Computer modelling and simulation Books

Book SynopsisPossibly the most comprehensive overview of computer graphics as seen in the context of geometric modelling, this two volume work covers implementation and theory in a thorough and systematic fashion. Computer Graphics and Geometric Modelling: Implementation and Algorithms, covers the computer graphics part of the field of geometric modelling and includes all the standard computer graphics topics. The first part deals with basic concepts and algorithms and the main steps involved in displaying photorealistic images on a computer. The second part covers curves and surfaces and a number of more advanced geometric modelling topics including intersection algorithms, distance algorithms, polygonizing curves and surfaces, trimmed surfaces, implicit curves and surfaces, offset curves and surfaces, curvature, geodesics, blending etc. The third part touches on some aspects of computational geometry and a few special topics such as interval analysis and finite element methods. The volume includes two companion programs.Table of ContentsIntroduction Raster Algorithms Clipping Transformations and the Graphics Pipeline Approaches to Geometric Modelling Basic Geometric Modeling Tools Visible Surface Algorithms Colour Illumination and Shading Rendering Techniques Curves in Computer Graphics Surfaces in Computer Graphics Intersection Algorithms Global Geometric Modelling Topics Local Geometric Modelling Topics Intrinsic Geometric Modelling Computational Geometry Topics Interval Analysis The Finite Element Method Quaternions Digital Image Processing Topics Chaos and Fractals Appendices: Notation Abstract Program Syntax IGES GM - AS Geometric Modelling Program - available at http://extras.springer.com (search 978-1-85233-818-3) SPACE - A Manifold Exploration Program - available at http://extras.springer.com (search 978-1-85233-818-3)

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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 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 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 SynopsisFritzson covers the Modelica language in impressive depth from the basic concepts such as cyber-physical, equation-base, object-oriented, system, model, and simulation, while also incorporating over a hundred exercises and their solutions for a tutorial, easy-to-read experience. The only book with complete Modelica 3.3 coverage Over one hundred exercises and solutions Examines basic concepts such as cyber-physical, equation-based, object-oriented, system, model, and simulation Table of ContentsPreface v About the Author v About this Book v Reading Guide vi Acknowledgements vii Contributions to Examples ix Contributors to the Modelica Standard Library, Version 3.2.1 xii Contributors to the Modelica Standard Library, Versions 1.0 to 2.1 xiii Contributors to the Modelica Language, Version 3.3 xiii Contributors to the Modelica Language, Version 3.2 xiv Contributors to the Modelica Language, Version 3.0 xv Contributors to the Modelica Language, Version 2.0 xvi Contributors to the Modelica Language, up to Version 1.3 xvi Modelica Association Member Companies and Organizations 2013 xvii Funding Contributions xviii Part I Introduction 1 Chapter 1 Introduction to Modeling and Simulation 3 Chapter 2 A Quick Tour of Modelica 19 Part II The Modelica Language 79 Chapter 3 Classes, Types, Declarations, and Lookup 81 Chapter 4 Inheritance, Modifications, and Generics 137 Chapter 5 Components, Connectors, and Connections 189 Chapter 6 Literals, Operators, and Expressions 269 Chapter 7 Arrays 313 Chapter 8 Equations 349 Chapter 9 Algorithms and Functions 423 Chapter 10 Packages 497 Chapter 11 Annotations, Units, and Quantities 521 Part III Modeling and Applications 567 Chapter 12 Cyber-Physical System Modeling Methodology 569 Chapter 13 Discrete Events, Hybrid and Embedded System Modeling 593 Chapter 14 Basic Laws of Nature 747 Chapter 15 Application Examples 795 Chapter 16 Modelica Library Overview 909 Part IV Technology and Tools 977 Chapter 17 A Mathematical Representation for Modelica Models 979 Chapter 18 Techniques and Research 993 Chapter 19 Environments 1029 Appendix A Glossary 1063 Appendix B Modelica Formal Syntax 1071 Appendix C Solutions to Exercises 1083 Appendix D Modelica Standard Library Samples 1093 Appendix E Modelica and Python Scripting 1123 Appendix F Related Equation-Based Object Oriented Modeling Languages 1153 Appendix G FMI – Functional Mockup Interface 1163 G.1 Summary 1163 G.2 Overview 1164 G.3 FMI for Model Exchange 1168 G.4 FMI for Co-Simulation 1169 G.5 Literature 1172 References 1175 Index 1197

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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 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 SynopsisThis book brings together computer models and simulation approaches that allow the investigation of a wide range of innovation related issues, and hence will be of interest for academics and researchers from a variety of innovation related disciplines.'- Mercedes Bleda, Journal of Artificial Societies and Social SimulationChristopher Watts and Nigel Gilbert explore the generation, diffusion and impact of innovations, which can now be studied using computer simulations.Agent-based simulation models can be used to explain the innovation that emerges from interactions among complex, adaptive, diverse networks of firms, people, technologies, practices and resources. This book provides a critical review of recent advances in agent-based modeling and other forms of the simulation of innovation. Elements explored include: diffusion of innovations, social networks, organizational learning, science models, adopting and adapting, and technological evolution and innovation networks. Many of the models featured in the book can be downloaded from the book's accompanying website.Bringing together simulation models from several innovation-related fields, this book will prove a fascinating read for academics and researchers in a wide range of disciplines, including: innovation studies, evolutionary economics, complexity science, organization studies, social networks, and science and technology studies. Scholars and researchers in the areas of computer science, operational research and management science will also be interested in the uses of simulation models to improve the understanding of organization.Trade ReviewThis book brings together computer models and simulation approaches that allow the investigation of a wide range of innovation related issues, and hence will be of interest for academics and researchers from a variety of innovation related disciplines. --- Mercedes Bleda, Journal of Artificial Societies and Social SimulationTable of ContentsContents: Preface 1. Why Simulate Innovation? 2. The Variability and Variety of Diffusion Models 3. Diffusion and Path Dependence in a Social Network 4. Explore and Exploit 5. Science Models 6. Adopting and Adapting: Innovation Diffusion in Complex Contexts 7. Technological Evolution and Innovation Networks 8. Conclusions Bibliography

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Book SynopsisTo analyze complex situations we use everyday analogies that allow us to invest in an unknown domain knowledge we have acquired in a known field. In this work the author proposes a modeling and analysis method that uses the analogy of the ecosystem to embrace the complexity of an area of knowledge. After a history of the ecosystem concept and these derivatives (nature, ecology, environment ) from antiquity to the present, the analysis method based on the modeling of socio-semantic ontologies is presented, followed by practical examples of this approach in the areas of software development, digital humanities, Big Data, and more generally in the area of complex analysis.Table of ContentsIntroduction ix Chapter 1. Use of the Ecosystem Concept on the Web 1 1.1. For marketing 2 1.2. For personal data 4 1.3. For services and applications 5 1.4. For dynamic interactivity 7 1.5. For pictorial analogies 8 1.6. For the information and communication sciences 12 Chapter 2. Ecosystem Modeling: A Generic Method of Analysis 15 2.1. Hypertextual gardening fertilized by the chaos of John Cage 16 2.2. An entrepreneurial experience 17 2.2.1. Objectives 18 2.2.2. Principle of the game 18 2.2.3. Motivations 19 2.2.3.1. Why model a cognitive ecology? 19 2.2.3.2. The relevance of the garden analogy 20 2.2.4. Strategic interests and potential benefits 23 2.3. The maturation of a research project 24 2.3.1. Evaluating index activity 24 2.3.2. Folksonomies explorer 28 2.3.3. Tweet Palette: Semantic mapping 34 Chapter 3. Fundamental Principles for Modeling an Existence 41 3.1. Key concepts for thinking about knowledge ecosystems 42 3.1.1. The noosphere 42 3.1.2. Enaction 44 3.1.3. Complexity 45 3.1.4. Trajective reason 46 3.1.5. Agency 47 3.2. Spinozist principles for an ethical ontology 48 3.2.1. Spinoza: ethical ontology 49 3.2.2. Limitations of Spinozism 50 3.2.3. Three dimensions of existence and three kinds of knowledge 51 3.2.4. Spinozist symbol politics 55 3.2.5. Spinozist ethics for the Web 57 3.2.6. The ontological principles of Descola 58 3.2.7. Principles of ontological matrices 59 3.2.8. The Web as analogist ontology 63 3.2.9. Principles of computer models 67 3.2.10. From Zeno to Turing via Spinoza 68 3.2.11. The search for the perfect language 74 3.3. Semantic knowledge management 77 3.3.1. The boundaries of ontologies 77 3.3.2. The semantic sphere IEML 78 Chapter 4. Graphical Specifications for Modeling Existences 89 4.1. Principles of graphical modeling 90 4.1.1. Unified modeling language 90 4.1.2. Graphic partitions and diagrams 92 4.1.3. Fixed image versus dynamic diagram 94 4.2. Semantic maps 97 4.2.1. Maps of physical spaces 97 4.2.2. Time maps 99 4.2.3. Maps of conceptual spaces 101 4.2.4. Interpretation maps 107 4.3. Graphical modeling rules 110 4.3.1. Physical dimensions 110 4.3.2. Actors 111 4.3.3. Concepts 111 4.3.4. Relations 112 4.3.5. Calculating the complexity of an ecosystem 113 Chapter 5. Web Platform Specifications for Knowledge Ecosystems 117 5.1. The generic management of resources 119 5.1.1. Non-digital resources 119 5.1.2. Digital resources 122 5.1.3. Management of digital resources 131 5.2. Principles for developing a Web ecosystem platform 138 5.2.1. Databases as a model of the ecosystem 138 5.2.2. Algorithmic platform to manage the ecosystem 153 5.2.3. Editorial platform for controlling collaborative practices 157 5.2.4. Client applications to explore ecosystem views 162 5.2.5. From technical specification to the organization of collective intelligence 171 Conclusion 173 Appendix 185 Bibliography 201 Index 217

