FROM 2005 TO 2010 BUSINESS LOGIC AND DATA

Tìm thấy 10,000 tài liệu liên quan tới từ khóa "FROM 2005 TO 2010 BUSINESS LOGIC AND DATA":

LUẬN VĂN TỐT NGHIỆP MBA DEVELOPING BUSINESS STRATEGY FOR VIETTEL MOBILE IN VIET NAM FOR THE YEARS 20112015

LUẬN VĂN TỐT NGHIỆP MBA DEVELOPING BUSINESS STRATEGY FOR VIETTEL MOBILE IN VIET NAM FOR THE YEARS 20112015

Luận văn tốt nghiệp MBA Developing business strategy for viettel mobile in Viet Nam for the years 20112015Luận văn tốt nghiệp MBA Developing business strategy for viettel mobile in Viet Nam for the years 20112015Luận văn tốt nghiệp MBA Developing business strategy for viettel mobile in Viet Nam for the years 20112015Luận văn tốt nghiệp MBA Developing business strategy for viettel mobile in Viet Nam for the years 20112015Luận văn tốt nghiệp MBA Developing business strategy for viettel mobile in Viet Nam for the years 20112015ObjectiveTo conduct the complete topdown and competitive analyses, develop alternative strategies and select a prioritized set of strategies that will ensure a competitive advantage for Viettel mobile in the period of 20112015.Subject Scope of the study Subject: Viettel group other competitive companies in the mobile telecommunication industry. Scope: Basing on specific statistics on the macro environment, industry and organizational environment of the Viettel from 2005 to 2010 and orientation until 2015. Study ApproachesQualitative quantitative; Statistical analytical analyses Chapter 1: Theoretical foundation of strategic management Concept role Stages of strategic management Models and matrices usedChapter 2: Analysis of business activities of Viettel Introduction to Viettel Group Business situation 20052010 Business environment analyzeChapter 3: Strategy selection Implementation solutions Vision mission Strategic identification selection Implementation solutions processes
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VIETNAM FREIGHT TRANSPORT REPORT Q3 2009

VIETNAM FREIGHT TRANSPORT REPORT Q3 2009

... of any information hereto contained Vietnam Freight Transport Report Q3 2009 © Business Monitor International Ltd Page Vietnam Freight Transport Report Q3 2009 CONTENTS Executive Summary ... leave Vietnam a second-rate economy for an indefinite period © Business Monitor International Ltd Page Vietnam Freight Transport Report Q3 2009 Business Environment Ratings The freight transport. .. International Ltd Page 23 Vietnam Freight Transport Report Q3 2009 Transport Outlook Table: Transport And Communications Data And Forecasts, 2005-2013 2005 2006e 2007e 2008e 2009f 2010f 2011f 2012f

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Weather based forewarning model for yellow rust of wheat in scarcity zone of Jammu and Kashmir, India

Weather based forewarning model for yellow rust of wheat in scarcity zone of Jammu and Kashmir, India

A forewarning model of stripe rust of wheat for predicting the disease initiation in Jammu sub-tropics was developed by the analysis of disease severity data pertaining to the years from 2005-06 to 2012-13 obtained from AICRP research experiments available in the Division of Plant Pathology. The analyzed data was validated during rabi season 2014-16.

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HIS chapter 2 ( Tin y học trong bệnh viện)

HIS CHAPTER 2 ( TIN Y HỌC TRONG BỆNH VIỆN)

Reinhold Haux Alfred Winter Elske Ammenwerth Birgit Brigl Strategic Information Management in Hospitals An Introduction to Hospital Information Systems 2 2 Basic Concepts 2.1 Introduction Each domain usually has its own terminology which often differentiates from the ordinary understanding of concepts and terms. This chapter presents terminology for hospital information systems and its information management, as used in this book. It is, therefore, essential to read this chapter carefully. All relevant concepts can also be found in the thesaurus at the end of the book. After this chapter, you should be able to answer the following questions: • Which basic concepts are needed in order to work on hospital information systems? • What terms do we use? 2.2 Data, information and knowledge Data constitute a reinterpretable representation of information, or knowledge, in a formalized manner suitable for communication, interpretation, or processing by humans or machines. Formalization may take the form of discrete characters or of continuous signals (e.g., sound signals). In order to be ‘reinterpretable’, there has to be agreement on how data represent information. For example, Peter Smith or 001001110 are data. A set of data which is collected for the purpose of transmission and which is considered to be an entity is called a message. There is no unique definition of information. Depending on the point of view, the definition may deal with the syntactic aspect (the structure), the semantic aspect (the meaning) or the pragmatic aspect (the intention or goal of information). We will simply define information as specific knowledge about objects such as facts, events, things, persons, processes, or ideas. For example, when a physician knows the diagnosis (facts) of a patient (person), then he or she has information. Information as specific knowledge contrasts with general knowledgeabout concepts (for example diseases, therapeutic methods). The knowledge of a nurse, for example, comprises how to typically deal with patients suffering from decubitus. For the sake of simplicity, we will often use the term information processing, when we mean processing of data, information and knowledge. 2. Basic Concepts 19 2.3 Information systems and their components Systems and subsystems Before talking about information systems, let us first define the concept system. As defined here, a systemis a set of persons, things andor events which form an entity, together with their relationships. We distinguish between natural systems and manmade systems. For example, the nervous system is a typical natural system, consisting of neurons and their relationships. A manmade system is, for example, a hospital, consisting of staff, patients and relatives, and their interactions. If a (manmade) system consists of both human and technical objects, it can be called a sociotechnical system. A system can, in principle, be divided into subsystemswhich comprise a subset of all objects and the relationships between them. For example, a possible subsystem of the nervous system is the sympathetic nervous system. A subsystem of a hospital is, for example, a ward with its staff and patients. Subsystems themselves are again systems. Models of systems When dealing with systems, we usually work with modelsof systems. A model is a description of what the modeler thinks to be relevant of a system. In the sciences, models commonly represent simplified depictions of reality or excerpts of it (see Figure 17). Models are adapted to answer certain questions or to solve certain tasks. Models should be appropriate for the respective questions or tasks. This means that a model is only ‘good’ when it is able to answer such a question or solve such a task. For example, a model which only comprises the patients of a ward cannot be used for nurse staffing and shift planning. Information Systems An information systemis that part of an enterprise which processes and stores data, information, and knowledge. It can be defined as the sociotechnical subsystem of an enterprise which comprises all information processing as well as the associated human or technical actors in their respective information processing role. ‘Socio’ refers to the people involved in information processing (e.g., health care professionals, administrative staff, computer scientists), whereas ‘technical’ refers to information processing tools (e.g., computers, telephones, patient Figure 17: A model of a computer is not the real computer. 20 Strategic Information Management in Hospitals records). The people and machines in an enterprise are only considered in their role as information processors, carrying out specific actions following established rules. An information system which comprises computerbased information processing and communication tools is called a computersupported information system. The subsystem of the information system where computerbased tools are used is called the computersupported part, the rest is called the conventional part of the information system. Components of information systems When describing an information system, it can help to look at the following typical components of information systems: Enterprise functions, business processes, application components and physical data processing components. An enterprise functiondescribes what acting human or machines have to do in a certain enterprise to contribute to its mission and goals. For example, patient admission, clinical data management or financial controlling describe typical enterprise functions. Enterprise functions are ongoing and continuous. They describe what is to be done, not how it is done. Enterprise functions can be put together in a hierarchy of functions, where a function can be described in more detail by refined functions. Enterprise functions are usually denoted by nouns or gerunds (i.e. words ending with ing). An activity is an instantiation of an enterprise function working on an individual object. For example, Dr. Doe admits patient Jane Smith is an activity of the enterprise function patient admission. Just as enterprise functions, they can be put together in a hierarchy of activities. In contrast to enterprise functions, activities have a definite beginning and end. To describe the chronological and logical sequence of a set of activities, business processesare useful. They describe the sequence of activities together with the conditions under which they are invoked, in order to achieve a certain enterprise goal. Business processes are usually denoted by verbs (for example, dismiss a patient, document a diagnosis or write a discharge letter). As they are composed of individual activities, they also have a definite beginning and end. We will only refer to enterprise functions and business processes in respect to information processing. Whereas enterprise functions and business processes describe what is done, we now want to have a look at tools for processing data, in particular at socalled application components and at physical data processing components. Both are usually referred to as information processing tools. They describe the means used for information processing. Application components support enterprise functions. We distinguish computerbased from conventional application components. Computerbased application components are installations of software products on computers. A software product is an acquired or selfdeveloped 2. Basic Concepts 21 piece of software which is complete in itself and which can be installed on a computer system. For example, the application component patient management system stands for the installation of a software product to support the enterprise functions of patient admission, management, and discharge. Conventional application components are realized by conventional means such as organizational plans which describe how to use conventional data processing components. For example, the application component nursing documentation organization contains rules how and in which context to use the given forms for nursing documentation. The communication respectively the cooperation among application components must be organized in such a way that the business processes can be executed. Physical data processing components, finally, describe the information processing tools which are used to realize the computerbased and the conventional application components (see Figure 18). Physical data processing components can be human actors (such as the person delivering mail), conventional physical tools (such as printed forms, telephones, books, patient record), or computer systems (such as terminals, servers, personal computers). Computer systems can be physically connected via data wires, leading to physical networks. Architecture and infrastructure of information systems The architecture of an information system describes its fundamental organization, represented by its components, their relationships to each other and to the environment, and by the principles guiding its design and evolution. 13 The architecture of an information systems can be described by the enterprise functions, the business processes, and the information processing tools, together with their relationships. There may be several architectural views of an information system, e.g. a functional view looking primarily at the enterprise functions, a process view looking primarily at the business processes, etc. Architectures which are equivalent with regard to certain characteristics, can be summarized to a certain architectural style. 13 Institute of Electrical and Electronics Engineers (IEEE). Std 14712000: Recommended Practice for Architectural Description of SoftwareIntensive Systems. September 2000. http:standards.ieee.org. Figure 18: Typical physical data processing components on a ward. 22 Strategic Information Management in Hospitals When the focus is put onto the types, number and availability of information processing tools used in a given hospital, this is also called infrastructureof its hospital information system. 2.4 Hospital information systems With the definition of information systems in mind, a hospital information system can easily be defined. A hospital information systemis the sociotechnical subsystem of a hospital, which comprises all information processing as well as the associated human or technical actors in their respective information processing roles. Typical components of hospital information systems are its enterprise functions, business processes, application components and physical data processing components. For the sake of simplicity, we will denote ‘enterprise functions of a hospital’ as ‘hospital functions’. As a consequence of this definition, a hospital has a hospital information system from the beginning of its existence. Therefore the question is not whether a hospital should be equipped with a hospital information system, but rather, whether its performance should be enhanced, for example, by using state of the art information processing tools, or by systematically managing it. All groups of people and all areas of a hospital must be considered when looking at information processing. A sensible integration of each hospital function and of different information processing tools in a hospital information system is important. Hospital staff can be seen in two roles: In one, they are part of the hospital information system. For example, when working in the department for patient records, or as operator in an ICT department, they directly contribute to information processing. In the other role, they use information processing tools (e.g. a nurse may use a telephone or a computer), in other words, they are users of the hospital information system. Each employee may continuously switch between these two roles. The goalof a hospital information system is to sufficiently enable the adequate execution of hospital functions for patient care, including patient administration, taking into account economic hospital management as well as legal and other requirements. Legal requirements concern e.g. data protection or reimbursement aspects, other requirement can be, e.g., the decision of a hospital executive board on how to store patient records. In order to support patient care and the associated administration, the tasksof hospital information systems are: • to make information, primarily about patients, available: current information should be provided on time, at the right location, to authorized staff, in an appropriate and usable form. For this purpose, data must be correctly collected, stored, processed, and systematically documented in order to 2. Basic Concepts 23 ensure that correct, pertinent and uptodate patient information can be supplied, for instance, to the physician or a nurse (see Figure 19); • to make knowledge, for example about diseases and side effects and interactions of medications, available to support diagnostics and therapy; • to make information about the quality of patient care and the performance and cost situation within the hospital available. In addition to patient care, university medical centers undertake research and education to gain medical knowledge and to teach students. When hospital information systems make available • the right information and knowledge • at the right time • at the right place • to the right people • in the right form • so that these people can make the right decisions, this is also described as the information and knowledge logistics of a hospital. Hospital information systems have to consider various areasof a hospital, such as • wards, • outpatient units, • service units: diagnostic (e.g. clinical laboratory, radiological department), therapeutic (e.g. operation room) and others (e.g. pharmacy, patient records archive, library, blood bank), • hospital administration areas (e.g. general administration, patient administration and accounting, technology, economy and supply, human resources), • offices and writing services for (clinical) report writing. In addition, there are the management areas, such as: hospital management, management of clinical departments and institutes, administration management and nursing management. These areas are related to patient care. They could be broken down further. For university medical centers, additional areas, needed for research and education, Figure 20: Different people working in a hospital (here: an emergency department). Figure 19: A health care professional accessing patient information. 24 Strategic Information Management in Hospitals must be added to the above list. Obviously, the most important peoplein a hospital are the patients and, in certain respect, their visitors. The groups of people working in a hospital (see Figure 20) are • physicians, • nurses, • administrative staff, • technical staff, • health informaticians, health information management staff, etc. Obviously, within each group of people, different needs and demands on the hospital information systems may exist, depending on the tasks and responsibilities. Ward physicians, for example, will require different information than physicians working in service units or than senior physicians. 