ĐĎॹá>ţ˙ _ a ţ˙˙˙J K L M N O P Q R S T U V W X Y Z [ \ ] ^ ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ěĽÁk řż˝bjbjŽŽ TK}Ä}ÄÍŐ˙˙˙˙˙˙lPPP"rśśś¤ć ř!ř!ř!#DL#\,ć y´OĘ~P" P P P'T>eTyT řúúúúúú$ ŻŞś…TSš'T…T…Tkśś P Pí3kkk…Tâś Pś Přk…Třk$k)qŢ4ŤXśśܲ P¨O đĺŘ Âć ř!gj|Œą¨Ü˛_I0y4˛¨Yăj"YܲkZ lĆ ŽlúźśśŮ FEDERAL STATISTICAL WEBSITE USERS AND THEIR TASKS: INVESTIGATIONS OF AVENUES TO FACILIATE ACCESS Carol A. Hert July 18, 1999 Final Report for Purchase Order #B9J82764 1 PROJECT OVERVIEW AND EXECUTIVE SUMMARY INTRODUCTION Advances in web technology, the ongoing imperative of agencies to provide access to Federal data, and increasing awareness on the part of the public of the availablity of statistical information, has led to increasing use of Federal statistical websites. Such usage has raised issues associated with appropriate interface design (and G. Marchionini has explored in a series of investigations), user behavior (Hert and Marchionini) and customer service activities. The task of improving access to statistical data necessarily involves investigations on all three fronts as well as the integration across the three. The project detailed here focused primarily on aspects of user behavior but also touched on customer service. In previous work, we conducted investigations of user groups and user tasks (via a variety of methods) associated with Federal statistical websites in order to provide redesign recommendations and prototype alternative interfaces for these websites. This work provided evidence that expert terminology may be difficult for users, that subject access (i.e., tasks in which beginning from the perspective of finding statistics on a particular topic is appropriate) is difficult via currently available tools and that users (and intermediaries) could often benefit from access to various components of statistical metadata in order to better accomplish their objectives. This results of this project provide insights in those three areas. In addition, the project researcher included a component related to customer service. Earlier investigations provided a picture of intermediaries as actively engaged with user information needs; they often provided interpretive and consultation services to help users reframe information needs, provide explanations of data structure and available information. There was also evidence that these intermediaries were being inundated with requests, often felt that they needed additional information to resolve user inquiries etc. Given this, a study which explored customer service initiatives was proposed with the assumption that enhancing intermediary effectiveness and efficiency was another avenue to improving user access. The specific studies that compose this project are: An analysis of FedStats search engine logs with deliverables as follows: an interactive webpage for exploration of queries, summary of usage of the search engine for November 1998, an analysis of user terminology compared to agency terminology and agency terminology extended with terms from thesauri, and a feasibility assessment of procedures used for comparison and implications for agency terminology enhancement along with set of rules which would need to be incorporated into those procedures A Relevance judgement study of CPS metadata with the following deliverables: a qualitative analysis of interviews with CPS expert users concerning metadata lacks, possible enhancements, and their use of metadata in support of various analytic tasks, preliminary specification of a user study of metadata usage (to be conducted Fall 1999), and recommendations for enhancements to existing metadata for use in online environment. A participant observation study of customer service activities with a sourcebook of information on products/services/ etc which could be used in support of various customer service integration/enhancement activities Specific research questions for each activity are provided in the detailed sections on each activity. EXECUTIVE SUMMARY OF THE PROJECT The three studies all investigated aspects of user access to statistical information. Earlier work had examined that phenomenon at a less detailed level by focusing on user tasks and goals. This work provided more detailed pictures of some of the tools available to provide access to users: the FedStats search engine, the FERRETT system, and customer service management within BLS. These three threads are distinct and no attempt is made at this point to synthesize the findings across the three. However, it is clear that supporting user access is complex and that many vehicles are available to do so; each of which may warrant individual study. The study of the FedStats search engine provided insight into the most common search queries on the part of users. As is the case on most search engines (web-based or otherwise) it was found that only a small number of queries are searched frequently and that Boolean operators are little used. As part of the study, user terminology was compared to agency terminology for a concept. Terminology employed by users does not overlap with agency terminology to any great extent. A number of terms employed by BLS for the “wage and pay” concept are not used in queries by users while users use a variety of terms that the agency does not use. The same holds true for the relationship of user terminology to terms in the FedStats A-Z index leading to some recommendations about possible enhancements to the index. The feasibility of automating the comparison technique employed in the study was also considered. While a number of programs would be needed and a set of explicit rules developed, the process can be automated-however it is suggested that further information on results of search queries be gathered prior to using the process further. The relevance judgement of metadata study has yielded a rich qualitative picture of how experts use metadata to determine variables to include in analyses. The process is characterized by complexity and situationality. Which variables seem appropriate may change as the expert thinks about the task at hand or about the variables. The study provided details on how experts make their decisions and the information used from the metadata. Universe statements, valid codes, and the type of variable (i.e., weighted, recoded, etc.) are all frequently used. The study has also enabled the researcher and John Bosley of BLS to specify the methodology for a related experiment with non-expert users of metadata. The participant observation study will generate a sourcebook of materials on technologies that may have the potential to add value to existing activities. These technologies include software for real time interaction with customers, helpdesk and knowledge management software, and tracking and logging facilities. 1.2.1 Recommendations This section provides the full set of recommendations that are provided in the sections that follow. Recommendations related to search log analysis and user terminology investigations are: The FedStats task force assess the extent to which the most commonly searched concepts (via the search engine) have related documents at agencies. For those that do, the A-Z index terminology might need to incorporate terminology employed by users in place of existing terms or use additional cross-references. TheFedStats task force clarify the type of document to which the A-Z index refers and provide a brief statement both on the A-Z index and the search engine web pages. For example, if the intent of the A-Z index is to point to the most commonly requested information or the “best” information on a topic, a note to that effect on the search engine might steer users to the A-Z index which would get them to materials more quickly. Ongoing analysis of search term logs to get a better picture of queries and their frequency. Techniques to bring together related terms (including the technique used in this study) should be employed to understand the frequency with which concepts are searched for by users. This information might be used to provide additional links to the most commonly requested materials, develop instructional materials in those areas, and provide other user aids. A log analysis of the FedStats A-Z index pages in comparison to the search engine logs might illuminate the differences in the tools’ usage and point to additional ways in which use of the tools might be differentiated. Investigate documents/information retrieved via the searches. The real test of the utility of user terminology inclusion will be the extent to which user terms retrieve information that is relevant to their query and whether they retrieve the same information as they might retrieve with agency terminology. Consider the feasibility of ongoing tracking of user terminology. This study has indicated that comparing user terminology to agency terminology is feasible and could be automated. As with most aspects of websites, one can anticipate that this terminology will change over time and agency terminology or related mappings will need updating. Qualitative analysis of user terminology is also suggested. The data set used here contains information on actual terminology employed. These data might be examined for typical mistakes made (such as spelling errors, syntax errors, etc.) and other aspects of query formation. The finding that there is a low use of agency terms, with some terms not used as all by users, has implications for any indexing of agency documents that might be done. There may be little value in using terms that are not used by users. The addition of terminology in areas of high frequency of searching might also be of value. While it may be unreasonable to provide a rich set of terminology in all concept areas, those concepts that are highly used might be further enhanced in an effort to assure that users gain access to relevant information in those areas. The study of metadata relevance judgement led to the following recommendations: Recommendation 1: Eliminate Abbreviations and Coded Information Perhaps the most straightforward improvement to the metadata would be the elimination of abbreviations (which could probably be automatically accomplished) throughout the metadata (including metadata field names) and the elimination of coded variable names and variable categories in universe statements. The use of codes caused analysts to have to do look-ups in other portions of the metadata, a process that is inefficient. Recommendation 2: Provide a Universe Statement for Each Variable Analysts relied heavily on the universe statements as a source of understanding and when it was missing had to attempt to recreate the skip pattern that would have led to the question concerned. Recommendation 3: Include Information on the Purpose of a Variable Knowing why a question was asked, or a variable created was helpful to the experts in determining usage. This information may be difficult to recreate for existing metadata but as new variables are added to surveys, the rationale for their creation might help users. There is some information available in the existing internal documentation on variable purpose that might be included in existing metadata. (New variables for some surveys apparently do included this information.) Recommendation 4: Include Periodicity Information in Date Field Even expert users found themselves guessing on how frequently data on some variables were included. The date field currently only includes date of first use, but not frequency with which a question is asked or tabulated. Recommendation 5: Include a Glossary of Terms Unusual or highly technical usage of common-looking words should be explained or avoided. Examples, “topcode” and “out” when the latter means an “output variable.” Some of the experts didn’t even know what “out” meant. Implication: Here as always, be careful to use clear, plain English or provide easy access to a glossary, e.g. hyperlink “topcode” to its definition. Recommendation 6: Clarify Valid Item Values Don’t abbreviate category labels so much that they become unrecognizable. Better explanation of both particular variables’ valid ranges would be helpful as would the inclusion of general orientation (such as in a glossary) to such broad categories as “missing data,” “flags,” etc. and why these are or are not useful or important to the user—or under what circumstances they become significant, e.g. how much “missing data” before the user should worry. Recommendation 7: Provide Mechanisms for Establishing Variable Context As more survey data are made available online, there will be an increasing need to provide within survey and across survey context. Currently there is no information in the variable metadata about the survey--such information needs to be included. Within survey context might be added by providing an online version of the survey instrument, with links to the variable metadata so that a user could see the actual question in context. Analysts did use paper versions of the survey for such a purpose in the study. Inclusion of new field that provides the survey from which the data come would also provide necessary context. Recommendation 8: Reexamine the external and internally available documentation for the metadata and determine whether internal information can be added to the public documentation. The analysts used metadata not available to the public to make their decisions. While some of this must naturally remain confidential, others might not. Additionally, one analyst indicated that it was sometimes difficult to talk to the public and reconcile the two sets of documentation to help the user. Recommendation 9: Consider Providing a Limited Set of Variables for Use The current online system (FERRETT) does limit access to the data to some extent (by not providing non-edited variables, for example). Given the complexity of the metadata and variables, an approach such as that taken with the American Community Survey where users who are less expert can retrieve a limited set of variables (for example, perhaps only recodes) to perform the most common analyses might be considered. The amount of statistical literacy and context necessary to perform some analyses may not be reasonable to assume for some users and might be difficult to provide. In order to pursue such an approach it will be necessary to identify a commonly used/wanted set of analyses and variables. DISSEMINATION ACTIVITIES The results of this project (and of earlier activities) are being disseminated via this report and its posting on a website ( HYPERLINK http://istweb.syr.edu/~hert http://istweb.syr.edu/~hert) and through conference proceedings and journal articles. May 1999 American Society for Information Science, MidYear Meeting, Pasadena California John Fieber: A Study of Caching Behavior (on the BLS website) Rachael Taylor: FedStats Evaluation Activities Carol A. Hert: CoChair of Meeting and Panel Moderator for session on Initiatives on the Evaluation of Federal Websites Summer 2000 Presentations tentatively scheduled as the American Statistical Association and the International Conference on Establishment Surveys. Journal Articles Hert, C.A., Jacob, E. and Dawson, P. Evaluating Indexing Practice In The Networked Environment: An Exploratory Study. Submitted to Journal of the American Society for Information Science. Referee comments received and paper now under revision. Targetted resubmission date: Sept. 1999. 2. FEDSTATS SEARCH ENGINE LOG ANALYSIS AND ASSOCIATED TERMINOLOGY STUDY INTRODUCTION An important source of information on user behavior on websites can be found in the logs generated via the search engine of the site. These logs, which record information on user queries and number of results found for those queries (though not information on what was actually found) can provide insights into commonly requested information and the terms used. The work reported here utilized the November 1998 logs from the FedStats search engine (a Verity search engine) in order to identify: The most commonly searched words or phrases (including their variants) The extent of use of Boolean operators The logs also provide a picture of how users express concepts of interest in the form of queries. As organizations place more of their information (and services) on the web in an effort to attract and service customers, they have begun to recognize that how they conceptualize and name concepts may not map completely to how their customers might describe similar topics. The result of this disconnect may be that users are unable to locate relevant information even though it available. This problem is not new-library and information scientists have developed indexing systems, controlled vocabularies, and thesauri, all in an attempt to guide users to information that may be relevant even if the information uses different terminology. To date, however, efforts to develop metadata, thesaural, or indexing systems for web-based information have made slow progress particularly in specialized disciplines such as that considered here. Developers of indexing systems explore how concepts are represented in texts or in real language as a source for terms (often referred to as sources of warrant in the information science domain). On the world wide web, a potential source for real language terminology employed by users in the logs of a search engine of a site. The second part of the search log analysis had the intent of exploring the relationship between user terminology for a concept (as represented in a search engine’s log) and the terminology employed by BLS (as represented in its published documents). The specific objectives were: To determine the extent of the overlap between agency (the United States Bureau of Labor Statistics) terminology for the concept of “pay” and user terminology for the same concept as identified in user inputs to a search engine. To determine the extent of the overlap between agency terminology expanded with related terms from two electronic thesauri (WordNet and Webster’s) for the same concept and user inputs. To compare the extent of the two overlaps. To consider the feasibility of this approach for automatically enhancing agency terminology and/or user queries. Along with the search engine, users also have an index of terminology available to provide access to relevant documents. The final component of this project examined the relationship between user terms and the terms available in the FedStats A-Z index. 2.2 METHODOLOGY The researchers used several sources of data for the analyses: the search logs from the search engine for November 1998, a set of agency terminology for a particular concept, and the entries of the existing A-Z index. Prior to conducting the analyses to address the research questions, several preliminary activities were needed including parsing the search engine logs, and developing list of agency terms and extending that list with additional terms. These are described below. 2.2.1 Parsing of Search Engine Logs The research team received the November 1998 logs from the FedStats search engine. These logs include the IP address associated with a given query, a time stamp, the search query, and databases searched (the FedStats engine enables a user to specify which agency websites to search), and information about the results received (number of pages found). The logs were examined by John Fieber, Indiana University, in order to understand their structure for parsing purposes. An example of the entry format (reformatted for ease of reading) is presented as Figure 2-1. The following aspects of the log files are relevant to the understanding of our log analysis. Certain log entries provided information which enabled the team to determine that the entry represented the user requesting an additional page of search results (those entries which showed a 0 hit after an entry with the same query showing hits). However, the logs do not indicate whether the next page command is for a previously viewed page or a new page of results. Since we were not currently investigating how persistent users were in investigating query results, this limitation was not a problem for our analyses. Some entries represented “ill-formed queries,” such as inappropriate use of quotation marks around Boolean operators or search strings, however were not easily identified in the logs without recreating the search. At this point, we were less interested in results from searches than in the terminology employed so our inability to recognize these was not a problem in this analysis. As with all logs, the IP address may represent multiple users. Caching of pages on local clients also prevented the team from exploring instances in which a user returned to a previously displayed list of results. Finally, no information on the actual pages retrieved are available in the files. Figure 2-1: Example of Search Engine Log File Entries host time hits coll. query terms 1 207.43.27.42 00:00:43 1245 ALL welfare 2 207.173.24.166 00:30:13 9 bea_web Economic report of the president 3 207.173.24.166 00:30:54 2892 \N President 4 208.18.175.189 01:00:06 0 ALL 501-88-1104 5 208.18.175.189 01:00:17 0 ALL 501881104 6 129.252.188.197 01:00:55 0 ALL Eating Disorders 7 206.214.143.165 01:07:57 56 ALL contingent workers Several definitions are necessary to clarify the discussion that follows. A query is the word or phrase (normalized as explained below), along with any Boolean operators, that appears in the log. There can be multiple instances of a query appearing in the log. An instance of a query is one entry in the log file. A term is the phrase or word used by the agency for a concept as well as a part of a query that is separated by a Boolean operator from another part of a query. Terms may consist of single or multiple words. Thus the user query “Catholic priests and salaries” consists of two terms, “Catholic priests” and “salaries.” 2.2.2 Query Parsing In order to identify user queries and their frequency, it was necessary to remove instances that represented a user displaying additional pages of results. This was done by sorting identical queries by IP address and removing those that met the criterion specified above. Once these were removed, the analyst normalized the query strings by removing extra spaces and by making all entries lower case. No additional normalization or stemming was done in this first analysis. 2.2.3 Session Identification For additional analyses, it was necessary to parse the queries into sessions. A session was defined as a series of inputs from an IP address with each input occurring less that 30 minutes from the last input. The identification of sessions is always problematic in log analysis. An analyst must make the assumption that entries from one IP address occurring within a reasonable time period (30 minutes is the standard time period used in this context) represent entries of the same user, from one search session. It is, however possible that such a session may represent multiple users from the same IP address. It is also possible for a single user session to span multiple IP addresses (if WebTV is used, for example). 2.2.4 Development of Agency Terminology Lists In this study, the researcher and Stephanie Haas investigated the relationship among user terminology and agency terminology for a particular concept—pay and wages. Dr. Haas developed two sets of terminology -one of terms/phrases used within the Bureau of Labor Statistics relating to the concept and another list which expanded that set of terms via the use of two online thesauri. The details of this process are provided in her report entitled Knowledge Representation, Concepts, and Terminology: Toward a Metadata Registry for the Bureau of Labor Statistics (Final Report to the Bureau of Labor Statistics (Purchase Order #OPS-184298)) The term lists that were developed are provided in appendices 2-1 and 2-2. 2.2.5 Analytic Activities 2.2.651 Search engine tabulations After the preliminary parsing and manipulation of the search logs, the analyst developed summary counts of actions recorded in the logs, the number of queries (number of actions minus number of next page commands), the frequency with which each normalized query string appeared in the log, and the number of unique queries (total queries minus all duplicate queries). An interactive website was developed to present summary statistics as well as provide the ability for a user to search the logs for a particular word (and see all queries that included that word) and to search the sessions for a particular word or string. This site is currently located at http://fallout.campusview.indiana.edu/~jfieber/fedstats/ . Plans are underway to place the site on the FedStats administrative server (probable URL: http://www.fedstats.gov/admin/usability/searchlogs/index.html). . 2.2.5.2 Comparison of Agency Terminology with User Terminology In order to compare agency terminology with user terminology, it was necessary to find user queries that represented searches for the concept of interest (pay and wages). Given the total number of queries and our inability to understand user intent from log entries, the researchers defined user queries related to the concept as those queries which were part of a user session in which an agency term for the concept was employed. If a user used no agency terms, the session was not identified. At this point, we have no measure of how many such sessions might exist—to identify them would involve extensive use of thesauri and clustering algorithms to bring together potentially related words and phrases, activities which were beyond the scope of this exploratory analysis. An example may clarify the process of identifying relevant user queries. A term on the agency list was “salary.” Searching the database by session, 94 sessions are found that included the term “salary”. The queries that make up these sessions are all considered to be relevant to the concept of “salary.” Thus the analysts searched the session database (via the website listed above) for all terms on both the agency list and the expanded agency list of terms. The identifier of each session (source, session #) that utilized agency terminology was entered into an Exel spreadsheet and the number of queries, total terms, and agency terms was recorded for each session, as were the set of terms employed by the user during the session. The coding rules for the sessions are included as Appendix 2-3. Since sessions might use multiple agency terms (and thus would be identified more than once by the above process), duplicate sessions were removed from the database prior to further analysis. In order to simplify coding, analysts were instructed not to interpret user queries in an effort to “understand what the user was doing.” A limitation of the use of logs is that we can never know exactly what the user was thinking, why he or she input the various terms, etc. so rather than assume that there are some cases in which we can tell, the set of coding rules purposely attempted to limit analyst interpretation of the queries. Data from the spreadsheets were then used to develop frequency distributions and scatterplots. 2.2.5.3 Comparison with the A-Z index. In order to investigate the relationship between user queries and terms in the A-Z index, the analyst checked all user queries with a frequency of at least 10 occurrences against the FedStats A-Z index (as of June 15, 1999, presented as Appendix 2-4) to determine whether there was an exact match in the index (exact match), a match where the user query was a truncated form of the index term (root match), or a match where the beginning of the query is an exact match with an index term (reverse root). In addition, the analyst identified any term in the user query which matched (as an exact match or root match) in the index. The definitions were: Exact match: The query phrase is exactly the same as an index phrase, or the only difference between the terms is that one is plural and the other singular, or one is an adjective of the other. Thus, product/productivity, banking/bankruptcy, and housing/household do not count as exact matches. Root: The entire query phrase exactly matches the first part of an index phrase precisely as far as the query phrase goes. (Or the query phrase exactly matches a heading in the index under which are 2 or more subheadings.) Reverse Root: This is when the query has more than one word, and the first word or words is an exact match of an index phrase (e.g., ‘crime policy’ reverse root matches with crime). If the first word(s) of the query constitutes a root match of an index phrase, then there is NOT a root match (e.g. the query ‘international economic statistics’ does not constitute a root match with ‘international trade’). Matching words: A list of words that are part of the query phrase, but not all of the query phrase, that appear as an exact match, root match, or both somewhere in the index. The researchers identified exact, root, and reverse root matches based on the assumption that if a user could find a close starting point in the index even if it was not identical with the query, he or she could likely gain access to relevant materials. For example, a user with the initial query, “crime statistics” would be able to use the A-Z index to find the term “crime” which might provide access to statistics in that domain. Matching words were identified, not because they currently provide access (a person with the query “murder in families” would not easily scan the entries available in the A-Z index and find the term “family,” which does appear in the index) but because they represent instances were index enhancement might be appropriate. The coding rules lead to some cases where a user might actually get to a index term successfully (Such as the case where a user with the query “child abuse” would likely find the term “children”) however, the researchers chose not to incorporate all such instances as rules in order to simplify the coding. 2.3 FINDINGS 2.3.1 Query Analysis Table 2-1 provides summary counts of the queries during the month of November 1998. Table 2-1: Summary Counts for FedStats Search Engine Log—November 1998 82443 total transactions 34552 "next page" transactions 47891 queries 28248 unique queries 10313 unique single word terms 7858 boolean "and" queries 319 boolean "or" queries 18313 unique hosts It can be noted that 16.4% of the queries used the Boolean operator “and” and 1.1% used the operator “or” for a total of 17.1% of the queries using Boolean operators. This finding is similar to other studies of the use of Boolean operators, either on web search engines or other information retrieval systems. 59.0% of the total frequency of queries represents those queries which appeared more than once. Thus approximately 19635 queries appeared only one time. Table 2-2 reports the frequency of queries as input by users during the month of November 1998. In this table, the queries have been normalized for capitalization and white space. Table 2-2: Nov. 1998 FedStats Queries and Their Frequency Table includes only those queries that were input over 30 times by users # query 544 population 229 divorce 228 inflation 215 income 198 gdp 180 family income 179 population and income 162 unemployment 153 consumer price index 145 crime 123 119 cost of living 113 welfare 111 abortion 106 inflation rate 104 teen pregnancy 104 gross domestic product 99 education 99 child abuse 96 cpi 92 suicide 92 religion 84 capital punishment 81 life expectancy 77 unemployment rate 76 internet 70 poverty 69 juvenile violence 69 interest rates 69 alcohol 68 immigration 66 teenage pregnancy 66 affirmative action 62 voting 60 gross national product 60 death penalty 59 domestic violence 57 homeless 57 customer satisfaction survey 52 national debt 52 marriage 52 adoption 51 drugs 51 census # query 51 aids 50 smoking Employment 49 divorce rate 48 sexual harassment 48 population, income 47 divorce rates 46 gnp 46 breast cancer 44 statistics 44 gun control 43 deaths 42 marijuana 42 budget 41 women 41 drunk driving 40 rape 40 cancer 39 mortality 39 juvenile crime 38 literacy 36 tourism 36 retail sales 36 prime rate 36 population and age 36 inflation rates 36 height 36 computers 34 insurance 34 guns 34 diabetes 34 death 33 depression 32 infant mortality 31 social security 31 salary 31 minimum wage 31 health 31 exports 30 wages 30 race 30 personal income 30 per capita income 30 military 30 hate crimes 30 firearms The 90 queries shown in Table 2-2 account for 13.9% of the total queries, and 3.2% of unique queries, demonstrating that there are very few high frequency queries with a rapid drop off into queries that are input only a small number of times. To say this a different way, most queries are input infrequently with only a few queries being input a substantial number of times. As stated earlier, the results above do not bring together variants of a term such as instances of such similar queries as “children” and “child” or “inflation rate” and “inflation rates”. A stemming algorithm will be run against the query database (targeted completion date: August 1999) that will truncate query terms in order to combine such queries. It is important to note that the stemming process used will not bring together synonyms (such as “cpi” and “consumer price index”). A much more elaborate process involving the use of networks of words and their meanings would be necessary to perform such an analysis. In a later part of the section, we report on such a process for one concept. 