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Ambient Intelligence and Internet of Things : Convergent Technologies
| Ambient Intelligence and Internet of Things : Convergent Technologies |
| Autore | Mahmood Rashid |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (421 pages) |
| Altri autori (Persone) |
RajaRohit
KaurHarpreet KumarSandeep NagwanshiKapil Kumar |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9781119821830
9781119821236 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Ambient Intelligence and Internet of Things: An Overview -- 1.1 Introduction -- 1.2 Ambient Intelligent System -- 1.3 Characteristics of AmI Systems -- 1.4 Driving Force for Ambient Computing -- 1.5 Ambient Intelligence Contributing Technologies -- 1.6 Architecture Overview -- 1.7 The Internet of Things -- 1.8 IoT as the New Revolution -- 1.9 IoT Challenges -- 1.10 Role of Artificial Intelligence in the Internet of Things (IoT) -- 1.11 IoT in Various Domains -- 1.12 Healthcare -- 1.13 Home Automation -- 1.14 Smart City -- 1.15 Security -- 1.16 Industry -- 1.17 Education -- 1.18 Agriculture -- 1.19 Tourism -- 1.20 Environment Monitoring -- 1.21 Manufacturing and Retail -- 1.22 Logistics -- 1.23 Conclusion -- References -- Chapter 2 An Overview of Internet of Things Related Protocols, Technologies, Challenges and Application -- 2.1 Introduction -- 2.1.1 History of IoT -- 2.1.2 Definition of IoT -- 2.1.3 Characteristics of IoT -- 2.2 Messaging Protocols -- 2.2.1 Constrained Application Protocol -- 2.2.2 Message Queue Telemetry Transport -- 2.2.3 Extensible Messaging and Presence Protocol -- 2.2.4 Advance Message Queuing Protocol (AMQP) -- 2.3 Enabling Technologies -- 2.3.1 Wireless Sensor Network -- 2.3.2 Cloud Computing -- 2.3.3 Big Data Analytics -- 2.3.4 Embedded System -- 2.4 IoT Architecture -- 2.5 Applications Area -- 2.6 Challenges and Security Issues -- 2.7 Conclusion -- References -- Chapter 3 Ambient Intelligence Health Services Using IoT -- 3.1 Introduction -- 3.2 Background of AML -- 3.2.1 What is AML? -- 3.3 AmI Future -- 3.4 Applications of Ambient Intelligence -- 3.4.1 Transforming Hospitals and Enhancing Patient Care With the Help of Ambient Intelligence -- 3.4.2 With Technology, Life After the COVID-19 Pandemic -- 3.5 COVID-19 -- 3.5.1 Prevention.
3.5.2 Symptoms -- 3.6 Coronavirus Worldwide -- 3.7 Proposed Framework for COVID-19 -- 3.8 Hardware and Software -- 3.8.1 Hardware -- 3.8.2 Heartbeat Sensor -- 3.8.3 Principle -- 3.8.4 Working -- 3.8.5 Temperature Sensor -- 3.8.6 Principle -- 3.8.7 Working -- 3.8.8 BP Sensor -- 3.8.9 Principle -- 3.8.10 Working -- 3.9 Mini Breadboard -- 3.10 Node MCU -- 3.11 Advantages -- 3.12 Conclusion -- References -- Chapter 4 Security in Ambient Intelligence and Internet of Things -- 4.1 Introduction -- 4.2 Research Areas -- 4.3 Security Threats and Requirements -- 4.3.1 Ad Hoc Network Security Threats and Requirements -- 4.3.1.1 Availability -- 4.3.1.2 Confidentiality -- 4.3.1.3 Integrity -- 4.3.1.4 Key Management and Authorization -- 4.3.2 Security Threats and Requirements Due to Sensing Capability in the Network -- 4.3.2.1 Availability -- 4.3.2.2 Confidentiality -- 4.3.2.3 Integrity -- 4.3.2.4 Key Distribution and Management -- 4.3.2.5 Resilience to Node Capture -- 4.3.3 Security Threats and Requirements