-
Browse Book Series
- Adhesion and Adhesives: Fundamental and Applied Aspects
- Advances in Additive Manufacturing Technologies
- Advances in Antenna, Microwave and Communication Engineering
- Advances in Biofeedstocks and Biofuels
- Advances in Cyber Security
- Advances in Data Engineering and Machine Learning
- Advances in Disease Diagnosis, Drug Delivery and Treatment
- Advances in E-Mobility
- Advances in Hydrogen Production and Storage
- Advances in Intelligent and Scientific Computing (AISC)
- Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Advances in Membrane Processes
- Advances in Nanotechnology and Applications
- Advances in Natural Gas Engineering
- Advances in Pipes and Pipelines
- Advances in Production Engineering
- Advances in Solar Cell Materials and Storage
- Advances in Surface Engineering and Materials Processing
- Applied Functional Materials
- Artificial Intelligence and Soft Computing for Industrial Transformation
- Astrobiology Perspectives on Life in the Universe
- Bioprocessing in Food Science
- Computational Intelligence and Biomedical Engineering
- Computational Intelligence in Industrial Mathematics
- Concise Introductions to AI and Data Science
- Cyber Systems and Quantum Engineering
- Decentralized Systems and Next-Generation Internet
- Diatoms: Biology and Applications
- Digital Convergence in Engineering Systems
- Emerging Trends in Medicinal and Pharmaceutical Chemistry
- Engineering Systems Design for Sustainable Development
- Fintech in a Sustainable Digital Society
- Human Centric Sustainability and Next Generation Technology
- Industry 5.0 Transformation Applications
- Infectious and Rare Diseases
- Innovations in Materials and Manufacturing
- Leading-Edge Breakthroughs in Artificial Intelligence
- Lifeline Molecules: Exploring Antioxidants and Their Impact
- Machine Learning in Biomedical Science and Healthcare Informatics
- Marine Nanoscience and Nanotechnology
- Materials Degradation and Failure
- Mathematics and Computer Science
- Next-Generation Computing and Communication Engineering
- Performability Engineering Series
- Robotics and Automation for Modern Applications
- Rotating Machinery Fundamentals and Advances
- Semiconductor Devices and Their Applications
- Separation Science
- Studies in Computational Intelligence and Smart Technologies
- Sustainable Biopolymer Technologies and Applications
- Sustainable Computing and Optimization
- Thermoplastic Bionanocomposites Series
- Transformative Innovations in Materials Science
Browse Subject Areas
For Authors
Submit a ProposalEngineering Thermodynamics and Recovery Management
One Line Description
Master the exact thermal engineering techniques and adaptive frameworks you need to slash operational costs, conquer strict environmental regulations, and transform complex waste management challenges into high-efficiency energy solutions.
Description
The role of smarter waste management and energy systems in industry development, waste management, and energy efficiency is a critical issue across industries today. The growing need to handle increasing waste volume while meeting strict environmental regulations has made innovative approaches essential. Industries face challenges like reducing operational costs, minimizing environmental harm, and complying with sustainability goals, while the demand for clean energy solutions that optimize resources and reduce waste is higher than ever. This book aims to bridge the gap between the theoretical principles of engineering thermodynamics and the practical challenges of municipal waste management. It explores how thermodynamic concepts can be applied to enhance energy efficiency and recovery in waste treatment systems. By incorporating advanced thermal engineering techniques such as heat exchangers, solar cooling, and trigeneration systems, the book offers actionable insights for creating sustainable and energy-efficient waste management solutions. These methods provide a strong foundation for addressing the energy demands associated with waste treatment while minimizing environmental impacts.
The book addresses the inherent uncertainty and complexity of decision-making in waste management. Using fuzzy logic principles, it provides a framework for accommodating incomplete data and subjective judgments, enabling municipalities to adapt to variable scenarios such as changing waste compositions and fluctuating energy demands. This book is a comprehensive resource for academics, researchers, policymakers, and industry professionals, providing a blend of theoretical knowledge and real-world applications, making it a valuable tool for solving complex problems in municipal waste management.
Back to Top
The role of smarter waste management and energy systems in industry development, waste management, and energy efficiency is a critical issue across industries today. The growing need to handle increasing waste volume while meeting strict environmental regulations has made innovative approaches essential. Industries face challenges like reducing operational costs, minimizing environmental harm, and complying with sustainability goals, while the demand for clean energy solutions that optimize resources and reduce waste is higher than ever. This book aims to bridge the gap between the theoretical principles of engineering thermodynamics and the practical challenges of municipal waste management. It explores how thermodynamic concepts can be applied to enhance energy efficiency and recovery in waste treatment systems. By incorporating advanced thermal engineering techniques such as heat exchangers, solar cooling, and trigeneration systems, the book offers actionable insights for creating sustainable and energy-efficient waste management solutions. These methods provide a strong foundation for addressing the energy demands associated with waste treatment while minimizing environmental impacts.
The book addresses the inherent uncertainty and complexity of decision-making in waste management. Using fuzzy logic principles, it provides a framework for accommodating incomplete data and subjective judgments, enabling municipalities to adapt to variable scenarios such as changing waste compositions and fluctuating energy demands. This book is a comprehensive resource for academics, researchers, policymakers, and industry professionals, providing a blend of theoretical knowledge and real-world applications, making it a valuable tool for solving complex problems in municipal waste management.
Back to Top
Author / Editor Details
Sadhan Kumar Ghosh, PhD is the Director General of the Sustainable Development and Circular Economy Centre at the International Society of Waste Management, Air, and Water. He has published ten books, 55 edited volumes, and more than 300 national and international articles and book chapters, and delivered more than 1,000 keynote, plenary, and invited speeches in several countries. His research interests include waste management, circular economy, and supply chain sustainability.
Sadhan Kumar Ghosh, PhD is the Director General of the Sustainable Development and Circular Economy Centre at the International Society of Waste Management, Air, and Water. He has published ten books, 55 edited volumes, and more than 300 national and international articles and book chapters, and delivered more than 1,000 keynote, plenary, and invited speeches in several countries. His research interests include waste management, circular economy, and supply chain sustainability.
Rahul Kar is part of the School of Mathematics at Kalyani Mahavidyalaya, Kalyani, Nadia, West Bengal, and is currently pursuing a Ph.D. in Mathematics from Bankura University. With eight years of teaching experience, he has published many research papers and edited 15 books. He is a member of the Indian Mathematical Society and an honorary member of the Carmels Research Institute, Dakar, Senegal.
Gunjan Mukherjee, PhD is an Associate Professor in the Department of Computational Sciences at Brainware University with more than 20 years of teaching experience. He has authored a textbook series, edited several books, and published six Indian patents. His research interests include computer vision, machine learning, soft computing, and image processing.
Pushan Kumar Dutta, PhD is an Assistant Professor at Amity University Kolkata, West Bengal, India. He has more than 300 publications to his credit, including books, book chapters, and journal articles. His research interests include machine learning, power electronics, data analysis, and pattern recognition.