2.3.1.1 Conclusions of the Query Analysis The analysis of query frequency indicated that only a few queries were used with high frequency, these were general single word terms, and there is a rapid drop off in frequency. These results are not unexpected—many previous studies of search engine usage have shown similar results. Boolean operators are infrequently used, with the “and” operator used much more frequently than the “or” operator or other available operators (such as “not”, if the engine makes available) and that there are few high frequency searches. It is anticipated that an analysis that included more data (e.g., additional months of log data) would demonstrate similar patterns, though the actual queries that are of high frequency may change. Examining only the high frequency queries over many months might demonstrate monthly cycles, etc. and enable the site to provide additional guidance in searching those concepts. Additional information from user observations might be necessary to clarify the intent of the searches. 2.3.2 Comparison of User and Agency Terminology for the “Pay and Wage” Concept The first analyses that were done compared user terminology with the unextended list of agency terms. Appendix 2-4 presents the spreadsheet of data with the sets of terms used in each session. Table 2-3 provides the frequency distribution of the proportion of agency terms to number of terms used in the session. Table 2-3: Frequency Distribution of Agency Term Proportion Proportion of agency terms to total number of terms usedFrequency0.058810.062510.076920.083320.090940.100050.111150.1250150.1429270.1667490.2000590.222210.25001040.285730.33332010.375010.4000100.428610.50002870.555610.6667180.800011.000076 . Most sessions’ (89% of sessions) terminology consists of 50% of less agency terminology. The same analyses were done comparing the user terminology with the extended list of agency terms. (The full data set is not provided but is available on request.) In this case, the proportion that was calculated was the proportion of terms in the extended list to the total terms. Table 2-4 provides the frequency distribution. Table 2-4: Frequency Distribution of Proportion of Extended Agency Terminology ProportionFrequency0.055610.058820.062510.071410.076920.083320.090940.100050.111130.1250160.1429270.1667500.2000710.222230.230810.25001280.272720.285780.33332300.363620.375020.4000130.428610.50003590.571410.6000220.6667400.7500190.800030.833320.857111.0000253 In the case of the extended term set, 73% of the sessions use 50% or less of the extended agency terminology. 19.8% of the extended term set sessions use all extended terms compared to only 8.7 of the sessions used only agency terms (from table 2-3). Conclusions of the Comparison and Recommendations The results indicate that users are employing many terms for the pay and wage concept and that many of these are not used by the agency itself. While this study has not assessed the user terminology in a qualitative fashion to determine the nature of the additional terms (how many are misspellings, incorrect terms, etc.), it does appear that there is a mismatch between the two terminologies. (A finding not detailed in this report is that many terms from the agency list do not appear in any user queries at all.) When the agency set is extended there appears to be increased matching but it is still low. Generally, what this would suggest is not that either party is performing ineffectively, but that instead, mappings between the two sets of terminology might be made. A strategy employed in many information retrieval systems is to include a controlled vocabulary of terms—a set of terms that the agency uses to describe its documents- and to make this available to users. Users thus have the option of searching freetext (as they currently do) or using the vocabulary as used by the agency. A less-visible strategy is to translate behind the scenes without informing the user but this requires that a user query be interpreted. It should be stressed that these results are limited in several ways. First, because we have only examined one concept, we can not assess whether these findings would be true for all concepts. Thus, we recommend that these results be considered as illustrative of a process by which agency and user terminology might be compared. The second limitation is our lack of data on the outcomes of the searches. It might be that user terminology actually retrieves the same documents as agency terminology. The search logs did not provide information on documents retrieved so without extensive manual researching it is difficult to determine the answer to this question. There might be no need to enhance the terminology set. Given these results, it is recommended that this set of results serve as representative of a process (See section 2.4 for additional discussion) and that additional work be done to examine the outputs of user searches. Dr. Haas’ report also provides recommendations in this area. 2.3.3 Comparison of User Queries and the A-Z Index The researchers compared the 366 queries that were input into the search engine at least ten times with terms in the A-Z index to preliminarily assess the success users might have had using the A-Z index with these queries. Table 2-5 provides the results of the comparison among the terms used in the FedStats search engine and the terms provided via the A-Z index for the top 90 queries as indicated in Table 2-2. Of the 90 queries compared in this table, 21 matched an A-Z entry exactly, 10 were root matches, and 8 were reverse matches. Thus out of the 90 queries (which it is important to remember does not represent the number of times the queries were used) there were “reasonable matches” for 43.4%. When the frequency of usage is considered, 2917 of the queries had reasonable matches out of the 6622 queries represented on the table or 44.1%. Of the top queries on Table 2-2, with frequencies of 100 or more for the month of November, only 7 of the 16 had matches, all of which were exact. These were the terms: population, divorce, income, unemployment, consumer price index, crime, and gross domestic product. Those without A-Z index matches were: inflation, gdp, family income, population and income, cost of living, welfare, abortion, inflation rate, and teen pregnancy. The results for all queries which had a frequency of at least 10 are provided in appendix 2-6. Of the 366 queries compared, only 43 of these matched an A-Z entry exactly. An additional 35 queries were root matches and a further 37 were reverse root matches. Thus out of the 366 queries (which it is important to remember does not represent the number of times the queries were used), there were “reasonable matches” for 31.4% of the queries. When the frequency of usage of queries is considered, we find that out of the total of 10869 user queries represented, 3690 or 33.9% would have found some type of match in the A-Z index. TABLE 2-5: COMPARISON OF USER TERMINOLOGY AND A-Z INDEX exactroot reverse words match:words match:Queryqueriesmatchmatchrootexactrootabortion111nnnadoption52nnnaffirmative action66nnnaids51nynalcohol69nnnbreast cancer46nnnbudget42nnncancer40nnncapital punishment84nnncensus51nnnchild abuse99nnychildrenchildrencomputers36nnnconsumer price index153ynncost of living119nnncpi96nnncrime145yyncustomer satisfaction survey57nnndeath34ynndeath penalty60nnydeathsdeaths 43ynndepression33nnndiabetes34nnndivorce229ynndivorce rate49nnydivorcesdivorce rates47nnydivorcesdomestic violence59nnndrugs51nnndrunk driving41nnneducation99yynemployment50yynexports31nnnfamily income180nnnincomefamily, incomefirearms30nnngdp198nnngnp46nnngross domestic product104yyngross national product60nnngun control44nnnguns34nnnhate crimes30nnncrimecrimehealth31yynheight36nnnhomeless57nnnimmigration68yynincome215yyninfant mortality32ynninflation228nnninflation rate106nnninflation rates36nnninsurance34nnninterest rates 69ynninternet76nnnjuvenile crime39nnncrimecrimejuvenile violence69nnnlife expectancy81ynnliteracy 38nnnmarijuana42nnnmarriage52ynnmilitary 30yynminimum wage31nnnwagesmortality39nnnnational debt52nnnper capita income30nnnincome incomepersonal income30yynincome incomepopulation544yynpopulation and age 36nnypopulationpopulationpopulation and income179nnypopulation, incomepopulation, incomepopulation, income48nnypopulation, incomepopulation, incomepoverty 70ynnprime rate36nnnrace30nnnrape40nnnreligion92nnnretail sales36nnnsalary31nnnsexual harassment48nnnsmoking50nnnsocial security31nnnstatistics44nnnsuicide92nnnteen pregnancy104nnnpregnancyteenage pregnancy66nnnpregnancytourism36nnnunemployment162ynnunemployment rate77nnyunemploymentvoting62nnnwages30ynnwelfare113nnnwomen 41nnn 2.2.3.1 Conclusions of this Comparison and Recommendations Relating to the A-Z Index The results indicate that if a user were to go to the A-Z index instead of the search engine, he or she might not have been able to identify a term that might lead to related information in a majority of cases. This may not be a problem if the A-Z index is used differently or serves to fill a different role on the site or if the information requested by the user is not to be found at the various websites indexed. At this point, we do not know which of these cases is the reality. Thus it is recommended that: The FedStats task force assess the extent to which the most commonly searched concepts (via the search engine) have related documents at agencies. For those that do, the A-Z index terminology might need to incorporate terminology employed by users in place of existing terms or use additional cross-references. TheFedStats task force clarify the type of document to which the A-Z index refers and provide a brief statement both on the A-Z index and the search engine webpages. For example, if the intent of the A-Z index is to point to the most commonly requested information or the “best” information on a topic, a note to that effect on the search engine might steer users to the A-Z index which would get them to materials more quickly. A log analysis of the A-Z index pages in comparison to the search engine logs might illuminate the differences in the tools’ usage and point to additional ways in which use of the tools might be differentiated. FEASIBILITY OF THE TECHNIQUES EMPLOYED This study explored the relationship between user terminology and agency terminology for a single concept. We have been able to gain a reasonable picture of user terminology (though additional clustering of terms may be necessary) and have found that there are quite large differences in how users search a query in comparison to how the agency refers to a concept. Whether the technique employed in this study is scalable is a notable question as agencies begin to consider whether to incorporate user terminology into their thesauri, indexes, or other dissemination and finding vehicles. It is important to note that the process used in this study only quantified the extent of the difference between agency terminology and user terminology. It does not indicate whether additional terms should be employed or whether any or all of the terms employed retrieve the same set of documents (and it is recommended that these be investigated). As a comparison technique, while somewhat unwieldy, at least on the user terminology side, would be fairly easily automated. How a set of agency terms might be generated is a different question. The steps which were employed to analyze user terminology and compare it to a set of agency terms are summarized in Table 2-6 Table 2-6: Steps in Terminology Comparison StepEffort RequiredIssues1. Collect user queriesLowFrom search logs2. Identify queries vs. other actions found in logMediumLog format needs to be understood and rules written 3.Normalize queriesGenerally LowIf minimal normalization done, very easy, however will miss some important variants 4. Stem terminology in queriesMediumStemmer software needed, stemming rules need to be checked, output should be examined prior to use5. Perform preliminary countsLow6. Parse into sessionsMedium to HighSubject to typical session parsing issues7. Search session list for each agency term (stemmed or unstemmed)HighRules for determining match necessary (see Appendix 2-3)8.Generate list of sessions using terminologyLow9.Remove duplicate sessions LowDuplicates occur due to fact that sessions may employ multiple agency terms each of which is searched in step 7.10. List terminology used in sessionMediumSet of rules needed for what constitutes individual terms, duplicate terms, separation of terms11. Calculate number of agency terms and total number of termsHighEach term in list (step 10) must be compared against the agency list12. Calculate proportion of agency terms to total termsLow All steps are capable of being automated thus while somewhat complicated, the technique appears feasible. The set of programs that might be written to accomplish the tasks above would be specific to a specific search engine as the sets of rules for comparison depend on the syntax of the engine search algorithm and the log file. 2.5 RECOMMENDATIONS The results of the study reported here indicate several next courses of action to further understand user terminology and its relationship to agency terminology. These are: Ongoing analysis of search term logs to get a better picture of queries and their frequency. Techniques to bring together related terms (including the technique used in this study) should be employed to understand the frequency with which concepts are searched for by users. This information might be used to provide additional links to the most commonly requested materials, develop instructional materials in those areas, and provide other user aids. Consider adding additional “See refs” to the A-Z index to better capture user terminology for concepts. Investigate documents/information retrieved via the searches. The real test of the utility of user terminology inclusion will be the extent to which user terms retrieve information that is relevant to their query and whether they retrieve the same information as they might retrieve with agency terminology. Consider the feasibility of ongoing tracking of user terminology. This study has indicated that comparing user terminology to agency terminology is feasible and could be automated. As with most aspects of websites, one can anticipate that this terminology will change over time and agency terminology or related mappings will need updating. Qualitative analysis of user terminology is also suggested. The data set used here contains information on actual terminology employed. These data might be examined for typical mistakes made (such as spelling errors, syntax errors, etc.) and other aspects of query formation. The finding that there is a low use of agency terms, with some terms not used as all by users, has implications for any indexing of agency documents that might be done. There may be little value in using terms that are not used by users. The addition of terminology in areas of high frequency of searching might also be of value. While it may be unreasonable to provide a rich set of terminology in all concept areas, those concepts that are highly used might be further enhanced in an effort to assure that users gain access to relevant information in those areas. 3. METADATA RELEVANCE JUDGEMENT PROJECT 3.1 INTRODUCTION The U.S. Federal government administers a multitude of surveys, some of which include as many as 2000 survey questions. Many users of Federal statistical data (and other data sets) are interested in using the collected data in analyses of their own design. A number of tools exist on Federal websites to support these analyses (such as DADS on the U.S. Bureau of the Census site and FERRETT on the Current Population Survey site). It is often difficult for users to determine which variables in any given data set are relevant to their interests. Currently, there is little empirical understanding of how users (ranging from novices to experts) determine which variables might lead to relevant data. What cues do they use to make such relevance decisions? Understanding this process could inform design of new systems and/or enhancement of existing systems by specifying which information is helpful to users in this judgement process. This study is approaching the issues above by investigating the metadata or codebook data associated with variables. Systems such as FERRETT rely on information in the codebooks for sorting procedures when users request variables with certain characteristics, and also users may view variable metadata in order to determine which variables might be of interest or to understand more about those variables. The specific research questions of the study are: What information from the public codebooks (metadata) do users employ to determine which variables (from the CPS) to work with in analyses? Which of 3 “levels” of metadata provides the best results in selection of relevant (as determined by experts) variables? The study is proceeding in phases. In the first phase (which has been completed), five expert users (BLS staff) were observed and interviewed as they worked through three scenarios, and three of the experts were interviewed a second time to develop a preliminary picture of how one set of users employed the codebooks. This phase was particularly useful in clarifying key issues in codebook use and in identifying potential problems to be addressed prior to phase two of the study. We will conduct phase two of the study in Fall 1999. In the second phase, we will provide less-expert users with three scenarios of use, each of which will have selected variables presented with different levels of metadata. Phase two addresses the second research question above. The findings of phase one are reported here and the methodology of phase two detailed. The findings and analysis of phase two will be provided in Fall 1999. The research is being conducted by the author and John Bosley, BLS with assistance from Jeff Pomerantz and Steve Paling, doctoral students at Syracuse University. The study methodology draws on two genres of studies in information science: relevance judgment studies and information retrieval system evaluation studies. Prior to specifying the methodology of this study, an overview of those studies and the necessary data collection instruments is in order as much of the effort to date on this project has gone towards developing these instruments. 3.1.1 Overview of Relevance Judgement Studies Relevance judgement studies investigate how users make judgements on the relevance or potential relevance of informational units. Traditionally those information units have been articles and books, and users examine representations of those units (such as citations) and indicate those they consider relevant or non-relevant. Users are asked about the criteria they are using in the judgements and how they make those judgements. The intent of this line of work has been to understand the phenomenon of relevance judgement, provide typologies of relevance criteria, and in some cases to suggest enhancements to the representations of the information units (See for example, Park, T. 1993. The Nature of Relevance in Information Retrieval: An Empirical Study. Library Quarterly 3(3):318-351.). For example, if users indicate that having information on the chapter titles in a book is helpful, it may be suggested that such information be added to the description of the book. As previously stated, the vast majority of work of this type has looked at books (using information on records in online library catalogs) or articles (using periodical databases with or without abstracts). Users may be asked to examine different representations of the same item such as a citation, a citation with an abstract, or the item itself. Only recently have other types of information entities such as maps and meteorological data been considered. He and Gey allude to the value of the codebook data in choosing variables in a paper that discusses a system that might facilitate browsing of such data. The second set of studies are those in information retrieval evaluation. There is a long stream of research which starts with the assumption that an information retrieval system (such as a card catalog) should be designed to provide users with all the documents (or document representations) relevant to their query and none of the non-relevant documents. A critical methodological issue in these studies is the determination of a document’s relevance to a query. A variety of approaches have been taken to identify relevant representations. Early studies used experts to assess each document’s relevance to a query. This approach doesn’t scale well to the size of current databases. It was also found that there was little overlap in the judgements across the experts (which was the impetus for the relevance judgement studies above). The approach generally used in current studies (see Harman for an overview of these studies) is to “pool” the set of relevant documents from different judges. Thus if five judges all found a document relevant, but only two found another document relevant, the first one would be considered “more relevant.” To summarize, a standard metric by which information retrieval system performance is measured is the extent of relevant documents retrieved. Translating the previous discussion to the context at hand-an investigation of how users employ "cues” in variable representations (i.e., the codebook data) requires the existence of those representations. In this case, we used the existing representation (the current codebook data) but also needed to create other “enhanced” representations (described below). The evaluation of a retrieval system designed to support access to the variables required that we determine “relevant” variables to queries (or as we call them below, scenarios). A component that has been lacking in the work cited above is an explicit metric which can assess the difference the levels of representation have on the users’ abilities to choose relevant variables and we developed one for this study. The study therefore has the potential to not only better facilitate access to variables via systems such as FERRET, but also to significantly add to the two literatures in information science mentioned above. 3.2 METHODOLOGY The discussion above pointed out the need for a methodology which 1) identified appropriate queries to the system, 2) provided a set of variable representations for users to consider in relationship to those queries, 3) established which variables were “relevant” to the queries, 4) provided a rationale for the enhanced document representations that would be tested, and 5) developed a metric for assessing the relative effectiveness of the representations. Given our limited understanding of the CPS metadata and how it was used in making decisions about variables, the first phase of the study was to explore, in an open-ended, qualitative fashion, how experts worked with the metadata and the limitations they experienced. The results from this phase of the study provides a preliminary answer to the first research question as well as enables the specification of the methodology for the experimental approach which will be used in the second phase of the study. 3.2.1 Phase One Methodology The intent of the first round of interviews was to gain understanding of the CPS metadata, how experts used the metadata, to gather their perceptions of their utility in making variable choices, as well as to provide information that would help the researchers develop the methodology for phase two. Bosley identified five BLS staff who routinely use the CPS in their work and they were interviewed in January 1999. To focus the interviews, the experts were provided with five use scenarios (appendix 3-1). The scenarios were based on comments and questions submitted by actual users to the FERRETT online help address. We identified approximately 20 potentially useful queries and wrote brief descriptions. These 20 draft scenarios were shown to several experts who assessed the ability of data in the CPS to address them. Five were chosen from the original 20 and the researchers then identified a set of variables that might potentially be used to address the scenarios. These scenarios and variable names and metadata were provided to the experts. Appendix 3-2 provides a brief overview of the structure of the metadata. The experts proceeded through the scenarios in the presence of the researchers. A free-form interview took place as the expert worked with the scenarios. The researchers kept track of the decisions made by the experts about variables, rationales for those decisions, as well as information about how the expert would identify relevant variables, which information available as metadata was being used in the decision-making process, etc. The experts also suggested additional variables to consider. These open-ended interviews provided a rich, qualitative picture of metadata usage by these experts. In addition, the researchers received valuable guidance is reframing the scenarios, and the variables that might be provided in conjunction with the scenarios. The data from this round of interviews were then synthesized to identify key themes and strategies of use on the part of the experts. Additionally, the researchers used the data to identify a set of “potentially” relevant variables to be pooled, and to specify potential enhancements to existing metadata (to be used in developing rules for the construction of new representations). Finally, the interviews enabled the researchers to further modify the scenarios. During May and June 1999, the researchers engaged in several meetings/interviews with one expert as they modified the variables to be included and their metadata, and then interviewed two additional experts to identify the final set of variables to be used during phase 2 of the study. Throughout this process, the researchers continued to be attentive to aspects of metadata use and incorporated new aspects into the findings presented below. 3.3 FINDINGS As indicated earlier, the results of phase one include the rich qualitative picture of metadata use as well as sufficient information for the researchers to develop descriptions of two additional “levels” of metadata, refine the scenarios, and finalize the set of variables. In this section, we first report on the picture of use, then describe and provide the rationale for the enhanced layers of description. 3.3.1 Use of Metadata by Experts Experts employ a variety of strategies in determining variables for analyses. If the analysis is one that they do frequently or is a variant of such, they indicated that they rely on “the standard variables”, those variables that other analysts would use in the agency context. However, in cases where the analysis is less familiar to them, they attempt to understand the nature of the variable by examining the metadata (and other tools). These activities are described further below. In the interviews, it was often difficult to distinguish an activity associated with variable understanding from variable choice so in the list of themes that follows, these activities are not separated. Context matters. How questions relate to one another is used by the analysts to understand who might have been asked the question and the skip patterns. Analysts may have to go back several skip patterns to understand the question however, making it difficult to include this information in the metadata about the variable in question. Additionally, some variables, such as recodes or other manipulations, may not exist on the questionnaire itself. At another level, the experts relied on their understanding of the survey and its purpose to determine whether the variable might be appropriate. For example, knowing that one doesn’t get to answer PEMLR unless over 15, or that while question is about hourly work, the real focus of the survey is on weekly wage, are examples of how the nature of the survey enabled the analyst to understand the variables. Variable naming conventions are used. Analysts indicated that they rely on their knowledge of variable naming conventions as a quick guide to variables. Knowing that a variable name starts with an H (related to households), P (related to individuals), or a G (geographic), for example, is a quick first clue. The coding associated with recodes, edited or unedited, and weighted variables was also used. Universe statements matter. Knowledge of the number of people or proportion of the sample a given question reaches is extremely important information. All of the analysts reported that knowing the universe was critical. In some cases when a universe statement was lacking, they attempted to recreate the skip interval to determine who had been asked the question. Universe statements are currently rather terse, and the analysts occasionally could not determine the universe from the statement and had to backtrack to identify the meaning of the various variable names and category codes. Valid item values need to be clearly written. The way the metadata file presents valid item values was perceived as not always clear or salient. In particular, the very terse provision of a valid range of 0-NNNN was often overlooked. Analysts also commented that it would be helpful to have a reminder of the units in these continuous variables—dollars, years, months, etc. without having to look back to the question to figure out the units. Standard variables and recodes are often preferred. Analysts rely on their knowledge of variables (and associated naming conventions) to identify and chose variables. Recodes are often used as are variables identified as those standardly used in BLS analyses. Analysts also indicated that unedited variables are seldom used (and in fact are not available on public tapes). Non-public information is used: Analysts at BLS have access to some information about variables that is not available to the public. Information about whether the variable was used for tables or another purpose (from the “purpose” column in the internal documentation) enabled one analyst to determine whether the variable was one she would use. The documentation used by analysts is also organized differently and includes an index which several analysts used during the scenario task to make their decisions. Coding categories help analysts interpret the question. When the question might be unclear to the analysts, the available coding categories was used to provide additional information on the nature of the question. A variety of strategies are employed: In addition to using the metadata, the analysts reported on additional activities they may use to understand the variables better. These were: Look at questions in context using paper form of survey Check numbers which result from calculations with published numbers to see if they are in same ballpark: if yes, the analysts considered that she had used the correct variables Look at multiple options/choose from different variables rather than want to see just one. This strategy was mentioned in conjunction with the length of time it took to download data-rather than return to download data associated with another variable, the analysts indicated that they would get more data then they would need to avoid going back again. Explore data collected via the question/variable: Frequency distributions, crosstabs, and other descriptive statistical techniques might be used. General knowledge of the survey: Read footnotes in published surveys about variables used Limitations of the existing metadata were indicated. In conjunction with the scenarios or during other portions of the interviews, the analysts articulated some of their perceptions of the limitations of the existing metadata, or what they wish they could have. These perceptions included: Unclear terminology: The experts sometimes had trouble understanding some of the terminology. For example, the terms, “topcode” and “out” (when the latter means an output variable), were unclear to some of the experts. One analyst suggested that a glossary of terms would be helpful. Frequency Of Question: Date information available does not indicate how frequently the question is asked which some respondents commented would be helpful. Inconsistency In Available Metadata: the extent of the metadata varies across the variables: key pieces (Such as the universe statement) may be missing, items may be wrong, etc. Wishlists: Respondents asked for: a glossary of terms, objective statements (why was this question asked), display of retrieved variables in the order they appeared on survey, other items noted above. The general picture that emerged from the first phase of the investigation is one represented by complexity and situationality. Even experts have difficulty using the metadata to make variable choices. In addition, throughout the interviews, the analysts continually revised their senses of which variables were relevant to the scenarios as they added richness to the scenario or richness to the analysis they might perform to accomplish the scenario. They provided information about how they interpreted the scenario that led them to chose particular variables, and they indicated a variety of different analytic paths to the ends suggested by the scenario. Thus, we might assume that there is no one set of variables that would support a particular scenario, particularly for the expert users who can bring significant expertise and knowledge to their choices. 3.3.2 Recommendations for the Metadata We can make several recommendations based on the findings reported above. Some of these concern the content of variable and/or survey metadata and some about how to facilitate metadata use in an online system such as FERRETT. 