Computer modelling and simulation Books

Book SynopsisTrade ReviewPraise for the first edition "Biologists . . . have been relatively slow to take advantage of enhanced computing power and unlock the potential of these techniques. This book removes any excuse."—Frontiers of Biogeography"This volume would be an excellent text for an introductory course in modeling as science, or for self-study by a mature researcher interested in learning about this important new way of doing science."—H. Van Dyke Parunak, JASSS"This book represents something I have been [awaiting] for some years now: a good and solid introduction to the field of individual- and agent-based models. . . . The book is not only a practical guide but also serves as a good introduction to the basics of 'healthy' programming. These authors are the right ones to do this as they have a strong background in the philosophical aspects as well as the practical issues of modelling."—Basic and Applied Ecology"Agent-Based and Individual-Based Modeling has the potential to foster an appreciation of the value and place of individual-based models in our field in the next generation of emerging ecologists."—Christopher X. Jon Jensen, Ecology

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Book SynopsisSimulation with Arena provides a comprehensive treatment of simulation using industry-standard Arena software. The textbook begins by having the reader develop simple high-level models, and then progresses to advanced modeling and analysis. Statistical design and analysis of simulation experiments is integrated with the modeling chapters, reflecting the importance of mathematical modeling of these activities. An informal, tutorial writing style is used to aid the beginner in fully understanding the ideas and topics presented. The new edition now reflects Arena version 16.2 (from version 14.5 in the prior edition), which contains many new and useful features.The new edition of Simulation with Arena is also available in McGraw Hill Connect, featuring Adaptive Learning Assignments, the MHeBook, Instructor Resources, and more!Table of Contents1) What Is Simulation?2) Fundamental Simulation Concepts3) A Guided Tour Through Arena4) Modeling Basic Operations and Inputs5) Modeling Detailed Operations6) Statistical Analysis of Output from Terminating Simulations7) Intermediate Modeling and Steady-State Statistical Analysis8) Entity Transfer9) A Sampler of Further Modeling Issues and Techniques10) Arena Integration and Customization11) Continuous and Combined Discrete/Continuous Models12) Further Statistical Issues13) Conducting Simulation Studies

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Book SynopsisThis book provides an introduction to quantum information science, the science at the basis of the new quantum revolution of this century. It teaches the reader to build and program a quantum computer and leverage its potential. Aimed at quantum physicists and computer scientists, the book covers several topics, including quantum algorithms, quantum chemistry, and quantum engineering of superconducting qubits. Written by two professionals in the experimental and theoretical fields of quantum information science and containing over 200 figures and 100 exercises with solutions and summaries at the end of each chapter, this book is set to become a new standard in the field.Trade ReviewManenti and Motta provide a tour-de-force of quantum information science. This is the first textbook that I have seen that comprehensively begins with mathematics, moves on to quantum physics, and via quantum algorithms ends up in the discussion of hardware implementations. With detailed explanations, modern references, and further reading tips this book is poised to become one of the classics in every quantum information scientist's bookshelf * Alan Aspuru-Guzik, Professor of Chemistry and Computer Science, University of Toronto and Scientific Advisor, Zapata Computing *Manenti and Motta have navigated the vast field of quantum information science to create a well-rounded and accessible textbook. Though the subject is too broad to be covered in its entirety, the authors have carefully selected key topics and provide clear explanations including advanced topics on quantum simulation and superconducting devices. This is an excellent resource for anyone starting a career in this field. * Jay Gambetta, IBM Fellow and Vice President of IBM Quantum *The text 'Quantum Information Science' is an admirable attempt by these two authors, a theorist and an experimentalist in the quantum information field, to guide readers from the very basics to the frontiers of research. The unusual breadth of topics ensures that every reader will learn something new and the inclusion of a large number of problems with detailed solutions means that this work is suitable for instructional use in graduate classes. A much needed and unique tour-de-force. * Garnet Kin-Lic Chan, Bren Professor of Chemistry, Caltech *Manenti and Motta have made a great effort to introduce the basic concepts in the rapidly growing field of quantum information science and technology. With numerous exercises and references, this book will not only be a valuable resource for current students, but also serve as a foundation for the next generation of quantum engineers. * Yasunobu Nakamura, Professor of Quantum Information Physics, University of Tokyo *Table of ContentsPART I - FOUNDATIONS 1: Mathematical tools 2: Computational models 3: Linear algebra 4: Quantum mechanics 5: Quantum circuits PART II - MODERN QUANTUM MECHANICS 6: Density operators 7: Quantum maps 8: Decoherence PART III - APPLICATIONS 9: Entanglement 10: Early quantum algorithms 11: Quantum simulation of Hamiltonian dynamics 12: Quantum simulation of Hamiltonian eigenstates PART IV - QUANTUM ENGINEERING OF SUPERCONDUCTING DEVICES 13: Microwave resonators for superconducting devices 14: Superconducting qubits Appendix A: The rotating wave approximation Appendix B: Advanced quantum mechanics Appendix C: The quantum Fourier transform Appendix D: The molecular Hamiltonian in second quantization

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Book SynopsisArtist Jon Stuart shows how he produces computer-generated images. The process starts with a simple block of cubes, which are added to, given texture, before being bent, stretched, twisted into the correct shape and size. He then adds background, lighting and colour to bring the whole scene to life.This is a Band 06/Orange book in the Collins Big Cat reading programme which offers varied text and characters, with action sustained over several pages. This is an information book with a flow chart on pages 22 and 23 that summarises the process of producing CGI. Contents are listed on page 1 while a glossary and index are detailed on pages 20 and 21. This book supports learning around art and design, and investigates different kinds of art, design and craft. It also supports ICT education, and is an introduction to modelling and creating pictures. This book has been levelled for Reading Recovery. For more guided reading books in this Collins Big Cat band, try Pompeii (9780007461875) writte

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Table of Contents1. Introduction I - WAVE ENERGY CONVERTER MODELLING TECHNIQUES BASED ON LINEAR HYDRODYNAMIC THEORY 2. Frequency-Domain Models 3. Time-Domain Models 4. Spectral-Domain Models II - OTHER WAVE ENERGY CONVERTER MODELLING TECHNIQUES 5. Nonlinear Potential Flow Models 6. Computational Fluid Dynamics (CFD) Models 7. Identifying Models Using Recorded Data III - WAVE ENERGY CONVERTER ARRAY MODELLING TECHNIQUES 8. Conventional Multiple Degree-of-Freedom Array Models 9. Semi-analytical Array Models 10. Phase-Resolving Wave Propagation Array Models 11. Phase-Averaging Wave Propagation Array Models IV - APPLICATIONS FOR WAVE ENERGY CONVERTER MODELS 12. Control Optimisation and Parametric Design 13. Determining Mean Annual Energy Production 14. Determining Structural and Hydrodynamic Loads 15. Environmental Impact Assessment

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Book SynopsisDigital computer simulation helps study phenomena of great complexity, but how much do we know about the limits and possibilities of this new scientific practice? How do simulations compare to traditional experiments? And are they reliable? The author seeks to answer these questions.Trade Review"This is the first book-length study of the role of simulation models from the standpoint of philosophy of science. It will be required reading for all who follow." - Ronald Giere, University of Minnesota"

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Book SynopsisA comprehensive and hands-on introduction to the core concepts, methods, and applications of agent-based modeling, including detailed NetLogo examples.The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach—with hundreds of examples and exercises using NetLogo—enables readers to begin constructing models immediately, regardless of experience or discipline.The book

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Book SynopsisThe second edition of Building Energy Simulation includes studies of various components and systems of buildings and their effect on energy consumption, with the help of DesignBuilderTM, a front-end for the EnergyPlus simulation engine, supported by examples and exercises. The book employs a learning by doing methodology. It explains simulation-input parameters and how-to-do analysis of the simulation output, in the process explaining building physics and energy simulation. Divided into three sections, it covers the fundamentals of energy simulation followed by advanced topics in energy simulation and simulation for compliance with building codes and detailed case studies for comprehensive building energy simulation.Features:Focuses on learning building energy simulation while being interactive through examples and exercises.Explains the building physics and the science behind the energy performance of buildings.Encourages an integrated design apTable of Contents1. Getting Started with Energy Simulation. 2. Geometry of Buildings. 3. Material and Construction. 4. Openings and Shading. 5. Lighting and Controls. 6. Heating and Cooling Design. 7. Unitary HVAC systems. 8. Heating Ventilation and Air Conditioning – Central Water Side. 9. Heating Ventilation and Air Conditioning – Central Air Side. 10. Natural Ventilation. 11. Simulation Parameters. 12. Renewable Energy System. 13. Costing, Sensitivity and Uncertainty Analysis. 14. Building Energy Code Compliance.

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Book SynopsisThis book provides a comprehensive overview of potential opportunities and the business value position related to implementing physics-based real-time simulation to production. The objective of real-time simulation is to provide value for all three dimensions of sustainability: economic, social, and environmental. By reviewing actual industrial cases and presenting relevant academic research, the book examines the topic from four interrelated viewpoints: the industrial need for sustainable production, the development of game-like virtual environments, capturing customer value and enhancing the user experience, and finally, establishing business value. It offers a framework that will enable a rethink and shift in mindset to appreciate how real-time simulation can change the way products are manufactured and services are produced. This book will appeal to researchers and scholars in areas as diverse as strategic management, manufacturing and operations management, marketing, industrial economics, and product lifecycle management.Table of Contents1 Creating Value with Sustainable Production based on Real-Time Simulation Minna Saunila, Juhani Ukko, Janne Heikkinen, Scott Semken, Aki Mikkola; Part I: Industrial needs of sustainable production; 2 Identifying industrial needs for real-time simulation and digital twins Lea Hannola, Ilkka Donoghue, Kirsi Kokkonen, Kalle Elfvengren, Jorma Papinniemi; 3 Company capabilities and implementing real-time activities Mina Nasiri, Juhani Ukko, Minna Saunila, Tero Rantala; 4 Real-time simulation strategies: Implications for operational excellence and sustainability performance Minna Saunila, Mina Nasiri, Juhani Ukko; 5 Selling Digital Twins in Business-to-Business Markets Tuija Rantala, Kirsi Kokkonen, Lea Hannola; Part II: Game-like virtual environments; 6 Accelerating Design Processes Using Data-Driven Models Emil Kurvinen, Iines Suninen, Grzegorz Orzechowski, Jin H. Choi, Jin-Gyun Kim, Aki Mikkola; 7 Gamification and the marketing of agricultural machinery Suraj Jaiswal, Anssi Tarkiainen, Tuhin Choudhury, Jussi Sopanen, Aki Mikkola; 8 Added value from virtual sensors Janne Heikkinen, Emil Kurvinen, Jussi Sopanen; 9 The Technical-Business Aspects of Two Mid-Size Manufacturing Companies Implementing a Joint Simulation Model Manouchehr Mohammadi, Kalle Elfvengren, Qasim Khadim, Aki Mikkola; Part III: Capturing customer value and user experience; 10 Implementing Digital Twins to Enhance Digitally Extended Product-Service Systems Ilkka Donoghue, Lea Hannola, Antti Sääksvuori; 11 The expected benefits of utilizing simulation in manufacturing companies: Insights from a Delphi study Kalle Elfvengren, Manouchehr Mohammadi, Ville Kalliola, Lea Hannola; 12 Integrating the User Experience throughout the Product Lifecycle with Real-Time Simulation-Based Digital Twins Qasim Khadim, Lea Hannola, Ilkka Donoghue, Aki Mikkola, Esa-Pekka Kaikko, Tero Hukkataival; Part IV: Value for business; 13 The digital twin combined with real-time performance measurement in lean manufacturing Mira Holopainen, Juhani Ukko, Minna Saunila, Tero Rantala, Hannu Rantanen; 14 Using Real-time Simulation in Company Value Chains and Business Models for Value Creation Maya Kristina Cheikh-el-Chabab, Olli Kuivalainen, Ulf R. Andersson, Roope Eskola, Aki Mikkola; 15 A model and propositions for the implementation of a digital twin Juhani Ukko, Tero Rantala, Mina Nasiri, Minna Saunila; 16 Managing Digital-twin lifecycle – Recognition and Handling of Business Risks Tero Rantala, Minna Saunila, Juhani Ukko, Aki Mikkola, Juha Kortelainen, Akhtar Zeb

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Book SynopsisSince the beginning of time, running a business has involved using logic by which the business operates. This logic is called the business model in management science, which increasingly is focusing on issues surrounding business models. Research trends related to business models include value creation, value chain operationalization, and social and ecological aspects, as well as innovation and digital transformation. Business Models: Innovation, Digital Transformation, and Analytics examines how innovation, digital transformation, and the composition of value affect the existence and development of business models. The book starts by addressing the conceptual development of business models and by discussing the essence of innovation in those models. Chapters in the book investigate how: Business models can analyze digital transformation scenarios Individual business model elements effect selected performance measures as well as Table of ContentsChapter 1. Innovation in Business Models. Chapter 2. Business Models in the Digital Transformation Era. Chapter 3. Value Composition for Business Models og High-Growth Enterprises. Chapter 4. The Variety of Aspects of Business Models in the High-Growth and High Tech Enterprises: An Estonian Case. Chapter 5. External Conditions of Profitability of Business Models of High-Growth Enterprises. Chapter 6. Analyzing the Employer Branding Business Models Based on Primary Research Results. Chapter 7. Models of Responsible Business: CSR from a Social and Economic Perspective. Chapter 8. Cyber Protection: Industrialized Assessments for Analyzing Cyber Risk. Chapter 9. Applied Data Analytics.

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Book Synopsis1 Complexity and Criticality.- 2 The Discovery of Self-Organized Criticality.- 3 The Sandpile Paradigm.- 4 Real Sandpiles and Landscape Formation.- 5 Earthquakes, Starquakes, and Solar Flares.- 6 The Game of Life: Complexity Is Criticality.- 7 Is Life a Self-Organized Critical Phenomenon?.- 8 Mass Extinctions and Punctuated Equilibria in a Simple Model of Evolution.- 9 Theory of the Punctuated Equilibrium Model.- 10 The Brain.- 11 On Economics and Traffic Jams.Table of Contents1 Complexity and Criticality.- 2 The Discovery of Self-Organized Criticality.- 3 The Sandpile Paradigm.- 4 Real Sandpiles and Landscape Formation.- 5 Earthquakes, Starquakes, and Solar Flares.- 6 The “Game of Life”: Complexity Is Criticality.- 7 Is Life a Self-Organized Critical Phenomenon?.- 8 Mass Extinctions and Punctuated Equilibria in a Simple Model of Evolution.- 9 Theory of the Punctuated Equilibrium Model.- 10 The Brain.- 11 On Economics and Traffic Jams.

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Book SynopsisBioengineering is a broad-based engineering discipline that applies engineering principles and design to challenges in human health and medicine, dealing with bio-molecular and molecular processes, product design, sustainability and analysis of biological systems. Applications that benefit from bioengineering include medical devices, diagnostic equipment and biocompatible materials, amongst others. Computer Modeling in Bioengineering offers a comprehensive reference for a large number of bioengineering topics, presenting important computer modeling problems and solutions for research and medical practice. Starting with basic theory and fundamentals, the book progresses to more advanced methods and applications, allowing the reader to become familiar with different topics to the desired extent. It includes unique and original topics alongside classical computational modeling methods, and each application is structured to explain the physiological background, phenomena that arTable of ContentsContributors. Preface. Part I: Theoretical Background of Computational Methods. 1. Notation - Matrices and Tensors. 2. Fundamentals of Continuum Mechanics. 3. Heat Transfer, Diffusion, Fluid Mechanics, and Fluid Flow through Porous Deformable Media. Part II: Fundamentals of Computational Methods. 4. Isoparametric Formulation of Finite Elements. 5. Dynamic Finite Element Analysis. 6. Introduction to Nonlinear Finite Element Analysis. 7. Finite Element Modeling of Field Problems. 8. Discrete Particle Methods for Modeling of Solids and Fluids. Part III: Computational Methods in Bioengineering. 9. Introduction to Bioengineering. 10. Bone Modeling. 11. Biological Soft Tissue. 12. Skeletal Muscles. 13. Blood Flow and Blood Vessels. 14. Modeling Mass Transport and Thrombosis in Arteries. 15. Cartilage Mechanics. 16. Cell Mechanics. 17. Extracellular Mechanotransduction: Modeling Ligand Concentration Dynamics in the Lateral Intercellular Space of Compressed Airway Epithelial Cells. 18. Spider Silk: Modeling Solvent Removal during Synthetic and Nephila clavipes Fiber Spinning. 19. Modeling in Cancer Nanotechnology. Index.

