnQLQZWXuQowVylu3RRgQSy8L1Wgd0ik0tMIKnPvQRziTtexYuAEUjql4DZp6MIVCqBLLxbm4695UVikycecU2efNTFHRHQ3P/p5EFeJIe3kUJ4VhOfr26oUo7lD5f/9kdnqzwWiPGOv57u8zGcm8CReuPyh+0sNP6euRN9meGKVxx83bWcPrqKvWE7o/VLMIfp6bwSCWThe60S+0U2l5ZAUp97CebS9rtKF4GAQFK/BAIDdSICFUogC+8Wgvkvnujj/uKJR6ofR/fxQjIa/5NJAx1CMxq495NJj98qJyZ/x4XTqgnsZ159qUr9UcEcrT+4jim/SjtC8XdNG1XFXOrAFgRxUF6UQyi/lbvleNU5z1UKTclFFp77lHRVrZqh/q0VNmWqcAJJfe4lmCv8wZL5lUMgqV8ClUNQlopAPIEsvFsb33nL/cXoPu4/jepd9PHBpXMDcA8ufS5v/v82ZYTbfeZ4kJ4TBHbzCQOq5QsL5trYFdee/7NgalB3v7WLatVeyo0uEhYUmqKTLDz3Kequopui/i0amTKkgEBSn3sJ5hQ8XGpCZRBI6pdAZdCTlSKQm0AW3i0EM6K0Np+wCOU8rgwE8dOvvFgF8pGPPnC50lNGVDDHlVvsvbCtxeYNp5dgrtKVuqhAAln4XqvAbpHJJSaQ1OdegrnEHa/iRcAIJPVLwOzTUQQqlUAW3q2N7xx0fz7yF+7PR/zixnHkL9x/4jr6iYm/f/6UoGvvf35KkMfy9139a/f+xVuLejH3+bE1v66x/D03PdEcAzsi9SPCbZExjj1+MydIa/UHeUf+wmGfhbj4QtsvwWwUdaxUAln4XqvUvpHdpSOQ1Odegrl0fa6S60jgm2++cevXr3dvv/12HUtKRvakfgkkg46sEIHaE8jCu7Xx6EF324iHg8+fh87D9+08HP+Pzz8TwOWcNBb/43H9quypPG/PNsc9i7fyokfibeg2x7j4qMfajPj7WWNzlh+2NVpm+Lom+za/85ZVl9pjFp771HZeAQ1T/xYASUlSRyCpz32mBHO/fv0cHRH+NGnSxN17771u2bJltX7orl+/7g4cOOA++OCDWpcRzTh27Fhv58iRI6NRBV1/9tln3qYrV64UlL6ciXLZunbtWs/g937v99zvfve7cppYL3Un9UugXhqnQkSgjASy8G55wTz8Yfdnw3t5ceqPBV6HRagJ5nD+kRtWePH798+OLap8E8xxXX/4w7OBZxnbqddW3F5+YM+NemLsD9t6W0x8oe3fJMEc1y26V0EEsvC9VkHdIVMbiEBSn/tMCeY+ffp4Ada8eXPXtWtXd99997mmTZsGArpDhw4Or2ax4eDBg76MIUOGFJs1Z3qEMg/NiBEjcqbJFzF9+nSff+PGjfmSJSIul63Hjx93zZo1c927d0+EnXU1IqlfAnVtl/KLQLkJZOHdevnoQfdnw3q6PxvWq+jjP/7q6aCL/vFXzxSdP1e9+QRzUKFzru+qBd7uZQd2+9vky9UO7LOQq95C7kswG0UdK5VAFr7XKrVvZHfpCCT1uc+kYJ42bVqVnt65c6dr3LixF5izZs2qElfIxYYNGxInmAcOHFgxgrmSbC3keciVJqlfArns1X0RqBQCWXi3Xj7yphe6/3Hov7r/OKynizsiJOPudwgJ5g7znio6v9UXLd8EM55jyh350nL31NZ/c09tW++P5lFm7vKu08eCx2nES8tj7aR8yrFg9doxWr/dt2M4ftNRDck2jjpWJoEsfK9VZs/I6lISSOpzL8F8s9dXrlzpBSbDf83LfOTIEdejRw/vheY+nXj33Xc7BDLh2rVrbtiwYe7OO+/0cbfddpu75557/OfEiRM+zdSpU12rVq0CQY4wHz58uDt//vzNmuMPcR5mhGW7du0cQ5gHDRrkbr/9dl8v98yTfPToUcfQc2zB3hYtWnh72rdvH1T03XffuSlTpvg40pC2b9++7vPPPw/ScLJ161Z31113+XIYuo7d/KBAG9977z2fdvHixf6aehcuXOj5WF1w4txsgSE8Dx8+7PPWZOupU6d82bQnHE6fPu3uv/9+z5Qy4f/888+Hkzi8/ti5fPlyt2nTJn/eqFEjbwvtgGFDh6R+CTQ0B9UnAvVNIAvvFoL5Pwz515wfhHKu+H+Ye0uEch6XLl9+0sfF774pgjnGxd8xaYi78uUXVbr77Q/P5q0/bGvYzrjy88VvOnqwSr1pvMjCc5/Gfiu0TerfQkkpXZoIJPW5l2C++ZQhIk0Uv/vuu/7uhAkTvFik8+64444gnutDhw65jz/+OIjnXvizZ88eX4bdQzSawOUegi9fiBPMJl6tTIaWm80cEfqrVq2qYoel5WihS5cuQRraZWnCNm3bti24j1imLkvH8Y033vDFmZ2ksXiEKaFt27b+HtfhevjR4KuvvqrR1jfffNPnJ68FFgCzNlNOmGlYWL/yyiuBPdiFDeHh9/yQ0dABOxREQATqn0AW3q0bgvkh96eDH3L/YUhxx3+YOzmAPmnLuoLy/2ziYLfhyAE3ecu/5UwfFsy57PrR6Efd8BeXuude2+KPudLZfeyzUGw7w+mZN532kIXnPu19mK999K8+YpDFZyDfe1GuuLL8BV+uL3mbwxwdkm3wTRS++uqr/ta5c+fcyZMnHWKawBGvJfYz79aCzcGNm8OMsAwvvLVv3z6fH9GXL5gQDc9hNsHcu3fvwEOKlxsxiE14hC3g2eWeeZ7t/t69e/19BLwtUnbp0qVAhNo9E7ijRo2yrA6vuXmLo4IZG+bMmeNXtD579qzPgwf5o48+CvJ/8cUXQT14gC3ksjVOMBv/hx9+OOgXbKGtfI4duzHszwQzonrLli1WlaPvSYdXuqED9SqIgAjUP4EsvFsI5j8d9KD708EPFn380ahHqnh6d516x/1s4qCc5UzavC5YsIvh1D+bMCi2XhtmzbE2doXbgz3YZQHPdDi+2PIlmI2kjiIgAiIgAnUlUJa/4Mv1x01Ngrl169ZeTDGMN1dAFGJ/WMjmE8xx5ZjA/frrr+Oi/b18gtnEqmU2j/GCBQvslh8KjZ1RwcxwZO5TfjiwCBr3+bEAgc85H0RuODC8nPtmg9n51FO3hvyF00fPO3bs6PPjwbZQqGDGK212RYdU9+zZ08c988yNBWNMMCOww4GFxCgDId3QgXoVREAE6p9AFt6tDW8fcN8f9KD7/uM9anX8vyY8XmUeMb2AMP7LkX2C8u57brI7dO79oIMQrcP+bUkQH60/LJhraxf1Y0c4UC72Rusr5nrjkVs/yobLTtN5Fp77NPWX2iICIlC5BMryF3y5vuRrEsw2ZHfXrl1Bj+Kd7NWrl587jGeXYcvYX6hgvnjxomOLKPJ16tTJzwO2IcX1JZixC5sKEczYQFraisfaPghI7uOlxjNsaQIQN09yCWYTquH0DBFnbnG3bt28gO/fv38wb7o2ghnvMXYx/DsaZs+e7ePwPBNyCWYbRi/BHCWoaxGoXAJ8L6Q9IJgbP/4vrvHjPep07LZwRhVv8/ufnXd9lv3KLdn/WhWE1NdiwsC89ZlHmGNt7MIW6reAQOdefbRz45E3rdjUHrPw3Ke289QwERCBiiJQlr8yyvUln08wI16xi48NS16yZElwD4Flw5FJU4hgRjCG5/ayRZKJZcooh2BmgTDqZsg1nt3ohwW1zAsbJ0yLEcwPPfRQwI+yzLNO/bURzDZEG47RwI8FlIs4J0gwRwnpWgTSS4B3P+3BC+aB/+L+ZOADrjHHATmOBcQ3HdHHzdm5KRbZ+xfPu/azJ9ZY/g+H93aHzt1Y/JEj196uAupvMX6ge/Hwb6vUjz3YVR/tww6GsKc9ZOG5T3sfqn0iIAKVQaAsf2WU60s+n2CeN2+eF1wIY+YqI3ZNIK9bd2u4GKtCY38hgnnRokU+LfNlmWtswcoth2B+9NFHvU0rVqwwc6odaTtt5IOHPBwKFczM/bYy7AcIymGVbO7XRjAzDJu8/Ojw7bffhs1yzLUmzuZcSzBXwaMLEUg1Ad79tAcEMyK5Pj//45mRVeYNw3DipjU11tFtwXT3XsgzTD6uuV+TfZR/+ctb/x8ePve+w46a8hUbL8Gc9jdC7RMBERCBhiNQlr8yyvXHTS7BzPZH5vlFEBPC83jDYnfmzJlemIUFs4nt7t27V+m5wYMH+7RsPWXhyy+/DDytpRTMnTt39nWz1VM4zJ8/399nvvbvfve7cFSV85YtW/p0CFwENIH9qs1LHJ3DHB2Szdxp+jm8wjVldOjQwd8PC+ZctppHOVyG/dgQFvwwtfsvvviit1WC2WPQPyKQCQLl+j+lIeG+dPi37o/7dy/JZ+ja31QZpo34vXfWhGp1/d3Y/u61k0erNPu10EJdRBBPuqitlGceadIx/Jp6o+nq6/rlt+VhrtJRuhABERABEag1gUwKZrYiYpErhiOHh0yzeJatiA1RW5GZeb/MkbXFtfjjLCyY2ZOYe3wQmHwQlPv37/f3EJksijV+/PhA2JG2lILZPOF4zPmhANtZwOv69euBDXi+2Y+ZD/HMa/7000/9w8SwbPsRAVtNKFs7axLMLNBl+YcOHepXFbdVvikjLJhz2RonmJctWxawZl9qhLptLYWwth8BJJhr/Z2gjCJQcQT4Tkl7QDD/Ub9/LtnnB8Medr/Zt7MKxhf27XTc5zNh4+oqca+dfMfd++x4b8//8/Rwx3U4kB57yUs54UBbuF/K9rz89oFwlak8z8Jzn8qOU6NEQAQqjkBZ/soo15e8eZip3z4IZoTz2rVrq3Ueeym3aNGiSlpbZTosmMmIF9lEJUIRbyyBOk04UierOVuZhQjm0aNHB3bZcOgDB6r+IfDII494G807Tga2irIFvqgXD+znn3/uy2K4tJVlHLDx3nvv9fmswrfeessNGDDAe4nxDK9ZsybYT5l9qAnYRxlx+xqz4BeC3erAq80cY67DgjmXrdRB2ugWUHjJw0xJQx+a2McuVvvmPnO2w8EW/YJHQwfsURABEah/All4t7xgfuyf3fce6+aFpj+W4Pp/zxxXZSGuy19cc3wssM1U7yVzb9gRqZ/7xFvAUx3OywJflI9QLpX9xkeC2XpBRxEQAREQgboSKMtf8JX2xw0LYYXn4eaCzrzaCxcuVItmyDArT3Ns6IBX+erVq7HVMicYLy4CupCA991+FMhVZrQcPL6HDx+uNhc6mo7rfLZG0zNMHC84+0pfvnw5Gp3I60p77hMJUUaJQAyBLLxbLx36rfte367ue327Nchxwsurq4hfsM96daNrMqRX3vqJj3qqyUt5DWk/c77THrLw3Ke9D9U+ERCByiAgwVwZ/dTgVp47d869++67wRBnxLUtrIWHXKF4AvrjpnhmyiEChRDIwrv14qE33B/27er+8Jc/b7DjT594zP1m7w5H3T8d3beoetvNGOvnM/u8TzzWoHbDacNhCeZC3h2lEQEREAERqJmABHPNjDKZYsyYMX5IM0OfbY4wf5Ty2bdvXyaZ1LXRWfijvq6MlF8EakMgC+8WwrPRoz+/9fll6Jz7ur7F5tGfuw2Rbatq81wlPU8Wnvuk94HsEwERyAYBCeZs9HPRrWQhs2effdbPg2a+b/PmzR2rgJ86darospThBgH9caMnQQRKQyAL79aLbyGYu7g/eKSLjgVwkGAuzbumUkVABEQgiwQkmLPY62pzWQhk4Y/6soBVpZknkIV3C8H8B306uz94JPTRdU4eGw79NvXvRRae+9R3ohooAiJQEQQkmCuim2RkGgjoj5s09KLakEQCWXi31r+13/37Pp3dv+/9TzoWwIFF0tIesvDcp70P1T4REIHKIJAJwRzeW7kyuqU4K1npmuHStgdxcblrTs3q3xMnTvRbSrGtVkMG9nNmhXGOFmgnfRr+lKrtVmd9HPXHTX1QVBkiUJ1AFt4tBPPv9+7kfr/3P+lYAIeXDr1R/UFJ2Z0sPPcp6zI1RwREoEIJpF4wP/nkk65p06YFdc/mzZuD/ZMLylAPidirmPnCdQnr16/3i3EhbPMFhOeSJUvyJYmN2717ty9/0qRJfi/m2EQluGl7PPNHAR9+FCDMmDHDX9t9jn379i2BBfVbJHYqiIAI1D+BLLxb6w/ud7//i07u3/2i443jwzePuo7l8dJbEsz1/6apRBEQARHIJoGy/AXfkH/cILoKFcwdOnRwDe1BZRXqrVu31unpK1Qws4jXnXfeWXRdzzzzjGvSpEnR+eqSAa85z8nMmTP9PsvsuYxwJ1y7ds2dPXvWf0aMGOHTvfFG8v84asjnvi7slVcEKo1AFt4tBPO/e7ijPgUykGCutLdY9oqACIhAcglkSjCfPHnSr/Y8b948d8899zjE6oMPPuh7p0uXLv66UaNGrlmzZm7btm3u0qVLbsKECV4scg/BaWHdunXurrvucqwgjcgm/oMPPnBWB15QVpZ+4YUXvEeUdPxRxxEv70cffeRat24d3CM/9SEM8ZZiB/FbtmyxKqscqbNx48a+XoQ+ZeNh3rFjh7/HNe0bMGCAzzdlyhSfnvvUxRDrXGnDFc2ePdvbbPn69Onjli1b5jp27Ogee+wx/2MENu/cudN17tzZ19mtWze3Z88eX4zxoH7jhVcdbzXbVdGGOK83tlEnx1xh0aJFvr58Yrkmu8L9lKue+rpPexREQATqn0AW3q1/O7jPjVm/XJ8CGbBIWtpDFp77tPeh2icCIlAZBMryF3xDfsmHPcxvv/22F2GIzSNHjngxiy3cZ7skhGSvXr28aP3888+9WMYjy5DpXbt2+bwI3cuXL/tzROcnn3zi5syZ469Pnz7ty6LM++67z61cudKdO3fOHThwwB0+fNhdvHjRDRs2zIvE69ev+/ukXbBgga8Twdu2bVuHKL1w4YKbPHlyrHccoUi+NWvWuE8//dQNHjzYX5P/zJkzbu/eve6zzz5z5nmmbR9//LH/cQCRisDF7lxpw48u+RDdCH3y8aPArFmzfH0I5g0bNvi6EOdwJU3//v294P/mm28CHghk+AwZMsTnnT9/vnv33Xcd4jpuBADzk2FIO++9917HDxThecqvvPKKj4MXfRM3HJ15zzXZFe6ncLtLcU5bFERABOqfgN6t+meqEpNPQM998vtIFoqACKSDQFn+gm/IL/k4wWx7CePRxZZXX33V92aLFi2qDMlGQCPIKIMP4gsRjEeUfF9//bXPx9xgrsOC2eogAcKNYddTp0715ZMWMcnQYs5tSDbil+uePXv6+vA0c424DQdEZsuWLYNbJowRjYhKBPr06dOdCevt27f7tAjr8JDsfGmDwp3zwj08JBvBjGfYAmIWO2kPgR8GuGZOuP1IYTw2btzo42wRL7z9cM0VEMMPPPCAz9OmTZsgGcwXLlzofywgP2yjoRi7onlLcQ0TBREQgfonoHer/pmqxOQT0HOf/D6ShSIgAukgUJa/4BvySz6fYDZPcS7BjEhkqDYi2T54mPEI04YrV674p6AmwYwXEzGOqHvooYd83jjBjPeWcsePHx/UR72kDYdOnTq5Vq1aBbdMMOOVJS/DuceNG+eHPVNeLsGcL21QeAGCefny5d5uE8FXr17119hVk2BmeDb21hTwZNOWgwcPVkuKRz4s6C1BMXZZnlIesV9BBESg/gno3ap/piox+QT03Ce/j2ShCIhAOgiU5S/4hvySL0Yw48FEjOJ5/uKLLxwLSiF0GWaMGD1x4oQfjs1QYtqAmMaLiceX6zgPM2KXOOYCIyTHjh3rr7mPh5o45kYzhBoPMfN8mVeNt5lh23EC0YZE461FwPfr18+XQ37acP/993uRvWnTJn/fBDMCmaHVDPfmx4J8acOPN0PDw4I06mE2Tzk/CPAjAvXg9cX+2grmDz/80M+LPn/+vLeX4d+UyTVM8KJTL+fYRh9EQzF2RfOW4pq+VhABEah/Anq36p+pSkw+AT33ye8jWSgCIpAOAmX5C74hv+TDgpl5y9Rtw4MRd1ybhxlvLqKMe6tXr3bvv/++X7iLaz6ITQQgATHKolws7NWjRw8fj4c4WgdpR40a5eMpgyHeHM1rPGjQoCCO+c4Mz8bjanVSfjQgjFlgy9Lcfffd/pz7eHWtDQy/5twEM8IfcUk+RHa+tOE6o3OpEf/hIdmkZTVrs4cjQ60JUR4m4hHThFwe5n379gWLjVEec69XrVrl81C/tZEjP1wgsONCoXbF5a3ve7RDQQREoP4J6N2qf6YqMfkE9Nwnv49koQiIQDoIlOUv+CR/ySM6bW6ydTFe3Og8YovjOG3atNiFq8Jp8OiykFhcQDyGF7TCBrzYudJbGXisv/zyS7sMjtiPhzpXsKHTxNeUNlcZcfcpFy+7ieG4NMXewxvODxFxgR80on0Vl64UdsXVU9O9JD/3NdmueBFIMgG9W0nuHdlWKgJ67ktFVuWKgAiIQFUCEsxVeRR8xdDp3r17e+8m/2mxbZKCCOQjoD9u8tFRnAjUnoDerdqzU87KJaDnvnL7TpaLgAhUFgE+L2FRAAAgAElEQVQJ5lr2F/v7sq3UmDFj/NzmWhajbBkioD9uMtTZamqDEtC71aC4VVlCCOi5T0hHyAwREIHUE5BgTn0Xq4FJIaA/bpLSE7IjbQT0bqWtR9WeQgjouS+EktKIgAiIQN0JSDDXnaFKEIGCCOiPm4IwKZEIFE1A71bRyJQhBQT03KegE9UEERCBiiAgwVwP3cSiVF27dvXbHxVb3BtvvOG3lCo2XyHpWXGb1bALCcOHD/dbZBWStpA0M2bMCFbKLiR9FtLoj5ss9LLaWA4CerfKQV11lpuAnvty94DqFwERyAoBCeZ66OnoXsP5imQbpffee88nYWVstkVCrJYiRPdLzlcH2zaxBVdNIWx/vrTsBR23N3K+PGmP0x83ae9hta9cBPRulYu86i0nAT335aSvukVABLJEQIK5Hnq7GMGMQMbza4F9ii9dumSX9XoshWCO2p/LYAnm6mT0x011JrojAvVBQO9WfVBUGZVGQM99pfWY7BUBEahUAhLMeXqOraNYCZtjnz59vLCdMGGCa9KkiWvWrJl79tlnfe6oYMaz2qhRI8d/ZnhuDx486NjLuXXr1v7ebbfd5vMjlNu2beuWLFniyzl58qQfQk3e8H0izZbu3bt7rzRl2V7LcfWRJ59gPnz4sGvZsqUvq0uXLt5e8zDHlZfL/ri01I1gbtGihd92C5Hds2dPd+bMGd/OHTt2+PbDh7gBAwb4+/Do1KmTvwfjefPm+fvHjx93ffv29TbS7i1btvj7cIMvZbRq1arGfat9pjL+oz9uyghfVaeagN6tVHevGpeDgJ77HGB0WwREQATqmYAEcx6gCDEE2dy5cx2eYMTynXfe6YdU79q1y4tfhGRUML/yyivu1KlT7pNPPnFt2rTxQ5O//vprd+DAAZ9nwYIFDhH47bffusaNGzvm+xLuvvtu165dO1/XCy+84NMibAnYct999znqPXr0qI9DEBPi6uN+LsHMUHBEe/v27R0i/c0336wimOPKy2V/XFrqRjDDDnHMDwZwQ5gTEM579+71c7fXr1/v2wIv+GIXwvncuXPB0HV+POAHiwsXLrjJkye7pk2bum+++cbnmzNnjrt27Zo7dOiQ5+krSOg/+uMmoR0jsyqegN6tiu9CNaAWBPTc1wKasoiACIhALQhIMOeBhkhF3FrAq4x4wxPLh/iVK1dWE8yfffaZW7FihRfCCEc8uQSEHf/BhYdkm2DGW0wcAtQCgnPYsGH+MmpL8+bNgznHuerLJZhPnz7t63r11VetKi9uzcOcq7w4+3OljQ7JxltMGxDeCHZ+PJg+fbpbtGiRt2X79u3+xwAYkNe8yMYFDzX24WkmDT9G3HPPPf4Hh0mTJgXe9qBBCTzBbgUREIH6J6B3q/6ZqsTkE9Bzn/w+koUiIALpIFCWv+Ar5Us+KlIZJoyXFJFsn6iHGe8o+RB906ZN88OwCxHMeFThggfZwh133OH69+/vL6O2IKYRkPnqyyWYjx075uvauXOnVeUF8xNPPJG3vKhgzld3VDDPnz/fc8EzPH78eO/RHjdunEPs0m4EMwHP+9ChQ31avMqsQE48eYw5R8rhs3TpUu/Fhw/e8iSHSnnuk8xQtolAHAG9W3FUdC/tBPTcp72H1T4REIGkEJBgztMTUZE6YsQIPy8XUffdd9+5EydOuMuXL3uRx39ciN3XXnvNCzyGcCP2GEZtghnvKumY+4xnNjokm3m4CHLi8EKTdv/+/d7CqC0mmG1oeFx95tVF2IYDtjNPGlGLjQhnrhHg+cqL2k8+bIyrm7KxkW2zGG7NHOwePXp4MximTjyCd9OmTb4MBDOrh9N2wqhRo/xccc7J++CDD3ov8vXr1/0Qb/LSDxyvXr3qy7C54L6ABP4DKwUREIH6J6B3q/6ZqsTkE9Bzn/w+koUiIALpIFCWv+Ar5Us+KlLff/99x1Bo7OfDfFvmLyNA8QZz7+zZs97jaWkQeyaYeWQGDRoU5MerzJDsmTNn+qfJhKvlNYFJZNQWE8wMb0aAWp5wfQhVhDCe8WhgbjFx5KMdzAtGMOcrjzLC9iO2c9WNIGaBLuOCDXi2Ccxbpj3UzdxmzhHMzGHmHtfYtmrVKp+eHw/MVuLpA+YzUybXfJj/jZhOcsBOBREQgfonoHer/pmqxOQT0HOf/D6ShSIgAukgUJa/4Cv9S55h2