Tabla de Contenidos: “…Chapter 7 - Connectivity of Ad hoc 5G Wireless Networks under Denial of Service Attacks -- 7.1 Introduction -- 7.2 Problem Definition -- 7.3 Connectivity Analysis -- 7.4 Results and Discussions -- 7.5 Conclusions -- References -- About the Authors -- Chapter 8 - Optimal Signal Design forWavelet Radio TOA Locationing with Synchronization Error for 5G Networks -- 8.1 Introduction -- 8.2 Wavelet Signal Design -- 8.2.1 Design Procedure -- 8.2.2 Filter Bank Implementation ofWavelet Packets -- 8.3 Problem Statement -- 8.4 ImportantWavelet Properties -- 8.4.1 Wavelet Existence and Compact Support -- 8.4.2 Paraunitary Condition -- 8.4.3 Flatness/K-Regularity -- 8.4.4 Degrees of Freedom to Design -- 8.5 Formulation of Design Problem -- 8.5.1 Design Criterion -- 8.5.2 Wavelet Domain to Filter Bank Domain -- 8.5.3 Transformation of the Mathematical Constraints from Non-convex Problem to a Convex/linear One -- 8.5.3.1 Compact support or admissibility constraint -- 8.5.3.2 Double shift orthogonality constraint -- 8.5.3.3 K-Regularity constraint -- 8.6 Results and Analysis -- 8.6.1 Frequency and Impulse Response of Designed Filter -- 8.6.2 Evaluation of Designed Filter under Loss of Time Synchronization -- 8.7 Conclusions -- References -- Appendix: Sum of squares of cross-correlation -- About the Author -- Chapter 9 - TV Broadcast and 5G -- 9.1 Introduction -- 9.2 Traditional TV Broadcast -- 9.3 Disruptive Changes -- 9.4 Technology Drivers -- 9.4.1 IP-based Communication -- 9.4.2 Broadcast vs. …”

Tabla de Contenidos: “…5.2.2 Linear impact model -- 5.2.3 Instantaneous impact cost model -- 5.2.4 Impact arbitrage -- 5.3 Cost of carry -- 5.4 Market‐wide impact and elasticity -- Chapter 6 Portfolio Construction -- 6.1 Hedged allocation -- 6.2 Forecast from rule‐based strategy -- 6.3 Single‐period vs multi‐period mean‐variance utility -- 6.4 Single‐name multi‐period optimization -- 6.4.1 Optimization with fast impact decay -- 6.4.2 Optimization with exponentially decaying impact -- 6.4.3 Optimization conditional on a future position -- 6.4.4 Position value and utility leak -- 6.4.5 Optimization with slippage -- 6.5 Multi‐period portfolio optimization -- 6.5.1 Unconstrained portfolio optimization with linear impact costs -- 6.5.2 Iterative handling of factor risk -- 6.5.3 Optimizing future EMA positions -- 6.5.4 Portfolio optimization using utility leak rate -- 6.5.5 Notes on portfolio optimization with slippage -- 6.6 Portfolio capacity -- 6.7 Portfolio optimization with forecast revision -- 6.8 Portfolio optimization with forecast uncertainty -- 6.9 Kelly criterion and optimal leverage -- 6.10 Intraday optimization and execution -- 6.10.1 Trade curve -- 6.10.2 Forecast‐timed execution -- 6.10.3 Algorithmic trading and HFT -- 6.10.4 HFT controversy -- Chapter 7 Simulation -- 7.1 Simulation vs production -- 7.2 Simulation and overfitting -- 7.3 Research and simulation efficiency -- 7.4 Paper trading -- 7.5 Bugs -- Afterword: Economic and Social Aspects of Quant Trading -- Appendix -- Index -- Question Index -- Quotes Index -- Stories Index -- EULA…”

Tabla de Contenidos: “…5.2.5.3 Theorized rating category: Morality -- 5.2.5.4 Theorized rating category: Reasoning -- 5.2.5.5 Theorized rating category: Social role management -- 5.2.6 A unified model of IC -- Chapter 6 Study three: The IC test and accompanying questionnaires -- 6.1 Methodology -- 6.1.1 Participants -- 6.1.1.1 Main testing test-takers -- 6.1.1.2 Main testing test-taker peers -- 6.1.1.3 Main testing IC test raters -- 6.1.2 Instruments -- 6.1.2.1 The IC test -- 6.1.2.2 Test-taker background questionnaires -- 6.1.2.3 Self and peer-assessment questionnaires -- 6.1.2.4 Rater training materials -- 6.1.3 Procedures -- 6.1.3.1 Administering the IC test and questionnaires -- 6.1.3.2 Training raters -- 6.1.3.3 Rater rating -- 6.1.4 Data analysis -- 6.2 Results and initial discussion -- 6.2.1 Rasch analyses of IC test scores -- 6.2.1.1 The Wright map -- 6.2.1.2 The candidate measurement report -- 6.2.1.3 The rater measurement report -- 6.2.1.4 The criterion measurement report -- 6.2.1.5 The item measurement report -- 6.2.1.6 The rating scale category functioning -- 6.2.1.7 The dimensionality of the data structure -- 6.2.2 Correlation between IC and LC -- 6.2.3 Rasch analyses of questionnaires -- 6.2.3.1 The disaffiliation control sub-section -- 6.2.3.2 The affiliation promotion sub-section -- 6.2.3.3 The morality sub-section -- 6.2.3.4 The reasoning sub-section -- 6.2.3.5 The social role management sub-section -- 6.2.3.6 Overall results of self and peer IC questionnaires -- 6.2.4 Correlation between the IC test and questionnaires -- 6.2.5 Rasch analyses of extrapolation and attitude items -- 6.2.5.1 Explicit extrapolation questions -- 6.2.5.2 Test-taker attitude questions -- Chapter 7 Validity argument and overall discussions -- 7.1 The domain description inference -- 7.1.1 Domain description assumption 1 -- 7.1.2 Domain description assumption 2.…”

