This book will help future scientists to become more intelligent users of computing technology in their practice of science. The content is suitable for introductory courses on the foundations of computing and the specific application of computers in different areas of science. The text presents a set of modules for use in existing science courses in order to integrate individual aspects of computational thinking, as well as a set of modules introducing the computer science concepts needed to understand the computing involved. These modules guide science students in their independent learning. The book covers computing applications in such diverse areas as bioinformatics, chemical kinetics, hydrogeological modeling, and mechanics of materials, geographic information systems, flow analysis, the solving of equations, curve fitting, optimization, and scientific data acquisition. The computing topics covered include simulations, errors, data representation, algorithms, XMS, compression, databases, performance, and complexity.

This textbook is intended for practical, laboratory sessions associated with the course of quantum computing and quantum algorithms, as well as for self-study. It contains basic theoretical concepts and methods for solving basic types of problems and gives an overview of basic qubit operations, entangled states, quantum circuits, implementing functions, quantum Fourier transform, phase estimation, etc. The book serves as a basis for the application of new information technologies in education and corporate technical training: theoretical material and examples of practical problems, as well as exercises with, in most cases, detailed solutions, have relation to information technologies. A large number of detailed examples serve to better develop professional competencies in computer science.

Numerical Algorithmic Science and Engineering (NAS&E), or more compactly, Numerical Algorithmics, is the theoretical and empirical study and the practical implementation and application of algorithms for solving finite-dimensional problems of a numeric nature. The variables of such problems are either discrete-valued, or continuous over the reals, or, and as is often the case, a combination of the two, and they may or may not have an underlying network/graph structure. This re-emerging discipline of numerical algorithmics within computer science is the counterpart of the now well-established discipline of numerical analysis within mathematics, where the latter’s emphasis is on infinite-dimensional, continuous numerical problems and their finite-dimensional, continuous approximates. A discussion of the underlying rationale for numerical algorithmics, its foundational models of computation, its organizational details, and its role, in conjunction with numerical analysis, in support of the modern modus operandi of scientific computing, or computational science & engineering, is the primary focus of this short monograph. It comprises six chapters, each with its own bibliography. Chapters 2, 3 and 6 present the book’s primary content. Chapters 1, 4, and 5 are briefer, and they provide contextual material for the three primary chapters and smooth the transition between them. Mathematical formalism has been kept to a minimum, and, whenever possible, visual and verbal forms of presentation are employed and the discussion enlivened through the use of motivating quotations and illustrative examples. The reader is expected to have a working knowledge of the basics of computer science, an exposure to basic linear algebra and calculus (and perhaps some real analysis), and an understanding of elementary mathematical concepts such as convexity of sets and functions, networks and graphs, and so on. Although this book is not suitable for use as the principal textbook for a course on numerical algorithmics (NAS&E), it will be of value as a supplementary reference for a variety of courses. It can also serve as the primary text for a research seminar. And it can be recommended for self-study of the foundations and organization of NAS&E to graduate and advanced undergraduate students with sufficient mathematical maturity and a background in computing. When departments of computer science were first created within universities worldwide during the middle of the twentieth century, numerical analysis was an important part of the curriculum. Its role within the discipline of computer science has greatly diminished over time, if not vanished altogether, and specialists in that area are now to be found mainly within other fields, in particular, mathematics and the physical sciences. A central concern of this monograph is the regrettable, downward trajectory of numerical analysis within computer science and how it can be arrested and suitably reconstituted. Resorting to a biblical metaphor, numerical algorithmics (NAS&E) as envisioned herein is neither old wine in new bottles, nor new wine in old bottles, but rather this re-emerging discipline is a decantation of an age-old vintage that can hopefully find its proper place within the larger arena of computer science, and at what appears now to be an opportune time.

Health Information Technology Basics gives your students an introduction to the fundamental concepts of the health information technology profession. Perfect for introductory courses where core material in the health information profession is being introduced, this book is written for associate degree level HIT programs at technical, community, or career colleges. The text begins with an introduction to the U.S. health care system and explores career opportunities within the health information profession. The health record is dissected and its many components are carefully reviewed. The book also examines various formats of the medical record and analyzes the advantage and disadvantages of the EHR. Finally, the text covers medical terminologies and classification systems and outlines the basics of reimbursement systems. Features: Each chapter begins with learning objectives and key terms to give the reader a synopsis of what he/she should expect to learn. Additional resources are listed at the end of each chapter for further exploration of the information covered in the chapter. A glossary is included for quick reference of main terms presented throughout the text. An accompanying Instructor’s Manual provides review exercises which recap the important points as well as lab assignments that allow students to apply the information in a practical setting.

