Mathematics in Computational Science and Engineering

Inbunden, Engelska, 2022

2 196 kr

Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt över 249 kr.

Beskrivning

MATHEMATICS IN COMPUTATIONAL SCIENCE AND ENGINEERING This groundbreaking new volume, written by industry experts, is a must-have for engineers, scientists, and students across all engineering disciplines working in mathematics and computational science who want to stay abreast with the most current and provocative new trends in the industry. Applied science and engineering is the application of fundamental concepts and knowledge to design, build and maintain a product or a process, which provides a solution to a problem and fulfills a need. This book contains advanced topics in computational techniques across all the major engineering disciplines for undergraduate, postgraduate, doctoral and postdoctoral students. This will also be found useful for professionals in an industrial setting. It covers the most recent trends and issues in computational techniques and methodologies for applied sciences and engineering, production planning, and manufacturing systems. More importantly, it explores the application of computational techniques and simulations through mathematics in the field of engineering and the sciences. Whether for the veteran engineer, scientist, student, or other industry professional, this volume is a must-have for any library. Useful across all engineering disciplines, it is a multifactional tool that can be put to use immediately in practical applications. This groundbreaking new volume: Includes detailed theory with illustrations Uses an algorithmic approach for a unique learning experiencePresents a brief summary consisting of concepts and formulaeIs pedagogically designed to make learning highly effective and productive Is comprised of peer-reviewed articles written by leading scholars, researchers and professorsAUDIENCE: Engineers, scientists, students, researchers, and other professionals working in the field of computational science and mathematics across multiple disciplines

Produktinformation

Utgivningsdatum:2022-05-24
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Antal sidor:448
Förlag:John Wiley & Sons Inc
ISBN:9781119777151

Utforska kategorier

Tillämpad matematik inom Naturvetenskap och teknik

Mer om författaren

Ramakant Bhardwaj, PhD, is an associate professor of mathematics in Amity University, Kolkata, India with 15 years of teaching experience. He has published 135 research papers in reputed journals. He is also the co-author of six mathematics books, which are not only for mathematicians but written for practical applications for engineers and scientists.Satyendra Narayan, PhD, is a professor of applied computing at the Sheridan Institute of Technology and Advanced Learning in Oakville, Ontario, Canada. He has more than 35 years of teaching experience and has published several research papers in the field of computing in reputed journals. He is also the co-author of several books. Jyoti Mishra, PhD, is an associate professor in the Department of Mathematics, Gyan Ganga Institute of Technology, Jabalpur, India. She has more than ten years of teaching and research experience and has published close to 50 research papers in reputed journals. She is also the co-author of several books.