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Book SynopsisThis new addition to the popular Essentials series provides a broad, general introduction to the topic of simulation within clinical education.Table of ContentsContributors vii Foreword x Glossary and abbreviations xii Features contained within your textbook xvi 1 Essential simulation in clinical education 1 Judy McKimm and Kirsty Forrest 2 Medical simulation: the journey so far 11 Aidan Byrne 3 The evidence: what works, why and how? 26 Doris Østergaard and Jacob Rosenberg 4 Pedagogy in simulation-based training in healthcare 43 Peter Dieckmann and Charlotte Ringsted 5 Assessment 59 Thomas Gale and Martin Roberts 6 The roles of faculty and simulated patients in simulation 87 Bryn Baxendale, Frank Coffey and Andrew Buttery 7 Surgical technical skills 111 Rajesh Aggarwal and Amit Mishra 8 The non-technical skills 131 Nikki Maran, Simon Edgar and Alistair May 9 Teamwork 146 Jennifer M. Weller 10 Designing effective simulation activities 168 Joanne Barrott, Ann B. Sunderland, Jane P. Nicklin and Michelle McKenzie Smith 11 Distributed simulation 196 Jessica Janice Tang, Jimmy Kyaw Tun, Roger L Kneebone and Fernando Bello 12 Providing effective simulation activities 213 Walter J. Eppich, Lanty O’Connor and Mark Adler 13 Simulation in practice 235 Jean Ker Simulation for learning cardiology 236 Ross J. Scalese Assessing leadership skills in medical undergraduates 238 Helen O’Sullivan, Arpan Guha and Michael Moneypenny Simulation for interprofessional learning 240 Stuart Marshall Use of in situ simulations to identify barriers to patient care for multidisciplinary teams in developing countries 242 Nicole Shilkofski Clinical skills assessment for paediatric postgraduate physicians 244 Joseph O. Lopreiato The challenge of doctors in difficulty: using simulated healthcare contexts to develop a national assessment programme 246 Kevin Stirling, Jean Ker and Fiona Anderson Simulation for remote and rural practice 250 Jerry Morse, Jean Ker and Sarah Race The use of incognito standardized patients in general practice 252 Jan-Joost Rethans Integration of simulation-based training for the trauma team in a university hospital 253 Anne-Mette Helsø and Doris Østergaard Conclusion 254 14 The future for simulation 258 Horizon scanning: the impact of technological change 259 Iliana Harrysson, Rajesh Aggarwal and Ara Darzi Guiding the role of simulation through paradigm shifts in medical education 267 Viren N. Naik and Stanley J. Hamstra The future of training in simulation 273 Ronnie Glavin Index 283

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Book SynopsisThis second volume of the series ''Reviews in Computational Chemistry'' explores new applications, new methodologies, and new perspectives. The topics covered include conformational analysis, protein folding, force field parameterizations, hydrogen bonding, charge distributions, electrostatic potentials, electronic spectroscopy, molecular property correlations, and the computational chemistry literature. Methodologies described include conformational search strategies, distance geometry, molecular mechanics, molecular dynamics, ab initio and semiempirical molecular orbital calculations, and quantitative structure-activity relationships (QSAR) using topological and electronic descriptors. A compendium of molecular modeling software will help users select the computational tools they need. Each chapter in ''Reviews in Computational Chemistry'' serves as a brief tutorial for organic, physical, pharmaceutical, and biological chemists new to the field. Practitioners will be intereTable of ContentsA Survey of Methods for Searching thr Conformational Space of Small and Medium-Sized Molecules (A. Leach). Simplified Models for Understanding and Predicting Protein Structure (J. Troyer and F. Cohen). Moleculaar Mechanics: The Art and Science of Parameterization (J. Bowen and N. Allinger). New Approaches to Empirical Force Fields (U. Dinur and A. Hagler). Calculating the Properties of Hydrogen Bonds by ab Initio Methods (S. Scheiner). Net Atomic Charge and Multiple Models for the ab Initio Molecular Electric Potential (D. Williams). Molecular Electrostatic Potentials and Chemical Reactivity (P. Politzer and J. Murray). Semiempirical Molecular Orbital Methods (M. Zerner). The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure-Property Modeling (L. Hall and L. Kier). The Electron-Topological Approach to the QSAR Problem (I. Bersuker and A. Dimoglo). The Computational Chemistry Literature (D. Boyd). Appendix: Compendium of Software for Molecular Modeling (D. Boyd). Author Index. Subject Index.

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Book SynopsisTHIS VOLUME, LIKE THOSE PRIOR TO IT, FEATURES CHAPTERS BY EXPERTS IN VARIOUS FIELDS OF COMPUTATIONAL CHEMISTRY. TOPICS COVERED IN VOLUME 20 INCLUDE VALENCE THEORY, ITS HISTORY, FUNDAMENTALS, AND APPLICATIONS; MODELING OF SPIN-FORBIDDEN REACTIONS; CALCULATION OF THE ELECTRONIC SPECTRA OF LARGE MOLECULES; SIMULATING CHEMICAL WAVES AND PATTERNS; FUZZY SOFT-COMPUTING METHODS AND THEIR APPLICATIONS IN CHEMISTRY; AND DEVELOPMENT OF COMPUTATIONAL MODELS FOR ENZYMES, TRANSPORTERS, CHANNELS, AND RECEPTORS RELEVANT TO ADME/TOX. FROM REVIEWS OF THE SERIES Reviews in Computational Chemistry remains the most valuable reference to methods and techniques in computational chemistry. -JOURNAL OF MOLECULAR GRAPHICS AND MODELING One cannot generally do better than to try to find an appropriate article in the highly successful Reviews in Computational Chemistry. The basic philosophy of the editors seems to be to help the authors produce chapters that are complete, accurate, clear, Trade Review“The editors have done an excellent job and the book is a must on every book shelf of computational chemistry literature.” (ChemPhysChem, 2005; Vol. 6; 7) "…this volume continues the traditions and standards of this series as a prime resource for anyone with an interest in theoretical and computational chemistry…a welcome addition to any library collection." (Journal of the American Chemical Society, March 9, 2005)Table of Contents1. Valence Bond Theory, Its History, Fundamentals, and Applications: A Primer (Sason Shaik and Philippe C. Hiberty). Introduction. A Story of Valence Bond Theory, Its Rivalry with Molecular Orbital Theory, Its Demise, and Eventual Resurgence. Roots of VB Theory. Origins of MO Theory and the Roots of VB–MO Rivalry. The ‘‘Dance’’ of Two Theories: One Is Up, the Other Is Down. Are the Failures of VB Theory Real Ones? Modern VB Theory: VB Theory Is Coming of Age. Basic VB Theory. Writing and Representing VB Wave Functions. The Relationship between MO and VB Wave Functions. Formalism Using the Exact Hamiltonian. Qualitative VB Theory. Some Simple Formulas for Elementary Interactions. Insights of Qualitative VB Theory. Are the ‘‘Failures’’ of VB Theory Real? Can VB Theory Bring New Insight into Chemical Bonding? VB Diagrams for Chemical Reactivity. VBSCD: A General Model for Electronic Delocalization and Its Comparison with the Pseudo-Jahn–Teller Model. What Is the Driving Force, s or p, Responsible for the D6h Geometry of Benzene? VBSCD: The Twin-State Concept and Its Link to Photochemical Reactivity. The Spin Hamiltonian VB Theory. Theory. Applications. Ab Initio VB Methods. Orbital-Optimized Single-Configuration Methods. Orbital-Optimized Multiconfiguration VB Methods. Prospective. Appendix. A.1 Expansion of MO Determinants in Terms of AO Determinants. A.2 Guidelines for VB Mixing. A.3 Computing Mono-Determinantal VB Wave Functions with Standard Ab Initio Programs. Acknowledgments. References. 2. Modeling of Spin-Forbidden Reactions (Nikita Matsunaga and Shiro Koseki). Overview of Reactions Requiring Two States. Spin-Forbidden Reaction, Intersystem Crossing. Spin–Orbit Coupling as a Mechanism for Spin-Forbidden Reaction. General Considerations. Atomic Spin–Orbit Coupling. Molecular Spin–Orbit Coupling. Crossing Probability. Fermi Golden Rule. Landau–Zener Semiclassical Approximation. Methodologies for Obtaining Spin–Orbit Matrix Elements. Electron Spin in Nonrelativistic Quantum Mechanics. Klein–Gordon Equation. Dirac Equation. Foldy–Wouthuysen Transformation. Breit–Pauli Hamiltonian. Zeff Method. Effective Core Potential-Based Method. Model Core Potential-Based Method. Douglas–Kroll Transformation. Potential Energy Surfaces. Minimum Energy Crossing-Point Location. Available Programs for Modeling Spin-Forbidden Reactions. Applications to Spin-Forbidden Reactions. Diatomic Molecules. Polyatomic Molecules. Phenyl Cation. Norborene. Conjugated Polymers. CH(2II) + N2 -- HCN + N(4S). Molecular Properties. Dynamical Aspects. Other Reactions. Biological Chemistry. Concluding Remarks. Acknowledgments. References. 3. Calculation of the Electronic Spectra of Large Molecules (Stefan Grimme). Introduction. Types of Electronic Spectra. Types of Excited States. Theory. Excitation Energies. Transition Moments. Vibrational Structure. Quantum Chemical Methods. Case Studies. Vertical Absorption Spectra. Circular Dichroism. Vibrational Structure. Summary and Outlook. Acknowledgments. References. 4. Simulating Chemical Waves and Patterns (Raymond Kapral). Introduction. Reaction–Diffusion Systems. Cellular Automata. Coupled Map Lattices. Mesoscopic Models. Summary. References. 5. Fuzzy Soft-Computing Methods and Their Applicationsin Chemistry (Costel Saˆrbu and Horia F. Pop). Introduction. Methods for Exploratory Data Analysis. Visualization of High-Dimensional Data. Clustering Methods. Projection Methods. Linear Projection Methods. Nonlinear Projection Methods. Artificial Neural Networks. Perceptron. Multilayer Nets: Backpropagation. Associative Memories: Hopfield Net. Self-Organizing Map. Properties. Mathematical Characterization. Relation between SOM and MDS. Multiple Views of the SOM. Other Architectures. Evolutionary Algorithms. Genetic Algorithms. Canonical GA. Evolution Strategies. Evolutionary Programming. Fuzzy Sets and Fuzzy Logic. Fuzzy Sets. Fuzzy Logic. Fuzzy Clustering. Fuzzy Regression. Fuzzy Principal Component Analysis (FPCA). Fuzzy PCA (Optimizing the First Component). Fuzzy PCA (Nonorthogonal Procedure). Fuzzy PCA (Orthogonal). Fuzzy Expert Systems (Fuzzy Controllers). Hybrid Systems. Combinations of Fuzzy Systems and Neutral Networks. Fuzzy Genetic Algorithms. Neuro-Genetic Systems. Fuzzy Characterization and Classification of the Chemical Elements and Their Properties. Hierarchical Fuzzy Classification of Chemical Elements Based on Ten Physical Properties. Hierarchical Fuzzy Classification of Chemical Elements Based on Ten Physical, Chemical, and Structural Properties. Fuzzy Hierarchical Cross-Classification of Chemical Elements Based on Ten Physical Properties. Fuzzy Hierarchical Characteristics Clustering. Fuzzy Horizontal Characteristics Clustering. Characterization and Classification of Lanthanides and Their Properties by PCA and FPCA. Properties of Lanthanides Considered in This Study. Classical PCA. Fuzzy PCA. Miscellaneous Applications of FPCA. Fuzzy Modeling of Environmental, SAR and QSAR Data. Spectral Library Search and Spectra Interpretation. Fuzzy Calibration of Analytical Methods and Fuzzy Robust Estimation of Location and Spread. Application of Fuzzy Neural Networks Systems in Chemistry. Applications of Fuzzy Sets Theory and Fuzzy Logic in Theoretical Chemistry. Conclusions and Remarks. References. 6. Development of Computational Models for Enzymes, Transporters, Channels, and Receptors Relevant to ADME/Tox (Sean Ekins and Peter W. Swaan). Introduction. ADME/Tox Modeling: An Expansive Vision. The Concerted Actions of Transport and Metabolism. Metabolism. Transporters. Approaches to Modeling Enzymes, Transporters, Channels, and Receptors. Classical QSAR. Pharmacophore Models. Homology Modeling. Transporter Modeling. Applications of Transporters. The Human Small Peptide Transporter, hPEPT1. The Apical Sodium-Dependent Bile Acid Transporter. P-Glycoprotein. Vitamin Transporters. Organic Cation Transporter. Organic AnionTransporters. Nucleoside Transporter. Breast Cancer Resistance Protein. Sodium Taurocholate Transporting Polypeptide. Enzymes. Cytochrome P450. Epoxide Hydrolase. Monoamine Oxidase. Flavin-Containing Monooxygenase. Sulfotransferases. Glucuronosyltransferases. Glutathione S-transferases. Channels. Human Ether-a-gogo Related Gene. Receptors. Pregnane X-Receptor. Constitutive Androstane Receptor. Future Developments. Acknowledgments. Abbreviations. References. Author Index. Subject Index.

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Book SynopsisThis Element introduces the philosophical literature on models, with an emphasis on normative considerations relevant to models for decision-making. It establishes the need for strategies to manage value judgments in modelling, including the potential for public participation in the process.Table of Contents1. Introduction; 2. Adequacy for purpose; 3. Inadequacy for purpose; 4. Models and values; References.

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Book SynopsisHealthcare simulation is the modern way to educate healthcare providers to achieve high performance and to improve patient safety. It encompasses mannikin based training for teamwork and nontechnical skills, task trainers for procedural skills, simulated participants for communication skills, and virtual/augmented reality simulation. Based on an award-winning postgraduate course, this text provides the background knowledge required to: run a healthcare simulation centre; use simulation for training and education; and support simulation-based quality improvement and research activities.*Presents a focused and highly practical approach to course material *Offers a detailed guide for anyone who uses healthcare simulation for education, quality improvement, or research *Shows a practical focus for teaching, quality improvement, and researchTable of ContentsIntroduction, History, and Key Concepts. Learning Theories. Instructional Design. Scenario Writing. Physical Safety in Simulation. Psychological Safety and Prebriefing. Debriefing. Assessment. Running a Simulation Facility. Using Simulation for Research. The Use of Simulation for Quality Improvement. Team Scenario Development Template. Standardised Participant Case Development Template. Index.

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Book SynopsisThis book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies.Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book Table of Contents1. Understanding the Oil & Gas Sector and its Processes: Upstream and Downstream 2. IT technologies Impacting the Petroleum Sector 3. Data Handling Techniques in Petroleum Sector 4. Predictive Modelling Concepts in Petroleum Sector 5. Supply Chain Management in Oil and Gas Business 6. Prescriptive Analytics and its Application in Oil and Gas Business 7. Future Challenges in Petroleum Sector 8. Oil & Gas Industry in context of Industry 4.0

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Book SynopsisThis accessible, non-technical book reveals how, starting in the 1800s, scientists have used mathematical models and computer simulations to demonstrate that climate change is real and accelerating. Readers will learn where the key scientific ideas came from, how they were tested, and what future these models forecast for our planet.Trade Review'Numerical climate models are a critical tool for assessing the threat posed by climate change and investigating the options available to mitigate that threat. Yet, an understanding of these models-how they work, what they tell us, and how their tested and validated-has remained evasive for all but the most math and physics-literate. In Computing the Climate, computer scientist Steve Easterbook takes us on a journey through the world of climate modeling, making the science accessible to lay readers, and showing us why we should trust the models and heed their warnings, before it's too late.' Michael Mann, University of Pennsylvania, author of The New Climate War'Computing the Climate provides an impressively detailed history of how climate models evolved from simple equations calculated by hand to giant programs running on supercomputers. Avoiding jargon, this book explains to a general audience how the laws of physics and the principles of software engineering are combined to build climate models.' R. Saravanan, Texas A&M University, author of The Climate Demon'Computing the Climate takes a unique look at the history of computational modeling the Earth's climate system, the processes represented in these models, their evaluation, and how they are being used to project the potential changes in the future of our climate. When combined with more detailed analyses of concurrent issues being addressed in these models such as cloud and convection processes, this would be an excellent book for a university course on climate modeling.' Don Wuebbles, University of Illinois'I teach several courses in climate change and climate modeling for general and specialized audiences, and I am so excited to incorporate this new text by Easterbrook into those classes. While climate models are derived from first physical principles, climate models are developed by people and communities. I think that this book's approach of the tracing of revolutionary ideas and herculean efforts by generations of scientists to develop deep understanding and predictive capability for weather and climate does the topic justice. The logical progression of concepts, chapter by chapter is excellent as is the extensive, but not obtrusive, referencing throughout. Many difficult concepts, including: the greenhouse effect, chaos and predicability, computational instability, parallel computing, the difference between predictions and projections, are explained very well and accessibly. This book will be compelling reading both for students and people who simply want to know more.' Matthew Huber, Purdue University'Easterbrook's non-technical survey of climate modeling uniquely expands the climate change genre. Students will benefit from its broad scope and equation-free conceptual explanations, and climate modelers will appreciate its historical approach linking nineteenth century experiments and ideas to twenty-first century breakthroughs.' Baylor Fox-Kemper, Brown University'This is a very readable personal account of climate model development throughout history. It focuses on several individuals and modeling groups/countries. It often refers to 'you' and 'we'. I learned a lot and enjoyed the book, and I recommend it to anyone faced with making decisions involving the future climate.' Kevin Trenberth, University of Auckland, author of The Changing Flow of Energy Through the Climate System'This engaging, beautifully written book brings alive the scientists who created climate models, how they did it, and what the models can (and cannot) tell us - all in straightforward, nontechnical language and enlightening illustrations. If you want to understand how modern climate science works, start here.' Paul N. Edwards, Stanford University, author of A Vast Machine: Computer Models, Climate Data, and the Politics of Global WarmingTable of Contents1. Introduction; 2. The world's first climate model; 3. The forecast factory; 4. Taming chaos; 5. The heart of the machine; 6. The well-equipped physics lab; 7. Plug and play; 8. Sound science; 9. Choosing a future; References; Index.

