{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Introduction","metadata":{}},{"cell_type":"markdown","source":"Artificial intelligence (AI) is rapidly changing the way we live and work. AI has transformed industries, including healthcare, banking, telecom, retail, manufacturing, hi-tech and transportation, to name a few. AI is assisting in automating time-consuming and repetitive procedures, enhancing decision-making, and increasing company efficiency in general. It is also being utilized in healthcare to diagnose illnesses and create individualized treatment regimens. AI is being utilized in finance to prevent fraud and enhance investing choices. And AI is being utilized in transportation to manage traffic flow and improve road safety. It is expected that the impact of AI technology on sectors will increase exponentially as the technology develops, opening the door for previously unimaginable innovations and advancements.\n\nSince the creation and application of AI systems can have a substantial impact on people, society, and the environment, ethics in the context of AI are an important factor to take into account. The practical design of AI systems, however, differs significantly from AI ethics ideas. Due to the fact that many of the translational tools and approaches currently in use are either too flexible or too rigid to be useful, this gap makes it difficult to include AI ethics into the algorithm design process. Privacy, justice, responsibility, accessibility, environmental sustainability, and transparency are important challenges in AI ethics. To ensure that AI systems are developed and applied in a way that fosters the welfare of individuals and society at large, these challenges must be addressed. The creation of AI-based translation or summarization tools is a problem in this regard because they can be subject to biases and errors that could have detrimental effects on users. In order to overcome these obstacles, it is essential to create practical tools and techniques for putting AI ethics concepts into action as well as to make sure that AI systems are created and used in a way that respects human rights and values.\n\nThe essay begins by discussing the ethical challenges of AI, such as bias, awareness, education, and incompetent systems. It then goes on to explore ethical guidelines for responsible AI development and deployment, including key considerations for implementation, implementing ethical requirements in software engineering, introducing watermarks to safeguard against manipulative AI-generated texts, infusing morality into AI with the MEC algorithm, promoting equality, inclusion, and human rights, and ethics as a service. The essay concludes by discussing the importance of addressing these ethical challenges in order to ensure that AI is used for good and not for harm.","metadata":{}},{"cell_type":"markdown","source":"# Unveiling the Ethical Challenges of AI\n\nSince the creation and application of AI systems can have a substantial impact on people, society, and the environment, ethics in the context of AI are an important factor to take into account. The practical design of AI systems, however, differs significantly from AI ethics ideas. Due to the fact that many of the translational tools and approaches currently in use are either too flexible or too rigid to be useful, this gap makes it difficult to include AI ethics into the algorithm design process. **Privacy**, **justice**, **responsibility**, **accessibility**, **environmental sustainability**, and **transparency** are few important challenges in AI ethics.\nTo ensure that AI systems are developed and applied in a way that fosters the welfare of individuals and society at large, these challenges must be addressed. For example, the creation of AI-based translation or summarization tools is a problem in this regard because they can be subject to biases and errors that could have detrimental effects on users. In order to overcome these obstacles, it is essential to create practical tools and techniques for putting AI ethics concepts into action as well as to make sure that AI systems are created and used in a way that respects human rights and values.\n\nThe following sections explore various aspects of AI ethics and its impact on different sectors, shedding light on the importance of addressing **bias**, **raising awareness**, **considering ethical issues in education**, and **mitigating the potential harm caused by incompetent AI systems**.\n\n#### Bias\nAlthough AI has the potential to change almost any industry, there are also issues with how we are using it. For instance, **Amazon's hiring algorithm** systematically discriminated against female candidates, demonstrating how AI has the ability to reinforce biases and introduce inequities. The design of the predictive algorithm itself or biased input data can contribute to discrimination being introduced at the core. It is crucial to make sure that AI systems are developed and tested with diversity and fairness in mind in order to overcome these issues. We need to carefully examine the data used to train AI models as well as monitor closely and evaluate their performance regularly to ensure unbiased outcomes. For AI systems' decision-making processes to be understood and checked for biases or errors, it is also crucial that they be explained.\n\n#### Lack of awareness\nTen SE executives from Finland who hold middle and upper level management roles were recently interviewed as part of a research study. The objective was to learn more about their perspectives on the moral prerequisites for using AI. The results showed that these executives lack awareness of ethical standards. Many perceive privacy and data governance as legal requirements rather than ethical matters. These executives understood the need to ensure AI systems are dependable, secure, and able to perform successfully. However, they considered technological robustness and safety as key risk prerequisites for their implementation. They recognized the need to ensure AI systems contribute to more general social goals such as societal well-being and environmental sustainability as critical sustainability requirements. These findings show the importance of raising awareness and educating people about the moral prerequisites for integrating AI, especially among middle and upper level management positions that have a significant impact on the design and implementation of AI systems.\n\n#### Ethical issues in education\nAn article highlighting the necessity of ethical issues in the use of AI in education was published in the International Journal of Artificial Intelligence in Education. The paper highlights the significance of concerns including equity, responsibility, openness, bias, autonomy, agency, and inclusion in the design and implementation of AI systems in education. Additionally, it makes a distinction between **\"doing ethical things\"** and **\"doing things ethically\"** acknowledging that although many researchers acknowledge the need to interact with the ethics of their work, there is sometimes a lack of awareness and understanding about what this entails in reality. The study makes the case that, as these systems proliferate and have a bigger influence, more attention must be devoted to the ethical implications of AI in education. It is possible to make sure that AI is utilized in a way that benefits all stakeholders and respects their rights and values by engaging with these ethical considerations.\n\n#### Impact of incompetent AI\nThere have been several instances in recent years where AI systems have harmed people as a result of design errors. Loan denials and incorrect diagnoses in the medical field are two famous instances. When it comes to loan denials, financial institutions' use of AI algorithms to evaluate creditworthiness has been found to provide biased results that disproportionately affect some groups and maintain existing inequities. Similar to this, AI-based medical diagnosing systems have been known to produce mistakes that could have major negative effects on patients' health and wellbeing. These incidents have sparked doubts about the fairness and dependability of AI systems, which has intensified attention on morality and the creation of trustworthy AI. Research has been done in response by governments, NGOs, academics, and industry stakeholders. In order to ensure the appropriate and ethical use of AI, governments, NGOs, academia, and industry players have responded by conducting research and adopting guidelines and frameworks. This increasing body of research, frequently referred to as \"Trustworthy AI,\" seeks to address these issues and advance the creation of AI systems that put people's welfare and the well-being of society at large as their top priorities.\n\n\n#### Unveiling the Dark Side of AI\nThere have been several instances in recent years where AI systems have harmed people as a result of design errors. **Loan denials** and **incorrect diagnoses** in the medical field are two famous instances. When it comes to loan denials, financial institutions' use of AI algorithms to evaluate creditworthiness has been found to provide biased results that disproportionately affect some groups and maintain existing inequities. Similar to this, AI-based medical diagnosing systems have been known to produce mistakes that could have major negative effects on patients' health and wellbeing. These incidents have sparked doubts about the fairness and dependability of AI systems, which has intensified attention on morality and the creation of trustworthy AI. Research has been done in response by governments, NGOs, academics, and industry stakeholders. In order to ensure the appropriate and ethical use of AI, governments, NGOs, academia, and industry players have responded by conducting research and adopting guidelines and frameworks. This increasing body of research, frequently referred to as \"Trustworthy AI,\" seeks to address these issues and advance the creation of AI systems that put people's welfare and the well-being of society at large as their top priorities.","metadata":{}},{"cell_type":"markdown","source":"# Ethical Guidelines for Responsible AI Development and Deployment \n","metadata":{}},{"cell_type":"markdown","source":"#### European union\nThe European Commission expert committee has suggested seven standards for reliable AI. It needs to include human oversight, which indicates that people should continue to be in charge of and influence AI systems' decision-making. In order for AI systems to be dependable, secure, and able to function even in the face of unforeseen problems or faults. The importance of privacy and data governance cannot be overstated when it comes to ensuring that AI systems respect users' rights to privacy and data protection. Transparency is also equally important since it makes sure that AI systems can be explained and understood by people. Fairness, non-discrimination, and diversity should be taken into account by default when designing and testing AI systems in order to prevent downstream issues. Fairness, non-discrimination, and diversity should all be taken into account while developing AI systems in order to prevent the continuation of existing prejudices and inequities. The well-being of society and the environment should also be accounted for to make sure that AI systems support more general societal objectives like justice and sustainability. Finally, it advocates holding AI systems accountable for their actions and putting safeguards in place to mitigate any unintended consequences. By following these seven guidelines, AI can be developed and applied in a way that is reliable, moral, and beneficial to society.\n\n#### OECD\nFive ethical guiding principles have been suggested by the OECD for the creation and application of AI. According to this, AI should be created and deployed in a way that benefits society as a whole and contributes to more general goals like economic growth, sustainability, and social well-being. The principles include inclusive growth, sustainable development, and well-being. Fairness and human-centric principles are also essential for ensuring AI is designed and applied in a way that respects human rights, freedoms, and dignity while preventing the perpetuation of pre-existing prejudices and inequities. To ensure that AI systems are understandable and can be checked for biases or errors, transparency and explainability are vital. AI systems must be dependable, safe, and able to function even under adverse conditions.   In order for AI systems to be dependable, secure, and able to function even in the face of unforeseen occurrences or mistakes, robustness, security, and safety are also crucial. It is crucial to make sure AI systems are held accountable for their deeds and that there are checks in place to deal with any unforeseen effects they may have. These moral guidelines can help AI to be exploited in a way that benefits society and upholds human rights and values. There are important ethical concerns that need to be resolved as AI technology develops. The separation between creators, implementers, and users of AI systems is one such. This distinction is key because each group is tasked with different duties and obligations when it comes to guaranteeing the ethical and responsible development and application of AI systems. As AI systems frequently depend on substantial volumes of personal data to operate, privacy and data protection concerns are particularly crucial. To prevent mistakes or unwanted repercussions, it is also crucial to ensure the dependability of AI systems. Concerns about bias are another ethical problem associated with AI. Prejudices, such as gender, can be reproduced by machine learning systems unintentionally or intentionally.","metadata":{}},{"cell_type":"markdown","source":"# Key Considerations for Implementation\n\nThere are a number of important considerations that must be made when putting an AI application into place to guarantee that the system is created and used in an ethical and responsible manner.  \n**Data bias** is one of the most crucial elements since the calibre and diversity of the data used to train the AI system will determine how accurate and impartial it is. Another important factor to take into account is **algorithm bias**, which may lead to unexpected consequences or biased outcomes. \n**Fairness** is another crucial element because, in order to prevent the perpetuation of current prejudices and inequities, AI systems should be created and tested with diversity and fairness in mind. Additionally, **explainability** is crucial since it guarantees that AI systems are transparent and comprehensible to people. Furthermore, **privacy and security** are essential because AI systems frequently use personal data and must be safe from breaches and unauthorized usage. The decision-making processes of AI systems must be **transparent** in order to be checked for potential biases or inaccuracies. It is possible to make sure that AI applications are created and deployed in a way that benefits society and respects human values and rights by taking into account these crucial\n\nIn addition to that, some principles and guidelines came as part of research in recent years\n\n#### 1. Implementing Ethical Requirements in Software Engineering\n\nAs society becomes increasingly reliant on Artificial Intelligence (AI) and AI-enabled systems, the implementation of ethical requirements in software engineering (SE) is gaining momentum. However, a gap exists in middle and higher-level management, where ethical requirements are often overlooked due to their perceived limited impact on human lives and financial value. It is observed that lack of priority given to ethical requirements, except for privacy and data governance, which are primarily seen as legal obligations. We need a practical approach utilizing an Agile portfolio management framework and the ethical risk requirements stack.\n\n#### 2. Introducing Watermarks: Safeguarding Against Manipulative AI-Generated Texts\nTo retain the distinction between text created by humans and text created by machines, watermarks are crucial in AI-generated material, especially in natural language outputs. This is important because machine-generated language lacks empathy, which can result in manipulation, the propagation of incorrect information, or emotional discomfort. Recent research has demonstrated that AI agents can learn to nudge or manipulate depending on their quantitative parameters and can create misleading methods independently of their creators' intent. This distinction can be enforced by using a watermark code based on equal-distance letter sequences. While there is no such code in writings authored by humans, its presence in texts created by machines would be beneficial from an ethical standpoint. It is possible to ensure that AI systems are open and accountable and that people \n\n#### 3. Infusing Morality into AI: MEC Algorithm\nPhilosophy and artificial intelligence have both researched the idea of moral AI, and recent breakthroughs have made it more imperative than ever to integrate morality into AI. The Maximizing Expected Choiceworthiness (MEC) algorithm, a strategy for forming proper moral judgments under moral uncertainty, is one approach to this. Recent studies have concentrated on creating useful applications of moral AI, even though the majority of the studies now available are just theoretical. According to experimental findings, MEC can create output that is as appropriate as or better than that produced by current approaches and that coincides with common sense morality. To make sure that AI systems are created and used in a way that respects human values and rights and encourages fairness and equality, it is crucial to build Moral AI. It is conceivable to develop a more moral and responsible technology that serves society as a whole by infusing moral judgment into AI systems.