Tabla de Contenidos: “…Charts -- Utility of Charts -- Variety of Charts -- Preparation of Charts -- Graphics Aids -- Evaluate Yourself -- Summary -- References -- Additional Reading -- Chapter 9: Programmed Learning -- Concept of Programmed Learning -- Definitions of Programmed Learning -- Historical Perspective of Programmed Learning -- Programmed Learning and Its Principles -- Programmed Learning and the Teacher -- Principles of Programmed Learning -- Principle of Behavioural Analysis -- Principle of Small Steps -- Principle of Active Participation -- Principle of Immediate Confirmation or Reinforcement -- Principle of Self-Pacing -- Principle of Validation -- Types of Programming -- Linear Programming -- Features of Linear Programming -- Important Features of Linear Programming -- Branching Programming -- Mathetics -- Comparison of Programming Styles -- Unit of Presentation -- Step Size -- Number of Steps -- Error Rate Permitted -- Response -- Reinforcement -- Subject-Matter Suitability -- Paper Programming -- Machine Programming -- Cost of Programme and Machines -- Target Population -- Subject-Matter -- Objective -- Programme Writer -- Books or Machines -- Evaluate Yourself -- Summary -- References -- Additional Reading -- Chapter 10: Programmed Instruction Material -- Phase 1: Preparatory Phase -- Selection of the Topic or Units to be Programmed -- Writing Assumptions About Learners -- Developing Specific Outlines of Content -- Writing Objectives in Behavioural Terms -- Writing the Initial Behaviour of the Learners -- Developing a Criterion Test -- Phase 2: Development Phase -- Designing of the Frames -- Sequencing of the Frames -- Phase 3: Evaluation and Revision Phase -- Individual Try-Out -- Small Group Try-Out -- Field Try-Out -- Evaluation of the Programme -- Validation of the Programme -- Evaluate Yourself -- Summary -- References -- Additional Reading…”

Tabla de Contenidos: “…Objective justification arguments raised in Google Search (Shopping) -- aa) Google's arguments regarding technical constraints -- bb) Rejection of objective justification by Commission and General Court -- d) No objective justification criterion in Article 6(5) DMA -- V. Consequences where no fair equivalent can be found -- C. …”

Tabla de Contenidos: “…-- 7.3.2 What Evaluation Criterion Has Been Used to Evaluate Model-Based Prioritization and How are The Results Reported? …”

Tabla de Contenidos: “…-- How to Contact the Publisher -- Chapter 1 Security Operations and Administration -- Comply with Codes of Ethics -- Understand, Adhere to, and Promote Professional Ethics -- (ISC)2 Code of Ethics -- Organizational Code of Ethics -- Understand Security Concepts -- Conceptual Models for Information Security -- Confidentiality -- Integrity -- Availability -- Accountability -- Privacy -- Nonrepudiation -- Authentication -- Safety -- Fundamental Security Control Principles -- Access Control and Need-to-Know -- Job Rotation and Privilege Creep -- Document, Implement, and Maintain Functional Security Controls -- Deterrent Controls -- Preventative Controls -- Detective Controls -- Corrective Controls -- Compensating Controls -- The Lifecycle of a Control -- Participate in Asset Management -- Asset Inventory -- Lifecycle (Hardware, Software, and Data) -- Hardware Inventory -- Software Inventory and Licensing -- Data Storage -- Implement Security Controls and Assess Compliance -- Technical Controls -- Physical Controls -- Administrative Controls -- Periodic Audit and Review -- Participate in Change Management -- Execute Change Management Process -- Identify Security Impact -- Testing/Implementing Patches, Fixes, and Updates -- Participate in Security Awareness and Training -- Security Awareness Overview -- Competency as the Criterion -- Build a Security Culture, One Awareness Step at a Time -- Participate in Physical Security Operations…”

Tabla de Contenidos: “…-- Doing it in Python -- Model selection -- Stepwise procedures -- The backward elimination -- The forward selection -- The stepwise regression -- Criterion-based procedures -- Time for action - model selection using the backward, forward, and AIC criteria -- What just happened? …”

