This is a MIME encoded message
--minaeibi1030835336
Content-Type: text/plain
minaeibi Sat Aug 31 19:08:56 2002 EDT
Added files:
/modules/minaeibi Datamin14.doc
Log:
MS-Word Document for LON-CAPA data mining
--minaeibi1030835336
Content-Type: text/plain
Content-Disposition: attachment; filename="minaeibi-20020831190856.txt"
Index: modules/minaeibi/Datamin14.doc
+++ modules/minaeibi/Datamin14.doc
ࡱ > x q r s t u v w z F } 3 1 5@ 0 `0 bjbj22 4 X X ' | | | @ 0 " " " " 8 < < " 1> \ \ 8 ' ' ' ' qu 7 < s = = = = = = = $ B R D = " N e qu N N = ' ' > = N J& 6 ' " ' = N = F . * " f " ' P ,O X , 0 > 0 1> , h
E B E " " E " e M = = " " " " " Data Mining for an Educational Web-based SystemPh.D. SurveyBehrouz MinaeiA31054289Adviser: Dr. Bill PunchGenetic Algorithms Research and Applications Group Department of Computer Science & EngineeringMichigan State UniversityAugust 2002 Chapter 1Statement of problemThe ever-increasing progress of network-distributed computing and particularly, the rapid expansion of the web, have had a broad impact on society in a relatively short period of time. Education is on the brink of a new era based on these changes. Online delivery of educational instructions provides the opportunity to bring colleges and universities new energy, students, and revenues. Many leading educational institutions are working to establish an online teaching and learning presence. Several systems with different capabilities and approaches have been developed to deliver online education in an academic setting (Mallard; WebCT; CouseInfo; eCollege; TopClass; Virtual University). In particular, Michigan State University (MSU) has pioneered some of these systems to provide an infrastructure for online instruction (Multi-Media Physics; CAPA; LectureOnline; PhysNet; Kortmeyer and Bauer, 1999; Kashy et al., 1997, LON-CAPA). My research is being performed on a part of the latest online educational system developed at MSU, the Learning Online Network with Computer-Assisted Personalized Approach (LON-CAPA). In LON-CAPA, we are involved with two kinds of large data sets: 1) educational resources such as web pages, demonstrations, simulations, and individualized problems designed for use on homework assignments, quizzes, and examinations; and 2) information about users who create, modify, assess, or use these resources. In other words, we have two ever-growing pools of data. As the resource pool grows, the information from users who have multiple transactions with these resources also increases. The LON-CAPA system logs any access to these resources as well as the sequence and frequency of access in relation to the successful completion of any assignment. We study the possible data mining methods for extracting useful knowledge from the large database of students who are using online educational resources and their recorded paths through the web. In this study, we aim to answer the following two research questions:Can we find classes of students? Do groups of students exist who use these online resources in a similar way? If so, can we identify a prediction for any individual student, such as which group they belong to? Can we use this information to help a student use the resources better, based on the usage of the resource by other students in their groups?Can we classify the problems that have been used by students? If so, can we show how the different types of problems impact students achievements? Can we help instructors to develop the homework more effectively and efficiently?We hope to find similar patterns of use in the data gathered from LON-CAPA, and eventually be able to make predictions as to the most-beneficial course of studies for each student based on a limited number of variables for each individual student. Based on the current state of the learner in a learning sequence, the system could then make suggestions to the learner as to how to proceed. This survey is organized as follows: Chapter 2 provides information about current online education systems and a background on intelligent tutoring systems. The second part of this chapter presents a system overview of LON-CAPA in detail to show the amplitude of growing homework and student data in LON-CAPA and the inadequacy of current statistical measures for classifying the students efficiently. Chapter 3 introduces the basic concepts of data mining clustering methods and describes the important algorithms and methods for data classification. Chapter 4 explains how to use fuzzy clustering for classifying students to improve the quality of learning objects. Chapter 5 will present a conclusion of the survey and indicate future work. Chapter 2BackgroundIn this chapter we review briefly intelligent tutoring systems and discuss online education systems, especially the LON-CAPA online education system. In this survey, we are not going to develop a complicated intelligent tutoring system; instead we apply the main ideas of intelligent tutoring systems in an online education system using possible data mining methods. Intelligent Tutoring Systems (ITS)Intelligent tutoring systems are computer-based instructional systems that attempt to determine information about a students learning status, and use that information to dynamically adapt the instruction to fit the students needs. (UrbanLurain, 1996; Petrushin, 1995; Benyon and Murray, 1993; Winkkels, 1992; Farr and Psotka, 1992; Venezky and Osin, 1991; Larkin and Cabay, 1991; Goodyear, 1990; Frasson and Gauthier, 1988, Wenger, 1987; Yazdani, 1987) ITSs are often known as knowledge-based tutors, because they have separate knowledge bases for different domain knowledge. The knowledge bases specify what to teach and different instructional strategies specify how to teach. (Murray, 1996) One of the fundamental assumptions in ITS design is from an important experiment in the theory of learning, which states that individualized instruction is far superior to class-room style learning. Both the content and style of instruction can be continuously adapted to best meet the needs of a student. (Bloom, 1984) Educational psychologists report that students learn best by doing, learn through their mistakes, and learn by constructing knowledge in a very individualized way. (Kafaei and Resnik, 1996; Ginsburg and Opper, 1979; Bruner 1966) For many years, researchers have argued that individualized learning offers the most effective and efficient learning for most students (Juel, 1996; Woolf, 1987, Bloom 1956). Intelligent tutoring systems epitomize this principle of individualized instruction. Recent studies have found that intelligent tutoring systems can be highly effective learning aides. (Shute and Regine, 1990) Shute evaluated several intelligent tutoring systems to judge how they live up to the main promises of intelligent tutoring systems to provide more effective and efficient learning in relation to traditional instructional techniques. Results of such studies show that tutoring systems do accelerate learning with no degradation of the final outcome. Learning Enhancement in the ITS In one study, Bloom (1984) stated that conventional teaching methods provide the least effective method of learning. As instruction becomes more focused and individualized, learning is enhanced. He compared students scores on achievement tests using three forms of instruction: conventional teaching, mastery teaching, and individualized tutoring. Mastery teaching is an instructional technique whereby a teacher supplements a lecture with diagnostic tests to determine where students are having problems, and adjusts the instruction accordingly. The result of this comparison is shown in figure 2. Students receiving conventional teaching scored in the 50th percentile, students receiving mastery teaching scored in the 84th percentile, while students receiving individualized tutoring scored in the 98th percentile. EMBED Word.Picture.8 Figure 2.1: Distributions for different learning conditions (Adapted from Bloom, 1984)Bloom replicated these results four times with three different age groups for two different domains, and thus, provided concrete evidence that tutoring is one of the most effective educational delivery methods available.Since ITSs attempt to provide more effective learning through individualized instruction, many computer-assisted instruction techniques exist that can present instruction and interact with students in a tutor-like fashion, individually or in small groups. The incorporation of artificial intelligence techniques and expert systems technology to computer-assisted instruction systems gave rise to intelligent tutoring systems, i.e., systems that model the learners understanding of a topic and adapt the instruction accordingly. A few examples of systematically controlled evaluations of ITSs reported in the literature are shown in the following table: ITSLiteratureObjectiveprogressLISP tutor(Anderson, 1990)Instructing LISP programming33-66% less timeSmithtown(Shute and Glaser, 1990)Teaches scientific inquiry skills1/2 time, same knowledgeSherlock(Lesgold et al.,1990)For avionics troubleshooting1/5 time, same knowledgePascal ITS(Shute, 1991)Teaches Pascal programming1/3 time, same knowledgeStat Lady(Shute et al., 1993)Instructing statistical proceduresMore performanceGeometry Tutor(Anderson et al., 1985)Teaching Geometry theoremsBetter solvingTable 2.1: Different Specific ITSs and their affects on learning rateShute and Poksta (1996) examined the results of these evaluations, which show that these tutors do accelerate learning with no degradation in outcome performance. The tutors should be evaluated with respect to the promises of ITSs. In all cases, individuals using the ITSs learne faster, and perform at least as well as those learning from traditional teaching environments. The results show that these tutors could not only reduce the variance of outcome scores, but also make learning less dependent on aptitudes, thereby providing every student with a fair chance at learning.Basic Architecture of an ITSThere is no standard architecture for an ITS. Nevertheless, four components emerge from the literature as part of an ITS (Wasson, 1997; Costa, 1992; Polson and Richardson, 1988; Yazdani, 1987; Wenger, 1987; Sleeman and Brown, 1982). These are the student model, the pedagogical module, the expert model, and the communication module or interface. These four components and their interactions are illustrated in figure 2.2. The student model stores information of each individual learner. For example, such a model tracks how well a student is performing on the material being taught or records misconceptions. Since the purpose of the student model is to provide data for the pedagogical module of the system, all of the information gathered should be usable by the tutor. The pedagogical module provides a model of the teaching process. For example, information about when to review, when to present a new topic, and which topic to present is controlled by this module. As mentioned earlier, the student model is used as input to this component, so the pedagogical decisions reflect the differing needs of each student. The expert model contains the domain knowledge, the information being taught to the learner. However, it is more than just a representation of the data; it is a model of how someone skilled in a particular domain represents the knowledge. By using an expert model, the tutor can compare the learner's solution to the expert's solution, pinpointing the places where the learner has difficulties. This component contains information the tutor is teaching, and is the most important since without it, there would be nothing to teach the student. Generally, this aspect of ITS requires significant knowledge engineering to represent a domain so that other parts of the tutor can access it. The communication module controls interactions with a student, including the dialogue and the screen layouts. For example, it determines how the material should be presented to the student in the most effective way. This component the least researched of the group.These four components, the student model, the pedagogical module, the expert model, and the communication module interact to provide the individualized educational experience promised by ITS technology. Current research trends focus on making tutoring systems truly intelligent. The evolution of ITSs demands more controlled research in four areas of intelligence: the domain expert, student model, tutor, and interface.The domain knowledge must be understood by the computer well enough for the expert model to draw inferences or solve problems in the domain.The system must be able to deduce a students approximation of that knowledge.The tutor must be intelligent to the point where it can reduce differences between the expert and student performance.The interface must possess intelligence in order to determine the most effective way to present information to the student.For ITSs to have a great impact on education, these and other issues must be resolved. To take advantage of newer, more effective instructional techniques, ITSs of the future will have to allow for increased student initiative and between-student collaboration (Shute and Psotka, 1996). ITSs must also assess learning as it transfers to authentic tasks, not standardized tests, and establish connections across fields, so topics are not learned in isolation. A more fruitful approach for ITS development may be to develop specific cognitive tools, for a given domain or applicable across domains. Such a transition would allow future ITSs to be everywhere, as embedded assistants, to explain, critique, provide online support, coach, and perform other ITS activities.Learning and Cognition Issues for ITS Development and UseThere are some findings in the areas of cognition and the learning process that impact the development and use of intelligent tutoring systems. Many recent findings are paving the way towards improving our understanding of learners and learning (Bransford, Brown et al., 2000). Learners have preconceptions about how the world works. If their initial understanding is not referenced or activated during the learning process, they may fail to understand any new concepts or information.One key finding regarding competitance in a domain is the need to have a more than deep knowledge base of information related to that domain. They must also be able to understand that knowledge within the context of a conceptual framework. They must also be able to organize that knowledge in a manner that facilitates its use. A key finding in the learning and transfer literature is that organizing information into a conceptual framework allows for greater transfer of knowledge. By developing a conceptual framework, students are able to apply their knowledge in new situations and to learn related information more quickly. For example, a student who has learned problem solving for one topic in the context of a conceptual framework will use that ability to guide the acquisition of new information for different topic within the same framework.A relationship exists between the learning and transfer of knowledge to new situations. Transferring is usually a function of the relationships between what is learned and what is tested. For students to transfer knowledge successfully across domains, they must conceive of their knowledge base as growing continuously, instead of discrete steps.Recent research by Singley and Anderson indicates that the transfer of knowledge between tasks is a function of the degree to which the tasks share cognitive elements (Singley and Anderson, 1989) In their study, Singley and Anderson taught students several text editors, one after the other. They found that students learned subsequent text editors more rapidly and that the number of procedural elements shared by the two text editors predicted the amount of transfer. Their results showed that there was large transfer across editors that were very different in surface structures but had common abstract structures. Singley and Anderson were able to generate similar results for the transfer of mathematical competence across multiple domains.Emerging computer-based technologies hold great promise as a means of supporting and promoting learning. There are several ways that such technology can be used to help meet the challenges of developing effective learning environments (El-Sheikh, 2001):Bringing real-world problems to the learning environment.Providing scaffolding support to students during the learning process. Scaffolding allows students to participate in complex cognitive experiences, such as model-based learning, that is more difficult without technical support.Increasing opportunities for learners to receive feedback and guidance from software tutors and learning environments.Building local and global communities of teachers, administrators, students, and other interested learners.Expanding opportunities for teachers learning.Learning environments need to be developed and implemented with a full understanding of the principles of learning and developmental psychology. In addition, these new learning environments need to be assessed carefully, including how their use can facilitate learning, and the cognitive, social, and learning consequences of using these new tools. Online Education systemsSeveral Online Education systems such as Blackboard (CouseInfo), WebCT, Virtual University (VU), and some other similar systems have been developed to focus on course management issues. The objectives of these systems are to present courses and instructional programs through the web and other technologically enhanced media. These new technologies make it possible to offer instruction without the time and place limitations of traditional university programs. However, these systems tend to use existing materials and present them as a static package via the Internet. There is another approach that is pursued in LON-CAPA to construct more-or-less new courses using the new network technology. In this model of content creation, college faculty, K-12 teachers, and students interested in collaboration can access a database of hypermedia software modules that can be linked and combined (Kortemeyer and Bauer, 1999). The LON-CAPA is the primary focus of this chapter. LON-CAPA, System OverviewLON-CAPA is a distributed instructional management system, which provides students with personalized problem sets, quizzes, and exams. Personalized (or individualized) homework means that each student sees a slightly different computer-generated problem. LON-CAPA provides students and instructors with immediate feedback on conceptual understanding and correctness of solutions. It also provides faculty the ability to augment their courses with individualized, relevant exercises. It also allows faculty to develop and share modular online resources. LON-CAPA aims to put this functionality on a homogeneously distributed platform for creating, sharing, and delivering course content with emphasis on cross-institutional collaboration and intellectual property rights management. LON-CAPA TopologyLON-CAPA is physically built as a geographically distributed network of constantly connected servers. Figure 2 shows an overview of this network. Every user in LON-CAPA is a member of one domain. Domains could be defined by departmental or institutional boundaries like MSU, FSU, OHIOU, or the name of a publishing company, students, authors and other users. These domains can be used to limit the flow of personal user information across the network, set access privileges and enforce royalty schemes. Thus, the student and course data are distributed amongst several repositories. Internally, all resources are identified primarily by their URL. Every user in the system has one library server, which is their home server. It stores the authoritative copy of all of their records.LON-CAPA currently runs on Redhat-Linux Intel-compatible hardware. The current MSU production setup consists of five access servers and two library servers. Three of the access servers are set up on a round-robin IP scheme as frontline machines, and are accessed by the students for user session. The current implementation of LON-CAPA uses mod_perl inside of the Apache web server software.Data Distribution in LON-CAPALearning objects in LON-CAPA could be simple paragraphs of text, movies, applets, and CAPA-style individualized homework problems. Online educational projects at MSU have produced extensive libraries of resources across disciplines. By combining these resources, LON-CAPA will produce a national distributed digital library with mechanisms to store and retrieve these objects. Participants in LON-CAPA can publish their own learning objects in the common pool. LON-CAPA will allow groups of organizations (departments, universities, schools, commercial businesses) to link their online instructional resources in a common marketplace, thus creating an online economy for instructional resources (lon-capa.org).LON-CAPA would enable faculty to combine and sequence these objects at several levels. For example, an instructor from Community College A in Texas could compose a page by combining a text paragraph from University B in Detroit with a movie from College C in California and an online homework problem from Publisher D in New York. Another instructor from High School E in Canada might take that page from Community College A and combine it with other pages into a module, unit or section. Those in turn can be combined into whole course packs. Participants in LON-CAPA can publish their own learning objects in the common pool.Resource Variation in LON-CAPALON-CAPA provides three types of resources for organizing a course. LON-CAPA refers to these resources as Content Pages, Problems, and Maps. Maps may be either of two types: Sequences or Pages. You will use these LON-CAPA resources to build the outline, or structure, for the presentation of your course to your students. A Content Page displays course content. It is essentially a conventional HTML page.These resources use the extension .html.A Problem resource represents problems for the students to solve, with answers stored in the system. These resources are stored in files that must use the extension .problem.A Page is a type of Map which is used to join other resources together into one HTML page. For example, a page of problems will appear as a problem set. These resources are stored in files that must use the extension .page.A Sequence is a type of Map, which is used to link other resources together. Sequences are stored in files that must use the extension .sequence. Sequences can contain other sequences and pages.Authors create these resources and publish them in library servers. Then, instructors use these resources in online courses. The LON-CAPA system logs any access to these resources as well as the sequence and frequency of access in relation to the successful completion of any assignment. All these accesses logged.LON-CAPA Strategy for Data StorageAll data are stored as URLs distributed over a wide area network. Internally, the student data is stored in a directory:/home/httpd/lonUsers/domain/1st.char/2nd.char/3rd.char/username/For example /home/httpd/lonUsers/msu/m/i/n/minaeibi/Figure 2.3 shows a list of a student data; files ending with .db are GDBM files (Berkeley database). Files with a course-ID as name, for example msu_12679c3ed543a25msul1.db, store performance data for that student in the course.ls -alF /home/httpd/lonUsers/msu/m/i/n/minaeibi-rw-r--r-- 1 www users 13006 May 15 12:21 activity.log-rw-r----- 1 www users 12413 Oct 26 2000 coursedescriptions.db-rw-r--r-- 1 www users 11361 Oct 26 2000 coursedescriptions.hist-rw-r----- 1 www users 13576 Apr 19 17:45 critical.db-rw-r--r-- 1 www users 1302 Apr 19 17:45 critical.hist-rw-r----- 1 www users 13512 Apr 19 17:45 email_status.db-rw-r--r-- 1 www users 1496 Apr 19 17:45 email_status.hist-rw-r--r-- 1 www users 12373 Apr 19 17:45 environment.db-rw-r--r-- 1 www users 169 Apr 19 17:45 environment.hist-rw-r----- 1 www users 12315 Oct 25 2000 junk.db-rw-r--r-- 1 www users 1590 Nov 4 1999 junk.hist-rw-r----- 1 www users 23626 Apr 19 17:45 msu_12679c3ed543a25msul1.db-rw-r--r-- 1 www users 3363 Apr 19 17:45 msu_12679c3ed543a25msul1.hist-rw-r----- 1 www users 18497 Dec 21 11:25 msu_1827338c7d339b4msul1.db-rw-r--r-- 1 www users 3801 Dec 21 11:25 msu_1827338c7d339b4msul1.hist-rw-r----- 1 www users 12470 Apr 19 17:45 nohist_annotations.db-rw-r----- 1 www users 765954 Apr 19 17:45 nohist_email.db-rw-r--r-- 1 www users 710631 Apr 19 17:45 nohist_email.hist-rw-r--r-- 1 www users 13 Apr 19 17:45 passwd-rw-r--r-- 1 www users 12802 May 3 13:08 roles.db-rw-r--r-- 1 www users 1316 Apr 12 16:05 roles.histFigure 2.4: Directory listing of users home directoryCourses are assigned to users, not vice versa. Internally, courses are handled like users without login privileges. The username is a unique ID, for example msu_12679c3ed543a25msul1 every course in every semester has a unique ID, there is no semester transition. The userdata of the course includes the full name of the course, a pointer to its top-level resource map (course map), and any associated deadlines, spreadsheets, etc., as well as a course enrollment list. The latter is somewhat redundant, since in principle, this list could be produced by going through the roles of all users, and looking for the valid role for a student in that course.ls -alF /home/httpd/lonUsers/msu/1/2/6/12679c3ed543a25msul1/-rw-r----- 1 www users 17155 Apr 25 16:20 classlist.db-rw-r--r-- 1 www users 60912 Apr 25 16:20 classlist.hist-rw-r----- 1 www users 12354 Jan 4 16:40 environment.db-rw-r--r-- 1 www users 82 Jan 4 16:40 environment.hist-rw-r----- 1 www users 103030 May 15 14:47 nohist_calculatedsheets.db-rw-r----- 1 www users 13050 May 9 21:04 nohist_expirationdates.db-rw-r--r-- 1 www users 6 Jan 4 16:40 passwd-rw-r----- 1 www users 17457 May 9 21:04 resourcedata.db-rw-r--r-- 1 www users 8888 May 9 21:04 resourcedata.histFigure 2.5: Directory listing of courses home directoryAn example of course data is shown in figure 2.5. classlist is this list of students in the course, environment includes the courses full name, etc, and resourcedata are deadlines, etc. The parameters for homework problems are stored in these files.To identify a specific instance of a resource, LON-CAPA uses symbols or symbs. These identifiers are built from the URL of the map, the resource number of the resource in the map, and the URL of the resource itself. The latter is somewhat redundant, but might help if maps change. An example is msu/korte/parts/part1.sequence___19___msu/korte/tests/part12.problemThe respective map entry is <resource id="19" src="/res/msu/korte/tests/part12.problem" title="Problem 2"> </resource>Symbs are used by the random number generator, as well as to store and restore data specific to a certain instance of a problem. More details of the stored data and the their exact structures will be explained in chapter 4, when we will describe the data acquisition of system.Resource Evaluation in LON-CAPAOne of the most challenging aspects of the system is to provide resource users with information concerning the quality and effectiveness of the various materials in the resource pool in terms of their effects on student understanding of concepts and their knowledge of procedures. These materials will include web pages, demonstrations, simulations, and individualized problems designed for use on homework assignments, quizzes, and examinations. The system generates a range of statistics that can be useful in evaluating the degree to which individual problems are effective in promoting formative learning for students. Each exam problem contains attached metadata that catalog its degree of difficulty and discrimination for students at different phases in their education (i.e., introductory college courses, advanced college courses, and so on). To evaluate resource pool materials, a standardized format will be required so that materials from different sources can be compared. This will help resource users in selecting the most effective materials available. LON-CAPA has provided questionnaires, which are completed by faculty and students who use the educational materials to assess the quality and efficacy of resources. In addition to providing the questionnaires and using the statistical reports, we investigate methods to find criteria for classifying students and grouping problems. We plan to include information, such as time spent on a particular resource, resources visited (other web pages), due date for each homework, and the difficulty of problems (observed statistically). Thus, we should gather floods of data on individual usage patterns. Especially as students go through multiple steps to solve problems, and choose between multiple representations of the same educational objects like video lecture demonstrations, a derivation, a worked example, case-studies, and so forth. As the resource pool grows, multiple content representations will be available to students. There has been an increasing demand for automated methods for evaluating resources. One such method is data mining, which is focus of this survey. So, in this survey we are to use some automated methods using data mining to evaluate the resources in LON-CAPA, whether we would be able to have a good perspective of system description to figure out the association rules and dependency model of the system objects.Chapter 3Literature ReviewPresently, the amount of data stored in databases increases at an amazing speed. This gives rise to the expressive need for new techniques and tools to aid humans in automatically and intelligently analyze huge data sets to get useful information. This growing need gives birth to a new research field called Knowledge Discovery in Databases (KDD) or Data Mining, which has attracted more attention from researchers in many different fields including database, statistics, pattern recognition, machine learning, and data visualization. In this chapter well indicate the definition of KDD and Data Mining describing its tasks, methods, and applications. Our motivation in this study is gaining the best technique for extracting useful information from large amounts of data in an online educational system, in general, and from the LON-CAPA system, in particular. We aim to obtain an optimal predictive model for students who take part in such systems.What is Data Mining?Data Mining or KDD is the process of analyzing data from different perspectives and summarizing it into useful information. It has been defined as "the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data" (Frawley et al., 1992; Fayyad et al., 1996). It uses machine learning, statistical, and visualization techniques to discover and present knowledge in a form that is easily comprehensible. The word Knowledge in KDD refers to patterns, which are extracted from the processed data. A pattern is an expression describing facts in a subset of the data. Thus, the difference between KDD and data mining is that KDD refers to the overall process of discovery knowledge from data while data mining refers to application of algorithms for extracting patterns from data without the additional steps of the KDD process (Fayyad et al., 1996). However, since Data Mining is the crucial and important part of the KDD process, most researchers usually use both terms indistinguishably.Figure 3.1 presents the iterative nature of the KDD process. Here we outline some of its basic steps as are mentioned in (Brachman & Anad, 1996):Providing an understanding of the application domain, the goals of the system and its users, and the relevant prior background and prior knowledge. This step in not specified in this figure.Selecting a data set, or focusing on a subset of variables or data samples, on which discovery is to be performed.Preprocessing and data cleaning, removing the noise, collecting the necessary information for modeling, selecting methods for handling missing data fields, accounting for time sequence information and changes.Data reduction and projection, finding appropriate features to represent data, using dimensionality reduction or transformation methods to reduce the number of variables to find invariant representations for data.Choosing data mining task depending on the goal of KDD: clustering, classification, regression, and so forth.Selecting methods and algorithms to be used for searching for the patterns in the data.Mining the knowledge: searching for patterns of interest.Evaluating or interpreting the mined patterns, possible return to any of previous steps.Using this knowledge for promoting the performance of the system and resolving the potential conflicts with previously believed or extracted knowledge.Data Mining TasksGenerally, the objective of data mining in practice is prediction and description. Prediction is to predict unknown or future values of the attributes of interest using other attributes in the databases while description is to find patterns to describe the data in a manner understandable and interpretable to humans. Predicting sale amounts of new product based on advertising expenditure, or predicting wind velocities as a function of temperature, humidity, air pressure, etc., are examples of predictive tasks in data mining. The relative importance of description and prediction could vary in different applications. These two goals can be fulfilled by the following data mining tasks: classification, regression, clustering, summarization, discrimination, dependency modeling, prediction, and change and deviation detection. Predictive tasksClassification is to segregate items into several predefined classes. Given a collection of training samples, is to find a model for class attribute as a function of the values of other attributes.Regression is to predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. This is studied in statistics and neural network fields. Deviation Detection is to discover the most significant changes in data from previously measured or normative values. Descriptive tasksClustering is to identify a set of categories or clusters to describe the data. (Jain & Dubes, 1988)Summarization is to find a concise description for a subset of data. Tabulating the mean and standard deviations for all fields is a simple example of summarization. There are more sophisticated techniques for summarization and are usually applied to automated report generation and interactive data analysis (Fayyad et al., 1996).Dependency modeling is to find a model, which describes significant dependencies between variables. For example, probabilistic dependency networks use conditional independence to specify the structural level of the model and probabilities or correlation to specify the strengths (quantitative level) of dependencies (Heckerman, 1996).Discrimination is to discover the features or properties that distinguish one set of data (called target classes) from other sets of data (called contrasting classes).There are some tasks in data mining that has both descriptive and predictive aspects In other Mixed tasksThere are some tasks in data mining that has both descriptive and predictive aspects. Using these tasks, we can move from basic descriptive tasks toward higher predictive tasks. Here, we indicate two of them:Association Rule Discovery: Given a set of records each of which contain some number of items from a given collection; Produce dependency rules which will predict occurrence of an item based on occurrences of other items.Sequential Pattern Discovery: Given a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events. Rules are formed by first discovering patterns. Event occurrences in the patterns are governed by timing constraints. Data Mining Approaches/MethodsThere are different methodological approaches to data mining including machine learning, statistics, database-oriented, knowledge representation and visualization, neural network and so forth. Machine learning approaches include learning from examples, conceptual clustering, decision tree induction, and so forth. Mathematical and statistical approaches include Bayesian inference, and rough set theory, while database-oriented approaches include attribute-oriented induction, a priori, and so forth.This survey focuses on methods and algorithms in the context of data mining according to the descriptive and predictive tasks. First, we describe clustering methods in data mining. Then we study the classification methods developed in the related research and extend them for predictive purposes.ClusteringData clustering is a sub-field of data mining that is dedicated to techniques for finding similar groups in a large database. Data clustering is a tool for exploring data and finding a valid and appropriate structure of objects for grouping and classifying the data (Jain & Dubes, 1988). A cluster indicates a number of similar objects, such that the members inside a cluster are as similar as possible (homogeneity), but the data belong to different clusters that are as dissimilar as possible (heterogeneity) (Hoppner et al., 2000). The property of homogeneity is similar to the cohesion attribute between objects of a class in software engineering. Heterogeneity is similar to the coupling attribute between the objects of different classes in software engineering. Unlike data classification, data clustering does not require the category labels or predefined group information. Thus, clustering has been studied in the field of machine learning as a type of unsupervised learning, because it relies on the learning from observation instead of the learning from examples. We use data clustering methods in LON-CAPA, because we have a large amount of data without any category labels for the students and learning objects. Two general categories of clustering methods are partitioning method, and hierarchical method:Partitioning MethodsA partitioning algorithm assuming a set of n objects in d-dimensional space and an input parameter k organizes the objects into k clusters such that the total deviation of each object from its cluster center is minimized. The deviation of an object in a cluster depends on the similarity function, which depends on the criterion employed to distinguish objects of different clusters. Clusters can be of arbitrary shapes and sizes in multidimensional space. Every particular clustering criterion enforces a specified discipline on the data. Many criteria are introduced in related literatures. Here, we study some of the most popular partitioning methods: such as the square error approach, mixture density model, density or mode estimation, graph connectivity, and near neighbor relationship.The most common approach in these methods is to optimize the criterion function using an iterative, hill climbing technique. Starting from an initial partition, objects are moved from one cluster to another in an effort to improve the value of the criterion function (Jain & Dubes, 1988). Each algorithm has different way for representing its clusters. k-mean AlgorithmThe k-means algorithm (McQueen, 1967), uses the mean value of the objects in a cluster as the cluster center. The mean value of a cluster is computed according to this function:EMBED Equation.3= EMBED Equation.3where ni is number of patterns in cluster Ci, (among exactly k clusters: C1, C2, , Ck) and x is the point in space representing the given object. The total squared-error is computed in this way:E = EMBED Equation.3The steps of the iterative algorithm for partitional clustering are as follows: 1. Choose an initial partition with k < n clusters (EMBED Equation.3, EMBED Equation.3, , EMBED Equation.3 are cluster centers and n is the number of patterns).2. RepeatGenerate a new partition by assigning a pattern to its nearest cluster center EMBED Equation.3.Recompute new cluster centersEMBED Equation.3.Loop Until no change in EMBED Equation.3.6. ReturnEMBED Equation.3, EMBED Equation.3, , EMBED Equation.3 as the mean values of C1, C2, , Ck.The idea behind this iterative process is to start from an initial partition assigning the patterns to clusters and to find a partition containing k clusters that minimizes E for fixed k. In step 3 of this algorithm, k-means assigns each object to its nearest center forming a set of clusters. In step 4, all the centers these new clusters are recomputed with function E by taking the mean value of all objects in every cluster. This iteration is repeated until the criterion function E no longer changes. The k-means algorithm is an efficient algorithm with the time complexity of O(ndkt), where n is the total number of objects, d is the number of features, k is the number of clusters, and t is the number of iterations such that t<k<n. The weaknesses of this algorithm include a requirement to specify the parameter k, inability to find arbitrarily shaped clusters, and it is very sensitive to noise and outlier data. Some of the noise data can influence the mean value. Because of this, Jain & Dubes, (1988) have added an step before step 6: Adjust the number of clusters by merging and splitting the existing clusters or by removing small, or outlier clusters. Graph ConnectivityA graph can represent the relationship between patterns. Every vertex represent show a pattern and every edge would represent the relation between two patterns. The edge weights are distances between two adjacent vertices. The criterion function here is that the pairs of patterns, which belong to the same cluster, should be closer than the pairs belong to different clusters. Several graph structures, such as minimum spanning trees (MST) (Zahn, 1971), relative neighborhood graphs (Toussaint, 1980), and Gabriel Graphs (Urquhart, 1982), have been applied to present a set of patterns in a graph in order to detect the clusters. Here we only indicate the first algorithm. The basic idea of Zahns algorithm consist of the following steps:Construct the MST for the set of given n patterns. Determine the inconsistent edges in MST.Delete the inconsistent edges from MST. The remaining components are our clusters.This algorithm can be applied recursively on the resulting components to determine the new clusters. The crucial point of this algorithm is the definition of inconsistency. Zahn presents several criteria for inconsistency. An edge is inconsistent if its weight is so much larger than average of nearby edge weights. Other definitions of inconsistency are the standard deviation by which an edge weight differs from the average of nearby edge weights, and the ratio of the edge weight differs from average of nearby edge weights can be considered. More details about the comparison amongst MST-based algorithms and other structures of graph theory could be found in Jain & Dubes, (1988). Nearest Neighbor MethodIn this method every pattern is put in the same cluster as its nearest neighbor. Lu and Fu, (1978) have presented a simple clustering algorithm, which is based on the nearest neighbor method. This algorithm partitions a set of {x1, x2, , xn} patterns into a set of k clusters {C1, C2, , Ck}. The user should specify a threshold r, which determines the nearest neighbor distance. Let i = 1 and k = 1. Assign pattern x1 to cluster C1. Mark pattern x1.i = i + 1. Find the nearest neighbor of xi among the patterns already assigned to its nearest neighbor. Let dj denote the distance between pattern xi and its nearest neighbor in cluster j.If dj EMBED Equation.3r, then assign xi to Cj; otherwise k = k + 1 and assign xi to a new cluster Ck. Mark pattern xi .If all the patterns have been marked, stop; else go to step 2.The number of cluster k, which are generated, is a function of the parameter r. As the value of r is increased the number of clusters k is decreased. Mixture DensityIn this approach we assume that the patterns are drawn from the k known number of populations. The chance that a pattern comes from EMBED Equation.3 or the prior probabilities P(EMBED Equation.3) for each population are known where EMBED Equation.3 is ith population or cluster, and i=1, 2, , k. The populations do not label the patterns. The probability density function for population EMBED Equation.3 is p(x|EMBED Equation.3,EMBED Equation.3), where EMBED Equation.3 is a vector of unknown parameters for EMBED Equation.3. The mixture density can be written as:p(x |EMBED Equation.3) = EMBED Equation.3Where EMBED Equation.3 = (EMBED Equation.3,EMBED Equation.3, , EMBED Equation.3). The conditional densities p(x|EMBED Equation.3,EMBED Equation.3) are called the component densities, and the prior probabilities P(EMBED Equation.3) are called mixing parameters. We know that EMBED Equation.3Our goal is to use the patterns to estimate the parameter vector EMBED Equation.3 so that the mixture can be decomposed into its component clusters (Duda et al., 2001; Jain & Dubes, 1988). This approach is model based, but the model is Gaussian. The parameter vectorEMBED Equation.3is usually estimated by the maximum likelihood or Bayesian approach. Maximum Likelihood EstimationThis approach is also called density estimation or mode seeking. Clusters are considered in the regions of pattern space in which the patterns are found dense. The regions that include less numbers of patterns separate the clusters. One of the simplest ways to determine the modes in the data is to construct a histogram by portioning the pattern space into k number of non-overlapping regions. Regions with relatively high frequency counts are the modes or cluster centers (Jain & Dubes, 1988). The advantage of density estimation method is that it does not require knowing the number of clusters and their prior probabilities. The disadvantages of this approach are that the process of the looking for the peaks and valleys in the histogram is difficult in more than a few dimensions and requires the user to identify the valleys in histograms for splitting interactively. Fuzzy k-means clustering In all partitioning algorithms, i.e. in the previous k-means algorithm, each data point is allowed to be in exactly one cluster. We put away this condition in the fuzzy clustering algorithm and assume that each pattern has some fuzzy membership in a cluster. That is, each object is permitted to belong to more than one cluster with a grade of membership. Fuzzy clustering has three main advantages: 1) it maps numeric values into more abstract measures (fuzzification); 2) student features may overlap multiple abstract measures, and there may be a need to find a way to cluster under such circumstances; and 3) most real-world classes are fuzzy rather than crisp (usual). Therefore, it is natural to consider the fuzzy set theory as a useful tool to deal with the classification problem (Dumitrescu et al., 2000). Some of the fuzzy algorithms are modifications of the algorithms of the square error type such as k-means algorithm. The definition of the membership function is the most challenging point in a fuzzy algorithm. Baker, (1978) has presented a membership function based on similarity decomposition. The similarity or affinity function can be based on the different concept such as Euclidean distance, neighborhood, or probability. Baker and Jain (1981) define a membership function based on the mean vectors of clusters:EMBED Equation.3(x) = EMBED Equation.3Where EMBED Equation.3is the Euclidean distance, between pattern vector x and the mean vector EMBED Equation.3 of cluster Ck .The parameterEMBED Equation.3 indicates the neighborhood size and cluster membership. This fuzzy partitional clustering has the same steps of squared error algorithm.Hierarchical MethodsHierarchical methods decompose the given set of data items forming a tree, which is called dendrogram. A dendrogram splits the dataset recursively into smaller subsets. A dendrogram can be formed in two ways: Bottom-up approach, which is called agglomerative approach, starts with each object forming a distinct group. It successively merges the groups according to some measures like the distance between the centers of the groups and this is done until all of the groups are merged into one, the top most level of hierarchy. Top-down approach, which is called divisive approach, starts with all the objects in the same cluster. In every successive iteration, a cluster is split into smaller according to some measures clusters until eventually each object is in one cluster, or until a termination condition is met.Hierarchical methods are popular in biological, social and behavioral systems, which need to construct taxonomies. Dendrogram are impractical when the number of patterns exceeds a few hundred patterns (Jain & Dubes, 1988). Partitional techniques are more appropriate in the case of large data sets.Classification and Predictive ModelingClassification is to find a model that segregates data into predefined classes. This classification is based on the features present in the data. The result is a description of the present data and a better understanding of each class in database. Thus, it provides a model for describing future data (Duda et al., 2001; McLachlan, 1992; Weiss and Kulikowski, 1991; Hand, 1987). Prediction helps people to make a decision. Predictive modeling for knowledge discovery in databases is to predict unknown or future values of some attributes of interest based on the values of other attributes in a database (Masand and Shapiro, 1996). Different methodologies have been used for classification and developing predictive modeling including Bayesian inference (Kontkanen et al., 1996), neural net approaches (Lange, 1996), decision tree-based methods (Quinlan, 1986) and genetic algorithms-based approaches (Punch et al., 1995).Bayesian Classifier One of the major statistical methods in data mining is the Bayesian inference. The naive Bayesian classifier provides a simple and effective approach to classifier learning. It assumes that all class-conditional probability densities are completely specified and this assumption is often violated in the real world (Jain et al., 1999; Duda et al., 2001; Wu et al., 1991). The Bayes classifier shown in figure 3.2, could be explained as follows: A set of patterns aj, j = 1,,n, is given, every pattern is sensed by a sensing device which is capable of capturing the features. Each pattern is considered in terms of a measurement vector xi. A pattern aj belongs to a classification set EMBED Equation.3, which includes all the possible classes that can be assigned to pattern aj. For simplicity all feature measurement are considered identical and each pattern belongs only to one of the m possible classes EMBED Equation.3, i = 1,,m.To classify a pattern into one of the m classes, a feature space is constructed according to the measurement vector x, which is considered as a measurement of true values damaged by random noisy data. The class-conditional probability density functions estimated from a training data set represent the uncertainty in discovered knowledge.EMBED Equation.3, i = 1,,m.Bayes decision theory states that the a-posteriori probability that an event may be calculated according to the following equation: EMBED Equation.3 , i = 1,,m.Eventually, the decision criteria can be applied for classification. To gain the optimal solution, the maximum likelihood classification or the Bayesian minimum error decision rule is applied. It is obtained by minimizing the misclassification and errors in classification. Thus, a pattern classified into class EMBED Equation.3 with the highest posteriori probability or likelihood:EMBED Equation.3The quadratic discriminant function using the Bayesian approach is the most common method in supervised parametric classifiers. The feature vectors in our datasets are assumed to be Gaussian distributed, and the parameters of the Gaussians are estimated using maximum likelihood estimation. The discriminant function decision rule and the a-posteriori probabilities for each classification are calculated for each sample test x using the following equation (Duda et al., 2001):EMBED Equation.3where is the mean value and EMBED Equation.3 is the covariance matrix of the training set. To obtain the optimal solution, the maximum likelihood classification or the Bayesian minimum error decision rule is applied. The sample is then assigned to the type that produces the highest a-posteriori probability. It is obtained by minimizing the misclassification and errors in classification. Decision tree-based methodThe Decision tree-based methods are the most popular methods in data mining context. The decision tree classifier uses hierarchical or layered approach to classification. Each vertex in the tree represents a single test or decision. The outgoing edges of a vertex correspond to all possible outcomes of the test at that vertex. These outcomes partition the set of data into several subsets, which are identified by every leaf in the tree accordingly. A leaf of the tree specifies the expected value of the categorical attribute for the records described by the path from the root to that leaf. Learned trees can also be represented as sets of if-then-else rules. (Mitchell, 1997)An instance is classified by starting at the root node of the tree. At each level of the tree the attributes of instances are matched to a number of mutually exclusive nodes. The leaf nodes assign classes to the instances. The classification of an instance therefore involves a sequence of tests, with each successive test narrowing the interpretation. The sequence of tests for the classifier is determined during a training period. Given some new data, the ideal solution would test all possible sequences of actions on the attributes of the new data in order to find the sequence resulting in the minimum number of misclassifications. Tree-based classifiers have an important role in pattern recognition research because they are useful with non-metric data in particular. (Duda et al., 2001) Decision tree methods are robust to errors, both errors in classifying the training examples and errors in the attribute values that describe these examples. Decision tree can be used when the data contain missing attribute values. (Mitchell, 1997) Most of algorithms that have been developed for decision trees are based on a core algorithm that uses a top-down, recursive, greedy search on the space of all possible decision trees. This approach is implemented by ID3 algorithm (Quinlan, 1986) and its successor C4.5 (Quinlan, 1993). C4.5 is an extension of ID3 that accounts for unavailable values, continuous attribute value ranges, pruning of decision trees, rule derivation, and so on. We used C5.0, CART, and Quest software for tree-based classification, whose results will be explained in the next chapter. What is the best feature for splitting?The first question that arises in all tree-based algorithms is which properties tested in each node, are binary-valued or multi-valued? The second question that arises in all tree-based algorithms is that, which property should be tested at a node? In other words, which attribute is the best classifier? We would like to select the attribute is the most informative of the attributes not yet considered in the path from the root. This establishes what is a "Good" decision tree. Entropy is used to measure a nodes information. Claude Shannon (1984) introduced this notion in Information Theory. Based on entropy, a statistical property called information gain measures how well a given attribute separate the training examples in relation to their target classes. Entropy impurityEntropy characterizes impurity of an arbitrary collection of examples S at a specific node N. Sometimes (Duda et al., 2001) the impurity of a node N is denoted by i(N). Entropy(S) = EMBED Equation.3where EMBED Equation.3 is the fraction of examples at node N that go to category EMBED Equation.3. So, if all the patterns are from the same category the impurity is 0, otherwise it is positive; if all categories are equally distributed at node N then the impurity has its greatest value 1. Now we come up with the key question at a sub-tree down to node N, what value of feature should we select for the test at node N when property query T is used? Duda et al. (2001) suggested a heuristic to select a query that decreases the impurity as much as possible.EMBED Equation.3where EMBED Equation.3 and EMBED Equation.3 are the left and right descendent nodes, and the EMBED Equation.3 and EMBED Equation.3 are their impurities respectively, and EMBED Equation.3 is fraction of patterns at node N that will go to EMBED Equation.3 when the property query T is used. The goal of the heuristic is to maximize the EMBED Equation.3, thus minimizing the impurities corresponds to an information gain, which is provided by the query.Gini impurityOne of the CART activities is to rank and order each splitting rule on the basis of a quality-of-split criterion. The default criterion used in CART is the Gini impurity; essentially a measure of how well the splitting rule separates the classes contained in the parent node (Duda et al., 2001). EMBED Equation.3As shown in the above equation, it is strongly peaked when probabilities are equal. So what is Gini trying to do? Gini attempts to separate classes by focusing on one class at a time. It will always favor working on the largest or, if you use costs or weights, the most "important" class in a node. While this approach might seem short sighted, Ginis performance is frequently so good that you should experiment and see how well it does. Gini is the default rule in CART precisely because it is often the best splitting rule. Twoing impurity An alternative criterion also available in CART is Twoing impurity. The philosophy of Twoing is far different than that of Gini. Rather than initially pulling out a single class, Twoing first segments the classes into two groups, attempting to find groups that together add up to 50 percent of the data. Twoing then searches for a split to separate the two subgroups (Duda et al., 2001). This is an ideal split. It is unlikely that any real-world database would allow you to cleanly separate four important classes into two subgroups in this way. However, splits that approach this ideal might be possible, and these are the splits that Twoing seeks to find.Avoid overfittingIf we continue to grow the tree until each leaf node corresponds to the lowest impurity then the data is typically overfit. In some cases every node corresponds to a single training input. In such cases we cannot expect an appropriate generalization in noisy problems having high Bayes error (Duda et al. 2001; Russell and Norvig, 1997). On the other hand if we stop splitting early, then a high performance tree classifier will not be met. There are several approaches to avoid overfitting in the training phase of tree-based classification:Cross-ValidationCross-validation is a technique to eliminate the occurrence of overfitting. The main idea of cross-validation is to estimate how well the current hypothesis will predict unseen data (Duda et al. 2001; Russell and Norvig, 1997). This is done by dividing randomly the data into two subsets, training and test. Usually, the test subset is a fraction of all of the data, i.e. 10%. The hypothesis induced from the training phase is tested on the rest of data to get the prediction performance. This should be repeated on different subsets of data, and then the result averaged. Cross-validation could be used in order to select a tree with good prediction performance.Setting a thresholdAnother method is to consider a small threshold value in minimizing the impurity. We stop splitting when the impurity at a node reduces by less than the considered threshold. The benefit of this method over the cross-validation is that the tree is trained using all the training data. Another benefit of this method is that leaf node can lie in different levels of the tree.PruningThe principal alternative of stop splitting is pruning (Duda et al., 2001). One approach, called reduced-error pruning (Quinlan, 1987), is to set every nodes in the decision tree to be candidate for pruning. Pruning a decision node consist of removing the subtree rooted at that node, making it a leaf node, and assigning it the most common classification of the training examples affiliated with that node. Nodes removed only if the resulting pruned tree performs no worse than the original over the validation set. (Mitchell, 1997)In C4.5, Quinlan (1993) applied a successful technique for finding high accuracy hypothesis during the pruning process, which is called rule post pruning. It involves the following steps:Induce the decision tree from the training set, growing the tree until the training data is fully fitted as well as possible, allowing overfitting to occur.Convert the learned tree into an equivalent set of rules by creating one rule correspond to a path from root to a leaf node.Prune each rule by deleting any preconditions that lead to promoting the estimated accuracy.Sort the pruned rules by their estimated accuracy, and set them in a sequence for classifying the instances.Why we prefer short hypothesis? Create small decision trees so that records can be identified after only a few questions. According to Occam's Razor, Prefer the simplest hypothesis that fits the data (Duda et al., 2001). Neural Network ApproachA neuron is a special biological cell with information processing ability (Jain et al., 1996). The classification approach based on Artificial Neural Network (ANN), which is called connectionist model, generates a lower classification error than the decision tree approach in several cases, but it needs longer training time (Quinlan, 1994; Russle and Norvig, 1995). A neural network is usually a layered graph with the output of one node feeding into one or more nodes in the next layer. These results with a complexity in understanding the classification rules from the structure of graph and weights assigned to the links between the nodes. The Multi-layer Perceptron (MLP) is a basic feedforward artificial neural network using the back-propagation algorithm. That is, during training, information is propagated back through the network and used to update connection weights. According to Ruck et al., (1990), the multi-layer perceptron trained using the back-propagation learning algorithm approximates the optimal discriminant function defined by Bayesian theory. The output of the MLP approximates the posterior probability functions of the classes being trained. The Sigmoidial activation function is used for learning the input weight vectors in the training phase as follows:EMBED Equation.3Tuning the learning rate, the number of epochs, the number of hidden layers, and the number of neurons in every hidden layer is a very difficult task far obtaining a good performance for MLP. In each epoch the input data are used with the present weights to determine the errors, then back-propagated errors are computed and weights updated. A bias is considered for the hidden layers and output.Describing the problem how to adopt the data mining techniques is not possible without representing the data in an explicit way. Lu et al.(1995) made an effort to overcome this obstacle. They use a three-layer neural network to perform classification. ANN made up of a lot of simple computational elements (neurons), which are densely interconnected. Among the network topologies the multilayer perceptron is useful for classification purposes. Figure 3.3 shows a three-layer feedforward network, which has an input layer, a hidden layer, and an output layer. A node (neuron) in the network has a number of inputs and a single output. Every link in the network is associated with a weight. For example, node Ni has EMBED Equation.3, , EMBED Equation.3as its inputs and ai as its output. The input links of Ni has weights EMBED Equation.3, , EMBED Equation.3. A node generates its output (the activation value) by summing up its input weights, subtracting a threshold and passing the result to a non-linear function f (activation function). Outputs from neurons in a layer are fed as inputs to next layer. Thus, when an input tuple (EMBED Equation.3, , xn) is applied to the input layer of a network, an output tuple (EMBED Equation.3, , cm) is obtained, where ci has value 1 if the input tuple belongs to class ci and 0 otherwise.Their approach uses ANN to mine classification rules through three steps explained as follows:In the first step, a three-layer network is trained to find the best set of weights to classify the input data at a satisfactory level of accuracy. The initial weights are selected randomly from interval [-1, 1]. Then, these weights are updated according to the gradient of an error function. This training phase is terminated when the norm of the gradient falls below a threshold.The redundant links and nodes without any effects on performance are removed and therefore obtained pruned network.Comprehensible and concise classification rules are extracted from the pruned network in the form of: if (a1 EMBED Equation.3 v1) & (a2 EMBED Equation.3 v2) & & (an EMBED Equation.3 vn) then Cj where an ai is an input attribute value, vi is a constant like vi, and EMBED Equation.3 is a relational operator (=, EMBED Equation.3 EMBED Equation.3, <,>) and Cj is one of the class labels.k-Nearest Neighbor (kNN) Decision RuleThe k-nearest neighbor algorithm makes a classification for a given sample without making any assumptions about the distribution of the training and testing data. Each testing sample must be compared to all the samples in the training set in order to classify the sample. In order to make a decision using this algorithm, the distances between the testing sample and all the samples in the training set are first calculated. In this survey, the Euclidean distance is calculated, but, in general, any distance measurement may be used. Euclidean distance metric requires normalization of all features into the same range. At this point, the k closest neighbors of the given sample are determined where k represents an integer number between 1 and the total number of samples. The testing sample is then assigned the label most frequently represented among the k nearest samples (Duda et al., 2001). The value of k that is chosen for this decision rule has an affect on the accuracy of the decision rule. The k-nearest neighbor classifier is a nonparametric classifier that is said to yield good performance for optimal values of k. To test this, different values of k were tested in this survey. Finally, the value k =3, in kNN is chosen.Parzen Window classifierIn this approach a d-dimensional window is formed around all the training samples and then, based on the number of patterns that fit in those windows, the probability estimates of the different classes are made. This can be stated as follows (Duda et al., 2001):EMBED Equation.3where EMBED Equation.3is a d-dimensional hypercube in the feature space, and EMBED Equation.3 is a general probability distribution function. EMBED Equation.3 is the probability that the pattern fits in the given class. It is necessary to choose the form of EMBED Equation.3. In our case, the multivariate normal distribution is assumed forEMBED Equation.3. The windows are centered on the training points, hence, the mean is known, but there is no predefined method to determine the variance. Depending upon the problem under study, the variance is estimated by minimizing the error rate and maximizing the classifier performance. Therefore, it needs to be determined by trial and error. In all datasets we assume that the classes are independent and thus the covariance matrices for the Gaussian distribution are diagonal. Genetic Algorithm (GA)Many times the learning procedure is considered as a search through the space of representations. Genetic algorithm presents a powerful alternative to traditional search techniques. It has been inspired by a similar principle in nature. Evolution in nature is controlled through the following principles:Survival of the fittest: the strongest specimens have the highest chance to survive and reproduce, while the weak ones are likely to die before the reproduction stage.Reproduction: the fittest specimens recombine their genetic information, thus creating new specimens with somewhat new characteristics.Mutation leads to random changes in genetic information.The success of this search technique in the nature inspired some researchers to propose methods and develop algorithms that could be encoded in computer programs. A clear and simple introduction to the discipline of genetic algorithm has been made by Goldberg (1989). To start a successful attempt to cast a real problem in a setting that its solution is leveraged by means of a genetic algorithm, two important step are be paved: encoding the search space into chromosomes; defining a fitness function that plays the role of an evaluation function in heuristic search. The implementation of chromosome is typically in the form of bit strings.GA-AlgorithmMichalski et al. (1998) has presented the main steps of GA, that is reproduction, recombination, and mutation in the following algorithm:Construct the initial population as set of binary strings generated randomly or by some pre-specified mechanisms.Replicate the specimen in the population into a set of survivors by a mechanism that ensures that specimens with a higher fitness value have a higher chance of survival.Pair up all the survivors such that each has a mate. Next specific chunks of encoded data is swapped between mates. Mutation arises when a single bit flip-flops. If the fitness function has not improved through several cycles, stop; otherwise go to 2.Using the GA in pattern classificationAn important question arises in relation to classification: How to use Genetic Algorithm for pattern classification? There are two different approaches to applying GA in pattern recognition:Apply GA directly as a classifier. (Bandyopadhya and Murthy, 1995; Srickanth et al., 1995)Use GA as an optimization tool for resetting the parameters in other classifiers.Most application of GA in pattern recognition applies GA as an optimizer for some parameters in the classification process. Many researchers used GA in feature selection and feature extraction. GA is applied to find an optimal set of feature weights that improve classification accuracy. First, a traditional feature selection/extraction like Principal Component Analysis (PCA) is applied, then a classifier like k-NN is used to calculate the fitness function for GA. (Seidlecki, 1989; Pie et al., 1998). The combination of classifiers is another area that GA is used for optimization. Kuncheva and Jain (2000) used GA for selecting the features as well as selecting the types of individual classifiers in their design of Classifier Fusion System.Feature Selection and ExtractionFeature extraction and selection is a very important task in classification process. Why we need to use feature selection or extraction? Jain (2000) presented an insightful review of feature selection. We can denote to the benefits of feature selection and extraction as follows:Minimizing the costIn many real world applications, feature measurement to find the feature vectors is very costly, especially with a large sample size. Pie, Goodman, and Punch (1998) presented in the context of a biological pattern classification that the most important 8 out of 96 features gives 90% classification accuracy. They showed that feature selection has a great potential effect in minimizing the cost of extracting features and maintaining good classification results.Data VisualizationMany times for explanatory purposes we need to project high dimensional data in two or three dimension. The main concern is that how to protect the distance information and deployment of original data in two or three dimensions. The traditional approach in data visualization is linear projection. A more convenient choice is projecting the data onto a graph (Mori 1998). Chernoff Faces is another method that projects the feature space to cartoon face, by which one can visualize more than three features (Chenoff, 1973). Curse of DimensionalityGenerally, more features mean more information. Based on an ideal situation that we have an infinite number of training samples, the classification would be more accurate. Also, having more features from classification patterns improve the performance. Nevertheless, in real applications with a finite number of sample size, there is a maximum of performance is inversely proportional to the number of features (Duda and Heart, 1973). The demand for a large number of samples grows exponentially with the dimensionality of the feature space. This limitation is called the curse of dimensionality (Duda et al., 2001). Trunk (1979), has shown the curse of dimensionality problem through an exciting and simple example. This puts a limitation on non-parametric decision rules such as k-nearest neighbor. So, it is often desirable to reduce the dimensionality of the space by finding a new set of bases for the feature space.Feature Selection A through review of feature selection has been presented in Jain (2000). Jain et al. (1997) presented a good taxonomy of available feature selection algorithms based on pattern recognition or ANN, sub-optimal or optimal, single solution or multi-solution and deterministic or stochastic. What are the criteria for choosing a feature subset? A subset of features might be chosen in regards to the following points:The relevance to the classification result: that is we remove the irrelevant features intuitively. Langley (1994) has a useful review on relevance and feature selection.The correlation with the other features: The high correlation among features will add no more efficiency to classification, as it is explained by regression theory in statistics. If two features are highly correlated, one of the features is redundant, even though is relevant.John et al. (1994) has presented the definitions of Strong Relevance and Weak Relevance considering the correlations among feature samples. Hall and Smith (1998) formulated measure of Goodness of feature as follows:Good feature subsets contain features highly correlated (predictive of) with the class, yet uncorrelated with (not predictive of) each other.Feature Extraction Feature extraction is a linear or non-linear transformation of the original feature space. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are the most commonly used techniques for feature extraction. PCA is an unsupervised technique, which is intended for feature extraction. The idea of the PCA is to preserve maximum variance after transformation of original features into new features. The new features are also called principal components or factors. Some factors carry more variance than other. If we limit the total variance preserved after such a transformation to some portion of original variance we can generally keep a smaller number of features. PCA performs this reduction of dimensionality by determining the covariance matrix. After PCA transformation in d-dimensional feature space the m (m < d) largest eigenvalues of the d x d covariance matrix are preserved. That is, the uncorrelated m projections in the original feature space with the largest variances are selected as the new features, thus the dimensionality reduces from d to m (Duda al et., 2001).LDA uses the same idea in the supervised learning environment. That is, it selects the m projections using the criterion that maximizes the inter-class variance while minimizes the intra-class variance (Duda al et., 2001). Chapter 4ExperimentsOur objective is to predict the students final grades based on their features, which are extracted from the homework data. We design, implement, and evaluate a series of pattern classifiers with various parameters in order to compare their performance in a real dataset of LON-CAPA system. This experiment provides an opportunity to study how pattern recognition and classification theory could be put into practice regarding to the logged data in LON-CAPA. The error rate of the decision rules is tested on one of the LON-CAPA dataset in order to compare the performance of each experiment. Results of individual classifiers, and their combination as well as error estimates are presented. The most difficult phase of the survey is properly pre-processing and preparing the data for classification. Some perl modules are developed to extract and segment the data from the logged database and visualize the useful data in statistical tables and graphical charts. More details of these tasks will be explained in part of this chapter for data acquisition and data representation. Dataset and Class LabelsAs the first step in our study, in order to have an experiment in student classification, we selected the student and course data of a LON-CAPA course, PHY183 (Physics for Scientists and Engineers I), which was held at MSU in spring semester 2002. This course integrated 12 homework sets including 184 problems. About 261 students used LON-CAPA for this course. Some of students dropped the course after doing a couple of homework sets, so they do not have any final grades. After removing those students, finally it remained 227 valid samples remained. You can see the grade distribution of the students in the following chart (figurer 4.1)We group the student regarding their final grades in several ways, which allows us classify the students in 3 ways: First: Let the 9 possible class labels be the same as students grades, as shown in table 4.1Class123456789Grade00.511.522.533.54# of students2010282343524128Percentage0.9%0.0%4.4%12.4%10.1%18.9%22.9%18.0%12.4%Second: We can label the students in relation to their grades and group them in three classes, high grades 3.5 and 4.0, middle grades 2.5 and 3, and low who has got the grades less than 2.5. 1HighGrade >= 3.56930.4%2Middle2.0 <Grade < 3.59541.8%3LowGrade <= 2.06327.8%Third: We can also categorize the students in regard to two class labels Passed grades higher than 2.0, and Failed for grades less than or equal to 2.0, as shown in table 4.3. 1Passed Grade > 2.016472.2%2FailedGrade <= 2.06327.8%Obviously, we can predict that the error rate in the first grouping should be higher than the others, because the distributions of the grades over 9 classes are so different. It is clear that we do not have enough data to model the first three classes in the training phase, and so the error rate would be high in the training phase. Before selecting any classifiers to learn the training data and choosing any model for prediction, we should discuss some pre-processing steps such as how to find the useful data from the logged data. Also, in what way we can visualize the various types of data. Well justify that the extracted data should undergo a normalization process before classification begins. Data Acquisition and Extracting the FeaturesThe problem is whether we can find the good features for classifying students! If so, we would be able to identify a predictor for any individual student after doing a couple of homework sets. With this information, we would be able to help a student use the resources better. As the first step to data mining, we want to make an initial effort to classify the students. Preprocessing student databasePreprocessing and finding the useful student data and segmenting may be a difficult task. As mentioned earlier, LON-CAPA, has two kinds of large data sets: Educational resources such as web pages, demonstrations, simulations, and individualized problems designed for use on homework assignments, quizzes, and examinations; Information about users, who create, modify, assess, or use these resources. Homework data has the following structure:resource.partid.opendate #unix time of when the local machine should let the #student inresource.partid.duedate #unix time of when the local machine should stop #accepting answersresource.partid.answerdate #unix time of when the local machine should #provide the correct answer to the studentresource.partid.weight # points the problem is worthresource.partid.maxtries # maximum number of attempts the student can haveresource.partid.tol # lots of possibilities here # percentage, range (inclusive and exclusive), # variable name, etc # 3% # 0.5 # .05+ # 3%+ # 0.5+,.005resource.partid.sig # one or two comma sepearted integers, specifying the # number of significatn figures a student must useresource.partid.feedback # at least a single bit (yes/no) may go with a # bitmask in the future, controls whether or not # a problem should say "correct" or notresource.partid.solved # if not set, problem yet to be viewed # incorrect_attempted == incorrect and attempted # correct_by_student == correct by student work # correct_by_override == correct, instructor override # incorrect_by_override == incorrect, instructor override # excused == excused, problem no longer counts for student # '' (empty) == not attempted # ungraded_attempted == an ungraded answer has been sumbitted and storedresource.partid.tries # positive integer of number of unsuccessful attempts # made, malformed answers don't count if feedback is # onresource.partid.awarded # float between 0 and 1, percentage of # resource.weight that the stundent earned.resource.partid.responseid.submissons # the student submitted string for the part.responseresource.partid.responseid.awarddetail # list of all of the results of grading the submissions # in detailed form of the specific failure # Possible values: # EXACT_ANS, APPROX_ANS : student is correct # NO_RESPONSE : student submitted no response # MISSING_ANSWER : student submitted some but not # all parts of a response # WANTED_NUMERIC : expected a numeric answer and # didn't get one # SIG_FAIL : incorrect number of Significant Figures # UNIT_FAIL : incorrect unit # UNIT_NOTNEEDED : Submitted a unit when one shouldn't # NO_UNIT : needed a unit but none was submitted # BAD_FORMULA : syntax error in submitted formula # INCORRECT : answer was wrong # SUBMITTED : submission wasn't gradedThe original data are stored with escape sequence codes like this:1007070627:msul1:1007070573%3a%2fres%2fadm%2fpages%2fgrds%2egif%3aminaeibi%3amsu%261007070573%3a%2fres%2fadm%2fpages%2fstat%2egif%3aminaeibi%3amsu%261007070574%3amsu%2fmmp%2flabquiz%2flabquiz%2esequence___1___msu%2fmmp%2flabquiz%2fnewclass%2ehtml%3aminaeibi%3amsu%261007070589%3amsu%2fmmp%2flabquiz%2flabquiz%2esequence___5___msu%2fmmp%2flabquiz%2fproblems%2fquiz2part2%2eproblem%3aminaeibi%3amsu%261007070606%3a%2fadm%2fflip%3aminaeibi%3amsu%261007070620%3a%2fadm%2fflip%3aminaeibi%3amsu%261007070627%3a%2fres%2fadm%2fpages%2fs%2egif%3aminaeibi%3amsu%261007070627%3a%2fadm%2flogout%3aminaeibi%3amsu To sense the data we use the following Perl module function: my $str; my $line; open (LOG ,$file);while ($line =<LOG>) { my ($dumptime,$host,$entry)=split(/\:/,$line); my $str = unescape($entry); my ($time,$url,$usr,$domain,$store,$dummy)=split(/\:/,$str); my $string = escape($store); foreach(split(/\&/,$string)){ print "$time $url $usr domain \n"; }}sub unescape { my $str=shift; $str =~ s/%([a-fA-F0-9][a-fA-F0-9])/pack("C",hex($1) )/eg; return $str;}After passing the data from this filter we have the following results:1007070573 /res/adm/pages/grds.gif minaeibi /res/adm/pages/stat.gif 1007670091 /res/adm/pages/grds.gif minaeibi /adm/flip 1007676278 msu/mmp/labquiz/labquiz.sequence___2___msu/mmp/labquiz/problems/quiz1part1.problem 1007743917 /adm/logout minaeibi 1008203043 msu/mmp/labquiz/labquiz.sequence___1___msu/mmp/labquiz/newclass.html minaeibi 1008202939 /adm/evaluate minaeibi /adm/evaluate 1008203046 /res/adm/pages/g.gif minaeibi /adm/evaluate 1008202926 /adm/evaluate minaeibi The student data restored from .db files in student directory and is fetched into a hash table. The special hash keys keys, version and timestamp were evaluated from the hash. The version will be equal to the total number of versions of the data that have been stored. The timestamp attribute is the UNIX time the data was stored. keys is available in every historical section to list which keys were added or changed at a specific historical revision of a hash. We extract some of the features from a structured homework data, which is stored as particular URLs. For example the result of solving homeworks problem by students could be extracted from resource.partid.solved, the total number of the students for solving the problem could be extracted from resource.partid.tries, and so forth. One of the difficult phase to data mining in LON-CAPA system, is gathering the student and course data, which are distributed in several locations. Finding the relevant data and segmentation phase may be complicated as well. Preprocessing Activity.logSince spring semester 2002, LON-CAPA has logged every activity of every student who has used online educational resources and their recorded paths through the web. This information is stored in activity.log which is located in a courses directory. The data stored in activity.log includes user name, time and resource URL. We can divide these data into six types of URLs, listed below, regarding their importance for data mining: 1. problems: are the most useful data. i.e. msu/mmp/kap14/kap14.sequence___33___msu/mmp/kap14/problems/cd418a.problem 2. html pages to those are some links in the problems. i.e. msu/mmp/kap14/kap14.sequence___5___msu/mmp/kap14/cd396.htm 3. The images, which are loaded in above html pages. i.e. /res/msu/mmp/kap14/picts/backsoun.gif .4. loncapa routines: i.e. /adm/navmaps, or /adm/roles, or /adm/logout. 5. Posted data by students: i.e. resource.0.11.submission=27.11 6. remote control gif files: i.e.: /res/adm/pages/v.gif So, the activity.log usually grows faster, when students have more access to the educational resources. We brought a sample of different types of data, which are logged in activity.log after a preprocessing phase as follows: 144) 1010955846: studentX --> /adm/navmaps 145) 1010955205: studentX --> /res/msu/mmp/kap14/picts/beta_eqn.gif 147) 1010955988: studentX --> /adm/navmaps 148) 1010955998: studentX --> msu/mmp/kap14/kap14.sequence___5___msu/mmp/kap14/cd396.htm 149) 1010955999: studentX --> /res/msu/mmp/kap14/picts/velocity_eqn3.gif 150) 1010956000: studentX --> /res/msu/mmp/kap14/picts/time_eqn.gif 151) 1010954609: studentX --> /res/adm/pages/grds.gif 152) 1010954611: studentX --> /res/msu/mmp/wordproc.gif 153) 1010954626: studentX --> /res/adm/pages/i.gif 154) 1010955717: studentX --> msu/mmp/kap14/kap14.sequence___1___msu/mmp/kap14/cd392.htm 155) 1010955717: studentX --> /res/msu/mmp/kap14/picts/backsoun.gif 156) 1010955920: studentX --> msu/mmp/kap14/kap14.sequence___3___msu/mmp/kap14/cd394.htm 157) 1010955921: studentX --> /res/msu/mmp/gifs/demo.gif 163) 1010955754: studentX --> msu/mmp/kap14/kap14.sequence___2___msu/mmp/kap14/cd393.htm 164) 1010955756: studentX --> /res/msu/mmp/kap14/picts/asound.jpg 166) 1010955999: studentX --> /res/msu/mmp/gifs2/example.gif 173) 1010955687: studentX --> /res/adm/pages/u.gif 174) 1010955688: studentX --> /res/adm/pages/e.gif 175) 1010956528: studentX --> msu/mmp/kap14/kap14.sequence___33___msu/mmp/kap14/problems/cd418a.problem 176) 1010956536: studentX --> msu/mmp/kap14/kap14.sequence___33___msu/mmp/kap14/problems/cd418a.problem 178) 1010956536: studentX --> msu/mmp/kap14/kap14.sequence___33___msu/mmp/kap14/problems/cd418a.problem 179) Sent data resource.0.11.submission=27.11 180) 1010956702: studentX --> msu/mmp/kap14/kap14.sequence___33___msu/mmp/kap14/problems/cd418a.problem Figure 4.2: A sample of Activity.log data, which is extracted some preprocessing phase Extractable FeaturesWe can explore the following features from the data, which are stored by the LON-CAPA system: Total number of correct answers. (Success rate)Getting the problem right on the first try, vs. those with high number of tries. (Success at the first try)Total number of tries for doing homework. (Number of attempt before correct answer is derived)Hours until correct. Time at which the student got the problem correct. Usually better students get the homework completed earlier. Time spent on the problem untill solved. (Difference between time of last successful submission and the first time opened the problem).Total time spent on the problem whether got correct answer or not. (Difference between time of the last submission and the first time opened the problem).Participating in the communication mechanisms, vs. those working alone.Reading the material before attempting homework vs. attempting first and then read up on it.Submitting a lot of attempts in a short amount of time without looking up material in between, vs. those giving it one try, reading up, submitting another one, etc.Giving up on a problem versus students continuing trying up to the deadline.It might be interesting to group students with time of the first log on (beginning of assignment, middle of the week, last minute) and correlate this with the number of tries or number of solved problems. A student who gets all correct answers will not necessarily be in the successful group if they took an average of 5 tries per problem, but it should be verified from this research. At this time we were able to extract the first six features in course PHY183 SS02 that we have chosen for the classification experiment.Visualizing the data in LON-CAPA (Statistical Information)Usually, instructors or course coordinators wish to assess the students educational situation or evaluate the problems presented in the course, after the students used the educational materials. We developed some perl modules that provide the statistical information for instructors and course-coordinators. Student AssessmentWe extract from student data some reports of the current educational situation of every student as you can see in table 4.4. A Y shows that the student has solved the problem and N shows a failure. A - denotes an un-attempted problem. The numbers in the right column show the total number of tries of the student in solving the corresponding problems.Top of Form# Homework Set TitleResultsTries1 msu/mmp/phy183.sequence 2 msu/mmp/kap1/calckap1.sequence YYYYYYYYYYYYY 1,1,1,2,1,1,1,1,1,7,9,3,23 msu/mmp/kap2/calckap2.sequence YYNYYYYNNYYYYYYYY 10,1,0,1,1,2,1,4,5,1,1,1,3,2,1,1,14 msu/mmp/kap3/calckap3.sequence YYYYYYYYNYYYYYYYYYY 4,3,5,1,1,8,2,3,20,1,1,1,1,1,2,3,2,2,35 msu/mmp/kap4/calckap4.sequence NYYYYYYYYYYYYYYY 20,1,1,1,3,3,2,3,4,2,3,2,1,1,2,56 msu/mmp/kap5/calckap5.sequence YYYYYYYYYYY-YY 5,2,1,9,12,1,3,12,1,2,1,,1,37 msu/mmp/kap6/calckap6.sequence YYYYYYYYYYYYYY 3,2,4,2,1,1,2,1,1,9,2,3,2,28 msu/mmp/kap7/calckap7.sequence YYYYYYYYYYYYYYYYYY 4,1,3,1,10,4,1,1,2,1,1,2,1,2,1,3,1,39 msu/mmp/kap8/calckap8.sequence YYYYYYYYYYYYYYY 4,3,1,2,3,3,4,3,3,1,1,4,1,1,710 msu/mmp/kap9/calckap9.sequence YYYYN-NYYNY 1,1,1,2,1,,2,2,1,6,411 msu/mmp/kap10/calckap10.sequence YYYYYYYYYYYY 2,1,1,1,1,1,1,1,1,1,1,112 msu/mmp/kap11/calckap11.sequence ----------------- 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0Table 4.4: A sample of a student homework results and triesLON-CAPA also provides a quick review of students tries on different problems of a course. The instructor may monitor the number of tries of every student in each map and its problems. The number of solved problems in a map is shown in the end of each map. The overall solved problems and total number of problems in the map are shown for every individual student. A small sample of this report is shown in figure 4.3.username1:msu ! 001 ! 1*11*121 8 1231.31423212 12 2111211284.131 13 231113112221 12 162 / 188 username2:msu ! 003 ! 12113162 8 1+11 1 21x111 10 11211322246132 14 ############ 0 149 / 188 1..9: correct by student in 1..9 tries *: correct by student in more than 9 tries +: correct by override -: incorrect by override .: incorrect attempted #: ungraded attempted : not attempted x: excusedFigure 4.3. Chart of map, problems, and students tries, and a quick statistics of solved problemsStatistics TableLON-CAPA presents a lot of statistical information about every problem in a table like shown in table 4.5, we call it Stats Table. Every part of multi-part problems is distinguished as a separate problem. The multi-instance problem is also considered separately, because a particular problem or one part of it might be used in different maps. Finally, the array, which includes all computed information from all students, sorted according to the problem order, underlying homework sets order. Therefore, in this step we can compute the following statistical information:#Stdnts: Total number of students who take a look at the problem.(Let #Stdnts is equal to n)Tries: Total number of tries to solve the problem (EMBED Equation.3where EMBED Equation.3denote a student try).Mod: Mode, Maximum Number of Tries for solving the problem.Mean: Average Number of the Tries. EMBED Equation.3=EMBED Equation.3#YES: Number of students solved the problem correctly.#yes: Number of students solved the problem by override. Sometimes, a student gets a correct answer after talking with the instructor. This type of correct answer is called corrected by override.%Wrng: Percentage of students tried to solve the problem but still incorrect. EMBED Equation.3 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/mmp/kap1/calckap1.sequence" \t "_blank" Homework Set 1 P#Homework Set Order#StdntsTriesModMean#YES#yes%WrngDoDiffS.D.Skew.D.F. 1stD.F. 2nd1 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/01_Math_1/msu-prob11.problem" \t "_blank" Calculator Skills25626731.0425600.00.040.25.70.030.002 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/01_Math_1/msu-prob10.problem" \t "_blank" Numbers256414171.6225500.40.381.65.70.110.023 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/03_Units_Scaling/msu-prob22.problem" \t "_blank" Speed256698132.7325500.40.632.21.90.060.024 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/01_Math_1/msu-prob13.problem" \t "_blank" Perimeter25638871.5225500.40.340.92.4-0.000.025 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/devolibrary/type-math/fraction-rlt-235.problem" \t "_blank" Reduce a Fraction25631541.2325600.00.190.52.30.010.006 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/devolibrary/type-math/fract-add-sub-div-p11.problem" \t "_blank" Calculating with Fractions25639371.5425500.40.350.92.00.150.027 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/01_Math_1/msu-prob04.problem" \t "_blank" Area of a Balloon254601122.3724702.80.591.81.8-0.05-0.028 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/01_Math_1/msu-prob07.problem" \t "_blank" Volume of a Balloon252565112.2424303.60.571.92.0-0.06-0.039 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/01_Math_1/msu-prob12.problem" \t "_blank" Numerical Value of Fraction25626841.0525600.00.040.23.40.01.0010 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/03_Units_Scaling/msu-prob17.problem" \t "_blank" Units2561116204.3624603.90.784.21.90.180.0311 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/06_Vectors_Scalars/msu-prob07.problem" \t "_blank" Vector versus Scalar254749112.9525101.20.662.21.1-0.05-0.0512 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/06_Vectors_Scalars/msu-prob10.problem" \t "_blank" Adding Vectors2531026204.0625001.20.763.61.80.140.0013 HYPERLINK "http://deepthought.lite.msu.edu/res/msu/physicslib/msuphysicslib/02_Math_2_Trig/msu-prob13.problem" \t "_blank" Proximity249663192.6623913.60.642.32.80.11-0.10Table 4.5: Statistics table includes general statistics of every problem of the courseS.D.: Standard Deviation of the students tries. EMBED Equation.3Skew.: Skewness of the students tries. EMBED Equation.3DoDiff: Degree of Difficulty of the problem. EMBED Equation.3As you see Degree of Difficulty is always between 0 and 1. This is a good factor for an instructor to determine whether a problem is difficult, and what is the degree of this difficulty. Thus, DoDiff of each problem is saved in its meta data.Dis.F.: Discrimination Factor is an standard for evaluating how much a problem discriminates between the upper and the lower students. First, all of the students are sorted according to a criterion. Then, %27 of upper students and %27 lower students are selected from the sorted students applying the mentioned criterion. Finally we obtain the Discrimination Factor from the following difference:Applied a criterion in %27 upper students - Applied the same Criterion in %27 lower students.Discrimination Factor is a number in interval [-1,1]. If this number is close to 1, it shows that only upper students have solved this problem. If it is close to 0 it shows that the upper students and the lowers are approximately the same in solving the problem. If this number is negative, it shows that the lower students have more successes in solving the problem, and thus this problem is very poor in discriminating the upper and lower students.We compute the Discrimination Factor from two criteria:1st Criterion for Sorting the Students: EMBED Equation.32nd Criterion for Sorting the Students: EMBED Equation.3Graphical chart Two important features of the stats table might be presented through the graphical charts. That is, a user could see the content of %wrong column and degree of difficulty of problems in the graphical chart as shown in figure 4.4 and 4.5 for homework set 1 in course PHY183 SS02. These graphical charts are produced dynamically by calling a CGI scripts, (graph.gif) which is located in /home/httpd/cgi-bin/Figure 4.4: Degree of difficulty graph Figure 4.5: %Wrong graphProblem AnalysisConceptual option response problems, in which the students are given several concepts that are randomly assigned to each student, are more difficult than numerical simple problems. Instructors usually want to see the students regarding every particular concept separately. LON-CAPA provides all response option problems in one table as shown in the table 4.6. # Problem Title Resource 1 Numbers /res/msu/physicslib/msuphysicslib/01_Math_1/msu-prob10.problem 2 Speed /res/msu/physicslib/msuphysicslib/03_Units_Scaling/msu-prob22.problem 3 Units /res/msu/physicslib/msuphysicslib/03_Units_Scaling/msu-prob17.problem 4 Vector versus Scalar /res/msu/physicslib/msuphysicslib/06_Vectors_Scalars/msu-prob07.problem 5 Adding Vectors /res/msu/physicslib/msuphysicslib/06_Vectors_Scalars/msu-prob10.problem 6 Traveling Car /res/msu/physicslib/msuphysicslib/05_1D_Motion/msu-prob16.problem 7 Atwood Machine /res/msu/kashy/Testing/randomlabel/atwood3T2M.problem 8 Sliding mass concepts /res/msu/physicslib/msuphysicslib/10_Motion_W_Friction/msu-prob32.problem 9 Work, Power, Energy concept /res/msu/physicslib/msuphysicslib/12_Work_Power_Energy/msu-prob27.problem10 Bead on a Wire /res/msu/physicslib/msuphysicslib/13_EnergyConservation/msu-prob32.problem 11 Atwood Machine /res/msu/physicslib/msuphysicslib/20_Rot2_E_Trq_Accel/msu-prob23.problem 12 Flinstone Bowling /res/msu/physicslib/msuphysicslib/21_Rot3_AngMom_Roll/msu-prob38.problem Table 4.6: Option response problems in course PHY183 SS02When a particular option response problem is selected, all data about this problem is restored, for every student. Different versions of students submissions are evaluated. The results are presented in a graphical chart as well as a numerical table, as shown in Figures 4.6, and 4.7, and table 4.7. For example, if we select the analysis of:/res/msu/kashy/Testing/randomlabel/atwood3T2M.problem INCLUDEPICTURE "http://deepthought.lite.msu.edu/cgi-bin/graph.gif?Atwood_Machine&Concepts&Answers&1780&6&1342,585,1263,1087,757,1354&433,1190,512,688,1018,421" \* MERGEFORMATINET Figure 4.6: Atwood Machine option response problem in HW3 # Concept Correct Wrong 1Two masses have same acceleration if the two the string does not stretch. 1342 433 2Weight of the two masses is greater than the tension of the string attached to the ceiling. 585 1190 3The top tension is equals the two bottom tensions. (massless pulley) 1263 512 4Tension holding the two masses are equal if mass of pulley=0 1087 688 5Sub-System accelerates upwards or downwards accordingly 757 1018 6Center of mass accelerates downward 1354 421 From:[Thu Jan 24 00:46:22 2002] To: [Mon Feb 4 23:59:59 2002]63884245Table 4.7: Table of student tries for Atwood Machine Problem according to every concept in one time interval.The data of students tries, the correct answers and the wrong answers are shown in a table 4.7. In the last row of the table you can see the time interval of this data and the overall correct and wrong answers separately. If an instructor wants to see the students tries in different time intervals, he/she could set the number of intervals from 1 to 7 time intervals, and then recompute the analysis. Thus, an instructor would be able to check whether the students have more wrong answers during the first days of opening the homework set, and how many students have tried during the first or the second interval. Since the problems are individualized he/she might be able to see how many students have tried to solve the problem after communicating with each other and understanding the concept. The charts and tables of students tries can be shown in 2 or more time intervals. So if the homework should be done in one week, an instructor would be able to observe the distribution of students tries every day separately after choosing the 7 time intervals. ClassifiersPattern recognition has a wide variety of applications in many different fields, such that it is not possible to come up with a single classifier that can give good results in all the cases. The optimal classifier in every case is highly dependent on the problem domain. In practice, one might come across a case where no single classifier can classify with an acceptable level of accuracy. In such cases it would be better to pool the results of different classifiers to achieve the optimal accuracy. Every classifier operates well on different aspects of the training or test feature vector. As a result, assuming appropriate conditions, combining multiple classifiers may improve classification performance when compared with any single classifier. Non-Tree ClassifiersThe scope of this survey is restricted to compare some popular non-parametric pattern classifiers and a single parametric pattern classifier according to the error estimate. Six different classifiers over one of the LON-CAPA datasets are compared in this study. The classifiers used in this study include Quadratic Bayesian classifier, 1-nearest neighbor (1-NN), k-nearest neighbor (k-NN), Parzen-window, multi-layer perceptron (MLP), and Decision Tree. These classifiers are some of the common classifiers used in most practical classification problems. After some preprocessing operations were made on the dataset, the error rate of each classifier is reported. Finally to improve performance a combination of classifiers is presented.Combination of Multiple Classifiers (CMC)In combining multiple classifiers we want to improve classifier performance. There are different ways one can think of combining classifiers:The simplest way is to find the overall error rate of the classifiers and choose the one, which has the least error rate on the given dataset. We call it an offline CMC. This may not really seem to be a CMC, however, in general, it has a better performance than individual classifiers. The output of this combination will simply be the best performance in each column in Figures 4.9, and 4.11.The second method, which we call online CMC, uses all the classifiers followed by a vote. The class getting maximum votes from the individual classifiers will be assigned to the test sample. This method intuitively seems to be better than the previous one. However, when we actually tried this on some cases of our dataset, the results were not better than the best result in previous method. So, we changed the rule of majority vote from getting more than 50% votes to getting more than 75% votes. We got a significant improvement over offline CMC. Table 4.10 shows the actual performance of the individual classifier and online CMC over all three datasets.Woods et. al. (1995) suggested a third method, which is called DSC-LA (Dynamic Selection of Classifiers based on the Local Accuracy estimates). This method takes a particular test sample, investigates the local neighborhood of that sample using all the individual classifiers and the one, which performs best is chosen for the decision-making.Besides CMC, we also show the results for an Oracle which chooses the correct results if any of the classifiers did so, as Woods et al. (1995) has presented in his article. NormalizationHaving assumed in Bayesian and Parzen-window classifiers that the features are normally distributed, it is necessary that the data for each feature is normalized. This ensures that each feature has the same weight in the decision process. Assuming that the given data is Gaussian distributed, this normalization is performed using the mean and standard deviation of the training data. In order to normalize the training data, it is necessary first to calculate the sample mean EMBED Equation.3 , and the standard deviation EMBED Equation.3 of each feature, or column, in this dataset, and then normalize the data using the following equation:EMBED Equation.3This ensures that each feature of the training dataset has a normal distribution with a mean of zero and a standard deviation of one. In addition, the kNN method requires normalization of all features into the same range. However, we should be cautious in using the normalization before considering its effect on classifiers performance. Table 4.4 shows comparison of Error Rate and its Standard Deviation using the classifiers in both normalized and un-normalized data in the case of 3 classes.3-ClassesWith NormalizationWithout NormalizationClassifierError rateS.DError rateS. D.Bayes0.49240.07470.55280.03741NN0.52200.03440.58640.041KNN0.51440.04360.58560.0491Parzen0.50960.04080.7280MLP0.45240.02850.6240CMC0.29760.03990.38720.0346Oracle0.10880.03230.16480.0224Thus, we tried the classifiers with and without normalization. As you see in table 4.8 we have improvement in most classification results after normalization. Here we have two important findings:1. Parzen-Window classifier and MLP do not work properly without normalizing the data. So, we have to normalize data for using these two classifiers.2. Decision tree classifiers do not have any improvement on their classification performance, so we ignore normalization in tree classifiers. We will study the decision tree classifier later. We did not introduce the Decision Tree classifiers results in Table 4.4.Comparing 2-fold and 10-fold Cross-ValidationIn k-fold cross-validation, we divide the data into k subsets of approximately equal size. We train the data k times, each time leaving out one of the subsets from training, but using only the omitted subset to compute the error threshold of interest. If k equals the sample size, this is called "Leave-One-Out" cross-validation. (Duda et al. 2001; Kohavi, 1995). Leave-One-Out cross-validation provides an almost unbiased estimate of true accuracy, though at a significant computation cost. In this survey we used both 2-fold and 10-fold cross validation. In 2-fold cross-validation, the order of the observations, both training and test, were randomized before every trial of every classifier. Then every sample was divided amongst the test and training data, with 50% going to training, and the other 50% going to test. This meant that testing was completely independent, as no data or information was shared between them. It is noticeable that the portion of data from each class was the same portion of classes in original dataset as well. Then we classified the test sets after the training phase of all the classifiers on the same training and test data. We repeated this randomly cross validation ten times for all classifiers. In 10-fold cross-validation, the available data (here, the 227 students data) are divided into 10 blocks containing roughly equal number of cases and class-value distributions. For each block (10% of data) in turn, a model is developed using the data in the remaining blocks (90% of data, the training set) and then evaluated on the cases in the hold-out block (the test set). Each case is thus tested on a model that was developed without reference to it and when all the tests (10 tests) are completed, each sample in the data will have been tested exactly once. The average performance on the tests is then used to predict the true accuracy of a model developed from all the data. For values like 10 or more, this estimate is pretty reliable and is much more accurate than a resubstitution estimate. 3-Classes10-fold Cross-Validation2-fold Cross-ValidationClassifierError RateS.D.Error RateS.D.Bayes0.50.08990.55360.02191NN0.49570.06860.58320.0555KNN0.51740.08060.5760.0377Parzen0.53910.0850.49920.036MLP0.43040.08060.45120.0346CMC0.3130.0840.32240.0354Oracle0.19570.05520.14560.0462Table 4.9 shows comparison of Error Rate and its Standard Deviation of using the classifiers in both 2-fold and 10-fold cross-validation in the case of the 3-Classes. You can see that the 10-fold cross-validation in relation to individual classifier has slightly better accuracy than 2-fold cross validation, but in relation to combination of classifiers (CMC) there is no a significant difference. Therefore we selected the 10-fold cross validation for error estimation in this survey.Results, Error EstimationThe experimental results were averaged and presented in some graphical charts. They show the effect of selecting the data randomly on the average error rate. The average of error rate and its standard deviation, which is associated with each classifier, is shown in the tables as well as the graphical chart. Each table summarizes the results of all the classifiers on our dataset. The standard deviation of error rate shows the variance of the error rate during cross validation. The error rate is measured in each round of cross validation by:EMBED Equation.3After 10 rounds, the average error rate and its standard deviation are computed and then plotted. This metric was chosen due to its ease of computation and intuitive nature. Figure 4.8 and 4.9 show the comparison of classifiers error rate when we classify the student into two categories, Passed and Failed. You can see that the best performance is for kNN with 82% accuracy, and the worst classifier is Parzen-window with 75% accuracy. CMC in the case of 2-Classes classification has 87% accuracy.It is noticeable that these processes were done after we had found the optimal k in the kNN algorithm and after we had tuned the parameters in MLP and after we had found optimal h in Parzen-window algorithm. Finding the best k for kNN is not difficult, and you can see its performance is the best in the case of 2-Classes, though is not as good as the other classifiers in the case of 3-Classes, as is shown in figure 4.11. Working with Parzen-window classifier is not simple because finding the best height for its window is not easy. The MLP classifier is the most difficult classifier to work with. Many parameters have to be set properly to make it work optimally. For example, after many trials and errors we found that the structure of the network in the case of 3-classes, the 4-3-3 (one hidden layer with 3 neurons in hidden layer) works better, and in the case of 2-classes if we have 2 hidden layer with 2 or 4 neurons in each hidden layer would lead to a better performance. There is no algorithm to set the number of epochs and learning rates in the MLP. However, sometimes MLP has the best performance in our datasets, as you can see in the Table 4.7, MLP is slightly better than the other individual classifier. In the case of 9-Classes we could not set the MLP to work properly, so we have not brought the result of MLP classifier into the final result in table 4.10.As predicted before, the error rate in 9-Classes is much higher than the other cases. The final result of the five classifiers and their combination in the case of 2-Classes, 3-Classes, and 9-Classes are shown in Table 4.8In the case of 9-classes 1-NN works better than other classifiers. Regarding the final results in Table 4.8, CMC has the better performance in relation to individual classifiers. In the case of 2-Classes it got 5% improvement, in the case of 3-Classes it got 20% improvement, and in the case of 9-Classes it got 22% improvement in relation to the best individual classifiers in the corresponding case.Error RateClassifier2-Classes3-Classes9-ClassesBayes0.23640.51430.771NN0.23180.49520.71KNN0.17730.49520.725Parzen0.250.5190.795MLP0.20450.4905-CMC0.13180.29050.49Oracle0.08180.1619-One important finding is that when our individual classifiers are working well and has a high level of accuracy; the benefit of combining classifiers is small. Thus, CMC has little improvement in classification performance, but when we have weak learner classifiers CMC has a significant improvement in accuracy.We tried to get better performance considering the degree of difficulty of problems. By choosing some specific conceptual subsets of the students data, we unfortunately did not obtain a significant increase in accuracy with this parameter. In the next section, when we explain the results of decision tree classifiers on our data set. We also will discuss the relative importance of the features, and which features have a higher correlation with category labels. Decision Tree-based softwareDecision trees have proved to be valuable tools for the description, classification and generalization of data. Many users find decision trees easy to use and understand. As a result, users more easily trust decision tree models than they do "black box" models, such as those produced by neural networks. Many tools and software have been developed to implement decision tree classification. Lim et al. (2000) has a insightful study about comparison of prediction accuracy, complexity, and training time of 33 classification algorithms; 22 decision tree, 9 statistical, and 2 neural network algorithms are compared on thirty-two datasets in terms of classification accuracy, training time, and (in the case of trees) number of leaves. In this survey we used C5.0, CART, QUEST, and CRUISE software to have tree-based classification. We also used some statistical software for multiple linear regression on our dataset. First we have a brief view of the capability, features and requirements of these software packages. Then we gather some of the results and compare their accuracy to non-tree based classifiers. C5.0Decision tree learning algorithms, for example, ID3, C5.0 and ASSISTANT (Cestnik et al., 1987), search a completely expressive hypothesis space and are used to approximate discrete valued target functions represented by a decision tree. In our experiments the C5.0 inductive learning decision tree algorithm was used. This is a revised version of C4.5 and ID3 (Quinlan 1986, 1993) and includes a number of additional options for implementation. For example, the Boosting option causes a number of classifiers to be constructed - when a case is classified, all of these classifiers are consulted before a decision is made. Boosting will often give a higher predictive accuracy at the expense of increased classifier construction time. For our experiments however, dataset boosting was not found to give any improvement in prediction accuracy. When a continuous feature is tested in a decision tree, there are branches corresponding to the conditions: Feature Value EMBED Equation.3 Threshold and Feature Value > Threshold, for some threshold chosen by C5.0. As a result, small movements in the feature value near the threshold can change the branch taken from the test. There have been many methods proposed to deal with continuous features (Quinlan, 1988; Chan et al., 1992; Ching et al., 1995). An option available in C5.0 uses fuzzy thresholds to soften this knife-edge behavior for decision trees by constructing an interval close to the threshold. This interval plays the role of margin in neural network algorithms. Within this interval, both branches of the tree are explored and the results combined to give a predicted class. Decision trees constructed by C5.0 are post pruned before they are presented to the user. The Pruning Certainty Factor governs the extent of this simplification. A higher value produces more elaborate decision trees and rule sets, while a lower value causes more extensive simplification. In our experiment a certainty factor of 25% was used. If we change the certainty factor we may obtain different results.C5.0 needs four types of files for generating the decision tree for a given data set, out of which two files are optional:The first file is the .names file. It describes the attributes and classes. The first line of the .names file gives the classes, either by naming a discrete attribute (the target attribute) that contains the class value, or by listing them explicitly. The attributes are then defined in the order that they will be given for each case. The attributes can be either explicitly or implicitly defined. The value of an explicitly defined attribute is given directly in the data. The value of an implicitly defined attribute is specified by a formula. In our case, data attributes are explicitly defined. The second file is the .data file. It provides information on the training cases from which C5.0 will extract patterns. The entry for each case consists of one or more lines that give the values for all explicitly defined attributes. The '?' is used to denote a value that is missing or unknown. Our dataset had no missing features. Also, 'N/A' denotes a value that is not applicable for a particular case. The third file used by C5.0 consists of new test cases on which the classifier can be evaluated and is the .test file. This file is optional and, if used, has exactly the same format as the .data file. We gave a .test file for our dataset. The last file is the .costs file. In applications with differential misclassification costs, it is sometimes desirable to see what affect costs have on the construction of the classifier. In our case all misclassification costs were the same so this option was not implemented. After the program executed for the PHY183 SS02 dataset we got the output results for both the training and testing data. A confusion matrix is generated showing the misclassifications. The confusion matrices for three types of classification in our dataset that are, 2-Classes, 3-Classes and 9-Classes are in appendix B. You can also find some rule set samples resulted from the rule-set option in C5.0, as well as a part sample of the tree produced by C5.0 in appendix B. Using 10-fold cross validation we got 79.3% accuracy in 2-Classes, 56.8% accuracy in 3-Classes, 25.6% accuracy in 9-Classes. One important and exciting experiment using C5.0 is to use a training and test set; and thus at least a 10-fold cross-validation to get a trustable result. For example, if we select one split in 3-Classes we might get about 75% accuracy in training set, and with boosting process we could get accuracy unto 90% or 95%. Unfortunately, this is overfitting, or overtraning, and we would not be able to generalize this result and this complex training model to test the unseen data.CARTCART uses an exhaustive search to identify useful tree structures of data. It can be applied to any dataset and can proceed with no parameter setting. Comparing CART analyses with stepwise logistic regressions or discriminant analysis, CART typically performs better on the learning sample. Listed below are some technical aspects of CART: CART is a nonparametric procedure and does not require specification of a functional form. CART uses a stepwise method to determine splitting rules. Thus, no advance selection of variables is necessary, although certain variables such as ID numbers and reformulations of the dependent variable should be excluded from the analysis. Also, CART performance can be enhanced by a proper feature selection and creation of predictor variables. There is no need to experiment with monotone transformations of the independent variables, such as logarithms, square roots or squares. In CART, creating such variables will not affect the trees produced unless linear combination splits are used. Outliers among the independent variables generally do not affect CART because splits usually occur at non-outlier values. Outliers in the dependent variable are often separated into nodes where they no longer affect the rest of the tree. CART does not require any preprocessing of the data. In particular continuous variables do not have to be recoded into discrete variable versions prior to analysis. While the CART default is to split nodes on single variables, it will optionally use linear combinations of non-categorical variables. For each split in the tree, CART develops alternative splits (surrogates), which can be used to classify an object when the primary splitting variable is missing. Thus, CART can be effectively used with data that has a large fraction of missing values.One of the advantages of CART is presenting the importance of independent variables in predicting both classification mode and regression mode. Each variable in the CART tree has an importance score based on how often and with what significance it served as primary or surrogate splitter throughout the tree. The scores reflect the contribution each variable makes in classifying or predicting the target variable, with the contribution stemming from both the variables role in primary splits and its role as a surrogate splitter (Dan and Colla, 1998). In tables 4.11 and 4.12 the importance of the six features (independent variables) are scored in the case of 2-classes with Gini splitting criterion and 3-classes with Entropy splitting criterion respectively.VariableTOTCORR100.00||||||||||||||||||||||||||||||||||||||||||TRIES56.32|||||||||||||||||||||||FIRSTCRR4.58|TOTTIME0.91SLVDTIME0.83DISCUSS0.00 The results in table 4.11 and 4.12 show that the most important feature which has the most correlation with the predicted variables is Total number of Correct answers, and the least useful variable is Number of Discussion. If we consider economical aspect of computation cost we could remove the less important features.In our experiment we used both 10-fold Cross-Validation and Leave-One-Out method. We found that in the case of 2-Classes and 3-Classes the error rates in training sets are not improved, but the misclassifications in the test sets are improved when we switch from 10-fold Cross-Validation to Leave-One-Out, but in the case of 9-Classes both training and testing sets are improved significantly as it shown in Table 4.13, Table 4.14, Figure 4.12, and Figure 4.13. Why? Splitting Criterion2-Classes3-Classes9-ClassesTrainingTestingTrainingTestingTrainingTestingGini17.2%19.4%35.2%48.0%66.0%74.5%Symmetric Gini17.2%19.4%35.2%48.0%66.0%74.5%Entropy18.9%19.8%37.9%52.0%68.7%76.2%Twoing17.2%19.4%31.3%47.6%54.6%75.3%Ordered Twoing17.2%20.7%31.7%48.0%68.3%74.9%Splitting Criterion2-Classes3-Classes9-ClassesTrainingTestingTrainingTestingTrainingTestingGini17.2%18.5%36.6%41.0%46.7%66.9%Symmetric Gini17.2%18.5%36.6%41.0%46.7%66.9%Entropy17.2%18.9%35.2%41.4%48.0%69.6%Twoing17.2%18.5%38.3%40.1%47.1%68.7%Ordered Twoing18.9%19.8%35.2%40.4%33.9%70.9% Discussion:In the case of 2-Classes, there is no improvement in the training phase and a slight 1% improvement in the test phase. It shows that with Leave-One-Out method, we can have more reliable model for classifying the students. It shows that the model we obtained in training phase is approximately complete. In the case of 3-Classes, when we switch from 10-fold to Leave-One-Out the results in the training phase becomes slightly worse, but we get about 7.5% improvement. It shows that our model was not complete in 10-fold for predicting the unseen data. So it is better to use Leave-One-Out to get a more complete model for classifying the students into three categories.In the case of 9-Classes when we switch from 10-fold to Leave-One-Out the results in both training and test sets improve significantly. However, we cannot conclude that our new model is complete; because the big difference between the results in training and testing phase shows that our model is suffering from overfitting. It means that our training samples are not enough to construct a complete model for predicting the category labels correctly. CART has a lot of useful text and graphical reports, some of which we presented in appendix B. It is noticeable that CART does not any description files to work with. You can read data as a text file or any popular database or spreadsheet. QUEST, CRUISEQUEST is a statistical decision tree algorithm for classification and data mining. The objective of QUEST is similar to that of the algorithm used in CART and described in Breiman, et al. (1984). The advantages of QUEST are that it uses an unbiased variable selection technique by default, it uses imputation instead of surrogate splits to deal with missing values, and it can easily handle categorical predictor variables with many categories. If there are no missing values in the data, QUEST can optionally use the CART greedy search algorithm to produce a tree with univariate splits. QUEST needs two text input files: 1) Data file: This file contains the training samples. Each sample consists of observations on the class (or response or dependent) variable and the predictor (or independent) variables. The entries in each sample record should be comma or space delimited. Each record can occupy one or more lines in the file, but each record must begin on a new line. Record values can be numerical or character strings. Categorical variables can be given numerical or character values. 2) Description file: This file is used to provide information to the program about the name of the data file, the names and the column locations of the variables, and their roles in the analysis. The following is our description file:phy183.dat"?"column, var, type 1 1stGotCrr n 2 TotCorr n 3 AvgTries n 4 TimeCorr n 5 TimeSpent n 6 Discuss n 7 Class2 x 8 Class3 d 9 Class9 x 10 Grade xIn the first line we put the name of data file, and in the second line we put the character used to denote missing data. In our dataset we have no missing data. The position, name and role of each variable come next with one line for each variable. The following roles for the variables are permitted: c stands for categorical variable; d for class (dependent) variable; only one variable can have the d indicator; n for a numerical variable; and x which indicates that the variable is excluded from the analysis.QUEST allows both interactive and batch mode. By default it uses discriminant analysis as a method for split point selection. It is an unbiased variable selection method described in Loh and Shih (1997). However, in advanced mode, user can select exhaustive search (Breiman et al., 1984) which is used in CART. The former is the default option if the number of classes are more than 2, otherwise the latter is the default option. If the latter option is selected, the program will ask for the user to choose the splitting criterion including one of the following five methods which are studied in Shih (1999):1 Likelihood Ratio G22 Pearson Chi23 Gini4 MPI (Mean Posterior Improvement)5 Other members of the divergence family The likelihood criterion is the default option. If instead the CART-style split is used, the Gini criterion is the default option. In our case, we selected the fifth method of exhaustive search which was optimal regarding the misclassification ratio. QUEST asks for the prior for each class. If the priors are to be given, the program will then ask the user to input the priors. If unequal costs are present (like in this example), the priors are altered using the formula in Breiman et al. (1984, pp. 114-115). In our cases the prior for each class is estimated regarding the class distribution. This asks for the misclassification costs. If the costs are to be given, the program will ask the user to input the costs. In our cases the misclassification costs are equal. The user can choose either split on a single variable or linear combination of variables. We used split on a single variable.QUEST also asks for the number of SEs which controls the size of the pruned tree. 0-SE gives the tree with the smallest cross-validation estimate of misclassification cost or error. QUEST enables user to select the value of V in V-fold cross-validation. The larger the value of V, the longer running time the program takes to run. 10-fold is and 226-fold (Leave-One-Out) are used in our cases.The classification matrices based on the learning sample and CV procedure are reported. Some samples of these reports are shown in Appendix B. You can see a table gives the sequence of pruned subtrees. The 3rd column shows the cost complexity value for each subtree using the definition in Breiman et al. (1984, Definition 3.5 p. 66). The 4th column gives the current or resubstitution cost (error) for each subtree. Another table gives the size, estimate of misclassification cost and its standard error for each pruned sub-tree. The 2nd column shows the number of terminal nodes. The 3rd column shows the mean cross-validation estimate of misclassification cost and the 4th column gives its estimated standard error using the approximate formula in Breiman et al. (1984, pp. 306-309). The tree marked with an * is the one with the minimum mean cross-validation estimate of misclassification cost (also called the 0-SE tree). The tree based on the mean cross-validation estimate of misclassification cost and the number of SEs is marked with ** (See appendix B).QUEST trees are given in outline form suitable for importing into flowchart packages like allCLEAR (CLEAR Software, 1996). Alternatively, the trees may be outputted in LaTeX code. The public domain macro package pstricks (Goossens, Rahtz and Mittelbach, 1997) or TreeTEX (Bruggemann-Klein and Wood, 1988) is needed to render the LaTeX trees.CRUISE is also a new statistical decision tree algorithm for classification and data mining. It has negligible bias in variable selection. It splits each node into as many sub-nodes as the number of classes in the response variable It has several ways to deal with missing values. It can detect local interactions between pairs of predictor variables.CRUISE has most of QUEST capabilities and reports (See appendix B). We have brought the results of tree-based classification with QUEST and the CRUISE into the final reporting table (Table 4.13), which includes all tree-based and non-tree based classifiers on our dataset in the cases of 2-Classes, 3-Classes, and 9-Classes.Final ResultsThe overall results of classifiers performance on our dataset are shown in the table 4.13. Regarding individual classifier, for the case of 2-classes, kNN has the best performance with 82.3% accuracy. In the case of 3-classes and 9-classes, CART has the best accuracy of about 60% in 3-classes and 43% in 9-Classes. However, considering the combination of non-tree-based classifiers, the CMC has the best performance in all three cases. That is we got the 86.8% accuracy in the case of 2-Classes, 71% in the case of 3-Classes, and 51% in the case of 9-Classes.Error RateClassifier2-Classes3-Classes9-ClassesTree ClassifierC5.020.7%43.2%74.4%CART18.5%40.1%66.9%QUEST19.5%42.9%80.0%CRUISE19.0%45.1%77.1%Non-tree ClassifierBayes23.6%51.4%77.0%1NN23.2%49.5%71.0%kNN17.7%49.6%72.5%Parzen25.0%51.9%79.5%MLP20.5%49.1%-CMC13.2%29.1%49.0%Chapter 5Conclusions and Future WorkIn this chapter, we summarize our survey and present directions to future work on data mining aspect of LON-CAPA system, ConclusionsIn this survey we select students and course data from a LON-CAPA course, PHY183 which was held at MSU in spring semester 2002. Developing some perl modules in the pre-processing phase, allow us to extracted 6 features for 227 students and group them in three ways 2-Classes, 3-Classes, and 9-Classes. Five non-tree classifiers and four tree-based classifiers are used to segregate the students by the class labels. The error rate of the different trained models resulted from different classifiers is estimated on unseen data. A combination of multiple classifiers leads to a significant accuracy improvement in all three cases. Using multiple linear regression we realize the importance of the features. We can find that 3 of the 6 features have more correlation with the class labels in all three cases. Since the most relevant feature is a discrete variable, the tree-based classifiers are classifying more accurate in most cases. The successful implementations of classifying the students in 2 and 3-Classes demonstrate the merits of using the LON-CAPA data for pattern recognition. However, using 10-fold Cross-Validation and Jackknife method we are able to show that these data are suffering from overfitting in the case of 9-Classes. Future workFuture perspectives on an automated system for a data mining aspect in the LON-CAPA system will focus on the following issues:Gathering more sample data by combining one course data during several semesters to avoid overfitting in the case of 9-Classes.Finding the paths that students usually choose to solve the different types of the problems from activity log to extract more relevant features.Comparing the classification results relating to groups of data i.e. regarding the different type of problems (Mathematical, Optional Response, Numerical, Java Applet, etc) .Comparing the classification results in relation to every individual problem in order to find which problem would be the best predictor.Making ANOVA to evaluate the effect of interaction between groups of problems and groups of students.Using PCA and LDA for feature extraction.Weighting features vs. feature selection.Using Genetic algorithm for extracting and weighting the features.Implementing fuzzy classification for grouping students.Clustering the student database in order to describe the student behavior and evaluating the type of problems effects on students effort. Finding the association rules and dependencies among the groups of problems. References:Baker, E. (1978). Cluster Analysis by Optimal Decomposition of Induced Fuzzy Sets. Delft University Press, Delft, The Netherlands.Baker, E., and Jain, A. K. (1981). A Clustering performance measure based on fuzzy set decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI 3, 66-75.Bandyopadhyay, S., and Muthy, C.A. (1995). Pattern Classification Using Genetic Algorithms, Pattern Recognition Letters, Vol. 16, pp. 801-808.Barchman R. J., and Anand T. (1996), The process of Knowledge Discovery in Database, 37-57, AAAI/MIT Press.Bloom, B. S. (1956). Taxonomy of Educational Objectives. New York, NY, McKay.Bloom, B. S. (1984). The 2-Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-one Tutoring. Educational Researcher 13: 4-16.Bransford, J. D., A. L. Brown, et al. (2000). How People Learn. Washington, D.C., National Academy Press.Breiman, L., Freidman, J.H., Olshen, R. A., and Stone, P. J. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth International Group. Bruggemann-Klein, A. and Wood, D. (1988). Drawing Trees Nicely with TEX.Chan, K.C.C. Ching, J.Y. and Wong, A.K.C. (1992). A Probabilistic Inductive Learning Approach to the Acquisition of Knowledge in Medical Expert Systems. Proceeding of 5th IEEE Computer Based Medical Systems Symposium. Durham NC. CAPA, See http://capa.msu.edu/Cestnik, B. Konenenko, I. Bratko, I. (1987). ASSISTANT 86: A Knowledge Elicitation Tool for Sophisticated Users, in Bratko, I. and Navrac, N. (eds), Progress in Machine Learning, Sigma Press, UK. CLEAR Software, Inc. (1996). allCLEAR Users Guide, CLEAR Software, Inc, 199 Wells Avenue, Newton, MA.Chernoff, H. (1973), The use of Faces to represent Points in k-Dimensional Space Graphically, Journal of American Statistical Association, Vol. 68, page 361-368, June 1973. Ching, J.Y. Wong, A.K.C. and Chan, C.C. (1995). Class Dependent Discretisation for Inductive Learning from Continuous and Mixed-mode Data. IEEE Transaction. PAMI, 17(7) 641 - 645. CourseInfo TM, Blackboard Inc., Washington, D.C. (CourseInfo is a trademark of Blackboard, Inc., see http://product.blackboard.net/courseinfo/; http://www.blackboard.com). Dan, S.; and Colla. P., (1998) CART--Classification and Regression Trees. San Diego, CA: Salford Systems, 1998.Duda, R.O., Hart, P.E. (1973). Pattern Classification and SenseAnalysis . John Wiley & Sons, Inc., New York NY.Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification. 2nd Edition, John Wiley & Sons, Inc., New York NY.Dumitrescu, D., Lazzerini, B., and Jain, L.C. (2000). Fuzzy Sets and Their Application to Clustering and Training. CRC Press LLC, New York.El-Sheikh, E. M. (2001). An Architecture for the generation of intelligent tutoring systems from reusable components and knowledge-based systems, (Ph.D. Dissertation), 2001, MSU.eCollege, See http://convene.com; http://eCollege.com (real education); http://www.e-education.com (jones knowledge) Frawley, W., Piatetsky-Shapiro, G., and Matheus, C. (1992). Knowledge Discovery in Databases: An Overview. AI Magazine, Fall 1992, pgs 213-228.Fayyad, U. M., Pitatesky-Shapiro, G., Smyth, P., and Uthurasamy, R. (1996). Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press.Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, MA, Addison-Wesley.Goossens, M., Rahtz, S. and Mittelbach, F. (1997). The LaTeX Graphics Companion, Addison Wesley.Hall, M. A., and Smith, L.A. (1997), Feature subset selection: a correlation based filter approach, Proceeding of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, Springer, Page 855-858. Hand, D. J. (1987), Discrimination and Classification, John Wiley & Sons, Chichester U.K. Heckerman, D. (1996), The process of Knowledge Discovery in Database, 273-305, AAAI/MIT Press.Hoppner, F.; Klawnn, F.; Kruse, R.; Runkler, T.; (2000). Fuzzy Cluster Analysis: Methods for classification, data analysis and image recognition, John Wiley & Sons, Inc., New York NY.Jain, A. K.; and Dubes; R. C. (1988). Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice-Hall.Jain, A. K.; Mao, J.; and Mohiuddin, K.; (1996). "Artificial Neural Networks: A Tutorial," IEEE Computer, March. 1996.Jain, A. K.; Zongker, D.; (1997). "Feature Selection: Evaluation, Application, and Small Sample Performance" IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 19, No. 2, February 1997.Jain, A. K.; Duin R. P.W.; and Mao, J.; (2000). Statistical Pattern Recognition: A Review, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000.John, G. H.; Kohavi R.; and Pfleger, K.; (1994). Irrelevant Features and Subset Selection Problem, Proceeding of the Eleventh International Conference of Machine Learning, page 121-129, Morgan Kaufmann Publishers, San Francisco, CA.Kashy, E., Thoennessen, M., Tsai, Y., Davis, N.E., and Wolfe, S.L. (1997), "Using Networked Tools to Enhance Student Success Rates in Large Classes," Frontiers in Education Conference, Teaching and Learning in an Era of Change, IEEE CH 36099. Kohavi, Ron (1995) A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, Proceeding of International Joint Conference on Artificial intelligence (IJCIA), 1995.Kontkanen, P., Myllymaki, P., and Tirri, H. (1996), Predictive data mining with finite mixtures. In Proceeding 2nd International Conference Knowledge Discovery and Data Mining (KDD96), pages 249-271, Portland, Oregon, August 1996.Kortemeyer, G., MSU LectureOnline software package, 1997-2000. Kortemeyer, G., and Bauer, W. (1999), "Multimedia Collaborative Content Creation (mc3) - The MSU LectureOnline System", Journal of Engineering Education, 88 (4), 421.Kuncheva , L.I., and Jain, L.C., (2000), "Designing Classifier Fusion Systems by Genetic Algorithms, IEEE Transaction on Evolutionary Computation, Vol. 33 (2000), pp 351-373.Lange, R. (1996), An empirical test of weighted effect approach to generalized prediction using neural nets. In Proceeding 2nd International Conference Knowledge Discovery and Data Mining (KDD96), pages 249-271, Portland, Oregon, August 1996.LectureOnline, See http://www.lite.msu.edu/kortemeyer/lecture.htmlLoh, W.-Y. & Shih, Y.-S. (1997). Split Selection Methods for Classification Trees, Statistica Sinica 7: 815-840. Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (2000). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms. Machine Learning, Vol. 40, pp. 203--228, 2000.See http://www.stat.wisc.edu/~limt/mach1317.pdf)LON-CAPA, See http://www.lon-capa.orgLu, S. Y., and Fu, K. S. (1978). A sentence-to-sentence clustering procedure for pattern analysis. IEEE Transactions on Systems, Man and Cybernetics SMC 8, 381-389.Lu, H., Setiono, R., and Liu, H. (1995). Neurorule: A connectionist approach to data mining. Proceeding 21st International Conference: Very Large Databases, pages 478-489, Zurich, Switzerland, September 1995.Masand, B. and Piatetsky-Shapiro, G. (1996), A Comparison of approaches for maximizing business payoff of prediction models. In Proceeding 2nd International Conference Knowledge Discovery and Data Mining (KDD96), pages 195-201, Portland, Oregon, August 1996.Matheus, C.J.. and Piatetsky-Shapiro, G. (1994), An application of KEFIR to the analysis of healthcare information. In Proceeding AAAI94 Workshop Knowledge Discovery in Database (KDD94), pages 441-452, Seattle, WA, July 1994.Michalski, R.S., Bratko, I., Kubat M. (1998), Machine Learning and Data Mining, Methods and applications, John Wiley & Sons, New York.Mitchell, Tom M. (1997), Machine Learning, McGraw-Hill.McLachlan, G. (1992), Discrimination Analysis and Statistical Pattern Recognition, John Wiley & Sons, New York.McQueen, J. B. (1967). Some methods of classification and analysis of multivariate observations. Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297.Mori, Y.; Kudo, M.; Toyama, J.; and Shimbo, M. (1998), Visualization of the Structure of Classes Using a Graph, Proceeding of International Conference on Pattern Recognition, Vol. 2, Brisbane, August 1998, page 1724-1727.Murray, T. (1996). From Story Boards to Knowledge Bases: The First Paradigm Shift in Making CAI, Intelligent, ED-MEDIA 96 Educational Multimedia and Hypermedia Conference, Charlottesville, VA.Murray, T. (1996). Having It All, Maybe: Design Tradeoffs in ITS Authoring Tools. ITS '96 Third Intl. Conference on Intelligent Tutoring Systems, Montreal, Canada, Springer-Verlag.Murthy, S. K. (1998), "Automatic construction of decision trees from data: A multidisciplinary survey, Data Mining and Knowledge Discovery, vol. 4, pp. 345--389, 1998.Multi-Media Physics, See http://lecture.lite.msu.edu/~mmp/PhysNet, See http://physnet2.pa.msu.edu/Pei, M., Punch, W.F., Ding, Y., and Goodman, E.D. (1995) "Genetic Algorithms For Classification and Feature Extraction", presented at the Classification Society Conference , June 95.See http://garage.cse.msu.edu/papers/papers-index.htmlPei, M., Punch, W.F., and Goodman, E.D. (1998) "Feature Extraction Using Genetic Algorithms", Proceeding of International Symposium on Intelligent Data Engineering and Learning98 (IDEAL98), Hong Kong, Oct. 98.Punch, W.F., Pei, M., Chia-Shun, L., Goodman, E.D., Hovland, P., and Enbody R. (1993) "Further research on Feature Selection and Classification Using Genetic Algorithms", In 5th International Conference on Genetic Algorithm , Champaign IL, pp 557-564, 1993.Quinlan, J. R. (1986), Induction of decision trees. Machine Learning, 1:81-106.Quinlan, J. R. (1987), Rule induction with statistical data, a comparison with multiple regression. Journal of Operational Research Society, 38, 347-352.Quinlan, J. R. (1993), C4.5: Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann.Quinlan, J. R. (1994), Comparing connectionist and symbolic learning methods, MIT Press.Ruck, D.W. , Rogers, S.K., Kabirsky, M., Oxley, M.E., and Suter, B.W. (1990), The Multi-Layer Perceptron as an Approximation to a Bayes Optimal Discriminant Function; IEEE Transactions on Neural Networks, vol. 1, no. 4.Russell, S.; and Norvig P. (1995). Artificial Intelligence: A Modern Approach. Prentice-Hall. Shih, Y.