Note: Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- 1 Occurrence and Production -- I. HISTORY -- II. SOURCES -- III. MINING AND ORE TREATMENT -- IV. METAL EXTRACTION PROCESSES -- V. RECOVERY FROM RESIDUES AND SCRAP -- VI. USE OF ENERGY -- VII. ECONOMICS AND PRICES -- REFERENCES AND FURTHER READING -- 2 Properties -- I. GENERAL PHYSICAL AND MECHANICAL PROPERTIES -- II. METALLURGICAL STRUCTURE -- III. ALLOYING ELEMENTS AND IMPURITIES -- REFERENCES AND FURTHER READING -- 3 Early Uses and Development -- I. EARLYUSES -- II. COATINGS -- III. CASTINGS -- IV. WROUGHT MATERIALS -- V. BRASS -- VI. OTHER ZINC USES -- REFERENCES AND FURTHER READING -- 4 Specifications and Testing -- I. NOMENCLATURE -- II. SPECIFICATIONS -- III. ANALYSIS AND TESTING -- REFERENCES AND FURTHER READING -- 5 Corrosion Resistance -- I. GENERAL PRINCIPLES AND SURFACE REACTIONS -- II. ATMOSPHERIC CORROSION -- III. CORROSION IN WATER -- IV. CORROSION BY CHEMICALS -- V. SOIL CORROSION -- VI. BIMETALLIC CORROSION -- VII. CORROSION FATIGUE -- VIII. CONTACT WITH OTHER MATERIALS -- IX. CORROSION TESTING -- REFERENCES AND FURTHER READING -- 6 How Zinc Can Protect Steel -- I. CORROSION PROCESSES -- II. BARRIER COATINGS -- III. SACRIFICIAL EFFECT -- IV. COMBINED EFFECT -- REFERENCES AND FURTHER READING -- 7 Anodes for Cathodic Protection -- I. PRINCIPLES OF CATHODIC PROTECTION -- II. SACRIFICIAL ANODE SPECIFICATIONS -- III. PRACTICAL CONSIDERATIONS -- IV. TYPICAL INSTALLATIONS -- REFERENCES AND FURTHER READING -- 8 The Family of Zinc Coatings -- I. TYPES -- II. GENERAL PROPERTIES AND RELATIVE MERITS -- III. DESIGN FOR COATING -- IV. JOINING -- V. DUPLEX COATINGS -- VI. USES AND STATISTICS -- REFERENCES AND FURTHER READING -- 9 Alloy Coatings -- I. REASONS FOR ALLOYING AND BACKGROUND -- II. 5% ALUMINIUM -- III. 55% ALUMINIUM. , IV. INTERMEDIATE ALUMINIUM CONTENTS -- V. ZINC-IRON -- VI. ZINC-NICKEL -- VII. ZINC-COBALT -- VIII. OTHER ALLOY COATINGS -- IX. COMPOSITE COATINGS -- REFERENCES AND FURTHER· READING -- 10 Products Galvanized after Fabrication -- I. PROCESS -- II. MANUFACTURING PRACTICE -- III. NATURE OF THE COATING -- IV. EFFECT OF ALLOYING ADDITIONS IN THE BATH -- V. EFFECT OF ALLOYING ADDITIONS TO IRON -- VI. TREATMENTS TO DEAL WITH CONTINUOUSLY CAST STEEL -- VII. GALVANIZING OF THREADED WORK -- VIII. DESIGN FOR GALVANIZING -- IX. SPECIFICATION, INSPECTION AND TESTING -- X. PROPERTIES OF GALVANIZED PRODUCTS -- XI. USES -- REFERENCES AND FURTHER READING -- 11 Tube Galvanizing -- I. INTRODUCTION -- II. PROCESS 'FOR