MyhjQY8sBaID1/bfY4IO4RpXMDr/O6777orV67ERee8l6s+yuOTK5w/fz42Kld5JI7any8t6c1rHK4INrbKd/g+tuJpjvIxLp9//nk4uRfOFy9erHIvqReV/twnlavsEgG9Ww3zDDCih0Un+fEz3/8rDWNNw9XSq1cv/+MvbU9S0HOfpN6QLSIgAmkmIMGc5t5V2xJFQH/cJKo7ZEyKCNTnu8VaDTYCZvDgwSmiVPumwITpQnAOfxhBZOtP1L70uuW0hSGxi10TcgWbQkQ6pgoVExi5VZt8xdRRm7TYpCACIiACIlB6AmX5ttWXfOk7VjUkj4Ce++T1iSxKB4H6fLeWL18eiEKmgjDlJuuB9TVgzA8JnTp18rsVMB0HPgsXLiwrnrBgxp4vv/wy1h6mRtEGPhLMsYh0UwREQAREIAcBCeYcYHRbBOqbQH3+UV/ftqk8EahkAvX5brFdHeWxvgLH8FZ/lcyotrazdgcc+Jw5c6ZaMdHpM9USlPhGWDBj49SpU6vVaItoWjskmKsh0g0REAEREIE8BCSY88BRlAjUJwH+WFMQARGofwL19W6Z+GrRooVfnJByw4svmuUDBw507dq1c4jJhx9+OFiAkIUOWQyRfeZZ+JBFHfF6slVeeC0HdhagXBZbtOHfLFy4YcMGq8ItXrzY7zWPgI9+Ro4cGaQ7ffp0UBdlIfSff/75IJ6TgwcP+jLwnrMzAeVhFws+Dh8+vIptVTI653bv3u3FMulrmreM/e3bt/flwg57aOfhw4erFPvFF184tg2kftLgrX711Ve9XWwhGA7shsBuE7C09oWHgVufEW91Rte6CC8+SZqwYEZg40G3/BxhEl7fI25I9tq1a729tNfW0WDtEdpFv1JPs2bNHNs72o8KzAGHPeWzVgfPDmWH+z3c9prOqUNBBERABESg9ATK8m2rL/nSd6xqSB4BPffJ6xNZlA4C9fVuseUfZT311FPuq6++8gINkYbACwcTUKQlnl0LOLdrO4Z3VQh7Pm1HANIh5ijD8h86dMhXxa4Fdi96RHQR2KXB8iL0wnb069cvMBkvebgMxK+JOu6HbQsy3TxB5FkdPXv29AstRtPYddu2bX09lG8ilfKxDZ4WbIg3caQjvdnHDxEWEPp2nzLCOyOYCDfBTNsRoqQfN26cFeFefPFFf4+h5Ahv4sOC2cpHvIf58YOHBetvy7dt27bALoQzgXneZh+8+NHFyp49e7ZPA0vuER9uc/QHDqu3piNlKYiACIiACJSeQFm+bfUlX/qOVQ3JI6DnPnl9IovSQaC+3i0TeTb0uFu3bl7g4JkNBxNQTz75ZCAEzROLLXiHzRs7b948XwZ5LLCl4MmTJ4P50cyTRgSTd/r06ZasyhFvtglX87BaHjyVNtcaUUc5fGwrPxPMiM4tW7YE5U6bNs2nwyudLyCorUxseOSRR6p5jcnPKtLsImGBHxrMZsQvYfPmzb4sbMEbT4DVqFGj/P2wYMbrTr2TJ08OeCKGuYd4J4QFM1586uOD1xfPrv1oAYs4wQyv8K4U+/bt8+VThgXrb9Lu37/fx2PDCy+8YEncpEmT/P0OHTo483Dv2rXL30NIE0wwkxdb6EdGCCC2axMoR0EEREAERKD0BMrybasv+dJ3rGpIHgE998nrE1mUDgL18W4x9Jdy8DJaMO9kWMQRFxZQlhbRR34+JpiIQxhzD4GYL7DCM+lGjBhRLRneWfN+mjeYe1ZfeLg3mRGTxD3zzDO+LBPM5pm2CqzNNdlGeoQkQ4ytTo6sIh72HFu54WPHjh19HryyhAEDBvjrMWPGhJO5HTt2+PvGmsW7rK44niziRQgLZq5NeONtXrVqlS+ja9euPm2cYPYRkX/M+2tbQ1p/L1iwIPgB4LnnnquSK9ecd9jSDsoKC+YqmWt5QbkKIiACIiACpSdQlm9bfcmXvmNVQ/II6LlPXp/IonQQqI93y4QWghXvJJ+PP/44EG01zWmFpHlTwx7DDz/80JfBkN9wwNPL/r4IxN69e/t5yLQjTjAzD5i4zp07B0XgMeWeeS+DCOccQ4CJw/NMyCWYrX2FCGbKwWPLXGPz/FLHQw89FFSNIGR4MZ555vb2798/GJpsghkPLPlWrlwZ5OMkKpitfaRFsIY/3DOveFQwX7p0KegHmJP2xIkTvq44wUw/jx071vNn2DZC2/oxKpgpiw/e5GgwkY1dYVutrOvXr0swR6HpWgREQAQqhIAEc4V0lMysfAL8oaUgAiJQ/wTq+m4hBE1cmSiKHlm8yYJ5HG1Oq903cVSTYF6yZIkXXtSBWA3XHRXMNqSbocXhct98801fBl7faMATStkIV0J9CeZwPSbKabMNP0c8GzeEvIlI7plgNo/zsmXLwsVVE8wsnEY+ykd8Rz+2OFhUMFMo3muzo3v37kE9UcGMwLd5x6SHpfUh17kEMz8ERIPVF7WTa4Q4QR7mKDVdi4AIiEBlECjLX/D8x6IgAlkjoOc+az2u9jYUgbq+W3v27PECC/GKVzb8MZHF/GYLdRHMiCYTyOvWrbMi/bxn2hEWzDZfFhHHXNdwYBg26cOC1eLNW86RUArBfPXq1UCUYpsNPccmm5tM3eYdN8FsC54xnDscoh7mCxcu+PJZnCxfiBPMzElm+DmLkJ06dSrIbn1pP3QsWrTI14FXOPxjhPVPVDCHh2SHFxajAhuujtc+V5BgzkVG90VABEQg2QTKolzr+sdNspHKOhGIJ6DnPp6L7opAXQnU9d1iSDRlREUQdjGX1ryONrS3LoIZMUddfMIibebMmf6eCWaGgNv8V7aCigsm7FasWBFEY6/dZw42oS6CGe87w4mjYe7cud5e2BC/ceNGfx3+YYE8NgTbBDMLXdF28uFFJiC+GZ7OfZvDzH1r/86dO326uH/iBHNcOu5FBTOinTqHDRsWZIGfecajghmhbT+ukI955xaYJ809FoLLFSSYc5HRfREQARFINgEJ5mT3j6xLEQH+mFIQARGofwJ1ebcQRSaIbVXpqIW2WjZbPRHqIpjJb6tbM1SXoc1dunTxYot2mGB+4IEH/D1sYxGv8IdVowkMayYPH/aGZpEvWxwM4YrYJdRFMNvWTnhQ4cCe0rbAFfUOGTLE18HiX8aRNKz2bZxIZ4KZxPYDBfdNFHPOJyyYmQ9t99kmi4W2aCf7XZvIrYtgNg8+ApmtxBjmbT82UG+cYMZ++3GANEuXLvXtD8+5pl8Zwo94vvfee/0+0ySSYPao9I8IiIAIVByBsvwFz38yCiKQNQJ67rPW42pvQxGoy7tl4iduLrDZv2HDBi/cbIEt20fYPKSWzgRjeN9mW1gLIWYBL2V4n17KtT2ETTAz95V2xX1shWjKmz9/fiBULS15EZIWWKiLuLAYJS7ONstjR4ZXk8+8rlYHbUUQ2vxl0iNwwwIYYWs/NoQFM8IR8YudiG9EMFtxUTZeYAtslTVjxoxqDPhRgOHRBBuaHl7d3PJHj+ytTB02JJv4Pn36VOHHDxPWNyaY4/o7PA+dHyQIW7durSK4qYt+f+yxx3y8BLPHoH9EQAREoOIIlEW58p