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Book SynopsisA comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques An Introduction to Statistical Computing: Fully covers the traditional topics of statistical computing. Discusses both practical aspects and the theoretical background. Includes a chapter about conTrade Review"The exposition is quite clear, intuitive, and is a useful complement to more abstract treatises on stochastic calculus and simulation." (MathSciNet, 1 December 2015) “Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course.” (Zentralblatt MATH, 1 March 2014) “Statistical computing in its broadest sense is an ever-growing field far too extensive to be covered in a single text. The current book has a far more manageable scope, notwithstanding its title. Its focus is on the use of Monte Carlo methods to simulate random systems and explore statistical models.” (Mathematical Association of America, 1 January 2014) Table of ContentsList of algorithms ix Preface xi Nomenclature xiii 1 Random number generation 1 1.1 Pseudo random number generators 2 1.1.1 The linear congruential generator 2 1.1.2 Quality of pseudo random number generators 4 1.1.3 Pseudo random number generators in practice 8 1.2 Discrete distributions 8 1.3 The inverse transform method 11 1.4 Rejection sampling 15 1.4.1 Basic rejection sampling 15 1.4.2 Envelope rejection sampling 18 1.4.3 Conditional distributions 22 1.4.4 Geometric interpretation 26 1.5 Transformation of random variables 30 1.6 Special-purpose methods 36 1.7 Summary and further reading 36 Exercises 37 2 Simulating statistical models 41 2.1 Multivariate normal distributions 41 2.2 Hierarchical models 45 2.3 Markov chains 50 2.3.1 Discrete state space 51 2.3.2 Continuous state space 56 2.4 Poisson processes 58 2.5 Summary and further reading 67 Exercises 67 3 Monte Carlo methods 69 3.1 Studying models via simulation 69 3.2 Monte Carlo estimates 74 3.2.1 Computing Monte Carlo estimates 75 3.2.2 Monte Carlo error 76 3.2.3 Choice of sample size 80 3.2.4 Refined error bounds 82 3.3 Variance reduction methods 84 3.3.1 Importance sampling 84 3.3.2 Antithetic variables 88 3.3.3 Control variates 93 3.4 Applications to statistical inference 96 3.4.1 Point estimators 97 3.4.2 Confidence intervals 100 3.4.3 Hypothesis tests 103 3.5 Summary and further reading 106 Exercises 106 4 Markov Chain Monte Carlo methods 109 4.1 The Metropolis–Hastings method 110 4.1.1 Continuous state space 110 4.1.2 Discrete state space 113 4.1.3 Random walk Metropolis sampling 116 4.1.4 The independence sampler 119 4.1.5 Metropolis–Hastings with different move types 120 4.2 Convergence of Markov Chain Monte Carlo methods 125 4.2.1 Theoretical results 125 4.2.2 Practical considerations 129 4.3 Applications to Bayesian inference 137 4.4 The Gibbs sampler 141 4.4.1 Description of the method 141 4.4.2 Application to parameter estimation 146 4.4.3 Applications to image processing 151 4.5 Reversible Jump Markov Chain Monte Carlo 158 4.5.1 Description of the method 160 4.5.2 Bayesian inference for mixture distributions 171 4.6 Summary and further reading 178 4.6 Exercises 178 5 Beyond Monte Carlo 181 5.1 Approximate Bayesian Computation 181 5.1.1 Basic Approximate Bayesian Computation 182 5.1.2 Approximate Bayesian Computation with regression 188 5.2 Resampling methods 192 5.2.1 Bootstrap estimates 192 5.2.2 Applications to statistical inference 197 5.3 Summary and further reading 209 Exercises 209 6 Continuous-time models 213 6.1 Time discretisation 213 6.2 Brownian motion 214 6.2.1 Properties 216 6.2.2 Direct simulation 217 6.2.3 Interpolation and Brownian bridges 218 6.3 Geometric Brownian motion 221 6.4 Stochastic differential equations 224 6.4.1 Introduction 224 6.4.2 Stochastic analysis 226 6.4.3 Discretisation schemes 231 6.4.4 Discretisation error 236 6.5 Monte Carlo estimates 243 6.5.1 Basic Monte Carlo 243 6.5.2 Variance reduction methods 247 6.5.3 Multilevel Monte Carlo estimates 250 6.6 Application to option pricing 255 6.7 Summary and further reading 259 Exercises 260 Appendix A Probability reminders 263 A.1 Events and probability 263 A.2 Conditional probability 266 A.3 Expectation 268 A.4 Limit theorems 269 A.5 Further reading 270 Appendix B Programming in R 271 B.1 General advice 271 B.2 R as a Calculator 272 B.2.1 Mathematical operations 273 B.2.2 Variables 273 B.2.3 Data types 275 B.3 Programming principles 282 B.3.1 Don’t repeat yourself! 283 B.3.2 Divide and conquer! 286 B.3.3 Test your code! 290 B.4 Random number generation 292 B.5 Summary and further reading 294 Exercises 294 Appendix C Answers to the exercises 299 C.1 Answers for Chapter 1 299 C.2 Answers for Chapter 2 315 C.3 Answers for Chapter 3 319 C.4 Answers for Chapter 4 328 C.5 Answers for Chapter 5 342 C.6 Answers for Chapter 6 350 C.7 Answers for Appendix B 366 References 375 Index 379

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Book SynopsisComputer modeling and simulation (M&S) allows engineers to study and analyze complex systems. Discrete-event system (DES)-M&S is used in modern management, industrial engineering, computer science, and the military. As computer speeds and memory capacity increase, so DES-M&S tools become more powerful and more widely used in solving real-life problems. Based on over 20 years of evolution within a classroom environment, as well as on decades-long experience in developing simulation-based solutions for high-tech industries, Modeling and Simulation of Discrete-Event Systems is the only book on DES-M&S in which all the major DES modeling formalisms activity-based, process-oriented, state-based, and event-based are covered in a unified manner: A well-defined procedure for building a formal model in the form of event graph, ACD, or state graph Diverse types of modeling templates and examples that can be used as building blocks for a complex, real-lifeTable of ContentsPREFACE xvii ABBREVIATIONS xix PART I BASICS OF SYSTEM MODELING AND SIMULATION 1 1. Overview of Computer Simulation 3 1.1 Introduction 3 1.2 What Is a System? 4 1.3 What Is Computer Simulation? 6 1.4 What Is Discrete-Event Simulation? 9 1.5 What Is Continuous Simulation? 11 1.6 What Is Monte Carlo Simulation? 12 1.7 What Are Simulation Experimentation and Optimization? 15 1.8 Review Questions 16 2. Basics of Discrete-Event System Modeling and Simulation 17 2.1 Introduction 17 2.2 How Is a Discrete-Event Simulation Carried Out? 17 2.3 Framework of Discrete-Event System Modeling 23 2.4 Illustrative Examples of DES Modeling and Simulation 32 2.5 Application Frameworks for Discrete-Event System Modeling and Simulation 38 2.6 What to Cover in a Simulation Class 40 2.7 Review Questions 42 PART II FUNDAMENTALS OF DISCRETE-EVENT SYSTEM MODELING AND SIMULATION 43 3. Input Modeling for Simulation 45 3.1 Introduction 45 3.2 Empirical Input Modeling 46 3.3 Overview of Theoretical Distribution Fitting 48 3.4 Theoretical Modeling of Arrival Processes 50 3.5 Theoretical Modeling of Service Times 53 3.6 Input Modeling for Special Applications 57 3.7 Review Questions 59 4. Introduction to Event-Based Modeling and Simulation 69 4.1 Introduction 69 4.2 Modeling and Simulation of a Single Server System 70 4.3 Execution Rules and Specifications of Event Graph Models 72 4.4 Event Graph Modeling Templates 75 4.5 Event Graph Modeling Examples 82 4.6 Execution of Event Graph Models with SIGMA 91 4.7 Developing Your Own Event Graph Simulator 99 4.8 Review Questions 106 5. Parameterized Event Graph Modeling and Simulation 107 5.1 Introduction 107 5.2 Parameterized Event Graph Examples 108 5.3 Execution Rules and Specifications of the Parameterized Event Graph 110 5.4 Parameterized Event Graph Modeling of Tandem Lines 112 5.5 Parameterized Event Graph Modeling of Job Shops 115 5.6 Execution of Parameterized Event Graph Models Using SIGMA 122 5.7 Developing Your Own Parameterized Event Graph Simulator 137 5.8 Review Questions 142 6. Introduction to Activity-Based Modeling and Simulation 143 6.1 Introduction 143 6.2 Definitions and Specifications of an Activity Cycle Diagram 145 6.3 Activity Cycle Diagram Modeling Templates 150 6.4 Activity-Based Modeling Examples 156 6.5 Parameterized Activity Cycle Diagram and Its Application 163 6.6 Execution of Activity Cycle Diagram Models with a Formal Simulator ACE® 171 6.7 Review Questions 183 7. Simulation of ACD Models Using Arena 184 7.1 Introduction 184 7.2 Arena Basics 185 7.3 Activity Cycle Diagram-to-Arena Conversion Templates 197 7.4 Activity Cycle Diagram-Based Arena Modeling Examples 209 7.5 Review Questions 223 8. Output Analysis and Optimization 224 8.1 Introduction 224 8.2 Framework of Simulation Output Analyses 225 8.3 Qualitative Output Analyses 228 8.4 Statistical Output Analyses 230 8.5 Linear Regression Modeling for Output Analyses 234 8.6 Response Surface Methodology for Simulation Optimization 241 8.7 Review Questions 247 PART III ADVANCES IN DISCRETE-EVENT SYSTEM MODELING AND SIMULATION 253 9. State-Based Modeling and Simulation 255 9.1 Introduction 255 9.2 Finite State Machine 256 9.3 Timed Automata 261 9.4 State Graphs 267 9.5 System Modeling with State Graph 271 9.6 Simulation of Composite State Graph Models 283 10. Advanced Topics in Activity-Based Modeling and Simulation 299 10.1 Introduction 299 10.2 Developing Your Own Activity Cycle Diagram Simulators 300 10.3 Modeling with Canceling Arc 310 10.4 Cycle Time Analysis of Work Cells via an Activity Cycle Diagram 313 10.5 Activity Cycle Diagram Modeling of a Flexible Manufacturing System 322 10.6 Formal Model Conversion 329 11. Advanced Event Graph Modeling for Integrated Fab Simulation 338 11.1 Introduction 338 11.2 Flat Panel Display Fabrication System 339 11.3 Production Simulation of a Flat Panel Display Fab 343 11.4 Integrated Simulation of a Flat Panel Display Fab 350 11.5 Automated Material Handling Systems-Embedded Integrated Simulation of Flat Panel Display Fab 362 12. Concepts and Applications of Parallel Simulation 371 12.1 Introduction 371 12.2 Parallel Simulation of Workflow Management System 372 12.3 Overview of High-Level Architecture/Run-Time Infrastructure 378 12.4 Implementation of a Parallel Simulation with High-Level Architecture/Run-Time Infrastructure 383 REFERENCES 395 INDEX 400