-S. (1999), Families of splitting criteria for classification trees, Statistics and Computing, Vol. 9, 309-315. Shute, V. and J. Psotka (1996). Intelligent Tutoring Systems: Past, Present, and Future. Handbook of Research for Educational Communications and Technology. D. Jonassen. New York, NY, Macmillan.Shute, V. J. (1991). Who is Likely to Acquire Programming Skills? Journal of Educational Computing Research 1: 1-24.Shute, V. J., L. A. Gawlick-Grendell, et al. (1993). An Experiential System for Learning Probability: Stat Lady. Annual Meeting of the American Educational Research Association, Atlanta, GA.Shannon, C.E., (1948) A mathematical theory of communication, Bell System Technical Journal, vol. 27, pp. 379-423 and 623-656, July and October, 1948.Shute, V. J. and J. W. Regian (1990). Rose garden promises of intelligent tutoring systems: Blossom or thorn? Proceedings from the Space Operations, Applications and Research Symposium, Albuquerque, NM.Shute, V. R. and R. Glaser (1990). A Large-scale Evaluation of an Intelligent Discovery World: Smithtown. Interactive Learning Environments 1: 51-76.Siedlecki, W., Sklansky J., (1989), A note on genetic algorithms for large-scale feature selection, Pattern Recognition Letters, Vol. 10, Page 335-347, 1989.Singley, K. and J. R. Anderson (1989). The Transfer of Cognitive Skill. Cambridge, MA, Harvard University Press.Sleeman, D. (1987). PIXIE: A Shell for Developing Intelligent Tutoring Systems. Artificial Intelligence and Education. R. W. Lawler and M. Yazdani. Norwood, NJ, Alex Publishers: 239-265.Sleeman, D. H. and J. S. Brown (1982). Intelligent Tutoring Systems. London, UK, Academic Press.TopClass TM, WBT Sytems, San Fransisco, CA. (TopClass is a trademark of WBT Systems, see http://www.wbtsystems.com HYPERLINK http://www.wbtsystems.com/ ). Toussaint, G. T. (1980). The relative neighborhood graph of a finite planar set. Pattern Recognition 12, 261-268.Trunk, G.V. (1979), Aproblem of Dimensionality: A Simple Example, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. PAMI-1, No. 3, July 1979.Urban-Lurain, M. (1996). Intelligent Tutoring Systems: An Historic Review in the Context of the Development of Artificial Intelligence and Educational Psychology, Michigan State University.Urquhart, R. (1982). Graph theoretical clustering based on limited neighborhood sets. Pattern Recognition 15, 173-187.VU, (Virtual University), See http://vu.msu.edu/; http://www.vu.org.Wu, Q., Suetens, and Oosterlinck, A. (1991) Integration of heuristic and Bayesian approaches in a pattern-classification system. In Piatetsky-Shapiro G., and Frawely W.J., editors, Knowledge Discover yin Database, pages 249-260, AAAI/MIT press.WebCT TM, University of British Columbia, Vancouver, BC, Canada. (WebCT is a trademark of the University of British Columbia , see http://www.webct.com/ )Weiss, S. M. and Kulikowski C. A. (1991), Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, Morgan Kaufman.Woods, K.; Kegelmeyer Jr., W.F.; Bowyer, K. (1997) Combination of Multiple Classifiers Using Local Area Estimates; IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 4, 1997.Yazdani, M. (1987). Intelligent Tutoring Systems: An Overview. Artificial Intelligence and Education: Learning Environments and Tutoring Systems. R. W. Lawler and M. Yazdani. Norwood, NJ, Ablex Publishing. 1: 183-201 Appendix A:Online Education SystemsHere are a few more profiles for comparison some institutes that have presented online delivery instructions. Based on what LON-CAPA has, there seems to be 4 major divisions: Institute Products: (1) Assessment Tools: (e.g. WebAssign and Homework Service) (2) Lecture Development Tools (e.g. Mallard)Corporate Products: (3) Complete Learning Management Systems [LMS] (e.g.Blackboard, Saba and WebCT) (4) Corporate Training Systems (e.g. PeopleSoft) The University of TX World Lecture Hall also was on the list, but this is basically a collection of course lectures in html format that you can browse. Some are current and you can enroll at the hosting institution to take the course for credit or just browse through the complete course. More info to follow... WebAssignCorporate OfficesP.O. Box 8202North Carolina State UniversityRaleigh, NC 27695Web Address HYPERLINK "http://www.webassign.net" www.webassign.netEstablish Date1997Mission StatementNot AvailableNumber of UsersNearly 40,000 (2000-2001 Academic Year)Countries ReachedU.S., IsraelInstitutions LicensedSee school listing belowDelivery Capability (Web and PC?)Web Based (NO PC)Languages AvailableEnglishCorporate PartnersNo Info AvailableService PartnersNo Info AvailableAnnual RevenuesNo Info AvailableVenture Capital (if applicable)No Info AvailableHits per MonthNo Info AvailableCapabilities/FeaturesHelp DeskInstant E-Mail NotificationRecord Keeping (for the term)Transaction Database Design (Real-time)Authentication with Institutions Database of Student ProfilesAutomated Class Registration and EnrollmentCustom Color and LayoutAdministrator Authorization to Key PersonnelProducts AvailableBasic service is to create, deliver, collect, grade, and asses student progress through the use of web-based practice exercises, quizzes, and homework assignments.Publishers contributing to the textbook and question database include: - Addison-Wesley - Benjamin Cummings - Thomson Learning - Duxbury - Freeman - Glencoe McGraw-Hill - Harcourt - Houghton Mifflin - Prentice Hall - WileyTextbook subjects include: - Astrology (1 book) - Biology (7) - Business (2 pending) - Chemistry (26) - Engineering (4) - Mathematics (8) - Physical Science (3) - Physics (31)(for a detailed list of books by subject matter, go to HYPERLINK "http://www.webassign.net/info/textbooks.html" http://www.webassign.net/info/textbooks.html)If a specific textbook is not currently provided a registered user my request that one be added. Three cost-sharing models are given for coding the questions of a new textbook including Professor/Institutional Coding, WebAssign Coding, and Joint Coding.Types of Questions:NumericalMultiple ChoiceMultiple SelectEssaySubmit FileFill in the BlankSymbolicPricing:Start-up Fee (for the first teacher in a department) $250 first yearRenewal Fee (for the first teacher in a department) $100 per yearAdditional Teachers (in the same department) $50 per yearTeachers may have as many classes and students as needed.Student HYPERLINK "http://www.webassign.net/info/AccessCodeInfo.html" WebAssign Access Codes Collegiate $5 per student per class per semester (or $3.33 per quarter)Pre-College (6-12) $3 per student per class per yearTechnical Specifications:Multi-institutional server:Sun Ultra Enterprise 450 with 1280 MB memory, two processors,nine 4.2 GB drives, and two 18.0 GB drivesWeb Servers:5 Dell PowerApp Web 100sThe WebAssign program is written in Perl, interfaces with a Sybase 11.5 database, and is delivered with Apache Web Server 1.3.12. Universities and Colleges Alabama State University Auburn University Augustana College Baylor University Carroll College Catholic University of America Chattahoochee Technical College Chattanooga State Technical Community College Christopher Newport University Clarke College Clemson University Coastal Carolina University Coastal Georgia Community College De Paul University Delgado Community College Delta College Drury University Duke University Duquesne University East Carolina University Eastern Kentucky University Eastern Michigan University Eastern Nazarene College Elizabethtown College Emporia State University Florence-Darlington Technical College Friday Center at UNC Gainesville College Georgia Institute of Technology Guilford College Harvard University Blackboard (BB), Inc.Corporate Offices1899 L Street NW, 5th Floor Washington, DC 2003622601 North 19th Avenue, Suite 200 Phoenix, AZ 85027Web Address HYPERLINK "http://www.blackboard.com" www.blackboard.comEstablish DateJune 1997Mission StatementTransforming the Internet into a powerful environment for teaching and learningNumber of Users1900 CustomersCountries ReachedOffices in:Australia, Canada, Germany, Greece, Netherlands, United KingdomInstitutions LicensedListed below.Delivery Capability (Web and PC?)Web based delivery.Languages AvailableMultiple (See digital content partner Parlo)Corporate PartnersPartner Groups include: Digital Commerce: Barnes & Noble (Booksellers) CampusTech (Software Seller multi-seat licensing) Edudex (Non-print educational materials, e.g. video) NowDocs (e-Document receipt, print, delivery) Obik (Career intelligence company, e.g. career guidance) SurfDiensten (Dutch Institution education material purchasing non-profit corp) Digital Content: Academic Systems (Math and English Programs) HYPERLINK "mailto:AOL@School" AOL@School (K-12 Content) Apex Learning (K-12 Content) Atomic Dog Publishing (Higher education content) Caliber (Corporate curriculum) Economy.com (Financial/Business Information) edu (College recruiting tools) Harcourt College Publishers Houghton Mifflin John Wiley & Sons, Inc. Kaplan (Test Prep) Labyrinth Publications (Computer skills content) McGraw-Hill MetaText (eBooks provider owned by NetLibrary) Parlo (Language courses) Pearson Education Thomson Learning W.W. Norton XanEdu.com Technology: Dell.com Embanet (Application hosting and management site) FT Knowledge (Management, Business, Finance) Griffity University (Australia) HorizonLive (Web-base video delivery) iGroup (Asian print, software, e-services delivery) IMS Global Learning Consortium, Inc. (developing open interoperability specifications) Infinet (Educational software apps developers) iORMYX (eCommerce solutions provider) K-World (Web portal developer) KPMG LLP (Consulting company) Microsoft Corporation netLibrary (eBooks, Internet based content management) Oracle PeopleSoft (LMS) Saba (Human capital management and development solutions) Smarthinking (Online tutorial and educational assistance) Stoas (Netherlands based multimedia and training systems developer) Sun Microsystems Swapdrive (Internet based file management) Tegrity, Inc. (Develop group-learning processes) Trevantis [by Lectora] (Content development software) VA Linux Systems (Linux based web-services provider) Yellowbrix (Automated information provisioning)Service PartnersSee above12/19/01 Questionmark offers testing services through the Blackboard Building Blocks (BB) Initiative.11/15/01 Blackboard forms Government Solutions Group, assisting government groups to expand their online learning and training solutions.10/29/01 Campus Pipeline partners with Blackboard, linking the backoffice operations of Campus Pipeline with the service offerings of Blackboard 5.10/29/01 Blackboards BB program provides the industrys first open set of Application Programming Interfaces (APIs) and developer services for creating extensions to Blackboard. From institution-developed innovations that extend Blackboard into new pedagogical frontiers, to commercial applications from 3rd party providers who speed their growth through Blackboards platform technology and client base, Building Blocks represents a springboard for innovation in the e-Education market.Annual RevenuesVenture Capital (if applicable)More than $100 MillionFrom investors that include AOL Time Warner Inc., The Aurora Funds Inc., The Carlyle Group, Dain Rauscher Wessels, Dell Computer Corporation, Internet Capital Group, Kaplan Ventures, Merrill Lynch KECALP, Microsoft Corporation, Morgan Keegan, Novak Biddle Venture Partners, Oak Hill Capital Partners, L.P., and Pearson Education.Hits per MonthNot AvailableCapabilities/FeaturesBlackboard 5 Learning System: Customizable (e.g. Kaplan, Bigchalk.com, Pearson Education)Blackboard Community Portal System: Blackboard Transation SystemCommerce and Access Solutions Integration with student ID cards (debit accounts) Future integration with cell phones, PDAs, etc. Blackboard Optim 9000 Blackboard EnvisionBuilding Blocks Initiative: An initiative focused on allowing customers (instructors, students, institutions, etc.) to build on the existing BB platform by integrating proprietary and 3rd party software, content, and services. Early adopters include Carnegie Mellon University and Princeton University.Products AvailableNo physical products offered other than through Corporate Partners.Course and Portal Clients:Adelaide University Australia InternationalArizona State University Higher EducationArkansas Tech University AR, USA Higher EducationAvon Old Farms School CT, USA K-12Belfast Institute of Further and Higher Education United Kingdom InternationalBook Promotion and Service Thailand InternationalBristol-Myers Squibb NJ, USA Corporate/Association HYPERLINK "http://company.blackboard.com/clients/cases/viewcs.cgi?csid=2016849" California State University at Dominguez Hills CA, USA Higher EducationCalvin College MI, USA Higher EducationCarnegie Mellon University PA, USA Higher EducationCase Western Reserve University OH, USA Higher EducationCherry Creek School District CO, USA K-12City College Manchester United Kingdom InternationalCity of Sunderland College United Kingdom InternationalCity University of Hong Kong Hong Kong InternationalColumbia University Teachers College NY, USA Higher EducationCornell University NY, USA Higher EducationDallas County Community College District TX, USA Higher EducationDuke University NC, USA Higher EducationEducation Queensland Australia InternationalErasmus Universitet of Rotterdam Netherlands International HYPERLINK "http://company.blackboard.com/clients/cases/viewcs.cgi?csid=66866702" Estrella Mountain Community College AZ, USA Higher EducationFlorida State University FL, USA Higher EducationFT Knowledge United Kingdom InternationalGeorgetown University DC, USA Higher EducationGriffith University Australia InternationalHouston Independent School District TX, USA K-12Huddersfield University United Kingdom InternationalInstituto Tecnologico Y De Estudios Superiores De Monterrey (ITESM) Mexico InternationalJames Madison University VA, USA Higher EducationJohns Hopkins University MD, USA Higher EducationWebCTUsage Statistics: Institutional use: 2,273 Active Users: Countries Reached: 76 Page Views per Month: Institutions Using Bb Web: Launch Date: e-Learning Platforms Release: WebCTs Vision:(Found at HYPERLINK "http://www.webct.com" www.webct.com)WebCT's vision is to work in concert with leading institutions to go beyond online course delivery and actually transform the educational experience. To that end, we will deliver state-of-the-art educational technology that supports a full range of teaching and learning styles and optimizes intellectual and technical resources. WebCT (WCT) provides users with access to e-Packs - Publisher content that is WCT-ready - Content Partners include: - McGraw Hill - Pearson - Pearson Canada - Thomson Learning - Bedford, Freeman & Worth Publishing - EMC Paradigm - Vivendi - John Wiley & Sons - Jones & Barlett - Mayfield Publishing - W.W. Norton & Company- Licensed materials that users have trial-access to for 30 days. Upon expiration, users can adopt the e-Pack - The e-Pack can be downloaded to an institutions server as long as they have a valid WebCT license - OR, the e-Pack can be hosted on WebCTs servers - An e-Pack example: Title: College Accounting 1-12 Description: For use with WebCT 3.x This e-Pack is a companion website for EMCParadigm's Paradigm College, Accounting 1-12 textbook. This Class Connection features: Course objectives, Syllabi, Assignments, Chapter-specific Self-tests, Glossary, and course Resources, featuring links to book-specific extra material and file downloads. Author: Dansby, Kaliski, Lawrence Publisher: EMC Paradigm Publishing Availability: *Available Now! Student Access Code Price: $31.19 (if purchased at WebCT.com) - COBALT, the next generation of WebCT (to be released 1st Quarter 2002), will include: - Best-of-breed learning tools - Robust content management capabilities - Dynamic learning information management - Complete personalization of the learning experience - Enterprise-class platform architecture Leveraging Technology to Transform the Educational Experience A Web CT White Paper June 2001 - COBALT will incorporate two forms of data mining: - Content Management: - An institution will only have access to content available on their server/platform - Internally developed content - Content e-Packs - Content will be tagged and stored with the intent of future reuse, reassembling, and sharing - Content will be tracked and monitored to: - Identify the use of content and improve pedagogical approaches - Control the use and reuse of copyrighted or licensed material - Additional key points re: COBALTs content management- A New Media Library will be included for managing audio, video, graphics and other media content- A commercially developed search-engine will be the first of its type to be utilized in a system developed for content management- COBALT will be developed to comply with major standards including: SCORM, IMS Content and Assessment Specifications Suport- The enhanced File Manger and method for developing a hierarchy for content will improve the storing and sharing of content across educational sections.- Role based access will allow instructors to designate scope of access by the role of a particular operater (e.g. Learner vs. Teaching Assistant vs. Instructor) - An institution or instructor will also be able to mine for information re: the student population- Students are encouraged to utilize the notes and chat functions of class space. Data is collected and then analyzed to identify areas for improvement- Student records are available, in a single location, for trend and regression analysis- Additional tracking information will be collected as students engage with the COBALT Learning Platform this information can be used by the instructor to monitor and enhance the acquisition of deep knowledge - The Benefits of COBALT:- For Students: Easily accessed, personalized, high quality education - For Faculty: The power to offer superior courses as efficiently as possible - For Administrators: A cost-effective platform for improving quality and reaching more students - For CIOs: An integrated, manageable, scalable platform that will meet critical e-learning needs, today and tomorrow Leveraging Technology to Transform the Educational Experience A Web CT White Paper June 2001. Appendix B: Some Tree Classifiers OutputC5.0Using C5.0 for classifying the students: This result shows the error rate in each fold in 10-fold cross-validation, and confusion matrix.In 2-classes (Passed, Failed) Fold Rules ---- ---------------- No Errors 0 9 18.2% 1 9 22.7% 2 12 27.3% 3 5 30.4% 4 8 17.4% 5 7 21.7% 6 10 13.0% 7 8 17.4% 8 4 17.4% 9 8 21.7% Mean 8.0 20.7%SE 0.7 1.6%In 3-classes (High, Middle, Low) we got the following results:Fold Decision Tree ---- ---------------- Size Errors 0 7 36.4% 1 12 45.5% 2 6 45.5% 3 7 47.8% 4 10 34.8% 5 9 34.8% 6 6 47.8% 7 8 43.5% 8 10 47.8% 9 9 47.8% Mean 8.4 43.2% SE 0.6 1.8%In 9-classes we got the following results:Fold Decision Tree ---- ---------------- Size Errors 0 57 81.8% 1 51 63.6% 2 55 63.6% 3 61 78.3% 4 48 73.9% 5 56 73.9% 6 58 69.6% 7 53 87.0% 8 56 78.3% 9 56 73.9% Mean 55.1 74.4% SE 1.2 2.4% (a) (b) (c) (d) (e) (f) (g) (h) (i) <-classified as ---- ---- ---- ---- ---- ---- ---- ---- ---- 1 1 (a): class 1 1 3 2 2 2 (b): class 2 1 2 7 6 2 7 3 (c): class 3 4 2 5 2 4 3 3 (d): class 4 2 5 3 12 11 8 2 (e): class 5 7 5 9 15 9 7 (f): class 6 4 6 3 15 5 8 (g): class 7 1 1 7 3 2 14 (h): class 8 (i): class 9Here, there are a sample of rule sets resulted the from C5.0 in 3-class classificationRule 1: (8, lift 2.9) FirstCorrect > 64 FirstCorrect <= 112 TotalCorrect > 181 AvgTries > 1270 TotalTimeSpent <= 87.87 Discussion <= 0 -> class High [0.900]Rule 2: (5, lift 2.8) FirstCorrect > 93 FirstCorrect <= 99 TotalCorrect > 181 AvgTries <= 1270 Discussion <= 14 -> class High [0.857]Rule 3: (15/2, lift 2.7) FirstCorrect <= 112 TotalCorrect > 181 Discussion > 0 Discussion <= 14 -> class High [0.824]Rule 4: (8/1, lift 2.6) FirstCorrect <= 112 TotalCorrect > 174 TotalCorrect <= 180 AvgTries <= 1768 Discussion <= 0 -> class High [0.800]Rule 5: (3, lift 2.6) FirstCorrect > 112 FirstCorrect <= 117 TotalCorrect > 180 TotalTimeSpent > 14.01 Discussion <= 1 -> class High [0.800].Rule 15: (3/1, lift 2.2) FirstCorrect <= 112 TotalCorrect > 180 TotalCorrect <= 181 Discussion <= 0 -> class Low [0.600]Here, there are a sample of rule sets resulted the from C5.0 in 2-class classificationRules:Rule 1: (158/25, lift 1.2) TotalCorrect > 165 -> class Passed [0.838]Rule 2: (45/8, lift 1.1) Discussion > 1 -> class Passed [0.809]Rule 3: (7, lift 3.2) FirstCorrect <= 78 TotalCorrect <= 165 -> class Failed [0.889]Rule 4: (2, lift 2.7) TotalCorrect <= 165 AvgTries > 669 Discussion > 1 Discussion <= 4 -> class Failed [0.750]Rule 5: (47/15, lift 2.4) TotalCorrect <= 165 -> class Failed [0.673]Default class: PassedEvaluation on hold-out data (22 cases): Rules ---------------- No Errors 5 3(13.6%) <<And a sample of tree which is produced by C5.0 in one of the folds in 3 classes:TotalCorrect <= 165::...AvgTries > 850: Low (13/2): AvgTries <= 850:: :...Discussion > 2:: :...TotalTimeSpent <= 20.57: Low (2): : TotalTimeSpent > 20.57: Middle (3/1): Discussion <= 2:: :...TotalTimeSpent > 22.63: Low (8): TotalTimeSpent <= 22.63:: :...AvgTries <= 561: Low (7/1): AvgTries > 561:: :...TotalCorrect > 156: Low (2): TotalCorrect <= 156:: :...TotalCorrect <= 136: Low (3/1): TotalCorrect > 136: Middle (6)TotalCorrect > 165::...AvgTries <= 535: :...TotalCorrect <= 177: Low (5) : TotalCorrect > 177: High (5/2) AvgTries > 535: :...FirstCorrect > 112: :...TotalCorrect <= 172: Middle (6) : TotalCorrect > 172: : :...TotalCorrect > 180: Middle (38/13) : TotalCorrect <= 180: : :...TimeTillCorr <= 23.47: : :...TotalCorrect > 178: High (2) : : TotalCorrect <= 178: : : :...TotalCorrect <= 174: High (4/2) : : TotalCorrect > 174: Middle (8/1) : TimeTillCorr > 23.47: : :...FirstCorrect > 129: Middle (2) : FirstCorrect <= 129: : :...TotalCorrect > 175: Low (7/1) : TotalCorrect <= 175: : :...FirstCorrect <= 118: Low (2) : FirstCorrect > 118: High (2) FirstCorrect <= 112: :...TotalTimeSpent > 87.87: Middle (5/1) TotalTimeSpent <= 87.87: :...TotalCorrect <= 169: High (5/1) TotalCorrect > 169: :...TotalCorrect <= 174: Middle (8) TotalCorrect > 174: :...Discussion > 7: Middle (5/1) Discussion <= 7: :...TotalCorrect <= 177: High (5/1) TotalCorrect > 177: :...TotalCorrect <= 181: :...AvgTries <= 1023: High (3) : AvgTries > 1023: Middle (9/2) TotalCorrect > 181: :...Discussion > 0: High (15/2) Discussion <= 0: :...FirstCorrect > 99: Middle (5/1) FirstCorrect <= 99: :...FirstCorrect > 89: High (7/1) FirstCorrect <= 89: :...AvgTries <= 1355: Middle (4) AvgTries > 1355: [S1]Evaluation on hold-out data (22 cases): Decision Tree ---------------- Size Errors 32 7(31.8%) <<CARTSome of CART report for 2-Classes using Gini criterion: File: PHY183.XLS Target Variable: CLASS2 Predictor Variables: FIRSTCRR, TOTCORR, TRIES, SLVDTIME, TOTTIME, DISCUSSTree SequenceTree NumberTerminal NodesCross-Validated Relative CostResubstitution Relative CostComplexity1230.873 0.0990.317-1.0002220.984 0.1040.3171.00E-0053151.016 0.1040.3970.003490.762 0.0890.4760.004570.778 0.0910.5080.004650.841 0.0930.5560.0077**30.667 0.0900.6190.009820.714 0.0880.6830.018911.000 6.73E-0051.0000.088 * Minimum Cost ** Optimal Classification tree topology for: CLASS2Error CurveGains for 2Gains Data for 2NodeCases Class 2% of Node Class 2% Class 2Cum % Class 2Cum % Pop% PopCases in NodeCum liftLift Pop13570.0055.5655.5622.0322.03502.5222.5222770.0011.1166.6726.434.41102.5222.52232112.5733.33100.00100.0073.571671.0000.453Variable Importance Variable TOTCORR100.00||||||||||||||||||||||||||||||||||||||||||TRIES56.32|||||||||||||||||||||||FIRSTCRR4.58|TOTTIME0.91SLVDTIME0.83DISCUSS0.00Misclassification for Learn DataClassN CasesN Mis- ClassedPct ErrorCost11641810.980.112632133.330.33Misclassification for Test DataClassN CasesN Mis- ClassedPct ErrorCost11642112.800.132632133.330.33Some of CART report for 3-Classes using Twoing criterion: (10-fold Cross-Validation):Tree SequenceTree NumberTerminal NodesCross-Validated Relative CostResubstitution Relative CostComplexity1420.802 0.0500.230-1.0002380.808 0.0500.2360.0013370.808 0.0500.2380.0014360.794 0.0500.2420.0035350.786 0.0500.2470.0036270.778 0.0500.2890.0047240.762 0.0500.3110.0058230.762 0.0500.3190.0059220.761 0.0500.3270.00510210.731 0.0490.3360.00611180.734 0.0490.3660.00712140.727 0.0490.4070.00713130.740 0.0490.4180.00714110.732 0.0490.4440.00915**100.694 0.0490.4570.0091680.720 0.0500.5000.0141760.743 0.0500.5450.0151850.741 0.0500.5740.0191940.728 0.0500.6050.0212030.745 0.0500.6610.0372120.758 0.0350.7510.0602211.000 0.0001.0000.166Classification tree topology for: CLASS3Error CurveGains for 1Gains Data for 1NodeCases Class 1% of Node Class 1% Class 1Cum % Class 1Cum % Pop% PopCases in NodeCum liftLift Pop9480.005.805.802.202.2052.6322.6322480.005.8011.594.412.2052.6322.63263162.0044.9356.5226.4322.03502.1382.0404857.1411.5968.1232.606.17142.0901.88071327.0818.8486.9653.7421.15481.6180.8915112.501.4588.4157.273.5281.5440.41110211.762.9091.3064.767.49171.4100.3878211.112.9094.2072.697.93181.2960.366148.005.80100.0094.7122.03501.0560.263300.000.00100.00100.005.29121.0000.000Variable Importance Variable TOTCORR100.00||||||||||||||||||||||||||||||||||||||||||TRIES40.11||||||||||||||||FIRSTCRR24.44||||||||||TOTTIME23.22|||||||||SLVDTIME21.67||||||||DISCUSS14.44|||||Misclassification for Learn DataClassN CasesN Mis- ClassedPct ErrorCost1692231.880.322953435.790.363631523.810.24Misclassification for Test DataClassN CasesN Mis- ClassedPct ErrorCost1693550.720.512955254.740.553632133.330.33Some of CART report for 9-Classes using Entropy criterion: (10-fold Cross-Validation)Different tree topologies for: CLASS-9Entropy Gini TwoingDescriptive Statistics in CART for 3-Classes Variable N Mean SD Min Max Sum ------------------------------------------------------------------------------------------- Overall FIRSTCRR 227.00 106.242 20.462 47.000 150.000 24117.000 TOTCORR 227.00 171.678 18.155 80.000 184.000 38971.000 TRIES 227.00 977.987 450.898 265.000 3095.000 222003.000 SLVDTIME 227.00 36.620 24.837 2.590 130.870 8312.700 TOTTIME 227.00 37.948 25.434 3.000 130.870 8614.170 DISCUSS 227.00 1.330 3.034 0.000 23.000 302.000 CLASS3 = 1 FIRSTCRR 69.00 103.145 19.598 57.000 149.000 7117.000 TOTCORR 69.00 179.290 7.900 141.000 184.000 12371.000 TRIES 69.00 1088.406 439.742 487.000 2227.000 75100.000 SLVDTIME 69.00 39.060 22.209 2.590 98.840 2695.130 TOTTIME 69.00 39.764 22.797 3.000 99.200 2743.720 DISCUSS 69.00 1.493 2.988 0.000 14.000 103.000 CLASS3 = 2 FIRSTCRR 95.00 108.505 20.973 54.000 150.000 10308.000 TOTCORR 95.00 175.453 12.412 118.000 184.000 16668.000 TRIES 95.00 984.937 443.874 392.000 3095.000 93569.000 SLVDTIME 95.00 36.866 27.353 4.100 130.870 3502.240 TOTTIME 95.00 37.916 27.865 4.130 130.870 3602.010 DISCUSS 95.00 1.537 3.596 0.000 23.000 146.000 CLASS3 = 3 FIRSTCRR 63.00 106.222 20.482 47.000 147.000 6692.000 TOTCORR 63.00 157.651 24.763 80.000 184.000 9932.000 TRIES 63.00 846.571 446.210 265.000 2623.000 53334.000 SLVDTIME 63.00 33.577 23.605 4.870 107.100 2115.330 TOTTIME 63.00 36.007 24.561 5.920 114.210 2268.440 DISCUSS 63.00 0.841 1.953 0.000 9.000 53.000A Sample of CART tree for 3-Classes using Entropy criterion: (10-fold Cross-Validation) QUEST Summary of numerical variable: FirstCorr Size Obs Min Max Mean Sd 226 226 0.470E+02 0.150E+03 0.106E+03 0.204E+02 Summary of numerical variable: TotCorr Size Obs Min Max Mean Sd 226 226 0.960E+02 0.184E+03 0.172E+03 0.171E+02 Summary of numerical variable: AvgTries Size Obs Min Max Mean Sd 226 226 0.193E+01 0.169E+02 0.551E+01 0.246E+01 Summary of numerical variable: TimeCorr Size Obs Min Max Mean Sd 226 226 0.249E+01 0.942E+02 0.280E+02 0.185E+02 Summary of numerical variable: TimeSpent Size Obs Min Max Mean Sd 226 226 0.260E+01 0.942E+02 0.281E+02 0.185E+02 Summary of numerical variable: Discuss Size Obs Min Max Mean Sd 226 226 0.000E+00 0.140E+02 0.912E+00 0.201E+01 Result for 2-classes Summary of response variable: Class2 class frequency Failed 62 Passed 164 ------------- 2 226 Options for tree construction Learning sample estimated priors are Class prior Failed 0.27434 Passed 0.72566 Size and CV misclassification cost and SE of subtrees: Tree #Tnodes Mean SE(Mean) 1 26 0.1947 0.2634E-01 2* 16 0.1903 0.2611E-01 3 13 0.1947 0.2634E-01 4 12 0.2035 0.2678E-01 5 6 0.1947 0.2634E-01 6** 4 0.1947 0.2634E-01 7 3 0.2301 0.2800E-01 8 2 0.2434 0.2854E-01 9 1 0.2743 0.2968E-01 CART 0-SE tree is marked with *CART SE-rule using CART SE is marked with **use 10-fold CV sample pruning SE-rule trees based on number of SEs = 1.00 subtree # Terminal complexity current number nodes value cost 1 26 0.0000 0.0885 2 16 0.0022 0.1106 3 13 0.0029 0.1195 4 12 0.0044 0.1239 5 6 0.0052 0.1549 6 4 0.0111 0.1770 7 3 0.0177 0.1947 8 2 0.0354 0.2301 9 1 0.0442 0.2743 Classification tree: Node 1: TotCorr <= 156.9 Node 2: Failed Node 1: TotCorr > 156.9 Node 3: TotCorr <= 168.8 Node 18: Discuss <= 1.279 Node 20: Failed Node 18: Discuss > 1.279 Node 21: Passed Node 3: TotCorr > 168.8 Node 19: Passed Classification matrix based on learning sample predicted class actual class Failed Passed Failed 35 27 Passed 13 151 Classification matrix based on 10-fold CV predicted class actual class Failed Passed Failed 33 29 Passed 15 149 Result for 3-classes use 10-fold CV sample pruning SE-rule trees based on number of SEs = 1.00 Size and CV misclassification cost and SE of subtrees: Tree #Tnodes Mean SE(Mean) 1 47 0.5354 0.3318E-01 2 30 0.5265 0.3321E-01 3 24 0.5354 0.3318E-01 4 10 0.4735 0.3321E-01 5* 9 0.4425 0.3304E-01 6 8 0.4513 0.3310E-01 7 6 0.4602 0.3315E-01 8** 4 0.4735 0.3321E-01 9 2 0.4823 0.3324E-01 10 1 0.5796 0.3283E-01 CART 0-SE tree is marked with * CART SE-rule using CART SE is marked with ** Following tree is based on ** Structure of final tree Node Left node Right node Split variable Predicted class 1 2 3 TotCorr 2 * terminal node * Low 3 26 27 1stGotCrr 26 28 29 TotCorr 28 * terminal node * Middle 29 * terminal node * High 27 * terminal node * Middle Number of terminal nodes of final tree = 4 Total number of nodes of final tree = 7 Classification tree: Node 1: TotCorr <= 165.5 Node 2: Low Node 1: TotCorr > 165.5 Node 3: 1stGotCrr <= 117.5 Node 26: TotCorr <= 181.5 Node 28: Middle Node 26: TotCorr > 181.5 Node 29: High Node 3: 1stGotCrr > 117.5 Node 27: Middle Classification matrix based on learning sample predicted class actual class High Low Middle High 34 4 31 Low 2 34 26 Middle 21 11 63 Classification matrix based on 10-fold CV predicted class actual class High Low Middle High 27 10 32 Low 4 37 21 Middle 25 15 55Bagging (Leave-one-out method) estimated priors are Class prior High 0.30531 Low 0.27434 Middle 0.42035 minimal node size: 2 use univariate split use (biased) exhaustive search for variable and split selections use the divergence famliy with lambda value: 0.5000000 use 226-fold CV sample pruning SE-rule trees based on number of SEs = 1.00 Size and CV misclassification cost and SE of subtrees: Tree #Tnodes Mean SE(Mean) 1 67 0.5354 0.3318E-01 2 61 0.5354 0.3318E-01 3 31 0.5177 0.3324E-01 4 24 0.5000 0.3326E-01 5* 10 0.4204 0.3283E-01 6 9 0.4425 0.3304E-01 7** 8 0.4469 0.3307E-01 8 6 0.5000 0.3326E-01 9 4 0.4956 0.3326E-01 10 2 0.4823 0.3324E-01 11 1 0.5796 0.3283E-01 CART 0-SE tree is marked with * CART SE-rule using CART SE is marked with ** Following tree is based on ** Structure of final tree Node Left node Right node Split variable Predicted class 1 2 3 TotCorr 2 * terminal node * Low 3 32 33 1stGotCrr 32 34 35 TotCorr 34 36 37 TotCorr 36 * terminal node * High 37 * terminal node * Middle 35 70 71 TimeCorr 70 * terminal node * High 71 * terminal node * Middle 33 104 105 TimeCorr 104 * terminal node * Middle 105 132 133 TotCorr 132 * terminal node * Low 133 * terminal node * Middle Number of terminal nodes of final tree = 8 Total number of nodes of final tree = 15 Number of terminal nodes of final tree = 10Total number of nodes of final tree = 19 Classification tree: Node 1: TotCorr <= 165.5 Node 2: Low Node 1: TotCorr > 165.5 Node 3: 1stGotCrr <= 117.5 Node 26: TotCorr <= 181.5 Node 28: TotCorr <= 169.5 Node 30: High Node 28: TotCorr > 169.5 Node 31: Middle Node 26: TotCorr > 181.5 Node 29: TimeCorr <= 52.44 Node 56: High Node 29: TimeCorr > 52.44 Node 57: Middle Node 3: 1stGotCrr > 117.5 Node 27: TimeCorr <= 24.49 Node 80: Middle Node 27: TimeCorr > 24.49 Node 81: TotCorr <= 180.5 Node 104: Low Node 81: TotCorr > 180.5 Node 105: 1stGotCrr <= 130.0 Node 110: TimeCorr <= 27.16 Node 112: Low Node 110: TimeCorr > 27.16 Node 113: Middle Node 105: 1stGotCrr > 130.0 Node 111: High Classification matrix based on learning sample predicted class actual class High Low Middle High 38 5 26 Low 3 47 12 Middle 13 13 69 Classification matrix based on 226-fold CV predicted class actual class High Low Middle High 31 9 29 Low 5 43 14 Middle 19 19 57 Result for 9-classes Classification tree: Node 1: TotCorr <= 104.0 Node 2: 0 Node 1: TotCorr > 104.0 Node 3: TotCorr <= 165.5 Node 4: 3 Node 3: TotCorr > 165.5 Node 5: TotCorr <= 181.5 Node 44: TimeCorr <= 2.565 Node 46: 8 Node 44: TimeCorr > 2.565 Node 47: TimeCorr <= 71.06 Node 48: AvgTries <= 3.145 Node 50: 4 Node 48: AvgTries > 3.145 Node 51: 1stGotCrr <= 77.00 Node 56: 5 Node 51: 1stGotCrr > 77.00 Node 57: AvgTries <= 12.52 Node 58: TimeCorr <= 40.50 Node 60: 5 Node 58: TimeCorr > 40.50 Node 61: 6 Node 57: AvgTries > 12.52 Node 59: 3 Node 47: TimeCorr > 71.06 Node 49: 3 Node 5: TotCorr > 181.5 Node 45: AvgTries <= 2.630 Node 108: 4 Node 45: AvgTries > 2.630 Node 109: 1stGotCrr <= 111.5 Node 110: 1stGotCrr <= 55.50 Node 112: 5 Node 110: 1stGotCrr > 55.50 Node 113: TimeCorr <= 57.44 Node 114: AvgTries <= 5.330 Node 116: 6 Node 114: AvgTries > 5.330 Node 117: 8 Node 113: TimeCorr > 57.44 Node 115: 6 Node 109: 1stGotCrr > 111.5 Node 111: 6 predicted class actual class 0 2 3 4 5 6 7 8 0 1 0 0 0 0 0 0 0 2 1 0 8 0 0 1 0 0 3 0 0 21 2 2 3 0 0 4 0 0 7 5 8 2 0 1 5 0 0 6 0 32 3 0 2 6 0 0 5 0 19 24 0 4 7 0 0 2 1 16 15 0 7 8 0 0 2 0 3 4 0 19 elapsed time: 386.25 seconds (user: 386.14, system: 0.11)CRUISEHere, some output results of CRUISE for 3-classes: CV misclassification cost and SE of subtrees: Subtree CV R(t) CV SE # Terminal Nodes (largest) 0.549823 0.3313E-01 82 1 0.539823 0.3315E-01 70 2 0.544248 0.3313E-01 67 3 0.539823 0.3315E-01 59 4 0.526549 0.3321E-01 56 5 0.544248 0.3313E-01 41 6 0.553097 0.3307E-01 38 7 0.553097 0.3307E-01 23 8 0.561947 0.3300E-01 21 9 0.557522 0.3304E-01 15 10 0.535398 0.3318E-01 9 11 0.504425 0.3326E-01 8 12* 0.460177 0.3315E-01 6 13 0.504425 0.3326E-01 2 14 0.579646 0.3283E-01 1 * denotes 0-SE Tree ** denotes given-SE Tree * tree is same as ** tree Following tree is based on ** Splits of the Tree: Node Split variable 1 TotCorr 2 * terminal * 3 TotCorr 8 TotCorr 24 * terminal * 25 * terminal * 9 TotCorr 27 * terminal * 28 TimeCorr 84 * terminal * 85 * terminal * Tree Structure: Node 1: TotCorr <= 163.156 Node 2: Terminal Node, predicted class = Low Class label : High Low Middle Class size : 3 28 11 Node 1: TotCorr > 163.156 Node 3: TotCorr <= 171.059 Node 8: TotCorr <= 168.639 Node 24: Terminal Node, predicted class = Low Class label : High Low Middle Class size : 2 7 1 Node 8: TotCorr > 168.639 Node 25: Terminal Node, predicted class = Middle Class label : High Low Middle Class size : 3 4 9 Node 3: TotCorr > 171.059 Node 9: TotCorr <= 183.206 Node 27: Terminal Node, predicted class = Middle Class label : High Low Middle Class size : 34 18 53 Node 9: TotCorr > 183.206 Node 28: ABS(TimeCorr - 35.4849 ) <= 19.1162 Node 84: Terminal Node, predicted class = High Class label : High Low Middle Class size : 22 4 11 Node 28: ABS(TimeCorr - 35.4849 ) > 19.1162 Node 85: Terminal Node, predicted class = Middle Class label : High Low Middle Class size : 5 1 10 Detailed Description of the Tree: Nodes No. Subnode Split Split Split label cases label stat. variable value 1 226 2 F TotCorr <= 163.16 3 < infinity # obs mean/mode of TotCorr Class High : 69 179.290 Class Low : 62 158.903 Class Middle : 95 175.453 2 42 **** terminal, predicted class: Low # obs Class High : 3 Class Low : 28 Class Middle : 11 3 184 8 F TotCorr <= 171.06 9 < infinity # obs mean/mode of TotCorr Class High : 66 180.576 Class Low : 34 175.176 Class Middle : 84 179.226 8 26 24 F TotCorr <= 168.64 25 < infinity # obs mean/mode of TotCorr Class High : 5 167.600 Class Low : 11 167.182 Class Middle : 10 169.800 24 10 **** terminal, predicted class: Low # obs Class High : 2 Class Low : 7 Class Middle : 1 25 16 **** terminal, predicted class: Middle # obs Class High : 3 Class Low : 4 Class Middle : 9 9 158 27 F TotCorr <= 183.21 28 < infinity # obs mean/mode of TotCorr Class High : 61 181.639 Class Low : 23 179.000 Class Middle : 74 180.500 27 105 **** terminal, predicted class: Middle # obs Class High : 34 Class Low : 18 Class Middle : 53 28 53 84 Levene ABS(TimeCorr - 35.5 ) <=19.116 85 < infinity # obs mean/mode of TimeCorr Class High : 27 34.7652 Class Low : 5 36.5700 Class Middle : 21 36.1519 84 37 **** terminal, predicted class: High # obs Class High : 22 Class Low : 4 Class Middle : 11 85 16 **** terminal, predicted class: Middle # obs Class High : 5 Class Low : 1 Class Middle : 10 Number of nodes in maximum tree = 153 Number of nodes in final tree = 11 Number of terminal nodes in final tree = 6 Classification Matrix : Predicted class High Low Middle Actual class #obs Prior --------------------- High 69 0.305 22 5 42 Low 62 0.274 4 35 23 Middle 95 0.420 11 12 72 Total obs = 226, # correct = 129 Resubstitution misclassification cost = 0.4292 S.E. of resubstitution misclassification cost = 0.3055E-01 Cross-validation error cost from pruning = 0.4602 S.E. of CV misclassification cost = 0.3315E-01 Elapsed system time in seconds: 5.54 In appendix A, there are more profiles of some institutes that have presented online delivery instructions. ID3 got this name because it was the third version of interactive dichotomizer procedure. This name has been got from administration office of Michigan State University for evaluating the exams problem. Here we expanded this expression to homework problems as well. The first five classifiers are coded in MATLABTM 6.0, and for the decision tree classifiers we have use some available software packages such as C5.0, CART, QUEST, CRUISE. We will discuss the Decision Tree-based software in the next section. In this section we deal with non-tree classifiers. We have not implemented this method in this survey yet. All the code was written in MATLABTM 6.0 Weak learner means that the classifier has accuracy only slightly better than chance (Duda et al., 2001) It is the commercial version of the C4.5 decision tree algorithm developed by Ross Quinlan. See5/C5.0 classifiers are expressed as decision trees or sets of if-then rules. RuleQuest provides C source code so that classifiers constructed by See5/C5.0 can be embedded in your own systems. CART(tm) (Classification And Regression Trees) is a data mining tool exclusively licensed to Salford Systems (http://www.salford-systems.com). CART is the implementation of the original program by Breiman, Friedman, Olshen, and Stone. We used CART version 5.02 under windows for our classification. QUEST (Quick, Unbiased and Efficient Statistical Tree) A classification tree restricted to binary splitsCRUISE (Classification Rule with Unbiased Interaction Selection and Estimation) A classification tree that splits each node into two or more sub-nodes These new software are developed by Wei-Yin Loh at the University of Wisconsin-Madison, Shih at university of Taiwan, and Hyunjoong Kim at University of Tennessee. These are vastly-improved descendant of an older algorithm called FACT. Survey / PAGE 111Interpretation/ EvaluationData MiningKnowledgeTransformationPreprocessing / CleaningPatternsTransformed DataSelectionPreprocessed DataTarget DataDataFigure 3.1: Steps of the KDD Process (Fayyad et al., 1996)Bayesian Classificationg1EvaluationLikelihoodFunctionsPatternDecisiong2xjMaximumSelector ngmFeatureExtractionaj{j=j,n}gi{i=i,m}Figure 3.2: the Bayesian Classification Process (Adapted from Wu et al., 1991)Figure 3.3: A Three Layer Feedforward Neural Network [Lu et al., 1995]Input LayerHidden LayerOutput LayerFigure 4.7: Graphical chart of student tries for Atwood Machine Problem according to every concept in 1 interval time.A frictionless, massless pulley is attached to the ceiling, in a gravity field g. Mass Ma is greater than mass Mb. The tensions Tx,Ty, Tz, and the constant g are magnitudes. (For each, select: Greater than, Less than, Equal to, True, or False) EMBED Word.Picture.8All machines in the network are connected with each other through two-way persistent TCP/IP connections. The network has two classes of servers: library and access servers. A library server can act as a home server that stores all personal records of users, and is responsible for the initial authentication of users when a session is opened on any server in the network. For authors, it also hosts their construction area and the authoritative copy of every resource that has been published by that author. Library servers can be used as backups to host sessions when all access servers in the network are overloaded. An Access Server is a machine that hosts student sessionsExpert ModelCommunication ModuleStudent ModelPedagogical ModuleLearnerFigure 2.3: A schema of distributed data in LON-CAPATable 4.3: selecting 2 class labels regarding to students grades in course PHY183 SS02Table 4.8: Comparing Error Rate of classifiers with and without Normalization in the case of 3 classes Table 4.10: Comparing the Performance of classifiers, in all cases: 2-Classes, 3-Classess, and 9-ClassesUsing 10-fold Cross-Validation in all cases.Table 4.2: selecting 3 class labels regarding to students grades in course PHY183 SS02Table 4.1: selecting 9 class labels regarding to students grades in course PHY183 SS02Figure 4.1: Graph of distribution of grades in course PHY183 SS02Table 4.9: Comparing Error Rate of classifiers 2-fold and 10-fold Cross-Validation in the case of 3 classes Figure 4.8: Comparing Error Rate of classifiers with 10-fold Cross-Validation in the case of 2-Classes Figure 4.9: Comparing Error Rate of classifiers with 10-fold Cross-Validation in the case of 2-ClassesFigure 4.10: Comparing Error Rate of classifiers with 10-fold Cross-Validation in the case of 3-Classes Figure 4.11: Comparing Error Rate of classifiers with 10-fold Cross-Validation in the case of 3-Classes (a) (b) (c) <-classified as---- ---- ---- 29 30 10 (a): class High 18 62 15 (b): class Middle 5 20 38 (c): class Low(a) (b) <-classified as---- ----147 17 (a): class Passed 30 33 (b): class FailedTable 4.14: Comparing the Error Rate of CART, between 2-Classes, 3-Classess, and 9-ClassesUsing Leave-One-Out method all learning and testing test.Table 4.11: Variable Importance in 2-Classes Using Gini CriterionVariable TOTCORR100.00||||||||||||||||||||||||||||||||||||||||||TRIES58.61||||||||||||||||||||||||FIRSTCRR27.70|||||||||||SLVDTIME24.60||||||||||TOTTIME24.47||||||||||DISCUSS9.21|||Table 4.12: Variable Importance in 3-Classes Using Entropy CriterionTable 4.13: Comparing the Error Rate in CART, between 2-Classes, 3-Classess, and 9-ClassesUsing 10-fold Cross-Validation in all learning and testing test.Figure 2.2: Components of an ITSTable 4.13: Comparing the Error Rate of all classifiers on PHY183 dataset in the cases of 2-Classes, 3-Classess, and 9-Classes 9 : ; < A N P i l u # 8 źҦ҇sfTD hy h4+ 5CJ( OJ QJ aJ #hy hX, 5CJ( OJ QJ ^J aJ$ hy 5CJ OJ QJ ^J h4+ 5CJ OJ QJ ^J h4+ 5CJ h4+ 5CJ OJ QJ h4+ 5OJ QJ h4+ 5CJ OJ QJ h4+ CJ OJ QJ h4+ CJ OJ QJ h4+ 5OJ QJ \ h4+ 5CJ OJ QJ \ h4+ OJ QJ
h4+ CJ h4+ CJ( OJ QJ h4+ CJ( OJ QJ h4+ OJ QJ h4+ : ; < = > ? @ A N O P _ i j k l $a$ d dh ~= z9 !v% ` _0 l # 8 9 : ; 2 $d a$ gdX, d gdy $a$ 8 9 : ; K # G O q s y | } - 1 8 < ԼԼԲԲ墕v h4+ CJ \]
hTf CJ hy h4+ 5CJ( OJ QJ aJ hX, 5CJ OJ QJ aJ hy hX, 5CJ( OJ QJ aJ h4+ B*CJ ph h4+ CJ h h4+ B*CJ ph h4+ 6CJ
h4+ CJ h4+ 5CJ OJ QJ hn 5CJ OJ QJ aJ hy 5CJ OJ QJ aJ *2 3 <
$ d a$ gdX, $ a$ gdX, $ a$ gdy
& F d d $d a$ j! k! l! ! $ !$ b$ d$ $ $ $ $ $ $ $ $ $ $ $ 6% & ( ( ( ƷtfYMG
h4+ CJ h4+ CJ OJ QJ aJ h4+ 5CJ OJ QJ aJ j h4+ 5OJ QJ U ,j@
h4+ CJ OJ QJ UVmH nH u h4+ 5OJ QJ j % h4+ 5OJ QJ U h4+ h4+ H*OJ QJ h4+ OJ QJ h4+ 5CJ OJ QJ ^J aJ h4+ 5CJ h4+ 5CJ OJ QJ
h4+ CJ h4+ 6CJ h4+ 5CJ OJ QJ aJ h4+ 5CJ OJ QJ aJ a b 9 : k! l! ! $ $ $ 6% & & ( ( ( ( ( ( $$If a$ 1 8 $
& F `a$ # $ $ ` a$ 7
& F d d
& F
8S d ` ( ( ( ( ( ( ( ( ) ) q) z) ) ) * * i* x* * * * + + G- d- / / / / / / / / / / / / / "/ #/ $/ %/ &/ '/ +/ ȾyӴ h4+ CJ mH nH sH u j h4+ UmH nH sH u j h4+ UmH nH u h4+ OJ QJ h4+ CJ aJ
h4+ CJ h4+ h4+ CJ OJ QJ h4+ 5CJ OJ QJ h4+ 5CJ OJ QJ h4+ OJ QJ ^J h4+ 5OJ QJ \ h4+ 5OJ QJ \^J ,( ( ( ( ) ) } } } } $$If a$ x kd $$If l \ s;$Y ' 6 4 4
l a ) ) ) 5) W) p) } } } } $$If a$ x kd $$If l \ s;$Y ' 6 4 4
l a p) q) z) ) ) ) } } } } $$If a$ x kdi $$If l \ s;$Y ' 6 4 4
l a ) ) ) ) ) * } } } } $$If a$ x kdL $$If l \ s;$Y ' 6 4 4
l a * * * 4* W* h* } } } } $$If a$ x kd/ $$If l \ s;$Y ' 6 4 4
l a h* i* x* * * * } } } } $$If a$ x kd $$If l \ s;$Y ' 6 4 4
l a * * * + + G- d- / / / / / / w r p n n n n n 7 ;
& F $d a$ + 8
& F x kd $$If l \ s;$Y ' 6 4 4
l a / / / / / #/ %/ &/ '/ 0 0 1 1 4 4 4 5 5 m6 n6 J7 K7 7 '8 8 <
& F3 ^ ` d 8
& F 8 $
& F a$ 5 d $d a$ +/ 8/ 0 0 1 1 5 m6 n6 K7 9 < V< <> ? ? B E E F F F F I lJ J J J J N N RN SN nN |N N N O P P ȽԳϦzoe h4+ B*CJ ph h4+ 5CJ OJ QJ h4+ 5CJ OJ QJ h4+ CJ aJ j h4+ 0J0 CJ Uh4+ 5CJ OJ QJ aJ h4+ 5CJ OJ QJ aJ h4+ CJ OJ QJ h4+ CJ OJ QJ ]h4+ CJ ] h4+ ]h4+ OJ QJ ]h4+ CJ ]aJ h4+ h4+ OJ QJ
h4+ CJ h4+ 6CJ '8 9 9 < < V< <> => ? ? A A B B E E E F G G rH H I 1
& F d 7 1 :
& F @; ^ ` $d a$<
& F3 ^ ` I I lJ J RN SN nN |Q }Q Q #R &R (R )R T T 0V 1V OV Y Y [ [ $
& F d ^ ` a$
& F d ^ ` d $d a$
& F d $d a$ P }Q Q Q #R $R %R &R 'R (R [R bR R R S T U 1V NV OV wW W [ [ [ \ "\ $\ ,\ 2\ 6\ Y\ b\ f\ k\ \ \ nnnnn] h4+ 5CJ PJ \aJ nHtH h4+ 6CJ PJ ]aJ nHtH h4+ CJ PJ aJ nHtH h4+ 6CJ h4+ 5CJ \aJ h4+ CJ aJ h h4+ 6CJ aJ h h4+ CJ aJ j h4+ CJ UmH nH u j h4+ Uj h4+ UmH nH u h4+ CJ aJ h4+ 5CJ OJ QJ \^J aJ
h4+ CJ $[ [ \ \ C] n] o] ^ !^ _ _ _ _ a a )a a d $d a$ d 7$ 8$ H$
& F d 7$ 8$ H$ hd 7$ 8$ H$ ^h
& F d 7$ 8$ H$ d 7$ 8$ H$ ^
& F d 7$ 8$ H$ $d 7$ 8$ H$ a$
& F d ^ ` \ q] x] #^ '^ 5^ 8^ _ _ _ _ G` a (a )a a a a a a a a a a a a a Vb Yb ^b bb b b b b c $i %i ʿۚیۀztng h4+ 5CJ
h4+ CJ
h4+ CJ
h4+ CJ h4+ CJ OJ QJ aJ h4+ CJ OJ QJ ^J aJ h4+ OJ QJ h4+ 6OJ QJ aJ h4+ OJ QJ aJ h4+ OJ QJ h4+ 5CJ \aJ h4+ 5CJ OJ QJ \^J aJ
h4+ CJ h4+ 5CJ PJ \aJ nHtH h4+ CJ PJ aJ nHtH%a a b b b b c 0c 1c uc c d Td d d )e oe e e 7f f f # $d %d &d 'd N O P Q ! $! d a$ ! d d f 2g g g h dh h h $i %i \i ]i k k ,l -l ql l l Em m m &n mn $d a$ $dh a$ # $d %d &d 'd N O P Q %i 0i \i ]i k k n n n n $o -o Vo ao o o o o q ]q zq q r s { | | | Ƽ䪢ufW hy 5CJ( OJ QJ ^J aJ$ hn 5CJ( OJ QJ ^J aJ$ h4+ B*CJ ph h4+ 5CJ OJ QJ \^J aJ h4+ CJ aJ h4+ OJ QJ aJ h4+ OJ QJ
h4+ CJ h4+ CJ OJ QJ aJ h4+ OJ QJ aJ h4+ CJ aJ h4+ CJ OJ QJ
h4+ CJ
h4+ CJ
h4+ CJ h4+ h4+ CJ aJ h4+ 5CJ aJ mn n n n n o o q ]q ^q zq q q q r r s Cw Dw z d $a$
& F d d ! d $d a$ $a$ # $d %d &d 'd N O P Q z z | | | | | ] ^ s ނ
& F d gd} d $ d a$gdy d gdy $d a$ | | | | ] ^ r s ~ } E O ނ R T U ! 3 ˾ѪѪѪќѪёцѪ h4+ 5CJ OJ QJ h4+ 5CJ h4+ j h4+ UmH nH u h4+ 6CJ h4+ 5CJ OJ QJ aJ h4+ 5CJ OJ QJ aJ
hn CJ
h4+ CJ
h1 CJ hn h4+ 5CJ( OJ QJ aJ #hy hy 5CJ( OJ QJ ^J aJ$ 4 T U Y / / ! 3 r s
& F ^ ` $
& F d ^ ` a$ $
& F d a$ $
& F d a$ $d a$ s K U V ( ; & d x # - | [ Ǔ v ϖ Ř
y $ ֝ ʞ h4+ B*CJ ph h4+ 5CJ OJ QJ aJ h4+ 5CJ OJ QJ ^J aJ h4+ 5CJ OJ QJ \^J aJ h4+ 5CJ \ h4+ CJ OJ QJ ^J aJ h4+ h4+ 5CJ h4+ 56CJ h4+ CJ OJ QJ ^J aJ
h4+ CJ h4+ 6CJ 3 K ( d [ Ǔ v $
& F# d a$ d $
& F d ^ ` a$
& F
hX d ^ $
& F hd ^a$
& F ^ `
& F d $
& F d a$ ϖ Ř ̞ + , A Z [ У $d a$ $
& F S d a$ $a$ $d a$ $
& F d ^ ` a$ d $
& F d ^ ` a$ ʞ , A l m V ` ϣ У ԣ գ ô}pf j h4+ EHUj9@
h4+ CJ UVj h4+ EHUj9@
h4+ CJ UVj h4+ CJ Uh4+ 56CJ h4+ 5CJ OJ QJ ^J h4+ 5CJ OJ QJ ]^J h4+ 56CJ OJ QJ ^J h4+ 5CJ h4+ 6CJ
h4+ CJ h4+ 5CJ OJ QJ ^J aJ h4+ B*CJ ph & ؤ ٤ ڤ
r s v w ܥ ! " # $ & ' 7 8 9 : @ A Q R ꭣꖌ j:@
h4+ CJ UVj h4+ EHUj :@
h4+ CJ UVj
h4+ EHUj9@
h4+ CJ UVh4+ OJ QJ j- h4+ EHUj9@
h4+ CJ UVj h4+ CJ Uh4+ 6CJ
h4+ CJ h4+ CJ H*h4+ 6CJ H*. r ܥ , Z ͧ ȩ a r e $ S d a$ $ @@dh ^`a$ $
& F$ @dh ^`a$ $
& F$ @@dh ^`a$ $ dh ^`a$ $ dh ^`a$ d $d a$ $d a$ R S T m n ' ( ) * D E U V W X c d t u v w y z 庰壙匂uk j h4+ EHUj:@
h4+ CJ UVj h4+ EHUj:@
h4+ CJ UVj h4+ EHUj:@
h4+ CJ UVj h4+ EHUj:@
h4+ CJ UVj h4+ EHUj:@
h4+ CJ UVh4+ 6CJ
h4+ CJ j h4+ CJ Uj h4+ EHU % ç ħ ʧ ˧ ` a z { > ? ̩ ͩ F G c e a b u ֲ ؿؿظءث h4+ 6OJ QJ h4+ OJ QJ h4+ 6B*CJ ph h4+ B*CJ ph h4+ 5CJ OJ QJ ^J h4+ 5CJ h4+ 6CJ ] h4+ 6CJ H* h4+ 6CJ
h4+ CJ j h4+ CJ Uj h4+ EHUj:@
h4+ CJ UV 1a b u [ ֲ T U Y յ
& F* h88d ^8 d $
& F S d ^ ` a$ d $
& F' d a$ $
& F n d a$ $d a$ Ƴ dz " T U Y Z c d y z | Ĵ Ŵ ƴ
/ 0 1 V W \ ] _ ` p q r s t 䶦 j h4+ CJ EHOJ QJ U j :@
h4+ OJ QJ UVj h4+ OJ QJ Uh4+ H*OJ QJ h4+ CJ
OJ QJ h4+ OJ QJ h4+ 6OJ QJ h4+ 6H*OJ QJ A е ѵ ҵ + , b c A B R S T U q r s t ŷ Ʒ ˾شاشؐشyo j" h4+ EHUj :@
h4+ CJ UVj h4+ EHUj :@
h4+ CJ UVj h4+ EHUj
:@
h4+ CJ UVj h4+ CJ Uh4+ 5CJ OJ QJ ^J
h4+ CJ h4+ 6CJ
h4+ CJ
h4+ CJ
h4+ 6OJ QJ h4+ OJ QJ h4+ 6H*OJ QJ + E u 4 $a$ $
& F d ^ ` a$ d ' d $ *$ $d a$ $d a$ $
& F d ^ ` a$ M N ^ _ ` a e f g h i j z { | } ~ ָ ) ĺu j, h4+ EHUj:@
h4+ CJ UVj* h4+ EHUj:@
h4+ CJ UVj( h4+ EHUj:@
h4+ CJ UVj& h4+ EHUj:@
h4+ CJ UVj$ h4+ EHUj:@
h4+ CJ UVj h4+ CJ U
h4+ CJ h4+ 6CJ ,) * + , 0 1 A B C D K L \ ] ^ _ c d t u v w x y ¹ ù Ĺ ش؝؆|oe^ h4+ 6CJ j9 h4+ EHUj:@
h4+ CJ UVj7 h4+ EHUj:@
h4+ CJ UVj 5 h4+ EHUj:@
h4+ CJ UVj93 h4+ EHUj:@
h4+ CJ UVj0 h4+ EHUj:@
h4+ CJ UV
h4+ CJ j h4+ CJ Uj. h4+ EHUj:@
h4+ CJ UV $Ĺ Ź ƹ ǹ ع ٹ ڹ ۹ ܹ 1 2 3 4 D E F G T e u v ˺ ̺ ܺ ݺ ĺzk j :@
h4+ OJ QJ UVj h4+ OJ QJ Uh4+ OJ QJ j&A h4+ EHUj5*@
h4+ UVj#? h4+ EHUj:@
h4+ CJ UVj = h4+ EHUj:@
h4+ CJ UVj ; h4+ EHUj:@
h4+ CJ UVj h4+ CJ U
h4+ CJ h4+ 6CJ $ݺ ߺ " # H I w x Ͽ п 0 1 ȸۢۜwpۢf j h4+ CJ Uh4+ 6CJ h4+ 6B*CJ ph h4+ B*CJ ph h4+ 56CJ OJ QJ ^J
h4+ CJ h4+ 6OJ QJ h4+ 5CJ OJ QJ ^J jbE h4+ CJ EHOJ QJ U j :@
h4+ OJ QJ UVh4+ h4+ OJ QJ j h4+ OJ QJ U jC h4+ CJ EHOJ QJ U #
O P e f v w x y 6 K Ƽدؘхxn_ h4+ 5CJ OJ QJ ^J aJ j3P h4+ EHUj#:@
h4+ CJ UVh4+ 6CJ H* jRN h4+ EHUj":@
h4+ CJ UVj0L h4+ EHUj!:@
h4+ CJ UVj6I h4+ EHUjh*@
h4+ UVh4+ 6CJ
h4+ CJ j h4+ CJ Uj4G h4+ EHUj :@
h4+ CJ UV $4 5 6 K \ n o 3 6 : d $
& F d ^ ` a$ $d a$ $ d a$ $
& F 7 d ^ ` a$ $d a$ $
& F- h *$ ^$ *$ $
& F d a$ K & A O K N X o X d S T U V W [ \ ` 1 ýðsss h4+ 6B*H*OJ QJ ph h4+ 6B*OJ QJ ph h4+ B*OJ QJ ph &h4+ 5B*CJ OJ QJ ^J aJ ph h4+ 5CJ OJ QJ aJ
h4+ CJ h4+ B*CJ ph h4+ 6B*CJ ph "h4+ 5B*CJ OJ QJ aJ ph h4+ h4+ 6OJ QJ h4+ OJ QJ '1 2 B C D E # $ % & , - 1 3 5 6 9 : B C I J L wgYYYYY j h4+ UmH nH u jS h4+ 6B*EHUph %j(P@
h4+ CJ OJ QJ UVaJ h4+ 6OJ QJ h4+ OJ QJ h4+ 6B*H*OJ QJ ph h4+ 6B*OJ QJ ph h4+ B*OJ QJ ph jQ h4+ 6B*EHUph %jP@
h4+ CJ OJ QJ UVaJ h4+ 6B*ph j h4+ 6B*Uph : C J M O Q R K m M N $d 7$ 8$ H$ a$ $
& F d ^ ` a$ $d a$ 5 d $d a$ d L M N O P Q x y K L \ ] ^ _ b f g k m zzshVzz #jW h4+ 6B*CJ EHUph j@
h4+ UVh4+ CJ h h4+ B*ph h4+ 6B*ph #jU h4+ 6B*CJ EHUph jʑ@
h4+ UV j h4+ 6B*CJ Uph h4+ 6B*CJ ph j h4+ UmH nH u "h4+ B*CJ mH nH ph sH u j h4+ UmH nH sH u h4+ B*CJ ph ! xox\JxCx h4+ CJ h #j^ h4+ 6B*CJ EHUph %j6@
h4+ 6B*CJ UVph h4+ 6CJ h h4+ CJ h
h4+ CJ h4+ 5CJ OJ QJ j\ h4+ B*EHUph j*Q@
h4+ UVh4+ B*ph j h4+ B*Uph h4+ B*CJ ph j h4+ 6B*CJ Uph jZ h4+ 6B*EHUph j(P@
h4+ UVh4+ 6B*CJ ph ) * + , [ 5 P i M Y j
d M N # & 6 O ƴƦƞƦƦ~rj`j`j h4+ 0J OJ QJ h4+ OJ QJ h4+ B*CJ aJ ph 'j h4+ 0J0 CJ PJ UaJ nHtHh4+ B*CJ aJ ph h4+ CJ aJ h4+ CJ PJ aJ nHtH "h4+ 5B*CJ OJ QJ ^J ph h4+ B*CJ ph jb h4+ CJ EHUh j6@
h4+ CJ UVh h4+ CJ h j h4+ CJ Uh ! e g 2 $d a$ 2 $d a$ ( $d a$ $dh a$
& F n d 7$ 8$ H$ d 7$ 8$ H$ $
& F d 7$ 8$ H$ a$ $d a$ $d 7$ 8$ H$ a$ O _ o p ӼǦӇyǚjW %j' A
h4+ CJ OJ QJ UVaJ h4+ 6CJ OJ QJ ]aJ jcf h4+ CJ EHUh %jY A
h4+ CJ OJ QJ UVaJ h4+ CJ OJ QJ aJ h4+ CJ aJ jc h4+ CJ EHUh j A
h4+ UVj h4+ CJ Uh h4+ CJ h h4+ 6CJ ]h h4+ 5\^J h4+ OJ QJ h4+ 6OJ QJ ] T U & ' ( ) [ \ l m n o ߹ߣߘqfX jp h4+ CJ EHUh jo A
h4+ UVjn h4+ CJ EHUh j A
h4+ UVjm h4+ CJ EHUh j A
h4+ UVh4+ CJ aJ jwj h4+ CJ EHUh j A
h4+ UV h4+ 6CJ OJ QJ ]aJ h4+ CJ OJ QJ aJ h4+ CJ h j h4+ CJ Uh j~h h4+ CJ EHUh "o t u
% & ^ _ o p q r Ⱥ~vmv] h4+ B*CJ OJ QJ aJ ph h4+ 5\^J h4+ OJ QJ jx h4+ CJ EHUh j A
h4+ UVjv h4+ CJ EHUh j A
h4+ UVh4+ 6CJ ]h jt h4+ CJ EHUh jo A
h4+ UVjr h4+ CJ EHUh j A
h4+ UVj h4+ CJ Uh h4+ CJ h ! $ 7 8 I $ I J K S k ' $d a$
& F $d a$ d
& F
& F S ( $d a$ ( $d a$
& F d 7$ 8$ H$ ! " # 7 8 I $ I J S ĸĀnnf^nZnTNn
h4+ CJ
h4+ CJ h4+ h4+ CJ aJ h4+ OJ QJ #h4+ B*CJ OJ QJ ^J aJ ph h4+ B*CJ OJ QJ aJ ph jz h4+ CJ EHUh %jA
h4+ CJ OJ QJ UVaJ h4+ CJ h j h4+ CJ Uh h4+ B*CJ OJ QJ aJ ph h4+ CJ OJ QJ aJ "h4+ CJ OJ PJ QJ aJ nHtH h4+ CJ PJ aJ nHtH H L ! " ʸݒyyiVDi #jj} h4+ 6B*CJ EHUph %j6@
h4+ 6B*CJ UVph j h4+ 6B*CJ Uph h4+ 6CJ h4+ 5CJ OJ QJ h4+ CJ \
h4+ CJ h4+ CJ OJ QJ h4+ 6B*CJ ph h4+ B*CJ ph "h4+ 5B*CJ OJ QJ ^J ph h4+ CJ aJ h4+ 6CJ ]aJ h4+ CJ aJ h4+ CJ OJ QJ aJ h4+ 6CJ OJ QJ ]aJ ' A # $d a$ $d a$ $
& F d 7$ 8$ H$ a$ $d d d [$\$a$ ( $d a$ ( $
& F d a$ "
) ʽ֣֮֮ւsfʽ֮h4+ 6B*CJ H*ph j h4+ B*EHUph "j(:@
h4+ B*CJ UVph j h4+ B*EHUph j@
h4+ UV j h4+ B*CJ Uph h4+ 6B*CJ H*ph h4+ 6B*CJ ph h4+ B*CJ ph j h4+ UmH nH u h4+ 5B*CJ ph
h4+ CJ ( , -
~ ( ) P % & ? F [ $d a$ $
& F d ^ ` a$
& F d ^ ` d $
& F d a$ $d a$ ) * + , 1 2 B C D E X Y i j k l r s ƻƠƕРyn_РyƠyƠy j h4+ B*EHUph j@
h4+ UVh4+ 6B*CJ H*ph jχ h4+ B*EHUph j>@
h4+ UVh4+ 6B*CJ ph j h4+ B*EHUph jw@
h4+ UVh4+ B*CJ ph j h4+ B*CJ Uph j߃ h4+ B*EHUph "j):@
h4+ B*CJ UVph "
! " $ % & / 0 1 3 4 D E F G I J K U V a b c d ǻזǻ{זǻkזזז j@ h4+ 6B*EHUph jo h4+ 6B*EHUph j^@
h4+ UVh4+ B*CJ ph j h4+ 6B*EHUph js^@
h4+ UVh4+ 6B*CJ ph j h4+ 6B*CJ Uph h4+ B*CJ H*ph h4+ 6B*CJ H*]ph h4+ 6B*CJ ]ph * ( P ̙~scV h4+ 5CJ OJ QJ h j֔ h4+ 6B*EHUph j`@
h4+ UV j h4+ 6B*EHUph jg`@
h4+ UVh4+ B*CJ ]ph j h4+ 6B*EHUph j^@
h4+ UVh4+ 6B*CJ ph j h4+ 6B*CJ Uph h4+ B*CJ ph h4+ B*CJ H*ph h4+ 6B*CJ ]ph P f 9 > Q % & ? R T F G W X Y Z [ a b r s t u ւ}pd}}W j6@
h4+ CJ UVj h4+ CJ EHUj6@
h4+ CJ UVj h4+ CJ Uh4+ 6CJ OJ QJ #j h4+ 6B*CJ EHUph %j6@
h4+ 6B*CJ UVph h4+ 6B*CJ ph j h4+ 6B*CJ Uph h4+ 5CJ OJ QJ h4+ 5CJ OJ QJ h h4+ 6CJ
h4+ CJ h4+ CJ h i j z { | } ~
# + \ E ˿زؙ{wh\h\h\w h4+ B*CJ \ph h4+ 6B*CJ \]ph h4+ "h4+ 5B*CJ OJ QJ ^J ph j h4+ CJ EHUj6@
h4+ CJ UVjI h4+ CJ EHUj6@
h4+ CJ UVjJ h4+ CJ EHUj6@
h4+ CJ UV
h4+ CJ h4+ 5CJ OJ QJ j h4+ CJ Ujz h4+ CJ EHU # # \ { 9 k k k $
& F p@ H d a$ $
& F p@ H d a$ $ p@ H d a$ A $
& F p@ H d a$ $
& F p@ H d a$ $
& F p@ H d a$ A $
& F p@ H d a$ E U { 1 # 7# P$ d$ 5& H& U( m( * * * * <+ , , , - - - - i. m. y. . / йаyn`nn`n h4+ 6>*CJ \]aJ h4+ >*CJ \aJ h4+ CJ aJ h h4+ 6CJ aJ h h4+ CJ aJ h h4+ 5CJ OJ QJ ^J aJ h4+ 5CJ OJ QJ h4+ CJ \aJ h4+ 5CJ OJ QJ aJ h4+ B*CJ ph h4+ B*CJ \ph "h4+ 5B*CJ OJ QJ ^J ph h4+ CJ \h4+ h4+ 6]"9 { ( ) # # 7# O$ P$ d$ t $
& F p@ H d a$ $
& F p@ H d a$ $ p@ H d a$ $
& F p@ H d a$ $
& F p@ H d a$ $ p@ H d a$ $
& F p@ H d a$ d$ 4& 5& H& T( U( m( ,
, , , , @- A- - i. ~/ / Y0 $
& F p@ H d a$ $
& F p@ H d a$ $ p@ H d a$ $
& F p@ H d a$ $ p@ H d a$ / / / / 80 K0 [0 0 0 0 1 1 3 4 4 =4 >4 b4 c4 d4 e4 f4 g4 4 4 15 25 65 75 .6 /6 06 16 ;6 F6 ȿ}m] h} h4+ 5CJ( OJ QJ aJ h} h} 5CJ( OJ QJ aJ h} 5CJ( OJ QJ aJ h} 5CJ OJ QJ aJ h4+ 5CJ OJ QJ aJ h4+ CJ OJ QJ ^J aJ h h4+ 6CJ ]aJ h h4+ CJ aJ h h4+ CJ aJ h4+ 5CJ OJ QJ ^J aJ *h4+ CJ \aJ h4+ CJ \aJ h4+ 6CJ \]aJ "Y0 Z0 0 0 0 1 M5 N5 -6 .6 06 16 ;6 G6 H6 8 8 $d 7$ 8$ H$ a$gd} $d 7$ 8$ H$ a$
$d a$gd} $
& F p@ H d a$ $ p@ H d a$ $ p@ H d ^a$ $ p@ H d a$ F6 G6 H6 \6 c6 8 8 %9 &9 '9 (9 09 9 9 9 A: I: V: Z: : : : : = != "= #= = = = = > h> ûû߳߳ߨyuhh^ h4+ OJ QJ ^J h4+ 5OJ QJ \^J h} j h4+ UmH nH tHu h4+ j h4+ Uh4+ 6OJ QJ ] h4+ OJ QJ h4+ 5CJ OJ QJ h% CJ aJ hX, CJ aJ h CJ aJ h4+ CJ aJ h h4+ 6CJ ]aJ h4+ CJ aJ h} CJ aJ h} h4+ 5CJ, OJ QJ aJ$ 8 : : = "= $= %= &= = = = = > > > > > > > > > $$ & #$/ If a$b$ + d ! $a$ d $
& F 7 d a$gdz2 $d 7$ 8$ H$ a$gd > > > > > > "> &> (> ,> .> 2> 4> 5> C> E> G> J> M> P> S> V> Y> \> ]> h> Ffѵ FfG Ff $$ & #$/ If a$b$ h> m> r> w> }> > > > > > > > > e? g? l? y? |? ? $$d $1$ If a$ $$If a$ $! d a$ ! d Ffa $$ & #$/ If a$b$ h> q> r> v> w> |> }> > > > > > > > > > > > > > > > > ? ? ? 9? <? e? g? l? y? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ɸɰyyu h4+ h4+ CJ aJ
h4+ CJ h4+ 5OJ QJ \^J h4+ 5CJ OJ QJ \^J aJ h4+ 6CJ ]aJ h4+ CJ aJ h4+ 5CJ OJ QJ \^J aJ h4+ 5CJ \aJ &j h4+ OJ QJ U^J mH nH u h4+ OJ QJ ^J h4+ CJ OJ QJ ^J aJ -? ? ? ? ? ? ? O F F F F F $$If a$ kd $$If T l 4 r k
| s d 0 6 4
l a f4T ? ? ? ? ? ? ? O F F F F F $$If a$ kd $$If T l 4 u r k
| s d 0 6 4
l a f4T ? ? ? ? }@ @ @ Q I I I 5 5 $$ & #$/ If a$b$ $d a$ kdq $$If T l 4 r k
| s d 0 6 4
l a f4T ? ? ? ? ? @ @ 7@ =@ }@ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ B B B B B uog_g_W_ hZ CJ aJ h4+ CJ aJ hz2 CJ aJ
h4+ CJ j h4+ UmH nH u h4+
h4+ CJ h4+ CJ aJ h4+ 5CJ OJ QJ \^J aJ h4+ CJ OJ QJ ^J aJ h4+ 6CJ \]aJ h4+ CJ \aJ h4+ 5CJ OJ QJ ^J aJ h4+ 5CJ aJ h4+ 5CJ OJ QJ %j h4+ 5OJ QJ UmH nH u @ @ @ @ $$ & #$/ If a$b$ $ & #$/ If b$ @ @ @ @ @ @ F 2 2 2 2 $$ & #$/ If a$b$ kdX $$If T l 4 r
S u - 6 0 6 4
l a e4f4T @ @ @ @ 2 * $d a$ kda $$If T l 4 r
S u - 6 0 6 4
l a e4f4T $$ & #$/ If a$b$ @ @ @ @ B B B C C C /E 0E OE E F F F $! d a$ $
& F8 d a$ $
& F d @& $
& F 7 d a$gdz2 $d a$ $ p@ H d a$gdZ $ p@ H d a$ $ d a$ B B B B C C C C C C C C C /D 8D D D D D 0E OE E
F F F F G G [S \S S U 7V ĵ𗏈{qme[ h4+ CJ OJ QJ h4+ OJ QJ h4+ h4+ CJ OJ QJ h4+ 5CJ
h4+ CJ h4+ CJ h h4+ CJ aJ h4+ CJ OJ QJ aJ h4+ CJ aJ h h4+ 6CJ aJ h4+ 5CJ OJ QJ \aJ h4+ 5CJ OJ QJ \ h4+ 5CJ OJ QJ h4+ 5CJ OJ QJ h h4+ CJ \h4+ CJ aJ h CJ aJ F G \G }G G G 8H yH H H 0I sI I I I I I J gJ J J AK K K L AL L L M 2 $d a$ M JM M M N _N tN N N O bO O O P -P oP P P :Q Q Q Q #R oR R R S [S \S $ @& 2 \S ]S S U 7V KV ^V uV V V W &W GW qW vW xW yW W W W W W +X ,X Y Z 5 ( $ a$ ( $d a$ 7V W +X ,X Z Z Z Z Z Z Z Z Z [ ([ Y[ ][ \ \ \ ] ] ] *] ^ ^ (^ ^ L_ U_ W_ [_ `_ l_ _ _ _ ` ` O` Ԭssg hz2 h4+ 5>*CJ hz2 h4+ 5>*CJ hz2 h4+ CJ h4+ 6CJ aJ h4+ CJ aJ h h4+ CJ OJ QJ aJ
h4+ aJ h4+ CJ aJ h4+ CJ OJ QJ aJ h4+ 6OJ QJ aJ h4+ 6CJ aJ h4+ CJ aJ h4+ OJ QJ h4+ h h4+ CJ h mH sH h4+ CJ OJ QJ h 'Z ^ ^ (^ _ +a a a b Fi Gi i i i j Ej $
& F6 d a$ d $a$ ^ & $d %d &d 'd N O P Q gdz2 d gdz2 $! d a$ $
& F d @& $d a$ $ 7 d a$ O` Q` T` ^` ` ` ` ` ` a (a .a >a Ea Ra Xa ca ha sa xa a a a a a b b b Bc Dc nc pc c c d d Wd d d d Ue We e e e e .f 0f @f f } h4+ 5CJ h4+ 5CJ
h4+ CJ h4+ 5CJ h4+ 6CJ
h4+ CJ h4+ >*CJ h4+ OJ QJ
h4+ CJ hz2 h4+ 6>*CJ hz2 h4+ >*CJ hz2 h4+ 6CJ hz2 h4+ 5CJ hz2 h4+ 5CJ hz2 h4+ CJ hz2 h4+ CJ 1f f f f g sg g g g g g g g g Ch Eh Uh ^h bh ch h h h h Di Fi Gi Qi i i o [p p p q q ߳sja h4+ OJ QJ h h4+ CJ aJ h h4+ CJ OJ QJ aJ h4+ OJ QJ aJ h4+ CJ aJ
h4+ CJ h4+ CJ OJ QJ aJ h4+ CJ h h4+ 5CJ h h4+ 5CJ OJ QJ \^J aJ h4+ 6>*CJ h4+ 5>*CJ h4+ 5>*CJ
h4+ CJ h4+ 5CJ h4+ 5CJ h4+ 6CJ
h4+ CJ #Ej j k l l l \m n Nn o o [p p q q q q Is d $
& F @& $
& F 7 ;d ^`;a$ $d a$ $hd ^ha$ $
& F6 d a$ q q q Is Us Xs ys s s t t ct xt t t u *u ku {u u u v )v lv yv v v 8w Bw uw vw y y <y =y Ny Ry cy fy vy xy zy y y y y y y y y y y ȿ h4+ B* eh ph r h4+ B* eh ph r h4+ eh r h4+ CJ h h4+ CJ h h4+ h mH sH h4+ CJ h h4+ 5CJ h
h4+ CJ h4+ 5OJ QJ ^J h4+ 5
h4+ CJ h4+ h4+ OJ QJ 3Is Us Xs ks ss ys zs 9 kdj $$If \ L B6$ ~ 0 4
aj $$If a$ '3 L$d %d &d 'd N O P Q `L zs }s s s s s s s s s c kdN $$If \ L B6$ ~ 0 4
aj dh $If s s s t t ?t l c c c c dh $If kd. $$If \ L B6$ ~ 0 4
aj ?t @t Ct ct xt t l c c c c dh $If kd $$If \ L B6$ ~ 0 4
aj t t t t t t l c c c c dh $If kd $$If \ L B6$ ~ 0 4
aj t t t u *u Gu l c c c c dh $If kd $$If \ L B6$ ~ 0 4
aj Gu Hu Ku ku {u u l c c c c dh $If kd $$If \ L B6$ ~ 0 4
aj u u u u u u l c c c c dh $If kd $$If \ L B6$ ~ 0 4
aj u u u v )v Gv l c c c c dh $If kdn $$If \ L B6$ ~ 0 4
aj Gv Hv Lv lv yv v l c c c c dh $If kdN $$If \ L B6$ ~ 0 4
aj v v v v v v l c c c c dh $If kd. $$If \ L B6$ ~ 0 4
aj v v v w w 7w l c c c c dh $If kd $$If \ L B6$ ~ 0 4
aj 7w 8w 9w uw vw y y y l g b ` X X V $d a$ 5 $a$ $a$ kd $$If \ L B6$ ~ 0 4
aj y y y y { { j{ k{ |{ | | P| ^| } } } ~ ~ ~ ~ L~ M~ ]~ ^~ _~ `~ f~ g~ w~ ƾth[R h4+ 6CJ H* j h4+ 6CJ H*Uj h4+ CJ EHUj+:@
h4+ CJ UV
h4+ CJ j h4+ CJ Uh4+ 6CJ aJ h h4+ CJ aJ h h4+ 5CJ h h4+ 6CJ h h4+ CJ h h4+ OJ QJ h4+ CJ h
h4+ CJ h4+ 5CJ h4+ h4+ B* eh ph r h4+ B* eh ph r y Fz ~z z { { i{ j{ k{ |{ } ~ ~ ~ T j P $
& F; d a$ $
& F; a$ $d a$ $
& F d @& $dh a$ $a$ ' $d %d &d 'd N O P Q ] w~ x~ y~ z~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ! T Y j k { | } ~ ƽƶͬͬ͆͟zƽͶvͬi] j h4+ CJ EHUj0:@
h4+ CJ UVh4+ j h4+ CJ EHUj/:@
h4+ CJ UVj h4+ CJ EHUj.:@
h4+ CJ UVj h4+ CJ Uh4+ 5CJ h4+ 5CJ h h4+ CJ h
h4+ CJ j h4+ 6CJ H*U j h4+ 6CJ EHH*U j-:@
h4+ 6CJ H*UV $j " ' - 4 9 ? H Q R T $If Ff? $$If a$ $a$ ݀ ހ ߀ Q S T U ́ ́ ߁ ľĩľľĩľ j h4+ CJ Uh4+ 0J CJ j h4+ CJ U
h4+ CJ j h4+ CJ U
h4+ CJ h4+ 5CJ OJ QJ ^J aJ
h4+ 0J j h4+ Uj h4+ Uh4+ =  Ȃ ɂ ˂ S W [ ^ Ff Ff $If  ǂ Ȃ ʂ ˂ ̂ J K L Q R S V W Z [ ] ^ b c f g h i l m q r u v y z ~
! $ % ) * - . 1 2 7 j h4+ CJ Uh4+ 0J CJ j h4+ CJ U
h4+ CJ j h4+ CJ U
h4+ CJ h4+ M^ c g i m r v z ! % * . 2 8 = > @ ф Ff Ff $If 7 8 < = ? @ A τ Є ф Ԅ Մ ل ڄ ۄ ߄ ą Ņ ȅ Ʌ ͅ ΅ ҅ Ӆ Յ օ ׅ N j h4+ CJ Uh4+ 0J CJ j h4+ CJ U
h4+ CJ j h4+ CJ Uh4+
h4+ CJ Iф Մ ل ۄ Ņ Ʌ ΅ Ӆ Ff' $If Ӆ ԅ օ c g k n s w y } ( , 0 3 8 < > B G K Ff? $If Ff< N O P a b c f g j k m n r s v w x y | } & ' ( + , / 0 2 3 7 8 ; < = > A B F G J K N O T U Z [ ] ^ _ և ׇ ؇ ڶj h4+ CJ Uj h4+ CJ U
h4+ CJ h4+
h4+ CJ h4+ 0J CJ j h4+ CJ Uj h4+ CJ U HK O U [ \ ^
$ % ( ň Lj FfE FfB $If
# $ ' ( ) Ĉ ň ƈ Lj ʈ ˈ ψ Ј ӈ Ԉ ؈ ܈ ݈ g h i } ~ j3, h4+ CJ Uh4+ 0J CJ j"$ h4+ CJ U
h4+ CJ j h4+ CJ Uh4+
h4+ CJ MLj ˈ Ј Ԉ ؈ ݈ I M R U Ffk0 FfV( $If 7 8 9 G H I L M Q R T U Y Z ] ^ _ ` c d h i l m p q u v z { ~ " # ' ( + , / 0 4 5 : j]< h4+ CJ Uh4+ 0J CJ jH4 h4+ CJ U
h4+ CJ j h4+ CJ U
h4+ CJ h4+ MU Z ^ ` d i m q v { | # ( , 0 5 ; < = $a$ Ff@ Ff8 $If : ; = F ɋ ʋ ڋ ۋ ܋ ދ $ & : L M ] ^ _ ` a T [ \ q r s j h4+ 0J0 CJ UjtK h4+ CJ EHUj3:@
h4+ CJ UVj=G h4+ CJ EHUj2:@
h4+ CJ UVjjD h4+ CJ EHUj1:@
h4+ CJ UVj h4+ CJ Uh4+ 6CJ h4+ 5CJ h4+ CJ h
h4+ CJ h4+ 5CJ h4+
h4+ CJ * ɋ ދ L a T A < y ȑ b $d a$ $
& F d a$ $h^ha$ $h`ha$ 6 d $hd `ha$ $
& F; d a$ P $d a$ $a$ $
& F; a$ $dh a$ A < J d e u v w x y ȑ b c d e o 𗐆zqicqicX h4+ CJ OJ QJ h
h4+ aJ h4+ CJ aJ h4+ 5CJ aJ j c h4+ CJ Uh j*S h4+ CJ Uh4+ CJ h h4+ 5CJ OJ QJ ^J aJ h jP h4+ CJ EHUj5:@
h4+ CJ UV jM hX, hX, CJ EHU j{A
hX, UVnHtHj h4+ CJ Uh4+ 6CJ
h4+ CJ h4+ 6CJ aJ b e & ) 8 B $$If a$ $If $d a$
& F d d ` d & B O M e ə ʙ ( ! W X Y Z N Q R o N ۹}uumu}f`fۋ[f h4+ 5
h4+ CJ h4+ 5CJ j h4+ Uj h4+ Uj h4+ UmH nH u h4+ 5CJ OJ QJ ^J aJ h4+ 56CJ ]aJ h4+ 6CJ
h4+ CJ
h4+ aJ h4+ 5aJ h4+ <CJ OJ QJ aJ h h4+ CJ OJ QJ aJ h4+ h4+ 5CJ OJ QJ ^J aJ
h4+ CJ h4+ CJ OJ QJ h #B C F O u o o e
( $If $If kdq $$If T F
$ 0 6 4
a T O I I I $If kdr $$If T F
$ 0 6 4
a p T 3 O I I I $If kds $$If T F
$ 0 6 4
a p T 3 4 7 M O I I ?
( $If $If kdt $$If T F
$ 0 6 4
a p T O I I I $If kdu $$If T F
$ 0 6 4
a p T I O I I C + $If $If kdv $$If T F
$ 0 6 4
a p T I J M ] O I I I $If kdw $$If T F
$ 0 6 4
a p T O I I I $If kdx $$If T F
$ 0 6 4
a p T e O I I : 3 $&d If P $If kdy $$If T F
$ 0 6 4
a p T e f j z Ƙ O I I I $If kdz $$If T F
$ 0 6 4
a p T Ƙ ǘ ˘ ۘ % O I I I $If kd{ $$If T F
$ 0 6 4
a p T % & * = O I I I $If kd| $$If T F
$ 0 6 4
a p T O I @ @ $$If a$ $If kd} $$If T F
$ 0 6 4
a p T ș ə ʙ ! W X O J J B B B B $d a$ $a$ kd~ $$If T F
$ 0 6 4
a p T X Q R U ^ g n $$If a$ $ @& $a$ n o q ǜ l f f f f $If kd $$If \ s h )" 0 4
a ǜ Ȝ ʜ ' , 2 < 6 6 6 6 $If kd $$If \ s h )" ( 0 4
ap( 2 3 5 { < 6 6 6 6 $If kdĆ $$If \ s h )" ( 0 4
ap( ǝ ͝ ҝ < 6 6 6 6 $If kd $$If \ s h )" ( 0 4
ap( ҝ ӝ ՝ < 6 6 6 6 $If kdF $$If \ s h )" ( 0 4
ap( A G L < 6 6 6 6 $If kd $$If \ s h )" ( 0 4
ap( L M N < 6 6 6 6 $If kdȋ $$If \ s h )" ( 0 4
ap( 2 3 4 l j j j j e j e j j d kd $$If \ s h )" 0 4
a
2 4 6 B 6 K | ϧ ѧ z 2 U ±¥}iZK h4+ 5CJ OJ QJ ^J aJ h4+ 5B*CJ \aJ ph 'j h4+ 0J0 6B*CJ UaJ ph h4+ B*CJ ]aJ ph h4+ 6B*CJ aJ ph h4+ B*CJ aJ ph h4+ B*CJ aJ ph h4+ 5CJ OJ QJ \^J aJ h4+ CJ aJ h4+ 5CJ OJ QJ \^J aJ h4+ CJ aJ h4+
h4+ CJ h4+ OJ QJ h4+ 5
h4+ CJ 4 B 4 5 6 K 1 2 \ u v g h $ d ^`a$ $
& F d a$ $d a$ $
& F d a$ $
& F d 7$ 8$ H$ a$ $d 7$ 8$ H$ a$ $
& F d 7$ 8$ H$ a$ U \ . I O j d e g ï Ư ' 8 9 L M 浰棛zlW (jڮq@
h4+ CJ UVaJ mH nH u j h4+ CJ EHUaJ (jJq@
h4+ CJ UVaJ mH nH u j h4+ CJ UaJ h4+ CJ aJ h4+ 5CJ OJ QJ aJ h4+ h j h4+ 0J0 Uh h4+ B*CJ aJ h ph h4+ 6CJ ]aJ h h4+ 6CJ aJ h h4+ CJ aJ h h4+ 5CJ OJ QJ ^J aJ h h ' ̲ ƴ ٴ $$If a$ d 7$ 8$ H$ $d 7$ 8$ H$ a$ $
& F d 7$ 8$ H$ a$ $d 7$ 8$ H$ a$ $d a$ M N O Ȳ ɲ ʲ ˲ ̲ Ŵ ƴ ش ٴ
˽~sdUdUPF9 h4+ 5OJ QJ \^J h4+ OJ QJ ^J h4+ ; h4+ ;CJ OJ QJ ^J aJ h4+ 5CJ OJ QJ ^J aJ h4+ ;OJ QJ ^J h4+ OJ QJ ^J nHtH h4+ OJ QJ 'jG h4+ 6B*CJ EHUaJ ph (jq@
h4+ CJ UVaJ mH nH u h4+ 6B*CJ aJ ph #j h4+ 6B*CJ UaJ ph h4+ CJ aJ j h4+ CJ UaJ j h4+ CJ EHUaJ
l f f ] ] ] $$If a$ $If kdu $$If l 4 F $$f $P
t$ 0 6 4 4
l a f4
" ) 0 7 > ? C J Q X ^ _ c j q x ĵ ˵ ҵ ٵ ڵ e 𤝔} h4+ 5CJ OJ QJ aJ h h4+ OJ QJ h4+ CJ aJ h h4+ 5CJ j h4+ UmH nH u h4+ 5CJ OJ QJ \^J aJ h4+ CJ OJ QJ ^J aJ h4+ 5CJ OJ QJ \^J aJ h4+ CJ OJ QJ ^J aJ h4+ h4+ 5OJ QJ ^J 1 " ) 0 7 > L F = = = = $$If a$ $If kd3 $$If l r $$f $ $
t$ 0 6 4 4
l a > ? C J Q X ^ L C C C C C $$If a$ kd $$If l ;r $$f $ $
t$ 0 6 4 4
l a ^ _ c j q x L C C C C C $$If a$ kd $$If l ;r $$f $ $
t$ 0 6 4 4
l a L C C C C C $$If a$ kd $$If l ;r $$f $ $
t$ 0 6 4 4
l a L C C C C C $$If a$ kdǗ $$If l ;r $$f $ $
t$ 0 6 4 4
l a ĵ ˵ ҵ ٵ L C C C C C $$If a$ kd $$If l ;r $$f $ $
t$ 0 6 4 4
l a ٵ ڵ L C C C C C $$If a$ kd $$If l ;r $$f $ $
t$ 0 6 4 4
l a Ŷ [ L J B = 0 d ^ ` d $d a$ kdv $$If l ;r $$f $ $
t$ 0 6 4 4
l a [ e f ú ĺ n o $$If a$ d 7$ 8$ H$ $
& F |d ^`|a$ $d a$ vd ^v` ʹ Ǻ ͺ ' ( 4 M o r y پ ttj\ h4+ OJ QJ ^J nHtH h4+ CJ OJ QJ h4+ 6OJ QJ ]h h4+ 5OJ QJ \h h4+ OJ QJ h h4+ 6CJ aJ h j h4+ 0J0 CJ UaJ h h4+ CJ aJ h h4+ 5B*CJ \aJ ph h4+ B*CJ ]aJ ph h4+ 6B*CJ aJ ph h4+ 6B*CJ ]aJ ph h4+ B*CJ aJ ph ' . 5 < = A H O U \ ] d k q x ~ ̥̥̥̥̥̥̥̥̥̥̥̥̥̥ᕍ h4+ CJ aJ j h4+ UaJ mH nH u "h4+ CJ OJ QJ ^J aJ nHtH (h4+ 5CJ OJ QJ \^J aJ nHtH (h4+ 5CJ OJ QJ \^J aJ nHtH h4+ PJ nHtH (h4+ 5CJ OJ QJ \^J aJ nHtH1 r i i i i i $$If a$ kd[ $$If l F $ $P $ $
t 0 6 4 4
l a L C C C C C $$If a$ kd $$If l ;r & $ $P $W $^ $ $
t 0 6 4 4
l a ' . 5 < L C C C C C $$If a$ kd $$If l ;r & $$P $W $^ $ $
t 0 6 4 4
l a < = A H O U \ L C C C C C $$If a$ kdƝ $$If l ;r & $$P $W $^ $ $
t 0 6 4 4
l a \ ] d k q x ~ L C C C C C $$If a$ kd $$If l ;r & $$P $W $^ $ $
t 0 6 4 4
l a ~ L C C C C C $$If a$ kd $$If l ;r & $$P $W $^ $ $
t 0 6 4 4
l a L C C C C C $$If a$ kdc $$If l ;r & $$P $W $^ $ $
t 0 6 4 4
l a L C C C C C $$If a$ kdB $$If l ;r & $$P $W $^ $ $
t 0 6 4 4
l a g L D D D 9 D 2 $
& F d a$ 2 $d a$ kd! $$If l ;r & $$P $W $^ $ $
t 0 6 4 4
l a g h i j k l ! " # $ H N U [ ½皌}i^^NFB h4+ jw h4+ U j h4+ UaJ mH nH u h4+ 6CJ ]aJ 'jz h4+ 6B*CJ EHUaJ ph jHA
h4+ UVnHtHh4+ 6B*CJ aJ ph #j h4+ 6B*CJ UaJ ph h4+ 5CJ OJ QJ \^J aJ h4+ h j h4+ CJ UaJ 0j h4+ 5OJ QJ U\^J aJ mH nH u h4+ CJ aJ h4+ 5CJ OJ QJ \^J aJ g h k l % ! q r $$ &#$/ If a$ $a$ $d a$ 2 $d a$ 2 $d a$ q r ; < B U V Z h m n r y čččnčnččnččn h4+ 5CJ OJ QJ \^J aJ h4+ CJ OJ QJ ^J aJ h4+ 5CJ OJ QJ aJ h4+ 5CJ OJ QJ \aJ h4+ CJ aJ h h4+ CJ aJ h h4+ CJ aJ h h4+ j> h4+ Uj h4+ Uh &j h4+ CJ UaJ mH nH tHu h4+ CJ aJ h4+ 6CJ aJ + ' 1 ; u u u u $$ &#$/ If a$x kdI $$If l e0 !
w 0 4 4
l a ; < B I P U a O O O O $$ &#$/ If a$ kdnJ $$If l e\ n !
m m 0 4 4
l a U V Z a h m a O O O O $$ &#$/ If a$ kd K $$If l \ n !
m m 0 4 4
l a m n r y a O O O O $$ &#$/ If a$ kdK $$If l \ n !
m m 0 4 4
l a a O O O O $$ &#$/ If a$ kdL $$If l \ n !
m m 0 4 4
l a a O O O O $$ &#$/ If a$ kd2M $$If l \ n !
m m 0 4 4
l a a O O O O $$ &#$/ If a$ kdM $$If l \ n !
m m 0 4 4
l a a O O O O $$ &#$/ If a$ kdN $$If l P \ n !
m m 0 4 4
l a a O O O O $$ &#$/ If a$ kdEO $$If l \ n !
m m 0 4 4
l a - . a u M N O 5 v ಭ|eWOOWOW h4+ CJ aJ h4+ CJ PJ aJ nHtH ,h4+ 5B*CJ OJ QJ \]^J aJ ph $j h4+ 0J0 6CJ U]aJ h h4+ 6CJ ]aJ h h4+ CJ aJ h h4+ OJ QJ h h4+ h j h4+ UmH nH u h4+ 5CJ OJ QJ \aJ h4+ 5CJ OJ QJ \^J aJ h4+ h4+ CJ OJ QJ ^J aJ h4+ 5CJ OJ QJ aJ a O O O O $$ &#$/ If a$ kdO $$If l \ n !
m m 0 4 4
l a - . a Y Q I Q I Q $ d *$ $d a$ $dh a$ kdP $$If l \ n !
m m 0 4 4
l a t v { Z $
& F d 7$ 8$ H$ a$ $ d *$ 7$ 8$ H$ $d d d [$\$a$ d d d [$\$
& F 8d $d 7$ 8$ H$ a$ $
& F d ^ ` a$ v z { J R 5 C D T U V W b h H b c j q w سإyfTسإسسسس "jXQ h4+ 6B*EHU]ph %j< A
h4+ CJ OJ QJ UVaJ h4+ 6B*]ph j h4+ 6B*U]ph h4+ 6OJ PJ QJ ]nHtH h4+ OJ PJ QJ nHtH h4+ 6CJ PJ ]aJ nHtH 'j h4+ 0J0 CJ OJ QJ U^J aJ h4+ CJ PJ aJ nHtH h4+ 5CJ \ h4+ 5CJ OJ QJ \^J aJ w H I J K ] c 8 = V W [ i ͥtffff hZ CJ PJ aJ nHtH h*7k CJ PJ aJ nHtH #h4+ B*CJ PJ aJ nHph tH h4+ 5CJ PJ \aJ nHtH &h4+ CJ OJ
PJ QJ
^J
aJ nHtH &h4+ CJ OJ PJ QJ ^J aJ nHtH &h4+ CJ OJ PJ QJ ^J aJ nHtH h4+ 6CJ PJ ]aJ nHtH h4+ CJ PJ aJ nHtH& V W 6 7 < @ / \ ] Z [ X Z c d $$7$ 8$ H$ If a$ $7$ 8$ H$ a$ $ b d 7$ 8$ H$ a$ $ *$ 7$ 8$ H$ $d d d [$\$a$ ( $d a$
(
& F ( $d 7$ 8$ H$ a$gd}Z $d 7$ 8$ H$ a$ $ 1 % ) 5 6 < @ A Z [ X ±䛆v_vWSGSW h4+ B*CJ aJ ph h4+ h4+ CJ aJ ,j h4+ 0J0 B*CJ OJ QJ UaJ ph h4+ B*CJ OJ QJ aJ ph )h4+ 5B*CJ OJ QJ \^J aJ ph +h4+ B*CJ OJ PJ QJ ^J nHph tH h}Z h4+ CJ PJ aJ nHtH h}Z h}Z CJ PJ aJ nHtH h4+ 6CJ PJ ]aJ nHtH h4+ CJ PJ aJ nHtH h}Z CJ PJ aJ nHtHX Y O P % & X Y Z D E T x y z |
֯o j h4+ UmH nH tHu h4+ CJ OJ QJ ^J aJ h4+ 5CJ OJ QJ \^J aJ h4+ h4+ 5CJ OJ QJ aJ 'h4+ B*OJ PJ QJ ^J nHph tHh4+ OJ QJ h4+ 6CJ ]aJ h4+ CJ aJ h4+ CJ OJ QJ ^J aJ &j h4+ CJ UaJ mH nH tHu+d e f n u l kd*S $$If < 4F Q S 4
< a p $$7$ 8$ H$ If a$ 5 ] kdT $$If < F Q S 4
< a $$7$ 8$ H$ If a$ ] kdT $$If < F Q S 4
< a ] kd U $$If < F Q S 4
< a $$7$ 8$ H$ If a$ 5 ] kd4V $$If < F Q S 4
< a $$7$ 8$ H$ If a$ ] kdU $$If < F Q S 4
< a
O P % & x x f x Y d 7$ 8$ H$ ^ $ b d *$ 7$ 8$ H$ $ b d 7$ 8$ H$ a$ $7$ 8$ H$ a$ ] kdV $$If < F Q S 4
< a $$7$ 8$ H$ If a$ & : D N X Y Z c k t V kdLW $$If l e\ $$ 0 6 4 4
l a $$If a$ t | $$If a$ & $$If a$ kdX $$If l ֞ $- $ K K J 0 6 4 4
l a $$If a$ & $$If a$ kdY $$If l ֞ $- $ K K J 0 6 4 4
l a $$If a$ & $$If a$ kdZ $$If l ֞ $- $ K K J 0 6 4 4
l a $$If a$ & & $$If a$ kd[ $$If l ֞ $- $ K K J 0 6 4 4
l a & , 2 8 > D $$If a$ D E T Z & $$If a$ kd \ $$If l ֞ $- $ K K J 0 6 4 4
l a Z ` f l r x $$If a$ x y { | & $ $ kd"] $$If l P ֞ $- $ K K J 0 6 4 4
l a | } ~ I kd&^ $$If l e\ $$ 0 6 4 4
l a $$If a$ d 7$ 8$ H$ ^ $$If a$ & $$If a$ kd^ $$If l ֞ $- $ K K K 0 6 4 4
l a
$$If a$
, > D E M S Y _ q r y H I 딌~se h4+ CJ OJ QJ ^J aJ h4+ 6CJ ]aJ h4+ CJ OJ QJ ^J aJ h4+ CJ aJ h4+ 5CJ \aJ jq h4+ Ujd h4+ U h4+ 5CJ OJ QJ \^J aJ j h4+ UmH nH tHu h4+ CJ OJ QJ ^J aJ h4+ 5CJ OJ QJ aJ h4+ h4+ 5CJ OJ QJ \^J aJ ' & & $$If a$ kd_ $$If l ֞ $- $ K K K 0 6 4 4
l a & , 2 8 > D $$If a$ D E M S & $$If a$ kd` $$If l ֞ $- $ K K K 0 6 4 4
l a S Y _ e k q $$If a$ q r y & $$If a$ kda $$If l ֞ $- $ K K K 0 6 4 4
l a $$If a$ & $$If a$ kdb $$If l ֞ $- $ K K K 0 6 4 4
l a $$If a$ & $ d kdc $$If l P ֞ $- $ K K K 0 6 4 4
l a J K ; K Y Z $d 7$ 8$ H$ a$ d (
& F dh b d 7$ 8$ H$ $ b d 7$ 8$ H$ a$ $ b hd 7$ 8$ H$ ^ha$ $
& F b d 7$ 8$ H$ a$ $ b dh 7$ 8$ H$ a$ I J ; < I J K ; C b c ) * m L һtdVHh4+ CJ OJ
QJ
^J
aJ h4+ CJ OJ
QJ
^J
aJ h4+ OJ PJ QJ ^J nHtH &h4+ CJ OJ PJ QJ ^J aJ nHtH h4+ 5CJ \aJ h4+ 6CJ ]aJ h4+ CJ aJ )h4+ 5B*CJ OJ QJ \^J aJ ph -j h4+ 0J0 5CJ OJ QJ U\^J aJ h4+ 5CJ OJ QJ \^J aJ h4+ h4+ OJ QJ h4+ 5CJ OJ QJ \^J aJ , > P b c m $ N O K L d 7$ 8$ H$ d 7$ 8$ H$ $d 7$ 8$ H$ a$ 7$ 8$ H$ L N O _ g h D v R W a f ) * 0 6 ̾}}o}o h4+ CJ OJ QJ ^J aJ h4+ h4+ 5CJ OJ QJ aJ h4+ 5CJ OJ QJ \aJ h4+ 5CJ OJ QJ ^J aJ #h4+ B*CJ PJ aJ nHph tHh4+ CJ OJ
QJ
^J
aJ h4+ CJ OJ
QJ
^J
aJ h4+ 6CJ ]aJ h4+ 5CJ \aJ h4+ CJ
aJ h4+ CJ aJ ( ^ _ C D $$If a$ 7$ 8$ H$ 2 $d a$2 $
& F dh a$ d 7$ 8$ H$ d d 7$ 8$ H$ $d 7$ 8$ H$ a$ ~ ~ ~ ~ $$If a$x kd} $$If l e0 !