+IgghkjYCe+6z1uNrbUAQq9d1i7m94vm9teSHE2CJq79697vLly7UtpsZ8Z8+e9aLw8OHDLvyjQDgjXm3iWX26mGCLiA0aNKhaNtrHcHh+oAgPY6+WsJY3GIbNHtIc6xpoP32K9zr8o0Vdy43LX6nPfVxbdE8EREAEkkygLMpVX/JJfiRkW6kI6LkvFVmVm3UCercq6wlghW8TvghMhL6tVo03X6EwAnruC+OkVCIgAiJQVwISzHUlqPwiUCAB/XFTICglE4EiCejdKhJYmZPTX3wQyeEh3OHh2GU2sSKq13NfEd0kI0VABFJAoGyC2f7D1DF+jpq4pJNLCr4z1AQRSBwBCYfEdUleg9avX+8Yes38ZQRzmzZt/IrTzFlWKJyAnvvCWSmlCIiACNSFQFkEc10MVl4REAEREAERCBOQcAjT0HlWCOi5z0pPq50iIALlJiDBXO4eUP0iIAIiIAJ1IiDhUCd8ylyhBPTcV2jHyWwREIGKIyDBXHFdJoNFQAREQATCBJIiHBhSbJ+wfTWds80R2yMRHn74Ybdr167YLCyW1b1792B/5dhEdbwZtqWORdU6Oytwd+3a1a/8XVMhbMe1bt26mpLljGcrKPZ4rsSQlOe+EtnJZhEQAREohoAEczG0lFYEREAERCBxBJIgHK5cuVJlv+A77rgj2Cs4HzBWiWZPY4QfgXPbYziaj7m/tDW893E0DdebN292O3fujIvKey9qS97EJYxkayzayf7RNQX2Xx49enRNyXLGz5o1y8+jzpkgwRFJeO4TjEemiYAIiEC9EZBgrjeUKkgEREAERKAcBJIgHEwwb9y40X388cdu2rRpXvS9//77NSI5cuSIu3Tpkk9XH4K5Q4cOgQCvsfJIgrAtkagGu5RgLgx1Ep77wixVKhEQARGobAISzJXdf7JeBERABDJPIAnCwQTz7t27fX98+umnXjDbcGE8oXh+LTRv3txt27bNX7Zt29YtWbLEn0cFM55nVpJu1qyZQwjT1nwe5i5dungvdaNGjXwe6ujfv78bOHCgY9umFi1aOGy76667fDrKu+eee9znn39ezRbSTJw40Q8Dx67WrVv7vNaGuOOMGTPcbbfd5u3kaO1atmyZa9++vZs8ebK3gTbNmTMnKGL58uXeXuzu2bOnzx/nYT58+LBr2bKlt522kt48zMePH3d9+/b197B1y5Ytvnza36tXL9etWzefj/MzZ874uKiHGc98586dfTrS79mzx6dr166dZ2EGr1ixwq/yna8vLG2pjkl47kvVNpUrAiIgAkkiIMGcpN6QLSIgAiIgAkUTSIJwMMGM2EK0IXDZNunatWu+PdiIyLLA9dq1a/0l4hGhSQgL5kWLFnnhuGbNGi9UBw8eXKNgPnXqlK8bUYiARAjff//9XkSOHTvWD9XGpu3bt7sPP/zQkT5cZ9QWRDZzqo8ePerrRmDmCwcOHHCI2osXL7phw4Z5sc9Qb/LR5vnz57v33nvP9enTx9dL3IkTJ3zcyJEj3UcffeS5kDYqmEmLCEd4nzx50jGnOyyY+eGBci9cuOCFedOmTb2ptJ8fLHbs2OEOHjzo+4U50oSwYP7qq6+8TfxIATuENuV/8803vjw7J1+rVq38fHNfSJn+gZGCCIiACIhA6Qno27b0jFWDCIiACIhACQkkQTiYYMaTO3fuXL+QFELUPKzYWKxgxsOJN9VCoXOY8SLbnGjyIhgpKxzeffddN2/ePG8r4n7UqFE+OiqYw/Op8YqbNzdcVvgc0clCWlOnTvU20G4EZ1iYkj7cFuogHYt9EXINyT59+rRPFxbSNofZPPp4p7GRHy0o85NPPqnWftpN33z99ddV7GI0AHnsRw5EP9eMDECEc47dCH7O33rrrXDTG/wcGxREQAREQARKT0DftqVnrBpEQAREQARKSCAJwsEEsw3JprmsaI14JWBjsYK5U6dO3pNp6ExkshJ3vlCTYMZjjD14rBG2eG0LEcwmTvPVbcO+Kfehhx7y9dQkmPmBAXu+/PJLX7QJZjzC4XDs2DGfLrygGTY98cQT7oMPPvBx48ePdytXrgw+1B39wQAvN4I5ahfDwrED0U+4evWqv4Y7oUePHn5Y+7hx47yX2t8s4z/YqiACIiACIlB6Avq2LT1j1SACIiACIlBCAkkQDiaYWfSLLaL27t3rmjRp4ufj0nSG87J9EV7UDRs2eCEWNySbdJMmTQrEHG2jTIYqk59r5s0ytBjhFxfatGnjENssJEZ9UcGI4KMePKwMa8bOYgUz3lg8yeGAAMW+2bNne7HJEHCuo8KUPCb+aQtDuEmH2D1//rz3ynMd9iSThx8KsJv2IJARzlyb15s51w8++KAfvn79+nXPiHykR1izZRZD0EmH+CWYtxlWeJapF7FPf2IPwpqyCLAinjpfeOEFf6+c/2CLggiIgAiIQOkJ6Nu29IxVgwiIgAiIQAkJJEE4mDcSW/jgtUXgIgAJY8aM8feJQ9AiumxBMIZBz5w506cbMGCAT7d06VIvjJkTbWXefffd/hyROXToUC8C47DiYUXokW/16tXVBDNDihHJxFM3c33DgtlsoYzwkOywh5lts5jjGw2UY/Yyp5hzBDMimrosvPjiiz7OFs1CpJrNLNhFvqhgJi9eZ9gRD2NsN8GMgLc44hlCTkAwkw7PO/dpO95qAgKaPNwj0HbS2AdBHQ7MS8dO80KH4xr6HBsVREAEREAESk9A37alZ6waREAEREAESkigUoQD82DxPtcU8PyGA2LchivbfcQoK1jnCgjRaDnRtCz6xUJaxQbKhvnrr78em5Uh1bbqdmyCHDexl3nDhQT7ISKaFtuYnx2uP+xhj+NPHj4WEMPMlzbPst3niHeaeepJCJXy3CeBlWwQAREQgboQkGCuCz3lFQEREAERKDuBLAoHvLBnz54tC3vqpf5KCWHBXFubGUL+wAMPeO8yw+OTELL43CeBu2wQARHIHgEJ5uz1uVosAiIgAqkiIOGQqu6s98Ygdpk3XpfAEHKGy+/fv78uxdRrXj339YpThYmACIhATgISzDnRKEIEREAERKASCEg4VEIvycb6JqDnvr6JqjwREAERiCcgwRzPRXdFQAREQAQqhICEQ4V0lMysVwJ67usVpwoTAREQgZwEJJhzolGECIiACIhAJRDIgnCYMWOG3wIprj9Y5Or48eNxUUXfY7umrl27OhYoa4jA9k179uyp16rYfoo51kuWLPHl5tq3mgXPwnHhczNo0aJFflXzuDhLU65jFp77crFVvSIgAiIQJiDBHKahcxEQAREQgYojkAXhkG/hqnbt2jm2O6qP8Pbbb/sVsNluqSHCtGnTfH3sF11fge26eCZYEdu2r2Jv63Bgqyu2mrKtrixdeHVt0vPDAWUxDzppIQvPfdKYyx4REIFsEpBgzma/q9UiIAIikBoCWRAO+QQzXmH2Vq6P0NCCGYF68ODB+jA9KIOtnx5++GF/bUKYvZPDW1HNnTvXC+GaBDOF9OrVy28nFVSQkJMsPPcJQS0zREAEMk5AgjnjD4CaLwIiIAKVTiApwmHZsmUOb+