Nature-Inspired Computing Paradigms in Systems: Reliability, Availability, Maintainability, Safety and Cost (RAMS+C) and Prognostics and Health Management (PHM) covers several areas that include bioinspired techniques and optimization approaches for system dependability. The book addresses the issue of integration and interaction of the bioinspired techniques in system dependability computing so that intelligent decisions, design, and architectures can be supported. It brings together these emerging areas under the umbrella of bio- and nature-inspired computational intelligence. The primary audience of this book includes experts and developers who want to deepen their understanding of bioinspired computing in basic theory, algorithms, and applications. The book is also intended to be used as a textbook for masters and doctoral students who want to enhance their knowledge and understanding of the role of bioinspired techniques in system dependability.

Project management has become a widespread instrument enabling organizations to efficiently master the challenges of steadily shortening product life cycles, global markets and decreasing profit margins. With projects increasing in size and complexity, their planning and control represents one of the most crucial management tasks. This is especially true for scheduling, which is concerned with establishing execution dates for the sub-activities to be performed in order to complete the project. The ability to manage projects where resources must be allocated between concurrent projects or even sub-activities of a single project requires the use of commercial project management software packages. However, the results yielded by the solution procedures included are often rather unsatisfactory. Scheduling of Resource-Constrained Projects develops more efficient procedures, which can easily be integrated into software packages by incorporated programming languages, and thus should be of great interest for practitioners as well as scientists working in the field of project management. The book is divided into two parts. In Part I, the project management process is described and the management tasks to be accomplished during project planning and control are discussed. This allows for identifying the major scheduling problems arising in the planning process, among which the resource-constrained project scheduling problem is the most important. Part II deals with efficient computer-based procedures for the resource-constrained project scheduling problem and its generalized version. Since both problems are NP-hard, the development of such procedures which yield satisfactory solutions in a reasonable amount of computation time is very challenging, and a number of new and very promising approaches are introduced. This includes heuristic procedures based on priority rules and tabu search as well as lower bound methods and branch and bound procedures which can be applied for computing optimal solutions.

This book presents a comprehensive survey of the Vesta system for software configuration management (SCM). Vesta, unlike other SCM systems, is specifically designed to handle very large software projects comprising tens of millions of lines of code and beyond. Researchers in the field of software engineering and specialists in the construction of software development tools will especially benefit from this work, but it will also appeal to those responsible for designing and deploying configuration management solutions for large software systems.

Three important but hard-to-achieve properties lie at the heart of Vesta's unique approach to software configuration management:

Every build is repeatable

Every build is incremental

Every build is consistent

To realize these properties in a practical SCM system, Vesta provides a novel repository to store the versions of the files that make up an evolving software system and a flexible language for writing modular configuration descriptions that define how the system is put together. This book explains in depth these facilities and the suite of tools that supports them, together with a methodology for applying them in practice.

Readers who seek more information about Vesta may download the entire system as well as other publications, reference documents, and user documentation from the Vesta home page at http: //www.vestasys.org.

Presenting theory while using "Mathematica" in a complementary way, Modern Differential Geometry of Curves and Surfaces with Mathematica, the third edition of Alfred Gray's famous textbook, covers how to define and compute standard geometric functions using "Mathematica" for constructing new curves and surfaces from existing ones. Since Gray's death, authors Abbena and Salamon have stepped in to bring the book up to date. While maintaining Gray's intuitive approach, they reorganized the material to provide a clearer division between the text and the "Mathematica" code and added a "Mathematica" notebook as an appendix to each chapter. They also address important new topics, such as quaternions.

The approach of this book is at times more computational than is usual for a book on the subject. For example, Brioshi's formula for the Gaussian curvature in terms of the first fundamental form can be too complicated for use in hand calculations, but"Mathematica "handles it easily, either through computations or through graphing curvature. Another part of "Mathematica" that can be used effectively in differential geometry is its special function library, where nonstandard spaces of constant curvature can be defined in terms of elliptic functions and then plotted.

Using the techniques described in this book, readers will understand concepts geometrically, plotting curves and surfaces on a monitor and then printing them. Containing more than 300 illustrations, the book demonstrates how to use "Mathematica" to plot many interesting curves and surfaces. Including as many topics of the classical differential geometry and surfaces as possible, it highlights important theorems with many examples.It includes 300 miniprograms for computing and plotting various geometric objects, alleviating the drudgery of computing things such as the curvature and torsion of a curve in space.

This first book in the series will describe the Net Generation as visual learners who thrive when surrounded with new technologies and whose needs can be met with the technological innovations. These new learners seek novel ways of studying, such as collaborating with peers, multitasking, as well as use of multimedia, the Internet, and other Information and Communication Technologies. Here we present mathematics as a contemporary subject that is engaging, exciting and enlightening in new ways. For example, in the distributed environment of cyber space, mathematics learners play games, watch presentations on YouTube, create Java applets of mathematics simulations and exchange thoughts over the Instant Messaging tool. How should mathematics education resonate with these learners and technological novelties that excite them?

