Kooperativer Bibliotheksverbund

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Format: 1 online resource (282 pages) : , digital, PDF file(s).

Edition: Second edition.

ISBN: 1-139-17332-4

Content: This is a new edition of a very successful introduction to statistical methods for general insurance practitioners. No prior statistical knowledge is assumed, and the mathematical level required is approximately equivalent to school mathematics. Whilst the book is primarily introductory, the authors discuss some more advanced topics, including simulation, calculation of risk premiums, credibility theory, estimation of outstanding claim provisions and risk theory. All topics are illustrated by examples drawn from general insurance, and references for further reading are given. Solutions to most of the exercises are included. For the new edition the opportunity has been taken to make minor improvements and corrections throughout the text, to rewrite some sections to improve clarity, and to update the examples and references. A new section dealing with estimation has also been added.

Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , Cover -- Half-title -- Title -- Copyright -- Contents -- Preface to first edition -- Preface to second edition -- Introduction and mathematical preliminaries -- 1.1 Introduction -- 1.2 Summation notation -- 1.3 Factorial notation n! -- 1.4 Combinatorial notation -- 1.5 Power notation -- 1.6 Differentiation -- the slope of a curve -- 1.7 Maxima and minima -- *1.8 Functions of more than one variable -- maxima and minima -- 1.9 The exponential function e x -- 1.10 The natural logarithm function In x -- 1.11 Exercises -- Elementary probability -- 2.1 Introduction -- concept of probability -- 2.2 Joint and disjoint events -- intersection and union -- 2.3 Conditional probability -- 2.4 Independence of two events -- 2.5 Exercises -- Random variables and their distributions -- 3.1 Discrete random variables and their distributions -- 3.2 Continuous random variables and their distributions -- 3.3 The area under a curve -- integration and differentiation -- 3.4 Exercises -- Location and dispersion -- 4.1 Measures of location - mean, median and mode -- 4.2 Dispersion - variance and standard deviation -- **4.3 Expectations and moments -- **4.4 Conditional means -- **4.5 Conditional variances -- **4.6 Skewness -- 4.7 Exercises -- Statistical distributions useful in general insurance work -- 5.1 The normal distribution -- 5.2 The Central Limit Theorem -- 5.3 The log-normal distribution -- *5.4 The Pareto distribution -- *5.5 The gamma distribution -- 5.6 The Poisson distribution -- 5.7 Normal approximation to the Poisson distribution -- *5.8 The binomial distribution -- **5.9 The negative binomial distribution -- heterogeneity of risk -- 5.10 The importance of theoretical distributions in general insurance -- Exercises -- Inferences from general insurance data -- 6.1 Hypothesis testing -- 6.2 Point estimation and method of moments -- *6.3 Maximum likelihood. , *6.4 Confidence intervals -- *6.5 Risk factors -- multivariate models -- least squares -- 6.6 Exercises -- The risk premium -- 7.1 Risk premium -- claim frequency and claim size -- 7.2 Claim frequency rate -- exposure -- 7.3 Claim size -- pitfalls -- 7.4 Claim settlement pattern -- *7.5 Excesses and excess of loss reinsurances -- 7.6 Exercises -- Experience rating -- 8.1 Introduction -- 8.2 Credibility theory -- 8.3 Full credibility -- 8.4 Partial credibility -- *8.5 Bayes' Theorem -- **8.6 A Bayesian approach to the updating of claim frequency rates -- 8.7 No claim discount (NCD) -- Exercises -- Simulation -- 9.1 Random numbers and simulation -- *9.2 How many simulations? -- 9.3 Computer generation of random numbers -- *9.4 Linear congruential generators -- 9.5 Random observations on the normal distribution -- 9.6 Random observations on the log-normal distribution -- 9.7 Random observations on the Poisson distribution -- *9.8 Random observations on the negative binomial distribution -- 9.9 A simulation example -- 9.10 When to simulate -- 9.11 Simulation of an NCD system -- 9.12 Limitations of the model -- sensitivity analysis -- 9.13 Exercises -- Estimation of outstanding claim provisions -- 10.1 Delays in claim reporting and claim settlement -- run-off -- 10.2 The run-off triangle -- 10.3 Chain-ladder method without inflation adjustment -- 10.4 Does the chain-ladder model fit the data? -- 10.5 Chain-ladder method with inflation adjustment -- 10.6 The separation method (direct future payments approach) -- *10.7 The separation method (two other approaches) -- 10.8 IBNR, and the chain-ladder and separation methods -- 10.9 Alternative methods of assessing outstanding claim provisions -- 10.10 The tail -- 10.11 Estimation of IBNR claim provisions -- 10.12 Exercises -- Elementary risk theory -- 11.1 Introduction. , 11.2 Portfolio with constant (fixed) claim size -- 11.3 Variable claim size -- **11.4 The expectation and variance of C -- 11.5 The assumption of normality -- 11.6 Summary and further reading -- 11.7 Exercises -- References -- Solutions to exercises -- Author index -- Subject index. , English

Additional Edition: ISBN 0-521-65534-X

Additional Edition: ISBN 0-521-65234-0

Language: English

URL: Volltext  (lizenzpflichtig)

URL: Inhaltsverzeichnis

URL: Inhaltstext

URL: https://doi.org/10.1017/CBO9781139173322

URL: https://doi.org/10.1017/CBO9781139173322

UID:

Format: xiv, 546 Seiten : , Diagramme ; , 254 mm.

Edition: Second edition

ISBN: 978-1-4625-4986-3

Series Statement: Methodology in the Social Sciences

Content: The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website (www.appliedmissingdata.com) includes datasets and analysis examples from the book, up-to-date software information, and other resources.New to This Edition*Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables.*New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics.*Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples.*Most of the book is entirely new.

