Recent achievements in hardware and software developments have enabled the introduction of a revolutionary technology: in-memory data management. This technology supports the flexible and extremely fast analysis of massive amounts of data, such as diagnoses, therapies, and human genome data. This book shares the latest research results of applying in-memory data management to personalized medicine, changing it from computational possibility to clinical reality. The authors provide details on innovative approaches to enabling the processing, combination, and analysis of relevant data in real-time. The book bridges the gap between medical experts, such as physicians, clinicians, and biological researchers, and technology experts, such as software developers, database specialists, and statisticians. Topics covered in this book include - amongst others - modeling of genome data processing and analysis pipelines, high-throughput data processing, exchange of sensitive data and protection of intellectual property. Beyond that, it shares insights on research prototypes for the analysis of patient cohorts, topology analysis of biological pathways, and combined search in structured and unstructured medical data, and outlines completely new processes that have now become possible due to interactive data analyses.

This is a book for people who love mechanics of composite materials and ? MATLAB . We will use the popular computer package MATLAB as a matrix calculator for doing the numerical calculations needed in mechanics of c- posite materials. In particular, the steps of the mechanical calculations will be emphasized in this book. The reader will not ?nd ready-made MATLAB programs for use as black boxes. Instead step-by-step solutions of composite material mechanics problems are examined in detail using MATLAB. All the problems in the book assume linear elastic behavior in structural mechanics. The emphasis is not on mass computations or programming, but rather on learning the composite material mechanics computations and understanding of the underlying concepts. The basic aspects of the mechanics of ?ber-reinforced composite materials are covered in this book. This includes lamina analysis in both the local and global coordinate systems, laminate analysis, and failure theories of a lamina.

Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods.

This book features 13 papers presented at the Fifth International Symposium on Recurrence Plots, held August 2013 in Chicago, IL. It examines recent applications and developments in recurrence plots and recurrence quantification analysis (RQA) with special emphasis on biological and cognitive systems and the analysis of coupled systems using cross-recurrence methods. Readers will discover new applications and insights into a range of systems provided by recurrence plot analysis and new theoretical and mathematical developments in recurrence plots. Recurrence plot based analysis is a powerful tool that operates on real-world complex systems that are nonlinear, non-stationary, noisy, of any statistical distribution, free of any particular model type and not particularly long. Quantitative analyses promote the detection of system state changes, synchronized dynamical regimes or classification of system states. The book will be of interest to an interdisciplinary audience of recurrence plot users and researchers interested in time series analysis of complex systems in general.

A collection of surveys and research papers on mathematical software and algorithms. The common thread is that the field of mathematical applications lies on the border between algebra and geometry. Topics include polyhedral geometry, elimination theory, algebraic surfaces, Gröbner bases, triangulations of point sets and the mutual relationship. This diversity is accompanied by the abundance of available software systems which often handle only special mathematical aspects. This is why the volume also focuses on solutions to the integration of mathematical software systems. This includes low-level and XML based high-level communication channels as well as general frameworks for modular systems.

This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data. Over 170 figures are included in the book.

These are the proceedings of the 22nd International Conference on Domain Decomposition Methods, which was held in Lugano, Switzerland. With 172 participants from over 24 countries, this conference continued a long-standing tradition of internationally oriented meetings on Domain Decomposition Methods. The book features a well-balanced mix of established and new topics, such as the manifold theory of Schwarz Methods, Isogeometric Analysis, Discontinuous Galerkin Methods, exploitation of modern HPC architectures and industrial applications. As the conference program reflects, the growing capabilities in terms of theory and available hardware allow increasingly complex non-linear and multi-physics simulations, confirming the tremendous potential and flexibility of the domain decomposition concept.

An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages. All data for exercises can be found here: https://github.com/tbrown122387/r_and_python_book/tree/master/data. Key features: - Teaches R and Python in a "side-by-side" way. - Examples are tailored to aspiring data scientists and statisticians, not software engineers. - Designed for introductory graduate students. - Does not assume any mathematical background.

Formally validated training material to pass Microsoft certification Exam 70-178: Microsoft Project 2010, Managing Projects! Designed for the busy, practicing project manager, Dynamic Scheduling With Microsoft Project 2010 will help you get up to speed quickly with the new features of Project 2010 and enable you to create effective schedules more efficiently using best practices, tips & tricks, and step-by-step instruction. Through the use of helpful screen shots, hands-on exercises, illustrations, and review questions, this guide instructs you on how to build dynamic schedules that will allow you to explore what-if scenarios and dramatically decrease the time you spend making static schedule changes. ""A must read, re-read, and use daily for all project managers"" is what PMI's Project Management Journal had to say about the previous edition. This updated version is even better.

Discusses the usage, debate, and conceptual and theoretical issues of e-collaboration support of distributed project management.