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Book SynopsisModel-based Systems Architecting is a key tool for designing complex industrial systems. It is dedicated to the working systems architects, engineers and modelers, in order to help them master the complex integrated systems that they are dealing with in their day-to-day professional lives. It presents the CESAMES Systems Architecting Method (CESAM), a systems architecting and modeling framework which has been developed since 2003 in close interaction with many leading industrial companies, providing rigorous and unambiguous semantics for all classical systems architecture concepts. This approach is practically robust and easy-to-use: during the last decade, it was deployed in more than 2,000 real system development projects within the industry, and distributed to around 10,000 engineers around the globe.Table of ContentsPreface ix Acknowledgments xv Introduction xvii Chapter 1 Introduction to CESAM 1 1.1 CESAM: a mathematically sound system modeling framework 1 1.2 CESAM: a framework focused on complex integrated systems 8 1.3 CESAM: a collaboration-oriented architecting framework 12 1.4 CESAM: a business-oriented framework 16 Chapter 2 Why Architecting Systems? 19 2.1 Product and project systems 19 2.2 The complexity threshold 22 2.3 Addressing systems architecting becomes key 25 2.4 The value of systems architecting 31 2.5 The key role of systems architects 34 2.6 How to analyze a systems architect profile? 36 Chapter 3 CESAM Framework 39 3.1 Elements of systemics 39 3.1.1 Interface 39 3.1.2 Environment of a system 41 3.2 The three architectural visions 42 3.2.1 Architectural visions definition 42 3.2.2 Architectural visions overview 46 3.2.3 Relationships between the three architectural visions 52 3.2.4 Organization of a system model 55 3.3 CESAM systems architecture pyramid 57 3.3.1 The three key questions to ask 57 3.3.2 The last question that shall not be forgotten 59 3.4 More systems architecture dimensions 60 3.4.1 Descriptions versus expected properties 60 3.4.2 Descriptions 62 3.4.3 Expected properties 73 3.5 CESAM systems architecture matrix 78 Chapter 4 Identifying Stakeholders: Environment Architecture 83 4.1 Why identify stakeholders? 83 4.2 The key deliverables of environment architecture 85 4.2.1 Stakeholder hierarchy diagram 85 4.2.2 Environment diagram 87 Chapter 5 Understanding Interactions with Stakeholders: Operational Architecture 91 5.1 Why understand interactions with stakeholders? 91 5.2 The key deliverables of operational architecture 94 5.2.1 Need architecture diagram 94 5.2.2 Lifecycle diagram 95 5.2.3 Use case diagrams 97 5.2.4 Operational scenario diagrams 99 5.2.5 Operational flow diagram 101 Chapter 6 Defining What the System Shall Do: Functional Architecture 103 6.1 Why understand what the system does? 103 6.2 The key deliverables of functional architecture 105 6.2.1 Functional requirement architecture diagram 106 6.2.2 Functional mode diagram 108 6.2.3 Functional breakdown and interaction diagrams 109 6.2.4 Functional scenario diagrams 111 6.2.5 Functional flow diagram 112 Chapter 7 Deciding How the System Shall be Formed: Constructional Architecture 115 7.1 Understanding how the system is formed? 115 7.2 The key deliverables of constructional architecture 117 7.2.1 Constructional requirement architecture diagram 118 7.2.2 Configuration diagram 120 7.2.3 Constructional breakdown and interaction diagram 121 7.2.4 Constructional scenario diagram 123 7.2.5 Constructional flow diagram 124 Chapter 8 Taking into Account Failures: Dysfunctional Analysis 127 8.1 Systems do not always behave as they should 127 8.2 The key deliverables of dysfunctional analysis 134 8.2.1 Dysfunctional analysis from an operational perspective 135 8.2.2 Dysfunctional analysis from a functional perspective 136 8.2.3 Dysfunctional analysis from a constructional perspective 138 Chapter 9 Choosing the Best Architecture: Trade-off Techniques 141 9.1 Systems architecting does not usually lead to a unique solution 141 9.2 Trade-off techniques 143 9.2.1 General structure of a trade-off process 143 9.2.2 Managing trade-offs in practice 145 Conclusion 149 Appendices 157 Appendix 1 System Temporal Logic 159 Appendix 2 Classical Engineering Issues 163 Appendix 3 Example of System Model Managed with CESAM 177 Appendix 4 Implementing CESAM through a SysML Modeling Tool 199 Appendix 5 Some Good Practices in Systems Modeling 209 References 211 Index 219