x 0 4 4
l a # ) ^ U U U U U $$If a$ kdI~ $$If l 4 e\ n !
m m 0 4 4
l a f4 ) * + 0 6 < B K B B B B B $$If a$ kd $$If l 4 r n ! m m 0 4 4
l a f4 6 B C J \ ] e w x | } ~
' ( ) + , . 8 T U zq h4+ 5CJ aJ ht h4+ 5CJ( OJ QJ aJ ht hH 5CJ( OJ QJ aJ haY 5CJ OJ QJ aJ h4+ 5CJ OJ QJ h4+ CJ PJ aJ nHtH j h4+ UmH nH tHu h4+ CJ OJ QJ ^J aJ h4+ 5CJ OJ QJ aJ h4+ h4+ 5CJ OJ QJ \^J aJ (B C D J P V \ K B B B B B $$If a$ kd $$If l 4 r n ! m m 0 4 4
l a f4 \ ] ^ e k q w K B B B B B $$If a$ kd $$If l 4 r n ! m m 0 4 4
l a f4 w x y z { | } K B B B B B $$If a$ kdL $$If l 4 P r n ! m m 0 4 4
l a f4 } ~ K B B B B B $$If a$ kd $$If l 4 r n ! m m 0 4 4
l a f4 K B B B B B $$If a$ kdԂ $$If l 4 r n ! m m 0 4 4
l a f4 K B B B B B $$If a$ kd $$If l 4 r n ! m m 0 4 4
l a f4 K B B B B B $$If a$ kd\ $$If l 4 r n ! m m 0 4 4
l a f4 K B B B B B $$If a$ kd $$If l 4 r n ! m m 0 4 4
l a f4
K B B B B B $$If a$ kd $$If l 4 r n ! m m 0 4 4
l a f4 ! ' K B B B B B $$If a$ kd $$If l 4 P r n ! m m 0 4 4
l a f4 ' ( * + , - K B B : /
$d a$gdH 2 $dh a$ d d [$\$ kdl $$If l 4 r n ! m m 0 4 4
l a f4 - . 8 T U S T " " " F# # W$ $
& F d a$gdl< $
& F d a$gdFJ
$d a$gdaY
$d a$gd(M
$d a$gd]A $
& F d a$gd >@
$d a$gdt $d a$
$d a$gdH U [ q $ D g k | T ! J! K! ! ! " " )" _" k" " " " " " ƽ h(M CJ \aJ h ? CJ \aJ h]A CJ \aJ h_ CJ \aJ hTf CJ \aJ h_ CJ aJ h4+ CJ aJ h4+ CJ \aJ #ht h4+ 5CJ OJ QJ ^J aJ #ht ht 5CJ OJ QJ ^J aJ ht CJ \aJ ht ht CJ \aJ 1" " " " " " " " " " # # F# # W$ $ $ $ $ % % d% q% r% {% l& m& n& o& ' ' "' *' =' N' ' ' ' ' ' ' Ⱦ|pfh4+ CJ OJ QJ h4+ CJ OJ QJ aJ h4+ 5CJ OJ QJ aJ hTf 5CJ OJ QJ aJ hTf 6CJ \]hU 6CJ \]h 6CJ \]h4+ 6CJ \]hl< 6CJ \]hFJ 6CJ \]h4+ CJ \aJ h(M CJ \aJ #h >@ h4+ 5CJ OJ QJ ^J aJ hV4 CJ \aJ (W$ % % % & I& & & R' ' ' ' 0( ( w) ) 1* * 9+ % ^ ` % ^`
$d a$gd_ $
& F d a$gdU $
& F d a$gd $
& F d a$ $
& F d a$gd ' ' ( ( ) ) ) * G* * * * + }+ + + + , , , , - - - "- $- Q- - - - . . Q. R. . . / / / / / / / 0 Ǽްޠ{sެn h4+ H*h4+ OJ QJ
h4+ CJ h4+ nHtH h4+ 6PJ ]nHtH h4+ PJ nHtH h4+ 6CJ h4+ 6h4+ h4+ CJ OJ QJ aJ h4+ 6CJ ]aJ h4+ CJ aJ h4+ 6CJ OJ QJ ]aJ h4+ CJ OJ QJ aJ h4+ CJ OJ QJ h4+ 6CJ OJ QJ +9+ + , , - - #- $- - - Q. R. / / / / h0 i0 0 0 K1 $ b 7$ 8$ H$ a$ ~= z9 !v% ^` ( $^`a$ ( ^ ` 7$ 8$ H$ % ^` 0 d0 e0 h0 0 #1 |1 1 1 1 1 92 U2 n2 2 3 3 3 \4 4 4 4 4 4 5 =5 ]5 l5 m5 5 H6 c6 d6 x6 6 6 6 6 7 Y7 p7 q7 7 8 8 G8 K8 S8 x8 8 8 8 8 8 8 ,9 i9 9 9 9 9 9 %: F: ĶĶհըհը h4+ B*ph h4+ OJ QJ
h4+ CJ h4+ CJ PJ aJ nHtH h4+ 6CJ PJ ]aJ nHtH h4+ 6CJ h4+ 6CJ OJ QJ h4+ CJ OJ QJ h4+ H*h4+ h4+ 6 ?K1 L1 1 1 T2 U2 3 ~3 3 4 4 4 4 5 m5 n5 c6 d6 6 6 7 7 ~= z9 !v% ^` ^` # $d ^`a$ $^`a$ 7 7 7 F8 G8 8 8 9 9 E: F: 1; 2; &< '< < < = = > > > > o? p? f@ 7$ 8$ H$ ^ ` ^` $^`a$ F: J: R: : : : }; ; 9< ;< < < O= Y= Z= [= ]= v= x= = = = 3> 4> ;> >> k> l> > > > > > "? #? %? Q? ? ? ? ? ? @ @ 5@ n@ v@ z@ @ A A A A A 9B QB B B YC fC hC C ND XD YD ZD \D uD wD D LE VE XE E E $F ]F
h4+ 0JD h4+ 0JD 6] h4+ H*] h4+ ] h4+ 6H* h4+ 6] h4+ 5 h4+ 6h4+ h4+ B*ph Lf@ g@ @ @ A !A A +B ,B RB SB B B C C D D E E CF DF |F }F $^`a$ $7$ 8$ H$ ^a$ $7$ 8$ H$ ^`a$ 7$ 8$ H$ $^`a$ ]F mF F F QG G %H cH H H H I I @I kI I I I J vJ J J J J K K K K K kL L M M M M M M M N )N 4N 5N 6N N N N N N N N N O ,O -O .O EO zO O O
h4+ CJ h4+ 6B*ph h4+ B*ph h4+ 6H*] h4+ 6] h4+ 5h4+ 0JD 6]
h4+ 0JD h4+ 6CJ OJ QJ ] h4+ CJ OJ QJ ]h4+ CJ OJ QJ h4+ ]h4+ h4+ 6 :}F F F G G H H WI J J J J J K K K L L L L M M 5N $^a$ $^`a$ % ^`% Z ^` $a$ $^`a$ 5N 6N N N -O .O O O gP hP P P CQ DQ R ~R =S S T 9U U JV W fW % ^` % ^ ` d d [$\$ $a$ 7$ 8$ H$ $^`a$ O O 2P VP P P Q /Q DQ Q Q KR tR uR vR R R =S }S S S S CT U .U /U 0U 9U ]U U U U V V JV V V V V ,W HW fW pW rW W W W X X ౢౢ{ j0 h4+ 6U j h4+ 6U h4+ H*h4+ >*CJ OJ QJ h4+ 6CJ OJ QJ ]aJ h4+ CJ OJ QJ aJ h4+ 6CJ OJ QJ ] h4+ 5CJ OJ QJ h4+ 6CJ OJ QJ h4+ CJ OJ QJ h4+ 6] h4+ 6h4+ h4+ 5 0fW gW X X {X |X Y Y UZ VZ Z Z [ [ 0\ 1\ \ \ ] ] ^ ^ ^ $a$ $a$ ^ ` 7$ 8$ H$ $ ^ `a$ % ^` $^`a$ X X ZX pX |X X X UY Y Y 4Z JZ tZ Z Z Z S[ r[ [ [ \ -\ [\ \ \ h] ] ] ] ] O^ ^ ^ ^ ^ ^ ^ ^ n_ _ ` ynfnh4+ CJ aJ h4+ 5CJ \aJ #h4+ 5>*CJ OJ QJ \^J aJ h4+ 5CJ0 OJ QJ \^J aJ0 #h4+ B* CJ PJ aJ nHph tH h4+ 5h4+ 5CJ h4+ 6h h4+ h h4+ H*h4+ 6CJ OJ QJ h4+ 6CJ OJ QJ ] h4+ CJ OJ QJ h4+ 6h4+ j h4+ 6U(^ ^ _ ` w` b b b b $b 2b Rb eb \ kd$ $$If l 4 $ h% 0 4
l a f4 $If ^ ` $ d @& $a$ ` &` b b b b rb sb b b b b b c c kh lh h h h h h i i Fj Oj Uk Vk k k k k k 1l Kl Ll hl l l l l ˺˺˺銂tfh4+ 0J) 67OJ QJ ]h4+ 0J) 57OJ QJ \h4+ OJ QJ h4+ 6OJ QJ ] j֖ h4+ U h4+ 5\ j h4+ Uh4+ 6>*]
h4+ 0J jT h4+ Uj h4+ Uh4+ 5CJ \aJ h4+ 5CJ( \aJ( h4+ h4+ 5CJ \aJ h4+ CJ aJ (eb fb rb b $If l kd $$If l 0 j$ 0 4
l a b b b b $If l kd7 $$If l 0 j$ 0 4
l a b b b b $If l kd $$If l 0 j$ 0 4
l a b b b c $If l kdw $$If l 0 j$ 0 4
l a c c 0c =c $If l kd $$If l 0 j$ 0 4
l a =c >c Tc mc $If l kd $$If l 0 j$ 0 4
l a mc nc c c $If l kdW $$If l 0 j$ 0 4
l a c c c c $If l kd $$If l 0 j$ 0 4
l a c c c c $If l kd $$If l 0 j$ 0 4
l a c c c
d $If l kd7 $$If l 0 j$ 0 4
l a
d d d -d $If l kdב $$If l 0 j$ 0 4
l a -d .d Nd `d $If l kdw $$If l 0 j$ 0 4
l a `d ad pd d $If l kd $$If l 0 j$ 0 4
l a d d d d d d e De pe e e $If l kd $$If l 0 j$ 0 4
l a
e e e mf nf f f f f g g )g 7g Mg `g kg lg $If l kdW $$If l 0 j$ 0 4
l a lg g g g g g g h "h 4h h h i i i i i l kd6 $$If l 0 j$ 0 4
l a $If i i j j j *j <j Ej Fj Oj Pj j j k k Lk Mk k k 0l 1l Kl Ll hl l l l l l , ` l l {m m p p p p q q Gq Hq Iq [q \q r r t t t t t t t w y I d ڄ ۄ , - . \ ] B C ޮޣޕޮފޅ~ޮsޮh j h4+ Uj h4+ U h4+ 5\ h4+ H*jԞ h4+ Uh4+ 6>*]
h4+ 0J j h4+ Uj h4+ Uh4+ 5CJ \aJ h4+ 5CJ( \aJ( #h4+ B* CJ PJ aJ nHph tHh4+ h4+ 0J- 57\h4+ OJ QJ h4+ 0J) 7OJ QJ (l {m m p p p p p p p p q a kd $$If l 4 $ h% 0 4
l a f4 $If 7$ 8$ H$ $a$ , q q q ]q $If q kdM $$If l 0 & $ 0 4
l a ]q ^q mq wq $If q kdԙ $$If l 0 & $ 0 4
l a wq xq q q $If q kdt $$If l 0 & $ 0 4
l a q q q q $If q kd $$If l 0 & $ 0 4
l a q q r r [r $If q kd $$If l 0 & $ 0 4
l a [r \r rr r $If q kdT $$If l 0 & $ 0 4
l a r r r r $If q kd $$If l 0 & $ 0 4
l a r r r r $If q kd $$If l 0 & $ 0 4
l a r r s 's <s Zs s s s .t t t t t u Ou $If q kd4 $$If l 0 & $ 0 4
l a Ou ou u u u u v v Kv Xv v v v v v v v v .w \w }w w w 2x bx x x x x y $If y y 1y my y y z ,z ^z z z z z { { { q kd $$If l 0 & $ 0 4
l a $If { { { | | | | ~ ~ ~ ~ q kd7 $$If l 0 & $ 0 4
l a $If
~ ~ ~ ~ + $If q kdנ $$If l 0 & $ 0 4
l a + , ; I $If q kdw $$If l 0 & $ 0 4
l a I J ` ~ ! W ׁ $If q kd $$If l 0 & $ 0 4
l a ׁ H q kd $$If l 0 & $ 0 4
l a $If H I d m ڄ x ܅ E z z z z z z z z z z z z ^ ?! ^ q kdW $$If l 0 & $ 0 4
l a E ~ 1 a Ӈ B < o ҉ e ъ ӊ Ԋ ڊ ۊ 5 N 7$ 8$ H$ ^ ъ Ԋ ي ڊ ۋ ܋ : o ď Ə ݏ ߏ 1 9 : S X b ~ ܑ 7 r v } h4+ H*h4+ B*ph h4+ 6] h4+ 5\ h4+ 5B*\ph h4+ OJ QJ h4+ 6CJ OJ QJ ]aJ
h4+ 0J j h4+ Uh4+ 5CJ$ \aJ$ h4+ 5CJ( \aJ( #h4+ B* CJ PJ aJ nHph tHh4+ j h4+ U *N k ~ : ; o Í ύ 0 < R f ~
o ~= z9 !v% ( ޏ . U { ݑ ޑ 7 X k w x œ > S & i |^`| F E q r % d q r s t u v $a$ 7$ 8$ H$ ^ ^ ` ^` ` ^`` ^ v y 4 7 @ S ȷlbSAS- 'h4+ B* OJ PJ QJ ^J nHph tH#h4+ 5>*CJ PJ \aJ nHtH h4+ >*CJ PJ aJ nHtHh4+ PJ nHtH 8h4+ 5>*B* CJ$ OJ PJ QJ \^J aJ$ nHph tH & *h4+ 5>*CJ( OJ QJ \^J aJ( h4+ 5>*OJ QJ \^J aJ( h4+ OJ QJ ^J h4+ 5CJ( OJ QJ \^J aJ( h4+ 5CJ OJ QJ \^J aJ( h4+ 5CJ0 OJ QJ \^J aJ0 )h4+ 5B* CJ PJ \aJ nHph tH 4 S n ܝ - H c ~ О d 7$ 8$ H$ d 7$ 8$ H$ d 7$ 8$ H$ $d 7$ 8$ H$ a$C $ 7$ 8$ H$ [$ \$ a$ ʞ Ϟ О Ҟ * ʠ ͠ ֠ נ ϻϤϻϒqcGϻϤϻσqc 7j h4+ B* OJ PJ QJ U^J mH nH ph tHuh4+ CJ PJ aJ nHtH #h4+ 5>*CJ PJ \aJ nHtH h4+ >*CJ PJ aJ nHtH#h4+ B* CJ PJ aJ nHph tH-h4+ 5B*OJ PJ QJ \^J nHph tH'h4+ B* OJ PJ QJ ^J nHph tH'h4+ B*OJ PJ QJ ^J nHph tH7j h4+ B*OJ PJ QJ U^J mH nH ph tHu О * + H e ՟ & A \ w ɠ ʠ d 7$ 8$ H$ ^ d 7$ 8$ H$ d 7$ 8$ H$ ` / L M h ԡ
% @ [ \ w % t ã a N d 7$ 8$ H$ L ^ b q v y { % 9 ? E K c t | ã ˣ ף ݣ , 2 J P a o Ĥ ֤ ܤ + 1 7 = N b ۥ E h4+ CJ PJ aJ nHtH 'h4+ B*OJ PJ QJ ^J nHph tH-h4+ 5B*OJ PJ QJ \^J nHph tH'h4+ B*OJ PJ QJ ^J nHph tH'h4+ B* OJ PJ QJ ^J nHph tH :N E F \ v Ŧ 4 N i ֧ ק ' > W d 7$ 8$ H$ E F \ u ū 5 Ԭ * R S ŭ ƭ 1 5 ӋӋӋӋӋӋӋw_G /h4+ B* CJ OJ PJ QJ ^J aJ nHph tH/h4+ B*CJ OJ PJ QJ ^J aJ nHph tH'h4+ B*OJ
PJ QJ
^J
nHph tH'h4+ B* OJ PJ QJ ^J nHph tHh4+ CJ PJ aJ nHtH &h4+ CJ OJ PJ QJ ^J aJ nHtH "h4+ CJ OJ PJ QJ aJ nHtH 'h4+ B*OJ PJ QJ ^J nHph tH/h4+ B*CJ OJ PJ QJ ^J aJ nHph tH W w x Ǩ 4 5 K f ԩ 2 M i $ *$ 7$ 8$ H$ d 7$ 8$ H$ 9 [ \ u ū 5 Q h Ԭ d 7$ 8$ H$ d 7$ 8$ H$ ^ Ԭ ) * R S n ŭ ƭ ǭ ȭ ɭ ʭ 1 P e } ۮ d 7$ 8$ H$ d 7$ 8$ H$ ^ 5 P T e m } ۮ , E U p ԯ 8 k o 0 4 P X ӱ 5 I q ز < P v > B P T " & [ _ /h4+ B* CJ OJ PJ QJ ^J aJ nHph tH/h4+ B*CJ OJ PJ QJ ^J aJ nHph tH R E p W k ̰ ( H { ˱ - i в 4 n ܳ d 7$ 8$ H$ ܳ 6 g < q ֵ ; z 0 e 3 s / L i d 7$ 8$ H$ & \ j k ? @ A D P Q U a b 绩sbQ jq h4+ B*CJ UaJ ph jf h4+ B*CJ UaJ ph jv h4+ B*CJ UaJ ph h4+ CJ OJ QJ ^J aJ h4+ OJ QJ ^J h4+ 5OJ QJ \^J #h4+ 5>*CJ$ OJ QJ \^J aJ$ & *h4+ 5>*CJ( OJ QJ \^J aJ( /h4+ B* CJ OJ PJ QJ ^J aJ nHph tH/h4+ B*CJ OJ PJ QJ ^J aJ nHph tH i [ \ j k w ̺ $$7$ 8$ H$ If a$ $7$ 8$ H$ a$
$$7$ 8$ H$ a$ 7$ 8$ H$ b 7$ 8$ H$ $a$ d 7$ 8$ H$ ̺ ͺ Ϻ Һ ? 1 1 1 1 $$7$ 8$ H$ If a$ kdH $$If T < 4r H L @ @ @
2 4
< a p2 T n kdT $$If T < r H L @ @ @ 4
< a T $$7$ 8$ H$ If a$ % + 1 | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T 1 2 4 6 D J P | n n n n n $$7$ 8$ H$ If a$ kdx $$If T < r H L @ @ @ 4
< a T P Q S U c i o | n n n n n $$7$ 8$ H$ If a$ kd
$$If T < r H L @ @ @ 4
< a T o p r t | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T | n n n n n $$7$ 8$ H$ If a$ kd. $$If T < r H L @ @ @ 4
< a T » Ȼ λ | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T λ ϻ ѻ ӻ | n n n n n $$7$ 8$ H$ If a$ kdR $$If T < r H L @ @ @ 4
< a T ? @ B C D P | v v v v k b b v v k $7$ 8$ H$ a$
$$7$ 8$ H$ a$ 7$ 8$ H$ kd $$If T < r H L @ @ @ 4
< a T P R S T U a c d e f g x } ż Ӽ ܼ Ff6 $$7$ 8$ H$ If a$
$$7$ 8$ H$ a$ 7$ 8$ H$ $7$ 8$ H$ a$ b g w x H i þ ;
, - c | nb h4+ B*CJ aJ ph jK h4+ B*CJ UaJ ph j[ h4+ B*CJ UaJ ph /j h4+ B*CJ UaJ mH nH ph tHu j5 h4+ B*CJ UaJ ph j; h4+ B*CJ UaJ ph jD h4+ B*CJ UaJ ph h4+ CJ OJ QJ ^J aJ h4+ 5OJ QJ \^J h4+ OJ QJ ^J & $$7$ 8$ H$ If a$ kd $$If T <
d$ d$ d# ( ( ( ( 4
< a T # ) / 5 : = C I $$7$ 8$ H$ If a$
I J kd $$If T <
d$ d$ d# ( ( ( ( 4
< a T J L O U [ b i o s y $$7$ 8$ H$ If a$
$7$ 8$ H$ a$ kdT $$If T <
d$ d$ d# ( ( ( ( 4
< a T S kd $$If T < 4F Q S 4
< a p T $$7$ 8$ H$ If a$ $7$ 8$ H$ a$
$$7$ 8$ H$ a$ 7$ 8$ H$ \ kd $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ 7 \ kd $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ \ kd| $$If T < F Q S 4
< a T $ % & / 4 5 \ kd $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ 5 6 > C D E 7 \ kd $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ \ kd, $$If T < F Q S 4
< a T E F G H i j p x $$7$ 8$ H$ If a$
$$7$ 8$ H$ a$ 7$ 8$ H$ $7$ 8$ H$ a$
? 1 1 1 1 $$7$ 8$ H$ If a$ kdL $$If T < 4r O z + H g +
2 4
< a p2 T n kd $$If T < r O z + H g + 4
< a T $$7$ 8$ H$ If a$ ¾ þ | s m m b s T T T $$7$ 8$ H$ If a$
$$7$ 8$ H$ a$ 7$ 8$ H$ $7$ 8$ H$ a$ kd> $$If T < r O z + H g + 4
< a T 1 kd $$If T < 4r O z + H g +
2 4
< a p2 T $$7$ 8$ H$ If a$ % & ( + . 4 9 n kd0 $$If T < r O z + H g + 4
< a T $$7$ 8$ H$ If a$ 9 : ; < = > ? @ | s g g g g g g g \
$$7$ 8$ H$ a$ b 7$ 8$ H$ $7$ 8$ H$ a$ kd $$If T < r O z + H g + 4
< a T
߿ $$7$ 8$ H$ If a$ $7$ 8$ H$ a$
! ? 1 1 1 1 $$7$ 8$ H$ If a$ kd $$If T < 4r H L @ @ @
2 4
< a p2 T ! ( ) + . < B H n kd $$If T < r H L @ @ @ 4
< a T $$7$ 8$ H$ If a$ H I K N \ b h | n n n n n $$7$ 8$ H$ If a$ kdF $$If T < r H L @ @ @ 4
< a T h i k n | | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T z l l l l l $$7$ 8$ H$ If a$ kdj $$If T < r H L @ @ @ 4
< a T | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T | n n n n n $$7$ 8$ H$ If a$ kd$ $$If T < r H L @ @ @ 4
< a T " ( | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T ( ) , / = C I | n n n n n $$7$ 8$ H$ If a$ kdH $$If T < r H L @ @ @ 4
< a T I J M P ^ d j | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T j k n q | n n n n n $$7$ 8$ H$ If a$ kdl $$If T < r H L @ @ @ 4
< a T | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T | n n n n n $$7$ 8$ H$ If a$ kd" $$If T < r H L @ @ @ 4
< a T
| n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T $ * 0 | n n n n n $$7$ 8$ H$ If a$ kdF $$If T < r H L @ @ @ 4
< a T 0 1 4 6 D J P | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T P Q T V d j p | n n n n n $$7$ 8$ H$ If a$ kdj $$If T < r H L @ @ @ 4
< a T p q t v | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T | n n n n n $$7$ 8$ H$ If a$ kd $$If T < r H L @ @ @ 4
< a T | q q h q h b q h h h 7$ 8$ H$ $7$ 8$ H$ a$
$$7$ 8$ H$ a$ kd $$If T < r H L @ @ @ 4
< a T - 2 @ R \ j t z FfW $$7$ 8$ H$ If a$ $7$ 8$ H$ a$ kd $$If T <
d$ d$ d# ( ( ( ( 4
< a T $$7$ 8$ H$ If a$
kd $$If T <
d$ d$ d# ( ( ( ( 4
< a T " ( $$7$ 8$ H$ If a$
( ) kdu $$If T <
d$ d$ d# ( ( ( ( 4
< a T ) + - 3 9 ? E J M S Y $$7$ 8$ H$ If a$
Y Z kd/ $$If T <
d$ d$ d# ( ( ( ( 4
< a T Z \ _ e k q w } $$7$ 8$ H$ If a$
kd $$If T <
d$ d$ d# ( ( ( ( 4
< a T $$7$ 8$ H$ If a$
kd $$If T <
d$ d$ d# ( ( ( ( 4
< a T $$7$ 8$ H$ If a$
kd] $$If T <
d$ d$ d# ( ( ( ( 4
< a T $$7$ 8$ H$ If a$
kd $$If T <
d$ d$ d# ( ( ( ( 4
< a T ! & + 2 8 > A G M $$7$ 8$ H$ If a$
M N kd $$If T <
d$ d$ d# ( ( ( ( 4
< a T N P R W \ c j o r x ~ $$7$ 8$ H$ If a$
~ $7$ 8$ H$ a$ kd $$If T <
d$ d$ d# ( ( ( ( 4
< a T S kdE $$If T < 4F Q S 4
< a p T $$7$ 8$ H$ If a$ $7$ 8$ H$ a$
$$7$ 8$ H$ a$ 7$ 8$ H$ \ kd# $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ ! 7 \ kdC $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ \ kd $$If T < F Q S 4
< a T ! ' 1 2 ; A J \ kd $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ J K S Y _ ` 7 \ kd $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ \ kdc $$If T < F Q S 4
< a T ` a b c $$7$ 8$ H$ If a$
$$7$ 8$ H$ a$ 7$ 8$ H$ $7$ 8$ H$ a$
? 1 1 1 1 $$7$ 8$ H$ If a$ kd $$If T < 4r O z + H g +
2 4
< a p2 T n kd $$If T < r O z + H g + 4
< a T $$7$ 8$ H$ If a$ | n n n n n $$7$ 8$ H$ If a$ kdu $$If T < r O z + H g + 4
< a T / | s m m b s T T T $$7$ 8$ H$ If a$
$$7$ 8$ H$ a$ 7$ 8$ H$ $7$ 8$ H$ a$ kd1 $$If T < r O z + H g + 4
< a T / 9 > ? A D 1 kd $$If T < 4r O z + H g +
2 4
< a p2 T $$7$ 8$ H$ If a$ D G M R S U X [ a f n kd# $$If T < r O z + H g + 4
< a T $$7$ 8$ H$ If a$ f g i l o u z z l l l l l $$7$ 8$ H$ If a$ kd $$If T < r O z + H g + 4
< a T z { | | s g a V T T T T
$$7$ 8$ H$ a$ 7$ 8$ H$ b 7$ 8$ H$ $7$ 8$ H$ a$ kd $$If T < r O z + H g + 4
< a T ! " O P p - H I U m (
7$ 8$ H$ ` 7$ 8$ H$ O ` a b g h i µµ{iZF/ ,h4+ 5CJ OJ PJ QJ \^J aJ nHtH &h4+ CJ OJ PJ QJ ^J aJ nHtH h4+ 5OJ QJ \^J aJ0 #h4+ 5>*CJ0 OJ QJ \^J aJ0 & *h4+ 5>*CJ0 OJ QJ \^J aJ0 h4+ 5CJ. OJ QJ \^J aJ. )j h4+ 5CJ. OJ QJ U\^J aJ. h4+ 5OJ QJ \^J &h4+ CJ OJ PJ QJ ^J aJ nHtH ,h4+ 5CJ OJ PJ QJ \^J aJ nHtH $h4+ 5OJ PJ QJ \^J nHtH N f - E ` h i $a$ $a$ b 7$ 8$ H$ 7$ 8$ H$ i F < 2 5 ` U d 7$ 8$ H$ 7$ 8$ H$ l m ) k n ( 1 2 3 V W I z j u ~ < мvvvvvvb &h4+ CJ OJ PJ QJ ^J aJ nHtH $h4+ 5OJ PJ QJ \^J nHtH h4+ PJ ^J nHtH h4+ OJ PJ QJ ^J nHtH ,h4+ 5CJ OJ PJ QJ \^J aJ nHtH &h4+ CJ OJ PJ QJ ^J aJ nHtH ,h4+ 5CJ OJ PJ QJ \^J aJ nHtH /h4+ 5>*CJ OJ PJ QJ \^J aJ nHtH 5 U r u W ( R | J K d 7$ 8$ H$ 7$ 8$ H$ K i C m ? i l m : ] y d 7$ 8$ H$ 7$ 8$ H$ ) J k n 2 3 S d 7$ 8$ H$ $ d 7$ 8$ @& H$ ~= z9 !v% 7$ 8$ H$ 7$ 8$ H$ : d 6 ` " a H ~ 7$ 8$ H$ * - L ` ~ @ Z ] * T W ~= z9 !v% 7$ 8$ H$ 7$ 8$ H$ I j k / r D m ? $ 7$ 8$ @& H$ 7$ 8$ H$ ? i ; > ` : o W O w 7$ 8$ H$ w ? B n ( L r 7 ] { d 7$ 8$ H$ 7$ 8$ H$ ( D i . X w z { F p s v = g d 7$ 8$ H$ g h i
> a 8 b < _ 7$ 8$ H$ d 7$ 8$ H$ d 7$ 8$ H$ ` _ ) D f % C o @ m G L 7$ 8$ H$ L Q < = D E x { S + a 9 o d 7$ 8$ H$ ^ $a$ d 7$ 8$ H$ 7$ 8$ H$ < = C D E x n o " # R U J K о~j~`\`\NF`\A\`\`\ h4+ H*h4+ CJ aJ j h4+ 0J0 CJ UaJ h4+ j h4+ 0J0 U&h CJ OJ PJ QJ ^J aJ nHtH &h4+ CJ OJ PJ QJ ^J aJ nHtH )h4+ 5B*CJ PJ \aJ nHph tH-h4+ 5B*OJ
PJ QJ
\^J
nHph tH#h4+ 5>*CJ0 OJ QJ \^J aJ0 & *h4+ 5>*CJ0 OJ QJ \^J aJ0 5h4+ 5B*CJ OJ
PJ QJ
\^J
aJ nHph tH o ' B ] _ # A U s 5 _ 7$ 8$ H$ _ , Z D F i $ & K " $ a 7$ 8$ H$
%
'
g
2 e 3 R q < o H 7$ 8$ H$ O Q J i S W v 7$ 8$ H$ ! e ) + h % D c ; = o 7$ 8$ H$ : m o G z n " I J ? @ m o ( $ *$ 7$ 8$ H$ $d 7$ 8$ H$ a$ $7$ 8$ H$ a$ / / $a$ 7$ 8$ H$ " @ A B F J m o p q v r ] ^ _ ` j s t z { ~zrh^X^
h4+ 0J j h4+ 0J Uh4+ OJ QJ ^J h4+ OJ QJ h h4+ CJ aJ h4+ CJ aJ h4+ 5CJ OJ QJ \aJ j h4+ 0J0 CJ UaJ h4+ CJ aJ h4+ CJ H*OJ QJ aJ h4+ CJ OJ QJ aJ h4+ B*ph h4+ PJ nHtH h4+ 6] j h4+ 0J0 Uh4+ h4+ CJ H*
h4+ CJ ! r _ ` $a$ h]h &` ( { ~ ! ! ! ! ! !! 4! >! p! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! }}Ì h4+ 6CJ h4+ OJ QJ h4+ 6CJ h4+ 6CJ OJ QJ h4+ 6CJ H*OJ QJ h4+ 6CJ OJ QJ
h4+ CJ h4+ 5\ h4+ 6h4+ 5CJ
h4+ CJ h4+ 56 h h4+
h4+ 0J j h4+ 0J Uh 0J mH nH u 1 ! ! ! ! ! !! -! .! 3! 4! o! p! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ( ( $ a$ $a$ $a$ ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! " " " " " " f" g" " " $a$ ( ( $ a$ ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! " " " "
" " " G" T" g" q" " " " " " " " " " " " r# s# t# u# j$ k$ m$ n$ $ Ÿбתױױױx j h4+ CJ Uj h4+ U
h4+ CJ
h4+ CJ h4+ 5CJ h4+ 5\
h4+ aJ h4+ 6H* h4+ 6CJ h4+ 6CJ H*OJ QJ h4+ 6CJ OJ QJ h4+ 6CJ h4+ 6h4+ 6CJ H*OJ QJ h4+ 6CJ OJ QJ h4+ h4+ OJ QJ -" " " " " " "
# =# s# t# u# j$ $ $ $ -' .' ;' <' Q' R' `' a' t' u' }' $dh a$ 5 5 ` $a$ $ $ $ $ $ $ $ $ $ 7% E% S% ^% & & ' ,' -' .' ;' <' Q' R' `' a' t' u' }' ~' ' ' ' ' ( ( ( >( B( G( O( P( ]( y( ( źźźŮ֥֥֖֝֝֝։}vvvo h4+ 5CJ h4+ 5\ h4+ 5h4+ \aJ h4+ 5aJ
h4+ CJ h4+ 5CJ h4+ OJ QJ h4+ 5OJ QJ h4+ 6CJ ]aJ h h4+ 6CJ aJ h h4+ CJ aJ h h4+ CJ aJ h4+
h4+ CJ j h4+ CJ Uj&: h4+ Uj9:@
h4+ CJ UV +}' ~' ' ' ( ( x( y( ( ) ) i) j) ) ) * * v* w* * * G+ H+ + + , , ?, d 7$ 8$ H$ ^ $a$ ( ( ( ( ( ( ( ( ) ) j) s) ) ) * * * 8* >* C* [* w* * * * * * =+ F+ H+ S+ + + + + , , , P, S, _, s, v, |, , , , , , , , , , , , κΦκΔκ h4+ PJ nHtH #h4+ B* CJ PJ aJ nHph tH'h4+ B*OJ PJ QJ ^J nHph tH'h4+ B*OJ PJ QJ ^J nHph tH'h4+ B* OJ PJ QJ ^J nHph tH
h4+ CJ h4+ 5CJ h4+ 5\ h4+ h4+ 5 6?, P, s, , , , , , , , - #- $- - - - - - - . . . $$7$ 8$ H$ If a$ $$ 7$ 8$ A$H$ a$ $a$ d 7$ 8$ H$ d 7$ 8$ H$ ^ , , - - - #- $- .- 0- [- d- f- p- v- - - - - - - - - . . . . / / / / &/ (/ R/ [/ ]/ g/ m/ w/ }/ / / / oj h4+ \ j E h4+ B*CJ UaJ ph h4+ 5OJ QJ ^J &h4+ CJ OJ PJ QJ ^J aJ nHtH h4+ 5OJ QJ \^J h4+ 5\ h4+ 5h4+ 5CJ h4+ 'h4+ B*OJ PJ QJ ^J nHph tH'h4+ B* OJ PJ QJ ^J nHph tH'h4+ B*OJ PJ QJ ^J nHph tH ) . . . . F. { m m m $$7$ 8$ H$ If a$ kd@ $$If T < 4F Q S 4
< a p T F. G. M. S. l. m. v. 7 \ kd9B $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ \ kdA $$If T < F Q S 4
< a T v. |. . . . . . \ kdB $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ . . . . . . . 7 \ kdC $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ \ kdYC $$If T < F Q S 4
< a T . . . . . . / / / / / w/ / / / } } { $a$ $$ 7$ 8$ A$H$ a$ \ kdyD $$If T < F Q S 4
< a T $$7$ 8$ H$ If a$ / / / / / / / _0 `0 &h4+ CJ OJ PJ QJ ^J aJ nHtH h4+ 5h4+ 5CJ h4+ 5OJ QJ \ h4+ 5\ h4+ / / 70 ]0 ^0 _0 `0 7$ 8$ H$ $a$ 2
00 P /R / =!"#$% D d
, B
S A ? 2 q 3FA D ``! q 3FA G 4 x yǟۻ3.@M`*#FmK rĠ4|K}w5T-R*$" DjS,JHREj$T5iU&DD
>3vogfg3;ߏ5;}sޞo4O4{4h ["KZgZ];|1}l龵գ ķgyEm[/k$Fhܱz|miqs|ۍehgֻR e.-/ Z]^X HzOqi/$5ѿ3oVnu j41qI^ۯ4~9i&Gt _ NҼ3a^.>x{'x{-u~&o?;؈z5["-t7Qkt_\ N~:kt>S|=ͷN,{[_,p6IoDop7ｐ$۟f2%x3qa>-\St#{rA\xx kkۧ̕D ;]({3 ڽ}ogG;7!E7 ' w ;9]<<w/͇V S 6o\#Dxz)Ss߿nDp'8]) o>BGt<\ʈExk9zOs9t_}x+ =tt~;+xX=wFrx[K}< :?|Ə/ߠyK&IkwuKCbOP7@ boƞ)͆7+2^j֞ 2l1Zԛ *Yl6fzSE{{-<xS&˃6F5n lNs
2ly~rE{{VW7O%$J*n{CƊ"!<?v{5G3csCw:U9x7\ |;[F78Wi#mIj͠3i0G)1"o rX) %M>oZ7j( m ic)zAYʢ7'$d0pЭɀ9H/|z !ե6f
կ*Zm{%7vx%kw8x$6. odzjxqBTEW'Ŏզ@ ^n*UNr8)T9K98686'/x[n{q]Qˍ{or29*oX%У6>8fڼ8(y7y\nxq,WGˍ'opB^ӵ7ǜ]nMu(4x88fo ѵx rɛu("mDmܩN e=O82)ϤgYwO7,*`:o N)&&`)oMѹgrj)\xSgMZ`T/o NjS x1ȡHęr3`&7' #m 9HPmPxSS
4N0r3@4 o C-I7ὁ81Ԑ7gxS.oe 67g[nc A
,8}J>v7 9e or KM ݽ*= MRcAc4ojfބr7COx#&|pXԦvh# #4dAqÛQ4d妎 ˼ ]0Nͨ7A>z":RxŲ6<O)Ixc%1<RH/& oВ"&-9<a[z'7 xc5A< +@O_nMpMQo(SDTF78˼
xc Ua !#CU7orѕ86uF[ 7(cűq<V6& C<4x3uo .5ioNkLd x} )H0)H7Q 1f 3b2$=)h eP&Ee(.7Ǵ /7Tb'tb6xSd^n D8_n DhU ÑRhU ~o+4-7À7eћ 7zx4BÛG+lBsbhVF@g4VqpSQ n"ҫYnFRt7Н. roО/xވў/ xSIE}4섆7h},x ǰޘ?^xA"2˼qƐ*d (
7ޘR}</7&J{79T#eQD5RK
o,P7yT$&F5rZL oP67VD|*fDxc
7Qj@Oo NGXCy0˼ꬁxS a-G7666jhMk;ҸF9:Z?6*Zol1om7
Oo/0;o\+rXmM 4evAџs8BMh7G &qxS =ς7Uxρ%xhxRKκWLyt:77FD\|z3] aһx Lz Xmx#s OL7aJyx#J6cpA8x(koe;)18y{ xcW
76P\?}\t>7Vp6O?7p?(|Q~5-abXh7?='xcK=܌sl roFiޘixuҎuZ
]V ިBt:$_<\;9T΄{1FЛBc얳L7 T⯪zӇ(u42ȁ7FAdMJoEMŰ],7xoDX. v&lTu7kxS ,N"*o"lNNPo9
TJx3= :y3#1Jbz~ B 7ż|UZn5LgV w o1&\. ojYM~To*QM7 N5z)o*"&<*7:ڠKxcބ9xcQ+ A~Aw 77@DB >MVd%!FY8T)FY8 %jI0 ŋ o_!ѦKI_CM;7)XM+G+'\sӓe#
R6Iwd x㌉vqqDŽR-ݸ8o9o@gx6Ȫj~i$uI)Ax9/eؔ_
N@y7~ I o oxD x$ 97@0hsSo9
)? כ00H7@"JQvB"JN H7@J o@x$`ooxDx %fހH(
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h j k l m n o p y | } ~ R o o t E n t r y F ]ZO @ D a t a
i YK W o r d D o c u m e n t 4 O b j e c t P o o l f麟O]ZO _ 1 0 8 4 5 2 7 6 0 0 F f麟Of麟O 1 T a b l e { C o m p O b j h O b j I n f o K
[ 4 @ 4 N o r m a l CJ OJ PJ QJ mH < A@ < D e f a u l t P a r a g r a p h F o n t # N e # N e l , b $ e`8v*Ƴ @ t ( UUUdddd
6
A
N
3 N
3 N
3 N
3 N
3 N B
S D N B
S D N B
S D N
3 B
S ? ( $ t t H $ t t 8 T t h t 8 D t x t 4 t 4 t t g j @ u ` @ G T i m e s N e w R o m a n 5 S y m b o l 3& A r i a l S T i m e s T i m e s N e w R o m a n " h 6f9f ! >0 I n t e l l i g e n t S y s t e m s L a b A h m a d
" ' , 1 6 ; @ E J O T Y ^ c h m r w z { ~
F Microsoft Word Picture
MSWordDoc Word.Picture.8 9q
F Microsoft Equation 3.0 DS Equation Equation.3 9q s
i O b j e c t P o o l
f麟Of麟O W o r d D o c u m e n t S u m m a r y I n f o r m a t i o n ( D o c u m e n t S u m m a r y I n f o r m a t i o n 8 Y bjbjWW = = ]
} } } $
b } M 0 } } } S } } \ J V
4 Number ofStudents 50% 84% 98%Percentiles: Summative Achievement ScoresConventional (1 : 30)Mastery Learning (1 : 30)Individualized Tutoring (1 : 1)
. X Y n p CJ CJ OJ QJ j UmH - . X Y o p $ - . X Y o p N N!h"\ # $% n z e`8v*ƳPNG
IHDR 6 ߠ0 gAMA PLTE 6 0 f w W c4 <j $k/ l @@\P o F 8 8 u @$ %qW >n"X w W 4 f l m ~ r f|3 # k H
0 qw W 04 H 3 $ T 8 %h Y dq 6f l + pHYsN IDATxZy| }Œ%k9q"VҐ?> ί6.z>Fr=xhx<\O< h' m4h 8@p6 mڀh 8@p6 mڀh 8@p6 mڀh 8@p6 mڀh 8@p6 (#Lco4A}6N:hڈח^8hvR,+FǐQCaS6 [h#IOۅ6m ; A#BU.6ڀ6 mD;h\mvTv m4AҴ ikFaFhv_x6Hc/ ApڋfG[6ڀhv;6 ڀN5m@ci 1mhl}ha|Om@mڀ;.|0wmh#ڀ {<8P Gwcm^Ez xzP7Y h:66 mmۮ~{l ܕG[AxU#q(i ';8 ڤb8#|s|wn=oz77Kn4}`hEEndqY d6I4s;8|x&rn fVm磜AQ}ͯS^߳M ܬ,IPKJ ѵ]hڀ c%7C m±Và 0hs u,=̏焃6*#66`YEM6CHj#G)
mmNսVh33eGA$z&,ͼ6ҭMѳ-ѦzToڄQڄ6`'xhAoGJQDn
m^vD/q66`'ph O#J^е#,Blf[m&#jhh3|_4)6 $lq6 I),yCS1r {P kAyE\$ڔ\ħz=r~V6lqpmiB5ߞX.hGɬ T<W=6Xh@-\mjFrЦ <.~wg{kBM)Vm ڔQ5B"&ècm"=2M R')@g@n;Rh3m:<6}h^0}7F*ͽhg1<6^[KU$G'u7F*6>
h"~h3GEuة{={N]Da&"ޠA;nwHqݴo\ :k͋@n oavmbtHzh7w36"sA$dBiG&tAfʟ{69# d9!$iC/p|" G/0裘7<D#wofF3nBN~&F5-H#68Dm[\!56 ڄ! 54 ڄ! hܠ9Ikڜ#m hdD͋I m"g`H a AFIbD &6Ȓޠ!s",*2E S!*,t2C?F9` 7h@ &LP`A?1&ێyC} 7h|=ЦF7ڼ)E=!ƉxЦxgDG 6nt7htKЦpWFEc3țu蝕5q#m^vRnr6 mGkm# Fq6&0bܠ lXd<1N7hj.;w7ڈrw,hc@4 h, ]6h>nTn|zd zmZ CAOv 7hbN& L Pem#6F
䢗(qMzp \#( ID.P yޠ\8&<xmP
$!'ٽA&88mNJ d&$oF*LhT$|Lj6'HuhsR<&,xmJƸA3F'I6hNt 6@¸A 7h hJAmP1ǟ8ĚڈD c #F74R %X1%Dj&Fᝫ/a:K\ڼl"1ñ 5<q3
* 7i#q6ˀ6~a; Л)P
X
6*6nVfykڄk>LEA &7`AfmXoѦf +6XK6k h.Fڈ
`hc _- |Z7AiI)kv@ᑶ<=n ;nn T³H6{X9:+j5ưO7wՆA/iPٸ)fv3<nFqsOm9DiPhoޞpߛi5.6EkRõ64xmsV0l86cWU`7k\(k3yj3ƍ7K'Q ڤpc<`MZf6:qx36H ڀ7k5q_ц8v܌k8fkskj`M '{k"9s!17@Pgk4Hdm&v06XEq<P?n2, LqTQ&Oygriri7WcM2G|̸цT>fmf{'5ch7shjuk4h<OMʛhmf,mOŵf8%O 7 MyKu9CU1ޛ0mo0hY)1dU)u)uӦ?r博6 ڐuidi;խ(q~ z\:A1t4zܗM6X#6DyB1Fl_mpF_ xnR7 mZI/hb$_ n{^}oz1=_ G^zaO]oozѿ=zߏkS6'"Y^laƦMn^6 Y ? *#7C IENDB`
Oh +'0 `
( 4 @ H P X s s Intelligent Systems Lab nte Normal g Ahmad g 2 ma Microsoft Word 8.0 @ G @ ĥV
@ V
՜. +,D ՜. +,D h p Michigan State University D j Title 6 >
_PID_GUID A N { A F 1 5 A D 0 0 - 2 D F 0 - 1 1 D 2 - 9 6 5 2 - 0 0 0 5 0 2 7 0 A D 1 9 } > (< ox$ \0L8eF#H(Kox$ l1xx$ t0&OG 7Z8 oxDLxҘ0!a ި 3o@:3xbހ,3'd='g ި&sRB7pdMHWY >YF9|v%\ b|bLWf
T xD ύJI H7@ ox$ H7@ ox$ H7@ ox$ H7@ ox$ H7@ ox$8ƶ5kOy<x=ǃ7JS (mOy<x=8o ( ox$ /Zr%4GLZ|ŷl,gwG\=~Dߤó+s{FxaoI3|yMfq=۳/oLZڶ?
CGRV^l[ʋ홡VneOj67?ƯܖFT5Jw\ʹMnoqKzmN-- lo~Nk# d}Cc}+ǖ, P ǖN,v٥|WVҭ~z7]F>Lwם_ry+~]Q;{vcTGŪzsnOe.-/ ܽ`ʠsc} A};I}=Օq}WWtG=Wdojt$Wez@trǎ^ 'g[C}Pֶ'm} Ee|F*ouQ=ko2xeCck|tqameiu jux{jq_c26cGZDz{v~B9~6~ӓݘ'sx ^:tw=~lu-)cR{w_f\x_<"[z)ߚTmԴHE.>HeD/ +\_3\[wpjzvrn3כk3 }{fx~48Fj| ]5{Isavo#?X7u7'5nz[~/_Ϗe=cd4u~,˭[l%~.^ύ2W6j?ט_7vScI& }oȬˍ7FVs7[{g~up dƳˣGm_ ^?>Nno8h-_귻3i7 jcy|~> wϿYz _()DI $$If !v h5 j5u 5 5 #v j#vu #v #v :V l 6 , 5 Y55'5/ / / / / / $$If !v h5 j5u 5 5 #v j#vu #v #v :V l 6 , 5 Y55'5/ / / / / / $$If !v h5 j5u 5 5 #v j#vu #v #v :V l 6 , 5 Y55'5/ / / / / / $$If !v h5 j5u 5 5 #v j#vu #v #v :V l 6 , 5 Y55'5/ / / / / / $$If !v h5 j5u 5 5 #v j#vu #v #v :V l 6 , 5 Y55'5/ / / / / / $$If !v h5 j5u 5 5 #v j#vu #v #v :V l 6 , 5 Y55'5/ / / / / / $$If !v h5 j5u 5 5 #v j#vu #v #v :V l 6 , 5 Y55'5/ / / / / / D d (D4 Z
c 6 A o v e r v i e w R R̀gU?@ F R̀gU?@ JFIF H H ,Photoshop 3.0 8BIM H H Adobe d
s !1AQa"q2B#R3b$r%C4Scs5D'6Tdt&
EFVU(eufv7GWgw8HXhx)9IYiy*:JZjz m !1AQa"q2#BRbr3$4CS%cs5DT
&6E'dtU7()euFVfvGWgw8HXhx9IYiy*:JZjz ? v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]g!2-{ bܷEm{v8<o:x=HթF* N 6Xr~b '~j K ,U?15%y *ߟ>vO[5mt kF nI_yřE{_2 c+ 0iڕSYV+dY(`*^N1Wb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]^63:G0k:ۨyĢ5%C74uk^[Z :jmwMo( (ej t U x aW yU| H^ *Լf =z>iVZjf Q^[ϧ-S$2+5qW y &/o<q-y[6U7zMt6rrbJኳʿ{){ %>Tf|'N*.a+PV &`ZHȍ.M ) J|Ο2M ?_3Cua:Iu%{H?O*~>Ej~By ֳ6>zV& Yx[Q _W-+ lbbUJz~m 9hU(/!Xkp;֦{i4ֵX# ƁG%Y ?? ʩ3
^W|*y
_|smuM04)Fe8U5K{>c6i o#IG*5@n ^ 1Tf*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb_ ~w ժb|+/M)Yt0o * ͥl ^*gfҿ6|m$s%yXYb3)PVVu#bWyq_ ~Sw|8< [յiO nn*r
C
UB yE3K# 5/*ll5 >g c&I
B<Ⴊ}=a_- 0ݠ~N_ˬ_yz7:%34F2 Kf6p ~3 vѿ+|ھw'm8Iq=w 2؋V=@6*ߟ,-yϚkGR-JԯVNr
KD@k 5s/^VtO &t1<Ӫ$WVrڪooC'.lO }5_{]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*Uثo w~Z \5=ZY>V b?ZjQ:M ω^&1Vp/ !W~ # a xUߧ H /za /t.N->kk<64-C=H75 Po$~UѼk Vʺ5ՊjȉXx*> L+S VIʝ^D 10 Zs]ML9 G)7iXȣqbU& ?zw֗_/7jߙAy?_Q^8/tRH.J
P\Oo=iUyȟ_LNN:k|Ι>okt<FTLtQ] ?掛e1yW XFY581.UFG#m B s^ut-.}w̚JYXrMu3O
IJJI#r%UWԘWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v* ?W*Uث - Ͽ'ww?")4 ʯ9 =D~bib'H4EޜU 3wX2y˾x?0'GJ5P.3n-$ Q2lU_ yq9h6?mM˞Q@.<b>o}"Zh
Tde 0 - 9DlM^6"KmS_,t/W
Q m {}oo?h~`έ-?w~e<'O g)8g6P_E ' > а~Iyss]8 7_?~w- }fS}CZWnUثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]|]4 K<诒=by-Z7IEj4ccU_:_U] * b/ / w UqT.?.?$:l?4K_֞hi;'X变',(+lSZHZY"qpy"_$JhJ`\U ?.y??GLV:W&uz\lZ']r2AҠ4C}Qv*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v* qo $O uKU*Uث 6Ue ) ث??R yK_h imiczm=3mu W{ur^,|UG'H=۳ꖘ UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثW "| o?]⯠1Wb^ / Mgf_ k [OEW "| nZb1Wb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb_? - / w]y J̿5|-O &]yD$G>usgd\hH[[K9 ӣSeqWR?i]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]| 8 'ȿ *t_*kߙ~q7k$]j,!me6w٦G<T'K] g %__@͊ %_7@Ê r $O uKLU*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*Uث ſ>E .W7#^sܸ= oMG3S>2Hr+ZK-,ZUd!@U*U ?* 2 lUߔ* -?ѿ U "| nZb1Wb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb_#\h^N<ck_;
[gj[/&F|t7@*&q_89 S ϗ rG S~P~ZDo6]c izP~HwImR/e^= Uy /חd֬-Aտ25xk'$ ..F*ɿ)_3ygU/UJ> +#\ u!_sS_ʾY_ʫe}wVFH S ? \+NF'yjr rkn8m+.yh|jZMΓ,z ky%\y#*]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*OϿ7k>k+⽓AHҚug \5GU淾Q<?Ư'ح5/{4<"O+S1ЍJEU5U.*Uث$qtͻF* MuH ?qrY'j&]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]Rx-`dFV9335
qW-O_O3i:/.~ByYǗ"`G.<Ǩs?kUzzG{ y䕇̷qjߘ~mߚ5y\^'qol~żQ'^]| 9'a}?F?~t["\ ЏYbj{ceQ_iڌX^<r#A U=WTӴ=/Rֵt#GTn G%G;*)bO@1V?/)զGa=1 5][%VaFxQ\U;P^^oT%ihrY]?Ak7GUw.4V,YiM֪|/.6:亄Wm7Y[[&tn`tdB'eWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]e< 8ߣO$iw 7obcYwVԎ2k_HHÊ 7?;q鶰[]C7; 5 qt{Q]v*H9"5)T8 (Ab_89JUMG | ե{«5nZHqVW 9aj?1^B+($Mc$.ʤ U ߙnҮ</̯.~Y^Z[ Iwu[Ex4M4f!uȀUT苶X?1oC9u Ө y|jdhSy#c9}s|UBG >r喛?o/2<:/`Y5ֶMx RJ[,qV7/2 '|k>C>O?oo|(4F鐛FyaUim&gz*| 8};ZMSO8j6zy6αA C%A@fnHGUt*y_u<%|MH/-
rDU6hF&kyehuU |O1Oqb$4O.y(֨s%$>|u"X n@UwȒ:3kwZjW&<unV=S]&F80USHW]g~m]k^EߟL4Z_Zp 6T[R;?.6?=//|1ipXy̆}M,6g4`g: $Fw"z42]TN +hjB ?1F J$iTr S }sKS3t7~XXܞh?105RNVO/f 1K]v*UثWb]v*UثWb]v*UثWb3'o<˟*K<2W YՁ UHsК *UثWb]v*UثWb]v*UثV;5>E|<}>SoޤGomBnAEQ_ jr3oxrOrkY/~!]$^_' մK wwio$] UYO/yIQFZ̍q}0٦I?*ϱWb]|~70^c?,?2AOvBi9*Wa|\wyn #P}^98 t>IX kUSC~^e W]Ӵɧ=IP Hv(b{_ߗpޅ -H<[8MQ.E YqqXS vQzo?0h~fү>o/jks5J$H;UkF /崞e?<ayyoK) xmn$7nד \Aߊ 0h7 w%a<z}w10cX%! :SHcg75IuԏYAm4.+k6U[9LfAn @U ~es||κeֱkfu>^ =
}[Ju;KmOQ~,ȓ#sLC?KgE\s%G)yfDYTYvQ,iVkPkAqA#}]KVZ!xWmTv*UثWb]v*UثWb]0WNC?vZ]MNn Q I 5y]@Y*N*j MƛS~C隅wvz!' S 7*h >gx<'%PIs.]hM:nA;T⬓;qc a WЧ,4U *ŏʯ#t?#] B 8 U n dx|o_ /y_ U4 -s/ G7v6仅 ^ZOOhYU~g 2h~R~o &~~by:?(SM{Eŀ\_Qy5c銽+KP U揟/;tkO4[q4 &ҼYͭa
g 9#q1bk 9?-{̚_`a<mlРYf EՕ`>wѿ! +<Nǯhw6o*^4[Ӭ l:JKs}aq<hNoqeE^?ο8`~G{|<:wkwuw]4Z f d% fkWb]v*UثWb]v*U 1ph ?RiraWqq FH I\/a)9]q !c?W GM ?% " ,`oռ!s]v*Uث&}ZWfqz D>'e,gP{bw7z埗1@^tMƁPbg}?U雘 ?#b? 5[[o1>8 MIo=+yh7"NTS'zwZ瓿<ukMNTH.Xe-B4r5
dO.Ryw Z- $?G|zUZjZ+Y9*jslUw%=畼O-^yzXݦ7 OPxgol#f`OBʽ;?>mo<#Fq?>y#Rkk#]yEhL4_2R(_0Կ ?/ͦ+y˾P]&95On)knֶ%z 8?; ,uu.o?+[4y2 @K(>e Nn-:h!x1
q~KOkj@u(Ty6K,RMn}=⒫qV@:2Ź{ F7V_{mSzT7v+I4Oujcu7-ZES_.7=o~P4jEo4ijҋ;FZV1XB~ }o|mY/< .+]C] <^_E qrԮp#_xWb]v*UثWb]v*U 1,j/<K@R7 x q gϺyʶ76B ]H܈H銲/_O UOʽ>P|c 4=.ΩDV-zb1Wb^gk}B^kKu&i>ds\jg$mqdPVXg & U~e sȘ|àjz柨jA 1KqjIČZ3QO U8]v*UثWb]v*UثWbRۭgI4&R52ttm՛ьO@RF* 6?1, +|{)5QV/[]7N˛0z-K7¤⯚uM ?W߬~Ej~wv?|ĕ"JZXd6PWV·-h PΟ ,i^ bw4USثWb]v* 2 9]]^Z?#4k/7y^S2G#ct]Iʣ}[ϾX|Ѣy.o3y,4 H- ;H}G
$hКNjQV"&]?T<fuZO`.Km+x$D[V`IU4KMNMfi>eϚ$- /GrZKAHţ*: GL 3U&F/fծ4J k-RtKeT>ldrVg
A^åjVƗCiykō: g)]U?v*UثWb]v*UثWb]v*UثW~M ͫ n*Uت3pKyx&x0REwVRv ]?{o IǯyO6[Xi:mռ|IK2qN( Jb_$Zy_)HmE?u]1 SBr6vb˿<h?z提 Ώ $NSku6sv fK4Q2Jh5@Pv_ 'g[,[jߝwkk}6eK{@o3#U_Jf~|E5[qKBߗ^s:6WѥԼmk*-KihL2@Nj*O4 Rhyvo͗P~fVf$<k.jӋ"hӬᴲXE˷_FX~tQ~lVh?7w -~Yy7p:v!]SKVfHo$ ֔"]X_Wb]v*UثWb]{ Q'@i 3FWf!kt5=X U[n~vj e5 l療KvRr牏E#܃FS?>f>u ֬9[E[^s-Zod/YY/d{h-tH:8 Wb]v*Uثs/ :~ey|e [ʿ>rn{tR˚ϕJ
4JSn|*/y3:~a 9yNRt 1\KrV/2,_J
FGՄ0dh
/[>VGu+}E/^"{}R-BⴾH7Pk2ȕ#tU:'y?UU-F昼*K[_6][BA͑djBk :ԶO)<֦KѢK4E1}RU +G|j˟zo|<G//˒[N5{ P[7'T|H 1W٘Wb]v*UثWb]v*UثWb]x yο p6~ +P"H"nmEcEP`1W o?0y**h[ _3jV8n49S?* | Oo3~kyqȟPX7yOFā?IXoA̫ټ 8e#>N<Ci>MEh 97VI#u C1V 87/]i~x嘞8y_L+K=hHSՉM0 ʯ}R -~ZGϞUm<qy3AMSHa .-h.- ˺5C*ŏʯ#t?#YG ! #yj F//k tVBMsa;#i4ѡhؕ%A#a~M' *D4Mc=i[qZ4 T/Pիok+o1Iy|k"YVpȶ\(@$ 9 F*?8yi<$ i|Bʚk}#8|?6*o=gu>Zh_IeGj &Q!@<@1Tv "hzh| fLDVZmi} s k!. .\SeWb]v*UثWb]. dU=rCտ1|iaqwOV՚X!-=y$5poB<] ~U
?Þ#۩n{xwzv]:E_^y;q L4;~i~nbh瞃6h8x1pE
]v*UثWb,/}W_V3Z7̚v WYKUR l b<+}o;v\/(y滰HWب f8#lUثWb]v*UثWb]v*UثWb]v*Uث9<7G7cƣo/j5p?ԭ MpR5jQ_ G?KvaOxMZ{h's'@1N* Uث_9~[NΥ7Ae
_X32g-SHE#Ӟ%^w??,yI4֗0i}a{n.,'TC X域.t6yXV '&q$Ez5ۍREU, , 3th|ˤ[}b#ɦkv@ڧT7vđ%=kf>>W]v*UثWb]P ,U 0zo 2}7Pe'Xm-m.CHbg*/) UE}]cTmѤy7P䆯Yy:;-7MmGQy%^AwbkCxm/%qW_r _] :7YMerFi;=X. f# Up 1Ovɮ6O,[yEeɴ> ֳ,ҵۿ9<x|8_/o{?(i~Y n7ַ֛m-RJ:vwO)uE** ?zF_Ky Ț+Io<PKg4Z2#9q8Jϴ?k@˷37|k8]~Y J6Xh/.漤V0:/7?-Ԓ Egm1F]x],=P%IRE*i~grUoOTg74V] CX%j.JX mו/<yy_SA>X8c4x iքT ~%ċ*7v*UثWb]v*So?D?ߙ$T4[ңUB|ثLUثWb]v*UثWb]v*UثWb]v*Uث ?0?'>FO}A \kDc<_.Hmժ}Av*U'43\~r~JI_^muZ~t^)kY'k|^=I(?$,u ̝/GzΝoy;QZ S[ $-f`v<&}Xxzogo5t841Gsg\Cʞkk=_DQHMOR=QԘW
3w懚<='[H<ɫJm?/?5mZo"G@:WIhdf^q«,UثWb]v*Uث>u{l@Etad#ԎNQbx@͵':ߘϦ~TNƓ {5ENs.6wwv^ܗ *_;Cy??ŧg>z6"C]օ-ftѳZFSy|U7t<j~bZys>^5G[I1%]ԯf̗o-lcNL@g3Z^gFӼۥiiX=
,$!4NoƱ<mn슪[Z*yCqg>NA6kZw"<-N.{D ef+M,N>C74_#Vߗum*-y#ζhRv~[[ФK k+X~&I*~tkWW? B?6a/7;F5 ?ʚrD+qN%,\\+U(&y?[JG/Flmk.-O.pGoNɺ)>꾳]v*UثWb]c u 'd7Z X ,m"hdu`phFyߛ5~ZX? @_4t)>-pc LUw<gߗ:ιi-t--o56zZ|emT 1W1WS * *_<_ch @ CԼ9'.u kHˍVntعo*OqsR4ko/^vy`{qj< UثWb]v*UثWb]v*UثW~o~d[VyQ?u|h,ƻJn= Ӻno?oˋ?$ŦZwǘ2ΪPj~`Ԉ{MJBX-П Q*UثWb_8vD?- ugiVe5h.(c ;_[gѤER?O}G_]+m#旔.cR};ۆ*٬W+4cr$YŇǛ /.l.[1k:lŝdA 7| O,y O /̟-NyO,mq֭dJcA%kvZ&KӒʫUzG?ɭ[Ryև¡x|pbǻ⯢P8E0g se A=}UثWbX w/ )|Oo5-ywʶu}F5q JH7ZҴy 9? /Xyyoɤ_\dzg4omzhYR);??% glu)Yj.02ie^w|ᬦID4=W
X_^R̞B.|kP t^AaX\[ i\U;_䟑vx?Sߜ\-`gԱ{j=#Ҿrh9* C ޝ
{gçMYy6MbRGTԬ r̷2 **"yM_-ym
zwGA<Ūv]̍xN-14[J̯BI>`ϗ5,hc+%scxC{5~0?Z e5^jgS^m{_S`F4vUDR|o6?< 9eo5fe xBco//A ֣=иٸdo!WI~^j IX|wC^}"wʩkl[~*UثWb]v*UثWbq 9- X| 9z|DFhv+Cm*j a j UثW QoojLi ''⯫|/eV^WytMJ{{Y."0^k)ꄒ o,U#|ˠMr U$֧&r1, Ή2;WO1Wb |ˠL3VZҾ}~չ6kȹxqJqTv*UثWb]v*Uث(θ/ɻ .REgkz2[zmxY⯩qWb]Km*\ҵ=UZfi5mɓԂ r4*Xv8 V "E exտ/Y䵜 ߛn.4+uxl5xfȊBG*#@>sռ #'+K=SHeA{G$?cX?m<Q٥<l|+IlI;_6i&7Jf#Wi8تP>i ]fЭ5 ; 7}u[$lļzRl] Ju.?-֚G{626(hT%YX*b]v* Ώ[ߕp3 %韫}o&n^:t1xO 12 ]?QXha-zkN`=[>[)')'$¡0^ET_w51&Ʊ/hΗ&.kK{2]ۤ- iU7
V 5kc m?0([Ytt)D:z0yƭ
9z U%:6yYoO5Jh>X[|ĺjCi U8侚[poUW뿓>mka?4J^h\П\5M3Nż<-G[B%!z)
pʺw涩m[kpyy Mg̐jD%<o{Hh4N^*+SRw懞42hz"Ӯ4,h [ė k
oHj ?.tu_O}H7- VowC9H|ukXʽ`[%WG !ݧ? rhXuO>4[\I-F*v*UثWb]v*UثW~7Y垪Sd .m|p[jn)w鵬R*cz//ٌ 5]WW$XE'..u`T1WWb /'W bOOej W^57 7|!~l~X_^M|oGp4ԯ<a.7ZI-d{tBъ UJ4 *y@ L&1/ɚ6Gm (꺦鴈k u[ XDM
|X<UGivgɚOΣ_O/| -]sކ[k}Fa~$ {cWXU;v, j~ZyKm;S@$|"[sm[ZkZ<QmiGD疿~~a~vTqY %?0<{ ˿Ԡњ rnDnef2rXQW^MҼ +b #Ug8<q 9 K{E Q1Y/Mוׂ4$G+{4]_`? u{Tϐ5 ;H+};N"b^,/} Uv*UثWb]|0=(?/4 .,,5r juxTku;^U|/y3zi>[mhzld5$ij*I$'d8Wb]v*U Omw w =Fi// 14.^:ึŬe 8VU^um_?XDwwnc*w}V/C$I=6 ٥e"W˚ %}W zHּ\˨bBDR&S;$6wy`jAvI$9]W ?ZgaIo0x䷹\YZΩ5.
