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Bifurcations, Instabilities, Degradation in Geomechanics
Leading international researchers and practitioners of bifurcations and instabilities in geomechanics debate the developments and applications which have occurred over the last few decades. The topics covered include modeling of bifurcation, structural failure of geomaterials and geostructures, advanced analytical, numerical and experimental techniques, and application and development of generalised continuum models etc. In addition analytical solutions, numerical methods, experimental techniques, and case histories are presented. Beside fundamental research findings, applications in geotechnical, petroleum, mining, and bulk materials engineering are emphasised.
Biased technical change and economic conservation laws
Makes use of Lie groups to shed new light on the analysis of economic conservation laws. Economic conservation laws are not simply abstract concepts; this book shows that they are tools of empirical analysis that can be applied to such topics as analyses of macro performance and corporate efficiency.
Beyond the Worst-Case Analysis of Algorithms
There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.
Beyond Cartesian Dualism : Encountering affect in the teaching and learning of science.
There is surprisingly little known about affect in science education. Despite periodic forays into monitoring students’ attitudes-toward-science, the effect of affect is too often overlooked. Beyond Cartesian Dualism gathers together contemporary theorizing in this axiomatic area. In fourteen chapters, senior scholars of international standing use their knowledge of the literature and empirical data to model the relationship between cognition and affect in science education. Their revealing discussions are grounded in a broad range of educational contexts including school classrooms, universities, science centres, travelling exhibits and refugee camps, and explore an array of far reaching questions. What is known about science teachers’ and students’ emotions? How do emotions mediate and moderate instruction? How might science education promote psychological
Best practices in software measurement : How to use metrics to improve project and process performance
Not everything that counts can be counted. Not everything that is counted counts. Albert Einstein This is a book about software measurement from the practitioner’s point of view and it is a book for practitioners. Software measurement needs a lot of practical guidance to build upon experiences and to avoid repeating errors. This book t- gets exactly this need, namely to share experiences in a constructive way that can be followed. It tries to summarize experiences and knowledge about software measurement so that it is applicable and repeatable. It extracts experiences and lessons learned from the narrow context of the specific industrial situation, thus facilitating transfer to other contexts. Software measurement is not at a standstill. With the speed software engine- ing is evolving, software measurement has to keep pace. While the underlying theory and basic principles remain invariant in the true sense (after all, they are not specific to software engineering), the application of measurement to specific contexts and situations is continuously extended. The book thus serves as a ref- ence on these invariant principles as well as a practical guidance on how to make software measurement a success.
Bernoulli potential in superconductors : How the electrostatic field helps to understand superconductivity
The motion of electrons in superconductors seems to defy our imagination based on daily experience with Newtonian mechanics. This book shows that the classical concepts, such as the balance of forces acting on electrons, are useful for understanding superconductivity. The electrostatic field plays a natural part in this balance as it mediates forces between electrons at long distances. Due to its classical interpretation, the theory presented in this book is suitable for introductory courses.
Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series
This book discusses the statistical methods most often applied for such adjustments, ranging from ad hoc procedures to regression-based models. The latter are emphasized, because of their clarity, ease of application, and superior results. Each topic is illustrated with many real case examples. In order to facilitate understanding of their properties and limitations of the methods discussed, a real data example, the Canada Total Retail Trade Series, is followed throughout the book.This book brings together the scattered literature on these topics and presents them using a consistent notation and a unifying view.
Behavioral consultation and primary care : A guide to integrating services
Comprehensive, wise, and incredibly practical, this exciting volume walks through the joys and challenges of an entirely new vision of behavioral health consultation in primary care settings. Gently, and with good humor, the authors show how to avoid key errors, and provide a detailed, point by point guideline for success in an effective and needed new form of practice. You will forever think differently about the proper role of behavioral health providers in health care delivery. Even better, you will be prepared to do something about it. -- Steven C. Hayes, University of Nevada The Primary Care Behavioral Health (PCBH) model is fast emerging as the future of integration between mental health and primary care services.
Beginning xml with C# 2008 : From novice to professional
Beginning XML with C# 2008 focuses on XML and how it is used within .NET 3.5. As you'd expect of a modern application framework, .NET 3.5 has extensive support for XML in everything from data access to configuration, from raw parsing to code documentation. This book demystifies all of this. It explains the basics of XML as well as the namespaces and objects you need to know in order to work efficiently with XML. You will see clear, practical examples that illustrate best practices in action. With this book, you'll learn everything you need to know from the basics of reading and writing XML data to using the DOM, from LINQ and SQL Server integration to SOAP and web services.
Beginning relational data modeling
Data storage design, and awareness of how data needs to be utilized within an organization, is of prime importance in ensuring that company data systems work efficiently. 'Beginning Data Modeling' leads readers you step by step through the process of developing an effective logical data model for a relational database model.
Beginning Object-Oriented Programming with VB 2005 : From novice to professional
Beginning Object-Oriented Programming with VB 2005 is a comprehensive resource of correct coding procedures. Author Daniel Clark takes you through all the stages of a programming project, including analysis, modeling, and development, all using object-oriented programming techniques and Visual Basic .NET. Clark explores the structure of classes and their hierarchies, as well as inheritance and interfaces. He also introduces the .NET Framework and the Visual Studio integrated development environment, or IDE. A real-world case study walks you through the design of a solution. You can then transform the design into a functional VB .NET application. The application includes a graphical user interface (GUI), a business logic class library, and integration with a back-end database. Throughout the book, you'll explore the fundamentals of software design, object-oriented programming, Visual Basic .NET 2.0, and the Unified Modeling Language (UML).
Beginning Java Objects : From concepts to code
Learning to design objects effectively with Java is the goal of Beginning Java Objects: From Concepts to Code, Second Edition. Plenty of titles dig into the Java language in massive detail, but this one takes the unique approach of stepping back and looking at fundamental object concepts first. Mastery of Java—from understanding the basic language features to building complete industrial-strength Java applications—emerges only after a thorough tour of thinking in objects. The first edition of Beginning Java Objects has been a bestseller; this second edition includes material on the key features of J2SE 5, conceptual introductions to JDBC and J2EE, and an in-depth treatment of the critical design principles of model-data layer separation and model-view separation.
Beginning deep learning with TensorFlow : Work with Keras, MNIST data sets, and advanced neural networks
Stats with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications
Beginning Database Design : From novice to professional
Beginning Database Design: From Novice to Professional provides short, easy-to-read explanations of how to get database design right the first time. Through the help of use cases and class diagrams modeled in the UML, youll learn how to discover and represent the details and scope of the problem in question.
Beginning Ajax with PHP : From novice to professional
Beginning Ajax with PHP: From Novice to Professional is the first book to introduce how these two popular technologies can work together to create next-generation applications. Author Lee Babin covers what you commonly encounter in daily web application development tasks.
Bayesian reliability
Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods. The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and degradation models. The authors pay special attention to Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. Throughout the book, the authors use Markov chain Monte Carlo (MCMC) algorithms for implementing Bayesian analyses--algorithms that make the Bayesian approach to reliability computationally feasible and conceptually straightforward.
Bayesian networks and Influence diagrams : A guide to construction and analysis
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty.
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams.It contians two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems.
Bayesian Methods in the Search for MH370
This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was used to define the search zone in the southern Indian Ocean. The book describes particle-filter based numerical calculation of the aircraft flight-path probability distribution and validates the method using data from several of the involved aircraft’s previous flights. Finally it is shown how the Reunion Island flaperon debris find affects the search probability distribution.
Bayesian core : A practical approach to computational Bayesian statistics
This Bayesian modeling book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models.
