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• 1. What is computation?
• 1.1. Defining computation
• 1.2. The roles of computation in scientific research
• 1.3. Analog computing
• 1.4. Further reading

• 2. Computation in science
• 2.1. Traditional science : celestial mechanics
• 2.2. Scientific models and computation
• 2.3. Computation at the interface between observations and models
• 2.4. Computation for developing insight
• 2.5. The impact of computing on science
• 2.6. Further reading

• 3. Formalizing computation
• 3.1. From manual computation to rewriting rules
• 3.2. From computing machines to automata theory
• 3.3. Computability
• 3.4. Restricted models of computation
• 3.5. Computational complexity
• 3.6. Computing with numbers
• 3.7. Further reading

• 4. Automating computation
• 4.1. Computer architectures
• 4.2. Programming languages
• 4.3. Observing program execution
• 4.4. Software engineering
• 4.5. Further reading

• 5. Taming complexity
• 5.1. Chaos and complexity in computation
• 5.2. Verification, validation, and testing
• 5.3. Abstraction
• 5.4. Managing state
• 5.5. Incidental complexity and technical debt
• 5.6. Further reading

• 6. Computational reproducibility
• 6.1. Reproducibility : a core value of science
• 6.2. Repeating, reproducing, replicating
• 6.3. The role of computation in the reproducibility crisis
• 6.4. Non-reproducible determinism
• 6.5. Staged computation
• 6.6. Replicability, robustness, and reuse
• 6.7. Managing software evolution
• 6.8. Best practices for reproducible and replicable computational science
• 6.9. Further reading

• 7. Outlook : scientific knowledge in the digital age
• 7.1. The scientific record goes digital
• 7.2. Procedural knowledge turns into software
• 7.3. Machine learning : the fusion of factual and procedural knowledge
• 7.4. The time scales of scientific progress and computing
• 7.5. The industrialization of science
• 7.6. Preparing the future
• 7.7. Further reading

In the course of only a few decades computers have revolutionized scientific research and more and more scientists are writing computer programs for doing their work. In spite of the ubiquitous use of computers in science, few researchers in the natural sciences have any schooling in computer science, software engineering, or numerical analysis. They usually acquire their computing knowledge 'on the job' and often feel overwhelmed by the amount of computing knowledge they must absorb. Computation in Science provides a background in computation for scientists who use computational methods. The book explains how computing is used in the natural sciences and provides a high-level overview of relevant aspects of computer science and software engineering with a focus on concepts, results, and applications. The goal of this book is to explain these basic principles, and to show how they relate to the tasks of a scientist's daily work in a language familiar to them. Its unique feature is in connecting the dots between computational science, the theory of computation and information, and software engineering. It will compensate for the general lack of any formal training in computer science and information theory allowing readers to better understand how they use computers in their work, and how computers work. Readers will learn to use computers with more confidence, and to see computing technologies in a different light, evaluating them based on how they contribute to doing science. This new edition has been significantly updated and extended to reflect developments in scientific computing, including new examples and references. It also includes a new chapter on reproducibility which reflects the importance that computational reproducibility plays. Accompanied by a website maintained by the author which hosts companion code and supplementary material, it is intended for both graduate students and experienced scientists. Some hands-on experience with computing is highly desirable, but no competence in any specific computing technology is expected

• Chapter 1: Operating systems and software
• Chapter 2: Hyper-V vs VMware
• Chapter 3: Creating Your First Domain
• Chapter 4: Active Directory
• Chapter 5: Domain Name System (DNS)
• Chapter 6: Certificate Authority
• Chapter 7: Group Policy
• Chapter 8: Joining the Machines to the Domain
• Chapter 9: Installing SQL server
• Chapter 10: Installing SharePoint
• Chapter 11: Installing Dev Tools
• Chapter 12: Troubleshooting
• Chapter 13: Finishing touches.
• (source: Nielsen Book Data)

This book is a step-by-step guide to building your own SharePoint farm in a home lab setting. Learn how to build a windows domain and then join servers into the domain in order to create your own testing and learning environment. After you get the domain stood up, where you go from there is up to you. This book will help you learn how to spin up SharePoint in a least privileged fashion. This isn't strictly a SharePoint book, though. For example, if you're not a SharePoint professional and are just looking to create a working windows domain for other purposes; the home-lab domain that you'll create will work great for Exam preparation for non-SharePoint purposes. You could even use it for learning how to install Exchange Server. After all, it's your Home Lab domain. In this book you will build your home-lab domain and you'll have a great place for learning how to administer SharePoint and develop SharePoint Apps. Here are just some of the tasks you'll complete in easy to follow exercises: Create a windows domain and a certificate authority, so that you can run SharePoint on SSL Join servers to the domain and configure other technology on the member servers as needed Install SQL Server, and correctly partition the server in accordance with best practices Install and configure a SharePoint farm Spin up Host Named Site Collections (HNSC's) Install and configure Visual Studio Who This Book Is For: This book is suitable for both developers and administrators. No technical knowledge is assumed beyond a general familiarity with computers and computing terminology. The resulting domain will be suitable for both IT and developer testing needs.
(source: Nielsen Book Data)

