Beyond Entertainment: How Value Perceptions Influence Production and Consumption of Earthy Short Videos Among Chinese Small-Town Youth

Beyond Entertainment: How Value Perceptions Influence Production and Consumption of Earthy Short Videos Among Chinese Small-Town Youth | 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 Article Beyond Entertainment: How Value Perceptions Influence Production and Consumption of Earthy Short Videos Among Chinese Small-Town Youth Guo Pianpian, Bahiyah Omar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6853397/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Previous studies of earthy short videos have neglected value factors. To study the value factors affecting the production and consumption of earthy short videos by Chinese small-town youth, we propose the concept of prosumption and a logical path model connecting perceived value factors and prosumption. We developed a scale to measure subcultural recognition and verified the important influence of subcultural recognition on the behavior of producing and consuming earthy short videos. We analyzed data from 548 samples and found that usefulness and subcultural recognition significantly predicted Chinese small-town youth's production and consumption of earthy short videos. Self-presentation significantly influences production, but not consumption. Enjoyment predicts consumption but negatively affects production. Social interaction has a non-significant impact on both production and consumption behaviors. The findings suggest that we should pay attention to youth subculture, enhance the usefulness of earthy video content, and focus on the sustainable development of small-town youth groups. Humanities/Cultural and media studies Social science/Cultural and media studies Social science/Psychology earthy short videos value factors prosumption production consumption youth subculture Figures Figure 1 Figure 2 Figure 3 1 Introduction Earthy short videos, often filmed in rural or underdeveloped towns and posted on Kwai and TikTok, are characterized by dialect, vulgar content, clichéd plots, and exaggerated performances. Prosumption is a synthetic term for production and consumption, referring to the actual behavior of individuals who produce and consume short videos in this study. Production includes users' behaviors of shooting, editing, dubbing, and publishing short videos. Consumption includes users' behaviors of watching/browsing, searching, favoriting, downloading, liking, commenting, and forwarding/sharing short videos. Small-town youth are the main producers and consumers of earthy short videos. They are a group of youth aged 14–35 born or living in underdeveloped towns or rural areas. In recent years, due to the close interaction with short videos, small-town youth have developed into a unique social group. Small-town youth spend their time on TikTok and Kwai, recording and showing their lives by making earthy short videos. The earthy video content they post receives online attention for being offbeat and not in line with mainstream values. In 2019, 230 million small-town youth on Kwai posted more than 2.8 billion short videos in a year, with more than 2,600 billion video plays (Kwai Big Data Institute, 2019). Some scholars believed that over 200 million small-town youth gave birth to Kwai and TikTok (Finance Stories, 2019 ). Small-town youth are a group of individuals with similar interests who unite through earthy short videos (Liu & He, 2022 ). However, this subcultural group has gradually transcended its initial scope of interest, evolving into a social class similar to the migrant worker group (Gao, 2021 ). Therefore, this study focuses on this group and explores the factors that influence their production and consumption of earthy short videos, with the aim of providing a framework for society to better understand and manage this group and earthy short videos. 2 Literature Review and Hypothesis Development 2.1 Prosumption of Earthy Short Videos Most studies on short video production behavior in the existing literature use quantitative methods. Liu and Huang's (2019) study found that perceived usefulness and enjoyment can positively influence short video users' content production behavior and that perceived usefulness has a more significant effect than perceived enjoyment. However, Tao and Zhu's (2020) study found that entertainment demand only significantly affects consumption behavior, and the impact on producing short video content is not significant. Yan and Sheng's (2021) study found that social reference and media attraction positively influence the content production behavior of short video creators. Cheng and Shao (2020) found that short-video content producers place a greater emphasis on pursuing unique self-presentation. Chen (2021) did not use quantitative methods for her study, but she analyzed the causes and characteristics of mimicry in short-video content production. She concluded that the behavior of mimicry production is influenced by audience identity and social culture. Based on these research findings, this study concludes that the factors influencing the production behavior of earthy short videos include usefulness, enjoyment, social factors, self-presentation, and cultural recognition. Some scholars have studied short video consumption behavior. Chen and others (2024) summarized information participation, novelty gratification, entertainment and enjoyment, stress relief, social interaction, escapism, self-compensation, self-expression, and social identity as the motives behind problematic short video consumption through a three-level meta-analysis. Moreover, Nguyen and Veer's (2024) meta-analysis summarized that attitude, perceived usefulness, and satisfaction influence users' intention to continuously watch videos, whereas entertainment, escapism, information seeking, and social interaction are important motivators for users to watch videos. The findings of these analyses provide an important reference for developing the variables of this study. Academic research on earthy short videos is dominated by discussion and critique. Pianpian and Omar (2023a) reviewed existing literature on earthy short videos from seven perspectives: social distinction, memetics, carnival theory, appreciation of ugliness, uses and gratifications, cultural capital, and youth subculture. In addition, they summarized the genres, themes, and other characteristics of earthy short videos through case studies (Pianpian & Omar, 2023b). However, no academic research using quantitative methods examines the factors influencing the engagement behavior of earthy short videos. Therefore, this study adopts a quantitative approach to explore the factors that affect the production and consumption behaviors of earthy short videos. 2.2 Perceived Value The concept of value usually plays a significant role in both academic research and marketing management. This study posits that value lies at the core of behavior, reflecting the actors' purpose and demands. Small-town youth produce and consume earthy short videos because they perceive their value as a solution to their dilemmas or demands. Therefore, this study adopts the perceived value theory to investigate the factors affecting the prosumption behavior of earthy short videos among small-town youth. Perceived value refers to a customer's overall evaluation of the utility of a product or service after weighing the perceived benefits against the costs incurred in acquiring the product or service (Zeithaml, 1988). Perceived value reflects customers' subjective and comprehensive perception of the positive meaning and usefulness of a product or service. Thus, it frequently serves as a driving force behind the adoption of information technology in non-work domains, particularly in the realms of online television, e-reading, and social networking sites that connect to subcultural experiences. Users produce and consume earthy short videos for various purposes. Perceived value is subjective and personalized. Specific dimensions of perceived value require specific analysis (Holbrook, 1982). Users produce and consume earthy short videos for various purposes, including entertainment, information, insight, self-presentation, social interaction, and cultural experience. Different motives reflect users' different perceptions of the value of earthy videos. So, when looking at what makes earthy short videos valuable to users and how that affects their creation and consumption, this study will consider factors like usefulness, enjoyment, self-presentation, social interaction, and subcultural recognition, based on previous research (Figure 1). 2.3 Hypothesis Development Usefulness In this study, usefulness refers to the extent to which short videos can help users solve problems and satisfy their needs. Malik and Annuar (2021) confirmed that perceived usefulness directly affects Malaysian users' intention to use e-wallets. Prastiawan and his colleagues (2021) verified that perceived usefulness affects e-banking usage among Indonesian users. Jiang (2021) argued that earthy short videos are a manifestation of emotional catharsis for the masses at the bottom of society. Gao (2022) found that short videos are an important channel for rural youth to obtain information, and they learn trendy dressing and skills such as singing, dancing, painting, and video editing through short videos. The featured news explanations in earthy short videos enable small-town youth to learn about current news in an entertaining way. The content of earthy short videos covers a variety of knowledge and skills, including cooking, beauty, games, singing, dancing, musical instruments, acrobatics, and so on. By watching earthy short videos, small-town youth can not only increase their knowledge and broaden their horizons but also learn skills and enhance their cognition. Tao & Zhu (2020) found that cognitive demand can influence the usage behavior of mobile short video users. Therefore, this study considers perceived value as a variable that influences small-town youths' prosumption of earthy short videos. Hypothesis 1a: Usefulness affects the production of earthy short videos by small-town youth. Hypothesis 1b: Usefulness affects the consumption of earthy short videos by small-town youth. Enjoyment In this study, enjoyment refers to the degree of pleasure that users experience when producing and consuming earthy short videos. Hasan and her colleagues (2021) found that perceived enjoyment positively predicts Turkish customers' intention to shop online. Mohamad and others (2021) confirmed that perceived enjoyment significantly influences Malaysian consumers' mobile hotel booking behavior. Xu and Thien (2025) proved that perceived enjoyment positively influences Chinese students' intention to use ChatGPT to learn English. Davies (1989) suggested that individuals who experience joy or pleasure in using technology will be more likely to adopt the product than others. Yang (2020) pointed out that earthy short videos satisfy the entertainment needs of small-town youth. Gu (2019) and Zhou (2019) argued that grassroots groups disseminate earthy videos to meet the intrinsic enjoyment needs. Kim and his colleagues (2007) verified the positive effect of enjoyment on users' perceived value and adoption of mobile Internet. Therefore, this study considers enjoyment as a variable that influences small-town youths' prosumption of earthy short videos. Hypothesis 2a: Enjoyment affects the production of earthy short videos by small-town youth. Hypothesis 2b: Enjoyment affects the consumption of earthy short videos by small-town youth. Self-presentation In this study, self-presentation refers to the efforts of users to present themselves in earthy short videos. With the development of network technology, the decentralized communication mechanism satisfies the demand of the rural marginalized groups to display themselves, which has been suppressed for a long time (Sun, 2021). Participation in earthy communication can win high returns and attention with lower costs and investment (Jiang, 2021). As a result, many short video users have started to present themselves in the Internet space, expressing their discourse by acting ugly and extreme (Jiang, 2021). They present and consume their bodies through performance and appreciation in earthy short videos (Yao & Liao, 2021). Zhang (2021) argued that rural groups realized psychological satisfaction and redemption by creating earthy short videos. Jiang (2021) believed that earthy short videos satisfy the psychological desire of content producers for self-presentation. Therefore, this study considers self-presentation as a variable that affects small-town youths' prosumption of earthy short videos. Hypothesis 3a: Self-presentation affects the production of earthy short videos by small-town youth. Hypothesis 3b: Self-presentation affects the consumption of earthy short videos by small-town youth. Social Interaction Social interaction is a social activity in which individuals interact with each other and engage in material and spiritual exchanges. Social interactions and social relationships create society and are essential to human existence (Okumdi & Akporaro, 2022). Sokolova and Kefi (2020) found that parasocial interactions positively influence French consumers' purchase intentions on YouTube and Instagram. Ghahtarani and his colleagues (2020) confirmed that social interaction significantly influences the Iranian users’ knowledge or information-sharing behavior on social commerce sites. Baber (2022) demonstrated that social interaction could predict the effectiveness of online learning during the pandemic of Covid-19. Jiang (2021) argued that grassroots groups spread earthy short videos based on their interpersonal needs, and they try to expand their friend circle by presenting themselves. Gao (2022) found that rural adolescents watch short videos to develop common topics and enhance peer relationships. Zou (2020) discovered that users utilized memes in earthy short videos to cope with social crises. Liu (2018) concluded that rural users use earthy short videos to address marriage and dating needs. Dai and Gu (2017) found that social interaction influences users' engagement behavior in mobile short video apps. Therefore, this study considers social interaction as a variable that affects small-town youths' prosumption of earthy short videos. Hypothesis 4a: Social interaction affects the production of earthy short videos by small-town youth. Hypothesis 4b: Social interaction affects the consumption of earthy short videos by small-town youth. Subcultural Recognition Subcultural recognition in this study refers to the recognition by an earthy short video user that he belongs to a specific social subcultural group, as well as the emotional and value significance that being a member of the group brings to him. Franklin and his friends (2022) argue that subcultural identities are understood and characterized through physical characteristics, gender expression and perceived norms, sexual preferences and gender roles, interests and hobbies, and social interaction dynamics. Moreover, subcultural identities can filter and regulate social associations and interactions. A study by Liu and others (2022) found that subcultural recognition can play a key role in mental health outcomes by interweaving social support and reputation. Yin and Jiang (2020) argued that the dialect culture in earthy short videos gave viewers a sense of group belonging and identification. Yang (2020) believed that small-town youth gained subcultural recognition in the interaction of earthy short videos. Liu (2018) pointed out that rural users build a virtual communication space on Kwai by posting and watching rural-themed short videos, forming a unique subculture ecosystem. Guo (2020) attributed the prevalence of earthy short videos to participants' attempts to seek identity and subcultural recognition. Therefore, this study considers subcultural recognition as a variable that influences small-town youths' prosumption of earthy short videos. Hypothesis 5a: Subcultural recognition affects the production of earthy short videos by small-town youth. Hypothesis 5b: Subcultural recognition affects the consumption of earthy short videos by small-town youth. 3 Methodology 3.1 Measurement Items This study measures seven variables: five dimensions of perceived value (usefulness, enjoyment, self-presentation, social value, and subcultural recognition) and two dimensions of earthy short video consumption behavior (production and consumption). The study designed measurement items for each variable. We adapted a total of 33 measurement items from existing questionnaires to construct the questionnaire (Appendix). The five measurement questions for usefulness were adapted from Lou and others (2000). Kim and his colleagues ( 2007 ) provided the three measurement items for enjoyment. The five measures for self-presentation were adapted from Tao and Zhu ( 2020 ). Sanchez and his friends (2006) provided the five questions for social interaction. Rintamäki and her colleagues ( 2006 ) provided the five measuring questions for subcultural recognition. The ten measurement items for production and consumption were adapted from Dai and Gu ( 2017 ). The study set at least three questions for each variable to reflect it as comprehensively as possible. Each item was measured using a five-point Likert scale ranging from 1 = strongly disagree, 2 = slightly disagree, 3 = neutral, 4 = slightly agree, and 5 = strongly agree. 3.2 Pilot Test This study conducted a pre-test before formally distributing the questionnaire, including an expert panel and a pilot test. Once we finalized the questionnaire, we invited two experts in the field of short video user behavior to review and evaluate the scales. Based on their feedback, we modified the questionnaire to improve its content validity. The pilot study was conducted in the Nanyang area of Henan Province, China, in March 2024. The sample of the pilot survey included 64 small-town youth users of earthy short videos. The results (Table 1 ) show that the Cronbach's alpha values of all variables are above 0.70, which means that all variables in this study are reliable and the measurement questionnaire has a high degree of internal consistency. It also indicates that respondents understand the survey's goals and content, the questionnaire is clear, and there is no information overload. Therefore, the questionnaire can be used for formal data collection. Table 1 Reliability Test of Scaled Variables for Pilot Test Variables No. of Items N Cronbach’s α Cronbach’s Alpha Based on Standardized Items Usefulness (USF) 4 64 0.737 0.737 Enjoyment (ENJ) 3 64 0.840 0.846 Self-presentation (PRE) 5 64 0.822 0.825 Social Interaction (SOC) 6 64 0.870 0.870 Subcultural Recognition (SUB) 5 64 0.862 0.862 Production (PDC) 3 64 0.864 0.865 Consumption (CSM) 7 64 0.873 0.873 3.3 Data Collection Youths from secondary vocational schools living in the central province of China are representative of Chinese small-town youths and are also the main producers and consumers of earthy short videos. They can provide sufficiently rich information about the adoption of earthy short videos for this study, and the data sampled from this group are typical and representative. Therefore, this study employs the purposive sampling method for this particular group. The study included secondary vocational school students aged 14 to 35 in Nanyang, Henan Province, China. The study excluded non-standard samples using a screening question: "Have you watched earthy short videos?" Before data collection, the study was reviewed by JEPeM-USM and was approved for implementation under the approval code USM/JEPeM/PP/23090696. Response rates are an important indicator for assessing the representativeness of sample data. Higher response rates represent less bias and high representativeness of the sample to the whole (Babbie, 2020 ). In Babbie's review, the sample must have at least a 50% response rate to analyze and write a report; a 60% response rate is considered satisfactory, and a 70% response rate is excellent (Babbie, 2020 ). We sent out a total of 952 questionnaires, of which 612 were returned for this survey. The response rate for this survey was 64.3%, which meets the 60% standard. The result indicates that the sample for this survey is sufficiently representative of the small-town youth population. To ensure that the sampling covered all areas of the district, we divided the sampling population into groups based on the schools and districts they belonged to in this study. We distributed the minimum number of samples evenly among the groups after calculating the required minimum sample size. We then collected data from each group in accordance with the determined number. The total number of secondary school students in the Nanyang area is about 165,000 (Headline Nanyang, 2022 ). When the total number of target population N is large, the total sample size required for the study is n = Z²S²/d² = 385 at a 95% confidence level and 5% sampling error. There are a total of 34 public secondary schools, distributed in various counties and districts across Nanyang. This study divided the sampling group into 34 groups, each representing a different school. The number of samples to be taken from each group is 385/34 = 11.32 ≈ 12. To reduce the sampling error and improve the sampling precision, we took 18 samples from each group and increased the total sample size to 18 × 34 = 612. The study used an online questionnaire to collect data. The survey instrument for this study was a structured questionnaire in self-administered form, and an online questionnaire was used to collect data. A total of 952 questionnaires were sent out, and 612 were returned. Following data collection, we eliminated unqualified and invalid data samples, ultimately leaving 548 valid data samples. The study used IBM SPSS 27.0 and SmartPLS 4.0 for data analysis. 3.4 Common Method Variance (CMV) The results of the one-way test indicated that the largest single unrotated factor explained 29.920% of the variance, which is below the 50% criterion. This suggests that the common method bias is not significant, allowing for further analysis of the data. 4 Results 4.1 Measurement Model Assessment The test indicators of the measurement model include indicator reliability, internal consistency reliability, convergent validity, and discriminant validity. The results (Table 2) demonstrate that the outer loading values for all items, with the exception of PRE5 and SOC1, are greater than 0.6, indicating the reliability of the items' indicators. The values of CA and rho_a for the usefulness are all greater than 0.6 and less than 0.7, indicating that the reliability of this variable is acceptable. Additionally, the CA, rho_a, and CR values of all other variables in the research model are greater than 0.7 and less than 0.95, demonstrating their reliability. Therefore, the model of this study has satisfactory internal consistency reliability. The AVE values of usefulness, social value, and subcultural recognition are between 0.440 and 0.496, which is higher than the threshold of 0.36, indicating that the convergent validity of these three variables is acceptable. The AVE values of the remaining four variables were above the threshold of 0.5, indicating that these variables had sufficient convergent validity. Table 2 Results Summary for Reflective Measurement Models Table 3 shows that the square root of the AVE values on all diagonals is greater than the correlation coefficients in the lower left corner of the diagonal. This says that there is excellent discriminant validity between the latent variables in the model. Table 3 Discriminant Validity: Fornell-Larcker Criterion USF SUB ENJ PDC PRE CSM SOC USF 0.694 SUB 0.514 0.704 ENJ 0.534 0.346 0.833 PDC 0.364 0.446 0.104 0.852 PRE 0.450 0.555 0.242 0.451 0.734 CSM 0.485 0.526 0.397 0.594 0.412 0.744 SOC 0.550 0.646 0.458 0.399 0.600 0.491 0.663 Notes : USF->Social Interaction, SUB->Subcultural Recognition, ENJ->Enjoyment, PDC->Production, PRE->Self-presentation, CSM->Consumption, SOC->Social Interaction 4.2 Hypothesis Testing The results show (Table 4) that the t-values for USF -> PDC, USF -> CSM, ENJ -> CSM, PRE -> PDC, SUB -> PDC, and SUB -> CSM are all greater than 1.96, and the p-values are all less than 0.05. This suggests that these six hypothesized relationships are statistically significant at the 5% level. The evidence indicates that usefulness and subcultural recognition significantly affect production and consumption. Self-presentation significantly affects production. Therefore, H1a, H1b, H2b, H3a, H5a, and H5b are accepted. Although the t-value for the relationship between enjoyment (ENJ) and production (PDC) exceeds 1.96 and the p-value is less than 0.05, the path coefficient indicates a negative value. This result indicates that the effect of enjoyment on production is negative. Therefore, H2a is rejected. The t-values of PRE -> CSM, SOC -> PDC, and SOC -> CSM are less than 1.96, the p-values are greater than 0.05, and the confidence intervals are held at zero, indicating that these three hypothesized relationships are not significant at the 0.05 level. This result indicates that the effect of self-presentation on consumption and the effect of social interaction on both production and consumption are not significant. Therefore, H3b, H4a, and H4b are rejected. Table 4 Summary of Direct Effect Test Relationship Beta (β) STDEV T statistics P values BCa 95% CI Decision L2.5% U97.5% H1a USF -> PDC 0.187 0.056 3.353 0.001** 0.072 0.289 Accept H1b USF -> CSM 0.174 0.055 3.165 0.002** 0.065 0.280 Accept H2a ENJ -> PDC -0.171 0.048 3.592 0.000*** -0.262 -0.076 Reject H2b ENJ -> CSM 0.142 0.047 3.048 0.002** 0.048 0.233 Accept H3a PRE -> PDC 0.229 0.054 4.217 0.000*** 0.119 0.333 Accept H3b PRE -> CSM 0.087 0.047 1.837 0.066 -0.004 0.179 Reject H4a SOC -> PDC 0.096 0.066 1.447 0.148 -0.039 0.216 Reject H4b SOC -> CSM 0.101 0.060 1.668 0.095 -0.025 0.216 Reject H5a SUB -> PDC 0.220 0.055 4.009 0.000*** 0.116 0.329 Accept H5b SUB -> CSM 0.274 0.052 5.308 0.000*** 0.175 0.376 Accept Notes : Significant at p<0.001***, p<0.01**, pUsefulness, ENJ->Enjoyment, PRE->Self-presentation, SOC->Social Interaction, SUB->Subcultural Recognition, PDC->Production, CSM->Consumption 4.3 Coefficient of Determination (R²) and Predictive Relevance (Q²) Table 5 shows that the R² for production is 0.290, which means that the five perceived values explain 29.0% of the variation in user production behavior. This suggests that the model has a moderate level of explanatory power for the production behavior of earthy short videos. The R² for consumption is 0.370, which means that the five perceived values explain 37.0% of the variation in user consumption behavior. This suggests that the model possesses significant explanatory power for the consumption behavior of earthy short videos. The results show (Table 5) that the Q² for both production (0.204) and consumption (0.201) is greater than 0, indicating that the structural model of this study has satisfactory predictive accuracy. Table 5 Model Results for R ² and Q² (cv-redundancy) Dependent Variables R-square Q-square Production (PDC) 0.290 0.204 Consumption (CSM) 0.370 0.201 4.4 Assessment of Goodness of Fit (GoF) As shown in Table 6, the mean value of Q² for all