Suchergebnisse

TÁRKI Data Bank

1 Reliability -- 1.1 Introduction -- 1.2 What is Reliability? Why Want Reliability? Principles of Achieving Reliability -- 1.3 Quantifying Reliability -- 1.4 The Methods of Systems Reliability Synthesis -- 1.5 Need for Reliability Data -- 2 Principles of Reliability Data Bases -- 2.1 Purposes -- 2.2 Construction -- 2.3 Data Acquisition -- 2.4 Physical Implementation -- 2.5 Development and Operation -- 3 Analysis Methodologies -- 3.1 Restrictions Owing to Data Base Assumptions -- 3.2 Constant Fault-Rate or Failure-Rate Methods -- 3.3 Non-Constant Fault-Rate Methods; The Weibull Distribution -- 3.4 More General Data Structures -- 4 Some Achievements Due to the Development of Data Banks -- 4.1 Introduction -- 4.2 The Nuclear Industry -- 4.3 Aircraft Industry -- 4.4 Electronics Industry -- 4.5 Chemical Industry -- 4.6 Data -- 4.7 Computational Considerations -- 4.8 Data Needs or Requirements -- 4.9 Other Factors Worthy of Consideration -- 4.10 Staffing -- 5 Facts: Most Comprehensive Information System For Industrial Safety -- 5.1 Introduction -- 5.2 The TNO Organization -- 5.3 Looking Back on the Start of FACTS -- 5.4 Information Handling -- 5.5 Cause Classification -- 5.6 Data Base Structure -- 5.7 Storage of Original Documents -- 5.8 Applications and the Use of FACTS -- 5.9 New Advances in FACTS -- 5.10 Latest Developments -- 5.11 PC-FACTS -- 6 Reliability Data Collection In Process Plants -- 6.1 General Remarks -- 6.2 Data Collection -- 6.3 Data Treatment and Examples -- 6.4 Uncertainty, Applicability and Caution -- 7 The Centralized Reliability Data Organization (Credo); an Advanced Nuclear Reactor Reliability, Availability, and Maintainability Data Bank and Data Analysis Center -- 7.1 The Basis for CREDO -- 7.2 CREDO—An Historical Perspective -- 7.3 Data Initially Identified for Inclusion -- 7.4 CREDO Component Description and Classification -- 7.5 Design of Data Input -- 7.6 CREDO'S Data Base Management System -- 7.7 Statistical Data Analysis and Processing -- 7.8 CREDO Development Experience -- 7.9 Achievements and Future Directions of CREDO -- 8 The Fabrication Reliability Data Analysis System Dante-QC1 -- 8.1 Introduction -- 8.2 Concept of the DANTE Code System -- 8.3 Data Base Configuration -- 8.4 Processing Function -- 8.5 Application of DANTE for PIE Data Analysis -- 8.6 Future Directions -- 9 Reliability Data Banks at Electricite De France (EDF) -- 9.1 The Origins -- 9.2 History and Objectives of the EDF Data Banks -- 9.3 SRDF -- 9.4 Conclusion -- 10 IAEA's Experience In Compiling A Generic Component Reliability Data Base -- 10.1 Introduction -- 10.2 IAEA's Generic Component Reliability Data Base -- 10.3 Problem Areas Connected with Generic Data Bases -- 10.4 Conclusion -- Appendix: Data Sources Included in the Generic Component Reliability Data Base -- 11 The European Reliability Data System—Erds: Status And Future Developments -- 11.1 Introduction -- 11.2 General Description of ERDS -- 11.3 The Component Event Data Bank (CEDB) -- 12 Development of A Large Data Bank -- 12.1 Introduction -- 12.2 The Data Bank System -- 12.3 Student Collection Scheme -- 12.4 The Item Inventory -- 12.5 Coded Storage -- 12.6 Output Data -- 12.7 Generic Reliability Data Output Enquiry and Reply Service -- 12.8 Reliability Improvements -- 13 Reliability Data Banks—Friend, Foe or A Waste of Time? -- 13.1 Introduction -- 13.2 The Personalities -- 13.3 The DataBase Design -- 13.4 The Component Inventory Data -- 13.5 The Component History Data -- 13.6 Dependent Failures -- 13.7 Data Analysis -- 13.8 Pooled Reliability Data -- 13.9 The Successes -- 13.10 Conclusions -- 14 Developments -- 14.1 Introduction -- 14.2 Changes in Data Handling -- 14.3 Data Base Software -- 14.4 Methodology and Technology Led Changes -- 14.5 New Data Bases -- 14.6 R & M 2000 -- 14.7 Changes in Attitude -- 15 Overview; Into the Future -- 15.1 Forty Years of Always Being Wrong and Always Being Right -- 15.2 The Next Forty Years -- 15.3 Using External Data Sources and Making Up Data -- 15.4 Justifying a Reliability Data Base.

AbstractThe article discusses different examples of data-driven policing, its legal provisions and effects on a society's understanding of public security. It distinguishes between (a) the collection of classical data such as fingerprints or DNA, which serve to identify suspects and to collect evidence, (b) the processes and the impetus of big data, and (c) the networking of files from different security authorities. Discussing systematic forecasting tools, the article works out a significant difference between the prediction of incidents such as home burglary in the case of predictive policing, and the identification of individuals deemed to be at risk of involvement in various forms of crime in the case of risk control programs. Data and personality protection are interrelated issues.

"The National Trade Data Bank (NTDB) is the U.S. Government's primary repository of trade related information"--P. vii. ; Includes index. ; Shipping list no.: 98-0073-P. ; Mode of access: Internet.

This article suggests criteria for suitable research designs for use with large data bases. The advantages and disadvantages of several types of quasi-experimental designs are compared. Among the more interesting designs are those trying to minimize the possibilities for selection differences between treatment and control groups by considering assignment to treatment in creative ways. When the probability of receiving a treatment is largely outside an individual's control, possible selection bias should be reduced. These designs often use data on populations rather than on the particular recipients of an intervention. Such analysis of impact on a population, rather than of effect on recipients, makes it comparatively difficult to find that an intervention makes a difference. Examples are taken from our research with the Manitoba Health Services Commission data.

In the course of my current research I have made machine readable a variety of socioeconomic data that was not available before. Because most of this data bank can be displayed in graphic form, and because it covers a vast amount of information, I want to describe its contents and make it available to the scholarly community. I have used the arrondissement and department codes employed by the Inter-University Consortium for Political and Social Research, and I hope that everyone doing quantitative work on France will use this system, so that all of our efforts will be compatible. Anyone who is using this coding system will be able to add data easily to my four sets, and ideally we will be able to develop a data bank with even more variables and a wider time frame.