The quick way to learn Microsoft Project! This is learning made easy. Get more done quickly with Microsoft Project. Jump in wherever you need answers. Brisk lessons and detailed screenshots show you exactly what to do, step by step. Quickly start a new plan, build task lists, and assign resources Share your plan and track your progress Capture and fine-tune work and cost details Use Gantt charts and other views and reports to visualize project schedules Share resources across multiple plans and consolidate projects Manage Agile plans using the new task boards to create backlogs, plan sprints, and hold scrum meetings Learn more about integrating Project with Microsoft 365 solutions for collaboration Master project management best practices as you learn Project Easily identify the features and lessons you need

This is a fully revised edition of the best-selling Introduction to Maple. The book presents the modern computer algebra system Maple, teaching the reader not only what can be done by Maple, but also how and why it can be done. The book also provides the necessary background for those who want the most of Maple or want to extend its built-in knowledge. Emphasis is on understanding the Maple system more than on factual knowledge of built-in possibilities. To this end, the book contains both elementary and more sophisticated examples as well as many exercises. The typical reader should have a background in mathematics at the intermediate level. Andre Heck began developing and teaching Maple courses at the University of Nijmegen in 1987. In 1989 he was appointed managing director of the CAN Expertise Center in Amsterdam. CAN, Computer Algebra in the Netherlands, stimulates and coordinates the use of computer algebra in education and research. In 1996 the CAN Expertise Center was integrated into the Faculty of Science at the University of Amsterdam, into what became the AMSTEL Institute. The institute program focuses on the innovation of computer activities in mathematics and science education on all levels of education. The author is actively involved in the research and development aimed at the integrated computer learning environment Coach for mathematics and science education at secondary school level.

Inspired by the author's need for practical guidance in the processes of data analysis, "A Practical Guide to Scientific Data Analysis" has been written as a statistical companion for the working scientist. This handbook of data analysis with worked examples focuses on the application of mathematical and statistical techniques and the interpretation of their results.

Covering the most common statistical methods for examining and exploring relationships in data, the text includes extensive examples from a variety of scientific disciplines.

The chapters are organised logically, from planning an experiment, through examining and displaying the data, to constructing quantitative models. Each chapter is intended to stand alone so that casual users can refer to the section that is most appropriate to their problem.

Written by a highly qualified and internationally respected author this text: Presents statistics for the non-statisticianExplains a variety of methods to extract information from dataDescribes the application of statistical methods to the design of "performance chemicals"Emphasises the application of statistical techniques and the interpretation of their results

Of practical use to chemists, biochemists, pharmacists, biologists and researchers from many other scientific disciplines in both industry and academia.

SAS Programming: The One-Day Course provides a concise introduction to the SAS programming language that gives readers not only a quick start in SAS programming, but also in the basic data manipulations and statistical summaries that are available through SAS. Unlike other introductory texts on the market, this is a pocket-sized reference that does not clutter the presentation of programming techniques by trying to teach statistical methods at the same time. Strong on explanations of how to carry out data manipulations that real-life data often call for, it also contains a short "workbook" appendix, complete with solutions. Datasets and the programming code are available to download from the Web.

This book presents algorithmic tools for algebraic geometry and experimental applications of them. It also introduces a software system in which the tools have been implemented and with which the experiments can be carried out. Macaulay 2 is a computer algebra system devoted to supporting research in algebraic geometry, commutative algebra, and their applications. The reader of this book will encounter Macaulay 2 in the context of concrete applications and practical computations in algebraic geometry. The expositions of the algorithmic tools presented here are designed to serve as a useful guide for those wishing to bring such tools to bear on their own problems. These expositions will be valuable to both the users of other programs similar to Macaulay 2 (for example, Singular and CoCoA) and those who are not interested in explicit machine computations at all. The first part of the book is primarily concerned with introducing Macaulay2, whereas the second part emphasizes the mathematics.

Economists can use computer algebra systems to manipulate symbolic models, derive numerical computations, and analyze empirical relationships among variables. Maxima is an open-source multi-platform computer algebra system that rivals proprietary software. Maxima's symbolic and computational capabilities enable economists and financial analysts to develop a deeper understanding of models by allowing them to explore the implications of differences in parameter values, providing numerical solutions to problems that would be otherwise intractable, and by providing graphical representations that can guide analysis. This book provides a step-by-step tutorial for using this program to examine the economic relationships that form the core of microeconomics in a way that complements traditional modeling techniques. Readers learn how to phrase the relevant analysis and how symbolic expressions, numerical computations, and graphical representations can be used to learn from microeconomic models. In particular, comparative statics analysis is facilitated. Little has been published on Maxima and its applications in economics and finance, and this volume will appeal to advanced undergraduates, graduate-level students studying microeconomics, academic researchers in economics and finance, economists, and financial analysts.