• 1. Introduction
• 1.1. The importance of Modelling and Simulation
• 1.2. Important Concepts
• 1.3. Structure of the book
• 2. The Recipe to build a mathematical model
• 2.1. Three fundamental concepts used for modeling
• 2.2. The recipe for the development of a model
• 2.3. Proposed problems
• 3. Lumped-parameters models
• 3.1. Some introductory examples
• 3.2. Some concepts about convective heat exchange
• 3.3. Some concepts about chemical kinetics and reactors
• 3.4. Proposed problems
• 4. Distributed-parameters models
• 4.1. Some introductory examples
• 4.2. Concepts about mas s and energy diffusion
• 4.3. Variations in two and three dimensions
• 4.4. Proposed problems
• 5. Solving Algebraic Eq uations Systems
• 5.1. Problems involving Linear Algebraic Equations
• 5.2. Problems involving Non-Linear Algebraic Equations
• 5.3. Numerical method for Linear and Non-Linear Equations Systems
• 5.4. Proposed problems
• 6. Solving Ordinary Differential Equations Systems
• 6.1. Concepts about Runge-Kutta Family
• 6.2. Different approaches to solve Ordinary Differential Equations using Excel (R)
• 6.3. Propos ed problems
• 7. Solving Partial Differential Equations Systems &n bsp--
• 7.1. Concepts about Finite Difference Method
• 7.2. Reducing a PDE system in an algebraic equation system
• 7.3. Reducing a PDE system in an ODE system
• 7.3. Proposed problems
• 8. Conclusions
• 8.1. Intro duction
• 8.2. Special Scenarios
• 8.3. Main Conclusions and tips
• 8.4. Proposed problems.
• (source: Nielsen Book Data)

This book treats modeling and simulation in a simple way, that builds on the existing knowledge and intuition of students. They will learn how to build a model and solve it using Excel. Most chemical engineering students feel a shiver down the spine when they see a set of complex mathematical equations generated from the modeling of a chemical engineering system. This is because they usually do not understand how to achieve this mathematical model, or they do not know how to solve the equations system without spending a lot of time and effort. Trying to understand how to generate a set of mathematical equations to represent a physical system (to model) and solve these equations (to simulate) is not a simple task. A model, most of the time, takes into account all phenomena studied during a Chemical Engineering course. In the same way, there is a multitude of numerical methods that can be used to solve the same set of equations generated from the modeling, and many different computational languages can be adopted to implement the numerical methods. As a consequence of this comprehensiveness and combinatorial explosion of possibilities, most books that deal with this subject are very extensive and embracing, making need for a lot of time and effort to go through this subject. It is expected that with this book the chemical engineering student and the future chemical engineer feel motivated to solve different practical problems involving chemical processes, knowing they can do that in an easy and fast way, with no need of expensive software.
(source: Nielsen Book Data)

• Section A. Performance benchmarking and optimization ; Algebraic multigrid on a dragonfly network : first experiences on a Cray XC30 / Hormozd Gahvari, William Gropp, Kirk E. Jordan, Martin Schulz, and Ulrike Meier Yang
• Performance evaluation of scientific applications on POWER8 / Andrew V. Adinetz, Paul F. Baumeister, Hans Böttiger, Thorsten Hater, Thilo Maurer, Dirk Pleiter, Wolfram Schenck, and Sebastiano Fabio Schifano
• SPEC ACCEL : a standard application suite for measuring hardware accelerator performance / Guido Juckeland, William Brantley, Sunita Chandrasekaran, Barbara Chapman, Shuai Che, Mathew Colgrove, Huiyu Feng, Alexander Grund, Robert Henschel, Wen-Mei W. Hwu, Huian Li, Matthias S. Müller, Wolfgang E. Nagel, Maxim Perminov, Pavel Shelepugin, Kevin Skadron, John Stratton, Alexey Titov, Ke Wang, Matthijs van Waveren, Brian Whitney, Sandra Wienke, Rengan Xu, and Kalyan Kumaran
• A CUDA implementation of the high performance conjugate gradient benchmark / Everett Phillips and Massimiliano Fatica
• Performance analysis of a high-level abstractions-based hydrocode on future computing systems / G.R. Mudalige, I.Z. Reguly, M.B. Giles, A.C. Mallinson, W.P. Gaudin, and J.A. Herdman

• Section B. Performance analysis and prediction ; Insight into application performance using application-dependent characteristics / Waleed Alkohlani, Jeanine Cook, and Nafiul Siddique
• Roofline model toolkit : a practical tool for architectural and program analysis / Yu Jung Lo, Samuel Williams, Brian Van Straalen, Terry J. Ligocki, Matthew J. Cordery, Nicholas J. Wright, Mary W. Hall, and Leonid Oliker
• Modeling stencil computations on modern HPC architectures / Raúl de la Cruz and Mauricio Araya-Polo
• Performance modeling of the HPCG benchmark / Vladimir Marjanović, José Gracia, and Colin W. Glass
• On the performance prediction of BLAS-based tensor contractions / Elmar Peise, Diego Fabregat-Traver, and Paolo Bientinesi

• Section C. Power, energy and checkpointing ; Assessing general-purpose algorithms to cope with fail-stop and silent errors / Anne Benoit, Aurélien Cavelan, Yves Robert, and Hongyang Sun
• A case for epidemic fault detection and group membership in HPC storage systems / Shane Snyder, Philip Carns, Jonathan Jenkins, Kevin Harms, Robert Ross, Misbah Mubarak, and Christopher Carothers
• Analysis of the tradeoffs between energy and run time for multilevel checkpointing / Prasanna Balaprakash, Leonardo A. Bautista Gomez, Mohamed-Slim Bouguerra, Stefan M. Wild, Franck Cappello, and Paul D. Hovland
• On the energy proportionality of distributed NoSQL data stores / Balaji Subramaniam and Wu-chun Feng.