latent variables is 0.307, and the mean value of R² for all endogenous variables is 0.330. The root sign of the product of the two yields the GoF value of the model as GoF = = 0.318. This result indicates that the predictive ability of the research model is 31.8%. The value of 0.318 is above the threshold of 0.25, which indicates that the research model has moderate predictive power. Table 6 Summary of Q² (cv-communality) and R² Variables SSO SSE Q² (CV_com.) R² Usefulness (USF) 2192.000 1873.543 0.145 Enjoyment (ENJ) 1644.000 1020.444 0.379 Self-presentation (PRE) 2740.000 1862.301 0.320 Social Interaction (SOC) 3288.000 2578.308 0.216 Subcultural Recognition (SUB) 2740.000 2064.424 0.247 Production (PDC) 1644.000 929.372 0.435 0.290 Consumption (CSM) 3836.000 2288.124 0.404 0.370 Average 0.307 0.330 4.5 Importance-Performance Map Analysis (IPMA) Table 7, Figure 2, and Figure 3 show the results of the importance-performance analysis. For production, the most important variable was self-presentation (0.229), followed by subcultural recognition (0.220), and social interaction was the least important (0.096). For consumption, the most important variable was subcultural recognition (0.274), followed by usefulness (0.174), and self-presentation was the least important (0.087). The most significant performance variable was enjoyment, with a score of 75.468. Table 7 Results of Importance-Performance Analysis [PDC & CSM] Constructs LV Performance Importance (total effects) PDC Importance (total effects) CSM Usefulness (USF) 63.068 0.187 0.174 Enjoyment (ENJ) 75.468 -0.171 0.142 Self-presentation (PRE) 48.988 0.229 0.087 Social Interaction (SOC) 58.823 0.096 0.101 Subcultural Recognition (SUB) 51.147 0.220 0.274 5 Discussion The purpose of hypotheses 1-5a (H1a, H2a, H3a, H4a, and H5a) was to test the relationship between perceived value and production behavior of earthy short videos. The results indicate that social interaction has no significant effect on production. Apart from that, usefulness, enjoyment, self-presentation, and subcultural recognition have a significant effect on the production of earthy short videos. The purpose of hypotheses 1-5b (H1b, H2b, H3b, H4b, and H5b) is to test the relationship between perceived value and consumption behavior of earthy short videos. The results indicate that self-presentation and social interaction do not have a significant effect on consumption. Perceived value, enjoyment, and subcultural recognition significantly influence the consumption of earthy short videos. This study found that usefulness significantly influences both production and consumption (H1a, H1b). The finding suggests that small-town youth strongly recognize the benefits that earthy short videos can provide in terms of cognitive improvement, skill development, and efficiency enhancement and will produce and consume earthy short videos for these purposes. This suggests that earthy short videos are useful to users in terms of emotional relief, session anxiety, and satisfying cognitive needs. This conclusion is consistent with the findings of previous studies (Zou, 2020 ; Jiang, 2021 ). Some users gain emotional catharsis and relief from ridiculing earthy short videos (Jiang, 2021 ). Some users watch earthy videos to resolve their anxiety through self-cathartic insults and self-projection, which in turn improves their performance in life (Zou, 2020 ). This study's findings further support these assertions. Improvements in cognitive skills and efficiency can enhance individuals' development and improve their performance at work and in life. The knowledge and practical skills taught in earthy short videos satisfy their thirst for knowledge and provide them hope for a better life. Gradually, consuming and producing earthy short videos have become their main activities. As a result, Chinese youth from small towns place significant importance on the useful value of earthy short videos. Enjoyment significantly affects consumption (H2b), but the effect on production is negative (H2a). This suggests that small-town youth recognize the entertainment value of consuming earthy short videos that bring them fun, joy, and enjoyment, and will consume earthy short videos for this purpose. This is in line with Zou ( 2020 ). He believes that one reason users watch earthy short videos is to seek entertainment. However, small-town youth do not believe that producing earthy short videos can bring these hedonic values. This suggests that small-town youth do not produce earthy short videos to satisfy their hedonic needs or to entertain themselves, but for other purposes. This is inconsistent with the findings of Zhang ( 2021 ) and Guo ( 2020 ). The reason for this result may be the different study populations. Zhang's (2021) analysis is based on the psychology of the urban group intending to obtain pleasure by imitating and reposting earthy short videos. Guo ( 2020 ) argues that the pushers behind earthy short videos are business and capital, and they produce them with the purpose of conveying simple happiness. However, small-town youth do not produce earthy short videos for pleasure. This finding updates society's view of the motives of small-town youth groups in producing earthy short videos. Self-presentation significantly affects production (H3a), but not consumption (H3b). This suggests that small-town youth will not watch, comment on, and retweet earthy short videos to display their thoughts. However, they will shoot and produce earthy short videos to show themselves. This is consistent with Jiang ( 2021 ) and Sun ( 2021 ). As a grassroots and marginalized group in society, they have not been able to gain a voice in mainstream media (Yang, 2019 ). When short video platforms target this group, their long-suppressed desire for expression is awakened, and excitedly and without measure, they begin their performances in earthy short videos. Earthy short videos provide a stage for small-town youth from the bottom of society to present themselves. In order to attract attention, they do not hesitate to play as ugly, act as crazy and stupid, and even abuse themselves in earthy short videos. This reflects their anxious psychology of thirsting to gain recognition by presenting themselves. This is also the embodiment of the self-presentation value of earthy short videos in the minds of small-town youth. The effect of social interaction on both production and consumption was not significant (H4a, H4b). This finding suggests that small-town youth perceive the social interaction of earthy short videos weakly. This means that small-town youth rarely rely on earthy short videos to make new friends or maintain friendships. For them, earthy short videos cannot significantly contribute to socialization. This conclusion is inconsistent with Hu and Xiang's (2021) view. The reason for the inconsistency may be the different populations analyzed. Hu and Xiang's (2021) view is based on the analysis of earthy Internet influencers. Earthy Internet celebrities become famous due to their earthy short videos. And by producing and consuming these videos, they gain fans, interact with them, and obtain commercial benefits. As a result, they strongly recognize the social interaction of these videos. However, a significant number of ordinary small-town youth have limited followers and struggle to reap tangible benefits from their interactions with earthy short videos. Therefore, they are unable to fully understand the social interaction value of earthy short videos. Subcultural recognition significantly influences production and consumption (H5a, H5b). This indicates that small-town youth attach considerable importance to the subcultural recognition value of earthy short videos. Through earthy short videos, they aim to make a lasting impression on others. They believe that they belong to the consumer group of earthy short videos, that earthy short videos match their interests, and that earthy short videos can bring something important to them personally. This suggests that small-town youth care very much about their subcultural identity and desire social recognition of their identity. This is in line with Qin and Zhou ( 2019 ). Small-town youth, having spent a long time at the bottom of society and neglected by the mainstream, deeply yearn for recognition from society (Qin & Zhou, 2019 ). Short video platforms, on the other hand, provide small-town youth this opportunity. The de-inhibition and decentralization of the Internet (Yang, 2019 ; Jiang, 2021 ) enable small-town youth to be rarely constrained and interfered with by their real-life identities, social statuses, and academic qualifications on the Internet, which greatly encourages them to participate in the dissemination and proliferation of earthy short videos. The Internet empowers traditionally disadvantaged groups, allowing the public to see and notice the underclass through earthy short videos. This approach enables small-town youth to develop group belonging and social identity in the virtual space of earthy short videos. And this subcultural recognition directly affects their prosumption of earthy short videos. 5.1 Practical Significance First, the government should accommodate and guide the development of subcultures. The results of the study found that subcultural recognitions significantly influence production and consumption. The important performance analysis (Figs. 1 and 2 ) also demonstrates the significant influence of subcultural recognitions on production and consumption, albeit at a relatively low performance level. This indicates that small-town youth pay attention to the subcultural recognition of earthy short videos, but the representation of subcultural recognition in these videos is inadequate and requires further enhancement. Video platforms and producers should produce and publish more content that reflects subcultural recognitions. Short video platforms should do a decent job as gatekeepers, strengthen content auditing, and disseminate high-quality subcultural content. In addition, mainstream society should take a more tolerant attitude towards earthy subculture and youth subculture. Previously, the mainstream culture regarded all kinds of phenomena and behaviors of earthy short videos as transgressions and suppressed, curbed, and reined in earthy subculture through moral panic and public opinion (Liu, 2018 ). However, this approach to addressing the issue fails to resolve the long-standing conflict between dominant and subordinate cultures, nor does it align with the principles of fairness and justice in governance. This is because the existence of subcultures is an indication of attempts to resolve social conflicts (Haenfler, 2023 ). Therefore, it is recommended that mainstream culture and earthy subculture increase communication and dialogue, jointly utilizing the strengths of the subculture to promote the development of disadvantaged groups and the progress of society. Second, short video platforms should increase the production of cognition-enhancing content. The study found that small-town youth value the usefulness of earthy short videos. Therefore, content producers should enhance the usefulness and practicality of their content so that users can learn practical life skills and improve their cognitive level. Short-video firms should be duty-bound to take on a broader corporate responsibility and output content with more public value. For instance, they should encourage farmers to showcase the beauty, food, folklore, and skills of the countryside, thereby highlighting the distinct earthy content of various regions. It is also recommended that platforms provide more traffic support and policy guidance to small-town youth groups to contribute to improving the survival of the underclass. 5.2 Limitations and Recommendations This study investigated the factors that influence Chinese small-town youth's prosumption of earthy short videos, using the perceived value theory as a framework. Despite the achievement of the research objectives, certain limitations remain. First, the limitation of sampling scope. Due to limited sampling capacity, the study sampled from a typical area in central China. We suggest future studies expand the sampling scope to the whole of China, covering all regions and provinces, to increase the diversity and comprehensiveness of the sample. Future studies should also take into account the consumption of earthy videos in other countries and draw comparisons with studies conducted in China. Secondly, we must acknowledge the limitation of variable settings. The research model did not include economic value as a measurement variable. At the initiation of the study, certain activities on short video platforms failed to capture our interest. However, these days, the features of rewarding, gifting, and earning gold coins for watching videos on short video apps are extremely popular. Many users consume and produce short, earthy videos to earn money. Therefore, we suggest that future studies investigate the monetary factor as a variable that affects users' prosumption. Despite these limitations, the findings of this study are still meaningful. The study effectively confirmed the factors that impact the target population's prosumption of earthy short videos, and the results hold practical significance for both governments and short video companies. 6 Conclusion This study contributes to the field of earthy short video research by examining the value factors that influence Chinese small-town youth's production and consumption of earthy short videos, adding to the understanding of earthy short video prosumption. This study proposes a logical path model connecting perceived value and prosumption with reference to perceived value theory. PLS-SEM evaluated the model, revealing sufficient reliability, validity, and moderate predictive power. Our study found that usefulness, self-presentation, and subcultural recognition have significant positive effects on the production of earthy short videos. Enjoyment negatively affects small-town youth producing earthy short videos. Usefulness, enjoyment, and subcultural recognition significantly predicted small-town youth's consumption behavior of earthy short videos. Social interaction had no significant effect on either production or consumption behavior. Based on the findings, the study suggests some practical recommendations for government departments and short video platforms. We suggest that government departments increase communication and understanding with subcultural groups and be more tolerant and wiser in dealing with the issue of the primary and secondary culture dichotomy. Meanwhile, we suggest content producers increase the usefulness and practicality of earthy short videos. It also suggests that short video platforms provide more support to rural youths in terms of policy and more guidance in terms of content. Although this study has limitations in sampling scope and variable setting, it is still important. It offers theoretical, methodological, and practical insights. This study developed a scale to measure subcultural recognition. It is the first to transform subcultural recognition into a measurable variable for assessment, successfully verifying its significant influence on the prosumption behavior of earthy short videos. Declarations Ethical approval This study was approved by the Human Research Ethics Committee of Universiti Sains Malaysia (JEPeM-USM) (Date: February 2, 2024 / Ethics Approval Number: USM/JEPeM/PP/23090696). The procedures used in this study were in accordance with the Declaration of Helsinki, International Conference on Harmonization (ICH) Guidelines, Good Clinical Practice (GCP) Standards, Council for International Organizations of Medical Sciences (CIOMS) Guidelines, World Health Organization (WHO) Standards and Operational Guidance for Ethics Review of Health-Related Research and Surveying and Evaluating Ethical Review Practices, EC/IRB Standard Operating Procedures (SOPs), and Local Regulations and Standards in Ethical Review. Informed consent This research is a non-interventional study. Prior to collecting questionnaire data (March–April 2024), researchers obtained written informed consent from participants or their legal guardians. The scope of informed consent included the content of the study, its purpose, procedures, potential risks, data processing, confidentiality, publication of results, and declarations of interest. 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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-6853397","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":499999006,"identity":"a8b16477-144c-4bfe-bb19-4760ac3654da","order_by":0,"name":"Guo Pianpian","email":"data:image/png;base64,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","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Guo","middleName":"","lastName":"Pianpian","suffix":""},{"id":499999007,"identity":"0cde8c79-5123-4add-80db-265105caae5d","order_by":1,"name":"Bahiyah Omar","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Bahiyah","middleName":"","lastName":"Omar","suffix":""}],"badges":[],"createdAt":"2025-06-09 10:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6853397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6853397/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89372765,"identity":"38e0084b-e769-4f16-a76b-e23cb9bb2362","added_by":"auto","created_at":"2025-08-19 10:27:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83736,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6853397/v1/9a9c9b2bf8c2b54f270c4630.png"},{"id":89373031,"identity":"577c99ae-27df-4655-8612-76283b27732b","added_by":"auto","created_at":"2025-08-19 10:35:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66133,"visible":true,"origin":"","legend":"\u003cp\u003eIPMA Representation of Production (PDC)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6853397/v1/43125d6fed97843a750bc40d.png"},{"id":89372767,"identity":"107369da-646d-43e0-8bbf-cd9a0be68ad3","added_by":"auto","created_at":"2025-08-19 10:27:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29633,"visible":true,"origin":"","legend":"\u003cp\u003eIPMA Representation of Consumption (CSM)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6853397/v1/8ce6bd2bdee7555513065426.png"},{"id":89374585,"identity":"eaadd04d-4a70-4c57-887a-b017314e693b","added_by":"auto","created_at":"2025-08-19 10:51:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1129401,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6853397/v1/a7b1f8be-dfe1-4114-8228-cd00ed4b1b88.pdf"},{"id":89372768,"identity":"1810701d-2deb-4e61-9a24-6a1ae0d95ee6","added_by":"auto","created_at":"2025-08-19 10:27:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":31559,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6853397/v1/38a36628487f914a9e6ff212.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond Entertainment: How Value Perceptions Influence Production and Consumption of Earthy Short Videos Among Chinese Small-Town Youth","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eEarthy short videos, often filmed in rural or underdeveloped towns and posted on Kwai and TikTok, are characterized by dialect, vulgar content, clich\u0026eacute;d plots, and exaggerated performances.\u003c/p\u003e\u003cp\u003eProsumption is a synthetic term for production and consumption, referring to the actual behavior of individuals who produce and consume short videos in this study. Production includes users' behaviors of shooting, editing, dubbing, and publishing short videos. Consumption includes users' behaviors of watching/browsing, searching, favoriting, downloading, liking, commenting, and forwarding/sharing short videos.\u003c/p\u003e\u003cp\u003eSmall-town youth are the main producers and consumers of earthy short videos. They are a group of youth aged 14\u0026ndash;35 born or living in underdeveloped towns or rural areas. In recent years, due to the close interaction with short videos, small-town youth have developed into a unique social group.\u003c/p\u003e\u003cp\u003eSmall-town youth spend their time on TikTok and Kwai, recording and showing their lives by making earthy short videos. The earthy video content they post receives online attention for being offbeat and not in line with mainstream values. In 2019, 230\u0026nbsp;million small-town youth on Kwai posted more than 2.8\u0026nbsp;billion short videos in a year, with more than 2,600\u0026nbsp;billion video plays (Kwai Big Data Institute, 2019). Some scholars believed that over 200\u0026nbsp;million small-town youth gave birth to Kwai and TikTok (Finance Stories, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Small-town youth are a group of individuals with similar interests who unite through earthy short videos (Liu \u0026amp; He, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, this subcultural group has gradually transcended its initial scope of interest, evolving into a social class similar to the migrant worker group (Gao, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, this study focuses on this group and explores the factors that influence their production and consumption of earthy short videos, with the aim of providing a framework for society to better understand and manage this group and earthy short videos.\u003c/p\u003e"},{"header":"2 Literature Review and Hypothesis Development","content":"\u003cp\u003e2.1 Prosumption of Earthy Short Videos\u003c/p\u003e\n\u003cp\u003eMost studies on short video production behavior in the existing literature use quantitative methods. Liu and Huang's (2019) study found that perceived usefulness and enjoyment can positively influence short video users' content production behavior and that perceived usefulness has a more significant effect than perceived enjoyment. However, Tao and Zhu's (2020) study found that entertainment demand only significantly affects consumption behavior, and the impact on producing short video content is not significant. Yan and Sheng's (2021) study found that social reference and media attraction positively influence the content production behavior of short video creators. Cheng and Shao (2020) found that short-video content producers place a greater emphasis on pursuing unique self-presentation. Chen (2021) did not use quantitative methods for her study, but she analyzed the causes and characteristics of mimicry in short-video content production. She concluded that the behavior of mimicry production is influenced by audience identity and social culture. Based on these research findings, this study concludes that the factors influencing the production behavior of earthy short videos include usefulness, enjoyment, social factors, self-presentation, and cultural recognition.\u003c/p\u003e\n\u003cp\u003eSome scholars have studied short video consumption behavior. Chen and others (2024) summarized information participation, novelty gratification, entertainment and enjoyment, stress relief, social interaction, escapism, self-compensation, self-expression, and social identity as the motives behind problematic short video consumption through a three-level meta-analysis. Moreover, Nguyen and Veer's (2024) meta-analysis summarized that attitude, perceived usefulness, and satisfaction influence users' intention to continuously watch videos, whereas entertainment, escapism, information seeking, and social interaction are important motivators for users to watch videos. The findings of these analyses provide an important reference for developing the variables of this study.\u003c/p\u003e\n\u003cp\u003eAcademic research on earthy short videos is dominated by discussion and critique. Pianpian and Omar (2023a) reviewed existing literature on earthy short videos from seven perspectives: social distinction, memetics, carnival theory, appreciation of ugliness, uses and gratifications, cultural capital, and youth subculture. In addition, they summarized the genres, themes, and other characteristics of earthy short videos through case studies (Pianpian \u0026amp; Omar, 2023b). However, no academic research using quantitative methods examines the factors influencing the engagement behavior of earthy short videos. Therefore, this study adopts a quantitative approach to explore the factors that affect the production and consumption behaviors of earthy short videos.\u003c/p\u003e\n\u003cp\u003e2.2 Perceived Value\u003c/p\u003e\n\u003cp\u003eThe concept of value usually plays a significant role in both academic research and marketing management. This study posits that value lies at the core of behavior, reflecting the actors' purpose and demands. Small-town youth produce and consume earthy short videos because they perceive their value as a solution to their dilemmas or demands. Therefore, this study adopts the perceived value theory to investigate the factors affecting the prosumption behavior of earthy short videos among small-town youth.\u003c/p\u003e\n\u003cp\u003ePerceived value refers to a customer's overall evaluation of the utility of a product or service after weighing the perceived benefits against the costs incurred in acquiring the product or service (Zeithaml, 1988). Perceived value reflects customers' subjective and comprehensive perception of the positive meaning and usefulness of a product or service. Thus, it frequently serves as a driving force behind the adoption of information technology in non-work domains, particularly in the realms of online television, e-reading, and social networking sites that connect to subcultural experiences. Users produce and consume earthy short videos for various purposes. Perceived value is subjective and personalized. Specific dimensions of perceived value require specific analysis (Holbrook, 1982).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsers produce and consume earthy short videos for various purposes, including entertainment, information, insight, self-presentation, social interaction, and cultural experience. Different motives reflect users' different perceptions of the value of earthy videos. So, when looking at what makes earthy short videos valuable to users and how that affects their creation and consumption, this study will consider factors like usefulness, enjoyment, self-presentation, social interaction, and subcultural recognition, based on previous research (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3 Hypothesis Development\u003c/p\u003e\n\u003cp\u003eUsefulness\u003c/p\u003e\n\u003cp\u003eIn this study, usefulness refers to the extent to which short videos can help users solve problems and satisfy their needs. Malik and Annuar (2021) confirmed that perceived usefulness directly affects Malaysian users' intention to use e-wallets. Prastiawan and his colleagues (2021) verified that perceived usefulness affects e-banking usage among Indonesian users. Jiang (2021) argued that earthy short videos are a manifestation of emotional catharsis for the masses at the bottom of society. Gao (2022) found that short videos are an important channel for rural youth to obtain information, and they learn trendy dressing and skills such as singing, dancing, painting, and video editing through short videos. The featured news explanations in earthy short videos enable small-town youth to learn about current news in an entertaining way. The content of earthy short videos covers a variety of knowledge and skills, including cooking, beauty, games, singing, dancing, musical instruments, acrobatics, and so on. By watching earthy short videos, small-town youth can not only increase their knowledge and broaden their horizons but also learn skills and enhance their cognition. Tao \u0026amp; Zhu (2020) found that cognitive demand can influence the usage behavior of mobile short video users. Therefore, this study considers perceived value as a variable that influences small-town youths' prosumption of earthy short videos.