in AmI and IoT Based on Sensor Network -- 4.3.3.1 Availability -- 4.3.3.2 Confidentiality -- 4.3.3.3 Confidentiality of Location -- 4.3.3.4 Integrity -- 4.3.3.5 Nonrepudiation -- 4.3.3.6 Fabrication -- 4.3.3.7 Intrusion Detection -- 4.3.3.8 Confidentiality -- 4.3.3.9 Trust Management -- 4.4 Security Threats in Existing Routing Protocols that are Designed With No Focus on Security in AmI and IoT Based on Sensor Networks -- 4.4.1 Infrastructureless -- 4.4.1.1 Dissemination-Based Routing -- 4.4.1.2 Context-Based Routing -- 4.4.2 Infrastructure-Based -- 4.4.2.1 Network with Fixed Infrastructure -- 4.4.2.2 New Routing Strategy for Wireless Sensor Networks to Ensure Source Location Privacy -- 4.5 Protocols Designed for Security Keeping Focus on Security at Design Time for AmI and IoT Based on Sensor Network -- 4.5.1 Secure Routing Algorithms. 4.5.1.1 Identity-Based Encryption (I.B.E.) Scheme -- 4.5.1.2 Policy-Based Cryptography and Public Encryption with Keyword Search -- 4.5.1.3 Secure Content-Based Routing -- 4.5.1.4 Secure Content-Based Routing Using Local Key Management Scheme -- 4.5.1.5 Trust Framework Using Mobile Traces -- 4.5.1.6 Policy-Based Authority Evaluation Scheme -- 4.5.1.7 Optimized Millionaire's Problem -- 4.5.1.8 Security in Military Operations -- 4.5.1.9 A Security Framework Application Based on Wireless Sensor Networks -- 4.5.1.10 Trust Evaluation Using Multifactor Method -- 4.5.1.11 Prevention of Spoofing Attacks -- 4.5.1.12 QoS Routing Protocol -- 4.5.1.13 Network Security Virtualization -- 4.5.2 Comparison of Routing Algorithms and Impact on Security -- 4.5.3 Inducing Intelligence in IoT Networks Using Artificial Intelligence -- 4.5.3.1 Fuzzy Logic-1 -- 4.5.3.2 Fuzzy Logic-2 -- 4.6 Introducing Hybrid Model in Military Application for Enhanced Security -- 4.6.1 Overall System Architecture -- 4.6.2 Best Candidate Selection -- 4.6.3 Simulation Results in Omnet++ -- 4.6 Conclusion -- References -- Chapter 5 Futuristic AI Convergence of Megatrends: IoT and Cloud Computing -- 5.1 Introduction -- 5.1.1 Our Contribution -- 5.2 Methodology -- 5.2.1 Statistical Information -- 5.3 Artificial Intelligence of Things -- 5.3.1 Application Areas of IoT Technologies -- 5.3.1.1 Energy Management -- 5.3.1.2 5G/Wireless Systems -- 5.3.1.3 Risk Assessment -- 5.3.1.4 Smart City -- 5.3.1.5 Health Sectors -- 5.4 AI Transforming Cloud Computing -- 5.4.1 Application Areas of Cloud Computing -- 5.4.2 Energy/Resource Management -- 5.4.3 Edge Computing -- 5.4.4 Distributed Edge Computing and Edge-of-Things (EoT) -- 5.4.5 Fog Computing in Cloud Computing -- 5.4.6 Soft Computing and Others -- 5.5 Conclusion -- References. Chapter 6 Analysis of Internet of Things Acceptance Dimensions in Hospitals -- 6.1 Introduction -- 6.2 Literature Review -- 6.2.1 Overview of Internet of Things -- 6.2.2 Internet of Things in Healthcare -- 6.2.3 Research Hypothesis -- 6.2.3.1 Technological Context (TC) -- 6.2.3.2 Organizational Context (OC) -- 6.2.3.3 Environmental Concerns (EC) -- 6.3 Research Methodology -- 6.3.1 Demographics of the Respondents -- 6.4 Data Analysis -- 6.4.1 Reliability and Validity -- 6.4.1.1 Cronbach's Alpha -- 6.4.1.2 Composite Reliability -- 6.4.2 Exploratory Factor Analysis (EFA) -- 6.4.3 Confirmatory Factor Analysis Results -- 6.4.3.1 Divergent or Discriminant