This volume contains the 14 papers that were presented at the 5th International Workshop on Performance Modeling, Benchmarking, and Simulation of High Performance Computing Systems (PMBS 2014), which was held as part of the 26th ACM/ IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2014) at the Ernest N. Morial Convention Centre in New Orleans during November 16-21, 2014. The SC conference series is the premier international forum for high-performance computing, networking, storage, and analysis. The conference is unique in that it hosts a wide range of international participants from academia, national laboratories, and industry; this year's conference attracted over 10,000 attendees and featured over 350 exhibitors in the industry's largest HPC technology fair. This year's conference was themed HPC Matters, recognizing the immense impact that high-performance computing has on our lives. Specifically, SC 2014 was focused not only on the very visible way in which HPC is changing the world around us, but also on how HPC is improving every aspect of our lives in the most unexpected ways.

• Preface
• Introduction
• Looking for Tangibility
• Concepts and Tools for Designers
• Innovation
• Complexity
• Flexibility
• Maturity
• Stability
• Sustainability
• Arts
• Conclusion.

Distinguishing between tangible user interfaces (TUI) and tangible interactive systems (TISs), this book takes into account not only the user interfaces but also looks at how interaction can be enabled by using digital information through the physical environment. TISs go far beyond the concept of tangible user interfaces, addressing large complex systems in the framework of human-centred design and putting the human at the center of the design process from the start. How can human-centered designers grasp the real world with computers? This question is explored by looking at concepts such as innovation, complexity, flexibility, maturity, stability, sustainability and art to see whether we can assess both physical and figurative tangibility during the design process before product delivery. Concepts like creativity, design thinking and team spirit are fundamental to TIS?s human-centered design, and are presented together with human-systems integration (HSI), agile development and formative evaluations to build a greater understanding of this new area of research. Tangible Interactive Systems would be an essential read to designers, academics and other professionals concerned with product design within HCI, industrial design, virtual engineering and other related areas.

• Introduction.- Complex Systems and Economics of Information.- Heterogeneity in Complex Communication Systems.- A Model of Adaptive and Selective Communication.- Intra versus Interspecies Communication: Boundaries and Advances.- Emergence of Language.- The Future of Communication. Conclusions.
• (source: Nielsen Book Data)

This book integrates current advances in biology, economics of information and linguistics research through applications using agent-based modeling and social network analysis to develop scenarios of communication and language emergence in the social aspects of biological communications. The book presents a model of communication emergence that can be applied both to human and non-human living organism networks. The model is based on economic concepts and individual behavior fundamental for the study of trust and reputation networks in social science, particularly in economics; it is also based on the theory of the emergence of norms and historical path dependence that has been influential in institutional economics. Also included are mathematical models and code for agent-based models to explore various scenarios of language evolution, as well as a computer application that explores language and communication in biological versus social organisms, and the emergence of various meanings and grammars in human networks. Emergence of Communication in Socio-Biological Networks offers both a completely novel approach to communication emergence and language evolution and provides a path for the reader to explore various scenarios of language and communication that are not constrained to the human networks alone. By illustrating how computational social science and the complex systems approach can incorporate multiple disciplines and offer an integrated theory-model approach to the evolution of language, the book will be of interest to researchers working with computational linguistics, mathematical linguistics, and complex systems.
(source: Nielsen Book Data)

• Simulation
• Graphics
• Visualizations
• Efficiency
• Recursion
• Projects
• Modeling, Part I
• Modeling, Part II
• Postscript.

• Intro; Preface; Contents; 1 Simulation; 1.1 Random Walk; 1.2 Air Resistance; 1.3 Lunar Module; 2 Graphics; 2.1 Pixel Mapping; 2.2 Scalable Format; 2.3 Building Software; 3 Visualization; 3.1 Geospatial Population Data; 3.2 Particle Diffusion; 3.3 Approximating π; 4 Efficiency; 4.1 Text and Language; 4.2 Babylonian Method; 4.3 Workload Balance; 5 Recursion; 5.1 Disease Outbreak; 5.2 Runtime Analysis; 5.3 Guessing Games; 6 Projects; 6.1 Sliding Tile Puzzle; 6.2 Anagram Scramble; 6.3 Collision Detection; 7 Modeling, Part I; 7.1 Laws of Motion; 7.2 Collisions in 1-D; 7.3 Collisions in 2-D

The second edition of this introductory text includes an expanded treatment of collisions, agent-based models, and insight into underlying system dynamics. Lab assignments are accessible and carefully sequenced for maximum impact. Students are able to write their own code in building solutions and Python is used to minimize any language barrier for beginners. Problems involving visualization are emphasized throughout with interactive graphics, image files, and plots of generated data. This text aims to establish a core learning experience around which any number of other learning objectives could be included. The text is presented in eight chapters where each chapter contains three problems and each problem develops five specific lab assignments, plus additional questions and discussion. This approach seeks to leverage the immediate feedback provided by the computer to help students as they work toward writing code creatively. All labs will scale to available hardware and free software could be used for the entire course, if desired.