Back to Top
Table of Contents
Preface
1. Integration of Thermodynamic Exergy Analysis and Fuzzy Multicriteria Decision-Making Models for Sustainable Waste-to-Energy Systems
Vishal Jain, Archan Mitra, Sanchita Paul and Rajesh Sisodia
1.1 Introduction
1.1.1 Background
1.1.2 Problem Statement
1.1.3 Research Gap
1.1.4 Research Objectives
1.1.5 Significance of the Study
1.2 Literature Review
1.2.1 Thermodynamic Exergy Analysis in Waste Management
1.2.2 Fuzzy Multicriteria Decision-Making
1.2.3 Hybrid Approaches in WTE Optimization
1.2.4 Role of AI and IoT in WTE
1.2.5 Research Gaps
1.3 Methodology
1.3.1 Research Design
1.3.2 Exergy Analysis Model
1.3.3 FMCDM Techniques
1.3.3.1 Fuzzy AHP
1.3.3.2 Fuzzy TOPSIS
1.3.4 Integration Strategy
1.3.5 AI and IoT Model Incorporation
1.4 Results and Discussion
1.4.1 Exergy Analysis Results
1.4.2 F-AHP Weight Assignments
1.4.3 Fuzzy TOPSIS Ranking
1.4.4 Integrated Analysis and Insights
1.4.5 AI and IoT Impacts
1.4.6 Policy and Practical Implications
1.5 Conclusion
References
2. Developing Fuzzy Multicriteria Decision-Making Model
Poulomi Das, Shubham Dutta, Ankit Sen, Sayan Bhattacharya and Abhishek Mukhopadhyay
2.1 Introduction
2.2 Related Work
2.2.1 Fundamentals of MCDM
2.2.2 Limitations of Traditional MCDM Methods
2.3 Framework for FMCDM
2.3.1 Fuzzification of Input Variables
2.3.2 Construction of Fuzzy Decision Matrix
2.3.3 Weight Assignment Using Fuzzy Techniques
2.3.4 Aggregation of Fuzzy Information
2.3.5 Defuzzification Process
2.3.6 Ranking and Final Decision-Making
2.3.7 Validation of the Model
2.4 Aggregation and Weighting Methods
2.4.1 Fuzzy AHP
2.4.2 Fuzzy TOPSIS
2.4.3 Fuzzy VIKOR
2.5 Use Case
2.5.1 Fuzzy Logic: A Human-Like Brain for ACs
2.6 Challenges and Limitations
2.6.1 Computational Complexity
2.6.2 Dependence on Expert Judgment
2.6.3 Handling Uncertainty in Large-Scale Problems
2.7 Conclusion
References
3. Performance Metrics and Monitoring System for Optimizing Waste Management
Manjushree Nayak and Pidugu Sai Naresh
3.1 Introduction
3.1.1 Importance of Waste-Recovery Management
3.1.2 Role of FMCDM in Waste Recovery
3.1.3 Performance Metrics and Monitoring in Waste Recovery
3.2 Theoretical Foundations of Waste-Recovery Management
3.2.1 Thermodynamic Principles in Waste Recovery
3.2.2 Exergy Analysis for Waste-Recovery Efficiency
3.2.3 Role of MCDM
3.2.4 Performance Metrics and Monitoring in Waste Recovery
3.3 FMCDM Approaches
3.3.1 Introduction to Fuzzy Analytic Hierarchy Process, Technique for Order of Preference by Similarity to Ideal Solution, and VIKOR
3.3.2 Applications in Waste-Recovery Systems
3.4 Real-Time Data Collection and Model Implementation
3.4.1 IoT-Enabled Sensors for Data Collection (Theoretical Representation)
3.4.2 Cloud Data Management Using AWS S3, PostgreSQL, and MongoDB
3.4.3 Real-Time Data Processing and Model Deployment
3.5 Predictive Analytics and Optimization Models for Waste Management
3.5.1 Machine Learning Models
3.5.2 Time-Series Forecasting with ARIMA and Prophet
3.6 Implementation of Performance Metrics Models
3.6.1 Model Development and Performance Metrics Calculation
3.6.2 Optimization Techniques and Deployment
3.6.2.1 Mathematical Problem Formulation
3.6.2.2 Optimization Algorithms
3.6.2.3 Readiness for Deployment
3.6.3 Real-Time Monitoring and Overall Performance Analysis through Power BI Dashboards
3.7 Case Studies and Real-World Applications
3.7.1 Industrial Waste-Recovery Performance Analysis
3.7.2 Predictive Waste Generation and Resource Planning
3.8 Conclusion and Future Scope
References
4. Smart Management Systems for Sustainable Waste Recovery: Bridging Industry 4.0 and Fuzzy Evaluation Techniques
Ritu Raj Choudhary, Udit Mamodiya, Ankur Srivastava and Prerna Srivastava
4.1 Introduction
4.2 Review of Literature
4.3 Research Gap and Motivation
4.4 Framework Overview
4.5 Data Analysis and Interpretation
4.5.1 Role of IoT and Sensors in Waste Stream Monitoring
4.5.2 AI Integration in Decision-Making and Process Optimization
4.5.3 Fuzzy Evaluation in Operational Management and Human Decision Support
4.5.4 Qualitative Use of Thermodynamic Concepts in Facility Design
4.6 Findings
4.6.1 Enhanced Waste Visibility and Responsiveness through IoT Integration
4.6.2 AI-Powered Automation and Predictive Waste Analytics
4.6.3 Fuzzy Logic Evaluation Served as a Fundamental Tool to Track Operational Performance That Gave Organizations Access to Multiple Organizational Levels
4.6.4 Thermodynamic Awareness in Facility Layout and Process
Optimization
4.6.5 Synergistic Impact of Integrated Systems
4.7 Future Directions
4.8 Conclusion
Bibliography
5. Foundations of Quantum Theory and Quantum Computing for Predictive Electronics
Shaon Bandyopadhyay
Introduction
5.1 Quantum Mechanics Principles Underlying Computing
5.2 Qubits, Quantum Registers, and Quantum States
5.3 The Quantum Circuit Model
5.4 Quantum Computational Complexity
5.5 Summary of Theoretical Foundations
5.6 Evolution of Quantum Hardware and Architectures
5.7 Quantum Algorithms and Complexity: Landmark Results and Recent Advances
5.8 Quantum Software and Programming Frameworks
5.9 Interdisciplinary Applications and Domain-Specific Studies
5.10 Gate-Based Quantum Computing Workflow
5.11 Quantum Annealing and Adiabatic Computing
5.12 QEC and Fault-Tolerance Frameworks
5.13 Frameworks for Hybrid Quantum–Classical Computing
5.14 Cryptography and Cybersecurity
5.15 Chemistry and Materials Science
5.16 Optimization, Finance, and Logistics
5.17 QML and Data Analysis
5.18 Healthcare and Medicine
5.19 Other Notable Application Areas
5.20 Scaling Hardware and Improving Qubit Quality
5.21 QEC and Fault-Tolerance Milestones
5.22 Algorithms and Software: The Search for the “Next Big Algorithm”
5.23 Integration with Classical Systems and Networks
5.24 Human Capital and Interdisciplinary Collaboration
5.25 Ethical, Societal, and Economic Considerations
5.26 Future Directions
Conclusion
References
6. Experimentation and Characterization of AA6082-T6 Alloy Using Bobbin Friction Stir Welding with Fuzzy Multicriteria Optimization
Dipali Pandya and Rahul Soni
Introduction
6.1 Tool Geometry and Development of Bobbin Tool
6.2 Experimental and Simulation Analysis
6.3 Fuzzy Multicriteria Optimization for BFSW
6.4 Results and Discussion
Conclusion
References
7. Artificial Intelligence for Sustainable Development Goals: A CSR Perspective
Anuradha Jain, Divyangna Beriwal and Hammouch Hind
7.1 Introduction
7.1.1 Contribution of Emerging Technologies for SDGs
7.1.2 Relevance of AI in the CSR Landscape
7.2 Conceptual Framework with CSR: Understanding AI and Its Components
7.3 Mapping AI to the SDGs: AI for Good: Sectoral Contributions to Key SDGs
7.4 CSR-Led AI Initiatives: Case Studies and Industry Trends
7.5 Data Privacy and Informed Consent with Ethical and Legal Dimensions: Ethical Use of AI in CSR
7.6 Policy Recommendations and Strategic Framework: Need for a Global Framework for AI–CSR–SDG Nexus
7.7 Challenges and Viable Solutions
7.8 Conclusion
7.9 Future Pathways
References
8. Lifecycle Cost Savings through Circular Product-as-a-Service Models: An Intrapreneurial Perspective on Innovation, Efficiency, and Customer Value
K.V.N. Lakshmi, Vidya C.M., Puja Roshani and Nabanita Ghosh
8.1 Introduction
8.2 Literature Review
8.3 Research Methodology
8.4 Limitations of the Study
8.5 Data Analysis
8.6 Findings
8.7 Conclusion
8.8 Scope for Future Research
References
9. Industry 4.0 in Applications of Waste Management
Anjana Devi S. C.