(Y*UثWb]v*Uث އe2yfSjͮeORRDvэgHbA;*X ykgNAkc5f˘Ol`mk$檾 +<#i7311uKP/4w2I4Y*UثWb]v*UثWb_^e *h־g4jy_4XI >71}@45ʨ sۥ(Wd4q o LU4q o LU4q o LUߖ, / #XD~nZƟՎ=-Q=Yum.,P#In/oOo(RyC SԼybk{nX%o! qv}W EgVw ]_l/do"Ib0đȓ$nTjц*1Wb]v*UثWb]v*U:F}qino-ޣ;P$;P*I=*N?!i-yJRi>Ig}"s_ lCL⯤Wb]v*UثWb:极K{]j^Un0MJ s9T3)Y$FhG
;6[oӿ0Z)^xUgn;$EJAcUH |HUϟ)u3~n~Iyo.t|x 8}U"ݪ_4XwO ͍>R>rWUW$Nk jJR/E>8hԱWbXG3?- -mo10| ;d&gE3-9XV^m CO 8 '掕ыqz~ "{rhi 嚧brq"Ks2& ږEˠ! ]튳//x~JW>fiEc]'maOBw?q^| f~[ =jda:&MYїI^H;O-P]q !c?W GM ?% " ,`oռ!s]v*UثWb]v*UثWĿC5}Gk >Zt \)/-+o-W~҆q7Eכ4#z4U<7MIi,S"TWh^]E*wo #C SkV7_y]c9XԱ
Ԛ*#CG_O_1R̚u;/,V8+| 8>^X^t .>e-t{u _--O63 $H #j =b1Wb]v*UثWb]v*U礒~cy |l<̿#n2 ,d"1xc+_Tc5y&_'^ZZǔ?+DZ ~Zҭ xVqi=Ndɺ-٢Z(UQriKH[Լe3T76Fkz[و_Z[vզUhJ2[UDPݶlt c5ɿ Gg!`kG^Ѭ/K d\=ͬ%6Je Z x}E_K]v*UثVw/-\W:`4%7h/4 k{ Ȗ<6^9?6\yG:ֹ)[eM7RX ug-8#U*o |}Z_o^ ^`vviV 2/8Uc7u{ˏ=/ ~s~: d ƃt^XPe:#q!?֖>o]to'6ieX ?P*9< 9 H|ؿ ?rlPjk#h0%jm%2㳮* /$u}wHo|Uzo?IVQ$UġX-(P}z*Uث Dg愔*j]bJO֥A*?*4R^Z լ7 l
Ƕ*?̯wM;y+y2ywPGQj
KYy k M$\U还Z^u_ _y W^oVVjM7Hǝ2m%Dn +qUo,~Kց,^^Ɵml_b [H(WsEQ8}v*UثWb]v*UثWb-5#@5mwUt/32[Jx?4R;,UKNo-NPԿ5! kw,j$9fbzqT7_ V] +cp ._ | UV_7TT* G~SiZ5
$݀.* ˏ_)nO"kNm}\nFbE(ИлS7e 9uKo˛[]f4IyhZ # vPQЧ\U?6? ߕ_YhYqc b[ -1Vwoou?-4<>ɫCnV{T[91(KR4銽v*UثWb]v*Uثϛ'?aŏO-K6Ɓ\:Ы{~`Ga=݆hʉ<7&>WD:jƗ'/0Ů[[j6QV:y: )5}2(a OVX"R>h2d
? I&s4/,ܽEޱy&rOV/:\,ga$0b_75SVsz }WPiw G݂f%W]v*UثWbc~Lu&Du}OI`U::20`*Gǘ ; #s~Xjck12 1#%";qӸ#U< 5>H?;_}8
O1^u n**hG t Jyb ,~ *}*M^Eɩ;pwR։bOVZC/˟? -~ -]韝~L <g~c $>$V)Z C'g&&y+O8['=C [mVT1HRT#b]]v*o} ?yJ~ ҎUe02c⯗{_\\Ǎ74׆xh Z$,>2r\UhT qO|}\Go뿘 `kgT{xaV
8C j6x*?%~Hi?W?2 my1nt+K['veGӬc+5bV#n1ES9pC Ye? 'ԛϚ Ce vn UO .imViO.j^U~qц̎
8
|UثWb]v*UثWb]7+? Zǖ6y~XGm2۸H2H8\~SI8~dcj0)*zu8qѓmN`qͨW Qqq)mc@ vk;Q-gR!]wj*6zWf,jhM^v (PBby 9C$-\|:G4o1>H K'q7);*Ƶ_Ř2lfֿ;<y.il 1hAiF :^o sRKyP3
S? 7w_ _^~Bɾohy7̚c5癢 Kk >(BB s
v*UثWb]v*U?>fM;o+}Su{_&p*k^cK UkĐ8[w U'/Yӯ;P5ޕ 偙"ŕy =Ώy'C ]ן|g
^Hӯ=pYO&J%UP~~շyWP״_8Rk74kK9"$wHZ2( eR 6syGlǫ~E^c[]-G=K~Ekygx!N3 *?: ?FǪy[B_<y[7dC+(5nU.e@Mw_ yFE_O+#Ѵ;yj-Z9.5 H Ȭ. WظWb]v*U>sԟW \+N7<&WK ȿ v#Hr}PGo 8ۧyh1CmJm<ei |SUzb~'/q>oƛPksȦ9 D@ o=ՀsWպnk:}_jVwZng*Ooq 9a2AVRAU;[_ei~`y/Es,$$-*D@!ІqW'㵾 ߙ?鹃Lt
Yf85`~BVO$ Ii䮙MR.f KJi>`еGPn~ Uy ?0sWIo;L0T$ד䗜$o(7*=qgy ONM*OM`yGj>s(#Ͼ`B-uv}* U??шDߒ Zʾa?$W Zhr/9GGmdWxW͞w'<˩nB-mE?~\m q[=L(7ܬƫ?-2| /Y-neӼF/jz>mAsj6ē **CѲ*Wb]v*UثWb]v*UثWb]v*UثWb]v*UثUI 1,,QDUUQRI; *F_o/t$j2V~k5q^XmD{)PpoZY=U^,Y}O&dMyɧPk.6}!XXCq >Y<a(eLEAi o6Թ˒>FG) ^M@*qV9gQ[hWW -|cJquvFuPYmP&Rh1TK7t;}D~l\.X g j!}NURˑ<w
!Ӵ+TaQF4;^SYMtfu[{N,
! # " $ % & ( ' ) + * , . - / 1 0 2 4 3 5 7 6 8 : 9 ; = < > @ ? A C B D F E G I H J L M x O N P S Q R T U V Y W X Z [ \ ^ ] _ a ` b c d f e g i h j l k m o n p q r s t v u w y z | { } ~ Vo"q<ws Y ]v*UثWt֗>VG<܍?Y1<^^}1D XM V\IE^uy)Ni<廿^P 5̄4'gDXVe;*SRW? 5;7~N>inee->$z:K˦JjI};W:_<]#/g[)+$R<RऑȪ૨`F*ʱWb]v*^c}'3R}76oyyFX`A_Ղ6yHCEث<.~^yO@Ot|8t,cA;-$ĻijĜUb]jg<Y? ?*tٵ 5ZmJZ"8Qثrmok#p^%^/6y{~Y=R-k~d GYaTUXF XVXbv*UثWb]v*UثWb]v*UثWb]v*UثWb]_] ,aF~C+<zq?O@~?riW>4zgO:/el<msc)%Ƅ7tQ3u势EqWb]v*UثWb]| |I0i4 !><<zR[uQNI<\PbqWb]m叚|mOo"+t6 ?6,X,w wA$?+~jk~[{{:4gPB4JԒ"u`C<lUz&*UثW~mg~Yhi_<ytoˏ#A"γ̥X 1* (U umK̺y;wk~fyc1 #@Ak? kVʽv*UثWt##tHmE\\hp^zd{e;}j(tavr}Ov*UثWb]v*UثWb]v*UثWb]v*UثWb^ 9/0~Vy;@}7n3[ >w{ⱰgE?CJ{d ="Dk&E#kk UOPUWb]v*UثWb^5 9 Oɟ z0aѼ)$r(˨b>SҼ) BC7F4m>- F1VOv*Uث3?)umG]SVw m ][G f]+^*z7
=kf<,$R+?6̻mSOnIRϿڻGKIeXإŬ㷹M WXo/)suPYa.nn'a4Oq+qF qW~Ry:yf c<$K&h0ipȟ sY/i R$WWb]x/<1i 2oy*L˩Q Mv،U>LE?:i`35C CPK A*qWb]v*UثW 9Aou{f~I뻋BPVxH IНT i?"z f di?"z f di?"z f di?"z f di?"z f di?"z f di?"z f d< l>F$Z_ux$bCt& S81Wb]v*UثWb]|uze7 oͼmޥoqʟz8UثWb]v*UثWb_;x< 8W pڴ` ?)iR.ob *UF*UثWb]gK/;i~qO 1-wX M`%EIbhTUYW'5o$yCo"oyF_s$3ۻմ{|$F *6y˞E7k f{1-`K3UAfbAb* ʾe>]yrGI/C(p e:&,vVWW8Wb]v*Us) j>b'[hyTn(A } v*UثWb] b Ci _+ձT 9OO[z5F^ci } 4TňTF ~]Թ U_~i;>=}.^=;n ]+K=j 8v81Wb]_B `_@b]v*UثWb]C^^ZwzZYX%ԧEJ]݉A$ eԠo[JM#C[V-e?hUF*UثWb]v*UثWbo1TgQcR ym^Dfd$WWb]v*Ug<sߛ|7^r7[O*5 &N)EVW,2ؕqt'鲪}gͿr[E_#^<Vw ,I2 Jvkrd}v*UثWbTn.!Am,ED U MO,15B/pCB1\Z5A{Ž2\O #G3*r ʌί"yzT40>b-Xq -±$ Z1V3 CK#=?CyS"B< OFEZ Ǚk ~-颹x; hWj{rvU (~ ?ʯ& > ,hҤ 9oUlyCr[Q<O&h1[q\j Z {Nt<[hz
]@m_\|yV2|^k`y-ؚԛ;=J~o٠ b M+KF",t1L!:"rX@$UW ? P_d:\:ܟZݛ$W"rG = S ϗ 'k ^oj[.{{{ L旤1wkRP#v*OȽ[_п%|n NR_is
jvModKQPb厁7ZMyKmGV W%qswI%zͿqV'h Ϗ< '<]v*UثWb]v*rj{cG&rm}vjJ=w)T0{k,)I
F* khR+{x"" ⪪ ( UqWb]v*UثWb]|21&<ۭߞ켹 Դ/q:[mqyN;P_Db]v*UثͿ2 5|Yku<& : h4H5耰UL_ϥm~}ed7?VroD*i}by$`W?/33LYu+*ʷ-~_~`骇Pu feżxG eTCUTא 0<uW[6ws;ii/%LW93 h%<ҬqWb]v*U>aʟ b'vm)TgjBPz
튰_.(^>]t _?::.kg7|%" Yj6
~@@d0+ 8Eq 䟔5؛7p)FIu ^y'ɞTDQ| j4Ҭ-¨PiAY>*UثWb]# . +~q>'6<FlC" A?i?"z f di?"z f di?"z f di?"z f di?"z f di?"z f dK@3ROzv>'Mc_[ĕ'5$իBNyA矚u./&Uk[h丶mq> E P܆*v*UثWb]v*Uث0&<ūbm ˱ysK(|楃Rl~ԀSU_H]v*UثWb]v*U03]ԋ2~carVV)U%>*v*UثWb}# շT7h VIca1 v3"uK<y\ s\[뿘)WDz}6 D? M-d*v*_ -Kh~r5y$A i`GԽ?oJG:R$YW^Pi ^tt;2hzͨ_i\6%9,M 9^v*UثWWYho6y:BK[j>m`$ti[銾]v*UثWb]v*UثWb]v*UثWb]v*UتKO0i>RzX~YӮmfCie O<TBqW 7h:UpIo >f-7dƃ᷒+5EFqWb]v*UثWb]v*U' rmUڝٵ[b}$U*UثT̞e-Sk|5Jx-^,ҕUsn:(X.1+r<4-g 2@a[̼YO(jG<oO*y#Ad$vVV,#<M+K+3UeWb_5jC>KA $ռgg毐(ԖC
OO hyT/D-~`y[Bhu,; TdAѕdu U]v* r+QˏھA Ć}v*UثWb]v*UثWb]v*UثWb]v*UثW c}-l{k2D*HVSw0[-|j} v*UثWb]v*UثWb _*[0+.} -nZs UثW~`~w>Uח %1CuIƉ}6cM *?z+ ŧݣ.$<ɇؒo?픔_E]v*Uث37wo̽*d֢o?7(t_6эŤG$] ,f(T dh2:0 PF*v*UV0R4/}Q* wCoBZ*v*UثWb]v*UثWb]v*UثWb]v*UثWb?_ w/SG.<9N~k~c#ZsA =WWb]v*UثWb]v*qE wZ' *6]:qh7JV+O<yClϘ<?u xWwF!Qv!Ti'ST~hHARWM)HEOq{g 忒+#Qh|ܤcYn'i'ݱVqv*UثWbTn--紻;[i<rF#Xz⯔s?fbߑv[?ɍ~^DyoT *^Jj>)MnWb]{ 8pꆼ|ˍuF
]S(Z7S, DGQ&:)o)w#JSjE>(Wb]v*UثWF|߮&u嘬oi}TSK o1W + b WW 8] + 0qWa__& w¿M *_5 U0綍yBM_^KM- \}B14*04* UثWb]v*Uث 4ˏ#`r'4{gS(ZfH;7,@U~Ay/V/?43Q%ǘ0&گzMSU]\%z" bc]v*UثWb]v*UثWE)OϏ-Hތ sӼc]Y˷whN-CSז |Vm+ߙ4y$b |Mo^ATߑZv`ek?6|OFa =IBzsuk((1Wb]v*UثWbXϜ +kJ2Z=X,?WGR# ) ^'W 2y3ˬy[~idCXEAOQJܢy%_Ib]Kr^=OqrM8o5K"S=+QP*o<_̯)Pyz'4N WϜ+8,MqW ЯZiU4PMPc `4=)J4iD
ml6'h/rC&Wп0lчq_HteaF'Nz U翚?ߘ>O7խϘ /M~).[xdMF wp*bN hd˚o垵k%ŧu!
,6}H/'f?*UثWb_?] Gr ~RO'VDtK)[X}~ID0gzmԠހ^D㊻W'/wĿ/?QR~\ο:W̚%י&ͭݮuޢ>Β# j1Wb]| q Ŋ]v*UثWb]o/1N~Kh<E/!^Mxu;Jw)>u'`vتCv*UثWb]v*UثWb^o -sT7~wTǗdlt KLNpF&*K͚/ n [7?*O.y&NPfYjVQ2,v^6U~ciV6fĐXi֑$0AcG q Fb]v*UثWb]v*oO- W u˭Unռ8j:&iSkYX+FGe*[~_bZZh_ߗ~uѭ ;i-zY_zl - C*ʽv* A, hx -MȊ wG!@Wi6?3L}xP'.54+J⩆*U W ;yk_tص-L4{[R*C
ApA_9~CC^Ϯip//4kZo-?z|JM55CKp'G%UU$o;6y;Z[i^ƫcJ#:L9smTp^'U7 (.-MMK}6f~\bMZWrOiӵ=7XP5 mRTT2v=* ˯3| emG:TEa[ .gcQRkVGY<2\i7 >JtGm[ukG ˫ 3EY2?*~L 5/9jy[[+)5M#VW^lYvUF 2\i+E?3iK̞IuKmJН:J<nJHKa U \ w -?4 i?TG_ʚ 4] S -Z=?Шؖ4{*U!S~X GM.Ђ!r#?K !h 8] *M?JFcsy~e=Cʗ_j坩욈Jʁk 'K}v*UثWb]|`G::4 }+~V0xIclen'^ԪWb_1 qo3b -_[.iqqɪ u*Y P<N*ʻ ؋ GK ]{E ᣥ \U =" *WhWwo4tU_:_U] * b/ /m /hWV]2MNxŞ},sۻ5D'}ICο>Qο?g K %T"}h J@Wim"~x!iIe.&^a m}xiPڭ'uq:ו~Q4,y{KOON4h- NUQjw4$>r]5}Z\u,nկ{kb{*GWE.ɥCq5 ~;[!^~tyAvEq/ɘmi|Ѩy;R)Yc52rIB"_& v 9?#Rp_E? 7 WȈ\A ?V u#* Dѿ*<+}ϗC_&soYG5մNĜ [8ySXו 5<7ˑcEg%gkļ-U,}RoxO^=f~YǗ/50k~4}IHj>"_E_` Jl7[KmB/=jVizP[\A9#~|[&FkplKi<kn_[uyximm,vDRYd Uaw~NRaoo jMu[[#(]BqY:yCo!g&'lt=5~|E78 O-Y~F Cv7g/z֡mLWn4>`a*b U蟗_^%##B-B=cPӡ\<bYr*z Uث?7 ,u/6C˿?i kn]P %VE1<qTCLXO1ri |E+_hZͰ Y@j9W&I&7S;| 9>ɱ{5_¤ڞhߨOKP)R|UثWb]\^gyoݥ4W6" `FB qqQQ4z-ymX9y䷊6m̚vqVoʟ(^E[ xio4R\N|U⯓ϙˏο=Nξf<5.Bc$?Y\JbWa__& w¿M *_5 U
j 7 g o1U#G'棣庂n7*ZSO?/AtKt(Ch8 N*3? 埝|y>/2K b҅}z8 EU38"_Xb]v*UثWb>川 0nm[Q>T.o}vEt)S U-9 -?+<i5jJK nWq{<ҟWb]_B `X٦{A&za9-wZ4KTwi\ {
- 7 c pcqV5ay ʺg2|O.yR<cxR+"M$[K L/$ NTb1WbR?.Dnߗu[W@g(svTop Z3Z{[\Zlu=ǖ5XH- h*y%"Ay G?;7?5䙈d.j zcX LXN\v^!7B>EՇ;vi+Wy,wBf'|U]pTNdd m0!Lz UثOk ^t b)4/L~GF ɏ,>kw-o/RXTkuEBƁqW~m?6| _'<ͦi^Vü-Y,/Z `V;SW?J]qagʞN7.| #j[K& %[<7b[}y0?c~UҦ:ߟpu[z9lWiMY6~tT~KϿw? ;?ZAaPqqiʄ5^IMgr3?jW |e12q|rT d{%9&ݿ 4-~`r H ryZwWZW 'NՌvb
|2S~E6Um͖? 埐gֱy/K l4gV&v6> }$1Wk? @ iU,,||B6u{GI[۩JV` QEP y'&6[k 9Gy/ʿ/] ڽw^^AM2ZΊNYUc5_W_L?|6tX1<DO80raeȽE~;g"9yF_,bWDH.O@UdXX4rF::0 ^%O +;r7Fѭʿv,ӭA#Yi77H;}n|UثWb]v*Uثѿ+|yjT;3Jд]CRԵ[,,lH#^N+VePHU._9WA}j>LA~vȲe0`FWW:+~O ]Mr :Io,w7ڬ#a~Lʾ y~=?z7u )h^ ([u2N(G * ^ʷv 0a /MKMӾyVS:A9٤fݿ* 2t!h ^ZYҵ_\-5̶͕wҵDF9 ZCv*UثWb]v*Uث#/2$(]:Wq@ҼMaRuPia%U} v*U >?*<ny<xywz^}*B8Ky$`T
V9 B 8 U n dx+o_ή`?5{G7K6gio^v* />X5:]/RO1 8zݳNC*8#I 5?o0 WE5|U*UثWb?8 O Zs4O,&m-P/v* 9P7Ͼwry~P<,I;aipg$1=_o 9 o--`Zڱ1m:Qd$uFe;1ȐO(;^j/UЁ/եi5)OcڼsFy
be%V?+#++\~g~cb_ |[b" IՄp$@A 2 TR_Ư#Ry y6*_gX_,STt 51z(@Tʿ ^K4ym03mm<~3cHR@~W+ U ?_ _RŽϜn䙤,HDLHqVįo:T<_?ym<0]hp['j 81. A QVE 2~O{W̿Z/i}m⻙]^K%Ġ
ĝP U?>M'#u.W^WO(Mn ٪^~wXo+?0|enQr}2ZmkTL)oo$79Tj U XBƕ>Sov*Cͷ W-_%c^o/ ny{yeOEPk7 %/ϐ2Whg>}|`q5P",B@81Wb]v*UثWb^{q~ky6%0O Z7}*,6ȼӋ+) _qqyR&;y Ov-4++h$3w/^AR4US2 :he VϞ<Gӊ3K'~-I AG̶jtMMƓ ZO^ E/M- 8ʭb?9γmI|g 4En?T|$Rj:t7 F8 }!-<yqn!,VVtr;qA&bhjjJv*UثWb]v*Uث/ʚy-eߖU{JDV5; ?WP~ȢqWb]_~t 6%<kҴ/ZқD#qA*=ë)?]pmT ]{E ᣥ \U =" * 'C/0eh?gqɵbm6Q0@ pWʻ ؋ GK ˼ y~C>AHў/*io=j=H8mm ZT>!<_"kߟP[_L_%y|o0iK&t2k n-R w U^ * b/ /zo .<_8_9A #DAQ/9Ivr; x} v*UثϿ5 g川GOE͞!쯊B*C\*JiF*__<[ sUӖ/5cokvNz;^4'qWb]v*UثWb]v*Uy͞\/57yX<[5ޯݷh;Y 3 1W Mֿ5<q`ӮOV ϡhWRQj ߍk"ߐ@| 8Ms7ꟕZӴj>Id#}tAFlU*UثWb]v*UثWb]v*UثWb]v*Uثϛ y+{\Ǣ/Euw[is-+߄f }ɯ hp]ޝo_T {u1 U옫Wb]I<-# \<v۽[[-< e ^O 8_A癭ͯ|u ^-WR6?܋u5`1W⯜0?%~{MbE 5KCORNO! ﻛ)퓯y\U )MO0jGAjIB'94{Ln]#RQG3V~Nb "yN,'|ܛJ״پfIݕ%R?&Iz*UثWb]mO GOQɿ|o>
]ɷ[ѿ`,UثWb]v*UثWbX _?+49O,:Pe4N<W(!Wycr.ץ.yvt?* $q(ޱ7ZXcBadUu/ Bk ɻu/m&J5
~ueH 1W;ͺ|:yZ5?.O{.PHU`NjdWb]v*UثWb]v*UثWb]v*UثWΟkY/傹~pyCJ~)49 ]֣fs9bWb]v*U_A_87TL zrޜK 䭾b1Wb ^?8]yu !JO0h7jC떱#I8'4/$GzcFִ m`'Eqٔ=v5Lu^}?. =.aӼԈ-YeV f$|RD6*v*UثWb_:CSʖ3j^jXmWyMz0W U F|Šjj g [İኦWb]v*UثV59Cz5Ϙ'ֵ^AcjV4SMqW?7 2x~AyL7-nt.Ӥ5=T|>Cn]b OK<zGbh o=q#WmbM'BSזf Wb]ZA DޛPo# + n:`*:tb g` ߹&]v*UثWb]v*UثWb]v*UثWb]tyrG4{ "kh宓H<)ok/.WxWb]v*y [ ~S %̟5x -O!olF*Wv*Uw7?7'&eX -wX$ <]nݒW7qWyɚ'o3@^iO/̋*fJ 5 7 U sWɞ}e pk-
_KRxY uW]v*Uثm&? 5MmzD x-ϑ|3| ]
i,ebmZU*UثWbZ$(,*1 1Wk Z&>eǚmI;4$4WmoSSQT1??< d OsH r 8ԏR-'C]^gyxUSc0οz,2nt˯/i$,tn]&=G~U wouvdכn˭E# yO@mv*UتtI" ,ƀ$^+m+&/4ukymS>tĖej2RH^DثƼϕn-{?^H9E2^}pD*⪾]v*UثWb]v*UثWb]v*UثWb_=~XWY Ac"Z<k-A E[eFcHZWиWb]| ]nߓ>Ff?0 9-5Fƅ[ w^^Z V8En{U]//~VZV m/D!%x"(NqWb_7ȯo9SEҵo35fk_FC@+{ U:7o| 9 GdyZr]f$k*m $E6# #qUثWb]@zf>jFo%^ė6)Ia#!lqWΐM~aZouղSZj),z*40FR;tQLU槗բ t$M =GNNǡ 麩QgTW 4J- *´_6H d5Z EFw施+xRȿui6B%*V7掳7q͌ SCDŽ7d)j 9AWV),mߚu%xu]X֫OB 8-jik~ijzyVJyh+eH /nt//>W-O-h ]Ѭ%[Eik
(1Tv*UثWb] <Z_'K +[KgiMfks5+T;⯎|/O so)O~`yD^wӣ4i7z-֍qg.kg+w,rI Q~b熕 Y^wq *̾Y }f_3~feb(41V1I_[K{7y6 z6O%ReѮ}'Xȡ0 bZgǙ/=U|w!Sk uCK+W>KI"Uy8 |NU;rP^gm)鷚g<yKϷZfyuvh"xL5* f^Kd| hZ>gR^M6iD<oⲚfS$lmߚJ5_o/ ){2OwZQ{qw:05ā V*̼ 9yg>[w6<IAY~[ve fIeTR ˚|}rwt+KcQP/ZLdf**?3> m}W˚<z^i" \4iӽ1$X qFi"3!Y5^wa?O/JXF,&diiYmֲ9ȴW<<wVz =[k2ٿ7EK_J)]n Xzr)z7y e+ ';~y9;Ii1O3+_1?sAǐ?+|} &h ecv`nfK;%k[K,UAy"z+<s#Clo(Vg<yE#Pݬ fW9*Jߊ2 9'};n[ɾW<Ow4}sS4I;+Ò,f ;;x&Ѯͤ5-.maqw"8 $/* /0wɳr `<V!OQ[KYNpZY]TqǗU{GM ,2U%wٗcbUثWb]{ r{̿2 {ڮFuDI?:
*v*UثV5 7s VR-~Y Xe
;(,B,@^?'?1__ L}+ .("|p+/<lK!܁9.7zHɺE} v*U_>Z<CũDV(k~[V0<QH*H^4i>xO EMB$M >WﴭR-CMl'A$S2DjWRA_<~BjN7㿘տ*"&t||.9v1Ia95%IXqWxWb]v*UثWb]v*UثWb]v*U:G<O3@~hc щ4' $n7WUa^o m_?NiS_-eS#Y4f;Z$,XqT_ 0b֭_ѵ0} m62VЪgqH̗zo˨;y͚>3hokVI,jFMV_3hW7^e _Fַw+R^YfXBsN2E
V?HӦ MX_<0MI=亵֘4+ ZZCZc
eB .-?DsU[˖Y>Xk-*
선ʑ#q@E *I!Ӵ-6WЯ_1y/t7% 2Ǵg(тyyGYԯ,<[kF[Rբ¹";ّUBF/eе.\id:Z<(Cf<~vX
mȏ#[ԵJmjkF-ROu
X$ӯ.miaJHu5? Y/ -t//>|0.%tn3% +5ð&WU
? -ƍ}Wʚ6sii*_ZP̰ *<H(B=<U74 ]gWyKFt /ͩ\X6pˊmɾV 5 {cF4AV4Z"yJD» +X|i|w7Oܵ3i fѓ
D
5~P7S`\p]/_(y9>ϩ,@a\UA'ϛ>kv:'F&oS}uda*Y(dfb{Of{-cX[X^iWBZ XFG Uߨ S,yoEo.Y7U-4}NVwV0A7VI4*UثWb_>+? X1D7d5;j =ñ#|U *UثU{;y6f q,U//䯜4>@/,4Y! ltF z0amU*UثWb_8:H\zD nЭumW[xQzJG5EF*?|Z}?:Kj[-]ia.` m bo4o6h'<ūvI5 F *1Tv*UثWb]v*UثWb]v*UثWb]^_6ysrGJd}K_yFy0_EssP+w H^p~Tb]N z0AVWFS=A+u~fy 7ƯM (ZZXSVYo'kfH\GY~qm?}S?Қ)b--uE'OQmXCB Υ ߒ_4h~USξQ=]UӴRe8ZB*_ߛڿ|u%Ak/\'\XEqnۛ]t[i=P.
lo+h憥eaq四_ѺLI R7kG9AVU}ʚWz~MZh:䫛[fH4 ]-ͽAZ{ >N$4 -ʬWV ֵ7W-[ifGDj 5ğ[B˻ MzW ||g:/um7:i5{ _ɶ ^y5^늻v*UثWb]v*UثV?c=_3kүu\y<^{ 8 /?Fh [CΞU9JرWbX|!C\y͚O!/i5[E *M '|5 9jmf` rž:[ PZ9.c$Km+,N[ѻ[PNa($c*"( @*v*UثW>e/ 3å~t~zӍ\kcҭhO} |a;w_, lnP/Aop#OWثWb]v*UثWb]v*UثWb]v*Uygi^C% )bJ<nPH _"|} /7WVMQ I/S "W_ֱb|>L:-ŵ{o-LZ|c!295s C>m>LдK?-O=ח-l4khH%HQ ʒ 05⫗I[k;5~}aj4a S}oztHJ % i A'Cʚ ڰjZ[m>\"ֵ5OR0) T/{/XyFW Td<&`}ĥCja1#v*jk⬲~abьomFZQp4yVG X֣Ych:˟+XI|yOaEb$HZ(UP 1VWci:d֛iZ_kw6GQCf2(2ȶDqW
8Wb]v*UثWb]xީqrXo櫨ןY.T= :\Ѵ] GM& k)k쏇aM1Wb__'G?O <\%Ƣ>ycRBVƍK!vb@U}[/˭z*~X/P6QJ WgxWb]v*oO/:.y[ >>3If<pЊE e c5/M/_0[jzEf5K42)dpF*b k'˗_u|aNxĚYn-[9 \Y_姟 #ywZ Sktw~ц*αWb]v*UثWb]v*UثWb]v*Uث(:_ 5<` wR^_bMcLk:jp?b]wt >W˾tj~]Nop\92 *1Wb]v*UثWb]v*UثWb]| 9SW |ѴRRiơqWʕ*1W8Wbz˩/3^XӬ uImkQɦV) ReWn^[jVDxlY]Xwc L.|G wI]%K.$"F \(-Z(bv*UثWb]p hC+yzw>䇚oDčpi[]b_G_0[1%r*Kk~w1hiuRk%ȪWbr&w72%Ť[2#); ~D rS1W~D rS1W~D rS1T??, 4i_ ^nI@yMím,+ƿalUF*UثWb]v*UثWb]|o|^T V/|ǘhwW ae~ ;O*jƀ^'4k# 7U /\ yǖ'-vZ7|X/nâyi 1FQ -wkV)Qu zgr ,W|s LU?_a1VgAC3:_m:jZ%Vi֙/w%J֥ԴhhWzf*UثWb]v*UثWb]v*U eCEH){7i z Jw1WXWbo̽V>K'>~5گ7d5M.+I/\Q/"y7FG//i_É&m cPTUثWb]v*U~%Ouh_^SΣB/<a
zƟurad
*Cv*? "MIHt1[7ZNe"ڕ`@8JJ1TO 1 #ʷk?iݬ<L6O?b]_? 1䡴 ث UثW > yw bv*UثWb]v*UثWb] r; 6#*^UǗ9nJ[\I Ėt9B1;SdO T OgW?# S ?Y'?6< % _M?KhV\[3CTdz)܌U 1䡴 ث UثWb]v*UثWb]v*Uث?%]ys 6??/b]+~`<Ⱥ?1 7QO'A-Υmu3N-03^&{^^G,YEaY.89HVy ՝ؖf%IYF*UثWb]v*U톢+C{_F BAI XyQC^jV:Λ]^隵Wuu,)4e`F*]_ !f 86w -vP&ußؚݘW /6 [}v*PO. tU_b]v*UثWb]v*UثW Gչ {B^7 9 !o,yc_ _ tiu85]ap6SB?j ?yS p OGe<_5yWUiYɤkvm[ܤ Cf 4/#O?PWoJlU*UثWb]v*UثWb]v*U h5НE~6" eޔ;1W_9h*s 8Y _,iRДѽ-_̼5eOZ3m}v*UثWb]v*H9"5)T8 (Ab_;2ɥ \i?H~DP$i1H!瞍wgVܷ8Uث ot <_>LNӧ\Z!!f9) ApA^Czm/?~| SD0]gL3&oVnc11W~H~zVrSdC8LMYS#Te!XH U4?=ܳT LU4?=ܳT LU4?=ܳT LU > A _ >o.Sk~eRCerlzݛ248]WWv*UثWb]v*UثWb_:N^CW${ ۉʊyoCvh1T> 9+yGҵGQѵ85'P˄=k,27 t`F* + b WW 8Yg֍yAyF->X<ɧ>qpAZLU 1䡴 ث UثWb]v*UثWb]v*Uث?%]ys 6??/b]x uF 8<"[ ^qie\y~V2hMxǢ@4WWb]v*UثWb_8rvM> "s .kC,nUثW̿ ߖ$4h$~_[ɤ~mX@ Td& ~Ks5B%Gn 2'~Y 7ʫ+KsO FtdvI** - ӊ5W m/lU:?-km< 9/0][OmDy TJk +H翘[m&̚6Y;yf76
UGbyY c?7090iƴ.,Mb)a0/ Hî*v*UثWb]v*UثWb_/]q 9i V _E^ 8 ; 5 mv*
y NywJO
^]v*UثWb]v*UثWb] ?* i{ԭ-4[ռT@04;⯠1Wbܪ2R@)%v9,X⯠Wb]v*UثWb]w *V,?0H^V jk {T.qU}kso{moyg:\]ēZHPbA*UJx kkR+yT::8hA_>B fX~Z~hZ=6VK{o51ϝ6hOŊp< w yS =*˿, Z,5.i_/ݼBVjyt⩵,{;VSӯto+ZyetwR[&MQ텝0\ \EH,)JU?ȯG?.?j ,U*UثWb]v*UثWb]uPտ&.A6ߙ4<%ކC}qLh/掝hvpiNȎ kh(b@m∠ U2 W
O7] * 3Wo]VFk 1\tZ}P: 3 U6UMԆ mr
K(~g~^~[;t:^yVoAT0tr t
9^i_^ * i5ס *\^~tiZR^YC ? tա<} [ԭ.5 (neXVG iԞ<]v*UثWb]v*UثWb?* m)ҫx ԇ=1WxWuOʩ\WL&9y[xDH:]v*UثWb]v*ltK{YG$)T2^ 8=݇ne $ By۔ZGF,DS;Ov*U~J bi}5-|NZ$oiooM[Ȟh)b$r.de-Hl{R eO8y rzZ P f#M֙Zxd9DJ
,!TIݨ?3<<VF|^d/- T5 N*q7>S yJgI@e?qS}kGO-پ//8h:|`/?,-aP:I)^)j Zm, 57?(~`Y;C'4$=,LФ:iLF(qTs^mO8 6ZnKe \[z~ymY"z5$ 8TUW ٪ zR0 ?3_?/ HzM_ }^>xA?J O? ~~av*UثWb]v* ;R?ڷ?hX UثWbR ]OF/tMV 1dմۙ g$:KfF :CR?"zΩ{귾BI/mJk˻VI'wWnX'f_ #_gVث1Wb]v*UثWb]v*Uثƿ#4y< 8Dˮ~^yO!Ub:MBjNث1c˞aPk]P).I#t'OWϟT(?7@)VЪv }bWb]v*UثWb]|h VK(U K[=i шqW8Wb]a>ak/3I-=Y5m*'&|U)?%'<xM:ptiEzf*UثWb]v*UثWb]| ) wm[G,U*UثWtoI|U ?ߗ zH/Y U* UثWb]v*UثWb]v*F cǘ(״8b ^-G*=lO\zcQ6%0
r4x.jWb(ɱ N/mz* -ո=O3Tw*]v*UثWb]v*UﯠyCڱ+̖ w$o {˶rUUb:H$l GSPAGZ⯚toο6zɿ2^</-Z3yOk|^h3 y#.9PMxN?b~{:ݥ ,U?= ? *XwiK| ?6ow 8mSg̚ˬ]_\Z6ewy0" }MHA銿NWb]v*UثWb]v*UثWb ## ͫs ]v*Ȓg *]4 K[%]VV[Bʉ:`%?/g_R}.mn;cfO+JkcS#\USq $wk , *{*UثWb]v*UثWb]v*q凖W~ve> h-? &k5>6:_G⯟< 93ۧ(A #2XOE;E уbWb 1_"s62 忘(twumWaI.L+&⬳o'_Wo'_Wo'_W V'O@u#گPt7W9Nw>U ~A\\^~DJ%א5^I$}2ݙݘʼn'*UثWbPz^֩}jYjv2[!Xڔٕ8uBZyQ,?9˳K[[j툷ӱU?. Wc F qW] I * ѵU_b]v*UثWb]v*UثWb_?GG 9 V J? 7Oͫ+[Su[[u{gM_Gh/`V-v;P_v*U JO
^͟WaW = [b`]v*UثWb]v*UثWb_0q;U`CrhWr%JupdQHF72`bA ) *7 Uot 愼e@hj~+p<UثK Zɿ u}v*h?*?_oWH/Y U* UثWb]pO# Li?^XͺZӊs] a>iZ@ U0 i u?ڿ 0Ê v*U O W bv*UثWb]v*UثWb]v* ;R?ڷ?hX}Oi/:-x :;k]9Z ۪,UثW _(m?+%z*6+oe^ ɇ^ 8 ; 5 mv*UثWb]v*UثWb]A:}iZii1Oc)cpUՊ"m^!ˢCS'kS}Oqs0"_+E"@ʽ_O2+f{KmӸxnm1tuVU? ֹk[aʉVwh$eS?6qGյ(/AS,W?Uy*v*U ?~T_bǯ_ tlUWb]| 9"˚8rH: :ɣv)}-C ^Q <~H"jG뿞<=66$ fNYZ]XϣzC~8$ [ *] C;?T ϑ늻wo #| 8 8So + PyrkP~UtcMv[a5b~bƂ{Okk /-^^ʐYY,R8e,@
UثWb]v*UثWb]v*U\?ߝZG.{}'A#b>]=/-<ץ"d?ԎDT⩯sr /'W{Ms U_5nW?) ~U?] U \ aD ~X;?-n"5MIf oWW.5)Դ~wx,qP$ԕY& OOӧH5 ymx -u%BT=Z 1W^yS# _4/Ҟֱzޗ98sqԭ*zv*UثWb]v*UثWb]v*<)cgTw;o0wW@};20xԇXX^7ѕ8o>yqt:5+8QF< $&[19qF8u$UOXd䪳/#\y_ m&ȞeY]'[\hŽk y-ZU't*O ?F|tt<S
{KBAVLsFVX^5 9=]Y<y~c/?BԵB [+JeV j=+Cy
ߜE w
ߜE w
ߜE fE5MW>W5-j MROKfK)
+7c3 1W1VY-kKOlr]CZ5;]5{A.]3||_-qWb]v*O|W^ 'YnZ^O Y U]6= O<hȾD(MsPԯ-.lmR\]S*$**{ a'AX XĿ >k
jS(j^lgatMG_/,"Үl-o 2R@ GW*<(~tӡ#Niqj>Zv뺍l(}/SkƼbgv2:b?+|'yIkk=uth֊JPt_eb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]h pA_?5~]C]>A&5Ns\fҮ$b*^k7bk檼o_6 _ƉyEte|啴z*uu{9 &6G"p 88M[ˊ]cUm:4zrrgnOUeX? / %m:5Xdk>c Ϋeiw v&4=T R}-/ȱT :6l24o1]UԿ2ۻ;y8=ك"ʩm :'bqySPkfQGƠ߸u/ -}D0]1LFy~-@Z+U 1䡴 ث UثWb]O?1 7"<m-HwJGL*<.4 o'_Wo'_W&~FU^(yJ-''OJlySs4VI-bf(^iè+oʾdt_1j_e%anҴvyDr4~+4^}+? TРkm Nl4v^}w%@*MO|U*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]U CO'ot| 艩Ǧ5+B!fݭ0Hpj$b%/ M e;W.Pn:B1Vr qe$gÜ$^夲ߊ튬s#凓|n58k_~YXI%ε_g
&ƮPﱢTsi_?"4_͍FdFRZ4;A4#YA?'/)&;zF +2)"$ U'&|cW^u/ j}F5CV֒?3]2K}$KXdh,}4pvT*_ O 㡊S /_ bw } o|'[ My_>(zG)WWb 8kew?7yF ,2<\k:?2\-Xkvх SEr'( *ƭ 8[p;>Ya9s~`:N]XuhKJWvlciG"'˟W 8JڹX;mQK 4j/8/-qX 5 CЭ E.|#&* \PB 8 86ubI~(N@4iȪ lԴVKLԿ+)= YA$S[Ժߧ*2YbСVT> 8+G/3yEoO8E攱{9|w%լM Ǭ^y@Z P Uߊm+Sҿo+IH~Cz7Vm %y#PЀqWSпUϖ~e}w}R? \+NF'dWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]S" I9C#
24 % !_6y{N%LnrB ` Asϓ\C\~R{ 0%wkc]>b> }inX6m:d mvQ$DR(*"( P ;v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*U _ 1 0 8 4 4 3 9 0 3 6 F O tO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 6 _ 1 0 8 4 4 3 9 0 3 7 F tO tO O l e
C o m p O b j f
F Microsoft Equation 3.0 DS Equation Equation.3 9q f
1 n i
x i i = 1 n i " O b j I n f o E q u a t i o n N a t i v e _ 1 0 8 4 4 3 9 0 3 8 F tO tO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 8 4 4 3 9 0 3 9 % F tO tO
F Microsoft Equation 3.0 DS Equation Equation.3 9q
x " i
x "C i "
2 i = 1 k "
F Microsoft Equation 3.0 DS Eq O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 6 uation Equation.3 9q o
1
F Microsoft Equation 3.0 DS Equation Equation.3 9q H 4
2 _ 1 0 8 4 4 3 9 0 4 0 " F tO tO O l e
C o m p O b j ! # f O b j I n f o $ E q u a t i o n N a t i v e 6 _ 1 0 8 4 4 3 9 0 4 1 * ' F tO tO O l e
C o m p O b j & ( ! f
F Microsoft Equation 3.0 DS Equation Equation.3 9q o
k
F Microsoft Equation 3.0 DS Equation Equation.3 9q O b j I n f o ) # E q u a t i o n N a t i v e $ 6 _ 1 0 8 4 4 3 9 0 4 3 , F tO tO O l e
% C o m p O b j + - & f O b j I n f o . ( E q u a t i o n N a t i v e ) 6 _ 1 0 8 4 4 3 9 0 4 4 C 1 F tO tO s
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q s
i O l e
* C o m p O b j 0 2 + f O b j I n f o 3 - E q u a t i o n N a t i v e . 6 _ 1 0 8 4 4 3 9 0 4 5 6 F tO tO O l e
/ C o m p O b j 5 7 0 f O b j I n f o 8 2
F Microsoft Equation 3.0 DS Equation Equation.3 9q s
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q E q u a t i o n N a t i v e 3 6 _ 1 0 8 4 4 3 9 0 4 6 4 > ; F tO tO O l e
4 C o m p O b j : < 5 f O b j I n f o = 7 E q u a t i o n N a t i v e 8 6 _ 1 0 8 4 4 3 9 0 4 7 @ F tO tO O l e
9 o
1
F Microsoft Equation 3.0 DS Equation Equation.3 9q H 4
2
F Microsoft Equation 3.0 DS Eq C o m p O b j ? A : f O b j I n f o B < E q u a t i o n N a t i v e = 6 _ 1 0 8 4 4 3 9 0 4 8 9 M E F tO tO O l e
> C o m p O b j D F ? f O b j I n f o G A E q u a t i o n N a t i v e B 6 uation Equation.3 9q o
k
F Microsoft Equation 3.0 DS Equation Equation.3 9q 7 <
d" _ 1 0 8 4 4 3 9 0 4 9 J F tO tO O l e
C C o m p O b j I K D f O b j I n f o L F E q u a t i o n N a t i v e G ) _ 1 0 8 4 4 3 9 0 5 0 H R O F tO tO O l e
H C o m p O b j N P I f
F Microsoft Equation 3.0 DS Equation Equation.3 9q P7
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q O b j I n f o Q K E q u a t i o n N a t i v e L 6 _ 1 0 8 4 4 3 9 0 5 1 T F tO tO O l e
M C o m p O b j S U N f O b j I n f o V P E q u a t i o n N a t i v e Q 6 _ 1 0 8 4 4 3 9 0 5 2 / Y F tO tO o
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q |q
i O l e
R C o m p O b j X Z S f O b j I n f o [ U E q u a t i o n N a t i v e V 6 _ 1 0 8 4 4 3 9 0 5 3 ^ F tO tO O l e
W C o m p O b j ] _ X f O b j I n f o ` Z
F Microsoft Equation 3.0 DS Equation Equation.3 9q B
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q E q u a t i o n N a t i v e [ 6 _ 1 0 8 4 4 3 9 0 5 4 \ f c F tO tO O l e
\ C o m p O b j b d ] f O b j I n f o e _ E q u a t i o n N a t i v e ` 6 _ 1 0 8 4 4 3 9 0 5 5 h F tO tO O l e
a P7
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q (9 <
i
F Microsoft Equation 3.0 DS Eq C o m p O b j g i b f O b j I n f o j d E q u a t i o n N a t i v e e 6 _ 1 0 8 4 4 3 9 0 5 6 a u m F tO tO O l e
f C o m p O b j l n g f O b j I n f o o i E q u a t i o n N a t i v e j 6 uation Equation.3 9q (9 <
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q P7
i _ 1 0 8 4 4 3 9 0 5 7 r F tO tO O l e
k C o m p O b j q s l f O b j I n f o t n E q u a t i o n N a t i v e o 6 _ 1 0 8 4 4 3 9 0 5 8 p z w F tO tO O l e
p C o m p O b j v x q f
F Microsoft Equation 3.0 DS Equation Equation.3 9q (9 <
F Microsoft Equation 3.0 DS Equation Equation.3 9q O b j I n f o y s E q u a t i o n N a t i v e t ) _ 1 0 8 4 4 3 9 0 5 9 | F tOO O l e
u C o m p O b j { } v f O b j I n f o ~ x E q u a t i o n N a t i v e y _ 1 0 8 4 4 3 9 0 6 0 k F OO ҄ I I
p ( x | i
, i
) P ( i
) i = 1 k "
F Microsoft Equation 3.0 DS Equation Equation.3 9q O l e
| C o m p O b j } f O b j I n f o E q u a t i o n N a t i v e ) (9 <
F Microsoft Equation 3.0 DS Equation Equation.3 9q (9 <
1
F Microsoft Equation 3.0 DS Eq_ 1 0 8 4 4 3 9 0 6 1 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 6 _ 1 0 8 4 4 3 9 0 6 2 F OO O l e
C o m p O b j f uation Equation.3 9q B
2
F Microsoft Equation 3.0 DS Equation Equation.3 9q h \k
k O b j I n f o E q u a t i o n N a t i v e 6 _ 1 0 8 4 4 3 9 0 6 3 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 6 _ 1 0 8 4 4 3 9 0 6 5 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 6
F Microsoft Equation 3.0 DS Equation Equation.3 9q P7
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q _ 1 0 8 4 4 3 9 0 6 6 F OO O l e
C o m p O b j f O b j I n f o (9 <
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q o
i E q u a t i o n N a t i v e 6 _ 1 0 8 4 4 3 9 0 6 7 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 6 _ 1 0 8 7 3 1 8 5 8 1 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e l _ 1 0 8 4 4 3 9 0 6 9 F OO
F Microsoft Equation 3.0 DS Equation Equation.3 9q P
P ( i
) = 1 i = 1 k "
F Microsoft Equation 3.0 DS Equation Equation.3 9q O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e ) (9 <
F Microsoft Equation 3.0 DS Equation Equation.3 9q (9 <
_ 1 0 8 4 4 3 9 0 7 0 W F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e ) _ 1 0 8 4 4 3 9 0 7 1 F OO O l e
C o m p O b j f
F Microsoft Equation 3.0 DS Equation Equation.3 9q (
F C K
F Microsoft Equation 3.0 DS Equation Equation.3 9q O b j I n f o E q u a t i o n N a t i v e D _ 1 0 8 7 3 1 8 6 3 2 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 8 4 4 3 9 0 7 3 F OO 2
1 "( 1 / ) x " k
2
k "( 1 / ) x " i
2 i "
F Microsoft Equation 3.0 DS Equation Equation.3 9q O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e \ @ yI I
x " k
2
F Microsoft Equation 3.0 DS Equation Equation.3 9q @I nI
k _ 1 0 8 4 4 3 9 0 7 4 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 8 _ 1 0 8 4 4 3 9 0 7 5 ~ F OO O l e
C o m p O b j f
F Microsoft Equation 3.0 DS Equation Equation.3 9q mI yI
F Microsoft Equation 3.0 DS Equation Equation.3 9q O b j I n f o E q u a t i o n N a t i v e , _ 1 0 8 9 3 5 9 8 8 0 = F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 6 _ 1 0 8 9 3 5 9 9 1 2 F O`O n
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q n
i O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 6 _ 1 0 8 9 3 1 1 1 7 8 F OO O l e
C o m p O b j f O b j I n f o
F Microsoft Equation 3.0 DS Equation Equation.3 9q n/ h 1
p ( x | i
)
F Microsoft Equation 3.0 DS Equation Equation.3 9q E q u a t i o n N a t i v e K _ 1 0 8 9 3 1 1 1 3 0 8 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 8 9 3 6 0 1 7 0 ) F `O`O O l e
n x
p ( i
| x ) = p ( x | i
) p ( i
) p ( x )
F Microsoft Equation 3.0 DS Equation Equation.3 9q C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 8 5 0 2 7 9 8 3 e F `O`O n 0
g i
= m a x j
{ g i
} , j = 1 , . . . , m .
F Microsoft Equation 3.0 DS Equation Equation.3 9q
g i
( x ) O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e = ( "1 / 2 ) ( x " i
) t
" "1
( x " i
) "( 1 / 2 ) l n | "| + l n p ( i
)
F Microsoft Equation 3.0 DS Equation Equation.3 9q _ 1 0 8 5 0 2 7 9 8 2 F `O`O O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e , _ 1 0 9 0 5 7 5 6 1 7 V F `O`O O l e
C o m p O b j f mI yI
"
F Microsoft Equation 3.0 DS Equation Equation.3 9q z (
i ( N ) = " P ( j
) j "
l o g 2
P ( j
) O b j I n f o E q u a t i o n N a t i v e _ 1 0 9 0 5 7 5 9 6 1 F `O`O O l e
" % ( + 0 5 8 9 : = @ C H M R W \ a f k p u x y z }
F Microsoft Equation 3.0 DS Equation Equation.3 9q z' 0
P ( j
)
F Microsoft Equation 3.0 DS Eq C o m p O b j f O b j I n f o E q u a t i o n N a t i v e C _ 1 0 9 0 5 7 6 1 6 7 F `O`O O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e
6 uation Equation.3 9q z
j
F Microsoft Equation 3.0 DS Equation Equation.3 9q z `
i ( N ) = i _ 1 0 9 0 5 8 2 6 7 3 F `O`O O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 9 0 5 8 2 7 1 9 F `O`O O l e
C o m p O b j f ( N ) "P L
i ( N L
) "( 1 "P L
) i ( N R
)
F Microsoft Equation 3.0 DS Equation Equation.3 9q z hx o
N L / ; < O b j I n f o E q u a t i o n N a t i v e 6 _ 1 0 9 0 5 8 2 7 6 4 F `O`O O l e
F Microsoft Equation 3.0 DS Equation Equation.3 9q z
N R
F Microsoft Equation 3.0 DS Equation Equation.3 9q C o m p O b j f O b j I n f o
E q u a t i o n N a t i v e 6 _ 1 0 9 0 5 8 2 8 9 5 F `O`O O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e ! C z'
i ( N L
)
F Microsoft Equation 3.0 DS Equation Equation.3 9q z' U O
i ( N R
)_ 1 0 9 0 5 8 2 9 4 0 F `O`O O l e
# C o m p O b j $ f O b j I n f o & E q u a t i o n N a t i v e ' C _ 1 0 9 0 5 8 3 1 5 1 F `O`O O l e
) C o m p O b j * f
F Microsoft Equation 3.0 DS Equation Equation.3 9q z
P L
F Microsoft Equation 3.0 DS Eq O b j I n f o , E q u a t i o n N a t i v e - 6 _ 1 0 9 0 5 8 3 9 8 5 F `O`O O l e
. C o m p O b j / f O b j I n f o 1 E q u a t i o n N a t i v e 2 - _ 1 0 9 0 5 8 6 6 2 3 ! F `O`O uation Equation.3 9q z
i
F Microsoft Equation 3.0 DS Equation Equation.3 9q z
i ( N ) = P O l e
3 C o m p O b j " 4 f O b j I n f o # 6 E q u a t i o n N a t i v e 7 ( j
) j `"i "
P ( i
) = 1 " P 2 j "
( j
)
F Microsoft Equation 3.0 DS Equation Equation.3 9q O DJ
f ( x ) = 1 _ 1 0 8 5 0 2 7 9 7 4 t & F `O`O O l e
; C o m p O b j % ' < f O b j I n f o ( > E q u a t i o n N a t i v e ? k _ 1 0 8 9 4 0 6 2 1 5 + F `O`O O l e
A C o m p O b j * , B f 1 + e ( "x )
F Microsoft Equation 3.0 DS Equation Equation.3 9q n
x 1 i
F Microsoft Equation 3.0 DS Eq O b j I n f o - D E q u a t i o n N a t i v e E ; _ 1 0 8 4 4 3 9 0 8 0 0 F O O O l e
F W y 9mE<WR.syh0uM.i^Fc]ŹFX MSz\/!y~g?VN-VDRj#Dάꊳ_ew ǾZ/̟]s\mNz$66pvRby?ߜ|ҿ-&hn?,`י.tO-+kRmBK;wkk36+ޫHYH.ʽsʟy=_'|k_۰?4;tAfT Y^ z(X3Z&}_Igmz,)ӹYa)]$Ҍ3^v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*/GV$? +'9gǏO=Oz_Y_N
oC5 +9oV^u~?{a[ _'W_Mv*/SLUZG C~ ??}kЮއ.U=Wkw%O}eԽz=z~=O^~f C5 ) Q S~=n=V;WTJTI L~~ __x^]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثWb]v*UثW D d
h B
S A ? 2 m _`/bBl/ I ``!A _`/bBl/ @ x | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗ/djg`b`* F\:3+!5C2 d^penR~\ /͂#c 60:0ͭ䂺 CFD@a
{aĤ\Y\PnϜ2(c -GK D d
B
S A ? 2 5< Bĵ ``! 5< Bĵ 8 xOP]˯B"
1&A:0$bRĂ (8D\]G]X EzG+kޏ}A@0z8pЉ11m[D3lTeЙvDo JyK0O Vk"+T A}C-i&qbVD1+~և\bPGԭ6yШ <o6j<0Zs+E K^rxCzN|]q(拖YI.r*)Lp}zqI路Rd cn )h\k ur>Mu>moO .Ar,Zq~ȮĽa+Jմ l qg Ǭb D d
N
s * A ? ? 2 .\NnQ q ``! .\NnQX xSKA~v EDZÃQv( \Pz $7ms] .{:u06ofB$w}}f 8 DȘ:#y6r#n]:<yGa
*U Q,| V0c䈜BH0= uЫɈr_'6V
FҶyP,φHլ jXP: H?i<2'c(:_y(/NS:vν)O}H T:e\E?euΛɛRt;gEwf߂/ wq[rqX3 q'륯wN־~pqM!6p"~~Q=oi
BVi8K93p)iz{C|fuIQa+ D d
T B
S A ? 2 r π 4qI N ``!F π 4qI K xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0d @Hfnj_jBP~nb 횷ߗq]a`b` F\E.7f P d^penR~ %Y0@|y`F\Ps}v&"0=0adbR
,.Ie(_Œvd>PƆ b#383 FF D d
,T B
S A ? 2 p zڮ L & ``!D zڮ K xcdd``> @c112BYL%bL0Yn &B@?6 5 bn ĒʂT +~3);a&br<5o//gZT T 0j]nT 1 <(ȼܤ Z痀f1ps @VrA]!ؙPÄI)$5%G\v.Fl@9v] I D d
,h B
S A ? 2 m %S9vsn^ I , ``!A %S9vsn^꾤 @ | xcdd``> @c112BYL%bL0Yn &B@?6 5=bn ĒʂT+~3);a~&br<5o//?4NHq=`̮<&yI9 Y.͂#c 60:0ͭ䂺 CFD@a
{aĤ\Y\P9Md>PƆ b#32 H[ D d
h B
S A ? 2 n s` J / ``!B s` @ x | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗ/djg`b`* F\:3+!5C2 d^penR~\ /͂#c 60:0ͭ䂺 CFD@a
{aĤ\Y\PnϜ2(c .G D d
h B
S A ? 2 n s` J 3 ``!B s` @ x | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗ/djg`b`* F\:3+!5C2 d^penR~\ /͂#c 60:0ͭ䂺 CFD@a
{aĤ\Y\PnϜ2(c .G D d
h B
S A ?
2 n s` J 7 ``!B s` @ x | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗ/djg`b`* F\:3+!5C2 d^penR~\ /͂#c 60:0ͭ䂺 CFD@a
{aĤ\Y\PnϜ2(c .G D d
T B
S A
? 2 o epQF* K ; ``!C epQF* K xMOJC1ƿV_Z w-x /*<B @ o#t-\tVĭ/ 4 &K)PydQ1QPZڴ: Oˉg<uQ5lú14z,eI z8/6Noά7{wFQvPn6(^.>iVDZ-6Yʟȓ]vgk`7Q<Wȯ_KnIp\mTp }ģb8(fs^ 9~Mw* Ed D d
,T B
S A ? 2 o %=1<_ ܅I K @ ``!C %=1<_ ܅I K xcdd``> @c112BYL%bL0Yn &B@?6 5 bn ĒʂT +~3);a&br<5o//T T 0j]nT 1 <(ȼܤ E.K@` ss+LE@a
{aĤ\Y\P%͕d>PƆ b#32 $"F D d
,h B
S A ? 2 o E9]= K E ``!C E9]= @ | xcdd``> @c112BYL%bL0Yn &B@?6 5=bn ĒʂT+~3);a~&br<5o//?T T 0jufWBkd ȼܤ b痀f1ps @VrA]!#t]"0=0adbR
,.Ie(ő2(c ~I D d
B
S A ? 2 : OİT?z%2 J ``! OİT?z%2L @ h R xMPAQ ZA xBB"71MGbv=M~i}=`O1Q=ϳFVk7(+M Oǐ(覙 9ɠ'vo>S(Y/^lÌL '#` 1AX 0-j*v XUBvϑV;1;l/֤{ R >3 D d
h B
S A ? 2 o "d]t9M(8E K ``!C "d]t9M(8E @ | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗsrE00p1 s i#`!2/27)?;%Y0@|y`Fy\Ps}6 b(L`8L+KRs]XA`l!v1203 3 ;GU D d
h B
S A ? 2 m n`9P'@ I ! ``!A n`9P'@彤 @ | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗ_de`b`* F\:3+!5C2 d^penR~5f.K@` s+l:.P p021)W2 53t1g0Cbd`f@ TGq D d
h B
S A ? 2 m ?EyFb I "# ``!A ?EyFb @ | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗwsq``b`* F\-:3+!5C2 d^penR~PK@` s+l:.P p021)W2gO[2(c .I7 D d
h B
S A ? 2 o "d]t9M(8E K %% ``!C "d]t9M(8E @ | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗsrE00p1 s i#`!2/27)?;%Y0@|y`Fy\Ps}6 b(L`8L+KRs]XA`l!v1203 3 ;GU D d
h B
S A ? 2 n -RR#SSL * J *' ``!B -RR#SSL * @ | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗwsq``b`* F\-:3+!5C2 d^penR~PK@` s+l:.P p021)W2gO[2(c /bIz D d
h B
S A ? 2 o G}CZ&eZ K .) ``!C G}CZ&eZ @ R | xcdd``> @c112BYL%bL0Yn &F! KA?H Z
@MO7$# !L
a A $37X/\!(?71v %9Xt9Ư !2/27)?"+%Y0@|y`FQ\Ps}6 b(L`8L+KRsﰧdgbaǹ H D d
h B
S A ? 2 m ?S߯]=+ I 3+ ``!A ?S߯]=+ @ R | xcdd``> @c112BYL%bL0Yn &F! KA?H Z
@MO7$# !L
a A $37X/\!(?71v 1s20p1 s i#_@:3+!5C2 d^penR~ +K@``s +l:.P p021)W2se3t1g0Cbd`f@ 'G D d
h B
S A ? 2 o "d]t9M(8E K 6- ``!C "d]t9M(8E @ | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗsrE00p1 s i#`!2/27)?;%Y0@|y`Fy\Ps}6 b(L`8L+KRs]XA`l!v1203 3 ;GU D d
B
S A ? 2 < O=v WPyU ;/ ``! O=v WPyUL ` ! xcdd``^ @c112BYL%bL0Yn B@?6 uZ7$# La A 27)?8 ǥ巁BrZHq%02VX F;D@~=`321)Wd.b# af@ 732p D d
B
S A ? 2 nƳ!Yzx 1 ``! nƳ!Yzx @ | xJ@ggӴIFAOAPP/
gEl_BjC A^< zPГoիqwv4Fdfl8 Lrb8(gE&IBH^]đuMق=Hbv-Fpk<جwjV0o'#p& j/m +"ZU@.yҺ Y +^F-5 l`.FT&3e7</.yV u' 6|% wkez<%rWx-SֳIђ+yI!vk%Õ)Ym5 % ϩ>.tj\!OtKCڝ0R+sO D d
B
S A ? 2 < O=v WPyU }3 ``! O=v WPyUL ` ! xcdd``^ @c112BYL%bL0Yn B@?6 uZ7$# La A 27)?8 ǥ巁BrZHq%02VX F;D@~=`321)Wd.b# af@ 732 D d
T B
S A ? 2 o ħAˀ K O5 ``!C ħAˀڤ R K xcdd``> @c112BYL%bL0Yn &F! KA?H Z
@MG7$# ! L
a A $37X/\!(?71v ˝f10p1 Us i#@Z+!5e\p,y ><F0nn(J.>;vP p021)W2si3t1g0Cbd`f@ G D d
T B
S A ? 2 q cNT %We\ M T7 ``!E cNT %We\ K xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0d @Hfnj_jBP~nb 횷ߗ+qrd`b` F\3UBk3(2/27)?aW%Y0@|y`FQ\Ps}v&"0=0adbR
,.Ie(J{2(c H D d
h B
S A ? 2 o Fw:r9"!@ K [9 ``!C Fw:r9"!@ @ | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗrY20p1 s i#@:+!5C2 d^penR~Я\[/͂#c 60-䂺 CFD@a
{aĤ\Y\Pvd>PƆ b#383 SH! D d
h B
S A ? 2 n -RR#SSL * J `; ``!B -RR#SSL * @ | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗwsq``b`* F\-:3+!5C2 d^penR~PK@` s+l:.P p021)W2gO[2(c /bIz D d
h B
S A ? 2 m ޭio5 I d= ``!A ޭio5 @ R | xcdd``> @c112BYL%bL0Yn &F! KA?H Z
@MO7$# !L
a A $37X/\!(?71v %9Xt9Ư !2/27)?"+%Y0@|y`FQ\Ps}6 b(L`8L+KRsﰧdgbaǹ HV D d
h B
S A ? 2 m n`9P'@ I g? ``!A n`9P'@彤 @ | xcdd``> @c112BYL%bL0Yn &B@?6 17TobIFHeA*C 0l? @Hfnj_jBP~nb 횷ߗ_de`b`* F\:3+!5C2 d^penR~5f.K@` s+l:.P p021)W2 53t1g0Cbd`f@ TGqj D d
P
!
S A ? " 2 {מ{8p__ jA ``! {מ{8p__ @ 4 h xcdd`` @c112BYL%bpu<L,,56~) @ k#/`g` UXRY loN` A 27)?(pˊ7k30 I] Hqd2g < y?b7L0\[;!2Bm`Ɗl.½ Q!
~
Ay Ĺ){+a/3LSY |tf/@*?ylv!Ƚ,sa|!p|hl pN"\1]=<Ĥ\Y\ t0 .?2~ D d
B
"
S A ? ! 2 < O=v WPyU C ``! O=v WPyUL ` ! xcdd``^ @c112BYL%bL0Yn B@?6 uZ7$# La A 27)?8 ǥ巁BrZHq%02VX F;D@~=`321)Wd.b# af@ 732 D d
B
#
S A ? " 2 < O=v WPyU E ``! O=v WPyUL ` ! xcdd``^ @c112BYL%bL0Yn B@?6 uZ7$# La A 27)?8 ǥ巁BrZHq%02VX F;D@~=`321)Wd.b# af@ 732 D d
| B
$
S A ? # 2 l + -x{ H xG ``!@ + -x{ ` 0 xcdd`` @c112BYL%bL0Yn &! KA?H Z
@@UXRYP vo`0L ZZpmظ00p1 s i#. -`ޕ6Ok<F0nF1 PsΫ5ѕd[%4T!6 b(`8+KRsʥN3t1g~`f@ X D d
4 P
%
S A ? " $ 2 V N\,OtZi 2 zI ``!* N\,OtZi @ X 8 xڥT=OA[9X` )D#HDHG((99hL"ARtQ~@Nih %&JIk{3ޭ
.x *`&$}.[)hHJ
TAo@[&F){ATkLe|.E9"r q93ז O9*0ď/ D(-Dj-4XtqSKIQ i>n2'opWC[ T1x]dq,븈YG%IuK:fyQ+p/ցFG;̜ϲg3 *sR. w b >{'9Z Oy>@bYfcdUs|s?WZ;Zת;XcџgnO"< ёw⣅hs̭DSwh
X" D d
H B
&
S A ? % 2 `C ?\W 5t h tL ``!` `C ?\W 5tx @ $ @C . xKPǿwIզ?@B
vrE[\!賅4,:O(]^Ā^xqϻ.* }ab,oi82ӎf\u:O]ٰ:v'{Ȗ%j8{(F 8W5t&-~FGM>2/gq's0ޅ8Nfp<dO<RB@X{Xґ%##M(>a75+py'Sz_zKמܴ^cX]bntd ؓ Y# D d
@h B
'
S A ? & 2 K @иm0Y A ' N ``! @иm0Y A` @ | xMPKjA}U3@jpNlB=hHH:
2;%td@ꚉICUzՄ?W-YӣD;Ӄ";_^)ےuI.$KB:JuOl:F%?Ɉo,Y'>wғ[oj*yOA !ex.|wRJ3ƍVݖ;|2'$4wέ43` D d
@ B
(
S A ? ' 2 Y%®qtU5S wP ``! Y%®qtU5S R x5O
P =I}'qtp_&Pp(إ[GG?A =5I념pn #JSGQ"C6dNcCL^TgbZIeTITIM!?K
lׂ.EO` H_z{ZC3$Z5 ؍VmHӸi.o82' D d
h P
)
S A ? " ( 2 T *cg㌰#V 0 )R ``!( *cg㌰#V @ H | xcdd``> @c112BYL%bpu<LL,56~) @ k7 zP5< %! `3);aV&br<Яb@Vg$u m 3b^?$@W&0; Eظ?m`a (䂺 C.] ` p021)Wx ], 0v] `q (`=> D d
h P
*
S A! ? " ) 2 T c*PˢKo> 0 !T ``!( c*PˢKo> @ H | xcdd``> @c112BYL%bpu<LL,56~) @ k7 zP5< %! `3);aV&br<Яb@Vg$u m 3b^?$@W&0; E3ظc7L( RQ^u=؇]=0adbR
,.I@X` .>6( D d
h P
+
S A" ? " * 2
.},`j ` V ``!X
.},`jr @ | & xcdd``dd``ba V d,FYzP1n: &B@?b ʞㆪaM,,He`H @201d++&1~-b p i#O, J _盁Q O?>Fc >`,ۛYF1K`*b '27)?(µ8701TWrAc ]=8gdbR
,.I@X` sdo D d
<
P
,
S A# ? " + 2 w){T;"rK AX ``! w){T;"rK ` @ Hb xڝS;K@DsD/EQ("X Xji
AFOc")vV{D {>tavgfA( 0 |1+S#D!y.qS1й4mx]j@?\ W;c3h=Jj}c! Ģo$we2Cyvmqy|C*Q 0
v 緣-X|{ L3Q䚴ύh#
gW Îfi xSz6u}JGOi
kH>EEK6;E'n3) -Oj|J!H^UݤN^wt|G7$E<o-ֶ7!f}Ӯv_ފ :N%?´[76&[L%sy` t)kn=8"8KEix D d
h P
-
S A! ? " , 2 T c*PˢKo> 0 Z ``!( c*PˢKo> @ H | xcdd``> @c112BYL%bpu<LL,56~) @ k7 zP5< %! `3);aV&br<Яb@Vg$u m 3b^?$@W&0; E3ظc7L( RQ^u=؇]=0adbR
,.I@X` .>6^ D d
(
| P
.
S A$ ? " - 2 SۗU \ ``! SۗU @ ` 1 0 \ xڝ1K@4FED$8uV(V-P
G A'x({{< 19=VD D<WW-u>;``RFNf!'1etO$Y'l,-AlI7M^;`|?-
, c2"|r !R>iVE.qP" [\|urj}q0悧8I[⣒A CwIъ Q C\B/1)rc7UQg3ܼޑEI<dlu{IJDl(O=[j+p D d
/| N
/
s * A% ? ? . 2 | hD
\-, X <_ ``!P hD
\-, # ` h7 0 xOQpe9AN % Z^ gb)'XBIL,,,,06Ɯ;W nv3ٙ' ($ R2%d]4rYnM+J2#c0d+kGO16^.5ee}"~S B\BvkΝtW6`pS!TtXMG <XFȟFO/Iyx:<n:M<sWɚc?z
wQ~fxy.IM
C~=yKFg.ͻ܂ :_f|Nh~g+Ms D ɳ[]]Zl7+_VOgj8z'Crߤ5 %DZ /U")_}&{g_KCk 2:zh#òEK{2ب.4y/ڳ. ' D d
, B
0
S A& ? / 2 @,Y*}-6h Zb ``! @,Y*}-6h x xcdd``````bb V dX@`FE%"LPL0}<L0Qcgb qP2 憪aM,,He`p `g11W&00r i#F+ \PӸ62uC``Ĥ\Y\ .w P 3?@* D d
@ 0 P
1
S A' ? " 0 2 ދ:`-" r d ``! ދ:`-" r k xڥSO@~wm - d0:@ %*`lC,& +Dbk/ P}~ Pity#4d1KK^(0a N! ,Sa9!'{YӿFPRmW:tdfHuPٿ@5c@v<GX39^/踧vFx#;t:~".)&8r,gf_Kdg3CRNLb?Ok=b8c_Vr`Uvu4|퀷f5} OK , ?'oϣf[h8yߨQ7]#|G5QE _'=g%ŭIof1 D d
| P
2
S A( ? " 1 2 w 0?bARw9 S f ``!K 0?bARw9R @ ` 0 xcdd``dd``ba V d,FYzP1n: &! KA?H1 Z00sC0&dT20 KXB2sSRsxC1XP] Hq502d4+a|5*!+H`ܞ@**H!2 R\&I9 @\
Ĺqe\D.v0o8N+KRsA<.f8 ;]U D d
@| P
3
S A) ? " 2 2 U ;BWxpi 1 h ``!) ;BWxpi ` 0 xcdd``> @c112BYL%bpu<L L LB@?b b熪aM,,He`( I? 01d++&1~-bF: p i#`Y!2/27)?(ZՍ<F0nn HEy%\` BtA``ÄI)$5batCbd` 9*<ܘ D d
T P
4
S A* ? " 3 2 @W:W*u j ``! @W:W*u e% XJ xڕ?H@&6Vq
CPtRڊ8IJ.:;888uprI0]'
6pK B &@\: G !s]WFyעc) M00G|2 eѾ'~iJ2'~-H
z`[Vޱʻv ͏Ni9=!;:>?Q<L)x^K8F~N կӽz-ׯeZ8ʎϖs]jrgq{@Q(zdoŨ_RMσS"z/uV {qԍ|R8r*05M%\pi0z{5!$N1dlu[xJ7b D d
hT P
5
S A+ ? " 4 2 @ ='\?,sVqtp; Sm ``! ='\?,sVqtp; @ | XJ xcdd``> @c112BYL%bpu<L
LLB@?b =47$# ! lo&`'0L ZZ\ X. 0VuTBkGy`F
J.>% #RpeqIj. n = D d
hT P
6
S A, ? " 5 2 @ U\]F2:_ 7o ``! U\]F2:_ @ | XJ xcdd``> @c112BYL%bpu<L
LLB@?b =47$# ! lo&`'0L ZZ\ X. 0VuUBkGy`F
J.>% #RpeqIj. n S= D d
lT P
7
S A- ? " 6 2 c _0Iv ? q ``!7 _0Iv
XJ xcdd``fed``ba V d,FYzP1n: &B@?b 030 UXRY 7S?&,e`abM-VK-WMcZ.` ,@R F\ JQ 1_?|Fg <`cDw7#w?Ϭ䂆8d!@``ゑI)$5b2B16?1Q^ D d
T P
8
S A. ? " 7 2 c [RCHTbՔ ? "s ``!7 [RCHTbՔ
@2 XJ xcdd``fed``ba V d,FYzP1n: &&! KA?H1 ZㆪaM,,He` @201d++&1~-b p i#FSff%ר f>#I3p{ 01TUDwCe l z`gVrAC S v 0y{qĤ\Y\ te P D d
,T P
9
S A/ ? " 8 2 @ 7E, y )u ``! 7E, y XJ xcdd``> @c112BYL%bpu<L
L,56~) @ k/ t jx|K2B* RvfRv,L !
~
Ay _ŀH9 @ڈ#XO%ļ?m`tc 䂺 C.] ` p021)Wx ], 0v] `q > D d
hT P
:
S A+ ? " 9 2 @ ='\?,sVqtp; w ``! ='\?,sVqtp; @ | XJ xcdd``> @c112BYL%bpu<L
LLB@?b =47$# ! lo&`'0L ZZ\ X. 0VuTBkGy`F
J.>% #RpeqIj. n = D d
, P
;
S A0 ? " : 2 O
A,ߏ3 + x ``!#
A,ߏ3̒ H xcdd``> @c112BYL%bpu<L 0
Yjl R A@1 ^ 4jx|K2B* R vfjv,L !