• Preface
• Introduction
• An elastic Wave Propagation (AWP)
• Green's Functions
• Data Sensitivity Kernels
• Optimization Algorithms
• CVM-S4.26
• Index.

This book introduces a methodology for solving the seismic inverse problem using purely numerical solutions built on 3D wave equations and which is free of the approximations or simplifications that are common in classical seismic inversion methodologies and therefore applicable to arbitrary 3D geological media and seismic source models. Source codes provided allow readers to experiment with the calculations demonstrated and also explore their own applications.

• Preface
• Part I: The Concept of Anti-Fragility: 1 Introduction
• 2 Achieving Anti-Fragility
• 3 The Need to Build Trust
• 4 Principles Ensuring Anti-Fragility
• Part II: Anti-Fragility to Downtime: 5 Anti-Fragile Cloud Solutions
• 6 An Anti-Fragile e-Government System
• 7 Anti-Fragile Cloud-Based Telecom Systems
• Part III: Anti-Fragility to Malware: 8 Robustness to Malware Spreading
• 9 Robustness to Malware Reinfections
• 10 Anti-Fragility to Malware Spreading
• Part IV: Anomaly Detection: 11 The Cortical Learning Algorithm
• 12 Detecting Anomalies with the CLA
• Part V: Future Anti-Fragile Systems: 13 Summary and Future Work
• About the Author
• References
• Index.

This book introduces a novel approach to the design and operation of large ICT systems. It views the technical solutions and their stakeholders as complex adaptive systems and argues that traditional risk analyses cannot predict all future incidents with major impacts. To avoid unacceptable events, it is necessary to establish and operate anti-fragile ICT systems that limit the impact of all incidents, and which learn from small-impact incidents how to function increasingly well in changing environments. The book applies four design principles and one operational principle to achieve anti-fragility for different classes of incidents. It discusses how systems can achieve high availability, prevent malware epidemics, and detect anomalies. Analyses of Netflix's media streaming solution, Norwegian telecom infrastructures, e-government platforms, and Numenta's anomaly detection software show that cloud computing is essential to achieving anti-fragility for classes of events with negative impacts.

• Foreword; Preface; Overview and Goals; Organisation and Features; Target Audiences; Suggested Uses; Acknowledgements; Contents; Contributors; Part I Theory; 1 Data Quality Monitoring of Cloud Databases Based on Data Quality SLAs; 1.1 Introduction and Summary; 1.2 Background; 1.2.1 Data Quality in the Context of Big Data; 1.2.2 Cloud Computing; 1.2.3 Data Quality Monitoring in the Cloud; 1.2.4 The Challenge of Specifying a DQSLA; 1.2.5 The Infrastructure Estimation Problem; 1.3 Proposed Solutions; 1.3.1 Data Quality SLA Formalization; 1.3.2 Examples of Data Quality SLAs.

• 1.3.3 Data Quality-Aware Service Architecture1.4 Future Research Directions; 1.5 Conclusions; References; 2 Role and Importance of Semantic Search in Big Data Governance; 2.1 Introduction; 2.2 Big Data: Promises and Challenges; 2.3 Participatory Design for Big Data; 2.4 Self-Service Discovery; 2.5 Conclusion; References; 3 Multimedia Big Data: Content Analysis and Retrieval; 3.1 Introduction; 3.2 The MapReduce Framework and Multimedia Big Data; 3.2.1 Indexing; 3.2.2 Caveats on Indexing; 3.2.3 Multiple Multimedia Processing; 3.2.4 Additional Work Required?

• 3.3 Deep Learning and Multimedia Data3.4 Conclusions; References; 4 An Overview of Some Theoretical Topological Aspects of Big Data; 4.1 Introduction; 4.2 Representation of Data; 4.3 Homology Theory; 4.3.1 Simplicial Complexes; 4.3.2 Voronoi Diagrams and Delaunay Triangulations; 4.3.3 Vietoris and Čech Complexes; 4.3.4 Graph-Induced Complexes; 4.3.5 Chains; 4.4 Network Theory for Big Data; 4.4.1 Scale-Free, Small-World and Random Networks; 4.5 Conclusions; References; Part II Applications.

• 5 Integrating Twitter Traffic Information with Kalman Filter Models for Public Transportation Vehicle Arrival Time Prediction5.1 Introduction; 5.2 Communication Platform on Twitter; 5.3 Communication for Data Collection on Twitter; 5.4 Event Detection and Analysis: Tweets Relating to Road Incidents; 5.4.1 Twitter Data: Incident Data Set; 5.5 Methodology; 5.5.1 Time Series and Temporal Analysis of Textual Twitter; 5.6 Proposed Refined Kalman Filter (KF) Model-Based System; 5.7 Conclusion; References; 6 Data Science and Big Data Analytics at Career Builder.

