Decomposition analysis of health poverty vulnerability and influencing factors among rural women of childbearing age-an empirical analysis based on 2-period panel data in rural Ningxia, China 

Decomposition analysis of health poverty vulnerability and influencing factors among rural women of childbearing age-an empirical analysis based on 2-period panel data in rural Ningxia, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decomposition analysis of health poverty vulnerability and influencing factors among rural women of childbearing age-an empirical analysis based on 2-period panel data in rural Ningxia, China Ximin Ma, Hui Qiao., Zhaoyan Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2627219/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The health of rural women of reproductive age is crucial to the sustainable development of individuals, families, and society, and conducting research on the identification of health-poor vulnerable groups and influencing factors is an important basis for adjusting and implementing health poverty alleviation policies, but there are few studies on the health-poor vulnerability of rural women of reproductive age. Method: Based on the panel data of the Ningxia "Rural Household Health Inquiry Survey" for 2019 and 2022, the four dimensions of household physical capital, financial capital, social capital, and human capital were incorporated into the SLA analysis framework, and the health poverty vulnerability of rural women of reproductive age was measured by using the three-stage feasible generalized least squares method and the Tobit model and Shapley decomposition to analyze the influencing factors of health poverty vulnerability and decompose the contribution of each influencing factor to health poverty vulnerability. Results: The health poverty status of rural women of reproductive age is not promising, with health poverty vulnerability rates for rural women of reproductive age above 20% in both 2019 and 2022 under different poverty line criteria. Shapley decomposition found that the top four contributors to health poverty vulnerability impact factors in 2019, using poverty line 1 and poverty line 2 as criteria, were household size, annual per capita household income, gift expenditure, and Respondents' self-rated health status. Using poverty line 1 and poverty line 2 as criteria, the top four contributing factors of health poverty vulnerability in 2022 are annual per capita household income, gift expenditure, respondent age, and household poverty. Conclusion: Strengthening the ex-ante intervention of health poverty among rural women of childbearing age, establishing an early warning mechanism for the risk of returning to poverty due to illness, improving the accurate identification of highly vulnerable rural women of childbearing age, and improving the medical insurance system for rural women of childbearing age can help improve the health poverty of rural women of childbearing age. women of childbearing age health poverty poverty vulnerability Shapley decomposition health poverty vulnerability Introduction Global poverty is the greatest challenge of the 21st century(1). It is included by the United Nations as a major global issue for social development, with health-related poverty being particularly prominent. Data released by the World Bank in 2018 show that 736 million people still live on less than $1.90 per day, with about 100 million of them living in poverty due to illness(2). Poverty eradication is one of the world's greatest challenges. The first of the 17 Sustainable Development Goals (SDGs) set by the United Nations is to eradicate all forms of poverty by 2030. As a developing country that once had the largest number of rural poor people in the world, poverty in China is of particular concern(3). China has made great strides in poverty alleviation since its reform and opening-up policy in 1978, and as of 2020, China has achieved a comprehensive victory in the battle against poverty, making a significant contribution to the cause of global poverty reduction. Current data in China indicate that by the end of 2020, 98.99 million rural poor people have been lifted out of poverty, and the new century goal of truly eliminating poverty and building an all-around well-off society has been achieved(4). The total elimination of poverty is the bottom-line task and landmark indicator for China to build a moderately prosperous society and achieve the first 100-year goal. The implementation of the health poverty alleviation project is an important measure to win the battle against poverty and achieve the elimination of poverty among the rural poor, and is an inherent requirement for comprehensively promoting the construction of a healthy China. By the end of 2019, a total of 980,000 households (2.66 million people) were not out of poverty in China, of which 375,000 households (968,000 people) were poor due to disease, accounting for 38.4% of households, and from 2016 to 2019, poverty due to disease was always maintained at a level of about 40%(5), and the proportion of poverty due to disease and poverty due to disease was still relatively high. Therefore, the focus and difficulty of poverty alleviation work lie in health poverty alleviation, and it is still difficult to stabilize and consolidate the effectiveness of health poverty alleviation in the future. The marginal poor and potentially poor people have become the target objects of poverty alleviation in the new era, and the ex-ante measurement of future exposure to health risk shocks and prediction of the probability of falling into poverty in the future are the keys to reducing the health poverty vulnerability of the rural population and the new poverty governance in the new era. With the feminization of agriculture and the increasing trend of rural young adults leaving for urban work, women have become the mainstay of the rural household workforce, and the level of female health poverty is critical to family and social development(6). Since 1993, the China Health Services Survey has been conducted six times. The surveys show that although the level of health service utilization among women of childbearing age in China has improved considerably over the past decade or so, the health status of women of childbearing age in rural areas is still at a low level (7), and this phenomenon is more pronounced in rural areas of western China. Poverty among women of reproductive age is a general state of life and development of women of reproductive age due to many factors such as physical, psychological, and environmental, including health poverty, educational poverty, and economic poverty(4). Numerous studies have shown the multidimensional nature of health poverty among rural women of reproductive age, and that rural women of reproductive age are highly vulnerable to health risks (8-11), leading to health deprivation and loss of health opportunities and presenting a state of health poverty vulnerability(12). Due to the remote location of most rural areas in western China, the lack of medical resources, the low health awareness of rural women of childbearing age, and the multiple responsibilities of rural women of childbearing age in caring for their families and performing labor, rural women of childbearing age are a high prevalence group of health poverty. Therefore, rural women of childbearing age play an important role in rural revitalization and consolidation of poverty eradication in the post-poverty alleviation era. Health poverty vulnerability is the probability that an individual, group, or organization will fall into poverty after a health risk shock, and the risk of falling into and returning to poverty due to disease needs to be identified in addition to the elimination of existing poverty (13-15). Some scholars have conducted studies on health poverty vulnerability, mainly related to the measurement of health poverty vulnerability of different populations and the analysis of influencing factors(16)(17)(18, 19). In general, scholars have studied health poverty vulnerability measures, with the following main shortcomings: studies on health poverty vulnerability have mainly focused on the elderly and the chronically ill, while studies on health poverty vulnerability of women in their reproductive years are rare. Research on rural women's health poverty vulnerability has mainly focused on the analysis of health poverty vulnerability influencing factors, and less research has been conducted on the contribution of each influencing factor to rural women's health poverty vulnerability. Current research lacks empirical analysis of the factors influencing health poverty vulnerability among rural women of reproductive age and the contribution of each influencing factor to health poverty vulnerability. Therefore, this paper measures the health poverty vulnerability of rural women of childbearing age by using the three-stage feasible generalized least squares (FGLS) method using panel data from the "Rural Household Health Inquiry Survey" in Ningxia, China, for 2019 and 2022, and compares the actual poverty and health poverty vulnerability, identifies the key influencing factors through Tobit regression analysis, and finally decomposes the contribution of each influencing factor to health poverty vulnerability based on Shapley decomposition. Methods Study design and sample Data were obtained mainly from the projects innovating payment systems and improving health benefits, which were jointly carried out by Harvard University, Oxford University, Fudan University, and Ningxia Medical University(2009, 2011, and 2012). The data from 2015、2019 and 2022 were extracted from the National Natural Science Foundation of China(from a follow-up study of the project) (20). A multi-stage stratified cluster randomized design was used to obtain a representative sample from each county. We selected a total of four counties in Ningxia Province, including two project counties(Haiyuan and Yanchi) and two control counties(Pengyang and Xiji). In each county, all the villages were divided into three economic levels, 40%of the sample villages were selected. Then, using the household head roster, 33 households(20 households in the control counties) in each village were selected by systematic sampling. Members of the sample households who had been living there for more than 6 months were selected as respondents. Data from two follow-up surveys in 2019 and 2022 were selected for this study. The questionnaire mainly includes data on household physical capital, financial capital, social capital, and human capital. Residents whose gender was female, marital status was married, and aged 18-49 years were selected for the study, and after removing responses missing key variables, a total of 6225 rural women of married reproductive age in 171 villages in four counties of Ningxia, western China, were surveyed in 2019 and 2022. Health poverty vulnerability measurement Vulnerability to health-related poverty predicts the probability that families will fall into poverty in the future due to unexpected health issues. The most common measurement method is expected poverty vulnerability(VEP)(21), which mainly uses three-stage feasible generalized least squares(FGLS) to quantify the family’s vulnerability to health-related poverty in three following steps(22, 23): First, Ordinary Least Square(OLS) is used to estimate the income equation: Main variables Based on the SLA framework(25), combined with the questionnaire used in the survey, livelihood capital in this study is measured in terms of the dimensions of household physical capital, financial capital, social capital, and human capital. Among them, physical capital is measured by the type of housing, type of drinking water, type of toilet, and separation of housing and kitchen. Financial capital is measured by registered poor households, household borrowing, and annual per capita household income levels. Social capital was measured by gift expenditures. Human capital was measured by respondent's age, respondent's education, respondent's occupation, household size, respondent's self-rated health status, respondent's chronic disease prevalence, and respondent's outpatient and inpatient service utilization. Table 1 shows the definitions of the main variables. TABLE 1 Variable definition . Variables Definition 2019年 2022年 Mean Standard error Mean Standard error Explained variable (Y) Health poverty vulnerability Measure with expected poverty vulnerability (VEP) Explanatory variable (X) Material capital Housing type 1=brick soil concrete, 2=brick wood, 3=Civil Engineering, 4=full brick, 5=other 2.660 1.174 2.860 1.185 Type of drinking water 1=tap water,2=cellar water, 3=well water, 4=other 1.274 0.569 1.064 0.351 Toilet type 1=water flushing type, 2=toilet, 3=dry toilet,4=other 2.827 0.545 2.792 0.609 Separation of housing and kitchen Yes = 1 and No = 0 0.665 0.472 0.732 0.443 financial capital Registered poor household Yes = 1 and No = 0 0.475 0.499 0.495 0.500 Loan Yes = 1 and No = 0 0.743 0.437 0.643 0.479 Annual per capita household income I group=1，II group=2，III group=3，IV group=4，V group=5 3.000 1.414 3.000 1.414 Social capital Gift expenses(log) Continuous variable (logarithm) 9580 34478 7573. 12875 Human capital The age of the interviewee continuous variable 8.440 1.213 8.329 1.985 Educational attainment of the interviewee 1 = no schooling, 2 = primary school, 3 = junior high school, 4 = senior high school or above 2.198 0.953 2.294 0.979 Agricultural workers Yes = 1 and No = 0 1.623 0.900 1.574 0.903 Family size continuous variable 5.028 1.640 5.018 1.708 Self-rated health 1=very good, 2=good, 3=average, 4=poor, 5=very poor 2.435 0.843 2.608 0.996 chronic disease Yes = 1 and No = 0 0.147 0.354 0.120 0.325 Outpatient service utilization of the interviewee Yes = 1 and No = 0 0.064 0.245 0.033 0.178 Inpatient service utilization of the interviewee Yes = 1 and No = 0 0.104 0.305 0.090 0.287 Results 2.1 Actual poverty and health poverty vulnerability of rural women of childbearing age Column (1) of Table 2 shows the poverty incidence and health poverty vulnerability rates of rural women of reproductive age for different poverty lines in 2019. Using poverty line 1 as the standard, the poverty incidence and health poverty vulnerability rates are 21.06% and 21.10%, respectively. Using the poverty line2 as a criterion, the incidence of poverty and health poverty vulnerability is 46.23% and 40.55%, respectively. Columns (3) and (4) of Table 2 show the actual poverty and health poverty vulnerability transfer matrices. Using the poverty line1 as a criterion, 94.97% of those who are poor in 2019 are in a vulnerable state of health poverty, meaning that 94.97% of these people may still not be able to move out of health poverty in the future, 1.45% of those who are non-poor in 2019 are health-poor and vulnerable, meaning that 1.45% of these people are likely to fall into poverty in the future as a result of a health risk shock. Using the poverty line2 as a criterion, 87.26% of the people in poverty in 2019 are in a vulnerable state of health poverty, meaning that 87.26% of these people may still not be able to move out of health poverty in the future, 0.74% of people within the non-poor in 2019 are vulnerable to health poverty, meaning that 0.74% of these households are likely to fall into poverty in the future as a result of a health risk shock. Column (1) of Table 3 shows the poverty incidence and health poverty vulnerability rates of rural women of reproductive age for different poverty lines in 2022. Using poverty line 1 as the standard, the poverty incidence and health poverty vulnerability rates are 19.62% and 20.02%, respectively. Using the poverty line2 as a criterion, the incidence of poverty and health poverty vulnerability is 45.43% and 40.57%, respectively. Columns (3) and (4) of Table 3 show the actual poverty and health poverty vulnerability transfer matrix. Using the poverty line1 as a criterion, 100.00% of those who are poor in 2022 are health-poor and vulnerable, meaning that 100.00% of these people may still not be able to move out of health poverty in the future, 0.56% of those who are not poor in 2022 are health-poor and vulnerable, meaning that this 0.56% of households may fall into poverty in the future due to health risk shocks. Using the poverty line2 as a criterion, 88.60% of those who will be poor in 2022 will be in a vulnerable state of health poverty, meaning that 88.60% of these people may still not be able to move out of health poverty in the future, 0.75% of those who are not poor in 2022 are health-poor and vulnerable, meaning that 0.74% of these people are likely to fall into poverty in the future due to exposure to health risk shocks. TABLE 2 Transfer matrix of actual poverty and health poverty vulnerability of rural women of reproductive age, 2019 Poverty line Poverty Incidence / Health Poverty Vulnerability Rate(%) 2019 2019 Vulnerability(%) Non-vulnerability(%) Poverty line 1 21.06/21.10 Poor(%) 94.97 5.03 Non-poor(%) 1.45 98.55 Poverty line 2 46.23/40.55 Poor(%) 87.26 12.74 Non-poor(%) 0.74 99.26 TABLE 3 Transfer matrix of actual poverty and health poverty vulnerability of rural women of reproductive age, 2022 Poverty line Poverty Incidence / Health Poverty Vulnerability Rate(%) 2022 2022 Vulnerability(%) Non-vulnerability(%) Poverty line 1 19.62/20.02 Poor(%) 100.00 0.00 Non-poor(%) 0.56 99.44 Poverty line 2 45.43/40.57 Poor(%) 88.60 11.40 Non-poor(%) 0.75 99.25 2.2 Analysis of factors influencing the health poverty vulnerability of rural women of reproductive age Tables 4 and 5 show the factors influencing the health poverty vulnerability of rural women of reproductive age at different poverty lines in 2019 and 2022, respectively. Tobit regression showed that annual per capita household income, gift expenditure, respondent's age, and respondent's education significantly and negatively affected health poverty vulnerability at both poverty line 1 and poverty line 2 in 2019; Type of drinking water, housing, and kitchen separation, and the number of household members at poverty line 1 significantly influenced health poverty vulnerability; poor households, respondents' self-rated health status, and respondents' chronic diseases at poverty line 2 significantly influenced health poverty vulnerability. In 2022, household borrowing, annual per capita household income, gift expenditure, and respondent age significantly and negatively affect health poverty vulnerability at poverty line 1 and poverty line 2; housing type and household poverty significantly affect health poverty vulnerability at poverty line 1; and respondent inpatient service utilization significantly affects health poverty vulnerability at poverty line 2. TABLE 4 Analysis of Factors Influencing Health Poverty Vulnerability of Married Women of Reproductive Age in Rural Ningxia, China, 2019 Variable Poverty line 1 Poverty line 2 Coefficient SD Coefficient SD Housing type 0.003 0.004 0.000 0.003 Type of drinking water 0.015** 0.007 0.009 0.007 Toilet type -0.001 0.008 -0.007 0.007 Separation of housing and kitchen -0.037*** 0.009 -0.011 0.009 Registered poor household 0.006 0.008 0.017** 0.008 Loan -0.016 0.010 -0.005 0.009 Annual per capita household income -0.196*** 0.003 -0.339*** 0.003 Gift expenses(log) -0.049*** 0.003 -0.032*** 0.003 The age of the interviewee -0.001** 0.001 -0.001** 0.001 Educational attainment of the interviewee -0.011** 0.005 -0.012** 0.005 Agricultural workers 0.001 0.005 0.006 0.005 Family size 0.013*** 0.003 0.009 0.003 Self-rated health -0.008 0.006 -0.012*** 0.005 chronic disease -0.011 0.013 0.007** 0.012 Outpatient service utilization of the interviewee 0.006 0.017 0.017 0.017 Inpatient service utilization of the interviewee 0.011 0.014 0.006 0.014 ***p < 0.01; **p <0.05; *p < 0.1. TABLE 5 Analysis of Factors Influencing Health Poverty Vulnerability of Married Women of Reproductive Age in Rural Ningxia, China, 2022 Variable Poverty line 1 Poverty line 2 Coefficient SD Coefficient SD Housing type 0.008* 0.004 0.000 0.004 Type of drinking water 0.014 0.014 0.012 0.012 Toilet type -0.009 0.008 -0.006 0.007 Separation of housing and kitchen -0.009 0.011 -0.002 0.010 Registered poor household 0.017* 0.010 0.013 0.009 Loan -0.023** 0.010 -0.023** 0.009 Annual per capita household income -0.200*** 0.004 -0.341*** 0.004 Gift expenses(log) -0.021*** 0.004 -0.018*** 0.003 The age of the interviewee -0.002** 0.001 -0.002** 0.001 Educational attainment of the interviewee 0.001 0.006 -0.002 0.005 Agricultural workers -0.009 0.006 0.003 0.005 Family size 0.002 0.003 0.005 0.003 Self-rated health 0.001 0.005 0.004 0.005 chronic disease 0.005 0.016 0.021 0.014 Outpatient service utilization of the interviewee -0.002 0.028 0.029 0.024 Inpatient service utilization of the interviewee -0.015 0.017 -0.029* 0.015 ***p < 0.01; **p <0.05; *p < 0.1. 2.3 Shapley decomposition of factors influencing health poverty vulnerability among rural women of childbearing age Considering that the running speed and computation time of Shapley decomposition is greatly affected by the number of explanatory variables, it is generally difficult to compute reliable results with more than ten variables. Therefore, in this study, among the sixteen risk factors, only those variables that were significant in the Tobit regression were selected for Shapley decomposition with α < 0.1. Shapley decomposition was performed by including the type of drinking water, housing, and kitchen separation, household poverty, annual per capita household income, gift expenditure, respondent age, respondent education, household size, respondent self-rated health status, and respondent chronic disease prevalence in 2019. 2022 incorporating housing type, household poverty, borrowing, annual per capita household income, gift expenditure, respondent education, and respondent hospitalization service utilization. Shapley's decomposition for 2022 incorporates housing type, household poverty, borrowing, annual per capita household income, gift expenditure, respondent education, and respondent inpatient service utilization. Tables 6 and 7 show the contribution of factors influencing health poverty vulnerability of rural women of reproductive age at different poverty lines in 2019 and 2022, respectively. The results of the Shapley decomposition show that the highest contribution to health poverty vulnerability in 2019, as measured by the poverty line1, was made by the number of people in the household, followed by annual per capita household income, gift expenditure, and respondents' self-rated health status, with relatively low contributions from other variables. Using the poverty line2 as a criterion, the highest contribution to healthy poverty vulnerability in 2019 was made by annual per capita household income, followed by the number of household members, gift expenditures, and respondents' self-rated health status, with relatively low contributions from other variables. The results of the Shapley decomposition show that the highest contribution to health poverty vulnerability in 2022, as measured by the poverty line1, is made by annual per capita household income, followed by gift expenditure, age of the respondent, and household poverty, with relatively low contributions from other variables. Using the poverty line2 as a criterion, the highest contribution to health poverty vulnerability in 2022 was made by annual per capita household income, followed by gift expenditure, respondent age, and household poverty, with relatively low contributions from other variables. TABLE 6 Decomposition of risk factors for health poverty vulnerability of rural women of reproductive age in 2019. Variable Poverty line 1 Poverty line 2 Shapley Contribution (%) Shapley Contribution (%) Type of drinking water 0.398 1.38 0.110 0.93 Separation of housing and kitchen 0.112 0.39 0.043 0.36 Registered poor household 0.420 1.46 0.083 0.71 Annual per capita household income 8.425 29.23 5.011 42.67 Gift expenses(log) 6.330 21.96 2.175 18.52 The age of the interviewee 0.557 1.93 0.178 1.51 Educational attainment of the interviewee 0.257 0.89 0.044 0.38 Family size 9.049 31.40 3.657 31.14 Self-rated health 1.594 5.53 0.353 3.01 chronic disease 0.162 0.56 0.075 0.64 TOTAL 28.820 100.00 11.743 100.00 TABLE 7 Decomposition of risk factors for health poverty vulnerability of rural women of reproductive age in 2022. Variable Poverty line 1 Poverty line 2 Shapley Contribution (%) Shapley Contribution (%) Housing type 0.222 0.71 0.039 0.32 Registered poor household 1.189 3.79 0.393 3.16 Loan 0.725 2.31 0.214 1.72 Annual per capita household income 13.819 44.09 6.898 55.46 Gift expenses(log) 11.213 35.77 3.709 29.82 The age of the interviewee 3.791 12.09 1.059 8.51 Inpatient service utilization of the interviewee 0.386 1.23 0.119 0.96 TOTA 31.346 100.00 12.436 100.00 Discussion Although China has eliminated absolute poverty, poverty vulnerability as a predictor of future poverty provides a critical new perspective for consolidating poverty reduction gains. According to this study of health poverty vulnerability among rural married women of reproductive age in western China, it was found that the health poverty vulnerability rate in 2019 and 2022 increased as the poverty line standard increased. In formulating future anti-poverty policies, we can use health poverty vulnerability indicators to identify groups that are likely to fall into poverty due to illness and return to poverty due to illness in the future and adopt targeted ex-ante interventions to eliminate health poverty at the root. This study shows that the incidence of poverty and health poverty vulnerability decreases over time regardless of the poverty line criterion. The incidence of poverty among rural women of married reproductive age decreases from 21.06% in 2019 to 19.62% in 2022 under the poverty line1 criterion. The health poverty vulnerability rate decreases from 21.10% in 2019 to 20.02% in 2022. Under the poverty line 2 standard, the incidence of poverty among rural women of married reproductive age decreases from 46.23% in 2019 to 45.43% in 2022, and the health poverty vulnerability rate from 40.55% in 2019 to 40.57% in 2022. The decreasing trend of poverty incidence and health poverty vulnerability in this study compared to other studies may be related to the implementation of a series of poverty reduction strategies in China. Thus, poverty alleviation not only reduces the incidence of poverty but also reduces the likelihood of future health poverty. Nonetheless, China's achievements in poverty eradication do not mean that the risk of future poverty is eliminated. Notably, we find that 20.02% (poverty line 1) and 40.57% (poverty line 2) of rural women of married reproductive age are still likely to fall into poverty in the future in 2022. This is because rural women of married reproductive age face an increased risk of disease and lack the ability to withstand risk shocks, and are therefore more likely to fall into health poverty. The Tobit regression and Shapley decomposition for 2019 and 2022 found that the highest contribution to health poverty vulnerability in 2019 was made by the number of household members, followed by annual per capita household income, gift expenditure, and respondents' self-rated health status; and the highest contribution to health poverty vulnerability in 2022 was made by annual per capita household income, followed by gift expenditure, respondents' age, and households in poverty. Annual per capita household income (26, 27) negatively affects health poverty vulnerability under the common criteria of poverty line1 and poverty line2, and the level of household income has traditionally been considered an important resource against disease risk, with high household income levels representing an individual's greater ability to acquire wealth and therefore to resist health risks(28). Gift spending (29, 30) negatively affects health poverty vulnerability, probably because rural residents obtain money flow in human interactions through holiday gift-giving among themselves, through which they consolidate and enhance social capital and reduce expenditure risk, which ultimately alleviates health poverty vulnerability. Unlike other studies(31), this study concludes that respondent age negatively affects health poverty vulnerability, which may be explained by the fact that with increasing age, rural women of childbearing age experience pregnancy and lactation, during which they become