Mathematical modelling Books
Society for Industrial & Applied Mathematics,U.S. A First Course in Options Pricing Theory
Book SynopsisAmong the many branches of applied mathematics, options pricing theory occupies a unique position: it utilizes a wide range of advanced mathematical concepts, making it appealing to mathematicians, and it is regularly applied at financial institutions, making it indispensable to practitioners. The emergence of artificial intelligence in the financial industry has led to further interest in mathematical finance and has increased the demand for literature on this subject that is accessible to a large audience.This book presents a self-contained introduction to options pricing theory and includes a complete discussion of the required concepts in finance and probability theory; an introduction to basic models, emphasizing both critical thinking and practical applications; and over 200 exercises, several Python codes for the analysis and application of the options pricing models, and numerical projects intended to help close the gap between theory and practice. A First Course in Options Pricing Theory is suitable for an advanced undergraduate course on financial mathematics and options pricing theory in engineering, computer science, and applied mathematics programs. The reader is assumed to be familiar with the standard material in calculus and linear algebra. Stochastic calculus is not used in the book.
£67.15
Information Age Publishing Multilevel Modeling Methods with Introductory and
Book SynopsisMultilevel Modeling Methods with Introductory and Advanced Applications provides a cogent and comprehensive introduction to the area of multilevel modeling for methodological and applied researchers as well as advanced graduate students. The book is designed to be able to serve as a textbook for a one or two semester course in multilevel modeling. The topics of the seventeen chapters range from basic to advanced, yet each chapter is designed to be able to stand alone as an instructional unit on its respective topic, with an emphasis on application and interpretation.In addition to covering foundational topics on the use of multilevel models for organizational and longitudinal research, the book includes chapters on more advanced extensions and applications, such as cross-classified random effects models, non-linear growth models, mixed effects location scale models, logistic, ordinal, and Poisson models, and multilevel mediation. In addition, the volume includes chapters addressing some of the most important design and analytic issues including missing data, power analyses, causal inference, model fit, and measurement issues. Finally, the volume includes chapters addressing special topics such as using large-scale complex sample datasets, and reporting the results of multilevel designs.Each chapter contains a section called Try This!, which poses a structured data problem for the reader. We have linked our book to a website (http://modeling.uconn.edu) containing data for the Try This! section, creating an opportunity for readers to learn by doing. The inclusion of the Try This! problems, data, and sample code eases the burden for instructors, who must continually search for class examples and homework problems. In addition, each chapter provides recommendations for additional methodological and applied readings.
£63.90
Information Age Publishing Multilevel Modeling Methods with Introductory and
Book SynopsisMultilevel Modeling Methods with Introductory and Advanced Applications provides a cogent and comprehensive introduction to the area of multilevel modeling for methodological and applied researchers as well as advanced graduate students. The book is designed to be able to serve as a textbook for a one or two semester course in multilevel modeling. The topics of the seventeen chapters range from basic to advanced, yet each chapter is designed to be able to stand alone as an instructional unit on its respective topic, with an emphasis on application and interpretation.In addition to covering foundational topics on the use of multilevel models for organizational and longitudinal research, the book includes chapters on more advanced extensions and applications, such as cross-classified random effects models, non-linear growth models, mixed effects location scale models, logistic, ordinal, and Poisson models, and multilevel mediation. In addition, the volume includes chapters addressing some of the most important design and analytic issues including missing data, power analyses, causal inference, model fit, and measurement issues. Finally, the volume includes chapters addressing special topics such as using large-scale complex sample datasets, and reporting the results of multilevel designs.Each chapter contains a section called Try This!, which poses a structured data problem for the reader. We have linked our book to a website (http://modeling.uconn.edu) containing data for the Try This! section, creating an opportunity for readers to learn by doing. The inclusion of the Try This! problems, data, and sample code eases the burden for instructors, who must continually search for class examples and homework problems. In addition, each chapter provides recommendations for additional methodological and applied readings.
£97.85
ISTE Ltd and John Wiley & Sons Inc Banach, Fréchet, Hilbert and Neumann Spaces
Book SynopsisThis book is the first of a set dedicated to the mathematical tools used in partial differential equations derived from physics. Its focus is on normed or semi-normed vector spaces, including the spaces of Banach, Fréchet and Hilbert, with new developments on Neumann spaces, but also on extractable spaces. The author presents the main properties of these spaces, which are useful for the construction of Lebesgue and Sobolev distributions with real or vector values and for solving partial differential equations. Differential calculus is also extended to semi-normed spaces. Simple methods, semi-norms, sequential properties and others are discussed, making these tools accessible to the greatest number of students – doctoral students, postgraduate students – engineers and researchers without restricting or generalizing the results.Table of ContentsIntroduction xi Familiarization with Semi-normed Spaces xv Notations xvii Chapter 1 Prerequisites 1 1.1 Sets, mappings, orders 1 1.2 Countability 3 1.3 Construction of R 4 1.4 Properties of R 5 Part 1 Semi-normed Spaces 9 Chapter 2 Semi-normed Spaces 11 2.1 Definition of semi-normed spaces 11 2.2 Convergent sequences 15 2.3 Bounded, open and closed sets 17 2.4 Interior, closure, balls and semi-balls 21 2.5 Density, separability 23 2.6 Compact sets 25 2.7 Connected and convex sets 30 Chapter 3 Comparison of Semi-normed Spaces 33 3.1 Equivalent families of semi-norms 33 3.2 Topological equalities and inclusions 34 3.3 Topological subspaces 39 3.4 Filtering families of semi-norms 43 3.5 Sums of sets 46 Chapter 4 Banach, Fréchet and Neumann Spaces 49 4.1 Metrizable spaces 49 4.2 Properties of sets in metrizable spaces 51 4.3 Banach, Fréchet and Neumann spaces 55 4.4 Compacts sets in Fréchet spaces 57 4.5 Properties of R 58 4.6 Convergent sequences 60 4.7 Sequential completion of a semi-normed space 62 Chapter 5 Hilbert Spaces 65 5.1 Hilbert spaces 65 5.2 Projection in a Hilbert space 68 5.3 The space Rd 70 Chapter 6 Product, Intersection, Sum and Quotient of Spaces 73 6.1 Product of semi-normed spaces 73 6.2 Product of a semi-normed space by itself 78 6.3 Intersection of semi-normed spaces 80 6.4 Sum of semi-normed spaces 83 6.5 Direct sum of semi-normed spaces 89 6.6 Quotient space 93 Part 2 Continuous Mappings 95 Chapter 7 Continuous Mappings 97 7.1 Continuous mappings 97 7.2 Continuity and change of topology or restriction 100 7.3 Continuity of composite mappings 102 7.4 Continuous semi-norms 102 7.5 Continuous linear mappings 104 7.6 Continuous multilinear mappings 107 7.7 Some continuous mappings 111 Chapter 8 Images of Sets Under Continuous Mappings 115 8.1 Images of open and closed sets 115 8.2 Images of dense, separable and connected sets 117 8.3 Images of compact sets 119 8.4 Images under continuous linear mappings 121 8.5 Continuous mappings in compact sets 123 8.6 Continuous real mappings 124 8.7 Compacting mappings 125 Chapter 9 Properties of Mappings in Metrizable Spaces 129 9.1 Continuous mappings in metrizable spaces 129 9.2 Banach’s fixed point theorem 133 9.3 Baire’s theorem 134 9.4 Open mapping theorem 136 9.5 Banach–Schauder’s continuity theorem 138 9.6 Closed graph theorem 139 Chapter 10 Extension of Mappings, Equicontinuity 141 10.1 Extension of equalities by continuity 141 10.2 Continuous extension of mappings 142 10.3 Equicontinuous families of mappings 146 10.4 Banach–Steinhaus equicontinuity theorem 148 Chapter 11 Compactness in Mapping Spaces 153 11.1 The spaces F(X; F) and C(X; F)-pt 153 11.2 Zorn’s lemma 154 11.3 Compactness in F(X; F) 157 11.4 An Ascoli compactness theorem in C(X; F)-pt 161 Chapter 12 Spaces of Linear or Multilinear Mappings 163 12.1 The space L(E; F) 163 12.2 Bounded sets in L(E; F) 165 12.3 Sequential completeness of L(E; F) when E is metrizable 167 12.4 Semi-norms and norm on L(E; F) when E isnormed 169 12.5 Continuity of the composition of linear mappings 171 12.6 Inversibility in the neighborhood of an isomorphism 174 12.7 The space Ld(E1 × ··· × Ed; F) 178 12.8 Separation of the variables of a multilinear mapping 181 Part 3 Weak Topologies 187 Chapter 13 Duality 189 13.1 Dual 189 13.2 Dual of a metrizable or normed space 193 13.3 Dual of a Hilbert space 196 13.4 Extraction of ∗ weakly converging subsequences 199 13.5 Continuity of the bilinear form of duality 203 13.6 Dual of a product 205 13.7 Dual of a direct sum 206 Chapter 14 Dual of a Subspace 209 14.1 Hahn–Banach theorem 209 14.2 Corollaries of the Hahn–Banach theorem 211 14.3 Characterization of a dense subspace 212 14.4 Dual of a subspace 213 14.5 Dual of an intersection 215 14.6 Dangerous identifications 216 Chapter 15 Weak Topology 221 15.1 Weak topology 221 15.2 Weak continuity and topological inclusions 224 15.3 Weak topology of a product 225 15.4 Weak topology of an intersection 226 15.5 Norm and semi-norms of a weak limit 228 Chapter 16 Properties of Sets for the Weak Topology 231 16.1 Banach–Mackey theorem (weakly bounded sets) 231 16.2 Gauge of a convex open set 233 16.3 Mazur’s theorem (weakly closed convex sets) 235 16.4 ˘Smulian’s theorem (weakly compact sets) 237 16.5 Semi-weak continuity of a bilinear mapping 240 Chapter 17 Reflexivity 243 17.1 Reflexive spaces 243 17.2 Sequential completion of a semi-reflexive space 247 17.3 Prereflexivity of metrizable spaces 248 17.4 Reflexivity of Hilbert spaces 250 17.5 Reflexivity of uniformly convex Banach spaces 252 17.6 A property of the combinations of linear forms 256 17.7 Characterizations of semi-reflexivity 257 17.8 Reflexivity of a subspace 261 17.9 Reflexivity of the image of a space 261 17.10 Reflexivity of the dual 263 Chapter 18 Extractable Spaces 265 18.1 Extractable spaces 265 18.2 Extractability of Hilbert spaces 266 18.3 Extractability of semi-reflexive spaces 267 18.4 Extractability of a subspace or of the image of a space 269 18.5 Extractability of a product or of a sum of spaces 270 18.6 Extractability of an intersection of spaces 271 18.7 Sequential completion of extractable spaces 271 Part 4 Differential Calculus 273 Chapter 19 Differentiable Mappings 275 19.1 Differentiable mappings 275 19.2 Differentiality, continuity and linearity 277 19.3 Differentiation and change of topology or restriction 279 19.4 Mean value theorem 281 19.5 Bounds on a real differentiable mapping 284 19.6 Differentiation of a composite mapping 286 19.7 Differential of an inverse mapping 289 19.8 Inverse mapping theorem 290 Chapter 20 Differentiation of Multivariable Mappings 295 20.1 Partial differentiation 295 20.2 Differentiation of a multilinear or multi-component mapping 298 20.3 Differentiation of a composite multilinear mapping 300 Chapter 21 Successive Differentiations 303 21.1 Successive differentiations 303 21.2 Schwarz’s symmetry principle 305 21.3 Successive differentiations of a composite mapping 308 Chapter 22 Derivation of Functions of One Real Variable 313 22.1 Derivative of a function of one real variable 313 22.2 Derivative of a real function of one real variable 315 22.3 Leibniz formula 319 22.4 Derivatives of the power, logarithm and exponential functions 320 Bibliography 325 Cited Authors 331 Index 335
£125.06
ISTE Ltd and John Wiley & Sons Inc Continuous Functions
Book SynopsisThis book is the second of a set dedicated to the mathematical tools used in partial differential equations derived from physics. It presents the properties of continuous functions, which are useful for solving partial differential equations, and, more particularly, for constructing distributions valued in a Neumann space. The author examines partial derivatives, the construction of primitives, integration and the weighting of value functions in a Neumann space. Many of them are new generalizations of classical properties for values in a Banach space. Simple methods, semi-norms, sequential properties and others are discussed, making these tools accessible to the greatest number of students – doctoral students, postgraduate students – engineers and researchers, without restricting or generalizing the results.Table of ContentsIntroduction ix Familiarization with Semi-normed Spaces xiii Notations xv Chapter 1. Spaces of Continuous Functions 1 1.1. Notions of continuity 1 1.2. Spaces C(Ω;E), Cb(Ω;E), CK(Ω;E), C(Ω;E) and Cb(Ω;E) 3 1.3. Comparison of spaces of continuous functions 6 1.4. Sequential completeness of spaces of continuous functions 10 1.5. Metrizability of spaces of continuous functions 11 1.6. The space K(Ω;E) 14 1.7. Continuous mappings 20 1.8. Continuous extension and restriction 22 1.9. Separation and permutation of variables 23 1.10. Sequential compactness in Cb(Ω;E) 28 Chapter 2. Differentiable Functions 31 2.1. Differentiability 31 2.2. Finite increment theorem 34 2.3. Partial derivatives 37 2.4. Higher order partial derivatives 40 2.5. Spaces Cm(Ω;E), Cmb (Ω;E), CmK(Ω;E), Cmb (Ω;E) and Km(Ω;E) 42 2.6. Comparison and metrizability of spaces of differentiable functions 45 2.7. Filtering properties of spaces of differentiable functions 47 2.8. Sequential completeness of spaces of differentiable functions 49 2.9. The space Cm(Ω;E) and the set Cm(Ω;U) 52 Chapter 3. Differentiating Composite Functions and Others 55 3.1. Image under a linear mapping 55 3.2. Image under a multilinear mapping: Leibniz rule 59 3.3. Dual formula of the Leibniz rule 63 3.4. Continuity of the image under a multilinear mapping 65 3.5. Change of variables in a derivative 69 3.6. Differentiation with respect to a separated variable 72 3.7. Image under a differentiable mapping 73 3.8. Differentiation and translation 77 3.9. Localizing functions 79 Chapter 4. Integrating Uniformly Continuous Functions 83 4.1. Measure of an open subset of ℝd 83 4.2. Integral of a uniformly continuous function 87 4.3. Case where E is not a Neumann space 92 4.4. Properties of the integral 93 4.5. Dependence of the integral on the domain of integration 96 4.6. Additivity with respect to the domain of integration 99 4.7. Continuity of the integral 101 4.8. Differentiating under the integral sign 103 Chapter 5. Properties of the Measure of an Open Set 105 5.1. Additivity of the measure 105 5.2. Negligible sets 107 5.3. Determinant of d vectors 112 5.4. Measure of a parallelepiped 115 Chapter 6. Additional Properties of the Integral 119 6.1. Contribution of a negligible set to the integral 119 6.2. Integration and differentiation in one dimension 120 6.3. Integration of a function of functions 123 6.4. Integrating a function of multiple variables 125 6.5. Integration between graphs 130 6.6. Integration by parts and weak vanishing condition for a function 133 6.7. Change of variables in an integral 135 6.8. Some particular changes of variables in an integral 142 Chapter 7. Weighting and Regularization of Functions 147 7.1. Weighting 147 7.2. Properties of weighting 150 7.3. Weighting of differentiable functions 153 7.4. Local regularization 157 7.5. Global regularization 162 7.6. Partition of unity 166 7.7. Separability of K∞(Ω) 170 Chapter 8. Line Integral of a Vector Field Along a Path 173 8.1. Paths 173 8.2. Line integral of a field along a path 176 8.3. Line integral along a concatenation of paths 181 8.4. Tubular flow and the concentration theorem 183 8.5. Invariance under homotopy of the line integral of a local gradient 186 Chapter 9. Primitives of Continuous Functions 191 9.1. Explicit primitive of a field with line integral zero 191 9.2. Primitive of a field orthogonal to the divergence-free test fields 194 9.3. Gluing of local primitives on a simply connected open set 195 9.4. Explicit primitive on a star-shaped set: Poincaré’s theorem 197 9.5. Explicit primitive under the weak Poincaré condition 199 9.6. Primitives on a simply connected open set 203 9.7. Comparison of the existence conditions for a primitive 205 9.8. Fields with local primitives but no global primitive 208 9.9. Uniqueness of primitives 210 9.10. Continuous primitive mapping 211 Chapter 10. Additional Results: Integration on a Sphere 213 10.1. Surface integration on a sphere 213 10.2. Properties of the integral on a sphere 215 10.3. Radial calculation of integrals 218 10.4. Surface integral as an integral of dimension d − 1 220 10.5. A Stokes formula 224 Appendix 227 Bibliography 239 Index 243
£125.06
ISTE Ltd and John Wiley & Sons Inc Earthquake Statistical Analysis through
Book SynopsisEarthquake occurrence modeling is a rapidly developing research area. This book deals with its critical issues, ranging from theoretical advances to practical applications. The introductory chapter outlines state-of-the-art earthquake modeling approaches based on stochastic models. Chapter 2 presents seismogenesis in association with the evolving stress field. Chapters 3 to 5 present earthquake occurrence modeling by means of hidden (semi-)Markov models and discuss associated characteristic measures and relative estimation aspects. Further comparisons, the most important results and our concluding remarks are provided in Chapters 6 and 7.Table of ContentsList of Abbreviations ix List of Symbols xi Preface xv Introduction xix Chapter 1. Fundamentals on Stress Changes 1 1.1. Introduction 1 1.2. Stress interaction 4 1.3. Stress changes calculation 12 1.4. Modeling of Coulomb stress changes for different faulting types 15 1.4.1.ΔCS for strike-slip faulting 15 1.4.2.ΔCS for dip-slip faulting 16 1.5. Seismicity triggered by stress transfer 21 1.5.1. Triggering of strong earthquakes 21 1.5.2. Aftershock triggering 23 1.5.3. Triggering of mining seismicity 28 1.6. Discussion on stress interaction 31 Chapter 2. Hidden Markov Models 35 2.1. Introduction 35 2.2. Hidden Markov framework 37 2.3. Seismotectonic regime and seismicity data 42 2.4. Application to earthquake occurrences 44 2.4.1. Two hidden states and three observation types 45 2.4.2. Three hidden states and three observation types 48 2.4.3. Model selection and simulation 50 2.4.4. Steps number for the first earthquake occurrence 53 2.5. Conclusion 54 Chapter 3. Hidden Markov Renewal Models 57 3.1. Introduction 57 3.2. Semi-Markov framework 58 3.3. Hidden Markov renewal framework 65 3.4. Modeling earthquakes in Greece 66 3.4.1. Hitting times and earthquake occurrence numbers 69 3.5. Conclusion 73 Chapter 4. Hitting Time Intensity 75 4.1. Introduction 75 4.2. DTIHT for semi-Markov chains 76 4.2.1. Statistical estimation of the DTIHT 78 4.3. DTIHT for hidden Markov renewal chains 83 4.3.1. Statistical estimation of the DTIHT 85 4.4. Conclusion 87 Chapter 5. Models Comparison 89 5.1. Introduction 89 5.2. Markov framework 90 5.2.1. HMM case 92 5.2.2. HMRM case 92 5.3. Markov renewal framework 93 5.3.1. HMM case 95 5.3.2. HMRM case 96 5.4. Conclusion 97 Discussion & Concluding Remarks 99 Appendices 105 Appendix 1 107 Appendix 2 113 Appendix 3 117 References 119 Index 137
£125.06
ISTE Ltd and John Wiley & Sons Inc Discrete Time Branching Processes in Random
Book SynopsisBranching processes are stochastic processes which represent the reproduction of particles, such as individuals within a population, and thereby model demographic stochasticity. In branching processes in random environment (BPREs), additional environmental stochasticity is incorporated, meaning that the conditions of reproduction may vary in a random fashion from one generation to the next. This book offers an introduction to the basics of BPREs and then presents the cases of critical and subcritical processes in detail, the latter dividing into weakly, intermediate, and strongly subcritical regimes.Table of Contents1. Branching Processes in Varying Environment. 2. Branching Processes in Random Environment. 3. Large Deviations for BPREs. 4. Properties of Random Walks. 5. Critical BPREs: the Annealed Approach. 6. Critical BPREs: the Quenched Approach. 7. Weakly Subcritical BPREs. 8. Intermediate Subcritical BPREs. 9. Strongly Subcritical BPREs. 10. Multi-type BPREs.
