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Adhesives in biomedical applications / / edited by K. L. Mittal and S. Neogi
Adhesives in biomedical applications / / edited by K. L. Mittal and S. Neogi
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (345 pages)
Disciplina 610/.28
Collana Adhesion and Adhesives: Fundamental and Applied Aspects Series
Soggetto topico Polymers in medicine
ISBN 1-394-20989-4
1-394-20988-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: General Topics -- Chapter 1 Historical Developments of Various Adhesives for Biomedical Applications -- 1.1 Origin of Adhesives -- 1.2 Prominence of Biomedical Adhesives in Wound Healing and Drug Delivery -- 1.2.1 Wound Healing -- 1.2.2 Drug Delivery -- 1.3 Generations of Bioadhesives -- 1.4 Timeline of Developments and Advances -- 1.5 Current and Future Applications -- 1.5.1 Wound Healing -- 1.5.2 Drug Delivery -- 1.5.3 Tissue Engineering -- 1.6 Summary -- References -- Chapter 2 Global Industry Development and Analysis of Adhesives for Biomedical Applications -- 2.1 Introduction -- 2.2 Research Landscape of Bioadhesives -- 2.3 Sources of Bioadhesives for Biomedical Applications -- 2.3.1 Natural Bioadhesives -- 2.3.1.1 Protein Bioadhesives -- 2.3.1.2 Carbohydrate-Based Bioadhesives -- 2.3.2 Synthetic Adhesives -- 2.3.2.1 Poly(ethylene glycol) -- 2.3.2.2 Polycyanoacrylate -- 2.3.2.3 Chondroitin Sulfate -- 2.4 Biomedical Applications of Bioadhesives -- 2.5 Latest Industrial Developments -- 2.5.1 Novel Adhesives -- 2.6 Summary -- Acknowledgment -- References -- Chapter 3 Biomedical Adhesives -- 3.1 Introduction -- 3.1.1 Definition of a Biomedical Adhesive -- 3.1.2 Proposed Mechanisms of Adhesion -- 3.1.2.1 Mechanical Interlocking -- 3.1.2.2 Electrostatic Attraction -- 3.1.2.3 Adsorption -- 3.1.2.4 Diffusion -- 3.1.2.5 Chemical Bonding -- 3.1.2.6 Weak Boundary Layers -- 3.1.3 Considerations for Development of Biomedical Adhesives -- 3.2 Types of Biomedical Adhesives and their Components -- 3.2.1 Soft Tissue Adhesives -- 3.2.1.1 Cyanoacrylate-Based Adhesives -- 3.2.1.2 Fibrin Glue-Based Adhesives -- 3.2.1.3 Protein-Based Adhesives -- 3.2.1.4 Polyurethane-Based Adhesives -- 3.2.1.5 Poly(ethylene glycol) (PEG)-Based Adhesives -- 3.2.1.6 Mussel-Derived Protein-Based Adhesives.