more conscious of their health and therefore mitigate health poverty vulnerability. Respondents' educational attainment (31, 32) negatively affects health poverty vulnerability, and many studies have shown that education, as an important way to enhance the value of human capital, plays a crucial role in alleviating health poverty vulnerability, while families with lower educational attainment tend to fall into poverty traps and form intergenerational transfer of poverty(33). Only under the poverty line1 criterion, does the type of drinking water(29) positively affect health poverty vulnerability, and drinking safe and clean tap water helps to reduce the occurrence of rural water-related diseases and protects villagers' health and normal life, however, in the mountainous areas of southern Ningxia, China, tap water is not yet widespread in some areas, and long-term drinking of unpurified tap water increases the possibility of disease. The separation of housing and kitchen affects health poverty vulnerability, which may be attributed to the fact that the separation of housing and kitchen to some extent reflects the economic status of the household, and households without separation of housing and kitchen tend to have lower economic levels and therefore lower health capital investment and a higher likelihood of health poverty. The finding that household size(27, 31, 34-36) positively affects health poverty vulnerability can be attributed to the negative effect of household size on household per capita consumption and welfare. As previous studies have shown a positive relationship between household size and poverty, a larger household size dilutes household consumption per capita and increases the likelihood of household poverty. Housing type affects health poverty vulnerability, and similar to the previous section, housing type also reflects the economic level of the household to some extent, so housing type indirectly has an impact on health poverty vulnerability. Only under the poverty line2 criterion, poor households positively affect health poverty vulnerability, and people in poor households also have poorer household economic levels and are less able to withstand disease shocks, and therefore have a higher likelihood of falling into health poverty. Respondents' self-rated health status influences health poverty vulnerability(31, 37, 38), and deterioration in health status increases health poverty vulnerability by reducing the ability to create human, physical, and social capital, which in turn reduces the long-term earning capacity of the population(39). Respondents' chronic diseases influence health poverty vulnerability(27), and studies have found that chronic diseases and the resulting financial burden of illness are important causes of poverty(40). Households with chronic diseases have a higher financial burden of illness and are significantly more likely to incur catastrophic health expenditures than those without chronic diseases (41). This study has two strengths. First, the data for 2019 and 2022 can predict subsequent future groups likely to fall into health poverty; the other advantage is the use of population-based data, which provides a sufficient sample size to explore the factors influencing health poverty vulnerability. However, our study has three limitations. First, household income and expenditure are self-reported and may suffer from recall bias. Second, we only used data for two periods, 2019 and 2022, and could not fully explore the long-term patterns of dynamic changes in health poverty vulnerability. Panel data of sufficient length are needed to analyze the dynamics of health poverty vulnerability among rural women of reproductive age. Third, the sample for this study was drawn from one province, so the main findings of this study may not be generalizable to other provinces because economic levels and anti-poverty strategies vary considerably among provinces. Although absolute poverty has been eliminated in China, our analysis shows that a proportion of rural women of childbearing age are still vulnerable to health poverty; therefore, future anti-poverty and welfare policy formulation should prioritize these vulnerable groups and adopt targeted poverty prevention and alleviation measures based on the factors influencing the health poverty vulnerability of rural women of childbearing age. Conclusion This study shows that a proportion of rural women of childbearing age remain vulnerable to health poverty. the highest contribution to health poverty vulnerability in 2019 was made by household size, followed by annual per capita household income, gift expenditure, and respondents' self-rated health status; The highest contributions to health poverty vulnerability in 2022 were annual per capita household income, followed by gift expenditure, respondent age, and household poverty, and the above variables were the most important factors contributing to health poverty vulnerability among rural women of reproductive age. To sustain poverty alleviation achievements and prevent rural Chinese women of childbearing age from falling into health poverty, anti-poverty policies should focus on these groups and adopt targeted poverty alleviation measures based on the main influencing factors of health poverty vulnerability among rural women of childbearing age. Policy suggestion Studying the vulnerability of rural women of reproductive age to health poverty has a practical role in accurately identifying vulnerable groups with high health poverty and discerning health-causing factors, and can help deter future health poverty among rural women of reproductive age from a health perspective. Based on the above findings, this paper makes the following recommendations for consolidating and expanding poverty reduction achievements and healthy poverty prevention governance in rural areas in the post-poverty era. First, establish an early warning mechanism for health risks, precisely identify the health poor and highly vulnerable groups, and shift the focus of work to consolidate the results of poverty eradication to the precise identification and effective assistance for highly vulnerable rural women of childbearing age. Second, we will improve health services in rural areas from a gender perspective, coordinate and improve the medical protection system for rural women of childbearing age, optimize the allocation of health service resources, and build a more complete health and hygiene system. Third, strengthen health education and health management to enhance the ability to sustainably escape poverty by improving the health literacy of rural women of childbearing age. Through health education lectures and health knowledge promotion, rural women of childbearing age are guided to develop good healthy living habits and thus improve their health literacy level. Declarations Acknowledgments This study is a population-based survey, and we thank all the respondents who volunteered to participate in the study. Author contributions HQ conceptualized the research idea and design. XM participated in the research design, drafted the manuscript, analyzed, and interpreted the data. ZH helped revise the manuscript and interpreted the data. All authors contributed to the article and approved the submitted version. Funding This paper was supported by the National Natural Science Foundation of China (No. 72164033), and the National Natural Science Foundation of China (No. 71864030). Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s. Ethics approval and consent to participate Ethical approval was granted by the Ethics Committee of Ningxia Medical University, Approval number, No. 2021-G152. All the par-ticipants provided signed informed consent at the time of participation. The study methodology was carried out in accordance with approved guidelines. Consent for publication Not applicable. Conflict of interest The authors declare that they have no competing interests. References Rivera JA, Castellanos-Gutiérrez A. 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Health-related financial catastrophe, inequality and chronic illness in Bangladesh. Plos One (2013) 8(2). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2627219","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":187371536,"identity":"9eda616c-ed60-4263-be82-95d25e0dcf65","order_by":0,"name":"Ximin Ma","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ximin","middleName":"","lastName":"Ma","suffix":""},{"id":187371538,"identity":"abbb2729-3511-4879-8947-1f347d5f9c5d","order_by":1,"name":"Hui Qiao.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACZjCSqG9jb2x8+IEULYx9PIebjSVIsIiBcZ5EepsADzHK+Y4zP3xc2GbBzCb5sI1BgsFOTreBgBbJw2zGxjPbJNjYpBPbHhQwJBubHSCgxeAwg5k0b5sED1BLu4EEw4HEbYS1sH8DaZFgkzwI1EicFh6wLQZsEoxEapE8zFNszHNOIoGNJxEYyAZE+IXv/PGNj3nK6hLk248/fPihwk6OoBYGVAUGhJRjahkFo2AUjIJRgAUAAE11OVSYGIeMAAAAAElFTkSuQmCC","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Qiao.","suffix":""},{"id":187371540,"identity":"86059dc2-5d23-4e0b-9373-41c6c4e8875e","order_by":2,"name":"Zhaoyan Hu","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhaoyan","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2023-02-25 08:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2627219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2627219/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46853520,"identity":"385e2abe-9893-4891-b284-04bd7511a1b0","added_by":"auto","created_at":"2023-11-21 14:00:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":540621,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2627219/v1/e13c7408-a753-41a2-a321-523755b3740e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decomposition analysis of health poverty vulnerability and influencing factors among rural women of childbearing age-an empirical analysis based on 2-period panel data in rural Ningxia, China ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal poverty is the greatest challenge of the 21st century(1). It is included by the United Nations as a major global issue for social development, with health-related poverty being particularly prominent. Data released by the World Bank in 2018 show that 736 million people still live on less than $1.90 per day, with about 100 million of them living in poverty due to illness(2).\u0026nbsp;Poverty eradication is one of the world\u0026apos;s greatest challenges. The first of the 17 Sustainable Development Goals (SDGs) set by the United Nations is to eradicate all forms of poverty by 2030. As a developing country that once had the largest number of rural poor people in the world, poverty in China is of particular concern(3).\u0026nbsp;China has made great strides in poverty alleviation since its reform and opening-up policy in 1978, and as of 2020, China has achieved a comprehensive victory in the battle against poverty, making a significant contribution to the cause of global poverty reduction.\u0026nbsp;Current data in China indicate that by the end of 2020, 98.99 million rural poor people have been lifted out of poverty, and the new century goal of truly eliminating poverty and building an all-around well-off society has been achieved(4).\u003c/p\u003e\n\u003cp\u003eThe total elimination of poverty is the bottom-line task and landmark indicator for China to build a moderately prosperous society and achieve the first 100-year goal. The implementation of the health poverty alleviation project is an important measure to win the battle against poverty and achieve the elimination of poverty among the rural poor, and is an inherent requirement for comprehensively promoting the construction of a healthy China.\u0026nbsp;By the end of 2019, a total of 980,000 households (2.66 million people) were not out of poverty in China, of which 375,000 households (968,000 people) were poor due to disease, accounting for 38.4% of households, and from 2016 to 2019, poverty due to disease was always maintained at a level of about 40%(5), and the proportion of poverty due to disease and poverty due to disease was still relatively high.\u0026nbsp;Therefore, the focus and difficulty of poverty alleviation work lie in health poverty alleviation, and it is still difficult to stabilize and consolidate the effectiveness of health poverty alleviation in the future. The marginal poor and potentially poor people have become the target objects of poverty alleviation in the new era, and the ex-ante measurement of future exposure to health risk shocks and prediction of the probability of falling into poverty in the future are the keys to reducing the health poverty vulnerability of the rural population and the new poverty governance in the new era.\u003c/p\u003e\n\u003cp\u003eWith the feminization of agriculture and the increasing trend of rural young adults leaving for urban work, women have become the mainstay of the rural household workforce, and the level of female health poverty is critical to family and social development(6).\u0026nbsp;Since 1993, the China Health Services Survey has been conducted six times. The surveys show that although the level of health service utilization among women of childbearing age in China has improved considerably over the past decade or so, the health status of women of childbearing age in rural areas is still at a low level\u0026nbsp;(7), and this phenomenon is more pronounced in rural areas of western China.\u0026nbsp;Poverty among women of reproductive age is a general state of life and development of women of reproductive age due to many factors such as physical, psychological, and environmental, including health poverty, educational poverty, and economic poverty(4).\u0026nbsp;Numerous studies have shown the multidimensional nature of health poverty among rural women of reproductive age, and that rural women of reproductive age are highly vulnerable to health risks\u0026nbsp;(8-11), leading to health deprivation and loss of health opportunities and presenting a state of health poverty vulnerability(12).\u0026nbsp;Due to the remote location of most rural areas in western China, the lack of medical resources, the low health awareness of rural women of childbearing age, and the multiple responsibilities of rural women of childbearing age in caring for their families and performing labor, rural women of childbearing age are a high prevalence group of health poverty.\u0026nbsp;Therefore, rural women of childbearing age play an important role in rural revitalization and consolidation of poverty eradication in the post-poverty alleviation era.\u003c/p\u003e\n\u003cp\u003eHealth poverty vulnerability is the probability that an individual, group, or organization will fall into poverty after a health risk shock, and the risk of falling into and returning to poverty due to disease needs to be identified in addition to the elimination of existing poverty\u0026nbsp;(13-15).\u0026nbsp;Some scholars have conducted studies on health poverty vulnerability, mainly related to the measurement of health poverty vulnerability of different populations and the analysis of influencing factors(16)(17)(18, 19).\u0026nbsp;In general, scholars have studied health poverty vulnerability measures, with the following main shortcomings: studies on health poverty vulnerability have mainly focused on the elderly and the chronically ill, while studies on health poverty vulnerability of women in their reproductive years are rare.\u0026nbsp;Research on rural women\u0026apos;s health poverty vulnerability has mainly focused on the analysis of health poverty vulnerability influencing factors, and less research has been conducted on the contribution of each influencing factor to rural women\u0026apos;s health poverty vulnerability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCurrent research lacks empirical analysis of the factors influencing health poverty vulnerability among rural women of reproductive age and the contribution of each influencing factor to health poverty vulnerability. Therefore, this paper measures the health poverty vulnerability of rural women of childbearing age by using the three-stage feasible generalized least squares (FGLS) method using panel data from the \u0026quot;Rural Household Health Inquiry Survey\u0026quot; in Ningxia, China, for 2019 and 2022, and compares the actual poverty and health poverty vulnerability, identifies the key influencing factors through Tobit regression analysis, and finally decomposes the contribution of each influencing factor to health poverty vulnerability based on Shapley decomposition.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were obtained mainly from the projects innovating payment systems and improving health benefits, which were jointly carried out by Harvard University, Oxford University, Fudan University, and Ningxia Medical University(2009, 2011, and 2012). The data from 2015、2019 and 2022 were extracted from the National Natural Science Foundation of China(from a follow-up study of the project)\u0026nbsp;(20). A multi-stage stratified cluster randomized design was used to obtain a representative sample from each county. We selected a total of four counties in Ningxia Province, including two project counties(Haiyuan and Yanchi) and two control counties(Pengyang and Xiji). In each county, all the villages were divided into three economic levels, 40%of the sample villages were selected. Then, using the household head roster, 33 households(20 households in the control counties) in each village were selected by systematic sampling. Members of the sample households who had been living there for more than 6 months were selected as respondents. Data from two follow-up surveys in 2019 and 2022 were selected for this study. The questionnaire mainly includes data on household physical capital, financial capital, social capital, and human capital. Residents whose gender was female, marital status was married, and aged 18-49 years were selected for the study, and after removing responses missing key variables, a total of 6225 rural women of married reproductive age in 171 villages in four counties of Ningxia, western China, were surveyed in 2019 and 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth poverty vulnerability measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVulnerability to health-related poverty predicts the probability that families will fall into poverty in the future due to unexpected health issues. The most common measurement method is expected poverty vulnerability(VEP)(21), which mainly uses three-stage feasible generalized least squares(FGLS) to quantify the family\u0026rsquo;s vulnerability to health-related poverty in three following steps(22, 23):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, Ordinary Least Square(OLS) is used to estimate the income equation:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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zfat7Gq1qgPQ8/l8z+pk7BkmXwJATyaTPSm7GztTVbVDJ/P5vA7gjvJRvRyXQRKkX4uLi3eUDhB288Ig9CObzZpihbW1NR2Ank6njXvS6bQOQFdVta9t0/XNeCaXyxnxzp1Er+YUVVUNea6trZk+i8fjOgB9fn4+9Hp7AcW7i4uLg26KiTvVr20FSqVSz/zZMMWBYeInXruTn0P8jP+gYuB+xeO9fPbtli39xtVW5/Dhw1AUBQAQi8VMn12/fh0A8Le//Q0TExN9b9uguHr1KsbGxgbdDBPNZrNnb+IUi0Xepsf0nW7s7MSJEz1qDbPVeeONNwbdhL7Ty3nBL6urq/j000+NWIHihz179hj3PPvss4hGoxgdHfVVdq1WC/TWd61Ww65du6CqKqLRaFdlMJ2Mjo4iHo8DAMbHx43rjUYD9Xod+Xwehw8fHlTzfEH6es899wy4JWbuRL+2Vbh27RoA+PZnDHM70I94fFifT3nhasCk02kAMOWW0TQNBw8exPz8PCYnJwfVNAabY0FjFDaUn41hhp1isTg0D+nMcDE1NXVnvJ4u0Mt5oRvW19dNP3BdvHgRAPDggw8a1+655x7s3Lmz7207fvw4Pv74YxSLxYHUfzvz6quvAoCxHVDTNBw6dAjZbBbT09ODbNqW5070awzDDD/9iMeH+fl04AtX1v34tVqt45q4fz+VSrnmfSoUChgZGcHIyAgWFhY6Pi+Xy0ZugJGREUxNTUHTNACdibGLxSJGRkZM+acajUaoicMfffRRAMD//u//Gtd++9vfYu/evVvm1zLAnCRf0zRjnMSAij63S6JI91cqFVQqFdsxpsRzdok7rYnpZmdnEYlEjMR8jUbDGPexsTFkMhnPpIPNZhO7du0y8k1ZddGpTaRPbpTLZezZswftdtvor11C30ajYcgmlUrZrn67ycWrf7Ozs6ZkfpSUkWxApn6yGbKp2dnZDhnY2Y04tlbbExOHj42NGTZWLBaNvqZSqY56vPSsW9zs3uqvxHEUPxPHpdsx88JLH2XszK7M/fv3AwBmZmY6+kKUy2WjbrsJr9uxcfPLdnkA6Q0Rq716+ShxbghDd5rNppHgtFarmeSTSqVs5w27+Um0uUajYSQDBW7NdyRzGmtxnhTHym/eRC+ZpFIpI4DavXu3Ua7Vj1jx0lOrb/LSLSeKxWJHsnQ6hMEqQ6eYwYrMvADc0nfqo5283eKRIKyurgKAaTFrYmICKysrgcv2y8rKiumNIFm6sZ8gMYKdrhBedklYfZzdHBUmTzzxBADgs88+AwAcOXIEsVgMp06d6lmdIl4yBdx1nHz17t27AdzyIeIYeMXc4oEIjUYDY2NjpgNnxOTdqVSqI5+sHU5+za1Pft5MCDrXiHFQIpFAKpXq8C+9sgWv2KWbWNtJNnaypZhqZmYGwC3/K1uH35id9Mcp0beXfnnFPU5xCY2JXSwjxjg0RlY78YorRR3KZDLSfqrbOM5vnOxlI93qsF/b8xp/J2TWCrrxTWHE49YxtPpl2efTgTHovYqtVkvPZrM6AD2XyxnXaX+toigd+Z/i8bhpLzztR83lcsaedNrjL+79X1xc1OPxuLEnmnJMUT6KarVqtCWZTOrpdFpXFMXY67q2tqYrimLsH61Wq3o0GtWj0WhHG/0QjUb1eDyu6/rmHlX6/62Cqqp6qVTSo9GokVeD5JjP5/VWq6XH43Ej34Kqqsb4WPfiJpNJ2/wg2WxWz2azeqvVMukM7fEtlUpGmclk0sj5QWVFo1FDZ6rVqq4oivTebaf9zslk0laf/IwfbPKhkO6THKvVqlG2mLNERi5OtFotvVQqGfot6jTlQpGpP5fLmWRAe6zFfdHUH7vxL5VKxj3UdrHexcVFww8sLi7quVzOdK/oM/zomRN2Yy1j99Rvu7HP5XImeciOmZ1ueLVdVh+d7MwJp3wH4njn83nT2Ij5VYKMjZdfJn0Vy1FV1fBH9G83H0U5gah/qqrqiqLo0Wi0oz2y41IqlYy25XI5PZlMGv7dbm7L5/OmPEWinqmqqquqavguKpPkLvaLoPEXx6zVahk6bpW7tV+yMrHLqyD23aozMnoq+iYv3XKiWq0abaN+kR7g/+UG8ooZ3HCaF6gv2WxWL5VKht5Z5eYVj3QL5QsKK88W2X4YeTP85A7xaz9BYgQ7XSG87JIgH0djKrahl6F2Op3Wo9Gons1m9Xg8HigW9YtX3CWr40465jX3rq2tmfwHzWtU1vz8vCETMc+aDE75YmT1wQk/c43T96PRqOGnKEYS29krW5CJXYLE2n5kS230g2yMJD5T0h/NsWIs56VfXnGPV1xCdgGbPMg0hnQ9Go3q6XTaaAv5TnE+E+M1mldpnrX6DruYL2iMLRsny9hINzosa3uy4y/2Sey/zDNDEN8UNB6nGNr63GaNq8KMI8Jk4AtXun4r0LJ7KIcl+R4F3iKkZKLRUXBKA9FqtWydnzWIFxcN6Hv0nWQy2ZE8kgY8SAI7UbH6HXiECcmSHCX9N5/POyYgtxtLu6DGLpG0NSC0Gh8Fj1SXVT+CLFxRsGDVJ9mFI7EPTkGcuCij65sLtqKDlZWLG3ZO161+sVy7YJMWDERbVhSlY/wpsazoyJ3qJZnSIpfYT1F2fvTMCbuxlrV7mgCt9ptMJk19lB0zP+32q49hL1yJY2Pnz4OOjZtfdnrAcBpLOx9F5dsFedaFDD/jYreQq+udyZPJHqx6RuMq1kd9EO8VF+qsfZJN4GmtR1YmTuXZ1e9HT910y4/uOs0zTjFD0B807AJzCkpJbrLxSDf47YcXg1q40nV5+wkaI4j3i7rixy7p4cbq/8VF9l5A4x30B9RucZKpHx130jHZuRcwL2SSnSWTSds4xk+/rPGNrD444WeucWqXnX8Rn2F6YQsy5QaJtf3K1u/ClZ+5xxon6Potny1e99Iv2bjHKS4R22e3gEpzI7VNXHiw/mBM37FboKD67b5vnZODxtiycbKf2MOPDsuUKzv+4vfF8ZHxW0F8U9B4nH4AFrHeQ9eGceFq4FsFgc3E5Ol0GktLS6bXFSmR57lz54xr586dw8GDBx3LIe666y4AwA8//AAAuHTpEtrtNhRFMb2uSEeAf/nll6ayHn74YaPM0dFRaJqGSqWC/fv3m75Pr6t+8cUXXff/6aefBgAcO3YMp0+f7kjUvtWgbQH03+XlZczNzZnkRq+GyxzB/vnnn0NVVdfXZkUor8fo6ChisRgeeOABAJuv0dMrx0Fzh/35z3+2TXRL2zs/+OCDQOUDwN133236dywWMx276lcuYdQv8tFHHwEwb0cZHR3FxsYGisUigM1XTlVV7dgqMjo6inQ6jXa7jfPnz7vWe9999wG4ZdNOBNUzO/zY/fHjxzv602g0DD0Eejdm/dBHN8Sxob5+//33xrWwxsbql7vF6qMmJiag6zrGx8fRbDYxNTVlHMX+008/dV0P8fzzz5v+/dZbbwEALly4AAA4c+YMABi+ishkMohGo6hUKh1bUTKZjPH/o6OjyGazAIB//OMfgdsL9EYm3eipnW6FhV3MEHa5wC2fRnLzG4/44e9//zsAYPv27V2XMWx42U/QGMEJP3Y5NzeHnTt3dpTX68NmaJugoigDjR2tMg2q435j7rGxMaP/5Nd37NiBubk5Ix4BgH379nXdx278tJWgfnX79u2o1+umLW5PPvmk8XmvbEGm3CCxdhiydaObuUeMW2OxGP74xz8CuDXHeumX37jHGpcAwGuvvQbgVrwtlk2yjcVi2NjYwOHDh6FpGgqFgvGcTM/AImLuQwDI5/MAbvlTJ8KI42TiZL82IqvDfsv1Gn87ZP1W2L5JxCseX1lZMeqlNBzWe4aZoVi4AoAXXngBwK1FKk3T0G63kUwmTQ7l9OnTpoBdFjqBQt98y6zjzyuRJZ16Uq1Wbb8fRu6IbDZrmwsi6Kk+g4ZOuHGSvRdXr15FMpl0/L7XqYuxWAylUgmrq6tQFMXYMx8keWm9XjdOhBSh8etHUs+gcgmKTBBBdveLX/yi4zNahLhx40Yo7QmqZ3b4sfsnnngC0WjUdBLRwsKCKVddr8ZsGPTRjV6MTdjQvv+9e/fiscceMxaCesGuXbsA3AoUrl69CsD+VC1KaP3dd9+5lkn5Eu0C1W4JWybDrqf9Img84gbpEumYH8TcaNYHE8r14/Tg2k/s7KcXflXWLmu1GtrtNh5//PGu6umWhYUFrK6uIpvNol6vu+ZbpTwy/WIYYu5XXnkFe/fuxf79+zE2NoZisRiKbQXx00Awvzo5OYl8Po+ZmRncf//9KBQKeOCBBwwd76UteJUbJNYOS7ZOhDH30AIJzbFe+hVG3DM6OopkMom5uTkj5i4Wizh06FDHvYVCwfCNx48flyofuCUDr4WLMPojEycDvYvHgpRrHX87ZP1W2L7JL5QD6+LFizh9+nTf6g2DoVm4mpychKIo+NOf/gRgcwHr+eefx8GDB6GqKorFImq1Gvbu3RuonqBHO968eTPQ9+2gCZ4ePKx8/fXXAMxvtmiahkgk4hqo0IKXG7Q6L5MULgjffvttoO+vr68H+v7k5CS++eYb5PN5FItFKIpiWunuBkrOO0iCyqVfkI73mqB65oSM3cdiMWSzWaiqinK5DE3T0Gw2OxajezVmw6CPbvRqbMKg2Wzi/vvvB7A5PplMpuPNvzBx+lWQgp5uCPONIaB3Mhl2Pe0nvThqulKpIB6Pd/X2zcrKSkegXa1WATgH4oPArm+9nAtl7bKfPq5cLuPYsWP4+OOPjYc+8ZAfK1999RWSyWRHGXRIQa8YZMwdi8VQLBZRrVYxNjaG/fv3I5FIBE6YH8RPh+FXp6enoaoqMpmMsYAlPgv0yhZkyg0aaweRrRdhzz0y+hWGTzh69CiAW2+lnT17Fr/5zW9M9yQSCSwvL+PSpUuYnp62XQAMg6D9kYmTexV79DPO8/JbvfJNMszOzuLAgQP4+OOPcerUqa4OTxkkQ7NwBQAvvfQSVFVFrVbDhQsXMDExYbwmevbsWXz00Ud48cUXuyqbXpsXtx0SdGqVG/T66tmzZ20/lzmNyAl6ddDpF9LDhw93BIjUjyAKVywW8dvf/hbvvfdeT18xj8fjKBaLtgYpI7cdO3YYemGlXC5LB0axWAzT09P45ptvEI/HsX///q6dBP1y47RwaA0Qe0FYcukWeuXa7kQOsqd7770XwGbQ7MQjjzwSSnuC6pkdfu2e/NPJkydx5syZjm3NvRqzYdBHN3oxNmFy4sQJtNttvP32232pj8Z5x44dAG7ZEv1IYYfXL+UUKIW1RawXMhl2Pe0XQeMRJ8iv9PqHqEFjtZ9e+VVZu/zZz34GALhy5UpX9fil0WjgwIEDxkPH+Pg4FEVxPSK9WCx2vKX04YcfGm+zhM0wxdx0oubi4iLq9Tref/996e+KhOGnw/Kro6OjOHXqFNbW1gDAeAOnV7bgp9xuYu0wZOtGGHMPzbHWe530K6y4h17smJubQ7lc7tgSVywWUa/X8dZbb3X1LEdj9+tf/9r1vrD64xUn9yoeC1qu0/iL+PVbYfkmWZrNJt59913HHV5bgaFauKIV5IMHD+LZZ581rmcyGWN/c7d5TXbt2oVoNIqZmRmT4miahiNHjnhO3rFYDMlkEpVKpePI6kKhEGjVtlKpOOYnoOMqxeMuM5kMfve73wG4dRRsN9sIf/jhB6ysrOD111/vuu0y7Nu3D+12G6lUyjS51Wq1jklkfX29wynS/v1nnnnG1M9ms4mTJ0966kStVjMFSbFYzMiR0e2veS+99BKATidE/XPKwxYmQeUSFMrNdvToUdOYaZqGixcvArj1WvDS0lJHwPTtt99CUZTQtjT60TNZ/No95RqqVCpYXl7u2NbcqzHzq492dtZLejE2BC18irasaZqvX53tgvkwt9z9+OOPpn9fvnwZwK0AjnL40BvHIuvr67avs1vH7+LFi1AUxch7QXZl/YXUbRFZpBcyGQa/OQwEjUecoE5kJcUAACAASURBVLxBWzUgdcLLfnrlV2XtkhaO6vV64De5vdA0Dc899xyy2axpfqGckdYfkhqNRkec2Gw2kUgksLS0ZBx3HvY2Qj86Tr5bHOcwYm7rMfCZTAbxeLzrLcnd+GkrQf1qoVAwlTE+Pm68wQL0zhZkyg0Sa/uVLc1rsnFMGHPP2bNnEY/HDf/qpV9hxj2UG+rAgQNG3ivCLt2G1We6ce7cOUSjUTzxxBOu94XVH684uVfxWNByreNvh6zfCts3yWK33bafzwKhoA8ZdEyjCB1hac3Sr+v2pynp+q1jTsUs+XSqBP7fCSTJZFKPRqO2JxnRsaLWdlBddOwmldMt1E7r0c5i/2BzokQ8Hu84kcAKnTzgRT6fD3xyAB0ratdWOkKVZE9H0lpPlqBxto6lrt86WQO4deyxWJd4/Kv1pAU6ipT0h+6VPcGB6iqVSnoulzNOJbE7GYyOQZeFxp6OLdX1WydQibrbarUM+dqdOOIkF9m+2Z08IvbDqX6SOR15nMvldEVRTPfQqUfWY4jFI53pmrXfYh3WU9TIDqlMWT1zwunUEL9273TiByEzZlSGk1+wQ1Yf3ezMCfFUPzoliE54teoPlS+2PejYuPll0oV4PK6XSiV9cXHR8GnA5imVa2trrj5KtDmyRVGeNJZ+x0U8/tl6NLLVf1MbrMeNW0+apX6l0+mOY72tJ2/SCT6Li4uG/xL7Svfb9UtWJuJpOYuLi4adimMmIqundqcn2umWG3b9Eu1cPGHKyf84YTcvkK+zto98mGgnMvGIH0QfbT01KghhnSootk+2LD/2EyRGEPtpHTtZu6Sxp/LpSHbStfn5eb1arRr3iado+ZEh2Y/1u3bHvxOka9ay7HyG3/a4yVRWx6kMq+3JzL3iuNmdYCj6SbpXps9Ofk1WH5yQ9atu7RLtgeY10R56ZQte5QaNtWVl262vk517SDbk16kd1pjVS79k4h63uESE5i07+xZjbIrPaGyy2ayhGzTHJZNJox90zekUPOtzSJA4TsQtTpaxkW50WNb2ZMdf1+2foWT8VhDfFCQeJ79Pdrq4uGj0Nx6P6/Pz80Yf7Z5Ph4GhW7hyOjrVzvHR4Il/osGKxiWWT4qqKIppgK3fs070ur6pBDTIFDx1ewQxOQZxYnfqo4hbwGHXBydZiO0IsnDlNA7WNpPhWx+63NptbSc5eHHi1nXdtZ/VatVwrvQ5TYwykBMSJ2TqEx2BTePn96GDAjta7LGTg518xb67ycUNGdtxql/spyiDdDptuxhCY0Dft97nZHt242q1G2tA5qZnTnj10a/dewWxbmPm1j83ZPRRxs6coKCVHrhkx0ZsXzdjI+OXFxcXDRulIC2ZTOrZbFZfW1vzHF9x0o/H4/ra2lrHA2Y34yIuAtN4W+cdkfn5eeO+aDRq66fEBTmSpZPdr62tGeXRoh/ZOc0fTv2SkQnJjmxblL2TP5bRU7927yZ7GV8mo2N2shXnBT/1EW7xiF/E+Q0Ib/Eq6MJVLpezlS89+Lrh1366jRHc/Jauy9mlrm+Op9jOUqlkPKBQW8QfK+1+jHWCHrLtxle0c6uOUf+ssiZb7jZ+1XV3mYr1OOm4nX34mXu9fAItmtBnNCYy2Pk1QlYfnMqV8atOWP2V02J3r2zBrdygsbaue8vWrn2y/kk2Zqd50muMZfTLLe6ReXYSyeVyjvpLizLkV+ilDusPyYuLi66xuK67x1zdxnF2OMXJMjbSjQ77iWlkxt+tDV7PDEF8k64Hi8ft4mWyW3FOsj6fDgsRXR+SI50YW2ZnZ3H16lVTboJyuYynnnoKrVbLdT9zrVbD7t27PROoFgoFfPHFF6GcjMgwDMNsUigUMDMzg2q1GtqW2FQqhUqlMjSnMTK3NxRHhKnDsvTCfoaFQqGAe++9t6tTsv2SSCSwb98+06lVs7OzWF1d7VteLoZhGIYJylDluGI6WV1dNRKQEn//+9+RTCZ7mlCdYRiGYZg7m4mJCeO4eyYcNE3DV1991ZdFK03TUK/XOw5tWF1dxb59+3peP8MwDMOEBS9cDTn1eh2/+tWvUCwWjaSHV69eNZKpLSwsBEoGSgHU5cuXe34KHcMwzJ0EJUJ3OynJD2LS+aBJ7Rlm2AnbfoaBYrGIM2fO4K9//Wtf6rt+/ToA4Je//CWmpqaMOK9er+OHH36Apmmm6wzDMAwzrPDC1ZBDR8n+85//NH6dO3jwIFRVxcjICH744Yeuf7VLpVLYtm0blpaW0G63oShK6CfLMAzD3IlEIhEsLS0BAH73u98hlUoFKq9Wq2Hbtm3GqTMPPfSQ6bRZhrmdCNt+hoVMJoPp6em+vTH/85//HNFoFPF4HI899phxqlwul8O7776LXbt24emnn+75KcQMwzAMExTOccUwDMMwDMMwDMMwDMMMJfzGFcMwDMMwDMMwDMMwDDOU8MIVwzAMwzAMwzAMwzAMM5TwwhXDMAzDMAzDMAzDMAwzlPDCFcMwDMMwDMMwDMMwDDOU8MIVwzAMwzAMwzAMwzAMM5TwwhXDMAzDMAzDMAzDMAwzlPDCFcMwDMMwDMMwDMMwDDOU9H3hqlarIZFIIBKJYGRkBFNTU9A0DZqmYXZ2FiMjI4hEIkgkEiiXy/1uniONRgNTU1MYGRnpS32DlEcikUAikTBdKxaLSKVSSKVSodbVbDYxMjKC2dnZUMvdamiahoWFBYyNjaFQKAy0Lc1m09C9Wq3WdRm9Htcw2mlHsVhEIpHoGAc7uxgETj6015TLZUQiEZMf6tUY2KFpGgqFAsbGxnpe1+2En7lrEP642WzyuAqEPQZ2dttv+h0/Af58k6Zpjn4/LPzOH1vNLrrVs1qthkwmIxVb9ioO7ZZBxulUb7PZNK7VajVMTU0hEon0pQ3M4Oi1v2KCY2ejMvgZ22Hzif2grwtX5XIZBw8exMrKClqtFrLZLObm5nDz5k2kUince++92NjYQLVaRbvdxocfftjP5jnSbDZx/fp1zM3Nod1u96XOYZJHuVzGjRs3UKlUBlL/ncD58+dx4cIFqKo60HZomobLly9jaWmpb7reDb1qZ7FYxCeffIJ6vR5amWHi5kP7Tb915cyZM1heXh64jWwlBjF3+eXy5cv44IMPeFxvUwahg35906VLl4bO71++fPm293e1Wg1ff/01lpaWPO8dxjh0mOL0RqOBmzdvYm5ubiD1M/1j2ONUpnvK5TIuXrwoNbbD6BP7gt5H4vG4ns1mO66XSiUdgK6qak/rt6vbicXFRb1arZquJZNJvR8i65c8/AJATyaTg27GbUu1WtUB6Pl8ftBN0fP5vA6gwwaGjV60c5jGwYqTDx0k/dSVraKXw0a/5q5u2arjahcnDJJha4/IIHTQTa+sfnQY/f5WtQu/+IkthyUOHdY4fdh9PRMOw+ivBsmwxcVB8Du2w+IT+0Vf37iq1+u47777Oq5fu3YNADA6OtqzuovFoq/X9d54442etcWLfsiDYZith5MPZRim/wwyTrBj2NozrPiNBxnGCsfpDDMcsD+/s7gjkrNTfgVZpqambuvXsxmGYRiG6Z5hixOGrT3Dit94kGEYhhlO2J/feQReuNI0zUi66ZQsOBKJGMkCZ2ZmjH8XCgVEIhHMzMyY7hMTkpXLZc9ExM1m09SGRCJhJLMsl8vYs2cP2u02KpUKIpGIaxKzVCpl7BHfvXs3IpGIbWLMQqGAkZERjIyMYGFhoeNzmXbblekmD7FMu0SQ1mSeYhLnVCpl1C8mk7QmgKXkjl5JVGu1mtEO+hPl5HTdSrFY7EjMaW2Dl6wpuSfVl8lkOlbfrXqaSCRQLBZN91gTyFKydFFOmqYZdY2MjHSUAXQ39m7YtZ3G3W4cRLnQNVG+jUajQ16NRqPr9tlhN66ULJf0s1wuG/K1m3jc7NoJO72zysiKdbw++uijjnvs7MJvf6yJXN3sx6lfgNmHUrlePrhcLhvjIbZD1OFGo2HIYWxszKQTdHiATMLIVCpl6peoA+TjvPyweD/JdGxsDF999ZXUfWIbw/CLdnK28yGE6Dusf4VCoaNNs7OzpnqtNmrXJ6rDqnupVMrVnt38qZ3dEjJ+1glKNkr6OTs76+gTvexIHGs7/yX6cU3TDH0kn+n1fZk2e8UJbnOA19g7jUGxWDTanUgkkEqljDqd2uNmt+JhB6RjdvrgVGcQgsZPMj7PDtl4sNFoGHqTSqU89dya3L1YLGJkZMRIxu4UV1l9biaTcfWvbvO9Fdn4jAhiV256JmtvJEfyMWNjY47+1Q4ZnRFjt0wmg0wmI12+2B+3ON3Ld1t1pdlsmvpMshFtT5yngM7Yw4kw4nQvfwWEI3uv+dUpdhDjDZKzlz2SbLzmsyCxstd3RT9PfScdsBsLmThVRo5WnSgUCq56Isq9UCj05TnJ6k+c/LHf5/t++kQnG/U7d4lzRCKR8PWs5hYXW8uWmX+GhqB7DdPptK4oirHPm/bkz8/Pd9wLhz2b9B0ri4uLejweN8qen5/v2MupqqoejUaN+lRV1RVF6dh7bv2eG055BWjveC6XM3JJxONxHYC+trbmq90y9Yusra2Z5Ef9jEajxj2lUsloTzab1fP5vF6tVvVcLmdcy+Vy+vz8vKntpVLJqIP27duNh7UP1WpVj0ajtrJqtVodcrFSrVaNvlK5qqqa2uAl61KppEejUaN+2husKIqpLdFoVM9ms3qr1dJbrZaezWaN8sV6qT9ivTTu8/Pzejab1RcXF033tloto64gY2+3r7nVaunxeNyk3yQHa5+tbSH5xONx4/ra2pquKIrpu9FoVI9Go6bvBsmtYTeu1BayTVE/aSxEnyFr13btTKfTHdeoPKteLy4umuoWdZrGwcku/PRH1zf9h+gr6b50Oi0tWzsf6uWD19bWDD2kHFmlUsnU/lKpZFwX7xX7SnK11m8dA9G+7PQ+l8tJ9TmbzZpsm9plHdtsNmtr29TOoH6R+uTlQ5zkQTrm5KuTyaTRV5JXNBrV0+m0URfJnnyfOB65XE5PJpN6Pp83ylQUxWTPMnOXk91SfV5+1olcLmfyiVSHmJNCvOZmR6QD1naQPln9eDqdNumD1/e7abPVR3rNAW5j7zQGa2trejQaNcaKdEqs2649bnZLvsCqY4uLi9J1+iHM+Ek27nQaIzsdF3Uhm83q1WrVqNvLX4n6mkwmjfZR35ziqmg0asi7Wq3qiqKYxsmu/dls1tAVN8Q5U/Rlun7Ln5Hcg9qVk57J2hvNN+l0Ws/n80bZoj6K91rHTlZnyH5VVdWTyWSgvDBOzy1evlvUFVHXyL4URdEXFxf1XC5nupfmmVarZZq7vXLxBo3TveaqMGQvO7/Sc5B13GguJ91zs0fqk9d8Jhsr2+H13bW1NUN/rDEZ3SciE6f6kaNdjCf6C6s+5HI5PZvN9u05qVqtGnYj449ln7H65RNzuZyjjcrMXWJd5BNJvmIb3frvFRfruvf8M6wEXrgip2Aq1GGitwuedN1+AiAlsiY+pMEjRbCbfMiJiYMrq9hie5wmBNEAKSAhpZNtt0z9IqTIYp/oPrtrVqMkAxLb7pQAzim5o50MybitwQU5HhnsynWTNbWXZG0XkIuOP5vN2j5g2TkVu3pVVe1w8rpu/8AeZOztxoOCOLv7RJmRU7LWQUGl2BbrWFE/vAJmv9iNq51+UvAk3itr13bt9LJfghazrPpDQYKMXcj2x7rwTNgtprnh1C4ZH2ynw9RX6+KLXV+d/IWdvFutlq4oiq3dJZNJ10BZ12/ZunUMrXpOk60V60NiUL/ox4eIQT1ht5hqDVhUVTWCDHHRVmyTeM1uMUXXdeMhQ7xXZu4SZWf3UOHlZ+2gdtstIov66WZH1vusdSaTSdtroq+g/8p832+brXovMwc4jT1hHQNalBShB1u39oj9sfp36wOYVR9k6vRDmPGTrM/rZuHK6gtl9Fz8PrWh1WqZ+mG3oGAnD7d5eH5+vqN9bjjNZbRwL9YTxK7E/tjpmVe5tGhg54ft7rXzT146Y+dfwl64kvXdTrpG85t1nnKLpbziHF0PHqd7zVVBZe9nfnWzXVH3nOxRdj6TjZXtkP2uXUxGcS712U+c6keOdn6NyrS2PZlM2v4Q1svnJDd/bNVvP8/3/fSJdjYqM3c5LfBTeV4LdzJxscz8M6wE3iq4srJivAZIr2YCwPfffx+o3EuXLqHdbkNRFNMrfHTs45dffolms4lKpYLHH3/c9N133nkHuq5jfHw8UBuciMVixv/fddddAIAffvhBut3dMDExYfSJtlC99957AICffvqp435qFzE2NtbR9jA4fPgwFEXpSAp7/PhxvPzyy4HLt5M1cf78ebTbbTzyyCOm61euXMHGxgaAzdcy5+bmEI/HO8r+/e9/DwC2r9yK9VLyTS/Z9WLsl5eXMTc3Zypv9+7dAGA6AvW1116z7cvy8jImJycBbMqiUqlg//79pvLolfcvvvjCd/u6RRxLq1z7ZddO+nPPPff4LsutP4C9jQLAzp07fddlxY8PtraN+nr33XcHboe1nuPHj0NV1Y7X1AF4jiEdKT4xMWG6bk1M//nnn0NVVcftM9ZX7rvxi359SLvd7rjv4Ycfdiz/wQcfBLDpZ2KxGGKxGDY2NnD48GFjO9fBgwcB3JpnRJ5//nnTv9966y0AwIULFzrudZu7nJDxs06QXMRxHB0dxcbGhu32ATs7EvV4enraqJO2elQqFVuZA7f0jP4r832/bRbxOwdYx96J7du3o16vo1AoGFsKnnzySde2uPHBBx9g586dpjonJyeh6zoOHz7ckzqJMOKnXsWdQKcvjMVijvplB9l6LBZzTdz9wAMPAACOHDlibH2hudrKjz/+iKmpKdx999145513pNuSyWSgKAo++OAD0/W//OUvePHFF41/B7UrJ/yUOzY2ZtKNiYkJpNNptNtt1+0xsjoTj8fx5ptvGnNCLBbriDGC4td3W3WN5jfrPBWUsOJ0q78KQ/bdxuiyWO1R9rmh21jZ73etft+qE7Jxql85Hjp0CO1225RuhvTu7NmzxrVGo+E4P/XjOcnOHwdh0D7Rz9xljRunp6cRjUaxurrqWodMXOxn/hk2QknOTjkpLl68iNOnT4dRpHFih775VljH3/T0NL777rtQ6goTmXZ3C+2N3bt3Lx577DFks9mwmh0IekClXBXNZhPr6+sdD51hc+PGDc97rl+/DgBQFKXjM5qEwzqNohdjX6/Xkc/nHcskRkdHkUwmMTc3Z/SnWCzi0KFDxj0ki2q1alvWyspKkO6HRr/s+v/+3/8LoHNxpBf8/Oc/BwB8++23puuaptnqpl964YODQgHCG2+8YTz4fvTRR0Yg78bS0hKSyaTnfVevXkUymXS0jzDG1q8PURQF6+vrpvvooYX0QIZCoYBdu3YB2PSxstB3wniIB+T8rBO9OOmH8l0cP34cL7zwgpSe+Pl+kDb3av6fnJxEPp/HzMwM7r//fhQKBTzwwANd67dMEvew65TBj/yG0ef5IRaLoVQqYXV1FYqiYGpqCs1m01ZHjh49auQw88tLL71k+gGB9Nu6qBbUrpwIUi49uDn98API6wzpyO7du41cbUHicTe69d29pBdxehiy72eMDvh7bugmVg47zpaNU/3K8Te/+Q2AWz8S0v/n83lUKhXj3oWFBePHjDDo5TOyLIP2iUHmrp07d3r+kCITF/uZf4aNwAtXs7OzOHDgAD7++GOcOnUq9LectuoRl2G3u9ls4v777wcArK+vI5PJhP6WRLfQA+qxY8egaRpOnDgxNJM1cfXq1b7VFfbYWxc7nDh69CgA4MyZMwA2fzWhyUnk5s2b4TXuNqAfPmZ0dBT5fB5zc3PGr47FYhH1eh0nT54MVHavfXAQKFg+c+YMms0mVldXpRPiWhd/gt4XFFkfcvLkSWxsbJiS9c7NzSGXy0kfnZ5IJLC8vIxLly5henra11uAYb9VO0yUy2U89NBDOHjwIFZWVnz/Qhj0+7L0wqdMT09DVVVkMhljMSnsQzWGoU7AW37D7PP8MDk5iW+++Qb5fB7FYhGKoti+1Xfy5EnE43Hs37/ft/zpLYI///nPADbjA+tbNr2yi37ZG+CtM+Pj47hy5QoWFxexvr6O3bt39+Q0siC+u5f0Mk4PQ/b9jNFlCRIrhx1ny84psnKMxWLIZrNYWlpCs9lEo9HAww8/bDwznDhxApqmodls9sS/DvLZfpA+sV9zl0xcLDv/DBuBFq6azSbeffddZLPZ0IW/fft2AMC5c+c6PqMTI+jX6+XlZdsyBjEAMu3uhhMnTqDdbuPtt98O1L5ecfz4cbTbbbz//vsoFot44oknel7nvffeC2DztUgrzWYTtVrN0JHLly87lhPW6+K9GPt4PI5isWh74oT1NKbJyUkoioK5uTmUy+WO13vp1VDxNWC38gZFv+z6P/7jPwAA//rXv0Ipz4sXX3wRyWQSBw8eRCQSwRtvvIFSqRRoQuylDw4DCpbfe+89nDhxAi+99JLU96LRKFRV9TwlbMeOHVBV1fEUnjCCI78+ZHJyEplMBsvLy4hEItizZw9ef/116S0+tKD51ltvdbUIRX3esWOH7+/aIeNnnaCFOnE7AtGNHR89ehTxeLyr08Bkvx+kzb2a/8W2nTp1CmtrawBgeqPWD4qioFKp2NqXtY1h1SmDjPyG3ef5JRaLYXp6Gt98842xOGUdl7vuugunT59GNBrFnj17fPk1ekCt1+sol8tYXV3teHsjqF05EbTcb7/9FtFo1PVtE782l8lksL6+jmw2a8RKYRHUd/easOP0MGTfzxgdkJvPgsTKYcfZsnFqN3KkNAPnzp3DwsICXnzxRYyOjiKdTqNYLOLcuXNSb8j7oddzpAyD8olhzF3r6+tIp9Ou9/iJi2Xmn2Ej0MKV3ZYepw6ToOz2edMbJeJ3d+3ahWg0ipmZGZOha5qGI0eOYOfOnRgdHUU8Hke9Xu9wBvRae7+RabcXdvKwC1S8cpP0E3pAJaPsx6RNk+67777b8StkLpfDxMSEsYWu3W53OET6RcTuraRuCGPsrezbtw/tdrvjSO5arWb7yysFJgcOHDDyXhGxWAzJZBKVSqXj+NVCoTA0b/AFtWvKBSD+4qVpWscvEM8++ywA4M033+yLo06lUjh69CjW19eh6zrW19d9LVrZ+VA/PnhQkE7Ozc2Zcgi4QcHCH/7wB9f7KOfOM888Y5qkm80mTp48Kf2Gkxt+fcjs7Cyi0SiuXLkCXdexsbHh6/Vru60MP/74o+P91s8ocJWVtRcyftaJp59+GsBmEGg9Hv3ixYu+22K3xc2Pvst8P0ibezEHAJt+T/T/4+PjyGazUlv+7KDA98iRI6brtVrN8C9h1ymDjPy2gs+ToVarmfxJLBYz8tPZva0xPj6OTz/9FO12G+l02lefKRY4cOAAXn311Y7Pg9qVE0HKpYdYr5QYsjZnfcOHfgT+97//LdUeK3Zxul/f3W/CjtPDkL3f+TWZTHbEcl9//bV0m2XmsyCxcthxtmyc2s2zzsTEhCnfE+nDs88+i3a7jWPHjoX+EkKv5ki/DMInBp27arUaVFXFCy+84HqfTFzsd/4ZKnwkcu+ATv2hIxUXFxeN05Pi8bg+Pz/fcbSk9eQQOn0KNqcbUfZ/+h5l9Bez3tNpXcCto7TpyFYRKoOOSXVDPP2B+kUnO8Byqpnd8Zwy7XaTqZ086KSJdDpt9EE8PYrKJvmL3xXLFE9yoHaKJ0bQiQ/WforHxTodBUvlWU+LcMOuXLENYll2sqZr0WhUz+Vyej6fN44TJmjsrMffikeBWuUk9p10LB6Pm/pud8xskLG3OzGi1WoZ40xjRaeB2I0Dyc7phA064pxklkwmjXaKiEfbEqSDMqcaOekLlSvKneQr3itr13btFE83KZVK+uLiouk42VwuZ4wvyZbuLZVKxn3xeNxop5NdyPZHlAedmEjH3M7Pz3ser6zr9j5U1gfbtUksU5SrqHPiSUBOJ5rYjYEVRVE8/a6I6G/J5y0uLhr2ScdH6/otO6Q+253iGdQvyvoQXddNtkV/+Xxez+fzRj3iMelW30AnrZFOirqbzWYN+xOP07a2SbRRP3OXk93K+FknqJ901HIul9MVRTHaQidMWcfHTmdJL+m0VPGYc7J18chr64lbMt+XabMofzFO0HXvOcBt7J3GgE74E0+aUhSl4xQku/Y4zSvUb9JPiin81ElzkZcehBk/+Yk7nXyTXTwoxlh2cvI6CVWUs9Wf280fVv9BeiGeymXXfvJ34nHyMpAc7eaaoHZl7b+fcnVdN3wrzYWqqurxeLwj5nLyTzJxF7VD1G/xdDM/8Y1TnC7ru+30Xtdv+R1r/Er6bndKpXVOs7MzEb9xupe/CkP2fuZX0v98Pm+cBEfyJPunOpzsUWY+k42V7ZD5LumyVcftYhXZONWPHK2ysOpDNBq1jdn69Zxkd2qyU912/lyGXvtEq412E69TnEvj6HTKvNUnesXFMvOPH5/YTwItXOn6pgKSoVDnrMEMCU/8o2DJ7jNRAUqlkqFAiqLYPhytra2Z7nFzrtbg0w7xwY2Of7a2UZyQxD75abcVN3mIwX08HtfX1taMSZIesOxkLNt2qsfuml277IxU9mhdt/76aa84tuQ8yLFbUVXVcBB29znVK9vGoGPv1r9Wq6Vns1nTg7xb0JHL5WxlQKytrRmyILu1HsVu17d0Om3Yulv9djJzKtdNt7zs2m0M7PwSLXyJ9k+ypTJoQYTqa7Vajnbhtz80KdjZuNPk6dRXsb9ePtipTbI+wM3XuY2BSDKZ9PS7VtbW1ox6Kaihh2m7o6ZFH2A9PjuIXyS8fAgxPz+vK4pi2Ktf/6HrtwIGCqYpIBXnLzGwo75b/Y2f8XSyW0LGzzqRy+VM/ssadMq2ZW1tzWgDPZCIwZVTf0Wdcvu+bJt1PLlI6AAAIABJREFUvTNOEHGbA9zG3qnf1jGz+zHErj1uMYr4QEoBseiDZOoknbQ7epvoRfzUTdxpnR+obNHPeNmpXdxjJ2fRdpzKqVarRpvpujgGfvyWDKVSyfEBJKhdOY2lrL2pqmqKcezme6841EtnSE/FNoo2HSS+Edvi5buddEXWDzqNhaxu+I3T3ewoLNnruvz8avcsVK1WDXmLn9vZIyEzn3nFym64fdfvc4VMnOpXjlZ5WrHGyrrev+ckP7Gprvt7vhfppU90a6vX3EWy9hpHL5/oFhd7zT+6Lu8T+01E14WjyRgmAFNTU3j66ae3zJGaTHekUimcOnUqlG1YdwqNRgMLCws4depUx2fNZhO5XA4vv/xyX0447DfNZhPpdBpXrlwZdFN6Dr3u/te//rVjG4amacbBCWGc3FIoFDAzM4NqtXpb6g2zdRgbG+vbIQkM00tu9/iG43SGYfwwbD4x8KmCDANsPpivrq7yZHib02g0sGPHjqFxYFuFQ4cOOSZjHB0dxcjIyEBy8vWDXC5nm0PgduT9998HYH+yXywWw913323kYGOY24GFhYXAJ6MyzDBwu8c3HKczDOOHYfSJ/2fQDWC2LvSLv6Io2NjYwKeffjroJjE9hBJLyp6OxtxCVVUcO3YMN27cwKOPPoq77roLwGYSxE8++QTHjh0bylOIukHTNNx///1ot9uIx+OIxWKhn1Y1rKiqiqWlJYyMjOCxxx4zjkL/8ccf8dlnn+Gxxx4L7e2or776CsBmYlp+44rpN81mE2fOnMGTTz7J+sdseW7X+IbjdIZhumFYfSJvFWS6plwu46mnnoKiKDh58iT/isMwDjSbTZw4cQKrq6vGaSWKoiCdThtHEN9OJBIJ1Ot1ZLNZvP3227fNopwXtB1weXkZ9XodABCNRpHJZPD888+H9oAfiURM/04mk1hZWQmlbIZhGOb2gON0hmFuJ3jhimEYhmEYhmEYhmEYhhlKOMcVwzAMwzAMwzAMwzAMM5TwwhXDMAzDMAzDMAzDMAwzlPDCFcMwDMMwDMMwDMMwDDOU8MIVwzAMwzAMwzAMwzAMM5TwwhXDMAzDMAzDMAzDMAwzlPDCFcMwDMMwDMMwDMMwDDOU8MIVwzAMwzAMwzAMwzAMM5T0fOEqkUggkUhI399sNlEoFDA2NoZardbDljHMJn51dKujaZpvGxuUXRaLRaRSKaRSKc97y+UyMpmM1L0Ms5WYnZ3FyMgIms2mr+8Vi0UkEgkUCgXX+zRNw8LCAsbGxjzvZeyRnUdqtVpP/FS3OtIPNE2T1sUw4Diy/wyz/olomma0NRKJIJFIoFwuD7pZntzuPrpWq2FqagqRSCS0MpvNJkZGRjA7Oxtamd1SLpcRiUT6qmvNZhNTU1MYGRnx1ZZarYZEIoFIJIKRkRFMTU1B07Qtazu3M8Ok4/1i6N64unz5MpaXl6Gq6qCbwjC3JWfOnPFtY5cvX8YHH3zQV7ssl8u4ceMGKpWK5721Wg3Xrl3D0tJSH1rGMMNPuVzGxYsXUa/XPe89f/48Lly4MLTzbrPZxOzsrBFMU9BcKBSgaRoymcygmyhFrVbD559/fsf5qUuXLuGTTz6R0sUwGMR8xWwNUqkU7r33XmxsbKBaraLdbuPDDz8cdLM8GXYfHYRGo4GbN29ibm5u0E25bWg2mzh37hyKxSLa7bb098rlMg4ePIiVlRW0Wi1ks1nMzc3h5s2bW9Z2mNsMfQjJ5/M6AL1arQ66KYwk2Wx2S5V7p9ONjQ3KLgHoyWSy47qdbjjdy9izFexrK7RxWKlWqzoAPZ/Ph3pvP1lcXNSj0agej8f1UqlkXFdV1fBJQxrKOHIn+Cmr3fZbv3oxX7Ev8g/Z6TBQKpV0ALqqqoNuSlcMq48Oi2Qy2bUvHyY9Gyb8yjQej9v6ua1kO7ern2Yd32To3rhith7NZrMnv5T0qlxm68O6EZytIMNisTj0W0+Y3lEsFrF//37s3LkTV65cweTkpPHZ6Ogopqensbi4OMAWMnbcjna7FfzlMHLixIlBN8Hg2rVrADZ9B3N7MUx6tpWp1+u47777Oq5vFdu5nf006/gmvHDFBELTNKTT6S1TLrP1Yd0IzlaQYaPRwNTU1KCbwQwITdOM8T916pTjfZlMBslk8rZbKNmq3I52uxX85TBSLBZv24dIZnhgPWOA29tPs47fItDClTXhZrFYxMjIiJGglJLtWRPDNRoNI1fF2NgYMpmMa7LBWq1m5LWgPxkogBoZGYGmaUilUqaEdBQYuyWaKxaLGBsbMz5PpVJGss9Go2EkqgOAQqFglEXJ7KxQwlDqO+XoEPsqyozKHBkZwcLCQof8M5mMkUAvk8l05PpoNBrGPZFIBJlMBo1GQ0p+XjJoNpvYtWuXkbdCHBsv2YtysLbLrVzqt9e4ATASWVp1JxKJGPrmpKPA5l5vuwSFfuTfDWK9dv2jfC+UeLZcLhv9dHpgsOrdV199FbiNbnXKjJFXP+3w0g1r/dRfvwl63XyUqEdUrtVHiX2cmppCKpUytWlkZMTk83rhS6wJimdnZw1bk5GhE07+wM5Pi3K3Xnezn3K5jD179qDdbqNSqSASiZiSWXv5NTFRv5hQdGRkBMVi0XaMZf2i23wkfkbttdqCnS8R29toNDA2NmYkOrbaOyFTrojY30Qi4WsesNM1p7Jl5nQZzpw5g3a7jXQ67fkr79GjR/Hdd98Z/7bzPzTuhJ+51qt/bvOIVVedkqh6+Uy3OdVJR4rFoskOvHyizJzphpfdin2h9qdSqY5FR9k5XpYg81XQeMQrFqL2WWMNu4VYMWmy9U+Us1eb/Og+9cHN39rpX6FQwP79+wEAMzMzRhv9zBGyeM2FhUIBkUgEMzMzpnpkdNppHrWOg7Uu65i4+XhrH7qN1a3QM1oqlUKhUDDZ3djYmK1N2Y21dd6RKdNJDqIOyMjf2h7r/GOnZ/S56P+seNkcJcOn51svn+WGtSzCrx3K1CPaPfl9mbaIekxyJFl62Y7bs5LXOgHg7l9kx0H22cANaif1Y3Z2tkN+XvbhpN9Ozw0y49+tjg8ibusLQfYZVqtVPZvNGvka0um0riiKDkBfW1sz9sRaq4lGo/ri4qJRhqIopn2bdrkJstmsnkwmpfMVqKqql0olPRqN6gD0dDpttDWfz+utVkuPx+P6/Py8cX88HjfVu7a2pkejUX1tbU3X9c1cG/S5qqpG2wHouVxOz2azej6fN9Upkkwm9Xg8buwRnp+f1wHo8Xjc1GaSWS6X0xcXF/VqtWq0jdqi67rRJ/puMpk05c1YW1vTFUUx+lOtVvVoNKpHo1G91WpJydFNBmK/xDH2kj31W2yXk7ysuiMzbrreqUPU7mg0auqbk44uLi7ajpUoXy/5d8Pa2poph4GqqrqiKKZ2l0olQ+9I50RbJNkQ2WxWj0ajhiyoL1aZeUEy9apT1ra8+klY5a7rzvv2yZ6y2axeKpUMPbQr1w0vH5VOpzvkp6qqofPUR9Iv0o1cLqfncjlD/vPz8z3xJbq+qSck92QyaegBybKbfBJe/oB8jJ1utVotkw+TsR+7sffya2trayZ5iLpAMi6VSsZ1O9l5QT4LQIcvJbnTdZoXaazIjsg+rO0lOQDQP/vsM1O7RZl6lSu2M51O6+l0Ws/n80bZ4jiK91pzKGSzWT2bzeqtVktvtVomX0542Us3kO76LafVaunRaNS2zblcTtd1/3OtW//c5hGyF2qL6AtFvfbymW5zai6Xs9WRtbU1QydkfKLMnCmLnd2KupjNZvVqtWrovejfZOd4L8Kcr3S9u3jEKxaidorzs+jPxHwyVvsk3bLqqmybZHXfy9+2Wi1HH2XnU/zMETLIzIUE6YQf3ObRVqtl9Nv6HdEG3Hy8aAdeMbHfHFfVatVkB/F4XM/n80b8YpU1lW+nO5Rf0E+ZFOdZfQHFQNZ+2NlYNBrV0+m0oWtUj9fcJbbTWr+MzVWrVaMuL5/lRalUMsoSY14/dugF2T3ZgjjviTK1a4uI03Un2/F6VnJbJ9B1b//idxy6zZOWy+VM/RB1nJCxD1EGohxbrZbJ5v2Ov18dH1Tc1g8CZzS1OlgKzgirEtH9YrBfKpVcF67m5+eNgNMvVL/oSKkOp+ShpAD5fL5j8svlcrbBDA2+rpsfYK0PeNbEdnbKRGVaZWS9z2osZBhiOWK7qE9+Jj4/MrDiJntrMJxMJm2vWcuVGTdd100P6ITdYoNdPfTgYx0r0enourf8u4H6IjosGjO7a9b6rXIgvbH2mfSumwcBuzrFyUNmjGT7qevdLVyJUJAkGwR046Pc2kUPf2L9NC6izvfCl1gXMyigcWqrFzL+gCZtq++hAESUi5f9OI29jF+z0wWSnXU+6UYWTjaUz+dN/aJgzdovu2uKohjjY2cbVr/rVa7TAxCVZ3evNTBSFKWj72IwLGMv3UB1WMfai2w2a9tmuwdrmblWtn92OkQPqSKqqnbotey85jSnUhl2+ujmE8V7/cyZXtjZLfXHanvxeNzkB2Vl4UWY85WuB4tHnMaNdMGq4+SnrD+UWeu3e0Dy2yavOFPW39rpn9NCi+wc4YWfuVBso1+6mUft9NXJx8vGxN0kZ6fviD+m6PotHyDKW1GUDt0hHRV/8PZTppsv8PKhFIvb/Rhjd81pIUas34/Nufksv3rk1WcvO/Qil8vZvpRgt7DqJS/ZhSvZZyW3dQIZ/+JnHLqJ5ah8ux+ig9iHk//xG4O4lanrnTo+yLitH4SW4+rhhx8GAMRiMdfX+h944AEAwJEjR4xX/MSEqyI//vgjpqamcPfdd+Odd94J1L7x8XHTf5eXlzE3N2d6fW/37t0AgEqlAgDYvn076vW66bXjJ5980rZ8cYvY6OgostksAOAf//gHAODPf/4zotFoh2wOHz4MAPjggw86yozFYsb/33XXXR2fx+NxvPnmm8Zrh7FYDI8//jiAzdcrK5UK9u/fb+ojve75xRdfOEjKjB8ZOGGV/fT0NDY2NgBsviqZyWRQqVSkjmyVGTcAtmWRjnpx6dIltNttKIpiqofK//LLLwG4y79bJiYmoOs6xsfH0Ww2MTU1hffeew8A8NNPP3XcL+qFqC8EHVU7MTFhum6XfFEWuzq///5745rMGPntpx+scrj77rt9levHR8myc+dOQ/+pvHg8jna73bEdoBe+5MEHHzTKs9MTWWT8weHDh6EoCt544w3T9ePHj+Pll182/t2N/fj1a9a+3nPPPQBu6UQQXnvtNQDARx99ZLq+vLxs0peVlRVjmxq9ig6YbYYYGxsz2izqix1+yrX6vunpaUSjUayurrrW8fnnn0NVVdetkb2wFxEaMxk0TcPc3Bzi8XjHZ7///e8BdI4X4D7Xdtu/RqOBer3eodN28ZHsvEZY51QvnHyiSJA50w/WumOxmKluv7LwIoz5yomg43bmzBkAt3SMyGQyiEajqFQqhs7Z2TX59SBtctP9sOJIK7JzhBfdzIVBCGMetfPxQWJiWfbt22dq8yuvvAIAxhxQLpehqmqHTxkdHUU6nUa73cb58+d9lRmUWCyGjY0NHD58GJqmoVAo4ODBgwCAH374oasy/dgcYeezwsbrec+Lubk57Ny5s6NtY2NjgdvmhOyzEmFdJ/DrX3o1DhQTiM9Jo6Oj2NjYMGKsbuzDD0HHX2QY4rZe0vfk7LFYDKVSCaurq1AUxdhXPD093XHv0aNHe5aMrF6vI5/PQ99866zjD9gcxHw+j5mZGdx///0oFAp44IEHOhYB7Hj00UcB3HKu9XodiqJ03EdGoKqq7z6cPn0aALB7924jzwzJ8fr16wCAarVq27+VlRWpOoLIwA3ap3z8+HG88MILSCaTUt+TGTcAUBQF6+vrpu/SWPz85z93rYNOz3Cqg2TsJv8g0D71vXv34rHHHjMWLrphaWlJWrZhITtGYfYzTPz4qCDs3bsXgPeCWj98iSyy/uD48eNQVdXYp99sNrG+vm66rxv7CcuvhcHo6CiSySTm5uaMib9YLOLQoUMd91KOnYsXLxr9DoMg5e7cudPzwejq1atIJpOOtjwxMdEzeyEdtwa/bpB+2NkHPXT6TeLebf8oeH/kkUc865D1mb0kyJwZJv2WRZD6grb16tWrAOwXZ3fu3AkARu42WpwR9ffHH38EANx7772htUmkl/5WZo7wYpBzYdh0GxN3SywWM348A27Fvb/4xS867qUFhxs3bvgqMywKhQJ27doFYFNvguDH5rYKtVoN7XY78A/nfpF9VnJiWOI5mZggDPvoF4OM2/rBQE4VnJycxDfffIN8Po9isQhFUToSpwLAyZMnEY/HsX///sBJCu349ttvPe+Znp6GqqrIZDLGw5pMW+xWTClpXFiMj4/jypUrWFxcxPr6Onbv3t2RePTmzZuB6+lWBk6Uy2U89NBDOHjwIFZWVnyv8sqM28mTJ7GxsWEkmms0Gpibm0Mul5M+ztXLmcnI3y/NZhP3338/AGB9fR2ZTCbw2yHWh5F+4DVGvehnmMj6qCDI9rcfvsQPMv4gk8lAURQcO3YMmqbhxIkTHQFnEPsJw6+FwdGjRwHc+hX37Nmz+M1vfmO6Z3Z2FgcOHMDHH3+MU6dOSb8l40WvyrUi4z96YS+0sCvj763Qw0lYBOmfrK52088wCWPODIt+yyJIfWG0lR7g3HjttdegKApyuRw0TYOmaXjzzTcRj8c7DoUJW3698Lcyc4QMg5wLwyJoTNwtdm+s0AN6mGUGIZFIYHl5GZcuXcL09LSvN3DdkLG5rcag5pCgJ/oOSzwnQ1D76BeDitv6wUAWroBN5zY9PY1vvvnGWJyyZu+/6667cPr0aUSjUezZsyfU467j8TiKxaLtCUzW0xxGR0dx6tQprK2tAYDtL+pWyBC3b98O4NYvwE4LPkF+XclkMlhfX0c2m8Xc3BzK5bLxGuDZs2dtv+P3xIpuZODE0aNHbYMtGWTHbXJyEplMBsvLy4hEItizZw9ef/11qS2nNGbnzp3r+IxOyBCxk3+3nDhxAu12G2+//XbXZYhEo1Goqup40lgvkBmjsPvZC2R8VBAoyLC+sm6ln75EFhl/cPz4cbTbbbz//vsoFot44oknbMvyYz9h+7WgTE5OQlEUo93WLSTNZhPvvvsustlsqAtLYZS7vr7ueXT0jh07oKqq7QlfdJodEba9PP/88wDg6EtEGo0GyuWy8WbQ5cuXHe/t9ldpv/2jhel//vOfnmX7iUd6RZA5M0z6LYsg9QVtKy0Ifv3114730BtIo6Oj+P3vf4/vv/8e27Ztw7Zt25BIJDreSghTfr32t7JzhBPDMBeGQZCYOAjr6+vGtmp6a8/txGmZt0fFMoNSLBZRr9fx1ltvhbYg5sfmtgo/+9nPAABXrlzpa71+n5WsDEs8RzphF3tSH8Kyj34wyLitH/R94apWq5mUORaL4a233gJgv+o6Pj6OTz/91DgWOyyB7tu3D+12u+NY01qtZkyChULB9Nn4+Diy2azt68fWdl28eBGKohi/nLz00ksAOg2Ryqd9236wvp1AiwD//ve/EYvFkEwmUalUOo5ILxQK0m97+JGBLDLyc0Jm3IDNtxGi0SiuXLkCXdexsbEh/Qrkrl27EI1GMTMzYxovTdNw5MgR43ViN/l3i93ibLd7+YFb+ZL+8Ic/dF2GX2TGKOx+homMj6IJSvRZmqY5/sphp9+rq6vIZrMdAdkgfIksfvwB/aJOCyzWfnZjP2H5tTChh68DBw4Yea8Iuy0HYcxhQcut1WpQVRUvvPCC632Uv+yZZ54xBUHNZhMnT57E6OiolL1omubreHtg8+Ehl8uh3W7jyJEjjv2j47InJyeN7ZvtdrsjaKa2WN+I88JvzELQQ/jc3JznG8qy81ovCTJnhkm/ZRGkvqBtpcXZP/3pTx2f0WI+US6Xcfr0aaysrBhbPk6dOtXhV8OUX6/9rdcc4YXfuZB+LArzwYwWwkVZ+5VzkJhYFmt81Ww2oaoqXn31VQCb/ioajWJpaakjPvv222+hKErHgo5XmcDm4qE1LnJbNBKx23pF22O7xY/NbRXGx8ehKArq9XqgN2Zo3O1icTvbkX1WcmJY4rmnn34awOYCstgGTdNw8eJFAP7sg/77/7P3xi9uXWf+/1tf9tcm1ag/7XbDMleBDckyH2qNvWTHAQdiqd6ytKRZyWkohpQ4mpT+kKSeRpMlhDrZSJvkB7OdGRsbzNKNNOuUlqWSZ2yIIVLD2pHDiCS4UEuEtPQnXStt/oDz/cF9rs+9ulf3SLoayfb7BUNi6d5zn/Oc5zznOUf3nMf7BtygRa8oiSpum1nCTm8PQ89O5M1mIBkHoGVTkVSXkkVA0pvqWW/klH09a4RkCdHTVYbRbrd9MwnJcyUjAf5yIr9kt5F6SAYtuVfK0zMbiKzZbLYvHaiezUapWxkQvKk09SwFus70evql/sRfMgzo8uoZHvR0yZIxyLKsoTPzmOqgWq2qQqGg2u32QN3rac4lNauepl5sw69ck3YT3Uid5a9YLKpisejSq5+NKnUrwwj+kgVGMrx4s5YN0r+eEtnbN4LQs7JIGlndbuT5fhnoJPWwnrVGz0onZZbLZUffkiI9DD2Vq94v/Z5p0kam9ZTMF3r5ev112xB9e6+VbEy63IMw8VGSSSSVSjk2WywWHbkKhYJjT6IHsRVJTevNxjIJXzIo7bGfDsMw8Qc6QVmfRC+D+o9cI6mSpW4mfs3PLpW6pU+vnvxSGpsiPsTPr0oWM7GncrnstImkrN/Z2XHZuZ+uvH3PtFxdD3o683g83pcdxy/ToFK3xl4p2y9jUFh/GbYP+j3fsixVLpddfqZcLvf5V/F5funOdX9pOtaa1C9oHBE/J/frqcHlM9NxbdCYqpR/3BSU/cuvPUzHTBP8+q1fxrFut+vUSfRmOsYPIurxSqnR45GwdhO9eFOXe9tMnq+3T6FQUMVi0VWuiUzDxJmmceQg+8tms4EZqwaNESaYjIVSZ2mHYfxQ2Dgq8kssI3Gt6EvGuEE+3jQmDvLRg5Dn6j5MdOTNNipy6/OsjY2NvqzIw5SpZ3is1+uqUCg4diZjlejZ60N1ecR+xM7y+bwTdwTZWVD8aNrnRN/eeMHrs0zwa7th+mEYoitd1xJTic2LnwiyI3muV1+D+o7JXGnQOoGJfxmmHUaJa5W6NSZalqWKxaIqFArKsizfbOBh/UOpmxkIpX+ILPoYWK1Wh2r/YW08irhN5A2K76fFWAtXogj9TxBl6n/1el3V63VnEJXPxXkopfruSafTvmWFpWw0uUefQIpB6MajO0l9EPLTgaQiFSPxC1C63a7rOukgYTIH6dmyLKfjiq68nWdnZ8fpkPF4XBUKBePgz1QH4nj04HyQ7nd2dhy5xZH5dRBvuabtptTNji+OwyuLbot+nwvVatUJKCzL6nPYYfrXBwHTlO56wJ1KpdTOzk7fAphfHxHH7leXnZ0dp0xpP1mAMJXL9JmmbWRSz7A66bYRdO2wfkMpFeqjhHK57MggdptOp1U+n3fZgdRR+mGQzUbpS4LaTCeofw3CxB94dRkUfJn4LwmKvAHEIL8WZAt+fnRUG/FSKBQCF7387ETsa5DtCkHyhZUrSBvI/TIJ0Bk0lotOpa28NmnSX8S/j7IwqNdBf4ZfGm2h3W4PrPMwY21Y/cLGEX3yIDYuC4denxjkM8Ps1O87U58ofsFkzDTF229N+548w2SMH0TU45VSo8Ujpv5FdC/+zG+8kefrPjOo3FFkGuQDwuLIQbLIBCroB7xBY4QJJmOhX9ub2nRQf9ER/6bHejI2ttvtUB9vEhOH+egg9AmvxLOD5gLi7+QZ2Wy2b1wepky/WK9er7t+iBjUT7y6lR8mvDGB184G9XelwvtclPGCt6xB89pR21mpm3MWqZP8WJVOp51F1SBZlOq380F+02u/g+ZKJvUZ5F+GbYdR4lpB9yN+dq+UWf8QObx9Wtq3Wq2O1P7D2vi4cZvEXN4flKdNTKldSllzh5LJZLC9vb1rmX9IOPKa6s9+9jPfbVhykPJubYMolUq47777dv38AjIbxGIxpNPp0Awpd6IvWV5exre+9a3bJs0uIXcjszZmkn6Wl5dx9OhR3zPtarUaTpw4satZVaOCY8RkaTQa2L9/P4rFYmT9dxJlEkJmk0wmg7W1tV1P0hLE1A5nJ2RSvP322wD8s5skEgnce++9u3aInm3b+Oijj7hoRe46Wq0WLly4wAkJITPOLI2ZpJ9arYZKpRKYiOFv/uZvRk46ME04RhBCyOzSarWwZ8+emVm0AoC/mrYAtzP6YcytVmti6cjJcLTbbWxubmJubg6PPPKIkz73z3/+M37961/jkUce2ZWsIZVKBZ9//jl+9rOfTfxZZDaRQ1qvX78O27YDD5+9U3xJqVTCysoKLMvCjRs38L//+7/TFokQEsKsjJnEn9///vfOgevf+c538OCDDzrfnT9/HgB2PfvjqHCM2F3kIPQoD4aeRJmEkNlCDsiftbGFWwVHRF6V1eFrs7OBbG04d+4cms0mACAejyOXy+HJJ59kAE52BQnQder1ep/93Um+pFar4Z//+Z9hWRZOnDjBX9IJuQ3gmDn7VCoVnD17Ftvb285n2WwW3/72t2+rN7o5RuwecvyAzrhTvkmUSQghpnDhihBCCCGEEEIIIYTMJDzjihBCCCGEEEIIIYTMJFy4IoQQQgghhBBCCCEzCReuCCGEEEIIIYQQQshMwoUrQgghhBBCCCGEEDKTcOGKEEIIIYQQQgghhMwkXLgihBBCCCGEEEIIITMJF64IIYQQQgghhBBCyEzChSsAnU4HpVIJyWQSjUYj9PrV1VXMzc2h0+nsgnTTxbbtoXQzDIuLi1hcXHR91mg0sLy8jFgs5vq8Uqkgk8kgk8lEKsMs0mq1sLy8jLm5uWmLMjYHlvRqAAAgAElEQVSmfaXT6Uy8zp1Ox5Enalu+U7ibfFsYjUYDuVwuMp9TqVSwuLiIUqkUSXlRYtv2TMt3O1Kr1RCLxVCr1aYtysxxO/rikydPIplMIhaLIZlMDuwnfrENmSydTgdzc3NYXV2dtihjc/LkScRiMSwvL09blF0livlGrVaLdNyeJrMUPwfNzcgt7jS/H3UMHBVcuAJw5coVnDp1Cu12e9qizBxnzpzBuXPndkU3rVYLf/jDH7C+vu76vFar4fPPP8f29vbEZZg2nU4H165dw/r6Onq93rTF2RU6nQ7effddVCqVidXZtm1cuXIFm5ubd41eyeg0Gg2cP38em5ubkZRXqVTwq1/9Cs1mM5Lyouby5cszLR+5c7gdffHq6iouXbqE69evo91uI5lM4o033pi2WOQOpNPp4KWXXkKxWMT6+rrvAo5t2zh58iQymQzm5uYQi8UwNzeH5eVl2LYdWG4sFkMul5t0FUZm3PlGo9HAJ598Etm4fTuwG/Fz0NyM3LlEHQNHioqAdrutisViFEVNjWKxqACoer0+bVFmjt3WTTqdVn6mCUCl0+ldkWE3yefzfZ8F6eBOZjfqzH5OhiFKn1Ov1xWAmRkrvX5n1uQj41Eul2faz90uvrjb7SoAqlwuT1sUcheQTqcd31wul5VlWarb7bquicfjCoDa2NhQSt200Xw+rwCoVCrlW265XHbdM6sM6xf84uc7da4wiN2In+/Gecndziz2pUjeuHrzzTejKIaQu45Op8NfMQghu0qlUuF20Ducl19+edoi3BFcu3YNAPD1r399ypKQOx3xyWtrawCAXC6HgwcPOjYo9Ho95PN5HD16FACQSCSwtrYGy7LQbDZ939LK5XJQSjn33Akwfibk7mPshatKpULHQcgI2LaNbDY7bTEIIXcRcoYeuXNZXl7m0QeE3GbMz89ja2vL9dna2hqWlpZcnymlnMUtnWQyOVH5ZgnGz4TcnYy1cFUqlXD48GEAwMrKCmKxmOvAylarhVwuh1gs5uyt1n8JKJVKznexWAyNRqPvM/0guEwm47q2Uqk4B4fZtu0cHOd3sF+r1cLi4qLzfS6XCzxcs1arOYdwegN8OVRUf0an03EO7mw0Gq77M5kMWq2Wq4xKpeJ8v7i4iEwmM/JBhF4dew8Nlb3wcuhuq9Vy9JjJZHx/dZfDEaW8jz76yEgWb7vpdfK2pxz0N+5hgrVazWlX0affYbiNRsN1nf6nHzznZ7Pe9pMDE+XeUQ7j63Q62Ldvn3OmjNfWdUqlEubm5jA3N4eTJ08O1EHYOQeC9zDmSqWCubk5V13G1YVfX9GfL+0v9+oy635Abx/dB3jtXC/PVA9hOpL6z83NIZfL9Z0PoR8mqvc/CSCHrYdJf46i3fwIai8TPfjpLgo5bdt22ViQj5mE3oIO+fXaWpDP8fbLd955Z6DOdMLqI3IM2y61Wg0HDhxAr9fD9vZ2n13qzx80TpjqIEiGQT671Wo5bQ7c8n8yHkufljbNZDJ9Y1symXSV6U3Asrq62tfGfn5Ur7feZ3W9+dmjd9zVn6HHLFLPubk5VCoVp/56rOLtE2H6y2Qyzo+J+/fv7xuL/fxVGMOM7aP6Yr/yG42Gbyw4Tl2EsPhUnr1//34At3QZdlCtX2zj9a2DYkzAf1wxHft1m/XavR4H6WOu7mP9/N4oY4o3ltf1pn8nbTauD9fnA4NkHzdGNm2bUW1T6qjr2vTt2OvXrwMAHnjgAeczvQ+Ne8hymO8Z1s6B0eYbpvFz2LzQW6co4+ewcsPG792Mn03ijWExmXOHxc9B8gaNRdOMn4PmtKPEacOOhSZzUhN/ZBoDT51x9xoGnYshn8t+6na7rVKplAKgqtWqUsq9L7tQKPTd67e3O5VKqXK5rHZ2dpy90KlUSuXzeVWtVlW1WlXxeFzF43HXffF43DmjoF6vK8uyXDJLWfl8XhWLRVWv1x3Z9H3k1WpVWZbl2oNdrVZVNpt16pFOp1WxWHTqq9djZ2dHxeNxtbOzo5S6te981HMe4vG4ymazqtvtqm6368gh5dfrdeezbDar8vm8qtframNjw/lMJ5/Pq3g87sgj15nI2G63Hd1IGwvdbtep987OjqpWq065XkzPuNrZ2XHZnjzf2/ZeG5U20PUkn1uW5dSzXq87tiTtt7GxoVKplKPvQqEw1p7voLrK54VCwTmrROxJl7lcLqtUKqXa7bYjn1dPfuj2nU6nVTabddouCl0E9RX5LpVKOXLrfkDXhbSvty7yHL3/ivyiB+nP3vMchjk/QfqLUjdtK51Ou2SRemSz2T79j1oPk/48brv5Mai9wvTgR1RyptNpV7tKmV6/FYXe4vG4yufzqtvtusYrvzb3G9d0nXnPE5H6+Y2VfoTVZ9R2EfzsUfxk2DhhqgM/wnx2u912xmfxfzImi/5Elnq97hq3U6mUKhaLjq50fVWrVVd7yjgnOpDy/exRbE8pd/8WO5WYQ6+7HhPo447c741Z9HFTPtevNdWfEOTn8vm8Y+O63w2zSdOxXanxfLHoTP+s3W47bR9FXZQyi0+915qMGUGxjd7Gg2JMQezD2//1c7ZMbFa3e/25Mvbk83lVKBTUxsaGK8bQdTDqmCIyem1YkL4tjOPDdV/g9WtRx8gmbTOqbRYKBVebyrgUdG6Vjow5fs8Qmxzn/EIT3zOsnY8z31Bq8FzBZF44qfjZpNxB4/dux88m8cYgffsRNuc2jZ/lM++9Iouum2nFz4PmtKPGaaZjocmc1MQfmcbAs8DEFq4sy+o7NK/dbisALiOQgy/9FlAAuIJGMVZXBXwGRWk43fj0QFOpmw7Wb+FKH7CDZPMLuPQAWkcaXhy2LGh55R1l4UoCRn0w8AZk+mf64qAumyAdzy/YNR1IggZPCZZ0whZtvHg7kNRLd67SDt4Jnrc8P2eYTqf7DmCV8uS6dDrtq8dRCdOB12b1tpX21/uIfm9Ye+kTVSlPyopKF359RYIz70CgDyDCoAm2t+28/dS07wbh5w90WfL5vLIsq+8+vzY1qcew/XnUdhuEn37C9BDEuHJ6A2XBO3BHobdUKtVXJxmv9M+LxWLgoeZynQQXXrkHTSx0TOszarvIvUH26Nen9YDfRAdBmPps6UO6jehBmx6ISgyg+xOJAfT+711AkaBf2tlrj9JeQYG/BHjpdLovwNdl87a3X8wizwobI03159ePZfLgxS/Y9sN0bB/HFwf5Zz89jFMX0/hUnmU6ZgTJq9TgGNPrY7wyeMf+YWzW77miA2+METS2jjOmyOKGd7xPp9OuecC4Plypfr8WdYxs0jaj2mbQeCe6HpQcQHyj30HluoxeGxiGYX1PmJ1HMd8IW7jSEb8tsk4qfjYt12T83o342bSP6HUIw2TObRo/B42jXt3MQvwcFPuPEqeZjoVh8zBTf2QaA88CkRzO7qVWq6HdbmNhYcH1+fz8PLLZLHq9Hi5evAjg5qGC2WwWm5ubrlcd4/E4AODdd991Pnv33Xdx5MiRvuclEgnXv++9917Xv+W12eeee8555fbQoUO+st9zzz195X7xxRcDauvmySefdP371VdfBQBcunQJAPDQQw+h2WyiVCo59f3mN79pXL5OIpHAjRs3cPToUeeVS9HPn/70p77rvXrx6u2//uu/AKBvP/3f/d3fGcuUy+VgWRZOnTrl+vw///M/8fTTTxuXY8LS0hKUUlhYWECn08Hy8rKTovrLL790rvNrvwcffND1b9u2sb29jcOHD7tex1xZWQEAvPfeewCAPXv2YH193dnWAQBPPPFEpPXS0dtIbFPa9vLly+j1erAsyyXz9vY2AOCDDz4wesY3vvEN51nz8/MT18X6+jr27t3bZ3/jnM+wtbXlyCGvAgPD9V0vqVQKr7zyivOqbiKRwKOPPgrgpr2sr6/jmWeeGbl8L8P251HbbVgG6cGEUeXU+7DO3r17Xf8eV2+tVgvNZrOvTvPz8333njt3Duvr6y65ZSuR9LuLFy+i1+vh4Ycfdt1rerizaX3GbZcg/MYJPc22iQ6CMPXZgv5K/fz8PPL5PADg//7v/1zXPfHEEy5/8vzzzwMALly40Fem+P75+XkkEgmcOXMGgHt7jTw7Ho9je3vbtVXntddeQyqVwvr6Ovbt24eXX365L9YJw+v7xDa8uvcyrP50zp8/j3a73bftTwg7rsB0bJ+EL/YyTl2GiU8ngV+MqXPq1Km+8fHQoUOug7WHtVnvc4Fb462fDEIUY8rx48f7dNpqtZz+JzKM48ODiDpGNmmbUW1TfKd33JB/f/755773yVlPlmX5nnsFAL/5zW8AAPv27fP93oRhfU+YnUcx3xhE0LxQZJ1U/Gxa7qjjd9Tx87B9xISwOTfj5/HjNCFsHmbij4aJgWeBiSxcffLJJwCAv/3bv+37ToxFd8Lf//73AdxapLJtG71eD+l02hUknT59OnRvqB+JRALVahUXLlyAZVnOOQDHjh0buqxhkYFCgrZDhw6hWCxiZWUF999/P0qlEh544IE+5z0MpVLJec7x48dHLmdzcxPpdHrk+4VnnnkG7Xbb6UjiuCbRCWSv98GDB/HII484Exsdea4eyP35z38GANx3330AbmUOqtfrUDffRHT9yYGZzz//PA4ePIjDhw8jmUyiUqnsih35If3MT16l1MhyTVIXjUYDvV4vUsctyHkK77//Pk6fPj12eVLG/v37nXPopH7eLD9RMmp/Nm23YRmkh1EwlfOv//qvAQCfffaZ637btmFZVl+5o+otaMLgR7PZRLFYDOxzAPDLX/4SQH9APixh9Ym6XUwx0cEgTHx2EP/0T/8EIDygTiQSSKVSrgW3IK5evQrAf2FRFkn/+Mc/uj7f3NxEPB7HjRs3+hYPJs2o+rt69SrS6XRgu5nYq+nYHrUvjrIuw8anu43Jofqj2OwoRDGmPPbYY4jH464slydPnvTNbhdVLDupck3bZhTblImxnKXm/VHAb+Js27ZzhtGgtpC5z6BFShPG8d1eoppvjMqk4mfTckcZvycZP0fZ98Lm3Iyfo4vTwuZhJv5omBh4FpjIwpUgHTiMQ4cOwbIs/Md//AeAmwtYTz75JI4cOeIESY1GAwcPHhxZlkOHDuF3v/sdisUiKpUKLMtyrVBOCr+B4tixY2i328jlcs4Clsnhb34sLi7i3LlzuHz5Mo4dOzZ2ymY53HEc5NfXt956C8DNXwd/+MMfjl2ul06ng/vvvx/ATblzuZzvL9YvvvgiLMtCoVCAbduwbRuvvPIKUqlU30LoH/7wh4HPTCQSqFQqqNfrSCaTOHz4MBYXF8c6BHxcJpXWfpK68C5GjMvq6iqeeuop/OIXv8Da2trQb0D4sbCwgA8//BDlchnXr1/H/v37J56NLYr+HNZuwzIpPYTJOT8/j2KxiPX1defXqkqlgmaziRMnTriu3U29mdruOP3SpD7TsE9h1P5r6rOD8L41MohhJ2nDBNNffvklLMtCr9fD9773vaGeMw7j6m/c8d1kbJ+EL/Zj3LqYxqezzCQngDrjjCmJRAL5fB7tdhu1Wg22baPT6fTZRdSx7KTLHcQ4thk0wfSbOOuLVkH+zrZtNJvNseZPwPi+x48o5hvjMqn4OazcccbvqOPnSfSRacy576b4WTCZh5n2s6jrPikmsnAlb7EMyg7hXdmTX/IajQYuXbqEpaUl57Xns2fP4p133hl7q1kikcCxY8fwu9/9DqlUCocPH574goM4rz179rg+n5+fx9raGnZ2dgAAP/jBD4YuWyZxr7766ti/pAA3t2e22+2xdSKBSrPZRK1Ww4ULF8Z++8CPN998E71eDz/96U8HXjc/P48f//jH+OKLL/C1r30NX/va17C4uOgKBORX87Nnz/qW4c0as7S0hK2tLZTLZTSbTbz99ttj1mZ4HnroIQDu7bSCZM8YhUnq4itf+QoA4MMPPxxJNj86nQ5ef/115PP5iUyScrkcrl+/jnw+j/X1dVcWHdOMmyaM25+HbbdhGaSHYRhGzqeffhrpdBpHjhxBLBbDyy+/jGq16nrtfFy9SfD98ccfh16bSqVQqVR8faTI/dWvfhUA8Nvf/nZoWYDh6xNVu5hiooMgTH12EBJYie8bxPXr15FKpUKvk7eFPv3008Br9PHLtm2srKxga2sL+Xwe29vbY/ctU8bR3549e5wYy0utVjOawIWN7ZP2xcI4dRklPt1NLMvC9va2b/+SMX1Ymx2VqMYUid1PnDiBM2fO9B35EXUsO6lyTdpmVNuUOYLpj9gy+R20aAXc3BIHYOz+OK7v9hLVfGNUJhU/D1vuMOP3JOLnSfU9IHzOzfg5OoLmYSb+aJgYeBaYyMKVvBq8ubnZ56Q/++wzWJbVN6h+97vfBQAcOXIE3/72t53Pc7mcs19/1K1mjUbD5SwSiYRz9lTUK4yyBU24cuUKgFsDd6lUculkYWHB+TVqWPxeZ/c+fxjk7aN/+7d/G7kM4cUXXwQAPPXUU3jhhRfGLs8PvwDAbwtJrVbD6dOnsbW15fx6tba25nJuiUQC6XQa29vbfWlrS6WS07G9K+W5XA6pVGqk9huXffv2IR6PY2VlxeVUbdvGc88913cWkCmT1MXCwgIsy0Kz2TQKDNLpdN+vBd5g3W9LRBTBkLd+ErD9/ve/x9LSkuPj9EDTtm3fXzdM6jFufzZtt2EZpIdRGEbOTCaDH/3oR7h+/TqUUrh+/Xrf+YTj6u2xxx4DcPPsiLBJwxNPPIFer9eXJl3OCQDgjF+vvPLKSHZoWp+o28UUEx0EYeqzBa/+3n//fViW1WcD3jI6nQ7a7bbR2CPnUsob3zoSaOp873vfQ7FYRCKRwNraGlKpFJ599tmR35oehmH1pyNnaf7Lv/yLK4jtdDo4ceKEcXw1aGwf1xfLgpEel/n51HHqMmx8KrKME1sNQzabBXDzfBidRqPhtPWwNjsqUY0pcj7d9vY2zp071/eme9Sx7KTKNWmbUW1TtkGHnRMI3Ixp19fXcfr0aVcca9s2crmcK9W9vFnoPdd1WMbxPX5EOd8YhUnFz6bljjJ+TyJ+nkTfC5tzDxM/iy/2vmXmXfS60+Jn07EwbB5m4o+GiYFngpDD20PRT+jXswZIxghvSlA9bbIXSU2pI5k2/DJqBGUUkixy3lTkUoakyZST9/XUmnr2AXm2ZVl9Kdq91+ppf72pNPUT/yWroFwjKWXlGskOYllWoJ4EXceie5FN0hzrsukZR7rdrpOFQp6jZ23KZrOqXq+rcrnsSm87TFaSdDodmEZUMkDozx/0ubS13hZ65ihJc6pncdSz3+EvmRHkr1AoOKl6BUkHir9kFkqn08qyLFdGBcmYJDYtcolehmk/XbZqtaoKhYJqt9u+2bOUupUJUc/OJFmERDei82EyyEn6WJ0odKHXT+8retpYaQM93b2k5lZKudK2SvYqPU38xsaGk7FG+ni5XHZ8gFwjevSTJwh5ruhGZJT66rqX1M/iV7yu1aQew/bnUdttEH76CdNDEOPKqfd56bfZbFYVi0Wn3ZUa3g/6ySO+RGxIT5ssn0nGIGlf8SmSdU8vU64RmarVqiNTKpUamCHKtD6jtovcK2mfZVzwy8TnN06Y6sCPYX22X6ps3b+Ijejju6Rx1jPr6Kmx/XyjN1OgpIzWYwv5zJvBR/yAN7bR7U0Iiimkbt4xWnQjdTbVn54JSXyiUrf8kNiXaQYtL0Fj+7i+WLIYie2Xy2WX/RcKBef+ceoyTHwq8nuzIgYRFMP4Zcr0swe9z6XTaSeLZ1DmzEE2q8uv61l/hu4v9KyEUsa4Y4oQlB1MqWh8uF+MGHWMbNo2o9qm+FHdRmTs0NtJZAj6032knp1M4kyv3vX2DmJY3x1m51HMN/ziZ6mT18f69YNJxc8m5ZqM37sRP5v2kSC/5kfYnNuro7D4WTKTlstlp611e5QYa5rxc5B+Ro3TTMdCk3mYiT8yjYFngbEXrpS6pRSv86vX664AN5vNDjR4b7pMwZvWVKlbxqf/SYPpf+l02pFDjEof7JVSvvf4le93rTdQlBSf4rC8E2Td8MQg9DrrDj4ota2OGJtlWY5h6Qsn+rMG6UlPQS/3iGyy2DZosuWHd5AU/J5fr9cD5Qpqa33BMZVKqZ2dHd+BWJxR0GDv1b8McLLo6B38dJu2LMvlIIZtP5FNdxZ+8nnb0ZtWWGTyszk//OzCT7ZxdDFIz9Vq1dVPJDiTAEbwa2NJ71oulx15yuWyo0exOW8Q6NfPB2FZlstm/NLey3N1n+KXEte0HqP052HbLYig9jLRg5eo5PTamP6nT5yj0Jse/EkdJcW4PmDLBFEPur0Dulyjjzdi83oQE0RYfUZtF0GCxmHHCT31dJgO/DD12XpwJs/Qf/AR9B/OxE78bMmk729sbDj6lHb3+8FK7xtBOvLz10Hj2DBjtKn+9EUv7xhcLBadevrp1ISgsV2p8X2x3/3pdFrl8/k++x6nLmHxqV8beMcxvzJN2nKQPSjlXmj1s0UhzGb9nmsaY+g+ctQxxYt3gUdnHB8eFK+PWu6gGNm0bUaxTa9fjcfjfTFRkF362ZFS7hjTK6ssYohuwmQL8z3D2vm48w1v/Gw6L/TGopOIn8PKDRu/w2SOMn4O6yNhOvQSNucWTOPnnZ0dp76y4CQ+S59zTCt+HhQrjRunhY2FYfMwwcQfmcbA0yamlEEaIBJKqVTCysoK6vV6JGcLNBoNvPPOO4HpbYk5y8vLOHr0qO8e/1qthhMnToycMSIItt/dSyaTwfb2tlGGNRJMq9XCyZMnfftQp9NBoVDAD3/4w4mcn0emi2kfajQa2L9/P4rF4tSyuxJCyO1MqVTCfffdN1LWdkKihPEzCWOiWQXJ6LzzzjvOWRJkdGq1GiqVSuDBlH/zN38zkdSybD9CxuMHP/hBYL+dn5/H3Nycc6AmIYQQQobDtm189NFHXLQihNwW/NW0BbhTkIPiPv3007HeAGg0Gjh//jxefPHFkQ+jJ7f4/e9/7xwk/J3vfMd1QOX58+cBAK+99lpkz2P73d3ohye2Wq2JZta602m323jppZfw+eef45/+6Z9wzz33ALh5WOWvfvUrvPTSS5FnwSHTZ5g+JAfNRpmdiBBC7gYqlQo+//xz/OxnP5u2KIQwfiZGcKtgBMRiMde/0+l05FvPyOhUKhWcPXvWla0lm83i29/+Nn9lIpEh25Z0uIVpdDqdDt58801cuHDByZBiWRay2SyefvppLgzfgQzTh2RLgQ7DGUIIIeT2gvEzMYULV4QQQgghhBBCCCFkJuEZV4QQQgghhBBCCCFkJuHCFSGEEEIIIYQQQgiZSbhwRQghhBBCCCGEEEJmEi5cEUIIIYQQQgghhJCZhAtXhBBCCCGEEEIIIWQm4cIVIYQQQgghhBBCCJlJuHBFCCGEEEIIIYQQQmaS227hqlarIZfLIZPJTFuUyOl0OlhdXcXc3BwajcbQ98u9nU5nAtIF02g0ZrJNFhcXsbi4ONQ9tm2jVCohmUyGtoFt2zh58iSSySRKpdI4opLblFarheXlZczNzU1blDuGafmxWWBUX7obvkjKj8ViU/V5pvbR6XQm3jfHHbPvZnK5HGKxGCqVyrRFmSidTgdzc3NYXV2NtEw/267VaojFYqjVaiOVO8z9lUoFi4uLE/MDUfu0TqdjHNvdDURtl+PaHiGEmHBbLVw1Gg188skn2NzcnLYokWPbNq5cuYLNzU30er1pi2NMo9HA+fPn75g2uXjxIs6dO4d2u2107aVLl4yuJXcenU4H165dw/r6+m3VZ8lsMo4vnbQvWl1dxaVLl3D9+nW0220kk0m88cYbE3lWFHQ6Hbz77ruoVCoT65u365g9C9RqNVy4cAGFQgHLy8uwbbvvGlkUzGQyiMViRgumlUoFsVgMJ0+enKT4U2U3bDuMSqWCX/3qV2g2mxN7RtQ+7cqVKzh16hTjNUIIuZ1RM04+n+/7DIBKp9NTkGbyFItFBUDV6/VpizIUd1KbDNMG9XpdAVDFYjGy55fL5duu/cfBr4/vJsPo2+/adDqtbgNXSm4TRvWlk/BFSinV7XYVAFUulyMtdzfYjb55u47Z06Lb7ap4PO7YUz6fV9ls1nXNzs6OAqDi8bij13a7rVKplAKgCoWCb9n5fF4BUDs7OyPLN+3xyJRpjzuT8jeTfMbt2ldnLSacNXkIIXcPM/3GVafTwfr6+rTFIGRXefnll6ctwq5RqVSmviVsGH3fTW1DCABcu3YNAPD1r399ypKQO4HLly8jn88jl8sBAH7605/iiy++cL119eWXXwIA1tbWsLS0BACYn5933kZ8/fXXfcteW1uDUgoLCwsjyTYL4xEhXmYt7pg1eQghdw9/NW0BgrBtG9lsdtpiELKrLC8v3zWvssv5UHv37p2aDMPo+25qG0IImQSHDh3CoUOHnH8nEglsbW25rllaWoJSqu/e+fn5ick1C+MRIV5mLe6YNXkIIXcXY79xZdu2c0hkLBbD3Nyc75kFcoBpLBZDJpMZeGh2p9PBvn37nP3zcr5B0LPl7AO/AxdrtRoWFxcHymZSr8XFRefQwUaj4cgkf41GA6VSyfm3HK7b6XScAyYbjQZqtZpzwG0mk0Gr1RpaFr96yHkQXj1UKhXnsF8TfcnhlYPOk2i1Ws7BqrFYzOhwRz+d+X0nevM+wyuLbduuw0ErlQrm5uYcu2o0Gr6Hl4aV66VSqTj6yOVyRrYj8gXZTxCZTMZ5w3D//v2OXQl+tmz667B+mKvIY9r+g+xMt+fl5eW+8oL6fa1Ww4EDB9Dr9bC9ve18P6hdvf0N6LcrL1KW6Gx1ddWpT5i+h2kbXX9zc3OYm5vzPWfFa3+5XC7UB+gJKVqtFpLJpOtwalNb0w/X9v5JOw/qU6byh/n6Qd8H+TFdv4Ps09vvw9pjkK5t23ZknZubc33A4f8AACAASURBVA6RbrVajk0lk0nf9jPtq6a+dBR/YlLXQTJK39q/fz+AW3YfdHD8btjPIPvw6kjaUPAbnwG4zlDyjjEmsc0wmMRBfj4rk8mg0WgE+rugusl3er/36lxPMGHbtqMPsa+obU9PgOLnlwch9plKpfrq6NeGpgSNR95nDPI9QegxmDCsnwqzbbnm5MmTrgPTdb146+TVmd/9Xh3pdvnOO++46hO1XY7CoPHerz5BsYtJ3w8bl03rN06MMihe9x5Gv7q66hpj/OxSPpcyFxcXHd8zSJ5BtuPt78lkss/GBz0TuBm78OB3QgiA8TfIZ7NZZVmWarfbSqlbe8g3NjacazY2NlQqlVLdbld1u11VKBSM9uYH7eEHoFKplMrn86parapqtari8biKx+Ou68rlskqlUo5sGxsbRueHdLtdlUqlnDroZyuEnbcgcnW7XaWUUtVqVWWzWee6dDqtisWic69lWc61uv70/eNhOu52u6parSrLslz37uzsONea6Cufzzuyd7td57wI/XyBnZ0dFY/Hnet0PYTpVc4rAOCqs+hJbEQppeLxuMpms44sokM5u6JerzvypdNpR0dyTbVadZ6lE1aurl/RR7FYdMrWZdTrpOvIxH6CCDqDoVgsus77qNfrThuKXQTRbrdVPB53ySP10e9Np9O+/SWVSjnX6HYmutHbYth+77WbQe2qlHLaS9eP1M9bdqFQcNVHb9cwffsRdK34qUKh4Jz9IO2t29XOzo6yLMu3Db39Qb9Hb4d0Ou08r16vG9uaV/ZyueycIWOqexP5w9p80PdBfkzX8yD7bLfbrn4f1h5hutb9pchUrVadz/36h+japK+a+lLTNh7mPJhh/ImUG9ZHJm0/g+xDdCT2oY9fuv3J2UnesUqeo+vOJLbRPw/Tj4k/zOfzKpVK9elIL7/b7br0Kojt63UTG9XLA+CcKSV9Rp6RzWZd4/44Y1kQejspdcsXBZ1b5dVP0LNFn+OcvRMUx5iMjX7U63XHPqTcYf2UqW3rsaZux3oM6KVQKDjjYdD9St1qI7ED3S7l2ijtUtefqU8bZrwPi13C+n7YuGxav3FilLB4XWJq0X0+n1fxeFyl02lfu5R6xeNxx/6k3QfFEvKsINvR50R6zC1n3Jk806+NCCF3J2MvXEmA6irU46DT6XRfUBI24Mt9gxaudCRoEecnB4B6g3B9cAmiWCz2HdApA4/u5GXCLE63XC77HuzpNxgppZxBRXfGfoOCiY6D7pVrg/SlD6yWZfXJ7g2OZJDWabfbRgtXSgUHnsViUVWrVaXUrbbT9SL69/tM9CCTP8FrP6blBk1QpDy/+72Boon9+OHXhqJf7+HIMsCHlSlBlY63v0hZ3v7it3gpMkp7KXXrAGf9OSb93k/+Qe0atnjkLcNvgUvvN1EuXOmLTxKs67aSTqf72lDKDAvMAfcit7Sbqa1JwKrjtwg4SPcm8oe1uYlN+Ol5GPsc1B4mEyA/fxk0ufba3TB91dSXmrax6SRvWH9iunClXzsp+9Hv0eUpFAq+C8B+E+lBdfTa0TjjrpewugXZeFBMEBQbeW3H+wNVOp32/UwfD4b1L6Z4J/Te5w/6EUb6cFCSABnTgn4EMMGvXsP4nmHKNfVTw9h2kA+wLMu3jHQ6HfpDnIybQYtZfr43rP6mdmnq04Yd7/1iF++cxaTvDxqXw+o3ToxiGq97Y1lZ+NSv97aL31whbOFKr483VvTanTc2MnmmlE8IIWNvFdza2nK2UMgrrwDwxRdfONfs2bMH6+vrznUA8MQTT4z13EQi4fr3vffeC+DWoZ6XL19Gr9eDZVmuV5i3t7cBAB988EFg2efOncP6+rrrPtkuIfcDN89b+PnPf45er4fHH38cZ8+exdraWmC5Tz75pOvfr776KgDg0qVLA+tqouMwgvQlnD9/Hu12e+B2vlarhWaziUcffdR17zDnTrz44osA4HrNHLipczn3IpFI4MaNGzh69KjzmvGRI0cAAH/605/6yvzGN77h3DdIlmHLffDBB13/LhaLAMLby9R+TDlz5gwA4IEHHnB9nsvlEI/Hsb29HbhlsNPpYHt7u6/NXnvtNdchtm+99Rbi8Xif/o4ePQoAOHXqVF/Z99xzj/P/XvsCxu/3pu3qh9iXHOwL3LTTGzduuOSJEl0HohuxK9u2sb29jcOHD7vsYmVlBQDw3nvvhZafTCadZ0i7mdqaX9p00a8fXt2byh/W5qPaxCj26dcepnjtWQ4m9/pNL6Z9dRhfOkv+xJRJ2U8Q6+vr2Lt3b1+7JZPJkesQxbirE1a3t956C+l0OtJznI4dO4YbN24AuDmG53I5bG9v+/oD4JZfGda/mPLLX/4SgNsvA3D6wR//+Eff+1qtFp566ikUCgXnUHcvFy5cQCqV8h2LxmEU3zMMYX4qCts+fvw4er0eLl686HzWarUwPz8fqq+LFy+i1+vh4Ycfdn0+TrKGYe0yjGHHe7/YRe/Xw/R9v3HZpH7jxCgm8bqOxLJh7f3QQw+h2Wy6tsF+85vfHCjLIE6dOtVnu4cOHYJSyuk/ps/0+gxCyN1JJFkFZa/4+++/j9OnT/d9//zzz+PgwYM4fPgwkskkKpUKjh07FsWjA/nkk08AAOrmW2V9f4Oe32w2USwWA+/VOXToEAqFAtrtdt8kJIx9+/YBMAuEw3Q8LlevXkU6nQ6s89LSkhOoegOYYZifn0c6ncb6+rozOapUKvjBD37Qd22pVHJ0dPz48ZGfGVW5EpSEtdcw9mPC1atXAfgHinKQbFDAH/S5n8yWZfV9LnUe5TDOafR7YdYyQ0lmtnq97msT3sOJTTG1NcuycP36dde9sqj213/915HJH9bmo9rEJOxzEpj21WF86Sz5k1GJyn78aDQa6PV6Q4+/JkQ57obVTc70jJpWq4VMJoPjx4/j+9//PtLptPG9Udue2L13wi0LmH4/KLZaLRw4cAC5XA6vvfaab7m2baPZbA48O3VUpul7orLtxx57DPF43JUN7uTJk87iwSCCFhvHZRy79DKJ8X7cvh9Wv3FkNonXR+HQoUMoFotYWVnB/fffj1KphAceeGDk8kz6RtTPJITc2Yy9cLW6uoqnnnoKv/jFL7C2tuabhjiRSKBSqaBeryOZTOLw4cNYXFwc65BTU0YdHD777DPja+XNrpWVlcDDnf0w/WXQRMdR4J3YBvGHP/xhrOf86Ec/AnDrl/+zZ8/iu9/9ruuaxcVFnDt3DpcvX8axY8ciS8U+qXK9DGM/psjkb1JEPXGaZr+fVcbtO36Y2NqJEydw48YN5+DUVquF9fV1FAqFod7wCJM/rM3HsYlJTewngWlfNbWH29Gf+DGu/Qwiah1FPe5Owx/WajX8v//3/3DkyBFsbW25svmZErVeB024vYuUnU7HWbQa9Db75cuXAQCPPPJIpLIK0/Y947ZBIpFAPp9Hu91GrVaDbdvodDpD2XSUi0NR2OUkGbfv70b9TOP1YTl27Bja7TZyuZyzmDTuofmz+ExCyO3JWAtXnU4Hr7/+OvL5vJFjX1pawtbWFsrlMprNJt5+++1xHj+Qhx56CADw7rvv9n0n2Y+CSKVSqFQqvgGlNxtGqVTCI488gl/84heIx+M4cuSIcSAqgcCePXsGXjOMjkdlz549aLfbgZkZO52Os03m448/HutZhw4dgmVZWF9fR61W63t9uVKpoNls4tVXX430tf9xy5X2+s53vjPwumHsxwRZWPj0008Drwn6dUrepjl37pzv99IP5BfloGBhnF9Dd7PfC6Izvyw0k9oqOAjZlnX27Fnf70exC8Dc1g4dOoRcLodz584hFovhwIED+MlPfhL4BoOXYeUPa/NhbWKS9hklpn11GF86S/5kVKK2H52vfOUrAIAPP/wwImknO+4OqtuVK1cifdaPfvQjpFKpwO11YURte6lUyriOtm0jm82GLloBwG9+8xsA/dtfo2CavidK23766acB3PwR48yZM84xCWF89atfBQD89re/HVsGYVy79BLleB9F3zep3zgym8Tr4zA/P4+1tTXs7OwAgO+OCBMsy8L29rav//DWMapnEkLubMZauPLbTuDnoLypZnO5HFKp1ERfsd63bx/i8ThWVlZcAZZt23juueecLRF+PPHEE+j1eshkMn3pwfXgpVar4bPPPkMul8PCwgJ+/vOfo91u47nnnvMt989//rPr3xLASUDhh6mOx0X2lP/Lv/yLazDsdDo4ceIE5ufn8dhjjwG4eebCuL+GyJkLTz31lHPulfD555/3Xe/V3SiMW+67776LeDzu6CEIU/sxRc5G+4//+I++765fv458Ph947/z8PFKpFJrNpu+iqwT6zzzzDID+yYjIbxrk6kyi38vWKv3NDdu2+359/Na3vgXgZgCp9xfbtvH++++P/PxRSSQSSKfT2N7e7kurXSqVQs9OCsLU1lZXVxGPx/Hhhx9CKYUbN24MtW3TVP6wNh/VJiZhn5PAtK8O40un7U+kr43jg6OyHz8WFhZgWRaazabRJDWdTvf5C+8i3iTG3bC6ZbNZ9Hq9Phv/6KOP+sqSrWO6PfjZgp/ehqlH1LZ38OBB9Ho9ozfT3377bfR6Pfz0pz91fd7pdJBMJl1lyPbXSfy4N03fM6xtD2J+fh75fB7b29s4d+6c8aLRt7/9bQDAK6+8Emo7u2WXXqIc76Po+yb1G0dmk3h9FEqlkqvtFhYWnDf1RiGbzQJA35yo0Wg4RxWYPNO27aF2sxBC7mBGP9f9VjaOeDyuyuWyKpfLTqYqSaG8s7PjZOiQrCySfULP7OGHZCipVquqUCi40gjrmTyUupUhS8/iJVlP5HrJ6hGWoUTSD8u9kgpZz44h6YCDMr34pbP1S3MdlKVK6mGqY797dV1LGu5B+tJTLEvGK/hkb9Ll0dMJy2eDMgPpOvbLdKbUrawjqVRKVatVVSwWHVny+byjM9FrNpvtawcpH1qmJNNyJfNROp127pXPgrJi6VlgTOwnCD3LlrS3rndv+mNvu/ohKeDlfql3WJZLSX3uvU50putCnqH3S5N+L/eIHXl16q2bZESTNiyXy652LBQKTpuJjVuWpYrFoioUCsqyLFeq8SB9m7aNZAHS7UypW/ai24WkfZa+kk6nHb80CNGbnp5bMLU1/ZnyVywWVbFYdJU5SPcm8oe1uYlN+PkxpczsU+/3er382sMPPzvW7/c+S2TS5Tftq6a+1LSN/XxREMP4E+lHJuVO2n7kGq99iG+X/inp3uV5GxsbzjimZ4Or1+uqUCi4UttvbGyMPe76EVY3XT/SP7PZrG/2T4ltstmsU1fRgcQ4ut3I99ls1olRxH+2223nM2/mrnHGMj/EX+qxULfbVeVyuS9jszwz6E9/vp7NTvzzKPiNR0qZj41+6P5bZB7GTw1j22E+ICxLX9D9Un8Zd6vVqqPzVCrl6DtKuzSpj07YeN/tdn37qtfnm/b9QeOyaf3GiVHC4vVut+uU79fefnYpGf6kDPEN+jwlSJ6gWFjqLT7NO86YPFPqGuZjCSF3PmMtXCl1c6CKx+OuRRgJbHTnrAc/lmWFLlopdSuQk7LEMep/4nz1P91JV6tV59mWZRk7PgnkJTjwCzjlT5CBQw/ylLrl0PP5vOPE/WTx1kPuN9Gxnw5M9eVNhysy6oOJjh40yeJOPB5X+XzeaNFKKBQKgXYgEyvLslwLBDKoe3Wtt4NfHaUeYeUK5XLZZbPZbNb1vVL97a3LMMh+BqEHdd5FzY2NDadtRN+mk4ednR1XP/ALZLrdrpN6O+g6P9sJsjOTfi+Brkm76vd4+4MsxHnbSK+PXxsO0reffvRrg3xPkB+QdpBgUuQf1IZBuvXKFWZrYjtyjV+ZJroPkz+szcO+H+TLw+zTtD386jVI1373h407pn3V1JeGtbFpHXXCZPSro7eeOrthP4NkqVarrvFVJtcyidZ1qU+6ZRIqY4LIM8q4O2gR2sQf7uzsuCZ6Ozs7gennvWOZUspZHBDb0MuTxUS5b5AP0xl1LAtCtwGR32vvfn1xkG3p40jYD5OD8I5HgsnY6McwMeug/mNi24PGHZ2gH7sG3S82IN/l83lHJlmMEqKwSz95THzaoPHer6/6tY9S4X0/bFw2qZ+JzKL7oBhlULw+yDcF1dtrh34/9PvJE2Y7us/3jjMmz5QFXZN5IyHkziam1AipYchQlEolrKysoF6vM1MGIWRXke3RP/vZz/rOdrNt20mSsFsZHwkh5jB+IIQQQggB/mraAhBCCJkccvizX0KCRCKBe++9Fw8++OBui0UIIYQQQgghRnDhaheQw1U//fRT/mJKCNlV2u02Njc3MTc3h0ceeQRf//rXAdw8bPvXv/41HnnkEfolQmYUxg+EEEIIIQC3Ck6YWCzm+nc6ncbW1taUpCGE3G3IdsBz586h2WwCAOLxOHK5HJ588klOhgmZURg/EEIIIYTchAtXhBBCCCGEEEIIIWQm+f+mLQAhhBBCCCGEEEIIIX5w4YoQQgghhBBCCCGEzCRcuCKEEEIIIYQQQgghMwkXrgghhBBCCCGEEELITMKFK0IIIYQQQgghhBAyk3DhihBCCCGEEEIIIYTMJFy4IoQQQgghhBBCCCEzCReuCCGEEEIIIYQQQshMwoUrQgghhBBCCCGEEDKTcOGKEEIIIYQQQgghhMwkXLgihBBCCCGEEEIIITMJF64IIYQQQgghhBBCyEzChStCCCGEEEIIIYQQMpNw4YoQQgghhBBCCCGEzCRcuCKEEEICqFQqiMViaDQa0xaFEEIIIYSQu5KYUkpNWwhCCCFkFkkmk3jmmWfw3nvvYWtra9riEEIIIYQQctfBN64IIYQQHyqVClKpFI4dO4br16/zrStCCCGEEEKmAN+4IoQQQnxIJpM4e/YslpaWUKlUcPbsWb51RQghhBBCyC7DN64IIYQQD5VKBclkEktLSwCAXC7Ht64IIYQQQgiZAnzjihBCCCGEEEIIIYTMJHzjihBCCCGEEEIIIYTMJFy4IoQQQgghhBBCCCEzCReuCCGEkBHpdDpYXl5GMplELBZDMplEpVKZtliEEEIIIYTcMXDhihBCCBmR5eVlAMDly5fR7XaRSqVw+PBhdDqdKUtGyN1DpVJBq9UaqwzbtlEqlWDbdkRSEUIIISQquHBFCCGEjMjW1hbW1taQSCSQSCTw/e9/HwDw5ZdfTlkyQu58bNvG4uIiPv74YywsLIxVViKRwH333YdMJjP2IhghhBBCooULV4QQQkhE/PrXv0YqlRp7Eh0Vtm1jeXkZc3NzzlbGaU3Kc7kcYrGY89doNAAAjUbD9fnq6upU5CO3F51OB/fffz9eeOEFvPbaa4HX6HaXyWQcu/Mjl8vhhRdewIEDB7h4RQghhMwQXLgihBBCIqBSqeDDDz/E1tbWtEUBcHPSvm/fPnz44YdoNptQSiGVSuHAgQNT2Q5VqVRgWRYAYGNjA0tLSwCApaUlFAoFAEA+n8fzzz+/67KR249sNot///d/Ry6X8/3etm2kUikAQLFYRDabxfb2Nvbv349arRZYbi6Xw9raGh5//HFuGySEEEJmBC5cEUIIIWNSqVTw1ltvYWtrC4lEYtriALg5sQdubmecn58HAPzwhz9Er9fDtWvXBt7barWQyWQGTtxt2x56W5XI9OCDD7rK2dzcRDqddrZdjou8xTXo7Rpy+1IqlZBIJHD06NHAa86cOYO1tTVUKhUcO3YMlUoFGxsbAIAf/ehHA8vP5XJIpVJ47rnnIpWbEEIIIaPBhStCCCFkDGZx0apSqaDZbOLs2bO+Mn366aehZVy5cmXg4tVzzz2HK1euDCXX008/DQA4f/6889m//du/IR6P47//+7+HKovcvbzxxht4+eWXB15z77339r2NdfToUViWhXa7HfqM119/HZubm9wySAghhMwAXLgihBBCfJAsY8lkErFYDHNzczh58qTrmkajgeXlZdei1erq6tTf9Hn55ZdhWZazHU/44IMPAAB/+7d/O/D+hYUF/O///i+azSa+973v9X2/vLyMzc1NXLp0aajzvObn55FKpbC5uQkAOHnyJCqVysws+tm2jdXVVec8sE6n45wTJjZQqVSmLeZdTaPRQK/X67NtL0FvYyWTScTj8dDnzM/PI51O43/+539GkpMQQggh0cGFK0IIIcRDq9XCvn37cO7cOZw9exZKKezduxfPPvssOp2Oc92RI0fQ6/Xwta99zTkA+vXXXzd6hvdQcpO/UqlkJHu73caPf/zjvu8+++wzAMA999wTWs7S0hLK5TK2t7exvLzsfL68vIz19XWUy+WRDqH/wQ9+gHa7jdXVVbz00ku4dOnSTCxaATe3l33zm99EtVpFu93Gb3/7W3zve9/Dk08+iW63CwAuXZDd54MPPkA6nR75ftu2A8/F8vLoo4/i6tWrIz+LEEIIIdHwV9MWgBBCCJklbNvG448/jhs3buDy5cvOosqjjz6K7e1t/PGPf3TOjLp+/frIz1laWoJSKhKZdeQNkWeffRbPPvus7zUPPPCAUVkywT98+LDzmSxamU7+vXz3u9/Fs88+i9dffx31en1mMjACwLFjxwDAeavqlVdewenTpx0Z9+7di+3t7anJR8aj0+mg2Ww6b/wRQggh5PaAC1eEEEKIxpkzZ9But1Eul11vAr333nuIx+OhW5Smjbwh4l0U63Q6sCwLlmUN9YaTd/FqY2Nj5EUrALh48aLz/6YLaLvNxx9/DODm22H6wtqVK1ecTHVkOjz88MN44403Rrq3UChgY2PDWXgmhBBCyO0BtwoSQgghGqdOnQIAPPbYY7BtG7VaDZlMBleuXMHa2tqUpQvnypUrvlupZMFIMvuNyjiHVbdaLRw+fBjFYhEA8O677wZea5oZMJPJ9G2p3L9/PwBg//79vlsuw7hw4QLi8bjrnKROp4Ner4eDBw/2XW+6jZOMzwMPPIBer+fasmtCpVLB3NzcwEyEXv70pz/hq1/96rAiEkIIISRiuHBFCCGE/AXbtp2MY1/72tdw//3348SJE9izZw+azWbfm0alUsloIcSPSZ1x1ev1fD8/ffo0AOBf//Vfh5KzUqng8OHDyOfzyOfzWF9fH+mA8k6ngwMHDqBcLuPYsWOIx+OOTH5I5sOwN9y2traglHL91et1AEC9Xu/7zmR7ZrPZxN69e12fSQbFf/iHf3B9Lgt5Dz/8cGi5ZHwSiQTy+TzefPNN43tarRbOnj079MLz5uYmDhw4MKyIhBBCCIkYbhUkhBBC/sK1a9cAAPl83miS+9FHH418UPSkzrgCbp7HpdNoNNBsNpFOp4c6U0pftNL1IdsGTbcM2raNbDaLXC7n3JPL5bC+vo5Wq+Ur09GjR4d6OyYq5A0vrw5l+6B3QWthYWFi7Uj8efHFF5FKpfDII4+E2mCr1cLKygr++7//e6hnlEol3LhxA9/97nfHEZUQQgghEcA3rgghhJC/IGcuxePxvu9yuVzf9qRKpYKtra1dkW0YJHugcPz4ccTj8aHeOKnVar6LVmtra8jn8zh8+LDxm1fPPfecc6/wrW99C8Ctw+R15ubmprb97oMPPgDQ/wbVhQsXYFmW63ykXC6HWCyGTCazqzLe7czPz+PSpUtYXl7GyZMnA6/TF62857qZ3Pfzn/98ZjJeEkIIIXczXLgihBBC/kIikUA6ncbm5qazSNVqtbC4uIi5uTln0aLVajnb98LOYNptstksKpUKOp0ObNvG8vIytre3sba2ZnwodavVwlNPPRX45tna2hpSqRSWl5dDz7xaXl7G5uYmXn31Vdfn99xzD4CbWQpt23Z912w2AUxn+91HH30EoP/g+Gaz2Xcwe6VSgWVZfW9nkcmzsLCAS5cu4fTp01heXu77vtVq4cCBA9izZw/OnDmDUqnk/GUyGdx7772+5cp95XIZhw4dmnQ1CCGEEGKCIoQQQohDt9tV2WxWAVAAVCqVUtVqte+6crmsZnEY9cqfzWbVzs7OUGXs7OyobDYb+px0Oj2wbK8c3nvlu3Q67bqvXq+PpVu5v16vD31vPB5XlmX5lrexsdF3/ajPIdHQ7XZVsVh02WG73VbxeNyxL7+/brfbV1a5XFbxeFyVy+XdrAIhhBBCQogpxYMZCCGEkGEplUp47733ZnKr4O3O6uoqrl69OrJuG40G9u/fj3q9Hnq4uwmlUgkrKyt95dVqNfzzP/8zz7i6A7BtG2+//Taef/55bg8khBBCZgwezk4IIYSMwLlz5/DEE09MW4w7kgsXLuDgwYMj3x/1wfeyfdC7CPab3/ymb/sguT1JJBJ47bXXpi0GIYQQQnzgGVeEEELIkNi2jWaziYceemjaotyRNJtN/MM//AMqlYrxAfCT5MKFC77ZI69evYrFxUW0Wi2srq5OQTJCCCGEkDsfLlwRQgghQ3Lt2jUAwN///d9jeXm5L9sgGY9UKoXDhw/j448/Ri6Xm6osnU4HvV7P9wD2PXv2YH19HSsrK3j++eenIB0hhBBCyJ0Pz7gihBBChqTT6ThbxNbW1qa+uEIIIYQQQsidCheuCCGEEEIIIYQQQshMwq2ChBBCCCGEEEIIIWQm4cIVIYQQQgghhBBCCJlJuHBFCCGEEEIIIYQQQmYSLlwRQgghhBBCCCGEkJmEC1eEEEIIIYQQQgghZCbhwhUhhBBCCCGEEEIImUm4cEUIIYQQQgghhBBCZhIuXBFCCCGEEEIIIYSQmYQLV4QQQgghhBBCCCFkJuHCFSGEEEIIIYQQQgiZSbhwRQghhBBCCCGEEEJmEi5cEUIIIYQQQgghhJCZhAtXhBBCCCGEEEIIIWQm4cIVIYQQQgghhBBCCJlJuHBFCCGEEEIIIYQQQmYSLlwRQgghhBBCCCGEkJnktly4sm0blUoFi4uLKJVK0xZnIiwuLmJxcXHaYoyN2qhQWQAAIABJREFUbdsolUpIJpNoNBrTFgcA0Ol0sLq6irm5uZmRabeYVL/pdDoz185R0+l0MDc3h9XV1dBrJ+2fbNvGyZMnkUwmZ9oH1mo1xGIx1Gq1aYsyNo1GA7lcDplMZtqiOFQqFWQymZmSaRRs23Z8ciwWw+Li4h1hM3crnU4Hy8vLmJubm7YoA9nN+GRavnBSfutOiMN3K26RsToWi838mO1Ho9HA8vIyYrHYtEXpo1aroVKpDHXP6uoqOp3OhCSKnkn4qVarNbQdNhoNnDx5MpLn363s9tjYarVui7F4GGZi4SoWixn/lUolXL58Gb/61a/QbDanLToJ4cyZMzh37hza7fa0RQFwcwC4cuUKNjc30ev1pi3OrlKpVCbWb65cuYJTp07NTDtPk0nqWbh48SIuXbpEfe8SjUYD58+fx+bm5rRFcajVavj888+xvb09bVHGJpPJ4L777sONGzdQr9fR6/XwX//1XyOVValUXDGDd6FZFsfkj0RLp9PBlStXUKlUZn6MvXjx4kzFJ1EzSb91J8ThuxG3rK6u4tKlS7h+/Tra7TaSySTeeOONiT0valqtFv7whz9gfX192qL0sbq6ij//+c/I5XJ931UqlcCFntdeew1vvvkmWq3Wbog5NlHPoyqVCv7nf/4Hx44dcz7z/niUTCb7FgSXlpbwj//4j1heXo5EjiBs23YWW+SHrJMnTzqf3650Oh28++67uzY2djodXLt2Devr6zM/Fg+FmgEsy1I7OzvOv+v1ugKg0um081m321X5fF4Vi0XXNfLv25l8Pj9tEfqIUqZisagAqHq9HlmZ4zKLMu0GUfWbcrncp7u7Vad+7IZ/mjUf6GcT0yZqmbzj0iwwizINQ7VaVQBUu92OrMxsNqsAqFQqFfi8VCoV6TOJm3Q6rWYkxBzIsOPWLMZrYUTlI7x1n7UxaBQmGbd0u10FQJXL5cjL3m1mrT9vbGyoQqHQ9/nOzo7a2NhQ8Xh8YLt2u12VSqVcc89ZJio73dnZ8R0X0+m0SqVSqlgsqnw+7+jPT8cbGxsT84PdblfF43HX+Lyzs+OM6bdzrCNMsi/5xbyz1nfHZSbeuDp+/DgWFhYGXpNIJLC2toZ77713l6TaHSqVysy9sjqLMpHZ4uWXX562CGTGmEWbmEWZiJtPPvkEADA/Px9Zma+//jqAm3GD3/Pi8Tg2NzcjfSa587mbY6O7ue6jcu3aNQDA17/+9SlLcmfRarXw7LPP4umnn+77bmFhAUePHsVPfvKTgWUkEgm88MILePzxx2Hb9qREnSls28bjjz+OF154wfV5o9HA/Pw8PvzwQxw7dgxra2u4dOkSgJtjqbffHz16FBcuXJjItsG3334bvV4PW1tbzvi8sLCASqWCfD4f+fPuNO6GmHcmFq78XvMM4ujRoxOUZHeRvaezxCzKRGaL5eXlO3ZrBRmNWbSJWZSJ7A7z8/NIpVK4cuWK6/NWq4WVlRVcunSJi1ZkKO7m2OhurjuZPVZWVpDNZsf24blcDu12G2fOnIlIstnmzJkzuHHjRt+c+9NPP8Xa2prrs4WFBWeh6I9//GNfWc888wxeeumlyBf9rl69Gvjdiy++GOmz7jTulph3JhauxqXVaiGTySAWiyGTyfj+KiQH20V5OGKtVsPi4iJisRjm5uawvLzs6sS2bSOXyznf53I5x2HUajUcOHAAvV4P29vbjuzArYMQ9cPUvIdINhoN59mZTMZ5rr5H2Xu2h3ffsFfmQTKJnqU+sVgMuVzOd4+4V9cfffTRWHr2nnOm71n3nlUSVkeTZ0j5jUZj4FkoUduUV7/eMuVAbjkM1cTuvTb6zjvvjCVjJpNxzjrYv39/X3vozxXd+AW8prZkSq1Wcw6gbbVaSCaTmJubc3Tit2fee0iunIkg32cyGVfdKpVK4CG3g/QcZEelUsn5zFumbltR6GcQ4+omyCa89irobaWfqzA3N+ecqdBqtRx9JpPJvrrr+vaTOcxOTfqu10ZNDuUfhN7e3jbXvxNZwuoY9gy9fPETevlB9YzK1vx0rPthkXVlZQUAAuUblYMHD6LX6zk6s20bBw4cQLlcDn3D+3ZAP+RV7MPri8P8iDfWKJVKTl+M8td0vY/79SMTHzQKfjHaoLeGdD+Xy+WMY6MwPQf5Qn1MER1If/EbV6fht8Lqrj83KB7RDwi2bdu5Tm/jQW01yhjqPbNnUBypyzAobjFF5N2/fz+AW2OQLqOfzXvPFBqkN++hy/oB8NLm3jmIlD/KOOFHWMzqJ4M+DxqFVquF7e1tfOMb3xi5DJ10Oo1Tp06NVUbYWC0JoaRfm9hZ1PMoAHjjjTewd+/evs+DXgj5u7/7OwDAV77ylb7vHn74YfR6PVy8eHFsuXS+853vAIBrXivMz89jz549ffeEzcUBs/ESCI9bhh0zvf3cr15RYDo3C5N3UvFgpEx7r6IffmdcBV2TzWZVPp9X9XpdbWxsOJ/p5PN5lc/nVbfbdc7Kwpj78svlsmsPrjxbl1lkU0qpdrut0ul0X5289+zs7DhncOjNU61WVSqVUgCcs77q9boqFArOZ4VCQW1sbKh6ve5cW61WXfJYluXILHumNzY2BsokclmW5eydrdfrKh6Pq3g8rrrdrnOd7I2W60QvGGNvdrvdVpZl9dVHqVv7oWWfumkd/faLyx5q/bN2u+3s9daZhE3F43GVzWadMkUeqVu9Xnc+M7H7crnsqru02bhyBu21l891+xS96Po3tSVT5EwD/OXcGulnIqOcZSAytNttp3+IDDs7Oy47Et3pMkr9vH3DRM/dbtexYR3p63qZUhf92X7tG8X5IlHoRil/m6hWq469iozetsrn86parapqterq4/K5fq2ws7PjKlP8Qzwed9UtyE5N+q7UWa7T9TLOGQsik98ZE4VCwRkvTOuolP8Y4ienjBXeekbZFwU5L8M7PvrVW3QSNaIHOaMjnU77ntdxOyLjkt5vpf94dR7kR9rttivWKBQKzvkYYuvjnP8iPrhQKDh/4hf181FMfNAoFItFVyyi27Z+tpk+bsnYJbpMpVKufuDXr0z8dZAv1P2B7gtFTp1p+q2gupvE4WJn0vZyrS67SVsNM4YqddP+9FhQnukdR03jllEQ/XjtWOJWv/YUHzVIb2Ir+jlE0nel38k5ROVy2XWt2PMw44To06v7sJhVKbN50DCYnvVkep3Ud1RfZzJW6/GNiZ1NYh41SrxYKBSUZVm+38n5bd7+NC4yHgBwjXFBmMzFTcZLpcLjlmHHTKmLlKn380nEPEE2r4/Fg+SdVDwYNbf9wpU3EE2lUi6HUa/XfTveOIYjg473YFd9sizP0Bdaut1u6MKVtywdMUrv4o10QN2w/JxUOp3uczJ+jicoCPAeMCnyyDOkQ/tNFMcNQmWy7HW6sniny2lSR78OHtbp9WdOyqZ0Jy1t6PeZn93rzxZH7dVXkB6HIWzhymvzXv2b2NIoAFCWZbkCMyk76FBZsfNisdg3qS4UCn11DBoQTfQcdECit0wJ4nXS6XTfZ1EsXEWlmyCbCJLRbxFDdOa1bb/+5x1w5fl+n+kymfZdWQDVabfbkUwAJaj1BgLpdNr5zLSOIrvJuBI0JkTdF6UdveNj0OL+pBaulLqpa8uyXIuCdwJ+kz/vBMzUj0j/0u1RxvJxfIuU6w3kZQI9jH8eFumrXtsW2/T6W7/Jo74AIPjJNK6/9vOF0pb6BGLafmuQTwmLw5Xqtwf57zBtZTqGehcTBL8fIU3jllEIWrjK5/O+7SmxvH59kN707/S+K/r02pTfeGg6TujPEkxjVpN50DB451lBDLvANaqvGzYe8bMzXR+TmkeNUk/LsgYmFpjUAox3gSdIDtO5uMl4OUzcYjpmyo813ljPbwE+CsLmsH7y6n11UnOzqLnttwp6D2tPJBKutI/nz59Hu93ue01Y8HuVLozLly+j1+vBsixXmZKW/IMPPgAApFIpvPLKK84zEokEHn300aGf5+Wee+5x/TuZTDrlD2Jra8t5VbhSqWBxcREA8MUXXwy8z7ZtbG9v4/Dhw676yhaP9957DwCc9OVLS0uu++V103HI5XKwLKvvld7//M//dB3QOGodh2ESNpVIJHDjxg0cPXoUtm2jVCrhyJEjAIA//elPfdf72b3OxYsX0ev18PDDD7s+341DQnX7FLlE/6a2NCrJZNJ5pmwHOnfuHNbX113Pk1f4pc8+9NBDaDabrteCv/nNb4Y+bxJ6PnbsGG7cuAHgpi3lcjlsb29PJJ3tJHUThtdmRWdhCTiWlpaglMLCwoLz+rekF//yyy8H3mvSd1utFprNZp+vjupMpOPHj/e9Yt9qtTA/P+/oZJw6mjKpvvjWW28hHo/36Uu2I4y7LWMYstks2u02Lly4gJ/+9Ke79txJ0ul0sL293Wefr732mmMzwPB+RO+P3hhjHPRtmYlEAv/+7/8OAPi///s/AGY+aFjkzJoHHnjA9Xkul0M8Hsf29nbflsEHH3zQ9e9isQgAziHFQUThr72+0OsDZ8FvDSIsDtcRe5D/jtJWYQT5SL9tUsKguCVKbNvG+vo6UqlU33c//vGPAcD3SAev3nR0+5H2DpsPjItpzDqpeZDXXkblvvvuG+v+YcdqPzvTmeQ8CrgZz5lw8uRJpFKpgds6LcuKRCYvkoytXq8jnU6j3W7j8OHDWFxcdG1bM5mLm46Xo8QtYWPm+vo69u7d29fOMmffbfzklb466blZlNz2C1dhXL16Fel0Gurm22V9f17nYIJkQQoq89ixYwCA06dPA7i531TOhJHvpoXsrX7//fcd+cKQzCj1et23vltbWwCAzc1NpNPpicn+zDPPoN1uOwtTEsx4Hc0odRyGSdiUUCqVsG/fPgA3J7ij8stf/hJA/+A3bUxtKUqazSaKxWJgewHAoUOHUCwWsbKygvvvvx+lUgkPPPBAqP4mpWc5L+T48eP4/ve/P7F+NUndTBI5N+DgwYP/P3t3/9rGle8P/C3YX7fdsfLT3t1SNArc0haXjZyU1imkEEvrltKlzVdyW0ogoY6UEuiTnMgJJWySjbRpLwTih8UBU7rVuEloKdeK5UAKK21ZJ0qQaEsX1hpCG/qTJ2p3/4D5/pB7JqPRSJrRgyU77xeYNtI8nDlzzpkzH82cg2eeecbxbDNO6q7o9FiDkZ2ye/duSJJUNfvL7OxszTgTrR6jU92qi4VCwbZDKzqIvRg89Pjx47Y3CWIsmlZ+bOgVu4Fy61mvdsQNESASHWYnbZBbYoBfux8QRPCiWT6K8uokeNHtfO6HdqtbOnGurH79618DAG7dulX1uaZpXbvZdkq0u3bpEHVjI83e2KzP2un7IFHOOxWY68SPuZ28VnfrPkoEHpz8KFEqlfDZZ5/h3LlzDZfrdgBmeHgYS0tLWFxchCzLKBQK2LVrl/EDqpN7cadtR6f7Lfl8HpVKpSNB2vXQi3uzVm36wBUArK6udmW7zS4ug4ODuH79OtLpNFZXV7Fz586ezswyOTmJ1157DZcuXcLU1JTrAWpv377ddJlu5TUA48mqDz74AMDdX+refPPNqmXaPUanunGcQ0NDuHDhAlZWVhCPxztyQe3XDpCTstRJ1g6snXg8jnK5jEgkYgRpnA5K2Ml8zmQyeOKJJ7B3714sLS1hdHS0Y9u20+286TRVVbF161YAd+thJBJp+pSWmdO6260y6vV6EY1GUS6XkclkoGkaVFWtaqvaPUY3unGchUKh49tshbgpFjdWVt9++y2A6sCzpmnweDwNy3cqlbIdoNpMPIUgnvpdb+vdjrTDSRvUCtEZ76b1yudet1vd1slz5fP5kEwmMT09bQSlFUVBoVDA2bNnO7afdjSaPW2jcNJn7fR9kAjq9EvfthvX6m7cX4gASrP2QdM0TExMYGpqqutP7dmxGzB8dHQUKysrCAQCqFQquHjxYtX3nSoL3ei3dOva1i0b4fqx6QNX27ZtQ7lcrjuDSCsFXjxqaa08wN1Kb50ZJBKJYHV1FdFoFNPT0x2ZLcctVVVx6tQpRKNR18Ec8Uju/Py87feioZEkCeVyuSszJgD3bvgKhQIymQyWl5erbjjaOUY3ulGmRKeq3pMBbv3qV78CAPzzn/9se1ud5LQsdVIgEICiKLbl0ro/n8+HqakpFItFAMD+/fsbbrsb+Xzo0KGmj2h3SjfzplvOnDmDSqXS0qtfTuqu6HR+/fXXbae1HhGEP3v2LM6fP2+8YiG0c4xOdasuil8t6wV+1vOpn2w2i0AgULdNHR8fr3mqR1zX27mGKIqCV199FadPn+545188TXLhwoW6+wbWtx1xQ3SMRTlw0wY5JZ7CFoFJO82eGBXXcTHLVT3rkc/90m51QyfOlZ19+/YhGAxi79698Hg8OHr0KBYXF3sewBX199q1a3WX2QhPabjts3b6PsjtU3j1iKFdWtXpa3W376O+//77ut9pmoZXX30VyWTS0SvGrb7K3cjPP/9s23fwer04fvy4sQzg7F7c6fWy0/0WMRPj9evXXa3XK724N2vVpg9cibFYXnjhhaqLvqqqOHv2bEvv/+/YsQOSJGFiYqLqZGqahoMHDxqPN1t/VRAN2w8//OB6n+2ya+SdNoxerxfBYBDZbLZmmtFUKmV0mETH7dixYx1Isb13330XAPDaa6/hnXfeqfqunWME7j1ib444a5pW8+tHN8qU3cXk3//+t+vtCC+++CIA4P333+/aBbAVTstSJ+3ZsweVSqVmim4xLojYt/m7wcFB46mYRtzks+iMWqcJt7LbZ7fOYTfzplvsAsN248DZcVJ3d+/eDeDu+ATdeqrM5/MhGo0im83iwoULNTe97RwjcLeTZW23rDeG3aqLb7zxBoDajo44JmuQTvwi2ekyLs5vvSeexPTU5unbI5EIDhw4AADGGA+tvEb4888/Y2lpCYcPH24h5Y35fD4EAgEUCoWaPBav8QLr2464MT8/j0AgYAQGnbRBbr3yyisAgD//+c8134mb52YuXrwISZKM9qCe9cjnfmm3usHNuXJ6DQXuTg9/6NAhrK6uQtd1rK6urnvQSvQlzX05n8+HYDCISqVS8yO3WP7ll19el/Q5uU7U47TP2un7oG4F9Vp9xbbda7VVt+6jmh2fOWhl/dGmUdDCGtDJ5/Ntt39iHEQrUb7EuGRO7sWdXi/d9luaGRwcNF5vtNbzftSLe7OWuR7OfR2IKVXNM4RZiRkBzLN+mKfLNc/mYJ6dQMy6gg7NcifSKWaRMY+8j/8biV8cg5h9xjxrgVhfTI0qjsM68445X8yzAJiP2bxd84wsYqpa4O70oul0Wk+n08b2xFTUYl92aRLTLIttiKmG7WZYE+cll8vp6XS6ahpY64yIrRB5bS0bbo7RbsYg82wsi4uLejqd1pPJZNVUomL9TpcpMcOD2Ld5v9Fo1Ji1xzx1s/m47cq9mGlQbHNxcdHYZiAQaDhbSCPmWSZEPouZYqx5Kmb3sc7216wsuSVmdjFPfy2Yp9cVdULMemSum4FAwDh/Yrpc82xJ5n2Yy57TfBZ1UtSNZDJpnHfRdpjTKr4Ph8PG+RXl0nwe2pn5qFN5Y1cm6qXRrkzo+r2ph61lW6RPtB3mtl+0UWKZmZmZqinW7dLkpO6KfYi2xDx1tfjMWs7cajQrpNNjrFcmzTPhiJlXzVM7izrajbqo63pVWnX9bpkJBAI1s8eZ2652p563Em1/vfoh2ntrmx0IBGpmSbMS7XMzTpdzS9QhUV/Efsz566QdMfc1zOVZlJV22haR/4lEomoqcEmSbKcMb9QGtULUoWg0avSBRD2ya3eCwaCRLvGZ9Rpp1zdqp70W9deaJru+Xq/bLbtjd9oPN08/b9dHcnqunF5Dze2imFEsHA7ryWRSn5mZMbbppt/SinptkOgrS5JUM/W8ucw1yrd6/T6RdmveiXJgbtucXifs7kmc9lmd3Ae5IdrtZrOc2dUhO+Ka1yqn12qRN+bza1fOunkfVe/aLtpgUUfMf9Fo1HZGXlHHzMfTieuGaCeDwWDVcabTaV2SpJq0OLkXd3K91HVn/RY310xRR8x1TJR/sZ9cLmecc1mWa2aNbiXvzH1ec3kyb9suvd3qD3ZaXwWuREWw/lkzTTQA5j+7da3TtYuKb74Ja8fi4qJR0GVZrmkgxVTc5uOwFkpR6USBrXccdnli97n54mH+E/sSF0txUREdRHPjY02TUCwWjYuB2Ib1ol4sFo39i8ZD3Pi2Giixy/d6NxZOjrFR+bJbXzRw1nPX6TIlLoBi6ldrY+a03IsG2zqlrLjgybJc1ZFwy3yjYe6cWPNUNKLWcqjrzsqSU3b7sevkiZsm0VibLzrWemO98DU6Fjf5bD3Huq4b+xLpKRaLRrkKh8P62tqasZ65TNZLj1vt5o3YhrVMWNNYr0zkcjlXZdt8sxEIBPRisWh0EESgvl6azMfUrO6aOxii7RYdp3aDVoL15sKcn82OsVG5t1s/l8sZ5c68z07WRXP6xVTQorxby4xd+u3qbius5alRx9uabgC2NwZ2abVrd63H2K1OX7FYrOp/WPffrB1x239wS5RRkQZRd+qV90ZtUKtmZmYc7T+dTlcFz8LhsO0NhF3fqJX2ulFb2Kh/0st2y3rsTtvsc+fOOaorTs+Vk2uort8NXJjPqflPBEXt8rrRtd6pevcy5uMul8tGuyvOp7ndadS3q/ed2zLl5DrRKB3N+qy67uw+yC0RjHST9/XY9WfccHKtdlrORH3u1n2U2KdVvXoi/uyuhzMzMzU/LojjbvbDTyPiPlr8CCDSYHeNM++30b24rje/Xup6835LK9dMcS8gtid+2BZBSZE28b1dX8Upa5/XaXrNdakb/cFO8+h6i9O2EBEREW1Ak5OTuHHjRtVsOZlMBs899xzW1tYajtuSSqVw9erVpjPtOF2OiDqnVCphdnYWU1NTNd+pqopEIoE333yz72ZdJmdKpRKeeOIJlMvllobmEDKZDA4dOoSVlZWeDES+3jRNw44dO3DixIm2x+QbGhrC/v37a2ZDptbl83l88skntu0W3bPpx7giIiIiMlteXsa2bduqPvv73/+OYDB4X9zEEG1W+/fvrzu5gs/nw8DAgDG2DW08g4ODSKfTSCQSbW3n/fffx6VLl+6b9t7r9eLSpUv44IMP2hqHKpPJQJZlBq067JNPPjHGcab6ftHrBBARERGtp0KhgHfeeccYODUSieDGjRtGh352dhYPPvhgy79Ma5qGmzdv4tq1a1BVta0nA4jIuXK5jCNHjuD777/H008/jQceeADA3cHPP//8cxw5cuS+CVZsVqJdVhSlpTZ6cnISx48f7+oM5P1ocHAQc3Nz+PDDD3Hy5EnX65dKJfzv//4vzp0714XU3Z/y+TwuX76Md999l/0EB/iqIBEREd1XhoaGUCgUkEgkjA68oijGLFiHDx9GPB63XbfZK4ChUMh2qnB2t4i6T1VVnDlzBsvLy8asj7IsIxwOY9++fbw53ETy+Txu377tKng1OzuLJ5988r4LWpmpqoqLFy/WvcbZKZVK+Mc//sEnrainGLgiIiIiIiIiIqK+xDGuiIiIiIiIiIioLzFwRUREREREREREfYmBKyIiIiIiIiIi6ksMXBERERERERERUV9i4IqIiIiIiIiIiPoSA1dERERERERERNSXGLgiIiIiIiIiIqK+1FbgStM0KIqCoaEhpFKpTqXpvpbP5xGJRBAKhdZtn5OTkxgYGICqquu6LnUfz8/GomkaUqkU/H4/8vl8r5PTV4aGhjA0NNTSuplMpmvtqqZpRj3zeDwYGhpCJpPp+H7WS7/mczPsi9TKZDLweDwbujy61U456GX5vV+pqopYLIaBgYF126emaZidnYXf7+9pe1EqlVzvf3Z2tu/6Br04h81strbP7XlXVRWpVAqapnUxVXfvWYeGhuDxeDAwMIBYLFZ3n6qqIhKJwOPxwOPxIBKJoFQqdTV93aSqKgYGBjA5OdnrpNxX2gpcrays4PPPP0ehUOhUeu5r+Xwely9fxsLCQq+TQkQ9cP78eVy4cAHlcrnXSdk08vk8vvnmm661q6FQCA899BDu3LmDXC6HSqWCjz76qCv76mfdzudGFEVhX4TaKge9LL/3K1VVcfHiRSiKgkqlsm77vXLlCr788sueXmcVRcGnn36KeDxe9XkqlTJ+BPH7/TWBl/HxcXz77bdQFGU9k1tXr87h/ULTNEQiETz55JMYHh6uu9zQ0FBVwN3n82Hfvn04ePBg14JXmUwGe/fuxdLSEtbW1hCNRjE9PY3bt2/XLKuqKgKBAI4cOQJd1zEzM4Pl5WX84x//6EraaBPT25TL5XQAejKZbHdThnQ6redyuY5tb6MBoAeDwa5sOxqNdmW7VuVyuaNlwol+KDfrlb90T6/Pe6fLejKZ1AH0vCxvNm7aVaf1eHFxUQegl8vldpK2qXQjn53oRl+ENh435cCu/HWz/7VZdPqaFwwG9Q7cjrjSy/aiWCzqgUCg5vNEIqEHg0E9mUzq0WhUB1C3LxAOh/V0Or0eyXWkF+fwfhAOh/XFxcWGy4g+o127Jcra2tpax9MWCAQcX8MTiYQuy3Jb++vFfSX1n74c4+ro0aO9TsKmpKoqpqen12VfZ86cWZf9mPW63CiKwlfyeqDX570XZZ26x007+c033wC4++smubOe1yMiK5a/1vGa1zpN0/DSSy/hnXfeqfpcVVU89NBDWFpaQjwex9TUFGZmZgAAX331Vc12Tp06hbGxsQ39qhU1Njs7i0KhgNHR0brL5PN53Lx5s+73g4ODGBoawsGDBzuevkKhgIcfftjRsjdu3IDf729rf2x3COjDwdljsRhfk+kCTdMQDofXZV+Koqx7h7DX5aZUKiEWi/Vs//erXp/3XpR16p71bCfvZ8xn6iWWv9bxmtee8+d0KUXJAAAgAElEQVTP486dO4hEIlWfP/jggxgfH6/67NFHHzW+s/L5fAgGg5iYmOheYqlnNE3DkSNH8N577zVc5q233sK5c+cabuuVV17BwsJC342N5gbbHTK0+8iW+XHbYrFoPC4aDAZtX59IJpO6LMs6AF2W5arH/sS65j/zI7KN1hWPEMqyrOdyOT2RSOgA9EQiYSxTLBb1cDhsbDscDtc8gru2tmYsI0mSHg6H9XA4XLOMNS0zMzOujrVemkS6zY98rq2t6dFoVJckyUhXNBqtefQzkUgYywSDQeNR5HK5bKTD/Ce+E49wWvOiXC5X7TcQCFQtY7eueGTV/NfsvNb73JpPgUDAyMtwOGzkZ73t1ysT4nis+WzejvVcNcqLxcXFqm3aHbNYVtQX8zkQxye2v7a2ZqxvfkTYSXmyptnu3Dop43bS6bRxDiRJ0hOJRE0ZdFLH0um0Hg6H9WAwaJRtcUx2y4pjDgQCejAYNJapd97r5aVIu5vznsvlao5HtGt2Zd28vl1eFIvFmny1nlexTqNXBdttE9rdrpm13Fs/M+eLufybz8Hi4mJV2bLuM5fLGemyyuVyVefWLj0iTY3KXKN20sru3Ds5/9Zzuri4aNSFYrGoy7KsS5Jke+28X/LZWgYDgYDtqxJ2x2JXh8061c42y4u1tTWjvUwmk3o6ndYlSaqqf07ah2b1t1H7uLa2ps/MzBhpsLI7TnNZsF4/FhcXjeVbfcXTeszW/LWm2Umf0m05aFb+mpXfZnnYTKPybe0fiOuAub0Rda1cLuszMzO250e0J62kt51rXrP2xe74Rbmu19bqeu21vt59g7kfaj7HdnXL+qpgvX6A9VyYOTleK0mSHL+KKupCPeJ8NHtVvVl+WNv9ZDKpS5KkS5Jke2/TD+ewUVveqO3rdF40u0+yy6961zSzmZkZ2zJnZn6N0HodtnLabjvpt9hd9+qde7t+ukirXf/Brh46ua8U7NpKXW/e13LaXzcz38sI3Sg/YjvNrtfm+11zH0Vcz3S9uk9hjo8IreTDeutY4CocDuvRaFTP5XJGhbPeDEejUaNhN3cKzA1LvfFdmq1rvoAEg0GjIIgCJdIpCk65XDaWNzcg4jjEMsFgsKYxEO/1irSIk2x+39zJsRaLxaqLnTlN5n2Gw2FdluWazoO5EohGWuxPBGnMrO+gr62tVXV2rEEpc0Uzd/bK5XLDde3GDsjlckZlsWvUATSsHJIkGfmby+VqOl125aZRmSgWi7YNvcg3a1C0UV4IdtuzC0KI7YlzUS6Xq4Jfogya0+GkPFnztN75cVLGrRKJhB4IBGrKoPlC6KSOFYtFY11RjxYXF43jN98wi/ohykU6na45Fut5b5aXbs672I416Gh+T7/eOBniwmheVxyf+WIjyqRYTrSdTjosnWgTWtmulblui2Mzt4vWdUW9FMuKG3yxT5EH4hwVi0VjLClr+q35L8qMXXvipMzpuruxOkT+2OWJk7ogjlUEHcS+7c79/ZDPa2treiAQsM03c56ItkAsZ86bRgGLTrSzTvJCdF5F/op6JY7XSfvQrP42ax9FR90uT8SNprUsmG+SrYGqZDJZdVyN2oR6xA8l1r6TOd/EZ076lK2UA3Me2NVdp+XX7TVZ152Vb/Nn5hsLc79T16vPr3lsJPMPfNZrTbP0tnPNa9a+mI9fLGdOR6M211xW7G62AoGA0T+03vCb71HM7I5DrGsNptq1y06O18rpuFrmm9lGgTDRXjeqi83yQ7SL4hwkEglj3FBRlsxtfD+cw2Zteb22r9N54eTewOk1zapZP2RxcbEqH5uVvUAgYPuDlJnT+2TzPp2OOWV3n+GmP25Xd9bW1qquq4I4h+a+TaO+ltP+ujWvrGOLdaP8iLxzcr0W+zBfr0VeRqNRPZFI6DMzM1XpMZ/XVvKhFzoWuLI2RNZKIoINNQmAfbTUGghxs665EIjMlmW5JtpcLpd1AFUnxXoixUXLvA/rSbRePJymV1QguzRZI9DWi671QhwMBm3PgVm9htAuz+0aGVEBzBWv3vmya9BEZbMOKCk6q/WI7Vnz3EnAs1GZsGsw7dLuNC/stlcvXXbnQnwmtmnuzDspT3bs9t+sjFuJPLELvpnPm9M6JtJgLZ8iT82/uNkt0yhwJdTLS7HvZud9bW1NlySppgzbtWv1bgyt5Vyk1Rxst0u76IA169R0ok1oZbt27OqDOPfWMmE+hyKfrb8Y290o2NUZ0Tk1s7v5EMdRr8yZ092JwJXbumC+yWwUwN/s+SwGJTYTdczcObSrmyKI4TRg0Wo76zQvrDdF4scpkYZm7UOz+uukfbRrn0Q+WctCox8SrdeLZjdJdkQZNN9kW2+UzJ/ZHbs53ztVDqyclN9Wr8lOyrf52ERgMp1O2z4tYfcDkq7fyys3fdJ2rnlO2xfxa7/1Jsju5tPKrm8s9m3XR7emw/pZvR9Y7cqOtZ/hpj21206jsinSIP4CgUDda4Jd2bHbp5P8EGm3u7cxp7efzmGjfp7duex0Xji5N3Ba560atSXiB2drP6LR9pqVTV13128R+2wncFUv3W7qZqN23Lrden0tJ9fjeuz208ny0+71Wtfv1U3r0/V26Wk1H9ZTx8a4sr6D7fV6q6ZGvXz5MsrlMjweT9Wf0OjdW7frivfCfT4fvF4vMpkMyuUyBgcHq5bz+XwIh8OoVCq4cuUKACAQCOD99983tun1evHss88a6/zlL3/B9u3b4fV6jc9GR0eh67rxfrqT9JZKJRQKhaptizRZLS0tGVPfKoqCoaEhAMBPP/1kLLNt2zZMT09XTZG7Z88e+wxtQlVVZLPZmrSdPHkSuq7X5KNT4+PjkGW5ZjDtEydO4M0336y73iOPPAIAOHjwoDH4eaPBCu1Yy4RT3cqLRsQ2xX/bqTt2mpVxq08++QQAqqbi9fl8uHPnjlHe3NQxwXoerG3IY489hkKhgFQqZUzn+/vf/97NodbkpVNXrlxBpVLBU089VfX59evXcefOnYbrapqGbDaLsbGxqvMlxqK4evUqAOCjjz4CgJopjp0MdtmtNsHJdu38v//3/wAAn376qfGZOJ+KolRNx7y8vGwc88rKCiqVCmRZrsqrbDYLwH5QWjO7dIm6bqdemfvPf/7TcD9utFIX/H6/kbZGZXWz5/OFCxcwPT1dlcadO3cCgJHWenXzN7/5TcNtW7XazrrNi9/97ncA7uaJz+dz3D40q7+tto8ffPABJEmq6WuI/stf/vKXmnUeeOAB4//dXD/NvF4v7ty5g/HxcWiahlQqhb179wIAfv7555rl7fqUZp0qB/XSapcWUX5bvSY7Kd/A3bbi448/RqVSwUsvvYT5+XlMTU3VTe8rr7xS9e/jx48DAL788kvH6W3nmue0fZmenq7pPwNwNGizz+dDNBpFNputmgTn4sWLRjkCgHg8bqQ3n88jEokgm81W3Y+0q9329LHHHqv73fDwMHRdx+LiIgKBAAqFAvbv32+77K9//eumaXWbH+ZzY673Qj+eQ6f9vE7mhdN7A6d13k4gELD9PBaLIZlMumqLm00g00q/ZSOy9rWcXo9b0Yny0+71GrhXNxuVl27mQ6et2+DsN27cQDAYhH73Ka+aP+vNW6fWBe7N/PTb3/625jvRqfz+++8BAHNzcwCAnTt3IhQKIZ/PIx6PG8s7GQjaSXpFg2XtJNSTyWTg9/vxt7/9zUij2dtvv42RkRGMjY3B7/dDUZSqdLvx448/trSeEydOnEC5XMbs7CyAu5V3dXW14Tn0er1YXFzE8vIyZFlGLBaDqqotH58b3cwLp9ot/1bNyriVk5kS3dQxp0ZHR5FMJjExMYGtW7cilUrhkUcecX28rXCbVrPvvvsOAJDL5WzP19LSEgBgYWEBwWCw5f10q01otl07g4ODCAQCVYNnnj9/HjMzM1UdHkVRqjrhotzUK9vN0isu5uYy+u9//xsA8NBDDzlKe6d1oy4Imz2fC4UCkslk3XQCwGeffQagNuDbLqftbLt54bR9aFZ/W20fC4UCZFmu+Vx0lLs92UUqlcKOHTsA3O0PtKpb5cCJVq/JTsq3MDo6ikQigXK53PCHJTsif0WQ1Ul627nmOWlf8vk8KpWK62MxEwE68+xic3NzNQOdl0olhEIhnDhxAq+//npb11k7rban4ubPLiBkNTo6iuvXr0OWZRQKBdvZA53OZtup/NgM57BT23V6b+CmzgvmH5WtUqkU/vCHP7j+MbbZD6Ld7Lf0M6fX405zU37W43rdq3xoxbrOKri6utqTdQVRMRsZHBzE9evXkU6nsbq6ip07d7Y0W5zT9N6+fbvpMpOTk3jttddw6dIlTE1N2TZYXq8XiqIgl8vB7/djbGwMQ0NDVb/A94NIJAJZlnHkyBFomoYzZ8446ryOjo7iX//6F5LJJBRFgSzLVb9Eb3adKP9Cp8q4HSd1zI14PI5yuYxIJGLcoG2U6Z+d1O1Wz2u32gQn263nnXfeQaVSMerlzZs3a56ynJ+fx8svv1yzrpPgqJ13330XsiwjkUhA0zRomob3338fgUCgpiO83jpdF4TNns+3bt1ytFyrx9KIk/rYqbxo1j44qb+tto+FQsFxOjtpaGgIFy5cwMrKCuLxeEeejupGOXCi1bbbafkGYDzVMzEx4erJarsb3k72Iepxci7cHL/V8PCwEbjXNA2ZTKbmKeJMJoMnnngCe/fuxdLSkuun891wW/ZEwMdJ30AQ/eNWnwruRn5s1HO4nmXDzG1+icC3XZ2dmJjAgQMHbJ+ezGaz8Hg8SKVSLae1W/2WfuemTq639bxe93M+COsWuNq2bRvK5bLtxTeTyTS8ALSzLnDvV9CbN2/WXcb65FMkEsHq6iqi0Simp6eRyWQAALIsI5vN2t78iRsJJ+kVj55//fXXDdOuqipOnTqFaDTq6CZyeHgYS0tLSKfTKBQK+PDDD5uuYyUeP75w4YLt9+0GjE6cOIFKpYIPP/wQiqJg9+7djtbzer2Ix+P417/+hUAggLGxsa4H5rqdF060W/7rqVfGrcSvenbfi+NvpY455fP5MDU1hWKxCAB1H5vvJHE8ly9frvlOVdWGNxHi1db5+Xnb78XThpIkoVwuuy7D3WoT3G7Xavfu3ZAkCfPz81AUBa+//joA4L333kO5XIaiKDWv6opXJi5evFizPU3TmtYvn8+H9957Dz/99BO2bNmCLVu2YGhoqKe/DnWzLgCbO58DgUDNK4+CqDe/+tWvAAD//Oc/O7pvp+1su3nhtH0QmtVft+2j+PW2XoCr00+nCIqioFAo4Pjx4y2/bmjWrXLgRKvXZCflW0ilUnjmmWdw6dIlSJKEvXv3Or5WiP1v27bNcXrbueY5aV9++ctfArj76mE73nnnHQB3nzQ9e/ZsTYD+0KFDXf/hot321M2TKyK4a/daoJNgZifzY6Ofw05u1+m9gZs6b2X3NE0ymbT9A+627clk0rZ/0exVr273W/qV2+txpzgtP+t1ve5VPrRi3QJXYuyFF154oaqxVVUVZ8+ebfjIazvrAvc6+gsLCzUdilu3bkGWZSPCbX3y5I9//CMA4IcffgAAhMNhAHfHWzLL5/PGOA1O0iuCNdPT0w1/IbV7nNCuAbSmOxKJIBAItPQYoc/nM96tt+tMiQLeKvHUlbhJbtaJzefzVR0Br9drjOHQTnQ4GAzW/KLx7bffVv273bwQDb05nZqmufr1s93yb9WsjFs9//zzAO5e9M1lT9M0/O1vfwPgro45lUqlqrY1ODiIaDTa9qOxTs67qJ+nTp2qqZ+JRKLpq63BYBDZbBaxWKwqz1KplBG0Fp2nY8eOuUp/t9oEp9utx+v1GmNGzM/PG79mirwcGxurGYtlx44dkCQJExMTVfVL0zQcPHgQ27dvb7jPTCaDubk5LC0tGY80T01NdeTG2AnxS6o5n7pRF8w2cz7v2bMHlUoFoVCoKu/EuJAA8OKLLwIA3n///Y7+cOG0nW03L5y2D83qb6vt4xtvvAGgtiMqtmUea6aT7G7WxSuWrehWOXCi1Wuyk/IN3C1jt27dQiQSweDgID7++GOUy+Wafqdgzcdr164BAPbt2+c4ve1c85y0L4ODg8Zrb+384Cf6j6dPn4bP56vJa7vy77R8iGO0PiFjvZlvtT1t5cb/q6++QjgcbtjPa/TqXjv5YbURzmEjndyu03sDp3Xeql5AIh6P2/4Bd8cyisfjDetqve/c9lvEMnbjE9pZXV21zWsn/fFGRNk3p9nNWxlOr8ed5rT8rNf1ulf50BK9TWIEfPOsU+YpKs2zPJinTBUz6gH209snk0k9nU4bI9w3W9c81ard6PdiRH/rlJLm6aR1/d4sCWL0fTELhXlqU3FsYvrhaDRaNe2402MVeSdJkjFlplhPfHbjxo2qf6fTaeM4xRSrxWLRmA1MpFPMGGCeXUCkQUyjap7hCJaZfcQ0pcC9qTWDwWDNjBN265pnU7LO/ifUmynBjpiSU5QFca6tMyxZy02zMmGelUFMG2qeZlQck9O8wP/NWCHOo67fm5EjEAjoi4uLejqdNtbH/82cVCwWq6ZBtZv1w0l5smN3fpqVcTsiH2VZ1pPJpJ5IJHRZlqvqjtM6JsqHtc6IfYi0ilmzzNOEy7JcNduU3Xl3mpfNzrv4TJIkPZFI6MlkUpdluWrWjXplXUxVL9YPBoO6LMu2M0eJ9XO5nJ5Op6umoLebgljM6tWJNqGV7TYi6op1ZpJwOGw7q5Wu32sLRPkSs/zYzVplvaaI8i1mZxEzsYlzK4iyaZ0i3lrmzNu0tpN2+SXOlXU6crd1QZZlR22hsFnzWUwdLtIppoG2zmgklhHt6uLiorE987TqVp1oZ53mhWibwuFwzWxMTtqHZvXXTftonRXUOuucmPa83vXdnJ+i7JnPsXn68XrTZpvrhGgrxfbFVN3mNJvTUq9P2Wo5MB+bufy5Kb+tXJOdlG/R36k3a5s5X0RemcuBWN86K6OT9LZzzXPSvpinihf1RfQ/RP4269OYj9tuWZG/yWRSX1xcNNpEca7FsdSrG7IsG9dAUTbM9zmi/jk5XjvWei6I2frMfbOZmZmafpJdPjS6djjJD3O7b96WKA/mPOqXc9isLbc7v53OCyf3Bk6vaVaifDnJS11vPqugte2w47TfIj63ayvtmPPJuh2n/fFm7Y7oQ4tzK9oxUZ8a9bWcXI/tmLcp8qDT5UfXnV+v7a5T5munOT0i38zX7FbzYb21FbgSFz7zn3UqV2vFExdC68VWMFdy64W30brWfdpldC6Xq2pAwuFwTSWSZdnYh9iOdRlzQESSJD0ajdpW3GbHKpYRBUXsS2xTFLJ0Om1MjSzyRDR84iIsAjnmC6n1BlVs27yeNd/MF91isWhsUwQszBqtKxqjeh3ZXC5X02GoR5w3kU+ikpu3a1dumpUJMQWvOD/FYtGYNlpcwJzmha7fawisAR278ycaJ7HPRnkpOClPZvW26aSM2xEdq3p1R9eb1zHRmWjWZoigsLmNsesUWs+7k7x0c97NgSRxc2RVr6wXi8WqdiKRSNjewIq0iOMTN6SNbro61Sa0st1m7Op8Op2uCe6YLS4uVtUvu0C43TVFtGnm8mw9907LnDXo2OiY7bZpvda1Whec2qz5vLa2pkej0aq2xtrRFMuYrweLi4tG21zvmtOJdtZJXtj1jayatQ/N6m+z9tGaBvP1b21trao9d3J9F22y3Tk238Q0aidEAEAsJ4L34prptE8p0tpKObCeR1H+3JZfJ2XFTqPybd1fs3NpDvKJdFjrtZmT9LZzzWvUvpiXMadVBBvFjacT4rpvp1gsGtsXQWNR7sz9r3r10259UdeseeHkeK3EOWuULyLvm5XhcDjc9KayWX7UK+ON8qjX57BZvaxXX7qRF07uDZxc0+zWcRIIFaxtvDU/zdfzRpzcJ9td9+rt2y7vzPnnpj9er92xXld0XTfyrlwuO+prOemvm7m5XnSi/LRyvW41PW7yoRc8ul5nWgOiLorFYnj++efXbXBEItp8YrEYxsfHbcfkymQyOHv2bF/NhrJRMZ/vYV7Ul0ql8NBDD/V8YoT7RSqVwsTEBHK5XE9mVyT3NE3Djh07cOLEibbqiaZp2LJlC4rFYktjUlL/m52dxZ///Oe2J1aYnJw0xr8k2ujWdVZBIuDu+8fLy8sMWhFRyzKZDBRFqdtp/6//+q+2pu2mu5jP9zAv6tM0DTdv3mTQiqgBr9eLS5cu4YMPPmhr3KYPP/wQMzMzDFptYuPj4xgZGWlrYGxVVbG8vIxz5851MGVEvfOLXieA7g/il0FZlnHnzh188cUXvU4SEW1gP/zwgzHo6R/+8Ac8+uijxndiZqyTJ0/2KnmbBvP5HuaFPUVR8P333/PmaJ2JQcO//fZbPnG1gQwODmJubg4ffvhhS+2Foih48MEHMT4+3oXUUT+ZmprC5OQkSqWS6yClpmk4c+YM5ubm1m3CGqJu46uCtC4ymQyee+45yLKMs2fP8mkrImqboiiYn59HNps1PguHw3jxxRf55EcHMZ/vYV5QP/B4PFX/DgaD9+0rqhuVqqq4ePGiMSucE5lMBgDYh77PKIqC3/zmN44D1Kqq4sqVK3j55ZcZtKJNhYErIiIiIiIiIiLqSxzjioiIiIiIiIiI+hIDV0RERERERERE1JcYuCIiIiIiIiIior7EwBUREREREREREfUlBq6IiIiIiIiIiKgvMXBFRERERERERER9iYErIiIiIiIiIiLqS20HrvL5PGKxGDweTyfS01WqqmJgYACTk5NNly2VSojFYhgYGFiHlN1fhoaGMDQ01OtktE3TNKRSKfj9fuTz+V4nh4iIiIiIiGjT+UU7K5dKJdy+fRvT09OdSk9fUFUV33333aY7Luqs8+fP48KFCyiXy71OChEREREREdGm1NYTV4ODg4hEIggGg51KT1f5fD7cuXMHJ0+erPkuFotVLbeRjqufmfNVuH79Oq5fv96D1Nxll6ZWxONx7NmzpyPbIiIiIiIiIqJaHOMKd5+w4tNVnacoClRV7XUyqvRjmoiIiIiIiIjI3n0fuNI0DeFwuNfJ2HTEGGH9pB/TRERERERERET1dTVwVSqVEIlE4PF44PF4EIlEbAex1jQNk5OTGBgYMJY1/4l1MpkMhoaGjM+HhoaQyWSM7aiqWjVY9uTkJDwejzEYu6IoiEQiCIVCxvI7duxAoVAAAGO7dlKpFAYGBjAwMIDZ2dm6+8zn80YaQ6EQNE0DgKrjczI4vNin3++vyr9SqVSVv2IAeU3TEAqF4PF4qvLEvA2/349UKuVo3+a8HhgYQCwWM44FuHvOxLkdGBhAJBJBJBIx1t21axcqlQqy2ayRF8C9wfzNg953Ig81TTO2a5fmRmkSeWktq+a8rpefN2/erFkmn887ngSAiIiIiIiIiBrQOyAYDOrWTeVyOR2APjMzo+u6rpfLZT0QCOgA9MXFxZr1ZVnWy+Wyruu6Ho1GdQB6OBw2likWizoAPZlMGtuTZVmXJMlYZnFx0dhHMBjUo9GoLkmSHgwG9VwupyeTSeO7Zuk3f55IJPR0Oq3ncjlj+8VisWaf0WhUTyaTei6X0xOJhPFZIpHQZ2Zmqta35oHVzMyMDkDP5XJV+SnypFwu64uLi7okScbnIt9EHkWjUT0ajepra2v62tpazff1pNNpPRAIGOdDpMWcb2J/Ii3BYLAmX63rFItFfXFxUQdQld+dyMNwOFxVhsS5FuWvXppEumRZrsprSZJ0SZL0tbU1YzlRnsRyIl/M50nkn7X8EhEREREREZF7XQtcybJsBDaEcrmsA6gKCFgDUoIIyAgicCMCRrp+Lzhh95k5YGYOPrQSuDKvLwIv5oCI2Kc1GCXLcs364jiaBY+SyWRVUE6kx+4zcx6I/+ZyOV2W5ZrtWoNGVmtra7okSUYAyLofEaCxHu/a2lrTwJV1W9bjbScPg8FgTaDILnhU7/yn02nb9Ih9iPNuDlDp+r0gq/XzYrFYlWYiIiIiIiIicq8rrwpmMhmUy2UMDg5Wfe7z+RAOh1GpVHDlyhUAwH/+8x/bbWzfvr3q38PDw9B1HYODg1BVFbFYDKdPn667jUcffdTYp9frbet4zOs/8MADAICff/65ZjnxneD3+2vWdyoej+POnTsA7r56FolEkM1mUalUbJcXeS3+e/nyZZTL5ZrXLgW7VzYBYGVlBZVKBbIsV62XzWYBAF999RUAIBAI4P333ze24/V68eyzz7o+TqtW83BpaQmKogC4+0ro0NAQAOCnn35quJ6machmsxgbG6s63omJCQDA1atXAQAfffQRgLvl0Ozhhx+23e7g4GDb5Y6IiIiIiIjofteVwNU333wDAPjtb39b893vfvc7AMD3338PAPj1r38NALh161bVcpqmQZblms9isRhGRkbwzDPPIBqNdjzt/aRUKiEUCuHEiRN4/fXXEQwGHa9748YNBINB6Hefqqv5swZgBHHu6q0Xj8cBAHNzcwCAnTt3IhQKIZ/PG9/1SiaTgd/vx9/+9jcjfc189913AIBcLmd7vEtLSwCAhYUFV/lPRERERERERO3r6uDsIgjSiM/nQzKZxPT0tPH0jqIoKBQKOHv2rLGcqqrYunUrAGB1dRWRSAQPPvhgdxLeBzKZDJ544gns3bsXS0tLGB0ddb2N1dXVlvevqmrD7wcHB3H9+nWk02msrq5i586dPZ2xb3JyEq+99houXbqEqampmqf9mrl9+3bTZdrJTyIiIiIiIiJyryuBq4ceeggAbGdcE5566inj//ft24dgMIi9e/fC4/Hg6NGjWFxcrArWnDlzBpVKBX/84x+7keS+c+jQIQQCAWOmPre2bduGcrls+0pgJpOpG5h67LHHAAAXL16s+U7TNON1PCESiWB1dRXRaBTT09NVMxquF1VVccKmmbAAACAASURBVOrUKUSjUdcBq0ceeQQAMD8/b/u9mEFSkiSUy+WqmRWJiIiIiIiIqLu6ErjavXs3JEnCwsJCTYDk1q1bkGW56lW1UCiEQ4cOYXV1FbquY3V1teYJI7tAi904U5tFuVyu+cxN0OT3v/89AOCFF16oCl6pqoqzZ8/C5/PZrrdjxw5IkoSJiQkjaCP2ffDgQWPsMevTVSKg+MMPPzhOY6f8+OOPNZ85zSuv14tgMIhsNotYLFa1XiqVMp7qEwHEY8eOOdpuPp9nkIuIiIiIiIioTW0HrjRNw7Vr1wDcHZMJuBsM+PjjjwEA4XDYCDrNzs5CURRcunTJWD+fz6NQKODQoUMIhUIIhUKIRCJIpVKYnZ01bv63bdsGADh48CDy+TxisRiWl5cBAN9++y1SqRQ0TTOe8hKDiJuJAM7q6qptUCGTyWBychKqqkJV1ZrjEvsCqp8mE/8vvhP5Il4tMwfdxCtpV69ebRjYCAQCKBQKSKVSyGQyiEQixsDsmUwGiqJAVVVjH9Ynq4aHhxGNRlGpVLBz504MDQ0hFApBlmUcPXq07n69Xi+mpqYAAAcOHIDf70coFMLWrVvxu9/9zgh4TU9PG3kOAOfPn4ckSdi9e3fV9lZXV43zJfLFLl/byUPx1NT09DQURYGiKDh48KCxjdnZ2ap9WdOUTCYhSRKmp6exdetWhEIh+P1+XL161QhYvfvuu8YykUgE+XweiqLgL3/5CwDgk08+MZ42UxQFO3fuNNJARERERERERC1qZ0rCXC6nA6j6SyaTVd8HAgHju3A4rBeLxZrthMPhquXMf5Ik6Wtra/ra2poeDAZ1AHogENCLxaK+uLioA9CDwaC+trZWs24wGDT2kUwma74XisWiLkmSLkmSnk6n6x6X2L95+3b7dLp+o+wvFou6LMtGvq2tremJREIHoCcSiaZ5bz5usZ1AIKDncjlH53ZxcdE4J7Is6zMzM1Xfy7JsbFcct/XcptNpY/1isWibZrvPWsnDdDptnMNEIqHruq4HAgHjnNZLkzm/w+GwUeYSiYS+trZWc07E/iVJ0pPJpJ5MJvVAIFC1j1wuV5UOIiIiIiIiImqNR9d1vZ3AV7tKpRJmZ2eNp3zMVFVFIpHAm2++WXcWPCIiIiIiIiIi2py6OqugE/v37687oLbP58PAwIDxKhgREREREREREd0/ev7E1cDAAAAgGo3i6aefxgMPPADg7jhGn3/+OY4cOeJ6pjgiIiIiIiIiItr4eh64UlUVZ86cwfLysjGTnizLCIfD2LdvX93Z74iIiIiIiIiIaHPreeCKiIiIiIiIiIjITs/HuCIiIiIiIiIiIrLDwBUREREREREREfUlBq6IiIiIiIiIiKgvMXBFRERERERERER9iYErIiIiIiIiIiLqSwxcERERERERERFRX2LgioiIiIiIiIiI+hIDV0RERERERERE1JcYuCIiIiIiIiIior7EwBUREREREREREfUlBq6IiIiIiIiIiKgvMXBFRERERERERER9iYErIiIiog1AVVV4PB6kUqleJ4WIiIho3TBwRURE1CEMLFA3nTlzBslkEqdPn4amab1ODhEREdG68Oi6rvc6EURERJtBLBbDww8/jNOnT+Nf//oXvF5vr5NEm4SqqggEArhz545RzuLxeK+TRURERNR1DFwRERF1AAML1E3mMiXKGoOjREREdD9g4IqIiKgDGFigbrErTwyOEhER0f2CgSsiIqI2MbBARERERNQdDFwREREREREREVFf4qyCRERERERERETUlxi4IiIiIiIiIiKivsTAFRERERERERER9SUGroiIiIiIiIiIqC8xcEVERERERERERH2JgSsiIiIiIiIiIupLDFwREREREREREVFfYuCKiIiIiDYdVVUxOzvb9nYURUE+n+9AioiIiKgVDFwRERGts1QqBY/HU/WXz+cRi8Wq/k3N5fN527zcjEqlEiKRiHGcfr8fmUym18nqS4qiYGRkBE8++WTb29q9ezfeeustpFKpDqSMiIiI3GLgioiIqA12QSjr39DQkO26uVwOuq5D13UMDw9jamoK0Wi0q+lVVbVh2syBEbdBIJEXbtaptz9rQGpyctJ2/eHhYSMPk8mk4/12Uz6fx8DAgJH2UChkfBcKhaqOS9M0R9sslUrYtWsXBgYGsLa2Bl3XMTIygueee27TBupaNTk5iQ8++AArKysYHBy0XaZUKlWdi1gsVvdceL1eLC0t4cKFC4jFYt1MOhEREdlg4IqIiKgN8Xgc4XAYAJBOp40giq7rWFtbgyRJdQNXdh5++OFuJRUA4PP5MDMzY/x7aWmp6vtz584BACRJQi6Xw/DwcFfToygKZFkGAMzMzBj7Gx4eRiKRAABEo1G8/fbbXU1HJ4kgJADIsoy//vWvxnfi/0X+er3eptvTNA27du2CLMuYmpoy1nnllVcAAF999VWnD2HDymQyWFhYwNLSUt28VVUVL730ErZt24ZkMglZljE9PY1XX3217nZF8Or69esdef2QiIiInGPgioiIqEO2b99e9W+v14vt27fjmWee6VGK7I2PjxvBIuvN/XfffQcA+Pjjj7setBJE4O/RRx81PtM0DQsLCwgGg1XBmo1i9+7dAAC/31+V9osXLwIAvvjiC8f5e/HiRVQqFRw/frzzCd1ENE3Da6+9hvn5+Ybl5eLFi1hZWcHJkycRj8exsrICWZaRzWYbbt/r9WJubg4HDhxAqVTqdPKJiIioDgauiIiI2lQulyHLMnw+X813S0tLiEQiPUhVYyJYZL4B1zQNe/fuxczMDEZHR9ctLfv27QMAXL582fjs2LFjkCSp6mmljcTr9SIajSKbzRqvoJVKJRw5cgTpdNpVUHBubg6SJNWcE/Gk1WOPPda5hG9gFy9exPbt25vm7b59+6oCW16vF36/H5IkNd3H4OAgotEo/vSnP7WdXiIiInKGgSsiIqI2aJqGQqGAQCDQle2XSiX4/X5jMG5N0xCLxYwxlEKhkONxksyefvppAMCnn35qfPbqq69iZGQE4+PjHUu/Ez6fD4FAAAsLCwCA2dlZKIrS8HWvjeD5558HAFy5cgWapuGll17C4cOHXQUyRfkaGRmp+rxUKuH06dMIBoPrGmTsZ5999hmeffbZpstZy5SmaVhdXcXHH3/saD+vvPIKFhYWWqp3RERE5B4DV0RERG0Qr9bt2rXL+ExRFHg8no5sf3BwECsrK8a/jx07hvHxcdy5cwfJZBLZbBbHjh1zvd3R0VFIkoTl5WUAdwdW1zTNGJtpve3fvx/lchmTk5M4cuQIvvzyy64GrexmI2z253ZWOZHH8/PzCIVCGBkZQTwed7UNa/nSNA2KomDXrl0YGRnZsE+kdUM2m8VTTz3lah1FUbBjxw688cYbjgOA4okucW6IiIiouxi4IiIiaoN4XevAgQNGgGNsbAzBYLBj+xABnHK5jHfffdeYKU0EQa5fv97SdiORCAqFAmZnZ3HhwoWagdrX08svvwwAOHXqFL744ou6s8F1ink2Qqd/boNOAIzXBb1eb0tBQVG+Hn30Uaiqii1btiAWi+GLL76Aoigb+om0XguFQhgbG0O5XMbExITrwCQRERGtDwauiIiI2nD16lUAqApwyLLs6JWlVtiNo1UoFFralniV7ciRI5ibm+tpEOTKlSvG/z/yyCM9S0cnaZpmPNG2bdu2lrYhytfw8DB8Ph/K5TIikQheeOEFDhBuEQwG8e233zpefmlpCeVyGclkEgAwMTGBTCbTreQRERFRixi4IiIiakM2m60Z38rv97t+ZamXotFo3SecWnlFzq1SqYSxsTEjgCBm3rMjXvHL5/NdTVMnHDx4ELIsIxAIYHp6uuGyoVAIoVCo5vNsNlv19J7P58PU1BQGBgbw0ksvdTzNG9m2bdtcB/N8Ph/i8TjS6TQA4KOPPmq6jhjb6pe//KX7RBIREZFrDFwRERG1SNwkWwfOXlpacjVrXK988803AO4N1G4ljq+bQThVVbFr1y6k02nE43FIkoS5ubm6y4snatrN326PcTU5OYlyuYxz585h//79qFQqDZ/muXbtWs1TeiL/7Z7W8vv9KJfLjtNzP9i3bx+mp6ehqqrrdSORCGRZxk8//dR02StXrkCSpK6/zkpERER3MXBFRETUon/84x8AgMcff7zmO0VRbJ+g6SfiNbQdO3bYfj84OAhd17sWhNM0DeFwGJFIxJhpT4y7Ve/JmfHxcei63va+uznGlaIomJ6eNl6/3L17N4DGT/PcuXOnZvuNytfq6qrTQ71v+Hw+JBIJhMPhlmb88/v9tq/imolZPQ8fPtxqMomIiMglBq6IiIha9OWXXwIAtm/fXvPd0aNHuzbOVadks1nIsmw7tlUkEoHH4+lq8O3gwYMAUDVouRh369NPP61ZfmBgYF1eXWyHeO3x448/Np7I8fl8CAQCWFhYqAmozM7OGk902W0LqC1f+Xwe5XIZ4XC4S0excZ08eRJDQ0MIhUKunrzSNA3Xrl3D+Ph4w+XE65+tDNRPRERErWHgioiIqEXLy8uQJKnqKQ3xREa5XG7pFbtbt24BAG7fvl21TTf/78Ts7GzDdRVF6eog87FYDAsLCzh+/HjV5w888AAAYHp6uiZdYhD6fh0/rFQqYdeuXQgEAhgdHa36TgQHz58/X/X5+Pg4otGo7SyUYmD3M2fOGHmRyWTwwgsvQJIknDp1qhuHseFNTU1hz549CAQCNU/uqaqKgYEBhEIhY5w0VVURCoUwNTXV8PU/Ua97OfsmERHR/YiBKyIiIpc0TUMkEkGlUkGlUqkaB2nLli3GQNxuZ8eLxWLGumNjY1AUBUD1q3xbt25t+v/NpFIpHDhwAABQLpfrvirYavCtmUgkYhyn+fU5TdNw4sQJAEClUsGrr75atd6PP/4IoP3xrbohn89j165dqFQqKJfLVYPHK4qCa9euAQBOnz5dM9aVqqo1AUJN06qeqtq6dSs8Hg9ee+0143XKZq+13c/i8Ti++OILZLPZqs99Ph9GRkaQzWaxc+dODAwMIJFI4H/+53+M11WtNE3D0NAQVFXF0tJST2ffJCIiuh959E4MFEFERESOpVIpTExMIJfL9WUQBrj7ZM9zzz3najypbh/X5OQkbty4UfeJl42Qr1aapmHLli01aRb5n06n6wZUaH0oioIHHnig5ik6IiIiWh+/6HUCiIiIqP/8/e9/RyAQ6HUyqiwvL9fM4LjRraysAKh9Ok/M+Gg3fhqtLwYOiYiIeouvChIREVGNGzduYGhoCKVSCZOTk47WicfjXZ2FsFAo4PHHH4eiKMZrlBvdN998A1mWAVQHSK5evVozfhoRERHR/YiBKyIiIqqxbds2TE9PY2JiAm+//XavkwMACAQCGBsbw9dff71pnoJ57LHHjHHGjhw5YnyezWY33dNlRERERK3gGFdERETrTIzFZLaRxmXqJ/l8Hjt37qz6jHlJREREtHkwcEVERERERERERH2JrwoSEREREREREVFfYuCKiIiIiIiIiIj6EgNXRERERERERETUlxi4IiIiIiIiIiKivsTAFRERERERERER9SUGroiIiIiIiIiIqC8xcEVERERERERERH2JgSsiIiIiIiIiIupLDFwREREREREREVFfYuCKiIiIiIiIiIj6EgNXRERERERERETUlxi4IiIiIiIiIiKivsTAFRERERERERER9SUGroiIiIiIiIiIqC8xcEVERERERERERH2JgSsiIiIiIiIiIupLDFwREREREREREVFfYuCKiIiIiIiIiIj6EgNXRERERERERETUlxi4IiIiapGqqvB4PEilUr1OClFf8/v9CIVCvU4GERERbUAMXBEREbXozJkzSCaTOH36NDRN63VyiPqSoigYGRnB6uoq8vl8r5NDREREG4xH13W914kgIiLaaFRVRSAQwJ07dxCLxfDwww8jHo/3OllEfcfv9+PSpUv47rvvMD8/j6WlpV4niYiIiDYQPnFFRETUgjNnzuDw4cMAgHfffZdPXRHZUBQFfr8fg4ODiEQifOqKiIiIXGPgioiIyCVVVaEoCvbt2wcA8Pl8iEQiOH/+fI9TRr2iKAo8Hg+DMhZHjx7F0aNHjX+fOHECJ06c6GGKiIiIaKPhq4JEREREbfL7/XjjjTdw9epVvgpHRERE1EF84oqIiIioDYqiIBAIIB6P81U4IiIiog7jE1dEREREbfD7/Zifn8fw8DAUReEA5EREREQdxCeuiIiIiFokBh8fHh4GAA5ATkRERNRhfOKKiIiIiIiIiIj6Ep+4IiIiIiIiIiKivsTAFRERERERERER9SUGroiIiIjWgaZpSKVSGBoagsfjwcDAACYnJ3udLCIiIqK+xsAVERER0To4f/48Lly4gIWFBei6jsOHD+PUqVNQFKXXSbsvqKqK2dnZtrejKAoH3yciIlpHDFwRERF1WSgUgsfjqfoDAL/fX/Vvqi+fz9fk4UYLHsTjcVy/fh0+nw8AsG/fPgDAzz//3Mtk3RcURcHIyAiefPLJtre1e/duvPXWW0ilUh1IGRERETXDwBUREZEDqVSqKmgSCoWqvrcGp+xuanVdN/4AYHV1FbIsdzXdqqpWpWtoaKjq+0gk0lIwSOSHm+BRvX1Zg1J2r88NDw8beZdMJh3vs59duXIFkiRh9+7dvU6KoZ1zVE8mkzFej3S7bidMTk7igw8+wMrKCgYHBx2tMzQ0VFPHBa/Xi6WlJVy4cAGxWKyTSSUiIiIbDFwRERE5EI/HEQ6HAQCyLOOvf/1r1fd//etfjSBUIpFAPB53tF2/39/ZhFr4fD7MzMwY/15aWqr6/ty5cwAASZKQy+UwPDzctbQoimLk0czMjLGv4eFhJBIJAEA0GsXbb7/dtTT0i1KphKNHj+LLL780nsDqB50+R6lUCs899xz2798PXddRLBZx6tSpdXtaKZPJYGFhAUtLS/B6vY7WSaVSKBQKDZcRwavr16935PVDIiIiqo+BKyIiIodefPFFAMAbb7xRcxMs/h0MBnHy5Ml1T1sj4+PjRjDCmu7vvvsOAPDxxx93NWgliODfo48+anymaRoWFhYQDAYxNTXlOMCwUZVKJbz00ku4dOmS4yeA1lOnzlEmk8HExARmZmYwPj4OABgcHEQwGMTVq1e7k3gTTdPw2muvYX5+3nGZyufzuHnzpqNlvV4v5ubmcODAAZRKpXaSSkRERA0wcEVEROTQb37zm7rfKYqCcrmMqampdUyRcyIYYb7B1jQNe/fuxczMDEZHR9clHWJcp8uXLxufHTt2DJIk1TzFthn1e9AK6Nw5OnToEILBoBG0Mrt27Vr7CW3i4sWL2L59u+OArKZpeOutt4ynEJ0YHBxENBrFn/70p1aTSURERE0wcEVEROTSrVu3aj47evQokslk2699lUolY9B2v98PTdMQi8UwMDBgjK2laZrr7T799NMAgE8//dT47NVXX8XIyIhtYKFbfD4fAoEAFhYWAACzs7NQFMXVq1z9RlEURCKRqsH2xV8kEjGW0zQNu3btwvz8vBG0ymQyfTfIdyfOkQjk7t27t+a7bDbb9bHdAOCzzz7Ds88+63j5gwcP4vjx467L4SuvvIKFhYWW6iURERE1x8AVERGRS6qqVv1bBB7EkyrtGBwcxMrKivHvY8eOYXx8HHfu3EEymUQ2m8WxY8dcb3d0dBSSJGF5edlIs6ZpPXlCbP/+/SiXy5icnMSRI0fw5ZdfdjVoZQ0mNfurNyi3lQgyHj16FK+//jpWV1cRCASQTCaNgeQVRTGWP3bsGCqVCnbu3Gns67nnnnN9PHazVDb7c6vdc/T5559DkqSqwB1wr+6sR5Aym83iqaeecrRsJpOBLMstPXkonugSr90SERFRZzFwRURE5JDdK0eqquL06dM4e/Zsx27GxXbK5TLeffdd4+kcMeD79evXW9puJBJBoVDA7OwsLly4UDNQ+3p5+eWXAQCnTp3CF1980fVX5syzOTr5c5Iv4ukpSZKwsrJiBDz27NmDiYkJ26dvpqambPfndCB/YWlpyfUxudXOORLjYVUqlZoAmnjSatu2ba7T1C2qquLs2bP3xaQAREREGxEDV0RERG1IJBIYGRnp2hhRdq8eNpvxrJ7nn38eAHDkyBHMzc317NW8K1euGP//yCOP9CQN7RJPTy0sLNjm40Z/+qadcySO3fzkmfiLRqMAgMcff7xzia0jGAzi22+/bbpcLBZDMpncsK+qEhERbXYMXBEREbUon89jeXkZp06d6nVSXIlGozVP0KRSqZZeKXOrVCphbGwMyWQSwN0BtO2sV3paoWkapqenEY1G2x7TrB85PUfA3Trg8XiQz+eNz0SwyO41PfHq5O7duzuZZFvbtm1rOttfKpXCH/7wh7ae+hNP1/3yl79seRtERERUHwNXRERELXrrrbdw+PDhDRO8+OabbwDcG6h9vamqil27diGdTiMej0OSJMzNzXV9v50e40o8UWQX7Lh69SoA90EMN4G6bo5x5fYciSCV+TXan3/+2XbZTCaDSqWCcDi8Lk837du3D9PT0zVj0plNTEzgwIEDtvmVzWbh8XiaDp5/5coVSJLUt7NEEhERbXQMXBEREbkQDAahaRpmZ2dRqVRcj0/USyKosmPHjprv4vF4S2MhOaVpGsLhMCKRiDFgtxhzy+6pmE6mpxtjXAHAo48+WvVvTdOMGfPcBjFu3ryJYDDoaNlujXHl9hwBwPj4eN3tW8eEO3v2LADgzTffdJSedvl8PiQSCYTD4boz/iWTSds/AJBlGclksuEA72LWz8OHD3flGIiIiIiBKyIiItcKhQKOHDmC+fn5XifFFRFUsT7tIp726eareQcPHgSAqlkMxZhbn3766bqnpxNu375d9e/z588DAN577z3X21IUpWeD5QtuzhEADAwMNHwiyfykUz6fRzabRSKRsJ3koFtOnjyJoaEhhEIh2yev4vG47R8A+P1+xOPxhuk9ePAgZFneUAFsIiKijYaBKyIiohaMjIx05AZ8dXUVQPVNvvnpECf/78Ts7GzddePxuDFodjfEYjEsLCzg+PHjVZ8/8MADAIDp6emqNHU7Pe0aHh5GIBDABx98AE3TjCfwJiYmEI1GMT4+XrNOPp9HJBIxAnIi2FMqlYzPzONErTe35wi4N0mA9Ykk8eSYCOTl83m88MILCAQCPZm5b2pqCnv27EEgEGg65pUbsVgM5XK55wFHIiKizY6BKyIiIpckSerIgOx+vx/lchkAqm6qza/ybd26ten/N5NKpXDgwAEAQLlctn1V8OGHH3aXeIcikQimp6cBAB999JHxuaZpOHHiBACgUqng1VdfXZf0dMrCwgJkWcaWLVuwZcsWzM3NIZ1OVz2tJCiKghdeeAEvvvgidF1HIpHAxMQEgLvjZKXTaQC1r9atl1bP0Y8//gigNt3imBYWFuDxeLB3714cPnwY169f79nMffF4HF988QWy2Wzb29I0DUNDQ1BVFUtLS5yNkIiIqMt+0esEEBERbSSdfLpCPG3l9PM7d+60tB/z60/rTVEUYyY5M6/Xu6GfVPH5fHWPzUyMgTQ1NYXdu3cjk8lgeXm56omy77//3vH4Vt3Q6jm6fPly3XSbx8nqF8PDw46Dg43GBbty5QqOHz+O0dHRTiWNiIiIGmDgioiIiKhLLl68iEqlgrGxMQB3X6Pbv39/1euEFy5cwJ49e3qVxJYtLy9jZGSk18lYd/0WkCMiItrs+KogERER4datW66WF7P+dev1Nrfp6Vc///wzAoFA1YyFu3fvNr7XNA2FQgGPPfZYD1PZmkKhgMcff9zRk2dERERErWLgioiI6D6XSqWMMY7Mg7j3Sr+lpx0PPvggCoWCMfi+oigYGRkx/v3dd98BAP77v/8bsVjMdua7fhUIBDA2Noavv/6aTyER0f9n731/3LiuvM8vgedF5kWsVNMv5vF6gjxdNDBCjG2vzZYGjtp4nIVFpsdjOIg1pPxoDS1kSGE7qwXspNtiK2MYljUmIzkYPaPulsYCBM+Mmx1pMMbApLulXXsh8jEsqW00Y3szQLo4gmNk35CiE/8BtS/ap3Tr8hbrFqvYZEvnAzRsFevHveeee865t+rewzAM0zdidrdF/AzDMAzDhCadTndsCs3uNxi1Wg0TExOuY9VqdWAbmuvSarXw3HPPYWlpCYZhIJvN4pVXXnE29G40GkgmkwA2st/xBBDDMAzDMIwbnrhiGIZhGIZhGIZhGIZhhhJeKsgwDMMwDMMwDMMwDMMMJTxxxTAMwzAMwzAMwzAMwwwlPHHFMAzDMAzDMAzDMAzDDCU8ccUwDMMwDMMwDMMwDMMMJTxxxTAMwzAMwzAMwzAMwwwlPHHFMAzDMAzDMAzDMAzDDCU8ccUwDMMwDMMwDMMwDMMMJTxxxTAMwzAMwzAMwzAMwwwlPHHFMAzDMAzDMAzDMAzDDCU8ccUwDMMwDMMwDMMwDMMMJTxxxTAMwzAMwzAMwzAMwwwlPHHFMAzDMAzDMAzDMAzDDCU8ccUwDMMwDMMwDMMwDMMMJTxxxTAMwzAMwzAMwzAMwwwlW3bianx8HOPj4327f6vVwpkzZ5BIJFAsFp3jjUYDs7OzGBkZQa1W69vzu5WrVCphfHzcVS5mc6nVapiamkIsFnMdbzQaGBkZwezs7IBKBkc/G43GwMogM+h+0wu1Wg3ZbBbpdHrQRek7w6gz/aBSqWzZNvXyScNGEP+kspfd6nmn6CnTO41GA8ViEYlEQsvX3C46dTvHhY1GA1NTUxgZGen5HpsdO1cqFcRiMVQqlb4/axgZpn7lFa8zwekWy/d7XB41rVbLqUssFsP4+LirvxaLxaEbr3STbxR2cisw8ImrYrGIWCym9beZXL58Ge+//z4sy3KOtVotXLt2DUtLS2i3peEtbQAAIABJREFU25taHuLq1at4++23sbq6OpDnM0C9XscXX3yB+fn5QRdlSzAM/SYotVoN7777LpaWlgZdFCYiarUaPv300y3bpiqfNGxUKhVcuXIllH/aCvVkNg/d+DAWi6FYLOLatWs4e/bsHaU/pVLpto0LG40GLl68iFKpFCp+4Nj5zoTj9ejYirF8N9LpNL797W/j5s2bqFaraLfbePPNN50J7pmZGUxMTGB8fBxnzpzxnISt1WpIp9OBJ8Tr9Tqy2azjv6amptBqtbqe7zVxFZWd3BLYQ0Cz2bQzmYwNwC4UCh2/l8tl2zCMTS9XtVpVlqlQKNgA7Gq1uinlyOVyWuViOrEsq29ySqVS9pB0oS3BZvSbxcXFSO8PwE6lUtrn91PfokK2J8NAv8qkum/QNh0mtoLtj6KMQe/R7343jH1mGIja3qowTdNeW1tz/k26IfbhZrNp53I5Rwc2O0brhah1aivYhjD0Em9t9dh5M/pXFGwF+3gnxuv9apetYF/9KJfLNgDbsizX8Xw+bxuGYVerVbtQKNjVatWZg5DtRrPZtBcWFuxkMhnYrqytrdmGYdgLCwvOv5PJpJ1MJu1ms6m8Jp/P2+Vyuet97wQ9H/gXVwAQj8fx4IMPev4+OTmJF198cRNLNDyUSqWh+NR2q3LixIlBF4HZRI4ePTrQ5w+7vjUajaF789ivMg1jXZn+0M9+xz7Ym82wt8eOHcPY2FjXc+LxOObm5rBt27a+lycKWKf6z+0g40HHMzqwnx1Obgf97yeffvopAGB0dNR1/Pjx43jxxRexa9cu59jk5CT+7d/+reMe8Xgchw4dwi9/+cvAz//Rj36EbDaLQ4cOAQDGxsbwy1/+Equrq/j5z3+uvObSpUuYnJwM/KzbjaGYuNJhenp60EXYdOr1OqampgZdjC1LqVRih3oHMTU1NdDlIcOub61WC5lMZtDFcNGvMg1jXZn+0M9+xz7Ym82yt9lsVvtcGgQMM6xT/ed2kPGg4xkd2M8OJ7eD/g+SGzdudBwTJ7LCUqvVYFkWvvOd73Q8wzRNzM/PdywZ7LZM8E5j6Ceu6vV6x+ZotNGeuAEZbRhHG3JWKhUkEgln3agMbd5Ja0uz2Szq9XpPZVTts0Ck02nlcT8qlQoeffRRtNttrKysIBaLKTcUrtfrzjPS6XTHDHur1XJkpdp8zq8MU1NTSKfTzn1isRhGRkaUdVE9q1QqOb/L+5mJ9RF/E+9dqVQwPj7uPFdcAyxvtlkqlTAyMuL8e+/evQCAmZkZ53lyW4m65XU8CKVSqWPz56C6Ka97DqKb8rOIVqvl3HNkZATZbFZ7QKBzbZgy6+ootTWVY3Z21tGFdDrtDF4nJiY62lDs716bW8t1CLLBvkrfxGeo5BNUx3RkLG40mU6nHUfXaDSwc+dOZ38Pcd9Alc7IfavRaDjPTiQSznNLpZIjV7ITsly87Gy3MlEZerFdfveV7091UrWHjt6o0LUzXv4hiC2S/WGxWMTIyAhGRkZw5syZjvN7tamizKhN5OtlKNii9pP1VWUvvVDpaT/tvJ8P9vN33SA5074Yoh9PJBJKPfezIXK9veIQXbsob3I+Ozvr2EWVvVXJnajVagPbr7Sbv91sf+mlUyqd9JOZ3I/feust5zf52l77QFD7JPqD8fFxpNPpjvt3sz+iDMW+pfItXkQVOwO9239KMCFvBC/au27+J0w8063fRjlOChpTiLLxs5tBfJpsF6NIXlKv151YSnw+yUmli6o2ofNU/VGsqxgPeNVLjvfERDP1eh2JRMKRj5+N6eYfepWdalwu94N+jldFucj2RXwOjTVnZmYAoKPeNHGk8uVRfUDz2Wefef6WTCYBAL/5zW9cx3/1q1/h8ccfdx3TtZOiTGS5RuUrNpVBr1UkaM2svEY0l8u51tGura05a1PF4pfLZds0TRuAs9dBtVq1c7mcDcBZR2rbtr2wsOBan0vr3jOZjOvZuntcWZbVdY1rMpl0PT8IUOzHIpaX5EN1EuvQbDZdzxbL6bc2WZRzKpWyU6mUnc/n7Xw+78herFOz2bQNw7BzuZzdbDadPScA2Pl83jmPZJdMJjuemc/nXWuyFxcX7WQy6axBpjqSPMT2TaVSdiaTcXRAlJPYJtVq1TYMQymDZrNpA3Dtp9ENeS0xrYmW2yyIbq6trdmmabp00zAM2zAMz3XPYvnFZ4n1I12x7Q09oDbVwe9a3TKr1sXr6mg+n3fpAt1L1Bevdfe5XE6pl6Je0HpzOk8sh66cvOwFHVfV0W+9ulg+PxnTWnuqJ/VVEVlnvXRG1E/RziwuLtoAbNM07cXFRTufz7vOFfu6rp1VrckPY7u63de2bcf+5HI5u1wuO/sXyPso6uiNF5ZlOTKV25hsJdkZkpN432az6ZRfrK+sY5ZlufxhPp939kUheYn2LKxNpX/L/VC0YWI7ZzIZu1AoOHUR6+1lL1X17Gbb+m3nVeXT9XdeiHXP5XJ2Mpm0C4WCs9enXD4dGyLuFaoqQzKZtBcXF51/++l3uVx22cBcLmcbhuHIQmVvSZ9U/oraz8+P6cjOzy6LsvXyt4Pyl7at1ilqO7EclmU5eixCdpjqIuo7tV9UfUDXPpEPpXtSGcVn+9kfujft9WJZlks3gwxZwsTOth3O/pfL5Y59e9fW1lzxr5//6TWe6dZv+zFO0o0p6Dc/uxnUpxmGYWcyGed+JHdZt3X3/rEsy65Wq075aVxSKBSc/qSSgUqvxXEOyQ+A0jaKexv5xXtra2uuZ5CNEeUt63+U41RZN73G5dVq1WmPfo5XxXLR/lSy3OS9rKgOMmTHyI/pPDvo3nlUf5Wf9ur38phZ106ura11xIymabrsTZTx0mYwdBNXqj+V4qiMEN1DHCiQ0MUOTMot308+FmRzdlIOeTO8ZrNpm6apKYVOujlfWemTyaSrDoVCwXNzSt0gTB5s2PatTe3EZ+VyOWU9Vc6LOo1svFOplHOMHJxsbGTjLDtTmnQQf5Pbj4yGGMTT+bJD6ka3gbEsX13dTKVSHeXymtT1QqWfqmcH0YFu1+qWWVUuHR2lf6sCerG9VPenAERVJ7HtyPmLWJYVqK946Ztpmh11pHvrTEjatp6MaXJZRHZ2XjrrJTuVnSHnKE/I9GpnvWx5WNvlN3ElQpN84qSKjt50gwZuqolMUaZB/IzXuVRXUZfITouTP2FtKk1myTIRj3kNcKg+quv9Jq56kUlUdl5VviD+zgsqt7wZK+miWEZdG+Jls2Sbravf8sQkBcjib3JdaaCiGij0+gJPJMjElcrfeunlZvlLOscrRpDlKdsx8n2yvqvsTRR9QLcvFgoFpV2l33XtD22OLPtFcQJdhzCxcxT230tu3fyPHBv1Gs/o9NuoxklBYoogdjOIT1O9OJFtTdBNq+l8se+IE8nyBK2s16qJTjom9/FCoeBqD92YGoDrZYA4TlPpf1TjVF1bZdvd+5yss2FiPvJ9styofbzsrQqaOCT/6veCOejEFbWDSuaqF5Zra2sdstG1k1Q2UTeo7uKxqOKlzWDolgoWCgXYGxNqsG0b+Xw+8D3uuusu5//j8XjH79PT07h58yaAjc/kstksVlZWQqWQHBsbQy6Xw/z8vOuzxHPnzuHgwYM937cb8kak8XjcVYcLFy5gfn7e9anfxMQEAGBlZUX7OTt27HBtjjo5OYlkMol2u416vY5Wq4X5+XnnE0eRn/3sZwDg+oz92LFjaLfbuHz5snOsXq9jdHTUaa+rV6+i3W7DNE1X+ancH3zwges5tLl/PB7v2GxP5tChQzBNs2Pjy2PHjuEnP/mJrzzC0E03W60WVlZWsHfvXled6ZPW9957r+fnJpNJvPTSS86nnvF4HN///vdDXxu2zDo6SrojrjEfHR3FzZs3fZfmvPvuu7Asq+tn2vV6Haurqx3y8NMjHSqVCizL6thceHR0FJlMpqMfqNCV8UMPPdTxifOePXtC10G2M7QuX9RlFWHsbFS2ywu571Edv/rqKwB6euNHNpuFaZo4e/as6/jf//3f48CBA2GroESsl9w+UdjU5eVlR79oSSEAfPnllx1lkROuTE9PwzAMXLp0KUwVtemXnQ/q7/zYs2ePq92ef/55AHDkFMSGjI6OIpfLYWVlxRWHXLx4Efv373f+HVS/v/vd7zr3V8VUIsePHwfg3jC/1Wrh7NmzeOqpp3ykES0qf6vSVZl++csouHz5MtrtNh5++GHX8Xvvvbfj3M2Mde6//36srq66lkj94Ac/cH7XtT/z8/PYsWNHh54lEonIyuoXO0dh/7vh5X/8iLLfbvY4qVe72c2nxeNx3Lx5E4cOHUKr1UKxWHTs3B/+8IeeyikjLhEm+woAH374IQDg5MmTMAyjI16k/fZE///Tn/4UQGc9L1y44Gy6HTSmTiQSjoz8klgMYpxKqPqcSNiY79y5cwCA7du3u45ns1kYhtHhE7sxNjaGq1evOv71L//yLwNtHaJz/0wmA8uynKWntH3AtWvXAAD33HOPc75qmaCundy1axds28bY2BgajQampqbw2muvAbgV7wKDHRcHZegmrmT6FeDTWttjx47hmWeeQSqVCn1PMkriZNsggjVidXW1YyJQ/AvD7t27AWwoPq3FNU2z4zxynKLBeOyxx2AYhquDnDlzxrWxKmV88Cp72LXGx44dg2VZznr5RqOB9fX1SDfgCwrJsVqtKuu8vLzc873feOMNABv7JdC+E7oy7HZt2DLr6GiYzCgfffQRUqmU5/137drlOEV5IBAFpMd/9md/1vEbDew///zzrvfQlfHzzz+P3bt3Y+/evUgkEiiVSgNPatGrne2n7dJBR290OHjwICzLciZ7SJejmBQNSlQ2lfZFuXLlimMbdNmxY0eoF0RB6YedD+rvghKPx50XQ0BwG/L0008DcE8cvfHGG64BWFT6rYIGd/Pz886eLJcvX8bu3bt9J72GhX75yyj413/9VwD6mwVvVqwzOTmJQqGAmZkZ3HfffSgWi9i+fbvzHB37U6vV0G63I53o64V+9o+tVK4ox0n9tJvFYhE7d+4EsKHv/eR73/segFsTY6urq8o60SSSuMH+6OgoUqmUa+KoVCrh2Wefdc7p5zgAGL5xKhE25vvoo48AqCfwd+zYAQD4/e9/r12eeDyOBx98EAsLC0gmkzh+/Hikk1enT59GPp9HqVTC3Xffjeeeew4A0G63kUwmXTGinE0wqJ2kvbB2796NRx55xJl8lRnGcbGKoZ+4Gh0djVxolUoFDzzwAPbv34/l5eXI0ktSwLa0tIRarYZSqYTdu3cPZJBCqLIjRIHqDREZDj/i8ThyuRwsy0KlUkGr1UKj0VC+LehXOlf6GuLIkSNotVo4ceJE3x2eLl988UXk9xwbG8P169exuLiI9fV1TExMaGcd0bk2TJn7paPE+vq61nn9kDtBQXsY/MoXj8dRKpVQrVaRSCSwd+9ejI+Pa29qGzVh7Wy/9cIPXb3pBr14OXnyJICNt4KDfnsVxqbOzs5i3759+Jd/+RfMzc35vuEdNP2087r+rhdUEzy6NmTXrl1IJpNOZqJKpaL88jIK/faCBkf0lcDRo0edY1uBfvvLKNDtx5sZ60xPT8OyLGSzWWcCS07IoFPuQdt+oL/9IwybVa5+jZOitpvj4+O4cOECrl69iunpaeXERZSovjSnDep1OHz4MIBbXwidP39eOWnUr3h0GMepRBT9Xt7UPCzf/e53sby8DNM0cfz48cji6Xg8jldffRU3b96EbdsolUrOC6gXXnjBOa9bNkEdeTUaDdx3330ANmxHNpv1/MJzmMfFIkM/cdUPDh8+jGQyGSjNsi4UnB07dgwnT5503n4OgmQyiVKppOxoqmxTQaAOs337dueTRvrEUYU8M0wDulOnTuHcuXOuZQzAxmfnwMYSBxn6pDIstGTx9ddfR6lUwmOPPRb6nmGgT1zPnz+v/D1smwEbhml9fd15Ix4kY4fq2rBl1tFRcqhemQa78dBDD8GyLOWn/ZVKBY1GwzHin3zySdd79cK3v/1tAMDHH3/seY7fl15BZbxr1y4sLy9jcXERq6ureP3114MUOTLC2Nl+2i4ddPRGB5qkX11dRaVSwaVLlwb29iqsTW00Gjh+/DhyuVzPE1br6+ubnj49ajvfi78Lyvr6urOkphcbQoHvuXPncOrUqY6BUVT67YW4ZJGyiw3DwCgoUfvLKPjWt74FAPj3f/937Ws2M9YZHR3F3Nwc1tbWAMD5mkTH/nzzm98EAFy/fr1v5dOh3/2jVzazXFGPk/phN0ulElZXV/Hyyy9v2tecNKFE+kxfW3ll0Za/UpucnHQy11UqlY5lnJsxDhimcSoRNuYj/9ItY59f7EUTeTLxeNxZShn1xBhRr9cxMzODTCbj6nOqZYJB7OSJEyfQbrfxyiuvaJVj2MbFKu7IiSvx000iqllUMWAD9D/n7gd79uxBu93uSDtK+/roopLNpUuXkMvlnP1PUqkU2u12R6cnIy8HzqKcLly40OEcd+7cCcMwMDMz4zJarVYLzz33nPPpZxhodpkGY4NexhCPx5FKpbCystKRcrdYLGrvg6BCfuNLRux3v/tdqGvDlllHR8loHz582HX/VquFK1eudL0/7bHxxBNPuIK9RqOBU6dOYXR01DHM4tKWqKBlsUtLSx1B5Y0bN2Capq+N0JWx3E7ZbBbJZFJp7zaDMHY2KtvVKzp6owsFifv27XO9SSOo/eW3Z90mKnohrE1VfWYfxG/WajVYloVnnnlGv9ARELWd78XfdUPei6XRaMCyLEdXerEhVOfXXnsNo6OjHfoapX57QXp//Phx5ysD8Tmb0Y97pV/+shs0+Sh+adFqtTq+sHnyyScBAC+99JJ2/wvTB3TtU7FYdOkn7adDfkDH/oyNjcE0TayurkbycrJXNqN/DHu5oh4nRW03AfU2C3/84x97LqMKuc5XrlyBaZrOF2g0oSFPrlBfkF/KA7cmB/bt29fxJWo/xwHEMI1TibAxH02+/eIXv+j4jV4giJA9k9vXa8Lwxo0bMAyjJ1m1Wq2u++LV63U8+uijSCaTOH36tOs3eZkggEB2UjWZ3W3/t2EbFyvx3b59ExBTmMoZdrzOlzM72LY6C4S4ez/dV0wJSmlrxdTldL1XFiR6jleGHK/sBpQ5RCddNkFlp5S14n3kVKJUB5IJpcvE1xlHKGWrbiYzej7JSkxfK2eHoWwbqlSkshwIv0wMlA2CZEAZTVSZ6igdrur+mUzGLpfLyud4ZQTxw0sH6Zly2m9d3ZRTsaZSKafuuqj0U2xD276VMUan3n7X6pZZVS5dHSX7YJqmXSgU7Hw+b5umqcyUUSgU7MXFRUfWYopYyh4IKWsH9SnSVzFFNB3zk5WXvlEmHDldspytsxs6MqaMb3JWTTlbDR3L5/OubHFy23ilCae2kLPtUNnonrp2VlWmKGyX6r7UFnL/pDqJ9dfRG13IdnmVnTKtLi4uOuUV7Ty1oconibZI1FFV+4WxqZR5isq5uLjo8tsLCwv22tqay65RuckXeGUNktsjiO/tp523bbUP7sXfyVC5xWsoBbicdawXG+KV+Ynw028xLlPJ1Mveys9QZU5S+U5dRF+g6k+UxU/WE5W/te3N95d0jkqnqC3IPhYKBad8+XzekRfZRjq3XC475yWTSWVbhOkDOvaJsgqS/lDadTHe1bE/pOskx2q16siQ2knHBoeJnW07vP1X2TCvTKIq/9NrPOPXb6MeJwWJKXTtpq5PE+0i6SA9N5fLObrnFa93g+4jxlT0fDnLHMlKzOKYTCY7/J1cP6+4XifeE32nqk+r9F/Ea5yqg1csr5Ix6bFYhn6NV6l/53I513hV7m/i871iibW1NUf3vTLuEV6xMqHq3/Q86s9UZhFVNkFC106KNo+eJ+pr1PHSZjDwiStSavnPazKDFEv8Ux1LpVLKe1erVVeqSwrO5Ukl6pjin23byufIeKUWzWQyjtHWVQhSIBqkq8rlJRMqC000yUZYB3IK1PG63cOyLNd55Ey64TdRWS6XnU5mmqar43u1kQgZhVQqpXxOL6k+VfIuFApKfbNttc546aZtbxgrkqNhGHY+nw880Sj3I9M0HZ2nMug6cJ1r/crcrd/o6iilfxUdi4jo+OTJ4UKh4NRBDK7lc+j+VEcabOv2GS99q1arLqesKr8ffjLOZDKuZ4gTB+I9yAaRE1bpTBD7F8bOepXJtsPbLvm+XuXs5nt09EYHCuq7lVWWFbUDtaGu7e/WflSWXm3q4uKiI1OqDwWXYtuRXe3mC7zspa7uqdKMR2nnxTqLPpjoxd/JZaL2pvboZu+D2hCyh93opt/dbLZ4f5W9Fe+vesFHOhOkP6t0XVUuXRulOnez/KWXTqn6VyqVsnO5nOs8so30jFwuZ5fLZefFTtR9QMc+yXZHnpAiutkf8Rx6HvkxejGja4PDxs5Up17sv/ysbn7SS597jWe69Y/N8t/dbLWf3Qzq06hMpmk6LxgNw3Da3c/H+7WhGHd66QDJhs6jftiNfD7f1V90i/e6jR8ILxsjllk1TvVDpUNe/ShIzEJlChPz2fbGJBLpLcXvqpdiqnI1m03nhRyVgcrh1fe97iVCOiq+yKPydbu3n47o2EnxRU4ymbTX1tacSa9+xEubQcy2NyFF0x1GqVTCJ598gldffVX5ezqdxtzc3JbY8yEWiyGVSoXOZDGsTE1N4fHHH49s40mGYRhmuBhGO1+r1TAxMYFCoTDw7J/9IpFI4OrVq8O53OAOYxj7AMMMK+l0GisrK5uSxXgQ+I1TmY2lmQ8//PBQLKXcTIbdV9yRe1z1k1arhaNHjzqbj8vU63U89NBDW2LS6nanXq8r1w8zDMMwtwds5wfDmTNnsHv3bp60GgK4DzAMQ/iNU5kNDhw4cMdNWm0FX8ETVxFQq9UQi8UwMjKCnTt34uDBg8qJqWKxiA8//HDLzHDThnjr6+uRbV4/aIrFImKxGBKJBB599FHPjfgYhmGYrclWsPOU/SjqTfgHSTqdRiwWw/j4OI4cOaKdyYiJnq3QBxhmGBGTIgxzEokg6I5TmVvcKS9dtpqv4ImrCLjnnntgGAaAjQwTXp/9T09P49ChQ5tZtJ4pFot44IEHAGxkF7n77ru7ZkXYKlAKWwD4p3/6pztuNp1hGOZ2Z9jtfDqdxo9//GMAwNLSEmKx2IBLFA0PPfQQgI2A//33379jAv9hZNj7AMMMI7VaDXfffbeTVfGBBx5AsVgccKnCoztOZe48tpqv4D2uGIZhGIZhGIZhGIZhmKGEv7hiGIZhGIZhGIZhGIZhhhKeuGIYhmEYhmEYhmEYhmGGEp64YhiGYRiGYRiGYRiGYYYSnrhiGIZhGIZhGIZhGIZhhhKeuGIYhmEYhmEYhmEYhmGGEp64YhiGYRiGYRiGYRiGYYYSnrhiGIZhGIZhGIZhGIZhhpItMXFVq9WQzWaRTqc9fy8Wi5tcqu5MTU2hXq8rf2u1WigWi0gkEqjVaptcss2l0WjcMXUdNhqNBmZnZzEyMtIhezreaDQGVDp/arUaxsfHEYvFMDIygqmpKbRarUEX67ahUqkgFouhUqkMuigO9XodU1NTGBkZGXRRAtFqtXDmzBkkEomh80UipVIJ4+PjWmVU6Ue3em4Fm8Los1yp4EKp1Jd7f1Cr4Y0zZ/py7260Wq1AfWBYqNVqmJqaQiwWG3RRtGg0Gshms4jFYojFYshms8p4uFKpdI3t+0EQWW5Vf3QnUiqVkE6nN1WXguI3lhVpNBoYGRnB7Oysc6yb/x3GeG4YaDQayj68mfIaHx/H+Pi4b5mGjSD6upkM/cRVrVbDu+++i6WlJeVv2WwWExMTmJmZQSKRwOzsrNJBFotFjIyMIBaLIZFI9KSspVJJewLm+vXrGBsbU/527tw5XLhwAZZlBS7DVuPatWs4e/bsHVHXYaLVauHatWtYWlpCu90edHECU6lUsH//fiwvL6PZbCKXy2F+fh5ffPHFoIvG9IlGo4Hf/OY3mJ+f33I6e/nyZbz//vtDbecqlQquXLmC1dXVnu+xFerJhOel2Vl89cc/Yk826xz7j0YD/1s2iz+JxfAnsRieSKfxgUYs9B+NBv7zyAheFwZaD+/ahR1/8Rf4P6emQpUznU47kyN+f+l0GlevXsXbb78dqg9sNvV6HV988QXm5+cHXRQtGo0Gkskkjhw5Atu2sbCwgEuXLuHDDz90nVer1fDpp58qY/t+EUSWW9kf3WlUKhV8/vnnWFlZGXRRPKnVavjss89C6Tv732A0Gg1cvHgRpVJpaPpwo9HAtWvXhqpMKrrNvQwce4sAwE6lUs6/y+WyDcBeWFiwq9WqXSgU7LW1NTuZTLrOs23bzufzdiqVsguFgp3L5WwANgC7Wq1qPXttbc1eWFiwDcPQuq5cLtv5fL7rOYVCIVAZtgqWZdmFQsF17Hata9TkcrnI77lVZZ9MJvsiD2a4WFxc7NDNVCplbyHX5FCtVm0AHfZvmIiijEHvofIJUaDSnTudKGT9DwsL9t9I8Uur2bT/1DDsfZmMfbJQsPdlMvY3APsbgP1uudz1fn+VStnfAOyTinL9w8KCfTiEnU+lUnZZej7FdyILCwtOXLgV+qmKrWIX8/m8bZqm9vlybL8ZeMnydvJHUdEv+90PBqFLQQlbxl7s150eS0fdh6OQZ7/sStRx0TD2qaH/4sqLU6dOIZVK4dChQ86xsbExLC8v41vf+pZzrNFo4Nvf/jaWl5cxPT2Nubk5LCwsAAA++OADrWeNjY3h0KFDePHFF7XOf+edd/DXf/3XAWpz+3DixIlBF2FL0mg0tswb1c1gdXUV3/nOdwZdDKbPHD16dNBFYPpMv3wC604nYWX963od/8ePf4z9Bw64jp8/dw6n5ubwj6USnp+exj+WSviqIApwAAAgAElEQVTvX8dRLxw+7Hm/14tFbBPiMZlnDx3C/3XpUs/LBvfv34/JyUnf8w4dOoQf/vCHPT2DCcZHH32ERCIx6GL0BNuUTjim39qUSiVevh8hwy7PO8GGbdmJKwBYX1/vOBaPx/Hggw86/962bZtrcgsAvvvd7zq/9YNuywRvZ0qlEk++9ECr1UImkxl0MRhmU5mamuJP3m9z+uUTWHc6iULWR2dm8FQmg/8yOuo6fte2ba5lg8DGpNOoaaLh0Q6/rtfx/7z3HnI/+UnXZx44eBA/P3IEN3vYvzArlakbchzIMCJsUzrhmH5rQ3u0MdEw7PK8U2xY6ImrVqvlbDLmtYlyqVRyNvii82mvKdV+UfV63bWxo7g5HTE6OgrLspRKND097fx/PB7v+P2zzz5DMpnsSyBTqVSwe/fujuO0QTnV++OPP+44R663vAGfvGeD+Ju410O3TUd12gu4tdEu7Q0hbiynolgsYu/evQCAmZkZz3JUKhVHDqq2k8s3Pj7uux9Zo9FwNiys1WquZ6TTac9NQeWNv8VZdHnvDC9Zk/76lVvc5LPVajn3OHPmDHbu3OnsuSHuxyHvzyHKWnVcF9q0Xex/tOkjbVpbr9edMqTT6Y43DDrtRHvC0e/pdNp3fziqF3BLj+RNVOW+VCwWXforJwSYnZ31tCOEuElsq9Vy9H9kZASlrzcortfrjs4kEgnPvfTkson49XGSLZ0zMjKCbDbrDNBEnSBZ1mo113HxWSqdq1QqHe1NBLXV1O9Ue8p0s0PpdNoJiCcmJlz1keVJ7XBG8VWGqh/7beAvl1N8rtznVHXxulZFUHl2q4+8sXSpVMLIyIhjm3VtOyHq8/j4uEufvfTDC5VNUfkE1Z5EXvL3kq2f7vjZdi8qlQqmpqY62mpkZEQpA5UNLAmbmcv9Uuyb4m+iDe+1/aOQ9a/rdVxeWcH/Irz4I571iJXMRALfMoyO4zdbLeSefRZ/NzfnJ3b8xcMP48t2G//35cu+50aNn58D/G26TNB21/EJfs8Qy+oVG/QSW/nJQbQtJMeVlRWsrKxo20i5fN3sYxT+VUVU/kimXq87sYR4PcW/sm3uh02RZSJvlC/GPvV6HYlEwrE9qpi+m5/XGYfoxtZe5wTxv7QRvyx/r/brVVZkN0T9VF0vIsZ5iUTC1c6AO3bwQ+WvK5UKHn30UbTbbac/quQbtv1Ux8Qyy+f2awwqykK8P/lzP3n5PddLnn5xmayDKsTxhjhOCRKD+tkwHT+mM/cyFIRda5jJZGzTNG3LsmzbvrWnzsLCgm3bG/tD0THat6ZcLtvlctk2DMM2DMN1v7W1NdswDDuXy9nNZtO2LMtOJpMd6yybzaZz3DAMrfW+lmXZuVzONgzDbjabgeuqs19QLpez19bWOo4ZhuFct7CwoNxnyzAMO5PJ2M1m0242m3Ymk7EBOPcTZaGqbzKZdOTuhV97UfmSyaRTjnw+r7UW12vtNT0jl8vZhULBrlarzl5j4nOpTemYWN9uMi+Xy46sxP3M6FrTNF3tXSgUXO1RrVYdXSS5iPJX7VeWTCbtxcVFrXJbluXoOwA7k8k49SdZyeudm82mc45qfXE+n7czmUyX1nDLnurabDbtcrlsm6bpOl6tVp36Uvmq1aqjq+KzdNqJ+jHp7uLiYqC9trx0PJVK2clk0mknKl8ymXTOKZfLLptB/c9rnTbtYaeyUSSncrnsHFc907Y3+jnZLbH9xHr49XHbth35k2xTqZSr7HSNKEvLshz9on930zmxz1D5gtpqWbeojeXzvPCyp9QX8vm8s16f2lOU0+LiolIX/NbjW5blaleRZrPp0lu6p9iGzWbTKaNYdtn+BZWnX31Eu5lKpRxbTm2uY9upjJlMxs5kMnahUHDqItZbpR9e9fSyKapz6ZjXfpHNZrOjnVV46Y6ObVextrbm7JtJ/S2fzzu+T+WrxDhF7O+iv6D6A+iIOchO0fGw7R9W1icLBfsbgP0/AuyP8XAyqdyj6m/yeWfvq/9RrXrucWXbG/tnfQOw92n4Mx1I3l6IfaCbn7NtPZve7Rk67a7jE2xbHSOI7S/e3yteDhpbic/287viuUH2RNG1j1H5VyqjLLew/kjGsiy7Wq06bZTP550YWPTLRD9sytramm2aptIeNpvNjtiH2o7koLIpYcchQWLrIP7Xtt378VAMRLLya7+wsqLfxOtVNoWuJ/8r7rtM9aZ9m1XxjKpNuvlr+R5h20+8XoyfqLxyfeWYqp9jULJzZKvEPiRe7yUvnefK8uzml8W4Qr6PaFfoj+yCuIdWkD7gZcN0bKfu3MswEHriihrKddMunVWElEIUsmpzdcuylMITG88vmBCNPZXFLziW0Zm4kutISqtSJPE4dW7VIEM8tra21qHYdL3Ohpg67UVBe7d6qfCbuBKNHAXN4nPJiKvu6ddxxMkxudyiDEmXyEEQNOiWDXw3vQtabtJV0jtR/1SBFLWpql1TqZSW/nYb4HkNMlVtL5ZNp740cSiSz+dDTVxRG8kDUJURlp0hOTG/Z8plpmfKMpHbiwJU1T3pPN0+ruorsr51C7BVx1Q659VfdW21ajJQNanmhV89xPYSk3HY9i1ZyrrgFdDKULuqJmVUgwSdhBNh5KlbHzkYpgCDzvWz7V7BtCro9KpPFDKhYEy2wzSB7ofqWUFsuxeAewLPtm/pnjiQzuVyyv6umryTfb1YB+rnUbR/WFnTJuq6E1cNy7K/AdgNqczvlsuuySy/iSvbtp2N3qNANUgQ6ebnxDbWsendCNLufj7Btrvbd1UZe4lRVATxu1SmXiauRMg+Uj+M0r9SGWW5hfFH3aDrxf4nvmiiOkZtU+jZcr+neortBrhf8IpyV7Vx2HGIbmwdxNdQPeR7dms/8b5hZUUTkvKz5WPy9WI9VefqTFx1O666R9j289J/1XilXC53yLlfY1Ca/JFjfdUEv0peOs/t1iYqv0z39YvN6TrZLgTpA14xmI4fCzL3MmhCLxVcXl52PnGkz+UA4Msvv+w4V162J+8xVa/Xsbq6iu9///uu46PSfgvi/ZaXl1EoFABsfM6q+iwQAHbt2gXbtlEul5FMJrG6uopnn31Ws5Z6qJYJvvnmm87zReSNp+PxOG7evIlDhw6h1WqhWCxi//79AIA//OEPznljY2PI5XKYn593fdZ+7tw5HDx40LeMOu310EMPYX5+3vXp6p49e3zv7cddd93l/D/pgvjcCxcuYH5+3vUZ5MTEBABop7l9+umnXf9++eWXAQDvv/8+gA05AcD27dtd52WzWRiGgZWVFUeuo6OjyOVyrmMAcPHiRadteik37X/mtw9aPB7HsWPHYFlWx+fiOtf3itwv5X6rU9/7778fq6urruUEP/jBD0KV6+TJkzAMo8Me0JLfs2fPdlxD+9mNjo4qlw3LyOfce++9APz3w3v33XdhWVbXz/h1+3gymcRLL73ktHM8Hu+wiUHR1TnCz1YDUKbyfVCxzKhXxDKQ7SA5Xb16Fe12G6ZpuuRN+ueXeCObzcI0zQ6d+fu//3sckDamjgI/eQatD8k5Ho87/SGIL5bbaXp6GoZh4NKlS71WMRCHDh2CaZodG4keO3YMP/HZE8mLILa9Gzt27HD1k8nJSSSTSbTbbdTrdbRaLczPzyOZTHZc+7Of/QwA8NZbbznHfvrTn3YcAzbsKG0sHkX7exFU1n8uyc+Lv8nn8d8XFlz7Yd1stfDKSy/hpVde0boHMWqagc6PApWfE22ajk3vhk676/qEsISJrXrxu0Hxso9fffUVgGj9axRllP2RDuJ+bBRbAsCHH37YF5vSarWwsrKCvXv3umQ2MzMDAHjvvfdc1yYSCaeOfnFC2HGIbmwdJar2I6KQ1fT0NG7evAlgQx+z2SxWVlaUcZJ4PbAxPsxkMo6P6Tdh229ychKGYeCNN95wHTcMA5ZluWzjm2++iaeeesr5dz/HoPPz89ixY0eHPdFNFhF27BvELxNiX4vH4/jbv/1bABt2IQp0bGfQuZdBE8nm7LSn0JUrVzoUOQjkQB9++OFA1z388MPI5/PIZDJYWVnpuiZ4cnIS169fh2maWF1djdRIqLIJLi0tIZVKad+jWCxi586dADYCSxXktPL5vHPs7NmzLuPQDb/2ev7557F7927s3bvXWXst7hvWL1ZXV1EoFGBvfAnY8dcLJEsyih999BGAWxMSIjt27AAA/P73v3eO0USYmFnljTfecAUh/Sg3QQPso0ePOhNAb731Vt+cuw469Z2cnEShUMDMzAzuu+8+FItFbN++vWMCN+hzTcUAhwz/IDcl/Oijj5BKpTxlItbbr49Tn5yYmHD2BduM/hcU0zQ7EmRQIH/PPff09dmffvopAHjKW0deBw8edE0KixPWm00U9QHC+eIdO3Yog+x+QZPytNdIo9HA+vp6zzYiqG0PAr2Q+uqrr/Cb3/wGAJS2iCbKxcHA6OgoUqmUa5BQKpVcL86ian8vdGR9+ev4a0Rjgv/C1/t4yHtf7f9v/w3zb7yhdQ8Rcwiz0AWx6Sp02p3QifvCECZGGQa/G6V/HRa+973vAdjwmf2wKXTParWqlNny8nKo8ocdh+jE1ptFVLKiPfOOHTuGZ555JtDYjyY9aLK234Rtv1wu5xpD12o17NmzB4ZhOBOqXjFVP8agtVoN7XY71EveQY19Rai/R/XiQsd29jr3MihCT1zNzs5i3759+Jd/+RfMzc1F8hXIF198Efiabdu2oVQqIZPJYHV11XfTSXJoURoJr2yCquyHKsbHx3HhwgVcvXoV09PTygAcuPW2YmlpCbVaDaVSCbt379YacOm0VzweR6lUQrVaRSKRwN69ezE+Pu676XEU3LhxI9L7eX1lQ47Kj127diGZTGJ+fh6tVguVSkU5Ax91uUVo0HHu3Dk0Gg1cunRpIM5dRKe+09PTsCwL2WzWmcAKO1FMm9gPIzr9XKePj42N4fr161hcXMT6+jomJiaGMpPJqVOncPPmTWeTx3q9jvn5eeTz+U2b/AmTlpi+rDp58iSAjbeNvX7tExVh6tMPX9xPaFL+yJEjaLVaOHHiRCQDTV3bHgTVF4c0UabD4cOHAdz6Kuz8+fPKAUK/0mzryPqxrwdZ/+FThl/X6/jH8+c7Nl7/oFbD5ZUV7HzgAfxJLOb8/a9ff9kzOzODP4nF8IHmZt3DgG7s5oVOu+vGfWEJE6MMg9+Nyr8OC/JXP0B/bEov4ykdwoxDAP3YejMJI6tKpYIHHngA+/fvx/LysvPl27AStv0ofqKXIW+99RYOHDiAbDbrTKhevHgRzzzzjOu6fo9Bw9i5QY59+4muH+uXrYiaUBNXjUYDx48fRy6XiyRIpuDwk08+8T3XayBHAw96g+kFObSovgzwyiZIn076KX6pVMLq6ipefvllrSVNNFt+7NgxnDx5smOJnIqg7bVr1y4sLy9jcXERq6ureP31132vCUMymUSpVFLKSieDiwoaCDz00EMAbs38f/bZZ57XyG9SX3jhBQAbAcKpU6c6goN+lFuEBh2vvfYaTpw4ofUpbz8JUt/R0VHMzc1hbW0NAEItz6W3kV6TX0HebkXNQw891PGJNFGpVNBoNAL38Ww2i/X1deeT7iAZoDaDyclJZLNZXLhwAbFYDI8++ihefPFFvPrqq31/9v333w9gY2mBDGV58SMejztvDSuVCi5duhTqi8AwhK1PFL54fX0dmUymp2t75dixY2i323j99ddRKpXw2GOP9XyvXmy7LhQMb9++3YkZrl275nm+/NZ3cnISpmk6/VheuhyFPvuhK+v/r8tXab+u13F0Zgbn//mfO377z/fcg1cLhY6/g18viXoslcKrhQL+syLmuqy5FcBmomPT/fBr96A+oVfCxCjD4Hf74V8HDQ0S77///r7YFFoyff78eeX9oohNexmHiPjF1ptFFLI6fPgwkslkzy+Vb9y4AcMwNjUGCdN+9NVfqVRCvV6HYRiIx+POEuKLFy/ivffec03g9XMM+s1vfhPAxgckYdnssa8I2YWo7KqO7Qwy9zIMhJq4Un12H2ZmkgKp+fl53y8zvJywbqN/8MEHyGQyHbPLtVqtpzqolgkCt9a1//znP+96/eeff95x7I9//KPn+eIacUAvINdtL3lSMJvNIplM9v2T8D179qDdbnekpaY1uDrIMqNAgN4OkGH+xS9+0XEtTRLIiBNHo6OjHToTRbn9oEHH/Px8X/bgCYJOfYvFous3WlOvo0N0nfypLE3YyUEEnT/I5ZO0f9cTTzzhchCNRgOnTp3C6Oiodh+X+98rX+8X87vf/Q7Arc95xbcjrVYr9NcBQZmdnYVhGLh+/Tps28bNmzc37bPqnTt3wjAMzMzMuPSh1Wrhueeec5aG+UGB2759+5wgWoTsqvwW7+OPP+616ErC1iesL67VarAsq+PtaL8h20rBbJgBZy+2XYVKbpcuXXLKRwF7u93umFCiPqkagJEN37dvn6N3RFT63A0/Wf9XnyUW4qSVvBTwjTNn8F9GR/H89HTHX+brdvmv3/8+np+edu2JJfKYFLP1GotFhY5N16FbuweN+2RoMkP0taqYI0yMEtTvrq+vR95uUfrXQSHL5MqVKzBNE5OTk32xKfF4HKlUCisrK5iamnI9v1gs+u7bqUMv4xARv9h6s/xvFLJSxba6/YBeTuj6qKgI23779+9Hu93Gj370I2dcMjY2BtM0MTMz0zHZ2s8xKD13dXW15xc9gxr7ipw/fx7JZNKZ2AvbB3RsZ5C5l6Gglx3dCcoMZxiGvbi4aC8uLjpZpSil5tramrMrvpim1bZvZaASMxNQNhG6p5hqko7RTvfJZNIul8tO6lBKoytmSqAsA4VCwXm2mPJSxCslcrfyEl4ZD8TsIZlMxq5Wq/bi4qKT5UBMASzWSUxTnsvllGljvbIoeaHbXpT1Qc5WJKeOlxEzK1AdxMyPquyIYnYNSmWKr7MdUApmVZYIGTHtvJzOVpYd6ZicHlSlE/L9VdnKdMptWZYyOwxBMiqXy3Y+n1embjdNsyMLiB8q2XsdV2VmFFNuU5YLnfpSVkGqK9Vfpccy1A/lzCu2rc4SSam0xTJ7pQb2QqWPYlnk+6vSAYtpdylDh9jeun2cyi3KUsw4Jtu/xcVF173y+by9trbmq3PdMsnp2GqyJZRFKpVK2YVCwS4UCkr99Xp+oVBw7JEq25LYDmJZKdsVtRtl8NFtc4Ku8+r7pmk6NpP6J9kQsnVifXqVp059xGeI99O17aKeU7nJTsq2xSu9tddxlU1R+QQZr6xl3VDpjm33ZttFSP7U/+h6OeMf6alhGB3+xssfU8Ygrww9Ydqfnh9G1pQlUJX9r762Zv+pYdh/k8/bJwsF199fpVL2r7rEIH5ZBel38R5+sZgXdJ1sP0TEvkuo/Jxt+9t0Hbq1u65PUGWbsu1b7UmxJcXBZAtEPe41trJtPb9r27f8aDf5q+ov+12VfYzKv3rJMqw/8oLKIMbUdL0YP/TDplCKe9FXk20hyG6YptlhF3RsStBxiIxftnZd/yvWQxxTUP3FuqnaL6ysqI9QH8xkMo5NoTjNtm2njRcWFpzsc8lkssNHqeojykvXL9M9aCwtE7b9VLEDyVeWUb/HoGQDqB3IJlK7LiwsOHqmkpfOc1Xy7OaXvewN1ZvGe2KsIdvOoDGoHBfp+DHduZdhIHT+4cXFRacjkmMgh0gDKhIY/ZEyiH9y6l5q6FQq5RiUXC7nCK5cLjvpY8UGkTtfuVzuOEccFMrnkiKJqMoLwHVdtwE5dUQxmKCBvVheUhzTNF1O0zRNZRCgm7pUxK+9bHujQ4lBjjjI8YMUPZVKOUZKbmeVToh1os4rO/tuiJMu1N6maXqmK15YWHDOI93qFsBR4Nftd69yq/RHlV6Y2sXLgVBf0MWrj6nKQvrp10+p3H7tJN9Td0JB1c9E29BsNp3JaGpjVfpfr+tVeNmooDKhe5FeiRN3hE4fp5TCYvnldlf141QqZedyOdfLAq9yynXz6pfdbDX1IWoL1bXdEAdT+Xzes8yqshLlctm5R7f+3g0d+03tQYEJlYvsokpXevF93eqjeoaIjm237Q17REET6als371k7lWGbrom+wQZKk8QZN0RCWrbRUgeony8fJBlWb5ylMnn813PCdP+th1e1n+VStl/JdnLhmXZf2oY9jcAz79WF/n6TVz9w8KC/aeG4bqHVyzWDZUNkmWka9PlFOPdbLoO3drdzyf42XL5etu2HX8r6m2vsRVd6+d3VbJV6Sihax+j9K9+MU1Yf6RCfKFE8vPSo37YlLW1Neee5BvEl2J+ftvPpvQyDpGv7xZb6/hfVT1Onz6t1X6ijoaRlaqcpI+iHbMsy9UPVX3Ja4zkVfZuOkmT21GOI0Wor4iQHqvo9xhUHPPTdTQhReX0kpfOc2V5dtOnbr6F9FgnVtHpA3RPr7hIx4/pzL0MAzHbDpn2bAio1Wr44IMPhjLzVj8plUr45JNPNmVPmWGnWCxiZmYG1Wp1YPvU9JNGo4FMJhPJ+m2GCQstYTp9+nTHkqNWq+VsGHun2WQmOFNTU3j88ceHYjPbWCyGVCoVOuPWsOIn61/X69j5wAP4fy3Lc0lf1HxvfBz/+7PPdmQoZJjbgXQ6jZWVldAZpocVHodsbbj9mK3Gfxp0AaJg+/btt+VkRTdarRaOHj2KS5cuDboozCaQz+eVe/AwzCCgzSpVexLF43Fs27bNSevLMF7U63VcunQJc1KGOiZ6dGT9P4+N4c3FRfxNPo9/jGBDeD+WKxWMmiZPWjHMFoTHIVsbbj9mKxJqc/ZhYStkEImCWq2GWCyGkZER7Ny5EwcPHty0tPPDDm1U1y2j1Fai1WphZGQEsVgM4+Pj+PLLL3vOVsIwUWNZFpaWljA1NYVSqYRarYZarYZKpYKpqSls27btjnuZwOhRLBYRi8WQSCTw6KOPemZy2mxoU9J+bC49KHqR9Z5sFk88+SQu9Hni6tf1Ot595x383enTfX0OwwwKMWnKltj0WAMeh2xtuP2Yrc5tMXF1p3DPPffAMAwAG5leeBnOBrFYDEtLSwCAH//4x0in0wMuUXji8biThnp8fBz/rEg/zjCD4vTp0ygUCrh+/Tr27t2LiYkJPPHEE3jnnXfw9NNP8yQr48n999/v/P8//dM/DcUEZ7FYxAMPPABgY1L27rvvVqaP3mr0Kus92Sz+p3vv7dvk1a/rdVz78EP83dxcR4ZChrkdqNVquPvuu52MZA888ACKxeKASxUeHodsbbj9mK3ObbHHFcMwDMMwDMMwDMMwDHP7wV9cMQzDMAzDMAzDMAzDMEMJT1wxDMMwDMMwDMMwDMMwQwlPXDEMwzAMwzAMwzAMwzBDCU9cMQzDMAzDMAzDMAzDMEMJT1wxDMMwDMMwDMMwDMMwQwlPXDEMwzAMwzAMwzAMwzBDCU9cMQzDMAzDMAzDMAzDMENJqImrVquFYrGIRCKBWq0WVZmYO4RGo4GpqSmMjIwMuigDpVKpIJvNIp1OD7oofafVaqFUKmF8fBzFYnHQxdkysMyYQTA7O4uRkRE0Go1Ne+aw6nqj0UA2m0UsFkMsFkM2m0W9Xt+U546MjGB2dtY51mq1cObMGSQSiQ45VSoVxGIxVCqVvpeNyhKkzcKUr9FoaMecjUbD0d+tEJ+WSiWk0+mhjgNqtZp2rLIV6sMMB1GMBTi23JpsFb86iFgoLLplDuJXh4FQE1eXL1/GhQsXYFlWVOVh7hAajQYuXryIUqmEdrs96OJERjqddgyw3186nUatVsOnn36KpaWlQRd9U7h69SrefvttrK6uDrooW4ZSqRSZzGq1GsbHxxGLxZBIJAIPHkulUoceT01NhS4XwwDR6nqUNBoNJJNJHDlyBLZtY2FhAZcuXcKHH344kPJcvnwZ77///lDEXptp069du4azZ8/61rvVauHatWtYWlraEvFFpVLB559/jpWVlUEXxZNarYbPPvtMK1bZCvVhhoOoxgK3W2zZarWciQeKs1qtVqB7TE1NdcRrpVKpTyUODvvV4UDXrw4NdkgKhYINwK5Wq2Fv5bC4uBjp/ZjhJZVK2RGo4dCQSqXscrnsOgago44LCwt2KpVynSP++3amWq3aAOxCoTDoomwZZJmp9MyPcrlsA7AXFhZs296wswDsxcVF7Xskk0k7lUq5/izLClQOxrZzudygizC0DKN9yOfztmmagy6Gi2GS02aWJUjM2Y/4tJ9shTggSBn7VR+2n2q28tgp6FjAsqwOezNMNjEsyWTSTiaTtmVZdrPZdP7dbDa1rrcsyzZN0xWrZTKZPpc6GOxXB4Oq72wlXzmUe1wdPXp00EVgmJ7Yv38/Jicnfc87dOgQfvjDH25CiZjbkf379+Oll15CIpHQeoPWarWwb98+pFIpHDp0CACQzWaRy+W03+TVajXs3r0by8vLrr/R0dHQ9bmTaDQamJ+fH3QxmAB89NFHSCQSgy4Gw9zxsP305k4aO504cWLQRYiMer2OqakpZ3lasVjE6uoqfvnLX2J0dBTxeBxLS0tYXV3FuXPntO558eJFnDp1yhWrDdPXVgD71UGx1fvO0E1cTU1NbZ3P1RhGIpvNap9LEwgME5RsNovr16/j/PnzePvttzEyMoJiseg5AXX58mW0223s37/fdfzxxx9Hu93GxYsXfZ957NgxHDhwIJLy36m0Wi1kMplBF4NhGGbLwfbTmztp7FQqlW6LyUvaB+5v//Zv8fTTT2N6ehoAcPbsWZimiV27djnnjo6OIplM4rXXXvO9b6vVwoULF7ReojN3FrdD34l04qpUKiGRSDgbrKkGUbQBGO2xIm6Alk6nHYFOTEwgFothbm7OtT63WCyiVqt1rNulDcWKxaLrXJ3nEpVKxdn/ZWRkxPUlAm3aRhv/1et1Zz+jdDqtvfYzVgYAACAASURBVGFbtz2PxJnnVqvlbFYYi8UwPj7u2o+GZuhHRkbQarWcsojnqOoTZGM58XpVGWjzU9rQrVKpODJW7Xsj1ymdTgdes62Si/wWoVaruTZ6LBaLGBkZwcjICM6cOdNxz3q9PpDNAVVQ/UgfVBvl6bRrq9Vy6jQyMoJsNtsxqeanY17I+3ipjokbssrniuj0o7B6LNql8fFxZ28x+RzxGbOzsx26qdIT+T7iRvv1eh2JRMK1OaKuzOU6v/XWW8q67dq1C6VSCaurq7hx4wbuu+8+zM7Odsjn7bffBgDce++9ruN//ud/DgD413/9164yrNVqWFlZQTKZVNbbD5VuyPaPbLJo30U98uunfrIP28/FDWSp7WQ5iH5Gfkaj0cDOnTudPTjkPqGrG7R5qMqHiH4trL7K9l3VlmF8mFgGHV33ws9PkSzEvd2y2azWBr5km1ZWVrCysqKMN7zam54r+iKx7WhTWNlWy/6sVCppb4gtxynyb37toWMr/ehm0/3KJ+7p4hXjifjFHDLd+ovow7rphuzrvO7hdY5fnUSijmX8bKSfPouIeqv75S+hE4/L+NlPr/vqxJi0sXc6ne6I7732gvSzr7p6Il7TTS7yBsqzs7OOHVGNnVS6SMhjKD+GaSxQLBaxd+9eAMDMzIxnf9WJLXX0Rcd3dBs7qupeLBYxPj6OTz75BHNzcyiVSs4kVb1eh2VZyi+RxsfH0W63fe3GuXPnsLq6ikQiETheVtXpTverXrEQPacffjXsOEtVZt2+E9Svbjph1xrSushcLmfncjm7UCjYpmnaADrW49I5zWbTbjabdi6X61hLqlpnubCw4NyPoPW7AOy1tTVXmfL5vGsNvM5zFxcXnfXE4jNpfX61WrUzmYwNwM5kMnYul7Or1apznu7aYdM0O9Yo032pHrSemfaisSzLTiaTjlwsy7LL5bJtGIarPGKdCoWCbRiGI8dqtWobhmEbhqG1J83a2prrfiRvwzCcc8rlstMG1PbVatUpC5VfrJO4ZpvO01XDZrNpG4ahbMt8Pu+Uk/byoeO07p9kKOrL2tqabZqmUk66a8n90Kkj6Xcul7PL5bLTvqK8bVu/XUknSCa0xp3w07FuiOeK+yxR35X7ArUbyZ3WkOv0oyj0WHw27esk1jGfz7v6vmjTCCqzSl4kg7W1NZetIpnT83RlTmWk86jOss1S0Ww2HZnlcjmn3tRPVToNoEPPZBYWFpyy0l8qldLuI2Id6Jpms+nYPtFW2PaGbRH9h18/9ZN92H5uWZZtGIar7Uimss8Qn6HSadVeHrq6IftH0hW5/cLq6zvvvOOy72IZovBhRBhdpzr4+Snbtm3DMJy93KrVqm2aZqA9LGT7adv+7S37adEXkZwXFhbsXC5nLy4uus4l+VarVafN5eer9uIol8tOW4jHddpDx1Z6oWvTvcpHMjZN0+lP5NtVPkEn5hDPpTqI9Va1vygjL0S7RXGHfA/SNZKB3BaibRIR2znqWMbPRuraL7o+k8nYhULBFcfJ+yWq9FYnHu+G115IqVRKGcOL4wYvxH6Wy+XsZDJpFwoFp51lWevY1yB6oiMX8okk01wuZxuG4ci329hJ5efIvvv5v2EcC3jtQxQkttTVFz/f4Td2JCzLsnO5nG2apr2wsOApd7K7Kp2hNvazUfQcMV5T3c8L9qu36t5sNj1joX761TDjrG5l9uo7QfzqoIls4kqumKhAtn1LqTsKIBkrrw3CkslkR6fx2lxYHFDpPJcaXB4Iy8EFNbhsAEi5dJCVhTqpKD8KBkTo2WIno/JRh6D/WpallAvJS2ezTHqe6KypbVTHxI7VbDY7npPP55XOkzqWDmSMZVSdk2QjPo+CQLENUqlUh5yoTlFtzqfjkFUOM5/Pu+QdpF1VbSL+rqtjXsgbfROmaXa0UblcdsmyWz8S+3gUelwoFJRylfu0qDs0SSE6BtM0O+RF5ZP1GoArIKT205E5PVvWPapz0KCA9K6bDgYJGKks4ssJXWR9tu1bMpTlIrYR1UWnn3rJPmw/VwVYcn1owlC+TnVMlrdufxQHKgQNjuTJpbD6SuWS7x2VD4tC13X8FJ0j+4KwAXbQ9hafT20h9x+v+EcnwO52XKc9/GxlN3Rtulf55IESQQMOkW4xh9ckl1gHepYsj2azqb1RMLWf3Cayn/UbJOi0c9SxTDf/pKPP8vViPVXnyvLQGQd0Q2U/yWbIMXyQSTGqg/zCnex8L/FAED0JMj4SB8r0LC+dIv8gy6ZQKGgNSIdxLOBn+/zskK6++PkOnbEjTajoJtTpFpcEHZuILzJVcvGC/aqeze63Xw0zzvIqs1/9dPzqoIlsqeB3v/td178LhQIA4P333wcAvPvuu7Asq+unq9149tln0W63XZ/g3XXXXQCA8+fPO8fq9bqzmZ3uc69evYp2uw3TNF3nUCrfDz74wFWWbdu2uf5Nz9KB1jBTWX/84x8jk8m49ju6cOEC5ufnXWWZmJgAAGV64bGxMdd/afO+7du3u87LZrMwDAMrKyu+n47u2rULtm1jbGzMWSJDa6u/+uqrjvOpLQC1PObn57Fjx46O33Q35mu1Wpifn0cymez47Wc/+xkAKJeYiM8Ty0j3XFlZwd69e12ynpmZAQC89957WmWLClk2pGck7yDtmkwm8dJLLzn9Kh6P4/vf/75zTVAdk5mcnIRhGHjjjTdcxw3DgGVZrv785ptv4qmnnuq4h6ofiemQo9Dj+++/H6urq65PwH/wgx84v5POyHsJ3Lx50/m0uFKpwLIsp3+J52UyGbTbbVy+fNn1WyKRcNqTrtOROe1F9fDDD7vuJy/x84I+Q08kEhgdHUW1WtW6LgjZbBZXr15FMpnE6uqq9hKRv/7rvwYA/OpXv3KOkQ6USiXXp/WXLl1y2iRoP5VlH7afNxoNrKysuPoPALz66quOjQQ2bPvNmzcBbPiVbDaLlZUVrRTfuv1Rda8HH3zQ9e+o9NWLqHxYWF0H9PwU2Y/nnnvOsRdR7P0RtL1F+04JDYLEDmHQaQ8/W6mDn033QhVTAMCOHTs8r1HFHF9++aXvs8bGxpDL5TA/P+/yH+fOncPBgwd9rwc22i+Xy3X4oIsXL3bsIxgVUcYyqv4eRJ/F64GNfki2pdvy67DjAC9OnjwJwzA6EoWQTTp79qz2vfbs2eOq2/PPPw9gwycBweyrrp4ElQuNt8SxjhfHjx8H4N6QudVq4ezZs8q4TGbYxgI6+NkhXX3x8x1Bx45//OMfo6ieNvF4HNPT03j//fdhGAaOHz+utWyQ/aoe/farUYyzgtKrX91M+rY5Oxl1qvBHH32EVCoFe+Mrr44/ceCoghrkzTffdI69+eabKBQKLqdw5swZVwCt89xPP/0UADzPEQP1qGi1Wnj22WdhGAZOnz7t+m11dRWFQsGzPH589NFHANQDAAoEf//732uVcWpqCrt378YjjzyCXC6nU7UOarUa2u12x8AvCL/5zW8AAKZpdvxGTjzoOm66Z7VaVcp5eXm55/L2gyDtSoZuYmLCWU8t6nFYHQOAXC6H1dVVJ1Ct1WrYs2cPDMNwJoSoTXrJPBeFHk9OTqJQKGBmZgb33XcfisUitm/f7tgbHZ0h+/Bnf/ZnHb/RpMHnn3/uex8dmdNeU372UIaCi507d+LGjRu4dOkS5ubmnPsYhtH1er/fZSjLDQB88sknWteMjY0hmUy6NoY8d+4cFhYWXMF+qVTCs88+65wTtp+GvV7HVhK0r8axY8fwzDPPIJVKaV2n2x9N08T6+rrr2j/84Q8AgHvuuQdAdPrqR1gf1quuq8rRzU/F43GUy2VcunQJpmk6e35E4dd7be/NRqc9/GxlPyHdvXHjhut4q9VS+vyw/PSnPwUA5PN555juQJ54+umnAbgnBN54441ACVqiIqpYJow+k23xmoQEwo8DvFhdXVXqCY1DwmxaHo/HkUwmnYFzUPuqoyf9kgtwa/Jsfn7eidUuX76M3bt3aw/wh2ksEAW6+uLnO3TGjrt27cLy8jLm5uZw5coVZ38/rz2w5Em3Xs+R6zU3NwcA+Pd//3eta9iv+rMZfrXf46ytyKZmFZQD7iDE43HkcjksLS2h0WigXq/jwQcfdAKNEydOoNVqodFodLwJ0X1uLxvY9crrr7+O1dVV/Nu//ZvSecgBXC9QMNMLjUYD9913H4AN+WWz2cDGUiaKOtFkRpR88cUXkd+zn+i069jYGK5fv47FxUWsr69jYmKiY5O9sO1BGeZok9i33noLBw4cQDabdd5mX7x4Ec8880yo54TRY2DjDY5lWchms47z6GXzfQpSwqArc11bRG+kkskkvvOd7+Dq1auYm5vrcGA00SfXm97YdPuywYvR0VGkUimtLyqIF154Ae122/lK6+OPP8ahQ4dgmqaTyvv8+fPKAWTYftrvfl6pVPDAAw9g//79WF5eDvz2UUc3Tp06hZs3bzobatbrdczPzyOfz3e0eRT62o2ofFgYv6vrpyYnJ/Hb3/4WhUIBpVIJpmmGTg0etr03G532iMpWBmV0dBSFQgHz8/OOTaKkE6dOnerL8yierNVqKJVK2L17d6DAf9euXc5EfKvVQqVSwZ49eyIvaxDC2LjN0ucw44Bu0Kbt/UBl33Ttq66e9EsuwK2JWvoC7+jRo84xP4Z1LBAWXX3R8R06Pmx0dBRzc3NYXl7Gtm3bsHPnTuXG6fQi/uOPP+64B305Ka9w0oEmSnW++mK/qk+//epmjbO2En2buKLO+MMf/hAA8NBDD3V82kZUKhWtjk9vLi5evIgzZ87gwIEDzue5pVJJ+Zm2znPvv/9+574ylG0kSiqVCo4fP45CoaCcdU0mkx1LZwhVJhkZCr4+++wzz3P8ZntPnDiBdruNV155xfd5fnzzm98EAFy/fr3ne9Ab2WvXrnmeE/QtDn3qKi41FdGR9WbSS7tms1msr687b9xoqW1YHaPypFIplEol1Ot1GIaBeDzufPF48eJFvPfeez07nij0WLzX3Nwc1tbWAMD5ooeeocoaRP3+29/+NgB1IEHIy51U6Mj8W9/6FgD/t2KUBXH//v148skncfPmTUxPT3u+QSU7LE8C0kCHfu+FRx55RPvcxx57DIZh4Pz58yiVSo6z/dnPfgbLslAqlTqWP4Ttp2GvJ9tz4cIF5e+kJ4cPH3ayLgZFtz9OTk4im83iwoULiMViePTRR/Hiiy/i1Vdfdc6JSl+7EYUP09X1bgTxU7Rs4re//S2SyST27t0bOKutSJj23myC2HsvW9lvDhw4gFQqhf379yMWi+Ho0aMol8t9G7jQwP3YsWM4efKkE18G4YUXXgCw8eXoqVOnIlmq0QtRxDJh9fnGjRswDKOrT45iHKCCvp7xGgyG/WJjfX3d2aaiF/vqpyf9kgshLlmkTGO6k7TDNhaIgqD64uU7ehk7Uoy8vr6Oxx9/HFNTU0in004MumvXLhiGoZxYW19f9+1jfui8pGS/qsdm+NV+j7O2In2buLp48SIMw8Bjjz0G4NaazieeeMJlnBuNBk6dOqVlRHft2gXTNJ31xzS4efLJJ9Fut3HkyBHneYTOc3fu3AnDMDAzM+NStlarheeee66nrxG8aDQa2LdvH5LJZMcnlWS49uzZg3a73ZHCtVarac3SUgD2i1/8ouM3msTQKacMLUkJytjYGEzTDLQfjoz4dYd8Dxp8Bw0a4/E4UqkUVlZWOtLXFotF1xuGer2+qV/kqQjSrvLXVeSAfve73wEIr2PE/v370W638aMf/ch5M0DtPTMzE+qT8Cj0uFgsuupH+5vQp+CPP/44gA1nKbZ/q9XClStXANyabKGvPUVu3LgB0zS1AgkdmT/55JMAgJdeeqmr8z9//jxefvll522YH0899RQMw8Dbb7/tOn7lyhUYhuHqO61WS2uvkUaj4aQc1iUejzt7Fpw/f95xtmS39+7d2zGADNJPvZ4Z5vrR0VFnPy85GKHPvgH1chTdAE63P87OzsIwDFy/fh22bTsTliJR6asXUfkwXV33K4uM7KfoixoiHo/j5ZdfBqD/hcr6+npHGcO092aj0x5+trLfpNNpHD58GOvr67BtG+vr630NxsXBPNDbktVsNgvTNPHaa69hdHS0I46le8pv5btNevRCWBsHhNNnGqT7+eQoxgEqaG8y2T6TPgfZd0y2H41GA5ZlOZNPvdhXPz3pl1xEaKL2+PHjOHz4sPZ1wzYWiAJdffHzHWHHjpOTk84ywnfeecf5kvrFF1+EZVku3096+OKLL7ruoTs2KZVKyi+zVbBf1WOz/Go/x1lbEs9t2zURU39StgE6Jmc4EVOeUjpeKHbpx9c73i8uLnbcg+4tZ3Gg9O8qdJ5LWSbwdcYUymIgZ+aBlI2GMtFAysDgBd1XLr9lWc7O/ZRik8pDaVvlbCWqbHoEZUKR0+vKGVO8EDOpUFpMKtPCwoIjF5Kl2E6UtUfMPCOmdaZUm2K2i4WFBa30oJTeWU75LD6fMn3IeqJKi0upSvF1NhhKyS1mmqD6yFktdKBndtMPko2cqYeywYgZJXTbleQsZpwR9U5Hx3RR9T2vfirWQWwHr34UVo8po4eYDt00TVd2FZIzpfLN5/O2aZquclAbyWmPxTS3tn0rY4eY1l2so47M6ZxkMmmXy2W7XC47/UxOoR0EqoOcql2+n6x3pP+ZTMapU7VatTOZjJbNk6H7qZ7rldVLp592k73O9TplJl0sFApOSnKC2q1QKNjlctmpD77O1EL1pbYsl8t2Pp93MkPp6IZYfvorFAp2oVBw1TkKfRXLKtqgqHyYKLNedV3HT8k+gtLU62bEFNtelJ1Oe3vZNbqnbMcoVhHtk9hG4rleabFVx3XaQ8dW6rSD+ExV3VXlE+tIep3JZJzMZ1RGysYm66Qq5rBttf6KeGWuDYJXxiqC0shTavZ8Pu+SF2VxUrVz1LFMt/6ua78oBqN2obTtsi576a1OPN4Nlf0Uyy+npvcaF8iImRGprmLdRHTtq4ifnvjJhewWtZHX/b3GTvQM3cyZxDCOBaitqP9QGYLYIR190fEdOmPHXhD7lFcfU41NyDaI8f/i4qKdyWS0Y3v2q51+VeVL+u1XRYKOs7zKrOo7Qf3qoAk9cWXbG51CbLxuA5pCoeBKpS4bKFERVA0rp5Elcrlc10GU33Nte8Og0rNN0+wI1ql+9EcKIP51M1biJIbqT6wXDdDJmMuDRp3nLiwsOHUmpddVPlGRk8mkvba25jicVCrlpMmUy0+dXpYTyZfKY5qmM0gh46gDDY5EBy+m7/SSjar9iLW1NeeehmHY+Xy+I8WqYRjaBpnwamcRL3l1a1+ddqV0qWLbyP2jm44FQZXaVRzEiuj2I/F+YfRYbnuvgIJSNHezX9Vqtaud66b7hI7M6Ry6Ry6Xc/qOGIz0gtgHvewgBS3Ur8SAma4LWw6yISKLi4td03N366c6svfr536sra25/IOsR2tra45sKUgkWYq+jAaYqgl3P92gvkDndKtzWH1V2aCofJh8Tq+6ruOnSA6izHRtiMpekQ33a28vO65r8738qVe5VMeDtIeurdSRkZdN71Y+GvSo5E0DgSAy6qabokyCDuRV9+gWG6j0hGRNNlZVh9OnTyv1p9dYxq+/69ovy7JceqSyhX7P0onHu8nTy36KflxVrm6IAzrSw25+ws++yvjpiW13l4ufPvuNnej+3XysV7mHcSxAfoPKEDS21NEXXd/RbezYK6Jv9NJD1dhEjFNIPkEn5tmvdvclop70y6/KBBln+ZVZ7jtBZDQMxGxbM4UYwzAMwzADgZYfnD59umMvs1arhXPnzgFAX7LgMkw/qdfrOHPmjJP5SqTRaCCfz+MnP/lJ5BkOS6USPvnkE9ceccydSa1Ww8TEBAqFwm1rQxOJBK5evaqdTZBhGGbY2NSsggzDMEx4YrGY9h9ze/D6668DUGe4isfj2LZtW+hN1xlmEDz77LMd2aCJ0dFRjIyMOHvJRUWr1cLRo0edPUMY5nbmzJkz2L17N09aMQyzpflPgy4AwzAMEwz+UPbOw7IsLC0tYWRkBI888gjuvfdeABvprd955x088sgjkX+RwjCbgWVZOHLkCD7//HN873vfw1133QVgY5Pft99+G0eOHIlkwE1f1RiGgZGRERw8eDCSja+ZrQ9lL4564/xBkk6nsbKygmQyCcuy8Nvf/nbQRWIYhgkFLxVkGIZhmCGHlgNeuHDBSZVtGAay2SyefvppnrRitiyNRgMnTpzApUuXnGxLpmkik8ngwIEDkU0uNRoNJJNJABuZu27XJWFMMGiCR+R2GBrNzs7i+PHjSKVSKBQKnl81MgzDbBV44or5/9u72xc3rnsP4F/Bfduks+qrkgazI8M18WWDM7JDujY4kJXqXsIF25WclmCwyVZyCdw43Y13N4QQx7VUpxdMsw/GBnNpIm1tQyhdZbWGLERqiG05rIiDL2RnML6hrzRWbP8Bc1/sPZOZ0Uia0fPa3w8sbaTRmTPnYebMzzPnEBERERERERENJM5xRUREREREREREA4mBKyIiIiIiIiIiGkgMXBERERERERER0UBi4IqIiIiIiIiIiAYSA1dERERERERERDSQGLgiIiIiIiIiIqKBxMAVERERERERERENpIEMXGWzWUSjUUSj0X5npa5isYh4PO45j+FwGOFw2PZZvePM5XIIBALI5XIdyy9tTrquI5vNIhwOI51O9zs7m165XEYymcTQ0FBf8+GnTjVNw9DQEKanp3uQM2oX66u1a7imaYjH4wgEAggEAojH4yiXy13M5Q/7ddaXrutYWFhAKBSq6aOP4/VZ0zSk02mEQiEUi8Wu7WN6ehpDQ0Nd28ejrFgsIplMIhAItPx7tzGtqBNN0zqRzbrc+qHfcXa7enWs9Gjrx72dpmmex7aDeK7txdh8EI+7Ebe4gZtBua/plYELXOVyOdy9exf5fL7fWamrWCzim2++weLiYstpbIbjpM4TN2Ve/tLpNK5du4ZPPvkEpVKp31nf9DRNw+3btzE3N4dqtdq3fORyOXz++eesU3oktXJt0zQNiqLgxIkTMAwD8/PzWFlZwZdfftnFnNZ39epVrK6uQlXVvux/0Fy/fh3nzp3rWnnouo7r169jcXGxr+fmzapcLuO7777D3NxcS78vFov49NNP2xrTdlonxtlEvdaPeztN03D9+nVks9mm589enGuLxaKve52//vWvXR+bP6rXmEG5r+kpY0ABMCKRSL+z0VAn8ug3jUQi0db+epXmZpbJZIxCodCVtGVZNtbW1sz/LhQKNW2gUqkYiUTCSKVStm3Ef1N7IpGI0e9T36DVqaqqA5MXejT4ubZNTU0Zsix3OUf+DFof7SW380EqlTIAdO3a2Kt9PMravbZ1ctzdqWtKt+4FOO6lburHPayf/t/Nc22hUDBisZihqmrN/qznBFVVDUVRzDz0Ymz+KFxj3O5RB+G+plcG7okrqi+bzXb8EeZupLnZzczMdC3tkydPYmRkpOE2wWAQs7OzePLJJ7uWDyKrM2fO9DsL9Bi7efMmQqFQv7NB/4/nA2rXILchTdNafjqNiJo7deoUhoeHG24zPDyM8+fP9yhHj45u3qNuBgxcbRLiHdZBT3OzSyaTXX09JB6Pe952fHy8a/kgErLZLAfxRASA5wNq3yC3IV3XEYvF+p0NokfW6Oho06CVMDIygtHR0S7n6NHR7XvUzaCtwFU0Gq2Zk8ftOzFhZCAQsE1WZ92m2STFYuJJMflYOp3G0NAQhoaGsLCwULN9uVxuONFrLpczJ30sl8sIhUK2SRnFRKReJorVdd3cVygUQjabbZj3RsSksNaJm3O5HPbu3YtqtYp8Pm+Wo9ucSF7Ltl6a1nyIPAcCAYTD4Zrjcsu7mJQwnU6jXC6b+QiFQq4TErrVk3XSvHQ6bTtGax6t31mP0Vp3zsl1nZPMTk9PIxAIYHp6GtFo1Bxs7d69u245C873uHvBWqbRaNT1ablGx18vTVHXuq6b6Vvrq1ma5XIZ4XDY/D4ej7tu02zy5Wb9rlledV2vyavb+cG6v0bnEbeyqnec1rYg2rCXNmJNMxwO15SJ9Vzllv9mdS0muLWWqfU8d+jQIQDA5OSkrS95qVMrt35a7xxlLRfrceVyOXOfQ0NDSCaT0HUdQO1CBdlsFkNDQ7bJK720MSfnuc65X+t2YmJPt3kaRJ1ns9ma+vJ7/XJOVF7vHORU77pm7VPWOnTLf7M+Zq0j0WZbnWxWpJ3P583rkDUvfs4HAMwJ1cU5HbBfn4eGhmquY271VY/b9dn6nfOa6SyXbDZr5i8cDiMajbY0SayXdJpdW900Oh9Y5XI5c/9u//jlpSz88DMesbYBt/OPsw5bvaZazw+NxhXO74rFotl/otGomY713OJc2MFZl16u680403RbTEJMZuyclN/Zp+LxuPmPcvXaUKMyApr3w0bjbK/XXk3TsGvXLnN+Sed3bscq9t2s/fk9x1u1e+300j4a1Vm98rZeb63XpFAoZJ6Hrecia3u2atZ/OjEGddPsWlUul81+J/Yh6tht7OHn3satPBtdO5qNW/1ed8Vx+V0oppUxVDe0e4/vl9drjNdxYLM+2Ww86xY3cLtHdbum1yu7TvbpvmrnPcNKpWLEYjEDgDE1NVXzvaIoRiaTMQzDMNbW1lzf+Z2amnKdR8K6raqqxtLSkgHA3Jd4x1NRFAOAbd6gtbU1Q5Zl8x3QQqFgSJJkSJJkVCoVY21tzZifnzcAGIqiGJFIxHw/tFAomN9Zfw/AiMViNXlUFMWIxWJGKpUyEomEmUfrcVvz7uQsk6WlJbNMG5WJKBdx/G5zCSiKYszPz9d83ihNw9ioV0mSjEQiYVQqFXPOpXr1LBQKBfP94UQiYSiKYqRSKfN4nPUkylXk0Xo8S0tL5nYiTUVR5EycGwAAIABJREFUavY5NTVlm6sgkUi45luUz9LSkrmPSCRiJBIJQ5Ikswzc3n8W7UG0H6ulpSVDluWaz71ym+Oq3jaxWMxIJBK2Nupsk82O30n0LUmSbPuw/sZLmpIkmW2+UCgYsizbvm/WJw3DaNrvvORVURRbXkXbE3kzjB/eBW92HnHT7DjF/qztR1VVM89udSrOHyJfkiSZ+bCeq5xtxEu9iPJylql1PqF6c/k0O1YnUacAzDq11oHzXCT6otg2k8kYiqKY8yI4j7tQKJjHGIlEjFgsZsiybJarlzbmRqQj9ivOAc78RiIR23YiL9Y+aD0Htnr9EuffWCxm1q3YrtH53Ot1zVqHlUrFtk2zPiau4yINVVUNWZYNSZJq8tPsvOYsW+e2fs8H1nIVxzQ/P28kEgkjk8nYthXtwa2+rHXp1p/crs+ijtyuZSL/a2trtr6dyWRammvDSzper61u6p0PrNf2VCpl64/WdumlLOpxu/76GY84fy/Kxto+xfwrXq+pkUjE9bxkHY80GldYv7OWnRj7JhIJY2pqypifn7edE6z15DwfiPw7r1de5zkRbUiUqbWORD+oVCrm+MZZJ6LcDGOjfp39t17fqVdGjfqhl3G2yJOXa69bOTU6Vi/tz+853qnda6eX9tGsztzyZL3GiX4i+pQsy0YmkzGmpqZs2zr7ZLP+06kxqFOza5Wqqua4RuRb9E9rXkRZ+Lm3MQx/93bNxq1er7vWsa34E8finNPN7Vzb6hjKC7c5rpy8js3byWe71xgv40DDaN4nG41nG8UN6s3R5aXsOtWn+63tmbxUVXW92IgBsW1nHgeH9bYVFWNtmKJyrb+PRCK2C5phuHcaUVEiPVG54sTl3LfzM+fvrcdT74Ti1G6ZiBOa86RUqVQ8TXZb74bY7bduF3UnkXfrRdUwfghQWju3LMs1+RbtyXkCEidp50kpEonYbkLc8u3s/M4bU1VVzTTqnRTEScc62aDYvllwsBE/gSvnyUNRlJoBuZfjdyPapzVg4jVNkT9nv/TbJ732u3p5Fb93Oz9Y66jReaRRXXo5zmYXFWdazoud+L1bkNx5Y9CsXsTF2G2Q5Gw3zvONl2N1I/q5dZAh+rSzr4uLozWvzv5lDapY8yXKR9x0iW29nPedxKDBylkHzoGj4HZTJH7f6vXLGbCx7t9LIKjeda1ZQMK6v0bnA2f9it83G7g34nYT5fd8YC1X0eac/9hRr3/6uQ67fS5uqN22s/6jiDM/1j7glZd0/FxbnZq1E2tApVKp1PQVL2XR6Nic9eNnPGL9RyjBLaDR6Jpq7c9iQO88L7ndOHsZVziDhuIY3MaQIm1xbrRem5yBScFr4EoEtq3qjeXd6sStHTQLXFnTcisjka7bfYOXcbbXa2+9z+ql4af9eT3Hu2n32tmsfTSrMzf1+olo/8727Kw/P/2nnTFoo7w3u1aJ/VrHDtaAp/Pa5+Xexq0srGk4zxvNxq1+j8XtH8Lq/d7ZflsZQ3nhJ3DlZQzfaj7b6eNex4Fe+2Sj8ay1PJrl37pts7Jrt08PgrbnuBoeHkYikUA+n7c9Yn358mUcPny43eRdBYNB8/8/8cQTtu90XUc+n8ehQ4dsj+9NTk4CAD777DPb9qFQyExPTJo9MTGBe/fuAfjhNZt8Pu+61KT198DGu72xWAzVarUnj1eOjIwgkUhgbm7OVv4XLlzAa6+95js9XdcxNzcHRVFqvvv9738PAPj444+bpnPw4EFbubzxxhsAgJWVFQAbj72qqlozUfnw8LBZflevXjU/P3nyZM1n5XIZw8PD5n4+/fRTqKpa97Ua5yOVzzzzjLlPa17dnDp1CoB9wlFd13Hu3DkcOHCgSWl0hnOy9mAwaGuTfo/fjagP8b9e0ty2bRsA4NixY2Yb3Ldvn7mN1z7pp9+55fXcuXPYuXOnrS737dsHwzBc5wtzO4/cv3+/btk0O85W7Nixw/bfExMTkCTJ7Cf1eKmXq1evolqt4oUXXrD99saNG2Y519Pqsf7qV78CAPz1r381PxPtNpvN2h45XllZMec2uHbtGqrVKmRZth2PWFL6iy++sO1HlFswGMTw8LDv877V8vKy+Ui4eIwaAL7//ntzm4cPH7r+dufOnc2KpEaj6xfg3gabLejg5HZda4Wzj42OjsIwDIyMjEDTNCSTSZw+fRpA/TJqld/zgbVcxfwazc7rnXLp0iXMzc3Z2t7u3bsBwGzD27dvR6lUsr0m84tf/ML3vpql4/fa6pe1zYrytfYVL2Xhld/xiFvbcJ5jrdyuqVYffPABJEmqma9FXE/OnTtXk2ajcYWzv4sFCRq102AwiHv37mF8fNx8pUiMrRtdr+opl8solUp48cUXbZ97nZMGABRFwTvvvGOOKYLBYE16jfgZewH9G2e3Oh5udo530+q102v7aKfOnP1ky5Ytno6tlf7TyhjUjd9rlfW1SXFfCwBffvmlbbtm9zZ+eRm3+j0W67k/GAziD3/4g+uxWLUzhuq0RmPzTufTTx/3Og70e852jmfb4fW+ptU+PQg6Mjn7K6+8AsB+U3/+/HlfE1F3yu3btwEAhUIBxsYTZba/5eVlT+mId5hPnjyJV199FZFIxHMeRCPs9EC+njfffBMAMDU1ZX7WakBFlJ8syzXficFGK6sQBoNBKIpiDixv3boFAPjZz35Ws60ov7t375qfvfTSS5AkybaawsLCgi0gcfPmTUQiEdd6NwyjrQkAxYVsbm7OHChdvXoVY2NjPbs5aqYbx+8lzWAwiKWlJaysrECWZSSTSWiahomJCQD++mQ7/a7bExY2O85O2blzZ92bc8FLvVj7j1+tHuvIyAgURbFNzHvhwgXMz8/bbpiz2SyOHj1qbiPOB/WOp9l+2z3vizl7Pv/8c9dVbn76058CAO7cuWP7XNd113NlO55++mkAwHfffWfbD+Dv5rJbxFwQY2Nj2LNnjznA74Z2zge9VCqVkEql6rZfYONmJJVKYXJyElu3bkU6nca2bdt8n5ebpeP32tppXsrCK7/jEVmWsb6+bttODNpFH/ajVCq57lvcGPZyktx0Oo1du3YB2PiHvFaJ4KHzHzT8EOfI3bt3m/Ordfo62EwvxtndGg+7afXaKTRrH/2os070n3bGte1cq37+858DaB4cdt7b+OX1HNLOsYi22uhYOnXv3G2dzqefPu53HNipczbZdSRwNTo6ap5wdV1HLpfDwYMHO5F0y6yDfr9yuRyeffZZHD58GMvLy20/VdFtIqiyuLiIYrGIbDaLsbGxtm5ybt682cEcbnAL8IhBtpffJhIJqKqKXC4HXdehaVrNvyo7B62dJAKEIrI/MzNjfjYounH8XtLct28fvv32W6RSKWSzWciyXDOxYbM+uRn6nZfj7JVutnWg9WM9fvw4qtWque1XX32F8fFxyLJsBp4vXrzoGlhv9yaglfP+9PQ0fvOb3+DKlSuYnZ11fUJpeHgYqVQKc3NztknYS6USzp4921aeneLxOCKRCGZmZszyePvttyFJUt/PN5qmYevWrQA22l88Hq/5l7tO2QznAyvnYNbNxMQEVFVFPB43A0+tPDHiJR2v19Zu8FIWfngdj5w9exb37t2zLTAxNzeHqamplsdDYiLvfgqHw7h06RKuXbuGiYkJPPXUU22n2c4YeWRkBDdu3EAmk8H6+jp27979SK9Q3Y3xsJtWr51e2ke/6qwT/aeVsU671yo/T550+x+ve3ndbee80EudzqeXPu5nHNiNczZt6EjgCtg44QIb/0Jw9uzZnr0+5SRecbl48aLr915WDnv99dehKErLT4zduXMHkiT1dIlPcUNz8uRJfPDBB+ZTcH6JiPL169frbuPnkXCr9fV183FM8VTBV199VXd7578IHjlyBMDG4PTChQs1r6I+99xzUFXV9dHhXC7X9k2x9bVYsfrMIDwBIXTj+P2kGQwGMTExgW+//RaKouDQoUPQdd1zn2y338myjHw+77oCRieDS/WOs1PW19ebLtftpV5EH/v0009rttE0zdOro60cq3g68uLFi8hms3j11VcBbDx2raoqstlszSsi27dvB7DxirmTWAmlkVbP+5qm4dSpU0gkEk1fqTty5AgikQgOHz6MQCCAmZkZLC0tdSWgkkqlIEkSFEVBIBCApmlYXV3t+/nmzJkzqFareO+997q+r3bPB72kKErN6zyCs+0NDw9jdnYWa2trAOD69IQX9dJp5draSX7Kohm/45F9+/YhHo/j0qVLCAQC2Lt3L9566y28//77vvYriH9Frxdc7MUTgOLG6N133+3IDbK44f3666/bTisej2N9fd18Gr2dlSP96sU4u5vjYTetXDv9to9e1lkn+k+r49p2r1UiMCLGJo1Y72388jJu7dSxNCrvTtw790Kn8+m3j3sZB3b6nE12HQtcxeNxyLKM06dPY3h42HWAHYlEaiLn33zzTaeyAGDjRisSiSCfz9csaZ5Opz1Fqd0e3fR6Yypusrr5+oQba1AFQMsX8+HhYUQiEdu/+gji5OclKOl8JFXTNKiqagY4xQV6cXGx5sJz584dyLJccwzWY7x06VLNDY2Y5+Pll1+2XeQ0TcPZs2c7ctMnAoSnTp3C66+/bvtO1/WWljbvlG4cv5c0xVN+QjAYxLvvvgtgo8147ZPt9DsAZrDn2LFjts+LxWJLc4E4NTtO4IcbQudrXl7/xbBYLEJVVXPAWo+XennppZcAbLRV58Bxamqq4TnCy7HWEwwGzfmILl68aF7QRX4OHTpUE1jftWsXJEnC5ORkzfK9x44dazqXVKvn/X/+8581n9Vrc9FoFK+//jrW19dhGAbW19e7ErTSNA179+7F8vIy7t27Zz7+3s5cVcAP1wTn0zCNAhxueXPqRN9aX1+vKfd2zwe9dPDgQVSrVUSjUVsZFYtFs++J5acFMT+l39fNmqXTyrW1k7yUhVd+xyPT09OQJAk3btyAYRi4d+9eW69DiXlCnTdD4ri6NY+rldtrnQ8ePGg5PXEetk574JfzSR1xQ/2///u/LefLD7dxdrvXXjedGg971cq102v76EeddaL/tDqu9Xutcl5bPv/8c8iyXHONb3Zv45eXcWu7192LFy9CUZSGYwg/Y6hyudyxV2T96sQ9vpXfPu5lHNjpczY5+J3NvZF6s90L1pUkxHLA1qVRnSsoWFcSsa6MYJ11320JY7HUL/5/1RGxfKXbqifWZS0FsbJMKpUylzAVqwssLS2ZqxmI5Tfn5+dtSwo7V52ot6qD23Fay9FtlQqxBKhzlRHD+GEFEudqC424pSlW1JAkqWa50WZpW1d7Edtay8VKrHjgXCrXutR3vfTrrRxhXSpZrJxjbZPWZUnd0rCuTJHJZFyPt94KFPWWLm5E/MbZBqzcVi0Rq0Y621Sz43cjltatt12zNJ1tQ5Sxtb699Ekv/a5RXq1lEolEzNWtrP3RbbUYw3A/jzh5OU7ramYiz6lUyrZU7dramrk6iSzL5ioeIn23vi2OyU+9WI9LkiRjamrKSKVS5pK31uMSxy5WDvRyrI2I43P2H1GnbsQKRKJcxApybit3iWWGnfts1sacxKpo4lgzmYzZJ8U1aW1tzXauFivgieXZxfnfWZ6tXr/EMYo6FUvHi3NSI42ua4bxw+qsmUzGWFpaMpfMttZ/oz5m3VZcM0S/nZ+fN+uq3rXNjWgrzj7p5XxQ7zwo0nRei92Wd/Z7HXb7vFKpmPkVfVWs3mldWU5RFLNMRTmLvIhzkyzLda9/XtIxjNaurc7ysJ4PxApkzuub9TxmbevNyqIet334GY9Y+774S6VSRiqVsvUH69L2Qr22ZG3fIj9i+XrrbxuNK9zGBtb9WfMmzoNixWRrXYr6EOWUSCTMeq83znQj+rEoQ+vS5+Iz62qtzryL47S2beuqsPXaUKMyqtcPvY6zvV57rcckzoGNjtVr+/Nzjm/E77XTa/toVmdu6uVd1KNzJT7Rfqxpeuk/7Y5B3Xi9Vom0YrGY7Vwp2ofg597GzzXFy7jV67GIehFtulKpGIlEwvW8X+983mwMJdqnc3XfRqzHWK8f+BmbtzLWa3TcXvu413Gg1z7ZaDzbbDVI6z2qn7LrRJ/ut44GrsSApdH3ovIURTFvCMSNVKVSMSvF+vfhhx/WfGZtCNY/YW1tzawIcdPmXJrY+udcetzaySqVinnisA4OVVU1TwqiMddbOtu5r3p5cB6T29Ky9Qa24uTgR700VVU1y8/aAZuxDljEidVZ/s7trYPcWCzWdNDlHLA4iZtzkW/nMs71ytcw7INu53Kh1vTdglOijfgpp2b5cWvj9dqUl+P3ko96Ab16aYo6FP1AnJzdAgv1+qT4vlG/85JX6+BYBIHEPur9vlGfc5aVl+PMZDLmRVC0IRF8cAauvfaxevnyUteZTMa2jds+xMBQ3Cx5PdZGRFrOvDQK7C4tLZn9T5blmiWQ653vhWZtzI1bfYljt17Erec055+4Ka93Xvdz/RLn8Xr7qtc+m13XRPk4+5jIy9LSUtM+5nYNF4M0Ud9e8tGoTkU5tHo+8FoH4ibBbd/16qbRucJ6kyDy7AyWWH/vDMpag0BuwWuv6QitXFsF5/nAT9l5KQs3jdq51/HI/Py8GZx1a1duY6xGfVQcy9TUVMNxXqO8u33n95wg2r0YKzuDnF6v41YicCHyJG4GE4mEWVf10pRl2eyb1t/7bUPWvNRrS17G2YLXa684Vus5vlH5NWt/fuuzGb/XzmbtwzC81ZkzD25593ousP5jcaP+04kxqBsv1yrrcVrz6Ja+13ubVu7tGo1b/RyLuP6KcnJLq14dCs3GUKJtef1HTLfycNZxK2PzVsZ6nbjGeBkHGkbzPtno3NDoHs95j+qn7DrVp/stYBg+l3mhgZXNZvH111+3PJ9DJxSLRezevRupVKrnq8z0SigUwrVr1/juMvVMIBDA1NRUX/v246xcLmNhYQGzs7M132mahqmpKfzud7/ryCtY2WwW9+/ft62Yas3H/v37uz4xP/VPsVjExx9/7NrWqD7xWvGHH35Yc23WdR0XLlwAgEd2XEJE/kWjUeTz+aYrnj4O9zbUWC/HgVRfx+a4ov7SdR0zMzPmBObUHQsLCxgbG2PQinru3/7t3/qdhcfW0aNH684PMTw8jKGhIXPS0HZomoZkMonnn3/e9funnnoKY2Njbe+HBtfHH3/c99UjN6M//elPANxX+AoGg3jyySe7Oik9ERE9uno1DqTG/qXfGaDWiX8BkCQJQ0NDeO211/q+6pSYbN/PhL+DTvyLjKIoUFUV3377bb+zRI+4eDyOUqmEK1eu4OHDh1AUxZyglXpPVVWcOHECd+/exc9//nNzqezvvvsOn3zyCU6cONGRYPbDhw9RrVaxf/9+vPbaa7Yb7W+++Qarq6v48MMP294PDZ5isYhPP/0Ub775Zt+v45uRqqpYXFzE0NAQ9uzZYy4//uDBA/z973/Hnj17+C/hRGSyTt5fLpcbTl7+KN7bkD+9GgdSY3xVcBPTNM1cgvWtt97q++OrIsBj9Sg0r+npaZw6dQqRSASpVKrt1b2Imkmn05icnASwseqM2+sv1DuapuHMmTNYWVkxV2+TZRmxWAxHjhzpaKChWCziz3/+M1ZWVlCtVgEAiqLg6NGjOHDgANsBkQvxOuClS5dQKpUAAJIkIR6P45VXXmHQiohM4h/+req9Bvio3tuQP70cB1J9DFwREREREREREdFA4hxXREREREREREQ0kBi4IiIiIiIiIiKigcTAFRERERERERERDSQGroiIiIiIiIiIaCAxcEVERERERERERAOJgSsiIiIiIiIiIhpIDFwREREREREREdFA6kvgStM0xONxBAIBBAIBxONxlMtlT7/N5XKIx+OIRqNdziVZFYtFJJNJBAIB2+eapmFoaAjT09N9ypl32WwW0Wi0pu3kcjkEAgHkcrm299HJtNwsLCwgFAohEAggFAohnU53ZT/dUq8dtWJhYQHFYtHz9pqmIZ1OQ9f1tvdNREREREREvdHzwJWmaVAUBSdOnIBhGJifn8fKygq+/PLLpr8tFou4desWFhcXe5BTEsrlMr777jvMzc31fN/FYtEMcFr//AYuc7kc7t69i3w+36Wcdt/09DRWV1exvr4OVVURCoVw+vTpfmfLs061I13XEY/H8fzzz2N0dNT2XTabrWkryWQSADA8PIwjR47g2LFjDF4RERERERFtEgHDMIxe7nB6ehqLi4tYX1/3tH0ymcTs7Kzts0AggEgkguXl5W5k8ZGQzWbx1FNP1dzYtyMajSKfz6PHTQbARrs5deoUJElCqVTC8PBwS+ls1raj6zp+8pOfIJPJIB6P9zs7bWm3HcXjcbz66qvYt29fzXfhcBjBYND22ezsrK29lMtlHD16FMvLyzXbEhERERER0WDp+RNXN2/eRCgU8rStpml9ecrnUTAzM9PvLHTUL37xCwDAzp07Ww5abWa3b98GADz11FN9zkl/LSwsoFQquQatisUixsbGsLy8bPtztpeRkRGEw2EcO3asV9kmIiIiIiKiFg3s5Oy6riMWi/U7G5tSMpmEqqr9zgZRR+m6jhMnTuD3v/+96/cnT57EkSNHPKX1yiuvYHFx0dccWURERERERNR7HQlc6bqOZDKJoaEhBAIBhMNhZLNZ2zbRaBSBQAD5fB75fN6cf8btxlHTNOzatQulUgkAzG3r7VdMVO2WVi6XQzgcRiAQwNDQEJLJZMP5bXRdNyfxTqfTKJfLZt5DoZDrpNvlcrlmsnlrXsTvxZ91Qm3rd9bfpNPpupNwi0mmxTFPT08jEAhgenoa0WjUfEpt9+7d5nxQznl/BOccUn5ls9mayfI1TcP09LSZv1wuZx6LmG+oWfl5nazfun8v7cFJ13UsLCwgHA7XTHQ+PT1ttuloNIpwONxSWu3kUdTP7t27Adjr1LrfZv3PuqhBuVxGKBTC0NAQNE2zfafrunncQ0NDZjrlctnsR6FQyKyfdDrtOu+Ytc15mUDe2QbcJp6/fPkyqtUqnnnmGddyyufzUBSlpv+5Ea/Qfvzxx7bP4/G4WS5EREREREQ0AIw2VSoVQ5IkI5FIGJVKxahUKkYikTAAGFNTUzXbRyIRIxKJeEo7EokYblkEYCiKYiQSCWNpaclYWloyJEkyJEmybZfJZAxFUQxVVQ3DMIz5+XkDQMP9FwoFI5VKGQCMRCJhKIpipFIpIxaLGQAMAMba2pptewDG/Py8YRiGoaqqoSiKAcBYWloyy