Validity -- 6.4.4 Structural Equation Modeling -- 6.5 Discussion -- 6.5.1 Technological Context -- 6.5.2 Organizational Context -- 6.5.3 Environmental Context -- 6.6 Conclusion -- References -- Chapter 7 Role of IoT in Sustainable Healthcare Systems -- 7.1 Introduction -- 7.2 Basic Structure of IoT Implementation in the Healthcare Field -- 7.3 Different Technologies of IoT for the Healthcare Systems -- 7.3.1 On the Basis of the Node Identification -- 7.3.2 On the Basis of the Communication Method -- 7.3.3 Depending on the Location of the Object -- 7.4 Applications and Examples of IoT in the Healthcare Systems -- 7.4.1 IoT-Based Healthcare System to Encounter COVID-19 Pandemic Situations -- 7.4.2 Wearable Devices -- 7.4.3 IoT-Enabled Patient Monitoring Devices From Remote Locations -- 7.4.3.1 Pulse Rate Sensor -- 7.4.3.2 Respiratory Rate Sensors -- 7.4.3.3 Body Temperature Sensors -- 7.4.3.4 Blood Pressure Sensing -- 7.4.3.5 Pulse Oximetry Sensors -- 7.5 Companies Associated With IoT and Healthcare Sector Worldwide -- 7.6 Conclusion and Future Enhancement in the Healthcare System With IoT -- References -- Chapter 8 Fog Computing Paradigm for Internet of Things Applications -- 8.1 Introduction. 8.2 Challenges -- 8.3 Fog Computing: The Emerging Era of Computing Paradigm -- 8.3.1 Definition of Fog Computing -- 8.3.2 Fog Computing Characteristic -- 8.3.3 Comparison Between Cloud and Fog Computing Paradigm -- 8.3.4 When to Use Fog Computing -- 8.3.5 Fog Computing Architecture for Internet of Things -- 8.3.6 Fog Assistance to Address the New IoT Challenges -- 8.3.7 Devices Play a Role of Fog Computing Node -- 8.4 Related Work -- 8.5 Fog Computing Challenges -- 8.6 Fog Supported IoT Applications -- 8.7 Summary and Conclusion -- References -- Chapter 9 Application of Internet of Things in Marketing Management -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Customer Relationship Management -- 9.2.2 Product Life Cycle (PLC) -- 9.2.3 Business Process Management (BPM) -- 9.2.4 Ambient Intelligence (AmI) -- 9.2.5 IoT and CRM Integration -- 9.2.6 IoT and BPM Integration -- 9.2.7 IoT and Product Life Cycle -- 9.2.8 IoT in MMgnt -- 9.2.9 Impacts of AmI on Marketing Paradigms -- 9.3 Research Methodology -- 9.4 Discussion -- 9.4.1 Research Proposition 1 -- 9.4.2 Research Proposition 2 -- 9.4.3 Research Proposition 3 -- 9.4.4 Research Proposition 4 -- 9.4.5 Research Proposition 5 -- 9.5 Results -- 9.4 Conclusions -- References -- Chapter 10 Healthcare Internet of Things: A New Revolution -- 10.1 Introduction -- 10.2 Healthcare IoT Architecture (IoT) -- 10.3 Healthcare IoT Technologies -- 10.3.1 Technology for Identification -- 10.3.2 Location Technology -- 10.3.2.1 Mobile-Based IoT -- 10.3.2.2 Wearable Devices -- 10.3.2.3 Ambient-Assisted Living (AAL) -- 10.3.3 Communicative Systems -- 10.3.3.1 Radiofrequency Identification -- 10.3.3.2 Bluetooth -- 10.3.3.3 Zigbee -- 10.3.3.4 Near Field Communication -- 10.3.3.5 Wireless Fidelity (Wi-Fi) -- 10.3.3.6 Satellite Communication -- 10.4 Community-Based Healthcare Services -- 10.5 Cognitive Computation. 10.6 Adverse Drug Reaction. |
| Record Nr. | UNINA-9910646196503321 |
Mahmood Rashid
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data mining and machine learning applications / / editors, Rohit Raja [et al.]