Tabla de Contenidos: “…Foreword by Takehiro Nakamura xiii -- Preface xv -- About the Authors xvii -- 1 Introduction 1 -- 1.1 3GPP 1 -- 1.2 Evolutionary Path of 3GPP Systems 4 -- 1.2.1 GSM 4 -- 1.2.2 GPRS/EDGE 6 -- 1.2.3 UMTS 7 -- 1.2.4 HSPA 8 -- 1.2.5 LTE 9 -- 1.3 Market Trend 10 -- 1.4 Requirement of LTE 11 -- 1.5 Overview of LTE Architecture 14 -- 1.5.1 Network Architecture 14 -- 1.5.2 QoS Architecture 18 -- 1.5.3 Radio Protocol Architecture of LTE 21 -- 1.6 UE Capabilities 24 -- References 26 -- 2 Idle Mode Procedure 27 -- 2.1 Idle Mode Functions 27 -- 2.2 Services and Cell Categorization 29 -- 2.3 UE States and State Transitions 30 -- 2.4 PLMN Selection 31 -- 2.4.1 Triggering of PLMN Selection 32 -- 2.4.2 Search of Available PLMNs 32 -- 2.4.3 PLMN Selection 32 -- 2.5 Location Registration 34 -- 2.6 Cell Selection 34 -- 2.6.1 Cell Selection Criterion 35 -- 2.6.2 Cell Selection upon RRC Connection Release 36 -- 2.7 Cell Reselection 36 -- 2.7.1 Measurement Rules 37 -- 2.7.2 Reselection to a Neighboring Cell 39 -- 2.7.3 Mobility State Dependent Scaling 41 -- 2.8 Access Verification 43 -- 2.8.1 Cell Barring Status and Cell Reservation Status 43 -- 2.9 Paging Reception 44 -- References 45 -- 3 Radio Resource Control (RRC) 47 -- 3.1 RRC Functions and Architecture 48 -- 3.2 System Information 50 -- 3.2.1 Scheduling of System Information 50 -- 3.2.2 System Information Update 54 -- 3.3 Paging 55 -- 3.4 Connection Establishment 56 -- 3.4.1 Step 1: Access Class Barring 57 -- 3.4.2 Step 2: Transmission of the RRCConnectionRequest Message 58 -- 3.4.3 Step 3: Receipt of the RRCConnectionSetup Message 59 -- 3.4.4 Step 4: Transmission of the RRCConnectionSetupComplete Message 59 -- 3.5 Security 60 -- 3.6 RRC Connection Reconfiguration 63 -- 3.6.1 SRB2 Establishment 63 -- 3.6.2 DRB Establishment 63 -- 3.6.3 Parallel Procedure with Security Activation 64 -- 3.7 UE Capability Transfer 65 -- 3.8 Intra-EUTRA Handover 65 -- 3.8.1 Handover Preparation 66 -- 3.8.2 Handover Execution 69 -- 3.8.3 Handover Completion 71.…”

Tabla de Contenidos: “…References 67 -- PART II COMPUTATIONAL ATTENTION MODELS 73 -- 3 Computational Models in the Spatial Domain 75 -- 3.1 Baseline Saliency Model for Images 75 -- 3.1.1 Image Feature Pyramids 76 -- 3.1.2 Centre-Surround Differences 79 -- 3.1.3 Across-scale and Across-feature Combination 80 -- 3.2 Modelling for Videos 81 -- 3.2.1 Extension of BS Model for Video 81 -- 3.2.2 Motion Feature Detection 81 -- 3.2.3 Integration for Various Features 83 -- 3.3 Variations and More Details of BS Model 84 -- 3.3.1 Review of the Models with Variations 85 -- 3.3.2 WTA and IoR Processing 87 -- 3.3.3 Further Discussion 90 -- 3.4 Graph-based Visual Saliency 91 -- 3.4.1 Computation of the Activation Map 92 -- 3.4.2 Normalization of the Activation Map 94 -- 3.5 Attention Modelling Based on Information Maximizing 95 -- 3.5.1 The Core of the AIM Model 96 -- 3.5.2 Computation and Illustration of Model 97 -- 3.6 Discriminant Saliency Based on Centre-Surround 101 -- 3.6.1 Discriminant Criterion Defined on Centre-Surround 102 -- 3.6.2 Mutual Information Estimation 103 -- 3.6.3 Algorithm and Block Diagram of Bottom-up DISC Model 106 -- 3.7 Saliency Using More Comprehensive Statistics 107 -- 3.7.1 The Saliency in Bayesian Framework 108 -- 3.7.2 Algorithm of SUN Model 110 -- 3.8 Saliency Based on Bayesian Surprise 113 -- 3.8.1 Bayesian Surprise 113 -- 3.8.2 Saliency Computation Based on Surprise Theory 114 -- 3.9 Summary 116 -- References 117 -- 4 Fast Bottom-up Computational Models in the Spectral Domain 119 -- 4.1 Frequency Spectrum of Images 120 -- 4.1.1 Fourier Transform of Images 120 -- 4.1.2 Properties of Amplitude Spectrum 121 -- 4.1.3 Properties of the Phase Spectrum 123 -- 4.2 Spectral Residual Approach 123 -- 4.2.1 Idea of the Spectral Residual Model 124 -- 4.2.2 Realization of Spectral Residual Model 125 -- 4.2.3 Performance of SR Approach 126 -- 4.3 Phase Fourier Transform Approach 127 -- 4.3.1 Introduction to the Phase Fourier Transform 127 -- 4.3.2 Phase Fourier Transform Approach 128.…”