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Book SynopsisThis book is devoted to the most used methodologies for performance evaluation: simulation using specialized software and mathematical modeling. An important part is dedicated to the simulation, particularly in its theoretical framework and the precautions to be taken in the implementation of the experimental procedure. These principles are illustrated by concrete examples achieved through operational simulation languages ​​(OMNeT ++, OPNET). Presented under the complementary approach, the mathematical method is essential for the simulation. Both methodologies based largely on the theory of probability and statistics in general and particularly Markov processes, a reminder of the basic results is also available.Table of ContentsLIST OF TABLES xv LIST OF FIGURES xvii LIST OF LISTINGS xxi PREFACE xxiii CHAPTER 1. PERFORMANCE EVALUATION 1 1.1. Performance evaluation 1 1.2. Performance versus resources provisioning 3 1.2.1. Performance indicators 3 1.2.2. Resources provisioning 4 1.3. Methods of performance evaluation 4 1.3.1. Direct study 4 1.3.2. Modeling 5 1.4. Modeling 6 1.4.1. Shortcomings 6 1.4.2. Advantages 7 1.4.3. Cost of modeling 7 1.5. Types of modeling 8 1.6. Analytical modeling versus simulation 8 PART 1. SIMULATION 11 CHAPTER 2. INTRODUCTION TO SIMULATION 13 2.1. Presentation 13 2.2. Principle of discrete event simulation 15 2.2.1. Evolution of a event-driven system 15 2.2.2. Model programming 16 2.3. Relationship with mathematical modeling 18 CHAPTER 3. MODELING OF STOCHASTIC BEHAVIORS 21 3.1. Introduction 21 3.2. Identification of stochastic behavior 23 3.3. Generation of random variables 24 3.4. Generation of U(0, 1) r.v. 25 3.4.1. Importance of U(0, 1) r.v. 25 3.4.2. Von Neumann’s generator 26 3.4.3. The LCG generators 28 3.4.4. Advanced generators 31 3.4.5. Precaution and practice 33 3.5. Generation of a given distribution 35 3.5.1. Inverse transformation method 35 3.5.2. Acceptance–rejection method 36 3.5.3. Generation of discrete r.v. 38 3.5.4. Particular case 39 3.6. Some commonly used distributions and their generation 40 3.6.1. Uniform distribution 41 3.6.2. Triangular distribution 41 3.6.3. Exponential distribution 42 3.6.4. Pareto distribution 43 3.6.5. Normal distribution 44 3.6.6. Log-normal distribution 45 3.6.7. Bernoulli distribution 45 3.6.8. Binomial distribution 46 3.6.9. Geometric distribution 47 3.6.10. Poisson distribution 48 3.7. Applications to computer networks 48 CHAPTER 4. SIMULATION LANGUAGES 53 4.1. Simulation languages 53 4.1.1. Presentation 53 4.1.2. Main programming features 54 4.1.3. Choice of a simulation language 54 4.2. Scheduler 56 4.3. Generators of random variables 57 4.4. Data collection and statistics 58 4.5. Object-oriented programming 58 4.6. Description language and control language 59 4.7. Validation 59 4.7.1. Generality 59 4.7.2. Verification of predictions 60 4.7.3. Some specific and typical errors 61 4.7.4. Various tests 62 CHAPTER 5. SIMULATION RUNNING AND DATA ANALYSIS 63 5.1. Introduction 63 5.2. Outputs of a simulation 64 5.2.1. Nature of the data produced by a simulation 64 5.2.2. Stationarity 65 5.2.3. Example 66 5.2.4. Transient period 68 5.2.5. Duration of a simulation 69 5.3. Mean value estimation 70 5.3.1. Mean value of discrete variables 71 5.3.2. Mean value of continuous variables 72 5.3.3. Estimation of a proportion 72 5.3.4. Confidence interval 73 5.4. Running simulations 73 5.4.1. Replication method 73 5.4.2. Batch-means method 75 5.4.3. Regenerative method 76 5.5. Variance reduction 77 5.5.1. Common random numbers 78 5.5.2. Antithetic variates 79 5.6. Conclusion 80 CHAPTER 6. OMNET++ 81 6.1. A summary presentation 81 6.2. Installation 82 6.2.1. Preparation 82 6.2.2. Installation 83 6.3. Architecture of OMNeT++ 83 6.3.1. Simple module 84 6.3.2. Channel 85 6.3.3. Compound module 85 6.3.4. Simulation model (network) 85 6.4. The NED langage 85 6.5. The IDE of OMNeT++ 86 6.6. The project 86 6.6.1. Workspace and projects 87 6.6.2. Creation of a project 87 6.6.3. Opening and closing of a project 87 6.6.4. Import of a project 88 6.7. A first example 88 6.7.1. Creation of the modules 88 6.7.2. Compilation 92 6.7.3. Initialization 92 6.7.4. Launching of the simulation 93 6.8. Data collection and statistics 93 6.8.1. The Signal mechanism 94 6.8.2. The collectors 95 6.8.3. Extension of the model with statistics 95 6.8.4. Data analysis 98 6.9. A FIFO queue 98 6.9.1. Construction of the queue 98 6.9.2. Extension of MySource 101 6.9.3. Configuration 103 6.10. An elementary distributed system 105 6.10.1. Presentation 105 6.10.2. Coding 107 6.10.3. Modular construction of a larger system 114 6.10.4. The system 115 6.10.5. Configuration of the simulation and its scenarios 115 6.11. Building large systems: an example with INET 117 6.11.1. The system 117 6.11.2. Ethernet card with LLC 119 6.11.3. The new entity MyApp 121 6.11.4. Simulation 125 6.11.5. Conclusion 126 PART 2. QUEUEING THEORY 129 CHAPTER 7. INTRODUCTION TO THE QUEUEING THEORY 131 7.1. Presentation 131 7.2. Modeling of the computer networks 133 7.3. Description of a queue 133 7.4. Main parameters 135 7.5. Performance indicators 136 7.5.1. Usual parameters 136 7.5.2. Performance in steady state 136 7.6. The Little’s law 137 7.6.1. Presentation 137 7.6.2. Applications 138 CHAPTER 8. POISSON PROCESS 141 8.1. Definition 141 8.1.1. Definition 141 8.1.2. Distribution of a Poisson process 142 8.2. Interarrival interval 143 8.2.1. Definition 143 8.2.2. Distribution of the interarrival interval Δ 144 8.2.3. Relation between N(t) and Δ 145 8.3. Erlang distribution 145 8.4. Superposition of independent Poisson processes 146 8.5. Decomposition of a Poisson process 147 8.6. Distribution of arrival instants over a given interval 150 8.7. The PASTA property 151 CHAPTER 9. MARKOV QUEUEING SYSTEMS 153 9.1. Birth-and-death process 153 9.1.1. Definition 153 9.1.2. Differential equations 154 9.1.3. Steady-state solution 156 9.2. The M/M/1 queues 158 9.3. The M/M/∞ queues 160 9.4. The M/M/m queues 161 9.5. The M/M/1/K queues 163 9.6. The M/M/m/m queues 164 9.7. Examples 165 9.7.1. Two identical servers with different activation thresholds 165 9.7.2. A cybercafe 167 CHAPTER 10. THE M/G/1 QUEUES 169 10.1. Introduction 169 10.2. Embedded Markov chain 170 10.3. Length of the queue 171 10.3.1. Number of arrivals during a service period 172 10.3.2. Pollaczek–Khinchin formula 173 10.3.3. Examples 175 10.4. Sojourn time 178 10.5. Busy period 179 10.6. Pollaczek–Khinchin mean value formula 181 10.7. M/G/1 queue with server vacation 183 10.8. Priority queueing systems 185 CHAPTER 11. QUEUEING NETWORKS 189 11.1. Generality 189 11.2. Jackson network 192 11.3. Closed network 197 PART 3. PROBABILITY AND STATISTICS 201 CHAPTER 12. AN INTRODUCTION TO THE THEORY OF PROBABILITY 203 12.1. Axiomatic base 203 12.1.1. Introduction 203 12.1.2. Probability space 204 12.1.3. Set language versus probability language 206 12.2. Conditional probability 206 12.2.1. Definition 206 12.2.2. Law of total probability 207 12.3. Independence 207 12.4. Random variables 208 12.4.1. Definition 208 12.4.2. Cumulative distribution function 208 12.4.3. Discrete random variables 209 12.4.4. Continuous random variables 210 12.4.5. Characteristic function 212 12.5. Some common distributions 212 12.5.1. Bernoulli distribution 212 12.5.2. Binomial distribution 213 12.5.3. Poisson distribution 213 12.5.4. Geometric distribution 214 12.5.5. Uniform distribution 215 12.5.6. Triangular distribution 215 12.5.7. Exponential distribution 216 12.5.8. Normal distribution 217 12.5.9. Log-normal distribution 219 12.5.10. Pareto distribution 219 12.6. Joint probability distribution of multiple random variables 220 12.6.1. Definition 220 12.6.2. Independence and covariance 221 12.6.3. Mathematical expectation 221 12.7. Some interesting inequalities 222 12.7.1. Markov’s inequality 222 12.7.2. Chebyshev’s inequality 222 12.7.3. Cantelli’s inequality 223 12.8. Convergences 223 12.8.1. Types of convergence 224 12.8.2. Law of large numbers 226 12.8.3. Central limit theorem 227 CHAPTER 13. AN INTRODUCTION TO STATISTICS 229 13.1. Introduction 229 13.2. Description of a sample 230 13.2.1. Graphic representation 230 13.2.2. Mean and variance of a given sample 231 13.2.3. Median 231 13.2.4. Extremities and quartiles 232 13.2.5. Mode and symmetry 232 13.2.6. Empirical cumulative distribution function and histogram 233 13.3. Parameters estimation 236 13.3.1. Position of the problem 236 13.3.2. Estimators 236 13.3.3. Sample mean and sample variance 237 13.3.4. Maximum-likelihood estimation 237 13.3.5. Method of moments 239 13.3.6. Confidence interval 240 13.4. Hypothesis testing 241 13.4.1. Introduction 241 13.4.2. Chi-square (χ2) test 241 13.4.3. Kolmogorov–Smirnov test 244 13.4.4. Comparison between the χ2 test and the K-S test 246 CHAPTER 14. MARKOV PROCESS 247 14.1. Stochastic process 247 14.2. Discrete-time Markov chains 248 14.2.1. Definitions 248 14.2.2. Properties 251 14.2.3. Transition diagram 253 14.2.4. Classification of states 254 14.2.5. Stationarity 255 14.2.6. Applications 257 14.3. Continuous-time Markov chain 260 14.3.1. Definitions 260 14.3.2. Properties 262 14.3.3. Structure of a Markov process 263 14.3.4. Generators 266 14.3.5. Stationarity 267 14.3.6. Transition diagram 270 14.3.7. Applications 272 BIBLIOGRAPHY 273 INDEX 277