\n\n#### 4. Promoting Equality, Inclusion, and Human Rights\nIn order to ensure that these systems are created and used in a way that promotes equality and fairness for all users, the ethical use of data in AI applications is essential. The data that is used to train the algorithm determines how useful AI is, however it is crucial to understand that this data may not be objective. In order to solve this, AI systems should be created and deployed in a way that considers the possibility of bias and discrimination and that supports inclusion and diversity. Any implementation of an AI system should put the demands and viewpoints of its users first, taking these into account as the system is being developed. Leaders must determine whether deploying AI systems is a violation. It is possible to guarantee that AI systems are used in a way that helps society while upholding human values and rights by giving ethical considerations a high priority during the development and implementation of AI systems.\n\n#### 5. Ethics as a Service: Bridging the Gap between Ideals and Actions in AI\nEthics as a Service (based on Platform as a Service model) could involve distributing responsibility across different components. One component is an independent **multi-disciplinary ethics advisory board** responsible for developing an ethical code through discussion with stakeholders, defining principles, and providing translational tools for ethical design. They also conduct regular audits of the company's behavior to ensure ethical conduct. Another component could be **AI practitioners** themselves who customize the software, define principles, use appropriate tools, document the design process, and justify decisions. Implementing this approach could address some limitations of current approaches, but its effectiveness in practice remains uncertain. Continuous audits are necessary due to the evolving nature of AI systems, but they impose financial and administrative costs. To strike a balance, a proportional level of AI governance based on risk levels should be introduced. Further research is needed to evaluate translational tools and the impact of AI ethics initiatives for improvement. A pilot of \"Ethics as a Service\" is being trialed by Digital Catapult, and others are encouraged to develop similar pilots to address important questions regarding board selection, compensation, audits, decision-making responsibility, implementation across different companies and sectors, and the varying benefits and drawbacks in different contexts. Public reporting of successes and failures is essential to establish a knowledge base of best ethical practices.\n\nEthics as a Service can ensure that AI systems are created and used in a way that promotes fairness, transparency, and accountability by offering ethical guidance and tools for AI practitioners. Although this strategy still has some drawbacks, it marks a significant advancement in bridging the ethical concepts of AI with the actual construction of AI systems. It is conceivable to produce a more moral and responsible AI that serves society by continuing to develop and improve Ethics as a Service.","metadata":{}},{"cell_type":"markdown","source":"##  Current level of AI readiness in countries \n![image.png](attachment:34cbcd9f-c873-4a39-a15f-9f5e54d44dd1.png)\n\n- At the government level, Singapore was the leader followed by US in 2021. In the technology sector, United States was the leader followed by Germany in 2021. At the data and infrastructure level, US was the leader followed by UK in 2021. Then, US, UK, and Germany are more favorable for investment, business, and research in AI.\n- In terms of AI readiness, United States maintained the first position followed by United Kingdom from 2020 to 2021. China and Canada experienced the highest growth (resp. +5.34 and +4.572 increase) from 2020 to 2021. The AI readiness of Germany and Republic of Korea decreased significantly (resp. -1.714 and -1.145) from 2020 to 2021. Then, China and Canada have a high potential of increase in the next years, also making them favorable for investment, business, and research in AI.","metadata":{},"attachments":{"34cbcd9f-c873-4a39-a15f-9f5e54d44dd1.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAW0AAAE+CAYAAABGG0F7AAAgAElEQVR4nOydd3gVxfrHP7tnT08njRR670WkF+kgICqKyhUvFgQUEBt2wIJcRFERRQEbKqJU6SpNeu8lJIQQEtJ7cuqend8fJyAtlCv8LtHzeR4ecnZnZ2ffnf3uzDsz70pCCIEPHz58+CgXyP/rAvjw4cOHj2vHJ9o+fPjwUY7wibYPHz58lCN8ou3Dhw8f5QifaPvw4cNHOcIn2j58+PBRjvCJtg8fPnyUI3yi7cOHDx/lCJ9o+/Dhw0c5wifaPnz48FGO8Im2Dx8+fJQjfKLtw4cPH+UIn2j78OHDRznCJ9o+fPjwUY7wibYPHz58lCN8ou3Dhw8f5QifaPvw4cNHOcIn2j58+PBRjvCJtg8fPnyUI3yi7cOHDx/lCJ9o+/Dhw0c5wifaPnz48FGO8Im2Dx8+fJQj/rJoZ5+xc+RYMXHxNmxucSPKVCbC46HY7rkheZXkOTlwsJicIu3cNneJm0MHi8gu0a5w5I3BXeLm8IEiMoo13MUudmwuIN1+8897S6N5OHa0mHyntx45Clzs21fI3oPF5DvOq1tCI/loETv3F5N7vs2ERuLxYnbtLSQxzX1J9u4CB4fi7bg1AEFGUjHx6erNvSYfPm4wf0m0V89O5J579tCs1XbadN/DE6+eJOlmCZ7LxfsT4/hmffENyExj8efHub3TLt6fn39ua8aebHp13828fa4bcI4rk7I7m9537GHWJie5CXmMGHyENamXCs0/ieSdKXS/7yjxBQJHch6jnzjIv0YfZ/jQgzwy9iRJRQCClTPjGfjvw4x66jAPvZRIhgqoHua8F0e/hw8zZmwc9z1wkM+22i/I/8MxB7jzqZOkFXpfABt+SqTX/ce5+Xfbh48bx38t2hm7zzDs7dMotUNZuLQ5bzzkz9KZCfR7JuVGlu9PNDtffpePf4zlr+dV7GTvIQftWprYtSOfTKd3s8GsEB1jIsgs/fVzXIXQEAOVKxnx1wsimkSw60RrBtUw3vTz3sqs/DyNap0jaB4us+iT43y52cPs75qweHYdctakMXerDfKKmTwji4EfNWT1nJqI7RlMX+PElpbJuHFnaPVoDTYub8KgxoJJr572ZixUfpxwlNk73dSuYkSvA5Doe08MsSU5/HDof3nVPnxcH/+laDv48K3TVGgVzm/f1KZ3+yBGv9OYdQtuY9N7MQC4Chx88tp+giPWENJoMw9/cMZ7aFI2jSr/xoC30gCIW5qAZFrL+zs9sCcJKWg9Q8cdZ0CnzSiV/2DqknwoLqZDqz0YIvWM77WBe74oZNPMwwRGbuTxV/cTVWUj9eqvI6jZQfJKS7jpne1I0dvZnHhp6zV+Tw6LDxmYPrsm2Rsy2RTnOLdPcBkXT3YR93dcT4Ne++jRZxNRbY9RqKrMfu0AEZXWElJpC++vLkAFcLuZ9vIBoqqsIzB8Dd0fjed4aefg1P4MWt+2kYja23l7bg5Ogw6DWSZ5Uypm6Ve+PKkS90si/qbfee2rJLrWXY+p1ia+2+TNQMsu5vUhO5AC1xHTfDeLj3jLbU8sYMTArQRVWk9A7CbuH1tqa01l8vBdBNVYR0D4H3T49wnSLuNdSt+RQZe2GwiptJ6galt554c/ex/LvjhCnWob8K+4lg5jEogrdSck7Mrmrjs2ERi5lpp372fxfm9ZNnxxhNDoDQwee5CQsA28t6wYx4lsWjdahz5wHW0Hx3H6ch4Jdw7vrPVw/10hyMCDk1viTr2dlrEKflYJi6QRf8pFTkI2+51W7qprISDWn9trGdm6LBtLbEUS7V2Y9WQF0OsICNLjSSlCBaY8tY8Ju520qGXC44azt9hcw4/b65tY/N1pHJcpkg8ftyL/nWiXqGQXefCvF4buvM3NO4cQECQDGgtmn+DNZW5+WNOGpe9WYvdn8XyyQYUgPdVr+BEbbgDAFGCiTl0LYVYZ/PXUqmrAWWLgja+a8GpHPV99l0GqwY+ZM2pCscYjExsze3AAwmygUpRMYFA0G7e2YuWsWtTQSvj1iPeJnP27St+Ho2hQRX9R4QUHt+Xi3yyA2hVDaF3Fw7LtJQCU2b7Wy1St5QclgkdHNWb7yuqcWHCS6VthybpO7F5WlZlj41h91A35dpJcJmYvasXhjY3J3ZfFvN8KAJj6UhzOBtGs+70hlQ1Osh0aMmDy11M7xkKwRcYSbKR+AwOZySY+X9KIIQ0U3v8mCxDMeu84vxSE4iroxKePWXnpmQSygR++OMniOBO/b2vH7p9rElVSxElg73dJTP1DZcbXrTi5vQFtQ93sPH7xS0zjjTHxFFWLZueutqyaGk1+YgGZgONgOiOn5vLo+w3Zt7QBjlVprDlgA5w8++xRXE1i2barDcNrqTw7MoE0F4RFmqlUUYcmKrBzX0seb6rx+BPHaD2qBa7cdtxeksfIt9MucUkUbylCC9ITGXBplfzx00SOymb63uGHI0fFowe7SwNJYLIIHPnOC9In/5HCtwvzieobjQK06FOZhfMa07uyDrtTO+9GG2gUZOREqp1s+8Vn9eHj1uS/E21Nw60JUC4vc2qejTWriuhwbwy9Gphpe2ckD/e2svzHM2CW0QHi/AatVvpbAw/QrGsIjapaub2JFVuhm4ISqFbbiOoUhFW2EmyCEqeGqkkMGBFK9Yp6KrcOoXKIhx3b8iAvm50FOvreEULgxVfoVFn1ezEtW/gDEp27BLJ1YTYAklS2W8Tt8BDTKJD+3f2JDVBYuaUIl9PJa2P28cSrqeSl2vltVxGEBfD++7VoZijiiymp2BQdDrsEahGb9kjc/1Ao9WItDOgbSYQk8JQOAQhRagMBtkIPHR+OoHqtIGpXMVFQ6AGXk72nVHQFefTuu5f3f84n62gBGw6r1KhsQikp4fkRh5i4vJjH3q5BVSA82kSAcPHhuCMM+yiTO4ZWpl/di19iMrWq6jm1N4thw4/wQ5LEq69VJhxY/3Mm+moh9OsYQvXbwlm6rgVDuwdwel06cXkGnhwcRd0YE48+WZWK2bn8dsqNnyJQJR39HgujerSJ4pQSTrgVdi8+Tre7D7Exyc3htTkcL7hw7CMt3YM50EDF0AtdREs/OsSERcXU7xFOtxoKxXYNiVLdlSRkGXS68+5bQhb3P5vEIT9/pjxVAYCOvcOoa5bIK/Fc8mauWlWhOMlJZoFvQNJH+UD5r47yN1OvqpE9W9JIowIVAYSbBQuzia4VQstKMi6nwGA8q5gSBr0OV6ELp8crTga99+kxGr1CffZZkiWFYKu3WG5VeHfIoDo0BOBRS9VegISRKOvZQpkZ3j+Qt7Zm8YNNh1+YmY7dz+08R86xdLalC0o+PITxVQ8hkSb8dDK/ZELnsqwhgVuFID89XknRcLkF/jF+DHusIsGKB50kE9MokIK4dHrcFYfcOIKh9wcTcizN63KRQPWAWV9qEx1IEpdxxghUzUhsZOnfHtDJ3r/ddhW/hpFMesCPPCfohETtaB1Rw2uz4fYKfLk0m63bs7mjdipvL27HsC4xrF9gZd7KTNZuKeTR7jvo9nQjvno++IIzPv9dc1qvSOfndXnsXHCahl9l8dNvzdBJMooCOgQgERpiQAc4nRpCkzGX2ktn1KGXPOTZQJIEOvREVvBep8uj4ShR6TysDj2jVTRFRlEUKvld+DYtKfEgyRLyOVEVzPswjmc/zaNJr8p8O7kSfgCBCkanhlAkcAvycwSmGG+vLetgNg8OP06+fyBff16PDtUufEFdbm6TrAO0yzrFfPi4Jfkvfdp6BgwOxxaXx+jRJ/hizhmmTIzn+eeOMnlJAVKgmTpNjGxbnUWyDfIT81i1oZBGHSti1IEmCZLSnHiAP1YXoFll78MqeYVM07yPkCRJyJKEBGgSqKo4V2Cd7E1/fme/db9I2FfApwuyiWgWQa3LNJzX/ZKPrpIfQ0ZWZ+p/avHKo5FUCRGs/yUfxSKjlNHalmRAgLd9qKNZFSN5OS4qNQ/ijkYKa3YVodPL7PstgyMBQXw5pw4DquvILBEoMqCz0KiGxrK1hQDs3ZpLLpJ3HyCXXrvXBpr3hVVqAwRgMFIrQk/KKTvNW4UQbfWw/Zgdsz+sWJDK3CMyb42vy6IPqlAnGrKSbRzZk8VX6+w8+GxtfplXhw5VdGSdLLnoylx8+OFpTvgH8+F7Dfny5QgMbhc5KSq3d7SSHl/M5hNOKLHx3OA9vL+skKhmwQTrHSz+NReAtUtTSJQD6FJDodjlvQ5PaRciIspMpE6Q64H2nULJSCrhSI6GWXdhKWJjFZxFbnIKvHc0cXMaX87PpWqzCOZ8VJkKpenCqwVTw1PCksN2yLZz4LSbZh0DADefTj9FfIaO8dPr0a+WnotlWjpr4/M4k+zBFGU411Dw4eNW57+uqdU6VeLzF+2Mnn6axXNUzFYDnXvH8O7QEEDiyaeqEf/ycVo13YQxUKJig2gm/9sKaDzYO4hhHxyj0W8Z1KugknHKQW6JBno3xxNs5BR5R8vyMh3En9JR4gCrxZ9mgR5eHLSV4++14l5NJT6h5IIBJGtMID0aa0z+VeWzryIuLbStmGnfZ+DXpTrPPx2LCQAXSTsz+f6HFB6qF0pykoMc20XTFt0aaadtnFbcaHjfdL2HVmHVtsPc224TuFVqdanEk3po2DGc2p8cp2OzzbRoHESgx8m+oyVAIM9PqMKAMQnUWnWKBtEKKckOsgs1nMJNfIqdPLuGo9BFQoKdIheARtYZOwmnZEBmyNNV2flMHGF1stCcKvc9WZtgnUSsSeX19w/z+fsKFpdGVJ1gHhnoR8QJF5uWxPHxZycJlgSBIRbeeDbqIqMYqC6XMOKp3bypKOhtHu4YWIlWTRUCqczIFpmMe2Q3b6HiCfXjTT8ZS3gAk5+LYdQHJ6j+5XE8YUZGvVubBlaJjdlOTiTaKCy9MX5VgnnjuWgeHrePSu/K2IWeqbObcLGTpkJTP9wF6STnuAETX3+SyPqjKhH5OXRps5XMM25uf6IWi16N5NVnw3h56G6+0GQqd4lkVF9/Etec5OMfC9D8Fd4dvpvx+SrZBQp7klpRqfQcWaftnCg24TrnCVE5XOCmUqw/Yf7XVO19+PifIwkh/mLPUCUvX6DoZfytukv25uW60UwyFSwX7ivKc+ExGgiyQG6WC0uIAZOkkZmnEhCgx6SXcNpUCh2C4BB96dvFQ1aWin+AAb2skZevERKmv6C78P3b+3ltp5WTS2pcUhbNo5Gb7cYaZMBs/LPJpTpUcos1goMVivJVLEF6TOf7SYWgqMCNppMJ9L/wPVeQ50JFokLweTKkqmTkaUSEGcDpJssmERZc6vJxeSgqEYQEKxTlulAC9JglQXaOSmC4HkXVyMn1EBiuRw/YilVKVImwIB1eX5EgPcON0aoQfIGLwUNWtgdJkgitcL4kamTnqghNIiz0Yqk8/3A3WXkCRZEJDrrwGh2FbopUQUipe+TPYzSyct34hxkwlTq4VKeHgiIPARX06M9r1nocbrILNALDjZjKGDoY030zxf1rM3NEKIW5TlSdjAx4PALhAZ1FIdjqvWZPkZtcVSYs2FsitcRNnhsM8p8uNLcqCA83nnO92Qtd2DSZkEDF2+JOK6Dfgwdp+9JtjO1pKts2PnzcQtwA0b41KE4p4PU3kli6rYTHJjXg5X4B/+si+bhOji2Lp89EOytXNKBm0M2PsLB5bjz3vVnIwaPNz7lffPi41fnbOPL0FoWmrULo9O+qdGnrE+zySPU7KjHZmY/p/yUijiCydggfz4zyCbaPcsXfpqXtw4cPH/8EfFH+fPjw4aMccVPdIxc34q+0eMXH/4bLdbR89+kaEGXP7b5W+52zfem01r9YIP7M7ta7f2V16G90Wc+e52r5nl+e671f/2v7Xpdov/TSS2RnZ2Mymcq8CWeRJB0GoxGjQUGSJDSPG5vNjkfzeWNuFjqdjrS0NPz8/PDz80PTrhxxUZJk9AYjRoMeWZYQmgeXy4nT6f7HLjaRZZm0tDQqVKiAXq8vo55L6I0mDIp80X4JSRI4HQ5Uz1WiXUoyZqsfZoNMSVERTvdfCzms6I2YrVYMkouc3OIrxGS4cciyTFZWFkajkYCAgDLrm6xTMBgMpQunvAu1JEBV3bjdKtoN9NDqjWb0ssDhcFCW1Jyt94pOAiFwu1241avZX8LkF4BVL1FUUIDrBumYJEmUlJTQuXNnHn744Ws65rpE+9ChQ/z0008YjUZUtexlvzq9EUWGzIT9bNl/HIcbAmPr0KttQ4TbdcMuGLwVQq/oEB4Vt+r5x4qNJEkYDAamTp1Ky5YtadOmDU6ns8z0sqyg1+soSotn445DFNqdmPzDadGmDdHBJlwuJ3+n0Y6zdVJ1ufCUcWGyLOPxeHj33Xd58MEHqVWrFm73RbFaJAlFEmSmJpKcXoBiMOEX4I/sspFXXILATLU6NQg2G9A07bL1UZJkFNnB3k0b2JuQR8fefakWYcWjXj79lZHQ6STysk9xYNtGDmVH8ebbPa54728EZ+vbzJkziY2NpWfPnpc5p4QsSziKcjiVkonBbMHtKMJud+KRTFSp14AIq4LwuHGplxd8SdZh0CsgNJyuskMXS5KEYjBQlHKUxCI/mtaJxuVWL9PblxGqjeSTyaiKEc3twFohiujQQITQLl/nJRmjQc/Jzd8zY0MWw194ixoW1RvK4y9iMBjYs2cPK1euvOZjrku0JUnCZDIhyzI63aVzsv8knRcG3MXsfTB8yP2EKUXMfGMIr9Z5jk2/TOAGBFf9E08ha5esIbx5RxrEhNzInMslOp0Og8G7rNtovHKo1zcfasu0P3IZOHgQNUL1JOz6hsGDdvDhljQerff3ChPrzjnKnJWJ3PWvO7nS3KLz67Ysy2XYMJ+vxw9nQWoFmlY0sWbJQmzV2zOoTRQrl+zhue/n8ViHulcpkeDXGa/wyjwHKw7cSc0oPVd8pK6CLes444c+zhblUUY/3uOq9/5GoSgKer13/n/Z5yzk+7eG8NlvJ+k4YBitKkvs3byGFasP0e7xl/l55qv4l3ntbnb/vpz8iAZ0aVj5imVRjy/lkREfcTD+AINmbOfNXlUvn1Cn58jqz3ly4rdYq/VixjfTqVLxCmsYgGPLp/DofzbSpEVjXDoZWW/gRllYlq9zaFFcB3379hUul+uq6QZUQVDrAZFxwdZ94s42LwtH6S/VUSwKi+2XHqx5rpK7Ktzan7/iF4wXECCW7c25NKWqXj4LTbv89r8BU6dOFdu2bbtKKo94767qAnMdsTHtQlv8OG6U+O7A+bbURGF+vjhnyYttd+5+/bm9pLhElGVhz0U73C5nGSk14XT9WReczkvrnctWJBzuyx+tXlRPJ3UOEnLEIFFGjfgzT5dLjB8/Xhw5cuQKqXLFkp9+EGdyhRDODNFOh6j32hohhBB/rFwidh1LF0JzCpvz0rqsOkqE3eUtRdaSlwXUFX+csgkhyqr3mnC5L7Wmai8UBcWOC7b9564GwlrlqQu22YsLRMllymEvumj7xbdVK2PHRcycOVP89ttvV0wjhBC7ZzwnADFr95/5nVj+ntCBiLl/2p8JPe4Lrjd/7xIRDWLU3EPnl044nRfdeM9p0TEI8XmuEB8Pbi3unbHvygVKWyciJES3F7+7dJ/HfUE9safuEg0MiGGr8y6b1bk6rbmFw3X92rJ7927x9ttvX3P6Gz4QKXa9zfwkePbDoYRfsKc+X/w4FiMaa759jx+2pGL12CkMbMT0KSOxxW/l61nfkmrozOQ3uvDTtClsPGlkwtRxFGxbzJfzVmONrkVx3Ab2lYQx4oVx9G8Rw5HDcYCOBT/9SGNdMxYvWcABatKieBM7i6OJsghScnPpMHAk9zV0MfbFqVS68wmeGdjx7zNJ/TpxHFzA60tOcMfTc2gXeaHzs/cz41AsQQC4U3cx5vVpWEMCOJ7p5MmXJtMzOpXXX/mAM1Iwvf41gp4RuTz3ykc0HPQsT/epz3dT32H74ZOkucyMff9zmmrHmPn1t8RlqgQaVA6a2/BCrWS++T2Nlp1iWDTjR/xa3c9/3hpDYMo6ps9cwAlzU1poG/lucyYPjRiDX+IvzPplPzUHvMisZ/oAcHTdt0z7YQvFJbm0/NcrPNW7Lou++IBlG1No36cOy2bPx12hJV/NnUyIO5sTuQqylsy3P6/mvl6d8fO7csvqygTT774HAbClF+DUwF2YA0D7nv1I3reCF0cvI6MgjypdnmDC4M6A4PCa75kyawGnzshM+m4OlVwewMYfK2Ywde5CrK3v4Z03xhCQso7psxdyxq8hde3b+HFLGnePeJnn7u8AuFj3zQd8szULqycHV3hbprzzBIG4sTlc5/myi5j68oucdFopSMuietd/8fpj3ZCwMfe9cayNd1JYmEPDdt1oUqsSxuJ45vyynYjKDRn9wnBOLP2Q2VvyGfPiqzSNLXuN/7UOytnc3ti3joJ8wBuwrFrvgQzpMpVZP31Gwbynydm8mI9mzWHPyRL6DX+ZFwZ2JOVUPG7g+K/fsrXmCOqE5fDJR9P5fXsC9XoP5YOXBmGWATmYqtXCmP7waO6+YwifDggnIysXRbiwuyEyKvLCoKR2Gx4ZVLvXpbNn+RzmLN1KbIM6nNq8gmNSLZ4Z+zK9GlfkwJZVZLlg+1fvM6+kHbUrKSyd8zMFoU0xnVhKzTEz6WbbzOQZ37M31UOvwS/y4sPtbt7UvOt5I1xLSzvhwzsFGMXr849fdn/ihk9EAIj3NuSJ06s/FP4g7vn8kBAFB0TXCIRf83FCCCGm9K0q0N0mjruFsO39SZhA1L3zebHj4AoRC6LpgIlCE0J8O6aPgAri6f/MESUZp8SYATUE+ItRL7wg3p7+jVj5em8BiA+3FAohEkW9yBpiRcrVewvllWtpacctnywA8fCkK6XLEU82jxCR7UaLTKGKkQ0Rfk3uFSfsQsx9to2AiuL7nS4hRKFoaqkgVucKseaFtqJq92dFocgT/cIR9XpMEkLki9HtwwUEiCeeGCzun7ROrJ80QADi2a9Xip/ef0IA4t3lCULk7BVdqxkEwb3Fvh0rRIcqikBXS/z82x/i8fbVBTQUQgiRvnWmCCdC7BdCfDq0h4BKIksIsfCNuwUgxi/dKr57c7AAxJtr3EI4UsVDdQKFIbKL+PnXzcJmL7u9fW0t7T8pSTsuWsiImqN/EkII4TyyQtQNDBQLk11i2aSRAhRxQAhx4NuXhbnKneJk3A7xSK+u4uf1SSJr8csCLOLZGUvEws9GCUC8vzxOiPyDoktlSRDbT2zeskYMal9BUKGbsAshEtdOE4EgPj+RKRJ/+UiEgnjiix1CCCHG96glrFWfFkIIsXbKQAHRYvVpTWx77x4B/mLaXo9IXPiKMOgNYr1biO9G9hCAmPD9LuFynxBNQFTq9pAoFEKc+epuUb33O6KsftBZZs2adU0t7Y3TnhKA+GRd7nlbC8Ur9zYTECbihBDzZ38utu7fJ0Z2ryzQtRdZbiEOL35PBIDoO/xNcfT4KbFs/hyxevM+8cVzdwpAzDuR780qb6/oHoHA2EActAvxw7MDRYWIcFElpqKo2W6IyL64QIkrRJgOcceo2UIIIVLXfilkEG0ffkts3fqjCAfR9rGPhSaE2D73FREKov3w6WLLjkMi7fha0UBGmKNaiycee1z8sO+0WPflBLFiR6IY36uCILSZ2HRpx79MrrelfcNfBjEN6wFO8o8fvcxeF2vmLaSQigzoEERM94H0aRzE2kmfQUBD6taKwGz0tn9b1A1HNltAA3OTZoQBbTv0pUWDXjzXHnKy0igAOnVuB4Rz3933YQmvRKN6MVB7MB9NnsyrIwbT880fqA38Om8+h+bPpPGoGfSK/iutrPJPWHgUJiA58cC5ga/4LYsZO/Ix7u3fh+enLiI/cStzdmfQpf+jhKFj4msPULxvA7/uKOCB978nijQWbFhO2pdDiJqwk+7B6YycuY3crASmvTMDXfWqBFkL0AikUUwUAfU7858vvmHe2E6EV64O1GFIv57c98hIWiuQeCYbQmpTr2o4xo4P0rhFL+65pxOE1WZA1/Y8/eTdwAnygVXz5pGJjcVfzCCuQCU4Ohy7B+o2qg/oeKJPKwY9+QSVjJCckADGKJpXCiE45nYGdGuD2fQXnMdl4n2Ulq6Yw9GCAn6f9SmbT6ZQMbAy6Vm5THvvXWIHPk6VWi34esVvDOhYmaISO0jVGNi7D3c/PoHmwKn0XAisT73qoUR2vJ82rTsz+F+PQk46TmDj8l8ooA5Dq4VRte9dNGviz7dzfgJAJ8ugU4B8ZsxejbVRHzrHSLR8/hkCKGLXD0tIz0jB5TFhViDILGMOa8vgh5qjV6oxc9ZjJP+2i53HUnnx49N8v/wVDDfMPuKC/7zkkZ6bDGFdqQX06d8TR1Ym/iHR4CmkSIOqNesTCXS893Hq1KzEnXf2JkDNQbV4W+v5mgESVxMd3IIXDgheb3aIpg3votf7P/JC9yq8/fMxjm/88oqrXgUQ1boVYUC3Tt1p1Wogz7aBzMw03ECjDncRZYYGd/aldYv6RNasR+VQAw3ufZMvZs3kwcYxNOvxEMG2U1gr1wK1iOKLg2neQG64aBs7j6QWMOvnOZy8ZK8Bg8kIZFNYukUIDTUiErDjcKoIzTsrpaREh6zoUHQA3sD3btXblbF7QG8woQdKiksAHYo3oXf6kPv82H+BPPdYNVYsmMqY6ccYOqTjjb7kckfwbV25p5rEhpXzOOL9qA412/TngQYyC5cs57jcgCCjggIU52UCZz/DpsPPKgFVeHlgDL9Of4UuHzr44vmqgEyApsPfL5b+Qx5n2uLdbF4w0RvwSZLQXG6KzhZACMCD3aXisdtxCjCZjHi/hgG4vfdZdWsgvLMK7A4bYMQIGC0moIgW/Yfy0pRvOJO8k1gduFQ3oGEHsNtxe8Bk8gaCsjlVZOnGv6x1Ol/grs0AACAASURBVB2KDIrBm7dZ7x1mb97zQUaPm0Z8TgLdQgWqGY5vPHzBsXpFAeHB4XKCvRgnZwfzBJqQ0Ert4Dj/2k0WIO3P50cSBAZ5B+AVnYQk6wAFk1mPqyTLawskBFBiCqD1sA+4u2YQn7/7DQkVOrBq43KqlObVvNdgqnCcsUMGkdVxJC1vqJ28jTG96c/AXLm71jF3XTZPfPgapG+jU/VaLD3tpGXbpqBTMMrgdrlxAQaTFcjgye61eWpeIo1bdyQEsFpkTiYmcQaVY6lO3ly3hw7FK7m99V0khXahW6vSYWfPRbPdFAVZAkWveD1KHo9XY9zebyoVub0a4w1YpqIJ8JydSVRaJz0uW+nvI9SrU5+ZW/Po3qYOSBIGC2BLYdvu4zfQil5ugtslll9+/xDT3vlU86vNrNX7yc9N54/5Uxnz2le0f2AQOtx8MOsQzoRNLD9QyMhxQwCF6IoBZG+bxOBRr/DG91tQ85NJzwEyU0gGUtPTAbDlQkpKPPke8Pc3AYeY8M57OPOzST6TCkknyDnvTdd/2OsYUg5yOqoTnSL/qZ7s84lg5sYtVDy9hgb1OrDuqNeuZ6cwBQT5Q3Q7HmhmZck3k0gFPp66FKXNQHo09T4E3YZMoPjkUXS9HsEb7DWccY81ImXjdD6YsZSM+D/4ZM4GwMWJpBMUZySR4Q2/zcnjCUAyCUVOJLebYg8kJCYBRSSknsF54gQA2WdSISOBLMBlcwB5HAV69uwLwOjBz3Lk1Em+nvE5RUD6qTOAIAnA46BIhfjEUwD4+7lJ2/kp0+b+hsN24756n5FyipMqHD10BICe3XoSBrz85Ej2nkjimxmzyZcq0Kdff9jyGg+9OZc/1qznpB2Sk08CZ0goKEGogkLg+KkkIIfTyZlkltrB7rYDCcTlQdsefYECnl2YhufwBjbvLWbkU48DBRyLT6c44Tjgx9MPdsd9YiEL9rk4Me9righnyFNdKNi7nA2n00g/eYCExEQWffkxO5K83yCVoloxenB1dm3bzYin7r9hNgJIO+29lkMH95CSeIAZEx6narth3D1hLl88VI+TcQfYn+/k4L7tzPl+PnhOEG8Hi0GPAZg2ZTxbl/7K3uPZHD68nwXffkMucOiIjaotWtAmDEY2i+DTNRqvThhNwrZfmDF1Mm0aN6VVt4Hszbnwuc9NOkGGCkePHKcA0FKTSAeS87xjE/Z8SE6OJx8oSTvGGQecjM8CQM08Q0Kmi9ykRG9mjn2kFrlIPrCR9z/9GfKKKMpz8fnwTrTueh+rLm29/iV048ePH3+tiefOncvAgQOvMt0PQqu1Yuyro6kf5mTx15/xxZxFJDgqMv6dZ6hUqRFD+7dmw/cfsmhbDs9O+5HRnSsBOjrd1R9XbjZ+de9g4tghRFWvhCJ5SD+RRMMW7akVriOyXlNq144iICgUQ1g1mrfrQBV9EanuIFrUCuBUTgV6dK1CXIqDho2rowDWijVIXLOah155naaxwVcse3ln27ZtxMbGEhMTc8V0ev8Ynhv/Gq2CMpn+0Sf8tGgRB53VmPjRTMb2qQwY6PvkGPwzdjJn9rfInZ7jj6/G4lc6mBMaE0PdOnUZ88h9hJSG3a3RYygdYnVs2bCCVTuzeOCpYfil7SJBX50e7RuTXyRoXMfCus2n6NynB/YcGy3at6BSbAhGhwehK8YS0phut/mTW1SCPiiWTp3qke2Oov3tDYmMqkxWios2ve9j9P0dOL59Bct+3Ub1Tg/RoaqBP/acpn33nhQm5lK/RRvqVY8hVC4krE5Teve9gzOnktBXak67lrXKHITWNI2NGzdSv359wsLCrmLtfOZ+PZdmfR+gS3AWaboqNLitHaMG9yQnaRuLFq8jqHFXejerQd0OA+nVpCLrFn7LzjSFbq1j2HUwj449OuC2CVo2r0ulKCM6h4xOyUMf1pTONQ14KtalefVYYiNDOZWt0K1Pf0Y+0I7fpo3jp93FvDpjEcM6RhK/dz15/tXp2aMqOfpI7rjvMTpWMbNkznQ2nInlm1WraB8OuqJEfly8lwphfiDcrPz8Y6Yu38a9jzxCuFGHosrsK6zMe6P6XtP6nL179xIUFES1atXKTKPZsojP8tCqUw84uYElv21HiWnFrLkLGNK9AQDBMbWpF6kjOz+IF8ePo0nNEIqLAmjWtj0NavqRdCSRDiPG82DrGArPZDPohQl0bVYTCvK4rXVnnnhyCBEWJ0vm/UBGZB92/P4zHapZcGhmhoz7lE5V/3T0qIVnWPDrdjr06E+zSB0evwBO7j1Ao3adiTRpxDZpSo1qkQT6BWGtVIfkTb9Tp+s9xBYfQY6tTd7xPYQ0bEWLaioF+orUrNyBBsoZcqz1GffOS0QEBRESEUHHNi3xj2nCA32aXHF6YFpaGgkJCXTo0OEaLM7NmfJ3q+FM2CCeHP6SSHJcPW1559qm/Pkoi+sdiCxfFImXezUQD3577NyWXVM7CmPD+0V66e+f33lOTP5u6zXneK0DkT7K5qZO+RNCXLWVfUuRvIV+jzzD8Uw3z336C5X/XutFLoskSdc/Wd/HORTFG3ahXNXza8aPxg0jeXfMA3RUxmHJPcTHX2Qx85tlZE0fSsPJa2nUrj8/vtLqmnP01be/jixfHA7hylyXaJtMJtLS0lDVS5eHnkVcEovh4u3ebWWlu3FIGPURPPX0KPTVO9E8RiEpKelGn+SWwmw2Y7PZKCgoIC0t7dIl2D6uiCRJaJqG0+nkzJkz+Pn5XTFcQ7lDkuk5bhknBm5iyZrdSBXrsGDTboLlEnIMDzGlQh86dOuG58xpktWri4jBYMDlcpGdnU16ejoul+v/4SL+Xuj1ejIyMs6tYr4Wriue9qhRo/Dz88NoNOLxXC7AiozV3w+97B2tVl12SmwuZIMBq8WMTgK3w47N4cZgDSA40IosVAry8ihx3ASBkRSsVit47Njsrr99XBIhBJqm0bhx43MCdKW0JosfCh5sdkepbQRICmaLCeF24nC6L/0Sbmk6nWJAh4brvEA7QoDBaER43FcPmHQLIkkSQgiKi4sJCgq6qmALwGy2IovShS2U2sBsQS9p2EuDFp1vQSFAMRgwGRQcdjuqR3hNLATICgZFwuW+mS8KGaPFgtmgoHlUHHYbLlVDZzBjNeqw20pwe67tSdHr9ezdu5fw8HCio6Ov2EgQgMlkQSd5sNsdiNIZLXqDCaMi47DbULXzq5uEojdgNCqI8z707Smtl0KSMRgUVLe7XMfIkWWZ/Px8LBYLgwYNuqZjrqulnZyczA8//IAsy5cRBAmDyclvC1ahi66FrvAUanAjujSvQlFaAr9vjycswI0nuB5dWtSm+Mgynp/4NY6Qhrw0/jXq+KvY/mKks38yZwUnLi6O0NDQK65UE7KB6Khw9v86j8POSgwa2IqslNNgDSXUVMyyJasJbdSN1tWtpGbmX5iXEOitIVjUTBKzXISF+KN5NHQGCxHhIRzbvg6HXyUig01o5SyioyRJOJ1OcnNziYmJuYJoC2SDH1HhFtb9soiSkAYMaFOL9IxcwiNDSd63hZP2EHq2bYAtM41Cl3c6mRCCgOAItIJEth7KoGvPvpjt2WQUuAgKqUCAzsXhk2lUCPBDvUqExr+KEOIvhxg1Go2kpKRQu3ZtmjVrhsPhuGw62WAmKjyE3WsWcUqrTL9eDSnIysYvLJLiU/vYk1hMj759MRRlk5nvQJK8geDsuSmcyijCaDGj0wQujxudKYi6tasTEmAk8fgJ9FY/5CuEyb3VURSFY8eOsW3btms/5npOoGkaRqPxCv4+M73u60/3ADOOfpP547u7AQiOrc29x6djHReDfdPdrJ5wF68ndGLbd/NxH55NWOUOxBVt5ioxW3xcA5qmYbfb0el0l4+VrRjwl07xaIceBA58nduyPsD/6WJ2HlyJZ+NH9H97FcNeHMpPQzvw2m3Ps3TcXaTlFJ1rLepNAZC0iIDmDzN0+hom3FsLTYWsI78zZvAn+LcYxNMPV8Fpd5QZTe9W5axo2+12HA5HmaKt6I2opzbR595JdBz6AvaFTzP0tcYkrhzLW//qzMyt2QTYjpKkb86iFYtoFODGoWqY/ENYM3MET7+9kshI2HvkDO/M38fINkbmfvYpH018hyMNn6V40VjScgove+5bDZfLhcvlwuFwXFa0Zb0RKW0/d987lloDxhCbOoWW7wWy5KfJzB/ZlRe+30+woYQzuXqmLFzPg7eHYbO7CAoOZOGnIxj56X7CwwNANpCZfoZH3vyaBxN3MG3SqyyPb8CRw3MweRzlNuSzXq/H6XRelxvuuicta5p2lUEaE01bRlJYuc4FW+WGDWnZqgGwmxHv7WFF8RJkwFj/MX75sSah11sQH5dwce/n4paUEAKD0UDSunl8E1cd8dQ9QCdmvRfFgRw3J76fQ1iviQy+uztt5H20nLgfvfG+c4ItyQYMIpv5q3bToGIk4YEm9HoT2cfXMXbCTAa//TX331aBlJRs3DegJXcrcDkb6k1GdqxaxomQDix7uD+eVjIfdvqAlKIcgpo9xK6Zw4h2H6Njo7asO5zCbe2jcaguJOEm3xHA7PXH6NXQn7FdKvL7qq081bY9gVVa8ezQQUyKM56LA10e7HdxGc//LYTX77126TwyK3Zi9uMDCHQ0YnXHu/hhUxzmgKasj1tKixA7Q3vUZ8HPyxnY6gkkyY3qtGOq2pklG36iU9MwrCQz7N8TeXRQR/L27Gf4c8M4NOVAmeUoT1xv2W/KsK+qqqgXr0ByulHdTtAELk8Kr0xadq5L0+nODvga2f8/eDwaoVUaEZG5gAET5rLp56+pNvpT7qwK1WrEsPTtf/HZsq1sOKYyYeyDFBUUnzvW6m9i3/LF6Br3onG4DpcqsPiHsOizNwls8y9aRno4mVaCxc+MDOW2y3o1NI+gcs2qnFo+iWc+WcqqrQkMf2kEQQTT7+5eKCU5xMcfpXbXhxnYqjqO0jjQDlsJ7e55kobhTooyj+GI6MCTj3bFoQXSrXNbrJIb1VN+u/qXIIEsCdIz47AJBZ0ioTMHEBLoZNeuNIaMGUWYM5Mcp4W6japhNphLPd0Cm0OlTY8HqRsto+k8/DjuRcy9RlDb30CzTp2Issq4/qHx8///5upIEsLtAPk2tv36PYmfPIBOkpEkmTsefYuMf6L1/weoDifBze5j4YxhLHhzEO3vf4mgQAN5OS7ufu59RnQO46m+bRj+0UoUs6X0ayMgG0wUJe5gt7MqI+5sh9PpweJfAbM+jdlzD5F1aguvPvMINavGMn7eDsxWC1I5c49cK06nndq9hjBtzL18NqYffYZOpEgzo9MbsBjzmPfJFJ4Z/Qr70ovISM9G0Xt7pkIIzCY9e1d+x9PDhrFs/xkyU0/jQofT4cBTOgBYftuMFyEEqgfatrmTE/M/5JXJs/hh9kzW7EglKCQEg1FBMlhRU9axdLPCqNEDofTjG5oQGK3+mCwmTv82i2kJtRn3YD0KCmw4HXbcZXw04Z/ATRFtIUCWLs5aIJUGS49u/xB7U4qxFeeyf+23nJz3BpO/PXIziuLjAiSMZgMJyz7hpV+tpGRnsWX+W3z90r/5ZvNR5kyZCD0nkplylHF9Ahh+z8NkBEdgQMKk11jwzUx279nGR9M+4GhGPqt+/J6TcWvIwZ87HxjO14s38O0LXfj5x18ocivI8t9Gfs5DwmQ0sHHeZ6wubkbSmVTmjr+Pac/cxYqMYCw6fzrd+xAzflxMezYzcsJsJKvV24IUMsgy1Zt34Y1pPzHlgVCeH/U6p/M1lPPihv5dXnUS4HbaqdzmXr7/8i1cp49ShAVzeA3at4zF5pIw2k8y+Z3Z3PvmNFpXlHCcnYwgvLFuDO5Mpn+5igFDBiIKC/BQvl0hN4KbIto6yUO+O/fCjXsOU6FRY9CSWLs/GwCTNYhGd/yLh5uE4MR8M4ri43wkMMgqfyz6lcA27YjWS7S4dywvdq3Asd07Wb7mBC27t8RojeaFl18lxJFBjmwgsmIUAYqgYu2m1IiwUlziQNJJaC4HLsIJBYJDg3A4VOrWbYCfpPK37bhKEgbZxuY/1hJZuw1BJj8eeOYF+kbr2XEwmTybSoWwEAIqVKRO47ro3E4MJn+ioqIINOVw4HAe4VFhmIwBtGhzO1bhRkWHTpKRFR2SLKPo5NK1DP/ri/2LSN7vjjpVmeZ3DuGLLz4mtPAwoe0G0qO6P7LOybyvf6TRE//h6T5NCPDzx2A0ER4Vhb9Zj8kvgEMrv2CbvRr9W1ehyFY6D1zy2kiWdeh0unMzc/4p3BTRfujxocz/chJTf1jNvn372L5sJs3Hnebzx0KhOInnn3qaXQfjSEw6RfyvH7ItegRTHinj00A+bhwC3Ohp3qkRB5esJDHfzqn9S/mjuCljRj5Is2qw6qe1FBRlsGThclqOfpsWuTvpUrcqb6/PZOCjY3hl7PMMG3I3gZqgadeuxFbrzsP3VuSXn1aRl5vBwjVH6D+gH4EGT7mb8ndNCIEbC/Xr1OXA1mWk5OWzbdkScls9y7DYbbw0/hPiU/NJ3Lee7Sf1TJz4Clm7v6ZetdYcdoYyb8IQFu/NxJEbx+ffbmX42+9QP1ImNzON5DPpZKamkpqZi0f8PYRbkiQkzU1RTgY/vDeK6Tv9+ew/ozB5Cvho1EAmztvK+m/G0+uOdnR6eDxFual0rx/D11tzMOXvYvgzn3PP448TgAOP8E4FVG25nEw6Q3bGGZJT0vBoIJdOef0ncFMCRlVs1JXedfUsXbiQ9Vt3cCRV47n3p9AwxATGMGJ0afy4ZB2paSnE50fy/pTRN/a7kf9QhBCkp6djNBovu7RYQqCqGrGN2tFIOcFPfxzkRFwa9zz3Kg38ZRq2vI30vWvZFXcau19NRv/7Ttw2J+aAMBo2bkig4qEgM5EVS9cT1rglVmceZnMgXfv1J/fwRvbFnSSicQ8e6tsGzWH3LiwpZ11Zj8eD3W4nMDAQTdMuU36BR4XaTW/DrzCB9TuOcMZmZeiwh4gOCSI3YTdrt+0nM99Dz4efonUVMyVOQUhENA0ataCyNZNflv/ByeQz1Ov9KIM618bhcBC3Yz3JIpSW1YPIKvIQFR2DSScQooz1TbcAiqJw+PBhQkNDiYyMRFXVS+0lSQhnEdvWriDD3JRxrwzD6rFhyzvN4WQ7DerVwj8ggPCwMCKq1KBV07oEhoTSsGkTRH4qxmqtuLt7K2S3d6GSzmCk4MRONie6aN+8BhnZRVSpVAmTUYdWDmcsybJMTk4Oqamp1xww6rpWRPbr148FCxac+5DnX0PDo8nofGELbhiaprFnzx4CAgLKnKcNgKTDYjXjLClCMlhQNBd2l4rOYMRs0FFS7MBkMaG6nHg0CYufBY+9BIeqeb+ObTRhNhkQqosSmx0he2Mfl7hUrGYTToeNcrgg8tw87ZycHGJjY684d1bSKVjNBmzFJejMFiS3C7cmYzHrcdgc6EwmZI8Lh9ONrBixWAw4SkpQzP4IVzEqeoyKwF66UlcxmDCbTCg6gcNmx+lye2PD38IYjUbmzZtHnTp1aNKkSZmLawCQFaxmPQ6bHY8ASVaw+lnR63RIMqAJPKqLYpsDk8WCai9BRY/VosdWXMz5446yYsBiNqFXdLiddmx25y1vq7LQ6/UcO3aMnTt38tprr13TMde9uObGCDaAT7BvNLIsYzabMZvNVwziI4RAU1UMJm//Rkh6LBYDQgg8GpgtJhACRW9AoTT4u2LArJcAgRAeHHZveH1FbyjNQ8Ji0nnriMGEoZy1eM4iSRImkwmz2Yzb7S6z5SaEQFUFRrMFEAhFjxHwaKL0Qx8aQlYwWfTeWRRuFcVgBI8LWTFioHSGhMl0bjWr2+XADSCVfgX+Freh0WhEr9ej1+vPfWyiLCTA4xHojaZz03tVlxOVs70J77Xq9Xo8bjeSYkAPuFwqisF0iVCpbhdq6ap5w//Tl+dvBnq9HoPBcPMCRgUEBLB169Zzy9ivVKEv5k//3M0IDuUDvKJ99OhRFEXBaDT+Y3x8NwpJklBVlfT09L99cLEbgcFgICUl5dwq6asHjLpQoOEirZCky093FALxN9UNnU5HcnKyN0bSNXJdoh0WFsayZcswmUxlBIwCkDCYLfhZLOgVCdXloLCwGLcmYbEYcTm8QXJ83FgkScLj8aAoCl27dr1quEdJVjCZDMhC8y7ZPj+wkazDbDTgcl66PFjSKRgNBnSSwOlwoGoCkFAMRox6BdXlwHlTAx7dXDRNIyAggJCQkGt+6Sl6I4rk/Xyaty8Csqxg0Ct43M5SG52PAEnGYDAiPC5cbg+yosdo9NrVbrNTHsZwjUYjYWFhHDx4kJycnKu6k4wGI4pOwu2w41Q9KAYjVov53Girx+Wk5FzwMi+6UrvIlNbTv5l2SJJEcXExdevWveZjrku04+PjWbFixVXTFaXEsX7LLjIL7IRWacLIXm0AOLx7P3WaN+bvGKn4ViE+Pt47DarMZomEwWyGwlT+2L4Pu+RP+zva4a/ZsKsCSdKhl13s232A6LrNqGDh3IOiN5pxF51h394D5LsNNL29NdF+3rWPyXH72J2YQXiNJjSuVQWPy1EuhOd8zvq0AaKjo68aD0LSGfEzw/E9O0mRw2lZNxqHW8Pq509JRiIH4pOJqVOfGLOCet4LQG+2oncXsmfXHgwV61K/dhTunNNs37mPfJeB9j26EaDZ/pyzfItiMpmQZZnBgwfTsuXVvijpJiUhgYwiJ7ENmhCuB60kkw2b9oLJhNvuIqJuUxpXviighTOHY4kpFDmtNGlS42+5cvro0aPMnz//mtNfl1dZUZSrdoFsh+cyYPgkLDVvo3vnFuz8dBjv/OYADnDfPY+yNtEX4/lm4fF4KCgoQFVVNE275J/H40FS9LhPb2X4U+NxhsRiOLmcf4+eis0SiKR5MFj92Dt/Enc/+hrJJQJZeI9D0uNM38d/Jn1KhghCO7OTsS+9TZbkz+Yf3+OrVYepWDGIryY+x/drj6LXK2gez2XL8X/snXd0VFXXh59779zpaYQkhJIQei+h996kKb2JgAKi8AoIKoJUQWyAoHRFuii9FwXpJfQSIECAACEQ0pPpd+73xwSE96UEBSV8PmvNWjA5Z+aeM3f2nLPP3r/9oj4URUHJvOb7//2wdioa/PQWZn86goW/ngKNBpfixmAyE717CaO/nMPVFAWdJOJSFJTMfqKsw3b9MBPGTmDfxSRMXj5ok88wqN9QrmmCCXAcosebY7jtNiDhfuQ1vAiPu9dmzTzfeBRqxg3Gf/QRv0Xexhr1G91f60O0Crs2TGbQ4FFMnvwVEyZMZPG6vdz/M2W/fpgBA8dwKcnFlQNTafPGZyQ/36/QP4LVan2qQhJPLRj1pJCab/oNIXfv/TQoHwLAuCmTOIQOKENk9CF4KSuCvBgIgvD4z0cQ0MsqK2Z9Q3ShDvRoUgNeycc35jKsONKbnhV8cd48zMrdiYTlMyNKEuBZbWp0ek7/upjdCXmZ0rklzpi8rFz7LnE2lWXTZ1B97iWa1gvh+o4lbD15htcbFwer/e8Z+N+JqoIo4W12M2fImxzM24cFH7fEmpiEXdKTfvoX+ozdyPTlCyjl5yYlJR2XW/W4niQtsuUGHw6bQHjfL+jbqASK28G2z0ayz9SW5R1r4aYuP4zWsXRvdwbWCcZpeUxERjbhxrEtzNuXzMWJdYByeH3dlJMJSeTwDmHcNz/TsmHIQ/sdXv8NBxOrMK16Baien2/H1yYyeRjVff/e63/ReObxG/45c/Pj2AFsPHqG05FR2EMbUqugwIFln1K0fF123XCxZ9kEyhavw1dzJ9OodF5yhFVn62WPcUi6GMGk8SN4/61XqdqoC5OmzmDz8ViSL0Twn26vUibUj2pdxuEAjqz9lvCiVfhqwXw61CpJQJ4CjJm/x3Mh1hvM/vQDhn44mFfq1GL0/AOAysJP36Byw87M/PRdcuWrwBknrJj+GQPeG0DrVzvwy+5nXDr5BUJAwO1WiL1xG1uaEyQRVTCRwwiRF2MwyBksXPwbLXp1w1d14L7Pu6i4nIRUrIVt21e0H/wt27dvokzrd6gS4kuZkjkZ1fMNVm7ewFlLbvp1bYH7v3yTLwsqoNMbubJ9MRP2yYzvVY2Th49x2ybiKyUy+oMp1B0wkJxJ54mMvoUoaz2JH4DZ25+tC0ZxxVCNxkWNnDgVheJWib8Zi8NlA2RUJLy84NyFG6iC9FIIkQQUrkix2/Op3W8Ke1ZtoEyvYbya0w+bzcLxXUsY0KMjQ7+eT5z1wTjR4pWbcX7VMIbOXMPOuV/R6tvV/+8NNjwHo91n4QL+UzaDge1eoXGDGpSs3obVx+Op2rEDcacOEW/RULNjTyzndhGtq8G2U9cZHX6dIR99DsCwNo2wlOvP13NXUzFxGT9siaV08WBuRZ6iz5zVnLx6lOtLRzJrdzoVWr2F6doh9lwxMn/3GXbPHcyM/q+y8pydNSM7scVahS8/n8S6H0eyoF9LZkfE82qXFpz9bTMJQfWYMXEAvwzrw8rEQkz7ZhrjOubn9S4DSXrWk/KCoKoqbmSadm7FrcXDeO/zH1g8cxo7EmwUyBvMpUO78K3elWYlA3Aj4x8YgF70mF6Xw06e8BYM7teBc8s/pf3bo0mWNaQkptJx6GRq5Iql5ystWH7KgsNhR5AeEQmQ7RGQZYUDBw+jig5++f4bPujXhXot+hOXHMuRK/HYTv7GF5+Nom2LBny97jhmkw5UMJi1/PbTJpJtSSyZO403W9Wl68Tl1On+PurmD3j946ksXfAtWy8I5A40AepLIUSiCyrDuNGjSN83m/qde3Di8i1cQE6TkUSrSM1G9dk37T2a9ZvygHskR3gXvnyvASvH96Zu74X4GB282F7+v4dnHyltKs43y7dx5OgxTp0+weu+Bxj88TSgCHnQNHW0WgAAIABJREFUZB4UBxGc00ztupUBaN6+ERkp8QBEnkrBqnp+cZ2SL+369iGPTqBo61fZOLwvc9bHUKWQjqQ78YCe4FxG6jdpigEo1qw3PesEcPXGQWbNPUKNhvUBkMLq0bW2yIqtEej8QyiQrxgt2rXjta5duLJvHbeObGPK5Eks25dAnWpFXuIbQ8Vuc1CwQR+W/jgOd1IKYXmCkNUclM5jY8KYSWxcOZuBI77kYsxFxr/3H05ZfTHIIjqjniPLZ7PXWYzNEadYM3UwSwb3YufpGOZPm07rUSs5e/4gpZNW0G/iYhyi8aUM0QIBUXUSmxpDUNHK/GfEF2xZ+SO5rq9l5qrLuL1y0rBdb6YvWcmYzjVZMHcpNq0BjSghy1aioqFq87Z8/NksVi8axpG507GEtOSnhZPxs6aRp0QoWqB00YII6stRIi/uxBam7XWyYc8JruxewuE5fXh/aRThjfox+csP6Ni1LwtmjOTqoW0k3ecNOrliDIcM7blw7RYnV/aid+NmrD2Z8M8N5AXhqX3aT+Loz4sJ7NCVvL45AOg+uBM753kBICCh1QGISAL3ZD9tLhWtVocD2B69mpotm3Nrc316r02gSi6AJKp6+dN8jUrv+nB6pKcCC+DJrtJk+slTr5KoKUSXClX53Wkl+vIVqOcHgCoHUTQ0AI2kImm1mYk9LkRVpnjrHgzsUf1ZT8ULhyAIqG4XFpuGKq3fpkWXDCZ0b0mjMQtpEqohtUtPkt1aSDCQY+8pipcri4/sxmqxYzZ5s2nrbwTVGEJuLz15Xx9Eh03HUOLXsGqHgyUzSuJjUpg4sj89f0zEoagv5Uk/eHYr/sYADDodBq2IMSiYvIH+5MgXiNlgwMfLgNstExoWRvDpZDSopNksuFz5KFBQQJIMSG43/gVLkN9fT2qGi0qt+9Kws5tFb9cnZOASOlXyJSku8YVPsMkKB/fsQPQvTLCXDJXa8Um7Juy+egFFKYwkecbnlLxoWK86Jj1Y0lIwevmw6rtF5P30KCICpV8by7D6i4iJzwD8/9kB/cM8c6N9bNko5h9M57N2pUm5eZlNqxMZN2YUqZeXEUkaO3fspkTZZHbFpxO2+Xc6vFUXwZ7IuYMXuJgK6q61WHIWpnixwuxZ+BXXq7Sgbe0C+Jhg/bwJ1PGuw+/H7OQ9cAhavobGZWHOyPfQv1YNZ3oc5ToPpbSvliFj29B6dH8alV1I7rjfOe9dl6+alWfX2o85dukES9bsZUL3GnR/qw1NPuhBqGsc+fRpOP3K0rV5pZd0a68iCCKi4OJixEa+XLWBmwGv8lXvcOLT7TTr+DqO5BtsWXqeqzFxGHOG4eO4wHtdO1Pl8y282rAKQ5cvY1t4LpSzv5LiXZKqdV+nfMhsvv9hGW/U8GP+6nO07dwHs+jE5gnhfslQcThF6jZ4hemD5rBqVxu8IpcRF9qaPm2qkLxY5rv5ywnqVJxfdkbSvutwLNEbad55Dt+uW03H3l0YsWQWhyp7c3zxQkKbdaNoiInIfRvYvGETp10NWTK8Gem343HzckxfeMWKTBuxgPWHahNmP8DvSmk+7FyCUX17UKTtm4Sotzl04jZvv/M2BncCjSrXpu+ivbTt1YreX4yh/eT+XNu3gfjivXivzsMPLf8/8cy1R9JiL7B67QZiMwSKlA6nXJlyhOXyIj7mPBdupuKXMwdGFG4mJKPzDaJUoVBIvcTB82lUrFKOcwsGMfTnOzSsX4mEqF/5bu4RZh85QiufWDbsOUfZeq+gi79Iqik35QrnplNBPV79ZtO0YA5Klq9Isfy5Mq/Ezt6Nqzlw/jYlwqsQXrY0Qb4Grp4/zo0UJ1qzPxVKFEDAwaVDm5i/9hgFK9SgTo0q5A/0/qvz+o/gdrs5duwYXl5eDxf1UlWQNJAey2/7zlGwZCny5wtGdjtxKG5EUcSScosrNxJwK25UNBQqVZSbkUcxFgynoI/IqUN7iYiMISisBGXKliTYz5vUuEh2/n6IVNmXcmUrUDg0ENXlzJYCPv+tPfKwcmOCIKLVSsRFHWbjrtMEFixJhfCy5PMzYMu4xa/rtxKPL2XKlqNUkVBclluciIylSMmS5NArHN25maNXMyhSqgxly5bGX7jF2vUR5C1TjkIhedCpDhzKozOOXxR0Oh1LliyhcuXK1K1b9zEtFa6fPczvB05jyl2EsuXDKRBoImr/RtbujqJo5RpUKFmU3AHeoDo4vPcI+UpXIMhH5eD2rZyJSSGkUDHKh1fA3/hiz8mf4ejRo2zatInhw4dnqf0/KBj1EDIWI5jHk6JGctdsjmpXE2OfWXzYuORDu7QNM1F/VSLvlsu++gPPiqwKRgmiBqNBh8Nuw6W4H2gniBpkjZhpMFRcDieirMXt9BwC6XQG9DoZl8OGze5AcatIGhm9wYgGFxabp0hpdsygfxrBqLtZoCaTHsVux2a341Lc94SQJBRsNhsOpwsQ0Wo1OB0O3IjoDEb0WgmnzeYp6oqE2ajH4bCjuJRsI36k1+tZvHgxVapUeYLR/pfH8bRG+x8UjHoIpkZ0qvMNP24+Te/aedm5ahnO8t3o1+DhBhsySEyzEH/zFpT7d9skiiImkwmj0fjEYH2PsJPuiX5nyZB5i2j+KFLhcDhAkNDp/3jO7XLgAGRZi5wpIpXdEAThnuiWyWTC6XxyIpjDZgcEtDo9d0etZP7ASRotBs0fc3FvLlFw2BUQJXQGIzrufre0PM+v17NGp9Oh1Wqfr034f4Asy89PMMpsNrN//370ev1TlXzPKpLWzNCxI1i3bhaD1qgUKl+D1q+U4cqpI9id98dwiug1Dg78upnQZl25+es8NpmaksskPUTn4f8PgiBw+vRpNBoNWq32f6qzP6QDQqaIz598wwdU+u+q1WVX7gpGxcfHEx0d/eT5u78vzy46L/NTeUav9vzQarVcvXoVrVaLKIpZEIx6xCxlodrDs5zfFwmNRkN0dLRH1TGrfZ7mDQICAti6detjtt4ikkaD3qBHcDs922eXgiBKaLRatBI4HE4QNOj0WhSnHYfDU4FapzegEdw43eAbUpRAScCaeo1fN17kYd8dFRGzVw6q1AgGl50Te7YRoagvxcHNn+XuZ1KjRo3H+kNVFSRZRiMAggiqC7vDhSRr0Wu19wIWVMWJxep44DTsD5+uFrfixKUomS4VDaqqIgoqDoczswDCcxzsc0JRFPR6fZYEo1RV9eiLyxrciguXG3R6PZq7YVECqIqCzWrDrXrMsCjJHqEuQcCtOLHZHJkaLR7dF61Wxu12orjcL/xBpCzL5MmTB5PJhNVqfezORJJlpEyhLNXtwulSQBCRZRlBVREkKVO//cE518haBMEzN6riwOlUUAUBrVaHRnBjszuztTEXRc8cBAcHZ7nPUxnt6Ohopk2b9tg2MZHHOHr2Aorel/LV6lIghxawcWL3Ds4laqhepTzpN89zOuoqppwFqFG3Kj4SJETtZeuRG9Rs1YF8WVcp/Jf/4ty5c4/V01ZVFY3eiJRyjV8PniIjw0nxGvWpkNeXhItHWLl1J25Jh+pMw56zAgM71iAlzXJvFS1IWrxNIsf37iJJE0R46WLIaganjxzhxp0M8hSrROnSgbhsNrLbT6ggCNhsNu7cuUNoaOjjd5Oqiipq8TaqHN21k2TvwtQuYmTTmvVcSXaikzUIqhuLlINOHVriLSooSEhksHfTGo7FWKnSsDlVyxbDabHgFiSMepkrZw5xIUFHlcplkFX7Cz2Her2eiIgIgoKCKFWq1D2xrf9G1umw37nKoVNRZDg0lK5SjXxeMkgC16OOczrqGpJ3CDVrVkB22VAyfyxlvYHkmEiOn7tMmkWkSqPG5DIIGLyNxJ7az8ErdupWD0dUs29FUlmWOXPmDHv37s1yn6cy2qIo4nQ6H+vDylsoH4NbhWOrPYkmzRsD4EZPcfvvtPk+N5daN8IqXqV+eFc6zDvIKxKo6ft5te07tB83C/9/3WN/GrfbTXp6OqIoPnI3JGh0GFMvMHDQROq+O4Qw12Z6d17JT3s2YD+2ju8W7KFEiB9JCTdRSxoZ1Kn6vW2vIOkI9ofvhrzNMW0FurfLj87sz88j3uC4VyveqJuTz4b3p+sn39GwiIl0W/aSaBUEAYfDgdPp9BwQPsZoq5Kewvn0fNL5NS7nbUrX9uVIv32G2VO/RcldBB+DDjX5CjvigmnfqTV2RwZGnyCWj+7MjAgjRfRXmDB+Gp8uXU/furkQtGY2Tv+A1VE6ur/RDcVpw628+KvIu8JRDofjoUZb0uhQYk/y0ZgZ1O/cG6/4X3nv/b0sWTKepL1L+XpZFN26NWbR5x9w8PIohnWrjD3DhigbcV09wJgvfqJVr954XZ9Pn3dP8POigXz7Tn+O3IhhZ0wwh7aUx6A4/meFnl1wu924XK7nJxiVlRAkUZuTAvkDSQ0uhPnuc4C2VCHyFCgAgCGwPCEGHUXKhUPMBmq/Npmv956gcvaMtHuhuP8zeli4mlYvc37TEhYfdrOoWS2gCPmGl+L0dYVCGhP9vlzBhy1DUFQR1ZrI5RsJntcRRLzNbia83orI8sNZ+Ulr7txOxhC/gCHTr7Dj8luEBwl02rCI6d8vptakgYj21Bd6pZgVHhryJ8r46NMYULcOqV2Xs6hPeRJu3+baJS3dR8ykc7uGmA0CP08ZScOgxgRqnCTbQHGloXiXZ8H6yZTPCd92K8G0hfPo03E62z9pyYyL1fl16TAct66TkGZ74YX/HyZQ9mCBA9DpZVb+NIc4U1Fea1abnGohVv1Yi2mb+qBdNJ1CXWZTv1ZZTDFteP3bWbzVoSregoDerGfHD9OJSCzD903rYKylZ+qctzkb+z79vvietKMzaDh0D3cdSC96eOTjeNprfy4FvxTFjaL81yrF6fSUrfL8B1ErcGX/csb/eJqpm37912D/TbgcCnnLN6G6ZiP1enzElC9mUm/EXJoXdmPLSGfT3LF0almfhq17s/lsAt56z9ZHozMSd3A5q67m4vVwmDzpO6KS3ZisaTgEN06ngl6rRZSdxCVk4HJl3y/Rk9DqDVzauYr1GSXpUfAak6fMIirZQe5cRalaPpTbCclcPPALa46kUq9acdItnggTa4aNBp3fwjv1Msk2G1a3jtyh4QQkH+STqfvp1L0eC6Z8zZFrVsxepmxyHPkEBAGHPZlr8XYMegOCKCLqJW5dOsClWwq5vI2kp6bh42fGZbFiy3AhiOCwOSlRvSW2iEn0HDSOKdM28Mbn31LKH9xOC8mpGbzgcuPPjedWpfF/vrIC/KGKL6FVbHw18HWm7kylfODzuop/uR8BUBwOvArXZ/LYt9k9/0sGfTiaTaeuo5EkBH0AZavV4r0hQ8gdt5F2bcZgDvZHUFVknZYTu3ZxPiqK3/ae5PjWH6hXvyOH/TtS33iRN3r0Ze7sqUxdtA1ToPElVuAVkDUKR48c5cq5U2zafYK9q76iZtm2pAbmw9dkJCCHwPdfzqB4656E6h2ZEU0qbrcbvdGARu+F++qvLDtpZPiQ9pzc8AtRqWmcOrCdy0dX0bBpczadS8Ggl58YVfFio+JyualWozG3t02i57CvmDH5a7YduYGvUSbF6vIcaKtuJJ0OjaCC6gYBHHYr+Wt04oPu9dg2fSSDho3jVFwGskbCnWlHsvHi+i/xXIy2W/FEGDz4pBvBcLf4pwub5MsPu87T0TYDoWQPbj2PC/mXB1AR0Bt1nFk8gnYLtLhUhYyoDUTN6E+/aTup2ONDxvRqQlipWnw9fTyumHWcUk0YNBpkSSA65iplOnXnq/GfMG/5Chp6H2XNz8fZcDmK/rX8SZByUaFyReqWqohBcGRve/NIBETBQUziJcq3e5svRn/C8tWrqKg/xJdrU8id25+jPwxni1CfPg3DSM2wIwgikkZCEECU9WhTzzHkg9l88OMK6uZViLpwBnNwe74d8zGfL9jD0LALzFy4AyRttjZMggAOq5XQWt2I2LOO/EochtzFyFe4Bq1eKYYlw+pRhBQEXFYnOrM3oiwhChq8fMxsnPgWG9SOJNpVzm/6ku/6t2LOnhgMBpnMgJv/lzwXo+3tLROdcv2B565tOULRmuUz/6dHdFlI1xVg6t4zdPX5lZLVenI27Xlczb/cQwBZUDiy5wwlm9eBjCQMhV+hf11/bCkJnIk8jxWPgU68eYegss0pplqwOBVUVSA4MABXmoXUVAtpNgfmwHzo9CJOTRCDx0+hQ2gsB24G0qZ5FZw2+0v6nVJxqzIBxly4M1LJcDpQFTdGrYHQUF/EhON89NUG+g56EyEjFbcqIIoCqsOBJJswqoksmL+KBgO/4NWSZixOLfny5sWqpJIG2NwQFJKHHL5eL3a8X5YQEHBjSc/Ap2gdvpw8AcuZHYQ06kDVPHnIZVa4fCcVk5cXyakWcuUJxtekxel2opXcHNt9nGJ1KmJJSaJg0/50K+uD4gJJ1mMy6pF1eswmo0ev/OVcITyU52K0u/Xty54Z/eny4TS2bF7PzPHv0XCmhcltfEGxcv7gMg5kONj08zIyCGbRb7toYllLCe9i/Hb65vO4pH8BUMGhSlRrUpUT30/n96ib7F71Db/TiE9eL8mirz5j8Ya9HNy+ljkbLjBm4oe4rhyiZ7NSfLnvGq926Yb7yAaW7zrBoU0ruaUtRu06pbkZfZIfv/qA3l/sYeLcmRT3VTzJUNl5mfhIVOxOkYZNWnBn/2o2HDjH+qXziSvamXcrWhn1Xm8O5GhDh3JepFmdSFozCZfXU7p6Z87bnIztUJ2PJs7ii0EdKVywAGVqdMOv8utUkrYzYd5eLh/8iWXngnmzc21Upz3771YEAUFQiI3cx+j33uL39DKMG9wWq8VAm7bVWTlzBkfPnGHD9r3UadIeb/tVmharwrwdsdR/rQ47pk8j4uJV1s0aS0rRrrxWvQiJlw6zes0OYo7tZvXG37E63EiSkP3nKos8N+0Ry41TzJ6zgKhEJ4XK1qVHz1fJIQK2VCLPR+EStLicCrkLFyeXtx5QOH1gNzbvglQokS/7LzL+AZ4oGIWKqoqYfLxIitzJTzvPkjMglPotmpHb4OLaqb0sXreX4GIVqV69CvlzGLDY0om5EINXvjDy5PQl+XIEyzYcwD+sOLVq1yLILHF851pinHlo1rQmmoxE0h1KZrZl9uNJglHgiSDRmbyxxUSwcP0BggqF07B+VUyuNKLOnseUvyR5jCq2zELJqiudmJg7hBXLzdHtB5Fy5EBS3ThddhTVQLlKVTHbolm4ZA1456F+q7aEGO2kWhwvdFSETqdj2bJlFC1alHLlymGz2f73egUBxZLEoYOH8QoJp2blwmQkJeBwi5gMOs7t38zukzcoVq0RNcvlx2lJ53L0RXyDi5A3wMzJXavYez6RwLzFaNKkDlpXBknXznP6Rjo+Bpl0BxQvUgSjQURRst86QaPRcP78eQ4dOsSIESOy1OfFEoz6l79EVgWjEAS0OiNaCdyoOG1WHC5PVIRBp8HtcuFw2nE6PVlrOl1m9qoCWp0enSzidqu4HDYcLjcarR6tBuxW+73EiOzI0wpGyXo9Oo2IW3HjdNhwuj2CWih27PeFNgiihE4n47A70BtNiKiZmaUCqlvBarUiaPQYdJ6sUqfdisOV9RT6f4q7RrtYsWL3jPbDEAQRrU6HqCrY7H8UdhAEEZ3BgEYAxe3CbrPjVgWPTIbThstNphCZihsVh9WGS1URNVr0WhlRBMXlwu5w3DuczG7Issy5c+eIiIjIstF+sQSj/uUvcVfsSK/XP3mFprpwZtokUaNFrwFQsNs9xkYQNWh1nttDvftF0QC4cWbqwAiSjE4CUFEU0Gi1z16g/W/kbtyxTqfLmr6O6sbl8hgLSdbhmQoFJBm9JP9XUxWtVntPTOp+tDo9oOJ0uRC4f65fbPR6PbIso9Vqs6CdoeIWRHR6/YPPKi5cggCqkDkPoKpuNLIOzQN/V9HodPfuL0VxoSgAnpT27Iosy+j1+ucnGOXt7c3vv/+OwWB4zA39cAGirG7zPBf/LJIKPNfxIm8vnwcXLlzAx8cHTaYWyN/FH+/1YieEPA5BEHA6nSQmJhIbG4ui/MlA4Eydkae699QHY7Kzw30ryzLXr3sCDmw2W5ZUEf+bB2SBs8GYnzWSJHHt2jXMZvOTG2fy1Cp/J0+evLcK+d9sMTB5+2A26AE3bjeIogCKk6TkZAStGY3bhsXmeKjzSVXBLzAYs1Yl/nY8NsfDfYpPQlVVZIMPwYE+ZCTGk5RmRf1/cEOoqkpKSgomk+mxBtujIaLBZDShEVWslgzPdl4QkLV6DHodquLAarHiesh5oqqC3mRGdNmw2J3oTT6YDXLmlh9sGelkWO3Zz8GI50u0Z88eypQpg6+vbxaUEkW0Oj2y4CLDakeQtHh5e6OT3KSlpmFz/PfiRkCj80Q/qE4bGRlWXKqEr78fOlHIDGVTSEtJxuZ8sV0ksizjdDpJSEjAx8fnsTsTQZQwmkxoJRGrJR2b3YVsMOPjbUDIrHDktGaQkm55yKJPwOTtg9uSjDV7KSM8EVEUsVqteHtnPbvwqYx2XFwcs2fPfmybiDVL2BebgLdWJD09lYx0C/qg4rz51tu4Lh/ktr4kRfM8+ldl1cT+TDni5tvvplI88M/vEW2Xt/FGr88o8+Ekhr9e7k+/TnbjzJkz9w4iH2W4NVojPno7635ZxuU0H9p1bUsONQNVZ8ISe5Z1W3ajz1+ZVg2qYk/L4EF3oYDJy5uYEzu5rQmjbolQLhxcw+97bqA36chIt1O4aj1qV8iP4nzxtTPu5+4C4cCBA7Rt25bQ0NAs9Uu7cpIjqT7ULeNpv3f1j5xKD6Rn97Y8dOOefo3Vq7bhVa4pHUrnBmDXysVEJbkxalQsTh2N2/YgxPu55b49MyRJIm/evFkqgnBy1waOx0Gb7u0wA0lxp1i3/hCi0YAj3UJgmZq0q1rsoX1jD28nrWg7ino94wG8AJw+fZo1a9Zkuf1T3xVP2gJVat2Fq0v6M3ndaV7r0ZO2zWtx/eQu4hJg/kcdmbB492P7t3qjO4mRO7mW8PBDjayiD2tExWCF41GX/tLrZCfcbjc2m+2xD7sCavwhmtduRXK+2tQKukC5wrU4LwVzbvVntB84k9K16xG9ZAh1+s7Gz6zFel9/t6Tl1pH5lKvTktWnE9AZ9OxeOZlZPyxn/fo1LF+1gVOX4nC6HE+8lhfxYbfb7wlGPZlkxr3dhQ/m7iQoKBAcdxjbsxmfrTzG8ZXD0ecsw+6r1gd6nFw3kbb9vqX8K69y8PM2tHjvR+A2g3sOZfnGNSxeOJ9la7ZyI9HyXO6RZ43NZnuy7z/5PH3bt+WoI4wagdcoU6QBUQ6ViG1zmTBxBit+Wcr8xT+x59gFHra3iF7/KXkqNWB/xnMZwj+Ow+F4fj7trBJSsDhh5oLk9fGFcrUZO64Eos7FwF+u/E9b1Z7MHZuRAJ/M6uqSjJfZzP0Ba0m349D658L031Fs9hTibToCfB483MCVgcUl4uWVA52YDU50nhMPFYzSaog5vo0d1/zZ3jgcCCT8P/OIt9k5sWEbppJDqRlegRwdmrJw5m2QNPdC9wSNHjE1mg0Hr1OtQF78jDKqw45/4Tp813MoTSrnxGlzkJKc5NHbyKZhf1kjjQld22NtM5kZbUt5nkm6TZWuoxjZsCqQQUqFMhyPiaNWaNi9Xr8vmYE5fDGh/jl4Z0AndkyOBiyUajuOH394858Zyl8gK+7LG5EH2Bat8k3DEugJomqelcSkpKLPVZoZS8dTr9Kjd972xMvsv+igkMEPb8Mjm2V7nsYN/HwEo9wKFocDN5Bw9Rxbth/AlRZJw