Introduction
Progression of Managing Waste for Eco-Friendly Society
Applications of Industry 4.0 Technologies in Waste Management
Modern Waste Management Models
Circular Model of Waste Recycling
Smart Waste Management System Architecture
Smart Waste Management Framework
IoT-based Smart Waste Management System
AI-Powered Smart Waste Management System
Process of Waste Management with Industry 4.0
Comparison: Traditional vs. Smart Waste Management Systems
Key Areas of Industry 4.0 Application in Waste Management
Noted Examples
Advantages of Industry 4.0 in Waste Management
Challenges in Application of Industry 4.0 for Waste Management
Conclusion
Bibliography
10. Circular Economy: Revolutionary Role in Attaining Sustainability
Nabanita Ghosh, John Benedict, Sukanya R. and Sunita Kumar
10.1 Introduction
10.2 Backdrop of Circular Economy
10.2.1 Influence of Climate Change on the Implementation of Circular Economy
10.2.2 Conversion Path from Linear Model to Circular Model
10.2.3 Factors Responsible for the Setup of Circular Economy
10.3 Mobilization of Resources in the Context of Climate Change
10.4 Use Cases as How Climate Change Induced the Emergence of Circular Economy
10.5 Pros and Cons in Implementing Circular Economy Models in Climate Change
10.5.1 Circular Economy as a Tool to Mitigate the Climatic Disaster
10.5.2 Conceptual Model Showing the Development of Climate Resilient Circular Economy
10.6 Examining the Relationship between the Circularity and CO2 Emissions at a Macrolevel
10.6.1 Examining the Relationship between the Rate of Circularity and CO2 Emissions in Different Countries for a Period of 8 Years
10.6.2 Examining the Relationship with the Use of Lagged Variable, Lagged Effect of Circularity on the Outcome Variable, CO2 Emissions
10.6.3 Summary of the Analysis
10.7 Conclusion
Bibliography
11. Life Cycle Assessment and Integration with Fuzzy Models in Waste Management for Environmental Sustainability
Archan Mitra, Rajesh Sisodia and Sanchita Paul
11.1 Introduction
11.1.1 Background
11.1.2 Problem Statement
11.1.3 Research Objective
11.1.4 Significance of Study
11.2 Overview of LCA
11.2.1 Traditional LCA Methods and Limitations
11.2.2 Fuzzy Logic and Fuzzy Multicriteria Decision Models
11.2.3 Integration of LCA and Fuzzy Models in Waste Management
11.2.4 Case Studies on Sustainable Waste Management
11.3 Research Methodology
11.4 Technical Analysis and Model Development
11.4.1 Development of Integrated LCA-Fuzzy Model
11.4.2 Modeling Uncertainties and Subjective Judgments
11.4.3 Validation and Calibration of the Model
11.5 Results and Discussion
11.5.1 Results from Case Studies
11.5.2 Impact of Fuzzy Integration on Decision-Making
11.5.3 Comparative Analysis
11.5.4 Challenges and Limitations
11.6 Future Directions and Innovations
11.6.1 Role of Fuzzy LCA Models in Promoting Circular Economy
11.6.2 Advances in Data Collection and Modeling
11.6.3 Enhancing Stakeholder Engagement in Waste Management
Decisions
11.7 Conclusion
11.7.1 Summary of Findings
11.7.2 Practical Implications
11.7.3 Policy Recommendations
11.7.4 Future Research Directions
Bibliography
12. An Optimistic Pythagorean Dissimilarity Measure and Its Smart Waste Management Application
Chinmayee Devi, Rituparna Chutia and Brindaban Gohain
12.1 Introduction
12.1.1 Motivation
12.2 Prerequisites
12.2.1 Existing Measures
12.3 Proposed Dissimilarity Measure
12.4 Numerical Study
12.5 Application
12.6 Conclusion
References
13. Cost–Benefit Optimization and Lifecycle Management in Circular Waste Systems Using Hybrid Thermodynamic-Fuzzy Models
Rakesh Aggarwal, Udit Mamodiya, Ankur Srivastava and Ritu Raj Choudhary
13.1 Introduction
13.2 Literature Review
13.2.1 Thermodynamic Approaches in Waste Recovery
13.2.2 Lifecycle Analysis and CBA in Circular Waste Systems
13.2.3 Fuzzy MCDM
13.2.4 Integrated and Hybrid Models
13.2.5 Research Gap and Motivation
13.3 Theoretical and Methodological Framework
13.3.1 Layer I: Thermodynamic Performance Evaluation
13.3.2 Layer II: Lifecycle-Based Cost–Benefit Evaluation
13.3.3 Layer III: Fuzzy MCDM
13.3.4 Integrated Model Flow and Architecture
13.4 Case Study and Simulation
13.4.1 Scenario Description and Decision Context
13.4.2 Model Application: Hybrid Evaluation Process
13.4.3 Interpretation of Results
13.5 Results, Discussion, and Policy Implications
13.5.1 Synthesis of Key Findings
13.5.2 Model Strengths and Theoretical Contributions
13.5.3 Managerial Implications
13.5.4 Policy and Governance Implications
13.5.5 Limitations and Sensitivity Considerations
13.6 Conclusion and Future Directions
References
14. Strategic Waste Recovery Management: Integrating Fuzzy Multicriteria Decision Models with Thermodynamic Efficiency Metrics
Prerna Srivastava, Vineet Pandey, Ankur Srivastava and Udit Mamodiya
14.1 Introduction
14.2 Literature Review and Theoretical Background
14.2.1 Strategic Waste Recovery and the Circular Economy Paradigm
14.2.2 Fuzzy MCDM Approaches
14.2.3 Thermodynamic Efficiency Metrics in Waste Recovery
14.2.4 Toward an Integrated Framework
14.3 Conceptual Integration Framework
14.3.1 Rationale for Integration
14.3.2 Structure of the Framework
14.3.3 Framework Visualization
14.3.4 Benefits and Evaluation of the Framework
14.3.4.1 Embrace Uncertainty: Fuzzy Logic to the Rescue
14.3.4.2 Thermodynamic Rigor
14.3.4.3 Built for Stakeholders: Inclusive and Transparent
14.3.4.4 Scalable and Adaptable
14.4 Methodological Blueprint
14.4.1 System Boundary and Scope Definition
14.4.2 Criteria Identification and Fuzzification
14.4.3 Thermodynamic Evaluation
14.4.4 Weight Derivation and Aggregation
14.4.5 Decision Synthesis and Ranking
14.4.6 Decision Dashboard and Interpretation
14.5 Application of the Framework: Strategic Evaluation of WtE Plant Siting in Vietnam
14.5.1 Case Overview
14.5.2 Applying the Framework: Step-by-Step Integration
14.5.3 Strategic Learnings
14.5.4 Adaptability and Reusability
14.6 Discussion
14.6.1 Comparative Advantage Over Conventional Methods
14.6.2 Trade-Offs and Conflicting Objectives
14.6.3 Model Limitations and Practical Challenges
14.6.4 Policy Implications and Strategic Outlook
14.7 Conclusion and Future Work
References
15. Secure Cloud Authentication for Thermodynamic Monitoring and Recovery Management Using Time-Based OTP and Single Sign-On
Vivek Kumar Prasad, Pronaya Bhattacharya, Subrata Tikadar,
Saubhik Bondopadhyay, Pushan Kumar Dutta, Bharat Bhushan and Suparna Biswas
15.1 Introduction
15.1.1 Novelty
15.1.2 Research Contributions
15.1.3 Article Layout
15.2 Literature Review
15.3 Secure Cloud Authentication Framework for Thermodynamic
and Recovery-Management Platforms
15.3.1 SSO Operation
15.3.2 TOTP Operation
15.3.3 The Framework
15.4 Use Case: Secure V2X Authentication for EV Energy Recovery and Battery Thermal Management
15.4.1 Factor Mismatching and Recovery-Oriented Access Policy
15.5 Cybersecurity for Recovery Management in Cloud-Enabled Energy Systems
15.6 Conclusion and Future Scope
Bibliography
16. Circular Economy Principles in Recovery Management
Preeti Goel and Dhruv
16.1 Introduction
16.2 Core CE Principles
16.2.1 Narrowing Resource Loops
16.2.2 Slowing Resource Loops
16.2.3 Closing Resource Loops
16.2.4 Regenerating Resource Loops
16.3 Integration of CE in Recovery Management
16.4 Case Study: Healthcare Adaptations During COVID-19 Using CE Principles
16.4.1 Challenges in Healthcare During COVID-19
16.4.2 CE Strategies in Healthcare Adaptations
16.4.3 Outcomes and Lessons Learned
16.5 Challenges and Opportunities in Adopting CE Principles in Recovery Management
16.5.1 Barriers to Adopting CE in Recovery Management
16.5.2 Opportunities Enabled by CE
16.6 Policy Frameworks and Stakeholders’ Collaboration for Effective Implementation
16.7 Future Perspectives in CE Principles for Recovery Management
16.8 Conclusion
Bibliography
17. Transforming Demand Forecasting through Predictive Analytics and Machine Learning
Dileep Rai
17.1 Introduction
17.2 Classical Demand Forecasting Models: Foundations and Limitations
17.2.1 Key Limitations of Traditional Models
17.2.2 Consequences in Modern Supply Chains
17.3 Why these Limitations Matter More Today
17.4 The Rise of Data-Driven Forecasting
17.4.1 Key Enablers of Data-Driven Forecasting
17.5 Common Predictive Analytics Methods in Forecasting
17.6 Expanded Industry Case Studies and Comparative Analysis
17.6.1 Additional Case Studies
17.6.2 Comparative Table
17.7 ML Algorithms for Demand Forecasting
17.8 Standard ML Models in Forecasting
17.9 Hybrid and Ensemble Models
17.9.1 Comparative Table
17.10 Critical Considerations in ML-Based Forecasting
17.11 The Future of Demand Forecasting: Real-Time, Autonomous,
Context-Aware
17.11.1 Emerging Trends and Innovations
17.12 Conclusion
References
18. Accelerating Product Development Cycles Effect on Standardization in Digital Twin Ecosystem by Mediation of IoT Integration in Horn of Africa
Shashi Kant, Mohit Verma and Thompson Xavier Ananth
18.1 Introduction
18.2 Background of Investigation
18.3 Statement of Problem
18.4 Theoretical Substructure
18.5 Definition and Advent of Concepts
18.6 Empirical Literature Review
18.7 Framework
18.8 Methodology of Investigation
18.9 Scale Development
18.10 Data Analysis
18.11 Discussion
18.12 Conclusion
18.13 Management Implications
18.14 Practical Implications
18.15 Theoretical Implications
18.16 Recommendations
18.17 Future Directions
18.18 Key Concepts with Definitions
References
19. IoT Architecture for End-to-End Visibility
Vugar Abdullayev, Nazila Ragimova, Bahar Asgarova, Mahbuba Shirinova, Swapan Banerjee, Mohammed Almaayah, Triwiyanto Triwiyanto and Khushwant Singh
19.1 Introduction
19.2 Fundamentals of Predictive Analytics
19.2.1 Core Concepts: Data Mining, ML, Statistical Modeling
19.2.2 Types of Predictive Models: Classification, Regression, Clustering
19.2.3 Data Sources in Consumer Electronics: Usage Data, Behavior Tracking, Sensor Data
19.3 Predictive Analytics in Consumer Electronics: Opportunities and Functions
19.3.1 Anticipating Consumer Preferences
19.3.2 Enhancing Product Features Based on Usage Patterns
19.3.3 IoT-Enabled Diagnostics
19.3.4 Preventing Failures through Predictive Algorithms
19.3.5 Demand Forecasting
19.3.6 Personalization through Predictive Models
19.4 Case Studies and Industry Examples
19.5 Evolution of AI and Predictive Algorithms
19.6 Conclusion
References
20. IoT-Based Real-Time Smart Water Management for Sustainable Resource Use
P. Kavin, K. Avinash and A. C. Shantha Sheela
20.1 Introduction
20.2 Related Work
20.3 Existing System
20.4 Proposed System
20.5 Requirement Analysis
20.6 Conclusion
References
21. A Digital Twin–Based Carbon Footprint Monitoring System
Harinipriya M., Arun Karthik B. and Manikandan T.