| Data mining and machine learning applications / / editors, Rohit Raja [et al.] |
| Pubbl/distr/stampa | Beverly, MA : , : Scrivener Publishing LLC, , [2022] |
| Descrizione fisica | 1 online resource (488 pages) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Machine learning |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-79250-9
1-119-79252-5 1-119-79251-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555106803321 |
| Beverly, MA : , : Scrivener Publishing LLC, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining and machine learning applications / / editors, Rohit Raja [et al.]
| Data mining and machine learning applications / / editors, Rohit Raja [et al.] |
| Pubbl/distr/stampa | Beverly, MA : , : Scrivener Publishing LLC, , [2022] |
| Descrizione fisica | 1 online resource (488 pages) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Machine learning |
| ISBN |
1-5231-5578-7
1-119-79250-9 1-119-79252-5 1-119-79251-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Matter -- Introduction to Data Mining / Santosh R Durugkar, Rohit Raja, Kapil Kumar Nagwanshi, Sandeep Kumar -- Classification and Mining Behavior of Data / Srinivas Konda, Kavitarani Balmuri, Kishore Kumar Mamidala -- A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects / Rakhi Seth, Aakanksha Sharaff -- Stream Mining: Introduction, Tools & Techniques and Applications / Naresh Kumar Nagwani -- Data Mining Tools and Techniques: Clustering Analysis / Rohit Miri, Amit Kumar Dewangan, SR Tandan, Priya Bhatnagar, Hiral Raja -- Data Mining Implementation Process / Kamal K Mehta, Rajesh Tiwari, Nishant Behar -- Predictive Analytics in IT Service Management (ITSM) / Christa IL Sharon, V Suma -- Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques / K Ramya Laxmi, Sumit Srivastava, K Madhuravani, S Pallavi, Omprakash Dewangan -- Inductive Learning Including Decision Tree and Rule Induction Learning / Raj Kumar Patra, A Mahendar, G Madhukar -- Data Mining for Cyber-Physical Systems / M Varaprasad Rao, D Anji Reddy, Anusha Ampavathi, Shaik Munawar -- Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining / Vivek Parganiha, Soorya Prakash Shukla, Lokesh Kumar Sharma -- Human-Machine Interaction and Visual Data Mining / Sinha Upasana, Gupta Akanksha, Samera Khan, Shilpa Rani, Swati Jain -- MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection / Lokesh Singh, Rekh Ram Janghe, Satya Prakash Sahu -- New Algorithms and Technologies for Data Mining / Padma Bonde, Latika Pinjarkar, Korhan Cengiz, Aditi Shukla, Maguluri Sudeep Joel -- Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier / Sudesh Kumar, Rekh Ram Janghel, Satya Prakash Sahu -- An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques / Nanda R Wagh, Sanjay R Sutar -- Conclusion and Future Direction in Data Mining and Machine Learning / Santosh R Durugkar, Rohit Raja, Kapil Kumar Nagwanshi, Ramakant Chandrakar -- Index |
| Record Nr. | UNINA-9910829956803321 |
| Beverly, MA : , : Scrivener Publishing LLC, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Industrial and Manufacturing Designs : Quantitative and Qualitative Analysis
| Industrial and Manufacturing Designs : Quantitative and Qualitative Analysis |