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Book SynopsisAn introductory approach to the subject of large strains and large displacements in finite elements. Large Strain Finite Element Method: A Practical Course, takes an introductory approach to the subject of large strains and large displacements in finite elements and starts from the basic concepts of finite strain deformability, including finite rotations and finite displacements. The necessary elements of vector analysis and tensorial calculus on the lines of modern understanding of the concept of tensor will also be introduced. This book explains how tensors and vectors can be described using matrices and also introduces different stress and strain tensors. Building on these, step by step finite element techniques for both hyper and hypo-elastic approach will be considered. Material models including isotropic, unisotropic, plastic and viscoplastic materials will be independently discussed to facilitate clarity and ease of learning. Elements of traTable of ContentsPreface xiii Acknowledgements xv PART ONE FUNDAMENTALS 1 1 Introduction 3 1.1 Assumption of Small Displacements 3 1.2 Assumption of Small Strains 6 1.3 Geometric Nonlinearity 6 1.4 Stretches 8 1.5 Some Examples of Large Displacement Large Strain Finite Element Formulation 8 1.6 The Scope and Layout of the Book 13 1.7 Summary 13 2 Matrices 15 2.1 Matrices in General 15 2.2 Matrix Algebra 16 2.3 Special Types of Matrices 21 2.4 Determinant of a Square Matrix 22 2.5 Quadratic Form 24 2.6 Eigenvalues and Eigenvectors 24 2.7 Positive Definite Matrix 26 2.8 Gaussian Elimination 26 2.9 Inverse of a Square Matrix 28 2.10 Column Matrices 30 2.11 Summary 32 3 Some Explicit and Iterative Solvers 35 3.1 The Central Difference Solver 35 3.2 Generalized Direction Methods 43 3.3 The Method of Conjugate Directions 50 3.4 Summary 63 4 Numerical Integration 65 4.1 Newton-Cotes Numerical Integration 65 4.2 Gaussian Numerical Integration 67 4.3 Gaussian Integration in 2D 70 4.4 Gaussian Integration in 3D 71 4.5 Summary 72 5 Work of Internal Forces on Virtual Displacements 75 5.1 The Principle of Virtual Work 75 5.2 Summary 78 PART TWO PHYSICAL QUANTITIES 79 6 Scalars 81 6.1 Scalars in General 81 6.2 Scalar Functions 81 6.3 Scalar Graphs 82 6.4 Empirical Formulas 82 6.5 Fonts 83 6.6 Units 83 6.7 Base and Derived Scalar Variables 85 6.8 Summary 85 7 Vectors in 2D 87 7.1 Vectors in General 87 7.2 Vector Notation 91 7.3 Matrix Representation of Vectors 91 7.4 Scalar Product 92 7.5 General Vector Base in 2D 93 7.6 Dual Base 94 7.7 Changing Vector Base 95 7.8 Self-duality of the Orthonormal Base 97 7.9 Combining Bases 98 7.10 Examples 104 7.11 Summary 108 8 Vectors in 3D 109 8.1 Vectors in 3D 109 8.2 Vector Bases 111 8.3 Summary 114 9 Vectors in n-Dimensional Space 117 9.1 Extension from 3D to 4-Dimensional Space 117 9.2 The Dual Base in 4D 118 9.3 Changing the Base in 4D 120 9.4 Generalization to n-Dimensional Space 121 9.5 Changing the Base in n-Dimensional Space 124 9.6 Summary 127 10 First Order Tensors 129 10.1 The Slope Tensor 129 10.2 First Order Tensors in 2D 131 10.3 Using First Order Tensors 132 10.4 Using Different Vector Bases in 2D 134 10.5 Differential of a 2D Scalar Field as the First Order Tensor 137 10.6 First Order Tensors in 3D 141 10.7 Changing the Vector Base in 3D 142 10.8 First Order Tensor in 4D 143 10.9 First Order Tensor in n-Dimensions 147 10.10 Differential of a 3D Scalar Field as the First Order Tensor 149 10.11 Scalar Field in n-Dimensional Space 152 10.12 Summary 153 11 Second Order Tensors in 2D 155 11.1 Stress Tensor in 2D 155 11.2 Second Order Tensor in 2D 158 11.3 Physical Meaning of Tensor Matrix in 2D 159 11.4 Changing the Base 161 11.5 Using Two Different Bases in 2D 163 11.6 Some Special Cases of Stress Tensor Matrices in 2D 167 11.7 The First Piola-Kirchhoff Stress Tensor Matrix 168 11.8 The Second Piola-Kirchhoff Stress Tensor Matrix 169 11.9 Summary 174 12 Second Order Tensors in 3D 175 12.1 Stress Tensor in 3D 175 12.2 General Base for Surfaces 179 12.3 General Base for Forces 182 12.4 General Base for Forces and Surfaces 184 12.5 The Cauchy Stress Tensor Matrix in 3D 186 12.6 The First Piola-Kirchhoff Stress Tensor Matrix in 3D 186 12.7 The Second Piola-Kirchhoff Stress Tensor Matrix in 3D 188 12.8 Summary 189 13 Second Order Tensors in nD 191 13.1 Second Order Tensor in n-Dimensions 191 13.2 Summary 200 PART THREE DEFORMABILITY AND MATERIAL MODELING 201 14 Kinematics of Deformation in 1D 203 14.1 Geometric Nonlinearity in General 203 14.2 Stretch 205 14.3 Material Element and Continuum Assumption 208 14.4 Strain 209 14.5 Stress 213 14.6 Summary 214 15 Kinematics of Deformation in 2D 217 15.1 Isotropic Solids 217 15.2 Homogeneous Solids 217 15.3 Homogeneous and Isotropic Solids 217 15.4 Nonhomogeneous and Anisotropic Solids 218 15.5 Material Element Deformation 221 15.6 Cauchy Stress Matrix for the Solid Element 225 15.7 Coordinate Systems in 2D 227 15.8 The Solid- and the Material-Embedded Vector Bases 228 15.9 Kinematics of 2D Deformation 229 15.10 2D Equilibrium Using the Virtual Work of Internal Forces 231 15.11 Examples 235 15.12 Summary 238 16 Kinematics of Deformation in 3D 241 16.1 The Cartesian Coordinate System in 3D 241 16.2 The Solid-Embedded Coordinate System 241 16.3 The Global and the Solid-Embedded Vector Bases 243 16.4 Deformation of the Solid 244 16.5 Generalized Material Element 246 16.6 Kinematic of Deformation in 3D 247 16.7 The Virtual Work of Internal Forces 249 16.8 Summary 255 17 The Unified Constitutive Approach in 2D 257 17.1 Introduction 257 17.2 Material Axes 259 17.3 Micromechanical Aspects and Homogenization 260 17.4 Generalized Homogenization 263 17.5 The Material Package 264 17.6 Hyper-Elastic Constitutive Law 265 17.7 Hypo-Elastic Constitutive Law 266 17.8 A Unified Framework for Developing Anisotropic Material Models in 2D 267 17.9 Generalized Hyper-Elastic Material 267 17.10 Converting the Munjiza Stress Matrix to the Cauchy Stress Matrix 274 17.11 Developing Constitutive Laws 279 17.12 Generalized Hypo-Elastic Material 288 17.13 Unified Constitutive Approach for Strain Rate and Viscosity 292 17.14 Summary 293 18 The Unified Constitutive Approach in 3D 295 18.1 Material Package Framework 295 18.2 Generalized Hyper-Elastic Material 295 18.3 Generalized Hypo-Elastic Material 299 18.4 Developing Material Models 302 18.5 Calculation of the Cauchy Stress Tensor Matrix 302 18.6 Summary 312 PART FOUR THE FINITE ELEMENT METHOD IN 2D 315 19 2D Finite Element: Deformation Kinematics Using the Homogeneous Deformation Triangle 317 19.1 The Finite Element Mesh 317 19.2 The Homogeneous Deformation Finite Element 317 19.3 Summary 326 20 2D Finite Element: Deformation Kinematics Using Iso-Parametric Finite Elements 327 20.1 The Finite Element Library 327 20.2 The Shape Functions 327 20.3 Nodal Positions 330 20.4 Positions of Material Points inside a Single Finite Element 331 20.5 The Solid-Embedded Vector Base 332 20.6 The Material-Embedded Vector Base 334 20.7 Some Examples of 2D Finite Elements 337 20.8 Summary 340 21 Integration of Nodal Forces over Volume of 2D Finite Elements 343 21.1 The Principle of Virtual Work in the 2D Finite Element Method 343 21.2 Nodal Forces for the Homogeneous Deformation Triangle 348 21.3 Nodal Forces for the Six-Noded Triangle 352 21.4 Nodal Forces for the Four-Noded Quadrilateral 353 21.5 Summary 355 22 Reduced and Selective Integration of Nodal Forces over Volume of 2D Finite Elements 357 22.1 Volumetric Locking 357 22.2 Reduced Integration 358 22.3 Selective Integration 359 22.4 Shear Locking 362 22.5 Summary 364 PART FIVE THE FINITE ELEMENT METHOD IN 3D 365 23 3D Deformation Kinematics Using the Homogeneous Deformation Tetrahedron Finite Element 367 23.1 Introduction 367 23.2 The Homogeneous Deformation Four-Noded Tetrahedron Finite Element 368 23.3 Summary 377 24 3D Deformation Kinematics Using Iso-Parametric Finite Elements 379 24.1 The Finite Element Library 379 24.2 The Shape Functions 379 24.3 Nodal Positions 381 24.4 Positions of Material Points inside a Single Finite Element 382 24.5 The Solid-Embedded Infinitesimal Vector Base 383 24.6 The Material-Embedded Infinitesimal Vector Base 386 24.7 Examples of Deformation Kinematics 387 24.8 Summary 392 25 Integration of Nodal Forces over Volume of 3D Finite Elements 393 25.1 Nodal Forces Using Virtual Work 393 25.2 Four-Noded Tetrahedron Finite Element 396 25.3 Reduce Integration for Eight-Noded 3D Solid 399 25.4 Selective Stretch Sampling-Based Integration for the Eight-Noded Solid Finite Element 400 25.5 Summary 401 26 Integration of Nodal Forces over Boundaries of Finite Elements 403 26.1 Stress at Element Boundaries 403 26.2 Integration of the Equivalent Nodal Forces over the Triangle Finite Element 404 26.3 Integration over the Boundary of the Composite Triangle 407 26.4 Integration over the Boundary of the Six-Noded Triangle 408 26.5 Integration of the Equivalent Internal Nodal Forces over the Tetrahedron Boundaries 409 26.6 Summary 412 PART SIX THE FINITE ELEMENT METHOD IN 2.5D 415 27 Deformation in 2.5D Using Membrane Finite Elements 417 27.1 Solids in 2.5D 417 27.2 The Homogeneous Deformation Three-Noded Triangular Membrane Finite Element 419 27.3 Summary 438 28 Deformation in 2.5D Using Shell Finite Elements 439 28.1 Introduction 439 28.2 The Six-Noded Triangular Shell Finite Element 440 28.3 The Solid-Embedded Coordinate System 441 28.4 Nodal Coordinates 442 28.5 The Coordinates of the Finite Element’s Material Points 443 28.6 The Solid-Embedded Infinitesimal Vector Base 444 28.7 The Solid-Embedded Vector Base versus the Material-Embedded Vector Base 447 28.8 The Constitutive Law 449 28.9 Selective Stretch Sampling Based Integration of the Equivalent Nodal Forces 449 28.10 Multi-Layered Shell as an Assembly of Single Layer Shells 455 28.11 Improving the CPU Performance of the Shell Element 456 28.12 Summary 462 Index 463

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Book Synopsis. a much-needed handbook with contributions from well-chosen practitioners.Table of ContentsForeword xi List of Contributors xiii Notes on Contributors xvii List of Acronyms xxxi Part I Overview and Introduction 1. Overview and Introduction to Modeling and Simulation Support for System of Systems Engineering Applications 3Larry B. Rainey and Andreas Tolk 2. The Role of Modeling and Simulation in System of Systems Development 11Mark W. Maier Part II Theoretical and Methodological Considerations 3. Composability 45Michael C. Jones 4. An Approach for System of Systems Tradespace Exploration 75Adam M. Ross and Donna H. Rhodes 5. Data Policy Definition and Verification for System of Systems Governance 99Daniele Gianni 6. System Health Management 131Stephen B. Johnson 7. Model Methodology for a Department of Defense Architecture Design 145R. William Maule Part III Theoretical and Methodological Considerations with Applications and Lessons Learned 8. An Agent-Oriented Perspective on System of Systems for Multiple Domains 187Agostino G. Bruzzone, Alfredo Garro, Francesco Longo, and Marina Massei 9. Building Analytical Support for Homeland Security 219Sanjay Jain, Charles W. Hutchings, and Yung-Tsun Tina Lee 10. Air Transportation Systems 249William Crossley and Daniel DeLaurentis 11. Systemigram Modeling for Contextualizing Complexity in System of Systems 273Brian Sauser and John Boardman 12. Using Modeling and Simulation for System of Systems Engineering Applications in the European Space Agency 303Joachim Fuchs and Niklas Lindman 13. System of Systems Modeling and Simulation for Microgrids Using DDDAMS 337Aristotelis E. Thanos, DeLante E. Moore, Xiaoran Shi, and Nurcin Celik 14. Composition of Behavior Models for Systems Architecture 361Clifford A. Whitcomb, Mikhail Auguston, and Kristin Giammarco 15. Joint Training 393James Harrington, Laura Hinton, and Michael Wright 16. Human in the Loop in System of Systems (SoS) Modeling and Simulation: Applications to Live, Virtual, and Constructive (LVC) Distributed Mission Operations (DMO) Training 415Saurabh Mittal, Margery J. Doyle, and Antoinette M. Portrey 17. On Analysis of Ballistic Missile Defense Architecture through Surrogate Modeling and Simulation 453Tommer R. Ender, Philip D. West, William Dale Blair, and Paul A. Miceli 18. Medical Enhancements to Sustain Life during Extreme Trauma Care 479L. Drew Pihera, Nathan L. Adams, Tommer R. Ender, and Matthew L. Paden 19. Utility: Problem-Focused, Effects-Based Analysis (aka Information Value Chain Analysis) 515Thomas W. O’Brien and John F. Sarkesain 20. A Framework for Achieving Dynamic Cyber Effects through Distributed Cyber Command and Control/Battle Management (C2/BM) 531John F. Sarkesain and Thomas W. O’Brien 21. System of Systems Security 565Bharat B. Madan Part IV Conclusions 22. Toward a Research Agenda for M&S Support of System of Systems Engineering 583Andreas Tolk and Larry B. Rainey Index 593

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Book SynopsisMathematical and Computational Modeling Illustrates the application of mathematical and computational modeling in a variety of disciplines With an emphasis on the interdisciplinary nature of mathematical and computational modeling, Mathematical and Computational Modeling: With Applications in the Natural and Social Sciences, Engineering, and the Arts features chapters written by well-known, international experts in these fields and presents readers with a host of state-of-theart achievements in the development of mathematical modeling and computational experiment methodology. The book is a valuable guide to the methods, ideas, and tools of applied and computational mathematics as they apply to other disciplines such as the natural and social sciences, engineering, and technology. The book also features: Rigorous mathematical procedures and applications as the driving force behind mathematical innovation and discovery Numerous eTable of ContentsList of Contributors xiii Preface xv Section 1 Introduction 1 1 Universality of Mathematical Models in Understanding Nature Society and Man-Made World 3Roderick Melnik 1.1 Human Knowledge Models and Algorithms 3 1.2 Looking into the Future from a Modeling Perspective 7 1.3 What This Book Is About 10 1.4 Concluding Remarks 15 References 16 Section 2 Advanced Mathematical and Computational Models in Physics and Chemistry 17 2 Magnetic Vortices Abrikosov Lattices and Automorphic Functions 19Israel Michael Sigal 2.1 Introduction 19 2.2 The Ginzburg–Landau Equations 20 2.2.1 Ginzburg–Landau energy 21 2.2.2 Symmetries of the equations 21 2.2.3 Quantization of flux 22 2.2.4 Homogeneous solutions 22 2.2.5 Type I and Type II superconductors 23 2.2.6 Self-dual case κ=1/ √ 2 24 2.2.7 Critical magnetic fields 24 2.2.8 Time-dependent equations 25 2.3 Vortices 25 2.3.1 n-vortex solutions 25 2.3.2 Stability 26 2.4 Vortex Lattices 30 2.4.1 Abrikosov lattices 31 2.4.2 Existence of Abrikosov lattices 31 2.4.3 Abrikosov lattices as gauge-equivariant states 34 2.4.4 Abrikosov function 34 2.4.5 Comments on the proofs of existence results 35 2.4.6 Stability of Abrikosov lattices 40 2.4.7 Functions γ δ (τ),δ >0 42 2.4.8 Key ideas of approach to stability 45 2.5 Multi-Vortex Dynamics 48 2.6 Conclusions 51 Appendix 2.A Parameterization of the equivalence classes [L] 51 Appendix 2.B Automorphy factors 52 References 54 3 Numerical Challenges in a Cholesky-Decomposed Local Correlation Quantum Chemistry Framework 59David B. Krisiloff, Johannes M. Dieterich, Florian Libisch and Emily A. Carter 3.1 Introduction 59 3.2 Local MRSDCI 61 3.2.1 Mrsdci 61 3.2.2 Symmetric group graphical approach 62 3.2.3 Local electron correlation approximation 64 3.2.4 Algorithm summary 66 3.3 Numerical Importance of Individual Steps 67 3.4 Cholesky Decomposition 68 3.5 Transformation of the Cholesky Vectors 71 3.6 Two-Electron Integral Reassembly 72 3.7 Integral and Execution Buffer 76 3.8 Symmetric Group Graphical Approach 77 3.9 Summary and Outlook 87 References 87 4 Generalized Variational Theorem in Quantum Mechanics 92Mel Levy and Antonios Gonis 4.1 Introduction 92 4.2 First Proof 93 4.3 Second Proof 95 4.4 Conclusions 96 References 97 Section 3 Mathematical and Statistical Models in Life And Climate Science Applications 99 5 A Model for the Spread of Tuberculosis with Drug-Sensitive and Emerging Multidrug-Resistant and Extensively Drug-Resistant Strains 101Julien Arino and Iman A. Soliman 5.1 Introduction 101 5.1.1 Model formulation 102 5.1.2 Mathematical Analysis 107 5.1.2.1 Basic properties of solutions 107 5.1.2.2 Nature of the disease-free equilibrium 108 5.1.2.3 Local asymptotic stability of the DFE 108 5.1.2.4 Existence of subthreshold endemic equilibria 110 5.1.2.5 Global stability of the DFE when the bifurcation is “forward” 113 5.1.2.6 Strain-specific global stability in “forward” bifurcation cases 115 5.2 Discussion 117 References 119 6 The Need for More Integrated Epidemic Modeling with Emphasis on Antibiotic Resistance 121Eili Y. Klein, Julia Chelen, Michael D. Makowsky and Paul E. Smaldino 6.1 Introduction 121 6.2 Mathematical Modeling of Infectious Diseases 122 6.3 Antibiotic Resistance Behavior and Mathematical Modeling 125 6.3.1 Why an integrated approach? 125 6.3.2 The role of symptomology 127 6.4 Conclusion 128 References 129 Section 4 Mathematical Models and Analysis for Science and Engineering 135 7 Data-Driven Methods for Dynamical Systems: Quantifying Predictability and Extracting Spatiotemporal Patterns 137Dimitrios Giannakis and Andrew J. Majda 7.1 Quantifying Long-Range Predictability and Model Error through Data Clustering and Information Theory 138 7.1.1 Background 138 7.1.2 Information theory predictability and model error 140 7.1.2.1 Predictability in a perfect-model environment 140 7.1.2.2 Quantifying the error of imperfect models 143 7.1.3 Coarse-graining phase space to reveal long-range predictability 144 7.1.3.1 Perfect-model scenario 144 7.1.3.2 Quantifying the model error in long-range forecasts 147 7.1.4 K-means clustering with persistence 149 7.1.5 Demonstration in a double-gyre ocean model 152 7.1.5.1 Predictability bounds for coarse-grained observables 154 7.1.5.2 The physical properties of the regimes 157 7.1.5.3 Markov models of regime behavior in the 1.5-layer ocean model 159 7.1.5.4 The model error in long-range predictions with coarse-grained Markov models 162 7.2 NLSA Algorithms for Decomposition of Spatiotemporal Data 163 7.2.1 Background 163 7.2.2 Mathematical framework 165 7.2.2.1 Time-lagged embedding 166 7.2.2.2 Overview of singular spectrum analysis 167 7.2.2.3 Spaces of temporal patterns 167 7.2.2.4 Discrete formulation 169 7.2.2.5 Dynamics-adapted kernels 171 7.2.2.6 Singular value decomposition 173 7.2.2.7 Setting the truncation level 174 7.2.2.8 Projection to data space 175 7.2.3 Analysis of infrared brightness temperature satellite data for tropical dynamics 175 7.2.3.1 Dataset description 176 7.2.3.2 Modes recovered by NLSA 176 7.2.3.3 Reconstruction of the TOGA COARE MJOs 183 7.3 Conclusions 184 References 185 8 On Smoothness Concepts in Regularization for Nonlinear Inverse Problems in Banach Spaces 192Bernd Hofmann 8.1 Introduction 192 8.2 Model Assumptions Existence and Stability 195 8.3 Convergence of Regularized Solutions 197 8.4 A Powerful Tool for Obtaining Convergence Rates 200 8.5 How to Obtain Variational Inequalities? 206 8.5.1 Bregman distance as error measure: the benchmark case 206 8.5.2 Bregman distance as error measure: violating the benchmark 210 8.5.3 Norm distance as error measure: l 1 -regularization 213 8.6 Summary 215 References 215 9 Initial and Initial-Boundary Value Problems for First-Order Symmetric Hyperbolic Systems with Constraints 222Nicolae Tarfulea 9.1 Introduction 222 9.2 FOSH Initial Value Problems with Constraints 223 9.2.1 FOSH initial value problems 224 9.2.2 Abstract formulation 225 9.2.3 FOSH initial value problems with constraints 228 9.3 FOSH Initial-Boundary Value Problems with Constraints 230 9.3.1 FOSH initial-boundary value problems 232 9.3.2 FOSH initial-boundary value problems with constraints 234 9.4 Applications 236 9.4.1 System of wave equations with constraints 237 9.4.2 Applications to Einstein’s equations 240 9.4.2.1 Einstein–Christoffel formulation 243 9.4.2.2 Alekseenko–Arnold formulation 246 References 250 10 Information Integration Organization and Numerical Harmonic Analysis 254Ronald R. Coifman, Ronen Talmon, Matan Gavish and Ali Haddad 10.1 Introduction 254 10.2 Empirical Intrinsic Geometry 257 10.2.1 Manifold formulation 259 10.2.2 Mahalanobis distance 261 10.3 Organization and Harmonic Analysis of Databases/Matrices 263 10.3.1 Haar bases 264 10.3.2 Coupled partition trees 265 10.4 Summary 269 References 270 Section 5 Mathematical Methods in Social Sciences And Arts 273 11 Satisfaction Approval Voting 275Steven J. Brams and D. Marc Kilgour 11.1 Introduction 275 11.2 Satisfaction Approval Voting for Individual Candidates 277 11.3 The Game Theory Society Election 285 11.4 Voting for Multiple Candidates under SAV: A Decision-Theoretic Analysis 287 11.5 Voting for Political Parties 291 11.5.1 Bullet voting 291 11.5.2 Formalization 292 11.5.3 Multiple-party voting 294 11.6 Conclusions 295 11.7 Summary 296 References 297 12 Modeling Musical Rhythm Mutations with Geometric Quantization 299Godfried T. Toussaint 12.1 Introduction 299 12.2 Rhythm Mutations 301 12.2.1 Musicological rhythm mutations 301 12.2.2 Geometric rhythm mutations 302 12.3 Similarity-Based Rhythm Mutations 303 12.3.1 Global rhythm similarity measures 304 12.4 Conclusion 306 References 307 Index 309