£125.06
ISTE Ltd and John Wiley & Sons Inc Structural Equation Modeling with lavaan
Book SynopsisThis book presents an introduction to structural equation modeling (SEM) and facilitates the access of students and researchers in various scientific fields to this powerful statistical tool. It offers a didactic initiation to SEM as well as to the open-source software, lavaan, and the rich and comprehensive technical features it offers. Structural Equation Modeling with lavaan thus helps the reader to gain autonomy in the use of SEM to test path models and dyadic models, perform confirmatory factor analyses and estimate more complex models such as general structural models with latent variables and latent growth models. SEM is approached both from the point of view of its process (i.e. the different stages of its use) and from the point of view of its product (i.e. the results it generates and their reading). Table of ContentsPreface ix Introduction xi Chapter 1 Structural Equation Modeling 1 1.1 Basic concepts 2 1.1.1 Covariance and bivariate correlation 2 1.1.2 Partial correlation 5 1.1.3 Linear regression analysis 7 1.1.4 Standard error of the estimate 10 1.1.5 Factor analysis 11 1.1.6 Data distribution normality 18 1.2 Basic principles of SEM 21 1.2.1 Estimation methods (estimators) 27 1.3 Model evaluation of the solution of the estimated model 36 1.3.1 Overall goodness-of-fit indices 36 1.3.2 Local fit indices (parameter estimates) 43 1.3.3 Modification indices 44 1.4 Confirmatory approach in SEM 45 1.5 Basic conventions of SEM 47 1.6 Place and status of variables in a hypothetical model 49 1.7 Conclusion 49 1.8 Further reading 50 Chapter 2 Structural Equation Modeling Software 53 2.1 R environment 54 2.1.1 Installing R software 55 2.1.2 R console 55 2.2 lavaan 58 2.2.1 Installing the lavaan package 58 2.2.2 Launching lavaan 58 2.3 Preparing and importing a dataset 60 2.3.1 Entry and import of raw data 60 2.3.2 What to do in the absence of raw data? 63 2.4 Major operators of lavaan syntax 65 2.5 Main steps in using lavaan 66 2.6 lavaan fitting functions 68 Chapter 3 Steps in Structural Equation Modeling 69 3.1 The theoretical model and its conceptual specification 70 3.2 Model parameters and model identification 71 3.3 Models with observed variables (path models) 73 3.3.1 Identification of a path model 74 3.3.2 Model specification using lavaan (step 2) 76 3.3.3 Direct and indirect effects 78 3.3.4 The statistical significance of indirect effects 80 3.3.5 Model estimation with lavaan (step 3) 81 3.3.6 Model evaluation (step 4) 82 3.3.7 Recursive and non-recursive models 83 3.3.8 Illustration of a path analysis model 85 3.4 Actor-partner interdependence model 90 3.4.1 Specifying and estimating an APIM with lavaan 92 3.4.2 Evaluation of the solution 93 3.4.3 Evaluating the APIM re-specified with equality constraints 94 3.5 Models with latent variables (measurement models and structural models) 95 3.5.1 The measurement model or Confirmatory Factor Analysis 97 3.6 Hybrid models 148 3.7 Measure with a single-item indicator 149 3.8 General structural model including single-item latent variables with a single indicator 151 3.9 Conclusion 152 3.10 Further reading 155 Chapter 4 Advanced Topics: Principles and Applications 157 4.1 Multigroup analysis 157 4.1.1 The steps of MG-CFA 162 4.1.2 Model solutions and model comparison tests 166 4.1.3 Total invariance versus partial invariance 171 4.1.4 Specification of a partial invariance in lavaan syntax 172 4.2 Latent trait-state models 172 4.2.1 The STARTS model 173 4.2.2 The Trait-State-Occasion Model 197 4.2.3 Concluding remarks 211 4.3 Latent growth models 213 4.3.1 General overview 213 4.3.2 Illustration of an univariate linear growth model 223 4.3.3 Illustration of an univariate non-linear (quadratic) latent growth model 228 4.3.4 Conditional latent growth model 232 4.3.5 Second-order latent growth model 240 4.4 Further reading 249 References 251 Index 269
£125.06
ISTE Ltd and John Wiley & Sons Inc Numerical Simulation, An Art of Prediction,
Book SynopsisNumerical simulation is a technique of major importance in various technical and scientific fields. Whilst engineering curricula now include training courses dedicated to it, numerical simulation is still not well-known in some economic sectors, and even less so among the general public. Simulation involves the mathematical modeling of the real world, coupled with the computing power offered by modern technology. Designed to perform virtual experiments, digital simulation can be considered as an "art of prediction". Embellished with a rich iconography and based on the testimony of researchers and engineers, this book shines a light on this little-known art. It is the second of two volumes and gives examples of the uses of numerical simulation in various scientific and technical fields: agriculture, industry, Earth and universe sciences, meteorology and climate studies, energy, biomechanics and human and social sciences.Table of ContentsForeword ix Introduction xi Chapter 1. Agriculture 1 1.1. Feeding the world 2 1.2. Agriculture is being digitized 7 1.3. Decision-making support 10 1.4. Environmental impact 16 1.5. Plant growth 23 Chapter 2. Air and Maritime Transport 31 2.1. The long march of globalization 32 2.2. Going digital! 35 2.3. Optimum design and production 50 2.3.1. Lightening the structures 50 2.3.2. Mastering processes 53 2.3.3. Producing in the digital age 58 2.4. Improving performance 63 2.4.1. Increasing seaworthiness 63 2.4.2. Limiting noise pollution 68 2.4.3. Protecting from corrosion 76 2.4.4. Reducing energy consumption 78 Chapter 3. The Universe and the Earth 87 3.1. Astrophysics 88 3.1.1. Telling the story of the Universe 90 3.1.2. Observing the formation of celestial bodies 105 3.1.3. Predicting the mass of stars 109 3.2. Geophysics 114 3.2.1. Earthquakes 115 3.2.2. Tsunamis 120 3.2.3. Eruptions 127 Chapter 4. The Atmosphere and the Oceans 133 4.1. Meteorological phenomena, climate change 134 4.2. Atmosphere and meteorology 137 4.2.1. Global and local model 138 4.2.2. Scale descent 142 4.3. Oceans and climate 145 4.3.1. Marine currents 145 4.3.2. Climate 155 Chapter 5. Energies 165 5.1. The technical dream 165 5.2. Combustion 168 5.3. Nuclear energy 173 5.3.1. Dual-use energy 173 5.3.2. At the heart of nuclear fission 176 5.3.3. Developing nuclear fusion 183 5.4. New energies 188 5.4.1. Hydroelectricity 189 5.4.2. Wind energy 193 Chapter 6. The Human Body 199 6.1. A digital medicine 200 6.2. Medical data 206 6.2.1. Medical imaging 206 6.2.2. Genetic information 211 6.3. Mechanical behavior of muscles and organs 215 6.4. Blood circulation 216 6.4.1. Blood microcapsules 218 6.4.2. Angioplasty simulation 219 6.5. Cosmetics 227 6.6. Neurosciences 228 Chapter 7. Individuals and Society 237 7.1. Calculated choices 238 7.2. A question of style 241 7.2.1. Assigning a work to its author 243 7.2.2. Understanding a pictorial technique 245 7.2.3. Discovering a personality type 247 7.3. The shape of a city 253 7.3.1. Transport 254 7.3.2. Sound atmosphere 256 7.3.3. Businesses 260 7.4. A question of choice 263 7.5. What about humans? 272 Conclusion 281 Glossary of Terms 287 References 317 Index 353
£125.06
ISTE Ltd and John Wiley & Sons Inc Geographical Modeling: Cities and Territories
Book SynopsisThe modeling of cities and territories has progressed greatly in the last 20 years. This is firstly due to geographic information systems, followed by the availability of large amounts of georeferenced data both on the Internet and through the use of connected objects. In addition, the rise in performance of computational methods for the simulation and exploration of dynamic models has facilitated advancement. Geographical Modeling presents previously unpublished information on the main advances achieved by these new approaches. Each of the six chapters builds a bibliographic review and precisely describes the methods used, highlighting their advantages and discussing their interpretations. They are all illustrated by many examples. The book also explains with clarity the theoretical foundations of geographical analysis, the delicate operations of model selection, and the applications of fractals and scaling laws. These applications include gaining knowledge of the morphology of cities and the organization of urban transport, and finding new methods of building and exploring simulation models and visualizations of data and results.Table of ContentsIntroduction ixDenise PUMAIN Chapter 1. Complexity in Geography 1Denise PUMAIN 1.1. A first bifurcation in the epistemology of geographic modeling 3 1.1.1. “Vertical” explanations for the “science of places, not people” 4 1.1.2. “Horizontal” explanations for the science of the spatiality of societies 5 1.1.3. The discussed status of modeling 7 1.2. Modeled regularities 10 1.2.1. Proximity and distances 11 1.2.2. The scale 15 1.2.3. Concentration and accumulation: geographical inequalities and scaling laws 19 1.2.4. Spatial change and trajectory dependence 21 1.2.5. Territorial drifts, space-time compression, and globalization 25 1.3. Conclusion 29 Chapter 2. Choosing Models to Explain the Dynamics of Cities and Territories 31Lena SANDERS 2.1. Introduction 31 2.2. Explaining by reasons or laws: choosing an epistemological framework 32 2.3. The modeling approach: diversity of models 36 2.4. Explaining through statistical relationships or mechanisms 38 2.5. Choosing the level of abstraction for the phenomenon to be explained: general versus particular 41 2.6. Choosing the level of abstraction for the model: stylized or realistic, KISS or KIDS 44 2.6.1. Modes of representation of space: from a stylized space to a realistic space 45 2.6.2. Formalizing spatial mechanisms: from stylized to realistic 48 2.7. Conclusion 50 Chapter 3. Effects of Distance and Scale Dependence in Geographical Models of Cities and Territories 53Cécile TANNIER 3.1. Three fundamental principles for modeling cities and territories 55 3.1.1. Effects of distance 57 3.1.2. Effects of scale dependence 58 3.2. Role of distance in spatial simulation models 61 3.3. Modeling scale dependence 76 3.3.1. Scale dependence as a result of processes acting at different scales 77 3.3.2. Scale invariance for the description of geographical phenomena 83 3.3.3. Scale dependence as a generative mechanism for simulated spatial configurations 88 3.4. Conclusion 93 Chapter 4. Incremental Territorial Modeling 95Clémentine COTTINEAU, Paul CHAPRON, Marion LE TEXIER and Sébastien REY-COYREHOURCQ 4.1. The map and the territory 96 4.1.1. Modeling as one map: selection and schematization 96 4.1.2. The representation of territory as an input of the model 100 4.1.3. The representation of territory as an output of the model 102 4.2. Generality and specificity: explaining by ways of geographical models 106 4.2.1. Historical contingency and non-ergodicity 106 4.2.2. General/specific/singular 109 4.3. Incremental territorial modeling 110 4.3.1. Identifying the object, scale, configuration, and stylized facts 111 4.3.2. Gathering the different theoretical explanations 112 4.3.3. Hierarchizing the interaction processes between agents 113 4.3.4. Hierarchizing the interaction processes between agents and their environment 114 4.3.5. Implementing mechanisms and their formal alternatives 115 4.3.6. Combining, simulating, and comparing 116 4.4. Challenges and limits of multi-modeling 117 4.4.1. The combinatorial curse 118 4.4.2. Human and technical costs 118 4.4.3. Subjectivity in the choice of building blocks 119 4.4.4. Comparing models of different structures 119 4.4.5. Sharing and accumulation of knowledge 121 4.5. Conclusion 121 Chapter 5. Methods for Exploring Simulation Models 125Juste RAIMBAULT and Denise PUMAIN 5.1. Social sciences and experimentation 126 5.2. Geographical data and computer skills 127 5.3. New generation simulations 130 5.3.1. A virtual laboratory: the OpenMOLE platform 131 5.3.2. The SimpopLocal experiment: simulation of an emergence in geography 134 5.3.3. Implementation of SimpopLocal, from NetLogo to OpenMOLE 137 5.3.4. Calibration and validation 139 5.4. Other examples of OpenMOLE applications: network–territory interaction models 143 5.5. Perspectives 147 5.5.1. Methods 147 5.5.2. Tools 148 5.6. Conclusion 149 Chapter 6. Model Visualization 151Robin CURA 6.1. Introduction 151 6.2. Visualization as modeling 153 6.2.1. Visualization as a tool for interdisciplinarity 155 6.2.2. Visualization and reproducibility 160 6.2.3. Visualizing a model means learning 162 6.3. Visualize to evaluate 163 6.3.1. Visualize before modeling 164 6.3.2. Visualize during the simulation 166 6.3.3. Visualizing after the simulation 169 6.4. Visualizing to compare 172 6.4.1. Which models should be compared? 172 6.4.2. How should visual comparison be done? 174 6.5. Visualizing to communicate 178 6.5.1. Visualizing to disseminate 179 6.6. Some obstacles inherent in model visualization 182 6.6.1. Producing and visualizing massive data 183 6.6.2. Visualization of aggregated data 187 6.7. Conclusion 191 References 193 List of Authors 221 Index 223
£125.06
ISTE Ltd and John Wiley & Sons Inc IGA: Non-conforming Coupling and Shape
Book SynopsisIsogeometric analysis (IGA) consists of using the same higher-order and smooth spline functions for the representation of geometry in Computer Aided Design as for the approximation of solution fields in Finite Element Analysis. Now, about fifteen years after its creation, substantial works are being reported in IGA, which make it very competitive in scientific computing.This book provides a contemporary vision of IGA by first discussing the current challenges in achieving a true bridge between design and analysis, then proposing original solutions that answer the issues from an analytical point of view, and, eventually, studying the shape optimization of structures, which is one of the greatest applications of IGA. To handle complex structures, a full analysis-to-optimization framework is developed, based on non-invasive coupling, parallel domain decomposition and immersed geometrical modeling. This seems to be very robust, taking on all of the attractive features of IGA (the design–analysis link, numerical efficiency and natural regularization), giving us the opportunity to explore new types of design.Table of ContentsContents Preface ix Chapter 1: Introduction to IGA: Key Ingredients for the Analysis and Optimization of Complex Structures 1 1.1 Brief introduction 1 1.2 Geometric modeling and simulation with splines 2 1.2.1.Parametric representationof geometries 2 1.2.2 B-spline and NURBS technologies 5 1.2.3 Design features and shape parameterization 15 1.2.4 Spline-based finite element analysis: isogeometric principle 21 1.3 Improved CAD-CAE integration for robust optimization 23 1.3.1 Returning to the original motivations behind IGA 23 1.3.2 An ideal framework for parametric shape optimization 25 1.4.The analysis-suitablemodel issue 27 1.4.1.The trimmingconcept 28 1.4.2 Non-conformingmultipatch parameterization 30 1.4.3 Imposingshape variation 33 1.5 Computation of non-conforming interfaces: a brief overview of usual weak coupling methods 35 1.5.1.Governingequations 36 1.5.2.Penalty coupling 38 1.5.3.Mortar coupling 39 1.5.4.Nitsche coupling 41 Chapter 2: Non-invasive Coupling for Flexible Global/Local IGA 45 2.1 Brief introduction 45 2.2 The standard non-invasive strategy 46 2.2.1.Origin 46 2.2.2 Non-invasive resolution of the coupling problem 47 2.3 Interest in the field of IGA 54 2.3.1 Global/local modeling in IGA 55 2.3.2.Challenges 57 2.4 A robust algorithm for non-conforming global/local IGA 59 2.4.1 Reference formulation: non-symmetric Nitsche coupling 59 2.4.2 A Nitsche-based non-invasive algorithm 64 2.4.3.Validation 72 2.5.Summaryand discussion 84 Chapter 3: Domain Decomposition Solvers for Efficient Multipatch IGA 87 3.1 Introduction 87 3.2 Benefiting from the additional Lagrange multiplier field for multipatch analysis 89 3.3 Case of multipatch Kirchhoff–Love shell analysis 91 3.3.1 Kirchhoff–Love shell formulation: basics 92 3.3.2 Formulation of the coupled problem 97 3.3.3 Preliminary results: monolithic resolution 100 3.4 On the construction of dual domain decomposition solvers 103 3.4.1 Formulation of the interface problem 104 3.4.2.Solvingthe interfaceproblem 106 3.4.3 Null space and pseudo-inverse 110 3.4.4.Preconditioning 111 3.5 Numerical investigation of the developed algorithms 115 3.5.1.Standardsolid elasticity 116 3.5.2 Heterogeneous plate bending 121 3.5.3.Scordelis–Loroof 123 3.5.4.Stiffenedpanel 125 3.6.Summaryand discussion 128 Chapter 4: Isogeometric Shape Optimization of Multipatch and Complex Structures 131 4.1 Introduction 131 4.2 Isogeometric shape optimization framework 133 4.2.1.Optimizationflowchart 133 4.2.2 Multilevel design 133 4.2.3.Design variables 135 4.2.4.Formulationandresolution 136 4.3 Unify the DD approach and multipatch optimization: towards ultimate efficiency 145 4.3.1 DD computation of the response functions 145 4.3.2 DD computation of the sensitivities 146 4.3.3.Non-designparts 147 4.3.4.Fast re-analysis 148 4.3.5.Optimizationalgorithm 149 4.4 Innovative design of multipatch structures: focus on aeronautical stiffenedpanels 149 4.4.1 Geometric modeling: embedded entities 150 4.4.2 Analysis: an embedded Kirchhoff–Love shell element 156 4.4.3.Two preliminary examples to illustrate the design capabilities 159 4.5 Application to solid structures and first interests 168 4.5.1.Simple extensionof themethod 169 4.5.2.Atest case in 2D 171 4.6 Advanced numerical optimization examples 175 4.6.1 Global shell optimization: stiffened roof 176 4.6.2.Local shell optimization: curvedwall 179 4.6.3.Designingan aircraftwing-box 185 4.7 Towards the optimal design of structural details within isogeometric patches 192 4.7.1.Asimple but instructive test case 192 4.7.2 Unify the non-invasive global/local approach and the optimizationof local details 193 4.7.3.Preliminaryresults and perspectives 197 4.8.Summaryand discussion 198 References 201 Index 229
£112.50
ISTE Ltd and John Wiley & Sons Inc Modeling and Use of Context in Action