| Autore | Sahu Atul Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (423 pages) |
| Altri autori (Persone) |
RautRakesh D
RajaRohit SahuAnoop Kumar SahuNitin Kumar |
| ISBN |
1-394-21266-6
1-394-21265-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Demonstrating the Role of Qualitative and Quantitative Information in Industrial and Manufacturing Designs -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Decision-Making (DM) and Framework -- 1.4 Directory of Cases -- 1.4.1 Role of Qualitative or Quantitative Criteria Toward Solar Panel Evaluation -- 1.4.1.1 Summary and Discussions Toward Evaluating Solar Panels -- 1.4.2 Role of Qualitative or Quantitative Criteria Toward Optimization of Automatic/Robotic Welding Systems -- 1.4.2.1 Summary and Discussions Toward Evaluating Welding System -- 1.4.3 Role of Qualitative or Quantitative Criteria Toward Selection of Smart Alloys and Materials -- 1.4.3.1 Summary and Discussions Toward Selection of Smart Alloys and Materials -- 1.4.4 Role of Qualitative or Quantitative Criteria Toward Logistic Service Provider Evaluation -- 1.4.4.1 Summary and Discussions Toward Evaluation of Logistic Service Provider -- 1.4.5 Role of Qualitative or Quantitative Criteria Toward Machine Tool Evaluation -- 1.4.5.1 Summary and Discussions Toward Evaluation of Machine Tool -- 1.4.6 Role of Qualitative or Quantitative Criteria Toward Industrial Robot Selection -- 1.4.6.1 Summary and Discussions Toward Selection of Industrial Robot -- 1.5 Critical Aspects -- 1.6 Implication and Discussions -- 1.7 Conclusions -- References -- Chapter 2 Sustainable Supply Chain Management Practices in Developing Economies: A Qualitative Mapping Approach -- 2.1 Introduction -- 2.2 Literature Review -- 2.2.1 Sustainable Supply Chain Management -- 2.2.2 Sustainable Supply Chain Management Practices -- 2.2.3 Challenges of Integrating SSCM -- 2.2.4 Strategies for Enhancing SSCM Integration -- 2.3 Methodology -- 2.3.1 Data Collection -- 2.3.2 Data Analysis -- 2.4 Results -- 2.4.1 SSCM Practices.
2.4.1.1 Green Packaging -- 2.4.1.2 Green Production -- 2.4.1.3 Stakeholder Engagement -- 2.4.1.4 Supplier Collaboration -- 2.4.1.5 Risk Mitigation -- 2.4.1.6 Social Sustainability -- 2.4.1.7 Innovative Infrastructure and Technology Systems -- 2.4.2 SSCM Challenges -- 2.5 Discussion on Results -- 2.6 Conclusion and Recommendations -- References -- Chapter 3 Advocating Lean Practices and Strategies in Decision-Making for Reinforcing Industrial and Manufacturing Designs -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Lean Tools, Motivation, and Methodology -- 3.4 Lean Theory and Practices -- 3.4.1 Lean Practices (Segment 1) -- 3.4.1.1 Value Stream Mapping (VSM) -- 3.4.1.2 Kaizen -- 3.4.1.3 5S -- 3.4.1.4 KANBAN -- 3.4.1.5 Six Sigma -- 3.4.1.6 Total Productive Maintenance (TPM) -- 3.4.1.7 Total Quality Management (TQM) -- 3.4.1.8 Overall Equipment Effectiveness (OEE) -- 3.4.1.9 Plan-Do-Check-Act (PDCA) -- 3.4.1.10 Inventory Management -- 3.4.1.11 Production Leveling -- 3.4.1.12 Zero Defect (ZD) Concept -- 3.4.1.13 Bottleneck Analysis (BA) -- 3.4.1.14 Root Cause Analysis (RCA) -- 3.4.1.15 Just in Time (JIT) -- 3.4.1.16 Time and Motion Study -- 3.4.1.17 Single-Minute Exchange Dies (SMED) -- 