3.2.2 Hard Tissue Adhesives -- 3.2.2.1 Dental Cement -- 3.2.2.2 Bone Cements -- 3.3 Advances in Adhesives Development for Biomedical Uses -- 3.3.1 Bioglue in Surgery -- 3.3.2 Wound Dressing -- 3.4 Summary -- 3.5 Acknowledgements -- References -- Chapter 4 Bioadhesion: Fundamentals and Mechanisms -- 4.1 Introduction -- 4.2 Bioadhesion in Biological Systems -- 4.3 Bioadhesion/Mucoadhesion -- 4.3.1 Specific and Nonspecific Bioadhesion -- 4.4 The Mucosal Layer and Barriers to Drug Delivery -- 4.5 Barriers to Mucosal Drug Delivery -- 4.6 Factors Affecting Mucoadhesion -- 4.6.1 Bioadhesive Interactions -- 4.7 Mechanisms of Bioadhesion -- 4.7.1 Interface Energetics -- 4.7.2 Chemical Interactions -- 4.7.3 Mechanical Effects -- 4.8 Theories of Bioadhesion -- 4.8.1 Wetting Theory of Bioadhesion -- 4.8.2 Electrostatic Theory of Bioadhesion -- 4.8.3 Diffusion Theory of Bioadhesion -- 4.8.4 Adsorption Theory of Bioadhesion -- 4.8.5 Fracture Theory of Bioadhesion -- 4.9 Stages of Mucoadhesion -- 4.10 Modulation of Mucoadhesion -- 4.11 Molecular Biology in Bioadhesion -- 4.12 Administration of Bio- and Mucoadhesive Drug Delivery Systems -- 4.13 Prospects -- 4.14 Summary -- References -- Part 2: Specific Adhesives, Characteristics and Applications -- Chapter 5 Fibrin Glue: Sources, Characteristics and Applications -- 5.1 Introduction -- 5.2 Evolution of Fibrin Glue -- 5.3 Types of Fibrin Adhesives and their Working Mechanisms -- 5.4 Production Methods of Commercial Fibrin Adhesives -- 5.5 Comparison of Some Commercial Fibrin Adhesives -- 5.6 Recent Developments and Future Trend of Fibrin Adhesives -- 5.7 Summary -- References -- Chapter 6 Herbal Bioactives-Based Mucoadhesive Drug Delivery Systems -- 6.1 Introduction -- 6.2 Mucous Membrane -- 6.3 Theories of Adhesion -- 6.3.1 Electronic Theory -- 6.3.2 Adsorption Theory -- 6.3.3 Wetting Theory.
6.3.4 Diffusion Theory -- 6.3.5 Fracture Theory -- 6.4 Mucoadhesive Polymers -- 6.5 Mucoadhesive-Based Drug Delivery Systems (DDS): Administration Routes -- 6.5.1 Ocular Mucoadhesive DDS -- 6.5.2 Nasal DDS -- 6.5.3 Oromucosal DDS -- 6.5.4 Pulmonary Mucoadhesive DDS -- 6.5.5 Gastrointestinal Tract Mucoadhesive DDS -- 6.5.6 Vaginal Mucosal DDS -- 6.5.7 Rectal Mucoadhesive DDS -- 6.6 Clinical Studies -- 6.7 Patents on Herbal Bioactive-Based Mucoadhesive Drug Delivery Systems -- 6.8 Summary -- References -- Chapter 7 Adhesive Hydrogels -- 7.1 Introduction -- 7.1.1 Hydrogels -- 7.1.2 Conventional Adhesives in Biomedicine -- 7.1.3 Need for Adhesive Hydrogels -- 7.2 Mechanisms of Adhesion -- 7.2.1 Mechanical Interlocking -- 7.2.2 Wet Adhesion -- 7.2.3 Diffusion -- 7.2.4 van der Waals Force -- 7.2.5 Electrostatic Interaction -- 7.2.6 π-π Interaction -- 7.2.7 Hydrogen Bonding -- 7.2.8 Ionic and Covalent Bonding -- 7.3 Design Principles for Adhesive Hydrogels -- 7.4 Commonly Used Adhesive Hydrogels -- 7.4.1 Natural Polysaccharides -- 7.4.2 Poly(amino acid)s and Their Analogues -- 7.4.3 Adhesive Proteins -- 7.4.4 Synthetic Polymers -- 7.5 Prospective Applications of Adhesive Hydrogels -- 7.5.1 Biomedicine -- 7.5.2 Textiles -- 7.5.3 Food Science -- 7.5.4 Bioelectronics -- 7.5.5 Cosmetics -- 7.6 Summary -- References -- Chapter 8 Adhesives in Dermal Patches -- 8.1 Introduction -- 8.2 Types of Dermal Patches -- 8.2.1 Microreservoir Type Dermal Patches -- 8.2.2 Adhesive Dispersion Controlled Dermal Patches -- 8.2.3 Membrane Permeation Controlled Dermal Patches -- 8.2.4 Matrix Diffusion Controlled Dermal Patches -- 8.3 Evolution of Adhesives in Medical Applications -- 8.4 Types of Adhesives Used in Dermal Patches -- 8.4.1 PIB-Based PSAs -- 8.4.2 Silicone-Based PSAs -- 8.4.3 Acrylic PSAs -- 8.4.4 Other PSAs -- 8.5 Testing Physical Properties of PSAs.