Book SynopsisThis book brings together current research and adopts a pragmatic approach to modeling and using context to solve real-world problems. The editors were instrumental in creating - and continue to be involved in - the interdisciplinary research community, centered around the biennial CONTEXT (International and Interdisciplinary Conference on Modeling and Using Context) conference series, focused on studying context and its implications for artificial intelligence, software applications, psychology, philosophy, linguistics, neuroscience, as well as other fields. The first three chapters lay the foundations, looking at the lessons learned over the past 25 years and arguing for a continued shift toward more pragmatic approaches. The remaining chapters contain contributions to pragmatic context-based research from a wide range of domains, including technological problems - such as subway incident management and autonomous underwater vehicle control - identifying emotions from speech without understanding the words, anonymization in a world where privacy is increasingly threatened, teaching in context and improving management teaching in a business school.Table of ContentsPreface xi Patrick Brézillon and Roy M. Turner Introduction xxi Patrick Brézillon and Roy M. Turner Chapter 1 Pragmatic Research on Context Modeling and Use 1 Patrick Brézillon and Roy M. Turner 1.1 Introduction 1 1.2 Pragmatic research on context 2 1.3 Role of context in AI systems 3 1.3.1 Data, information and knowledge 3 1.3.2 Contextual knowledge 6 1.4 Three examples of pragmatic research on context 8 1.4.1 Introduction 8 1.4.2 Contextual graphs (CxGs) 9 1.4.3 Context-based reasoning (CxBR) 11 1.4.4 Context-mediated behavior (CMB) 12 1.4.5 Conclusions and lessons learned 14 1.5 Conclusion 18 1.6 References 19 Chapter 2. Modeling and Using Context: 25 Years of Lessons Learned 23 Patrick Brézillon 2.1 Introduction 23 2.2 Knowledge in action 25 2.2.1 Operational knowledge and contextual knowledge 25 2.2.2 Operational knowledge and mental models 26 2.2.3 Modeling operational knowledge 27 2.2.4 Indirect modeling from experience reuse 29 2.2.5 Lessons learned 31 2.3 Context in action 32 2.3.1 Conceptual modeling 32 2.3.2 A typology of contexts 33 2.3.3 About contextual elements 34 2.3.4 Implementation of the contextual graphs formalism 39 2.4 Using context in real-world applications 40 2.4.1 Context and focus processing 40 2.4.2 Context and actors 42 2.4.3 Extension of the CxG formalism 43 2.5 Conclusion 46 2.6 References 49 Chapter 3 Toward Pragmatic Context-Based Intelligent Systems 53 Roy M. Turner and Patrick Brézillon 3.1 Introduction 53 3.2 Evolution of AI systems 55 3.2.1 Formal versus pragmatic acontextual approaches 55 3.2.2 Formal consideration of context 56 3.2.3 Pragmatic consideration of context 57 3.3 Pragmatic context-based intelligent systems 62 3.3.1 Explicit context representation 63 3.3.2 Context assessment mechanism 66 3.3.3 Context transitioning mechanism 68 3.3.4 Context-based intelligent assistant systems 68 3.3.5 Context-based intelligent autonomous agents 73 3.4 Conclusion 80 3.5 References 81 Chapter 4 Activating the Context for Learning and Teaching: Findings from the TEEC Project 87 Claire Anjou, Thomas Forissier, Jacqueline Bourdeau, Valéry Psyché, Lamprini Chartofylaka and Alain Stockless 4.1 Introduction 87 4.2 Theoretical framework 89 4.2.1 Internal and external contexts for education 89 4.2.2 Modeling external context 91 4.3 The research focuses 95 4.4 Methodology 98 4.4.1 DBR methodology 98 4.4.2 Data collection and analysis 99 4.4.3 TEEC organization 99 4.5 Results and findings 101 4.5.1 Context effects identification and specification 101 4.5.2 Using the digital technologies 105 4.5.3 Learning as an evolution of mental representations 106 4.5.4 The development of digital tools 107 4.6 Discussion and interpretation 114 4.6.1 Context effect and affective dimension: learning with contexts, contexts effect and cognitive conflict 114 4.6.2 Digital education and context 117 4.6.3 Mazcalc needs to interact with the scripting tool 118 4.7 Conclusion and related work 118 4.8 Acknowledgment and credits 120 4.9 Appendices: description of the TEEC experiment 120 4.9.1 Historical event/social realities 120 4.9.2 Geothermal energy 121 4.9.3 Literature 122 4.9.4 Sustainable development: sugar 122 4.9.5 Sustainable development: fruit 124 4.10 References 125 Chapter 5 Pragmatic Reasoning in Context: Context-Mediated Behavior 131 Roy M. Turner 5.1 Introduction 131 5.2 Context-mediated behavior 133 5.2.1 CMB for autonomous agents: Orca Project 137 5.2.2 Contextual schemas 138 5.2.3 Context assessment 144 5.3 CMB and planning 146 5.4 CMB in multiagent systems 149 5.4.1 Context-appropriate organization and reorganization 149 5.4.2 An ontology for contextual knowledge and contexts 151 5.4.3 Trust in context 154 5.5 (Deep) learning in context 155 5.6 Conclusion 162 5.7 Acknowledgments 162 5.8 References 163 Chapter 6 Using Context to Help Identify the Emotional State of a Human in a Conversation 169 Andreas H. Marpaung and Avelino J. Gonzalez 6.1 Introduction and background 169 6.2 Use case and research hypothesis 170 6.3 Related works 172 6.4 Sentiment analysis as a way to model context 174 6.5 Our approach to the problem 176 6.5.1 Our overall approach to paralinguistic affect recognition 176 6.5.2 A (very) brief description of phase I (context-free classification) 177 6.5.3 Phase II – the context-centered process 178 6.6 Example application: smart phone 189 6.6.1 Phase 1: context-free process 189 6.6.2 Phase 2: context-centered process 190 6.7 Summary and conclusion 191 6.8 References 192 Chapter 7 Context-Driven Behavior: A Proactive Approach to Contextual Reasoning 197 Christian Wilson 7.1 Motivation for a proactive model 197 7.2 Challenges associated with a proactive model 199 7.2.1 Coping with uncertainty 199 7.2.2 A lack of initial knowledge 202 7.3 Context and contextual knowledge 203 7.3.1 Problem-solving contexts 203 7.3.2 Contextual schemas 204 7.4 A framework for context-driven agent 208 7.4.1 Defining a problem-solving scenario 209 7.4.2 Predicting future contexts 210 7.4.3 Identifying context-inappropriate behavior 215 7.4.4 Strategy modification 217 7.5 Conclusion 219 7.6 References 219 Chapter 8. Context-Based Personal Data Discovery for Anonymization 221 Hassane Tahir and Patrick Brézillon 8.1 Introduction 221 8.2 Personal and sensitive data 223 8.3 Procedure of personal data discovery 224 8.3.1 Objective of personal data discovery procedures 224 8.3.2 Role of a DPO in personal data discovery 225 8.3.3 Description of procedure of data discovery 225 8.4 Specifying personal data in the context of an anonymization process 228 8.4.1 Definition of anonymization 228 8.4.2 Motivation for data anonymization 228 8.4.3 Examples of techniques of anonymization 229 8.4.4 Anonymization process 231 8.4.5 Contextual elements in personal data discovery 232 8.5 Related work 235 8.6 Procedure contextualization for data discovery 236 8.6.1 The concept of context 236 8.6.2 Conceptual graph approach 236 8.6.3 A case study 238 8.7 Conclusion 242 8.8 References 243 Chapter 9 Situated Management Learning 245 John Hegarty and Régis Maubrey 9.1 Introduction 245 9.2 Management practices, values and theoretical insights 246 9.2.1 Management practices 247 9.2.2 Management values 251 9.2.3 Management insights 253 9.2.4 Toward a dynamic model of situated management learning 256 9.3 Situated learning – an application in an accounting classroom 257 9.3.1 The rules of the learning game 257 9.3.2 The accounting decision-making situation 258 9.3.3 Learning teams 259 9.3.4 Deliverables by the learning teams 259 9.3.5 Feedback to the learning teams 260 9.4 Results 260 9.5 Discussion, outlook and related research 261 9.6 Acknowledgments 262 9.7 References 263 List of Authors 267 Index 269
£112.50
ISTE Ltd. Numerical Methods for Strong Nonlinearities in
Book SynopsisNumerical Methods for Strong Nonlinearities in Mechanics deals with recent advances in the numerical treatment of contact/friction and damage phenomena. Although physically distinct, these phenomena both lead to a strong nonlinearity in the mechanical problem, therefore limiting the regularity of the problem, which is now non-differentiable. This has two direct consequences: on the one hand, the mathematical characteristics of the problem deviate from wellestablished forms, requiring innovative discretization schemes; on the other hand, the low regularity makes it particularly difficult to solve the corresponding large-scale algebraic systems robustly and efficiently. In addition, neither the uniqueness, nor the existence of solutions, remain assured, resulting in bifurcation points, limit loads and structural instabilities, which are always tricky to overcome numerically.
£118.80
Edward Elgar Publishing Ltd Tax Policy and Uncertainty: Modelling Debt
Book SynopsisPresenting innovative modelling approaches to the analysis of fiscal policy and government debt, this book moves beyond previous models that have relied upon the assumption that various age-specific rates and policy variables remain unchanged when it comes to generating government expenditures and tax revenues. As a result of population ageing, current policy settings in many countries are projected to lead to unsustainable levels of public debt; Tax Policy and Uncertainty explores models that allow for feedbacks and uncertainty to combat this.Applicable to any country, the models in the book explore the optimal timing and extent of tax changes in the face of anticipated high future debt. Chapters produce stochastic debt projections, including probability distribution of debt ratios at each point in time. It also offers important analysis of fiscal policy trade-offs as well as providing advice on when and by how much tax rates should be increased.Economics scholars focusing on fiscal policy will appreciate the improved models in this book that allow both for uncertainty and feedback effects arising from responses to increased debt. It will also be helpful to economic policy advisors and economists in government departments.Trade Review’This book develops important innovations in addressing two problems in determining short term fiscal policy according to long run fiscal projections. The first problem is the difficulty of modelling the complex interactions of macroeconomic variables that generate feedback effects from policy decisions. Second is the potential sunk costs of making irreversible tax and spending decisions in the face of significant uncertainty about future phenomena such as population ageing and climate change. The authors build their analysis carefully and in a very readable style. It should provide a useful manual for fiscal policy makers around the world.’- Ross Guest, Griffith University, Australia -- ’Anyone seeking to understand tax policy modelling under uncertainty will certainly want to consult this book.’- James R. Hines Jr., University of Michigan, USTable of ContentsContents: 1. Introduction I Deterministic Projection Models 2. Projecting Tax Revenues 3. A Debt Projection Model II Uncertainty in Tax Models 4. Tax Policy under Uncertainty III Debt Projections and Uncertainty 5. Stochastic Projections and Debt 6. Optimal Tax Policy Bibliography Index
£86.00
ISTE Ltd Enhancing Stochastic Petri Nets with
Book SynopsisThis book explores the world of reconfigurable stochastic Petri nets (RSPNs), a powerful method for modeling and verifying complex, dynamic and reconfigurable systems. As modern discrete-event systems become increasingly flexible, requiring structural adaptability at runtime, classical Petri nets are proving insufficient. This book presents innovative extensions to Petri nets, offering enhanced modeling capabilities for reconfigurable systems, while ensuring efficient verification. Through a structured approach, this book introduces reconfigurable generalized stochastic Petri nets (RecGSPNs), an advanced framework that integrates reconfigurability while preserving crucial system properties such as liveness, boundedness and deadlock-freedom. This book systematically explores modeling techniques, including stochastic reward nets and dynamic topology transformations, demonstrating their effectiveness through quantitative and qualitative analyses. By addressing challenges in state-space explosion and computational complexity, this book provides essential methodologies for researchers and practitioners working on reconfigurable systems, and serves as a valuable resource for those working in network security, manufacturing systems and distributed computing, where dynamic reconfigurations are essential.
£118.80
CABI Publishing Mathematical Modelling in Animal Nutrition
Book SynopsisMathematical modelling is increasingly applicable to the practical sciences. Here, mathematical approaches are applied to the study of mechanisms of digestion and metabolism in primary animal species. Farmed animals - ruminants, pigs, poultry and fish are comprehensively covered, as well as sections on companion animals. Common themes between species, such as energy and amino acid metabolism, are explored with a worldwide approach. Leading researchers from around the world have contributed to France and Kebreab's volume to provide an integrated approach to mathematical modelling in animal nutrition.Table of Contents1: Linear Models for Determining Digestibility 2: Nonlinear Functions in Animal Nutrition 3: Interesting Simple Dynamic Growth Models 4: The Dilemma in Models of Intake Regulation: Mechanistic or Empirical 5: Models to Measure and Interpret Exchange of Metabolites Across the Capillary Bed of Intact Organs 6: Modelling Methane Emissions from Farm Livestock 7: Supporting Measurements Required for Evaluation of Greenhouse Gas Emissions 8: Models for Enteric Fermentation and Stored Animal Manure 9: Data Capture: Development of a Mobile Open-Circuit Ventilated Hood System for Measuring Real- time gaseous emissions in cattle 10: Efficiency of Amino Acid Utilization in Simple-Stomached Animals and Humans: A Modelling Approach 11: Compartmental Models of Protein Turnover to Resolve Isotope Dilution Data 12: Assessment of Protein and Amino Acid Requirements in Adult Mammals, with Specific Focus on Cats, Dogs, and Rabbits 13: Mathematical Representation of the Partitioning of Retained Energy in the Growing Pig 14: Aspects of Energy Metabolism and Energy Partitioning in Broiler Chickens 15: Modelling Phosphorus Metabolism 16: Methodological Considerations for Measuring Phosphorus Utilization in Pigs 17: The Prediction of the Consequences of Pathogen Challenges on the Performance of Growing Pigs 18: Factors Regulating Feed Efficiency and Nutrient Utilization in Beef Cattle 19: Models of Nutrient Utilization by Fish and Potential Applications for Fish Culture Operations 20: Integrated Approaches to Evaluate Nutritional Strategies for Dairy Cows 21: Modelling Lactation Potential in an Animal Model 22: The Diary of Molly 23: Modelling Sugarcane Utilization by Dairy Cows in the Tropics 24: Simulation Exercises for Animal Science MSc Students: Rumen Digestion and Pig Growth
£153.18
Edward Elgar Publishing Ltd Augustin Cournot: Modelling Economics
Book Synopsis"If Augustin Cournot had still been alive, he could have won the Nobel Memorial Prize in Economics on at least three different occasions", exclaimed Nobel Laureate Robert Aumann during the 2005 Cournot Centre conference.From his earliest publications, Cournot broke from tradition with his predecessors in applying mathematical modelling to the social sphere. Consequently, he was the first to affirm the mathematization of social phenomena as an essential principle. The fecundity of Cournot's works stems not only from this departure, but also from a richness that irrigated the social sciences of the twentieth century. In this collection, the contributors - including two Nobel laureates in economics - highlight Cournot's profound innovativeness and continued relevance in the areas of industrial economics, mathematical economics, market competition, game theory and epistemology of probability and statistics. Each of the seven authors reminds us of the force and modernity of Cournot's thought as a mathematician, historian of the sciences, philosopher and, not least, as an economist.Combining an epistemological perspective with a theoretical one, this book will be of great interest to researchers and students in the fields of economics, the history of economic thought, and epistemology.Trade Review'. . . readers, especially those with a methodological orientation, will find in this book useful material of a kind not so frequently available in more traditional HET books and journals.' -- Nicola Giocoli, Storia del Pensiero Economico'Augustin Cournot: Modelling Economics is not a biography, but rather a reflection on those ideas of Cournot that persist today and what we can still learn from this great thinker. One cannot help but wonder at the wide range of accomplishments detailed in this book, but the discussion of Cournot's missteps is an unexpected highlight. . . this book should appeal to those who would like to learn more about Cournot as well as the various settings in which his thoughts have been embraced or rejected.' -- Lauren E. Feiler, Journal of Economic Literature'This rich and fascinating collection of essays helps enormously to establish the reputation of Augustin Cournot as a diverse and powerful thinker, whose numerous contributions range far beyond his widely acknowledged model of oligopoly. Cournot is revealed not merely as a mathematician, but one who was engaged in philosophical debates concerning epistemology and the nature of science. Anyone with the preconception that the development of modern economics was confined to the Anglophone world - from Smith through Marshall to the Nobel Laureates of today - will be amazed by the details of Cournot's contribution revealed here.' -- Geoffrey M. Hodgson, University of Hertfordshire, UKTable of ContentsContents: Preface About the Series: Professor Robert Solow The Complete Works of Antoine Augustin Cournot Chronological Biography of Antoine Augustin Cournot Introduction Thierry Martin and Jean-Philippe Touffut 1. Cournot as Economist: 200 Years of Relevance Jean Magnan de Bornier 2. Cournot’s Probabilistic Epistemology Thierry Martin 3. The Functions of Economic Models Bernard Walliser 4. From Cournot’s Principle to Market Efficiency Glenn Shafer 5. War and Peace Robert J. Aumann 6. Cournot and the Social Income Robert M. Solow 7. Comparing the Incomparable: The Sociology of Statistics Alain Desrosières Index
£90.00
Edward Elgar Publishing Ltd Augustin Cournot: Modelling Economics