\u003c/p\u003e\n\u003cp\u003eHypothesis 1a: Usefulness affects the production of earthy short videos by small-town youth.\u003c/p\u003e\n\u003cp\u003eHypothesis 1b: Usefulness affects the consumption of earthy short videos by small-town youth.\u003c/p\u003e\n\u003cp\u003eEnjoyment\u003c/p\u003e\n\u003cp\u003eIn this study, enjoyment refers to the degree of pleasure that users experience when producing and consuming earthy short videos. Hasan and her colleagues (2021) found that perceived enjoyment positively predicts Turkish customers' intention to shop online. Mohamad and others (2021) confirmed that perceived enjoyment significantly influences Malaysian consumers' mobile hotel booking behavior. Xu and Thien (2025) proved that perceived enjoyment positively influences Chinese students' intention to use ChatGPT to learn English. Davies (1989) suggested that individuals who experience joy or pleasure in using technology will be more likely to adopt the product than others. Yang (2020) pointed out that earthy short videos satisfy the entertainment needs of small-town youth. Gu (2019) and Zhou (2019) argued that grassroots groups disseminate earthy videos to meet the intrinsic enjoyment needs. Kim and his colleagues (2007) verified the positive effect of enjoyment on users' perceived value and adoption of mobile Internet. Therefore, this study considers enjoyment as a variable that influences small-town youths' prosumption of earthy short videos.\u003c/p\u003e\n\u003cp\u003eHypothesis 2a: Enjoyment affects the production of earthy short videos by small-town youth.\u003c/p\u003e\n\u003cp\u003eHypothesis 2b: Enjoyment affects the consumption of earthy short videos by small-town youth.\u003c/p\u003e\n\u003cp\u003eSelf-presentation\u003c/p\u003e\n\u003cp\u003eIn this study, self-presentation refers to the efforts of users to present themselves in earthy short videos. With the development of network technology, the decentralized communication mechanism satisfies the demand of the rural marginalized groups to display themselves, which has been suppressed for a long time (Sun, 2021). Participation in earthy communication can win high returns and attention with lower costs and investment (Jiang, 2021). As a result, many short video users have started to present themselves in the Internet space, expressing their discourse by acting ugly and extreme (Jiang, 2021). They present and consume their bodies through performance and appreciation in earthy short videos (Yao \u0026amp; Liao, 2021). Zhang (2021) argued that rural groups realized psychological satisfaction and redemption by creating earthy short videos. Jiang (2021) believed that earthy short videos satisfy the psychological desire of content producers for self-presentation. Therefore, this study considers self-presentation as a variable that affects small-town youths' prosumption of earthy short videos.\u003c/p\u003e\n\u003cp\u003eHypothesis 3a: Self-presentation affects the production of earthy short videos by small-town youth.\u003c/p\u003e\n\u003cp\u003eHypothesis 3b: Self-presentation affects the consumption of earthy short videos by small-town youth.\u003c/p\u003e\n\u003cp\u003eSocial Interaction\u003c/p\u003e\n\u003cp\u003eSocial interaction is a social activity in which individuals interact with each other and engage in material and spiritual exchanges. Social interactions and social relationships create society and are essential to human existence (Okumdi \u0026amp; Akporaro, 2022). Sokolova and Kefi (2020) found that parasocial interactions positively influence French consumers' purchase intentions on YouTube and Instagram. Ghahtarani and his colleagues (2020) confirmed that social interaction significantly influences the Iranian users’ knowledge or information-sharing behavior on social commerce sites. Baber (2022) demonstrated that social interaction could predict the effectiveness of online learning during the pandemic of Covid-19. Jiang (2021) argued that grassroots groups spread earthy short videos based on their interpersonal needs, and they try to expand their friend circle by presenting themselves. Gao (2022) found that rural adolescents watch short videos to develop common topics and enhance peer relationships. Zou (2020) discovered that users utilized memes in earthy short videos to cope with social crises. Liu (2018) concluded that rural users use earthy short videos to address marriage and dating needs. Dai and Gu (2017) found that social interaction influences users' engagement behavior in mobile short video apps. Therefore, this study considers social interaction as a variable that affects small-town youths' prosumption of earthy short videos.\u003c/p\u003e\n\u003cp\u003eHypothesis 4a: Social interaction affects the production of earthy short videos by small-town youth.\u003c/p\u003e\n\u003cp\u003eHypothesis 4b: Social interaction affects the consumption of earthy short videos by small-town youth.\u003c/p\u003e\n\u003cp\u003eSubcultural Recognition\u003c/p\u003e\n\u003cp\u003eSubcultural recognition in this study refers to the recognition by an earthy short video user that he belongs to a specific social subcultural group, as well as the emotional and value significance that being a member of the group brings to him. Franklin and his friends (2022) argue that subcultural identities are understood and characterized through physical characteristics, gender expression and perceived norms, sexual preferences and gender roles, interests and hobbies, and social interaction dynamics. Moreover, subcultural identities can filter and regulate social associations and interactions. A study by Liu and others (2022) found that subcultural recognition can play a key role in mental health outcomes by interweaving social support and reputation. Yin and Jiang (2020) argued that the dialect culture in earthy short videos gave viewers a sense of group belonging and identification. Yang (2020) believed that small-town youth gained subcultural recognition in the interaction of earthy short videos. Liu (2018) pointed out that rural users build a virtual communication space on Kwai by posting and watching rural-themed short videos, forming a unique subculture ecosystem. Guo (2020) attributed the prevalence of earthy short videos to participants' attempts to seek identity and subcultural recognition. Therefore, this study considers subcultural recognition as a variable that influences small-town youths' prosumption of earthy short videos.\u003c/p\u003e\n\u003cp\u003eHypothesis 5a: Subcultural recognition affects the production of earthy short videos by small-town youth.\u003c/p\u003e\n\u003cp\u003eHypothesis 5b: Subcultural recognition affects the consumption of earthy short videos by small-town youth.\u003c/p\u003e"},{"header":"3 Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Measurement Items\u003c/h2\u003e\u003cp\u003eThis study measures seven variables: five dimensions of perceived value (usefulness, enjoyment, self-presentation, social value, and subcultural recognition) and two dimensions of earthy short video consumption behavior (production and consumption). The study designed measurement items for each variable. We adapted a total of 33 measurement items from existing questionnaires to construct the questionnaire (Appendix). The five measurement questions for usefulness were adapted from Lou and others (2000). Kim and his colleagues (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) provided the three measurement items for enjoyment. The five measures for self-presentation were adapted from Tao and Zhu (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sanchez and his friends (2006) provided the five questions for social interaction. Rintam\u0026auml;ki and her colleagues (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) provided the five measuring questions for subcultural recognition. The ten measurement items for production and consumption were adapted from Dai and Gu (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The study set at least three questions for each variable to reflect it as comprehensively as possible. Each item was measured using a five-point Likert scale ranging from 1\u0026thinsp;=\u0026thinsp;strongly disagree, 2\u0026thinsp;=\u0026thinsp;slightly disagree, 3\u0026thinsp;=\u0026thinsp;neutral, 4\u0026thinsp;=\u0026thinsp;slightly agree, and 5\u0026thinsp;=\u0026thinsp;strongly agree.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Pilot Test\u003c/h2\u003e\u003cp\u003eThis study conducted a pre-test before formally distributing the questionnaire, including an expert panel and a pilot test. Once we finalized the questionnaire, we invited two experts in the field of short video user behavior to review and evaluate the scales. Based on their feedback, we modified the questionnaire to improve its content validity. The pilot study was conducted in the Nanyang area of Henan Province, China, in March 2024. The sample of the pilot survey included 64 small-town youth users of earthy short videos. The results (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) show that the Cronbach's alpha values of all variables are above 0.70, which means that all variables in this study are reliable and the measurement questionnaire has a high degree of internal consistency. It also indicates that respondents understand the survey's goals and content, the questionnaire is clear, and there is no information overload. Therefore, the questionnaire can be used for formal data collection.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReliability Test of Scaled Variables for Pilot Test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of Items\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCronbach\u0026rsquo;s Alpha Based on Standardized Items\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsefulness (USF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnjoyment (ENJ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-presentation (PRE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Interaction (SOC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubcultural Recognition (SUB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduction (PDC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumption (CSM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.873\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data Collection\u003c/h2\u003e\u003cp\u003eYouths from secondary vocational schools living in the central province of China are representative of Chinese small-town youths and are also the main producers and consumers of earthy short videos. They can provide sufficiently rich information about the adoption of earthy short videos for this study, and the data sampled from this group are typical and representative. Therefore, this study employs the purposive sampling method for this particular group. The study included secondary vocational school students aged 14 to 35 in Nanyang, Henan Province, China. The study excluded non-standard samples using a screening question: \"Have you watched earthy short videos?\" Before data collection, the study was reviewed by JEPeM-USM and was approved for implementation under the approval code USM/JEPeM/PP/23090696.\u003c/p\u003e\u003cp\u003eResponse rates are an important indicator for assessing the representativeness of sample data. Higher response rates represent less bias and high representativeness of the sample to the whole (Babbie, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Babbie's review, the sample must have at least a 50% response rate to analyze and write a report; a 60% response rate is considered satisfactory, and a 70% response rate is excellent (Babbie, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We sent out a total of 952 questionnaires, of which 612 were returned for this survey. The response rate for this survey was 64.3%, which meets the 60% standard. The result indicates that the sample for this survey is sufficiently representative of the small-town youth population.\u003c/p\u003e\u003cp\u003eTo ensure that the sampling covered all areas of the district, we divided the sampling population into groups based on the schools and districts they belonged to in this study. We distributed the minimum number of samples evenly among the groups after calculating the required minimum sample size. We then collected data from each group in accordance with the determined number. The total number of secondary school students in the Nanyang area is about 165,000 (Headline Nanyang, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When the total number of target population N is large, the total sample size required for the study is n\u0026thinsp;=\u0026thinsp;Z\u0026sup2;S\u0026sup2;/d\u0026sup2; = 385 at a 95% confidence level and 5% sampling error. There are a total of 34 public secondary schools, distributed in various counties and districts across Nanyang. This study divided the sampling group into 34 groups, each representing a different school. The number of samples to be taken from each group is 385/34\u0026thinsp;=\u0026thinsp;11.32\u0026thinsp;\u0026asymp;\u0026thinsp;12. To reduce the sampling error and improve the sampling precision, we took 18 samples from each group and increased the total sample size to 18 \u0026times; 34\u0026thinsp;=\u0026thinsp;612. The study used an online questionnaire to collect data. The survey instrument for this study was a structured questionnaire in self-administered form, and an online questionnaire was used to collect data. A total of 952 questionnaires were sent out, and 612 were returned. Following data collection, we eliminated unqualified and invalid data samples, ultimately leaving 548 valid data samples. The study used IBM SPSS 27.0 and SmartPLS 4.0 for data analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Common Method Variance (CMV)\u003c/h2\u003e\u003cp\u003eThe results of the one-way test indicated that the largest single unrotated factor explained 29.920% of the variance, which is below the 50% criterion. This suggests that the common method bias is not significant, allowing for further analysis of the data.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results","content":"\u003cp\u003e4.1 Measurement Model Assessment\u003c/p\u003e\n\u003cp\u003eThe test indicators of the measurement model include indicator reliability, internal consistency reliability, convergent validity, and discriminant validity.\u003c/p\u003e\n\u003cp\u003eThe results (Table 2) demonstrate that the outer loading values for all items, with the exception of PRE5 and SOC1, are greater than 0.6, indicating the reliability of the items\u0026apos; indicators.\u003c/p\u003e\n\u003cp\u003eThe values of CA and rho_a for the usefulness are all greater than 0.6 and less than 0.7, indicating that the reliability of this variable is acceptable. Additionally, the CA, rho_a, and CR values of all other variables in the research model are greater than 0.7 and less than 0.95, demonstrating their reliability. Therefore, the model of this study has satisfactory internal consistency reliability.\u003c/p\u003e\n\u003cp\u003eThe AVE values of usefulness, social value, and subcultural recognition are between 0.440 and 0.496, which is higher than the threshold of 0.36, indicating that the convergent validity of these three variables is acceptable. The AVE values of the remaining four variables were above the threshold of 0.5, indicating that these variables had sufficient convergent validity.\u003c/p\u003e\n\u003cp\u003eTable 2 Results Summary for Reflective Measurement Models\u003c/p\u003e\n\u003cp\u003e\u003cimg 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hBzzaQNwKmX5LfUV77y3Z+0sezRdk0AjpCZWAmKk/RWyNdKwGDaiY+ZitoAtvulNb3oBrsT6ZrxWtRCLMpoyu8ZCfcyXXXZZrU+kGiByZUmO/WrQt956a5ids5sgMqYHJrVg66Bj0akot7BoUh7hzW9+c+2XYNy4cTFpukhbHo0Ag2VzEdP/Mdoyw9dQ+DcQ71l8/GwzpJuJKJvuU7l0odNv7br/VG1IjvdALKlToJslAv3Upz4VOn22JfT0GP0SSyxRi1fh7NrgZSkiAknPrIJ0596TjwZ24jKMfAQMTt67jpL9AEmt2Ss7gV//+tdhxm3Qp8NnoKldEakbpHxLvhmDmDjtjErR90O6oJ35JrUz7YpqT9vSDxi4+fNAWPgnsUKNXZRB/znUX/iXBMBgzOB87PQZb2eOZt9DytPZ95BMqSdfI1QPZdZu4KVC2X777YOaSt1IsAwopB76ETghWfvuu2988pOfrLcSufvuu2tv28idbQN8b2bZMFCW/ubb3/52+D71Z/ChNvBcZtKwU1++VGCgbGoKwOZzBgAAEABJREFUxJJRLOxMHJEkdi/6U7/ZWW/XfsM7gzm7EeX5TRNQxIn0S/8gzruAv/tK0Pd6B54RyTIJNHH1Lkk+tQ3llPxsPjjQ1N96r9oFVSNJgf5Hn13yOiI2JBvGDP2zvhfBoT6lFaB6I3UlNUV64OkdFvshZWSI8G1qYyVoO8azkYrNsBEdhETHMDNg2Tro0Ohs99prr/Dh6iyJmJEk4jbnGjdC48MzcPuodZIGcPpPOk35qW18oMTaDEnNKHzEOgUfN5GqPJ7Li9eZORfoNNlyUEcQM/voiQuRLGJaBADp8oGZnbj2EVMbub/bAkmaDoP4XidBhahDMtNF2MxcdeRmuPS+nh8mBhUfCBsIeLhP56SzFUdUr6Nna+WeDCMfAaJ0akA19S0a5A3cvhltbO21165VQuJ9P9qbwVsbkR/Bods3ICvDzN83qR0ZWH1fpAvarPZogqENykuNxtYDgfDtIxf99S0IVX/pyhR8u/qTZt9jpYvvmspAf6IuVI/IgXsMuCY9CIr7ECl55JWuXgZz0ivlIx0GHfioo4kRH0y+P6REXzVhwoQQT8KNLCiHjZlv1cQNFuoDU32lPlNfZrCHq76uqqraKN7EkA2GiYpyegvKg7l35/mQOvlcq5O66fuotPS50koYP358qHO5pubUR3geKihERd9Y0rUb5ER/ynDWe0SU9CklT/Po3Ze+yDtQNyo3bejEE0+siaNncA+8+IaBs6O4DKMTgWEjOosvvnitLySG1Dn1BT9piE7C4GsAJRql7y75pZk9lGtHBl8IilnlLrvsUnew7pfWGczqzDgM4KWj7sxTrg3mRLE6ZB8/vbaPrrNs8WYkOgkdlN8oZXTTUcfa+Tys681K4aHTNONqzsDEl3tKp1uuq6oKHSuMdKQ64ZLWlmM+59whYHDxPTqWkrT/qnpO9VHiDJDal8mIARNBMEEo6SYvBkyDPjsQ8b51kgrnJRjw5VUW8bt4UhNEwnlvoTwfqc7U6erw3vKI8737Td93b32PgVZZ8gqImP5G38bOkPSCvYO0Egz8yB2pFlJn4O/tOySVQgiQOBIhEzeSi1JOGczLtaNVSZ7T88ICdgzoSWWQQpNE2DATqKoZ34f7BcSDlJZEmhSE9Lt8//pp90tHOMV7b+7rK5DCkdaYQJIgux/uJT87EpJ7OHpnJEr6j5Le39EEVV2beRAkEj5kjQEuUkv11syT56MPgWEjOj54olsSFaJX0gPwkwr4gG688cbacZLORJwgr4Zt2a68/QXGbAiRWYiZTzOvD40YXVxVVeGDief/kRA1Oyd63ueTgmrLh0rs635pPqSS7ujZHXVO8iFqPEmK6+agXp5PJ22WRLyvjkgPYidtVoL7GZlbOqmDpc6blfsyz+hAwCDm+yPlQWCQHFIO9m3NAan5TfaHjH6DSoikgYGxvKQbjn0FEhREQJsniTHZklcfY6BFGHzbc9r3cDHgG7C02BJjZTcDCarfRoZIYfRTJd3vwgeh0w/pR0iVTBpIpku+3o4wRUZIlKTDFRYkOyQapCUmLvpe6X0FxNQKRb/n9/UBsEEikDlSNjZY+joSur7K8duwICHXB1JNiVNWuUefLg4x8c71sbPT35RyHEkQlYc8kx55djZbnlt6htGLwLARHZATNTNINitARog3BSJIDdaskOrEkY2NTsnsjQ7dTMhAjGiYKdAHK9MA7dp99MP0vDoWMzN6ch8n8TMxKUtzHa/OQcfjfh+5D9MMykoq+XQ07Aws1/Ox8utA/Hv22WfXHiiJTqmwlGe5vHSzNvYufp9oW9nDGdhE6XQ8g4/fChAdjo7FtQHHNSmUmZzZkHehPvAxALhXBw4L52ad8CZlo0v3DojcBbNU9TZ7lDfDyEHAt+F7VCPfjRUcJAbUlr4nBF/b0J5IIZAR3540Exi2FVQjpAcGeu2MihnhoNZRrnyOAvsBg7+BjE2Z9ioeIdGutT0TFJMK3y/iIL90+dyPQDgvgXQDGdeP6IM8gyBeGSZU7AEZKKtr6XtIi33npe/xPCQvyvX71E1UNZ6B1MU3IL+6+048U+k7SDL0TaRS1NueWZ/h+2NjiOh4Hs9luS8Co65+y3cIb+f8q+gvTSy8D6SPKshzw0we6hsDPum162ZAiHz/3pPvXF31k75jkhYqRMTHKif2Mc6Vzx7PeyrP5OhZSNeUT2onwMd7VX8BqdE2lK1vR1DV095yQsnrGbw7UmblKUe78ozuRyTViRRLm2Svo/7ycg+BOCM8rjOMbgSGleiAvnQyZhyusXpkhqjT7IGIlOGdARPJ8MESaSMWpDbHHntsIBrExD4+nSc9LtVJVVW1LxSzN/l1Fjov6heSByTEJmT0435bcL9nUp6P0O+ZlVFRSfcbrkmVkAS/Q+/tQ2UHRFxMIqKTq6qqXjaMmLl3uAOcPD+CqRP1PAy2GR4TkVdVFeyg4Gp/FOme3cxWPveahbPRca7ThIN3QNQtv9mj2ah8OkQduPihDOy/zDrZEnQGhLoMlL09k3uRPPfpLOVhS9LfzLXkcQ+bCh2yuGbQ4RuIdNyMWeWdPHlynUWHTdxeCGQd2YI/2oAJhe/A4/o+fU/UxuPGjav3/kIMpLGHk6Y9aROkGSQMjtILvuzdlOubEl+Cwdu3SNVBPSTP2LFjg20GkqR87VG89omglGvfYimneVSmZ6JmFo8seb7SF5EU9db3mAjpS0gptHNGw35XGTDQvzhXJ9IF+ZEGBEh+/REyJd63qC9iyK29wcP3Y1KlTN+lsqTrm6i7xJNKqb9z35y+Ut/j2nNXVRV+G5lyv3fCVgjxct1bUFdtEkGQrh5Ilm9Gv4zo6BsYO/s9+djT6Ku9S321vkL/6TnUTzt3Lr9vUl+hvsoXr+/RF8GcbZF2I00++OpftBd5mSJIsz+Ta2mwQhztKYfUwEge71x9S9sU120BcYUJjAS4mTB4TriIgwFiJ64ZTCCk66+pfBF57wnZQ3pL3mnTptVG7/J6d+LZsMHShMP1gIcuLHDYiY4PyAdFjysYMK0kKFghG8iENEHjlqYTM+thD6NB6zyk++iQIh+/dGmkC+4pv+Ne4lGrPdyjY0CE5PFB+n3xgnIc3SsN4XEt6Izc4+Mu+Xz8PnAzE7/PcLF0NvIOV9BZeB7PXUJVVaHjc63Ong0OOkxxQpltsl9yrfM0qDgXmnkNQgaNEqezUeZQB9I7pMLHb4atk9cJGBxI2hiMm8X29lzuJdqnTmVHIY9ZuI5cx+S6t2CANhtHisxEO/NYjWLmiszofEkdr7rqqjqbGb80M/k6YvofHsKnH7ryP3wMwN5/CWbn5VwbKe1F+0ewzbqlaxvalIrBTJxgEIW9e10L8gjem+9cHCKAPDnXDpVlYuQaCUAInPsmHQUTEBIaZTWDvsWkSB5BH+I7KXmk99b3MA4u+ZEq54LVV4ssskjt3da1uvtdEynXyvedIWfqI/he/J4+Qp5mX2TwFycY0PVjynQtlD7HuWeFn/7PtcA+Bwmw6gh5J631W50B+fIs7ilB/+jZTdpKHKzZJpZ36/fdW96ZZ4NBSWfDV2x0lFFwgHlVVbXjPHiX/FRvno2Uq/RViJT3537vXZml7Xnf8iNY8rsfUUOEqa2UK71bA2kUYqevIX3TTxk/PK9VbdqScUefLK4ZkEPv3GpX/Q0Joz7Kij3nJa+xSJtDWIsZQelvHEs+0rlyPhKPw050RiKoWafhRQAh1WEYPJwbaM1+dR46CB88MX1vTym/+xDwkm6wYEdQSHaJbx4NDKQIzbjmuVk/NYJBQcfVzGt2ReSuQ/c7CJZlsc378zwRmF0ESKgQEe3dLL9IaWe3nEHOP2DFk5L5Dqn1qqoK5HvACh+EgrwfZJ060PuhikRe/JR+iroXmWv2FdIE+fQjzgUk2ESMVAzJFyf4DWU5L4EwgZq4EF/qRvayJX0kHpPojMS3mnXqFQErbHzgZtFFxE2yQ+pjxsM2oPNGai7+RKSzByMlYifgWiCZac6grBxhtyTNfX5PGSQ0bL4YcTZ/g1iaTYT8ns9KH+oMNh7yszOTJrhXHufFbqFZVp4nAp0IkG5Qh1CNdqaNxGurPtVXvdtSP9Iq/QbJMVsjz22yg+Q4J/n1zQudEhvpAlsx6dTgSBObsHIfG1h5BPZT7DPZZLGpQq5IJdmHiee6RTklnT3VN77xjdDnub+tIYlOW99cPvcsIYCcsI+go9bZm/HwBm2WozMg6vZRW43CpoLxebNgJIY9gPutfEGGSIYY3CJInLY1dd305ToWpEQHxu5CeVRTOl+zJ9fNgOj4beTF/UTRJR0RYkvCtkwcoqUM9XI96kJWOBEYYQhQj1LXUWvrA1TPhIZU2qphdjgIDKN//RC7G3magbRYGlKij2AfqZ9TTlNF5R4ESF9kkqafkl+8wFic8bl+zbWgn3Nsc0ii0+a3l88+UwSIcdkNMAY2Y0Ic2Hy40WoyxqiMGH34OhPGfNJKoPZiP1Ku2RRYbot40K9bkUdiU9Lp1Rm0Mph0boZktlQMMEu+ciRZolajMqMus6SX7l5gY0C0zf7ATMwKI3nYU7BVKGXkMRFIBNqLgD7K5Ik6XT/FZqfYfyJApDhsCxnoI0O9TXLYPzURoIrShyBJ7EVLmgkelZhr6i/2O9Re1H48cOtzSJRN8EzgTAaV1fb+JomON55hpCJQbzDnY2Zn48NmsGeWZHkq6YkP2goPHzJDc7Yy/YFBX07vbdZDOmSVFvVXucdvMOKUR5zfYXfjfGahqqoZfDrF9H9+jxEmQ292PogVw17EaHpy/k8EEoERgADpL7JhMqZ/QnBUSz/iW7fiUD8lrqi3nPcWrPpEhtxnYoRI9ZZPutBMc03953dt22KBBCPvZp42nifRaeNby2eebQSsCLGUk+rJkmhqKB+wlSrSqKKor5CKvgpHWtjQIEUkO6RAnXmJgYmRSY50SFbLEEF35puVa2JnaizSIH5nPLtVXaQ+s3J/5kkEEoF2IIBYWDFILW3CVZ6abYzVdyY8PFKX+P6OJMlWW5m0kQQL/eWXpm/jB63kJekm4TYZJPGRp80hiU7z7eX5iECAlAWp0UmY2bDRcc7XBrUSh45sYqicSEmsSnFtSbp75SU54UyOw0QzLMDwlYQgWd3AcE+ngNiYhSE2SJMlxJzgmaExJiT2ZRxJDEx65Lc5bnSvToTtDr08A0LSJmVQlSFdftvvCtRXCI+l1K4zJAKJwMhBAJGxTN5yfKr2UjP2OPokfVSxH9Rn6DuouNjUUI8XHzlWnPEyrs8j2eHzqNjY6FPk5eSRSko/R9ps4uQ3LH4ov8vNgf7LsyBhJb6txyQ6bX1z+dx9IoAsIC1WIrBrYUzMDodayd49VhJwMsefCaKDqNikkFNEPj7sT0Zka+WDJd86CmSFEy+dkTI5/aM313mw17HUUyejYyFiRop0HFZ4kRxZLs5ImdM4hn4cB5IO0Z97Jrp3nbSFSDwAABAASURBVBPpj07LaggSI8tlVZRunVSHGs51hkQgERg5CFRVFfoaBsL6i1IzUh4O/hgXU5Xrh6ixSGxId/VlJmn6BmkMlvm5UhbbHosh9HXSTLLY8ugP9UX6PJMq1/o0xsz6Gb+trzQp5MfHddtDEp22v8F8/hcgQM+MMDDyLaGIXxGHEsfWhV6cpESco3sZILvWCfjwxbtWZjM/h2zidTpmZNLZ5/C3I56hsYdzD3G0OHnZ2zgv5bvPtd+hU3efaw7aGEdz0qazQrKkKzNDIpAIjCwE9BP6Bn1JqRn7Gn2HvkKafkH/QsriXNBfUVc5F6Tpx9znWp/j6JpfJfe7Fo/Q6ONc63c4ljRpo0KTV3p5ljYf54rofOu6G+LCK76dITHINjBIbeCO+38Vf3jmv+Pya78bv5z6+8R5kHDOfmyg+vEsp81t6cHfPRYvfcWrYpe9PxF/+uvfB7y/+fXU3w0LX5orojMsT5w/mgiMIgSo16i0bEA5iqqdVU0EEoFhQIDhM9W5LXOG4ecH7Sfniui8f4N1Y8uJ782QGGQbyDYwR20g+4/sP7MNjJ42sOTYxQeNzPRX8FwRnf4KzrREIBFIBBKBRCARSASGG4EkOsP9BvL3ZwOBzJoIJAKJQCKQCMweAkl0Zg+vzJ0IJAKJQCKQCCQCLUJgRBOdFr2HfNREIBFIBBKBRCARGAQEkugMAqhZZCLQRgRsOWF1F58dSyyxRHz605+O8847L2699dYBqQ4niZtuumlwlDinBfJEfeKJJ8bGG2/8giIuvPDC+MhHPhK8vL4gcS4iTj311IAJnyJ8kcBm/vnnDw4hebvuq+h//vOfccUVV4SVLLbykG/KlCnxiU98ItTDdWfgtp9HWg7iOtN45d5xxx3je9/7XvCkzWnle9/73uAlV157FHHd73yoAqw5w4QPXPh64vl7zz33HKpHmKXf8S44y+OhfJZuGLmZRmXNkuiMyteelU4EZkTgwQcfDHt+cRzG1TxPqgaGCy64ILiLnzH3nF0ZELmnn7O7n7vrkUceCR5fe3smztU4ceQI8rncA/OXp+yLL744OFq78cYbAzaW3/KU/aMf/ajPH+Gen3dt24mUTLYG4dmWY8kS1zwigbzdNuPKubrZJuQ973lP2EjWuylpjjznIqrOBd61HQcr2NPNbyJlnhsuzm1NUIjdYP327JbL0aZtFHhId69ntUWC8wwjH4EkOiP/HWcNE4GZImA3dp5QbRzIQ6sb1llnndh1112dDkjYY489arIwN4W9+c1vDlKM3oiCHZ95kZ6b8mf1Xpu/kubYCLGve+D5wQ9+MHiu7StPZ/z6668fZTDuTGteIz3eVTOueY4EkTg14+bovJ+bbCNAemM/N152ZeXl2zYniIXrbg077LBD2PagW58vn2tgEZhnYIvL0hKBRKBtCFBZ2bjU/jiF5KiDwYoqwsZ+1CM2HLRPFxXFtGnTwkam0l2vvvrqbqkDqQPidM4554S0Qw45JMz+68Tpf2xr8fa3vz1WWWWVWjoyPSq+8Y1v1Hnl32uvvcJmrOLt3SOveNIDcSXY60e8Z6JCcU5iIp3KDfHZf//9a1UZ1YX4gQqwWnjhhWvSVcq86aabeupAelDiy5EUSh2OOOKIWkpmfzOkxnMLnRIG+66Jt4eaMrwD+xH1pUq08Sv1nd8ghbL5o73W4KCcQlqV4XpuB3ptxtYqY8eO9Xg9wXYEpFYiqOI233zzGpc111wzbGArnmSO+pEzTPu3vfvd7w7tcOLEiXXekg+O9ny76qqr6vhtt922R1Xn+SdMmFDHkz4qV7A3nfppFyRq1ITK1laV692RzFFzUku6Z7vttqvLIdX83e+Gx3uv58gwOAgk0RkcXLPURKA1CPzhD3+oB97epCTikAiSCTP4pZdeuiYnZvB2akckqAHM5FddddUwsHzhC18IAwibkosuuihuueWWmhQBxOD+73//O37605/WtivsVcSdfPLJcfnllwcbFoMQEkWFduaZZwZixNZFWQYs5VCV3HzzzWFQk4+aCDGjtqIeu+SSS2rbIvv3PPDAA26Z6/D73/8+1lprraAi22233WqJgN9TsEF38uTJYWA+7rjj4n3ve1/85je/kdQT4HD44YcHVRYMqPKQDiom5VD7lMwHHHBAXe+jjz462LvA8rDDDotrr702nn322ZKt54jESIMDWx2kx+B9zz33xJVXXhlUSfvtt1+d34CPEL31rW+tr+f0DzzsywT3zjLgrt3AhPRLG2FfhADfdtttscYaa8QNN9wQDz30UMDB+9xqq63i2GOPDcfPfOYzNfFRfyRYe0HwEKfzzz+/xoD6UFtTNunaFltsESRsCDB7MASKalGc33v00Ufrd6fNeGblUPUhhWzHvDvtmESqsz553W4Ekui0+/3l0ycCc40Aw1odv4Gpt8IMWgYj+ahW5DE433///UG95forX/lKsPNRzk477RRbbrllGFiRAoO4PMKSSy4Zm222WT3gUHkYwP/7v/87GNGSEFC5IF7yGrCWWWaZQK5IJgyGRXowfvz4+PznPx/K9txNiQ2p1J/+9KcwYPk9kgPPpcy5CWx0rrvuuiiSAr9RykMmSGyQkauvvjo+9KEPvcAomtTLveUekgoSEcSoSLBKmgGfZAS+SKVNFknGVlhhhZJlhiP8SYdmiHz+YqmllgpqQ6QAySJd6yvv87fM0oFqTp0ZRvd2A2KBYJEESmdkPd988wXckArvFimjjmTMDBuqR5Iy+b3rfffdN9Zbb736XSIh3uXdd98dpGfaTsEDGb733ntr4uQ32U9pB6Q2Nsv1e8rsDNoaMqiteXewkb8zX163G4EkOu1+f+1++nz6rkCAmJ8KwGBtxtx8KJKK++67rxlVnxtMDHBF3YIM1Qmz+ecVr3hFGGyoqxApthOIjWKQFxIK57MTlGllFpJj3x5EApmanTL6ymugJoUiQUA8mvlg+LWvfS1KsE9ZM73z3DNSpah7GbA785RrdSnnc3JEIkjkqLMQDJKYOSmneQ+VJrUQgtuMd059pn2QwCGi4vwm/JzPTXjlK19ZSwiREqRJWcUOCvE99NBDa2kh6SCyS8InT38BaS7vjTSpv7yZ1j4Ekui0753lEycCA4qAmS/VEbsJKhnEww+YMZ900klh5u66GUgaLLc+8sgj62hqCASFpKWO6OMPNYIgGQlhh0I9Rv1ELSTe7zpusskmQYJC/eDaaiQzeef9BYOfQYtagqrIgIuY9HfP7KSROBhEqYqQHgMu1QdVCPWUsu66665a6uC8r0AdQ1JFglDqWPKS4DinCiSRIU1yPbuBtILNiWf2jCRmiGBVVbNb1AvyUw2RXFGbUUeVDCR5iBlJlOX4J5xwQvzrX/+qJTna0uyqzEhq1MF7teJOu6AaZLPEDszvksiQHMHJCrlJkyYFguv3Osm7/IJ2NnXq1ED82BGJE8444wyHDCMIgTYRnREEe1almxEgrTDAk3QIxOl8l5i9dvNzz82zGRDYNcwzzzz1bFi9N9poo2A4S7LCzsJs+eCDDw6DDpUUaYQBTl6zaOfsa5AMBrRsMthYsJFhiPra1742jjrqqBDnHgOh8pAdxIlqh+8e8YxTF1xwwUCk2ALJj7RUVVWruajNrL46/vjjg4qEDcbjjz/eA8HrXve6MNiybzEokgL0JM7mCXsjRrBuY/ei3p6N1IJd0bLLLhuLLLJIeBb5PKtB1gDK2NZzeVZkhkSFmsV9BR/5lYuEsEeRhjCJR/RIJeadd9445phjApbsXBBDq67gYLBXljjlU+1QMRr8GXMjUp7d+2T/8qY3vcnlXAcSGioibWSbbbapjXk9s/cIcxh5174n72LnnXcOAfGkjkJApFNpUYEhRCRP6qMu6uchERi4rrjiirUac9111w3SPxJI9fWb3gUbrpe+9KWB1Iljs4OMUQ/Cg3qQipT9F5UWg2n2TvwR+R33CMiS6wwjB4EkOiPnXWZNBgABszx2AWbCjBwFg48Bl93EzH6CmBwRmFm+bkw3IFiSrM6CAf3Vr351sJVAYsSx1SkDpwGFXY14hqLqZNCi6hJn9RXsnAvSERK2Nq5JQ8QJBilxCCXpkHM2GQiWc4HEhNSHfZBrAzki4VxAkJTlmZEBcZ6F3xnxcxoYq1LPKE89V1pppbooK3TECQZ1BtvOBc+K2JEuuCaJmDBhQm3I7dogTIrgXNC2PCtSp/DS/pBGJEocwiUvSRDy7VxQV1g79zvjxo0LpMa1NPcKCIcVWAOhPlJeCQyfGZT7PQEOVfWcxMg7Yfwr3rPBzrOX/IgHMifdezXBIA1zTbVXfoMkTxxCTQIoHolB6sQXGxyE1vsSR+2KQHoPrgVSRGpWBEmbRqiVVcqXhx2QuHaEfMpZQSCJzqyglHlGBQLE62bAVoKQNJRKm0kynDUYFZVCSWseST4M2Ndff30zOs8TgWFDgFpNuy2ru2ZmCzRsD9rLD1vVhSgjPlb39ZIloxKBWUIgic4swZSZRgMCiI5ZIIkFVU6zzkTt7DyI1RnMEnGT3FA3ODczNVtkoGppMJWJ+81WpQs8xoojNZGHOmfixIn1LF98hkRgoBGg8iJxsRSbeok0Y6B/Y7DKIzFkJEzaQqU1WL+T5Y58BJLojPx3nDWcRQT4NmGIy06l8xa6f3EGCqsyXCNGbB6oMKgFLOFlgyAd6WEsyXaAWN01w02qCJ03lQuSxD8MEqTsDInAQCOgLVN9jR07Nqh1Brr8wSzPN8bWyrOn3cxgIj3yy06iM/LfcdZwFhGoqirYhTBaZDDZvI0BY1VVwRajGd/fOaNSBpf8c3BghxRZZWM1EBuN7u+8+6tdpiUCiUAi0A4Ekui04z3lUw4BApzKscdho0Oy0/xJTuAYQVI1NeP7OzebtpqIFEewbHUgHLX195uZlggkAolAIjAjAkl0ZsQjr+YCgbbfWlVVWDVkJYbVGqU+bGtIZeyfxINriUeG+OHg54M6il+VkkaCw28JqY7VHeKtNGGz4zxDIpAIJAKJwNAgkERnaHDOX2kJAqQ27GdsY8B2RuDbxWqsIs0RR/Vk6TM3/ux2LD/nK4XEpiyLZRvhXj473MMvCP8zDJctoWbT0xJY5ugxGWvPzCOxVUEcwbF3mqMfGaKbPCdSK3B4yO/L008/PVe/blWR8th3KahcU5PaNHVm2HGFUO51/1AHfqU8f2eg+p2VZ4FjyUtVzB8Sg/++7uVhWZ17ayvakDT38o3jmayCdA1Pv+W87aHg5Kgu2iVHn+rr6Fo8jNgIioeLY28BNmwT3TOSwzASnZEMa9at7Qiwo+FTQ7jzzjtrR2XNOnFeJ81Scr5S5KeqQmrE85Himl8U1wK/L1ZakQC5RqiaZY6k80svvTT4c2GI3Ve9DFz8ovCnUwa8vvIOZ7z3hbBa4kyyZ88oRua8Sc/pc3GsyGUBR38wIOlzbUUfJ3zaFwlgf+VzPMg3TX95BjNekPkdAAAQAElEQVTNwGp1IbszdYCNfcy8z5kRMP6SeDgm8fSM9uDiJ4gfKtedwVYg8PEOnHemUy3DQzkcAXJQWMryjVqm3nlPG6/5DeKQkfNJz8+BpAmU/cJsJcJ7uHiEkaNJkzO+ryyQ4MeJ40XvSYDl6aef/oI92dw/0kISnZH2RrM+iUAiMGAIkKwYxEnt2FkJBlwSAoPqnPwQA3XEmCsC5SG/nNy5njJlSr0hqk063/72t/dbPGkiKWK/mfpJnNvB3+DqmTnk411YXUg2qXA5heznp1+QZK8qXrPn1ECfqhkeLyh4eoQNZpGv6af1NhT9kW95hjX08+PIiy1BLIgoJJtNoXP7qvEYTaKsCG4FSIy5FUCmERp5Saq9pxIskLC6zT0jOSTRGclvN+uWCAwTAmb2M/tpW0rYGNKeWTPLO1zpVIxE/gYNz+s5+HcxW7ZnlOvZDbaEoKYZM2ZM8NlkkEKcyjWfTbZXmN1yZyc/iREPy7Nzz6zkVR8DMXVJf/l5q15ooYX6yzJDGq/dvGLPENnLhd9eZZVVekl5LortHJu7567a9VeboZLiwoJEzCatamAJPkkgyVWRdslHvWVfsd6IDNLE+al30Fu6ckdSSKIzkt5m1iURGGIE2EawVzLA2cCRYzcdbPMxSD4QmgMPPDAQINIRdkolDxUWNZ4y7JHEtoD9B/WfOLN9+zyV/EN1ZO9gWwaSFbZVzd91PXny5HqzSvtN2VqBvxer7BAIdg+woR5gn2VbCgMLScekSZNqdQFMbNXg2m+5NgO3z5W9nHgEZgtkTyf7ZpF2yMs2iFpm7bXXjjPPPLN+LJitvPLKwVieegLm1BwM4m2rYNsFgbSFaofqjfTIDF9ZdSFz8Yfky15p7NM8AymK4jy/36RK8vwcZXq30kow6CIgVJ3suki8kEq4IS7UUiUvFac2JM1R21FXCwiUXfKVo7JgQMVm+w4qSNIN+32Rhvkd78D74tUciW3+XilnuI+ez/5fSA3STS1+1VVX9TwWiRbsqFlFwvQ3v/lNaDeuBeQHAUXckSR4KFfaSA9JdEb6G876JQKDiABJBJH5zTffHDpbg6sOt/mTZp/sOQzcNrv86le/GgYccfIhCgZH+2YZeHTQBm5xZqnE60Tt8g5lQNgMlP39pgGevYP9nOyHZZ8n0hIDMi/aTzzxRCAvBlP42ABU/dXTajwkwDXi5PrjH/94GMAMVH4XMUJIkCS2YqQ/CJRBueCH+NhDDE7IpLwIELsf5MNzObLXoLahxuEtGWmicjK4+625CSRcCJaBkyNMrhqUZ/8q+3BRcSHBjPp5OpZWAqLVfL/qDENqmgkTJtR4lLzaF5KmjcDW88OwL3smZRW7FbYtnoMqx/1Ud55ZqKoq2Boh4DYALb/XLUftwftHqD2TdqLexRaKqmr8+PHhncurXXZKBZFODkq1V2pCZEdZoyEk0RkNbznrmAhAYBCCWTppBM/PpBa9/QSP0Ow5kAAz6Q033DCofQxa8pNEMEw1OFJjWTVjxk6sbpA0eEqTdyiDZ/EM1AFmw739tlk2tcoiiywSVAAkOlRTiBrJDkmG+6hqkAHn/QXqKwTE6j/5zNDNypWNkOy3336hTIanMJUHkSElI/2Yb775ghSpEB9qHARLXWAu/2AEhIOLBdICfqjKbyAZ2kVVVVGevzx3yaMNHXTQQeUytAfEjOQFIfTsJVG9SPmqqgrEjVG/dkXqVvI0j0jLu971rmZUzzlP0byaI5ramPe29NJLR/P3ejIP84n2ZOsYGNu3zMQCUUF+PJr28f73vz8YKasHooskSitBmyLBQyq9L640StpIPybRGelvOOuXCAwiAiQuBiXidKqmWfkp6gbEwcDXV36zbWVbgcRmRUfeV97BijdLJq1CJAwqnb9DKmX2TL1ltizdIGkmjbAIJDCIi2BARorkm9XgGfojWsohDTLg+40SVlttNUlDGtZee+1A6EiwytJuxMt7RrbKsyG7/T2YFUKII6NvRK0vkus3SP0M4P2V11+aZ/ZOGH/b545NS3/5hysNcSGJsUErfE0sVlpppaDm1AarqgpkD1aki6SRiFxfzwszamLStTLh6CvvSIhPojMS3uLg1GFElUqVYLZZgsFjICvI5wmVjc7XIM5wkKRjIH+jG8tSV6oU+FI1GPDZTMBCGrWKjtizW4YPfzNTko8VVlghqC3cq4xyzo4AjtQLMNQRI0f8zChnqEJVVUEiwAbJ83IP4PkFqiiuBKgLSBisohJPVbLxxhvX+0oZ9EmkqKSkXXjhhYE0qSdstBFOJ6n/SBTM2uEFC9fSSCPUv2BnOTZVnzK0OfmXWmqpWgpCHeV3BIOi++EmLxWHvI7wNtBRy1FlFRXY7OLK1ob0wKBKVeJ6t912C9fIjDZA1XLjjTeGJc6ei+rq9ttvD3Uuz+95qKG0E21HOd61c8un4SEgdO5Tf8SOUTFJoHopSznu4RtGXdS7pPne1RcJhQk7HNI4BBqh2nfffWv7Junu7ZYAE4SfyokxtrbGFoxUTPuiHtXmYI70+aZMDrSFUgffkneh3r4t78H70q6olUkDS96RekyiM1LfbNZrBgSoAMwUx40bFwwTi8h3hkxzeKFjNdMyo9KRGJwZ1+qk57DI1tzG7sFgzG7CwGKWqeMlYqcWKAO7CsHeAMye4oQTTgiDEvWOWf93v/vd0JmTghC/E8MjF/KTapCsMKpVzlCHyZMnBzUK30CeR/B+vW/SCst7DRbiDSi77rpreGaEhxTH1h/SqGEQHIMsdR7bGwSgXFMpIB5840hHHBl4M/AVp4ypU6fW+7Gx0UEmYI4UsNdgoCqPwFZHnOcymLlf2zRwspViyAtHZMhvOJ/dgCCRHjAEZvPhG6Meg5f35ZvwLfA7xHbEc5H+eW4SO0dlkIghQKQ37LTgh3Acf/zxseiii9akEUbw5JeKdIMND8JM1YQIKKuqqlBX9SNBQkzhafk7bNgLWcqvvWmXjLPVWRsmAUOaXHdTQCR9G7A599xzAwn0fI6+E/XRbhA68eyNSBp9k64FBM/3SEoGf+9B0C7f+c53BvWsfCM5DBzRGckoZd1aj4COjHhax8kvis55sCplxQx/IvxWDNZvdEu5VoHAU2fLNgQZ4ROGaF2cQY8Nhuc1KImThz2J1VSuBT5Q2Gk4l47wOBeUi2jorJUzHIEXbM9SwhJLLNHzGNoU4iPt4IMPDgOKRBIIhq/iBaoC9jbq4ZrhK5UfA+1yjRwWHKw0g5FZukFJHkaosGOj49q9bHb8nusSSGwY3rpGKq0wci4YHNnBOFcv985JIFVQRglWjSlH2exeOABEIsq1fN4lmx0G0a5h4T75XcNLfZ2rs7alLSEu1IKkaNKE0q5I3FzLhyAiXa69C3gqxzUVKLINDzh6VoHEzfJs590WSGm8Y89PUoOkeUZHcSUUskL1BjN5StBeSr7OI7y105J3pB6T6IzUN5v1GjIEzJp11kP2gy37IR202bQBiPfalj1+Pu4IRIBqELlCWEnVEKTBqGaW2R0IJNHpjveQT9EFCBATM6jV+RHBU0N4LAZ/lp6OHTu2NvgjRtdRUhtYASG/JcHy0odb3ivOTFEenSgxvLKJ0c1i5WMvQY1G727mzpBQXvpzR0uRif2J35Xd1mBGyoZCvahy2lqPfO6Rg0BVVUGKw9aHXyESsJFTu6xJJwJJdDoRyetRiQBbAqtCEBedH1UEQ0qDMxsIqijSCKQEgWHrQH1AFC5/Ef8a0NkJsNtBhu65555g9MfpG5sFyzoZY0qXz1Jctg3sDZArxEbcYostFuyIxFHjjMqXMuoqnRVOBBKBwUAgic5goJpltg4B0huSG/YBHn6bbbYJBIM0hTSHYSijQPY30hnSkuxYleOa8aWjdNIYkhsGkGyD+P6gC2dLYeZIvy4vA0qEiWGhpaBVVdWGlwgW40wrWKxGQXrkz5AIJAKJQCIw+wgk0Zl9zPKOEYQA9RESY9mqFTGkM6V6/GtYLk20zR8Kg9lChEiASGwQnpJ/do8MB5EaZXJ+ZhUOwoPY8G9hySsDatIlkqLO8vM6EUgEEoFEYOYIJNGZOUaZYwQgwMcGNRTiwhbHMl2kxlJVqiOrsNiPWJpZlrsWmxk+KizNFSzPtJSTtMceOrzdKsvSaH4q9tlnnyAd8jukMVRUftu9jgiVfPyC8Bo777zz1k6/LMm21FM8lRaJDtLDvsWy5Da8AnZHiJ+j53W0fBg+AukVfyYIpfQS3APTcl2OVIblvC1HBFgdkeDOZ9bueH3ujB+oa21MWx2o8oaiHN+J919+yzX/QtqLtuObMRmBm7hm8D1bOl3ulY90tFznMREoCCTRKUiM+OPoriAywwjY0lqkhB8JvjiQEktgoUOigvCQ4FiJgdQgNGx3OK9jY8M/BaLCaZlVG3xxKKtIfNzLfw7368jKDTfcENRZ8rO5IT1id+OoE+eN1f0leAa/yeZHnLIsny3qLs/ZrYFzvPPPPz/gY9C1fJr6TT0Ey3+9A+mlDiRVlhxbZl3iyhHphJ9BrsR18xFZU0cq0E4yx1jd8mYbKQ5WHbQddmSI1mD9xkCW6zl9ZwJiiPBwI8CBn/ZilR5XA859pyYR1MCO4qiXLRPX1tzPbxBbN+UM5HNmWe1HIIlO+99h1mAWEGAr0+lDwrWOlJM7RfDTwe+LeEGcgPwYtBkSF/8fCBNpDJ8c8hqo5aF2ci6O/w4kybnAs6mjIA9vtwZz1wK/I6Q4VlsVfyEkRnxpeI5uDrDhCJBfFkQOeWHfBF91E6gCEUaSnVIXBNBMnNM3ErYS73j00UcHOyiO0Vx3e/D++SzxnkkDh/p57WOEOCOHnURrqJ9lVn7POyeVsRqxqqrgEI8X5fINckKp7ZOM8sHkW+Ck0VF7MlFBHElDq6oK3qqRaE4MZ+X3M8/oQaBPojN6IMiaJgLDgwAnbgyXLUUXbCdAEtQG6U0TMSvLDDz24LFM3mBFgrH33nvXG1CWvFtuuWVwmlfUK45WpPGCTIJli4SS1xFpJM0ycCND4ro18Pxr0OV6vzdVo3qr42A+P0kgz8GXXHJJjwfdwfy9uS0bkeGhmR8qZXHBgKwgNa5NJBBnKxtddwbOKpFnklFpJgnIHqkiCZC4DIkABJLoQCFDIjAMCFixxW6DJEOgXuNtdhgeZa5+0iBvR/IykHPjb2ZNEtYs2KCE7FiRJp7qQpCPiovhddPGoqqqmighQ7YqcE+3BtI4pNXeSWyTuCDgIXmrrbYKJJZaS7ytGKzQQ4aKpIpdFgzkY6dl6wKqmL7qyo5l5513Dls3+A2EUpz82g91IHLouluDPZrGjh0bhdQwvidR5Tm6qqqexxZHOlgikGN7grHVQYzkh11J1wZ5JFd+iRvAYxbVUgSS6LT0xeVjJwLdgIABlvrJgFueh82EAalc93VkjI3AGLARAkSAOqOZn2t7WwbwrNyM76ZzxAJpoWbxXEgeU0sWpAAAEABJREFUWxPqI8QOiSVtIXmAjX2ekCJqOca01KHUNfJRxwjOldVbgBnXBWyB+HKiImyqA6l3EK3e7u2WODZq3CuU56GeKuf9HRn3UxEi1rA877zz6v2wmveoP/LdjMvz0Y1AEp3R/f6z9onAXCFAAmOgpzYoBVEpmImTVJS43o7segzWnDQa3El37Ejdm/ExiUhvZXRDHLKC1LBN8jyO6rPAAgsEI3VxAozsvYQUInCMZkkmrMTjYkAetl8kX/0ROyo9gznDbuSJFMm9zTAz7Jt5h+McWUP+ym+TZiHNVq2VuN6OCy+8cBQbHauy+iJ0PJL3dn/GjU4EkuiMzveetU4EBgQBqgKrygxcpUDL7hnlIjIlzhEpQmR+9atfBTWdawaobHsEqisr0xioyt8MBrjmdTedIy2Mj0kYZve5DO7Ul8Wm5MUvfnEoC1nqqywYwpYhO/umJmEo9yCb5bwbjzyMI3jl2ZZffvmaFFK5dRJdRuklXznaW84WDtwvwK/El2ORrpXrPI5uBJLojO73n7XvHgRa+SRUKFatmV2XChjE2EhQ3xx88MFBaiEwWJaPKorzReoq9jpsNQSDXVU9t/qmkAb2GKQ5Vs2V8rvtiJgwLH/wwQfrR0Ne+IKhviNZcURkqJeoXkgbSIFIsHjLZoTufhhRgbGzWXvtteuy+vrjt/yOVVbKK+SQ+u+uu+6Kpt1KX2UMZ7z6PvzwwwEbz4HgITRcP7C9gYXAXYO2gfxoC6SHjkgy9R+STSIoL5Whskh5OOB0niERgEASHShkSAQSgTlGYIMNNqj35UJiSiEMbtlJGJT4PBFIZRjRGsx4heb3xL5e5R62JvwSucfKLfHsePguovZx3a3B5qy2CEFskDT+XNZZZ524+eabA6mhZlIPkixuBai6rCqSTpK1ySabBIxINNjskJT1VVf2KZMmTarzG+DhT7IhP2eXVFsrr7yyy64NiE5VVXW7KQ9pqxU+dWAICwERtvwcUbzooouCXY+ja5I0RuyIobz8XiF9HE1arVXKzWMikESnrW0gnzsR6BIEDD6HHnpoCJaal8eyooYUpwSep6VZAiyOcz2rjMQJpDziBYMbknPppZfW/lGsUpKnWwMbG0u7TzzxxKiqKmzboR777bdfWD6trq7ZlyBFVE+8clM9qZNVW9JJMwzc4voLDHLlZwvkHuekSAiWQb43u53+yhuONM7+kD7+l8rvaw+WnauPAFP2Xp1taamllqpvIekr2FJXwWXbbbcNbg7qDPknEZiOQBKd6SDk/0QgEZg7BHistQyYVIZEY+5Ke+5uPmnY7lCNVdX/LTl+LrX7/jI09qz86RSVzJw+JfUTA93ewgUXXBAkQp1lkxpxNMlhIZViZ3q3Xa+yyiqBlCCz/S2nn5Xndj//QcokHZuVezLPyEOgrxrNFdF57In/iEcefTxDYpBtINtAzP+KMbHZh7aI//77PwakPVxy2RXx93/8M9rSzzzxH3+MccuvGCuPXy0ef/IPc4XBQgsvEjff8qNewxprTYhHH3/yBeV/cv8D4zWvXXSuf3uo+nQYvWqhhWOrrbeN3z/2xAvqMzvP4f5NP7BZLPuO5eLJP/xprsqand/NvLM3/v/XX//WFxcZ1Pi5Ijqnn3N+HHfqmRkSg2wD2QayDWQbaFkbyLFrqMfve+7/xaASmr4Knyuic/gB+8ZJRx2aITHINpBtINtAtoFsA9kG+m0Dq6+8Ql9cZFDj54roDOqTZeGJQCKQCHQRAvkoiUAi0E4Ekui0873lUycCiUAikAgkAonALCCQRGcWQMosicDsI5B3JAKJQCKQCHQDAkl0uuEt5DMkAolAIpAIJAKJwKAgMOxEZ5999oniK2KrrbaqK2mjuh133LGOHzduXFx11VV1/Oz84aX0bW97W/BGapfgvu7lQZTb9b7Shyp+sH9nypQptVdRWHMPf//999c/+e1vf7vGWfwhhxxSx5100kk9cfyi8NbKERzHcPI1g/fD02194/Q/fudjH/tY8G46/bKV/3n45dmXl177CvVXiSeeeCK4oIcJ3yfyXnPNNWE7A/eff/75ourAfT2PtvJyGldH5p9EIBFIBBKBQUVg2InOMcccEzx5GgDsj6O29i85++yz63gk533ve5/oWQ4PPPBAXHbZZfHjH/84uAj//ve/H73tiosEcRduAJrlwluaEb4GXZsF2khx2WWXrWvCFXvx4nrEEUfELbfcElz3f+9734vf/va3weuqwZ5n0t/85jdh8ObdVZpw+eWXR3HPjyhxGmeDx94cmtU/2OV/uJA/6KCDwh47d999d/Dc+sMf/rDXp7Y55eqrrx52knauPd1xxx0BZ7j87Gc/i69+9athqwMOzXj+5eYebvZv0jbtTdRr4RmZCCQCicDoRmDAaj/sRMdmbgsssMCAVUhBNrp72cteFlyH77rrrvGFL3whets7pszA3TMagn1jZlZPbve9j0JeDNQ8vnpPnfc++eSTMWbMmCgbECJKJ5xwQme2Vl1rO+r0+te/PrQhnlYR5U5PtzYkJPWx7QE3/1zuIzP22yGxWXTRRcO2BbY9QBq550faea4FyJFHHhk83CKPrjMkAolAIpAIDA4Cw050ZrVaVCz2yDFY/OQnP+m5zSBDdXDGGWeE3YGpoew3QwphBl3Sza7tqSKvfWWox3oKef5k8uTJQe3gfvlskCeJxMemeeIOPPDAILEQb6Daa6+9goSEZEic59xjjz2COscOw+LaFEgnDL6w8/zIkf1kkMbOepBUlHp3prXxmhSKZGa++eariTE3+jZItIHgX//61xmqRL1HNYosaxekPqQzNqSktrO7svsRHtc2r5SOHCrIvjx+r1yLy5AIJAJzgUDemgj0gUAriM7UqVPD4PvJT36yVkexv1GfTTfdNLbccstAZhAge53YFI4EYsUVV4yDDz442PogNdOmTQuzdPYpN954YxiIlFECtcuxxx4bn/jEJ8IgZFZPRYM8ITLsU+y7Q7Jx9NFHx7333ls/C4kRG5ZCis4777xgo2LDPgNkKb8txxVWWCGQOjY8sJo4cWL85S9/6Xn8p59+OmzGR2oBq56EEXBCIqN+SB2SUqqkDSAp5fqpp54KxIbky35MNhjcbrvtQtvQ/m666aZ4/PHH6/2Iyr5PVIULLbRQaB/KYdvjmCERSAQSgURgcBHoGqJjdttfVdnckDCwa0Bs2OH89Kc/re142PBQGbCpYN9jQDEIISyve93rQpyyqSIWXnhhpy8I1C5m6CQ2BvdlllkmECSD2vXXXx+MpqlovvGNb4S4//zP/wznyAziQ42hUM+ABJ1++ulR4sR3SzCY94e1tPHjxwfJxs477xwkZIjbn//857oKCKDdhdmZIH515Aj5U1VVre6EQakSgoP0VNX/bSqJDLH5ohJll7P77rvXaqpLL700bCi4xRZbxAYbbFAbdO+7777BvmnBBRcMbQMZRxLFwVI7K7+VxyFFIH8sEUgERgkCw050DCLzzjtvmOEaVJq4Uxewcxg7dmwYdK+88spYZ511AomgPkJkDLglfP7zn2/ePiDnnsnARBVWfudrX/tarLHGGmGgnzJlSrz//e+vjZ8NkGw2vvnNb8aECROCGmtAHmKACkH4nn322SikpRRLYsHGxPXtt98eVlo5P/zww2syRzrRm4plo402CgO4vCMhaIvveMc7aoJLXel9qrf217TxgiPpDZWUesMAfiWPNqCtWO2H8JAwkhKxexJPjUpaxtAbKVdGhkQgEUgEEoHBQWDYic6LXvSisBKFBIFUplSTtMTg8apXvSoMNossskicdtppNbmw1Fn8008/HVOmTCm31Ctkei46TkhgDFy33nprlMEcienI9oJLq5Q8I9UYg1QqL2oqqh3PgPRsOV19ZrWNdIOfOHY6nvcFBQ5jxGte85p461vfGkgbiYRHMfBSw0hzjUBaNYR4uhZe/epX1zYrzpuBVII6sBnX9vM3v/nNNdFB7hDAO++8s5bMvexlL+upGqy22WabQGhEMmAmKaQudS1QUZHmUGnBXJzAiJkKliSI5EdchkQgEUgEEoHBQ2DYiY6qmd1SR1mRQnoiUEsdcMABkuOZZ56plz2zf7AcnQrJ4EHysPfee9cqAvewL6HiYjuCKMlDKrTqqqvWBGnJJZesy6KiMijJbzkxVYyZN6J11FFHhQHK8uIbb7yxVlntsMMOQX1lEOQfxbMhTY6Mdy+88MJaZWHWfvPNN8e4cePqZdmMnusKdNEfUgREDTYwMxCzdXLuMd/whjfU0qryLkitYAxHdkrqx0bnnHPOkX2GQNIGOwbdMJshsSUXiy++eO0Xh4Gx9sYQW/vx+MgNyaJz9SwYMpKn+kSAtBNYwsCKNWpV+QXxW2+9dd1WLGEXlyERSAQSgURgcBHoCqJDcmNAsDKFhEGw8smKH9VHMAy4lj4jIwxAqQ6saCGpkV9AZCznJZUQP3Xq1LBqxgoitjTyWC1jEBo7dmxtM0EKQ8XEdwxpDTsgS6RJmEg9+PPhOwapUh5JDgNnKg7PqEyqCKqqqqqCDcZvf/vb2hjVwOb5uykwnEXMGGd7dtKcNddcszak9ZwIILwsqZbORoeaDjlkEA4T+HoH8jfDbrvtFogj2yYSsGZaW86prxBvPpi0BfVEYD3/17/+9WBk7tzKLKQRRqSEcBO//vrr1/6HrMhDmkgDxQvyWjG4+eabh/vFZUgEEoFEIBEYXAS6gugMbhWz9EQgEUgEEoFEIBEYXgSG79eT6Awf9vnLiUAikAgkAolAIjDICCTRGWSAs/hEIBFIBBKB2Ucg70gEBgqBJDoDhWSWkwgkAolAIpAIJAJdh0ASna57JflAiUAiMPsI5B2JQCKQCPSOQBKd3nHJ2EQgEUgEEoFEIBEYAQgk0RkBL3FWqsCxIj84lrxbPm2ZvPssrRcn2O9LnCX4rgXL6u3lxUljuV98CXwG2cDUffzoiBcnv7huDf091yOPPFI7CeRWgHPF/vJyrMjnjnrfddddM2S9+OKLax9PPExbll8SuUhYffXVg4sCDgRLfB4TgUQgEUgEBh6B1hKd4svFfkMctdkqwpYQPNqC6ZRTTqk355Q2efLkMNDw82JAes973hMXXXRRcAr461//uh5wbENhXyzpAh8+Bmtej/nUmRUvyn63W8OkSZOCx2Oenm1SapNJz/re9743vvzlLwdvzpze3XLLLcGn0Pe+973aHwxfMQZ7XpD50TH42+yUTxjh8ssvD1sb8LtTVVV9z1ZbbRXK5aPIb7Qp8APEmZ+2YW8qZIWvod7qoF0gLJxGOuftWD6boG644YYBJ164eVC2PQQnk3wY2TLkyCOPDI4b+YhyT4ZEIBFIBBKBwUGglUTHjNvgwZkbj8o8EBtYSSk4EQTVnnvuWW8JYf8rRIdEw8BlcOb4jVdj+z5xMmgzRl5wSTekC5wKcuvv/uOOOy44Bfz3v/+t6EH5cbgAABAASURBVNYG5G1mD88p4wILLFCTF3l597VXEweNrpvB1hxjxowJnqrhvuuuu9bJEydOrO/npLGOaNEf2zmok60tXvayl8Uqq6wSSFwnaSOJWXfddettILRBTi9Vk6NKRImHaUSmuZcVZ4GIue0hJkyYIHuGRCARSAQSgUFGoHVEx2aLvM7y1ksFAx9eZklknPcVHn300ShSGftmIUq2PugtPw/IBjqDN++2tkOgnrH3UW/5R1Ic6QTJDe/IPAMjR7ZBKN6Bm3W99tpr4/e//30dZTuE+mT6H++DJE2Yftma/yQuJDOenwSGl2SetRE2W2A0K0K9x1s0dRXVVZH6INtIkPYo/uijjw57srmXhEd7sj2JNNJE8RkSgUQgEWgdAi164NYRnT/96U9hq4jlllsumu71Z4a5rSEMYtQLV199dS1xKOqb5r3/+Mc/wuBFWlHi2ZyQBo2Ggcn+X8cdd1wYsBHJidOlM1QxBQuDtr2uqPfsKVbim0dSIQM5yVAzvtvPSezUD6lDcsrzIriFJIt76qmnArFRP1JF22rYMwxReuCBB+rtP0h7PvWpT9VbgtgWhC2PvdPGjh0bH/vYx0I7s+2J8jIkAolAIpAIDB4CrSM6BhmqK+qF2YHFAE6t4H4SGsSHlKdZBvWXQcsmnwa9kvaqV72qtve57bbbSlRrj+plQO6rAtLGjx8fSKENLO11ZWD+85//XN8Cd/hQ79l/rI5s/EFESYTWWmutRmw7TquqCiQHBuWJERykp6qqEhXIEJsvaijEeffddw82YggNG5+J08nhxhtvHMsss0ywC2P3RCVGvbX//vsHgs32STuW1lNwniQCIxeBrFkiMGwItI7oMDamVqAaaKJGpWUQb8Y1z6lh2FkYwE877bSgntp6662bWYKNDrXDpz/96Z5NLpsZ/Hbzum3n888/f7BLKqSlPD+JhUHYNeNZK62cWy1EzaLeTQmXNGGjjTaKBRdc0GlPYLhM2kZy0RPZkhOExkoom5JqTwiPequLNleqAUc2S0idOBjAD9lBBKm5tDXlsdGR5qg8ZNs9iyyyiEOwA6pP8k8ikAgkAonAoCDQOqJDumKwNjgXRNhUmEmbfZc4R5IFNhHOv/Wtb9X2JAYWEp2jjjoqDOokPNJLMICttNJKwZi0xJWj3y7nbTyymWFLg4yQSKgDyQw1jDTXVrNZnUXV4lp49atfHc2BXpxgJRZbJufCAQccEFZ1rbrqqi7rVW31SYv+WAWF6CB32tidd95ZLzXXbko1YLXNNtvUhsjiSGsYrq+88srBngmJfuyxx2r1FOK8/PLLB9ytRjvnnHPcUqu+GMDDsI7IPzMikFeJQCKQCAwQAq0jOmbGBhWDUcGADQ0JD2PPEvfd7343GIyuueaadRRy1FzpwgbFwGRgrjM0/hjYm/HPPPNMMEhFgBrZWnlKZWI1kIGXnQ3bEkbZzlUIwWPojQyKo57ae++9g5SCH52bb7452OiUAds9gmX4lmRTc7lPsBRdWpsCYsy+SEBEEJdC3JAb6jz12XfffWvyop477bRTWJqPADHm3nbbbeNDH/pQnY5QwhiBRq6nTZtW+9axvJyBu6X5ysuQCCQCiUAiMDgItI7oICFWwjD6LKoq5IeUgsrFwCOccMIJcf311we1AeiQGkay0gRLhqmqpk6dGgZpkgx2O5aqy98MpB7uYVvRjG/jORskvlwMuOpFmoMMGojVZ8UVVwwrruAjnY0O4mOFEQmZgRtWjHDlL0GZDGwRQvcJm2yySUke7OOAlU/dxAj7xz/+cVB3qie7HT/ALcGZZ57pNEgRkUb1vPXWWwNuEhjIW47PoJ00B1Es0jBt19Jy92iviJF72hxMIBivI7Xslf74xz/2WR1qu7PPPju0wS222GKGfNoVVaiVfBJcc2Dpu+MCwvcuvi3BN8KXEuLMtQW1ZW/P7pshPWQXR21KVVzyucc19wV///vfS3R885vfDJJA2Oy4445BvdqT2MUn6spdhXpa0dqUGpfHlmfzzTevJwPqp6/nvkK6RRF77LFHLW2Xxi0IFbG0m266KUyAxZMsN/GS3qZAkuy9q4vvxOKb/p7fuEd9XvIQApAeu1+/3bQtZbbwyU9+MoyZJf9oOLaO6Fjp8uEPfzioFO64444Z3pE4g4hgZZW8JcP6668fpD7SBOlsKnSizftIJMo9jhrGZZddFuV+cRkSgUQgancNBhukBLE7+eSTw6DcqQ6GFcNrq/QQSCSPw07xJSDKpKzl2jfJsadvlbE3CVhJ6/ajAXnixIlx8MEHx7333ls74ORDqfO5DWhXXnllPbEgFSRpbdoNsgGjcldeuReRpKLnXRs2iCMXECW9m4+kwfCw+tC5SQSTg+Yzq9MxxxxTOx51zpEp6bI8Jl8IpH7fpIutpQmb9oYMce7pHhMMxMc9bQzaxBlnnFFjQDrMVrKveiDJSFEzHS7wgQX8SJJLuomEb7Bcj5Zj64iOF0NVRVrAK6+GLm62wmxkNmv3e+utt16Y7c/GrZk1ERjRCJDm8B5OxUdqReLJUNsg3FlxS+1JDXW6JhfNdBIxUh4SrxJPOlRUzezAOG4sad1+RNJIJkhKx4wZU0+SiiSw+ezIHwkHR5s77LBDM6k+N7Bbude0D+O1nGdzJKrO1KI/Blg2cN6z900q5d03q8A7PSmYOBiS5MHAtdWO2lmRsJJYlHjHYm5Axe66rUGbMGHw/L4B31hvrk1IrbS1TpMKOBWJDalxs/0g0qSAyh5NoZVExwvS8RHBEYG6Hqywzz77BJf9PrDB+o0sNxFoIwJWplFV6kw9P7Wdwcms23UJd999d5jFy0eFTKVDMiHdYK8MxMZ1CaStzqmbSYzYR7luQ+B1Hfkrz0qtQApRrsuRVJk6j20b8veZz3ymJAWplz7Hir8SSQJEjWzLFgbwXDggAiW924/aARvA8pzMCorD0RLnvYt3rR2pM6Lr2oBOnWyCS+KhX7btD2JE6kNlRQKGQFE/u6dtwffAnMKkwbObOCB2pH2um8GEAj5NTKWTAvne2KiSDhknxc8sjOT01hIdL0UHUj4K14MRiIVTkjMYyGaZbUeAjRwbieb3IY5tSbNuBjiDFQN4Kx2plC0ckE+nbsauQ2/e49zKNQTHZINqx2+J7/aA7CF9zefs7dmpsxAZ6ifqcasdbT1j8YMtZzodSurrDOzuYV9IYuReODZ/q1vP4dLZVqjh+npeNjxs4djpyGPwp8Zk+K/dIDOkhLAmBUGIkUBkuinFcG9bgnbiG1Kn5jN34mRxiEkA9W4TU/f4ntgCHnbYYYFIc/chfjSHVhOd0fzisu6JwHAjoDM2AzeAeRYdtA7ZDNR1CezcLLE36yZStyKNASX7DB0y0TyDXflsJlvE9AY4Az77HBIO9gilzG4+UleRdpVnpOLrjcghNGXFI4mWQYsqgnG3CRZMqHYMaHAq5ZWj1XwGdKqKEjd0x9n/JbiQQJQ7nXv+ct15JLHg0qPkIf0RLBhhoEsdqg2578QTT6wXlbCl5JTTveLbFrQT5A1p8+zUU74p0j3XJVhEQhXIRs43A0urONl1sVGSzu4Nme7PxqeUN9KPSXRG+hvO+iUCg4RAmW0XAoLw6KRJHJo/Sb3MMLLE6cytkHG0fL/E93W0CtCAJ/SVp5viqVgMMOWZDM48ZZfrckTkCnbikEDSrgkTJtQeusXNLJBqI5szy9cN6VwvMJItz2J1kFVB5brzyHCZR/sST4rlHmSGkS1phRVF1HgHHnhgkPrtueeeQaJDHVjua9ORepfkinG/56a+837Lqk5xgm+qiY24Eqw49n2xKyXh4rG9TB5KntF2TKIz2t541jcRGCAErGJkM0IKYdCxooNBqI66+RNWIJmZUs2IZ5tCJYXoTJo0KSY9HxAZUg32KvKVYIBjd6DDL3HdfCS9YidiGTiCh/Qga53PbGAmsZFHGrWVwWmdddbpwWT11VcPqgj1l6cZrFyTXiQezbRuPLcyisrJ1jveqTZRfFR1Pi+7LaorRsslzftnq2I5vbaDVJMSkXaQJpJsUO9R5RVD5XJvm4577bVXHHTQQfUjk9ggyQhQHfH8H+qo8t34ZtTbd8bQ23dUjN+ptaqqCqrj528dlYckOqPktRODW9GgU9Q5lqW8OpxFFlkkzBD4LdH5WNHhw9BZGJx02OwsrGbQqfjopAlmaRdccEHtUJAjQXFWqfG/00ZodZIGbvYPBisqA5j0Vhd52ZzwmzN27Nh6M1giZM4UGaPqpM0sqS7cLz8yYOAyWxXX5lBVVVj9SBJhdnn66aeHVYoGIPXTGVNRkfxYTqxdaR8CPx+ddSfhkFe8pejyCco69NBDRbciUN1dddVVYU84pAWh8X1RZ1EjqJs2ZZbOMFse9eSnqVPCYUA3cFVVFQy4rZiRV5gwXfIzKxKxbgFNG7GIxMBsMNdvGKBty6NvYUhcntV3wo2ItlTifGfsvPRf8ktnp8NuiRqHY1O4VFUVTcPucn9bjiYP+g110fatyvPspDzSSt8tTvDNMMh2LmhjCKT74cMgWb8vjYHy0UcfHSSHiFEh2dJGckiiM5LfbqNu2D9SY5BFXsqs20amxMA6HUZ+N9xwQ+0ozyzUR2DQJxrmEI7Y2UoPHbg0QX6ds5UmHC+Ks1QWQWj8/GyeDl92M0mdgVUdZo/qxct2b08EM6Jys216cXYWiM8tt9xSO6tkewJHq0SQHAPdWWedFfx+9FZeW+PYRLCZIJ0odUD01N8svsRpG4JVIKXjLWmOJD0Gb+c6enkFHbfOXHybArLv+TfYYIP6sdUBYVO3Un8euOURyjdZZ37+D3smhtsIAULJ+Fhewbf4fLbWHNgV6U88f3no0rdYRVXiDO7Na/EG589//vPhXqFprE1dJU7Qj8nf5sBporp43967uuhf9C2d7QSZ8a3JUwLfOu4XkMMSjwxauSaeTQ8yVNJG8jGJzkh+ux11I+btiHrBJZ0wi/6SoGPme0FHW+LK0QfDLsPM3UxNkMaQ0uzNedsCwmIWbQauzupkADcDb9ZFnJk5fTiP2maeyIwZGAmZWbiBjbdfnRPjQqJ3ztBIeppl5XkikAgkAonA4CEwIojO4MEz+komTiciN8CTZrC5YDNhhU0nGsTLRTpBpSXdTMEqkM7ZmLRuD4gKKQ6ig6RUVRX2orLMGVFpPv8pp5wSRPCWPpsVUe+5X93lJ72pqufuRxwtG23en+eJQCKQCCQCQ4NAEp2hwbk1v0IsylbA0UBOXMpi3yCuEgbyYovDT4O4EhgEUlVQZZB2lPi2HD0/CRUDvuYzq7u0EkfqhRAxxqVuoVogamefw7aH+kqg6uPQDUmsqqrcnsdEIBFIBAYbgSy/gUASnQYYo+GUTwaDeV91Jc2h4uJqnr8KEh02FcVfhbRio8PWoFlOVVXBVoMenbSnmdaG86qqQv3gU8gNyQwVFrJS6mCJqzwMHhlmM+pjvK05syIIAAAQAElEQVTOjAUtdaUjp/az+oEaj9qq3J/HRCARSAQSgaFDIInO0GE97L/EqM0KIE6lmg9DtVJUTxxtsT+Rzsj22GOPDYN65z3S2Z8Y4J2XYMWJ1RGW2Ja4thxJchhGIjdUVciOejPgbhIV5IYxqaWy6kbVxUaHTxPXHOMx8GaUDYdiwyMtQyLQlQjkQyUCIxiBJDoj+OV2Vs2qBaoVK6L4PZHOeNZqLIO3a2qZk08+OahdXLM3Ya/CuNZ1MzA6LoN7M95SYmqvZlxbzq1QoJ7j5I0Bso0VOShDZEodEJtPf/rTtYMycVbXWN5p2atrgbTHUmArQBgni8uQCCQCiUAiMPQIJNEZesyH9Re/8IUv1L9vpRQjWrY0nFM5l8AvDhsce8qIO+2008JgjRjxXcHnhUGcrxj5SyhGue7htn+PPfYoSa068iiKxPDzwT4JWYGVSnByt/POOzut3c1bqqm+VFRc0JPyXH/99SGOkTbDbEvv6xum/+Hy3wo2DtO4/retwfToNv7PZ04EEoFEoDUIJNFpzasamAclmbFiyCAs8CZKXVNKJ9Hgn4KNjnQ+ZKQLpDzihI9+9KPllvpooBcvWEJNelQntPAPckIqhdRx6FaqwEibzU25RnTUF4aclokn/REHY9fNABMr2aQLzbKb+fI8EUgEEoFEYOAQSKIzcFhmSX0hkPGJQCKQCCQCicAwIZBEZ5iAz59NBBKBRCARSAQSgcFHoBuJzuDXOn8hEUgEEoFEIBFIBEYFAkl0RsVrzkomAolAIpAItBeBfPK5QSCJztygl/cmAolAIpAIJAKJQFcjkESnq19PPlwikAgkArOPQN6RCCQC/4dAEp3/w2JEn/GDw5OvwBmefZlUmFdkcQIvwOI4zHMtFC/BnOe57gy/+93vQhnuK8Fv/eUvfymXrTvagJOvm2nTpgUPyb1VwDYQnVjAqORVf9iUa8cmru6Vzuu0tAyJQCKQCCQCg4NAEp3BwbXrSuW/Zc899wxbEhx99NHx+OOP1894//33Byd4u+22W9ig0l5XhxxySHAkaNNO+1rxH2NQdm0vp+222y6cC/Jec801dVn+ID2HH3549OZHRnq3B2TlyiuvrOs3efLk+MpXvhIw6XzuSy+9tM4DA8F+VgikfLBuOhoUJ3C+KG8J73znO3stW96hDflriUAikAiMXASS6IzcdztDzWzJYN+qN7zhDbH//vtHcXC38sor16TGRpQIzA033BA8+J5xxhlhvybbPJBq2NPKNc/Ju+yyS53mGqFZddVVe37r17/+ddx99931/lg9kS064RSRx2J4wODJJ58MzhObVSDNWXrppXswsIkpJ4tLLrlkTzabg/ZcTD/hKRlJhJmARE2YMCEWXXTR6an5PxFIBBKBRGCwEEiiM1jIdmG5ZePO/h7N7ubICtWLfMjPTjvtFPPOO6/LOpQ/t99+ey0ZQoLE/fvf/45bb701SDdctzFQKdnKwd5fL3nJSwIRtKUFslfqA8eVVlqpXMZtt90W48eP77l2D1LTEzH95BWveEU0CSGJ0BZbbDE9Jf8nAolAIpAIDCYCSXQGE90Wlr3aaquFQdlWEBdffHGtWjGwV1VV1+Zf//pXHYcQXHbZZVFsfRCk4447LtZZZ51eSVF9c5f/UYf77ruvrv9LX/rS+mmRHaqrZ599tr72Z5555omyySc8brnlloBb9PPvRS96Uc89bIBgu9VWW/VzRyYlAolAIpAIDAQCQ0B0BuIxs4yhQmDs2LFhPyfqrc9+9rNBMmEvp0JoGCcjOOxM7INVnusnP/lJvOMd7wh7YpW40XBktLzYYovFwgsvPMvVveuuu2LTTTed5fyZMRFIBBKBRGDOEUiiM+fYtfJO0gRkpa+HZ6NCTXPEEUcEWxKbVB5//PHxxBNP1Le8/OUvj2Kj8+lPf7qOY4B80kknBSnF9ttvH4x5r7jiimDnUmdoyR+SGqQFPqQ7VHFUeCRaRYLTWZUHH3wwSHVsltqZ1tf19ddfH6usskpfyRmfCCQCIx2BrN+QIpBEZ0jhHt4fY4hsEC/LyMvTGMypq1wjOmxSnLM7sVLrla98ZTz99NOiZggbb7xxLcGZb775wuosEiDh3e9+d0yYMCH22muvGfJ3+0VVVcGg2JJvBscIDGzGjRsXCyywwAseHxl65JFHAq5F1fWCTB0RjJIRyWIM3pGcl4lAIpAIJAIDjEASnQEGtJuLI5XYcsst6yXT9957b7Czufbaa8NKK6urPDsfOGeddVawO5H+wx/+MBAdxrmuESU2K/IxUDbAk4RYPTR27NgYOz1YcSS87nWvU2SrAtskUq8777wzSGvUn1SrqqqwAus//uM/eupjKfovfvGLFxhfI0fIJKJkGT/iVG5i6P3qV7864Fni8pgIzASBTE4EEoG5QCCJzlyA18ZbDzrooHqFEAkMO5ubb745SGEKKbGa6KijjgpLrKXzn2MpNOmF6ze96U1xzz33BF8xfdWfBEToK72b4xE0Kjnk74tf/GJ88pOfrKU8nvncc8+NCy64wGkdqqoKuMGkjnj+D0x/+ctfxgc/+MHa3gnpeT4pkEL+c4oErcTnMRFIBBKBRGBwEEiiMzi4dm2pVDB77LFHbX/DBofzwLI83ENTw7AfKTY6SI90Qf4S+OWRv7fwgQ98ICZOnNhbUiviFl988bCCjFH2iiuu2PPMn/rUp2Kfffbpuaay23vvvXuuywmfOu6F1aGHHhqLLLJISaodNvaHXU/GuTnJexOBRCARSAR6EEii0wNFniQCiUAikAgkAonASEMgic5Ie6OzX5+8IxFIBBKBRCARGLEIJNEZsa82K5YIJAKJQCKQCCQCs090ErNEIBFIBBKBRCARSARagkASnZa8qHzMRCARSAQSge5EIJ+quxEYdqLzhz/8ofbnwkdLMzz22GO1x9n+4OPLhQt+/kr6y5dpzyHAD0wT43LO1wsnduWarxjLyd3Fp0zzPltBcKZn2bn8HObxrVPy/v73vw/+ZVy3NaizdjVt2rSAS1/14E+oYND0lSM/Xzowcl4Cp4vylyC9876SN4+JQCKQCCQCA4PAsBOdb37zm7W/EV52LcXlq0WwBNpg0V81+XI5+eST+x2M+rt/tKWdf/75YSNJy5sPPvjggLNgqwYOAg8//PCYNGlSeBc2t4QPMnneeeeFJdP86SBBnA3yx7PEEkuEZegc6/32t7+tvSNPmDAh7rjjDre2MiBptrCAi/p+5StfqTcx7azMww8/HJwvyvfxj3+83v6i5NEud9xxx1huueVKVH087bTTejB3H386yFKdmH9GEQJZ1UQgERhKBIad6Oy888410VlvvfXijDPO6PHvYtC1r1J/YBiwjznmmJh//vn7yzbkadz833TTTVGkIkP+AH38oAF52223jdVXXz1OPfXUHqw5DOQv5qtf/Wq9P9Wf//zn2omgvZ7s4cRpHvLDLwx/Oquttlqdz8+4twzoI8Hbry0wOEtE/rRH0qzrrrtOVWcIX//618MeWDBB0F03M4wZM6Z5GdqEDVLlF5AopJBH6Rky5kUikAgkAonAgCIw7ESnt9o88MADgeTYXqC39G6Ps63CFVdc0XVEpzfcqK147kVopBu8d9ttt6C2+cxnPiNqlgLpzkc/+tFZytvNmaiVXvayl9VbNLzkJS+JlVdeOez91anCsp0Gtau6INq2dXAuuAepcV4CT8irrrpquYxLL700tthii57r/k4yLRFIBBKBRGDOEegaokNFYnB96KGH4sc//nGwZ1Atew5tsskm8fnPfz7e/va3x1JLLRX2H6Ji4H126aWXrlULrs2UDbjc8lOtKPNjH/tYvSHj2muvHWxLvve978VCCy1Uu+Y//fTTw95GX/jCF8LMmiqC+/5x48bVRIsNBakGe43NN988lL3CCivEXXfdVf/m2972tjjkkENi6623jgUXXDAuueSS+PnPf157zz377LPDsz3xxBOq0VXhr3/9a6ibQd1WB7BrPqB6fe5zn6tVUQbk0WIDRQJHZYeUFJKN7FAvPfvss02IYqeddgr2SaRkF154YS0BmyFDx8WLXvSiQI5EswG6+OKLazWi6wyJQCKQCCQCg4dA1xCdn/zkJ3HkkUfWNh8/+MEPempsoCXhIfo3s6auQlYM1FdccUVPPrY+VAxsRX70ox/F/fffX6vCuPK3A/dmm20WXPYXYmN2zRaDagLJce/VV18d7FjYq2y66aYxZcqUQApOOOGEsJ8RNY2jMqkqir0KsnPAAQcEcoOMUadREbFdabr/73nYQT2ZeeF33313HHvssbW9iDr3dgebHGQHYUQ2e8szmuO818MOOyxuu+228J7nnXfeWYYDUda+ZvmGzJgIJAKJQCIwxwh0DdFp2ujsuuuuQX2gVtQoNpr80Ic+FAaTMWPGiK4lO2bV9cX0P9dff30gQdNP441vfGMo76KLLqrtKN773vfGNddcU0t0fv3rXweJBULCPsjeTh/5yEfita99bZh1+22qiFe+8pX1BoykQs8880wtBVI26dIOO+wQu+yyS7BXQaCQJ/mrqpKl60PTRscGln098Cc+8Ylgj3P88cdHN0qm+nruOY234eZiiy0WVpGR7pDmMYinzivSmFL2rbfeWkvvSBGp/Xrb86rk7Txqq/YT64zP60QgEUgEEoGBR2CegS9y7ko02LBxYPNw1FFHzXJhVAvN1T5IkQFIAcqjzjIoUYu9613vEj1LAfmhxmA4OmnSpJg0PSBUBr9ZKqDLMyFpsCHJMrB3Pi6JDtUNqUVnmmsE0XEkhKqqYskllwxLvqk5qewQHarMBRZYYIYqnnLKKTXRhZ+VZwg0Sc0MmXq5IJlk74OM95KcUYlAIpAIJAIDjMCwS3QMouxxSE7Y6EydOjX40GEIi6AwlqU+ko/RrIFHfnFNLEhnvv3tbwd1kzJuvPHGoE6Sh50FSY2VXEsssURtK2GwUSa/MNRXgoGNOko8KY5BCfEiuTnxxBNrFcUvf/nL8DuWWLtXHkapnokkwP1m/86t3qF28wzdENTRiirYUf3Bid8bS6g9szSSG/nk8cxvectbguqvrKiiInz3u98dbHu22267WgomH6KpPCQBBp12P/K0ISAubGio65A79mDrr79+VFUVcFE39VhzzTVr9Z86I9iIUCHQ2qi2qj1pvzBxj4AQIfEFT3EZEoFEoKsQyIcZYQgMO9FhJ2NQgCvbFnYPglVX48ePD+qnhRdeONjNXHXVVbVaASlig+MeRIT0hqqKxMaybvdvueWWYUCWR7B8msqGCsw1wmKA8hvf+MY3gr0PNdR3v/vduPzyywM5MvCzHaKmWnbZZePoo48OhIdqix0R9YOB67LLLgsD2tixY4NhKlUbadKvfvWrWk3m97ohkNp4XoPyl770pdpGB1YGZDhQ71kxBmtErjzzxIkTY5tttqkHe8bY73jHO+pVWeecc07JEmyjEMl1uYfV1QAAEABJREFU1lkn2FAhqz2JLTqhGqXOQ+S++MUvhqX1pDyqcO6558YFF1zgNPh5kgY/y/JhWydM/8OgXfv64Ac/WBu9l/Y9PalWh/Kfw+DZdYZEIBFIBBKBwUVg2IkOGxwEpTMUQmFFlDSD6KTpaiPnAmkPaMzAEQ/nVErSBMa01E7iBTP05nLe5ZdfPkgy5GWLwheKc/YopEPOBfY9ZfBzzenbhAkT6pVVrjksJC066aSTwjVbDTZAyvnUpz7lp7smWCHkGTuDFW2LL754MKAuaYy1mw/+4Q9/uL5kjE1tgyjVEc//8R7KvdJJzp5Pat0BFgzOrepDkEsFvM999tmnXMb+++9fv3P1bqqitD33iteuGC6Xm7Q77bRc53EEIJBVSAQSga5GYNiJzuyiQ6VkgDGQso0466yz+i2CtEVezt+Qln4zZ2IikAgkAolAIpAIjCgEWkd0qF1IIKhKqJ7YO/T3RqgP5OUzh2Smv7yZlggMAQL5E4lAIpAIJAJDiEDriM4QYpM/lQgkAolAIpAIJAItRyCJTre/wHy+RCARSAQSgUQgEZhjBJLozDF0eWMikAgkAolAIpAIDDUCs/t7SXRmF7HMnwgkAolAIpAIJAKtQSCJTmte1dw/KP82HCp2Bg4Qp02bFvziSOMjiMNDv8hxoC0LxAscJPLFw5+Ma5tTcjrIj5Br4Z577nFrawOvyHwpnXfeebW/oL4qYhsI9eU7icPIko8PIvfaD41jxhJvjzH5S+B7h3PBkp7HRCARGAwEsszRjkDriQ4/Nny6bL/99tFb4Ol3r732qtOs1iovnNNBTt/cwwuzAckqLtclyC++3DMSjt///vfDknyeoUt9bExZ6vnTn/60xspWGSXdkUdkxxKQGzgZ4DnE43BRGhL0sY99LDhLdN22gNhZocfjMc/XfC1ZtddZDyRH+xH/i1/8Ik499VSn8fDDD8dNN90UvCtzHHjwwQeHsiRyRulYApzKeZuP1113XfAltd9++/VZDT6ItJcSON+UmXPKEufIp1XxJs7pojjhoYcekr1V4cgjj6y/Jf1Kbw/+yCOP1OnqJ+y+++5hkiEv7+ziBE5T7b0mXvCt7bjjjjF58mSXrQr6FZ7W1auvB9enSBf45NJXy9tsD9KUo++SplxxQlv7HvUo4ec//3ndNk444YQo9S9pncf77ruvzuubgp10TkxhYRslk1NxAue44vmva+M3pQ5zElpNdHjptdeSTTnt00QK8dnPfjZKsEcV9/v87vBUzO9OGbB5Xj7wwAODh1pelDl14/SOB2BefT/60Y+GLQ40iB/96Edzgm3X3cNRHQ/IPP3CjANGgSdgXp7Vmedoy/CRQx6T4WQLBJugSl9wwQXr/aA23HDD8I9vIttHyKMsDvXsnWWwl962oJP8zW9+Exwkqq89zXiM7qwH/0wwU+ett946bDkCL1thbLzxxiFee0O0dVqONoQVL/CezYGlzWA7y27TtW8DgdNelllmmeiN7JBkqXv5LnksRyKRbcSSh2lpSA68kUTfoO/SO3j/+98fdnsXNxTYDMRvILFcYSAvCEkZgJplG6A4GFV3QR8FF3lg5JsTr89CCMXbVsbgru/ynSJT4tsQbKlC+ul70R58Y709N0/0+mN1991ccskldTYTjn333benf/ftwAsBgpH8b3jDG6L0TfVNLfyD6Gvv6uPxTbYcewuIICwRXwRGmzPp0k7c7/tydK+Jl2/ONcevJvj80kkb6aGVRAd58bLsZeVjQXa8YGHs2LExdnqwbYSZuYYvGKB1PjoIHS+vyTzg6ix8VEiRTtYgXY7iMWoqBqqZkdgYDOo2o1R/9YMLSQX8ED0Dt/j+gu0xkCd5ECPvw3kbAxUeDLQLBG655ZYL2410dgjaGKmOAcy+Xrxh247E4ASLqqpCe9T24KAtyeNcMLOytYbztgZtAwm0JYh6q4/Zo++rWScDEk/bMPPN2TOM92g4L7300qH9SPONwhx+Bi4EZ8KECfGBD3wgVltttXr7lWa53XqOwBickRge3NdYY42wnUjn8/KmbX80dbefHiInP7JNwsoHmDT4Pvroo6ENXn311bHWWmvFUkstFTDUXhGIzrK78ZrkD5nTFjbffPPQ55ggNJ+VpJhUmJd2dTf5NFjLY3D2PYrXT1P72nDXVj086Ivn1d63W4ih+9oWjDnIv/rYloh0Rh/TrIeJve2Tzj777CCl18b0MeIRI9oI95O0Ij3aCC/vJg/iTbbgRzjQLHeknreS6LCBwO432GCDPt8L2xHstZnBTtxe9mGHHRZlc8Zmem/nOqNFF120VkdoRL3laVucjgR5mzJlSkyZHjqff7HFFqv39SKVoRo0a+rM09e1wU9HQ9rRV55ujaceIM5FUAy45TkNMMh1uXYk+UNitt9++zDYI93im4GEh8QQCWjGK48dU9tnnt71H/7wh4CD+r3sZS8LRNdg5boEnsmluUZyqDoNVOKkiRd+9rOfhbajDOoeecULpLe33367064Pnh1BKw/6lre8Jcymy3U5InjlnJRGvZE9AxbpGDuvqVOn1pjaH83ABPMykZDf/d6BY7cHkhfEtjynNkDVW64dESF46Xe0I99d+X7EyyP88Ic/DFv/INFIsDhBHAJVMBLXpsA+0P6KyKDn9m2ZeHcSN+1A321vQW3HxEn7QJCRF9+Y+00s3E+q7KhvE3/ppZcGaaMJreuRHuZpYwW9NB6RdZTN59f4t3/eVodhrY+kme78c5/7XL0xqL2IekuXpxn8BrLjN2clf/PeNp8jKvZqMrtgjDyrdTEbNSNvdkqzem+b8hmQ1l133XoQshFqb8TZrN7s2wyqWTcDtk1om3FtPPc96FBtqlueXwcslOvOI5JNGmGA6kyzkWohf1R7N954Y5A4ykdS5NiGYIBG2JrPOjMyQqVDTece2Hz5y18OM3Y2FgY90kC4stcwkMsnmMx1zvbFd2MglSrkzPNpPwZ25yUYjA855JAgLWVysNBCC4X6l/RyRAKpfst1OfoNg782VuLadNROvM/O9kNy3KwHDQPJofGJqpcEx36Lr3nNa8LEnEpLfvhKdy6QAFIvU1uZ1CFF4kd6aB3R8XHoSHQGRN/NF0SMSaUlEAFXVdVMrs/f/OY3ByM2DBgxqiNn8sfHSaxsxj+TrDNN7oYMiEjTRqevZ2LTw0aJWLgMOH3lFU/iZdAnlm4OftLaELQnHSSJi86BREbHYxb0kpe8ZIYqmBHpbAxIW221VQjNDKRh7qFeaMY7J0omanY+mgJMEWGzdh10s+6+L0bd8BfP5gAZhBOpj44c+ZE20gLDY7P45ZdfvqdqBivfqYHqtNNOi9mRqvYU0tIT5Ia0BwYkO76zZlVMKhgfk3I14xFBkg/9TzN+JJ7rj2FkLJs0aVKYkJIs669MsEjsfTcIMrVVkYrB9OMf/3iQSJvsM1sYifh01ql1RKezAs1rA5LZs2CQ1pmacTbzsKNgQMuegOqr6H+beaqqiqqqmlEj9pzem3RMo0dUOiu60047BemFFSLS4EWloOOdb775gppLvBm3wZ4dFGLoWnzbAukdQmsGhehoP6uvvnptb9Osi5UwyDbVlEFZW9NpuIdqlZEuMqm9wUy8+2EHNzMv120OyCy8yrtGZLQLRKa3ehm0dcTsLqpqxu+LTY7OF9l0r5m9WSkCpOM2+DF0l9btgd2Ngag8p4HagFOuO49sLJp1N2AbmMzQ2VuQ2pBEa2tUDYi4Msz8tS/xrrs9ICZISnlO35iBt1w7mshaEMG+kjSLqYHVRNJKMJmidiltpcRTfVJZUXOWuLYdkRP9MZWlZ/e9+K7Euy6Bal3wDVZVFdqcfkvQRrQNasF999039MlFZUgayB6MEbsFJMouZQ7jcdB/unVExyzZi6KP9FL7QshskLoJ0+0tD6NkRIdxn3Q2E1bSKBNhMoiJFwxUBvTOD0tamwI/OmZC1AcYPx2vcOyxx9aExayStIEKoXTUOiJLgxEidbX8daONNopzzz03dL7irBJguGwGalahTDNwaW0LVG7aF1WCehRRuHpQ4RmUnFMzwEAe7g1IEBlKssUgFtYJSSPxsRzY4OU+4uIxY8bESCA6CJt2wfYCwdFm1I3xrLp2BpILhqPwbabBhprKSqNmvG+RvYtVJDpm32AzvVvPSWa0GwMNVRN8GPj39rxwo8qcMGFCTzKpl7ojhvoiq2qQJX0S+x2SL8TZd8yGw3fXc3MXn4wbN652O6FebCzV3SSi+cik9TATTB6ofpsTMPfow/RBzft8m4zg9eni9XWObQxUcswGPLtJE9y0KdclkG6aDKizOG0MGSr9CsLIj5fJV28TBGQZSTJRcf9ID60jOl6I2Y6ZNtZKx8jy3OBMXVBsdMoRa+XcDonR2RR1lVmQDtQApUwdKgt1xMgqD1IM8X6DXtNvIlni2hzor0lpfCSlHtQrRQ+O+RuwS5qjNHrdqqqC7Q7io4MSJ13wcRa1Q7l2bFsgjaKuGzt2bOhoiXmRvc56GHyQGPE6l2OOOcZpbbOj/YgTQboBs4I340B2KOVanrYGZM7KIFIqbYqRv9Uy6m4gM5PUUZf6sbtgZOvbK3GO8hDD66hdCwi1bxjGpK8wFd+GoL14bsvl1UHdfC/6EhMM0lMDkbqYuetXfHeuBee+MfcKBisqCu2R5MekTDyph1U0pa9ybzcHfY93r5/1/FZIIb3IzQ477BAIDKx8f67lMUmwsrbUy0Rh/vnnr1edlTg4yI/caHPuM2kr6W07esf6B/VgYgEPdUBsrcYyHplkiLciSz5HbkJMNA499NCArXaiDepzEOerrrqq9rcjP2kZsphEB7JdGohAdYqWK/r4+RwgScDq2eeU4MWOHz8+sHxEh18TIr5SLS/5xBNPrFUzOgyiz8mTJwfVlk5cPjMps1DlVNWM4nbpbQrUefS5ncESRgOyOpY0M/Vm3Yo42CBGRdXsfIjlfZzlXkezkOb9bTqHBUwM2tpIeXakWWddrrUrdRXcQ41gpuW6BD5D2IWVe3Q6VDfluu1Hg7rVjKR7bEmoGtSJ9MVKtCb5RVi0HenNQAJEYtOMU87hhx8eCKTvkci9md7t5yQ0yA77NiTF8wtILtJW+heEGk4GrlIn5+IY3OrL9Eml39IeXSOVJmWwK/d1+9EgTLpgkCXtLBI8qhaDs7rAyEBMlVnqvvbaa/dUzbfEWLknYvqJb5VtDjzcI1DZTE9q5X99yXHHHVf7C9L+y2QcKVQv35y+Rn90/PHH1/mMbWWCigBpH4J2I69AOgYbwTfbxLWVQM3GQ7dSomMG5CViu2ZHPiAdRm9BXg2npFl+2MTHvRoBBq0TMaMonRB9MimQD8n9zfvyPBFIBJ5DgETBt6Mjfi4mamkYUmfQLnFUL83rEi9OWrl2pLLxzSHWvk9xcxiG7TZ4qIO+xUNUVRX6IvZGVfXcpMnAru5V9dy1fIIJnHuFZn5pbBnJlfYAABAASURBVDjEI5Ou2xS8V23F85fnhoFr0lRx5Vocm5vSH0uDC7ycl6CNyNsMpaySp21H45T6IIHFZELdtSn4qI+xjSqz5BMnaBcwhrVroaqqgJu8ArIkfrSEVhKd8nIsv/TRk7iUuIE88sVDLNr00zCQ5WdZiUAikAgkAolAIjC