21.1 Introduction
21.2 Related Work
21.3 System Design
21.4 System Evaluation and Comparative Analysis
21.5 Conclusion
Acknowledgment
References
22. Leveraging Digital Twins for Supply Chain Optimization and Resilience in IoT Networks in Africa
Udit Mamodiya, Shashi Kant and Randhir Singh Baghel
22.1 Introduction
22.2 Background of Investigation
22.3 Statement of the Problem
22.4 Definition and Origin of Notions
22.5 Empirical Literature Review
22.6 Investigation Methodology
22.7 Scale Development
22.8 Data Analysis
22.9 Discussion
22.10 Conclusion
22.11 Managerial Implications
22.12 Practical Implications
22.13 Theoretical Implications
22.14 Recommendations
22.15 Future Directions
22.16 Key Terms through Definitions
References
23. Urban Intelligence—Vision AI’s Role in Smart City Security and Innovation
Udit Mamodiya, Ashish Avasthi and Indra Kishor
23.1 Introduction
23.2 Foundations of Urban Intelligence and Vision AI
23.3 Vision AI in Smart City Security
23.3.1 Public Safety and Surveillance
23.3.2 Traffic and Transportation Security
23.3.3 Critical Infrastructure Protection
23.3.4 Crowd and Event Security
23.3.5 Integration with Emergency Services
23.3.6 Threat Scenario Examples
23.4 Vision AI–Driven Innovation across Sectors
23.4.1 Smart Traffic Management
23.4.2 Urban Planning and Analytics
23.4.3 Environmental Monitoring
23.4.4 Public Health
23.4.5 Retail, Advertising, and Smart Payments
23.4.6 Accessibility and Inclusivity
23.5 System Architecture, Data Flows, Privacy, and Ethics
23.5.1 Architecture Overview
23.5.1.1 Edge Devices/Smart Cameras
23.5.1.2 Edge Gateway
23.5.1.3 Cloud or Centralized Analytics Layer
23.5.1.4 Integration Hubs and Application Interfaces
23.5.2 Data Pipeline and Feedback Loops
23.5.3 Privacy and Ethical Considerations
23.5.4 Regulatory Environment and Standards
23.6 Case Studies
23.6.1 City A: Transithub Vision AI Deployment
23.6.2 City B: Critical Infrastructure Protection
23.6.3 City C: Traffic Security and Smart Enforcement
23.6.4 City D: Sustainable Innovation and Public Services
23.7 Diagrams and Table Summary
23.8 Future Perspectives and Conclusions
23.8.1 Emerging Innovations in Vision-Based AI for Urban Environments
23.8.2 Key Challenges in Deploying Vision AI in Urban Environments
23.8.3 Metrics and Evaluation Criteria for Vision AI in Urban
Applications
23.8.4 Steps for City Governments and Urban Planners
23.8.5 Conclusion
References
24. Study of Resilient SCADA Framework, Security Trends, and Intelligent Protocol Design: Fortifying and Enhancing Petrochemical Process Controls against Future Threats—A Review
Aldrin Karunaharan K. and Abdul Hameed Kalifullah
24.1 Introduction
24.2 Literature Survey
24.3 SCADA System Approach
24.4 Review of Resilient SCADA Framework
24.5 Conclusion
References
25. Change Management for Supply-Chain Transformation
S. B. Donaev, A. V. Alizadeh, M. Singh, R. G. Abaszade, V. H. Abdullayev, I. X. Normatov and Triwiyanto Triwiyanto
25.1 Introduction
25.2 Types of Consumer Devices Generating Data
25.3 Methods of Data Collection
25.3.1 Passive Data Collection
25.3.2 Active Data Collection
25.3.3 Automated Data Collection
25.4 Types of Data Collected
25.4.1 Behavioral Data
25.4.2 Biometric Data
25.4.3 Location Data
25.4.4 Environmental Data
25.4.5 Transactional and Interaction Data
25.5 Data Processing and Storage
25.6 Applications of Collected Data
25.7 Conclusion
References
26. Digital Supply-Chain Talent Development
R. G. Abaszade, A. V. Alizadeh, M. Singh, V. H. Abdullayev, R. E. Ismibayli, S. B. Donaev, I. X. Normatov and T. K. Asgarov
26.1 Introduction
26.2 Core Concepts of ML
26.3 Types of ML
26.3.1 Supervised Learning
26.3.2 Unsupervised Learning
26.3.3 Reinforcement Learning
26.4 Popular Algorithms and Techniques
26.5 Model Evaluation and Metrics
26.6 Tools and Libraries for ML
26.7 Challenges and Limitations in ML
26.8 Future Perspectives in ML
26.9 Conclusion
References
27. Ethical and Social Governance of AI-Enabled Supply Chains
F. G. Abaszadeh, E. M. Gadirova, F. G. Asadov, Davron Juraev,
Swapan Banerjee, Mohammed Almaayah and Rıfat Benveniste
27.1 Introduction
27.2 Fundamentals of Real-Time Processing
27.2.1 Latency, Throughput, Determinism, and Responsiveness
27.2.2 Differences between Real-Time and Batch Processing
27.2.3 Hard versus Soft Real-Time Systems
27.2.4 Relationship with Embedded Systems and Edge Computing
27.3 Real-Time Processing in Different Consumer Electronics
27.3.1 Smartphones and Tablets
27.3.2 Smart Home Devices
27.4 Core Technologies Enabling Real-Time Processing
27.5 Applications of Real-Time Processing in Consumer Electronics
27.5.1 Health Monitoring and Biometric Analysis
27.5.2 Navigation Systems and Autonomous Features in Vehicles
27.6 Conclusion
References
28. Resilience and Risk in the Postpandemic Era
M. Singh, R. G. Abaszade, V.H. Abdullayev, S. B. Donaev, M. Topuz, Swapan Banerjee, Rıfat Benveniste and Mohammed Almaayah
28.1 Introduction
28.2 Fundamentals of Quantum Computing
28.2.1 Basic Concepts: Qubits, Superposition, Entanglement,
Quantum Gates
28.2.2 Quantum Algorithms
28.2.3 Differences between Classical and Quantum Computing
28.3 Fundamentals of ML
28.4 Integration of Quantum Computing and ML
28.5 Core Algorithms and Methods in QML
28.6 Applications of QML
28.7 Conclusion
References
29. Sustainability Transformation Roadmaps
M. Singh, R.G. Abaszade, Sardor Donaev, Ibrohimali Normatov,
Fuad Abaszadeh, Sherzod Boltaev, Narmina Guliyeva and Davron Juraev
29.1 Introduction
29.2 Fundamental Principles of Quantum Mechanics for Computing
29.3 Basic Elements of Quantum Computing
29.4 Key Quantum Algorithms
29.4.1 Deutsch–Jozsa Algorithm
29.4.2 Grover’s Search Algorithm
29.4.3 Shor’s Factoring Algorithm
29.4.4 Quantum Fourier Transform
29.5 Quantum Computing Architectures and Models
29.6 Quantum Error Correction and Decoherence
29.7 Applications of Quantum Computing
29.8 Conclusion
References
30. Enterprise Mortgage Management System Using Power Platform
Himanshu Kumar, G. Ushaswi and Kamaleshwar T.