Tabla de Contenidos: “…Machine generated contents note: Preface xxi Acronyms xxv 1 OpRisk in Perspective 1 1.1 Brief History 1 1.2 Risk-Based Capital Ratios for Banks 5 1.3 The Basic Indicator and Standardized Approaches for OpRisk 9 1.4 The Advanced Measurement Approach 11 1.5 General Remarks and Book Structure 16 2 OpRisk Data and Governance 17 2.1 Introduction 17 2.2 OpRisk Taxonomy 18 2.3 The Elements of the OpRisk Framework 25 2.4 Business Environment and Internal Control Environment Factors (BEICFs) 29 2.5 External Databases 32 2.6 Scenario Analysis 33 2.7 OpRisk Profile in Different Financial Sectors 36 2.8 Risk Organization and Governance 43 3 Using OpRisk Data for Business Analysis 49 3.1 Cost Reduction Programs at Financial Firms 50 3.2 Using OpRisk Data to Perform Business Analysis 54 3.3 The Risk of Losing Key Talents: OpRisk in Human Resources 55 3.4 Systems Risks: OpRisk in Systems Development and Transaction Processing 56 3.5 Conclusions 59 4 Stress Testing OpRisk Capital and CCAR 61 4.1 The Need for Stressing OpRisk Capital Even Beyond the 99.9% 61 4.2 Comprehensive Capital Review and Analysis (CCAR) 62 4.3 OpRisk and Stress Tests 68 4.4 OpRisk in CCAR in Practice 69 4.5 Reverse Stress Test 75 4.6 Stressing OpRisk Multivariate Models 75 5 Basic Probability Concepts in Loss Distribution Approach 79 5.1 Loss Distribution Approach 79 5.2 Quantiles and Moments 84 5.3 Frequency Distributions 87 5.4 Severity Distributions 88 5.5 Convolutions and Characteristic Functions 93 5.6 Extreme Value Theory 95 6 Risk Measures and Capital Allocation 101 6.1 Development of Capital Accords Base I, II and III 102 6.2 Measures of Risk 105 6.3 Capital Allocation 130 7 Estimation of Frequency and Severity Models 143 7.1 Frequentist Estimation 143 7.2 Bayesian Inference Approach 155 7.3 Mean Square Error of Prediction 160 7.4 Standard Markov Chain Monte Carlo Methods. 161 7.5 Standard MCMC Guidelines for Implementation 174 7.6 Advanced Markov chain Monte Carlo Methods 182 7.7 Sequential Monte Carlo Samplers and Importance Sampling 194 7.8 Approximate Bayesian Computation (ABC) Methods 212 7.9 Modelling Truncated Data 215 8 Model Selection and Goodness of Fit Testing 231 8.1 Qualitative Model Diagnostic Tools 231 8.2 Information Criterion for Model Selection 235 8.3 Goodness of Fit Testing for Model Choice (How to Account for Heavy Tails!) …”

Tabla de Contenidos: “…-- 2.3 Green Nanotechnology -- 2.4 Green Synthesis of Nanomaterials -- 2.5 Advantages of Green Nanoscience -- 2.5.1 Green Nanoscience in Industries -- 2.5.2 Green Nanoscience in Automobiles -- 2.5.3 Green Nanoelectronics -- 2.5.4 Green Nanoscience in Food and Agriculture -- 2.5.5 Green Nanoscience in Medicines -- 2.6 Conclusion -- References -- Chapter 3 Optimization of the Process Parameters to Develop Green-Synthesized Nanostructures with a Special Interest in Cancer Theranostics -- 3.1 Introduction -- 3.1.1 Conventional Techniques in Nanoparticle Synthesis -- 3.1.2 Green Nanotechnology -- 3.2 Mechanism Underlying Green Synthesis -- 3.3 Green Synthesized Nanoparticles in Cancer Theranostics -- 3.4 Optimizing the Synthesis and Subsequent Characterizations -- 3.4.1 Approaches to Achieve Optimization -- 3.4.2 Characterization of Nanoparticles -- Acknowledgment -- References -- Chapter 4 Sustainability: An Emerging Design Criterion in Nanoparticles Synthesis and Applications -- 4.1 Introduction -- 4.2 Biotemplates -- 4.2.1 Plant-Based Biotemplates…”

Tabla de Contenidos: “…Preface xiii Table of Engineering Applications xvii Part 1 Introduction 1 Mathematica Environment and Basic Syntax 3 1.1 Introduction 3 1.2 Selecting Notebook Characteristics 4 1.3 Notebook Cells 8 1.4 Delimiters 12 1.5 Basic Syntax 12 1.5.1 Introduction 12 1.5.2 Templates: Greek Symbols and Mathematical Notation 15 1.5.3 Variable Names and Global Variables 18 1.6 Mathematical Constants 19 1.7 Complex Numbers 21 1.8 Elementary, Trigonometric, Hyperbolic, and a Few Special Functions 22 1.9 Strings 25 1.9.1 String Creation: StringJoin[] and ToString[] 25 1.9.2 Labeled Output: Print[], NumberForm[], EngineeringForm[], and TraditionalForm[] 26 1.10 Conversions, Relational Operators, and Transformation Rule 28 1.11 Engineering Units and Unit Conversions: Quantity[] and UnitConvert[] 30 1.12 Creation of CDF Documents and Documents in Other Formats 33 1.13 Functions Introduced in Chapter 1 34 Exercises 35 2 List Creation and Manipulation: Vectors and Matrices 39 2.1 Introduction 39 2.2 Creating Lists and Vectors 39 2.2.1 Introduction 39 2.2.2 Creating a List with Table[] 45 2.2.3 Summing Elements of a List: Total[] 46 2.2.4 Selecting Elements of a List 47 2.2.5 Identifying List Elements Matching a Pattern: Position[] 49 2.3 Creating Matrices 51 2.3.1 Introduction 51 2.3.2 Matrix Generation Using Table[] 54 2.3.3 Accessing Elements of Arrays 55 2.4 Matrix Operations on Vectors and Arrays 56 2.4.1 Introduction 56 2.4.2 Matrix Inverse and Determinant: Inverse[] and Det[] 57 2.5 Solution of a Linear System of Equations: LinearSolve[] 58 2.6 Eigenvalues and Eigenvectors: EigenSystem[] 59 2.7 Functions Introduced in Chapter 2 61 References 61 Exercises 61 3 User-Created Functions, Repetitive Operations, and Conditionals 69 3.1 Introduction 69 3.2 Expressions and Procedures as Functions 69 3.2.1 Introduction 69 3.2.2 Pure Function: Function[] 74 3.2.3 Module[] 78 3.3 Find Elements of a List that Meet a Criterion: Select[] 80 3.4 Conditionals 82 3.4.1 If[] 82 3.4.2 Which[] 83 3.5 Repetitive Operations 83 3.5.1 Do[] 83 3.5.2 While[] 83 3.5.3 Nest[] 84 3.5.4 Map[] 84 3.6 Examples of Repetitive Operations and Conditionals 85 3.7 Functions Introduced in Chapter 3 92 Exercises 92 4 Symbolic Operations 95 4.1 Introduction 95 4.2 Assumption Options 101 4.3 Solutions of Equations: Solve[] 101 4.4 Limits: Limit[] 105 4.5 Power Series: Series[], Coefficient[], and CoefficientList[] 108 4.6 Optimization: Maximize[]/Minimize[] 112 4.7 Differentiation: D[] 114 4.8 Integration: Integrate[] 120 4.9 Solutions of Ordinary Differential Equations: DSolve[] 126 4.10 Solutions of Partial Differential Equations: DSolve[] 136 4.11 Laplace Transform: LaplaceTransform[] and InverseLaplaceTransform[] 138 4.12 Functions Introduced in Chapter 4 145 References 145 Exercises 146 5 Numerical Evaluations of Equations 151 5.1 Introduction 151 5.2 Numerical Integration: NIntegrate[] 151 5.3 Numerical Solutions of Differential Equations: NDSolveValue[] and ParametricNDSolveValue[] 154 5.4 Numerical Solutions of Equations: NSolve[] 178 5.5 Roots of Transcendental Equations: FindRoot[] 180 5.6 Minimum and Maximum: FindMinimum[] and FindMaximum[] 182 5.7 Fitting of Data: Interpolation[] and FindFit[] 186 5.8 Discrete Fourier Transforms and Correlation: Fourier[], InverseFourier[], and ListCorrelate[] 189 5.9 Functions Introduced in Chapter 5 194 References 195 Exercises 196 6 Graphics 209 6.1 Introduction 209 6.2 2D Graphics 209 6.2.1 Basic Plotting 209 6.2.2 Basic Graph Enhancements 213 6.2.3 Common 2D Shapes: Graphics[] 217 6.2.4 Additional Graph Enhancements 222 6.2.5 Combining Figures: Show[] and GraphicsGrid[] 238 6.2.6 Tooltip[] 241 6.2.7 Exporting Graphics 244 6.3 3D Graphics 244 6.4 Summary of Functions Introduced in Chapter 6 253 References 254 Exercises 254 7 Interactive Graphics 263 7.1 Interactive Graphics: Manipulate[] 263 References 287 Exercises 287 Part 2 Engineering Applications 8 Vibrations of Spring Mass Systems and Thin Beams 293 8.1 Introduction 293 8.2 Single Degree-of-Freedom Systems 294 8.2.1 Periodic Force on a Single Degree-of-Freedom System 294 8.2.2 Squeeze Film Damping and Viscous Fluid Damping 298 8.2.3 Electrostatic Attraction 302 8.2.4 Single Degree-of-Freedom System Energy Harvester 304 8.3 Two Degrees-of-Freedom Systems 307 8.3.1 Governing Equations 307 8.3.2 Response to Harmonic Excitation: Amplitude Response Functions 307 8.3.3 Enhanced Energy Harvester 310 8.4 Thin Beams 315 8.4.1 Natural Frequencies and Mode Shapes of a Cantilever Beam with In-Span Attachments 315 8.4.2 Effects of Electrostatic Force on the Natural Frequency and Stability of a Beam 318 8.4.3 Response of a Cantilever Beam with an In-Span Attachment to an Impulse Force 323 References 326 9 Statistics 327 9.1 Descriptive Statistics 327 9.1.1 Introduction 327 9.1.2 Location Statistics: Mean[], StandardDeviation[], and Quartile[] 327 9.1.3 Continuous Distribution Functions: PDF[] and CDF[] 329 9.1.4 Histograms and Probability Plots: Histogram[] and ProbabilityScalePlot [] 331 9.1.5 Whisker Plot: BoxWhiskerChart[] 332 9.1.6 Creating Data with Specified Distributions: RandomVariate[] 334 9.2 Probability of Continuous Random Variables 334 9.2.1 Probability for Different Distributions: NProbability[] 334 9.2.2 Inverse Cumulative Distribution Function: InverseCDF[] 337 9.2.3 Distribution Parameter Estimation: EstimatedDistribution[] and FindDistributionParameters[] 337 9.2.4 Confidence Intervals: CI[] 340 9.2.5 Hypothesis Testing: LocationTest[] and VarianceTest[] 342 9.3 Regression Analysis: LinearModelFit[] 343 9.3.1 Simple Linear Regression 343 9.3.2 Multiple Linear Regression 347 9.4 Nonlinear Regression Analysis: NonLinearModelFit[] 351 9.5 Analysis of Variance (ANOVA) and Factorial Designs: ANOVA[] 354 9.6 Functions Introduced in Chapter 9 358 10 Control Systems and Signal Processing 359 10.1 Introduction 359 10.2 Model Generation: State-Space and Transfer Function Representation 359 10.2.1 Introduction 359 10.2.2 State-Space Models: StateSpaceModel[] 360 10.2.3 Transfer Function Models: TransferFunctionModel[] 362 10.3 Model Connections Closed-Loop Systems and System Response: SystemsModelFeedbackConnect[] and SystemsModelSeriesConnect[] 363 10.4 Design Methods 369 10.4.1 Root Locus: RootLocusPlot[] 369 10.4.2 Bode Plot: BodePlot[] 371 10.4.3 Nichols Plot: NicholsPlot[] 372 10.5 Signal Processing 374 10.5.1 Filter Models: ButterworthFilterModel[], EllipticFilterModel[], ... 374 10.5.2 Windows: HammingWindow[], HannWindow[], ... 381 10.5.3 Spectrum Averaging 385 10.6 Aliasing 388 10.7 Functions Introduced in Chapter 10 390 Reference 391 11 Heat Transfer and Fluid Mechanics 393 11.1 Introduction 393 11.2 Conduction Heat Transfer 394 11.2.1 One-Dimensional Transient Heat Diffusion in Solids 394 11.2.2 Heat Transfer in Concentric Spheres: Ablation of a Tumor 398 11.2.3 Heat Flow Through Fins 401 11.3 Natural Convection Along Heated Plates 405 11.4 View Factor Between Two Parallel Rectangular Surfaces 408 11.5 Internal Viscous Flow 411 11.5.1 Laminar Flow in Horizontal Cylindrical Pipes 411 11.5.2 Flow in Three Reservoirs 412 11.6 External Flow 416 11.6.1 Pressure Coefficient of a Joukowski Airfoil 416 11.6.2 Surface Profile in Nonuniform Flow in Open Channels 419 References 423 Index 425.…”

Tabla de Contenidos: “…Data enhancement -- 3.4.2. Criterion -- 3.4.3. Gradient optimization -- 3.5. Analyze error…”

Tabla de Contenidos: “…2.1.2.4 Practice and Opinio Iuris in Specific Cases -- 2.1.2.4.1 Southern Rhodesia -- 2.1.2.4.2 Katanga -- 2.1.2.4.3 Biafra -- 2.1.2.4.4 Bangladesh -- 2.1.2.4.5 Bantustans -- 2.1.2.4.6 Turkish Republic of Northern Cyprus (trnc) -- 2.1.2.4.7 Republika Srpska -- 2.1.3 Iterim Conclusions -- 2.2 Doctrine -- 2.2.1 Preclusion of the Emergence of Statehood Despite the Effectiveness of the Entity: A Legalist Position -- 2.2.2 Nullity of the State Due to Illegality of Its Creation -- 2.2.3 Illegality Without Drawing Any Consequences on the Status of the Entity -- 2.2.4 Non-recognition of Illegal States or Their Social Isolation -- 2.2.5 Classical Factualist Doctrine -- 2.2.6 Interim Conclusions -- 3 Role of DoI in Secession -- 3.1 Inherent Part of a Secessionist Attempt -- 3.1.1 An Intentional Act to Secede -- 3.1.2 Analogy with the Law of Acquisition of Titles of Territorial Sovereignty -- 3.1.3 Relevance of the Taiwan Example -- 3.2 Links between the DoI and Secession -- 3.3 Conclusion -- 4 DoI and Secessionist Attempt within the Purview of Peremptory Norms -- 4.1 General Observations -- 4.2 Applicable Law -- 4.3 Criterion of Connection with Violation -- 4.4 Addressees -- 4.4.1 Involvement of the Third State in the Secessionist Attempt -- 4.4.2 Secessionist Group as a Direct Bearer of Peremptory Obligations -- 4.5 Legal Consequences -- 4.5.1 Illegality of the Declaration of Independence and Secessionist Attempt -- 4.5.2 Invalidity of the Declaration of Independence -- 4.5.2.1 Invalidating Effects of Peremptory Norms on Unilateral Acts of States -- 4.5.2.2 DoI as a Legal Act -- 4.5.2.3 Invalidating Effects of Peremptory Norms on the DoI -- 4.5.3 Consequences in the Field of International Responsibility -- 4.5.3.1 Legal Basis of the Duty of Non-recognition -- 4.5.3.2 Interplay with Other Effects of Peremptory Norms -- 4.6 Conclusion -- 5 Conclusion…”

Tabla de Contenidos: “…. ♣ Least Squares Method and Linear Regression -- 7.1 Least squares method - Linear regression -- 7.2 Correlation and causality -- 7.3 Uncertainty in the adjustment parameters -- 7.4 Occam's razor or criterion of parsimony -- 7.5 Summary of important concepts -- 7.6 Exercises and problems -- References -- Chapter 8. …”

Tabla de Contenidos: “…Forecasting with statistical models -- 4 Modeling a moving average process -- 4.1 Defining a moving average process -- 4.1.1 Identifying the order of a moving average process -- 4.2 Forecasting a moving average process -- 4.3 Next steps -- 4.4 Exercises -- 4.4.1 Simulate an MA(2) process and make forecasts -- 4.4.2 Simulate an MA(q) process and make forecasts -- Summary -- 5 Modeling an autoregressive process -- 5.1 Predicting the average weekly foot traffic in a retail store -- 5.2 Defining the autoregressive process -- 5.3 Finding the order of a stationary autoregressive process -- 5.3.1 The partial autocorrelation function (PACF) -- 5.4 Forecasting an autoregressive process -- 5.5 Next steps -- 5.6 Exercises -- 5.6.1 Simulate an AR(2) process and make forecasts -- 5.6.2 Simulate an AR(p) process and make forecasts -- Summary -- 6 Modeling complex time series -- 6.1 Forecasting bandwidth usage for data centers -- 6.2 Examining the autoregressive moving average process -- 6.3 Identifying a stationary ARMA process -- 6.4 Devising a general modeling procedure -- 6.4.1 Understanding the Akaike information criterion (AIC) -- 6.4.2 Selecting a model using the AIC -- 6.4.3 Understanding residual analysis -- 6.4.4 Performing residual analysis -- 6.5 Applying the general modeling procedure -- 6.6 Forecasting bandwidth usage -- 6.7 Next steps -- 6.8 Exercises -- 6.8.1 Make predictions on the simulated ARMA(1,1) process -- 6.8.2 Simulate an ARMA(2,2) process and make forecasts -- Summary -- 7 Forecasting non-stationary time series -- 7.1 Defining the autoregressive integrated moving average model -- 7.2 Modifying the general modeling procedure to account for non-stationary series…”

Tabla de Contenidos: “…6.4.2.2 Gradient Tree Boosting -- 6.4.2.3 XGBoost -- 6.4.3 Stacking -- 6.5 Results -- 6.5.1 Bagging Meta Estimator -- 6.5.2 Random Forest -- 6.5.3 AdaBoost -- 6.5.4 Gradient Tree Boosting -- 6.5.5 XGBoost -- 6.5.6 Stacking -- 6.5.7 Comparison with Single Classifiers -- 6.6 Conclusion -- Acknowledgement -- References -- Chapter 7 Feature Engineering and Selection Approach Over Malicious Image -- 7.1 Introduction -- 7.2 Feature Engineering Techniques -- 7.2.1 Methodologies in Feature Engineering -- 7.2.2 Strides in Feature Engineering -- 7.2.3 Feature Extraction -- 7.2.4 Feature Selection -- 7.2.5 Feature Engineering in Image Processing -- 7.2.6 Importance of Feature Engineering in Image Processing -- 7.3 Malicious Feature Engineering -- 7.4 Image Processing Technique -- 7.4.1 Steps Involved in Image Processing Technique -- 7.4.2 Image Processing Task -- 7.4.2.1 Image Enhancement -- 7.4.2.2 Image Restoration -- 7.4.2.3 Coloring Image Processing -- 7.4.2.4 Wavelets Processing and Multiple Solutions -- 7.4.2.5 Image Compression -- 7.4.2.6 Character Recognition -- 7.4.2.7 Characteristics of Image Processing -- 7.5 Image Processing Techniques for Analysis on Malicious Images -- 7.6 Conclusion -- References -- Blog -- Chapter 8 Cubic-Regression and Likelihood Based Boosting GAM to Model Drug Sensitivity for Glioblastoma -- 8.1 Introduction -- 8.1.1 Glioblastoma -- 8.2 Literature Survey -- 8.3 Materials and Methods -- 8.3.1 Methodology -- 8.3.1.1 Generalized Additive Models (GAMs) -- 8.3.1.2 Model-Based Boosting - Boosted GAM -- 8.3.2 Datasets Description -- 8.4 Evaluations, Results and Discussions -- 8.4.1 Akaike Information Criterion (AIC) -- 8.4.2 Adjusted R-Squared -- 8.4.3 Discussion -- Conclusion -- References -- Chapter 9 Unobtrusive Engagement Detection through Semantic Pose Estimation and Lightweight ResNet for an Online Class Environment…”

