Resume_Screening_with_Python.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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      "cell_type": "markdown",
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        "df = pd.read_csv('UpdatedResumeDataSet.csv')"
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      "execution_count": 3,
      "outputs": []
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        "df.head()"
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              "       Category                                             Resume\n",
              "0  Data Science  Skills * Programming Languages: Python (pandas...\n",
              "1  Data Science  Education Details \\r\\nMay 2013 to May 2017 B.E...\n",
              "2  Data Science  Areas of Interest Deep Learning, Control Syste...\n",
              "3  Data Science  Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...\n",
              "4  Data Science  Education Details \\r\\n MCA   YMCAUST,  Faridab..."
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Strong analytical, problem solving & organizational abilities. \\u00e2\\u0080\\u00a2 Extensive experience in managing operations with demonstrated leadership qualities & organisational skills during the tenure. \\u00e2\\u0080\\u00a2 Managing customer centric operations & ensuring customer satisfaction by achieving service quality norms. \\u00e2\\u0080\\u00a2 Analyzing of all operational problems, customer complaints and take preventive and corrective actions to resolve the same. \\u00e2\\u0080\\u00a2 Receive and respond to Key customer inquiries in an effective manner and provide relevant and timely information. \\u00e2\\u0080\\u00a2 Deft in steering banking back-end operations, analyzing risks and managing delinquencies with dexterity across applying techniques for maximizing recoveries and minimizing credit losses. \\u00e2\\u0080\\u00a2 Analyzed & identified training needs of the team members and developing, organizing and conducting training programs and manage bottom quartile team to improve their performance. \\u00e2\\u0080\\u00a2 Preparing and maintaining daily MIS reports to evaluate the performance and efficiency of the process relate to various verticals. \\u00e2\\u0080\\u00a2 Measuring the performance of the processes in terms of efficiency and effectiveness matrix and ensuring adherence to SLA. \\u00e2\\u0080\\u00a2 Major Activities Define processes for Field Services were monitored and necessary checks were executed and controlled. Also measured Vendor SLA by analyzing the TAT of vendors & the Client SLA provided to us. \\u00e2\\u0080\\u00a2 As per company procedures, handling & ensuring vendor's payment issues to be sorted out &payments are processed on quarterly basis. \\u00e2\\u0080\\u00a2 Appropriately plan and execute each skill of operations in accordance with the department's policies and procedures. \\u00e2\\u0080\\u00a2 Manage relationships with business team, software development team and other services to achieve project objectives. Different software Worked till now: - a. CTL prime - Axis Bank Credit Cards b. Insight - For POS Machine technical operations for Amex (MID & TID Generation- ATOS (Venture Infotek) c. Ticket Management System - TATA Communications Private Services Ltd (ATM - NOC Operations) d. Branch Portal (Yalamanchili Software Exports Ltd) - Prepaid Cards (SBI Bank & Zaggle Prepaid Oceans Services Ltd) Zaggle Prepaid Ocean Services Pvt Ltd Oct, 2017 to Till Date Designation: Manager - Operations (Payment Industry - Prepaid Cards - INR) Education Details \\r\\n  Commerce Mumbai, Maharashtra Mumbai University\\r\\nOperations Manager \\r\\n\\r\\nService Manager - Operations (Payment Industry - Prepaid Cards - INR & FTC)\\r\\nSkill Details \\r\\nOPERATIONS- Exprience - 73 months\\r\\nSATISFACTION- Exprience - 48 months\\r\\nTRAINING- Exprience - 24 months\\r\\nNOC- Exprience - 23 months\\r\\nPOINT OF SALE- Exprience - 20 monthsCompany Details \\r\\ncompany - Zaggle Prepaid Ocean Services Pvt Ltd\\r\\ndescription - Card Operations\\r\\ncompany - Yalamanchili Software Exports Ltd\\r\\ndescription - 24*7 Operations Pvt Ltd) Dec 2015 to Feb 2017\\r\\n\\r\\nDesignation: Service Manager - Operations (Payment Industry - Prepaid Cards - INR & FTC)\\r\\n\\r\\nKey Contributions: \\u00e2\\u0080\\u00a2 A result-oriented business professional in planning, executing& managing processes, improving efficiency of operations, team building and detailing process information to determine effective result into operations.\\r\\n\\u00e2\\u0080\\u00a2 Ensuring PINs generation (SLA) is maintained and chargeback cases are raised in perfect timeframe.\\r\\n\\u00e2\\u0080\\u00a2 Managing email customer services properly and ensuring the emails are replied properly. Also, ensuring transaction monitoring is properly managed 24/7.\\r\\n\\u00e2\\u0080\\u00a2 Assisting Bankers (SBI & Associated Banks) for their BCP plans by getting executed in the system with the help of DR-PR plans & vice versa or any other business requirements.\\r\\n\\u00e2\\u0080\\u00a2 Expertise in maintaining highest level of quality in operations; ensuring adherence to all the quality parameters and procedures as per the stringent norms.\\r\\n\\u00e2\\u0080\\u00a2 Lead, manage and supervise the execution of external audit engagements and responsible for presenting the findings & developing a quality reports to the senior Management and Clients.\\r\\n\\u00e2\\u0080\\u00a2 Coach/mentor (20) team members to perform at a higher level by giving opportunities, providing timely continuous feedback and working with staff to improve their communication, time management, decision making, organization, and analytical skills.\\r\\n\\u00e2\\u0080\\u00a2 Providing the solutions and services to the client in their own premises with aforesaid count of team members.\\r\\n\\u00e2\\u0080\\u00a2 Also ensuring end to end process of PR & DR as per client requirements (PR- DR & DR -PR) by interacting with internal & external stakeholders.\\r\\n\\u00e2\\u0080\\u00a2 Determining process gaps and designing & conducting training programs to enhance operational efficiency and retain talent by providing optimum opportunities for personal and professional growth.\\r\\ncompany - Credit Cards\\r\\ndescription - Ensured highest standard of customer satisfaction and quality service; developing new policies and procedures to improve based on customer feedback and resolving customer queries via correspondence, inbound calls & email channels with the strength of (12-16) Team members.\\r\\ncompany - AGS Transact Technologies Limited\\r\\ndescription - Key Contributions: Lead - SPOC to Banks\\r\\ncompany - TATA Communications Payment Solutions Ltd\\r\\ndescription - To make ATMs operational within TAT by analyzing the issue is technical or non-technical and also by interacting with internal & external stakeholders.\\r\\ncompany - Vertex Customer Solutions India Private Ltd\\r\\ndescription - Key Contributions: \\u00e2\\u0080\\u00a2 Build positive working relationship with all team members and clients by keeping Management informed   of KYC document collection & con-current audit progress, responding timely to Management inquiries, understanding the business and conducting self professionally.\\r\\ncompany - Financial Inclusion Network & Operations Limited\\r\\ndescription - Key Contributions: POS-Operations \\u00e2\\u0080\\u00a2 Cascading the adherence of process is strictly followed by team members & training them to reduce the downtime.\\r\\n\\u00e2\\u0080\\u00a2 Managing Stock of EDC Terminals \\u00e2\\u0080\\u00a2 Managing Deployments of terminals through Multiple teams \\u00e2\\u0080\\u00a2 Would have worked with multiple terminal make & model \\u00e2\\u0080\\u00a2 Managing Inward, Outward & QC of applications installed in the POS machines.\\r\\ncompany - Venture Infotek Private Ltd\\r\\ndescription - Key Contributions: POS-Operations\\r\\ncompany - Axis Bank Ltd - Customer Services\\r\\ndescription - Aug 2006 to Oct 2009 (Ma-Foi&I- smart)\\r\\n\\r\\nDesignation: Team Leader/Executive - Emails, Phone Banking & Correspondence Unit (Snail Mails)\",\n          \"Skill Set: Hadoop, Map Reduce, HDFS, Hive, Sqoop, java. Duration: 2016 to 2017. Role: Hadoop Developer Rplus offers an quick, simple and powerful cloud based Solution, Demand Sense to accurately predict demand for your product in all your markets which Combines Enterprise and External Data to predict demand more accurately through Uses Social Conversation and Sentiments to derive demand and Identifies significant drivers of sale out of hordes of factors that Selects the best suited model out of multiple forecasting models for each product. Responsibilities: \\u00e2\\u0080\\u00a2 Involved in deploying the product for customers, gathering requirements and algorithm optimization at backend of the product. \\u00e2\\u0080\\u00a2 Load and transform Large Datasets of structured semi structured. \\u00e2\\u0080\\u00a2 Responsible to manage data coming from different sources and application \\u00e2\\u0080\\u00a2 Supported Map Reduce Programs those are running on the cluster \\u00e2\\u0080\\u00a2 Involved in creating Hive tables, loading with data and writing hive queries which will run internally in map reduce way.Education Details \\r\\n\\r\\nHadoop Developer \\r\\n\\r\\nHadoop Developer - Braindatawire\\r\\nSkill Details \\r\\nAPACHE HADOOP HDFS- Exprience - 49 months\\r\\nAPACHE HADOOP SQOOP- Exprience - 49 months\\r\\nHadoop- Exprience - 49 months\\r\\nHADOOP- Exprience - 49 months\\r\\nHADOOP DISTRIBUTED FILE SYSTEM- Exprience - 49 monthsCompany Details \\r\\ncompany - Braindatawire\\r\\ndescription - Technical Skills:\\r\\n\\u00e2\\u0080\\u00a2   Programming: Core Java, Map Reduce, Scala\\r\\n\\u00e2\\u0080\\u00a2   Hadoop Tools: HDFS, Spark, Map Reduce, Sqoop, Hive, Hbase\\r\\n\\u00e2\\u0080\\u00a2   Database: MySQL, Oracle\\r\\n\\u00e2\\u0080\\u00a2   Scripting: Shell Scripting\\r\\n\\u00e2\\u0080\\u00a2   IDE: Eclipse\\r\\n\\u00e2\\u0080\\u00a2   Operating Systems: Linux (CentOS), Windows\\r\\n\\u00e2\\u0080\\u00a2   Source Control: Git (Github)\",\n          \"IT Skills: Area Exposure Modeling Tool: Bizagi, MS Visio Prototyping Tool: Indigo Studio. Documentation: MS Office (MS Word, MS Excel, MS Power Point) Testing Proficiency: Smoke, Sanity, Integration, Functional, Acceptance and UI Methodology implemented: Waterfall, Agile (Scrum) Database: SQL Testing Tool: HPQC Business Exposure Education Details \\r\\n Bachelor Of Computer Engineering Computer Engineering Mumbai, Maharashtra Thadomal Shahani Engineering college\\r\\n Diploma Computer Engineering Ulhasnagar, Maharashtra Institute of Technology\\r\\n Secondary School Certificate  Ulhasnagar, Maharashtra New English High School\\r\\nSenior Business Analyst - RPA \\r\\n\\r\\nSenior Business Analyst - RPA - Hexaware Technologies\\r\\nSkill Details \\r\\nDOCUMENTATION- Exprience - 47 months\\r\\nTESTING- Exprience - 29 months\\r\\nINTEGRATION- Exprience - 25 months\\r\\nINTEGRATOR- Exprience - 25 months\\r\\nPROTOTYPE- Exprience - 13 monthsCompany Details \\r\\ncompany - Hexaware Technologies\\r\\ndescription - Working as a RPA Business Analyst\\r\\ncompany - BBH- Brown Brothers Harriman & Co\\r\\ndescription - is a private bank that provides commercial banking, investment management, brokerage, and trust services to private companies and individuals. It also performs merger advisory, foreign exchange, custody services, commercial banking, and corporate financing services.\\r\\n\\r\\nResponsibilities: \\u00e2\\u0080\\u00a2 Performed Automation Assessment of various Processes and identified processes which can be candidates of RPA.\\r\\n\\u00e2\\u0080\\u00a2 Conducting Assessment that involves an initial Understanding of the Existing System, their technology, processes, Usage of the tools, Feasibility of tool with automation tool along with automation ROI analysis.\\r\\n\\u00e2\\u0080\\u00a2 Preparing the Automation Potential Sheet which describes the steps in the process, the volume and frequency of the transaction, the AHT taken by SME to perform the process and depending on the steps that could be automated, Automation potential and the manual efforts that will be saved are calculated.\\r\\nCalculating the complexity of the Process which is considered for automation and depending on all these factors Number of Bots and Number of Automation tool Licenses are determined.\\r\\n\\u00e2\\u0080\\u00a2 Implementing a Proof of Concept (POC) to Validate Feasibility by executing the selected critical use cases for conducting a POC which will helps to identify financial and operational benefits and provide recommendations regarding the actual need for complete automation.\\r\\n\\u00e2\\u0080\\u00a2 Gathering business requirements by conducting detailed interviews with business users, stakeholders, and Subject Matter Experts (SME's) \\u00e2\\u0080\\u00a2 Preparing Business Requirement Document and then converted Business requirements into Functional Requirements Specification.\\r\\n \\u00e2\\u0080\\u00a2 Constructing prototype early toward a design acceptable to the customer and feasible.\\r\\n\\u00e2\\u0080\\u00a2 Assisting in designing test plans, test scenarios and test cases for integration, regression, and user acceptance testing (UAT) to improve the overall quality of the Automation.\\r\\n\\u00e2\\u0080\\u00a2 Participating regularly in Walkthroughs and Review meetings with Project Manager, QA Engineers, and Development team.\\r\\n\\u00e2\\u0080\\u00a2 Regularly interacting with offshore and onshore development teams.\\r\\ncompany - FADV - First Advantage\\r\\ndescription - is a criminal background check company that delivers global solutions ranging from employment screenings to background checks.\\r\\nThe following are the processes which were covered:\\r\\nEmail Process, Research Process, Review Process.\\r\\n\\r\\nResponsibilities: \\u00e2\\u0080\\u00a2 Requirement Gathering through conducting Interviews & Brainstorming sessions with stakeholders \\u00e2\\u0080\\u00a2 To develop decision models and execute those rules as per the use case specifications.\\r\\n\\u00e2\\u0080\\u00a2 To Test/validate the decision models against document test data.\\r\\n\\u00e2\\u0080\\u00a2 To maintain and enhance the decision models for changes in regulations as per use case specifications.\\r\\n\\u00e2\\u0080\\u00a2 Responsible for performing the business research that will make a business growth.\\r\\n\\u00e2\\u0080\\u00a2 Developing a clear understanding of existing business functions and processes.\\r\\n\\u00e2\\u0080\\u00a2 Effectively communicate with the onsite clients for the queries, suggestions, and update.\\r\\n\\u00e2\\u0080\\u00a2 Giving suggestions to enhance the current processes.\\r\\n\\u00e2\\u0080\\u00a2 Identifying areas for process improvement.\\r\\n\\u00e2\\u0080\\u00a2 Flagging up potential problems at an early stage.\\r\\n\\u00e2\\u0080\\u00a2 Preparing PowerPoint presentations and documents for business meetings.\\r\\n\\u00e2\\u0080\\u00a2 Using any information gathered to write up detailed reports.\\r\\n\\u00e2\\u0080\\u00a2 Highlighting risks and issues that could impact project delivery.\\r\\n\\u00e2\\u0080\\u00a2 Able to work accurately.\\r\\n\\u00e2\\u0080\\u00a2 To develop and maintain documentation for internal team training and client end user operations.\\r\\n\\u00e2\\u0080\\u00a2 To work efficiently with team members and across teams.\\r\\n\\u00e2\\u0080\\u00a2 To mentor and train junior team members.\\r\\ncompany - Clinical Testing, Lab Work and Diagnostic Testing\\r\\ndescription - IQVIA provides services to its customers this includes: Clinical Testing, Lab Work and Diagnostic Testing under clinical trial. These customers need to pay to IQVIA and aging details and invoices are generated for the same.\\r\\nThe following are the processes which were covered:\\r\\n\\r\\nTracking Payments, Automated Real Time Metrics Reporting (Dashboard), Past Due Notifications, AR Statements, Credit/Rebill.\\r\\nResponsibilities: \\u00e2\\u0080\\u00a2 Conducting meetings with clients and key stakeholders to gather requirements, analyze, finalize and have formal sign-offs from approvers Gather and perform analysis of the business requirements \\u00e2\\u0080\\u00a2 Translating the business requirements into the Business Requirement Document [BRD], Functional Requirement Document [FRD].\\r\\n\\u00e2\\u0080\\u00a2 Facilitating meetings with the appropriate subject matter experts in both business and technology teams \\u00e2\\u0080\\u00a2 Coordinating with business user community for the execution of user acceptance test as well as tracking issues \\u00e2\\u0080\\u00a2 Working, collaborating and coordinating with Offshore and Onsite team members to fulfill the BA responsibilities from project initiation to Post-Implementation \\u00e2\\u0080\\u00a2 Reviewing the test scripts with business users as well as technology team. Execute test scripts with expected results for the System Integration Test (SIT) and User Acceptance Test (UAT) \\u00e2\\u0080\\u00a2 Coordinating and conducting the Production Acceptance Testing (PAT) with the business users \\u00e2\\u0080\\u00a2 Creating flow diagrams, structure charts, and other types of system or process representations \\u00e2\\u0080\\u00a2 Managing changes to requirements and baseline through a change control process \\u00e2\\u0080\\u00a2 Utilizing standard methods, design and testing tools throughout project development life cycle \\u00e2\\u0080\\u00a2 Work closely with the operational functional teams, operations management, and personnel, and various technology teams to facilitate a shared understanding of requirements and priorities across all areas\\r\\ncompany - Eduavenir IT Solution\\r\\ndescription - Project: M.B.M.S\\r\\n\\r\\nM.B.M.S. - is an Inventory management application that allows user to manage inventory details of different warehouses, having different products located at various locations and help extract what goods have been procured, sold or returned by customers. It generates automated invoicesalong withcustomized reports. It also managescustomer complaint and resolution system implementation along with automated MIS on monthly basis.Sales and forecastingis also developed on MIS System and the streamlining of process of warehousing and dispatch along with online proof of delivery management system (POD documentation) is generated.\\r\\n\\r\\nResponsibilities: \\u00e2\\u0080\\u00a2 Participate in requirement gathering discussion with client to understand the flow of business processes \\u00e2\\u0080\\u00a2 Analyze the requirements and determine the core processes, develop Process Documentation and ensure to stay up-to-date in conjunction with on-going changes \\u00e2\\u0080\\u00a2 Participate in process flow analysis and preparing BRD, SRS.\\r\\n\\u00e2\\u0080\\u00a2 Coordinating with developers, designers & operations teams for various nuances of the project, communicate the stakeholder requirements from requirement /enhancement to implementation and finally deliver the same within estimated timeframe.\\r\\n\\u00e2\\u0080\\u00a2 Support UAT by reviewing test cases, manage version control of documents, software builds.\\r\\n\\u00e2\\u0080\\u00a2 Coordinate with the stakeholders for UAT sign off and coordinate internally for production movement till Golive stage of the application.\\r\\n\\u00e2\\u0080\\u00a2 Provide demo and training to internal and end user using PowerPoint presentation.\\r\\n\\u00e2\\u0080\\u00a2 Resolving project functional &technical issues during UAT.\\r\\n\\u00e2\\u0080\\u00a2 Prioritizing the Production bugs and resolving the same within the estimated timeframe.\\r\\n\\u00e2\\u0080\\u00a2 Preparing Project Status Report and Production Bugs Status to all the stakeholders.\\r\\n\\u00e2\\u0080\\u00a2 Promoting and Networking for online trading platform.\\r\\n\\u00e2\\u0080\\u00a2 Designing query sheet for obtaining and comparison of quotes from various vendors.\\r\\n\\u00e2\\u0080\\u00a2 Development of product codes / material codes for inventory management (Master Data Management)\\r\\ncompany - CAPGEMINI Head Office\\r\\ndescription - Type: Mobile and Device Testing.       Duration: January 2014 - August 2014\\r\\n\\r\\nFollet - An application which takes an electronic request from the user for the books he requires from a particular follet store. This detailed information about books that will include the name of the book, its price, the date of the transaction and the parties involved which will then be sent to follet stores. User then create request for one or more books for a given date. This request is then processed further and user gets a mail of the date when he will be provided with that book.\\r\\n\\r\\nResponsibilities: \\u00e2\\u0080\\u00a2 Understanding the needs and business requirements.\\r\\n\\u00e2\\u0080\\u00a2 Preparing BRD, SRS by eliciting all the requirements from the client and SMEs \\u00e2\\u0080\\u00a2 Understanding the dependency of the modules in the system \\u00e2\\u0080\\u00a2 Preparation of test plan for Unit level and Integration level.\\r\\n\\u00e2\\u0080\\u00a2 Preparation and execution of test cases.\\r\\n\\u00e2\\u0080\\u00a2 Defect tracking, Issue Resolution, Risk Monitoring, Status Tracking, Reporting and Follow-up.\\r\\n\\u00e2\\u0080\\u00a2 Preparation of Test Completion report.\\r\\ncompany - CAPGEMINI Head Office\\r\\ndescription - \\r\\ncompany - CAPGEMINI Head Office\\r\\ndescription - Humana is a health care insurance project of U.S. which deals with supplying various medicines to citizens as per the doctor's reference and patient's insurance policy. This application keeps track of all the medicines user has consumed in the past and generates a patient history. A citizen is given a drug only after the doctor's reference so the doctor's information is also linked with the patient's history.\\r\\n\\r\\nResponsibilities: \\u00e2\\u0080\\u00a2 Understanding the requirements and getting clarifications from client.\\r\\n\\u00e2\\u0080\\u00a2 Involved in writing test cases based on test scenarios and execute them.\\r\\n\\u00e2\\u0080\\u00a2 Ensuring Test Coverage using Requirement Traceability Matrix (RTM) \\u00e2\\u0080\\u00a2 Preparation of Test Completion report.\\r\\ncompany - CAPGEMINI Head Office\\r\\ndescription - Testing Trends WQR (World Quality Report) is an application which allows the users to take a survey on different methods and technologies used for testing. Users can choose to answer any type of questions under three different categories. Users have a facility to search, view and export the data to excel. Also, users get daily and weekly reports through email about the new trends in testing implemented around the globe. Testing Trends WQR app is available on Android and IOS platforms.\\r\\n\\r\\nResponsibilities: \\u00e2\\u0080\\u00a2 Understanding the requirements and getting clarifications from client.\\r\\n\\u00e2\\u0080\\u00a2 Writing test cases based on test scenarios and executed them.\\r\\n\\u00e2\\u0080\\u00a2 Performing different types of testing such as Functional, Integration, System, and UAT.\\r\\n\\u00e2\\u0080\\u00a2 Defect resolution and maintenance of the application.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxDZtMV4wAwg",
        "outputId": "e6d29457-9c2c-447c-bf44-6cc552835771"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(962, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 899
        },
        "id": "tp9A68YwwEzB",
        "outputId": "153fb213-24e4-4938-a768-edc12b237242"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category\n",
              "Java Developer               84\n",
              "Testing                      70\n",
              "DevOps Engineer              55\n",
              "Python Developer             48\n",
              "Web Designing                45\n",
              "HR                           44\n",
              "Hadoop                       42\n",
              "Blockchain                   40\n",
              "ETL Developer                40\n",
              "Operations Manager           40\n",
              "Data Science                 40\n",
              "Sales                        40\n",
              "Mechanical Engineer          40\n",
              "Arts                         36\n",
              "Database                     33\n",
              "Electrical Engineering       30\n",
              "Health and fitness           30\n",
              "PMO                          30\n",
              "Business Analyst             28\n",
              "DotNet Developer             28\n",
              "Automation Testing           26\n",
              "Network Security Engineer    25\n",
              "SAP Developer                24\n",
              "Civil Engineer               24\n",
              "Advocate                     20\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Category</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Java Developer</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Testing</th>\n",
              "      <td>70</td>\n",
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              "      <th>DevOps Engineer</th>\n",
              "      <td>55</td>\n",
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              "    <tr>\n",
              "      <th>Python Developer</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Web Designing</th>\n",
              "      <td>45</td>\n",
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              "      <td>44</td>\n",
              "    </tr>\n",
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              "      <th>Hadoop</th>\n",
              "      <td>42</td>\n",
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              "      <th>Blockchain</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ETL Developer</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Operations Manager</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Science</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sales</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mechanical Engineer</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arts</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Database</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Electrical Engineering</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health and fitness</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
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              "      <th>PMO</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
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              "      <th>Business Analyst</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
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              "      <th>DotNet Developer</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
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              "      <th>Automation Testing</th>\n",
              "      <td>26</td>\n",
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              "      <th>Network Security Engineer</th>\n",
              "      <td>25</td>\n",
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              "      <td>24</td>\n",
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              "      <td>24</td>\n",
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              "      <td>20</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.countplot(df['Category'])\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "K_6Mr7C2wGpK",
        "outputId": "fcd9b4e0-69a8-4dde-f40a-b52e963c8920"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Category'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qS0mCAbIwJEi",
        "outputId": "133727f2-6841-401a-95d5-a6f0c73cb42f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Data Science', 'HR', 'Advocate', 'Arts', 'Web Designing',\n",
              "       'Mechanical Engineer', 'Sales', 'Health and fitness',\n",
              "       'Civil Engineer', 'Java Developer', 'Business Analyst',\n",
              "       'SAP Developer', 'Automation Testing', 'Electrical Engineering',\n",
              "       'Operations Manager', 'Python Developer', 'DevOps Engineer',\n",
              "       'Network Security Engineer', 'PMO', 'Database', 'Hadoop',\n",
              "       'ETL Developer', 'DotNet Developer', 'Blockchain', 'Testing'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts = df['Category'].value_counts()\n",
        "labels = df['Category'].unique()\n",
        "plt.figure(figsize=(15,10))\n",
        "\n",
        "plt.pie(counts,labels=labels,autopct='%1.1f%%',shadow=True, colors=plt.cm.plasma(np.linspace(0,1,3)))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "7vvyNW34wLj_",