3.3.2.1 Recommendations on Metadata Content Recommendation 1: Eliminate Abbreviations and Coded Information Perhaps the most straightforward improvement to the metadata would be the elimination of abbreviations (which could probably be automatically accomplished) throughout the metadata (including metadata field names) and the elimination of coded variable names and variable categories in universe statements. The use of codes caused analysts to have to do look-ups in other portions of the metadata, a process that is inefficient. Recommendation 2: Provide a Universe Statement for Each Variable Analysts relied heavily on the universe statements as a source of understanding and when it was missing had to attempt to recreate the skip pattern that would have led to the question concerned. Recommendation 3: Include Information on the Purpose of a Variable Knowing why a question was asked, or a variable created was helpful to the experts in determining usage. This information may be difficult to recreate for existing metadata but as new variables are added to surveys, the rationale for their creation might help users. There is some information available in the existing internal documentation on variable purpose that might be included in existing metadata. (New variables for some surveys apparently do included this information.) Recommendation 4: Include Periodicity Information in Date Field Even expert users found themselves guessing on how frequently data on some variables were included. The date field currently only includes date of first use, but not frequency with which a question is asked or tabulated. Recommendation 5: Include a Glossary of Terms Unusual or highly technical usage of common-looking words should be explained or avoided. Examples, “topcode” and “out” when the latter means an “output variable.” Some of the experts didn’t even know what “out” meant. Implication: Here as always, be careful to use clear, plain English or provide easy access to a glossary, e.g. hyperlink “topcode” to its definition. Recommendation 6: Clarify Valid Item Values Don’t abbreviate category labels so much that they become unrecognizable. Better explanation of both particular variables’ valid ranges would be helpful as would the inclusion of general orientation (such as in a glossary) to such broad categories as “missing data,” “flags,” etc. and why these are or are not useful or important to the user—or under what circumstances they become significant, e.g. how much “missing data” before the user should worry. Most of the above recommendations might be easily provided as they could be implemented across the surveys and might require the creation of only one product (such as a glossary) which could be used in multiple instances. Some of them (such as spelling out abbreviations) might be easily automated. The inclusion of universe statements and variable purpose would be significantly harder as the metadata for each variable would have to be examined. However, the analysts repeatedly expressed the need for such information to make their decisions. 3.3.3.2 Recommendations for the Metadata System How the content is implemented online is also an issue in its usability. The next several recommendations relate to the system. Recommendation 7: Provide Mechanisms for Establishing Variable Context As more survey data are made available online, there will be an increasing need to provide within survey and across survey context. Currently there is no information in the variable metadata about the survey--such information needs to be included. Within survey context might be added by providing an online version of the survey instrument, with links to the variable metadata so that a user could see the actual question in context. Analysts did use paper versions of the survey for such a purpose in the study. Inclusion of new field that provides the survey from which the data come would also provide necessary context. Recommendation 8: Reexamine the external and internally available documentation and determine whether internal information can be added to the public documentation. The analysts used metadata not available to the public to make their decisions. While some of this must naturally remain confidential, others might not. Additionally, one analyst indicated that it was sometimes difficult to talk to the public and reconcile the two sets of documentation to help the user. Recommendation 9: Consider Providing a Limited Set of Variables for Use The current online system (FERRETT) does limit access to the data to some extent (by not providing non-edited variables, for example). Given the complexity of the metadata and variables, an approach such as that taken with the American Community Survey where users who are less expert can retrieve a limited set of variables (for example, perhaps only recodes) to perform the most common analyses might be considered. The amount of statistical literacy and context necessary to perform some analyses may not be reasonable to assume for some users and might be difficult to provide. In order to pursue such an approach it will be necessary to identify a commonly used/wanted set of analyses and variables. 3.3.2 Description and Rationale for the levels of metadata description A second outcome of the interviews was the development of the rules for creating enhanced metadata for each variable that would be used in phase 2 of the study. The literature on the structure of relevance judgement methods, while rich, has focussed almost exclusively on the use of expert assessment of relevance (the Cranfield studies), pooled relevance judgements (the TREC experiments), or, in the case of user-oriented research, on eliciting relevance criteria. During a literature review, the researchers found no empirical work in which different levels of descriptions were built for assessment in experiments. Most relevance criteria elicitation studies have relied on readily available levels of metadata (or content) for textual entities (e.g., citation, citation plus abstract, full text) thus the need to develop a rationale/strategy for creating levels of description was not an imperative. In the domain of statistical data, however, standard levels of metadata descriptions are not available, thus needed to be developed for this research. Our transformations are based on the results of our first round of investigation in which experts were asked to solve scenarios given the available metadata. Their comments informed our transformation strategies. In particular, expert comments about the lack of clarity caused by abbreviations and codes determined our level 1 enhancement, and the need for universe statements, survey metadata, related recodes, etc. determined the rules for our level 2 enhancement. We will employ three levels of description: The first level is the metadata for variables as currently available (termed level 0). Our first level of enhancement (termed level 1) is to add to the descriptions using a straightforward syntactic and lexical enhancement. That is we did not attempt to add additional meaning to the metadata but transformed it by spelling out abbreviations, and translating coded information into English expressions. Appendix 3-3 provides an example of level 0 and level 1 metadata for a set of variables.) The second level of enhancement adds information not currently present in the variable metadata. All transformations of the first level are included as well as information on the survey name, what the survey was intended to do and the creation of universe statements for all variables (whether or not they existed in the original metadata). 3.3.3 Phase Two Methodology In Fall 1999, the researchers will conduct the second phase of the study—an experiment in which users with some familiarity with statistical analyses and variable codebooks will be assigned to one of several test conditions (the levels of metadata) in assess the differences in effectiveness of the codebook representations. The set of respondents will be volunteers (receiving $20 for participation) solicited from advanced social sciences classes at Syracuse University or via BLS respondent solicitation channels. Volunteers will be screened to establish that they have knowledge of the structure and use of codebooks in statistical analyses and that they have some experience with subject matter and/or analyses that are within the scope of CPS data. Volunteers will be given three scenarios with associated variables and metadata (see Appendix 3-4 for scenarios and variables and Appendix 3-5 for interviewer instructions). For each scenario, they will be asked to identify variables that they would choose for the analysis described in the scenario and to report on how they made those choices. Additionally, they will be asked to report their confidence in their judgement for each variable. After performing all three scenarios, they will receive the same three scenarios and variables with a higher level of metadata for each variable and perform the same operations as before. Subjects will be randomly assigned to one of the following test conditions: Metadata level 0, followed by metadata level 1 Metadata level 0, followed by metadata level 2 Metadata level 1, followed by metadata level 2 The variables are those identified by the experts during phase 1 as being relevant or “seemingly relevant” to the scenario. “Seemingly relevant” variables are those that, at first glance, might be considered appropriate, but are actually not, perhaps because the related question was asked to too few CPS respondents, or because the categories are not useful for the scenario as written, etc. The researchers and experts worked together to create a manageable subset of all possible variables—thus the set does not include weighted variables nor the full extent of variables which through various combinations, etc. could be used to answer the scenario. Metadata levels one and two will be generated for each variable by the researchers. A pretest of the instruments was conducted in June 1999. Two volunteers who had been screened for their knowledge of codebooks and social science surveys worked on the 3 draft scenarios using level 0 and level 1 metadata. They were able to choose variables using the metadata and to speak about their decisions. They were both unclear about the meaning of the question asking about their confidence in their variable choices, and that question has now been revised. Our analysis will occur at the level of the scenario/metadata level ordering. Figure 3-1: Graphic Representation of Analytic Structure Analysis of Precision Scores Order of Metadata Level Presentation ScenarioLevel zero, level 1Level zero, level 2Level 1, level 2Scenario 1( matches from level 0 to 1 for all respondentsScenario 2Scenario 3 Analysis of Confidence Scores Order of Metadata Level Presentation ScenarioLevel zero, level 1Level zero, level 2Level 1, level 2Scenario 1( confidence rating from level 0 to 1 for all respondentsScenario 2Scenario 3 A total of 54 respondents will be recruited (6 per cell). For each respondent, we will calculate 2 “precision scores” for each scenario. For each level of metadata presented, the user relevance judgements will be compared to the expert judgements of relevance and seeming relevance and the total number of matches and mismatches counted. For each set of judgements, we will calculate the change in number of matches. A similar process will occur for each set of confidence judgements (though there will be no comparison to the experts-instead the difference in confidence across the 2 sets will be calculated—though how we do that is still in question). ANOVAs will be performed to test differences among the cells. The hypotheses that will be tested are: Higher levels of metadata will be correlated with higher average levels of confidence and higher precision (an increased match between the user relevance judgements and the experts’) in the relevance judgements. 3.4 CONCLUSION FERRET and systems like it significantly enhance access to the statistical data the Federal government creates. However, these systems are still largely designed for users with sophisticated knowledge of the data sets in question. As usage by non-expert users increases, further attention to providing streamlined access to the data will be necessary. The study reported here has highlighted both how a set of users (experts) use metadata as well as provided insights into limitations of the existing metadata. The findings have also enabled us to specify several possible enhancements to the metadata which will be tested in the second phase of the project. 4 INVESTIGATING AND FACILITATING THE INTEGRATION OF TECHNOLOGY INTO CUSTOMER SERVICE ACTIVITIES AT BLS INTRODUCTION As organizations begin to integrate web-based information resources and services, one of the first challenges many of them face is managing the increased demand for those resources and the increased demand for help finding and using them. The Bureau of Labor Statistics has recognized the need for a reconsideration of how customer service is provided via the web and also for a reconsideration of customer service functions throughout the organization as the web has focussed attention on the commonalities across departmental activities and information. In the last year, discussion has begun related to several issues associated with customer service that have emerged due to the Bureau's increased web-presence. These issues include: How to provide the best service to customers independent of the "door" through which they come. How to minimize the duplication of effort of customer service staff and analysts throughout BLS. How to track customer inquiries and respond efficiently and effectively How to maximize customer service staff and analyst knowledge of the range of BLS information and data. In conjunction with these discussions, the researcher proposed to Deborah Klein, Associate Commissioner, to: Provide assistance to the Bureau (via workshops) in considering these issues using a rich base of theory and practical advice available in the domain of library and information science where issues associated with providing customer service-related to information needs has a long history. Observe the process that is occurring as the web technology impacts a particular component of the organization (customer service) to provide general insight into the nature of the process for use by other organizations Enable the organization to develop mechanisms and strategies to continue to successfully integrate technological change into this component of organizational activity by identifying both strengths and barriers to this integration in general and by specifically investigating one such change-realtime interaction with web customers to determine its potential utility for BLS. These objectives developed out of several years of engagement with BLS and its website. This engagement has demonstrated that customer service activities (particularly for non-expert users) are increasingly important in maintaining the success of the BLS website, that BLS is invested in improvement of its website, and that technological change will continue to reshape the organization. As a research project, this work necessitated a “participant observation” approach in which the researcher maintained an observational presence in the organization and also (in the case of the workshops and other activities), actively participated. Participant observation projects thus unfold at the pace of the organization. In this particular case, an initial workshop was held in Feb. 1999. Workshop materials are presented as Appendix 4-1. Debriefing with D. Klein and her staff indicated that other team-building activities needed to occur before further workshops, thus no further workshops were held. Additionally, an investigation on tools to facilitate realtime interaction and customer tracking via the web started and will be completed in early Sept. 1999. This investigation will produce a resourcebook concerning three sets of technologies that have the potential to provide functionalities that might be used to enhance customer service aspects. These are: Technologies that support real-time interaction (including chat software, web-based videoconferencing, internet telephony): Intermediaries often work with users to reframe an information need, educate about resources, etc. Often this is best accomplished with one-on-one interactions in real time. A variety of technologies are now available to accomplish this. Technologies that support the gathering of information about user activity: A challenge for intermediaries attempting to help users is to understand where the user is on a website, what the user has already done, or is doing in response to intermediary comments. Tools which could provide snapshots or “videos” of user actions (either in realtime or batch mode) would simplify that task of understanding. Tools which also enable an intermediary to perform actions on a remote client might also be useful. Technologies to facilitate knowledge management: email and telephone inquiries, responses given to customers, etc. A set of technologies has long been available to manage telephone helpdesks by storing information on transactions, tools are now almost as well developed to support internet inquiries. The resourcebook will provide technological specifications and a critique, provide a list of vendors, and analyze how these technologies might develop in the near future. Other aspects of the project were postponed due to various events on both the researcher and agency sides. It is likely, however, that the imperative for the project remains and that such a research project could provide significant insight into the organizational impacts of technology. 4.2 RESULTS OF THE WORKSHOP Since the workshop was part of a participant observation project, the researcher kept a record of it including responses from the participants. Staff in D. Klein’s office also provided materials gathered during the workshop. These materials form the summary presented here. Appendix 4-2 presents the original summarization. There were 9 people in the workshop representing the Office of Publications and Special Studies (4), Office of Employment and Unemployment Statistics (3, from two divisions), and the Office of Compensation and Working Conditions (2). The audience appeared to be a mix of experienced intermediaries and new intermediaries. New people engaged in the discussion both by pointing out what was difficult for them as well as by providing numerous anecdotes about helping users. At least one of the experienced intermediaries commented throughout the workshop how simple it was to help users, that questions from users were stereotypical and that what was really needed was just more information about the agency to do the best intermediation possible. In addition to the anecdotes presented, the participants articulated several issues during the presentation. They perhaps reflect issue areas for customer service at BLS: User expectation management Dealing with negative users Transfer users around What data does the agency not have Terminology differences between users and agencies Delegated searching (getting good information when the person who actually needs it is not the person calling etc.) Tracking users for feedback Having a customer survey on Web would be helpful These issues had been previously identified by the agency as areas of investigation; the fact that no new issues emerged during the workshop was an impetus for not conducting further workshops. 4.2.1 Recommendations From the Workshop: Where might the agency go from here? The results of the workshop indicate that while BLS is well advanced in considering how to enhance customer service, it may be that this is not known to various personnel. Additional internal PR about agency initiatives in this area may be warranted. Additionally, it seems that many of the analysts and other personnel that interact with the public are tracking that interaction to a greater or lesser extent. Further information gathering on within agency FAQ’s and/or databases of questions/answers is currently in progress at BLS in order to understand these functions and to utilize the data more successfully. Intermediaries made a number of suggestions about how they could better help users by educating the users about what to know about their inquiries to get the best response from the intermediaries. Such information might be disseminated via the current website. My intent is to develop such a page for BLS in the next several months. CONCLUSIONS Facilitating customer service activities, both in terms of improved responsiveness to customers as well as in terms of internal processes, appears to be the next wave of website enhancement. The trends being observed at BLS have been observed by the researcher in several other settings. BLS has been proactive in responded to the trends and its activities in this area may be a model for other organizations. Continuing to track the changes may thus provide a useful blueprint for other organizations. There are no specific recommendations to be offered in this area as BLS staff is taking a leadership role in this activity. The resourcebook, when completed, will provide background on several technologies that may enable BLS to continue to work innovatively in this area. APPENDIX 2-1: BLS TERMS FOR PAY AND WAGE CONCEPT Apprentice rates At-risk pay Attendance bonus Back pay Base rate Beginner rate Bereavement pay Bilingual pay differential Blue circle rate Call-in pay Cash profit-sharing Commission Commission payment Compensation Contract-signing bonus Cost of living adjustment Deadhead pay Dismissal pay Double time Draw account Earnings Educational pay differential Entrance rate Experimental rate Flagged rate Flat rate Free room and board Guaranteed rate Hardship allowance Hazard pay Helpers rate High time pay Hiring rate Holiday bonus Holiday premium pay Hourly rate Incentive earnings income Journey level rate Knowledge-based pay Longevity pay Make-up pay Moving allowance Multiskill compensation Nonproduction bonus Out of line rate Overtime Paid absence allowance Pay in lieu of vacation Pay-for-knowledge Payments for income deferred due to participation in a salary reduction plan Penalty rate Per diem allowance Piece rate Portal to portal pay Premium pay Probationary rate Production bonus Profit-sharing Profit-sharing distributions Push money Red circle rate Referral bonus Reporting pay Retroactive pay Royalty Safety bonus Salary Scale Severance pay Shift differential Shift premium Skill-based pay Stint work Straight-time earnings Subsistence allowance Superannuated rate Supplemental pay Temporary rates Tips Tonnage rate Tool allowance Trial rate Tuition reimbursements Unearned income Uniform allowance Union rate Vacation pay Wage Year-end bonus APPENDIX 2-2: EXTENDED AGENCY TERM LIST Accumulation Adjustment Allowance Apprentice rates At-risk pay Attendance bonus Back pay Base rate Beginner rate Bereavement pay Bilingual pay differential Blue circle rate Bonus Call-in pay Charge Charge per unit Cleanup Commission Commission payment Compensation Contract-signing bonus Cost Cost of living adjustment Cost-of-living allowance Deadhead pay Deduction Depreciation allowance Discount Dismissal pay Disposable income Dividend Double time Draw account Earning per share Earnings Educational pay differential Emolument Experimental rate Fee Financial gain Flagged rate Flat rate Free room and board Fringe benefit Government income Government revenue Gratuity Gross Gross profit Gross profit margin Gross sales Guaranteed rate Half-pay Hardship allowance Hazard pay High time pay Hiring rate Holiday bonus Holiday premium pay Honoraria Honorarium Hourly rate Incentive Incentive earnings Income Index Issue Journey level rate Killing Knowledge-based pay Living wage Longevity pay Lucre Make-up pay Margin Markup Merit pay Minimum wage Moving allowance Multiskill compensation Net Net income Net income Net profit Net sales Nonproduction bonus Out of line rate Overcompensation Overtime Paid absence allowance Pay Pay in lieu of vacation Pay packet Pay rate Pay-for-knowledge Payment Payment rate Payments for income deferred due to participation in a salary reduction plan Payoff Payroll Penalty rate Per capita income Per diem allowance Percentage Perk Perquisite Personal income Piece rate Portal to portal pay Portion Premium pay Probationary rate Proceeds Production bonus Profit Profits Profit-sharing Profit-sharing distributions Push money Rate of pay Rate of payment Receipts Recompense Red circle rate Referral bonus Regular payment Reimbursement Relocation allowance Remuneration Reporting pay Retroactive pay Return Revenue Royalty Safety bonus Salary Salary straight-time earnings Scale Seasonal adjustment Severance pay Share Shift differential Shift premium Sick pay Skill-based pay Stint work Stipend Strike pay Subsistence allowance Superannuated rate Supplemental pay Take care, takings Take-home pay Temporary rates Tip Tips Tonnage rate Tool allowance Trial rate Tuition reimbursements Unearned income Unearned revenue Uniform allowance Union rate Vacation pay Wage Wage scale Wage schedule Windfall profit Workmen’s compensation Year-end bonus Yield APPENDIX 2-3: CODING RULES FOR THE AGENCY-USER TERMINOLOGY COMPARISON ANALYSIS INSTRUCTIONS You will search each term using the query function at  HYPERLINK http://fallout.campusview.indiana.edu/~jfieber/fedstats/ http://fallout.campusview.indiana.edu/~jfieber/fedstats/ For each term on your list, record the following information about its appearance in a session: Session identifier, # of queries in session, # of agency terms, total number of terms, terms used in session. Session identifier: record source # and session #. Example: on the search engine it will say: Source 40, session 2 of 8. Record this as 40 2/8 # of queries in session: Record the number of unique inputs (queries) within the session. You will need to actually look at the query column to identify. # of agency terms: compare the terms used within the session to the attached list of agency terms and record the number. A TERM IS A WORD OR PHRASE (new term starts after Boolean Operator or a comma) The following query consists of two terms: wage discrimination and salaries. This query consists of three: wage, salary and discrimination. A match of query term to agency term is identified as times when the query term matches the agency term exactly or when the query term matches the beginning part of an agency term. Thus if the query term were wage and the agency term was wage increases, the term wage would count as a match (because it would retrieve documents that used agency term wage increases). Total number of terms: count all terms in session (using definitions above—ignore duplicate terms). New terms used in session: record all terms used in session that are not in the agency list. A Few Additional Notes: case doesn’t matter—AND is always treated as boolean unless it is surrounded by Quotes. If the query says title, ignore those words (in, title) when counting terms. If the person has searched more than one website (check scope column) with same query, count terms used for all websites searched. APPENDIX 2-4: SESSIONS ASSOCIATED WITH PAY AND WAGE CONCEPT (IDENTIFIED VIA AGENCY TERM USAGE) Source #Session ## of queries# of agency termstotal termsProportion (D/E)terms used (separated by commas)93/94150.2000women, income, women average income, average, women's yearly income139/135150.2000salary of President, salaries, President, salary, annual salary1512/161120.5000population, income321/1162180.1111overtime working, overtime, overtime census, survey of overtime hours, overtime hours, single parent homes, working single parent homes, working single parent, percentage of single parent, single parent income, single parent hours, single parent, government, list, education, top priority, education agenda, education statistics403/82130.3333minooka population, income, population411/13140.2500casinos, income, IRS+casino, casino552/43150.2000disabled, incomd, population, income, current population reports586/75150.2000Pilot salaries, salary info, income for pilots, income, clara Reedy603/51111.0000income623/111120.5000population, income735/71120.5000income, wages762/64150.2000income, celebrity income, income of actors, actresses, movie income764/66160.16671999 salary increase projections, salary projections, salaries, Wisconsin salaries, cost of living 1998, cost of living826/102140.2500lottery, states, income, use954/54160.1667tax evasion, income, tax, evasion, (Income Tax Evasion), (tax evasion)997/102130.3333drugs, population, income1004/123240.5000education, income, education level, datasets1251/51120.5000president, salary1261/14150.2000population, income, furniture manufacturing, furniture building, manufacturing furniture1331/91140.25001997, 1997 federal income, federal, income1451/12130.3333family income, population, income1523/33130.3333income secretary of state, salary, Secretary of State1644/51120.5000African American, income1871/15160.1667pay scale, sex, income, equity, government, income disparity1955/138170.1429cost of living, cost of living index, c.o.l. index, cost of living adjustment, united states, family income, index cost of living2028/92120.5000average income in CA, income2082/72130.3333Bishop, california, income2111/13140.2500family income, household income, income, household2341/121120.5000population, income24212/152130.3333income, race, gender24329/291111.0000income2431/292120.5000population, income2437/292130.3333income, investing, investments2462/21120.5000personal, income2572/71130.3333population, income, age25914/151111.0000income2635/81120.5000women, income 2645/772120.1667UFO's, "unidentified flying object", unidentified flying object, flying, object, unidentified, plane, income, salary, wages, (occupation, job)2881/12130.3333poverty age distribution, population, income28912/205150.2000income, poulation, household, statistics, family income statistics2951/72140.2500annual family income, annual, family, income2991/11221.0000education, income 3025/73140.2500life expectency, purchasing power, population, income3083/103150.2000poverty, usa, race, welfare, income3203/101120.5000population, income3392/81111.0000income3439/122120.5000income, family income34311/123250.4000income, per, capita, Average, USA3717/84130.3333income, average, population3761/12130.3333population, income, African-Americans4062/102120.5000income, poverty4069/102120.5000salary, employee4141/61120.5000population, income4181/122120.5000income, social security4216/132120.5000family income, income4231/14160.1667televisions, income, crime, cops, media4435/114150.2000population steuben county in, population, income, indiana population, marijuna dealers4501/12120.5000salary, supreme court justice4682/101120.5000population, income4783/111130.3333income, civil, engineer4969/111111.0000income5083/81230.6667education, age, income5086/82130.3333Income, Family, individual person5107/72130.3333salary, cabinet, agriculture51510/181111.0000income5325/95150.2000income, foreign countries, income from foreign countries, foreign, foreign income5471/27160.1667fargo, labor, wage, building permits, number establishments, hotel rooms55410/182130.3333average, salary, washington58411/156160.1667Argentina's income, income, Argentina disposable income, Argentina, Argentina income, South America6093/91111.0000income6123/142120.5000households, income6168/82120.5000blacks, income6237/101120.5000population, income6309/123120.5000musicians, income6585/114150.2000populationand, income, population, income distribution, income distribution changes6751/15140.2500african american, income, family income, black6811/3221130.0769african american, family income, population, black, poverty, black familyies, black families, education, drug use, substance abuse, blacks, children, race6825/54150.2000homosexual, income, population, homosexual demographic*, homosexual market6893/62120.5000salary surveys, salaries7389/121120.5000population, income7422/102130.3333population, income, births7421/104160.1667income teachers, income education, teacher, income, professionals, salaries7426/101120.5000population, income7424/101120.5000population, income7423/104260.3333income, education, incomes, different educations, annual median/mean incomes from different levels of education, income differences due to education7442/91120.5000political affiliation, income7512/101120.5000children, income7521/31240.5000wage, salary, by, occupation7552/55170.1429workers, compensation, workers compensation, work injuries, illnesses, survey of occupational injuries, job training7755/164240.5000salary, salary survey, programmer, programmer salary survey7881/14150.2000professional compensation, professional, compensation, Los Angeles, Virginia7926/81140.2500Stae of Michigan, Oakland County, population, income8421/11120.5000overtime, over time8461/13130.3333long island, income, ny8621/11120.5000personal, income8821/11130.3333income, sex, marital8871/15160.1667population, income, ethnicity, irish american purchasing, demographics, U.S. Census9151/11120.5000poverty, income9461/11221.0000income, education9529/121120.5000Population, income9524/123150.2000occupation, wage, (occupation), (wage), (education)9682/111120.5000population, income9781/12120.5000income, per capita income9868/123130.3333income, family income, male income9862/1281110.0909poverty, income, guidelines, poverty guidelines, definition, for a family of four, 1998 poverty guidelines, 1997 poverty guidelines, 1996 poverty guidelines, 1998, official poverty guidelines10094/121120.5000population, income10102/43140.2500Latin America, Latin American Population, population, income10177/122130.3333dentists, income, income of dentists10174/126190.1111Population, income USA, income, income 