• 6.1 Carotene: A Job Title Classification System6.1.1 Occupation Taxonomies; 6.1.2 The Architecture of Carotene; 6.1.2.1 Taxonomy Discovery Using Clustering; 6.1.2.2 Coarse-Level Classification: SOC Major Classifier; 6.1.2.3 Fine-Level Classification: Proximity-Based Classifier; 6.1.3 Experimental Results and Discussion; 6.2 CARBi: A Data Science Ecosystem; 6.2.1 Accessing CB Data and Services Using WebScalding; 6.2.2 ScriptDB: Managing Hadoop Jobs; References; 7 Extraction of Bayesian Networks from Large Unstructured Datasets; 7.1 Introduction; 7.2 Text Mining; 7.2.1 Text Mining Techniques.

This important and timely text/reference reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: Describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures Examines the applications and implementations that utilize big data in cloud architectures Surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions Identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches Provides relevant theoretical frameworks, empirical research findings, and numerous case studies Discusses real-world applications of algorithms and techniques to address the challenges of big datasets This authoritative volume will be of great interest to researchers, enterprise architects, business analysts, IT infrastructure managers and application developers, who will benefit from the valuable insights offered into the adoption of architectures for big data and cloud computing. The work is also suitable as a textbook for university instructors, with the outline for a possible course structure suggested in the preface. The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Marcello Trovati serves as a Senior Lecturer in Mathematics, Dr. Richard Hillas a Professor and Head of the Computing and Mathematics Department, Dr. Ashiq Anjum as a Professor of Distributed Computing, Dr. Shao Ying Zhu as a Senior Lecturer in Computing, and Dr. Lu Liu as a Professor of Distributed Computing. The other publications of the editors include the Springer titles Guide to Security Assurance for Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.

• Screening
• CAD
• Mammography, Tomosynthesis, and Breast CT
• Novel Technology
• Density Assessment and Tissue Analysis
• Dose and Classification
• Image Processing, CAD, Breast Density, and New Technology
• Contrast-Enhanced Imaging
• Phase Contrast Breast Imaging
• Simulations and Virtual Clinical Trials.

This book constitutes the refereed proceedings of the 13th International Workshop on Breast Imaging, IWDM 2016, held in Malmö, Sweden, in June 2016. The 35 revised full papers and 50 revised poster papers presented together with 6 invited talks were carefully reviewed and selected from 89 submissions. The papers are organized in topical sections on screening; CAD; mammography, tomosynthesis, and breast CT; novel technology; density assessment and tissue analysis; dose and classification; image processing, CAD, breast density, and new technology; contrast-enhanced imaging; phase contrast breast imaging; simulations and virtual clinical trials.

• AI foundations
• Applications of AI
• Semantic web
• Information retrieval
• Constraint satisfaction
• Multimodal interaction
• Knowledge representation
• Social networks
• Ad-hoc networks
• Algorithms
• Software architecture
• Machine learning
• Smart modeling and simulation.

This book constitutes the refereed proceedings of the 14th Pacific Rim Conference on Artificial Intelligence, PRICAI 2016, held in Phuket, Thailand, in August 2016. The 53 regular papers and 15 short papers presented in this volume were carefully reviewed and selected from 161 submissions. Pricai covers a wide range of topics such as AI foundations; applications of AI; semantic web; information retrieval; constraint satisfaction; multimodal interaction; knowledge representation; social networks; ad-hoc networks; algorithms; software architecture; machine learning; and smart modeling and simulation.

• Part I: Evolutionary algorithms
• Introduction to Evolutionary Single-objective Optimisation
• Particle Swarm Optimisation
• Ant Colony Optimization
• Genetic Algorithm
• Biogeography-Based Optimization
• Part II: Evolutionary Neural Networks
• Evolutionary Feedforward Neural Networks
• Evolutionary Multi-Layer Perceptron
• Evolutionary Radial Basis Function Networks
• Evolutionary Deep Neural Networks.

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.
(source: Nielsen Book Data)

• Introduction.- Total Subsidence Analysis.- Tectonic Subsidence Analysis.- Thermal Subsidence Analysis.- Subsidence Visualization with BasinVis 1.5.
• (source: Nielsen Book Data)

This book provides a comprehensive introduction to techniques for quantitative subsidence analysis and visualization with example applications. Subsidence analysis is an essential step to understand basin evolution through geologic time and space in the study of sediments and sedimentary basins. Quantifying techniques have been developed and applied in many basin research projects to evaluate total, tectonic and thermal subsidence. They are also a pre-requisite for basin evolution modelling. Recent studies have applied visualization techniques to understand regional subsidence contexts and trends, which confirmed that three-dimensional visualization of the basin subsidence is highly helpful to gain insight into basin evolution. In this book, we show how geoscience and computer science can be effectively combined in advanced basin analysis, especially in terms of basin subsidence. Each type of subsidence analysis is introduced with example applications. In particular we present a study of the Vienna basin using BasinVis, a MATLAB-based program for analyzing and visualizing basin subsidence. Given its breadth of coverage, this book will benefit students in undergraduate and postgraduate courses and provide helpful information for research projects and industry applications.
(source: Nielsen Book Data)