Book Synopsis"If Augustin Cournot had still been alive, he could have won the Nobel Memorial Prize in Economics on at least three different occasions", exclaimed Nobel Laureate Robert Aumann during the 2005 Cournot Centre conference.From his earliest publications, Cournot broke from tradition with his predecessors in applying mathematical modelling to the social sphere. Consequently, he was the first to affirm the mathematization of social phenomena as an essential principle. The fecundity of Cournot's works stems not only from this departure, but also from a richness that irrigated the social sciences of the twentieth century. In this collection, the contributors - including two Nobel laureates in economics - highlight Cournot's profound innovativeness and continued relevance in the areas of industrial economics, mathematical economics, market competition, game theory and epistemology of probability and statistics. Each of the seven authors reminds us of the force and modernity of Cournot's thought as a mathematician, historian of the sciences, philosopher and, not least, as an economist.Combining an epistemological perspective with a theoretical one, this book will be of great interest to researchers and students in the fields of economics, the history of economic thought, and epistemology.Trade Review'. . . readers, especially those with a methodological orientation, will find in this book useful material of a kind not so frequently available in more traditional HET books and journals.' -- Nicola Giocoli, Storia del Pensiero Economico'Augustin Cournot: Modelling Economics is not a biography, but rather a reflection on those ideas of Cournot that persist today and what we can still learn from this great thinker. One cannot help but wonder at the wide range of accomplishments detailed in this book, but the discussion of Cournot's missteps is an unexpected highlight. . . this book should appeal to those who would like to learn more about Cournot as well as the various settings in which his thoughts have been embraced or rejected.' -- Lauren E. Feiler, Journal of Economic Literature'This rich and fascinating collection of essays helps enormously to establish the reputation of Augustin Cournot as a diverse and powerful thinker, whose numerous contributions range far beyond his widely acknowledged model of oligopoly. Cournot is revealed not merely as a mathematician, but one who was engaged in philosophical debates concerning epistemology and the nature of science. Anyone with the preconception that the development of modern economics was confined to the Anglophone world - from Smith through Marshall to the Nobel Laureates of today - will be amazed by the details of Cournot's contribution revealed here.' -- Geoffrey M. Hodgson, University of Hertfordshire, UKTable of ContentsContents: Preface About the Series: Professor Robert Solow The Complete Works of Antoine Augustin Cournot Chronological Biography of Antoine Augustin Cournot Introduction Thierry Martin and Jean-Philippe Touffut 1. Cournot as Economist: 200 Years of Relevance Jean Magnan de Bornier 2. Cournot’s Probabilistic Epistemology Thierry Martin 3. The Functions of Economic Models Bernard Walliser 4. From Cournot’s Principle to Market Efficiency Glenn Shafer 5. War and Peace Robert J. Aumann 6. Cournot and the Social Income Robert M. Solow 7. Comparing the Incomparable: The Sociology of Statistics Alain Desrosières Index
£33.20
ISTE Ltd and John Wiley & Sons Inc Mathematical Models
Book SynopsisThis series of five volumes proposes an integrated description of physical processes modeling used by scientific disciplines from meteorology to coastal morphodynamics. Volume 1 describes the physical processes and identifies the main measurement devices used to measure the main parameters that are indispensable to implement all these simulation tools. Volume 2 presents the different theories in an integrated approach: mathematical models as well as conceptual models, used by all disciplines to represent these processes. Volume 3 identifies the main numerical methods used in all these scientific fields to translate mathematical models into numerical tools. Volume 4 is composed of a series of case studies, dedicated to practical applications of these tools in engineering problems. To complete this presentation, volume 5 identifies and describes the modeling software in each discipline.Trade Review"An inventory of ground measurement instruments, which provide necessary input data for the various modeling tools described in the book, is drawn up, and mathematical models describing each field within the overall subject area are detailed by a series of system equations. These are then solved by the use of numerical methods adapted to the particular characteristics of the application in question." (Environmental Expert, 19 April 2011)Table of ContentsIntroduction xix Jean-Michel TANGUY Chapter 1. Reminders on the Mechanical Properties of Fluids 1 Jacques GEORGE 1.1. Laws of conservation, principles and general theorems 1 1.2. Enthalpy, rotation, mixing, saturation 13 1.3. Thermodynamic relations, relations of state and laws of behavior 20 1.4. Turbulent flow 26 1.5. Dynamics of geophysical fluids 30 Chapter 2. 3D Navier-Stokes Equations 35 Véronique DUCROCQ 2.1. The continuity hypothesis 35 2.2. Lagrangian description/Eulerian description 36 2.3. The continuity equation 37 2.4. The movement quantity assessment equation 38 2.5. The energy balance equation 41 2.6. The equation of state 41 2.7. Navier-Stokes equations for a fluid in rotation 41 Chapter 3. Models of the Atmosphere 43 Jean COIFFIER 3.1. Introduction 43 3.2. The various simplifications and corresponding models 44 3.3. The equations with various systems of coordinates 56 3.4. Some typical conformal projections 61 3.5. The operational models 67 3.6. Bibliography 69 Chapter 4. Hydrogeologic Models 71 Dominique THIÉRY 4.1. Equation of fluid mechanics 71 4.2. Continuity equation in porous media 72 4.3. Navier-Stokes’ equations 74 4.4. Darcy’s law 76 4.5. Calculating mass storage from the equations of state 80 4.6. General equation of hydrodynamics in porous media 82 4.7. Flows in unsaturated media 84 4.8. Bibliography 91 Chapter 5. Fluvial and Maritime Currentology Models 93 Jean-Michel TANGUY 5.1. 3D hydrostatic model 99 5.2. 2D horizontal model for shallow water 107 5.3. 1D models of fluvial flows 119 5.4. Putting 1D models into real time 131 5.5. Bibliography 151 Chapter 6. Urban Hydrology Models 155 Bernard CHOCAT 6.1. Global models and detailed models used in surface flows 156 6.2. Rainfall representation and rainfall-flow transformation 161 6.3. Modeling of the losses into the ground 164 6.4. Transfer function 169 6.5. Modeling of the hydraulic operating conditions of the networks 177 6.6. Production and transport of polluting agents 189 6.7. Conclusion 205 6.8. Bibliography 206 Chapter 7. Tidal Model and Tide Streams 213 Bernard SIMON 7.1. Tidal coefficient 214 7.2. Non-harmonic methods 215 7.3. Compatibilities 216 7.4. Tidal coefficient 222 7.5. Modeling 223 7.6. Tidal currents 226 Chapter 8. Wave Generation and Coastal Current Models 235 Jean-Michel TANGUY, Jean-Michel LEFÈVRE and Philippe SERGENT 8.1. Types of swell models 235 8.2. Spectral approach in high waters 242 8.3. Wave generation models 246 8.4. Wave propagation models 260 8.5. Agitating models within the harbors 266 8.6. Non-linear wave model: Boussinesq model 298 8.7. Coastal current models influenced or created by the swell 320 8.8. Bibliography 325 Chapter 9. Solid Transport Models and Evolution of the Seabed 335 Benoît LE GUENNEC and Jean-Michel TANGUY 9.1. Transport due to the overthrust effect 338 9.2. Total load 344 9.3. Bed forms and roughness 344 9.4. Suspension transport 346 9.5. Evolution model of movable beds 357 9.6. Conclusion 364 9.7. Bibliography 364 Chapter 10. Oil Spill Models 371 Pierre DANIEL 10.1. Behavior of hydrocarbons in marine environment 371 10.2. Oil spill drift models 372 10.3. Example: the MOTHY model 375 10.4. Calculation algorithm of the path of polluting particles 378 10.5. Example of a drift prediction map 379 10.6. Bibliography 379 Chapter 11. Conceptual, Empirical and Other Models 381 Christelle ALOT and Florence HABETS 11.1. Evapotranspiration 382 11.2. Bibliography 394 Chapter 12. Reservoir Models in Hydrology 397 Patrick FOURMIGUÉ and Patrick ARNAUD 12.1. Background 397 12.2. Main principles 399 12.3. Mathematical tools 401 12.4. Forecasting 403 12.5. Integration of the spatial information 405 12.6. Modeling limits 406 12.7. Bibliography 406 Chapter 13. Reservoir Models in Hydrogeology 409 Dominique THIÉRY 13.1. Principles and objectives 409 13.2. Catchment basin 410 13.3. Setting the model up 411 13.4. Data and parameters 412 13.5. Application domains 412 Chapter 14. Artificial Neural Network Models 419 Anne JOHANNET 14.1. Neural networks: a rapidly changing domain 420 14.2. Neuron and architecture models 422 14.3. How to take into account the non-linearity 429 14.4. Case study: identification of the rainfall-runoff relation of a karst 434 14.5. Acknowledgments 441 14.6. Bibliography 441 Chapter 15. Model Coupling 445 Rachid ABABOU, Denis DARTUS and Jean-Michel TANGUY 15.1. Model coupling 446 15.2. Bibliography 488 Chapter 16. A Set of Hydrological Models 493 Charles PERRIN, Claude MICHEL and Vasken ANDRÉASSIAN 16.1. Introduction 493 16.2. Description of the annual GR1A rainfall-runoff model 495 16.3. Description of the monthly GR2M rainfall-runoff model 496 16.4. Description of the daily GR4J rainfall-runoff model 500 16.5. Applications of the models 505 16.6. Conclusions and future work 506 16.7. Bibliography 507 List of Authors 511 Index 515 General Index of Authors 517 Summary of the Other Volumes in the Series 519
£194.70
ISTE Ltd and John Wiley & Sons Inc Statistical Models and Methods for Reliability
Book SynopsisStatistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical Models and Methods in Survival Analysis, and Reliability and Maintenance. The book is intended for researchers interested in statistical methodology and models useful in survival analysis, system reliability and statistical testing for censored and non-censored data.Table of ContentsPreface xv Biography of Mikhail Stepanovitch Nikouline xvii Vincent COUALLIER, Léo GERVILLE-RÉACHE, Catherine HUBER-CAROL, Nikolaos LIMNIOS and Mounir MESBAH Part 1. Statistical Models and Methods 1 Chapter 1. Unidimensionality, Agreement and Concordance Probability 3 Zhezhen JIN and Mounir MESBAH 1.1. Introduction 3 1.2. From reliability to unidimensionality: CAC and curve 4 1.2.1. Classical unidimensional models for measurement 4 1.2.2. Reliability of an instrument: CAC 6 1.2.3. Unidimensionality of an instrument: BRC 9 1.3. Agreement between binary outcomes: the kappa coefficient 10 1.3.1. The kappa model 10 1.3.2. The kappa coefficient 10 1.3.3. Estimation of the kappa coefficient 10 1.4. Concordance probability 11 1.4.1. Relationship with Kendall’s τ measure 12 1.4.2. Relationship with Somer’s D measure 12 1.4.3. Relationship with ROC curve 13 1.5. Estimation and inference 14 1.6. Measure of agreement 14 1.7. Extension to survival data 15 1.7.1. Harrell’s c-index 15 1.7.2. Measure of discriminatory power 16 1.8. Discussion 17 1.9. Bibliography 18 Chapter 2. A Universal Goodness-of-Fit Test Based on Regression Techniques 21 Florence GEORGE and Sneh GULATI 2.1. Introduction 21 2.2. The Brain and Shapiro procedure for the exponential distribution 22 2.3. Applications of the Brain and Shapiro test 24 2.4. Small sample null distribution of the test statistic for specific distributions 25 2.5. Power studies 28 2.6. Some real examples 28 2.7. Conclusions 31 2.8. Acknowledgment 32 2.9. Bibliography 32 Chapter 3. Entropy-type Goodness-of-Fit Tests for Heavy-Tailed Distributions 33 Andreas MAKRIDES, Alex KARAGRIGORIOU and Filia VONTA 3.1. Introduction 33 3.2. The entropy test for heavy-tailed distributions 35 3.2.1. Development and asymptotic theory 35 3.2.2. Discussion 39 3.3. Simulation study 40 3.4. Conclusions 42 3.5. Bibliography 42 Chapter 4. Penalized Likelihood Methodology and Frailty Models 45 Emmanouil ANDROULAKIS, Christos KOUKOUVINOS and Filia VONTA 4.1. Introduction 45 4.2. Penalized likelihood in frailty models for clustered data 48 4.2.1. Gamma distributed frailty 52 4.2.2. Inverse Gaussian distributed frailty 52 4.2.3. Uniform distributed frailty 54 4.3. Simulation results 55 4.4. Concluding remarks 57 4.5. Bibliography 57 Chapter 5. Interactive Investigation of Statistical Regularities in Testing Composite Hypotheses of Goodness of Fit 61 Boris LEMESHKO, Stanislav LEMESHKO and Andrey ROGOZHNIKOV 5.1. Introduction 61 5.2. Distributions of the test statistics in the case of testing composite hypotheses 63 5.3. Testing composite hypotheses in “real-time” 68 5.4. Conclusions 73 5.5. Acknowledgment 73 5.6. Bibliography 73 Chapter 6. Modeling of Categorical Data 77 Henning LÄUTER 6.1. Introduction 77 6.2. Continuous conditional distributions 78 6.2.1. Conditional normal distribution 78 6.2.1.1. Estimation of parameters 78 6.2.2. More general continuous conditional distributions 81 6.2.2.1. Conditional distribution 82 6.2.2.2. Normal copula 83 6.3. Discrete conditional distributions 84 6.3.1. Parametric conditional distributions 84 6.3.2. Estimation of parameters 86 6.4. Goodness of fit 86 6.4.1. Distribution of ˆX2 87 6.5. Modeling of categorical data 88 6.5.1. Contingency tables 89 6.5.1.1. General tables 89 6.5.1.2. Further examples 93 6.6. Bibliography 93 Chapter 7. Within the Sample Comparison of Prediction Performance of Models and Submodels: Application to Alzheimer’s Disease 95 Catherine HUBER-CAROL, Shulamith T. GROSS and Annick ALPÉROVITCH 7.1. Introduction 95 7.2. Framework 96 7.2.1. General description of the data set and the models to be compared 96 7.2.2. Definition of the performance prediction criteria: IDI and BRI 96 7.3. Estimation of IDI and BRI 97 7.3.1. General estimating equations for IDI and BRI 98 7.3.2. Estimation of IDI and BRI in the logistic case 98 7.3.2.1. Asymptotics of IDI2/1 for logistic predictors 99 7.3.2.2. Asymptotics of BRI2/1 for logistic predictors 100 7.4. Simulation studies 102 7.4.1. First simulation 102 7.4.2. Second simulation: Gu and Pepe’s example 104 7.5. The three city study of Alzheimer’s disease 106 7.6. Conclusion 108 7.7. Bibliography 109 Chapter 8. Durbin–Knott Components and Transformations of the Cramér-von Mises Test 111 Gennady MARTYNOV 8.1. Introduction 111 8.2. Weighted Cramér-von Mises statistic 111 8.3. Examples of the Cramér-von Mises statistics 113 8.3.1. Classical Cramér-von Mises statistic 113 8.3.2. Anderson–Darling statistic 113 8.3.3. Cramér-von Mises statistic with the power weight function 114 8.4. Weighted parametric Cramér-von Mises statistic 114 8.4.1. Covariance functions of weighted parametric empirical process 114 8.4.2. Eigenvalues and eigenfunctions for weighted parametric Cramérvon Mises statistic 116 8.5. Transformations of the Cramér-von Mises statistic 117 8.5.1. Preliminary notes 117 8.5.2. Replacement of eigenvalues 118 8.5.3. Transformed statistics 119 8.6. Bibliography 122 Chapter 9. Conditional Inference in Parametric Models 125 Michel BRONIATOWSKI and Virgile CARON 9.1. Introduction and context 125 9.2. The approximate conditional density of the sample 127 9.2.1. Approximation of conditional densities 127 9.2.2. The proxy of the conditional density of the sample 129 9.2.3. Comments on implementation 131 9.3. Sufficient statistics and approximated conditional density 131 9.3.1. Keeping sufficiency under the proxy density 131 9.3.2. Rao–Blackwellization 132 9.4. Exponential models with nuisance parameters 135 9.4.1. Conditional inference in exponential families 135 9.4.2. Application of conditional sampling to MC tests 137 9.4.2.1. Context 137 9.4.2.2. Bimodal likelihood: testing the mean of a normal distribution in dimension 2 139 9.4.3. Estimation through conditional likelihood 140 9.5. Bibliography 142 Chapter 10. On Testing Stochastic Dominance by Exceedance, Precedence and Other Distribution-Free Tests, with Applications 145 Paul DEHEUVELS 10.1. Introduction 145 10.2. Results 148 10.2.1. The experimental data set 148 10.2.2. An application of the Wilcoxon–Mann–Whitney statistics 149 10.2.3. One-sided Kolmogorov-Smirnov tests 150 10.2.4. Precedence and Exceedance Tests. 