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Book SynopsisThis book is the first introduction/reference to the computer simulation of glass materials, which are growing in their applications such as telephone technology, construction materials, aerospace materials and more.Trade ReviewModeling and simulation are crucial for understanding structure-property relationships in glass-forming systems and for accelerating the design of next-generation glassy materials. Atomistic Simulations of Glasses is a comprehensive volume dedicated to the topic of atomic-scale modeling of glassy materials, with particular emphasis on silicate glasses of practical industrial interest. As such, this book fills a critical gap in the literature, offering an excellent introduction for newcomers to atomistic modeling, as well as a comprehensive and state-of-the-art reference for practitioners in the field. Atomistic Simulations of Glasses, published by ACerS-Wiley, consists of 15 chapters written by experts from around the world. It is edited by two leading authorities in computational glass science: Jincheng Du (University of North Texas) and Alastair N. Cormack (Alfred University). The book itself is gorgeous, printed in full color on high-quality paper. It is designed in a reader-friendly format, including a comprehensive index, an extensive list of references at the end of each chapter, and a helpful table to decode every acronym used throughout the book. Each chapter is well written and has been carefully polished. The text also flows smoothly across chapters, which is sometimes a problem in edited volumes. The first five chapters are devoted to fundamentals of atomistic modeling techniques for glassy systems, including classical simulation methods (Chapter 1), quantum mechanical techniques (Chapter 2), reverse Monte Carlo (Chapter 3), structural analysis methods (Chapter 4), and topological constraint theory (Chapter 5). Each of these chapters does a great job at providing both foundational knowledge and discussing the state-of-the-art in methods and tools. The chapter on topological constraint theory is especially interesting because this is a family of techniques developed specifically for glassy materials. The latter 10 chapters of the book focus on application of these techniques for simulating various glass families of interest. These chapters cover a wide range of silicate, aluminosilicate, and borosilicate glasses, as well as phosphate, fluoride, and oxyfluoride systems. The coverage of transition metal and rare-earth-containing glasses is also a nice touch. There is a particular emphasis on bioactive glasses and glasses for nuclear waste immobilization. As a whole, the 10 application-focused chapters do an excellent job demonstrating the utility and versatility of atomistic simulation approaches for addressing problems of practical concern in the glass science and engineering community. These chapters also provide good perspective on specific needs for future developments in the field. There are a few missing topics that would have been valuable to include in the book. While reactive force fields are mentioned briefly, an entire chapter devoted to the principles and applications of reactive force fields such as ReaxFF would have been a nice addition, especially because reactive force fields are becoming increasingly important in the glass science community. Also, given the importance of thermal history in governing the structure and properties of glasses, it would have been worthwhile to include a chapter on accessing long time scales, e.g., using kinetic Monte Carlo, meta-dynamics, or the activation-relaxation technique, all of which have been applied to noncrystalline systems in the literature and can enable simulations to access experimental time scales. It also would have been helpful to expand the chapter on reverse Monte Carlo to include other Monte Carlo techniques more broadly; for example, Metropolis Monte Carlo is a computationally efficient alternative to molecular dynamics for calculating glass structure and static properties. Finally, given the large amount of research activity in modeling of metallic glasses, a chapter on atomistic simulations of metallic glasses would be a nice addition. Overall, Atomistic Simulations of Glasses is a very welcome addition to the literature and highly recommended for both students and professionals in the field of computational glass science.—John C. Mauro is a Dorothy Pate Enright Professor in the Department of Materials Science and Engineering at The Pennsylvania State UniversityTable of ContentsPreface Part I Fundamentals of Atomistic Simulations Chapter 1 Classical simulation methods Abstract 1.1 Introduction 1.2 Simulation techniques 1.2.1 Molecular dynamics (MD) 1.2.1.1 Integrating the equations of motion 1.2.1.2 Thermostats and barostats 1.2.2 Monte Carlo (MC) eimulations 1.2.2.1 Kinetic Monte Carlo 1.2.2.2 Reverse Monte Carlo 1.3 The Born Model 1.3.1 Ewald summation 1.3.2 Potentials 1.3.2.1 Transferability of potential parameters: Self-consistent sets 1.3.2.2 Ion polarizability 1.3.2.3 Potential models for borates 1.3.2.4 Modelling reactivity: electron transfer 1.4 Calculation of Observables 1.4.1 Atomic structure 1.4.2 Hyperdynamics and peridynamics 1.5 Glass Formation 1.5.1 Bulk structures 1.5.2 Surfaces and fibers 1.6 Geometry optimization and property calculations 1.7 References Chapter 2 Ab initio simulation of amorphous solids Abstract 2.1 Introduction 2.1.1 Big picture 2.1.2 The limits of experiment 2.1.3 Synergy between experiment and modeling 2.1.4 History of simulations and the need for ab initio methods 2.1.5 The difference between ab initio and classical MD 2.1.6 Ingredients of DFT 2.1.7 What DFT can provide 2.1.8 The emerging solution for large systems and long times: Machine Learning 2.1.9 A practical aid: Databases 2.2 Methods to produce models 2.2.1 Simulation Paradigm: Melt Quench 2.2.2 Information Paradigm 2.2.3 Teaching chemistry to RMC: FEAR 2.2.4 Gap Sculpting 2.3 Analyzing the models 2.3.1 Structure 2.3.2 Electronic Structure 2.3.3 Vibrational Properties 2.4 Conclusion 2.5 Acknowledgements 2.6 References Chapter 3 Reverse Monte Carlo simulations of non-crystalline solids Abstract 3.1 Introduction -- why RMC is needed? 3.2 Reverse Monte Carlo modeling 3.2.1. Basic RMC algorithm 3.2.2. Information deficiency 3.2.3. Preparation of reference structures: hard sphere Monte Carlo 3.2.4. Other methods for preparing suitable structural models 3.3 Topological analyses 3.3.1. Ring statistics 3.3.2. Cavity analyses 3.3.3. Persistent homology analyses 3.4 Applications 3.4.1 Single component liquid and amorphous materials 3.4.1.1 l-Si and a-Si 3.4.1.2 l-P under high pressure and high temperature 3.4.2 Oxide glasses 3.4.2.1 SiO2 glass 3.4.2.2 R2O-SiO2 glasses (R=Na, K) 3.4.2.3 CaO-Al2O3 glass 3.4.3 Chalcogenide glasses 3.4.4 Metallic glasses 3.5 Summary 3.6 Acknowledgments 3.7 References Chapter 4 Structure analysis and property calculations abstract 4.1 Introduction 4.2 Structure Analysis 4.2.1 Salient features of glass structures 4.2.2 Classification of the range order. 4.3 Real Space Correlation functions.Spectroscopic properties: validating the structural models 4.3.1 X-ray and Neutron diffraction spectra 4.3.2 Vibrational spectra 4.3.3 NMR spectra 4.4 Transport properties 4.4.1 Diffusion coefficient and diffusion activation energy 4.4.2 Viscosity 4.4.3 Thermal conductivity 4.5 Mechanical Properties 4.5.1 Elastic constants 4.5.2 Stress-strain diagrams and fracture mechanism 4.6 Concluding remarks 4.7 References Chapter 5 Topological constraint theory of glass: counting constraints by molecular dynamics simulations Abstract 5.1 Introduction 5.2 Background and topological constraint theory 5.2.1 Rigidity of mechanical networks 5.2.2 Application to atomic networks 5.2.3 Constraint enumeration under mean-field approximation 5.2.4 Polytope-based description of glass rigidity 5.2.5 Impact of temperature 5.2.6 Need for molecular dynamics simulations 5.3 Counting constraints from molecular dynamics simulations 5.3.1 Constraint enumeration based on the relative motion between atoms 5.3.2 Computation of the internal stress 5.3.3 Computation of the floppy modes 5.3.5 Dynamical matrix analysis 5.4 Conclusions 5.5 References Part II Applications of Atomistic Simulations in Glass Research Chapter 6 History of atomistic simulations of glasses Abstract 6.1 Introduction 6.2 Simulation techniques 6.2.1 Monte Carlo techniques 6.2.2 Molecular dynamics 6.3 Classical simulations: interatomic potentials 6.3.1 Potential models for silica 6.3.1.1 Silica: quantum mechanical simulations 6.3.2 Modified silicates and aluminosilicates 6.3.3 Borate glasses 6.3.3.1 Borates: quantum mechanical simulations 6.4 Simulation of surfaces 6.5 Computer science and engineering 6.6.1 Software 6.6.2 Hardware 6.6 References Chapter 7 Silica and silicate glasses Abstract 7.1 Introduction 7.2 Atomistic simulations of silicate glasses: ingredients and critical aspects 7.3 Characterization and experimental validation of structural and dynamic features of simulated glasses 7.3.1 Structural characterizations 7.3.2 Dynamic properties of simulated glasses 7.3.3 Validation and experimental confirmation of structural and dynamic properties 7.3.3.1 Diffraction methods 7.3.3.2 Nuclear Magnetic Resonance 7.3.3.3 Vibrational spectral characterization 7.4 MD simulations of silica glasses 7.5 MD simulations of alkali silicate and alkali earth silicate glasses 7.5.1 Local environments and distribution of alkali ions 7.5.2 The mixed alkali effect 7.6 MD simulations of aluminosilicate glasses 7.7 MD simulations of nanoporous silica and silicate glasses 7.8 AIMD simulations of silica and silicate glasses 7.9 Summary and Outlook Acknowledgements References Chapter 8 Borosilicate and boroaluminosilicate glasses 8.1 Abstract 8.2 Introduction 8.3 Experimental determination and theoretical models of boron N4 values in borosilicate glass 8.3.1 Experimental results on boron coordination number 8.3.2 Theoretical models in predicting boron N4 value 8.4 ab initio versus classical MD simulations of borosilicate glasses 8.5 Empirical potentials for borate and borosilicate glasses 8.5.1 Recent development of rigid ion potentials for borosilicate glasses 8.5.2 Development of polarizable potentials for borate and borosilicate glasses 8.6 Evaluation of the potentials 8.7 Effects of cooling rate and system size on simulated borosilicate glass structures 8.8 Applications of MD simulations of borosilicate glasses 8.8.1 Borosilicate glass 8.8.2 Boroaluminosilicate glasses 8.8.3 Boron oxide-containing multi-component glass 8.9 Conclusions 8.10 Appendix: Available empirical potentials for boron-containing systems 8.10.1 Borosilicate and boroaluminosilicate potentials-Kieu et al and Deng&Du 8.10.2 Borosilicate potential- Wang et al 8.10.3 Borosilicate potential-Inoue et al 8.10.4 Boroaluminosilicate potential-Ha and Garofalini 8.10.5 Borosilicate and boron-containing oxide glass potential-Deng and Du 8.10.6 Borate, boroaluminate and borosilicate potential-Sundararaman et al 8.10.7 Borate and borosilicate polarizable potential-Yu et al 8.10 Acknowledgements 8.11 References Chapter 9 Nuclear waste glasses 9.1 Preamble 9.2 Introduction to French nuclear glass 9.2.1 Chemical composition 9.2.2 About the long term behavior (irradiation, glass alteration, He accumulation) 9.2.3 What can atomistic simulations contribute? 9.3 Computational methodology 9.3.1 Review of existing classical potentials for borosilicate glasses 9.3.2 Preparation of a glass 9.3.3 Displacement cascade simulations 9.3.4 Short bibliography about simplified nuclear glass structure studies 9.4 Simulation of radiation effects in simplified nuclear glasses 9.4.1 Accumulation of displacement cascades and the thermal quench model 9.4.2 Preparation of disordered and depolymerized glasses 9.4.3 Origin of the hardness change under irradiation 9.4.4 Origin of the fracture toughness change under irradiation 9.5 Simulation of glass alteration by water 9.5.1 Contribution from ab initio calculations 9.5.2 Contribution from Monte Carlo simulations 9.6 Gas incorporation: radiation effects on He solubility 9.6.1 Solubility model 9.6.2 Interstitial sites in SiO2-B2O3-Na2O glasses 9.6.3 Discussion about He solubility in relation to the radiation effects 9.7 Conclusions 9.8 Acknowledgements 9.9 References Chapter 10 Phosphate glasses Abstract 10.1 Introduction to phosphate glasses 10.1.1 Applications of phosphate glasses 10.1.2 Synthesis of phosphate glasses 10.1.3 The modified random network model applied to phosphate glasses 10.1.4 The tetrahedral phosphate glass network 10.1.5 Modifier cations in phosphate glasses 10.2 Modelling methods for phosphate glasses 10.2.1 Configurations of atomic coordinates 10.2.2 Molecular modelling versus reverse Monte Carlo modelling 10.2.3 Classical vs. ab initio molecular modelling 10.2.4 Evaluating the simulation of interatomic interactions 10.2.5 Evaluating models of glasses by comparison with experimental data 10.3 Modelling pure vitreous P2O5 10.3.1 Modelling of crystalline P2O5 10.3.2 Modelling of vitreous P2O5 10.3.3 Cluster models of vitreous P2O5 10.4 Modelling phosphate glasses with monovalent cations 10.4.1 Modelling lithium phosphate glasses 10.4.2 Modelling sodium phosphate glasses 10.4.3 Modelling phosphate glasses with other monovalent cations 10.4.4 Modelling phosphate glasses with monovalent cations and addition of halides 10.4.5 Cluster models of alkali phosphate glasses 10.5 Modelling phosphate glasses with divalent cations 10.5.1 Modelling zinc phosphate glasses 10.5.2 Modelling zinc phosphate glasses with additional cations 10.5.3 Modelling alkaline earth phosphate glasses 10.5.4 Modelling lead phosphate glasses 10.6 Modelling phosphate based glasses for biomaterials applications 10.6.1 Modelling Na2O-CaO-P2O5 glasses with 45 mol% P2O5 10.6.2 Modelling Na2O-CaO-P2O5 glasses with 50 mol% P2O5 10.6.3 Modelling Na2O-CaO-P2O5 glasses with additional cations 10.7 Modelling phosphate glasses with trivalent cations 10.7.1 Modelling iron phosphate glasses 10.7.2 Cluster models of iron phosphate glasses 10.7.3 Modelling trivalent rare earth phosphate glasses 10.7.4 Modelling aluminophosphate glasses 10.8 Modelling phosphate glasses with tetravalent and pentavalent cations 10.9 Modelling phosphate glasses with mixed network formers 10.9.1 Modelling borophosphate glasses 10.9.2 Modelling phosphosilicate glasses 10.10 Modelling bioglass 45S and related glasses 10.10.1 Modelling bioglass 45S and related glasses from the same system 10.10.2 Modelling bioglass 45S and related glasses with additional components 10.11 Summary 10.12 References Chapter 11 Bioactive glasses Abstract 11.1 Introduction 11.2 Methodology 11.3 Development of interatomic potentials 11.4 Structure of 45S5 Bioglass 11.5 Inclusion of ions into bioactive glass and the effect on structure and bioactivity 11.6 Glass nanoparticles and surfaces 11.7 Discussion and future work Bibliography Chapter 12 Rare earth and transition metal containing glasses Abstract 12.1 Introduction 12.1.1 Transition metal and rare earth oxides in glasses: importance and potential applications 12.1.2 Effects of local structures and clustering behaviors of RE and TM ions on properties 12.1.3 Redox reaction and multioxidation states of TM and RE ions 12.1.4 Effect of composition on multioxidation states in glasses containing TM 12.1.5 The role of MD in investigating TM and RE containing glasses 12.2 Simulation methodologies 12.2.1 Interatomic potentials and glass simulations 12.2.2 Cation environment and clustering analysis 12.2.3 Diffusion and dynamic property calculations 12.2.4 Electronic structure calculations 12.3 Case studies of MD simulations of RE and TM containing glasses 12.3.1 Rare earth doped silicate and aluminophosphate glasses for optical applications 12.3.1.1 Erbium doped silica and silicate glasses: from melt-quench to ion implantation 12.3.1.2 Europium and praseodymium doped silicate glasses 12.3.1.3 Cerium doped aluminophosphate glasses: atomic structure and charge trapping 12.3.2 Alkali vanadophosphate glasses as a mixed conductor 12.3.2.1 General features of vanadophosphate glasses 12.3.2.2 Sodium vanadophosphate glass 12.3.2.3 Lithium vanadophosphate glass 12.3.3 Zirconia containing aluminosilicate and borosilicate glasses for nuclear waste disposal 12.4 Conclusions Acknowledgement References Chapter 13 Halide and oxyhalide glasses Abstract 13.1 Introduction 13.2 General Structure Features of Fluoride and Oxyfluoride Glasses 13.2.1 Structure Features of Fluoride Glasses 13.2.2 Structure Features of Oxyfluoride Glasses 13.2.3 Phase Separation in Fluoride and Oxyfluoride Glasses 13.3 Structures and Properties of Fluoride Glasses from MD Simulations 13.3.1 General Structures from MD simulations 13.3.2 Cation Coordination and Structural Roles 13.3.3 Fluorine Environments 13.4 MD Simulations of Fluoroaluminosilicate Oxyfluoride Glasses 13.4.1 Oxide and Fluoride Glass Phase Separation Observed from MD Simulations 13.4.2 Oxide-Fluoride Interfacial Structure Features from MD simulations 13.4.3 Correlation of Structural Features between MD and Crystallization 13.5 ab initio MD simulations of oxyfluoride glasses 13.6 Conclusions Acknowledgements References Chapter 14 Glass surface simulations abstract 14.1 Introduction 14.2 Classical molecular dynamics surface simulations 14.2.1 amorphous silica surfaces 14.2.2 Multicomponent oxide glass surfaces 14.2.2.1 Bioactive glasses 14.2.3 Wet glass surfaces 14.2.3.1 Reactive potentials 14.3 First Principles Surface Simulations 14.3.1 Silica glass surfaces 14.3.2 Multicomponent glass surfaces 14.3.3 Wet glass surfaces 14.4 Summary Acknowledgements References Chapter 15 Simulations of glass - water interactions Abstract 15.1 Introduction 15.1.1 Glass Dissolution Process and Experimental Characterizations 15.1.2 Types of Atomistic Simulation Methods for Studying Glass-Water Interactions 15.2 First-Principles Simulations of Glass-Water Interactions 15.2.1 Brief Introduction to Methods 15.2.2 Energy Barriers for Si-O-Si Bond Breakage 15.2.3 Reaction Mechanism for Si-O-Si Bond Breakage 15.2.4 Strained Si-O-Si linkages 15.2.5 Reaction Energies for Multicomponent Linkages 15.2.6 Effect of pH on Si-O-Si Hydrolysis Reactions 15.2.7 Nanoconfinement of water in porous materials 15.2.8 Oniom or QM/MM simulations 15.2.9 Areas for improvement/additional research 15.3 Classical Molecular Dynamics Simulations of water-glass interactions 15.3.1 Brief Introduction and History 15.3.2 Non-Reactive Potentials 15.3.3 Reactive Potentials 15.3.4 Silica Glass-Water Interactions 15.3.5 Silicate Glass – Water Interactions 15.3.6 Other glasses – water interactions 15.3.7 Areas for Improvement 15.4 Challenges and Outlook 15.4.1 Extending the Length and Time Scales of Atomistic Simulation 15.4.2 Reactive Potential Development 15.5 Conclusion Remarks 15.6 Acknowledgements 15.7 References