• 1 Introduction 71.1 On dynamic mixtures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2 General conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 Basic Models 132.1 Regression model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1.1 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.1.2 Point estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.1.3 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2 Categorical model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.2.1 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.2 Point estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.3 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3 State-space model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3.1 State estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Statistical Analysis of Dynamic Mixtures 213.1 Dynamic mixture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2 Unified approach to mixture estimation . . . . . . . . . . . . . . . . . . . . . . . 223.2.1 The component part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2.2 The pointer part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.2.3 Main subtasks of mixture estimation . . . . . . . . . . . . . . . . . . . . . 233.2.4 General algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3 Mixture prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3.1 Pointer prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.3.2 Data prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 Dynamic Mixture Estimation 294.1 Normal regression components . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.1.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.1.2 Simple program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.1.3 Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334.2 Categorical components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.2.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.2.2 Simple program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.2.3 Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.3 State-space components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384.3.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.3.2 Simple program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.3.3 Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Program Codes 435.1 Main program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435.1.1 Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.2 Subroutines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475.2.1 Initialization of estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 475.2.2 Computation of proximities . . . . . . . . . . . . . . . . . . . . . . . . . . 495.2.3 Update of component statistics . . . . . . . . . . . . . . . . . . . . . . . . 525.3 Collection of programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546 Experiments 556.1 Mixture with regression components . . . . . . . . . . . . . . . . . . . . . . . . . 566.1.1 Well separated components . . . . . . . . . . . . . . . . . . . . . . . . . . 576.1.2 Weak components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646.1.3 Reduced number of components . . . . . . . . . . . . . . . . . . . . . . . 646.1.4 High dimensional output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64<6.1.5 Big noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656.2 Mixture with categorical components . . . . . . . . . . . . . . . . . . . . . . . . . 696.3 Mixture with state-space components . . . . . . . . . . . . . . . . . . . . . . . . . 756.4 Case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 756.4.1 Static normal components . . . . . . . . . . . . . . . . . . . . . . . . . . . 796.4.2 Dynamic normal components . . . . . . . . . . . . . . . . . . . . . . . . . 807 Appendix A (supporting notions) 877.1 Useful matrix formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 877.2 Matrix trace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 877.3 Dirac and Kronecker functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 887.4 Gamma and beta functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 897.5 The Bayes rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907.6 The chain rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 917.7 The natural conditions of control . . . . . . . . . . . . . . . . . . . . . . . . . . . 917.8 Conjugate Dirichlet distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 917.8.1 The normalization constant of Dirichlet distribution . . . . . . . . . . . . 927.8.2 Statistics update with the conjugate Dirichlet distribution . . . . . . . . . 927.8.3 The parameter point estimate of the categorical model . . . . . . . . . . . 937.8.4 Data prediction with Dirichlet distribution . . . . . . . . . . . . . . . . . 947.9 Conjugate Gauss-inverse-Wishart distribution . . . . . . . . . . . . . . . . . . . 947.9.1 Statistics update for the normal regression model . . . . . . . . . . . . . . 947.9.2 The parameter point estimate of the regression model . . . . . . . . . . . 957.9.3 The proximity evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 968 Appendix B (supporting programs) 978.1 Simulation programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978.1.1 The simulation of pointer values . . . . . . . . . . . . . . . . . . . . . . . 978.1.2 The simulation of mixture with regression components . . . . . . . . . . . 988.1.3 The simulation of mixture with discrete components . . . . . . . . . . . . 998.1.4 The simulation of mixture with state-space components . . . . . . . . . . 1018.2 Supporting subroutines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1038.2.1 Scilab start settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1038.2.2 The point estimation of a normal regression model . . . . . . . . . . . . . 1038.2.3 The value of a normal multivariate distribution . . . . . . . . . . . . . . . 1048.2.4 Discrete regression vector coding . . . . . . . . . . . . . . . . . . . . . . . 1058.2.5 Kalman filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1068.2.6 Matrix upper-lower factorization . . . . . . . . . . . . . . . . . . . . . . . 1078.2.7 Transition table normalization . . . . . . . . . . . . . . . . . . . . . . . . 1088.2.8 The approximation of normal pdfs by a single pdf . . . . . . . . . . . . . 108.
• (source: Nielsen Book Data)

This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms.
(source: Nielsen Book Data)

• Introduction.- Sampling and Deterministic Modeling Methods.- Temporal and Point Uncertainty Modeling.- Classical Spatial Variation Models in Earth sciences.- Spatial Dependence Measures.- Spatial Modeling.- Spatial Simulation.- Index.
• (source: Nielsen Book Data)