Note: References S. 493-517, Author Index S. 519-528, Subject Index S. 529-545 , 1. Introduction to Missing Data; 1.1 Chapter Overview; 1.2 Missing Data Patterns; 1.3 Missing Data Mechanisms; 1.4 Diagnosing Missing Data Mechanisms; 1.5 Auxiliary Variables; 1.6 Analysis Example: Preparing for Missing Data Handling; 1.7 Older Missing Data Methods; 1.8 Comparing Missing Data Methods via Simulation; 1.9 Planned Missing Data; 1.10 Power Analyses for Planned Missingness Designs; 1.11 Summary and Recommended Readings; 2.- , Maximum Likelihood Estimation; 2.1 Chapter Overview; 2.2 Probability Distributions versus Likelihood Functions; 2.3 The Univariate Normal Distribution; 2.4 Estimating Unknown Parameters; 2.5 Getting an Analytic Solution; 2.6 Estimating Standard Errors; 2.7 Information Matrix and Parameter Covariance Matrix; 2.8 Alternative Approaches to Estimating Standard Errors; 2.9 Iterative Optimization Algorithms; 2.10 Linear Regression; 2.11 Significance Tests; 2.12 Multivariate Normal Data; 2.13 Categorical Outcomes: Logistic and Probit Regression; 2.14 Summary and Recommended Readings; 3.- , Maximum Likelihood Estimation with Missing Data; 3.1 Chapter Overview; 3.2 The Multivariate Normal Distribution Revisited; 3.3 How Do Incomplete Data Records Help?; 3.4 Standard Errors with Incomplete Data; 3.5 The Expectation Maximization Algorithm; 3.6 Linear Regression; 3.7 Significance Testing; 3.8 Interaction Effects; 3.9 Curvilinear Effects; 3.10 Auxiliary Variables; 3.11 Categorical Outcomes; 3.12 Summary and Recommended Readings; 4. Bayesian Estimation; 4.1 Chapter Overview; 4.2 What Makes Bayesian Statistics Different?; 4.3 Conceptual Overview of Bayesian Estimation; 4.4 Bayes’ Theorem; 4.5 The Univariate Normal Distribution; 4.6 MCMC Estimation with the Gibbs Sampler; 4.7 Estimating the Mean and Variance with MCMC; 4.8 Linear Regression; 4.9 Assessing Convergence of the Gibbs Sampler; 4.10 Multivariate Normal Data; 4.11 Summary and Recommended Readings; 5.- , Bayesian Estimation with Missing Data; 5.1 Chapter Overview; 5.2 Imputing an Incomplete Outcome Variable; 5.3 Linear Regression; 5.4 Interaction Effects; 5.5 Inspecting Imputations; 5.6 The Metropolis–Hastings Algorithm; 5.7 Curvilinear Effects; 5.8 Auxiliary Variables; 5.9 Multivariate Normal Data; 5.10 Summary and Recommended Readings; 6. Bayesian Estimation for Categorical Variables; 6.1 Chapter Overview; 6.2 Latent Response Formulation for Categorical Variables; 6.3 Regression with a Binary Outcome; 6.4 Regression with an Ordinal Outcome; 6.5 Binary and Ordinal Predictor Variables; 6.6 Latent Response Formulation for Nominal Variables; 6.7 Regression with a Nominal Outcome; 6.8 Nominal Predictor Variables; 6.9 Logistic Regression; 6.10 Summary and Recommended Readings; 7.- , Multiple Imputation; 7.1 Chapter Overview; 7.2 Agnostic versus Model-Based Multiple Imputation; 7.3 Joint Model Imputation; 7.4 Fully Conditional Specification; 7.5 Analyzing Multiply-Imputed Data Sets; 7.6 Pooling Parameter Estimates; 7.7 Pooling Standard Errors; 7.8 Test Statistic and Confidence Intervals; 7.9 When Might Multiple Imputation Give Different Answers?; 7.10 Interaction and Curvilinear Effects Revisited; 7.11 Model-Based Imputation; 7.12 Multivariate Significance Tests; 7.13 Summary and Recommended Readings; 8. Multilevel Missing Data; 8.1 Chapter Overview; 8.2 Random Intercept Regression Models; 8.3 Random Coefficient Models; 8.4 Multilevel Interaction Effects; 8.5 Three-Level Models; 8.6 Multiple Imputation; 8.7 Joint Model Imputation; 8.8 Fully Conditional Specification Imputation; 8.9 Maximum Likelihood Estimation; 8.10 Summary and Recommended Readings; 9.- , Missing Not at Random Processes; 9.1 Chapter Overview; 9.2 Missing Not at Random Processes Revisited; 9.3 Major Modeling Frameworks; 9.4 Selection Models for Multiple Regression; 9.5 Model Comparisons and Individual Influence Diagnostics; 9.6 Selection Model Analysis Examples; 9.7 Pattern Mixture Models for Multiple Regression; 9.8 Pattern Mixture Model Analysis Examples; 9.9 Longitudinal Data Analyses; 9.10 Diggle–Kenward Selection Model; 9.11 Shared Parameter (Random Coefficient) Selection Model; 9.12 Random Coefficient Pattern Mixture Models; 9.13 Longitudinal Data Analysis Examples; 9.14 Summary and Recommended Readings; 10.- , Special Topics and Applications; 10.1 Chapter Overview; 10.2 Descriptive Summaries, Correlations, and Subgroups; 10.3 Non-Normal Predictor Variables; 10.4 Non-Normal Outcome Variables; 10.5 Mediation and Indirect Effects; 10.6 Structural Equation Models; 10.7 Scale Scores and Missing Questionnaire Items; 10.8 Interactions with Scales; 10.9 Longitudinal Data Analyses; 10.10 Regression with a Count Outcome; 10.11 Power Analyses for Growth Models with Missing Data; 10.12 Summary and Recommended Readings; 11. Wrap-Up; 11.1 Chapter Overview; 11.2 Choosing a Missing Data-Handling Procedure; 11.3 Software Landscape; 11.4 Reporting Results from a Missing Data Analysis; 11.5 Final Thoughts and Recommended Readings; Appendix. Data Set Descriptions; Author Index; Subject Index; About the Author;

Language: English

Subjects: Psychology , Sociology

Keywords: Sozialwissenschaften ; Statistik ; Methodologie ; Fehlende Daten ; Datenauswertung

URL: Inhaltsverzeichnis

UID:

Format: 1 online resource (viii, 424 pages) : , digital, PDF file(s).