/w4cNdkyZNvMhasWKFX10ZYXb77be7t956y+NmiPVtt93mRRtHGz5M5N69e13Pnj2997Nly5beU4pgxovcr18/f7958+Zu165dvqwRI0a4xx57zJ/379/f9e3b1+8VTLnhdJcuXXITJkzwtjVr1sw9++yzPk/4n6hgzpVnx44djjJgjxhlBWkLCFb2iObYp08fB5f27du7yZMnuxYtWnj758yZ45PjuTXveL50eITxRsP1jjvu8O1t06aNVRkc2a8am+BOMMEMe2whsK91o0aNfL2FCObly5f7MtmfOUkhKc99kpjIFhEQAREoBQEJ5lJQVZkiIAIiIAINRiApwmHWrFlePK5evdq9//77rlWrVl4cIgq5ZgiwibYDBw64w4cPu4sXL7phw4b5dMypvX79ui+DdIjXN99803399dcOwYxAZbgxZSEauUdgf+D77rvPn+dLh1hGnOKNRmzDLbqncFQw58pz5swZL+wZ9kz7KMuGcSOgEah4cY8cOeLgQvz8+fN93bSNNLSXOcKcE/Kl27Rpky9j48aN7uOPP3Y9evTwzHzG0D/8IEFd8CWYYLb2Hjt2zPEDAz8kLFy4MCjD0kWHZFPGG2+84cukv5IUaKeCCIiACIhA6Qno27b0jFWDCIiACIhACQkkRTgg+MxjSXOHDBkSeE+57tKli+vYsaMngbdy69atburUqd4jTRvwopr4ZDGscEAId+vWLbiFpxnvMyEqmHOlQ3C3bdvWjR492n8QqitXrgzK5CQqmHPlQewiSqdPn+5FL/Zv377dl0W5CxYsCMqNcrE2fvvtt9UEc5hfOB0e97BHGQ91OK1Vxn7L2PLhhx/6WyaEuejUqZP3ehOPh3zx4sVBGZYuTjCfO3fOl1nXvZzNxvo60g4FERABERCB0hPQt23pGasGERABERCBEhJIinCICsM4wYxoI+ARZngygvmhhx7yggzBvHTpUn9+7dq1KsSigpmh14UI5nA6hjMj2hHJ9qnJw5wrz/jx4/2w5nHjxjkW1KIPSimY+REAr7qFXIIZLzC24BUmmBDmHM88Yt76oFDBvH//fl8m3vIkhaQ890liIltEQAREoBQEJJhLQVVlioAIiIAINBiBpAiHQgUzwhibZ8+e7efTjh071l9zn/m1xI0aNcovUnX27Fk/TLs+BDNDkRHpbEHFNkknTpxwly9frtJPxFE/Q5jxIufKg7cXm7DZhkuXUjDbPGJYcc786DgPM5577CcNISyYqzTUuVgP86FDh9zp06f9h2HnBPsRg+HySQq0U0EEREAERKD0BPRtW3rGqkEEREAERKCEBJIiHBDAYRE3dOjQakOyzbuJIMZuPgyT5oj4JMycOTOIY3Eq9lmOCmYW2SrEwxxOh4eVubtWL4uCMQQ7HBDSeHJJw+rbufIwXBpvLemYF815LsEc5WIiNjokO186bMQbz9xo2s3iatgfF5g7/uCDD/ooG6Idl46F1qy/LJ2xsSP5mC/Nvs5JC9ioIAIiIAIiUHoC+rYtPWPVIAIiIAIiUEIClSoc8O7GzZkFFWLyk08+KQk1hmHXVDYLjYVDXB7SfPrpp+FkDXbOqtu2gFq00s2bN3shHx1uHk1XyDVzoZeQybMAAAxESURBVHm+mG+etFCpz33SOMoeERABEaiJgARzTYQULwIiIAIikGgCEg6J7p56MY7VwhkGzmJn5pF//fXXc5bNQmhr1qzJGV9oBCue42FOYtBzn8RekU0iIAJpJCDBnMZeVZtEQAREIEMEJBzS39lffPGFn4/MFlws+MV2VlkPeu6z/gSo/SIgAg1FQIK5oUirHhEQAREQgZIQkHAoCVYVmnACeu4T3kEyTwREIDUEJJhT05VqiAiIgAhkk4CEQzb7Peut1nOf9SdA7RcBEWgoAhLMDUVa9YiACIiACJSEgIRD8VgZ4ty1a1e/xVS+3DNmzHDz5s3Ll6Re4tg3Od8waxbdYv5yNLCSN+24cOFCNCr113ruU9/FaqAIiEBCCEgwJ6QjZIYIiIAIiEDtCEg4FM+NFbrh9uqrr+bNHN3OKm/iWkay3zPbUg0fPjxnCdE9ri0h22LRjlOnTtmtzBz13Gemq9VQERCBMhOQYC5zB6h6ERABERCBuhGQcCieX5IEM9YfOXLEXbp0KWdDJJiro9FzX52J7oiACIhAKQhIMJeCqsoUAREQARFoMAJpFw533XWXGzlypLvvvvu8J/aJJ55wCxYscHfffbe/HjhwYMB6586drnPnzv5+t27d3J49e4K45cuXu2bNmrlGjRq5nj17VvEwb9++3XXs2NHnI//Jkyd9vrCHGUHbqVMnn6ZJkyaxQ7UPHTrkWrZs6dOwHVOHDh3c3LlzfVm33367Y49kC82bN3fbtm3zl2wVtWTJEn/+3nvv+fZSB2loU1gwnzlzxsFk3LhxLuxh3rhxo09vQ7u/++4716pVK7d48WKrMlXHtD/3qeosNUYERKCiCUgwV3T3yXgREAEREIG0CweGKzNPl72I169f74Vu3759vVf2ueee89cff/yx++qrr7xQZWjz8ePHXf/+/b04/uabb9yJEyd8OoT3Rx995NauXRsI5i+//NLnW7RokZ9HjEju06ePf7DCgnnChAnutttu857gc+fOOYRtODC0unHjxq59+/ZecGMv4nz06NE+Gf20YsWKIAvX2EEgH/OlCe3atXOtW7d2u3btcseOHXPnz58PBDPXpH3ooYfct99+W0UwMy8bVohrwo4dO3wbmeecxpD25z6NfaY2iYAIVCYBCebK7DdZLQIiIAIicJNA2oUDIhCPMuHatWteBG7ZssVfI4xp/9GjR926dev8OWkIFy9e9Nd4dclPOkQlITwk28QzwpYPgrdp06Y+XVgwI2Apg3tWv0908x/mERMfnheNV7kYwcziXZSBtzgcEMHcR4DzQwDinBD2MHM9aNAgR50EPNx45dMa4KEgAiIgAiJQegL6ti09Y9UgAiIgAiJQQgJpFw75BLN5jhHMDLmGBZ5mwtWrV/01XmmGRROHN5lgghkv7LJly3wc+VeuXOk/NlQ6LJjJh0AfOnSo9+SaF9oX6Jz3BlMHQ6gtIF4ZQk4griYPM95g0jFEPBwQzHDAw40I/vrrr310VDC/++67Pj9D0UkfJ+zD5VbyOZwUREAEREAESk9A37alZ6waREAEREAESkgg7cKhUMFs3uepU6e6K1euuPHjx3vReP36dXf48GEvJLnHEGfmC8MNb7AJ6zlz5ngPNGIaYUxgPjNDpBHaDMG2+cGjRo1yzDEOB+YM4wFGZCN8Ec7hIdmcszUUXu4NGzb4+uOGZDM/2YZ143GmTpvDjA2I5nvvvdfRLvOw4/02rzN5GbaNfXYvbGdaztP+3Keln9QOERCByicgwVz5fagWiIAIiECmCaRdOIQFM2KT9rIvMcE8zMztJcycOdPHk4ZPeA9lhDRlcZ85whxt+PRTTz1VJZ95j1etWuXv9+rVyzGHmTyUgfglLhrwWBNHOoQtaW1I9pgxY4I62rRp49MxjJyAwMV2AkKba8rgg9d79uzZ/h7xiGbi77nnHi+a77jjDp/O5irDhnxTpkzx5aX1H9qoIAIiIAIiUHoC+rYtPWPVIAIiIAIiUEICEg5V4TIk+/Tp015MVo1xfigzc5vjAvmYh8wiYeEQvmahLQRrTZ7bTz/91BcRnsPMDfMYh8vPdU4Z4bpzpeO+DdHm3AQzdaU56LlPc++qbSIgAkkiIMGcpN6QLSIgAiIgAkUTkHAoGlmDZYgK5lJW/Pnnn/vtqPBsDx48uJRVJaJsPfeJ6AYZIQIikAECEswZ6GQ1UQREQATSTEDCIbm9yx7Iu3fvbhADEczsSc3QblsNvEEqLlMleu7LBF7VioAIZI6ABHPmulwNFgEREIF0EZBwSFd/qjWFEdBzXxgnpRIBERCBuhKQYK4rQeUXAREQAREoKwEJh7LiV+VlIqDnvkzgVa0IiEDmCEgwZ67L1WAREAERSBcBCYd09adaUxgBPfeFcVIqERABEagrAQnmuhJUfhEQAREQgbISkHAoK35VXiYCeu7LBF7VioAIZI6ABHPmulwNFgEREIF0EZBwSFd/qjWFEdBzXxgnpRIBERCBuhKQYK4rQeUXAREQAREoKwEJh7LiV+VlIqDnvkzgVa0IiEDmCEgwZ67L1WAREAERSBcBCYd09adaUxgBPfeFcVIqERABEagrAQnmuhJUfhEQAREQgbISkHAoK35VXiYCeu7LBF7VioAIZI6ABHPmulwNFgEREIF0EZBwSFd/qjWFEdBzXxgnpRIBERCBuhKQYK4rQeUXAREQAREoKwEJh7LiV+VlIqDnvkzgVa0IiEDmCEgwZ67L1WAREAERSBcBCYd09adaUxgBPfeFcVIqERABEagrAQnmuhJUfhEQAREQgbISkHAoK35VXiYCeu7LBF7VioAIZI6ABHPmulwNFgEREIF0EZBwSFd/qjWFEdBzXxgnpRIBERCBuhKQYK4rQeUXAREQAREoKwEJh7LiV+VlIqDnvkzgVa0IiEDmCEgwZ67L1WAREAERSBcBCYd09adaUxgBPfeFcVIqERABEagrAQnmuhJUfhEQAREQgbISkHAoK35VXiYCeu7LBF7VioAIZI6ABHPmulwNFgEREIF0EZBwSFd/qjWFEdBzXxgnpRIBERCBuhKQYK4rQeUXAREQAREoKwEJh7LiV+VlIqDnvkzgVa0IiEDmCEgwZ67L1WAREAERSBcBCYd09adaUxgBPfeFcVIqERABEagrAQnmuhJUfhEQAREQgbISkHAoK35VXiYCeu7LBF7VioAIZI6ABHPmulwNFgEREIF0EZBwSFd/qjWFEdBzXxgnpRIBERCBuhKQYK4rQeUXAREQAREoKwEJh7LiV+VlIqDnvkzgVa0IiEDmCEgwZ67L1WAREAERSBcBCYd09adaUxgBPfeFcVIqERABEagrAQnmuhJUfhEQAREQgbISkHAoK35VXiYCeu7LBF7VioAIZI6ABHPmulwNFgEREIF0EZBwSFd/qjWFEdBzXxgnpRIBERCBuhKQYK4rQeUXAREQAREoKwEJh7LiV+VlIqDnvkzgVa0IiEDmCEgwZ67L1WAREAERSBcBCYd09adaUxgBPfeFcVIqERABEagrAQnmuhJUfhEQAREQgbISQDjoIwZZfAbK+uKpchEQARHICAEJ5ox0tJopAiIgAiIgAiIgAiIgAiIgAiJQHAEJ5uJ4KbUIiIAIiIAIiIAIiIAIiIAIiEBGCEgwZ6Sj1UwREAEREAEREAEREAEREAEREIHiCEgwF8dLqUVABERABERABERABERABERABDJCQII5Ix2tZoqACIiACIiACIiACIiACIiACBRHQIK5OF5KLQIiIAIiIAIiIAIiIAIiIAIikBECEswZ6Wg1UwREQAREQAREQAREQAREQAREoDgCEszF8VJqERABERABERABERABERABERCBjBCQYM5IR6uZIiACIiACIiACIiACIiACIiACxRGQYC6Ol1KLgAiIgAiIgAiIgAiIgAiIgAhkhIAEc0Y6Ws0UAREQAREQAREQAREQAREQAREojoAEc3G8lFoEREAEREAEREAEREAEREAERCAjBCSYM9LRaqYIiIAIiIAIiIAIiIAIiIAIiEBxBCSYi+Ol1CIgAiIgAiIgAiIgAiIgAiIgAhkhIMGckY5WM0VABERABERABERABERABERABIojIMFcHC+lFgEREAEREAEREAEREAEREAERyAgBCeaMdLSaKQIiIAIiIAIiIAIiIAIiIAIiUByB/x8QRZnluc8DIAAAAABJRU5ErkJggg=="
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   "source": [
    "## Building a Knowledge Graph Solution\n",
    "A knowledge graph is but one building block in a more complete knowledge management solution. Knowledge graphs are rarely used in isolation and knowledge graphs in Neptune are no exception. Constructing and maintaining large-scale high-quality knowledge graph applications requires integration across multiple sources, both on the front-end and backend, to develop an effective solution.\n",
    "### Knowledge enhanced applications\n",
    "Knowledge graphs are at the core of many human-facing applications such as search systems, as demonstrated in this notebook, but also question and answering bots, recommendation systems, chatbots, and other highly interactive interfaces. Each of these applications puts a unique set of requirements and scenarios on the application where the connected nature of data in a knowledge graph provides a robust mechanism for quickly and efficiently providing answers.\n",
    "### Knowledge graphs and machine learning\n",