Innehållsförteckning

Preface xvii1 Brownian Motion in EOQ 1K. Suganthi and G. Jayalalitha1.1 Introduction 21.2 Assumptions in EOQ 41.2.1 Model Formulation 41.2.1.1 Assumptions 41.2.1.2 Notations 41.2.1.3 Inventory Ordering Cost 41.2.1.4 Inventory Holding Cost 51.2.1.5 Inventory Total Cost in EOQ 51.2.2 Example 51.2.3 Inventory Control Commodities in Instantaneous Demand Method Under Development of the tock 71.2.3.1 Assumptions 81.2.3.2 Notations 81.2.3.3 Model Formulation 91.2.3.4 Numerical Examples 101.2.3.5 Sensitivity Analysis 111.2.4 Classic EOQ Method in Inventory 121.2.4.1 Assumptions 121.2.4.2 Notations 131.2.4.3 Mathematical Model 131.3 Methodology 151.3.1 Brownian Motion 161.4 Results 171.4.1 Numerical Examples 201.4.2 Sensitivity Analysis 201.4.3 Brownian Path in Hausdorff Dimension 211.4.4 The Hausdorff Measure 221.4.5 Levy Processes 221.5 Discussion 231.5.1 Future Research 231.6 Conclusions 24References 242 Ill-Posed Resistivity Inverse Problems and its Application to Geoengineering Solutions 27Satyendra Narayan2.1 Introduction 282.2 Fundamentals of Ill-Posed Inverse Problems 292.3 Brief Historical Development of Resistivity Inversion 302.4 Overview of Inversion Schemes 312.5 Theoretical Basis for Multi-Dimensional Resistivity Inversion Technqiues 322.6 Mathematical Concept for Application to Geoengineering Problems 402.7 Mathematical Quantification of Resistivity Resolution and Detection 432.8 Scheme of Resistivity Data Presentation 452.9 Design Strategy for Monitoring Processes of IOR Projects, Geo-Engineering, and Geo-Environmental Problems 472.10 Final Remarks and Conclusions 49References 513 Shadowed Set and Decision-Theoretic Three-Way Approximation of Fuzzy Sets 55M. A. Ibrahim, T. O. William-West and D. Singh3.1 Introduction 553.2 Preliminaries on Three-Way Approximation of Fuzzy Sets 573.2.1 Shadowed Set Approximation 573.2.2 Decision-Theoretic Three-Way Approximation 583.3 Theoretical Foundations of Shadowed Sets 603.3.1 Uncertainty Balance Models 613.3.1.1 Pedrycz’s (Pd) Model 613.3.1.2 Tahayori-Sadeghian-Pedrycz (TSP) Model 613.3.1.3 Ibrahim-William-West-Kana-Singh (IWKS) Model 623.3.2 Minimum Error or Deng-Yao (DY) Model 633.3.3 Average Uncertainty or Ibrahim-West (IW) Model 643.3.4 Nearest Quota of Uncertainty (WIK) Model 653.3.5 Algorithm for Constructing Shadowed Sets 653.3.6 Examples on Shadowed Set Approximation 663.4 Principles for Constructing Decision-Theoretic Approximation 733.4.1 Deng and Yao Special Decision-Theoretic (DYSD) Model 743.4.2 Zhang, Xia, Liu and Wang (ZXLW) Generalized Decision-Theoretic Model 773.4.3 A General Perspective to Decision-Theoretic Three-Way Approximation 783.4.3.1 Determination of n, m and p for Decision- Theoretic Three-Way Approximation 793.4.3.2 A General Decision-Theoretic Three-Way Approximation Partition Thresholds 813.4.4 Example on Decision-Theoretic Three-Way Approximation 833.5 Concluding Remarks and Future Directions 87References 884 Intuitionistic Fuzzy Rough Sets: Theory to Practice 91Shivani Singh and Tanmoy Som4.1 Introduction 924.2 Preliminaries 934.2.1 Rough Set Theory 944.2.2 Intuitionistic Fuzzy Set Theory 954.2.3 Intuitionistic Fuzzy-Rough Set Theory 964.3 Intuitionistic Fuzzy Rough Sets 974.4 Extension and Hybridization of Intuitionistic Fuzzy Rough Sets 1104.4.1 Extension 1104.4.1.1 Dominance-Based Intuitionistic Fuzzy Rough Sets 1114.4.1.2 Covering-Based Intuitionistic Fuzzy Rough Sets 1114.4.1.3 Kernel Intuitionistic Fuzzy Rough Sets 1124.4.1.4 Tolerance-Based Intuitionistic Fuzzy Rough Sets 1124.4.1.5 Interval-Valued Intuitionistic Fuzzy Rough Sets 1124.4.2 Hybridization 1134.4.2.1 Variable Precision Intuitionistic Fuzzy Rough Sets 1134.4.2.2 Intuitionistic Fuzzy Neighbourhood Rough Sets 1144.4.2.3 Intuitionistic Fuzzy Multigranulation Rough Sets 1144.4.2.4 Intuitionistic Fuzzy Decision-Theoretic Rough Sets 1144.4.2.5 Intuitionistic Fuzzy Rough Sets and Soft Intuitionistic Fuzzy Rough Sets 1154.4.2.6 Multi-Adjoint Intuitionistic Fuzzy Rough Sets 1154.4.2.7 Intuitionistic Fuzzy Quantified Rough Sets 1164.4.2.8 Genetic Algorithm and IF Rough