0j83q1MFEUxMpmM+d+JRMK1PFOplGEYhrG0tGTuIxKJGIlEwpAkyTwukf9CoWCmKY5dkiSjUqnY9r+0tGTIslzzuZOzPqxlZS1TkZ4ow1QqZRQKBfM4RFkZhmGsra0ZsiybeS0UCmZdWvMjytm6n7W1NXP/XtqDYRiueRV1I8pXlJeiKGYdTE1NubZFZzk602olj27E8Vvr1DC89b+1tTWz/hVFMfugqAvrd9b8iTpcWloyP7dua60Ht34lysxarobh3q8lSTJisZh5DKIcrX2t3vnAMH6oL9FHRX4atWlFUWrKXxyzdb9ERERERETUP20HrhKJhCHLcs3n4gbQeaPdycCVlbhJFjec4oZeBK2caTrzZSWCBCJw4dxHLBazHWcikbD9XlXVmiCR+Mx57JVKxfZZoVBwLU9xMy6IYIg1YCb25Ra4MgzDDAY4yySVStmCSfU0qg/ncYk8iOCdOFbntpFIxBa0s/7WGvBwC1xZ91+vPTjLwC0NkbZ1f5FIpCbI6NyHG7e0/OaxUbrObf30PwC2AKU1OOOWv0wm4xpsdWsHXsvV7feir1rboDMgLPbhJdaeyWTM429UZ27ngkqlwqAVERERERHRAGnrVUFd1zE3NwdFUWq+E/PQOF/F6RTnamBPPvkkAODhw4cAgGvXrqFarUKWZdurcPl8HgDwxRdfNN3HwYMHbft54403AAArKysANl6/UlUVIyMjtt8NDw8jFouhWq3i6tWr5meJRAL5fN72GtLly5dx+PBh878//fRTqKra8NU+K/Ha1PDwcNMV0k6dOgUAOHPmjPmZrus4d+4cDhw40KQ0WvPEE0+Y/9+ZP13Xkc/ncejQIduxTk5OAgA+++wzz/up1x5a9dxzz2Fubs72yt3BgwfbSrPTeWyl/4VCITMfznbrzJ+YCL7dfDYTDAZx7949jI+PQ9d1pNNps0/cv3/ftq3bsTrF43Fcu3YNiqKgVCrVvDYpuE3yHwwGa8qFiIiIiIiI+qetwJVY6UyW5ZrvREClX3PF3Lp1CwBgbDxVVvM3MTHhO81gMAhFUVCtVm37+NnPflaz7Y4dOwAAd+/eNT975ZVXANgDR+fPn0c8Hjf/++bNm4hEInXzLebmaYUIns3NzZlzFF29ehVjY2NNg17dINpPoVBwPdbl5eWe50l44403MDY2hkOHDiEUCiGbzbbUZrppkPtfK9LpNHbt2gVgY6J1KxGw9dpOg8EgFhcXAQBff/216zZbtmxpNatERERERETUIx2ZnP3mzZudSKYrOn3j7nbjLAJYzYyOjkJRFMzNzUHXdeRyOdeneNbX19vOZz1vvvkmAJhPNc3MzJif9ct3333X1/27CQaDyGazKBQKCIVCOHToEMLhcMOJ/ftlkPufV+FwGJcuXcK1a9cwMTFhPu0liICtn74xPDyMSCRiBpqJiIiIiIho82krcPXTn/4UAHD9+vW627z44ovt7KJl27dvB7DxKp6TWDmwFevr6+brSk8//TQA4Kuvvqq7/QsvvGD77+PHjwMALly4gLNnz9a8ovfcc89BVdW6KyS2G4izvrIoVv5ze2WqF7Zt2wYAuHjxouv3CwsLvcyOq9HRUSwvLyOTyaBUKuFPf/pTv7NkGuT+50c2m0WpVMK7777b9IkqVVV9p78aE91LAAAcjklEQVRnzx7Xz/28ikpERERERET90VbgyvpEgzMQJJ6icQZm1tfXe/LUyq5duyBJEiYnJ20BEF3XcezYMezcubNpGs75dTRNg6qqZvDppZdegiRJWFxcrAko3blzB7Is17zaF4/HIcsyTp8+jeHh4Zqg0S9+8QsAwMsvv2wLXmmahrNnz3YkyCSesDp16hRef/31ttNrVTAYRCQSQT6fRzKZtLWLdDrd9bmVGkkmk7b/jsfjUBSlpcBJJ4j+9ODBA/OzVvpft0QikZqnob755htPv7W+TitYj9O6Dz80TYOu67ZXcd1Y+6imaeZrtERERERERNR/bb8qODs7C0mSkEwmzUBLsVhEMplEJpOxBVrK5TJUVUWpVPJ1c5jL5TA9PQ1N05DL5QDUBsDEU0/iZjkYDGJ2dhYA8Nvf/hahUAjRaBRbt27Fjh07PAWArJNza5qGWCwGRVHMG+FgMIi//OUvAIBYLGYGrxYWFpDNZnHlyhXXdF977TVUq1Vzziur0dFRJBIJVKtV7N69G+FwGNFoFLIsY2ZmBsBG8E0cb6NJ5r/44gtks9maoIZ46kqWZezbt69pOYh9iid7rHUn6txZH+JpFutrgOJ31m1TqRQkScLc3By2bt2KaDSKUCiEzz77zBZw+PTTT83fWoOEYv+6rjdsD43yKsrQ+uScpmmIx+PmvorFIkqlEl599dWG5eSWlp881vPJJ58AAP77v//b9rnX/mc9dmeQ1a1erPm6c+eO+Zn1GERfBDbalKqqSKfTKBaLmJ6eNr+7dOmSGTx2a0fi6ch33nkHuVwO6XQaZ8+eNfct0hITtjufRiyXywgEAjX1NTU1hfPnz9crUuTzeSQSCdtnY2NjePbZZxm8IiIiIiIiGhSdWJpQVVUjFouZy9UrimIsLS3ZthFLzzv/GllbWzMkSTIkSTIymYyRSqVqfl8oFGo+S6VSZhpLS0uGoigGAEOWZWN+fr7p8Yg0Y7GY+VtJkoypqSmjUqm4bi+2E79bW1urm36lUjEURWmYh1QqZciybJZnoVAwv3MebyQScU0fgDE1NVU3fS9lIY7PrYzd6qNe/urVnWFs1LNoP85ydtt3szT9bOtsl6IsrXUv2o6zTTu5peUnj17L3tnGm/W/RmXvtVya9bVKpWL+RlEUY21tzSgUCoYsy0YmkzEqlUrD309NTZnlnMlkDFVVDUmSDFmWzb5UqVQMSZJsxy4+dx5/KpVy7avC2tqarRyEWCxmSJJkqKrasK6JiIiIiIioNwKGYRjuIa3HV7FYxO7du5FKpQZuJblOCYVCuHbtWl9WEyRq1cLCAv74xz+2vYDB9PQ0VFVtea47IiIiIiIi6o2OrCpIm8vCwgLGxsYYtKJNZ3x8HGNjY21N3K9pGlZWVvDhhx92MGdERERERETUDQxcuRBz+zRaLXCziUajCAQCCIfDOHHiBN57771+Z4moJbOzs7h7925L81Dpuo4zZ87g/PnzDNwSERERERFtAgxcOUSjUfz2t78FACwuLiIQCPQ5R53x3HPPAdiYUH51dZU37bSpvf/++7h9+3bNRO2NaJqGy5cv47333sPIyEgXc0dERERERESdwjmuiIiIiIiIiIhoIPGJKyIiIiIiIiIiGkgMXBERERERERER0UBi4IqIiIiIiIiIiAYSA1dERERERERERDSQGLgiIiIiIiIiIqKBxMAVERERERERERENJAauiIiIiIiIiIhoIDFwRUREREREREREA4mBKyIiIiIiIiIiGkgMXBERERERERER0UBi4IqIiIiIiIiIiAYSA1dERERERERERDSQGLgiIiIiIqojHA4jnU73OxtERESPLQauiIiIiIhcZLNZHDx4EJcuXYKu6/3ODhER0WMpYBiG0e9MEBERERENmnA4jOXlZVy9ehV3797FxMREv7NERET02GHgioiIiIjIIZvN2oJVIogVDAb7nDMiIqLHCwNXRET0WCmXy/jDH/6AxcVFAIAkSYjH43jvvfd4Q0pERERENGA4xxURET02yuUy9u/fj//4j/9ApVKBYRgolUrYsmULotEo57AhIiIiIhowfOKKiIgeG6FQCFeuXMHIyEjNdwsLCyiXy5idne1DzoiIiIiIyA2fuCIioseCpmkYGxtzDVoBwPj4ODRN63GuiIiIiIioET5xRUREREREREREA4lPXBERUdtCoRCi0Wi/s0FERERERI8YBq6IiKgt2WwWY2NjWF9fR7FY7Hd2iIiIiIjoEcLAFRERtWVmZgbj4+M4efIkTp482e/sEFGPFYtFBAIBBAKBRy54res6ksmkeXzxeJyrjxIREfUYA1dERNSybDaLUCiEkZERxONxPnVF9BgaHR3F/Px8R9NcWFgwg0WtBorS6bSZxvT0NABgenrac5BN13VEo1FomoZKpYJKpYJSqYS33367pfwQERFRaxi4IiKils3MzGBmZsb8bz51RfR4euaZZzqa3vj4OKampiDLMoLBYEtpHDlyBIqiQJIkvP/++wCAX/3qVwCA+fl5jI6ONvz95cuXoaoqPvroIwSDQQSDQYRCIa4+SkRE1GMMXBERUcvW19dtN3/xeBzLy8t9zBERPSpUVcXY2FjLvw8Ggzh+/Diq1So0TYOu6zh69CgymQzGx8eb/v78+fOIx+Nm4EzXdVy/fh0vvvhiy3kiIiIi/xi4IiIiIqKOsr6mVywWEQqFEAgEEI1GPb/6t7Kygj179rSVj3g8DlmWcebMGUSjURw/fhzxeLzp73RdR6lUMvdfLBYRjUaxc+dOHDlypK08ERERkT8MXBERERFRR01MTCCRSECSJHz88ce4du0a1tbWkM/nceHChaa/L5fLqFar2LZtW9t5ee211zA3N4ejR4/WBK3ExPJOt2/fBgDs3LkT0WgUu3fvxtGjR7G8vNzyq4tERETUGgauiIiIiKjjtmzZgmq1itnZWQSDQYyMjHj+7ZdffglJkmy/sa5eWO/POeG6ruu4dOkSAOD+/fs1+xkdHYVhGDWff/HFF5BlGcPDw1heXsb8/DxOnDiBcrns+RiIiIioMxi4IiIiIqKBUi6XsXPnTttnIsjU6M854Xo0GkU4HEYqlcK5c+dq9iMCXk6fffYZFEUx/3t8fByyLGNhYaFDR+hNNpttO1im6zrS6XTLqzMSERH1GwNXREREHokbwFAoBADmvD1uN76Pm2w2a5ZFOp3u2b6i0WhX99WqbDaLeDxe8wTQo0LTNCSTSU/zRbViZWWl7UnQk8kkwuEwZmdnceDAAaiqimw2a9tmaWnJ9bfXr1/H3r17bZ+pqgpJksz/7mYd67qOcDiMr7/+2teTam6CwSCefvppRKNRPjFGRESbEgNXRET02LBOGG39GxoaQjweb3hTVy6XsWvXLty5cwfXrl0DsLGqoizLXc2zpmm2vIbDYdv38Xi84atSvRKPx5HJZHq2LxFweO655zz9JhqN+gowapqGcDhsK1vB2Y5yuZxrHn/3u9/h8OHDSCaTnve7GWSzWSiKgpGRETMQ9M033wAAHjx4YG53584dABtlCcB84kd83oiqqti+fTuy2azvNq3rOpLJJFZWVjA7OwsAGB4ehiRJmJmZsT159MQTT9T8XsyvJV4tFOlVq1X86le/MrfrVh1rmoatW7fi+PHjeP/99xtum0wma85nzuCcyOvx48exd+9eBq+IiGjzMYiIiB4jsVjMAGBkMhnzs0wmYwAwJEkyKpVKzW9UVTUkSTISiUTNd5FIxOj25XR+ft4AYACoyV+lUjHzXigUupqPZgqFggHASKVSXd/X0tKSAcBYWlrytH0r9aSqqlnua2trtu8URTEAGPPz8w3TqFQqhizLrm1nMxLlbu0/ot7Fn2EYRiqVMv87EokYhvFDHXhpI6J8W2lLU1NT5n5En7Dmx5qmyLuVOB9Y86soSt3+1ek6VhSlabsyjI32KcuyEYlEzL9YLNbwN5lMxpBl2fU8R0RENKgYuCIioseKCFypqmr7PJFI2G50rSKRiKEoimt6vQhcGYZhyLLsuh9x4+01gONXoVAwEomEpwBCLwNXIhDhrMd6Wq0nEUCxWltb83Wc3a6jXqlUKoYkScbU1FS/s9IxboGrRCJRt783S6fdOk6lUmagz8u2rewvFos1DXARERENEr4qSEREj5VSqQRJkjA8PGz7fMuWLa7bF4tF5PN5HD9+vBfZqysWiwGA7TUfXddx+PBhzM/PY9++fR3bl6Zp5lxe//mf/4k9e/bgyJEjHUu/Ez777DPXeuw0Ue/idUBd13H06FEkEglMTEx4SmN0dBSKouD111/vWj574cKFC6hWqwPXFtohXm209qsbN25gbGzMVzqdquPTp09jZmam6XZitcRW+v2pU6ewuLjIVwaJiGjTYOCKiIh8y2az5sTk1vl9crmc6+eDQtd1qKrqelP62WefAQB+9KMf2T7/85//DACeJ6Eul8tmGYRCIXN+nKGhIXMy8VZW9/r5z38OAPjrX/9qfvbrX/8aY2NjGB8f952ek67ryGaziEajUBQFd+7cwZUrV3Djxg3E43EEg8GW0rXOE2UNiInyafXmOZ/P+w4utOKll14CAPz9738HABw7dgzBYNCcO8mro0ePQlXVgQkWiInFBS/1cvr0aUQika4HC3vpl7/8JQBgcnISwEY/KJVKZn/zo906LhaLqFarNSsjurlw4QJKpRJCoRCSyaQ5j5gXw8PDiEQitnMJERHRIGPgioiIfEmn07h//z5WVlYAAGfPngUALCws4O9//7s5YfmtW7daTt9tAvVGf14nbxaTqjtXCyuXy8jn8+aE01YrKyuIRCKe8z8yMmLuBwDefvttjI+P4969e0ilUsjn83j77bc9pyfs27cPkiSZ5S6Wt/cbQHEqFotIJpPYunUrPvjgAxw+fBjffvstZmdn217NDNh4ekVRFADAmTNnsH37dqyvr6NQKEBVVezfv993miIwsGPHjrbz10wwGEQsFkM2m0UymYSqqvjoo498p/P8888D2Ai4eeG3D/hZXTGZTCKZTOLEiRMAgOnpaZw+fRoXL15EpVIB4N5HqtVq2yv9DRpjY9oMLC8vA9iob8MwWnqSyW8dO33xxReezzV37tyBLMtQVRVzc3OQZRnT09Oe9/Xiiy/i5s2bLeWTiIio1xi4IiIiXyYmJjA+Po5//vOfAGDeyB44cMAWRHnyySdbTl/cTHr98/KEAgD84x//APDDDaZ4ymjv3r2QJAnnz5+3bS9u1r2uXCeIp5NUVcWbb75pBoDEq2U3btzwlZ4Qj8dRKpWwsLCAS5cumTfbfum6bj5h8/LLLwMAVldX2366qh6R3p49e8yAwOjoKCKRCFRV9f0E2pdffgkAeOGFFzqaz3peffVVVKtVZLNZLC8vt1Q+og2IJ/ua8dsHvLaFZDKJbDaL1dVVjIyMoFwu49SpU3jrrbcwOjqKYDCI1157DdVq1fbkkCjz7du3+zzyx4ffOm7H7Ows1tfXUalUkEqlIEkSTp065St4RUREtFkwcEVERC0Rc8M8/fTTAH4ITmiaBlVVzVesBol4WunZZ59FIBDAT37yE8zMzCCRSODbb7+tecLo4cOHAFoPwgFwfa2qVCq1lNa///u/AwBOnDiB8+fPtxxgun37NiYnJxEKhVAqlTr2dFUzTz31VN38+LG6ugoAngOW7RKvCcqy3HZQb319vRNZakkul8Pc3Bz+8Ic/mPW9sLAAALZ5q+7fvw/gh/Zv/eyJJ56oSdfv02GPyl89rdbxCy+8gOvXr/v6TTAYxMTEBFZXV83glZ/XBomIiDYDBq6IiKgl4lVAZzDi8uXLSCQStoCNeP2vn8TcNYqi2J5UWV9fx/vvv9/xp4y6KZFI1ASa/JTxtm3bkEqlsL6+DkVRkEwmB2buJS9EPfbCwsICVlZWkEgkUCqV6pZTNBr19LqeqqqdzqJn77zzDiRJwoEDB8zPVlZWoCiKrf2LV8i2bdvmKV2/T4c9Kn/1tFrH27ZtQ7VabSnwNDIyYj7x+j//8z9Nt79//z5+/OMf+94PERFRPzBwRURELfnqq68A2G9udV3HuXPn8N5779Vs63Xulm7NcSXmnerFhN7dIoKFrUwcbSWe0lhfX8ff/vY3ABtzGoXDYWSz2ZYmj+8V8URfOBzu+r5yuRxOnDiBK1eumBPg92pC627McVUqlbBz586apyOtfULXdeTz+Y48XUb+BINBJBIJnDlzpqXfi8n2xdOwjSwuLtbMY0ZERDSoGLgiIqKWfP/99wBgu7k9duwYzp49W3PDK+YG8qJbc1x1KujTT2LunF27dtV8J8rNr9HRUczOzuLbb7/F8ePHcfHiRWzdunVgn8IST5Ps2bOnq/spl8v4zW9+Y75GOTIyAlmWMTc357r98vKypzYuy7Kn/XdrjisrUZbWPnHhwgUAwMmTJ32nRxu81rGbN998E9lsFtlstuU0du7c2fD7dDqNe/fu2Z68IyIiGmQMXBERUUvEa2ZCMpnEjh07bKtxlctl3yv/dculS5cAuAd96vnRj34E4If5ffqt3pMw1qfUWhUMBhGPx7G8vIxSqYQtW7Zg//79A/cUVr1XVDtJ13Xs378fiUTCfIoFAGKxGKrVKnK5nG37aDTq+amnUCjU8fx6pSgK8vm8WZdisQLRJ8rlMk6fPg1FUWzHDfwwz5uXp3k6pZWnLzs9X1Ur2qnj4eFhrK6uIplMmvOPeZXNZjE1NeU6r55QLpcxOTmJv/zlL3yijoiINg0GroiIqC2apiEej2PLli3mqnnCyMgIMpkMgN5NpO2mXC6bE6J/9913nn8n5pFqtGy8mIjZOi+NNcjj5f97Yb2Jdf52YmICiUTCV3qNDA8Pm68S/td//Rc+//xz80mcRkTZ3rlzx/a5yK8z6FHvc69yuVzHV1HTdR3RaBSqquKNN96wfSeCN2fPnrV9/tFHHzWdc0sEbv2uUNlJ58+fhyRJiEajKBaL5vxWwEb72rt3L2RZdn1665lnngHwQ+CwF+7cuYNCodDX+ar86FQdj4yMYHV1FefPn0cymaz5fmFhAYFAAOl02uxD2WwWn3zySU2btSqXy9i7dy8ymYztHxiIiIgGnkFERNSCqakpA4ARi8WMQqFQd7tUKmVEIpEe5sxOVVUDgO2vUql4/n0sFqv7G1mWzTQlSTLW1tZcPxckSXL9vJlUKmXLvyzLdbfpl0wmY8tjIpEwDMMwEomE7XNRRqJcnZ83s7a2ZkiSZEiSZExNTXmuy0gk0rR8VFU1FEUx8zQ/P2/br7VeU6lUTfqN2vn8/Lyv4+yWtbU1I5FI1LTFWCxmZDKZhr+VJMlQFKVHOd0o00bnlkHT6TquVCpGKpWqSW9tbc3WTiORSNO6y2QyhiRJTbcjIiIaRAHD6NA/MxEREbkIh8M4ePBgzdNYm0WxWMTu3buRyWRqXp8aJOl0GpOTkx17euRRE41Gkc/nu1Y+4jXBenNNhcNhVKtV8wm9fiuXy3j22WcxPz9vTjzfjGhjqqo2fB2tU6LRKD766KNN80rboNWxoOs6/vSnP+GNN97YNGVJRERkxVcFiYioa3RdR6lUwvbt2/udlZaNjo4iEolgZmam31mhTapYLKJUKtW8YthPX375JQDg+eef9/ybI0eOQJKklle98yufz2+aQMsg1rEQDAbx/vvvb5qyJCIicmLgioiIuub27dsAgH/9139FMpm0zQO1mczOzuLevXuu880MCue8UmS3vLzctaetdF2vO2eZrut4+eWXkUgkBmpeodXVVUiSZM7j5kUwGMRf/vIXzM3NtbXqnVftrM5XTzabRSgU6uhiA4Nax0RERI8KBq6IiKhrfvrTn0KSJCiKgj179vTk9aJuECt9raysIB6PD8wKe0I6ncbc3BwA+F6JjNr361//GqVSCdevX7etnlksFrF161bE43HMzs72MYe1SqUSxsbGfP9u3759yGQySCaTSKfTXcjZBk3TurIC4wcffIDf//73nhYb8GKQ65iIiOhRwTmuiIiIPNJ1HRcuXMC5c+cGbh4bGixilbff/e53fV1Rs1s0TcOZM2dw7969rjx9VSwWcfLkybpzhrUim83i7t27mJiYQDgcxvLycluvzz3qdUxERDQoGLgiIiIiooGSy+Vw69atji7qYA1WWYNYRERENNj+pd8ZICIiIiKyunXrVsfTvHHjhvn/B3mFUCIiIrLjHFdERERENHCefvrpfmeBiIiIBgADV0REREQ0UL766is89dRT/c4GERERDQAGroiIiIhooHz//ff9zgIRERENCAauiIiIiGig6LrOlfqIiIgIAFcVJCIiIqIBEwgEwCEqERERAXziioiIiIgGjCzL/c4CERERDQgGroiIiIhoYJTLZYRCoa6knU6nEQqFEAgEEI1Goet6V/ZDREREncPAFRERERENjIcPH+LHP/5xzefRaBSBQACBQAC5XA4AEA6Hzc+aSSaTuHTpEq5cuYJKpYL19XVcvny54/knIiKizvqXfmeAiIiIiEh48OABduzYUfN5KpXC9evXMTY2hn379gEAjh49ClVV8be//a1hmsViEXNzcygUChgZGQEAhEIhlMvlzh8AERERdRSfuCIiIiKigXHr1i3Xz0dGRhCPx1EqlQBsvFL4xz/+Eaurq01XIDx58iQikQhXKiQiItqEGLgiIiIiooGyfft218/ffPNNqKqKdDqN/fv348qVK+YTVPVomoZ8Po/Dhw/bPr9+/Tq2bNnSsTwTERFRdzBwRUREREQD46uvvsITTzzh+t3w8DAikQgmJyddg1bFYrFmvqvr168DAA4dOmTOhxUIBFCtVvH000935yCIiIioYxi4IiIiIqKB8f333+NHP/qR63flctkMRD18+LDm+9HRURiGYfvs7t27kCQJhmGYf5lMBgCwc+fODueeiIiIOo2BKyIiIiIaGLquu77+p2ka9u7di9nZWUQiEXz88cc127itMHjnzp2aANUnn3yCSCSC4eHhzmaeiIiIOo6BKyIiIiLquVwuh3A4DE3TbJ+LydetdF1HLBbD7Ows4vE4Dh8+jLm5uZrfLi0tNd1vuVzG4uIiZmZm2jsAIiIi6gkGroiIiIiop7LZLB48eIBqtYqpqSnbd4qi2P5b0zREo1GEw2HE43EAwLZt2wCg5rduc2ONjIxgfX0duq6jXC7j6NGjSCQSXGGQiIhok2DgioiIiIh6Kh6PIx6P4+TJk1hcXESxWASw8TRUMBi0bRuLxVAqlTA3N2d+Njk5CQC239Zz4MABKIqCn/zkJ9i7dy8OHjyI2dnZDh8RERERdUvAcM5gSURERETUI9FoFLqu48aNGygWi/jzn/+MbDbbUlrFYhG7d++umaCdiIiINi8+cUVEREREfZNKpVAqlczXB3fs2NFyWg8ePACw8eQWERERPRoYuCIiIiKivhkZGUEikUAymcQ//vGPttL65S9/CeCHVwmJiIho8+OrgkRERETUV7quY+vWrQCAv/3tb5w4nYiIiEx84oqIiIiI+ioYDOKtt95CtVrtd1aIiIhowPCJKyIiIiIaCKFQCCsrKxgeHu53VoiIiGhAMHBFREREREREREQDia8KEhERERERERHRQGLgioiIiIiIiIiIBhIDV0RERERERERENJAYuCIiIiIiIiIiooHEwBUREREREREREQ0kBq6IiIiIiIiIiGggMXBFREREREREREQDiYErIiIiIiIiIiIaSAxcERERERERERHRQPo/AXR6MorvMrYAAAAASUVORK5CYII=\" style=\"width: 750px;\" width=\"750\" height=\"393\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the SLA framework(25), combined with the questionnaire used in the survey, livelihood capital in this study is measured in terms of the dimensions of household physical capital, financial capital, social capital, and human capital. Among them, physical capital is measured by the type of housing, type of drinking water, type of toilet, and separation of housing and kitchen. Financial capital is measured by registered poor households, household borrowing, and annual per capita household income levels. Social capital was measured by gift expenditures. Human capital was measured by respondent\u0026apos;s age, respondent\u0026apos;s education, respondent\u0026apos;s occupation, household size, respondent\u0026apos;s self-rated health status, respondent\u0026apos;s chronic disease prevalence, and respondent\u0026apos;s outpatient and inpatient service utilization. Table 1 shows the definitions of the main variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 1 Variable definition\u003c/strong\u003e.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"51.02040816326531%\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.244897959183673%\"\u003e\n \u003cp\u003e2019年\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.244897959183673%\"\u003e\n \u003cp\u003e2022年\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eExplained variable (Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eHealth poverty vulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eMeasure with expected poverty vulnerability (VEP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eExplanatory variable (X)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eMaterial capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eHousing type\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e1=brick soil concrete, 2=brick wood, 3=Civil Engineering, 4=full brick, 5=other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e2.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e2.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eType of drinking water\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e\u0026nbsp;1=tap water,2=cellar water, 3=well water, 4=other\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e1.274\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.569\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;1.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eToilet type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e1=water flushing type, 2=toilet, 3=dry toilet,4=other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e2.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e2.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eSeparation of housing and kitchen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eYes = 1 and No = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003efinancial capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eRegistered poor household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eYes = 1 and No = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eLoan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eYes = 1 and No = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eAnnual per capita household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eI\u0026nbsp;group=1，II\u0026nbsp;group=2，III\u0026nbsp;group=3，IV\u0026nbsp;group=4，V\u0026nbsp;group=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e3.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;1.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;3.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.414\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eSocial capital\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eGift expenses(log)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eContinuous variable (logarithm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e9580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e34478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e7573.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e12875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eHuman capital\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eThe age of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003econtinuous variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e8.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e8.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eEducational attainment of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e1 = no schooling, 2 = primary school, 3 = junior high school, 4 = senior high school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e2.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e2.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eAgricultural workers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eYes = 1 and No = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e1.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eFamily size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003econtinuous variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e5.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e5.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eSelf-rated health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003e1=very good, 2=good, 3=average, 4=poor, 5=very poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e2.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e2.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003echronic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eYes = 1 and No = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eOutpatient\u0026nbsp;service utilization of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eYes = 1 and No = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eInpatient service utilization of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.02040816326531%\"\u003e\n \u003cp\u003eYes = 1 and No = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Actual poverty and health poverty vulnerability of rural women of childbearing age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eColumn (1) of Table 2 shows the poverty incidence and health poverty vulnerability rates of rural women of reproductive age for different poverty lines in 2019. Using poverty line 1 as the standard, the poverty incidence and health poverty vulnerability rates are 21.06% and 21.10%, respectively.\u0026nbsp;Using the poverty line2 as a criterion, the incidence of poverty and health poverty vulnerability is 46.23% and 40.55%, respectively. Columns (3) and (4) of Table 2 show the actual poverty and health poverty vulnerability transfer matrices.\u0026nbsp;Using the poverty line1 as a criterion, 94.97% of those who are poor in 2019 are in a vulnerable state of health poverty, meaning that 94.97% of these people may still not be able to move out of health poverty in the future,\u0026nbsp;1.45% of those who are non-poor in 2019 are health-poor and vulnerable, meaning that 1.45% of these people are likely to fall into poverty in the future as a result of a health risk shock. Using the poverty line2 as a criterion, 87.26% of the people in poverty in 2019 are in a vulnerable state of health poverty, meaning that 87.26% of these people may still not be able to move out of health poverty in the future,\u0026nbsp;0.74% of people within the non-poor in 2019 are vulnerable to health poverty, meaning that 0.74% of these households are likely to fall into poverty in the future as a result of a health risk shock.\u003c/p\u003e\n\u003cp\u003eColumn (1) of Table 3 shows the poverty incidence and health poverty vulnerability rates of rural women of reproductive age for different poverty lines in 2022. Using poverty line 1 as the standard, the poverty incidence and health poverty vulnerability rates are 19.62% and 20.02%, respectively.\u0026nbsp;Using the poverty line2 as a criterion, the incidence of poverty and health poverty vulnerability is 45.43% and 40.57%, respectively. Columns (3) and (4) of Table 3 show the actual poverty and health poverty vulnerability transfer matrix.\u0026nbsp;Using the poverty line1 as a criterion, 100.00% of those who are poor in 2022 are health-poor and vulnerable, meaning that 100.00% of these people may still not be able to move out of health poverty in the future,\u0026nbsp;0.56% of those who are not poor in 2022 are health-poor and vulnerable, meaning that this 0.56% of households may fall into poverty in the future due to health risk shocks.\u0026nbsp;Using the poverty line2 as a criterion, 88.60% of those who will be poor in 2022 will be in a vulnerable state of health poverty, meaning that 88.60% of these people may still not be able to move out of health poverty in the future,\u0026nbsp;0.75% of those who are not poor in 2022 are health-poor and vulnerable, meaning that 0.74% of these people are likely to fall into poverty in the future due to exposure to health risk shocks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2 Transfer matrix of actual poverty and health poverty vulnerability of rural women of reproductive age, 2019\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003ePoverty line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003ePoverty Incidence / Health Poverty Vulnerability Rate(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eVulnerability(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.25%\"\u003e\n \u003cp\u003eNon-vulnerability(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003ePoverty line 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e21.06/21.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003ePoor(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e94.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\"\u003e\n \u003cp\u003eNon-poor(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.56140350877193%\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.57894736842105%\"\u003e\n \u003cp\u003e98.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003ePoverty line 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e46.23/40.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003ePoor(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e87.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e12.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\"\u003e\n \u003cp\u003eNon-poor(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.56140350877193%\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.57894736842105%\"\u003e\n \u003cp\u003e99.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3 Transfer matrix of actual poverty and health poverty vulnerability of rural women of reproductive age, 2022\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003ePoverty line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003ePoverty Incidence / Health Poverty Vulnerability Rate(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"32.6530612244898%\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eVulnerability(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.25%\"\u003e\n \u003cp\u003eNon-vulnerability(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003ePoverty line 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e19.62/20.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003ePoor(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e100.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\"\u003e\n \u003cp\u003eNon-poor(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.56140350877193%\"\u003e\n \u003cp\u003e0.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.57894736842105%\"\u003e\n \u003cp\u003e99.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003ePoverty line 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e45.43/40.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003ePoor(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e88.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e11.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.85964912280702%\"\u003e\n \u003cp\u003eNon-poor(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.56140350877193%\"\u003e\n \u003cp\u003e0.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.57894736842105%\"\u003e\n \u003cp\u003e99.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Analysis of factors influencing the health poverty vulnerability of rural women of reproductive age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTables 4 and 5 show the factors influencing the health poverty vulnerability of rural women of reproductive age at different poverty lines in 2019 and 2022, respectively.\u0026nbsp;Tobit regression showed that annual per capita household income, gift expenditure, respondent\u0026apos;s age, and respondent\u0026apos;s education significantly and negatively affected health poverty vulnerability at both poverty line 1 and poverty line 2 in 2019;\u0026nbsp;Type of drinking water, housing, and kitchen separation, and the number of household members at poverty line 1 significantly influenced health poverty vulnerability; poor households, respondents\u0026apos; self-rated health status, and respondents\u0026apos; chronic diseases at poverty line 2 significantly influenced health poverty vulnerability.\u0026nbsp;In 2022, household borrowing, annual per capita household income, gift expenditure, and respondent age significantly and negatively affect health poverty vulnerability at poverty line 1 and poverty line 2; housing type and household poverty significantly affect health poverty vulnerability at poverty line 1; and respondent inpatient service utilization significantly affects health poverty vulnerability at poverty line 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4 Analysis of Factors Influencing Health Poverty Vulnerability of Married Women of Reproductive Age in Rural Ningxia, China, 2019\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"37.37373737373738%\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"38.38383838383838%\"\u003e\n \u003cp\u003ePoverty line 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"24.242424242424242%\"\u003e\n \u003cp\u003ePoverty line 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eHousing type\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eType of drinking water\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.015**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eToilet type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eSeparation of housing and kitchen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.037***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eRegistered poor household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.017**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eLoan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eAnnual per capita household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.196***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.339***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eGift expenses(log)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.049***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.032***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eThe age of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.001**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.001**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eEducational attainment of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.011**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.012**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eAgricultural workers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eFamily size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.013***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eSelf-rated health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.012***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003echronic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.013\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.007**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eOutpatient service utilization of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eInpatient service utilization of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.014\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.014\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e***p \u0026lt; 0.01; **p \u0026lt;0.05; *p \u0026lt; 0.