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Book SynopsisWireless networks represent an inexpensive and convenient way to connect to the Internet. However, despite their applications across several technologies, one challenge still remains: to understand the behavior of wireless sensor networks and assess their performance in large-scale scenarios. When a large number of network nodes need to interact, developing suitable analytical models is essential to ensure the appropriate coverage and throughput of these networks and to enhance user mobility. This is intrinsically difficult due to the size and number of different network nodes and users. This book highlights some examples which show how this problem can be overcome with the use of different techniques. An intensive parameter analysis shows the reader how to the exploit analytical models for an effective development and management of different types of wireless networks.Table of ContentsPreface ix Introduction xi List of Acronyms xv Part 1. Sensor Networks 1 Chapter 1. Fluid Models and Energy Issues 3 1.1. The fluid-based approach 4 1.1.1. Sensor density and traffic generation 5 1.1.2. Data routing 5 1.1.3. Local and relay traffic rates 6 1.1.4. Channel contention and data transmission 6 1.1.5. Mean packet delivery delay 7 1.1.6. Sensor active/sleep behavior 7 1.2. Network scenario 7 1.3. The sensor network model 11 1.3.1. A minimum energy routing strategy: computing u(r:r) 11 1.3.2. Channel contention and data transmission: computing s(r) and PR(r) 17 1.3.3. Mean packet delivery delay: computing q(r) 22 1.4. Results 24 1.4.1. Model validation 25 1.4.2. Model exploitation 28 1.4.3. Model solution complexity and accuracy 35 Chapter 2. Hybrid Automata for Transient Delay Analysis 37 2.1. Event detection in WSNs 37 2.1.1. The 802.15.4 MAC protocol 39 2.2. Model for single-hop network topologies 40 2.2.1. Single message transfer 40 2.2.2. Multiple message transfers 43 2.3. Solution technique 44 2.3.1. Time discretization 44 2.3.2. Transient solution 46 2.3.3. Performance metrics computation 49 2.4. Model for multi-hop network topologies 50 2.5. Model validation and exploitation results 52 2.6. Discussion 57 Part 2. Vehicular Networks 59 Chapter 3. Safety Message Broadcasting 61 3.1. System description 62 3.2. Dissemination of safety messages 63 3.2.1. The spatial differentiation approach 63 3.2.2. The safety application 64 3.3. Assumptions and notations 65 3.4. Model outline 66 3.5. Computation of the block probability 67 3.6. Computation of the probability of first reception 69 3.6.1. A Gaussian approximation to the transient system behavior 73 3.7. Performance evaluation 77 3.7.1. The impact of power capture 77 3.7.2. The case of occupation probability ρ = 1 79 3.7.3. The case of homogeneous occupation probability ρ < 1 80 3.7.4. The case of inhomogeneous occupation probability 83 3.7.5. The impact of the forwarding policy 85 Chapter 4. Modeling Information Sharing 89 4.1. System scenario 89 4.2. Modeling information exchange in IVN 90 4.2.1. Model description 91 4.3. Computation of the probability of successful information retrieval 93 4.4. Model validation and exploitation 98 Part 3. Cellular Networks 103 Chapter 5. Multi-RAT Algorithms 105 5.1. RAT network 106 5.1.1. Scenario 107 5.1.2. RAT selection strategy 108 5.2. Network model 109 5.2.1. Functional rates 110 5.3. Model solution 115 5.3.1. Analytical approach 115 5.3.2. Computation of performance metrics 117 5.4. Performance evaluation 118 5.4.1. Setting and results 119 Bibliography 123 Index 127

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Book SynopsisThis book provides the mathematical basis for investigating numerically equations from physics, life sciences or engineering. Tools for analysis and algorithms are confronted to a large set of relevant examples that show the difficulties and the limitations of the most naïve approaches. These examples not only provide the opportunity to put into practice mathematical statements, but modeling issues are also addressed in detail, through the mathematical perspective.Table of ContentsPreface ix Chapter 1. Ordinary Differential Equations 1 1.1. Introduction to the theory of ordinary differential equations 1 1.1.1. Existence–uniqueness of first-order ordinary differential equations 1 1.1.2. The concept of maximal solution 11 1.1.3. Linear systems with constant coefficients 16 1.1.4. Higher-order differential equations 20 1.1.5. Inverse function theorem and implicit function theorem 21 1.2. Numerical simulation of ordinary differential equations, Euler schemes, notions of convergence, consistence and stability 27 1.2.1. Introduction 27 1.2.2. Fundamental notions for the analysis of numerical ODE methods 29 1.2.3. Analysis of explicit and implicit Euler schemes 33 1.2.4. Higher-order schemes 50 1.2.5. Leslie’s equation (Perron–Frobenius theorem, power method) 51 1.2.6. Modeling red blood cell agglomeration 78 1.2.7. SEI model 87 1.2.8. A chemotaxis problem 93 1.3. Hamiltonian problems 102 1.3.1. The pendulum problem 106 1.3.2. Symplectic matrices; symplectic schemes 112 1.3.3. Kepler problem 125 1.3.4. Numerical results 129 Chapter 2. Numerical Simulation of Stationary Partial Differential Equations: Elliptic Problems 141 2.1. Introduction 141 2.1.1. The 1D model problem; elements of modeling and analysis 144 2.1.2. A radiative transfer problem 155 2.1.3. Analysis elements for multidimensional problems 163 2.2. Finite difference approximations to elliptic equations 166 2.2.1. Finite difference discretization principles 166 2.2.2. Analysis of the discrete problem 173 2.3. Finite volume approximation of elliptic equations 180 2.3.1. Discretization principles for finite volumes 180 2.3.2. Discontinuous coefficients 187 2.3.3. Multidimensional problems 189 2.4. Finite element approximations of elliptic equations 191 2.4.1. P1 approximation in one dimension 191 2.4.2. P2 approximations in one dimension 197 2.4.3. Finite element methods, extension to higher dimensions 200 2.5. Numerical comparison of FD, FV and FE methods 204 2.6. Spectral methods 205 2.7. Poisson–Boltzmann equation; minimization of a convex function, gradient descent algorithm 217 2.8. Neumann conditions: the optimization perspective 224 2.9. Charge distribution on a cord 228 2.10. Stokes problem 235 Chapter 3. Numerical Simulations of Partial Differential Equations: Time-dependent Problems 267 3.1. Diffusion equations 267 3.1.1. L2 stability (von Neumann analysis) and L∞ stability: convergence 269 3.1.2. Implicit schemes 276 3.1.3. Finite element discretization 281 3.1.4. Numerical illustrations 283 3.2. From transport equations towards conservation laws 291 3.2.1. Introduction 291 3.2.2. Transport equation: method of characteristics 295 3.2.3. Upwinding principles: upwind scheme 299 3.2.4. Linear transport at constant speed; analysis of FD and FV schemes 301 3.2.5. Two-dimensional simulations 326 3.2.6. The dynamics of prion proliferation 329 3.3. Wave equation 345 3.4. Nonlinear problems: conservation laws 354 3.4.1. Scalar conservation laws 354 3.4.2. Systems of conservation laws 387 3.4.3. Kinetic schemes 393 Appendices 407 Appendix 1 409 Appendix 2 417 Appendix 3 427 Appendix 4 433 Appendix 5 443 Bibliography 447 Index 455

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Book SynopsisThe Handbook of Dynamic Data Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies. Beginning with general concepts and history of the paradigm, the text provides 32 chapters by leading experts in ten application areas to enable an accurate understanding, analysis, and control of complex systems; be they natural, engineered, or societal: The authors explain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination. The Dynamically Data Driven Applications Systems (DDDAS) paradigm inspired research regarding the prediction of severe storms. Specifically, the DDDAS concept allows atmospheric observing systems, computer forecast models, and cyberinfrastructure to dynamically configure themselves in optimal ways in direct response to current or anticipated weather conditions. In so doing, all resources are used in an optimal manner to maximize the quality and timeliness of information they provide. Kelvin Droegemeier, Regents’ Professor of Meteorology at the University of Oklahoma; former Director of the White House Office of Science and Technology Policy We may well be entering the golden age of data science, as society in general has come to appreciate the possibilities for organizational strategies that harness massive streams of data. The challenges and opportunities are even greater when the data or the underlying system are dynamic - and DDDAS is the time-tested paradigm for realizing this potential. Sangtae Kim, Distinguished Professor of Mechanical Engineering and Distinguished Professor of Chemical Engineering at Purdue UniversityTrade ReviewThe Dynamically Data Driven Applications Systems (DDDAS) paradigm inspired research regarding the prediction of severe storms. Specifically, the DDDAS concept allows atmospheric observing systems, computer forecast models, and cyberinfrastructure to dynamically configure themselves in optimal ways in direct response to current or anticipated weather conditions. In so doing, all resources are used in an optimal manner to maximize the quality and timeliness of information they provide. Kelvin Droegemeier, Regents’ Professor of Meteorology at the University of Oklahoma; former Director of the White House Office of Science and Technology Policy We may well be entering the golden age of data science, as society in general has come to appreciate the possibilities for organizational strategies that harness massive streams of data. The challenges and opportunities are even greater when the data or the underlying system are dynamic - and DDDAS is the time-tested paradigm for realizing this potential. Sangtae Kim, Distinguished Professor of Mechanical Engineering and Distinguished Professor of Chemical Engineering at Purdue University Table of Contents1 Introduction to Dynamic Data Driven Applications Systems.- 2 Tractable Non-Gaussian Representation in Dynamic Data Driven Coherent Fluid Mapping.- 3 Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems.- 4 Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness.- 5 Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics.- 6 Markov Modeling of Time Series via Spectral Analysis for Detection of Combustion Instabilities.- 7 Dynamic Space-Time Model for Syndromic Surveillance with Particle Filters and Dirichlet Process.- 8 A Computational Steering Framework for Large-Scale Composite Structures.- 9 Development of Intelligent and Predictive Self-Healing Composite Structures using Dynamic Data-Driven Applications Systems.- 10 Dynamic Data-Driven Approach for Unmanned Aircraft Systems aero-elastic response analysis.- 11 Transforming Wildfire Detection and Prediction using New and Underused Sensor and Data Sources Integrated with Modeling.- 12 Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation.- 13 Photometric Steropsis for 3D Reconstruction of Space Objects.- 14 Aided Optimal Search: Data-Driven Target Pursuit from On-Demand Delayed Binary Observations.- 15 Optimization of Multi-Target Tracking within a Sensor Network via Information Guided Clustering.- 16 Data-Driven Prediction of Confidence for EVAR in Time-varying Datasets.- 17 DDDAS for Attack Detection and Isolation of Control Systems.- 18 Approximate Local Utility Design for Potential Game Approach to Cooperative Sensor Network Planning.- 19 Dynamic Sensor-Actor Interactions for Path-Planning in a Threat Field.- 20 Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction.- 21 A Dynamic Data-Driven Optimization Framework for Demand Side Management in Microgrids.- 22 Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods.- 23 Design of a Dynamic Data-Driven System for Multispectral Video Processing.- 24 Light Field Image Compression.- 25 On Compression of Machine-derived Context Sets for Fusion of Multi-model Sensor Data.- 26 Simulation-based Optimization as a Service for Dynamic Data-driven Applications Systems.- 27 Privacy and Security Issues in DDDAS Systems.- 28 Dynamic Data Driven Application Systems (DDDAS) for Multimedia Content Analysis.- 29 Parzen Windows: Simplest Regularization Algorithm.- 30 Multiscale DDDAS Framework for Damage Prediction in Aerospace Composite Structures.- 31 A Dynamic Data-Driven Stochastic State-awareness Framework for the Next Generation of Bio-inspired Fly-by-feel Aerospace Vehicles.- DDDAS: The Way Forward.