        "outputId": "370c1920-564c-495d-9188-7f92c79b7b89"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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ljBw58ryjRQshxNlIkSiEEEII8S9Tp07lwQcfZMWKFRw7dkzvOEIIUaOkSBRCCCGE+AeLxcLcuXO57777GDJkCNOnTy/z+BtvvEF8fDyhoaHceeedWK1W92OLFy8mICCA/Pz8Mts8/PDDXHLJJe7v58+fT5s2bfD396dhw4aMHz++zPo2m42nnnqKxMRE/P39adq0KVOnTgXA5XJx55130qhRIwIDA2nRogXvv/++e9uxY8cyY8YMvvvuOxRFQVEUli9fDsDhw4e58cYbiYiIICoqimHDhpGRkXHhL5oQwqdIkSiEEEII8Q/z5s2jZcuWtGjRgltvvZXPP/+cv4ZwmDdvHmPHjuX1119n06ZN1KlTh48++si97aWXXkpERATz5893L3O5XMydO5dbbrkFgM2bN3PjjTdy0003sXPnTsaOHcvzzz9fphi97bbbmD17Nh988AF79+7l008/JSQkBABVValfvz5ff/01e/bs4YUXXuDZZ59l3rx5AIwZM4Ybb7yRwYMHk5mZSWZmJr1798bhcDBo0CBCQ0NZuXIlq1evJiQkhMGDB2O326v7ZRVCeBEZuEYIIYQQ4h/69OnDjTfeyMMPP4zT6aROnTp8/fXX9O/fn969e9OpUyc+/PBD9/o9e/bEarW6B6555JFH2LlzJ0uXLgVOtS5effXVHD9+nIiICG655RaysrJYvHixex9PPvkkP/30E7t372b//v20aNGC3377rdyjGT/wwAMcP36cb775Bjh1T2J+fj4LFy50r/Pll1/y6quvsnfvXhRFAcButxMREcHChQu5/PLLL+RlE0L4EGlJFEIIIYT4U3JyMhs2bGDEiBEAmEwm/vOf/7i7eu7du5cePXqU2aZXr15lvr/llltYvny5+17GWbNmMWTIECIiItz76NOnT5lt+vTpQ0pKCi6Xi23btmE0GunXr99Zc3744Yd06dKF2NhYQkJC+Oyzzzh06NA5n9v27dtJTU0lNDSUkJAQQkJCiIqKwmq1kpaWdv4XRwhRa8g8iUIIIYQQf5o6dSpOp5O6deu6l2mahr+/P5MmTSrXPrp160aTJk2YM2cO9913H99+++1p9zWeS2Bg4DkfnzNnDmPGjGH8+PH06tWL0NBQ3n77bdavX3/O7SwWC126dGHWrFmnPRYbG1vufEII3ydFohBCCCEE4HQ6mTlzJuPHjz+t6+U111zD7NmzadWqFevXr+e2225zP7Zu3brT9nXLLbcwa9Ys6tevj8FgKDOVRqtWrVi9enWZ9VevXk3z5s0xGo20a9cOVVX5448/ztjddPXq1fTu3ZvRo0e7l/27JdDPzw+Xy1VmWefOnZk7dy5xcXGEhYWV4xURQtRW0t1UCCGEEAL48ccfycvL484776Rt27ZlvoYPH87UqVN5+OGH+fzzz5k2bRr79+/nxRdfZPfu3aft65ZbbmHLli289tprXH/99fj7+7sfe/zxx1m6dCmvvPIK+/fvZ8aMGUyaNIkxY8YA0LBhQ26//XZGjRrFwoULSU9PZ/ny5e6BaZo1a8amTZtYtGgR+/fv5/nnn2fjxo1ljt+wYUN27NhBcnIy2dnZOBwObrnlFmJiYhg2bBgrV6507/ehhx7iyJEj1fjKCiG8jQxcI4QQQggBDB06FFVV+emnn057bMOGDfTo0YPt27fz448/MmHCBKxWK8OHDyc+Pp5Fixa5B675S48ePdiwYQO///47AwYMKPPY/PnzeeGFF0hJSaFOnTo8+OCD7iIRwGq18uyzzzJnzhxycnJISkri2Wef5Y477sBms3Hvvffy7bffoigKI0aMIDw8nF9++cWdISsri1tuuYW1a9disVhYtmwZ/fv35/jx4zz11FP8/PPPFBUVUa9ePS699FLeeecdaV0UQrhJkSiEEEIIIYQQwk26mwohhBBCCCGEcJOBa4QQQngNp1MlL7eU/HwrublW8vOt5OWVkvfX/3NLKSiwUVLswGpzYre5sFqd2GwubLY//7U6sdn//PfP5ZoGBoOCwaBgNP71r+HUMuO/lhsM+AcYCQ42ExTk9+e/ZoKCzYSE+BEa6kdomD+hoX6EhfkTFuZPVHQgsbFBxMYFExkZ4J6jTgghhPBE0t1UCCGE7pwFxeSeKGJzqoXjmRYyM4v+/NfCieMWcrJLycuzYrHY9Y56wcxmAzExQcTFBxMTG0RcXDBxccGnisj4U/+vVy+UpAbhBAWZ9Y4rhBCiFpIiUQghRLVzldiwHc3GejQb29FsbCfysJ/Mx56Vjz27EM3mQE1M4NIvS/WO6lHi4oNJSgqnQcNwGjaMoEHDcBr8+W9iYhhms1HviEIIIXyQFIlCCCGqhGq1Yz2Wg+1Yzp8F4Z//HsvGmWc57/aG6DD6LZBumOVlMCjUrRtKg4bhNGseRctWMbRqHUurVjEk1AnRO54QQggvJkWiEEKICtE0DVtmDqUHjlN6IJOSA5mUph/HfjIfLuBPimIyMuD3YDRNCsULFREZQKtWMbRqHfNn8RhDq1axxMUH6x1NCCGEF5AiUQghxFm5Sm2Uph+nNP34qWLwQCalGcdRS6vn3sB7UuJJOWatln0LiIoOpFWrGNp3iKdj5wQ6d06gWfNoDAYpzIUQQvxNikQhhBAAaC6V0oMnKN57iOK9h7DsO4ztaPYFtQ5W1HhLEj9uLqix4wkICfGjfYc4rv9PPLeNsmEydMSg1Nc7lhBCCB3JFBhCCFFLOQuKsew7VRAW7ztMcfLhamshLK/GMTKaZ02zWOysWX2ELj2yKHVNBBcoxGBUOmI0dMSkdMSodEBRpKuqEELUFlIkCiFELWE9kkXRjnQsuzMo3ncI29EcvSOdJjbApXeEWqtD58Pu/2tk49SW4HQtwQaAEaPSFpPSC6OhFyalK4oSqFdUIYQQ1UyKRCGE8FG2YzkUbT9A0Y40inak48gp1DvSeRVnHgZkZE49tO+0/RyPunBp23Fp20H9BPDDqLTHpPTCZOiFUemEovjXVFQhhBDVTIpEIYTwEc6CYgq3pVG0NZXCranYT+TpHanCEoIMekeoleLjA6lTP70CW9hxaZtwaZuwqROBAIxKJ0yG3piUnn92T5VTDCGE8FbyCS48xsiRI8nPz2fhwoXVepyxY8eycOFCtm3bViX7mz59Oo888gj5+flVsr8L0b9/fzp27Mh7772ndxRRAzRVpXjvIQrW76NgcwqlBzJrdJCZ6hBtllE29dCpi98F7sGKS1uLy7X2z+6pwZiUrpgM/TAbBspAOEII4WXkkq04q5EjR6IoCvfee+9pj91///0oisLIkSNrPtgFGjNmDEuXLq3RYyqKcsavOXPmVOlxFixYwCuvvFKl+xSexVViI2/lTtLfmceOEa+T/PinHJ/3B6Vpx7y+QAQI1eSeRD107GKp4j0W49T+wOp6mSJHX4ocV2B1jsepbkcGVRdCCM8nLYninBITE5kzZw4TJkwgMPDUIAVWq5WvvvqKpKQkndNVTkhICCEhNX/P07Rp0xg8eHCZZREREVV6jKioqCrdX2U5HA7MZhmlsqrYjudSsG4v+ev3YdmVjubw3UIq2KXqHaFWat8po1r3r2rJ2LRkbOqHKMRhMlyC2XApJuUiuZdRCCE8kLQkinPq3LkziYmJLFiwwL1swYIFJCUl0alTpzLrqqrKuHHjaNSoEYGBgXTo0IFvvvmmzDq7d+/mqquuIiwsjNDQUC6++GLS0tLKrPPOO+9Qp04doqOjuf/++3E4HO7HvvjiC7p27UpoaCgJCQncfPPNnDx50v348uXLURSFpUuX0rVrV4KCgujduzfJycnudcaOHUvHjh3LHPPzzz+nTZs2+Pv7U6dOHR544AH3Y++++y7t2rUjODiYxMRERo8ejcVS8avuERERJCQklPkKCAgATnVZjYiIYNGiRbRq1YqQkBAGDx5MZmame3un08lDDz1EREQE0dHRPPXUU9x+++1cc8017nX69+/PI4884v6+YcOGvP7664waNYrQ0FCSkpL47LPPyuQ6fPgwN954IxEREURFRTFs2DAyMjLKrDNlyhRatWpFQEAALVu25KOPPnI/lpGRgaIozJ07l379+hEQEMCsWbMq/PqIsor3HeLI57+y+54J7Br5Noc/+ZGirak+XSACmFSN6CBpaapp7TptrrFjaZzEoc6hxHk3hY7OFDv+i901D1XLrrEMQgghzk2KRHFeo0aNYtq0ae7vP//8c+64447T1hs3bhwzZ87kk08+Yffu3Tz66KPceuut/PHHHwAcPXqUvn374u/vz++//87mzZsZNWoUTqfTvY9ly5aRlpbGsmXLmDFjBtOnT2f69Onuxx0OB6+88grbt29n4cKFZGRknLHL63PPPcf48ePZtGkTJpOJUaNGnfX5ffzxx9x///3cc8897Ny5k++//56mTZu6HzcYDHzwwQfs3r2bGTNm8Pvvv/Pkk09W5CUsl5KSEt555x2++OILVqxYwaFDhxgzZoz78TfffJNZs2Yxbdo0Vq9eTWFhYbnu3xw/fjxdu3Zl69atjB49mvvuu89dNDscDgYNGkRoaCgrV65k9erV7gLVbj81X96sWbN44YUXeO2119i7dy+vv/46zz//PDNmzChznKeffpqHH36YvXv3MmjQoKp7YWqRktRjHPn8V3aOfIt9j3zMiXl/YD108vwb+pgkz2gQrzWSGvgTEaXX71kpTu03Sl1PU+ToicVxPTbXFFTthE55hBBCgHQ3FeVw66238swzz3Dw4EEAVq9ezZw5c1i+fLl7HZvNxuuvv86SJUvo1asXAI0bN2bVqlV8+umn9OvXjw8//JDw8HDmzJnj7orYvHnzMseKjIxk0qRJGI1GWrZsyZAhQ1i6dCl33303QJlir3HjxnzwwQd069YNi8VSpgvpa6+9Rr9+/YBTxcuQIUOwWq3ulrt/evXVV3n88cd5+OGH3cu6devm/v+/W+ZeffVV7r333jKtaeUxYsQIjEZjmWV79uxxd9t1OBx88sknNGnSBIAHHniAl19+2b3uxIkTeeaZZ7j22msBmDRpEj///PN5j3vllVcyevRoAJ566ikmTJjAsmXLaNGiBXPnzkVVVaZMmYKinBowZNq0aURERLB8+XIuv/xyXnzxRcaPH891110HQKNGjdizZw+ffvopt99+e5nX6a91RPmVZhwn948d5K3Y4ZHzFuqhXqjKVoznX1FUiTr1Dp9/pRqh4tK24HJtwep6A6PSAz/D1ZgNV6AoYXqHE0KIWkWKRHFesbGxDBkyhOnTp6NpGkOGDCEmJqbMOqmpqZSUlHDZZZeVWW63293dUrdt28bFF198znvV2rRpU6aQqlOnDjt37nR/v3nzZsaOHcv27dvJy8tDVU/dv3To0CFat27tXq99+/Zl9gFw8uTJ0+6jPHnyJMeOHePSSy89a6YlS5Ywbtw49u3bR2FhIU6nE6vVSklJCUFBQWfd7t8mTJjAwIEDyyyrW7eu+/9BQUHuAvGv3H91pS0oKODEiRN0797d/bjRaKRLly7u1+Bs/vlaKIpCQkKCe7/bt28nNTWV0NDQMttYrVbS0tIoLi4mLS2NO++8012ow6mur+Hh4WW26dq16zlziL9Zj2SR98cOclfsxHpQWkz+rW6wdDetSZ27eeL8mSoubS2lrrWUul7EpPTHzzgMk3KJ3MMohBA1QIpEUS6jRo1y36f34Ycfnvb4X/fo/fTTT9SrV6/MY/7+p/6g/zXwzbn8u4BUFMVdBBUXFzNo0CAGDRrErFmziI2N5dChQwwaNMjdNfJM+/mrhexMxdT5MmVkZHDVVVdx33338dprrxEVFcWqVau48847sdvtFSoSExISynRj/bczPfeqGAXwXK+pxWKhS5cuZ7yHMDY21v1znTx5Mj169Cjz+L9bRYODgy84qy9zFhST8/tWcpZsPTUSqTir2AApEmtS996Z519JV3ac2mKczsVAKGbDIPwMwzAqvVAUuWtGCCGqgxSJolz+ukdNUZQz3m/WunVr/P39OXTokLub57+1b9+eGTNmVHrky3379pGTk8Mbb7xBYmIiAJs2barwfv4pNDSUhg0bsnTpUgYMGHDa45s3b0ZVVcaPH4/BcOpkZN68eRd0zMoIDw8nPj6ejRs30rdvXwBcLhdbtmw5bRCeiujcuTNz584lLi6OsLDTu3OFh4dTt25dDhw4wC233FLp49RWmkulcNN+shdvomD9PjSnbw86U1Vi/GSE05piMGj06H1A7xgVUIRD/QaH+g0K8ZgNQ04VjIZ2egcTQgifIkWiKBej0cjevXvd//+30NBQxowZw6OPPoqqqlx00UUUFBSwevVqwsLCuP3223nggQeYOHEiN910E8888wzh4eGsW7eO7t2706JFi/NmSEpKws/Pj4kTJ3Lvvfeya9euKpkTcOzYsdx7773ExcVxxRVXUFRUxOrVq3nwwQdp2rQpDoeDiRMnMnToUFavXs0nn3xSqePk5+dz/PjxMstCQ0PL3QL34IMPMm7cOJo2bUrLli2ZOHEieXl57pbSyrjlllt4++23GTZsGC+//DL169fn4MGDLFiwgCeffJL69evz0ksv8dBDDxEeHs7gwYOx2Wxs2rSJvLw8HnvssUof25dZj2SRvXgzuUu34sjxxK58ni3SKEViTamX6CI0zKZ3jErROIFd/Ry7+jkGpTl+hhsxG67DoEToHU0IIbye9NMQ5RYWFnbG1qa/vPLKKzz//POMGzeOVq1aMXjwYH766ScaNWoEQHR0NL///jsWi4V+/frRpUsXJk+eXO5WxdjYWKZPn87XX39N69ateeONN3jnnXcu+HndfvvtvPfee3z00Ue0adOGq666ipSUFAA6dOjAu+++y5tvvknbtm2ZNWsW48aNq9Rx7rjjDurUqVPma+LEieXe/qmnnmLEiBHcdttt9OrVi5CQEAYNGnTGwXjKKygoiBUrVpCUlMR1111Hq1atuPPOO7Fare6f9V133cWUKVOYNm0a7dq1o1+/fkyfPt39cxWnuEptZC/axL7HPmH3Xe9yYt4fUiBWUihSJNaUVq2L9I5QJVRtP1bXqxQ5elHifBSnul7vSEII4dUUrSpuehJC1DhVVWnVqhU33nhjlbSoisopSTnKyR/XkbdiB2qp/fwbiPOyGxUG/S6jWdaEJ57bz0NPLtc7RrUw0Bg/402YDddL66IQQlSQdDcVwkscPHiQxYsX069fP2w2G5MmTSI9PZ2bb75Z72i1jmp3krdyJ1k/rKV4n6dMH+A7/Fwawf4axbbKd6UW5dO91yG9I1QblQNYXa9jdY3HbLgKP8OtmAwd9I4lhBBeQYpEIbyEwWBg+vTpjBkzBk3TaNu2LUuWLKFVq1Z6R6s1VC2Twu3rODhuN86CYr3j+LQGERp7TkiRWJ3Mfhqde2ToHaMG2HCo83Go8zEq7fAz3ILZcDWKUvmu+kII4euku6kQQpyHU92EXZ2OQ10M9oakXt9F70g+b1xmKIv3y23z1alZCzu/b5iudwxdKITjZ7gJP+MdGJQ4veMIIYTHkb/AQghxBprmwO76FovjaoqdN+JQfwac4JdK5NDTR/gVVatuiFy/rG6t2xboHUE3GgXY1E8pcvSlxPkULi1N70hCCOFRpEgUQoh/0LQSbK7PKXL0p9T1OC5t12nrRF2XUfPBapm4QBnhtLp16npC7wgewI5D/RqL43KKHffgVDfqHUgIITyC3JMohBCAquVhd03Hrn6BRv65V45cj1onFkOm3NNUXWL8pEisbt37HNQ7ggfRcGpLcDqXYFQ642+8G5NyGYoi19KFELWTfPoJIWo1VTtKqfNlihwXYVMnnr9ABBSDC2VIcvWHq8WijNLdtDoFBWm0aXdU7xgeyaVtocR5HxbHZdhds9E0m96RhBCixkmRKISolVzqfkqcj1PkGIBdnQ6UVmj7pEsPovm5qiWbgDBFXtvq1KSZFYOcAZyTSjqlrucoclyM1fUhmlZ77+EUQtQ+8idCCFGruNRdFDvuweK8Aof6LeCs1H78g0sxXHm8asMJtxBVuptWp7bt8/WO4DU0srG5xlPouBir8100rVDvSEIIUe2kSBRC1AoudR/FjnuxOK/GqS0BLrw7Y/Sg/RceTJxRgFPFLF1Oq02nbnKBo+Is2NRJFDn6/dmyKHOlCiF8lxSJQgif5tLSKHE+hMU5BKe2uEr3HVHvJFon6YJWHRQgMULvFL6r58UH9I7gtTQKsLnGU+Toh801GU2z6h1JCCGqnBSJQgif5NIOUuJ8HItjMA71R6qi5fBMQq5JrZb9CkiMkJbE6hARqdKocY7eMbyeRi5W1ziKHP2xuWaiaXa9IwkhRJWRIlEI4VNU7SglzmewOC77857D6h0AJa5dOmqsjH5YHeqGSJFYHZq3qNggTeLcNE5idY2lyHEJdtccNK1y9zkLIYQnkSJRCOETVC2HUudYihyX4lDnUtkBaSrKaHLhf92hGjlWbZMQJEVidWjXMVfvCD5J4xilrmf/nDpjAZomgy8JIbyXFIlCCK+maTZsrk/+nMpiJlDzXb7i+iajmuSEsKrF+strWh269JD5EauTykFKXWP+7Oq+VO84QghRKVIkCiG8kqZp2F3fU+QYiNX1FmDRLUtgaDHGQSd0O76vipLCu8opikYvGbSmRqikUuK8m2LH7bhUGQlZCOFdpEgUQngdp7qJYud1lLoeQcMzWkWirpCTwKoWrkiRWNXi4lViYvW7oFIbObWVWJxDKHW+gKrl6R1HCCHKRYpEIYTXcGkHKXaMpth5Iy5tu95xyohMOo7WRibZrkqhck9XlWvRSub204cLu/olFscl2FzTZHAbIYTHkyJRCOHxNK2AUuerWByDcGq/6h3nrIKvlW58VSnQ6aK6pi6prTp0ytY7Qq2mUYDV9QoWxxU41GV6xxFCiLOSIlEI4bFO3Xc4lyLHpdjVz9FjUJqKiO+Uihrp2Rm9iUGDeuF6p/AtXXse0TuCAFTSKHHeSbHjDlyazLUqhPA8UiQKITySS91JsXM4pa5n0PCOIfuNZhf+1x7WO4ZPSYqQlsSqYjBq9Ogtrd2exKn9gcVxJaXOl1C1fL3jCCGEmxSJQgiPcqpr6f+wOK/FpW3TO06FxfZPRjPKvXRVpW6YFIlVJTHRSXCotHR7Hid2dQYWxyXYXXPQNPmdF0LoT4pEIYTHsLsWUOQYiF39CvDOQisoogjlkiy9Y/iMhCA5Ya4qrdoU6R1BnINGPqWuZyl2/kemzBBC6E6KRCGE7lxaKhbHCEpdY9DI0TvOBYsckqJ3BJ8RF+CdFws8UYfOcvHCG7i0TVicQ7E630bTrHrHEULUUlIkCiF0o2k2rM53sDiG4NLW6x2nykQ3PorWXKYaqApRJmlJrCrdeh/SO4IoNwc29WMsjsE4HKv1DiOEqIWkSBRC6MKpbsbiuAqb+hHg0DtOlQu6Lk3vCD4h0uDSO4JP8PPT6NztoN4xRAWpHCLz659Jf3sezkK58CSEqDlSJAohapSmlVLqfJli539Q8d1CKr5rKlqY7xW/NS1Uk+6mVaFRYztms7yW3kbL7UXWTJXcpVvZfc975P6xQ+9IQohaQopEIUSNcarrsDiuxK5Ox1sHpikvk58D0zCZk+5CBbl8+/ekprRuV6h3BFFRWihHX2nk/taZbyF93GxSX/oCe478PIUQ1UuKRCFEtdM0C6XO/1HsvAWV2tPlLfaSZDTknroLYVI1YoLlNbxQnboe1zuCqKCSVZdjTTu9u3XB2j3suWcCWT9vkOkyhBDVRopEIUS1cqgrKHIM/nNai9p1QhMSXYDS3/tHa9VbUqTeCbxfz4sy9I4gKqK0LcfeMZ31YVexlUMffEvK01OwHc+twWBCiNpCikQhRLXQtCJKnE9S4hyJxjG94+gm/CqZ7+xC1QuTLqcXIjhYo1XbTL1jiHIzc3x8h3JdUyvafoA9oz8g5/et1R9LCFGrSJEohKhyTnUjRY4hONRv9I6iu5hmh9Ealugdw6vVke6mF6Rpc5lrz5vY9wzCssFZ7vXVEhsZb80j/c25uIrlZy2EqBpSJAohqoymObE636XYeTMaMmgLgKJA4PB0vWN4tbgAaUm8EG3b5+kdQZST4mzA4ZdCKrVt7rJt7Ln/Ayx7ZT5MIcSFkyJRCFElXFoGxc4bsamTAJnb7p/ieuxHDZLXpLKizdKSeCE6dZeupt5BIXtaH7TSyv++24/nkTzmUzJnLUWTkYGFEBdAikQhxAWzu+ZhcQzFpW3TO4pH8guwYx52VO8YXivSKCe7F6L3xQf0jiDKIXNjS/J+qIKLSS6VY18sYf+Tk7GfzL/w/QkhaiUpEoUQlaZq+RQ77qfU9TRQrHccjxYzcJ/eEbxWmI/PqVmdIqNUEhtId1NPV1oUSMGEFlW6T8vuDPaMfp/cP3ZU6X6FELWDFIlCiEpxqmuwOK7Eqf2idxSvEBqbh9ZbpsOojGCXdNWtrBatZNAkb3BsVndMFv8q36/LYiV93Gwyxn+Dq9RW5fsXQvguKRKFEBWiaeqfg9P8HxoyQXdFhF+dqncEr+Tn0gj1l/sSK6NtB5lDz9Nl7WuA9nNCtR4j57fN7L1/IsXJh6v1OEII3yFFohCi3FQtm2LnbX8OTiMn7RUV0/IgWn0Zor4yEiPl960yunSXe2E9mcNuJu/9jjVyLNuxHJIf/5TMOcvRVOnCLYQ4NykShRDl4lQ3/jk4zRq9o3gtg0EjQKbDqJT6YXon8D6KotFLBq3xaEe+64ThaECNHU9zujg2fRH7n56KPaugxo4rhPA+UiQKIc7L5ppMsfMWNE7oHcXrxfXaj+Yv99hVVN0QaUmsqIQ6KtExMqCUp8o/Gofzy4a6HNuy4wB7H5xE0S65aCWEODMpEoUQZ6VphRQ77sXqGgc49Y7jE/yDrBivknnrKio+ULrHVVTL1ha9I4izUF0GsiZ2RdEU3TI48y2kPD2Vkz+s1S1DdRs5ciTXXHPNacuXL1+Ooijk5+e7///XV2xsLFdeeSU7d+6s+cBCeBApEoUQZ+RS91DkuBqntljvKD4nelCy3hG8ToyfFIkV1b5jtt4RxFkcXd4WZU+o3jHQnC4Of/g9B99bgOqo3RcCk5OTyczMZNGiRdhsNoYMGYLdbtc7lhC6kSJRCHEau2suFudwNA7pHcUnhSdkQzeZu64iIo3S3bSiuvWWkSw9UXFuONZPm+kdo4zsXzey/6kpOHKL9I6im7i4OBISEujcuTOPPPIIhw8fZt8+md9W1F5SJAoh3DTNQanzeUpdzwAyp1Z1Cr06Te8IXiVMkfs4K8Jo1OjWS+4380SZk7uhWE16xzhN8Z6D7H1wUq2fJqOgoIA5c+YA4Ofnp3MaIfTjeZ9SQghdqFoOJc4HcGnr9Y5SK8S2Tacwvi3KiZob2dCbhciQ/RWS2MBJUJBD7xjiXzI3N4PV0XrHOCtHTiHJYz4j6cFriLm8i95xqsSPP/5ISEhImWUu1+kXnerXrw9AcfGpwZ6uvvpqWrZsWf0BhfBQ0pIohMCl7qXIPkwKxBpkMKr4Dz+odwyvEeBUMUuX03Jr3aZQ7wjiX2wlAVgmttM7xnlpDicH3/2Gwx//gHaGYsrbDBgwgG3btpX5mjJlymnrrVy5ks2bNzN9+nSaN2/OJ598okNaITyHtCQKUcv98F0yIVFv0LnHMb2j1DpxF+3n0OSmGBxGvaN4PAVIioC0HL2TeIcOXU7qHUH8y5E53VByzXrHKLeT362hNOM4jZ+9GVN4sN5xKi04OJimTZuWWXbkyJHT1mvUqBERERG0aNGCkydP8p///IcVK1bUVEwhPI60JIpaYfr06UREROgdw+O889Yabh3xLbfd0IwDKTJbeU0LCCnBeIXMPVleiRHSklhePfrIoFOeJDu1PtrCunrHqLCi7QfY+9CHlByoXdP23H///ezatYtvv/1W7yhC6EaKROEVsrKyuO+++0hKSsLf35+EhAQGDRrE6tWr9Y7mlex2F/fc+T0vv7gCTYOCAhN33HQdxcVyk35Nix68X+8IXqNuiBSJ5eEfoNGxixSJnsLpMJH7Xme9Y1Sa/UQeyY9+TO4fO/SOUmOCgoK4++67efHFF9E0+dwRtZMUicIrDB8+nK1btzJjxgz279/P999/T//+/cnJkb5nFZWTU8qVl89kzld7yiw/kOrHPbdcp1Oq2iui/gm0DgV6x/AKCUEyeE15NG5ixyj3b3qMoz91xHAoUO8YF0S1OUgfN5sjn/+KVksGkXrggQfYu3cvX3/9td5RhNCFFInC4+Xn57Ny5UrefPNNBgwYQIMGDejevTvPPPMMV199NQDvvvsu7dq1Izg4mMTEREaPHo3FYjnnfr/77js6d+5MQEAAjRs35qWXXsLpPDWZsKZpjB071t1yWbduXR566KFqf67VLXlfNhf3msyG9Wfu4rhiWRgvPTO4hlOJkGtkOozyiPGXwqc8WreViw6eouB4DPYZjfSOUWVOzPuDA6/PRrU79Y5SLtOnT2fhwoWnLe/fvz+aphEREVHm//+UmJiIw+HgxhtvrJmwQngYKRKFxwsJCSEkJISFCxdis5157j6DwcAHH3zA7t27mTFjBr///jtPPvnkWfe5cuVKbrvtNh5++GH27NnDp59+yvTp03nttdcAmD9/PhMmTODTTz8lJSWFhQsX0q6d549Kdy5LFqfR/+JpHDlccs71pn6cyLxZvjH0ubeI63AANdqudwyPF2WqHS0YF6pj1+N6RxCApiqcnNQNxeVbp1r5q3aR8r9puIqtekcRQlQjRZPO1sILzJ8/n7vvvpvS0lI6d+5Mv379uOmmm2jfvv0Z1//mm2+49957yc7OBk5dTXzkkUfIz88HYODAgVx66aU888wz7m2+/PJLnnzySY4dO8a7777Lp59+yq5duzCbvWc0urP5aNI6nnt6OeUdzTwgQGPOD4vp0l2maKgpB3/ogmNy0/OvWIsd9zMz4rcgvWN4vCVrv6dFaykU9XZkRVus77TRO0a1CWxSh2av3IE5KlTvKEKIaiBFovAaVquVlStXsm7dOn755Rc2bNjAlClTGDlyJEuWLGHcuHHs27ePwsJCnE4nVquV4uJigoKCTisSY2NjsVgsGI1/Tz3gcrnc2+Tk5NCnTx80TWPw4MFceeWVDB06FJPJ+2aNefqJn/ho0s4Kbxcb6+KXlV8TX0fmW6sJpUXBHLntSp9rdahKxWYjVy0JOf+KtVhIqMbeI5P1jlHrlRSEcuTeyzEUe9/fjIrwS4ik2eujCKgbo3cUIUQVk7MR4TUCAgK47LLLeP7551mzZg0jR47kxRdfJCMjg6uuuor27dszf/58Nm/ezIcffgiA3X7mLnwWi4WXXnqpzOS6O3fuJCUlhYCAABITE0lOTuajjz4iMDCQ0aNH07dvXxwOR00+5QuiaRp33DavUgUiQFaWkduuvwabTebwqwmBocUYLpe57c4l0OnCoMh1zXNp1rxU7wgCOPZ5N58vEAHsx/NIfuxTilOO6h1FCFHFpEgUXqt169YUFxezefNmVFVl/Pjx9OzZk+bNm3Ps2Lknhu/cuTPJyck0bdr0tC+D4dTbIjAwkKFDh/LBBx+wfPly1q5dy86dlSu4aprT6WL4sC+Y//WBC9rPnl0BPHTnNVUTSpxX5BUyHca5GDSoG653Cs/Wpn2+3hFqvRM7GsOyWL1j1BhnvoX9T35G4ZZUvaMIIaqQ71/mEl4vJyeHG264gVGjRtG+fXtCQ0PZtGkTb731FsOGDaNp06Y4HA4mTpzI0KFDWb16NZ988sk59/nCCy9w1VVXkZSUxPXXX4/BYGD79u3s2rWLV199lenTp+NyuejRowdBQUF8+eWXBAYG0qBBgxp61pVXWmrnqsHT2Lghr0r29/MP0bz7+iU89uzvVbI/cXZRDTPJaVWEslfu8TmbxHCNI/mK3jE8Vpdu575AJqqX3epPwfvta90VeLXUTuqL02n4+A1E9e+gdxwhRBWobZ9jwguFhITQo0cPJkyYQN++fWnbti3PP/88d999N5MmTaJDhw68++67vPnmm7Rt25ZZs2Yxbty4c+5z0KBB/PjjjyxevJhu3brRs2dPJkyY4C4CIyIimDx5Mn369KF9+/YsWbKEH374gejo6Jp4ypWWk1NEvz4fV1mB+Jf332nCL9+3rdJ9ijMLvu7CWn99Xb0w6W56Lr37yu+Pno7M64Ihy1/vGLrQHC7S35zLyYWr9Y4ihKgCMnCNED4iPf0EQ6/4gkMHq2f+qpAQjW8X/0DLNjJqYnVyOkwcGHU1hgLvH1W3Osy3BjNpvXSCOZPoGJVtaVP0jlFr5abXJefhi1CQlu6EG/tRb5TMuSuEN5OWRCF8wJYtqVzWf3q1FYgAFovCyBuvJC83sNqOIcBkduJ33WG9Y3isuACZK/Fsmrc89xyoovq4nEayP+gsBeKfjs/7g4x356O55P0qhLeSIlEIL7d40WauueprTp6s/k4BR4+YuOM/w3G55ESoOsX2T0aTUTzPKMokJ51n065jjt4Raq0jiztgSAvWO4ZHyVm8ibSXv0S1ec+o4EKIv0mRKISX0jSNObOXMfLWReTn1VzRtnlDEE8+OLTGjlcbBUcWolyapXcMjxRhkCLxbLr1lGkI9FCUFYltamO9Y3ikgvV72f/MVJxFMjWLEN5GikQhvJCmaUybupiH71+DxVLzb+N5sxKY8mGfGj9ubRIxJEXvCB4pVJMi8UwURaNHnzS9Y9Q6mgbHP+6OwSHzyZ5N8Z6DpDwrhaIQ3kaKRCG8jKqqTPzgB54as5nSUv1OTF5/sTUrfm+u2/F9XXTjI6hNLXrH8DhBco/TGdWp6yIySk7Ca9qxta1QNkXoHcPjlaQcJeWZKVIoCuFFpEgUwou4XC7efGMBLz2/C5tN37evw6Ew+o6+pKd59rQg3kpRIHh4ut4xPI5J1YgJlkLx31q2LtY7Qq1TWhRMyaTWesfwGiWpx/4sFGWAJSG8gRSJQngJh8PBC8/P4a3X9+NweMZbtyDfwMgbrqa42E/vKD4prlsKWkj1jVjrrZIi9U7gedp3lntYa9rRmV1RLDIdS0WUpB5j/9NTpVAUwgt4xpmmEOKcbDY7Tz4xiw/fP4TL5Vlv2wNpZu6++Tq9Y/gks58D0zVH9I7hceqHyciv/9a9l0ybUpNO7mkIixL0juGVStOkUBTCG3jW2aYQ4jSlpVYefmgGn3+Wiap65tQTK5eH8dLTV+gdwyfFXpqMhhRF/5QQLK/HP5lMGl17StfkmuKwmcl/v4PeMbyaFIpCeD4pEoXwYBZLMY8/MoPZX2SjaZ5ZIP5l6if1mftFF71j+JyQ6HzoK/Pf/VN8gBSJ/5TU0EFgoHRLrilHvu2CITNA7xherzTtGCnPTMVVbNU7ihDiDKRIFMJDFRQU8dSTM/nqy1yPLxABNE3huTGd2byhgd5RfE7EVTIdxj9Fm2Xgmn9q1aZI7wi1Rt7heJyzk/SO4TNKUo+R8tznuEptekcRQvyLFIlCeKCcnDyee3Y6X83M89gupmdisyrcffNAMo+F6x3Fp8Q0P4yaJEPH/yXCKEXiP3XsclLvCLWC6jKQ9UFXFC+4aOdNivcdJvWFGahWu95RhBD/IEWiEB7mxIlsxr4wk6++KPK4QWrKIyvLyMjrr8Fmk8mlq4pi0Ai8/oDeMTxGGFIk/lOP3gf1jlArHFnaDkNyiN4xfJJlZzqpL3+Bapdu00J4Cu87AxXChx09eoJXXp7J7FkWnB4yzUVl7Nntz4N3Xqt3DJ8S3zMFNcildwyPEOyS1+EvAQEa7TvLyKbVzZITjm1yM71j+LSiLakceG0WmlPe30J4Au89CxXCx2RkHOW1V2cwb3YJdh9ohfvlhyjGv36J3jF8hl+ADfPQo3rH8Ah+Lo1Qfxm8BqBxUxtGo7wW1e34p91RfOBz2dMVrN/HgTfmoLmkt4AQepMiUQgPkJKSwZtvzGTB13aspb4zOfMH7zThp4Vt9Y7hM6IvS9Y7gsdIipTCCKBNuwK9I/i8zI3NYV2U3jFqjfxVu8h49xu9YwhR60mRKITO9uxJ5d3xX/L9t05Kin2nQARQXQqPj+7F3l119I7iE8LicqFnrt4xPEKijI0EQKdux/WO4NOsxYEUTWyjd4xaJ3fpVo5OX6R3DCFqNSkShdDR/v3pfDhpNr/+pFJUaNY7TrUoLla44z9XkJsbpHcUnxB2dareETxCQrC0JAL0vChd7wg+7eisrhjy/fSOUSsdn7OcrF826B1DiFpLikQhdJKRcZRPPp7DkkVOcrJ9+yTk6BETd9x4HS6XDB1/oWJaZ6DWlekw4gPlnqXQMJVmLWT6i+qStT8R7ce6eseo1Q5N/I6CjdLNXgg9SJEohA6OHj3Bxx99xW+/lpB5LFDvODViy8Ygnnjgar1jeD2DQSNguEx5EOMnLYnNWlj1juCznHYzee930juGUFUOvP4VJSkyaJcQNU2KRCFq2MmTOXzy8WyWLM7jYEaw3nFq1NdfxfPZxIv0juH14vsko/nX7mHiI03Skti2fZ7eEXzW4R86YThcOy7geTq11E7KCzOwnZDfdyFqkhSJQtSg3Nx8Pvl4NksXZ5KSHKZ3HF2MG9uKP5Y21zuGV/MPsmK8MlPvGLoKp3YXyQBdehzTO4JPKjgWi/PLBnrHEP/gzCsi9fnpOIukq70QNUWKRCFqSEFBEZ99OpelSzLYsztS7zi6cToV7r+jL+lp0XpH8WrRg/frHUFXIaq0JPa6+IDeEXyOqiqcmNgVxSWnR57GeugkaS9/gWp36h1FiFpBPgWFqAHFxSVMnfI1S35LZue2GGr7+W1BgYGRN1xNcZFvD9hTncLrZKF1ztc7hm78nSr+ptp7X2JsrIs6dWWOxKp29I+2GHbXzl4e3sCyM52M8V+jabX3vS9ETZEiUYhqZrPZ+fzz+Sxdup0d2+JwykVQAA6kmbn71uv0juHVQq9J0zuCbhQgMbL2nig2b1WidwSfU5wXRunHLfSOIc4j748dHP38V71jCOHzpEgUoho5nU5mzfqeZUs3sHdXHaxyO0UZK5eHMfapK/SO4bXi2h1Ai7PpHUM3ieF6J9BPu445ekfwOcemdsNgNeodQ5TDia9XcPKHtXrHEMKnSZEoRDXRNI1vF/zGol9XkZ5Wl+wsvRN5ps8/rc/cmV31juGVDEYV/1o8HUbdkNrbktit5xG9I/iU41uboqyI0TuGqIDDH/9I/rq9escQwmdJkShENdA0jUW/rmThwt/IOhFDWqpMIn82mqbw3BOd2LRORhOsjLiL9qPW0ukg4oNq5/M2GDR6XiSD1lQVW0kAhR+00zuGqChVJX3cbIqTD+udRAifJEWiENVg9eotzJnzE8WWMDZvNOkdx+PZrAp33zqQzGO1uP9gJQWEFmO84rjeMXQR61c7WxLr1nMRFm7VO4bPODq3C4YcGUTLG6k2B6kvzpQ5FIWoBlIkClHFtm3by8wZ31JaYmTtqgBkELbyyc4ycvv112CzyT1BFRVVS6fDiKqlLagtW1v0juAzcg7UQ/22vt4xxAVw5ls48OosVLtD7yhC+BQpEoWoQikpGUyd8jWFhVY2bYigVAaqqZC9u/154I5r9Y7hdSITT6C1K9Q7Ro0LV2pnkdihi9zgXBVcDiO573XWO4aoAiUpRzk06Xu9YwjhU6RIFKKKHDlynMmfzSUrK5e0lHiyTtbOE9gL9etPUbzz2iV6x/A6IdfWvukwQlSX3hF00a2n3INVFQ7/0hElI0jvGKKK5CzeRNYvG/SOIYTPkCJRiCqQn1/I5M/mkp5+lKKC+iTvrZ0nr1Vl4vgm/LSwrd4xvEpchzS0qNrV3SrQpWJQald/bpNJo2vPdL1jeL3CE9E4pjXWO4aoYoc/+oHi/TLyrxBVQYpEIS6Qw+Hgiy++Y/fuFAIDGrBqhVPvSF5PdSk8ProXu3fU1TuK1zCaXZivq13TYRg0qBehd4qa1bCRA39/uQh1ITRV4cRH3VBccgrkazSHkwOvzsJZWKx3FCG8ngy7KMQF0DSN779byso/NhETnciCrx0yUE0VKS5WGHXTYH5ZNYeoqBK943iFuH7JHJ3WuFad/CaGaxzOq9gUMzbHEuyupWWWGZRYgv0fO+s2DtdO7M7fULU8DEo0/qbBmIwt3Y/bnSuwO1cA4Gfqh5/pYvdjLvUQVsd3BPmNRlEubGCmVm1r372nVe3YmlYoW2UkZV9lP5nPgTfm0OzVO1AMteezUIiqJu8eIS7A2rXb+O67pURGRbH4VwWrjEpfpY4dNXHHDdfhcsk8k+URFG5BGVi7BjWpF1q5qzIGJZ5g/2fdX4F+/z3rui71IFbHHMzGrgT5PYjJ0JpSx5e41ON/Pp6JzbmEAPMIAsw3YXMudj+maS6sjoUEmK+54AIRoGOXkxe8j9qstDCE4g9b6R1DVLOiLakcm7lE7xhCeDUpEoWopLS0Q3w163sMBgObNwaTnSUD1VSHLZuCeOL+q/WO4TUir6xd02EkBFW26d6AQQn9x1fwWde0O1djNDTDz9QXoyEOf/PlGJS6OFxrAVC1LAxKAiZjE0zGphiUBFTtVLFud63EaGiE0ZBYyZxl9eyTUSX7qa2OTuuGoVg6UdUGx+cuJ3/dXr1jCOG1pEgUohLy8gqYPm0B2Vl5nDwRx/59ch9idfp6djyfTbxI7xheIbrRMdSWRXrHqDGxAZW7OKNq2Visr2OxvUWpfQ6qln/WdV3qIUyGpmWWmQzNcKmHAP4sCrNRtXxULQ9Vy8agxKOqOThcm/A3XV6pjP8WGKjRtqMMylFZJ3Y1gqVxescQNUXTyHh7HtZj2XonEcIrSZEoRAXZ7Xa+mLmQPXtSMZsTWb2ido0oqZdxY1uxfElzvWN4heDrDugdocZEmypeJBoNiQSYbyDQ7w4CTNeganmU2D5F02xnXF/DgqKElFmmKCFomuXP/cXhbxpEiX0qJfbP8TcNxmiIw+r8Fn/TFTjV/RTb3qPY9gFOtfIjkzZpZkNusaocu9Wfwvc66B1D1DBXsZUDr8xCtdr1jiKE15E/N0JUwF8D1axatYWYmEQW/SwD1dQUp1PhgVF9SU+N0TuKx4vvnIYaVjsuXoQbKl4kmowtMBvbYTTUwWRsTpDfSDRKcbh2VDqHn6kHIf6PE+L/OH6mHjhcm1Hwx2hIwupYQKD5VvxNQ7DaZ6Nplet50KZdfqXz1XZH53dGOemvdwyhg9L04xx8/1u9YwjhdaRjvhAVsGbNVr7/7neio6NZ9LOC1SoVYk0qKDBw+w1D+WXFLIJD5crw2Zj8HJivPYJrRqMq2d/0A2uZlPIHI5K68nirgWdc554Ns9iSd/ok731imvB+lxsA+CJ9PTMz1gNwe6Me3Nqwh3u9XfnHeGPvIqb3uB1TBZrLQrULvxdYUQIxKDFoWs6ZH+fvVsO/aNrprYt/UbVibM6lBPn9F1U9jEGJwWCIwUAMOFRULRujklDhnJ27Ha/wNgJyD9bBOTcRGf6q9spdto3gVonEXd1b7yhCeA0pEoUop78GqjEaDezaHkR2ltyHqIf0A2buvvU6vvpujt5RPFrcgGSOzWyIol3YqfHugkwWHNlGs5DYc673dsfrcGh/z99X4Cjl5jWfMzChBQApRSf5JHUl73W+Hg14dMs39IxuRNPQOJyqyut7fuW5NldUqEAECHJdeJGoaTZULRdFCT3j40ZDEk41DT/+vi/WqaZiNCSdcX2b40f8jBdhUMJxcQT4+3XRcAGVy9zzosp3Va2tXE4jOe93QZESsdY78tnPBDWtR0jrBnpHEcIrSHdTIcohNzef6dMWkJOdh9OZwPZtUiDqaeXyMF588gq9Y3i04KgClP4XNmBDidPO8zu+57k2VxBqDjjnuuF+gcT4h7i/1mdnEGAwMzD+1FyCGcU5NAuNo1t0Q7pHN6RpaCwZxbkAfJGxns6RibQJr1PhjCZVIzakYkWX1fEzTvUAqpqHSz1IqeNLFAyYjKfuWSu1z8Pm+NW9vp+pDy51P3bnSlzqSWyOJajaUczGXqft2+lKQdWyMRt7AmA01EfVsnC6krE7N6BgwKCcu+A+k/BwlcbNatf0JlXh6G/tUVLPPnKtqD00p4sDr3+Fs7BY7yhCeAUpEoU4j38OVFOvXkMW/3LmwS1EzZr2WX1mz+imdwyPFnFVygVt/+bexfSJbUKP6IYV3va7ozu4vE4rAk1+ADQNieVQcS7HSwvILC3gUHEuTUJiOFKSxw9Hd3Bfs76VzpkUUbH1Na0Aq30OxfbxlNq/QiGIIL/7MPzZfVTT8lG1v0eINRoaEGC+CbtrAyX2D3Cquwg034rRkPCv/TqwOr8nwHwtinLqz6tBCcffdDVWxzfYncsIMN+Aopgr/BybtSit8Da1XVF2JNYpTfSOITyII7uQgx8s1DuGEF5BupsKcQ6apvHtgt9YtWoLTZok8suPTkrlXM0jaJrC8092pFmLk3TteVDvOB4puulhcpsUY0ireEvKosw97Cs8wcyet1d42135x0izZPF8m79bexuFxDC6eT9Gb5oLwP3N+59atnE2DzUfwNrsdD5LW4VJMTCm5UA6R525K+eZ1AvT2FyBfIF+I875eJD/PactMxvbYTa2O+d2imImxP/x05b7mbrhZ7qwCxptO+Rd0Pa1jabB8Y+7YXAY9Y4iPEz+ql3kLN1K9KWd9I4ihEeTlkQhzmH16i38+OMyEhJi2LfHzMEM1/k3EjXGZlW4+9aBHDsSrncUj6QoEHRdxe9jO15ayPh9S3i1/VD8jRW/lvjd0R00DYmlbUTdMsuvT+zEgovvYcHF93B9Yid+PLqTIJMf7SLq8eruX3in43U81uJSnt3xPXa1/F266wT7/gBSXXoc1TuCV8lc3xJlY6TeMYSHOvzR99iz8vWOIYRHkyJRiLNIScngq1k/YDKZ0NRwVv4ho2l6ouwsIyNvvAabTVoMziS+ewpqcMXuod1XeJxcewm3rp1Gj8Vv0mPxm2zJO8ycQ5vosfhNXOcYUbTUaWfx8b0Mq9/+nMfIt5cwOW0VT7S6jF0Fx0gKiiIpOIqu0Q1wqi4O/Xm/YnnEBfh+kdj74toz9+WFslqCsExqrXcM4cFcxVYyxn+DJnNYCXFW0t1UiDP4a6Ca3Nx8mjZtzKyZVlzSiOix9u725/47rmXKV9/oHcXjmP3tmIcdxfVV+Uf06xbdgDm97yyz7OVdP9EgOJrbG/XEqJz9+uKSE/twqE6uqNP2nMcYv28pIxp0Iz4gjD0FmTj/MTKqS1NxVeDkLdp84SOcerK4eBdxCUXnX1EAcPSLbhgKK37fp6hdiralcXLhauKvvej8KwtRC0lLohD/YrfbmTljIcnJ6TRv3pAVyx3kZPv2SagvWPRTFO+8cqneMTxSzMBkNMpfdAWb/GkaGlvmK8BoJsIcSNPQUyNzvrDzBybtX37att8d2UG/uOZE+AWedf/rstM5VJLLjUldAGgdXoeDxbmszkpjweFtGBQDDYKjyp030ujb788WrS58NMZxr+QR4Z9e5qtbuyNnXX/IZZmnrR/hn86Nw/6eq3HiuwU0rX+QpvUPMnFCQZntN22w0q/nUZzOmm2pydrbAO2Xis9BKWqno9MWUXrwhN4xhPBI0pIoxL/8+utK1qzeSpMmSSQUOehqsLFN3ipeYeKExrRse5Krrt2pdxSPEhqTx4mLcmBVTJXt83hpIYZ/zT2XUZzDtvwjTOryn7NuZ3U5eGvvb4zrMAyDcmr7+IAwnmh1GS/v+hmzwchL7YYQYCx/S1BYJecd9BbtO+VUyX5atTaz8B8FlMl09rkDv5wbh93+d4GXm6tyUdejDBt+ahCkXTvtvP5yHnO+jQcN/nPtCS65LJA2bf1wOjUefSCH9z+KOecxqprDZibv/Q5y9VuUm2Z3kv7WPFq9PxrFJLcsCPFPcuYrxD/s2ZPK998tJTomgnB/fzrsPUGPYJUeA/353wZ/ThTKhMyeTHUpPH5/Txo1yaFN+2N6x/EoYUPTKLyAIvGz7rec83uAhsHRbBr09Dn3E2A0s+Di00cPvaZ+B66p36FS2YJ8vC941x5nb/GrCKNJIT6hfH/2I6PKnjDPn1dAUJDCNX8WiSnJdtq086PfgFMtxm3a+ZGS7KBNWz8+eLeA3hcF0Lmrf5XkLq8j33XGcOzsLdhCnElp2jGOfbmUeiMv1zuKEB5FLrgJ8afCQguzv/qR4uJSEhJiaJueT4DjVAtFU4eNad0sXNPGt09GfUFJscKomwaTmxukdxSPEtviIGqib87f4ufSCPXRwWsMBo0efapm0JoDqQ5aNjxEhxaHufv2kxw+VP4Bjb6cXsR1N4QQHHzqtKF1Gz9SUxwcPuTk0EEHqSkOWrU2k57mYNaMIv73Us2OLJp/NA7nrPLfdyvEPx2f9weWPTKVkhD/JEWiEJyaD/Gbb35l7940mjZtQJ1cK/Wyy55QBzpVHo6x8O4lNkL9ffOE1FccO2pi5PXX4XJJy+9fFING4PUVnw7DWzSM9M33ZL1EF6FhtgveT9du/nw0JZZvfkjg3YnRHMxwcsWlxygqOn9X3c0bbezZ7eC2UaHuZS1a+fHCy5Fce+VxrhtyghdfiaRFKz8euT+bl16P4vffSunV6QgXdz/K6pXVe3FCdRnI+qAriibvd1FJqkrG2/NwWWUUcyH+IkWiEMCaNVv5felaEhPrEIxC2wP5Z123k8vKrIuL6dvEt++D8nZbNwfx+OhhesfwKPE996MFVGw6DG9RL0zvBNWjVeuqGdX0ssFBXDM8mLbt/Lj08iDmfRdPYb7Kt9+cf1CcL6YV0bqtmS7dynYfHXVPGJt21WfTrvqMuieMr74oIiTUQPeeATx4bzZfzovntbeiuPP/srDZqq+IP7qsHcre0POvKMQ52DJzOfLZT3rHEMJjSJEoar3MzJN8Pe9nTEYTkZFhtDuQj7/z3AVgqMPFi/WLeLGvA7PRN1swfMH8OXF8NlGGN/+LX6AN41WZeseoFnVCfPOiTYfOWdWy34gII02aneoeei7FxSoLvrbwfyPPXYTlZLt489V83poQzaYNVpo2M9GkmZm+/QNxODRSU859nMqy5IZj/bRZtexb1D7ZP2+gYMM+vWMI4RGkSBS1mt1uZ/ZXP3L06AkaNqpHvawSEnKt5drWAPRXSvjqkhLa1fXNE1RfMG5sK5YtbqF3DI8RMyhZ7wjVIt5H70ns3utQtezXYlFJP+AkPuHcIzounF+MzQb/uTnknOs980QOox8Ko159Ey4VHP+oCZ1OcLmq5+eTObkbik1GpRRVJ2PCApwFFz7tjBDeTopEUastXrSadeu206RJEoEOjTbp+RXeR4zdybstLDzU0wkVmItO1AynU+HBuy4mPbXqpn/wZmHxOWg98vSOUeVi/HzvvWf20+jcI6NK9vW/p3JYtaKUgxkO1q+1cusNJzAa4fr/nCr+/jsqi5f+l3vadl9OL2LI1UFERZ+9EFu2pJS0FCd333eqz2/nLv6kJDv47dcSpk8pxGiEZs2rfnL7zE3NUVZHV/l+Re3mzCvi4MSFescQQndSJIpaa8/23XwxaQoGp42goEDaH8jDXMmr3SZV41r/YmZeZiUpyvdOVr1dQYGB224YSnGRn95RPELY1al6R6hykSbfG3m4YSMHfn5V00vh2FEXd92WRbd2R7jjlpNERRtZsqIuMbGnir8jh50cP172NUxJtrN2te2cXU1LS1WeeCSHCR9GYzCcGjimXn0Tb02I5v57snnnzXw+nhJLYGDVnm7YigMpmtimSvcpxF/yV+0ib6XMtytqN0XTNDmjFbWOtaSULyd+yu8//kaexUqn2EYMN8RWyb7tRoUvcoP5cpt0gfI0ffoWMeeH2XrH0J3qMpA6eiiGzAC9o1SZbD8TN/wWrHeMKjVseBaTPv9W7xge6cCUi1C/r6d3DOHDzNFhtPnsUYzBvvM5KURFSEuiqJXWLP6d9L376TeoPx3atGCgVnVDI/q5NO4Mt/DRQCtRQXINxpOsXhHKC09cqXcM3RmMKgHDM/SOUaVCXL53X3Cnrif0juCRslMSpUAU1c6RU8jRGYv1jiGEbqRIFLXOgb37Wbf0D6JiYzH7+dM3uC7hRv/zb1hBrRw2vuhlYXAL3zt59WbTJ9fjq+nd9I6hu/g++9H8fKeLpr9Lxd/kWxdluveRyb3/zekwkfteJ71jiFoi68d1FCcf1juGELqQIlHUKiUWC0u+/RFbqZXI2Gj8i53EHqq+UcyCnCpPJhTxRn87gWbfOoH1Vpqm8MKTHdmwtlGNHG/C2/lE+Kfz9OM551wvP9/FmIeyadHgEHGh6XRpc5jFv5S4H58320KbJodoEH+QZ58ou6+DGQ66tDlMYWH5L0j4B5diuPJ4xZ6MB1OAxEjfeY8FBWm0aXdU7xge58iPnTAcDtQ7hqgtVI2DH3yL5oM9FYQ4HykSRa2haRorf17Cwf2p1GvUAIB6+wswVPN5pQL00Er5qn8JPZLkD40nsNkU7rn1Uo4dCa/W42zZZGPa5CLatDv3gDl2u8a1Vx7n0EEnM2bHsXFnfd7/OIY69U7d15qT7eKhe7N55Y0ovv0pgXmzLfz6098F5JiHcnjxtSjCwir2kR49aH/Fn5QHSwr3nSKxSTMrBvkLXUZBZgyOmQ31jiFqmdK0TE4uXK13DCFqnM/+CVq+fDmKopCfn3/O9Ro2bMh7771XI5nOZ/r06URERJxznX379tGzZ08CAgLo2LEjGRkZKIrCtm3baiSjN0vdtZeNy1cRkxCPyWwm/GQpoXn2Gjt+hN3J642LeLqPA6PiOyez3ion28DtN1yDrZrmWLNYVO6+/SQffBxDROS5P2q/nF5EXq7KrG/i6dk7gAYNzVzUN5B27U91g85IdxIWbuC6G0Lo3NWfi/sFsn/fqd/db+ZaMJkVrr6m4oO2RNQ7idapoOJPzkPVPfdUfl6lbft8vSN4FFVVOPlhNxSXz562CA927Isl2E/m6x1DiBpVo5+2I0eO5JprrjlteXkLugtRngLMG7z44osEBweTnJzM0qVLSUxMJDMzk7Zt2wI181p6I0thEUu//Qmn00l4VCQGp0rdlMIaz2HQYJCphC8HltIyTloV9bZvjz+jR15XLfse83AOl18RRP9Lz9817pcfS+je058xD+XQLPEgvTodYfyb+e4JyJs0NVFaorJ9m428XBdbNtlo086P/DwXr43N4+33Kj9XXMg1vjMdRnyQ77ynOnXzna7AVeHYijYoO6pugDEhKkSBHyev0TuFEDVKLsl5mbS0NC666CIaNGhAdHQ0RqORhIQETCaT3tE8lqZprPhxEYcPpFO3YRIA8RkW/Gz6nVAm2B180MbCPd2cumUQpyz+OZK3Xrm0Svc5f56FHVttvPhqZLnWz0h38t2CElyqxtffJfDEsxFMeq+At8flAxARaeSjqbHcNyqLS/oc46ZbQ7j08iD+93Qud98XxsEMBxd3P0qvTkf4bkHF7rGNa5eOGmur6FP0SDF+vtNC3/PiA3pH8Bgl+aGUfNJS7xiilipu1IB7d0Ux8tUdLF6UpnccIWqMxxaJq1at4uKLLyYwMJDExEQeeughiov/Pvn54osv6Nq1K6GhoSQkJHDzzTdz8uTJM+5r+fLl3HHHHRQUFKAoCoqiMHbsWPfjJSUljBo1itDQUJKSkvjss8/Ome3XX3/loosuIiIigujoaK666irS0v7+4PirC+iCBQsYMGAAQUFBdOjQgbVr15bZz/Tp00lKSiIoKIhrr72WnJxzD2yhKAqbN2/m5Zdfdj+Hf3Y3zcjIYMCAAQBERkaiKAojR44EoH///jz00EM8+eSTREVFkZCQUOY1AMjPz+euu+4iNjaWsLAwLrnkErZv3+5+fPv27QwYMIDQ0FDCwsLo0qULmzZtAuDgwYMMHTqUyMhIgoODadOmDT///PM5n09NObAnmS2r1hFb51Qx7V/sIOZw9Q1WU15mVWNEUDFTLyulTpjvnNx6ow/fbcyPC9pXyb6OHHby9OM5fDYjjoCA8n3EqqpGbJyB9z+KoWNnf667IYTHn4pg2uQi9zpDhwWzZkt9tu5N5JnnI1m1opTdO+2MvCuUO/8vi3HvRDFzThwP/jeLrJPlH7XUaHLhf92hCj9PTxRl8o2WxIhIlUaNz/33oDY59nl3DCUy76yoWYbYSGYZG3PV9Hz2HikF4MnHl2CzycVdUTt4ZJGYlpbG4MGDGT58ODt27GDu3LmsWrWKBx54wL2Ow+HglVdeYfv27SxcuJCMjAx3QfRvvXv35r333iMsLIzMzEwyMzMZM2aM+/Hx48fTtWtXtm7dyujRo7nvvvtITk4+a77i4mIee+wxNm3axNKlSzEYDFx77bWoatkTlOeee44xY8awbds2mjdvzogRI3A6T324rF+/njvvvJMHHniAbdu2MWDAAF599dVzvi6ZmZm0adOGxx9//LTnAJCYmMj8+fMBSE5OJjMzk/fff9/9+IwZMwgODmb9+vW89dZbvPzyy/z222/ux2+44QZOnjzJL7/8wubNm+ncuTOXXnopubm5ANxyyy3Ur1+fjRs3snnzZp5++mnMZjMA999/PzabjRUrVrBz507efPNNQkL0v0HIZrWx4qfFOB0OwiIjAKi3v7DaB6upiMZ2O593szC8ne9MR+BtVFXh8Qd6sGt73Qve17YtNrJOqvTrcZTooHSig9JZvcLKpx8WEh2U7u5C+k/xdUw0aWrGaFTcy1q0NHPiuAu7/fT1bTaNMQ/lMOHDGA6kOXE6NS7qG0izFn40aWZm04aKtQzG9U1G9YECK1zx/ucA0LxFqd4RPMbx7U1geYzeMUQtoviZSElsxpAfYcrvZS/WHEjLY+L7G3RKJkTNqvE+ij/++ONpxYPLVfbkeNy4cdxyyy088sgjADRr1owPPviAfv368fHHHxMQEMCoUaPc6zdu3JgPPviAbt26YbFYTtu/n58f4eHhKIpCQkLCaZmuvPJKRo8eDcBTTz3FhAkTWLZsGS1atDjjcxg+fHiZ7z///HNiY2PZs2eP+95AgDFjxjBkyBAAXnrpJdq0aUNqaiotW7bk/fffZ/DgwTz55JMANG/enDVr1vDrr7+e9bX7q1tpSEiI+3lkZ2e7HzcajURFRQEQFxd32j2Y7du358UXXwROvaaTJk1i6dKlXHbZZaxatYoNGzZw8uRJ/P1PDZbxzjvvsHDhQr755hvuueceDh06xBNPPEHLli3d+/jLoUOHGD58OO3atQNO/Uw8wbY16zmwbz/1GjUEIOJEzQ5WU14BTpUHoiz0vSSAF9b4UWBVzr+RqFIlxQqjbhrMLyvnEh1T+ZbmfpcEsmZL2Ym+7787i2YtzDwyJqJMIfiXnr38+XpuMaqqYTCcejw1xUFCHSN+fqev//a4fC69PJCOnfzZvs2G8x8Xth0OzliInktgaDHGQSfQfqpToe08TYjqGxda2nXM1TuCR7CX+lP4XnvPvJotfJKjYX1eWKux7rcz90wDeOfNtdw0oi31E+UeWeHbavyzd8CAAWzbtq3M15QpU8qss337dqZPn05ISIj7a9CgQaiqSnp6OgCbN29m6NChJCUlERoaSr9+/YBTxUpFtW//dzezvwrJs3VdBUhJSWHEiBE0btyYsLAwGjZseMZj/3O/deqcOvn6a7979+6lR48eZdbv1atXhbNXxD/z/JXprzzbt2/HYrEQHR1d5nVPT093d6V97LHHuOuuuxg4cCBvvPFGmS62Dz30EK+++ip9+vThxRdfZMeOHdX6XMoj92Q2qxf9TlBoCH7+froNVlMR7V1WvriomAFNfaNFxNtkHjNxx43X4nJVvkgPDTXQuo1fma+gYANRUUZatzk1FcZ/R2Xx0v/+LgRG3RNGfq6Lpx7LJXW/g0U/l/DuW/ncde/pJyH79tr59msLz7546n7H5i3MGAwwc1oRi34uISXZQeeu/hXOHXWF90+HEehSfWLk4C49ZH5EgCNfd8GQc+7pY4SoCobIUH4KacrlM4pYt99yznVLShw8+9TSGkomhH5qvEgMDg6madOmZb7q1St71d1isfDf//63TCG5fft2UlJSaNKkCcXFxQwaNIiwsDBmzZrFxo0b+fbbbwGw2yveSvRXl8m/KIpyWtfRfxo6dCi5ublMnjyZ9evXs379+jMe+5/7VZRTJ53n2m91O9fztFgs1KlT57QCPjk5mSeeeAKAsWPHsnv3boYMGcLvv/9O69at3a/7XXfdxYEDB/i///s/du7cSdeuXZk4cWLNPsF/0DSNlb/8Rs7xk8TWOdXqGp9uwWz3/OIr1OHi+XpFvNzPjr/J+094vc3WzUE8ft+waj3GkcNOjh//u9WrfqKJ+T8msHWzjT5dj/LUYznc+0A4jz5Rdh5HTdN4ZHQ2r70VTXDwqY/vwEADH02O5a3X83jw3mzefi+auvUq3kkkMuk4WhvPvohyPgYN6kXoneLCKIpGLxm0hpz0uqjfJOodQ/g4FVjrULh2iYl3fsoq93YLv01m2e8Z1ZZLCE/gkUNidu7cmT179tC0adMzPr5z505ycnJ44403SEw89UfkrwFUzsbPz++0bq2VkZOTQ3JyMpMnT+biiy8GTg2yU1GtWrVyF5d/Wbdu3QXn8/M7ddW1os+1c+fOHD9+HJPJ5G4ZPZPmzZvTvHlzHn30UUaMGMG0adO49tprgVP3RN57773ce++9PPPMM0yePJkHH3yw0s/lQqTt3seOdZuIq1cHg8GAv8VB7BH9B6spLwW4mFJmDXDwyp4gth+V7qc1af7cOFq1vZj/PrSySvb30291zvk9QPeeASxZee57IhVF4ddlp68zeEgQg4ckXVhIIPjaA5Ts7njB+9FTYoTGoTzvfb/ExavExJ67JcPXuRxGcj7oLN1MRbXKCTDz9t4A1h8ycKpcrJgnH/+NNRtGYTbLoErCN3nkZ/BTTz3FmjVr3IO6pKSk8N1337kHrklKSsLPz4+JEydy4MABvv/+e1555ZVz7rNhw4ZYLBaWLl1KdnY2JSUllcoWGRlJdHQ0n332Gampqfz+++889thjFd7PQw89xK+//so777xDSkoKkyZNOuf9iOXVoEEDFEXhxx9/JCsrC4ulfCcbAwcOpFevXlxzzTUsXryYjIwM1qxZw3PPPcemTZsoLS3lgQceYPny5Rw8eJDVq1ezceNGWrVqBcAjjzzCokWLSE9PZ8uWLSxbtsz9WE2zWW388dNinE4HoRGnWmLq7y/EG3uhRdudjG9WyKO9nIAXPgEv9sZLLVm2+Mz3Jfuq+E6pqJGed89uRdQL9e73SYtW3nMxq7ocWdQBQ1qw3jGEj3KYFL4pDeaGxYF/FoiVk7wvh48nnbuBQghv5pFFYvv27fnjjz/Yv38/F198MZ06deKFF16gbt1TV9BjY2OZPn06X3/9Na1bt+aNN97gnXfeOec+e/fuzb333st//vMfYmNjeeuttyqVzWAwMGfOHDZv3kzbtm159NFHefvttyu8n549ezJ58mTef/99OnTowOLFi/nf//5XqUz/VK9ePV566SWefvpp4uPjy4wIey6KovDzzz/Tt29f7rjjDpo3b85NN93EwYMHiY+Px2g0kpOTw2233Ubz5s258cYbueKKK3jppZeAUy2X999/P61atWLw4ME0b96cjz766IKfT2VsXb2O9H37qZN0qmUl4ngpIfnee+Jr1OBqv2K+uMxKo2jvPgH2Jk6nwgN3XcyBlFi9o9QYo9mF/7WH9Y5xQeKDvPs90qFT9vlX8mGFJ6Owfd5E7xjCB2nAXnMAt20I4cMNJjTtwnscvPH6ak6ekAs7wjcpmqZ5919UIf4h50QW08dPwmGzn+pq6lRpuS7LK+5FLA+7UeGr/GBmbJHuLTWlYWMHP//xFaFhvjHh/PmU5Idy9I7BKC6PvIZ4XisJ5IU/vHewkxnzVnDJoH16x9CFpkHqS4NQtkToHUX4mEJ/ExMPBLIkteo/1+4d3YW3xl9W5fsVQm/eeRYgxBlomsaqX38j90QWMXXiAYg9VOwzBSKAn0tjZKiFTwZaiQn2neflyTIOmLn7luv0jlFjgiKKUC4p/wAOnibai+d7NBg1evSuvYPWHFvTWgpEUaVcRoVfnUEMXxJULQUiwLSp2zh8qKBa9i2EnqRIFD4jdddedqzbTFz9uhgMBkx2F7GHfbMbSAuHjRk9ixnSyntPiL3J6hWhPD/mSr1j1JjIISl6R6i0CIP3vicSE50Eh3pv1/gLUVoUTMmH+tzHLnxTur8/d28P5c3VZpxq9Q1mZbO5eHPc6mrbvxB6kSJR+ARbqZUVPy3G5XISGn5qbrm4DAvGCk4q7k2CnCqPxxXx1gAbQX6++zw9xYwp9fhqWne9Y9SI6MZH0Zp75wWWUM17i8RWbYr0jqCbYzO6oVg8csB14WVK/Ix8kB3CqMUBpOfUzEjHX325i9TU3POvKIQXkSJR+IQtq9aSnpxCnaRTU6KYS51EH63cCLbeRAG6qVa+6ltM74bee3LsDTRN4YWnOrB+TSO9o9SIoOvS9I5QKUHOC5/qSC8dOntvN98LcWJ3I7TF8XrHEF5ONZy6J/n65cF8u7tm79t3OlVef6Xi06EJ4cmkSBReL/vESVYvXkZIeDjmP+eJTEi3YKhFjWvhDhevNCzi2YscGL1xrg8vYbMp/Pf/LuXYkfDzr+zl4rumooU59I5RYUYN4kO984JJt96H9I5Q4xw2Pwrfa693DOHlMgP8eHhvKC/84UepQ595Uud/vYddO0/qcmwhqoMUicKraZrGyp9/Iy8rm9g/B6vxtziIPF6qc7KaZ9DgMmMJXw0spXW8d54ke4OcbAO3XX8NpaW+3TXO5OfANOyI3jEqJTFC7wQV5+en0bnbQb1j1LgjCzqjnAjQO4bwUjazgWkFIdy8KJBdx/U9pdU0ePXlFbpmEKIqSZEovNr+nXvYuX4z8fXqoiinrh7WOVCEPtcRPUOc3cH7rS2M7u7UO4rPSt7rz/13XKt3jGoXe0kyGt7XMl0vzPsyN2psx2yuXRd38g4l4JydpHcM4YU0BbYYA7hpdQgzt3nOlFA//5jKxg3H9I4hRJWQIlF4rb8Gq1FVlZA/B6sJKrATnl075rM7F5OqcUNgMdMuK6VeuPedMHuD336J5K2XB+odo1qFRBeg9M/RO0aF1Qn2vt/51u0K9Y5Qo1xOI1kTu6DU6kt6ojJyAsw8cyCUx3/3J7/U835/Xhn7h94RhKgSUiQKr7V97UYO7k+lTlJ997I6abV3dMAzaWi3M7WLhRvbe+9gHp7swwmN+GG+b99PFX7Vfr0jVFhcgPcViZ26Htc7Qo06urQdhuQQvWMIL+IwKXxTGswNiwNZf8hzT1+XLzvIij9qX9dx4Xs8910mxDlYCotY9/sfBIWEuAerCcm1EZJfO+cYOxd/l8p9kRY+uNRGRJD3nTx7MlVVGPNgD3Ztr6t3lGoT0+wwWkPvGik42uR93TZ7XpShd4QaY8mJwDa5qd4xhJfQgL3mAG7bEMKHG0xomue1Hv7byy/KvYnC+0mRKLzS1lXrOHHkGLF1E9zL4tMtOibyfO2cVr7oXcylzaRVsSqVFCuMuukKcrKD9Y5SLRQFAoen6x2jQiKM3lUkBgdrtGqbqXeMGqFpkPlJdxS759xHJjxXob+J14+GMnqJP8eLvOeUdcP6o/zyc6reMYS4IN7zjhPiT/nZOWxYvoqwyAiMxlMnGiG5NkIKpBXxfEIcLp6ra+HVfnb8TdKqWFUyjxkZecN1OBy++ZEa12M/apD3XFwIw7uKxKbNrXpHqDGZG1ugrI/UO4bwcC6DwiJnEMOXBLEk1Ts/V199aQWaJn9nhffyzneeqNU2rlhD7sksouPj3MsS0uVexPJSgD6U8tWAEjrX966TaU+2bUsgY0ZfrXeMauEXYMc87KjeMcot2Ok9BS1A2/Z5ekeoEVZLEJaJbfSOITxchr8/d+8I5Y3VZpyq53ctPZudO07y7fx9escQotKkSBReJSvzOFtWriUyJhqD4dSvb0iujeAC75v0W29RdidvNS3i8d4ODIpc7awKC+bF8fGEi/WOUS1iBnrPyY5Z1Qj3osFrOnWvHV1Nj87qiqHArHcM4aFK/Ix8kB3CHYsDSM/x3uLwn157ZSUul1yMFd7Jt2eDFj5nw7KVFObl07hVC/cyaUWsPKMGV5lL6DzQzAtbAknzkT/Menrr1Za0aJ3FJYO8p6gqj9DYPI73zkFZE613lHJJitLYecw7fp97X3xA7wjVLis5Ce2nOjV+3KF/fESm9fTpRW5I7MxTrS8/bfnvJ5KZdmAth0vycGoqSUGR3NKwO0PqtnWv80X6emZmrAfg9kY9uLVhD/dju/KP8cbeRUzvcTsmg1yHLw9VgTVaIOOWmylxeMd7trxS9ucye9Yubr3Nt0fBFr5JikThNY5lHGL7uk3EJMSjKKf+kITkSCtiVahrd/BxeydzCoP4fLN8LFwIp1Phwbsv4oelOTRulqV3nCoVfnUqhV5SJNYPg51eMKd1ZJRKYgPf7m7qtJvJe6+TLl2XZvYaiUv7uyUnzZLN/ZvmcGlCizOuH2YOYFTjXjQMjsZsMLIyK5WXd/1ElF8QvWIak1J0kk9SV/Je5+vRgEe3fEPP6EY0DY3Dqaq8vudXnmtzhRSI5ZQZ4Mfr2/3Zddx3X683XlvFjTe1wc9PBmsS3sV335XCp2iaxrqlf1BSVExYZIR7ubQiVh2zqvF/IcV8NtBKfKh0j7kQhQUGbrvhKooK/fWOUqViWh5Eq+8dg6zUCfaO3+HmLb1repHKOPJ9JwxHA3Q5dqRfEDH+Ie6vVSdTqR8YQZfIpDOu3zWqAQPiW9AoJIb6QZGMaNCNpiFxbMs7AkBGcQ7NQuPoFt2Q7tENaRoaS0ZxLgBfZKync2QibcJrvsXU29jMBqYXhnDzokCfLhABDh0qZNrUbXrHEKLCfPudKXzGodQD7Nmyndi6CWVbEQulFbGqNXPYmNa9mKvbeNfgH57mYLqZu26+DtU7apVyMRg0ArxkOoz4QO+4J7Fdx1y9I1Sr/KOxOGY10DsGAA7Vxc+Zu7m6fnv335Fz0TSNDTkZHCzJpVNkIgBNQ2I5VJzL8dICMksLOFScS5OQGI6U5PHD0R3c16xvdT8Nr6YBW4wB3LQ6hBlba0/L2rtvr8XhkL+pwrtIvzLh8TRNY+OyVdhKrdRtEOZeHndI5kWsLoFOlUdjLPS7JICxq/0osvnWfSI1Zc3KUF54Ygivjv9J7yhVJq7Xfg590hzF5tkneNF+3lEkdunuPaPGVpTqMpA1qSuKyzOuRy8/uR+L08rQuu3OuZ7FYeWKPz7ErrowKgpPtbqcnjGNAGgUEsPo5v0YvWkuAPc3739q2cbZPNR8AGuz0/ksbRUmxcCYlgPpHHXmFsvaKCfAzDv7Alh30DN+H2pSZqaFb+fv48abZHRf4T2kSBQe72BKGvu27SS2ToJ7mX+xg9A8mRexunV2WfnyYgdvpwWxKt0z/7DbnMuxOxdhNvYmwDz0vOs7XNuxOuZgMrQm0O//3MvtzhXYnSsA8DP1w8/09yilLvUQVsd3BPmNRlEqVhzNnFqX1m27c/MdGyq0nafyD7JivCoTdX59vaOcU5TB86/aK4pGLx8etOboH21Qdoedf8Ua8t2RHfSOaUxsQOg51wsy+fNVr1GUuOxszM1gQvLv1AuKoGvUqRbR6xM7cX1iJ/f6Px7dSZDJj3YR9Ri+6jNm9rydk9Yint3xPd/3vRc/Q+0+1XKYFL63BPHhCiOaVnsvOH40aZMUicKreOZZnxB/0jSNDctWYbNaCQn/+2Qj9rDv38fjKcIcLl5KKuL5ix2YDJ7VOuNSD+NwbcCgJJx/ZUBV87A5fsaoNPzXfjKxOZcQYB5BgPkmbM7FuNTjAGiaC6tjIQHmaypcIJ7aXuGFpzqwfk2jCm/rqaIHJesd4bzCFM/v55tQRyU6pljvGNWiOC+M0k/OPDiMHjJLC9iQk8Gw+h3Ou65BUUgMjqRFWDy3NuzBpfEtmH5g3RnXzbeXMDltFU+0uoxdBcdICooiKTiKrtENcKouDhX7dnfic9GAveYAbtsQwqT1plpdIAJs2ZzJ+nW+23NA+B4pEoVHO7g/leTtO4mt+/dAAEaHSuRxKRJrkgG4xFDCV5eW0DbBM06+Nc1GqWMuAabrUAgsx/oqpY65+JkGoihRZR5TtSwMSgImYxNMxqYYlARU7dTIpHbXSoyGRhgNiZXOarMp/PfWSzl2OKLS+/Ak4QnZ0M2zR+QM9oKbQVu29t0u85lTumGwek6X5O+P7iDSL4iLYppWeFsVDbvqPONj4/ctZUSDbsQHhKFqKk7t7xZsl6bi0jzrwlpNKfQ38frRUEYv8ed4kZxq/uXjSRv1jiBEuck7V3gsTdPYsHw1dquNkLC/uwdFHy3B4Pnnfz4p1u5kQisLD/Rwcuo6sX6sju8wGVpiMpbvpM/uXIpBCcbP1O20x04VhdmoWj6qloeqZWNQ4lHVHByuTfibTp9PraJycgzcdv0wSkt9o+tZ6NVpekc4J3+nSoDJs0/Q23XM1jtCtcjc0hRWxugdw03VNH44upOr6rU7bWqKF3b+wKT9y93fTzuwlnXZ6RwpySfdks2XGev5+dhurvzHPIl/WZedzqGSXG5M6gJA6/A6HCzOZXVWGgsOb8OgGGgQHHXadr7MZVBY5Axi+JIglqTKKea/ff/dfo4cPn3eTiE8kW+crQifdKZWRFSN6KO+2T3LW5hUjeEBxXS9zI8XNgVwKK/muxA5XNtRtWMEme8v1/pONQOHaxNB/g+d8XGjIQ5/0yBK7FMB8DcNxmiIo8Q+BX/TFTjV/didSwED/uahmAyV6zqavM+f+0dex+dz51Vqe08S2zadwvi2KCf0mdrgfBQgKVJjf5bndnHr1uuw3hGqnK0kAMsH7fGkV31DTgbHrYVcXe/0Cc2PlxZi+EfaUpeDN/cu5qS1CH+DiYYh0bzSbiiX12lVZjury8Fbe39jXIdhGP4cKTU+IIwnWl3Gy7t+xmww8lK7IQQYzdX75DxIhr8fL28OID3Hk376nsXpVPnsk828/NoAvaMIcV6KptXSvhDCo2maxrxPprFr4xYatWzuXh5xopQGu/P1CybKsBkVpucEM2d7zXUrU7V8SmwfEug3CqPh1AWEEttnGAx1zjhwjabZKLa/T4BpGCbjqXukSu1fA9YyA9f8m8O1GadrD/7mayi2vUuw3/2oWgFWx1yC/Z9EUSp/je2BRw/w1Nglld7eUxz6tSP2jzznvrN/e+VoCL+nek6Xx38yGjX2HJlOUJBvTeNzYNpFqN/W0zuGqEElfkamHgtkwW7PfK95mojIAPal3k9QUO25gCC8k/QFEB4pIzmV/Tt2E1e37KTEMYelFdGT+Ls0/hth4cNLrUQG1cz1Jpd6FA0LJfZJFFmfo8j6HC4tHYdrLUXW59C0sn2RVS0HTcuj1DHTvb5T3YpT3UuR9TlUNee0Y6haMTbnUvzNV6OqhzEoMRgMMZiMTQAVVbuwboIfvd+I7745/wAani7uov2oZs8dRbRuiN4Jzi6xgdPnCsTstPpSINYiqgKrCOSG5cFSIFZAfp6V2bN26R1DiPOS7qbC42iaxpZVa7HbbAT/417EoAI7wYW+dVLlK1o7bXzR28HEw8EsSq7ea08mQ1OC/B4us8zq+AaDEoufqR+KUvb4BiX2tPXtzt/QsOFvugpFCT/tGDbHj/gZL8KghOPiCPB3IaThAi7splhVVXjywe40bpZNuw7eO9pdQEgJxitOoH1fV+8oZxQfpAKeefLauo1v3ZfkdJjIfb+TXHmuJTID/Hh9uz+7jstPvDI++WgTo+7qiKJI11zhueTdLTzOyaOZ7N+xm+j4uDLLpRXRswU7VJ5KKOL1/vZqHTBEUfwxGhLKfCn4oShBGA2npsIotc/D5vj1z/XNp60PASic2s+/u406XSmoWjZmY08AjIb6qFoWTlcyducGFAwYlNgLfh4lJQp33jSY7CwPbu4qh+jB+/WOcFaxfp57N0WHLif1jlCljv7cAUNGkN4xRDWzmQ1MLwzh5kWBUiBegOR9OSxdkq53DCHOSd7hwuPs2rgFS2ERoRF/t/CYbC4isqw6phLloQC9tFK+GlBCt0T9hqDVtHxUragS2zmwOr8nwHytu0XSoITjb7oaq+Mb7M5lBJhvQFGq5l6SzGNGRt54LQ6H934UR9Q/gdahQO8YZxRl8txhkHv0OaR3hCpTeCIa+/TGescQ1UgDthoDuGl1CDO2embrvLf5aNImvSMIcU4ycI3wKEUFhXz22nhcDicxdeLdyxPSCok/KC2J3sSlwGJHEO+sMaHW8kmUz+faG7L4YMq3eseotMzNzSh+qbPeMU5zws/MTb95XuuWv7/G3qNTMZs9t4gtL01VSBk7CMO207ttC9+QE2DinX2BrDvovRezPJGiwMatd9O8RbTeUYQ4I3nHC4+yd8t28rKyiYz7e44txaURfaxEx1SiMowaXGEq4cuBpTSL9f6T4er07dexfDzhYr1jVFpchwOo0Xa9Y5wmVPXMQXUaNbH7RIEIcHRVaykQfZTDpDDfGsQNi4OkQKwGmgYffyiticJzybteeAyH3c7W1esICAzEaPy7O0vk8VJMDmnw9lZ17A4+bGvhrq5OvaN4tLdebcnSX1udf0UPZDS58L/O87pPBjhVjIrnfXa0aeeZ3XMrqqQghJKPW+odQ1QxDdhnDuD2jSFMWm9Gk54g1Wb2rF3k5cmtNMIzSZEoPEbq7n0cO3i4TDdTgNgj0s3U25lVjVuCi5ky0Ep8mOedtHsCp1Phwbv7cCDlwgfF0UNc/31oRs9qHTMA9SL0TnG6jl2P6x2hShyb1h1DsQyS7ksK/U2MOxbKfUv8ySyUU8TqVlLiYMbn2/SOIcQZySeA8AiaprF19XrQwM/f3708JNdGQLG0QPmKJg4b07tZuK6tZ3YD1FtRoYHbrr+KokL/86/sYQJDizFc7nkjdiZGeN5FiV4XZegd4YKd2NkYfvfOCxridC6DwiJnEMOXBPFbipwa1qTPPtmC0+lZF9iEACkShYc4kn6QA3uTiUn4VyuiTHvhcwKcKg9GW5hwiY3QAM87gdfbwQwzd44YjuqF5wyRV3jedBj1Qj3rdywkVKNFa+9uSbRb/Sl4v73eMUQVyfD34787QnljtRmnKl1La9qRI4X88J3nfXYKIUWi8Ag712+mtLiEoNC/54zzK3ESmmPTMZWoTh1dVmZdVEy/Jl5YDVWztatCeH7MEL1jVFhUw0y0VhWfeqQ6JQR5VpHYrHmp3hEu2JFvOmM46X2t3aKsEj8jE7NDuGNxIGk5Uhzq6aNJG/WOIMRppEgUusvLymHXxi1ExsagKH//oYo+WoL82fJtoQ4XL9QvYmxfB2ajZ53M6+2Lz+vyxdQeeseosODrDugdoYxYf8+6CNGmfb7eES5IbkYdXPMS9Y4hLoCqwGoCuWF5MAt2y5yHnmD9uqNs3eLdPQyE75EiUehu9+ZtFOTmEREd9fdCVSPyhPdfcRfnZwD6KSV8dUkJ7ep61gm9njRNYezT7Vm/qpHeUSokrnMqarhD7xhu0SbP+p3q0u2Y3hEqzeU0kv1BZxS5fOe1jgeYeSQ5lP/94UeJQ36OnmT2rJ16RxCiDCkSha6sJaVsXb2O4NBQDIa/fx1Dc22Y7Z51cieqV4zdyYTmRTzc08mpQdiF3a7w39su5djhCL2jlJvJ7MTvusN6x3ALN3jW50jvvp7V0loRRxa3x5Aacv4VhcexmQ3MKAxmxKIgdmbKqZ8nWjB/Hy6XZ31eidpNPimErpJ37OLkseNEx8eVWR6VKa2ItZFRg2v8i5l5mZWGUVIoAuTkGPi/66+htNR7phqI7Z+M5iHzE4ZqnnPSFR2jUjcxX+8YlVKUFYltahO9Y4gK0oCtxgBuWh3C9K3e8xlSG508UczvSzP0jiGEmxSJQjcul4utq9djMpkw+5ndy40OlbBsmVy2Nku02/m0YxG3dpKpMgD27/Nj9O3X6R2j3IIjC1EuzdI7BgBBTs/5HWreskTvCJWiaXD8424YHHL/mjfJCTDxbEYoj/3uT36pdC31BvPm7NY7ghBuUiQK3Rzcn8ahlLTTpr2IOFGKwTMaIYSO/Fwad4ZZ+HiglZhg+YVYsiiCN8cO1DtGuUVcmaJ3BOBU63R8qGe0JrbrmKN3hErJXNcSZVOk3jFEOTlMCgusQdywOIh1B+U0z5v89EMKJSWec0+3qN3k00PoZvu6jTgcTgKDg8osl66m4p9aOmzM6GnhipaecaKvp4/eb8R333TQO0a5RDc5gtrUoncMABIj9E5wSreeR/WOUGHWomCKP2ytdwxRDhqwzy+A2zeGMHG9GU2T1kNvY7HY+fF7mTNReAYpEoUuTh7LJHnbTqJiY8os9y92EFQkV9FEWUFOlSfii3hzgJ0gv9rbqqiqCk8+2J2d2+rrHeW8FAWCh6frHQOA+mH6/84oikaPPml6x6iwo190RSk0n39FoatCfxPjjoVy32/+ZBbKqZ03mzd3j94RhACkSBQ62btlO0UFhYRFRpRZLq2I4mwUoLtayqx+JfRIqr2tiiUlCqNuGkR2luePMhnXLQUtxKl3DOp4QHflOnVdREZ51+fbyb0N0X5N0DuGOAeXQWGxK4jhS4L4LUVO6XzB70vSyc7yzvuXhW+RTxRR42xWGzvWbyYkLAxF+Wd3GI2YTgdQAzxnoAnheSLsTl5vXMQzFzkwesgImjXteKaRkTdci8Ph2R/hZj8HpmuO6B2D2AD9f09ati7WO0KFOGxm8t/zjq7NtdVBfz/+uyOUcavMOFXpWuornE6V+d/s1TuGEFIkipqXsT+V7MwTRP6rq2ls/SM0v3M5jb78juAXt8BF2R4zjL7wLAYNLjeWMGtgKS3jamer4vatgTx23zC9Y5xX7KXJaDrPexlt1v93pH1nzxjttbyOfNcZQ2aA3jHEGZT4GZmUE8LIxYGk5Uhx6ItklFPhCaRIFDVu39YdqJqGn79fmeWJLZKBU60Pdbqk0PTJpdT/8icCHtuN1qZQj6jCw8XbHUxsY+G/3fTv0qiHhV/H8tGEvnrHOKeQ6Hzoq++onhEG/YvE7r0O6x2h3PIOx+Oc1UDvGOJfVAVWE8gNy4OZv0umI/FlGzcc48CBPL1jiFpOikRRowrz8tm/YzcRUWWHU1cMLuo1O33I/MDQYur330Wzcb8QN+U3zHemotb1rvt6RPUyqRo3BRXz+WWl1A2vfS3Pb7/agqW/ttI7xjlFXKXvdBhh6FskmkwaXXt6xiA+56O6DGRN7IIiI2N6lOMBZh5JDuV/f/hR4pCfTW0grYlCb1IkihqVunsfBbl5hP+rSExomIFfgO2c24bF5dJg2Gaaffw9ke+vxDD8iEcMiiE8QyO7nc+7WLi+Xe26p9XpVHjw7j6kJsfqHeWsYpofRk3S7+JOsFPf34mkhg4CA73js+rI7+0w7AvVO4b4k81sYEZhMCMWBbEzU07ZapN5c2SUU6Ev+cQRNUbTNHZt3ILZzw+DsWxXmb+6mpaHokB0o2M0vn01jWcsJPTVjTDwJKpJ/y5lQl/+LpX7oyy8f4mNcA8YrKSmFBUaGHnjVRQWeOY9ZIpBI/D6A7od36xqRATq9/vQqk2RbseuCEtuOLbPmukdQ3BqzsOtxgBuWh3C9K0mveMIHaSm5LJ5U6beMUQtJkWiqDGZh45wOC39tLkRjSYHdRpX7gTSaHYR3/4ATR9aRoMvfyDwqZ1onfOrIK3wZu1dVr64qJhLmtaeVsWDGWbuGnEdqodeK4nvmYIapN/PIzFSvyKxY5eTuh27IjI/64Zik3vd9JYbYOK5jBAe+92f/FLpWlqbSZfTmjF27Fg6duyodwyPI0WiqDEpu/ZQYikmKLTs/G51mqRhMl94Vyz/ICv1+uyh2dhFJEz/Fb/79qM1lLmGaqtQh4v/1bPwcj87/qba0aq4dnUI/3t8iN4xzsgvwIZ56FHdjp8Yptuh6dH7oH4HL6fMjc1R1kTrHaNWc5gUFliDuH5xEGsPSrEuYP43e3G5POfK38iRI1EUBUVRMJvNxMfHc9lll/H555+jVvAK5fTp04mIiKiSXOnp6dx8883UrVuXgIAA6tevz7Bhw9i3b1+5th8zZgxLly6tkiy+RIpEUSNsNisrln2PXSvC5XSUeSyx+f4qP15IVAFJV2yl2Qc/EP3hcow3H0SLcpx/Q+FTFOBiSpk1oIRO9WtHofjltLp8MbWH3jHOKPqy8ncrr2p1gvU50QoI0Gjf2bNHNrUVB1I0sY3eMWotDUj2C+D2jSFMXG9Gk0GDxJ9Onihm2e8ZescoY/DgwWRmZpKRkcEvv/zCgAEDePjhh7nqqqtwOmv+3muHw8Fll11GQUEBCxYsIDk5mblz59KuXTvy8/PLtY+QkBCio+Ui2b9JkShqxNGjKeRY93PSvos1275m287fOXHyIEZzCfENqvcqe2TiCRrdtI4mUxcS/uY6lCuPo/nXnm6IAqLtTt5uWshjvRyg85x91U3TFMY+3Z51KxvrHeU0YXG50DNXl2PH6XRPYuOmNoxGz/6dOzK7K4Z8v/OvKKpcob+JN46Fcu9v/mQWyimZON3c2Z7V5dTf35+EhATq1atH586defbZZ/nuu+/45ZdfmD59unu9d999l3bt2hEcHExiYiKjR4/GYrEAsHz5cu644w4KCgrcLZNjx44F4IsvvqBr166EhoaSkJDAzTffzMmTZ++yv3v3btLS0vjoo4/o2bMnDRo0oE+fPrz66qv07NnTvd6RI0cYMWIEUVFRBAcH07VrV9avXw+cubvplClTaNWqFQEBAbRs2ZKPPvrI/VhGRgaKorBgwQIGDBhAUFAQHTp0YO3atWX2sfr/2bvv8Cqq9IHj35nb781N7yEhgYQeei8iKMXCYkURFdbe1rW7a1s79gKuDVTAxZ/Yu4ggRbHRe0J6Qnrvt878/ogGYnpP4Hyeh0dzZ+bMyU0yd94557zv9u2cfvrpmM1mfHx8mDNnDsXFNaVNFEVh6dKlREVFYTKZGDFiBB999FGrfx6dSVyRhC6RnHIA/9AAxk+bSWj/UGy6HOKObaZSXodG2zUBm6xRCBicRv8bttL33S8wP7AXJhV1e6FvoWtoVJinr+J/s6qJ8ju5f+YOh8T1i2dyLN2n+Z27mOffErvlvP767hlJHBpb2i3nbamCo+GoX4R2dzdOOW5ZYoPbzIUbzWxIELdiQuO+/jKBqqqePRNq5syZjBgxgk8++aT2NVmWWbZsGYcOHWL16tX88MMP3HPPPQBMnjyZl156CU9PT7Kzs8nOzuauu+4CakYGH3vsMfbt28dnn31GamoqS5YsafTcAQEByLLMRx99hNvd8P1kRUUF06dPJzMzky+++IJ9+/Zxzz33NDpFdu3atTz00EM88cQTHDlyhCeffJIHH3yQ1atX19nv/vvv56677mLv3r0MGDCAhQsX1o6m7t27lzPOOIMhQ4bwyy+/8NNPPzFv3rzaPi5dupQ1a9bw+uuvc+jQIW6//XYuv/xytm7d2rI3vQtIqqqe3HdLQrez2ap4e9UDOBx2/P1qbkYUVaEwP4fxU35i2MjuzfxXVepBwc5+2NaHI8d7NH+A0Os5NBL/V2Jh1e6Te93PgEEOvtryvx5VfkFRJBJvmoecZerS86bo9Vz1fdeeE+DJF/ZxxdW/dfl5W8Ll1JJ829nIGV3/vpzK0gx6HttlJKlQTCsVWuadNfO58OLur4e7ZMkSSkpK+Oyzz+ptu/TSS9m/fz+HDzdcuuOjjz7ihhtuoKCgAKhZk3jbbbc1OyV0586djBs3jvLycjw8Gr5H++9//8s999yDRqNh7NixzJgxg0WLFtGvX82MmjfffJO77rqL1NRUfH196x3/8MMP89lnn7F3714AoqOjeeyxx1i4cGHtPo8//jjffPMNP//8M6mpqURFRbFy5UquvvpqAA4fPszQoUM5cuQIgwYN4rLLLiM9PZ2ffvqp3vnsdju+vr5s3LiRSZMm1b5+zTXXUFVVxXvvvdfke9JVxOMrodOlZ8RRUpKPt9fxrKayJBMQGMyAwY5u7FkNs1cFEWfsZ8CzXxPw5ka0S5JRA5uu2Sj0bnq3ymJrBa+facO/m9aqdYWjcXpuWnxBd3ejDllWMV7Y9YlcPOmen/PEqSndct6WOPblKBEgdqEqvYZXCj1YssEkAkShVdZ/2z0zMFpDVVUk6fjv9caNGznjjDMICwvDarVyxRVXUFhYSFVV0wkFd+3axbx584iIiMBqtTJ9+nQA0tPTGz3m5ptvJicnh7Vr1zJp0iQ+/PBDhg4dyvfffw/UjOqNGjWqwQDxryorK0lKSuLqq6/Gw8Oj9t/jjz9OUlJSnX2HDx9e+/8hISEAtVNj/xxJbEhiYiJVVVXMmjWrzjnWrFlT7xzdSRTfETpdYtJeFFVBq6275sXHLx+9oWcFY17BhXhdUIh63k4KU8Io3RqJ+/sg5Erxp3IyGui0s3qik9eyzHx15OQcVdz4nTdPPTyLfz38fXd3pVbQlHjS34zp0nILHt2QIdDqqRAzsGeWvyjNDsD5biQiVOl8igS/qCae3KKjyinecaH1Nn6fjKKoyHLP/f05cuQIUVFRQM2avXPPPZcbb7yRJ554Al9fX3766SeuvvpqHA4HZrO5wTYqKyuZM2cOc+bMYe3atQQEBJCens6cOXNwOJoeVLBarcybN4958+bx+OOPM2fOHB5//HFmzZqFydTyh2F/rptcsWIFEybUTQKn+UuNb51OV/v/fwbIf05hbeqcf57j66+/JiwsrM42g8HQ4r52NnHnK3SqyqoykpL24WmtnzUqKKTnZvyTZBX//sfw738M1xVaCg5GUvFDX9SffJHcYgC+KauSf+GVhK0sjBjLnYPPbHCfTzP28nXWQZIq8gEY7BnMTTHTGeZ9fG3Uuym/sSa1Zpre4qgJXB55/GJ9sCSLp458x6oJi9HK7ft5mF0KdwZWMD3YyMM/66m099wP4bZ67eVIBg0ZyXkL9nZ3V4CacjWas7NRPu3Tded0Kxi1KjZX1/18YwbauuxcraEoEnmvjBXXsi6QY9SxdL+R/dnivRbarrCgmh2/ZTJhUtddM1vjhx9+4MCBA9x+++1AzWigoig8//zzyH98Rn/wwQd1jtHr9fXWEMbFxVFYWMhTTz1FeHg4UDPdtLUkSWLQoEH8/PPPQM2I38qVKykqKmp2NDEoKIjQ0FCSk5NZtGhRq8/9p+HDh7Np0yYeeeSRetuGDBmCwWAgPT29dqS0JxJXLaFTpaUdpqysAC/P+kFiYHDPDRJPpNW5CB6VSPSdmwh/92uMdx1EHV7W3d3qkQ6VZvPJsb3EeAQ0ud+u4nTmhAzh9XGX8c6EKwkyenLLrnXk2WrWpyaU5/F64o88OfxvPDH8b7yW8COJ5TUjMi5F4cnD6/n3kLntDhBPNFaxsXZaJZMjT77pp4oicc+t4ziwp+fcYPjN7fjSN83p69u1S/CHDS/u0vO1VNa2oUgHurFw5CnArpNZU25h4XdmESAKHeK79T1jGqLdbicnJ4fMzEx2797Nk08+yfz58zn33HO58sorgZo1fU6nk+XLl5OcnMy7777L66+/XqedyMhIKioq2LRpEwUFBVRVVREREYFer6897osvvuCxxx5rsj979+5l/vz5fPTRRxw+fJjExETeeust3n77bebPnw/AwoULCQ4O5rzzzmP79u0kJyfz8ccf18tG+qdHHnmEpUuXsmzZMo4ePcqBAwd45513eOGFF1r8Pv373/9mx44d3HTTTezfv5+4uDhee+01CgoKsFqt3HXXXdx+++2sXr2apKQkdu/ezfLly+slx+lO4soldKrExL2AhEZTd9DaYKzCy6d7UuG3h9Gjij6nHSLm8W8JensDuusSUMKru7tbPUKVy8GD+7/g/qFnYdUZm9z38eF/4+KI0Qz0DCLSw48Hhp2Fqqr8XpgKQGplITHWQMb5RTLeL5JoawCplTW/L++m/sZon3CGeoV0+Pfg5XTzWGQ59091opVPrpxe1dUSVy2cQ0F+z0jO5BWSjzq6pEvP2cera3+mYyZkden5WqKyxErV64O6uxsnLRXYozFy2c8evLNbTNYSOk5PCRLXr19PSEgIkZGRzJ07l82bN7Ns2TI+//zz2umYI0aM4IUXXuDpp59m2LBhrF27lqVLl9ZpZ/Lkydxwww1ccsklBAQE8MwzzxAQEMCqVav48MMPGTJkCE899RTPPfdck/3p06cPkZGRPPLII0yYMIHRo0fz8ssv88gjj3D//fcDNaOWGzZsIDAwkLPPPpvY2FieeuqpetNH/3TNNdewcuVK3nnnHWJjY5k+fTqrVq2qnU7bEgMGDGDDhg3s27eP8ePHM2nSJD7//HO02prrwmOPPcaDDz7I0qVLGTx4MHPnzuXrr79u1Tk6m8huKnSasrJC3ln9H7QaLV5edUeWwiOPMnLcj93Us46lqlCcHkLJj1G41gcjl+maP+gk9J8DX+GpM3LnoDO57ve1DLQGNTrd9K8qXXZmb17OUyPOY1pgNCkVBVz92/94b/LfUYHLfn6btydcgU7WcOuuD3h30hIs2s6dt5+n1/HIASOHc06uZ2nDR1bz2ca16HTdP2KaszeaiofGdNn53i7z4N09XbcO8vcj6wgJ7VklMBKfPwO2+je/o9BqRUYtz8UZ+SXt5FzfLHS/Iwk3EdZHzAIQusbJdfcj9Chp6UcoLy/Caq0//zuwB69HbC1JAt++2fS7/Gf6r/ocz6W/wexcFF3X1H/sCb7LPkxcWS63xJzepuOXH92Cv8GD8X6RAER5+HPTgOnctHMdN+9cx80DTifKw58nD63n1gEz+KUghQXbV3LZz2+zu6jxjGftEehw8vLgCm4a33PKR3SE/XtN3H79/O7uBgCBsV2bSTjI1HWBsX+Au8cFiDl7o0WA2AmcWolPbWYu2mAWAaLQqTZ8l9zdXRBOIWIuhNBp0tPjkGUNslz3Q1OSFAKCMrupV51Lo3UTODSVwKGpOK42kL8viqrvI2CHN9JJmkcwp7qM5+M28t+xl2LQtP6Ssir5FzZkH+GN8ZfVOf6i8FFcFD6q9uuvMg9g1uqJ9Q7jwp/eZM3ExeTZyrlv/xd8cdoN6OWOv5xpFZWLTZWMm6XnwR1GjpWcHD/Dzz8OYPDQ07j5zm3d2g9Zo2C4MA3HawO65Hz+hq6bODNwcNNp3ruao9pI2cux4slwB1KBo3oDj/6uJ6tUvLNC51v/bSJ/v3pkd3dDOEWIIFHoFDZbFamph/CweNfb5uufi07n7PpOdTG9yU7YxDiYGEdlsSeFv0dhWx+OnGTp7q51qLiyHIocVVz+yzu1r7lVlT3FGXyQsYufZ92NRmr4BurdlN9YlfIrr469lBhrYKPnKHFUsSLpJ94cv4iDpVlEmH2JsNT8cylu0iuLiG7i+PaKdDhYOdrFOwVm1u0/OUYKnntyIAOH5HPmWUe6tR+BU4+SviIa2dX5N9m+mq4bSYwdWdhl52qJYx+MQS7UN7+j0CLlBi2vpJjYkCCCQ6HrbN2chs3mwmgUt+9C5xO/ZUKnyM5Jpqy8kOCgyHrbektW045k8SnDMmcfzNlHSWYAxT9H4fwm7KS4aRvn15f3J19d57VHD35NX4sfi6MmNhogrk75lbeTf+GVMQsY0kwSmufjNrGw7ziCjJ4cLs3GpR6fyutWFdxdsLTa4Fa4waeCqWcYefAXPSVVvXtU0eWSuPXaKXyxqYDogfnd1g+jtRLNWTmoX4Y2v3M7eUldFySOm3isy87VnMLkUJSPe05m297MLUv84DTxzEYtLqV3XwOE3qeqysmP29KZNbtfd3dFOAWIR2BCp8jMTMTldqLT1U8uEhjSc26euoN3WD5RF/9O9MrP8XruZ6R5WSjG3rt+0aI1EG0NqPPPqNHhrTMRba1JWPTQgS955eiW2mNWJf/K6wk/8tDQswgxeVFgr6DAXkGVq36x3F8LUkivKmJBRE2CkyFeIaRVFrE9P4lPMvYiSzJ9LU3XPepIw1w23p1cyZkxvfdn9qfycpnFF59LWWnT2Wg7m28XlcPwULrmZybLKhOn9oy1Q26nhqKXR3d3N04KaQY91++38uRPOhEgCt1m86aU7u6CcIoQI4lCh1MUhaSkfZgM9adVGk0VeHr1zNphXU3WKAQMyCBgQAbOK3UUHIykclME6nY/JPXkugHJqS5DPmFN5scZu3Gqbu7d91md/a7tP4Xro6fVfm1zO3nmyPcsHTEfWao5Psjoyd2DZ/HowW/QyRoeiT0Ho6ZrM8p6ON3cF1rBjFATj23XdWmB9o6Wnqbj6oUXsO6r9+jAspOt4hOeS0FsWafX7jO6FDSSiruT/75Cw9x4etk69RwtdWz9CKSUk2uKe1er0mt4O9vExwdPjqnmQu+2+YfU7u6CcIoQJTCEDpdfkMmadx/Bw+KNxeJVZ1vffnEMH7O9m3rWO1SXWyjY2Q/bhj5Ih0Sq656uSK/lySMmdh3r3RMzLv97Fktf+qrbzp+9cwCVj45qfsd2WnLAk7Sizg0Sz5xTwjsffNCp52iJslw/cm6c2SXrPU9GigS/qiae/FVHpb33PggSTj4JKbcQFNwzat4KJy/xySF0uKysRKqrKzCb6wc4p+J6xNYyWSsJn3GAmKXfErjye3RXJaGE9IxRCaE+X4eLp6PLuWuyE1nqvc/c1q4K4d2VE7rt/IEjklB9Oz+hVbhX5/+Mho/uvjWef1IVidxXx4oAsY1yjDpuj7dy/1a9CBCFHmfz5tTu7oJwCuixnx6SJPHZZ5+1eP/IyEheeumlNh/fWVatWoW3t3d3d6NLpaUdQZY1SFLdD1ZJcuMflNVNveqdPAOL6HveTmJe+wKfl39EvvAYqsfJVbfvZKBR4RxdFe+eWU10QPcXqW8LVZV4+N/D+fXH7kmIoNG50V2Q1unnCbN2fpA4flL3PwzL+mUw0h7v7u5Gr2PXyawpt7DwOzP7s3vsLZJwitu8KbW7uyCcArrlCpiTk8M//vEP+vXrh8FgIDw8nHnz5rFp06bafbKzsznrrLNa3OaOHTu47rrrWrz/kiVLkCSp3r+5c+e26ntpziWXXMLRo12TlKEnqK6uIC39MFYPn3rb/AJy0GpFgNMWkqziF5VFv8Xb6bf6Mzwe3wEz81C0vTMgOVmFOpy8OqyCq8b0zt9zh0Pi+sUzOZZe/++3KwROj0ft5DIVwebODRK1WpWxE7s3sUR1mQeVrwzu1j70NiqwV2Pksp89eGe3SNcg9GxbxLpEoQt0+ZUwNTWVKVOm4O3tzbPPPktsbCxOp5PvvvuOm2++mbi4OACCg4Nb1W5AQECr+zJ37lzeeeedOq8ZDPWzcbaHyWTCZDJ1aJtt4XA40Os7v9xCVnYS5eXFhIbUH40ICun+p+snA43OTfDwZBiejP06IwV7+lG1IVyMGvQQOkXlCo9KJp5p4MHf9OSW967RiKJCmSsvms9XW9ZiNndtPVOzVwXSmfnwXVCnnSPA0LlBaGSUE4OhezPfZq4ai1wpAp2WKjJqeSHeyPZUkZhG6B2ysyuIO1LAoMH+3d0V4STW5XcvN910E5Ik8fvvv3PhhRcyYMAAhg4dyh133MGvv/5au9+J00UnT57MvffeW6ed/Px8dDod27ZtA+pPN20Jg8FAcHBwnX8+PsefoEuSxMqVKzn//PMxm83ExMTwxRdf1Gnjiy++ICYmBqPRyIwZM1i9ejWSJFFSUgLUn2768MMPM3LkSN59910iIyPx8vLi0ksvpby8vHYfRVFYunQpUVFRmEwmRowYwUcffVTnvAcPHuSss87Cw8ODoKAgrrjiCgoKCmq3n3766dxyyy3cdttt+Pv7M2fOnFa9N22VlZWEorjRausHpP5BmV3Sh1OJwWwjbMphYh75juBV69HfeBQ1sqq7uyUAMU4774yv5Lyhva9URkK8npsWn98t5/Y5u3NnXvjqOjdIHDysrFPbb07ewSjY2HlB9snEpZH41Gbhog1mESAKvc4PohSG0Mm6NEgsKipi/fr13HzzzVgs9VNyN7Z2b9GiRbz//vucmIh13bp1hIaGMm3atAaP6SiPPPIICxYsYP/+/Zx99tksWrSIoqIiAFJSUrjooos477zz2LdvH9dffz33339/s20mJSXx2Wef8dVXX/HVV1+xdetWnnrqqdrtS5cuZc2aNbz++uscOnSI22+/ncsvv5ytW7cCUFJSwsyZMxk1ahQ7d+5k/fr15ObmsmDBgjrnWb16NXq9nu3bt/P666934LvSMEVRSEjci8lYP+OWTmfH6lnS6X04lXn4lhJx1h5iln2J33+3oFmYhuJTv+6g0HVMLoV/+lfwwkw7VkPvSmqzaYM3Sx+a1eXn9YvKQhlU3vyObeQldW6QOHJMXqe23xSHTU/py8O77fy9hQrE6w0s3uXBst+0qCdZySHh1CBKYQidrUuDxMTERFRVZdCgQa06bsGCBWRlZfHTTz/Vvvbee++xcOHCeslRWuOrr77Cw8Ojzr8nn3yyzj5Llixh4cKFREdH8+STT1JRUcHvv/8OwBtvvMHAgQN59tlnGThwIJdeeilLlixp9ryKorBq1SqGDRvGtGnTuOKKK2rXY9rtdp588knefvtt5syZQ79+/ViyZAmXX345b7zxBgCvvPIKo0aN4sknn2TQoEGMGjWKt99+m82bN9dZ/xgTE8MzzzzDwIEDGThwYJvfp5YqKMikqCgbq7V+YXMfv1za8aMSWsknPJeohb8S/fbneD39K9JZOajdPAXuVDbKbeN/0yqZ1q93rSF9fXkkn30wssvPa7mg8wrRW9XO/RlMmJLaqe035dgnY5Byjd12/t6g3KDl6WwPbvjeSFZp75oKLggn2v5jBk6n+FwXOk+XLlpoa0nGgIAAZs+ezdq1a5k2bRopKSn88ssvtUFTW82YMYPXXnutzmu+vnUDnOHDjz+VtVgseHp6kpdX86Q4Pj6ecePG1dl//PjxzZ43MjISq9Va+3VISEhtm4mJiVRVVTFrVt0n+A6Hg1GjamqI7du3j82bN+PhUX/ELikpiQEDBgAwZsyYZvvSkTKzEqm2VRIU2LfeNl//7nu6fiqTNQoBg9MIGJyG4+96CvZHUbUpAvUXHyRE1N6VPJ1uHg4vZ2sfM0/+pMWl9Pz3X1Ek7rl1HFHRBYwYfazLzhs0OolkzyHIZboOb9vs6rybKqNJJXZk171PJypKD8b9frj4q26EW5b4wWnimY29429PEJpTUeHg4IF8Ro1uXQ4PQWipLg0SY2JikCSpNjlNayxatIhbb72V5cuX89577xEbG0tsbGy7+mOxWIiOjm5yH52u7k2KJEkoSvueRDfVZkVFBQBff/01YWFhdfb7M6lORUUF8+bN4+mnn67XdkhISO3/NzSltzOlpR1Gq9E2OLrr65/bpX0R6tMbHYSOj4fx8VSVelC4ox/V34Ujx4uCvF1FBmZIVQw7Q8tjB00c6AUp9qurJa5eOIdvf1xHQGBFl5xTq3eiO/8Y7tVRHd62RoUgq9IpCYWiY+zI3fAjdbs0FC4bKx78NCLNoOex3UaSCsT7I5xcdu/KFkGi0Gm69OPM19eXOXPm8N///pfKysp62/9M9tKQ+fPnY7PZWL9+Pe+99x6LFi3qxJ62zMCBA9m5c2ed13bs2NGuNocMGYLBYCA9PZ3o6Og6/8LDwwEYPXo0hw4dIjIyst4+XR0Y/qmyqoz0jDg8Gih9IUkK3j7dX1xaOM7sVUH4mfsZ8OzXBLy5Ce2SZNRAe3d365QR4HDxwqAK/jHBRc0KqZ4tN0fDkovPx+nsuo+MwBnxqFLnvDd9vTulWYbGlnROw83I3BiLdLR7rv09WbVew6uFHizZYBIBonBS2rVT1J4WOk+XP/P873//i9vtZvz48Xz88cckJCRw5MgRli1bxqRJkxo9zmKxcN555/Hggw9y5MgRFi5c2O6+2O12cnJy6vw7MUNoc66//nri4uK49957OXr0KB988AGrVq0CaPNaSavVyl133cXtt9/O6tWrSUpKYvfu3SxfvpzVq1cDcPPNN1NUVMTChQvZsWMHSUlJfPfdd/z973/H7e6e+enZWUlUVBQ3WB/Ry6cAjVbMm++pvIILiLxgB9Fvfo73C9uRzstEsfTOOn+9iVZRucBYyepZNiJ8en6guH+viduvP6/LzmfxLUU6veXX49YI9eyc93v0uJxOabcp5QU+2Fc2PSPmVKNI8LNk4uJtFj48KLKWCiev3bu6/pojnDq6PEjs168fu3fvZsaMGdx5550MGzaMWbNmsWnTpnrrA/9q0aJF7Nu3j2nTphEREdHuvqxfv56QkJA6/6ZOndri46Oiovjoo4/45JNPGD58OK+99lptdtP21Ft87LHHePDBB1m6dCmDBw9m7ty5fP3110RF1Uy9Cg0NZfv27bjdbmbPnk1sbCy33XYb3t7eyN0x1wk4lpn4R+mL+muIfP3EVNPeQJJV/KOP0f+qn+i35nM8HtkF0ws6vbj5qS7C4eDNUeVcNqLnP0j5/GN/Xnluepedz/vchE5pN8TSOUHixKldm5JeVSHnjXFIDhEI/SnXqOP2eCv3b9FTaRejh8LJLe5IARUVIou50Dkkta3ZZIQGPfHEE7z++utkZJw6hePdbjer1vyHkpJ8ggLrB+9jJ20ipE9q13dM6BC2CjMFu/tRvSEcab9nd3fnpHZYa+DBXwwUVfXcm1uNRmXF/35i1tlHOv1cqgoJd5yLnNSxUym3qGYe2daxSXG8vBQOpq/s0Dabk/XbIKqeGNGl5+yp7DqZdUUm3tndpakWBKHbffv9ZUyZ2v6BE0H4q56fNaGHe/XVV9mxYwfJycm8++67PPvssyxevLi7u9WliotzKSnJw8Pi3eB2H5G0plczelTR57SDxDz+LUFvb0B3bSJKeHV3d+ukNMRlZ83kCuYM7Lmjt263xD+vm0JCfGCnn0uSwHxBx4/O+ek6/v2NGdi1fxO2CjMVy4d06Tl7IhXYqzVy2c8eIkAUTkm7dmR3dxeEk5S4orZTQkICjz/+OEVFRURERHDnnXfy73//u7u71aXy8zOorq4gwD+83jaTuRyjUQQUJwurfzHWebtQz91FcXoIJT9G4Vof3CmlCk5VFqfCvcHlnB5i4tHtOqqdPW9UsbxcZsnF5/Dtj/+Hp5etU88VND6BFMsg5MqO+7jyljs+SBw2orjD22xK5v/GnfJ/d0VGLS/EG9meKqbbCqeu3btEkCh0DhEkttOLL77Iiy++2N3d6FZ5+Rmgqg2uh/T2FVlNT0aSBL59s/Htm437Ug2FcRGUb+mLe7M/slPcsLWXBExUq3nvdCdL4038nt7zJn2kp+m46tIL+ODr9zq17IPO4EA3PxP3e/Xrr7aVVe349Z9jJmR2eJuNyY/ri/rNqZv23qWR+KrSzPJtGhS15z1EEYSuJIJEobP0vDsPoVdRVZX0jDgMxobXDHn7dk52QqHn0GjdBA5Lof8tW4j835eY7tuHOq4YtReUdujpvB0ulvYr594pTuROKgfRHr/97MF9t5/b6efxPzO+Q3+fLO6OH0mcPC25w9tsiNOho/jlkV1yrp4oXm9g8S4PXv5NKwJEQQBSU0spLKjq7m4IJyERJArtUlFZQmFhNhZzwwlNRH3EU4veZCdsYhwxD24gdPV69DfHofSvXxNVaDlZhbnaKtaeWc2AwJ63VnHtqlBWr5jYqeew+hcjTS3ssPZ0ioq3ueOCzsAgN4HB5R3WXlOOfT4KOdPYJefqScoNGp7K9uCG741klYpbF0E40S4xmih0AnGlFdqlIP8YVVVlmBsMElW8fTruxk7oXSw+ZUTM2ceAF7/C/7Uf0FyRguInUnW3VbDDyStDK7hubM+rYfnIfbH8/GP/Tj2H57ykDm0vwrvjgsSBg7vmQUhJZiCu/0V2ybl6CrcssdFt5oKNFr47KqayC0JDdu0UQaLQ8USQKLRLXv4x3I3UR/TwLEGrc3ZDr4Sexjssn6iLfyd65ed4Pfsz0rwsVGPPC3Z6Op2istBSycpZNkI6qSB8WzgdEjdcOYOMNJ9OO0fAwLQOzarbpwOruQwf1fkPwxS3TP7ysUin0BTLNIOe6w9YeeInHS7l1Pm+BaG1xLpEoTOIIFFol8ysRHSahjPsiammwl/JGoWAgRn0v/ZHIt/9Ast/dsOUQtQeuN6uJ+vvsPP2uAouGNbxCVjaqrhI5sqL5lNV1TkZNyVZxXRRx5XDCLF03NTdsROOdVhbjcncMgzpsLXTz9MTVOs1vFrkwZINJpIKRHAoCM0RQaLQGUSQKLSZw2EjOyu5kammImmN0DSdwUnImASi791In/99jeH2w6hDy7q7W72G0aXwD78KXpxpx2rsGUF24lE9N1x5Qae1HzTxaIeNQAeZOuY9k2WVCVM6N2lNZZEXtjdiOvUcPYEiwc+SiYu3WfjwgJhaKggtlZ9XRXpaaXd3QzjJiCBRaLOCgkwqKkuwWBoLEsVIotAyJmsl4TMOELP0WwJXfo/uqiSUkM6tv3eyGOm2sXZqJdP794ykNpu/9+LJh2Z3Stt6kx3NuR3zxNxf3zHvV1i4G6unvUPaakz2inFItpO7YlWuUccdR63cv0VPpV2MHgpCa4l1iUJHE0Gi0GYFhVk4nXb0elO9bZLkxtOrqBt6JfR2noFF9D1vJzGvfYHPSz8iX3gM1UOsX2yK1enmoT7lPHyaA52m+0cVX1/Wl0/WjeqUtv3nxHdIO96ajgkSBw/p3Kym2btiYLtfp56jOzl0Mv+rsHDpd2b2ZYlbEkFoKzHlVOhoJ/ejSaFTFRRkAhKSVP+pr4e1FE0H3YQJpyZJVvHrl4Vfvyxcl2kpPBJB+Q99Ubb5I7vEzeRfycB0qZqhM508ftjMvszuG41RVYl//XMs/WPyGTG6Y9freQYVkjuhGOm39iXJ8aRjrk8jRnfejAl7lZGK5bGcjONqKrBPa+Sxn/UUVZ2M32H7qKqCw7URp7IXVS1HkjzRaUaj18xs8DMXoNrxIS5ld73XZSkQi+F2AJzuPdid36FiR6cZg1F3vM6pohRT5XwLi/4WJOnUK7PS2+3amdXdXRBOMiJIFNpEVVUyMxMwGswNbvewirnxQsfR6lwEDU8maHgy9uuMFOzpR9WGcKQ93t3dtR7H3+Hi+Zgyvgq38NKvGuimEKO6WuLqhXP49sd1BARWdGjbnn9LpPy3ce1qw8PdMUHi+EnpHdJOQ469Pw6pqHMSAXWnYqOW5+ONbE8V6w4b43Bvxen+DaPuYmQpCLd6DJvzIySM6LVTGjzGqJuHytwTXlGotL+MVo6t+UqtxOb85I82fah2rMYl90erGQyAzfUZBu1cESD2Unv35KIoKrIsHroIHUM8jhfapKqqjKLiXExmjwa3iyBR6CwGs42wKYeJeeQ7gletR3fDUZTIqu7uVo+iUWG+oZI1s2xE+nbf9NPcHA2LLzofh6NjP2oChqS2e82qwa1g0rXvvdHpVUZPSG1XG40pSOyD+llop7TdXVwaic/sFi7aYBYBYjPcShpazRC0mkHIsg86TSxaOQa30vjIvCQZkSVr7b+afW3otGMAUNUiJIzoNMPRyOFo5H4oas1IuNO9FwkNOs2wrvj2hE5QUeHgaLyoTS10HBEkCm1SWJRNdXU5ZlPDKdktIkgUuoCHbyl9z97DgGVf4vffLWgWpqH4OLq7Wz1GuMPBGyPLuWJU95XKOLDPxO3Xn9ehbcoaBeOFqe1up69P+4LEyCgn+g5KgHMil1NL0UujO7zd7nRUb2DxLg9e/lWLcgrVemwrjdwXlzsRRakJ4txK9h+B44AWt+F070Qj90eWaqZmy5I/Kk7cShaqWoVbPYYsBaOq1dhd32PQ/a1Tvheh6+zZk9PdXRBOImK6qdAmhYVZOF1OdDpDg9vFSKLQ1XzCc/FZmIuyQKbgaATlW/qi/BCAZD+1Ryz0bpWrPCuYfKaBB38xUFDZ9TfoX3ziz6Ch0/nHXVs7rM2gKUdJfzMGydH2n28fL5W4vLb3YciwzrnOHft6JHJ6/YRgvVG5QcOrqSbWHz21/w5bS6+ZDqqdSseL1EwZV9FrZ6PTtCwhlKKW4VaOYtRdUvuaJJkw6i7G5vwAFSc6eTRazQBszo/RayahqMVUO9YACnrtGeg0sZ3yvQmdJylRJAwUOo4IEoU2yc8/hiw1PhAtRhKF7iJrFAIHpxI4OBXH3/UU7I+iamME6q8+SCdlCpCWGeS0s3qik/9mWvgmrusnkTz/5AAGDclj1tlHOqQ9g6Ua+ewc1M/C2txGaMOz5Vts1Njc9jXQgNIcf5yro3r9b6pbltjsNPHsD1ocrt7+3XQ9l3IAp3svRt0lyFIQipqFzfkVsmRFpxnT7PFO924kjGjlIXVe12mGotMMPeE8ybiVbAz6eVTan8OovxQZK5WO/6KRo5Cldv6RCF0qOamku7sgnETEdFOh1VRV5dixBIxGS4Pb9YZq9Hox5U/ofnqjg9Dx8UTf9z1h736D4dYjKAM7NolKb2J2KdwVVM7TM+yY9V27VtHtlvjndVNIiA/ssDb95hxt1/GBpvZNFR0/Ja1dx/+VqkjkvTIOyd27P5rTDXpuPGDliZ90IkBsI7vzW/Ta6eg0I9DIwTWZTbVTcbiaH41XVRWneydazSgkqfGxAFV1YXd+jlF3PopaCCho5X7IcgCy5I+iZHTgdyR0hZTk4u7ugnAS6d2fREK3KCsrpLSssNH1iB4eYhRR6HnMXhWEn7mfAc9+TcCbm9AuSUYN7Nwi6D2RBIxXbKydXsnEvl1bpqa8XGbxxedSWtwxUym9w/JQR7X9ehNgaHugbDarDI3NbPPxDcn8aSjSfs8ObbMrVes1vFrkweINJhIKRHDYHioO6mcmllFbULrFraSgqoXoNGOb3M/h+gGNPACNHAaof2lbadG5hJ4lJaWku7sgnEREkCi0WlFxDtXV5ZhMDU9DEVNNhZ7OK7iAyAt20P/Nz/F+YTvSeVko5u5L7tIdvB1unogq576pTjRS140qZqRpuWrh+SgddP/pcV5im4/1aUct1/4xNuQO/AStKrVS9drAjmuwCykS/CKZuHibhQ8PiLWHHUErD8bh2ozLHYeiFON0H8Lp+gmdfHyqqN25nmrHB/WOdbp3IEvhaOTgRtt3K7k4lf0YtLMAkKUAJCQcrh0151Tz0ch9Ov4bEzpVUWE1paXty/wsCH8SQaLQamVlhSiKG6224fpdImmN0FvIsop/9DH6X/Uj/dZ8jscju2B6AWo7gofeRFZhlqaKtWdWMyio677n33/x4L7bzm1+xxYIjE1BCWjbiLCX1PbvedjwkjYf25Cst8chV/a+NAG5Rh13HLVy3xY9lXYxethRjLq/odUMw+b8nErHC9hd36DTjEf/R1AHoKjlqGpJneNU1YZLOdTkKKKqqtidn2LUnoMk6QGQJB1G3UU4XD9gc36MQfs3ZMmrU743oXOJdYlCRxFBotBqZWVNZ88SQWLv9eKzJXgbUvjXnY3XWlr9Vhlnzcyib1AafYPSmD83m1076t6kL3+hlOg+aUT3SWP5i3V/H3b+bmP6xExcru6r39cQrd5J8KhEou/cRPi7X2O86xDq8LLu7laXCHI4WT6kghvGu7rsnGtXh7LqzYntbkejdWO4oG0F7T2Uto8ejxrXcanmc/f3g80BHdZeV3DoZP5XYeHS78zsyxK3Eh1NkgwYdfPwMN6L1fgYHoa7Mehm11ljaNJfjNlw3V+OM2I1PopeO76JtiXMhhvQagbXeV2rGYyH8V48jPej147r2G9I6DJiXaLQUcSVXWi1goJMtFp9o9vFdNPeafdOO++sKGdobOM/W4Cfttm4cIEHX24I5vutIYSFazn/nByyMmsCjIMHHDz5aDFvvRvIW2sCeeLhYg4drElk5HKp3H5LIS++4o9W23NHHYweVfQ57SAxj39L0Fsb0F2bgNrn5J7Co1VULjFV8vasakK9uiaAf/T+WLZvjW53O4GnxaNoWz8qaHIpaOW2fa8TpyW36bi/ctgMlL48vEPa6goqsE9rZOEvHry1q/eNfArCyU6sSxQ6iggShVZRVZWCgkwM+oYTT0iSgsWjvIt7JbRXRYXCtYvzWPaaP94+TV8WVqwO5JobPBk+wsCAQXqWv+6Pqqhs3VwNQEK8g6GxeqbPMDF9pomhsXoS4p0ALHuhlMlTjYwe23B9zZ7IGlBM33m7if7v5/gu34a8IB3F09nd3eo0UQ4Hb4+p4OLYzl+j6XRI3LjkdDLSfNrVjslaiWZO68tRSEC4d+vP5+2jENWv8dH21jj2wRjk/N7x91Bs1PJgmge3bTJQ1A31NgVBaF5ykhhJFDqGCBKFVqmqLqequhyDoeEg0WwpR5ZPjfVcJ5O7/lnI7LPMnH5G67NOVlWpOJ3g41OTsGLIUD2JCU4y0l2kpzlJTHAyeIiOlCQna1eX88Aj7QsIuoskgW/fbPpd/gv9V32O55O/I83ORdGdfAlvDG6Fm3wrWHaGHW9z544qFhfJXHnhfKqqGl7j3FK+Z7WtHEYf79Z/fwMGVrfpXH9VlBKK+6OenxzEpZH43G7hog1mtqeKxDSC0JOJ6aZCRxFBotAqZWWF2O1VjQaJFlH+otf5+IMK9u+x85/H2xa8PXxfEcEhGk4/wwjAwMF6HnrUh/PPzuGCc3L5z2M+DBys57abC3jkSV9++L6aSaOOMW18Jtt/7Jib7a6m0boJHJZC/1u2EPm/rzD/ex+MK0alZ62zbK9Yl413J1cyM7pzA+HEBD03XHlBu9rwichBHdr6NaShHq3/mcWObHpddku4XRoKlo1GqlfmoGc5qjewZJcHL/2qRVF7dl8FQYCU5JLu7oJwkhALCoRWKSsrwuGwodMZG9wuktb0LscyXPzrzkI+/SYEo7H1z4xefLaEjz+s5Kvv6x5/1XWeXHXd8Xpv771bjodVZvxEI2Njj7F5eyiZmS6uviKfffHhGAy99+ZTb7IROikOJsVRWexJ4e/9sK3vg5xk6e6udQgPp5sHwiqYEWbi8e067J1UHH3z91488eBs7n9sQ5vbsJyfTNWhka06JrgNI6Vjxre/PuKx70b06N+RCoOG/6aaWH9UjBwKQm+SlVWOzebCaBS3+EL7iJFEoVXKywuRkJAbKRAmgsTeZe9uO/l5CtMnZOJnTsHPnML2bTbe+G8ZfuYU3O7Gb6CXv1DKi8+W8unXwQxrItlNYYGbpx8v4ZkX/dj5u43oGC39Y3ScdroJp1MlMeHkWd9n8SkjYs5eBrz4Ff6vbUZzeSqKn6O7u9VuEjCVatbOqGJ0n84bLX1jeV8+fn9Um48PGpWI4tO69zvA0Lrp8ZKktjtpTVmeL/a3+rerjc7iliU2us1c+INFBIiC0AupKqSK5DVCBxCPGYRWKSnJp6kZRyKzae8yfaaJn3eH1Xnt5mvziRmo47a7vNFoGv5hv/xcCc8/XcLHXwUzakzTSTf+fXchN93qSVgfLbt32XGeEBO6XDQZiPZm3mF5eC/IQ7lQpjCxD2Vb+6JsDESy9d7Lrp/DxTPRZXwbYebFXzp++qGqSvzrtrH0jylg5JiMVh+v0bkxnJ+B8+2WB2C+utYFiYFBCgGBFa3tWi1VhdzXxyG7et4z2nSDnsd3G0ko6L0j+4Ig1KxLHDTYv7u7IfRyvfduRegW+QXHMDQy1RTAw3pq1JU7WVitMkOG1h0FNFtkfH01ta9ff1U+oaEa/vO4LwAvPVfCk48Us2JNIBF9teTm1JS+sHjIeHjUvfHdvLGapAQXr79VUwNu9BgDCfFOvl9fReYxFxoNxAxoX8KSnk7WKAQMTCdgYDrOxToKDkRRuSkC9WdfpF64xkujwrm6KkadqePB3SZSCjv2e7BVS1xz2Wy+2foBgcGtz5QccHo8maujkNwtC8K8pdYFiQMHV7a6TyfK+mUw0k7vdrXR0ar1GlbnGFl3QNwSCMLJIFmsSxQ6gPhEEFrM7XZRXJSLwWBucLtG68BoquriXgmd7ViGixNnF7/1ZjkOByy+NK/Ofvc+4M2/Hzye/Ka6WuHu2wp5e20AslwTSIT10fLMi37cfF0BegO8tjIAk6nnjah0Fp3BScjYozD2KNXlFgp29sP2XTjSYWt3d63VwhxOXh/h4v9KLKza3bHTEnNzNCxZcD6fbfwfen3rgjizdznSzHz4PqhF+3uorWt/xKiCVu1/oupyC1WvDOkxqWoUCX7DxBPbdFTae0qvBEFoL1EGQ+gIkqqqJ+dcL6HDlZTk8faqhzCbPfGweNXb7uFZzIw5n3RDzwShdyvL9aP4lyjs34YhZzc+Ut9THdUZePA3PXnlHRvwzzu/gFdXtf6aUpgcRvFtU1u0r1uCM7d4QgtDt9UfbGPmnLhW9wkg8b/T4bvgNh3b0XKNOp46YGRv1qnzkEYQThVnzu7HJ58v6O5uCL2c+HQQWqysvAi7vRpjI+UvjEYxiigIbeEZVEjf83YS89oX+Lz0I/KFx1A9XN3drRYb4LSzanwl5w7p2FIZX37qz7JnTm/1cX79MnFHt2yqqkaFYM/m9wOQNSoTJrctaU3e4cgeESA6dDJrKyxc+p1ZBIiCcJIStRKFjiA+IYQWKystxO12otE0vIbMYOydNe8EoaeQZBW/fln0W7ydqNWfY318B8zMR9W0bkpkdzC5FO4MqODZGQ4sho6boPLCUzF89UnrM4F6XJTS4n37eresv+HhLizW1merddp1lLw8otXHdSQV2K81svAXD1buEitNBOFklp5Witvd8z83hJ5NBIlCi5WV1xSQlqSGp2WJkURB6DhanYug4clE3/YDEWu/xHTvAdRRJd3drWaNVapZO62SKVEdc4Pidkvc/Y8Z7N/TwuG+PwSNTcRhsrVo3zDPlgWJg4a2PpEOwLFPx3TrNOJio5YH0zz45yYDRZVi7aEgnOycToWMDJFIUGgfESQKLVZUlINGbvwJtEEkrRGETmEw2wibcpiYR74j+J316G44ihLZc//evJxuHu1bzgPTnGjl9o8qVlTI/P2ScyksaHkmXK3eiX3mkRbtG2xuWR9Hjs5v8fn/VJwRhOv/Ilp9XEdwaSQ+t1u4aIOZ7ami5qEgnErS00RJMqF9RJAotFh+fgb6RtYjgphuKghdwcOvlL5n72HAsi/x++8WNAvTWl1AvivIKpwhV7H2jGqGBLd/VDEv14NLzz0DpRXLHiPOTUdpQfbSQGPLgsRxk9NbfnJAccvkLxvbLaVOjuoNLNnlwUu/dnw9S0EQer7iInFPJrSPCBKFFrHZqiivKMbQRJBoFEGiIHQpn/Bcohb+SvTbn+P59G9IZ+WgGjo2eUx7BTqcvDy4gpsntD8RT9yRCG64fEqL9/cLs1E6svm1ib665gNJvV5l9Li0Fp8b4NimWOR4j1Yd014VBg1PZ3tw/fdGMkvFR7wgnKqKi1s23V4QGiM+QYQWKf8js6lB31SQ2HOnvwnCyUzWKAQOTqX/jVvp++4XmB/YCxOLUOkZFY60ispFxkpWzaom1LN9weK33wzl9efHtnh/r3OONruPt9x8kBjVz4GuBcHknyoKvbG/GdPi/dvLLUtscpu58AcL64+KqaWCcKorLhJBotA+IsWZ0CLl5UU4HLYmRxLFdFNB6H56o4PQ8fEwPp6qUiuFO/phWx+OdNTS3V2jr8PB22NdvJ4p81l82/vz1BMjGDq8nGmz4pvdN3p8GXF9K9GnNX4+T7X50dchsa1LApHzxjgkR9cEaxkGPY/tNpJQIKaVCoJQo6REBIlC+4iRRKFFqqorUFUVWW74pkdV3RQVGlFExmVB6DHMXuWEn7mPmOe+IuCNTWgXp6AE2ru1Twa3wj+DXTw1oRCrrm1rKd1uDTdeNY20JL9m95Vl0MxLbHIfcwtSxY8cm9Pi/mXvGAC/+rZ4/7aq1su8XmThyg0mESAKglBHcbF4cC+0jxhJFFrEZqtocntxkYaxgy5F1qhYPVQ8vRS8fVz4+Djw8bfj72/DL6CKwMBKAkPKCQ4pIzi0BB9fcREThK7gFVKA14UFKOdLFCX1oXRbJO7vg5Crumdq4gSjllUTy3k2UeLX7NYHVKVlMosvnM8329/FbHE2uW/E1CTS3xqGprrh71WnqHga3JTZG38vJk5JbVG/bJUmypcP7dQnsIoEv2Pi8W06Ku0iOBQEob4SsSZRaCcRJAotUl1dAU2sb6qsrPmv4pYoLZUoLZXJSNcCTdcG0+lUrJ4qXt5uvL2d+Pg68fWz4RdgIyCwkoCgSoKCygkOLSU4rBSLpedlcRSE3kSWVfxjMvCPycB1hY78Q32p/KEv6k++SO6unVziK2t5IkZlQ0gmyw6EUO1s3fmTUrRcd8nF/O+r95rcz2R2os7LhA8aL0UR6mGnzG5ucJvFojIkNrtFfcpcOxa5RN+ifdsi16jjqQNG9maJiUCCIDROJK4R2ksEiUKLlFeUNDrVFKCy6YHGRjmdEkWFEkWFMvBnDTSvRvc3GmtGKb283Hj7OvHxdeDnZ8M/oBq/wEqCgioJCikjOLSUoJBS9Hox/1UQGqPVOwkZlQijErFdb6ZgVz+qN/RBOtD432BHkyWJuVYPRk4qYNkxH35JbnktRICtP3rw6N1n89Cz3zS5X9DseAqbCBIDjHbiaDhIjB7Qsput/KPhqF+Ftmjf1nLoZD4sNrFyq/jYFgSheWK6qdBe4tNGaJHyskJ02safjldUdM2UJ5tNwmbTkJerAfRA48koJEnFbAZPLzfePm68fZz4+Nrx87cREFCFf1AVQcHlBAaXERJaRkBQGbJ4OC+cooweVfSZfhCmH6Q834eiX6NwfBuOdKzp2QAdJVgy8Fh4BVujjDzxgwFFbfkf48oVfRg8bBwXL97R6D4+gUVkTyxC38hawQBD47MUhg0vbrYPLoeO4pdHdfg0UxU4oDXyyC96iirF1FJBEFpGTDcV2ksEiUKLlFcUo20iSPxzumlPoqoSlZVQWaklO0sLGIDGa5bVrqf0VvD2rrue0j+gigCxnlI4RVgDirHOK0Y9dzfFaSGU/BSFa30wclnrRvlaS4PMTJeDEee4eGCXmbjslq2XVFW47+5R9B+Yx+iJjdcy9PxbIrZfxze4LaCJ+pKjxjc/1TTjy1HIGY1nf26LYqOWF4+a+DFFPL0SBKF1xHRTob1EkCg0y+l0YKuubDJIrOqBQWJr1VlPmdb+9ZSBQZUEBpcTHCLWUwq9kySBb2Q2vpHZuC/VUBgXQdnmvihb/JGdnZfwxq9c4ZUhFXzaz8h/txtadIzNDtcsms23P64jKLThchWhQ1KJDxuOLrP+33agsfE115OnJTd57tKsAFz/60tHjfO5NBJfV5lZtk2DoorRQ0EQWq+83IHLpaDViodMQtuIIFFols1WgdPlwGRsfBSurWsSe7NOW08ZVkpwSGmrCncLQmfTaN0EDkshcFgKjmuMFOyNpPL7CKSdPp1zPhdcpLUx4Vwn/9puIqu4+aA0v0Bi8fkX8sW2NegbGBmUZRXdBSmwfHC9bYGmhm+kfHwVwvs2Pt1UUSRyl49F7qCkPwl6A4/8riezVNzYCYLQPiXFNvwDGl5rLQjNEUGi0Kzq6kqKiytwmGUkSY/RaESjqXsDU3ESjCR2tvaup/T1s+PrV3c9ZVBIGcEhYj2l0LX0Jhuhk+JgUhyVxZ4U/t4P2/o+yEmN/063VXi5m3fGVfJ2oZF1u5rPGnooTsc/Fl/IG+9/0OD2sClHyXhjAPJfCt376xsOQgcMqmryfJlbhyEf8my2X82pMGh4Nc3Et/HdU5JEEISTT3FxtQgShTYTQaLQrOzsIn7ckk9VdRqSVPM03mjU1PwzaTEZtaSnDQM6Z0ThVNSZ6ylDQkvwFusphQ5i8SnDMmcvzNlLSWYgRdsjsX8ZjK6049bn6e0qN3hUM/0cF//6wUhZddNPRL751puXHjuD2x7cVG+byWzDfU428qd96rzuLTW83jJ2ZFGj56ks9qT6tYHtSlbjliW2uEw884MWh0tMLRUEoeOIdYlCe4ggUWiW06nBxyeKQYN8sdmqsdlt2G12bHYbZSUOCp0OSkvE0+/u1JnrKUP6lGA2N12sXBAAvMPy8F6Qh3KhTG5cMFlf+mPe0R+ds2PqBg6ucPJ/p7t4MdXExiNNJ9F56cX+DBqWx9zzD9TbFjgnnuK/BIlGRcasV6hy1A35xozPbPQcWW+NQ7a1/dqXYdTz+G4jR/NFcCgIQscTQaLQHiJIFJplq7ah1eoICQlpdJ+M1Cqqq8Qaut6izespvf8sJfLHesrAavwDKwkMFOspheNkjULI0CxChmZhqzpE4jZvqrdF43WoL3I7E7GYq1TuC6rijAg9/9lkwOFqeBzP7Ybbb55EZP8CBg2vm53UL7SA3DEl6Hd513m9n7/KwazjX0uSyqRGktbk7IlG2ubfpu+hWi+zOtfMuv3i4ZogCJ1H1EoU2kMEiUKzqqpsSFLTN3aKu/HMgELv19r1lBYLWD3FekoBjGY3w+YWwtxCivN2kflrJPJPA9DHWdvcpqTCRJuDD+a4eOSAiT3pDX+UVVTC3y85hy+3rcY/oO5ouMffEnHsGlvntb6+CgezjgduwSEKfv71F1zbq4yULYtt9TRTRYLfMfHkNh3ldjF6KAhC5xK1EoX2EEGi0KzqaltNIbImuMXAkfAHVZWoqICKitatp/TyduPl7cLHxynWU56kfAJd+PwtEf6WSGG2LwW/9EdZH4oup+np0Y3xqlB4LrqS7/rpeWaLARoI245lySyYdTbf7/oczQkDd2HDU0gIHIY27/i5w6x1L2SDhjSctjlz3RjkwtZNoc0z6lh6wMjeLPFERBCEriGmmwrtIYJEoVmVVdXQTAUwRQSJQhuduJ6SNB3QdMKT+uspHfj52/H1P2E9ZUgZISFlBIeJ9ZQ9lV9IEX4XFKGcJ5GbHErJtijk74PQVLbuY0l2w1k4GHpmBff9ZiazvP4od0JKEFcvmMGqjzfXvqbRKEjnpcKbg2pfCzLXvZDFjiyo11ZhchjKX9YzNsWhk/mw2MTKreLjVhCEriVGEoX2EJ9aQrPKSivQaJteO6PUL0kmCJ2i/nrKptN7i/WUPZssq4REZxISnYnzCi3ZB/tSsbkv2h/9WlV7MMKpZ+XoalbnOXn/iHe97Zs2xvDai05uvP2n2tdCp8Vx7K1odO6aj0J/Q90ZE+Mmptf52u3UUPTS6GYemdVQgQNaI4/8oqeoUkwtFQSh64k1iUJ7iCBRaFZpWTk6XdO/Kooi1iQKPVNHrKf087fh7y/WU3Y2nc5FxKgkGJVE9fUmsnf2w7YxHP2+xpMqnciIjusD4Yw+1fz7ZyMFfwnOnnl8CIOG5jNjdjwAVi87pZOO4P9TLAA+8vEHBBqNyrjJdZPWZHw7Eim1+ZpjJUYtLxw18WOK+OUQBKH7lJXZu7sLQi8mgkShWeVllei0Tf+qiDWJwsmgw9dTBlUQGFwh1lO2gclSTb/ph2D6IUoLvMn9tR/u9WHo0psP0qIdDt6d5OK1TDNfHDk+C8Llgpuvms5XWwroF10IQOh5qTj+CBKtJ6y9Dutjx2I5PkWiLNcP5zv9mhxFdGkkvqk28/I2DUo7s7gKgiC0l8slHuALbSeCRKFJqqpSUVHZ/EiimG4qnIJavZ5Sr+JpVfH8y3pKv4Bq/AOqxHrKRnj5l+B17m44dzd5aUEkf+WLx0/9MVY2PipsdCrcFljB6SFG7tumw/ZHqYzycrj8gvP4fvu7WKwOIgZUkTi2GHb6YHYev6GKiDxW+/+qIpH76jikJqa/JugNPPK7nsxSMXooCELP4BZP8IV2EEGi0CS73YHT6Wp+TaK4DglCs5wOicJCicIOWE8ZEFhJQFAFQcHlp9R6ysC+uQTenIvzuiMk7fJEs38YbAhFctS/RknAKLeN96fbeXC3yoHimmmrGWkarrjoAj757n0ArBckUr5zHDqHilaj4HLLxA7PqW0nc/sQpD0NT3mtMGh4Nc3Et/Gi5qEgCD2LKE8mtIcIEoUmud1uFEVBp9M1uZ8IEgWh43XGesrgkDKCQ0vxDyzv1espdToYNLEMJv6Mc4mOwsN9qfi+L+qPfkh/mRTq5VJ5IVbh64oylu+x4lYldvzqyQ1LxvH6qh34D06jLGA4Ur6Bvn4KSXkyp51RMx21qtSDqlcH1Suu4ZYltrpMPP2DFodLTC0VBKHnESOJQnuIIFFoktutoKogSY3fBLnFkypB6HbtXk/p68TH74T1lME1o5RBweU9fj2lTu8keGQijEyk+kYLGT/5Y/uqL+b0kNp9tJLMfKvKhDOreXifkfg8ma8/HcmHs9xcvGg3hktScLwyiCBTJWkaCxOm5gOQtWoc8l/KcmQY9Ty+28jRfBEcCoLQc4n7M6E9RJAoNElRFBRFQZYbvxkSo4iC0Pt01nrK4JBSgkOL6yR96Uomj0oGzK2EuWnkp3lQuXMIjm9DkfMMAAQ7nCwf6uItLwfrEvy5744xRA8oYPCUBNJfiyHQYCc8XINOp5B7MAo2Bda2Xa2XWZNr5v39YmqpIAg9nwgShfYQQaLQJLfbjaqqTY4kiqQ1gnDya8t6SqunG7OlCrO5HKu1Cl9fG0FBCn36KISFOwgKLSekTwnBfUo6ZT1lQN8KAvr+jnK+RNoeTwo2R+K5IwZdtYYbQnXMCMvj8X1+XLNoFt9sW0fVtCOEfBaNNMSOw2ag7KURSIAiwe+YeHKbjnK7GD0UBKF3ENNNhfYQQaLQJEVRmw0SxTVIEIS/qllPqQU8//jXOAkwm8HTquDlVTNS6evrwC/Ahn9ANf6BFfgHluLrn0+fyApC+lSjacVgniyrRI0pJWrMPhyOw2Ts6UPeFz7EHOrH22MrWVVsZME5c3hr5bf03TKSkHGpZH48GinPQJ5Rx1MHjezJ7MULOAVBOCWJkUShPUSQKDSpRSOJirgICYLQdipQWQWVVTLZuc2PVEoo6PVOPL1lfH1VvHyceHlXo9HmEBJWyZChMsGhlXh75xHetwq/AEftsXq9k/4TUug/IYXqysMUH4jk71+FMXynjheWTmXe4Er8+1dQvXIwH1WaWbFVfEwKgtA7KWI9kNAO4tNPaJKiqKiK2vSaRDHdVBCELqQiY3cYyM+D/Dw4nvnVv8H9dVoVk9GFVltJcB8TWl0R3j6lDBlmxM+/EktkCpb+buaX9Ce/0Enl+v488YuVgkoxtVQQhN5LjCQK7SGCRKFJLRtJ7MIOCYIgtJLTJeGs0AHeFJUAhAAhHPxNITaokn5mb4YFWZCnFqFUa7F5Orj58lwqpGoSs6pJTTFTUOhFeaUX5WUyqrjvEgRBEE5yIkgUmqQoCoqqIkmNr8cRaxIFQejJNJKboYFuIg2lDAs14+cqJdrTgKdbi9NkQzk3AWd/hQ+fmMLAoSXYKjSY/TLY/1UQZ/YxEbWoCN8pCQQNzERR3STEBRF/OIikBF/SU73IzDCTm2OgsEBDdc+tFCIIwilGoxGzIYS2E0Gi0KSaOokqTQwkoojpDL2W3bUFh+s7dJrJGHXzGtzH4fodl3sPbjUHAI0chkE7B40cfsI+23C4tgGg105Hr51Wu82tpGNzfo5ZfxOSJEoHCJ0r0OxgdB+JMF0ZkUYbg3xNeDtAi4aaaakuMFhweVfjOO9XIs/I5qdNg3jkyim8dkEOazMVZk87zNZ3BrP4gUO89t5YbCv6snijnSqqcY8vwTwhh1lzkrnosj31zl+Q58GRQ8EkxAeQkuhDRpoHWZlm8vN0FBdJuMX0fEEQuogsi4RbQtuJIFFokqIof6xJbPxCI6ab9k5uJQOn+3dkKbiZ/ZLRaoZjkOcBWhzubVQ53sZiuA1Z8sKtZGN3bcSkWwyoVDtXo5Fj0MjBqKobm/MzjLrzRYAodCiT1slQ/ypiA2GIr55wg4qX3YFR/fOJlhbwAEfd4yr8i3HP38PwswvR6RRefnIiLz43nOXTK5En72X3AyO5aISVlBATJWumcf+dO9ibZuS2u8cTYzVwNWYGbvcjn6Fk9atEnpKH1+hMAiKz0WgU/AMrmBaYyLQZifX6rCiQlBDA0cNBJCb4k5rsRWaGBzlZBvLztVSUd/rbJgjCKUSMJArtIYJEoUktGUkUeh9VtVPtXIdRewEO1w9N7mvSX1rna6N0ARXug7iVJGTNaBQ1H1kKRqvpD4DsCkZR89EQjMP9Ixo5qs6ooyC0hiQpDApWGR3mZqCPmwiDG3+XgrlaQeKPC5PqABtA4xeqyqhMdPMOMmx6CVoduN1w7SWns379AC4aUM4A7ISenkVW0Zl4JMvc/dBv3HThWbzx9ChG3r2PrTv/jxsWz+emjRYGBem5fqiT2BTQJEdR8W4UpVYn7imFWMZlEzgkA5Ol/rxTWYaYgfnEDMxvuI/leo4cCuFoXCDJiT6kp3iSlWkmN0dPUaGMw9HgYYIgCA1qKumgIDRHBIlCk1qyJlGrExeh3sbm/BytPAitJrrZILE+J+BGwgSALAWjqAUoagmgoqgFyFIQilKI070Ti/4fHdx74WTla3Ezpo+D2ECV/laVEEnB06agcf2xg4MTRgZbdt0piknFcuEhRkyuOP5agZZLzplNXFwfQsw2lgQ5KJ+SSmW1mbJKHZpsI336l+Dbz8aqAgvXPRVL2d0SH327ljeXTeHZJ4Zy+w8GInz13DDcyTjJhrZch2Z9MM71wRyTRuIcWYZ+Ug6+IzLwDSlsUV8tVgdjJ6YxdmJag9uPZXgTfyiIhKMBpCZ5k5HmQU6Wifx8LaUlkpjVIQhCHWIkUWgPESQKTWpJdlOt+C3qVZzufShqFmbdzW063u76FknyRCNHA6CRAzFo51DleAsAg3YuGjmQKsdKDNqzcClHcbg2ATIG3Ty0clRHfStCL6WV3fT3tjHAq5p+FieRJpUIgw5fjf74TpVtb19FoWJSHP7nH2X8IHudbQf3enDZ+XMoLvIDFB6KLcUiG/E4P5c9OyIAcEsSRYnB/OPOHVy18DSmnGFi2LOxJLllrrt1O2eeFc/1V55L3GE9923RE2jVcv1IN1N11ejdKpIqod/jBXu8KGIgeSE2mJaHdXQWgTGZ6HSuBnrdvD7hJfQJL+GMufH1ttntGhLjgzh6JJDEo76kpXofT6iTr6Gqqk2nFHogu2sLLvdBFDUfCR0auS8G7VxkOaDRY9xKLg7X97iVTFRKMGjPQa+dWmcfp3sPdud3qNjRacZg1J1bu01RiqlyvoVFfwuSZOy0703oWBqNWJMotJ24vRea5HYrzdZJFEFi76GoJdidX2HSX4Uk6Vp9vN21Bad7P2b9tXWO12snoNdOqP3a6d6FhAGNHEGl/QUs+ptR1FJsjv/DYrgHSRK/NKeKCB+V4cEKg3wUIk1uQmQ3Xk4XGhVA98e/juGWXJTOOELEhUnEhNvrbf9snT933DQHp8sCwFVDSxlkNGIPr6BfVCb73q158OEEyg74MGvJDiIiJ/DAdh1rJtvxfGEocQ43g2YfYf1Pq3js/pmsWtGfvHKZx36U8TRo+PtwB7M8HFiU4wm9tNlG+CCC6g8iSDa4sI8rwDgum6ARmXj5tiMaPoHB4Gbo8CyGDs9qcHtBgYkjB4KJP+JHcqIPGalWsrPMFOTrKSnW4HaLEYfewq0ko9dMQpb7AAp213d/rBO/HUnSN3KUA0nyxaCLxe78ut5WRa3E5vwEo+5iZMmHasdqXHJ/tJrBANhcn2HQzhUBYi8jRhKF9hB3au0UGRnJbbfdxm233dbdXekUNdNNlSZHEnVacRHqLWqeIldQ5XjlhFcV3O5UnO5f8TA81ujU4poMplsx669GI4c0eg5FrcTu2oRZfz2KkoEs+SPL/sj4g1NBUQvQNJMsR+h9PAwqI0JVhvop9LcqhGld+LvdGE6skdO2AbRmufROKs8+StS8BAYG1A8OAZ75Tz9eeXk6qloTlEZ5lHCJb00gVzJxN7IMv26v2bfCbkfzswWWwIULf+HFpafzyD6FZ4eC9pXh/JxfxuRFmTz81A/MnL2PGxafQXmZN2V2mZd3GHlV0jK/byEXhcgE6evetGvsWsw/BcNPweQzirjQHByj0giakEf/2Co0nZTfyd+/mmkzUpg2I6XeNrcb4g9b2bvbm7hD3iQnepKVaaUg30JFmQWn00BLp/cKnc+sv6rO10bdRVTan8CtZqKVGp6poZHDa9eG253r621X1SIkjOg0w//Yvx+Kmg8Mxunei4QGnWZYx34jQqeTxUii0A6tChKXLFlCSUkJn332WSd1p3mnn346W7duBUCv1+Pv78/o0aP5+9//zgUXXNBt/TpZud0KqDQ93bTjBgKETqaVozHr/1nnNZvzI2QpAL12eqMBot21FYdrM2b9VWjkPk2ew+78Cr1mak3mU44Bx3P+q7gBsXCqN5MllYGBKrFBKgO83EQYFIJwYXW6j4cRCvWyinYGl7cD6aIUwmccwWJtODgEuH7RGL75ajR/BjoaycVDQ8vRy564ZBdDzq4p75KV6QmAU1Xwzw6gvFTmn3cfZcUr49ld6M2GqlLmWiBw3VQOeuxn2PwjnDazkD2JH3HbdXP4+vM+qKqEU9XyUWoQn6YpLBhi4/xAJwGN/Nr7ZQVDVjB8DfFedhzjs7GMy6JPbA4mi7ND36/GaDQwJLacIbHlQEa97RUVWg4fCCTusD9JR31IS/EkO9NCfp6RkmItTqe4Ee1Wqg2gdp14W8iSPypO3EoWsuSNWz2GThqLqlZjd32PWX9tR/VW6EIicY3QHr1yJPHaa6/l0UcfxeVycezYMT799FMuvfRSlixZwptvvtnd3esWTqcTna7jozVFUZp9gCzLErIsSmH0BpJkqDeKJ6FHksxo5JrXqx0fIEueGHRzgT8DxO8x6i5FknxQ1PITjjPUacvlTkBRCzBqLgZAI/dBUfNxueNR1FIkZGSp8XUzQs8SaFUYGQKDfBWizG5CNW58XS60J0ylpGvimDqq/ctxn7ufQXMzMZobr9NaXqZhwTkzObi/7ujKLQPTiTT6AVA6+giD/GraKCux1uygU5AlmZxEKzFjSrngkv2sWTmRF3Z7MGhCPpF6E8a3hrOz0s7Yy5IxGBReW/0t3309kHv/MY3Cwpqgya3K/N8hM/93SOWiWIWLg+0EOhp/w/SlBvTfR8L3kWRoFFxjStFPzMF/RAbeAcXteMfax8PDxfhJWYyf1PBU1uwsT+IOhpB41J+UJG8y0qxkZxnJz9NRUiwS6nQmVVWwub5CI/WtvYa3hSSZMOouxub8ABUnOnk0Ws0AbM6P0WsmoajFVDvWAAp67RnoNLEd900IncZoEKWnhLZr8+O/9evXM3XqVLy9vfHz8+Pcc88lKSmpdvvkyZO599576xyTn5+PTqdj27aaotvvvvsuY8eOxWq1EhwczGWXXUZeXl6z5zabzQQHB9OnTx8mTpzI008/zRtvvMGKFSvYuHFj7X4ZGRksWLAAb29vfH19mT9/PqmpqQBs2LABo9FISUlJnbb/+c9/MnPmzNqvf/rpJ6ZNm4bJZCI8PJxbb72VysrG15Ckp6czf/58PDw88PT0ZMGCBeTm5tZuf/jhhxk5ciRvvPEG4eHhmM1mFixYQGlpaZ12Vq5cyeDBgzEajQwaNIhXX321dltqaiqSJLFu3TqmT5+O0Whk7dq1zb5vbeF2u4+nmW+CWJd48lDVktpAEMDp+hVwY3OupdL+ZO0/h+vHvxznxOb64o+aiDWXFlnywqD9GzbnRzhcmzHqLm7TWkihcxm0KmP7KCwe7eax6Q5Wzarm6zPKWTe6nH+HlHO+oZKRbhuBDmfdALGLlYfkYbtpI4Pf+IaRFxxrMkBMTjQzY/x59QLE8YF5/C3Qt/Zr66xUAIoKdFRW1qxVdKk1UU310ZqRxfsePYyH1Y1T0fBCqhcuqWa79/vj+P1/x8u7zDknnk2/r2bkmL9O6ZT46ICGS74383SOlUx9838DsltG/7sPLBtMwdWzOXLLPOLen0D20T64XT3rxi8ktIwZs+O59pbtPPni17z7yfts/HUV+5JXkJi3ku+3f8Hylb9y+70JnL8gj/ETK+kb6cJi6e6e93521xcoSi5G/cJ2t6XTDMViuA0Pw90YdGfiUpJxK9noNOOxOf4Pg+5cTLpF2Jwfo6gVzTcodDurp6H5nQShEW2+ta+srOSOO+5g+PDhVFRU8NBDD3H++eezd+9eZFlm0aJFPPPMMzz11FO1UxXXrVtHaGgo06ZNA2pGvx577DEGDhxIXl4ed9xxB0uWLOGbb75pdX8WL17MnXfeySeffMKZZ56J0+lkzpw5TJo0iR9//BGtVsvjjz/O3Llz2b9/P2eccQbe3t58/PHHXH311UBNQLRu3TqeeOIJAJKSkpg7dy6PP/44b7/9Nvn5+dxyyy3ccsstvPPOO/X6oChKbYC4detWXC4XN998M5dccglbtmyp3S8xMZEPPviAL7/8krKyMq6++mpuuumm2kBv7dq1PPTQQ7zyyiuMGjWKPXv2cO2112KxWFi8eHFtO//61794/vnnGTVqFEZj5ywmV1t4P6jVSjgc3XfzKLSd2XBdk197GOs+7GmMJOnwMNxZ73W9dhx67bi2d1DoUFF+NYlkBnorRBrdBP+RSEY+8c+3h9XjqxyYhfclRxk5OpcmqvHU2r41mGsWzaaivO510UPn4N4YFfmPz6QqvwKGja9J+/nzj378OW3C7tCAEaQDEbAwA4vFxay5+/n0w1EcyDbx3QCFc/5YYOn7wWRS/Y8QOXc/AH7+Tr784Xveej2GpQ9Nwm6v24f18TLr482MCSrnyv52YnXaFj2I06Wb4b1IKt+LJNHsQplciGl8NgFDM7BYe27qUp1OYdCwHAYNy2lwe0mxibhDwSTEBZCU6EtGmpWsY2bycnUUF8k4u2GkurewOT/H5Y7DrL8OWfLq0LZV1YXd+TlG3QIUtRBQ0Mr9gJqpqYqSgfxHUhuh57JaG0tkJAjNa3OQeOGFF9b5+u233yYgIIDDhw8zbNgwFixYwG233VY7Egfw3nvvsXDhwtqg8aqrji++7tevH8uWLWPcuHFUVFTg4eHRqv7IssyAAQNqRwrXrVuHoiisXLmy9nzvvPMO3t7ebNmyhdmzZ3PppZfy3nvv1QaJmzZtoqSkpPZ7W7p0KYsWLapNShMTE8OyZcuYPn06r732Wr3AbNOmTRw4cICUlBTCw2ueLq9Zs4ahQ4eyY8cOxo2ruVG22WysWbOGsLAwAJYvX84555zD888/T3BwMP/5z394/vnna9dYRkVFcfjwYd544406QeJtt93W6eswNRqZloR+YiRREHoWq1FlVKjKEH+FfhY3YVo3/m4XevcJf9GdlEimw0zPx/fCI0RHZrf4kA/eHcm9t43G5ap/Ubp7aB6+Wmvt1/bp8ch/DMrt3+NT+7pNrfnM0B0JxumQ0ekV/vWfQ3z5SSwut5bntpoYdHo2/dWazynXq4NJUzX0PWtPbRtX35DA2X/L4x9Xn8NvP9f/PNuVa2VXrpUBPmVcGVXORA8LmibWfp9IU6VFszEI18YgshiBM7Yc3eQcfEZk4heWRwub6RG8faqZODWFiVPrJ9QBSE32Jf5w8B9TWb3IzPAgO8tIQb6WslKpxQ8yTyaqqmJ3fYHLfRiz/lpk2bf5g1rJ4foBjTwAjRyGW8lCrbOWXPnL10JP5SlGEoV2aPOtfUJCAg899BC//fYbBQUFNWvXqJluOWzYMAICApg9ezZr165l2rRppKSk8Msvv/DGG2/UtrFr1y4efvhh9u3bR3FxcZ02hgwZ0uo+nVjPb9++fSQmJmK1WuvsY7PZaqfFLlq0iIkTJ5KVlUVoaChr167lnHPOwdvbu7aN/fv315nKqaoqiqKQkpLC4MF1n6IdOXKE8PDw2gARYMiQIXh7e3PkyJHaIDEiIqI2QASYNGkSiqIQHx+P1WolKSmJq6++mmuvPb5Q3OVy4eVV90nh2LFjW/0etZZWqwVVbbZWok4vQYvCSUEQOpJGUhkcpDIsUGWAt0KE3k0gLjy6IZFMR1A1CvLZOfjPO4xXcMuK0P/psftn8uYr/WloIfW86GxO8zr+eaCgEDX3ePCZnHD8+lr1x2eR1q3l2GETUSMrCQ2vYs6cUr7+xg+Q+c8uP1aMrsD0RwkP52sDOFppZ8BFh2vbCQkt5aNv3+ONZdN47slB2Krr9+tosScPFHsyINDONYPLGYUGbStWgkhI6A94wgFPShhAQYAdpuZjGZtF0IBj6A29eygusl8Rkf2KmNPAtqoqHQlHgjgaF0hSgi9pKV5kHTOTm6OnsECDzdbl3e0SdtfnON37MOmvAMlwwjpxY+10/r+uLVdVF4r653IeN4pahlvJQkKPLPvXad+t5OJU9mPR3wqALAUgIeFw7UCWrChqfrMJzISewcNDjCQKbdfmIHHevHn07duXFStWEBoaiqIoDBs2DIfj+J3IokWLuPXWW1m+fDnvvfcesbGxxMbWLHaurKxkzpw5zJkzh7Vr1xIQEEB6ejpz5syp00ZLud1uEhISagOxiooKxowZ0+BavYCAmsQZ48aNo3///rz//vvceOONfPrpp6xatap2v4qKCq6//npuvfXWem1ERES0uo8tUVFRM89/xYoVTJgwoc42zV9yo1u6YEGHTqdFluVmg0S9uA4JQqcL8lQZFaIyyEchyuImRHbh43J3eyKZjqAa3GgvOEbg3MNYfMpadazdruGay+axZWNgg9tDrZXcEFb3IlU+/igDgo9n3k1PPp5QSTlh7q0tIxJGHgLghts38c23F6OqEpnlRt4v1fJ3r+Nr1OU1sewtq2LkVal1znX9rT8yY/ZRbrlqLkcONfxk/2iegXvyDET6ufnHODsjnE40bRjp1eYb4NM+2D/tQ7J2NPbRBZgm5RI0Kgsv39a9rz2d2exkxJhjjBhzrMHteTkeHDkUQmJ8AClJ3qSnWcnONNUm1HG7Gzysx3O6fwOg2rGizutG7UXotGOAP9aWn/CwRFXLqXIsP6GNH3G6f0QjRdVZYqCqKnbnpxi159TWXJQkHUbdRdicXwAuDNq/dfj0VqFznEprEh9++GE+++wz9u7d291dOWm0KUgsLCwkPj6eFStW1E4l/emnn+rtN3/+fK677jrWr1/Pe++9x5VXXlm7LS4ujsLCQp566qnakbedO3e2pTsArF69muLi4tqpoqNHj2bdunUEBgbi6enZ6HGLFi1i7dq19OnTB1mWOeecc2q3jR49msOHDxMdHd2iPgwePJiMjAwyMjJqv6fDhw9TUlJSZ2Q0PT29dvQS4Ndff0WWZQYOHEhQUBChoaEkJyezaNGiVr8PHU2r1SBrZNxuBVlu/Om2wdCL5jcJQg9n1P5RczBAob+HQh+9mwDFhcnV+TUHu5ri6cRwSRpBMw9jtFS3+vi8HCsLzj2HpITGrvMKj4wuxkzdWSXeZyXV+To78/hNr8Gir63UUrbDBPNq/n/k+BIGRGcSn1AzirJmr5apswzEOI6X3/D4bAL73Hpirz7KiZfMAYNy+XbbGpb+ZzZvvR6By9XwNTO1UMOd680EeSr8Y4KdCaoDbRsDf61Li/b3YPg9mHxGkBiaj3NsBoET8ogYXIZWe3LP/ggMriAwOIHpZyTU2+ZySSQnBBJ3OJDkBH9Skz05luFBbraBgnwtFT04L4vVuLTZff66tlyWfVp0nCRJmA031HtdqxmMh1iD2OtYPbvvCX5TAwsA//nPf3j44Yfb3Pann37KeeedV/vaXXfdxT/+8Y82tSc0rE1Boo+PD35+frz55puEhISQnp7Ov/71r3r7WSwWzjvvPB588EGOHDnCwoXHs29FRESg1+tZvnw5N9xwAwcPHuSxxx5r0fmrqqrIycmpUwLjxRdf5MYbb2TGjBlATfD37LPPMn/+fB599FH69OlDWloan3zyCffccw99+vSp3e/hhx/miSee4KKLLsJgOP7U5d5772XixInccsstXHPNNVgsFg4fPsz333/PK6+8Uq9fZ555JrGxsSxatIiXXnoJl8vFTTfdxPTp0+tMDTUajSxevJjnnnuOsrIybr31VhYsWEBwcE366kceeYRbb70VLy8v5s6di91uZ+fOnRQXF3PHHXe06D3qKDUjiVLtVODG6EWQKAhtEu2vMDxYZYCXQqTJTZDkxrOHJ5LpCGqgHeNlSQRPiUPXximR+/f0YdH5Mygpbrw+3HWx2UT/JUB0+FQwcMTxKKC6Wqb0hCQ3pZVVYKoJGj3iQ3G7qS1yf9+TR1l88fGpdndv1bJ2mgOL6/gPzPLlKFKMPvRb9FudRDsarcoDT3zHzDlh3HHTLDIzGr+Byy2TeeB7E14mA7dMcnCa1oHe3r6gzisrAL4IQPkC4sxVlA5Pxjwum/7jSvD0PrXWmGm1KgMG5zJgcG6D28tKDMQdDjkhoY4nWcdM5OXqKSqUacOEJ0Hocp7W7htJzM4+Pp1/3bp1PPTQQ8THx9e+1trcI83x8PDo8DZPda0qgaEoClptzfTD999/n127djFs2DBuv/12nn322QaPWbRoEfv27WPatGl1pmgGBASwatUqPvzwQ4YMGcJTTz3Fc88916J+rFixgpCQEPr3788FF1zA4cOHWbduXZ0yEWazmW3bthEREcEFF1zA4MGDufrqq7HZbHVGFqOjoxk/fjz79++vN3I3fPhwtm7dytGjR5k2bRqjRo3ioYceqh0B/CtJkvj888/x8fHhtNNO48wzz6Rfv36sW7euzn7R0dFccMEFnH322cyePZvhw4fX6fs111zDypUreeedd4iNjWX69OmsWrWKqKiov56y0/35824uSDScOjMaBKFNvM0qM6IVbhrv4rkZDv5vVhUbZpSxYmg5//CrYI62ioFOO96OvwSIJxmlfyXm+/fR7/UvCZ95oM0B4lefxnLe7FlNBojDg6q52LeB7XOT0JzwiHTHL4GoJ3wcutXjFzRDtYn8zONtzJydSHTM8T6XOrQ8sd+N8pc12eqHkaS8OwVVqf8AbfJpmWz8dQ2zzzqCJDX9wy6tlnniByPnbfbgc9VAtaljHsgZq8wE/ToM6/JZ5C6+kP0PTOPA5xFkp3VOpuzextPbzvjJqSy6agcPPfkdK9Z+yNdb17AjbiVJ+W/y8/4Pefv/tnHfI4dZeGU2004vJ2aAE28ftVclDhJObt05khgcHFz7z8vLC0mS6rz2/vvvN1rqzeFwcMsttxASEoLRaKRv374sXVozEh4ZGQnA+eefjyRJtV//WWLuT0uWLOG8887jueeeIyQkBD8/P26++WacJ6RMzs7O5pxzzsFkMhEVFcV7771HZGQkL730Ume/Pb1Cq0YS8/LyaqdennnmmRw+fLjOdrWBNGNnnXVWg68DLFy4sM7oYmNtnOjEUhLNCQ4OZvXq1c3u99tvvzW6bdy4cWzYsKHR7X9mU/1TREQEn3/+ebPnvPHGG7nxxhsb3X7ZZZdx2WWXNbgtMjKy2fepo+h0WjQtCBL1evGpKAhQk0hmaIhKbKBKjKdChN5FAG48nCcsgOpFiWQ6ijq8DM9L4wgckorUzij4haWn8dLTA1DVxp9z6rUq/x5Uipa6QaKiKoSdkV7ntd9+9q7ztdbgDRzPeqLkRkDE8SfgV163k4funlT79S/5PnxbmMM5fn8518d9SHZPod+S7fW+Zw8Phbfe/5GP/pfCg/eeRkVF00/Aq50yL20z8oqs56oJTub5OPCo7JjRP0mVMe8Phf2hVAIJEVVoT8/BPCIDv8g8tLpTa5SxJcL7FhPet5hZZ9ffZrdrOHokiKNHgmoT6mQeM5ObbaCwQEN162dVC0KbWLtxJLEpzZV6W7ZsGV988QUffPABERERtUu5AHbs2EFgYCDvvPMOc+fOrZev40SbN28mJCSEzZs3k5iYyCWXXMLIkSNrE0NeeeWVFBQUsGXLFnQ6HXfccUeL6rWfKloUJBYXF7N9+3a2bNnCDTfUn6sunLy0Wg2yXLMmsSliTaJwKgrzUhkRojLI102kSSFUduHtdKE5MR7opYlkOsyUQnwuOoJf/8x2N+V0ytxy1Tl880VIs/s+NKmSYOqPIpYMSWVAYN26gnGHvet8XVRWDT7Hr2klOyyEnlDq88qrD/Dc4yMoKzXXvrY8LogxU0oI/ssEHfWzMJKVqURd9RNyA8HxRZcf4/Q5n3L9pTP5fWdYve1/5VJk3vzFwApJx8IxLi4KsuNT0bFBnJRuxr2mH+X0o8zkQj6tAPOkbLwHHsNk6bk1GXsKg8FN7MgsYkdmNbi9IN9C3MEQEuL9SUnyIT3VSnZWzVTW4qLem1BH6Hm8fXrmzIDmSr2lp6cTExPD1KlTkSSJvn371h77Z/JJb2/v2mVajfHx8eGVV15Bo9EwaNAgzjnnHDZt2sS1115LXFwcGzduZMeOHbVLwlauXElMTEwnfde9T4uCxKuuuoodO3Zw5513Mn/+/M7uk9CD6HRaZI2Ybiqc2sz6mmBwqL9Cf6tCH50bf8WF8SRMJNMRVElFnpuL//zDeIXmd0ibRUVmFs47l8MHvZvdd84AN5NkFw2VwrDMTqz3Wk5W3RIAJZV2OOHmSt1Rd7tGAxdftpe3Xptc+5rdJfFUojfPx5TVfUgAqF+EkuKeStQ125E19a+l/gHVfLzpa959fSKPPzyMqurmV4Koqsx7O/W8h555sU4u72snsLzjowupWov6XTCV3wVTySgYXor+tGw8h2fhFVjQ7lHhU5F/QCVTZyQydUb930VFgeSEAOKPBJF41J+0ZC+OZXiQk22gIE9Debl4ICu0nG9D0+27WWVlZbOl3pYsWcKsWbMYOHAgc+fO5dxzz2X27NmtPtfQoUPrjDSGhIRw4MABAOLj49FqtYwePbp2e3R0ND4+PvXaOVW1KEj89NNPO7sfp4yHH364zdmcukNL1ySK6abCyUFlQKDK8KCaRDJ9jW6CcOF5Ys1BlVNuqmhLqXo3mvOyCDzrEB5+pR3WbvyRIC6dN4uCfHOz+/qaVf4ZUYXsrH9Ncvk4GTCtpN7rhfl+db62qXWnLxkKfSjK0+AbeDwIu/c/caxdNRpb9fFgcl+mxBfhFs43VPJX6tehHKkYx+DbdjQYKAJcccOvTJ9zlJuvnMfe/S1/8vblAR1fHtAxY6CLv8fY6FPubiA87iD7vXDs96KAQeT729GekYtlTBY+/bLR6cUfRnvJMkQPzCd6YMMPVyrL9cQdDuFoXADJib6kpXiSdcxMXq6ewgKRUEc4TqeT8eyBJTBaUupt9OjRpKSk8O2337Jx40YWLFjAmWeeyUcffdSqc+l0ujpfS1LziRiF49pcJ1E4NRgMejQaDS5X00+oxXRTobfxt6iMCFUY7KvSz+ImVOPG1+VCdxLUHOxqisWF/pI0gs84gtFaP0Bqj43rB3Hj4onYbC1LwLB0ih2To+GbAOncY+j0dUe+XC7Iy63btmzyAOx1Xis+6o9v4PFMmCaTi8v/nsvKV/vW2W/ZrxrGzNIT0cDdumFrJLur7Iz69z40jZSfiIgq4ssfV/PCI2fy3+X9cLTid3BzvJbN8R6MjXRxwzA7/SpcNJMXp12kAgPudRGUrYugVKMgTSnCNDkbryGZeHh33EMC4TiL1cGYCWmMmZDW4PbMDC/iDgeT+MdU1mPpHmRnGcnP01FaIiHuj08dvn49bxQRaHGpN09PTy655BIuueQSLrroIubOnUtRURG+vr7odDrc7ZyXPXDgQFwuF3v27GHMmJr6oomJiRQXF7er3ZOJCBKFJun1OrRaDU5n03PpTGYRJAo9k06jEhsCQwMUYjzdhP9Rc9By4lRR9x//hFZR/BwYL0smeFoceqO9+QNa6fWXJ7P04cEoSuOJCU505Ug3AxyN9yPo9Pr18g7t98b5l1FHJzr+GiRKKf1hat1yCbfc+RP/ezsCm+3E4yUe2GlkxSgnBnf9CM17x0B2PQqjHthXL2A90R3/2ciseX245e9zSE5t2ff/p52pWq5J1TI4xM3No+wMrnYid3JwILll2OZP9TZ/qolFja5APz0X66hMvMJy0TQyeip0rLDwUsLCSzljTny9bU6n/EdCnUCSEvxqEupkWMjJqUmoU9Wxz3eEbubn1/zMi+7SXKm3F154gZCQEEaNGoUsy3z44YcEBwfj7e0N1CRw3LRpE1OmTMFgMLRpiuigQYM488wzue6663jttdfQ6XTceeedmEymZms8nipEkCg0SZZljEYDtuqmbwCtVvEHJXS/CB+VESEKA70VokxugmU3Xn9NJCOmY7WbElGN5bIEgscdRaPr+OhaUeCOG8/m4/f7NL/zHyJ9VS73q2x0bWjViHy8A+o/Id71W/2bi+oGCt279/jAFXVf8/Ov5Kx5+Xz6YWCd1zOKJd7OM3KjX8NpLH33DmTfk3qG/3sHekPjgWLs6GN8//sqHr7zHNauDW71KNCRbA23ZJsJ93Xxz/EORjqdaLpo7ayU6IEz0YMi+lNgdaI9PR/z+Cx8BmRhMIn0nt1Bp1MYOjybocOzG9xeXGjiyKEQEuIDSEn0JT3VSlamibxcHcVFMi6x7rpX8fXtmUlroKbUm9ls5tlnn+Xuu+/GYrEQGxvLbbfdBoDVauWZZ54hISEBjUbDuHHj+Oabb5DlmvXazz//PHfccQcrVqwgLCysXqWBllqzZg1XX301p512GsHBwSxdupRDhw5hNPbc964rSWpX1VIQeq1HH1lOamoWkZGNZ95zuVReek48hhS6htWgMjz0j0QyHgphWhf+bleDIzdCx1GHlGO9NJ7A4ckNZursCOVlBhadP489O31bfIwkqaydbSPE1vgTAPn+XfSbUD9RyJ03jOaD/xtb57UpfR08Hlk/kAl//3MMZlud1xLjAzhj0nko7vqB5bOTixmrazwJTfXkXAbevg29ofnob9uGgdx58zRy8lpV3rgOP7OTmydUM1WrouumhyUqKtLYEgxTc/CKzcTqXyjqCvYCigKpyX7EHw4mMcGPtCRvMtI9yMk2UpCvobxMQtxN9izzzxvIu/93fnd3o1c5duwY4eHhbNy4kTPOOKO7u9PtxEii0Cyz2YSrmUeIWq2EySxRXSU+JYSOI0sqA/9IJBPjpRBhcBOMC48TE8mcgjUHu9zEIrwvisN/QEanniYl2Y9LzplLdpalVcfdOcnVZIBoM1UxcHRqg9uSE+qPJJZWNrwQsDQ1mMAhdduJHpjPtOllbP3Bq97+j+ywsHpiOb5ywx+1pp+DSGQmMXdsQadv+hp72ux4Nu9K5a7r5/P1N95N7tuYwiodj27W4aF3ctWIIuZ66TE52x50toWEBDt9sO/0IY/B5IbY0M3MqUl+0zcbrU4MV/VEsgz9ogvpF13Y4PbKSh1HDwdzNC6wTkKd3BwDhQUy9o6fjS40o6euSexJfvjhByoqKoiNjSU7O5t77rmHyMhITjvttO7uWo8ggkShWRaLqUULhD2tIkgU2i7QqjAyBAb7KkSZ3YT8kUhGKxLJdAsVFWlWHn7nHcEnPLf5A9pp+9b+XLVwKlWVrcvGNyZM4SxDVc3DgkZUnJaArpHgIyuz/ohldSOJb8oP+tQLEgGuv3UbW3+YV/+8Tj1PJ0gsHaAiNzJcpv/ZjwT3DKLv3oJe3/QvuIenndf/7wM+XTuaB/81htKytg3BVTh0LNsRxOsaF5cOzuX8AB3eSvdkQZSyjbjWRlK6NpISvRt5WgGmSdl4D87EbK3olj4JrWexOBk1LoNR4xp+kJSTZSXuUE1tyNRkHzLSPMjKNJGfp6OkWCTU6Qx+IkhsltPp5L777iM5ORmr1crkyZNZu3Ztvayopyox3VRo1prVn/L111sYOrTpAqOfflRNUqLI/iE0zaBViQ1RGRag0t9ak0gmUHFhcom7hJ5A0Spo/5ZFwDmHsTawhq8zvLtyHA/eMxy3u3UJWvRalQ9mVuFlb3r0yfjSV/Tp1/B0+P7+V+Nw1j1vH48q3h1TP2CriM5k5As/NdjOjHEXkHjUv8Ft94zN5ixL00kkHOOKib5nM3pDy56E5GZ58o8lf+OX39qfnEIjuTk/Jo+LQlSCaN0obqcaXIZ+eg7WEVl4heR12hRnoXs5nTJJR2tqQyYd9SP1j4Q6udkGCgq0VIpnBW3y5FMzueWf47u7G0IvJkYShWaZzSZastjA6ikjUkQKJ4ryUxkRrDDAWyHS5CZYqkkkI4tEMj2OYnajvyidoNmHMXl23V3Z/XfMZs1bfWmo8H1zHp3mbDZALI7KYFwjAWJqon+9ABHAgZaGhq2NyUE4bDJ6Y/0HGlddv5P77pzb4HmW7Qti7GnFBLga/8jV7/Ah8amZRP9rM3pD838UQaFlfLDhf6x44TSefXoQ1bZmD2mUW9Xw0dEQPjqqcG7/EhaE2QiXekCweMQTxxFPChlAgY8DzYw8LOOy8InOQm8Q8xdPFjqdwqChuQwa2vCMhZJiE3GHg0n4ozZkeqqV7EwzeTk6iopknGKGSYMCg3rA37DQq4kgUWiWwaCnJTdwVk+RfeBU5WVUGRGmMsRPob/FTZjWjZ/bhd4tpor2dKqvE8OlyQRPj0Nvakek0UpVVTqWLJjHLz82PPrWnDkD3IxXms+SKc862ui2A7sazp5qdzf80ahVtBw7ZKbfmPpB9BXXpPP8k8UUFtZf42hzyjyT5MVTURVolMavk/pd3iQ+MYP+923GYGzZ05Nr79jGzLPjuWnx2RyOa+8UKZmvknz5KglmR9tZGFZGJC2rT9nZpGI9yid9KP+kD2UaBWl8Mcap2XgNzcTqW9Ld3RM6kbdPNROnpDBxSkqD29NTfTlyKLhmFPLPhDpZRvLztZSVnroJdSIi6q+TFoTWEEGi0CyDQY/agvhPlME4+WkklSHBKkMDVQZ4KUTo3QSKRDK9khJmw3xZIsET47s8WUhWhjcLzj2LtFRrm473Nav8s28VUjO/Z26Tk8EzGk60AXDkYFCDr1c2MUhVfMQPGggSAa66NoVnn2q4XtfOVC3fRxmZS9MjYPq93iQ9NpN+D2zGaGrZaFn/Qbl8+8s7PHXfXFasiOiQUgUbEg1sSAzgtCiFJf2riHS6apLO9ACSW4Zf/LD94oeNYeT0rUI3IwePUVn4hOeg0Z5cM1peeKaELz+rJCHeidEkMX6ikUee8CFmYOMB/No15dx8bUGd1wwGidyyyNqvl79QyssvlADwzzu9+cftx4OKnb/buPPWQjb9FIpW2zN+7o2JiCwiIrIIzqm/zWbTcPRIMPGHg0hO8CUt1bNmKmuunsJ8Dbauey7W5cIjPLu7C0IvJ4JEoVkeVgu04Emc1dq1WfKEzhXkqTIqRGXQn4lkZBc+LrdIJNPLKQMr8FwYT+CIZORuKHC+67cIrrjodMrL2l6HaukUO6ZGksucyH5mAmZL4xevxKMNP2l3KTKKpCI38HRMnxYFpDV43A137OH1V2Ipr2h4RO+ZLXpGneMiqLzpIEZ/wIvkR2fS78HNGM0tu4uVZbjvqfXM+Vskt157BunHWre+szHbUmS2pXgwKkzhhiEOop12etrSQCnNjGtVP0pW9aPY5EI+PR/zhGx8BmZitFR1d/fabfs2G9fc4MnosQZcLpXHHizm/HNz+G1vHyyWxj93PT0ldhw4Plp+YoHwgwccPPloMe9/GgQqXHJ+LjNnmRg6TI/LpXL7LYW8/Kp/jw8Qm2M0uhk+KpPhozIb3J6X40Hc4RAS4vxJTfIhPd1KdqaJ/FwdxcUSLcjZ1yPpdDIhoW17CCcIfxJBotAsT08LGo2My+VCq238V0ZMN+2djFqVEaEqQwMUoq0KfXRu/P+aSEZkpe/11LHFeC+Ix39gWrfVpfvovZHc/Y/RuJpYm9ecK0e6GeBo2Qib74zkJrenpDS+ZkfVSA3+3usP+OF2S2g09SMlvcHNZZfn8Mbr4Q23qcr861cTK0ZWoG3mAYv+kCfJj8wk6j8/YGphoAgwZnIqG3es5v5/zOOjjwM6bKrdnkyZ6zONDAgwcPNwB8MUG3IPzDUlVWtRvw2h8tsQKhkNI0oxnJaNNTYLr8ACpJ4W4bbAx18F1/n61ZUBRPdJZ+9uO1OmNZHBUpIICm74by0h3sHQWD3TZ9QcPzRWT0K8k6HD9Cx7oZTJU42MHts9GW+7UmBwBYHBCZw2M6HeNpdLIjkxgPjDNQl10lK8OJbhQU5WTUKdivJu6HALhYVZkWVxTya0jwgShWZ5enpgMOix2x1NB4liummPFx2gMDxIrUkkY3QThAtPp5vaZ9EqYqroSURFRZpZgN8Fh/GJyOnWvjzxwEzeeKUfqtr2GQeRviqX+1W26KGFOqyMPtENJ6z5U2Zm42v43BoJjat+QKGp0lKc7Y9/n/wGj7v5nq2sWXV5o4lkUgs0rC03stjYfOCnP2Il9eGZRP5nMyZL8+sv/2Qyu3jhAvK45AABAABJREFUrU+Z87ch/Ou2KRQUddy1+Wi+xD83GQj31nHLKBejJRtadw8OvPZ5Yd/nhZ1B5AfY0c7MxTI2C5+oLHTNlBzpqcpKa6JzH9+mR4srKxSGxaSjKjB8pIGHHvNh8JCaKapDhupJTHCSke5CVVUSE5wMHqIjJcnJ2tXlbPk1rNO/j55Oq1UZMCiPAYPyGtxeXmbgyKFgEuMCSfojoU7WMTN5uXqKCmUc3fhZ2idcTDUV2k+UwBCaVVpazj13P4PJZMDX17vJfV9dVkmVqJXY7bzNKqNCVQb7KvTzUAjTuPBzudAp4mdzKlA1CppzsvGfdwTPoMbX5HUFh0Pm2kXz+GFDw+v/WkqSVNbOthFia9mdl+n+vYRNiG90e26WJ2MHX9ro9m/mlmGqbvjvRbrjEP1PP9josbf9/QI+/qTphDwrzq0kurxlQ/TOmAr6PvIDZo+WB4p/KsgzcvvVf2PLNu9WH9sS/haVW8Y4mayzoWsgqO6pVJ2CPLkQ4+SamowW77Lu7lKLKIrKwgtyKS1VWL85tNH9fv/VRlKik6GxespKFZa/WMovP9n4ZU8fwvrUPOx9+80yXl1W833fdKsnV13nyfy52Vx7oyduNzz1WDFancRTz/s2PWIpNOhYujdxh4JJPOpPSpI3GenWmoQ6eVpKSzo3oc7CRcN4Y+W5nXcC4ZQggkShWW63mztuX4rD4SQkJKDJfd9dVUVuTg+cg3SS0soqQ0NgWIBCjKebCIObQNWNxdlLF1II7aIaXWgvOkbQ7MOYvbt/LlR+ngeXnHMuCUfb/1T7rslOztG1bH2ZanQTseaLJjOEfv/FUK66Ykqj2788uxyPyoavZc7ZOQy+ZWujx6YcDWDGxPObXM/ka1FYO60CYyOB6F85oivp+8gPWKxtW2P35gtDeeHZSVRWdc7acatB5caxLmaYbBidve8zQI2uQD+jpiajd1hut6zXbYk7bing+w3VrP8hpDbYawmnU2X88GNceIkHDzzccHKl994t5+svqnjxFX/Gxh5j8/ZQMjNdXLckn33x4RgMYrZQR7HbNSTEBdUk1En0/WMqq5m8HAMFBRqq27mU9p5/T+aBh07rmM4Kpywx3VRolkajwdfXi7TUhhd+n8hqlcjt3lltJ60+3iojQlQG+biJNCmEyC68nS40oubgKU/xcmK4NJXg049gaMW0xM50cF8ol513BsVF7R+BGBOmMNdQVZM5twW087KaLSFxeH9gk9vdTd0P7274JvtPUQPyOW1qBZu3ejS6T1GlzLIME3cHVCG1IE7UJ1pIf+AMIh77AYtn09NoG3LdHYeYeXYq1y+cw9HktpUdaUq5XeKZ7Tpe1mq5doybuZ42LI7e87BKSvTAmRhNEdEUejrRnJ6HeXw2PjGZGLqwNExT7v5nAd99W8XXG1sXIALodBLDR+pJSWp4im1hgZunHy/hm00h7PzdRnSMlv4xOvrH6HA6a6ajDh3WM8qhnAwMBjfDRmQxbERWg9sL8i3EHwrmaFxATUKdNCtZmSby8/QUF0nNZjAW5S+EjiCCRKFFAgJ8OHq04RpFJ6rJcNp7bgx6IrP+j0Qy/gr9PY4nkjGKRDLCXyghNswLkwiaHN+j1ld98/lQ/nHteBz29tbtA71W5cGh1WhaUTvde2ZSs/vEH/FucruriSBRV2CgtNALL7/SRve59d5f2Lx1VpPn+PaQjplz9YytbtnTHV2KmfT7ZxL++GY8vBouw9GU6EGVbNrzCQ/dNpJ314zB5e6YDKgnsrskXvlNy6uShStHujk/wI6nvXddsKQyHcoXYVR8EUY5Y5DGFWOcmoPnsEw8A4q6vD+qqnLPbYV89UUVX20IITKq9X9XbrfK4YNOZs1t+KHNv+8u5KZbPQnro2X3LnudAvUuV83xQtfxD6jE//Qkppxe/1qmKHA0zoe87AEU5keRnupDRpqFtFSJ9FQn2dlVhIsgUegAIkgUWiQgwA+Xs/kPepHhtDVUBgaqxAbV1Bzse0IiGen4LmJ0UKhHia7AujCBwFFJPa4m3MtPT+P5pQPblaDmRI9Oc+LViiBDHViOT1hus/ulJDc+ygfgaqYmYHFicJNB4tgpKYyItbPvQNMZIh/cZGDdbBeeFS0bJtWlmcm4byZ9ntiMtY1Tih99aS9/W5DO9Ytmklfk26Y2mqOoEqv2aFmFlouHubkkzI6frec8yGgpCQl2+GLb4YuNIeSGVaOdkYvH6Cx8IrPRajs/AL7r1kI+XFfJex8F4mGVyM2pOaenl4zJVPN3dv1V+YSGavjP4zU/z6efKGbceAP9+usoKVVY/kIpGekurryqflmEzRurSUpw8fpbNctJRo8xkBDv5Pv1VWQec6HRQMyA9j/wETqGLMOgIcUMGvIb8Fu97TabCW/joq7vmHDSEUGi0CKenh6oLcibLzKcNszfojIyVGGwr0qUxU2oxo3vXxPJ9L77J6GLKSNL8b40joDBqd1WxqIxbrfEzX8/h68/bzyZRmvNGeBmvNK66bPm81JbtN+xzKanzjX351h92B+aSIwDcMM/93LjNROa3MfmlHnskImn+1UitzDe12WYOHbfDMKe2IKnT9sSroydXMQv8Z/wz79P4uuvB3dYUN+QDw9q+PCgmbkD3FwZ5Whx8qGeSMo04f5fJKX/i6TE6EaeWoBpUjbegzIxW1s/utsSb71Z8zDg3Fl113L8d4U/i66sCfqOZbiQT/gRlhQr3HpTAXk5brx9NIwcpee7rSEMGlz39766WuHu2wp5e21AbcmEsD5annnRj5uvK0BvgNdWBtQGo0LPZzQ60etCursbwklAJK4RWuTnn/fw0ourGDo0uk5B3r86luHm/bU9Y01Ud9BpVGL/SCQT7ekmXO8mUHFhdvXMJAhCL3FaAT4XHsEvquH1K92tuMjEwvnzOLTfu8Pa9DWr/G9KBSZHy/92VJ1CxNovMTRTXqKoQMeI/kugidHCt8+tJKqJ7KOOyEqGLPuq2T5NHXY1aRnNT+u8d4aNuUor5tQCzlAboU9saXJEsyU2fRvJrddOoay88bqRHWlKpJtrBjjoa3c0M17bywwpRz89G+vwLLxC8pB7YU1GofeTicCq39Ld3RBOAmIkUWgRT08LGo2M2+1uslaixyk0khjhqzIiWGGQt0Kk2U2w5Mbb6UIWiWSEDqBqFOS5ufj/7TBeIQXd3Z1GJcQHcsm82eTnmju03aVT7K0KEAE0Z2U3GyAC/PajP00FiADOZu7v9akWqirMmD2aTkN41fXx/OeBIc326Zktekad4yKovOXTh3VZRrL+NR2WbsPLv6TFx/3VGWel8vuRLG5cfBabNwXS3HvTXttTNWxPNTEi1MCNQx3EOO2cFPHUYSuOw1YKGUC+rx3tjDws47Lx6Z+F3tC6BwCC0FayFNHdXRBOEiJIFFrEy8uKwaDHbnc0GSR6ekpoNDSZ+r23sRpqEskM+SORTJjWhb/bhcEtpooKHU81uNGcl0nQWYex+LZvhKiz/bBhINdfOQlbdcdmPbxypJsBjtbfVHvNSm7Rfnt+92t2H4fafKBUmByMeXjT57zihp9Z9vxgCoubbk9VZf71q4kVIyvQtuJ6oss1kXH3NNRnfsQ7oKTlB/6FxepgzSef8/H/jeKBu8ZQUdH50wv3ZcnckGUkxt/AzSMcDFNs9NDKE60mFxlQPg6n/ONwyjQK0sQiTFNy8BySidW3pLu7J5zERJAodBQRJAot4unpgcGgx2ZzYLE0PmIgyxLePjKFBb3vk16WVAadkEgmwlCTSMbjxEQyCmJ0UOgUqqcT3YI0gmcewdjM6FRPsOKViTz+4FAUpWMzZEb6qlzuV9nqDL5q30p8I1o2HTfuUNMlLADsLRjZqjoSAM0EiTqdwqWL0vnvK32bbS+1QMP/s3ee0VFVXRh+7vRJ7ySEkASSQAi9915CR6oICnbsgKhYAFFE7Ip+NkAQwd5FpVcp0nvopEASSO/T7/cDiURImCTTEu6z1qwFM/ecsyeZ3Dn77L3fvbJAwyQroqHXoslyI+XJLlje2IFfnZxKjf0vo8YfoGvPMzw0eTh7dtk2OlwepzMFpm5QE+qt5LFWJtrIdChqkZqmYJbB9gBKtgdQQlPSw4tQ9r6EZ6tUfMLSkctr0amqhNORCRHONkGiliDVJEpYhdlsZvq0VzHoDYTUrbi/2G8/6zh5wrUlz4M8LbQMgVg/C5FuZkL+EZJRWKQ/BwnHYgnSox1/juCuJ1CqXf8EwmKBGY8M5Lsvw2w+tyCIrOyvq5KwiXr6ccJ6HrHq2h4thnAusWKBnfn9i+mkrzikZ2iWT5NX/rzpevk5WtrF3kmxleXai4YUEVVBPWR5FHsWEPL6dgJDbROB/ujdrrz9ahN0Dm4T6Ocu8mhrI11UelS1vJ5bdDch75GBtkMavo0uonFz/QMiCdfGTbEIpayPs82QqAVIkUQJq5DL5fj5eZOUePGm1/oHuI4Kmloh0ixEpGmgSJTnFSGZQIsJ7bUbDzNSa0cJhyNGFON2x2mC2552uTYW5VFUoGLCbcPYt8c+bROe7GSqkoMoyi0Etrt5b8SrXL7sddNrigw3d9IUxzww6JWo1BU7k16+JQwdmsE33wZaZd8zm7Ss7FaIpqRyh1ZuBZ6kPdUV4/wt1I2ovtLmQ1P/ou/Ak0y5azCnTjiukXp2kcBL21S4q5U81MZEHzcdGmPtdBaFIgWWP0Io+iOEIlpDq1xU3dLwbpaKV1Cmy6kYS7g+ciHG2SZI1BIkJ1HCaoKC/Dl58vxNr3OWkxjpf0VIppGPhXDtFSEZb0lIRsLFEJvl4zXuJEFNzyPUILWOxPP+jBsST+oF+yhgtgq1EK8uvpLSXUlk/S+hcbcuAlNcrKSw+OZplIU6I6gr/oqUWWRkJdchJPrCTed7/Lkt/PDjaExWBAizi2S8l6zh6aAShEp+RNwKPch6tgemlzZSP7r6StPRjTJYt3MZLz8fz7JP61tlv60o0gu8uUPJQoWC+1qbGeSjw11fMw5UqswBHwwHfMgglst19Ch6pePRNg2fyFSUSqn4XeJmuCNQz9lGSNQSJCdRwmoCAnwxWbFDCLCzk+itEWkZKtLE30IDdzOhCjP+ZhMqSUhGwoURu2ThO+oEAVE3dyhcjR1bG3LP7V0pKqq4MXxVUSlE5sSVIK+iAKR3f+sEawCOHAgFbn6PKtKbbuokAhScCLLKSawfmU3PHvms33DzKCbA6uMqetYz0KEKTpG2yIOC2X1JnLOOiMbVzxWVyWDOq6sZNDycx+7ty8ULtq1DvRkGk8CHuxV8LLgzsYWFkUE6vPWuXdJgC4RLasxfh5P3dTi5SguyLlloO6fiHZuKu3fV+mNK1G7kQsVtyiQkKoPkJEpYjZeXB9bkvvj62UbhVC6INAkWaRokEu1tob7qipCMuyQk41CWndvJB6e3ML5+W56M7XvDa35KOcjvqUc5W5gBQKxXMA9H96Cpz791X1+c/5vliX8DMCmyAxMj/m0yfjQ3lQUJa1jWYRIKmeukK1cXURARBlwmYPhxfEIvO9ucKrFyaVuef7IFZrP9HIOXuhmrvOkXQ0vwi7x5GvxVDh+ouBbxKkV660Ka+oPeMNS6tZ94dgcbNsZjrRLA7A0avu1fhHcVytTURW4Uv9Sf87PWEhlrm6LCdh2T2LxvGU8/NpSfvwuy+n3YCososPygnOW4MyrOzO319ATobo0TQcEoQ9wcSPHmQIppgRhdgKpXOp4tUvGpexlZbZGFlagWMinVVMKGSE6ihNV4enmACKIoVnhSJZMJ+PrJyMyw/ksrxEukRYj4r5CMzISv0YT82k3IrbEXcCmO5aXx44WDRHtUXEu1LyeZASFNaO4Tilqm4PPzu3h03zd82+U+gjSenC64zMdntvFu69GIwLT939PRP5IozyBMFgvzj6/m+biBtcZBtCjNKIanEjT4OB7+uc42p8rMmtGPZYsisGffvAExZtpbqp4WqRmZVKmm5SePB1h1XYmVPqvikA9mswy5FZv0lu2SadVCx/6DGqvmNpgVPL8PFsaBrAo+gKpQi+HlgZx/YTWRTaqfegqg0ZhZuOhnBg2L45knOpOd5ZyoxQ/H5PxwzI3+0RYmNdBTtwq1rDUZ4bQnxtOeZBNNlpcRea/LuLVPwzf6olW9QiVqJ1I9ooQtkZxECavx9vZAIZdhMplQKpUVXuvvf2MnUasUaR4iEhdoIcrTQj3lFSEZzbVCMrU/i6hGUGwyMOvwrzwfN5AlZ7dXeO285sPK/P+FpgPZuOEku7MSGRLajMSiLKI9g2jnHwFAlGcgiUXZRHkG8UXi37T2DSPOO8Reb8VhWNxNqMYkU6ffcbSeRc42p8qUlCi4e+wwtm+1zqGqKn5uIk+EFyNUcX8vIuLf0XrBGoBzZ6xL99SZrDuwUBpUZF7wo054plXXPzR1P/dP7mzVtQDHLnvzXehFxnl5WD3mWuSFKozzBpI8ey31G1dfzOYq8UOP0aHzOR67bzhbNlr3M7UHa0/LWHtaS6dwFfc3MhKh19vxSMM1EfKVWH4JpfCXUApkbRDa5qDumo5304t4BWQ72zwJByJFEiVsieQkSliNv78Pbm5aiot1eHtX7CQGBAqYsyw0ryNeEZLRXEkV9TKa/60GEpFSRV2Y1xLW0iWwIR38I27qJP4XndmISbTgrdQCEOURSHJRNukleYhAclE2DT0CuFCcw28XD/NFp8m2fwMOxOJvQDP+HMHdTqDSVrGwzkVIS/Vm7OBBJJ7ztPtar3bRozVUPU1O1jsDN8/KOT4pydZF8fRm678e0w54WO0kxt92lAYvduBcovXpux8fCKFdjzQaUDVHUVaoRDd3AGkvbyQkqnp9FK/F17+EFT99zYrP2jNvVkuKbOeDVpqdSXJ2JslpHqLioaZGYow6apAulM0QLALs9kO/24/LNOFSvRKUvdLxaJ2KT3g6CoV0ClubkSKJErZEchIlrCYgwBd3DzeKi0rw9q54Azki1kJbWcG/T0ipojWKNWnHOZF/ieUdJ1Vp/PunNhOg9qD9P5HDSI8AHo7pwcN7vwHgkZieV57b8xWPx/RiZ+Z5Pj37FwpBxozGfWntV99Wb8WuWMJKcL/jDHXan0KhrPmbr/2763Pn6J7k51nnSFWHO1uaiTFUz6H2HGi9YA2AySSQmWFdhFBvVmGt1GrxIR8YYb0dE+8+yEtz2lg/ABlz9vvyaZtitGLFB3TlzlCkoPD53mS8to3ACNvWx068Zzc9+57iwTuHcdjKVFp7cThNxkNpahoGqHi0uYFmoh75Ldz/VrigxfRFJLlfRJKjMSPrnoG2Yzo+jS5W+oBFwrUR8EEm1HG2GRK1iNpRACThEBQKBaGhdSiyoiN0vlvVNjISzie9JJ+3TqxnXvOhqOWVP0dadm4na9MSeLPVyDLjR4e14sduD/BjtwcYHdaKVReP4KZQ0cwnlHnH/uTNliOZ3qgPzx3+FYPFtR0uMbYAj5f2EfX+b4R2OV4rHMQfv2nBqEF9HeIgRviJ3OlfvXRcMUhPQFRKpcYkHA2xuoWDwWL9PczzZGil7Lj7kX0E+FUugnqhwI3FF6unBiaUKMh9phtZSbZP7a5XP5fftyxn2jOnUTmupWK5nM0UmLZRzV37PdgpaDHJb7Uk1OsRdHLEtcEUv9SS1AmDOf3sQJJXtyQ3NQiLRfr51HRkQrSzTZCoZUiRRIlKUT+8Lvv2Hr3pdSUaBUa5gNJ8657g1lRO5KeTbShm4s6lpc+ZRZEDOSl8m7KPHf2eQi7c+Hzpi/N/s+z8Lj5sezvRnkHlrpFrKGbR2b/4tP0EjualUt/Nj/ruVx4mi5nkf+oVXQ2xfQ4+YxIIbFQ558TVWTCnFx++1xBRtP+5oSCIzG+nQ6mr3r1BPTKp0oqOB/dZ3z9MZ1YA1kU6tYUepCapqRtu3fUKJQwZephln7e02h6AH0/XoXPABdqoqp4KLJQoyH6qC7y+E/8I61VhrWX6c5sYMOQkD02O5/xZ528xUvNkPLdZhZ+bkkfamOiq0qEySUqgAMIxLwzHvMikEZf99Ch7X8a9XSo+DVJRqaVakJqGlGoqYWucfweXqFHUCfJHtFL3PN9diX++9EVT02jnH87Xne8t89xLR38n3N2fSZEdy3UQPz+/i8/O7eSDNmNpchMRmrdObGB8eDvqaLw4npeGSfw3QmIWLZgdra1fASIiQt/L+N+WgG/YJWebY1MMBhkP3jmU9asdl6L0ZCcTITZQovTvXDnBGoATxypW6b2WEiuFa66SfszTaicRYPqc/Xz9dTN0+sq1FnnlYF2Wts/Em6r3rBR0crJndEJ8fRcBDWzftzOueSobdy9l1ozBfLW8brXbIdmC7GKBl7cpcVcrmNLaRF8PHZpq1MPWNmTZaszfh5H/fRh5cguyjtmou6bh0yQVD99cZ5snYQWSaI2ErZGcRIlKERDoh1whx2g0oVRW/PHJk5zEGom7Qk2UZ9nNtEauxEepLX1+9pHfCFJ78mhMTwCWndvFJ2e2Ma/5UEK03mTqr9S6uMlVuCnK5p7tyjxPcnE2c5sNAaCJdwhJRdlszzjLJV0BMkFGuLufnd/lzbEoLCiGphE45BiegbYT+3AVMjM8GDdkMKdOeDtszVahFuLVxdaW+pVPl0zcfSrfTPzsaevfa3El66gtZ4IA68RrAHz9TfTofpI165pUap2cEhkfXvTlmdAiZNXQ8RQMcrKf7oi44G8CK5m2aw0Khcir765i0PAopj7Yk8uXXKO6pUgv8NZOJe8rFNzTysxgXx0eehfwYl0IwSxD3B6AbnsA6TRDjCxC1Ssdj5ap+IRdQi6Xfl6uiFyo3L1EQuJmSE6iRKUIDPTF3U1LcfHNxWvy3VVAzW0DIFE+6SX5ZTaoP6TsxyiaeebQz2Wuu79hFx6M6lb6f53ZyOsJ63i1xXBk//TarKPx4qnYfrx09A+UMjlzmw1GI3deTavFzYxyVDJ1+yfg5l1w8wE1kGOHQ7hjRF+ys7QOW1OlEJkTV4LcBuKvnoPPV2lcSpL177dQXzkHzONkfeB4pcY8O/8A6zY0wmKpXDRx7RklvULVdKymPLTMICf3mY6wQCAwOrlac5VHt15n2HogiWkPDOfPVc4//LmKwSTw8R4FnwruTGhhYWQdHT66ml9bbA+E8+4Yzzckh4Zku5uQ97yMtn0avo0uonGzTf9NieqiQC7EOdsIiVqGIFqbOyghAZhMJp6cvgCD3kBI3X9rxkRRRFdcTG5mNgW5uRTl5+NbYuIRzygnWishYT0WXwOa2xOp0yMBtVvtbUb9569xPHpfewx6xzriC3oa6CBWf0Mp+hposORX5IrKRzOigh5Ab6WTKggiG7tXLloZsPR7fPwrZ9eInn3Zd6BBpcYAqBUiX/cqxMcGKZOi3IL3gt0ENUqq9lwV8fO3LXh+Rnvy81xTJOW2JmbGh+kJ1DlWjnvpuZ1sunSSxKJs1HIFzX1CeSymJxHu/uWO+e3iYeYe/aPMcyqZnB39nir9/xfn/2Z54t8ATIrswMSIDqWvHc1NZUHCGpZ1mIRCVrUor4gIrfLQdE/Dq2kqXkGZCK75q631yIRYPJW/O9sMiVqGFEmUqBQKhYK6dYPYu2s/JXk5FOXloSsoxFRSDGYjMgGUchlu7lqU3u6YLSLyW661sURNwlK3BLc7zhLc8RQKVe3u1bLwja68+UpjhwjUXMuAGDPtbeAgAqhuS6mSg3jmZKDVDiKAKApYBCrVay83KRAf//RK2fX8/MOMHBgJlbxP6k0C846781p0AfJqHvUKZhl5M9vDfIGg2MTqTVYBI8YeomvPs0yZNJy/d7jbbZ2q8tNxOT8dd6NvlIXJDfSE6h1TLrE/O5kx9VvTxDsEs8XC/05v5dG93/Bdl/vQKsqXinVXqPmh6/2l/xeu+QydLrjMx2e28W7r0YjAtP3f09E/kijPIEwWC/OPr+b5uIFVdhBL1zvgg/6ADxnEcrmOHkXvdDzapOITmYZSWbvvp66EXGjmbBMkaiGSkyhRadzlFoqTz6ADNFoV/u5ueAXXxdvPG19/H7y8vZH988Wj35OBW4GUwiPheogxRXiMP0mdlmcrrZJZ0zCbBR6/bzC//ljX4Wv7uYk8EV6MYKP9tl+3ygvWABzab72y6VUsMgFZJRSaxcRQaF05J7Fd58s0bHCZs+cqLx6076KMtRFuDFQUV3rsfxHMMvKea4f4sow6TSvXf7IyBAQV8v2fK1n0QRfemBdHiQtmK64/I2P9GS0dw9Xc38hApF5v16PO99uOK/P/F5sNpt+mhSTkp1fYM1YAAtQeN3wtsSiLaM8g2v3TqzbKM5DEf1Sjv0j8m9a+YcTdRGCssgiX1Ji/Cifvq3ByVGZkXbLRdkrFp8lF3L1qZ+q+qyAXmjvbBIlaiGtUkkvUKDp1bE7j6HAGj4xn4PB4uvftTsv2LYmMisTH17fUQQQo8ZD6JUq4FmLrXLxe+5uoN1YR0uZ0rXcQ83K0DO09xikOIsCrXfRobaUi2T4bT/+qiQgdO1L5lipmWeVcA/MB69VTr+Wp2SerNA7gjR0K0jW2uc8KZhn5L7Ql/UhDm8xXEfc/up3V236kcRPXFTfblSTj3rUanjjlSYJSg6NaCRYar4S8vZQV19CWmA0M2fIhg7f8j+n7v+dsYUbpa1EegSQXZZNekkdaSR7JRdk09AjgQnEOv108zEPR3e36HmQGOWwKpGR+C9ImDuL0U4NIWtWanAvBUk9GO6Bw8UhiYmIigiBw8OBBl5hv2bJl+Pj4VNsOQRD4+eefqz2PqyI5iRKVJjAkGC8fT7DigL3Y2wW6KktIAPTKwG/hVqJfXENQbOItUTtz5mQgvTuM4shBH6esf2dLMzEGGyjV/IP7kKoJ1gCcOeVT6THmSn5IxCNeGA2Vd9gG33aCkJDCSo+DK2mxz+21XbN4wSJQMKsNaQftX0/eIDqTdTuXMeXxRBQunNd0JE3Gw+vV3HfEiwNyTaUPDyqDRRR56+R6WvjUu05l+lrC3f2ZFTeIt1qN4qVmQxERuefvFVzSXamjjfQI4OGYHjy89xse2fsNj8T0JNIjgPnHVvN4TC92Zp5n7PbF3LHjM/Zn20e06FqEk54YP40m6+EenJ00gnOfdeXS0QbodRq7r1370SATGjvVgsmTJyMIQunD39+f+Ph4Dh8+7FS77E1aWhoDBw50thl2Q3ISJSqNX1AgWnd3Sopurlxa6CM5iRLOQ5RbEIamErRoPVHTNuIXkeZskxzG5nUxDOoxmMuX3JyyfrivyJ3+tlM3tngaCYxLrPL45MTK/xwq6yQKFoH81MpHLAEenla1NFqA81kCK3JsV+MnWAQKZ7cmbb9j+q49//Jaflj9J2H1Xbu1wvksgekb1dy5z4Md2M4xv5bXEtZytiCD+S2GVXhdc59QhoQ2o5FXHdr41eeNliPxVWr5MeVg6TWjw1rxY7cH+LHbA4wOa8Wqi0dwU6ho5hPKvGN/8mbLkUxv1IfnDv+KweK4shAhT4Xl51AKnmtH8u3DOPNKPy5saUp+pq/DbKhNyIU4BMH5WVvx8fGkpaWRlpbGhg0bUCgUDBkyxNlm2ZXg4GDU6qr3rHV1JCdRotK4e3rg7e9LSdHN62AMbgqMKuljJuFYRI0J+R1J1P1sDQ3v34ZXnSxnm+RQlnzYkUlju1FS4pxDGkEQebW9DqXJduLZqhEXUCirvpFNS638JspUBR+g6FRA5QcBE+/djV8llVGv5fMDck6rbLdZERAofLElqXsa2WzOimjdLoVNe5cxevwlqqGl4hDS8mU8v0XFmJ2erDe7YVDaxuDXjq/lr4wzfNzuDupovCo1ViGT08irDinFN07HzjUUs+jsXzwV24+jeanUd/Ojvrsfbf3DMVnMJBdl2+ItVBrBIsDffujeiuPyPf05/chQEr9vR+a5ephNlWsNc6siF1o52wQA1Go1wcHBBAcH07JlS2bOnElKSgoZGRk3vH7Lli20b98etVpNSEgIM2fOxGT69x5vsVh4/fXXiYqKQq1WU79+fV555ZUbzmU2m7nnnnto3LgxyclXIuO5ubk8+OCD1KlTB41GQ9OmTVm1alWZcWvWrCE2NhYPD49SJ/cqe/bsoV+/fgQEBODt7U2PHj3Yv39/mfHXppteTYH98ccf6dWrF25ubrRo0YKdO3dW+mfpKrj4rVjCFREEgXoR4ZQUWyeWUOgrRRMlHIPFy4jy/jOELf2DyNt34e5b+YbrNRmLBZ56NJ4Xn21e6d57tuTJTiZCdLatNfPtUfVIW+oFb4qrENQ0VUGuxHCgak6iQiFyx13Va0Exc7uaEhseygkIFL/ckou7HJPKplabeefjX1i8civ+Aa7fnSu3ROCVv5TctsWDXw3uVf7Zi6LIa8fXsvnyKT5qO55QN59Kz2EWLZwpzChXyOatExsYH96OOhovLKIFk2guM9bsIt3QhBQ3TMsbkDu1C+cmjODc/3qQujeGkkLXU8N1FRSyls424ToKCwtZsWIFUVFR+Ptf38rl4sWLDBo0iHbt2nHo0CE++ugjlixZwrx580qvefbZZ1mwYAGzZs3i+PHjfPnll9Spc73Al16vZ8yYMRw8eJBt27ZRv359LBYLAwcOZPv27axYsYLjx4+zYMEC5PJ/vxeLi4t58803+eKLL9i6dSvJycnMmDGj9PWCggImTZrEX3/9xa5du4iOjmbQoEEUFFQswvT8888zY8YMDh48SExMDOPHjy/j/NYkXLgKQMKVCawbjLUtNgt9VPheqr195yScj1hHh+aOcwR3PoFSfWvKrhcVqJg4ahh7/3Zuw/JWoRbi1cVgSz2gFnl4B1U9GnxgX1iVxlXFSRT3+WI2y5BXQRDp0Se3svTTBliRyX9DsosFFia58XRIoU3VOEvmt+DiswKhnRJsOGv59Bt0gq2dz/PofSPYtM7bIWtWh2KjwDs7Fbwv8+De1maG+Onw0FsfFX4tYS2r047zVqtRuClUZOqv1Kd6KNRo5Fci4LOP/EaQ2pNHY3oCsOjMXzTzCaWemy+FJh3Lz/9Nekk+I+q1uG7+XZnnSS7OZm6zK6l/TbxDSCrKZnvGWS7pCpAJMsLdnXvfuBFCiQLLmmCK1wRTTCvEZnmou6fj1TwV7zoZCJXpT1OLcZVI4qpVq/DwuHJIUVRUREhICKtWrSojZniVDz/8kLCwMD744AMEQaBx48akpqbyzDPPMHv2bIqKinjvvff44IMPmDRpEgANGzaka9euZeYpLCxk8ODB6PV6Nm3ahLf3lfvF+vXr2b17NwkJCcTEXEmbb9CgbD9ao9HIxx9/TMOGV4S6Hn30UV566aXS13v37l3m+k8//RQfHx+2bNlSYRrtjBkzGDx4MABz584lLi6OM2fO0Lixc+tGq4LkJEpUCf+gQGQyGSaTCcVNFAeKfGpvvraEc7E0LMJj/GnqtD5Tpd55tYWUJF/GDBrIxQs3jiI4CpVCZE5cCXLbadUA4Da86oI1AMcOVb69BICxCntQQSen4HIAPiGXKz3W3dPAkBHpfLMyuPIL/8PqU3J6h2ppZ7Ftb4mSV5tz4WkZ9boes+m85eHlo2f599/w9fK2zH22NYVV0/VxKCaLwCd7FXwquHNHcwujg3X46G4eQfg+5QAAD+75sszzc5oOYmjoldYG6SX5yK5x/fNNOuYd+5MsfRFeSg2NvYJZ0mEiDTzKRrJ1ZiOvJ6zj1RbDkf1TY1tH48VTsf146egfKGVy5jYbXOqMujLCEW8MR7zJpBEZAXoUvS7j3jYV3wapKNWuq5JrTwSCkQm2bWVSVXr16sVHH30EQE5ODh9++CEDBw5k9+7d112bkJBAp06dEK6p++7SpQuFhYVcuHCB9PR09Ho9ffr0qXDN8ePHU69ePTZu3IhW+68a8MGDB6lXr16pg3gj3NzcSh1EgJCQEC5f/ve+fenSJV544QU2b97M5cuXMZvNFBcXl6azlkfz5v+2IwkJufK7uXz5suQkStw6+AUF4ubuRklRMZ7eFddO6N2v1CUqbSWDL3HLIzbPx/v2EwQ2SbzlT5N3/RXJ5HHdKSp0/mHM3K5GvPW2TasRtSYCm1fPSTx9smpREmMV43FFZwOr5CQCTH9uCz9+Ow5jNQLis7Yp+aa37X8XutebkmIWCOtx1KbzVsTtd+2le+/TPHjXMA7uq7glhKsgigIrD8lZecid4bFm7qivJ0hX/i9074CZN53z0/YTyvz/ycZ9ebJx35uO08iV/NjtgeueH1GvxQ2jjjUFIVON+bsw8r8LI09uQeicjbZzGt5xF/HwyXO2eQ5DIWvnbBNKcXd3JyrqX1XkxYsX4+3tzaJFi7jvvvsqNde1Dl9FDBo0iBUrVrBz584ykT9rxiuVZQ9GBEEokyE3adIksrKyeO+99wgPD0etVtOpUycMhooPJK6d96oTbLHUzP2vVJMoUSW8/X1x9/K0SrwGoEhSOZWwBV0z8X33L6Ln/UlQ0/O3vIP45dI23D6st0s4iP2jLXQQbd8ZXTEsFaWqelGCxHNVq2cyVLGfm+5Q1eoSAerWy6NH79wqjwfQmwRePqbFYodveP1bcaRsdGzj7rr18vht4xfMeP4UNU1I8JcEOePWuDHvoicXNCprOkdJVBLBLINtAZS81oz0u+I5PXUIST+3JTspBLO5dm9zFUInZ5tQLoIgIJPJKCm5/nshNjaWnTt3lnHKtm/fjqenJ/Xq1SM6OhqtVsuGDRsqXOOhhx5iwYIFDBs2jC1btpQ+37x5cy5cuMCpU6eqbP/27dt5/PHHGTRoEHFxcajVajIzM6s8X02kdv/1SNgNmUxGaER9SqzMAZJaYUhUFVEQEQalE/jJRqKe3oB/g4vONsklmPNMX56Z2hqz2fkKgH5uIlMjimxaB3cVn15VF6y5StrFqqXSVSXdFMCy24/q6IBMf/6vavfx3HdRxhqDfdqf6N+NJXm9Yx1FgCee3syqjb/SMLrm1R1vOCPjzjVaZp735KxaLTmLdkQ4547xs4ZkP9ads3eO4Nyn3Ug/GIWuuGZEoiuDXNbR2SaUotfrSU9PJz09nYSEBB577DEKCwsZOnToddc+/PDDpKSk8Nhjj3HixAl++eUX5syZw/Tp05HJZGg0Gp555hmefvppli9fztmzZ9m1axdLliy5bq7HHnuMefPmMWTIEP766y8AevToQffu3Rk1ahTr1q3j/Pnz/Pnnn6xevdrq9xMdHc0XX3xBQkICf//9NxMmTLA6wllbkNJNJapMvcgI9v+1C1EUy+SV3wjJSZSoLKLKjHxEKkGDjuHhd+ukD92MkhIF994+lG2by2+07Whe7aJHa4d0cjE2H9+6VUvbvEpOlpa8vKp5XFWNJAo5KopyfPDwy63S+GYtUmnVpoT9e6u3IXljh4JW/ZUEV5DqWFUMC2NJNsuoP+CgzeeuiMZN01m/axlzZw7ki8/qYa5hpci7k2XsTtYQF6zi4aZGGpt03OIJEXZFVqjEsqouhavqUkBrhNa5qLun4dU0Fa/ArGofxjgTgRDkQoSzzShl9erVpTV4np6eNG7cmO+++46ePXuSmJhY5trQ0FD++OMPnnrqKVq0aIGfnx/33nsvL7zwQuk1s2bNQqFQMHv2bFJTUwkJCWHKlCk3XHvq1KlYLBYGDRrE6tWr6dy5Mz/88AMzZsxg/PjxFBUVERUVxYIFC6x+P0uWLOGBBx6gdevWhIWFMX/+/DLqp7cCgmitRKWExH9IPnOOZW++T2DdENQaTcUXiyJxf11GYayZedkSjkP0MKEam0SdvsfReFiXznyrkJbqzbghgzh/1tPZppRyZ0sz93jbR1VE++zhaitqblrbiLvG9KjS2Hk9DHShaim07nMOENKm6qlOG1Y3ZvK47lUef5VIf5FPWxSgMNvnq1415RT1Bx2wy9w3Y8fWBjzxQG/S02puUlSEn8ijLQ20FPXILdJ2zJGIwTqUvdNxb5OGb0RqtfqwOgOl7DbcFG852wyJWkzNvbNKOJ06oXXx8PamML/injEACIIUTZSoEEugHvXjJwj/7DfCR+yVHMT/cHBvPfp2HO5SDmK4r8id/lXs13ATLCozAa2qJ1gDcORg1ZVCdVWMJALojlW9LhGgT/wJGkRVf9N6PktgRY79eswZPo4h6dc2dpu/Ijp3P8eW/Z8zZETNjQglZgvM2Khm4l4PtqPFJK+hb6QGIqRrMH0ZQd6TnTg3fgRn3+1F6t+NKc53nXtsRSgE10k1laidSE6iRJVRazWENYigMM+6huWSeI3EjbBEFOP27CEafvwbYX0PodLcmlLmFfHzty0YGd+f/LybROwdiCCIvNpeh9Jkn+iHYnAaak31+6ueOnF9I2drqUSru+sw7a76ule5/xHbqIh+fkDOaZX9FF+Mi6NI/Mk5KotubkY++vwH/vfZTnx8am4kLr1AxgtbVIze4ck6sxt6pbQ9cyQygxxxYxDFr7QgdeIgTj89kKTfW5NzoQ6WahwW2ROFzHVFayRqB9JdSKJahEVFYjZZd9otRRIlrsUSl4/ny3uJencVdTudQK6sYcVFDuL1uT15/IF2GI2uVUL+ZEcTITr7OfQ+fc7ZZJ7zZ6veO1JXjY+kkOxGSWH1Inh3TN5NYJBtUvRnbldTorLfV75paQMSf2hXLcGe6jB05BE27f2Szt1qQEPFCsjTCcz/S8ltWzz4Re9Oicr5wlS3IsIJL4yfRJP1cE/OTh7B+aVduHS0AQada8jrCoQiE+o52wyJWo7kJEpUi+CwUJQqJQb9zbtn6zwUmBSueSIn4UA6ZePz1g5iXv2TOi3OIpNUG26IwSDj3vHDeP/tGETRtW7VrUItxGvslw4sNijCNyzNJnNdvFD1TZ3eXL37VX5yULXGy2QwYbJtnOXsYoGFSW52VdU0fd6AxO86OM1RDAgs4ptVXzJ3wRHc7CPs6jBKjALv7lIwbIM7Xxa5U6CWnEVnIeSqMP9Uj4Ln2pF0+3DOzO/Lha1NKcjydZpNChdSNZWovbjWzkOixhFcLxQPLy/rUk4FQUo5vUUREaH/JQI+3EzUs+sIiE5xtkkuTVamO4O6j2HtH1Wvp7MXKoXInLgS5HbUoNKOTLRJjVlRkZLsrKpPVGKqnhElCdWrSwSYMnUbHlUPhpZh9Sk5e2X2lXA3r4gg8euOiE5M0bvnoZ2s2f4Dcc1ufnjp6pgsAov2Khi+zp1Pcz3I0bhWRsGthmARYJc/ujfjuHR3f04/MpTEH9qRdS4Us8lxjrwr90eUqD1ITqJEtdC4aanXINw68RqklNNbDYvCgmzUBYI/W0fUo5vxqXfJ2Sa5PMePhNCnw0hOJng725QbMrerEW+9/VQARbmFgLbV740IcORAvWpFtYqr+TaNe6vvJLq7Gxk2KrXa81xl1jYleWr7Ohrmr8I5/6VzHcWIBlms/utzHpl2DmXV2mS6FKIo8NUhOSPXuPP2JQ8uaWrBm6oFCClumD5vQM7UrpybMIKz/+tB2v4YSorsG8qWIokSjkByEiWqTf2oBpiM1vXhKvJ1jXx+CfticTehmHSesGV/0mDSdjwDcpxtUo1gzaomDOsTT1amazbs7R9toYNYtZYQ1iKPT0fjZps1jhysW63xJdVtL3jME4O++gdj05/dgspG52t6k8C841osdv72t3xbn/NfdHK66MfMF9fz45o/qB9ee2qefzsh5/Y1brx0wZMUjcquKcQS1iOUKBDXBFP0Yisujh/KmRfiSV7bgtz0QJsemMgIRyZU794mIWENkpMoUW2Cw+qhUCoxGm4uYlHiocAs1SXWWkQ/I6pHThLx2SoiRu1G61WzRSQcyQdvdeH+Ozuj17tmhMBHKzI1ogh7//V69bdNDR7AiePVUxitbrqpgED+xerVJQLUCSmgVz/bHbTsvSBjjcH+RXuWH8JIXNbF6Y5iyzYX2LxvKeMmpiGrRbueTWdl3LVGy9NnPTmjUkvOoqtx2BvDB43JfKA3Z+4bzvmVnbh8IhyjoXonPgpZ1fq+SkhUllp0u5RwFnXq1cXDy9PqukQp5bT2IdbToXn6CJGLfqX+gIOotDW/FshRmM0Cj907mNdeikO0d3inGizoqkdrsGMhIiCGFeMfbrvUynNnvKo1vri6kUSg+GRg9ScBZjy3zaYOzhs7FKQ7IGXR8nMoiZ91wWJ27mdbqbTw5v9+Y9k3WwgIrF3u1N4LMu5fp+GRE54cU2pw0Y4NtzRCphrzN/XJf7ojieOGc+bNPlzc0YTC3MqXFShkPW1voITEDXDdHYlEjcHNw526EfWtrkvM93edXm8S1cPSqBD3ufto+P5v1Ot6HIXSfrVqtZH8PA3D+4zm5+9DnW1KhdzZ0kwjg/0df83IJAQbqt1eSK7evabIBh0+DAer3y8RoHHTdNq0t52irCgKPLfXMc3bLb+Gkri4C2YnO4oAvfqfZOv+5fQdkOtsU2xOwiUZj65Xc88hL/bKtJhlkrfoighmGWwNoGRBM9Lviuf0tMEk/dKW7OQQK/5GNJJojYTDcP4dW6JWEBHT0Kp0U4D8ACktpsbTLgfv13cS/frvhLQ6g8yeUpe1lHOnA+ndfhSHDjhPRt0awn1F7vQvsvs6okwkoINtBGsAjEYZmRnV+4orMlZ/k23Z52Mz1cMnnvrbJvNc5XyWwMocx/SKsPxel8SPu2E2O7+Vg6eXnqXffsvbH+7F09PZ1tiepByBpzapuGOPB9vQYpRKPFwa4awHxiUNyX60O2fvGsGZjzuRsjcM0Xz9d4NC6IggSNoOEo5BchIlbEKdeqHIFQqrHEWTWk6xl2vWXUmUj4gIvS/j98EWomatJbBxsk3aFNyKbNkYTXz3wVxKr16zdXsjCCKvttehNNn/WEfW+zJaD9s5owlH6mKqZmC7SF/99y0zyMm/VH2VU4AefU8T3cgGObDXsOyAgtMqx2w6xTXBJH7oGo4iwJgJ+9mw+2ubRmhdicuFMmZvUTFmuydrTW7oldKWz9WRFSjhj/roX+rM2ZH9yXj3DgzHhoC+ESBIqaYSDkW6Y0jYhJD6oXh4e1qfchognYTVFES5BWFEKnUWrydq6ib86qc726QazWcfd+CuUd0pKXb92tzpHU2E6GyQc2kFXvG2E6wBOHSg+up/togkAhSdsU1dIsCDjx622VxXmbldTYnKMdsBcV0dEt/v7tCechURUjefn9etYOacE2hsUAmhN22mSP8BBbo5FOrmUWL4Aosl46bjRLEEnfEXCnXzKdC9QKH+TUzmE6WvG80HKNQtoEA3F51xVZmxFksOhfo3EUXdDefO0wm8ul3JbVs8+EnnRrHKNX72EjdBhLyNZpKfdefMmJZceGI8spIBzrZK4hZCchIlbIKbhwchYfWsE68B8gOkukRXx6IxI5+YSOjS1TS8ZxteQdnONqnG88zjA5jzTHMsFtffpLUKtTBQ45gIi1hHj3/DCzadM+Fo9R2zYhv5x/rDtokkAoy7ax/BIbZN784uFliY5OawMgBxYxCJC3u4jKMI8Mj0rfy++ZdqR2rNlnOo5J1wUz2MVnUvImaKDZ8hiuV/mETRRLFhCRYxB43qDtxVT6JRjkQQroiaWMQidMYfUSsH4aa6B5P5ICZzQul4neln1Ip4BKHi79USo8DCv5UM3+DOikJ38u3cL1PCxogBKD3qONsKiVsIyUmUsBkRjaKtrkvUeSjRa1xngyDxLxYfA8oppwlftorIsX/j5mNddFiifIqKVIyKH8WXn4eD3ZtIVB+VQmROXAmOKjVVj0yyeV3r2dOVVw38L6Io2KSfoGW3r037pE28+4zN5rrK6lNy9soc159T3BzI+Xd6YjK6jqMSE3uJ9buWcu+UFBRVNMtNdQ9KRRvksjrIZSFolKMRycUsXix3jNG8D5EStMo7UcgikMl8UcgaIJeFACCK2QhoUMqbI5eFIZc1wCJm/DP2IAJylPKmVttosggs2adgxDo3Ps7xIFvjOr8DifLx6RDrbBMkbjEkJ1HCZoSEhSKTyzEZrTuJlVJOXQtLiA7Nk8dosHgV4YP2o3a7ceqSROVISfKlb8fR7N5pG5VLRzC3qxFvvWOUakVE/Dvb3ulJSbaNw2MLhUghT0VBtk/1jfmHB5/YhqeX7eN+s7YpyXNkdGlbAIlv93ApR1Emgxdf+5Ovf11LSKgNDi7+SQEVKP/zaLIcRy7UR2/6hULdKxTp30Vv2oQoXllfJgQgYsRsSUUUizGLF5AJwYhiCXrTOtTKYVUzTRT45rCcUWvceTPdwyEtUSSqjndHyUmUcCySkyhhM+qEheLp7UVhnrV1iVLKqStgiSrEfdYBoj78jXo9jqJQ2VYY41bm7+2R9O88jAvJHs42xWr6R1voIJY4bD2hexbu3raPVl9Ks02mgsVG6kyF54NsMg+ARmPmtjG26yd5Fb1JYN5xrU2ip1azPYDEN3piNLqWg9KhSyKb937O8FGZVRboEkULOtMq5EI4cllwBdflYLIcBUS0qsmoFL0xmLZhMG8EQBC0aJRj0Bm/pcjwP5Sy1ijkMehNf6CSd8Ii5lCkX0iR/l2M5iNVsvX3k3LGr3HjxRRPktUqSYHcxVD4euDeqJ6zzZC4xZCcRAmb4eHlSd2I+uTn5lp1faGPCpMkze00xFZ5eC34m+i3fiek3SmpjYWN+eaL1tw+rBeFhTUnYu6jFZkaUeTQhFjPQbZre3GV0ycDsTLz/aaYbeQk6o7ari4RYNrMLajsoH2094KMNQbHtMUoZZc/Sa/1xGhwLTEnNzcjH3z2I598vh1f38q7TXrTr1gsl9Coxld4nYgFAXfUituQy0JRypujVvTCaNpdeo1SHoe7eioe6qdQK/tispzDbElDKW+PzvAVauUQtMoJ6Iw/YBELK23rVbackzFprZanznhyWqVGlL6iXQLvdo0RZNKWXcKxSJ84CZsS3TQWk8GAKFrxhSoTyPeXookOp0cmfu9tI3ruaoKaJDq8jcU7b+Tioz7PzCezyr3m8yX5DOydSnidJMLrJDE8Po19e8o2c3//7Tyi6iURVS+J99/JK/Pa3t06enS8iMkBrRtuxNyZfZnxaBtMJtdJo7OGBV31aA2OOyyw+OsJaJRi83kP7Quz2VwmG/2BmHfbNt04IKiQvvHl/w1Vhzd2KByferjbj6QFPTHoXctRBBg4/Bib9q2kW0/rI9464y+YzCdwU92PTKi4PlaGFzJZAILw75ZMJgQhUoAoXp/2LYom9MZf0ChvwyJmARYUsgbIZIHIhAAslur/Te27KOOBdRoeOubJEYXGsdFlievw6dLE2SZI3IJIf/YSNqV+VAO0Hu4UF1rX7ywvSHISHYEotyAMSSXw0w1EPbkBv0jbp6pZw/69epYuKiCuWcUbwb+26hg11oPf1gazbksIoWEKbhucTurFKxumo0cMzH8phyVfBLFkeRCvvJjDsaNXQkcmk8i0R7N454MAFA6OVOv1cibcNoLFHzWgJgjUXMudLc00MuhvfqENUd+Wglxutvm8x47YLrXTbKNfo3BRS3GBbdOOZ7ywFXsEF0RR4Lm9WkxyB3+G9/qSNL8XBr3rRd/9/Yv58pevmPfmIdwqaG8qiuI/DuJx3FT3IZP53XRuuSwciyWrtAYRwCJmIuCJIFx/0GQwbUQui0EuC+VKVe+1BzuW//y/epzMkPH4BjV3H/Bkj0yL2dGfCQnkHhq8Wkc72wyJWxCXdxI3b96MIAjkWpnCWJuZPHkyI0aMcLYZFRIUGkJQ3WDysnOsur7ATy196dgRUW1GPj6Zup+toeED2/AOznSaLYWFFu6fdJmFHwXg41vxrWfR50HcN8WL5i3UxDRW8f7HAYgWkS2brtTKnT5pIK6Zih69tPTorSWumYrTJ6/UUi58O4/OXTW0buvYjealNC/6dx7L1o22c1AcRbivyJ3+tmtkby3+3Wyfagpw5pSPzeYy2tDZL0iy7WcjulEG7TvZ5/d2PktgZY6D004B4YAPyS/3Qq9zzQPESff/zfqd39GsxY0PVPSmXzCaD6JRjQNBjUUswCIWIIr/1nqXGL5Fb1xd+n+logMiJehNq7BYMjCZT2AwbUap6HTd/GbLJYyWw6gV/QCQCYEICBhMezCZT2ARM5DLbF+7lpwr4+lNKsb/7clWUYtRKhVxGD6d45Apa1ZWikTtoFpO4uTJkxEEofTh7+9PfHw8hw/brtlv586dSUtLw9u7+nLm9qSkpAQ/Pz8CAgLQ6x17Gl8Vli1bho+Pj83nlclkxLRoiq7Iuv5qolwg39/1To1rOqKXEeX9Zwhb+geR43fi7mtd/0p7MuOJLPoPdKNnn8qrThYXixiN4Ot7RYykSZyKM6eNpCSbSE4ycua0kdgmSs6fNbLy8wJemOtra/Mr5ND+evTpOIJzZzwduq4tEASRV9vrUDo6NbdjFh6+uXaZOjnRds6NyYZOYvHx6vdu/C9Tn9lp8zmvsuyAgjMqJ9yfD3uT/JLrOoph4Tn8sfVznnjqzHV1oUbz34COEsMiivTzSx8m87/7IlHMxSL+m7oqE3xwU92N2XKBIsNCdKbfUCo6o5L3KDO3KIrojT+hUQxGEK4sLAhKNMrRGEwb0Rl/QK0YdtP01uqQUSQwZ6uKUds9WW1yQ690+VhDjce3e3NnmyBxi1Lto4n4+HiWLl0KQHp6Oi+88AJDhgwhOTm52sYBqFQqgoPLVwVzFX744Qfi4uIQRZGff/6ZcePGOdskpxEe1QClWolep0OtufmXfF6QBt/LUrsFWyAG6dHccZbgLidRqm2k3GEDfvi2kMMH9GzcUbdK4198LpvgEDk9+1z5PDWKVTH7JV9uG5QOwJyXfWkUq2J4fBpz5/uxcV0JC17OQaEUWPCWH1262a//2y/fN2PaQ20xGlxLndFapnc0EaJz/GfFY8h5u82dnmq7U3ejDX1n014/GGu7+QC69DhH4yYGThy3Ty3fMzvUrOhsdGitKoBw1IvkF3tTf84m1FrHqe1WhhkvbGTAkBM8PDmexPNXPnOemldvOs5N/cB1z8ll4birH65wnCAIuKmnXPe8Qh6Lh9yx7REKdAKvbVfyrkLB/W1MDPTS42awfer4rY7cyw2vVg2dbYbELUq1j4DUajXBwcEEBwfTsmVLZs6cSUpKChkZVxq93ihd9ODBgwiCQGJiIgBJSUkMHToUX19f3N3diYuL448//rjh+KsRsDVr1hAbG4uHhwfx8fGkpaWVsWvx4sXExsai0Who3LgxH374YelrBoOBRx99lJCQEDQaDeHh4bz66pUbuyiKvPjii9SvXx+1Wk3dunV5/PHHb/pzWLJkCRMnTmTixIksWbLkutcFQWDx4sXcdtttuLm5ER0dza+//lr6utls5t577yUyMhKtVkujRo147733yl1v+fLl+Pv7Xxe1HDFiBHfeeScAhw4dolevXnh6euLl5UWbNm3Yu3cvmzdv5u677yYvL680Cvziiy/e9D1aS92I+vgG+JOXnWvV9fn+GinltJqIkUW4PXeIBh//RljvIy7lIF5IMTHzySw+/TwIjabyt5x33sjlh++KWPFdnTLj73nAi71H67H3aD3uecCLL78owMNTRvuOGh6bksmKb+vwyut+3HtnBnq9faJkb77cg8fu61BjHcRWoRYGaqyL+tsS0dtAQJMku8x9IcWHYhu+JVummwonvOwSHZvy+CGbz3mV7CKBhUluTmmJIBz3JHl2L3TF9jvkqS7NWqaycc8y7piUapf6UFdHbxL44G8lwze4s7zAnXxH9tm8BfDtEocgt007HwmJymLTW1phYSErVqwgKioKf3/rldweeeQR9Ho9W7du5ciRI7z22mt4eJRf4F9cXMybb77JF198wdatW0lOTmbGjBmlr69cuZLZs2fzyiuvkJCQwPz585k1axaff/45AAsXLuTXX3/l22+/5eTJk6xcuZKIiAjgSkTwnXfe4ZNPPuH06dP8/PPPNGvWrEL7z549y86dOxk7dixjx45l27ZtJCVdvwGaO3cuY8eO5fDhwwwaNIgJEyaQnZ0NgMVioV69enz33XccP36c2bNn89xzz/Htt9/ecM0xY8ZgNpvLOJqXL1/m999/55577gFgwoQJ1KtXjz179rBv3z5mzpyJUqmkc+fOvPvuu3h5eZGWlkZaWlqZn191UanVRDePoyjfuhRHUS5Q4CelnFYFsVk+nq/sIeqd36nb8QRyheud5B7cryfjsoUeHS7i73Yef7fzbN+q45P/5ePvdh6zufzt5/tv5/HOG3n89HswTSsQu8nKNPPavFxef8efvbt1REUraBitpHtPLUajyJnTtu39aDTKuG/CMN57sxGiWDN3hiqFyOy4EpzR+UQ54gIKxfWqjbbg0F7b1mMZLbY9wMq/YPuU01HjD9im8Xs5rD4lZ6/MOY6acNKTlNm90BU5vj7SWpRKC68tXMUXP2wiqM6t2UrIZBFYul/B8LXufJTtTpaj1XFrKVKqqYQzqfaRz6pVq0oduqKiIkJCQli1ahWyShypJScnM2rUqFJnrEGDBhVebzQa+fjjj2nY8EoI/tFHH+Wll14qfX3OnDm89dZbjBw5EoDIyEiOHz/OJ598wqRJk0hOTiY6OpquXbsiCALh4eFlbAkODqZv374olUrq169P+/btK7Tns88+Y+DAgfj6XqmDGjBgAEuXLr0uOjd58mTGj7/SL2n+/PksXLiQ3bt3Ex8fj1KpZO7cuaXXRkZGsnPnTr799lvGjr0+P0mr1XLHHXewdOlSxowZA8CKFSuoX78+PXv2LH0vTz31FI0bNwYgOvpfdSxvb28EQbBbKm9ETBQ7123CZDShsKLgOi9Ig0+GlHJqNV2y8B2dgH/Di8625Kb06K1lx/7QMs89cn8G0Y2UTJ3hg7ycKPJ7b+by1mu5/LAqmFZtKj5EePapLB5+3IvQegr279NjvMYnNJmo0BGtLNnZbowbMpQTx1y7TvpmzO1qxEdvH0ftZvh1t49gDcCxI3VsOp+tg9DFJwMhyvZtPybde4oFLzW2+bxXmbVNyTe9jXg74TMjnPIk5YVe1Ht5M1oPxwssWUv33qfZui+JJx4czprfHVsX7Up8e0TBt0cUDIwxc2ekwSnp7LUBhY8Hns0r3g9LSNiTah+B9+rVi4MHD3Lw4EF2797NgAEDGDhw4A0jaeXx+OOPM2/ePLp06cKcOXNuKnzj5uZW6iAChISEcPnyZeCKo3r27FnuvfdePDw8Sh/z5s3j7NkrG5PJkydz8OBBGjVqxOOPP87atWtL5xozZgwlJSU0aNCA+++/n59++gmTqfwvRbPZzOeff87EiRNLn5s4cSLLli3DYil7oti8+b8nQu7u7nh5eZXaDfC///2PNm3aEBgYiIeHB59++mmFtZ33338/a9eu5eLFK47CsmXLSsWEAKZPn859991H3759WbBgQen7dwT1oyLx8vEh30pV2vwAtdSH6SaIgogQn07ARxuJemZ9jXAQATw9ZTSJU5V5uLnL8POT0yTuSnTwwXsymPtCdumYd9/M5ZW5Obz/aSD1wxVcSjdxKd1EYeH1p/Sb1pdw9rSJ+x/yAqB1GzWnTxpZt7qYZYvzkcshOsY2p9onjgXTp/3IGu8g9o+20EF0Tp2X2CoXr8Dsm19YRU6duHnLgcpgsHEk0XjAtv0Sr3Lfo9vx9rFfUqjeJDDvuNZp92nhrAcXnu9l8zYitsbd08DiL7/jvU924+XtnD6trsKfp+TcsUbL7GRPktQqp6Qs12R8u8YhyKWNkYTzqPanz93dnaioKKKiomjXrh2LFy+mqKiIRYsWXVngn4jitc3VjcayqV/33Xcf586d48477+TIkSO0bduW999/v9w1lcqyGz5BEErnLywsBGDRokWlzuvBgwc5evQou3btAqB169acP3+el19+mZKSEsaOHcvo0aMBCAsL4+TJk3z44YdotVoefvhhunfvfp3NV1mzZg0XL15k3LhxKBQKFAoFt99+O0lJSWzYsOGmdl91JL/++mtmzJjBvffey9q1azl48CB33303BkP5J3CtWrWiRYsWLF++nH379nHs2DEmT55c+vqLL77IsWPHGDx4MBs3bqRJkyb89NNP5c5nS9w9PWnYpBEFOblWXW+Ry8j3d00lO2djUZqRjUkh5LO1NHx4Cz6hGc42yeZcSDGRnv5vquySTwswGGDS7ZdpFJ5S+nj/nbwy40pKLDw1NYt3/uePTHZlMx9aT8Hr7/jzyAOZvPlaLh8tDkSrrf4X7bo/GjOk90AyM1w37c0afLQiUyOKnNbF0WOY/QRrAJLOV9DErgrobZw9KB7wwWSyfd2WWm1m1LgLNp/3WvZekLHG4LzPv3DenYvPub6jCDDy9oNs2v0V7Tu6buTTUWw7L2PyWi3TT3tyUqVGlCQIrEJKNZVwNjb/phIEAZlMRknJlVPqwMAr9RdpaWml6ZgHDx68blxYWBhTpkxhypQpPPvssyxatIjHHnus0uvXqVOHunXrcu7cOSZMmFDudV5eXowbN45x48YxevRo4uPjyc7Oxs/PD61Wy9ChQxk6dCiPPPIIjRs35siRI7Ru3fq6eZYsWcLtt9/O888/X+b5V155hSVLltCvXz+r7N6+fTudO3fm4Yf/VTezJvJ333338e6773Lx4kX69u1LWFhYmddjYmKIiYlh2rRpjB8/nqVLl3LbbbehUqkwm+1bvxbVtAn7//obs9mM3IrC6+wQrZRyeg0WdxOqscnU6XscrWft2mj8vi6kwv8fOVX2c1weWq2MvUevr0G76x5P7rrHdu0oPnynMwteaoJYC8LdC7rqHa5UeRXR3URAM/s6iakXbVsLpTcLYEPdCMEoIz8tAL+wdNtN+g+PP7OFlcsmYs8uTG/sUNCqv5JgnW3rfK1FSHLj4sxe1H11M+5eBTcf4ESCggv5Yc1KPn6vK2/Nb4LuFv96O5gqY0qqhphANY80N9DUokN2a5Zw3hSlnyceTSOcbYbELU61nUS9Xk96+pUvu5ycHD744AMKCwsZOnQoAFFRUYSFhfHiiy/yyiuvcOrUKd56660yc0ydOpWBAwcSExNDTk4OmzZtIja26nLOc+fO5fHHH8fb25v4+Hj0ej179+4lJyeH6dOn8/bbbxMSEkKrVq2QyWR89913BAcH4+Pjw7JlyzCbzXTo0AE3NzdWrFiBVqstU7d4lYyMDH777Td+/fVXmjZtWua1u+66i9tuu63U8bwZ0dHRLF++nDVr1hAZGckXX3zBnj17iIyMrHDcHXfcwYwZM1i0aBHLly8vfb6kpISnnnqK0aNHExkZyYULF9izZw+jRo0CICIigsLCQjZs2ECLFi1wc3PDzc22J8QRMQ3x8vUmPycX34Cbp1gV+KsxqGWobH10X8Ow+BvQjD9HcPcTqDSu33OzNmOxwNQHB/HTt7ZvTu0MJrY008jgvM+UYthFlCr7ORdZWW7k59k2TKE3Y1MnEaD4TKBdnER//2IGDM7k1x8DbD73VURR4Lm9Wj5tYUJhw1rfyiCkuJH6TC/qLtiMu7fze8DejClP/EXf+JNMmTSYkwn2aVVSkziVIfDEBjVhPkoeaWWijaBz2mfJVfHt1gzhVpTLlXApqv0JXL16NSEhIYSEhNChQwf27NnDd999VyqeolQq+eqrrzhx4gTNmzfntddeY968eWXmMJvNPPLII8TGxhIfH09MTEyZlhWV5b777mPx4sUsXbqUZs2a0aNHD5YtW1bqcHl6evL666/Ttm1b2rVrR2JiIn/88QcymQwfHx8WLVpEly5daN68OevXr+e33367oVrr8uXLcXd3p0+fPte91qdPH7RaLStWrLDK5gcffJCRI0cybtw4OnToQFZWVpmoYnl4e3szatQoPDw8GDFiROnzcrmcrKws7rrrLmJiYhg7diwDBw4sFcfp3LkzU6ZMYdy4cQQGBvL6669bZWdl8PTxpkFsI/Kyraw/EgRygmt2Kl91sISVoJ15mIaf/kr9/ockB9HJ5OdpGNZnTK1xEMN9Re7yd25E2reXfeuiD+61LgJdGXQ2rkkE0B2ynxP35PNbsLdi/vksgZU5zr1XCxe1pD7di8LcmlEfHNUog7U7lvHAo0kopC4RAKTkypi5ScW4vz3ZbNFiUEhO0VX8+16fuSYh4WgE8dpiQYkaSZ8+fYiLi2PhwoXONuU6Du/ay3efLqN+dEOrUk5VJSYa78xwWr2UMxCbFOB5+0mCmp9DJpP+HF2B82cCGDt0AOmptq1vcxaCILKyv86pKoNikwKiF/xh1zXee70nb74SY9M572tjYoKNFTUtHkaiVvxkt7/324eOZ/tW26Vbl8eifjqinBiZBhCDdYS8tgUP31yn2lEZ9uwK57H7+nIxRep/dy2eapGH2pjo5aZDY7x1M4q0kcE0+egJZ5shIWHbPokSjiUnJ4effvqJzZs388gjjzjbnBsS0SgKTx9vCnLybn4xYNAqKPS9NdJxxA45+Ly5g+gFfxDc8qzkILoI2zZGE99tSK1xEAGmdzQ5XYbebcQ5u69xKsG2yqYAOpPtj6xkhUoKM21v61Wmzdxpt7mv5ZkdanQq524jhHQNaU/1pCDbx6l2VIZ2HZPYvHcZt429jHArnYjehAK9wOs7lAzf7MH3Je4UqW5NJ1qKIkq4CpKTWINp1aoVkydP5rXXXqNRo0bONueGePn6ENk4mrwc6yXvs0Nqb8qpiAh9L+H/v81EP7+WgBjb90uTqDqff9qOiaO7U1xcew4qWoVaGKgpdqoNospMYCv7CtYAnD9ne9XLYju1BSw4F2ifiYEOXRKJa2b/CF92kcDCZDentzYQLqtJn9GT/Mya05tQozGzcNHPLFrxF37+zv4JuhYGk8D/disYtt6dz/PdyVPfQvm5chl+fVo52woJCUByEms0iYmJ5OXlMWPGDGebUiExzeOwmC1YrFRTzQvSYFLUruNVi8KC7LYLBC9ZR9Tjm/ENu+RskyT+w7NT+/PCUy2xmGvP6bVKITI7rgS5kzO35ENTUant77SkXlDbfM4SO+ns6I/ary4R4KGpB+w6/1X+PClnr0zrkLUqQshUkz6jB/kZ9ovQ2oMBQ46zZd8KevZxfQEeR2MRBZYdUDBirTv/y/IgU2Nb5WJXxLttI5Q+rt/iReLWQHISJexOREwUHt5e5Odal3IqygRygp2/6bAFFjczirvOU2/pnzS4ezuegTnONkniPxQVqRgzaBQrlkZALauGndvViI/eTqGwSuDbx/6ppkVFSrKzbP/7K7ZDuimA+W/7OonDRx+mXph92xxdZdY2pUtEe2TZai7N6EHe5ZurabsSPr4lfPHj17z27gHcJf/ghnx/VM6YNW4sSPUgVVN7Mj3+S8CANs42QUKiFMlJlLA73n6+NIiNIS+rEimndWt2yqnF14Dq4VNELF1FxOjduHkXOtskiRtwIdmXfp1Gs2t7zdpUWkP/aAsdxBJnm4EYVYhvPdu3e/gvh/eHYQ8ZtmI7RRKFS2qK8uwrLjPpgZN2nf8qepPAvONaXKGNqJCj4vKT3clNt68Tbg/uuHsP63d9S8vWt3hDxQpYc1rOhDVaXkjyIFGtcnqqsy1R+Hrg3aGxs82QkCjFBW7pErcCcW1bISJiNFgnnqHzUFLkVfNSSyyhOjQzjtJg8W/Ujz+ASit92bsqe3aF06/zMFKSat/RvY9WZGpEkUvERbUjEx2yzpEDIXaZ115OIkBBYpD9JgfufWgHvr6O2UbvvSBjrcE1DveEPBUZM7qTm1a5n+/br+fSq/NF6vknElUviTtGX+L0yYq/swb3S8NHff66x9jh/x6MvP92HlH1koiql8T775TNqNm7W0ePjhcxma78nuqF5fLbpuVMn3kaVe0NmFWb7Yly7l6rZdppT06qNNihU43D8e/bGsHe/WskJCqB5CRKOISGsY0ICA4mOyPT6jE1ScDG0qgQ9zn7ifrgN+p1P4ZC6fwUP4ny+XZFa8YO7kNhge1r2FyBBV31aA3Ol5AXlRYC29i3N+JVThy3T+SoyGC/3WfJcfuJ1wAolRZG35Fs1zWu5fUdCtJdpG5MyFdy+cmu5FysY/WY7Vt13DfFi3Xb6vLTH8GYjCK3DUmnqKj8v6UV3wRxMims9LHzQChyOQwfdUUd+egRA/NfymHJF0EsWR7EKy/mcOzoFcfTZBKZ9mgW73wQgOI/dfjTnt3Ebxt/o0FD6bukIg6lypiyTs0DR704qNBgrsG72oD4ds42QUKiDDX4z0miJqHWamjRsQ2FuflY25ozt44Gs9y1jwfFtrl4v7aL6Nd/J6TNaWTOVgiRuCkvP9+bJx9pg8nk/BoqezCxpZlGTu5ddxX5wDTUDoqmnztrn9TNIjv+KE177J/m/PhTW9E6qMRbFAWe26vF5CL3bVmhkswZ1juKP6wKZsJdnsQ2UdGsuZoPFwdyIdnMwf3lfwh8/eTUCVaUPjatL8HNTWDEP07i6ZMG4pqp6NFLS4/eWuKaqTh98kp4euHbeXTuqqF12xsfVjVplsaG3Uu5856LSAGmijmbKTBtg5q79nuyS3Cdz6C1eDSLRBNa81KkJaxn8+bNCIJAbm6us02xGslJlHAY0c3icPP0oCi/wKrrLQoZuUEaO1tVRXpl4Pf+VqJnryEwNknqdVUD0Ovl3DlqBJ9+EEVtE6i5SrivyF3+tm38Xh28+9q/7cVVLqbY515RaMf2ksIZD/Ql9vXgfHxLiB+aYdc1ruV8lsDKHNfJAhGKFGRO70p2SnClx+bnXTn08/Wz3kNbsayAkWM8cHe/sr1qEqfizGkjKckmkpOMnDltJLaJkvNnjaz8vIAX5lbctkOhEJn/zu+s/HEDdYKlQ8ibkZon49nNKsbt8mSjxQ2DomZsc50dRZw8eTKCICAIAkqlkjp16tCvXz8+++wzLJbKfe5efPFFWrZsed3zERERCILArl27yjw/depUevbsafX8iYmJCILAwYMHrbru6sPT05O4uDgeeeQRTp8+bfV6tzI1469HolYQUr8eETENyb5ciZRTFxKwEeUWZMMuErRoPVHTNuIXnuZskySs5HK6JwO6jGHzevvWgDkTQRB5tb0Opck1pBzE8GL86qc6ZC2jUUbGZft8nZUYBbuKY+Sn2P8z+eRzWxwaiVp2QMEZleukcgslCrJmdCE7yfq6VYtF5NkZWXTsrKZJnHXFgfv26Dl+zMhd9/wb1W4Uq2L2S77cNiidkYMvMedlXxrFqpj6SCZz5/uxcV0JnVpdoFv7i2zfVr7QVJeeZ9my/3MGDrVeAO5WJrtY4OVtSkb+5cHvBjd0Ktfd7iq83fHt1szZZhAfH09aWhqJiYn8+eef9OrViyeeeIIhQ4ZgMtkm7Vmj0fDMM8/YZC5rWb9+PWlpaRw6dIj58+eTkJBAixYt2LBhg0PtcBUMVmqDgOQkSjgQQRBo2r41ZrPZ6htOsbeKEnfnpgWKGhPyOxKpu3Q1De77C686WU61R6JyHD5Qjz4dR3D2tJezTbEr0zuaCNHZMexVSTQjkxBkjnFYjx2ui5VtWKuERWa/yHPxCfunmIVHZtOlu2P78D2zQ+1SG3OhREHWU13IPB9q1fUzHs/i+HEjS76w3on/YmkBTZoqadOurIN8zwNe7D1aj71H63HPA158+UUBHp4y2nfU8NiUTFZ8W4dXXvfj3jsz0OvL/5txdzfy6YrveX/R33j7uMZhkKtTpBd4c6eS4Zs8+LbYnSK16+XtBgxsj0zl/PIHtVpNcHAwoaGhtG7dmueee45ffvmFP//8k2XLlpVel5yczPDhw/Hw8MDLy4uxY8dy6dKV3s/Lli1j7ty5HDp0qDSCd+3YBx54gF27dvHHH39UaMvixYuJjY1Fo9HQuHFjPvzww9LXIiMjAWjVqhWCINw0Cunv709wcDANGjRg+PDhrF+/ng4dOnDvvfdivuaL45dffqF169ZoNBoaNGjA3LlzS/eqd9xxB+PGjSszr9FoJCAggOXLlwNgsVh49dVXiYyMRKvV0qJFC77//vsKbfvhhx+Ii4tDrVYTERHBW2+9Veb1iIgIXn75ZcaPH4+7uzuhoaH873//K3NNbm4u9913H4GBgXh5edG7d28OHTpU+vrVyO7ixYuJjIxEo7E+68Z17uAStwTRTWPxCwogN8N6Rys7xDk9Ey3eRpQPnKb+0j+IvP1v3H2sS5OVcB1W/dSMEf37kZtTO/pulkerUAsDNcXONqMUUW4hoP0Zh613eL91G/+qYs/WDoZ9jqlDmv7sToesc5XsIoGFyW4u1aJA0MnJeaoTmefqVXjdU09ksubPYn5bE0xoPes270VFFn78rpA7J1dcG5uVaea1ebm8/o4/e3friIpW0DBaSfeeWoxGkTOnby6nO2LsITbv+YpOXV0ntdzVMZgEPtqjYNg6d5bmeZDrAn09AZDLCBza0dlWlEvv3r1p0aIFP/74I3DFERo+fDjZ2dls2bKFdevWce7cuVIHaty4cTz55JPExcWRlpZGWlpaGecqMjKSKVOm8Oyzz5abxrpy5Upmz57NK6+8QkJCAvPnz2fWrFl8/vnnAOzevRv4N0J41TZrkclkPPHEEyQlJbFv3z4Atm3bxl133cUTTzzB8ePH+eSTT1i2bBmvvPIKABMmTOC3336jsPDfdmZr1qyhuLiY2267DYBXX32V5cuX8/HHH3Ps2DGmTZvGxIkT2bJlyw3t2LdvH2PHjuX222/nyJEjvPjii8yaNauMUw3wxhtv0KJFCw4cOMDMmTN54oknWLduXenrY8aM4fLly/z555/s27eP1q1b06dPH7Kz/806OHPmDD/88AM//vjjTdN0y/ysrL5SQsIGuHl4ENe2Jfk51jeVzwlxc6i8tSVEh2b6MSKX/Eb4kP2o3Z3fa06i8rz9ancevrsDRoNrqC3aC5VCZHZcCa6kmSTrexmtu+OcVnspm17FbM+i48NemIz237C26ZBEs5aObcnz50k5e2WudUAjGORkP92RjDNh170miiJPPZHJql+L+XV1CBGR1t87fv6hCL0ext1RcUudZ5/K4uHHvQitp8BsAeM1PqHJBGazdW51QFAh3/6+kjnzj6J1naoMl8ciCiw/KOe2te68n+lBppPVeH27NkXl79pZLo0bNyYxMRGADRs2cOTIEb788kvatGlDhw4dWL58OVu2bGHPnj1otVo8PDxQKBQEBwcTHByM9j/KWS+88ALnz59n5cqVN1xvzpw5vPXWW4wcOZLIyEhGjhzJtGnT+OSTTwAIDLyiCn01Qujn51el9wSUvq+5c+cyc+ZMJk2aRIMGDejXrx8vv/xy6ZoDBgzA3d2dn376qXSOL7/8kmHDhuHp6Yler2f+/Pl89tlnDBgwgAYNGjB58mQmTpxYOsd/efvtt+nTpw+zZs0iJiaGyZMn8+ijj/LGG2+Uua5Lly7MnDmTmJgYHnvsMUaPHs0777wDwF9//cXu3bv57rvvaNu2LdHR0bz55pv4+PiUiWIaDAaWL19Oq1ataN68udU/J8lJlHA4jVs2Q63VUlxo3SmoWSkjP9D+AjaWhkW4vXCQhv9bRb2eR1Gq7NggTcJuGI0yHrxzKO8saIwo1v5b3NyuRnz0riWT7zXgnEPXO3va267z29NJFMwy8tPs2wrjKo9M3++Qda5l1jYlea4StfkHmUFO7jMdyThVv8zzMx7P4puvilj0eSAengKX0k1cSjdRUvLvCcyD92Qw94Xr6wJXLCtg8DA3/PzLT2fctL6Es6dN3P/QFaegdRs1p08aWbe6mGWL85HLITqmck7LfY/sYPW2H2jcxHVSzWsKPx6TM2aNG/NTPbmocU5TyqDhnZ2ybmUQRRHhn3tgQkICYWFhhIX9e8jSpEkTfHx8SEhIsGq+wMBAZsyYwezZs6+rjysqKuLs2bPce++9eHh4lD7mzZvH2bO2a6d0VWX/6vs6dOgQL730Upk177//ftLS0iguLkahUDB27NhSx7aoqIhffvmFCRMmAFcidcXFxfTr16/MHMuXLy/X7oSEBLp06VLmuS5dunD69OkyabCdOnUqc02nTp1Kf9aHDh2isLAQf3//MuueP3++zLrh4eGlznVlcK07t8QtQb0GEdRrEE7ymfO4eURaNSYz1A2fy/Y5Bbe0zMNn3IkrKqUOqqGSsA/Z2W6MHzqE40d9nG2KQ+gfbaGD6FqRbjFEh3+DCw5dMyXJvtEqu0YSgaLTgfjVt78Q1uDhRwmPaE9SouO++vUmgXnHtbwWXYDMhaLdglFG7jMdEBcIBDVKAmDJp1dKCob0Sy9z7f8WBTDhritppBdSTMj+c/Z0+qSBndv1/PR7+UqlJSUWnpqaxWcrA5H9U+MaWk/B6+/488gDmajU8NHiQLTayh9sNYjKYt3OZcyf3Y/FH0aWiU5K3Jx1p2WsO62lU7iK+xsZidDrHaJ/7RYdikeTcAesVD0SEhJK6wBtxfTp0/nwww/L1BoCpemcixYtokOHDmVek9tQfeuqk3X1fRUWFjJ37lxGjhx53bVXa/gmTJhAjx49uHz5MuvWrUOr1RIfH1/G7t9//53Q0LLlD2q1/US8CgsLCQkJYfPmzde95uPjU/pvd3f3Ks0vOYkSDkcmk9G8Q1vOHj+FxWxGZsUffpGvmmJPBW4FNoyYdM/Ed2QC/g0co8AoYV9OJtTh9qH9yMy4NXKvfLQiUyOKEFwsgKAemYTMwYctl9LtK0ZhsvOO0XAwAPrYd42r3P3gCV58tqljFvuHvRdkrA13I17hOnWzcCWKmzezPeIrAnWaJJKrv/lG+Pd11yukRjdS3XSsVitj79HrayHvusezjBpqdXjupXUMHBrGI/f2JyXJ9QRaXJ2dSXJ2JslpFqLioTgjjUw67HkrCxrW6eYXOZmNGzdy5MgRpk2bBkBsbCwpKSmkpKSURhOPHz9Obm4uTZo0AUClUpWJhN0IDw8PZs2axYsvvsiwYcNKn69Tpw5169bl3LlzpVG6/6JSXYn63myN8rBYLCxcuJDIyEhatWoFQOvWrTl58iRRUVHljuvcuTNhYWF88803/Pnnn4wZMwal8krkv0mTJqjVapKTk+nRo4dVdsTGxrJ9+/Yyz23fvp2YmJgyDvF/W4bs2rWL2NjYUrvT09NRKBRERERYtW5lkJxECacQ3awJ3v4+5GZl4xdkXQg8I8yD8OO51VpXlFuQxV8iYNhxvEOsb8Uh4dqsX92YhyZ1RKdzTsqQM1jQVY/W4EKhGUBEJKCT7VKCrOHUiSAqoehdJUx2jitYdvtiMcuQOaCwdNIDO1n4ZhzZWY7tFfr6DgUt+ysJ1rlWmEswy8h/vh3iyzKCmzo2TdoetGqXwqY9y3j2iSH88E0dKtniTgI4kibj4TQ1Df1VPNLCQHNRj9xiW29R4eOBb48WNp2zuuj1etLT0zGbzVy6dInVq1fz6quvMmTIEO666y4A+vbtS7NmzZgwYQLvvvsuJpOJhx9+mB49etC2bVvgiiLn+fPnOXjwIPXq1cPT0/OG0bQHHniAd955hy+//LJM1HDu3Lk8/vjjeHt7Ex8fj16vZ+/eveTk5DB9+nSCgoLQarWsXr2aevXqodFo8PYuv+QgKyuL9PR0iouLOXr0KO+++y67d+/m999/L3XGZs+ezZAhQ6hfvz6jR49GJpNx6NAhjh49yrx580rnuuOOO/j44485deoUmzZtKn3e09OTGTNmMG3aNCwWC127diUvL4/t27fj5eXFpEmTrrPrySefpF27drz88suMGzeOnTt38sEHH1wXXd2+fTuvv/46I0aMYN26dXz33Xf8/vvvpb+PTp06MWLECF5//XViYmJITU3l999/57bbbiv9nVSV2l+wI+GSePn6ENuqOXnZ1gvY5AZpMFRRvlpUmZGNSyZkyVoaPrhVchBrER+/15l7x3e5pRzEiS3NNDLonW3GdQi9MnHzcqwK8KF9FStV2gKjnZ1EoURBQWblxReqgkIhMm5iokPWuhZRFHhurxaT3LHOqTUIZhkFL7Ql/XBDZ5tiE9RqM29//AuffbWVgACphKKqnM0SmL5RzZ37PNgp2PazGzCwnUu0vbiW1atXExISQkREBPHx8WzatImFCxfyyy+/lDpTgiDwyy+/4OvrS/fu3enbty8NGjTgm2++KZ1n1KhRxMfH06tXLwIDA/nqq69uuJ5SqeTll19GpytbSnTfffexePFili5dSrNmzejRowfLli0rTQ1VKBQsXLiQTz75hLp16zJ8+PAK31ffvn0JCQmhWbNmzJw5k9jYWA4fPkyvXr1KrxkwYACrVq1i7dq1tGvXjo4dO/LOO+8QHl42HXjChAkcP36c0NDQ6+oJX375ZWbNmsWrr75KbGws8fHx/P777+Wm6rZu3Zpvv/2Wr7/+mqZNmzJ79mxeeuklJk+eXOa6J598kr1799KqVSvmzZvH22+/zYABA4Arv48//viD7t27c/fddxMTE8Ptt99OUlISderUqfDnYg2CeLV6U0LCwZw9fpIV731EYN0QNFrraooCkgsJPWP9JlT0MKEal0idPgloPFwr1UmielgsMP2hgfzw9fUqhbWZcF+RRa0LUJpc79bt9foughonOXTNuc/Gs/jD+je/sBos7V9ChN6+4UrtzCOEdj5u1zWuUpCvpm2jSRQ74ZY4uZWJSV6u2bpBRMRj7n5CWjmufYu9yc9V89j9I9i41r7iTrcCfm4iD7cx0U2tQ2WseohWUMhp+vnTLq9qKuF8IiIimDp1KlOnTnXK+lIkUcJphMc0JKR+GNmXMqwek13XDZPi5qd5liA9qicSCP/sN8KH75McxFpGQb6aEf1G33IOIsD89jqXdBDFAD3+0SkOX/fMSR+7r2EU7R/90h/xt/saV/H00jN4+CWHrXctyw4oOKOyn5BDdRAQKJzTmrR9Mc42xWZ4+ej5/LtvePP9fXjYpvTxliW7WGDeNiW3bfHgN4M7JaqqbaF9usRJDqJEjUByEiWchkKhoHnHtpQUF5fbVPW/WBQysuuWL0xiiShG+9whGn78G/X7HEalcTFVD4lqc/6cP306jObAXsek57kST3YyUlfnmp9p1ahk5E5o1picaH+hIoMDnETz3/44Mq9n+vNbUDgp2+2ZHWp0Vdxg2xsBgaK5rUjd3cjZptiUcXftY8Oub2jd1rXUkGsixUaBt3cqGLbBg2+K3CmsZBlMnVHd7GSZhIRtcc27tMQtQ0yzOLx8fcjPybV6TEaYO5b/7NnEZvl4zNtD1LurCO14ArmiaqpXEq7N9i0Nie86hLTUqsk512RahVoYqHHdiLh/F8cK1lwlPc3+no4j9IGETDVFeY5LCawXlku3XnkOW+9asosEFia74Xrx8H8pnteSi7saO9sMm1K3Xh6/bPiCp2edxI6q/LcMJovAx3sVDFvnzpI8D3I1N78XebZsiHuM/euoJWoHiYmJTks1BclJlHAyfkEBxDRrQk5mltVjTGo5uXWu1DCKnbLweXs70a/8SXDzcw6X3pdwHF8sbseE23pSXHTr7W5UCpHZcSU4IVBnHZ2z8PBxvMNxIdnHIXV1hv+eStmJgvOVb3ZcHWY8tx07t4Aslz9Pytkns29/y+pSMr8FF3c0cbYZNuexGVtYtfkXGka7ltJsTUUUBVYclHPbGnfey/AgQ6Ms99rgsT0dZ5iERDWRnEQJp9O0XWvkcjm6YuvTYDIaavH/cDPRz64nIMqxjbslHM/z0/vz3JMtMZur1/tLb9pMge5ZdMbfyr3GYNpNsf4TCnRzKdDNpdiwGLMl5T/XbKVQN49C3TwMpm1lXjNbkinSv48o2i6aPberER+9DXuE2hjPwc5pHXBgr2NqUvUOOnvSHXesk9i89QVatHZe+uEL25Tkq11L4fG/lCxoxoVtcc42w+Y0bnKJ9buWMfn+C9iwR/ktz8/H5Yxd48accyrS3MsegrhFh+LVuvw+fBISrobkJEo4ncjYGMKjG5CRlm71GJ1aTbHOw45WSbgCxcVKxg4ZyfIlEVDNNgRmSwpG825kQvBNrjuHQt4cN9X9uKkeQhB8KDZ8hkXM++f1NPSm9WiU49Eob0dvWovZcuWzK4pmdMaf0ShHIAi22Xn1i7bQQXTdOiLRx0BArGMVTa9y/HD1Jb6tQW92TLjNtMdx4jVXeWzGXoeveRW9SeDl41osLr4T0b3RlJTNzZxths1RKERefvMPvvplPSF1XTVNoWayNUXLHX+oeDUvjMLICJDJCB5jXZN1CQlXwcVvzRK3AgqFgjbdOmMymTBWoiv2id3t7WiVhLNJTfGhf6fR7NwWUO25RFFPifEbNIqRCFSc4qZV3Y5K0Qm5rC5yWRAaxUhAxGy5UnNnETOQCcEo5A1RyKOQCcFYxCsKvQbzNuSySOQy20S4fLQi0yKK7dylr3oob7vgtBrgUyd9HbKOzkFOonDOHV2x/YV4rqX/oAQiGzgvSr33goy1Bse+56qgf7sJKRuaO9sMu9Cp2zk27/ucobdlOi39uLay9mA+Q5fl8KGiMT5da19EWqJ2IzmJEi5Bo5bNCAkLJTPdeln2Sxe8OJfgYz+jJJzGvr/r07fzMJISbaPZrjP+gkLWGIW8Kqk+RsBc6lxecQozsYi5WMQcLGImMqEOFksWRvNe1Ir+NrEZYEFXPVqDa4sw+XVzXk+5pPOOETDSO/BXkJ/s2JRTgHsfPubwNa/l9R0K0iuo43IV9O/FkrKuhbPNsAtubkY+XPYjHy3dgY+vVNtva3qNboEgk7bcEjUL6RMr4RKoNWradO9MSVExZnPFO7LikgKOJ2zn70M/supn163Tkqga33/ZktGD+lKQr7HJfEbzISxiKmrFgCqN15v+RBC8kMuuOJhyWRBqxQCKDUsoNnyGWhGPXBaEzvQTasVATJZTFOnfpUi/EJPlfJXtntjSTCODvsrjHULbHLwCcpy2fFqqYxyLEgfeZooTghy32D/cee/fBAQ6zzEQRYHn9moxyV0/jKV/vzHJq1s62wy7Mfi2o2zet5Iu3QucbUqtoX59L26/o6mzzZCQqDSSkyjhMjRp0xL/oECyL2fc8PX8gmwOHd3EnqM/k206S3BkHeqEDCYrwzF1SRL255UXejP94baYTLYRs7CIueiNq9AoxyEIlXco9KbNGM2H0SonlhmvUnTAQ/0kHuonUSk6YDTvQ0CNXFYfnfFHtMqJqBWD0Rm+QhQr72GE+4rc5V9U6XGOxn1o1Z3g6pKV5UZ+nmOcihIHpZsCmPY7vi5RJoPxdzlHfOgq57MEVubcPO106bmd3LVzGd3Xv02/TQt58sAPJBZVrI5tsphZdOYvhm/9mM7r3mD89iXsyCj7fv9MPcbgLf+j14Z3ePvEhjKvpZbkMnLbJxSarhzaGD5sRNLvrSv5DmsO/v7FfP3bV7z02mHcXD8T2OWZ+mRHFAppuy1R85A+tRIug6e3Fy27dCA/Jxfxmq7S2dlp7D+0lgMnf6NQuED96HC69BpI0xYdcHP34HRCS+cZLWETDAYZk8YM5+P3oxBF292WzJaLiBRSbPiAAt3zFOiexyyex2jeSYHueUSxfLEGg2krBtMW3FT3IJeFlHudRSxCb9qAWjkMiyUFmRCATBaAQt4QsGARMytt9/z2OpQm1075sngYCWzqPCfx4J76DlurxIGdAsQjXhgNKsct+A+PTNuGu5Pbjy47oOCMquIWN/uzkxlTvzVLO97J/9qMw2Sx8OjebygxlV/P/uHprfx44SBPxfbj2y73MyqsFU8d/JET+VcEp3INxcw79idPxPTmg7a382faMbZd/jeNesHxtTwa0xMPxb+2GT+JJunXNtV8x67N3VN2sXbH98Q1c/GMBhcmJMSDOyfVzlpWidqP5CRKuBTNOrTBy9eHnMwsLl1KYveB3zl8bg0GVQYNYmPo1nsIjeJaoVb/m4qYcakeudnVFzeRcA4Zlz0Y0GUsG9faPiKskEXhpnoCN9VjpQ+ZEIpC1gI31WMIwo1vgXrTFvSmjbip7kYuq7jxsd64CpW8KzLBGxER+DddWsQMVE41cHonE3V11gs4OQvl8IsolM5L9z56qGKVWlviyHRTwSKQn+r4ukR3TwNDR6Y5fN3/8swONTpV+VuT99uOY2hocxp6BBLjVYcXmw0mXZdPQn756th/pB3j7gad6BrYkHpuPoyu35rOAQ1YmbgHgAvFuXgo1PQPiSXOO4S2fvU5/090cnXacRSCjN51Gl03r3FxFIk/tUN07fOcahEemc3qvz7n0elnUbp+2ajL8djU9qhdvM2LhER5SE6ihEsRUCeIRq3iOH5uMycubAL3Ahq3aE7X3kNoGNMUheLGN9vTCbVTTKC2c+RQKH063MaZU152mV8Q1MhlwWUeAioEwQ257IqTUWL4Fr1xdekYvWkLBtM6NMrRCIIvFrEAi1iAKF5/mm4yn8YiZqKUdwRALquHRczAZD6JwbQbARkywfoNf8u6FgZpXD/NFMCv51mnrn8ywc9ha5UYHVsrV3TKOYde05/b4nRHILtIYGGyG9b6XYXGK3+XXsryVYuNFhMqWdnvDo1cycGcK/1P67v7oTMbOZGfTp6hhON5aUR7BpJv1PHx6a08HVu+GJVpaQOSfqzdjiLAM3M28PPaPwiPkHQArCUkxIN772/lbDMkJKqMdLwh4XLEtW/OjkNeePqEER7ZGFk50Z5rSU8NJz/XFy8f54loSFSOVT815YkH2mEwOHdXKoq5WK5pMmE07QLM6Iwry1ynkvdBrex7zTgjOtOvaJXjSyOSMsEbtWIYOuP3gAKNcozVtZAqhcicpiXIa0Bml9g8D+86lU+jtSWJ52yjfGsNxQ5MNwUwHAgA24nkWk1I3Xx69sll3Wofxy9+DX+elNO7rpa2lor7g1pEkbdOrqeFTz2iPMs/jOno34AvE/fQ2jeMem6+7M5KZOOlk1j+8ey8lBpebDaYOUd+R282MqhuUzoFNOClo38wtn4bUktymX7ge0yihQcadqVvcOMy85s+b0CiSUbE2L9rdQuJ5q0vsGnvMp6dOpjvvgzBIrVWrJCnn+2CViuFXyVqLoIo1vbzL4mahiiK/LbqEw4f2UpEuPV9hYKCU+jQba0dLZOwFe+91o23Xm1k0/rDms6rPQ10FCveFLsKbi8cpG77k061oUWD+8nOcsyOPDbIwoexjlN7FDVmGnz1I3K543fhxw7XZWD3IU6PjKkVIt/2LsZLX37k6tXja9iRcZbFHSZSR1N+NkLOPzWH2y6fQRAgVOtLB/8Ifr14mO39ZtxwzL7sZN47uZFP209gxLZPeKX5MPzV7kzatZyfuj6An/r6Ak757UlE3P43gqz2b6s2r4th+sM9yLhci73iahDZwIe9B+9HqZQ72xQJiSoj7dAkXA5BEGjZsicqlZaionyrx11ODyPjUvkCIxLOx2wWmHLXEN6cHys5iNfQL9pChxriIIpaE4EtnCdYA1BUpCQn23Gb0yIHl4gKOjkFl52TchrXPJXW7Yqdsva16E0CLx/XYinnNvHa8bX8lXGGj9vdUaGDCOCrcuOtVqPY1vdJfuv+MD90vR+tXEmo1ueG1xssJl47vpbn4uJJKc7BLFpo41efCHd/wt18OZqXesNx5q/DSVzZCYul9jtOPfudYsu+5fQbKGXv3IjnZ3WTHESJGo+0S5NwScLqNSKqYQsysy5WalzC4fZOPwGXuDE52VoG9xzD77/UdbYpLoWPVmRaRDE1ZVupGJKKUu1cYZ1D+8Ic+nde6OCaRICis44Xr7nKE0/tcdra17L3goy1hrI9GERR5LXja9l8+RQftR1PqJuP1fOp5QqCNJ6YRQsbL52kR1D0Da9bcnYHnQIiaewVjFm0YL4mr9IkWkrTVG+E+bswEpd3viUcRU8vPZ99/R3vfLQXTy/pi/cqTZsFMmZcE2ebISFRbSQnUcIlEQSBVi17oVAoKSkptHpcXm4AF5Mb2tEyiapw+mQQfTqO4thhH2eb4nIs6KpHazDf/EIXwae3c/vpARw+6NiDhiIn1InqDjlPsblX/5M0jHZwIWY5vL5DQbrm37qu1xLW8mfaMeY1H4abQkWmvpBMfSE687/2zj7yGx+c2lz6/6O5qWy8dJILxbkcyEnhsX3fIiJyV2SH69Y7V5jJuvQEpkR1AyDC3R9BEPj5wiH+yjhDYlEWTbwrzlix/FiPxGVdbglHEWD0HfvZtPtr2rR3fgTaFZj1Yg+E2lycKnHLIDmJEi5LeHgcEeFxZGReqNS4E0fbYDZLaR6uwsa1jRjUYxAZl6SuzP9lYkszjQw1QKnmH8RGBfiGXnK2GZw67tiG83qTYLXapq2w7PZzalbEA48cdd7i1yCKAs/t1WKSX9l0f59ygEKTngf3fEn85g9KH+vSE0rHpJfkk6n/93BRbzHx0emtjN2+iBkHfiRQ48Hi9hPxVGr+s5bIK8dWM61RH7SKK70qNXIlLzYdzOKz23n56J88HdufIM3NRZMsP4eSuKQrFvOtsc2qE1LAz+tW8OyLCWg0N7++ttKhYygDB0U52wwJCZsgCddIuDSnzxzg+x/eJTAgFI3G+k7Psc13E9XoiB0tk7CGRR90ZN6sOCwWyWn/L+G+IotaF6A01ZxbsPaZI4R2Oe5sMxjRdyL79jj20GF9r3zkFsf+ruosW4OnX65D17yKxQLtGt/H5Uuu4eTc3drEXZ41oz3MtcgGpRF+/19OESFyFqdPBjLlziGcOnnrKXv+ue4OunSt72wzJCRsgmvc/SUkyqFBZDPC68dWOpp4OqEFBr3aTlZJ3AyLBaY/NJCXnm8uOYjlML+9rkY5iBaVmYA2zk81BbiQ4vhQhVnm+PSxwvNBDl/zKjIZTJjs3F6Y17J0v4Izqpp3T7f8EULix91uqeyW6EYZrNu1lHsfSqac1sa1kj79IiUHUaJWITmJEi6NXK6gVaveiCLoDdarP5qMak4ntLSfYRLlUlSgYmT/0Xz3ZZizTXFZpncyUVfnXPGXyqIYmI5ao3O2GRiNMjIzHP/VVZ7Kpj3RHXNeXSLAlKl/4eG4dpQ35ZkdanSqmrdtEdcEk/hhN8ymW8dRlMngxQWr+ea3tdQNrf1RVEGAOXN7ONsMCQmbUvPuthK3HNFRrahfvxGXL6dUatz5M7EUFbrQDucWIPG8P707jmHfHj9nm+KytKxrYZCm5qXN+fR1jajS0UN1MTtB58fsBCEK027H1l7+Fzc3IyNG37jdgzPILhJYmOzm8PpQWyCuq0PiB91vKUcRoH3nRLbsX8qI0RnUZi2XEbc1pmWrYGebISFhUyQnUcLlUSiUtG83EEEQKC62vqG1KMo5cbSNHS2TuJYdWxsS32UIqResrx291VApROY0K6GmlSeJkUX41k9zthkAHD4Q6pR1TU5oUiIku1FS6Ny/p2kzN6NSOdWEMvx5Us4+mdbZZlQJcWMQiQt7YDLdQjmYgEZj5v0lP/HJ59vx9auJLn7FqFRyZs3t7mwzJCRsjuQkStQIohq2JCa6NZczU6iM1lJqSgNysp2bsnUrsHJpW+4Y0ZOioppXM+RI5nY14qMzOduMSqMZmegyUYATx5zTP9DkpB9AfrLz6hIBgoIL6d0/26k2/JcXtinJV9dMR0vcHEjiOz0wGWum/dVh4PBjbNm7gu69rD/srQk8MKU1UVFS9oxE7UNyEiVqBDKZjPbt4tGq3SkoqMyGReD4ofZ2s0sCZs3ox8yprW4pYYaq0C/aQgfR+rpaV0GUWwhs5xqCNQBnT3s7ZV1nufYlCc4/5Hry+W3IXGi3oDcJzEvQOqVO1CZsCyDxrZ63pKPo61/Cyp+/Yv7bB3GvBUkn/gFannmui7PNkJCwCzX1FitxCxIaGk1cXGcys1IrFU3Mzgwh/aKkOGZrSkoU3D50JMsWRYITUvFqEj5akWkRxTXypyTrfwmNm+s0yU5Jck4TNmekmwIY9zrfSWzc5BJtO7hWHe2eFBlrDTW49+oOfxLf6InRcOu1iQC4897drNv5Hc1aOl8Mqzo8P6sb3t63cGNIiVqN5CRK1BgEQaBd2wF4eweQk1O5ht4JR9phsdTELbprkpbqTf9OY9i+1fkb2JrAgq4GtAYnqK3YAO8BrhNFtFjg8iXnRKyNopPuH8c8MeidXxQ49Zm/nW3Cdby+Q0G6pgY7Wbv8SXq9J0aD83+/ziAsPIc/tizniafOuFTdq7U0iQvk7ntbOtsMCQm7ITmJEjUKf/8QWrfqQ15+JuZKSBwWFviQfL6RHS27ddi/uz59Ow4n8bykHGsNE1uYaWSomaflYmgJfhEXnW1GKadP1MHgpM4hBifpbQgI5F90bl0iQLdeZ4hpfP0PX2/aTJH+Awp0cyjUzaPE8AUWS0aFc4miGb1pA4X6NyjQzaJI/x4m88ky1xjNByjULaBANxedcVWZ1yyWHAr1b2Kx6HlurxaTvAYfAO72I+nVni5xEOAsZrywkZ/XryKiQc2q13719T7I5dI2WqL2In26JWocLVv2JDAglMysC5Uad/JYa0zGGnzq7AL8+E0LRg3qS36elF5jDeG+IncFuFaaXmVQj0pEJnMdNcKD+52jbApgcFYkESg+6Ryxnv/ywGOHr3vObDmHSt4JN9XDaFX3ImKm2PAZoli+N28wrcVo2o1GMRR31TSU8g6UGFdgtlxpt2ERi9AZf0StHISb6h5M5oOYzAml43Wmn1Er4hEEDeezBL7MrcFppwD7fEl+pRcG/a0r/NWsRSqb9ixlwuRUl6p/LY9hw2Po1TvC2WZISNiVGvCnKCFRFk8PX9q1HUBJSSFGo/VhBYNey5mTzexoWe1mwZxeTH2w3S0ptlBV5rfXoTS5jpNVGUSZSEAH10k1BUg46ryImsGJbUsMB53bL/Eq4ybuJzik7A/CTXUPSkUb5LI6yGUhaJSjEcnFLJYfgTaaD6BS9EQhb4xM5odK0RGFrBEG0zYARDEbAQ1KeXPksjDksgZYxIx/xh5EQI5S3rR0vqX7FZxR13AH66APyS/3Qq+7dQ/gFAqRBe+t4osfNhFUx3X7BGm1Cua/1sfZZkhI2B1ptydRI2natCuHj/zF5YxkQutGWT3u3Klm1I88hZt7oR2tq10YDDIevHMo61fXcbYpNYrpnUzU1d34EOP75P18n3KAtJI8ABp4BHBfwy50CWx4w+sf2L2S/Tkp1z3fJaAh77UZA8AX5/9meeKVurFJkR2YGNGh9LqjuaksSFjDsg6TUFh5TC/rdRk3T9f6Ozlz0sdpaxucWNNs2eeD2SRHrnB+Xeud95zmjVcqSN0Xr6RWC5Tfy1DExPXbDyVmSyIAMiEAESNmSyoywQezeAGl0BZRLEFvWoeb6v7r5nxmu5qVnY1onOnNV5fD3iTP7UX92ZtQa2tmirot6N77NFv3JTF1yjBWr3K91hJTn+xI/XDnqCxLSDgSKZIoUSNRq7V06DAQs9mMTm+98qLZrODIgU52tKx2kZnhwcBuYyQHsZK0rGthkKb8NNMgjSePxvTki06TWd5pMm39w3nywA+cLbxxLdcbLUeyuuejpY9vutyLXBDoG3xls3664DIfn9nG/ObDeKX5MD46vY0zBZcBMFkszD++mmebxFvtIAJ4xp+vxDt2DMlJzksr1DvR95AZ5ORfcg2RqAce/wsv7xtHx0XRgs