ISBN: 1-108-62032-9 , 1-108-61736-0 , 1-108-75552-6

Content: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

Note: Title from publisher's bibliographic system (viewed on 29 Jan 2020). , Cover -- Half-title -- Title page -- Copyright information -- Contents -- 1 Introduction -- 2 High-Dimensional Space -- 2.1 Introduction -- 2.2 The Law of Large Numbers -- 2.3 The Geometry of High Dimensions -- 2.4 Properties of the Unit Ball -- 2.4.1 Volume of the Unit Ball -- 2.4.2 Volume near the Equator -- 2.5 Generating Points Uniformly at Random from a Ball -- 2.6 Gaussians in High Dimension -- 2.7 Random Projection and Johnson-Lindenstrauss Lemma -- 2.8 Separating Gaussians -- 2.9 Fitting a Spherical Gaussian to Data -- 2.10 Bibliographic Notes -- 2.11 Exercises -- 3 Best-Fit Subspaces and Singular Value Decomposition (SVD) -- 3.1 Introduction -- 3.2 Preliminaries -- 3.3 Singular Vectors -- 3.4 Singular Value Decomposition (SVD) -- 3.5 Best Rank-k Approximations -- 3.6 Left Singular Vectors -- 3.7 Power Method for Singular Value Decomposition -- 3.7.1 A Faster Method -- 3.8 Singular Vectors and Eigenvectors -- 3.9 Applications of Singular Value Decomposition -- 3.9.1 Centering Data -- 3.9.2 Principal Component Analysis -- 3.9.3 Clustering a Mixture of Spherical Gaussians -- 3.9.4 Ranking Documents and Web Pages -- 3.9.5 An Illustrative Application of SVD -- 3.9.6 An Application of SVD to a Discrete Optimization Problem -- 3.10 Bibliographic Notes -- 3.11 Exercises -- 4 Random Walks and Markov Chains -- 4.1 Stationary Distribution -- 4.2 Markov Chain Monte Carlo -- 4.2.1 Metropolis-Hasting Algorithm -- 4.2.2 Gibbs Sampling -- 4.3 Areas and Volumes -- 4.4 Convergence of Random Walks on Undirected Graphs -- 4.4.1 Using Normalized Conductance to Prove Convergence -- 4.5 Electrical Networks and Random Walks -- 4.6 Random Walks on Undirected Graphs with Unit Edge Weights -- 4.6.1 Hitting Time -- 4.6.2 Commute Time -- 4.6.3 Cover Time -- 4.7 Random Walks in Euclidean Space -- 4.7.1 Random Walks on Lattices -- 4.7.2 Two Dimensions. , 4.7.3 Three Dimensions -- 4.8 The Web as a Markov Chain -- 4.8.1 Pagerank -- 4.8.2 Relation to Hitting Time -- 4.8.3 Spam -- 4.8.4 Personalized Pagerank -- 4.8.5 Algorithm for Computing Personalized Pagerank -- 4.9 Bibliographic Notes -- 4.10 Exercises -- 5 Machine Learning -- 5.1 Introduction -- 5.1.1 The Core Problem -- 5.1.2 How to Learn -- 5.2 The Perceptron Algorithm -- 5.3 Kernel Functions and Nonlinearly Separable Data -- 5.4 Generalizing to New Data -- 5.4.1 Overfitting and Uniform Convergence -- 5.4.2 Occam's Razor -- 5.4.3 Regularization: Penalizing Complexity -- 5.5 VC-Dimension -- 5.5.1 Definitions and Key Theorems -- 5.5.2 VC-Dimension of Some Set Systems -- 5.5.3 Shatter Function for Set Systems of Bounded VC-Dimension -- 5.5.4 VC-Dimension of Combinations of Concepts -- 5.5.5 The Key Theorem -- 5.6 VC-Dimension and Machine Learning -- 5.7 Other Measures of Complexity -- 5.8 Deep Learning -- 5.8.1 Generative Adversarial Networks (GANs) -- 5.9 Gradient Descent -- 5.9.1 Stochastic Gradient Descent -- 5.9.2 Regularizer -- 5.10 Online Learning -- 5.10.1 An Example: Learning Disjunctions -- 5.10.2 The Halving Algorithm -- 5.10.3 The Perceptron Algorithm -- 5.10.4 Inseparable Data and Hinge Loss -- 5.10.5 Online to Batch Conversion -- 5.10.6 Combining (Sleeping) Expert Advice -- 5.11 Boosting -- 5.12 Further Current Directions -- 5.12.1 Semi-Supervised Learning -- 5.12.2 Active Learning -- 5.12.3 Multitask Learning -- 5.13 Bibliographic Notes -- 5.14 Exercises -- 6 Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling -- 6.1 Introduction -- 6.2 Frequency Moments of Data Streams -- 6.2.1 Number of Distinct Elements in a Data Stream -- 6.2.2 Number of Occurrences of a Given Element -- 6.2.3 Frequent Elements -- 6.2.4 The Second Moment -- 6.3 Matrix Algorithms Using Sampling -- 6.3.1 Matrix Multiplication Using Sampling. , 6.3.2 Implementing Length Squared Sampling in Two Passes -- 6.3.3 Sketch of a Large Matrix -- 6.4 Sketches of Documents -- 6.5 Bibliographic Notes -- 6.6 Exercises -- 7 Clustering -- 7.1 Introduction -- 7.1.1 Preliminaries -- 7.1.2 Two General Assumptions on the Form of Clusters -- 7.1.3 Spectral Clustering -- 7.2 k-Means Clustering -- 7.2.1 A Maximum-Likelihood Motivation -- 7.2.2 Structural Properties of the k-Means Objective -- 7.2.3 Lloyd's Algorithm -- 7.2.4 Ward's Algorithm -- 7.2.5 k-Means Clustering on the Line -- 7.3 k-Center Clustering -- 7.4 Finding Low-Error Clusterings -- 7.5 Spectral Clustering -- 7.5.1 Why Project? -- 7.5.2 The Algorithm -- 7.5.3 Means Separated by Ω(1) Standard Deviations -- 7.5.4 Laplacians -- 7.5.5 Local Spectral Clustering -- 7.6 Approximation Stability -- 7.6.1 The Conceptual Idea -- 7.6.2 Making This Formal -- 7.6.3 Algorithm and Analysis -- 7.7 High-Density Clusters -- 7.7.1 Single Linkage -- 7.7.2 Robust Linkage -- 7.8 Kernel Methods -- 7.9 Recursive Clustering Based on Sparse Cuts -- 7.10 Dense Submatrices and Communities -- 7.11 Community Finding and Graph Partitioning -- 7.12 Spectral Clustering Applied to Social Networks -- 7.13 Bibliographic Notes -- 7.14 Exercises -- 8 Random Graphs -- 8.1 The G(n,p) Model -- 8.1.1 Degree Distribution -- 8.1.2 Existence of Triangles in G(n, d/n) -- 8.2 Phase Transitions -- 8.3 Giant Component -- 8.3.1 Existence of a Giant Component -- 8.3.2 No Other Large Components -- 8.3.3 The Case of p < -- 1/n -- 8.4 Cycles and Full Connectivity -- 8.4.1 Emergence of Cycles -- 8.4.2 Full Connectivity -- 8.4.3 Threshold for O(ln n) Diameter -- 8.5 Phase Transitions for Increasing Properties -- 8.6 Branching Processes -- 8.7 CNF-SAT -- 8.7.1 SAT-Solvers in Practice -- 8.7.2 Phase Transitions for CNF-SAT -- 8.8 Nonuniform Models of Random Graphs. , 8.8.1 Giant Component in Graphs with Given Degree Distribution -- 8.9 Growth Models -- 8.9.1 Growth Model without Preferential Attachment -- 8.9.2 Growth Model with Preferential Attachment -- 8.10 Small-World Graphs -- 8.11 Bibliographic Notes -- 8.12 Exercises -- 9 Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models -- 9.1 Topic Models -- 9.2 An Idealized Model -- 9.3 Nonnegative Matrix Factorization -- 9.4 NMF with Anchor Terms -- 9.5 Hard and Soft Clustering -- 9.6 The Latent Dirichlet Allocation Model for Topic Modeling -- 9.7 The Dominant Admixture Model -- 9.8 Formal Assumptions -- 9.9 Finding the Term-Topic Matrix -- 9.10 Hidden Markov Models -- 9.11 Graphical Models and Belief Propagation -- 9.12 Bayesian or Belief Networks -- 9.13 Markov Random Fields -- 9.14 Factor Graphs -- 9.15 Tree Algorithms -- 9.16 Message Passing in General Graphs -- 9.16.1 Graphs with a Single Cycle -- 9.16.2 Belief Update in Networks with a Single Loop -- 9.16.3 Maximum Weight Matching -- 9.17 Warning Propagation -- 9.18 Correlation between Variables -- 9.19 Bibliographic Notes -- 9.20 Exercises -- 10 Other Topics -- 10.1 Ranking and Social Choice -- 10.1.1 Randomization -- 10.1.2 Examples -- 10.2 Compressed Sensing and Sparse Vectors -- 10.2.1 Unique Reconstruction of a Sparse Vector -- 10.2.2 Efficiently Finding the Unique Sparse Solution -- 10.3 Applications -- 10.3.1 Biological -- 10.3.2 Low-Rank Matrices -- 10.4 An Uncertainty Principle -- 10.4.1 Sparse Vector in Some Coordinate Basis -- 10.4.2 A Representation Cannot Be Sparse in Both Time and Frequency Domains -- 10.5 Gradient -- 10.6 Linear Programming -- 10.6.1 The Ellipsoid Algorithm -- 10.7 Integer Optimization -- 10.8 Semi-Definite Programming -- 10.9 Bibliographic Notes -- 10.10 Exercises -- 11 Wavelets -- 11.1 Dilation -- 11.2 The Haar Wavelet. , 11.3 Wavelet Systems -- 11.4 Solving the Dilation Equation -- 11.5 Conditions on the Dilation Equation -- 11.6 Derivation of the Wavelets from the Scaling Function -- 11.7 Sufficient Conditions for the Wavelets to Be Orthogonal -- 11.8 Expressing a Function in Terms of Wavelets -- 11.9 Designing a Wavelet System -- 11.10 Applications -- 11.11 Bibliographic Notes -- 11.12 Exercises -- 12 Background Material -- 12.1 Definitions and Notation -- 12.1.1 Integers -- 12.1.2 Substructures -- 12.1.3 Asymptotic Notation -- 12.2 Useful Relations -- 12.3 Useful Inequalities -- 12.4 Probability -- 12.4.1 Sample Space, Events, and Independence -- 12.4.2 Linearity of Expectation -- 12.4.3 Union Bound -- 12.4.4 Indicator Variables -- 12.4.5 Variance -- 12.4.6 Variance of the Sum of Independent Random Variables -- 12.4.7 Median -- 12.4.8 The Central Limit Theorem -- 12.4.9 Probability Distributions -- 12.4.10 Bayes Rule and Estimators -- 12.5 Bounds on Tail Probability -- 12.5.1 Chernoff Bounds -- 12.5.2 More General Tail Bounds -- 12.6 Applications of the Tail Bound -- 12.7 Eigenvalues and Eigenvectors -- 12.7.1 Symmetric Matrices -- 12.7.2 Relationship between SVD and Eigen Decomposition -- 12.7.3 Extremal Properties of Eigenvalues -- 12.7.4 Eigenvalues of the Sum of Two Symmetric Matrices -- 12.7.5 Norms -- 12.7.6 Important Norms and Their Properties -- 12.7.7 Additional Linear Algebra -- 12.7.8 Distance between Subspaces -- 12.7.9 Positive Semi-Definite Matrix -- 12.8 Generating Functions -- 12.8.1 Generating Functions for Sequences Defined by Recurrence Relationships -- 12.8.2 The Exponential Generating Function and the Moment Generating Function -- 12.9 Miscellaneous -- 12.9.1 Lagrange Multipliers -- 12.9.2 Finite Fields -- 12.9.3 Application of Mean Value Theorem -- 12.10 Exercises -- References -- Index.