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Book Synopsis3D Mesh Processing and Character Animation focusses specifically on topics that are important in three-dimensional modelling, surface design and real-time character animation. It provides an in-depth coverage of data structures and popular methods used in geometry processing, keyframe and inverse kinematics animations and shader based processing of mesh objects. It also introduces two powerful and versatile libraries, OpenMesh and Assimp, and demonstrates their usefulness through implementations of a wide range of algorithms in mesh processing and character animation respectively. This Textbook is written for students at an advanced undergraduate or postgraduate level who are interested in the study and development of graphics algorithms for three-dimensional mesh modeling and analysis, and animations of rigged character models. The key topics covered in the book are mesh data structures for processing adjacency queries, simplification and subdivision algorithms, mesh parameterization methods, 3D mesh morphing, skeletal animation, motion capture data, scene graphs, quaternions, inverse kinematics algorithms, OpenGL-4 tessellation and geometry shaders, geometry processing and terrain rendering. Table of Contents1 Introduction.- 2 Mesh Processing Basic.- 3 Mesh Processing Algorithms.- 4 The Geometry Shader.- 5 Mesh Tessellation.- 6 Quaternions.- 7 Character Animation.- 8 Kinematics.

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Book SynopsisThis book is a collection of papers compiled from the conference "Algorithms and Computer-Based Solutions" held on June 8-9, 2021 at Peter the Great St. Petersburg Polytechnic University (SPbPU), St. Petersburg, Russia. The authors of the book are leading scientists from Russia, Germany, Netherlands, Greece, Hungary, Kazakhstan, Portugal, and Poland.The reader finds in the book information from experts on the most interesting trends in digitalization - issues of development and implementation of algorithms, IT and digital solutions for various areas of economy and science, prospects for supercomputers and exo-intelligent platforms; applied computer technologies in digital production, healthcare and biomedical systems, digital medicine, logistics and management; digital technologies for visualization and prototyping of physical objects.The book helps the reader to increase his or her expertise in the field of computer technologies discussed.

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Book SynopsisCommissioned by the Society for Modeling and Simulation International (SCS), this needed, useful new ‘Body of Knowledge’ (BoK) collects and organizes the common understanding of a wide collection of professionals and professional associations.Modeling and simulation (M&S) is a ubiquitous discipline that lays the computational foundation for real and virtual experimentation, clearly stating boundaries—and interactions—of systems, data, and representations. The field is well known, too, for its training support via simulations and simulators. Indeed, with computers increasingly influencing the activities of today’s world, M&S is the third pillar of scientific understanding, taking its place along with theory building and empirical observation.This valuable new handbook provides intellectual support for all disciplines in analysis, design and optimization. It contributes increasingly to the growing number of computational disciplines, addressing the broad variety of contributing as well as supported disciplines and application domains. Further, each of its sections provide numerous references for further information. Highly comprehensive, the BoK represents many viewpoints and facets, captured under such topics as: Mathematical and Systems Theory Foundations Simulation Formalisms and Paradigms Synergies with Systems Engineering and Artificial Intelligence Multidisciplinary Challenges Ethics and Philosophy Historical Perspectives Examining theoretical as well as practical challenges, this unique volume addresses the many facets of M&S for scholars, students, and practitioners. As such, it affords readers from all science, engineering, and arts disciplines a comprehensive and concise representation of concepts, terms, and activities needed to explain the M&S discipline.Tuncer Ören is Professor Emeritus at the University of Ottawa. Bernard Zeigler is Professor Emeritus at the University of Arizona. Andreas Tolk is Chief Scientist at The MITRE Corporation. All three editors are long-time members and Fellows of the Society for Modeling and Simulation International. Under the leadership of three SCS Fellows, Dr. Ören, University of Ottawa, Dr. Zeigler, The University of Arizona, and Dr. Tolk, The MITRE Corporation, more than 50 international scholars from 15 countries provided insights and experience to compile this initial M&S Body of Knowledge.Table of Contents1. Preliminary.- 2. M&S BOK Core Areas and the Big Picture.- 3. Simulation as Experimentation.- 4. Simulation as Experience to Enhance Three Types of Skills.- 5. Simulation Games (Simulation as Experience for Entertainment).- 6. Infrastructure.- 7. Reliability and Quality Assurance of M&S.- 8. Ethics.- 9. Enterprise (Economics of M&S).- 10. Maturity.- 11. Supporting Domains: Computers and Computation.- 12. Supporting Science Areas.- 13. Supporting Engineering Areas.-14. Supporting Social Science and Management Areas.- 15. Philosophy and Modelling and Simulation.- 16. History.- 17. Core Research Areas.- 18. Trends, Desirable Features, and Challenges.