3.4.1.18 DMAIC -- 3.4.1.19 Poka-Yoke -- 3.4.2 Lean Practices (Second Segment) -- 3.4.2.1 Redundancy -- 3.4.2.2 Digitalization -- 3.4.2.3 Health, Safety, and Allowance for Continuous Flow -- 3.4.2.4 Simplification and Standardization -- 3.4.2.5 Teamwork and Partnering -- 3.5 Lean Strategy: Discussions and Implications -- 3.6 Lean-Based Case Investigations and Discussions -- 3.6.1 Lean Manufacturing is a Vital Tool to Enhance Productivity in Manufacturing -- 3.6.2 The Linkage Between Lean and Sustainable Manufacturing for Attaining Refined Performance -- 3.6.3 A Conceptual Model of Lean Manufacturing Dimensions for Sustainability. 3.6.4 Lean Practices Align Toward the Health and Safety of Workers in Manufacturing Industries (MIs) -- 3.6.5 The Linkage Between Lean and Agile Manufacturing for Work-In-Progress (WIP) Control -- 3.6.6 Adaptations of Lean Practices in SMEs to Support Industry 4.0 in Manufacturing -- 3.6.7 Implementation of Lean Practices in the Water Heater Manufacturing Industry for Value Adding -- 3.6.8 Lean Practices in Indian Machine Tool Industries for Receiving Productivity -- 3.6.9 Lean Manufacturing (LM) Practices for Influencing Process-Based Innovation and Performance -- 3.6.10 The Implementation of Lean Manufacturing in the Furniture Industry -- 3.6.11 Implementation of Lean Manufacturing in the Electronics Industry -- 3.7 Modeling of Lean Under Industrial and Manufacturing Sphere -- 3.7.1 Lean Modeling in Manufacturing Industries -- 3.7.2 Lean Modeling in Academic Institutes -- 3.7.3 Lean Modeling in Managerial Structure and Service-Related Organizations -- 3.7.4 Lean Modeling in Social Fields -- 3.7.5 Lean Modeling in Environmental Science -- 3.7.6 Lean Modeling in Economics -- 3.7.7 Lean Modeling in the Automobile Industry -- 3.8 Conclusions -- References -- Chapter 4 A Qualitative Study to Rank Non-Conventional Energy Sources for Industrial Sustainability and Energy Management Decisions Using MoSCoW Prioritization Method -- 4.1 Introduction -- 4.1.1 Major Non-Conventional Energy Sources -- 4.1.1.1 Solar Energy -- 4.1.1.2 Wind Energy -- 4.1.1.3 Hydroelectric Power -- 4.1.1.4 Biomass Energy -- 4.1.1.5 Geothermal Energy -- 4.1.1.6 Tidal and Wave Energy -- 4.1.1.7 Hydrogen Fuel Cells -- 4.1.2 Significance of Non-Conventional Energy Source -- 4.1.2.1 Environmental Benefits -- 4.1.2.2 Energy Security -- 4.1.2.3 Economic Benefits -- 4.1.2.4 Resource Sustainability -- 4.1.2.5 Climate Change Mitigation -- 4.1.2.6 Technological Advancement. 4.1.3 Scope of Non-Conventional Energy in Industrial Sustainability -- 4.1.4 Problem Formulation -- 4.1.5 Objectives of Chapter -- 4.1.6 Methodology of Chapter -- 4.1.7 Organization of Chapter -- 4.2 Review of Literature -- 4.2.1 Solar Energy -- 4.2.2 Wind Energy -- 4.2.3 Hydropower -- 4.2.4 Biomass and Bioenergy -- 4.2.5 Geothermal Energy -- 4.2.6 Tidal and Wave Energy -- 4.3 Current Scenario of Non-Conventional Sources in Industrial Sustainability -- 4.3.1 Wind Energy -- 4.3.2 Hydroelectric Power -- 4.3.3 Biomass Energy -- 4.3.4 Geothermal Energy -- 4.3.5 Tidal and Wave Energy -- 4.3.6 Hydrogen Fuel Cells -- 4.3.7 Energy Storage -- 4.3.8 Policy and Regulation -- 4.3.9 Integration and Grid Management -- 4.4 Overview of Indian