Additional Edition: Online version: Blum, Avrim, 1966- Foundations of data science New York, NY : Cambridge University Press, 2020. ISBN 9781108755528

Additional Edition: ISBN 9781108485067

Additional Edition: ISBN 1108485065

Language: English

Subjects: Computer Science

URL: https://doi.org/10.1017/9781108755528

URL: Volltext  (lizenzpflichtig)

UID:

Format: 1 online resource (819 p.)

Edition: 1st ed.

ISBN: 1-281-05756-8 , 9786611057565 , 0-08-053696-4

Content: This is the first comprehensive book for all aspects of monodispersed particles, consisting of four parts: Part 1 for the fundamentals of the elementary processes; Part 2 for the preparation of monodispersed particles, including the general principles, explanations of almost all known monodispersed systems on the basis of their classification, and techniques for controlling their mean size, shape, internal structure, composition, heterojunction, surface modification, etc.; Part 3 for the analytical methods for the formation processes and the characterization of monodispersed products; Part 4 f

Note: Description based upon print version of record. , Front Cover; Monodispersed Particles; Copyright Page; Preface; Contents; PART 1: FUNDAMENTALS; Chapter 1. Nucleation; 1.1. Surface Energy; 1.2. Equilibrium Concentrations of Three-Dimensional Nuclei; 1.3. Nucleation Rate; 1.4. The Nucleation Process in Closed Monodisperse Systems; 1.5. The Nucleation Process in Open Monodisperse Systems; References; Chapter 2. Growth; 2.1. Equilibrium Concentrations of Two-Dimensional Nuclei; 2.2. Nucleation Rate of Two-Dimensional Nuclei; 2.3. Growth Rate by Surface Reaction; 2.4. Growth of Polyhedral Particles by Surface Reaction , 2.5. Diffusion-Controlled and Reaction-Controlled Growth Modes2.6. Criteria for the Distinction of Growth Modes; Chapter 3. Habit Formation; 3.1. Surface Chemical Potential of a Crystal Face; 3.2. Stable Forms; References; References; Chapter 4. Recrystallization; 4.1. Phase Transformation; 4.2. Ostwald Ripening; 4.3. Self-Recrystallization; 4.4. Reversed Ostwald Ripening; 4.5. Contact Recrystallization; References; Chapter 5. Solid-Solution Formation; 5.1. Equilibrium Compositions; 5.2. Conversion by Intra-Particle Recrystallization; References; PART 2: PREPARATION , Chapter 6. General Principles for the Formation of Monodispersed Particles6.1. Separation of the Nucleation and Growth Stages; 6.2. Inhibition of Random Coagulation; 6.3. Reserve of Monomers; 6.4. Choice of Growth Modes; 6.5. Introduction of Alternative Mechanisms; References; Chapter 7. Monodispersed Systems; 7.1. Classification of Monodispersed Systems; 7.2. Homogeneous Systems; 7.3. Heterogeneous Systems; References; Chapter 8. Control of Particle Characteristics; 8.1. Size Control; 8.2. Shape Control; 8.3. Internal Structure Control; 8.4. Composition Control , 8.5. Layered Structure Control8.6. Hollow Structure Control; 8.7. Porous Structure Control; 8.8. Heterojunction; References; PART 3: ANALYSES; Chapter 9. Analyses of Formation Processes; 9.1. Electron Microscopy; 9.2. X-ray Diffractometry; 9.3. Infrared Spectroscopy; 9.4. Ultraviolet-Visible Spectroscopy; 9.5. Potentiometry; 9.6. Inductively Coupled Plasma Spectrometry; 9.7. Gas Chromatography; 9.8. Ion Chromatography; 9.9. Radiochemical Analysis; 9.10. Seeding Analysis; References; Chapter 10. Characterization of Products; 10.1. Transmission Electron Microscopy , 10.2. Scanning Electron Microscopy10.3. Electron Diffractometry; 10.4. Energy Dispersive X-ray Spectrometry; 10.5. Powder X-ray Diffractometry; 10.6. Oriented Particulate Monolayer X-ray Diffractometry; 10.7. X-ray Photoelectron Spectroscopy; 10.8. Infrared Spectroscopy; 10.9. Ultraviolet-Visible Spectroscopy; 10.10. Photon Correlation Spectroscopy; 10.11. Turbidimetry; 10.12. Coulter Principle; References; PART 4: APPLICATIONS; Chapter 11. Application to Fundamental Studies; 11.1. Determination of the Avogadro Number; 11.2. Measurement of Zeta-Potential , 11.3. Determination of Hamaker Constants , English