~
Ay _ŀH9 @ڈ+Q3<W@W&0; ֱrE`7L@2¥ z.! v 0y{aĤ\Y\ t0 !v120= D d
0 P
<
S A1 ? " ; 2 & ݠc1k\ݸn0 z ``! ݠc1k\ݸn0 x$ k xڥK1_kwE Т]qH[qpBmmur\ :8PA u:ADƞP wM{$| `h၅--VM}~ 6$#Ԛq0 CrZWm% ckZj2@~O a+r#+RzTz ` 4NSoV̙>1L AU3V zd]Џ2Evd^u"{^|vr ftOU=EbM\r#$g@fyv}Z˴֯qh8~o=E~{cz6| v8GNEǭ˒<ߝꋏWA2|]}[FDx8;a<]0rF L D d
l N
=
s * A2 ? ? < 2 7f=:4=& * } ``! 7f=:4=& * p xcdd``>$d@9`,&FF(` TN! KA?H1 @1@0&dT0p E1At 2Bab`b Yٜ wfRQ9A $37X/\!(?71AD<+ 8U` 5l= 0~9;Q 1 y3p{W0S4 L @DBoؿ#\L'nj{`&??I9 @B^W
8t\ g+mA|J.hnp`-F&&\A D,Ġt O | D d
C o m p O b j / 1 G f O b j I n f o 2 I E q u a t i o n N a t i v e J ; _ 1 0 8 4 4 3 9 0 8 1 . y 5 F O O uation Equation.3 9q (
x n i
F Microsoft Equation 3.0 DS Equation Equation.3 9q Ģ
w 1 i O l e
K C o m p O b j 4 6 L f O b j I n f o 7 N E q u a t i o n N a t i v e O ; _ 1 0 8 8 8 4 5 7 3 5 : F O O O l e
P C o m p O b j 9 ; Q f O b j I n f o < S
F Microsoft Equation 3.0 DS Equation Equation.3 9q ` d
w n i
F Microsoft Equation 3.0 DS Equation Equation.3 9q E q u a t i o n N a t i v e T ; _ 1 0 8 9 4 0 6 2 7 0 G ? F O O O l e
U C o m p O b j > @ V f O b j I n f o A X E q u a t i o n N a t i v e Y 7 _ 1 0 8 9 4 0 6 3 6 8 D F O O O l e
Z n 4.
x 1
F Microsoft Equation 3.0 DS Equation Equation.3 9q n <
c 1 C o m p O b j C E [ f O b j I n f o F ] E q u a t i o n N a t i v e ^ 7 _ 1 0 8 9 4 9 4 6 4 3 B Q I F O O O l e
_ C o m p O b j H J ` f O b j I n f o K b E q u a t i o n N a t i v e c )
F Microsoft Equation 3.0 DS Equation Equation.3 9q W
F Microsoft Equation 3.0 DS Equation Equation.3 9q _ 1 0 8 9 4 9 4 7 0 0 N F O O O l e
d C o m p O b j M O e f O b j I n f o P g E q u a t i o n N a t i v e h ) _ 1 0 8 9 4 9 5 1 4 3 L S F O O O l e
i C o m p O b j R T j f W
F Microsoft Equation 3.0 DS Equation Equation.3 9q W
d",
F Microsoft Equation 3.0 DS Eq O b j I n f o U l E q u a t i o n N a t i v e m - _ 1 0 8 9 4 9 5 2 5 3 X F O O O l e
n C o m p O b j W Y o f O b j I n f o Z q E q u a t i o n N a t i v e r ) _ 1 0 8 5 0 2 7 9 8 1 ` ] F O O uation Equation.3 9q W
e"
F Microsoft Equation 3.0 DS Equation Equation.3 9q pI
p n
( x ) O l e
s C o m p O b j \ ^ t f O b j I n f o _ v E q u a t i o n N a t i v e w = 1 n 1 v n i = 1 n "
( x "x i
) h n
F Microsoft Equation 3.0 DS Equation Equation.3 9q =
v n _ 1 0 8 5 0 2 7 9 8 0 b F O O O l e
{ C o m p O b j a c | f O b j I n f o d ~ E q u a t i o n N a t i v e 6 _ 1 0 8 5 0 2 7 9 7 9 o [ g F O O O l e
C o m p O b j f h f
F Microsoft Equation 3.0 DS Equation Equation.3 9q 9
F Microsoft Equation 3.0 DS Equation Equation.3 9q O b j I n f o i E q u a t i o n N a t i v e ) _ 1 0 8 5 0 2 7 9 7 7 l F O O O l e
C o m p O b j k m f O b j I n f o n E q u a t i o n N a t i v e C _ 1 0 8 5 0 2 7 9 7 6 $ j q F O O ' S
p n
( x )
F Microsoft Equation 3.0 DS Equation Equation.3 9q 9
O l e
C o m p O b j p r f O b j I n f o s E q u a t i o n N a t i v e ) _ 1 0 8 5 0 2 7 9 7 5 v F OO O l e
C o m p O b j u w f O b j I n f o x
F Microsoft Equation 3.0 DS Equation Equation.3 9q 9
F Microsoft Equation 3.0 DS Equation Equation.3 9q E q u a t i o n N a t i v e ) _ 1 0 8 4 4 3 9 0 8 3 { F OO O l e
C o m p O b j z | f O b j I n f o } E q u a t i o n N a t i v e X _ 1 0 8 4 4 3 9 0 8 5 3 F OO O l e
r< I I
x i i = 1 n "
F Microsoft Equation 3.0 DS Equation Equation.3 9q r mI yI
x i C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 8 _ 1 0 8 4 4 3 9 0 8 6 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e 0
F Microsoft Equation 3.0 DS Equation Equation.3 9q r I I
2x I
F Microsoft Equation 3.0 DS Equation Equation.3 9q _ 1 0 8 4 4 3 9 0 8 7 F OO O l e
C o m p O b j f O b j I n f o fK
1 n x i i = 1 n "
F Microsoft Equation 3.0 DS Equation Equation.3 9q fp @
1 0 0 * ( E q u a t i o n N a t i v e g _ 1 0 8 4 4 3 9 0 8 8 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 8 4 4 3 9 0 8 9 F @O@O O l e
n "( # Y E S + # y e s ) n )
F Microsoft Equation 3.0 DS Equation Equation.3 9q f 0
1 n "1 ( x i
"2x ) 2 i
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : G = > ? @ A C B D E H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~ h P
>
S A3 ? " = 2 Q `s[=l<Y3wƍZ - 0 ``!% `s[=l<Y3wƍZ @ | xcdd`` @c112BYL%bpu<LL ,56~) @ k;' P5< %! `3);aV&br<Яb@Vg$u mA3b^?c .`a Dw'5\J.hpC b ; LLJ% H X, -NX D d
| B
?
S A4 ? > 2 h ZZ*O[ D % ``!< ZZ*O[ ` 0
xcdd`` @c112BYL%bL0Yn B@?6 Ā깡jx|K2B* Rͤ `YB2sSRsn-| W P.P16 ֙Y 30yk~?K`ܞ~ **l:.P p121)W23qcgba~ q,C YI D d
,h B
@
S A5 ? ? 2 m .FgzLBN I # ``!A .FgzLBN @ | xcdd`` @c112BYL%bL0Yn &B@?6 5}bn ĒʂT +~3);a~&br<5o//ƥT T0ufVBkGy?#c .`t` P^NGv'\F LZ`VrAC r`ca"+ kF&&\\i ] ـr06 PY D d
@| P
A
S A6 ? " @ 2 I 3TmȻ}~m % & ``! 3TmȻ}~m ` 0 xcdd`` @c112BYL%bpu<L L LB@?b 熪aM,,He`H I? 01d++&1~-bF: p i#/ `>W 1 y3p{0:0TWrA}1D.v0o8+KRsA<.@ .Ff8 i? D d
h P
B
S A7 ? " A 2 D K<ԍ{iy ``! K<ԍ{iy @ H | xcdd``> @c112BYL%bpu<LL,56~) @ k;7 P5< %! `3);aR&br<Яb@ V`$u m$cX 1 <Fc .`[Qu=؇]=0adbR
,.I@X` > D d
h P
C
S A8 ? " B 2 D A'&66G,dQ ``! A'&66G,dQ @ | xcdd``> @c112BYL%bpu<LL ,56~) @ k;' P5< %! `3);aR&br<Яb@ V`$u mt$cX 1 <Fc .`4\u=؇]=0adbR
,.I@X` >s D d
P
D
S A9 ? " C 2 , ʱ }a%! V ``! ʱ }a%! V: @ H xEN=Q;_w'^"Rh5
=!\RBhTgߺ%vf% ф=9gRD$.Sѐ
HFM2$姞 a5K/>um* 3fh(LyjR \;Bތ.O7eWݝ[xX)W_SYi 0> D d
P
E
S A: ? " D 2 - ]}c[t ``! ]}c[t: @ H xENQ=3cQJ#_TlAlNP J;7;{!@ ц=y)2DlQԧf
_iHuAB-k/*f|t*g
Fx9I7!h/]' bB2x4H} {BJތ|me)nq2|]twb] F)G_S ~1OT0 D d
P
F
S A: ? " E 2 - ]}c[t ``! ]}c[t: @ H xENQ=3cQJ#_TlAlNP J;7;{!@ ц=y)2DlQԧf
_iHuAB-k/*f|t*g
Fx9I7!h/]' bB2x4H} {BJތ|me)nq2|]twb] F)G_S ~1OT0 D d
P
G
S A: ? " F 2 - ]}c[t U ``! ]}c[t: @ H xENQ=3cQJ#_TlAlNP J;7;{!@ ц=y)2DlQԧf
_iHuAB-k/*f|t*g
Fx9I7!h/]' bB2x4H} {BJތ|me)nq2|]twb] F)G_S ~1OT0 D d
, P
H
S A; ? " G 2 P rK̿ݪ$d , & ``!$ rK̿ݪ$d xcdd``> @c112BYL%bpu<L0
Yjl R A@3 N t jx|K2B* Rvfjv ,L !
~
Ay _ŀH9 @ڈˁ^J,1 Ma(w
رp5b7L@2ׄbq%\` BtA``ÄI)$5batCbd` y=N D d
P
I
S A< ? " H 2 . 3ׁG +PH
``! 3ׁG +PH: @ R xcdd`` @c112BYL%bpu<LB@?b P5< %!
`35;aR&&ܤ r&f+_ŀH9 @ڈˁdJ.-\`@TC``Ĥ\Y\ te`v0@Af8 s[1m D d
N
J
s * A= ? ? I 2 P O ޔ!'Hn , ``!$ O ޔ!'Hn ` x/Aݮv[Z%"B,Q AF"DH8#&J:mĩNC?@CI\ŅfT#eپ}f }/g!!Eq nv"8(tÂ!FuZgtLEUD`~ug}N@zPdkfPS|]Aw |'닲~pĊEމ[ȤB4s,\tK`ĭM8Ә V_#,3k D:p<j|E9Z8N|\w l#ӻ^T\_'2mp Up#x\G|?>dsz6 k9k"o[QD#RVf-uzOs Ϥ[[;M
wPk_j_Qn5OJ}6ClMKqcVV :AMYt) z]Cb¬Al֭ D d
h <
K
C A> J 2 P q;0+^G , ޙ ``!$ q;0+^G @ x | xcdd``> @c112BYL%bpu<LL ,56~) @ k' zP5< %! `3);aV&br<1@pT9 P.P16z3b^?c 60-䂺 CF=0adbR
,.I Ӊr!v120CF} D d
<
L
C A? K 2 @ QZ{HWc SW ``! QZ{HWc SW< ` ! x xcdd`` @c112BYL%bpu<L ,56~) @ KH+ :P5< %! `35;aV&&ܤ r(7e1@pT9 P\Hq100ܛU F;=`321)Wx\ ]` ]O4NC`( q$ .l2 D d
lh <
M
C A@ L 2 o #=bl+.KrZD K ``!C #=bl+.KrZD @ p | xcdd``fed``ba V d,FYzP1n: &B@?b 030<UXRY 7S?&,e`abM-VK-WMcs>Q.
N300 Us i#F9fVJ 3d>#dOȄ
=Y ~ jF} y\P,8xa&#RpeqIj.ŠV "1h:(G3ı Z D d
<
N
C A? M 2 @ QZ{HWc SW ``! QZ{HWc SW< ` ! x xcdd`` @c112BYL%bpu<L ,56~) @ KH+ :P5< %! `35;aV&&ܤ r(7e1@pT9 P\Hq100ܛU F;=`321)Wx\ ]` ]O4NC`( q$ .l2 D d
<
O
C A? N 2 @ QZ{HWc SW ] ``! QZ{HWc SW< ` ! x xcdd`` @c112BYL%bpu<L ,56~) @ KH+ :P5< %! `35;aV&&ܤ r(7e1@pT9 P\Hq100ܛU F;=`321)Wx\ ]` ]O4NC`( q$ .l2
D d
0
P
# AA O "
=SPC".Z - `@= =SPC".Z9 S K D1 xmhTWɌM:w~mRƴ~,ê,ۡDn(u Z1X*"Pea~ֵ-}s余fɻs9{{̍upqS @o /^ w ǐ'҃R8 C 5!&7M!=؎#xYR .Ghg{SaMq}՞d*5m ]EE7;㹘:lݶ$Dc ;ń
<|[Ki$ Y(} ;n/9|n':ʷհV_d'}^Y0$wO[8~yCs G71|O{J}`GM=t89^ʅ _D 9ߛt2sq/A{a K:uJMs&c6vT~Y|H-}236}aGHR}j[{,HO~gJ wJ9N31ݎ|_s_܉sy3gys'n}HƧ9\?;<Q<99ߜKͽ XHgJ v#2^ ]>4}d _&`Nn,3(J>F Q y?4z4|^KQKb>_Y?M{H) cn#'6WKF:d!+U`٣ |QYu8SpxUVoJ϶˶oiK"lvX}Cڐm]4>H)g.AEu-KҸӬ uf}b덣U7& 7+N3?d+fO ML[6,k< 3NRB~mZ{AP `~FAbk^fMʼ oS>Sǎ8lOm6n aOᷴZeݾy wu[/oFWo#mԺ{eٓi]mlx֚a46 [/262/W.؍) IBDv# z Ժqmذ~pm^=482zՏ}'0N'4^Dq`՛C<uN«'mnlH6 +mz.#\E2«He W5NQ?ce?mG̻\. ckiנz ~@&W3NذI@'|짍yڌ}1N5^D\ߦuN«'mnl8^qm>VRȼ
T+o)iƫH_'rv!ޥuN«'mnlXwm>V\~jh8x5S?Ӯ4it^3 @'>cѠ [Vδ{.?t-OM .
61ݮߍ iʬypQcwv- 'Ҹ 2-TBtZ+~cs r?(CK Z)xm P rw7= (w3<3δV>m]7 pNfw`jA6jv!Rݛ?Yזyկmax
? z CSw
zF3?-)AO交3ٳ &fKC).:?K /AiK?s O?#,C [JؿCٿ!K P~1?aXϽ|1unssJ;rdC [bSZ 1M1HR_73 f& RL=|? s%^G|ѥB紆ql|oKҜYk'cJc pJ?]$W&@SOt=I=7 \e_{b^ ss&n^5Us[ z oҧ+c|~fg,<oN92{#ub r^KXd-u&=oމ s@=k8\s4gM4<QoӸu>o.<YPO)5e6XJHGO\ '0NM9jg~V>: 1T^gNJ Ӻ śN$$If !v
h5 5 5=5=55555 5
#v #v #v=#v#v#v#v#v #v
:V l
t 6 0 6 ,
5 5555555 5
e48kdm $$If l
Tq F Y!$
t 6 0 6 ( ( ( ( 4 4
l a e4N$$If !v
h5 5 5=5=55555 5
#v #v #v=#v#v#v#v#v #v
:V l
t 6 0 6 ,
5 5555555 5
e48kd $$If l
Tq F Y!$
t 6 0 6 ( ( ( ( 4 4
l a e4N$$If !v
h5 5 5=5=55555 5
#v #v #v=#v#v#v#v#v #v
:V l
t 6 0 6 ,
5 5555555 5
e48kd $$If l
Tq F Y!$
t 6 0 6 ( ( ( ( 4 4
l a e4T$$If !v
h5 5 5=5=55555 5
#v #v #v=#v#v#v#v#v #v
:V l
t 6 0 6 , ,
5 5555555 5
e48kd $$If l
Tq F Y!$
t 6 0 6 ( ( ( ( 4 4
l a e4 $$If !v h5 s5d555#v s#vd#v#v#v:V l 4 0 6 , 5 s5d5554 f4T $$If !v h5 s5d555#v s#vd#v#v#v:V l 4u 0 6 , 5 s5d5554 f4T $$If !v h5 s5d555#v s#vd#v#v#v:V l 40 6 , 5 s5d5554 f4T $$If !v h5 u555-5#v u#v#v#v-#v:V l 46 0 6 , ,,5 u555-54 e4f4T $$If !v h5 u555-5#v u#v#v#v-#v:V l 46 0 6 , ,,5 u555-54 e4f4T $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj $$If [!v h5 55~ 5 #v #v#v~ #v :V 0 , 5 55~ 5 / 4 4
aj D d
0 B
Q
S AB ? P 2 >q4w\ \ ``!T >q4w\P @ `m " xcdd``Vgd``be V dX, XĐ ɁYRcgb 63G 憪aM,,He`x V_C&,`Bܤ &@Q muɜ)T 0X-A,@!
~
Ay 0@Lb4ؤ<`~&$ 6I lRHt{f# \и h
0y{1Ĥ\Y\ 1 ug0 >YG D d
h B
R
S AC ? Q 2 < 1ඇunE%& ( ``! 1ඇunE%&h @ R | xPQ=3Kb#jБ> >bEN+|ZSTj5wvdrgΝs% {r%Y֢DcRC S]/&Y;_EƒJ},1CT0ZDasubϨN2*Co&Xuґ}NoUU SiJ{8CG sUEAM̭! 1*$d J: D d
B
S
S AD ? R 2 ; S;Ã(Jc` S ``! S;Ã(Jc` SL ` ! x x5OQ=3Kb㑈j |
4:M(JЩ>A3*VXsg$s9K+@R96()D!P7-B3* j#4"r zJ;":IcNv19l<m͑/6)rSEeAD&߈{&'ɰ'K sFk !+kÊSXVWo'b/L_?E-m D d
N
T
s * AE ? ? S 2 ɩ: ``! ɩ:P @ ] m x=KAggOϜC@0 v!?Kc - &Eu{g}fn$ 䵠
a8YļM⒘"+Mj}r!V";"uGeHcbP(7+y{oE)4{]̳%7>t*IC~
A 췃%n l\;#Synovy+_;U^5/p~թh=P ynX5vcmsV cwG]YG wKqsx: :W
lyJ1l4=_Y,c={`i g~ D d
l N
U
s * AF ? ? T 2 b}cX@|{f$ 8 ``! b}cX@|{f$P h p xRJAݻ%3$Q"_ JJ-LI"$r X K kB$VVQH "=v웷 `hp<8cbKk)_r ͞ʠ2yS0X[h Inq\<{))*Q-rٝn= .`tM4: P0I3ɚF"=Ndssvi8" (^[>qE_s u++ /0$g
SIX y;z}Rޱ$ qj\6䴿5n8l˷ƫ@*Ys"܍.8;S,: =nf/zCLXKL oh b D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / m m p / k a p 1 / c a l c k a p 1 . s e q u e n c e $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
akd $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
a- D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 1 1 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kd $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap - D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 1 0 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kd $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap ; D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 3 _ U n i t s _ S c a l i n g / m s u - p r o b 2 2 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kd $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap - D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 1 3 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kd# $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap 5 D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / d e v o l i b r a r y / t y p e - m a t h / f r a c t i o n - r l t - 2 3 5 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 8 4 4 3 9 0 9 0 F @O@O = 1 n "
F Microsoft Equation 3.0 DS Equation Equation.3 9q f
1 n ( x i
"2x ) 3 i = 1 n "
S . D . ( ) 3
O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e = 1 n ( x i
"2x ) 3 i = 1 n "
1 n "1 ( x i
"2x ) 2 i = 1 n "
( ) 3
F Microsoft Equation 3.0 DS Equation Equation.3 9q _ 1 0 8 4 4 3 9 0 9 1 F @O@O O l e
C o m p O b j f O b j I n f o rԈ I I
1 " ( # Y E S + # y e s x i i = 1 n "
)
F Microsoft Equation 3.0 DS Equation Equation.3 9q E q u a t i o n N a t i v e _ 1 0 9 2 1 4 2 9 7 1 F @O@O O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 8 4 4 3 9 0 9 3 F @O@O O l e
,¯ h
"
P a r t i a l C r e d i t A w a r d e d x i i = 1 n "
F Microsoft Equation 3.0 DS Equation Equation.3 9q C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 8 1 1 9 1 7 5 4 F @O@O rԀ I ̚I
( "
# Y E S + # y e s ) x i i = 1 n " a
F Microsoft Equation 3.0 DS Equation Equation.3 9q LI ~I
R 0 kd. $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap ? D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / d e v o l i b r a r y / t y p e - m a t h / f r a c t - a d d - s u b - d i v - p 1 1 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kdC $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap - D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 0 4 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kdF $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap - D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 0 7 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kdI $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap - D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 1 2 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kdL $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap ; D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 3 _ U n i t s _ S c a l i n g / m s u - p r o b 1 7 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kd]% $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap ? D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 6 _ V e c t o r s _ S c a l a r s / m s u - p r o b 0 7 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kdr- $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap ? D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 6 _ V e c t o r s _ S c a l a r s / m s u - p r o b 1 0 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kd5 $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap 7 D y K y K h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 2 _ M a t h _ 2 _ T r i g / m s u - p r o b 1 3 . p r o b l e m $$If !v h5 555A55X5d5A5 5
5
5 5 5#v #v#v#vA#v#vX#vd#vA#v #v
#v
#v #v #v:V 0 , 5 555A55X5d5A5 5
5
5 5 5/ 4 4
ap kd= $$If 8h ! 2/ + !$ A X d A 0 8 8 8 8 4
ap D d
N
V
s * AG ? ? U 2 1 ዦQ F?'[9G D ``! ዦQ F?'[9G @ h 8 xTJAsgb&`H T4>A0
>jA!hT0Ng-]gNsι̲ '@@3+V K2f+A5X. %"É:j I}U]h jIRauAn ZOH;v-8tVUJqL;I Pɬf3;E{ :qJs2R>҇ꗜq wƊ^Z9he gz'IϾ8uŭ {?'rj-+ g.Y,s;LsK;'ߔ<YWm|1+K=ΊEF*<=وs
xhvM02cZǼ$D26/w
F¾ij>{蕟ph7 D d
' N
W
s * AH ? ? V 2 h]:uf=v q G ``!i h]:uf=v @ % ( 7 xKHTQ99;w hA ,Y4|e Q GZEAV-,p!Ԣ"ʰZ"Q=afs=ށҩj@><i!y B@[\ <aڥ6P b`c^pP 5~949җ= Lg3zrGQnuAGkKZȵpAgYx 5DkBAkfSgρL+Q-˙J,= Dt=giDBkoygJSG!FRej+3. Xǃ@q͈ 5}ӒQ66oEY_hvcxF<+vżsw 5g}NǜNqo ~ Toh*9Jf=㩡1P_pph2^4لrTnWC&r^dw:r[VOT^GaW}bi87DN`TO#sp_ժi.VDN!Cׯ2+Cw7H| h 0٧3u^x xo](9Vy rG46|Y/9r1o ):`|2.qJݏǢ +,ys:{)riE^+K CPϫUgN鴻nD2xD OsJm@ԯj]1_:{k",0/l`PG@vf'&)B^P.QaP)
3 G D d
B
X
S AI ? W 2 :XQ1n7 K ``! :XQ1n7 @a ] xS=KA|]<W"E"^¤FHrT[S+H! Jr̭!\ {7ox3-+ `
Њ@(gϙU1˙<
Nw ٲ&UN<T UX]n `M#gvrE U@ϩl\}cK4|`D+3>zTT5mH1Ie;=2G] {}Xߕ31nV=JK9>a?_Rs 4[]럝=-wvNoW4xS!3yt'N3uㆊ<1IoFK:80.wyc&3tj a_50 D d
4 h
q
s * Ab ? ? 3 " ` ? X 2 %{XDw#J=v =N ``! %{XDw#J=v @ p xڥSK@L` 8(*B'I[p5@!i!fSG."/CXDpG}wI:Kxޅ 0L.?a!Ju Gy2" ]jT,L`A178AxPx^g`x\\sDz]hs~ƷT1 U(W)d]Z3\O8kՕ >ے \k_W]֙RChUJFU4,mR[nK]u_Bmo΄+Lr9I*'at88OKy{@yH^E ͎͂ Z3rī>6\.fzk-ݬ"a]f⾋ ӫV!DƮ2p*zZ*>Xv D d
4 B
Y
S AJ ? Y 2 xe?W- P ``! xe?W-l
p xR=KA|m" jPl<"",+-L@DH@F,llBψ6baeswv=4{ݼa̛a7L MyaHY6B<2/^أ PD F!$pn*VK pҤmLJbp10Qf..5 |7-z h ;/b'&lyDZb9C-䣗G?cn\Nܿ^Ųuݨ
JVy[hN%RJlKPګRCPIES=[+}5M8ߥWY&MED#|?IvO%2ہ;~+tvÍL| D d g
R J
c & A b U ,yrYзًIJP 1 nS n ) ,yrYзًIJPPNG
IHDR g M sRGB pHYs od IDATx^흍r*FsɳN2ۍg~JMk y}@KRT~ 6^ P `#jxQ@5 aEi@ ՠ@ 6! T> 6^ P `#jxQ@5 aEi@ ՠ@ 6! ^.}[݇N抻N?JBT,b1[5e6g*__J?O?} Wwm5\{AE[Mhp"Pށ&t#;p>VYO\3p8@+V.C2>zWُ5c`]c]k|-yXgC`>]F2w֦\ ] ﻅe }+Mi>7ä W(q zG N)}~:{ aP{w;5WC !GAr>nPiVC컑szW6co5 wg!87a1IgE5&In@`229z ۾)X
w&jtNsX ˧ Й 8A`y)$ t&jtNsX |
'j,B@gFg4 ˧ Й 8A`y)$ t&jtNsXa}y C`F@2?x!|DĔ3n&05Ngzny<8TC3;QAovx5XݠC`Nb5Hd-!0 q1 k܀ N t @F հ4 j TƋ A lP/JC } @5l(=}C[H?" jdA'V#%,@`~B٦iޑ2?P< amW(F^Ѹ"E>Z1a +5wyynO
W,ԙp>] LP *' !дq:-Z噄 F 09iR4ȃ<ٸ $#k V' 8k% Ur_FzӢ=@`[h9 B _% N\c4$5-!6w'x߈!f(hx\CmB6xA -4k$Cwy LN 6OOG2$ԣy7O<ymwu0pW(k~W5MZ\CǛ?טH>pudǏwuJ̵|PkT 0@5Uɿ?*څ FjhFHiN 莜!8jxl" PrY
A ШƲ8 `&WʧW͞R@j$Hw@jY+?|T#{P棇GH@3hU>wg[c&8 ?F>& # N`Mjo|m@XJ`Mj{/0G,!Ъ 2j(J#K}c(V$CI˨uY5(Rʈ 4}{(>1̒!@j܄r&oyҟ zhXbeI5T{(˓On?7w:XTýT !OvA>B\:OVCXM T,&)˦qjIH[MF!0@j.AYY$Фq52g FhUhh@j'wrh# 4HiD f!,V`]c8T\5g4F, !$ SgT V#j1h >V#j1h2հkt8 ['NTTPx>&x> " TCu
y "Ф[3DX.Yj4yP.d "z!E@哯;,! j>S& <wH F1+pPJ8*vSy:t>)(va74] Ņ*Vi ; cTthy Q֯J\EhZ[DR-O=@5 /`ĉ˧ n$QE4 # SKX &jLc&A̢=f v3>zj̑9 y2WZ'e j7w< 0'jb vLTr;P%҄@5s([Liueщҋ+@56 ʠ1 Ш{( GTI8H _
}
T=P=`!Q"'A jpgWBTbz >Ԃ@ ȫ _Or㪧'%!Г@5ʇPڧ <3j UޭT lRk+請1ާq0 F9֭j-.__ fW-Fn8өF>J92$Ǽ숢@\s^yކxQZ@`j| 0@5F6!Љ@56OI`Sh#hT6& ! PO ,K@Ǝ F D& P{@{(P!V8ԼP
84F~Fy5T hR~4 0 1<B`Uƪox 4-^lxA=,CI5G! TCKA Ոg c% XMq<>V5~X& {%vgGƝఁ&U f # |~3C<x ѼןxY4C` h}Ϲ~
̕/Uǭ?H]sT5愅WFP `#|>]1]ٲ[%jJd|p-J*|.0UOS^:m#\Z]1Y;i]*h밥nWqcJVeE{\÷ꫵxXuT_-_Mߢ(-GԴ$x%knZtw!$Uy)w·љZO16[9**j4} k%T;R2#0o:6-L =LX +($T ]9S<YJIjgSZ-p S_wW %M]E >pV W(M@51PPP&X@(Ft,PDL@ T#T "& * TC E n ! "jJ7B@@ @B@5B`! j bM@51PPP&X@(Ft,PDL@ T#T "& * TC E n ! "jJ7B@@ @B sl"<j IENDB` D d h R J
c & A b 2 "=gMD " ac n "=gMD "PNG
IHDR h ҫU sRGB pHYs j IDATx^r:Ex~<w<DS*]ٖ \t MX || 4%@8 02 @8 02 @8 02 @8 02 @8 02 @8 02 @8 02 @8 02 @8 02 @8 02 @8 02 -g 9~yh0s[ȼ cQ!lg47 c %jm>7$=j/~]/wfM] eun8Br+z|fmΝYƁF! xxg?wc TYGj "E#z 1/7lY/ S'υ<wsD8M +Rl,IlÍ5JenlS*㈗5w )z UkO>.ũGG/giG4X RG%1̨h nl/_7PP TIGQN kCѾ,(T?!#Kc%8*@ Lp1@ !@ Lp1@ !@ Lp1@ s ~Ӊ mH8Uë$Q8pT%1JA{8Jc~ XZ#3~[Ogs|9)~rGb Tx g pfyD±qXlt@3B o8z'9 a#9 a#9 a#9 a#9 a#9 a#9 a#9 a#9 a#9 a#$
ǰ݁_e$% p
Bwnp,Ig N $'Q2-It Q;OO;O T G[;$
gׅ# "pBr<XT7CQ*'B.!0:X 2RQi-Go6Af&T8 u283*)&1hE o\oh5AŒ( ([9ޡch2@+y%1zQK5Z7 9܌@ 2 @bc;][:ﴭA$KpT'Ǣ:DXyũJ)cA 3cdhUd U'_xypQ>Ӷ|L83R_8)b>7y,Μ|C@b 7f\@bIϪ\%yWZ?h f%X\U6@9,`[Dj ek]d;:dqŒ% ^~t\ >S[,±ؕj SM9gO od8V?P)3>__5=aXᢇ0N)+.,c,SAj@/w=8Ɓ .(ͺ;!WHcn8@bI<UJjGqH"% *"M<"p,8* "@dC p7k˫P?@bI$vy, -G[,*. .E @p,@#,8 p]YލH 8` k@6hpc糞8@b:U0 . .E @pTyXvG 2YQ+qi$"X ʹ7+&
G4HrX E otQяX| ZE o'VX\+'xGyEq#|@`Ip H9UyEANW M oZ$mT~%' *ϪdTU #P@G_ylt] m^sRU?r[Pt \4oH_ngӐrk ;C;a'" B W8C&ՠdHr+, QYѩ90;}^gc?h_ q(p< g; XR8>&P\! .a#PL K8ӓ)q2D cpqЎ4rwEu GHcsTE c<I@(YqT<E n& E K87D5.IcQ8]zI4u@'c]shqDuWUO-ssȟ#,8Gt@ #N7,ᘞUX7G!, mLlWCALlWxG 8o H"% ;Unq4E`,[L*[ [K5zI'bI|}2-6o?\MZ8^>PrB zņ }d`m4/ /*^n1$@ @}= wLo- Ƕc]Xm?FD @}xq J::G1\'pd/ '@8 p=zsc'@8 p=zsc'@8@ņg'qmC[@6 nPc'@Hp8bF`'± sz$@8zu1# 9= =κIp g]$@8vd@ +畷+aru9gş wX4bW-wu;`|y!v hmXLC+'o8U)2c@N- XF#Жycɍv!yc!!KkنN Bɠ\@:MZtb[,u@e!Е+c DZ8"h (cm$nC(;`7kٖYYf5)3n{e!C W}RT>w&LņUp/=m`E .xTpcsÐņn.8ʮ˔YZ>Tv L=_PۿKT7CG]9Sn58bq
U8E±V)^v0
0\eeTG#&Xaub (Uw]cuٔGkm\Q<bXc;Ӻp;
3UIciM|B!Cj8YfX`NS`a=-C)r2 ]~Lz@ 9*G r G r G r G r G r G r G r G r G r 7 IENDB` $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p T $$If !v h5 5 5#v #v #v:V 0 6 , 5 / 4 4
p O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e , _ 1 0 8 1 1 9 2 1 5 4 F @OO O l e
C o m p O b j f O b j I n f o
F Microsoft Equation 3.0 DS Equation Equation.3 9q I I
F Microsoft Equation 3.0 DS Equation Equation.3 9q E q u a t i o n N a t i v e , _ 1 0 8 1 1 9 2 0 8 1 F OO O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e d _ 1 0 9 0 9 3 0 8 1 6 F OO O l e
H I yI
x i
= x i
"
F Microsoft Equation 3.0 DS Equation Equation.3 9q . <
E r r o r R C o m p O b j f O b j I n f o E q u a t i o n N a t i v e _ 1 0 9 1 3 6 9 2 7 6 F OO a t e i n e a c h r o u n d = T o t a l m i s s c l a s s i f i e d o f t e s t e x a m p l e s T o t a l n u m b e r o f t e s t e x a m p l e s
F Microsoft Equation 3.0 DS EqT D d \
s 8 A g r a p h b v)g)+`lɽg H n v)g)+`lɽgPNG
IHDR Y PLTE T bKGD H cmPPJCmp0712 Om 0IDATxv ༽]`;At{z@ cWMU'2No q pӽ(Z ~p"q5ǫqб^ݿţ=}r[~ x ' U :Y}|>[>;~\ ?6!#T~*Njūa\':V_p]zY7 w1CS8|>B|q!t1.}5j|}搯_3,R_Z# 888וӣrp: r$T zU&uv|6]خ[u &sGp wA | t8 ;Pǎd psTNp3aޔ 2u1 ,GxQz&TRUJؼ1V捵RBǷK' />'9p0G/|̲] ?c*Gy<01iqZҹ]N_: gq?jaz;fW_/CԕvȬiGHG O# ?sJjl#/k2~z< ,ŶΫ8j2p2c|?*Ǜ9Cf 88ڬoqg^> r>R'mt<r|-m#_O/.!888Q)8:{dl;V?qq#0OQ5r>3\<r#_U>]lU|WUƢkm&W# RF#u:Vɭtl po9ǙJ8_ IENDB` $$If !v h5 555#v #v#v#v:V 0 , 5 555/ 4 4
a?$$If !v h5 555#v #v#v#v:V ( 0 , 5 555/ 4 4
ap( ?$$If !v h5 555#v #v#v#v:V ( 0 , 5 555/ 4 4
ap( ?$$If !v h5 555#v #v#v#v:V ( 0 , 5 555/ 4 4
ap( ?$$If !v h5 555#v #v#v#v:V ( 0 , 5 555/ 4 4
ap( ?$$If !v h5 555#v #v#v#v:V ( 0 , 5 555/ 4 4
ap( ?$$If !v h5 555#v #v#v#v:V ( 0 , 5 555/ 4 4
ap( $$If !v h5 555#v #v#v#v:V 0 , 5 555/ 4 4
a D d
B
]
S AN ? ] 2 ="%Z'O$ - ``! ="%Z'O$ R x5O;
P Vbi. xA4RZzbay`afg + R>Sb! d!uߩ! ƘO陈^RaljUm}.1?|-n-q^# A3k9[1=,#1ReT Eϴ# D d
B
^
S AO ? ^ 2 VY3U[i`5 ݏ ``! VY3U[i`5 ` R ! x5O;
P V"V" @A!iҥKd`afg ; R>Qb!sdj!u&/kݤas(O c~Z<[b1m /g@^ ̑L܊ѷiy2˛ߥ-x?^t". D d
Ll B
_
S AP ? _ 2 ~}6 QçN t ``!l ~}6 QçNR
: x}QJP=&6,j@BCu +,4VfQPo }i7rɻ2m<TMK1NmdɱWxkՃUl0"T{z
O4Sa{p/,b0_lu`;n]&|mw2G.j oogdpy Úsק5 >g z__&Y|\ǽ}LZgvNo4?`q:/fj n KK^u\k I~ UR $$If !v h5 l55#v l#v#v:V l 4
t$ 0 6 , 5 f5P59 f4 $$If !v h5 l5555#v l#v#v#v#v:V l
t$ 0 6, 5 f555 59 9 $$If !v h5 l5555#v l#v#v#v#v:V l ;
t$ 0 6, 5 f555 59 9 $$If !v h5 l5555#v l#v#v#v#v:V l ;
t$ 0 6, 5 f555 59 9 $$If !v h5 l5555#v l#v#v#v#v:V l ;
t$ 0 6, 5 f555 59 9 $$If !v h5 l5555#v l#v#v#v#v:V l ;
t$ 0 6, 5 f555 59 9 $$If !v h5 l5555#v l#v#v#v#v:V l ;
t$ 0 6, 5 f555 59 9 $$If !v h5 l5555#v l#v#v#v#v:V l ;
t$ 0 6, 5 f555 59 9 $$If !v h5 l5555#v l#v#v#v#v:V l ;
t$ 0 6, 5 f555 59 9 $$If !v h5 B55c#v B#v#vc:V l
t 0 65 P559 $$If !v h5 B5P5s5152#v B#vP#vs#v1#v2:V l ;
t 0 6,5 P5W5^559 $$If !v h5 B5P5s5152#v B#vP#vs#v1#v2:V l ;
t 0 6, 5 P5W5^559 $$If !v h5 B5P5s5152#v B#vP#vs#v1#v2:V l ;
t 0 6, 5 P5W5^559 $$If !v h5 B5P5s5152#v B#vP#vs#v1#v2:V l ;
t 0 6, 5 P5W5^559 $$If !v h5 B5P5s5152#v B#vP#vs#v1#v2:V l ;
t 0 6, 5 P5W5^559 $$If !v h5 B5P5s5152#v B#vP#vs#v1#v2:V l ;
t 0 6, 5 P5W5^559 $$If !v h5 B5P5s5152#v B#vP#vs#v1#v2:V l ;
t 0 6, 5 P5W5^559 $$If !v h5 B5P5s5152#v B#vP#vs#v1#v2:V l ;
t 0 6, 5 P5W5^559 zG D d
0
`
# AQ ` b F EȻۓ _ F D ` n F EȻۓ _PNG
IHDR 0 sRGB FsIDATx^]z++-M̚Σc~H 仓%A!? joh ~ * !0 @ | T BRa( $ @ @ P @H P I @ @
3@ Yv9͟]2qd+IQ3 % udZ N՞rY[CU
$GGn8U4 `&GJ>4sWQ^!-WVJ!Yd'H1AN-ܴ+QH5yǟlOy/H ɭhXa*U,
"шc0ܜeA ^X DTǛc2Dgci,(By> { RNt`NFX
BH]eU)bv<ܔ[}Mӗ`]/2W
J˚I(Ғ%~7+P@8[ "M9A ߖ;K#4Gz CoȖL6Gtx)hq4s&9Zn+9q[e,
Q&I6
DYb(r %U*I6]FMԟVkc]}VPԅ gqAl=9uG%{$划u GH։:+hrSOyJNX Еwj6.ںZ۱%N ^ٝhjĆMVb\着eU>#QO$xC\uf?}_E1\/V,bDͭ |zv(_l@"&e!
_: (&i*T9$V 0 )^]&W#:eSO4 Ip
*@NWM*[ԪzQ@G7 u V63u)}l isOVBRTb4bMrիZ`D1J݂FgH2 $BUNUǝ-@LsUKV5 s'A!6: nub(x$
]\+H0 ʺ]sFr&(o_&[Q#۠@~o}͵>}M蹙DLܐq=
C^xERR+8j3'm@H;&p(dID'4ov%$?DNu M=ꓛKG 6h ZCy X bCJDuXJ#F숺@ "$6(! FP !QN?5 0b`@ 삀^B
Қ'ew1 @ Rv PƄd
KB lLHyI!> @lTd
wtS@ k qD9SOǘ\:O 1jS BJ'ƄV lLHȵ лdP=*?~Լ) gLuP>[ 2DB͎ ii; DpfB8e7hT@<̄C`;3@ K@H;Z: D tQ% vt@ @K@ D >F1!dM
E Fr7@ <)± @@ @|+ȏma }/uX !aiFS%KҫavD/ "+2J~_7||#zX {\/z-.i+u+"I #ܪY&f·( BLP 6l¹O;>Bˡ= #Ze5[uι/[uۨ<i#cAU 8(<ݯ*urb/ۨ0i#cAU N.Bl~0)\Z%4 B%K . * !0 @ | @PM=!18"D p,DWOCeLIWe_qEqkp\ I9K%s~%TDHc'_O~iN ,|A!quF X Du"n'AމdA2>l :0k ]9[)$n'3 ̃$i;3ZɽARm)UwG D1]RsBѠIj
F$D mL'Q= 9rXCl @=?!gml -a( p..係/ɜTblr(w16)DHΝгp*] wX !I
@@3 $g iPP # @ pnh Iҹ| $a! LC j;m ;,l|4~8;["BR3 G P871 B4Y| vC6"FqΝSaO!@ f> i CH U ӔBZ+ZU Ip V ZmQHmf.ƶ@ Թ]{PI[;"5ZEn,]"/KQj2eRPHu[z\I8$C@`(I-w q V$Ҩ> @ BbB @`( H " ^@ c ! kZt $n{u
q rf0" bC-lAH̅ I5 !ޝc Ѓ@dzr3~NB +~OٽLA=M AY Xʵ(%d8((6Bnӑ#^r $5}SbpwVߓj)L^Bėg3hK\o) jKRů:x2 ̉<?"sឃ S@ @I;b7!Uã { !g엮V"`sXkU ڼw YiQ A|Bf 8sOV
[j(1!udE!P lLHpӎ6b@ SKH`o4FBWTo˧x sp`ai%`/"AH5!C]Ee S ע\
9_oԂeqPâ[@ $ !;a! l@>Y oQBz @y T SB+ޢJ k> ~^Z9! 1PER?siP)vQOBRo}IMF}Nm<iSAm FQ I ]D?z{RGgT!aG ,n(TH){=hN@&i iHX U"
~1$ 0N@ 4d4Fٹd Mz@ Q61M 5C1=tyQz$Qԋ|]@;]ȽR]@*{(i:[@HX Y}lM @ ^ * KH5':(W@ déx摆 F$$FépN'C' wף$[8YwqV4!bo( N"6$߁|}X HB YifĵPk<g t+x:CIGDcD :=鞒3Sy >@og|BpUz ] w'i8 ځZiXdE;P6iN#$g((< ڎvչUIvvDρAu?aMRdK_q"$=);&M ta2ɄX[
1S! d if P2' |q?'S >! Nbnȧ 8 ̒{n߃խ&q PO i8 uw^׃lt".C $W%Q
]=) \9<R5JG>`68؋` +~ZDb0}l&%a RdK<sp.翽U)d@z! G?Kߊ(' XVl=\^.&錗&!pP6KQė3`bxjB2_%0hJbq N<_GʄP(@ BAH-"ʾjւ$p"c^,QO5*!r^ )6B$R^7 dP=duPRr{HZIeB2-R9I30;c S%jLQ
σh=!; );. l& 2"6ñPԅ آFmq^% MHP #NRb8ay TtvI0õ@-] 't= eo EΈUIlM, lmg|.i!KُOQ>* Cr+˖%Faq0.-)q vPXC Q4 ] SxN$W;o U,< $햯zyneA8x|NOJ&~ʎ.0~^%AH {m${L3~L]:Tn˪%}Ɗ /0$Fx +v.;
lpI}# L N$_#CIXUrE\4pRMD.;k7Ƙ β{BJ }z@ iW--z=x0&FN`! &xTx
[m\VC⤯qgplft*dќHIgg %h< R$73e*&$=} [+?9Ud*gR3Ifj?C-Djm(W^K@C U`6x6!]Cd >7-F\F-
, @ S8)jF֮sDF
e`CpBIu:tPR!d?wdAey^ARQ v1ևp %d)J)FH}٦۲O.PW;[{,p- w`AFLhnj6VW) {>|p ,\$I WmtdABРa @4T $ M՟ T#K=H.Nco!h*CJG#<BKHN|
iR ` m&qRaj9zq
#PNH:2 mu6<ww3)g8EW]tZQؘ(ĜdtdKTe#3 j\uP4 =!C)I9Fs1,PJ:@ ɽl"&$AL넴f 3s I eF3K3dV &R[TNUY/ #)5O; 3@0Ai'| >7[ȟ >j4 r"ͤjI3Y;u 9PL{HWQ$It3)֠*j}njf'|`EFvY=$ՄT\ xRCIogqNAI.%V]2 I('Sv\?On){m٢[O J.AN #s24I[vN Љ !%?
>IY I2 `Gh"${=='o8)H'fSF@o|XIWxI5w V!xBIўO%ra ؙĺjH+ ScASޒ}NBW^9]>U~\{oc߂ꄔ7Y),
8I`Pv億]]q8w8X5:78RS~.3I-Y18~c7R6Et[DoTys&#t^3ʮ@<SvH_6^yL/oNJ?~G?%+ew0LSKT[);~Bh@ŷ BHy tԿygAfveDϵu)(LpBqL,CC9!I&CB$z=I]OaBg! sZltf2KGH/{T~Bify^+ٵg?=}GY F{hzji'): $- u4M'^>11=DhuWYJű^ب۱.}}̬ '冥|#.m+COz!9 ߵJAY=-ĩ9M--Ó|ge~y]}"$xoCw |Hu!4 f.WޮRyzY^Ttb-vvXmWpt-()xa9,CBl7/o0G&6@)F"e-;%m\EGk ѭC5t㥢b~`_PIa6x_ѵ "wdNcQv(jS܋bS Z5 䬝NF]@r5mџ _u!Wx!k9 $KH}]%!u{He$sF-Ws:oLj
_*tt % ?GHAwkGFVQN"I \.<JG "!Tb wVruZ -NH֖}U'p jӺn st?D6z^IMҦЭUIEA]p~aJrX@SvqNGd ۳}I<\b@\BjboBJ_0B>|,HVtCf):Xɝ'ZݬzK{05WhN_$*I6~l #
UOr(qeɦwY"{c> oNRJgy{ yyaWiĉvr-6UbN5gWa.7{urgo;'}_tfHdlZ3*Kd wVW&tɺx-ݨ
/7z6:']s^wM:8+mNG7MO/);~~K#)y:H+6G~!xs4@+-nF4AtC{]On*RtpWɟ}3B
بjXs^ Y V=U
HWh僤6-=-#iC| bc]5liDqOnv6ߥ*6)GTadWQv}ܫlԧ{{tO )Bbr]FCMpxbP6 ꛝ^- RqYs!SvLޫ= sO}|4s. 2dTm:B`MKE&wUX c*'
r]H.5|̕$ؿį}7%$a q2E˯3k&#V. ջvN<^dtJ}mJU硄ӄjYY朡IPV"Fa}ao%εH5=} פY R!);A< 箒N8WrzN a+n&?{WiɎ儜$HHա\ƜFVߵ \DN}y46z"x(3)B
^֧mTj* Iۀh*LfO
1¥ZF)sãA{ez0W+mw)0
]xeU]nx _(Lfٔ_~2!H9)ׯ3 <mˢ+ e^/)k4w'F.FM #DU 4%Gegr l=lRǾqouvujyV; 4Gk ]Yhej;vKR;YYH&*csAݦgc*$@} 9 }V&_iVrM=wu(E ;"$ƿoͶ)lr9Eo>vk#ܨw7Ѧsc:_ݛָptKrC@I I*eWV$Fo8sčGBn6
2 ߓ.jz { >4 T4 vxUy CJ{(^9gHi*RR룃k-_V0ꩦ_ M)~=(YC_.='I4#nn p ){GH>'wvw|4l *")wnSR
,dXK x=_a>_Fjr(n4_bv;_,Ts>VKևT6V(5z {HV|6&{1#!yl&) hor.N6-PkǠz{=$w:7$^-|ԡta]3En7),0rTG Estg ;KM)Ewj#)EH{RnqP.kgd
Ԏ {"gmת#F#.hRRĥjG%oqv}l`~c~/ ]1GE]R))ɹӾ=HflS)ܔg!7LJ/"E9I|d/= mM"(dO_!m(_"IH\|^- ;}]ٝ=
Q<=1LXj;mhhBE GPߢ9dQ4< ))8 3xlh$kWSPNҽB( Ȯ:yܖa>`ʵ: x}b ] 3! klcpء<xN 1ڐ_aHeЧ j椭/`g&>.uB$ _6T$ÿbд Kf{!u<aUW v ^Fv\k-sth *͚J1Y<J%1'6ɦ9'C p I Fm1NUwkoJU#MPXuǨ:'p8"GB:+T$YԖJ
禄J)6Zd z]s9p{ BPI~A=C#\^Kv`fK
MoXtb`aT>Jz1֗[.PDK\ L)Xʼn` 5 ؈NHM%)~!!)2#VhQI:{H̄gd,(ƯAKGѦM `9bD*1<+Q{xu<[5@E 8;ĆǙrB8eo:Ko u}!UDjўP.=w39FD XXlcBss6_C*| L 5!Zãk̽Z+L2z zx!_ձqءTiCT)WdiyY"}r`䫖/9ZSBʽ1$= C%Nvy\rEWuG2#<❰>h${($(x-GYBʱ Pimqc
00
~qRTwߚßyIT`#Q*~/C%~ &,꓾7aՇɺ DNG)X\{f7rpMH%I>柳xbTlK)S9z'Y\O` GC2M{u$_^讒蒒HjyU( 5aɨpI I>uy=EEmо!&AFHVAG{e~ҕ yEI5w]8I9!흲ęUZvvvh3|wDv9mكw ͼ_GZ9KNrJ6mG,6[!ԷZ(HWDޙOphkr $~;)= -W <`g
Q D!-]")KrRq\7lzxjS$R\\Lc7:aw XbZo6Z)[> ),IM6 v芊>0H!ܴwh*fg9R%hڄeC
2 dn+D#|=5}=߆͔HENN41I_*m$] Yha N(& n/twʤ FeUk
m.IkxF@} J [s\trt=Dm.>Ye2V6)8/H!IJ,ׅJ''np]5F&6^Ri]ⶑ!=Pζc?H6Q]?Lgc\u)P+ TyT6[wW@0\2қohY6MRMtً8 4ù Ɲя6hA qڛYa " T f~b+YwnO@HԑUnފf ?nC]!Foa~fSp[DCWȄEwN: $qN,O7\qR$9E*ըG QJ %svCtr`^)739OXpuҌrf3 yv7Λ#ptRY捣"sPS-."\~sH
=GB3d48TuWWI(R/ Xh975WJh2xo&'ъ,jF 8rt "F Q:3xt=gj5n,O59&s s:ÇH l+8 FlE\ǔI$C
5ϩw'^.tJtdB" "✤$ag )CGrP;!jp\FNcB%Ⓣx<ƵpZ_[H20j]Ue#t]b [1yAkU eF :0:Ө\`1To%>&T
8i5G' $ܲQAdIJSܷ H20+7s̹_$utԋe
L$N!u1[ f,%YeoљD iUZ݁'?=wV HrkK ϒw+e'cD1U2"ڱ[f#']Nfwc
$*h8&35> Tk_%y~IkIM ~]j\RP2ՃKD|{q837ᮥ;
tLb?~@^O `hȽ?OfT2'=FEdi}qz'+ }uWb}x~_|\jLJo D=3@$=0ZPl8I!Q F*9w m1 z}d/|LC`b#vWhCkNT69ӟt }q E8Ή-L)R-mn;JDk+!%3>ڐ ART<8%Jn7>Fr-.S= [\o*$MH՝3pv>
Q\)h(׳(,
9YoUld(T767&$Y@C
pu$宻*NJ_!kUUH:#Dݎuh]B%kG .Ƅ$sz IM@FɃ^\q@!Gy>@^( lrS50RFۅJs)1!NW&v |xTH P^F DJFZC%$u?Z.I%/
cFvc V={ʗ/M;վүg]$fc`Vp Q<>)kR˪2m}RZc_7|_BHnrPpT;yTj ' P ԋYs:%ś {RQ7mkÐ9 hz?Z?zSSvM<0f](lgW)$ks3ID{E!n* ?8ov'=z{o\&%$m;L/W9lDVN
mk|{3;%:qCH =뎺U' )HÇC$$FJ81q֨;PY'zb/1"qa+>ս\.T@ D\^ok%mlLI>g!i |yi5uJq${n٥6/KqD#Rhi>4}ΌN1vF uMqw^Coqgyb5E[]k(/6Z]lݒ_5Md~SkLB2vz`uv <FT v>(.KJUphwC4 7q,Gk57ܩZCE&ki?CY^Kk#@͙*tQU_礀[WQF"5 Z8]:إ'2s w\B@H\HR|U WC^=5?: Q$#jy"[g/(<T ].?HCd/rE.9SR$dZլ}g-W 4UaP~ רm;ÇRZ4qtl YzN4.0Qe:-w9k]c $F06ݴ Yr>q 68K:^V#Z
T.䒓v ȶJIr YpF6Gً DLöKN
vh9#vv ^4r 6>/mAo\^ \%'ǹRj|nm4i8bk;յ BpX rrPI <Zxjף`(S@cU*42>-0* va 98WZZ1q) @xd}<|SŹ!*zv-8쭃!\rӆ2! c!D&V۔WHUTh^..S9<63c*:I{P~\|}Al[_ F5\ 16M'$ߓ@Hwrɡ#*M Ӧ6iswolbDY1 BRg8CP. P۱שnb\C]AH
̫*M ۹8N |ߕu|"VC+pA5!GO4ىR.jOdd`KHf{;':wriCYk%MtUSz#,Bz %-J py'f nV'S[*Fv>Pބt0Y_.9=#/TZ &Ym] }MHܯ)r)R9 [HقF+{Pd NHv5d?8PFz(<CH3g53iB d,?)˟d
Ȉ=jHl`)KN;PI1J2#UÇksb*Xe\ϲԺ:h(dY Iute\2d5Y&a1xS냩wjz(d|aRVB/!tXwԋG A>sA8 g|d9 , ѭT o V(b>9mkIb܈ KѼT硥inCմlKB: BZd#Vl2b-1bkDiFH[8|%WL/ݛ}ղ,
a#
Kli.n<jέ B϶\Yo֙hH0+HZnY%Zk[X G ԯ-t'ʏfi& #~R%c&4a$dJAt!uy%n :/-x& ,Am2p!j5@ BpUy[KAjh6!`&aVt;#nWiMOFA`dGU z + 6Pd [h2Iq.TzB1q҈r9Dd#9kL@u@H܈ ! Dt"BsqO2gDJug !^ P)=yjIa2Q%@H lZ;r 2z+d=d$N+B:Ǣ3vqt䍀_3i 6cT~<ңg_2Yu8æ w*0;
ʁ&M6aO4'54;JS uQحw
$4vVj]WZm Ђ -Ж# ^xZ{z}<d075k52x "1>~`2
ۙ5b-Lε10zі VlD7QOWXDjbs~D4}C+֖!*#I-/&XQ $EƐPerI:T07~ѱKl_ >TkwʅJb[G Z5uRI,i_UX;@Ŧi b>Q stS+ 4a4(`P0QOw@\$3-L|V ZQwRvݶF6Fv:vrvRہچVB㖻1!#.$gwv<mQQ_Sv loƄdSy |bj#dD5cߪ{yroĶx9ſEE2 ~~nis!z嬈]B6Q|3_vz)0YɈ'@7SfQbC,6&$?\?0rx7% &f2mQ I3|k,]%ajnaX+iYTC2z16=q~l6LFsӸ8?*V{s=QoJltmti!2W`PD.6 - lrfB8eL赿#!*ӭU=Q Ҝ@7ԆGFs4PݐۼQeQ9Lm,$! 9ڗ;0@ -ּMB~!0T9[6 `2F #
t)9;0TV⯶uZ,VlasLmMp I0
,BzW1@@ $}6QP km9g
`29 tgtAamYۀX<P@
uIUMʾX &`ݛ !nk0Ͷp{00 Bzy;*TI !C:X*aiu`uo2i3Rw~m I0|"i_i|ZMzddPB ?`@`~Ġ4D !=/*ah9`98
$kPmi[$lvT մ*ajnH?l;)Q!*Vi U INUb<TZ{;߀ɶ3 AHzlqm>Q`2m&K ^UێP SvƆɶ36AH,r>#ұlرja&_Ѽ fJ%B%_ k%=DJ0^R5$٘ y OpCC@5TFT8H0,lLH,UB%Lm,.L *C%mɨel0v61fj63ySڻ- `]L?ff Ec% > JZj 9dB($#`W
{wnA#e(OTɞ|bϷ3L*Bkh x]" , !7!g XN&*34DrqvQb|&L r*}ΠֱPxdԦFw#D /xZzҪ fBUSߙ4 k3d>9X0uGLH晄K2zF MT( Rnf3 IhPϽc;G>Vث þT*oVTYQwh0z`ޠ&"`2 h'HU8uj [W%=KT9)>Ozh I倶}rL2 @ < f !pUWیllImhg-h߅TLr=$=9xS5.Wx%7|Rycu?a 'dnGs@ 0 Rv#. @ ! Bb @`z lؠ A 4 $6(! F !к,Jh{D\ zAit(f;ک V(q$nNW3&{1U j6R 6mKMYW/ {kB:_Z~FuRZ@ F D
B"(ΉS[Nk%u/&黦@C
\@ER~پq MXq,diB Yz<μ9>py/<Y'i[q_$Dd!6ՠMQ] $U8\JPB)CQŸXZɒVu *jF :g~|ߜB哐RpvA Y'H\ K" BDySj} 7%9iDIBڍLkHֶPx@HǛxM 5nSU<I]D>6
薏O@ +O5rS^K#"@Nl*V`__s
%Zi}'!TNucQKeF /;ظ @`'!d-
8E׀ ;! BZ p0 vB + @`@H]@ i'kAW 6. Nւ@ ! l\t ] # B:ظ @`' .)zP IENDB` D d
s b
a
s * AR ? ? # " ` a 2 G H`3"+ EC # ``! H`3"+ ECJ ' h< d xmR=OA3>[pC!6F DE壉B! =p"UD"?P@IcJ"+jU>uAXifye 3Q τej#J F:Z% VRME4\Eg ? Uc8UNPR W)Br讇Q]E?/}s3ͧK3 G/ق{f4v:S ؕqKנ/XeJ5۶̤D/ iUz[Cϭ8Id9q =׃ ܃͛VjM~5kj:E@Vueh otۣ_r_W=q,:vVT#PU:*j%1f8s;Hy
WI'RN?ڏ=S|m?[jƶ?5[+y/&uE2tq/S acxx]Qދo'a, D d
E$$ 0
b
# AS b "
?pn\x1 ,
`@=|
?pn\x1 ,P l 'Y ; J
x\}lT}w:Mj;ZsJإ < +qP\+Ih &RJbH 5QS+R"%j EJ"" *Q+RjGQPUJSTUN$gfonYg8sVYogk~3ޝ ^ `D@ cKj m ߌe0y!` G0]$d"8R5˩-]\N74;ްK7C29===q&jN;T/R9R]Fv+/cQ %[w`,Gyx,~]eXLI~{#rښmw^Y,:x;3}亚퇪%O(HE
X ̧2|/K $?4y2U9Sه8]sjg.Q(}W@j
I;%_o(B4OȶTGͽQݗ9L)@|*u6 `ޔm8\lv3 +}oΡa /u0\? 1<^iȿ"<L_<ׅ RVY<e~skly,C;9oچpL%?A|7 ]>/~WK<x
M|v=p3z~ͦ7ޣ-O^k76s\qc8f"U "W 5{6:<cلcY%
xn5:j: > ?n= [_l4Lzbj >~l'eg nt_:G徘6Fbhzm^Ar~|ӃHH6 ƜQI8,`Z~+$\IIP4^c"#_'N=+$Nw ^|{"WtKH Ax 䏈xO#/4)I,i2-_*G5NF)k$^R0s>{T[dvsޗQIK' +rsJ+WA*$"&*-O5AIHH
Ǹ51CI0Ft;%EC
<apʵjk;D$WAs_Ǧ/~{d9WKMwZ_kue'Q>?n=̽vq8n^|s)>ϥ31,x5'<7xq*,$=>h_[o%FcNN=A !-LBĿ0M] tִ6̭kl A#Mx5`T_>]hfR(y$KJNߌJ6¤pG{_z^^ K z Q;+PQR}&}b~W/qU eĈ6VRc%tTh6uW#EŲr)c_%i3GQ_D~Guu*^8G"da_A~%Fh{,?3p3Q9,ǰ2vs=ưr +}m<1v14c(5Naw6i;`?dڨoq_4t7q?ng7 g;J7r܊ԅOb"Ku|^\{ҷ, 3a:M!n<xaQ}$Ӷ~RXSU"~yo_q_({FoY{Ԅa'>\侐}gBa f3'>K B}y :" {x64½ӗ͌ψ>ݼGf2rͼz?" ր;N,X3>}g|ȧ3ƀw|M ./6,sRlGli@l >*rr<r<g6g'q ?8A>xm$Ǒ/YƑEkG "| #.&1r%UYp؈%X_!-?T9MrfG&8 #9 #m*\ωM\T6M303 XE 8r28r#^X%gf 8 ӌ>|zxwdǓCd?Ǔ d>k1{ǓCd?Ǔ =>4Xē /6d?Ǔ =>1 \Cs~>=UˋMBw=!߈Ic+ 9+m# 0wa.3 +0Њ)<szmD
;zUp}qp-h\r\i{ m\ vp11=X1 muY~ ۹ 9cWsK\! ]:-cBng =wO ]݈2-a9/]z ,Y _.Y6μt`~ocCwRK寥$Cwq3mXz e FF D d
0
c
# AT c b E 8 E]{xok E ` n E 8 E]{xokPNG
IHDR 0 sRGB E?IDATx^] #-M5k 'JBW_t%t@ Y- @ B ? @ LB > & !0 @ | L B2a $ @ @ d @H 0 Ʉ @ YvoIOޮa e,١"gJ;V:Wd,'lSS-qp }ѨnӫQZ2ޠlM3x"Z Rϰ~q&zwe/iCF%>S?%
gq{;D8;ѢX?y` 5ȩ| ("z<6]ecC4dZOdΦ2W})h
.v Rp2m` 0nQe dieJA<Q4M)% exzOao-K-݀QK?c]H~= Ei8Z/IS*pKS d%^Om]Y
H4 gSp18>kHr&uԉw88^\ww6UD_ևB:)E,EC9R(J{J T';u`DViV"c\ OhD6b6#L.gWSof/N%ո8(åQz!:%Ñb?`s yu?DpI? ')Ԝـ8bi"Q6EtSqui\P,1"J㤍FNE)`@"&u!2Y72H
S$sbw0HH2o̳SS{%rro; K7@_ЗeĺNYg6GR Җ\-͟١]|*@WIZJ%)uof (}+6^}@ɮ8Ok;׀R(ۜ.VbcD} NIJ֭@]P9}/KH64?>>QΒyKdGVR@/E:=0:JKkCF^yERD})U 5ԓ붋Z!$\4LO
+%nJ@TT/#b U @ H !IPC :\bI%R'5 =BzɠQ! @ =@ 5LgH5ɟS j@@ %Yrک5D@ F %;p@ {8 86T@ ܃א*Qz BS @ˇϐyÒ8A9HkM$ȳ Y W,d@ ؛X1 @ XF.!GBOxy˰B6 "`PWCf @ <c:͐=@ p e v !ٵ$@ \ *sCY ]@Hvmɀ W! BP `X<,Kc O l e
C o m p O b j f O b j I n f o E q u a t i o n N a t i v e ) uation Equation.3 9q
d"
F Microsoft Word Picture
MSWordDoc Word.Picture.8 9q Oh +'0
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q b T U V W X Y Z [ \ ] ^ _ ` a | d e f g h i j k l m n o p r s t u v w x y z { 1 t V %߃:=RӮH= $ @@& б;2: 'X\ ?*ˠ%!B-yph $\
-=S@$LfZ( d 1@ޑ$!4vPtwE< #wC!ѱti{ВY𗊣%=yIBkz)4 FQ#bB"%iFg#?:hI4 4;8b"m@K|@&IMAHM>Z $ ݀DWDaOBb!E -pW:: $ BZ E 'UBʃx2pǻ9ж lĚi: 1Zq$I%[5N-Bc Џ'emBw# _][QR
ZR3$98fSwVX%n Zvҁ+tQ:SCU,_`Dυ@TlS!ZָTF/ B`F]t)i
m$@`pǘBǜ)Hbki cdHrSr]* yCԶhg-{q(=0T|iAOIğP 'i98@٫Ps+Z"7;=曍~b7ݑNv@*kBL/c:
MTp $x"xoWMݡ_?`Vz#
S,uq-cQ (8uX]3h-1ֺ mq<#[v{$.qJTy8 '^!o$q- ( .A"'sJvsh%bss}SQFPS6,7ˊ4!$]X[*t^tRe#$Ns*JմXzwn'p>3}sm Tt1 {
9G̲BNڛe$rC}/q@'U xui'ʮ8MdA0V4-,7ބԼn>HqR
=b/ 9s<^#[bиmS O⊦6U~1z@Y5ޛ4PrUm2 TEVy=%%wUYb.cqVIx~N$؈aAHmT)+X*Z{y|@7~!G~Ŀ X:fdއ 5h6Leɦ.ԭW)f1-^!6eSKV 2~ iB"LIZ~f y0.b_-pGmӢD TY %cx@eT2q9 $!.dϘz
-nM6:)c~vW>rV~+'6 *4Ih S~ƺ9;\Kl( ATp0%vqDR ,7 P`}腲xѷG,1"$R&27X |40>}vuiX,/Iăvn'i`
9݆lxL F@p8ޑiLsR J3==T]e aF$ʖp%Lki /E˶=s
=oUl %~LY}Q8n5vɕyO8Y'zs]/OK%<%x *H- C_L 6!c2'Y=XQv`^qeg4Ky?btzش2UHC^M>PPt4Xl !5$FP.( sh^Lz!YlkHޢ<p6%Ioeé\<fة]r8@ <ξH3" f7^ZxQ R>S-tBS)ڶW9; sGnZĝztWEeT^.JFi&}=d$JvwGnbMO,ڷr^L:)3BPso.,apn턔fRl,'IdrN~p"XS$I%+!@M@7qnn'lD U'j( 6J@ ^+ X[T &+!~
t gKjB}QT`%~/ghQ^V j%FopmF$#P^BFpipGv]`Yīob5*
9ɈodNO{ )ĭ+2f@ 9N|Dxax/I%f yFz.knUsJ\JHazo5V5OP Y,Fh(@EMȟK}8zsn$l4݄N(`#{kZ4TDZ-н_$э "f.IYM,V*[$E&
wǦTU^j?5_GHc2#歅;zc˘㮦$R5`oME D,e:XP<.BFM:]-% h%1E<VzT *
9I2ݗ v䤋ɳ$ R3h&I,E##m:5m1$EF%:SV"ʚA/"$V`*AXn
k~@L}5s
]8tbKKO[ɥG̕,}`u~$;-1wq}SYVz*Uq/N|.7$7RUzY6ϊf G[#f(韾QgiqyWg
ц^sz=.;u 0qʉb|V%te_2EWW'`u֊WWON̐oH{Y?Ms0)G__rF۽-#[F[,s}"}[H[z>V p; F:'҃OVf}Hf5t~/ysYpǩW˧EH p-%ܣ}]/߽lZu `*.=ҋL~#wn1?|$uBm2˼z[(*E){4H[u-dע~{oLʊ:I:'JRV.~٦8"Fu2?W}4泎e%5 ͼ# ֳC227Hp CdC
t<6
*t=`2ڿgSH͙]p \x # 9imx1ܩc7 |hC-"rS[3!7'6ͤ ]-#?<ѓ!9NrWUf =?ߵ 7-=cP.te אxv[5W^3He jIbp.}A>8<ǠAjF|1*;[2Rx?;E/SKٛb|Kl6> b# }A] 9o}_3(KismttYi]-ˬR3 Opy*5o~Wl܅b "$UmnG+9'o-~*PixR\Y})W#e/]%D6Gf27KOj+M5ǥ5aCM ɂ6xeItvR#D`.(PNhSxG3IoQ vMIMzUo9.] $ : &՚g^\q,+:hҜKWݙ:a%8dצ}u !r?t5A-u(iH3/զx]2q h >d6zr[UZNcBGi;_l!MSI6 Tg_z`"4ͥv I_n=7&$WP٘`,%U=+n-]~وX;
&L4A *]Pnd@WM>E&"D Tv\vw9q'NCA;֟ IF6E%J)qҫ qvG+vrl9 fT4g<ƄD9>'_e glvj,nIR 9*F8iK6Os2c<2WӍ I~+I'S U_1z|X+MdliRb4<˥SdT4 lLH2D}WYؔ(e] L"/
s6њFi雒y8(Zh$iK^+{tp]=_%$ Ш*Wn uU,&$'YG(g,o~;AP6;t+3>C2MH(;MFFH8QR.6C1z >]3t 7j`1㒝F|ќ+IeG LD=7oC݅ T[H :̥'h!0 |GIuFfqyw}-hƸ7 \$ ݅jY41Bm -,qo$. O[=H]}:P{t^m*2=z,
_]$pA8s;z1'eQ1:&*?+v''yA>"ثME.jI oDGJ$}I2,D69)0< U? BåS{n6B4Xg+
[hN*ǝ!O~p?Ϟ 4%7~(ޮ;owӀǠZNQ{keȐ7fc
j)٨ &Us:q1m/er}: Dž\Pw]F$ewnd[J
ʐa1nQ[$F}u#`?鍞 u,aaG$^ Ip"NR @ Ow59;x{6ۋB~ t{cjk>l :칤]CI~AxQ6̹Cڋd;Go@HJ$I_It }&/^ImNS'`j3\M"p¢f8hNzldfA=Dl)IvI }'s3 ,ZќI#(Lڔkfk>/<cJSFBN !Xt+m6)EyRNj}7!uġ& $y^ߏ*_Z3FjA_6=&#vC 4cjPAb6U>8iTD۵ eO 2(1ńv Z tŘRx- /6BNLŅ.VB2^GyVWp,]Z0-N<`3Օ4UIRlNNJ!jj.' N{vHk ~p{*{IQF;fSo yB Y5fLfR\ >GCM2ȒP#!l=UYpg܌̘?"Us!H0Sΰ8@d I*ldc 1I vTQܛiG]Q'9PEN]q@-^WU6iJ+d /эI4Mu! |uJ/kkr]aa'"*Dz3| ب]t !aNm%-n&IR>2.+Zfk!kI,5zLe.'ui}Z.?#AHvrT8c)5E6"9!&'(ٽdg3x'4< 4 lf$7uuN n0%4КIr =`sR(<Ƕ+ Ғ]\B$i@K@3F:TsоAnO&DD4ar2'xjܾQO͵@)VLF@ ;9XQ,Wt{gOI}$c 8.P&j^? nbѫ)*t_ZtZp9M mu$},'=J^wB[zLHG>CPj %I6̀jwTiE\L ~ݨH\| No27@* 5Ʉ;0b#n:NDaF45"I'vz ʹ ӹRy&T'PUvӢɄJC8N0Sq8k4/8g3]I4WKTӕIy--d,|AQeH4&@U5!i9m,$Unx!<]CFӣS5doBRq6 d"Y;|NR:~kI̵wHd8&Tk˳iW uD"r؝iq Age!&# B ӑc>G;8iI6'!?y]
"TQ 6bܔik&za4!|p>{s. 5'oZSȒȋNZ
` |*s3#,z{as%Nt
d K*g(xjeQY? 2h(i"[6srMH ٸD6ҷz@e'l褦>&exhHL%$_s=DI$EѨgʘXwX-@V`pI+}ʹ:(ŽzN%pO^|n4,.Q}@Hyx8,7|, 3ބԔX4кPDcuKQVWdLixf|*!+ӹ z.h@QBfGrP5Kw3?=0TЈe1d , Č$
Fu=ytX[$S[dө ѻ:g}k 5b6?!\ͧSQ.uy&°ԸI|h$`ETȓnZ etV]!=po?x 84g@Cm#dmQ
fpח5`gH NkJvD7,Wa1u.&zh@"CѯZ g`zqX"LiCw1IήUODk_R$"X vtCJ@ۅi1;j0C )O4(F WBh8\ryɛ&FdHkwʐ)PVVT4yD]#\78X2Ծ:53$cөR Ch5)9eGOpSOz~'dلug}S ='RP6i? P$ K>kQ<awSl[WijZY̯@2S"ٓ!3aL2qhvS"+Jv*ޖ dhF c/x3c >Fهc L!)i1hX$w^`F^'$iߕlcDHytZF{k#>ӄ?uh5$b/#EL5I:~5 6"JrbƖUzm:&eHUV qMDN>l4)CJfHa]r3M.D`륱&C%[uD ,5A6?wJؿ1^EXdp $Q>w_uƈ#,ŌGƕzCŴt9"R tis} <WfńDĢҲ1Qp $"Ujn kY'$VyC kFPCa4]LN alXv6VI;<
h; *>gvZ 4 3#8,!pl*!u\:IQS7''FD$AOe#dH("F"7:x \Vq*z;!i_c%ި*^tQ1 鲡e KGu<t.Iز ?=d6܇oo 钻|Bz%d*TtaDJ~?!!hfkHtoXRK$W ~EՎwSך\o ΐݸz7CÕV=QQ;3_\6ZN6n
UrO:Qohԥ+m9kB9o:g9E%Hug5#2 մh8ZZ<}n,Jv {5淧lZ%k+p>tL 7fs8"O3hӅ! @۾;>6mM3K<P3JR*ukZ`k[RM-=RtZx z{z:2DU-Y|gR_I'dyE r@~6f|x,MCl)FPD^Mqe .OS W0XE ihd1jbUkE쇒T)Eql4Lѽ3?hhH1H+I*ז,G#EGiԬXۃsF)~u,}Er&g10*'^CpokP|yOꯕmβ5NHSKv!qA2U#M3Ӎ+Е6R En&rЩĵC`l8 rAEя_Ouܷr9 o9ejɎu)zPCvd#JnE 7:Wemq,+X#2|'GהŴ#t VqR=R"ANx<钝R8@f R+IYGK`,3)EUc2RtsZ{aj i6s uZ7f_MR W톺ۧ";I-M<a yXCĶ'·pH KzM^Kt :}*;ii{1yk4z'h`$s*V؎$I\e4z-o1!\3z_OxV9aEz]V`-]s:-kC}̂RND}2 Q=u22-}$xHKK9kvu|7Sq*ϲgb\S/!u pdXcij_h'Gil;{(o7Ef\ܴpiapHݲleZzh F _Z<]6{=Vݒ,A {STxeY=SoZ Mw:d-aAHǘr{E ZٙXNye^xTqa<{ɬLu !] Q<uBpK8&^$kqН}Dx.bF&`)CSQ<':,I:^8i@ swn7 PN]6mSTA%+:#QX2%R 3q:Tx.Pv! lHFGBE%<.E |h˃h(nȃԳkh;$MH@$lnS 0 u."$_'͜$iR-tyz^'z͈Th
8DP#-\L*_FHfILMB2eӅ@S V p!Q+_0;}zu. SJP#``=1q/ ,45@Rd$mOHDwN_q tQ*.'^g}@B:*T-CK z !ТN$u$qgG NHd,nNE%^vRO߿M7B^~oJ>i㬩+U?`)YNGL{-2$_̿G@fB^n2xCnܧ_َ2ڴ6{`DX EHlqv lT=vHnf+IH Ӭ$-IRE< u'w\:f|a(@ )ED1 Shgx `~l1:I<{fpiIT
%k9 -2\^ A:3C dӋa$y13Z #jʜ~s{ey3 i9pete##9T%$W' - њ<%6+I Iwf$a#I8?"]u0 NE \
F8iF|rN *!MvLTή]S 6ٱא!C%WÛ<˓Nr Bb ph #ӣsY=??1&F mITkHG#n®BL !p z>+&")Zj>$zUT˚vg XouAXt ϸƅS۷ Kij;!c2#3凫Z#,\Z$k1\;UrϦk \ iCy^ P,= StA`G6:$7˔Ki{if17昻 BQŦ ã6P" (!^ark̹?,b-< &vd$}~T9aF.)O{= AǡB[ rT&3!qg%ޞ*lq ;tg4= [xQx%H3%ބdX7ԚPXcNu6h9'gO:m>G}¿:3o GI -cV0E<%$W[*'2#fL ~@H n9[Mˬ>!PbaO[豠qBڻdcž
w\+nj-9չlMsf4%gM`DX#v]T!b<,^ "_wx «GXg!w@H;ZjwYm^hi-݆ .HG6o9w靳&EU AC T6pQ hBcݺ ;Q GE $xyt34\g: /X+Mvɚl ;F_p=Ibq8> <,/$>
8wfj` 2 $2T?.$,u^]eaUcUOY6 'YkVx1X<n}VÌG !9M Ñ0vT_ W2YM}-2$MOG`$I۷Ӛ2| @ܭB{XL @ DO!.mw$'لZj-) .,xL{ fE6rFEqKCf $- V;ɄhD]2 $֡v '5s)[j˾6tH7DQ}~ q`[rcu]ԙr(>dmT,AH,"p@\ f]05e'm% @Hט'qM#]gz;WEG~9OJmINDjM2['a]xW 3 {q
a~_5VT3%$WůNeC* jւd?~Ӆ⓽P{R}SRz-m>b $-$c|_NEVr 3ޚxd+Ċ_sp Tj趈n!AK*
MiFKTYRbbB5(FkT>" e
v NZ'kH\Lkn )ΥQB7"\Qiod#+XӈT#P$H'_klj}bŏ~;P}YZM-ǧ)y@H{ TԃF,'g??3#i &AE\N6W< @Ra; !_2]\)'\[+AH[oaJԼ(XV|*` Ϋ/ #}qjGuN-bD 0 =C*w(T4,I"Kebv˥lPfTޝEW=4~!:z T\N!kçP8>D+G(JUlӄoi%3U؋bY6 T 6]Bzho)
]ⵂhoLl- JNb$IKH( iQZ6K{#*<H [Í8yuC?E,7!R-N*Zb%x 8N cbە#TTrydnNs7۬ NH~/0|L| h MiI3'I w"OfwLaգFjÕiBRu8!IÂN&tYX4FsՄtpXkPL poE4 ulv 9A#i͐hva;Ǭs釺* &<R15~ a)F2b̦:K&mJEzfHK`¤@ |Ia ^K yC#}[%p)! c?(r$H| -闕o([HH"iyƱBһyڝڧBܬ? FBEі ㋙d>+N17h]ܨ;O !uEHv9~Q& ң{ejܓՃ!C'Iw$=&G_ [IyϻTY]j ] x??_YU&?_Oumt!%)8;J6pcS` W8!|Ϯ (2ߎ#p!99i{|B*ՠU
n [i{ !(*rn)yd7| !C# tȍԒ~YXGK %-w8vW wǎ%dGG-5 Q |0Q It4@ E|BZ/f*y X 6N3 KHMX78=:;mS;N>3 ɱ>@;QMESAAIH°@ ep?X&3 ߿iPn@,_ Qd%cIHa<' `$
' 2Ix.+~~JXB] Ȥu<p56{uAH 4@ %GAyA뛊bѹfWzEb9(@ Kʲo0ݩq; ! @X|+ᒽ-< (IO Iq@ \ T(]ڹI>bY @?+=QK/Gn !Y! ҅F@ ,Z2 .D tѡ2 @" $VL@ !]ht -ngz@ ܉@J9Z3ߔ.1@ Q<jV? M GE=Fxs
4^ }<`]&ttǜe<uHkM$3څ S?ӗ^-7&^CZ @ Euy 4$ӣ[ӻlLHt% `(\=
~?B҃F! jKDO쟲 1SB A [:]ϣ4\鿰~}U@ ].W_$9=2Is2!f@ <[d!9)Nٕ % ~) B$a2@ %*Ѿ.h/ݑ xH;A) NE -{#!V9zxB&gM o#&gdIϯ.ȚKg!KH!WW OL Ȧ&(άR^5|J,$$;<W-)>۷w3v6@Xӳ/ ^h OtQUI="y+v $CW X8t(I92{O_Rg zw%2 0(p ^DU!_.y1ģoz
vW% ne
BbΎk;⫬yr,nߎBp,l~m^ݚF!3<OgEъڔjwU*iR q~ 6*~dLӁ (Z, 4Vۗ᠘CЅk?[ sAǭx/ Tװ
ۤT].LA'" "w 8٢{Ỉf|˺ 7>2t@ ("&<U1B<1V0 @vº":>N א&) B&U(ـn bH BSG!M @ | L B2a $ A֞ɽ5j V?T"m"4J2es
r`Z@@ |yɸcAHֹn9]gv RFS6
Q
ĮMݽUJT;X5i>k5>"` ."2'.pɉ*fkPїn\)51zig)ec8E|J<>H}m"O *Zdǯ7 }㜁D>|*W|%8_ ~ʅE dkP.㾬iP~Ω]wS_FI4KUV)}KubiS] 'Oo+~ q1҆Pf2轲-ݝ=} [da2Hrmf~ AHb_ʏۘ[1(|˒-|J:n=}xJCJJK-7A $-Sb^ۗ<"@Su ƺ4.yXLjӗ;ב+CoEz7SA& *!c2W PN2PxE W|D_1%険,JWWpI` t@X5!/#=~eGw+ Nd^n:J>B:N Rh%(|z0GB2Eq.fk)* ÑU0X& @IDm;%yH.AN ): & $M41 @ !CG D &@ 1 $1t 4X@
2D8 &@d @H 0 Ʉ @ @ ` 3@ :Ҍ-v IENDB`
D d
! 0
d
# AU d " n
T͔Pڬv J
-? `@=B
T͔PڬvP B uS >8
x\]l\G>sw] bD4N^&Rnm%q7 J[+X HSA"R * *ǖDI[R %9gYk]Νsܙ͜3sw #Ř \
0G C1~bG!Ԋ (EL7 ,Gc{{Pr*}rOnqި<ݜ^XXfN߁i"U!ݛ]˫ᶨџ?[^;뱲"`g+y_qt%ݐ{vƦ~zD@{YK}U}Xe,)F26&5p@} Diq| _m,||*RݡeYvү]>!^s?x0&x&&᳓ .y?8@PG`
k˩IzI?7=~cw<M2]7yXE a~_.7|s7t'm*1oGsa!W7Wov~ 毕|s"˸.RXzx}֮"Uhf'5\ƌknh^/f~,Of|6.!ub+e&0!r[XgcCjSܖnl:'(^sq.C: |5g 3쭏>kƦd|ņ}6v~u;)?bV.C⺘>F,apO?g5W\!xS7Ɯ)I8-%O
q9M
IrI=Fo9Fq \()'ߋ2xMjb\+iԓ?)3Iq\qR O~efzMxOiI|5OcGy'rEDRLaDrYA 4NaIJ0h)9̙9I-<a Kk{X/$MzO0ٸN'EHIrERR' -I8#)'O4#䯉fqC+Ij &N ͒0ťU=6u)4wWKM8]:dX5?k1?fa\.\i^|})Z<|i! !M>"{AtfP-H>H a$ibzQ; !AӔO{5)^Q>x 뀭!R+Jaj<6FjZQ4w$e+ZMw5Wh:5z<KiyJWL+aъ?Y?;LwX(^[ tzhӘ& 6\F{{ٲɪd ^CHvve
U\6]L1c.h Rn{1A觴7kmx"ZYg3t*^$@>k8z#>~'Xu}G s>_E-o $OR_NFgv =]F:(N27 3S\^' ~177Mo(f (eVrGQx[<D/^+1g(^cOIɳQ= 4Ҙ+_CDZ!} UVW 8vauຐyz0- xO8+^E [0)'[KT!c0χEuy꒰ħAd1"x!>W3fx1K͘sBlncsr쌳xc
Np]3>> OgU]n\]lN(> Ցw[ 8#r5A})ݴ_J<|3_xy'G,Ƀ*Wē-ȳw;;1 몽<}8*viJƾm<x2mOr<'}R' Cd2 x2OrM S8Lr<'9Lēđ:C|,$Ǔ6f [ 21e2 2SO7l) S-cA)S>1ۊcS&Ĕ S|bb^-Zxy|穘 {q+^w\
kgenzbB{1 1`xދ^ q]ϋ,vŸ*^!\ g\B\K[ [ [k>7#O~11=XSwrܪגv.v62vu '~41^B~qL\_~BH߰PߟA +.weJ[Q&ڳlUoF:<YF_!VY6l`~˯j}^2A2zƻ8=gX)?bwM/ $$If !v h5
5w#v
#vw:V l e0 , 5
5w $$If !v h5
5m5m5#v
#vm#v:V l e0 , 5
5m5 $$If !v h5
5m5m5#v
#vm#v:V l 0 , 5
5m5 $$If !v h5
5m5m5#v
#vm#v:V l 0 , 5
5m5 $$If !v h5
5m5m5#v
#vm#v:V l 0 , 5
5m5 $$If !v h5
5m5m5#v
#vm#v:V l 0 , 5
5m5 $$If !v h5
5m5m5#v
#vm#v:V l 0 , 5
5m5 $$If !v h5
5m5m5#v
#vm#v:V l P 0 , 5
5m5 $$If !v h5
5m5m5#v
#vm#v:V l 0 , 5
5m5 $$If !v h5
5m5m5#v
#vm#v:V l 0 , 5
5m5 $$If !v h5
5m5m5#v
#vm#v:V l 0 , 5
5m5 D d
P
e
S AV ? " e 2 . qe2[7^VЮ
Q ``! qe2[7^VЮ: @ R xcdd`` @c112BYL%bpu<LB@?b P5< %!