30.1 Introduction
30.2 Literature Review
30.3 Proposed Method
30.3.1 Customer Interface and Application Processing
30.3.2 Backend Processing and Case Management
30.3.3 External Services and APR Calculation
30.3.4 Security and Access Control
30.3.5 Business Process Automation and Workflow Management
30.4 Results
30.5 Discussion
30.6 Conclusion
References
31. Additive Manufacturing and the Rise of Digital Inventory
Fuad Abaszadeh, Elmina Gadirova, Ibrohimali Normatov, Fargan Asadov, Swapan Banerjee and Triwiyanto Triwiyanto
31.1 Introduction
31.2 Types of Consumer Electronics and Data Exposure
31.3 Nature of Data Collected and Privacy Concerns
31.4 Core Quantum Algorithms Applied in Machine Learning
31.5 Key Privacy and Security Risks
31.6 Security Mechanisms and Privacy-Preserving Technologies
31.7 Best Practices for Enhancing Data Privacy and Security
31.8 Conclusion
References
32. Pharma of the World to Pharma Powerhouse of the World: Leveraging AI for Sustainable Growth in Indian Pharma Startups
K. V. N. Lakshmi, Guhashri Musalkol, Mahima Vishwanath, Veena Keerthi and Nabanita Ghosh
31.1 Theoretical Background
32.2 Statement of the Problem
32.3 Review of Literature
32.4 Research Gap
32.5 Scope of the Study
32.6 Objectives of the Study
32.7 Research Design
32.8 Limitations of the Study
32.9 Data Analysis
Construct Validity and Factor Structure
32.10 Findings
32.11 Conclusion
32.12 Scope for Future Research
References
33. Fuel Storage and Supply Unit of Ship Using Smart Plant Instrumentation
K. Prabhu, S. Vijayachitra, M. Dharani, R. Ghurucharan and M. Mathiarashu
33.1 Introduction
33.2 Proposed Methodology
33.3 Instrument Tagging
33.4 Connection Setup
33.5 Block Diagram
33.6 Wiring Diagram Generation
33.7 Specification Sheet
33.8 Instrument Index
33.9 Data Management
33.10 Validation and Quality Assurance
33.11 Results
33.11.1 Wiring Diagram Document Sheet
33.11.2 Browser Module
33.11.3 Specification Sheet
33.12 Conclusion
33.13 Future Scope
References
34. Future Horizons: Emerging Technologies and Models
Alex Khang, Vugar Abdullayev, Nazila Ragimova, Vasila Abbasova, Jale Agazade, Yegane Aliyeva, Shafi Danyalov, Ajesh F. and Fuad Abaszadeh
34.1 Introduction
34.2 Fundamentals of QC for ML
34.3 Overview of Classical ML Concepts Relevant to QML
34.3.1 Classical Supervised, Unsupervised, and Reinforcement Learning
34.3.2 Complexity Challenges in Classical ML
34.3.3 Potential Areas for Quantum Acceleration
34.4 Core Quantum Algorithms Applied in ML
34.4.1 Quantum Support Vector Machine
34.4.2 Quantum Principal Component Analysis
34.4.3 Quantum k-Means Clustering
34.4.4 Quantum Neural Networks
34.4.5 Hybrid Quantum–Classical Algorithms
34.5 Applications of QML
34.5.1 Quantum Chemistry and Materials Science
34.5.2 Financial Forecasting and Risk Analysis
34.5.3 Healthcare and Bioinformatics
34.5.4 Optimization Problems in Industry
34.6 Conclusion
References
Index
Back to Top
Preface
1. Integration of Thermodynamic Exergy Analysis and Fuzzy Multicriteria Decision-Making Models for Sustainable Waste-to-Energy Systems
Vishal Jain, Archan Mitra, Sanchita Paul and Rajesh Sisodia
1.1 Introduction
1.1.1 Background
1.1.2 Problem Statement
1.1.3 Research Gap
1.1.4 Research Objectives
1.1.5 Significance of the Study
1.2 Literature Review
1.2.1 Thermodynamic Exergy Analysis in Waste Management
1.2.2 Fuzzy Multicriteria Decision-Making
1.2.3 Hybrid Approaches in WTE Optimization
1.2.4 Role of AI and IoT in WTE
1.2.5 Research Gaps
1.3 Methodology
1.3.1 Research Design
1.3.2 Exergy Analysis Model
1.3.3 FMCDM Techniques
1.3.3.1 Fuzzy AHP
1.3.3.2 Fuzzy TOPSIS
1.3.4 Integration Strategy
1.3.5 AI and IoT Model Incorporation
1.4 Results and Discussion
1.4.1 Exergy Analysis Results
1.4.2 F-AHP Weight Assignments
1.4.3 Fuzzy TOPSIS Ranking
1.4.4 Integrated Analysis and Insights
1.4.5 AI and IoT Impacts
1.4.6 Policy and Practical Implications
1.5 Conclusion
References
2. Developing Fuzzy Multicriteria Decision-Making Model
Poulomi Das, Shubham Dutta, Ankit Sen, Sayan Bhattacharya and Abhishek Mukhopadhyay
2.1 Introduction
2.2 Related Work
2.2.1 Fundamentals of MCDM
2.2.2 Limitations of Traditional MCDM Methods
2.3 Framework for FMCDM
2.3.1 Fuzzification of Input Variables
2.3.2 Construction of Fuzzy Decision Matrix
2.3.3 Weight Assignment Using Fuzzy Techniques
2.3.4 Aggregation of Fuzzy Information
2.3.5 Defuzzification Process
2.3.6 Ranking and Final Decision-Making
2.3.7 Validation of the Model
2.4 Aggregation and Weighting Methods
2.4.1 Fuzzy AHP
2.4.2 Fuzzy TOPSIS
2.4.3 Fuzzy VIKOR
2.5 Use Case
2.5.1 Fuzzy Logic: A Human-Like Brain for ACs
2.6 Challenges and Limitations
2.6.1 Computational Complexity
2.6.2 Dependence on Expert Judgment
2.6.3 Handling Uncertainty in Large-Scale Problems
2.7 Conclusion
References
3. Performance Metrics and Monitoring System for Optimizing Waste Management
Manjushree Nayak and Pidugu Sai Naresh
3.1 Introduction
3.1.1 Importance of Waste-Recovery Management
3.1.2 Role of FMCDM in Waste Recovery
3.1.3 Performance Metrics and Monitoring in Waste Recovery
3.2 Theoretical Foundations of Waste-Recovery Management
3.2.1 Thermodynamic Principles in Waste Recovery
3.2.2 Exergy Analysis for Waste-Recovery Efficiency
3.2.3 Role of MCDM
3.2.4 Performance Metrics and Monitoring in Waste Recovery
3.3 FMCDM Approaches
3.3.1 Introduction to Fuzzy Analytic Hierarchy Process, Technique for Order of Preference by Similarity to Ideal Solution, and VIKOR
3.3.2 Applications in Waste-Recovery Systems
3.4 Real-Time Data Collection and Model Implementation
3.4.1 IoT-Enabled Sensors for Data Collection (Theoretical Representation)
3.4.2 Cloud Data Management Using AWS S3, PostgreSQL, and MongoDB
3.4.3 Real-Time Data Processing and Model Deployment
3.5 Predictive Analytics and Optimization Models for Waste Management
3.5.1 Machine Learning Models
3.5.2 Time-Series Forecasting with ARIMA and Prophet
3.6 Implementation of Performance Metrics Models
3.6.1 Model Development and Performance Metrics Calculation
3.6.2 Optimization Techniques and Deployment
3.6.2.1 Mathematical Problem Formulation
3.6.2.2 Optimization Algorithms
3.6.2.3 Readiness for Deployment
3.6.3 Real-Time Monitoring and Overall Performance Analysis through Power BI Dashboards
3.7 Case Studies and Real-World Applications
3.7.1 Industrial Waste-Recovery Performance Analysis
3.7.2 Predictive Waste Generation and Resource Planning
3.8 Conclusion and Future Scope
References
4. Smart Management Systems for Sustainable Waste Recovery: Bridging Industry 4.0 and Fuzzy Evaluation Techniques
Ritu Raj Choudhary, Udit Mamodiya, Ankur Srivastava and Prerna Srivastava
4.1 Introduction
4.2 Review of Literature
4.3 Research Gap and Motivation
4.4 Framework Overview
4.5 Data Analysis and Interpretation
4.5.1 Role of IoT and Sensors in Waste Stream Monitoring
4.5.2 AI Integration in Decision-Making and Process Optimization
4.5.3 Fuzzy Evaluation in Operational Management and Human Decision Support
4.5.4 Qualitative Use of Thermodynamic Concepts in Facility Design