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Book SynopsisA sleeker, more comprehensive approach to construction projects BIM and Construction Management, Second Edition is a complete integration guide, featuring practical advice, project tested methods and workflows, and tutorials for implementing Building Information Modeling and technology in construction.Table of ContentsIntroduction xvii Chapter 1 Why Is Technology So Important to Construction Management? 1 The Promise of BIM 2 Processes 4 Technologies 5 Behaviors 7 The Value of BIM in Construction 8 Where Does BIM Play a Role in Construction Management? 15 Team Engagement 16 Project Pursuit and Business Development 16 Planning for BIM Success 19 Using Contracts in Planning 19 Scheduling 20 Logistics 22 Estimating Cost 23 Constructability 25 Analyzing Data in BIM 27 Designing for Prefabrication 29 Coordinating Construction 31 Using Mobile Devices 32 Controlling Schedules 33 Controlling Cost 34 Managing Change 35 Material Management 37 Tracking Equipment 37 Closeout 38 Managing Facilities 39 Knowledge Platform Population 40 Where the Industry Is Headed 42 Leadership Buy-In 42 The Evolving Role of the BIM Manager 43 What Have Been the Results? 43 Summary 44 Chapter 2 Project Planning 45 Delivery Methods 46 Design-Bid-Build 47 Construction Manager at Risk 52 Design-Build 56 Integrated Project Delivery 62 BIM Addenda (Contracts) 63 AIA: Document E202 65 AGC: ConsensusDocs 301 65 DBIA: Document E-BIMWD 65 AIA: Document E203 66 Contracts Summary 66 The Fundamental Uses of BIM 67 Level of Development 68 Model-Based Coordination 69 Model-Based Scheduling 72 Model-Based Estimating 72 Model-Based Facilities Management 73 Model-Based Analysis 74 BIM Execution Plan 75 History of the BIM Execution Plan 75 Communication 77 Expectation 83 Organization 85 Summary 89 Chapter 3 How to Market BIM and Win the Project 91 BIM Marketing Background 92 Building Your Team 94 Marketing Your Brand of BIM 97 Does What You Are Proposing Show Clear and Demonstrable Value? 98 Is This a Proven Tool or Process, a Developing One, or an Innovative One? 99 Can You Show Real Results from the Impact of Implementation? 102 Is This What the Owner Wants? 104 Is This Something You Can Deliver? 105 Using BIM to Enhance the Proposal 108 Addressing BIM in the RFP 108 Project Pursuit Images 110 Project Simulations 112 Project Pursuit Virtual/Augmented Reality Simulations 113 Other Marketing Tools 116 Tailor-Fit Your Offerings 116 Client Alignment 117 Pushing the Envelope 118 Seeking Value and Focusing on Results 118 Summary 121 Chapter 4 BIM and Preconstruction 123 Leaning on the Past 124 The Empire State Building 125 Adopting New Technology 132 The Journey to BIM 134 The Kickoff 136 Getting the Right People in the Room 136 Creating the Vision 138 Opening the Lines of Communication 139 Accounting for the Expectation Bias 139 Scheduling Design 139 Design Structure Matrix 145 Scheduling the LOD 148 Constructability Review 149 Leverage the Plans 150 Leverage the Details 153 Leverage the People 158 Estimating 163 Revit Schedules for Estimating 164 Cost Trending with Assemble 171 Analysis 175 The 2030 Challenge 176 Overview of Sustainability and BIM 177 Sustainability Analysis with Sefaira 182 Logistics and Planning 188 Summary 190 Chapter 5 BIM and Construction 191 Overview of BIM in Construction 192 Model Coordination 194 BIM and Site Coordination 194 Clash Detection 196 Navisworks Conflict Exercise 196 Fabrication 208 BIM Scheduling 213 Scheduling Software 217 Completing the Feedback Loop 226 Systems Installation 228 Installation Management 228 Installation Verification 232 Construction Activity Tracking 234 Field Issue Management 235 BIM and Safety 236 Producing Better Field Information 238 Beginning with the End in Mind 239 What Information Do You Need to Build? 242 Model Redlining Exercise 242 Video Embedding Exercise 250 The Virtual Job Trailer 252 The Conference Room 252 The Plans and Specifications Hub 254 The Jobsite Office as a Server 254 The Jobsite Office as a Communication Hub 255 Setting Up the Job Trailer 255 Summary 256 Chapter 6 BIM and Construction Administration 257 The Battle for BIM 258 Training Field Personnel 261 Training Goals for Basic Skills 263 Advanced Training Goals for Model Creation 263 Training Courses for Additional Uses 265 Document Control 270 Creating a Digital Plan Room with Bluebeam Revu eXtreme 272 The Real Value of 4D 281 Developing BIM Intuition 284 Starting with a Door 284 Assemble Systems: Beyond the Basics 286 Importing Search Sets into Navisworks 288 Mapping Equipment to BIM 360 Field 291 Information Loading and QR Coding 295 Using 360 Field to Status Material 299 Visualizing Equipment Status in the Model 301 Endless Possibilities 304 Small Wins to Big Change 305 Summary 305 Chapter 7 BIM and Close Out 307 True Costs of Facility Operations 308 Artifact Deliverables 310 Constant Deliverables 315 Taking a Hybrid Approach 317 Owners and BIM 317 Owner Options 318 Integration of a Record BIM 320 BIM and Information Handover 325 Maintaining the Model 329 Ongoing Investment and Logistics for Facility Management BIM 330 Training 332 Model Maintenance 333 One BIM = One Source of Information 334 Summary 337 Chapter 8 The Future of BIM 339 What Will BIM Be? 340 Industry Trends 340 BIM and Prefabrication 342 New Processes and Roles 343 Interoperability 345 BIM and Education 349 BIM and the New Construction Manager 351 BIM and the New Team 354 BIM and the New Process 356 Future Opportunities 357 Future Relationships 359 Virtual Builder Certification 360 Summary 362 Index 363

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Book SynopsisA modern guide to computational models and constructive simulation for personalized patient care using the Digital Patient The healthcare industry's emphasis is shifting from merely reacting to disease to preventing disease and promoting wellness. Addressing one of the more hopeful Big Data undertakings, The Digital Patient: Advancing Healthcare, Research, and Education presents a timely resource on the construction and deployment of the Digital Patient and its effects on healthcare, research, and education. The Digital Patient will not be constructed based solely on new information from all the omics fields; it also includes systems analysis, Big Data, and the various efforts to model the human physiome and represent it virtually. The Digital Patient will be realized through the purposeful collaboration of patients as well as scientific, clinical, and policy researchers. The Digital Patient: Advancing Healthcare, Research, and Education addresses Table of ContentsList of Contributors xiii Preface xvii Part 1 The Vision: The Digital Patient—Improving Research, Development, Education, and Healthcare Practice 1 1 The Digital Patient 3C. Donald Combs Health, The Goal, 4 Personalized Medicine, 4 The Best Outcomes, 5 The Emergence of the Digital Patient, 5 The Human Physiome, 6 Enabling the Digital Patient, 8 P4 Medicine, 11 Conclusion, 11 References, 12 2 Reflecting on Discipulus and Remaining Challenges 15Vanessa Díaz]Zuccarini, Mona Alimohammadi, and César Pichardo]Almarza Introduction, 15 A Brief Contextual Background and a Call for Integration: Personalized Medicine is Holistic, 16 The Many Versions of the Digital Patient: On the Road to Medical Avatars, 18 Discipulus: The Digital Patient Technological Challenges and Main Conclusions, 19 The Remaining Challenges and Big Data, 24 Conclusion, 25 References, 26 3 Advancing the Digital Patient 27Catherine M. Banks Introduction, 27 The Digital Patient: Its Early Start, 28 Engaging the Digital Patient, 30 Conclusion, 31 4 The Significance of Modeling and Visualization 33John A. Sokolowski and Hector M. Garcia Introduction, 33 Modeling a Complex System: Human Physiology, 34 Medical Modeling, Simulation, and Visualization, 35 Modes and Types of Visualization, 40 Visualization for Patient]Specific Usefulness, 43 Conclusion, 43 References, 45 Part 2 State of the Art: Systems Biology, the Physiome and Personalized Health 49 5 The Visible Human: A Graphical Interface for Holistic Modeling and Simulation 51Victor M. Spitzer Introduction, 51 Education, 53 Modeling, 55 Virtual Reality Trainers and Simulators, 56 Conclusion, 58 References, 59 6 The Quantifiable Self: Petabyte by Petabyte 63C. Donald Combs and Scarlett R. Barham Introduction, 63 Smarr’s Quantified Self, 64 Extending Smarr’s Research, 67 The Quantified Self]Vision, Simplified, 69 Criticism, 69 Conclusion, 71 References, 72 7 Systems Biology and Health Systems Complexity: Implications for the Digital Patient 73C. Donald Combs, Scarlett R. Barham, and Peter M. A. Sloot Introduction, 73 Systems Biology, 75 The Institute for Systems Biology, 76 The Complexity Institute, 78 The Potential of Systems Biology, 81 Criticism, 82 Conclusion, 83 References, 83 8 Personalized Computational Modeling for the Treatment of Cardiac Arrhythmias 85Seth H. Weinberg Introduction, 85 Basics of Cardiac Electrophysiology, 86 Cardiac Modeling Advancements, 89 Regulation of Intracellular Calcium, 90 From Cells to Cables to Sheets to Tissue to the Heart, 91 Where Can we go from Here? What is the Cardiac Model in the Digital Patient? 95 References, 96 9 The Physiome Project, openEHR Archetypes, and the Digital Patient 101David P. Nickerson, Koray Atalag, Bernard de Bono, and Peter J. Hunter Introduction, 101 Multiscale Physiological Processes, 102 Physiome Project Standards, Repositories, and Tools, 103 Archetype Specialization, 112 Archetype Definition Language, 113 Linking Archetypes to External Knowledge Sources (Terminology and Biomedical Ontologies), 114 Archetype Annotations, 114 OpenEHR Model Repository and Governance, 115 Fast Healthcare Interoperability Resources, 115 A Disease Scenario, 116 Summary and Conclusions, 121 References, 122 10 Physics]Based Modeling for the Physiome 127William A. Pruett and Robert L. Hester Introduction, 127 Modeling Schemes, 128 Future Challenges, 142 Conclusion, 142 Acknowledgments, 143 References, 143 11 Modeling and Understanding the Human Body with SwarmScript 149Sebastian von Mammen, Stefan Schellmoser, Christian Jacob, and Jörg Hähner Introduction, 149 Related Work, 150 Multiagent Organization, 152 Designing Interactive Agents, 152 Speaking SwarmScript, 153 Answering Demand: The Design of SwarmScript, 153 Graph]Based Rule Representation, 153 The Source–Action–Target, 154 SwarmScript INTO3D, 154 A SwarmScript Dialogue, 155 Discussion, 159 Summary, 161 References, 162 12 Using Avatars and Agents to Promote Real]World Health Behavior Changes 167Sun Joo (Grace) Ahn Introduction, 167 Avatars and Agents, 168 Using Agents and Avatars to Promote Health Behavior Changes, 169 Conclusion, 174 References, 174 13 Virtual Reality and Eating, Diabetes, and Obesity 179Jessica E. Cornick and Jim Blascovich Introduction, 179 Virtual Reality, 179 Obesity and Weight Stigma, 184 Virtual Reality as a Tool for Combatting Health Issues, 185 Conclusion, 189 References, 189 14 Immersive Virtual Reality to Model Physical: Social Interaction and Self]Representation 197Eric B. Bauman Introduction, 197 Theory for Immersive Virtual Learning Spaces, 197 Conclusion, 202 References, 203 Part 3 Challenges: Assimilating the Comprehensive Digital Patient 205 15 A Roadmap for Building a Digital Patient System 207Saikou Y. Diallo and Christopher J. Lynch Introduction, 207 Approach, 210 Building the Digital Patient Through Interoperability, 211 Conclusion, 221 Acknowledgments, 221 References, 221 16 Multidisciplinary, Interdisciplinary, and Transdisciplinary Research: Contextualization and Reliability of the Composite 225Andreas Tolk Introduction, 225 Interdisciplinarity and Interdisciplinary Research, 226 Data Engineering to Support Interdisciplinarity and Interoperability, 228 Base Object Models to Support Transdisciplinarity and Composability, 233 Open Challenges on Reliability, 235 Summary and Conclusion, 237 References, 239 17 Bayes Net Modeling: The Means to Craft the Digital Patient 241Joseph A. Tatman and Barry C. Ezell Introduction, 241 Other Interesting Applications, 246 Conclusion, 251 References, 253 Part 4 Potential Impact: Engaging The Digital Patient 255 18 Virtual Reality Standardized Patients for Clinical Training 257Albert Rizzo and Thomas Talbot Introduction, 257 The Rationale for Virtual Standardized Patients, 258 Conversational Virtual Human Agents, 259 Usc Efforts to Create Virtual Standardized Patients, 260 Conclusion, 269 References, 270 19 The Digital Patient: Changing the Paradigm of Healthcare and Impacting Medical Research and Education 273V. Andrea Parodi Introduction, 273 Overview Digital Medicine Projects, 275 Personalized Patient Care Clinical Use, 279 Recommended Education and Training for VPH Project Participation, 281 From Flexner to the 2010 Carnegie Report, 284 Summary Statements, 286 References, 287 20 The Digital Patient: A Vision for Revolutionizing the Electronic Medical Record and Future Healthcare 289Richard M. Satava Introduction, 289 Applications of the Digital Patient as the EMR, 291 Discussion, 296 Conclusion, 297 References, 297 21 Realizing the Digital Patient 299C. Donald Combs and John A. Sokolowski Index 305