The Heinz Nixdorf Museum Forum (HNF) is the world’s largest c- puter museum and is dedicated to portraying the past, present and future of information technology. In the “Year of Informatics 2006” the HNF was particularly keen to examine the history of this still quite young discipline. The short-lived nature of information technologies means that individuals, inventions, devices, institutes and companies“age” more rapidly than in many other specialties. And in the nature of things the group of computer pioneers from the early days is growing smaller all the time. To supplement a planned new exhibit on “Software and Inform- ics” at the HNF, the idea arose of recording the history of informatics in an accompanying publication. Mysearchforsuitablesourcesandauthorsveryquickly cameupwith the right answer, the very rst name in Germany: Friedrich L. Bauer, Professor Emeritus of Mathematics at the TU in Munich, one of the - thers of informatics in Germany and for decades the indefatigable author of the“Historical Notes” column of the journal Informatik Spektrum. Friedrich L. Bauer was already the author of two works on the history of informatics, published in different decades and in different books. Both of them are notable for their knowledgeable, extremely comp- hensive and yet compact style. My obvious course was to motivate this author to amalgamate, supplement and illustrate his previous work.

Basic Category Theory for Computer Scientists provides a straightforward presentation of the basic constructions and terminology of category theory, including limits, functors, natural transformations, adjoints, and cartesian closed categories. Category theory is a branch of pure mathematics that is becoming an increasingly important tool in theoretical computer science, especially in programming language semantics, domain theory, and concurrency, where it is already a standard language of discourse. Assuming a minimum of mathematical preparation, Basic Category Theory for Computer Scientists provides a straightforward presentation of the basic constructions and terminology of category theory, including limits, functors, natural transformations, adjoints, and cartesian closed categories. Four case studies illustrate applications of category theory to programming language design, semantics, and the solution of recursive domain equations. A brief literature survey offers suggestions for further study in more advanced texts. Contents Tutorial • Applications • Further Reading

A concise guide to the origins and prediction of subsurface fluid pressures, emphasizing the interactions with geological processes.

This textbook presents a thorough foundation to the theory of computation. Combining intuitive descriptions and illustrations with rigorous arguments and detailed proofs for key topics, the logically structured discussion guides the reader through the core concepts of automata and languages, computability, and complexity of computation. Topics and features: presents a detailed introduction to the theory of computation, complete with concise explanations of the mathematical prerequisites; provides end-of-chapter problems with solutions, in addition to chapter-opening summaries and numerous examples and definitions throughout the text; draws upon the author’s extensive teaching experience and broad research interests; discusses finite automata, context-free languages, and pushdown automata; examines the concept, universality and limitations of the Turing machine; investigates computational complexity based on Turing machines and Boolean circuits, as well as the notion of NP-completeness.

This practically-focused textbook provides a concise and accessible introduction to the field of software testing, explaining the fundamental principles and offering guidance on applying the theory in an industrial environment. Topics and features: presents a brief history of software quality and its influential pioneers, as well as a discussion of the various software lifecycles used in software development; describes the fundamentals of testing in traditional software engineering, and the role that static testing plays in building quality into a product; explains the process of software test planning, test analysis and design, and test management; discusses test outsourcing, and test metrics and problem solving; reviews the tools available to support software testing activities, and the benefits of a software process improvement initiative; examines testing in the Agile world, and the verification of safety critical systems; considers the legal and ethical aspects of software testing, and the importance of software configuration management; provides key learning topics and review questions in every chapter, and supplies a helpful glossary at the end of the book. This easy-to-follow guide is an essential resource for undergraduate students of computer science seeking to learn about software testing, and how to build high quality and reliable software on time and on budget. The work will also be of interest to industrialists including software engineers, software testers, quality professionals and software managers, as well as the motivated general reader.