5IhDJ67E/fNvTQNL0T3nkMZPrAv4SF+9Pp8babou4rqdiNIMliuMGXUEIYO6klISBlWHrfBnTO0q1qAlt0G8f7A/pQPDaDbiAXcrZkRsXYOn4wcy7vd2/DxnDWIZuPzGGK2xeVSCC7dkCrqFsJffZ/Jn82k0ecLaRjkpnKdavw2uRu9R01l7TmZqePeJCXpbpqqgJdJYM/6zQTXbE2YWcWpCAiSBuvti8wY/Q6VSpXg9RFzuOPQoJXElzpL7drW+cy7npt+lURWrN5KEuDlF0qThlUBiNq5ihJdh/FmrbAH+tVo3pqfPmjJqNm/sPwQTJg8FrBz48Jmhr75GoWLlmfu75f//gE9R3KXqEy4ZQ9lmw1g+bKNNH5/JA0DfLCmxPLDxHdoUq0MLd+ZSKz1f/vu3LCRsAZtyGMW/yfq5v8rz8Voy1qRY7t+YcTYcbw/eBDbjsdjDihDz4oKp6PvIAbXoEUFb6JSfZkwYxnRkT+xcewQDlxXkDUSCJ4zLHtGBrU6DmHu4k0saG9k9OdfQs6S9K9l4tQtE1/PWMj1EwvYMvNL9sbA+cXvMnBxPGMnfM68nzfxYccaOC1/zc3ysqE4HBjzV2fSpCFc27eYoR+PZ9eefdxMVqnR8V0+6FyHbd8OZ9j4Wew/cR2d4a7Kn56bJ3dxUS5Kx1pFsdkUtDodgurCL6gQddv3Yc73kzg1eTADJi5DNJmy4zlkltm+5wDXok6zbOVWVk15hxxCOVIBuM3McWP4dNJ0Fi/5hW0nrj3Qr0KbjxnVrwbzx/Wj95CpHDsXA5ioUr0C3SfMY8OU9vSuV4AZe2//A6N6Pgi+xfni69F439xGly69mLV4O6luCPLLS7W2PVm8fi1++0ZRvcMw7i9YlnhmMxcMlaleOi8ul4pO/8i3+H/Fc/EdOGxuajTpwYSRfcHp5OixgwDoDZ7EAgCNwUyRMI++tkJRSocopDud94rBqYDOx5+zq37iaGQpZNEbUfHEuGpMXuQrWhAAt3dBCgcZ0Qjw0/y1FGqw7N6W3NucA/HPhm29lAjoDTpOL/2Ud9YKxN+O49apNdQq+ypT69Qk174pWOuN4soriCJ2AAAgAElEQVSSSvzyaW869OtK+15xBKRcQZKcrFz4PQfS8mG5tY8TN+M5N2cqNXMNpFWfkaRnWNF5B/LFmA4M2nOBDKfoibP9p4f8XHBwyxZD+Vd7M2Tg2zCwA1cC8zApAkZXCuTtT0bxNqOYO6A2b380jdabvrjX8+t32iB3WMKV6flZ9WVHevbuSbWI3/j08488DZp9zCcVPmXnkXP0q/FyyF9e2rmUwctjiTh+DpL2UaVIDQbMac/8vr24GyIw4du5VO2/klQb5NQDOJkybhxRAY2xnU4kKjmRH0dPJuiNDlQplucfHM0/z3NZaWtlCbcz8zdTlilfqYbnzUTQyJ7fCY34h1aAW3GiCCI6SQaNFo1GxuStY93Et/lph4Xe7RrSvG4xnIqKdLcvd/s6UFQBUQPeooOYG6fuXUdiWiyqMfsG3j9zBJAFJxG/HaVQ41qQnkDO0q3p1SAnKVcvcuB4MlXrFCU9ycmrr/fAW6NDFSUC84Tgo5Go/EoXXmtUk/AKFQj0NlGoXFWC0o6y+5oGnaCguhXSHCIlioRilJWXWAtCi68SiKDcPTYz4C0bsMfHk5Kacq+VnDMP+XI8aHjXbj5ChUr5AXjt7bFUNSRz8vR/uUMMAmWLFX+eA/hbOXX6JLnu+vX9qtOzeR0yLh8n0fLHsWO600XZsoUx3VtNy7R86yO6NK9B1Srh+BuMlKpWg/wBL2H4yFPyzI22IyWGk9E3iEu4QFSS5wT+rpP9xMXbJFy6huJOJ/JiHLevXyAVEDUaXOlpHI+9zu3jx7h2M5boS7cwB5g5dWkv63ft45u5W3Bb0oh1wfGoWFIvRXPLCYIkIdrucPZMNAN//JEz371P91Ez+GXeVLYdu8b1yDNYnj7m/+VEVXG4tTTu/goXF85jx7kY9q+byk5tW8Z9/Bav1MzJki/ncf7yKabP+IluX8+m1PXfKZdTy6Bt16jf5DVebd6AQnm9sFkc6Eze5KlciO96dWLdsYsc2LyUA+n5GfWfrqgWy0u6yvbQvV9v1OMr2XPpDodX/ICr9RQ+q3mZt3sPYlvEWU7vXUtkWhHW/jCEm+cWoREKcCwRhrzTnAXfLuBmYhLLZ8+iych5hMev4T9fLiTqYjQHV47H0W4LwxoH/NNDfGbUqluXuJM72HM2hphDq4kwN2b+mOZMee8tVuw9QeTJI2zad4WJEyZgcN8gjyDw1foLVGrYklZNGxIW6IdDUtGb8xLo928BWWn06NGjs9p46dKldOzY8RFiRB6unz+Hb4kG1C5TBFnnTZ4AT9B4yvXzOPLWpGZ4AfxcqRgKVKdi+fyY/IIJ9PejWKnC+Hr7IbrcVKpXH19ZR4N2PSiks3BHCmPwwN6E5A3E7LQghlSjVsWC+AYF4e8XSJlSxZH1OgqXqEnPDjWIv3gaU8lWfDLsbfKJCoacofiZXv4oElVViYuLQ6fTPbTmnAC4FAW//OWpV0jDrqMXSbYY6dL/TfztaRSuWBOTJZpzMQn4F6lG50blyHBpKFelHtVLFkBSXdhSbxN55gbhTVsR5uPGnKMsxYOTOXzyOqLBl5btOhJkcGJzKtkyw00QBCIiIqhQoQL+/v6PbKfJUYiW1cLYe+AIbp9ivD+gDQZDADnFVE6du4xiCqRtt14EmwT0xmCaNKlHiYL5KF2/BcaEKC5cicW3aF06NStLYNEC3Dq2n8t30lFyVmNAl8p/44j/GseOHcPX15cCmaUEH4YxsBA1iwVy4lQkCQ5vOvXsQIDZTP5AlQOHInEKInVa9aRYsAyCgdoN61GjYim89BosybGcjLpF/VfbEOhOx+gfhI9R+zeO8Plz8+ZNLl68SO3atbPU/l/BqJcIRVE4duwY3t7eTygUKqAz6FEdDlSNDC4HDpeCqJHRaTU4HU40Gg1Ohx03Ijq9DrfDhjOzuIWs1aGVJVS3C5vNgUZrAMWBIEkoTjtOJXuuse9qj0yfPp0+ffpQpEiRf/qSXnjmzp1LaGgojRo1+qcvJdty9OhRNm3axPDhw7PU/qmWn6qq/muwX2AkScLb2xsvL6+sVXfWZH78Wpn7D+Zljecz1mrvW9FozA+0UVUVUZIxmjLvB9nzV1l+8LWyE3d3BndFfP7lyRiNxn/n6i/yXAWjfH19mTt3LqqqPlaTIVNK4H+f9yg4ZTHhQkVV/5r40F35y7/Ks3qd54kgCIiiSEJCAjqdLkvXe/dGeVgSzsOe/6/e3P8pZ63Pi42QWQHlwoULLF26lMDAwCdrj8AjBaIed78/IIymPryA74s+l5IkceLECc6cOUN0dPQTMyPvGab75+S+sT98vH8I0D1Y6f0hr5UNkSSJmJgYfHx8stznqYy2wWDAbDZjNBof8QGJmH188TJosaankJJhzZxwAb3ZCx+zCUGxkZSUgv1hesGZH4CKiHeOAAK8ZOLiYsmwPX3YntE3gOAcRuLj4ki1ZCUl+eEIkgb/XHnxFi3ExN7BpbyYIj6CICDLMqdOnSIsLIwiRYo8NhVb1GgxGU3IkorFkoHN7gREZK0Wk9kEioOM9PRHuDpEjGYzKDYsVo97xGw2oZNUUlNTsTmc2TJyRBAEXC4X3bp1I2/evFlLZc8U2fIIRjnvLTJESYNOq8Vpt2YW9r2viyiiMxhxOyzYHQqS1oCftxlV/ePecjttpKZbeJFlomVZ5vbt2+TLl48CBQo8XjBK0KA36dFKAjaLFbvTBQhotDqMeh2K04bVZv+f8WpkHXqDDkFxkm6x4naDpJHx8vZCg0J6WtrDbUk2QZIkzGYziYmJWe7zVEb7xo0bdOrU6bFtTmxZw9a95ynTpAevlfkj3OnWmYMs27wDJWcpenZ6Fd8nROJFbJrD4I/XMmLVOpr8iXDVuL3z6DhoFR9Pmk6b8nmf/gXuY80XPem7PYCzm794cuN/mGvXrlGzZk1Kly792HZqyg32Hz7K9XiR0tUbUDy3Z4ubHHeFPXv3g18BGrdswKOOfE4c2I4UWI1SBfxQUq+yYd0mziYa6NypHSEB2feEX1VVzp49i9H4uExaz+pP0hqR1XTOHDrAdTE3TSoVwuZSMRp03Ll2nuOnLxBWvgr5vGWPSH/mbshlT+Ponh14FW9K+SJmkqMjmDXzVzQGM7KkojjdeOUpSdvWddGrSmam8IvH3QNvb29vQkJCHlK5xrNb1shaVOtt9u3ay+UEB+E161O2QE7cboWrZyL49cQFvPKUoFaNcIy4cLndngIJeh1xF46yd88ZnN6htGxWF7OsknrzHD/9sIBEfShtOrajkL8Wm8Pxl3fm/wQajYa0tDQiIyOz3OepQv4kSXqiYFTZJi04t/RDmlauxuqzf4jeBJWsguHqJizGoo802NaLvzFyzFwsQKVm7Uk8tIGL8X9OJSZXpSb4W6KIvvzX67yXCy/FhWPH+RML/r8dm82GxfIEsaGUE7Rt359E39KUznmVFrVf4QqQsH8BvT+cQVj5yhya/T4NP/jpod2TL6yiWrUGrDrqmdth7eozb8clLu2YRWhwOGtPJj3jUf19WK1W7HY7brcbRVEe+nC5FARJi956ifHDPmbd8Tjy5MqB0+XGqNewd9k3jPtmCRZ9AP4mDS6XgktREGUdFw6tY/70L+jaawDHb7sxmbXEHtnBL7uOEns1mssxt9i5dh47j8Wi0Ui4H3ENL8rjrkvpUfOkCiLOO5F8/MFo4r2KUiO/k6G9B3DKquX4ikl8+ctRqtSry9n1Uxg1bQWqRoNbUdDoDURtm8GAT5dSoHojfC4tpsuQH9A7L9Pvrfc5k6Jy6bdp1KrTkuNJIIt3CyP883PytA9VVbN2BpXJc4iDk6jc5n1U3995rU5H1Nvr7v2lcJlSGANyPLJn0v4fmbwyH2NHAfji72fA5P0nV21aMz7+foiavx6KHhpWhqCc+x70Oz7Kcf8PkxU/6NU9K1l1SGZlhfzAO4TFfsRl4PCcHwltOpOSBQry4Qc9GFd5Gnzx3zurW6zaeobqJQvjrdMBKQRWGsQX4/sDYC3jx84Ne2lVpsWzHtrfxmPnUFVBlPEyWPm0d39oN4mve9UgKT4Ou6rj+p4ZDF9ynbUb55BHzCA+IQlnpq/I5bATWKg6fSuUY8vqLZla83ZSlFyM+mYaHUuFYfbzZlCTGlR+53WMqoOMbHCe8mgEJI2GW+cOsOGckxkdGuClFCTs06XEWuyc/mUZQb1WUb1iFRzNGzBx/UWcqgiiiEFWWDb1O4r0XU+dUkURig1jSslurNzgS4V3J/Jpl9q4rQOoGVCclUeuEt44BKsjG6yqngHPISPSRZJFx5fbIxhb9hhCuYH3/uJ0erY+AL/9MI5ebw+gd5vqlH31EwB2Hr5M+rVtvDt+GolWCwYd7Px+FLWLByIIAlO33QDgyp6l9On5JkPf7USx8i05nWBh2SetEIRA3v+kE4JQFJCQ8ISogZOfJ39Ag1qVMIsCHcauBeDrt5simfIz7OO3yeMlIXiXZMv5ZABiT+/is2H9aNawJoWrNSP2tguDBElnNtO75xuMGTOEqhVrsyoi+1WPD23Qg655tuBdqx+Th71Lk03XqMdVDpyKJaSAJ67elK8EwRwl4oGeKqunLqF41/cpZrLhUFTAhyGZBhtA8A0itEyJv3M4fysqoDcaOLF8JvOuBdO5mMLc2T9w8GIa3kIswz76kZZ9XyNy/QKWbdyLRZDRiB6j61ZcGH2D8DN7Vt+gYk93ULZNV5qE5iDd7WDNB0250WIarYpAhu3PFQF5cVBxu1zkCm9IC98I/Io0ZuSoaZToN5pOhfxo1LkdM3vVovvwz1m7+xpDhw/D4Lajqp6TrbTUNK5evI5bI2FJSiHp9k1uh9RieMtSXImNx2ZLxe2Xi8oFg3E/SR72JeI5GG0B1ZnO9XiBT7btpfL1b2jxyS8ASAKIshFifqDDyB18MnMac1b+inPNpwxddZ2wIDM5ClTng3ffIIfBSEaaBd+y3dh19jbrh1Zn2tRvgTRea9OXukO+58vvfqK971beH7OZjsNGEiAkkb/BCLbv+RlU5Y8DINVOgQbv8NvuCA7Ne4vfls7hihveGfkZhfVWirX7ghtpCh9WSmXOxt2gRDOg7zBK9vqGTb/uYd+Kr9EaPK+1/ucZHFfyM3T0Vxw4/Bt1wrKhiqA+jB59ulPR9xqff7OQfct+AJsOvVtEe3dnIknoRPkBfePkS4egeD2q+hpQJS3evg/umi6vGEFysT70bP7oRIvsj4AsOjlx7jxuVxoHIg7z+/LvaN36Dc7diufKnSRSI09y9Nh+Rr3fjVELdmH0MtzblKmq8mARWgFcNisuQUPamS1MOBLI128UIv6OhRdyK/cUCHgUPw2+eWnxWlfqh7mZN2sOa5av41S8QplaLejaqj5Xtv7I7CXbiDhxCkWSEFQ3dkWm23s9OfJVTzr3+4jZC5ZwU8hBkEFDmsWOn4/E6m+nENxpBC2LemGxu8ju85VVnoN7xOM3cDudQCirNvxMuaodmNgwnXZBXtzSGri+7hcSLRaWTZqGxpVM4wFDaV0xL/mSciKbchHq6w0oIBsoWdqj01CiSjlc51xwfQvH4zM4vGYOcRtv46zYn15NS4I7GYM5H3XrlqI0gDsZNVN4CsFMwv7P6THbyOttSxJCLFYLyDo9OXMHUzLEs7osVLYQJ+0SyWfXsT81iCkFPcdwkiEQL70GJ9Ch52Dm1X8FkzCOMo1e54dFs3m0w+fF5MzycXx2qRzb101DtZymhKkSExtVRO8jYbFmmmmnjQyXH/nu6zd1ZH+WnhL5sWAgB45cYnGv1hSeP5P6tYpjO76cgRuN/Pz94Gwbp50lBAEBJ7fSb1KwQgt6vzuY/3SsS0zFany/4iKawBBaduhJg9I5KeEDb69chbV/c2TZjtPl0WNR792Y3L1BMehg3sKfqNn6LYwZaSSp2bJa2wOogFan5czWn1l8GmYuWoE+dh+vtWzPpAWNKH15IRV7TmRW/dwsGtOXAR99SZMd8wnQSdgyMijR4kPWhDXmjhhMyK2lTMhTkpIFcqE1qZzZPJttiUX46uP2ODPScL8E85VVnoPR1qDX6dDpPUkXuau0Z8+60RStG8ytkT1pVEAgKCwvyBoGDB7A/R7rM+kW/ogwk9AIIIqelHmn04kkSJA3P2Cg4Zu9eSXovs7JB1BF6Y9oB1GLLGvw9clB/MaxdPsukqsnV2CNXIITkGTQuHUICNzNync5PNKwxtwFsZ2bzJmLCvmKSFyPPkOCxY0M3DYXYXt0BpDCf9rW5MNRC/h1Ru9nP43PkcPbj+IdXh8AwViKQS39uJGjEGGh3hw5HAM1c5EYdRqpVgdyA7FXLmPOE0LvsT/SMl0kKJfIgLpHyDdgJPVrFSf57A6GLbzIj9M+RLSkkeLW4G02vJzrHlXFLegJ8c6LLS4VjUaDJEuIQgClqhfmyFIFFyqC6sk6DQ0xosPOtdjb+AQEotFoMJmNaDQCOoMRrQyy2ZdrOyezaI+Dn0bUJMOWwsuwahQQEAWRqOjziMZg/PQSXmVr0bZ2JS5rbxNx6iZvvFcAQRJp0PJVKp7chCRAfNxNtF4BCHotJas3htuH6DhoM59+v40ygRKRvy1ixq+JjBgzFKMzmQyngiz9vYWs/0mesXtEJWrPRtasW8tPG3ZhzQw0KdJiBJs+qcOUsfNxatzIr0ykVZ4j1Gr6JtNnfMekb2dxyQbFypbnzu7JjF+8nkP71nMq3sq2DVtxAaLg5tL+7ZxOD2dq13B6N6rH599NZ/K0mRw5f53DWzZzLeUiyzcfxqbA9f2r2X/0NKtXriAlR2n8EvfzzpgF7Dx4iOirl7gaeZnNK1Zw6ux5Vm7xeG4Fwcn+9StJk5vxUYcc9G3XlI9Gjmfj0Ri4dYo1e46zbd5E3hw+h6MRR/Av0Zg3O9R7tlP4N9Cid2fuLJrE2j3H2LpyBvuD3+G9OsH07NiV88sn8Nv+Q8xYeZrhI3uD7Qpv1KnOrMPXCC5YkvLF/Yk6cJjIyzc5e+YCzrgjNK9Tn2UrF9OqcU1KlyxGs/5TSfzzofEvOCo2m0q9Vu3wub6bH1ZsY9G8RWjrvEX3uuXoUCsfM2fNZ+/v61h3JI63evch5dxqXmvZnROJbsSMW6xe8hMnr8azecUijl1KIkC6yuB+Yyjf+T+EGF24FPVlsNmAiktRqFStBpaL+1i8bjtrv/+OY2J5+nZpT+2KuVn43Tfs2reLeSt20aJLdwK4Qa/WjVlz/A56IYPffp7FJxMX0mDoVF6v5MOVg6vo2K4fm3ZsoXvzOlSvXo3Bo5diNLzU+7sHeMbaIyoJ169w5WY8Bv88FM6fB/nez4KF3b/uJaxCPfL6aVBSr7H51x3ccvhRpXwZihYNRYONiD27cJnDCMup4f/YO/M4m+r/jz/PufudfTdmx5gZYx+Mfd8iItmL7JHshUgJUYkQUkSSCmWnkH3fd8YyjJlh9v3uy/n9cYdMyfL9EaOej8f94977+dxzPuec+z6f8/6836/3rdQsVG5+hJcORCy4xbG4NMpElMfTxcT+X7dwNjmfarE1iQgPIffWNVIydWjd/ShdKgBT2jUuJWcjql2ILhdO4ok9XC/QUqtmBPFn4/EPDcKQnUpqtg6NdxBRIb7kpl7jUrKeipWiUZHPib17uGHwoF7dimQlXEHmFYKXPZXf9xzBqvahSrWqlAl4ttTYPv/8c2rVqkVsbOx926VePs3Bc9fw8C5BdI1YvAofURLjTnPifDw+pSpRq1IYSGYun72Ia+ko/LQKMBdwPeEGWXkGULlSPtiF42euIVcpsNls2G1WlJ5BVIoIRl4MDY9er+fSpUu4urree+YmSUiCgFKlxpR2iV93n8Y7NJoqlcriJBMQBSOHd2znpsWJChUrUybAE4Muh1u3svEJCEBmKeDWrVQkhQbJasLZvQS+zhKXktIoGVQajWjGaiseESMqlYqffvqJiIgIKleu/Nc47cKMUIVKiS4lnv1Hz6L0CqVipXJ4axXYrAWcPnSQG7kQVaESpYN9sZuN3EpKxqVEMEpdMscvplCmQkUCPLSYTCZ0OekkpxegUSuw222YTQYUGh/Cgr2f2cS3+yGXy4mLi+Pw4cOMHz/+ofo8stFetWpVUU2K/3immDlzJrVr136g0f6Pe6PX64mLi/t7o30XolyBRqPEbrZgMpsd2XyCgEqtRS7YMZlNWK12EARkMhG71YYkCMhEWaH/VcJus2OXKIzJtj7TGZB/5s9G+/4ZuArUKiWSzYLJZMEuSSCIqFQq5DIBk8noOFaATCZHsluxC3JUChGLxXJn8VYQReQyWREZAEmy3elb3LhttI8cOfLQRvuRBaP+M9jPNi4uLnh4eDzt3Si2aLVafHx8cHFxwW63PzBmWwJQqnG6hy6GWvN8l1xTqVQ4Ozvj7u6Ot7c3RuPDlPZTo71H6oVa/RxX7b0PSqWS1NRHSwB8pJl29+7dCQwMRKFQPFBIRxDEwrug9FgXCG7LZ0p2+3Mtsv+/oFQqOXHiBP7+/vj6+j5QwMexVHQPsSLHiXtCe/nsIggCdrudtLS0/yYnD4FcLic+Ph5vb288PDwe4np7EpdWYX3CYoooimRnZ1OtWjWGDh36UH0eaaatVCp55ZVXUCqV2P629qKAWuuEqSALo0XExd0N0WrEaPn/ZyuJgA0BUbKCqESS/h0ZUA+LSqVCoVAQERFBZGQkZrP5b9vKFCqUog2LoEQpWdCbLIDDV4vNjEyhwmoyYLnreV2u0qBRKRw3TgDsmI0mjCYzEiBXqlHK7BgMlnvdCooFZrMZSZIICwvDbDY/cKYtyBSo1XJMehPIZCjkcuRy+R31Pttd7gBJkpDJlWjUysLUZTDq9dgEOWqVElEUHSbIbsdiNWOxWJ/pe6darWbVqlW4uLhQrVq1+7hHBOQKFQrBhhUZCpmEwWi6MzaZQolCLmIxGrEj3KUYKaLWaFHIRQQk9AUFf1yPgohSqUa0GTHZJaTi5Fe6C7lczrVr17h+/frD93mUDaSnp1O9evUHtju4ZjHHbtnQCHlcTDQx4O2xlHN/lC39HXYubFrAuG/3Mf3HZZR6jh89/1d27txJeHg45cuXv3/DxCMsO3QTMTsF37pdaVrVEasef3Q7R+PTyNWr6PZ6+yIhmZnntzN93i/oJYcfUrRYKdGoNyPbVQUg48x2Np+30LJ1C3yK6WK+1WolLi4Og8Fw/ydESQKlM+6yXH5ZvBJ7mYa8VEHFl7PncibFgFIuQxTAoAnmvTGD8JBZsSFD0qXx7Rfz2H4undhW3Xm9U1MMF35n/KzvMIga5KIEKChZoR59ujZHbbPyrHprZTIZ+fn5VKhQgcjIyPu2Ndw8z9Yj8ejz0nGv2IyWle5qn3GWxZvjaNK6JcGeRS+c3T/O4odt5wmo0pDhb3Ytcj0mHN3GvkR4pX3TvxU2Kw7cLl7ysDxSyJ8oig8UjEr79T1GLb9O29e60rVHf6rqfuPbAwWPspn77QFRMV6sXrWFjIdxn/0LsVgsD/Ytph2ieZ+5NGjRgjatAhjRpgVngdx9Cxiz8BCt2rTAcuxLGo1aUaTb2Z3rOZlsJigogLDgAM4d3kVijmNbR5ZNps/8I9SpHYOrvHjOesAx0zabzdjt9vu+kGsJcs5gYIfuxDuVo1pEIAVZ1zh49DI+JYMJDiuNh5TNkXMJODmpsNtsqJ1d2LL0A3bd9KNXh1hmvdGRWavPYow/zsE0OxGlQwgtE42UfYrz13JRKeTYpfvvx9N63a2p/yC3iCXtHEMnzCcwuhotq/gwY0A/dmc5rpu0Q8t5od9cKtatibdz0ai0y1vn89m6q7zatyN7Pu3Jy29+e+e7rXOH89GqM1SpXuVJJJv8o1it1qcrGPXRkC8IGn2QIGfHPbHLxz+QlK/g1o3rmJChdXbH19MFg74AldYZTHncSEoHpROBQSWQYyLhynUKLDLCo8rcuYOaspK4mKwj1NmGk7MrsjtjNHPjagI6C4RFhqMy60i+eQvXgDLorp3gplFLhYoRxfpO/Li5cXQLW4/cIshFDS4vECqMxwZM+nQRkb1X46zxYMCQPgwpOwHr9E53LhKf8s35pNMLVPAGck6TmWlm9Os1uLhtFsN+yWTfLzOf4qgeH7fXTe6FJEmIciXOJNOxbntiZh/jvSaeZKRmcivPie4jJtK6WU1ctHYWThpPZJfuuNoM5EkgM+rwL9uSqYN7EeWnIGHbSg5cuUx+bAU+m9uLpmGBqDS59Gu1n14je6OyZaOXnv1iCA/i1qWDbDpwgbH+JXB3qoWv59fYBDm6c2t5adhPrDmwFr979DN6RrN48UA8VfDb/DcJnnEOCbjx8yjG/ubK0XXD/+mhPBM8du2RFzrU5ceBrXjr/Y+Yu3A5F7I9CfRWcWnVB8Q07MiBZDsgMbJzZ7YlgiizMvetl/ho9UXk6Jkzfgwbjlxm7/IJvDpqKQDXDm3ki2/XcnDPr3w0ehYGxMIsRiOLprzHxv1n2fnDp/QY8RVZOVcY2bYO7YZ/wbpfvuetdg149YOVz+wj5tMguMHr9K98lvAmPRgzYjidF66lEsncSC3Ay9txSchc/PGR3+TUXf0iajclurDW7fIF83Cq+hJq4LtPP6FyzYrMnz6RTxZtIPvvXenPBUqVmnNb1nBcXYXy6esYMWoCW8+n4B9Ujurlg8gtMHBmy7fsTnKmYdVQdIXrBSaTiQr1WyBmnmfrii/YZ67K8H6tCKtcjXClRK4hm1Vj3sDywmhiPA0YTPbnIjc7uFpLupW6QbNm7Rg7ejZNBk+gkYec77/+Ap/aTdk671MmzlhCckHRf2mFmPq4CjquHN3OkO8z+W7ZJwjkMnDk1zR5uTlfTR3L58u2PqVRPT0eu9FuMXUFq2d148aeTcyeOIhyweF8tP4iDUZMQbx2Fq2fG9xawvwNm1i3dh/IPbA712b8kIbEL8XovJ0AACAASURBVBrExF1uvNn1RQZMGs/6zwbw8/FDDBkxndo9BzJg0FA+nvsOCpsZmRzSlg1i/G9mBr7WnoETBrNp5iA2WSpRpbScfKcKDBgznf2bx7By6hQuP0Bi+l+FUxA9X3uVQOEW82bO4cNZSwElcpOEWl2Y069UoBYE7n7wlckViAJw+RcW7XVh2EuRQCbfbrlJQkoOYZGl+H50G4bPWf7Pj+kfQ0Aht3Ly/EmSb1wnPkfClriDbo1bclVZEie1Ci8XPXM++566XXvgIxhx5HwULq5JVs4f3MHWQ6c5v/sX5n69GovGBY1GS+7pX/nomAsf9ahIXq4eieJdSusO6gA6de9LxQAZi2Z9yLvTl2LGzt51e0lOuoFHWFmOLB9N80Ef82fna+KVg6z7dQ9JV/cy9YMFmK4fZndiHgXZWQSE+fLZa63pt/j4UxnW0+Kxu0fsFhXtBk6i3UDH++2TG9J98kxGtlnAmGYiG1Zu4nyKhX3bv2bwh3O5Uj0Xr06DCAZmbj+ELl/k20VzSM82MXnOQjzj9nIix4mqnoX3F3dXJAGUwMb1x8nLK8PchfOx5BYw+qOPaeQHSyUIjazoaB9RHT/hS4z3d8X/qzi6cBSDDkRwctvHQB41Pd0Y/X09XPwU5OUWmmmDjjyxBOF/6Z3Na70m8cb3JwovnuvkUYLRo4ZTrySEL7pMnUl7SB7UjYDnMvRWQJDMpObeokbbnox8ox90rcfpHVEs/DWdr7t4s2bSa8SFdePjGG/yMrMRRBlKhRyrxYxdUBDbuhvNevoyuLE/IR2+oGfHVlQLtDF97lLadR+LQp9f6BZ52mN9PJzatID3fzewceXPQD4vR5dgwKxYzC6BDB0wnNaNS9Iw0E5g66mk546mpNsfc8mwck0YUa4JI8YPorRQhpnhbyFzqsDE4W3wBrZKx2k7+Svo9eVTG98/zWOfae/5ajqX7nqv9Quickx1VECLwQNZObwPGTUaULvhi5Q3H6XPl7/TvVVlACIDvFHKvOnZ5y1GjRrFyMHdqRXpgy5hP3vSHL9nPnUOK3LUQHSEO3LBjzf7DmTYyLd5b+xIAuUgl8mQ3wnWt4BMhbPz4x5p8SXu3DWCY2+XI3Olc0xJXIPLE+LlxqnTDs3yrPg4nGq3xRO4duEiWYVle7Z9Pozj3p3oGHL716Kp6JzNjdQ8APQmCxFRfjg/lwYbQMKOihCvIHJSkzECmHQYcKN65RLoLv3GiHnHGTW8G7aCXCRBBGsB1y5dR6bIZtPa/ShcnJAsdtIycoioXJGgsEASdi5i62U1LepVwPCczTBuJiXh4+df+M6FutXqU6pUGaL8lcRnO/7Yeoud8IhINGqRxGsX0Vus7NmwnjsPyJZkPEqXp0WbJjhZUrgdIKfTg/dzrN9+Lx77TPvy1hm8sOwiXw5rQeb5/RyI9+Hz+X0BCIioT76wkJpl/UBwp1bVEFYdEggplKR+4aMv8VCUJ7xxOj3repGlimbauF582OETmvl58mK3LrQrp8NecJWvl55m8sQFBE6KpHTdbHo1CybDHEz/1yJYujqRcwnvM635FEK8jKTqTzN74Q4+HdDovwVJoF2fbkxs1Z8fopahOr2c3UGDWFHPm2zzy1Tq0Yefq3zF/gXbGDVuFuRdpHvDKJpuSOA971N0Hb6Ut/ck3PVraoaNbMGU0aOJmPIKny48Qu/xX/HwtaWLGxImk0Tjlzoze9kopn3TAI9zPyI1GES3UikMb9Gbm5WG0DhEID3FjkzpQtrVNTRo+QWbLx1g12fVWXdpGq+Em9myK4MJH0+grOUggZ0mUWnCesLcJPJzng9f9m1qNWjMx93fZk5MJKWzt7NfWZNFbWpw1dCBzpPfpW7oh2yc8Q3t+r2HhzKNyqWieX/nTZRbJzPk63180KsiezZupP/876gSXooPuwXTt+NHLHgrgonf3OT9ZW8+7SH+ozxmwSgHqef2svt8BlE1G1M+yPWRd+rM7g0kycJ5oU7EHx8aUrlwSyKqVInCD2yAw/96Yf9vJNgCaFmvaGyy2Wr/Q9QfO3bEJ1H14ZniYQWjQOL39WvxiK5P1VJ3K4Ib2bZ+K+ENXyTE5eENR/6NE2w9b+DllrX/p/1+VnigYBSOCBKFxhUvtZ7VP2/Es1wDakWXJDc7B5lcjZNWRm5OPvbCjFOZQo2TVo7JaEMUdOzdsg2TVzma16+EXZ+P3mBC7eaG0mYlX6fHXkwK1KpUKpYvX06NGjVo2LDhA9sf3LoOMaQ2Ncp6//FhTjxrdl2icduWuN5jzOf3bibe5EOLJtW42+qkndvD0XQtrRrG/P8H8pQ5fvw4mzdvZty4cQ/V/j/BqOeM/wSj/n88tGCUJIFMjpNGjc1ixmh2uDQEUQD7X/NBRVHAbrMjypWoNSqwWTAYTNglR0k8URSQ7DakYrT0eNtox8bGPpTR/o97c/z4cTZt2vSfYNS/FRcXF1xdH/3p5j8caLVavLy8cHFxeQjNHMmRiq1UonV+SGN7W2RKoUCt+R+LVj8jqFQqnJyc0Gie2wWMfwS1Wv1IsfiPZLTd3NyYOnUqWq0Wm812nw1JSJJwV5JCoWhUoRbx45tLFG5HFBAFsNvsj/n3ixdyuZwDBw4QHx9PSEjIfbVHHCFoDk0HO49Lc/8ZLVH/iCQmJt4xRA/7IHq3bNGd/4X01xn37e+KSotKhWXM7kZ6pnVHABQKBWfPniU+Pp4jR47cV6tFuquq/B/aIn9te3e7e/UTChWn7vzPH7tN+WcRRZH09HS8vb0f3LiQR16IrFGjBnK5/L4qf4IgolRrUMkETBYzokyJWqXAqMt77GXuRVGOWqtBtFswGk1/FPP9l3Fboa5EiRL4+fndV25AkkCh1qAU7NgEGXLJis5gurP4JcoUaNQKTAZD0eMpiGi0TqgUMiTJjlGvw2RxFFSVJFBqtCgEKwaDuVjKRd2eZJw5c4Zq1aoRFBT0wBRtQRRRqdQINjMGsxWZXImTsxMKQUKv12E0WYocC42LO05KmUNP2lH6Fn2BHrnGCVXh+oskWdHl52OyPNspYQqFAlEUcXNzo0qVKveZJAjI5HJUKhWiKGAzGzGarSiUGpzvKktnMeop0BsLjw0gCMjlSpRKBXKZgEGvw2K1I4hy1BoVMlFAslrQ3yU+VdyQy+VcvXqV3Nzch+/zKBvIycmhSZMmD9U2fv8alm08itluITcrh7R8iWHTFtAo8HHfE3P5etxwFhx2Zd/Wz1E95l8vbpw5cwYXFxdktwtf/glJklA5u8Gtkyxas4u8HAM1X+lDoxhncvPNyORKZNYMfvphIzXadCXCXcRsdZS/cnJSc+q35XyydDMlqr7AkIGv4i0zYbRKeHp7cvPENn7eE0/9F1pTykuDxW4rdukhgiDg6elJjRo1CA0Nfag+u3/+lqzAerSrVwr015n67oecM/jw1thx1A0t6qraMW8kMw5k4+akRAJkGj/6jRrIhaXT2Xg5DxelHSM+jJj4ITX87r/g/yxw+fJlgoODqVOnzv0b6pJYtX4XKalZVGz1Gg3D3Um+uJmPpv2ETaPBqDNR7oUevNO1qG/cmnKWH9fvJCFLzoDRb+CYj9rY/MO3JGTl4R7dii4Nyz6p4f0juLu7s3bt2odu/9gFowBm9Iql47TddB89mclTPmbO/AXMqJvCxDVpj7K5h8SNNg2qkJ58iX970qPdbr8jeGQ2mzGZTH952QQZliubqP/yBBp1H8iQrlEMbNiAnala5JIVuVLG6o+HMOy9b0g3StgsZsxmExIyzm1byLSlh3i53wDyN46j+StjybKJaORWVn82jDdnb6Nagwb4OcnQGw2YTffeh2f1dfu4Wa3WB7iWCjFcpmfL5hyVwmkcUwopP4kxA0dA1c60K5tOzYhKrD2fUaTL7o0/YfGNJioygrKhHuzfvgutRwFLlx2lfLUKBJUMIDDQHwXFQ3b49nG7H+akw7Tv/g7BtV5iUIdohrZuyaF8IxdO7SOlwImy4aGUDAzAz01T5KnEkLid9n2nU7vnYDqFnCG8fBcKgHebVuBcYHveeLM320Y15v1fk57oGJ80D2NT7+axx2nnbH2bkT9YOJc7g9J3TXsDOk/mfZ1DFkaffp39xy/iVaoKVcL9kAw5XLmZS2ipYK4f3UmixYu6tSveianOTrrIkTPXCatch3B/lzu/mZt6jYs3sim4kYFSpb5zB7LkpbD/0Cm0QeWpHhnwuIdYbPizz1CSJORyGclXTnApRU9oqA/+odUp6++GzWZDqdWSeGQd56XKxFbSOSIhHL+E3WZBdIlg3NQu1KkURnXXiZwcfQBULpzaMJt5BwTWrZmFhyWHmymZmO3FX+jovtgS6d28M/XmH6RveceVqpOg08jPqFoxDKhN//X1Sc8vqrjYuNcUhrzSEw/g0ta55Hi8T1kt+Nfvy5hBr2LnCWS8PUHuJ651m/TEOE6eT8bTzxlRXY6oMoFIFitKr9KMGP0idarfu87qju+mQ4XBlFICXaZRcWgER68eZsXeZD4Kd1Rnql6lLOl/q+1ffHiU/8pjvz4WT11BcLvXKfdnP4VHRWoFwo29vzDhy5+JO7yJjo0b8ePFLKS047zRqR7N2vfix1/WMrRrfRq/8yMApzYtYdqiDZzZtYoWjdqyK8kh83pux4/MW7aJC8e389m8b7heoEAFZFzYzeTpX3L6xC66NWvMN3uuPO4hFmssJishNTvQNeQs5cvUoWuvLxi05BealLRiM6WwZttVXu7UBKvOWEQu0m6zUjIqhlKecO7wNhYtP8mH8z4nwseJX775muiqkXw6pCfvfbUFnNxRyoSHXsQrjsStXsFR91iiri2i1xtjOZ4FTq6BDoNtNbB+/nTCX/uYvrGBRfrVbNsFh7R8BtPm7eeVVxsBNs5umUbzBlVRy5S89cVvT2FET46AmCZ0DL1B1fAaDB+3hJ6TP6GmpzMFN08ztFdrokM8CK7RiaPJRZ+VLx07h1NgUOE7Z0J89VxOC2bWyNp0Ll2avoOHIXaYw/jWIX/d6HPMY59pJ6faUATf258KoI6oz+SKTVE7Q0HyUebP30WXWe15oXZZDnm+yHsTX2Fs15o4VZpE6iddCK7RmvE11WhVeVw93Zx1v12meksd/cd8w+LtWyjrBG3CXak9fCMmwD24PEPfqYqHWobq8kG+WrOX3vXKPO5hFlskuxXUJeg8aCRZq49ybN08NpxI4+CWhVz48Qd8W/SmrPosFruAzW4tnEnZkSTHAmX2md/4cstpMuOPsbjHAKL3TmDF3gyiSmfwdvumjOvcmZ1Hp7Dis76IBXnFckHyYdh1eh9n913l+uDu1PfeSoyXgouShQhSmPfRQpJzr7Lou6/Jd9/ExA6V7/RTKB2zma/feJWw/l9R0QmQVAyeNJce3RqA6SCCuhaeQXFMfKl4+2rvoCxJl8HvkrluP7/OHcuSdfs5sX8NkZXb8PWmqVQJVjPr9bLU6ziIjL1LcCqcK+Rm61BqbpsoO05aDQaTmUa9hjPwxkKO/r6SRV/9SsC547QK1z614f3TPHaj/WLrID5buYVbvIn/Pb6XZ51hwpL9NGpeG51Rg0rpKFFkNMvwLRSsEJUKvFxU2ADd1T3M2XCFJo1jMGvd8RSV5KVsIltWEu/CMFejyYAdCREwZV5j7sLVRNdqTI5NRLT//Q3k34eAWqPkyNIPGHskgPO//Yox7SRNy1fhw9k1EU4exXQkiR0Ft7iefJWpI4fxwdxvqeScg95sQy7Y8KjUkqn1uqA2vEaDqMbs2X0QpViCwUPfpFUVf7xm7uel+RfI10toBKHYrurfHzNZxnQa9BpJ9xZ1oUU5Vi71Ysk+mFqnBIMmOJIkopxfZtjcxUzsMKtI76yjC1lwLYq9LwQ7PhBC6dGtcNFTVZNP66s5mpAKPB9G+/Smr3h/bTLrFy2GhdPoGB3MyAWn+HlU4zttXuk7iTkjfsJgBqfC4jWenhqOZd4uoCJiFp3w1e2jXtef2XVkFc7