    "Knowledge graphs and machine learning are a natural complement in many applications. The connected nature of data in a knowledge graph is capable of providing unique and novel insight to machine learning applications. In turn, the output of many machine learning tasks can also be stored back into a knowledge graph to enrich the connections within the graph and enable it to better service user applications.\n",
    "### Knowledge graph architectures\n",
    "The following diagram shows how Neptune can be used with other Amazon Web services to build knowledge solutions. You can load data directly into Neptune using query APIs, or from relational databases using the Amazon Web Services Database Migration Service. Neptune also supports bulk loading data from Amazon S3. Neptune can be deployed in conjunction with Amazon SageMaker to train predictive models or Amazon Comprehend to extract additional entities from within the knowledge graph data. Finally, Neptune can then be leveraged to support a wide array of end user applications.\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
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   "id": "8639ea3d",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This notebook has shown how you can use Amazon Neptune to create a knowledge graph as part of a bigger contextual search solution. We've used a blog post dataset with post-centric and author-centric queries to do the following:\n",
    "1.\tFind and stitch data entities together\n",
    "2.\tFind unknown connections between them\n",
    "3.\tUse these stitched and unknown connections to provide contextually relevant answers to real world search questions\n",
    "\n",
    "Real-world data is tangled, messy and disconnected. We've suggested a knowledge graph data model and queries that can help provide better and more complete answers through the use of contextually connected graph data. This type of solution can be used to power many kinds of applications such as question/answer bots, real time chatbots, or web-based search engines."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96f44955",
   "metadata": {},
   "source": [
    "## What's Next?\n",
    "\n",
    "The examples in this notebook show how to develop a knowledge graph data model and accompanying queries. To build a knowledge graph solution that incorporates Neptune, we recommend the following resources:\n",
    "\n",
    "  - [Getting Started with Amazon Neptune](https://pages.awscloud.com/AWS-Learning-Path-Getting-Started-with-Amazon-Neptune_2020_LP_0009-DAT.html) is a video-based learning path that shows you how to create and connect to a Neptune database, choose a data model and query language, author and tune graph queries, and integrate Neptune with other Amazon Web services.\n",
    "  - Before you begin designing your database, consult the [Amazon Web Services Reference Architectures for Using Graph Databases](https://github.com/aws-samples/aws-dbs-refarch-graph/) GitHub repo, where you can browse examples of reference deployment architectures, and learn more about building a graph data model and choosing a query language.\n",
    "  - For links to documentation, blog posts, videos, and code repositories with samples and tools, see the [Amazon Neptune developer resources](https://aws.amazon.com/neptune/developer-resources/).\n",
    "  - Neptune ML makes it possible to build and train useful machine learning models on large graphs in hours instead of weeks. To find out how to set up and use a graph neural network, see [Using Amazon Neptune ML for machine learning on graphs](https://docs.aws.amazon.com/neptune/latest/userguide/machine-learning.html).\n",
    "  "
   ]
  }
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