Sets 1164.5 Applications of Intuitionistic Fuzzy Rough Sets 1164.5.1 Attribute Reduction 1164.5.2 Decision Making 1184.5.3 Other Applications 1194.6 Work Distribution of IFRS Country-Wise and Year-Wise 1234.6.1 Country-Wise Work Distribution 1234.6.2 Year-Wise Work Distribution 1244.6.3 Limitations of Intuitionistic Fuzzy Rough Set Theory 1244.7 Conclusion 125Acknowledgement 125References 1255 Satellite-Based Estimation of Ambient Particulate Matters (pm 2.5) Over a Metropolitan City in Eastern India 135Tamanna Nasrin, Sharadia Dey and Sabyasachi Mondal5.1 Introduction 1365.2 Methodology 1375.3 Result and Discussions 1385.4 Conclusion 143References 1446 Computational Simulation Techniques in Inventory Management 147Dr. Abhijit Pandit and Dr. Pulak Konar6.1 Introduction 1476.1.1 Inventory Management 1476.1.2 Simulation 1486.2 Conclusion 164References 1657 Workability of Cement Mortar Using Nano Materials and PVA 167Dr. Mohan Kantharia and Dr. Pankaj Mishra7.1 Introduction 1677.2 Literature Survey 1687.3 Materials and Methods 1717.4 Results and Discussion 1717.5 Conclusion 177References 1788 Distinctive Features of Semiconducting and Brittle Half-Heusler Alloys; LiXP (X=Zn, Cd) 181Madhu Sarwan, Abdul Shukoor V. and Sadhna Singh8.1 Introduction 1828.2 Computation Method 1838.3 Result and Discussion 1838.3.1 Structural Properties 1838.3.2 Elastic Properties 1858.3.3 Electronic Properties 1878.3.4 Thermodynamic Properties 1908.4 Conclusions 195Acknowledgement 196References 1969 Fixed Point Results with Fuzzy Sets 199Qazi Aftab Kabir, Sanath Kumar H.G. and Ramakant Bhardwaj9.1 Introduction 1999.2 Definitions and Preliminaries 2009.3 Main Results 201References 20810 Role of Mathematics in Novel Artificial Intelligence Realm 211Kavita Rawat and Manas Kumar Mishra10.1 Introduction 21210.2 Mathematical Concepts Applied in Artificial Intelligence 21210.2.1 Linear Algebra 21310.2.1.1 Matrix and Vectors 21310.2.1.2 Eigen Value and Eigen Vector 21410.2.1.3 Matrix Operations 21710.2.1.4 Artificial Intelligence Algorithms That Use Linear Algebra 21710.2.2 Calculus 21810.2.2.1 Objective Function 21910.2.2.2 Loss Function & Cost Function 21910.2.2.3 Artificial Intelligence Algorithms That Use Calculus 22210.2.3 Probability and Statistics 22210.2.3.1 Population Versus Sample 22410.2.3.2 Descriptive Statistics 22410.2.3.3 Distributions 22510.2.3.4 Probability 22510.2.3.5 Correlation 22610.2.3.6 Data Visualization Using Statistics 22610.2.3.7 Artificial Intelligence Algorithms That Use Probability and Statistics 22710.3 Work Flow of Artificial Intelligence & Application Areas 22710.3.1 Application Areas 22910.3.2 Trending Areas 22910.4 Conclusion 230References 23111 Study of Corona Epidemic: Predictive Mathematical Model 233K. Sruthila Gopala Krishnan, Ramakant Bhardwaj, Amit Kumar Mishra and Rakesh Mohan Shrraf11.1 Mathematical Modelling 23411.2 Need of Mathematical Modelling 23511.3 Methods of Construction of Mathematical Models 23611.3.1 Mathematical Modelling with the Help of Geometry 23611.3.2 Mathematical Modelling with the Help of Algebra 23711.3.3 Mathematical Modelling Using Trigonometry 23911.3.4 Mathematical Modelling with the Help of Ordinary Differential Equation (ODE) 23911.3.5 Mathematical Modelling Using Partial Differential Equation (PDE) 24011.3.6 Mathematical Modelling Using Difference Equation 24011.4 Comparative Study of Mathematical Model in the Time of Covid-19 – A Review 24111.4.1 Review 24111.4.2 Case Study 24611.5 Corona Epidemic in the Context of West Bengal: Predictive Mathematical Model 24711.5.1 Overview 24711.5.2 Case Study 24811.5.3 Methodology 25011.5.3.1 Exponential Model 25011.5.3.2 Model Based on Geometric Progression (g.p.) 25211.5.3.3 Model for Stay At Home 25311.5.4 Discussion 255References 25512 Application of Mathematical Modeling in Various Fields in Light of Fuzzy Logic 257Dr. Dhirendra Kumar Shukla12.1 Introduction 25712.1.1 Mathematical Modeling 25712.1.2 Principles of Mathematical Models 25912.2 Fuzzy Logic 26112.2.1 Fuzzy Cognitive Maps & Induced Fuzzy Cognitive Maps 26212.2.2 Fuzzy Cluster Means 26312.3 Literature Review 26412.4 Applications of Fuzzy Logic 26812.4.1 Controller of Temperature 26912.4.2 Usage of Fuzzy Logic in a Washing Machine 27012.4.3 Air Conditioner 27112.4.4 Aeronautics 27212.4.5 Automotive Field 27212.4.6 Business 27412.4.7 Finance 27512.4.8 Chemical Engineering 27612.4.9 Defence 27812.4.10 Electronics 27912.4.11 Medical Science and Bioinformatics 28012.4.12 Robotics 28212.4.13 Signal Processing and Wireless Communication 28312.4.14 Transportation Problems 28312.5 Conclusion 285References 28513 A Mathematical Approach Using Set & Sequence Similarity Measure for Item Recommendation Using Sequential Web Data 287Vishal Paranjape, Dr. Neelu Nihalani, Dr. Nishchol Mishra and Dr. Jyoti Mishra13.1 Introduction 28813.2 Measures of Assessment for Recommendation Engines 29413.3 Related Work 29513.4 Methodology/Research Design 29613.4.1 Web Data Collection Through Web Logs 29613.4.2 Web User Sessions Classification 30013.5 Finding or Result 30513.6 Conclusion and Future Work 306References 30714 Neural Network and Genetic Programming Based Explicit Formulations for Shear Capacity Estimation of Adhesive Anchors 311Tawfik Kettanah and Satyendra Narayan14.1 General Introduction 31214.2 Research Significance 31314.3 Biological Nervous System 31414.4 Constructing Artificial Neural Network Model 31714.5 Genetic Programming (GP) 32014.6 Administering Genetic Programming Scheme 32014.7 Genetic Programming In Details 32014.8 Genetic Expression Programming 32214.9 Developing Model With Genexpo Software 32214.10 Comparing NN and GEP Results 32514.11 Conclusions 326References 32715 Adaptive Heuristic - Genetic Algorithms 329R. Anandan15.1 Introduction 32915.2 Genetic Algorithm 33015.3 The Genetic Algorithm 33115.4 Evaluation Module 33115.5 Populace Module 33115.5.1 Introduction 33115.5.2 Initialisation Technique 33115.5.3 Deletion Technique 33215.5.4 Parent Selection Procedure 33215.5.5 Fitness Technique 33315.5.6 Populace Size 33315.5.7 Elitism 33415.6 Reproduction Module 33415.6.1 Introduction 33415.6.2 Operators 33415.6.3 Mutation 33815.6.4 Mutation Rate 33815.6.5 Crossover Rate 33815.6.6 Dynamic Mutation and Crossover Rates 33815.7 Example 33915.8 Schema Theorem 34115.8.1 Introduction 34115.9 Conclusion 34215.10 Future Scope 342References 34216 Mathematically Enhanced Corrosion Detection 343SeyedBijan Mahbaz, Giovanni Cascante, Satyendra Narayan, Maurice B. Dusseault and Philippe Vanheeghe16.1 Introduction 34416.1.1 Mathematics in NDT 34616.1.2 Principal Component Analysis (PCA) 34716.2 Case Study: PCA Applied to PMI Data for Defect Detection 34716.3 PCA Feature Extraction for PMI Method 34916.4 Experimental Setup and Test 35116.5 Results 35216.6 Conclusions 355References 35517 Dynamics of Malaria Parasite with Effective Control Analysis 359Nagadevi Bala Nagaram and Suresh Rasappan17.1 Introduction 35917.2 The Mathematical Structure of EGPLC 36117.3 The Modified EGPLC Model 36317.4 Equilibria and Local Stability Analysis 36417.5 Analysis of Global Stability 36517.6 Global Stability Analysis with Back Propagation 36717.7 Stability Analysis of Non-Deterministic EGPLC Model 37317.8 Discussion on Numerical Simulation 37817.9 Conclusion 38117.10 Future Scope of the Work 381References 38118 Dynamics, Control, Stability, Diffusion and Synchronization of Modified Chaotic Colpitts Oscillator with Triangular Wave Non-Linearity Depending on the States 383Suresh Rasappan and Niranjan Kumar K.A.18.1 Introduction 38418.2 The Mathematical Model of Chaotic Colpitts Oscillator 38518.3 Adaptive Backstepping Control of the Modified Colpitts Oscillator with Unknown Parameters 39518.3.1 Proposed System 39518.3.2 Numerical Simulation 40018.4 Synchronization of Modified Chaotic Colpitts Oscillator 40018.4.1 Synchronization of Modified Chaotic Colpitts Oscillator using Non-Linear Feedback Method 40218.4.2 Numerical Simulation 40418.5 The Synchronization of Colpitts Oscillator via Backstepping Control 40518.5.1 Analysis of the Error Dynamics 40518.5.2 Numerical Simulation 40818.6 Circuit Implementation 40918.7 Conclusion 412References 412Index 415