4CLSa6IDGUkKs1/lAByJns9KBLjfL62IE8tESgUQgEUgERhQCrSc6I+ptZGUSgUQgEUgEEoFEYEARSKIzd3Dm3YlAIpAIJAKJQCLQxQgk0enil5OPlggkAolAIpAItAuB7nvaJDrd904G7Yk4trN0szNYwWZpPV8U0nik5bDLgzD0toRRvMDnBa+/7Jdc8zPDXwMfGK532WWX4M/CvW0NvJFafskXBVz6qgffFeq81157BQ++zXyWcEqz1NPSV2kcT4oTuEMQlyERSAQSgURgcBFIojO4+HZV6bYk4EWVV0wExWAsWMLJ+yhfFvzl8OFRyAoHX/zJWNooneE3P0Z8F/GFwX+OexECZW288cbBESNHaNHCf08//XTtl4IfFM65zjjjjODQrrMq9rriz0Od7cfkWPIggiWOE6/iU4hre/m4K7BhIdJY7sljIjBaEch6JwKDjUASncFGuIvKR2j48eBjwmoy/hT4ZeDcjqM710gMT74cU3HZz4eF+4oPBj4c+Hjgv0HV+GPgvJH0w/0cVHFLz2O19LYF7vX5bVEPzsk497O3DjLXrIutHjh+U2fYTZs2rU7m8ZY7dj4skEI4w0si0iQ/Msh3DMmR+AyJQCKQCCQCg4dAEp3Bw7YVJRvYSWOaD2sfJ14zbevAM3QzrbdzJIjER5r9jtxrQHfdpoDMcLPOERdywlEXsqNOpGDNunDpb580e83Y7oCXUumcTNoAlffXAw88sHZrL15AKh1vu+22IF1DplxnmB0EMm8ikAgkArOHQBKd2cNrROS251DZE8aA3FkpThi5mSfR4dqfbUlnnt6uqWMM7vYTKzY+veXr1jgbJbK1IW1Bcspz/u1vf4v//d//LZf10bYEVFHsbexFZK8nCaRf7HumTJkSpEEHH3xwFDWeMmwWyqYHRvB1T4ZEIBFIBBKBwUMgic7gYdu1JbMZYYfDXsTmlL09KHWUvcNss2Fw7i1PZ9zee+8dVF72+GGn0pk+HNeD9Zvc+iNDbJUYb99xxx31T1H1Ue9RETLuJvn5+c9/XqfJT/Kz3377BbIJpzoh/yQCiUAikAgMGgJJdAYN2u4tuGmjs+6664bBuVM14+lJJ6zUYlvywAMPiHpBcG+JpJpht2Klkd1yS3xbjoiIjQVJo/7nf/4nSHieeOKJsJfOvPPOO0M1bGBJ1UXy5ZxR9t///vcZ8sCmbLQnoaqqeq8jG30yBicpEp8hEUgEEoFEYPAQSKIzA7aj74IxMcNh+3oZuDsRmDhxYliJ9fTTT9dJ7FFIev70pz/F6173umBwWyc8/wdBYIhrddfzUa06sC1CWBgKUzWxz2FzRILTrAgJDnsccVZYIUOdq6gszXf/29/+dtl6QikfyeyJzJNEIBFIBBKBQUEgic6gwNqdhZLOnHfeebXapNjosDE59thj401velN8/etfj6OPPjpOOOGEYDCrFlQwllkvs8wyLoOxLh86X/jCF+JrX/taHUdNJU5ZbFJWXnnlWHrppeu0tv2Bw1prrRVHHHFE7LrrrjFmzJhwrR7HHXdcnHjiiU5jn332CbY56ixQUyF3d955Z7gW2DetueaagTxNmzatJ56NDiPn9dZbry4r/yQCiUAi0LUIjIAHS6IzAl7irFbBSp8LLrigduh35JFH1v5i2OmcddZZwZbEsmdqGNeWmZdyDf6f//zno6qqMHDzscO+pAzU8jJEVhZj5A033DCox8r9bTpaPWa12eGHHx7UUkiJVWXqsN1228VWW23lNKi4vvOd79QYcqi4xx571PEIIRwEWIwfP76OJzkTJ3zmM5+JYrxcJ+afRCARSAQSgUFDIInOoEHbfQWzuSFd6AxsaxjQWm1V0jqJyoILLlhXCBHgG4avnDpi+h9LscW5lzrL8uzp0a39DwuYvPGNb4ymygp+TVUdX0LqLLhHheV3LSy22GKi6iBdnAC7tmNUVyr/JAIvRCBjEoGuQyCJTte9knygRCARSAQSgUQgERgoBJLoDBSSWU4ikAjMPgJ5RyKQCCQCg4xAEp1BBjiLTwQSgUQgEUgEEoHhQyCJzvBhn788+wjkHYlAIpAIJAKJwGwhkERntuDKzIlAIpAIJAKJQCLQJgRGNtFp05vIZ00EEoFEIBFIBBKBAUdgRBAdTvDszSTYLJF/k/7c6//4xz+OzTbbLLj6L4hed9119SaMyy+/fFxzzTUlesQcp0yZUjsFhFEJ7373u+v6XXnllfGud70rllhiiRnCt771rTr9L3/5S3zwgx+cIa3ktbmljTDrjNP/+B3OCHlOnn7Zyv+PPPJI8Fr8lre8JbSl3ipx6aWXvgAPDgSbeW+66aba6WAzTjsr2L3vfe9rJuV5IpAIJAKDjsBo/IHWEx0bJp577rlhULY9wRe/+MW47LLLorftDLxgexdxlvfTn/60J8/9998fl19+edx+++2xyy67xA9+8IOwLYL8IyVMmjQpbrjhhuAHB1Y/+clPwv5VHAVutNFGgSz+7ne/68GRQzvbPxjoOcyDKYeCq622WsC5BN6D/+u//quG6ZBDDgmOA22fYJ+oOrJlf2zrcNBBB4V2dPfdd4etMZDnZjXUzfYQBYOHHnoo+MxBFks+mHA2CIsS9+yzz4by77333hrDq666qiTlMRFIBBKBRGCQEGg90fnFL34RnLFxdgcj2xLYNNGGiq47Aydvdo/mtK2kITrzzTdfvbnlbrvtFlz9uy7pI/HI8R2Pvgbi++677wVVtKVBVVUB3xckPh9hoCf5KNs92DbhhBNOeD61nQf1HTNmTHAYiBSussoq8f3vfz8Qm1Ij0sJ11lmnXAZC1JTO2MsKQeJJuSfT9BMEB9m85557wp5g06PyfyLQ5Qjk4yUC7Ueg9URn2WWXDWoSBMUAYpCikjFIeT233nprzx5DU6dOFTVDkH7yySfHtddeW0tzehv0Z7hhBF3Yx6qv6nzlK1+pJV4bbLBBX1mCJK1s9tlnphYlkNT86le/CiSXh+N55pmnltSQdP31r3/tqQlP0DbxLBG2xFhxxRXLZS0RRP468aUqXWmlleLUU08NZLvnhjxJBBKBRCARGDQEWk902FGQItg5ev311w8z7aeeeqoGzEz7m9/8ZthfaNVVV63tLuqExp/lllsuqGgMQDaktNliI3lEn1LTGbDt21Qqap8qNiSkFnbeftvb3laS6iN7HumCjT3ryBHyh7oTcbM9A5JTqkX68s9//rNcznCUn0RxqaWWquORaXnZetURjT8TJkyI3XffPU466aS48cYbgz1TI3lEnGYlEoFEIBHoNgRaT3QMKsjKnXfeGfvuu29tdzJp0qR48skna4nDhRdeGGuvvXYcffTRYcZOTdV8CWbnCy20UCywwAJhbyMqL8aoU6dOjanTg0Gumb/t58hLITPzzjtvbc9koC71uuKKK2Lbbbet8aPmK/HluOmmm9b2JdRW7HhK/Eg4VlUVSI52UuqjfSE9VVWVqBmO8ESQxkxXd8lL9eV+tjh//OMfA2F0Lk3bUhZyiZxfffXVM5SVF4lAIpAIJAIDj8A8A1/k0JbI7qGskmJESuLw9NNPB3WDIzsUg3IJVF39PaHByU7dhx12WAiMl/vL37Y0O5EzMC542KiyWQfkh4HxM888E3bwbqZ1ntvlm+2Kwd5A3pk+8+vuyoGEkG4xJGaMjrAgzGPHjg2qrM6nRXAefvjheMUrXhEMtl2TLDLc1nYYfSM+F1xwQTRVX8phI9WJvfgMiUAikAgkAgOLQOuJjqXPBpJp06b1IGN2TVJDcnHOOefUkhmJd9xxRzTziesMiy66aLDZYdQsLL744p1ZRvz16173umBY/NWvfjU+97nPvWCQLgAw1LV67bbbbitRrT9SXSI6jz/+eG0wTFLI4LrYfDUrSNqHML7zne+so0lsdt5559BuBAbdK6ywQuyzzz41GaozTf+j/LPPPjssw59+mf8TgUQgEUgEBhGBVhGd3nCgdlpvvfVijz32qP2aMEq2rJfNBDXL+PHjwxJqNiUGn6qqgm2JAcxyaf5QPvnJT8bXv/71WGaZZWKkSXAKZlOmTAlG2qQvJDFIX0kjBdtyyy1r1Z40Egm2OQySLcV3Dj9L0OV1XoIVam9961vjRS96UZx++um1+pCEzSBfym/TEbHdfvvtawN2BMZyevZd6rDNNtsEIuNcsLrqZz/7WfRmjyO9MxTM1lhjjfjwhz9c+23qzJPXiUAikAgkAgOLQOuJDvscqhb2DmbXnLa95z3vqZeKm4WTSDz66KMh7ZRTTgkDGf8lfJpYccVxoCXWVsSwyVlkkUUGFuEuKW3SpEm1Oo8Ugv8ckobyaAghf0RUL9I4B5QmHk6kYPCDEWmE8xL44ykDPZLJHkUeGCujbYH6CiHkVPLBBx+Mj370o7Xdjnogw2eeeabTOlBZMXZn11NHdPxB9pDGEl0wu/vuuwOBKvF5TAQSgURgDhHI22YBgdYTnVmoY2ZJBBKBRCARSAQSgVGKQBKdUfris9qJQCIwChHIKicCoxCBJDqj8KVnlROBRCARSAQSgdGCQBKd0fKms56JwOwjkHckAolAItB6BJLotP4VZgUSgUQgEUgEEoFEoC8Ekuj0hUzGzz4CeUcikAgkAolAItBlCCTR6bIXMliPw48Or8jFl4ujZdR+j2+cd73rXbUfoiWWWKLnyLOvdE4ZLTlvppXz9773vfHYY4/JVvvRET9u3LiwwWod2cI/tgDhJNA+arxI91WFAw44oMbK8vqHHnqoJxuv3GuvvXbwy3Tdddf1xJ944ol1fhgJvDDzrNyTIU8SgUQgEUgEBhyB4SQ6A16ZoSjwtNNOi/333z9sETAUvzdQv8GPzg033BB8CyEw/N/wmcOZ4kYbbRScARqgpfH3Yh+riRMnhoHe9gb8wXCwyP+L9BL4MOI35/vf/35UVVX7K9pqq60CAbI9xEA9/1CVww+QrUS++MUvBn83F198cfzwhz98wc+fccYZ8Y1vfKOur32rkB6ZOJx0ftJJJwWnjBdddFHw12SLjJe//OV1ftj96le/Cn6JeGJ2X4ZEIBFIBBKBwUFgVBMdjgJnNqO2R5HZehm0d99997AX1nzzzTc4b2SISrXXkn3AYHDfffe94Fc5u6uqKuzV9ILE5yMM2CQfSy+9dNg6A+mRhCAhR4iT6zYF9R0zZky8/vWvr0mhbS6QuPL+S120m7XWWqu+lLekI4827bSNhjay4oorxo033hh//vOfAy71DdP/kPTkFhDTgcj/iUAi0EAgTwcDgVFNdCZPntzrbL0J9C233BL2JeJRuBmh2hvAAAAQAElEQVQ/Es5f9apX9VkN2z/wlLzBBhv0mefcc8+Np59+uk63DUR9Mv2PAd5gL0y/bM1/m3iStHh+m3jykrzYYovVHqUR3mZFSKy+973vxRVXXBGkY7aHKPe7V3A/Ajht2rSaNNmupJRx1llnhe02ynUeE4FEIBFIBAYHgWEhOma9XOubKbNVWH/99aNIFeydZN+lCRMmhD2oqEWoAuQTCgxIypJLLhlUC9/+9rdLdI8NhK0fkBO7Se+5555hc8+3v/3t9b5XtjvYbbfdwmBDDfWpT32qHszsbeQ3HBEcahsD2Kmnnhof+tCHanXV4YcfXm90WX7wy1/+cs9vFhXHr3/963jf+94Xf/vb3+q0Nddcs1aDlHu65bjLLrsEMsJWpDyTjVBh4Nnti2Wfq5LmyJ5HumDPMHGd4Ze//GW9V9QCCyzQmdTV14gd4mZLBySlPKx2RPVUrh2p/EjE7ItlA9APfOAD9V5hfd2PBLlPIOliL/XGN77RZYZEYI4RyBsTgURg5ggMOdFh2GqAtEeQTSARHEabbF9IEWwiidycf/75YSD56le/WqsRfvCDHwTpgn2s2D5QLyAsNuekFrDBohmzfOwibCyJACE5U6ZMCaTlpz/9aVCzXH755bXhLOJDDYVcXTF9Zo6cKA/Jcr3OOuvUaiokx/NeeOGF9YagTz31VI0sSc+PfvSj2u7C87///e8Pv/mlL30prr/++nrTRvUbN25cfd05WNaFDPEf5KWQmXnnnTfY3rz0pS/teQr13nbbbePJJ5+MRXrZ98v+V1RWAjuenhufP/nTn/4UfqOodZ6PbsWhqqp6X6smKfHOkJ6qqmaoAxstbcW7RnbZ61TV/92PNLmh3O+8hJtuuimae42V+DwmAolAIpAIDDwCQ0502G7svffeQcpBUjL//PPXhqtmuXZ0tgu5FUBUBqqLYNx4441x2GGHBTJjgJj3+QGaRMb1JptsUht8vuENb6jzIR3IkwHqhBNOCEa0JDfUCa997WsV+4JAwkR6gbxQRbwgw/SI7bd/blfr6af1/6OPPjqQAhfIA3UG4oBcIVH3339/PXB2k2SDJAHpQ1SETjxgy9bmmWeeCdIrdesrUL2wTUFsDOjyKdtGq2PHjnXZqqC9kG4h2ogMwoPwqYu206wMY2Wbek6aNCkQ4MmTJ9d2OO6Hnc1QYcK42f3UYe7Xhq1I025dD23IX0sEEoFEYPQhMOREpy+IDRoGms508VRYX/va10I49thjY9lll60HYQRiv/32i5NPPjkMLtQw8pQwceLEzuL6vFbGzTffHGussUZYNdRnxkYCQ172FyXK75fzNh8Z0h5xxBFBmkYF2GmfUurGUJeU7LbbbqujqBi9r1VXXbW+Rgbqkxb9sQoK0aGOorIitWFwbbVasxpUVNqLOCooK6qckxiSWiJIhdSQDJb7EfpXv/rVIcifIRFIBBKBRGBwERg2ovPHP/6xXn5r5su2hS+SzlmzqrOFIKGhnnJNRcB+xkz5Ix/5SL3Elx2O1S333ntvTXrkU76B2nl/wcz99ttvD0uvSYTY5/Cj0nkPSYWBrxn/8Y9/PE455ZQguRFvZm+1kvNuC9R3/OaQvpDEUP+VZ2R3wy6KBEMaqRTpVlElOmeTYwm6vM5LoPZjiLz11lsHYmMlUUnjh6b8RluOiy++eG1fRHqnLVhOX4gbe62dd965rspnP/vZILFRV22XCgvRRXomTZfyyEsyiTgjhPVN0/+wX0KKqFmnX+b/RCARSAQSgUFGYNiIjo6egbBB0uwXQTBQXnDBBfH1r3+9tnVRd8u5ESCifoMKQ9EFF1wwLO81EH3iE58IAwsphMFbGfLxDbPZZpsFMoTIHHLIIXHwwQcH+x0EyPJe6ewrHnzwwTjwwANr0rTyyivXNiZsfBAsgxS7HwMUsmCgZ+NjxQ11hTKorPymsg3uBn/EjBqDek2dlMX+RZ2GI8CINAFZYwwOz/Ic7G7YO7ErkcY5oDTxiCipFTUXvy+kHc5LoOYz0CN5//jHP2qj7pJGpaicNgVSRYTwxz/+cWgXVJranDpol2eeeabToIJF0NUVMWZQL8H9a6+9dpB0WcFFtVnul84OjOpWPtcZEoFEIBFIBAYXgQEkOrP/oJyqGSg4X7PUmerDtYA8KBGpYbQsTmDHQ3X16U9/OpAPJMasWV5kRx5BvMGoGAtfffXVwdBZmsCwWRkGeNII13youEZkDPj77LNPkGbIL69VSs4FRtF+08zetcDuyKyenY9rgYTDUUCK3JMhEUgEEoFEIBFIBIYGgSEnOqQC1E8IhZnt0FQzfyURSAQSgUQgERhiBPLnugKBISc67BOom7jFt2qpK1DIh0gEEoFEIBFIBBKBEYnAkBOdF73oRcE/y9ixY2Ps9DAiUc1KJQKJQCIw+wjkHYlAIjAICAw50RmEOmSRiUAikAgkAolAIpAI9IpAEp1eYcnIRKAFCOQjJgKJQCKQCMwUgSQ6M4UoMyQCiUAikAgkAolAWxFoLdHhlZiPE/tm2caArx3O2jj9s+eUjTU5+Xv9618f3O9bes7XjfRjjjmm3k7CS+MzxjJ0y9vZDMkj8KpsY0uO9PhD4RxO/raGKVOmBGd49rWChXP1feKJJ4JvIFtWVFUV0tR/zJgxYfk/Pzo8/cKJ12Nbdkgvgc+YqVOnBu/JHCiKt7eXZf1txIrTRN6QuQrg5ZiTRB6OO+vCnxAP2urLBUGpr7yM7N2r7fHSzXdR836OLWHfjGvrufbBbxT/URwkctTZWReYcLoIK4GN3oYbblhn40Nr8803D9+udmOPON+aezhhlN/3Wbxv1ze14I/+SR19Z/xvaVedj+2bsTWNdqKe9vezpYp8HKJyeeF+bbDzfn2Tfo6PL/nbEPjZ4sOMf7FFF1009D29Pbfvg+dwmPCDxjVHycd3FSee0viu4uvK9+U7E+c+2waV/G08qo+VyerDgetDDz3UazXgye2JvmTJJZcMvuJk1Hbca6zjx8s3Jl652iL89U++L9+vtJEeWkl0OGg79NBDA9GxBQSnfDaRtIEmp4E6Si9dHI/HOlmbbfpgECNO33hX9nJ1Ipzp2WaCE0J5BJ5xGU6XTT9/9rOfBYd67mljUEebSZa9rnQmCKLOEk4c/nFihxyq/4knnhj77rtv7bjRQMNbso+GE0HpJSCNHOJxkLfllluGeM4f4dpGnDhVRPD4dtKe1Ou73/3uC6rCa3RpLzoPOGgf7jHg88N07rnn1pvHcj5YCuBe4aqrrqr3xSpxbT36zi699NIw6N511121V3LfY9n0ttSLf6qLL764bhvahw7WAOZ+7U47lAcuBkIdM0eUOnD5Ean11lsvbBhbyuzmo4mBiRIHpb4zzknVp/OZtS1uNqSpp21otCn3+84+85nPhH3R9F38gjXv52zVBKwZ1+3ntkyBh70Lneu/DcrN50b+7HfIkStMVlpppdAu5JHm+0FkpCHFBmwYvfjFL67bl7Fgr732is5y3d+WYOKp/1FHfW5few6azJtoajPObdujjsY398IaGSpjnXZmQsbZrcmcyQMntp0kWhkjLfRNdLq0prZs4GmYk0AebDsfk8dlHnnvvvvuwGA70304pBo6k860ck1CMX78+LA/kRmVTlmD6K28ck/bjmOmS2x4g+bB2ayo8/nNlqqqCqSyM61c+3DM5EmBeJAWpBm4mp6XxbUl6DRIrcw4tRN14gqBhKFZB3tdcQ4pTpvTVpzbKwtx1PFqQwZrGEtDhGANe+RRXJuD2aBBhodyElWOPGFn8G7WC7lebLHF6ij3+DZNPhAde9S5B8lGmLUlGc1IP/CBDziNjTfeOOxrp/OuI7r8j0HEAEMq6F37lgw+nY+tXcw777yhnUnTdhxN1kh29HEcprofsZYmkOIst9xyIc11WwLCr46kLksttVQgKb6t5vMjM7CTR/zCCy8cXJI45yAWSbbfoesSSMQQAte2DHLtO3PdxoDsmzB49gkTJtRkF5FxXYL28a1vfStM3BHpEu9Isu7om9Q/+/Z8a4iPfk27kUbiCH8TFflHcmgd0TGrI8rzoZO4dL4cHaY4nUdVVU5nCD4SH4L7Z0h4/sJLNzs1c3g+KsaNGxc6587GVtLbetT4Dcg62846kOZg+jvuuGNnUs+1LS/gJcKH42gwQgJIily3KagviYyBl7qzqqp6q4fHHnusllo062IvKxIyki8dDt9QVVXFmOkEsmBRVc+pAnUu7oXV97///bBlRmmn4tsaTDpIwArh8z0apKhtmnWi8pQmziQCyXMPYmMw11HDxUyWR3REmdhdfqF8ywiV624PJDXNZzWwIC+dz60dUPWZuJFEkAIhAiYQzf7J/STK7rdXnQ1nTdhctynoQzvfq30Km3UwadB3mFwidIhP8UKvzcBJv4UIk0i4t3xfzrUpbW2w8PEbgxlMLH0jJg1+R1+kTvol1yVUVRW2kyE1JPnz3ZS00v9oR1TCsDDJMrl/9NFHw3er//EdUsHDs9w7Uo+tIzrE4hqDAaX5UkhbdLo6GR0H0a/BSh4iXg1Bo/FSqSLoNKWVcPzxx4dOxt5bPqYS78h+R4MpH5a4tgYDkcbuY6Jq8kE1O1V2AT4A+Bj4DVLNuhqMdDJCJ04+JqJUdgcG/uZ9bTj3/PDRCTSfFw7SmnFmSva+IlZG7JoYlnwGfJ0UqZB2Z1ZPrVekPyVfW48wMbNs4iUOXr3VSRoirO34FquqCtIvW7DYXsW3rVM2UJX7lUX0Tq2D8JT4bj5qQ806eFY4OTbD2LFja/XwKaecElR3BnQTD32ZYzOv+wWTLQRJu2qmt+EcLp1txXfRfHbpJM0kwrbgQWJIJOQhXYfZqaeeGrYLYp4AE2mC9mWSapLW1m9MfdSjs/104iTdWEVqSIJIsmriCQfBObJIDcjsQHsyqUB+kG6EWZsTX1UvFAgoYySF1hEdsxmMtVMKwbCNEajNM4nkvODSMRqU6LnZ2xDfGeQ7X2Kx0SHy3HPPPTuT62u/XZ+0+A+JmL3DDjvssCDutS9Xszr04exNfARsJZppzhFIg5VQxKvihaqqgrQNztdee62oVoWqek4io0M2wOpwzH60Ix1LszJUXOwMdCjsJxCeZjoyyaBQZy1eZ2RWrh3C2EwVUULOpbcxwIRaAV6eH1465L4GYeo/uJmZ62Ddw5ZJPCxMUo466qhAHKUJ2plvm/rGdRvCmOlSPe0m4rmnpa5D7J67+r+/pDnUMSZZ6si2RB9jYqV9lJzlfliYaFABsQtzP2mYiV/J281HuCBx5Rmd90ZILrnkkjA5YIepr/LNlHvKcddddw3SIdLWEscuR/9j8C9xbTtqJ74N0l/P7tvwTRnzXPcV4KQtlXRGzAjNdtttV9tZijepQCLZT8LWRBWhnFnZ7m17aB3R0Qn48H0kTfCxfSSH9T1xnpUdzXTnyI8ZI70m/bi4zuDDW3nllUPj0ok00/12XDE9QAAAEABJREFU87qN53D53Oc+F3ASdD7NehjUrSYyiyJ9aKZ1nvuIfIgkXWWwM8ixrTDr7Mzf7ddmk0ixQUpHg+wghssss0yPnUCpA4NlA7ZZOMNJG8GabUsnyWGka0YFR3HUD/I6HymBxFT9DM7qpA3opM26XXcGYnLYsM+Qpu0ginDUCfsup02bFsiPdKJ2BqcbbbRRbaMjrg1BH2PmXJ6VeoadUbkuR/1LSWODAz/qGvcjMyWfPOwO9T8rrrhiiW7d0cpOkoTy4NqDFYvl2tEEgfE5ibB+hLTPtyWtMxigiy0Pks3gvdjodOZty7VvgZSdZMoz+15MJmb23k06ED/3lEAFPHHixGh+j/DU70+aNCmQHOm+2XLPSD22jujQ4TJQ85HMyUuhVtGZkmjoaHsrw8fDlocKRzqDSTNvHZDrkR6QH9IxUh0fggGntzpTQRiIdF7Nj4Wko62DOnEwiYLBRftgO0Ka0KwfLAw6VkY4R5Cqqgq2X+7VkTCUp+40UyfB0WZ1LgLdus6LnQ+CrgyhbYFUlTictIpNGykeVaeOure6kEiYpMBJOtyQJUSZBMPApUyzWukHHXRQMFqGo+tPfepTDl0fkHyGtmV1ENJDNdf54OpJrU4i47yqqtD3GIDcjyz7tty/22671WnaTwn6QqrmzgGu83e65ZpamzQdmSGl8X15v83nM9E0aFtq7ygYsJt5nJNIkNz7jlxvv/32gQwU4gQj8W0MJHvavmfXBpBkBMh1b0EfQwrPFKGZbsJ2zTXX1Mb8zXgqPxIxE14T32baSD1vHdHREZhFmvVh8dj+3nvvHToMdhL3339/z7syY/Ix6GD5gbEaxMBiWaMldmbqZhmMSdlPmGkavAVMuhgUul9cXx14zw928QlR97rrrlv7D6KyItYsj0scLg42jnS6BiwdNd0vY2z199FYnui8BGJmBHDfffeNEkdFQ3pWym/TESHhm0JnY5k4Y2ztSh0Qk5133tlpTJ48Ocwe1Zk0Qodk4GY/QLKIIEqDOekjPOsbp/8hNSuD/fTL1v6vqipIGgy4Bmfic2oYpJCKGG4kFKWCCBHpjMFLHOlfIQC+NYO29uf8wx/+cCBOZvXsxWDpXvd1e1Av5NYKoXXWWSd22mmnYGti4KHiJK0wyCPQCLE86qePgoH7r7766npFjfaD5PQ22CsTMex2PMrzaSNUUiZBvi+2lL4FfbeVefphbYlbBupvmMCxrCIiqRcnkLozWlY29bCFASSopa3wzSStjcHk4Ytf/GLdn/qOkBL1IOWRZowz+YaDoN1Ysm+iYEI+YcKE+l7Eb+211w6rP91vPJTfZIz0uZBCaSM9zNO2ClpmqhPUaRqsdYSISAlNMqLTKPGOSI36brbZZqGxiOsrmHFoOFRcBn6dUnd1Kmoy62HSpElhdqi+Vj/odMrd7G58RCXNhyPNhyKuv8Cw2+zT6qOSz+zCe1JGG4MOgFRKB4wolzqwI0GIyzUbnFJn7c4ApTMucY7amfZW7nHUDi3rdD4SAikp24hmnZAVJFlHW+qow25eizewIeGw0i4nTO+kxVNDiGsGhEBaW4JJgefXD3lm0iuSCIMYyYX+xbU8QufgXO7nQ8j9ncEkr1Mi0pmn266pu0mK1bc8G3WxvqkQl3Itj2/OxFbeI488svaVI55UVJzQ7HukCRafSGtr0BbUgw2lb0Q9TLwY5utr9K/SBe1EumCyivSI7+x7tEPxjN/lHU2hdUTHyyFhMJP0oVNDiRusYIbq93Q2ZuyD9TtZbiKQCCQCiUAikAjMBQJ93NpKoqMuRKBWShHfuh6sQCxqpmH2NVi/keUmAolAIpAIJAKJwOAg0FqiAw4rWeh4nQ9WoBtPSc5goZvlJgKJQCIwbAjkD48SBFpNdEbJO8pqJgKJQCKQCCQCicAcIpBEZw6By9sSgURglCGQ1U0EEoFWIpBEp5WvLR86EUgEEoFEIBFIBGYFgSQ6s4LSCMjDoRtnXc3AKZ6qcdbG/0Izzbl46TwE834rrjNwwW5/FvlK8Ft9ORksebr5qD6cmlnRx0Nyb8/KYWUnFnyjyPv8/dHX/dIL9vJnSAQSgUQgERg8BJLoDB62XVWyfYR4lrVUnnM/m+Lxk8Mpm51xOcnjtEsar9H8VFjVhuBwdMafA4dtHJhJL8HeKQb0UlnkiFO0tvpqQFY4UFQ/TgG55renUKlfOXIQKE8J/BIhL+7n9qDE855sG4lynyM/F5wsOs+QCCQCiUAiMLgIJNEZXHxnvfRBzskbKW+jXH5z/c1JG+dsCA4vvQhLVVUhzRYGJ5xwQhjkedPkZp3bcA7beNmUXoIBnbfq8vg8bnL8Zt+eEtemI8dl3/nOdwIeSMqTTz4ZMGjWgTSHk8SCAVwRQ15ZH3744bAtBlx5SEYUOVUs9yvv9ttvD96SS1weE4FEIBFIBAYPgSQ6g4dtV5fMSyv3+9RM1FadD8s7cFVV8dRTT3Um9Vxzz44E2RtLpC05eMcl3XDdxjB16tTgtdbeMjwdczVvS4umCotPpaZExl5NCKD6IkqwRf6UA4tyPxUgImifLLjJnyERSAQSgUSgdwQGKjaJzkAh2cJy7BdmWwuDcnl8tikGe3s8cSde9pkp6VRT0gVu+tmbSPvnP/8Zxx13XJD6KFNc24I6UOPZl6r4Z0J2qK5sRlnqw6+S/ZxcIy/csq+22moBC/upvfKVr6zJkvRyPwkOaY4NGpGkqqokZ0gEEoFEIBEYZASS6AwywN1WPCNhNihUV7w+29fK3jLlOdnW2F7DfiqkEyW+HBEB6iqBCqbEG8Df8Y53RLOskjaSj4ihvXhsBtpfPRHEiy66KHbYYYf+smVaIpAIzDECeWMi0DsCSXR6x2VUxDIwPuigg2aoKxudI444Iu6444548MEHZ0hzQUVTbFN23313UWEQP+mkk4KEZ/vttw9E6oorrqjtXOoMLflDUoO0ML4m3aGKQwypqooEp7MqMCLV+f/s3Qe8HlW1P/z16F/Fq1dRUBFQg9IVjBSVnkiVDtKLHgSkSO9FIAFEOqFJx9B7L1KlSZNepQiGIsVLFRVfvVze8x3Zx8nDSUggT/LMycon+8zsMvPs/Zuyf7PW2mtRR334wx+O9uOpBh1PvXXGGWcEY29GzgyUN9poo7j77rsj/yUCiUAikAh0DoEkOp3DtivPTK2ywgorRE9PT5VMwvWOmtCXXXbZSgU1dOjQetW79jfYYINgu8JYd99994099tijSossskgMGTIkNt9883cd080FrVYrGBQzpGZwjMAgOlaqiRbc3ndk6JlnnokvfelLQdXVav37eCuvHK++HC9yMIkOjNhGuQ4Mlmedddb202Y+EUgEEoFEYAIikERnAoI57qea+C1JFix/Nom/8MILYTIuvSCRUUaCYZWQOqusBg0aFN/61rfivvvuCzY5zqGtfel//ud/gpTChK5tSYyTpemmm678RGO2s802W2VrQ9JCWnPTTTfFEkssEa1WK9jY/PnPf+4bC5x+//vfB4PjUviNb3wj2PPcc8898eijj8Ztt90Wiy++eCBKBR9SH4TyK1/5SrQTzXKe3CYCiUAikAhMGASS6EwYHLv+LPzoUDktvPDCQa1UX2nF7kbZj370o0rtdP/991fjOemkk2Lw4MFhqTmbHJOzQKr2SzKpM8StDnjnj2Okd7KN2kw55ZRB0nLttdfGwQcfHHwJkfIYxMknn1wRO/tSq9UKZI7/IXnJ8UiiJeaHHnpodfwMM8ygqi8x/l5llVX68rmTCCQCicBkj0AHAUii00Fwu+nU/OiceOKJgewceeSRMdNMM/V1j90NI+RSV4iLNsrGln7+859Xk33fyXp3VlpppVhxxRV795r532ozK8iOO+644FSxjILDRQbcJW8Z+RZbbFGyfVuSmgMPPDD44Zlrrrn6ysuO+oMOOqhkc5sIJAKJQCLQQQSS6HQQ3Dx1IpAIJAKJQEcQyJMmAuOMQBKdcYYqGyYCiUAikAgkAolA0xBIotO0K5b9TQQSgfFHII9IBBKByRaBJDqT7aXPgScCiUAikAgkAgMfgSQ6A/8a5wjHH4E8IhFIBBKBRGCAIJBEZ4BcyBxGIpAIJAKJQCKQCLwbgSQ678Zk/EsacATHdvziiHFV0nnnnVf1XEwrjv9Kedn+8Y9/rOr/+c9/xmWXXRalvL695JJLqhAQVcPeP7/5zW/62tWd6/VWNeY/r8YXXHBBnHLKKfHUU0+Nsd9wKVicc84572r3+OOPV1jwYfS///u/ffVvvPFGnH/++X353EkEEoFEIBHoHAJJdDqHbdedmSdjsa14SBaaYP31149f/OIXVT8FpxRwUp0CE3dPT08gSPKSif+YY46x25cE83SsAsRG3n5TE8/PRx99dOUJGpEx3kL42sfEKSBv0e3l8pwsigXWX/3FF18cwmdolykRSAQSgYGIQDeNKYlON12NDvZFaIMf/OAHIcaSWFebbbZZbLrppnHqqaeGsBBLLbVUFeZAHYLDK/Bvf/vbIKERx2mZZZYJcZl4AVZfEuJUPP8ecsghwYFeqfv85z/fwRF15tTCNpBwrbbaasFTtBANl19+eb8/duONN8ZPf/rTMN5VV121rw3niwgSB43qv/3tbwev0hogTcjkRz7yEdlMiUAikAgkAh1GIIlOhwHu1tOLtC3QpBAOImm391McJ2VTTDGFTb+J2oZkyKT93HPPxZlnnhnzzjtvHH744UE90+9BXV5IVQUboRyQk29+85vx0EMPvWs8AnSeffbZVfDTG264oSKLhkYCRhoGA0FBSdHEAlNHffWHP/whpplmmphiLLhqmykRmLwQyNEmAp1DIIlO57Dt+jObqD/1qU+FkAelsxdddFEgMFRa6667blBvlTpb0g71ElWWMonkg7SCzc4111wTu+++u+JGJYSEXY0AnEhO6TzSRupV8rYCfcJihx12CIkNk6CoiI4tsiPm1corrxxFnSeG2CuvvBJzzDGHU2RKBBKBRCARmAgIJNGZCCB300+IUr7bbrvFeuutF0cddVQ1SU8//fR9XTShM0ymdhLjqq/iPXZIQDQRAJPqhgEuaYaygZgEN/3Qhz4UVHpiYLG7IcER1X3QoEEV0aO6Ekfs3HPPDXZMIqEvv/zyAxGOiT6m/MFEIBFIBMYVgSQ644rUAGk31VRTxcYbbxx77LFHkEiwRakPjQRi+PDhwT7njjvuqFdV+3UbHRKfqrDtz5e+9KWYe+6536XuaWvWdVkqK4FMSXComRAXZI2Eh3puTB1eaKGFKvsmkhwqP0E7P/e5zwXbJgFSSXFIuvbcc8+YffbZY7nllguqPnZTDJrHdN4sTwQSgUQgEfjgCCTR+eAYNuoMJuzpppsuSB0kk3t9AKQUpBCMlRdccMF4/vnnw4Rfb1P2e3p6AhFg0GyCL+WWZ1u9hBSVskm7HfdfR1KosF577bVq3OyXFlhggUB2+jsLcos9N6sAABAASURBVEPdVQyOBw8eHFR4VlupQ5jY5Pzwhz8M51KH9Ew77bTVijYqsP7Om2WJQCKQCCQCEwaBJDoTBseuPwvbEX5zGA//+te/DkvBS6dJdq644oowMat74oknKtXW2muvHciO5dYjR46MRx55pJIC2S/J6iJSIseUMioxK5bK+Zu0nWWWWYIqj2G18VA5fe9736uGQPpiFZoMsqKeeo8xMimNciTxi1/8YqijumKXQ2rGhkl9pkQgEUgEEoGJi0ASnX7wHqhF1CmMjKmW2sdIymNirtftt99+wZ6nTNJUVRtttNFoh5JIzDfffKOVkYC0l43WoIszn/jEJyrV3qBBg+JjH/tYbL311oG4jKnL1FOrr756kNBow3Znm222qQy8Sc/UzTXXXKr6EqnRQQcd1JfPnUQgEUgEEoHOIZBEp3PYdtWZ2YOQsvT0qpvWXHPNYGxcOkjFpKzUMShWp42ysaUVV1wxGCKzPenpPbfELsUk7xxNTJ/97GeDzyHEDikpY0DqinSHBMdY11lnnZhxxhlLk2qLGJHiUFdZnl4V1v6QgK2yyiq1ktxNBBKBRKCrEWh055LoNPryZecTgUQgEUgEEoFEYGwIJNEZGzpZlwgkAolAIjD+COQRiUAXIZBEp4suRnYlEUgEEoFEIBFIBCYsAkl0JiyeebZEIBEYfwTyiEQgEUgEOoZAEp2OQZsnTgQSgUQgEUgEEoFJjUASnUl9BfL3xx+BPCIRSAQSgUQgERhHBBpJdF588cXYe++9K6d2Bx98cDz66KPx1FNPjeOQR2/2zDPPxD777BPOOXrNf3Kc7fm9f/zjH/8pbOCe+FNiXP30pz8N3nzrQTkNh5dj9SU99thjikdLcBIiorQRsFLiCVjDv//973HCCSfEhRdeKNvIBIedd945Ntxww7jzzjvHOIY333yzugdhwXdOvaEQG8qF2xBOQ52AqcpK2mSTTSrP0+oyJQKJQCKQCHQGgcYRHZMLz7tCDOy6667Bwd1aa60VCMsYIBpr8Re+8IX48Y9/HHyn9NdQCITtt98+eAD+17/+1V+TRpQhLSussEJwYGeCNTGfddZZfX2/9NJLq/hU6k3Sa6yxRgwdOrQiLX2Nenc4A+QjRxvpsMMOC9cDCeRZeYsttoitttoq7r333t7Wzfsv9INxDRkyJLbbbrs49thjg3fj/kYi9AXSqD08S5tbbrml8iitfJdddonBgwdXVcI/KCsJ/lNPPXVVl38SgUQgEUgEOoNA44iOEAakESZhju1WXXXV2H///Ssvtu8HIp5txSIyefd3POdvJjGEqr/6ppRdffXVIbwBh3ff+MY3QggDnpL1/6677oqddtopjjjiiFhqqaVi0KBBseSSSwYJxIEHHhiCe/7zn/8Mkz/cTd7aSHvttVfMMMMMIdZVq9WKww8/vJJyOG8TE+mdeF/f+ta3KkeAHC0Ki4HE1ccjnhcJGY/SkrAR6pFhoTLExuJw8ctf/nJ88pOfjBdeeCE4CYSZJKjnpptuGmO675wrUyKQCCQCnUFg8jpr44iOSWPKKacM8ZfOPvvs6motuuiiMe+881b7JAviEYk1JJH8VBW9f66//vpQdu655/bFenr22WfjmmuuCSqX3iZx+eWXV220E+dI2UBI8KFCEXrgoYceqoYkTpMdsZpM5Msuu6xsX5pnnnmqSfrKK68MUoobb7yx8hjc16B3BwEkKRI6oTfb6P8wELML+XCfffjDH67Ijlhgb7zxxmhjgwfyt/nmm1cEkSRIAzHESBepQ5HH2267TXEg04UMKYD9SiutZDdTIpAIJAKJQAcRaCTRMbn46mb/IF177bV9ELENQXQUIEJsLXx9X3zxxXHSSSeFyNQXXHBBnHHGGdVX9nrrrRcm/1deeSVINnbffXeHVtIM5VVmAPwRXVssKxOsMbOzMbGTQLBJEc+q1Wq9a6SkFeqporSXrzdCCqhmxqT6q7ft9v3/+7//C5KWj3/844HklP4iwaKQl7ytYKfIMDUUAn366acrDqpQ95w60iDq1VGjRlV15Q88ScDmnnvuUpTbRKCrEcjOJQJNRqBxRAfYs846a2U7YvJlQ7H++uuHyUadL+k11lgjenp64rTTTosbbrghzjzzzMqOxKTDHufII48McYzEHFp77bUdViUTE0Lka55aYky2GVXjBv4RewkeCy+8cAjuOWTIkPh//+//xRRTTBHv9Y9t1Hu1mZzqCzGcY445KlueW2+9tRo+PKlDET/3GpumBx98sKorf0iDlJd8bhOBRCARSAQ6h0DjiM7LL78cJDWtVisEXJQXKdsE/oc//CGoGEgZQCaStKjSVmW9+uqrwW6i1WoF1ZeJSDt2FNpK1F5Dhw4Nq4gYIJuwlA+EBLOHH364isTN7ob6jirrvvvuCyoVGJF81cdK6kDNJWAnUgQvBrX1NvYdR41jv8mJFAdxsYLMeEh42NawZRobGZx55pmre6t97EhPe1BP57zuuutiYBOddiQynwgkAonApEOgcUQHVCQtlv2aNHxZM0pGSkhiPv3pT1c2NyZpEzBJBAkGGwkSHsdLViG1qyNIg7773e/GAQccEI5zfm0HSmJsjPAZD4NkW7gxQF5sscVihx12iFJvy9D461//eiyzzDKBTG6wwQaVJOjee+91aJUQH8vU2aZUBQ3/M2jQoMqwmnrJ/cE+B/l1n/U3NG1uvvnmQATb66lDHV+wVs8mjCQRCZfPlAgkAolAItBZBBpJdNhBUBWYeKmo2FWsueaagcwMHz68sq9RzhaFimqJJZYIhqEjR46sVgQpZ4/z0ksvxSGHHBIPPPBAXHXVVdVkxb5H/d133x1W4LDBYNNDWrTbbrt19mp0+OzsdPbcc88KA/gcf/zxQVrD7oafIFIuYy8J0UP+SHx0bb/99ovFF188Nttss+oc2jEKn3/++SupkDZsVRh3s5Xi40jZe6Vuqv/qV78aiDE8Ntpoo0r6J6+PCPChhx5qN4YNG1ZhoM3TTz8d7jEV7hu4SIjiQgstFMiTOgnB/tSnPhWkRPKZEoFEIBFIBDqLQOOIji9hBMdki4SwNTEBlcnEyqGTTz65kjwo/9GPflQtPSepsXrIMZKVQnyYHHfccUGFs+KKK1ZLqm+66aZQT/phlZGJjG0Pmx1+VTp7OTp3dlKZnp6eSmpjfIX0lV9EeBhijxgxohq/rbzy0obqDxbIjHNIMKmrZ+Bo5RqiU7d/Kufo9u3HPvaxytcQQojMMHwnJdRvNk4Itf2f/OQnFU4wcj9ShyqfddZZq3LYINfuO+UlIYVIEElaKcttIpAIJAKJQOcQaBzR+dCHPhRWxZh82OggOFQBBSJ2FiQ7yiV2EqWOTxxlElWEOvsSaQYbFPsSqZHzs/Ep5yuSjXK+Jm3L8u86Bu39N8kbe0lT9GOkrA3fMKVNu2TCtSh1MGz/jSbkkRDX2vV3n5Q+G0+x6XJfGKc2VKaljfbKJW1KedmqJ9Ep+dwmAolAIpAIdBaBxhGdzsKRZ08EEoFEIBFIBBqGQHZ3rAgk0RkrPFmZCCQCiUAikAgkAk1GIIlOk69e9j0RSAQSgfFHII9IBCYrBJLoTFaXOwebCCQCiUAikAhMXggk0Zm8rneONhEYfwTyiEQgEUgEGoxAEp0GX7zx7bol8pY2//SnP43HH388OPqrn4OTPPUl8flSr7f/4osvBl9FpQ0v0hJvwurFE1O39dZbyzYywUG8qg033DA4phzbIDhW5IvJmAUE1faOO+4Iy9KFgODqQJnE27L4adr+8pe/VJQpEUgEEoFEoMMITHKi8/zzz1fO1maYYYaQikO2Do97nE/PGRxfPBzBjfNBXdgQaeE7aPXVV49NNtkkttlmmzjrrLP6enrppZeGIJPq+YARL4xH4BNOOKGvjR2hCyzD10Y67LDDqjhiwmdccskl8fbbb1d+ZJAfxzumSem1116r+j9kyJAqhpVYajxx9zeGv/3tb8GjtHsYMbIkfdSoUcH/Dn9O/Og4ljNKxyvnLRluv/71r4MzRuWZEoFEIBFIBDqHwCQnOvy6+OoVxkHwzS233HKsoxXawQQz1kYfoPLtt98O5/f17TR8xpBSzDXXXLKNTVdffXXMMsss8b3vfS+EJEBKig8cXqJNypwkCgfBB8ySSy5ZeZgWF4uEAh4mf+Rll112qbz9asf7L4LqugCnp6enqjOJc7iIBChvSuIN+0Mf+lB861vfihlnnDEEekVK3Bf1MQgvAkd47b///iHeFR9DYmNxrOg4Xpb52SEFgx8pzhZbbFHhs++++4bjhISonzf3E4FEIBFIBCYsApOc6MR4joe6xSQ9noeNc3Nf9EcddVQV/mGcD2pAw3nnnbfyAE118tBDD1U9/tWvflVtb7jhhkoSQwpRFbzzZ5555gnO8HiUvuWWWwJx+cEPfvBO7b83X/rSl4KkiEPC5ZZb7t+FvX85DjThc+7Ym23Ef2SG+onEyrg5n0R2xKt64403RhuD8BacVs4555wxcuTIECJEA04F//73v1chRcTBEjNNvDCYC6lRVHzK3nrrrSABclymRCARSAQSgc4g0JVE57777guhGUyu7BmEciDy55ZfEl/p4osvDhMFKZA2JEFFEnPGGWcElYPwDewnHnnkkSr0gXbbbrttPPXUU31oKpP83r/+9a/Ycccd48gjjwyu/cXAEkfLl/czzzxTHSPP/sIx+lUV9v4x2SFgAoeqEwbBxNZb1RX/xbk68cQTA7nRv+HDh1fkxpjZpPDY22q13tVXcbDUC+SJCMjXGyEFgwcPDp6l6+UmduqcEhqhXtet+66X64ucITmln4gL0lLypDmkPKQ2iMv1119fqQPVI35sc6hg3SfiZJEGkuwgfkJjaMe2xzZTIpAIJALtCGR+wiLQdUSHqoOxLBsS0ptNN900SBzYyDAOpSIgVTCBnHrqqUECw+bB5LTxxhsHsmFrQtlqq61ipZVWqoxuTcS77bZbICznnHNO+Ec9MWTIkBDDCFF59NFHo6enJ3xtO4evedIKxKlMTPojbpTfRHQQorPPPrsiSM5jUvzOd75TES3qCr/TLUn/YAk7McKG9I5dGIz+Qj2095lkor1sTPk//elPwSYI/mNq0+Tyl19+OdwP22+/fSA11HfuqyuuuCLgSfUlBhh1IXUVaZD7T17bGWaYIUiCkKlBgwY1GYrseyKQCCQCXY9A1xEdNjskAQgKSQo7B8E3kQZ1JA9sS0gKfDX//Oc/D3YjDGsF5/z+978f66+/fiAjSJGJRCBKASZFpL7uuuvCpH3eeecF6QRDY8fffPPNle2KuFbUMOIUIU/sh0xSriRC46ufXYbzMuAlUWLEixCZ3JCxmWaaKUx2JCCO64ak7w8//HDAkN3NueeeW6mySM8Y0SJ5JBX1vrK7IZkR2BMpIr354x//WG9S7TvO9ZFxzDXXXFMFxmSzoqwpCfGYY445gpTGeFxrNjePdqTrAAAQAElEQVTutzoZdG1brVal1nMMKY77kkE2Gx+BYRkpkyQqI110L8DRuWG96KKLVlJG2DcFn+xntyKQ/UoEEoGxIdB1RGdsnW2ve+2114KKyORbUnsbE83xxx8fljtTbyFB2lBz+dK2P66JtMkxJnPHCPBoMvQb8t2eGBuTROgnQ1pbASwRNxPzDjvsUEkqlGtH+kC6hTTON998scEGGwRJEDWWNhLcSd7+/Oc/y1bSM8SA4bPJnaFzVdGQPwis60tdR13FPgcRRmTKEBCbBRdcMKhQlbkvqKWQpJdeeqmy76ISRPSWXnrpClPESVs4WanmnmHUrSxTIpAIJAKJQOcQaCTRIZF58MEHwySy3377BYkCiEgtbOsJMfGFTW3zzW9+M0xc6qmtGJBSdcmzzeBnxr7ky9tqI/slLb744uF3yzHUaVQQ7C9Km27estNh47TeeusFFR0CSMpAskXaRXKlriQkzuqpInWANQw222yzKG1M9vPPP39o45zOQ6WonlqnaauKSBCp9oyDjRfJobzrSlVJimh/2LBhQUVnnAgLdauVg4ghiR5bL3iQFJImIj3aIpNIDqma80yKlL+ZCCQCicDkhMAkJzq+hk0EJgTqKsbCbB8uuOCCoAZiC0PNZNJkG8G+wWTNJsIkzCbGZK3c1zTpAt8vjmOPYvI2wTuXNiZvK2Ycz1YHAVJuUrKltjIRscWxtFw9o2iTvEnQUvOf/OQnlc+fkSNHVkbT1GZUWCYvExy7HSuVllhiia65l0hljNFECxt9N/GWDsJQv0eMGFH5kbGVV17akFpQA55++ulVG+ehrkMgtTH5X3/99ZV/GHUSVaK6piSEhC8h99iwXjLDoNjqKv13L6y55pp2K7XnMcccU+GAFK288spVufvNsUgRg2+qVUbLKuGhLcwQQ2WZEoFEIBFIBDqLwCQnOmxGrr322spuhlTF8me2JCQxyAN1i322EpYs+6KmLjGBfupTn4rzzz8/OPVThpz4Cn/99dcD6eEzhg0F4sSXiTZsU6hlSINIIuwr59SNysXy4IsuuiisuLJPqkOCZDUVOw3HWQHmGCu/9Ik6jATIuRAipI1qjCSps5dv3M+OwGkN70GDBlW+XOTrySQ/6J06W+ONqLeI0AYBVC/BrLSQb09UY6W+KVt9RkRI6uoqK/cDdWUZB2mP8ZKIMUIu5QhhwajeXlvn7Q/XcmxuE4FEIBFIBCYsApOc6EzY4eTZEoFEIBFIBBKBRCAR+A8CE5zo/OfUuZcIJAKJQCKQCCQCicCkRSCJzqTFP389EUgEEoFEYGAjkKObxAgk0ZnEFyB/PhFIBBKBRCARSAQ6h0ASnc5hm2dOBBKBRGD8EcgjEoFEYIIikERngsKZJ0sEEoFEIBFIBBKBbkKgsUSH7xq+doDJdw4fOJIl3WIO8a+jThJjSPRu9RwFXnbZZYpHS5wH8hasDa+3nAFusskmVVRq3oAtMR/tgAZmhKwwPkE4eSzmJ6c+DMv01ZdUd6BY2llWL9xFacN5osTHEU/IxV/RiiuuWA5p3JaPJSEaeHd2L41tANwe8EcEj3vuuWe0phxYKudXx/3oHurp6al8MHG6OFrj95/JIxOBRCARSATGgkDjiM5bb70Vv/rVr8JEzZcOT8XiUfFpYxLmDJBvHZOucavj+I2DPPX86AgYKs95oGSiFiiUzx5tOCgU/kCsIoE9+U8Rf8tk5ZxNTHfeeWdwdvfrX/86brzxxiqUQyEywhVwfocMqoeBCZ4DR5N0CV/AF5E2yrWRYC3oqXAJAlhy7KhcGAUEsWlYCdHgWruPhLpAVpDn/sYhZtUCCyxQxUizj0Rrx4+TsBruIX6YEEr+eDia5FwRPsKXCBzrfnZMpkQgEUgEEoHOINA4oiMIJXIjdhXHa4IocvDGcR+IkCCeljlwU8cLLW/AXPOr56af19qjjjqqCgdh8jZBc+FfXP0jNkceeWTlHM8xJm8O90x68k1MpA08O3/hC18IjhYRvuK5GfEZMWJEcJQ466yzVsMjzeDlmBdkHqY5QxT+QbwrUomqUe8fnn55nkaGVltttRATq7c4SMWKR2H5piT3DEeAHPtxjvid73wnfvOb3wQiVx8DCSCpDxLDm7d7UD3HkYiSeFicTk499dSKg5NJhHqttdaq8jwmI4glJElVmH8SgUQgEUgEJjgCjSM6JDe8zvLwCw0EhtrE1zESZJJaZJFFKpKCmMibiLUtyTGtViuEaTBhUdmIWfWhD/0HjmmnnTZ8kZdjfvSjH1WhDUz4paxJWyTloYceClIF49Z3ki1b0jETdTtOYjZNM800gej89re/reJ8CSXhmJLUk4YJfVC8JJOEmODXXXfd0qwRW9I9kpmPf/zjQQLjfnAfuD94uq4PQmBO4TEQSKqrIvWBsXsKCVdOqkV689RTTwXpDWLtPLwu+72SV5YpEUgEEoFEYMIj8J+Zvf9zd10pe5siddA5kgfSiGeffTZIKIR9KGTEpGWy+shHPqJpXzKR+Vr/n//5nzABkf4o62vwzg41zTu7QRLkGBNbKWvSVviBJ554IiRSheWXXz5MtFRM1DWITqvVeteQEEUhOEg6qAPl642E2CC9IPFSfuutt8aSSy4ZJB0kFsqakowPKXG/uG9Kv0mrkJSSd38hNkI9IMAIoThY8BS+RPw20h6hR0jOJOQa+WNP5jxse2wzJQKJQCKQCHQWgcYRHcSkqKlAYwJi98B4eKuttgoTDdWKL2VfzSYvbbQtSd6khLiYqO1Lpb6/rXYIEUPV/uq7vYxEQkwmOAk2edttt1VGsa1WK0hj2OnAqn0cyo1bUgc72zElqqvHHnss5p577mDMPaZ23VjearUCyanfC8aL9LRa/yGByBB7LXHZ2OUYJ6KH0CCNK664YoiJhpCTNJKGaU/SpQ0D5ZlnnjmQRm26EYvsUyKQCIwJgSxvGgKNIzpUCr6wC9D3339/XH755VWWbQQ7ExMRdYOvaoahJpiqwTt/TMQmHhKgeeaZJ0ziDETbJ3qGpNq9c1i16U/yU1V0+R8rzaiodNOKHyuwSMFgQypGCgED9SWZtB9//PFgp8Lwm23P7bffXqr7tqREpBylgG3OiBEjgsSjlDVhi9DMMccc8de//jWskEJ4EGbSMPddGcMnP/nJ6p6hMlX2mc98JkjEkB3kBalk0+N8pDjq7MMFxgzDqVcPPPDAUO8cmRKBRCARSAQ6g0DjiM7gwYPD5FPgQGSoSEh6SpnJxmS02GKLxcorrxzHH398ID7qTei+xK0cYpdD7cJ4+fDDD69WI2kjIUy33HKL3Sr5HRM/u4yqoIF/dtppp2iXSBk/QrjqqqvG+uuvH8iPodmyMVHHvme22WaL/fffv0oXXnihJlUiGYJVlan9ufnmm4M0o1bUiF2r7BAd9wlCTQIGA9K/MgC2SOuss06lnlNGrSdKORun+eefP0aNGhXPPfdckIax15lrrrkqmx9t5RnIkwStvvrqijINcARyeIlAIjBpEWgc0WH/wT6iwOaL2ES86aabVqoYk+vmm28e7Ca0MTn7ejZZURloyz6FIS41BaNRxyI7kjYSo9Kenp6+Ccpkxs7ChO+8TUwnnnhiRT6Mb9lllw3SGkbdiOE+++wTJD2kN+pt2Z/st99+QWJhvIgQkkhFqI2ERFqVpg2clUlWH+22224Oa1Ri6I7gSYgw4kIdZxDIDT9B9mHgXjBWK9GsPkOA+GJihI04qicRZL9Daqite4xBN+mj82RKBBKBRCAR6CwCjSM6yy23XKUSoUKhWmCfs9FGGwXVDLUAY2GSHBML6Eh2TOAmdfWWpiNDdVWEfZMVKYY20i677BJUDc5h0j7ppJPCxM1WR1nTkqXfJtqCE+mU5eZlHNRSq6yySvzpT38K47d1jPLSBqbsT0gstJEQHURJmxtuuKE6VvmWW24ZVDnKm5SomBA2kioqTmQPITaGU089NY477ji7QYVJ9WSspFdUoCrcH6SF7kPSmz322KOPLGvrvoWz47V/d2pWyT/+8Y/groH6k5TqpZdeetcAGLwji+4/yUo9fobqDRFC6lF+nJTL/+xnP6s+XiwEYHunvCnJQgdjRJyLz672vlMlq4eJZP+EE07oa+b9dtpppwX3BTAsFTAnwSZdXmihhYL6udR185aE08cW9bAPrDEZ5DNHYIcJEwsnPDfGRSrvA0Q5dXD78ezp+AMjuda+qYkk2XU3TlJfc9fYxnLdddf1fYzW25FKmy+tRlbufnLP+XhjG8h1iN9SN9BT44gOFYGJxITNoV+nLxCVFUeC/MK4QTr9e3n+RKApCJhYONdEShA76l+Tct1ey1hIQ7l6MGFJhxxySJjA1JV05plnBilqyVMZ+mDRnrE3v0Olrtu33hk+CHbdddcwaXPngOC295stIQJnjBLJHzVnaccGjDsN5ytl9vn4MuEjmKTb1Pmlvpu3/HXBg38u+z4ifHDV+8y+jYSdbzOYWPnK/5Q2jiVJVe7+8cGgvTqJuti8QPUs39TEFODYY48N4yQd5rR1TGNBZpCi9nrPpg9PH/alzgfqoYceGj7aPV9MM/gIK/UDeds4ouNieLCpEKid5DudGC1j1r7WO/1b43v+bJ8ITCoESBZ8LVLxkYpS7ZKg1l+u+kZyyB+RfceQQJDwyEskYlTNvuLlJdKhIUOG2A3OG6lSq0wD/phESC9IW0g7vT+KJLDefWOElzKSMBJEUhp5ySTky7tuH4Y0ITeIABWodk1JpKRs4Fxn19vKQ9e+3n/EBXbaKPdhWyTDVnKS4CtHqC0YQAblLSRBJkkWiwRWeRMTqZfx6btnwDNmwYd8PZHyudeQwXq5fQSIxAve8hJ3K0wMYGruRKqRHedRP5BTI4mOC8KI1gvUficT9RW1BLVNJ38nz50INA0BEw11AtskffchYJKitpEvifpTnbyvSs9UOYZxvHOY9NWXVCY3UlsSI/ZRpa7bt0KrIH+lnyYXKoOSL1tuHcq+uH0mtUJqhGBBHgcNGlSaBGnOVVddVbklEG+OCshCjL4GXb6D4Hpvl25SPzEXKHlbNpdsKElsqGQQH5I9ddRdthJsrOosBNoqUnkuNNQ3NXkePCM+GowBEUbceFmXrycfFFRPdUxL/fDhwysXF2XesgpUe88ikkNF7zlkouDjoxw3QLbvGkZjic67RpIFiUAiMFER8BXtBeqlWX5YGVuAkq9v1RHHsz3wAtfOS53UQr7e1j5VBILD3osNnd9S3u0J2SvErvR1bH2nZjBRWyGqPQedXBCwr5AvCdHxlc7GycTHoJ1Nii/10qabt3Bpv1eMvd5n9WxsSG9cd+SmkJnSzjHUNbyOm8jZUHIFYqFKadPUrfvEc9J+/xhzfUwIoI8AJBBmpc4zJQxS3Taw1BX8W63/+ATTvtQP5G0SnYF8dXNsiUAHEfAyJnnxAvUzXtBeyL5A5dsTETkDbdIcExT7DJ6iieYZ7IoH7bghpwAAEABJREFUds0111Teux1rgjPhs88h4SCOV97tibqKtKv00xdzf0Su1LPTgSUCo4ztjXh7MKHaMaHBydc7vEk7SEMYzZMW+Sp3XLcnuBhD6af9IsEqZbacalJV8q5+9NFHBxKovCSSHjgU9adJnXQfXggPPNmhlPbvue2iBu4TzwYppm55ZjxTJFjyJVFfUgWykfPMwJLbD9JEGCg755xzqsUl7LzcQyQ42iE3nlX3pfuo1foP8SnnH2jbJDoD7YrmeBKBiYSAlWPISCEgCI+XdF3dUu8KI1FqLfYZyr3Ux8XAX3w2ah7Jcd2e2EywFyn9pJ7hKbvk27eMc9kAlnIqrP7IIpUDfEl8tEWOkMb+yIL6bktcL9SD2Lof6jZJ+ou0IXrcf1h0wh0DtZ46iU0JPIsNi7KBIMkxDsn