4.6 Findings
4.6.1 Enhanced Waste Visibility and Responsiveness through IoT Integration
4.6.2 AI-Powered Automation and Predictive Waste Analytics
4.6.3 Fuzzy Logic Evaluation Served as a Fundamental Tool to Track Operational Performance That Gave Organizations Access to Multiple Organizational Levels
4.6.4 Thermodynamic Awareness in Facility Layout and Process
Optimization
4.6.5 Synergistic Impact of Integrated Systems
4.7 Future Directions
4.8 Conclusion
Bibliography
5. Foundations of Quantum Theory and Quantum Computing for Predictive Electronics
Shaon Bandyopadhyay
Introduction
5.1 Quantum Mechanics Principles Underlying Computing
5.2 Qubits, Quantum Registers, and Quantum States
5.3 The Quantum Circuit Model
5.4 Quantum Computational Complexity
5.5 Summary of Theoretical Foundations
5.6 Evolution of Quantum Hardware and Architectures
5.7 Quantum Algorithms and Complexity: Landmark Results and Recent Advances
5.8 Quantum Software and Programming Frameworks
5.9 Interdisciplinary Applications and Domain-Specific Studies
5.10 Gate-Based Quantum Computing Workflow
5.11 Quantum Annealing and Adiabatic Computing
5.12 QEC and Fault-Tolerance Frameworks
5.13 Frameworks for Hybrid Quantum–Classical Computing
5.14 Cryptography and Cybersecurity
5.15 Chemistry and Materials Science
5.16 Optimization, Finance, and Logistics
5.17 QML and Data Analysis
5.18 Healthcare and Medicine
5.19 Other Notable Application Areas
5.20 Scaling Hardware and Improving Qubit Quality
5.21 QEC and Fault-Tolerance Milestones
5.22 Algorithms and Software: The Search for the “Next Big Algorithm”
5.23 Integration with Classical Systems and Networks
5.24 Human Capital and Interdisciplinary Collaboration
5.25 Ethical, Societal, and Economic Considerations
5.26 Future Directions
Conclusion
References
6. Experimentation and Characterization of AA6082-T6 Alloy Using Bobbin Friction Stir Welding with Fuzzy Multicriteria Optimization
Dipali Pandya and Rahul Soni
Introduction
6.1 Tool Geometry and Development of Bobbin Tool
6.2 Experimental and Simulation Analysis
6.3 Fuzzy Multicriteria Optimization for BFSW
6.4 Results and Discussion
Conclusion
References
7. Artificial Intelligence for Sustainable Development Goals: A CSR Perspective
Anuradha Jain, Divyangna Beriwal and Hammouch Hind
7.1 Introduction
7.1.1 Contribution of Emerging Technologies for SDGs
7.1.2 Relevance of AI in the CSR Landscape
7.2 Conceptual Framework with CSR: Understanding AI and Its Components
7.3 Mapping AI to the SDGs: AI for Good: Sectoral Contributions to Key SDGs
7.4 CSR-Led AI Initiatives: Case Studies and Industry Trends
7.5 Data Privacy and Informed Consent with Ethical and Legal Dimensions: Ethical Use of AI in CSR
7.6 Policy Recommendations and Strategic Framework: Need for a Global Framework for AI–CSR–SDG Nexus
7.7 Challenges and Viable Solutions
7.8 Conclusion
7.9 Future Pathways
References
8. Lifecycle Cost Savings through Circular Product-as-a-Service Models: An Intrapreneurial Perspective on Innovation, Efficiency, and Customer Value
K.V.N. Lakshmi, Vidya C.M., Puja Roshani and Nabanita Ghosh
8.1 Introduction
8.2 Literature Review
8.3 Research Methodology
8.4 Limitations of the Study
8.5 Data Analysis
8.6 Findings
8.7 Conclusion
8.8 Scope for Future Research
References
9. Industry 4.0 in Applications of Waste Management
Anjana Devi S. C.
Introduction
Progression of Managing Waste for Eco-Friendly Society
Applications of Industry 4.0 Technologies in Waste Management
Modern Waste Management Models
Circular Model of Waste Recycling
Smart Waste Management System Architecture
Smart Waste Management Framework
IoT-based Smart Waste Management System
AI-Powered Smart Waste Management System
Process of Waste Management with Industry 4.0
Comparison: Traditional vs. Smart Waste Management Systems
Key Areas of Industry 4.0 Application in Waste Management
Noted Examples
Advantages of Industry 4.0 in Waste Management
Challenges in Application of Industry 4.0 for Waste Management
Conclusion
Bibliography
10. Circular Economy: Revolutionary Role in Attaining Sustainability
Nabanita Ghosh, John Benedict, Sukanya R. and Sunita Kumar
10.1 Introduction
10.2 Backdrop of Circular Economy
10.2.1 Influence of Climate Change on the Implementation of Circular Economy
10.2.2 Conversion Path from Linear Model to Circular Model
10.2.3 Factors Responsible for the Setup of Circular Economy
10.3 Mobilization of Resources in the Context of Climate Change
10.4 Use Cases as How Climate Change Induced the Emergence of Circular Economy
10.5 Pros and Cons in Implementing Circular Economy Models in Climate Change
10.5.1 Circular Economy as a Tool to Mitigate the Climatic Disaster
10.5.2 Conceptual Model Showing the Development of Climate Resilient Circular Economy
10.6 Examining the Relationship between the Circularity and CO2 Emissions at a Macrolevel
10.6.1 Examining the Relationship between the Rate of Circularity and CO2 Emissions in Different Countries for a Period of 8 Years
10.6.2 Examining the Relationship with the Use of Lagged Variable, Lagged Effect of Circularity on the Outcome Variable, CO2 Emissions
10.6.3 Summary of the Analysis
10.7 Conclusion
Bibliography
11. Life Cycle Assessment and Integration with Fuzzy Models in Waste Management for Environmental Sustainability
Archan Mitra, Rajesh Sisodia and Sanchita Paul
11.1 Introduction
11.1.1 Background
11.1.2 Problem Statement
11.1.3 Research Objective
11.1.4 Significance of Study
11.2 Overview of LCA
11.2.1 Traditional LCA Methods and Limitations
11.2.2 Fuzzy Logic and Fuzzy Multicriteria Decision Models
11.2.3 Integration of LCA and Fuzzy Models in Waste Management
11.2.4 Case Studies on Sustainable Waste Management
11.3 Research Methodology
11.4 Technical Analysis and Model Development
11.4.1 Development of Integrated LCA-Fuzzy Model
11.4.2 Modeling Uncertainties and Subjective Judgments
11.4.3 Validation and Calibration of the Model
11.5 Results and Discussion
11.5.1 Results from Case Studies
11.5.2 Impact of Fuzzy Integration on Decision-Making
11.5.3 Comparative Analysis
11.5.4 Challenges and Limitations
11.6 Future Directions and Innovations
11.6.1 Role of Fuzzy LCA Models in Promoting Circular Economy
11.6.2 Advances in Data Collection and Modeling
11.6.3 Enhancing Stakeholder Engagement in Waste Management
Decisions
11.7 Conclusion
11.7.1 Summary of Findings
11.7.2 Practical Implications
11.7.3 Policy Recommendations
11.7.4 Future Research Directions
Bibliography
12. An Optimistic Pythagorean Dissimilarity Measure and Its Smart Waste Management Application
Chinmayee Devi, Rituparna Chutia and Brindaban Gohain
12.1 Introduction
12.1.1 Motivation
12.2 Prerequisites
12.2.1 Existing Measures
12.3 Proposed Dissimilarity Measure
12.4 Numerical Study
12.5 Application
12.6 Conclusion
References
13. Cost–Benefit Optimization and Lifecycle Management in Circular Waste Systems Using Hybrid Thermodynamic-Fuzzy Models
Rakesh Aggarwal, Udit Mamodiya, Ankur Srivastava and Ritu Raj Choudhary
13.1 Introduction
13.2 Literature Review