Sage Accounts is a straightforward accounting package, enabling small businesses to control their finances in a quick and easy manner. It assists in the smooth running of the business by enabling you to compile graphs and tables to keep track of where the money is coming from and where it is going to. The package includes summary information allowing you to view details at a glance, and provides an efficient time saving way of controlling your stock and financial records. This new edition has been fully revised and updated to ensure all information is accurate and up to date.

'The Project Manager's Toolkit' provides a quick reference checklist approach to drive an IT development project as well as solve issues that arise in the process. It can be used proactively to set a project on the right course and reactively for solutions to problems. It will: * help identify what needs doing next on an IT project * provide quick reference 'to-do' lists for use throughout the lifecycle of an IT project * answer the need for material that can be used to quality-check project deliverables It has been designed so that those on the project team who are facing a problem can pick up the book, turn to a relevant checklist and use it as a "starter-for-ten" to find a solution. For example, how to analyse data for a data-conversion exercise, or how to measure the quality of a project deliverable. 'The Project Manager's Toolkit' therefore provides a fast way to reduce an insolvable problem/issue to a set of smaller solvable ones

A clear, comprehensive treatment of the subject, Environmental Statistics with S-PLUS is an ideal resource for environmental scientists, engineers, regulators, and students, even those with only a limited knowledge of statistics. It provides insight into what to think about before you collect environmental data, how to collect it, and how to make sense of it after you have it. This book addresses the vast array of methods used today by scientists, researchers, and regulators.

Through its convenient showcase of information and numerous data sets posted on the Web, Environmental Statistics with S-PLUS shows you how to implement these methods using the statistical software package S-PLUS and the add-in modules EnvironmentalStats for S-PLUS, S+SpatialStats, and S-PLUS for ArcView. This survey of statistical methods, definitions, and concepts helps you collect and effectively analyze data for environmental pollution problems.

A Simple Guide to SPSS for Political Science, International Edition is a supplemental text that can be used with another statistics or research methods text. Designed for Political Science majors, A Simple Guide to SPSS for Political Science, International Edition helps students navigate through SPSS while taking a statistics or research methods course. The text includes additional coverage of categorical dependent variables, sample problems, and data sets specifically for Political Science. The American National Election Studies (ANES) database is used for sample problems, providing students with well-known and widely used resources in Political Science.

Living with Robots: Emerging Issues on the Psychological and Social Implications of Robotics focuses on the issues that come to bear when humans interact and collaborate with robots. The book dives deeply into critical factors that impact how individuals interact with robots at home, work and play. It includes topics ranging from robot anthropomorphic design, degree of autonomy, trust, individual differences and machine learning. While other books focus on engineering capabilities or the highly conceptual, philosophical issues of human-robot interaction, this resource tackles the human elements at play in these interactions, which are essential if humans and robots are to coexist and collaborate effectively. Authored by key psychology robotics researchers, the book limits its focus to specifically those robots who are intended to interact with people, including technology such as drones, self-driving cars, and humanoid robots. Forward-looking, the book examines robots not as the novelty they used to be, but rather the practical idea of robots participating in our everyday lives.

This book presents strategies for analyzing qualitative and mixed methods data with MAXQDA software, and provides guidance on implementing a variety of research methods and approaches, e.g. grounded theory, discourse analysis and qualitative content analysis, using the software. In addition, it explains specific topics, such as transcription, building a coding frame, visualization, analysis of videos, concept maps, group comparisons and the creation of literature reviews. The book is intended for masters and PhD students as well as researchers and practitioners dealing with qualitative data in various disciplines, including the educational and social sciences, psychology, public health, business or economics.

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.

Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program.

Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations introduces the notion of chronotopologic data analysis that offers a systematic, quantitative analysis of multi-sourced data and provides information about the spatial distribution and temporal dynamics of natural attributes (physical, biological, health, social). It includes models and techniques for handling data that may vary by space and/or time, and aims to improve understanding of the physical laws of change underlying the available numerical datasets, while taking into consideration the in-situ uncertainties and relevant measurement errors (conceptual, technical, computational). It considers the synthesis of scientific theory-based methods (stochastic modeling, modern geostatistics) and data-driven techniques (machine learning, artificial neural networks) so that their individual strengths are combined by acting symbiotically and complementing each other. The notions and methods presented in Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations cover a wide range of data in various forms and sources, including hard measurements, soft observations, secondary information and auxiliary variables (ground-level measurements, satellite observations, scientific instruments and records, protocols and surveys, empirical models and charts). Including real-world practical applications as well as practice exercises, this book is a comprehensive step-by-step tutorial of theory-based and data-driven techniques that will help students and researchers master data analysis and modeling in earth and environmental sciences (including environmental health and human exposure applications).