Additional Edition: ISBN 0-444-62750-2

Additional Edition: ISBN 0-444-89569-8

Language: English

UID:

Format: 1 online resource (465 p.)

Edition: 2nd ed.

ISBN: 0-323-22168-8

Series Statement: Plastics Design Library

Content: Handbook of Thermoplastic Elastomers, Second Edition presents a comprehensive working knowledge of thermoplastic elastomers (TPEs), providing an essential introduction for those learning the basics, but also detailed engineering data and best practice guidance for those already involved in polymerization, processing, and part manufacture. TPEs use short, cost-effective production cycles, with reduced energy consumption compared to other polymers, and are used in a range of industries including automotive, medical, construction and many more. This handbook provides all the prac

Note: Description based upon print version of record. , Front Cover; HANDBOOK OF THERMOPLASTIC ELASTOMERS; Copyright; Dedication; Contents; Preface to the Second Edition; Preface to the First Edition; Acknowledgments; 1 - Introduction; 1.1 Elasticity and Elastomers; 1.2 Thermoplastic Elastomers; References; 2 - Brief History of Thermoplastic Elastomers; References; 3 - Additives; 3.1 Antioxidants; 3.2 Light Stabilizers; 3.3 Nucleating Agents; 3.4 Flame Retardants; 3.5 Colorants; 3.6 Antistatic Agents; 3.7 Slip Agents; 3.8 Antiblocking Agents; 3.9 Processing Aids; 3.10 Fillers and Reinforcements; 3.11 Plasticizers; 3.12 Other Additives , 3.13 Selection of Additives3.14 Health, Hygiene, and Safety; References; 4 - Processing Methods Applicable to Thermoplastic Elastomers; 4.1 Introduction; 4.2 Mixing and Blending; 4.3 Extrusion; 4.4 Injection Molding; 4.5 Compression Molding; 4.6 Transfer Molding; 4.7 Blow Molding; 4.8 Rotational Molding; 4.9 Foaming of Thermoplastics; 4.10 Thermoforming; 4.11 Calendering; 4.12 Secondary Manufacturing Processes; 4.13 General Processing Technology of Thermoplastic Elastomers; 4.14 Process Simulation; 4.15 3D Printing; 4.16 Product Development and Testing; References , 5 - Styrenic Block Copolymers5.1 Introduction; 5.2 Polystyrene-Polydiene Block Copolymers; 5.3 Styrenic Block Copolymers Synthesized by Carbocationic Polymerization; 5.4 New Commercial Developments; References; 6 - Thermoplastic Elastomers Prepared by Dynamic Vulcanization; 6.1 Introduction; 6.2 The Dynamic Vulcanization Process; 6.3 Properties of Blends Prepared by Dynamic Vulcanization; 6.4 Processing and Fabrication of Thermoplastic Vulcanizates; 6.5 New Commercial Developments; References; 7 - Polyolefin-Based Thermoplastic Elastomers; 7.1 Introduction; 7.2 Thermoplastic Polyolefin Blends , 7.3 Morphology7.4 Properties of TPOs; 7.5 Processing of TPOs; 7.6 Painting of TPOs; 7.7 New Commercial Developments; References; 8 - Thermoplastic Elastomers Based on Halogen-Containing Polyolefins; 8.1 Introduction; 8.2 Blends of PVC with Nitrile Rubber; 8.3 Blends of PVC with Other Elastomers; 8.4 Melt-Processable Rubber; 8.5 Thermoplastic Fluorocarbon Elastomer; 8.6 New Commercial Development; References; 9 - Thermoplastic Polyurethane Elastomers; 9.1 Introduction; 9.2 Synthesis of TPUs; 9.3 Morphology; 9.4 Thermal Transitions; 9.5 Properties; 9.6 Processing of TPUs , 9.7 Blends of TPUs with Other Polymers9.8 Bonding and Welding; 9.9 Use of Bio-Based Raw Materials in TPUs; 9.10 New Commercial Development; References; 10 - Thermoplastic Elastomers Based on Polyamides; 10.1 Introduction; 10.2 Synthesis; 10.3 Morphology; 10.4 Structure-Property Relationships; 10.5 Physical and Mechanical Properties; 10.6 Chemical and Solvent Resistance; 10.7 Electrical Properties; 10.8 Other Properties; 10.9 Compounding; 10.10 Processing; 10.11 Bonding and Welding; 10.12 New Commercial Developments; References; 11 - Thermoplastic Polyether Ester Elastomers; 11.1 Introduction , 11.2 Synthesis , English

Additional Edition: ISBN 0-323-22136-X

Additional Edition: ISBN 1-306-85975-1

Language: English

UID:

Format: 1 online resource (xii, 558 pages) : , digital, PDF file(s).

ISBN: 1-139-16670-0

Series Statement: Cambridge textbooks in linguistics

Content: Andrew Radford's textbook is written for students with little or no background in syntax, and introduces them to key concepts of Chomsky's minimalist programme (e.g. merger and movement, checking, economy and greed, split VPs, agreement projections), as well as providing detailed analysis of the syntax of a range of different construction types (e.g. interrogatives, negatives, passives, unaccusatives, complement clauses). Illustrative material is drawn from varieties of English (Standard English, Belfast English, Shakespearean English, Jamaican Creole and Child English). There is a substantial glossary and an extensive integral workbook section at the end of each chapter with helpful hints and model answers, which aim to get students to analyse phrases and sentences for themselves within a minimalist framework.

Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , Cover -- Half-title -- Series-title -- Title -- Copyright -- Contents -- Preface -- Principles and parameters -- 1.1 Overview -- 1.2 Grammar -- 1.3 Criteria of adequacy -- 1.4 Language faculty -- 1.5 Creativity -- 1.6 Principles -- 1.7 Parameters -- 1.8 Parameter-setting -- 1.9 Evidence -- 1.10 Summary -- Workbook section -- Categories and features -- 2.1 Overview -- 2.2 Morphological evidence -- 2.3 Syntactic evidence -- 2.4 Functional categories: determiners and pronouns -- 2.5 Auxiliaries and infinitival to -- 2.6 Complementizers -- 2.7 Parsing -- 2.8 Subcategorial features -- 2.9 Cross-categorial features -- 2.10 Summary -- Workbook section -- Syntactic structure -- 3.1 Overview -- 3.2 Forming phrases -- 3.3 Specifiers -- 3.4 Forming sentences -- 3.5 Tree diagrams -- 3.6 Configurational relations -- 3.7 Testing structure -- 3.8 Additional tests -- 3.9 C-command -- 3.10 Summary -- Workbook section -- Empty categories -- 4.1 Overview -- 4.2 PRO subjects -- 4.3 Null auxiliaries -- 4.4 Unfilled INFL -- 4.5 Bare infinitives -- 4.6 Null complementizers -- 4.7 Null determiners -- 4.8 Pronouns -- 4.9 Attributive adjectives -- 4.10 Summary -- Workbook section -- Checking -- 5.1 Overview -- 5.2 Interpretable and uninterpretable features -- 5.3 Checking -- 5.4 Phrases -- 5.5 Percolation -- 5.6 Determiner phrases -- 5.7 PRO subjects -- 5.8 Objective subjects -- 5.9 Bare phrase structure -- 5.10 Summary -- Workbook section -- Head movement -- 6.1 Overview -- 6.2 Auxiliary inversion -- 6.3 Traces -- 6.4 Verb movement -- 6.5 Strong and weak features -- 6.6 Negation -- 6.7 The syntax of have -- 6.8 Tense affix -- 6.9 Question affix -- 6.10 Summary -- Workbook section -- Operator movement -- 7.1 Overview -- 7.2 Wh-operators -- 7.3 Enlightened self-interest and shortest movement -- 7.4 Pied-piping -- 7.5 Embedded questions in Belfast English. , 7.6 Embedded questions in Standard English -- 7.7 Subject questions -- 7.8 Yes-no questions -- 7.9 Nonoperator questions -- 7.10 Summary -- Workbook section -- A movement -- 8.1 Overview -- 8.2 VP-internal subject hypothesis -- 8.3 Evidence that subjects originate in spec-VP -- 8.4 Argument structure and theta-marking -- 8.5 Case-checking via head-adjunction and attraction -- 8.6 Raising predicates -- 8.7 Differences between raising and control predicates -- 8.8 Passivization -- 8.9 Explanation -- 8.10 Summary -- Workbook section -- VP shells -- 9.1 Overview -- 9.2 Ergative predicates -- 9.3 Adverbs and prepositional particles -- 9.4 Ditransitive and resultative predicates -- 9.5 Three-place predicates with clausal complements -- 9.6 Object-control predicates -- 9.7 Monotransitive predicates -- 9.8 Unergative predicates -- 9.9 Unaccusative predicates -- 9.10 Summary -- Workbook section -- Agreement projections -- 10.1 Overview -- 10.2 Subject agreement projections -- 10.3 Evidence from other varieties of English -- 10.4 Object agreement projections -- 10.5 Exceptional case-marking -- 10.6 Indirect object agreement projections -- 10.7 Genitive DPs -- 10.8 For-infinitives and prepositional objects -- 10.9 Passives and unaccusatives reconsidered -- 10.10 Summary -- Workbook section -- Glossary and list of abbreviations -- References -- Index.

Additional Edition: ISBN 0-521-47707-7

Additional Edition: ISBN 0-521-47125-7

Language: English

URL: Volltext  (lizenzpflichtig)

URL: https://doi.org/10.1017/CBO9781139166706

UID:

Format: 1 online resource (553 p.)

Edition: 1st ed.

ISBN: 1-280-63063-9 , 9786610630639 , 0-08-045937-4

Content: Over the past decade, there has been rapid growth in bioengineering applications in the field of spine implants. This book explains the technical foundation for understanding and expanding the field of spine implants, reviews the major established technologies related to spine implants, and provides reference material for developing and commercializing new spine implants. The editors, who have a track record of collaboration and editing technical books, provide a unified approach to this topic in the most comprehensive and useful book to date.·Related website provides the latest info

Note: Description based upon print version of record. , Front cover; Title page; Copyright page; Table of contents; Contributors; Preface; 1: The Basic Tools and Terminology of Spine Treatment; 1.1 Introduction; 1.2 Which Way Is Up?; 1.3 The Spine; 1.4 Overview of the Handbook; 1.5 Acknowledgments; 1.6 References; 2: Synthetic Biomaterials for Spinal Applications; 2.1 Introduction; 2.2 Mechanical Properties and Mechanical Testing; 2.3 Metals and Metal Alloys; 2.4 Ceramics; 2.5 Polymers; 2.6 Composites; 2.7 Biological Effects; 2.8 Biocompatibility Testing; 2.9 Summary and Conclusions; 2.10 References , 3: Structure and Properties of Soft Tissues in the Spine3.1 Introduction; 3.2 Intervertebral Discs; 3.3 Intervertebral Disc Aging and Degeneration; 3.4 Ligaments; 3.5 Spinal Cord; 3.6 Conclusions; 3.7 References; 3.8 Review Questions; 4: Biomechanics of Vertebral Bone; 4.1 Introduction; 4.2 Trabecular Bone; 4.3 Mechanical Behavior of the Vertebral Body; 4.4 Noninvasive Vertebral Strength Assessment; 4.5 Acknowledgments; 4.6 References; 5: Musculature Actuation and Biomechanics of the Spine; 5.1 Spine Muscles; 5.2 Spinal Loading Estimation Techniques; 5.3 Spinal Loads During Various Activities , 5.4 References6: Spine Disorders: Implications for Bioengineers; 6.1 Introduction; 6.2 Scoliosis; 6.3 Osteoporosis; 6.4 Cancer: Metastatic Spine Tumor; 6.5 Rheumatoid Arthritis; 6.6 Trauma: Whiplash Injury; 6.7 References; 7: Historical Review of Spinal Instrumentation for Fusion: Rods, Plates, Screws, and Cages; 7.1 Thoraco Lumbar and Lumbo-Sacral; 7.2 Anterior Instrumentation; 7.3 Intervertebral Body Cages: Cervical and Lumbar; 7.4 Cervical; 7.5 Summary; 7.6 Acknowledgments; 7.7 References; 8: Clinical Performance of Rods, Plates, Screws, and Cages; 8.1 Introduction , 8.2 Anterior Applications8.3 Posterior Systems: Rods and Screws; 8.4 Intervertebral Body Devices: Cages; 8.5 Conclusions; 8.6 Acknowledgments; 8.7 References; 9: Biologics to Promote Spinal Fusion; 9.1 Introduction; 9.2 Osteoinductive Bone Graft Substitutes; 9.3 Bone Morphogenetic Proteins; 9.4 Demineralized Bone Matrix; 9.5 DBM: Pre-clinical Studies; 9.6 DBM: Clinical Investigations; 9.7 rhBMP-7 (rhOP-1): Preclinical Studies; 9.8 rhBMP-7 (rhOP-1): Clinical Investigations; 9.9 rhBMP-2 (INFUSE): Pre-clinical Studies; 9.10 rhBMP-2 (INFUSE): Clinical Investigations; 9.11 Calcium Sulfate , 9.12 Hydroxyapatite9.13 Tricalcium Phosphates; 9.14 Biphasic Calcium Phosphate (BCP); 9.15 Calcium Phosphate/Collagen Composite Matrices; 9.16 Conclusions; 9.17 References; 10: Nucleus Replacement of the Intervertebral Disc; 10.1 Introduction; 10.2 Intervertebral Disc; 10.3 Degenerative Disc Disease: Etiology; 10.4 Current Treatments for Degenerative Disc Disease; 10.5 Total Disc Replacement; 10.6 Nucleus Pulposus Replacement; 10.7 Historical Design Perspective; 10.8 Recent Design Concepts; 10.9 Future Directions; 10.10 References; 11: Total Disc Arthroplasty; 11.1 Introduction , 11.2 Pioneers of Total Disc Arthroplasty , English