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Book SynopsisThis book constitutes the refereed proceedings of the 18th International Conference on Computer-Aided Systems Theory, EUROCAST 2022, held in Las Palmas de Gran Canaria, Spain, during February 20–25, 2022. The 77 full papers included in this book were carefully reviewed and selected from 110 submissions. They were organized in topical sections as follows: Systems Theory and Applications, Theory and Applications of Metaheuristic Algorithms, Model-Based System Design, Verification and Simulation, Applications of Signal Processing Technology, Artificial Intelligence and Data Mining for Intelligent Transportation Systems and Smart Mobility, Computer Vision, Machine Learning for Image Analysis and Applications, Computer and Systems Based Methods and Electronic Technologies in Medicine, Systems in Industrial Robotics, Automation and IoT, Systems Thinking. Relevance for Technology, Science and Management Professionals.Table of ContentsSystems Theory and Applications.- Transdisciplinary Software Development for Early Crisis Detection.- Uncertainty and Ambiguity: Challenging Layers in Model Construction.- George J. Boole. A Nineteenth Century Man for the Modern Digital Era.- Improvement of Electromagnetic Systems by Werner Von Siemens.- Improvement of Electromagnetic Systems by Werner Von Siemens.- Theory and Applications of Metaheuristic Algorithms.- Multi-criteria Optimization of Workflow-based Assembly Tasks in Manufacturing.- Lightweight Interpolation-Based SurroImproving the Flexibility of Shape-Constrained Symbolic Regression with Extended Constraints.- gate Modelling for MultiObjective Continuous Optimisation.- Analysis and Handling of Dynamic Problem Changes in OpenEnded Optimization.- Dynamic Vehicle Routing with Time-Linkage: From Problem States to Algorithm Performance.- Dynamic Fitness Landscape Analysis.- A Relative Value Function Based Learning Beam Search for Longest Common Subsequence Problem.- Multi-day Container Drayage Problem with Active and Passive Vehicles.- On Discovering Optimal Trade-Offs when Introducing New Routes in Existing Multi-Modal Public Transport Systems.- A Mathematical Model and GRASP for a Tourist Trip Design Problem.- A Large Neighborhood Search for Battery Swapping Station Location Planning for Electric Scooters.- Shapley Value based Variable Interaction Networks for Data Stream Analysis.- Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization.- Comparing Shape-Constrained Regression Algorithms for Data Validation.- Shape-constrained Symbolic Regression with NSGA-III.- Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments.- Identifying Differential Equations to predict Blood Glucose using Sparse Identification of Nonlinear Systems.-Obtaining Difference Equations for Glucose Prediction by Structured Grammatical Evolution and sparse identification.- Model-Based System Design, Verification and Simulation.- Modeling Approaches for Cyber Attacks on Energy Infrastructure.- Simulation setup for a closed-loop regulation of neuro-muscular blockade.- Textile In The Loop as Automated Verification Tool for Smart Textiles Applications.- Orchestrating Digital Twins for Distributed Manufacturing Execution Systems.- Automata with Bounded Repetition in RE2.- Integrating OSLC Services into Eclipse.- Developing an Application in the Forest for New Tourism Post COVID-19.- GPU-Accelerated Synthesis of Probabilistic Programs.- Static Deadlock Detection in Low-Level C Code.- Applications of Signal Processing Technology.- 3D Ultrasound Fingertip Tracking.- An Artificial Skin from Conductive Rubber.- Neural Network Based Single-Carrier Frequency Domain Equalization.- Smooth Step Detection.- Optical Preprocessing and Digital Signal Processing for the Measurement of Strain in Thin Specimen.- Lower Limbs Gesture Recognition Approach to Control a Medical Treatment Bed.- Artificial Intelligence and Data Mining for Intelligent Transportation Systems and Smart Mobility.- JKU-ITS Automobile for Research on Autonomous Vehicles.- Development of a ROS-based Architecture for Intelligent Autonomous on Demand Last Mile Delivery.- Contrastive Learning for Simulation-to-Real Domain Adaptation of LiDAR data.- Deep Learning Data Association Applied to Multi-Object Tracking Systems.- A Methodology to Consider Explicitly Emissions in Dynamic User Equilibrium Assignment.- Sensitivity Analysis for A Cooperative Adaptive Cruise Control Car Following Model: Preliminary Findings.- On Smart Mobility and Data Stream Mining.- Smart Vehicle Inspection.- Computer Vision, Machine Learning for Image Analysis and Applications.- Impact of the Region of Analysis on the Performance of the Automatic Epiretinal Membrane Segmentation in OCT Images.- Performance Analysis of GAN approaches in the Portable Chest X-ray synthetic image generation for COVID-19 screening.- Clinical Decision Support tool for the Identification of Pathological Structures Associated with Age-related Macular Degeneration.- Deep Features-based approaches for Phytoplankton Classification in Microscopy Images.- Robust Deep Learning-based Approach for Retinal layer Segmentation in Optical Coherence Tomography Images.- Impact of increased centerline weight on the Joint segmentation and classification of arteries and veins in color fundus images.- Rating the Severity of Diabetic Retinopathy on a Highly Imbalanced Dataset.- Gait Recognition using 3D View-Transformation Model.- Segmentation and Multi-Facet Classification of Individual Logs in Wooden Piles.- Drone Detection Using Deep Learning: A Benchmark Study.- Computer and Systems Based Methods and Electronic Technologies in Medicine.- Continuous Time Normalized Signal Trains for a Better Classification of Myoelectric Signals.- A Comparison of Covariate Shift Detection Methods on Medical Datasets.- Towards a Method to Provide Tactile Feedback in Minimally Invasive Robotic Surgery.- Reference Datasets for Analysis of Traditional Japanese and German Martial Arts.- A Novel Approach to Continuous Heart Rhythm Monitoring for Arrhythmia Detection.- Indoor Positioning Framework for Training Rescue Operations Procedures at the Site of a Mass Incident or Disaster.- Designing sightseeing support system in Oku-Nikko using BLE beacon.- Systems in Industrial Robotics, Automation and IoT.- Mixed Reality HMI for Collaborative Robots.- A Digital Twin Demonstrator for Research and Teaching in Universities.- Robot System as a Testbed for AI Optimizations.- An Architecture for Deploying Reinforcement Learning in Industrial Environments.- Ck-continuous Spline Approximation with TensorFlow Gradient Descent Optimizers.- Stepwise Sample Generation.- Optimising Manufacturing Process with Bayesian Learning and Knowledge Graphs.- Representing Technical Standards as Knowledge Graph to Guide the Design of Industrial Systems.- Improvements for mlrose Applied to the Traveling Salesperson Problem.- Survey on Radar Odometry.- Systems Thinking. Relevance for Technology, Science and Management Professionals.- Systems Thinking. Relevance for Technology, Science and Management Professionals.- Crisis Management in a Federation – Cybernetic Lessons from a Pandemic.- Using Archetypes to Teach Systems Thinking in an Engineering Master’s Course.- Collecting vs Sharing of Personal Data: Examining the Implications to the Society.

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Book SynopsisThis book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on Modelling and Simulation for Autonomous Systems, MESAS 2022, held MESAS 2022, Prague, Czech Republic, October 2022.The 21 full papers included in the volume were carefully reviewed and selected from 24 submissions. They are organized in the following topical sections: Modelling, Simulation Technology, methodologies and Robotics. Table of ContentsM&S of Intelligent Systems - R&D and Application.- AxS/AI in Context of Future Warfare and Security Environment.- Future Challenges of Advanced M&S Technology.

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Book SynopsisThis open access conference proceedings contains all the papers presented at the ICTIMT 2023, the 3rd International Conference on Thermal Issues in Machine Tools. The event takes place in Dresden, the capital of Saxony, from March 21-23 2023. The conference is organized by the Chair of Machine Tools Development and Adaptive Controls of the Technische Universität Dresden.Table of ContentsThermal interactions between workpiece, tool, machine.- Testing and simulation methods to identify thermal errors.- Reference workpieces and assessment.- Energy efficient compensation and correction of thermal errors.- Improving thermal robustness of machine tools through design changes.- Thermo-energetic optimization of machine tools.

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Book SynopsisThis open access conference proceedings contains all the papers presented at the ICTIMT 2023, the 3rd International Conference on Thermal Issues in Machine Tools. The event takes place in Dresden, the capital of Saxony, from March 21-23 2023. The conference is organized by the Chair of Machine Tools Development and Adaptive Controls of the Technische Universität Dresden.Table of ContentsThermal interactions between workpiece, tool, machine.- Testing and simulation methods to identify thermal errors.- Reference workpieces and assessment.- Energy efficient compensation and correction of thermal errors.- Improving thermal robustness of machine tools through design changes.- Thermo-energetic optimization of machine tools.

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Book SynopsisThis book reviews the state-of-the-art efforts to apply machine learning and AI methods for healthy aging and longevity research, diagnosis, and therapy development. The book examines the methods of machine learning and their application in the analysis of big medical data, medical images, the creation of algorithms for assessing biological age, and effectiveness of geroprotective medications.The promises and challenges of using AI to help achieve healthy longevity for the population are manifold. This volume, written by world-leading experts working at the intersection of AI and aging, provides a unique synergy of these two highly prominent fields and aims to create a balanced and comprehensive overview of the application methodology that can help achieve healthy longevity for the population.The book is accessible and valuable for specialists in AI and longevity research, as well as a wide readership, including gerontologists, geriatricians, medical specialists, and students from diverse fields, basic scientists, public and private research entities, and policy makers interested in potential intervention in degenerative aging processes using advanced computational tools. Table of ContentsAI in longevity.- Automated reporting of medical diagnostic imaging for early disease and aging biomarkers detection.- Risk forecasting tools based on the collected information for two types of occupational diseases.- Obtaining longevity footprints in DNA methylation data using different machine learning approaches.- The role of assistive technology in regulating the behavioural and psychological symptoms of dementia.- Epidemiology, genetics and epigenetics of Biological Aging: one or more aging systems?.- Temporal relation prediction from Electronic Health Records using Graph Neural Networks and Transformers Embeddings.- In silico screening of life-extending drugs using machine learning and omics data.- An overview of kernel methods for identifying genetic association with health-related traits.- Artificial Intelligence approaches for skin anti-aging and skin resilience research.- AI in genomics and epigenomics.- The utility of information theory based methods in the research of aging and longevity.- AI for Longevity: getting past the Mechanical Turk model will take Good Data.- Leveraging algorithmic and human networks to cure human aging: Holistic understanding of Longevity via Generative Cooperative Networks, Hybrid Bayesian/Neural/Logical AI and Tokenomics-Mediated Crowdsourcing.

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Book SynopsisThis is the first book that sums up test-related modeling of digital circuits and systems by a new structural-decision-diagrams model. The model represents structural and functional information jointly and opens a new area of research.The book introduces and discusses applications of two types of structural decision diagrams (DDs): low-level, structurally synthesized binary DDs (SSBDDs) and high-level DDs (HLDDs) that enable diagnostic modeling of complex digital circuits and systems.Topics and features: Provides the definition, properties and techniques for synthesis, compression and optimization of SSBDDs and HLDDs Provides numerous working examples that illustrate the key points of the text Describes applications of SSBDDs and HLDDs for various electronic design automation (EDA) tasks, such as logic-level fault modeling and simulation, multi-valued simulation, timing-critical path identification, and test generation Discusses the advantages of the proposed model to traditional binary decision diagrams and other traditional design representations Combines SSBDDs with HLDDs for multi-level representation of digital systems for enabling hierarchical and cross-level solving of complex test-related tasks This unique book is aimed at researchers working in the fields of computer science and computer engineering, focusing on test, diagnosis and dependability of digital systems. It can also serve as a reference for graduate- and advanced undergraduate-level computer engineering and electronics courses.Three authors are affiliated with the Dept. of Computer Systems at the Tallinn University of Technology, Estonia: Raimund Ubar is a retired Professor, Jaan Raik and Maksim Jenihhin are tenured Professors. Artur Jutman, PhD, is a researcher at the same university and the CEO of Testonica Lab Ltd., Estonia.Table of ContentsChapter 1: Introduction.- Chapter 2: Overview of structural decision diagrams.- Chapter 3: Structurally Synthesized Binary Decision Diagrams.- Chapter 4: Fault modeling in digital circuits.- Chapter 5: Logic-level fault simulation.- Chapter 6: Test generation, fault diagnosis and testability.- Chapter 7: High-Level Decision Diagrams.- Chapter 8: Test generation for microprocessors with HLDDs.