152 10.3. Negative binomial limit laws 155 10.4. Conclusion 159 10.5. Bibliography 159 Chapter 11. Asymptotically Parameter-Free Tests for Ergodic Diffusion Processes 161 Yury A. KUTOYANTS and Li ZHOU 11.1. Introduction 161 11.2. Ergodic diffusion process and some limits 165 11.3. Shift parameter 168 11.4. Shift and scale parameters 172 11.5. Bibliography 175 Chapter 12. A Comparison of Homogeneity Tests for Different Alternative Hypotheses 177 Sergey POSTOVALOV and Petr PHILONENKO 12.1. Homogeneity tests 178 12.1.1. Tests for data without censoring 179 12.1.2. Tests for data with censoring 180 12.2. Alternative hypotheses 184 12.3. Power simulation 185 12.3.1. Power of tests without censoring 187 12.3.2. Power of tests with censoring 189 12.3.2.1. How does the distribution of censoring time affect the power of the test? 189 12.3.2.2. How does the censoring rate affect the power of the test? 191 12.4. Statistical inference 191 12.5. Acknowledgment 192 12.6. Bibliography 193 Chapter 13. Some Asymptotic Results for Exchangeably Weighted Bootstraps of the Empirical Estimator of a Semi-Markov Kernel with Applications 195 Salim BOUZEBDA and Nikolaos LIMNIOS 13.1. Introduction 195 13.2. Semi-Markov setting 197 13.3. Main results 201 13.4. Bootstrap for a multidimensional empirical estimator of a continuous-time semi-Markov kernel 205 13.5. Confidence intervals 208 13.6. Bibliography 210 Chapter 14. On Chi-Squared Goodness-of-Fit Test for Normality 213 Mikhail NIKULIN, Léo GERVILLE-RÉACHE and Xuan Quang TRAN 14.1. Chi–squared test for normality 213 14.2. Simulation study 221 14.3. Bibliography 226 Part 2. Statistical Models and Methods in Survival Analysis 229 Chapter 15. Estimation/Imputation Strategies for Missing Data in Survival Analysis 231 Elodie BRUNEL, Fabienne COMTE and Agathe GUILLOUX 15.1. Introduction 231 15.2. Model and strategies 233 15.2.1. Model assumptions 233 15.2.2. Strategy involving knowledge of ζ 234 15.2.3. Strategy involving knowledge of π 235 15.2.4. Estimation of ζ or π: logit or non-parametric regression 236 15.2.5. Computing the hazard estimators 236 15.2.6. Theoretical results 239 15.3. Imputation-based strategy 241 15.4. Numerical comparison 242 15.5. Proofs 244 15.6. Bibliography 251 Chapter 16. Non-Parametric Estimation of Linear Functionals of a Multivariate Distribution Under Multivariate Censoring with Applications 253 Olivier LOPEZ and Philippe SAINT-PIERRE 16.1. Introduction 253 16.2. Non-parametric estimation of the distribution 255 16.3. Asymptotic properties 257 16.4. Statistical applications of functionals 260 16.4.1. Dependence measures 260 16.4.2. Bootstrap 261 16.4.3. Linear regression 262 16.5. Illustration 263 16.6. Conclusion 264 16.7. Acknowledgment 264 16.8. Bibliography 264 Chapter 17. Kernel Estimation of Density from Indirect Observation 267 Valentin SOLEV 17.1. Introduction 267 17.1.1. Random partition 267 17.1.2. Indirect observation 268 17.1.3. Kernel density estimator 269 17.2. Density of random vector Λ(X) 271 17.3. Pseudo-kernel density estimator 273 17.3.1. Pointwise density estimation based on indirect data 273 17.3.2. Bias of the kernel estimator 274 17.3.3. Estimate of variance 276 17.4. Bibliography 279 Chapter 18. A Comparative Analysis of Some Chi-Square Goodness-of-Fit Tests for Censored Data 281 Ekaterina CHIMITOVA and Boris LEMESHKO 18.1. Introduction 281 18.2. Chi-square goodness-of-fit tests for censored data 283 18.2.1. NRR χ2 test 283 18.2.2. GPF χ2 test 284 18.3. The choice of grouping intervals 285 18.3.1. Equifrequent grouping (EFG) 289 18.3.2. Intervals with equal expected numbers of failures (EENFG) 289 18.3.3. Optimal grouping (OptG) 289 18.4. Empirical power study 290 18.5. Conclusions 293 18.6. Acknowledgment 294 18.7. Bibliography 294 Chapter 19. A Non-parametric Test for Comparing Treatments with Missing Data and Dependent Censoring 297 Amel MEZAOUER, Kamal BOUKHETALA and Jean-François DUPUY 19.1. Introduction 297 19.2. The proposed test statistic 299 19.3. Asymptotic distribution of the proposed test statistic 301 19.4. Acknowledgment 305 19.5. Appendix 306 19.6. Bibliography 309 Chapter 20. Group Sequential Tests for Treatment Effect with Covariates Adjustment through Simple Cross-Effect Models 311 Isaac Wu HONG-DAR 20.1. Introduction 311 20.2. Notations and models 313 20.3. Group sequential test 316 20.4. Discussion 318 20.5. Acknowledgment 318 20.6. Bibliography 318 Part 3. Reliability and Maintenance 321 Chapter 21. Optimal Maintenance in Degradation Processes 323 Waltraud KAHLE 21.1. Introduction 323 21.2. The degradation model 324 21.3. Optimal replacement after an inspection 326 21.4. The simulation of degradation processes 327 21.5. Shape of cost functions and optimal δ and a 329 21.6. Incomplete preventive maintenance 330 21.7. Bibliography 333 Chapter 22. Planning Accelerated Destructive Degradation Tests with Competing Risks 335 Ying SHI and William Q. MEEKER 22.1. Introduction 336 22.1.1. Background 336 22.1.2. Motivation: adhesive bond C 336 22.1.3. Related literature 337 22.1.4. Overview 338 22.2. Degradation models with competing risks 338 22.2.1. Accelerated degradation model for the primary response 338 22.2.2. Accelerated degradation model for the competing response 339 22.2.3. Degradation models for adhesive bond C 339 22.2.4. Degradation distribution and quantiles 340 22.3. Failure-time distribution with competing risks 341 22.3.1. Relationship between degradation and failure 341 22.3.2. Failure-time distribution and quantiles 342 22.4. Test planning with competing risks 342 22.4.1. ADDT planning information 342 22.4.2. Criterion for ADDT planning with competing risks 343 22.5. ADDT plans with competing risks 344 22.5.1. Initial optimum ADDT plan with competing risks 344 22.5.2. Constrained optimum ADDT plan with competing risks 348 22.5.3. General equivalence theorem 348 22.5.4. Compromise ADDT plan with competing risks 350 22.6. Monte Carlo simulation to evaluate test plans 352 22.7. Conclusions and extensions 353 22.8. Appendix: technical details 354 22.8.1. The Fisher information matrix for ADDT with competing risks 354 22.8.2. Large-sample approximate variance of ht (tp) and tp 355 22.9. Bibliography 355 Chapter 23. A New Goodness-of-Fit Test for Shape-Scale Families 357 Vilijandas BAGDONAVIČIUS 23.1. Introduction 357 23.2. The test statistic 358 23.3. The asymptotic distribution of the test statistic 359 23.4. The test 364 23.5. Weibull distribution 364 23.6. Loglogistic distribution 365 23.7. Lognormal distribution 366 23.8. Bibliography 367 Chapter 24. Time-to-Failure of Markov-Modulated Gamma Process with Application to Replacement Policies 369 Christian PAROISSIN and Landy RABEHASAINA 24.1. Introduction 369 24.2. Degradation model 370 24.2.1. Covariate process 370 24.2.2. Degradation process 371 24.3. Time-to-failure distribution 371 24.3.1. Case of a non-modulated gamma process 372 24.3.2. Case of a Markov-modulated gamma process 373 24.3.3. Stochastic comparison 374 24.4. Replacement policies 376 24.4.1. Block replacement policy 377 24.4.2. Age replacement policy 379 24.5. Conclusion 381 24.6. Acknowledgment 381 24.7. Bibliography 382 Chapter 25. Calculation of the Redundant Structure Reliability for Agingtype Elements 383 Alexandr ANTONOV, Alexandr PLYASKIN and Khizri TATAEV 25.1. Introduction 383 25.2. The operation process of the renewal and repaired products 384 25.3. The model of the geometric process 386 25.4. Task solution 387 25.5. Conclusion 389 25.6. Bibliography 390 Chapter 26. On Engineering Risks of Complex Hierarchical Systems Analysis 391 Vladimir RYKOV 26.1. Introduction 391 26.2. Risk definition and measurement 392 26.3. Engineering risk 393 26.4. Risk characteristics for general model calculation 395 26.4.1. Lifelength and appropriate loss size CDF 395 26.4.2. Probability of risk event evolution 396 26.4.3. Lifelength and loss moments 397 26.4.4. Mostly dangerous paths of risk event evolution and sensitivity analysis 399 26.5. Risk analysis for short-time risk models 400 26.6. Conclusion 402 26.7. Bibliography 402 List of Authors 405 Index 409
£146.66
ISTE Ltd and John Wiley & Sons Inc Basic Stochastic Processes
Book SynopsisThis book presents basic stochastic processes, stochastic calculus including Lévy processes on one hand, and Markov and Semi Markov models on the other. From the financial point of view, essential concepts such as the Black and Scholes model, VaR indicators, actuarial evaluation, market values, fair pricing play a central role and will be presented. The authors also present basic concepts so that this series is relatively self-contained for the main audience formed by actuaries and particularly with ERM (enterprise risk management) certificates, insurance risk managers, students in Master in mathematics or economics and people involved in Solvency II for insurance companies and in Basel II and III for banks.Table of ContentsINTRODUCTION xi CHAPTER 1. BASIC PROBABILISTIC TOOLS FOR STOCHASTIC MODELING 1 1.1. Probability space and random variables 1 1.2. Expectation and independence 4 1.3. Main distribution probabilities 7 1.3.1. Binomial distribution 7 1.3.2. Negative exponential distribution 8 1.3.3. Normal (or Laplace–Gauss) distribution 8 1.3.4. Poisson distribution 11 1.3.5. Lognormal distribution 11 1.3.6. Gamma distribution 12 1.3.7. Pareto distribution 13 1.3.8. Uniform distribution 16 1.3.9. Gumbel distribution 16 1.3.10. Weibull distribution 16 1.3.11. Multi-dimensional normal distribution 17 1.3.12. Extreme value distribution 19 1.4. The normal power (NP) approximation 28 1.5. Conditioning 31 1.6. Stochastic processes 39 1.7. Martingales 43 CHAPTER 2. HOMOGENEOUS AND NON-HOMOGENEOUS RENEWAL MODELS 47 2.1. Introduction 47 2.2. Continuous time non-homogeneous convolutions 49 2.2.1. Non-homogeneous convolution product 49 2.3. Homogeneous and non-homogeneous renewal processes 53 2.4. Counting processes and renewal functions 56 2.5. Asymptotical results in the homogeneous case 61 2.6. Recurrence times in the homogeneous case 63 2.7. Particular case: the Poisson process 66 2.7.1. Homogeneous case 66 2.7.2. Non-homogeneous case 68 2.8. Homogeneous alternating renewal processes 69 2.9. Solution of non-homogeneous discrete timevevolution equation 71 2.9.1. General method 71 2.9.2. Some particular formulas 73 2.9.3. Relations between discrete time and continuous time renewal equations 74 CHAPTER 3. MARKOV CHAINS 77 3.1. Definitions 77 3.2. Homogeneous case 78 3.2.1. Basic definitions 78 3.2.2. Markov chain state classification 81 3.2.3. Computation of absorption probabilities 87 3.2.4. Asymptotic behavior 88 3.2.5. Example: a management problem in an insurance company 93 3.3. Non-homogeneous Markov chains 95 3.3.1. Definitions 95 3.3.2. Asymptotical results 98 3.4. Markov reward processes 99 3.4.1. Classification and notation 99 3.5. Discrete time Markov reward processes (DTMRWPs) 102 3.5.1. Undiscounted case 102 3.5.2. Discounted case 105 3.6. General algorithms for the DTMRWP 111 3.6.1. Homogeneous MRWP 112 3.6.2. Non-homogeneous MRWP 112 CHAPTER 4. HOMOGENEOUS AND NON-HOMOGENEOUS SEMI-MARKOV MODELS 113 4.1. Continuous time semi-Markov processes 113 4.2. The embedded Markov chain 117 4.3. The counting processes and the associated semi-Markov process 118 4.4. Initial backward recurrence times 120 4.5. Particular cases of MRP 122 4.5.1. Renewal processes and Markov chains 122 4.5.2. MRP of zero-order (PYKE (1962)) 122 4.5.3. Continuous Markov processes 124 4.6. Examples 124 4.7. Discrete time homogeneous and non-homogeneous semi-Markov processes 127 4.8. Semi-Markov backward processes in discrete time 129 4.8.1. Definition in the homogeneous case 129 4.8.2. Semi-Markov backward processes in discrete time for the non-homogeneous case 130 4.8.3. DTSMP numerical solutions 133 4.9. Discrete time reward processes 137 4.9.1. Undiscounted SMRWP 137 4.9.2. Discounted SMRWP 141 4.9.3. General algorithms for DTSMRWP 144 4.10. Markov renewal functions in the homogeneous case 146 4.10.1. Entrance times 146 4.10.2. The Markov renewal equation 150 4.10.3. Asymptotic behavior of an MRP 151 4.10.4. Asymptotic behavior of SMP 153 4.11. Markov renewal equations for the non-homogeneous case 158 4.11.1. Entrance time 158 4.11.2. The Markov renewal equation 162 CHAPTER 5. STOCHASTIC CALCULUS 165 5.1. Brownian motion 165 5.2. General definition of the stochastic integral 167 5.2.1. Problem of stochastic integration 167 5.2.2. Stochastic integration of simple predictable processes and semi-martingales 168 5.2.3. General definition of the stochastic integral 170 5.3. Itô’s formula 177 5.3.1. Quadratic variation of a semi-martingale 177 5.3.2. Itô’s formula 179 5.4. Stochastic integral with standard Brownian motion as an integrator process 180 5.4.1. Case of simple predictable processes 181 5.4.2. Extension to general integrator processes 183 5.5. Stochastic differentiation 184 5.5.1. Stochastic differential 184 5.5.2. Particular cases 184 5.5.3. Other forms of Itô’s formula 185 5.6. Stochastic differential equations 191 5.6.1. Existence and unicity general theorem 191 5.6.2. Solution of stochastic differential equations 195 5.6.3. Diffusion processes 199 5.7. Multidimensional diffusion processes 202 5.7.1. Definition of multidimensional Itô and diffusion processes 203 5.7.2. Properties of multidimensional diffusion processes 203 5.7.3. Kolmogorov equations 205 5.7.4. The Stroock–Varadhan martingale characterization of diffusion processes 208 5.8. Relation between the resolution of PDE and SDE problems. The Feynman–Kac formula 209 5.8.1. Terminal payoff 209 5.8.2. Discounted payoff function 210 5.8.3. Discounted payoff function and payoff rate 210 5.9. Application to option theory 213 5.9.1. Options 213 5.9.2. Black and Scholes model 216 5.9.3. The Black and Scholes partial differential equation (BSPDE) and the BS formula 216 5.9.4. Girsanov theorem 219 5.9.5. The risk-neutral measure and the martingale property 221 5.9.6. The risk-neutral measure and the evaluation of derivative products 224 CHAPTER 6. LÉVY PROCESSES 227 6.1. Notion of characteristic functions 227 6.2. Lévy processes 228 6.3. Lévy–Khintchine formula 230 6.4. Subordinators 234 6.5. Poisson measure for jumps 234 6.5.1. The Poisson random measure 234 6.5.2. The compensated Poisson process 235 6.5.3. Jump measure of a Lévy process 236 6.5.4. The Itô–Lévy decomposition 236 6.6. Markov and martingale properties of Lévy processes 237 6.6.1. Markov property 237 6.6.2. Martingale properties 239 6.6.3. Itô formula 240 6.7. Examples of Lévy processes 240 6.7.1. The lognormal process: Black and Scholes process 240 6.7.2. The Poisson process 241 6.7.3. Compensated Poisson process 242 6.7.4. The compound Poisson process 242 6.8. Variance gamma (VG) process 244 6.8.1. The gamma distribution 244 6.8.2. The VG distribution 245 6.8.3. The VG process 246 6.8.4. The Esscher transformation 247 6.8.5. The Carr–Madan formula for the European call 249 6.9. Hyperbolic Lévy processes 250 6.10. The Esscher transformation 252 6.10.1. Definition 252 6.10.2. Option theory with hyperbolic Lévy processes 253 6.10.3. Value of the European option call 255 6.11. The Brownian–Poisson model with jumps 256 6.11.1. Mixed arithmetic Brownian–Poisson and geometric Brownian–Poisson processes 256 6.11.2. Merton model with jumps 258 6.11.3. Stochastic differential equation (SDE) for mixed arithmetic Brownian–Poisson and geometric Brownian–Poisson processes 261 6.11.4. Value of a European call for the lognormal Merton model 264 6.12. Complete and incomplete markets 264 6.13. Conclusion 265 CHAPTER 7. ACTUARIAL EVALUATION, VAR AND STOCHASTIC INTEREST RATE MODELS 267 7.1. VaR technique 267 7.2. Conditional VaR value 271 7.3. Solvency II 276 7.3.1. The SCR indicator 276 7.3.2. Calculation of MCR 278 7.3.3. ORSA approach 279 7.4. Fair value 280 7.4.1. Definition 280 7.4.2. Market value of financial flows 281 7.4.3. Yield curve 281 7.4.4. Yield to maturity for a financial investment and a bond 283 7.5. Dynamic stochastic time continuous time model for instantaneous interest rate 284 7.5.1. Instantaneous deterministic interest rate 284 7.5.2. Yield curve associated with a deterministic instantaneous interest rate 285 7.5.3. Dynamic stochastic continuous time model for instantaneous interest rate 286 7.5.4. The OUV stochastic model 287 7.5.5. The CIR model 289 7.6. Zero-coupon pricing under the assumption of no arbitrage 292 7.6.1. Stochastic dynamics of zero-coupons 292 7.6.2. The CIR process as rate dynamic 295 7.7. Market evaluation of financial flows 298 BIBLIOGRAPHY 301 INDEX 309
£125.06
ISTE Ltd and John Wiley & Sons Inc Mathematics for Modeling and Scientific Computing
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
£125.06
ISTE Ltd and John Wiley & Sons Inc Systems Dependability Assessment: Benefits of
Book SynopsisPetri Nets were defined for the study of discrete events systems and later extended for many purposes including dependability assessment. In our knowledge, no book deals specifically with the use of different type of PN to dependability. We propose in addition to bring a focus on the adequacy of Petri net types to the study of various problems related to dependability such as risk analysis and probabilistic assessment. In the first part, the basic models of PN and some useful extensions are briefly recalled. In the second part, the PN are used as a formal model to describe the evolution process of critical system in the frame of an ontological approach. The third part focuses on the stochastic Petri Nets (SPN) and their use in dependability assessment. Different formal models of SPN are formally presented (semantics, evolution rules…) and their equivalence with the corresponding class of Markov processes to get an analytical assessment of dependability. Simplification methods are proposed in order to reduce the size of analytical model and to make it more calculable. The introduction of some concepts specific to high level PN allows too the consideration of complex systems. Few applications in the field of the instrumentation and control (l&C) systems, safety integrated systems (SIS) emphasize the benefits of SPN for dependability assessment.Table of ContentsIntroduction xi Part 1 Short Review of Petri Net Modeling 1 Introduction to Part 1 3 Chapter 1 Autonomous Petri Nets 5 1.1 Unmarked Petri nets 5 1.1.1 Definitions 5 1.1.2 Drawing 6 1.1.3 Other definitions 7 1.2 Marking of a PN 7 1.2.1 Order relation on markings 8 1.2.2 Enabled transition 9 1.3 Dynamics of autonomous PNs 9 1.3.1 Firing of a transition 9 1.3.2 Transition matrix 11 1.3.3 Firing sequence 11 1.3.4 Reachable marking 12 1.3.5 Fundamental equation 12 1.3.6 Properties of PN 14 1.3.7 Other properties 14 1.3.8 Invariants in a PN 15 1.3.9 Reachability graph 16 Chapter 2 Petri Nets and Event Languages 19 2.1 Labeled PNs 19 2.1.1 Formal definition 19 2.1.2 Generated and marked languages 20 2.2 Example 21 Chapter 3 Comparison Petri Nets – Finite State Automaton 25 3.1 Language expression 26 3.2 Building of the models 27 3.2.1 Synchronization of submodels 28 3.2.2 Resource sharing 29 3.2.3 Construction by refinement 30 3.3 Compactness of the model 32 Chapter 4 Some Extensions of Petri Nets 35 4.1 PN with inhibitor arcs 35 4.2 Timed PN 36 4.2.1 P-timed Petri nets 37 4.2.2 T-timed Petri nets 37 4.3 Synchronized PN 38 4.4 Timed synchronized PN 40 4.5 Interpreted PN 41 4.6 Colored PN 42 4.6.1 Introduction example 42 4.6.2 Formal definition 45 4.6.3 A dedicated software CPN Tools 46 Conclusion to Part 1 51 Part 2 A Formal Approach to Risk Assessment 53 Introduction to Part 2 51 Chapter 5 Ontology-based Accidental Process 61 5.1 Preliminary definitions 61 5.2 Elementary entities: HSE and VTE 63 5.2.1 Hazard supplier entity (HSE) 63 5.2.2 Vulnerable target entity (VTE) 63 5.3 Elementary situations and elementary events 64 5.3.1 State versus situation 64 5.3.2 Initial situation (IS) 64 5.3.3 Initiating event (IEv) 64 5.3.4 Hazard situation (HS) 65 5.3.5 Exposure event (EEv) 65 5.3.6 Exposure situation (ES) 65 5.3.7 Accident situation 65 5.3.8 Hazardous (feared) event (HEv) 65 5.4 Conclusion 66 Chapter 6 Petri Net Modeling of the Accidental Process 67 6.1 Elementary process 68 6.2 Sequence of elementary processes 71 6.3 Modeling the action of a safety barrier 71 6.4 Modeling of a cumulative process 73 6.5 PN as a support for risk assessment 75 6.5.1 Modeling of the damage 75 6.5.2 Modeling of the event frequencies 75 6.5.3 CPN Tools implementation 77 6.5.4 Evaluation rule of the risk 83 6.6 Conclusion 86 Chapter 7 Illustrative Example 87 7.1 Functional description 87 7.2 Building of an accidental process 88 7.2.1 First elementary process 88 7.2.2 Second elementary process 91 7.2.3 Parallel process 92 7.2.4 The whole model 92 7.3 Conclusion 94 Chapter 8 Design and Safety Assessment Cycle 95 8.1 Five essential steps 95 8.2 Ontological interest 98 Conclusion to Part 2 101 Part 3 Stochastic Petri Nets 103 Introduction to Part 3 105 Chapter 9 Basic Concept 107 9.1 