2.5 Health information systems In many countries, the driving force for health care and for ICT in health care during the last years has been the trend towards a better coordination of care, combined with rising cost pressure. One consequence is the shift towards better integrated and shared care. This means that the focus changes from isolated procedures in one health care institution (e.g. one hospital or one general practice) to the patientoriented care process, encompassing diagnosis and therapy, spreading over institutional boundaries (see Figure 21). In the US, e.g. health care organizations are merging into large integrated health care delivery systems. These are health care institutions that join together to consolidate their roles, resources and operations in order to deliver a coordinated continuum of services and to enhance effectiveness and efficiency of patient care. The situation in Europe is also changing from hospitals as centers of care delivery to decentralized networks of health care delivery institutions which are called regional networks or health care networks. Enterprise boundaries are blurring. Hospital information systems will increasingly be linked with information systems of other health care organizations. The future architecture of hospital information systems must take these developments into account. They must be open to provide access or to exchange patientrelated and general data (e.g., about the services offered in the hospital) across its institutional boundaries. Figure 21: A general practitioner, contacting a hospital. 2. Basic Concepts 25 A lot of technical and legal issues have to be solved before the vision of transinstitutional computersupported health information systems will adequately support transinstitutional patient care. For example, general willingness to cooperate with other health care providers must exist; optimal care processes must be defined, and recent business processes be redesigned; accounting and financing issues must be regulated; questions of data security and data confidentiality must be solved, together with questions on data ownership (patient or institution) and on responsibilities for distributed patient care; issues on longterm patient records (centralized or decentralized) must be discussed; and technical means for integrated, transinstitutional information processing must be offered (‘telemedicine’, ehealth), including general communication standards. When dealing with hospital information systems, we will keep these aspects of health information systems in mind. 2.6 Information management in hospitals In general, management comprises all leadership activities that determine the enterprises’ goals, structures, and behaviors. Accordingly, information management in hospitals are those management activities in a hospital which deal with the management of information processing in a hospital and therefore of its hospital information system. The goal of information management is systematic information processing which contributes to the hospitals strategic goals (such as efficient patient care and high satisfaction of patients and staff). Information management therefore directly contributes to the hospital’s success and capability to compete. Information management encompasses the management of all components of a hospital information system: the management of information, of application components, and of physical data processing components. The general tasks of information management are planning, directing, and monitoring. In other words, this means • planning the hospital information system respectively its architecture; • directing its establishment and its operation; • monitoring its development and operation with respect to the planned objectives. Information management can be differentiated into strategic, tactical, and operational management. Strategic information management deals with information processing as a whole, and lays down strategies and principles for the evolution of the whole information system. Tactical information management deals with the execution of certain projects concerning just part of the information system, e.g. the introduction of an application component for a certain hospital function such as patient administration or documentation of operations. Operational information management, finally, must secure the 26 Strategic Information Management in Hospitals smooth operation of the information system, e.g. planning of necessary personal resources, failure management, or network monitoring. 2.7 Examples Example 2.7.1 Architecture of a hospital information system In the following, an extract of the description of the architecture of the hospital information system of the Plötzberg Medical Center and Medical School (PMC) is presented. As mentioned, PMC is a fictitious institution, which will be used in examples and exercises in this book. The hospital information system of Plötzberg Medical Center and Medical School (PMC) supports the hospital functions of patient treatment with patient admission and discharge, decision support, order entry, clinical documentation and service documentation; handling of patient records; work organization and resource planning; and hospital management. Those hospital functions are supported by some bigger and over a hundred smaller application components (partly computerbased, partly conventional). The biggest application component is the patient management system (PMS), the computerbased application component which supports patient admission and discharge, management of patient treatment, part of administrative and clinical data management, and handling of patient records. In addition, several computerbased departmental application components are used for work organization and resource planning (e.g. in the radiological department, in the laboratory department and in outpatient units). Nearly all computerbased application components are interconnected, using a communication server. Some computerbased application components are isolated systems without interfaces ... Conventional application components are used for special documentation purposes (e.g. documentation in operation rooms), and for order entry and communication of findings. … The application components are realized by physical data processing components. As computerbased physical data processing component, approx. 40 application and database servers are operated, and over 4,000 personal computers are used. Over 1,000 printers of different types are installed. Most computerbased physical data processing components are interconnected to a highspeed communication network. … As conventional physical data processing components, over 2,000 telephones and 800 pagers are used. Over 2,000 different paperbased forms are used to support different tasks. More than 400,000 patients records are created and used each year, a dozen archives are responsible for patient record archiving. A conventional mailing system allows for conventional communication between departments. …” 2. Basic Concepts 27 Example 2.7.2 Comments on the future of health information systems For the physicians of the 1990s and beyond, computer workstations will be their windows on the world. Much of the necessary technology already exists. Desktop or bedside, in the office or at the hospital, computers can respond to a simple click of a mouse pointing device. … In the future, the physician will be able to access the patient record largely by using the mouse and doing very little typing. Moreover, the record will include graphics and images as well as extensive text. Outpatient records will be integrated with inpatient data by using the capabilities of communications networks that link hospitals with the clinics and private offices of their medical staff members. … 14 “Through the further development of information systems at the university hospitals, the following goals are of special importance: • Patient based (facilitywide) recording of and access to clinical data for teambased care. • Workflow integrated decision support made available for all care takers through uptodate, valid medical knowledge. • Comprehensive use of patient data for clinical and epidemiological research, as well as for health reports. ... The following tasks shall have priority and will be worked on in the next years: • The introduction of a patient based, structured, electronic health record. • The stepwise introduction of information system architectures which support cooperative, patient centered and facilitywide care. Workflow support in the area of patient care. • The establishment of a suitable network and computer infrastructure in order to be able to, via the Internet, inform about the care offered at a particular hospital. • The introduction of efficient, usable mobile information and communication tools for patient care. ...” 15 From the experience gained so far ..., a number of direct benefits from health telematics can be identified: ... 14 Ball M, Douglas J, ODesky R, Albrigh J. Health care Information Management Systems A Practical Guide. New York: Springer; 1991. p. 3. 15 Deutsche Forschungsgemeinschaft (DFG): Informationsverarbeitung und Rechner an Hochschulen Netze, Rechner und Organisation. Empfehlungen der Kommission für Rechenanlagen für 20012005 (information processing and computer systems for universities; in German), Kommission für Rechenanlagen der Deutschen Forschungsgemeinschaft. Bonn: DFG; 2001. http:www.dfg.de. 28 Strategic Information Management in Hospitals • More people can be diagnosed and treated at their local clinics or hospitals, though without the facilities of urban referral hospitals. For the first time, it is technically feasible to contemplate the provision of universal health care. ... • Health telematics allows the global sharing of skills and knowledge. Access to international centers of excellence for various specialties becomes possible from many locations. Medical expertise can be available to anyone on request. ... • Cost savings can be achieve by reducing the transport of patients and travel of health care professionals, as well as by allowing home care of patients who would otherwise require hospitalization. ... 16 The future tasks of health care include: greater cooperation, more quality and economics and greater adjustment to the needs of patients. The information age offers great possibilities to solve these tasks, maybe even possibilities that we can’t begin to imagine today. The neuralgic point though in the discussion of telematics in health care is the uniting of data. Especially with regard to personal patient data, we are forthright dealing with the most personal of all data, and special caution is to be exercised when dealing with these data. Afterall, questions of power are raised through the uniting of data: greater transparency also means greater control. 17 2.8 Exercises Exercise 2.8.1 HIS as a system As introduced, a system can be defined as a set of people, things andor events, which form an entity, together with their relationships. Which people, things or events can you find when looking at a hospital information system? In what relationship do they stand to one another? To solve this exercise, take into account the components of hospital information systems as defined in section 2.8. Exercise 2.8.2 Goals of models Find two models which represent a city. What are the goals of these models? What are their components? 16 World Health Organization (WHO). A Health Telematics Policy, Report of the WHO Group Consultation on Health Telematics 1116 December 1997, Geneva. World Health Organization: Geneva; 1998. 17 Speech of German Minister for Health, Andrea Fischer, at the occasion of the first meeting of the symposium ‘telematics in health care’, August 19th 1999, Bonn. 2. Basic Concepts 29 Exercise 2.8.3 Information processing tools in a hospital Look at the following Figures 22 25, taken from a University Medical Center. Which information and communication tools are used? Which hospital functions may be supported by those tools? Exercise 2.8.4 Information processing of different health care professional groups Please have a look at the different groups in a hospital (e.g. : physician, nurse, administrative staff, hospital manager, patient, visitor), and describe some of their typical information processing needs. Exercise 2.8.5 Information and knowledge logistics Select one typical business process in a hospital (such as admitting a patient, requesting an examination, planning of therapeutical procedures, documenting diagnoses etc.) and find three examples how information and knowledge Figure 25: In a laboratoryunit. Figure 22: In the office of a senior physician. Figure 23: Admission at a general practitioner. Figure 24: In an intensive care unit. 30 Strategic Information Management in Hospitals logistics can fail. Which consequences may arise for the quality and for the costs of patient care from this failure? Exercise 2.8.6 Buying a HIS Is it possible to buy a hospital information system? Please explain your answer. What do ‘vendors of hospital information systems’ really sell? Exercise 2.8.7 Health information systems Please have a look at the statement on the comments for the future of health information systems (example 2.2). Which chances are discussed, and which problems? 2.9 Summary When working on hospital information systems, we must distinguish between data, information and knowledge: • Data can be defined as a representation of information, or knowledge, suitable for communicating, interpreting or processing. • Information can be defined in connection with objects which have a particular meaning in a specific context (specific knowledge). • Knowledge can be defined in connection with a certain discipline using specific terminology (general knowledge). Systems can be defined as a set of people, things andor events which can be regarded as an entity. Systems can be divided into subsystems and represented using models. Models commonly represent simplified depictions of reality or excerpts of it. Remember that models • usually form a simplified representation of reality, • should be adapted to a specific question or task, and • should be appropriate to provide answers for these question or tasks. A hospital information system can be defined as the sociotechnical subsystem of a hospital which comprises all information processing functions and the human or technical actors in their information processing role. Thus, when looking at a hospital information system, try to identify the following components or objects: • The enterprise, where it is located. • The hospital functions supported. • The business processes which take place. • The information processing tools used. • The human actors involved (both as part of the information system and as users). The goal of a HIS is to 2. Basic Concepts 31 • adequately enable the execution of hospital functions for patient care, • taking financial, legal and other requirements into account. Information and knowledge logistics means to make available • the right information (about patients, ...) and the right knowledge (about diseases, ...) • at the right time • in the right place • for the right people • in the right form • so that these people can make the right decisions. When working on a hospital information system, you must consider • all areas of a hospital, such as wards, outpatient units, service units, administration departments, writing services, management units, .... • all groups of people in a hospital, such as patients, visitors, physicians, nurses, administrative staff, technical staff, health informaticians, .. Information management in hospitals are those management activities in a hospital which deal with the management of information processing and therefore the management of the hospital information system. The architecture of an information system describes its fundamental organization, represented by its components, their relationships to each other and to the environment, and by the principles guiding its design and evolution.
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geological database draft