13.2.1 Thermodynamic Approaches in Waste Recovery
13.2.2 Lifecycle Analysis and CBA in Circular Waste Systems
13.2.3 Fuzzy MCDM
13.2.4 Integrated and Hybrid Models
13.2.5 Research Gap and Motivation
13.3 Theoretical and Methodological Framework
13.3.1 Layer I: Thermodynamic Performance Evaluation
13.3.2 Layer II: Lifecycle-Based Cost–Benefit Evaluation
13.3.3 Layer III: Fuzzy MCDM
13.3.4 Integrated Model Flow and Architecture
13.4 Case Study and Simulation
13.4.1 Scenario Description and Decision Context
13.4.2 Model Application: Hybrid Evaluation Process
13.4.3 Interpretation of Results
13.5 Results, Discussion, and Policy Implications
13.5.1 Synthesis of Key Findings
13.5.2 Model Strengths and Theoretical Contributions
13.5.3 Managerial Implications
13.5.4 Policy and Governance Implications
13.5.5 Limitations and Sensitivity Considerations
13.6 Conclusion and Future Directions
References
14. Strategic Waste Recovery Management: Integrating Fuzzy Multicriteria Decision Models with Thermodynamic Efficiency Metrics
Prerna Srivastava, Vineet Pandey, Ankur Srivastava and Udit Mamodiya
14.1 Introduction
14.2 Literature Review and Theoretical Background
14.2.1 Strategic Waste Recovery and the Circular Economy Paradigm
14.2.2 Fuzzy MCDM Approaches
14.2.3 Thermodynamic Efficiency Metrics in Waste Recovery
14.2.4 Toward an Integrated Framework
14.3 Conceptual Integration Framework
14.3.1 Rationale for Integration
14.3.2 Structure of the Framework
14.3.3 Framework Visualization
14.3.4 Benefits and Evaluation of the Framework
14.3.4.1 Embrace Uncertainty: Fuzzy Logic to the Rescue
14.3.4.2 Thermodynamic Rigor
14.3.4.3 Built for Stakeholders: Inclusive and Transparent
14.3.4.4 Scalable and Adaptable
14.4 Methodological Blueprint
14.4.1 System Boundary and Scope Definition
14.4.2 Criteria Identification and Fuzzification
14.4.3 Thermodynamic Evaluation
14.4.4 Weight Derivation and Aggregation
14.4.5 Decision Synthesis and Ranking
14.4.6 Decision Dashboard and Interpretation
14.5 Application of the Framework: Strategic Evaluation of WtE Plant Siting in Vietnam
14.5.1 Case Overview
14.5.2 Applying the Framework: Step-by-Step Integration
14.5.3 Strategic Learnings
14.5.4 Adaptability and Reusability
14.6 Discussion
14.6.1 Comparative Advantage Over Conventional Methods
14.6.2 Trade-Offs and Conflicting Objectives
14.6.3 Model Limitations and Practical Challenges
14.6.4 Policy Implications and Strategic Outlook
14.7 Conclusion and Future Work
References
15. Secure Cloud Authentication for Thermodynamic Monitoring and Recovery Management Using Time-Based OTP and Single Sign-On
Vivek Kumar Prasad, Pronaya Bhattacharya, Subrata Tikadar,
Saubhik Bondopadhyay, Pushan Kumar Dutta, Bharat Bhushan and Suparna Biswas
15.1 Introduction
15.1.1 Novelty
15.1.2 Research Contributions
15.1.3 Article Layout
15.2 Literature Review
15.3 Secure Cloud Authentication Framework for Thermodynamic
and Recovery-Management Platforms
15.3.1 SSO Operation
15.3.2 TOTP Operation
15.3.3 The Framework
15.4 Use Case: Secure V2X Authentication for EV Energy Recovery and Battery Thermal Management
15.4.1 Factor Mismatching and Recovery-Oriented Access Policy
15.5 Cybersecurity for Recovery Management in Cloud-Enabled Energy Systems
15.6 Conclusion and Future Scope
Bibliography
16. Circular Economy Principles in Recovery Management
Preeti Goel and Dhruv
16.1 Introduction
16.2 Core CE Principles
16.2.1 Narrowing Resource Loops
16.2.2 Slowing Resource Loops
16.2.3 Closing Resource Loops
16.2.4 Regenerating Resource Loops
16.3 Integration of CE in Recovery Management
16.4 Case Study: Healthcare Adaptations During COVID-19 Using CE Principles
16.4.1 Challenges in Healthcare During COVID-19
16.4.2 CE Strategies in Healthcare Adaptations
16.4.3 Outcomes and Lessons Learned
16.5 Challenges and Opportunities in Adopting CE Principles in Recovery Management
16.5.1 Barriers to Adopting CE in Recovery Management
16.5.2 Opportunities Enabled by CE
16.6 Policy Frameworks and Stakeholders’ Collaboration for Effective Implementation
16.7 Future Perspectives in CE Principles for Recovery Management
16.8 Conclusion
Bibliography
17. Transforming Demand Forecasting through Predictive Analytics and Machine Learning
Dileep Rai
17.1 Introduction
17.2 Classical Demand Forecasting Models: Foundations and Limitations
17.2.1 Key Limitations of Traditional Models
17.2.2 Consequences in Modern Supply Chains
17.3 Why these Limitations Matter More Today
17.4 The Rise of Data-Driven Forecasting
17.4.1 Key Enablers of Data-Driven Forecasting
17.5 Common Predictive Analytics Methods in Forecasting
17.6 Expanded Industry Case Studies and Comparative Analysis
17.6.1 Additional Case Studies
17.6.2 Comparative Table
17.7 ML Algorithms for Demand Forecasting
17.8 Standard ML Models in Forecasting
17.9 Hybrid and Ensemble Models
17.9.1 Comparative Table
17.10 Critical Considerations in ML-Based Forecasting
17.11 The Future of Demand Forecasting: Real-Time, Autonomous,
Context-Aware
17.11.1 Emerging Trends and Innovations
17.12 Conclusion
References
18. Accelerating Product Development Cycles Effect on Standardization in Digital Twin Ecosystem by Mediation of IoT Integration in Horn of Africa
Shashi Kant, Mohit Verma and Thompson Xavier Ananth
18.1 Introduction
18.2 Background of Investigation
18.3 Statement of Problem
18.4 Theoretical Substructure
18.5 Definition and Advent of Concepts
18.6 Empirical Literature Review
18.7 Framework
18.8 Methodology of Investigation
18.9 Scale Development
18.10 Data Analysis
18.11 Discussion
18.12 Conclusion
18.13 Management Implications
18.14 Practical Implications
18.15 Theoretical Implications
18.16 Recommendations
18.17 Future Directions
18.18 Key Concepts with Definitions
References
19. IoT Architecture for End-to-End Visibility
Vugar Abdullayev, Nazila Ragimova, Bahar Asgarova, Mahbuba Shirinova, Swapan Banerjee, Mohammed Almaayah, Triwiyanto Triwiyanto and Khushwant Singh
19.1 Introduction
19.2 Fundamentals of Predictive Analytics
19.2.1 Core Concepts: Data Mining, ML, Statistical Modeling
19.2.2 Types of Predictive Models: Classification, Regression, Clustering
19.2.3 Data Sources in Consumer Electronics: Usage Data, Behavior Tracking, Sensor Data
19.3 Predictive Analytics in Consumer Electronics: Opportunities and Functions
19.3.1 Anticipating Consumer Preferences
19.3.2 Enhancing Product Features Based on Usage Patterns
19.3.3 IoT-Enabled Diagnostics
19.3.4 Preventing Failures through Predictive Algorithms
19.3.5 Demand Forecasting
19.3.6 Personalization through Predictive Models
19.4 Case Studies and Industry Examples
19.5 Evolution of AI and Predictive Algorithms
19.6 Conclusion
References
20. IoT-Based Real-Time Smart Water Management for Sustainable Resource Use
P. Kavin, K. Avinash and A. C. Shantha Sheela
20.1 Introduction
20.2 Related Work
20.3 Existing System
20.4 Proposed System
20.5 Requirement Analysis
20.6 Conclusion
References
21. A Digital Twin–Based Carbon Footprint Monitoring System
Harinipriya M., Arun Karthik B. and Manikandan T.