Non-Conventional Energy Sector -- 4.4.1 SWOT Analysis of Non-Conventional Energy Sources -- 4.4.1.1 Strength -- 4.4.1.2 Weaknesses -- 4.4.1.3 Opportunity -- 4.4.1.4 Threats -- 4.4.2 Energy Management Decision in Indian Context -- 4.5 Qualitative Analysis Using MoSCoW Method -- 4.5.1 Research Design -- 4.5.2 Renewable Energy Technology Dimensions Based on Industrial Sustainability -- 4.5.3 MoSCoW Prioritization Approach -- 4.5.4 Results -- 4.6 Discussion -- 4.7 Conclusion -- 4.7.1 Limitations -- 4.7.2 Further Avenues -- References -- Chapter 5 Response Surface Methodology: A Statistical Tool to Optimize Process Parameters (Quantitative Data) to Maximize the Microbial Biomass and Their Bioactive Metabolites -- 5.1 Introduction -- 5.2 Conventional Methods for Multifactor Experimental Design -- 5.2.1 Full Factorial Design -- 5.2.2 Fractional Factorial Design -- 5.2.3 One-Factor-at-a-Time (OFAT) Design -- 5.2.4 Central Composite Design (CCD) -- 5.2.5 Box-Behnken Design -- 5.2.6 Taguchi Method -- 5.2.7 Latin Square Design -- 5.3 Response Surface Methodology (RSM) -- 5.4 RSM in Bioprocessing/Fermentation. 5.4.1 RSM for Antibiotic Production from Microorganisms -- 5.4.2 RSM in Enzyme Production -- 5.4.3 RSM for Bioethanol Production -- 5.4.4 RSM in Biosurfactant Production -- 5.4.5 RSM in Heavy Metal Pollution Elimination -- 5.5 Role of Quantitative Data in RSM -- 5.6 Conclusion -- References -- Chapter 6 Evaluating Mass-Spring-Damper Systems and Models for Reinforcing Engineering Designs: A Qualitative and Quantitative Approach -- 6.1 Introduction -- 6.2 Extensive Review of Existing Optimization Models for Mass Damper Systems -- 6.3 Use of Mass Damper Systems: Active and Passive -- 6.4 Brief Review of Optimization Models for Mass Damper Systems -- 6.4.1 Modal Analysis-Based Optimization -- 6.4.2 Optimization in the Frequency Domain -- 6.4.3 Time-Domain Optimization -- 6.4.4 Multi-Objective Optimization -- 6.5 Algorithm of Particle Swarm Optimization (PSO) -- 6.6 Benefits of Optimizing Mass Damper Systems -- 6.6.1 Vibration Reduction -- 6.6.2 Maintenance and Repair Costs -- 6.6.3 Health and Well-Being -- 6.6.4 Repercussions for the Natural World -- 6.7 Role of Qualitative Optimization and Discussions -- 6.7.1 Language of the Developer -- 6.7.2 Conceptual Understanding -- 6.7.3 Trade-Off Analysis -- 6.7.4 Identifying Critical Factors -- 6.7.5 Non-Linear Effects -- 6.7.6 Sensitivity to Assumptions -- 6.7.7 Incorporating Practical Constraints -- 6.7.8 Iteration and Iterative Learning -- 6.7.9 Interdisciplinary Collaboration -- 6.7.10 Communication with Stakeholders -- 6.7.11 Risk Assessment and Mitigation -- 6.8 Conclusion -- References -- Chapter 7 A Fuzzy Decision Optimization of Wire-EDM Process for Reinforcing Manufacturing Design Under Quantitative Data -- 7.1 Introduction -- 7.2 Review of Literature -- 7.3 The Significant Facts Related to Design, Implementation, and Importance of Total Productive Maintenance Programs in Manufacturing Operations. 7.4 Primary Objectives. |
| Record Nr. | UNINA-9910877139303321 |
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Sahu Atul Kumar
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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