Introductory example 107 9.2 Formal definition 108 Chapter 10 Semantics, Properties and Evolution Rules of an SPN 111 10.1 Conservatism properties 112 10.1.1 Conservatism of the mean marking in steady state 112 10.1.2 Conservatism of the flow in steady state 113 10.2 Mean sojourn time in a place of a SPN 113 10.3 Equivalent Markov process 114 10.4 Example of SPN for systems dependability modelling and assessment 116 Chapter 11 Simplification of Complex Models 121 11.1 Introduction 121 11.2 System modeling 122 11.3 Presentation of the quantitative analysis method 124 11.3.1 Steps to obtain an aggregated Markov graph 124 11.3.2 Toward a direct establishment of a reduced Markov graph 137 11.4 Example 137 11.4.1 Failure modeling 138 11.4.2 Study of the different functional and hardware solutions 139 11.4.3 Evaluation of the weighting coefficients from the Petri nets 144 11.4.4 Conclusion 147 Chapter 12 Extensions of SPN 149 12.1 Introduction 149 12.2 Relationship between stochastic Petri nets and stochastic processes 150 12.3 The transition firing policy 151 12.4 Associated stochastic processes 151 12.4.1 Temporal memory based on resampling 152 12.4.2 Temporal memory based on age memory or on enabling memory 153 12.4.3 Stochastic process underlying a stochastic PN 154 12.4.4 Embedded Markov chain of the stochastic process 157 12.4.5 Application to a case study 159 12.5 Synchronization problem in generalized stochastic Petri nets 162 12.5.1 GSPN with internal synchronization 162 12.5.2 SPN with predicates and assertions 164 12.6 Conclusion 168 Part 4 Applications of Stochastic Petri Nets to Assessment Problems in Industrial Systems 169 Introduction to Part 4 171 Chapter 13 Application in Dynamic Reliability 175 13.1 Presentation of the system and hypothesis 175 13.2 System modeling with Petri net 177 13.3 Methodology application 179 13.4 Construction of an aggregated Markov graph 180 13.5 Conclusion 185 Chapter 14 Classical Dependability Assessment 187 14.1 Availability study of a nuclear power plant subsystem 187 14.1.1 CPN modeling 188 14.1.2 Reliability and dependability assessment 192 14.1.3 Conclusion 196 14.2 Common causes failures in nuclear plants (safety oriented) 197 14.2.1 The Atwood model 197 14.2.2 Case study 199 14.2.3 Probabilistic dependability assessment 208 14.2.4 Conclusion 212 Chapter 15 Impact of Failures on System Performances 213 15.1 Reliability evaluation of networked control system 213 15.1.1 Statement of the problem 213 15.1.2 Reliability criteria of an NCS 215 15.1.3 Elements of modeling 216 15.1.4 Simulation and results 225 15.1.5 Evaluation of reliability 230 15.1.6 Conclusion 230 15.2 Railway signaling 231 15.2.1 Introduction 231 15.2.2 Interest 233 15.2.3 Signaling system specifications 234 15.2.4 Elements to be modeled 235 15.2.5 Architecture of the model 236 15.2.6 Example of an elementary model 237 15.2.7 Incident generation 239 15.2.8 Results 239 15.2.9 Conclusion 242 Conclusion 245 Appendix 247 Bibliography 251 Index 261
£125.06
ISTE Ltd and John Wiley & Sons Inc Data Uncertainty and Important Measures
Book SynopsisThe first part of the book defines the concept of uncertainties and the mathematical frameworks that will be used for uncertainty modeling. The application to system reliability assessment illustrates the concept. In the second part, evidential networks as a new tool to model uncertainty in reliability and risk analysis is proposed and described. Then it is applied on SIS performance assessment and in risk analysis of a heat sink. In the third part, Bayesian and evidential networks are used to deal with important measures evaluation in the context of uncertainties.Table of ContentsContents xi Foreword xiii Acknowledgments xiii Chapter 1 Why and Where Uncertainties 1 1.1 Sources and forms of uncertainty 1 1.2 Types of uncertainty 3 1.3 Sources of uncertainty 3 1.4 Conclusion 6 Chapter 2 Models and Language of Uncertainty 9 2.1 Introduction 9 2.2 Probability theory 11 2.2.1 Interpretations 11 2.2.2 Fundamental notions 13 2.2.3 Discussion 15 2.3 Belief functions theory 15 2.3.1 Representation of beliefs 16 2.3.2 Combination rules 18 2.3.3 Extension and marginalization 20 2.3.4 Pignistic transformation 20 2.3.5 Discussion 21 2.4 Fuzzy set theory 21 2.4.1 Basic definitions 22 2.4.2 Operations on fuzzy sets 22 2.4.3 Fuzzy relations 23 2.5 Fuzzy arithmetic 25 2.5.1 Fuzzy numbers 26 2.5.2 Fuzzy probabilities 28 2.5.3 Discussion 29 2.6 Possibility theory 29 2.6.1 Definitions 30 2.6.2 Possibility and necessity measures 30 2.6.3 Operations on possibility and necessity measures 32 2.7 Random set theory 32 2.7.1 Basic definitions 33 2.7.2 Expectation of random sets 34 2.7.3 Random intervals 35 2.7.4 Confidence interval 35 2.7.5 Discussion 36 2.8 Confidence structures or c-boxes 36 2.8.1 Basic notions 36 2.8.2 Confidence distributions 37 2.8.3 P-boxes and C-boxes 38 2.8.4 Discussion 40 2.9 Imprecise probability theory 40 2.9.1 Definitions 41 2.9.2 Basic properties 42 2.9.3 Discussion 44 2.10 Conclusion 44 Chapter 3 Risk Graphs and Risk Matrices: Application of Fuzzy Sets and Belief Reasoning 47 3.1 SIL allocation scheme 48 3.1.1 Safety instrumented systems (SIS) 48 3.1.2 Conformity to standards ANSI/ISA S84.01-1996 and IEC 61508 49 3.1.3 Taxonomy of risk/SIL assessment methods 50 3.1.4 Risk assessment 50 3.1.5 SIL allocation process 52 3.1.6 The use of experts’ opinions 53 3.2 SIL allocation based on possibility theory 54 3.2.1 Eliciting the experts’ opinions 54 3.2.2 Rating scales for parameters 55 3.2.3 Subjective elicitation of the risk parameters 56 3.2.4 Calibration of experts’ opinions 59 3.2.5 Aggregation of the opinions 61 3.3 Fuzzy risk graph 65 3.3.1 Input fuzzy partition and fuzzification 65 3.3.2 Risk/SIL graph logic by fuzzy inference system 66 3.3.3 Output fuzzy partition and defuzzification 67 3.3.4 Illustration case 69 3.4 Risk/SIL graph: belief functions reasoning 72 3.4.1 Elicitation of expert opinions in the belief functions theory 72 3.4.2 Aggregation of expert opinions 73 3.5 Evidential risk graph 75 3.6 Numerical illustration 77 3.6.1 Clustering of experts’ opinions 77 3.6.2 Aggregation of preferences 78 3.6.3 Evidential risk/SIL graph 79 3.7 Conclusion 81 Chapter 4 Dependability Assessment Considering Interval-valued Probabilities 83 4.1 Interval arithmetic 84 4.1.1 Interval-valued parameters 84 4.1.2 Interval-valued reliability 85 4.1.3 Assessing the imprecise average probability of failure on demand 86 4.2 Constraint arithmetic 90 4.3 Fuzzy arithmetic 93 4.3.1 Application example 95 4.3.2 Monte Carlo sampling approach 97 4.4 Discussion 99 4.4.1 Markov chains 100 4.4.2 Multiphase Markov chains 101 4.4.3 Markov chains with fuzzy numbers 102 4.4.4 Fuzzy modeling of SIS characteristic parameters 104 4.5 Illustration 105 4.5.1 Epistemic approach 106 4.5.2 Enhanced Markov analysis 113 4.6 Decision-making under uncertainty 115 4.7 Conclusion 117 Chapter 5 Evidential Networks 119 5.1 Main concepts 119 5.1.1 Temporal dimension 121 5.1.2 Computing believe and plausibility measures as bounds 123 5.1.3 Inference 124 5.1.4 Modeling imprecision and ignorance in nodes 126 5.1.5 Conclusion 128 5.2 Evidential Network to model and compute Fuzzy probabilities 128 5.2.1 Fuzzy probability and basic probability assignment 128 5.2.2 Nested interval-valued probabilities to fuzzy probability 129 5.2.3 Computation mechanism 130 5.3 Evidential Networks to compute p-box 131 5.3.1 Connection between p-boxes and BPA 132 5.3.2 P-boxes and interval-valued probabilities 133 5.3.3 P-boxes and precise probabilities 133 5.3.4 Time-dependent p-boxes 134 5.3.5 Computation mechanism 134 5.4 Modeling some reliability problems 136 5.4.1 BPA for reliability problems 136 5.4.2 Building Boolean CMT (AND, OR) 137 5.4.3 Conditional mass table for more than two inputs (k-out-of-n:G gate) 138 5.4.4 Nodes for Pls and Bel in the binary case 140 5.4.5 Modeling reliability with p-boxes 140 5.5 Illustration by application of Evidential Networks 145 5.5.1 Reliability assessment of system 145 5.5.2 Inference for failure isolation 153 5.5.3 Assessing the fuzzy reliability of systems 155 5.5.4 Assessing the p-box reliability by EN 162 5.6 Conclusion 169 Chapter 6 Reliability Uncertainty and Importance Factors 171 6.1 Introduction 171 6.2 Hypothesis and notation 173 6.3 Probabilistic importance measures of components 174 6.3.1 Birnbaum importance measure 175 6.3.2 Component criticality measure 176 6.3.3 Diagnostic importance measure 176 6.3.4 Reliability achievement worth (RAW) 177 6.3.5 Reliability reduction worth (RRW) 177 6.3.6 Observations and limitations 178 6.3.7 Importance measures computation 179 6.4 Probabilistic importance measures of pairs and groups of components 179 6.4.1 Measures on minimum cutsets/pathsets/groups 181 6.4.2 Extension of RAW and RRW to pairs 182 6.4.3 Joint reliability importance factor (JR) 182 6.5 Uncertainty importance measures 184 6.5.1 Uncertainty probabilistic importance measures 184 6.5.2 Importance factors with imprecision 186 6.6 Importance measures with fuzzy probabilities 188 6.6.1 Fuzzy importance measures 189 6.6.2 Fuzzy uncertainty measures 190 6.7 Illustration 191 6.7.1 Importance factors on a simple system 192 6.7.2 Importance factors in a complex case 195 6.7.3 Illustration of group importance measures 197 6.7.4 Uncertainty importance factors 200 6.7.5 Fuzzy importance measures 203 6.8 Conclusion 206 Conclusion 207 Bibliography 211 Index 225
£125.06
Edward Elgar Publishing Ltd Game Theory and International Relations:
Book SynopsisWhat is the origin of game preferences and payoffs, how are they aggregated and what are the implications of interdependent preferences? What is the importance of information for building game models? How can game models be used to analyse empirical cases? At the cutting edge of current modelling in international relations using non-cooperative game theory, this collection of original contributions from political scientists and economists explores some of the fundamental assumptions of game theory modelling. It includes a theory of game payoff formation, a theory of preference aggregation, thorough discussions of the effects of interdependence between preferences upon various game structures, in-depth analyses of the impact of incomplete information upon dynamic games of negotiation, and a study using differential games. Numerous illustrations, case studies and comparative case studies show the relevance of the theoretical debate. The chapters are organised to allow readers with a limited knowledge of game theory to develop their understanding of the fundamental issues.Containing theoretical discussion of the basic game theory assumptions - as well as means of going beyond them - Game Theory and International Relations will be welcomed by all those interested in the empirical application of game theory models in international relations.Trade Review” -- `Table of ContentsContents: Introduction Part I: Preference Formation and Aggregation Part II: Interdependent Preferences and Rational Choice Part III: Dynamic Games and Information Index
£107.00
Springer Nature Switzerland AG Optimization in Large Scale Problems: Industry
Book SynopsisThis volume provides resourceful thinking and insightful management solutions to the many challenges that decision makers face in their predictions, preparations, and implementations of the key elements that our societies and industries need to take as they move toward digitalization and smartness. The discussions within the book aim to uncover the sources of large-scale problems in socio-industrial dilemmas, and the theories that can support these challenges. How theories might also transition to real applications is another question that this book aims to uncover. In answer to the viewpoints expressed by several practitioners and academicians, this book aims to provide both a learning platform which spotlights open questions with related case studies. The relationship between Industry 4.0 and Society 5.0 provides the basis for the expert contributions in this book, highlighting the uses of analytical methods such as mathematical optimization, heuristic methods, decomposition methods, stochastic optimization, and more. The book will prove useful to researchers, students, and engineers in different domains who encounter large scale optimization problems and will encourage them to undertake research in this timely and practical field. The book splits into two parts. The first part covers a general perspective and challenges in a smart society and in industry. The second part covers several case studies and solutions from the operations research perspective for large scale challenges specific to various industry and society related phenomena.Table of ContentsPart 1.- Risk Based Optimization of Integrated Fabrication/Fulfillment Supply Chains (Nasim Nezamoddini, Faisal Aqlan, Amirhosein Gholami).- μθ-EGF: A New Multi-Thread Implementation Algorithm for the Packing Problem inspired by Electromagnetic Fields and Gravitational Effects (Felix Martinez-Rios and Jose Antonio Marmolejo-Saucedo).- The Vector Optimization Method for Solving Integer Linear Programming Problems. Application for the Unit Commitment Problem in Electrical Power Production (Lenar Nizamov).- An Outer Approximation Algorithm for Capacitated Disassembly Scheduling Problem with Parts Commonality and Random Demand (Kanglin Liu, MengWang, Zhi-Hai Zhang),- Multi-Tree Decomposition Methods for Large-Scale Mixed Integer Nonlinear Optimization (Ivo Nowak, Pavlo Muts, and Eligius M.T. Hendrix).- An Embarrassingly Parallel Method for Large-Scale Stochastic Programs (Burhaneddin Sandıkçı and Osman Y. Özaltın).- Part 2.- How to Effectively Train Large Scale Machines (Avan Samareh, Mahshid Salemi Parizi).- A Graph Search Algorithm for Solving Large Scale Median Problems on Real Road Networks (Saeed Ghanbartehrania, J. David Porterb, Mahnoush Samadi Dinania).- Solving Large Scale Optimization Problems in the Transportation Industry and Beyond through Column Generation (Yanqi Xu).- Dynamic Energy Management (Nicholas Moehle, Enzo Busseti, Stephen Boyd, and Matt Wytock).- An Approximation-Based Approach for Chance-Constrained Vehicle Routing and Air Traffic Control Problems (Lijian Chen).- Algorithmic Mechanism Design for Collaboration in Large-scale Transportation Networks (Minghui Lai and Xiaoqiang Cai).- Kantorovich-Rubinstein Distance Minimization: Application to Location Problems (Viktor Kuzmenko, Stan Uryasev).
£79.99
Springer Nature Switzerland AG Recent Developments in Mathematical, Statistical
Book SynopsisThis book constitutes an up-to-date account of principles, methods, and tools for mathematical and statistical modelling in a wide range of research fields, including medicine, health sciences, biology, environmental science, engineering, physics, chemistry, computation, finance, economics, and social sciences. It presents original solutions to real-world problems, emphasizes the coordinated development of theories and applications, and promotes interdisciplinary collaboration among mathematicians, statisticians, and researchers in other disciplines.Based on a highly successful meeting, the International Conference on Applied Mathematics, Modeling and Computational Science, AMMCS 2019, held from August 18 to 23, 2019, on the main campus of Wilfrid Laurier University, Waterloo, Canada, the contributions are the results of submissions from the conference participants. They provide readers with a broader view of the methods, ideas and tools used in mathematical, statistical and computational sciences.Table of ContentsS. M. Dastjerdi, A. HormoziNejad, K. Gharali and J. Nathwani, Numerical investigation of VAWT airfoil shapes on power extraction and self-starting purposes.- O. Abu and I. I. Ayogu, An Optimal Control Strategy for a Malaria Model.- L. Feng and X. Wang, Automate Obstructive Sleep Apnea Diagnosis Using Convolutional Neural Networks.- M. Rezaeian, M. Soltani and F. M. Kashkooli, On the modeling of drug delivery to solid tumors; computational viewpoint.- A. F. Ivanov and Z. A. Dzalilov, Oscillations and Periodic Solutions in a Two-Dimensional Differential Delay Model.- K. R. Green and R. J. Spiteri, Solving cardiac bidomain problems with B-spline adaptive collocation.- A. Sowa, Toral diffeomorphisms induce quantum superoperators via TAQS.- N. Mudalige, BOLD.R: A software package to interface with BOLD through R.- E. I. Verriest, Properties of the Zeros of the Scale-Delay Equation and Its Time-Variant ODE Realization .- M. Ashrafizaadeh, A. Ghavaminia, Development of a lattice Boltzmann model for the solution of partial differential equations, A performance comparison study with that of the finite difference method.- M. Ashrafizaadeh, F. Gharibi and S. M. Khatoonabadi, An extended pseudo potential multiphase lattice Boltzmann model with variable viscosity ratio.- M. Ahmed and S. A. Campbell, Effect of genetic defects in a cortical circuit model associated with childhood absence epilepsy.- P. C. Jentsch and C. L. Nehaniv, Exploring Tetris as a Transformation Semigroup.- W. M. Abdullah, S. Hossain and M. A. Khan, Covering Large Complex Networks by Cliques - A Sparse Matrix Approach.- T. Migot and Monica-G. Cojocaru, Revisiting Path-Following to Solve the Generalized Nash Equilibrium Problem.- H. Shaheen, R. Melnik and S. Singh, Analysis of Cortical Spreading Depression in Brian with Multiscale Mathematical Models.- M. Syed Usama and N. A. Malik, A Comparison of Turbulence Generated by 3DS Sparse Grids With Different Blockage Ratios and Different Co-Frame Arrangements.- R. Fallahpour and R. Melnik, Numerical Analysis of Nanowire Resonators for Ultra-High Resolution Mass Sensing in Biomedical Applications.- I. Farahbakhsh and C. L. Nehaniv, Spatial Iterated Prisoner’s Dilemma as a Transformation Semigroup.- L. Graham, M. Demers, Applying Neural Networks to a Fractal Inverse Problem.- D. St Jean, H. Kunze and D. Gillis.- Evaluating a logistic k-mer based model for classifying CO1 sequences of C. clupeaformis.- A. Egri-Nagy and C. L. Nehaniv, A Bestiary of Transformation Semigroups for the Holonomy Decomposition.- R. Xu and R. N. Makarov, High-Frequency Statistical Modelling for Jump-Diffusion Multi-Asset Price Processes with a Systemic Component.- M. M. Mukhopadhyay and R. N. Makarov, Calibration and Analysis of Structural Credit Risk Models with Occupation Time.- N. Mattia Marazzi, V. H. Huxley, R. Sacco and G. Guidoboni, Quantitative study of the coupling among cardiovascular system, lymphatic system and interstitial space.