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Book Synopsis.- M&S of Intelligent Systems R&D and Application..- Comparison of Frequency Cepstral Coefficients in Impulse Acoustic Events Detection..- Modelling and Simulation of hypersonic missile in VR-Forces environment..- Atlas Fusion 2.0 - A ROS2 Based Real-Time Sensor Fusion Framework..- UAS Flight Path Optimization Model for Effective Monitoring and Surveillance of the Buffer Zone in the UNFICYP Peacekeeping Mission..- A Model-Based Design Approach for a System of Systems based on an Integrated UAV Platform..- Practical applicability of tree spacing passability analysis on vehicle path planning..- Where to go and how to get there: Tactical terrain analysis for military unmanned ground-vehicle mission planning..- A Survey of Trajectory Planning Algorithms for Off-road Uncrewed Ground Vehicles..- Multi-physics and Multi-spectral Sensors Simulator for Autonomous Flight Functions Development..- Future Challenges of Advanced M&S Technology..- Conceptual Aspects of Counter-UAS Modelling and Simulation..- Challenges Associated with the Deployment of Autonomous Reconnaissance Systems on Future Battlefields..- The Key Challenges of SBAD M&S..- Development of Geoprocessing Tool for Wet Gap Crossing in Military Operations..- Digital Twin Modeling for Machine Vision Testing in Autonomous Systems..- A Situation Analysis Process in Computer-Generated Forces Team Behavior within Air Combat Simulations under Risk and Uncertainty: Concept and First Implementations..- A Tactical Planning Process in Computer-Generated Forces Team Behavior within Air Combat Simulations: Concept and First Implementations..- Survey on Sensing, Modelling and Reasoning Aspects in Military Autonomous Systems..- AxS/AI in Context of Future Warfare and Security Environment..- Camera based AI models used with lidar data for improvement of detected object parameters..- The Analysis of Point Cloud Registration Methods for Natural Environment in Autonomous Driving..- Hyperspectral Data Dimensionality Reduction: a Comparative Study between PCA and Autoencoder methods..- Utilizing a CNN for Automatic Detection of Military Reconnaissance and Surveillance Objects in Aerial Images: Concept and Challenges..- Multimodal Earth Observation Modeling using AI..- Statistical Evaluation of Simulation Study Data..- Mission: COMANND.  Conceptualizing an AI Assistant for Decision-Making..- Using Only Synthetic Images to Train a Drogue Detector for Aerial Refueling.

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Book Synopsis.- Applications of Signal Processing Technology..- Efficient Hardware Architecture for Random Forest Training..- Influence of Spike Encoding, Neuron Models and Quantization on SNN Performance..- Adaptive combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation..- Using of a Robotic Platform to Detect Acoustic Events for Indoor Environments..- Applied Data Science and Engineering for Intelligent Transportation Systems and Smart Mobility..- Modeling Wildlife Accident Risk with Gaussian Mixture Models..- Towards a Unified Incident Detection and Response System for Autonomous Transportation..- Computer and Systems Based Methods and Electronic Tools in Clinical and Academic Medicine..- Edge-Processing of Myoelectric Signals for the Control of Hand- and Arm-Prostheses..- AI-Driven Gesture and Action Recognition for Learning Medicine through Virtual Reality..- Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices..- A Survey of Machine Learning Methods for Analyzing Synovitis Arthritis in Human Joints..- Motion tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications..- Advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, Therapy, and Rehabilitation..- Systems in Industrial Robotics, Automation and IoT..- Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM..- Machine Learning based Parameter Estimation of Energy Models in Digital Production Environments..- Efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning Methods..- Machine Learning using a Hybrid Quantum Classical Algorithm with Amplitude Data Encoding..- Quantitative Trend Analysis of Reinforcement Learning Algorithms in Production Systems..- Using AutomationML for Advanced Simulation in Industrial Automation..- AR Digital Twin Demonstrator for Industrial Robotics Education..- Accelerating Manual Pick-and-Place Operations with AR Projected CAD Plans and AI-Assisted Object Recognition..- Systems Thinking: Applications in Technology, Science, and Management..- Variety Engineering - A Cybernetic Concept with Practical Implications..- Using a System Archetypes to Explore Business Model Challenges for Digital Textile Microfactories..- Interacting with the Water Cycle - Towards an Experimental Paradigm..- Systemic Thinking in IT Management of the Future: Where are the Benefits?..- Developing a Human Health Digital Twin for Cardiovascular Risk Assessment: Simulation Model and Dashboard..- The Human-Centered AI-DATA Model for Digital Customer Journeys in E-Commerce..- Using LLMs and Websearch in Order to Perform Fact Checking on Texts Generated by LLMs..- Data Science in Medical and Bio-Informatics..- Data Based Prediction of the Duration of the Postoperative Stay of Patients..- A Methodology to Build Spanish Trustworthy Question-Answer Datasets for Suicide Information..- Personalized ML-Assisted Respiratory Muscle Training for Patients with Paraplegia..- Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset using Genetic Programming..- Customization and Analysis of Orthopedic Aids..- Source Localization for Electrohydraulic Shockwave Devices..- Analysis of Fluorescence Images of C. elegans.

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Book SynopsisThis book focuses on the design, development, and analysis of augmented and virtual reality (AR/VR)-based systems, along with the technological impacts and challenges in social learning. Social Learning provides a comprehensive approach to researching methods in the emerging fields of AR/VR. The contributors of this book outline the state-of-the-art implementation of AR/VR for the Internet of Things, Blockchains, Big Data, and 5G within AR/VR systems.

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Book Synopsis1. Systemanalyse: Eine Einführung 1. 0 Überblick Unsere Wirklichkeit wird nicht so sehr geprägt durch die Einzelfunktionen ihrer vielen Bestandteile, sondern vielmehr durch deren Zusammenwirken. Manche Kom­ ponenten wirken stark aufeinander, andere nur schwach, weitere schließlich haben überhaupt nichts miteinander zu tun. Wir verwenden das Wort 'System', um damit eine Anzahl von Bestandteilen abzugrenzen, die untereinander relativ stark, mit ihrer gemeinsamen Systemumwelt aber nur relativ schwach interagieren und das so, daß man dem beobachteten Verhalten dieses Systems einen 'Zweck' zuordnen kann. Bei näherer Betrachtung ist unsere Realität voll solcher Systeme, und sogar voller Sy­ steme von Systemen: Menschen, Tiere, Pflanzen, Ökosysteme, Maschinen, Fabriken, Städte, Staaten. Um die Rolle der Systemanalyse zu diskutieren, befassen wir uns hier beispielhaft mit den komplexesten dieser Systeme: mit natürlichen Systemen (Orga­ nismen und Ökosystemen). Im Laufe der Evolution haben nur diejenigen natürlichen Systeme überleben können, denen es gelungen ist, Systemprozesse zu entwickeln, die ihre Erhaltung sichern, d. h. , die die Fähigkeit erworben haben, auch unter schwierigen und unerwarteten Bedin­ gungen zu überleben. Allerdings sind die meisten natürlichen Systeme nicht in der Lage, erfolgreich mit den schweren Störungen fertigzuwerden, die ihnen durch den hohen Ressourcenverbrauch und die Umweltbelastungen der modernen Gesell­ schaften aufgezwungen werden. Um die Zerstörung der ökologischen Basis und der natürlichen Ressourcen zu vermeiden, müssen wir lernen, diese Systeme in ihrem Verhalten besser zu verstehen und die Folgen unserer Handlungen zuverlässig abzu­ schätzen. Das Werkzeug für diese Aufgabe ist die Systemanalyse.Table of Contents0. Überblick und Vorbemerkungen.- 1. Systemanalyse: Eine Einführung.- 2. Grundwissen der Modellbildung und Simulation.- 3. Verhalten und Stabilität dynamischer Systeme.- 4. Simulationsmodelle.- 5. Anhang.- Anmerkungen zu den Programmen auf der Begleitdiskette.