INSIDE AND OUTSIDE GALVANIZING -- III. OUTSIDE-ONLY PROCESS -- IV. USES -- REFERENCES AND FURTHER READING -- 12 Wire Galvanizing -- I. INTRODUCTION -- II. GALVANIZING PRACTICE -- III. NATURE OF THE COATED STEEL -- IV. P'ROPERTIES, AND USES -- REFERENCES AND FURTHER READING -- 13 Sheet Galvanizing -- I. INTRODUCTION -- II. GALVANIZING PROCESSES -- III. NATURE OF THE COATED STEEL -- IV. PROPERTIES AND USES -- REFERENCES AND FURTHER READING -- 14 Electroplated Coatings -- I. PROCESSES FOR PRODUCTS AND FOR CONTINUOUS SHEET OR WIRE COILS -- II. NATURE OF THE COATING -- III. ALLOYING ELEMENTS -- IV. PLANT FACILITIES -- V. PROPERTIES AND DESIGN -- VI. USES -- REFERENCES AND FURTHER READING -- 15 Mechanical and Adhesive-Bonded Coatings -- I. INTRODUCTION -- II. PROCESS -- III. NATURE OF THE COATING -- IV. PLANT FACILITIES -- V. PROPERTIES AND DESIGN -- VI. USES -- REFERENCES AND FURTHER READING -- 16 Sherardizing -- I. PROCESS -- II. NATURE OF THE COATING AND ITS INSPECTION -- III. PLANT FACILITIES -- IV. PROPERTIES -- V. DESIGN FOR SHERARDIZING AND ITS USES -- REFERENCES AND FURTHER READING -- 17 Sprayed Coatings -- I. PROCESS. , II. NATURE OF THE COATING AND INSPECTION -- III. ALLOYING ELEMENTS -- IV. PLANT FACILITIES -- V. PROPERTIES -- VI. DESIGN AND FABRICATION -- VII. USES -- REFERENCES AND FURTHER READING -- 18 Zinc Incorporated into Coatings -- I. TYPES OF ZINC -- II. TYPES OF COATING CONTAINING ZINC -- III. ZINC-RICH SYSTEMS -- IV. ZINC-DUST/ZINC-OXIDE SYSTEMS -- V. PROPRIETARY SYSTEMS -- REFERENCES AND FURTHER READING -- 19 Fabrication and Joining of Zinc Coated Steel -- I. FABRICATION AND FORMING -- II. WELDING -- III. BRAZING AND SOLDERING -- IV. ADHESIVE BONDING -- V . MECHANICAL FIXING-INFLUENCE OF ZINC COATINGS ON FASTENING -- REFERENCES AND FURTHER READING -- 20 Duplex Coatings -- I. INTRODUCTION -- II. CHOICE AND PRETREATMENT OF ZINC COATING -- III. PAINTS -- IV. POWDER COATINGS -- V. LAMINATES -- VI. TESTING AND PROPERTIES -- VII. DESIGN, FABRICATION AND USE -- REFERENCES AND FURTHER READING -- 21 Economics of Protection by Coatings -- I. INTRODUCTION -- II. INITIAL COSTS -- III. MAINTENANCE COSTS -- IV. OVERALL COST ASSESSMENTS -- V. CASE HISTORIES -- REFERENCES AND FURTHER READING -- 22 Zinc Batteries -- 1. INTRODUCTION AND HISTORY -- II. PRIMARY AND SECONDARY ZINC BATTERIES -- II. ELECTROLYTES FOR PRIMARY ZINC BATTERIES -- IV. RECHARGEABLE ZINC BATTERIES -- REFERENCES AND FURTHER READING -- 23 Pressure Die-Casting Processes -- I. INTRODUCTION -- II. ALLOYS AND PROPERTIES -- III. PRESSURE DIE-CASTING PROCESSES -- REFERENCES AND FURTHER READING -- 24 Gravity Casting Processes -- I. INTRODUCTION -- II. ALLOYS AND PROPERTIES -- III. GRAVITY DIE-CASTING TECHNIQUES -- IV. CASTING OF SLURRIES (LIQUID-SOLID MIXES) -- V. PROTOTYPE PRODUCTION -- REFERENCES AND FURTHER READING -- 25 The Cast Zinc Alloy Product -- I. INTRODUCTION -- II. GENERAL PROPERTIES -- III. FINISHING CASTINGS -- IV. ASSEMBLY TECHNIQUES -- REFERENCES AND FURTHER READING. , 26 Designing for Production as Zinc Alloy Die Castings -- I. INTRODUCTION -- II. BASIC PRINCIPLES -- III. DESIGN DETAILS FOR CASTINGS -- IV. ZINC ALLOY CASTINGS TO REPLACE OTHER MATERIALS -- REFERENCES AND FURTHER READING -- 27 Rolled Zinc -- I. INTRODUCTION -- II. PRODUCTION -- III. ALLOYS, PROPERTIES AND FABRICATION -- IV. SHEET IN BUILDING -- V. PHOTOENGRAVING PLATES -- VI. SUPERPLASTIC ALLOYS -- REFERENCES AND FURTHER READING -- 28 Other Wrought and Special Products -- I. EXTRUSION AND FORGING ALLOYS -- II. WIRE -- III. POWDER METALLURGY PRODUCTS -- IV. DISPERSION-STRENGTHENED ZINC ALLOYS (COMPOSITES) -- REFERENCES AND FURTHER READING -- 29 Zinc Dust and Compounds -- I. ZINC DUST -- II. ZINC OXIDE AND CHEMICALS -- REFERENCES AND FURTHER READING -- 30 Zinc as an Alloying Element -- I. INTRODUCTION -- II. BRASS -- III. OTHER ALLOYS -- REFERENCES AND FURTHER READING -- 31 Zinc and Health -- I. THE NEED FOR ZINC -- II. TOXICITY AND INDUSTRIAL HAZARDS -- REFERENCES AND FURTHER READING -- 32 Prospects for Zinc -- I. AVAILABILITY -- II. DEMAND -- REFERENCES AND FURTHER READING -- Index.

Note: nIndex. - Contract BMBF 19 B 9817 M. - Verbund-Nr. 01011562. - Engl. title: Mobility in the Greater Stuttgart area , Differences between the printed and electronic version of the document are possible , Also available as printed version , Systemvoraussetzungen: Acrobat reader.

Note: Ebook , Epublication based on: 9781847559104 , Chapter 1: Introduction and Scope-- Chapter 2: Synthesis Techniques and Mechanism of Growth-- Chapter 3: Structural and Physical Properties of Elongated TiO2 and Titanate Nanostructures-- Chapter 4: Chemical Properties, Transformation and Functionalization of Elongated Titanium Oxide Nanostructures-- Chapter 5: Potential Applications Summary.

Note: Intro -- TABLE OF CONTENTS -- DEDICATION -- List of Illustrations -- Acknowledgments -- Introduction -- 1 -- 2 -- 3 -- 4 -- 5 -- 6 -- 7 -- 8 -- 9 -- 10 -- 11 -- 12 -- 13 -- 14 -- 15 -- 16 -- 17 -- 18 -- Appendix -- Bibliography -- BOOKS -- MAGAZINES -- NEWSPAPERS.