1880, 1940, United States, historical population, United States population, 188010201/11120.5000female population, income10321/14160.1667Family income, population, middle class, income, class, working poor10601/52120.5000overtime hours, overtime11002/33130.3333income, Las Vegas, census tract11003/31120.5000Las Vegas, income11181/21120.5000state, income11591/12130.3333Male population, income, population11962/21120.5000asian, income12076/113130.3333teen pregnancy, income, teen pregnacey12151/61120.5000population, income12167/94150.2000rolling meadows.IL, population, income, census, population'12184/132130.3333population an income, population, income12312/27180.1250*congress, congressional salaries, congress net worth, congressional, congress salary, salary, $171,50013217/121120.5000population, income13314/101120.5000income, population13381/14140.2500male population income, male income, female income, income13532/25150.2000income, health insurence, medical insurence, population, national population13666/132230.6667education, income, gender 13664/13141160.0625SAT scores, income, college education, parent income, personality traits, competitiveness, motivation, worker perseverence, worker motivation, employee motivation, employee personality, personality, race, minority, Women, Gender13665/131130.3333Eductaion, college, income13669/131120.5000Hispanic, income13668/131120.5000Hispanic, income13801/11111.0000income13854/61422.0000compensation, pay13911/14140.2500physician assistant, income, nurse practioner, mississippi14451/14150.2000population, flowers, demographics, income, gender14511/11111.0000income14555/92140.2500income, poverty, family income, National14865/113140.2500population, income, income statistics, income home page14884/72130.3333population, income, zip code14911/11120.5000engineers, income15173/102240.5000library, income, library technicians, salary15449/103140.2500population, income, population wyoming, cities15501/16160.1667income, human, capital, human capital, human capital statistics, income statistics16061/11120.5000income, inequality16191/11120.5000population, income16271/12120.5000income, median family income16291/11120.5000population of u.s. working women, income16329/121120.5000population, income16735/75160.1667cancer, lumg, lung, smoking, income, personal16827/71120.5000baby boomers, income16842/53130.3333income, hourl;y wage rates, income of popul17213/33130.3333population, income, gnp17522/23140.2500race, income, African, American17622/22160.1667world, population, protein, consumption, income, per capita17962/23150.2000employee compensation index, compensation, index, compensation cost, series180115/162130.3333wealth, index, income18041/12240.5000education, income, educational attainment, 199618332/21120.5000popualtion, income18521/11120.5000poverty, income18551/11120.5000gender, income 185712/144150.2000population, income, zip code, income per household, by zip code18651/12140.2500wage, compare, foreign, women18671/23130.3333income, state, county19111/41120.5000workers, compensation19131/11130.3333population, income, ohio19143/71120.5000black population, income19511/51120.5000population, income19832/113140.2500women/salaries, minorities/salaries, minorities, income19922/22130.333345204, population, income19932/63140.2500pharmaceutical sales, (income) Drugs, population, income20101/13230.6667jobs, income, earnings20191/24140.2500income, percentile, national income, individual income20255/51130.3333Sacramento, California population, income20684/45150.2000dow jones averages 1995-97, economic data, stock averages over time, income, income & data20811/11120.5000medium, income20871/12111.0000income21441/12111.0000compensation21591/12130.3333jobs, income, "jobs for 2004"21705/62120.5000income, gambling21881/12130.3333gender, income, management21982/23230.6667salary, income, individual22043/81120.5000women, income22221/92120.5000family income, income22381/95160.1667population, income, beverage industry, coca-cola, coke, beverages22471/11130.3333wage, gender, race22527/102120.5000wages for women, income22861/12120.5000population, income23032/51111.0000income23261/15240.5000medical secretary, income, medical secretary hourly income, hourly rate233710/112120.5000boston, income23377/111120.5000African American population, income23391/12120.5000elderly, income23401/51111.0000income23441/11120.5000sex, income23492/33130.3333population, income, income statistics23511/13170.1429race, income, hispanic, poverty, Butler, county, Missouri23522/74160.1667accountant, unemployment, database, data points, occupation, income23611/11120.5000population, income23835/52130.3333income, race, forecast24065/91111.0000income24233/42230.6667race, income, education24321/1101100.1000crime, crime +auto theft, crime +carjack, crime +theft, carjack, auto theft, grand theft auto, income, discretionary income, safety24366/72120.5000women, income 24581/12120.5000income, average family income24771/12130.3333population, income, 192024811/12120.5000income, family income24851/13130.3333income, teacher income, teacher's salaries24961/13140.2500family income 1970, family income, population, income25122/81130.3333population, income, florida25124/83130.3333economy, statistics, income25127/82130.3333income, poverty, 199825381/13130.3333income, income earnings with bachelor degree, graduate incom256113/131120.5000income, index25617/132120.5000income, real income26011/11111.0000income26079/91120.5000population, income26301/12120.5000income, individual income26481/1101100.1000"black income", "black family income", black family income, income, income among blacks, blacks, violence, BUSINESSES, CRIME, poverty26671/12120.5000income, 105th congressional district26701/12120.5000per diem, air force per diem26731/11120.5000income, 192026891/14130.3333income after college, income, salaries26901/14140.2500adult single mothers, income, lone mothers, single mothers27221/12130.3333income, family income, 198827521/12140.2500nursing home, age, population, income27813/56160.1667gdp, gnp, gross domestic product, population, income, projected gnp28484/42130.3333population, income, spending28511/13240.5000attorney, income, attorney salary survey, salary28611/16260.3333cozt of living, cost of living, cost of living adjustment, cola, 1995 cola, 1996 cola29152/92130.3333population, income, metropolitian29291/14140.2500birth rates, birth, income, population29432/114140.2500women, trends, income, female29439/112140.2500blacks, income, moles, human29901/14160.1667family income, average US family income, average, family, income, poverty level29911/12120.5000women income, income29961/58160.1667congressional, salary, congress, salaries, house representatives, congress salaries30001/11130.3333income, North Carolina, county30171/11111.0000income301911/133130.3333avionics, wage data, income30422/22130.3333population, income, age30635/51120.5000population, income30876/83150.2000women, income, incomes, men, income by gender30911/12130.3333population, income, Michigan31036/123140.2500cities, population, income, (cities)31181/11120.5000poverty level, income31192/32120.5000colorado population, income31255/74140.2500income, "presidents salary", salary of president, president income31481/13160.1667income poverty, value of noncash payments, Valuation of noncash benefits, poverty, income, 199731681/12120.50001910, income31761/12130.3333population, income, median income levels31971/12120.5000per-capitia income, income32101/17160.1667italian population, income, Italy, Italy consumers, Italian consumers, Italian spending32381/13130.3333wilmington ohio, population, income32405/101120.5000population, income326011/141120.5000population, income32862/21120.5000population, income32861/21120.5000population, income32901/13140.2500guam, income, economy, data32931/23130.3333phone calls, telephone, income33592/31120.5000population, income33672/24130.3333abortion, income, university budgets34471/11120.5000income, gender34604/103240.5000income, civil, engineer, salary34608/101120.5000population, income34631/11120.5000asian americans, income34745/93130.3333Hunary, hungary, income34891/11120.5000population, income34961/16160.1667engineer income, engineer, income engineer, egineer, income, average income35424/72120.5000income, family income 35635/82130.3333census tract, population, income36191/13130.3333teen pregnancy, income, welfare36354/71120.5000Population, income36641/12130.3333president, income, tax36711/11111.0000income36731/11120.5000African American, income37453/81120.5000wage, rate37663/61120.5000job, income38121/12130.3333socioeconomic status, native americans, income38201/51221.0000education, income 38531/16160.1667retirment income, income at retirement, income, statistics for people living below the poverty line at retirement, family income, retirement income38566/104140.2500COLA, cost of living adjustment, social security payments, social security data38621/11120.5000income, race 38634/61120.5000state, income38841/11120.5000greek, income38981/12130.3333median, income, (income)39011/11120.5000population, income39141/13120.5000income, mean391616/172130.3333population, income, city financial report39251/12130.3333salary, increase, increment39261/12130.3333franchise, population, income39601/15250.4000wage gap, male felmale wage gap, gender wage gap, income, wage39633/51120.5000income, mexico 39673/143130.3333income, Special Income, Social Security Income39679/141120.5000political party, income39851/13130.3333logistic function, logistic, hourly rate39941/11120.5000population, income40066/63140.2500telemarketing, family income, income, zip code40421/15150.2000population that wares parkas, popualation ohio, population, income, purching power population 40711/11130.3333wage, determination, tennessee41121/21120.5000population, income41122/21120.5000popualtion, income41281/12130.3333population, income, child population41391/62120.5000puerto rico, income41691/53130.3333income, household, statistics42141/11120.5000population, income42971/14130.3333President, pension, salary42981/31111.0000income43051/22240.5000compensation, "College Administrators", salary, college43371/11120.5000Congress, salary43561/16180.1250naval income, navy, income, statistics, naval, statictics, dod, office43572/32150.2000income, computer, consultants, software, consulting43651/45150.2000psychiatric, psychiatry, income, health costs, health43671/15250.4000incomes, education, income, post secondary education, education levels43911/1101110.0909government accounting, minimum wage, minimum wage mobility, mobility, gao, (minimum wage), nebraska, rural development, (rural development), wage, rural44021/11120.5000battered women, income44291/12120.5000income, teenagers44341/13260.3333salary, survey, wage, research, analysis, occupational employment statistics44371/23140.2500savings, income, taxes, savings rate44471/13130.3333women, employment, income44541/13160.1667poverty level, 1997, census, program participation, income, poverty44801/12150.2000population, income, state, economic, growth44951/13160.1667income, party, affiliation, party affiliation, democrats, employment45121/41120.5000population, income45475/82130.3333women, income, gender equality45481/11111.0000income45501/14240.5000income, race, black, education45521/31120.5000population, income45861/11120.5000income, internet45928/114170.1429hispanic, population, income, hispanics, dominicans growth, hispanic population, dominican46051/12120.5000salary, family income46111/113230.6667compensation, income, wages by occupation46277/94140.2500income, mexico, income mexico, statistics46352/29290.2222family income, education, standardized test, family status, Academic Achievement, Family Economic status, Standardized, income, assessment46381/12140.2500data, executions, income, race46573/62120.5000income, average income46751/12140.2500population, income, Kissimmee, Florida46843/33130.3333military payscale, payscale, salary47002/22120.5000florist, income47274/71111.0000income47281/11120.5000population, income47391/22111.0000income47422/29190.1111spend entertainment, spend, ANDentertainment, entertainment, county, income, percent income entertainment, percent, expenditure47471/12120.5000income, "income table"48192/42130.3333salary, public school, private school teacher salaries by state48371/18180.1250income, wages draftsperson, drafting, cadd, draftsmen II, draftsperson II, draftsperson, wages 48501/16150.2000population, income percentile, income, "income by percentile, "income by percentile"48831/12130.3333population, income, U.S. poverty statistics48931/12120.5000gs ratings, income49391/18180.1250income, labour income, total income of USA, debt, Government Debt, Population, Debt of Government, export49471/15160.1667income, seniors, senior income, income senior, population, poverty49491/16170.1429population, income, poverty, seniors, federal standard, federal standard poverty line, poverty line49701/12130.3333personal income, personal, income49781/12130.3333engineering, services, income49801/11120.5000income, degree49821/11120.5000income, investing49881/11130.3333workers, compensation, costs49971/11120.5000population, income50191/11120.5000population, income502410/143150.2000cigarette prices, population, income, michigan, counties51091/103130.3333income history, income, income history data51121/12130.3333Proverty, population, income51442/24140.2500Race, Race income, income, crime51471/41111.0000income51911/12230.6667income, education, median income52111/24140.2500population, income, women, women's labor force participation rates52437/71120.5000population, income52662/22120.5000statistics about poverty between 1997-1998, income52911/16160.1667mortgage rate, family income in the US, income, family income in the United States, family income, housing53012/22120.5000income, creatine53153/33130.3333income, poverty level, poverty level 199853352/52240.5000representative, salary, congress, pay53411/12140.2500minimum, wage, jobs, minimum wage53451/13130.3333personal income, household income, income53732/21120.5000strasburg, income53811/14130.3333income, family income, family income usa53901/12130.3333income, manhattan, tax54152/21120.5000condominium, income54401/11120.5000income, job55241/21130.3333wages, compensation, computer operators55242/21130.3333wages, compensation, computer operators55403/42130.3333birth rate, income, United States55402/42130.3333birth rate, income, birth rate & income55462/25160.1667consulting, salary, salaries, starting, starting salaries, banking55991/15380.3750education, income, postsecondary education, Bachelor, Graduated degrees, salary, income attained when graduating, income graduation56431/13130.3333national debt, amount of the national debt, income56501/12140.2500population, income, distribution, united states57431/22130.3333population, income, crime57432/22120.5000women, income57531/11120.5000occupation, income57601/391110.0909income, population, family income, age, income of the population 55, older, Table I, aged units, income sources by age, income age 62-64, age 62-6457684/71120.5000Household, income 57751/14250.4000cost of living, cost of living mountainview, income, mountainview, mountain view57921/13250.4000income related to formal education, education, income, leisure time, social class58021/22130.3333occupation by income bracket, occupation, income58651/13130.3333anesthesiology, compensation, RVU58661/22130.3333women in the workplace, women, income58901/11221.0000income, education 58961/11120.5000occupation, income59142/21120.5000salary, writer592910/111120.5000family therapist, income59671/15150.2000salary per hour, minimum salary per hour, income, minimum income, minimum salaries60241/18180.1250salary information systems, salary, salary + engineer, technical, engineer, information, career60341/12130.3333computers in households, income, computers60851/15150.2000racism, prejudice, police butality, low income, education60911/11120.5000population, income60931/11130.3333Connecticut, income, demographics61231/14250.4000income, race, sex, youth, education61673/44150.2000Boston, prison statistics, age 18, income, neighborhood61671/47170.1429"Boston" + "income" + "1985" + "1990", "Boston" + "Income", Boston, income, 1985, 1990, metro statistical area61672/46140.2500Boston, Metro statistical area, income, prison statistics616815/191111.0000salary62181/22120.5000climate, income62182/23130.3333agriculture, tables, income62561/12230.6667income, increase, compensation62721/15170.1429family income, native american family income michigan, family income michigan, family, income, michigan, native american62941/13140.2500high school graduates, population, income, wisconsin vocational education63023/41120.5000zip code, income63091/21120.5000population, income63092/21120.5000population, income63304/42130.3333population, income, economic class63302/43240.5000household, income, race, education63601/11111.0000income63811/12120.5000salary, salary surveys64261/12130.3333LDR, population, income64372/21212.0000salary, writer64433/57170.1429"two income family", family income, income, single family income, single parent, family, "family"64721/12120.5000cost of living adjustment, consumer price index65058/135150.2000top earnings, income, occupation, best occupations, top incomes65161/13130.3333income, family income, socioeconomic status65824/52130.3333family income, family, income66031/12130.3333population, income, family income66151/12130.3333military, income, milirary income66252/32120.5000median income, income66301/13140.2500family income, population, income, u.s. population66951/13130.3333cost of living adjustment, cola dc, cost of living dc67039/105150.20001932 Census, 1932 income, income, 1932 Census Annual Family Income, Annual Income67051/13140.2500hispanic, income, population, sipp67411/11111.0000income67711/15150.2000philadelphia smsa, population, income, philadelphia, smsa67915/102130.3333median, income, 199868734/73140.2500political party, income, democratic party, united states democratic party68736/71111.0000income68793/74130.3333population, income, 168802/81111.0000income68804/81111.0000income68903/35370.4286income, education, Does Education Pay Off?, salaries, high school graduates, high school diploma, salary69036/71120.5000income, gambling69111/11120.5000population, income70051/11111.0000income70221/16150.2000population, income, percentage of americans making $100,000, individual income of americans, individual income70311/13140.2500average, income, national AN, national70401/1121120.0833income, nevada, las vegas, las vegas + social security, social security, service, labormarket, unemployment, personal income, department, labor, hotel70581/12130.3333income, family, mushrooms70617/112130.3333women, income, gender discrimination70631/24130.3333womens income, women, income70632/21120.5000women, income70941/13120.5000income, per capita71061/22230.6667education, income, degrees earned71471/13130.3333income, Average Family income, Average income71491/13230.6667education, income, geography73471/13130.3333income, per capita, per capita income73731/35150.2000welfare dependancy, income, crime, drug use, illegal drug abuse74001/14450.8000pay, scale, government, gs, gs-1074821/13140.2500wages, anuual compensation, compensation, annual74875/63130.3333population, income, wages74872/67160.1667income, household, household income, computer based testing, supplimental education, computer assessment75181/21120.5000salary, teacher75392/43140.2500per capital income, income, cities, per capitia income75561/13130.3333gini, gini+income, income75621/12120.5000Distribution of income in U.S., income76262/34140.2500population, demographic description, income, ethnic mix76712/73130.3333hazardous duty, hazard pay, hazardous duty regulations76744/114150.2000income, counties, govinfo.library.orst, Economy, USA76881/12130.3333income, population, michigan77123/62130.3333jobs, income, careers for the educated77191/12120.5000consulting, income77631/11111.0000income77651/16170.1429income, family income, poor, temporary worker, part time worker, income inequality, working poor77674/74140.2500population, united states population, income, race78151/12120.5000alcoholism, income78941/12120.5000job, income80011/15170.1429southwest u.s. pop/income, population income, southwern U.S, population, income, southwestern U.S population income, southwestern U.S.80161/15160.1667position salaries, salaries, job, income statistics, income, administrative assistant80171/18170.1429cost of living, salaries cost of living, salary cost of living, cost of living salary, cost of living income, family income, family income cost of living80216/63140.2500employers, employees, worksites, income80481/32130.3333population, income, florida 80561/11120.5000income, household80691/12120.5000income, hours81261/11120.5000population, income81293/45170.1429family income, historical family income, income ranking, occupations ranked by income, occupations, rank, income81471/14140.2500population, population florida, florida, income81781/11111.0000income81861/12130.3333teacher, salary, Washington82071/12160.1667alpharetta, georgia, median, family, income, median family income82131/12130.3333u.s. population statistics, population, income82141/11120.5000income, managment analysis82312/22130.3333income, future, projection82711/14270.2857Congress, income, education, race, religion, demographics of Congress, education of Congress82891/12120.5000income, trade deficits82911/15160.1667population, income DC metro area, income, income MD, income in MD, poplulation MD83001/14140.2500population, income, per capita, per capita income83103/84160.1667population, income, zip code, wealthiest, zip codes, highest income83571/12130.3333immigrants, population, income83871/22130.3333population, income, fort wayne84321/11111.0000compensation84711/24140.2500gross regional output, per capita income, san mateo, income84773/81130.3333hope mills, nc population, income84778/81130.3333Age, income, marital status85201/12130.3333income, tax, amount of taxes paid in 199785231/11111.0000income85381/12130.3333personal, income, projections85422/22120.5000"income per capita""per capita income", income85641/14140.2500population, income, income cameron county texas, census data85861/11130.3333michigan, income, county85891/141100.1000household income city, household income city county MSA, income, city, county, MSA, household, Dallas, Houston, Texas86181/1152180.1111world population, US income among immigrants, Immigrant income, China's population, family income, college graduate income, college graduates, income, income levels, education, education level, alcohol abuse in college, alcohol, college, crime in East Palo Alto, Crime, East Palo Alto, Crime in California86411/1201170.0588spending power, las vegas, service, hotel, income, job, stats, nevada, labor, per capita personal income, wages, workforce, hotel industry, employment, unemployment, tourism, jobs86461/14140.2500wage statistics for Registered nurses, income, salaries, regional salaries86471/12120.5000income, foreign countries86511/12120.5000consumer, income86541/12120.5000income, COLA86661/12140.2500wage differences among men, women, population, income86671/13140.2500U.S., population, income, Census86763/63130.3333income, per capita, national income86811/12130.3333Population, income, Radio86901/11120.5000population, income87381/24150.2000University, college mail man, mail managers salary analysis, income, mail center magagement report87811/13130.3333average U.S. income, year, salary88461/11130.3333government, payroll, compensation88691/13111.0000cost of living adjustment89131/14150.2000homelessness, housing, 1990 census, population, income89481/13130.3333salary, increase, (salary)90071/11111.0000salary90261/12130.3333population, income, money magazine90301/12120.5000population, income90451/31120.5000women, income90453/32130.3333women income, women, income91011/14160.1667state, income, deciles, state income deciles, income deciles, income decile91211/12140.2500population, income, florida, 1996ANDincomeANDmelbourneANDflorida91221/11120.5000population, income91431/12120.5000income, library91521/13140.2500Japanese population, income, Japanese, Japanese American91611/11120.5000race, income91671/18180.1250family income, income distribution, US income distribution, American income distribution, population, income, personal income personal income for 199692101/13130.3333income, population, income distribution 92721/14150.2000head of household, metropolitan area, income by area, income, number of children93021/11120.5000occupation, income93311/11120.5000population, income93431/14140.2500income, income projections us, us income estimations, us income93561/12130.3333school teachers, income, teachers94141/19180.1250wine, consumption, price, (wine), income, wine consumption, wine statistics, disposable income94271/15260.3333education, income, cost benefit of secondary education, cost of secondary education, wages, wages over time94681/13240.5000farm income, income, grape, wage95061/11111.0000vacation pay95111/14150.2000income, population, income distribution, distribution, US95631/15170.1429minimum, wage, "minimum wage", history, "income tax", rate, "income tax rate"96002/26180.1250budget, NIH, pediatrician, income, malpractice, insurance, anesthesiol, anesthesiology96051/12130.3333population, san antonio, income96061/11111.0000income96341/18460.6667salary, ws, wage grade, payscales, pay, scales96481/13240.5000farm income, income, grape, wage96651/11120.5000county, income96694/62120.5000income, median wage96741/11120.5000college, income97101/14140.2500retire, population, income, retirement97431/11120.5000population, income97491/14130.3333divorce, socioeconomic status, income98011/11120.5000family, income98341/12120.5000annual income, income99031/11221.0000education, income99291/11120.5000medical specialty, income99861/17160.1667income, distribution, statistics, level, tax, bracket100001/11120.5000income, gender100351/18160.1667"El Paso Colorado income", income, "el paso", "el paso colorado", unemployment, "county unemployment"100641/15150.2000charge, figure, elizabeth ann hilden, wage, average hourly earnings100771/14150.2000population, income, united states, personal income, tax100831/18180.1250vital, northcarolina, Nash county vital, Nash county NC, Nash county Statistics, north carolina Statistics, fanily income, income100971/11120.5000population, income101241/14140.2500welth, population, income, family income101751/11120.5000population, income101971/11111.0000income102091/11120.5000population, income102101/12260.3333income, forestry, industry, forest, product, consumption102111/13130.3333income, average, income title102312/21111.0000income102311/21111.0000income102851/21120.5000herbs, income103101/11120.5000population, income103271/12130.3333Colorado population, income, Colorado103681/11130.3333population, income, ethnic104211/11120.5000income, national average104221/14150.2000income, salaries, wages, florida salaries, social worker wages104421/14140.2500national poverty level, poverty, poverty income, income104911/17590.5556gender, gap, pay, Protestant clergy, clergy, salary, wage, discrimination, differentials104921/11120.5000black, income105031/11120.5000population, income105051/41130.3333Sea Cliff, NY, income105071/11111.0000income105161/17180.1250annual income of single parent households, population, income, teenage pregnancy, single mothers, teenage single mothers, education of teen mothers, teenage mothers105191/16150.2000population, income, census, poverty, 1991105531/12140.2500household income, Florida, household, income105731/13130.3333income, Average Income Statistics, Income Statistics106801/13130.3333teenage, income, crime106862/23140.2500population, cities, income, oregon107101/11120.5000salary, buyers107231/14140.2500income, single person income, single income, income stats107351/13130.3333income, physicians, opthalmologist salaries107441/12130.3333hourly compensation of manufacturing workers, compensation, labor107611/44140.2500General Service Compensation, general service grade compensation, compensation, federal compensation107961/15150.2000income per year, income, eggs, united states, eggs eaten per year108181/11111.0000income108311/12120.5000population, income108571/11120.5000dentist, income 108731/13250.4000education, income, investment, child education, best investment108921/151100.1000Seniornet, 50+, computers, consumers, statistical data, seniors, Demographics, "statistical data", 1998, income109031/13130.3333income, income distribution by gender on wallstreet, income distribution by gender109191/12120.5000population, income109361/16160.1667income, distribution, population, disparity, "Current Population Reports", "Population income profile"109371/1111100.1000income, disabiliites, disability, personal, (sources of personal income), sources, (no title), (bureau of Economic analysis), personal income, sources of personal income109501/25150.2000income, population, gdp per capita, growth, gdp growth109541/12120.5000income, income stats110081/11120.5000population, income110501/12120.5000inflation, salary111711/11120.5000population, income111761/12130.3333population, income, average personal income113001/13130.3333pay scales, income, jobs113311/11120.5000income, county113581/11120.5000income, distribution114761/12130.3333population, income, income distribution115451/22170.1429personal, income, Sector, county, assets, expenditures, SIC116391/11120.5000population, income116831/15140.2500rainfall, income, average income, average117031/27170.1429ceo, compensation, executive, small business, entrepreneur, salaries, management117561/12120.5000income, personal income118161/11130.3333federal, attorney, salary118432/31130.3333women, men, income118433/31120.5000men, income 118431/32120.5000women, income119021/12130.3333Dentist, income, professional income119541/13140.2500population, income, statistics of income, statistics of income internal revenue service120031/12130.3333poulation, income, population120421/12130.3333blind wage, blind, income121072/52140.2500population, income, savings, personal121131/11120.5000population, income121781/11111.0000income122161/11120.5000population, income122481/14160.1667population, income, auto sales, New Mexico auto sales, New Mexico sales, new mexico122971/12140.2500household, income, oklahoms, oklahoma123292/31221.0000Education, income 123411/11120.5000population, income123971/12130.3333demographics, race, income123981/11120.5000population, income124181/11111.0000income124671/1101110.0909corrections officer income by state, corrections officer income, average family income, corrections officer, Correctional Officers salary, state, Correctional Officer salary, Correctional Officer income, Correctional Officers, correction officer, income124851/12120.5000income, usa statistics125001/13140.2500state, income, rank*, rank125161/14140.2500women, income, incomeand men, men125191/11120.5000women, income125471/12130.3333personal, income, 1990125912/31130.3333occupation, salary, statistics125961/11111.0000income126161/12230.6667education, income, education level126561/14140.2500manager of MLB, salary, salary of manager, major league baseball126611/13140.2500inflation, income, historical, "Consumer Price Index"127161/17160.1667librarians, librarian salary DC, librarian salary, librarian, nonprofit127721/12130.3333population, income, income statistics128121/13130.3333salary, salary by profession, profession128471/11120.5000china, compensation128761/1101120.0833wages, 1900, manufacturing wages, factory wages, 1900-1920, worcester lunch car company, income, 1880, census 1900, census, 1890, new england census129031/31120.5000population, income129221/15150.2000cosmetic, cosmetic in USA, cosmetic company USA, income, cosmetic market129281/14190.1111home, builders, income, oklahoman house builders, consumers buying new homes, consumers, buying, new, homes129651/31111.0000income130041/13130.3333population, population & income, income130301/12140.2500eduational attainment, income, race, eduation131161/13130.3333occupational, salaries, income131601/11111.0000income132492/22160.1667wage, determination, dept of labor, computer, data, librarian132491/22130.3333Wage determination for Computer Data Librarian, wage, determination133271/11111.0000compensation133361/14160.1667men, income, California, annual income, Los Angeles, average income133551/26170.1429income by age group, income, age group, household income, injuries, sports, wrist133671/23230.6667wage & benefits, wage, education133851/11130.3333black, family, income134041/11120.5000population, income134111/11111.0000income1341610/121120.5000capital gain, income134191/11111.0000income134321/13140.2500income, categories, revenues, occupation134851/12120.5000manhattan income, income135341/14160.1667attorney, per capita, income, (attorney), juvenile justice, Texas135871/12130.3333income, albemarle county, population135931/110270.2857popupation, women, population, income, shopping, education, health136001/12150.2000population, income, Philadelphia, african americans, blacks136021/12221.0000earnings, income136041/12130.3333age, income, billionair136091/12120.5000watchmaker, income136151/17170.1429income, income comparison, family income, per capita income, (per capita income), (family income), (national income)136961/12120.5000icome, income137201/11120.5000population, income137531/14140.2500state, tax, rates, income137691/11230.6667migration, income, education137951/11120.5000age, income137981/12120.5000national disposable income, income137991/14140.2500salary, income Missouri, salary missouri, missouri138301/13130.3333women, income, population138791/23140.2500car sales, population, income, income per capita139171/26140.2500women, population, income, age139381/13140.2500population, income, income