Fully updated for Microsoft Dynamics AX 2012 R3! Dig into the architecture and internals of Microsoft Dynamics AX 2012 R3 - with firsthand insights from the team that designed and developed it. Deepen your knowledge of the underlying frameworks, components, and tools - and deliver custom ERP applications with the extensibility and performance your business needs. Useful for Microsoft Dynamics AX solution developers at all levels, this guide will provide max benefit to those who understand OOP, relational database, and Transact-SQL concepts. Gain best practices, patterns, and techniques to: Exploit interoperability with Microsoft Visual Studio tools Work with MorphX and avoid common pitfalls with X++ code Use Enterprise Portal with ASP.NET and SharePoint for rich web-based apps Simplify process automation with built-in workflow infrastructure See how the runtime implements security and configuration Design and customize the user experience Gain greater control over complex batch jobs Customize the prebuilt BI solution and reporting Test applications, publish services, and optimize performance

This second volume in the series covers such topics as information systems practice and theory, information systems and the accounting/auditing environment, and differing perspectives on information systems research.

Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings.

This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader's analytical capability.

Water Resource Modeling and Computational Technologies, Seventh Edition provides the reader with a comprehensive overview of the applications that computational techniques have in various sectors of water resource engineering. The book explores applications of recent modeling and computational techniques in various sectors of water resource engineering, including hydroinformatics, irrigation engineering, climate change, hydrologic forecasting, floods, droughts, image processing, GIS, water quality, aquifer mapping, basin scale modeling, computational fluid dynamics, numerical modeling of surges and groundwater flow, river engineering, optimal reservoir operation, multipurpose projects, and water resource management. As such, this is a must read for hydrologists, civil engineers and water resource managers.

The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.
In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.

The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest.

Review of First Edition:

"The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)

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This book collects peer-reviewed contributions on modern statistical methods and topics, stemming from the third workshop on Analytical Methods in Statistics, AMISTAT 2019, held in Liberec, Czech Republic, on September 16-19, 2019. Real-life problems demand statistical solutions, which in turn require new and profound mathematical methods. As such, the book is not only a collection of solved problems but also a source of new methods and their practical extensions. The authoritative contributions focus on analytical methods in statistics, asymptotics, estimation and Fisher information, robustness, stochastic models and inequalities, and other related fields; further, they address e.g. average autoregression quantiles, neural networks, weighted empirical minimum distance estimators, implied volatility surface estimation, the Grenander estimator, non-Gaussian component analysis, meta learning, and high-dimensional errors-in-variables models.

After the introduction of Enterprise Information Systems (EIS) and Enterprise Resources Planning (ERP) applications to many business organizations, the functional issues such as technical problems, security threats, persistent end-user resistance, and the excessive hidden costs of these systems began coming to light. Cases on Enterprise Information Systems and Implementation Stages: Learning from the Gulf Region focuses on the Gulf region and its lead on the enterprise systems adoption which has exceeded those in many developed countries. This reference book highlights experiences implementing EIS, delivering a comprehensive multi-perspective account of EIS issues, and explores the concerns, risks, and critical success factors of EIS for a variety of organizations. It aims to be beneficial for both practitioners and academics alike.

Get fast answers to your G Suite questions with this friendly resource G Suite For Dummies is the fun guide to the productivity suite that's quickly winning over professional and personal users. This book shares the steps on how to collaborate in the cloud, create documents and spreadsheets, build presentations, and connect with chat or video. Written in the easy-to-follow For Dummies style, G Suite For Dummies covers the essential components of Google's popular software, including: Google Docs for word processing Gmail for email Google Calendar for scheduling and day planning Google Sheets for spreadsheet functionality Google Drive for data storage Google Hangouts and Google Meet for videoconferencing and calling capability The book helps navigate the G Suite payment plans and subscription options as well as settings that ensure your own privacy and security while operating in the cloud. Perfect for anyone hoping to get things done with this tool, G Suite For Dummies belongs on the bookshelf of every G Suite user who needs help from time to time.

This book opens the "black box" of software sourcing by explaining how dynamic software alignment is established and how it impacts business performance outcomes. By investigating how software-sourcing modes are related to value generation in the post-implementation phase, it shows researchers and managers the impact logic of on-demand, on-premises, and in-house software on dynamic fit and process-level performance outcomes in a client organization. It describes dynamic IT alignment as the key to success in a fast-moving digital world with software-as-a-service on the rise and highlights the fact that today companies can choose between developing software in-house (make) or sourcing packaged systems in an on-premises (buy) or an on-demand (lease) mode. This book is the first to explicitly compare these sourcing arrangements with each other in terms of alignment and business performance.

This new edition includes the latest advances and developments in computational probability involving A Probability Programming Language (APPL). The book examines and presents, in a systematic manner, computational probability methods that encompass data structures and algorithms. The developed techniques address problems that require exact probability calculations, many of which have been considered intractable in the past. The book addresses the plight of the probabilist by providing algorithms to perform calculations associated with random variables. Computational Probability: Algorithms and Applications in the Mathematical Sciences, 2nd Edition begins with an introductory chapter that contains short examples involving the elementary use of APPL. Chapter 2 reviews the Maple data structures and functions necessary to implement APPL. This is followed by a discussion of the development of the data structures and algorithms (Chapters 3-6 for continuous random variables and Chapters 7-9 for discrete random variables) used in APPL. The book concludes with Chapters 10-15 introducing a sampling of various applications in the mathematical sciences. This book should appeal to researchers in the mathematical sciences with an interest in applied probability and instructors using the book for a special topics course in computational probability taught in a mathematics, statistics, operations research, management science, or industrial engineering department.

John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved.