Recent Advances in Mathematics for Engineering

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Download Recent Advances in Mathematics for Engineering written by Mangey Ram in PDF format. This book is under the category Engineering and bearing the isbn/isbn13 number 0367190869; 0429200307; 0429575807/9780367190866/ 9780429200304/ 9780429575808. You may reffer the table below for additional details of the book.

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Specifications

book-author

Mangey Ram

publisher

CRC Press; 1st edition

file-type

PDF

pages

Pages

language

English

asin

B0863BGMS9

isbn10

0367190869; 0429200307; 0429575807

isbn13

9780367190866/ 9780429200304/ 9780429575808


Book Description

In present years; arithmetic has skilled astounding development in the engineering sciences. Mathematics kinds the frequent basis of all engineering disciplines. Recent Advances in Mathematics for Engineering (PDF) offers a complete vary of arithmetic utilized in quite a few fields of engineering for completely different duties like civil engineering; laptop science; structural engineering; and electrical engineering; amongst others. It offers chapters that develop the purposes of arithmetic in engineering sciences; communicates progressive analysis concepts; gives the true-world utility of arithmetic; and significance in the lifetime of teachers; researchers; practitioners; and trade leaders.

Includes

    • current findings from varied establishments
    • Provides worldwide research and findings in modeling and simulation
    • Stresses on the newest analysis in the sector of engineering purposes and many others
    • Recognizes the gaps in the data in the sector and offers the newest approaches
    • Provides varied mathematical instruments; strategies; methods; and strategies throughout completely different engineering fields

NOTE: The product solely consists of the ebook; Recent Advances in Mathematics for Engineering (Mathematical Engineering; Manufacturing; and Management Sciences) in PDF. No access codes are included.

Additional information

book-author

Mangey Ram

publisher

CRC Press; 1st edition

file-type

PDF

pages

Pages

language

English

asin

B0863BGMS9

isbn10

0367190869; 0429200307; 0429575807

isbn13

9780367190866/ 9780429200304/ 9780429575808

Table of contents


Table of contents :
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
Editor Biography
Contributors
Chapter 1 Statistical Techniques and Stochastic Modeling in Public Health Surveillance Systems Engineering
1.1 Introduction
1.1.1 Preliminaries
1.1.2 Definition of Biosurveillance
1.1.3 Objectives of Biosurveillance
1.1.4 Biosurveillance Systems and Processes
1.1.5 Objectives, Goals, and Challenges
1.2 State of the Art
1.3 Statistical Framework
1.3.1 Sentinel Epidemiological Surveillance System
1.3.2 Two Season Influenza Historical Data
1.3.3 Research Methodology
1.3.3.1 The Standard CDC and ECDC Flu Detection Algorithm (Serfling’s Model)
1.3.3.2 An Extended Serfling’s Model
1.3.3.3 A Mixed Model Including Auto-Regressive Moving Average (ARMA) Terms
1.3.3.4 A Mixed Effects Periodic ARMA Model Based on Change-Point Detection
1.3.3.5 A Distribution-Free Control Charting Technique Based on Change-Point Detection
1.4 Comparative Study
1.5 Concluding Remarks
Acknowledgments
References
Chapter 2 Assessment of Earthquake Hazard Based on Statistical Models, Probability Theory, and Nonlinear Analysis
2.1 Introduction
2.2 Earthquake Hazards
2.2.1 Strong Ground Motions
2.2.2 Seismic Wave Amplification
2.2.3 Liquefaction Hazard
2.3 Probabilistic Seismic Hazard Analysis
2.3.1 Assessment
2.3.2 Methods for Seismic Hazard Analysis
2.3.2.1 Deterministic Seismic Hazard Analysis (DSHA)
2.3.2.2 Probabilistic Seismic Hazard Analysis (PSHA)
2.3.3 Development of a Comprehensive Earthquake Catalogue
2.3.4 Catalogue Homogenization
2.3.5 De-clustering of Catalogue
2.3.6 Check for Completeness
2.3.7 Seismogenic Source Characterization
2.3.8 Seismicity Parameters
2.3.9 Ground Motion Prediction Equation (GMPE)
2.3.10 Formulation of PSHA
2.3.10.1 Spatial Uncertainty
2.3.10.2 Size Uncertainty
2.3.10.3 Temporal Uncertainty
2.3.10.4 Uncertainty in GMPE
2.3.10.5 Hazard Calculation Using Total Probability Theorem
2.3.10.6 Disaggregation
2.4 Ground Response Analysis (GRA)
2.4.1 Methods for Nonlinear Ground Response Analysis
2.4.1.1 One-Dimensional Approach
2.4.1.2 Two-Dimensional Approach
2.4.1.3 Three-Dimensional Approach
2.4.2 Procedure of GRA
2.4.3 Geotechnical Site Characterization
2.4.3.1 Site Class
2.4.3.2 Bedrock Definition
2.4.4 Estimation of Dynamic Soil Properties
2.4.4.1 Low Strain Shear Modulus (G[sub(max)])
2.4.4.2 Standard G/G[sub(max)]-(gamma) and D-(gamma) Curves
2.4.5 Selection of Input Earthquake Motion
2.4.6 Nonlinear Ground Response
2.4.6.1 Formulation of Ground Response
2.5 Liquefaction Potential
2.5.1 Simplified Procedure Based on SPT
2.5.1.1 Cyclic Stress Ratio (CSR)
2.5.1.2 Cyclic Resistance Ratio (CRR)
2.5.1.3 Factor of Safety (FS) and Liquefaction Potential Index (LPI)
References
Chapter 3 Multi-Model Approach in the Risk Assessment Tasks with Satellite Data Utilization
3.1 Introduction: On the Methodology of Satellite Data Utilization in Multi-Modeling Approach for Socio-Ecological Risks Assessment Tasks – A Problem Formulation
3.2 On the Methodology of Modeling: Selection of Variables to Assessing Risks
3.2.1 Data Utilization Approach to Variables Selection
3.2.2 Formal Models of Risk Assessment and Decision Support
3.3 Generalized Stochastic Model of Hydrological Threats
3.3.1 Analysis of Key Processes Forming Flood Emergency
3.3.2 Detailed Models of Moisture and Soil Water Content
3.4 Satellite Models: Spectral Response Models
3.4.1 Spectral Model of Surface Response to the Heat and Water Stress
3.4.2 Spectral Response to the Snow Melting: The Stochastic Approach
3.5 Satellite Data for Assessment of Hydrological Climate-Related Risks
3.5.1 Land Covers Classification Approach
3.5.2 Spectral Data Calibration Using in-Field Spectrometry Measurements
3.6 Risk Model: Method of to the Risk Assessments Using Bayes Approach
3.7 Conclusions
Acknowledgments
References
Chapter 4 Integral Transforms and Parseval–Goldstein-Type Relationships
4.1 Introduction
4.2 A Parseval–Goldstein-Type Relationship
4.2.1 A Parseval–Goldstein-Type Relationship for Laplace Transforms
4.2.2 Some Illustrative Examples
4.3 The L2-Transform and its Applications
4.3.1 A Parseval–Goldstein-Type Relationship and its Corollaries
4.3.2 Some Illustrative Examples
4.4 Solving Classical Differential Equations with the L2-Transform
4.4.1 A Technique for Solving Bessel’s Differential Equation Using the L2-Transform
4.4.2 A Technique for Solving Hermite’s Differential Equation Using the L2-Transform
References
Chapter 5 Numerical Solution of Cauchy and Hypersingular Integral Equations
5.1 Introduction Singular Integral Equations with Cauchy Kernel
5.2 Method of Solution for CSIEs over [–1,1]
5.3 Error Analysis
5.3.1 Well Posedness
5.3.2 Existence and Uniqueness
5.4 Illustrative Examples
5.5 Introduction of Hypersingular Integral Equations
5.6 Method of Solution to the Problem
5.7 Convergence
5.7.1 Function Spaces
5.7.2 Error Analysis
5.7.3 Well Posedness of Linear System
5.7.4 Existence and Uniqueness
5.8 Illustrative Examples
5.9 Conclusion
References
Chapter 6 Krylov Subspace Methods for Numerically Solving Partial Differential Equations
6.1 Introduction
6.1.1 Types of PDEs
6.2 Solution of PDEs
6.3 Numerical Solutions
6.3.1 Finite Volume Method
6.3.2 Finite Element Methods
6.3.3 Finite Difference Methods
6.3.3.1 Explicit Method
6.3.3.2 Implicit Method
6.4 Stationary Iterative Methods
6.5 Non-Stationary Methods (Krylov Subspace Methods)
6.5.1 Conjugate Gradient (CG) Method
6.5.2 Generalized Minimum Residual (GMRES) Method
6.6 Conclusion
Appendix
References
Chapter 7 The (2+1) Dimensional Nonlinear Sine–Gordon Soliton Waves and its Numerical Simulations
7.1 Introduction
7.2 Description of the Method
7.3 Implementation of Method to 2D SGE
7.4 Results and Discussion
7.4.1 Circular Ring Solitons
7.4.2 Elliptical Ring Solitons
7.4.3 Elliptical Breather
7.4.4 Superposition of Two Orthogonal Line Solitons
7.4.5 Line Solitonsin an Inhomogenous Medium
7.5 Conclusions
References
Chapter 8 Dynamical Complexity of Patchy Invasion in Prey–Predator Model
8.1 Introduction
8.1.1 Prey–Predator System
8.1.1.1 Cooperative Behavior of Hunting
8.2 Nonlinear Dynamics Preliminaries
8.2.1 Basics of Stability Analysis
8.2.1.1 Local Stability Analysis
8.2.2 Types of Bifurcations
8.3 Turing (Diffusive) Instability
8.4 Models Description
8.5 Spatiotemporal Model
8.5.1 Initial Density Distribution
8.5.2 Equilibria of System
8.5.3 Stability Analysis of System
8.6 Analysis of the Spatiotemporal Model
8.7 Numerical Simulations
8.8 Discussion and Conclusion
References
Chapter 9 Developments in Runge–Kutta Method to Solve Ordinary Differential Equations
9.1 Introduction
9.2 Development of Runge–Kutta Method
9.3 The Runge–Kutta Methods
9.4 Extension of Runge–Kutta Methods
9.5 Numerical Results
9.6 Conclusion
References
Chapter 10 A Criterion Space Decomposition Method for a Tri-objective Integer Program
10.1 Introduction
10.2 Literature Review
10.3 Preliminaries
10.3.1 Basic Concept
10.3.2 Review of Some Recent Approaches
10.3.2.1 The (epsilon)-Constraint Method
10.3.2.2 Boland, Charkhgard, and Savelsbergh Method (2017)
10.4 The Proposed Algorithm
10.4.1 Numerical Illustration
10.5 Computational Experiments
10.5.1 Before Relaxation
10.5.2 After Relaxation
10.6 Conclusion
References
Chapter 11 Link-Weight Modification for Network Optimization: Is it a Tool, Philosophy, or an Art?
11.1 Introduction
11.2 Broad Classification
11.3 Network Optimization by “Link-Weight Modified to Zero Value”: A Close Look at Some Problems in Category 1
11.3.1 A Classical Application: The Assignment Problem Solved by the Hungarian Method of Assignment
11.3.2 Unification of an Assignment and the Transportation Problems
11.3.3 Shortest Route in a Directed Network
11.3.4 Shortest Route in a Non-Directed Network
11.3.4.1 Label Associated with a Node
11.3.4.2 Notations and Definitions
11.3.4.3 Link-Weight Modification Using the Implied Direction
11.4 Link-Weight Modification Approach to Find a Minimum Spanning Tree with Node Index (Lesser than equal to) 2
11.4.1 Index Balancing Theorems
11.5 Numerical Illustrations
11.5.1 Unification of the Transportation and Assignment Models by the Hungarian Approach
11.5.2 Shortest Path in a Directed Network
11.5.3 Shortest Path in the Non-Directed Network by Labelling Approach
11.5.4 Minimum Spanning Tree
11.6 Concluding Remarks
References
Chapter 12 Residual Domain-Rich Models and their Application in Distinguishing Photo-Realistic and Photographic Images
12.1 Introduction
12.2 Need for Residual Domain
12.3 Rich Models of Noise Residual
12.3.1 Noise Residual
12.3.2 Truncation and Quantization
12.3.3 Co-Occurrences
12.4 Rich Models for Steganalysis of Digital Images
12.5 Rich Models for Distinguishing Photo-Realistic and Photographic Images
12.5.1 Existing Works
12.5.2 Feature Extraction
12.5.3 Classification
12.5.4 System Description
12.6 Experiments and Discussion
12.6.1 Datasets
12.6.2 Analysis for Different Color Channels
12.6.3 Robustness against Post-Processing Operations
12.6.4 Comparison with State of the Art
12.6.4.1 Comparison on Columbia Database
12.6.4.2 Comparison on RAISE versus Level-Design
12.7 Conclusion
Acknowledgment
References
Chapter 13 Swirling Subsonic Annular Circular Jets
13.1 Introduction
13.2 Computational Model
13.2.1 Geometry
13.2.2 Meshing
13.2.3 Physics Definition
13.3 Results and Discussions
13.4 Conclusions
References
Chapter 14 Computations of Linear Programming Problems in Integers
14.1 Introduction
14.2 Review on Terms Related to Linear Programming Problems and Simplex Method
14.2.1 Convergence of Simplex Method
14.2.2 Programming for Simplex Method
14.2.3 Code to Perform Optimality Test
14.2.4 Code to Print Simplex Table for Each Iteration
14.3 Two-Phase Method
14.3.1 Code to Check Feasibility of the Solution
14.3.2 Code for Two-Phase Method
14.3.3 Code for Two Phase Method
14.4 Integer Cutting Planes
14.4.1 Basic Terminology for Integer Programming
14.4.2 Cutting-Plane Algorithm for Pure Integer Programming
14.4.3 Code for Gomory’s Cuts
14.4.4 Codes to Display Gomory’s Tables
14.5 Discussion and Summary
Acknowledgment
References
Chapter 15 Fuzzy EOQ Model with Reliability-Induced Demand and Defuzzification by Graded Mean Integration
15.1 Introduction
15.2 Literature Review
15.3 Preliminaries
15.4 Notations and Assumptions
15.4.1 Notations
15.4.2 Assumptions
15.5 Formulation of Mathematical Model
15.5.1 Crisp Model
15.5.2 Fuzzy Model
15.6 Optimality Criteria
15.7 Numerical Example
15.8 Sensitivity Analysis
15.8.1 For Crisp Model
15.8.2 For Fuzzy Model
15.9 Conclusion
Appendix
References
Chapter 16 Inventory Model for Decaying Products with Shortages and Inflation under Trade Credit in Two Warehouses
16.1 Introduction
16.2 Assumptions and Notations
16.2.1 Assumptions
16.2.2 Notations
16.3 Mathematical Formulation and Solution of the Model
16.4 Total Cost Calculations
16.4.1 Present Worth Ordering Cost
16.4.2 Present Worth Holding Cost for RW
16.4.2.1 Case 1: When Q (Lesser than equal to) W
16.4.2.2 Case 2: When Q > W
16.4.3 Present Worth Holding Cost for OW
16.4.3.1 Case 1: When Q (Lesser than equal to) W
16.4.3.2 Case 2: When Q > W
16.5 Present Worth Deterioration Cost
16.6 Present Worth Shortage Cost
16.7 Present Worth Opportunity Cost
16.8 Interest Payable
16.8.1 Case 1: 0 (Lesser than equal to) M (Lesser than equal to) t[sub(d)]
16.8.1.1 When Q (Lesser than equal to) W
16.8.1.2 When Q > W
16.8.2 Case 2: t[sub(d)] (Lesser than equal to) M (Lesser than equal to) t1
16.8.2.1 When Q (Lesser than equal to) W and Q > W
16.8.3 Case 3: When M (greater than equal to) t1
16.9 Interest Earned (I[sub(e)]) from Sales Revenue
16.9.1 Case 1: 0 < M (Lesser than equal to) t[sub(d)]
16.9.1.1 When Q (Lesser than equal to) W
16.9.1.2 When Q > W
16.9.2 Case 2: t[sub(d)] < M (Lesser than equal to) t1
16.9.2.1 When Q (Lesser than equal to) W
16.9.2.2 When Q > W
16.9.3 Case 3: M > t1
16.9.3.1 When Q (Lesser than equal to) W
16.9.3.2 When Q > W
16.10 Present Worth Total Cost
16.11 Numerical Illustrations and Analysis
16.11.1 Example
16.12 Sensitivity Analysis
16.13 Observations
16.14 Concluding Remarks
Appendix I
Appendix II
References
Index

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