in ohio, median income139491/35250.4000salary, salaries, occupation salaries, occupation, wages139621/11120.5000population, income140001/12130.3333women's income, women, income140091/11111.0000income141061/11120.5000population, income141121/13240.5000population, income, relocation, cost of living141201/15140.2500income vs. voter turnout, income, voter-turnout, voter turnout141284/41111.0000income142081/11120.5000population, income142591/15140.2500president, president salary, salary, clinton142721/2102100.2000women, women salary, income, income by age, aging, children, education, gender, age, fertility143441/11120.5000population, income143751/13130.3333DIVORCE, SOCIAL CLASS, income144171/12150.2000congressional, salary, salaries, senator, annual144291/14140.2500sports, income, adventure sports, adventure144311/13130.3333cost of living, cost + of + living, per + diem144791/13120.5000ethnicity, income144831/13130.3333cardiologist, cardiovascular, salary, phisicians144901/12130.3333zip codes, population, income145153/51120.5000population, income145152/51120.5000population, income145621/24180.1250corporate income tax rates, construction cost index, construction, cost, index, state, bond, ratings146022/210160.1667debt service, consumer, debt, consumer debt, income, consumer debt service146211/11111.0000income146411/12120.5000income, ohio146422/31230.6667income, education, women146421/32230.6667income, education, women146423/32240.5000income, education, women, race147101/17170.1429confectionery, chocolate, chocolate consumption, confectionery consumption, 1998 disposable income, disposable income, income147601/191100.1000MEDICAL EMPLOYEES, income, MEDICAL INCOME, medical, medical employment, medical personel, medical worker, medical employee, medical sector, medical professional147621/12130.3333currency, income, federal reserve147651/13140.2500Women's Salaries, income, women, Computer Science148391/16160.1667wage ranges, salary, range, salary ranges, ranges, household income148941/14140.2500salary, salary employment, salary survey, salary computer148961/12130.3333accountants, income, family income149041/24150.2000income, (metropolitan statistical), (North Carolina), (Local Area Personal Income), Local Are Personal Income149051/12140.2500population, income, women, age149141/14140.2500gender bias in the workplace, womens income vs. mens income, income, income based upon gender149761/15150.2000world gross domestic product, population, income, world real personal income, world income150451/12130.3333Los angeles population, population, income150641/11120.5000income, population150941/14150.2000poverty, disability, poplation, income, population151631/16170.1429commissions, sales, commission, sales commissions, dealer commissions, dealer commission, dealer markup151671/13130.3333income, personnal income, personal income151821/13140.2500lawyer, income, lawyer population, average income152071/11111.0000hazard pay152081/11111.0000hazard pay152091/21111.0000hazard pay152751/12130.3333family income, population, income152981/11111.0000salary153061/12120.5000income, family income153501/11111.0000income153511/13240.5000population, income, education, Raw data on popualtion due to153641/22120.5000child support, income153661/13130.3333salary, wages, wage survey154011/11120.5000population, income154481/11111.0000income154541/14240.5000cost of living, cost of living increase for 1999, population, income156141/12140.2500population, income, ANDWisconsin, Wisconsin156641/11130.3333population, income, alcohol156941/13460.6667Government, pay, scale, job, classification, Texas157241/11120.5000race, income157551/15160.1667respiratory, income, respiratory theropy, theropy, theropist, respiratory theropist157571/13120.5000population, income157681/14170.1429affordable housing, income levels, income, housing developments, poverty, data, public housing authority '157781/13140.2500women, income, chilren, homelessness158451/14140.2500annual raise, raise, annual review, salary159421/16170.1429population, income, projections, (income), (projections), (personal income), personal income159561/12150.2000median, income, metropolitan, statistical, area160081/11111.0000income160161/21120.5000population, income160162/22130.3333population, income, travel160201/15180.1250population, taxes, estimated, tax, payers, estimated taxpayers for 1999, 1999 taxes, income160211/11130.3333Black, male, income160341/12130.3333income, personal, farm160601/11120.5000population, income160881/15250.4000cost of living percent increase, income, cost of living adjustment, cost of living index, cost of living161021/12130.3333women, salaries, income161281/14140.2500salary, x118, wage scale, salaries161591/11120.5000population, income162181/12120.5000income, household income162331/14170.1429median income, population with college degrees, population, college degrees, male, income, graduate degrees162681/13150.2000asian, indian, ethnic, minority, income163021/52130.3333population, income, pennsylvania163051/11120.5000population, income163401/16170.1429workers, comp, workers comp, "workers comp", fraud, workers compensation, "workers compensation fraud"163541/11120.5000population, income163711/11120.5000poverty line, income164011/13150.2000income, age, average income, unemployment, state164101/11120.5000disposable, income164171/15160.1667family income, income, average family net worth, net, worth, average164201/15150.2000salary, wages, engineer wages, engineer wage, engineer salaries164271/13150.2000personal + income + commerce, personal, income, commerce, department164591/11111.0000wage164651/12120.5000income family, income164861/11120.5000disposable, income165221/23140.2500family income, populationand income, population, income165281/13120.5000Income, average Household income165441/12120.5000overtime, establishment survey165481/21140.2500population, income, elderly, residence165482/21140.2500population, income, elderly, residence165733/31120.5000population, income165791/12120.5000household income, income165811/11140.2500population, age, income, housing166241/12240.5000presdident, salary, president, income166591/11120.5000financial manager, income166701/1111130.0769affirmative action, affirmative action results, affrimative action results, segregation, negro segregation in schools, black segregation in schools, segregation schools, income vs race, population, income, income blacks, whites, poverty level166761/12120.5000age, income167481/12130.3333rent, income, poverty167541/12130.3333family income, population, income167861/15270.2857"japanese americans", population, income, ethnicity, japanese, minorities, education169311/13160.1667statistics, income, bulletin, capital gain, capital, tax169411/13140.2500department of comm, population, income, age169471/18170.1429cost of living, cost of living comparisons, national cost of living factors, (Cost of living), salary comparisons, cost of living statistics, population169561/11221.0000education, income169661/12120.5000income, 20th century169721/12160.1667Green Book, earned, income, tax, credit, Current Population Survey170051/18180.1250beauty, target population, day spas, population, beauty treatmen, income, beauty services, family income fairfield county170181/32240.5000education, educational, attainment, income170182/31230.6667educational, attainment, income170381/24140.2500income, family income statistics, 1997 individual income statistics, individual income171941/12130.3333ssd, poplation, income172561/13120.5000income, national income172681/15180.1250population, income, sat scores, teachers, academic achievent, teacher, academic achievement, achievement172911/16170.1429Female victimization rates, income, female victimization, Crime Victims, Crime rates, The relationship betwwen female crime rates, victimization173101/13130.3333Welfare Satistics, income, family income173471/11111.0000salary173681/11130.3333population, income, job title173901/13140.2500minimum wage, pouplation, population, income173961/13140.2500cvus93, income domestic violence, income, violence174411/11120.5000population, income174751/12130.3333healthcare providers, income, pediatrician174861/11221.0000education, income 174981/15170.1429retirement, income, deceased, retirement income (deceased OR dead OR died), retirement income (deceased OR dead OR died)"100"year, individual savings, individual savings retirement175391/11120.5000retirement savings, income175401/11111.0000income175441/14140.2500average family income, average income, family income, income175531/14140.2500women income, distribution of income by sex, income, woman175641/13130.3333income, income+percapita, income+capita175941/11120.5000income, demographics177141/13170.1429U.S.A., population, spending, America's spending, population verses the world, income, consumption177281/15160.1667gini coefficient, labor, lorenz curve, work, income, sex177771/16150.2000family statistics, family income, income, social level, families,177971/11120.5000income, California178711/12130.3333restaurant, income, expenses178781/24160.1667population, income, women, wealth, (women), assets178802/21120.5000income, women179971/13250.4000lifetime, earnings, lifetime earnings, income growth, education180031/11111.0000income180801/22130.3333education, income, education level180802/21120.5000education level, income180891/11120.5000population, income181111/12120.5000income, per capita income181191/22130.3333population, income, statistics181791/12120.5000income, wages181931/11120.5000income, high school graduates182211/11120.5000age, income182531/11120.5000population, income182721/11120.5000population, income 183111/17180.1250teenage income, income, teenagers, drivers, driving, statistics, male, female183123/32140.2500"crime rate of Latinos", population, Latinos, income183371/12120.5000income, california183661/12120.5000income, donald kauffman183701/12130.3333poverty, level, income184661/11130.3333population, income, taxes APPENDIX 2-5: A-Z INDEX TERMS USED IN COMPARISON APPENDIX 2-6: USER QUERIES COMPARED WITH A-Z INDEX exactroot reverse words match:words match:queriesmatchmatchrootexactrootabortion111nnnabortions13nnnabuse20nnnaccident12nnnaccidents 22nnnadoption52nnnadvertising24nnnafdc11nnnaffirmative action66nnnafrican americans12nnnage15nnnaging11nnnagriculture11yynaids51nynair pollution11nnnalcohol69nnnalcoholism28nnnanorexia18nnnapparel 15nnnassisted suicide11nnnasthma16nnnautomobile19nnnautomobile accidents14nnnautomobiles16nnnaverage height11nnnaverage income19nnnincomebalance sheet 14nnnbankruptcy 25nnnbirth12ynnbirth control13nnybirthsbirth rate20nnybirthsbirth rates19nnybirthsbirth records14nnybirthsbirths26ynnbrazil11nnnbreast cancer46nnnbudget42nnnbudget deficit15nnnbusiness17nnncalifornia11nnncancer40nnncapital punishment84nnncauses of death19nnncensus51nnncensus bureau14nnnchild abuse99nnychildrenchildrenchild care11nnychildrenchildrenchild support28nyychildrenchildrenchildren16ynnchina12nnncigarettes14nnnclinton11nnncloning12nnncocaine11nnncola15nnncollege14nnncomputer25nnncomputers36nnncongress23nnnconstruction23ynnconsumer and price and index13nnnconsumerconsumer confidence25nnnconsumerconsumer price index153ynnconsumer spending18nnnconsumercorporate profits11nnncost of living119nnncost of living index23nnncpi96nnncredit card12nnncredit cards12nnncrime145yyncrime rate26nnycrimecrimecrime rates19nnycrimecrime?crime statistics26nnycrimecrime?crimes14yyncustomer satisfaction survey57nnndeath34ynndeath penalty60nnydeathsdeath rate15nnydeathsdeath rates15nnydeathsdeaths 43ynndebt15nnndeficit12nnndemographics28nnndepression33nnndiabetes34nnndisabilities14nyndisability20nyndisabled16nyndiscount rate19nnndiscrimination 24nnndisposable income26nnnincomeincomedivorce229ynndivorce rate49nnydivorcesdivorce rates47nnydivorcesdivorce statistics24nnydivorcesdomestic violence59nnndrinking and driving21nnndrug 15nnndrug abuse18nnndrug use22nnndrugs51nnndrunk driving41nnnearnings11yyneating disorders14nnneconomic growth13nnyeconomyeconomyeconomic indicators20nnyeconomyeconomyeconomy14yyneducation99yyneducation and income16nnyeducation, incomeeducation, incomeelder abuse13nnnelderly18nnnelection 22nnnelection results11nnnelections18nnnemployment50yynemployment statistics16nnyemploymentemploymentenergy12yynethnicity10nnneuthanasia19nnnexchange rate 14nnnexchange rates22nnnexports31nnnfamily 16nynfamily income180nnnincomefamily, incomefast food12nnnfbi14nnnfederal budget29nnnfederal funds rate25nnnfederal reserve13nnnfire 11nnnfirearms30nnnflorida 12nnnflorida population11nnnpopulationpopulationfood11nnnfootwear12nnnforeign aid14nnngambling27nnngangs17nnngdp198nnngnp46nnngross domestic product104yyngross national product60nnngun10nnngun control44nnnguns34nnnhandguns11nnnhate crimes30nnncrimecrimehealth31yynhealth care18nnyhealthhealth health insurance19nnyhealthhealthhealth statistics12nnyhealthhealthhealthcare20nnyheart disease12nnndiseaseheight36nnnhepatitis13nnnhigh school dropouts16nnnschoolhigher education12nnneducation education hispanics15nnnhiv21nynhmo10nnnhomeless57nnnhomelessness20nnnhomicide23nnnhomicides12nnnhomosexual12nnnhomosexuality11nnnhospital 15nnnhospitals21nnnhousehold income23nnnincomeincomehousing28yynhousing starts25nnyhousinghousinghunger19nnnhunting16nnnhypertension10ynnilliteracy14nnnimmigration68yynimmunization10nnnimpeachment11nnnimports13nnnincome215yynincome distribution15nnyincome incomeindustry15nyninfant mortality32ynninflation228nnninflation and rate17nnninflation index14nnninflation rate106nnninflation rates36nnninformation technology11nnninjury10nnninsurance34nnninterest rate18ynninterest rates 69ynninternational economic statistics11nnneconomyeconomyinternational trade12ynninternet76nnninternet usage11nnninternet use12nnninterracial marriages10nnnmarriagesinvestment17nnnjobs14ynnjuvenile and violence13nnnjuvenile crime39nnncrimecrimejuvenile violence69nnnlabor13nynlabor statistics10nnnlaborlawyers 11nnnjuvenile11nnnlead poisoning12nnnleading causes of death11ynndeathslife expectancy81ynnliteracy 38nnnliteracy rate12nnnlung cancer12nnnm210nnnmanaged care13nnnmanufacturing16ynnmarijuana42nnnmarriage52ynnmaternal mortality10nnnmedian income19nnnincome incomemedicare17nnnmental health22nnnhealthhealthmilitary 30yynminimum wage31nnnwagesmoney supply25nnymoneymoneymorbidity10nnnmortality39nnnmsa14nnnmurder25nnnmurder in families12nnnfamilynafta19nnnnaics14nnnnational debt52nnnnational income10nnnincome incomenursing home 13nnnnursing homes16nnnobesity28nnnoccupation11yynpension10nnnper capita income30nnnincome incomepersonal income30yynincome incomepersonal savings10nnnpharmaceutical10nnnpolice brutality12nnnpopulation544yynpopulation and age 36nnypopulationpopulationpopulation and income179nnypopulation, incomepopulation, incomepopulation and race24nnypopulationpopulationpopulation income13nnypopulation, incomepopulation, incomepopulation, income48nnypopulation, incomepopulation, incomepornography17nnnpoverty 70ynnpoverty level24nnypregnancy18nynprime rate36nnnprison19nnnprison population12nnnpopulationpopulationprisons21nnnproducer price index24nnnpricespricesproductivity13ynnprostitution12nnnpuerto rico15nnnrace30nnnracism17nnnrape40nnnrate of inflation 20nnnreal estate11nnnreal gdp23nnnrecidivism15nnnrecycling16nnnregistered voters15nnnreligion92nnnretail21nnnretail sales36nnnretirement12nnnrussia 16nnnsalaries22nnnsalary31nnnsavings12nnnsavings rate11nnnschizophrenia12nnnschool uniforms11nnnschool violence22nnnschools13nnnselected interest rates12nnnsex 25nnnsex education16nnneducationeducationsexual abuse15nnnsexual harassment48nnnsexually transmitted diseases11nnndiseasesic21nnnsic codes18nnnsids10nnnsingle mothers12nnnsmall area estimation 12nnnsmall business18nnnsmoking50nnnsocial security31nnnspecial education10nnneducationeducationspending13nnnstate population17nnnpopulationpopulationstatistical abstract16nnnstatistical abstract of the united states12nnnstatistics44nnnstd11nnnsteel12nnnsteroids10nnnstock market10nnnstress 17nnnsubstance abuse16nnnsuicide92nnnsurvey of current business20nnnsyphilis10nnntax10nyntaxes13nyntechnology10nnnteen pregnancy104nnnpregnancyteen suicide12nnnteenage13nnnteenage pregnancy66nnnpregnancytelecommunications16nnntelevision29nnnterrorism27nnntobacco27nyntourism36nnntrade12yyntrade deficit11nnytradetradetraffic accidents12nnntransportation19yyntravel15nnntreasury10nnntreasury bill14nnntuberculosis11nnnu.s. population11nnnpopulationpopulationunemployment162ynnunemployment and rate11nnyunemploymentunemployment rate77nnyunemploymentunemployment rates29nnyunemploymentunions10nynunionunited states population12nnnpopulationpopulationus population24nnnpopulationpopulationveterans10yynviolence18nnnvital statistics24yynvote 10nnnvoter registration12nnnvoter turnout28nnnvoters14nnnvoting62nnnwages30ynnwealth12nnnweather20nnnwelfare113nnnwelfare reform16nnnwelfare statistics12nnnwholesale price index10nnnpricespriceswomen 41nnnwomen and income21nnnincomeincomeworkers compensation13nnncompensationworking women10nnnworld population15nnnpopulationpopulationy2k25nnnyear 200010nnnzip code10nnn# of matches433537 APPENDIX 3-1: SCENARIOS AND VARIABLES USED IN EXPERT INTERVIEWS SCENARIO #4 You are interested in exploring the relationship between contingent workers obtaining health insurance through their employers, and the basis reported for part-time employment by part-time employees. (latest data). Using FERRET, you go to the CPS Contingent Worker Supplement and access all Labor Force and Contingent Worker variables by using appropriate checkboxes. You get a variable list from which you select three variables that look like promising candidates for describing reason for part-time employment. Then you go back and select three variables that relate to employer provision of health insurance. You access metadata files for all these variables of interest. Those files are listed below Please review the metadata for the candidate variables to be used in a bivariate exploratory analysis and tell us in your own words what parts of the metadata content help you most in reaching a decision about selecting just one variable from each set. If none of these selected variables meet your needs, what is it about the metadata that convinces you that you should look at other candidate variables? We are also interested in what additional metadata would be helpful in reaching this decision, and any other comments you may have on how the metadata information influences your thinking about the question you had in mind to begin with. ALTERNATIVE VARIABLES TO ACCOUNT FOR PART-TIME STATUS (LABOR FORCE) PEHRRSN1 (Jan 199401 - ) Labor Force-(part-timer)reason Some people work part time because they cannot find full-time work or because business is poor. Others work part time because of family obligations or other personal reasons. What is your Main reason for working part time? (probe If Necessary: What is your main reason for working Part Time instead of Full Time?) **Related Recodes: PRWKSTAT, PRPTREA, PRPTHRS Edited Universe: PEHRWANT=1 (PEMLR=1 And PEHRUSLT 35) Valid Entries 1 Slack Work/Business Conditions 2 Could Only Find Part-Time Work 3 Seasonal Work 4 Child Care Problems 5 Other Family/Personal Obligations 6 Health/Medical Limitations 7 School/Training 8 Retired/Social Security Limit On Earning 9 Full-Time Workweek Is Less Than 35 Hrs 10 Other - Specify  PEHRRSN2 (Jan 199401 - ) Labor Force-(part-timer)reason not full-time What is the main reason you do not want to work full time? **Related Recodes: PRPTREA Edited Universe: PEHRWANT=2 (PEMLR=1 And PEHRUSLT 35) Valid Entries 1 Child Care Problems 2 Other Family/Personal Obligations 3 Health/Medical Limitations 4 School/Training 5 Retired/Social Security Limit On Earning 6 Full-Time Workweek Less Than 35 Hours 7 Other - Specify  PRPTREA (Jan 199401 - ) Labor Force-(part-timer)specific reason Detailed Reason For Part-Time Valid Entries HYPERLINK "http://ferret.bls.census.gov/items/value/valu_59069.htm"-1 In Universe, Met No Conditions To Assign 1 Usu. FT-Slack Work/Business Conditions 2 Usu. FT-Seasonal Work 3 Usu. FT-Job Started/Ended During Week 4 Usu. FT-Vacation/Personal Day 5 Usu. FT-Own Illness/Injury/Medical Appt 6 Usu. FT-Holiday (religious Or Legal) 7 Usu. FT-Child Care Problems 8 Usu. FT-Other Fam/Pers Obligations 9 Usu. FT-Labor Dispute 10 Usu. FT-Weather Affected Job 11 Usu. FT-School/Training 12 Usu. FT-Civic/Military Duty 13 Usu. FT-Other Reason 14 Usu. PT-Slack Work/Business Conditions 15 Usu. PT-Could Only Find PT Work 16 Usu. PT-Seasonal Work 17 Usu. PT-Child Care Problems 18 Usu. PT-Other Fam/Pers Obligations 19 Usu. PT-Health/Medical Limitations 20 Usu. PT-School/Training 21 Usu. PT-Retired/Ss Limit On Earnings 22 Usu. PT-Workweek <35 Hours 23 Usu. PT-Other Reason  ALTERNATIVE VARIABLES TO DESCRIBE EMPLOYER ROLE IN PROVIDING HEALTH INSURANCE (CONTINGENT WORKERS) PES50 (Feb 199702 - ) Contingent-health insurance, covered by employer,y/n Do you receive this health insurance through (fill: employer's name, or your temporary help agency, contract company, etc.) Universe = Entry in PES49=1 and IO1COW not equal to 6, 7, or 11 -9 No response (N/A) -3 Refused -2 Don't Know -1 Not in universe 1 Yes 2 No  PES55 Contingent-health insurance,given by temp firm,not in plan Why aren't you in this plan? Universe = Entry in PES54 = 1 -9 No response (N/A) -3 Refused -2 Don't Know -1 Not in universe 1 Covered by another plan 2 Traded health insurance for higher pay 3 Too expensive 4 Don't need health insurance 5 Have a pre-existing condition 6 Haven't yet worked for this employer long enough to be covered 7 Contract or temporary employees not allowed in plan 8 Other-specify  PES53 Contingent-health insurance,given by temp/contract firm Does (fill: employer's name or your temporary help agency, contact company, etc.) offer health insurance to any of its (fill: temporary) employees? Universe = Entry in PES52A and IO1COW not equal to 6, 7, or 11 -9 No response (N/A) -3 Refused -2 Don't Know -1 Not in universe 1 Yes 2 No APPENDIX 3-2: OVERVIEW OF STRUCTURE OF METADATA  HYPERLINK http://www.bls.census.gov/cps/metaproc.htm http://www.bls.census.gov/cps/metaproc.htm The following describes the layout of the FERRET Metadata Interface File (MIF). (HYPERLINK "mifsample.htm"View a sample MIF.) This file is used to populate the FERRET database metadata repository, and to create the internal description files. These can then be used to create a data dictionary, either complete or customized (through FERRET). It also is used to drive the information passed to the customers through the FERRET front end. HYPERLINK "/cps/mifillustrate.htm"View five illustrations of how the MIF information is used by FERRET. The following pieces of item metadata are mandatory. Note that the one character delimiters should be in column one followed immediately by at least one space. The delimiters surrounded by colons allow for multiple line entries without having the delimiter at the beginning of each line. They should be at the beginning and ending of the text. M Item (variable) name or mnemonic name. S Short description or English label (limit sixty characters) C Concept label or topic label 1 T Time of item (when it began),e.g. 199401 for January 1994, and when it ended (if it has),e.g. 199401:199406 1 W Suggested weight variable name (e.g. BASEWGT), Yes(if item is a weight), or NONE (if there is no suggested weight)1 2 X Security Level(e.g. Public, Sponsor, Sworn, Census). 1 Y Variable type abbreviation as follows: 1 E = Edited U = Unedited W = Weighting R = Recode X = Allocation flag T = Topcoded S = Sample Control G = Geography Z Data type abbreviation as follows: 1 B = Binary (numeric) C = Character F = Floating point T = Military time (HH:MM) Ix.y = Implied decimal (user defines the x to be the total length of value including the decimal and y is the number of digits to right of decimal For example: I10.4 = Implied decimal (5 digits to the left and 4 digits to right of decimal) I5.2 = Implied decimal (2 digits to left and 2 to right) Note: the value line should then contain minimum and maximum value with a decimal. E.g. for Z I5.2, the value line should be V 0.00:99.99 V Value (mandatory for binary items only, however character items may have V lines) with description. Each value line should have a V at the beginning. The first 16 characters of two or more description lines cannot duplicate. V 1 Male V 2 Female or for a continuous range variable: V -1 Blank V 0:99 Years or for a continuous range with decimals (e.g. Z I10.4): V 0.0000:99999.9999 The following item is optional, but strongly recommended for items that are not allocation flags or topcoded items: :L: Long description. There may be a multiple line description with a :L: on the following line after the description. :L: The following items are optional: P CD-ROM or ascii file data start and end positions e.g. P 15 16 U Universe description 3 :A: Attachment type (e.g. Edit Specs, Recode Specs, Instrument Specs, Sampling, User Note, etc.) followed by the URL of the text, beginning on the next line, e.g. http://www.census.gov/mydir/myfile.htm (Please note: there is no :A: line after the URL line.) B Synonyms(Multiple words should either be listed separately, or comma delimited). I Iteration group size - (i.e. variable repeats 12 times, then 12 would be the group size). 1 _____________________________ 1Items may create global values using a two-character delimiter with "G" being the first character, e.g. GC, GT, GW, GX, GY, GZ, or GI. Global values should appear at the top of the MIF. However, a global value can be changed within the file by entering a new global value at the point in which the new value should begin. Also, global values are overridden by an individual value for any specific item. 2When entering new variables, please place variables used as Suggested Weight variables with their corresponding information at the top of the file. 3Universe descriptions should follow Long descriptions. _____________________________ HYPERLINK "mifsample.htm"View a sample MIF HYPERLINK "/cps/mifillustrate.htm"View five illustrations of how the MIF information is used by FERRET  Contact: (HYPERLINK "mailto:whazard@census.gov"whazard@census.gov) Bill Hazard-Census/DSD/SMPB Last modified: August 28, 1997 URL:  HYPERLINK http://www.bls.census.gov/cps/metaproc.htm http://www.bls.census.gov/cps/metaproc.htm SAMPLE MIF GC Displaced Worker Supplement Variables GT 199602 GW PWSSWGT GX Public GY E GZ B M PWSSWGT S Second Stage Weight (rake 6 Final Step Weight) C Weighting Variables (NOTE: This overrides the GC for this item.) W Yes (NOTE: This overrides the GW for this item.) Y W (NOTE: This overrides the GY for this item.) Z I10.4 (NOTE: This overrides the GZ for this item.) :L: Second Stage Weight :L: V 0.0:99999.9999 Weight Values :A: Weighting Specs http://www.bls.census.gov/specs/pwsswgt.htm M PEST16 S Jobtenure - same industry curr and prev job :L: Earlier you told me that the business or industry that you currently work in is (fill: name of industry from basic CPS). Is that the same industry in which you worked a year ago, in February :L: U [(PEST1A = 52 - 99) and (PEST1B = 2)] or U [(PEST1A = 12 - 99) and (PEST1B = 3)] or U [(PEST1A = 2 - 99) and (PEST1B = 4)] or U (PEST3 = 12-35) or U [(PEST5A = 52 - 99) and (PEST5B = 2)] or U [(PEST5A = 12 - 99) and (PEST5B = 3)] or U [(PEST5A = 0- 99) and (PEST5B = 4)] or U [(PEST7 = 1 or PEST8 = 1) and PEIO1ICD = 1 - 999] V -9 No response V -3 Refused V -2 Don't Know V 1 Yes V 2 No M PRSUPTYA S Dispwkr - supplement interview status W PWRCWGT (NOTE: This overrides the GW for this item.) Y R (NOTE: This overrides the GY for this item.) :L: Type of Supplement Interview- Displaced Workers :L: V 1 Interviewed,Displaced Worker (self-employed people included) V 2 Interviewed,Not a Displaced Worker V 3 Interviewed,But Displaced Worker Status V 4 Noninterview-Eligible for Displaced Workers, but NOT Interviewed V 5 Not Eligible for Displaced Workers M PESEH1OA S Dispwkr - current job hourly rate of pay T 199502:199502 (NOTE: This overrides the GT for this item.) Z I5.2 (NOTE: This overrides the GZ for this item.) :L: Out variable for hourly pay rate-current job Dollar Amount--Two implied decimals (Topcoded such that hourly rate is less than or equal to $1923.00 divided by usual hours) :L: U PUSCE2O=1 V -9 No response V -3 Refused V -2 Don't Know V 0.0:99.99 Range M PRSUPTYB S Jobtenure - supplement interview status C Job Tenure Supplement Variables (NOTE: This overrides the GC for this item.) Y R (NOTE: This overrides the GY for this item.) :L: Type of Supplement Interview - Job Tenure and Occupational Mobility :L: V 6 Interviewed, Job Tenure and Occupational Mobility V 7 Noninterview - Eligible for Job Tenure and Occupational Mobility, but not Interviewed V 8 Not Eligible for Job Tenure and Occupational Mobility APPENDIX 3-3: EXAMPLE LEVEL 0 and LEVEL 1 METADATA SCENARIO 2: LEVEL 0 (Existing Metadata) M HRHHID S Household Identifier - scrambled C Household Variables Y R Z C :L: Household Identifier - scrambled
Edited Universe:
All Households in Sample
:L: M PROLDRRP S Demographics Recode to collapse new RRP categories into old C Demographic Variables Y R :L: Recode which collapses the new relationship to reference person
categories back into the old format. This allow users to maintain
consistency when comparing to prior surveys.
Valid Entries
:L: V 1 Ref Pers W/ Rels V 2 Ref Pers W/O Rels V 3 Spouse V 4 Child V 5 Grandchild V 6 Parent V 7 Brother/Sister V 8 Other Rel Of Ref Per V 9 Foster Child V 10 Nonrel Of Ref Per W/ Own Rels In HH V 11 Partner/Roommate V 12 Nonrel. of ref per W/O Own Rels In HH M PULINENO S Demographics Line Number C Demographic Variables Y U :L: Line number
Response codes = 1-N (unlimited numbers may be assigned, but the
instrument will allow interviewer to collect data for a maximum of
:L: V -3 Refused V -2 Don't Know V -1 Blank V 1:99 Range M PEPARENT S Demographics Parent Line Number C Demographic Variables :L: Enter Line Number Of Parent Of [fill name (who)] -- Ask If
Necessary
<0> No One Listed Above
Edited Universe: all
Valid Entries
:L: V 0:99 Range M PESPOUSE S Demographics Spouse Line Number C Demographic Variables :L: Line Name Relation Mar
Enter Line Number Of Spouse Of [fill name (who)] -- Ask If Necessary
Edited Universe:
All
Valid Entries
:L: V 1:99 Range M PESEX S Demographics Sex C Demographic Variables :L: Enter Appropriate Sex.
Ask Only If Necessary: What is your sex?
Edited Universe:
Every Person
Valid Entries
:L: V 1 Male V 2 Female PEAGE M PEMARITL S Demographics Marital Status C Demographic Variables :L: Are you now married, widowed, divorced, separated or never married?
** Related Recode: PRMARSTA
Edited Universe:
PEAGE>=15
Valid Entries
:L: V 1 Married - Spouse Present V 2 Married-Spouse Absent V 3 Widowed V 4 Divorced V 5 Separated V 6 Never Married SCENARIO 2: LEVEL 1 METADATA HRHHID: Unique Household Identifier Short Description: Household Identifier - scrambled Concept Label: Household Variables Variable Type: Recoded Variable Data Type: Character Data Long Description: Household Identifier - scrambled Edited Universe: All Households in the Sample All Households in Sample PROLDRRP : Recode of Relationship to Reference Person Categories Short Description: Demographics Recode to collapse new RRP categories into old Concept: Demographic Variables Variable Type: Recoded Variable Long Description: Recode which collapses the new relationship to reference person categories back into the old format. This allow users to maintain consistency when comparing to prior surveys. Valid Entries VALUE1 Reference Person With Relations VALUE2 Reference Person without relations VALUE3 Spouse VALUE4 Child VALUE5 Grandchild VALUE6 Parent VALUE7 Brother/Sister VALUE8 Other Relation of Reference Person VALUE9 Foster Child VALUE10 Nonrelation of Reference Person with own relations in household VALUE11 Partner/Roommate VALUE12 Nonrelation. of reference person Without Own Relations In Household PULINENO: Demographics line number Short Description: Demographics Line Number CONCEPT Demographic Variables Variable Type: unedited Long Description: Line number Response codes = 1-N (unlimited numbers may be assigned, but the instrument will allow interviewer to collect data for a maximum of VALUE-3 Refused VALUE-2 Don't Know VALUE-1 Blank VALUE1:99 Range PEPARENT: Demographics Parent line number Short description: Demographics Parent Line Number CONCEPT: DemographicVariables Long description: Enter Line Number Of Parent Of [fill name (who)] -- Ask If Necessary <0> No One Listed Above Edited Universe: all respondents in survey Valid Entries VALUE 0:99 Range PESPOUSE : Spouse line number Short description: Demographics Spouse Line Number CONCEPT: DemographicVariables Long description: Line Name Relation Mar Enter Line Number Of Spouse Of [fill name (who)] -- Ask If Necessary Edited Universe: All respondents in survey Valid Entries VALUE 1:99 Range PESEX : Sex of Respondent Short description: Demographics Sex CONCEPT Demographic Variables Long description: Enter Appropriate Sex. Ask Only If Necessary: What is your sex? Edited Universe: Asked of every respondent in survey Valid Entries Value 1 Male Value 2 Female PEMARITL: Marital Status of Respondent Short description: Demographics Marital Status CONCEPT: Demographic Variables Long Description: Are you now married, widowed, divorced, separated or never married? ** Related Recode: PRMARSTA