GEOLOGICAL DATABASE DRAFT

OBJECTIVES • To become familiar with Surpac’s Geological Database module. • To learn about the minimum requirements for a geological database. • To learn to import data into a database from ASCII text files. • To learn to map a database • To create composite files • To view data and create sections Using these principles then to apply to interpreting geological sections

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statistical analysis methods for chemists a software based approach

STATISTICAL ANALYSIS METHODS FOR CHEMISTS A SOFTWARE BASED APPROACH

Most analytical experiments produce measurement data which require to be presented, analysed, and interpreted in respect of the chemical phenomena being studied. For such data and related analysis to have validity, methods which can produce the interpretational information sought need to be utilised. Statistics provides such methods through the rich diversity of presentational and interpretational procedures avail able to aid scientists in their data collection and analysis so that information within the data can be turned into useful and meaningful scientific knowledge. Pioneering work on statistical concepts and principles began in the eighteenth century through Bayes, Bernoulli, Gauss, and Laplace. Individuals such as Francis Galton, Karl Pearson, Ronald Fisher, Egon Pearson, and Jerzy Neyman continued the development in the first half of the twentieth century. Development of many fundamental exploratory and inferential data analysis techniques stemmed from real biological problems such as Darwin’s theory of evolution, Mendel’s theory of genetic inheritance, and Fisher’s work on agri cultural experiments. In such problems, understanding and quantifica tion of the biological effects of intra and interspecies variation was vital to interpretation of the findings of the research. Statistical
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DAIHATSU FAULT ANALYSIS