Additional Edition: ISBN 0-12-369390-X

Language: English

UID:

Format: 1 online resource (1519 p.)

Edition: 6th ed.

ISBN: 1-281-79591-7 , 9786611795917 , 0-08-052354-4 , 1-60119-452-8

Content: For ease of use, this edition has been divided into the following subject sections: general principles; materials and processes; control, power electronics and drives; environment; power generation; transmission and distribution; power systems; sectors of electricity use.New chapters and major revisions include: industrial instrumentation; digital control systems; programmable controllers; electronic power conversion; environmental control; hazardous area technology; electromagnetic compatibility; alternative energy sources; alternating current generators; electromagnetic transients;

Note: Previous ed. edited by G.R. Jones, M.A. Laughton, M.G. Say. 1993. , Front Cover; Electrical Engineer's Reference Book; Copyright Page; Contents; Preface; Section A: General Principles; Chapter 1. Units, Mathematics and Physical Quantities; 1.1 International unit system; 1.2 Mathematics; 1.3 Physical quantities; 1.4 Physical properties; 1.5 Electricity; Chapter 2. Electrotechnoloy; 2.1 Nomenclature; 2.2 Thermal effects; 2.3 Electrochemical effects; 2.4 Magnetic field effects; 2.5 Electric field effects; 2.6 Electromagnetic field effects; 2.7 Electrical discharges; Chapter 3. Network Analysis; 3.1 Introduction; 3.2 Basic network analysis , 3.3 Power-system network analysis Section B: Materials & Processes; Chapter 4. Fundamental Properties of Materials; 4.1 Introduction; 4.2 Mechanical properties; 4.3 Thermal properties; 4.4 Electrically conducting materials; 4.5 Magnetic materials; 4.6 Dielectric materials; 4.7 Optical materials; 4.8 The plasma state; Chapter 5. Conductors Superconductors; 5.1 Conducting materials; 5.2 Superconductors; Chapter 6. Semiconductor ,Thick and Thin-Film Microcircuits; 6.1 Silicon, silicon dioxide, thick- and thin-film technology; 6.2 Thick- and thin-film microcircuits; Chapter 7. Insulation , 7.1 Insulating materials 7.2 Properties and testing; 7.3 Gaseous dielectrics; 7.4 Liquid dielectrics; 7.5 Semi-fluid and fusible materials; 7.6 Varnishes, enamels, paints and lacquers; 7.7 Solid dielectrics; 7.8 Composite solid/liquid dielectrics; 7.9 Irradiation effects; 7.10 Fundamentals of dielectric theory; 7.11 Polymeric insulation for high voltage outdoor applications; Chapter 8. Magnetic Materials; 8.1 Ferromagnetics; 8.2 Electrical steels including silicon steels; 8.3 Soft irons and relay steels; 8.4 Ferrites; 8.5 Nickel-iron alloys; 8.6 Iron-cobalt alloys , 8.7 Permanent magnet materials Chapter 9. Electroheat and Materials Processing; 9.1 Introduction; 9.2 Direct resistance heating; 9.3 Indirect resistance heating; 9.4 Electric ovens and furnaces; 9.5 Induction heating; 9.6 Metal melting; 9.7 Dielectric heating; 9.8 Ultraviolet processes; 9.9 Plasma torches; 9.10 Semiconductor plasma processing; 9.11 Lasers; Chapter 10. Welding and Soldering; 10.1 Arc welding; 10.2 Resistance welding; 10.3 Fuses; 10.4 Contacts; 10.5 Special alloys; 10.6 Solders; 10.7 Rare and precious metals; 10.8 Temperature-sensitive bimetals; 10.9 Nuclear-reactor materials , 10.10 Amorphous materials Section C: Control; Chapter 11. Electrical Measurement; 11.1 Introduction; 11.2 Terminology; 11.3 The role of measurement traceability in product quality; 11.4 National and international measurement standards; 11.5 Direct-acting analogue measuring instruments; 11.6 Integrating (energy) metering; 11.7 Electronic instrumentation; 11.8 Oscilloscopes; 11.9 Potentiometers and bridges; 11.10 Measuring and protection transformers; 11.11 Magnetic measurements; 11.12 Transducers; 11.13 Data recording; Chapter 12. Industrial Instrumentation; 12.1 Introduction; 12.2 Temperature , 12.3 Flow , English

Additional Edition: ISBN 0-7506-4637-3

Language: English

UID:

Format: 1 online resource (447 p.)

Edition: 1st ed.

ISBN: 1-280-72875-2 , 9786610728756 , 0-08-046719-9

Content: This unique book about bioscience and the bioengineering of titanium materials is based on more than 1,000 published articles. It bridges the gap between the medical/dental fields and the engineering/technology areas, due to the author's unique experience in both during the last 30 years. The book covers Materials Classifications, Chemical and Electrochemical Reactions, Oxidation, Biological Reactions, Implant-related Biological Reactions, Applications, Fabri-cation Technologies, Surface Modifications, and Future Perspectives.* Provides quick access to the primary literature in this fi