Edited Universe: Asked if Respondent is 15 years or older Valid Entries Value 1 Married - Spouse Present Value 2 Married-Spouse Absent Value 3 Widowed Value 4 Divorced Value 5 Separated Value 6 Never Married APPENDIX 3-4: SCENARIOS FOR PHASE 2 WITH VARIABLE NAMES June 29, 1999 Scenario 1: Compute the civilian umemployment-to-popuation ratio for several states and metropolitan areas. Variables GECMSA GEMSA GESTFIPS GESTCEN PEAFNOW PEAGE PEMLR PRCIVLF PRPERTYP Relevant variables: GEMSA, either GESTCEN or GESTFIPS, PEMLR Scenario 2: Compute the fraction of individuals who have an elderly parent living in the same household. HRHHID HRHTYPE PEAGE PEPARENT PERRP PESEX PRFAMNUM PRFAMREL PROLRRP Relevant variables: hrhhid, perrp or proldrrp, peparent Scenario 3: Compute the fraction of workers who usually work fulltime (at least 35 hours a week) regardless of number of jobs held and who were at work last week. PEHRUSL1 PEHRUSL2 PEHRACTT PEMLR PESEX PHERUSLT PRWKSTAT PRTLF PRHRUSL PEHRFTPT PRUSFTPT Relevant variables: PESEX, PEMLR, PRHRUSL APPENDIX 3-5: Draft Interview Guide for Phase 2 Interviewer Instructions Interviewer Thank you for agreeing to participate in this study. Before we start, I 'd like you to read the consent form to make sure that you understand the study and what will happen. ( Give respondent the consent form. The respondent reads the form. Interviewer Do you have any questions right now? (Questions Interviewer If you are ready to start, Could you please sign this form? I will give you another copy of the form for you to refer back to. (Give respondent the second copy and get the signed one back. (Get tape ready. Interviewer (Say respondent number and date. Are you ready? ( Give instructions for the whole study. In this study, you will be given three scenarios or tasks to accomplish. Each task involves choosing variables that you think might be relevant to answering the scenario if you were considering doing the analysis suggested by the scenario. We will provide a set of variable names for each scenario and each variable will have some associated that we hope will help you make your decisions. For each scenario, we will ask you to make two sets of relevance evaluations; that is, we will ask you to decide which variables you think might be appropriate for the scenario and how confident you are in that decision for each variable. There are no right or wrong answers to these questions. To the extent possible, we hope you answer these questions just as you normally would do when conducting a search in statistics. At the end of the study, we may ask you to explain your reason for your answers to the questions. Do you have any questions? BEGIN STUDY Present scenario and the assigned first type of metadata for variables. The respondent may talk aloud and so we want to tape that. Interviewer: Here is the first scenario and the set of potentially relevant variables. Feel free to make notes on the variable lists. Once you are done making your choices of relevant variables, I will ask you 2 questions about each variable. FILL OUT THE ATTACHED TABLE For each variable, ask the respondent a) Based on what you know now about the variable (USE NAME), would you consider using it in the analysis suggested by the scenario? ____ Yes ____ No b) Please indicate your level of confidence in the judgement you just made by a percentage. By confidence, we mean the extent to which you are sure that the variable is relevant to the scenario ______________% c) Please comment briefly on your reasons for / reasoning behind answers to the above two questions. SCENARIO 2: Compute the fraction of married couples that are living with an elderly parent METADATA LEVEL RESPONDENT Variable nameRelevance yes/noConfidence in decision %comments SCENARIO 2: Compute the fraction of married couples that are living with an elderly parent METADATA LEVEL RESPONDENT Variable nameRelevance yes/noConfidence in decision %comments APPENDIX 4-1: Workshop Materials INTERMEDIATION WORKSHOP: HELPING USERS ACCESS AND USE STATISTICAL DATA Carol A. Hert, Ph.D. Syracuse University School of Information Studies February 23, 1999, 10 am Bureau of Labor Statistics OBJECTIVES: Provide information on empirical findings on how intermediaries help users of statistics To provide insights from Library and Information Science on that process and how to facilitate What we know about users What we know about intermediaries What we know about managing the process To further develop a list of probes which can be used across BLS when working with users To further develop specifications for a question/answer tracking system and its sharing across the agency To identify additional strategies/activities/etc. that might facilitate intermediary assistance of users SCHEDULE OF WORKSHOP Introductions Insights from Research and LIS Theory and Practice and Consideration of Similarities/Differences at BLS Breakout Groups to work on objectives 3-5 (.75 hour) Reports from Breakout Groups (.75 hours) Where to go from here? (.25 hours) INSIGHTS FROM LIS THEORY AND PRACTICE WHAT WE KNOW ABOUT USERS users experience gaps, anomalous states of knowledge and can not tell intermediaries what they need/want what is being sought is strongly related to potential use users attempt to determine potential relevance of items as early in the information seeking process as possible INSIGHTS FROM LIS THEORY AND PRACTICE AND BLS RESEARCH WHAT WE KNOW ABOUT INTERMEDIARIES Know relevant information sources, keep relevant materials close at hand, and know what is available in their local collections. Know other people to whom to refer users. Understand the specifics of data collection and dissemination tools within their domain. Understand survey methodology. Help users express and refine their information needs. Have technical and searching skills Intermediation involves understanding user needs, negotiation with users to match those needs to resources, good communication skills and knowledge of information sources. The knowledge and skills provided by intermediaries may be viewed, from the user perspective, as places were less experienced users experience lacks or breakdowns in the statistical information seeking process. Thus, we might infer that users: Lack knowledge of survey methodology (specifically mentioned by intermediaries). Lack an understanding of the structure of a domain and the people and information entities (both metadata and key publications that disseminate the information) within it which results in a mismatch between information needs and available information. Lack appropriate information handling and technical literacy skills. Understanding the User’s Information Need: Neutral Questioning and Probes Bill Katz (Introduction to Reference Work, 7th ed. New York: McGraw-Hill, 1997) indicates that gathering the following information can help intermediaries understand what a user is seeking: What kind of information is needed? (e.g., aspects other than topical) How much information is needed? How much information does the user already have on the topic? How is the information going to be used? What degree of sophistication in required? How much time does the user want to spend finding information, and it? When is the information needed? Neutral Questioning is a variant of open-ended questioning designed to understand user needs in terms of the context of the need (e.g., what the person already knows, has already done) and the intended use of that information. The goal is to allow the user to reveal his or herself and the information need without feeling threatened. Examples (from attached Dervin and Dewdney article) are: To assess the situation: Tell me how this problem arose. What are you trying to do in this situation? What happened that got you stopped? What kind of help would you like? What have you done so far? To assess the gaps: What would you like to know about X? What seems to be missing in your understanding of X? What are you trying to understand? To assess the uses: How are you planning to use this information? If you could have exactly the help you wanted, what would it be? How will this help you? What will it help you do? My dissertation work found that there were a number of dimensions of a user’s situations that influenced the information need and associated information seeking. It is possible to ask about these dimensions to gain a richer picture of the user’s situation. (see attached table from Hert, C.A. 1997. Understanding Information Retrieval Interactions: Theoretical and Practical Implications. Greenwich, CT: Ablex.) (NOT INCLUDED IN THIS APPENDIX) Probes in the Statistical Arena Intermediaries help users focus their information needs through the use of: Probes common across agencies (topic, time, geography, number of statistics wanted), Probes common to a class of agencies (e.g., demographic aspects for agencies where the individual is the unit of analysis), Probes specific to an agency. INSIGHTS FROM LIS THEORY AND PRACTICE WHAT WE KNOW ABOUT MANAGING THE INTERMEDIATION PROCESS sharing information on users, answers for users, and strategies and tools used can reduce individual effort. intermediaries need ongoing training/education: good intermediation is both an art and a science and aspects of it can be taught. electronic reference settings may need different management structures. BREAKOUT GROUP TASKS Group A: Develop a list of probes that might be used by intermediaries across BLS. Try to use neutral questioning strategies in your phrasing. Consider whether there are some probes that are specific to certain areas of BLS. Group B: Develop a list of information units (items) that would be helpful to share across the agency to facilitate intermediation activities, answer user questions, and track intermediary answers. Group C: Develop a list of activities/resources/etc. that exist or would be helpful to have to facilitate intermediation activities at BLS. Each group should plan on reporting (5-10) minutes on their activities. BIBLIOGRAPHY Dervin, B. and Dewdney, P. (1986). Neutral Questioning: A New approach to the reference interview. RQ summer 1986: 506-513. attached. Dewdney, P. and Michell, G. (1997). Asking “why” questions in the reference interview: A Theoretical justification. Library Quarterly 67(1): 50-71. Hert, C.A. (1998). Facilitating Statistical Information Seeking On Websites: Intermediaries, Organizational Tools, And Other Approaches: Final Report To The Bureau Of Labor Statistics. Available at:  HYPERLINK http://istweb.syr.edu/~hert/BLSphase2.html http://istweb.syr.edu/~hert/BLSphase2.html, Chapter 3 findings and recommendations were included as part of the workshop materials. They are not reproduced here. Lankes, R. D. (1998). _Building and maintaining Internet information services: K-12 digital reference services_. ERIC Clearinghouse on Information and Technology, Syracuse University, Syracuse, NY. (IR-106). Lankes, R. D. and Kasowitz, A. S.(1998). _The AskA starter kit: How to build and maintain digital reference services_. ERIC Clearinghouse on Information and Technology, Syracuse University, Syracuse, NY. (IR-107). Nolan, C.W. (1992). Closing the reference interview: Implications for policy and practice. RQ. Summer 1992: 513-523. APPENDIX 4-2: SUMMARY OF WORKSHOP March 2, 1999 23 February 1999—BLS intermediary workshop We had a group of 9 people representing the Office of Publications and Special Studies (4), Office of Employment and Unemploymnet Statistics (3, from two divisions), and the Office of Compensation and Working Conditions. The workshop started with a presentation by me on general theories/knowledge about users and intermediaries based in LIS literature. My sense was that this information, while relevant, did not connect well for the audience and if an additional session were to be scheduled, would change the order of the presentation. The audience appeared to be a mix of experienced intermediaries and new intermediaries. New people engaged in the discussion both by pointing out what was difficult for them as well as by providing numerous anecdotes about helping users. At least one of the experienced intermediaries commented throughout the workshop how simple it was to help users, that questions from users were stereotypical and that what was really needed was just more information about the agency to do the best intermediation possible. In addition to the anecdotes presented, several issues were articulated by the participants during the presentation. They perhaps reflect issue areas for customer service at BLS: User expectation management Dealing with negative users Transfer users around What data does the agency not have Terminology differences between users and agencies Delegated searching (getting good information when the person who actually needs it is not the person calling etc.) Tracking users for feedback Having a customer survey on Web would be helpful The original presentation was to include information on managing reference services. However, since the participants did not appear to be managers, I did not cover that material. Following the presentation, the group divided itself into several breakout groups. These were slightly modified from the original list. Development of the list of probes also did not seem to engage participant interest. From comments made during the session and from asking the direct question about whether participants felt that they had a good list of probes (answer-yes), I could not generate group interest in working on this one. ORIGINAL BREAKOUT GROUP TASKS Group A: Develop a list of probes that might be used by intermediaries across BLS. Try to use neutral questioning strategies in your phrasing. Consider whether there are some probes that are specific to certain areas of BLS. This section was crossed out. People seemed comfortable with this. Group B: Develop a list of information units (items) that would be helpful to share across the agency to facilitate intermediation activities, answer user questions, and track intermediary answers. One front door. Group C: Develop a list of activities/resources/etc. that exist or would be helpful to have to facilitate intermediation activities at BLS. The following breakout group was added based on discussion during the workshop. Group D: User expectations and how to manage. The following are brief summaries from each group. BREAKOUT SUMMARY Issues multiple transfers more control of incoming phone calls better training and publications from the offices on their stuff (and timely training). Website improvement (including an A-Z subject list) more information specialists terminology need more integration across programs (e.g., putting pages together which might express what are all the earnings data BLS as a whole collects) difference in how phone systems are structured in the offices Do we really know how often they are transferred? One person said “I still feel like I need to know more about how agency works” Expectations Users expect: one stop shopping- real English answers immediate response (preferable) analysis and assistance in interpretation Solutions - Be more clear about what we expect from the user and what we can do (Note from Carol: FYI, there is a small set of literature on what makes a successful user and one of the components is that they are prepared, and invested in their search. Some of the preparation we might give them is an explanation of what BLS intermediaries can do for them, what types of questions an intermediary might ask them (so they can think about it in advance instead of on the fly) Find out where are they coming from; who referred them. Ask for specific citations in documents/tables. This would help when a user said “I have this table and need to get the lastest number” To get the best service from us…know about periodicity, etc. – (That is, have something on the website that talks about what staff can do for users and what information helps staff help users. Consistency of service Sharing Some stuff on intranet which could be on public site (inconsistencies across units in terms of what information is on intranet). Training (using push technology rather than passive for distribution). Effective use of links Figure out common body of knowledge that everyone should have at X point Final Observations On My Part At points I felt like an apologist for BLS since people had what seemed to be "pent-up" frustrations coming out of service to users/or about how the agency as a whole may be handling customer service. Later, I suggested to Debby Klein that they may want to do some PR about how innovative BLS is. New workshop ideas Need to express what BLS is up to in this area Talk about why their jobs are harder than they were Natalie and Richard- have a database of questions/answers which might be looked at both for the questions and as a possible model. Where might the agency go from here? PR about how innovative/advanced the agency is in thinking about how to manage customer service better Further information gathering on within agency FAQ’s and/or databases of questions/answers. Following this information gathering, consider how to integrate on the website Develop page on how to get the best use of BLS intermediaries to go on website My recommendation is that doing more of these types of workshops is not necessary at this point, except perhaps for members of Debby’s nascent task force. Such a workshop might begin to consider management aspects and future directions.  Gluck, M. (1996). Exploring the Relationship between User Satisfaction and Relevance in Information Systems. Information Processing and Management. 32(1):89-104.  Schamber, L. (1991). Users’ Criteria for Evaluation in a Multimedia Environment. ASIS Proceedings 1991, pp. 126-133.  He, J. & Gey, F. (1996) Online Codebook Browsing and Conversational Survey Analysis. Social Science Computer Review 14(2): 181-186.  Harman, D. (1995). Overview of the Third Text REtrieval Conference (TREC-3). Proceedings of the Third Text REtrieval Conference.  This explanation is extremely simplified and does not take into account other aspects of system evaluation which have been considered (See Harter and Hert, (Evaluation of Information Retrieval Systems. 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$$1$Ifa$kŠtŠwŠyŠ{Š}Š~ŠŠ€ŠôôôôôěěHTŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$€ŠˆŠŒŠŽŠŠ’Š“Š”Š•ŠôôôôôěěH„Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$•Š˘ŠĽŠ§ŠŠŠŤŠ´ŠľŠśŠôôôôôôěHˆŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$śŠÄŠÇŠÉŠËŠÍŠÖŠ×ŠŘŠôôôôôôěHxŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$ŘŠęŠíŠďŠńŠóŠôŠőŠöŠôôôôôěěHHŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$öŠüŠ˙ŠŞŞŞŞŞŞôôôôôěěHhŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$ŞŞŞŞŞŞ Ş!Ş"ŞôôôôôěěHXŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$"Ş,Ş/Ş1Ş3Ş5Ş6Ş7Ş8ŞôôôôôěěH\Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$8ŞCŞFŞHŞJŞLŞMŞNŞOŞôôôôôěěHPŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$OŞWŞZŞ\Ş^Ş`ŞaŞbŞcŞôôôôôěěHźŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$cŞqŞuŞwŞyŞ{Ş‚Ş‘Ş’Ş›ŞôôôôôôôPTôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙ $$1$Ifa$ ›ŞžŞ Ş˘Ş¤ŞĽŞŚŞ§ŞŤŞôôôôěěHDôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$ŤŞŻŞąŞłŞľŞśŞˇŞ¸ŞźŞôôôôěěH@ôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$źŞżŞÁŞĂŞĹŞĆŞÇŞČŞßŞôôôôěěHôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$ߪăŞĺŞçŞéŞęŞëŞěŞôôôôěě?ŒŹ$$If–6”֞ňţŢ !1|Ž~#ě ?Kđ ÖÖ ˙˙˙˙öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$쪍ŤŤ Ť Ť ŤŤôôôôôěě$1$If $$1$Ifa$ŤŤŤŤ Ť"Ť$Ť%ŤR`GGGGG?$1$If $$1$Ifa$Ź$$If–6”֞ňţŢ !1|Ž~#ě ?Kđ ÖÖ ˙˙˙˙öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙%Ť&Ť'Ť,Ť/Ť1Ť3Ť5Ť6Ť÷SDHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If6Ť7Ť8ŤDŤGŤIŤKŤMŤSŤ÷SˆHHHHHH $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfSŤYŤZŤaŤdŤfŤhŤjŤkŤôPLôôôôôH$1$IfŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙ $$1$Ifa$kŤlŤmŤtŤwŤyŤ{Ť}Ť~Ť÷SLHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If~ŤŤ€Ť‰ŤŒŤŽŤŤ’Ť“Ť÷STHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If“Ť”Ť•ŤĄŤ¤ŤŚŤ¨ŤŞŤŤŤ÷S`HHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfŤŤŹŤ­Ť´Ť¸ŤşŤźŤžŤżŤ÷SPHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfżŤŔŤÁŤŇŤŐŤ×ŤŮŤŰŤÜŤ÷StHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfÜŤÝŤŢŤčŤěŤîŤđŤňŤóŤ÷S\HHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfóŤôŤőŤŹŹ Ź ŹŹŹ÷SpHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfŹŹŹ!Ź$Ź&Ź(Ź*Ź+Ź÷SpHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If+Ź,Ź-Ź7Ź:Ź<Ź>Ź@ŹAŹ÷SXHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfAŹBŹCŹSŹVŹXŹZŹ\Ź]Ź÷SpHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If]Ź^Ź_ŹhŹkŹmŹoŹqŹrŹ÷STHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfrŹsŹtŹƒŹ†ŹˆŹŠŹŒŹ’Ź÷S”HHHHHH $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If’Ź˜Ź™ŹŤŹŽŹ°Ź˛Ź´ŹľŹôPxôôôôôH$1$IfŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙ $$1$Ifa$ľŹśŹˇŹÇŹĘŹĚŹÎŹĐŹŃŹ÷SpHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfŃŹŇŹÓŹÝŹŕŹâŹäŹćŹçŹ÷SXHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfçŹčŹéŹóŹöŹřŹúŹüŹýŹ÷SXHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfýŹţŹ˙Ź­ ­ ­­­­÷STHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If­­­­!­#­%­'­(­÷SXHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If(­)­*­7­:­<­>­@­F­÷SxHHHHHH $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$IfF­G­H­R­U­W­Y­[­\­÷SXHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If\­]­^­l­o­q­s­u­v­÷ShHHHHH÷ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$Ifv­w­x­Š­­­‘­“­›­÷SŹHHHHHH $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If›­˘­Ł­ł­ś­¸­ş­ź­Ä­Ë­ôP¤ôôôôôôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙ $$1$Ifa$ Ë­Ě­×­Ű­Ý­ß­á­â­ă­[`PPPPPHH$1$If $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙ă­ä­ř­ű­ý­˙­Ž ŽŽŽ[ĐPPPPPPP[ $$1$Ifa$Ł$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙ Ž.Ž2Ž4Ž6Ž8ŽKŽ^Ž_ŽrŽôôôôôôôP ôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙ $$1$Ifa$ rŽuŽwŽyŽ{ŽŽŽĄŽ˘ŽŤŽŽŽôôôôôôPTôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙ $$1$Ifa$ ŽŽ°Ž˛Ž´ŽľŽśŽˇŽÂŽĹŽôôôěěH\ôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$ĹŽÇŽÉŽËŽĚŽÍŽÎŽÓŽÖŽôôôěěHDôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$֎؎ڎ܎ݎގߎäŽçŽôôôěěHDôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$çŽéŽëŽíŽîŽďŽđŽůŽüŽôôôěěHTôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$üŽţŽŻŻŻŻŻŻŻôôôěěHdôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$ŻŻŻŻŻŻŻ%Ż(ŻôôôěěHLôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$(Ż*Ż,Ż.Ż/Ż0Ż1ŻCŻFŻôôôěěHxôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$FŻHŻJŻLŻMŻNŻOŻWŻZŻôôôěěHPôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$ZŻ\Ż^Ż`ŻaŻbŻcŻsŻvŻôôôěěHpôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$vŻxŻzŻ|Ż}Ż~ŻŻŠŻŻôôôěěH\ôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$ŻŻ‘Ż“Ż”Ż•Ż–ŻžŻĄŻôôôěěHPôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$ĄŻŁŻĽŻ§Ż¨ŻŠŻŞŻšŻ˝ŻôôôěěH”ôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$˝ŻżŻÁŻĂŻÄŻÎŻĎŻáŻäŻôôôěôHœôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$äŻćŻčŻęŻëŻőŻöŻţŻ°ôôôěôHPôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$°°°°° ° °°°ôôôěěHhôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$°°°!°"°#°$°6°9°ôôôěěH¨ôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$9°;°=°?°L°M°N°U°X°ôôôôěHLôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$X°Z°\°^°_°`°a°g°j°ôôôěěHHôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$j°l°n°p°q°r°s°{°°ôôôěěHTôôŁ$$If–6”֞ňţŢ !1|Ž~#ě ?KđöÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö(˙$1$If $$1$Ifa$°°ƒ°…°†°‡°ˆ°°’°ôôôěěHLôôŁ$$If–6”֞ňţŢ !1|Ž~#ě 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öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ €†‡†¤†Ľ†¨†Ź†Ž†°†˛†š†ôôRpôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ š†‡‡‡ ‡ ‡ ‡‡‡0‡ôRŔôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ 0‡1‡5‡:‡<‡>‡@‡G‡t‡u‡]RRRRRRR]  $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö u‡y‡}‡‡‡ƒ‡Š‡œ‡‡Ą‡ôôôôôôôRźôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ Ą‡Ľ‡§‡Š‡Ť‡˛‡ ˆ ˆˆˆôôôôôôRôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ˆˆˆˆ!ˆLˆMˆQˆUˆWˆôôôôôRŕôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ WˆYˆ[ˆbˆ„ˆ…ˆ‰ˆˆˆ‘ˆôôôôR0ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ‘ˆ“ˆšˆЈшՈوۈ݈߈ôôôRźôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ߈ćˆ˙ˆ‰‰‰ ‰ ‰‰‰ôôRLôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ‰R‰S‰W‰\‰^‰`‰b‰i‰ë‰ôRdôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ë‰ě‰đ‰ô‰ö‰ř‰ú‰ŠŠŠ]ĚRRRRRRR]Ä $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö Š#Š'Š)Š+Š-Š4ŠOŠPŠTŠôôôôôôôR$ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ TŠXŠZŠ\Š^ŠeŠ˜Š™ŠŠ˘ŠôôôôôôR¨ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ˘Š¤ŠŚŠ¨ŠŻŠŠÊNJ͊ϊôôôôôR´ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ϊъӊڊďŠđŠôŠúŠüŠţŠôôôôR|ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ţŠ‹‹‹‹‹‹‹‹‹ôôôR¨ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ‹%‹8‹9‹=‹B‹D‹F‹H‹O‹ôôRŘôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ O‹n‹o‹s‹w‹y‹{‹}‹„‹•‹ôRœôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ •‹–‹š‹ž‹ ‹˘‹¤‹Ť‹Ëċ]¸RRRRRRR]| $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ċȋ΋Ћҋԋۋâ‹ă‹ç‹ôôôôôôôR”ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ç‹ë‹í‹ď‹ń‹ř‹ŒŒ ŒŒôôôôôôR˜ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ŒŒŒŒŒ­ŒŽŒ˛ŒśŒ¸ŒôôôôôR ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ¸ŒşŒźŒÌđŒńŒőŒűŒýŒ˙ŒôôôôRlôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ˙ŒKLPTVXZôôôR ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ZaŽ“—™›¤ôôR¤ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ¤ˇ¸źŔčƍ͍ŽôR0ôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ŽŽŽ ŽŽŽŽŽ>Ž?Ž]ěRRRRRRR]¨ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ?ŽCŽHŽJŽLŽNŽUŽhŽiŽmŽôôôôôôôRtôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ mŽqŽsŽuŽwŽ~Ž…Ž†ŽŠŽŽôôôôôôR´ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ Ž‘Ž“Ž•ŽœŽ˛ŽłŽˇŽ˝ŽżŽôôôôôRčôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ żŽÁŽÎʎěŽíŽńŽőŽ÷ŽůŽôôôôRČôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ůŽűŽ#')+-ôôôRđôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ -4Z[_dfhjqôôRœôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ q‚†‹‘˜ŠôR ôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ŠޏŽ˛´ś¸żҏӏ]¤RRRRRRR]ź $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ӏ׏܏ޏŕâéôôôôôôôR´ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$   ./37ôôôôôôRřôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ 79;=DlmqvxôôôôôR¸ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ xz|ƒڐېߐăĺçôôôôRĐôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ çéđ‘‘‘‘‘‘‘ôôôR¨ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ‘%‘8‘9‘=‘B‘D‘F‘H‘O‘ôôRźôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ O‘g‘h‘l‘q‘s‘u‘w‘~‘…‘ôRxôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ …‘†‘Š‘Ž‘‘’‘”‘›‘˛‘ł‘]´RRRRRRR]ŕ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ł‘ˇ‘ť‘˝‘ż‘Á‘ȑę‘ë‘ď‘ôôôôôôôRĚôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ď‘ó‘ő‘÷‘ů‘’’’"’(’ôôôôôôR|ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ (’*’,’.’5’<’=’A’E’G’ôôôôôR ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ G’I’K’R’¤’Ľ’Š’­’Ż’ą’ôôôôR|ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ą’ł’ş’“““““““ôôôRĐôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ““7“8“<“B“D“F“H“O“ôôRđôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ O“ł“´“¸“ź“ž“Ŕ““ɓГôRtôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ГѓՓړܓޓŕ“ç“ú“ű“]¨RRRRRRR]” $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ű“˙“””” ””” ”$”ôôôôôôôR¨ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ $”)”+”-”/”6”I”J”N”S”ôôôôôôR¤ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ S”U”W”Y”`”r”s”w”|”~”ôôôôôRŹôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ~”€”‚”‰”ݔޔâ”ć”č”ę”ôôôôRôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ę”ě”ó”"•#•'•+•.•0•3•ôôôRĚôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ 3•:•Օ֕ڕޕŕ•â•ä•ë•ôôR„ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ë•6–7–;–?–A–C–E–L–÷UźJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfL–e–f–j–o–q–s–u–|–ôR¨ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$|–––”–™–›––Ÿ–Ś–÷UČJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŚ–Á––Ɩ˖͖ϖіؖ÷UŒJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifؖ$—%—)—.—0—2—4—;—÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If;—N—O—S—X—Z—\—^—e—÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ife—x—y—}—‚—„—†—ˆ——÷U°JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If—$˜%˜)˜-˜/˜1˜3˜:˜÷UĐJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If:˜X˜Y˜]˜b˜d˜f˜h˜o˜÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifo˜€˜˜…˜‰˜‹˜˜˜–˜÷UĚJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If–˜ł˜´˜¸˜ź˜ž˜Ŕ˜˜ɘ>™ôR,ôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ >™?™C™H™J™L™N™U™‘™’™]LRRRRRRR]Œ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ’™–™š™œ™ž™ ™§™ô™ő™ů™ôôôôôôôR,ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ů™ý™˙™šš š?š@šDšôôôôôěJ¨ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$DšHšJšLšNšUšišjšnšršôôôôôôR¸ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ rštšvšxšš—š˜šœš šôôôôěJœôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ š˘š¤šŚš­šžšżšÚǚôôôôěJŹôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ǚɚ˚͚ԚéšęšîšňšôôôôěJ¨ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ňšôšöšřš˙šS›T›X›\›ôôôôěJ˜ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$\›^›`›b›i›y›z›~›‚›ôôôôěJ ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$‚›„›†›ˆ››Ą›˘›Ś›Ť›ôôôôěJ¨ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Ť›­›Ż›ą›¸›˛̛Л՛ôôôôěJ,ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$՛כٛۛ⛜œœ œ"œôôôôôR¨ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ "œ$œ&œ-œ@œAœEœIœKœôôôěJŔôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$KœMœOœVœpœqœuœzœ|œôôôěJčôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$|œ~œ€œ‡œŞœŤœŻœ´œśœôôôěJ`ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$śœ¸œťœœ‚ƒˆôôôěJŹôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$‘“š­ŽłˇšôôôěJPôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$šť˝ĝžžž žžôôôěJôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$žžžž>ž?žDžIžKžôôôěJ@ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$KžMžOžVžΞϞԞ؞ڞôôôěJÄôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ڞܞޞĺž˙žŸŸ Ÿ ŸôôôěJpôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ Ÿ ŸŸŸ[Ÿ\ŸaŸeŸgŸôôôěJŔôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$gŸiŸkŸrŸ‹ŸŒŸ‘Ÿ•Ÿ—Ÿ™ŸôôôôRÜôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ™Ÿ›Ÿ˘ŸŸßȟ̟ΟПôôěJ¤ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ПҟٟëŸěŸńŸőŸ÷ŸůŸôôěJ”ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ůŸűŸ       ôôěJěôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$   ' K L Q U W Y ôôěJ”ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Y [ b p q v { }  ôôěJüôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$  ˆ Ż ° ľ š ť ˝ ôôěJ¨ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$˝ ż Ć Ů Ú ß ă ĺ ç ôôěJLôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ç é đ ,Ą-Ą2Ą7Ą9Ą;ĄôôěJôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$;Ą=ĄDĄmĄnĄsĄwĄyĄ{ĄôôěJüôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa${Ą}Ą„ĄěĄíĄňĄ÷ĄůĄűĄýĄôôôRŹôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ýĄ˘˘˘˘"˘$˘&˘(˘ôěJŹôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$(˘/˘B˘C˘H˘L˘N˘P˘R˘ôěJHôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$R˘Y˘”˘•˘š˘ž˘ ˘˘˘¤˘ôěJôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$¤˘Ť˘ř˘ů˘ţ˘ŁŁŁ ŁôěJĚôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ ŁŁ+Ł,Ł1Ł6Ł9Ł;Ł>ŁôěJüôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$>ŁEŁ*¤+¤0¤5¤7¤9¤;¤ôěJĚôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$;¤B¤]¤^¤c¤h¤j¤l¤n¤ôěJ¤ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$n¤u¤†¤‡¤Œ¤‘¤“¤•¤—¤ôěJ¤ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$—¤ž¤Ż¤°¤ľ¤š¤ť¤˝¤ż¤ôěJxôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ż¤ƤͤΤÓ¤פ٤ۤݤôěJ¤ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ݤä¤ö¤÷¤ü¤ĽĽĽĽ ĽôôRHôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ĽHĽIĽNĽRĽTĽVĽXĽ_Ľ÷U$JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If_Ľ‘Ľ’Ľ—Ľ›ĽĽŸĽĄĽ¨Ľ÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If¨ĽŻĽ°ĽľĽšĽťĽ˝ĽżĽĆĽ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĆĽďĽđĽőĽúĽüĽţĽŚŚ÷U@JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŚ?Ś@ŚEŚIŚKŚMŚOŚVŚ÷UĐJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfVŚsŚtŚyŚ}ŚŚŚƒŚŠŚ÷U¤JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŠŚœŚŚ˘Ś§ŚŠŚŤŚ­Ś´Ś÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If´ŚáŚâŚçŚěŚîŚđŚňŚůŚ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfůŚ(§)§.§2§4§6§8§?§÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If?§’§“§˜§œ§ž§ §˘§Š§÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŠ§ź§˝§§Ƨȧʧ̧Ó§÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÓ§ć§ç§ě§đ§ň§ô§ö§ý§÷UĐJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifý§¨¨ ¨$¨&¨(¨*¨1¨÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If1¨Z¨[¨`¨e¨g¨i¨k¨r¨÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifr¨…¨†¨‹¨¨‘¨“¨•¨œ¨÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifœ¨ʨ˨ШÔ¨Ö¨بÚ¨á¨÷U°JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifá¨ö¨÷¨ü¨ŠŠŠŠ Š÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If Š9Š:Š?