DAIHATSU FAULT ANALYSIS

U. S. Department of Transportation National Highway Traffic Safety Administration National Center for Statistics and Analysis Washington, D.C. 20590FARS Analytic Reference Guide Table of Contents  1 Table of Contents 2010 FARSNASS GES Standardization......................................................................... 2 New in 2010 FARS ......................................................................................................... 4 Variables with Changes in Definitions and Attributes........................................... 5 New SAS Tables in 2010................................................................................... 32 Summary of the SAS Naming Changes in 2010 ................................................ 38 Trafficway Descriptor Data Elements 2010........................................................ 39 Preface ......................................................................................................................... 41 FARS Operations.......................................................................................................... 42 FARS Instructions......................................................................................................... 43 FARS SAS Data Sets.................................................................................................... 44 FARS Variable List........................................................................................................ 46 Variable Definitions and Codes ..................................................................................... 55 The ACCIDENT Data Set .................................................................................. 60 The VEHICLE Data Set ................................................................................... 110 The PERSON Data Set ................................................................................... 247 The CEVENT Data Set.................................................................................... 295 The VEVENT Data Set .................................................................................... 302 The VSOE Data Set ........................................................................................ 309 The FACTOR Data Set.................................................................................... 313 The VIOLATN Data Set ................................................................................... 315 The VISION Data Set ...................................................................................... 319 The MANEUVER Data Set .............................................................................. 321 The DISTRACT Data Set................................................................................. 323 The DRIMPAIR Data Set................................................................................. 325 The NMIMPAIR Data Set................................................................................. 327 The NMCRASH Data Set ................................................................................ 329 The NMPRIOR Data Set ................................................................................. 331 The SAFETYEQ Data Set ............................................................................... 333 The PARKWORK Data Set.............................................................................. 335 Appendices................................................................................................................. 419 Appendix A: Vehicle MakeModel Designation ........................................... 420 Appendix B: V23 Accident Type Diagram................................................... 526 Appendix C: Additional Variable Information .............................................. 527 Appendix D: FARS Variables by SAS Table and Year ............................... 559 Appendix E: Pedestrian and Bicyclist Data Availability Change.................. 579FARS Analytic Reference Guide 2010 FARSNASS GES Standardization FARS Analytic Reference Guide 2010 FARSNASS GES Standardization  2 2010 FARSNASS GES Standardization The purpose of this document is to inform users of NHTSA’s Fatality Analysis Reporting System (FARS) and National Automotive Sampling System General Estimates System (NASS GES) data about some of the more significant changes to the 2010 data as a result of the standardization of the data elements between the two systems. In addition to the changes outlined below, a listing of all specific data element changes can be found in the following table: Variables with Changes in Definitions and Attributes The FARSNASS GES Standardization began in 2006, with the second phase being implemented in the 2010 data collection year. The definition and element attribute changes introduced in 2010 are the most substantive and most numerous changes in one year in the reconciliation of the FARS and NASS GES data systems. In the 2011 data collection year – the third and final planned phase of the FARSNASS GES Standardization – nearly all remaining data element attribute and file structure differences will be addressed. As a single, unified data entry system, FARSNASS GES will be compatible with the Model Minimum Uniform Crash Criteria (MMUCC), the guideline used by nearly all States to develop and revise their crash forms and databases. Once complete, the FARSNASS GES Standardization will simplify crash data coding and analysis as well as reduce costs and errors. Probably the most notable changes were the introduction of precrash information in FARS (already collected in NASS GES) and a change to case structure or how the groups of related data elements are organized. For example, in 2009 a FARS case consisted of Crash, Vehicle, Driver and Person coding forms. In 2010, the Person level form was split into Motor Vehicle Occupant and NonMotor Vehicle Occupant forms, and the Precrash form was added (new to FARS, though not to NASS GES). These structure changes also include changes to how the data are now stored and made available. For example, for FARS, there are now 16 data tables rather than 4. This results from the changes in the number of coding forms and from changes in specific data elements. Several data elements that used to allow only a specified number of responses now have a “selectallthatapply” format. There is a separate data table for each of these data elements. At the Crash level, a Crash Events Table was added to FARS (and modified in NASS GES). In NASS GES, NonHarmful Events were added to the Crash Events Table. The precrash information represents not only a new coding form, but more importantly, largely a new concept for FARS, attempting to collect data about the conditions, events and driver actions that preceded and may have contributed to the crash. Precrash data is intended to improve crash avoidance research and has been included in NASS GES since 1992. The new FARS Precrash form information consists of 23 data elements, 9 of which were previously coded at the Crash level, 3 each at the Vehicle and Driver levels, and 8 new elements. Nine trafficway descriptor data elements were moved from the crash level to the new precrash level. These elements provide details about the characteristics of the trafficway selected for each vehicle.FARS Analytic Reference Guide 2010 FARSNASS GES Standardization FARS Analytic Reference Guide 2010 FARSNASS GES Standardization 
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NoSQL data models trungtt dhbkhn