1pLZi3C/veUHqmE/Il0TSRepV8mXrmXL/lHzZuldIThEoJBx5Yt/EBUY7iSrHDKRtEp2BdDVzLInARETAxGs1IoNI6gNLw9kAeFH31w1qK1+RjIvVeynzVVUSIkMUbzm5+pJ80Vt94oVfyibh9j1/mpEnQ1tf1MaM9CBr/R2ItLBdEaOv1NsvmCywwAJhgjJ+k5u8L3df5hynUmHUDU7LObpxO3To0MqZJjLjmpJWFKlM6S/SZlK29NlWIt1STyohnEohjVbzwcIy/oIXkok8WdHlmCYmfuCoJfWdxMZ4ESD5khhllzF7ZkhmrPRDJrkFUceXF5slNk8IjefSPUMl7L4hLXQs8lzOO1C3SXQG6pVtG5evbctS3fgMJb1o+B8qzdz4xKHq2VDYWhJsAittbH19emFpI/H7YZUANYR6yQuMvcFZZ50l26hErGvitmLGZEVlYDztgyAaN/6SvKB9hTme4akJyQoQtgQmJceb1IYNG1b5hrFMth1bbZqUWq1WWPnDgNTXJTUD30JenJYCexlTM5QxwZWvHJNXKatvSYdIiZT5oi/YOhc1hvImJNIYS6Ito0da2BchKb6kLRU2tnJPjRo1KnjUplbob2zIIALYarUCbpZeW5bu3nKefffdNwoRiC7/5x6hkjK5mswZUpug2RhRPVGxuJdOPvnkygO76w/HrbfeuhqZyfuoo46qQtKoY8OD1FSV7/xBtOH1TraRGx8PBx98cPWecO+X97T3i7q6GwYD9MzwwWS/njyH7g0YK59mmmmCbyqkG8HkABYxUjfQUxKdgX6F3xkfnwq8HNPrSuwinnrqqarWi5MdBIeIvj7LV6jJfMcdd4ziF0XkeM4WOVPURqK28GVggo/ef0Si8r5Se7ON+28F0GGHHRb8WDD4pPM27vpATFJeIMYveRlZQmzlEEwdzyAQBrfeemtce+211eGcKyKUjvnKV74S5QVeVTb4zw477FB52XbdyzCQafcYUlzKvLDr+VJetnygFLG7Fz2cJOTAy7y0a8qWmkX/i2rFGBA2Y0OMjcPKKbiUyUhZPfkq32uvvapYaqXcUnXndV/1t+KmtOvGLV9BJDH6X/qHtFFpkTwoK3ltOOlEgJV7bykryQo0HyPqSuKl3Xus5Ju65SPIOH1IIn/G4f1CokUyI18SWy3PWsmXLWLDbok0p5QhNp5TdkzeWaV8oG+T6Az0K/zO+IiLGaDJ0slyZibmkrwXJhGph6qoFWxN1upM9PTinJT5ChBGw3ESZ26kFyRG8laF+HJt6lcVY1lf0b7ATT6+QL0YkBvjK8mYyz6DWSFG5PlC8XWOCFHt0K2X4/k++cEPfqBZEK3DHFGsCvJPIpAIJAKJQEcQSKLTEVjf90k7dqAvBCQE+0do/BARsi3Rscmd+Fi+JI6lGI6S8vBkSrdbJvTSBqFZY401AjFQxtqfTUFT7Cn0uSRSKVIcWPj6brVaVRgQ42bEV9r5Guezo+R9WRIFk2Y5Xh2S02r953jqLOo9khzHwU17qgv5TIlAIpAIJAKdQSCJTmdw7bqzIh6kLlQKJDnsJzinIqmwrLfui6HeefYGfDtQyZAIydfrW61WFcuJPpgO2QTebkxab9/N+8YHo3YsECB1/fUdeSEiJgHTphzfav1nyabj4dxqtaL93MhO5L9EIBFIBBKBMSDwwYuT6HxwDBtxBpIDxrBWKTD243+DWoWkguSGnY6Jun0wykkoWP23Wq2waqa9jbzzIFCWLNILs+5nF8TAWX0TUqvVqrzOIivICTyo4aiwGIL2NwaqLmSFjxAkxrZ+PLwcT8/ufAiP88DLlnTINlMikAgkAolAZxBIotMZXLvurIxqGfPpGI+ryIj4OozSrIYwOVNRqS9J1Nyi7rK6BiFiMFrqy5bRJRseS2BLWRO3iApDSOQGEUF2ELcxqeLUMxhEAknMyvGOldSX4xkEMpxEjGBDQkbdp0w+UyKQCEwYBPIsiUA7Akl02hEZwHlEhC8KQxT3xBbJWXjhhWPDDTcM8XNKPbsdK2B4YWXXY1LmuMvy4REjRji0SpaEWnLOSZzlnyUxxnWcZdpVw4b8QUhIsRA30heBFa1OoJprH4Jl44iL1RClznJXTr4YfyM7bJsYbzt+++23Dwbe2lrlxlcGw2X5TIlAIpAIJAKdQSCJTmdw7bqzkjgwqhVHhg8KfixM1Ixm2d1YhXX55ZcHPyfqOeFCbCyHpXoxIAa3ljeSBmkjIQKWm5c22knsVvym/SYlUivjgQ8CY5UZeyZjEHcJIbQvkYIhNJZsykvGzVeF1WiclzneEmJ1PL2SDsGN5MgqNuWZJiUC+duJQCIw0BFIojPQr/A746N6olpho0PdIrXbnbDZUV4SR2bvHN63GTx4cPAPU9rUpTt9jXp3OAtcdtlle/ea959UhiSGVAvhKyPg04MPk5Jnu8TfTsmXLT8XCCHpWPGhoq4QStgNBF8fxpQpEUgEEoFuRyCJTrdfoexfVyGQnUkEEoFEIBFoFgJJdJp1vbK3iUAikAgkAolAIjAeCCTRGQ+wxr9pHpEIJAKJQCKQCCQCkxKBJDqTEv387UQgEUgEEoFEYHJCYBKMNYnOJAA9fzIRSAQSgUQgEUgEJg4CSXQmDs75K4lAIpAIJALjj0AekQh8YASS6HxgCJtzgr/97W8xatSoePrpp4MzPF57670X00p9ScIX1Ovt8x3z/PPPV+fRzjklYRD6q+erR3mTEhz4x+G9mNO//vr+17/+tQ8DOEgw1XZsx7/++uvVceKLaZspEUgEEoFEoLMIJNHpLL5dc3befnfYYYfYcsstY6+99opjjjkmtt56677+8e3CJ85WW20Vw4cPD9s999wzhIjoa9S7c+ihh1bHaSMddthhccQRR0QhTXzH7L333tU5HC/oZe9hjfmPrFx00UVV/4cNG1bhJFRG+wDOO++8qg0MJB6mizdljhaVSfzsFGwQTJgod+7f//737afN/AdFII9PBBKBRKANgSQ6bYAM1OzVV18dnNjxanzkkUfGjDPOGEIVGC/pBW++TzzxRBWiQJgCiYNBXoJN4NoddNBBgQTssccefe2+8pWvxDPPPBOkGNrcf//9UepPPPHEmH766RU3Jj355JNxxRVXBA/SSIrgpFdeeeVo/SfNEaMKRtL+++8f4od97WtfCyEhrrrqqoDbUUcdFaRfsHcCpFAoDccssMACsdlmmynOlAgkAolAItBBBJLodBDcbjo1IoLImLiFa1h66aUD6dFHE7ugnyZsUc2V2fLe+9BDD4VAnrwECwchbIF4UNpIyy+/fIiJRXX11FNPxRZbbBGrrbZaaK++y9O7ujeqV7X3sY99LD73uc8FT8a8Q995551RV2H913/9VwjvUA6+9dZb47vf/W6VRZQ+/vGPx1RTTRXOQ9JTjj/55JND3Kzo/ScchHLEqDeb/xOBRCARSAQ6hEASnQ4B222nXXPNNUMcpu985zuBwJA0CLz59ttvB4nNlFNOGZ/85CdH67aJWjgD6qubbrop3nzzzdEmeI0ds8oqq8SXvvSlaLValQrrhz/8YQwdOrSS/mjTlISsPfDAA/GpT30qkEH9Rnaorur2SiRdgnSq/7//+79KUjb//PNXUi3BUgXqhJ36cvztt98e7JVgrtz5HfvCCy/IZkoEEoFEIBHoEAJJdMYH2Ia3JT3YbrvtKgkN1cm5555bTc71SXxMQ6TmQorGVK/8y1/+sk38+Mc/rlRbG2+8cUWOqsIB+ofab9ppp43Pf/7zA3SEOaxEIBFIBJqNQBKdZl+/ce491RTDYNIcKivBKtdff/0gUaCmMWH3R2SUq2d/0mq14o033hin3xTM0uTPCHqcDuiCRiQ1SAvJFekOPKySoqoqEpz2bj722GNBMvPZz342BEltP/4vf/lLOP6rX/1qOJ8Vas7x6quvVhIwEjH5TIlAIpAINBGBJvQ5iU4TrtIE6CPbE8bITjXnnHPGAQccECZddjuLLLJIpa659NJLVfclNiQMb5EWtjgkNieddFJffdmxyshqq5IvW0vRkZ2S7/Ztq9UKhE6/jRuBQXQGDx4c//3f//2u7iNDDLGp7aiiWq1/H2/lluPVl+Nhx96HGtCJrMCaYYYZgmpQPlMikAgkAolAZxBIotMZXLvyrNtvv31cd911gfRY+WPyZUjMYHbHHXcMqiaGyeqtNFphhRWCqovh7RRTTBE33nhjXHLJJbHttttW59DuhBNOqPzyzDrrrJV9jjJp5513Dsa3pBxdCcYYOsXQmuH23XffHaQ1bJMYELdarWDITSpWDkVoLBGHXyn7xje+EVSB99xzT2WQfdttt8Xiiy9eSW+sxIIJfKxM22+//SqD53JsbhOBgY9AjjARmPgIJNGZ+JhPkl8cNGhQtfSZVIcfl7vuuqvKl85YTXXDDTfEWWedVfmHOfPMMytSRL1V2iBGjjfBDx8+vGqHFPDNo43VWaV8scUWi3nmmUdxoxKjbITk2muvjYMPPrgieqQ8BoGknHHGGXar1Gq1Yrrppgtqqaqg94/jLdW3xJzPIUSR5Ka3KhZaaKFYeeWVK9xWX331oD5UnikRSAQSgUSgcwgk0ekctl11ZmqTVqtVrbgizZHYpNQ7OdNMM4XykviKqdfbN6lzEFjabLTRRoqrZLVVKV900UWrsib+Qeio9o477rjRyBqJWCF1xmUZueX09uuJb6EDDzww+OGZa665+qrY+ayxxhoVxlbB9VXkzhgRyIpEIBFIBD4oAkl0PiiCeXwikAgkAolAIpAIdC0CSXS69tJkx8YfgTwiEUgEEoFEIBEYHYEkOqPjkblEIBFIBBKBRCARGEAITNZEZwBdxxxKIpAIJAKJQCKQCPSDQBKdfkDJokQgEUgEEoFEYDJEYEAOOYnOgLys/Q+Kg0A+XDirs0T85ZdfHq2hpeLqS+IPZrQGvRnO9MTJKm2cU+Icr7e67z+Py9rwDNxX2JAdOPAI/dRTT40WzLO/7hs3/zrGCtPS5rnnnqt8DcGa88BSbuv84ovZz5QIJAKJQCLQWQSS6HQW37wz89oAABAASURBVK45u1AMO+ywQ1gevddee8UxxxwTW2+9dV///vjHP8aIESNiq622qvy82O65555RPPmWhnzDOK74yznssMPCcvNCmpAePmQsO9eGw7xybBO2yMpFF11UYTBs2LAKJ0E9++s70mcJOl85u+++exUcVTtEcJ999qnOYXvvvfcq7kvXX3/9u4Kj9lXmTiLQJASyr4lAAxBIotOAizQhunj11VcHZ3/iXB155JEx44wzhkCdzk16wcmdcBDFD44tPzuc5xXpw0EHHRRIAK++6iU+Y4RBIKVwLuc/8cQTK0Klnldh5U1JTz75ZPAOvdNOO1V+cEhreInur/+8SV988cWxzTbbhHEXx4J33HFHRRiN/+ijj44FF1yw73DnU//Pf/6zryx3EoFEIBFIBDqHQBKdzmHbVWdGRBAZE624TEsvvXQ1OeukiV3Qz/333z8+85nPKKq2AoA+9NBDVbTzRx99NEgteFAWJqFq1PtHDCxhJKhwqHoKyVHWW924/6NGjYqPfexjIZDpRz7ykeBoUcyvv//976ON5bzzzqvwO+WUU+Jb3/pWIIUaOJ60bOjQoRVhUlaS2FmugQCgn/jEJ0rx5LTNsSYCiUAiMNERSKIz0SGfND/IE+8000wT3/nOdyrvyNQrX/jCF+Ltt9+uVC5CF3zyk58crXMmfEEnqa/EfHrzzTffpXJxzCqrrBICW5JqaCN0gtAIPCX3Z+cz2o90UQZZe+CBB6oAp8igriE7VFf1cSAsCN1KK60UJFZTTz11kHZpD1PhM5DHTTbZJH784x9Hkd4gmbfffnuFYavV0jxTIpAIJAKJQIcRSKLTYYC76fR/+MMfqthNYlItsMACce655wZJT30SH1N/qbmQojHVK2ebs+yyy8YvfvGLECvqN7/5TfUb6t5X6tKD/vznPwebJqoqYzzttNNCyAcSLWEh/vu//zsQRKSJ7RI7JRIhccQQny4dVnYrEUgEEoEBiUASnQF5Wd89KKopEzR1FMmLgJICdlodRU3DTqc/IqNcvUm91WrFG2+88e6Tv1NCYiG2E/uf2WefPahvTPbvVHf9hvpp2mmnDVIp0h14vP766/Ff//VfIU5VGUCpX2utteLTn/50UNN9/vOfDyqu0sYW6RHE85VXXgkSMQFBGXkPGzYsECAG23fffbemmRKBRCARSAQ6hMBAIjodgmhgnJbtCGNko5lzzjnjgAMOCCuk2Iwsssgilbrm0ksvVd2XTNyWRi+55JLBFufLX/5ynHTSSX31ZeeCCy6IRx55JBZaaKFg01PKSTaockq+27etVisQOqupjJuKCtEZPHhwGEvpPzUd8vPggw9WRVR8H/7wh6N9rI7X4Itf/GKQoJHoMOT+yU9+UhEkht6zzjqrJpkSgUQgEUgEOoRAEp0OAduNp91+++3juuuuC6THiiDEZciQITH33HOHFUQbb7xxsC1Rb6XRCiusUKm65p133phiiinixhtvjEsuuSS23Xbb6hzanXDCCcFXjAmb3QpVDhUWCRLitM4663QjFGPsE0Nr6jySlscee6ySxLDDabVaQWJFKka6Y5n9pptuWuFwzjnnxDzzzFOtrqKigotEivP4449XdlFsmQYNGhSDehOpkXNYsYYwjbEzWZEIJAKJwAdCIA+GQBIdKEwGyQTLRodUh3+bu+66K+TL0Ndbb7244YYbgtRh+PDhwaAWKaLeKm0QI8fzNaONhBTwzaNNq9WqjHKpaI466qjwG1Q66pqSppxyyiBpYWPEqJqPHFIe/T/55JPD2OwvvPDCcfjhh1e+cpCivffeW3El2YKLBM9SXlW+82eqqaYKBtzvZHOTCCQCiUAi0EEEkuh0ENxuOrVl0q1Wq1pxRZojsUmp93GmmWYK5SXNPPPM9epq/6tf/WrlILC0YWdSVbzz57vf/W7fOdj2vFPcqA1CR7XHGSBJTek8iVghdcpIvODA+LoQOjZKyiRkR7v2RJJTVmm112U+EZiUCORvJwIDEYEkOgPxquaYEoFEIBFIBBKBRKBCIIlOBUP+SQQSgfFHII9IBBKBRKD7EUii0/3XKHuYCCQCiUAikAgkAu8TgSQ67xO4PGz8EcgjEoFEIBFIBBKBiY1AEp2JjXj+XiKQCCQCiUAikAhMNAS6mOhMNAzyhxKBRCARSAQSgURggCIwyYnOk08+GUISSFzr33fffWOFmtfam2++uXLUNtaG41h59tlnhwCX49h8vJpxnieI43gd1MHGnNjBmS8Yju/EYBLSQewr5RJvyLrgWvCCrOyiiy6q/OvYb0+vvvqq5n1JAEu+du69996+sqbt8IrM2/Mpp5wS4lf1138+iNqxEA+s3lb4DL6I6mXCQcCzHbd6m9xPBBKBRKBfBLLwfSEwSYmOeD9bb711H2kRB0hwxLGN5Ne//nVwbsfz7tjajUvdZpttFn7z4YcfHpfm49XmwgsvDM72Tj311PE6rlONTboCSj777LNV6Ief//znlXM/v+c68PnCE7B8PfFuzNuvMhP/McccY7dK8rvuums4d1XQ++ewww6rro/x92Yb919YjKOPPrq6J5E24xXAs30gyHa9bNiwYfVstc/vDqeCVab3j3O7d6VnnnmmtyT/JwKJQCKQCHQagUlKdC6++OIQS2jnnXeOnp6e+NnPfjZa8MT+Bi9cwbe+9a3+qsa7bIcddhjtmHvuuSfOPffc0creb2bIkCHRn8O993u+D3rc5ZdfHmIvGTPCs9NOO1WERwynZZddNsRjWmqppapQBn5LQMof/ehHVXgI5auvvnoI88BhoGslkYZdffXV4dyOkVZbbbUqrpP9JqZHH300SBmNw/iFaKiPz5hIY4TFgIG06KKLhuCddQeJ7qPppptO874EU8S6eFruq8idRKAzCORZE4FEoBeBD/WmSfZ/+umnD7GAxFXytStYIo+yvqSfe+656qv67bffDuJ+aheSBROzuEFUWGIsSY4tgyhttTexl7xjhS5QTsWgjgfcchxVzrrrrlv9JqmHL25b7V566aWqXL/eeuutkHcOag1EjarDvnPrt2OEEjBJlvNP6q34SmIvUaXoL2JTJvBWqxWtVmu8uyj+lUCWxlsONtl/6lOfKtnGbV1HATpdP/GovvnNb1aBSqn4ymA+85nPxOyzz16yVQywOvl2T7rfxM3qa9S7wxO1e14Q0N5s/k8EEoFEIBGYCAhMUqKz+OKLx4orrhiiORPz+wo25hdeeCH23HPPmGWWWQKhYUMiMrb4Q+olE/Zee+0VQhDsv//+lWTo97//fTgPNcz3v//9IG0QhNLvUHkhLhtuuGEIxuir3HlKEuPJ71544YUxYsSI2HzzzWPo0KHxj3/8I0477bQQ2sD5/a5I3iQjfsdvCPCovbzf1a6ct1u2yyyzTCU1I82BGZUUEjm+/SPtKLYp4kCZzL/3ve+N72m6sj0Si3gj00hO6SSS4z4s+foWeSYFKqEi/vKXv/QFAq23a8R+djIRSAQSgQGIwCQlOvBkk2PiZNzJpkVARZKWH/7wh0HU/9GPfjTEFJpzzjk170skEkceeWQImnjrrbfGAw88UJGT+eefv4q15JwmLERqwQUXrI6jMmATVGXa/iAqYhBtsMEGoU/UFh/5yEeCVGbVVVcNcaCi9g8JOv744wOBEOCRLQcJBwnPI488UmvZPbsnnHBCHHvssUEyxU4EWfugvXvttdfGaLD7Qc/dhONJ/kh4pp566kAcEaVBgwZFiX3VhDFkHxOBRCARGMgITFKiQxqC4LABES0bwWDgev/9978n5nPMMUcgQcgJ9QmDUTY2RYXwne98J5Ce9zzReDbwuwgT+wuqCGSMEa/fQpbYsYznKSdKc1IxahnE7MQTT4w99tgjGA7XSRmJ1nt1pmajUxFCahirsxg0v9ex3V5PZYXQkuD87//+b2XTREWJMCO9/fWfNEf5VFNNVUkfqV4RY/fIjjvuGNSypIgDAR/jzJQIJAKJQNMQmKREhx2DiYD4/7Of/WysvfbawcaDXU0B0oRDAkF1wI5GW3XyvqCpCj7xiU/E4MGDg20ItVGxGamrp6glHMc+hU2PyczxyurJsdRVtn5bnd/0e+xvynmUS4jaFVdcEZYM1+021HVTMmbG37D89Kc/Xa2MMhYTMIkE3O++++5K2lP6bTWV49SXsvoWMTDBw6pe3uR9xBkuJFXGBZ8FFlggkJ32cbk/YIQcIb2I9xFHHBHuE8R7v/32iyWXXDKOO+64gFP78ZlPBBKBRCAR6DwCE4/ojGEs/OaQMIwcOTKsvppvvvmqlT5E/yZky3NJeRh48v9i4iFVufHGG8NxlgLPNddc1QonE4uJ5pe//GU4n3pf4tNMM031Ze1rm62O5eT8xbC9Qbb4fEGYtOMD5pprrqkIlwnLeU4//fTK147fIoV64oknqvMhPyQ6rVarWq2lrWOcWztqDW1JfMYw/IlajMjARD/ZMjG+NonrBJUhnz/sndRL1HxWZCFHJEKkP3UbHTZSbH2sUCoTuWX/yCZM4ejcTUrswhgMkzDCAJEpNkhXXXVV8I1UxoMAkz4W+5xSPqat+8X9h7AjnSRsY2qb5YlAIpAIJAITBoFJSnSomUaMGBEzzDBDNRp5E62v5xlnnDEQFtIEy7SVIypUQwxqGQSTKFAZMWamQjHh7rvvvmFVlhNaEs3Ght2NyarValWGyCaw5ZZbLhzv94skhi8US4V9obO7Yc/iPIgUg2O/74vf75VjqM223XbboNLRljrIEnj7W2yxRayzzjp2J3n69re/XRFDK9t0puTtSyVvkpeXEEdG2PYlxIghs30JTjBhQyUvkWqQzLGNkm9aIh3ceOONY9CgQeGeQvbYg/U3DiTasnIEub96BvTUmfU69wtSWce5Xp/7iUAiMPkgkCOdOAhMUqLD6JgkgI1OT09P9PQmE00ZOnsSZbaITZmkqaiUqVtxxRVL82q7yCKLVOfp6T1XmUzYS5h8lfF/MmTIkGoSW3PNNau2Vko5pzrtfNU72VprrdVX73eo1xzrPJKJzuoc9kDykvpVVlklrPRiUO0ciJrzTco0qHfibrValfG0fkqt1uhLyhFO5SXJ6zPyUvAudWWLEGpTEolcqVtsscVKcaO2rvMPfvCDQOwQ29L5JZZYIhDmkodLPV/KyxYRZLRe8u4X5LvgUz93aZPbRCARSAQSgQmLwCQlOhN2KHm2RCARSAQGMgI5tkQgEXg/CCTReT+o5TGJQCKQCCQCiUAi0AgEkug04jJlJxOB8Ucgj0gEEoFEIBGISKKTd0EikAgkAolAIpAIDFgEkugM2Es7vgPL9olAIpAIJAKJwMBDIInOwLumOaJEIBFIBBKBRCAReAeB90103jl+vDcc8/F1I9ZSPYkVxQHbeJ9wAh+gDxwJcoTHM2776TnO43SPE732um7Pc+YH85/+9KchJhNHjJzX7bnnnpWnZHVnnXVWNQyegfkrUrbV6aocAAAQAElEQVTbbrvFlltu2ddGWUnPPvts1b78EQ5CHf8zpaxpW2EfOK8UuoHzx7H1n/ftQw45pMKGc0htL7nkknAsf0ucNCqTeM+GTUmbbLJJ5YhSXaZEIBFIBBKBziAw0YkOPzn8zPAazCmgmEtLL7108IAsdWaY435WvlH41eHsjVv/9iMFCOXTh0+U9rpuzvPOzLEfPy4m2G222SaQGj5j+Mi56667wrj5ijEOTht33333ykM0X0DbbbddCHlgYnfNJHkOEZ3bMSZ4YTXU8bJc9yGjvgkJwdN//pCMmdNI3o/767vwGHwFPf/885VXb36bEBvesQVM5UcHQRRw1vHCQjh3SfAXDFRdpkQgEWg8AjmALkVgohMd3oiFd+CxmLNAjuwEQRTWgbRkUuPUarXC5C/8hL6QfBx55JEhVIS8cpNTq9WSbUwSkoIjRA7uvvGNbwRSIr4Vh4e8OiM2PPzyRG1QrpNrI6+cs0ZOFRFV5RKJkElevDLHSJzhqdtnn31CmA71ypuShK9AcHnpRsRnm222ELYBgauPQcw0OHJ2yWs3p5DILxw5l0RyNt100yr+GknPCy+8EAg+bCRkSL1j6ufN/UQgEUgEEoEJi8BEJzr9dV/8JBKeEjqhtPHFK1yDGEO+npVTfZU4RDfffLOiKpEqmHi1J02oCnv/kEAoK0m8rFGjRoUYW7feems8+OCDoe6OO+6oolVTSYl3JSq1dsIg+L3LL7+8kmhQ/2jbe+rqv691x0smL4Wvv/56RSRIPJSfe+658dJLL6maZInXZ+M66KCD4qGHHqr6IXxDtfM+/5BQID8Iq1MIq2ErKTPpiwUm34SEzCAlyIcwIsgesuP+FAS2PoaDDz44kN4555yzun8Ed0WQEBxhHrSVJ+VBDpFF+8ol2K+00kp2M02uCOS4E4FEYKIgMEmJzsknn1zZNuy9997hK3jHHXfsG/RRRx0VSA3igfCwb3jrrbdi+PDh1URNUuCLWGBEpATJYV8j6KdYQsW2gvoFMXEutikme+oFKoWenp6KkAhW+fOf/zzefPPNQGiUX3/99WHi6+tQ7w6pyHrrrRfIUm82/IYYWPapfthlkAhQd2jnPPp06qmnVuftz+bHsRMjlVhWJlh9g2P7+MalH7fddlt1zZxDorqivmk/Fpli5zLllFO2V3Vt3vVBVpEzJKd0lDQPaS150hxSHtIe95V7hTqw1Jet+1W8LLHSSpktGyD3RTuxV5cpEUgEEoFEYMIiMEmJDtsQ8YQQESoVqhTDQ15EDBcdmwqERATJoG4hQUFk2E/IUyMhQdRNiAUCY+I97rjj4h//+EcwKhZzCsnxG1Qw1A0mflGnkSVxjXyxm8xMPt/85jd1I8QiEt3bVzo7IvviWlWVvX/0wbn8LsmPiY0ER/wngSD180c/+lEIREp18X6IRe/PTLD/cLjhhhtC/37xi1/EkCFDxvvcVIzFxgRugpgih/UTifhNwrbVVlvViwfMPjJNUigCPINt9ymJ5BVXXDHaGA888MAQO410q15xyy23hHupXjYO+9kkEUgEEoFE4H0gMEmJjglg/vnnDxOmVVe+jo2B6gfhoFaiPpIniUAWfHX74qZeEBS01WqF8larFa1WK9icUBGYiBAP6gEkyUREXSXoJPsgahWJ+oGKodVq+elQRm1RZdr+UNNIpfiee+6pgoPKO+fss88eVBjOyai59M/5tZmU6eyzz46HH344EDATMPJIunXvvfeG/rVarUAw2/sIQ/WlnBqGjYm0+eabB6LHtsrqLW1IKq655ppg9EyaoawpiRQHkXMfkiS619xbCPgUU0zRNwx4tFqtcJ84Bnl2/RFrjRBm5Pu73/1u1ImxOue87rrrkugAI1MikAgkAhMBgUlKdIzPBOKr2ERpma6v5WmmmSYQE1++2vzrX/8KdhJUAIgEVZNyKgR2IiZv6gMqAZPMK6+8EgxFkSETDTXK8F6VF0mE6OaOHd+EBJj068cxWD3//PPD7yknxXi/53f8BE39nOyII44IBFAVfGwRMsSMrQkJWalXZ2yIo3r59vTiiy+G5eUMll1H9VSIiAHDZxIsKj3lTUkIHLJW7iX33dChQwORKWNAbKy+O/roo6sialQEGUly7G9+85sKZySQ2ss9ijhpDC8EH2bymRKBRCARSAQ6i8BEJzq+ltm1kCSw0TnggAOC5IMaCNFh93HBBRfEmmuuWS3ZlWf/YIkvokMFRVWknOrARL3GGmtUX9faKTc5URmpc84CIQkGO6ALL7ywspnxZa0v1GSkHdRYCBMj55NOOqkiW9RXN910U+gT1QTpkFVGpDm//OUvg7Sp9AdZYJDLpggx2njjjcMx1GckKoyfS18mxZa6rvjM0efjjz8+SKH0BZbUgPAriT0OyQxDcHZUJnBlpd4WQXVNSLqcUzs2SeoQWBO78zclWYFGtWccG220UbAxktd/96r7xf6wYcOCis44qShhgCQxXLe03DVX5xzuO8dIyDCskEH5TIlAIpAIJAKdRaAQnc7+Su3sVB+Mdi09RjDYjagm5md/w+Zj5ZVXroyTL7744iCFYVC85JJLVmopdh8mE+W77rprsJ/xhW0J+IgRI4K9hDoExXlN1OxRlElsJkh6kBGEBaEyyZMeMUj29U71om8kRb7ckRskyj41jURigSQgC/rs3BzrIW3aMlQ2WbKDMQ6qosGDB+vSJEnsoRARk7C+siUyEZfOzDDDDGEih6H6kpRTFa6//vrVMutrr722uibqET1jKzZNJn9EkV2VeolvnvIbTdhSt7lnEELjoZ4rEi33qvvFOKabbrqgbjVG19k9S6WF6CLS1IPq3FPUp0i346hq4V7yyjIlAolAIpAIdA6BiU502DRQTQ0aNCgG9aayFFc5wqLM1y6VSckjDyZbMCAp2khscZRJJiPkRjnDZGWkMux87Jfk65qKwTm15dOn3h/nUS6ZjCT7VA31uqLK8FvqJX00DueUl+rHsOko/ZjYWwTTbyJv+iXJ15M2ykuSV28Crx9X6m1dK20k+fYEP3VNSvrs3nI/leus/+5V19a+RNpjvEiP+7XValU+mJSV5D6rX3fnI9FxfKZEIBHoZgSybwMFgYlOdCYmcNRXVBHsc0oqJGdi9iN/KxFIBBKBRCARSAQmDQIDmuiAlHrLiq2ShHZQnikRSAQSgQmJQJ4rEUgEuhOBAU90uhP27FUikAgkAolAIpAITAwEkuhMDJTzNxKBdyGQBYlAIpAIJAITA4EkOhMD5fyNRCARSAQSgUQgEZgkCDSW6Fiebkk41Cx5tmRXssrK0u5Ro0YFvzjKJD5geKXlCI7fE2WS5b+29WSZOIeFzi32kaXoHMHJT6qUv5sIdCsC3DTwF7TtttuOsYtcONSfMS4f2htz08BfVns5dxLcG5xwwgntVV2dL++WMTnN9I6pY8ItBd9eZVBcXJR6vrxKedlyqSG2Xsk3YcsP12abbVbFyxtTf1977bWq3tg5OeV7TVuOWbnAUM4fmrJ64qeMWwft6uVN3DePGSd3H2X8YxrH008/Hfx41etPPPHECkOuPupOYEubG264oaoXt3FymNsaSXT4KeGXZuaZZw6EhlPAVVZZpfLvstNOOwXfNpb+2r/99ttDTKt55523CnVgmfiqq64a4k+p5xel1WqFZdI/+9nPwguZEzwvVl5tlXN65wHiobncKE3ccmLHLw5/Pl6+/BNxbCigqnLJg2FsMLBiTRkPv/Cz354EVNVe4gmZjyRtnFNZE5MJaNFFFw2+kjh87G8M5513XhhnPXEiWdqahIp/oVJWtl5iYl1ZAcjTdylv4tbHBj9T/A15pvojO5yD8uPEr5DEe3jxFF3GzHfVvvvuWzlhLGWePx8lHIuKYbfCCiuUqq7fmnh4y0Zehg0bFibv9k7zG7bFFltU7y24cFVRx4XjU+USL+z14wWVPfjgg0OIknp5N+9zlom08mXmflhttdX67e5PfvKTmGaaaSpcjM+7XkP3Gd9l8PD8mcyVS7yUuz+04eBUWVMT57XGYpzGwF+X7ZiSBTdIcan37vHecjwsbEudLUeuknuUfzruNJQ3MI1zlxtHdLjT93L14uPJ1z5/LyYOvkt89XHS5+LxYTLjjDNWJMZLByr8nfCH4lh+UuyrQ4zkPUi+Trn1JzXit8fL24Tut5yjiYmUC6nzgjQuX9S/+93vwph9NcGD3yETi/HxF+NF66WD9d9xxx2x9NJLB4d3roG0zjrrBGd4zu0YDgQ9QOp8JfgN5U1KCN7OO+8cJhETNI/W7V/TJINCjRin9Pjjj8e0004bZTIqzgZ9adXH7jgv+uWXXz587Zvo3J/1Nk3aF9vr8ssvD4RNSJEVV1yxCn0Bt/o4+JISY87z6ZkUOoRjxdJGKJe77747PIulzNbHhZf+scceG7yOu0eVd3tyXU3OSAxv5ByNup/a++3Zc8/Ahd8m0mPttRs+fHgsvvji4cNMfd0X0xtvvBGeOR9h2jYlkfwhc+4FH6be0ddff/1o3fdR+cQTT8S6665b+VmbbbbZwmStEWeuCBI8vGe0VS5tvPHG4dmy3/REiiNSgHGKA4nQeZ+2j8uHpQ8EH66lznsJMdp///0r/MxlSA+S6UPd+10IJRgTALgerda/4zyWcwzE7YeaNiihGDz0X/va16qumyh8FXPN78b3slxjjTVGi01UNRyPP76+TEom+3IYskPa4yuzlDVpS9pAQgMfDuu8iJdYYolqCIiil06VGY8/vFAjjkTGDvOF5iVm38vdBGe/SYkqAOk18fCSLFYatacXSBmHe+B73/teyYaJvT5xk5QRsfc1eGeHdPGggw4K8cCQ8XeKG7tBdKiCOfY0CHi5lzyH8iWReqmTR3LEs+NkUd6kTULrhSxfEqmaZ5pkzcvcy7rUdfuWtLR+fUkGfVy09/vrX/96X5FnCHaIzZtvvhlwIqneeuutK1LjfaSxkDPnnHNO8HAu36RkgiWFL312D3h3l7ytiRdehx9+eBVHz+SMRKsr72MfF55HZEf5OKcGNET6vSfKe9SzxckoR7ft3ReCxvPj46HU+bhy/3jGlKlzPCkyAk516AN15MiR4ZnyLtNuoKfGER2iSTGvyoXxNUlsbvLo6ekJ6ig3S6kf1y1RKHUVvSjRn61zl+M9oHS/bpZS1qStr8OHHnooSGh8Wen7NttsY/O+kwfSwb5MbcsXponf17evMuVNSSYTkjserr0ceIQmqfHyKF+VxoJol7HKm4h9HdkfW0IMvXhOO+20Sj/uq2ps7bu9ziTkhTrFFFP0dRX5kfoK2naMWdiWQoLlqSlM8vWmnkfBT5VRX5COXH/99bJdnxA9ZLneUYSwnm/fJ43g4LSUewcdd9xxlbSMasLkpQ4R93FH0izfpGRirl9n90/7u9pzx4RAvEFkznukTPrG6v3vHebjg7RP2UBK7hPSm/b7x8d3fZyIHkJN4kUjUeq8HOexwAAAEABJREFUl3ixJ6VXBl9tSX/gJY/ceEaptNi3ajfQU+OIjotVmL2LY1Ki86VeIOUhIjaB92eApf2Yki9HX+IuPnGyr6m6qsrvuKGoKcZ0jm4uJwY1qUhrrbVWUJ+Y2Me3zxdddFGfbQopBpzoect5fJnKw5LKq5Q3Yetl4IViEkFySp/ZTbz11lslO9pWe9Iwk/doFW0Zk7WJ2nWgG/eSh19bswGd9cJFJH21k/B4Rk3cnHiSDNYHf99991X36AYbbBCkqWKmkYbV23R4f6Kd/qWXXgofDeUDzrum1WqF940FFSYuqiqqLSp1cQHr9+dE6+hE+iHkhrTHuEl2jLn8NAkrG0zGtNQzJFylbnLasq30PPkAr4/bPUNaz2aHJJUpBrUVQoSAL7zwwkF66rmiFmO3+V7GzvXzN3W/cUSnHWi6fV/UHgq6SeI4TJd9ibYmKHYX9ktS1v6iwKBJJkxE7H+IS32Bl2OaviWR8PKAF/0/EaYHwSRexgaXsj+mLSM5omOJPRQ1odUy5VhfXx6sueeeOxhijuk83VjearUCyakTQONyr7Ra/eux3WcIkvtnbGPyskaIfKVSWbA/oZ7pTyQ9tvN0Ux1JjmcGYdEvL173EyIj3558Tfpi9dJttVohz2Zg1llnrcizjxiS1PPPP78K4At3k75roo0v3fZzdmPeh5L7ovTNtTfhlHz7lnSCOkGcvPY6hBABQgRJlH3EUe14dklOfbQ05aucnVX9Xfzaa6+F93Z9zD4ALBZhXGxhBElp3daP2t09N2rUqCCZqB87EPZdV5JM4zMez4vnSrl8SQJLs+PxcU+TgawwRaBxIAjwrFALCoLtwwohcn+5n7yH3E+OdW7vr3LegbptHNHxwqg/LC4wA1sidBeJDYqbwktS3oNicveSkCey8zVAxOcFqqw90Y97CJ2n1BEd04e+15d7ad9tWw8GAqhflndi8hi+l6Wb30NA2qO+njwwHox6WdknkSBNY9Rc8FdHLcGgzteFfFOSe4a60gTseiM87gXkl0i9fRxeEF4mXr6f/vSn26tHy5v8nc/9qoK6xhb2tk1MSAi7L7YXCI7JHeGDYX/jIbnwHLJ/Us/+AGEuybMtVMvKK68cJnDqC+oObb3ASV3td3tCTPTbveF6w4dkpr9+w42h/5AhQ/qrDiRJQp58fBWsbBmh+nJvCi76S6Ln+dJ/Y7eIpD5w7ySYSZ4NEgjPWb2NfdItxMm7Rn4gpdVXXz08B8bE3QLc3FPyJVHlwlAaOXJkeAddddVVlapTG4Tx1FNPDbaZPtwRG9Iw96Tn0AccEoUA+WBxzEBOjSM6bE0Ql3JRTEBsJvbee+/K7oEUhurEBdSGPpdeGOv1tUjK4IXsxjG5+7Km92UTUGx0tPMCx5idQyKlMFmN6SWuTbcn9kukCPV+mmxgCFcSMS+aUq89MTL8Sll9C5Prr78+TFwetHqdSWrjjTeuFzVin/2DF7HJxYvYvWYi8dJtH4B6Lxov3Pa69ryvUKverGxTR9XquHKfKmta8vJESpBBonCSQnZZPiLg4kvSi7qMyz1BMtN+r5T6+tYKG8am5bl1jN+ot+nWfVIKKxBJX7xLSFJN6D4aEBO2NyYi/ffl7oOrfh/4IPHecizVMDXzQJiMGPC7jlQnxsYVhXeHd45rTcoMK+8NeW0YqlvkACurGZVJPq7qvnQcS5XF6NYcoH1TE+med7JxsueDh7H4EDUnUVvJjykxG4At9a97kF2gtqTsVs2SonqWkEUfFP2927QfSKlxRMdyZhORi+5C+NJhrEclwL4GcWHUVyZnD46lvNQr6r2MrWRgZOp4LxRLZBEdddowbCY6JSLWRuLLw4PlBpT/gGmSHG6ZooeGpGrZZZcN9kZeyjrjgYAb1q9eMjl72OjBTWhwqdvo8IdC2gEzL2J5x0m+yDhedO4mJS8FY5YQES8G6jhj8ALyArEvmbjYTiDN8iUxBHRPwqCo+tQRxXtJwcc96SXe9C9S94/l+CaXo446qvJZZayeL89LXQJq+Tmyp76/ROpYl2y4H5EC95eXN5F+f8d1Y5lxIDvsikhQSUUlPqm8w5BE/fb8wIl0TF6i2nP/GDf7JEvOSRvV1ZMVqOXerJd36/6UU04ZpAueA1II7wt9pWpxfX1EwsizZ2WZ8Q8bNiyQXu08e8okE7XnSLnkWO9sZLqJ7x1jKMkHtVWbxsm3lOuvDimkivLMyZcER6Sm5L3jzWWS+bHcO+ZEJJNQAK7e+T50y3EDeds4ouPmJrFBaKiwfA25gC6+l4abwcNSv2i+ItRJJrK6SqUcp66eygTky8vLys3ny6p+3ibt+yqCncnE1zbsSK3KGGDohUzkrl4i/VIOC3YTJm4kU51E6mGy9gJzHipB5ZIXmmOVNyl5KXhx+EIksTLZuseMgSjYxGtfcl95IZd6ZVLBkXi4EEPlJmpkET5syUg+lDc9WRKNFHv2ylh8JXrW6hO4+6ieL23LFjmq1yMD5Zls4gcGPPS/9L3VaoX3CClpq/Vvmy/vKri0Wv/Ow0J7eDqWKp7kWXl78u7ygdFe3s15794yttJPGBhreV+UvDLjdx9oa7zKpNJWuSSvXBoIkzcthbEggeX6e6bcU/Ax5pI8M561kvccwRjWpaxs3S/wUV/e26Vu0m8714PGER1QUCWQxBSpjrJOJXpMNxeRYfuE1qnfzPMmAolAIpAIJAKJwIRBoJFEx9CJ5CaG2BY7ZhyGTfvdTIlAIpAIJAKTHoHsQSIwrgg0luiM6wCzXSKQCCQCiUAikAhMvggk0Zl8r32OPBGYjBDIoSYCicDkikASncn1yue4E4FEIBFIBBKByQCBJDqTwUXOIY4/AnlEIpAIJAKJwMBAIInOwLiO4zQK3pAtMedpk1Myviv4deHfRLnEx4WTleXnyvj+mHfeeSs3/fL1xNOp9u2J506/117ehDynilb28aNkGfjY+iyGFb8fMOGFVFt+mfjW4WJdgEJlEn9E2kmcMyrLlAgkAolAItBZBJLoTBB8u/8kHNtx1iZcxo033hjix/Auyp8CB3d8e/Day2um0fDfwIO0ZfxnnHFGcFO/9NJLBwd6/MBIHOhxfubcjpG4a3d+JEd8LWVNSggeB3gHH3xwCI9x9tlnBy/G/Y0ByePxlmt++1YC2nL2BUuxm/jS4YPI8Vbvwe2SSy4JjsAmhnsEv5spEUgEEoHJGYEkOpPJ1Sdt4CCQAy6O7kS4FQTO8DnbandCpfy9Em+4nHndeuutfU3FvOJY0Dn7Chu08/vf/z440uI7iUsBnqIFXRT8rj4MMWNIfUjFeK8t+JHacKDH4RdHX5xzia/mWA4HbXkLJtWxnykRSAQSgQGJQBcNKolOF12MTnZFLKuHHnooSGiuvPLK6qe4AK923uef22+/vTqSVKja6f3DBTsvnTzA9mYb9V/QTRIZnkZ5p+UlmR+lp59+OtqlU0KKCEKJQFJdFamPsSN/3P+LhcV79IILLljhQGpmh7dkQR6ptuQzJQKJQCKQCHQOgSQ6ncO2q87MnThViSSQm3AWJvbx7aRJmjRCWm655ULQQSE5nKcQAgRAvmmJ2u21114LHrCRnNJ/hIUKquRfffXVSp3FxboQESQ0P/zhD6tqEjOqL+op0iCxaZAflcKJiHEl3oxywQyVZ0oEEoFICBKBjiGQRKdj0HbXiUkkpptuurj77rtDsDexnJAVk3jpaX0yL2Xt2xKkkq3JCSecEGussUaQbjiPQIRseEzg1DXCZ7z00kvtp+jafKvVqkhOnQDCBOlptf4TiwgZ+vvf/x5ioLHLKTHBzjvvvKDiYptz6KGHhhhGSKX4YQaNQMGI/RLVnjbKMyUCiUAikAh0DoEkOp3DtqvOLJinlVY6JeikyRYhYXDLFoU9CWmP+noSobvYn9TL7ZPorL322sGoedSoUUFlg0RJTz31VLADKoa42nd7QmhEQRa49M033wyE58UXXwzSMKqs0n/2N8bKHkkZmxwYafPggw8GlZ7zsO2ZddZZY+TIkRUB0lZiwCzCMLIjn+l9IJCHJAKJQCIwjggk0RlHoAZCs5122iksna6PhbGsCZoNjyXPyE+p155dyZgm5Mceeyyuv/76YLhLPfOrX/0qSrI0m6SDTVA5XxO2M844YyA6bGtIqUjAGB0jg6X/n/vc58KKM4bIyhgwW6U2zzzzhGPuv//+6hzIEBy0qSfnv+WWW8IqrHp57icCiUAikAhMeASS6Ex4TLv2jCeeeGKQJFBZLbvssvH444/HF7/4xaq/DJPZm7ArUS8xMmZoayXRyiuvHKecckrUbXQWWWSRStqxxx57xBRTTFGdp0v/jHO3EDZjluacc85qOf18881XHY/cbLjhhtU+G5vZZput8i20wQYbxN577x0IEFXWMsssE6RdVrlZar/55psHI2eYSoyTlTm+Oln+SQQSgUQgEegYAkl0OgZtd514tdVWqyZlKiz2NfzFmIhLL0ltqLTYk6iXSGSUszWhgrJsnDRCnUTqceGFF4bl2OU8ZXvDDTc0UmJBfYXAsWEisUL+2NYYl+Xhxx13nN2wMuvAAw8MOFhpRpqjggpr++23r4gNdd6ZZ54ZlvN//etfr9pqf++998bCCy+seaZEIBFIBBKBDiOQRKc/gLMsEUgEEoFEIBFIBAYEAkl0BsRlzEEkAolAIpAIJAKdQ6DJZ06i0+Srl31PBBKBRCARSAQSgbEikERnrPBkZSKQCCQCicD4I5BHJALdg0ASne65FtmTRCARSAQSgUQgEZjACCTRmcCA5ukSgURg/BHIIxKBRCAR6BQCSXQ6hWyeNxFIBBKBRCARSAQmOQJJdCb5JZg4HRBQ8sgjjwzxrjjCEwJCcEmhGjgP5PmX88B99tmn6lDxsyMK+RJLLBHzzjtv5Q9G+ANO7yT+YYQ5cO7qoN4/jzzySAjyqZ6fHo7yeosn8P/OnU7YB96QF1pooZh55pkrJ4nCYLT/4kknnVT5JTJOCX733HPPaM1OO+204GixFO61116jHcM/0R/+8IdSndtEIBFIBBKBDiCQRKcDoHbjKW+44YbYb7/9giM86b777gskh/djTvAGDx4cZ511Vuyyyy5V94U0ePLJJ4PzO+V33HFHrLvuulEP6nnEEUfEpptuGs5dHdT7h7dlhIpjPMf3FwKht1nX/n/66aerIKXHHntsPPDAA1Ucr6uvvnq0/iI+X/jCF/ocAAr5gAzyilwaIjDIUMkLAvqNb3yj7xhxx3hGFnKitMltIpAIJAKJwIRHYLIgOhMetuadkcfjt99+u+q42Fa77rprbLfddlVe+AZxmarMePz54Q9/GP/93/8dYj05jBRIKATEqR4zS11TEoJCakW6xcuxkBg8HyM39TEssMACfdk777wzxMMqBbxHiwEmDEQpsxVPzFa65JJLQpgJ+5kSgUQgEUgEOodAEp3OYdtVZ15llVXihRdeCOENTjg+bloAABAASURBVDjhhKpviy22WLV9v39M1sJCkFQ4hwme9AdR8DsIgPKmJGorUhz9F+Kh1WoF9dJzzz0XJDJlHNRUCF7JX3XVVVHiYSm74IILqrzwGfIScum89iUBVEnI7GdKBBKBRGAiIzBZ/VwSncnkcpt02dKwzyHJ+eY3vxnPPPPMeI/+oosu6rMzWX/99ePwww+PueeeuzoPm5xBgwbFbrvtVqnAVl111Xjrrbequib8IfGCkXhX9f4iQOrqZWWfFGuaaaaJ6aefvipio0QaNNNMM1X5/v6QgJHuID/91WdZIpAIJAKJwIRDIInOhMOyq88kwOQbb7wRgnCefPLJ8ZnPfCYEonz11Vf7+l2XWvQVtu3UbXRIdLbddtvYf//94x//+MdoLZGg//3f/w1EYLSKLs60Wq0qQCmyU8jNm2++GVRYH/7wh/vtOVUXMiewqfHecsst8dvf/jZOP/30oPJiB3XttdeOho/8XHPN1e/5sjAR6EoEslOJQIMRSKLT4Is3Pl3/3e9+VxkiO4btyMiRI+Oll16Khx9+OEh7PvKRj4RJW309/e1vf6vq62Vln/3K97///bCyigqrlJctg2YEoOS7fUuSw3gauUH6kJ2XX345Zp111n4xUM/o+nOf+1xV32q1qpVapFpjGiv1HvslhshjapPliUAikAgkAhMOgSQ6Ew7Lrj8TmxCqFR198MEHbSq1E4nFOuusE3vssUeUepVDhw4NKhj18u3JSiyGx8jBVFNNNVq1Zeq77757sHUZraLLMwjIa6+9FsgIA2TL562o6s9Y+5///GdFDstqK1KfBRdcMHp6eqrEYNmqNobKDL4NnWQNVlNPPbXsQE05rkQgEUgEugaBJDpdcyk62xFSG0a1O+20U2Vjs/nmm4eJukzAG2ywQVgOTULDL4y09dZbx49+9KN4/fXXK38wl19+eVDNqJNWW221qt5KK5P8z372s+rc6tisUHN1dlQT/uykUJbYwweBoYJjz+SXkMENN9zQbpWouKxmK8bYVWHtD+Nj0p5aUWivjH+iennuJwKJQCKQCHQGgSQ6ncG16866zDLLBNUMGx3qFgk5qXeU40DlJS2//PJVtZVH559/fp8PmFJvO2LEiKqNPwiPMsnSc2XjlLqs0eyzzx433nhjpZJbaqml+np36qmnBglWKbDyir+dkm/fIoJWV9XLOVPs6ZX41MtyPxFIBBKBRKBzCCTR6Ry2eeZEIBFIBBKBRCARmMQINJHoTGLI8ucTgUQgEUgEEoFEoCkIJNFpypXKfiYCiUAikAgkAv0ikIVjQyCJztjQybpEIBFIBBKBRCARaDQCSXQaffmy84lAIpAIjD8CeUQiMDkhkERncrraOdZEIBFIBBKBRGAyQ2CSE53/+Z//iVGjRvUlLvM5bPsg14FLfgEsebj9IOdxLO+3PAgLnyDfxMTxHf8to2o42zcWYQuef/75PvyVl9Qe1kF7vmPq7XlOlmCuvuD1Qa+hc02qBBN4uRd5SB5TP9wXsBIzDC71dnwWqXv66aeDN+RSxyeR8ldeeaUUNWCbXUwEEoFEoLkITHKic84551TO6JZeeungSXeTTTapnNCZDN4PrCYp3mx59bV9P+eoH2Oyv+CCC+Kuu+6KMQV2rLfvxn1efgXfFHSTA8Dhw4fH/PPPH7/61a8qD8BHHnlkCOdQ6tTz5rvnnnvGnXfeOdqQDj300OBIUBvpsMMOiyOOOCKEStCQU8GVVlopRowYIdu4hBQKXGpsw4YNC35wEJr2gQiXscYaa4R28Dj77LP7miCCPEOrs7333nurOqQHpsqdW3DPqiL/JAKJQCKQCHQMgUlOdHiaXXnllUM0Z87XTKS+pk877bRo/0ruGAptJzYBCZGA2IgwrY9DhgyJVqvV1rIZ2a997Wux7777BgeBG220UUVwdtxxx+D198knnwyBOWecccbYeOONqzoEaOTIkXH77bfHDjvsEI8//ng10IMOOiiQAKEitJGEOCDRQDA1EiqheFuWb1qCxxVXXBE77bRTuB9ffPHFuPLKK981DM4D3RswQNDlSyOhMbbaaqsKy6OPPjqEhVCHFH7xi1+syoWH2GyzzRRnSgQSgUQgEeggApOc6LSPbdppp60mBoEi3w/RMdFyyT/NNNO0n3qc8lQVJ598cjXJIzrjdFADG5G68JT8wAMP9Nv7hRZaKEgpTNrXXntt5SXYhL7eeuuFeFDlIN6TkcCiulpkkUVGqy/tmrId1ave+9jHPhbCNAh0+u1vf7uSarkv6mNwn1G7KhPqQfwq+44nBRo6dGggTMpKcl+JmyUPf9IykiH5TIlAIpAIJAKdQaALiM7oAxN/iX2NL9+//OUvQT0g/tLcc88dBx98cLB9+MlPflLFVDK5+NomTaBy4J5fnCWSij//+c/ViakKuOo/77zz4p577gkEaNVVV61ULc4vppFjPvWpT4Wv+ZNOOil++ctfBonHuuuuW0kzFltssUqS4YSkF87vGL9/9dVXB1sgko4pppgirrrqqvjyl78c88wzT3SznYpxIHJf+tKXDKvfZHxzzDFHkHDddNNN1TjnnXfe0dqa5FdZZZUY23lGO6CLM8ga4ude+OhHP1r1FNmhuqLCrAre+SM2GAyprc4888wg5VL1hS98IeSRHGrYH//4x1VMMfeee1u9ds7Pnsm9Lp8pEUgEEoFEoDMIdA3Refjhh+OUU04Jon5SBAEhTa533313RWquv/762GabbeKAAw4IX9niKZ111lkVIRFokq2PvHLBJU1aINtyyy3DRGxfkMYTTzzRbpVMUlNOOWU4hp3JDTfcEGI09fT0xM9//vOqP/rw2GOPVe39ob5ZeOGFq2POOOOMSsVx2WWXVV/vCJe+6L8gmraO6aYkhhO1FJWUuEvf+973xto9JFED0gvEyH6mCIR5+PDhceutt4b7Y4pekguXj3/84wGzr3/964E0sV267bbbVGVKBBKBRGDcEMhWExSBriE6ZVQkOewekBmSl6IKMXlow9h18ODBdoOkZeaZZw7kgiSF9EcFaYvz2B9Tsvrl/PPPrwyhtUFwqGXs19Oyyy4b5feUX3PNNTHnnHPaDWoIBr5PPfVUrLnmmuHrf9iwYWHSo9qoGnXpH3ZHcJ6yl+iNrYsma2ocdj6tViuavPpsbONUR5VHdUpChygjdu4Ttjjt1/Pmm28Odlww/OxnPxtbbLGFU4yWkB72Z1ZYsWVyPivUNHr11Vcrmy8SMflMiUAikAgkAp1BoGuIjojRVEWkKSQm7RNLGT41AmJR8tQMJiKqBatdSvl7bU0wJjbne6+29XqSDatnShkC5vdLvtu3sIWxVNQoY+ozmxw4kfywxaGSo9prb29V2iOPPNJe3Lh8q9UKhI5tmCXhVEuIDqLrOtcHRAI4/fTTV/ZIe++9dzzxxBOVarTexvHySDcyhLyTXCpzDyHkJD/ymRKBD4BAHpoIJAJjQWCSEx1Egy0Lexmrrep9RSpIENT7ylZnNQtbHFKcUaNGVeoBai5fziNGjKhUCdQJJhL2ESYWEhYTMRscq2NIhdhRUGttt9121QQ1qvdcVF6tViuQLCoHS7JNdAxR2fyYuDbeeONqNY4VSY6htrBiTHtf7JZy20e8rNgp/db3SZXYL8FW/2AKz9IX6jb91EadMUmMZUkr2Cqxy4Ehtdcll1xSrdLSRjrhhBMC1rPOOmt1StIL19JvOG9V2KA/DK1hQmXq2lJdkty1Wq0wHveB4TDW3m+//QIGXA8gQlSj7hVlkmOtWLN03zG77LJLMEhWx6bH8aSA6jIlAolAIpAIdAaBSU50GPEiBoZ31FFH2fSlc889N4j/SWpMGCrY1SyzzDKB7LCRsDKIWoDaaq+99qpIiEnqBz/4QWUYPN9888U666wTDEfVL7fccrH//vsHEkRiYbm1Zb/OtdpqqwXpDAmG3zNxsbNhaItYIQOWYiM2bIkcc/rpp1fLtrWjvrI8nnqLSs1qJV/6+j0pE/KFtJGY3XvvvVH3L4SYwJDRMYNZY5KotEzgDK9L30l0fvvb3wYctJGQAoSxtDH2VqsVCCIXAaW8KVvjRkhcO8bviDApj/4jKeyy7G+66aahDgbsvtwHyhFqZRKbL9Ie5RJyhJCrW3311WOppZZSPPFT/mIikAgkApMRApOc6JCQkBxIBx544GjQW7Wi3CqoYhdDlWKllHIJySkHmXyUmVyQmW9+85tVFZ8liIk6ZOSnP/1pOI/Kcoy68hu+4OWpedZaa63K74k80kVVttVW//aRoszkTwXBMZw8Y2mrwuxLlrr7nUmZTNT86OiPZLIt/fn85z9fOWpU3p7UlXZlyyaJ2qa0RRRLnS2ng6WO8biypiXX1HVEpq2eK/3ffvvto07qGHSXsZb7cK655uq7XxCacqwtSSFC7hj3obJMiUAikAgkAp1FYJITnc4OL88+ARDIUyQCiUAikAgkAo1FIIlOYy9ddjwRSAQSgUQgEUgE3guBCU903usXsz4RSAQSgUQgEUgEEoGJhEASnYkEdP5MIpAIJAKJwOSJQI560iKQRGfS4p+/nggkAolAIpAIJAIdRCCJTgfBzVMnAolAIjD+COQRiUAiMCERaCzR4eeGM0Bxm+pJzCt+efhx4dukXmefHxQO29pB1J7fF2347+EY7rrrrqv8wdx5553BX0z7MU3JcwQo3IWx1RMHghwyitVVLy/7HOS1j5EzwEsvvTRKG3HCJA4SteWsUR3fPPJNTLwi8/Ys9lrdC3f7WISBMFbOJzlIbK/npNE91F7u/nSPuT/dd+31Tctz+inOHBzca2PqvzHDSyBevpjq7X79618HH031MkFQxbh76KGHwr1ar2vCPueRxmXMrvmY+uwe0cYzw2dYvZ13HN9UQpKUcvcax6eO4d+rie8mY9J/vrvKuNq33ivaSPZLPVyVSe6P8u6BEUeuynlwHwgBc8flXfSHP/yh731s7FJ5Du+7774qZqP7hWPXgqGtd5u2kne6soGaGkl0ymTqZWmi4BFZTCYXidNBATnLTS4IZ91BHqIjppWXS3l5is7NR0o5hxfJL37xiyqAqHOKo+VF7kGSb2ri/K8EmOQnBg5iLom/BDMviTK2Y489NtQbdymz5SNIxHb7EmeLRx55ZCBAjpeU89uz00472W1UggWfS8iwifaYY46pAri2DwLJ4YNJufsHBvbryT3FUWO9THwsx91///314g+0PykPRnL4uTKRe27gUF6y9X4df/zxlZNPZVdccUVwFGrffcMxo3tNG2UlccioHDFq4rPHweTvfve76tngu8uzVsZWtsgdv1TCjjz33HMVRqWdD5Cenp6Aj3vRMTytI0TyPkR23nnn4JiyvMu06fbkY5R3emPhU8rz01+f+anyQavOfcDxqX0exRFI+xdeeGEgTfadhy82+MFD3EEOUdU1MXkXebYQEtfbO7lO+MqY3ENl35YjXFsEiDdW2wQzAAAQAElEQVR7RNiW53/vNXXuN/OoayA/0FPjiA5Ji4dE2IfBgwcHb8Y8Fwvw6aXA+7EvcZ6QOfubbrrp4rvf/W6okxAhDv5MNqQZXrRezlNPPXXsuuuuVTsPFc/Kvq4+/elPx2abbRa+8B999NFG3g+CcnISKLYXJ4hwQGzgRFrF0zPngKVO/cUXXxww4XxRGwNXjlzC2L7EAZ7zehA9hIKFKoflIYccEq6XY5uSXGOhQtxXnB/ylO1+a+8/CZnQDsa69tprhy/LehvSC/devcz9hmy6H01Q7k/3V71N0/ZN5CYW3sclY/QCbR+HydpzBa8hQ4ZUgXi1+cQnPhHw49RTvp48d/PPP3+9qDH7JmWE2fVGcnjcRnzqAzDZ+AjjlRw2nlH4IY3aLbjggiGwMUel8hIpIQ/b2sOH93ZevJtCBJE5H0e77bZbcDbKqSvig5gYX0neOSZlDjrdM+p5mXc8Auh45Y5X7jghV8Tv8zEmSDMskUF1TUy8rMOgvIuE4fGRUB+LZ08MPVhI3/ve98LcNdVUUwVJ19JLL13NaXvuuWeYz3xoOd77mrTINXCc4NXKB2pqHNERawnTdXFbrda7rovJxQSNwLyrsrdAvZcEluslQ5zni3HRRRcN8Yp6m1QhHbxkVlllFdngDdnXJY/ITXmhVB3v98+/C8WvarVaVWiMf5eM/heGXszw8cUoHIS8yQwepTWyKWTGhz/84UB6TFzqhMCwbVry9WQsJiaejL1IqU68NOtjGTRoUJDqkGIgf7PPPntfNcyI18XN6ivs3UEQ3X9eRF7Ajustbux/EzUp6LTTTluFTvEitg8vdfWBzTTTTFUcOi9X5eX+gLXJqdw36kpyruLBvJQ1ZWvS5WF7+umnr94nvK0XKUQZgzhnxm5CI5k2+ciXMSPBnsNW6z/vuRlnnDHcV61WK2DmI6+crwlbUgZ9NjYfSMZSJt96/314wYNEx7PnY22aaaapxuxYHxakEe4f53OsNogQiaL3v/baqmti8i5yjxhD/V1UniFj+sxnPhP1dw/JDe/ssOWVn1f8VqtVzW0FJ8f54Ed+zIXUo8oGcmoc0UFKfNGM6aJ48bopPChjauPl46G49957Q+BGX+1eKO3tSSVKmQfSC5x4tJQ1eetF7CvJ1+TYxuGL0cRNJaV9maDKMR/72McCSfziF79YiqqtFw6JxdiuQ9Wwi/4gsV6sCK97qHTNi7Z94kb4vDi8KEh8BD/V3gsWtiY2+ZIQHzY5XloIjzhX1IelvolbmLg3PvvZz1aBcMsYYADLkrcVDoP0Rxwx6i5f3MoHahJvD1Grj88XdT1v30cCrKh5qWDE8TNBq3uvhCibCEm9TPjv1b4b6km6vDPqfWFm4N1SL0MSSbiE1qH+pQaeZZZZwvPjWaMal5Br0nzH+jhFEkmhqXk22GCDaL8G2jUhuSf6exd5tjx3/Y3B/UAi7SO2vR6+sEN+1JHiIDgEBssvv3wMlHnN2PpL70l0+jtoUpYhJ7566n3A7Ito3E3ui/m9JvByvEms7I9t6yYxabs5xtau2+vYnJic6Wu9MKhRxqXPvjjHpZ02RO9Ez3Tp8gMx+ZJC8HxV+1KnovMy8XIaNGhQuFfq4/Z1Rvqz7rrrBr27+xXR0b7ebqDuk/whw543eI3pZT1Qxz+mcblXTODIIlWySX9MbdvLqbFIphdZZJFotf4j9Wlv19Q8NQzpMQJoAi/jMDmbsKmsSDC8/9VR1yB9SAJ1GFKlfHJJzDXcR/19tFOlExC43+ChDVJIrYr0eCcpH6ipcUTHl/FHP/rR0a6HLwR6/j322CPc4Ng/YjJao1rGA0C86eX79a9/Pbx8nbfWZIy75aEaY4Mur/CVBCcvD0ZrpFlj67JVEb6uYNVqtWJcjPvYDPjKap/sx/Y73VDnq5iKxf1gAvm///u/cJ+Q8PhyrvfxvPPOq3TeXhACdEomb8aV7CqIjBFJEkh2SyYlUiL3m3P5knd+Kqz6eZu0bxzuDfcEvEwwCLHJCZb1sVD9uvcsHPChImBvvX6g7VMfkGqWcSEwiF7Jl637iJqUzUlJ4/p1zXibDZD7s5yv27ek6SQPpZ8kFMbfar2bqLEjtCjEghL2SMwWHOd5IhVleOy+89GgnMQQ+RHQWZ3nj2RN3QROHT+d56f9XUQS6tny3PXXASrQVqsVyE69numBOXO55ZarF/ftk7w3+T3UN5Cx7DSO6HzlK18JEoP6mOgjvTyxVS9eX9n1+vq+Ccdk5IFjhMsweb755qtWgfjiLm3tMxj1glLmJe6hoq6Qb2qisoOTROw7pnF4qKgXZp111mCo9v3vfz98LTEwhk05jr0UPD1kyqxQU8ZQV56xnG1TkvvLtTZGRMS9xlC2fTKxGg2Z9uIx2SDbVJtW0JjgGGaTaCE0xx13XCB+2rJ1ggXCjGSOq5rCMd2WvHBJTo3XhwKbClItUkJ1pb+IEONtagSY+Yosq/9Km4G29fVMreejCgG2ys59Uh8nzBhue38xOCapQJD6U3HVj0PEvZuoT+HpXoV7vU237rMxInlAiD1n7h1q3Pb+WvzBFgU5pD5HlKnPkUCJCt2zwy7QO934fWCZ0ElbLawgcWUT1H7upuS9i9w7sPIu8k72DiaJaR+DuQkOyJH7Sb1j3HdcECAzrVYrvJuVq5fs33HHHUFlKj9QU+OIDnJSJlUPgqWWHhwSCvY25UK5OdhEuPheqnwFSF6yyjB+hIi4c9tttw3ntJxRG8mE5eJ7mJzTl4IvEQ+dfJMSqYSJxk1N1EtKU/rvpWm5ONWLOmOX6G09FAzWiNa19/LxQLHU10ayYs0XBEIkzwbDJG5fImFzbFOSsSLBfMLov3uFCF3/kTgG7PatyGKcrQ3yZ5J6r3uDbw/JMWwPSNS8+J2vqcm4ETZqF3h4Mc8999zVcPjuQP7cH1bCGK+xk2JY9aiR9lw9eL5MSoxMlakrz7QXMZW1sqYk95Axk/gdeOCBgfCT9Bmb540hO9yGDh0a7qsTTzwx3HM+RExWxgkP7ag9YanMvkkfeSTBgCd1vXeT+m5PyO4WW2wRpO6wMRH7GGi1WtU7GBbeO+xtEBbvH2P04UAi4UOW/RtslbtfzAmtViuo8NxvbHo8ZyZ8xLHbMRlT/7xTLV7gEsVYEeDyLiIpLu8ix1thxai7PHve9SQ5pIQkyY5ngMytg7bykvuOXav3mfMM1NQ4omMC9qXkorsovmishvJlKd+ekJhyc6jDbF3gckMo8zBY8ugrQF6yfN2XvH2JvwbiUw+ffBPTCSecUC21b+87wsfwukhh1JcXiZeQvOQlTHqz4oorylbJ1zusqkzvHxh5gffuVnYDXu72m5JgsfHGGweJFynN1ltvHb4q2/uPBHtRK4cLw0j79eSrfvPNN+8rcn/BR4EXOdJtv8mJrh9pQWbKRwPJVfuYGNsyDlU+55xzRh0XZdQRpV5egr8XsHvQpKWsSYlEFBF0/5R7pb3/JmnvKBIwGLLhqkuNy32ivhyLHJFCl/yKK64YJBkl3+1b7wTLnklJXV9q3vY+e6bYsBXSByPvJ8+nyZuE2TEkHGV1LEyQQO/oVqsVnuMmf0gYKxVveReZ58qHt7HXk+u/0korRb2e5EcZLLV1f8GwvJ+VaYN02h/IqXFEx3JfNzTdbavVip6enr5EnFkuFgKE1PTU6u3zSeDFU9qVrS8FomBtJMSp1fq33hjrpcNcb731SvNGbd3o7COMS6pLHrxsTNrK25O69oE6F3uU0tbkXdqUsrL1EvPSKfVN2XohMGg36RAfl377kqyTZhNwGatjSruy9ZIe2vvFXvL07siPY7zoS3nbtnFZX53uCWoE90cZgOeJTVLJG7eEGFPVKDeBw0i5NGTIkFCmzmSmTPIxoqxJySSChOg/EqjvxobclI8oE5S8NvAbNGiQZlUySSsvSaH7UbtSZosMNY0IlvdR/V1EguEZM8Ebq7zxSYyPlUneKcqkentYwkK5dw+zBO2bnLxX+nsXeffU30XGXs+7H6y+gkVJ7FgLqSxl7iVzZZMxGpe+N47omCwweGJMou1xGeQHaUN8jOQQD/c38X+Qc+exiUAikAgkAolAItBZBBpHdAocvoJ8GZZ8p7a+qhj+lS/QTv3O+z5vHpgIJAKJQCKQCCQCY0SgsURnjCPKikQgEUgEEoFEIBGYbBFoH3gSnXZEMp8IJAKJQCKQCCQCAwaBJDoD5lLmQBKBRCARSATGH4E8YqAjkERnoF/h3vEVPzqW1dcTJ1v86Fx22WVRLy/7jLB7Dx/tP0+ml156aV97/i0kvhk05NvB8c4p38TEPxOfQXzD8FsypjHwcWKs/J9w2lZvd/HFF1cYnXbaacGgXR0fRHyoOKZp/oX0P1MikAgkAk1EIIlOE6/a++wzz6ycJzqcjwrL5Tky46CLu3WeR9VJguKp5wFYviT+dUzWJc//EKeBCJDglryWqrP8X0wj+01KsDj66KNj1KhRwdOv2GCIXPsYkBz+Y5QbMwzsS/L9ESTOujgz0+a3v/1t1I9RlqkZCGQvE4FEoFkIJNFp1vV6X73l24TfCr4V+L3p6ekJxIbEQvBNK9jEpSp16kkkOIOzrF4bP6ycB1ZxZuxL/DA4L1LAFxEypXyppZaKK664wmGNSoIHPvnkk8HfEl8cnGshcO2D4Gma8y1j5Z+Ch9bSBtHh2LLUWbmnjndbvi6U8/EEf+WZEoFEIBFIBDqHQBKdzmHb1WfmTKrVaoXwGf11FMnh0p8Xaq7ExWiS5wSu7vlWsE/Oq/g34tRriimmCDF8qLJ4M+3v3N1cRhJjLFNOOWVwnc5BJbfyVHz1fnPsRqpDZYX8zT777FU1kkRdxWMrydDLL79clfvz1a9+NYTfgA/JUROd4BlHpkQgEUgEmoRAEp0mXa0J2Nebbrop2OjwAD220/JciuxQa2lf9z7tOG76Bc8r3qYFQRVOQqwx6ixtmpIEGXz88ceDY0gkp/QbyRGjqORtET6u+qn3SHx23HFHxcFLMMmNYI3UU1R9AoOqFPYAlvwykQCNKSyAtpkSgUQgEUgEJgwCSXQmDI4T7CydPhGbE5Pzz372szA5i1U1Lr+JuIxLO22EPmCM/OMf/1h2QCZu6BE87urZIv35z3+uxon4cS5JjXfccccFydjdd99d1YlDQ00oiB4p0UsvvVSV559EIBFIBBKBziGQRKdz2Hblmdnq7LHHHiF8xu677x5sUMbWUaoWkzYVVavVClGTx9beZL7xxhuHAKLUXWNr2211VFZIGgmOFVIiAFuxRsIjRlG9v+edd14ILEsqI86TVK+3L/4MMkRNJS9mGoJkxRU1lhhPyjMlAolAIpAIjBGBD1yRROcDQ9isEzBMZl8isacZU+9JG374wx+GwHrLLrtsCLDI7uSQQw4JdinlOJP4r371q3jkkUdKUTBOpsqae+65+8qasiNoIhWWMSI61E4CMCI79TFYN8XXaQAAEABJREFUjSYYHnslBtgkOUhhvQ07JcSwBNI7/vjjK7WYAI+MwamxytLz+nG5nwgkAolAIjDhEEiiM+Gw7NozkUpYJWTivvHGGyuD2NJZ0gvLxale1PHxIlk1hMTsueeeMcsss1TN+ZYh6dhoo41CG8kSaRF2ESJSDmUSIlCWUlcHN+SPsU4//fTBN45x/OlPfworpXT/qquuCn6C7FuRxThbG+Tv29/+dhR7JmXSSSedVEnMih0U3Mox++67b6y77rpRVmQ5Z6ZEIBGYAAjkKRKBNgSS6LQBMpCz1En92eSwM2FAbLl0GT8jWpO1FVWljDSI9GbFFVcsReF8K6ywQl++7Ky++upRP18p7/YtLKjeSLxIabbeeusgnWrv9zLLLBPUVtH7Dy6IS+/uaP8ZK5OKlUI2SwyS5akDjzjiCLuZEoFEIBFIBDqIQBKdDoLbLac2EbPN6enpiZ7eRPJQ+kYlY9JW3p7UlXZl61zsUUpbvndK3Q9+8IPq/OpIeEp507YkVMZC4kKVVfq/xBJL9El3lCGBxio5RhkCIy8tt9xyivoSOx9l6qS+ityZlAjkbycCicAARyCJzgC/wDm8RCARSAQSgURgckYgic7kfPVz7OOPQB6RCCQCiUAi0CgEkug06nJlZxOBRCARSAQSgURgfBBIojM+aI1/2zwiEUgEEoFEIBFIBCYhAkl0JiH4+dOJQCKQCCQCicDkhcDEH20SnYmPef5iIpAIJAKJQCKQCEwkBCY60Xn44YdDrCVpu+22q0IKCEcwkcY7QX/mzjvvDDGj/vrXv07Q83bqZL/73e/6sD/00EODk8Arr7yy7+f4eXFd+NsRdFL9tdde23eMoJ59jd/ZefHFF2P48OFVm4MOOuid0ohf/OIXccopp/Tlm7TDwaLAmxtuuGG4xv31/eabb67GDK+SnnjiiarpJZdcEo7li6jEuVLBYeOll15aHXfGGWcoypQIJALvgUBWJwIfFIGJSnRMfGussUastNJKId7SqquuGvPMM0/89re//aDjmCTHzz777LHppptW3m8nSQfG40f333//4MOFMzzYDxo0qMJecEmn2WabbeJf//pXdV3kN9hgg/jLX/4S8847b3zzm9+MCy+8MAQEVVdPjz/+eEVqZphhhlhnnXVC6IhNNtkkeFS+7bbb6k0bsS/0A3yGDBkSiPixxx4b999//7v6LnSDdiXdcccdwaMyYvPcc8/FDjvsEOJmbbnlliGIpxPwPH3qqadWGN90003BIaPyTIlAIpAIJAKdQ2CiER1xgLbaaqsYMWJECC9gouU518ve5NK5IU64M/9//9//V01g4h85q4CY0047bRXbSb5b03XXXRcCSiIeMIc9b8Y8+5LIPPvss2Hi5QVYndhNPCVzcCeWEyeBa6+9diAwwkLUx4kACWPAYd4XvvCFKlr3UUcdFXXvyfX23b7/+9//vrqenCoa12yzzRaik9elWa+++moIyAkrSUwrDgbhxcmisSM5SDD8iqRHfCtk0DE//elPo5DLbsekWf3L3iYCiUAiMDoCE43oiB2EGJS4QaUbgwcPDlKekj/nnHOqL92LL744/vnPf1bFTz75ZNx6663x4IMPVnUmbpOLyhtuuCEuuuiiMImLw3T55ZfHueeeGyZwk9bIkSPj9ttv17RSQ5jQqXCUk0aY5O1TN5BoOIf6Rx99NE4//fRK/eKL3vm4+T/ttNNCW+oNajj71cl7/zz11FNV/5xPX02OyJHf9Du+5p3T7/Y2nyj/jUksKx5+pfqPCtbpeiApM888cyXBgDsys95660W9PenbdNNN1xf2wHn++Mc/VhIccZ7km55cL6QEYfnkJz8ZH/7wh6tYVO4/McHK+D7zmc8ED8glL/6V+1gwUwQH4VMnT8ojrITgnu4rUkB1X//618O9ViRqyjIlAolAIpAITHgEJgrRQUqI+n0htw/BF694Scr32muvPjUBqcB+++1X2ZFsscUWlV2DoJImDLYgjz32WJiATMxIiGNNRqRDVCyFTPgaR04QInYTpBImppIf0SthcgybEioGarSenp7q9/7+97/HfffdF7vttls888wzutiX7rrrrsoOY9iwYVWZ/uyyyy7VPgJg3yTGJqOn93wS4uZ3jz766EAmqsYd/gMTfa9PzPWfFMYAASXBEbYBwdFXxLLebsoppwxjgjfSps51ED7CflNTvd9saKidPv7xj1ckp9S5D5CSkq9v2We5f+aYY456cbX/1ltvhXhZc801VyDBrVYrkJ6qsvcPYuX43t38nwgkAolAItAhBCYK0THBj2miKONCHM4///xqMjXRsn048sgjw8Sz1FJLxdxzzx3sRsRZoh4wuZCqkFYsuuiicfzxx8enP/3pGDJkSJQvamoHNkB+w2RDbWNLbSDYIkPT7bffPvyeL3iEyRe3dqQdytlYkIqoQwpM+GxdxHhabLHFnLpKyIvjHIMQzDLLLJWdit+fc845Y6211qrIk3M8//zzQdJTHdjhPyZv/X+vnzHuAw44IM4777xAEqm2SCDqx1FpmZxJzUi8kFd419tMbvsIDDJDKtY+9gMPPDCo/Kaaaqr2qswnAolAIpAITCQEJgrRIbXxxWtSePPNN0cbmonYVy2pg8mf2kCDr371q+GLGMEwiSA3iIw6KgVbxAKhWHDBBStD2BdeeEFxv8nxki9qW7/TarUqUtRqtaLValXHqddfkxPVAwLERkU/I6om1R9SECqMKtP7x6T/0Y9+tHcvqq94/afa8lvO+fnPf776DeesGk2kP36fJA2+/REeEjI2J4xujQlxIXVigHzccceN1ksTOgJISsW+B2EsYx6tYUMz7iv3KSNs5Nw1d0+5/lNMMUW/oyLJg4F7tDRA6qlTSSrdo8rZ/Gjnfpb3LMhPM800spkSgUQgEUgEOoTARCE6+k7SQdJiNQ4pjTLp+uuvr4w9EQuTCvsa5aQ1JhhERr6/pI3l3c6BnJDqaGeSKpM6Ox3JxKJuXJOl1Sa6119/PZCz+oRkZRGVUP1cyMTZZ59dSaCU/+lPfwqSKPuTMiEnq6++erWMn5oOqSn9IUE766yzqqwVQeycZKhu2JA4Vr6eSLNcJ6uL7NfrBsL+oEGDKmmbewthoR4dOnRovyvr3GMI5De+8Y2+oSPrVKNwdv2pvagy3ZOkiFdddVXV1rJ9Rvnum6og/yQCiUAi0EQEGtDniUZ0vNCPOOKIykgY6WELIjH+XGCBBWKhhRYKdjnbbrttpeIh9mej46uXzQ0yY3k6dRb7ERIFNjWOYSTsi9vEQoKBULG5cX4qLteBbQk1F8Pgww47LE4++eRgO+T32PAwHj744IP7lgKbyPlBoUIjMTLxf+1rX6uIDEmHczGcRqAYWlNxMdbt6emp+o8cbbXVVtWybP1FMvheOfzwwyvDaeofRErfOp1IKYxNn3ve6R9skLliCG6yZrStXBtSNquCTMjw33333UOfYbD44ouHZer6Td0FU4bWbHZM7PzwOJcJ37J27ZqSSOKoJffee+/YaKONYsoppwx5/TdW/ofsS+4fqwmpQ+VJIN0LlpaTesHSOdh9qd98880rtaBy98KwYcMi/yUCiUAikAh0FoGJRnQMg52NCZ+aBIGQLLe1RFs9yYMJQLmluAxdSXVOOumkuOKKK8IS3s022yxMviad+eabLxgha2+SZQ9D1aSs/AYpD6nFuuuuW63Ouuyyy6pVXiYZtiiWVK+22mrhdxEYhExfTG6MkE14CA+1jr6Y2Nj1sM+xgoqdzxJLLBFWKCFh5XcRAytuVlxxxaq/+kEdhAQgAMY2MdVYyBqJk4kaXhKbG+o3KkFEjbG28n322ScQTZM+PzpIoeMQUoRyxx13jFVWWQVMQX2FQJngXUv17HuMkSE4KUbVsCF/SLHchySPw4YNC+QEPrpvLGzE7EtUksh4qXc9SbkuvPDCCj9Yuo/5jULYGYQj4Mrdv+4Z58mUCExGCORQE4GJjsBEJTpGR8VEPVASNYnykko58mPiYEtTyqaeeuqgQpL/0pe+FI5FPuRL+1arVX2FK5PYTkhT9n6Zm1gk9jLaq0dGnNe+RF3BRoNERju/M0XNPkMbEiPjcC5559d/ZfJSmfzUyUtImK2EYDhmYqZ6X/QBtn4fzrA0cSuX4KWuvzEpQwjUf/GLXwztJbg4Vx1P10u7JiWkxH3h+iK4pe/G554oeWOt51utVrRjjNy47o5ptf5Tr52yTIlAIpAIJAKdRWCiE53ODueDn53KyllIe9hn2M+UCCQCkwiB/NlEIBFIBD4gAkl02gBceuml41e/+lWV2La0VWc2EUgEEoFEIBFIBBqEQBKdBl2s7Op7IpANEoFEIBFIBBKB0RBIojMaHJlJBBKBRCARSAQSgYGEwORNdAbSlcyxJAKJQCKQCCQCicC7EEii8y5IsiARSAQSgUQgEZg8ERiIo24k0eGnhAfl4nCPI8EZZpghJLGkrrnmmrjjjjuq6/Xss88GB3fq+PDhvI9zQD5yOL/jG0a+alz7I6QDJ3qO4zPnxBNPDJ5wa01yNxFIBBKBRCARSAS6HIHGER1O7y655JL43ve+F4gOwnLGGWdUTvm42ud5mMO3sjScd18O/dTxDbPJJpsEd/ycCnLaJh4VZ3e82pZr5by8ADuGEz3ESciHTTfdtAoHUdo1aSskAc/U/Logb5z73XDDDcFjsnEIawEfdcbJ2zSv1ZwF8lUkDlTxMq19ScJrcIQohEdPT0/wtux45+GX57777quuU2nf7VvXnosBnrpnnnnmQKKFdWjvNyeWxlgSv0KcSSLDSDGSzOEih4q8RTvePcQJoWN4zeavSXmmRKC5CGTPE4HuR6BRROeRRx6pJh7eZTlc4yF55MiRwXuxSQXcJidhIRAX6Xe/+10oU2ciFs7ApCwv8XZLymPClpfEqSIJ4jROXuI5mATokEMOkW1UErfJhHvdddfF/fffHwicGGEmXZPw3/72t/jxj38cvPiq40W6xGSCD0mYpfY8RLcP3OTumOHDh4drce+994bjlW233XaxwQYbxPPPP99+WNfmn3766RAihIdrIUbcPzw/1zuM+HAeaIwSTJFpgTvdo8g4iaN9JFE4CMfzjg1rx3BGyCu38kyJQCKQCCQCnUOgUUSHW36hE4o3WiEXTBjiUNUhWnTRRUPiuZbayYRu4vK1LZq0Y0p7YSl4+r3lllv6JA9iEwknwVtwaWcr/ARpkvhW8k1J+vzQQw+F8BOkNvo966yzBsmDfWSHJObtt9+WDaE1SH8KzgrFbDJ5i2ElX5IwD6QfJY8QOV6eNI2H6f4kQeq7MVFZ8mTM4zMPye4X4UGQm3p/qTxLXhgQ95s8aeH/+3//LyT3X90ztPhswpho96Mf/SjOO++8RpFA/f6gKY9PBBKBRGBiI9AYokO1In4SYlJAMsEI4kltUMpsSWx8cbdaraCCWWSRRULwzplmmimoaxrERnAAABAASURBVLQpSbgCdj0mcZMydcJzzz0XpDcmqtLO1gSIVJFayDchCc4Jp8GDBwdCV+8zFcoWW2wR8Fp++eVj1113DQ4TSbRmn332gG1pb8IX/4tUopQhUEJkwLqUIQhl3/Ewq9eXum7cUluR4ugzkttqtUIoD/cDMlj67H4ztpIn/SrkTqw00sZC8kh1ll122UA0kclCsrUhcRw1alQ5TW4TgUQgEUgEOoBAo4iOr+X6ROqrWWyqseEinhPVC7WNCWyttdYKgT3jnX/IDKLDfoKKwcTz8ssvh4n+nSajbUg9XnjhhdHKujkjwvYrr7wSpBP99RP5gcEuu+wSorhTwyB5gng6thzDRkegz9tuuy0QJ9eCRE3Q0tKmfUuKhjwhk+1145afuK1cW1I/eNR/GQFSVy8r+8gLqU1RcyJGsHzxxRerGGA777xziJUGr1arFe3nRnYi/yUCiUAikAh0DIHGEB1f0a1WazRjYPYf7B18cdcRIpVRbhLZfffdqyqqKKurGM5SU1WF7/wh2VhwwQUrGxVSCl/nvujfqR5tY8LXl9EKuziDDApMSVo1pm6ahKlsSB7uuuuuYFvDXuess84a7RAYIoAXXHBBUFmRXswyyyyjtSkZZAhpXG655UpR129brVZQ7SE7hdzADUl03fsbANzcZ45T7967/vrrKyNv6imJHRMJDrJUVGBFQtSke8n4MiUCiUAi0DQEGkN0SHKoQBgJF5CtoGITwrCW3Yxykpnzzz8/Sp69zUUXXaSqSr6unavK1P5svvnm1TE33XRTDBkypFYz+i5pTjF8Hr2mO3NULEgclQzpQ3svrThjIHv66aeHiRjGyCHJFwlXe/vVVlstGOda3UYd2F4vT7WHMFEbyjclkbYgbsgNIoLskO6xZ7KCrH0c6hFqBselnooKsSEV23///WPo0KGBGJL6MIpHjJzH/ek+VCafKRFIBBKBRKAzCDSG6CAoVgDVSYsJhvpE3fzzz1/50UFSqErkfYVbam7iIbWRTFrrrbdeWJZuJZLl4yZty65N3Ouvv35YpbTCCitUdhXUNdtvv30lSUKy2K9897vf7czV6MBZYbDmmmuGcc8777zBZqT8jCX6xi1PtQcT+yZ6E7aVRPL1RNo1zTTTxHTTTVfhXa+zb+J3LrY+8pbv16+Zsm5Os802WzCedq1JX9iFwYFkrL3fRRJmtVWpI6GxPN091Gq1KlXVZz7zmUp16D4SMDZ6/5GYUWtRdfVm838ikAgkAp1AIM/Zi0BjiE5vX2OnnXaqpC6W/BbbnGmnnTasZkFATLIkCVQw1A2O2WGHHWLYsGHVkmr1++yzT/j65kfHsudnnnkmSHO0JclYddVVg8TG5GyiY9/C3479gw46KNhflHM7pgmJGo4ayhLy73//+xVBQfqsukIISTKMncGx8sGDB8dSSy1VLQ3nR4fEbMUVV4yjjz464L3yyitHWRqNfCI2sKGm4R/G8myGy87Fp06xX2kCVlaaucbuCQQG8aXu1He+hxBj+xIVl/uOzxx5aa655goYkIhR7ZHgINCtVitWWmmlmHXWWSv8SY7W6yXcjsmUCCQCiUAi0DkEGkV0wECCU4xmqVqUdTohRCb5OeecM8ry4E7/ZifOz9cNslfSoEGDqp8hVRjaq2JhT1PqyuSMyJSyjTfeuGrf09MTyJAMQljqOdGr55WzV6mvlHNMtyd2SDfeeGM8+uijFeEr/T311FPjuOOOK9lqVRqD676Cd3bWXnvtoCo0fn6b3ikOhvGMs5UX6Vmpy20i0BUIZCcSgQGIQOOIDokCY1nGnRPzeqy44opBYjExfzN/KxFIBBKBRCARSAQ+GAKNIzqGy3cLaUSr1ZLteGI0SuVAxdPxH8sfSASag0D2NBFIBBKBrkegkUSn61HNDiYCiUAikAgkAolAVyCQRKcrLsNk0okcZiKQCCQCiUAiMJERSKIzkQHPn0sEEoFEIBFIBBKBiYdANxOdiYdC/lIikAgkAolAIpAIDEgEkugMyMuag0oEEoFEIBEYeAjkiN4PAkl03g9qDT1G7KmRI0fGyN507bXXhnAZPCCX4fCDo07YDD5k1POkrEwq7epbsZ2EilAvNMQ//vGP4EBQ3vnqbZuy/9e//rUK23DKKadUDir76zdHgMZYT7wh19s+/vjjFdbFwSVPyqW9uGv1trmfCCQCiUAi0BkEkuh0BteuO+u5554bPPEKb6BzCAxPyVdeeaVscHzHC7LMbbfdFptttlmU2Fi33nprlb/88stVj5acx3kRJxWCed5///12g7fkc845p9pvyh9xvziHRAoREw7/OPhr7//NN988WhHv2/WCESNGhFhsiGAp32OPPapdca44ZHzppZeqfP5JBDqFQJ43EUgEIpLoTAZ3gdhLe++9dwjzILRBT09PCG2wyCKLxIsvvhjPPfdciMEk1IU6E7RAoKDhP0g4B2E1hEBQVk9XXHFFFVJD+IjFF1+8ioH1k5/8JJzHb1122WX15l2/T5L15JNPhuClHET+13/9V7QTvFdffTXEDTNGadFFFw1hMcReM8AjjjgiECQhNsRT+/a3vx1iZWmjvRhXMO+PQDk+UyKQCCQCicCEQyCJzoTDsivPJMI21ZL4XgiLIJ86KhAqqc1MM81UERVRzg8++OCK9AhMSfogAre2EmIk3pfJW14i/bjvvvtCcFB5SXwrsbWosBCCjTbaSHFjEmkLjKaccsqKnIhzRb1HjVcGIUinMBElL1yEuFjyv//974MUS6BY2FNnvfXWW6oqcmRHfCykEFmS756UPUkEEoFEYOAhkERn4F3T0UZEdUJyYGJutVqj1clssMEGIdbV9ttvH88++2zMN998ceihhwaphvqSBLtEjEziCIzyQw45JJZcckm7o6Unnngifv7zn4cAq00KgIqQsKtB+khgyqCQHAE8S76+Rf5IgeaZZ54Qew3RsYWTYKikOLfffnt1COJzxx13BLUVPNkCVRX5JxFIBBKBRKBjCCTR6Ri03XFiUd7ffPPN9+wMCQP1FVWLwKnsbkzg9QNN0KK5X3PNNRUpYrS8xBJL1Jv07SNW119/fQwbNiwc01cxwHaeeeaZIOGZeuqpA5EZNWpUDBo0qFITkn5997vfDfZRf//73/tGPuOMM8a+++4bZ511Vl9Z7iQCiUAikAh0BoEkOp3BtWvOSmU1zTTTxNgMX19//fVqMjZBb7311pVNCmnDcccdN9o4SCHY9Vx00UVx0kknxSqrrFJN8qM16s1Qea255prBMJktinP1Fnf9fyorqjwSHAQRcaF6IuH5yEc+0m//CxmcaqqpKokOCc9XvvKVYK9DmjX//PNXRI9hs1hp1FV77rlnbLzxxsGQu9+TZmEikAgkAonABENgIhKdCdbnPNF4IMBeZqWVVgp2JjfccEOYcMvhbEVIXEzoVk29/PLLYUKfeeaZ4wc/+EGQTpS2Zbv66qsH4+YzzjgjqL1KeX9bBGG22WarVGP91XdjGZJChWV1GlxgssACC4SxtPcXGYIhcoTEUHcNHjw4qAqpDKmwtEE0kZ5yvH1lX//610tRbhOBRCARSAQ6hEASnQ4B202nJYXZYYcdglpq//33j5EjR1aJHQ0j49JXKquRvXWWV7Ov2W677SqpA2Pbiy++OPh+MakzPu7p6akOu+qqq4JdCzsUfnR+85vfhHNIlqz/8Ic/bBTRmWWWWWL66aePM888sxoHImO8BmusxmdfIr2xlJ59jrxEVfXFL36xOpbqSr0VXIyzYSIph1lPT49DMiUCicDkikCOe6IgkERnosA86X+E9IUNjkm89IadyJe+9KVq1dWuu+4aSIk6S6oZ0pq05Rkoa2f/U5/6VEWYLL2WJzFCliyxlq+npZdeOsZkw1Nv1037VH3USoMGDQqrz6jyEJf++kj6xdiYdKbUs9fZZptt4stf/nIlHSMBm2uuuUp1tf3kJz8Z++yzT7WffxKBRCARSAQ6i0ASnc7i21Vn5+uGFKEkpEUHqVIQIEvDS11ZLs2PTin7xje+oXlQR7FBkeFfp9QzaCb9KPnSXrsmpc9+9rOV6m7dddcNqqzSd6TN+EoebvV8KUeMSHEQR8vTlTtnwWWNNdZolJRL/zN1BQLZiUQgEXgfCCTReR+g5SGJQCKQCCQCiUAi0AwEkug04zplLxOB8Ucgj0gEEoFEIBHIEBB5DyQCiUAikAgkAonAwEUgJToD99qO78iyfSKQCCQCiUAiMOAQSKIz4C5pDigRSAQSgUQgEUgECgLvn+iUM+Q2EUgEJmsEHn744Tj11FMr79pjAuJvf/tb8CEk3XbbbcGRYntboTLECHv++edHqxIf7OSTTw4hR0ar6PKMsB+//vWvq3FzPDmm7l533XVVm7PPPjvax+6Ye++9N4y/+HC69dZbK8/ksJQc1x+eju3WJIyMvt9zzz1j7CLHm9pI9ktDuCqTfve73wUHn+pgp6wkTk3bY/Zp16TEq/wFF1wQp5xySjz11FNj7DpHsGXcnqF6Qz7Q1J122mkhDmG9jhf48847r140IPeT6AzIy9r/oLwU1ltvvZAE7vzzn/8cV155ZV/jH//4x1XdCSecENdff32o5zFZe4mn377G7+y8+OKLMXz48Oq4gw466J3Sf2+8nDnZ+3euOX+Ffdh5551jww03rMJY9Nfzm2++uRozXEqqv0T22GOPqp5PnjJBizm21VZbVeVewv2dt2llSM7pp58eJh8hP9rDhpTxCBrrnpI//vjjA3mxL8Ft8803j/PPP1+2L3HIKNTIAQccEIhSX0VDdtz/njlesjnrLMFw6913bxxxxBEhaKxwKfAr7Yzf5DRs2LCw7zh1jz32WBVXTf6FF14IE5Xj5ZuQTMSXX355eB749hIIt79+c3LKsaY6z5igw+6Dvffeu3JSCldOTjkr1YZzTvehfQnJ/MMf/mB3oqUJ+UPG+stf/rIiOIgMB6x1wlf/Le9ZeNTL7Hs+x0aQhPPxntN2IKckOgP56tbGxiPycsstV8VYMglziMejr9AQmnFy52WpTp6DQQ+O2Ex8wVx44YVxzDHHqBoteRH94he/CD541llnnb46cZxMXmN6ifU17LIdoR9gMGTIkOAZ2svFC7S9m16g2pVk4uaLSLtbbrklFlxwwVC3yy67hLAQyrfccsvgaFF4DfGufGUpb2ry8vW1adzuF5M5Auja18fk5eyFijz29PSEWGjlvhAUddttt40vfOEL4aW+6qqrBj9EjjfJSzvuuGNstNFGFabKm5BMyjyMc6YJlymnnLKSytT77nkjpZljjjkq8sv5pC/sIv3xzCECJnz48tlkIl9yySWr9rDk1JMvK84r6+fu1n1k7rDDDovddtutuqbeLYhP+0cU0iwEjWfGONUjxN5XYvPtvvvuleNSseRIE43XPeTDQnvPGV9f5dlT37TkOYIBn1zGM8UUU8QVV1zR7zBuuummCg9j9wy3oDHuAAAQAElEQVSVRp6zFVZYIZSvvfba1bNX6jyXPL835d4p/X4/2yQ67we1hh1DNC7sAJUBp4GDBg0KN7+JhUTGS9mDwguwOi9nnpI9AJwKemF4SDbZZJN3fVl7GYvGzSGeFw1ofH16gfOoLN+k5MUgbhWHicbFOaIvQy/aMg5f1UOHDg1YScI7IC88KZu8vKDExhIElYdknpB9ecPPeU1Miy22WFx22WXllI3cGrf756tf/WrA7NOf/nSY0L2c6wNS7j6icjFRe2FzxAhTX/ZwM8mbwJBrx2pHCuRrU8BU0kXlTUmeJ9ceCRQslrNJ91G9/x/5yEcqgud+MXbE0TPkfvEM8Z7t42HaaacNE5Jjp5tuuiieuMViE6pl6qmnDnHW1Hd78oHAy7p7wj3j+RJapr3fnh14+JBC/ryDjJs0y8eIewaunJI+8MAD1eFIjXPKKKP2cR75JiaSGO9gWLm+SCGiZ1z18Zx11lkhIcLiGXoHqae2I231HvaRWgi0OqpO18JHBSyVDeSURGcgX93esbnpxaAysUi9RX3/TbgeDiRFIE8SDPpcIlOi4np7XwlesshROYEvgpdeeim+/e1vl6IqaCgVxUILLdRX1pQdE6+vRi8Xk40XKbLjheFlW8YhzINJrOSROi9ZeRMyKYVJaqeddgrkUrmXNJJjX5I3qdlvajIZ+0KHVRmDe6f9Rez+EldNyBFqKMSIxAumpF/wHjFiRKUqRLBNZGKNOS8bDtLG73//+2GSK7/T7VuBbxGUej/ZkNTz9kln2Ji4V9itLLPMMhWRoYrwbCFB6jxjJm/HlKS+1WqFZ7eUdfvWR1X7xOojwLNX77vnC/n1HB1++OGB9IpD51667777wrtHe/cg/OzXE2npmmuuWS9q1L4xIXk+mJCc0nkfAt7pJW+LRHtHwYj0z8eDcu9r0i7PmHiFP/nJT6KQHVJqH2yTS2DhJDruiAGcTCYmXi+O/obpRSu2lQlGuAcEh5jT13e9vS91ahhEyMtKHZscL2b7JXlp0b2bzEpZU7a+kE3c4nchOaXfpAu+gEq+vjWpm5ipH5QjL1Q0XrS+VuE6atQoVaMlX1NUGqMVvkemydWLLLJIII2+3pE/L2sSIfi5B9laHHzwwfHoo48GKQVVIBIOI1jONNNMsd9++zUZgn77TiJYJnAGp54fDaluxEijuqG+MplRhaoriS0ZSRCpUSkbSFvXnyQQAXRfGBvpBBW895SECCHE6kryPJJskF6XsoG89eFFkuXD1ccqNbHxIpTeZT5YqeBJ/nw4IDhsw+Co3eSQkugM8Kts8japvNcwZ5999vC1zbDRlwTVFkPK+nFUWr68fDF48ZishwwZUm8SbDTYDQiOOVrFAM0QL3uh+NI0RF9fYmDJM+5eYIEFwuSuriQqKxMXIlTKmrg1blILZEX/fYX6wm6feNR5qTIiRY7dO77S3Zte0EgMqY4J2/l8tZIMUXH4KjXZLbzwwn1f8c7X7QlJQZpLPxEYaoKSL1vPm48INislUZ/C1MeJY0xWAuS618pxtgghkuQ6yDchucbukdJX19r4W61WKerbkgKy/3PPbLbZZnHJJZeESV3AYeSXpM9zJnBu30G9O1T1PsDg1ptt5H8fWp4LH6o+sjwrJHieBc/KmAblOemvbooppgjE0XNlAYoFJN75yy+/fLXSzz7Jf3/HDoSyJDoD4Sr2O4Z/FxJ9+pIm1emP8LzyyiuB4WP8JDuIi68h+mArQP59ln//9UL11XDppZdWhqO+tk3q/66NQIKs5vLVxTiZuJTR7V577dW3aqS07catlwvJjJcv+wAvFxMU+wAviv76TJ0CAy/c9nqkB471cqoxEqL/n71zAdapauP4s79yaVCim9s4FSNJnSgpaqhOFBVCaHCEXMq1UpgkuhGSS2lQphmF6IJJurhVQ3fVdKEyxyVqJikq3cx857em9X77vOe4nM/Z7H3O31jvu9dtn7X+e797/ffzPOt5eBCHy5N4DCZIsBCbgxXSBR7E6eJw1DjMGyNkFm8MK3mzhAyjwqOO/tw/kCRscpDycB8iHQQbFn6uDcdJSKhut2zZYkg/+d2hKuBFITx27gMkp5A9FmXUUxAkVFxZWVnGiwZt6MP804kxUh+vMqVNEpJ/FnGvQIwhgy1btsw3dIzcmTNED5Uvkgq/u8rfNzk5OcbzDRLoT8B9xH3DAu7LkvqNJIZ7B6yYF0QH42t+IwXNid8Pz6OCJFlIT3nO8xvs1KmTU2Gh/kNCD8bszuKeK+i8xaFMRKc4XMUDzAFywhsPhAY7CG5235ztvBixkYeUoFLgmIcuixV9yYcTb+Ys/ixeHIfrgiBwNin8gEi8XbA7gjfVgs4V7huXY1QJvHGiFuBNikUcw2NIYPoYeQhBIDGITK8jD+b09/UsemzXZ8GGEIARNj20TWKC4LVt29aQ7EGUkQiywCOJ4aHMFnrmzJspixm78HgT5z5jJwmLFKQHTDCWxHASUsnijfF7w4YNzb+9s6hjLJ8UnJBcYBOBTduECRPcywR2btwzvDkj+eSe4t7CHofNAtglMX+PFy8V/HbADPUNtjp+/kh3MFDmTdyXJeEbid3AgQOdzQ3YQAAhtUEQGPZIYIHUARsupDe8KDF/fis8b/i9IAXj5QnJKOdiofZzBxfIITtKfVlSvzElQKLJ7wgMIMBIZZgPUhlsAzmGrFCPipPnuCd5EG3KSbhpgCD6ZxH9SlIS0UnE1T68QbKwYv/AAxb7mx49erjtqTw0YPecnR8Ab0zU0Ya3KUTDfhGCsKCy4o0A5t+3b1+6OXUXO0wwekPE7AoT/IFtEQQNNQtbmhGrk2dKLORIrDgm8ZaEZIK3cPIkCCAYkngYs/BDnjAk5Y3+iSeecLYm1OMfhT5JTiy0GH36RRtSnT4fCA33BtJC6ljY/MOYBQkVH+SaRR4yAwmkHeJ17l2O6Q+OHCcldevWzRnqsxCzqBc0bn5LbK9HHYFUsGvXrgZRpC32Obh34Bgs+I1y7FNS7x8IH1IY7guILs8UPyf/zb3A7w3SRxkYhSUVGGDzTEuXmLKYQ37ok/TEXNipyX3Pi+LgwYMNCej+5sWLB78/7rf0NtxT3Fvp5Zyb31Z6eXHLi+gUtyu6n/nwhoQHVRZqjBpJLLy8NbF9kYcqW3kpx8CPt1AWfR60OD6jH/YmZXN1vfg1ad++vftLqK8gULyNshXWFf77wVsEC/q/2UR88UDhYcGbJAsJUgjwYfAsXCzqHJN4EGEg6+sp4y0MDEm8gWOvRDkie6QWiOSpI2F/kOTtr8yLhOQFbPw9QRmGj2CFZMPnIdAkCA8PZcpJ3JuUI+XxJIdyFkLKSYjxKUtSQsXQpk0by87ONtRxjB1CA7nht0QeHMjThpeOjIwMil2CZFNGnV/wXUXuB3h4EpibTdx/dlQxL9RSfvD8drBd43dFGXnakDzJ4/eCKwcIEuSaduFEvW8bLk/qMeSX+UJSuOZ+HrwseOkOLw1gxPOX54xvg5qYchLSMF8e/ua+hHiGyxJzXIiBiugUAqykN+VHw4PUJx66zMnbCPCA8XU8ZKlj4fFlfkGnDEJAPW8Pvh57Dcp8Ik9bn0/KN4sPCzQPlvDDlPnwIPXzALdwnnLaezwQ01NGQmLhy/13uJ42SkJACAgBIVD0CIjoFD2mOqMQEAJCQAjECwGNpgQjIKJTgi++pi4EhIAQEAJCoLgjIKJT3K+w5icEhEDhEVAPISAEig0CIjrF5lJqIkJACAgBISAEhEA6AiI66YgoLwQKj4B6CAEhIASEQEwRENGJ6YXRsISAEBACQkAICIHDR0BE5/AxLPwZ1EMICAEhIASEgBA4IgiI6BwRmI/+HyH0w7Rp0wwnUsShwrkUMWHwLsrocNmP7xzq+vfvbw8//LARgwhngXgpJQ4UUYFpG04EIMQ7KY6ncExFHd6CmzVrZpyLb4IPUp6EREwZvBvj0Rjvq0SUJiRE+thxhsj8fMKvEPOmP/PFIRwBF+fOnWvEzaI/sWgIu4F/Hvrtz4kXbZWEgBAQAiUJgSjnKqITJboxOTdxm/BgvHLlSiO2DHFjiCGE51/iwhBbBjf8DzzwgFGHF05izjB8PN5OnjzZ8MJK7B7KwonFnT64qCemCmERCNRHSAjKV61aZXjODfeJ8zHxYaZMmWLEbiJsA4EV8fwcHjPEB+eBzI8Epnh0xcsrsXboT9yZ9evXG0EbCaNBf7DZvXu3i+lDvyVLllCsJASEgBAQAhEiIKITIbhxOTUL6ueff26zZ882pDaMC/fqBBLkGLJD3CskDuQvvvhiQ/oT9vpL3KeFCxca8bBo49OyZcsM6YfPIwWaOXOmESbClyXpmwCVuO7H4zMekomvQwBGyE14HkhsfJ6Ak1dccYXLEj0YD9IQIcJlEMvJ9ydWGAQKKQ+Yuw76EAJC4P9AQF2EwKEjIKJz6FglsiXBOVmIMzMzLT0cA+oTAuChdiJeysiRI41ge999950RrJFYQ37SLPgEt5w1a5YvMggUweJY1H0hoRMIiEmcLBJtfF3cv1E7IcWB6BCyIQgCg7QgoQoTE9RUYWyQfkEO9+3bZ/SnDpITBP/rv3fvXkOVRXBH2vs4NXHHROMTAkJACCQdARGdpF/Bg4wfdclPP/1kSCcKagr5IWbTiBEjDEkDahiiCRPEk76+DzY6BPpct26dQZywO5k+fboRtNC34RtiQFA9Inej8qIP9j/UxT0h0fr7778NPMJjhQBRFy7zx0jCiCgMwaON7x8EgW9i9CcDLpm5hJPgqhAhojNTrhQ9AvoLQkAIlFwERHSK+bU/9thjjUCTSBT2N1VICyqb1q1bG4a0d9xxh2GvM3/+/DxdUMMg6XnxxRcNlVWtWrWclCJPo1AGY1v6/PDDD6HS+B4GQeBUe5AVyAnEBdwgiRC9gkYObkhyUAlCkPgO94cs0p+6cH+MvbH/CZfpWAgIASEgBIoeARGdosc0VmdEjdK0aVOnUkH6kD64efPmGcbIzz77rLGwo4YaNWqUdenSxZYuXWrp/zp27GgY506dOtVuv/329Op8eVRALP75KmJYABlBvbR3715DVQXZ2blzp2HPVK5cuXwjph6jYnalUe/705dEve8P2Uw/Abu60suUFwJCQAgIgaJFQESnaPGM3dmQRHTu3Nkt1hdeeKHb8eMHiZ0I283JsyPrnnvu4dCQYmC8zE4iVxD6wBbltNNOs2rVqrnt46GqfIcY3tauXdsw7M1XGdOCunXrGtvot23bZhggr1ixwsAByVj6kL0kjN1Wvg6JF7vcsHOC7KxZs8aysrIs3J+t/qj0kmqw7eeqbyEgBIRAEhAQ0SmCqxT3UyBVQQ3FFvKrr77aERQMkdl1dckllzibFKQ4GBxTjh1Jy5YtrVevXoYfHbait2nTxmbMmGFVq1a1du3a2ZAhQ9y02X4OSZo4caJ1797d8MvDOUhBELgyJB2ucQI+bgByYAAADydJREFU2GmGvdKAAQMMAtOzZ0/DTxBDx/dQ7969OXQJFRWEBtsbV5D7AQkEr379+hnEkv5szc+tSuHODq1x48blMw6njZIQEAJCQAgULQIiOkWLZ6zPhq8bVC0+ZWRkuPFiQNy8eXPDpsbXoZYKgsAgMr6sb9++rn12drZBhsi8/PLLzvcObXCiN3To0FS+IL879Il7QiqDJGbDhg0G4fPjxTcQW+d9HrUgUiuf99/16tWzt99+27766itr0aKFL07hgkNCDL5TFToQAkJACBQvBGI1GxGdWF0ODUYICAEhIASEgBAoSgREdIoSTZ1LCAgBISAECo+AegiBCBEQ0YkQXJ1aCAgBISAEhIAQOLoIiOgcXfz114WAECg8AuohBISAEDhkBER0DhkqNRQCQkAICAEhIASShoCITtKumMZbeATUQwgIASEgBEosAiI6JejS4wE5JyfHcnITXpL/+ecf2717dwoByknEptqzZ4/5espIqYahA3zJ7Nixw50zPdQD+c2bN+f5G6GusT1k3vjHYew4/StooARLBZNwwsEgbYktRjn9CQFBGQnP02BCHU4FKVMSAkJACAiBaBEQ0cmPb7EswXfLsGHDDG+8+NN59NFHjTRr1iw337feestwJujriHfFQv3+++87XzqEQfjss89c2/DHF198YU2aNLFOnToZfmaog1AtXrzYOQskZlaSYjpBVvANBA6jR4+2J5980iB+zCucFi1aZLTxqWHDhoY35e3bt9u0adNcXY8ePWz27NmGB2X64pvnzjvvdHX4JPrggw8oVhICQkAICIEIERDRiRDcuJz622+/dQSHcA9PP/20kQYNGuSkMIQ7QALBgk2gSerw5oukhvHjxbdPnz5GvCwWbsrCCYIEscFrME4GkYBAnvCoDFnCod6VV14Z7hLr402bNtmrr75qd999tzF2JDDLly/PM2akOcSpAivS+PHjnbNFnABCatq2beswhkhCiMCYEwwePNh5m6ZPq1atrH///omTdjEPJSEgBEoaAsmer4hOsq/fIY0eCQVkhoXWB5ckVAELdPny5d05IChr1651x2eccYZNnjzZatas6fJ8ENIAqdCSJUvIphKxoCBDvuDLL7+01157zYYPH25JIjh+/Dk5OVamTBkjUGepUqWsUaNGhuQFfHwbMCS8g8+DW+PGjV0WzPCMTIbzUF65cmUjdhjeo+vUqUOVQQzBiuQK9CEEhIAQEAKRICCiEwms8TnpL7/8YqiXGjRoYCzc4ZERcRt1VqVKlYwYTkgaCH/wzjvvGMFAw4Eoa9WqZR07dnSxr5BacB4kN8THKlu2LFmXVq1a5Wx7iBVVo0YN69ChgwsS6ipj/rFv3z5DPXf88cdb6dKl3WjBDNVV2NaG2F0eGyKUE+6BmGF0CILAzR81Vvv27Q3JWRAEVBk2OuvXr3fH4MuBl5xxrCQEigsCmocQiBMCIjpxuhoRjIWFFFXLwU7do0cPw66mbt261qZNG0MNhdom3G/s2LGGGmz16tW2a9cuW7hwobVu3TrVhIWc+E6ocJYuXeokIVu3bnXtUo2K2QFGywQ6JRionxrG2UjEIElgiZQIKQ8YE8xzzpw5NmXKFAuCQIE9PWj6FgJCQAhEhICITkTAxuW0qE9OPPFEQ7KzvzGhlkEqgZoGqc706dPtzTffdNKbcJ/atWu7iNyowiA52O1Ur1493MRJMzBORkrE4n/ppZcaKps8jWKaQVIDadm7d68h3YG4gRuqKi/BSR/6xo0bDakO8/V1qK8mTJjgbHzAAKNu6lAVEiSVY1SAzZs3NzAlX7KTZi8EhIAQiA4BEZ3osI3FmbHBueqqq+zDDz80v+CGB8auKL/TiHLUNqhckD4guaEsnDp37mzY5bCzaMiQIeEqJ6GoUqWKbdmyJVVesWJFq5ibUgUxPgiCwJBGeSkYBAaik5mZaRUqVMg3csgQEitUdF7VFW5Uv359Q7XHNaCcNtnZ2XbBBRcYu7OQ7Bx33HFUKQkBISAEhEBECIjoRARsXE4bBIGxCwgVCgs2O4pQpZCQttxwww1uqBjLDh061CjHTuWNN96wW265xe0KwucLCzOLPos39j5ZWVmGjQ/qLVRjGDtz3LVrV9uwYYNBkjgXNiktW7Z0f6OoPqI8D6o7/OhgeI20hl1lEMUgCIz54X/I/30IIsbEbC33ZWDEvElIySCX3nCZMqRhkJ0ZM2YYkjbfT99CQAgIASEQDQL/iea0OmvcEJg4caKhkpo/f77z43Lfffc525mzzjrL7TJiazm+cCjHvuS2226z6667zvCj88knn9iCBQsMI2V2EHXv3t1tjWaO8+bNc4QH9RR+dCAKXbp0sblz59qYMWNcYis2bZOQkD6NGDHCqe4mTZpkbJFHysPY2TL/3HPPcehSEARWrVo1Y5caBUiAIEZgSOIYPzzUkSiD/IBTs2bNKFISAkJACAiBiBEoAUQnYgQTdHq2NGOD49Opp57qRo8NyrnnnuvULL6uXbt2ro6t477smmuucWVIc/ziPmrUKOczhjYYMNMACQg+aCiD+FCWpIS/oUceecRmzpzp1Ex+7Dj7YxeVz6N2GjhwoM8aNj4YZzNvEsQmVZl7QBnnhVzmZvVfCAgBISAEjgACIjpHAGT9CSEgBISAEBACsUGghA1ERKeEXXBNVwgIASEgBIRASUJARKckXW3NVQgIASFQeATUQwgkGgERnURfPg1eCAgBISAEhIAQOBACIjoHQkd1QkAIFB4B9RACQkAIxAgBEZ0YXQwNRQgIASEgBISAEChaBER0ihZPna3wCKiHEBACQkAICIHIEBDRiQza+J34vffeM0I7kB577DHDy+/y5ctTA7355ptd/ezZs23VqlWunphXtCcR+ynV+N8DvAXjL4Z6nBIS9PPWW29156EM78p4Wf63eSK+8AQ9fPhw6927twtMWtCgcZ7I/MKJuYfb4mF66tSpqaK//vrLwIg+jz/+eKpcB0JACAgBIRAdAskjOtFhUazPPH78eLv22mutb9++du+991pGRoZzhkfoByZO+AdiPFFHvlevXi78A+ELzjvvPHvppZcs7OWXNqSvv/7aHnroITv99NMNh4Tff/+9EQaC85D4O7///jtNE5F+/vlnh0+zZs2cV2QcH3766af5xv7NN9+4dsyRhAfp6tWr52kHcVy5cmWqbPTo0UaoDNovW7bMHnzwwVSdDoSAEBACQiAaBER0osE1VmdlsX3qqads3bp1zvsx5OP66683pApIZLZt22aEKyBIJ3U9e/a0kSNHWqlSpYwgnyeffLLddNNN1q9fP/vtt9/yzA0CVKtWLatUqZLhafmkk06yxo0bOyJVs2ZN27NnjyNUeTrFOEPsKjwcn3/++ca88OwMKQlLs3bt2mXNmzd3cwSvP/74w4gZVqZMGTczQkHgBZn4Yq4g9wNpDnjjSZk+hNyAfIJ9brX+CwEhIAT+bwTU8cAI/OfA1apNOgJIaV5//XWDdJDC8yFcweWXX+5ICvGoiOu0ePFiR2ZQr4Tbd+jQwcV1YrH25yC+1Y8//miNGjXyRVanTp3UMfU7duywqlWrpsrifACZQf0EwStfvrwdc8wxjuxs2rTJETY/doJxEibC51esWGGZmZk+ax9//LFB+KpUqZIqQ3IGAdq9e7crq1evnu3bt89ycnJcXh9CQAgIASEQDQIiOtHgGpuzIlHZunWrhRfm8OBatGhhxLpCgkMMJggO0bXXrl0bbmYVK1Y0gl1ChLwUAnuTVq1a5WkXzhC8kgCf4bI4H0NEtm/fbsSwguT4saJ6I6K5z4e/f/31V0PdVb9+fVeMhAzy0rRpU5f3H5DGChUqGBIwypAK8a0kBI48AvqLQqBkISCiU8yvN4s3Up2DTfPss882Ak4uWrTIsLtBtYXxcrgfKi2kHq+88opt2LDBsFPBliXcJnw8adIkO1B9uG1Sjzdv3myorFDdgTXSnxo1ajhiGJ4T9UjWxo4d6+yZCKIKmcrIyAg307EQEAJCQAgUMQIiOkUMaNxOhxQBWxOkOgURHoxjkS5gdItkB2KyevVqwwCZ6N3h+bCgo+5aunSps+9hd1Xp0qXDTVLH2LVAjPZXn2oYowOIB5IZ1EvY1EBcMK7GRqls2bIFjnTjxo3GHCEySH0WLlxoN954oxHd/a677jJ2tbF7a+fOnQaZ5NyQRKLCDxs2zNINmAv8I0e5UH9eCAgBIZBkBER0knz1DmHskBMWXgjN5MmTDVLju73wwgs2f/58l12zZo0zViaD6gYbEvqSDyd2brH4f/TRR24XV7gufMwC37Fjx3BRIo6RsPz555/GFnOICxIaDI8hgekTgDhCIM855xxXBeFBnYdtEmncuHGGahDCWLlyZdeGLf1TpkwxDJhRBbpCfQgBISAEhEBkCIjoRAZtfE6MlAI1EqoT7G+wwyGx6Hbq1MkNlMX63Xffdf5vaIPtCVvO8aPDtvJRo0YZKqszzzzTsrKy3DZ1OqLuYscW9jgs8mZm2PBgG3TRRRfRJFEJScxll11m999/v/Xp08ewTSLPJJgr/oc4JkFWMDxu0KAB2YMmMEeKQ78JEyYctL0aCAEhIASEwOEjIKJz+Bgm4gwYxy5YsMBYqPHjQkK1xA6iE044wQYNGuQc5FGOfxcWYhZ9/Og888wzrl+TJk0MFQ4qmfbt27t5o76CQM2bN8/50aHwlFNOMfpznLSEFAsJ2JgxY2z06NE2YMAAAx/m0a1bN+vcuTOHLpUrV86Q2vh6Vxj6QKI1Y8aMVAnYQqDY3SaVVQoWHQgBISAEIkXgqBOdSGenk+dBADsSVDM+sY2aBviNQV3Fwu3rkGRQhx8dX+YXdMogBNSzhdrX40eHMlQ4ECiOk5gYP0SEnVJhlRXzg8T5OYFbOO/L/Tdb1MP14MR5IYu+jb6FgBAQAkIgWgQOi+g8//zzNmfOHCVhoHtA94DuAd0DugeK5h4otjiyeSNaSlPw2Q+L6BR8SpUKASEgBISAEBACQiAeCBwW0cFbbnZ2tmUrCQPdA7oHdA8cnXtAuAv3hNwDeOA/GtTnsIjO0Riw/qYQEAJCQAgIASEgBA4VARGdQ0VK7YRA8UBAsxACQkAIlCgERHRK1OXWZIWAEBACQkAIlCwERHRK1vUu/GzVQwgIASEgBIRAghH4LwAAAP//7/R6vgAAAAZJREFUAwDXVOV2/Du/ggAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cp\u003eTable 3 shows that the square root of the AVE values on all diagonals is greater than the correlation coefficients in the lower left corner of the diagonal. This says that there is excellent discriminant validity between the latent variables in the model.\u003c/p\u003e\n\u003cp\u003eTable 3 Discriminant Validity: Fornell-Larcker Criterion\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUSF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENJ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUSF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENJ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: USF-\u0026gt;Social Interaction, SUB-\u0026gt;Subcultural Recognition, ENJ-\u0026gt;Enjoyment, PDC-\u0026gt;Production, PRE-\u0026gt;Self-presentation, CSM-\u0026gt;Consumption, SOC-\u0026gt;Social Interaction\u003c/p\u003e\n\u003cp\u003e4.2 Hypothesis Testing\u003c/p\u003e\n\u003cp\u003eThe results show (Table 4) that the t-values for USF -\u0026gt; PDC, USF -\u0026gt; CSM, ENJ -\u0026gt; CSM, PRE -\u0026gt; PDC, SUB -\u0026gt; PDC, and SUB -\u0026gt; CSM are all greater than 1.96, and the p-values are all less than 0.05. This suggests that these six hypothesized relationships are statistically significant at the 5% level. The evidence indicates that usefulness and subcultural recognition significantly affect production and consumption. Self-presentation significantly affects production. Therefore, H1a, H1b, H2b, H3a, H5a, and H5b are accepted. Although the t-value for the relationship between enjoyment (ENJ) and production (PDC) exceeds 1.96 and the p-value is less than 0.05, the path coefficient indicates a negative value. This result indicates that the effect of enjoyment on production is negative. Therefore, H2a is rejected. The t-values of PRE -\u0026gt; CSM, SOC -\u0026gt; PDC, and SOC -\u0026gt; CSM are less than 1.96, the p-values are greater than 0.05, and the confidence intervals are held at zero, indicating that these three hypothesized relationships are not significant at the 0.05 level. This result indicates that the effect of self-presentation on consumption and the effect of social interaction on both production and consumption are not significant. Therefore, H3b, H4a, and H4b are rejected.\u003c/p\u003e\n\u003cp\u003eTable 4 Summary of Direct Effect Test\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelationship\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta (\u0026beta;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;STDEV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBCa 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL2.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eU97.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eUSF -\u0026gt; PDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eUSF -\u0026gt; CSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eENJ -\u0026gt; PDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eENJ -\u0026gt; CSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003ePRE -\u0026gt; PDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003ePRE -\u0026gt; CSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH4a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSOC -\u0026gt; PDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH4b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSOC -\u0026gt; CSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH5a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSUB -\u0026gt; PDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eH5b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSUB -\u0026gt; CSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e5.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: Significant at p\u0026lt;0.001***, p\u0026lt;0.01**, p\u0026lt;0.05*, USF-\u0026gt;Usefulness, ENJ-\u0026gt;Enjoyment, PRE-\u0026gt;Self-presentation, SOC-\u0026gt;Social Interaction, SUB-\u0026gt;Subcultural Recognition, PDC-\u0026gt;Production, CSM-\u0026gt;Consumption\u003c/p\u003e\n\u003cp\u003e4.3 Coefficient of Determination (R\u0026sup2;) and Predictive Relevance (Q\u0026sup2;)\u003c/p\u003e\n\u003cp\u003eTable 5 shows that the R\u0026sup2; for production is 0.290, which means that the five perceived values explain 29.0% of the variation in user production behavior. This suggests that the model has a moderate level of explanatory power for the production behavior of earthy short videos. The R\u0026sup2; for consumption is 0.370, which means that the five perceived values explain 37.0% of the variation in user consumption behavior. This suggests that the model possesses significant explanatory power for the consumption behavior of earthy short videos.\u003c/p\u003e\n\u003cp\u003eThe results show (Table 5) that the Q\u0026sup2; for both production (0.204) and consumption (0.201) is greater than 0, indicating that the structural model of this study has satisfactory predictive accuracy.\u003c/p\u003e\n\u003cp\u003eTable 5 Model Results for \u003cem\u003eR\u003c/em\u003e\u003cem\u003e\u0026sup2;\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Q\u0026sup2;\u003c/em\u003e(cv-redundancy)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"416\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eProduction (PDC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eConsumption (CSM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e4.4 Assessment of Goodness of Fit (GoF)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Table 6, the mean value of Q\u0026sup2;\u0026nbsp;for all latent variables is 0.307, and the mean value of R\u0026sup2;\u0026nbsp;for all endogenous variables is 0.330. The root sign of the product of the two yields the GoF value of the model as GoF =\u0026nbsp;\u003cimg width=\"187\" height=\"23\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;= 0.318. This result indicates that the predictive ability of the research model is 31.8%. The value of 0.318 is above the threshold of 0.25, which indicates that the research model has moderate predictive power.\u003c/p\u003e\n\u003cp\u003eTable 6 Summary of \u003cem\u003eQ\u0026sup2;\u003c/em\u003e(cv-communality) and \u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"535\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u0026sup2; (CV_com.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eUsefulness (USF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2192.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1873.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eEnjoyment (ENJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1644.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1020.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSelf-presentation (PRE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2740.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1862.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSocial Interaction (SOC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3288.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2578.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSubcultural Recognition (SUB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2740.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2064.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eProduction (PDC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1644.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e929.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eConsumption (CSM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3836.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2288.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.307\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.330\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e4.5 Importance-Performance Map Analysis (IPMA)\u003c/p\u003e\n\u003cp\u003eTable 7, Figure 2, and Figure 3 show the results of the importance-performance analysis. For production, the most important variable was self-presentation (0.229), followed by subcultural recognition (0.220), and social interaction was the least important (0.096). For consumption, the most important variable was subcultural recognition (0.274), followed by usefulness (0.174), and self-presentation was the least important (0.087). The most significant performance variable was enjoyment, with a score of 75.468.\u003c/p\u003e\n\u003cp\u003eTable 7 Results of Importance-Performance Analysis [PDC \u0026amp; CSM]\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstructs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLV Performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImportance (total effects) PDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImportance (total effects) CSM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eUsefulness (USF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e63.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eEnjoyment (ENJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.468\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSelf-presentation (PRE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e48.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.229\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSocial Interaction (SOC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e58.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSubcultural Recognition (SUB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e51.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.274\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eThe purpose of hypotheses 1-5a (H1a, H2a, H3a, H4a, and H5a) was to test the relationship between perceived value and production behavior of earthy short videos. The results indicate that social interaction has no significant effect on production. Apart from that, usefulness, enjoyment, self-presentation, and subcultural recognition have a significant effect on the production of earthy short videos. The purpose of hypotheses 1-5b (H1b, H2b, H3b, H4b, and H5b) is to test the relationship between perceived value and consumption behavior of earthy short videos. The results indicate that self-presentation and social interaction do not have a significant effect on consumption. Perceived value, enjoyment, and subcultural recognition significantly influence the consumption of earthy short videos.\u003c/p\u003e\u003cp\u003eThis study found that usefulness significantly influences both production and consumption (H1a, H1b). The finding suggests that small-town youth strongly recognize the benefits that earthy short videos can provide in terms of cognitive improvement, skill development, and efficiency enhancement and will produce and consume earthy short videos for these purposes. This suggests that earthy short videos are useful to users in terms of emotional relief, session anxiety, and satisfying cognitive needs. This conclusion is consistent with the findings of previous studies (Zou, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Some users gain emotional catharsis and relief from ridiculing earthy short videos (Jiang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Some users watch earthy videos to resolve their anxiety through self-cathartic insults and self-projection, which in turn improves their performance in life (Zou, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study's findings further support these assertions. Improvements in cognitive skills and efficiency can enhance individuals' development and improve their performance at work and in life. The knowledge and practical skills taught in earthy short videos satisfy their thirst for knowledge and provide them hope for a better life. Gradually, consuming and producing earthy short videos have become their main activities. As a result, Chinese youth from small towns place significant importance on the useful value of earthy short videos.\u003c/p\u003e\u003cp\u003eEnjoyment significantly affects consumption (H2b), but the effect on production is negative (H2a). This suggests that small-town youth recognize the entertainment value of consuming earthy short videos that bring them fun, joy, and enjoyment, and will consume earthy short videos for this purpose. This is in line with Zou (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). He believes that one reason users watch earthy short videos is to seek entertainment. However, small-town youth do not believe that producing earthy short videos can bring these hedonic values. This suggests that small-town youth do not produce earthy short videos to satisfy their hedonic needs or to entertain themselves, but for other purposes. This is inconsistent with the findings of Zhang (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Guo (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The reason for this result may be the different study populations. Zhang's (2021) analysis is based on the psychology of the urban group intending to obtain pleasure by imitating and reposting earthy short videos. Guo (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argues that the pushers behind earthy short videos are business and capital, and they produce them with the purpose of conveying simple happiness. However, small-town youth do not produce earthy short videos for pleasure. This finding updates society's view of the motives of small-town youth groups in producing earthy short videos.\u003c/p\u003e\u003cp\u003eSelf-presentation significantly affects production (H3a), but not consumption (H3b). This suggests that small-town youth will not watch, comment on, and retweet earthy short videos to display their thoughts. However, they will shoot and produce earthy short videos to show themselves. This is consistent with Jiang (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Sun (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a grassroots and marginalized group in society, they have not been able to gain a voice in mainstream media (Yang, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). When short video platforms target this group, their long-suppressed desire for expression is awakened, and excitedly and without measure, they begin their performances in earthy short videos. Earthy short videos provide a stage for small-town youth from the bottom of society to present themselves. In order to attract attention, they do not hesitate to play as ugly, act as crazy and stupid, and even abuse themselves in earthy short videos. This reflects their anxious psychology of thirsting to gain recognition by presenting themselves. This is also the embodiment of the self-presentation value of earthy short videos in the minds of small-town youth.\u003c/p\u003e\u003cp\u003eThe effect of social interaction on both production and consumption was not significant (H4a, H4b). This finding suggests that small-town youth perceive the social interaction of earthy short videos weakly. This means that small-town youth rarely rely on earthy short videos to make new friends or maintain friendships. For them, earthy short videos cannot significantly contribute to socialization. This conclusion is inconsistent with Hu and Xiang's (2021) view. The reason for the inconsistency may be the different populations analyzed. Hu and Xiang's (2021) view is based on the analysis of earthy Internet influencers. Earthy Internet celebrities become famous due to their earthy short videos. And by producing and consuming these videos, they gain fans, interact with them, and obtain commercial benefits. As a result, they strongly recognize the social interaction of these videos. However, a significant number of ordinary small-town youth have limited followers and struggle to reap tangible benefits from their interactions with earthy short videos. Therefore, they are unable to fully understand the social interaction value of earthy short videos.\u003c/p\u003e\u003cp\u003eSubcultural recognition significantly influences production and consumption (H5a, H5b). This indicates that small-town youth attach considerable importance to the subcultural recognition value of earthy short videos. Through earthy short videos, they aim to make a lasting impression on others. They believe that they belong to the consumer group of earthy short videos, that earthy short videos match their interests, and that earthy short videos can bring something important to them personally. This suggests that small-town youth care very much about their subcultural identity and desire social recognition of their identity. This is in line with Qin and Zhou (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Small-town youth, having spent a long time at the bottom of society and neglected by the mainstream, deeply yearn for recognition from society (Qin \u0026amp; Zhou, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Short video platforms, on the other hand, provide small-town youth this opportunity. The de-inhibition and decentralization of the Internet (Yang, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jiang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) enable small-town youth to be rarely constrained and interfered with by their real-life identities, social statuses, and academic qualifications on the Internet, which greatly encourages them to participate in the dissemination and proliferation of earthy short videos. The Internet empowers traditionally disadvantaged groups, allowing the public to see and notice the underclass through earthy short videos. This approach enables small-town youth to develop group belonging and social identity in the virtual space of earthy short videos. And this subcultural recognition directly affects their prosumption of earthy short videos.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Practical Significance\u003c/h2\u003e\u003cp\u003eFirst, the government should accommodate and guide the development of subcultures. The results of the study found that subcultural recognitions significantly influence production and consumption. The important performance analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) also demonstrates the significant influence of subcultural recognitions on production and consumption, albeit at a relatively low performance level. This indicates that small-town youth pay attention to the subcultural recognition of earthy short videos, but the representation of subcultural recognition in these videos is inadequate and requires further enhancement. Video platforms and producers should produce and publish more content that reflects subcultural recognitions. Short video platforms should do a decent job as gatekeepers, strengthen content auditing, and disseminate high-quality subcultural content.\u003c/p\u003e\u003cp\u003eIn addition, mainstream society should take a more tolerant attitude towards earthy subculture and youth subculture. Previously, the mainstream culture regarded all kinds of phenomena and behaviors of earthy short videos as transgressions and suppressed, curbed, and reined in earthy subculture through moral panic and public opinion (Liu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, this approach to addressing the issue fails to resolve the long-standing conflict between dominant and subordinate cultures, nor does it align with the principles of fairness and justice in governance. This is because the existence of subcultures is an indication of attempts to resolve social conflicts (Haenfler, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, it is recommended that mainstream culture and earthy subculture increase communication and dialogue, jointly utilizing the strengths of the subculture to promote the development of disadvantaged groups and the progress of society.\u003c/p\u003e\u003cp\u003eSecond, short video platforms should increase the production of cognition-enhancing content. The study found that small-town youth value the usefulness of earthy short videos. Therefore, content producers should enhance the usefulness and practicality of their content so that users can learn practical life skills and improve their cognitive level. Short-video firms should be duty-bound to take on a broader corporate responsibility and output content with more public value. For instance, they should encourage farmers to showcase the beauty, food, folklore, and skills of the countryside, thereby highlighting the distinct earthy content of various regions. It is also recommended that platforms provide more traffic support and policy guidance to small-town youth groups to contribute to improving the survival of the underclass.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Limitations and Recommendations\u003c/h2\u003e\u003cp\u003eThis study investigated the factors that influence Chinese small-town youth's prosumption of earthy short videos, using the perceived value theory as a framework. Despite the achievement of the research objectives, certain limitations remain. First, the limitation of sampling scope. Due to limited sampling capacity, the study sampled from a typical area in central China. We suggest future studies expand the sampling scope to the whole of China, covering all regions and provinces, to increase the diversity and comprehensiveness of the sample. Future studies should also take into account the consumption of earthy videos in other countries and draw comparisons with studies conducted in China. Secondly, we must acknowledge the limitation of variable settings. The research model did not include economic value as a measurement variable. At the initiation of the study, certain activities on short video platforms failed to capture our interest. However, these days, the features of rewarding, gifting, and earning gold coins for watching videos on short video apps are extremely popular. Many users consume and produce short, earthy videos to earn money. Therefore, we suggest that future studies investigate the monetary factor as a variable that affects users' prosumption. Despite these limitations, the findings of this study are still meaningful. The study effectively confirmed the factors that impact the target population's prosumption of earthy short videos, and the results hold practical significance for both governments and short video companies.\u003c/p\u003e\u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study contributes to the field of earthy short video research by examining the value factors that influence Chinese small-town youth's production and consumption of earthy short videos, adding to the understanding of earthy short video prosumption. This study proposes a logical path model connecting perceived value and prosumption with reference to perceived value theory. PLS-SEM evaluated the model, revealing sufficient reliability, validity, and moderate predictive power. Our study found that usefulness, self-presentation, and subcultural recognition have significant positive effects on the production of earthy short videos. Enjoyment negatively affects small-town youth producing earthy short videos. Usefulness, enjoyment, and subcultural recognition significantly predicted small-town youth's consumption behavior of earthy short videos. Social interaction had no significant effect on either production or consumption behavior. Based on the findings, the study suggests some practical recommendations for government departments and short video platforms. We suggest that government departments increase communication and understanding with subcultural groups and be more tolerant and wiser in dealing with the issue of the primary and secondary culture dichotomy. Meanwhile, we suggest content producers increase the usefulness and practicality of earthy short videos. It also suggests that short video platforms provide more support to rural youths in terms of policy and more guidance in terms of content. Although this study has limitations in sampling scope and variable setting, it is still important. It offers theoretical, methodological, and practical insights. This study developed a scale to measure subcultural recognition. It is the first to transform subcultural recognition into a measurable variable for assessment, successfully verifying its significant influence on the prosumption behavior of earthy short videos.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Human Research Ethics Committee of Universiti Sains Malaysia (JEPeM-USM) (Date: February 2, 2024 / Ethics Approval Number: USM/JEPeM/PP/23090696). The procedures used in this study were in accordance with the Declaration of Helsinki, International Conference on Harmonization (ICH) Guidelines, Good Clinical Practice (GCP) Standards, Council for International Organizations of Medical Sciences (CIOMS) Guidelines, World Health Organization (WHO) Standards and Operational Guidance for Ethics Review of Health-Related Research and Surveying and Evaluating Ethical Review Practices, EC/IRB Standard Operating Procedures (SOPs), and Local Regulations and Standards in Ethical Review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is a non-interventional study. Prior to collecting questionnaire data (March–April 2024), researchers obtained written informed consent from participants or their legal guardians. The scope of informed consent included the content of the study, its purpose, procedures, potential risks, data processing, confidentiality, publication of results, and declarations of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author confirms that all data generated or analysed during this study are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBabbie, E. R. (2020). \u003cem\u003eThe practice of social research\u0026nbsp;\u003c/em\u003e(15th ed.). Cengage Learning.\u003c/li\u003e\n \u003cli\u003eBaber, H. (2022). 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Earthy Culture: self-identity of college students in TikTok short videos. \u003cem\u003eReporters\u0026apos; Notes,\u0026nbsp;\u003c/em\u003e(36), 98-99.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"earthy short videos, value factors, prosumption, production, consumption, youth subculture","lastPublishedDoi":"10.21203/rs.3.rs-6853397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6853397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious studies of earthy short videos have neglected value factors. To study the value factors affecting the production and consumption of earthy short videos by Chinese small-town youth, we propose the concept of prosumption and a logical path model connecting perceived value factors and prosumption. We developed a scale to measure subcultural recognition and verified the important influence of subcultural recognition on the behavior of producing and consuming earthy short videos. We analyzed data from 548 samples and found that usefulness and subcultural recognition significantly predicted Chinese small-town youth's production and consumption of earthy short videos. Self-presentation significantly influences production, but not consumption. Enjoyment predicts consumption but negatively affects production. Social interaction has a non-significant impact on both production and consumption behaviors. The findings suggest that we should pay attention to youth subculture, enhance the usefulness of earthy video content, and focus on the sustainable development of small-town youth groups.\u003c/p\u003e","manuscriptTitle":"Beyond Entertainment: How Value Perceptions Influence Production and Consumption of Earthy Short Videos Among Chinese Small-Town Youth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 10:27:29","doi":"10.21203/rs.3.rs-6853397/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"290984039728091937587809949134392936645","date":"2025-08-13T13:24:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T11:39:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-22T07:10:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-17T17:01:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T06:24:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-07-09T06:21:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3613f5ab-43a1-404d-a77f-7ace6a896835","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53114071,"name":"Humanities/Cultural and media studies"},{"id":53114072,"name":"Social science/Cultural and media studies"},{"id":53114073,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-04T11:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 10:27:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6853397","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6853397","identity":"rs-6853397","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}