Note: Description based upon print version of record. , Cover; Bioscience and Bioengineering of Titanium Materials; Copyright page; Contents; Prologue; Chapter 1. Introduction; References; Chapter 2. Materials Classification; 2.1. General; 2.2. Medical/Dental Titanium and its Alloys; References; Chapter 3. Chemical and Electrochemical Reactions; 3.1. Discoloration; 3.2. Corrosion in Media Containing Fluorine Ion Bleaching Agents; 3.3. Corrosion Resistance, Effects of Environmental and Mechanical-Assisted Actions; 3.4. Metal Ion Release and Dissolution; 3.5. Galvanic Corrosion; 3.6. Microbiology-Induced Corrosion; 3.7. Reaction with Hydrogen , ReferencesChapter 4. Oxidation and Oxides; 4.1. Formation of Titanium Oxides; 4.2. Influences on Biological Process; 4.3. Crystal Structures of Ti Oxides; 4.4. Characterization of Oxides; 4.5. Unique Applications of Titanium Oxide; 4.6. Oxide Growth, Stability, and Breakdown; 4.7. Reaction with Hydrogen Peroxide; References; Chapter 5. Mechanical and Tribological Behaviors; 5.1. Fatigue; 5.2. Fracture and Fracture Toughness; 5.3. Biotribological Actions; References; Chapter 6. Biological Reaction; 6.1. Toxicity; 6.2. Cytocompatibility (Toxicity to Cells); 6.3. Allergic Reaction , 6.4. Metabolism6.5. Biocompatibility; 6.6. Biomechanical Compatibility; References; Chapter 7. Implant-Related Biological Reactions; 7.1. Bone Healing; 7.2. Hemocompatibility; 7.3. Cell Adhesion, Adsorption, Spreading, and Proliferation; 7.4. Roughness and Cellular Response to Biomaterials; 7.5. Cell Growth; 7.6. Tissue Reaction and Bone Ingrowth; 7.7. Osseointegration and Bone/Implant Interface; 7.8. Some Adverse Factors for Loosening Implants; References; Chapter 8. Implant Application; 8.1. General; 8.2. Clinical Reports; 8.3. Surface and Interface Characterization , 8.4. Reactions in Chemical and Mechanical Environments8.5. Reaction in Biological Environment; References; Chapter 9. Other Applications; 9.1. Denture Bases; 9.2. Crowns and Bridges; 9.3. Clasps; 9.4. Posts and Cores; 9.5. Titanium Fiber and Titanium Oxide Powder as Reinforcement for Bone Cement; 9.6. Sealer Material; 9.7. Shape-Memory Dental Implant; 9.8. Orthodontic Appliances; 9.9. Endodontic Files and Reamers; 9.10. Clamps and Staples; References; Chapter 10. Fabrication Technologies; 10.1. Casting; 10.2. Machining; 10.3. Electro Discharge Machining (EDM) and CAD/CAM , 10.4. Isothermal Forming10.5. Superplastic Forming (SPF); 10.6. Diffusion Bonding (DB); 10.7. Powder Metallurgy; 10.8. Metal Injection Molding (MIM); 10.9. Laser Welding/Forming; 10.10. Soldering; 10.11. Heat Treatment (HT); References; Chapter 11. Surface Modifications; 11.1. Sandblasting and Surface Texturing; 11.2. Shot-Peening and Laser-Peening; 11.3. Chemical, Electrochemical, and Thermal Modifications; 11.4. Coating; 11.5. Porosity Controlled Surface and Texturing; 11.6. Foamed Metal; 11.7. Coloring; References; Chapter 12. Future Perspectives , 12.1. Titanium Industry and New Materials Development , English

Additional Edition: ISBN 0-08-045142-X

Language: English

UID:

Format: 1 online resource (281 p.)

ISBN: 1-282-28982-9 , 9786612289828 , 0-08-095571-1

Series Statement: Mathematics in science and engineering ; v. 61, 2

Content: Methods of nonlinear analysis

Note: Description based upon print version of record. , Front Cover; Methods of Nonlinear Analysis; Copyright Page; Preface; Contents; Contents of Volume I; Chapter 9. Upper and Lower Bounds via Duality; 9.1. Introduction; 9.2. Guiding Idea; 9.3. A Simple Identity; 9.4. Quadratic Functional: Scalar Case; 9.5. min u J = max v H; 9.6. The Functional l t o [u'2 + g(u)] dt; 9.7. Geometric Aspects; 9.8. Multidimensional Case; 9.9. The Rayleigh-Ritz Method; 9.10. Alternative Approach; 9.11. J(u) = lto [u'2 + f(t)u2] dt; General f(t); 9.12. Geometric Aspects; Miscellaneous Exercises; Bibliography and Comments , Chapter 10. Caplygin's Method and Differential Inequalities10.1. Introduction; 10.2. The Caplygin Method; 10.3. The Equation u' 〈 a(t)u + f (t); 10.4. The Linear Differential Inequality L(u) 〈 f ( t ); 10.5. Elementary Approach; 10.6. An Integral Identity; 10.7. Strengthening of Previous Result; 10.8. Factorization of the Operator; 10.9. Alternate Proof of Monotonicity; 10.10. A Further Condition; 10.11. Two-point Boundary Conditions; 10.12. Variational Approach; 10.13. A Related Parabolic Partial Differential Equation; 10.14. Nonnegativity of u(t, s); 10.15. Limiting Behavior , 10.16. Limiting Behavior: Energy Inequalities10.17. Monotonicity of Maximum; 10.18. Lyapunov Functions; 10.19. Factorization of the nth-order Linear Operator; 10.20. A Result for the nth-order Linear Differential Equation; 10.21. An Example; 10.22. Linear Systems; 10.23. Partial Differential Equation-I; 10.24. Partial Differential Equation-II; Miscellaneous Exercises; Bibliography and Comments; Chapter 11. Quasilinearization; 11.1. Introduction; 11.2. The Riccati Equation; 11.3. Explicit Representation; 11.4. Successive Approximations and Monotone Convergence , 11.5. Maximum Interval of Convergence11.6. Dini's Theorem and Uniform Convergence; 1 1.7. Newton-Raphson-Kantorovich Approximation; 11.8. Quadratic Convergence; 11.9. Upper Bounds; 11.10. u' = g(u, t ); 11.11. Random Equation; 11.12. Upper and Lower Bounds; 1 I. 13. Asymptotic Behavior; 11.14. Multidimensional Riccati Equation; 11.15. Two-point Boundary Value Problems; 11.16. Maximum Interval of Convergence; 11.1 7. Quadratic Convergence; 11.18. Discussion; 11.19. Computational Feasibility; 11.20. Elliptic Equations; 11.21. Parabolic Equations; 1 1.22. Minimum and Maximum Principles , Miscellaneous ExercisesBibliography and Comments; Chapter 12. Dynamic Programming; 12.1. Introduction; 12.2. Multistage Processes; 12.3. Continuous Version; 12.4. Multistage Decision Processes; 12.5. Stochastic and Adaptive Processes; 12.6. Functional Equations; 12.7. Infinite Stage Process; 12.8. Policy; 12.9. Approximation in Policy Space; 12.10. Discussion; 12.11. Calculus of Variations as a Multistage Decision Process; 12.12. A New Formalism; 12.13. The Principle of Optimality; 12.14. Quadratic Case; 12.15. Multidimensional Case; 12.1 6. Computational Feasibility; 12.17. Stability , 12.18. Computational Feasibility: General Case-I , English

Additional Edition: ISBN 0-12-084902-X

Language: English