£104.99
Springer Nature Switzerland AG Mathematical Descriptions of Traffic Flow: Micro,
Book SynopsisThe book originates from the mini-symposium "Mathematical descriptions of traffic flow: micro, macro and kinetic models" organised by the editors within the ICIAM 2019 Congress held in Valencia, Spain, in July 2019. The book is composed of five chapters, which address new research lines in the mathematical modelling of vehicular traffic, at the cutting edge of contemporary research, including traffic automation by means of autonomous vehicles. The contributions span the three most representative scales of mathematical modelling: the microscopic scale of particles, the mesoscopic scale of statistical kinetic description and the macroscopic scale of partial differential equations.The work is addressed to researchers in the field.Table of ContentsM. Herty et al., Reconstruction of traffic speed distributions from kinetic models with uncertainties.- M. Herty et al., From kinetic to macroscopic models and back.- R. Ramadan et al., Structural Properties of the Stability of Jamitons.- C. Balzotti and E. Iacomini, Stop-and-go waves: A Microscopic and a Macroscopic Description.- F. A. Chiarello, An overview of non-local traffic flow models.
£87.99
Springer Nature Switzerland AG Wavelets in Neuroscience
Book SynopsisThis book illustrates how modern mathematical wavelet transform techniques offer fresh insights into the complex behavior of neural systems at different levels: from the microscopic dynamics of individual cells to the macroscopic behavior of large neural networks. It also demonstrates how and where wavelet-based mathematical tools can provide an advantage over classical approaches used in neuroscience. The authors well describe single neuron and populational neural recordings.This 2nd edition discusses novel areas and significant advances resulting from experimental techniques and computational approaches developed since 2015, and includes three new topics:• Detection of fEPSPs in multielectrode LFPs recordings.• Analysis of Visual Sensory Processing in the Brain and BCI for Human Attention Control;• Analysis and Real-time Classification of Motor-related EEG Patterns;The book is a valuable resource for neurophysiologists and physicists familiar with nonlinear dynamical systems and data processing, as well as for graduate students specializing in these and related areas.Table of ContentsMathematical Methods of Signal Processing in Neuroscience.- Brief Tour of Wavelet Theory.- Analysis of Single Neuron Recordings.- Classification of Neuronal Spikes from Extracellular Recordings.- Analysis of Gamma-Waves in Multielectrode LFP Recordings.- Wavelet Approach to the Study of Rhythmic Neuronal Activity.- Wavelet-based Approach to Epilepsy.- Analysis of Visual Sensory Processing in the Brain and Brain-Computer Interfaces for Human Attention Control.- Analysis and Real-Time Classification of Motor-related EEG and MEG Patterns.- Conclusion.
£132.99
Springer Nature Switzerland AG Perspectives in Dynamical Systems II:
Book SynopsisThis volume is part of collection of contributions devoted to analytical and experimental techniques of dynamical systems, presented at the 15th International Conference “Dynamical Systems: Theory and Applications”, held in Łódź, Poland on December 2-5, 2019. The wide selection of material has been divided into three volumes, each focusing on a different field of applications of dynamical systems.The broadly outlined focus of both the conference and these books includes bifurcations and chaos in dynamical systems, asymptotic methods in nonlinear dynamics, dynamics in life sciences and bioengineering, original numerical methods of vibration analysis, control in dynamical systems, optimization problems in applied sciences, stability of dynamical systems, experimental and industrial studies, vibrations of lumped and continuous systems, non-smooth systems, engineering systems and differential equations, mathematical approaches to dynamical systems, and mechatronics.Table of ContentsNonlinear modelling and control of self-balancing human transporter (Makkar).- Nonlinear tourist flows in Barcelona (Trullols).- Convergence of dual infinity series (Klimenda).- Full spectrum analysis for studying the backward whirl in accelerated rotor systems (Alshudeifat).- Switched Reluctance Motor dynamic eccentricity modelling (Lorencki).- Harmonic transfer path analysis of a wine refrigerator (Hörtnagel).- Risk related prediction for recurrent stroke and post-stroke epilepsy using Fractional Fourier Transform analysis of EEG signals (Dulf).- Chaos, bifurcations and strange attractors in environmental radioactivity dynamics of some geosystems (Ternovsky).- Dynamics of chains as a tool to study thermomechanical properties of proteins (Weber).- Evaluation of the crane’s actuators strength based on the results obtained from dynamics model (Urbaś).- Nonlinear dynamics of atomic and molecular systems in an electromagnetic field: Deterministic chaos and strange attractors (Glushkov).- Deterministic chaos, bifurcations and strange attractors in nonlinear dynamics of relativistic backward-wave tube (Ternovsky).- Detection of chaotic behavior of the dynamical system using methods of deformable active contours (Ruchkin).- Dynamics of sensing element of micro- and nanoelectromechanical sensors as anisotropic size-dependent plate (Barulina).- Dynamic analysis and damage of composite layered plates reinforced by unidirectional fibers subjected low velocity impact (Soukup).- Identification of nonlinear joint interface parameters using instantaneous power flow balance approach (Rajan).- Numerical procedure for sensitivity analysis of hybrid systems (Pytlak).- Asymptotic stability of fractional variable order discrete-time equations with terms of convolution operators (Mozyrska).- Dynamics of circular plates under selected heat loadings (Doneva).- A Rulkov neuronal model with Caputo fractional variable-order differences of convolution type (Mozyrska).- Electrostatically actuated initially curved micro beams: analytical and finite element modelling (Mozhgova).- Numerical and analytical investigation of chatter suppression by parametric excitation (Dohnal).- Nonlinear study of a pneumatic artificial muscle (PAM) under superharmonic resonance condition using method of multiple scales (Kalita).- Two-mode long-wave low-frequency approximations for anti-plane shear deformation of a high-contrast asymmetric laminate (Kaplunov).- A study on the coefficient of restitution effect on single-sided vibro-impact nonlinear energy sink (Saeed).
£119.99
Springer Nature Switzerland AG Modeling, Dynamics, Optimization and Bioeconomics
Book SynopsisThis book, following the three published volumes of the book, provides the main purpose to collect research papers and review papers to provide an overview of the main issues, results, and open questions in the cutting-edge research on the fields of modeling, optimization, and dynamics and their applications to biology, economy, energy, industry, physics, psychology and finance. Assuming the scientific relevance of the presenting innovative applications as well as merging issues in these areas, the purpose of this book is to collect papers of the world experts in mathematics, economics, and other applied sciences that is seminal to the future research developments. The majority of the papers presented in this book is authored by the participants in The Joint Meeting 6th International Conference on Dynamics, Games, and Science – DGSVI – JOLATE and in the 21st ICABR Conference. The scientific scope of the conferences is focused on the fields of modeling, optimization, and dynamics and their applications to biology, economy, energy, industry, physics, psychology, and finance. Assuming the scientific relevance of the presenting innovative applications as well as merging issues in these areas, the purpose of the conference is to bring together some of the world experts in mathematics, economics, and other applied sciences that reinforce ongoing projects and establish future works and collaborations.Table of ContentsA. Afsar, F. Martins, Bruno M. P. M. Oliveira, and A. A. Pinto, Immune response model fitting to CD4+ T cell data in lymphocytic choriomeningitis virus LCMV infection.- U. Agyüz, V. Purutçuoglu, E. Purutçuoglu and Y. Ürün, Construction of a New Model to Investigate Breast Cancer Data.- I. Baltas, M. Szczepanski, L. Dopierala, K. Kolodziejczyk, G.-W. Weber and A. N. Yannacopoulos, Optimal Pension Fund Management Under Risk and Uncertainty: The Case Study of Poland.- M. Bujidos-Casado, J. Navío-Marco and B. Rodrigo-Moya, Collaborative Innovation of Spanish SMEs in the European context: A compared study.- G. G. de Castro, A. O. Lopes and G. Mantovani, Haar systems, KMS states on von Neumann algebras and C*-algebras on dynamically defined groupoids and Noncommutative Integration.- C. Çıtak, T. Aksu, Ö. Harputlu and Gerhard-Wilhelm Weber, Mixed Compression Air-Intake Design for High-Speed Transportation.- D. Czerkawski, J. Małecka, G. Wilhelm Weber and B. Kjamili, Social Entrepreneurship Business Models for Handicapped People - Polish & Turkish case study of sharing public goods by doing business.- H. H. Ferreira, A. O. Lopes and E. R. Oliveira, An iterative process for approximating subactions.- A. D. Garcia and M. A. Szybisz, "Beat the gun": The phenomenon of liquidity.- E. Gómez-Escalonilla and Laura Parte, Board Knowledge and Bank Risk-Taking. An International Analysis.- F. Jiménez-Delgado, M. Dolores Reina-Paz, Israel J ThuissardVasallo and David Sanz-Rosa, The shopping experience in virtual sales: A study of the influence of website atmosphere on purchase intention.- Kyung B. Kim and José M. Labeaga, European Mobile Phone Industry: Demand Estimation Using Discrete Random Coefficients Models.- A. O. Lopes and M. Sebastiani, On Bertelson-Gromov Dynamical Morse Entropy, Rogério Martins, Synchronisation of weakly coupled oscillators.- Z. Kamisli Ozturk, Y. Cetin, Y. Isik and Z. I. Erzurum Cicek, Demand Forecasting with Clustering and Artificial Neural Networks Methods: an Application for Stock Keeping Units.- O. Palanci, S.Z. Alparslan Gok and Gerhard-Wilhelm Weber, On the Grey Obligation Rules.- Juan Diego Paredes-Gázquez, Eva Pardo and José Miguel Rodríguez-Fernández, Robustness checks in composite indicators: A responsible approach.- Elena V. Ravve, Zeev Volkovich, Gerhard-Wilhelm Weber, A Logic-Based Approach to Incremental Reasoning on Multi-Agent Systems.
£112.49
Springer Nature Switzerland AG Recent Advances in Industrial and Applied
Book SynopsisThis open access book contains review papers authored by thirteen plenary invited speakers to the 9th International Congress on Industrial and Applied Mathematics (Valencia, July 15-19, 2019). Written by top-level scientists recognized worldwide, the scientific contributions cover a wide range of cutting-edge topics of industrial and applied mathematics: mathematical modeling, industrial and environmental mathematics, mathematical biology and medicine, reduced-order modeling and cryptography. The book also includes an introductory chapter summarizing the main features of the congress. This is the first volume of a thematic series dedicated to research results presented at ICIAM 2019-Valencia Congress.Table of Contents1 M. Berger, Asteroid-Generated Tsunamis: A Review.- 2 A. Bermúdez, Some Case Studies in Environmental and Industrial Mathematics.- 3 Z. Cai et al., Hyperbolic Model Reduction for Kinetic Equations.- 4 A. Cohen et al., State Estimation - The Role of Reduced Models.- 5 C. Conca, Modelling Our Sense Of Smell.- 6 L. Edelstein-Keshet, Pattern formation inside living cells.- 7 M. Garzon et al., Efficient Algorithms for Tracking Moving Interfaces.- 8 K. Lauter, Private AI: Machine Learning on Encrypted Data.- 9 C. Le Bris, Mathematical approaches for contemporary materials science: Addressing defects in the microstructure.- 10 H. Leng et al., An iterative thresholding method for topology optimization for the Navier-Stokes flow.- 11 K. Sako, Cryptography and Digital Transformation.- 12 H. Suito et al., Numerical Study for Blood Flows in Thoracic Aorta.- 13 J.A.C. Weideman, Dynamics of Complex Singularities of Nonlinear PDEs: Analysis and Computation.
£35.99
Springer Nature Switzerland AG Advances in Design Engineering II: Proceedings of
Book SynopsisThis book contains the papers presented at the XXX International Congress INGEGRAF, “Digital Engineering, its application in Research, Development and Innovation”, held on 24–25 June 2021 in Valencia, Spain.The book reports on cutting-edge topics in product design and manufacturing, such as industrial methods for integrated product and process design; innovative design; and computer-aided design. Further topics covered include virtual simulation and reverse engineering; additive manufacturing; product manufacturing; engineering methods in medicine and education; representation techniques; and nautical, engineering and construction, aeronautics and aerospace design and modeling. The book has six sections, reflecting the focus and primary themes of the conference. The contributions presented here will not only provide researchers, engineers, and experts in a range of industrial engineering subfields with extensive information to support their daily work; but also they are intended to stimulate new research directions, advanced applications of the methods discussed, and future interdisciplinary collaborations.Table of ContentsPrefaceOrganization Committee, Scientific Committee Part 1 - Engineering and Construction - New Methodologies BIM Part 2 - Teaching - Learning in Graphic Engineering Part 3 - Product Design & Development Part 4 - Manufacturing and Industrial Process Design Part 5 - Graphical Bioengineering Part 6 - Innovation in Design
£143.99
Springer Nature Switzerland AG Mathematical Modeling of the Human Brain: From Magnetic Resonance Images to Finite Element Simulation
Book SynopsisThis open access book bridges common tools in medical imaging and neuroscience with the numerical solution of brain modelling PDEs. The connection between these areas is established through the use of two existing tools, FreeSurfer and FEniCS, and one novel tool, the SVM-Tk, developed for this book. The reader will learn the basics of magnetic resonance imaging and quickly proceed to generating their first FEniCS brain meshes from T1-weighted images. The book's presentation concludes with the reader solving a simplified PDE model of gadobutrol diffusion in the brain that incorporates diffusion tensor images, of various resolution, and complex, multi-domain, variable-resolution FEniCS meshes with detailed markings of anatomical brain regions. After completing this book, the reader will have a solid foundation for performing patient-specific finite element simulations of biomechanical models of the human brain.Trade Review“The book represents an excellent introduction and hands-on guide to this important and exciting field, for applied mathematicians and image processing practitioners … . It is, perhaps, most beneficial, to electrical and computer science majors who wish to rapidly immerse themselves in the field, in a manner that is mathematically correct and sound, yet also practical.” (Emil Saucan, zbMATH 1501.92001, 2023)Table of ContentsIntroduction.- Working with magnetic resonance images of the brain.- From T1 images to numerical simulation.- Introducing heterogeneities.- Introducing directionality with diffusion tensors.- Simulating anisotropic diffusion in heterogeneous brain regions.- Concluding remarks and outlook.- References.- Index.
£23.74
Springer International Publishing AG Graph-Based Semi-Supervised Learning
Book SynopsisWhile labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / IndexTable of ContentsIntroduction.- Graph Construction.- Learning and Inference.- Scalability.- Applications.- Future Work.- Bibliography.- Authors' Biographies.- Index .
£26.59
Springer International Publishing AG Mathematical Modeling: A Dynamical Systems
Book SynopsisThis book provides qualitative and quantitative methods to analyze and better understand phenomena that change in space and time. An innovative approach is to incorporate ideas and methods from dynamical systems and equivariant bifurcation theory to model, analyze and predict the behavior of mathematical models. In addition, real-life data is incorporated in the derivation of certain models. For instance, the model for a fluxgate magnetometer includes experiments in support of the model. The book is intended for interdisciplinary scientists in STEM fields, who might be interested in learning the skills to derive a mathematical representation for explaining the evolution of a real system. Overall, the book could be adapted in undergraduate- and postgraduate-level courses, with students from various STEM fields, including: mathematics, physics, engineering and biology.Table of ContentsIntroduction- Algebraic Models.- Discrete Models.- Continuous Models.- Bifurcation Theory.- Network-Based Modeling.- Delay Models.- Spatial-Temporal Models.- Stochastic Models.- Model Reduction and Simplification.