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Book SynopsisAccessible VBA coding for complex financial modelling How to Implement Market Models Using VBA makes solving complex valuation issues accessible to any financial professional with a taste for mathematics.Table of ContentsPreface ix Acknowledgements xi Abbreviations xiii About the Author xv CHAPTER 1 The Basics of VBA Programming 1 1.1 Getting started 1 1.2 VBA objects and syntax 2 1.2.1 The object-oriented basic syntax 3 1.2.2 Using objects 3 1.3 Variables 5 1.3.1 Variable declaration 5 1.3.2 Some usual objects 7 1.3.3 Arrays 9 1.4 Arithmetic 10 1.5 Subroutines and functions 13 1.5.1 Subroutines 14 1.5.2 Functions 15 1.5.3 Operations on one-dimensional arrays 16 1.5.4 Operations on two-dimensional arrays (matrices) 16 1.5.5 Operations with dates 19 1.6 Custom objects 21 1.6.1 Types 21 1.6.2 Classes 22 1.7 Debugging 24 1.7.1 Error handling 24 1.7.2 Tracking the code execution 25 CHAPTER 2 Mathematical Algorithms 29 2.1 Introduction 29 2.2 Sorting lists 29 2.2.1 Shell sort 29 2.2.2 Quick sort 32 2.3 Implicit equations 34 2.4 Search for extrema 36 2.4.1 The Nelder-Mead algorithm 36 2.4.2 The simulated annealing 40 2.5 Linear algebra 43 2.5.1 Matrix inversion 44 2.5.2 Cholesky decomposition 46 2.5.3 Interpolation 48 2.5.4 Integration 57 2.5.5 Principal Component Analysis 60 CHAPTER 3 Vanilla Instruments 67 3.1 Definitions 67 3.2 Fixed income 67 3.2.1 Bond market 68 3.2.2 Interbank market 72 3.3 Vanilla derivatives 75 3.3.1 Forward contracts 75 3.3.2 Swaps 77 3.3.3 Bond futures 81 3.4 Options basics 84 3.4.1 Brownian motion 84 3.4.2 Ito integral 85 3.4.3 Ito formula 86 3.4.4 Black–Scholes basic model 89 3.4.5 Risk-neutral probability 90 3.4.6 Change of probability 90 3.4.7 Martingale and numeraires 92 3.4.8 European-style options pricing 94 3.5 First generation exotic options 95 3.5.1 Barrier options 95 3.5.2 Quanto options 102 CHAPTER 4 Numerical Solutions 105 4.1 Finite differences 105 4.1.1 Generic equation 105 4.1.2 Implementation 106 4.2 Trees 112 4.2.1 Binomial trees 112 4.2.2 Trinomial trees 116 4.3 Monte-Carlo scenarios 116 4.3.1 Uniform number generator 117 4.3.2 From uniform to Gaussian numbers 127 4.4 Simulation and regression 129 4.5 Double-barrier analytical approximation 134 CHAPTER 5 Monte-Carlo Pricing Issues 139 5.1 Multi-asset simulation 139 5.1.1 The correlations issue 139 5.1.2 The Gaussian case 139 5.1.3 Exotics 143 5.2 Discretization schemes 146 5.3 Variance reduction techniques 147 5.3.1 Antithetic variates 147 5.3.2 Importance sampling 148 5.3.3 Control variates 153 CHAPTER 6 Yield Curve Models 163 6.1 Short rate models 163 6.1.1 Introduction 163 6.1.2 Hull and White one-factor model 164 6.1.3 Gaussian two-factor model 180 6.1.4 Hull and White two-factor model 203 6.2 Forward rate models 204 6.2.1 Generic Heath-Jarrow-Morton 205 6.2.2 LMM (LIBOR market model) 216 CHAPTER 7 Stochastic Volatilities 233 7.1 The Heston model 234 7.1.1 Code 234 7.1.2 A faster algorithm 239 7.1.3 Calibration 248 7.2 Barrier options 254 7.2.1 Numerical results 257 7.2.2 Code 257 7.3 Asian-style options 260 7.4 SABR model 264 7.4.1 Caplets 264 7.4.2 Code 265 CHAPTER 8 Interest Rate Exotics 267 8.1 CMS swaps 267 8.1.1 Code 269 8.2 Cancelable swaps 272 8.2.1 Code 272 8.2.2 Tree approximation 276 8.3 Target redemption note 281 8.3.1 Code 282 Bibliography 287 Index 289

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Book Synopsis

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Book SynopsisThe engineering of materials with advanced features is driving the research towards the design of innovative materials with high performances. New materials often deliver the best solution for structural applications, precisely contributing towards the finest combination of mechanical properties and low weight.Table of ContentsPreface xiii Part 1 Engineering of Materials, Characterizations, and Applications 1 Mechanical Behavior and Resistance of Structural Glass Beams in Lateral–Torsional Buckling (LTB) with Adhesive Joints 3 Chiara Bedon and Jan Belis 1.1 Introduction 4 1.2 Overview on Structural Glass Applications in Buildings 5 1.3 Glass Beams in LTB 5 1.3.1 Susceptibility of Glass Structural Elements to Buckling Phenomena 5 1.3.2 Mechanical and Geometrical Influencing Parameters in Structural Glass Beams 8 1.3.3 Mechanical Joints 9 1.3.4 Adhesive Joints 10 1.4 Theoretical Background for Structural Members in LTB 14 1.4.1 General LTB Method for Laterally Unrestrained (LU) Members 14 1.4.2 LTB Method for Laterally Unrestrained (LU) Glass Beams 17 1.4.2.1 Equivalent Thickness Methods for Laminated Glass Beams 18 1.4.3 Laterally Restrained (LR) Beams in LTB 23 1.4.3.1 Extended Literature Review on LR Beams 23 1.4.3.2 Closed-form Formulation for LR Beams in LTB 24 1.4.3.3 LR Glass Beams Under Positive Bending Moment My 28 1.5 Finite-element Numerical Modeling 31 1.5.1 FE Solving Approach and Parametric Study 32 1.5.1.1 Linear Eigenvalue Buckling Analyses (lba) 32 1.5.1.2 Incremental Nonlinear Analyses (inl) 35 1.6 LTB Design Recommendations 38 1.6.1 LR Beams Under Positive Bending Moment My 38 1.6.2 Further Extension and Developments of the Current Outcomes 39 1.7 Conclusions 42 References 44 2 Room Temperature Mechanosynthesis of Nanocrystalline Metal Carbides and Their Microstructure Characterization 49 S.K. Pradhan and H. Dutta 2.1 Introduction 50 2.1.1 Application 50 2.1.2 Different Methods for Preparation of Metal Carbide 50 2.1.3 Mechanical Alloying 51 2.1.4 Planetary Ball Mill 51 2.1.5 The Merits and Demerits of Planetary Ball Mill 52 2.1.6 Review of Works on Metal Carbides by Other Authors 53 2.1.7 Significance of the Study 54 2.1.8 Objectives of the Study 55 2.2 Experimental 56 2.3 Theoretical Consideration 58 2.3.1 Microstructure Evaluation by X-ray Diffraction 58 2.3.2 General Features of Structure 60 2.4 Results and Discussions 60 2.4.1 XRD Pattern Analysis 60 2.4.2 Variation of Mol Fraction 65 2.4.3 Phase Formation Mechanism 69 2.4.4 Is Ball-milled Prepared Metal Carbide Contains Contamination? 71 2.4.5 Variation of Particle Size 72 2.4.6 Variation of Strain 74 2.4.7 High-Resolution Transmission Electron Microscopy Study 76 2.4.8 Comparison Study between Binary and Ternary Ti-based Metal Carbides 76 2.5 Conclusion 80 Acknowledgment 80 References 80 3 Toward a Novel SMA-reinforced Laminated Glass Panel 87 Chiara Bedon and Filipe Amarante dos Santos 3.1 Introduction 87 3.2 Glass in Buildings 89 3.2.1 Actual Reinforcement Techniques for Structural Glass Applications 92 3.3 Structural Engineering Applications of Shape-Memory Alloys (SMAs) 93 3.4 The Novel SMA-Reinforced Laminated Glass Panel Concept 94 3.4.1 Design Concept 94 3.4.2 Exploratory Finite-Element (FE) Numerical Study 96 3.4.2.1 General FE Model Assembly Approach and Solving Method 96 3.4.2.2 Mechanical Characterization of Materials 98 3.5 Discussion of Parametric FE Results 101 3.5.1 Roof Glass Panel (M1) 101 3.5.1.1 Short-term Loads and Temperature Variations 102 3.5.1.2 First-cracking Configuration 106 3.5.2 Point-supported Façade Panel (M2) 109 3.5.2.1 Short-term Loads and Temperature Variations 111 3.6 Conclusions 114 References 117 4 Sustainable Sugarcane Bagasse Cellulose for Papermaking 121 Noé Aguilar-Rivera 4.1 Pulp and Paper Industry 122 4.2 Sugar Industry 123 4.3 Sugarcane Bagasse 124 4.4 Advantageous Utilizations of SCB 129 4.5 Applications of SCB Wastes 130 4.6 Problematic of Nonwood Fibers in Papermaking 131 4.7 SCB as Raw Material for Pulp and Paper 134 4.8 Digestion 135 4.9 Bleaching 135 4.10 Properties of Bagasse Pulps 136 4.10.1 Pulp Strength 137 4.10.2 Pulp Properties 137 4.10.3 Washing Technology 138 4.10.4 Paper Machine Operation 138 4.11 Objectives 138 4.12 Old Corrugated Container Pulps 139 4.13 Synergistic Delignification SCB–OCC 141 4.14 Elemental Chlorine-Free Bleaching of SCB Pulps 150 4.15 Conclusions 156 References 158 5 Bio-inspired Composites: Using Nature to Tackle Composite Limitations 165 F. Libonati 5.1 Introduction 166 5.2 Bio-inspiration: Bone as Biomimetic Model 169 5.3 Case Studies Using Biomimetic Approach 172 5.3.1 Fiber-reinforced Bone-inspired Composites 172 5.3.2 Fiber-reinforced Bone-inspired Composites with CNTs 176 5.3.3 Bone-inspired Composites via 3D Printing 177 5.4 Methods 179 5.4.1 Composite Lamination 180 5.4.2 Additive Manufacturing 181 5.4.3 Computational Modeling 182 5.5 Conclusions 183 References 185 Part 2 Computational Modeling of Materials 6 On the Electronic Structure and Band Gap of ZnSxSe1–x 193 Ghassan H. E. Al-Shabeeb and A. K. Arof 6.1 Introduction 193 6.2 Computational Method 194 6.3 The k·p Perturbation Theory with the Effect of Spin–Orbit Interaction 197 6.4 Results and Discussion 202 Acknowledgment 205 References 205 7 Application of First Principles Theory to the Design of Advanced Titanium Alloys 207 Y. Song, J. H. Dai, and R. Yang 7.1 Introduction 207 7.2 Basic Concepts of First Principles 208 7.3 Theoretical Models of Alloy Design 211 7.3.1 The Hume-Rothery Theory 211 7.3.2 Discrete Variational Method and d-Orbital Method 216 7.3.2.1 Discrete Variational Method 216 7.3.2.2 d-Electrons Alloy Theory 218 7.4 Applications 219 7.4.1 Phase Stability 219 7.4.1.1 Binary Alloy 219 7.4.1.2 Multicomponent Alloys 222 7.4.2 Elastic Properties 223 7.4.3 Examples 226 7.4.3.1 Gum Metal 226 7.4.3.2 Ti2448 (Ti–24Nb–4Zr–8Sn) 227 7.5 Conclusions 230 Acknowledgment 230 References 230 8 Digital Orchid: Creating Realistic Materials 233 Iftikhar B. Abbasov 8.1 Introduction 234 8.2 Conclusion 243 References 243 9 Transformation Optics-based Computational Materials for Stochastic Electromagnetics 245 Ozlem Ozgun and Mustafa Kuzuoglu 9.1 Introduction 246 9.2 Theory of Transformation Optics 249 9.3 Scattering from Rough Sea Surfaces 252 9.3.1 Numerical Validation and Monte Carlo Simulations 256 9.4 Scattering from Obstacles with Rough Surfaces or Shape Deformations 258 9.4.1 Numerical Validation and Monte Carlo Simulations 263 9.4.2 Combining Perturbation Theory and Transformation Optics for Weakly Perturbed Surfaces 264 9.5 Scattering from Randomly Positioned Array of Obstacles 268 9.5.1 Separate Transformation Media 269 9.5.1.1 Numerical Validation & Monte Carlo Simulations 271 9.5.2 A Single Transformation Medium 273 9.5.2.1 Numerical Validation & Monte Carlo Simulations 275 9.5.3 Recurring Scaling and Translation Transformations 276 9.5.3.1 Numerical Validation & Monte Carlo Simulations 278 9.6 Propagation in a Waveguide with Rough or Randomly Varying Surface 278 9.3.1 Numerical Validation and Monte Carlo Simulations 283 9.7 Conclusion 287 References 288 10 Superluminal Photons Tunneling through Brain Microtubules Modeled as Metamaterials and Quantum Computation 291 Luigi Maxmilian Caligiuri and Takaaki Musha 10.1 Introduction 292 10.2 QED Coherence in Water: A Brief Overview 295 10.3 “Electronic” QED Coherence in Brain Microtubules 301 10.4 Evanescent Field of Coherent Photons and Their Superluminal Tunneling through MTs 305 10.5 Coupling between Nearby MTs and their Superluminal Interaction through the Exchange of Virtual Superradiant Photons 312 10.6 Discussion 316 10.7 Brain Microtubules as “Natural” Metamaterials and the Amplification of Evanescent Tunneling Wave Amplitude 319 10.8 Quantum Computation by Means of Superluminal Photons 325 10.9 Conclusions 329 References 330 11 Advanced Fundamental-solution-based Computational Methods for Thermal Analysis of Heterogeneous Materials 335 Hui Wang and Qing-Hua Qin 11.1 Introduction 336 11.2 Basic Formulation of MFS 338 11.2.1 Standard MFS 338 11.2.2 Modified MFS 340 11.2.2.1 RBF Interpolation for the Particular Solution 341 11.2.2.2 MFS for the Homogeneous Solution 342 11.2.2.3 Complete Solution 343 11.3 Basic Formulation of HFS-FEM 344 11.3.1 Problem Statement 344 11.3.2 Implementation of the HFS-FEM 346 11.3.4 Recovery of Rigid-body Motion 349 11.4 Applications in Functionally Graded Materials 349 11.4.1 Basic Equations in Functionally Graded Materials 349 11.4.2 MFS for Functionally Graded Materials 350 11.4.3 HFS-FEM for Functionally Graded Materials 353 11.5 Applications in Composite Materials 357 11.5.1 Basic Equations of Composite Materials 357 11.5.2 MFS for Composite Materials 360 11.5.2.1 MFS for the Matrix Domain 360 11.5.2.2 MFS for the Fiber Domain 360 11.5.2.3 Complete Linear Equation System 361 11.5.3 HFS-FEM for Composite Materials 362 11.5.3.1 Special Fundamental Solutions 362 11.5.3.2 Special n-Sided Fiber/Matrix Elements 363 11.6 Conclusions 365 Acknowledgments 366 Conflict of Interest 366 References 366 12 Understanding the SET/RESET Characteristics of Forming Free TiOx/TiO2–x Resistive-Switching Bilayer Structures through Experiments and Modeling 373 P. Bousoulas and D. Tsoukalas 12.1 Introduction 374 12.2 Experimental Methodology 376 12.3 Bipolar Switching Model 378 12.3.1 Resistive-Switching Performance 378 12.3.2 Resistive-Switching Model 383 12.4 RESET Simulations 389 12.4.1 I–V Response 389 12.4.2 Influence of TE on the CFs Broken Region 393 12.5 SET Simulations 398 12.6 Simulation of Time-dependent SET/RESET Processes 401 12.7 Conclusions 403 Acknowledgments 404 References 404 13 Advanced Materials and Three-dimensional Computer-aided Surgical Workflow in Cranio-maxillofacial Reconstruction 411 Luis Miguel Gonzalez-Perez, Borja Gonzalez-Perez-Somarriba Gabriel Centeno, Carpóforo Vallellano, and Juan Jose Egea-Guerrero 13.1 Introduction 412 13.2 Methodology 413 13.3 Findings 418 13.4 Discussion 427 References 436 14 Displaced Multiwavelets and Splitting Algorithms 439 Boris M. Shumilov 14.1 An Algorithm with Splitting of Wavelet Transformation of Splines of the First Degree 443 14.1.1 “Lazy” Wavelets 444 14.1.2 Examples of Wavelet Decomposition of a Signal of Length 8 447 14.1.3 “Orthonormal” Wavelets 450 14.1.4 An Example of Function of Harten 454 14.2 An Algorithm for Constructing Orthogonal to Polynomials Multiwavelet Bases 456 14.2.1 Creation of System of Basic Multiwavelets of Any Odd Degree on a Closed Interval 456 14.2.2 Creation of the Block of Filters 459 14.2.3 Example of Orthogonal to Polynomials Multiwavelet Bases 461 14.2.4 The Discussion of Approximation on a Closed Interval 463 14.3 The Tridiagonal Block Matrix Algorithm 464 14.3.1 Inverse of the Block of Filters 464 14.3.2 Example of the Hermite Quintic Spline Function Supported on [−1, 1] 465 14.3.3 Example of the Hermite Septimus Spline Function Supported on [−1, 1] 467 14.3.4 Numerical Example of Approximation of Polynomial Function 470 14.3.5 Numerical Example with Two Ruptures of the First Kind and a Corner 471 14.4 Problem of Optimization of Wavelet Transformation of Hermite Splines of Any Odd Degree 475 14.4.1 An Algorithm with Splitting for Wavelet Transformation of Hermite Splines of Fifth Degree 478 14.4.2 Examples 485 14.5 Application to Data Processing of Laser Scanning of Roads490 14.5.1 Calculation of Derivatives on Samples 490 14.5.2 Example of Wavelet Compression of One Track of Data of Laser Scanning 490 14.5.3 Modeling of Surfaces 490 14.5.4 Functions of a Package of Applied Programs for Modeling of Routes and Surfaces of Highways 492 14.6 Conclusions 494 References 494