NOSQL DATA MODELS TRUNGTT DHBKHN

011214 1 NoSQL data models VietTrung Tran is.hust.edu.vn~trungtv 1 Eras of Databases •  Why NoSQL? 2 011214 2 Before NoSQL 3 RDBMS onesizefitsallneeds 4 011214 3 ICDE 2005 conference 5 The last 25 years of commercial DBMS development can be summed up in a single phrase: one size fits all. This phrase refers to the fact that the tradi.onal DBMS architecture (originally designed and op.mized for business data processing) has been used to support many datacentric applica.ons with widely varying characterisHcs and requirements. In this paper, we argue that this concept is no longer applicable to the database market, and that the commercial world will fracture into a collecHon of independent database engines, some of which may be unified by a common frontend parser. We use examples from the streamprocessing market and the datawarehouse market to bolster our claims. We also briefly discuss other markets for which the tradiHonal architecture is a poor fit and argue for a criHcal rethinking of the current factoring of systems services into products. After NoSQL 6 011214 4 RDBMS vs. others 7 NoSQL landscape 8 011214 5 NoSQL raising 9 10 011214 6 Why NoSQL •  “The whole point of seeking alternatives to RDBMS systems is that you need to solve a problem that relational databases are a bad fit for.” Eric Evans Rackspace 11 Why NoSQL contd •  ACID does not scale •  Web applications have different needs –  Scalability –  Elasticity –  Flexible schema semistructured data –  Geographically distributed •  Web applications do not always need –  Transaction –  Strong consistency –  Complex queries 12 011214 7 NoSQL use cases •  Massive data volume (Big volume) – Google, Amazon, Yahoo, Facebook – 10100K servers •  Extreme query workload •  Schema evolution 13 Relational data model revisited •  Data is usually stored in row by row manner (row store) •  Standardized query language (SQL) •  Data model defined before you add data •  Joins merge data from multiple tables –  Results are tables •  Pros: Mature ACID transactions with finegrain security controls, widely used •  Cons: Requires up front data modeling, does not scale well 14 Oracle, MySQL, PostgreSQL, MicrosoW SQL Server, IBM DB2 011214 8 Keyvalue data model •  Simple keyvalue interface – GET, PUT, DELETE •  Value can contain any kind of data •  Pros •  Cons •  Berkley DB, Memcache, DynamoDB, Redis, Riak 15 Keyvalue vs. table •  A table with two columns and a simple interface – Add a keyvalue – For this key, give me the value – Delete a key •  Super fast and easy to scale (no joins) 16 011214 9 Keyvalue vs. locker 17 vs. Relational Model 18 011214 10 Memcached •  Open source inmemory keyvalue caching system •  Make effective use of RAM on many distributed web servers •  Designed to speed up dynamic web applications by alleviating database load –  Simple interface for highly distributed RAM caches –  30ms read times typical •  Designed for quick deployment, ease of development •  APIs in many languages 19 •  Open source inmemory keyvalue store with optional durability •  Focus on high speed reads and writes of common data structures to RAM •  Allows simple lists, sets and hashes to be stored within the value and manipulated •  Many features that developers like expiration, transactions, pubsub, partitioning 20 011214 11 •  Scalable keyvalue store •  Fastest growing product in Amazons history •  Focus on throughput on storage and predictable read and write times •  Strong integration with S3 and Elastic MapReduce 21 •  Open source distributed keyvalue store with support and commercial versions by Basho •  A Dynamoinspired database •  Focus on availability, faulttolerance, operational simplicity and scalability •  Support for replication and autosharding and rebalancing on failures •  Support for MapReduce, fulltext search and secondary indexes of value tags •  Written in ERLANG 22 011214 12 Column family store •  Dynamic schema, columnoriented data model •  Sparse, distributed persistent multidimensional sorted map (row, column (family), timestamp) > cell contents 23 Column families •  Group columns into Column families •  Group column families into SuperColumns •  Be able to query all columns with a family or super family •  Similar data grouped together to improve speed 24 011214 13 Column family data model vs. relational •  Sparse matrix, preserve table structure – One row could have millions of columns but can be very sparse •  Hybrid rowcolumn stores •  Number of columns is extendible – New columns to be inserted without doing an alter table 25 Bigtable •  ACM TOCS 2008 •  Faulttolerant, persistent •  Scalable –  Thousands of servers –  Terabytes of inmemory data –  Petabyte of diskbased data –  Millions of readswrites per second, efficient scans •  Selfmanaging –  Servers can be added removed dynamically –  Servers adjust to load imbalance 26 011214 14 •  Opensource Bigtable, written in JAVA •  Part of Apache Hadoop project 27 Hadoop? 28 011214 15 •  Apache open source column family database •  Supported by DataStax •  Peertopeer distribution model •  Strong reputation for linear scale out (millions of writes second) •  Written in Java and works well with HDFS and MapReduce 29 Graph data model •  Core abstractions: Nodes, Relationships, Properties on both 30 011214 16 Graph database (store) •  A database stored data in an explicitly graph structure •  Each node knows its adjacent nodes •  Queries are really graph traversals 31 Compared to Relational Databases OpHmized for aggregaHon OpHmized for connecHons 011214 17 Compared to Key Value Stores OpHmized for simple lookups OpHmized for traversing connected data Compared to Document Stores OpHmized for “trees” of data OpHmized for seeing the forest and the trees, and the branches, and the trunks 011214 18 35 36 011214 19 •  Graph database designed to be easy to use by Java developers •  Diskbased (not just RAM) •  Full ACID •  High Availability (with Enterprise Edition) •  32 Billion Nodes, 32 Billion Relationships, 
 64 Billion Properties •  Embedded java library •  REST API 37 Document store •  Documents, not value, not tables •  JSON or XML formats •  Document is identified by ID •  Allow indexing on properties 38 011214 20 Relational data mapping •  T1–HTML into Objects •  T2–Objects into SQL Tables •  T3–Tables into Objects •  T4–Objects into HTML 39 Web Service in the middle •  T1 – HTML into Java Objects •  T2 – Java Objects into SQL Tables •  T3 – Tables into Objects •  T4 – Objects into HTML •  T5 – Objects to XML •  T6 – XML to Objects 40 T1 T3 T2 T4 Object Middle Tier Relational Web Browser Database T5 Web Service T6 011214 21 Discussion •  Objectrelational mapping has become one of the most complex components of building applications today – Java Hibernate Framework – JPA •  To avoid complexity is to keep your architecture very simple 41 Document mapping •  Documents in the database •  Documents in the application •  No object middle tier •  No shredding •  No reassembly •  Simple 42 ApplicaHon Layer Database Document Document 011214 22 •  Open Source JSON data store created by 10gen •  Masterslave scale out model •  Strong developer community •  Sharding builtin, automatic •  Implemented in C++ with many APIs (C++, JavaScript, Java, Perl, Python etc.) 43 •  Apache project •  Open source JSON data store •  Written in ERLANG •  RESTful JSON API •  BTree based indexing, shadowing btree versioning •  ACID fully supported •  View model •  Data compaction •  Security 44
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AN EVALUATION OF TRANSLATION QUALITY OF LITERARY WORKS

AN EVALUATION OF TRANSLATION QUALITY OF LITERARY WORKS

The present study focuses on the evaluation of translation quality of EnglishVietnamese fictional prose, more specifically, of EnglishVietnamese short stories. To achieve this aim, we have taken the data from Australian Short Stories collection (2005). The data comprise five Australian short stories (with the total word count being 19,725) and their translations. The data for the research also collected from a survey questionnaire conducted on 370 native Vietnamese speakers, and a focus group interview to validate the naturalness of a number of sentences in the Vietnamese translations.The research methods employed in this study are a combination of qualitative analyses by means of House‟s (1997) functionalpragmatic model of translation quality assessment (TQA), the main method, quantitative analyses by adapting House‟s (2006) method, Multiplechoice Discourse Completion Task (MDCT), and focus group interview.The review of literature on translation evaluation shows that House‟s functional pragmatic TQA model is highly appropriate for assessing fictional prose translations because it, firmly based on contemporary research on literary criticism, takes into account both the source text (ST) and the target text (TT). Applying this model, the assessor can discover not only the merits and shortcomings of the translations, but also the cultural aspects of the STs and TTs, which are linguistically manifest.The findings reveal that all the five TTs are target text focused (covert translations), in which the translator has adapted the ST‟s norms to those of the TT. The findings have pointed out several mismatches between STs and TTs along the dimension of Tenor in House‟s model, which include the use of different Vietnamese personal pronouns and kinship nouns for the same English personal pronoun, the shifting of nouns in the STs into verbs in the TTs, and the transformation of a variety of passive sentences in the STs into active ones in the TTs.In addition, this study suggests that the Australian original texts and the Vietnamese translation texts are equivalent at the level of ideational functional component, and at genre level in House‟s model. However, the researcher argues that the interpersonal functional component in the TTs to some extent is more marked than in the STs.
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Tinh GHG Calculations and References

TINH GHG CALCULATIONS AND REFERENCES

Calculations and ReferencesThis page describes the calculations used to convert greenhouse gas emission numbers into different types of equivalent units. Go to the equivalency calculator page for more information.A note on global warming potentials (GWPs): Some of the equivalencies in the calculator are reported as CO2 equivalents (CO2E). These are calculated using GWPs from the Intergovernmental Panel on Climate Change’s Fourth Assessment Report.Electricity Reductions (kilowatthours)The Greenhouse Gas Equivalencies Calculator uses the Emissions Generation Resource Integrated Database (eGRID) U.S. annual nonbaseload CO2 output emission rate to convert reductions of kilowatthours into avoided units of carbon dioxide emissions. Most users of the Equivalencies Calculator who seek equivalencies for electricityrelated emissions want to know equivalencies for emissions reductions from energy efficiency or renewable energy programs. These programs are not generally assumed to affect baseload emissions (the emissions from power plants that run all the time), but rather nonbaseload generation (power plants that are brought online as necessary to meet demand). For that reason, the Equivalencies Calculator uses a nonbaseload emission rate.Emission Factor6.89551 × 104 metric tons CO2 kWh(eGRID, U.S. annual nonbaseload CO2 output emission rate, year 2010 data) Notes:•This calculation does not include any greenhouse gases other than CO2.•This calculation does not include line losses.•Individual subregion nonbaseload emissions rates are also available on the eGRID Web site.•To estimate indirect greenhouse gas emissions from electricity use, please use Power Profiler or use eGRID subregion annual output emission rates as a default emission factor (see eGRID Year 2010 GHG Annual Output Emission Rates (PDF) (1 p, 312K, About PDF)).Sources•EPA (2014) eGRID, U.S. annual nonbaseload CO2 output emission rate, year 2010 data. U.S. Environmental Protection Agency, Washington, DC.
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Ebook business plan for a fashion brand