8.5.1 Tack Test -- 8.5.2 Shear Adhesion or Creep Test -- 8.5.3 Peel Resistance Test -- 8.6 Prediction of Patch In Vivo Adhesive Performances -- 8.7 Adhesive Properties and Formulation Studies -- 8.8 Summary -- Acknowledgements -- References -- Chapter 9 Medical Adhesives from Extracted Mussel Adhesive Proteins -- 9.1 Introduction -- 9.2 The Mussel Byssus -- 9.2.1 Collagenous Thread Protein -- 9.2.2 Thread Matrix Protein -- 9.2.3 Mussel Foot Proteins (MFPs) -- 9.3 Mussel-Inspired Adhesion -- 9.3.1 History -- 9.3.2 Bioadhesives -- 9.3.2.1 Mechanisms of Biological Tissue Adhesion -- 9.3.3 Role of DOPA in Adhesion Process -- 9.3.3.1 DOPA Oxidation -- 9.3.3.2 Coacervation -- 9.3.3.3 Surface Drying -- 9.3.3.4 Catechol Modification -- 9.4 Mussel-Inspired Tissue Adhesives -- 9.4.1 Extraction of Mussel Protein -- 9.4.2 Synthesis of Catechol-Functionalized Polymers -- 9.4.2.1 Direct Functionalization -- 9.4.2.2 Polymerization of Modified Monomers -- 9.4.2.3 Methods to Synthesize Catechol Peptides -- 9.4.2.4 Recombinant Technology -- 9.4.3 Mussel-Inspired Medical Bioadhesives -- 9.4.3.1 Bioglues -- 9.4.3.2 Drug Delivery -- 9.4.3.3 Tissue Engineering -- 9.4.3.4 Cellular Engineering -- 9.5 Summary -- References -- Chapter 10 Dental Adhesives: State-of-the-Art, Current Perspectives, and Promising Applications -- 10.1 Introduction -- 10.2 Brief History of Dental Adhesive Systems -- 10.3 Classification and Composition of Adhesive Systems -- 10.3.1 Three-Step Etch-and-Rinse Conventional Adhesive System -- 10.3.2 Two-Step Etch-and-Rinse Conventional Adhesive System -- 10.3.3 Two-Step Self-Etch Adhesive System -- 10.3.4 One-Step Self-Etch Adhesive System -- 10.3.5 Universal or Multimode Adhesive System -- 10.4 Understanding the Challenges of Dental Adhesives Inside the Mouth -- 10.4.1 Host-Derived Proteolytic Enzymes -- 10.4.2 Adverse Chemical/Biochemical Interactions.
10.4.3 Mechanical Loading -- 10.5 New Approaches Targeting Longevity of Adhesive-Dentin Interfaces -- 10.6 Dental Adhesives Endowed With Antibacterial Properties -- 10.7 Summary -- 10.8 Acknowledgments -- References -- Chapter 11 Role of Adhesive-Based Systems for Diagnostic Imaging and Theranostic Applications -- 11.1 Introduction -- 11.2 Role of Adhesives in Diagnostic Imaging -- 11.2.1 X-Ray Imaging -- 11.2.2 Magnetic Resonance Imaging -- 11.2.3 Ultrasound Imaging -- 11.2.4 Endoscopy -- 11.2.5 Fluorescence Imaging -- 11.2.6 Other Applications -- 11.3 Theranostics -- 11.3.1 DOPA (3,4-dihydroxyl-L-phenylalanine) and Dopamine -- 11.3.2 Gelatin -- 11.3.3 Chitosan -- 11.3.4 Poly(lactic-co-glycolic acid) -- 11.3.5 Poly(acrylic acid) -- 11.4 Summary -- References -- Index -- EULA.