60CrkQjlwWXO4cClkMRvNfWCyZiKIFk/k0JssxRK70zxMELRrlGHTGbyky/A+lrDUKeQx60x+o5J2wiDkU6RdSpH8Xo/kIANlFAguT3aiZcft/EY55kfJib/Ql5TvZtwLungYWrfyehZ/+Xe7nzRmEh3sz7cmOzjZDQsIhSE6iRI2lUUxbGjRoxqVLSZUadzmtPmkXIuxjVC3i2OEQ+nS4jVMnpBPTyqBSiMxpVoK8Aqeie1A0XQMbUt/dj3B3Px6J7oGbXMWR3NQbXu+t0hKg9ih9/J2ZiEampG+dxgAkFmUR7RlEO/8I2vtHEOUZSGLRlX6iXyT+TWvfMOK8Q6x+D2KQnoCo6yOXziY9zXnJL3qzc8VRis64Rl2iRmNm5Jgbp5LqTb9isVxCoxpf4Rxq5RBkQgBFhrcp1M9CZ/oVpbwN17bSUcrjcFdPxUP9FGplX0yWc5gtaSjl7dEZvkKtHIJWOQGd8Qcs4pWI958n5eyT1QLnKsGT5Nm90BXXgvdSTW4bd4hNu7+ifSfXqO2e/1pvNBopCU/i1kByEiVqLHK5gg7tB6JUqigsyqvU2CMHOmKURGzK5c9f4xjWN57sLGmTUlle7GrER2e9Sp9ZtLAm7TglZiPNfawTZvnl4mH6h8SiVVxRRIzyCCS5KJv0kjzSSvJILsqmoUcAF4pz+O3iYR6K7l6p96AemYSsIi/XCVxI9qHEie0adU5uoaM/7BqRRIAnZm7hvyWAOuMvmMwncFPdj0yo+GBJJnigVd2Jh3ou7uqncVdNR0CFTLhxaqEomtAbf0GjvA2LmAVYUMgaIJMFIhMCsFj+PdB4YZuSfHXN38QLJz1JeaE3uqIaLspjA4KCC/lh9Uqef/k4GieWbA4Y2JChwyWVdIlbB8lJlKjRRITH0bhRezIyUxBF61NS9Dp3ThxtY0fLai4L3+jKg3d1wqCXnOjK0i/aQkexxKprzxRcptv6t+i87g1ePb6GN1qNpIHHzR2Bo7mpnC3MYHhoi9LnIj0CeDimBw/v/YZH9n7DIzE9ifQIYP6x1Twe04udmecZu30xd+z4jP3ZyTddw7/zWavegyPZvyfMqetXwu+3C5bdvogu0us1ILCIvvFZAIii+I+DeBw31X3IZNbXkAmC8h+H0oLRchSFrMkNrzOYNiKXxSCXhQIiItceYFjK/F9vEpiXoMVFflTVQjjjQcoLvSgpdHe2KS7BlMf/Ys1fP9K4ieP74Hh4qHjnvQEOX1dCwplITqJEjUYQBDq0H4iHuw95eRX35voviWeakJvtOqfzzsZsFnjk7iG8Ma8JoijdGiqLj1ZkWkQx1u5Nw939+bLTPSzrMInRYa148cgqzhVm3nTcLxcPE+URSFOfumWeHx3Wih+7PcCP3R5gdFgrVl08gptCRTOfUOYd+5M3W45keqM+PHf4VwyWCjyebpm4++Rb+S4cx/Ejzq2L1TlZM0bIU1GQ7eNcI67hyee3IpOB3vQLRvNBNKpxIKixiAVYxAJE0Vh6bYnhW/TG1aX/N1uSMZqPYrFkY7Kcp8S4FBBRKa6PeJstlzBaDqNW9ANAJgQiIGAw7cFkPoFFzEAuq1dmzJ4UGWuNtSMCJ5z14MLzvSgu8HC2KS5Bg+hM1mxfxgOPJqFwYMD4hTndqBfm5bgFJSRcAGknKFHjqVMnnObNupOdewmLpTIpcgKH9nXBUhuOnKtJXo6Wob1H8+uPdW9+scQNWdDVgNZgvSehlMkJc/cl1juYR2N6EuMZxFdJeyscU2IysDY9geH1mld4Xa6hmEVn/+Kp2H4czUulvpsf9d39aOsfjsliJvmfesUb4TnItdpeXOX0SV+nrl9icv59ovCc81qA/JfoRhl06FyI0fw3oKPEsIgi/fzSh8n8b09FUczFIhb8+39MGEzrKDK8g86wAgEv3FRTEISy6e2iKKI3/oRGMRhBuJJaLQhKNMrRGEwb0Rl/QK0YdsP01td3KEjX1I5sCOG8OxefkxzFq8hkMOuVNXz/xxrqhdn/9KZN2xCmPNzW7utI3Jxly5bh4+PjbDNuGSQnUaJW0KZNX/x8g8nKvrHwR3nk5wZw/syNU5xuFc6cDKR3h1EcOejcTXhNZmILM40M1ZOstyBirCjCB6y/dAKjxcTAkKYVXvfWiQ2MD29HHY0XFtGCSfx3I2UWLZjLSc0WfQ0ENLp5OqozSDrn3JS7EuPNr7E3+mOulfkwbeYuPDWv3vChVPybzu+mfgCtakzp/xWyBrirp+GpeRkPzSy0qrHIhOujNIIg4KaegkIeW+Z5hTwWD80zeGieR6Vod0PbRFHgub1aTHLnO/e2QEhy4+LMXhTleTrbFJehTYckNu9bxqhxlxDs9GtWKGS8/+FAZDLnf44mT56MIAjXPeLj49m8efMNX7v2sXnz5ko7WdfOK5PJ8Pb2plWrVjz99NOkpaXZ781KuASSkyhRK/DxDqRtm34UFuZiMlVuN3fyaBtKim/Nmo/N62IY1GMwly/VjtQsZ1Dfx8JdAZVT3vvg1Gb2ZyeTWpLLmYLLfHBqM/uyk4kPiQNg9pHf+ODU5uvG/XLhMD2CYvBRlS8otCvzPMnF2Yytf2WT3sQ7hKSibLZnnOXHlIPIBBnh7jeuG1ONTHGJXnw3IjXVuVGhYheIJJp2+zvbhDJ06naO2DjH14dZy/ksgS9za8+9TUhxI3VmL4pypbTHq6jVZt799BcWf7kNP3/bt8p4YloHmjZznQh+fHw8aWlpZR5fffUVnTt3LvPc2LFjr7u2c+fOVV735MmTpKamsmfPHp555hnWr19P06ZNOXLkiA3fXc3BYHDd+54tkZxEiVpDyxY9qRcaQ3p65fq7mc1KDu/rYierXJclH3Zk0thulJSonG1KjebVDnqUpsptTrINxcw5sopR2xbx0N6vOZ6XxvttxtExIBKA9JJ8MvVlG9knFmVxMPcCw0PLTzXVmY28nrCO55rEI/vnaL2OxounYvvx0tE/+OzcDuY2G4xGfmOHy6/bmUq9D0eRmeFOQb5znbRio/N7tQkXtS6Xcjjl8YPONqFClu5XcOa/Uqw1GOGiltRnelGYK7Umupb+gxLYsm8FPfvYrp66YZQvzzznWnsDtVpNcHBwmYevry8qlarMc1qt9rprVaqqf9cHBQURHBxMTEwMt99+O9u3bycwMJCHHnqozHWLFy8mNjYWjUZD48aN+fDDD0tf69y5M88880yZ6zMyMlAqlWzduhUAvV7PjBkzCA0Nxd3dnQ4dOrB58+YKbfvoo49o2LAhKpWKRo0a8cUXX5R5XRAEPvroIwYOHIhWq6VBgwZ8//33Za5JSUlh7Nix+Pj44Ofnx/Dhw0lMTCx9ffLkyYwYMYJXXnmFunXr0qjRraFyKzmJErUGjcadrl1HIMhkFBbmVmrs5fQwUhKj7GOYi2GxwFOPxvPis82xWOTONqdGM72Tibq6yp8ozm46iN96PMzO/k+xrtfjfNhufKmDCPBp+wm82GxImTER7v7sHTCzzHX/RSNX8mO3B2jkVVbkZUS9Fqzp9RirejxM18ByPufts/H0y630e3EEB/c6V9kUoNjo/EgiQEGS60Q1AEbefpC6oa7VLuW/PLNdjU5Ve7Y7QpqGtKd6UZjj42xTXAof3xK++PFrXnv3AB42OEt574N4qSdiOWi1WqZMmcL27du5fPkyACtXrmT27Nm88sorJCQkMH/+fGbNmsXnn38OwIQJE/j666/LKNF/88031K1bl27dugHw6KOPsnPnTr7++msOHz7MmDFjiI+P5/Tp0ze046effuKJJ57gySef5OjRozz44IPcfffdbNq0qcx1s2bNYtSoURw6dIgJEyZw++23k5CQAIDRaGTAgAF4enqybds2tm/fjoeHB/Hx8WUihhs2bODkyZOsW7eOVatW2e6H6cLUnrumhAQQ1bAlzZp25XJGSiVFbODYwY7odLW7L2BRgYpR8aP5+ov6zjalxtOyroVBGtdo8GwLPIZWLgLvSI4eCnG2CRS7SHZR8fFAZ5twHZPuP+lsEyoku0hgYbIbzo8F2w7hkpq0GT0pyJJqyf/LHXfvYd2ub2nZuup14hPvakb3HuE2tMo2rFq1Cg8PjzKP+fPnO8WWxo0bA5RG3ObMmcNbb73FyJEjiYyMZOTIkUybNo1PPvkEgLFjx5Kamspff/1VOseXX37J+PHjEQSB5ORkli5dynfffUe3bt1o2LAhM2bMoGvXrixduvSGNrz55ptMnjyZhx9+mJiYGKZPn87IkSN58803y1w3ZswY7rvvPmJiYnj55Zdp27Yt77//PnDFUbVYLCxevJhmzZoRGxvL0qVLSU5OLhPFdHd3Z/HixcTFxREXF2erH6NLIzmJErUKQRDo3GkoAQF1ychMufmAazAa1Rzd38lOljmflCRf+nQczd6/re9jJnFjVAqROc1KcLF+81XG4mkkoEmis80ol1MnnL8RLnIRJ9G0t+zf75JP8unc5gJhAYmEBSTSr3sq61YXlzs+4biBO8ddollMCj7q83y4MO+6a779qpC4hsmE10niuaeyyryWlGikTVwK+fn/fvjvfXgH3j6u7YL9eVLOPlntOgQUMtSkP9WD/Ezn/324GvXCcvlt03KefPYUlc02DgxyY96rve1jWDXp1asXBw8eLPOYMmWKU2y5GhEUBIGioiLOnj3LvffeW8aBnTdvHmfPXum7GxgYSP/+/Vm5ciUA58+fZ+fOnUyYMAGAI0eOYDabiYmJKTPHli1bSuf4LwkJCXTpUjYluEuXLqVRwqt06tTpuv9fvebQoUOcOXMGT0/P0jX9/PzQ6XRl1m3WrFm1UnZrIlIcXaLW4eMTRKeOQ/njzyXo9cWo1dYLF6RdjCT1QgR16yXaz0AnsOuvSCaP605RYe2pzXEmL3Y14uPs7uo2RDXiAgql676f82edr+hYaHCNdFPhhBd6nQa15kqUpG6oghfn+dEwSokowlcrCrhj9CW27g4ltsn1G5qSYpGISCUjRrnz3FPXt0LJyjTz+JRMPlwcQESkkrEj0uneU0v84Cv30RmPZzHnFT+8vP49Y1arzYwen8KSj1w7Q+GFbUq+7W3E6//s3Xd4VGX2wPHvnT6T3nuDkEAghN57R0AQpAkCgl0UFSyr7tpFVCyrrru/VYq9t7UjCiqg9B5CSCGE9Eb61Pv7IxKIoaVO4f08zzwkM3fuPTNMZu6Z97znNTrua72ppCIteSuGw7O/4hlQfPE7XGbuvH8T4yalcMuiiaQfu7RT3meeG4uvr2N+oeDm5kZsrGNMjTmdZEVHR1NZWTeH/r///S/9+/dvsJ1SeWZay7x587jjjjt4+eWXeffdd0lMTCQxMRGAyspKlEolu3btanAfAPfWqB8+j8rKSnr37l2fvJ4tIOBM5Yab2+XX4FCMJAouKbHbEGI79iA3L7NB/fulOLB7ECaj6yRT767tzZwrR4kEsZWM7WRjgFxj7zBale/wc39L6yhyTtr/29tKo70jOKM8+8yJy8TJBsZNNNCxk5rYODV/f8wXN3cFO/44d8C9+mh5/GlfZsxyR6NtnPhmZljw9FIwfaY7vfpoGTpcz9EjdcOoH39QiUotceW0xidLy+7ZjE7XSg+wjRgtEk8k63G1pXEVJVrylw/jVL5jLZHiKBISc9n4x1oWLDmJ8iLT8K+a3pkZM7tceCOBmpoa/u///o9hw4YREBBAUFAQoaGhpKenExsb2+ASE3NmHv3UqVOpra3lu+++4913360fRQTo2bMnVquVgoKCRvsIDg4+ZxxdunRhy5YtDa7bsmULCQkNlzb7/fffG/3epUvd/3OvXr1ITU0lMDCw0XG9vC7vBlEiSRRckkqlZuiQqzAYPCgrK2zSfU1GPYf29r/4hk7g4fvGcN+dvbBaRYOa1uCtl7kruhqXOsdMOoVnYN0IRFNLF8/2yYeVeGszuObq/AbXv/z8KWLDjxMbfpyXX2hY2rhzey3DB5zEcoHusFUVGkpL7P+MW2wSsv3DAKA65dzzEq1WmU8+rKS6yka/Ac37UqhjrIqaahv79hopLbGye6eRrokaykqtPPlIKc++eO5lOHz8ahg/qahZx2xPO04o+MHsOstinCaVaShYMZSyPJEonotKJfPk81/zzmc/Ehxy7nkCQcFuPP/P8e0cWdMYjUby8vIaXIqKmvZ3Z7VaG5Ws/rU8868KCgrIy8sjNTWV999/n8GDB1NUVMRrr71Wv82jjz7KypUr+ec//8nRo0c5cOAAa9eu5fnnn6/fxs3NjWnTpvH3v/+d5ORk5s6dW39bXFwc8+bNY8GCBXz66adkZGSwfft2Vq5cyddff33OuO655x7WrVvHa6+9RmpqKs8//zyffvopK1asaLDdRx99xJo1azh69CgPP/ww27dvZ+nSpUDd6Ka/vz9Tp07l119/JSMjg02bNnHHHXeQnZ3dpOfW1YhyU8FlhYZ2pE/vcWz65SM8PHxQqS59nbXsrE6ERqYTFOKcbxA1NSqWzJnCr5scr8mFM3t6iAm9yTHXEWwuw9QzDWuaWrp42vFMM3+/v4SBQxomJgcPmHjqsVLe/ywIZJh9VT6jxurp2k2DxSJz19JiXvqXPyrV+bOvvbsiaGIxQJuxKSSUVvsHY97tB5PO/H7ooIlxw3KorZVxc1fw9odBdO7SvNFXbx8l/3ojgFsWF1JTIzNnvjujxxlYelMhN9ziyfFMM3Nn5GMxy9z/dx+mTj8zqnj3A5v56vMZWB38T+SZrSp6jlMTVNu0NXUdnXRKQ+GKYfDsb3iHFNg7HIc0eHg6m3adYPktV/L1l76c/c3Py/+aiJ+fY5aZnvbdd98REtKwkVd8fDxHjhy55H1UVlbSs2fPBtd17NiRY8fOvwRSfHw8kiTh7u5Ohw4dGDduHHfffXeDEb7rr78eg8HAs88+yz333IObmxuJiYnceeedDfY1b948rrjiCoYNG0ZkZMMS9bVr1/LEE0+wfPlyTp48ib+/PwMGDGDy5Ibdvk+bNm0aL730Es899xzLli0jJiaGtWvXMmLEiAbbPfroo7z//vvceuuthISE8N5779WPNhoMBn755Rfuu+8+pk+fTkVFBWFhYYwePRpPz8t7TVJJbmotniA4kZqaSt7/4Fny8zMJD49r0n11uiqGj/sMjdaB6swuQW6OF7MnX+EQ87hcyfwkK0u8Ky++oROR9Rai3/ofas35u7JEBx/nsZW+LLju3K8nq1XmitG5zFvowbYttZwqs/Hux3VLcHz2cSWvvlTOj7+GAjB6SA633+XFtBluPP9MGfl5VlY9f+EF4v/90hCe/EfCBbdpL9+PrkBjsX+3IlltI+aDz1Cp6ubWmUwy2VkWysttfPFpFW+ureDrH0Mumigmxp3glqWe3HrHhUuqfvulhr/fX8LXP4bQKyGb198MIChIyeghOew6FEFA4JlKhblT5/LbJsd/74nxk/m/pApUDpD0tzabu5mAZ7fgE5Z/8Y0vYx++E8+qR8dQkG9mwaLuvPLaFfYOSWgDkiTx2WefMW3aNHuH4nREualwTo888gg9evSwdxgtpte7M3ToVSiUSioqGjdpuJDaWjf27BjWRpG1jb07wxkzYKpIEFtZpLeNBf6us9zFaaorc86bIF5q6eKqJ8vwD1CeM4lM6KrhWKqZE1kWso6bOZZqpkuCmow0M++sr+ChRy/elTHl8IWTyPZkdZBPTMmsoDz3TFmhRiPRIVZNj15aHn7Cl26JGv79cussKm40yqy4o5gXXvUnPc2CxSIzZJieTvEaOnZSs3N7wy/R7v7b1lY5blvLKJZ4t8z1yk4BFJVqilYMoTT73PO4hDqz5qWwaec73HZHACufGW3vcATB4TjIR55jKCws5JZbbiEyMhKtVktwcDDjx49vNCkWYNu2bSiVSiZNmtTotszMTCRJqr/4+fkxbtw49uzZc95jr1u3rn57pVKJj48P/fv357HHHuPUqcYtyoVL17FDEj2SRlJYdBKrtWld7QpyI0k/6hzr4Xz+YRLTJ4yj/JSDd49wQiv7G1FfYN6cs/IZ2bhhzaGDJsJ8Mwn0yOSupcUXLF3ctqWWt9dV8M/Xzj0PKr6Lhn885sNVV+QxfVI+Dz/uQ3wXDXfeVsSjT/ny04YaBvbMZmi/k2z59dzNgNKPOU65j1VykEmJQPWx85eS22Qwmlrn9frsyjJGj9PTo6cWq1XGctZbqNlc92XC2foOOE637s5RfbF2t4pjTV0fwUlIVSqKlg+mJMv+a4w6Mg/PCh5f2R0PD9d8HQhCS4g5iWeZMWMGJpOJ9evX06FDB/Lz89m4cSPFxY3bSr/xxhvcfvvtvPHGG+Tk5BAaGtpomx9//JGuXbuSnZ3NHXfcwcSJEzly5Aje3t7nPL6npycpKSnIskxZWRlbt25l5cqVrF27li1btpzzGK7OZDK1eF2a02snZp1IJi8/k7DQprWPPry/L74BeXj7OG578WceHcErL8Qiy+J7n9Z290ALobUOskhea+pSgXdo43lLneLU/Lo9rL508ZbrC89ZulhRYeOm6wp56V/++PmfvzHS4hs9WXzjmUTv3bcqcPdQ0G+Ajj6J2fy8JZSTJy0subaQfSkRaP/ScTP7hOOcvFn+bFn0cdZuPj6xh9yaui/wOrj7c33HwQwO6HjO+6VVFvLv1F85Up5Hbm05d8eP5provg22+TbnEK+kbqLaYmJKWHfu7nxmZCOnpoylOz/gzYGLcFfVPR+1+/xhJDz6UAljxusJj1BRWSnz8fuV/La5lk+/qhtFumlxIaGhSh5+om59RZNJ5khy3evZbJLJzbGyf58RdzcFHWIbzts+kmzis48q+WV7GABx8WoUCnhzbQVBQUpSU8z06tP4/+eWO/dw2+IBTXty7eS+LVreGWRGZ7J/GXFrk2pUFN8zCPmZbfhF5dg7HIekVdyMStH34hsKTkvMqms+cUb5p7KyMn799VdWrVrFyJEjiYqKol+/fvztb3/jyiuvbLBtZWUlH3zwAbfccguTJk1i3bp159ynn58fwcHB9OnTh+eee478/Hz++OOP88YgSRLBwcGEhITQpUsXlixZwtatW6msrOTee++t385ms7Fy5UpiYmLQ6/UkJSXx8ccf198WHh7eoOMUwJ49e1AoFBw/frz+8V5//fUEBATg6enJqFGj2Ldv33ljs9lsPPbYY4SHh6PVaunRowffffdd/e2nR0/ff/99Bg0ahE6no1u3bmzevLnBfg4ePMjEiRNxd3cnKCiIa6+9tkFnrhEjRrB06VLuvPNO/P39GT++dTqNubt7M3TwdGRZprKyrEn3lWUlu7aNwmy+9MY37cVkUrBk7pW8/HycSBDbQI9QG1foXK/MFEA/LeOc119q6WJGupms4xbmTM/Hz5CBnyGD99+u5NuvqvEzZJCR1rgpSHGRlVVPlPHMC37s3F5LbCcVHTupGTZCj9kscyy14X3MZgXFRQpMlt+pMr5ERe0jVNQ+QpXxX1isKed9bFZbPjWmt6msXUVF7d8wWX5rtI3ZuofK2qepqH2UWvNXDW6z2UqpND6HLNc23O+fI4mBOg+Wxo3grYGLeHPgIvr4RbF8zyekVZ67k3Kt1UK4wZulcSPw0zRePqLMVM0Th75lWdwoXukzh29zD/FrwZkmEk8f/oGlcSPqE0QA23YfbDaJwkIrNy8pom9iNlMn5LJ7p5FPvwpm5Ji6BhzZJyzk5Z3pJJObY2FYvxyG9cshL9fKyy+cYli/HG6/pWGHRFmWufPWIp58xg83t7r3Fr1ewb/+G8AzT5Vy+81FPPuiH6Fhjb9rvnLGfiIiHbx7zZ9KqiRezjLgqqeRUo2KkuWDKMoIs3coDkcpdUerXGbvMATBYYmzyj+5u7vj7u7O559/jtF44VKZDz/8kM6dOxMfH8/8+fNZs2bNRb+p0OvrPrBNpqaNSAQGBjJv3jy+/PJLrH+2jFu5ciVvvvkm//73vzl06BB33XUX8+fPZ/PmzSgUCubOncu7777bYD/vvPMOgwcPJioqCoCZM2dSUFDAt99+y65du+jVqxejR4+mpOTc8/ZeeuklVq9ezXPPPcf+/fsZP348V155JampqQ22u+eee1i+fDl79uxh4MCBTJkypX4ktqysjFGjRtGzZ0927tzJd999R35+PrNmzWqwj/Xr16PRaNiyZQv//ve/m/R8XUhcXG+6Jw6lsPAENlvTTmCqqzw5sHtQq8XSGoqL3Lhi2Ex++EbMO2kLGpXMw4k1KF1vgAFZYyWgZ/olbXu+0sW4eDVbd4fx644zl4mTDQwdruPXHWGERTROHv52TzG33uFJWLgKq62uXPE0i6Vx6eLBvWFYraCQvNCqxuOmWYqb5jZUio7UmN/CajtfYw4TkuSLVj0BicZzJW1yFbXmT9Gqr8CgWYzFuheL9UwL+FrL52hVE5CkhqXbp8MdFtiJIQEdiXTzJcrNl9s6Dceg1HCg7NyjNV29QlgWP4rxIQloFI1HXbOry3BXaRkX0oWuXiH08Y0ko6ruffO73MOoJAWjguIb3EdRqaayyJdX/hPAgaMRFFTEcCw7ii++C6lPEAG+3hDCa6+fKU2NilZTZoxpdPl6Q8OyREmS+O7nUCZMajhvb8IkAwdTIzmaFcmCxeef+7zoxkvvtmhv36Qo2aVw7K6WLSGZlJTcM5Ci9HB7h+JADOhVzyNJoqBOEM5HJIl/UqlUrFu3jvXr1+Pt7c3gwYN54IEH2L9/f6Nt33jjDebPnw/AhAkTOHXqVKMRs7OVlZXx+OOP4+7uTr9+/ZocW+fOnamoqKC4uBij0chTTz3FmjVrGD9+PB06dGDRokXMnz+f//znP0Bde+EtW7aQlZUF1I0Cvv/++/WLlv72229s376djz76iD59+tCpUyeee+45vL2960ck/+q5557jvvvuY86cOcTHx7Nq1Sp69OjBiy++2GC7pUuXMmPGDLp06cJrr72Gl5cXb7zxBgCvvPIKPXv25KmnnqJz58707NmTNWvW8PPPP3P06NH6fXTq1IlnnnmG+Ph44uMbnhi1hCRJDBk8jcCgKPLzjzf5/iezYjmR2anV4mmJwwdCGN1/OinJl/dCr23pkSFmvGubNofVWSgn56LRNf4y7NGHStjyaw3HM80cOmji0YdK+G1zLbPmuAN1pYuPPlT3RZJOpyChq6bBxctbgbtH3fUaTcOy0Z9/rCEt1cINt9SVnvbqrSU1xcyG76pZ93o5SmVdqevZ9u2pK7FXKbugUnZGofBHoQhAqx4PaLDass79+BQR6NRXoFYmAY2TMlkuQUKHWtkdpSICpaIDNrluFNBs3YuEErWyW6P7mc+xQqZVtvF97mFqrGa6ezdvtCbSzZdaq5kj5XmcMtVw+FQunTwCKDfX8u/UX7i3y7hz3q8i3XGXuLnu5m34+DjP+NxDv6op17puwqAwKSm5dwCFqRH2DsUh6JUPopQ62DsMQXBoIkk8y4wZM8jJyeHLL79kwoQJbNq0iV69ejUoJ01JSWH79u31C4CqVCpmz55dnwidbdCgQbi7u+Pj48O+ffv44IMPCAoKanJcp0cpJUni2LFjVFdXM3bs2PrRT3d3d958803S0uqaUPTo0YMuXbrUjyZu3ryZgoICZs6cCcC+ffuorKzEz8+vwT4yMjLq93G28vJycnJyGDx4cIPrBw8e3GgB1oEDB9b/rFKp6NOnT/02+/bt4+eff25wzM6dOwM0OG7v3r2b/BxdKk9PP4YMnobFaqa6uund/w7sHkhFuX0Ts++/SuDK0RMoLnLdb77tbUysjQHyuRupuALv0Y3/zoEmly5eqpoaG/fcWcwLr/qhUNQlWmHhKp55wY/bbiziuVVlvPZ6AHp9w4+kI4caJ0GybMNs3QeYUCoiG91+KRSSPzJmrLYcZLkaq5yNQgpGlmswWjagVV95zvuZz1pT7VhFAUN/XM2gDc+y8vD3PNtzOh3cm7eQuadaxyOJk3j4wNcs/H09V4R2Y6B/B15M+YlZkb3JqSnjmq1rmLXldX7MOzNCZzzouAunq9U2Zs1v+pdx9mK0SDyRrMfmOL2JWp3CpKTs/gEUHo2ydyh2pZImoFHOvfiGgnCZc92vzZpJp9MxduxYxo4dy9///neuv/56Hn74YRYtWgTUjSJaLJYGTWRkWUar1fLKK6/g5XUmgfjggw9ISEjAz8/vvM1qLkVycjKenp74+fmRnl5XIvb1118TFtbwW2vtWV3a5s2bx7vvvsv999/Pu+++y4QJE/Dzq2slX1lZSUhICJs2bWp0rJbEeTGVlZVMmTKFVatWNbrt7MVh3dwaz9lpTV069yctbR/79m0iKioBxTnKv87HalWz+/eRDBn9P5TK9p9z88rqwTzzRBdkm/h+p61462XuiqlGcsFeNQByx0p8wvPOedsr/7nwyNRfSxL/6uyyxrPp9Qp2Hmxc6rZgsccFSxbTj515P7Xa8qg2vQZYAA169XyUiqZ/6QYgSXp06pnUmj9Exoxa0QuVMo5a8ydolAOxyaXUmN4EbGhUo1ErEwEwnzUwFuXmx7sDF1NpMbIx/wiPHPiK/+s3r9mJ4sigeEaeVVK6qySLYxUF3NtlLNN+/Q9Pdr8SP60bC39/k14+Efhq3bD+4Q/XN+tw7eKOFZt5841oaqrtHcml2XFCwQ+RBiaonCTgZpDMCsru64e8EgI7O08S31oURGFQNT4HEQShMXGmeREJCQlUVdU1rrBYLLz55pusXr2avXv31l/27dtHaGgo7733XoP7RkRE0LFjxxYlXgUFBbz77rtMmzYNhUJBQkICWq2WrKwsYmNjG1wiIs6UkVxzzTUcPHiQXbt28fHHH9eXmgL06tWLvLw8VCpVo334+zc+wfH09CQ0NLTRUiBbtmwhIaHhIte///57/c8Wi4Vdu3bRpUuX+uMeOnSI6OjoRsdt68TwbAqFgmHDZhAUHE1u7qXNyzpb+Sk/Du9retlwS1itErcvmcSqx7qKBLGNPT3EhMHkHE03mkM/PRMHWsnhgrKOn5kTqJD8cdPcjkFzKxplf2rNH19gTuLFqZVdcdPeibv2HrTqMVhs6VhtuaiV/ag1vYdWPRm9eh615k+wyZUAmM4aZlIrlES4+dDFK5ilcSOI8wjkveM7m/9gz2KyWVh1+Ace6DqBE9WlWGUbvX0jiXbzI8rgw8FTdXMfpXwtVaccd01UT28jE6c07qDryJ7ZqiJf53hNylqTZFVw6m/9yD8cY+9Q2pkWg+pVJMlx/2YEwZGIs80/FRcXM2rUKN5++232799PRkYGH330Ec888wxTp04F4KuvvqK0tJQlS5bQrVu3BpcZM2acs+S0KWRZJi8vj9zcXJKTk1mzZg2DBg3Cy8uLp59+GgAPDw9WrFjBXXfdxfr160lLS2P37t28/PLLrF+/vn5f0dHRDBo0iCVLlmC1Wht0aB0zZgwDBw5k2rRp/PDDD2RmZrJ161YefPBBdu4890nOPffcw6pVq/jggw9ISUnh/vvvZ+/evSxb1rAz2Kuvvspnn33GkSNHuO222ygtLWXx4sUA3HbbbZSUlDB37lx27NhBWloa33//Pdddd119U5724u0VwKgRs1EoVZSVNf0kJjMtgdzs9inZKT+lY+roq/n8Y9Gdrq3NT7ISb6q9+IZOSlba8O/d9C9G7MFmg4K8M6P8kqRCofBHqQhDq56AQhGM2do6C7fLsgWj+Qt06quwycWADZWiAwpFAArJH5vtBAAXWnrQhozZ1jpzWN9I28pA/xg6ewZjlW1YbWe6J1lkG7azGqVVZAa2yjHbyooHN6NyopolWZZ4cKcei9JJvklpJsmqoPyBvuQfuHzm5emVD6NUJFx8Q0EQAFFuWs/d3Z3+/fvzwgsvkJaWhtlsJiIightuuIEHHngAqCs1HTNmTIOS0tNmzJjBM888w/79+/H0bN7iz+Xl5YSEhCBJEp6ensTHx7Nw4UKWLVvWYJ+PP/44AQEBrFy5kvT0dLy9venVq1d9nKfNmzePW2+9lQULFtR3V4W6uY3ffPMNDz74INdddx2FhYUEBwczbNiw886ZvOOOOzh16hTLly+noKCAhIQEvvzySzp1atjI5emnn+bpp59m7969xMbG8uWXX9aPTp4ejbzvvvsYN24cRqORqKgoJkyYgELR/t9XdOzYgwH9J7Fp84foDR5oNU2b47dv51C8fIowuLXdEgnpqQHMmjyO/Lz2G2m9XEV621jgX1VXzeiilBPz0BmcY67l0SNBDbqfNiYjy63zn2Wy/IRSEYdSEVY3T5GzW9ra6n83/jmS+MrRTQzy70Cw3pNqi4nvcg+zqySLl3vPBuAfB/5HoLZumQwAs81KemXdEhNm2UahsYKU8nwMSg0Rbj4NYkmvLGJDXjLvDLwOgGg3PyRJ4vPsffhr3cisKibB60zZb83hAEg69xxTRxARVcqQ4eVs2ti8z0V7SCuWeLfMwAIP11z+5jTJJlH+9z7Ij0oEO/BrqDWoFVehUc6xdxiC4FQkWawyKbSCzMxMYmJi2LNnDz169LB3OJfMZDby2Wcvc/TYLqIiEpqcrPr45TNoxNcoFK3/Z7T5p07cMG8wNdWai28stNg742sIrXXRiYh/8n7pN/xjTto7jEvywVu9WbG0romV0fwdSmU8CryRMWKx7sVk/QW9+jpUyk7UmD5EIXmiVU8A6kYGbXJdhUCNaR0qZQ/Uyh5IaFAoGpbUW2351Jjfwk1zB5KkQZbNVBmfRqOagELyoMb8Dm7aFSgkLx4eamKEoobHDn7DjuJMioxVuKu1dHIPYEHMAAb415Xv3bj9HUL1XjySOBmAnJoyrvyl8XI+vXwi+L9+Z6YCyLLM9dvfYVHMAIYGxtZf/2vBMVYl/4DZZuWWTsOYFp505j6xlXR6/uvWeMrbzJ4dEUwdOxFnO9v477haYi+yJJYrkJFxf3QPIT1TL76xE1JInXBXfYYkGS6+sSAI9USSKLQKZ00SAYqLc/nw4+eorDhFSEjT52h06ryXzom7WjWmNf/uz6N/64bNdulNdYTmu3ughSka1x41kCOqiX35K6Q2+EKjLTx830TW/LtunnWt+RMs1mPIVCChQ6EIRqMcjkpZV8lQbfw/JMkHvaaug7PNVkqV6ZlG+1RKMRi0N9b/LssyNab/oFENR6XsUn+9xZpMrflLwIJGNQ6Nqi8A9w02O2RTk4gPvkSrd+wR4knDF7B/r+7iGzoQXzeZdwZVojO54GKpfyEj4/aPvYT2OXrxjZ2KAXf15yil2ItvKghCA6LcVLjs+fmFMGLYLL786t9UVJTg4eHbpPunHknCLzCXgKBzL6TdVPfePoH33mxea3+h6XqE2rhCVwUufh6om37caRJEgLSjZ8r6deoZcIFeImcnfgAKhQ8eupUXPYYkSRi0Nze6XqXsgvtZSeNpRqvkkJ+a5ScCCYhz7E6VS+/exY0LBl98QwdSUiXxcpaBFcGV51gh07VISFQ/1pOchyRC+6XYO5xWo1c+IRJEQWgm0bhGaBXR0dHIsux0o4inde7cj969xlJYnIPZ3NTyIok924djrG3Zt+RVVRpmTJghEsR2pFHJPJxYg9LFE0RZIePf37nmHGVlOl5pWK2DzletPuK46yWeNnHqIaJiHPQJvIBvUpTsUlw+a9JWP9GDk793tncYrUKjmItGOc3eYQiC0xJJoiBQN6IwZPBUOsYkcjLnGE2twjbWGtj9xwhszVyJ+cRxH8YMuJrt2/yadX+heR4ZYsbbUc/8W5FidAF6d+cqp83LdbwhuxqrY44nmXY5fpIIsPjmZHuH0CwP/aqmXOt4r8e2UvNUEie3OHcXUAVd0Sn/Ye8wBMGpiSRREP6k07kxetQ1eHr6U1B4osn3LyoII3l/09dP/GNLDOMGXUl2lnuT7ys035hYGwNkx57H1Vo8JzjHshennTjuQ40D/tfUWhy0XHe/Jxaz4ycxC67/HT9/B30OL8BokXgiWU8zvwN0SjWrEsn+tau9w2gmTwzqV5Akrb0DEQSnJpJEQThLcHA0w4fNwGSqoarqVJPvn57ajayMThff8E8fvNWLOVeOpLJSfJi1J2+9zF0x1S4/zwhADq7Fr2PTv/Swpz07IuwdwjnVWBzzFSNZFZTnBtg7jItSqWTmXJth7zCaZccJBT+YHa8Eui3VPtuN7J8T7R1Gk8iyEoPqZZRS+6xjLAiuTCSJgvAXid2GktR9OPkFWVgsF1yo7Zz27xpMSdHFF7h+9P4xrFjaG4vF8UcAXM3KISYMJqu9w2gX2unH22SJlrZ06MC512u1t2qzYyaJAFWpjp8kAty+/BcMTrrs6zNbVeTrLtBByQXVvpDAiY3d7R3GJdOr7ketGGrvMATBJYgkURD+QqFQMHzY1URGdm7W/ERZVrJj6xhqqs99JmQ0Kpl31TRef60DXBZjWY5lfpKVzqZae4fRLmRk/AY5V8MagNQUn4tvZAfVZsdNtk17nWNeopuHicnT8uwdRrPIssSDO/VYlJfX+7bxpS5k/ZB08Q3tTK2YgVa5xN5hCILLEEmiIJyDm5sXo0fOxWDwoLgkt8n3Nxn1bN8yptEoYX6uJ+MGzeSXny4+0ii0vkhvGwv8nauBS0sohhfh5llh7zCa7HiGY5b1VTnwSKJtuw82q3N8pN/1t19QOWkBRVqxxLtljvn6bEumVzqT9W1Pe4dxXgp6oFc+Ye8wBMGlOMcniiDYQUREPIMHTaOqqoyamsom37+8zJ+9O86UvezbHc7oAdNIP+bZmmEKTbCyvxG1ozYfaQMeVzhXw5rTck9q7B3COVWbHDdJlGpUVBQ1bY1XewmPKGP4qDJ7h9Fsa3erOKa9/OaRm16L4/jXvewdRmNyIG7q/4hGNYLQykSSKAgX0LvXaLomDCY3LwOrtelz2HKzO3D0cA+++DiRq8aP5VRZy9ZSFJrv7oEWQmtN9g6j3dj8jPjFOVfDGoCiQjcqHHTws8qBy00BKtOcp0Jh+YO/ITluzn1R923RUqu5/E6hzP/pxPEv+tg7jHqyTYe7+g0UknPMyRUEZ3L5vcMJQhMolSpGjphFaGgHcnKbPj8RYPf2zjz+UAxm0+XV8MCRJIXauEJ3+ZSZAminn0CpdL7mPLt3RNo7hPOqMjp2VmM84DzrrCb2yKFHbwdc5+QSlVRJvJxlwLG/Nmgb5jc6kvlpX5rxcdiqZFnCTfMCSoWzLtUhCI5NJImCcBFeXv6MHnUNWq2BouKTTbpvQUEJn3y8DW/fZPwDnO+E3RVoVDKPJNagtNk7kvblN+SYvUNolkP7gu0dwnlVOfhAtPUPP7ufuDfF7ct32DuEFvkmRckupd7eYdiFZV0Hjn/cz66vN63ibtSK8fYLQBBcnEgSBeESxER3Y8TwmdTWVlFRUXJJ9zmWdoIvPt9JVaWeEcOHM3OOB15ejj0S4YoeGWLGu9Zi7zDa18Bi3H2avs6nIzh6xHHn1VlskkMvqC4Vaak65WXvMC7Z2CuO0KGjc/9tPvSLmnKtk3bhaSHLWzEc/7C/XRJFyToFvfq29j+wIFxGRJIoCJeoR9JI+vWdSFFxLrXG6vNuZ7PZ2LnzMD98exilwp8RI0bj4emJm5uC6TP1XIb9DuxmTKyNAbLzlrQ1l/sk51ywHCAz3d3eIVyQTeHAWSJQkeFcc7Ouv/WgvUNoEaNF4olkvUN/edCWLO9Ek/nuQOR2fAKs5kQ8dM+22/EE4XIlkkRBuEQKhYKhQ6eT0KU/OTlpWCzmRttYLBZ++mkn27Zk4+sXxbBhw9GelRX6+Su48iodCvGX1+a89TJ3xVRfditRyl4m/BOO2zuMZjuZ7ZidTU9z9CSx9rBzJYnzFm8nINCJamTPYccJBT+YL79lMU6zfhBJ5tsDsbVDomg2ReBjeAtJcuz3CUFwBeJUVRCaQKPWMm7stURGduZkTsNGNtXVNXz55VYOHSgntmNX+vXtj+Ic2WBUtIqx48VwYltbOcSEwXT5zQNVT8tGpXLOEr6qCg1lpY6dhFkdvCWnZYfzNK8BUCjgmoVp9g6jxZ7ZqiJfd/k2J7N+HEHmm4PaNFE0GX3wMbyPJIllpAShPYgkURCayMPDl/HjFuLt7U9uXl1ZX3FxKZ9+soXsLCu9evUnvnPCBfeRmKSm/8DL94Sirc1PstLZVGvvMOzCd5jznnDv3RXh8I1XLI6dIyKlu1Fb7VyjWrfc9Svujl1lfFGyLPHgTj0WpYO/QNqQ7dNwMtcObpNE0WzS461/D6UipNX3LQjCuYkkURCaISQ4htGj5iFJEgcPHebzT3dw6pSOIUNGEBYWfkn7GDJMQ+cul2fDg7YU6W1jgf/ltdzFaXLPMjwDLq2xkiPav9fxTwAdfSQRoDzLuUpO3dzMTJmea+8wWiytWOLdMudK0Fub7YswMt8Ygs3aeqeXFosKvfL/UKviWm2fgiBcnEgSBaGZ4uP6MHjQVLZvK8Bi8WLE8FF4e3tf8v0lSWLiZC0xHZVtF+RlaGV/I2qLgw9HtRH3qc7bsAYg5bC/vUO4KIsTzHKtTg60dwhNdtffNqN2geKKtbtVHLvMu5PZ/hdKxv8NwdoKiaLNJiFZVuKmH9wKkQmC0BQiSRSEZpIkib59JnDVVXMIDIxElpt+8qhUSky9SkdklEgUW8PdAy2E1jr4YnZtRHaz4J/o3EliRpqHvUO4KLMTJImW3c41LxEgJLSckWNL7R1Gq7hvi5ZazeV9eiV/G0Lmv4ditbbss81YeRe+njNaKSpBEJri8n4XE4QWUqvVXH/DPAYP7kVqaiZmc+OOpxejUklMm6EjNEz8ObZEUqiNK3SXZ5kpgOrKk6jVTX/9OZLsE44/AuMMLYHkA56YTc7X/XH5A7/hBNW8F1VSJfFyloHLs57hDPn7YDJfHYrV0rxE8VTxHIL9l7ZyVIIgXCpxVioILaTX67hu8QwSE+NIScnEZrM1eR8ajcSMmXqCgsWfZHNoVDKPJNagbPpT7zJ8RjpvwxoAo1FJUaHjv/7NzagYaG+STaI8x7nmJQIkJObSu9/516B1Jt+kKNml1Ns7DLuTfwwi45VhTU4UiwuGExH8ZBtF5XhGjBjBnXfeae8wBKEBx/9EFgQn4OPjxZLrZxIREUzq0cwGS2NcKq1O4urZevwDxJ9lUz0yxIx3rTOM8bQNuWs53sGF9g6jRQ7tD6UZ36+0O5MTJIkAVUcdf37nuSy7Z7u9Q2g1D/2iplwrmpPxUyCZLw3HYrm056IovyuRwf9FcrBh5UWLFjFt2rRG12/atAlJkigrK2v3mAShLYmzUUFoJRERIVy3eAbuHm4cP57TrH3o9RIz5+jw8XWsD0dHNibWxgC5xt5h2JXbNOeeiwiwb1eYvUO4JCYnSGQBTHucM0kcMfYoneKcu2z6NKNF4olkPe2wxrzDkzcHkPnCcCzmCyeKxYXRhAe9g0olkmtBsDeRJApCK+rWLY5rr52K1WIlL6+oWftwc1Mwa44eTy9xZnEx3nqZu2KqnaCVSNuRdVb8e6TbO4wWO+IEnU3BeUYS5V0+rdJd0h5uWHrA3iG0mh0nFPxgvryXxaj3qz+Zq0dgNp+7jW1RYRhBPh+i1Xi2c2Ctp7i4mLlz5xIWFobBYCAxMZH33nuvwTZVVVUsWLAAd3d3QkJCWL16daP9lJaWsmDBAnx8fDAYDEycOJHU1NQG23zyySd07doVrVZLdHR0o/1ER0fz+OOPM3fuXNzc3AgLC+PVV19t/QctuCzn/AQRBAc2eEhvpl89jpKSMsrKypu1Dw9PBbPm6nH3cI4TUntZOcSEwWS1dxh2pZqcg0br/B1d04952TuES+IsLzepVklFgXMk3n81+9qdBAY5yZDtJXhmq4p8nQus79Eatvpx/NkRmE0Nn4+igkD8Pd7HzeCcr9nTamtr6d27N19//TUHDx7kxhtv5Nprr2X79jNl1Pfccw+bN2/miy++4IcffmDTpk3s3r27wX4WLVrEzp07+fLLL9m2bRuyLHPFFVfUN8fbtWsXs2bNYs6cORw4cIBHHnmEv//976xbt67Bfp599lmSkpLYs2cP999/P8uWLWPDhg1t/jwIrkGSmzN5ShCEC7Jarbz15ud8/fUmoqPD8fBwa9Z+SoptvP9ODdXV4s/0r+YnWVniXWnvMOzO71+b8AnPt3cYLTag6xJOZjv+UjB/H2pmlMI5mqvo7z1I2JBD9g6jWV58eiSrV3aydxitpqOfzL+TKlBZxXs5AH1Kibp/E2qNieJCPzy17+HvH2vvqC5o0aJFvP322+h0ugbXW61WamtrKS0tPedayZMnT6Zz584899xzVFZW4ufnx9tvv83MmTMBKCkpITw8nBtvvJEXX3yR1NRU4uLi2LJlC4MGDQLqRigjIiJYv349M2fOZN68eRQWFvLDDz/UH+fee+/l66+/5tChur/56OhounTpwrffflu/zZw5cygvL+ebb75p7adHcEFiJFEQ2oBSqWTuNVMYPWYQGRnZVFY276TS10/BzDk6/vKZdNmL9LaxwP/yXe7iNDm20iUSRJsNCvIdP0EEMDrRAFftPucdlbnxjt/wcPxlMy9ZWrHEu2Wi7LTeTh+OPTaIvOwAdIrXHT5BPG3kyJHs3bu3weX111+vv91qtfL444+TmJiIr68v7u7ufP/992RlZQGQlpaGyWSif//+9ffx9fUlPj6+/vfk5GRUKlWDbfz8/IiPjyc5Obl+m8GDBzeIbfDgwaSmpmK1nil3GDhwYINtBg4cWL8PQbgYkSQKQhvRajUsWjSd4SP6kZ6eRXV185qrBAQquXq2Ho3jLyHXblb2N6K2iG/k9dMz7R1Cqzh6OJhmLDFqF7UW5ykBt233xVlrhQwGM9NmnrR3GK1q7W4Vx7Tijfw0Y6Y/qupXCQ1Jsncol8zNzY3Y2NgGl7CwM023nn32WV566SXuu+8+fv75Z/bu3cv48eMxmZx/SoBw+RFJoiC0IZ1Oy+LFMxg8pDfHjmVRU1PbrP0EhyiZMVOPWkxr4e6BFkJrxQeurLYR0Nu510Y8be/ucHuHcMlqnWROIoBUqqGy1NveYTTb3X/bjEZj7yha131btNRqxKlXhYcF/3sn07FbH3uH0qq2bNnC1KlTmT9/PklJSXTo0IGjR4/W396xY0fUajV//PFH/XWlpaUNtunSpQsWi6XBNsXFxaSkpJCQkFC/zZYtWxodOy4uDqXyTFXG77//3mCb33//nS5durTOgxVcnninEoQ25uZm4PrrZ9F/QBJHj2ZiNDYvwQkLVzJtho7LuTN4UqiNK3SizBRAOTEXrb55Xzo4muQDzrPwuzONJAJUZgTaO4Rm8w+sZPT4EnuH0apKqiRezjLgpAO8raLc3YzHslF07TPM3qG0uk6dOrFhwwa2bt1KcnIyN910E/n5Z6YEuLu7s2TJEu655x5++uknDh48yKJFi1AoFA32MXXqVG644QZ+++039u3bx/z58wkLC2Pq1KkALF++nI0bN/L4449z9OhR1q9fzyuvvMKKFSsaxLNlyxaeeeYZjh49yquvvspHH33EsmXL2ufJEJyeSBIFoR14eLhx442z6dOnGykpGc0uPYmKVjFlmg7FZfiXq1HJPJJYg9KJ5oS1Ja8xzr/sxWnHUr3tHcIlq7HYO4KmqTnovPMSAVY8+KvLvd99k6Jkl1Jv7zDsotzdhOqm/vQaPMHeobSJhx56iF69ejF+/HhGjBhBcHAw06ZNa7DNs88+y9ChQ5kyZQpjxoxhyJAh9O7du8E2a9eupXfv3kyePJmBAwciyzLffPMN6j/LiXr16sWHH37I+++/T7du3fjHP/7BY489xqJFixrsZ/ny5ezcuZOePXvyxBNP8PzzzzN+/Pi2fAoEFyK6mwpCOyopKeNfr77Dvr1H6NylQ/0bflOlHrXw1Re1WJ2o9K2lnhxhYpDcvHmdrkaOqib2pa+QFK7x9j2052Iy051jiHxyZyvLg5ynq64cUU2nV/9n7zBa5OqJ8/hja/M6RDsqrUrmw1HVeBqd7FuHFih3N2FblMSIifMalEQKbSM6Opo777yTO++8096hCE7Kxb6fEwTH5uvrzU03zyWhayxHjqRjsTTvBKFTnIrpM3WXzRzFMbE2BooEsZ5+eqbLJIgA+bnOkSACVJudq9xUOmGgptK5E6y77vv94hs5GaNF4olkPTbnejk1W6lnLZaF3Rgx8RqRIAqCkxBJoiC0s4AAX2659Rri4mI4kpzeoF11U0RFq5g5R+/yy2N462XuiqnmMjmXuihZacO/n2s0rAE4nuFLjRPl/9VOOPBTnuW88xIBBo9II76L6zWr2nFCwQaz6y+LUehdg2VBIiMnzkepdJ4vhAThcieSREGwg+DgAG659Ro6dIwgOTmt2YliaJiS2dfocXNz3RRq5RATBtNlVFd7EYoxBejcnGMx90uxd5fzdDYFqHbCXKUm2bnnJQLcdPt+e4fQJlZtVZGvc92SkBz/SqzzuzJ6/HxUKtd9nI4oMzNTlJoKLSKSREGwk/DwYG659RqiosJIOZKOzda8jiwBgUrmzNfj6el6ieL8JCudTa7RwbO1eI53nYY1AIf2B9k7hCapMjnf35l5p/MniTPn7SYk1PW6VsmyxIM79ViUzve6uphDmhyKRwYydty1qNUutpaJIFwGRJIoCHYUFRXGzbfMJSQ0kJSUDJrbR8rHR8Hca/X4+rnOiUakt40F/mK5i7PJobX4dci2dxitKjXFx94hNEmVyQnngh7ywGR0/pP0a68/evGNnFBascS7Za5VdpriVot+8ghmz1mORuPicyIEwUWJJFEQ7Kxjx0huvuUaAgJ8OdqCRNHDQ8GceQaCgl3jz/qp/kbUFic8IW9D2umZKFyoYQ1AVoZzNVVxxpFECYnyk849LxHgxqVb8PRyrdf/aWt3q0jTau0dRqs44FGDdmJPrrx2Lno310p+BeFy4hpnk4Lg5OLjY7jxptl4+3iRmnq82YmiwSAxa66eyCjn7h5310ALYbVOOPmrDcnI+A90nYY1p+XkONc8pUonfVlWpwTYO4QW02qtzJjtWiPpZ7t/i5ZajfOelsnALs9qPMb3YvK8WWj1YgRREJyZ874bCYKL6dYtjhtunIW7u4G0tKxmJ4parcSMWTo6d3HOLnJJoTYm6USZ6V9JIwsxeDjP+nyXoqjAncoKe0fRNLIsOeWyBaY9fvYOoVUsu38zLjLg1khRlcTLWQaccazUJsEf3lX4ju0tEkRBcBEiSRQEB9KjRxduuHE27u5uHD3a/NJTpVJi0pVaevd1rlEajUrmkcQalK7Xn6LFPCe6VsMagN07I+wdQrPYFM6XJdp2e2O1OHeFAYCfXzXjriiydxht5psUJbuUenuH0SRWBWzxriRodB+mzBcJoiC4CpEkCoKD6d27K7fedg1+fj4kJ6c1u+upJEmMHK1l+EjnaVjx8BAz3rVOuBBdG5P9jfh1OmHvMFrdwb3B9g6hWaxOmCQqTErK852/yynAigd/wZXXY3/oFzXlWueoBDGr4FefKmImDBQJoiC4GJEkCoID6tYtjqW3zyc8PJjDh481ex1FgL79NUyaokXh4H/to2NtDJSdaFX1dqSZkYXSBYdXjx7xtXcIzWKVnC9JBKg65vzzEgE6dCqi/yDXKr0+m9Ei8USy3uHLmqu1Epv8qki4cgRXzL1aJIiC4GIc/LRREC5fcXEx3H7HtXTsGMnhw2mYzc0fYevSVc2MWTo0Djqo6KWTuTumGgc/J7Ibv8Gu17AGIDPd3d4hNIuzJonG/a4xkghw1/3b7B1Cm9pxQsEGs+N2Bi01wGa/KvpNn8C4q6eidtQPF0EQmk0kiYLgwKKiwrj9jgUkJHQkOfkYRmPzWytGRauYfY0eNzfHO8F9eqgJg6n5o6UubXAR7t6n7B1Fm8g56ZwnlhbH+xO6JLbtPsiOPjx1iQYMySChm9HeYbSpVVtV5Oscb155jidsCzQycvZURkyZgNKVa38F4TImkkRBcHChoYEsvf1aevZMICUlnZqa2mbvKyhYyfyFeoJDHOdPf36Slc6m5j8mV+c+KcPeIbSJinItZaXOmbBYnHTMWzqloaLE295htJqb79hr7xDalCxLPLhTj0XpOK+3VB8rB4KsXHHN1QwaNxKFo89jEASh2cRftyA4gYAAX25bOp/+/XuQmnqcysrqZu/Lw1PBnHl6uibavzFCpLeNBf5iuYvzkb1NBHQ+bu8w2sTeXRE0s3mv3ZmdNEkEqEwPtHcIreaq2fsIC3ftCoS0Yol3y+xfdmpTwB5/M8eDVUy5dg69hw1CctKya0EQLo1IEgXBSXh7e3LLrXMZMbIfGeknKCsrb/a+VCqJiZN0jBqrsWtDm6f6G1FbnDRTaAfqq7JRqlzzJPjA3hB7h9BszvySNR5ynXmJAItuTLF3CG1u7W4VaXZcHNKsUfCbfw2nQgxctXgeif162S0WQRDaj0gSBcGJuLu7ccMNs5k0eQQnT+ZTUFDcov316q1h5hw9ekP7fyN810ALYbXNn2N5OfAbdszeIbSZlMPOu7i7M48kWv5wrSRxya1b8fZx4qz9Et2/RUutuv1P2ardVWz0KUcR4ceMGxYQl9i13WMQBME+RJIoCE5Gq9Vw7YJpzJo1kbKyck5m57VofxGRSq5dpCcouP3eDpJCbUzSNb9k9rLQpxQPv1J7R9FmMtI97B1Cs5mcuPmLlKOjusI5u8qei1pt4+q5rreG6F8VVUm8fMJAe6bDpf4afnQvwScmjJk3LCQ6LrYdjy4Igr2JJFEQnJBSqWT6jPEsXDgds9lCevoJ5BZM8PL0VDB3vp6Erm0/T1GjknkksQalzfW//W8Jtynp9g6hTZ08Yb/yuZYyOflLt+K468xLBFh272Z0l8ESfd+kKNml1Lf5cWTgZJiOnxWFRMTHMvOmRYRGR7b5cQVBcCwiSRQEJyVJEmPHDebGm+eg02k5ejQDm635C66rVBJXTNExcnTbzlN8eIgZ79rmr/l4ObC5mwnolmnvMNqM0aikqNB5P36ceSQRoPpwgL1DaFXePjVMnFxo7zDaxUO/qCnXtt2XeRaVRHKsjj+s+XTr35NZN19HQEhwmx1PEATH5byf0oIgADBgQA+W3j6fgAA/kpPTsFha1uikd18NV8/WoW+DL6xHx9oYKNe0/o5djHraSVRq102kD+4NpwXfZ9id0YljB7Ds9LV3CK3u7gd/4XJYrs9okXgiWU9bfE9R7aFiZ0clR2uKGDBmONMXX4unj3frH0gQBKcgkkRBcAHdusWx7M6FdOgQSfLhYy1aSxEgMkrF/EUGAoNa7y3CSydzd0y1E7f8aD++w9PsHUKb2r/HeTubAhidfCRROuKJsda16jOjOxQzaFiFvcNoFztOKNhgbt1lMYpC9PwWaKLYVMWY6VO4Yu7VaPWu9RoRBKFpRJIotKno6GhefPFFe4fRajIzM5Ekib1799o7lEZiYsK5e/l19B/Yg2OpxykpKWvR/ry86uYpdmmleYpPDzFhMLnmcg6tSe5+Cq+gInuH0aaSDzl3uaPRBV7G5dnO/X9wLsv/ttXeIbSbVVtV5OvULd6PTQHHO3uyTV2CQqPkygVzGHbFWJSXw7CsIAgXJJJEO9m2bRtKpZJJkyY16/6PPPIIPXr0aN2gWmDdunV4e3s3un7Hjh3ceOONbXbcRYsWIUnSeS/R0dEt2ve0adMaXBcREUFubi7dunVrWeBtJCDAl6VL53PltNEUFpaQlZXTooY2arXEpCk6RozS0JJ1k+cnWelsbtno5uXC7coMe4fQ5tKPedo7hBapdYEksTrF9ZLE3v2Pk5hktHcY7UKWJR7cqceibP4bs1GvJDnJi92nsvELDODqGxbSY2A/pJa82QuC4DJEkmgnb7zxBrfffju//PILOTk59g6nzQQEBGAwtG5ZzNleeuklcnNz6y8Aa9eurf99x44drXo8pVJJcHAwKlXbdwFtLp1Oy7x5V3L99bNQKBQcOZKO1dqys9o+/Zo/TzHS28YC/6oWHf9yIestBCS5fpKYneXcZWw1Vuc/iTbvdt51Ki/k1jt32zuEdpNWLPFuWfM+X8sCtOxLMJCSm0XHLvHMuW0JHRPiWzlCQRCcmUgS7aCyspIPPviAW265hUmTJrFu3boGt59rVO7zzz+v/3Zv3bp1PProo+zbt69+xOz0PrKyspg6dSru7u54enoya9Ys8vPz6/dzegRyzZo1REZG4u7uzq233orVauWZZ54hODiYwMBAnnzyyQbHf/7550lMTMTNzY2IiAhuvfVWKisrAdi0aRPXXXcdp06dqo/nkUceARqXm15qfG+99RbR0dF4eXkxZ84cKirOPdfEy8uL4ODg+guAt7d3/e/5+flMnDgRd3d3goKCuPbaaykqOlPK9/HHH5OYmIher8fPz48xY8ZQVVXFI488wvr16/niiy/qH9OmTZsalZtu2rQJSZLYuHEjffr0wWAwMGjQIFJSUhrE+cQTTxAYGIiHhwfXX389999/f5uOBCsUCkaOGsBdd19HZGQohw+1fJ5iVLSK+QsNBIc07W3jqf5G1BYnXzOgnSin5KDWmuwdRpuy2aAg37lL2Wotzp8kynu9sVgc98uu5po8/QARUS4w1HuJ1u5Wkaa99OVkZAlyYj3YFwo5uTn0HDyAWbeIDqaCIDQmkkQ7+PDDD+ncuTPx8fHMnz+fNWvWNKkkcPbs2SxfvpyuXbvWj5jNnj0bm83G1KlTKSkpYfPmzWzYsIH09HRmz57d4P5paWl8++23fPfdd7z33nu88cYbTJo0iezsbDZv3syqVat46KGH+OOPP+rvo1Ao+Oc//8mhQ4dYv349P/30E/feey8AgwYN4sUXX8TT07M+nhUrVjSKuynxff7553z11Vd89dVXbN68maeffropTzEAZWVljBo1ip49e7Jz506+++478vPzmTVrFgC5ubnMnTuXxYsXk5yczKZNm5g+fTqyLLNixQpmzZrFhAkT6h/ToEGDznusBx98kNWrV7Nz505UKhWLFy+uv+2dd97hySefZNWqVezatYvIyEhee+21Jj+e5ujSpSPLVyymX/8kUlOPU1JyqkX78/Kum6fYb4D6kspP7xpoIazWtZOe1uQz0rXXRgQ4cigEs9neUbRMjQs0npXMCspz/e0dRptYfFOyvUNoV/dv0VKrvvjpnFmj4FgPXw4qTlFRdorhUyYwdeEc3Dw82iFKQRCcjet9jegE3njjDebPnw/AhAkTOHXqFJs3b2bEiBGXdH+9Xo+7uzsqlap+9Axgw4YNHDhwgIyMDCIiIgB488036dq1Kzt27KBv375AXbK2Zs0aPDw8SEhIYOTIkaSkpPDNN9+gUCiIj49n1apV/Pzzz/Tv3x+AO++8s/440dHRPPHEE9x8883861//QqPR4OXlhSRJDeL5q40bN15yfOvWrcPjzw+ua6+9lo0bNzYa3byYV155hZ49e/LUU0/VX7dmzRoiIiI4evQolZWVWCwWpk+fTlRUFACJiYkNnmej0XjBx3Tak08+yfDhwwG4//77mTRpErW1teh0Ol5++WWWLFnCddddB8A//vEPfvjhh/qR2LYWGOjH0tvnExzsx3ff/kpVVTXh4cHNnneiVEoMG6ElOkbJt18Zqag49xcc3UNsTNJVg5MvF9Be5PgKfMLyL76hk9u/J8zeIbRYjZMnuadVHwvANyLP3mG0ukU3bePl1V0pKXb+Ed9LUVQl8Wq2gbuDKs/bPbrCV0N6nAeZJ47j5unBlGtn03NwfzH/UBCE8xIjie0sJSWF7du3M3fuXABUKhWzZ8/mjTfeaPG+k5OTiYiIqE/AABISEvD29iY5+cw3q9HR0fUJGEBQUBAJCQkozlpBPSgoiIKCgvrff/zxR0aPHk1YWBgeHh5ce+21FBcXU11d3ebxhYSENIjlUu3bt4+ff/4Zd3f3+kvnzp2ButHKpKQkRo8eTWJiIjNnzuS///0vpaWlTT4OQPfu3RvEC9THnJKSQr9+/Rps/9ff25per2Pe/KksXnI1kiSRktLyeYqRUSoWLDbQKb5x6aBKIfNo9xqUNlFmeqkMV2XaO4R2cfiA8zdMqTa7xol17T7XHElUqWRmzTtu7zDa1VdHlOxSNp40blPAyVgPDsZqSctMJywmirm3XU+vIQNEgigIwgWJJLGdvfHGG1gsFkJDQ1GpVKhUKl577TU++eQTTp2qKwVUKBSNyk/NrVifpVY3bJstSdI5r7P9udp1ZmYmkydPpnv37nzyySfs2rWLV199FQCTqfVLCS8US1NUVlYyZcoU9u7d2+CSmprKsGHDUCqVbNiwgW+//ZaEhARefvll4uPjychoeuOQs2M+/cHbnJjbkkKhYPSYQSy7cyHh4cGtMk9Rr5eYepWeUWMUKKQzSeejw8x417pATV47sWms+Pdy/VJTgGNHve0dQotVu8hL27bdB5uTr/l4Prcv34y+7XqmOaSHflFTrj1TIFbjpiK1jz8p2hrys3PpOXgA1yy9gcjYDnaMUhAEZyGSxHZksVh48803Wb16dYOkZd++fYSGhvLee+8BdR1BKyoqqKo60xHyr+vyaTSaRiNBXbp04cSJE5w4caL+usOHD1NWVkZCQkKz4961axc2m43Vq1czYMAA4uLiGnVkPVc8f9VW8Z1Pr169OHToENHR0cTGxja4uLm5AXUJ3eDBg3n00UfZs2cPGo2Gzz777JIf06WIj49v1GW1tbuuNkXXrp1YvmIJffolkpp6nNLSls1TBJCqd9M3dgchgTKjY20MlGtaIdLLh+qKPLS6y2OJkBPHm9Ei18FUu8g0W0WlmsoiX3uH0SY8vY1MutL1y7fPZrRIPJGsx6aAonADKb18SM3PxmwyMn7mVKYtmouHt5e9wxQEwUmIJLEdffXVV5SWlrJkyRK6devW4DJjxoz6ktP+/ftjMBh44IEHSEtL4913323UATU6OpqMjAz27t1LUVERRqORMWPGkJiYyLx589i9ezfbt29nwYIFDB8+nD59+jQ77tjYWMxmMy+//DLp6em89dZb/Pvf/24UT2VlJRs3bqSoqOicZahtFd/53HbbbZSUlDB37lx27NhBWloa33//Pddddx1Wq5U//viDp556ip07d5KVlcWnn35KYWEhXbp0qX9M+/fvJyUlhaKiomaP5t5+++288cYbrF+/ntTUVJ544gn2799v11KfoCB/7rhjAVOmjCQvr4jsE3nNXk8xKzUNY0khfXpHsPwmA9cMbOVgLwNeY9LsHUK7yct1/qnwVS5SbgpQke785b/ns/zBzTjwakVtIqVMyS+x/qRHaslIPYZvgD9X37iQIRPHoPpLlY5Q53SX8rKyMnuHIggORSSJ7eiNN95gzJgxeHk1/iZvxowZ7Ny5k/379+Pr68vbb7/NN998Q2JiIu+99179khJnbz9hwgRGjhxJQEAA7733HpIk8cUXX+Dj48OwYcMYM2YMHTp04IMPPmhR3ElJSTz//POsWrWKbt268c4777By5coG2wwaNIibb76Z2bNnExAQwDPPPNNoP20V3/mEhoayZcsWrFYr48aNIzExkTvvvBNvb28UCgWenp788ssvXHHFFcTFxfHQQw+xevVqJk6cCMANN9xAfHw8ffr0ISAggC1btjQrjnnz5vG3v/2NFStW0KtXLzIyMli0aBE6nX3XitPrdVy7YBpLllyNjMzRlIwmj5xWVVSSffgwQYG+xHbuiEolURHrzrFefhj1zr3MQXuRO1ThG5Fr7zDaRWa6H7UuMGBaZXSdubbGg645LxEgPLKMoSNaXinhLHp0VXLfrToUhkpOpmfSpXcPrrn9RuISu9o7tAtatGhR/VJTarWaoKAgxo4dy5o1a5o0beNcy4cJgtB8ktzc4QNBEJpt7NixBAcH89Zbb9k7FAAOHjzK+nWfkZmZTWxsFHr9xRNYm83Grp82oTFXM2rCCPSGhhOAFFYbIccq8D956c2NLke65YcIH37Q3mG0i88/TOL2G/rbO4wWc9PIfDWw3N5htAo5yEin/35u7zDazN5d4Vw5+gpc+UzHoIcZV2jolagkNysbm9XKwLEjGDZpPFrdpa+haC+LFi0iPz+ftWvXYrVayc/P57vvvmPlypUMHTqUL7/8EtUlDAmvW7eOO++8s8kjgps2bWLkyJGUlpaKJFMQziJGEgWhjVVXV/P8889z6NAhjhw5wsMPP8yPP/7IwoUL7R1avW7d4urWU+yXxLFjWeTlFV60/DT9YDK2ylMk9e7eKEEEsCkVnIz3Ij3JF5NWvNWci6y04d/38ik1PXQgyN4htIoqk4Sr5BxSvpaqU572DqPN9OidTVJPFxi+Po/4jgruvVVH9842MlNS0bsZuOq6eYyZPsUpEsTTtFotwcHBhIWF0atXLx544AG++OILvv322/rpNs8//zyJiYm4ubkRERHBrbfeWr+U1KZNm7juuus4depU/ajk6Qqst956iz59+uDh4UFwcDDXXHPNOTumb9myhe7du6PT6RgwYAAHD5758q64uJi5c+cSFhaGwWCor/I628cff0xiYiJ6vR4/Pz/GjBnToLfE66+/TpcuXdDpdHTu3Jl//etfrfwsCkLrEmdugtDGJEnim2++YdiwYfTu3Zv//e9/fPLJJ4wZM8beoTUQHBzAHcsWMG/+FIxGE0eOpGOxnLuNY1lxCQXpx4iIDCE8KvyC+63w03K0XwClQfYtr3VEyvH56AyXT5OfYyne9g6h1dhc6NOzItN15yUC3Hb3TnuH0Op0Opg5Wc0tC3QobRVkHUunQ+c4rll6A4n9e7vE8hajRo0iKSmJTz/9FKjr0P3Pf/6TQ4cOsX79en766SfuvfdeoG7Ky4svvoinpye5ubnk5uayYsUKoK47/OOPP86+ffv4/PPPyczMZNGiRY2Od88997B69Wp27NhBQEAAU6ZMqe9FUFtbS+/evfn66685ePAgN954I9deey3bt28HIDc3l7lz57J48WKSk5PZtGkT06dPr/+y9Z133uEf//gHTz75JMnJyTz11FP8/e9/Z/369W39NApCs4lyU0EQGjl0KJV33/kfR46kExUVirf3mZEGq8XCzg0bcVfZGD1xJCq15pL361VQQ9jRctQmx1oexF68X9yCf4dse4fRbkb1u47UFNdonvHD6HLUFtf4+FTOPU7M3N/tHUabGtpjMZkZrtHFpnsXJTOuUOPhzp/lpRb6Dh/CiCsnYHB3t3d4TbZo0SLKysr4/PPPG902Z84c9u/fz+HDhxvd9vHHH3PzzTdTVFQEXHq56c6dO+nbty8VFRW4u7vXl5u+//77zJ49G4CSkhLCw8NZt24ds2bNOud+Jk+eTOfOnXnuuefYvXs3vXv3JjMzk6ioqEbbxsbG8vjjj9evkQ3wxBNP8M0337B169YLxisI9uJC34UKgtBaunbtxD33Xs/EicPIzysiIyO7voFA8u59KM019Orfq0kJIsCpQD