21.1 Introduction
21.2 Related Work
21.3 System Design
21.4 System Evaluation and Comparative Analysis
21.5 Conclusion
Acknowledgment
References
22. Leveraging Digital Twins for Supply Chain Optimization and Resilience in IoT Networks in Africa
Udit Mamodiya, Shashi Kant and Randhir Singh Baghel
22.1 Introduction
22.2 Background of Investigation
22.3 Statement of the Problem
22.4 Definition and Origin of Notions
22.5 Empirical Literature Review
22.6 Investigation Methodology
22.7 Scale Development
22.8 Data Analysis
22.9 Discussion
22.10 Conclusion
22.11 Managerial Implications
22.12 Practical Implications
22.13 Theoretical Implications
22.14 Recommendations
22.15 Future Directions
22.16 Key Terms through Definitions
References
23. Urban Intelligence—Vision AI’s Role in Smart City Security and Innovation
Udit Mamodiya, Ashish Avasthi and Indra Kishor
23.1 Introduction
23.2 Foundations of Urban Intelligence and Vision AI
23.3 Vision AI in Smart City Security
23.3.1 Public Safety and Surveillance
23.3.2 Traffic and Transportation Security
23.3.3 Critical Infrastructure Protection
23.3.4 Crowd and Event Security
23.3.5 Integration with Emergency Services
23.3.6 Threat Scenario Examples
23.4 Vision AI–Driven Innovation across Sectors
23.4.1 Smart Traffic Management
23.4.2 Urban Planning and Analytics
23.4.3 Environmental Monitoring
23.4.4 Public Health
23.4.5 Retail, Advertising, and Smart Payments
23.4.6 Accessibility and Inclusivity
23.5 System Architecture, Data Flows, Privacy, and Ethics
23.5.1 Architecture Overview
23.5.1.1 Edge Devices/Smart Cameras
23.5.1.2 Edge Gateway
23.5.1.3 Cloud or Centralized Analytics Layer
23.5.1.4 Integration Hubs and Application Interfaces
23.5.2 Data Pipeline and Feedback Loops
23.5.3 Privacy and Ethical Considerations
23.5.4 Regulatory Environment and Standards
23.6 Case Studies
23.6.1 City A: Transithub Vision AI Deployment
23.6.2 City B: Critical Infrastructure Protection
23.6.3 City C: Traffic Security and Smart Enforcement
23.6.4 City D: Sustainable Innovation and Public Services
23.7 Diagrams and Table Summary
23.8 Future Perspectives and Conclusions
23.8.1 Emerging Innovations in Vision-Based AI for Urban Environments
23.8.2 Key Challenges in Deploying Vision AI in Urban Environments
23.8.3 Metrics and Evaluation Criteria for Vision AI in Urban
Applications
23.8.4 Steps for City Governments and Urban Planners
23.8.5 Conclusion
References
24. Study of Resilient SCADA Framework, Security Trends, and Intelligent Protocol Design: Fortifying and Enhancing Petrochemical Process Controls against Future Threats—A Review
Aldrin Karunaharan K. and Abdul Hameed Kalifullah
24.1 Introduction
24.2 Literature Survey
24.3 SCADA System Approach
24.4 Review of Resilient SCADA Framework
24.5 Conclusion
References
25. Change Management for Supply-Chain Transformation
S. B. Donaev, A. V. Alizadeh, M. Singh, R. G. Abaszade, V. H. Abdullayev, I. X. Normatov and Triwiyanto Triwiyanto
25.1 Introduction
25.2 Types of Consumer Devices Generating Data
25.3 Methods of Data Collection
25.3.1 Passive Data Collection
25.3.2 Active Data Collection
25.3.3 Automated Data Collection
25.4 Types of Data Collected
25.4.1 Behavioral Data
25.4.2 Biometric Data
25.4.3 Location Data
25.4.4 Environmental Data
25.4.5 Transactional and Interaction Data
25.5 Data Processing and Storage
25.6 Applications of Collected Data
25.7 Conclusion
References
26. Digital Supply-Chain Talent Development
R. G. Abaszade, A. V. Alizadeh, M. Singh, V. H. Abdullayev, R. E. Ismibayli, S. B. Donaev, I. X. Normatov and T. K. Asgarov
26.1 Introduction
26.2 Core Concepts of ML
26.3 Types of ML
26.3.1 Supervised Learning
26.3.2 Unsupervised Learning
26.3.3 Reinforcement Learning
26.4 Popular Algorithms and Techniques
26.5 Model Evaluation and Metrics
26.6 Tools and Libraries for ML
26.7 Challenges and Limitations in ML
26.8 Future Perspectives in ML
26.9 Conclusion
References
27. Ethical and Social Governance of AI-Enabled Supply Chains
F. G. Abaszadeh, E. M. Gadirova, F. G. Asadov, Davron Juraev,
Swapan Banerjee, Mohammed Almaayah and Rıfat Benveniste
27.1 Introduction
27.2 Fundamentals of Real-Time Processing
27.2.1 Latency, Throughput, Determinism, and Responsiveness
27.2.2 Differences between Real-Time and Batch Processing
27.2.3 Hard versus Soft Real-Time Systems
27.2.4 Relationship with Embedded Systems and Edge Computing
27.3 Real-Time Processing in Different Consumer Electronics
27.3.1 Smartphones and Tablets
27.3.2 Smart Home Devices
27.4 Core Technologies Enabling Real-Time Processing
27.5 Applications of Real-Time Processing in Consumer Electronics
27.5.1 Health Monitoring and Biometric Analysis
27.5.2 Navigation Systems and Autonomous Features in Vehicles
27.6 Conclusion
References
28. Resilience and Risk in the Postpandemic Era
M. Singh, R. G. Abaszade, V.H. Abdullayev, S. B. Donaev, M. Topuz, Swapan Banerjee, Rıfat Benveniste and Mohammed Almaayah
28.1 Introduction
28.2 Fundamentals of Quantum Computing
28.2.1 Basic Concepts: Qubits, Superposition, Entanglement,
Quantum Gates
28.2.2 Quantum Algorithms
28.2.3 Differences between Classical and Quantum Computing
28.3 Fundamentals of ML
28.4 Integration of Quantum Computing and ML
28.5 Core Algorithms and Methods in QML
28.6 Applications of QML
28.7 Conclusion
References
29. Sustainability Transformation Roadmaps
M. Singh, R.G. Abaszade, Sardor Donaev, Ibrohimali Normatov,
Fuad Abaszadeh, Sherzod Boltaev, Narmina Guliyeva and Davron Juraev
29.1 Introduction
29.2 Fundamental Principles of Quantum Mechanics for Computing
29.3 Basic Elements of Quantum Computing
29.4 Key Quantum Algorithms
29.4.1 Deutsch–Jozsa Algorithm
29.4.2 Grover’s Search Algorithm
29.4.3 Shor’s Factoring Algorithm
29.4.4 Quantum Fourier Transform
29.5 Quantum Computing Architectures and Models
29.6 Quantum Error Correction and Decoherence
29.7 Applications of Quantum Computing
29.8 Conclusion
References
30. Enterprise Mortgage Management System Using Power Platform
Himanshu Kumar, G. Ushaswi and Kamaleshwar T.
30.1 Introduction
30.2 Literature Review
30.3 Proposed Method
30.3.1 Customer Interface and Application Processing
30.3.2 Backend Processing and Case Management
30.3.3 External Services and APR Calculation
30.3.4 Security and Access Control
30.3.5 Business Process Automation and Workflow Management
30.4 Results
30.5 Discussion
30.6 Conclusion
References
31. Additive Manufacturing and the Rise of Digital Inventory
Fuad Abaszadeh, Elmina Gadirova, Ibrohimali Normatov, Fargan Asadov, Swapan Banerjee and Triwiyanto Triwiyanto
31.1 Introduction
31.2 Types of Consumer Electronics and Data Exposure
31.3 Nature of Data Collected and Privacy Concerns
31.4 Core Quantum Algorithms Applied in Machine Learning
31.5 Key Privacy and Security Risks
31.6 Security Mechanisms and Privacy-Preserving Technologies
31.7 Best Practices for Enhancing Data Privacy and Security
31.8 Conclusion
References
32. Pharma of the World to Pharma Powerhouse of the World: Leveraging AI for Sustainable Growth in Indian Pharma Startups
K. V. N. Lakshmi, Guhashri Musalkol, Mahima Vishwanath, Veena Keerthi and Nabanita Ghosh
31.1 Theoretical Background
32.2 Statement of the Problem
32.3 Review of Literature
32.4 Research Gap
32.5 Scope of the Study
32.6 Objectives of the Study
32.7 Research Design
32.8 Limitations of the Study
32.9 Data Analysis
Construct Validity and Factor Structure
32.10 Findings
32.11 Conclusion
32.12 Scope for Future Research
References
33. Fuel Storage and Supply Unit of Ship Using Smart Plant Instrumentation
K. Prabhu, S. Vijayachitra, M. Dharani, R. Ghurucharan and M. Mathiarashu
33.1 Introduction
33.2 Proposed Methodology
33.3 Instrument Tagging
33.4 Connection Setup
33.5 Block Diagram
33.6 Wiring Diagram Generation
33.7 Specification Sheet
33.8 Instrument Index
33.9 Data Management
33.10 Validation and Quality Assurance
33.11 Results
33.11.1 Wiring Diagram Document Sheet
33.11.2 Browser Module
33.11.3 Specification Sheet
33.12 Conclusion
33.13 Future Scope
References
34. Future Horizons: Emerging Technologies and Models
Alex Khang, Vugar Abdullayev, Nazila Ragimova, Vasila Abbasova, Jale Agazade, Yegane Aliyeva, Shafi Danyalov, Ajesh F. and Fuad Abaszadeh
34.1 Introduction
34.2 Fundamentals of QC for ML
34.3 Overview of Classical ML Concepts Relevant to QML
34.3.1 Classical Supervised, Unsupervised, and Reinforcement Learning
34.3.2 Complexity Challenges in Classical ML
34.3.3 Potential Areas for Quantum Acceleration
34.4 Core Quantum Algorithms Applied in ML
34.4.1 Quantum Support Vector Machine
34.4.2 Quantum Principal Component Analysis
34.4.3 Quantum k-Means Clustering
34.4.4 Quantum Neural Networks
34.4.5 Hybrid Quantum–Classical Algorithms
34.5 Applications of QML
34.5.1 Quantum Chemistry and Materials Science
34.5.2 Financial Forecasting and Risk Analysis
34.5.3 Healthcare and Bioinformatics
34.5.4 Optimization Problems in Industry
34.6 Conclusion
References
Index
Back to Top