£67.49
Springer International Publishing AG Operational Research: IO 2021—Analytics for a
Book SynopsisThis book provides the current status of research on the application of OR methods to solve emerging and relevant operations management problems. Each chapter is a selected contribution of the IO2021 - XXI Congress of APDIO, the Portuguese Association of Operational Research, held in Figueira da Foz from 7 to 8 November 2021. Under the theme of analytics for a better world, the book presents interesting results and applications of OR cutting-edge methods and techniques to various real-world problems. Of particular importance are works applying nonlinear, multi-objective optimization, hybrid heuristics, multicriteria decision analysis, data envelopment analysis, simulation, clustering techniques and decision support systems, in different areas such as supply chain management, production planning and scheduling, logistics, energy, telecommunications, finance and health. All chapters were carefully reviewed by the members of the scientific program committee.Table of ContentsA. R. Aguiar, T. Ramos, M. Isabel Gomes, Chapter 1 – A Biased Random-Key Genetic Algorithm for the Home Care Routing and Scheduling Problem: exploring the algorithm's configuration process.- B. F. Azevedo, F. Alvelos, Ana Maria A. C. Rocha, T. Brito, José Lima, Ana I. Pereira, Chapter 2 – An Integer Programming Approach for Sensor Location in a Forest Fire Monitoring System.- C. Bessa, R. Duque, A. Jesus, C. Silva, L. Eberle, S. Moniz, Chapter 3 – Capacity allocation incorporating market equity concerns: a Pharmaceutical Supply Chain case study.- I. S. Costa, R. Figueiredo, C. Requejo, Chapter 4 – The Shortest Path in Signed Graphs.- M. Dias, N. Lourenço, C. Silva, S. Moniz, Chapter 5 – The Break Point: A Machine Learning Approach to Web Breaks in Paper Mills.- M. M. Lima, F. Soares de Sousa, E. G. Öztürk, P. F. Rocha, A. M. Rodrigues, J. S. Ferreira, A. C. Nunes, I. C. Lopes, C. Teles Oliveira, Chapter 6 – A resectorization of fire brigades in the north of Portugal.- L. Magalhães, J. S. Guedes, J. Freire de Sousa, Chapter 7 – A Holistic Framework for Increasing Agility in a Production Process.- P. Nascimento, C. Silva, S. Mueller, S. Moniz, Chapter 8 – Nesting and scheduling for additive manufacturing: an approach considering order due dates.- E. Göksu Öztürk, Filipe Soares de Sousa, M. M. Lima, P. F. Rocha, A. M. Rodrigues, J. S. Ferreira, A. C. Nunes, I. C. Lopes, C. Teles Oliveira, Chapter 9 – Developing a System for Sectorization: An Overview.- M. Pascoal, M. T. Godinho, A. Moghanni, Chapter 10 – New models for finding K short and dissimilar paths.- M. T. Pereira, M. Oliveira, F. A. Ferreira, A. Barreiras, L. Carneiro, Chapter 11 – Time Windows Vehicle Routing Problem to on-time transportation of biological products on healthcare centres.- H. S. Rodrigues, Artur M. C. Brito da Cruz, Chapter 12 – The role of communication on the spread of dengue: an optimal control simulation.- A. Torrado, A. Paula Barbosa-Póvoa, Chapter 13 – Towards an Optimized and Socio-Economic Blood Supply Chain Network.- Clara B. Vaz, ngela P. Ferreira, Chapter 14 – A DEA Approach to Evaluate the Performance of the Electric Mobility Deployment in European Countries.- D. B. Viana, B. B. Oliveira, Chapter 15 – The art of the deal: Machine learning based trade promotion evaluation.
£127.99
Springer International Publishing AG A³N²M: Approximation, Applications, and Analysis
Book SynopsisThis volume collects papers based on plenary and invited talks given at the 50th Barrett Memorial Lectures on Approximation, Applications, and Analysis of Nonlocal, Nonlinear Models that was organized by the University of Tennessee, Knoxville and held virtually in May 2021. The three-day meeting brought together experts from the computational, scientific, engineering, and mathematical communities who work with nonlocal models. These proceedings collect contributions and give a survey of the state of the art in computational practices, mathematical analysis, applications of nonlocal models, and explorations of new application domains. The volume benefits from the mixture of contributions by computational scientists, mathematicians, and application specialists. The content is suitable for graduate students as well as specialists working with nonlocal models and covers topics on fractional PDEs, regularity theory for kinetic equations, approximation theory for fractional diffusion, analysis of nonlocal diffusion model as a bridge between local and fractional PDEs, and more.Table of ContentsCTRW approximations for fractional equations with variable order (Kolokoltsov).- Fractional Elliptic Problems on Lipschitz Domains: Regularity and Approximation (Borthagaray).- Regularity estimates and open problems in kinetic equations (Silvestre).- An optimization-based strategy for peridynamic-FEM coupling and for the prescription of nonlocal boundary conditions (Littlewood).- Nonlocal diffusion models with consistent local and fractional limits (Du).- A one-dimensional symmetric force-based blending method for atomistic-to-continuum coupling (Li).- A note on estimates of level sets and their role in demonstrating regularity of solutions to nonlocal double phase equations (Mengesha).- An overview of Almost Minimizers of Bernoulli-Type Functionals (Garcia).
£87.99
Birkhäuser Active Particles Volume 4
Book Synopsis1 Consistency of Semi-supervised Learning, Stochastic Tug-of-War Games, and the p-Laplacian.- 2 Discrete Minimizers of the Interaction Energy in Collective Behavior.- 3 Large-Population Limits of Non-Exchangeable Particle Systems.- 4 Models of Animal Behavior as Active Particle Systems with Nonreciprocal Interactions.- 5 Bayesian Sampling Using Interacting Particles.- 6 Aggregation-Diffusion Phenomena.- 7 Conservative Semi-Lagrangian Methods for Kinetic Equations.- 8 Large Population Limit of Interacting Population Dynamics via Generalized Gradient Structures.- 9 Adjoint Monte Carlo Method.
£103.99
Springer Mathematics for Sustainable Industry
Book Synopsis- Analysis of Carreau Fluid in MHD Natural Convection with Biot Heat Flux and Injection/Suction Effects.- Advanced Thermal Conductivity Enhancement of Hybrid Nanofluid Using Spherical Graphene and Strontium Titanate with CMC Base over a Riga Plate via Caputo-Fabrizio Fractional Derivative.- A Comparative Analysis of Classification Methods for Handling RFID Data Stream on Inventory Management.- Diffie-Hellman Key Exchange Protocol Based On Ring LWE with n Entities.- Spatial Global Numerical Computation of the Integral Equations for the Ruin Probability.- The Orientation of a Rotated Ellipsoid Based on Its Polarization Tensor.- University Teaching Assessment Using Fuzzy Delphi Method with Z-Number Approach.- Squeeze Flow of Ternary Casson Nanofluid with Magnetohydrodynamic, Viscous Dissipation and Thermal Radiation Effect.- Numerical Simulation of Aqueous Humour Flow during Descemet Membrane Detachment under External Heat Sources.- Indoor Transmission of COVID-19 using Advection Diffusion Equation.- Forecasting Electricity Demand from Battery Electric Vehicles Using Box-Jenkins Modeling.- Mathematical model and algorithm for grammatical editing of given words in Uzbek language.- Creation of a Multi-Functional Device for Real-Time Monitoring of Water Quality.- Valuation of Basket Options Accommodating Assets’ Correlation.- Monitoring The Quality of Water Production Process in Surabaya Using Max-MCUSUM Control Chart based on Residual Deep Learning LSTM Model.- Generalized Linear Model Approach on Dengue Incidence in Selangor.- Hybrid Genetic Algorithm for Multi-Period Inventory Routing Problem with Carbon Cap-and-Trade Policy.- Wave Attenuation by Seagrass.- Optimization of Green Transportation Routing Problem in Petroleum Logistics by Ant Colony Algorithm.- Several Functions of Matrix Metalloproteinases in the Formation of Invadopodia by Level Set Method.- Advanced Production Planning System for Multiperiod Production Smoothing Problem.- Unsteady Solute Dispersion through a Catheterized Artery with the Presence of Cosine and Sine-Shaped Stenoses.
£161.99
De Gruyter Homogenization Methods: Effective Properties of Composites
Book SynopsisAlmost all materials are inhomogeneous at the microscale. Typical examples are fiber- and grain structures made of anisotropic phases. These cannot be accounted for in detail in engineering calculations. Instead, effective, homogeneous material properties are used. These are obtained from the inhomogeneous structures by homogenization methods. This book provides a structured overview of the analytical homogenization methods, including the most common estimates, bounds, and Fourier methods. The focus is on linear and anisotropic constitutive relationships, like Hookean elasticity and Fourier’s law for thermal conduction. All sections are accompanied by example calculations, including program code that is also available online.
£60.30
Springer Fachmedien Wiesbaden Mathematische Modellierung: Eine Einführung in
Book SynopsisIm landläufigen Mathematikunterricht an Schulen und Hochschulen wird die Mathematik überwiegend als eine geistige Disziplin vermittelt, in der es um die Klarheit und Stringenz des Denkens geht. Systematik und formale Eleganz der Darstellung stehen im Vorder grund. Dabei wird aber ignoriert, daß die Mathematik nicht allein aus sich heraus lebt, sondern in andere Bereiche unserer Kultur eingebunden ist. Ohne allzusehr zu übertreiben, kann man sogar behaupten, daß sie ein integrativer Bestandteil unserer technologischen Welt ist und damit einen Einfluß ausübt, der weit über ihr Selbstverständnis hinausgeht. Mit der Sprache der Mathematik lassen sich auch nicht-mathematische Inhalte, zu mindest bis zu einem gewissen Grade ausdrücken. Man gelangt auf diese Weise oft zu Einsichten, die man ohne die Sprache der Mathematik nicht so klar und präzise aus drücken könnte. Man muß dabei allerdings auch beachten, daß mit der Umsetzung nicht mathematischer Sachverhalte in Mathematik eine starke Vereinfachung einhergeht als Preis für die mathematische Abstraktion. Diese Vereinfachung muß bei der Interpretation der durch Mathematik gewonnenen Einsichten berücksichtigt werden. Der Zweck dieses Buches besteht darin, die soeben skizzierte Rolle der Mathematik ins Bewußtsein der Mathematiker zu bringen. Danken möchte ich Frau A. Garhammer für das Schreiben dieses Buchtextes auf dem Computer und Herrn S. Bott für die Herstellung der Graphiken.Table of ContentsProblemstellung - Ein mathematisches Modell des Informationsbegriffes Modell des Informationsbegriffes - Entscheidungs- und Spielmodelle - Wachstumsmodelle - Zwei mathematische Modelle in der Medizin - Konkurrenzmodelle - Ein mathematisches Modell der Hämodialyse - Ein mathematisches Modell für Rüstung
£37.99
Springer Fachmedien Wiesbaden Angewandte Mathematik, Modellbildung und
Book SynopsisEs macht wenig Sinn, gerade wenn man an die Schulen denkt, Numerische Mathematik als Selbstzweck zu präsentieren. Wo ist der Sinn von Interpolation, Approximation und der Lösung linearer Systeme, wenn man nicht weiß, in welch vielfältigen Problemen diese Techniken anwendbar sind? Bei der Suche nach Anwendungen stößt man auf die Modellierung technischer, biologischer und ökonomischer Fragen. Des Weiteren muss das Modell in irgendeiner Form auf einem Rechner abgebildet werden, wozu man einige Kenntnisse aus der Informatik benötigt.Table of ContentsModellbildung oder: Wie hätte Leonardo modelliert? - Wie schnell wächst Fußpilz? - Wie wirtschaftlich ist mein Betrieb? - Wie sendet Asterix Geheimbotschaften an Teefax? - Was haben Tomographie und Wasserleitungen gemeinsam? - Wie fließt der Straßenverkehr? - Dem Zufall keine Chance? - Wie fängt der Hai die Beute?
£23.74
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Assessing Risk Assessment: Towards Alternative
Book SynopsisChristian Hugo Hoffmann undermines the citadel of risk assessment and management, arguing that classical probability theory is not an adequate foundation for modeling systemic and extreme risk in complex financial systems. He proposes a new class of models which focus on the knowledge dimension by precisely describing market participants’ own positions and their propensity to react to outside changes. The author closes his thesis by a synthetical reflection on methods and elaborates on the meaning of decision-making competency in a risk management context in banking. By choosing this poly-dimensional approach, the purpose of his work is to explore shortcomings of risk management approaches of financial institutions and to point out how they might be overcome.
£62.99
Springer Fachmedien Wiesbaden Mathematische Bildverarbeitung: Einführung in
Book SynopsisDieses Buch behandelt die mathematischen Aspekte der modernen Bildverarbeitungsmethoden. Besonderer Schwerpunkt liegt dabei auf der Präsentation von Grundideen und Konzepten. Es werden eine Vielzahl moderner mathematischer Methoden behandelt, welche zur Lösung wichtiger, grundlegender Probleme der Bildverarbeitung eingesetzt werden. Die Grundprobleme umfassen zum Beispiel Entrauschen, Scharfzeichnen, Kantenerkennung, Inpainting. Neben elementaren Methoden wie Punktoperationen, linearen oder morphologischen Filtern stellt das Buch insbesondere neuere Methoden wie partielle Differentialgleichungen und Variationsmethoden vor. Trade Review"[...] it belongs to the bookcase of any office where someone is doing research/application in image processing. It has the virtues of a good and handy reference manual." Zentralblatt MATH, 1220-2011Table of ContentsEinleitung: Was sind Bilder? Problemstellungen der klassischen Bildverarbeitung - Grundlegende Werkzeuge: Grauwerttransformationen. Glättungs- und Schärfungsmethoden. Lineare Filter. Morphologische Filter - Diskrete und kontinuierliche Betrachtungsweise: Interpolation. Die kontinuierliche Fourier-Transformation. Fourier-Reihen. Gefensterte Fourier-Transformation. Kontinuierliches Filtern und die Wärmeleitungsgleichung - Axiomatische Bildverarbeitung: Skalenraum-Axiome. Beispiele für Multiskalen-Analysen. Grundlegende Theorie. Die Standard PDE-Modelle - Variationsmethoden: Motivation und Vorbemerkungen. Anwendungen
£28.49
Springer Verlag, Singapore Modeling and Simulation of Complex Dynamical
Book SynopsisThis book highlights the practical aspects of computer modelling and simulation of complex dynamical systems for students. Mechanical systems are considered in the book as representative examples of dynamical systems. Wolfram SystemModeler, in combination with Learning Management System Sakai, is used as an instrument for studying features of various physical and technical phenomena and processes. Each of the presented virtual labs may be considered a stand-alone mini project to enable students to go through all the steps of mathematical modelling and computer simulation—from the problem statement to mathematical and physical analysis of the obtained result. The book is useful for teachers to organize the educational process, allowing gradual monitoring of the learning process and assessment of students’ competencies. It also allows tutors to design individual educational trajectories for students to achieve educational properties. The subject of the book is an extension of activity started by the international team of authors within the InMotion project of the European programme ERASMUS+.Trade Review“The authors went to great effort to produce beautiful figures, with color no less, to aid the reader. The tables are laid out logically, and the discussion for each model is, for the most part straightforward. I wish I could recommend the book … .” (David S. Mazel, MAA Reviews, August 22, 2022)Table of Contents1. Virtual Labs (76 pages) 2 Guidelines for performing virtual labs in Wolfram SystemModeler (63 pages)
£37.99
Springer Verlag, Singapore Artificial Intelligence for Automated Pricing
Book SynopsisThis book highlights artificial intelligence algorithms used in implementation of automated pricing. It presents the process for building automated pricing models from crawl data, preprocessed data to implement models, and their applications. The book also focuses on machine learning and deep learning methods for pricing, including from regression methods to hybrid and ensemble methods. The computational experiments are presented to illustrate the pricing processes and models.Table of Contents1. Pricing based on product descriptions: problem, data, and methods.- 2. Extract product data from descriptions by NLP techniques.- 3. Segmentation and Quantity the qualify features.- 4. Pricing prediction using machine learning and ensemble methods.- 5. Applications & Discussions.
£52.24
Springer Verlag, Singapore Methods of Mathematical Oncology: Fusion of
Book SynopsisThis book presents original papers reflecting topics featured at the international symposium entitled “Fusion of Mathematics and Biology” and organized by the editor of the book. The symposium, held in October 2020 at Osaka University in Japan, was the core event for the final year of the research project entitled “Establishing International Research Networks of Mathematical Oncology.” The project had been carried out since April 2015 as part of the Core-to-Core Program of Japan Society for the Promotion of Science (JSPS). In this book, the editor presents collaborative research from prestigious organizations in France, the UK, and the USA. By utilizing their individual strengths and realizing the fusion of life science and mathematical science, the project achieved a combination of mathematical analysis, verification by biomedical experiments, and statistical analysis of chemical databases.Mathematics is sometimes regarded as a universal language. It is a valuable property that everyone can understand beyond the boundaries of culture, religion, and language. This unifying force of mathematics also applies to the various fields of science. Mathematical oncology has two aspects, i.e., data science and mathematical modeling, and definitely helps in the prediction and control of biological phenomena observed in cancer evolution.The topics addressed in this book represent several methods of applying mathematical modeling to scientific problems in the natural sciences. Furthermore, novel reviews are included that may motivate many mathematicians to become interested in biological research.Table of ContentsPART 1: Mathematical Modeling: D. Guan, X. Luo, and H. Gao, Constitutive Modelling of Soft Biological Tissue from Ex Vivo to In Vivo: Myocardium as an Example.- T. Colin, T. Michel, and C. Poignard, Mathematical Modeling of Gastro-intestinal Metastasis Resistance to Tyrosine Kinase Inhibitors.- Y. Tanaka and T. Yasugi, Mathematical Modeling and Experimental Verification of the Proneural Wave.- D. Kumakura and S. Nakaoka, Exploring Similarity between Embedding Dimension of Time-series Data and Flows of an Ecological Population Model.- T. Hayashi, Mathematical Modeling for Angiogenesis.- S. Collin, Corridore and C. Poignard, Floating Potential Boundary Condition in Smooth Domains in an Electroporation Context.- N. L. Othman and T. Suzuki, Free Boundary Problem of Cell Deformation and Invasion.- L. Preziosi and M. Scianna, Multi-level Mathematical Models for Cell Migration in Confined Environments.- S. Magi, Mathematical Modeling of Cancer Signaling Addressing Tumor Heterogeneity.- N. Sfakianakis and Mark A.J. Chaplain, Mathematical Modelling of Cancer Invasion: A Review.- T. Williams, A. Wilson, and N. Sfakianakis, The First Step towards the Mathematical Understanding of the Role of Matrix Metalloproteinase-8 in Cancer Invasion.- PART II: Biological Prediction: T. Ito, T. Suzuki, and Y. Murakami, Mathematical Modeling of the Dimerization of EGFR and ErbB3 in Lung Adenocarcinoma.- H. Kubota, Selective Regulation of the Insulin-Akt Pathway by Simultaneous Processing of Blood Insulin Pattern in the Liver.- D. Oikawa, N. Hatanaka, T. Suzuki, and F. Tokunaga, Mathematical Simulation of Linear Ubiquitination in T Cell Receptor-mediated NF-κB Activation Pathway.- Y. Ito, D. Minerva, S. Tasaki, M. Yoshida, T. Suzuki, and A. Goto, Time Changes in the VEGF-A Concentration Gradient Lead Neovasculature to Engage in Stair-like Growth.- N. Hatanaka, M. Futakuchi, and T. Suzuki, Mathematical Modeling of Tumor Malignancy in Bone Microenvironment.- M. Yamamoto and Jun-ichiro Inoue, Signaling Networks Involved in the Malignant Transformation of Breast Cancer.- PART III: Data Science: R. Morishita, H. Takahashi, and T. Sawasaki, Cell-free Based Protein Array Technology.- Y. Nojima and Y. Takeda, Omics Data Analysis Tools for Biomarker Discovery and the Tutorial.- M. Oyama and H. Kozuka-Hata, Integrative Network Analysis of Cancer Cell Signaling by High-resolution Proteomics.- N. Nakamura and R. Yamada, Distance-matrix-based Extraction of Motility Features from Functionally Heterogeneous Cell Populations.- S. Kawasaki, H. Hayashi, and Y. Tominaga, Data Analytic Study of Genetic Mechanism of Ovarian Carcinoma from Single Cell RNA-seq Data.
£119.99