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 5 Analysis of Factors Influencing Health Poverty Vulnerability of Married Women of Reproductive Age in Rural Ningxia, China, 2022\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"37.37373737373738%\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"38.38383838383838%\"\u003e\n \u003cp\u003ePoverty line 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"24.242424242424242%\"\u003e\n \u003cp\u003ePoverty line 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eHousing type\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.008*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eType of drinking water\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.014\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.014\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eToilet type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eSeparation of housing and kitchen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eRegistered poor household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.017*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.013\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eLoan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.023**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.023**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eAnnual per capita household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.200***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.341***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eGift expenses(log)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.021***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.018***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eThe age of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.002**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.002**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eEducational attainment of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eAgricultural workers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eFamily size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eSelf-rated health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003echronic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.014\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eOutpatient service utilization of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.028\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.024\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003eInpatient service utilization of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-0.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e-0.029*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e***p \u0026lt; 0.01; **p \u0026lt;0.05; *p \u0026lt; 0.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Shapley decomposition of factors influencing health poverty vulnerability among rural women of childbearing age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering that the running speed and computation time of Shapley decomposition is greatly affected by the number of explanatory variables, it is generally difficult to compute reliable results with more than ten variables. Therefore, in this study, among the sixteen risk factors, only those variables that were significant in the Tobit regression were selected for Shapley decomposition with \u0026alpha; \u0026lt; 0.1. Shapley decomposition was performed by including the type of drinking water, housing, and kitchen separation, household poverty, annual per capita household income, gift expenditure, respondent age, respondent education, household size, respondent self-rated health status, and respondent chronic disease prevalence in 2019.\u0026nbsp;2022 incorporating housing type, household poverty, borrowing, annual per capita household income, gift expenditure, respondent education, and respondent hospitalization service utilization. Shapley\u0026apos;s decomposition for 2022 incorporates housing type, household poverty, borrowing, annual per capita household income, gift expenditure, respondent education, and respondent inpatient service utilization.\u0026nbsp;Tables 6 and 7 show the contribution of factors influencing health poverty vulnerability of rural women of reproductive age at different poverty lines in 2019 and 2022, respectively.\u003c/p\u003e\n\u003cp\u003eThe results of the Shapley decomposition show that the highest contribution to health poverty vulnerability in 2019, as measured by the poverty line1, was made by the number of people in the household, followed by annual per capita household income, gift expenditure, and respondents\u0026apos; self-rated health status, with relatively low contributions from other variables.\u0026nbsp;Using the poverty line2 as a criterion, the highest contribution to healthy poverty vulnerability in 2019 was made by annual per capita household income, followed by the number of household members, gift expenditures, and respondents\u0026apos; self-rated health status, with relatively low contributions from other variables.\u003c/p\u003e\n\u003cp\u003eThe results of the Shapley decomposition show that the highest contribution to health poverty vulnerability in 2022, as measured by the poverty line1, is made by annual per capita household income, followed by gift expenditure, age of the respondent, and household poverty, with relatively low contributions from other variables.\u0026nbsp;Using the poverty line2 as a criterion, the highest contribution to health poverty vulnerability in 2022 was made by annual per capita household income, followed by gift expenditure, respondent age, and household poverty, with relatively low contributions from other variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 6\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDecomposition of risk factors for health poverty vulnerability of rural women of reproductive age in 2019.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"39.795918367346935%\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003ePoverty line 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003ePoverty line 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.517241379310345%\"\u003e\n \u003cp\u003eShapley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.758620689655174%\"\u003e\n \u003cp\u003e\u0026nbsp;Contribution (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.517241379310345%\"\u003e\n \u003cp\u003eShapley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.206896551724135%\"\u003e\n \u003cp\u003e\u0026nbsp;Contribution (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eType of drinking water\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.398\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e1.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e0.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eSeparation of housing and kitchen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.112\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e0.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.043\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e0.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eRegistered poor household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.420\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e1.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.083\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e0.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eAnnual per capita household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e8.425\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e29.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e42.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eGift expenses(log)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6.330\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e21.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e2.175\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e18.52\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eThe age of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.557\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e1.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.178\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e1.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eEducational attainment of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.257\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.044\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e0.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eFamily size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e9.049\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e31.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e3.657\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e31.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eSelf-rated health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1.594\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.353\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e3.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003echronic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.162\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e0.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.075\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e28.820\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e100.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e11.743\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e100.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 7\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDecomposition of risk factors for health poverty vulnerability of rural women of reproductive age in 2022.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"40.4040404040404%\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003ePoverty line 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"31.31313131313131%\"\u003e\n \u003cp\u003ePoverty line 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.517241379310345%\"\u003e\n \u003cp\u003eShapley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.758620689655174%\"\u003e\n \u003cp\u003e\u0026nbsp;Contribution (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.517241379310345%\"\u003e\n \u003cp\u003eShapley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.206896551724135%\"\u003e\n \u003cp\u003e\u0026nbsp;Contribution (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\"\u003e\n \u003cp\u003eHousing type\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e0.222\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e0.039\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e0.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\"\u003e\n \u003cp\u003eRegistered poor household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e1.189\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e3.79\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e0.393\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e3.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\"\u003e\n \u003cp\u003eLoan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e0.725\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e2.31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e0.214\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e1.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\"\u003e\n \u003cp\u003eAnnual per capita household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e13.819\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e44.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e6.898\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e55.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\"\u003e\n \u003cp\u003eGift expenses(log)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e11.213\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e35.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e3.709\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e29.82\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\"\u003e\n \u003cp\u003eThe age of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e3.791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e12.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e1.059\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e8.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\"\u003e\n \u003cp\u003eInpatient service utilization of the interviewee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e0.386\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e1.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e0.119\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.816326530612244%\"\u003e\n \u003cp\u003eTOTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e31.346\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e100.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e12.436\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e100.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough China has eliminated absolute poverty, poverty vulnerability as a predictor of future poverty provides a critical new perspective for consolidating poverty reduction gains.\u0026nbsp;According to this study of health poverty vulnerability among rural married women of reproductive age in western China, it was found that the health poverty vulnerability rate in 2019 and 2022 increased as the poverty line standard increased.\u0026nbsp;In formulating future anti-poverty policies, we can use health poverty vulnerability indicators to identify groups that are likely to fall into poverty due to illness and return to poverty due to illness in the future and adopt targeted ex-ante interventions to eliminate health poverty at the root.\u003c/p\u003e\n\u003cp\u003eThis study shows that the incidence of poverty and health poverty vulnerability decreases over time regardless of the poverty line criterion. The incidence of poverty among rural women of married reproductive age decreases from 21.06% in 2019 to 19.62% in 2022 under the poverty line1 criterion.\u0026nbsp;The health poverty vulnerability rate decreases from 21.10% in 2019 to 20.02% in 2022.\u0026nbsp;Under the poverty line 2 standard, the incidence of poverty among rural women of married reproductive age decreases from 46.23% in 2019 to 45.43% in 2022, and the health poverty vulnerability rate from 40.55% in 2019 to 40.57% in 2022.\u0026nbsp;The decreasing trend of poverty incidence and health poverty vulnerability in this study compared to other studies may be related to the implementation of a series of poverty reduction strategies in China. Thus, poverty alleviation not only reduces the incidence of poverty but also reduces the likelihood of future health poverty.\u0026nbsp;Nonetheless, China\u0026apos;s achievements in poverty eradication do not mean that the risk of future poverty is eliminated.\u0026nbsp;Notably, we find that 20.02% (poverty line 1) and 40.57% (poverty line 2) of rural women of married reproductive age are still likely to fall into poverty in the future in 2022. This is because rural women of married reproductive age face an increased risk of disease and lack the ability to withstand risk shocks, and are therefore more likely to fall into health poverty.\u003c/p\u003e\n\u003cp\u003eThe Tobit regression and Shapley decomposition for 2019 and 2022 found that the highest contribution to health poverty vulnerability in 2019 was made by the number of household members, followed by annual per capita household income, gift expenditure, and respondents\u0026apos; self-rated health status; and the highest contribution to health poverty vulnerability in 2022 was made by annual per capita household income, followed by gift expenditure, respondents\u0026apos; age, and households in poverty.\u0026nbsp;Annual per capita household income\u0026nbsp;(26, 27)\u0026nbsp;negatively affects health poverty vulnerability under the common criteria of poverty line1 and poverty line2, and the level of household income has traditionally been considered an important resource against disease risk, with high household income levels representing an individual\u0026apos;s greater ability to acquire wealth and therefore to resist health risks(28).\u0026nbsp;Gift spending\u0026nbsp;(29, 30)\u0026nbsp;negatively affects health poverty vulnerability, probably because rural residents obtain money flow in human interactions through holiday gift-giving among themselves, through which they consolidate and enhance social capital and reduce expenditure risk, which ultimately alleviates health poverty vulnerability.\u0026nbsp;Unlike other studies(31), this study concludes that respondent age negatively affects health poverty vulnerability, which may be explained by the fact that with increasing age, rural women of childbearing age experience pregnancy and lactation, during which they become more conscious of their health and therefore mitigate health poverty vulnerability.\u0026nbsp;Respondents\u0026apos; educational attainment\u0026nbsp;(31, 32)\u0026nbsp;negatively affects health poverty vulnerability, and many studies have shown that education, as an important way to enhance the value of human capital, plays a crucial role in alleviating health poverty vulnerability, while families with lower educational attainment tend to fall into poverty traps and form intergenerational transfer of poverty(33).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnly under the poverty line1 criterion, does the type of drinking water(29)\u0026nbsp;positively affect health poverty vulnerability, and drinking safe and clean tap water helps to reduce the occurrence of rural water-related diseases and protects villagers\u0026apos; health and normal life, however, in the mountainous areas of southern Ningxia, China, tap water is not yet widespread in some areas, and long-term drinking of unpurified tap water increases the possibility of disease.\u0026nbsp;The separation of housing and kitchen affects health poverty vulnerability, which may be attributed to the fact that the separation of housing and kitchen to some extent reflects the economic status of the household, and households without separation of housing and kitchen tend to have lower economic levels and therefore lower health capital investment and a higher likelihood of health poverty.\u0026nbsp;The finding that household size(27, 31, 34-36)\u0026nbsp;positively affects health poverty vulnerability can be attributed to the negative effect of household size on household per capita consumption and welfare. As previous studies have shown a positive relationship between household size and poverty, a larger household size dilutes household consumption per capita and increases the likelihood of household poverty.\u0026nbsp;Housing type affects health poverty vulnerability, and similar to the previous section, housing type also reflects the economic level of the household to some extent, so housing type indirectly has an impact on health poverty vulnerability.\u003c/p\u003e\n\u003cp\u003eOnly under the poverty line2 criterion, poor households positively affect health poverty vulnerability, and people in poor households also have poorer household economic levels and are less able to withstand disease shocks, and therefore have a higher likelihood of falling into health poverty.\u0026nbsp;Respondents\u0026apos; self-rated health status influences health poverty vulnerability(31, 37, 38), and deterioration in health status increases health poverty vulnerability by reducing the ability to create human, physical, and social capital, which in turn reduces the long-term earning capacity of the population(39).\u0026nbsp;Respondents\u0026apos; chronic diseases influence health poverty vulnerability(27), and studies have found that chronic diseases and the resulting financial burden of illness are important causes of poverty(40). Households with chronic diseases have a higher financial burden of illness and are significantly more likely to incur catastrophic health expenditures than those without chronic diseases\u0026nbsp;(41).\u003c/p\u003e\n\u003cp\u003eThis study has two strengths. First, the data for 2019 and 2022 can predict subsequent future groups likely to fall into health poverty; the other advantage is the use of population-based data, which provides a sufficient sample size to explore the factors influencing health poverty vulnerability.\u0026nbsp;However, our study has three limitations. First, household income and expenditure are self-reported and may suffer from recall bias. Second, we only used data for two periods, 2019 and 2022, and could not fully explore the long-term patterns of dynamic changes in health poverty vulnerability. Panel data of sufficient length are needed to analyze the dynamics of health poverty vulnerability among rural women of reproductive age.\u0026nbsp;Third, the sample for this study was drawn from one province, so the main findings of this study may not be generalizable to other provinces because economic levels and anti-poverty strategies vary considerably among provinces.\u003c/p\u003e\n\u003cp\u003eAlthough absolute poverty has been eliminated in China, our analysis shows that a proportion of rural women of childbearing age are still vulnerable to health poverty; therefore, future anti-poverty and welfare policy formulation should prioritize these vulnerable groups and adopt targeted poverty prevention and alleviation measures based on the factors influencing the health poverty vulnerability of rural women of childbearing age.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that a proportion of rural women of childbearing age remain vulnerable to health poverty. the highest contribution to health poverty vulnerability in 2019 was made by household size, followed by annual per capita household income, gift expenditure, and respondents\u0026apos; self-rated health status;\u0026nbsp;The highest contributions to health poverty vulnerability in 2022 were annual per capita household income, followed by gift expenditure, respondent age, and household poverty, and the above variables were the most important factors contributing to health poverty vulnerability among rural women of reproductive age.\u0026nbsp;To sustain poverty alleviation achievements and prevent rural Chinese women of childbearing age from falling into health poverty, anti-poverty policies should focus on these groups and adopt targeted poverty alleviation measures based on the main influencing factors of health poverty vulnerability among rural women of childbearing age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy suggestion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudying the vulnerability of rural women of reproductive age to health poverty has a practical role in accurately identifying vulnerable groups with high health poverty and discerning health-causing factors, and can help deter future health poverty among rural women of reproductive age from a health perspective. Based on the above findings, this paper makes the following recommendations for consolidating and expanding poverty reduction achievements and healthy poverty prevention governance in rural areas in the post-poverty era. First, establish an early warning mechanism for health risks, precisely identify the health poor and highly vulnerable groups, and shift the focus of work to consolidate the results of poverty eradication to the precise identification and effective assistance for highly vulnerable rural women of childbearing age. Second, we will improve health services in rural areas from a gender perspective, coordinate and improve the medical protection system for rural women of childbearing age, optimize the allocation of health service resources, and build a more complete health and hygiene system. Third, strengthen health education and health management to enhance the ability to sustainably escape poverty by improving the health literacy of rural women of childbearing age. Through health education lectures and health knowledge promotion, rural women of childbearing age are guided to develop good healthy living habits and thus improve their health literacy level.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a population-based survey, and we thank all the respondents who volunteered to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHQ conceptualized the research idea and design. XM participated in the research design, drafted the manuscript, analyzed, and interpreted the data. ZH helped revise the manuscript and interpreted the data. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper was supported by the National Natural Science Foundation of China (No. 72164033), and the National Natural Science Foundation of China (No. 71864030).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the Ethics Committee of Ningxia Medical University, Approval number, No. 2021-G152. All the par-ticipants provided signed informed consent at the time of participation. The study methodology was carried out in accordance with approved guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRivera JA, Castellanos-Guti\u0026eacute;rrez A. HEALTHY AND SUSTAINABLE DIET. \u003cem\u003eAnnals of Nutrition \u0026amp; Metabolism\u003c/em\u003e (2020) 76.\u003c/li\u003e\n\u003cli\u003eYao SJ, Wang JF. Economic development and the road to anti-poverty in the 70 years since the founding of New China. Journal of Zhongnan University of Economics and Law (2019)(06):3-16. doi: 10.19639/j.cnki.issn1003-5230.2019.0074.\u003c/li\u003e\n\u003cli\u003eXian ZD, Wang QZ, Cheng JJ. 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Health-related financial catastrophe, inequality and chronic illness in Bangladesh. \u003cem\u003ePlos One\u003c/em\u003e (2013) 8(2). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"women of childbearing age, health poverty, poverty vulnerability, Shapley decomposition, health poverty vulnerability","lastPublishedDoi":"10.21203/rs.3.rs-2627219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2627219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe health of rural women of reproductive age is crucial to the sustainable development of individuals, families, and society, and conducting research on the identification of health-poor vulnerable groups and influencing factors is an important basis for adjusting and implementing health poverty alleviation policies, but there are few studies on the health-poor vulnerability of rural women of reproductive age.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e Based on the panel data of the Ningxia \"Rural Household Health Inquiry Survey\" for 2019 and 2022, the four dimensions of household physical capital, financial capital, social capital, and human capital were incorporated into the SLA analysis framework, and the health poverty vulnerability of rural women of reproductive age was measured by using the three-stage feasible generalized least squares method and the Tobit model and Shapley decomposition to analyze the influencing factors of health poverty vulnerability and decompose the contribution of each influencing factor to health poverty vulnerability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The health poverty status of rural women of reproductive age is not promising, with health poverty vulnerability rates for rural women of reproductive age above 20% in both 2019 and 2022 under different poverty line criteria. Shapley decomposition found that the top four contributors to health poverty vulnerability impact factors in 2019, using poverty line 1 and poverty line 2 as criteria, were household size, annual per capita household income, gift expenditure, and Respondents' self-rated health status. Using poverty line 1 and poverty line 2 as criteria, the top four contributing factors of health poverty vulnerability in 2022 are annual per capita household income, gift expenditure, respondent age, and household poverty.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Strengthening the ex-ante intervention of health poverty among rural women of childbearing age, establishing an early warning mechanism for the risk of returning to poverty due to illness, improving the accurate identification of highly vulnerable rural women of childbearing age, and improving the medical insurance system for rural women of childbearing age can help improve the health poverty of rural women of childbearing age.\u003c/p\u003e","manuscriptTitle":"Decomposition analysis of health poverty vulnerability and influencing factors among rural women of childbearing age-an empirical analysis based on 2-period panel data in rural Ningxia, China ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-30 11:37:35","doi":"10.21203/rs.3.rs-2627219/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fadfcb5f-9663-4737-9ef3-497a11a124a0","owner":[],"postedDate":"March 30th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-21T13:59:53+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-30 11:37:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2627219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2627219","identity":"rs-2627219","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}