1HBgRQFG5wmXK95pLDavCNPmnvMNpV7knXSBABbC4wUnOaZYefvUNoc0tuOWTvEFpMq6pl7pVWFs/RolUZyThyFE9vT6YvWcDEuTOcMkG8GFmW60dFf/zxR0aPHk1YWBgeHh5ce+21FBcXn7Ob+tl27drFlClTiIyMxMPDg+HDhwOQlZXVYLuBA8+05vb19SU+Pp7k5GQArFYrjz/+OImJifj6+uLu7s73339fv4+kpCRGjx5NYmIiM2fO5L///S+lpaUAVFVVkZaWxpIlS3B3d6+/PPHEE6SlXT7TDQTnI5JEQRDOydvbk8VLrubmW+bi7m7g0KFjZKVlUplzgg6x0QQENa9EzaZScDLOi9Q+flR7uE7S0FTaGZkoFJdPqlyQ586f04dcgtWFkkTpmDvGGudfv/JC5i/Zjr+/c/69SRIkJkn06LSTqoIdFObmkZt1gsR+vZi37GYS+/VyifLSc0lOTiYmJobMzEwmT55M9+7d+eSTT9i1axevvvoqACbT+RcuraqqYvz48Xh6evLOO++wY8eO+rWQL3S/v3r22Wd56aWXuO+++/j555/Zu3cv48ePr9+HUqlkw4YNfPvttyQkJPDyyy8THx9PRkZG/bzJ//73vw3WyD548CC//+7aI/iCcxNJoiAI56VQKBg6rC/33X8jffp2I+9YKt4eOrr17Nbifdd4akjt40d2nCdWlWue4JyPrJDx759u7zDa1Z6dkfYOoVVZXOykvPxEoL1DaFMqlcycBc73N+cfoGDufD3jJ7oR070LJWWVlJSUMXHOdK6+YSH+Qa77//bTTz9x4MABZsyYwa5du7DZbKxevZoBAwYQFxdHTk5Og+01Gk2jZZyOHDlCcXExTz/9NEOHDqVz587nbFoDNEjYSktLOXr0aP26yVu2bGHq1KnMnz+fpKQkOnTowNGjRxvcX5IkBg8ezKOPPsqePXvQaDR89tlnBAUFERoaSnp6OrGxsQ0uMTExrfFUCUKbcI0CfUEQ2lR4eDB33rmIcB89aXv3kZWaRlhMNGpNC0cCJYnicDdOBegITS3Hp8B1uxCeTTGqAIOHCw2rXYJD+12js+lpFtfKEak+4g9xx+0dRptaevevrP1PR85qOOmwVCoYMEhD3/5qlEqJwsISSipq6T5iBPPmX0li7+72DrFVGY1G8vLyGi2BMXnyZBYsWMDBgwcxm828/PLLTJkyhS1btvDvf/+7wT6io6OprKxk48aNJCUlYTAYiIyMRKPR8PLLL3PzzTdz8OBBHn/88XPG8Nhjj+Hn50dQUBAPPvgg/v7+TJs2DYBOnTrx8ccfs3XrVnx8fHj++efJz88nISEBgD/++IONGzcybtw4AgMD+eOPPygsLKxPMh999FHuuOMOvLy8mDBhAkajkZ07d1JaWsrdd9/ddk+sILSAGEkUBOGSaLUa5t14LQvvuoWoTh3JSj1GWXFJq+zbolWS1c2HtB6+GPXKVtmnI/OckGHvENrd0WRfe4fQqiy4VpZo2uVv7xDanJuHiclX5do7jIuKiFSycLGBAYM0yLKVlJR0KiuquPrq8fz9ibtdLkEE+O677wgJCSE6OpoJEybw888/889//pMvvvgCpVJJUlISzz//PKtWraJbt2688847rFy5ssE+Bg0axM0338zs2bMJCAjgmWeeISAggHXr1vHRRx+RkJDA008/zXPPPXfOGJ5++mmWLVtG7969ycvL43//+x8aTd28+4ceeohevXoxfvx4RowYQXBwcH0CCeDp6ckvv/zCFVdcQVxcHA899BCrV69m4sSJAFx//fW8/vrrrF27lsTERIYPH866devESKLg0ER3U0EQmqymqprfvvuR7T/9itlsJjQ68pIWO74Ukk0mMLOSwKxKFC7YBFUONNLxP1+iULrgg7uACUMWcuiA86zbdjHvjK8htPbS5zQ5OllpI+bDz1Cpz73sjavIyfZiSI/Z/LmygUPR6WD4SC2JSXUVGqWlpzhxIo9OnaKZM3cS3bvHu+zcQ0EQHI8oNxUEocn0bgbGTJ9CTHwnfvz0KzJTUgkMC8XT26vF+5YVEvkdPCgN1hOecgqPUtc5EQfQzDh+2SWIADnZTeuE6+jMLvb1qmRVUJ4TgG+U44+0tURo+CmGjyrjx++97R1KPUmCpJ5qBg/VoNdLWCxWMjJOgAxXTBrOjBnj8fLysHeYgiBcZkSSKAhCs0iSRGy3LgRHhvPLV9+z85etlJeUEhwZ3iqjiiaDivSefnjn1RB6zHXWVvQfdPm1PC8v01JW5lojIGbZtR4PQFWq6yeJAHc/+Bsbf5iMI9RRRUQqGTVGQ0BgXZl9UVEpOTkFdOwYyVXTx9K/f5IYPRQEwS5EkigIQou4e3owce4Mojt3YtP/vuN4yjF8A/3x9vdrlZObsmA95f5aQtIr8Muudu6ZYEOLcPMqt3cU7W7v7kiHOCFvTSYXTBJN+/xhjL2jaHuJSTn07FPD7h32W/bD01NixCgtcZ3rTsOMRhPp6VnodDqmThvNlCmj8Pb2tFt8giAIIkkUBKHFJEkioVcSUZ06sO3Hzez4+TcyU1IJiYpAp2/5idjptRWLQw2EHKvAs8TYClG3P49JzteCvzUc3Bds7xBancnFkl4A23YfbFbFZVEOfceKHSyaPazdj6vWQL/+dV1LVSoJWZbJzSmguKSMxMQ4Zlw9gYSEWDF6KAiC3YkkURCEVuPm4cGYqyYTn9SNzf/7jpT9h9Dp9QSFh6JQtLyZcq27mowevriXGAk9Vo6+0nmabNh8jfjHZ9k7DLs4ctjP3iG0OpPN9U7ipRoVFUW+eAUV2TuUNjd6whE6xA4i/Vj7nAZJEnRLVDFkmAY397r3wqqqatLTs/H19eLaBdMYO3Ywer2uXeIRBEG4GJEkCoLQ6iI6RDPn1iXs27aDX7/7kYwjRwkIDWmVxjYAlb5ajvb1xyevhuD0CjRGxx/50F6VjVJpvfiGLigjzfWabjjBS65ZKtMCL4skEeCG2w7yt7t6tPlxIqOUjBilITCobt6hzWYj63gONTVGBgxIYsbVE4iODmvzOARBEJpCLIEhCEKbKisq5tfvfmTvlu1YLGZCoyJRa1qv06VklfHPriLoeCVKi+O+nQWt+x4P3zJ7h2EXvTpdT2GBay3L++AQM2OU1fYOo9UpJuXQ4aZf7R1Gu7DZoE982702ff0kho/U0jH2zPfxZWXlZGXlEh4exLRpYxkytHerLR8kCILQmsQ7kyAIbcrb34/J82bRpUd3Nn/1PRkpqXh4e+EfHNQq825kpURhlDslIQaCjlfid7LK8dZX7F9y2SaItbVKiotcK0EEqLUCSntH0fqsf/gh31hXHunqFAqYtyiNF5/p1Kr79fKWGDhYQ0JXFQpF3RNpNlvIyMhGAsaNG8y0q8YSGOh6ZdiCILgOkSQKgtDmTi+XEdExhh2bt7Btw8+kJ6cQEhmOwd29VY5h1SjI6eRJYYQbQRkV+ObVIDnIwKL75Ax7h2A3B/aGY3O0pL0VGF1wTiKAVKSl6pQX7t6n7B1Ku7j5zt94/V+dqKxs+b48PCUGDtLQrfuZ5LC+MU1xGbGdoplx9Xh69+4qGtMIguDwRJIoCEK70ep1DJkwmk6JCWz+6nsO79yDQlnUamsrAph1SrK7eFMQ5U5wegXeBbV2XTbD5mHGPyHTjhHY14E9ofYOoU3UOE/PpCaryAjEveflkSS6uZmZenUO76xr/uvUzV1iwEA13XuoUSrPvNuUlZVzIisXP38f5l17JWPGDMLd3a01whYEQWhzIkkUBKHdBYWFcPUNCzjcK4lfvv7+z7UVA/D29221b9hNBhVZ3XwoqDATkl6BZ7F9ls3QTMtGpXbhjOIijhzyt3cIbaLG4rojQbWH/aFnqr3DaDd3/20TH717DSZT0+6nN0j0H6AmqacatfrM66G21khGRjZqtYrRYwcxZcoowsKCWjlqQRCEtiWSREEQ7EKhUNCtb0+i42PZ9uMmdm7aQsaRowSFh+Hm0TolqAC1HmoyknwxnDIRnFGBR0kTzwRbyHd4Wrsez9GkHXPNBcFdeSTRssMP5tk7ivYTGFzJyLGlfP+1zyVtr9NB3/4aevZWo9GcSQ6tVisnsnKprKqmW7c4pk4bQ/fu8aK0VBAEpyS6mwqC4BBOZhxnyw8/c2TPfqwWC8ER4WjbYM0wfYWZgOOVeBfWtv2cxR5lxD72fRsfxLH1T7ienJOu17hmQpyV+0JaYSKbgwp//3/oDK7XvfV8jhwMZvzQKy84f1arhd59NfTuq0arPZP4ybJMXl4RhYUlREaGMGnSCIYM7YNW23pdnAVBENqbGEkUBMEhhMVEcfUNC0g7dIStP/xEWvJRVCoVwRFhqNTqVjtOjYearG4+5NVYCMiqwje3us26oRquvHwb1gBYLBIF+a6XIIJrl5sClGcFoOt83N5htJvO3fLo06+a7b8bGt2m10PP3hp69VGj0zX8fy8tLSf7RC6+ft7MmXMFY8YOxtvbNUfPBUG4vIgkURAEh6FQKOiUmEBM504c2rWXbT/8TNaxDNw83AkICUKhbL01B0x6FSfjvciL8cA/uwr/7CpUrbjOoqy3END98k4SUw6HYHHRsswqs70jaFvVyYFwGSWJAMvu+4N5V42s/93TU6JPPzWJSQ3nHAJUV9dwPPMkWq2GMeMGM2nSCMLDg9s7ZEEQhDYjkkRBEByOSq0maUBf4hK7su/3Hfy+YRMZKal4+/nhG+jfqnN8rBoF+R08KIxywzenhoCsKjRGa4v3q5p6ErXGxTOJi9i3O8zeIbSZqvad2truLLv94Cp7R9G+ho1KpVP8EMpKtPQdoKZzlzNLWZxWU1NL1vEcZGSSenRhypWjSEiIFfMOBUFwOSJJFATBYendDAwYPZwuPbuz65et7PxlK+nJKXWdUP1arxMqgE2poCjCjaIwA94FNQQer0Jf1fxhMJ+R6a0Wm7NKdtHOpgDVJtdOCuQDnphNGtQaF8+Gz5KVEcn8hbVUlDduYFNbayQrKxer1UqXLh2ZMHEYvXoltNrSPYIgCI5GvLsJguDwvHx9GDVtEt0H9mXn5i3s3bKdjOQU/EOC8fD2at1v8RUSZcEGyoINeBTVEphVhXtZE0+Uu1TgHVLQejE5qbSj3vYOoc24+kiiZJMozwnAL/qkvUNpUxaLkqOH49mzvTdFhQGNbjcaTWRl5WA2W4iPj2H8hGH07dsNdSvOkxYEQXBEIkkUBMFp+AcFMmHWVfQY2I8/fvqFQzv3UJxfQEBoCO6eHq1+vAp/HRX+OgynTARkVeJVaORS0lH9VWIUESDruN7eIbQZV08SAapSXDdJrKnWcWBPEvt29aC6qvEC9yaTmRNZudQaTcTFRTN+/BD69e+ORiM6lgqCcHkQS2AIguCUZFkmO+M4f/y4meS9+zEZTQSGBOPWBsniaZpqC34nq/HNq0ZlPvdbp6yxEvX2/9DojG0Wh7PoFHQjtbX2jqLt/DT81CV9aeCspMFFdLxvo73DaFX5uUEc2JPIkUNdsFoajwaazWZOnMijuqqWjrGRTJgwlP4DktDptHaIVhAEwX7ESKIgCE5JkiQiOkQTfkMUmSnH+OOnXzh2MJn8kzn4BQXi6ePd6s0kTAYVuZ08yevgAalZ+OdXEWptOH9JOTlXJIhARpqfSyeIADaFhNLmut+z2nb5YLUqUCrbaI2YdlJTo+CnHzwoK5pEUcG5O5BaLBayT+RRWVlNTIcIxo8fwsBBPdG3wVqtgiAIzkAkiYIgODVJkojp3Ino+FhOpGWw+7ffSd69n6LcfLwD/PDx90OhaN21+mSlxC5jMpbgUkb1nIBvqhWvdDNKM3iPTmvVYzmrvTvD7R1Cm7MpwMnzpwuSapVUFPg77fza0uIAjqfHs2ObOx9/dJCuCTVERTfcxmQyc/JkPpUVVURGhTL3mikMGtwTN7fG6yUKgiBcTkSSKAiCS5AkicjYDkTGdmDgmBHs3bqd/X/sJOPIUTy8vfALDESpap11FmtqKqg0FxDdsSNmHxX5/VQU9NTgmWPEV+/CWUMTHD4YZO8Q2pxVklDjuiOJAFVpAU6VJJpNGrKzOpKVHk/5KT8AgkMgOCSV1GOpREXHAHVLWWRn52E0moiODmPOnEkMGtwLD4/G8xMFQRAuRyJJFATB5QSFhzJ+1jT6jRzKge272P3bNo4fTUXn5kZASBDqFjafOHEyBYVaJiK6U/11slriVJSOLT9Pwd2zlMjoo4RHHUOrc/Gay/NIPeJt7xDanPUyWBuvdp8/DLF3FBdXUhxAVnpnck50wGptfGrTPSma7749SkZGJkajFWSI7RTFmDGD6NM3EYPBdZssCYIgNEfr1mAJgiA4EJ8AP4ZNGsf1f7ubKxfMxTfAj+yM45xIy8BY07zkzWazUViWgbe/HzrtuU8sK8t9OLy/Pxu+msuOLaPJy4nAZnP9hOJsWZmuPyJjuQySRNt2Xxy1vZ3JpCE9NYFN31/Flp+u5ERm3DkTRFmWCQ72wMtbJj8/h+7dO3Pn3dfx93/cxrDh/ZwyQVy0aBGSJCFJEhqNhtjYWB577DEsFgubNm1CkiR8fHyo/cvE4B07dtTf72xWq5UXXniBxMREdDodPj4+TJw4kS1btrTnwxIEwYGIkURBEFyeu6cH/UYNJWlgX5L37mfX5q1kpWWALBMQGozB3f2S91VYnI1FqiI8ottFt5VlBXk50eTlRKPVVRMelUpYRAZePsUteThOITfH9deRs7h+johUqqGy1BsP3zJ7hwKA1aIkPzeSk1kdyM+NQJbPX0Jus9koKyugrLwQN4MX1103nQ4xA+nbtw9KZeuUntvThAkTWLt2LUajkW+++YbbbrsNtVrNwIEDAfDw8OCzzz5j7ty59fd54403iIyMJCsrq/46WZaZM2cOP/74I88++yyjR4+mvLycV199lREjRvDRRx8xbdq09n54giDYmVgCQxCEy47FbCb1YDK7ftlK+pGjmI0m/IKD8PDyvGhH1D37N2DUFDJk1CQUUvOKMQxu5YRGZBAa7poJY36uB306z734hk7u/fHVBNWa7R1Gm3N7eA8hvY/a7fg2m0RRQSgnszqSezIKq+XC5eIWi4niklyqqivw8Q6ga8IguiYMJDAwstU7HtvLokWLKCsr4/PPP6+/bty4cVRUVLBy5UpGjhzJQw89xO+//86GDRsAqKmpISQkhDvuuIPHH3+c06d/H3zwAXPmzOHLL79kypQpDY4zY8YMNm/ezPHjx3Fzc/3qAEEQzhAjiUKbkiSJzz77zC7fQo4YMYIePXrw4osvtsr+zvWhbC/2fF7/ypGel0ulUqvp0rM78UndOH40jV2/buPo/oMU5uTi6eONT4A/KlXjt0djbQ0VxjwioqKanSACVFd5cuxIEseOJLlkwrhnZ6S9Q2gXFpdeJfGMmoP+0M5JoixLlBQFkXMihpzsGEzGC5eEyrJMTU0FJaV5WK0WAgIiGDpkOp3j++Lp6ddOUduXXq+nuPjMe8i1117Ls88+S1ZWFpGRkXzyySdER0fTq1evBvd79913iYuLa5QgAixfvpxPP/2UDRs2OMTnjSAI7UckiUKzLVq0iPXr1ze6fvz48Xz33XdtcsymJEeffvopanX7lbxt2rSJkSNHnvO23NxcgoPPvT5Xc+Tm5uLj43PxDdvBSy+9hLMWJCgUivrlM3KPn+DQ7n0c3L6brNR01Bo1fkEBDUpRT+QcQdJYiYyJa7UYXDFhPLTf9TubApid82XfZNbtfrCw7Y9zdmKYezIaY+3Fl6GwWi2UlhVQUVGCTudGdHQ3uiUMIja2Jzrd5bGMhSzLbNy4ke+//57bb7+9/vrAwEAmTpzIunXr+Mc//sGaNWtYvHhxo/sfPXqULl26nHPfp68/etR+I8mCINiHSBKFFjk9J+JsWq3WTtHUMZlMaDQafH197XL8lJQUPD09G1wXGBjYqsdozYSzuaxWK5Ik4eXlZe9QWkySJEKjIwmNjmTwuJEc3X+Ifdt2kHUsg7wTJ/Hy9cHLz5fCkgw8g3zQ69um7MpVEsaUZPv87bU3s3x5jCRKJwzUVLqhd69q9X3bbHWJYW52DLnZ0RiNF0/sZFmmurq8ftTQ1zeYoUOmExfXm5DgGJcpKb2Yr776Cnd3d8xmMzabjWuuuYZHHnmEHTt21G+zePFili1bxvz589m2bRsfffQRv/76a6N9OesXfYIgtB3R3VRoEa1WS3BwcIPLhUa4Tpw4waxZs/D29sbX15epU6eSmZnZYJs1a9bQtWtXtFotISEhLF26FIDo6GgArrrqKiRJqv/9kUceoUePHrz++uvExMSg0+mAunLTO++8s36/RqOR++67j4iICLRaLbGxsbzxxhtAXcKzZMkSYmJi0Ov1xMfH89JLLzXrOQkMDGz0nJxezH3RokVMmzaN5557jpCQEPz8/Ljtttswm8/Ma8rNzWXSpEno9XpiYmJ49913iY6OblA2K0lSfXlnZmYmkiTx6aefMnLkSAwGA0lJSWzbtq1BXL/99htDhw5Fr9cTERHBHXfcQVXVmZM+o9HIihUrCAsLw83Njf79+7Np06b629etW4e3tzdffvklCQkJaLVasrKy6h/TaSNGjOCOO+7g3nvvxdfXl+DgYB555JEGsRw5coQhQ4ag0+lISEjgxx9/bPCY7Mng7k6PQf259q5bWbj8NoZMHINCqeTYkf2YpHICg8PaJY7TCeMvP05j4zczST7Qh7ISf4ftNHm24xmXx9wl02WSJAKUZ7XeF121NXqyMjqxc9sovv9iPts2TyIzLeGiCaLFYqao6CSZmQcpryihQ0wi06Yu5bqFjzFi+ExCQzpcNgkiwMiRI9m7dy+pqanU1NSwfv36RvMGJ06cSE1NDUuWLGHKlCn4+TUuvY2LiyM5Ofmcxzh9fVxc61VPCILgHMRIotBuzGYz48ePZ+DAgfz666+oVCqeeOIJJkyYwP79+9FoNLz22mvcfffdPP3000ycOJFTp07Vt+DesWMHgYGBrF27lgkTJjToTnfs2DE++eQTPv300/N2rVuwYAHbtm3jn//8J0lJSWRkZFBUVATUdcELDw/no48+ws/Pj61bt3LjjTcSEhLCrFmzWvV5+PnnnwkJCeHnn3/m2LFjzJ49mx49enDDDTfUx1lUVMSmTZtQq9XcfffdFBRcfDHrBx98kOeee45OnTrx4IMPMnfuXI4dO4ZKpSItLY0JEybwxBNPsGbNGgoLC1m6dClLly6tHwleunQphw8f5v333yc0NJTPPvuMCRMmcODAATp1qlsPsLq6mlWrVvH666/j5+d33hHS9evXc/fdd/PHH3+wbds2Fi1axODBgxk7dixWq5Vp06YRGRnJH3/8QUVFBcuXL2+lZ7f1KBQKImM7EBnbgcHjR3Ng505+++ND0JjIyDyIh7sP3t6BqFRtX9J89gijRltDYFA2ASHZBAadRKM1tvnxmyrnZMvWoXQWJpu9I2g/Ncn+kJDRrPvKskRpiT8FuREU5EZwqswPLnE+pyzLVFWXU1qah81mxccniGE9ZxIX14vgoOjLKin8Kzc3N2JjYy+4jUqlYsGCBTzzzDN8++2359xmzpw5XHPNNfzvf/9rNC9x9erV+Pn5MXbs2FaLWxAE5yCSRKFFTpe7nO2BBx7ggQceaLTtBx98gM1m4/XXX6//YF+7di3e3t5s2rSJcePG8cQTT7B8+XKWLVtWf7++ffsCEBAQAIC3t3ejckuTycSbb75Zv81fHT16lA8//JANGzYwZswYADp06FB/u1qt5tFHH63/PSYmhm3btvHhhx82OUkMDw9v8HtUVBSHDh2q/93Hx4dXXnkFpVJJ586dmTRpEhs3buSGG27gyJEj/Pjjj+zYsYM+ffoA8Prrr9cnaReyYsUKJk2aBMCjjz5K165dOXbsGJ07d2blypXMmzevfmS1U6dO/POf/2T48OG89tprFBQUsHbtWrKysggNDa3f33fffcfatWt56qmngLpE/1//+hdJSUkXjKV79+48/PDD9cd65ZVX2LhxI2PHjmXDhg2kpaWxadOm+v/HJ5980qFPQjy8PBk0ehQDRo4gJzeN1NQ9JB/5nZM5qUiSAh/vINzdvdvlhNVk1JOd1YnsrE6AjLdvIYHB2QQGZ+PtW4Qk2XeosbxMS1np5XHibryM1r407/SHGZe+vdGoozAvrC4xzA/DbNI16XgWi+nPuYalGAwedOyQRNeug+gQk4hef+lL1gjw+CHVwzUAADg5SURBVOOPc88995xzFBHqksSPPvqIhQsXNloC48svv+Sjjz4SnU0F4TIkkkShRUaOHMlrr73W4LrzzQXct28fx44dw8PDo8H1tbW1pKWlUVBQQE5ODqNHj25yHFFRUedNEAH27t2LUqlk+PDh593m1VdfZc2aNWRlZVFTU4PJZKJHjx5NjuXXX39t8Bj/2jyna9euDUY7Q0JCOHDgAFA3n1GlUjXoPhcbG3tJTWq6d+/eYJ8ABQUFdO7cmX379rF//37eeeed+m1kWcZms5GRkUF6ejpWq7VRSZHRaGxwYqHRaBoc51JiOR3P6dHQlJQUIiIiGiT6/fr1u+g+HYFCoSA8rBPhYZ0YOGAS6RkHSE7+g+NZyRQWn8Sgc8PLKwCdzq2dRjgkykoCKSsJ5OjhXqg1tfgH5hIQeBL/oBzc3CvaIYaGLpfOpgAmJyj9bTWHPDAZNWi0pnPeLMtwqtSfgrxw8nMjKCsJ4FJHC0+zWi2Ul5dQXlE3B9fXN5jevcYS16kXQUFRl/WoYUtoNBr8/f3Pe7skSXz44Ye8+OKLvPDCC9x6663odDoGDhzIpk2bGDx4cDtGKwiCoxBJotAil1LuclplZSW9e/dukKicFhAQUD9vr7lxXIhef+H26e+//z4rVqxg9erVDBw4EA8PD5599ln++OOPJscSExODt7f3eW//a9IoSRI2W8vr1s7e7+mTqdP7rays5KabbuKOO+5odL/IyEj279+PUqlk165djcp1zx4p1uv1l3Si1laP0ZHodG4kdBlAl879KSrOIS1tH0eO/EFBYTZGYzX6dk8YwWzS/dkAJAYAvaGCgKCT+Afm4B+Yi1ZX2+YxHNwX0ubHcBRGqwTOvyb7JZGQKD8ZiH+HbKCu4Uz5KV+KC4MpLgyhpDAYs7npTctsNhuVlaWUlRdhs1rw9PSjZ4+RdOzYg6jILpdNh9KmWrdu3XlvGzFixAUb0UybNq3R7SqVihUrVrBixYrWClEQBCcnkkSh3fTq1YsPPviAwMDARt0/T4uOjmbjxo3nXUpCrVZjtVqbfOzExERsNhubN2+uLzc925YtWxg0aBC33npr/XVpaWlNPk5LxcfHY7FY2LNnD7179wbq5luWlpa2aL+9evXi8OHD503oe/bsidVqpaCggKFDh7boWBcTHx/PiRMnyM/PJyiobqmEs7vxORtJkgjwDyPAP4y+fcaTn5/J8axkUlJ22DVhBKip9iArozNZGZ0BGQ/PUnz8CvDxK8THtwB3z1OtXp6acuTyWJMOoPYyShJtCijeF02ZyacuKSwKwnKRRe3Puy+bjaqqU5w6VYjZYsbd3ZuEzv2Jje1JdFQC7u7erRu8IAiC0GQiSRRaxGg0kpeX1+A6lUp1ztKWefPm8eyzzzJ16lQee+wxwsPDOX78OJ9++in33nsv4eHhPPLII9x888316ztVVFSwZcuW+rWfTieRgwcPRqvVXvJagdHR0SxcuJDFixfXN645fvw4BQUFzJo1i06dOvHmm2/y/fffExMTw1tvvcWOHTuIiYlp8nNSUFBAbW3DERs/P79LWrOxc+fOjBkzhhtvvJHXXnsNtVrN8uXLL3kE73zuu+8+BgwYwNKlS7n++utxc3Pj8OHDbNiwgVdeeYW4uDjmzZvHggULWL16NT179qSwsJCNGzfSvXv3+rmOrWHs2LF07NiRhQsX8swzz1BRUcFDDz0E4PTlZEqlktDQjoSGdqRf34kOlTCCREW5LxXlvn8mjaBSmfD2LcTHtxBvvwJ8fAtbPNqYcczj4hu5iNqmf1/lNCw6iRp/BdWBSmoClNT6KZAt8XCgeftrmBiacHPzIqZDInGdehMT3RVv79ZdJkgQBEFoGZEkCi3y3Xff1c9/Oy0+Pp4jR4402tZgMPDLL79w3333MX36dCoqKggLC2P06NH1I4sLFy6ktraWF154gRUrVuDv78/VV19dv4/Vq1dz991389///pewsLBGy2dcyGuvvcYDDzzArbfeSnFxMZGRkfUNdm666Sb27NnD7NmzkSSJuXPncuutt563G9yFxMfHN7pu27ZtDBgw4JLu/+abb7JkyRKGDRtGcHAwK1eu5NChQ/VLezRH9+7d2bx5Mw8++CBDhw5FlmU6duzI7Nmz67dZu3ZtfeOgkydP4u/vz4ABA5g8eXKzj3suSqWSzz//nOuvv56+ffvSoUMHnn32WaZMmdKix+hoHDthrGOxaCgqCKOo4MyyHga38gZJo6d3MUrlpZcKn8y27zqp7anGYu8IWketZMbkq8QSoKXWV0F1gBKzZ8tXyLLZrFRWnaL8VFGjxDAqsjM+PsFO/8WQIAiCq5JksYKqIDi07OxsIiIi+PHHH5vV1McZbNmyhSFDhnDs2DE6duxo73DalNVqPW/C6Onpj17v7lAnzgqFFU/vYnx8C/D2K8RqO0FYmAWVuvFHR22tkviQJbjY9NPzmptk5UbvSnuHcclkwKRXUuOupsZdRa27mlNaK7+kfExsYmdiOia0+BhGUw2VFaVUVpYhI+Pm5kVoaEeRGAqCIDgZkSQKgoP56aefqKysJDExkdzcXO69915OnjzJ0aNHL6lk1Rl89tlnuLu706lTJ44dO8ayZcvw8fHht99+s3do7eqvCWNRcQ41tVWolCrc3Xzw8PBGpXKcNQdra4288frPxHXqTt/+AfgFFOPnX1T3b0ARR5O9ufqKcfYOs91MS7CyLMAxk0SbAmrc1dS6q/5MCut+tqkajxBu2/UpHsFaevUb1vTj2GxUVZ+ioqIUo7EatVqLl6cfMTHdiYyMJzS0I16e/iIxFARBcDKi3FQQHIzZbOaBBx4gPT0dDw8PBg0axDvvvOMyCSJARUUF9913H1lZWfj7+zNmzBhWr15t77Da3dklqf37XUFR0UlO5hwjKyuZrKwj5OSmY7NZ0enc8HD3xWDwsOvJdknxKWxWBR4evpQU+VNS5E8qZ8qrbTYrCxdDSYlMSbGN0hIbJX9eTEa7hd1mqs32TXxkwKxTYtQrMRpUGPVKTAYVRr0Ko0EJl/hacdf5U1mRfcnHNZlqqaj8c7RQtmEweBIa2oHYjj0IDe1IcHAMGvXlU3YsCILgisRIoiAIggOqqakkJzeNnJw0jqXto6Q4l5raSiRJgZubF+7u3mg1LWto1FQHDx7jxx8ymDDhSjSapo1wVlbaKCuVqaiwUVEuU1EhU1Fuq/u3Qqa6yvk+iobG2Hgssm3XorSoJEx6JSadCpNO+efPf/6rVyErWv7/fyI7hfTCrQweNQGDofFC9TabjerqcioqS6mtrUKl0uDl5Ud0dDciIzoTGtoBH+8gMVooCILgQsRIoiAIggPS693p2CGJjh2SGDxoGsXFOeTmZZB9MpWs44cpKcnFZKpF+Wdpqru7F+o2Hr0pLa1ArdI2OUEEcHdXULfk5rnXjLBaTyeOMpUVNsorZCrL/0wqK2QqK2Vqa2SasQJOm6k+97ryF2VVSljUCiyaPy/1PysbXG/SKc9ZHtrafH1CSMtVUFyYhyEqFpvNSnV1BVVVp6gxVoEsY9B7EBQYSceOPQgPiyU4OAat9sLrzwqCIAjOSySJgiAIDk6hUBAQEE5AQDjdE4diNpsoLDxBbl4GWVlHyD55lLz8TKxWCwqFEoPBE4PeA53ODYWi9ZKMstKqNlvcXKmU8PaW8PaGCy0+aDbXJYu1tTI1NTK1tWCslTEa6y4mE5j+/NdokjEZZcxmkGWw2WRkG9hsYJNPXweyTa7/2SaDbKtLWkH6MzZQKUGlApVKqv9Z46GgykuNTSFhU0pYlQpsSqnB5VxJoKx0rBE3rdaACh15ucexyrUgKTAYPPD1CyEqsgshwTEEBkbi6yuazgiCIFwuRLmpIAiCk6utrSIvL5PComxyczPJyT1GZWUZtbVVgIxGY8Bg8MCg90Ct1jb7RP/tt35CpQihX/9LW87Fme3a9CuekpER40e6VGIkyzKmWiNVFRVUVVRiMZmRlAosqnK8o/T0HzyaoKAoggIj8fYOdKnHLgiCIFw6MZIoCILg5HQ6N6KjuxId3RUAq9VCSWk+RUUnKSrK5kT2UYqKTlJQeAKz2YRCoUCvd8eg90Cv90CpPP/I3Wlms4XychNRUZ5t/XAcglKpxGaRnTpJkmUZk9FITVU1NVXV1FZXI8syGp0WNw8P4rp3JTI2hsDQEALDQvD08XbqxysIgiC0HpEkCoIguBilUkWAfxgB/mFAP6CuEU5xcQ6FRSfJz88i+2QK5eUlFJfkYLPZUKu16PXu6LRu6HQGlMqGHw+lpaewWhV4e3m3/wOyA0mhwOpECz7arFZqqmuora5LCE1GI5IkodZo0LsZCAoLITQmkqCwUALDQggMCUar19k7bEEQBMFBiSRREAThMqDXuxMeHkd4eBxQ17GyvLyobrSxOIeTJ49RUJBFVVXZn4ljXYcYrUaPVudGXl4JVouEt7ePPR9Gu1GolNisjpckyrKMxWyuHx2sqapGluvi1LsZ0Lu70SmmCyFR4fgGBOAbGIBvoB96NzcxSigIgiBcMpEkCoIgXIYUCgXe3oF4ewcSG9sTqCtTPVVexKlTRZSVFVJaWkB+QSbFxblYzDbc3d1JO3YCSZLQaNUY9Dr0Bj0Ggw6tVuNSSYhSocRix5FEi9mCsbYWU20ttTV1/9psNpBBqVahdzPg7edLQu8kAsNC6pLBAD98/P1QudCaqoIgCIJ9iCRREARBAOrKVH19gvH1CW5wvdlsorS0gMzMAiorjBQWlZJ9IpeTJ/OprKymuKgUo8kMEkgyKFVKdFoNWq0GzZ//6nQalEql0ySSCqUSaxuOJNqsVkxGEyajEVOtEZPRiLHWWNdaVZJQKBRo9Tq0Oh2BYcEEhATjFxiAh48X3n6++AUG4O7l6TTPpyAIguBcRJIoCIIgXJBarSEwMJzAwPAG19tsNsrKyikuLqO4uIzyU5WUl1dSWFhCYWEJxcVl1NYaKT9VgdFowmK11a0qIcuo1Sq0mjOJpEajRqVSolarHCKZVKqUyE0YSZRlGavFisVs/vNiwWI2YzabsVrqfm7QTFyS0Gq1aHRatAYdwZFh+AUG4OXni7uXB57e3nj6eOHh7Y1W17brXwqOZ8SIEfTo0YMXX3zR3qG0iujoaO68807uvPNOe4fSLI888giff/45e/futXcogtBuRJIoCIIgNItCocDX1xtfX286dWp8u81mo6KiivLyyjOXPxPJoqLTieQpamuNVFZUYbZY6pIri7UuSZQkQEaWZRQKBUqlEpVSiVL1579KBUpVXUJ55sKfCeaZn8++re76M7/LsozNJmOz2f782YbNJlNrNGOz2SjKy6+73Wqtu63+Xxs22YZCkpBlkLGhUqpQqlWo1WpUajVagw5fD388vLzw8PbE4O6O3s2AVq/D3cMDdy9P3L08Mbi37nqWl7NFixaxfv16Vq5cyf33319//eeff85VV11FU1b9crbE5rPPPmPVqlUkJydjs9mIjIxk7NixDpFo7tixAzc3t/rfJUnis88+Y9q0aS3a74gRI9i8eXOj62+66Sb+/e9/t2jfZ1uxYgW33357q+1PEJyBSBIFQRCENqFQKPDy8sDLy+O821itViorq6mpqaWmxtjg39oG19VSXV1DVVUN1dU1VFfX3WaxWP4sC5XrkjVZPusCp5NMWQbOvo26baU/SzsVkoSkUKBQ1P3u7e+LTWFCrVGjOT3ip9Wh1mnQanVodVr07m7oDXp0BgM6g/4vPxvQ6Jq/JqXQfDqdjlWrVnHTTTfh4+NcjZZMJhMajabJ99u4cSOzZ8/mySef5Morr0SSJA4fPsyGDRvaIMpLd/rxBAQEtNkxbrjhBh577LEG1xkMhlY9hru7O+7u7q26z+Zo7utDEJpFFgRBEAQnZLPZ5Npao1xVVS1XVlbJ5eWVcllZuVxaekouLi6VCwtL5IKCYjkvr1DOycmXT57Ml0+cyJWPHz8pZ2Zmy+npJ+TMzGz5xIlcOTe3QC4sLJFLSsrk8vJKuaqqWjbW1so2m83eD1NogoULF8qTJ0+WO3fuLN9zzz3113/22WfyX095fv31V3nIkCGyTqeTw8PD5dtvv12urKyUZVmWhw8fLgMNLjabTfb395c/+uij+n0kJSXJwcHBDfap0WjkqqoqWZZl+fjx4/KVV14pu7m5yR4eHvLMmTPlvLy8+u0ffvhhOSkpSf7vf/8rR0dHy5Ik1R9/2bJl9dt99dVXsqenp/z222+f83EvW7ZMHjFixEWfn88//1zu2bOnrNVq5ZiYGPmRRx6RzWZz/e2lpaXyjTfeKAcGBsparVbu2rWr/L///a9BrGd74YUX5KioqPrfFy5cKE+dOlV+4okn5JCQEDk6OlqWZVmOioqSX3jhhfqfz35eo6Ki5IyMDFmSJHnHjh2N9h8ZGSlbrdZzPp6/Pk9/lZGRIQPyJ598Io8YMULW6/Vy9+7d5a1btzbY7v/+7//k8PBwWa/Xy9OmTZNXr14te3l51d/+18d++nE+++yzcnBwsOzr6yvfeuutsslkqt+mtrZWXr58uRwaGiobDAa5X79+8s8//9zguBd6DZ5+rh577DH52muvlT08POSFCxee97EKQmsT9S2CIAiCU5IkCa1Wg8Ggx83NgIeHG15eHnh7e+Lr642/vw8BAb4EBfkTEhJIaGgg4eHBREaGEhUVRkxMOFFRYYSHBxMcHIC/vw8+Pl54eLhhMOjRaMVIoDNSKpU89dRTvPzyy2RnZ59zm7S0NCZMmMCMGTPYv38/H3zwAb/99htLly4F4NNPPyU8PJzHHnuM3NxccnNzkSSJYcOGsWnTJgBKS0tJTk6mpqaGI0eOALB582b69u2LwWDAZrMxdepUSkpK2Lx5Mxs2bCA9PZ3Zs2c3iOXYsWN88sknfPrpp+ec8/buu+8yd+5c3nnnHebNm3fOxxMcHMyhQ4c4ePDgeZ+XX3/9lQULFrBs2TIOHz7Mf/7zH9atW8eTTz4J1JWHT5w4kS1btvD2229z+PBhnn76aZRK5QWf77/auHEjKSkpbNiwga+++qrR7Tt27ABg7dq15ObmsmPHDqKjoxkzZgxr165tsO3atWtZtGhRi8uxH3zwQVasWMHevXuJi4tj7ty5WCwWALZs2cLNN9/MsmXL2Lt3L2PHjq1/Ti7k559/Ji0tjZ9//pn169ezbt061q1bV3/70qVL2bZtG++//z779+9n5syZTJgwgdTUVODir8HTnnvuOZKSktizZw9///vfW/Q8CEKT2DtLFQRBEARBaA2nR3hkWZYHDBggL168WJblxiOJS5YskW+88cYG9/31119lhUIh19TUyLLccPTrtH/+859y165dZVmuG5Xr37+/PHXqVPm1116TZVmWx4wZIz/wwAOyLMvyDz/8ICuVSjkrK6v+/ocOHZIBefv27bIs141QqdVquaCgoMFxTo+QvfLKK7KXl5e8adOmCz7uyspK+YorrqgfmZs9e7b8xhtvyLW1tfXbjB49Wn7qqaca3O+tt96SQ0JCZFmW5e+//15WKBRySkrKOY9xqSOJQUFBstFobLDdX59LQP7ss88abPPBBx/IPj4+9THv2rVLliRJzsjIOO/jHj58uKxWq2U3N7cGl9MjrqdHEl9//fX6+5z+P0hOTpZlWZZnz54tT5o0qcF+582bd9GRxKioKNlisdRfN3PmTHn27NmyLNeNICuVSvnkyZMN9jt69Gj5b3/7myzLl/4anDZt2nkfvyC0JTGSKAhCsyxatKjFTQcEQRDayqpVq1i/fj3JycmNbtu3bx/r1q2rn2vm7u7O+PHjsdlsZGRknHefw4cP5/DhwxQWFrJ582ZGjBjBiBEj2LRpE2azma1btzJixAgAkpOTiYiIICIiov7+CQkJeHt7N4gpKirqnHP2Pv74Y+666y42bNjA8OHDL/hY3dzc+Prrrzl27BgPPfQQ7u7uLF++nH79+lFdXV3/mB977LEGj/mGG24gNzeX6upq9u7dS3h4OHFxcRc81sUkJiY2a97ctGnTUCqVfPbZZwCsW7eOkSNHEh0dfcH7zZs3j7179za4XHnllQ226d69e/3PISEhABQUFACQkpJCv379Gmz/19/PpWvXrg1GWUNCQur3eeDAAaxWK3FxcQ2e782bN5OWlgZc+muwT58+F41FENqCSBIFwY5OnDjB4sWLCQ0NRaPREBUVxbJlyyguLrZ3aPUyMzORJKlRGdRLL73UoLTGHqKjo5Ekiffff7/RbV27dkWSJLvHKAiCfQwbNozx48fzt7/9rdFtlZWV3HTTTQ0Si3379pGamkrHjh3Pu8/ExER8fX3ZvHlzgyRx8+bN7NixA7PZzKBBg5oU59ldP8/Ws2dPAgICWLNmzSV3Ze3YsSPXX389r7/+Ort37+bw4cN88MEH9Y/50UcfbfCYDxw4QGpqKjqdDr1ef8F9KxSKRnGYzeZLfjwXo9FoWLBgAWvXrsVkMvHuu++yePHii97Py8uL2NjYBhcPj4bNstRqdf3Pp0vIbU1Y4uZczt7n6f2e3mdlZSVKpZJdu3Y1eL6Tk5N56aWX6re5lNdgc59PQWgp0d1UEOwkPT2dgQMHEhcXx3vvvUdMTAyHDh3innvu4dtvv+X333/H19e3zY7f0i5pXl5erRhN80VERLB27VrmzJlTf93vv/9OXl6eS324iq52gtB0Tz/9ND169CA+Pr7B9b169eLw4cPExsae974ajQar1drgOkmSGDp0KF988QWHDh1iyJAhGAwGjEYj//nPf+jTp0/9+06XLl04ceIEJ06cqB9NPHz4MGVlZSQkJFw09o4dO7J69WpGjBiBUqnklVdeadJjj46OxmAwUFVVVf+YU1JSzvuYu3fvTnZ2NkePHj3naGJAQAB5eXn1XYGBZq8bqFarGz23ANdffz3dunXjX//6FxaLhenTpzdr/00RHx9fP0/ytL/+3lQ9e/bEarVSUFDA0KFDz7nNpbwGBcGexEiiINjJbbfdhkaj4YcffmD48OFERkYyceJEfvzxR06ePMmDDz5Yv210dDSPP/44c+fOxc3NjbCwMF599dUG+ysrK+P6668nICAAT09PRo0axb59++pvf+SRR+jRowevv/46MTEx6HQ6AL777juGDBmCt7c3fn5+TJ48ub4cBiAmJgao+9CTJKm+lOqv5aZGo5E77riDwMBAdDodQ4YMafBBu2nTJiRJYuPGjfTp0weDwcCgQYNISUmp32bfvn2MHDkSDw8PPD096d27Nzt37rzg8zhv3jw2b97MiRMn6q9bs2YN8+bN4//bu/Ooqqr2D+Bf8DoAXhzwTcAUBLwMqQSJiRhEwgJLcygRvQmYWMvSEAfkl6VIjoiYpCa5CpBIrVToDQHFIZRShpRMCUFBLQHL4c0bKqj79weLHUcQ0SzKvp+1zlrec/bdZ5/DXXgfnn2erVIp/w4WExODfv36wcjICD179sRrr70GnU4njyckJKBz587IzMyEvb09OnbsCF9fX1RUVMg2eXl58Pb2Rrdu3dCpUyd4eHjg22+/VZznhx9+wJAhQ9ChQwc4ODggKysLenp6SElJkW3Onj0LPz8/dO7cGV27dsXIkSNRXl4uj9ff38WLF8Pc3LzRl1wiurt+/fpBq9UiNjZWsX/u3Ln4+uuvMW3aNBw5cgQlJSVITU1VFA2xtLREdnY2fvrpJ/zyyy9y/9NPP41Nmzbh8ccfR8eOHaGvrw93d3ckJycrpoV6eXnJ83/77bfIzc1FQEAAPDw8WjyFUKPRYO/evdi6dWuz6zVGREQgLCwM+/btQ1lZGQ4fPoyXX34ZtbW18Pb2BgDMnz8fGzduxMKFC3Hs2DEUFRVh8+bNeOuttwDUTaV1d3fHCy+8gF27dqGsrAzp6enIyMiQ1/3zzz8jKioKJ0+exNq1a5Gent6i67idpaUldu/ejcrKSly6dEnut7e3x6BBgzB37lyMHz/+rtlNAKiurkZlZaVia9jn3UyfPh07duxATEwMSkpKEBcXh/T09D9UtEqj0UCr1SIgIADbtm1DWVkZcnNzsXTpUqSlpQFo2WeQqDUxSCRqBRcvXkRmZiZee+21Rv8JmpqaQqvVYsuWLYqpPStWrJAVzsLDwxESEqJYA2vs2LE4f/480tPTUVBQAGdnZwwdOhQXL16UbZqqovfbb79h5syZyM/Px+7du6Gvr4/Ro0fLaTO5ubkAgKysLFRUVGDbtm1NXlNYWBi2bt2KxMREfPvtt7CxsYGPj4/i/EBdlbmVK1ciPz8fKpVKMZ1Iq9Xi0UcfRV5eHgoKChAeHt5oSs/tunfvDh8fHyQmJgKo+8KwZcuWJqcp6evrIzY2FseOHUNiYiL27NmDsLAwRZvq6mpER0cjKSkJ2dnZOHPmDGbPni2PX7lyBYGBgThw4AAOHjyIPn364Nlnn8WVK1cA1K37N2rUKBgaGuLQoUP44IMPFAE/UDdFy8fHB2q1Gvv370dOTo4MSGtqamS7u1UJJKK7i4yMbDS1sH///vjqq69w4sQJPPXUU3BycsL8+fNhbm6ueF95eTmsra0Vzwx6eHjg5s2b8g9mQF0Adfs+PT09pKamokuXLnB3d4eXlxesrKzk9M+WsrW1xZ49e7Bp0ybMmjWryTYeHh44deoUAgICYGdnh2HDhqGyshI7d+6Uf2Dy8fHBl19+iZ07d8LFxQWDBg3CqlWrYGFhIfvZunUrXFxcMH78eDg4OCAsLExm/Ozt7bFu3TqsXbsWjo6OyM3NVfxuvBcrV67Erl270LNnTzg5OSmOTZ48GTU1NS2aagoAGzZsgJmZmWIbP358i8fi5uaG9evXIyYmBo6OjsjIyEBoaKj8Q+r9io+PR0BAAGbNmgVbW1uMGjUKeXl56NWrF4CWfQaJWlXr1s0h+nc6ePBgk9Xd6sXExAgAoqqqSghRV+HM19dX0WbcuHFi2LBhQoi6imjGxsaKSnZCCGFtbS3i4uKEEHeuone7n3/+WQAQR48eFUL8Xh3u8OHDinYNqwjqdDrRtm1bkZycLI/X1NQIc3NzERUVJYQQYu/evQKAyMrKkm3S0tIEAFnJTa1Wi4SEhGbH11B9xbyUlBRhbW0tbt26JRITE4WTk5MQQohOnTqJ+Pj4O77/s88+EyYmJvJ1fHy8ACBKS0vlvrVr14ru3bvfsY+bN28KtVot1xJLT08XKpVKVFRUyDa7du1S/LyTkpKEra2tYg2+69evCwMDA5GZmSmEuHOVQCKih1lkZKTo169fq44hODhYDBkypFXHQNTamEkkakWihcUIAMDV1bXR6/oKeYWFhdDpdDAxMVFUSisrK1NMHW2qil5JSQnGjx8PKysrGBsby0pyZ86cafHYTp48idraWri5ucl9bdu2xcCBAxtVFmyuytzMmTMRHBwMLy8vLFu2TDH25jz33HPQ6XTIzs7GRx99dMe/QGdlZWHo0KHo0aMH1Go1Jk6ciAsXLsjqfwBgaGioKBrQsGIdAFRVVWHKlCno06cPOnXqBGNjY+h0Onm/iouL0bNnT5iamsr33F4pr7CwEKWlpVCr1fJn1bVrV1y7dk1xzfdbJZCI6J9Gp9Ph+++/x5o1azB9+vS/9NzR0dHy9/J7772HxMREBAYG/qVjIPq7YeEaolZgY2MDPT09FBUVYfTo0Y2OFxUVoUuXLk2WRW+KTqeDmZmZXOS5oc6dO8t/N1XIZcSIEbCwsMCGDRtgbm6OW7duoW/fvoppjw9Sc1XmIiIiMGHCBKSlpSE9PR0LFizA5s2bm7xHDalUKkycOBELFizAoUOHZAn1hsrLyzF8+HBMnToVixcvRteuXXHgwAE5tcnQ0LDR+OrH2DCYDwwMxIULF7B69WpYWFigffv2cHV1vaf7pdPp8MQTTyA5ObnRsYY/84ep8A4RUXOmTZuGTZs2YdSoUS2eavqg5ObmIioqCleuXIGVlRViY2MRHBz8l46B6O+GQSJRKzAxMYG3tzfWrVuH0NBQxXOJlZWVSE5ORkBAgOLB+YMHDyr6OHjwIOzt7QHUVUmrrKyESqW665pSDV24cAHFxcXYsGGDrMB24MABRZv6TFZTlejqWVtbo127dsjJyZHPt9TW1iIvL6/ZYgtN0Wg00Gg0CA0Nxfjx4xEfH3/XIBEAXn75ZURHR2PcuHHo0qVLo+MFBQW4desWVq5cCX39ukkUn3766T2NDQBycnKwbt06PPvsswDqCtA0LGpha2uLs2fPoqqqCt27dwfQuFKes7MztmzZgkceeQTGxsb3PAYioodNQkJCqy1ZdD//FxA97DjdlKiVrFmzBtevX4ePjw+ys7Nx9uxZZGRkwNvbGz169MDixYsV7XNychAVFYUTJ05g7dq1+OyzzxASEgKgroqeq6srRo0ahZ07d6K8vBxff/015s2b12x10C5dusDExAQffPABSktLsWfPHsycOVPR5pFHHoGBgQEyMjJQVVWF//3vf436MTIywtSpUzFnzhxkZGTg+PHjmDJlCqqrqzF58uQW3Y+rV69i2rRp2LdvH06fPo2cnBzk5eXJQPhu7O3t8csvvyA+Pr7J4zY2NqitrcV7772HU6dOISkpCevXr29R3w316dMHSUlJKCoqwqFDh6DVahVBvre3N6ytrREYGIjvvvsOOTk5snpgfdCv1WrRrVs3jBw5Evv370dZWRn27duHN954Az/++OM9j4mI/hy3V3EmIvq3YJBI1Er69OmD/Px8WFlZwc/PD9bW1njllVfg6emJb775ptEaibNmzUJ+fj6cnJywaNEixMTEwMfHB0Bd8LFjxw64u7tj0qRJ0Gg08Pf3x+nTp2U2qyn6+vrYvHkzCgoK0LdvX4SGhmLFihWKNiqVCrGxsYiLi4O5uTlGjhzZZF/Lli3DCy+8gIkTJ8LZ2RmlpaXIzMxsMqvXlDZt2uDChQsICAiARqOBn58fhg0bhoULF7bo/UBdhvZOJdMdHR0RExOD5cuXo2/fvkhOTsbSpUtb3He9Dz/8EJcuXYKzszMmTpwol/1oeB0pKSnQ6XRwcXFBcHCwrG5aXy3P0NAQ2dnZ6NWrF8aMGQN7e3tMnjwZ165dY2aR7ltQUBD09PSgp6eHtm3bonv37vD29sZHH330hxcOv5PExES4uLjA0NAQarUaHh4ef0klXktLS3mtDbdly5Y90POsXr261bJbREStSU/cS+UMImoVlpaWmDFjxj1P3aQHKygoCJcvX1asd9gSOTk5GDJkCEpLSxVFcf6p9u3bB09PT1y6dEnxzCu1rqCgIFRVVSE+Ph43b95EVVUVMjIysHTpUjz11FP44osvGq0d+kfMnj0ba9aswaJFizBq1CjU1tbi448/xrJly7B69eo/db03S0tLTJ48GVOmTFHsV6vVD+WzvLW1tXddDoiI6EFiJpGIHjoNMyrt2rWDjY0NIiMjcePGjRa9v7y8HHp6enItyXu1fft27Nq1C+Xl5cjKysIrr7wCNze3+w4Q68dTv6nVajz22GN4/fXXUVJScl990sOpffv2MDU1RY8ePeDs7Iw333wTqampSE9PV2TELl++jODgYPznP/+BsbExnnnmGRQWFgIATpw4AT09Pfzwww+KvletWiU/wwcPHsTKlSuxYsUKzJ49GzY2NrC3t8fixYsxY8YMzJw5E2fPngVQ96xZ586dkZKSgj59+qBDhw7w8fGRx4G6ir+enp5Qq9UwNjbGE0880exUeaAuIDQ1NVVs9QHivn37oKenh927d2PAgAEwNDTE4MGDUVxcrOhj0aJFeOSRR6BWqxEcHIzw8HA8/vjj8vjt002ffvppvPHGGwgLC0PXrl1hamqKiIgIRZ/N3dt6qampcHZ2RocOHWBlZYWFCxcqfj/p6enh/fffx/PPPw8jI6NGjx8QEf3ZGCQS0UPJ19cXFRUVKCkpwaxZsxAREdFoKu2f5cqVK3j99ddhZ2eHoKAguLi4IDU19Q/3m5WVhYqKChQWFmLJkiUoKiqCo6Mjdu/e/QBG/c/zZ1Xgfdg888wzcHR0xLZt2+S+sWPH4vz580hPT0dBQQGcnZ0xdOhQXLx4ERqNBgMGDGhUfTc5ORkTJkwAAGzatAkdO3bEq6++2uh8s2bNQm1tLbZu3Sr3VVdXY/Hixdi4cSNycnJw+fJl+Pv7y+NarRaPPvoo8vLyUFBQgPDw8AeSOZs3bx5WrlyJ/Px8qFQqRdXM5ORkLF68GMuXL0dBQQF69eqF999//659JiYmwsjICIcOHUJUVBQiIyOxa9cueby5ewsA+/fvR0BAAEJCQnD8+HHExcUhISGhUSAYERGB0aNH4+jRo395tU8iIrTuMo1ERA9eYGCgGDlypGKft7e3GDRokNDpdEKtVovPPvtMcXz79u3C0NBQ/PrrrwKAYvPw8FD0u2LFCmFqaiq6du0qXnvtNVFTUyP7uXjxopg4caLo3LmzMDAwEL6+vuLEiRPyeHx8vOjUqZPIyMgQdnZ2wsjISPj4+Ihz587d8XrKysoEAHH48GHF/ps3b4qnn35aWFhYiBs3bsj9KSkpwsnJSbRv31707t1bREREiNraWiGEEOPHjxd+fn6KfmpqaoSJiYlITEyU/S5ZskRYWlqKDh06iP79+yvu1969ewUAcenSJbnv888/Fw4ODqJdu3bCwsJCREdHK85hYWEhIiMjhb+/vzA0NBTm5uZizZo1ijaXLl0SkydPFt26dRNqtVp4enqKI0eOyOMLFiwQjo6OYsOGDcLS0lLo6end8Z79GzX1ua83btw4YW9vL4QQYv/+/cLY2Fhcu3ZN0cba2lrExcUJIYRYtWqVsLa2lseKi4sFAFFUVCSEEMLX11c4OjrecSzGxsZi6tSpQoi6zzwAcfDgQXm8qKhIABCHDh0SQgihVqtFQkJCi6/VwsJCtGvXThgZGSm27OxsIcTvn9GsrCz5nrS0NAFAXL16VQghxJNPPilef/11Rb9ubm6K67r9nnp4eDRaZN3FxUXMnTtXCNGyezt06FCxZMkSxfGkpCRhZmYmXwMQM2bMaPH9ICJ60JhJJKJ/BQMDA9TU1MDIyAj+/v6NqqDGx8fjxRdfhFqtRm5uLoDfM3cNMzB79+7FyZMnsXfvXiQmJjYq2x4UFIT8/Hx88cUX+OabbyCEwLPPPova2lrZprq6GtHR0UhKSkJ2djbOnDmD2bNn3/M16evrIyQkBKdPn0ZBQQGAu2cptFot/vvf/0Kn08l+MjMzUV1dLZcaWbp0KTZu3Ij169fj2LFjCA0NxUsvvYSvvvqqyXEUFBTAz88P/v7+OHr0KCIiIvD22283KvixYsUKODo64vDhwwgPD0dISMg9ZWAAoLS0FFu3bsW2bdvuezrwv5EQQlbXLSwshE6ng4mJCTp27Ci3srIynDx5EgDg7++P8vJyufROcnIynJ2dYWdnp+izpVQqFVxcXORrOzs7dO7cGUVFRQCAmTNnIjg4GF5eXli2bJkcR3PmzJmDI0eOKLYBAwYo2vTv31/+28zMDABw/vx5AEBxcTEGDhyoaH/766Y07LO+3/o+W3JvCwsLERkZqTg+ZcoUVFRUoLq6WvZ7+7UQEf2VuE4iET3UhBDYvXs3MjMzMX36dABAcHAwBg8ejIqKCvkFb8eOHcjKygLw+4L2JiYmMDU1VfTXpUsXrFmzBm3atIGdnR2ee+457N69G1OmTEFJSQm++OIL5OTkYPDgwQDqvlz37NkTKSkpGDt2LIC6IhTr16+Xz3dNmzYNkZGR93V99V/ay8vLMXDgQCxcuBDh4eEIDAwEAFhZWeGdd95BWFgYFixYAB8fHxgZGWH79u2YOHEiAOCTTz7B888/D7VajevXr2PJkiXIysqCq6ur7OPAgQOIi4uDh4dHozHExMRg6NChePvttwHUrXV5/PhxrFixAkFBQbKdm5sbwsPDZZucnBysWrUK3t7eOHDgAHJzc3H+/Hm0b98eABAdHY2UlBR8/vnneOWVVwDUTTHduHGj/BlRyxQVFaF3794AAJ1OBzMzM+zbt69Ru/pCRKampnjmmWfwySefYNCgQfjkk08wdepU2U6j0eDAgQOoqamRa6nWO3fuHH799VdoNJoWjy8iIgITJkxAWloa0tPTsWDBAmzevLnZNVK7desGGxubZvttOGW1Pkj+o5Veb58Gq6enJ/tsyb3V6XRYuHAhxowZ06hNfQVkAA9lAR4i+udgJpGIHkpffvklOnbsiA4dOmDYsGEYN26cLDAxcOBAPPbYY0hMTAQAfPzxx7CwsIC7u/td+33sscfQpk0b+bphFqGoqAgqlQpPPvmkPG5iYgJbW1uZMQHqlsBoWMSmYR/3qj6b0zBL1FyWQqVSwc/PTz5v9ttvvyE1NRVarRZAXaauuroa3t7eij42btx4x+xOUVER3NzcFPvc3NxQUlKCmzdvyn31QWfD1/X3pSUZGACwsLBggHiP9uzZg6NHj+KFF14AADg7O6OyshIqlQo2NjaKrVu3bvJ9Wq0WW7ZswTfffINTp04pniH09/eHTqdDXFxco/NFR0ejbdu28nwAcOPGDUUhmuLiYly+fFmxDqpGo0FoaCh27tyJMWPG3HHN0wfF1tYWeXl5in23v75XLbm3zs7OKC4ubnTcxsYG+vr8WkZEfw/MJBLRQ8nT0xPvv/8+2rVrB3Nz80al/4ODg7F27VqEh4cjPj4ekyZNkoFWc5rLIrRUU33cy9S9huqDrIZZortlKbRaLTw8PHD+/Hns2rULBgYG8PX1le8HgLS0NPTo0UPx/voM35+hJRkYgNmVu7l+/ToqKysbLYExfPhwBAQEAAC8vLzg6uqKUaNGISoqChqNBufOnUNaWhpGjx4tpzmOGTMGU6dOxdSpU+Hp6Qlzc3N5HldXV4SEhGDOnDmoqalRLIGxevVqvPvuu+jZs6ds37ZtW0yfPh2xsbFQqVSYNm0aBg0ahIEDB+Lq1auYM2cOXnzxRfTu3Rs//vgj8vLyFEFmU65cuYLKykrFPkNDwxavNTp9+nRMmTIFAwYMwODBg7FlyxZ89913sLKyatH7m9KSezt//nwMHz4cvXr1wosvvgh9fX0UFhbi+++/x6JFi+773EREDxKDRCJ6KBkZGTU7Fe2ll15CWFgYYmNjcfz4cTk9E4CcPtcwC9YS9vb2uHHjBg4dOiSnm164cAHFxcVwcHC4j6to3q1btxAbG4vevXvDyckJgDJLcSeDBw9Gz549sWXLFqSnp2Ps2LEycHVwcED79u1x5syZJqeWNsXe3h45OTmKfTk5OdBoNIqsa/3zbQ1f12eSGmZgLC0tW3ReaiwjIwNmZmZQqVTo0qULHB0dERsbi8DAQJml0tPTw44dOzBv3jxMmjQJP//8M0xNTeHu7o7u3bvLvtRqNUaMGIFPP/0UH330UaNzvfvuu+jfvz/WrVuHt956C23atIGzszNSUlIwYsQIRVtDQ0PMnTsXEyZMwE8//YSnnnoKH374IQCgTZs2uHDhAgICAlBVVYVu3bphzJgxWLhwYbPXOn/+fMyfP1+x79VXX8X69etbdK+0Wi1OnTqF2bNn49q1a/Dz80NQUJB8Jvl+tOTe+vj44Msvv0RkZCSWL1+Otm3bws7ODsHBwfd9XiKiB641q+YQEf0Zmqvy2NCECRNEu3bthK+vr2J/bW2tMDAwEIsWLRKVlZXi8uXLd+w3JCREVj8VQoiRI0cKBwcHsX//fnHkyBHh6+srbGxsZAXU+uqmDW3fvl009+u4vrppVlaWqKioECdPnhSpqanC09NTGBgYiD179si2GRkZQqVSiYiICPH999+L48ePi02bNol58+Yp+pw3b55wcHAQKpVK7N+/v9ExExMTkZCQIEpLS0VBQYGIjY2V1Sdvr25aUFAg9PX1RWRkpCguLhYJCQnCwMBAxMfHyz4tLCyEsbGxWL58uSguLhZr1qwRbdq0ERkZGUIIIW7duiWGDBkiHB0dRWZmpigrKxM5OTnizTffFHl5eUKI36ub0j9LU5/5vysvLy/x0ksvtfYwiIhaHSe/E9G/1uTJk1FTU9NoDTKVSoXY2FjExcXB3NwcI0eObHGf8fHxeOKJJzB8+HC4urpCCIEdO3Y8kDXfvLy8YGZmhn79+iE8PBz29vb47rvv4OnpKdvUZyl27twJFxcXDBo0CKtWrYKFhYWiL61Wi+PHj6NHjx6Nnid855138Pbbb2Pp0qWwt7eHr68v0tLS5JTW2zk7O+PTTz/F5s2b0bdvX8yfPx+RkZGKojVA3fp5+fn5cHJywqJFixATEwMfHx8Av2dg3N3dMWnSJGg0Gvj7++P06dOK7BbRg1JdXY2YmBgcO3YMP/zwAxYsWICsrCzFrAIion8rPSHu80EYIqJ/uKSkJISGhuLcuXONKjTSg2VpaYkZM2ZgxowZrT0U+oslJCRgxowZuHz5cmsPReHq1asYMWIEDh8+jGvXrsHW1hZvvfVWk8/zEhH92/CZRCL616murkZFRQWWLVuGV199lQEi0Z8oKCioUVb578DAwEAue0NEREqcbkpE/zpRUVGws7ODqakp/u///q+1h0NERET0t8LppkRERERERCQxk0hEREREREQSg0QiIiIiIiKSGCQSERERERGRxCCRiIiIiIiIJAaJREREREREJDFIJCIiIiIiIolBIhEREREREUkMEomIiIiIiEhikEhEREREREQSg0QiIiIiIiKSGCQSERERERGRxCCRiIiIiIiIJAaJREREREREJDFIJCIiIiIiIolBIhEREREREUkMEomIiIiIiEhikEhEREREREQSg0QiIiIiIiKSGCQSERERERGRxCCRiIiIiIiIJAaJREREREREJDFIJCIiIiIiIolBIhEREREREUkMEomIiIiIiEhikEhEREREREQSg0QiIiIiIiKSGCQSERERERGRxCCRiIiIiIiIJAaJREREREREJDFIJCIiIiIiIolBIhEREREREUkMEomIiIiIiEhikEhEREREREQSg0QiIiIiIiKSGCQSERERERGRxCCRiIiIiIiIJAaJREREREREJDFIJCIiIiIiIolBIhEREREREUkMEomIiIiIiEhikEhEREREREQSg0QiIiIiIiKSGCQSERERERGRxCCRiIiIiIiIJAaJREREREREJDFIJCIiIiIiIolBIhEREREREUkMEomIiIiIiEhikEhERERERETS/wNzcS8EuL2P2gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Category'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Vo-mdMcHwNpn",
        "outputId": "9f790a6e-1b50-4393-a85e-e2d6abc441f3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Data Science'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Resume'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "T4NRSgflwRd1",