ŠCŠEŠGŠIŠPŠôRźôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$PŠhŠiŠnŠrŠtŠvŠxŠŠ÷UÜJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŠŸŠ ŠĽŠŠŠŤŠ­ŠŻŠśŠ÷ULJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfśŠňŠóŠřŠüŠţŠŞŞ Ş÷UŒJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If ŞUŞVŞ[ŞaŞcŞeŞgŞnŞôRźôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$nŞ„Ş…ŞŠŞŽŞŞ’Ş”Ş›Ş÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If›ŞËŞĚŞŃŞŐŞתŮŞŰŞâŞ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfâŞőŞöŞűŞ˙ŞŤŤŤ Ť÷UœJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If ŤŤŤ"Ť&Ť(Ť*Ť,Ť3Ť÷UœJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If3ŤCŤDŤIŤOŤQŤSŤUŤ\Ť÷UdJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If\ŤœŤŤ˘ŤŚŤ¨ŤŞŤŹŤłŤ÷UÔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfłŤŃŤŇŤ׍ŰŤÝŤߍáŤčŤôR´ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$čŤţŤ˙ŤŹŹ Ź ŹŹŹ÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŹ+Ź,Ź1Ź5Ź7Ź9Ź;ŹBŹôRŔôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$BŹ[Ź\ŹaŹeŹgŹiŹkŹrŹ÷UŔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfrŹ‹ŹŒŹ‘Ź•Ź—Ź™Ź›Ź˘Ź÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If˘ŹľŹśŹťŹŔŹÂŹÄŹĆŹÍŹ÷U@JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÍŹ­­ ­­­­­­÷UÄJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If­6­7­<­@­B­D­F­M­÷U@JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfM­†­‡­Œ­­’­”­–­­÷U¸JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If­´­ľ­ş­ž­Ŕ­­Ä­Ë­÷U8JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfË­ŽŽŽ ŽŽŽŽŽ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŽCŽDŽIŽMŽOŽQŽSŽZŽ÷UČJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfZŽľŽśŽťŽżŽÁŽĂŽĹŽĚŽ÷U˜JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĚŽŰŽÜŽáŽĺŽçŽéŽëŽňŽ÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfňŽůŽúŽ˙ŽŻŻŻ ŻŻ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŻŻŻ#Ż'Ż)Ż+Ż-Ż4ŻôRÔôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$4ŻRŻSŻXŻ\Ż^Ż`ŻbŻiŻ÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfiŻzŻ{Ż€Ż„Ż†ŻˆŻŠŻ‘Ż÷UČJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If‘ŻŹŻ­Ż˛ŻśŻ¸ŻşŻźŻĂŻ÷UČJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĂŻŢŻ߯äŻčŻęŻěŻîŻőŻôR”ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$őŻ°° ° °°°°°÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If°0°1°6°:°<°>°@°G°÷UdJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfG°‰°Š°°“°•°—°™° °÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If °ł°´°š°ž°Ŕ°°Ä°Ë°ôRŔôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$Ë°ă°ä°é°í°ď°ń°ó°ú°÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifú° ąąąąąąą$ą÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If$ą+ą,ą1ą5ą7ą9ą;ąBą÷U|JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfBąŠą‹ąą–ą˜ąšąœąŁąôR ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$Łą˛ąłą¸ą˝ążąÁąĂąĘą÷UđJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĘąîąďąôąřąúąüąţą˛÷UœJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If˛˛˛˛˛!˛#˛%˛,˛÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If,˛3˛4˛9˛=˛?˛A˛C˛J˛÷UŒJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfJ˛V˛W˛\˛`˛b˛d˛f˛m˛÷UôJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifm˛“˛”˛™˛˛Ÿ˛Ą˛Ł˛Ş˛÷UDJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŞ˛ä˛ĺ˛ę˛î˛đ˛ň˛ô˛ű˛÷UlJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifű˛?ł@łEłIłKłMłOłVł÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfVłiłjłołsłułwłył€ł÷U¸JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If€ł—ł˜łłĄłŁłĽł§łŽł÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŽłľłśłťłżłÁłĂłĹłĚł÷UźJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĚłäłĺłęłîłńłółöłýł÷UtJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifýł´‚´‡´‹´´´‘´˜´÷U˜JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If˜´§´¨´­´ą´ł´ľ´ˇ´ž´÷UÔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifž´Ü´Ý´â´ć´č´ę´ě´ó´÷UŔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifó´ ľ ľľľľľľ#ľ÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If#ľ9ľ:ľ?ľCľEľGľIľPľ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfPľ{ľ|ľľ…ľ‡ľ‰ľ‹ľ’ľ÷U4JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If’ľČľÉľÎľŇľÔľÖľŘľßľ÷UĚJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifßľűľüľśśś ś śś÷UĚJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifś.ś/ś4ś8ś:ś<ś>śEś÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfEś[ś\śaśeśgśiśkśrś÷UPJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfrśŻś°śľśťś˝śżśÁśČś÷UœJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfČśÖśלÜśáśăśĺśçśîś÷U°JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifˇˇ ˇˇˇˇˇ÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifˇ ˇ!ˇ&ˇ*ˇ,ˇ.ˇ0ˇ7ˇ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If7ˇJˇKˇPˇTˇVˇXˇZˇaˇ÷UÄJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifaˇ{ˇ|ˇˇ…ˇˆˇŠˇˇ”ˇ÷U|JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If”ˇ¸¸ ¸$¸&¸(¸*¸1¸÷UđJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If1¸V¸W¸\¸`¸b¸d¸f¸m¸÷UĐJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifm¸Š¸‹¸¸”¸–¸˜¸š¸Ą¸ôRôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$Ą¸Ž¸Ż¸´¸¸¸ş¸ź¸ž¸Ÿ÷UřJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŸě¸í¸ň¸ö¸ř¸ú¸ü¸š÷UHJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifš>š?šDšHšJšLšNšUš÷UĚJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfUšqšršwš{š}šššˆš÷UôJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfˆšŽšŻš´š¸šşšźšžšĹš÷UlJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĹš ş şşşşşş ş÷UĐJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If ş=ş>şCşGşIşKşMşTş÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfTş…ş†ş‹şş‘ş“ş•şœş÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifœşňşóşřşüşţşťť ťôRäôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ť+ť,ť1ť5ť7ť9ť;ťBť÷UřJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfBťiťjťoťtťvťxťzťť÷UŘJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfťŸť ťĽťŞťŹťŽť°ťˇť÷UÔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfˇťÔťŐťÚťŢťŕťâťäťëť÷UœJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifëť;ź<źAźEźGźIźKźRź÷U°JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfRźgźhźmźqźsźuźwź~ź÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If~źŇźÓźŘźÜźŢźŕźâźéźôRŘôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$éź˝ ˝˝˝˝˝˝˝÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If˝&˝'˝,˝2˝4˝6˝8˝?˝÷UÔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If?˝[˝\˝a˝e˝g˝i˝k˝r˝ôRźôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$r˝Š˝‹˝˝”˝–˝˜˝š˝Ą˝÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĄ˝´˝ľ˝ş˝ž˝Ŕ˝½Ä˝Ë˝÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfË˝ů˝ú˝˙˝žžž žž÷UĐJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifž-ž.ž3ž8ž:ž<ž>žEž÷UôJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfEžjžkžpžtžvžxžzžž÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifž—ž˜žžĄžŁžĽž§žŽž÷UĚJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŽžĘžËžĐžÔžÖžŘžÚžáž÷UhJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifáž$ż%ż*ż.ż0ż2ż4ż;żôRÜôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$;ż›żœżĄżĽż§żŠżŤż˛ż÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If˛żżżŔżĹżÉżËżÍżĎżÖż÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÖż˙żŔŔ Ŕ Ŕ ŔŔŔ÷UČJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŔ1Ŕ2Ŕ7Ŕ;Ŕ=Ŕ?ŔAŔHŔ÷UźJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfHŔ ŔĄŔŚŔŞŔŹŔŽŔ°ŔˇŔ÷UěJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfˇŔŰŔÜŔáŔćŔčŔęŔěŔóŔ÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfóŔÁÁ ÁÁÁÁÁÁ÷U°JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÁ2Á3Á8Á<Á>Á@ÁBÁIÁ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfIÁ\Á]ÁbÁfÁhÁjÁlÁsÁ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfsÁ†Á‡ÁŒÁÁ’Á”Á–ÁÁ÷UĚJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÁšÁşÁżÁĂÁĹÁÇÁÉÁĐÁ÷UŘJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĐÁďÁđÁőÁůÁűÁýÁ˙ÁÂ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÂÂÂÂ#Â%Â'Â)Â0Â÷UđJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If0ÂUÂVÂ[Â_ÂaÂcÂeÂlÂ÷U˜JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IflÂ{Â|†ˆŠŒ“Â÷UŕJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If“³´šžÂŔÂÂÂÄÂËÂ÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfËÂŢÂßÂäÂčÂęÂěÂîÂőÂ÷UźJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifő ĂĂĂĂĂĂĂ$Ă÷UźJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If$Ă<Ă=ĂBĂFĂHĂJĂLĂSĂ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfSĂfĂgĂlĂpĂrĂtĂvĂ}Ă÷UŒJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If}ĂÉĂĘĂĎĂÓĂŐĂ×ĂŮĂŕĂ÷U¸JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŕĂ÷ĂřĂýĂÄÄÄÄÄ÷UŕJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÄ/Ä0Ä5Ä9Ä;Ä=Ä?ÄFÄ÷UÜJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfFÄfÄgÄlÄpÄrÄtÄvÄ}Ä÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If}ĐđĖĚĜĞĠħÄ÷U¸JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If§ÄžÄżÄÄÄČÄĘÄĚÄÎÄŐÄ÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŐÄÜÄÝÄâÄćÄčÄęÄěÄóÄ÷UŔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfóÄ Ĺ ĹĹĹĹĹĹ#Ĺ÷UˆJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If#Ĺ.Ĺ/Ĺ4Ĺ8Ĺ:Ĺ<Ĺ>ĹEĹôRŒôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$EĹQĹRĹWĹ[Ĺ]Ĺ_ĹaĹhĹ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfhĹ—Ĺ˜ĹĹĄĹŁĹĽĹ§ĹŽĹ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŽĹÁĹÂĹÇĹËĹÍĹĎĹŃĹŘĹ÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŘĹlĆmĆrĆwĆyĆ{Ć}Ć„Ć÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If„ĆÔĆŐĆÚĆŢĆŕĆâĆäĆëĆôR”ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ëĆůĆúĆ˙ĆÇÇÇ ÇÇ÷U”JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÇÇÇ$Ç(Ç*Ç,Ç.Ç5Ç÷U”JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If5ÇCÇDÇIÇMÇOÇQÇSÇZÇ÷UŔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfZÇsÇtÇyÇ}ÇǁǃNJÇ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŠÇÇžÇŁÇ§ÇŠÇŤÇ­Ç´Ç÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If´ÇÁÇÂÇÇÇÍÇĎÇŃÇÓÇÚÇ÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÚÇČČ ČČČČČČ÷UĚJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfČ7Č8Č=ČAČCČEČGČNČôRÔôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$NČlČmČrČvČxČzČ|ČƒČ÷UXJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfƒČÂČĂČČČĚČÎČĐČŇČŮČôRœôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ŮČéČęČďČôČöČřČúČÉ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÉ0É1É6É;É=É?ÉAÉHÉ÷UŔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfHÉ`ÉaÉfÉjÉlÉnÉpÉwÉ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfwɠɥɌɪɏɎɰɡÉôR¨ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ˇÉĘÉËÉĐÉÔÉÖÉŘÉÚÉáÉ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfáÉĘĘĘĘĘĘ Ę'Ę÷UŘJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If'Ę†Ę‡ĘŒĘĘ’Ę”Ę–ĘĘ÷UŘJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĘźĘ˝ĘÂĘĆĘČĘĘĘĚĘÓĘôR¨ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ÓĘćĘçĘěĘđĘňĘôĘöĘýĘ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfýĘËËËËËË Ë'Ë÷UđJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If'ËLËMËRËVËXËZË\ËcË÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfcËwËxË}ˁ˃˅ˇˎË÷UÔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŽËŹË­Ë˛ËśË¸ËşËźËĂË÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĂËÖË×ËÜËŕËâËäËćËíË÷UČJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfíËĚ ĚĚĚĚĚĚĚôRxôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$Ě&Ě'Ě,Ě0Ě2Ě4Ě6Ě=Ě÷U<JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If=ĚuĚvĚ{ĚĚĚƒĚ…ĚŒĚĚôR ôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ĚžĚŁĚ§ĚŠĚŤĚ­Ě´ĚűĚ]xRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöűĚüĚÍÍÍ Í ÍÍFÍ],RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöFÍGÍLÍPÍRÍTÍVÍ]Í“Í]4RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö“Í”Í™ÍÍŸÍĄÍŁÍŞÍńÍ]xRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöńÍňÍ÷ÍűÍţÍÎΠΣΤÎ]ČRRRRRRR]¸ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ¤ÎŠÎ­ÎŻÎąÎłÎşÎŃÎŇÎôôôôôôěJ¤Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŇÎ×ÎŰÎÝÎßÎáÎčÎúÎűÎôôôôôôěJĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$űÎĎĎĎĎ ĎĎ^Ď_ĎdĎôôôôôôôRđôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ dĎhĎjĎlĎnĎuϚϛϠĎôôôôôěJÄôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ Ď¤ĎŚĎ¨ĎŞĎąĎËĎĚĎŃĎôôôôôěJlôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŃĎŐĎ×ĎŮĎŰĎâĎ&Đ'Đ,ĐôôôôôěJ ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$,Đ0Đ2Đ4Đ6Đ=ĐiĐjĐoĐôôôôôěJpôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$oĐsĐuĐwĐyĐ€ĐĹĐĆĐËĐôôôôôěJ¨ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ËĐĎĐŃĐÓĐŐĐÜĐďĐđĐőĐôôôôôěJŘôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$őĐůĐűĐýĐ˙ĐŃ%Ń&Ń+ŃôôôôôěJxôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$+Ń/Ń1Ń3Ń5Ń<ŃCŃDŃIŃôôôôôěJŘôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$IŃMŃOŃQŃSŃZŃyŃzŃŃôôôôôěJ¨ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŃƒŃ…Ń‡Ń‰ŃŃŁŃ¤ŃŠŃôôôôôěJ ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŠŃ­ŃŻŃąŃłŃşŃËŃĚŃŃŃôôôôôěJĚôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŃŃÖŃŘŃÚŃÜŃăŃ>Ň?ŇDŇôôôôôěJ´ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$DŇHŇJŇLŇNŇUŇkŇlŇqŇôôôôôěJôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$qŇvŇxŇzŇ|ŇƒŇ­ŇŽŇłŇˇŇôôôôôôRôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ˇŇšŇťŇ˝ŇÄŇîŇďŇôŇřŇôôôôěJˆôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$řŇúŇüŇţŇӐӑӖӚÓôôôôěJŘôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$šÓœÓžÓ Ó§ÓĆÓÇÓĚÓĐÓôôôôěJ¸ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ĐÓŇÓÔÓÖÓÝÓôÓőÓúÓţÓôôôôěJřôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ţÓÔÔÔ Ô2Ô3Ô8Ô<ÔôôôôěJěôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$<Ô>Ô@ÔBÔIÔmÔnÔsÔwÔyÔôôôôôRœôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ yÔ{Ô}Ô„Ô”Ô•ÔšÔžÔ ÔôôôěJxôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ Ô˘Ô¤ÔŤÔ˛ÔłÔ¸ÔźÔžÔôôôěJ¨ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$žÔŔÔÂÔÉÔÜÔÝÔâÔćÔčÔôôôěJxôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$čÔęÔěÔóÔúÔűÔŐŐŐôôôěJ\ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŐŐ ŐŐ‘Ő’Ő—Ő›ŐŐôôôěJ¸ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŐŸŐĄŐ¨ŐżŐŔŐĹŐÉŐËŐôôôěJ\ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ËŐÍŐĎŐÖŐÖÖÖ Ö"Ö$ÖôôôôRÜôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ $Ö&Ö-֍֎֛֓֗֙ÖôôěJ°ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$›ÖÖ¤ÖůÖúÖ˙Ö×××ôôěJ ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$× ××<×=×B×F×H×J×ôôěJ¨ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$J×L×S×f×g×l×p×r×t×ôôěJôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$t×v×}×ç×č×í×ń×ó×ő×ôôěJhôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ő×÷×ţ×AŘBŘGŘKŘMŘOŘôôěJěôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$OŘQŘX؟ؽŘÂŘĆŘČŘĘŘôôěJäôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ĘŘĚŘÓŘőŘöŘűŘ˙ŘŮŮôôěJÔôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŮŮ Ů*Ů+Ů0Ů4Ů6Ů8ŮôôěJ˜ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$8Ů:ŮAŮPŮQŮVŮZŮ\Ů^ŮôôěJ¤ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$^Ů`ŮgŮyŮzŮŮƒŮ…Ů‡ŮôôěJĐôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$‡Ů‰ŮŮ­ŮŽŮłŮˇŮšŮťŮ˝ŮôôôR¨ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ˝ŮÄŮ×ŮŘŮÝŮáŮăŮĺŮçŮôěJ¨ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$çŮîŮÚÚÚ ÚÚÚÚôěJHôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ÚÚSÚTÚYÚ^Ú`ÚbÚdÚôěJôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$dÚkÚ—Ú˜ÚÚĄÚŁÚĽÚ§ÚôěJĐôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$§ÚŽÚËÚĚÚŃÚŐÚ×ÚŮÚŰÚôěJŕôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŰÚâÚŰŰ Ű ŰŰŰŰôěJxôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŰŰ!Ű"Ű'Ű+Ű-Ű/Ű1ŰôěJŕôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$1Ű8ŰYŰZŰ_ŰcŰeŰgŰiŰôěJhôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$iŰp۳۴ۚ۽ۿŰÁŰĂŰôěJ¨ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ĂŰĘŰÝŰŢŰăŰçŰéŰëŰíŰôěJ(ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$íŰôŰ'Ü(Ü-Ü1Ü3Ü5Ü7ÜôěJôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$7Ü>܊ܪܯܾܳܡܚÜôěJ ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$šÜŔÜŃÜŇÜ×ÜŰÜÝÜßÜáÜôěJôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$áÜčÜÝÝÝÝÝ Ý"ÝôěJôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$"Ý)ÝOÝPÝUÝYÝ[Ý]Ý_ÝfÝôôRäôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ f݈݉ݎݒݔݖݘݟÝôRôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ŸÝÉÝĘÝĎÝÓÝŐÝ×ÝŮÝŕÝ÷U¤JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŕÝňÝóÝřÝüÝţÝŢŢ Ţ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If Ţ2Ţ3Ţ8Ţ<Ţ>Ţ@ŢBŢIŢ÷U¸JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfIŢ`ŢaŢfŢjŢlŢnŢpŢwŢ÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfwŢ‹ŢŒŢ‘Ţ•Ţ—Ţ™Ţ›Ţ˘Ţ÷UŒJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If˘ŢŽŢŻŢ´Ţ¸ŢşŢźŢžŢĹŢ÷UüJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĹŢíŢîŢóŢ÷ŢůŢűŢýŢß,ßôRüôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ,ß-ß2ß6ß8ß:ß<ßCßeß]äRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöeßfßkßoßqßsßuß|ߤß]üRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö¤ßĽßŞßŽß°ß˛ß´ßťßţß˙ß]hRRRRRRR]l $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ˙ßŕŕ ŕ ŕŕŕ™ŕšŕŸŕôôôôôôôR(ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ŸŕŁŕĽŕ§ŕŠŕ°ŕăŕäŕéŕôôôôôěJôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$éŕíŕďŕńŕóŕúŕ*á+á0áôôôôôěJÄôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$0á4á6á8á:áAá[á\áaáôôôôôěJ”ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$aáeágáiákárá€áá†áôôôôôěJ¨ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$†áŠáŒáŽáá—áŞáŤá°áôôôôôěJ°ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$°á´áśá¸áťáÂáVâWâ\âôôôôôěJ¨ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$\â`âbâdâfâmâ€ââ†âôôôôôěJ ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$†âŠâŒâŽââ—âčâéâîâôôôôôěJ¤ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$îâňâôâöâřâ˙âQăRăWăôôôôôěJ ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Wă[ă]ă_ăaăhă™ăšăŸăôôôôôěJäôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŸăŁăĽă§ăŠă°ăŇăÓăŘăÜăôôôôôôRôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ÜăŢăŕăâăéăääääôôôôěJ¨ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ääää&ä9ä:ä?äCäôôôôěJ¨ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$CäEäGäIäPäcädäiämäôôôôěJ˜ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$mäoäqäsäzä‰äŠää•ä—äôôôôôRČôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ —ä™ä›ä˘äťäźäÁäĹäÇäôôôěJ¨ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ÇäÉäËäŇä%ĺ&ĺ+ĺ/ĺ1ĺôôôěJÜôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$1ĺ3ĺ5ĺ<ĺœĺĺ˘ĺŚĺ¨ĺŞĺôôôôRôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ŞĺŹĺłĺŢĺßĺäĺčĺęĺěĺôôěJDôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ěĺîĺőĺ/ć0ć5ć9ć;ć=ćôôěJ¨ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$=ć?ćFćYćZć_ćcćećgćôôěJäôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$gćićpć’ć“ć˜ćœćžć ćôôěJěôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ ć˘ćŠćÍćÎćÓć×ćŮćŰćôôěJ<ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŰćÝćäćçç"ç&ç(ç*çôôěJôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$*ç,ç3ç˘çŁç¨çŹçŽç°çôôěJDôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$°ç˛çšçóçôçůç˙çččôôěJ€ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$čč čččččč!č#čôôôRœôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ #č*č:č;č@čDčFčHčJčôěJĚôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$JčQčmčnčsčwčyč{č}čôěJŘôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$}č„čŁč¤čŠč­čŻčąčłčşčôôR@ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ şč3é4é9é=é?éAéCéJé÷U„JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfJé”é•éšéžé é˘é¤éŤé÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŤéźé˝éÂéĆéČéĘéĚéÓé÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÓéćéçéěéđéňéôéöéýé÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifýéęęęęęę ę'ę÷UčJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If'ęJęKęPęTęVęXęZęaę÷UčJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifaę„ę…ęŠęŽęę’ę”ę›ę÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If›ę˘ęŁę¨ęŹęŽę°ę˛ęšę÷U¸JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfšęĐęŃęÖęÚęÜęŢęŕęçęôRźôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$çę˙ęëë ë ë ëëë÷U˜JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifë%ë&ë+ë/ë1ë3ë5ë<ëôRäôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$<ëžëŸë¤ë¨ëŞëŹëŽëľë÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfľëĺëćëëëđëňëôëöëýëôR`ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ýë=ě>ěCěGěIěKěMěTě÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfTě€ěě†ěŠěŒěŽěě—ě÷UÔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If—ěľěśěťěżěÁěĂěĹěĚě÷UäJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĚěîěďěôěřěúěüěţěí÷UäJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifí'í(í-í1í3í5í7í>í÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If>íTíUíZí^í`íbídíkí÷U(JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfkížíŸí¤í¨íŞíŹíŽíľí÷U4JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfľíëíěíńíöíříúíüíîôR¨ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$îUîVî[î_îaîcîeîlî÷UčJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Iflîîî•î™î›îîŸîŚî÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŚî­îŽîłîˇîšîťî˝îÄî÷UDJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÄîţî˙îď ď ď ďďď÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifď+ď,ď1ď5ď7ď9ď;ďBď÷U„JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfBďŒďď’ď–ď˜ďšďœďŁď÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŁďŞďŤď°ď´ďśď¸ďşďÁď÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÁď×ďŘďÝďáďăďĺďçďîď÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifîďőďöďűď˙ďđđđ đ÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If đđđđđđ!đ#đ*đ÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If*đ“đ”đ™đđŸđĄđŁđŞđôR ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ŞđťđźđÁđĹđÇđÉđËđŇđ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŇđĺđćđëđďđńđóđőđüđ÷UxJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifüđńń ń ńńńńń÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifń‰ńŠńń“ń•ń—ń™ń ń÷UřJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If ńÇńČńÍńŃńÔńÖńŮńŕń÷UŔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifŕńwňxň}ňňƒň…ň‡ňŽň÷UÄJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŽň¨ňŠňŽňłňľňˇňšňŔň÷UôJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŔňĺňćňëňďňńňóňőňüň÷UĐJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifüňóóó#ó%ó'ó)ó0ó÷U”JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If0ó>ó?óDóHóJóLóNóUó÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfUóhóiónórótóvóxóó÷UäJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfóĄó˘ó§óŤó­óŻóąó¸ó÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If¸óćóçóěóđóňóôóöóýó÷UĐJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifýóôô ô$ô&ô(ô*ô1ô÷UôJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If1ôWôXô]ôaôcôeôgônô÷U\JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfnôŽôŻô´ô¸ôşôźôžôĹô÷UäJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĹôçôčôíôńôóôőô÷ôţô/őôR ôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ /ő0ő5ő9ő;ő=ő?őFő`ő]ÄRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö`őaőfőjőlőnőpőwőŕő]RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöŕőáőćőęőěőîőđő÷őöö]œRRRRRRR]8 $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ö ööööööUöVöôôôôôôěJÄĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Vö[ö_öaöcöeölö†ö‡öôôôôôôěJřĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$‡öŒöö’ö”ö–ööÄöĹöôôôôôôěJ<Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ĹöĘöÎöĐöŇöÔöŰö÷÷ôôôôôôěJ8Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$÷÷÷÷!÷#÷*÷a÷b÷g÷ôôôôôôôR4ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ g÷l÷n÷p÷r÷y÷Ž÷Ż÷´÷ôôôôôěJĐôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$´÷¸÷ş÷ź÷ž÷Ĺ÷â÷ă÷č÷ôôôôôěJřôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$č÷ě÷î÷đ÷ň÷ů÷ ř!ř&řôôôôôěJ¨ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$&ř*ř,ř.ř0ř7řJřKřPřôôôôôěJxôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$PřTřVřXřZřařhřiřnřôôôôôěJŕôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$nřrřtřvřxřřŕřářćřôôôôôěJ(ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ćřęřěřîřđř÷ř*ů+ů0ůôôôôôěJ¨ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$0ů4ů6ů8ů:ůAůTůUůZůôôôôôěJŒôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Zů^ů`ůbůdůkůwůxů}ůôôôôôěJxôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$}ůůƒů…ů‡ůŽůúúúôôôôôěJ´ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$úú!ú#ú%ú,ú‚úƒúˆúôôôôôěJÄôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ˆúŒúŽúú’ú™ú3ű4ű9űôôôôôěJüôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$9ű=ű?űAűCűJűrűsűxűôôôôôěJĐôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$xű|ű~ű€ű‚ű‰űŚű§űŹűôôôôôěJ¤ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Źű°ű˛ű´űśű˝űĎűĐűŐűôôôôôěJ”ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŐűŮűŰűÝűßűćűôűőűúűôôôôôěJ¨ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$úűţűüüü üüü$üôôôôôěJ ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$$ü(ü*ü,ü.ü5üŚü§üŹüôôôôôěJôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Źü°ü˛ü´üśü˝üíüîüóüôôôôôěJxôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$óü÷üůüűüýüý ý ýýôôôôôěJĚôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ýýýýý"ý>ý?ýDýHýôôôôôôRdôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ HýJýLýNýUý—ý˜ýýĄýôôôôěJôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ĄýŁýĽý§ýŽýÝýŢýăýçýôôôôěJČôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$çýéýëýíýôýţţţţôôôôěJČôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ţţţţ&ţAţBţGţKţôôôôěJĐôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$KţMţOţQţXţľţśţťţżţôôôôěJ¸ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$żţÁţĂţĹţĚţăţäţéţíţôôôôěJ¤ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$íţďţńţóţúţL˙M˙R˙V˙ôôôôěJ$ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$V˙X˙Z˙\˙c˙•˙–˙›˙Ÿ˙ôôôôěJlôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Ÿ˙Ą˙Ł˙Ľ˙Ź˙đ˙ń˙ö˙ú˙ôôôôěJŘôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ú˙ü˙ţ˙&',0ôôôôěJŘôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$0246=\]bfôôôôěJôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$fhjls€†ŠŒôôôôôRLôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ŒŽ—ÓÔŮÝßôôôěJäôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ßáăę  ôôôěJĚôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$#?@EIKôôôěJôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$KMOV€†ŠŒôôôěJxôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŒŽ—žŸ¤¨ŞôôôěJÔôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŞŹŽľÓÔŮÝßôôôěJôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ßáăę#%ôôôěJPôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$%')0mnswyôôôěJŔôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$y{}„žŁ§ŠôôôěJ8ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŠŤŽľ+,158ôôôěJ,ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$8:=Dvw|€ƒôôôěJ4ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ƒ…ˆCDIMOôôôěJˆôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$OQSZĽŚŤŻąôôôěJÄôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ąłľźÖ×ÜŕâôôôěJ ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$âäćíţ˙ ôôôěJôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$  "#(,.ôôôěJ4ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$.029opuy{ôôôěJŕôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa${}†§¨­ąłôôôěJěôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$łľˇžâăčěîôôôěJÄôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$îđňůôôôěJ¨ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$!#*=>CGIôôôěJčôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$IKMTˇ¸˝ÁĂôôôěJäôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ĂĹÇÎđńöúüţôôôôRäôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ţ)*/3579ôôôRÄôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ 9@Z[`dfhjqôôR8ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ q¨ŠŽ˛´ś¸ż÷UČJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfżÚŰŕäćčęńřôRxôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ řůţ     2 ]čRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö2 3 8 < > @ B I \ ]¨RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö\ ] b f h j l s  ]”RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ‚ ‡ ‹   ‘ ˜ ´ ]ĚRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö´ ľ ş ž Ŕ  Ä Ë  ]ŒRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö   ! # % ' . o ]`RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöo p u y { }  † ™ ]¨RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö™ š Ÿ Ł Ľ § Š ° Ŕ ]œRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöŔ Á Ć Ę Ě Î Đ ×  ]@RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö       ' 4 ]RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö4 5 : > @ B D K â ]¸RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöâ ă č ě î đ ň ů " ]RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö" # ( , . 0 2 9 Š ] RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöŠ ‹  ” – ˜ š Ą ´ ]¨RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö´ ľ ş ž Ŕ  Ä Ë Ţ ]¨RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöŢ ß ä č ę ě î ő 5 ]\RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö5 6 ; ? A C E L n ]äRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aön o t x z | ~ … ä ]ŘRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöä ĺ ę î đ ň ô ű g] RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöghmqsuw~Ÿ]ŕRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöŸ ĽŠŤ­ŻśĂÄ]RRRRRRR]D $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö ÄÉÍĎŃÓÚôôôôôôěJ”Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ "$+yzôôôôôôěJ¸Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$zƒ…‡‰çčôôôôôôěJÜĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$číńóő÷ţôôôôôôěJxĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$$(*,.5<=ôôôôôôěJĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$=BFHJLS‚ƒˆôôôôôôôRŕôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ˆŒŽ’™şťŔÄôôôôôôR˜ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ÄĆČĘŃŕáćęôôôôěJŹôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ęěîđ÷  ôôôôěJœôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$"238<ôôôôěJřôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$<>@BIpqvzôôôôěJ¨ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$z|~€‡š› ¤ôôôôěJôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$¤Ś¨Şą×ŘÝáôôôôěJ˜ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$áăĺçîýţôôôôěJ´ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$   *+04ôôôôěJ¤ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$468:ASTY]ôôôôěJÄôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$]_acj„…ŠŽôôôôěJ4ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Ž’”›ŃŇŘÜôôôôěJœôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ÜŢŕâéřů˙ôôôôěJřôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ vw}ôôôôěJpôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ƒ…‡ŽŇÓŮÝßôôôôôR@ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ßáăę"#)-/ôôôěJhôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$/13:ź˝ĂÇÉôôôěJŹôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ÉËÍÔçčîňôôôôěJôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ôöř˙()/35ôôôěJŹôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$579@STZ^`ôôôěJ|ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$`bdkrsy}ôôôěJŹôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ƒŠž¤¨ŞôôôěJDôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŞŹŽľîďőůűôôôěJŘôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$űý˙$%+/1ôôôěJ|ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$135<CDJNPôôôěJ|ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$PRT[bcimoôôôěJ˜ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$oqszˆ‰“•ôôôěJŹôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$•—™ ł´şžŔôôôěJřôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŔÂÄËńňřüţôôôěJĚôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ţ $%+/1ôôôěJÄôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$135<UV\`bôôôěJ\ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$bdfmŹ­łˇšôôôěJ@ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$šť˝Äüý ôôôěJÄôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$   mntxz|ôôôôR˜ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ |~…“”šž ˘ôôěJŹôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$˘¤ŤžżĹÉËÍôôěJ¸ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ÍĎÖěíó÷ůűôôěJ|ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$űý  ôôěJôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$#ČÉĎÓŐ×ôôěJôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$×Ůŕ  ôôěJôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$"OPVZ\^ôôěJ4ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$^`gœŁ§ŠŤôôěJźôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Ť­´ËĚŇÖŘÚôôěJěôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ÚÜă ôôěJœôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$-.48:<>ôôôRHôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ >E€†ŠŒŽôěJôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$—ĂÄĘÎĐŇÔôěJhôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ÔŰ$(*,.5ôôRôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ 5š›ĄĽ§ŠŤ˛ôRhôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$˛ôőű˙ ÷U|JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If  "$+÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If+>?EIKMOV÷U¤JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfVghnrtvx÷U`JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfżŔĆĘĚÎŃŘ÷U$JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŘHIOSUWY`÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If`ł´şžŔÂÄË÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfËŢßĺéëíďö÷UüJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifö]^dhkmpw÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifw! " ( , . 0 2 9 ÷U<JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If9 p q w { }   ˆ ÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifˆ  ž ¤ ¨ Ş Ź Ž ľ ÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifľ Č É Ď Ó Ő × Ů ŕ ÷U¨JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifŕ ň ó ů ý ˙ !! !ôRŹôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ !!!$!(!*!,!.!5!÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If5!a!b!h!l!n!p!r!y!÷UÄJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ify!’!“!™!!Ÿ!Ą!Ł!Ş!÷UœJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŞ!š!ş!Ŕ!Ä!Ć!Č!Ę!Ń!÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŃ!ć!ç!í!ń!ó!ő!÷!ţ!÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m 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$$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If@$e$f$l$p$r$t$v$}$÷UŔJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If}$Ő$Ö$Ü$ŕ$â$ä$ć$í$÷UŘJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifí$ % %%%%%%#%÷UČJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If#%=%>%D%H%J%L%N%U%÷UřJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfU%{%|%‚%†%ˆ%Š%Œ%“%÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If“%Ś%§%­%ą%ł%ľ%ˇ%ž%÷U|JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifž%Ĺ%Ć%Ě%Đ%Ň%Ô%Ö%Ý%÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÝ%đ%ń%÷%ű%ý%˙%&&÷U°JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If&\&]&c&g&i&k&m&t&÷UřJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 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6aös.t.z.~.€.‚.„.‹.É.Ę.]XRRRRRRR]p $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö Ę.Đ.Ô.Ö.Ř.Ú.á.%/&/,/ôôôôôôôR”ôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ,/0/2/4/6/=/J/K/Q/U/ôôôôôôRpôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ U/W/Y/[/b/Ś/§/­/ą/ôôôôěJ¨ôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ą/ł/ľ/ˇ/ž/0000ôôôôěJäôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$000!0(0I0J0P0T0V0ôôôôôR¸ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ V0X0Z0a0w0x0~0‚0„0ôôôěJŹôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$„0†0ˆ00˘0Ł0Š0­0Ż0ôôôěJ|ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Ż0ą0ł0ş0Á0Â0Č0Î0Đ0ôôôěJźôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Đ0Ň0Ô0Ű0đ0ń0÷0ű0ý0ôôôěJ|ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ý0˙01111111ôôôěJôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$11 1'1P1Q1W1[1]1ôôôěJÄôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$]1_1a1h11‚1ˆ1Œ1Ž1ôôôěJhôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Ž11’1™1Ű1Ü1â1ć1č1ôôôěJôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$č1ę1ě1ó1222#2&2ôôôěJpôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$&2(2*212t2u2{222ôôôěJPôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$2ƒ2…2Œ2Č2É2Ď2Ó2Ő2ôôôěJ¤ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Ő2×2Ů2ŕ2ń2ň2ř2ü2ţ2ôôôěJŔôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ţ233 3!3"3(3,3.3ôôôěJŹôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$.3032393L3M3S3W3Y3ôôôěJ4ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Y3[3]3d3Ů3Ú3ŕ3ä3ć3ôôôěJ˜ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ć3č3ę3ń3˙344 4 4ôôôěJŹôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ 4444*4+4145474ôôôěJČôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$7494;4B4\4]4c4g4i4ôôôěJÔôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$i4k4m4t4‘4’4˜4œ4ž4ôôôěJôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ž4 4˘4Š4ľ4ś4ź4Ŕ4Â4ôôôěJěôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Â4Ä4Ć4Í4đ4ń4÷4ű4ý4ôôôěJ,ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ý4˙455;5<5B5F5H5ôôôěJČôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$H5J5L5S5m5n5t5x5z5ôôôěJ$ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$z5|5~5…5ś5ˇ5˝5Á5Ă5ôôôěJÜôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Ă5Ĺ5Ç5Î5í5î5ô5ř5ú5ôôôěJ(ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ú5ü5ţ567686>6B6D6ôôôěJDôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$D6F6H6O6ˆ6‰66“6•6—6ôôôôRŹôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ —6™6 6ł6´6ş6ž6Ŕ6Â6ôôěJŘôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$Â6Ä6Ë6é6ę6đ6ô6ö6ř6ôôěJ|ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ř6ú677 77777ôôěJŹôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$77 73747:7>7@7B7ôôěJôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$B7D7K7z7{77…7‡7‰7ôôěJ\ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$‰7‹7’7Ń7Ň7Ř7Ü7Ţ7ŕ7ôôěJ|ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŕ7â7é7đ7ń7÷7ű7ý7˙7ôôěJŹôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$˙78888"8&8(8*8ôôěJôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$*8,838`8a8g8k8n8p8s8ôôôRäôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ s8z8Ů8Ú8ŕ8ä8ć8č8ę8ôěJŹôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ę8ń899 99999ôěJŘôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$99:9;9A9E9G9I9K9ôěJ$ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$K9R9ƒ9„9Š9Ž99’9”9›9ôôRôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ›9Ç9Č9Î9Ň9Ô9Ö9Ř9ß9÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifß9:::::::&:ôR¨ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$&:8:9:?:C:E:G:I:P:÷U$JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfP::‚:ˆ:Œ:Ž::’:™:ôRŘôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$™:ˇ:¸:ž:Â:Ä:Ć:Č:Ď:÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfĎ:â:ă:é:í:ď:ń:ó:ú:÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifú: ;;;;;;;%;÷UôJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If%;Š;‹;‘;•;˜;š;œ;Ł;÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŁ;î;ď;ő;ů;ű;ý;˙;<÷U|JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If< <<<<<<<%<÷U”JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If%<2<3<9<=<?<A<C<J<÷UÄJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfJ<c<d<j<n<p<r<t<{<÷UÄJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If{<”<•<›<Ÿ<Ą<Ł<Ľ<Ź<÷UÜJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŹ<Ë<Ě<Ň<Ö<Ř<Ú<Ü<ă<÷UXJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifă<a=b=h=l=n=p=s=z=÷UčJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifz=>>">&>(>*>,>3>÷UčJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If3>U>V>\>`>b>d>f>m>÷U(JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifm>Ÿ> >Ś>Ş>Ź>Ž>°>ˇ>÷UpJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$Ifˇ>ű>ü>??? ? ??M?ôRHôôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ M?N?T?X?Z?\?^?e?ˆ?]ěRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöˆ?‰??“?•?—?™? ?@]RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö@@@@@@@&@E@]ÜRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöE@F@L@P@R@T@V@]@ť@]ŘRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöť@ź@Â@Ć@Č@Ę@Ě@Ó@.A]ĚRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö.A/A5A9A;A=A?AFAqA] RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöqArAxA|A~A€A‚A‰AœA]ŹRRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöœAAŁA§AŠAŤA­A´AçA],RRRRRRJ$1$If $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aöçAčAîAňAôAöAřA˙AgBhB]RRRRRRR] $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö hBnBrBtBvBxBBŠBŞBôôôôôôěJ(Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŞB°B´BśB¸BşBÁBóBôBôôôôôôěJŒĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ôBúBţBCCC CCCCôôôôôôôRŒôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ C!C#C%C'C.C9C:C@CDCôôôôôôRŒôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ DCFCHCJCQC\C]CcCgCiCôôôôôRčôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ iCkCmCtC–C—CCĄCŁCôôôěJ|ôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ŁCĽC§CŽCľCśCźCŔCÂCÄCôôôôR¸ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ÄCĆCÍCăCäCęCîCđCňCôôěJ|ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ňCôCűCDD D DDDôôěJTôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$DDDWDXD^DbDdDfDôôěJ¸ôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$fDhDoD…D†DŒDD’D”DôôěJĚôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$”D–DD¸DšDżDĂDĹDÇDÉDôôôRŹôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ ÉDĐDăDäDęDîDđDňDôDôěJ|ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$ôDűDEE E EEEEôěJtôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$EE_E`EfEjElEnEpEôěJôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$pEwEŁE¤EŞEŽE°E˛E´EôěJĐôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$´EťE×EŘEŢEâEäEćEčEôěJ,ôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If $$1$Ifa$čEďE"F#F)F-F/F1F3F:FôôR”ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ :FGFHFNFRFTFVFXF_F÷U°JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If_FłF´FşFžFŔFÂFÄFËF÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfËFŢFßFĺFéFëFíFďFöF÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IföFaGbGhGlGnGpGrGyG÷UôJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfyGžGŸGĽGŠGŤG­GŻGśG÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfśGáGâGčGěGîGđGňGůGôRÔôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ůGVHWH]HaHcHeHgHnH÷U JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfnHžHŸHĽHŠHŤH­HŻHśH÷U|JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfśH˝HžHÄHČHĘHĚHÎHŐH÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŐHčHéHďHóHőH÷HůHI÷UĚJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfIII"I&I(I*I,I3I÷UĐJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If3III–IšIœIžI I§I÷U°JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If§IťIźIÂIĆIČIĘIĚIÓI÷UźJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfÓIęIëIńIőI÷IůIűIJ÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfJJJJ J"J$J&J-J÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If-J–J—JJĄJŁJĽJ§JŽJôRŔôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$ŽJĆJÇJÍJŃJÓJŐJ×JŢJ÷UěJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŢJKKK KKKKKôRŹôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$K,K-K3K7K9K;K=KDK÷UÄJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfDK]K^KdKhKjKlKnKuK÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfuKáKâKčKěKîKđKňKůK÷UJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfůK!L"L(L,L.L0L2L9L÷UäJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If9LZL[LaLeLgLiLkLrL÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfrL…L†LŒLL’L”L–LL÷UüJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfLMM MMMMMMôRŹôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$M/M0M6M:MM@MGM÷U´JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfGM\M]McMgMiMkMmMtM÷U$JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IftMĽMŚMŹM°M˛M´MśM˝M÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If˝MĐMŃM×MŰMÝMßMáMčM÷UtJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfčM-N.N4N8N:NNEN÷U`JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfEN…N†NŒNN’N”N–NNôRtôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$NâNăNéNíNďNńNóNúN÷UtJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfúN˙NOO O OOOOôR¸ôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$O-O.O4O8O:OOEO÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfEOXOYO_OcOeOgOiOpO÷U@JJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfpO¨OŠOŻOłOľOˇOšOŔO÷UäJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŔOáOâOčOěOîOđOňOůO÷UÜJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfůOPPP#P%P'P)P0PôRüôôôôôôĄ$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö $$1$Ifa$0PWPXP^PbPdPfPhPoP÷UüJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfoP–P—PPĄPŁPĽP§PŽP÷UŹJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŽPÁPÂPČPĚPÎPĐPŇPŮP÷UÄJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfŮPňPóPůPýP˙PQQ Q÷UäJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$If Q+Q,Q2Q6Q8Q:QU?UEUIUKUMUOUVU÷UlJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfVU™UšU U¤UŚU¨UŞUąU÷UHJJJJJJ $$1$Ifa$Ą$$If–6֞Ę˙Č Tś#-ţI 7˛°m öÖ˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6aö$1$IfąU+V,V2V6V8V:VOJQJUjOJQJUjOJQJUmHnHu 5OJQJ >*OJQJOJQJ5>*OJQJCJOJQJh5CJCJOJQJh:|b}b°bąb˛błb´bľbśbˇb¸bšbşbŔbÇbŃbŢbëbúřđđđđđđđŐĐđđĘĘĘĘĘ $$1$Ifa$$$If–6”\ÖĘ˙Ţ !1|Ž~#4Ö 6$1$If$a$ëběbíbőbűbcc cc`˜XMMMMMM $$1$Ifa$$1$Ifž$$If–6”ä֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6cccc!c#c%c&c'c`XUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6'c(c2c5c7c9c;ccDcGcIcKcMcNcOc`HUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6OcPcYc\c^c`cbcccdc`TUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6dcecpcscucwcyczc{c`\UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6{c|c…cˆcŠcŒcŽccc`TUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6c‘cc c˘c¤cŚc§c¨c``UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6¨cŠcŽcącłcľcˇc¸cšc`DUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6šcşcÍcĐcŇcÔcÖc×cŘc`|UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6ŘcŮcëcîcđcňcôcőcöc`xUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6öc÷cűcţcddddd`@UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6dd ddddddd`HUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6dd%d(d*d,d.d/d0d``UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 60d1d6d9d;d=d?d@dAd`DUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6AdBdPdSdUdWdYdZd[d`hUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6[d\dddgdidkdmdndod`PUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6odpd{d~d€d‚d„d…d†d`\UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6†d‡dd“d•d—d™dšd›d`TUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6›dœdĽd¨dŞdŹdŽdŻd°d`TUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6°dądÂdĹdÇdÉdËdĚdÍd`tUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6ÍdÎdŐdŘdÚdÜdŢdßdŕd`LUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6ŕdádědďdńdódődöd÷d`\UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6÷dřd eeeeeee`„UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6ee%e(e*e,e.e/e0e``UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 60e1e@eCeEeGeIeJeKe`lUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6KeLe[e^e`ebedekele`„UUUUUUM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6leme|eeeƒe…e†e‡e`lUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6‡eˆe”e—e™e›eežeŸe``UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6Ÿe eŚeŠeŤe­eŻe°eWHLLLLLD$1$If $$1$Ifa$§$$If–6”֞Ę˙Ţ !1|Ž~# ÖÖ ˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6°eąe˛eŔeĂeĹeÇeÉeĐe÷X€MMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfĐeŃeŇeÝeŕeâeäećeíe÷XtMMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifíeîeďeűeţefff f÷XxMMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If f f fff f"f$f+f÷X€MMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If+f,f-f4f7f9f;f=f>f÷XLMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If>f?f@fGfJfLfNfPfQf÷XLMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfQfRfSfafdfffhfjfkf÷XhMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifkflfmftfwfyf{f}f~f÷XLMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If~ff€ff’f”f–f˜f™f÷XlMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If™fšf›f¤f§fŠfŤf­fŽf÷XTMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfŽfŻf°fťfžfŔfÂfÄfĹf÷X\MMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfĹfĆfÇfÎfŃfÓfŐf×fŘf÷XLMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfŘfŮfÚfífđfňfôföf÷f÷X|MMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If÷fřfůf g ggggg÷XpMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifggggg!g#g%g&g÷XLMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If&g'g(g6g9g;g=g?g@g÷XhMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If@gAgBgNgQgSgUgWg`g÷X MMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If`gigjgugxgzg|g~g‡ggôUœôôôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ g‘gŸg˘g¤gŚg¨gągşgťg`¨UUUUUUU`T $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 ťgÄgÇgÉgËgÍgÎgĎgĐgôôôôôěěMHž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ĐgÖgŮgŰgÝgßgŕgágâgôôôôôěěM\ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$âgígđgňgôgög÷gřgůgôôôôôěěMPž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ůghhhh h h h hôôôôôěěMPž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ hhhhhhh h!hôôôôôěěMPž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$!h)h,h.h0h2h3h4h5hôôôôôěěMDž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$5h:h=h?hAhChDhEhFhôôôôôěěMPž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$FhNhQhShUhWhXhYhZhôôôôôěěMTž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ZhchfhhhjhlhmhnhohôôôôôěěMXž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ohyh|h~h€h‚hƒh„h…hôôôôôěěMTž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$…hŽh‘h“h•h—h˜h™hšhôôôôôěěMdž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$šh§hŞhŹhŽh°hąh˛hłhôôôôôěěMĞ$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$łhĐhÓhŐh×hŮhÚhăhähôôôôôěôM ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ähřhűhýh˙hii i iôôôôôěôMˆž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ i!i%i'i)i+i,i-i.iôôôôôěěM˜ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$.i@iCiEiGiIiJiSiTiôôôôôěôMxž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$TifiiikimioipiqiriôôôôôěěMpž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$rii…i‡i‰i‹iŒiiŽiôôôôôěěM„ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ŽiŁiŚi¨iŞiŹi­iŽiŻiôôôôôěěM@ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$Żiłiśi¸işiźi˝ižiżiôôôôôěěM`ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$żiËiÎiĐiŇiÔiŐiÖi×iôôôôôěěMdž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$×iäiçiéiëiíiîiďiđiôôôôôěěMLž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$điöiúiüiţijjjjôôôôôěěM„ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$jjjjjjj#j$j0jôôôôôôôUŒôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ 0j3j5j7j9j?jFjGjXj[jôôôôôôU ôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ [j]j_jajgjnjojvjyj{jôôôôôULôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ {j}jj€jj‚jŸj˘j¤jôôěěM¤ôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$¤jŚj¨jŠjŞjŤjąj´jśjôôěěMHôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$śj¸jşjťjźj˝jËjÎjĐjôôěěM€ôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ĐjŇjÔjŰjÜjÝjčjëjíjôôôěMtôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$íjďjńjřjůjújk k kôôôěMxôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ k kkkkk k#k%kôôôěMPôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$%k'k)k*k+k,k1k4k6kôôěěMDôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$6k8k:k;ko@oBo÷÷XXMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfBoCoDoEoPoSoUoWoYo÷÷X\MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfYoZo[o\okonoporoto÷÷XlMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Iftouovowo†o‰o‹ooo÷÷XlMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifoo‘o’ošooŸoĄoŁo÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfŁo¤oĽoŚoŽoąołoľoˇo÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifˇo¸ošoşoČoĚoÎoĐoŇo÷÷XźMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfŇoŮočoéoóoöořoúoüoôôUXôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$üoýoţo˙oppp p p÷÷X@MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If p pppp!p#p%p'p÷÷XlMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If'p(p)p*p=p@pBpDpFp÷÷X|MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfFpGpHpIpYp\p^p`pbp÷÷XpMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifbpcpdpepkpnppprptp÷÷XHMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Iftpupvpwp€pƒp…p‡p‰p÷÷XTMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If‰pŠp‹pŒp•p˜pšpœpžp÷÷XTMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfžpŸp pĄp´pˇpšpťp˝p÷÷XĚMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If˝pČpÓpÔpŮpÜpŢpŕpâpôôUDôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$âpăpäpĺpîpńpópőp÷p÷÷XTMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If÷přpůpúpq q q qq÷÷X`MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifqqqqqq q"q$q÷÷XTMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If$q%q&q'q-q0q2q4q6q÷÷XHMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If6q7q8q9q=qAqCqEqGq÷÷XDMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfGqHqIqJqNqQqSqUqWq÷÷X@MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfWqXqYqZqqquqwqyq{q÷÷XMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If{q|q}q~q•q˜qšqœq÷÷OŒDDDD $$1$Ifa$§$$If–6”֞Ę˙Ţ !1|Ž~# ÖÖ ˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfœqžqŸq qĄqĽq¨qŞqôěěD@ôôô§$$If–6”֞Ę˙Ţ !1|Ž~# ÖÖ ˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ŞqŹqŽqŻq°qąq˝qŔqÂqôôěěM`ôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ÂqÄqĆqÇqČqÉqÎqŃqÓqôôěěMDôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ÓqŐq×qŘqŮqÚqăqćqčqôôěěMTôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$čqęqěqíqîqďqűqţqrôôěěMˆôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$rrr rrrrrrrôôôôULôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ r!r"r#r$r0r3r5r7rôěěM”ôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$7r9r@rHrIrZr]r_rarcrôôôU¤ôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ crjrqrrr„r‡r‰r‹rr”rôôU¨ôôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ ”r›rœr§rŞrŹrŽr°rąrôU\ôôôôôM$1$Ifž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ąr˛rłrÁrÄrĆrČrĘrŇr÷X„MMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfŇrÓrÔrŰrŢrŕrârärĺr÷XLMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifĺrćrçrńrôrörřrúrűr÷XXMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifűrürýrssssss÷XœMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifs#s$s5s8s:ssIsTsôUÄôôôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ TsUs_sbsdsfshsisjs`XUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6jsksosrstsvsxsyszs`@UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6zs{ss‚s„s†sˆs‰sŠs`@UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6Šs‹s”s—s™s›ssžsŸs`TUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6Ÿs s­s°s˛s´sśsˇs¸s`dUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6¸sšsÂsĹsÇsÉsËsĚsÍs`TUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6ÍsÎsŘsŰsÝsßsásâsăs`XUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6ăsäsďsňsôsösřsůsús`\UUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6úsűs t tttttt`hUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6ttt"t$t&t(t)t*t`XUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6*t+t5t8t:tt?t@t`XUUUUUMM$1$If $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6@tAtRtUtWtYt[tbtitjt`¤UUUUUUU`P $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 jtrtutwtyt{t|t}t~tôôôôôěěM¤ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$~ttt’t”t–tžtŚt§tŽtôôôôôôôULôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ Žtątłtľtˇt¸tštştÂtôôôôěěMPôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ÂtĹtÇtÉtËtĚtÍtÎtŰtôôôôěěMdôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ŰtŢtŕtâtätĺtćtçtňtôôôôěěM\ôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ňtőt÷tůtűtütýtţt uôôôôěěM`ôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ u uuuuuuu#uôôôôěěMdôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$#u&u(u*u,u-u.u/u;uôôôôěěM`ôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$;u>u@uBuDuEuFuGuOuôôôôěěMPôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$OuRuTuVuXuYuZu[ubuôôôôěěMPôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$bufuhujulumunuouƒuôôôôěěM´ôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ƒu†uˆuŠuŒu”u›uœuĽu¨uôôôôôôUTôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ ¨uŞuŹuŽuŻu°uąuÂuĹuôôôěěMtôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ĹuÇuÉuËuĚuÍuÎuŘuÜuôôôěěM\ôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ÜuŢuŕuâuăuäuĺuřuűuôôôěěM|ôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$űuýu˙uvvvvvvôôôěěMpôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$vvvvvv v/v3vôôôěěMpôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$3v5v7v9v:v;vy?yByEyGyIyôěěM<ôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$IyKyLyMyNy[y^y`ybyôěěMdôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$bydyeyfygyuyxyzy|yôěěMhôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$|y~yy€yy‹yŽyy’yôěěMXôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$’y”y•y–y—y yŁyĽy§yôěěMTôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$§yŠyŞyŤyŹyżyÂyÄyĆyôěěM|ôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ĆyČyÉyĘyËyŮyÜyŢyŕyôěěMœôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ŕyâyęyńyňyűyţyzzzôôôUTôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ zzzzzzzzz÷÷X˜MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifz%z,z-z7z:zz@zôôUXôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$@zAzBzCzPzSzUzWzYz÷÷XxMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfYz_z`zaznzqzszuzwzôěMŒôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$wz}zƒz„zŽz‘z“z•z—zôôUXôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$—z˜z™zšz¤z§zŠzŤz­z÷÷XXMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If­zŽzŻz°z´zˇzšzťz˝z÷÷X@MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If˝zžzżzŔzÇzĘzĚzÎzĐz÷÷XLMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfĐzŃzŇzÓzćzézëzízďz÷÷X”MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifďzđz÷zřzţz{{{{÷ěMHěěěěěž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$$1$If{{ { {{{{{{÷÷XHMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If{{{{*{-{/{1{3{÷÷XhMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If3{4{5{6{F{I{K{M{O{÷÷X¤MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfO{W{^{_{m{p{r{t{v{ôôUhôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$v{w{x{y{‡{Š{Œ{Ž{{÷÷XhMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If{‘{’{“{›{ž{ {˘{¤{÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If¤{Ľ{Ś{§{˛{ľ{ˇ{š{ť{÷÷X\MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifť{ź{˝{ž{Ć{É{Ë{Í{Ď{÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfĎ{Đ{Ń{Ň{ä{ç{é{ë{í{÷÷XŹMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifí{ő{ü{ý{ ||||||ôôU¤ôôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ |%|&|7|:|<|>|@|A|ôUtôôôôôM$1$Ifž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$A|B|C|R|U|W|Y|[|\|÷XlMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If\|]|^|o|r|t|v|x|y|÷XtMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ify|z|{|†|Š|Œ|Ž||‘|÷X`MMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If‘|’|“|§|Ş|Ź|Ž|°|ť|÷XĐMMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifť|Ć|Ç|Ý|á|ă|ĺ|ç|ú| }ôUôôôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ }}"}%}'})}+}6}A}B}`ĐUUUUUUU` $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 B}T}W}Y}[}]}p}ƒ}„}—}ôôôôôôôU ôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ —}š}œ}ž} }ł}Ć}Ç}Ó}Ö}ôôôôôôU`ôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ Ö}Ř}Ú}Ü}Ý}Ţ}ß}č}ë}ôôôěěMTôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ë}í}ď}ń}ň}ó}ô}~~ôôôěěMhôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$~~ ~ ~ ~ ~~~~ôôôěěMXôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$~~~!~"~#~$~/~2~ôôôěěM\ôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$2~4~6~8~9~:~;~B~E~ôôôěěMLôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$E~G~I~K~L~M~N~`~c~ôôôěěMČôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$c~e~g~i~t~~€~ˆ~‹~~ôôôôôUPôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ ~~‘~’~“~”~Š~Ź~Ž~ôôěěM´ôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$Ž~°~˛~š~Ŕ~Á~Î~Ń~Ó~Ő~ôôôôUdôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ Ő~×~Ř~Ů~Ú~ç~ę~ě~î~ôěěMdôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$î~đ~ń~ň~ó~˙~ôěěM`ôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$   ôěěMDôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$#&(*ôěěMLôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$*,-./479;ôěěMDôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$;=>?@SVXZôěěM|ôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$Z\]^_knprôěěM`ôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$rtuvw€ƒ…‡ôěěMTôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$‡‰Š‹Œ—šœžôěěM\ôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ž Ą˘Ł­°˛´ôěěMXôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$´śˇ¸šËÎĐŇôěěMxôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ŇÔŐÖ×ŕăĺçôěěMTôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$çéęëěóöřúôěěMLôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$úüýţ˙ €€€€ôěěMdôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$€€€€€#€&€(€*€ôěěM\ôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$*€,€-€.€/€7€:€<€>€ôěěMPôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$>€@€A€B€C€L€O€Q€S€ôěěMTôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$S€U€V€W€X€_€b€d€f€ôěěMLôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$f€h€i€j€k€s€v€x€z€ôěěMPôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$z€|€}€~€€Œ€€‘€“€ôěěMdôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$“€•€–€—€˜€Ś€Š€Ť€­€ôěěMhôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$­€Ż€°€ą€˛€€ŀǀɀôěěMpôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ɀˀ̀̀΀ހá€ă€ĺ€ôěěMpôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ĺ€ç€č€é€ę€ň€ő€÷€ů€ôěěMPôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ů€ű€ü€ý€ţ€ôěěMôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ !"'*,.ôěěMDôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$.0123ADFHôěěM°ôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$HJT^_loqsuôôôUdôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ uvwxŠ‘“÷÷XxMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If“”•–´ˇšť˝÷÷XÄMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If˝ŁƁǁˁ΁ЁҁԁôěM@ôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ԁՁցׁáäćčę÷÷XXMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifęëěíňő÷ůű÷÷XDMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifűüýţ ‚‚‚‚‚÷÷XlMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If‚‚‚‚0‚3‚5‚7‚9‚÷÷XŒMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If9‚:‚;‚<‚K‚N‚P‚R‚T‚÷÷XlMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfT‚U‚V‚W‚_‚b‚d‚f‚h‚÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifh‚i‚j‚k‚{‚~‚€‚‚‚„‚÷÷XpMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If„‚…‚†‚‡‚™‚œ‚ž‚ ‚˘‚÷÷XŔMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If˘‚Ź‚ś‚ˇ‚Ŕ‚ÂłǂɂôôUTôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ɂʂ˂݂̂ŕ‚â‚ä‚ć‚÷÷XÄMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifć‚ń‚ü‚ý‚ƒƒƒƒƒôôU„ôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ƒƒƒƒHƒKƒMƒOƒQƒ÷÷XŘMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfQƒRƒSƒTƒ_ƒbƒdƒfƒhƒ÷÷X\MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifhƒiƒjƒkƒoƒrƒtƒvƒxƒ÷÷X@MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifxƒyƒzƒ{ƒƒ„ƒ†ƒˆƒŠƒ÷÷XHMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfŠƒ‹ƒŒƒƒ–ƒ™ƒ›ƒƒŸƒ÷÷XTMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfŸƒ ƒĄƒ˘ƒŻƒ˛ƒ´ƒśƒ¸ƒ÷÷XdMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If¸ƒšƒşƒťƒÃƃȃʃ̃÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If̃̓΃σ߃âƒäƒćƒčƒ÷÷XpMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifčƒéƒęƒëƒóƒöƒřƒúƒüƒ÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifüƒýƒţƒ˙ƒ„„„!„#„÷÷XœMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If#„$„%„&„/„2„4„6„8„÷÷XTMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If8„9„:„;„?„B„D„F„H„÷÷X@MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfH„I„J„K„Q„T„V„X„Z„÷÷XHMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfZ„[„\„]„h„k„m„o„q„÷÷X\MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifq„r„s„t„ƒ„‡„‰„‹„„÷÷X”MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If„Ž„˜„™„Ś„Š„Ť„­„Ż„÷ěMděěěěěž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$$1$IfŻ„°„ą„˛„ş„˝„ż„Á„Ä÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfÄĄńƄ؄ۄ݄߄á„÷÷XœMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifá„â„ě„í„………… …÷ěM|ěěěěěž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$$1$If … … … …………… …÷÷X\MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If …!…"…#…-…0…2…4…6…÷÷XXMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If6…7…8…9…A…D…F…H…J…÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfJ…K…L…M…U…X…Z…\…^…÷÷XPMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If^…_…`…a…g…j…l…n…p…÷÷XHMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifp…q…r…s……„…†…ˆ…Š…÷÷XMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfŠ……–…—…Š…Ź…Ž…°…˛…ôôUxôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$˛…ł…´…ľ…ąDžɅ˅ͅ÷÷XlMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifͅ΅υЅׅڅ܅ޅŕ…÷÷XLMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifŕ…á…â…ă…ě…ď…ń…ó…ő…÷÷XTMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifő…ö…÷…ř…† † † ††÷÷XhMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If†††††"†$†&†(†÷÷XdMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If(†)†*†+†;†>†@†B†D†÷÷XŔMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfD†O†Z†[†h†l†n†p†r†ôôUhôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$r†s†t†u†‹†Ž††’†”†÷÷X¸MMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If”†Ą†˘†Ł†ľ†¸†ş†ź†ž†ôěM¨ôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$ž†ˆ̆͆ŕ†ă†ĺ†ç†é†ôěMŹôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$é†ö†÷†ř†˙†‡‡‡‡ôěM`ôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If $$1$Ifa$‡ ‡‡‡)‡,‡.‡0‡2‡÷ěMäěěěěěž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$$1$If2‡=‡H‡I‡W‡Z‡\‡^‡`‡k‡ôôU¸ôôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$ k‡v‡w‡€‡ƒ‡…‡‡‡‰‡Š‡ôUTôôôôôM$1$Ifž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$Š‡‹‡Œ‡•‡˜‡š‡œ‡ž‡Ÿ‡÷XTMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ 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6$1$If|ˆ}ˆ~ˆˆˆ’ˆ”ˆ–ˆ—ˆ÷XlMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If—ˆ˜ˆ™ˆŹˆŻˆąˆłˆľˆśˆ÷X|MMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifśˆˇˆ¸ˆΈшӈՈ׈ވ÷X¸MMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifވĺˆćˆíˆđˆňˆôˆöˆ÷ˆôULôôôôôM$1$Ifž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$÷ˆřˆůˆ ‰ ‰‰‰‰‰÷X¤MMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$If‰!‰"‰7‰:‰<‰>‰@‰M‰ôU´ôôôôôôž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$M‰N‰O‰]‰`‰b‰d‰f‰g‰÷XhMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifg‰h‰i‰z‰}‰‰‰ƒ‰Ž‰÷XÄMMMMMM $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$IfŽ‰™‰š‰ž‰Ą‰Ł‰Ľ‰§‰¨‰ôU@ôôôôôM$1$Ifž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6 $$1$Ifa$¨‰Š‰މ´‰ˇ‰š‰ť‰˝‰ž‰÷XXMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ !1|Ž~#Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙Ö˙˙˙˙˙˙˙4Ö 6$1$Ifž‰ż‰Ŕ‰ɉ̉ΉЉ҉Ӊ÷XTMMMMM÷ $$1$Ifa$ž$$If–6”֞Ę˙Ţ 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