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Rev. ed. of: QI/QM troubleshooter / Bonnie Foster.
Review Questions of Clinical Molecular Genetics presents a comprehensive study guide for the board and certificate exams presented by the American College of Medical Genetics and Genomics (ACMG) and the American Board of Medical Genetics and Genomics (ABMGG). It provides residents and fellows in genetics and genomics with over 1,000 concise questions, ranging from topics in cystic fibrosis, to genetic counseling, to trinucleotide repeat expansion disorders. It puts key points in the form of questions, thus challenging the reader to retain knowledge. As board and certificate exams require knowledge of new technologies and applications, this book helps users meet that challenge. - Includes over 1,0000 multiple-choice, USMLE style questions to help readers prepare for specialty exams in Clinical Cytogenetics and Clinical Molecular Genetics - Designed to assist clinical molecular genetic fellows, genetic counselors, medical genetic residents and fellows, and molecular pathologist residents in preparing for their certification exam - Assists trainees on how to follow guidelines and put them in practice
This collection of exercises proposes a relevant choice of the written tests assigned to the Information Engineering Courses of General Physics in the past Academic Years. An accurate selection of the problems has been done. They have been organised by item with the addition of a largely commented solution with the purpose to provide students with an advanced tool for the preparation for the written part of the examination. Every item is gradually introduced; but a sufficiently deep theoretical knowledge of the matter of study is anyway required in order to correctly understand the presented situations. The proposed problems are the ideal complement to the exercises solved by a Professor while lecturing or the ones offered in theory textbooks as worked out examples or problems to be solved.
This book examines seven key combinatorial engineering frameworks (composite schemes consisting of algorithms and/or interactive procedures) for hierarchical modular (composite) systems. These frameworks are based on combinatorial optimization problems (e.g., knapsack problem, multiple choice problem, assignment problem, morphological clique problem), with the author’s version of morphological design approach – Hierarchical Morphological Multicritieria Design (HMMD) – providing a conceptual lens with which to elucidate the examples discussed. This approach is based on ordinal estimates of design alternatives for systems parts/components, however, the book also puts forward an original version of HMMD that is based on new interval multiset estimates for the design alternatives with special attention paid to the aggregation of modular solutions (system versions). The second part of ‘Modular System Design and Evaluation’ provides ten information technology case studies that enriches understanding of the design of system design, detection of system bottlenecks and system improvement, amongst others. The book is intended for researchers and scientists, students, and practitioners in many domains of information technology and engineering. The book is also designed to be used as a text for courses in system design, systems engineering and life cycle engineering at the level of undergraduate level, graduate/PhD levels, and for continuing education. The material and methods contained in this book were used over four years in Moscow Institute of Physics and Technology (State University) in the author’s faculty course “System Design”.
In its thousands of years of history, mathematics has made an extraordinary ca reer. It started from rules for bookkeeping and computation of areas to become the language of science. Its potential for decision support was fully recognized in the twentieth century only, vitally aided by the evolution of computing and communi cation technology. Mathematical optimization, in particular, has developed into a powerful machinery to help planners. Whether costs are to be reduced, profits to be maximized, or scarce resources to be used wisely, optimization methods are available to guide decision making. Opti mization is particularly strong if precise models of real phenomena and data of high quality are at hand - often yielding reliable automated control and decision proce dures. But what, if the models are soft and not all data are around? Can mathematics help as well? This book addresses such issues, e. g. , problems of the following type: - An elevator cannot know all transportation requests in advance. In which order should it serve the passengers? - Wing profiles of aircrafts influence the fuel consumption. Is it possible to con tinuously adapt the shape of a wing during the flight under rapidly changing conditions? - Robots are designed to accomplish specific tasks as efficiently as possible. But what if a robot navigates in an unknown environment? - Energy demand changes quickly and is not easily predictable over time. Some types of power plants can only react slowly.
The focus of the papers presented in these proceedings is on employing various methodologies and approaches for solving real-life problems. Although the mechanisms that the human brain employs to solve problems are not yet completely known, we do have good insight into the functional processing performed by the human mind. On the basis of the understanding of these natural processes, scientists in the field of applied intelligence have developed multiple types of artificial processes, and have employed them successfully in solving real-life problems. The types of approaches used to solve problems are dependant on both the nature of the problem and the expected outcome. While knowledge-based systems are useful for solving problems in well-understood domains with relatively stable environments, the approach may fail when the domain knowledge is either not very well understood or changing rapidly. The techniques of data discovery through data mining will help to alleviate some problems faced by knowledge-based approaches to solving problems in such domains. Research and development in the area of artificial intelligence are influenced by opportunity, needs, and the availability of resources. The rapid advancement of Internet technology and the trend of increasing bandwidths provide an opportunity and a need for intelligent information processing, thus creating an excellent opportunity for agent-based computations and learning. Over 40% of the papers appearing in the conference proceedings focus on the area of machine learning and intelligent agents - clear evidence of growing interest in this area.
Offers a comprehensive guide to assisted reproductive technology surveillance, describing its history, global variations, and best practices.