This is a revised and updated second edition, including new chapters on temporal and point uncertainty model, as well as on sampling and deterministic modeling. It is a comprehensive presentation of spatial modeling techniques used in the earth sciences, outlining original techniques developed by the author. Data collection in the earth sciences is difficult and expensive, but simple, rational and logical approaches help the reader to appreciate the fundamentals of advanced methodologies. It requires special care to gather accurate geological, hydrogeological, meteorological and hydrological information all with risk assessments. Spatial simulation methodologies in the earth sciences are essential, then, if we want to understand the variability in features such as fracture frequencies, rock quality, and grain size distribution in rock and porous media. This book outlines in a detailed yet accessible way the main spatial modeling techniques, in particular the Kriging methodology. It also presents many unique physical approaches, field cases, and sample interpretations. Since Kriging's origin in the 1960s it has been developed into a number of new methods such as cumulative SV (CSV), point CSV (PCSV), and spatial dependence function, which have been applied in different aspects of the earth sciences. Each one of these techniques is explained in this book, as well as how they are used to model earth science phenomena such as geology, earthquakes, meteorology, and hydrology. In addition to Kriging and its variants, several alternatives to Kriging methodology are presented and the necessary steps in their applications are clearly explained. Simple spatial variation prediction methodologies are also revised with up-to-date literature, and the ways in which they relate to more advanced spatial modeling methodologies are explained. The book is a valuable resource for students, researchers and professionals of a broad range of disciplines including geology, geography, hydrology, meteorology, environment, image processing, spatial modeling and related topics. Keywords "Data mining - Geo-statistics - Kriging - Regional uncertainty - Spatial dependence - Spatial modeling - geographic data - geoscience - hydrology - image processing.
(source: Nielsen Book Data)

• Chapter 1 The Influence of Microstructure on Neural Tissue Mechanics
• Chapter 2 Modeling a Collagenous Tissues Using Distributed Fiber Orientations
• Chapter 3 Emergent Behaviors in Cell Mechanics
• Chapter 4 Histomechanical Modeling of the Wall of Abdominal Aortic Aneurysm
• Chapter 5 The Biomechanics of Fat: From Tissue to a Cell Scale
• Chapter 6 Glaucoma and Structure-Based Mechanics of the Lamina Cribrosa at Multiple Scales
• Chapter 7 From Stress-Strain Relations to Formulation of Growth and Remodeling Theories: A Historical Reflection on Microstructurally-Motivated Constitutive Relations
• Chapter 8 Relationship Between Structure and Mechanics for Membranous Tissues
• Chapter 9 Structure-Function Relations in the Coronary Vasculature
• Chapter 10 Biomechanical Basis of Myocardium/Vessel Interaction: Implications for Patho-Physiology and Therapy
• Chapter 11 Microstructure-Based Constitutive Models for Coronary Artery Adventitia
• Chapter 12 Structural-Based Models of Ventricular Myocardium
• Chapter 13 Structure-Based Constitutive Model of Coronary Media
• Chapter 14 Biomechanics of the Cornea and Sclera
• Chapter 15 Mechanical Modeling of Skin
• Chapter 16 Undesirable Anisotropy in a Discrete Fiber Bundle Model of Fibrous Tissues
• Chapter 17 Structural Models as Applied to Engineered Tissue Scaffolds
• Chapter 18 Finite Element Implementation of Structural Models
• Chapter 19 A Microvascular Model in Skeletal Muscle Fascia
• Chapter 20 Network Approaches to the Mechanical Failure of Soft Tissues: Implications for Disease and Tissue Engineering.

This book portrays the commonality of tissue micro-structure that dictates physiological function in various organs (microstructure-function relation). Tissue and organ models are used to illustrate physiological functions based on microstructure. Fiber scale properties such as orientation and crimp are described in detail. Structurally-based constitutive models are given throughout the book, not only to avoid ambiguities in material characterization, but also to offer insights into the function, structure, and mechanics of tissue components. A statement of future directions of the field is also given, including how advancements, such as state-of-the-art computational modeling and optical measurements of tissue/cells structures, are taking structure-based modeling to the next level. This book also: Provides a comprehensive view of tissue modeling across multiple systems Broadens readers' understanding of state-of-the-art computational modeling and optical measurements of tissue/cells structures Describes in detail fiber scale properties such as orientation and crimp.

• Part I Modes of Heat Transfer.- Thermal Circuits.- Lumped Capacity Systems and Overall Heat Transfer Coefficients.- Modes of Heat Transfer.- Part II Transient Conduction (with Convective/Radiative.- Boundary Conditions).- 2-Node Systems.- Multinode Systems.- Part III Steady-State Conduction (with Convective/Radiative.- Boundary Conditions).- Nusselt Number Correlations.- Heat Transfer Fins.- Steady-State Conduction.- Part IV Heat Exchangers.- Internal Flow Models.- Heat Exchangers.
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This innovative text emphasizes a "less-is-more" approach to modeling complicated systems such as heat transfer by treating them first as "1-node lumped models" that yield simple closed-form solutions. The author develops numerical techniques for students to obtain more detail, but also trains them to use the techniques only when simpler approaches fail. Covering all essential methods offered in traditional texts, but with a different order, Professor Sidebotham stresses inductive thinking and problem solving as well as a constructive understanding of modern, computer-based practice. Readers learn to develop their own code in the context of the material, rather than just how to use packaged software, offering a deeper, intrinsic grasp behind models of heat transfer. Developed from over twenty-five years of lecture notes to teach students of mechanical and chemical engineering at The Cooper Union for the Advancement of Science and Art, the book is ideal for students and practitioners across engineering disciplines seeking a solid understanding of heat transfer. This book also: * Adopts a novel inductive pedagogy where commonly understood examples are introduced early and theory is developed to explain and predict readily recognized phenomena * Introduces new techniques as needed to address specific problems, in contrast to traditional texts' use of a deductive approach, where abstract general principles lead to specific examples * Elucidates readers' understanding of the "heat transfer takes time" idea-transient analysis applications are introduced first and steady-state methods are shown to be a limiting case of those applications * Focuses on basic numerical methods rather than analytical methods of solving partial differential equations, largely obsolete in light of modern computer power * Maximizes readers' insights to heat transfer modeling by framing theory as an engineering design tool, not as a pure science, as has been done in traditional textbooks * Integrates practical use of spreadsheets for calculations and provides many tips for their use throughout the text examples.
(source: Nielsen Book Data)

• Part 1 Introduction
• Chapter 1 History and Future
• Chapter 2 What is Multiphysics
• Chapter 3 How to Do Multiphysics
• Chapter 4 Multiphysics in Porous Materials
• Chapter 5 How to Use this Book
• Part 2 Mathematical Background
• Chapter 6 Tensor and Field
• Chapter 7 Tensor Analysis
• Chapter 8 Index Notation and Tensor Notation
• Chapter 9 Partial Differential Equations
• Chapter 10 Numerical Solution of PDEs
• Part 3 Monolithic Physics
• Chapter 11 Thermo: Heat Transfer
• Chapter 12 Hydro: Pore Water Movement
• Chapter 13 Concentrato: Transport of Dispersed Mass
• Chapter 14 Mechano: Stress and Strain
• Chapter 15 Dyno: Dynamics
• Chapter 16 Chemico: Chemical Reaction
• Chapter 17 Electro: Electrostatics
• Chapter 18 Magneto: Magnetostatics
• Part 4 Multiphysics
• Chapter 19 Thermomechanics: Non-Isothermal Mechanics
• Chapter 20 Hydromechanics: Poroelasticity as a Simple Case
• Chapter 21 Thermohydromechanics
• Chapter 22 Electrokinetics
• Chapter 23 Electromagnetics
• Chapter 24 Fluid Dynamics
• Chapter 25 Hydrodynomechanics: Fluid-Structure Interaction
• Chapter 26 Thermoelectromagnetics
• Chapter 27 Electromagnetomechanics
• Part 5 Implementation Methods
• Chapter 28 Finite Difference Method
• Chapter 29 Finite Volume Method
• Chapter 30 Finite Element MethodReferencesIndex.
• (source: Nielsen Book Data)

This book summarizes, defines, and contextualizes multiphysics with an emphasis on porous materials. It covers various essential aspects of multiphysics, from history, definition, and scope to mathematical theories, physical mechanisms, and numerical implementations. The emphasis on porous materials maximizes readers' understanding as these substances are abundant in nature and a common breeding ground of multiphysical phenomena, especially complicated multiphysics. Dr. Liu's lucid and easy-to-follow presentation serve as a blueprint on the use of multiphysics as a leading edge technique for computer modeling. The contents are organized to facilitate the transition from familiar, monolithic physics such as heat transfer and pore water movement to state-of-the-art applications involving multiphysics, including poroelasticity, thermohydro-mechanical processes, electrokinetics, electromagnetics, fluid dynamics, fluid structure interaction, and electromagnetomechanics. This volume serves as both a general reference and specific treatise for various scientific and engineering disciplines involving multiphysics simulation and porous materials.
(source: Nielsen Book Data)

• Front Velocity Modeling Approach to Column Chromatographic Characterization and Evaluation of Ketamine Enantiomers Separation with Simulated Moving Bed
• Modeling Hybrid Systems with Petri Nets
• Automatic Tuning of Computational Models
• Enhanced Interior Gateway Routing Protocol with IPv4 and IPv6 Support for OMNeT++
• Simulating LTE/LTE-advanced Networks with SimuLTE
• Sensitivity Estimation using Likelihood Ratio Method with Fixed-sample-Path Principle
• A System Dynamics Simulator for Decision Support in Risk-based IT Outsourcing Capabilities Management
• Analysis of Fractional-order Point Reactor Kinetics Model with Adiabatic Temperature Feedback for Nuclear Reactor with Subdiffusive Neutron Transport
• Analysis of Model Predictive Control for Fractional-order System
• CFD Modeling of a Mixed Mode Boosted GDI Engine and Performance Optimization for the Avoidance of Knocking
• Real-time Radar, Target and Environment Simulator
• Computationally-efficient EM-simulation-Driven Multi-objective Design of Compact Microwave Structures
• Simulation-based Optimization in Design-under-uncertainty Problems through Iterative Development of Metamodels in Augmented Design/random Variable Space
• Social Aggravation Estimation to Seismic Hazard Using Classical Fuzzy Methods.

The present book includes a set of selected extended papers from the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2014), held in Vienna, Austria, from 28 to 30 August 2014. The conference brought together researchers, engineers and practitioners interested in methodologies and applications of modeling and simulation. New and innovative solutions are reported in this book. SIMULTECH 2014 received 167 submissions, from 45 countries, in all continents. After a double blind paper review performed by the Program Committee, 23% were accepted as full papers and thus selected for oral presentation. Additional papers were accepted as short papers and posters. A further selection was made after the Conference, based also on the assessment of presentation quality and audience interest, so that this book includes the extended and revised versions of the very best papers of SIMULTECH 2014. Commitment to high quality standards is a major concern of SIMULTECH that will be maintained in the next editions, considering not only the stringent paper acceptance ratios but also the quality of the program committee, keynote lectures, participation level and logistics.