AjK7lGT70c1ptevcfH9fT4rG7RxpOWYDP9XWM+nDjPb418vGoYaRVjuGFRo0ID3ZFlDtmHmqlHLnCYbTtVhsWQY0HOqa+9TbOVZvTvFETQtyUiHIBrXs4CaeOk1oYJZOUlkiGXoULsHjOWI6Yo3n5hcZUCvVEpvgvGegOAsgFG1dOXyUgpgKY8lH7VqZJlAdq38q8+8Ekhr01hDd7dyHIP5j+o8YQ5VJAXoEZjcrGlg2byLUKYDejy9Ej+gXiG96UEFUKtzJ12GwSVrMd/wA3FMrn2T2iJFgdSnbKrcL3RnRWPzwNV7lw9Q9dFp0JalWuAYB0Z7HOyLuj5tKhV1fUAFIB27du427Pd7zZnRZ1H1zWr7hwI+E6Xv6315b8qB1TFx/rZU5dSrnTJiMnjdh6tXBTgyQ5FuYiq5Xn2uEDhS2SSND5429K5Koqmtv6b207NqCE1/OrdHMvHn+lHkVF0m7upkmDlwnfVJt+3VrgIreReO4wUa99Tqumtek4fgbfWDM4Gp/G9fxTGBKDOR4fT0LqL6QMbYyPQkuo8w1+OZdL+ZphTPtkCsG61pxJTCNn16+4vjaCST3mULd2fQZ2aYUsNR0n+1V+vpBDbMXqTB77CbPLWkk4k06Sbj9pus74Fu/ks8eDJGGwyGj7Vl9+fmseC8KVaJJ2caHsO8x8vRaygixUKfGsP3KatHQDJ0+co0aQib7NGlJ74XkaZx7kzaEH6N62EUnnD9Np3CwahUdindybObM+ISi/CWv2ZjF25DhcJCMFz5G86J/pNmw4v7zyNos2N8D9ynpKDVvC27H5vNxhDA1ffYNgIZk077asGNWd5EvLCao6jX2XT+G8YyzrTJXY285hzBGcCdSfpUu/bfRp14CMuB1UnXicXlWLaZHNe9Ck7SusHDGNbzdGEqI7w9VS3flyTCem9G7P2hqdqextJy5RzowP30ZhSaKUsiyjNp1l0JgFRDfpwpz11fE7vYi6YxbS4OUwBnzbnDHf7uSV0By+2+rMooX/CUb9LQ8rGOXAwMk9uzmfmIO7ty8lAktRtVwIYOLk7l0QXIPKATKuJmTj4yYjPdeE3WbGKywST9FIYnISksqHYC+RA7v341W+AeFuOuKu5xEZGQZYOXdgBzesPjSpW5nMxMsYFX6E+Ttx7sAu9J7RVA935/q1BNz8y+ChLU5r8v8bdrudEydO3CdO25FBqtKoMaZcYt+5ZJxdvakQUwmtVY9ZEjAVZJGakYdSpcRoMBNQqgyGjFTUnr5obdkcPnKMLKuGqKhylAryxWI0IpcLXDp5iGSdnNIR5QjydcFiNBXLTDVBEJg3bx79+/enbNn7uydsWdfZduQSHv6liKlYBhlw7dR+Tl3LIqB0BOWjw9GIYLcaSU/LxqekP/qMG+TY3An0uzt+28y+3zaTJ/ciMDSCCqXvHU3xLLJo0SJCQkJo2rTpfdsZ069z6OxVlG7+VKgYhbNcICfxNLuOJeAXHEp0dAVcVAAS6SnJOHmVRKsQMWZe5/CJy4geIdSNKTwf5kx27zqGzNmDUuWr4u9SvF2gT1ww6uGN9n/80zzYaDuQAIVCiShISAjYrRasdodUkSDKkMtliIKAZLdjsVqRyeVIVit2UYZKqQCbDZtkx2qxOKqriCIKuQIkR8FXywOyCJ9VboevzZ0796GM9n88vNH+j7/niQpGAf8Z7GcYURTRaDSo1eoHVneWCtXlBEBUKItcCJJUqBUjiCiVSkfbQqEwe6HmjIAMperuG4OjvSBJqFTFMy9VEBx+eFEUi+0Y/mlUKtV/NuH/iUqlenKCUc7OzkyaNAmtVvtATYb/+GcRBAFRFElISECr1aJQKLDb7c93gssTQBRF4uLiWL58OWq1+qEWU/8QQOLe0TJ/4ya6WzSqqEBU8dHTPnToEC4uLhw/fvzhskj/owgymYyMjAz8/O5Vj/7ePJLRttvttG7dGrVafX/BKFGGUqlEoVDcedQGsJh0FOhMz23s7tNGqVSyZMkSKlWqRExMzN+mx8qVGlycNXcsjCAI2MwG8vVm5LfPGRImkxGL9e7zLCBXKFAoFMhEAavZiMlsvXM+5UoNGqWATqenOOp2CYKAwWCgcePGODk5PTi9WJIQ5Eo0Gg0yu4l8M/j4eKMSJcf4BQE5FlJupWOySUUMsUyuRKNVYzcZ0BnNKNRanLRqFKIMoyEPncGEJD3bllulUpGRkUGpUqWIjY19gGCUAqVSjiiK2K1mjMbbomICcrkcuVyGzWLGYit6vSmUShQKOTJRwGQwYJZkuLs63XUTFJDsFnT5+Tzj+lr3RC6Xc+7cOa5fv/7gxrf7PMoGCgoKqFq16kO0tHBq9zZ+2/Y7py9cJUtnQSGTaPDq+4zoXONRNvkfj0jJkiUJDw+nTJm/TyhKObWBseOWIbq5oUDAYsjDt2Y3pg56kQsHt7Lr5EXyRV/ad+5AGbeil0h2whm2bt/H1XQTjV5+nZpRf4Rb6S9u4vO1uYwa0bXYlnWzWCzExcXh/ICiopIEKq0Txsxr7N2xhRzvGLpUUzJ5/FtcyZfhpFaA1chNawm+mPUBJeVWzDYJQRTRqBWkXrvA3i3b8a7UjMYxpbl15gCbtx4nIz2biIadaFatNHbzsz3BUalUaLVawsLCHuj/16deYuv2vcTfzCayzgu8UPOPKlNZNy+z78AJQqs3oUKwZ5F+ty4e5fetB7iWbqfzkKGU115hcN93yBe0aJQysJqwu4YyZvL7RBTT/Bqz2cyFCxceuv2TEYzq3YL3lh6g1RsfsmzlajZt2sDKeW+w4qP55D/KBv/jkbFarQ88RxfOnCBH8qBSuUjKVamJ4dQ60tSlOLZ8AuO/v8jLvQYQkrKGFm/MK9JPl7CT4ePm4VP/VVqG5NK3Szcu5BZOb0wZjOrfhc9/OkJxLktrsViwWq3Y7XZsNts9X1arFZlSg5S0l7dHf8oNyZdaFUuRm5nItZt6wstGElEuGnfySM614q5RYLZasNlsqNRyTmxazEdf/IAisBIVy5REnnmMNwdNpmSTrgzq1YLPB7zC7/EGFHLH+fy7/XgWXpIkPdBVakw8TL+3JqMt14zebUrxXr8+7Ex1RKYfXzOb0VMXIy8RRZmAogZbn/A7g9+dS5UO/WhROo5GTQdivX6ePQlyqleJJrJcJTws1zl1w4RvMTXY4DjHT8yn/TAYd7/D2F+snE//kNJ3rU8og9qwdm11/pB7MpKWqsPXz+svv2E1Gu6qpg75mbdQevk7ZFf1WWTgiXeRk2Qn+UYifsEhfxmQMSuZfFUAPk7wvIj0//+w41yqJpM/rUd0sBqIZ9fafnzfuxwftttKVuQE3FVK6taKRXWpaM+jv6wiL6QCjUo7Q0AXQj/7iYMnbxHVwJcNa/fSpF0zft2ooXgHYN0fSZIcJcOsifTvO54mMzfyVsNQcrMyuJHnTLu+w2nVvBYeLjIWTUmlYkwPtHYj+XaQq7TkXdrDR4t28uG3K2gYpiYjx0TOvsPsibvFVzXCCSGcMq56DGbxublSM26cZtPhRBZX8kdJLcr4uCFXKsg9t4V35h7i61++J8zlr/32/DATc8SbRHupoct0wt8uw4rjLXl39hd0Lu/osPCdvdTsO4X7Pxc9Xzz24OXpI36gZIceRQz2bfxCHUV59y15n9femsbqH2cSUiKK32+ZWfNeUwTBgw/fbIUiujlLv56HWhCo3H4gw/t3Qy0I9J0wi7FvD6G8u0CdN77CBqSf3Mhb/fszedxAFILIN6ds6K/txE8QKNOsF++MG02Ys0Bs/3mAnbFNXBCEEFacyEfKPULNQA86vffT4z4MzzAi1Ws3KzTYJqb1H8/Lsz5AAMZ8/A5XPm1FWO2XGbU+j53LhhTpefrsRZw1hXdLtQfeHlos2XnkXD9Gjls4TaNLYDBZi5VK3f+CSqPh8IpF7KM0Ja6to0+f/iz69RSB4TVo1bgyNkSO/fwZezNDaFc/Cp3BDEi4uHuz8ot3MHtX5PwvM+gzaBwH4m4R1OINRjayESWUoGW9asRMPUyvWGcMRkeBieJOYJ1uTKqVj0oMo0vPd2n24RzqeshYMmk48gp1OblqFmM/nMnZmwVF+p3cexSX0MJUfzSU9irgmnP5Owb7wg+j2enyGi+VLf7H6FF47P+vXIOAYL97QcJOxs3rxF2+RnJaFiRu5/U359JxwgcMGPoB/csl8sb0g7QbOQEftYZm41aQvm8lPfoNokclBbFth7Dw5x2sHliZXUdvMnXuMq4c+46UXUu5rAfXgKp8MGs+8xctp181dzav3II2rCFD66soV7cTs+cv48a+Odzav4YzBhlT12wlJtQZDy81giaMnqMmsGBi58d9GIoFiQfWcV5bmfalHH5pm1MgXd8cRrfKTuz8aT69Rv5YpL3JakF2Zx4tIJOpcHKVOLH7CE1aROMisyFXaYqtP/vhEJCLJi7ciMdYkI9B60eIKoWR3TtxOE8LNglnWyJzFu2hzWsdURqycTiQRNRqO4e2JaAXBTxDyyFdWE3foZ9wy6iibExLur7eDlv2dRbPHs+2c1koVXJ4pr3aD4ldwL92O955tzO5F3fy0eCRXMjJ48iRRARbLm5hZUjdvZiX+k4tks5vMtiRy2+bKAmlQo1gc7hi7Jknmbwul1FDm/3jw3naPHaj3bKFK9e27CDvzicSBl0aK9/vROM2wzidlE6OXsaVPWtYsmARHi9PZn6/6ki5eZicSlGrpDPeJRwzcrtMg6+vQze3RIA/niV9AbApg/FWS9gFsOWcZUyf3nx/OJXKFcOxmByCJKKoxNfH4SNTeJTAVSli1AMu1WhXW83ytSfIP/8DBW5N8Xjep4Z/w4KPp1C7ZTu0coB03h3yPqV7jmTKvO849NO7rJnRj6t3tXdzUpMv3p4NmRCcveHMcjq/NY1urdrSYNjPZ+s5pwAAIABJREFUJG2fQfM3ppNh+OfH888gIGAlU59FudrN6dihAxMmT6VRiSx+2ZONf5A3W2eNITWqG82iXCkw2hAEEblcgUxmITMXGr/ckTYvvsLU+R/hnXKCLTPeYu7VKJYtnsfGIxcon7CCOSuPIspVxSL070EcX7+YVVcVfDx5GpsP7aCq/BCfLf4Fq3MJXm77Ko0btmbOjPfJvrCL7Lw/QkDc3BTkZN+WaxUx2CX83BzCtivnz8a7Zksquz7Pzrh789jNVZMZ6wi9tYruo5YVzhFkBIXXYNhL0VzKkFOuUjBa0gmp3Y7XBwxg0OBhNI3UYLLasJmLLmHZrBZshbFjdosZW2F9SbvVhNkm4KqB97o3p6DGKLrXjcBekIPVLha2MWO+3ddswmK148g3kdOtZz8O/TKG92en0aNXhcd9CIoFV5f3YW5ifXq2/CM1ODFFj7fW8ejpFxVN6chGhAJxJ4+SaYfqsdFcOXAFHUDaLfJNENZ+PMmpF9m6aR3LxrXEJ7Y3K+ePwvu5LdAtYbNrKOtdlszEGxglMOZnkWfxpm6dQNL2LqHP3DgmjO2GNS8PBBnYCrh04RK5BjXVYzXcuJSIxSaRn5ODZ2gwudmpiC4lES35mGTe1KlVkRA/D8D+XEy00zJSEcTbz18BxIZXIrJ8XSqGqoi7mghAZraeqPLRaNRw/sRR8uxQq2UTrvy+wdFNf454QxSxNfyw3dzEhC+P0aN7u6czoKfM4xeMohRHT6xm0Og5dH7jLC82rI6Pp5ZTuzJp36YNcm0t5nzSlzcb1udc/04422zU69Yby8EjuCnyWL79MC/Wr4Hu3C4S9GosZ/aT16AO28/nYMqN52S6jTLOrmitWWw9cJNWnbowbOlYFoYN46bVGUvOZS4nX+N8vjMpF4+QZq2Js5M7TuRx6vAlYlqUpVST1oQPGY952HIePqT9OaLgPP1HrWT8mhzu2FZlJEMGNuSr2bPx7dGQ0zt2Mv7Lz5EZrjO0SzuaLTvEyC5DqfvbKL5cup4A/QViXuxLzWA1Iipuxp9n894E1Pp8dhy8xIuxZZE/l08wEkaTlYadX2XDsenMW7QC97TDhPeaSMcSSUx6+xuiOo6kqquOjHQJUakh+/pm+vacxxfbttHz3Y8ZN2sZK9ZLJO3eQ7M+I+jVQGL765P4eKlIoDWeyz4deLNjVSzGgudi6bxBi7Zs2D+HxWu2EWSNJ6NieyY2K0Oy7E1GfLmMDWVtXNl5mjeGDsddmU2bV1rTe8kJer36PrU2Dmb2z7/je3UtLT+YR3kVTB88kqgeU4h5+ALmzxVPTnvElMPF8xdIzNDh7OGDl5cXwUGBqOUAdi6dOsTVFAMBoaUpFxFC7s0E8kx2BIWGkgElMGamkJmvR1Q44eOhIj0tG7sgw92nBB5akeSEa9i0/gT7Krhw+jwK3zKEuVlJztDjpFVQkK/HLirwK+GPVm4hKTEZmcYTf193IJtpH37J6++NpURx/0f8ic8//5xatWoRGxv7t23sJh0JSakElS71p7u2lRuXz5GYacY3qDThAZ6AjfSbqWh8SuCsEDEVZHD2bBx2J38qR5dCIQKSnbysdDJydAgiyDRuBPp5IRbDY6vX67l06RKurq73zoaUJCRBQK5UgS6Fg8ficPUvRWR4EDKLkeysLJTuvmhFm0PPRRARsJGfr0fr4oZWrSA94TwXE3PxDytDqQBfBMCYc4MTZ26gcvMkqlw0agyYLM92RqtKpWL58uXUqFGDhg0b3retISuFsxfjkbn6EhFZGie5Y1xpV08Sl2oiuHRpQvy8AYm0Wzdx9vJHqxQxZidyOi4RuUdJqkaEAlYS4xNxCwjFVfXsHptH4YkLRq1atQqlsvguNeny87lw5HeOpTszoPPzJ3Izc+ZMatWqRc2aNZ/2rhRL9Ho9Fy9e/HujfReiKEepUiBZLVisNkd9R1EEyY79rpTQ2xIDdrsNSRKQKRQo5CI2swWr3XanKpBSIUeSrFjMVuzPuBa5IAh3jHZsbOwDjfZ//D1PXDCqOBtsgJ9nfcA5ZQQj334+/WFeXl4EBPx7ixn/f9FqtYSEhODi4nJfqYZ/O4IgoFQq8fb2xsPD42nvTrHG3d39gQJvd/NIRlur1VK5cuU7YkR/RUCtdUKtUiDZbTiEbwRsNgtmkwmT+emKTMmVCrJvJeMSHMGxzd+Rk6d/HtZ57qBQKEhPT2fmzJkoFAps96lSLYhy1GoVMgHMJiNmqw1EGSqVGrVShsVkxGAyFxFAEuVyNBotMgFAAMmKXm/Aant+jJsoindcEg+T/SsIjpmzTJIwWSwIogy1RotCsGMwGrBa7UWuMUEQUak1qBQyTEZ9Ee0WEJAr1cgxYzQ/+xXGZTIZWVlZaLVatFrt/a83mRyVSolcELBYjJjuHp9MjkatwGIwYv2TaI0giChVKgS7FZPZUuRYyhQqVAowGEzFtqydKIrodDr69ev30H0eyWjbbDZOnjx53zZ51+M4mZiOSq1BJtgw6k2o3LwoFVUOr2dgkp4df4xVv56lVa+eBDyHEQ7Lli0jNjaW8PDwB7a9ceUC2XoZERXLcrtOSkHKRQ6dSyeyWl0C3P7qM7xy+jBpOgk5VsxKT+rGRD3mETx9Pv/8c7p06UKJwtDTB2FITSBbG0LJwqy+S8f2k2p1oU5shXuGZ6VcOcW5RD016tfiL/r9BQkkCSEEFpNKS2vXriUoKOihNIlSblwlLd9Mqeiov2Qw3ryRSMngoHv202fcxOxWEve/LKUVcD1ZIjTgHumUxYjr16+zcuXKh27/SOv7BoPhgToDanctM/s3ZfQn65AEifzsDI5sXkKLmFiW7nh4JasnhVGXy+yxQ9hyKvlp78oTISMjg+zs7Ps3Mt3kg0EDOXBTT37cKl5oM5Rc4MLqWbz65gesX/UVdapEsejgtSLd8i4e5J3Bg3hv4kTGjhnDxE+/Ii7X9OQG8xSwWq3k5uY++BgCIPHNlDFMWfIrSekZIBlZNGkQH8z7gW+m9ie6dk/OZRWdrW//YgDdBn3Agk+G4OdbhR8PpN35zp55iqaVQvlgfc5jHtWTIyMjg8zMzPs3MqTy+cR32XomiZwzm+nWcSiXDX88ne1d0J+KLftw9k/Dzok/xKzPPqR1vYaM/W530S+lXN5uW5t2Y75/TCN5emRlZWEyPfz/6JFm2qIoPnBxRukeRGSgO2lhFakeEwsxwEvteb3q2zi92JxSFy9R99431H8E/7BoIoI97lvZpbjzoHOUvGsJEzfbkebFADGoejuxJ28Wumw3Js34hgohWsJercac1QfpUzPsTj99roW6fUfyVo+uKAC73YYoPl/H8VGKEa8a044NroP4ZXQLAHIzLqIIacY377VHbbpM20qN2HDqKtGNIu/0uXrDwEerVlPTFSa1DWfml7PpWGsyMuDY3q1kZEKEUzGZZnO7mMb9oziSjm/my53pXHy/AVCZ2TOaczg1h/BQT3KunuBwYgZqSY7sT9ZI6RVKu67dubVnEynGoje/S0d3kJyai9z3+ail+ShRQk8kktYmCVjtRQ+ytvmnlNNf5rsdh8CSwMfjxjJu5EA6D5xGvsnIgmHtqdvnY9bPGUFESX/qd5+MDci8fIg3WtSk17TvmNi7GT5+kfx4/CrrZw2jXEhJBn++2bEBUxpfT3ubVvWqERrTkQQLoE/nyzGv0rrn20wf25+mL4wEBOyAWuMMeVd457UXmfDt/idxGJ5Z/CPrEa37ih7T13N23QzKT/yZF12hc+/XqRCixZh+kfySDZk9sE2RfpIoQ590mTmTR9N3yHgOX/n3ajam7V7Am+tFFg+rzNGjJ0nTS7h5R9KjR3vUko3Lx64R0XoAr9eJLNLv9UmLqFlYHjIyPAh3dQlkwM3Tm7lqjaBRpcC/qaTwbPIwxsa7VAWCr3/Li6MWcHjjNiq9NpLuoZ5gSWHl1nN0f7U9crPpL9ZI6+ZHSMnSOMngbvkse95ldp3T0aJJTWRC8TlWj4snmP7w15MZqAB1fgKv1m5DwICpTPlsPoEHp9Llmzgax0ay74fFeLZ8nzMnV3BzxXt8ut+MV3gsEU5p/LDhBIPm/8rPwyLoGvsSqrYTOL5nHsuGd2dTCmRcPk3wC++wac8emplX0//LK6D1oX1VZzYtXYmsXAu6d6gPkoRcrkSffJIv53xN9KsfMrFn7Sd3GJ5BxOB6fDd1EBs+6kmFlyYiqP5YFLp8ZBOffjybX/ed4NCxq0X6qT2UGDLTUQZEokraSZP6LThxqzgLsf7v7NpzBEmm47sFXzNuUCdqNejhyBQll43ff8ecedM5eS2VpJSiIkgKVaFjVn+a744reee9wUAOm7Yk8UqHNihEE1q3Z2Dx5zGi9o/hkyljuLxmCrEv9SAuy+EKuH74CP7VW+NXxgNkKlzuKa9qxy5Jdy1ASmxdsZ5mr3bHXxSQa4u3P/t/4QlkRN7mz3dAC+ctIrGGq6w6l07pbUtYYChAX7IM3jIFvoH+eFRuSp1wN6AeDaNLkBB/E2qHElzSg0oV2uOjkuHTsj7q2QaqhXmiph3VPDqRlKmnVfmmyBdOY8bxADwC/Tl6NQEog1+wD5rwWAa+1sGx2KZPR1ZwmV4t69H/hyQWtPj3hcdlHfuBsTv9SUtP5+bBbwip2x7/cgmMaORPYEQN3pzQiMYrx1L3lbb0kxJwL+znUboqkz+r5rgdd6/N2aiGHDxxgyr+pZ/iaJ4GZq7lXsIvqjmDh49ncK9mRHnVZMG57xgR7Ups09Y06dCC9/q/QJdRs7m84t0/9c/n/UGTaT9tGU0C4bdPR7HoiCuqrz5m98UczAs/4nf1yzSJjbzn1osbGed38MUB2Hb4IqYzK2jUrg/jg5zJWzsLWfVErqzeQerN00z59GdmDW+P8p6ptI5JYMKmGQxfeozRivls2XmcSwV6Vm0oySsv/nsmXk9kpq1SylEqioZm7J/YCqnZeN7uVAWDwUiH3q8z4M3BzN94jG/7lyNHZ0S8c7JsiDYBtcbxG4IIstvpdTIZglx1R2tOplTg7a/lx95VGXa+HCN6vUbD8qURbzvIBAFURWcuyhKVWLJyIRv7BvL2d6efxCF4plnz9Y+4tXwBuUxGcJ1+LH7RgxPnr5Ol06Fx9cbTVUNM9RicXHxRAeYCR2hkZkbWXc9PckpXqkbYnyqN/DtQ4qn0wd298HbmHkHZ0m6YM03YbSa8S/ig1vhTrVQMvmrHtWfU6Qr75rHgwylUGzOXXrHegJ0S1VrRsVYwVqUWjUqBk6vr3xiu4sn+Xb8heIYQ5KmlTIPXmdC5KZcv5dG6axdC3BRo3FxRKpQ4OWkcZcWMOv6ocieiVChRaR22wF4yhsEdamCwKXHWaNBoXNConuDc8xnkCYw2gx07kijQrWHNOhs5aWkkXT3FxoNubN00ERdNJp0bymnfbARzRrciPyWNyOatubzrMJl7L3PycjaVw13I1eVxfNfv5DZvxK49Vzjt+TtZumpc37UfQ9Ih9iYaaB2k4Vaqnq2/Hycq305G/EZ27Fby695DXFTsQ6IOx3cfwnA2jm2nb/JixZLEHdvJ9r2n8OtXg/Pbv6di61rEn5zJ8s/682+pv92gbQM+nTaL8w1nIiYd5DdFG6Z1C2TC0P6Ubj6AVuWUrFl2kHeXLkZjTqLPyy/RcN4GfH6bw0GhCi/XDuXU/gNEv9iXJuX/nYkVrVq+xPSBX3PgSidcziwlI3IAHUoeosdri+k56m08Mk9xLN+Nqe+/RXbSNl7o/BU/bPuJ1a83YMKOfF6MS2RhTgaiTzVmz5jCiEaQcn4j8/V6JJcwIkuXfNpDfGxUqFCF2WtXsv1sc0JNR9lrCGHMlJ5U8YIm+RnsWnuYvAIbURUrIbNl0qH+i3SduYLOdQK5fGIr206e4bq0jriGIURUbsigyg3Jv3WeM8s+I9XoTHTF+5c6e954/EbbYGbcD+vQeDpjM1lxLe1O2Uq16T8uGl8NgBdfrDvNt19+wbZt+6jVqCVhrnLEtm+xub4JpWgCPJiw/FdSJWd0BWY6f7qaNnINel0B6tpD2LqlL34WHaBh0a8bMPi5EfPVLzgvWYlRU5q3F+3hpTTIz8lH1XA827YYcFNYsALq/2PvvMOjqLo4/M7ubEs2vZNCCC0Qeu9FQKRKkw4KCChSREGlKIgCNhREEBTpVZHeeyd0AoReQkIIIb1tn53vjw1NIZIPUIO8z7PPk92Zuztz5+bMnXPP+R3fYvyybhMqXw2uVbuyY0shImOF5164/36KNh/MavfNbFr9G/7+wXw2bQqFPZwZ9GZnlq3byg5DRZq/NZqyYQFgy6HPh+MoFOBNofY9uTx3GTuOyjSo35xSpYvxGCo0zyWF6nRn82xffluzhKCiFVnwS23CfGTeaHWaXStXUqFOPd4Z2ZrC3hoshjKM+XgI/joo3+Z9lrzhjdIuYTZlYxO90aslsNvJMLowcdkObJJItsmGzz99kk+JInU6MOMzH3buWU1iQAk+GjeeUrkFq7KzMtCHdGbzpk5otNnYFd4MHTOW0BI+gIQseDDyx9+xC2pku4QEKIGMbCMdPplGS7NETlYO+LnncQTPF89OMOoF/wiPIxj1gkdjtVqZMGECHTt2pFSp5y9x6Gnzyy+/ULhwYRo3fv50fP4u8isYle8JZn7iWF/w9yPL8otr9IS86L/Hx263v+ivp0B++jBfRvuOSMwL/r14e3u/EIx6AlQqFX5+fvcWGV+QJ4GBgS/66glxdXXNV7JfvnzaKpWKRo0aodfrHykOoxRVaLVaFNixSXYEhRJBljCZTHer0DwpgqBA6+SEKNvIMZrvk7F06Bw7adQYc7KwyQIarRbZasZsexIBHgGVRoNStmOTJKTc2YWgFNFqtIhKsJiNWKwSgqBEq9Ui2yyYrLa/dRaiVCqJiYlh1qxZuLm5PfIayTiuk0opIKBAlqyYrTaUKg06rRpBEBwBmzYz2cb76n0KCjQaLSqVEtluw2Q0YbML6Jydc3W1ZRCE+7YVvBmYKIqcPXuWNWvW5CGM5hDc0jnfqTwvIMg2snIMICjRqERkBARBxmI287BuEBQiOq2I2WhGkmWUohpRdFwPyWrGWgBEuFQqFVeuXEGv1+Pr65unxIVCFFErlSA4xpvFKqEQVTjp7i3/SxYTRov1vtwiAVGlQqkQEBS5/WKzIyhVqFW5Rk6yYbb+s0J0T4JCoSArK4tmzZo9dpt8C0atX78ejUbzUIOgFEVuRu1j95nrKFQ6dFoF2enpFCr/Eg3LhyBJtidO9hIEBXZrDhvnTGJDWlmWjGyPZHMopYkKiDm5lakz1tBh9mJq6myc2r8bZVhNIvw12KT/78dFUcSWcJoPP55Aqc7jeLNxceyynZS4c0RGniTdqqRQ8bJUr1AavVpi05J5ZLmW4OWXa6EX5L9NSdBut3P+/Hnc3NzylBUVNTpE8232HTxFlkkmomZdivk4kRpzhk37T4JSjWwxYPaIoHfLymRmGwEFOic1N6IPsf1gNP6la1C/RjlcNBY2L15Igk2HWlQgYMeOCzVfbkiAFqz/Z5//EwiCQ5VyxowZ9O3bl7CwsIcaIkGhxG5IYMfmXSSaBNSChRSTF2/1bolStnLx1DGuxKfiV7QilUoG3B2f930BotLInq3HKd2wLt4iYExk74Eo0k0qGrZoiF628UTzjL8BURSZO3cuQUFBNG7c+JFGWyGKKKyZHD18gsQME6Wr16eIl5bbcVFs3XYCQavBajLjW6oaTaqVQrBLuRMLEUtqLIdPnifDoqFek/o4SkKaOL5vH4lZEqVqNiLUXYnt395Zj0AURaKioti4ceNjt8mXe0SSJNRqx0xMFMU/vQQgsHwFDsx+n8m/7iWsXCV8xCT6NqjG9G1XUCr/3Ca/L6VSgUrrgtp4kZ+X7AAcF1cURVCIBPl5sHH2T5y3ARiZNqIfUzdfB0H5f/8m6ed5f+LvdHhzKLWKO5a9FYICn5BQss8soW/fyQRXi0CvFtg/tT9j5h8lrFRJ3FTKu8f2d7zUajWSJD1SUlSWZVCqcLbe4NPhn5Ko9cfHGsWbr3/IbSdfMqLW8uX3s1mxbDFzZs1k8eaTaLVqZFlG1OiI27+YQR+MZ1/kboa0a8zwb9fh4qnh++EfMWvpKlb8voLlC37k428XkSOrEOS/74b1NLizHmCz2e5OSh46BhUCxoQoJk34igWL5jFz+vdM/mUdSmDbz1/x69FbFA3QMPOTASw+FP/nMaBUcPKHfrzaYyRpIkAi/V7rTZxLcUKlHbTo/AUW4e8bN//3/wUPao88aj+FNYlvxn7GZYMzftIV+r0xlNvAhSO/MfHL75k/bzZTf5jJhsgLKBXC3f4SMs7x0ehvMHsWQRf7O136TQJgxoDOrIh1o6hHBm+178l1Hm6PCsILHJOtvxLiu598h/xJkvQXgt16igf7c905jLKhIZQNfZ8Ov03ht10HGdDYkTlnNmSRbbTh6uWBCjCbTIhqLUrZzO3kLNz9vFEDsuTQ0FWoNaiVCmwWE2ZJgbNOTWDhYvh6ONJhU1NScfbwRKMA0T+YEF8/FBKACzP3PJiKjdXA7dQsNM7uuOn/HJktmQ2kZ+UgOrnh5uTw3x/fOpc1lzVMqVHtD3s7U7pIMAoXD0oAW7/py1cXynNo4+AH9jJlZ5BltODq5YNGgcN9YLKhc9JiNWZhFfU4qQRM2ZkYLHbcPN3vJg/ZTFkkpebg7uuHTsxb58Fut999CYLwUNeMRi1yYdMCZh60Iy9vDJTj+zElOHxNoqSg5Z1JqxjRJgyLTcZuSOXGrRQQBOx2K9dumhjy7UI61yvJ6Q3fUK3DdxwYVJW2QyfRe8J7+ADrfviIk7p6lHKHrKyCNfu5M9N+nMXcTLPIq8Nm8E6XexWC7MnnGDttNR8tfI8SZXXUK7eMA/vP0LX6g2sMOdGbWZHmjYt4Cxdgxyevsc7zA36qVgT4jBLvCkxY35mxLUKfwVk+XSRJ+stiEdcj1/DTzniOja+CCyXwndSag7fS8HQJZca8bdSp/vAErb0LP2NHQkW+q1AcKoxm8i892LNSyXtbvDBMrwpUpW/Jcbw+YhO7Jr7yDM7u7+FxRLfu55mkEllz/b4AZzf+xKGkUgx7zdGplyI3sutsMhpTPFG3Xflk1Ous+HwgOzICaVjKm61bNnFbGcakn7+nWPoBhr83nvAB3zLk5dJs+GE4E/c5c3DFFyAoEEw3WTdvMstWryfOGMqs33+mmEbO9RGCNS6SQcM/IbjTDEa1DcNw8wxLV+/i5rUzbL3mxOZl36K97/6Tenk/89ZHUchby/49B6nUZThvNCjB6UPHSTxn5NupITTv3I1wn3vKYpJkR6GU2L19EXvsddn6c88H+uLUpvnsumbDXZHKruO3Gf7FFxTJieTdIZMp/EodTi9cQPBb83mvbAKrIuNR5dzgWIo/08f1IeHcXtZsP0bMhaOcTArghxlfUzQfaz5/HAiyLGOzSviXaUBlS3taD/yCV0tDjWE/0rwoRO8ysHvZFK4uucptdXEGDRtEaXcRg1XCZjFTrmFblDqR2PgkJFFPSLA3WpsrzTs0JjvuNnbzORZsj2PgxApYTKaHHsPzgqiwEr1lHh8fm8P5WyI9RnxO64gwmoXbGPbWmzCqIya/mrz7Wv0HG1qS+e1QKn061WH29JOIQGJ6Ooq0exKtoiAQE58A/PuN9p0bXV4UKlmJ8NQRdOr3OQObFqPOmx/yqr8Hm/fdYsWyz1n8YyyENGLk8H4E3ScwHlG3JWmj+vP+JF8aBGbR87s5+MZNwZp9X185q0m7fOVhP/vc8kxySgSFBqUlnj5NKtF7pcz6HRtoUd4bU8Ixho+bTrG67ejZpysZaz5j/IYYKpZXsGjFXorX78gPP3xLdds+mvWZij6sDOq08xy9mgQIlAjzJTLyOOBwiaRcjiGsSWe+mzaZkJjltBu1BpRaBGQkCVTBpTCfPcrRG2YghiEDRuHZ4DU+HDWUmqV9yTHed9C2NEa/9zFioap06tabTrX8ebd9T1KBoEJ+uAVF0LxFc4LcHoxRV6j15CSsp0nrgfjVeXAxwZawhYEfL6BM81707N2XEhn7aD/wB7RBVXBK3c3SfdkM/WoCFc0neeeTWTRo140+vdtweu4Qvj6ajE1QUKFxTyZ+8w2Frizjp03Hnuy6AJLZgltEc375tj9rp43gzXdGcCpVwk2rRMIJ/yLFeK1bF2xHFtCq29d4BnghyCDb7ah1WhSIuHuqWfPLT9Tu9S4hTiJ6D1e8/N1YMXE43q8MpqSnHYvVTsGvI/5olJKAs39ROrzRn9KKY7xaphFmNIya8h0u1zfSqkUbJv56BHe3B6VDb1w4hm+JGhQu4o4sqFEDHbr0Jn5dHz6es5FNi6az6KqMr9tD1ZMKJCrfisz67gP2LJpAiw5dWHsqGQCdUk1QuTq8/e5gLi0eQMdRU7l/zu5ZrivfDW7Id8PeoHWXQSTYNIR3eg194jpajvqFPesXMnHuCXQBz0sa0uPxTIy2LJlQuZXil4VjOfTzAOYdc1jH66d2smdvNMcOrOHX5btwr9qQEFdPvAMDKVWyIuUjCuEVVJrBQ/qQevQA4IF/Id+7Ogx+waF4ujoGs2Q24Vm2LqUL+eMdEMGEse2J2XMIcnP0ZDuAG0HeHuj1TsStXsH6S1peLeWHyq0kX3z6EV73yRZnX9rG6qg0IqpVBaB2916Eq44xJwGCiwbj7B5AeFgAevWDoTmSOQu34t25enwKQ2r78umKs3e3HZ43g6tuwdQqLIDKjR7vduPcqgWYrBr8vEvQuEElqlVvSjFNMtt3nmHr5lUsXxNJRI26eEh6ggMDiTm8nLnLtiE5qUlOSX2y64KAxknLhaVjaD7doY2eHrWMvRN70Hfqbmq+NY7v3+9MhXptmL3oGyzRizkh6NEOAEoGAAAgAElEQVQqc90GKHBxVrLqyyFcDh/O1CENMGUZUDnpubx4GOMvNeKr/uXIyTDmRk880eH+q/Ep24yvJw6jfJlKfDp/KZVcTrDw3C3eeud75p5MRZZlmhiWUa71x/e1ymBg57c5cOoIP3y5iOSkaKbM/B2p5rvkRK8hbsca3GqUoZg+gKZ1y/9j5/a0idm/jE5zb5GdY0A2HOHmDz3p/ON56rX9gI8Ht6NshXrMn7eEK1u3kHLfROrgj/1ZYO6CXZZJ2TuVIfULs/hYBGn28xRP2Mt5QqkSFkCrdo3+uZP7B3gmRlurUWMzG8CvNSvHtmJIs5acMYGXtxeS1pN6DVrQvFUbJs5czMCGARgyDMgK5V1fzZmoyzTu0Q6wY7OZUagcsxWrKQvDXTepfNcFA3DkZDwvdXwFsKNSqNDm6lUJghKVWouLixZDfDSPqlcjurgjpyeTnZpbPsNkwKoKpoIr2BUKFKLyoYtqarUGS3oWQSV7cnHDV3zWvhwj5x4GIMDPg5RbydypgWLOMeNToiQqlYBSKSDbHVZNoZCxatxp1boVL7d4jem/buTN6iIfdOvIgQQf3ujZnVcqBD65q0EAtcLKoe0nqNihKWSl4FKuI0Pre5KelMiFi5fJtsoIdon022l4l2pCSSzIKi0aUYW7uzMnNi3lnEtTZo7tgmCxonF2RpV9hVGT9/D+qC6YbyQiCc+3wcaSRczVq5juvM9Kwhb8CuWlcyRoQvD0dnzct/9HBGvvX2Ay0bzfe4Q6mXFxcUGjdsbVzQVzhhmn0q2Yu+BHbPM/x+2dmTR6fqRHOHvmDMUjcrNLdVXo3akBYtJ5UrPuVWvJMuZQtVYFnO/Tmdu5Zg/lWzQHwLPOQAbVcCUlPRmEknw3ey6ttTvZ7dqRoQ28/s7T+cd56kbbnH6D3QdOEHUxkosZMm3GzKdX6YuULdkeRZHGNPS5SN+Pp5B4O54ty3/nbJoVrd6Jc9uX8d2Czexau5D1Cd5MGNIOUOLu586sj0azesM6vp85D9Plw1xKsWFTQNK+n5m5ejfbVv7ETrkR84fXJefyEY7cvMH+XWfBFkvU1asc3LkF95deo4X/VWo1H8aJ6FNs3B6J9b5nMW1QLfrW1fLeyC9Is1jZtex3CrXoRyNnOLo1kkv7DhKbYnjgXCVrErv37sGYtJMjBjtFmw3n7LrPmdyrOm1GzCW04/uUubmNEd9sxGpJY/HvRxj84VsorQlEHjnAkVOnSLFA5ZcaUJFDdHp/GhnpN1k9dzE3LCZS4qI5ei6KfdvXsOC3PZw7de3JojFksNpVVGlUgaNzZ3M8IZOTO2azy1qXUd0jmDfpa9YfOMO5Y7uYvfYcn3w5AjnmAD1fDmf62QzOLBlHqzc+YePyH2lQqTRBpeqw/Wg8a2YMY6c9mOalfMmwSDzfFhtQixxYNYupC7ZzM/YCs39cQ4/PJ1AlNARFwgk27byEKeUyaw9fYMDAPiTHrsW7cH32XNfTb8hA3nyjG+ULa8kyxBNaoSJubhoybl1k2sdvMzm1Geu+aPXXx1CAKFW6FNG713M8LpPk6A3szynL6G5VmTJ8CFtPX+NK9FFW7r3A4AGDcbLdorJ7CEv2J9GsQ302T51MssHMuR0zSSrejU6NgrHnJLHixzEMmJvG4gVf8/w4kh6Pp649EnPiIOeTcxAk8AgOp1pEEFjT2bZmPV7VO1ExyMScL8ZxNMWJBi+3oH2TqsRsG0ntfvsZ9Nar+IWVp0eHRtzNuzSls3LBTM6Zg+n7ehviTh3A6lyEkkU9SL4cxZJfN+FTuTl9O9RHiZ3r0Ye4GJ+JQuVKkI8LiUm3sUh2ilVpTKi7kUXfjGNfrJ0ydV5hQMeGf/C6Wlk7azIn052oXK0WzetVBHsqe7YexISAZ3B5qpS+FwmQdvMCx0/HglJGdAuhasVwnETIij3B2oMxNG7dFm/5EtO+XYTsV5Ta9ZtQuYQ/adePc/hcCrIdgstWIiLYC+QUfhr3OacNHtRq1IIuL1dGyrjGjz8uo3jLftTziGPreTON61TBSfPwe63dbufEiRO4uLg8IsNKRpYV6F1duHF4Lb8eisXLzZdG7V6juIfEhQObmLP2EMHhFahRpy5lgtzJyU4n+sR5/MuWI+Psfq4kWVCpRWS7lQyDnVp1GpJ9LRKbXyVKBblgLaDxsncQBIHp06fTr18/SpR4tHqcISGaGTPnowisQN2GL1G5mB8AtrSLzF+yBVnvQ8XaDahU1A+bIYVjJy4TUbU6erVMUux5TkRfR1CAoPGmQe2irPt1Hd4Vm1Cn9OMVE/638LjaI7ej9/LbzihcvENp1Kopgc4qrh1czs9roilRoRyNmrQh2FMAbByPPEChUjXxd1NxfNMC9l014uNfhDbtmqBDJnL1UhI8ytKmXpnnYtUkv9oj/wrBqItbP6Ld1zaObvmG56Pi2z/DXxttQJaRBQVaJx2CXQJBid1ixGSV0eh0qEUB2S5js1mxWKygUKLVapDMZgS1Fq36voAj2Y7BaEAh6lDaLRjNFmRBKND/SI9rtF/g4IVg1JOTX6Od75C/vGO0/z/scZe5EGcnwwraFwKC/zcKhQJBEFAoFI8uwpxrVC1GY25MsiNNXakUsFlMSFbBYdgBQaEAZMy54XuYjVgfUjRakgxYc/cv6Ab7zutZjPPnkRd99eTkt//yXY09MzMT4C8D6h8HQSliT7vMltSaTOonsnXPUVpVLnZX2+MFj8+dRUqbzYbZbH6i6yPzPAfrPZo7Bttms5GRkUFmZuYjs0v/69wRj5MkiaysLAwGA2bzQ+7oL8gTtVpNZmZmvgx3voy23W5nwoQJuLi4PEKMSEDn4oKTWnX3vSCALFnIzMx+qAiOQtTg5uaCKMjcOrqFb/eszc8hvSCXOzOeqKgovL298fT0xGq1PjLiRBAUaJ31qBUSOdlGbHYJUKBx0uOsETEYsjCbbX9e+BQEFEoNOpWdbIP5uUueUSqVHDx4EFEU8xjn9yMgqtSoFHaMZisOcTEdzloVJmMOZstD+hDH9VJptAh2C2aLBAgo1Wo0ChmjyVIg0v81Gg0HDhzA1dWVw4cP532Dy3XJqRUCRqPxgbUPQVCg0mgQJAtm673PFUoVznpnlIKMjACSjZycbGyyEme9HlGwO94XIH2bP6JUKklMTMyXKy5fRlsQBD744APg4TNtlUZF5PIlnDWr8VDZSEpKxmi24VaoNF3b1cNFp8T+EMmzO7Pq580A/N04OTkxbdo0qlevTqVKlbBYLA/dT1Bq8PR0JXLNPE5n+fNhp0YYMzPQuHuRcmYHaw7dpFvH9gRpJVIy7wXOKpQqdM56nKQE9p41Uraod4FfeLwfQRCw2+1oNBqaNm1KeHh4noZIUIi4uLmSGXuW44lKqpYMQKd3J/vqPpZtv0ir196kuIed9Jz7Z6AOJUoXFxfiTu0kVgqmXFFvNDpnxJxkohKMhPnnLfj1b0GtVuPj44OLiwvly5fHbH7ITVyWEVQ6fN3VHNy0hlMpKtq2fwV3wUC22YZap8fVSeT0oX0YdEGUCPHELtkRRBVKQzK790ViQIcom5HUPjRs2YBQHyU7ViwhlsL06tUEITOFbLOtQNoPlUrFhQsXOHfu3GO3yZfRlmUZLy+vPKfyzd98h821lMz1fpvVsz4h+8pRfpq9ltuNGxMW8t+Kp/wn0Ol06PV63NzcHr2T/SpdG7Sg8cSF1JXn4B0yidibW1jzyeuMXHGWoj7wdr8ejFhxjglt71UEN6ZdZvlPU5n88ScYmy3k7JJuf8MZ/b3Y7XaUSuXdp5W8MfP9h29xzBzKu8OG4u2tZ9304SxPKMW4Dr60a1yJt+fupU/NgAdama4fZvSwyVC6FR8MaYSXmMLc779m9ncTiSw7BsuaD57dCT5lNBoNOp0ONzc3TCbTnwynoFQh5lzng35jKNp+KDU8D9CmZQ9mb1xOlUIKbp7bxYjv5uFR+VV6dwrFVS1jt8sondyxntrNuPFfUCi0GGZDJh7hjWn9WkPGdWnLUVVRPG/9yKhh37L//DZC9dmYbHKBc+uJooher8/XTfoZCEZBkZLlKOpciqK+vuDbnEkR1bFrHv0PYDbb0TwkjE22mRHEh5TblR1RD/djkUD9+Drizy2yLP/lALi5bSlLzgWxuGZhYCw1+vhzIsdCVmhj9p6ch78Ic9+uQZ8Z6x8w2nalMw06DcE9bS+jrz6fnS1JErIsP4ZbxMTUvu25WmsM83o5hMRsKaf4ec1lRk6fQEiYikFdljF79u/0qTnwbis57QKDhn5OnQ9+4fUauenXJoHqzXsSSCLvHS9YK/F3xtv9QmX3b1OJGhKj97MtVs2X3VoQoAin2rQtXM2UqWg7yYhPZtBmzAz61A/hRlwCptwy7IIkYbYJdB+zkG8Gt8BmsZBjMGC+sg2PtuOJHNwS0RJFMfdqnIzNIKy4ErskFbjZ9v9T+eeZLPtKkg1T7qDPTrjG1l2HsKWdpEml4vQdNpbKpYP4ZPJuYs9sZlCfrtSpEETLAVOwAqs+70t4tTZ89/U4qkYUxadUM/bccGQp2jJjmPHZMF6uW4mwWp04nw63D//KBx+N4JO3O9Bm4NQC4Qv8pylUqQnVjEt5edD3bFg2jSYzt9DAWU237l3wz72N6919KFvK74F2zq4BBPu6IluMT62gRUElYcdcppx146PWoew/cBwJyLl9kfgcGbWrw/CGehXjVuzFB9ptWzSOBI961HS/xdHoGMeHWk9KFQtBYTNhtT1P/Spgt9vwKFqWomkHadhuIIsXbqJc73fpGO7G6lmTyC7ejHDhGut2RiFonBCVijtNEQQ753ctoVPzelRt1Il91zKhUA36tynP7fh4Dq3bSs03v6JdJW9MpoJbCCG/PBOjrVIrOL77Vz4ZP5F3hw5m49FEXP0r0is8lc3HMpm9fDVdG/hz2+LB+JmLObh9K4lrv2BFdBptBgxEd3M39ojXOXrhBiOrxzFgzBqw3KJ/hx54vfYJW/ZFsWTcm7jc+o2qrb+gx5iJfPHTMlLnDabfvGvP4pSeL7yr8vuisRxZ+iktOg/k3KkowCHbCmCK2clv51yY93Hnhzb/j9trALbt2s+tuCvMn/0zXwxqhejbnGzRFUGAOxX5nJwUID8YUbH828Wci73MqtW/0qN6ESr1+Jw7ebYPW+8p2MhINgmXkKp8OX4QtuiVdOs9iI17L2C2J7F+xR6SYk+wZt1K+reqSav3f0ZQa1EIINusSKInQSHBvP7+WF4LuUrL2k05kqTEGSOLpozg468XEnlgG3ujb6HWFKwnlCfhmRhti9lO9cbdGDdqBLPmLqHHy2EAaF29KFe3PuVLVya8Qkn8xWzmzZzBxsgzePh5kmMyg6jBLTicysUcj47BparhbJJIvr2THdddqRLsCkD1xk1wOrqaWJPI8fWbWLvmV6p37UW9oIL1ePRPELNpMq8t1ZKWlEL2xa3sGteTj1ZddWyUEvh22jLafPQl5b2eiXLvc4CFBHMc4Y278sHwUaw9FkkNcSPTt9lw19i5E/lmsNhxc39wbeFyHHQYMIwPPvyM00d/JXHTSk5eeV5D5RxhgRd3LWXcuiR2nLzE5b3zMWyawJhftmJRetCqx0A+/3oGa+dP4NyOtdzKkFAqBCSrEfcyDfn0o3epXLYSw39eRlnhOmfikkDtRPsBE1i1aw+vuRzio08Wo9FqHZFq//Qp/w08m5m2QoA7lRi0emrUruv4WwAER7fakk7Su9eH6Is1pFXbVyjtp3GE9SiVCLKMXXYYX8kmoVCpEAUNidduYr7vKUilFkB2oVG7V2hQrxkTZ86mR6N/vwbxP832lbsIa9kEAOfijRnRzIO4BMd8b/nUnyjf9we61Ah+ZHu1qERQ/ndmNn9GjYvNE93dItcueIgueJUpjJSRijHdMUgTs1IICYl4oGVwEMi5JS4krzBC3QQkhWPdRlSJiOrn6EYpgEKp4PSZ07j6B6OXLRSu04E3WlUkPVONl4sKUaPCbMrGxd0bP18dSpWGgJDCeOgEzkWdxaoSMZuM2AQF/n6eGG/fJksScFKBHT0linmicnVxGOv/gsXmGRhtmymJC7G3SEyP4brR4dcWBMfPnL+eTOrVKySZAKWMJTmeqJir7F02mzVHb5CYmEry5WgSEm9x9ep1xxeqlCRePYqTTwtGvJJGi/Zvcyr6LEfPXEHf8Qeaao/S7Z1JxKelcXD9Uo7EPK+zlqfHK92bcernWSSlZ5FybQtb1F2Y/nYZpr/dgJ5frWDR6PbUrV6BoIBqnJduU89L5JPdDqH5nIybnL58C0vcGbIMhieu+VlQ6f5GD3KOreTErSzOrF5MRu3hvFc3gjZlXJm1aQXpcSc5dd3C4MGduX1hCe5iYY4nw/DvPubo/ClcTslhx/xZlH5zGHWLgCk1nugLN4i/cJ6bKZkPiJkVWGQZyWancoVKJF48yt5TlzmxaSkHzRUZMaADHbu9xJaFizh7+Tqrth6gZac+BCpjKOms4uf9SVzeNYuZS/Zw4+YVFowbT60vt9LP/yTt3p7IpZu3OLplLmtvV2fB5D6YsnP+M0lhyrFjx4593J2XLFlCp06d8iz3fu34URSFa1CtRGEEpSuF/R2lVtKuR5PsVp6qxd1RuAUSHBhG40ZlORt5lIh2Q+jTNBxZ7YYqO5MSVWvhqnUivEQwvn4hhLor0YRVpEOPN/BJO8n6QxewK50IKx7BmwPakn7mAAdPXkLtU5qGNYs+m3I8BYTIyEiCg4MJCgp65D6uhavQKCibNYfOczPWQO9xH+FvSybZ4kydOtUoElaCilWqUL1GTerUqEWFclWoWbk8XjqJCyeOYvSqRJ0K3hhsOkICfHiespjtdjt79+4lIiICH59Hi+tr/ErRvKIfm3dEYtAF8cHwXuhFBdWbNMZ2JZoz15Op2qo7dYt7I2q9qFKzJmVLFyUkoiFh2kROnrmIsnBtBvZqgRq4fvowKc6FeblCIDmSksDAQgUiGioqKgqNRkNgYCA224Ox0gIg2e14hoRTsZCWoyfPkJgl0rJbF4rq7PiVqYO/PZbj52PxL1qFDm0aIdplwitWpny5MkQU8+Pwju0kpGXjVakNPRuGILgVwin5DHuirmOQnOkxeBDBYjYGS8GLHAFHlnlKSgrx8fHUq1fvsdr8KwSjXvD0mDx5MjVr1qR69eqPsbedZ+QhK7BYrVYmTJhAx44dKVWq1GO0eHgfynLeCrV/tb2gMHfuXNzc3KhatSomk+nhOwkKtFotdosZu1JEKUuYrTYEhYhWI2IxW1Fp1NjMJiRZQKvTYbMYkQURUSEgAaLgyBRFKeKk1WA1m1GoVNitZqy2gvtYcie55vDhw4wePfqx2vwrBKNe8PTIn4DPi2v5Rx6n5uGDPLwP/+orngeDDdwVJ7vzehQWc27ijWTFKt+xI3bMZguCIGAxm7kje2E23cnCtWHNLRRilmVHG9mOKVfszG42I1OwbdJf9dvDyJfRViqVebpGXvDPo9VqXzwJPQGiKKJWq9Hr9f/0oRQI1Go1KpUKURRfjLv/gzv9JoqPb4rzrT1Ss2ZN9Ho90p+yj2RkWUDn6oKzWgPYMeVkkW20FEhfU0FEFEUuX76Mp6fnXwtGKUV0Wh1KhYzZZMRisTlkWxVKtBoNdpsFk+XP7UVRjVqjQYENo9H03CXZiKLIuXPn2LRpE6IoPjRbTZZlFEo1Lq56lLndIyCRlpoJSiUajRaVqMBiNmK2/CHpQ1Cg1TnhpBUxGQz3xKEEAZVKi1phI8doKRBTcVEUiY2NRaVS4erqmncWqaBEq9OhUoLZaMRstePs6oZGFO72sUIJORmZmKz2u6ev0jihd9JgMxvJNpiwyw4NHL2bHtEukZ2TVaATkgRBICsrix49ejx2m3wZbUmSOHjwYJ77JEef4uDVKyA6UbZGU0I98vMLL3hSFi1aRMOGDSlU6C+KDMpZXL58jZR0mZAy5Qm4W5vPysWzcQQUCcNF9+dmdmMWl69cIdPuQpVyRZ/24f8rmDlzJq1btyYgICCPvSSO7NlGYg6oMZNk8qNbW8c6QtK1C1xPziYkoiK+Tn9+9L0WtZ8TVzOoVKMOoQGudz9Pj79IvFMoER7qP7X5t7Jlyxbc3NyoXr36I422UqkEawaH9kUSm2ahfM2XKBHgTOS6hRy/YcBJqwYBDBkS9bp3pYynBkkCpVJB3KndrN99Cv+y9WjTwFHsWMqMY9mSlWRqgmnVoQ2BegHJZisQN7o/olQqiY6OZvXq1Y/dJt+CURaLBbX60YPKOyKc37tWJLrYaHY1a5qfr3/BU+D27dvExsbmbbQzz9Kjx1jafjCW0KzNvFR1FFvOrMN+cCOrDh/npzHj6DjzKGM6lX2gWeKhpQydto1uQz/EsnM6E6e6MGvGWDyeI4+Z1WolISGB9PT0PI128pl1fDTya/yLFsGak0GqUIlubSP4bOBbmCK68nplM0O7jqPt2F/oUMH7brs9897j69+SqVhRQ5+OrzFm+WnefVXPrKnz2LF4BtsD+5K4/KO/41SfCvHx8WRnZxMXF/fQhUiFUo2QdZXPx/9AudZ9KGU/TefmP/Dt79NZ+uWnHHcuTYiziCXzJjuO5rCkdl3c05VIah2xkcv4ZMpaylcrzZSRg/n5rUWsGBZG69YDcQ6vQNaJzxg4cjybtq6ipLMZUwGMkxRFkbi4uGcrGPXXrg41xYuGYQwIxTm/X/6CJ+ZxFtJi9/3Gwn0qFqwuDZQmqN0ojqfDK8XK0qV0Xa78OpOHLbDNnzkT33Lv0qJicQjrxezmvdgbPYTW5Z6vx6nHceeZZC0t+37B0Nfr3P0scc9c1pyzs/z75hRWQJNyy1k4fy0dKvS6u49dG8aU5d8SpoXA2AOs27iPd1/tTpM2XSlkjyHqeMGZZcO9akn3v+4gyzKiWkXiuYOsOWvlu1+b42YL58dJv3PudjZlGvVi4DtvU1irIXrHXIocs1PeX4/ZZESDlWSjyPsTZtCybgnOFk+l6rRtXGvzCsW6jeH7Aa9gzRxMLZ/y/HYsls9eDsFs+7PK4L+d/2ch8pksu9okCbvd8aiUdf0IAzu8Qrmi3riGNiYqB27tnkOgXkGvkZN546VSKASRpgOmkAqYkqIY2aMF5Uv4oQ+px5F0SDy8gFC9QNuBn9Pr5fIIgsD7c/c+i0P/TxBS6zWae66kROfPWTLpI+osOcOr7qDxCcLXTf9IDen05FvEJiY73tgtZCYlcDsh82888n8PCoXM7ZOrGdS9OVUbd2TliSRUmLkRc5OMHMc+VqOZC9euPNCuQaeBFCaH5LhDHJHqMOG7noCCwsGBqK1m/lJcsIBht0n4latDHdUuitXsytTJ8yje6wM6Fw+gde/u6HNyMKZf5uc562nSth0KmxlZljGZbFRu0I4yQRB9cg8zNyUxafpnBJSsx8h25Yi5cRur3Y7Ky4vyIX7YbS8Eo56Qe3c7c3YS/adt4tSVZGpmbOenZUfwr9+L1mU0XEt2Zs6Oc9hv7iFl1ceMX3gYBIG+X60i6mIijVV7mTbvCH7VetC4iJ4cMYw5W6I4s3wYy774jCuGPA7hBY/GvTSjhvbF/eZm+n44nUv7N5N13+ZHLet069eHA/M+5/UPJvLznFWcv2XGzcfl7zjifx2ueg9cfArxav+RdAg30q5SPaR6nWjheYGe7bsza84vbDwUiYf/H5OcbJw/sZcFc37j8tVzbFy+gzt2uuAupz0cAYfip7NPUV7v9ibh4jU+/fwbdu08xLUMG4Jsx9lNx6apI7HWGEjNUNFRuUYQQJCRJBtJ16NYPn8ZZ5JSObB6MTEZElarhJuLyIYZ3+HediRty7pjNNv4b+RD/h/ukfziHVGFyf27sL7+QBq8VITojAwAXNw8qVCttqObA2rRrX0NDl9MQNu9BQsHvolTtTYUrV6V1HTHzM7N1ZXiZSoCUKhcSwI0BzD8d26uT5Ur6yfxcVRJDu+Zgi1tP6Ge9ZhQ8yUmti2ZZ7vSLd9jz6ZqXEwWKCQkE1C4BGUK51Fs4TlGX7g6I0c6Fh4b1/Rn8azirEtxZ9amfWzYH01oeDFO/+ZJcK2Kf2gpUqJifUrXfIXG5T+gcp9Padz4JSrddZ8/P6ZbRkClUXN+10rmnpKY/es6FDHbebVNdyYsaMCSDxuReGIZn21UsHZXbQxpt5EFAaVCgSyDSqXGK6gsXQfV5e239lO8ZGdK1qnPOy+V4dLOX1h1zY9vxnTDnp2B9JwkKz0Oz2imfW/g9SjkR2q1z/ioa228lBKC6IjltEt2pDu/boshNlXH2wNeZXQ5FRfLDuX9nm0IcpIRFA4fn6hQIAiOe4xksaBUq9EWLPffv4Y9a/fiXqsyAKJHbca96kum8d5qokalQqvJDR2RJW7Hx5NlcdSQLFGpHi1frsSa2V/S+uMfKeX1HK1CPi7WLC6dP8vdZbe0W6jC29DGC/AqSvPWrUk7uZjzLq35vEd1kI0kxN8GUti49hgqrQ4B8PDyJbxkCF65BZ20ThrU2oeE7BRglAoF0WdPo3D2wVuvoUjNVvR6uQoqUYNKSmDw2+NpNfxDCtkzMdsFFAKkp6Ui260c2r4Vq2sgni469O5eBHkGUjysGKmnVzJl1TVGjxuJt8KA0SKjUAjP0e0ub57JTFsGRMFhnMMiApj5wwgaBL/HoTOxHBEOwqD6OClMzPlmEL7Jr6AWBcq2e596/nAkxJ8ZP4xmWaH+HDp9kbMJ+0m97sK2ozeQQ9aT+sa7KASZqycOsmrXRYa/8vgFMV/goGWv9kwf+gWbI8ajjt/PdvdefNO1GJgT2b5hI5sOxuG3dA4NKg6jumcy3RvW4eUFkQyrXoijW9exaftuNE3G82HHcv/0qfwzqDSc3LSAH+Z606JhCU4fO8M734zDA0i6fIwNGzZw8oYz3/8wHL/glfgAACAASURBVBcg/sIG6jf+mk2XI9n0RQW2J0yleTDs3XaDMd99TmE1XDu8kXmrd3L69HnW7a5Gg9pV0D8HIjo2SaJKteosnrCQZZuqUCjnFJHWcgzvUY01095hTYI3e2qFkZFtyK0LGUevFi3oPXM70r7ZzNl/gz5Ni3Fmz3rajP+RarY9tGjenRjXslw4tY2k5HQq1enNtEn9MOQY/xOiUc9kWAiCiEfxYgCM/m0/tY9exLdYaZptPsX5dEdMiUV0pmHbN6hbPRifoJJEFHU8Hw5eFEm5wxfxDqvIj6uOcD1Zgeis5ec9kSidvFDbZZyL12HrgV3oAn2fxeE/9/hU78HKWWWIPHsWL78IJk3viz+A0oXiZauz8GAkSo0z/m4a0Acz5de1uJcMxJqRjE3vSZv+IylT5L/c92pa9XoH5drNZMoedOjZh8JBviBbSMmyUrR6WzoOKoMu13r4hjbk11UlCNHCqF+WsHH/OTLFsrz+wThC/Rxx2h5BJRk4cQF9rOAfGMhDqu8VOARAspjxKdOYH74J5cChU6T6hTNiTAf8lNlo6vVjewMvAlUWLFYZBBuyzo+pi1fgEeSNU9GJiOu2E5OQTc2OQylTPICMm9f45rct6NQqZLuExWJGo/HEYvrvqHs+VcEoa8whPpu2iFRFMKPGDycgj1vCsJf9sXXZw+ReL2bKT5P8CUa94I/kXzDqv828efNwdXXNUzBKEAQUogqdRoMsWTCZrUh2OwqliFKQsUnSXYlfQRBQiiJ2yQaCEp2TM0rZkX1rkyRQKFGJyrtZlI4QPwmLpWCG3TxzwShZlvOMg1Tp3SlUtDID+72Obx4zBTk7kas307FfOoOdEi9ki17wr6Ggxfn+09xvPPPqO7vNSrbVkZ4v5O4v2yUcsQTCA4uI0p3wPVnCkJXhcHkIgmOlUbZjfUgSTUG9bvkXKMun0dZoNHkLm3iX5K238o5AALDmGHlvxhaUGq/8/PwLHgMfH588daBfkDeiKOLt7Y2X14ux+Tj4+Pjg4uKCp6cnZvN/x0XxtFCpVLi7u+eZZf5H8mW0FQoFZcuWRaVSPTztUhBQKpTonJ3BZiHHYMxd0RUQlQpUWieQrNgVSodPSrZhyDZglQpe+um/EZVKRVJSEq6urqhUqjwFfJQqNRqVEgQFss2cG+fqQKFUodOpMRsN2KR73jNBUKDVOaPTqrDbzGTnGLHdd+0UKh1OajAYjAWy+K8gCCiVSpKTk/npp59QKpWPTC8WRBV6Zz2qO4pRso2MtCxkUUSjFpElEAQJk9nyQHUfhVKFk7MzapWA1Wgkx2jCLitxcXdBlZsZJ8s2DFk5mG3/7kd+URTJyMhAqVTi4uLypyIId5FBoVajVYmAgGy3YDRZ724UFGqcndSYDDncCdO+g1qjQykIKJVgMhmw5kqMiBodOhXk5Bgd4X5/xwk/AwRBICcnh/79+z92m3wZbZPJxOnTp/Pcxxh7mTU7dxJYpz11inre22DJYMOq1UjBNWlVs3h+fvYF+WD+/Pk0bNiQ4OBH13gEwBTH9uO3UWfewrNaCyI8H9x8cPdeqtev+yfX1ZXIdfy+8wxB5evRtXmtP3znRTafgKY1C/Y6xbRp02jfvj3+/v557GVk0+JFXEqzohYsJNsKM2pwG8DAkaPnkC1GnIPKERHi+od2NrYsmcWJmzZqvNyW+mUDAdi/Yg6HbxhwUoFV4UWXvp3xKgB+w/Xr1+Pn50eVKlXy3tGSxOHoeKzpSbiG16VsgPa+jTls3XmaJg1r/KlZ6pXjXEqTMWQYadjonmRA5pXD7EvQ07xO6ad0Jv8cV69eZenSpY+9f76HhcViyXO7LiSQTd/3o274SxzMuG+WonYj4fBiomPybv+CJ+NO6aI8yTxNuw6j8YsoTYXQLLrVbsT5+yZ1B7/rRq3W7xGV9uDjbtTab+j32UKcXAQmvlabl96Zyr2raWfISzXpPXo2OU/zhP5mrFYrSUlJpKWl5blfSvR2ps9ZwfX4a5w5eYKT0RcBM6Pf7Mfp2zLF/NRMfrcrK84mP9BuwbsN+XbzDZSJu2lQLpzxy64ANxk7YgZXU29x9nQUJ06dJi6hYPRiQkLCX/aVPfUi7w4Zh0XtQzHXbIZ26MyRjHtja+mHr9L6zY84k/6H7z74Mz1HLcO/WGn0l+bRqP+PgMSYnl0ZPG4M3d6ZwK2C0U15kp6e/k8LRumo3WksIRUOUsuvKtdNxwjJ3RJWsiiaQvdmL8kJcaRnG9F5BhLo5YxsMZCYmoW7lxeG1CRkhQrJZsZqV+Di7YurWsBotaFTq8nJTCM9PR2l3g9/Tyeyk+K4ejMN7+BiFPJ0wpJxm4RMKypRjV+AD8bURFIyTLgHBOKmfQ4CYB/B4yxsxOxdwcrIVFa46cCtM4XNY7iRCeEeYLi2meXXitGo0i2QH7yny7oSfDv3fcr7CHSsUY3yjQZz5rP+VPIU2bRwOZU7N2bDSoGCnm7zOAtDBjS06jeOvq/dm2Fe3zSFdVdgTfMqeAINK/ky9asFtJs79O4+3hU6s+yNd3ADyoj1Gb78B0Z1GkJQnbeZOvaNZ3A2z5bHGW83ovewcu8Vhk0OxE9TH3/9LyRlWcBNw5lt67CFlSFQcwnxD0FpE/u9S4lJcRR211H4rSmYvIL4/Z0+fDp/McnH57P/rS3PTQJpfhYjn8EDmI3UTDOvf7WEobWuUqvVCO4WD7LZkHL9dLsXfcW0JZs5e3Qr/dq3YcWZ2whSKiO7NaDLWx/SvVUDvvrmN+Z+05/W70wmByUZpzfT88OpAEjXd9G5Qx+OJecQd3wj02YvZfXC72nfphsHE2wY4w/StUldvpq3BQVwZfsCeg2ZyE3Dv9tP+HcQWr8HXYvspVLbd/jy05F0/HEFjT0AUpi3/AJ9ezXCnGn8U7uyDZpTzscxuFJzLlCobHmC3dVkxUQR71qIpkVcybHaC6x/MT+o7UZ2LfqS/j3bUrN2I77fEY+rhxOxV2Ox5HadNSeFm9kPzkKbdO3HncT/awlGihdvAEDMqWW8P7AHlcpXZdyCHTxPqzzBFRvTyucsTZt1YuL42TQZOJrmQS7IqZfYd91C00bVMBuNyH8YOJLdwI1zMbnv4rmVI2M0pACQZcjEVoB92U/CM5hyCkhmA9myB9/uOMEuZRG6jK7Oqs/boAQEUQvWXbw+/HfWXjxEWT3ozQdo0XkgsWd+pU1RA8Ni1Py+aAPuam+Sdl5n8p5bBGhg74HVLJ88k7PfvU9xs5LwNv1oUcKHuGsleH1AM/xd0rhYtxJL1h7j+36v0rr0p2zNcaQMp6KmXZfOlPLUPP1TLmjow/jw/QH0nLiJkatOENHZjW5NIzi/eQfhXd8g3BaFqNMT4P7g1Ed5X+TQ7O/n8/rHv+GjgMhjkdRu9jZ+J5ehcXHhv9DDGic/GrTvSf3apTi05HN6NqpGO/ksrdw+okbNtozpU5Wl6w7iWafZA+1EdW6fxm1gybWizJn1KnCNl1u15dXOjXiny0aK1mmDb9gF3qqdVxGGgoOgD+WdwQM5N20DE0YPx7v5B3RrVYOrUSeIqNcRn2LrUWmd8HB6sN3bn7xH2c416HnjE8L154mXPfB0ubNG8F801w6egdF2PK9IZisQyuHYTbgGvcL7Vc7T18+NW2odtpXTuOFagfDcMnxVG75HUL82XLgNbh6eFPasSNnijqoowa82Qz/yLfbFXSNFVZYp3SvxxsidTGpmpVe7ugC42xL4Ysx0ilSrhlOhwsi5olRvfjiAcS2nYPioMZeuGnjp7QZP/3QLIKfmj6Dr5gDORB0HEqno7s+AqcEYd8zH7L6deTnxHI86SMfW3Zm+ciFlHrDddhaMfRv/N+YxuGkg3FhCq97f0a7/DbKOrifmkETfz0L5Znh33LSPOIDnAPei1embW7inxKhP+f6LeWxNcWPeqVvs27AGZUAZYnetx/2Vhn9unHqM/qNXM2PzEooqAYow4pN+uV82kK/qDGL7oYvPjdGO3jKLYWsy2L5tB3CbliVC6DSsEG4nF2IsdpBlKYeJuXiKHv2/ZvXU93FSOxwAZTpNIrlyJ/ZcVVENOz+VU1MtwpFRLT8vfpH/g2dgtEVc9Hp0esd/uhjYlD0LPqRq1/YIHzSiYREFYngFpGu/csUG4SJY7TdRFS5OsC/cVKsQ7z8sj3p0q+zMsMHDeG/EEga38WGYb2+2hv/Ee1UCQc5gdO83oP9vvNm5Iok7Z5Guc9yyvaq347XQCfT4bB7t69aj9B8X8v+jnDp+keIvtcl950ev6oW47BnB+yNHEJ8uY407zKkLN+n1endCVHA77gZOAUHoRdj08wSSy7zJe62KY7PLiE5VmbXoO/SuPsj+0WxNd6Jb+zo4P89iXtZMzp2PITSiHDoFkBiHVOJVWnoBqKjTvD3bfhrNJW0D5rUqCXYzCfHJ+AYHojTH8uXkNfSeMJnSGkAycDrqHMUrVebOPS5eUZxX6v5RHbDgEhtzHf+gO9FMvjSp24BLhUvx9msTicvUoLF4sPygkU6dWuOkFridEIuzZyDOGiVexarRNjCG15uu45MfN3OnBpCzkwsanTM6p0f96nOMnA9atWolWyyWPPawy+e3LZFDnFXygB82yAbrvS2r3q8hg0Zeeswsy7IsdyyJHNFmpHzuyjl59OtN5fFLomR75m25gy+yNqSpvDk65W7b4z+2kVE0ls2579s5Ib/86cbcd1ny6BZhslikjjz55zlys2pq2avWgLtt4xf1lqGUfDzjf+ydZ2BURReGn7s9u+kh9BZ6kd4hNKki5QMRpUuRLoqKKKAgUkSUpqB0kKZ0EZAmvZcAIQQISagpkLYpu9k+348NgUCARAkQ5PkDe3fm5s7cuWfvzJzznuy0NPcyY8YMcezYsceWiT+1QpQqUU/8dTZU7F45RbTvOlro074zxV4R0z/uJQAxfMoqYTCEi9eLIKYE3hT7v+8uAOHpm1946NQCPMTKrUFCCCFuXD4rvm6lEVBMLN4dKKz2HG5oDmGxWMT48eNFcHDwY0pZxcpvh4nuwyeL7VvWirGfjhS/HgwRQggRF35CTBszTAz49FtxzeAsfS14pZDpqooLKVbRpzgCEL55fYVKIYkiNTqK9dNGiXYDxoq/tm0RM7/5RHy79FDON/QpsXDhQrFr167HlokJ3C4a1a4vlu4+LQ6t+UF0fu8LEWF0fhd//YKYN7KRABcxatkRIYy3RElcxOwtIUIIs9j922zRv1cf8fNfQennSwo9LD7qXFeAixj30xqRbLTlYAtzntOnT4uJEydmufxTNtpC6GPihNFkEnEJemG1OTJ8d+n0CRGlv1f/7N/rxMJVm8SdtBtoStELvUUIq1Ev4pNM9yrazSLp/j9rTxFGs8hA8OnjIirF+f9kfdK9L4y7xIgv1mW1ibmerBhtJ7Fi7aqVYu+psAxHzcYkEROfKBzCLuLu3BZWIYTd6ryPKYlxIsWYKuw2u7BZTSI+IUFYrA4hhF0k62NFfLJVCJtJxMbphe2lNtpCCEey2Lt+uVi/+5RIH6m2VHH+1DERdsfwcHGb07DERMcIi80ubDa7MKemiIREZ9nT29eIFRt3i1vxj3++XjSyYrSFEEI4ksX29avErpMZx1tqUryIiTcIISwiJiY+razzbc8Ue1UcPHb+oVNZkuPE7YQUIYRNxMbECavV8VCZ3ER2jfZTXx7xyOOM0nBRP7wdVbZ6rQyfq7z+FlXu+6zWeTg3sZQeZMg6KFPhdr+fi0yHywPT7/LVa6f/39XjXjaVTbN20XrY1Gy14b+BD527dnvoqMrFjTxpks7evmlKfgrnpo/O/f4IHDVennfvsYSrx92wbwU+/4XNXsmVJp16ZDwm1/BajcyFuiS50xEyT757SX7lch2qtDWR6q3epnqOXGjOIrKqNye50qpT14cOa9y80peF8uRJe+rTdPPVPsXxz0RNQOnqzV2NSZ883g8XeMnJlsufw+F4pMLfi4Y+fAPVfLxYZqpDy8ckJn/ZkMvlueYevYgolUrkcvnjNXZekY5CoUAuz+2e+c8XhUKR9R8/srkR6erqyrlz51Cr1VmM4JF4Ht7vMrkCa5InXyxaQ/Vq5bh86SL23CiGkQ0kSUKtVhMfH8+1a9dwc3N7ZILenLsGyMbYe+GQyWTYbDYSExMJCwtDCIHtKSSMzfwpuHf0+Twl/w5JklAoFMTGxiKTyQgLC3s2glG5fZA9gFKpJCwsDBeXrGcsypbRliSJTZs2odVqHy0OcxchQKbCw8OFlMQkbI5nl8NNplDh5eOLymFg0+8BSEotLipBSrIBO9JL6+GpUqkIDAxMN9xmsznTeyQEKNQatCoFyGTYLakYUy0gU+Dq7o6LSo7FZCQlxZh+3wQClcYNDzct9yd2sqUmk2iyoVFrnPfXYcVoNOHIpf2sUCgIDg5Gp9MRGBiY+cuJEEgKNR4e7mmCURKSsBCXkIKrhwdqhSzNrkjgMJMQn4hVkNZvclQaNSqFHLlMwpiSjEPhgqeHa4Zpr81sJDHZ8EILb6lUKgICAvD09CQmJgaLxfLo8aZS46JSIMnkOKypGExWVGpX3N2d40mSwGJMITHlrsicQCBD6+qOzkWFsJpJTErCJqnw8nRHeV96MYfNQlJiIhZH7vPelslkxMbGPkHnJiPZMtoGg4Fx48Zlo0YSc2dtZuSoIdn5M/8aS0QQg9/vjaLJF8z7bBDxlw+wM0TGwHb+uT7E+kmo1Wrq1atH9epPWiGN47dfN3A7wUHbgZ9QMu2H/tiGOSzecYFWfT5hcN1iGWrEBu1m5rItmOUuKBUyMKXiUbcPozpX4vTu9Zy8HEPNDu9Rs5hrzjTuGWGxWOjSpQulSpV6dCHDNX6ZuYDgRIFaMhJlLsWSmZ+w8ocvORVhRqtRIpMc6KVCTJ34Ofd7pumvnWHjlj04CtZl8P8aIKLP8uV3izApXVErwOGQyFexCUOGtORF78nly5dTuHBhmjbNxB/9foSevzZuIyw6hSbvDqNSfjWRIXv4ae5mhFaHyWCkVJO3GdUxowhZ+OHfmbFsH6Vb9ODTQQ3AGsGMybO5kQwuajkICa13MXoM/ZziuhxsaA4SGBjIn3/+meXy2Q5jz850UX9oEUM/6sm8c9n9K/dzh0mfjmB/WNaVYVSFXqN/DTh58Q4AN4IOsHnbEZL+zWXkEqxW65PvUexh6lfvQYUO7zO4nS/+eStwGdj27VAmbb5GqyYl+LBecfouOpahWtDeTRy4cAeNSomL3M7uPzZjcVExq1dd1t4swKABbZnSvQGzt13OuQbmMDabLUt9GHP1HH/uOYtKBWaTCeQKTPFBbNhyAJtMhVzlQuyl46w/EJTBYF/Z8xNDJv1B52EfE7ViIP8buYo7AXvYGBiJm0aJXONJyKHVHDwfR26wQRaL5bESwAD222fo+tYgVGVa0Kd5Abo1akZAqpXggJ2cCr6NTLJhMtkQD8xqQnfNYeCkP/BvXZ/f3ven9uCVkHCcmZuC8HB3QabQYgjdz687LuKTGzrrEWR3CS7buy1ZXzCPY/lRIx+1Lc+SmT/Qd8knKAGEjSR9IgabRH5fbxxmI/FJySjVHni6a8AYw6Wr0VhlrpQv74fCdovlS9dS5f1vARvJSclYbUrcXW3cvGPDr3AeLIlRnLt4Dd+SlSnum3b3lC7I07SOy7b+lGXtlNzdnrMnRXE66Bq+pSvh5/uiv8tknyfdo4iAQxy9aaeyB+DRmip5xqO3gWeDnqz7vC5qwHB0D7/HxmWol7fyG/z4diuq5FdAcjgyTWE+bXiTQj1tHF1SH+QwuG01pm39g0FtPiM3xtfc7bsn9aFZ0tFh4FcM6HLPW0QfH8zgCXNp3vA1AP6YK6dphYweE2tnTqdsnx24ITF03Aj8+/5GULNRrNg4nGpuzsdx7JXzdBnbNddN9R9FVPh5jl+O4/uKvuikOlTy88GYakLlU4avpnyGf43MPUAsXhVYtGIoRb2hrf0gry0JAUdLJixYRe+aTgWXVV9H8HrnSbhleoaXkxxT7I07spUwRR0mzJhJ6IYlnI1J2xQzJ7JmcncathpBHGC4sZ82NSrzzeoLwG0+GvYNwXdiWTmhDz/vN3Lj0D6i9LHMGfchc35dz7xxPalTvzv9uzWkRPvZGCKOMH7ybP5aM4ta1evx13XnnxFCIMnkgJGxPf1pOmQ0CUDK1d18Mno6e7YvoHaVWvwV8mw3614ECjV9h25591Kqfje+HPc9H/65nToKqN/QabBvnf+bSz5NWT7izQz1ytZvwWv5nIZl+/wpqEq2Qe0aR1xCPOa09IDCnkB4tAHjM27Ts0YpjOyYN4bWjaqSv2glpu26iqd3BRrXc+aVTAzdzNaLZlo18ctQ7/jxG+Qr5PRjk3v5oU2IRlOqarrBDpjWg6jWk6j8ElmhwlWb8IbnRSpXeoNJ362my9dT8Pd2w3DnMl9+8DY1yxakcssBXIjJuJFZoWZTinqDIeIMk/do2Lz5a8hfj67VnaHNKUd/4vfoGnSo+B8Li8yOE3hWgmucJIlpY4aL1XvOCVNcqOhX01e8NWnrva+jfhZqz5bidloAxjctCot+s84KcXCkoGA3cT0+WQj7dXE1SghhOi4qFSkn9t9wlrXuHCvQVhXXhRARUXqRGn1LXItPFkII8XWrQqLdZ5uEEEIcGV9f1Ow7RwghRMj6b0TV5r1EjBDCFnNenL/hbMMvnQuKRp+uzE4XvPBkNbgmeOsPokHVWqKgGlG+w6j0iMjjf8wRwwYMFFWK+IimHyzJtG5y4BLRuNPY9M+f1VaLwlXai58X/CjqFFSK5p/NENZMa774ZDW4JiH8pFi+Yb8QQojjS4YLQFxI/zZVDGnTTGwMe7hePY1SLDvjDP5KuXVE+JerJQ6GOiPFEgN+F7XfGvOUWvJsyGpwzYUt80X7tq1FXiWiWIP3RKRBiEtHN4mdpyKFEEJ81S6/8GoyWDwYlnTx9Drx2ZDB4vWahUX1jmNE9N2graRz4t3274mg5Nw60u6R3eCaHHnTTgo/Q/CVGGLCz/Lb1qMUrlGD9dPn3itgd6BSyNK3elVqBXarEfwn8VG1qzSqXoVytftyKioW1ApkkkiXbTRazLjmq0phoGB+D1ISw/l99gw27juBVeOKTDycEdrmEEiSAhlgMxjYuvgbVu08hd3NF8n233vTDlwxhndXyzh05gQRxki89kxl1K+XAKjdfgg/zvuFo39MZe+P/Xk4nUIswz/4mfenfJN+ZOrxWH75tA0FyjenXt0aNKnsnxOiNi8Unn416dGxEQC13xtBTXc4kpbv4OScYYSX68v/Sjxcz0trR290arfa7Mk4VJ5o3FSAmTnzV9Oj/7vPqAXPjuAdi/h03U3++PMvbluuUvH2UobPO0fZuh1oUcMpijVg1Ew8kqIwPvD4lqv+FlPnzOXvk0eI3DiFdfudU+mNC37By78dFV1f9pH2MDlitM/s+YsKXUbxQb9uvPNON8ZPmkTVpK3MDL37VwXGVBu6NEN8+UY8Oq0WQkMYueUIwWdP8UsXFe8P+w5QYLUI3NOCpVxUcnDI0i98+rBOBGib0bFJbaoV9kA4nDfRRa1AliaD6aJWoVDIcQemDXqXgxZ/urWsSZ1iOqz/wVzwpw6ep0xLp8FBVoAe/j6Ybt8k4Mql9DIWowF8WlDogbqHfhzHfmVjumfIKObKmz0G0tT8G9uiK9Gv+xNST+V2UmMIOHXy3sZ23A3iCrSiZx4g6RTvfb2Tz8Y9HG0KULW8guCQ2wCYYq9g9y2EX14wXFrOssM23vR/7Zk04VkSGhpKgSJ3PZGK07p+U3SJwVy5lZheJt6QQM061XFLV4Z0sH3thvucSwV5NF4UL14MuMnUWTvp0LHVs2rCC8VTt1i3Tmxk6LjfcPVWAQo0Khl2uY7Xm5biyyZtOJsgoMD/qMZBWncfway5s4gwyomIusz5Q4vo9tECouJiibCUYMJXw4EC1CxjYli/jzgYeJWdZ66hsYbwV5hzk6xCxfKcWTmV5Zv+4I+TtzAmhHHbbGX3yRukXL5EQnIS248EEBl3jcAkB1VrVyZw83QWbtjGr4ciSL0Zzu2H9f5farqPGsrteZM5eCGMU9tmc6zk1ywd6c+ScaP4ZPoqju3dwrQ/YggI/wvigmiQT2LM0Ztw6xCfT1rH17O+zHC+2Ovn+G3WKHovV3L58Hyy7nGaS3Hx5dbJzYz9cjr79+/i+3mHWbp9HWrMfDtsMIouM2h6n6LkteDlSKpyHLpl45O5v3J11TccDDzPosWnGDJpOj4k0qbOYNqOmUGJl29fnGZvtic1eCdr953mxJ8LuVCwC0u/epdVX/Zjxupt/L39T3acMzBj8peorTcpLClZtD8GR+jvdBoykyMnjjD384l8uieMN/1gctPKqDtOolWpXOwy8i+QhMh6eFH79u1Zv379Y8KkHdy6fIFbehNuPvkp7VcElRwS79zgRrQeq82MV4Ey+BXwIC78JPtO3qC0f3NKKeIJibVRIr+WfX/vw6H1wbdwGRpUdc4vkyOC2HHkKrWb+GONiyDZZEXt6kvpUoVR2PTs3bEXXfnGVPMxcfFWEnm93bh9OwabpKJsIQ+u3rqD3QFu+ctQsqDE3m07UZZoiH/hVI6FJlGxTGnctC9H6PfMmTOpV68edepkroFxl8Srp9kXHIubzoNaTeriBsSGn2PPkSBc8xWlUo1aFPHWADauh1zDw68EWmssl8KSqfBaybtyJABEXDpGaJInjWuXy9G2PQusViuTJ0+mS5culC9f/pHlLPpb7Nq5D+HjR4UKFSlRwBMcNq5evICmaBkKuN2LcLNbUrh+/TaFS5REJYebF08QGBZD3hJVqVWhEFj0nA2+TsmKVXDLZcNw0aJFFCtWjObNmz+2XOLNixw7fw2VZz6qVK+Mt0ZBzJVj7Dp5AeUiXwAAIABJREFUk4LFivJalTrkcQVwcCPsCl5FyqIzXWfTlkMoPH0pVr4mVfy8ATPBZy7hW6oivm4vx9JIQEAAf/31F2PGjMlS+adstF/xvMmq0X5F5mTVaL/CycKFCylWrBgtWrR43peSawkICGDbtm2MHTs2S+VfWsGo/ypKpRJ1JgqLr8gaSqUShULxapxnEbVa/aqv/iXZ7b9szS88PT05ePAgSqXyyYJRQiAQ92m7SE5tCiEQkgxZJpEDzpd+6ZlplLxsqNVqoqOjCQoKwmq1PlEwKn2SJd3TCbl/4vWgjsSjJmWSJN33Xe69f3fbERcXR2BgIHFxcU+M9gPSxnrG/hJCPFKbJ/M+vvesZCcz9/NEoVBw8+ZNjEYjOp3u0eNNZJ4cTEoTfxL3f860usjw/cPjMPeOOYVCQUhICK6uWd/MyLZg1K5du9DpdI8RjBIgKdBodbi7u6LVaJDLBFaLmbiYWDTeBfF1sXDlahQOuwOHJCEhEEKG1t0DlS0FvdGaawbui4RKpSI4OJikpCSioqIeIeDjFOJRKpVodW6oFJCakowh1YxMoUDr6om7VoXJmExScgpW+11hNYFG54mPlys4nD/IMrkCmzGeO4kWtFotLioFJmMyhlRLrlOtu4tcLufKlSu4urpy+fLlJ0RGCiSZEo2LBqWwkmgwIZPJUShV6NxcMCQkYLHfZ7yFQFKqcHP1QKdVYkxKJMWYis0Oaq0WL093ZDYL8Ql6zFbbs1NY+4eoVCqCgoLw9PQkMTERq/Xh51YIgcrFFR9vT2SI9KA3hzmFmPgkFBodOhc1wmYiJcWA1Z6xv2UKJS5aHXJbKkkGEwIJN998eKnk2ACZJEiOjyXRmLlY1YuMECJdKbFQoQf9tB5Ntoy2Xq9nwoQJWSp7/cw+du07yrGQKySZleg88vHppAlErPqQt4YGczV2Fw/GMYXvXsWFwn0YWi7rMoWvyMjixYvx9/enTJkyjy1njYvg8KkTRMTaqNfiXSr5eQBWTm//nc1Hr1OjSXsGNa2UoY4+dB8zl+xA4eqOUi7HkhyDh/8HfNEqL+dOHOVSaAy1Wr9NtWK5W5heqVTy1ltvUaxYsScXxsiFYwHofSrQoLQ3INDfucn+A2dpNXAwD+c2TuXA1jXsPnaDBm/1pFXV4gDcvnSa39ZtIDlPDd7/YCD5cskjsG7dOgoWLEj9+vUfWcYWe4XZcxZhUHigVsqxm40oizRk1OfNuHXxJCeDwlB7VaZZc2dEboa6qUkEHj9AarGGNPBzhq4HrpnBssAU8rrJ0cem8MawCTQqlns3JS9evMiGDRuyXD5ba9qSJGGxWJ5Y7sdBzen59e+UbzuQ2T8vZuniefR020qfZUb8mzXEkhTJnYdq2Th9ZDdbd53JziW94gESExOJj49/fKE7h2nefhCKUk1oXslO27pNuQxs/WEU3208QyFvOx+2rEmfhUczVDu7fT0HL0RgMaViMSbw57r1JItkpvdsyeqz0LZlVab0asmP24JzroE5jNVqJSkpCaMxC4H4CecZ9t4HbLsQQwEvF7Cb2LtxOfNnfMZb3T4mNJMqW2cMZtZvF/ErIqdfXT9GLTwPMQfp/elEbF7F0W/7guJ1+xKXSd0XEb1ej8HweDG3y6f3sePIeQzGVMwOGwdXL+BAtIXTv03iq/mHaNi6FUFbptF/7GLuX3TVXz/Ngl9m8X63zszaciL9+Kaf5hJilpEcF0NikoGU5IQcat2zITU1NeeSIMCT19vsx8YzYmE0p+J2U9Xj3vG6ozfws1ULEXZUWld8wKn8nn46BW9/tZi3MzlnhmJ3j9kdSPL/XmDMk5Ak6Yn36NqpvzlwwoJ/SS/gXUqZPybaBnnrd2XRx7VwlUAXfpLF0dEZ6nmVasTkDu2oU0QDieHYRV4+aBBFqd5mji9vig4Y2rYKU/7aysA2FXKlYBRkdU05gaGd+1Fx2h6GVE9bjxR2ajTvTFP/wvw0/1Kmb0SFqnZk/ogO+ACy0+vZfCUce4ofExaspHYBLQwtyXRpMLECfHLBbD8r402Xpxyjp82iceVSQCQjo02M+KAFSzsOwr3PBvK4edG2XSuCtsRig/Rxo8tXjv4flSPu+E6u3pdJyL1IE36dNiZjSsJcTnaWdp661ZswaAGFew3PYLAB0BSjvBsIIUcrT+S7H8fToFReJEnFpmtgiTxMI5WE5p3NYLpCMw8Jefn/8dGQLvhIEvJqA7EBXN/Nu+/25K1W1ZAkiZWXs7BR9IoMFG8znNFNziF51aNv57fofjCSxgqoVa8WyuTbHNu+ir9SSvP75x0z1KvS+m2nwQZ+/W40hRr1xt3VRsydEC6EOO/D1eshhEcbX3rBqNOLv2OdoTT+piN8N+U7tpy8BpIcdzctOKzYHpG9oGrTDmgS7hC4ezEbExoxb2oH5H6VqV1Azc2QU3zz9td8f+oyZXOBwc4qxWs0TDPYsGb6LIo1b09BnYIuY0fyY7eaNO/7CUu2RfDdtIzKkEqNDqWkw2Gx4LjvTTQ5Low+g7pSwtuN6p1HEZ2VJFovEU/daBuschQ8phcFmIxGWvcdz+GwO8xok5eJiw+iKtiAjwZUp6i3EjSl+bxvLQqVqc3MuWuIt54h36VlbLgNQvgwZfVyNuz+mwG1fFix6sDTbsJ/AB3Vm79Lr+7VCTqxl8WfDyEiLSo0IiyIC+Ex2G/sp8fgeZnW1p9bxqrwivRu5g1Sa0a3ykefN+sy+ttZbDtzi6LF3F548f5/y7nrYZiT9VxPcWC7eZh2/g0zLIc8+sXJys2QQM5dT0GK2sfICb9iBmzGZE4eOIiydAmWjuzKoRsv4c+ePpCtJ+Np2dApc5C3YHlGDBpAaUsoyxbMZ+H6g5lUcjy0qV23Wzc+Gf49R0/+jfuR7xgyeW+OX/qLxFM32q1buBG2YwePXGUSduyqvPilRaCWLuuLMdEpySgTchzCafAddhu+BdM2ghR5KeSuxpACUgE1X735Br/siKRWpaKkGvVPuwkvPccXjWFOXB2W/TSHE9fDUO/6ha/XBQFQoloz+g35kHlTBrJj4VCuPlQ7geGDZ9Nv8pfpb0UT/zjCrpWz6fdef7y1SvyrNX3JBaMs3DbcpmqzTrRr2ZrRcxfRLM8t5mfJdigpV6c5PfsN58fpPfht1s9cuAMKrSed+o/g88lLqBzyG9+vPvrkU+UyfhrYDe+2X1DGA3Ak8uXnU6nUbxY/r/iD1dO6MWX0RGIfISlxf/K6Fr3607BCIfKVrM2kz1pz8cipZ9OAF4SnbrSbz9xCkchNdBo6hwzquI5kopNAUssR92VqsJrNCIXz8ZdJAiGcj7scgT3dF9yCxarA0xdGNX2NyA7rGNSqMo4kA0ivHPuzS+j5q7gUzef8IHnR4Y0i5HHo2X30IHfvTEJMPL6V++MHXDl/jmiD85t1I3tyqkgP3r5PJlpSe1O1dj1i//yEkHz9GNm92jNtz7NHRTmv0sRG3Ej7nILRkQ//qmkf1SocQqQn3bCZEzh/7jIQyfyZm9PPknwniVp1q3Lj1N9E3PdibVa507xBRs+d3E7Euo8YcaoWM7o7pSmENZqwqwkUKuBcbqtRtxlN65RHIXNw5cI5YlPu+nzLkEsyUDntQuCBPVyKu+cPHhwGXft3eqZted7kwAuRH8dOrOb9j6fT7M2jtH29FjqlRFxEOFXeHkPRi6cRtjtsOXyZAQ3KovUpSNzxnVy/4snJG8lYTXu5EFqUg1dNJBiPEhjVmcoFlLi6yjm54xyvd+zB4tndWJZvCNesWuKvHCfK1JYCD/tWveIRtBv2Pr8Pn8/68i5o9ae4UukLfuxdnbHv92XzX+doXTMv587B7xu/B0M4Q9u/Ses1J/i4yBVmbr7MyBW/Zjhf5MUj7Nixj4txpVmz4pNckSbr3/Jmn/f57eDXLN66H4+rByg74Fvae0Hsrcvs2bAJgyWetb/v4v32LbDf3Mtbb05ia8hp7uwdxUBTIu3Kawk8rWDWgp/wPDWd7j03MLR3G2wRp6j15VqG+ud93k18eiRdZujojYyYczj9kKQuS6936vP79Km4dK7HqT3H6DV4GJ6qeNq2a8Z7iwLo37QgAX+v50T4baL3bOZ0w5KUKWCi94iRdGzXCJUxGkPVfnzWqeRzbNyzJ+e0R0wJnD8TwMWbCXjnz493noJUr1CCO7eukmAwo3L1xa+QD+bE24RHJ+Pr5U5KSiI2h4TO3RVTcgoWB/jkLUIeDxW3rl7BpvKieEF3zp88jqzga5T2sHM1OpmCRYvjpn7lSQJZ1x5JvnWZ4yGReLr5UL5WZXRA4u1wjh4PQuNVgApVKpPXXQ3CRuSNW7gWLIrWnsi1WwZKlCqcYYoWGXqGSKMXNSsXz9G2PQuyoz1iiovg0KkLeBQoQZXKpVABqclxRETGYEdCkqspVKwoWsnEjVtxFC5eBGPEBQ4FhKL1LUq5ipXI56YAh4FTB/cTZdZSuJAf1SpmxT/8xSArglEOi4Gw8GiKlytJBsshLASfPEG0SU6JUqUoXtAXcBBx/Tru+YrhphFEXb9GksmGcIBbnnwU8vUkNGAfwXccFC1agkoViuf6ZN2vBKP+47wSjPp3vBKMyh4LFy6kePHiT1T5e8Wjya7RfiUY9ZIhl8tRqXKrh/TzR6lUIpfLkctz+/vbs0GpVCKTvZrl/hvkcvmTtZzuI1tr2nny5OHixYuo1epHpn3P6CR+v2DUs0WSyZBJEg67PdfqYGQXjUZDamoqkZGR+Pj4YDI9nHotIxKSlMk9kiBbnZYm/PPIz7mEu4EiNpuNmJgY5HJ51gSjyKwLpGxFuT14Hf+07rNErVaTnJxMfHw8t27demy09F0BLQlwPCCm9WhxLefYlCRZ2v8fLcKVW5HL5cTGxuLp6ZnlOtlaHunRowfu7u6PVvmTZDisFpQ6d5TCjNHsQCETPCLOIMeQyWWYU00ImRy1SonIxq9YbkalUnHx4kXy5MmDt7f3Yw2OQuWCSmbB7NCgUdgwGM3IlWrcXF1w2BxIEqSkJGPP5OZJMgUuGhU2iwmbkHBx0aJQyEkTcUTYzKSaTA+J/+QG5HI558+fx8/PD51O98g3ILlShYtajUKpQDgcCLsNo9GAXVKi08gwGO1odWpMBgOZdYNcpcFFKcNgSOWuzp0kk+PiosWSmowDCcezfnCyiVKpJDw8HIVCgZeX1yNf5AQSGq0LmI2kOhS4u6gwpBrTf+SUGi0KYcZotmeMfJZkuLhoMBtSEEotWpXAYDC98EJa2UEul6PX62nYsCEffvhhlupk6007MTGRFStWPLZMyvULLPx1KQm6CvTp0YcKeZ/Pcsr25d+y9LCZX2aNI+u/YbmfRYsW0ahRI0qXLv3YcvbQA/x2KRWXmKtIlTrTsWYeMEcw97vvOXo9mSrNujGx1+uPqO1g4+IFFGz0NnVKebPokx4cjnNDrZCQySRwLc+k6cPwzKWz5hkzZtC5c2eKFCny6EIiktkTvicwIhWV3MIdS3nWLfoWSGT3jkMYE2OJU5ehT4d6j6h+lsX7QmnWtjPF70tNtnv9SihSn+a1/TKt96KxadMmPDw8qF+//iNmdhJqFw2xFw+z9eQ1LIm3cS3fjJ7NK5GSasXDw43bQXtYuesyDdt0oEIhN+x2ByChddVwbvdGAm4mo4+IpHrnwTQp44rB+GT9o9yCUqkkKCiInTt3ZrlOtqVZLRbLY9dMXYtVJOHUb+x27c+Yj5/f+neFKmUJnPQtUSnj8HzZw/PuIykp6cmCUdd2UrPPKv7evgCN9RR1yzWkRPRFdn04gpulO9O7bQw9ejVjv343fw5vlrFu4nn6dBtJg5GzaZzPFRBs336c+uNnUlxuIyZ4O8vP3STVTq402larlcTERFJSUh5b7vaFAE5ds9L+f81JiY0lwVYAsDH705Goa/Sgb8viDOnVkyuG5UzuVjFD3bBdc/lwyTWmjh+Kr8aBc2vJzJhe7+DRuj/vlSuQY+172uj1eoQQ3LlzB5PJ9NDyhUyhxhK8ixGT1/PZuAn4SRfo2X0gYsUG3i6pY/f8SXz9RxyTvvkQD5mR6GgjCIFS607I33MZ9WsEM2eNQXl1LZ07duanP9dTUqnH8pJMnhUKBQkJ2RO8euqCUQAe7l54uHk8JBgUFX4ZvcOFkiWKIjPruXotApe8pSjkbuB0wFVKVK2BtxqMCZHc0Mso55cfh1lP6NVIzGYHfq+9hqstkcvXbuPtm4e4G1dIEJ7UrlaWpOsXOHc9mYo1auKrU+Cq1uLp7oY5PpLj58LR5i9FpZJ3U87auBF+lbj4RDQ+hSnvlx97ajxh4VG4FixOQS8dsTevEGlUUblsMRLvXOfWHUHJcl5cCrxOiUqVcZclcyXkBsmpdoqWLoXaYUGtc0eleL6WKiv351ZoAGdPXMFbpwQqUdzHB8lkpuZ7Y/modmUUMlh3bS8N152A+422iKBf5/5U+WYv/eveFdbV02P0z3R42+k9sObKfj7+5AMK5OL96qz0oQU19Zu/TadW1UlBh7tGwnjzIBsCbvPdx41Q+kCbhvX5Zt1qJnebmF4vLmQb733zNyv/Xk/R+/ro5951uN1gCZO65a7ApLt9dXc/4MG1arlCTkpEGMfOXUTy8KJ4nuIUKJgfT62WiBO/8/H8YNbt2URxSU9sQhImiw0hSag1Sq5eOEZQiDueefKTV1mSvF5uqBQiTT0u9yY+uJ+sCG49SI5YGIdwZLqRsm/hJBq07sX5GJASQvm8e2P6zT6IJIvnh0/exb/HVADmTZvApqPXITmEbyd9x44TF9n82xT6fDiXRIORrzpWofk7n7Jx23aGd2lClw+/ZtXGP5k/cQDNu44h1uFc3zVEhLBm5TKWzJnCG/41+W7LZQA2zPqK+au3cinwIJ/27cSETecRKTcZ0b4OHy7ZA8CJ1ROo4t+VRCDswFIaVq7H6Olf0+nd9zkRcJZvvxjPkcshrJvxGU3feJOpS7YRlWR+qM0vIoUb9Wd8ez2uldrQr9dQOi/eRGWNmiZ1nQYbrJwIMdCra8apfciWlRxM8qP4td/55KOPWb4zEPCkQ3enwTaGrGXbtQK0rFv4mbfpWaMSRg6u/ZHBA3pQv25Nhv58BKUjgSSzhCYt0Cufhy/JKRlFVrf8NAZ5kUrsXzqdD0dO4OwtCyTsYMyaaGqVNDDuw0H8tP7Y49R7chV2q5UC1VsxuLaVti1a0uOjH6jY80Pal3bh93nz8ShfkZ1zxvLh2OkERRpRpUU+phpM1PnfMMqm/knTFu3oPWQpg2bNoby7BYv9pVrWzjbPVCKidZ/38NsxDovVjrxwTRo0rsyO1BSQl2L1+sWUKdqc97/zoEW9XnRpV4dNkzqz7WJVDk14CxKL4eXZmGNjhtC7YUHGUYsvRg+mY1E9jSYdZOKF3Qzt3Y5q5VpwJGQcLV3ALHnQ5J0RtPziC5aMbcNnk5cwpEE7Jv98kHlHDlLDG0qqr1Gnz8d8lbCLNxpXZq/NuXnXqE0HCi38CYuA6o1aU9htKiXeGEbAwG+J/vMD/rfPg5TvOkJDFVPydGT6nr0UU77YG0fpqPJQsfrrdC+i5MiSuey4oqD54UUUTPsJjwzazoHEEszp3SRDteATh4lKcODwLUuVvCcZ0rEd+QMu0KKsK2BlRN+JtF977j8REenmW5ZuQ0dQvWo5gv+cTvN+HfjfyTW4KCXuegsqlQ5kUsYxsf23s8Q160SJqvU5vaYrnT41s76LmgS7EheNC82blqZRxzcQ+8/zQaPc/ePn9BRxoNS6U6pyczp7GQjYuoL1h+No06wYp45ewtqoGXlL1mT3xtH0CYxj/7KvUEg2HA4HbnkK4d+sOREW2Lx8LdGuFag4cSBucik9o9J/kZyZyz/YmXYbZgcoZXIUCgVS2p9VqdT3ckXma8ielR+w6IvJlGjlzIIRfPoYd0LPMHP290yc8zcz122iWQGwyhR45XOu++k8ClKkUBG8ZIDMjWJ5VVhsNuw48Mjng99dbYPXGpPfU4s+aDsXFfnIl7bOXalmG/Lpg7iEAxelAlmaRrdS5YJKIXPucAsHDopSt3wJPD1UFK3ZjAIRf7HpUhyH1x+k02e/0kJFrhlF+34YyA/xTZg3fTrnE0xUv7aYkb84ReZNMef5bsYOpq2cQ6EHUgvdjo2jftfX6dSsPr1GT6WzvxtBx88DsO3Lblx5/Qc65Z7l2H+OEGjzl+PNFvUp4OtNs77vUy+fnQi9CzJ7KoY0HRF9qp18+fNnqBqbAr0GDKRBrbpMX7eewue2c+bseQqW7EYv/xo0/N8nrOqtYEOmine5CyFJqFVqjq5byKorSmbNXUBg2CU6eB1jwo9/IPMqTOee/ejYsQuzvxuL6coRYpJsaF11eHtq+PWz3lwtM4Bff11LbOwJbq78hhXHwlGpVUj/GUfeh8kRo+3iosFV43bvQNx1Vu08htJFgyE+FrVaApIIDgzFxdMHgIRLO1kQ7c/iz8rT/c2PEUCFsqURPn58NPxTxo4eRe+3WqAAtBolGo1zxdzVXYtSqXCmKbI7cMjUeLvqkElgMArUaWmbjp4NomP3/+FZvDqy0FOExjpvekxUOJpab1AOGWbJzJ0458Q08nIA15Mk8sgArRa1UoU2bV6iLdeFUV1K8defu1E3/pj1U9/NiW7MMaIj43Ev4pv+uX75ApR/rSokB/PlmJ9o/fm3FEyOIjTkOg5sBOzbT7QNXqtWgajgOJxzEQNC8qJIhZJw+zCj1kUy7YNHp5x6qbAncuLIAZLv6haFXUJWrRc9GhdBpb9DQrxTefJWfCQ1ajcAkji416m70cDfk0uXwgEw6aOQFyxLo9avo48+mi7WFRUn57XK5Z5tm3ICAZJcIj5Rj1qjQ7KawMWDssUqUrFyHUrnh5Br0cgUcvSJBoqUKIOHVnDq8AHuJJqIj07APb83NlMqMs9S1K1ZnMLe3v+p2IvMePrLIyKEmQtOE6oZS4dbuyA1gVMHT1Hj0/n0afU6pVThVM/rRoc+76OxWdg4dTL76/di3OiFjJ7/By3L1GBJiaIUaqXj2u/zmV69Ap7lTtG5XgHcy7dg4BvlGDXzAmcK/Uxg96L8/OW3HD+oZ/a6DxjXwoPIwBCGf7mAQ+PycfXSAar6VaJmxYq07TmIMe9URk05Rv9vPO2bNOfX2cP5a/kBvvnBmfeyUp16DO3bkSLra/NO+2qIiD18PPk3/D33EhB3nmHDpjF/wseU8o5jxbJNnNZd4vDiMZhlvkxetom3a+Z76t2ZE3QZMYQZDfsyJe9PuAauIajeNFbUSaBPk/osPZbIrKWLsFqdpvm4OYbP3mqC/59XmfjeSMot7sT7U8rjb92NteK7tK+Zl8mtipBa/BMq+D6Y9fMlReHJzcO/M/yrn+jSqjKXryczceZoFMq8DO9RgQ8Gj2Dq4Mr8dVnLN5+0IOriShq/PoZDkeEMmTSdZr2Hs6zgWA4unkmLIbMpVf81hteZQqthP/JB2TA2qfuxoV/u2pDMHIHVaqd+sxYsHfg5X8zwpVjiKc64NWR66xpE043u40YxRzeUo6vX0rHfJHxFBE3eepdBq07RbcRgOgzrzw+mYUTtW4t7x4m0f80Tc6qBh6fz/x1yhfZIQsQlYmQlKFPgH4RnOywgy6Re0i0OXoijYb0q2T7l1f3L+fFvPYOH9qCwu5o9i4bx9uRgAoKPUe45O4VnR3sk5NRh3MrXo4AuexOua2ePI4rWwS935+/NlOxojyTeuEgkBSlf9IE0TY47HDujp26NRyVXNnP8SBB16tfIeL6rZ7ilqkjFQrlHhmDZsmW4u7tTq1atTF3+hBAo1G4Uyqvl2N87cRSoTo3S3sTcjkXtkQedNYpdB0Oo07Il7tY44gwOihQrQvLtmxhxxVudyN/7zuJXoyHF3WzciXMGHuWSlcgnolAouHz5MidOnGDs2LFZq5PD1/RU8CpU7p/ng8vMYAO4F6ZhvX+20bNm2gSutVlN6XzOq2rWoglV16vI8wJE8WQn/LlMzQb/6G8Ur/pKjArAo2h5HsyqB4AsL3VrPE5aVf2QwQbw8KuW+fleYB4cb5mNP5s5mWs3kylWuQ7YLERF3UGSJIwJd0iVa6jXoDrmuFvcSfPmuxZ6BSQJCRORBgXVatXEatETFSvSjPXzk8fICbIrWZAto223218JRgGjfpnHW4O+ZWXpkZS0XWb19kus3PELeZ73heHUH3FxcXnel5FrUSqVKJXKV6JbWUStVqPRaHB1dUWheII5EQKh0KLSPHhYoNU9OgJOCIFC+3L6JCkUCrRabc5lYy9QoABr167F1dUVq9X6sFO4ECCT4ZR3ARA4HE9wqkiro5DLcdjt2B2OfyUKI5MrkEkiTQfh6c+hhBCo3Xz4/Iv+7Nn5B1d0xXinZyfC92wjyGJ7rh4kKpWK8PBw7HY70dHRmU5X73L3eLoIjxA4g4cfz92y4r7/Z6VebkEulxMREcGJEye4dOlSttTX/muo1WouXryIQqEgIiLikdoj6aQJRmUwUJkdy1Dl4eWWrNTLLcjlcm7fvo2XV9bXErJltM1mM3FxccTExGTaWZIkA4cdrYc3CocJo9mMzSYe27GSJCEp5JhTrWhcFAibI0Pm5azj1L2QOSykOlSoFTiFfP7BmZ7MTSSZAt/iZZCElbPHjyFeALOlVqtJSEjAw8ODq1evPuIhklCqtXh4uCKX7hltkzGF5BQjDiGQK1W4ajWkpqRgsTvS66m0rni4aZGlG20wG5PRJz8isV8u466BSElJITQ0FF9f3yeq/MlkCjQ6F7CYSDVbEZIMldoFnUZOSlIy1gdFnyQJhUKFTqfFYkwkNS0eW5LJcdG5ohIWEg2puWL6r1Ao0Ov1aDQaHI7MA+ruIldq0KhlCIcMSVgwplqQKVS4eXigkkuYDMmkGFMziMtJChWeXp4o0h7DC3IIAAAgAElEQVQtu9VEUlISNkmNTqPA4QAcVoym3KtF4nA4MJvNWVaThGwabb1ez6BBgx5bJvrMfmYtXEyMuhQDBw6mVtknLxrYIk7Q/b1PqDFgJp+9/fBaX3ZYO+49vjhZkNBtk//VeXIrNpuNhg0bUrFixUeWSbyyiyEffU+MQ4ZSkpApVVRq3Z/JQ9o6C6Tc4ce5K+n88QgK3DdCbp9Yw7Dxv5AkVCjkcuQ2O35vj2HW0H+2Nv6ikpyczDvvvEPx4lnJxCNYNfNbNPV70Ke2U2Dq2vENLNgey6hxQ3DPrEpcMJNnr6bNsG+oes/zkr9+mcRZ7xZ80bv202jGM2H16tUUKVIEf3//x5azRQSwZOcFzHE3yVOvK70a+IExnFEDhrH3up2+n01hUK/qGSulXuOD9wZwKVGFQqTiWaYps34cS177JWbN2oVOk4prlR70blAwB1uY8wQHB7Nhw4Ysl8+2YJTVan3sunb+ao1xv92TE74VsmSwARSFqvJaCRVhYbezczmZ0vINf4Zt2f2vz5NbMZlMTxQ7On38FLqKLenf8jXQ+bL3u97o1YXSvhWs+Kobwxck4D9gKAU8763tXgw4jXe1dgxoUgGFw8CyOT/jV+AfbxG/kFitVkwmE6mpT5492BKP07blKN6bu4ZONfKCKYEZ48eSrJUz69uddB03gNceqLN/2Vi2XNCzZM5C8nX4iqq+Skg4TY+B09Fab7FFoeCLLrnHaBsMhif2lTF0D22HL2XFhl8pqAimQcXO5N+3g9Mj+6J58xsWeAVS640anP5xNwuG3dO6sYadIkJbhZH9mpEYH4/CuzQ+5iCqlujMT5cv4e+ayjvl/YhecomP6r4AXgD/kMctY2ZGjniPqLVeqHHuNty4EECUTUcVPy27DoXSuE1T3PXX2HEkGFy8adi0Lloc2B0SCnnahVsS2bt9C6F6F1q3bU8Rb4mQwDOkKL0pqUvht20nqNK0DXXL3vuFjbx8giOBN0kNPwfKV1l+H42DfFWaM6FtLfJ7AqmB/OLSkaV9nX7Bt06u51qeTrSssRbJkXEgFa3dnpHv1qeUp0RqxHH21mhF31YVnkMbXgDMIXRs2JfBuy/Q4a6jiMaLEd/OAdMhFszan2nkWuPeE2lMLMf/2Ij87oPqVYMVa1Zy+JdhHDqcuzb6syJ4lBAXSWjQeW7ooWB+T4pXrIirTE3zwdOo0qAW0JDfJ27kx5PnETRLX2i0O+Q0bNuNlo1KEGd1xcdNTsiyoQS5v4m/K4ALA7uWYeSCrXxUt3sOt/TFIWcEoxz2dGH3yyf+pEvDZnQdMpCuXftw9vQ5ho6aR/7ypbi4ZTY9Ri4DNMhwruuBkZnfTSHakZfQPybS6cNvsCDnwt+zqFunOaPn/U1C2N80rdOC3RdiATjy+/fM23aRfN4qdmzcxJ3YnGjVy4KMChVrkNcTwM7sUTNoM6I/Tn+TSNb8fYeBb1XCkGRB9oA3gF/VupT0dD5S62ZPxavOu7j/R7NynVu7jpslGyPWfELNBu3YeMl470u9Aftj9y+TsdoeLmBMTn3mCUOeBYXqtGdEYzkNinvT8p2pDPjme2rmc0sz2E7CLkRSvl7VDDtDMqz89GEbKlWpTB53N75Zf4G8hfJjv7SBxLQyl4JCcSTrn2l7njc56KftHH3NWnelgMfPDJyylY0rJEa9Xo5rrSZQrnBpPJtWYnSXCdye1huNUoZw2AEtH3w+DoNZol7ewazvtZRTEV/Tpms/ys28wYixIyjlArYbtVl14QK1ZbcY8MNhNp/YSAmguoeeg8O351yzXgIkmQwJuLBzASddarC4VlHAweqpiyjXZSCWiL1YgZSoaHAvcF89p4WO+HMMC2PasL917l5L/OfYOXj5CIGHY3H/9g9+9ppG7fI6DgqBP4D0D52IXpaIkQexy6jUph+Ta1g4tvwbmjTcz6GLx2mQTw1AyNbxHPYYxvpBGQXKHPlq8eexUCoU1sGVpUhlXqdR7E2m9fiT/PmqMrhfW47uj8Lvi5chejTr5JBg1L3Bl2pKRePmhUpYAAN6KRVb4nVO7NvBWXMJflm9iHzYMNscSDIFkMr8KWP56dctRJlUeLu5IBNgNifjUMgwpQLYkXBFp1VivHQVu6s3d708k1IMuTLN1fNg/vT5vN6mPUrAFnmGbQf3sXjsSD6bvpSLIef4dOhwAh/cmBehDJm0h7FT+z+PS35BcGBITeb13sN4vWw+anUfQzs/2HR3K0Vk1RXtgTJCgPTyjd2Tm5cw50A8oz76kI2nb9K/zC2mzncKlEWf2cCP+1xZ/POQh4yRJp+f02ADlH6PdoVTCLucwqfLD3N02xLGf9wFN21Rurf/j2jepJEjb9o6tRyF5FybU6lUqBQSHhoVIKeYhzt/hPrQcHLr+2rYUauUuPkUwBB2gGnLTrI79AdKXF1LitmBQg1Kmw6lTIFaDSCwW+0o1Qo8y5bg6tH5XIyEvAUhJOAUUUnqnGjWS8X1JX1YE1uXKY2LAiDzfY1p85Zgl+swXtjGtUgDM3+cSWUVhASex7tUBfJo5Sz7fATXSvaihe8T/sBLjZLy3qVZcvpK2ucEIvQFGNY47aOnG3KU6RK1dnMSIZdv4le+IholgA4kGa4eGdevte5alC4vX5ql+NjbaNJnbC4U96tIodqlcdw+weRfg/jiu6/wcphJMYGrVkXopfPkL1aKv9etp03PnigBzIcwF21NozpO7YSqNaoxd3BzfAYu5K0Sz6tlz4en/qYdcmADWwIiiLx1iP2nLrJ/71ZCIh3MnLEQKwpGz1tO5YsjqdNpEGNGjWLa8r3cvnyAY8FX2Ld5NpHqivh7h9Cz3SCWH7qBxpbMub2H2LN1PTcs8azZdgKQ41syL3sWrSK1ZDd+G92YdjXK06XnYI4lu1LRcZ5Vf5592k17eYg5StuvdjF+5VzuSjzJlGryFyqKm+MWm3Ye52boHZZv2Emi8RqjutdnweVoLGE7WLgyhG+n9Hyul/8i0P6jUTSxHGXS0j+YN2kujScvp6USokIDmDn+J2zeScyesYKbyQ7u3NhNg9bvczLKACnRbJzzAzaVYPm0iZwMugXAhV2rmL/5PNcP/sHvW/aiz72uxw/R/K3e+ETs5ftlG/h97iSSan/C+FZq+rZ7g1XrF1KzcB5c1BrcqvYmPj6ad+u1ZtPpFAoSSKeuHzNv4c+M+3oP03esp5QcrhzazKfDBmNpPoXVn7d43s179ohs0K5dO2GxWB5bxpCUJIxmmzAZjSLVYBTGlCRhsVlEYpxe2NJLpYqDO7eLk0HhwiqEcJhSRIrRKowpic4yxlgRHHrLWdRuEakGgzAmG4TVbBJJKalpx60iNTlRmNNOmnDzkjh3OdL5lc0kDMnG7DTtpWHGjBni2LFjjy1jt9lEqsGQ6XdWk0HoE5OE1W4RiQkJwiaEsFvtznoOizCabZnWe1mwWCxi/PjxIjg4OAulU8TRA/tF8PXY9CPm1BSRkJAibFaLSNInCpPN2XcOmzXtX4tISEgQJotdGJITRYrRLIQQIjUxXiQZUoXNYhT6xGSRVu2FZ+HChWLXrl1PLmg3iDPHDonA0Mi0A1ah1ycLk8kibHa7sJqMItmQ9mw7rGllTCLg8F5xMihMGBx3T+QQV4POiduZD99cyenTp8XEiROzXP6pL49o3e7qaGfUv1B63y+Fo8G/Rat7H9W6tKlkWiiCiw/lS6Z9J1Oi0d6bRrrddRuWKdC43gtd8CxcNj3rukyuRvvyzTKfGjK5HI02cxlVhVqLR9rqkrtnWo+m5b2USUpcXnJJjuxJKOio27BRhiMqjS5dW8PtvuUPSa5I+1eJ591+Vd4bvxp3L+46qnrkIq8/kdXQTZmWqnXuD8JS4OFx30Oqdknfl0K6a5bUVKufcXMSJIpXrPyPrvVFJsv9yD9YHnklGPVio1KpXokd/QsUCgVKpRKN5pWvf1bQaDRPFop6xWNRq9U5F1yTP39+fvnlF9RqdZZi5cVdMaJXPBNUKhXnz58nPj6e8+fPY7E8bmE0o7zl/QJS9x1NO565/ObLdm8lSUIulxMWFsaWLVvQ6XRPFIwS9wkY3RNKy84fJc3bBCRJkJukt1QqFadPnyY0NJTr169jtVofUdLZrvvHVpbHjrNjHnkss69zE3K5nJiYGLy9sy5Ony2jnZKSQvHixdFqtZmKEQkhw7dQATTC8f/2zju+pvv/489zd272NkKIiBpBxV5Fau9dfNVWigqlZm2K2rVq1tbWrD3bKrV3jRBbJLJ37jzn98cVSawk/YlK5Pl45ME99/25534+95z3+az3641ZJkerUaNLSkKSyZEZEwgOi0l/UUsCti5uaMxxhEcn56Tr9b1ErVZz5coV3Nzc8Pb2RqfTvcJKQGPrSD4XBwREy0Uvk6NPiMEgqLGz0SCaLdKMcpmMqKcPiEowIQAKpRWu+dyxUghICEimJJ6GhmGSW2GjVSMT5GDWEROXgDkHRokIgoBMJuP06dN4eHjg5OT02s6JQm2Nu7srKrkACMikZO7fD0VupcXGytJzEk1G4uLjXhloI1fb4u6iJSo0DJ0ow97ZBUdbawTJSERYKPFJr3OA7w9qtZpbt27h7OyMt7c3er3+1XZaO9zdXJALFvE4mUyOMTmG0PB4rGxsUMgF5DJIiI0l2ZDqVwRBgY29LUqZ5VpMiI8mWS+htXfA1ckBmWAiLiqS6JjEHJu9PmVkFx4envkyWTlBfHw8jRo1eqPNnO6fccGxMAWNERw7E8jHtWohD7/DQ49W7Jz2cqjpwbkDOJrvS6Z3qpaVr5LHa7hy5QpVqlShXLnXZ+R5fHojn3dagNzREZUgQ58YTdmWX1JOf4olv13C0cEWuUJG2MM79F5xjb71LEE1UuRdBvfvy6UIAaU5CZNrRTb+PB/nsOts23eMyJg4tEWq0K1N3ZyRXeM1nDhxAl9fX7y9vV9rI0Wcp0+PXtzRqVGaYrmn8+X2yWXE3rnAb8fOEREVhatPNbo0r/fqvkjkCXoHbGbcutkUBg6snMw3K3eRYF+ShSuWUy+HZK+5f/8+Hh4e1KjxetGw8OuH6NOzH3FKF6zUCgxxYVh/3JMd83twZOsvBD6JJNaopm3Xvvi4pg2xNXFsxwbOP0kkNlZHi/5DqeYAgX+sY/g3S3iid6PfxJl098/ZSTmuXr3Kzp07M23/lgWj4km2Kc30Od9if3oZhRZtYfrWY1RJPs20o09eWcKhoDeeOWnl5T1Hp9O9poedSuCdh3hXa0GT2j4IWlcOTe2C1suHh3+fovVnvfAu5IRN3BW6jgujQaXUmyjq4WP0zt50a98QK10cok0hXOVJDGzXgdLf/sbgni4MbN6IYP1sxuXQh7DRaHz+9ybCQkMwOJZhQOu6GBJiSZB7IsbdZGDAOBqOXMKQSiL9On1OsHERI9q8KBuVwPxx41m9PZoxQMyhxXx3JJLR06Zz5adRVK7enNuBB/DOAdPqycnJGUzDQXjwE1xK16bDJzWxdVCwe/p4VFXKcXTRSHbFVWfeqADWzxzIt98tZMOcwc+d0smlA5h0tgS7Vg4l+OAk2rYO4Py2jvSZfpghI6cRf/onun1ag3wPntKocPbECb4LXpmb4A285Q6RNQMnD8FeAJzs0FjZ4+IkR2NdjdE+ArrH/3DgbBCeFepS3tMeoy4Zn3od8dYWwJQUw8On0WiUCrRO+XDQKomJiMbBxZHY8CdExsSjcsyPh4sdT2+e4+8bIfjWaIS3mxJdbCTBMUaKFXbg1JFDJNqVwr9ysQy/bW4k4x9fJF/pOoz+tCJF3BRgOsGyZf3Z0+pjjrgqqFmtAmoZnNtyn0HfLyGtOKler6BK0470bFY39WD4WlbetON+I0uEQ5f6vow4dJhhnaqRm9P8GgQNtZt1ol3r1Gi8O7tmcyXRgZk1LBKtDSqXYcHOfS857dN791Dg04Z47NyLEohy9GP10i8oaienQZ1NjBeKc/2hiLfP+++IMiMY5VTEj4BvGlDGMz9EX2d/xS5M7lyNZc3acbuCZXeIr48nZ40O6UYlP83bQKXFj7ACvBuMwq2PF7/+3YHv1/xIFXcN1K/DzoUyDl97RKPCntlXyfeMt3xVyLB3fLZxxywiSiKWDouAcHwRn//wJ0kP/qRSEQdWXwd53H26lShIiYCDKDRxjKzjRcHWS7HRKkk+PJR8FfqQCKjv7KKUX3Pu6RVcWj+bWQcuE3xqPcXd3TlthKirmyhZJD+Ne0/irz8P0qWKN7VGf7jyrG9GRulyVS0OmwRG91nKlwsGAUr8a1gcduKjk6w+8pA+LSumK6nUmjiweByNG9TETq2m0bCfwbU0QtQZjhx/CkicOneakGSJDHKY5HjUYgK/LRpDs/pVsBJkNBi5k4KlSxB88RR/nY8Cojh78wyPkpLSlYsPvc4/0Q60/8QXg9HSSl4Vq1A0RXnr3Aasyn1BnRzgsDNLvuJlLA4b+GnRj1Rq0BgHoNe8H7k2uRUuJSsy73eYOqYbaSdHChZSsu2nXwEIf7iHMw8Twb24xWEDJPzJTauKdPvkw3HY8A4S+6Y8OWXFWjFrrC2qhDD+t2oNe/eepMewavTvWYHbRgFkhdn801j2fPkYBbD0oEhJ4QRbroq0VTgy/sdN1CqoJcz/c761k5MY6sXmddv47aiZqQ078GnJWTTpHsDAWm50Kw8eg1YjTvs0m8RVcgc3D/xMaL7K+BdIL9W/+ccluNQYQr4XhudW+YsSMHEJ1SqVIfzkaso0GMqRLx6x8ZvGTOjXmptd2nEnKJ4ydVxeLf6fi7BxL8uwmYuoXaUUDw/Op0jDVlydJPF9n2VM7N+GG21bcvNOFCVrpJeuPfjzOjzrjYTEvYiSSKIOUjZoizG3+GLyJdbv2Jor2y/+9h4O3pIza6QlQUdCaAK9J47CkGTg1w3T6ZXPk82jOjz3Gf1m/8TBrkNp3PU2rSvpEJwLUdDtmX6CmMD8MavoMWMZvrl5SPcKss+nCQJCmlT38Y9/p1+Xr7mVbEOlko4Ynq00myQRQbIYyer2wSXoN5Zu3o5Tx8GM7vgRW1ev49j1MBo2tmS0CT21gi4DFhOndqeUuxsGXSJgwmRS4mRrCeixtrVHLRh5/9ff/1sWz/ieOo3boEk7Jg3awg974hn0v5eV02xcC1GtkmWo71qtAfX9nLlzN4QOM3ZxYPtKAgYNwkYuo2a13B9arHXzonYVi0Mu3KA1flq4ZYQeM3eyd+NSerZtiqPMjuaN087t32TCuMV8O6wrDbtMIyz8JP07fcn9x5be+Io5C6gzYjaNMpMwJwcyufeXlOo6gQIKwPSUKZOXUKLDNCZNn8UvC4axd8VyIhJT7d3KtuTQ0T+ZO3UsjZx02JdtTDWLVA5HVs4kuWI3hnUo/5/U5b8k25y2qNdh0CWhNwNE06dFV2y7L6e2Vz7CnySje7aVSp+oIzE5ZeGsMKPqx9H/2zVU9SlKq77Dub6mN5ueFKeCAxB/k05tR9N0zlhKeDgRGh+H3iQCZhINCSQ9W4AzGfQk63VkPuvah8eF75uwUexEl9oFUw9KT2hdtwuN5q5LzSxvTOLK2eMEJydw4NeNBIZaHEzMjRtY+9ShbjVLBoBCxYtxbk57oqtN4Ovmxd9tZd41ujD2bdvIlRDLFRZz7gSaJmPpYg0g4FnMi0NLv0FsMIYvantg0kVw/ORF4owluBoby8kDv3Fg87c4OFZi3fbFFPFQsXxYYwJdW1HXw8T58+d58DTuP63i2+avme1YmNyVsfWfjSHEBKJidaTs1bNx8qZJg+poVGYuHj9FSLTlXrZy9iBf3GHazXrA/qPTUSJxet1wllxxpmOD4ly+cI6bd0L/o1r9N2TL9IhRF8GBk3cpUdqDo7t2U6JbM4ZP/oouU5uxWjEQoUg5rOODuP+kKEFJ+chvOs+TkNoUyG+H/9fj+d9uLfltQWlTm17NGuFa89mcla0no0d0YeZnvXELaER+348x3z9FyGUDNkU9CDx1hLiqn6F0KkY575ucvBeNfxHHvP3fLxL3D2NX32f2zr3p5hCvb1nKo6Jd2VwnNbzYFP+Y6UN6U2flH9SzjWXGtEnUqFIOMSmebiPGUNxOyaPLR9hz6AThjl3ZMa7tu6/Pu0bjho3pKQunjOSTWn5ERyWzZNEYAO5c+p1Dh/4iqdwAVne1jDjCg0/Rr/8SVu3+hcoe1kQ8uc2Rbacp7KXi0IGz1DT+yfgtQfiWmUXbtWGERsbRcfQKpvd+MYQ7ZyJFB7J4bzCzV61LPagqxpCv2rLhp6nY1Pfj1uW7DAwYjK0yhhG9+tB9+R46V9fy289buXA7msUHD1ASSAo8wKBJ21F4fUS3VusJDQvFt9Foti7q/5/V710jSFkIem/RogVbt27NMJRdNOuJjo5DEmSIooS9swtqmciju3dQuXjibmUkIt6AlVqFTqdDkkS01vZorVSIogGdTkKrVQMSuqQk5CotypSUzJKe+3ceYu9RFFtJR2yyGY1CQmcwIwL2zs6oRD0R0XEoNbbY2Wg+KJ89b948qlWrRpUqr9+7Khn1RMbE4uzqlq5tdHExJMk0ONmkncyWSIiNR21nhxI9QTduY1TZks/dFUdby2RibNgDYsx2eOaCfJFGo5Fp06bRoUMHSpYs+Vo7yZjEnVu3MGmcKZDfHTutCiQzkU9DSMKGQvnS5CwUTcTHJ2FtZ4dMAENyPDFxycjlMhDkaBQSSQbR8lsIIIkiGlsHbHOA0MuKFSsoUqQIn3766WttJLOBqKhEnFwdX7gXJZ4+uEO0XsA9Xz4c7SwKRAnxMaitHVAY4rgfGot7oUJon/UuDEnxxCbqnrWVgCSaUWhscbTLuRPbFy5cYN++fYwZMyZT9tnS05bJ1Ti7vCi4LKOQV8qwWYWLk+UHsra2Tm8lU5GqZSSg0aZ/H0FNEe+Uz7HB+ZkuVToruRqXl87/YZCZZ7CgVOPi6vbScY2dAy9vDRawsU9ZFlPjXerFPcdg7+aJ/UtHczeCUot36RfmUwU5zvk8cH7RWKbA1j51aVFlZYublW06E+sXy+QgMrrmBLkKZ9dXPYAE3D29cX/hqI3tsweexo6iRdIvyaq0trhqbfmQydKctslkyhOMes/RarXY2n7YF/X/hxSxKO1rVBDzSI+NjQ1WVlYZG+bxWjQaTYYaN2nJ0vTIwIEDKVCgAAqF4jUnEZDJ0gyAJAnxmWjU8w34z46lFhEsmjnpxGRkqSIwad5P+cvj1SiVSgIDA3FxccHJyemV+jCQJiBCsqRfTvm/5Xex/IYZtbWAYBnKvygCJElZF016T0hplxs3buDp6YlWq33DzSQgyAQEJCQs/4piqpCWAOmv85fOA5KYpq3S/iY55BpXKBTcu3cPKysr3N3dMxSRs9zXUrp6p7S5JIqvuW6etXMav/G8zDPfIkkiOaTJXkImkxEdHY23tze9evXKVJksTY9ERkbSunVrrKysXukQZHKIj0lAY++IYExAb1JgrVFhNutJ1InYqCWSzTK0GhWSaJHnUimUSAoBs16PKD4LldclYlJYYaOE+PhERAQUKg3WaiWiJObKjNVvA41GQ1hYGN7e3pQpU+bV4eyCDIVcjiATkMvkz5yOiNFoRlAo0aplRIZFYePghCDq0RlevhEFuRJrGxX6+CTMEiBXoNFokZuSSTKaML85Ffl7S4ozePjwIWXLln2jI1IoFMgEGYJcjuxZ+4GETGONwpyMQVKh1chIjE8vnqVUa9EoBRJ1RmztrdHFxaE3WaYJzfoEUNmhkpJJSNK/9yJIKpWKxMREXFxc8PPze304uyCgsbJBTIom3qzEyd6a5KREFBpr1IgkGU3Y2FiRFBePMe21I8iwtlKTGBeNSe2Ik1okXmdEKVcgyCziXmaTEbMEosmUIzsLSqWSu3fv8vTp00yXybJglL+//xtt7l/8i1nTRiMUb0L/gMGUclOBqOPshmlM+cuK2TMC8HZMHU493DeT2YHVmR+QKiafFBbIggnTOJ3gRPP6lZCSY7l58TjFW06nb6NCWfnKHxznz5+nYsWKlC37eqH40PNb6Td6CZLWDrkAJp0Bn2YDmdW7LGvX7cHOyZlr14No1vNLvF4Z5ZHA1KHjKNNuKC2re2CMDebE0b1s33qREesXk9NztP/111+ULVv2jYJRRF1mcMBYbkfLUYrxPDaU4/yhOdw/uZXvlmwnRpdEpXYDGNrBP90cpC70ItO+W8T1++F4VvNnUMBXFNHA7sWTWH/0IokqV7oETOaz2i/O9L6f3LlzBw8PD6pWrfpGu+t/7uZ8qBFZzAMuanz5pps/4f/sZ/yMVQSFROLXujdD+3bC9YXZ1/N71vMkUc3DS0fwbhVAm5r52T2hOzNPROKihehoPb3nbKJL9ZdWEnIMNjY22ScYJZPJMhCMgiIf18J07zgJhb6wOGwAmYZKtRy5d9QrncMGMxsWzGHBX78yMuAsKak/tW4l+Eh3hu8etWJj184oDGYCy5o4GJT44unyeAGdTkdycvIbbf65eRvPqq3o1KAcCrt87BndFLOnDz+MGciNol+yuPWn7Jj8OW2+VnJpeZ+XygceWcHEuXOZUrsfAEe3byQq9ikLNmynbw532kajEYPBkKFgVGjwIxJtfBk7sDlJ0RHEmtzRxT1ixfrD1O8WgIf+Au07foaT9yV6VkjdC79j2RREry+YO8yZ/1WqyDRTGZa1i2bRRSOzp88jZNtQPm3UgUpP/6RYDlg+yoxgVNyt3xm5cDczF87nI6vL1Ks1mI/rl+X6vIV4Nh1Ov0IRdKjbjhiDG0uGpHYK4679yrerzrBmywJ0Pvfwa9GDqg/3c+vcHdqMWUolczCR8Xp8PHKAstYb+I8FoyzY2Dkiql5oSIUGO+v0x+4cXoVVxwV0vt+NsfPPsXJwqtaFlVaNtYO9JWmZSsStRH+GVtRD4scAABUESURBVM3Jgp/vhswIRhUsX58RTfwo4AgkHuBb+wHsa6rn0/7/MPhsLQBadW5J++ITkJb3SbdNK+72UY6F5aNb7TLIBcs+rIbdh0PkXgaN2fHByAaYZGrKflwT7wL5iXMvirdnPszGWAZPmYurowZM7nRqvgt7pTpduTpdZ/JZUYuYWe+uLbia3xazmy9blrfFGig1vCfeG37GOgc4bMicYFRyXDCnTp0lIjoRnB3xKu9HAZUWjwGzKPnxRwBMm9aUBVevIuH//Hrbu+p7NOXn4SoA5b+hsjiZv68mYu9amrK1CuN0W0kZLx/s3/+dkW+VbLnHJEnipeWolxZYYth/LphqjTowcXJXdq+aw5M0nRuZSklk4Bm2HjrO9llf0mP6juz4qh8gMkqWfuawpVjGDdzEoKn9QLRC6xDD6rXHADj++1FMUiyxaYsaIvll9w3qd/gM4qPSR5wmJ2POqatB/wKlOZGTu1czYdwwGlTzo3bf5ciV9rg6wrnfjzJlyFfcz9+ENr4u6crlK1qMRzcvsHnBcBZedGFg90rInVyx0iXy547ldPAbRp9FC8n3H9UrO3Cv2JmlXQtRq2QxqjcKoEyXQZR2sX7usAFO/xlMsxZN0nUQ7l9/hMY9ZX5OxN1VRXRCAjGRQcwaMZ6BXZtSqmoTzoR/ONcdZGMYu/BiSItCjmRKXWSIe/IPF29GQ3wopmJNkF//jWNXI5+/L0kiCq0D+Qt6ULRwIWzVH0of7t1x/+Qe7jmUol5hNciKMH3qcA5804DCfk05ePUBWH9MmhAR7l48Q+GarSgiByutDU6OaffCf0ghTOBYpBqjZ3zHnOW/cmD1WP5a3pejz2YJFFpr6jbvhO7IVAYuPPhCSQlBrsS7RluqKf+mfY/xxAKCDDQOxRg6uSeT21fml0sR77pK2YcYxyOZDyt/WUk5+T2GNG3KocepjvbOhgCuFB7A0FY+6Yrp9UaUytTRtVKhIC4hgS7T5zBv7FT2nb5AE3EfwydtfWdVeR/IHk8oSZiEF3aXPA1H65m6iLh/+QqUBQtx5fBWjl+MpUWzEgybuOD5+6LRjINHcaqXKkL5DuNYOrQRBl1inp7IWyOWrweMpUPvgc8Fo0o1H06iJPHw/B6cdSb6Lh6brsSmxVPp1+ETfMt/xIoTt+jT6GN27LuQavCh+G3RjNzaFd+PvNHIBYo3bExlVydikwA0lK9ShRoNOtCzcwu2/7o7TUEDCYkSHsV9qehXle9WTSRo9x4uBekRVNZUqVOPqk2HM8QlkDV7Lv1HlXv7HF69gLNSUXq2b8OSA9cZ11LNzAWHAAjcs4gpQdXZt6j3S87Iw9OJkHspuypE4kwKCtg5k69UWfI7WIHMln5fdSQ5IvOpunID2eK0S31UmJ2HdxL9/IiBaRMO0rmT77PXjzgYZMv0qcPo038AvXt2Y960MYTvXcDtZxYqBMyy1KesgyaB9Ss3k16dOI9/y7FZXTjj1IOmpV8OIjm4IICDyX5M7lQJTLH8vHQRZ0MSGL3sAFeu3eDqpZu0K52fGZsP0apxBUshaw2CyZyrEx88R4zh9307eBpveRl38TSOjYZSNeESW4+ce2Zk5lrQfbp+3hEx+T7LF68jkSSWzVj4/GMu7T5K9XYdSLy4nQvPO9YxHI32pE/7Wu+yRtmKTCERHxP//LXRVIA2bfyIPb+Bib8E0addKU4eO8qB45cRTcls+HE+tyKh2qf1uHNs17NCF3go+xinuHOcSDOPuudINH0Hd3zXVfpPyZaVvc8nbuB4pzbkt3HCq2gBkhOMfPLFDEYXBaLv8u0X7fj5rA3+fwXSqVYJMCWxd+9hnFUxtGk5nPkBtVn0dwQJT9fQuv0ZJF0Cd28FUbf3D/TMji/8gWF8dJz+3+5h8s3d6Z7asXf/ZuqMZViVacG6H1vjpAQpJoy1c2dT/+NmVMrviU3UdeZMWcyfj6K5tGAKTXxnU0C6xsxR09FLEQR8NZXRX31BJW+X154/x6NwRhX1D63qL6Rm3UoI2kLMmzsaV/Eiq775Hxs+bkpx62Q8ag1nTNcahN3ayYI5i6nboyvipe8o1eA8rco6IrpW5edl3dH/uYiSviXp2LM9iU/CmHLkBHV81Bl/jxxCvbafc/DIYAZMUFLEeA91y8H0qRJFU+++HA5R88eBjSRGhxFfrANRpxay7ofZmEq3p1uXGbT/rTV9Rk/H6vEF+q3eRsMSl6lZrQbetT7Bxqin7qA5tK2a+UzmuYFsEYxKxUR0TBKODrlR0v39JDOCUa8jOTYGlb1DOuW/D43MCkYBoIslWrTFUZt+wBoREoxD/oKv7RGZY58Sb+WOQ7pdDyJPQmMokC9nOaCVK1fi6en5RsGoFBLDH2Oy98jybo/EyKcond1JW+xpeDTurjlfoAzegWBU1kJsFXkO+x3z/wmBtrJ3yNjoAyDTbaix51VuwyV/wVccTUVu787LLS3LcQ4byJK0hLWrx786h7Xzy4FGucVh/xuyLBilUn1gmyJzGDY2Ntjbf2iae28PpVKJtbV1nghSJrGzs8trq/8nWRWMylJP29vbm0mTJqHRaF6rySBXKpEDEgKSZMZkenv7PQRBjkqtQoYJnc6YI7UGshOFQsHNmzcJCgrCxcXltYJR6RGQy4UX9EIyFo16ldBPxuI/7zeCYNGzuHr1KjqdDpVKlbmbSZAhF8Asishk8nSiaaJofi4kldZeIZchiWaLLsmz1+mQJMxm83vdjkqlktu3b2NnZ8fJkyffIFAms2iHp0PCZDI/e0+OaDa+pCkkCAJyeZrJuudtIiCTy5Hx/rdRRshkMuLi4vDy8sp0mSwLRrVt2/a5wtaLCDI5MSFPEBxc0YgJJBuVONhr35JqmYBkTObp0yfEJSjxq+qFaMjLApkWjUZDdHQ0JUuWxMvLK0OnLcgV2NpaER4ajZWV4vlNo1CrUSkUGJKTML1KnUumxNZKQWJ8PGZBiSBYbJRae9TmeJKNUjqRpJxCynUdEhKCj48PBQsWzFC5TiZXYWslIzpej1whIJMrUcoVqBQyjCYDomjCoE91LIJMgY1WSXhIGDYFPVHpYzGKMhBkqNUqBAFEoxGdQW/Z3mp+f9tRqVSi1+txcHCgQoUKrw1nl8sVyGWWDpcggGQ2kqQ3oJAr0aolHgVH4upZCHNcLMY09ZUp1SgEno3uRQxGA6LZkvTARtCThAalpCc24c2yDe8zcrmcoKAgYmJiMl9IygJNmzbN0CY26JzUu3llqW6XodKlh3FZ+fhMYJB2TmolFS838C1/bu5h4cKF0qVLlzJpHS9N/qqbNHfrxXRHkx5el3p27ivdTnpduWhpaJd20uqjN9Mdjd0/QWo5eIlkyvK3fr+YOnWqFBQUlEnrh9LQ/3WSVh+/L0mSJP02s7fkam8rOTs7SRq7/NKYdRdesDdLK78fLy1d/oP0eeee0olwSZLu7JK8HewkBycnydnZSXJzKyy1DvhBin+blcom1q5dK/3+++9vtAm/sluqW8JRsndwkpydXSR3NzfJv9c8SZKipSkjh0krViyShn4zVYp8sWDkVcnfx1ayd3SWHO1spTJ1u0oPJEm6vXmEZKVQSrYuXtLEX//Jppq9O65cuSJNnjw50/ZZmtNOEYx6E3bF/CjrnIiVbWHKFXrbYvxKmlQvyaOIaPRv+ZNzC0aj8dWSrC9geHqa6mX9qR2whIA2aTOwmFg8ohurjl4hSf/yb/3k+Er8/DrQc+2vdK9bIvWNyLN0HTOP81cfk5PHP0aj8bloVEbEnFlGmXID+eKnjXSv4QmGMB6Knuy9HEJE6BOe3PuHUe3Sqy3+NKQmF9Wf8kXvgXz/pQ9D+37FpWvBdF57lOjISCIiIlk0uBZ16vlj85rzvk/o9foMfUJYRBLNR60hIjqSiIhw5n3Rgo49W/FNk1q41P2aXr2+pInXY9p3m0basaHh8Q18+64hJiqCx48f8cfuVRS+u54mP8lJMhp4sCeA8e3LsOR8QvZWMpvJqmBUtgTX6I0CYooasJTEno0rWLV4Jku2ncRojOXAr2vZ8/c1zh/awrpNRwE4u3s5Q/r3Z+n2k2nmqBLYs3kVC+fN4fANiwpGUrIRmSwvpP3/RxyDmjaj847T1C6afhHpxsG1KOsMpoG3mpdkseNu8tXY9Uz45SCl0/4ExhjW73vKiIBWKEymDyMwMuoMzXqsYNPlnfikTLuaJezcCiJFPeDIsbPIbJyw1qTfQGmIfcLuQwcACHkUR5Ua/uT3/ZRe/uUsBsYLrD+dn67NM9hu+J6QGcGofN5+tGxYFQWgv/8Hx8yV6Fo6kr2BMop+ZFFZ8SpehfDHV4lP4/9FFBRwd+Lu5b+5dDcKZ60CSVGBX9ZPAsCx8iDaOqt4FB37irPmXrLJ+6W63VUDmnFKWZ/POnUhet8y7intMZxZTrMm7Zm6aDW7918m+PwiNp2xY+iobizo3Jj5Rx4DMKRde0Lz1aFLU28G+NfmUBxYKT8Il5CthPyxnLWBpXD762sKOdjRbvwWyxtR5/jtph296hYmIdGA4oX9+Jd3reWSwYM7y7vj5lSArxbsByD46imsChXF11WNXpJlT8TWe8afG3cS7ubD4eHtcXEqSP+lp8DKnpDjG+g/ZBjjv/oMh/xV+O16ZLpyXaftosj1GcjVtgw/4c7cr1viXqQYhbUKQMfkz0bR+YeJr9xKmFNxKuSFVz6LTs1Pa7ZQuZ4/GnsXNI+vcPD4NQAunNzH9SdSOockiAZWjO3OoDHjaVTei7aDlmAoXIryzs8ehLH7uaipQPe6b95imdvIxvvL4lwf3rjGphuT8LHqSb+FS3ECfOqVwmoPbNvx43PrUZ53iYpX4l8BLgQ+BtVM5h3Rc+Y7W2QuRSmqvMWObfeoXySHaFa+x1w9ewqdYKJS29k8atcZN5uKTKgcRJFrR/i4SSuCbhxDL4mEXP0Hn+plSInNu3LuLHFGB+oO+5W+PXZQuFRr6lU/zf3Nu/EbNJvgo1FIRok7UfF4O9oiy7XPVxOXQ84SGmFH+6lb6NZxNa6VqtH0fxKD523nm2c5Omf3LE/AuEW02DLuecngwHPUH7SCqR9BrxbdKPUkgjNbJ2EHXPhlIae9O/Ft4dwpBhB7aSX7bzmybrxFmvaX3fNp2M2fKztbU0weg1+Z8mjS3N4K7wb8fbMjzhog6Sgq65Zs+19bOlVxAzGYEYPXMHnf4dSRzgdC9vS0BYGU3vbYzUf5vLTI5pk98SndkpPhYNbrkTvaPp/7/GPZtwwYPJ/HSWrqlCuMUZSIfBCKzEpOyOObHDt2l8ErDjChS1GSdEaEHL3J579HMOmgYluK2gDWfkzwd+PM8f3cenSdVd9NY8bq7dy+/Q/jx00gMM0ko9lsomz9qpRzAW2JOrRrWZ0nu9ay79I9Vk37mrE/nSX0wh6mzt1CXC5fdDDoJSo3bI6HCpwqtqFFUYET50GdJqnyR2Ua4aFN3y+aNGQUxfw7Ub3h51w4vw3x4mH+eQSYw1m8YT99OjR+xzV5V0QwsOc0mo8c/Xyu3qv+V9wOfszBn5fgJsTQpFkb0gXvax0tDhtAW49mXkqMcsuBjYtWUaLTt3T2zcl57P8d2eK0bTVylDJLEM7POx4ybuFq9hwLpJV0gD+umZA7qBGVGlIeqnNHTiF/g6H4l3bj6IVHWKlVODdogBhyAeeytWjesiWN69dELQOttQoENblHmeHdU8avKvy9//nrU7diqN2sO9PmLWXN2jUsHNsf3zJ+bPhlE2XlSWxbsZLr8WbKVivPw/MhlkVgMYq4BAP5W37F3v3bWPbjQjaN8se9VjdWT+6BQ85OJpIBCioU+ojAq1ctL00PCDKUoaZDIH9fCnxmY+LukxA6f94OKfkha1dswAzoE2IQZZYr3+ykpngJH7wLwYXFAzie6Etdv5yRZiyrHJ/Vg22q7vT0fSEQR1CwdfLnnLVuz/BulYEkfl3zI3fD4ti5cinPlxgfbSWhdHc6VrTj2KLBHA4rTN3S9lw+d5rLgcHvuDb/LW99euTUpvmsPhZKfP6D7Dnvx6Wfv+HXm//QwOk+9oMPMaxGJCM7XMcjOJbJqz9hVI/6TJ4/gOpDqvH0yVLaVC7O9B3reNpnHodmHKZpGU+qVi6PvWNpJk3rx5wlf5DfNoZ5uy8zqGk55Ll2CJ595G8whm2D/6ZOv1m0sruMw5g/GFnD0mOJu3GUcfNX8uh+CF8PnciSRQPZsWY85Ss2ZGin7+l/vD09R8ymouoepbtMp3WFwoCeCwc2MnPiKRzjBUb8UIWJ/VugzcWT2/UGT6TX2S70mbAQj9hr9Fq5jaYfqejd8nOWeVfCjiRqfjaeDjW9eBK0i4lz1uHbojOLNi6n69cdSOjoz7ljVxi/5ifcxEt8MuMmw7asJjeKPiQ9PMWUTRdZsWtbuuOhgX8wbfKPFGgygL3f1rQc1MXww7h5DPioHWUcHvNJ1eY0qFecKKMne3+bQ8ThRfSevR29aSt7lw1HpzfRuPFQNm0a+4oz51Kysp+wefPmksFgyOo2ROnRrWvSvah/u3s3QXoc9rb3e+de5s6dK506dSpTtknBt6XQ1+7Ffj0RD25L4YlZL5cTMBgM0oQJE6Tr169nyj70bqAUl+6WMEsP7j/OoJROun03I5ucwYoVK6RDhw79i5KiFB8TKRkzsEqOCpbC4s3/5qvlGM6fPy9NmTIl0/ZZ7gtlZT9hCh7FS2W5TCrWFHTN2CqPVDL7G1kV8ObfqEY4F35DlvIcTsp20sy2oXtRnxeOyCjsmdFuBjXeRXPPjod/4xNAwMY+Y4EsjWMBcvVMG7w2wvx1ZMlpS5KEyWRCkqQsCZzk8W5Qq9XPLwBJkjIVIJJHKoIgIIri8yAyURQzDBz5kFEqlekC7vT6XL76nA2oVKosB9dkSU97yJAhqNVqNBrNW9ITyeNtIpfLuXXrFk5OTri4uGSom5HHy8hkMm7cuEGRIkXyrvMMkMvl3Lt3D61Wi7u7e9719i8QBIHY2Fh8fX3p2TNzKV6y5LSjoqKIior6l8OhPN4FCoUCURTzRkL/khRlubw2zBwpKnx5DvvfIwgCbm5u2NhkTrggS047jzzyyCOP/5Y8EY888sgjjxxEntPOI4888shB5DntPPLII48cRJ7TziOPPPLIQeQ57TzyyCOPHMT/AXS+R/DH4X3PAAAAAElFTkSuQmCC"}}},{"cell_type":"markdown","source":"# Conclusion","metadata":{}},{"cell_type":"markdown","source":"A number of concrete steps can be taken to uphold ethical standards in AI, including:\n1. Establish clear ethical standards: Establish principles and guidelines that clearly state the ethical factors that should be taken into account while developing and implementing AI systems. These rules ought to be developed with consultation from all relevant parties, including professionals in ethics, technology, and policy\n2. Integrate ethical considerations into the design process: From the very beginning, the design of AI systems should take ethical issues into account. Conducting ethical effect analyses, spotting potential biases, and include a variety of stakeholders in the design process are a few examples of how to do this\n3. Encourage accountability and transparency: AI systems should provide comprehensive explanations of how they operate and how judgments are made, as well as both. This may entail making the data used to train the system accessible and enabling independent auditing\n4. Make sure there is fairness and non-discrimination: Fairness and non-discrimination should be ensured in the design and implementation of AI systems. This may involve seeing potential biases in algorithms and data sets and taking action to counteract them\n5. Provide continuing education and training: To make sure that developers, engineers, and designers are aware of ethical considerations and best practices, continual education and training on AI ethics should be given to them\n6. Create regulatory frameworks: It is important to create regulatory frameworks to guarantee that AI systems are developed and applied in a way that promotes ethical concerns. This may involve creating criteria for accountability, justice, and transparency as well as penalties in cases of non-adherence\nBy implementing these, it will be feasible to make AI more ethical and to develop a system that is reliable and beneficial to society as a whole.\n","metadata":{}},{"cell_type":"markdown","source":"### Use of Genrative in essay\nWe have used generative AI (chatgpt) \n1. To paraphrase the content\n2. To generate title","metadata":{}},{"cell_type":"markdown","source":"# Reference\n1. Ethics as a Service: A Pragmatic Operationalisation of AI Ethics. https://link.springer.com/article/10.1007/s11023-021-09563-w \n2. Ethical Issues of AI. https://link.springer.com/chapter/10.1007/978-3-030-69978-9_4\n3. A high-level overview of AI ethics. https://www.sciencedirect.com/science/article/pii/S2666389921001574\n4. Ethics of AI in Education: Towards a Community-Wide Framework.https://link.springer.com/article/10.1007/s40593-021-00239-1\n5. Towards Theory-based Moral AI. https://arxiv.org/pdf/2306.11432.pdf\n6. The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis. https://arxiv.org/pdf/2206.03225.pdf\n7. Implementing AI Ethics: Making Sense of the Ethical Requirements. https://arxiv.org/pdf/2306.06749.pdf\n8. A Framework for Ethical AI at the United Nations. https://arxiv.org/ftp/arxiv/papers/2104/2104.12547.pdf\n9. Z-Inspection®: A Process to Assess Trustworthy AI. https://ieeexplore.ieee.org/document/9380498\n10. What does it mean to be an AI Ethicist: An ontology of existing roles. https://arxiv.org/pdf/2205.03946.pdf\n11. The Ethical Need for Watermarks in Machine-Generated Language. https://arxiv.org/ftp/arxiv/papers/2209/2209.03118.pdf\n12. ESR: Ethics and Society Review of Artificial Intelligence Research. https://arxiv.org/pdf/2106.11521.pdf\n13. AI-Ethics by Design. Evaluating Public Perception on the Importance of Ethical Design Principles of AI. https://arxiv.org/pdf/2106.00326.pdf\n14. Responsible Artificial Intelligence – from Principles to Practice.https://arxiv.org/pdf/2205.10785.pdf\n15. Towards the Right Kind of Fairness in AI. https://arxiv.org/pdf/2102.08453.pdf\n16. Signs for Ethical AI: A Route Towards Transparency. https://arxiv.org/pdf/2009.13871.pdf\n17. Ethical Use of Data in AI Applications. https://www.intechopen.com/online-first/1121510\n18. Toward Ethical AIED. https://arxiv.org/ftp/arxiv/papers/2203/2203.07067.pdf\n19. MORAL DILEMMAS FOR MORAL MACHINES. https://arxiv.org/pdf/2203.06152.pdf\n20. Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education. https://arxiv.org/pdf/2109.02866.pdf\n21. Ethical AI for Social Good. https://arxiv.org/pdf/2107.14044.pdf\n22. An Ethical Framework for Guiding the Development of Affectively-Aware Artificial Intelligence. https://arxiv.org/pdf/2107.13734.pdf\n23. Reconfiguring Diversity and Inclusion for AI Ethics. https://arxiv.org/pdf/2105.02407.pdf\n24. AI and Ethics — Operationalising Responsible AI.  https://arxiv.org/pdf/2105.08867.pdf\n25. AI Certification: Advancing Ethical Practice by Reducing Information Asymmetries. https://arxiv.org/pdf/2105.10356.pdf\n26. A Practical Guide to Building Ethical AI. https://hbr.org/2020/10/a-practical-guide-to-building-ethical-ai\n27. An overview of ethical issues in using AI systems in hiring with a case study of Amazon's AI based hiring tool. https://www.researchgate.net/publication/337331539_An_overview_of_ethical_issues_in_using_AI_systems_in_hiring_with_a_case_study_of_Amazon's_AI_based_hiring_tool\n","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}