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Book SynopsisDieses Buch gibt eine umfassende Einführung in die verschiedenen Aspekte der modernen Computergraphik. Neben der Diskussion grundlegender Fragestellungen (Koordinatensysteme, Rasterung, Farbmodelle) werden dabei sowohl die geometrische Modellierung dreidimensionaler Objekte als auch deren graphische Darstellung behandelt. Weiterhin wird die Rolle der Computergraphik in aktuellen Anwendungen wie Animation, Visualisierung oder Virtual Reality beleuchtet. Unterstützt durch zahlreiche, z.T. farbige Illustrationen erhält der Leser so einen Überblick über die einzelnen Arbeitsschritte und Techniken auf dem Weg zum photorealistischen Bild.Trade Review"Es ist leicht lesbar und erfordert keine besonderen mathematischen Vorkenntnisse." Monatshefte für Mathematik, 02/2003Table of ContentsGrundlagen und graphische Grundfunktionen - Geometrische Modellierung dreidimensionaler Objekte - Graphische Darstellung dreidimensionaler Objekte - Ausgewählte Themen und Anwendungen - Schnittstellen und Standards - Graphiksoftware - Aufgaben

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Book SynopsisShape interrogation is the process of extraction of information from a geometric model. It is a fundamental component of Computer Aided Design and Manufacturing (CAD/CAM) systems. This book provides a bridge between the areas geometric modeling and solid modeling. Apart from the differential geometry topics covered, the entire book is based on the unifying concept of recasting all shape interrogation problems to the solution of a nonlinear system. It provides the mathematical fundamentals as well as algorithms for various shape interrogation methods including nonlinear polynomial solvers, intersection problems, differential geometry of intersection curves, distance functions, curve and surface interrogation, umbilics and lines of curvature, and geodesics.Trade ReviewFrom the reviews: "... Currently there are several excellent books in the area of geometric modeling and in the area of solid modeling. The major contribution of this book lies in its skilful manner of providing a bridge between these two areas that is guaranteed to make the target audience cry out aloud with delight. Apart from the differential geometry topics covered, the entire book is based on the unifying concept of recasting all shape interrogation problems to the solution of a nonlinear system. Indeed the book is quite compulsive; No study of shape interrogation can ignore Patrikalakis and Maekawa's. Nearly 460 references to the literature make the book widely welcomed. ..." Current Engineering Practice 2002-2003, Vol. 45, Issue 3-4 "... It provides a comprehensive coverage of the fundamental concepts that shape interrogation techniques rely on as well as of the various techniques and algorithms for interrogation of shape features. ... Containing 408 pages, the book can be an indispensable reference for anybody with interest in this field of computer aided geometric design and software development. Nick Patrikalakis and Takashi Maekawa, researchers at MIT, managed to presnet all related concepts in an insightful way. The careful arrangement of the topics and the endeavor of the authors to recast all shape interrogation problem to the numerical solution of a nonlinear system of equations impressed the reviewer. ..." I. Horváth, Structural and Multidisciplinary Optimization 2003, Vol. 24, Issue 6 "…this is a very detailed and complete book on topics that are important in both the theory and practice of geometric modeling. It is a welcome addition to the literature. Reading it and experimenting with the techniques it describes should be a rewarding experience." Luiz Henrique de Figueiredo, MATHEMATICAL REVIEWS "... This book by Patrikalakis and Maekawa is the first thorough, long overdue, look at this curicial area. The book presents an original and inclusive summary of advanced computational topics that relate to the geometry of freeform shapes. Research in these computational areas has matured to a point where such a compendium is no longer nice to have on one's shelf, but a necessity for the serious investigator. The book handles computational problems that represent fundamental components in any solid modeling environment, filling a vacuum in the literature. It will serve well any researcher, either in academia or industry, working in the area of freeform design or manusfacturing. This work continues from the point where the traditional geometric design and solid modeling books stop. ... Shape interrogation and computational geometry of freeform shapes have been a part of the geometric design and manufacturing community for a long time. This book makes efforts and is likely to become the 'Bible' for this area. As a high-quality produced book, it is a must reference for any advanced researcher or developer who works with splines and freeform representations. If you consider yourself one, this book should probably be on your bookshelf. I eagerly await what the first revision of this book may yield." Gershon Elber, Computer-Aided Design 35 (2003) 1053 "‘Shape Interrogation’ in general means the process of extracting information from a geometric model. … The aim of this text is to provide an exhaustive list of tools and algorithms useful for shape interrogation of freeform curves and surfaces. Their effectivity depends on the end user’s capability of solving systems of nonlinear equations, which is one reason for the author’s focus on robust polynomial solvers." (Johannes Wallner, Zentralblatt MATH, Vol. 1035, 2004) "‘Shape Interrogation’ is the process of extracting information from a geometric model. … This book provides a bridge between the areas of geometric modeling and solid modeling. Apart from the differential geometry topics covered, the entire book is based on the unifying concept of recasting all shape interrogations problems to the solution of a nonlinear system. … The book can serve as a textbook for teaching advanced topics of geometric modeling for graduate students as well as professionals in industry." (deslab. mit.edu, October, 2003) "This book gives a detailed description of algorithms and computational methods for shape interrogation … . The book can be used in a course for advanced graduate students and also as a reference text for researchers and practitioners in CAD/CAM. … is a very detailed and complete book on topics that are important in both the theory and the practice of geometric modeling. It is a welcome addition to the literature. Reading it and experimenting with the techniques it describes should be a rewarding experience." (Luiz Henrique de Figueiredo, Mathematical Reviews, 2003 a) "Shape interrogation and computational geometry of free-form shapes have been a part of the geometric design and manufacturing community for a long time. This book makes a first triumphant attempt at summarizing these research efforts and is likely to become the ‘Bible’ for this area. As a high-quality produced book, it is a must reference for any advanced researcher or developer who works with splines and freeform representations. If you consider yourself one, this book should probably be on your bookshelf." (Gershon Elber, Computer Aided Design, Vol. 35, 2003) "The book focuses on the topic of getting shape information from the geometric models of sculptured objects. … Containing 408 pages, the book can be an indispensable reference for anybody with interest in this field of computer aided geometric design and software development. … the text is sufficiently illustrated with figures and the production of the book is of good quality. … The book can be offered as a textbook for teaching advanced topics of geometric modeling for graduate students." (I. Horváth, Structural and Multidisciplinary Optimization, Vol. 24 (6), 2003) "This book provides the mathematical fundamentals as well as algorithms for various shape interrogation methods including nonlinear polynomial solvers, intersection problems, differential geometry of intersection curves, distance functions, curve and surface interrogation, umbilics and lines of curvature, geodesics, and offset curves and surfaces. … The book will inform and enlighten professionals in industry and therefore remains essential reading for them too." (Current Engineering Practice, Vol. 45 (3-4), 2002-03)Table of ContentsRepresentation of Curves and Surfaces.- Differential Geometry of Curves.- Differential Geometry of Surfaces.- Nonlinear Polynomial Solvers and Robustness Issues.- Intersection Problems.- Differential Geometry of Intersection Curves.- Distance Functions.- Curve and Surface Interrogation.- Umbilics and Lines of Curvature.- Geodesics.- Offset Curves and Surfaces.

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Book SynopsisAn komplexe Karosserie-Blechformteile werden seitens der Automobilindustrie allerhöchste Anforderungen hinsichtlich Funktionalität und Oberflächenqualität gestellt. Um diese Anforderungen zu erfüllen, wird ein entsprechender Methodenplan entwickelt. Das geplante Werk führt zunächst in Grundlagen von Karosseriebau, Umform- und Werkstofftechnik, Werkzeugtechnik und Pressentechnik ein, soweit diese für die Herstellung von Karosserieteilen relevant sind. Auf Basis dieser Grundlagen wird im Hauptteil die Thematik der Methodenplanung behandelt, wobei der komplexe Planungsprozess zunächst auf ein sequentielles Gedankenmodell herunter gebrochen wird. Schließlich wird anhand von Praxisbeispielen aufgezeigt, wie die zuvor sequentiell behandelten Planungsschritte zum Teil gleichzeitig, zum Teil nacheinander in mehreren Iterationsschleifen in der Praxis abgearbeitet werden. Bei allen Ausführungen steht stets die Erfüllung der qualitätsmäßigen Anforderungen, die heute an moderne Karosserieteile gestellt werden, im Vordergrund.Table of ContentsEinleitung.- Karosserietechnik und Karosseriewerkstoffe.- Plastizitätstheoretische und werkstofftechnische Grundlagen.- Verfahrenstechnische Grundlagen der Karosserieteilherstellung. Werkzeugtechnik und Werkzeugherstellungsprozess.- Grundlagen der Maschinen- und Anlagentechnik.- Fertigungsplanung und Fertigungsstrategien.- Methodenplanung.- Sachwortregister.- Literaturverzeichnis.

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Book SynopsisDieses Buch richtet sich gezielt an die große Gruppe der Prozesseigner, die in ihrem Unternehmen für einen oder mehrere Geschäfts- und Produktionsprozesse verantwortlich sind. Ihre Rolle ist wichtig, damit Prozessmanagement insgesamt gelingt. Geschulte Prozesseigner ermöglichen Standards zu etablieren und dezentral erkannte Verbesserungspotenziale zu erschließen. Hierzu müssen sie Führungsverantwortung übernehmen, ohne über disziplinarische Durchgriffsmöglichkeiten zu verfügen. Dieses Buch bietet Prozesseignern praxisnah konkrete Anleitungen, wie sie ihrer Aufgabe gerecht werden.Table of ContentsWarum Unternehmen Prozessmanagement nutzen - Prozesse schrittweise entwickeln - Der Fall FULLSERVICE-STRUKTURBETRIEB - Methoden der Prozessintensivierung - Prozesse bewerten - Prozesse von Prozesseignern

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