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Book SynopsisWorkflows are being rethought and remodelled across the architecture, engineering and construction (AEC) spectrum. The synthesis of building information modelling (BIM) platforms with digital simulation techniques and increasing access to data, charting building performance, is allowing architects to engage in the generation of new workflows across multidisciplinary teams.By merging digital design operations with construction activities, project delivery and post-occupation scenarios, architects are becoming instrumental in the shaping of buildings as well as the design process. Workflows expand the territory of architectural practice by extending designers' remit beyond the confines of the design stage. The implications for the AEC industry and architecture as a profession could not be greater. These new collaborative models are becoming as important as the novel buildings they allow us to produce.Contributors include: Shajay Bhooshan, John Cays, Randy DeutsTable of ContentsAbout the Guest-Editor 05Richard Garber Introduction Digital Workflows and the Expanded Territory of the Architect 06Richard Garber Sketching with Glass A Return to the Hand-Driven Workflow 14Sean A Gallagher Geologic Workflows The Metamorphosis of the Great Rock 22Péter Kis and Sándor Bardóczi The Fifth Dimension Architect-Led Design–Build 28Stacie Wong Mashup and Assemblage in Digital Workflows The Role of Integrated Software Platforms in the Production of ArchitectureAdam Modesitt Putting BIM at the Heart of a Small Practice 42David Miller Encrypted Workflows The Secret World of Objects 48Rhett Russo Understanding Architectural Workflows in Global Practice 56Randy Deutsch Expansive Workflows Downstream Coordination in the Design of Sporting Facilities 68Jonathan Mallie From Pencils to Partners The Next Role of Computation in Building Design 74Ian Keough and Anthony Hauck Collaborative Design Combining Computer-Aided Geometry Design and Building Information Modelling 82Shajay Bhooshan Ruptured Flows An Argument for Nonlinear Workflows 90Kutan Ayata Life-Cycle Assessment Reducing Environmental Impact Risk with Workflow Data You Can Trust 96John Cays Coming Full Circle New Ruralism 104Richard Garber Ecological Workflows Zhangdu Lake Farm, Hubei Province, China 114Richard Garber Advanced Engineering with Building Information Modelling Establishing Flexible Frameworks for the Design and Documentation of Complex Buildings 120Ken Goldup, Zak Kostura, Tabitha Tavolaro and Seth Wolfe Sinuous Workflows MAD Architects, The Harbin Opera House 128Richard Garber Counterpoint Architects at the Mixing Desk Workflows Cutting Across the Whole-Life Process 136Dale Sinclair Contributors 142

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Book SynopsisAn authoritative guide to computer simulation grounded in a multi-disciplinary approach for solving complex problems Simulation and Computational Red Teaming for Problem Solvingoffers a review of computer simulation that is grounded in a multi-disciplinary approach. The authors present the theoretical foundations of simulation and modeling paradigms from the perspective of an analyst. The book provides the fundamental background information needed for designing and developing consistent and useful simulations. In addition to this basic information, the authors explore several advanced topics. The book's advanced topics demonstrate how modern artificial intelligence and computational intelligence concepts and techniques can be combined with various simulation paradigms for solving complex and critical problems. Authors examine the concept of Computational Red Teaming to reveal how the combined fundamentals and advanced techniques are used successfully for solving and testing complex real-world problems. This important book: Demonstrates how computer simulation and Computational Red Teaming support each other for solving complex problems Describes the main approaches to modeling real-world phenomena and embedding these models into computer simulations Explores how a number of advanced artificial intelligence and computational intelligence concepts are used in conjunction with the fundamental aspects of simulation Written for researchers and students in the computational modelling and data analysis fields,Simulation and Computational Red Teaming for Problem Solvingcovers the foundation and the standard elements of the process of building a simulation and explores the simulation topic with a modern research approach.Table of ContentsPreface xi List of Figures xv List of Tables xxv Part I On Problem Solving, Computational Red Teaming, and Simulation 1 1. Problem Solving, Simulation, and Computational Red Teaming 3 1.1 Introduction 3 1.2 Problem Solving 4 1.3 Computational Red Teaming and Self-‘Verification and Validation’ 8 2. Introduction to Fundamentals of Simulation 11 2.1 Introduction 11 2.2 System 14 2.3 Concepts in Simulation 17 2.4 Simulation Types 21 2.5 Tools for Simulation 23 2.6 Conclusion 24 Part II Before Simulation Starts 25 3. The Simulation Process 27 3.1 Introduction 27 3.2 Define the System and its Environment 27 3.3 Build a Model 29 3.4 Encode a Simulator 30 3.5 Design Sampling Mechanisms 32 3.6 Run Simulator Under Different Samples 33 3.7 Summarise Results 33 3.8 Make a Recommendation 34 3.9 An Evolutionary Approach 35 3.10 A Battle Simulation by Lanchester Square Law 35 4. Simulation Worldview and Conflict Resolution 57 4.1 Simulation Worldview 57 4.2 Simultaneous Events and Conflicts in Simulation 64 4.3 Priority Queue and Binary Heap 68 4.4 Conclusion 72 5. The Language of Abstraction and Representation 73 5.1 Introduction 73 5.2 Informal Representation 75 5.3 Semi-formal Representation 76 5.4 Formal Representation 82 5.5 Finite-state Machine 86 5.6 Ant in Maze Modelled by Finite-state Machine 89 5.7 Conclusion 99 6. Experimental Design 101 6.1 Introduction 101 6.2 Factor Screening 103 6.3 Metamodel and Response Surface 113 6.4 Input Sampling 116 6.5 Output Analysis 117 6.6 Conclusion 120 Part III Simulation Methodologies 121 7. Discrete Event Simulation 123 7.1 Discrete Event Systems 123 7.2 Discrete Event Simulation 126 7.3 Conclusion 142 8. Discrete Time Simulation 143 8.1 Introduction 143 8.2 Discrete Time System and Modelling 145 8.3 Sample Path 148 8.4 Discrete Time Simulation and Discrete Event Simulation 149 8.5 A Case Study: Car-following Model 151 8.6 Conclusion 154 9. Continuous Simulation 157 9.1 Continuous System 157 9.2 Continuous Simulation 159 9.3 Numerical Solution Techniques for Continuous Simulation 164 9.4 System Dynamics Approach 172 9.5 Combined Discrete–continuous Simulation 174 9.6 Conclusion 176 10. Agent-based Simulation 179 10.1 Introduction 179 10.2 Agent-based Simulation 181 10.3 Examples of Agent-based Simulation 185 10.4 Conclusion 194 Part IV Simulation and Computational Red Teaming Systems 197 11. Knowledge Acquisition 199 11.1 Introduction 199 11.2 Agent-enabled Knowledge Acquisition: Core Processes 202 11.3 Human Agents 203 11.4 Human-inspired Agents 208 11.5 Machine Agents 211 11.6 Summary Discussion and Perspectives on Knowledge Acquisition 215 12. Computational Intelligence 219 12.1 Introduction 219 12.2 Evolutionary Computation 223 12.3 Artificial Neural Networks 232 12.4 Conclusion 239 13. Computational Red Teaming 241 13.1 Introduction 241 13.2 Computational Red Teaming: The Challenge Loop 242 13.3 Computational Red Teaming Objects 243 13.4 Computational Red Teaming Purposes 244 13.5 Objectives of Red Teaming Exercises in Computational Red Teaming Purposes 245 13.6 Discovering Biases 246 13.7 Computational Red Teaming Lifecycle: A Systematic Approach to Red Teaming Exercises 247 13.8 Conclusion 251 Part V Simulation and Computational Red Teaming Applications 253 14. Computational Red Teaming for Battlefield Management 255 14.1 Introduction 255 14.2 Battlefield Management Simulation 256 14.3 Conclusion 261 15. Computational Red Teaming for Air Traffic Management 263 15.1 Introduction 263 15.2 Air Traffic Simulation 263 15.3 A Human-in-the-loop Application 270 15.4 Conclusion 271 16. Computational Red Teaming Application for Skill-based Performance Assessment 273 16.1 Introduction 273 16.2 Cognitive Task Analysis-based Skill Modelling and Assessment Methodology 274 16.3 Sudoku and Human Players 276 16.4 Sudoku and Computational Solvers 280 16.5 The Proposed Skill-based Computational Solver 283 16.6 Discussion of Simulation Results 293 16.7 Conclusions 300 17. Computational Red Teaming for Driver Assessment 301 17.1 Introduction 301 17.2 Background on Cognitive Agents 303 17.3 The Society of Mind Agent 306 17.4 Society of Mind Agents in an Artificial Environment 312 17.5 Case Study 325 17.6 Conclusion 330 18. Computational Red Teaming for Trusted Autonomous Systems 333 18.1 Introduction 333 18.2 Trust for Influence and Shaping 334 18.3 The Model 335 18.4 Experiment Design and Parameter Settings 342 18.5 Results and Discussion 344 18.6 Conclusion 347 A. Probability and Statistics in Simulation 349 A.1 Foundation of Probability and Statistics 349 A.2 Useful Distributions 369 A.3 Mathematical Characteristics of Random Variables 390 A.4 Conclusion 396 B Sampling and Random Numbers 397 B.1 Introduction 397 B.2 Random Number Generator 400 B.3 Testing Random Number Generators 408 B.4 Approaches to Generating Random Variates 413 B.5 Generating Random Variates 416 B.6 Monte Carlo Method 423 B.7 Conclusion 432 Bibliography 435 Index 459

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Book SynopsisNeuroendocrinology with its well defined functions, inputs, and outputs, is one of the most fertile grounds for computational modeling in neuroscience. But modeling is often seen as something of a dark art.Table of ContentsList of Contributors, vii Series Preface, ix Preface, xi About the Companion Website, xv 1 Bridging Between Experiments and Equations: A Tutorial on Modeling Excitability, 1David P. McCobb and Mary Lou Zeeman 2 Ion Channels and Electrical Activity in Pituitary Cells: A Modeling Perspective, 80Richard Bertram, Joël Tabak, and Stanko S. Stojilkovic 3 Endoplasmic Reticulum- and Plasma-Membrane-Driven Calcium Oscillations, 111Arthur Sherman 4 A Mathematical Model of Gonadotropin-Releasing Hormone Neurons, 142James Sneyd, Wen Duan, and Allan Herbison 5 Modeling Spiking and Secretion in the Magnocellular Vasopressin Neuron, 166Duncan J. MacGregor and Gareth Leng 6 Modeling Endocrine Cell Network Topology, 206David J. Hodson, Francois Molino, and Patrice Mollard 7 Modeling the Milk-Ejection Reflex, 227Gareth Leng and Jianfeng Feng 8 Dynamics of the HPA Axis: A Systems Modeling Approach, 252John R. Terry, Jamie J. Walker, Francesca Spiga, and Stafford L. Lightman 9 Modeling the Dynamics of Gonadotropin-Releasing Hormone (GnRH) Secretion in the Course of an Ovarian Cycle, 284Frédérique Clément and Alexandre Vidal Glossary, 305 Index, 315

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