`35;aR&&ܤ r73kЯb@Vg$u mhaq%.K !v0y{ngdbR
,.I@X]0; 3X @v1 $$If !v h5 55S#v #v#vS:V < 4 5 55S/ 4 4
< p $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< $$If !v h5 ! 5o 5m 5k #v ! #vo #vm #vk :V l e0 6 , 5 555$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55J$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55J$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55J$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55J$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55J$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l P 0 6 , 5 55K55K55J $$If !v h5 ! 5o 5m 5k #v ! #vo #vm #vk :V l e0 6 , 5 5$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55K$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55K$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55K$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55K$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l 0 6 , 5 55K55K55K$$If !v h5 ! 5 5d5 5d5 5b#v ! #v #vd#v #vd#v #vb:V l P 0 6 , 5 55K55K55K D d
E$$ 0
f
# AW f " & Da .ZW 4e `@= Da .ZW4B l 'Y ; x\}lTw>> ΥS5 *R1VD_ט:QJ( rj%q*"%JNEEB)Ý7<;sg;m μ~b_X<f = zCs,-k㡏 Sv1& 0-TeHVYdI&}Qֽ>8*NljCN>*.9+X·s nh[8 t.]nqQ.~!mK{ f&\t5GqXFK0~햎<#`Ӯb\p~í.U6?;E%͆ UCɭ̂cN|ǹ> MTX#O t'JPѷ.e8 _Jr}pUߕucWbԾ?[ S5x]n]WE"<Zpk_JqA-3]E[cвm9Rٕسw5&_8=pz oot, M >.y-ز%Y-l øS"?K>lPo3K ڂwY-~>=\« _(yc_v _|`J ¾a-C˶ `6Ï;{ʟ b FqA-)p֏>ؤc)ެoR?x\DZYϙB.8|B?YB? 捤w<No̹u>q|z&g%Ͱe(kaH z0&c5Yq Z軾UV+,qYXv?AYSԉoD8:KVA+U͐o3=\P,r&k]L&],~>53/5/n\` Ghny1?ki㕶M2?}|h?R?m/PtCƣ.QUUnhK.Z`=~D=QCsfAh )^"Gߊ<Q ś*i*5.wj@\qE)XU0U~Z+Hcl:eCOWԗm
_xB~p%^al[ 5ȯQħϹ}ιSlDŃmkuPQx_zc[:BJԿOֿ<Qx,c[D'K HK-c[DKH,c[%Dkě~ 淤'mձtC%p%^ꗲߖ;D
W93yK
W~}@xޖ y
WRg,c[)D+ħmn~zdƶj2:YW49 emuTQxSxG0D
W>3<a"O]JԧFoBhXX'C1G5/^k@5R |g+]de+;Rrem-#=<n O4y~?{y!?`͆|!7˷c{wm/A^/=^pVVd٫Ծ=٢v.pvj6$?[F{)uv ߇g`=j
TQSwef R>1S,YԲ͝>s+,Wk1հfhAڈ\lr;Jg"$UR(~E#\a*' wTy1KneUm1q#=!6u(߀Mj#VV+AX#.*8CXmviv$(1_~/LjM|^"[ˊ3jv/R-泿?ĸFZ˽te%Yl'i^s45iojj,;\iY6{u9k4bէi>ӼbOBeK[ fw$KE0{gԓ i<_#[=8#Q m-#h{rvuiEv@z/@a&M(\_`]B˼mu) XJc`ƺin 6"ZeGRgb0}濳7NRGuIr2[ f a?m0<n;Gp4I߷Py@ʹ߸ӥ?+H_{R=8n6\%b$=-_^^)0zz狷}h}rty?/"w3 J' ~A}.}~9eBŸe]b`Ou@}1WRo#t^=r-u>뙁ԖeNϭm:e,>9 -Z-s#Zd^H0 >' yܩ yAzNZw6B xx弄FX^D]84Ϝo_;clT&*3QlLGR٫>n#$e+Y߹ރ-l^qǩnvdke{ ]-ȾO'k(ϙP<|?>ǟn_.,2=g=)~qN0 c`~t3c`;! ?~q{W\肏q1jMͰǾy_jv|IWࣂ/b+vmtWgo+b:_wo ѾUH<uCuN5 ޙ`oK5.m \%ӈ^{za憁Yֹۮ 9K4&ex.w 9JƐ10 D d
E$$ 0
g
# AX g " Cc $lIM3@ t q `@=l Cc $lIM3@4@ l 'Y ; : x[olTww>g LMB!I"4āN5h+DiF҆U A$I$ZԂP5֠ĨiJ>3sww̝y= 6#1m i] p`~ ߨS C be[v1Ɏ `0 T W"9'zz&7XX9;._X@R\Ȫa(<Ã08 tJn pYcti[ګg03M80z$%2J_D+9cvOq2gi73ل ?Xf|}ŀOQ>\}] 7FE; xx@ӵ!Mb StzvW09\>N$v:ˆӉ&DߢPB-śr
Qco|qq_BT ~oB$)!_ӉPwCE&_^ ScE2:L._V3}"uM@1>PQx/MR@2뿬C-S8QYBwx G !_Xv(S־@/ɍ,*ph۱}m'Eۦx>ԏxƁ< fgI6cvҌێ
MN39='nU>/tIvaTqng05ܾs!Zāhz,a.˟,0|` p<~ 7kDW9TN K%?cM=βgt8tN&mlrV Y ұcGfo,bx\bl}o^hmcw<5~{%&pJ砢&ZHiׁ4CL%H`{c~7u':* Z?D1|PR+Zos"OԆ6'2sғ9_+]1W
߸i~5Z+N `le@~@W4m
߸){^*| W&v M
_ [Kz%o\ђPcCK~wcߏ<рxEk Xw!OtRJoU1U/ oBhxo{ 'm1K\:o\Q]
VeU~,Qxz-K D
W~'ω\N䉺K=4N;cyv+R w _<Qa~?E=iŪ}NJ 9/(*S/bsD}
W>E?b:K] WD{K6r4Z-Dďƚ)e:ByIdX/2}<~澲A+oỹGʽ5Rru{k%[*;<_X9|JR-yKa;,NKӒ[]}Pv.@~Ζo37 X}\K!ٵȨZeA~s=-j`>`܅cQ} jihQ X(~)Up0Q;.8.eYa\"̤c3aCE"i
/n#wYXٔ XV!U[#[b4z`*yOE6it<%yWƾߍVs FVېjU>brь3N-# guV^ IJ^O{s7i>aȲ[fN }[ G'yÞgGg9TIgw<OJ~mbķ}UOFVԂ )(⌏NbS-FGv+{p 2_m}^P2¶
O} e~e^϶ (<y>ZͶ.qkm2Zĭle2aq;ɽSO9/QX>{o?INx$9Z;~s` .q,?Gჹoh<hV2J3z寷zd3P+Ykd- W"(,,TBXO>JayߥC T y'8 N>U~a9.__/0~b_U*bI% t^=r-5b2'$0bғfd,> m`\I;n%nB)e>dy<aiŜE
9nwq6iHw/C x% LYso@G He
ޯ</ZパUdhb~lMj{k0LX{^RU>;K>ߎoo4O{۫}Nhs͋}8c > z| ?m?d߽ ce9z65{h YS(`,\FL=;?~~4sܻe?c{g K
UFקʘ |q^>J-d&79|9*C:tog>chS$/#%֕|v)$0)Z \$({liptya`u:uCr `[=¤ o!\W~/ ! $$If !v h5
5x#v
#vx:V l e0 , 5
5x $$If !v h5
5m5m5#v
#vm#v:V l 4e0 , 5
5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 4P 0 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 4P 0 + , 5 5m5f4 $$If !v h5 5 5m5m5#v #vm#v:V l 40 + , 5 5m5f4 D y K h t t p : / / w w w . w b t s y s t e m s . c o m / y K 6 h t t p : / / w w w . w b t s y s t e m s . c o m / 0 P OGR M F LES W OF E W w $$If !v h5 h%#v h%:V l 40 5 h%/ 4 f4 $$If !v h5 5#v #v:V l 0 5 5/ 4 D y K w w w . w e b a s s i g n . n e t y K 4 h t t p : / / w w w . w e b a s s i g n . n e t / $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 ? D y K - h t t p : / / w w w . w e b a s s i g n . n e t / i n f o / t e x t b o o k s . h t m l y K Z h t t p : / / w w w . w e b a s s i g n . n e t / i n f o / t e x t b o o k s . h t m l $$If !v h5 5#v #v:V l 0 5 5/ 4 D y K y K d h t t p : / / w w w . w e b a s s i g n . n e t / i n f o / A c c e s s C o d e I n f o . h t m l $$If !v h5 h%#v h%:V l 40 5 h%/ 4 f4 $$If !v h5 5#v #v:V l 0 5 5/ 4 D y K w w w . b l a c k b o a r d . c o m y K 6 h t t p : / / w w w . b l a c k b o a r d . c o m / n @ a $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 D y K A O L @ S c h o o l y K $ m a i l t o : A O L @ S c h o o l n N $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 $$If !v h5 5#v #v:V l 0 5 5/ 4 D y K y K h t t p : / / c o m p a n y . b l a c k b o a r d . c o m / c l i e n t s / c a s e s / v i e w c s . c g i ? c s i d = 2 0 1 6 8 4 9 D y K y K h t t p : / / c o m p a n y . b l a c k b o a r d . c o m / c l i e n t s / c a s e s / v i e w c s . c g i ? c s i d = 6 6 8 6 6 7 0 2 ; D y K w w w . w e b c t . c o m y K , h t t p : / / w w w . w e b c t . c o m /
$$If !v h5 55@5@5@#v #v@:V < 4
2 5 5@/ 4 4
< p2 T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T D d
X 0
h
# AY h " l 5 TTkw;T H `@=@ 5 TTkw;T r ` g v xڝSJAq(M KSk/?;<;]N#c0ޞꩭq m|3@9_ԀgLȷ`0yߖh@CJq0~ |+w8Jy ) 07V\P383ۄoޣb2|. z5e>Lz[֖StUR--TAxZ7s oj="z2æ@xMJI|8P] C4 D d
$ 0
i
# AZ i " >- rRF c `@=[ >- rRFXP ǝ^ ) x\klUfm)Rjۢ bR+EcE"kE<4M@|$
j&
E@|5!d7ӻv q:ιs ̜3ٽBhXM429(e06cW0#@T%~Pt#R5lZ |1ޯʍD/ȕP| ɯl0B hIFyD ԉ3 SMA<Uƞݕv44(OS"*>.Hh:}0-wL0r|dž ЮUGBgҬ#!ukM].o$C~o8Hk ?bê yoY{'<,: :ldFKY JR CFQrZPZqtQu&rνVX^V&K
(n2xV~U9LǽyqRٹڜ6oz{^4^˛ǯ1fDP+8u8D~25~r;66&w1r~>4Ey^ ֑OAǪOEZݪIHYomϕ>gρj++A`\& S`*;GǭS.U.m_,^ˤi*4#Wp{q6ggr-ߢ; ~7 >𩀏|xkAo/|5o | C +{$'?9/ ~!NO\u|# D>2 : _"G | 6w}c?3?(G? ~p퉫8^x!ŀ^ x%s x7,xl sN8sU8s]8d.NB;sD<ٯbޫFR5ޟm}_0yv'9j!~_2 9h78shw[9G^> mwNk/tnWkt=JYZ> mmx?O˧3
gE5kݛ+_Wti-u|T| \aT=4ʤ hgNg0ޫw~Z-B֢8k/ZL4h1RxQv (uW;uʱxZG<D|I2e^5
+_g? FO'a-5ے$?Z_OsL1K}I2%3}I2(ꋡȿl'aRlJK'!Y1M4?Mw}*{=Hi0[4h0h0Z^>dCJ{D֠{QhaРZ%Eϋ <o ߋ ۭ5(( // 2hZrߋ/E |iLߋ_Ds~֠$h0yk( /
Q&4PdߋhhPn`%%EբAAjkJ}/JK,`E [ C0ΈWevJֆiq6mqԚNor&>o9G'GhoQ(]!;t 7-
u5FW[NA*8y C긷S.~YWxBZُNe~[Ξ}clϾgHDFkax696J9PDX%UR?Z-{ [/"|5#?R84sߏUKއꞻ cU|2[H01i:{aH %:a;62z0[.-/$dum~ߐ i]\&@0@}#b|ۼ| :xE{(XH~tVe[._)7 # D d
r 0
j
# A[ j " :/Oԗ]X { `@=s :/Oԗ]XhA $ r A x[olE{'@%ԳP0Hk%9JB@OGҤXZ41I$MODc"TB"јuMocw+Vv//73ٷάAD)srVTJtBHEO ٭* )"Ze$8~,AjH]Q=K{TT QΈk"JJ_gNOR5=|(d:K~pZÜm]F iҝGJ.q/)c}K܈RյdWtn`\^H$r#B3'ܿsՏO~Wry 2 _9)]HR+bh7?D7a3JJV|t5)hm I*ۻSްA 2R7x\P\ Qa\P\ A 5 Dw xW乏S|@oX
ym\ Y^Ӳ^Y,+Ǎ}q_OIKXKK{q![f\iOoʆhb>!O-K uḮypmA?7tڞ}FHB
弎x 81cG h܇m ><c:lvNb==^~O2D[ oN{ |A1!!U?EWPlp[TWx/ཀ' O xm x3q^ x 倗 <cL[}{Ԁ/. _} ; 6o<x Zk r#GXqmr;NWd? <_x 8 _x -o| w <x= 2|ʕ+㤬릯ص/;o*-N* ʎ|Df}to;jh ҙ~>85.}I o'sVtssQB>W&d1߃fbU5BuP-Y 2iP>~8KYLj3LHn2ǓJs7g_6xn J K ֯xMZ? ~n0
mONūns l`Yp!gqHqP 4Yp考 (XT ,`Xw,6 xoQ0 jLcRs<_{t.$v̟ygs#oF뙃M wȁ?7*ln9xނ 9FŢn9F͍3Xpp9F$S՞`ZTaW8]5FK;ףtO߄*/EQ!H
nh,oK?Mݚ^&]Ǯ'KFTqus~Or{e6}Cɽ99<0 .;]'˟LKȷBь+G~fAÞN5 `NY` \6ڭ݂}~y F7bݫȐBw};skg6^)_=m/0)>WuJ ⵈw-"<\9Eg1TZWfbtZ !gqHqP 4YpZ 2 T2,8w-Ebv`
WgYpZopZ<'miN4~__\#uOj_ZZW7s
G\S1sq~~~˵݇6̡k iSc Pms/՚TkA--}>| _ jjϠ$$If !v
h5 55555555 5
#v #v
:V < 4 d 5 5
/
4 4
< pd T kd $$If T < 4
d$ d$ d# d ( ( ( ( 4
< a pd T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v h5 55S#v #v#vS:V < 4 5 55S/ 4 4
< p T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T 4$$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 4
2 5 +55H5g5+/ 4 4
< p2 T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T 4$$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 4
2 5 +55H5g5+/ 4 4
< p2 T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T
$$If !v h5 55@5@5@#v #v@:V < 4
2 5 5@/ 4 4
< p2 T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T $$If !v h5 55@5@5@#v #v@:V < 5 5@/ 4 4
< T D d
= \z 0
k
# A\ k " s yNEdV F O `@=G yNEdV F # x xڽV=KA5QTB_8*HElJ0Xi3Vj:*ARZiffp7۷yvg6
v0 1>0S x×i>B G> ?%HMccH 91 N175094ad0zU4gy!^6x5O6 ^ca+z8ow"uRKyb?& /fz~"x~YbW|M짪x?X~qRB8NjY//d?i@+og~5!%Z[[%>Ƭ1֛MSWP [ x]5Ʃ0zp|1wms`m 鏨Hi YxZ?s⍴.y-n-g>{XW=355ޫyn +srx]g[^m _e6u|u?onk\Wuk{xÕ:>־/Mǹe D d
$ 0
l
# A] l " v s^$q5 R `@=J s^$q59 ǝ^ x[}lEwףKJh b%~ | QjJH!
Q #PPsw7}ݻoי7ovޛy7;c QH\]&q?Rl<@aK U3 ƠlVU1E W :>AjX ڐ
laVE)cBZ̿~8a.C` =|VX _$^laCøqfN-;aQ\ c9'֍KE=}v~ c 5=;7P_{HfǗniw, ^5nQ{_*[w!8 fc|!wpx 8Ipy0 F ̰ 8z0XH xe& YϘ@B p^#Z8O:+i>xbрsG}<h~ jyz>:І|=; J4)>_W6@a*}D6l#(KiSpdSDkګ|-UiЦ=m ag~ O9_v8Hت|L#]LyX}>ξZg23̍XS70 [e ķ/ܶo۬BT\(6uBNȗ r!_,䋅TKXȋ|O?a7MfsX F1hN!B~Ro -!/2ʉk|w y3B~O y ('^7 y3c/F<Vb.egQ,iGjAKkw5'˪` hT 0 4Y .bY/zTnTAV i#~ zp),[<MalZY o4]HUHo#kw"Q#"-x/tCF:Ԡ^>>>>??UOid^x/ts<R{y]p]9'ϱ+ α1=uaݳ<]fXu* Ն`X܊BRֵu/PU6L+5 ݧQW g{<ꚃO.x/tfcz}x/t_f] c:O=l̥uXv4Vؾ5<i9a} t+~')T\7(s.8fFn j< 0 uסxcC<*RWū QW+Hoz^dx4^Oǧh w!_s!ҏIT#$$sA8,a : b0l,r n3`p!(Xִ0h1c@2',Ƣa٧R^H5teO{m1ha8syEao>:ﺅBN?oJ걳A$@cף01+ФQ*W:1h*J =pFP_=P0cҾnoo_}m;v ]rzש9ߢq܄lZ,m#-3}~-\{]94oOx1="%m*[]va܈6،Waڊ%;4 gENLp xdPy(y^jBB<
}4gEEW]ЩU5v~v~$ԻH<gҍnuĥ!h|I%O6/ͅHDƏ<8] D d
r 0
m
# A^ m " f[Ae { y `@= f[Ae {B $ r x\olEwz Zmz(,`S1g[++! MO)NKRSb-_ h D'c"TR"Ƙݛvؽۻ+y33{ x Ǝ(gO (%:"V!sP /x F ?gP +F
aT+JVT8J!q(yRT=gP>AyL?Le
3(-)J zVNOB]#ZlV' [a(1Fo|f\\ďݾqnDYYj p~Z.ԧKk|9!m{nHv}Qn~:
0t^F+9w]C3ꢾuCl8#_ <Ll. K.8^ |@v\@\p |&<5 `&s1&g^ E'@
~c(P SuEN2qt(5N d ǟ{'і&uِ'GO']R[Mt~5pkd#q#a A>;T(1Ŕ?Jyoax ã 2<C )M 2} Dj{Ǔ>j%֨sr}In(S?M obx;.1p 8ƽqɩ~N[ fOivSR^nw3<0 2<*W1<Ë^Ci5 Kl35c ocx[F72<(ë^# /bxC e0_1U;ݾew0_=$
^_E 2<e _&71|-2 1<.w1 2}Kz8)t2e[RIsẋ7u CZk1)-3<x "Ű=FhcFZ<g^' +fѹ0 ?u=HMyRϥT/ԡ,Ei
B(Z߀8J+ʓ[iۂRlA#(TPtU*T&ʷ 3пg֓ՖcnmsjXbϣvbJ&R =jȗ=4Vz|ft߃Mwv=i__c 0ArL@RrYw"vO堉8Xg ,4C䠆8h08pcQz84`Xq,@6%1
i / y1ٍIύf ~S wnh<!ؿ-'Vp"(&`,rE>:-r "5qIrH| UmOh +7pڔ@=wPQ>>1}%T4-~-mN/70\2~ sk\ߏVTV= ?kZ<Q[Al߁ᱶ/ #?+ǃI= e ʂIx%8@v߇ҵjPڱA GiCzwq'Stwv8 O ֩nV㺻>oZ{kE< ">0Xb-ݗ}*M:2]p㐵8$9! 8pr-Ec d"khqKi da{癞2ߡzo[cߓܗV0ҜBE&NףK]S1sѷHn 9yoHJthf¬/TK?ȴOI { W|$$If !v
h5 55555555 5
#v #v
:V < 4 d 5 5
/
4 4
< pd T kd $$If T < 4
d$ d$ d# d ( ( ( ( 4
< a pd T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v
h5 55555555 5
#v #v
:V < 5 5
/
4 4
< T $$If !v h5 55S#v #v#vS:V < 4 5 55S/ 4 4
< p T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55_ 1 0 8 4 4 3 9 0 9 7 F O O O l e
1 T a b l e S . C o m p O b j h
[ $ @ $ N o r m a l mH < A@ < D e f a u l t P a r a g r a p h F o n t # . 1 4 9 N T W n q -
! # . 1 4 9 N T W n q
- - - w ' - , l " ! " , 2 $ KuTo9;Om% C @ . ! ( ( P
r
6 H
C ? r
6 r
6 r
6 r
6 r
6 r
6 r
6 r
6 r
6
r
6 r
6 r
6 r
6 r
6 r
6 r
6 r
6 r
6 r
6
r
6 r
6 r
6 r
6 r
6 r
6 r
6 r
6 r
6 r
!
6 B
S ?
- ! ^ t = t % t % O t % t J % t % t % b t W N t W N t ] t G t t t t t $ t O $ t $ t = $ 7 t X ? O t X O t X O t
w X O t " X O t O X p O t " X O t X e O t X - O t Q t S t / : z . / : < = C L T V \ c d h n q . @ ̫u - p @ G T i m e s N e w R o m a n 5 S y m b o l 3& A r i a l " q h ҁeҁe 20 A h m a d A h m a d O b j I n f o O b j e c t P o o l O O O l e P r e s 0 0 0 c \ W o r d D o c u m e n t q , { 4 & & Word Microsoft Word Courier New - @ Times New Roman - - " - ? - " - ? @ Times New Roman - - ' - " - - - - - ~ $ > - - ~ $ > @ Times New Roman - & > . 2
& > 1 & . - ' - - $ b - - $ a - & a . 2
& a ) . T{ - ' - - $ y - - $ y - & y . 2
& y The - & ! . T{ - ' - - E$ - - D$ - & . 2
& acceleratio ! ! ! ! ! ! & . T{ - ' - - d$ $ - - c$ # - & # . 2
& # n & . T{ - ' - - $ G - - $ G - & G . 2
& G ( . T{ - ' - - $ p - - $ p - & p . 2
& p magnitude : ! % & & & ! . T{ - ' - - $ - - $ - & . 2
& ) . T{ - ' - - $ - - $ - . T{ - ' - - y - - y - { . 2
{ of& . T{ - ' - - y - - y - { . 2
{ Mb C & . T{ { K . 2
{ K is .... . T{ { . 2
{ that of & ! & . T{ - ' - - @y - - ?y - { . 2
{ Ma. C ! . T{ - ' - - Py ! - - Oy ! - ! T{ - ' - - 7 > - - 6 > - > . 2
> 2) & . T{ . 2
Tz+Ty - ! * . % . T{ n . 2
n is .... . T{ . 2
Tx- % . T{ d . 2
d . . T{ - ' - - 7 _ - - 6 _ - _ T{ - ' - - u$> - - u$> - &> . 2
&> 3) (Mb& B & . T{ & . 2
& )g is .... % . T{ & . 2
& Tz- ! . T{ &S . 2
&S . . T{ - ' - - $S - - $S - .S T{ - ' - - ty> - - sy> - z> . 2
z> 4) (Mb& B & . T{ z . & 2
z )g + (Ma)g is .... % * C ! % . T{ - > . 2
> Tx- % . T{ . 2
. . T{ - ' - - }yO - - }yO - O T{ - ' - - 6> - - 5> - > . 2
> 5) & . T{ . 2
Tz - ! . T{ . 2
is .... . T{ . 2
Ty- $ . T{ . 2
. . T{ - ' - - 6 - - 5 - T{ - ' - - $> - - $> - &> . 2
&> 6) The center & - & ! ! ! & ! . T{ - ' - - $ - - $ - & . 2
& - . T{ - ' - - $ - - $ - & . 2
& o & . T{ - ' - - "$ - - !$ - & . 2
& f . T{ - ' - - 7$ - - 7$ - & . 2
& - . T{ - ' - - $ - - $ - & . 2
&
mass of Mb: ! & C & . T{ - ' - - z - - z - { . " 2
{ and Ma does not ! & & C ! & & ! % & . T{ - . 2
accelerate! ! ! ! ! ! ! . T{ - ' - - z} - - z| - | T{ - ' - Y - bjbjWW = = ] & & & & & 2 & F b b b b b $ > 1 1 b b b d * b b
: b N ?x& & 1)The acceleration (magnitude) of Mb is .... that of Ma. 2) Tz+Ty is .... Tx. 3) (Mb)g is .... Tz. 4) (Mb)g + (Ma)g is .... Tx. 5) Tz is .... Ty. 6) The center-of-mass of Mb and Ma does not accelerate ! # $ & ' ) - / : < = ? A C L N O Q R T W Y l n r t u w
& ( ) - B*CJ h nH B*CJ h nH j UmH > " # % & ( ) . / ; < > ? B C M N P Q S T X Y m n s t v w
! " # $ % & ' ( ) - . / : ; < = > ? A B C L M N O P Q R S T W X Y l m n r s t u v w dw '
& ' ( ) * + , ' ( * + , - N N!"""#"$"% S u m m a r y I n f o r m a t i o n ( ~ D o c u m e n t S u m m a r y I n f o r m a t i o n 8 1 T a b l e aI S u m m a r y I n f o r m a t i o n (
Oh +'0 P
$ 0 8 @ H s s Ahmad f hma Normal Ahmad 1 ma Microsoft Word 8.0 @ @ $TV@ $TV
՜. +,D ՜. +,, h p | MSU j Title 6 >
_PID_GUID A N { 7 A 0 0 C A 2 1 - 6 8 9 B - 1 1 D 6 - B A 3 2 - 0 0 C 0 4 F A 3 B A C F } S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T 4$$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 4
2 5 +55H5g5+/ 4 4
< p2 T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T 4$$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 4
2 5 +55H5g5+/ 4 4
< p2 T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T $$If !v h5 +55H5g5+#v +#v#vH#vg#v+:V < 5 +55H5g5+/ 4 4
< T D d
O 0
n
# A_ n " l :baif H `@=@ :baifL 7. Fn xX=hQg1 T,bKFmBUHm$k`cXi4u3uyLxEoyovH g ߲,^+;eI D.u5gsߑJw^~G7E5- /0m(Ǟ7&wq=jrl8#4 MxʯqJxHxʿ㸚^zM+1S3
_̧5yx0굅"Yre cb=O"XS|_(X^W?#`ԫJ<'b:u4y1?4 Ũ <^"ԛ^(ɧ%Uy+>.3Ī ?9v&EKqX-Z68vs, =Voʨ.ں ;yl<Ag.o98sr1>>& _9n9=|oK ÊxaPu>~mjX7Y!~'ze/ΐFlzwM|\ {b{˓ۡSFY9a>R 1Sg/ Ή O#c[T=gΣziL)^ZgW՟'.}ط9WEe̜sc Zgb_7~;o_:?Fl[Ưq=;oBW D d
% 0
o
# A` o " {)F< `@= {)F< | | Ku m> xXjA1V,iV`K4R6Z1EE-,Xj,/N^,7ps̞93"p
CC_/}:O QQ<9 S#NҨsїoU?'6WLI$WN< MI71%1?V*]+"&3b܌x/FzRS+` c܉x/Fo2~^z++V~q/:+VU{1m 8~{=W&ŨX 㣈b{ %IJW{JJ Wϻ{1xŨy2Ũwg%IJkWj[ QnZjtoHơo4V4MצKG?Y /;=O=*jgk>x F :_O y^ Ň }uk
Uڬ7EQ=ނzcZK|]m7kݡm_}=;oED{]Y8>xhf}}} -he Y$T<6z_k+ϚV<n=Qx=Z@?g>x_ՆdVVl#dmu0 D d
" / 0
p
# Aa p " = Eʘuz@jq 1 `@= Eʘuz@jq|d O $ &U Ct
x\>wv5j(lJ\d.XZq--4ؕXZHShR) mJ
FMG a*6miնoy̛3ٟG.3w93sw{w 48L
' l` i,OLa7Y~-_k anW720p[ǖ
ܛ ;3]0
salf:nS ބnX?~L ot:BjXLt1]ExZ?ƥlF<1>'?4OJV<. ar9B|>ހ <yn'բϚ!srP[9l1}bBnA7u tQK/\Ymw5>zިkY'Iy<ܭ^5އȯ0v+ȿ3?oKF[Bw }P8^Ta~7s7*M<?yNț{OYP>-!o߾~Bț~K]AųF8i<8Gۍ7Η(|O >õкV_Z'82V;4䏵U6}Ah ֝e i5Io)^<o}qo >Լ1ô!pAׄ~qWkJx7
uMEk.=x }x1ah |%2Zo|v;p&JjGJ s~" DK1M!/
\6ƹ& 7X}8)GSF_)LOﭱK1 eYRƘϳbte6%O(?s%?]EmRH?vn*-L?.3<' ~/nѤ[0#gp`˴6q .n|eqqoPCֵōI Kpo}_%o-vks %o Z=y*sN+VݏL.k^bR_卣^U1pWHi0uCcX[ECj;c v+G+fp::k 9}~Daܶa>+ 4^= uĝ &a`H"ٝ0_'6bo̾&6 Axt":`?h.^<}FϑK R:Zx]qݾH,Ӈ27Y9I#m#:G\dU岠~j0efn#~o)c+{ٗ=zEך-ch-|ek_m1v
u?^'{i+Hc]Ơ`7 OM'.mYH0{ӻ4a\cT ø1F±1k/V h.. [<fƵ00ۦ
q?0면.kk
Ed;RrO86 6my,c4+MWa<f~
L|-[p`7܂1n=-REܢ2j宙W !Ub&,TɶAw S,^1n0dLlT_/x9]%G/AmC%G/o6}]m\?H^9^^%M4~B-J \)GX}Ip*:Mٗ&d
qW,R [OQ5nб֝,.TMxӄƝD5W{gZUqsY01gC=Eq_J {( 3^ 3_ +c t
z>G\fl_p; q9e"7ER$N߮4eӎmځ ڑCɟ{+93ֵ mI<2">-ϱ&cu4g0N DACks6n[6fnFxjsU [רhWW#g0:dA}WA7F :&,`nrĴyؓdK䲁<ڊ'܍ ۣ;?Q=?Σ ?0?pl18%1N;+ { h@ Bk ^"ܗB{lpO8Vݳw] $[<O "ι3}=Am:a.oԦ`7MH.s宋ͥ2 +ؚ-EKM8:,<a ϫpZy@F sZge3AU/sY\| :ܴ$-[}}`}P沠0 `6<C.1ܩ^+c<ɈzGkzcP51pnQ_aLjFX 7Z>r .scCКIo2\dTkrhג~kG b&U=L. ҇fah<'@e})soR o@oh -:(N-Z7cuԺw/ݑ{5`?֬,xaoZ/2|1 x D d z ^
c : A ? f i e 2 0 0 2 A t w b A-6&!' n A-6&!' PNG
IHDR +\ sRGB CIDATx^_\Uǫa" ڼV ܀yÚ UqYpp%$aM!IDg`6 6dic _Mխs6ms]@y4Zj
sje;??_^Uvﾋ|ܹ:`U|ĉ"N"Yk?x2ʑd <xܹV KߗFZ^vl5: GXԷoӧO*ͺJ {,v6$Fauï;NӓcPdNC6 xf438wA_oݺ?ٳgL:wDaռe0ds*L!L?˝l[0dgsW]r0l2,w
Kn&SM]C6wɭd
ܹ+`.ULC6~;w %
)`&r简!Va2 dY0ds*L!L1zeRY
5dpݨΪڔk֦rl&=⢦!
dz'5dQ<H}`F+ĘXjjFU,Fjl ҥCNA)ŐMWK[wC6tS2T&_jM5dZںw!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,l!ᮀ!뮕,ln7|? /zÅ7qʲ<0dS@[\on3괸:s^_7w?I\1Ȑ<_'8eh7fr5ך_͍_Fo#|wnt*'8uhfmcMlx v=??zʹ?iiiI^6ۚ nmL~ ZY't .n {K.]vMm|5{/qk({oFؕPXֶ(]v ?~\q /) .^Y6lcFfeC s12?ՙ7n={#,,,[s=sǎ |+ CvR Mf:->A; ԩSO<D$RG@} *<VY.{&Cֳz$&> dBXNcq1۟yUZz~uGh1( pH 8p ]XNX6öm۰xWʘ,z:G\ t>>q ӧO``QA"+/ !+K][[c<w9k,-\Ifi!5#g`BO0L ce*Z7l,Y d߀T쫛V駂#GOf\$#y xkcz>JTjkX ,<q?Lز6lj<A5,h=0[?2[ۗajBǪWbv Tt.]5CkȪbb Y ۑ60OZV*bjdeeDk;8ޒK ƟO0qɡ- KO.8 Ů# "fЮr\ K`ժ|X1ߟ _-olɾ%hVuqqQg dj{i|6M9bЅFfh0fj}{V}^r,6>U/ \g(#ۼI kis= ]|&.8uA`uu&F#=+W=ݾ'p5j|L\mIn6: 0݁zl%tpux|O``սM8#3%Wpӗcꁬ7 _L] ~o 7Qzsmż=s挬:處ө1eF9F22Ook~ /Z#{'fQDžb[^5ݮx ?Ԁq.FT^A0`AcuB4#Q- ƀlE[n۳L3kN`Mt[' +7ma;S^yC]55Y*pqNUP.z}dG#;4$Q>E2[_LxG&y ̖G*/6#ŭ2y¨~Gvڗ)ϸͧ{pWɡ ` bHDRRO :""d!V:3v)P}+)*V$~bge (+++ux2 z衫W&4ﷀ%T FI: _,au*.3'^I27|#cU[dqL<g[cY`ߋp7?9ϖT7ARї=BH'|`̈́j$s" RFakD[Y{de6os Ə2Vp%&}JK =
T Y]ܹs.?\
Qb
K\{2.[z < &1 d)#/,;+nDNz 뽔G&Mu%Y.8NKyx/W1:*. 4ݐyx䥗H/e}Z.8 Kyy~Y3%eOP +Ȫope'.t`A|@M vذ/'{i+rAd5MDkm%ݩϒ$b®0,.ۯۉŊyk4'{\0` W.|y<~H'q3gXҪ`0a62kF8~0Z5 V #i Z_n 1_upi[_+P/dCk{y-n,ߐv=#)*&P/d#`X%lg7LG"ڄ}|a]CNvj8:vCض˻j))klƚ,kL0IЕYݪ+̪ˁumuD֛0y<_e B^yZ^]݆A"6py^R['GI|@rm`C/; SO0A4'N e; Y7<'Ic Z7Vk* }Vko<2̖ά2} jozi#o@֎1^ܲc^~n.F1!dG }ￗrxLY٠d:_?2EmbpXIQ氀
+=0rs(Yٻ~)!]zoa>*7GM.7 [l;iכ[oJ8v_#ؼJG֠]ۚ~Baty %pD;y_GgV2@gk]kfGTٴ(s%KԈ2cx1" ʘLeq-;^XV ,e&C-nv56H9:
5f࡙u5K] S۪b)9*VHgV6(Y٤&CþeGno;)hem_eT#HqQ<l-7mȖjvC^6d{l![nsf"6ڐ(Xa봥6'saUC ٬r3RHlk_fێYnB, z3:s-ېU * :s=* DMWs ictH+C6Sxf\xL,3*+lhٌɼX
j?g^+0dgq 0ZR%m!櫼5d^vUr॒(^na(^i!VJn
Im!VJn
:ϭKZQAEڮ/)I5&UT.
lWݗ [[Pd'1毚8Cʇ쨛;C( 3jC6W%W*^Jd ZXJdUâ%q ܶ";nսjEL:=cH8-js'Gn~~T&N{L~n^*y!Ev HEܹe Gp8ޡݻ99}ȟ8Kj~Xi>wV-vRZJF.X9?x(%R0QNs&8*rC q'(UxŔnp (6|{k^b2F7I347 :grV(ռF]-pRi"J͵8 ~Rɍ\:|kbnٜ#xKk
<o> rUc=ţG<9okZ3)y͛ l ]ȧ6'>-
B<x_1նl:W[Uد5dIѣR
_uO > ecfK1
ɊdY { Uuː
Y>F]CLΫ6|L9Z m%C6+d\xjA5d3A k$\ai! zWJބj#<3}LpvkekW\SStylR"^, _QhӬ0CK9xգ N cȦeV; pO鏷&L ^{m&wOa%)̧ :8C 7Ч> ސu%= cW?`˹5j4o`:w~ } ߛ4TʌZ!U)hy ߖ¦Y!`W}vVP ٔP\B4kwuN>6aAalbz%< 2IeȦ,+ȆLn4%n!ڞ/\E9) \!,[E Hz-D^XXpi@I.!#JL㣆y54 oa.rld&gw-1j3qM6 jPh3OmEJaģy#q<T)! q9N W*J{2dcK8X`:K<BܕH8C6}MqeE:])9
4+M(;)U(yt^8J}aV6Ĥ= &N{JoKBż$5dG_Y̬ rMnd^GJ~~xC6^)XBmu`g&+ 8EbV6!WdC%$WzlLY٬x\a :ӛVOҮ]g/[
l J^=kV6C^)b/gk1SN=ތJ7+.]}Q7L]8$udf,cS;HS}ũS WedY~Al,2sB*ڋ/Nkty@y}UceNs'InXoetf7z5|4$9 x zȑjW
o~1s"o+mU ?Ο;煤<T?\LPD7ŊW8 ʝ`2l Yj L*/''9>r٫W[=yՐQ`c\uH;<XTK.5]s&N]}MwpGji^O~̿~8qJS@Y PÚb2#\9Rs sݻo/xG/ѿ(jY|%ekO6`67i`{ڐ#y˕J֚|З-y+xu O1`㜄?Z?1`S7R( ` BƱ9{"l.զ5gPɗQ氆:{B-'9+hL߁K0
_lG#[h,ϙ؍g KpIRU֛oo D ᚣb]2T7dfho95\x5CkW)x YJx/^+ 7O B6K;pq_!ow٨%VOy\&졑qX`hA?Li^]]jbS98Ju]R p0.$܋po@"dRpgj© 7%kj:- PKsjWWo̒;hheW9YIu<O`>$˸QݒUY>W"ZOt ^U0&7LMPhft"&#ȳѯRvw 2d]K"mܳnʺO|9NԠQnV6 kltzԶqrوW!>6.,AZ
A6|j "&S{ pjh^} wI|ZGsTCyb+/hY2Ubݛ~9 ˍ-u4*E$t:Ci$
r : ZJFzTt2ΊM@99HeP5UQkuشc&vt3IPdԩ_ SֱcOk f@iUV% ь}fbgKeB!&YCCMXq_ '٥,@ɐIQj%"vB[y%zf_y(*nWbCbL} f_n)Uj ڍM6jf !c}; V -V>mk Rez!^9]ܶma\zT J|QCֈ2JG/>l $ %E-{#r 5̧ڠѱ!U"܆iyq/Y A"e_lzZi+`f.UlzZi+`f.UlzZi+`f.UlzZi+`f.UlzZi+`f.UŇ,x>k <xC6)p,a4YBjȦ% JA,\+*`P2
7 6{p IENDB` D d
X B
s
S Ad ? 2 O E?
3E j: ``! O E?
3E4 % , xXMlE~3$vHIiZ& PD-qDxX"vd; 5&U. BrR ^B $Rs! 7kgqv-go~f@u7@']NW Q끉TX.NWL')>=G=~|V/)~;M;%xg2c~X
r$Oq}t?J e?018}P1cb; 懍-q $7MGΣD|"͇[uuȶq6 ] _@.R㽧`xh:exS[ fqb=3@m:h:G"ɐVc<6fl1|G` 8{2'I00zUy*0ؐ<E=J'Ԕ]*"rw^} :O&>)Ĝ8U9gB$xE3 {qJD$jH\g<'9sK<k*+9F9w UC :bVH #Jyg*-ê
FxeUxmO
?p~&8o4Uzb҄kDޮqt*>e3g +'= {bxX8A!^5pEb]̮Q_f fva 45mVy%w%F㩲Fs,j.8 =eWXvuRiUYSyBy 1 s̢git\3Z3hkX%Kr 4IBv
F.Eś#湉IG]NԅX/E]/2]i/1O!qBR-BO!t;fp퇖HĬԯ#*H*F5D!,1: :
yi$WfyJqtrf="Frz$ZPoWMEAKj"wpn{2ĴcЍ5]e/˔> Cr FV]MiHjvHԚ8OD? ' aiRH0h{4* t+ 139e*vI9oI3?`/Gw#mv)}Tz`UHw$}¸1M0^cc\|#Cmn ] Rt U36D$V=U3 AzBa<k˸5'&y^o 6wǍe~kԨ\c>Nz<f9brev,+x$C|g&ĩW_<%'vyO;1H,O`qG $$If !v h5 55S#v #v#vS:V < 4 5 55S/ 4 4
< p T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T $$If !v h5 55S#v #v#vS:V < 5 55S/ 4 4
< T P D d
r | 0
t
# Ae " @V}!a| ME `@= @V}!a|8( Ì( n xZohU >00BF+W^ܰCJ tcRR0.80*DB 4h
`P` ႃs|8~/&]8{? sn"P wd<V^s[y7Tv`wp~?;݉o:ϵ<՜{rφpoS( ݏ<Bz><kHx¯7 6- oŬW&^%0xW ޖĻBPޠZ0/Xk oŬOഽޥZ0/X'ފYox+ ̩O)8_8bW}^^^:88_ڳFR,'^x+f\q⭘o^ܞo|*3_|zx+gS7E)[1?G%YԞ/i|;CbŬ A 9⭸Sx'ފ=$ފ9_8.os9\Yzg ^k׳bֻBxX1]'^pg }?m_]{{Z1-/XyמSҷ`[ ފ/:Vϼlߤ"=+fe◩KT%iŬL_ӊsYWrƓ>aQܯx*\ J_^/K<:ڷ<V<:=Cʓ/RNO7vOV /Eo@?_0z r(;cC*~|Pes; w- Cѱbz1]ӽ&/e j_ &bw<\DVvxX;JE5/:[}# \0곹@?q䳸Xb>j~/v>`|.ȗt0è.^Cvo^{ oQuLMƳxECȟ#
o= v[Eznc
oAxءp,u؉~挢oz =*
1+O@(Ai].Go#g-u \|:j<, 0P bzX9=[x@ UEc,& * a=/z#ƺ,KOg֥`r]=xJEϓ} |~9> ֯_ 淯
o_`R9iϘR#lߦ1Uo(<|pΰ, g+=2"W/(=+3폢_]R|>ޕ?ߐK7DKD [ 8 @ 8 N o r m a l _HmH sH tH B @ B H e a d i n g 1 $$@& a$ CJ aJ < @ < H e a d i n g 2 $@& CJ aJ H @ H H e a d i n g 3 $$@&a$ 5CJ \aJ N @ N H e a d i n g 4 $$d @&a$ 5CJ \aJ B @ B H e a d i n g 5 $@& 5CJ \aJ F @ F H e a d i n g 6 $$d @&a$ 5\B @ B H e a d i n g 7 $@& 5CJ \aJ @ @ @ H e a d i n g 8 $$@&a$ 5\F @ F H e a d i n g 9 $$@&a$ 56\]D A@ D D e f a u l t P a r a g r a p h F o n t V i@ V T a b l e N o r m a l :V 4 4
l a ( k ( N o L i s t 0 U@ 0 H y p e r l i n k >*B*< O < H 3 $d d @& 5CJ \aJ < O < H 4 $d d @& 5CJ \aJ @ O @ H 1 $d d @& 5CJ0 KH$ \aJ0 < O < H 2 $d d @& 5CJ$ \aJ$ 8 O 8 A d d r e s s 6CJ ]aJ b O Rb P r e f o r m a t t e d ( # ~= z9 !v% OJ QJ . X@ a. E m p h a s i s 6]@ V@ q@ F o l l o w e d H y p e r l i n k >*B* D B@ D B o d y T e x t $a$ CJ OJ QJ aJ B C@ B B o d y T e x t I n d e n t $a$ : Q@ : B o d y T e x t 3 CJ aJ 4 O 4 H 5 $d d @& 5\4 @ 4 F o o t e r ! . )@ . P a g e N u m b e r 4 @ 4 H e a d e r ! * W@ * S t r o n g 5\. (@ . L i n e N u m b e r @ Y @ D o c u m e n t M a p ! -D OJ QJ f O 2f t i t l e + " $$$$ *$@& `a$ 5CJ OJ QJ \aJ T O BT a u t h o r # $d *$`a$ CJ OJ QJ aJ P O P a b s t r a c t $ $d *$a$ CJ OJ QJ aJ d O Rd r e f e r e n c e i t e m % $ *$^ `a$ CJ OJ QJ aJ L # L T a b l e o f F i g u r e s & p^`p : O r: c o n t e n t
' d d CJ aJ L ^@ L N o r m a l ( W e b )
( d d CJ OJ QJ aJ < O < w a 1 0 1 7>* CJ OJ QJ Y( aJ d $@ d E n v e l o p e A d d r e s s ! * @ &+D/ ^@ CJ aJ : %@ : E n v e l o p e R e t u r n + 4 O 4 w a 1 0
, d d OJ QJ 8 O 8 w a 1 7>* CJ OJ QJ Y( aJ 4 O 4 b i g t e x t 5CJ$ \aJ$ 6 @ 6 F o o t n o t e T e x t / @ &@ @ F o o t n o t e R e f e r e n c e H* J O J B u l l e t I t e m 1 $d *$a$ CJ aJ t e@ "t H T M L P r e f o r m a t t e d 7 2 2 (
Px 4 #\'*.25@9 h \@ h z - T o p o f F o r m 3 $&d a$& <CJ OJ QJ _HaJ h mH sH tH \ ]@ \ z - B o t t o m o f F o r m 4 $$d a$ <CJ OJ QJ aJ R ORR F i r s t P a r a g r a p h 5 CJ OJ QJ aJ mHsH\ R@ b\ B o d y T e x t I n d e n t 2 6 $hdh ^ha$ CJ aJ B O B p 1 a 7 $d *$a$ CJ OJ QJ aJ t O t f i g u r e l e g e n d 0 8 $$
& F? *$^ ` a$ CJ OJ QJ aJ | O r| h e a d i n g 1 < 9 $$$$
& FG `d *$@& ^``a$ 5CJ OJ QJ \aJ x O rx h e a d i n g 2 7 : $$$
& FG @ d *$@&^a$ 5CJ OJ QJ \aJ O r h e a d i n g 3 ? ; $$$
& FG xd *$@&^`xa$ 5CJ OJ QJ \aJ O N u m b e r e d I t e m ` <
& F>T . O I t e m = $
&
F d *$>T - ^ `a$ CJ OJ QJ aJ x O rx h e a d i n g 4 7 > $$$
& FG p` d *$@&^a$ 6CJ OJ QJ ]aJ > O> h e a d i n g 5 ?
& F: @&` < Z@ <
P l a i n T e x t @ OJ QJ aJ b P@ b B o d y T e x t 2 " A $ p@ H d a$ B*CJ \ph L `@ "L H T M L A d d r e s s B 6B*CJ ]aJ ph ^ O 2^ c 8 C d d [$\$/ 5B* CJ OJ PJ QJ \^J aJ nHph tH O A h < < T O C 1
E x x 5;\aJ P . P T O A H e a d i n g F x 5CJ OJ QJ \^J aJ 6 6 T O C 2
G ^ :aJ 8 8 T O C 3
H ^
6]aJ 2 2 T O C 4
I X^X aJ 2 2 T O C 5
J ^ aJ 2 2 T O C 6
K ^ aJ 2 2 T O C 7
L ^ aJ 2 2 T O C 8
M x^x aJ 2 2 T O C 9
N @^@ aJ T , T T a b l e o f A u t h o r i t i e s O 8^ `8 X S@ X B o d y T e x t I n d e n t 3 P $hd ^ha$ CJ :
: I n d e x 1 Q 8^ `8 : : I n d e x 2 R 8^`8 : : I n d e x 3 S X8^X`8 : : I n d e x 4 T 8^ `8 : : I n d e x 5 U 8^`8 : : I n d e x 6 V 8^`8 : : I n d e x 7 W x8^x`8 : : I n d e x 8 X @8^@`8 : : I n d e x 9 Y 8^`8 6 ! 6 I n d e x H e a d i n g Z B M q d @ I `(
n { ) 4 D ^ h j | ( 1 ; ? C Z ^ t ( 5 C Q - = Y
+ 6 4 x K 2 T `( $ ) , - / 0 2 3 6 7 8 : = @ A C z y x w | ) 4 D ^ h j | ( 1 ; ? C Z ^ t ( 5 C Q - = Y
+ 6 4 x K 2 T
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; `( : ; < = > ? @ A N O P _ i j k l # 8 9 : ; 2 3 <
a b 9 : k l 6 ! ! ! ! 5! W! p! q! z! ! ! ! ! ! ! ! " " " 4" W" h" i" x" " " " " " # # G% d% ' ' ' ' ' ' ' ' ' ' #' %' &' '' ( ( ) ) , , , - - m. n. J/ K/ / '0 0 1 1 4 4 V4 <6 =6 7 7 9 9 : : = = = > ? ? r@ @ A A lB B RF SF nF |I }I I #J &J (J )J L L 0N 1N ON Q Q S S S T T CU nU oU V !V W W W W Y Y )Y Y Y Z Z Z Z [ 0[ 1[ u[ [ \ T\ \ \ )] o] ] ] 7^ ^ ^ 2_ _ _ ` d` ` ` $a %a \a ]a c c ,d -d qd d d Ee e e &f mf f f f f g g i ]i ^i zi i i i j j k Co Do r r t t t t t ]x ^x sx z z z z z z z z { { { { { { { { | | T} U} ~ ~ Y / / ! 3 r s K ( d [ Nj v ώ Ő ̖ + , A Z [ Л r ܝ , Z ͟ ȡ a b u [ ֪ T U Y խ E u ͺ κ Լ 4 5 6 K \ n o 3 6 : C J M O Q R K m M N e g $ 7 8 I $ I J K S k ' A # , -
~ ( ) P %
&
?
F [ # \ { 9 { ( ) 7 O P d 4 5 H T U m $
$ $ $ $ @% A% % i& ~' ' Y( Z( ( ( ( ) M- N- -. .. 0. 1. ;. G. H. 0 0 2 2 5 "5 $5 %5 &5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 "6 &6 (6 ,6 .6 26 46 56 C6 E6 G6 J6 M6 P6 S6 V6 Y6 \6 ]6 h6 m6 r6 w6 }6 6 6 6 6 6 6 6 6 e7 g7 l7 y7 |7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 }8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 : : : ; ; ; /= 0= O= = > > > ? \? }? ? ? 8@ y@ @ @ 0A sA A A A A A B gB B B AC C C D AD D D E JE E E F _F tF F F G bG G G H -H oH H H :I I I I #J oJ J J K [K \K ]K K M 7N KN ^N uN N N O &O GO qO vO xO yO O O O O O +P ,P Q R V V (V W +Y Y Y Z Fa Ga a a a b Eb b c d d d \e f Nf g g [h h i i i i Ik Uk Xk kk sk yk zk }k k k k k k k k k k k l l ?l @l Cl cl xl l l l l l l l l m *m Gm Hm Km km {m m m m m m m m m n )n Gn Hn Ln ln yn n n n n n n n n o o 7o 8o 9o uo vo q q q Fr ~r r s s is js ks |s u v v v w Tw w x jx x x x x y y y y y "y 'y -y 4y 9y ?y Hy Qy Ry Ty y y y y y y y y y z z z z z z z z z z z z z z z z z z z z z S{ W{ [{ ^{ c{ g{ i{ m{ r{ v{ z{ { { { { | | | | | | !| %| *| .| 2| 8| =| >| @| | | | | | | | | | | | | } } } } } } } } } } } } } } } } } } c~ g~ k~ n~ s~ w~ y~ }~ ~ ~ ~ ~ ~ ~ ~ ( , 0 3 8 < > B G K O U [ \ ^
$ % ( ŀ ǀ ˀ Ѐ Ԁ ݀ I M R U Z ^ ` d i m q v { | # ( , 0 5 ; < = Ƀ ރ L a T A < y ȉ b e & ) 8 B C F O 3 4 7 M I J M ] e f j z Ɛ ǐ ː ې % & * = ȑ ɑ ʑ ! W X Q R U ^ g n o q ǔ Ȕ ʔ ' , 2 3 5 { Ǖ ͕ ҕ ӕ Օ A G L M N 2 3 4 B 4 5 6 K 1 2 \ u v g h ' ̪ Ƭ ٬
" ) 0 7 > ? C J Q X ^ _ c j q x ĭ ˭ ҭ ٭ ڭ Ů [ e f ò IJ n o ϸ и ۸ ' . 5 < = A H O U \ ] d k q x ~ ƹ Թ ۹ ͻ g h k l % ! q r ' 1 ; < B I P U V Z a h m n r y - . t v { Z V W 6 7 < @ / \ ] Z [ X Z c d e f n u
O P % & : D N X Y Z c k t | & , 2 8 > D E T Z ` f l r x y { | } ~
& , 2 8 > D E M S Y _ e k q r y J K ; K Y Z , > P b c m $ N O K L ^ _ C D # ) * + 0 6 < B C D J P V \ ] ^ e k q w x y z { | } ~
! ' ( * + , - . 8 T U S T F W I R 0 w! ! 1" " 9# # $ $ % % #% $% % % Q& R& ' ' ' ' h( i( ( ( K) L) ) ) T* U* + ~+ + , , , , - m- n- c. d. . . / / / / F0 G0 0 0 1 1 E2 F2 13 23 &4 '4 4 4 5 5 6 6 6 6 o7 p7 f8 g8 8 8 9 !9 9 +: ,: R: S: : : ; ; < < = = C> D> |> }> > > ? ? @ @ WA B B B B B C C C D D D D E E 5F 6F F F -G .G G G gH hH H H CI DI J ~J =K K L 9M M JN O fO gO P P {P |P Q Q UR VR R R S S 0T 1T T T U U V V V V W X wX Z Z Z Z $Z 2Z RZ eZ fZ rZ Z Z Z Z Z Z Z Z Z [ [ 0[ =[ >[ T[ m[ n[ [ [ [ [ [ [ [ [ [ [
\ \ \ -\ .\ N\ `\ a\ p\ \ \ \ \ \ \ ] D] p] ] ] ] ] m^ n^ ^ ^ ^ ^ _ _ )_ 7_ M_ `_ k_ l_ _ _ _ _ _ _ ` "` 4` ` ` a a a a a a b b b *b <b Eb Fb Ob Pb b b c c Lc Mc c c 0d 1d Kd Ld hd d d d d d {e e h h h h h h h h i i i ]i ^i mi wi xi i i i i i i j j [j \j rj j j j j j j j j k 'k <k Zk k k k .l l l l l m Om om m m m m n n Kn Xn n n n n n n n n .o \o }o o o 2p bp p p p p q q 1q mq q q r ,r ^r r r r r s s s s s t t t t v v v v v v v +x ,x ;x Ix Jx `x ~x x x x x y !y Wy y y y y y z z z { H{ I{ d{ { { { | m| | | x} } } ~ E~ ~~ ~ ~ 1 a B ׀ < o ҁ e т ӂ Ԃ ڂ ۂ 5 N k ~ : ; o Å υ 0 < R f ~
o އ . U { ݉ މ 7 X k w x ŋ > S & i F E q r % d q r s t u v 4 S n ܕ - H c ~ Ж * + H e & A \ w ɘ ʘ / L M h ԙ
% @ [ \ w % t Û a N E F \ v Ş 4 N i ֟ ן ' > W w x Ǡ 4 5 K f ԡ 2 M i 9 [ \ u ţ 5 Q h Ԥ ) * R S n ť ƥ ǥ ȥ ɥ ʥ 1 P e } ۦ E p W k ̨ ( H { ˩ - i Ъ 4 n ܫ 6 g < q ֭ ; z 0 e 3 s / L i [ \ j k w ̲ Ͳ ϲ Ҳ % + 1 2 4 6 D J P Q S U c i o p r t ³ ȳ γ ϳ ѳ ӳ ? @ B C D P R S T U a c d e f g x } Ŵ Ӵ ܴ # ) / 5 : = C I J L O U [ b i o s y $ % & / 4 5 6 > C D E F G H i j p x ¶ ö % & ( + . 4 9 : ; < = > ? @ ߷
! ( ) + . < B H I K N \ b h i k n | ¸ ȸ ɸ ˸ θ ܸ " ( ) , / = C I J M P ^ d j k n q ǹ ι ӹ ֹ
$ * 0 1 4 6 D J P Q T V d j p q t v ĺ ʺ к Ѻ Һ - 2 @ R \ j t z ǻ Ȼ ʻ ̻ һ ݻ " ( ) + - 3 9 ? E J M S Y Z \ _ e k q w } Ǽ ̼ Ҽ ؼ ݼ ! & + 2 8 > A G M N P R W \ c j o r x ~ ߽ ! ' 1 2 ; A J K S Y _ ` a b c ž ƾ Ⱦ ˾ ξ Ծ پ ھ ܾ ߾ / 9 > ? A D G M R S U X [ a f g i l o u z { | ҿ ӿ ! " O P p - H I U m ( N f - E ` h i F < 2 5 ` U 5 U r u W ( R | J K i C m ? i l m : ] y ) J k n 2 3 S : d 6 ` " a H ~ * - L ` ~ @ Z ] * T W I j k / r D m ? i ; > ` : o W O w ? B n ( L r 7 ] { ( D i . X w z { F p s v = g h i
> a 8 b < _ ) D f % C o @ m G L Q < = D E x { S + a 9 o ' B ] _ # A U s 5 _ , Z D F i $ & K " $ a % ' g 2 e 3 R q < o H O Q J i S
W
v
! e ) + h % D c ; = o : m o G z n " I J ? @ m o r _ ` ! - . 3 4 o p f g
= s t u j - . ; < Q R ` a t u } ~ x y ! ! i! j! ! ! " " v" w" " " G# H# # # $ $ ?$ P$ s$ $ $ $ $ $ $ $ % #% $% % % % % % % & & & & & & F& G& M& S& l& m& v& |& & & & & & & & & & & & & & & & & ' ' ' ' ' w' ' ' ' ' 7( ]( ^( a( 0 0 0 0 0 0 0 ( 0 ( 0 ( 0 ( 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 70 0 #0 80 70 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 80 80 +0 0 ( ;0 70 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 50 G% 80 G% 0 G% 80 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 0 G% 3 <0 G% 3 <0 G% 3 <0 G% 3 <0 G% 0 G% 0 G% 0 G% :0 10 4 70 4 70 4 0 4 0 4 0 4 70 4 0 4 0 4 70 4 70 4 70 4 10 4 10 4 10 4 10 4 10 4 0 4 0 4 0 4 0 4 0 4 0 lB 4 0 4 0 4 0 lB 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 lB 4 0 4 0 4 0 4 0 4 0 lB 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 lB 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 lB 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 X 0 4 X 0 4 X 0 4 X 0 4 X 0 4 X 0 4 X 0 4 X 0 4 X 0 4 X 0 4 X 0 4 0 { 0 { X 0 4 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | X 0 ! 4 ( 0 4 ( 0 4 ( 0 4 0 ( X 0 ! ( 0 0 0 0 X 0 ( X 0 ( 0 ! 0 ( 0 4 ( # 0 4 0 v 0 ! v 0 v X 0 v 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 , Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő $ 0 Ő $ 0 Ő $ 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 , Ő 0 Ő ' 0 Ő ' 0 Ő ' 0 Ő ' 0 Ő 0 Ő 0 Ő 0 , Ő 0 Ő 0 Ő * 0 Ő * 0 Ő * 0 Ő * 0 Ő 0 Ő 0 Ő 0 Ő 0 , Ő 0 Ő 0 Ő 0 Ő 0 Ő $0 Ő '0 Ő 0 , Ő $0 Ő 0 Ő 0 Ő 0 , Ő 0 Ő 0 Ő 0 Ő 0 Ő $0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő $0 Ő - $0 Ő - $0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 50 Ő 50 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő 0 Ő (0 Ő (0 Ő (0 Ő 20 Ő 20 Ő 0 Ő 0 Ő (0 Ő (0 Ő (0 Ő (0 Ő 8 0 v (0 8
! " # $ % & ' ( ) * + , - . / 0 1 2 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~ 8 0 v 8 0 v 0 0 8 0 v 0 0 8 0 v 0 0 8 0 v 8 0 v (0 K (0 K (0 K (0 K (0 K (0 K (0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K A0 K 0 K 0 K 0 K A0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K +0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 K 0 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 ; 8 0 8 8 0 8 8 0 8 0 8 0 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 20 8 0 8 0 8 0 8 (0 8 (0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 50 8 0 8 (0 8 (0 8 0 8 0 8 0 ; 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 ; 8 0 8 6 0 8 6 0 8 6 0 8 6 0 8 6 0 8 6 0 8 6 0 8 6 0 8 6 0 8 6 0 8 0 8 0 8 ( 0 8 0 [h 0 [h 0 [h [h 0 [h 0 [h 30 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 50 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h [h 0 [h ; 0 [h ; 0 [h ; 0 [h ; 0 [h ; 0 [h ; 0 [h P0 [h ; 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h 0 [h ; 0 [h 0 [h ; 0 [h 0 [h ; 0 [h 0 [h P0 [h ; 0
[h 0 [h 60 [h 0 [h 0 [h 0 [h 0 [h 0 [h [h 0 [h 0 [h 0 [h 8 0 [h 8 0 [h [h 0 0 0 0 0 0 0 (0 0 0 0 0 0 0 0 0 0 0 0 (0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 20 20 20 6 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 $0 0 $0 0 0 4 0 0 0 0 0 0 0 0 $0 0 0 0 0 0 0 0 0 0 0 (0 (0 (0 0 0 0 0 0 $0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 $0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0 0 0 0 p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 %0 p %0 p %0 p %0 p %0 p %0 p %0 p %0 p 0 0 p (0 (0 0 0 p (0 p 0 p 0 0 p 0 0 p (0 p 0 p 0 0 0 p 0 p 0 0 p 0 p 0 0 p 0 ( #0 p 0 p 0 p 0 0 p 0 p 0 p #0 p 0 0 p 0 p 0 0 0 p 0 p 0 p 0 p 0 p 0 p 0 p 0 p 0 p 0 p 0 p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 0 8 0 8 0 %0 8 %0 8 0 0 @ 0 @ 0 0 @ 0 @ 0 @ 0 0 @ 0 @ 0 @ 0 @ 0 @ 0 0 @ 0 @ 0 @ 0 @ 0 @ 0 0 0 @ 0 @ 0 @ 0 @ 0 @ 0 @ %0 H %0 H %0 %0 H %0 H %0 %0 H %0 H %0 %0 H 0 H 0 0 H 0 H 0 %0 H %0 H 0 0 P 0 P 0 0 P 0 P 0 P 0 P 0 P 0 0 P 0 0 0 0 p 0 P 0 P 0 P 0 P 0 P 0 P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ` 0 ` 0 ` 0 ` 0 ` 0 0 ` 0 ` 0 0 ` 0 0 0 0 0 ` 0 0 0 0 0 0 ` 0 ` 0 0 ,0 ,0 ,0 ,0 ` ,0 ` ,0 ,0 0 0 0 0 ` 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 x 0 x 0 x 0 x 0 0 0 0 0 0 0 0 0 x 0 x 0 x 0 0 x 0 x 0 x 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 p C0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 $0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 p @ /0 @ /0 @ /0 @ 0 @ /0 @ /0 p @ 0 p @ /0 @ /0 p @ 0 @ /0 @ $0 p @ /0 @ (0 @ (0 @ 0 0 X @ 0 @ 0 p @ 0 @ 0 @ 0 @ 0 @ 0 0 X 0 p 0 0 p 0 X 0 p X 0 0 p 0 3 0 p 0 3 X 0 p X 0 0 p 0 g 0 p 0 g p 0 H x 0 g 0 p 0 ` 0 p h 0 g 0 p 0 0 p 0 0 p 0 (0 p (0 (0 p 0 p 0 p (0 p (0 0 p 0 0 p 0 (0 p (0 (0 p (0 0 p 0 p 0 p 0 p (0 p (0 (0 p (0 0 p 0 p 0 p (0 p (0 (0 p 0 p (0 p (0 0 p 0 p 0 0 H 0 0 H 0 0 p 0 0 p 0 0 p 0 0 p 0 p 0 p 0 p 0 50 H 50 p 0 p 0 0 H 0 0 p 0 0 p 0 p 0 p 0 p 0 H 0 p 0 p 0 0 p 0 0 p 0 0 H 0 0 H 0 p 0 0 p 0 0 H 0 0 H 0 0 H 0 0 H 0 0 H 0 0 H 0 0 H 0 0 H 0 0 0 0 0 0 0 0 0 0 0 0 0 H 0 p 0 0 H 0 p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 p 0 p 0 0 H 0 0 p 0 p 0 p 0 p 0 2 0 H 0 0 2 0 # # & & & ) 8 ( +/ P \ %i | ʞ R ) Ĺ ݺ K 1 L O o " ) P E / F6 h> ? B 7V O` f q y w~ 7 N : U M
v w X
I L 6 U " ' 0 F: ]F O X ` l v E 5 b < { ! $ ( , / `0
5 7 9 ; E F P R \ ` a c d { > A N U W Z \ ^ ` f l 2 ( ) p) ) * h* * / 8 I [ a f mn z a 4 : ' 9 d$ Y0 8 > h> ? ? ? @ @ @ @ F M \S Z Ej Is zs s ?t t t Gu u u Gv v v 7w y j ^ ф Ӆ K Lj U b B 3 I e Ƙ % X n ǜ 2 ҝ L 4 h > ^ ٵ [ < \ ~ g ; U m d & t & D Z x | & D S q ) B \ w } ' - W$ 9+ K1 7 f@ }F 5N fW ^ eb b b b c =c mc c c c
d -d `d d e lg i l q ]q wq q q [r r r r Ou y { ~ + I ׁ H E N О N W Ԭ ܳ i ̺ 1 P o λ P I J 5 E 9 ! H h ( I j 0 P p ( ) Y Z M N ~ ! J ` / D f z i K ? w g _ L o _ ! " }' ?, . F. v. . . / `0 ! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 6 8 : < = > ? @ A B C D G H I J K L M N O Q S T U V W X Y Z [ ] ^ _ b e f g h i j k l m n o p q r s t u v w x y z | } ~
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = ? @ B C D E F G H I J K L M O P Q R S T V X Y [ ] _ a b c d e g _0 v ! # & 7 9 @ Q S ' ) D U W c t v y _ p r A R T s M ^ ` i z | ~ ְ ) + 0 A C K \ ^ c t v x Ʊ ױ ٱ ۱ 3 D F u ˲ ܲ Լ e v x 1 B D # % K \ ^ ) + & ( [ l n t ^ o q " ! ) + 1 B D X i k ! 3 D F F W Y a r t i z | Lv ]v _v fv wv yv v w w w w w jx {x }x x x x Ty y y z z z z K{ Q{ { |
| @| | | } } } } O~ a~ ~ & ^ ( h } 8 G Ƀ ڃ ܃ L ] _ d u w Y 8 L N Ȫ ʪ ! # C T V O P rZ Z Z k` ` ` Uc c c i Hi [i l l l | -} \} B ܃ `( :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::XXXXXXXXXXXXXX:::::C:::::X XXXXXXXX ) ! : ? b $ y<˫ۿ! b $ %"+cj b $ t)qVjՎ_, # @ B= < (
V "
# " * V "
# " V "
# " V "
# " V "
# " b
3 G n ` f! u wL `T f! `T 2 `T B `T AU wL AU A 2 f! " . b
)
C ) " 7 b
(
3
! " # $ % & ' ( ) * + , - . / 0 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~ G n ` f! u wL `T f! `T 2 `T B `T AU wL AU A 2 f! " 6 b
3 G n ` f! u wL `T f! `T 2 `T B `T AU wL AU A 2 f! " - b
!