EBOOK BUSINESS PLAN FOR A FASHION BRAND

INTRODUCTION 1.1 Background When the decision of writing the thesis brought the authors together, the common point was a love and passion for the fashion industry. Taking a closer look at the list of the world’s richest people, it can be seen that within the top 10, three of them are billionaires in the field of fashion industry (Forbes 2011). On the other hand, both authors have a strong belief in Finnish design, but dislike the high prices of products made in Finland. The International Council of Societies of Industrial Design, also known as ICSID, nominates a design capital of the world every other year. Their reasoning for this comes from the fact that more than half of the world’s population now lives in urban areas and because of that design has become a fundamental tool to make cities more attractive and liveable. The World Design Capital designation is essentially a promotion project to celebrate the accomplishments of cities that use design as a tool to improve social, cultural and economic life. The World Design Capital in 2012 is Helsinki, partnered with Espoo, Kauniainen, Vantaa and Lahti. This promotion gives the authors strong belief that now is the time to utilize the current day atmosphere. (WDC Helsinki 2012.) One of the authors comes from a family of entrepreneurs and her mindframe has always suggested that she would not suit following others. Having a creative personality and desire to be in charge lead the author to want to create something new and unique that would fit the Finnish market. Yet she was well aware of the fact that competition is fierce in Finnish fashion industry and having a relatively low price would give a company a competitive advantage. The other author has always seen the potential of his home country, China, which has the reputation of being the world’s top manufacturing country (Marsh 2011). Yet for Chinese brands, China does not have something equally well known to offer the world. There are variable reasons for this, and one of the main reasons speculated by the author is the lack of good design and innovation of Chinese domestic brands. Thus when the opportunity of combining Finnish design with Chinese manufacturing knocks, the author is strongly confident in the cooperation. While in the process of deciding the thesis topic, the authors found that in the current day modern people are more and more willing to shop online (Skarda 2010). This opens the doors for the authors to target more than just the Finnish market in the future. The combination of the two authors brings together knowledge of the Finnish market and innovative design with knowledge of Chinese potential and entrepreneurial mindset. 1.2 Objectives The main objective of the thesis is to research the Finnish fashion market and to find an appropriate niche to satisfy an unfulfilled need of the Finnish consumer. With the research data that the authors will collect, they will then proceed to perfect their business idea. The secondary objective is to complete a viable business plan based on the business idea. As the business plan is a secondary goal, the authors will rather concentrate on the part of the business idea and some financial information.
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Data Just Right. Introduction to LargeScale Data Analytics

Data Just Right. Introduction to LargeScale Data Analytics

The growing fields of distributed and cloud computing are rapidly evolving to analyze and process this data. An incredible rate of technological change has turned commonly accepted ideas about how to approach data challenges upside down, forcing companies interested in keeping pace to evaluate a daunting collection of sometimes contradictory technologies. Relational databases, long the drivers of businessintelligence applications, are now being joined by radical NoSQL opensource upstarts, and features from both are appearing in new, hybrid database solutions. The advantages of Webbased computing are driving the progress of massivescale data storage from bespoke data centers toward scalable infrastructure as a service. Of course, projects based on the opensource Hadoop ecosystem are providing regular developers access to data technology that has previously been only available to cloudcomputing giants such as Amazon and Google. The aggregate result of this technological innovation is often referred to as Big Data. Much has been made about the meaning of this term. Is Big Data a new trend, or is it an application of ideas that have been around a long time? Does Big Data literally mean lots of data, or does it refer to the process of approaching the value of data in a new way? George Dyson, the historian of science, summed up the phenomena well when he said that Big Data exists “when the cost of throwing away data is more than the machine cost.” In other words, we have Big Data when the value of the data itself exceeds that of the computing power needed to collect and process it.
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20849 READING TEST

20849 READING TEST

Effects of TechnologyModern Technology has had a tremendous effect on the lives of people and their entertainmenthabits. Today, the Internet has undergone a phenomenal global growth. It has become such animportant data-gathering and communication source that few can't afford to ignore. The Netencircles the globe.Young people spend a lot of time on their computers because it’s exciting and they have found inthe Net new ways of meeting a basic human need: the desire to communicate with other people. Email sends electronic messages from one person to another – like letters, but capable of crossing theAtlantic in 15m. File transfers move bulk data from one computer to another with these capacities,the Internet becomes post office, printing press and meeting-place all in one.Some people are making a fortune in cyberspace. Most companies have their own websites; othersexist only on the Internet. They are something called “dot com” companies. Some of the mostsuccessful Net entrepreneurs are teenagers who are still at school. They are called internet nerds.To become a successful entrepreneur all you need is: to start a webpage of your own, have a goodidea for a business, think of a catchy name and find someone to lend you money. And remember!English is the most used business language!A. Read the text and say if the following statements are True or False. Correctthe false ones.1. The Internet doesn't interfere at all with people’s lives._______________________________________________________________2. The English language is not necessary to be successful on the Net._______________________________________________________________B. Complete the following sentence with the ideas from the text:1. Nowadays the Internet has become tremendously ______________________________________________________________________________________.C. Answer the following questions, using your own words as far aspossible and expressing your point of view:1. what is so exciting about the net? ________________________________________________________________________________________________.
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SCIENTIFIC RESEARCH

SCIENTIFIC RESEARCH

3. The research objectives, mission and scope. 3.1. The research objectives. The objectives of the study are applying the Multiple Perspective Tool to give multidimensional viewpoints relating to Climate change, equipping for students necessary skills in using this tool to analyse and assess not only this problem but also other ones especially environmental pollution and sustainable development. 3.2. The research mission This thesis focused on addressing the following missions: • Gain and choose data, documents; examine the geographical, historical and cultural – social characteristics of Thai Binh. • Analyse and integrate the evidence, reasons, impacts and adaptive solutions to climate change in Thai Binh. • Based on the previous results, the author used the Multiple Perspective Tool to assess Climate change in Thai Binh 3.3. The research scope. About the content: After analysing the characteristics of nature, economy, society , history and cultural diversity in Thai Binh, the author applied the Multiple Perspective Tool to appraise climate change under 8 perspectives of this tool. About territory: the study was done in Thai Binh. About methods: there are several methods used such as: quantitative, map and chart, field trip, comparative, especially using the Multiple Perspective Tool in assessing climate change in Thai Binh. About time: The series of climatic data was gathered in the period from 1961 to 2011, and the socio economic data was gained from 2005 to 2011.
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Israel information technology report q1 2010