Note: Statistical Analysis Techniques in Particle Physics -- Contents -- Acknowledgements -- Notation and Vocabulary -- 1 Why We Wrote This Book and How You Should Read It -- 2 Parametric Likelihood Fits -- 2.1 Preliminaries -- 2.1.1 Example: CP Violation via Mixing -- 2.1.2 The Exponential Family -- 2.1.3 Confidence Intervals -- 2.1.4 Hypothesis Tests -- 2.2 Parametric Likelihood Fits -- 2.2.1 Nuisance Parameters -- 2.2.2 Confidence Intervals from Pivotal Quantities -- 2.2.3 Asymptotic Inference -- 2.2.4 Profile Likelihood -- 2.2.5 Conditional Likelihood -- 2.3 Fits for Small Statistics -- 2.3.1 Sample Study of Coverage at Small Statistics -- 2.3.2 When the pdf Goes Negative -- 2.4 Results Near the Boundary of a Physical Region -- 2.5 Likelihood Ratio Test for Presence of Signal -- 2.6 sPlots -- 2.7 Exercises -- References -- 3 Goodness of Fit -- 3.1 Binned Goodness of Fit Tests -- 3.2 Statistics Converging to Chi-Square -- 3.3 Univariate Unbinned Goodness of Fit Tests -- 3.3.1 Kolmogorov-Smirnov -- 3.3.2 Anderson-Darling -- 3.3.3 Watson -- 3.3.4 Neyman Smooth -- 3.4 Multivariate Tests -- 3.4.1 Energy Tests -- 3.4.2 Transformations to a Uniform Distribution -- 3.4.3 Local Density Tests -- 3.4.4 Kernel-based Tests -- 3.4.5 Mixed Sample Tests -- 3.4.6 Using a Classifier -- 3.5 Exercises -- References -- 4 Resampling Techniques -- 4.1 Permutation Sampling -- 4.2 Bootstrap -- 4.2.1 Bootstrap Confidence Intervals -- 4.2.2 Smoothed Bootstrap -- 4.2.3 Parametric Bootstrap -- 4.3 Jackknife -- 4.4 BCa Confidence Intervals -- 4.5 Cross-Validation -- 4.6 Resampling Weighted Observations -- 4.7 Exercises -- References -- 5 Density Estimation -- 5.1 Empirical Density Estimate -- 5.2 Histograms -- 5.3 Kernel Estimation -- 5.3.1 Multivariate Kernel Estimation -- 5.4 Ideogram -- 5.5 Parametric vs. Nonparametric Density Estimation -- 5.6 Optimization. , 5.6.1 Choosing Histogram Binning -- 5.7 Estimating Errors -- 5.8 The Curse of Dimensionality -- 5.9 Adaptive Kernel Estimation -- 5.10 Naive Bayes Classification -- 5.11 Multivariate Kernel Estimation -- 5.12 Estimation Using Orthogonal Series -- 5.13 Using Monte Carlo Models -- 5.14 Unfolding -- 5.14.1 Unfolding: Regularization -- 5.15 Exercises -- References -- 6 Basic Concepts and Definitions of Machine Learning -- 6.1 Supervised, Unsupervised, and Semi-Supervised -- 6.2 Tall and Wide Data -- 6.3 Batch and Online Learning -- 6.4 Parallel Learning -- 6.5 Classification and Regression -- References -- 7 Data Preprocessing -- 7.1 Categorical Variables -- 7.2 Missing Values -- 7.2.1 Likelihood Optimization -- 7.2.2 Deletion -- 7.2.3 Augmentation -- 7.2.4 Imputation -- 7.2.5 Other Methods -- 7.3 Outliers -- 7.4 Exercises -- References -- 8 Linear Transformations and Dimensionality Reduction -- 8.1 Centering, Scaling, Reflection and Rotation -- 8.2 Rotation and Dimensionality Reduction -- 8.3 Principal Component Analysis (PCA) -- 8.3.1 Theory -- 8.3.2 Numerical Implementation -- 8.3.3 Weighted Data -- 8.3.4 How Many Principal Components Are Enough? -- 8.3.5 Example: Apply PCA and Choose the Optimal Number of Components -- 8.4 Independent Component Analysis (ICA) -- 8.4.1 Theory -- 8.4.2 Numerical implementation -- 8.4.3 Properties -- 8.5 Exercises -- References -- 9 Introduction to