3 G n ` f! u wL `T f! `T 2 `T B `T AU wL AU A 2 f! " / 6
" % V 2
# " #
3 r f,k,kXX? P "
3 r f,k,kXX? P "
3 r f,k,kXX? P " P
3 " b
C " b
$
C $ " 2 b
C
" +
b
C " ) b
C " " B
%
B D Ԕ ? " 3 B
#
B D Ԕ ? " 1 B
B D Ԕ ? " ' B
B D Ԕ ? " B
B D Ԕ ? " n 2
0 ? " n 2
0 ? " , n 2
0 ? " $ n 2
0 ? " ! t B
"
6 D ? " 0 t B
6 D ? " t B
6 D ? " t B
6 D ? " t B
6 D ? " & B
+
H D Ԕ ? " 9 B
N D o ? " B
N D Ԕ ? " B
'
N D Ԕ ? " 5 B
&
N D Ԕ ? " 4 B
N D Ԕ ? " (
,
< , ? " : n 2
*
0 ? " 8 6
9
" G V
.
C " < 6
5
" C \ B
?
S D " M 6
1
" ? \ B
>
S D " L \ B
;
S D " I \ B
4
S D " B \ B
<
S D " J N B
B
S D P x
C
< C ? Q
0
< 0 ? " >
2
< 2 ? " @
A
< A ? " O
@
< @ ? " N
8
< 8 ? " F
-
< - ? " ;
= @
< = ? " K
3
< 3 ? " A
7
< 7 ? " E
/
< / ? " =
6
< 6 ? " D
:
< : ? " H V
w
# % w " % V
x
# $ x " $ H
y
# # y # H
z
# " z " ( 2
{
T
|
C & | & T B
}
c $ D
T B
~
c $ D b B
@
c $ D " b B
c $ D " b B
@
c $ D " b B
c $ D " b B
@
c $ D " x
< ! ? ) !
< ' ? " ' J 2
# " T J 2
# " R J 2
# " S 6 2
" b 6 2
" d 6 2
" c J 2
# " j J 2
# " k \ B
S D " X \ B
S D " ^ \ B
S D " ] \ B
S D " \ \ B
S D " [ \ B
S D " Z \ B
S D " Y \ B
S D " W \ B
S D " V \ B
S D " h \ B
S D " g \ B
S D " a \ B
S D " f \ B
S D " e \ B
S D " ` \ B
S D " l \ B
S D " m
< ? " n
< ? " U
< ? " _
< ? " i x
< * ? { * x
< ) ? u ) x
< . ? v . x
< / ? w / x
< 0 ? x 0 x
< 1 ? y 1 x
< 2 ? z 2 x
< 3 ? 3 x
< 4 ? 4 x
< 5 ? 5 x
< 6 ? ~ 6 x
< 7 ? | 7 x
< 8 ? } 8 ~
B 9 ? " 9 x
< : ?
: x
< < ? < x
< - ? o - x
< , ? p , x
< + ? q + x
< ( ? r
( T
C ; ; V
# " s
< ? " t B
S ? ' ' ' ' ' ' ' ' ' ' ' ' !' #' #J &J z z z z z z z z z z z z z z z z z z z z z z z z z z z { { { { { { { {
{ { { { { { { { 3 4 6 7 8 : ; < = > ? @ A C D E F G H J K M O "5 6 7 8 X h X y ( `( z 6 t t w 8 0 t x 0 t t t ~ m t t t y 8 S t } * ( t { t | n @ t { t O& t t ' p t c t 8" w (& g t x G t 8" H' t n - t { K t H { h t
% t [ 8" t _ t ! / t 8" # 8" t P C t - > m . t ^ t p p k t < c t 0! t t \ t 8 [ t W
' t { h t c t / t _ t @ t b t >
] ^ @ x c
@ ! g
@ " . C S t # H C t $ h t % g t & Z + Z ; t ' ( t ( h k @ ) fx 6( t * X 6 t + \ \ t , t - V t . . t / P t 0 p ] t 1 m m
t 2 t 3 $ t 4 t 5 f t 6 V t 7 f t 8 p t 9 v t : N t ; $ t < t = p ~ t > t ? @ t @ @ F t A p ` f t B t C e " t ` t ` t P t 0! t > t t t t t P > t t t t 0! t t t f t @ f t ` f t ~ n t ~ n t ~ n t ~ n t 0! t @ ^ t
^ t $ t 6 t N t l t 8 {! t ! W! t n ! t t h & 8 t M # t ] $ t ! t W " t m <! t & F ! t y# t ^ # t L k # t % t 8 # t > e # t $ }" t # t < + # ; t #
t _ T o c 1 7 2 4 5 4 5 1 _ T o c 1 7 7 9 1 9 7 8 _ T o c 1 7 8 2 3 0 5 6 _ T o c 1 8 2 3 5 1 0 9 _ T o c 1 8 4 0 9 2 8 9 _ T o c 1 7 2 4 5 4 5 2 _ T o c 1 7 7 9 1 9 8 0 _ T o c 1 7 8 2 3 0 5 8 _ T o c 1 8 2 3 5 1 1 0 _ T o c 1 8 4 0 9 2 9 0
_ 9 6 3 9 9 7 4 5 3
_ 9 6 3 9 9 7 8 4 7
_ 9 6 3 9 9 8 5 5 0
_ 9 6 3 9 9 8 7 1 0
_ 9 6 3 9 9 8 9 2 5
_ 9 6 3 9 9 9 0 6 0
_ 9 6 7 5 4 5 8 6 5 _ 1 0 5 6 7 9 7 6 0 1 _ 1 0 5 6 7 9 9 9 0 7 _ 1 0 8 4 5 2 7 6 0 0 _ T o c 4 2 7 0 5 1 0 5 6 _ T o c 4 2 7 0 5 1 5 8 7 _ T o c 5 2 2 0 7 4 8 8 7 _ T o c 4 2 7 0 4 9 4 8 1 _ T o c 5 2 2 4 2 8 6 6 6 _ T o c 1 7 2 4 5 4 5 3 _ T o c 1 7 7 9 1 9 8 1 _ T o c 1 7 8 2 3 0 5 9 _ T o c 1 8 2 3 5 1 1 1 _ T o c 1 8 4 0 9 2 9 1 _ T o c 4 2 7 0 5 1 0 5 7 _ T o c 4 2 7 0 5 1 5 8 8 _ T o c 5 2 2 0 7 4 8 8 8 _ T o c 5 2 2 4 2 8 6 8 0 _ T o c 1 7 2 4 5 4 5 4 _ T o c 1 7 7 9 1 9 8 2 _ T o c 1 7 8 2 3 0 6 0 _ T o c 1 8 2 3 5 1 1 2 _ T o c 1 8 4 0 9 2 9 2 _ T o c 1 7 2 4 5 4 5 5 _ T o c 1 7 8 2 3 0 6 1 _ T o c 1 7 2 4 5 4 5 6 _ T o c 1 7 8 2 3 0 6 2 _ T o c 1 7 2 4 5 4 5 7 _ T o c 1 7 8 2 3 0 6 3 _ T o c 1 7 2 4 5 4 5 8 _ T o c 1 7 8 2 3 0 6 4 _ T o c 1 7 2 4 5 4 5 9 _ T o c 1 7 8 2 3 0 6 5 _ T o c 1 7 2 4 5 4 6 0 _ T o c 1 7 8 2 3 0 6 6 _ T o c 1 7 2 4 5 4 6 1 _ T o c 1 7 8 2 3 0 6 7 _ T o c 1 7 2 4 5 4 6 2 _ T o c 1 7 8 2 3 0 6 8 _ T o c 1 7 2 4 5 4 6 3 _ T o c 1 7 8 2 3 0 6 9 _ T o c 1 7 2 4 5 4 6 4 _ T o c 1 7 8 2 3 0 7 0 _ T o c 1 7 2 4 5 4 6 5 _ T o c 1 7 8 2 3 0 7 1 _ T o c 1 7 2 4 5 4 6 6 _ T o c 1 7 7 9 1 9 8 3 _ T o c 1 7 8 2 3 0 7 2 _ T o c 1 8 2 3 5 1 1 3 _ T o c 1 8 4 0 9 2 9 3 _ T o c 1 7 2 4 5 4 6 7 _ T o c 1 7 7 9 1 9 8 4 _ T o c 1 7 8 2 3 0 7 3 _ T o c 1 8 2 3 5 1 1 4 _ T o c 1 8 4 0 9 2 9 4 _ T o c 1 7 2 4 5 4 6 8 _ T o c 1 7 7 9 1 9 8 5 _ T o c 1 7 8 2 3 0 7 4 _ T o c 1 8 2 3 5 1 1 5 _ T o c 1 8 4 0 9 2 9 5 _ T o c 1 7 2 4 5 4 6 9 _ T o c 1 7 8 2 3 0 7 5 _ T o c 1 7 2 4 5 4 7 0 _ T o c 1 7 8 2 3 0 7 6 _ T o c 1 7 2 4 5 4 7 1 _ T o c 1 7 7 9 1 9 8 6 _ T o c 1 7 8 2 3 0 7 7 _ T o c 1 8 2 3 5 1 1 6 _ T o c 1 8 4 0 9 2 9 6 _ T o c 1 7 2 4 5 4 7 2 _ T o c 1 7 7 9 1 9 8 7 _ T o c 1 7 8 2 3 0 7 8 _ T o c 1 8 2 3 5 1 1 7 _ T o c 1 8 4 0 9 2 9 7 _ T o c 1 7 2 4 5 4 7 3 _ T o c 1 7 8 2 3 0 7 9 _ T o c 1 7 2 4 5 4 7 4 _ H l t 1 6 4 4 4 5 1 3 _ H l t 1 6 4 4 4 5 1 4 _ T o c 1 7 2 4 5 4 7 5 _ T o c 1 7 8 2 3 0 8 0 _ T o c 1 7 2 4 5 4 7 6 _ T o c 1 7 8 2 3 0 8 1 _ T o c 1 7 2 4 5 4 7 7 _ T o c 1 7 8 2 3 0 8 2 _ T o c 1 7 2 4 5 4 7 8 _ T o c 1 7 8 2 3 0 8 3 _ T o c 1 7 2 4 5 4 7 9 _ T o c 1 7 8 2 3 0 8 4 _ T o c 1 7 2 4 5 4 8 7 _ T o c 1 7 8 2 3 0 8 5 _ T o c 1 7 2 4 5 4 8 6 _ T o c 1 7 8 2 3 0 9 3 _ T o c 1 7 8 2 3 0 9 4 _ T o c 5 1 5 0 8 8 2 9 4 _ T o c 1 7 8 2 3 0 9 5 _ T o c 1 7 8 2 3 0 9 6 _ T o c 1 7 2 4 5 4 8 0 _ T o c 1 7 7 9 1 9 8 8 _ T o c 1 7 8 2 3 0 8 6 _ T o c 1 8 2 3 5 1 1 8 _ T o c 1 8 4 0 9 2 9 8 _ T o c 1 7 2 4 5 4 8 5 _ T o c 1 7 8 2 3 0 9 2 _ 4 . 2 _ D a t a _ R e p r e s e n t a t i o n _ T o c 1 7 2 4 5 4 8 4 _ T o c 1 7 8 2 3 0 9 1 _ H l t 1 8 0 9 2 8 3 9 _ H l t 1 8 0 9 2 8 4 0 _ H l t 1 8 0 9 2 8 4 1 _ R e f 1 7 3 7 2 4 6 7 _ T o c 1 7 2 4 5 4 8 8 _ T o c 1 7 2 4 5 4 8 9 _ T o c 1 7 2 4 5 4 9 0 _ T o c 1 7 8 2 3 0 9 7 _ T o c 1 7 8 2 3 0 9 8 _ T o c 1 7 8 2 3 0 9 9 _ T o c 1 7 8 2 3 1 0 0 _ T o c 1 7 8 2 3 1 0 1 G% G% G% G% G% G% G% &' &' &' 4 4 4 4 4 4 z z z z z z z z { { { { { { { { { { | | s t K K K K K ( ( ( ( ( [ [ v v v v v Ő Ő 8 8 J J l7 l7 ; ; 0= > V a [h [h [h [h [h i i b V W X X I a(
@ @ @ @ @ @ @ @ @ @ ! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M O N P Q R S T U V W X Y Z [ \ ] @^ @_ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { @| @} @~ 9 9 9 9 9 c% c% c% c% c% c% c% '' '' '' U4 U4 U4 U4 U4 U4 z z z z z z { { { { { { { { { { { { S} S} J J J J J & & & & & Nj u u u u u G G # # R R x7 x7 ; ; N= > 'V a h h h h h i i b t ! W X vX Z g a( w- > w- X# w- w- L# w- ? w- W# w- K# w- L# w- TL# w- +# w- +# w- ,# w- | w- w- w- < w- | w- w- w- < w- | w- w- w- < w- | w- w- w- < w- |
w- w- w- | w- < w- w- w- w- t w- w- t w- w- w- < w- w- < w- w- L w- ̐ w- L w- ̑ w- L w- w- !w- d "w- #w- $w- , %w- &w- | 'w- (w- )w- , *w- l +w- ,w- -w- , .w- l /w- , 0w- 1w- , 2w- l 3w- 4w- 5w- , 6w- l 7w- 8w- 9w- , :w- l ;w- <w- =w- , >w- l ?w- @w- Aw- , Bw- l Cw- Dw- Ew- , Fw- l Gw- Hw- Iw- , Jw- l Kw- Lw- Mw- , Nw- l Ow- Pw- Qw- , Rw- l Sw- Tw- Uw- , Vw- l Ww- Xw- Yw- , Zw- l [w- \w- ]w- , ^w- l _w- `w- aw- , bw- l cw- dw- ew- , fw- l gw- hw- iw- , jw- l kw- lw- mw- , nw- l ow- pw- qw- , rw- l sw- tw- uw- , vw- l ww- xw- yw- , zw- l {w- |w- }w- , ~w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- , w- l w- w- w- d w- w- 4 w- t w- w- w- 4 w- t w- w- w- 4 w- t w- w- w- 4 w- t w- w- w- 4 w- t w-
w- w- 4 w- t w- w- w- 4 w- t w- w- w- 4 w- t w- w- w- 4 w- t w- w- w- 4 w- t w- w- w- 4 w- t !w- "w- #w- 4 $w- t %w- &w- 'w- 4 (w- t )w- *w- +w- 4 ,w- t -w- .w- /w- 4 0w- t 1w- 2w- 3w- 4 4w- t 5w- 6w- 7w- 4 8w- t 9w- :w- ;w- 4 <w- t =w- >w- ?w- 4 @w- t Aw- Bw- Cw- 4 Dw- t Ew- Fw- Gw- 4 Hw- t Iw- Jw- Kw- 4 Lw- t Mw- Nw- Ow- 4 Pw- t Qw- Rw- Sw- 4 Tw- t Uw- Vw- Ww- 4 Xw- t Yw- Zw- [w- 4 \w- t ]w- ^w- _w- 4 `w- t aw- bw- cw- 4 dw- t ew- fw- gw- 4 hw- t iw- jw- kw- 4 lw- t mw- nw- ow- 4 pw- t qw- rw- sw- 4 tw- t uw- vw- ww- 4 xw- t yw- zw- {w- 4 |w- t }w- ~w- w- 4 R R ! ; ; ; ; g= g= Q Q Q Q R R XR XR R R jZ jZ b[ [ [ B\ \ \ ] Z] ] ] ^ h^ ^ _ c_ _ ` L` ` ` a ^d d d .e ve e f Wf f "& "& X } # # ! " " &" # # # !# !# *# # # # $ $ $ ;% F% % % % 3& 3& E& M& ' ' ' ( ( ( ( >) >) G) ) ) ) J* J* - - . . . / / / !0 !0 5 5 5 G8 G8 Q8 8 8 ; ; ; < < < = = = = 9> 9> > > f? f? BA BA SA A A A C C D D D G G G G G G G G I I I 0K 0K 9K L L L M !N !N ,N 0N 0N 8N N N N JO JO RO Q Q Q Q [R [R cR S S S S S S S S S T nV nV wV gX $Z $Z 2Z 2Z AZ GZ RZ RZ [Z _Z Z 0[ 0[ 6[ 6[ h h h h h h h i i i xi j j &j &j .j .j 7j 7j ?j ?j Lj Lj m m m ]o ]o fo ro ro 2q q q s s t t z z z z z z z d{ d{ {{ { { { { { { { { { { | P| P| | | | | | | _} c} x} x} } } } } } } } } } } } } } ~ ~ ~ ~ 8~ <~ E~ E~ J~ R~ R~ a~ a~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 1 1 H L a a ( ( ׀ ׀ < < V Z o o Ł Ɂ ҁ ҁ P P e e ۅ ۅ 8 8 A H H R R a(
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; = < > ? @ A B D C E F G H I K J L N M O Q P R T S U W V X Z Y [ \ ] ^ _ ` c a b d f e g i h j k m l n p o q r s t u v w x z y { | } ~
! $ " # % & ' ) ( * + , / - . 0 1 2 5 3 4 6 7 8 ; 9 : < > = ? @ A B C E D F H G I J K N L M O R P Q S V T U W Z X Y [ \ ] ^ _ ` a b c d e h f g i j k n l m o q p r u s t v x w y z { ~ | } X X ! ; ; ; ; o= o= Q Q Q Q $R $R `R `R R R rZ rZ g[ [ [ G\ \ \ ] _] ] ] ,^ m^ ^ _ h_ _
` Q` ` ` a cd d d 3e {e e f \f f )& )& a . . ! ! ! $" (" (" # # # )# 1# 1# # # # $ % % =% H% % % % C& K& O& O& ' ' ' ( ( ( ( F) I) I) ) ) ) R* R* - - . . . / / / *0 *0 5 5 5 O8 W8 W8 8 8 ; ; ; < < < = = = = A> A> > > n? n? QA VA VA A A A !C !C D D D G G G G G G G G I I I 7K <K <K L L L
M *N .N .N 7N BN BN N N N PO TO TO Q Q Q Q bR mR mR S S S S S S S S T T uV yV yV kX ,Z 1Z @Z FZ QZ QZ YZ ]Z dZ dZ Z 4[ 4[ <[ <[ h h h h h h i i i i i $j $j ,j ,j 5j 5j =j =j Jj Jj Zj Zj m m m eo po po {o {o 6q q q !s s t t z z z z z z z w{ { { { { { { { { { { { | | ^| ^| | | | | | | a} f} ~} } } } } } } } } } } } } } } ~ ~ 4~ :~ ?~ ?~ I~ Q~ Q~ \~ \~ o~ o~ ~ ~ ~ ~ ~ ~ ~ ~ C J O O $ $ 3 3 ŀ - - Q X ] ] ǁ ́ ́ V V } @ H H M M W W a(
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; = < > ? @ A B D C E F G H I K J L N M O Q P R T S U W V X Z Y [ \ ] ^ _ ` b c a d f e g i h j k m l n p o q r s t u v w x z y { | } ~
! # $ " % & ' ) ( * + , . / - 0 1 2 4 5 3 6 7 8 : ; 9 < > = ? @ A B C E D F H G I J K M N L O Q R P S U V T W Y Z X [ \ ] ^ _ ` a b c d e g h f i j k m n l o q p r t u s v x w y z { } ~ | 9 *urn:schemas-microsoft-com:office:smarttagsplace 9 *urn:schemas-microsoft-com:office:smarttagsState 8 *urn:schemas-microsoft-com:office:smarttagsCity B x *urn:schemas-microsoft-com:office:smarttagscountry-region 8 u *urn:schemas-microsoft-com:office:smarttagstime 8 t *urn:schemas-microsoft-com:office:smarttagsdate = *urn:schemas-microsoft-com:office:smarttags PlaceName = *urn:schemas-microsoft-com:office:smarttags PlaceType ; 2 *urn:schemas-microsoft-com:office:smarttagsaddress : 1 *urn:schemas-microsoft-com:office:smarttagsStreet > *urn:schemas-microsoft-com:office:smarttags
PostalCode 11011121314151617191999202000200121232425262944045475598DayHourMinuteMonthYear x u t t u u u u u u t t
u u u u u u u u u u u u u u u u u u u t u x x x 2 1 x x x x x 2 1 x x 2 2 1 x x x x x x x x t t t t x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 6 = ` g o s I O T Z o t # ` d ! "! {! ! ! ! " %" " " # # # # # # $ $ $ $ & & 3& :& . . 1 #1 1 1 !2 &2 +2 12 :2 >2 3 3 L5 U5 W6 c6 ;
; ; ; ; ; [= b= B B B B F F L L M N Y Z [ [ [ [ [ /[ 2[ 4[ h[ t[ v[ x[ [ [ [ [ [ \ \ \ H\ S\ U\ W\ \ \ \ \ \ \ \ \ ] (] *] ,] `] n] p] r] ] ] ] ] ] ] ] ] -^ 6^ 8^ :^ ^ ^ ^ ^ 3_ 5_ _ _ _ _ _ _ ` ` ` ` R` c` e` g` ` ` ` ` ` ` ` ` a #a jb rb c c c c .d 0d dd pd rd td d d d d d d d e 4e De Fe He |e e e e e e e e f %f 'f )f ]f lf nf pf f f $g -g g g 7h <h i i i i y y <} D} G} K} n s ڜ 0 4 * 0 ū ǫ Y Z
\ ^ ů ȯ i } ~ = B c p Q V / 9 0 6 S U ( ) b c 6 < T [ [ a f l q s I K T V a c
= @
s { C J O, Z, q1 u1 ? &? )? -? }? ? ? ? ?
@ @ @ y@ @ @ @ @ A B /B DB MB B B B B C C C C C C D %D SD fD D D \E nE uE }E E E E E tF F F F F F F G TG aG bG G N $N ;N >N }N N N N N N N N O O )O 0O \O _O aO dO }O O O O O O O O O O O O 8P NP OP WP YP oP |P P P P P P Q Q Q $Q uQ }Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q R T T U U V 'V V V 6W BW .Y 5Y FY QY YY \Y iY lY Y Y Y Z Z Z Z Z Z Z [ [ U[ ][ c[ n[ [ [ [ [ %\ -\ j\ r\ x\ \ \ \ \ \ \ \ \ \ ] ] h] p] ] ] ^ ^ ^ .^ A^ I^ ^ ^ ^ ^ _ $_ *_ =_ P_ X_ ^_ q_ _ _ _ _ V` ^` ` ` `a la c c li pi r r u u v v x x y y (y ,y -y 3y # " ( T Y . 7 S \ * 3 i q c f y y | x } & ) { - 4
'
a
h
N U a d 3 8 _ g i q s x } 0 3 l p ! w! ! ! ! " " 9# @# V# \# # # '$ ,$ $% +% 0% 9% >% D% % % % % % % & & R& Z& ' ' D' R' ' ' ' ' v( {( ( ( ) ") ) ) ) ) + + l+ q+ + + + + + + , (, E, O, - - - - (- 2- C- H- . . / '/ -/ 3/ D/ K/ / / a0 j0 0 0 1 1 S2 Y2 b2 i2 23 73 =3 H3 '4 -4 4 4 4 5 5 5 5 5 5 5 6 !6 x6 6 6 6 g8 t8 8 8 9
9 9 9 -9 09 ; ; %; .; ; ; ; ; < < < < = = = = = = }> > ? ? ? ? B B B B C !C D D D D D D E E 'E -E G G G G G G yH H DI II UI [I I I J J ~J J J J K K K K L L 9M BM HM PM M M JN QN N N O O gO oO xO ~O O O O O P P $Q *Q Q Q R R R R !S *S ;S BS S S S S BT LT T U U U eV lV {V V W W WX eX pX uX Z Z a a c c d d d d d e e e e e j j [k ek k k k k k k /l ;l yl }l m m Yn an cn in ~n n n n n o ]o eo ~o o o o 3p :p cp ip p p p p nq zq q q r
r -r 4r _r hr mr tr r r $s 0s Pt Zt <w @w Aw Iw Jw Qw x x y y y y ҁ ށ & . / 9 Ԃ ق ; @ 4 ; ^ e a f * 7 : @ B I 3 4 К њ J K I J m n d p ~ ͞ ۞ < H V b q } Ϡ ۠ S _ n z * : F U a & 2 ͣ ٣ = I Y a ܤ ( 5 = T \ Ħ , : U ] ԧ 8 D W c o w Ш ب 4 @ X d $ I U ƪ + P \ ˫ ! - B P s ̬ ج ( 4 & 2 _ g ܮ կ ! _ k z } ߷ " % > E Y \ 9 ; O R V _ s v % ( R T h k M U a g v y ; B n u ( : B N T 2 9 w o v ; B a h L T Y ` d f [ ] . 5 P W v ~ Q Y * 2 ~ V ] { \ d o p x y / 0 J K k l p q M T R Y w ~ 4 ; ` h 9 A = D R W a f J M ] d N Q T W g n + 2 e h k n ~ r
u
} @ C w z U a ` u j a( , #, 3 3 ; ; [ [ 2[ 5[ v[ y[ [ [ \ \ U\ X\ \ \ \ \ *] -] p] s] ] ] ] ] 8^ ;^ ^ ^ ^ ^ 3_ 6_ _ _ _ _ ` ` e` h` ` ` ` ` Eb Jb c c .d 1d rd ud d d d e Fe Ie e e e e 'f *f nf qf $g -g i i j j $ & ܞ A D w y ϣ ѣ " B ī Э ԭ 4 7 q s e g ± ı ^ ` - 1
i k Z ^ c e l p [ ` f i ) 1 " " A- E- !. %. >7 A7 = = > > )? -? ? ? @ @ FA PA A A B B B 2B ~B B C C E E E E qF sF F F F F G G G G H $H VH [H H H H H XI aI I I J J ?J JJ J J J J K K 6K AK 7N 9N KN ON ^N cN xN zN N N O O )O 1O NO SO yO |O O O O O O O ,P P YS ]S T T W W X SX TX XX X X Y *Y .Y 5Y Y Y \a la j j %q )q ,q 1q 7q =q q q q q q q ~r r r r s s s s ރ I M T [ r t ޱ ( 1 : C L U ^ j t " 0 R S ! ! v! ! ! ! ! ! " " 0" 1" E" F" " # 8# 9# }# ~# # # # # $ $ K$ L$ $ $ $ % % & & & & ' 2' ' ' i( |( ( ( ) $) %) J) L) {) |) ) ) ) ) ) ) :* U* m* n* }* + + + + + + , [, \, , , - - >- d. . . . / X/ / 0 0 0 G0 w0 x0 0 0 0 1 1 F2 v2 23 $4 4 K5 6 6 6 6 7 7 8 8 !9 79 \9 j9 9 9 S: s: t: w: : $; ; J< < < < < D> {> }> > ? P? @ @ WA iA A B C C F F .G G G G hH H H H H H H AI DI cI I I I J ~J J J <K =K K K K DL L L L 9M M M M N N !N IN N N N N O +O ,O IO JO eO Q 6Q R R S S T T U U QV mV lW mW X X ^ ^ b b d d ~ ߐ 3 O Q ' ) ǟ ` h % > @ ݡ B J ( * 1 = e w } E ] k w P d ө 5 U q ~ ت < \ ˫ > P P ^ " 2 [ g ! 9 ; O Z R T h s w z K N
5 8 K P m v
1 4 t w d g > B " ' y E I . 3 J T ` e ~ B I l s = D k r ' . Z d < F \ ` 5 9 * 8 j o J M y N Q f l T W 1 9 e h } k n
r
u
} @ C X ^ x } N V [ ^ ` ] d N P u i ) 1 ! \! h! ! ! ! " #" +" "$ +$ $ $ R% \% a( 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 8 l lB B i i t t ^x sx ! 3 ( Nj , A Л r , b u 6 K o K m 8 I $ K S Z j G
P d 5 H ( ) .. /. 1. G. 2 2 0= O= 7N KN V (V a a c %c l l m Hm q 2q iq q s s x x ȉ & ) F O M ] ː ې * = R U 5 B 6 K ' ĭ и ۸ ͻ v { 7 < Z k t | ; K r D U V ] ( ' ' H H Q 7Q U U Y $Z RZ n[ [ \ ] ] 1d Ld h h i i i j \j j j j j k o 3p r s v v ,x `x z { т ҂ v c ƥ 1 0 \ D P U a g ; Ѻ > ? { G I J l m ' ( M N e f - D E $ i q ` b s ! , . 2 p j , . : < P R _ a s u | $ $ % "% % & a( ` a( M o h s e n M a r y a m Z a h r a M o h s e n M a r y a m Z a h r a M o h s e n M a r y a m Z a h r a M o h s e n M a r y a m Z a h r a M o h s e n M a r y a m Z a h r a M o h s e n M a r y a m Z a h r a M o h s e n M a r y a m Z a h r a M o h s e n M a r y a m Z a h r a M o h s e n M a r y a m Z a h r a r j e 9 : ; > wwZ _< Bh/ +z6Ԡ i? Q2^O 6] ~ ez5 -wH D h>8
9t L;Nz4W o SF ؊V c
l m
k q0 Xh %t 4oY * \d Ŭ , F %C~ &?8 0o . )%>!h7 ?:Qo E Xn" BX0 '2^O FRu'$ :xF(V %7 o hj Ei5 @H? <E)t r_u HqdC0m K *&ޘT oԠ uN` F j4 q 5 F dPj ym S ) ^ ,N n k IE [
8 Kn Vw ;!Vj !%V o!؊V P&"" |"| f +rv$ \ +$q O
$ zA%L>X >[%5zj q % Et$&x9v !=& ELf&5zj z#&N*L '&匀 Mm' & Z',N e( A?(q t*vTF
l+bk <, _- 20ҫ /)61 v17 ]16: !2cM kA2z$ >32\# (?3` dG3 T@4
E4 6F GV7 F X\8
S< V :P J=5: ;:" tc;6 s|>,N k>>< h; ?!f XQ? F K?*@@X {z@
S< @Vw uA` G1A3 +pA,p mB F a-CKtY wC Q D F %7Dpv P 'FT @rF؊V ? wF2^O F `F [PlG & )HVw -H@z @d-Jk |qK_E8 pjK 3 L< h6L,zJP eM3 WN 'vO Q ]Od pQO ,lPҢ AP GkQ bsQ؊V ]SQ KQt qBR"(8 A~R lS &T 4T`:< eO3UE S #OV V` 4CW FX "hYrD! BZjv @Z F sZ"
R\ \\Hd x] '] _]8| o]S [_0 .` F fbV8? k(b wMd>!h7 :dt We _fTX t|f
6i&L Fli^0H >ipv j OSXkVw hlh etl`n4 #m }@m:X %~ nʜc Kyorn( Upꃮ sbJq êJ :q^0H o-qVPy 0Sr uKTr 2Lsd& XSsj #2sx
uVw S<uV1V $Lu 4Sulh D|uTX
u0 ~Zul Pv v Pw'$ Mxw F x _\x "y4r .yıt ey 6hz{B ?)z Yz2^O -Kz؊V r\z2^O <{ fy{{T C . |&F | +B| >}Vw 9~z ~nCf +~~i hN xL 9,H lb p) @ OJ QJ o( @ OJ QJ o( @ OJ QJ o( @ OJ QJ o( @ OJ QJ o( @ OJ QJ o( * h h^h`OJ QJ o( ^` . ^` . p^p` . @ ^@ ` . ^` . ^` . ^` . ^` . h h^h`OJ QJ o( @ ^ ` . @ ^ ` . @ ^ ` . @ ^ ` . h ^h`
C h a p t e r . P ^`P . ^` . . x p^`x . . . @ ^`
. . . . .
X ^
`X . . . . . . ^ ` . . . . . . . 8 H^`8 . . . . . . . . ` ^`` . . . . . . . . . h h^h`OJ QJ o( k ^`kCJ o( 0 ^`0CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . ` ^``CJ o( . . . . ` ^``CJ o( . . . . . ^`CJ o( . . . . . . p p^p`CJ o( . . . . . . . ( ^ `(CJ o( . . . . . . . . h h^h`o( . h ^`OJ QJ o( h H h ^`OJ QJ ^J o( h H o h p p^p`OJ QJ o( h H h @ @ ^@ `OJ QJ o( h H h ^`OJ QJ ^J o( h H o h ^`OJ QJ o( h H h ^`OJ QJ o( h H h ^`OJ QJ ^J o( h H o h P P^P`OJ QJ o( h H h ^`OJ QJ o( h H h ^`OJ QJ ^J o( h H o h p p^p`OJ QJ o( h H h @ @ ^@ `OJ QJ o( h H h ^`OJ QJ ^J o( h H o h ^`OJ QJ o( h H h ^`OJ QJ o( h H h ^`OJ QJ ^J o( h H o h P P^P`OJ QJ o( h H ^` CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . 8 8^8`CJ o( . . . . ` ^``CJ o(
. . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . ( ^ `(CJ o( . . . . . . . . . h h^h`o( 1 P ^`Po( . . ^`o( . . . x p^`xo( . . . . ^`o(
. . . . .
X ^
`Xo( . . . . . . ^ `o( . . . . . . . 8 ^`8o( . . . . . . . . ` ^``o( . . . . . . . . . h h^h` . h h^h`o( ) h h^h` . l ^`lo( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o( . . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . h ^`OJ QJ o( h H h ^`OJ QJ ^J o( h H o h p p^p`OJ QJ o( h H h @ @ ^@ `OJ QJ o( h H h ^`OJ QJ ^J o( h H o h ^`OJ QJ o( h H h ^`OJ QJ o( h H h ^`OJ QJ ^J o( h H o h P P^P`OJ QJ o( h H ^`o( h H . h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( h ^` . h ^` . h pL p^p`L . h @ @ ^@ ` . h ^` . h L ^`L . h ^` . h ^` . h PL P^P`L . : :^:`o( 0 ^`0o( . 0 ^`056 CJ OJ QJ o( . . 8 8^8`56 CJ OJ QJ o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ^`o( . . . . . . ^`o( . . . . . . . p p^p`o( . . . . . . . . h h h^h`OJ QJ o( h 8 8^8`OJ QJ o( ^`o( ) ^ `o( . ^ ` . xL x^x`L . H H^H` . ^` . L ^`L . 0 ^`0o( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . ^` . ^`B* OJ QJ o( ph p p^p` . @ @ ^@ ` . ^` . ^` . ^` . ^` . P P^P` . h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( / ^`/CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . 8 8^8`CJ o( . . . . ` ^``CJ o(
. . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . ( ^ `(CJ o( . . . . . . . . . *> *^*`>5 OJ QJ ^J o( . ^` . pL p^p`L . @ @ ^@ ` . ^` . L ^`L . ^` . ^` . PL P^P`L . h h^h`o( . ^`o( 80 8^8`0CJ o( . 80 8^8`0CJ o( . . ^`CJ o( . . . ` ^``CJ o( . . . . ` ^``CJ o( . . . . . p p^p`CJ o( . . . . . . ^ `CJ o( . . . . . . . @ ( @ ^@ `(CJ o( . . . . . . . . h h^h`CJ o( 0 ^`0CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . ` ^``CJ o( . . . . ` ^``CJ o( . . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . h h^h` . @ ^ ` . ^` o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . ` ^``o(
. . . . . ` ^``o( . . . . . . ^`o( . . . . . . . p p^p`o( . . . . . . . . p p^p`o( . . . . . . . . . " ^`"5OJ QJ ^J o( . " ^`"5OJ QJ ^J o( . " ^`"5OJ QJ ^J o( . . " ^`"5OJ QJ ^J o( . . . 8 8^8`5OJ QJ ^J o( . . . . 8 8^8`5OJ QJ ^J o( . . . . . . ` ^``5OJ QJ ^J o( . . . . . . . ` ^``5OJ QJ ^J o( . . . . . . . . ^`5OJ QJ ^J o( . . . . . . . . . ^`o( ^`o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . ^`o( . h ^`OJ QJ o( pL p^p`L . @ @ ^@ ` . ^` . L ^`L . ^` . ^` . PL P^P`L . ` ^``CJ OJ QJ ^J o( ` ^``CJ OJ QJ ^J o( . ` ^``CJ OJ QJ ^J o( . . ` ^``CJ OJ QJ ^J o( . . . ` ^``CJ OJ QJ ^J o( . . . . ` ^``CJ OJ QJ ^J o( . . . . . ` ^``CJ OJ QJ ^J o( . . . . . . ` ^``CJ OJ QJ ^J o( . . . . . . . ^`CJ OJ QJ ^J o( . . . . . . . . h h h^h`OJ QJ o( h H h 8 8^8`OJ QJ ^J o( h H o h ^`OJ QJ o( h H h ^ `OJ QJ o( h H h ^ `OJ QJ ^J o( h H o h x x^x`OJ QJ o( h H h H H^H`OJ QJ o( h H h ^`OJ QJ ^J o( h H o h ^`OJ QJ o( h H ^`B* OJ QJ o( ph 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . ^` CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . 8 8^8`CJ o( . . . . ` ^``CJ o(
. . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . ( ^ `(CJ o( . . . . . . . . . h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( h h^h`B* OJ QJ o( ph ^`OJ QJ o( o p p^p`OJ QJ o( @ @ ^@ `OJ QJ o( ^`OJ QJ o( o ^`OJ QJ o( ^`OJ QJ o( ^`OJ QJ o( o P P^P`OJ QJ o( h h h^h`OJ QJ o( h 8 8^8`OJ QJ o( o h ^`OJ QJ o( h ^ `OJ QJ o( h ^ `OJ QJ o( o h x x^x`OJ QJ o( h H H^H`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h h^h`o( . h h^h`OJ QJ o( h ^` . h ^` . h pL p^p`L . h @ @ ^@ ` . h ^` . h L ^`L . h ^` . h ^` . h PL P^P`L . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h h h^h` . h 8 8^8` . h L ^`L . h ^ ` . h ^ ` . h xL x^x`L . h H H^H` . h ^` . h L ^`L . h h^h`OJ QJ o( h h^h`o( . h h^h`o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o( . . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . ^`o( . ^`^J o( . 80 8^8`0^J o( . . ^`^J o( . . . ^`^J o( . . . . ` ^``^J o( . . . . . ` ^``^J o( . . . . . . p p^p`^J o( . . . . . . . p p^p`^J o( . . . . . . . . ^`OJ QJ o( ^`OJ QJ o( o p p^p`OJ QJ o( @ @ ^@ `OJ QJ o( ^`OJ QJ o( o ^`OJ QJ o( ^`OJ QJ o( ^`OJ QJ o( o P P^P`OJ QJ o( h ^`OJ QJ o( h H h ^`OJ QJ ^J o( h H o h p p^p`OJ QJ o( h H h @ @ ^@ `OJ QJ o( h H h ^`OJ QJ ^J o( h H o h ^`OJ QJ o( h H h ^`OJ QJ o( h H h ^`OJ QJ ^J o( h H o h P P^P`OJ QJ o( h H @ ^ ` . h h^h`o( . 8 8^8` . L ^`L . ^ ` . ^ ` . xL x^x`L . H H^H` . ^` . L ^`L . @ ^ ` . l ^`lo( l ^`lo( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . h h^h`OJ QJ o( 8 8^8`OJ QJ o( o ^`OJ QJ o( ^ `OJ QJ o( ^ `OJ QJ o( o x x^x`OJ QJ o( H H^H`OJ QJ o( ^`OJ QJ o( o ^`OJ QJ o( h h^h`o( . { ^`{5CJ OJ QJ ^J o( . { ^`{5CJ OJ QJ ^J o( . . 0 ^`05CJ OJ QJ ^J o( . . . 0 ^`05CJ OJ QJ ^J o( . . . . 8 8^8`5CJ OJ QJ ^J o(
. . . . . 8 8^8`5CJ OJ QJ ^J o( . . . . . . ` ^``5CJ OJ QJ ^J o( . . . . . . . ` ^``5CJ OJ QJ ^J o( . . . . . . . . ^`5CJ OJ QJ ^J o( . . . . . . . . . / ^`/CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . 8 8^8`CJ o( . . . . ` ^``CJ o(
. . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . ( ^ `(CJ o( . . . . . . . . . 0 ^`0o( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h ^`OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h ^`OJ QJ o( h p p^p`OJ QJ o( o h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h P P^P`OJ QJ o( o h ^ `OJ QJ o( { ^`{o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . ` ^``o(
. . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . h h h^h`OJ QJ o( ^`B* OJ QJ o( ph ^ ` . ^ ` . x x^x` . H H^H` . ^` . ^` . ^` . h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( ^` . ^` . p p^p` . @ @ ^@ ` . ^` . ^` . ^` . ^` . P P^P` . h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( M ^`Mo( D D^D`o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( ` ^``o( . ` ^``o( . . ` ^``o( . . . ` ^``o( . . . . ` ^``o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . h h^h`OJ QJ o( h h^h`o( h h^h`o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o( . . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( ` ^``o( . ` ^``o( . . ` ^``o( . . . ` ^``o( . . . . ` ^``o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . k ^`ko( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . ` ^``o( . . . . ` ^``o( . . . . . ^`o( . . . . . . ^`o( . . . . . . . p p^p`o( . . . . . . . . h
^`h H . h
^`h H . h
pL p^p`Lh H . h
@ @ ^@ `h H . h
^`h H . h
L ^`Lh H . h
^`h H . h
^`h H . h
PL P^P`Lh H . ^`56 o( T a b l e . ^` . pL p^p`L . @ @ ^@ ` . ^` . L ^`L . ^` . ^` . PL P^P`L . { ^`{5CJ OJ QJ ^J o( . { ^`{5CJ OJ QJ ^J o( . . 0 ^`05CJ OJ QJ ^J o( . . . 0 ^`05CJ OJ QJ ^J o( . . . . 8 8^8`5CJ OJ QJ ^J o(
. . . . . 8 8^8`5CJ OJ QJ ^J o( . . . . . . ` ^``5CJ OJ QJ ^J o( . . . . . . . ` ^``5CJ OJ QJ ^J o( . . . . . . . . ^`5CJ OJ QJ ^J o( . . . . . . . . . h h^h`OJ QJ o( . I I^I`o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . h h^h`B* OJ QJ o( ^`OJ QJ o( - 8 8^8`OJ QJ o( ^`OJ QJ o( ^`OJ QJ o( p p^p`OJ QJ o( ^ `OJ QJ o( @ @ ^@ `OJ QJ o( ^ `OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( k ^`ko( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . ` ^``o( . . . . ` ^``o( . . . . . ^`o( . . . . . . ^`o( . . . . . . . p p^p`o( . . . . . . . . h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( h^`o(
C h a p t e r . xP x^x`Po( . ( ^(`o( . . x ^ `xo( . . . ^`o(
. . . . . X p^`Xo( . . . . . . @ ^`o( . . . . . . . 8 ^ `8o( . . . . . . . . @ ` x^@ ``o( . . . . . . . . . h h^h`o( ) h h h^h`OJ QJ o( h 8 8^8`OJ QJ o( o h ^`OJ QJ o( h ^ `OJ QJ o( h ^ `OJ QJ o( o h x x^x`OJ QJ o( h H H^H`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( ^`o( . ^`o( . . ^`o( . . . 8 8^8`o( . . . . 8 8^8`o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . ^` . ^` . pL p^p`L . @ @ ^@ ` . ^` . L ^`L . ^` . ^` . PL P^P`L . ` ^``CJ o( . ` ^``CJ o( . . ` ^``CJ o( . . . ` ^``CJ o( . . . . ` ^``CJ o(
. . . . . ` ^``CJ o( . . . . . . ^`CJ o( . . . . . . . ^`CJ o( . . . . . . . . p p^p`CJ o( . . . . . . . . . ^`o( ^`o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . h h h^h`B* OJ QJ o( ph h 8 8^8`OJ QJ o( o h ^`OJ QJ o( h ^ `OJ QJ o( h ^ `OJ QJ o( o h x x^x`OJ QJ o( h H H^H`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( ^` . ^` . p p^p` . @ @ ^@ ` . ^` . ^` . ^` . ^` . P P^P` . h h^h`B* OJ QJ o( ph 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . h h^h`OJ QJ o( ^`5OJ QJ ^J o( . ^`5OJ QJ ^J o( . . ^`5OJ QJ ^J o( . . . ^`5OJ QJ ^J o( . . . . 8 8^8`5OJ QJ ^J o(
. . . . . 8 8^8`5OJ QJ ^J o( . . . . . . ` ^``5OJ QJ ^J o( . . . . . . . ` ^``5OJ QJ ^J o( . . . . . . . . ^`5OJ QJ ^J o( . . . . . . . . . @ ^ ` . ^`B* OJ QJ o( ph 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ^`o( . . . . . . ^`o( . . . . . . . p p^p`o( . . . . . . . . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h h^h`B* OJ QJ o( ph P ^`P . . ^` . . . x p^`x . . . . ^`
. . . . .
X ^
`X . . . . . . ^ ` . . . . . . . 8 ^`8 . . . . . . . . ` ^`` . . . . . . . . . 0 ^`0o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . ` ^``o(
. . . . . ` ^``o( . . . . . . ^`o( . . . . . . . p p^p`o( . . . . . . . . p p^p`o( . . . . . . . . . h h h^h`OJ QJ o( h 8 8^8`OJ QJ o( o h ^`OJ QJ o( h ^ `OJ QJ o( h ^ `OJ QJ o( o h x x^x`OJ QJ o( h H H^H`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( { ^`{5CJ OJ QJ ^J o( . { ^`{5CJ OJ QJ ^J o( . . 0 ^`05CJ OJ QJ ^J o( . . . 0 ^`05CJ OJ QJ ^J o( . . . . 8 8^8`5CJ OJ QJ ^J o(
. . . . . 8 8^8`5CJ OJ QJ ^J o( . . . . . . ` ^``5CJ OJ QJ ^J o( . . . . . . . ` ^``5CJ OJ QJ ^J o( . . . . . . . . ^`5CJ OJ QJ ^J o( . . . . . . . . . 0 ^`OJ PJ QJ ^J o( - 0 ^` . 0 p p^p`OJ QJ o( 0 @ @ ^@ `OJ QJ o( 0 ^`OJ QJ o( o 0 ^`OJ QJ o( 0 ^`OJ QJ o( 0 ^`OJ QJ o( o 0 P P^P`OJ QJ o( h h^h` . @ ^ ` . D ^`Do( - D ^`Do( - . 0 ^`0o( - . . 0 ^`0o( - . . . 8 8^8`o(
- . . . . 8 8^8`o( - . . . . . 8 8^8`o( - . . . . . . ` ^``o( - . . . . . . . ` ^``o( - . . . . . . . . : :^:`o( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ^`o( . . . . . . ^`o( . . . . . . . p p^p`o( . . . . . . . . h ^`OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h h h^h`OJ QJ o( v h 8 8^8`OJ QJ o( o h ^`OJ QJ o( h ^ `OJ QJ o( h ^ `OJ QJ o( o h x x^x`OJ QJ o( h H H^H`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( l ^`lo( l ^`lo( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o( . . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . 0 ^`0CJ o( 0 ^`0CJ o( . 0 ^`0CJ o( . . 0 ^`0CJ o( . . . 8 8^8`CJ o( . . . . 8 8^8`CJ o( . . . . . ` ^``CJ o( . . . . . . ` ^``CJ o( . . . . . . . ` ^``CJ o( . . . . . . . . @ ^ ` . h 8 8^8`OJ QJ o( h ^`OJ QJ o( o h ^ `OJ QJ o( h ^ `OJ QJ o( h x x^x`OJ QJ o( o h H H^H`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h h^h` . @ ^ ` . h h^h` . h ^` . h ^` . h pL p^p`L . h @ @ ^@ ` . h ^` . h L ^`L . h ^` . h ^` . h PL P^P`L . ^`OJ QJ o(
! " # $ % & ' ( ) * + , - . 2 k 3 4 5 6 7 8 9 D ; < = > ? @ A B C E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j ^`B* OJ QJ o( ph ^ ` . ^ ` . x x^x` . H H^H` . ^` . ^` . ^` . ^` CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . 8 8^8`CJ o( . . . . ` ^``CJ o(
. . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . ( ^ `(CJ o( . . . . . . . . . h h^h`o( ) h h^h` . ` ^``CJ o( . ` ^``CJ o( . . ` ^``CJ o( . . . ` ^``CJ o( . . . . ` ^``CJ o(
. . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . ( ^ `(CJ o( . . . . . . . . . h ^`OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( 0 ^`0o( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . / ^`/CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . 8 8^8`CJ o( . . . . ` ^``CJ o(
. . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . ( ^ `(CJ o( . . . . . . . . . h ^`56 o( F i g u r e . h ^`OJ QJ o( h pL p^p`L . h @ @ ^@ ` . h ^` . h L ^`L . h ^` . h ^` . h PL P^P`L . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( w w^w`o( - 0 ^`0o( - . 0 ^`0o( - . . 8 8^8`o( - . . . 8 8^8`o(
- . . . . ` ^``o( - . . . . . ` ^``o( - . . . . . . ^`o( - . . . . . . . ^`o( - . . . . . . . . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h V V ^V `OJ QJ o( h & & ^& `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h f f^f`OJ QJ o( o h 6 6^6`OJ QJ o( ^`OJ QJ o( ^`B* OJ QJ o( ph ^`OJ QJ o( o ^ `OJ QJ o( ^ `OJ QJ o( x x^x`OJ QJ o( o H H^H`OJ QJ o( ^`OJ QJ o( ^`OJ QJ o( o ^`OJ QJ o(
^`h H .
^`h H .
pL p^p`Lh H .
@ @ ^@ `h H .
^`h H .
L ^`Lh H .
^`h H .
^`h H .
PL P^P`Lh H . h h^h`o( h h^h`o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o( . . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . ^`OJ QJ o( ^`OJ QJ o( o ^ `OJ QJ o( ^ `OJ QJ o( x x^x`OJ QJ o( o H H^H`OJ QJ o( ^`OJ QJ o( ^`OJ QJ o( o ^`OJ QJ o( h h^h`B* o( ) h h^h`OJ QJ o( ^`B* OJ QJ o( ph ^`B* OJ QJ o( ph ^ ` . ^ ` . x x^x` . H H^H` . ^` . ^` . ^` . h h^h`OJ QJ o( N ^`OJ QJ o( N ^`OJ QJ o( o N V V ^V `OJ QJ o( N & & ^& `OJ QJ o( N ^`OJ QJ o( o N ^`OJ QJ o( N ^`OJ QJ o( N f f^f`OJ QJ o( o N 6 6^6`OJ QJ o( ^ `o( . ^ `o( . . 0 ^`0o( . . . 0 ^`0o( . . . . 8 8^8`o(
. . . . . 8 8^8`o( . . . . . . ` ^``o( . . . . . . . ` ^``o( . . . . . . . . ^`o( . . . . . . . . . h h^h`OJ QJ o( ^`o( - ^`o( - . 0 ^`0o( - . . 0 ^`0o( - . . . 8 8^8`o(
- . . . . 8 8^8`o( - . . . . . 8 8^8`o( - . . . . . . ` ^``o( - . . . . . . . ` ^``o( - . . . . . . . . h h^h`OJ QJ o( . 8 8^8`o( . 8 8^8`o( . 0 ^`0o( . . ^`o( . . . ^`o( . . . . p` p^p``o( . . . . . p` p^p``o( . . . . . . ^ `o( . . . . . . . @ @ ^@ `o( . . . . . . . . ^ `o( . ^ `o( . . ^ `o( . . . 8 8^8`o( . . . . 8 8^8`o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . h h^h` . h h h^h` . h 8 8^8` . h L ^`L . h ^ ` . h ^ ` . h xL x^x`L . h H H^H` . h ^` . h L ^`L . h h^h`o( . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( w w^w`o( - 0 ^`0o( - . 0 ^`0o( - . . 8 8^8`o( - . . . 8 8^8`o(
- . . . . ` ^``o( - . . . . . ` ^``o( - . . . . . . ^`o( - . . . . . . . ^`o( - . . . . . . . . @ ^ ` . ^ ` B* CJ OJ QJ o( I I^I`o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . $ ^`$5B*OJ PJ QJ ^J o( ph . $ ^`$5B*OJ PJ QJ ^J o( ph . . $ ^`$5B*OJ PJ QJ ^J o( ph . . . $ ^`$5B*OJ PJ QJ ^J o( ph . . . . $ ^`$5B*OJ PJ QJ ^J o( ph
. . . . . $ ^`$5B*OJ PJ QJ ^J o( ph . . . . . . $ ^`$5B*OJ PJ QJ ^J o( ph . . . . . . . $ ^`$5B*OJ PJ QJ ^J o( ph . . . . . . . . ^`5B*OJ PJ QJ ^J o( ph . . . . . . . . . h h h^h`OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h h^h`OJ QJ o( w w^w`o( - 0 ^`0o( - . 0 ^`0o( - . . 8 8^8`o( - . . . 8 8^8`o(
- . . . . ` ^``o( - . . . . . ` ^``o( - . . . . . . ^`o( - . . . . . . . ^`o( - . . . . . . . . h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( ^ ` B* CJ OJ QJ o( @ ^ ` . h ^`OJ QJ o( h p p^p`OJ QJ o( o h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h P P^P`OJ QJ o( o h ^ `OJ QJ o( h h^h`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( pL p^p`L . @ @ ^@ ` . ^` . L ^`L . ^` . ^` . PL P^P`L . N ^` . N ^`OJ QJ o( o N V V ^V `OJ QJ o( N & & ^& `OJ QJ o( N ^`OJ QJ o( o N ^`OJ QJ o( N ^`OJ QJ o( N f f^f`OJ QJ o( o N 6 6^6`OJ QJ o( ^`OJ QJ o( h h^h`o( . 8 8^8` . L ^`L . h ^ ` . ^ ` . xL x^x`L . H H^H` . ^` . L ^`L . h h^h` . h h h^h`OJ QJ o( h 8 8^8`o( . h ^`OJ QJ o( h ^ `OJ QJ o( h ^ `OJ QJ o( o h x x^x`OJ QJ o( h H H^H`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^` . h ^` . h pL p^p`L . h @ @ ^@ ` . h ^` . h L ^`L . h ^` . h ^` . h PL P^P`L . @ ^ ` . ^`B* OJ QJ o( ph 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( 0 ^`0o( 0 ^`0o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o( . . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ` ^``o( . . . . . . . . 0 ^`0o( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . ^`OJ QJ o( 8 8^8`o( )
^`h H .
L ^ `Lh H .
^ `h H .
x x^x`h H .
HL H^H`Lh H .
^`h H .
^`h H .
L ^`Lh H . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( 0 ^`0o( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . ` ^``o( . . . . ` ^``o( . . . . . ^`o( . . . . . . ^`o( . . . . . . . p p^p`o( . . . . . . . . ^` . ^` . p p^p` . @ @ ^@ ` . ^` . ^` . ^` . ^` . P P^P` . h ^`OJ QJ o( h ^` . h pL p^p`L . h @ @ ^@ ` . h ^` . h L ^`L . h ^` . h ^` . h PL P^P`L . ^`o( . 80 8^8`0o( . 80 8^8`0o( . . ^`o( . . . ` ^``o( . . . . ` ^``o( . . . . . p p^p`o( . . . . . . p p^p`o( . . . . . . . ^ `o( . . . . . . . . h h^h`o( . @ h ^`OJ QJ o( ` ^``o( . ` ^``o( . . ` ^``o( . . . ` ^``o( . . . . ` ^``o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . h N N^N`OJ QJ o( h ^ `OJ QJ o( o h ^`OJ QJ o( h
^
`OJ QJ o( h ^`OJ QJ o( o h ^ ^^^`OJ QJ o( h . .^.`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( ^`5CJ OJ QJ ^J o( ^`5CJ OJ QJ ^J o( . ^`5CJ OJ QJ ^J o( . . 0 ^`05CJ OJ QJ ^J o( . . . 0 ^`05CJ OJ QJ ^J o( . . . . 8 8^8`5CJ OJ QJ ^J o( . . . . . 8 8^8`5CJ OJ QJ ^J o( . . . . . . 8 8^8`5CJ OJ QJ ^J o( . . . . . . . ` ^``5CJ OJ QJ ^J o( . . . . . . . . h ^`OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h h^h`OJ QJ o( h h h^h`OJ QJ o( h 8 8^8`OJ QJ o( o h ^`OJ QJ o( h ^ `OJ QJ o( h ^ `OJ QJ o( o h x x^x`OJ QJ o( h H H^H`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h h h^h`OJ QJ o( 8 8^8` . L ^`L . h ^ ` . ^ ` . xL x^x`L . H H^H` . ^` . L ^`L . 0 ^`0o( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . 0 ^`0CJ o( 0 ^`0CJ o( . 0 ^`0CJ o( . . 0 ^`0CJ o( . . . 8 8^8`CJ o( . . . . 8 8^8`CJ o( . . . . . ` ^``CJ o( . . . . . . ` ^``CJ o( . . . . . . . ` ^``CJ o( . . . . . . . . h h^h`OJ QJ o( h h^h`OJ QJ o( h ^` ) h ^`B* OJ QJ o( ph h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( @ ^ ` . : :^:`o( 0 ^`0o( . 0 ^`0o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ^`o( . . . . . . ^`o( . . . . . . . p p^p`o( . . . . . . . . ^` . ^` o( ^` o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o( . . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( h h^h`o( )
h h^h`5CJ o( . h h^h`o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o( . . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( ^` CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . 8 8^8`CJ o( . . . . ` ^``CJ o(
. . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . ( ^ `(CJ o( . . . . . . . . . h h^h`B* OJ QJ o( ph 8 8^8`B* OJ QJ o( ph ^` . ^ ` . ^ ` . x x^x` . H H^H` . ^` . ^` . ^` CJ o( . 0 ^`0CJ o( . . 8 8^8`CJ o( . . . 8 8^8`CJ o( . . . . ` ^``CJ o(
. . . . . ^`CJ o( . . . . . . ^`CJ o( . . . . . . . p p^p`CJ o( . . . . . . . . ( ^ `(CJ o( . . . . . . . . . h h^h`OJ QJ o( ! ^`!o( ! ^`!o( . ! ^`!o( . . 8 8^8`o( . . . 8 8^8`o( . . . . ` ^``o( . . . . . ` ^``o( . . . . . . ^`o( . . . . . . . ^`o( . . . . . . . . h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h p p^p`OJ QJ o( h @ @ ^@ `OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( h ^`OJ QJ o( h ^`OJ QJ o( o h P P^P`OJ QJ o( ^`OJ QJ o( h ^`OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( X X^X`o( X X^X`o( . 0 ^`0o( . . 0 ^`0o( . . . 8 8^8`o( . . . . 8 8^8`o( . . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . h h h^h`OJ QJ o( h 8 8^8` ) h ^`OJ QJ o( h ^ `OJ QJ o( h ^ `OJ QJ o( o h x x^x`OJ QJ o( h H H^H`OJ QJ o( h ^`OJ QJ o( o h ^`OJ QJ o( ` ^``o( . ` ^``o( . . ` ^``o( . . . ` ^``o( . . . ` ^``o(
. . . . . ` ^``o( . . . . . . ` ^``o( . . . . . . . ^`o( . . . . . . . . p p^p`o( . . . . . . . . . ^`CJ OJ QJ o( ^`CJ OJ QJ o( o p p^p`CJ OJ QJ o( @ @ ^@ `CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( ^`CJ OJ QJ o( P P^P`CJ OJ QJ o( k ^`ko( ( ) ^` . pL p^p`L . @ @ ^@ ` . ^` . L ^`L . ^` . ^` . PL P^P`L . w w^w`o( - 0 ^`0o( - . 0 ^`0o( - . . 8 8^8`o( - . . . 8 8^8`o(
- . . . . ` ^``o( - . . . . . ` ^``o( - . . . . . . ^`o( - . . . . . . . ^`o( - . . . . . . . . # t# #OV `F # 4CW 0Sr o lS K?*@ Hq S ) %t
E4 6hz BZ l _] 3 L r_u <{ ~ $Lu Pv #m hl ]SQ t|f wC hj v $ wwZ uKTr ;: +pA ~Zu 4T F Bh/ ez G1A /)61 q % 9~ , h6L KQ o] &T e( _\x GkQ AP ey pQO D A~R We '] |qK k(b h; ? WN l$ $ % % % ( 4 @
\ h t + Data Mining for an Online Education System o Data Mining, Online Education at Behrouz Minaei l ehr Normal rje 745 Microsoft Word 10.0 @ @ :ƒO@ , @ :ƒO u
F Microsoft Word Document
MSWo D o c u m e n t S u m m a r y I n f o r m a t i o n 8 : C o m p O b j j 8& & ' d' ' ( ( ( sZ fb eM ;! Mm' L) %7D O
$ [_ %7D ) >i :d XSs 20 P 'F @Z XQ? Mxw Q D GV7 q 5 k>> uN` .` mB
6i _f D|u <E V j ym tc; t* +B| *& a-C ,lP 9 4Su -H
u ]1 d qBR "y o-q % )% Fli :q "hY +$ P&" v1 wMd L;N h> FRu Pw Et$& }@m ?)z | . | OSXk zA% etl dG3 +rv$
u @ )H Kn >} ~ K _- c
uA (?3 SF @H? sbJq !=& -Kz FX |" .y [
Kyo pjK V : %~ n
l+ bsQ @rF o! x] :x !2 o +z6 E 0]O hN fy{{ #2s S<u '& fy{{ ) o! * x] T* q0 0o {z@ ^ s|> \d Z' ' x ! ? wF r\z 'vO Q Yz z#& >[% ELf& kA2 @d-J m
n [PlG \\ 6 IE &? R\ j % T@4 BX0 E eO3U +~ A?( i? -w ?: 2Ls J=5: X\8 4oY
9 >32 _ <, o Up # @ h ^`OJ QJ o( # @ h ^`OJ QJ o( ($ @ OJ QJ o( $ l @ ^ ` OJ QJ o( -(% ^ ` B* CJ OJ QJ o( % @ CJ$ OJ QJ o( n% D @ CJ( OJ QJ o( " D& @ ^ ` OJ QJ o( & @ ^ ` OJ QJ o( ' @ ^ ` OJ QJ o( -p' m @ > >^>` . ' @ ^ ` OJ QJ o( ,( @ ^ ` ՜. +,D ՜. +,p , p x AI GARAGe A
+ Data Mining for an Online Education System Title D 8 @ _PID_HLINKS A D [ h t t p : / / w w w . w e b c t . c o m / ]
X E h t t p : / / c o m p a n y . b l a c k b o a r d . c o m / c l i e n t s / c a s e s / v i e w c s . c g i ? c s i d = 6 6 8 6 6 7 0 2 n = U D h t t p : / / c o m p a n y . b l a c k b o a r d . c o m / c l i e n t s / c a s e s / v i e w c s . c g i ? c s i d = 2 0 1 6 8 4 9 i R m a i l t o : A O L @ S c h o o l 6 0 O h t t p : / / w w w . b l a c k b o a r d . c o m / L 2 h t t p : / / w w w . w e b a s s i g n . n e t / i n f o / A c c e s s C o d e I n f o . h t m l R S I - h t t p : / / w w w . w e b a s s i g n . n e t / i n f o / t e x t b o o k s . h t m l P F h t t p : / / w w w . w e b a s s i g n . n e t / * & D h t t p : / / w w w . w b t s y s t e m s . c o m / z c h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 2 _ M a t h _ 2 _ T r i g / m s u - p r o b 1 3 . p r o b l e m { m g h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 6 _ V e c t o r s _ S c a l a r s / m s u - p r o b 1 0 . p r o b l e m z j g h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 6 _ V e c t o r s _ S c a l a r s / m s u - p r o b 0 7 . p r o b l e m e h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 3 _ U n i t s _ S c a l i n g / m s u - p r o b 1 7 . p r o b l e m ~ 6 ^ h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 1 2 . p r o b l e m { 7 ^ h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 0 7 . p r o b l e m x 7 ^ h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 0 4 . p r o b l e m j l g h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / d e v o l i b r a r y / t y p e - m a t h / f r a c t - a d d - s u b - d i v - p 1 1 . p r o b l e m ^ b h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / d e v o l i b r a r y / t y p e - m a t h / f r a c t i o n - r l t - 2 3 5 . p r o b l e m 6 ^ h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 1 3 . p r o b l e m
e h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 3 _ U n i t s _ S c a l i n g / m s u - p r o b 2 2 . p r o b l e m | 6 ^ h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 1 0 . p r o b l e m } 6 ^ h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / p h y s i c s l i b / m s u p h y s i c s l i b / 0 1 _ M a t h _ 1 / m s u - p r o b 1 1 . p r o b l e m % ' C h t t p : / / d e e p t h o u g h t . l i t e . m s u . e d u / r e s / m s u / m m p / k a p 1 / c a l c k a p 1 . s e q u e n c e OJ QJ o( ( @? @ ^ ` OJ QJ o( -( @ ^ ` OJ
QJ
o( X) @ h h^h`OJ QJ o( @ 50 ,f@T Z: B
z@&, X\'Xo cz On B
B
^ǠN:6d:: ̴L"( p B
z@&, X\'Xo кsB) [<no.8SMB B
B
B
B
XP. XP. B
z@&, X\'Xo B
XP. B
B
z@&, X\'Xo :6d:: ̴L"( B
bFD thk<| 6{0ltJ$ B
B
B
z@&, X\'Xo ` }Z n % z2 ? >@ UA (M Tf *7k -s l< H y t +b } 4+ ]A . U V4 Z FJ X, 1 aY _ 9 ! ! ! ! 5! W! p! q! z! ! ! ! ! ! ! ! " " " 4" W" h" i" x" " " " " c% U4 K 0 2 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 "6 &6 (6 ,6 .6 26 46 56 C6 E6 G6 J6 M6 P6 S6 V6 Y6 \6 ]6 h6 m6 r6 w6 }6 6 6 6 6 6 6 e7 g7 l7 y7 |7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 }8 8 8 8 8 8 8 8 8 8 8 8 8 Uk Xk kk sk yk zk }k k k k k k k k k k k l l ?l @l Cl cl xl l l l l l l l l m *m Gm Hm Km km {m m m m m m m m m n )n Gn Hn Ln ln yn n n n n n n n n o o 7o 8o vo js u w x x x y y y y y "y 'y -y 4y 9y ?y Hy Qy Ry Ty y y y y y y y y y z z z z z z z z z z z z z z z z z z z z z S{ W{ [{ ^{ c{ g{ i{ m{ r{ v{ z{ { { { { | | | | | | !| %| *| .| 2| 8| =| >| @| | | | | | | | | | | | | } } } } } } } } } } } } } } } } } } c~ g~ k~ n~ s~ w~ y~ }~ ~ ~ ~ ~ ~ ~ ~ ( , 0 3 8 < > B G K O U [ \ ^
$ % ( ŀ ǀ ˀ Ѐ Ԁ ݀ I M R U Z ^ ` d i m q v { | # ( , 0 5 ; < & ) 8 B C F O 3 4 7 M I J M ] e f j z Ɛ ǐ ː ې % & * = ɑ R U ^ g n o q ǔ Ȕ ʔ ' , 2 3 5 { Ǖ ͕ ҕ ӕ Օ A G L M N 4 Ƭ ٬
" ) 0 7 > ? C J Q X ^ _ c j q x ĭ ˭ ҭ ٭ ڭ IJ ϸ и ۸ ' . 5 < = A H O U \ ] d k q x ~ ƹ Թ ۹ ͻ ' 1 ; < B I P U V Z a h m n r y Z c d e f n u & : D N X Y Z c k t | & , 2 8 > D E T Z ` f l r x y
& , 2 8 > D E M S Y _ e k q r y # ) * + 0 6 < B C D J P V \ ] ^ e k q w x y z { | } ~
! ' ( Z Z Z $Z eZ fZ rZ Z Z Z Z Z Z Z Z Z [ [ 0[ =[ >[ T[ m[ n[ [ [ [ [ [ [ [ [ [ [
\ \ \ -\ .\ N\ `\ a\ p\ \ \ \ ] ] ] a a h h h h i i i ]i ^i mi wi xi i i i i i i j [j \j rj j j j j j j j j k r r s v v v v v v +x ,x ;x Ix Jx `x z z { H{ I{ k w ̲ Ͳ ϲ Ҳ % + 1 2 4 6 D J P Q S U c i o p r t ³ ȳ γ ϳ ѳ ӳ x } Ŵ Ӵ ܴ # ) / 5 : = C I J L O U [ b i o s y $ % & / 4 5 6 > C D E j p x % & ( + . 4 9 : ߷
! ( ) + . < B H I K N \ b h i k n | ¸ ȸ ɸ ˸ θ ܸ " ( ) , / = C I J M P ^ d j k n q ǹ ι ӹ ֹ
$ * 0 1 4 6 D J P Q T V d j p q t v ĺ ʺ к Ѻ - 2 @ R \ j t z ǻ Ȼ ʻ ̻ һ ݻ " ( ) + - 3 9 ? E J M S Y Z \ _ e k q w } Ǽ ̼ Ҽ ؼ ݼ ! & + 2 8 > A G M N P R W \ c j o r x ~ ߽ ! ' 1 2 ; A J K S Y _ ` ž ƾ Ⱦ ˾ ξ Ծ پ ھ ܾ ߾ / 9 > ? A D G M R S U X [ a f g i l o u z { n ` . a u % % % & & & & & & F& G& M& S& l& m& v& |& & & & & & & & & & & & & & & a( =Q =Q Ms 5ѐ 5ѐ mU mU i0 mJ 9 5 @ < `( P @ U n k n o w n G z T i m e s N e w R o m a n 5 S y m b o l 3& z A r i a l 7& V e r d a n a A& A r i a l N a r r o w ; S i m S u n [SO ?5 z C o u r i e r N e w C S y m b o l , I t a l i c ? S y m b o l , B o l d ;& z H e l v e t i c a 71 C o u r i e r 5& z ! T a h o m a A& T r e b u c h e t M S 9 M S S e r i f 7 T m s R m n ; W i n g d i n g s ] M o n o t y p e S o r t s C o u r i e r N e w 3 z T i m e s " A h fhhn u u ! 2 d 3Q H ( ? * D a t a M i n i n g f o r a n O n l i n e E d u c a t i o n S y s t e m D a t a M i n i n g , O n l i n e E d u c a t i o n B e h r o u z M i n a e i r j e
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~ rdDoc Word.Document.8 9q
--minaeibi1030835336--