        "outputId": "f72c5d4c-bf6e-4178-a036-01e84660321d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Skills * Programming Languages: Python (pandas, numpy, scipy, scikit-learn, matplotlib), Sql, Java, JavaScript/JQuery. * Machine learning: Regression, SVM, NaÃ¯ve Bayes, KNN, Random Forest, Decision Trees, Boosting techniques, Cluster Analysis, Word Embedding, Sentiment Analysis, Natural Language processing, Dimensionality reduction, Topic Modelling (LDA, NMF), PCA & Neural Nets. * Database Visualizations: Mysql, SqlServer, Cassandra, Hbase, ElasticSearch D3.js, DC.js, Plotly, kibana, matplotlib, ggplot, Tableau. * Others: Regular Expression, HTML, CSS, Angular 6, Logstash, Kafka, Python Flask, Git, Docker, computer vision - Open CV and understanding of Deep learning.Education Details \\r\\n\\r\\nData Science Assurance Associate \\r\\n\\r\\nData Science Assurance Associate - Ernst & Young LLP\\r\\nSkill Details \\r\\nJAVASCRIPT- Exprience - 24 months\\r\\njQuery- Exprience - 24 months\\r\\nPython- Exprience - 24 monthsCompany Details \\r\\ncompany - Ernst & Young LLP\\r\\ndescription - Fraud Investigations and Dispute Services   Assurance\\r\\nTECHNOLOGY ASSISTED REVIEW\\r\\nTAR (Technology Assisted Review) assists in accelerating the review process and run analytics and generate reports.\\r\\n* Core member of a team helped in developing automated review platform tool from scratch for assisting E discovery domain, this tool implements predictive coding and topic modelling by automating reviews, resulting in reduced labor costs and time spent during the lawyers review.\\r\\n* Understand the end to end flow of the solution, doing research and development for classification models, predictive analysis and mining of the information present in text data. Worked on analyzing the outputs and precision monitoring for the entire tool.\\r\\n* TAR assists in predictive coding, topic modelling from the evidence by following EY standards. Developed the classifier models in order to identify \"red flags\" and fraud-related issues.\\r\\n\\r\\nTools & Technologies: Python, scikit-learn, tfidf, word2vec, doc2vec, cosine similarity, NaÃ¯ve Bayes, LDA, NMF for topic modelling, Vader and text blob for sentiment analysis. Matplot lib, Tableau dashboard for reporting.\\r\\n\\r\\nMULTIPLE DATA SCIENCE AND ANALYTIC PROJECTS (USA CLIENTS)\\r\\nTEXT ANALYTICS - MOTOR VEHICLE CUSTOMER REVIEW DATA * Received customer feedback survey data for past one year. Performed sentiment (Positive, Negative & Neutral) and time series analysis on customer comments across all 4 categories.\\r\\n* Created heat map of terms by survey category based on frequency of words * Extracted Positive and Negative words across all the Survey categories and plotted Word cloud.\\r\\n* Created customized tableau dashboards for effective reporting and visualizations.\\r\\nCHATBOT * Developed a user friendly chatbot for one of our Products which handle simple questions about hours of operation, reservation options and so on.\\r\\n* This chat bot serves entire product related questions. Giving overview of tool via QA platform and also give recommendation responses so that user question to build chain of relevant answer.\\r\\n* This too has intelligence to build the pipeline of questions as per user requirement and asks the relevant /recommended questions.\\r\\n\\r\\nTools & Technologies: Python, Natural language processing, NLTK, spacy, topic modelling, Sentiment analysis, Word Embedding, scikit-learn, JavaScript/JQuery, SqlServer\\r\\n\\r\\nINFORMATION GOVERNANCE\\r\\nOrganizations to make informed decisions about all of the information they store. The integrated Information Governance portfolio synthesizes intelligence across unstructured data sources and facilitates action to ensure organizations are best positioned to counter information risk.\\r\\n* Scan data from multiple sources of formats and parse different file formats, extract Meta data information, push results for indexing elastic search and created customized, interactive dashboards using kibana.\\r\\n* Preforming ROT Analysis on the data which give information of data which helps identify content that is either Redundant, Outdated, or Trivial.\\r\\n* Preforming full-text search analysis on elastic search with predefined methods which can tag as (PII) personally identifiable information (social security numbers, addresses, names, etc.) which frequently targeted during cyber-attacks.\\r\\nTools & Technologies: Python, Flask, Elastic Search, Kibana\\r\\n\\r\\nFRAUD ANALYTIC PLATFORM\\r\\nFraud Analytics and investigative platform to review all red flag cases.\\r\\nâ\\x80¢ FAP is a Fraud Analytics and investigative platform with inbuilt case manager and suite of Analytics for various ERP systems.\\r\\n* It can be used by clients to interrogate their Accounting systems for identifying the anomalies which can be indicators of fraud by running advanced analytics\\r\\nTools & Technologies: HTML, JavaScript, SqlServer, JQuery, CSS, Bootstrap, Node.js, D3.js, DC.js'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the original category distribution\n",
        "print(\"Original Category Distribution:\")\n",
        "print(df['Category'].value_counts())\n",
        "\n",
        "# Get the largest category size (i.e., the category with the maximum number of entries)\n",
        "max_size = df['Category'].value_counts().max()\n",
        "\n",
        "# Perform oversampling\n",
        "balanced_df = df.groupby('Category').apply(lambda x: x.sample(max_size, replace=True)).reset_index(drop=True)\n",
        "\n",
        "# Shuffle the dataset to avoid any order bias\n",
        "df = balanced_df.sample(frac=1).reset_index(drop=True)\n",
        "\n",
        "# Check the balanced category distribution\n",
        "print(\"\\nBalanced Category Distribution (After Oversampling):\")\n",
        "print(df['Category'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSJqTs_uwS8O",
        "outputId": "cf2b0d03-3fec-4b29-aa9a-4a6b2ae9da94"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Category Distribution:\n",
            "Category\n",
            "Java Developer               84\n",
            "Testing                      70\n",
            "DevOps Engineer              55\n",
            "Python Developer             48\n",
            "Web Designing                45\n",
            "HR                           44\n",
            "Hadoop                       42\n",
            "Blockchain                   40\n",
            "ETL Developer                40\n",
            "Operations Manager           40\n",
            "Data Science                 40\n",
            "Sales                        40\n",
            "Mechanical Engineer          40\n",
            "Arts                         36\n",
            "Database                     33\n",
            "Electrical Engineering       30\n",
            "Health and fitness           30\n",
            "PMO                          30\n",
            "Business Analyst             28\n",
            "DotNet Developer             28\n",
            "Automation Testing           26\n",
            "Network Security Engineer    25\n",
            "SAP Developer                24\n",
            "Civil Engineer               24\n",
            "Advocate                     20\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Balanced Category Distribution (After Oversampling):\n",
            "Category\n",
            "Civil Engineer               84\n",
            "Python Developer             84\n",
            "Advocate                     84\n",
            "Sales                        84\n",
            "Health and fitness           84\n",
            "Testing                      84\n",
            "DotNet Developer             84\n",
            "ETL Developer                84\n",
            "Web Designing                84\n",
            "DevOps Engineer              84\n",
            "HR                           84\n",
            "Business Analyst             84\n",
            "Blockchain                   84\n",
            "Electrical Engineering       84\n",
            "SAP Developer                84\n",
            "Network Security Engineer    84\n",
            "Mechanical Engineer          84\n",
            "Operations Manager           84\n",
            "Java Developer               84\n",
            "Data Science                 84\n",
            "Hadoop                       84\n",
            "Automation Testing           84\n",
            "Arts                         84\n",
            "Database                     84\n",
            "PMO                          84\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-58fa04a1b064>:9: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  balanced_df = df.groupby('Category').apply(lambda x: x.sample(max_size, replace=True)).reset_index(drop=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "def cleanResume(txt):\n",
        "    cleanText = re.sub('http\\S+\\s', ' ', txt)\n",
        "    cleanText = re.sub('RT|cc', ' ', cleanText)\n",
        "    cleanText = re.sub('#\\S+\\s', ' ', cleanText)\n",
        "    cleanText = re.sub('@\\S+', '  ', cleanText)\n",
        "    cleanText = re.sub('[%s]' % re.escape(\"\"\"!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\"\"\"), ' ', cleanText)\n",
        "    cleanText = re.sub(r'[^\\x00-\\x7f]', ' ', cleanText)\n",
        "    cleanText = re.sub('\\s+', ' ', cleanText)\n",
        "    return cleanText"
      ],
      "metadata": {
        "id": "3ZkbcjX6wUxP"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleanResume(\"my #### $ #  #noorsaeed webiste like is this http://heloword and access it @gmain.com\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "v-cN7TgnwYVt",
        "outputId": "3e2ae2c3-8f48-46e8-8004-459791ff6a76"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'my webiste like is this and a ess it '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Resume'] = df['Resume'].apply(lambda x: cleanResume(x))"
      ],
      "metadata": {
        "id": "Pu7-_m9awaRj"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Resume'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "03E2jpHYwbxA",
        "outputId": "7b787c26-72d9-4d23-9ee8-30170b24864e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'SKILLS 1 Autocad 2 Pro v 3 Catia 4 word excel 5 Photoshop INDUSTRIAL EXPOSURE Industrial Visit at Larsen Tubro L T 3 months industrial training Inplant Training at Ashoka Buildcon Training for infrastructure setup and maintenance Education Details January 2017 Masters in structure Nashik Maharashtra Universal University Nashik Civil Engineer Civil Engineer Skill Details Autocad Exprience 1 months Catia Exprience 6 months maintenance Exprience 48 months photoshop Exprience 1 months training Exprience 6 monthsCompany Details company Color Sky Decor LLC Dubai description Role Civil Engineer company Girija Construction And Devolopers description Role Civil Engineer company Showrooms Villa s Hotels description 1 Exterior and Interior Of buildings villa s etc 2 Construction of new building and villa s 3 Preparing bill of quantities scheduling etc company Showrooms Villa s Hotels description Maintenance and Interior outfit works of apartments Showrooms Villa s Hotels etc Description 1 Analyze photographs drawings and maps to inform the direction of projects as well as the overall budget constraints 2 Ensure project feasibility through continual evaluation of structural integrity and design practicality 3 Create schedule for project completion 3 Perform and adjust quantity calculations for practical and budgetary purposes 4 Communicate with team members as well as customers and vendors to ensure maximum cohesion and fluidity on projects 5 Forecast design and construction time frames 6 Inspect project sites to ensure they meet relevant codes and are progressing properly company Sonia Buildcon description Role Civil Engineer company description 8 day to day management of the site including supervising and monitoring the site labour force and the work of any subcontractors Duration 2015 2016 Role Civil Engineer Construction renovation interior designing of buildings malls commercial complex Villas etc Description 1 Undertaking technical and feasibility studies including site investigations 2 Using a range of computer software for developing detailed designs 3 Undertaking complex calculations 4 Liaising with clients and a variety of professionals including architects and subcontractors 5 Compiling job specs and supervising tendering procedures 6 Resolving design and development problems 7 Managing budgets and project resources 8 Scheduling material and equipment purchases and deliveries 9 Making sure the project complies with legal requirements especially health and safety 10 Assessing the sustainability and environmental impact of projects 11 Ensuring projects run smoothly and structures are completed within budget and on time company Grandeurs Realetors India description Role Civil Site Engineer company description Description Responsibility 1 Evaluated overall cost of materails labours subcontractors and tools 2 Regulated subcontractor work schedule and equipement delivery 3 Provided required documents for owners and subcontractors 4 Cheack regular basis project progrees and devolopment 5 act as the main technical adviser on a construction site for subcontractors craftspeople and operatives 6 ensure that all materials used and work performed are in a ordance with the specifications 7 liaise with any consultants subcontractors supervisors planners quantity surveyors and the general workforce involved in the project Responsibility '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "metadata": {
        "id": "HRK9tBh5wdGB"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le.fit(df['Category'])\n",
        "df['Category'] = le.transform(df['Category'])"
      ],
      "metadata": {
        "id": "W3QyttEqweuA"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.Category.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y7QkeP-Kwf2G",
        "outputId": "c2c63874-5256-4881-944e-8a05f6e18c45"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 5, 11,  7,  1,  2, 13,  6, 15, 18, 16, 17, 21,  3, 20,  4, 12,  8,\n",
              "       24, 10,  9, 23, 14, 22,  0, 19])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ['Data Science', 'HR', 'Advocate', 'Arts', 'Web Designing',\n",
        "#        'Mechanical Engineer', 'Sales', 'Health and fitness',\n",
        "#        'Civil Engineer', 'Java Developer', 'Business Analyst',\n",
        "#        'SAP Developer', 'Automation Testing', 'Electrical Engineering',\n",
        "#        'Operations Manager', 'Python Developer', 'DevOps Engineer',\n",
        "#        'Network Security Engineer', 'PMO', 'Database', 'Hadoop',\n",
        "#        'ETL Developer', 'DotNet Developer', 'Blockchain', 'Testing'],\n",
        "#       dtype=object)"
      ],
      "metadata": {
        "id": "esV6o41KwhnT"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf = TfidfVectorizer(stop_words='english')\n",
        "\n",
        "tfidf.fit(df['Resume'])\n",
        "requredTaxt  = tfidf.transform(df['Resume'])"
      ],
      "metadata": {
        "id": "j54etY0kwjQp"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "XaFdzxnywk50"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(requredTaxt, df['Category'], test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "UQypoiE8wmjr"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3TON7eg1wnnH",
        "outputId": "37db5aec-e906-4d7f-a1c3-a8b8aa5f2c38"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1680, 7335)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "soOmN9bXwoyw",
        "outputId": "283523c8-a352-4313-ab4a-43e7a6c4ec8e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(420, 7335)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "# Ensure that X_train and X_test are dense if they are sparse\n",
        "X_train = X_train.toarray() if hasattr(X_train, 'toarray') else X_train\n",
        "X_test = X_test.toarray() if hasattr(X_test, 'toarray') else X_test\n",
        "\n",
        "# 1. Train KNeighborsClassifier\n",
        "knn_model = OneVsRestClassifier(KNeighborsClassifier())\n",
        "knn_model.fit(X_train, y_train)\n",
        "y_pred_knn = knn_model.predict(X_test)\n",
        "print(\"\\nKNeighborsClassifier Results:\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred_knn):.4f}\")\n",
        "print(f\"Confusion Matrix:\\n{confusion_matrix(y_test, y_pred_knn)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_knn)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jf1UcntXwrsY",
        "outputId": "9bfeda90-ab27-4117-aeb5-244c046ca1b8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "KNeighborsClassifier Results:\n",
            "Accuracy: 1.0000\n",
            "Confusion Matrix:\n",
            "[[17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0 15  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0 18  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0 10  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0 13  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 18  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 16  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 18  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 16  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 15  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 21  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 22\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "  23]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        17\n",
            "           1       1.00      1.00      1.00        14\n",
            "           2       1.00      1.00      1.00        17\n",
            "           3       1.00      1.00      1.00        22\n",
            "           4       1.00      1.00      1.00        15\n",
            "           5       1.00      1.00      1.00        19\n",
            "           6       1.00      1.00      1.00        18\n",
            "           7       1.00      1.00      1.00        14\n",
            "           8       1.00      1.00      1.00        14\n",
            "           9       1.00      1.00      1.00        14\n",
            "          10       1.00      1.00      1.00        17\n",
            "          11       1.00      1.00      1.00        10\n",
            "          12       1.00      1.00      1.00        13\n",
            "          13       1.00      1.00      1.00        18\n",
            "          14       1.00      1.00      1.00        16\n",
            "          15       1.00      1.00      1.00        14\n",
            "          16       1.00      1.00      1.00        19\n",
            "          17       1.00      1.00      1.00        18\n",
            "          18       1.00      1.00      1.00        16\n",
            "          19       1.00      1.00      1.00        17\n",
            "          20       1.00      1.00      1.00        17\n",
            "          21       1.00      1.00      1.00        15\n",
            "          22       1.00      1.00      1.00        21\n",
            "          23       1.00      1.00      1.00        22\n",
            "          24       1.00      1.00      1.00        23\n",
            "\n",
            "    accuracy                           1.00       420\n",
            "   macro avg       1.00      1.00      1.00       420\n",
            "weighted avg       1.00      1.00      1.00       420\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Train SVC\n",
        "svc_model = OneVsRestClassifier(SVC())\n",
        "svc_model.fit(X_train, y_train)\n",
        "y_pred_svc = svc_model.predict(X_test)\n",
        "print(\"\\nSVC Results:\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred_svc):.4f}\")\n",
        "print(f\"Confusion Matrix:\\n{confusion_matrix(y_test, y_pred_svc)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_svc)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dAIbLg6hwtk3",
        "outputId": "7f86f606-ade9-409f-f680-e4ba46f6f60b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "SVC Results:\n",
            "Accuracy: 1.0000\n",
            "Confusion Matrix:\n",
            "[[17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0 15  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0 18  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0 10  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0 13  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 18  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 16  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 18  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 16  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 15  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 21  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 22\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "  23]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        17\n",
            "           1       1.00      1.00      1.00        14\n",
            "           2       1.00      1.00      1.00        17\n",
            "           3       1.00      1.00      1.00        22\n",
            "           4       1.00      1.00      1.00        15\n",
            "           5       1.00      1.00      1.00        19\n",
            "           6       1.00      1.00      1.00        18\n",
            "           7       1.00      1.00      1.00        14\n",
            "           8       1.00      1.00      1.00        14\n",
            "           9       1.00      1.00      1.00        14\n",
            "          10       1.00      1.00      1.00        17\n",
            "          11       1.00      1.00      1.00        10\n",
            "          12       1.00      1.00      1.00        13\n",
            "          13       1.00      1.00      1.00        18\n",
            "          14       1.00      1.00      1.00        16\n",
            "          15       1.00      1.00      1.00        14\n",
            "          16       1.00      1.00      1.00        19\n",
            "          17       1.00      1.00      1.00        18\n",
            "          18       1.00      1.00      1.00        16\n",
            "          19       1.00      1.00      1.00        17\n",
            "          20       1.00      1.00      1.00        17\n",
            "          21       1.00      1.00      1.00        15\n",
            "          22       1.00      1.00      1.00        21\n",
            "          23       1.00      1.00      1.00        22\n",
            "          24       1.00      1.00      1.00        23\n",
            "\n",
            "    accuracy                           1.00       420\n",
            "   macro avg       1.00      1.00      1.00       420\n",
            "weighted avg       1.00      1.00      1.00       420\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Train RandomForestClassifier\n",
        "rf_model = OneVsRestClassifier(RandomForestClassifier())\n",
        "rf_model.fit(X_train, y_train)\n",
        "y_pred_rf = rf_model.predict(X_test)\n",
        "print(\"\\nRandomForestClassifier Results:\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred_rf):.4f}\")\n",
        "print(f\"Confusion Matrix:\\n{confusion_matrix(y_test, y_pred_rf)}\")\n",
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred_rf)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0UF9khhGw6nq",
        "outputId": "3895f23a-1945-43f5-cbe9-35d9a010bcb4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "RandomForestClassifier Results:\n",
            "Accuracy: 1.0000\n",
            "Confusion Matrix:\n",
            "[[17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0 15  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0 18  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0 10  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0 13  0  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 18  0  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 16  0  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 18  0  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 16  0  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 15  0  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 21  0\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 22\n",
            "   0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
            "  23]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        17\n",
            "           1       1.00      1.00      1.00        14\n",
            "           2       1.00      1.00      1.00        17\n",
            "           3       1.00      1.00      1.00        22\n",
            "           4       1.00      1.00      1.00        15\n",
            "           5       1.00      1.00      1.00        19\n",
            "           6       1.00      1.00      1.00        18\n",
            "           7       1.00      1.00      1.00        14\n",
            "           8       1.00      1.00      1.00        14\n",
            "           9       1.00      1.00      1.00        14\n",
            "          10       1.00      1.00      1.00        17\n",
            "          11       1.00      1.00      1.00        10\n",
            "          12       1.00      1.00      1.00        13\n",
            "          13       1.00      1.00      1.00        18\n",
            "          14       1.00      1.00      1.00        16\n",
            "          15       1.00      1.00      1.00        14\n",
            "          16       1.00      1.00      1.00        19\n",
            "          17       1.00      1.00      1.00        18\n",
            "          18       1.00      1.00      1.00        16\n",
            "          19       1.00      1.00      1.00        17\n",
            "          20       1.00      1.00      1.00        17\n",
            "          21       1.00      1.00      1.00        15\n",
            "          22       1.00      1.00      1.00        21\n",
            "          23       1.00      1.00      1.00        22\n",
            "          24       1.00      1.00      1.00        23\n",
            "\n",
            "    accuracy                           1.00       420\n",
            "   macro avg       1.00      1.00      1.00       420\n",
            "weighted avg       1.00      1.00      1.00       420\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "pickle.dump(tfidf,open('tfidf.pkl','wb'))\n",
        "pickle.dump(svc_model, open('clf.pkl', 'wb'))\n",
        "pickle.dump(le, open(\"encoder.pkl\",'wb'))"
      ],
      "metadata": {
        "id": "Uyt2XMACxTq3"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Function to predict the category of a resume\n",
        "def pred(input_resume):\n",
        "    # Preprocess the input text (e.g., cleaning, etc.)\n",
        "    cleaned_text = cleanResume(input_resume)\n",
        "\n",
        "    # Vectorize the cleaned text using the same TF-IDF vectorizer used during training\n",
        "    vectorized_text = tfidf.transform([cleaned_text])\n",
        "\n",
        "    # Convert sparse matrix to dense\n",
        "    vectorized_text = vectorized_text.toarray()\n",
        "\n",
        "    # Prediction\n",
        "    predicted_category = svc_model.predict(vectorized_text)\n",
        "\n",
        "    # get name of predicted category\n",
        "    predicted_category_name = le.inverse_transform(predicted_category)\n",
        "\n",
        "    return predicted_category_name[0]  # Return the category name\n"
      ],
      "metadata": {
        "id": "vETWu82yxeNx"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "myresume = \"\"\"I am a data scientist specializing in machine\n",
        "learning, deep learning, and computer vision. With\n",
        "a strong background in mathematics, statistics,\n",
        "and programming, I am passionate about\n",
        "uncovering hidden patterns and insights in data.\n",
        "I have extensive experience in developing\n",
        "predictive models, implementing deep learning\n",
        "algorithms, and designing computer vision\n",
        "systems. My technical skills include proficiency in\n",
        "Python, Sklearn, TensorFlow, and PyTorch.\n",
        "What sets me apart is my ability to effectively\n",
        "communicate complex concepts to diverse\n",
        "audiences. I excel in translating technical insights\n",
        "into actionable recommendations that drive\n",
        "informed decision-making.\n",
        "If you're looking for a dedicated and versatile data\n",
        "scientist to collaborate on impactful projects, I am\n",
        "eager to contribute my expertise. Let's harness the\n",
        "power of data together to unlock new possibilities\n",
        "and shape a better future.\n",
        "Contact & Sources\n",
        "Email: 611noorsaeed@gmail.com\n",
        "Phone: 03442826192\n",
        "Github: https://github.com/611noorsaeed\n",
        "Linkdin: https://www.linkedin.com/in/noor-saeed654a23263/\n",
        "Blogs: https://medium.com/@611noorsaeed\n",
        "Youtube: Artificial Intelligence\n",
        "ABOUT ME\n",
        "WORK EXPERIENCE\n",
        "SKILLES\n",
        "NOOR SAEED\n",
        "LANGUAGES\n",
        "English\n",
        "Urdu\n",
        "Hindi\n",
        "I am a versatile data scientist with expertise in a wide\n",
        "range of projects, including machine learning,\n",
        "recommendation systems, deep learning, and computer\n",
        "vision. Throughout my career, I have successfully\n",
        "developed and deployed various machine learning models\n",
        "to solve complex problems and drive data-driven\n",
        "decision-making\n",
        "Machine Learnine\n",
        "Deep Learning\n",
        "Computer Vision\n",
        "Recommendation Systems\n",
        "Data Visualization\n",
        "Programming Languages (Python, SQL)\n",
        "Data Preprocessing and Feature Engineering\n",
        "Model Evaluation and Deployment\n",
        "Statistical Analysis\n",
        "Communication and Collaboration\n",
        "\"\"\"\n",
        "\n",
        "pred(myresume)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "RSaJd2vsxgL8",
        "outputId": "25680789-d948-48e9-8178-a70974644ee3"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Data Science'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "myresume = \"\"\"\n",
        "Jane Smith is a certified personal trainer with over 5 years of experience in helping individuals achieve their fitness goals. Specializing in weight loss, strength training, and sports conditioning, Jane has developed personalized workout routines for clients of all ages and fitness levels. She has extensive knowledge in nutrition and exercise science, and uses this to create holistic health and fitness programs that are tailored to individual needs.\n",
        "\n",
        "Jane holds a degree in Exercise Science and is a certified trainer through the National Academy of Sports Medicine (NASM). She has worked with athletes, seniors, and individuals with chronic health conditions, helping them improve their physical well-being and overall quality of life.\n",
        "\n",
        "Her expertise includes:\n",
        "- Weight Loss and Body Composition\n",
        "- Strength Training and Resistance Exercises\n",
        "- Cardio Conditioning\n",
        "- Nutrition Coaching and Meal Planning\n",
        "- Injury Prevention and Rehabilitation\n",
        "- Functional Movement and Flexibility Training\n",
        "- Group Fitness Classes\n",
        "\n",
        "Certifications:\n",
        "- Certified Personal Trainer, NASM\n",
        "- CPR and First Aid Certified\n",
        "- Yoga Instructor (200-Hour Certification)\n",
        "\n",
        "Education:\n",
        "BSc in Exercise Science, ABC University, 2014-2018\n",
        "\n",
        "Work Experience:\n",
        "- Personal Trainer at XYZ Fitness Gym (2018-Present)\n",
        "- Fitness Coach at Wellness Center (2016-2018)\n",
        "\n",
        "Languages:\n",
        "- English (Fluent)\n",
        "- Spanish (Conversational)\n",
        "\"\"\"\n",
        "\n",
        "# Now, test the model with the Health and Fitness-focused resume\n",
        "pred(myresume)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "oHLCU3qixi4E",
        "outputId": "ae90e30d-145f-4313-dae7-cb6e8b61016f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Health and fitness'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Sb8-VYuaxlGp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}