Classification -- 9.1 Loss Functions: Hard Labels and Soft Scores -- 9.2 Bias, Variance, and Noise -- 9.3 Training, Validating and Testing: The Optimal Splitting Rule -- 9.4 Resampling Techniques: Cross-Validation and Bootstrap -- 9.4.1 Cross-Validation -- 9.4.2 Bootstrap -- 9.4.3 Sampling with Stratification -- 9.5 Data with Unbalanced Classes -- 9.5.1 Adjusting Prior Probabilities -- 9.5.2 Undersampling the Majority Class -- 9.5.3 Oversampling the Minority Class. , 9.5.4 Example: Classification of Forest Cover Type Data -- 9.6 Learning with Cost -- 9.7 Exercises -- References -- 10 Assessing Classifier Performance -- 10.1 Classification Error and Other Measures of Predictive Power -- 10.2 Receiver Operating Characteristic (ROC) and Other Curves -- 10.2.1 Empirical ROC curve -- 10.2.2 Other Performance Measures -- 10.2.3 Optimal Operating Point -- 10.2.4 Area Under Curve -- 10.2.5 Smooth ROC Curves -- 10.2.6 Confidence Bounds for ROC Curves -- 10.3 Testing Equivalence of Two Classification Models -- 10.4 Comparing Several Classifiers -- 10.5 Exercises -- References -- 11 Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression -- 11.1 Discriminant Analysis -- 11.1.1 Estimating the Covariance Matrix -- 11.1.2 Verifying Discriminant Analysis Assumptions -- 11.1.3 Applying LDA When LDA Assumptions Are Invalid -- 11.1.4 Numerical Implementation -- 11.1.5 Regularized Discriminant Analysis -- 11.1.6 LDA for Variable Transformation -- 11.2 Logistic Regression -- 11.2.1 Binomial Logistic Regression: Theory and Numerical Implementation -- 11.2.2 Properties of the Binomial Model -- 11.2.3 Verifying Model Assumptions -- 11.2.4 Logistic Regression with Multiple Classes -- 11.3 Classification by Linear Regression -- 11.4 Partial Least Squares Regression -- 11.5 Example: Linear Models for MAGIC Telescope Data -- 11.6 Choosing a Linear Classifier for Your Analysis -- 11.7 Exercises -- References -- 12 Neural Networks -- 12.1 Perceptrons -- 12.2 The Feed-Forward Neural Network -- 12.3 Backpropagation -- 12.4 Bayes Neural Networks -- 12.5 Genetic Algorithms -- 12.6 Exercises -- References -- 13 Local Learning and Kernel Expansion -- 13.1 From Input Variables to the Feature Space -- 13.1.1 Kernel Regression -- 13.2 Regularization -- 13.2.1 Kernel Ridge Regression. , 13.3 Making and Choosing Kernels -- 13.4 Radial Basis Functions -- 13.4.1 Example: RBF Classification for the MAGIC Telescope Data -- 13.5 Support Vector Machines (SVM) -- 13.5.1 SVM with Weighted Data -- 13.5.2 SVM with Probabilistic Outputs -- 13.5.3 Numerical Implementation -- 13.5.4 Multiclass Extensions -- 13.6 Empirical Local Methods -- 13.6.1 Classification by Probability Density Estimation -- 13.6.2 Locally Weighted Regression -- 13.6.3 Nearest Neighbors and Fuzzy Rules -- 13.7 Kernel Methods: The Good, the Bad and the Curse of Dimensionality -- 13.8 Exercises -- References -- 14 Decision Trees -- 14.1 Growing Trees -- 14.2 Predicting by Decision Trees -- 14.3 Stopping Rules -- 14.4 Pruning Trees -- 14.4.1 Example: Pruning a Classification Tree -- 14.5 Trees for Multiple Classes -- 14.6 Splits on Categorical Variables -- 14.7 Surrogate Splits -- 14.8 Missing Values -- 14.9 Variable importance -- 14.10 Why Are Decision Trees Good (or Bad)? -- 14.11 Exercises -- References -- 15 Ensemble Learning -- 15.1 Boosting -- 15.1.1 Early Boosting -- 15.1.2 AdaBoost for Two Classes -- 15.1.3 Minimizing Convex Loss by Stagewise Additive Modeling -- 15.1.4 Maximizing the Minimal Margin -- 15.1.5 Nonconvex Loss and Robust Boosting -- 15.1.6 Boosting for Multiple Classes -- 15.2 Diversifying the Weak Learner: Bagging, Random Subspace and Random Forest -- 15.2.1 Measures of Diversity -- 15.2.2 Bagging and Random Forest -- 15.2.3 Random Subspace -- 15.2.4 Example: K/pi Separation for BaBar PID -- 15.3 Choosing an Ensemble for Your Analysis -- 15.4 Exercises -- References -- 16 Reducing Multiclass to Binary -- 16.1 Encoding -- 16.2 Decoding -- 16.3 Summary: Choosing the Right Design -- References -- 17 How to Choose the Right Classifier for Your Analysis and Apply It Correctly -- 17.1 Predictive Performance and Interpretability. , 17.2 Matching Classifiers and Variables -- 17.3 Using Classifier Predictions -- 17.4 Optimizing Accuracy -- 17.5 CPU and Memory Requirements -- 18 Methods for Variable Ranking and Selection -- 18.1 Definitions -- 18.1.1 Variable Ranking and Selection -- 18.1.2 Strong and Weak Relevance -- 18.2 Variable Ranking -- 18.2.1 Filters: Correlation and Mutual Information -- 18.2.2 Wrappers: Sequential Forward Selection (SFS), Sequential Backward Elimination (SBE), and Feature-based Sensitivity of Posterior Probabilities (FSPP) -- 18.2.3 Embedded Methods: Estimation of Variable Importance by Decision Trees, Neural Networks, Nearest Neighbors, and Linear Models -- 18.3 Variable Selection -- 18.3.1 Optimal-Set Search Strategies -- 18.3.2 Multiple Testing: Backward Elimination by Change in Margin (BECM) -- 18.3.3 Estimation of the Reference Distribution by Permutations: Artificial Contrasts with Ensembles (ACE) Algorithm -- 18.4 Exercises -- References -- 19 Bump Hunting in Multivariate Data -- 19.1 Voronoi Tessellation and SLEUTH Algorithm -- 19.2 Identifying Box Regions by PRIM and Other Algorithms -- 19.3 Bump Hunting Through Supervised Learning -- References -- 20 Software Packages for Machine Learning -- 20.1 Tools Developed in HEP -- 20.2 R -- 20.3 MATLAB -- 20.4 Tools for Java and Python -- 20.5 What Software Tool Is Right for You? -- References -- Appendix A: Optimization Algorithms -- A.1 Line Search -- A.2 Linear Programming (LP) -- Index.

Note: Intro -- Practical Immunology -- CONTENTS -- Foreword to the first edition -- Acknowledgements -- 1: Isolation and structure of immunoglobulins -- 2: Monoclonal antibodies: production, purification and enzymatic fragmentation -- 3: Antibody interactions with antigens -- 4: Antibodies as probes -- 5: Immunoassay -- 6: Isolation of cells -- 7: Phagocytosis, complement and antibody-dependent cytotoxicity -- 8: Lymphocyte structure -- 9: Lymphocyte function -- 10: The cytokines -- 11: Immunological manipulations in vivo -- APPENDIX A Buffers and media -- APPENDIX B Basic techniques and useful data -- APPENDIX C Equipment and manufacturers index -- Index.

Note: Cover -- Title page -- Contents -- Introduction -- Population dynamics -- Random processes -- Microbial ecology -- Propagation of epidemic diseases -- Population genetics -- Stratifications of disease systems -- MATLAB® programs -- Bibliography -- Index -- Back Cover.