Tabla de Contenidos: “…4.3 Nonconcentrator collectors -- 4.3.1 Flat plate collector -- Applications -- 4.3.2 Evacuated tube collectors -- Application -- 4.4 Concentrator collectors -- 4.4.1 Compound parabolic and Fresnel lens collectors -- Application -- 4.4.2 Parabolic trough collector -- Parameter calculation -- More equations for the model -- Application -- 4.4.3 Parabolic dish reflector -- Application -- 4.4.4 Central receiver or heliostat field reflector -- Applications -- References -- 5 - Solar photovoltaic thermal systems -- 5.1 Introduction -- 5.2 Photovoltaic thermal technology -- 5.3 Solar cell or PV cell -- 5.3.1 Crystalline solar cell -- 5.3.2 Thin-film solar cell -- 5.3.3 Amorphous solar cell -- 5.3.4 Organic and polymer solar cell -- 5.3.5 Dye-sensitized solar cell -- 5.3.6 Hybrid solar cell -- 5.3.7 PV cell electrical parameters -- p-n junction -- Short-circuit current (Isc) -- Open-circuit voltage (Voc) -- Fill factor -- Solar cell efficiency -- Detailed balance -- Boundary conditions -- 5.3.8 Performance of PV cell -- Effect of temperature -- Solar to electricity system -- Solar to fuel system -- Solar electricity to fuel system -- 5.4 Energy conversion in different types of PVT systems -- 5.4.1 Energy conversion in PVT/water system -- Evaluation criterion of the PV/T system -- 5.4.2 Energy conversion in glazed PVT/water system -- 5.4.3 Energy conversion in unglazed PVT/water and PVT-PCM systems -- 5.4.4 Energy conversion in PVT/air system -- References -- Further reading -- 6 - Solar thermal power plant -- 6.1 Introduction -- 6.2 Basic concept of solar thermal power plant -- 6.3 Solar thermal power generation technologies -- 6.3.1 Solar tower power plant -- 6.3.2 Parabolic through solar power plant -- 6.3.3 Parabolic dish solar power plant -- 6.3.4 Linear Fresnel reflector solar power plant -- 6.3.5 Solar chimney power plant…”

Tabla de Contenidos: “…2.6.1 Use of Texture and Context -- 2.6.2 Using Ancillary Multisource Data -- 2.7 Epilogue -- References -- Chapter 3 Dimensionality Reduction: Feature Extraction and Selection -- 3.1 Feature Extraction -- 3.1.1 Principal Component Analysis -- 3.1.2 Minimum/Maximum Autocorrelation Factors -- 3.1.3 Maximum Noise Fraction (MNF) Transformation -- 3.1.4 Independent Component Analysis -- 3.1.5 Projection Pursuit -- 3.2 Feature Selection -- 3.2.1 Greedy Search Methods -- 3.2.2 Simulated Annealing -- 3.2.3 Separability Indices -- 3.2.4 Filter-Based Methods -- 3.2.4.1 Correlation-Based Feature Selection -- 3.2.4.2 Information Gain -- 3.2.4.3 Gini Impurity Index -- 3.2.4.4 Minimum Redundancy-Maximum Relevance -- 3.2.4.5 Chi-Square Test -- 3.2.4.6 Relief-F -- 3.2.4.7 Symmetric Uncertainty -- 3.2.4.8 Fisher's Test -- 3.2.4.9 OneR -- 3.2.5 Wrappers -- 3.2.5.1 Genetic Algorithm -- 3.2.5.2 Particle Swarm Optimization -- 3.2.5.3 Feature Selection with SVMs -- 3.2.6 Embedded Methods -- 3.2.6.1 K-Nearest Neighbor-Based Feature Selection -- 3.2.6.2 Feature Selection with Ensemble Learners -- 3.2.6.3 Hilbert-Schmidt Independence Criterion with Lasso -- 3.3 Concluding Remarks -- References -- Chapter 4 Multisource Image Fusion and Classification -- 4.1 Image Fusion -- 4.1.1 Image Fusion Methods -- 4.1.1.1 PCA-Based Image Fusion -- 4.1.1.2 IHS-Based Image Fusion -- 4.1.1.3 Brovey Transform -- 4.1.1.4 Gram-Schmidt Transform -- 4.1.1.5 Wavelet Transform -- 4.1.1.6 Deep Learning for Image Fusion -- 4.1.2 Assessment of Fused Image Quality -- 4.1.3 Performance Evaluation of Fusion Methods -- 4.2 Multisource Classification Using the Stacked-Vector Method -- 4.3 The Extension of Bayesian Classification Theory -- 4.3.1 An Overview -- 4.3.1.1 Feature Extraction -- 4.3.1.2 Probability or Evidence Generation -- 4.3.1.3 Multisource Consensus…”