ISRAEL INFORMATION TECHNOLOGY REPORT Q1 2010

... any information hereto contained Israel Information Technology Report Q1 2010 © Business Monitor International Ltd Page Israel Information Technology Report Q1 2010 CONTENTS Executive Summary... International Ltd Page 28 Israel Information Technology Report Q1 2010 Israeli IT Industry - Historical Data & Forecasts (US$mn, unless otherwise stated) IT Sector, Q1 2010 2007 2008 2009f 2010f 2011f 2012f... in the eurozone, could undermine demand for Israeli exports © Business Monitor International Ltd Page 10 Israel Information Technology Report Q1 2010 Israel Business Environment SWOT Strengths
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ĐỀ THI THỬ MOS WORD 2010

ĐỀ THI THỬ MOS WORD 2010

Họ và tên: Số báo danh: Đề phần 1: 1 Văn bản sau đây đã được định dạng, hãy tạo mục lục cho tài liệu này (các mục lớn là các từ ghi hoa tiêu đề của cả đoạn) 2 Tạo Header có nội dung “Handbook” và Footer có nội dung “Microsoft Word 2010 Advanced Demonstration” 3 Đánh số trang bắt đầu từ 3 > hết (không có số trang trên trang đầu tiên, số trang bắt đầu từ trang thứ 2 và bắt đầu bằng số 3) Introduction CHANGES IN POLICY This Manual supersedes all previous employee manuals and memos that may have been issued from time to time on subjects covered in this Manual. However, since our business and our organization are subject to change, we reserve the right to interpret, change, suspend, cancel, or dispute with or without notice all or any part of our policies, procedures, and benefits at any time. We will notify all employees of these changes. Changes will be effective on the dates determined by the Company, and after those dates all superseded policies will be null. No individual supervisor or manager has the authority to change policies at any time. If you are uncertain about any policy or procedure, speak with your direct supervisor. EMPLOYMENT APPLICATIONS We rely upon the accuracy of information contained in the employment application and the accuracy of other data presented throughout the hiring process and employment. Any misrepresentations, falsifications, or material omissions in any of this information or data may result in exclusion of the individual from further consideration for employment or, if the person has been hired, termination of employment. EMPLOYMENT RELATIONSHIP You enter into employment voluntarily, and you are free to resign at any time for any reason or no reason. Similarly, Two Trees Olive Oil is free to conclude its relationship with any employee at any time for any reason or no reason. Following the probationary period, employees are required to follow the Employment Termination Policy (See Section 3.13).
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Oracle Essbase Oracle OLAP: The Guide to Oracle’s Multidimensional Solution

Oracle Essbase Oracle OLAP: The Guide to Oracle’s Multidimensional Solution

We explain what OLAP is and why it is important. Realworld case studies highlight Oracle products, but can also help you envision how OLAP in general enhances business intelligence in an organization. We introduce general OLAP concepts and design principles before showing how they map to Oracle products. Productspecific information includes architecture, application design, application building, and maintenance considerations. We also cover enduser analysis tools, reporting tools, and other frontend applications that can leverage OLAP data. You do not need to have a technical background to understand the concepts we cover in this book. OLAP benefits everyone in the organization, and we try to make the information in this book accessible to all. Whether you work in the IT department or in the line of business, such as finance, sales, research, or marketing, you stand to gain a better understanding of OLAP concepts in general and Oracle’s OLAP solutions in particular. Because this book is intended for people in a wide variety of roles, including DBAs, architects, planners, business analysts, and potential consumers of OLAP results—from salespeople to CEOs to marketing managers—the level of detail in the book varies from highlevel overview down to technical details. Most chapters begin with introductory material suitable for anyone, and then delve into technical product details.
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PLASTIC REPORT STUDY SUMMARY

PLASTIC REPORT STUDY SUMMARY

Executive SummeryThis study has been commissioned by KATALYST- a project of European Donors for Bangladeshwith a goal to improve competitiveness of business within sectors having ample opportunities.Plastic is one of the sector where KATALYST is working. The potentials of recycling of plasticwaste have made it a growing business worldwide, both from economic and environmental pointof view. In Bangladesh, especially in Dhaka, plastic waste recycling is based on rudimentarytechnology and dominated by informal sector. However, there is a dearth of information aboutcomposition of plastic waste and demand-supply scenario of recycled granules/ pellets.In order to obtain in depth information about plastic waste composition as well as plastic wasterecycling scenario in Dhaka, this study was launched and contracted out to Waste ConcernConsultants- a specialized organization working in waste and environmental sector in Bangladeshfor more than a decade.In view of the above facts, this may be considered as a baseline study on plastic waste recyclingin Dhaka city in particular and Bangladesh in general. This study is mainly based on primary data,as there was an acute lack of secondary data. The key findings of this study are as follows:1.In DCC area 3315 tons of solid waste has been generated per day during 2005, of which4.15% is composed of plastic materials. As such, 50,214 tons of plastic waste is disposed inthe city at the rate of 137.57 tons/day.2.Comparing the previous available date of 1992, with 2005 survey results, it indicates anincrease of 10.43% per year in the amount of plastic waste. This also signifies that with thegrowth of economy of the country, the amount of plastic waste is also increasing. This trendin the growth of plastic waste is expected to continue in near future also.
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ACCOUNTING INFORMATION SYSTEMS 10TH EDITION GELINAS TEST BANK

ACCOUNTING INFORMATION SYSTEMS 10TH EDITION GELINAS TEST BANK

The system facilitates the sharing of services for efficiency and consistency.PTS: 16. List four pros and four cons of ERP packages.ANS:The pros should include any four of the following:One package across many functionsBest practicesModular structureNo development needed (unless modifications are required)ConfigurableReduced errors (business rules, enter data once)The cons should include any four of the following:Complex and inflexibleBest practices are shared by all who use itDifficult to configureLong implementationBest of breed might be better than single ERP packageCan’t meet all needs (developed for many user types)PTS: 17. Why might a firm decide to implement only certain modules in an ERP system rather than a completeimplementation?ANS:The answer should include such items as:Cost considerationsImplementing additional modules one at a time or on an as needed basis with additional modulesadded at a later timeThere may be legacy systems that they do not want to changeThe firm may want to add on other modules from a combination of ERP vendors using enterprise
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AN INTRODUCTION TO R FOR QUANTITATIVE ECONOMICS

AN INTRODUCTION TO R FOR QUANTITATIVE ECONOMICS

This book gives an introduction to R to build up graphing, simulating and computing skills to enable one to see theoretical and statistical models in economics in a unified way. The great advantage of R is that it is free, extremely flexible and extensible. The book addresses the specific needs of economists, and helps them move up the R learning curve. It covers some mathematical topics such as, graphing the CobbDouglas function, using R to study the Solow growth model, in addition to statistical topics, from drawing statistical graphs to doing linear and logistic regression. It uses data that can be downloaded from the internet, and which is also available in different R packages. With some treatment of basic econometrics, the book discusses quantitative economics broadly and simply, looking at models in the light of data. Students of economics or economists keen to learn how to use R would find this book very useful.
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