Record Nr. UNINA-9910830374603321
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biofuel extraction techniques : biofuels, solar, and other technologies / / edited by Lalit Prasad, Subhalaxmi Pradhan, and S. N. Naik
Biofuel extraction techniques : biofuels, solar, and other technologies / / edited by Lalit Prasad, Subhalaxmi Pradhan, and S. N. Naik
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (629 pages)
Disciplina 662.88
Soggetto topico Biomass energy
ISBN 1-119-82952-6
1-119-82951-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Plant Seed Oils and Their Potential for Biofuel Production in India -- 1.1 Introduction -- 1.2 Background -- 1.3 Non-Edible Oil as Feedstock for Biodiesel -- 1.3.1 Jatropha -- 1.3.2 Pongamia -- 1.3.3 Mahua -- 1.3.4 Nahor -- 1.3.5 Rubber -- 1.3.6 Lesser Explored Non-Edible Oils for Biodiesel Feedstock in India -- 1.4 Fuel Qualities -- 1.4.1 Cetane Number -- 1.4.2 Acid Value -- 1.4.3 Ester Content, Glycerides, and Glycerol -- 1.4.4 Phosphorus Content -- 1.4.5 Iodine Value -- 1.4.6 Oxidation Stability -- 1.4.7 Linolenic Acid Methyl Esters -- 1.4.8 Polyunsaturated (≥ 4 Double Bonds) Methyl Esters -- 1.5 Conclusion -- Author Contributions -- References -- Chapter 2 Processing of Feedstock in Context of Biodiesel Production -- 2.1 Introduction -- 2.2 Feedstock in Context of Biodiesel -- 2.3 Processing of Oilseeds -- 2.3.1 Pretreatment -- 2.3.2 Decortication -- 2.3.2.1 Characteristics of Oilseeds Required for Decortication -- 2.3.2.2 Decortication Method -- 2.4 Oil Extraction Methods -- 2.4.1 Aqueous Method -- 2.4.2 Hydraulic Press -- 2.4.3 Ghani (Animal or Power-Driven) -- 2.4.4 Solvent Extraction Method -- 2.4.5 Mechanical Extraction Method -- 2.4.6 Microwave Assisted Oil Extraction -- 2.4.7 Ultrasonic Assisted Oil Extraction -- 2.4.8 Supercritical Assisted Oil Extraction -- 2.5 Catalyst -- 2.5.1 Homogeneous Catalyst -- 2.5.2 Heterogeneous Catalyst -- 2.5.3 Biocatalyst -- 2.6 Production Process of Biodiesel -- 2.7 Techniques for Biodiesel Production -- 2.7.1 Catalytic Transesterification Technique -- 2.7.2 Pyrolysis -- 2.7.3 Microwave Assisted -- 2.7.4 Ultrasonic Assisted -- 2.7.5 Supercritical Assisted -- 2.8 Advantages & -- Disadvantages of Using Biodiesel -- 2.9 Current Challenges and Future Perspectives of Biodiesel -- 2.10 Summary -- References.
Chapter 3 Extraction Techniques for Biodiesel Production -- 3.1 Introduction -- 3.2 Direct Use and Blending -- 3.3 Microemulsion -- 3.4 Pyrolysis -- 3.5 Transesterification -- 3.5.1 Homogeneous Catalyzed Transesterification -- 3.5.2 Heterogeneous Catalyzed Transesterification -- 3.5.3 Enzyme Catalyzed Transesterification -- 3.5.4 Supercritical Alcohol Transesterification -- 3.6 Intensification Methods for Biodiesel Production -- 3.6.1 Ultrasonic Method -- 3.6.2 Microwave Method -- 3.6.3 Cosolvent Method -- 3.6.4 Membrane Technology -- 3.6.5 Reactive Distillation -- 3.7 Conclusions -- References -- Chapter 4 Role of Additives on Anaerobic Digestion, Biomethane Generation, and Stabilization of Process Parameters -- 4.1 Introduction -- 4.2 Anaerobic Digestion Process -- 4.3 Metallic Additives -- 4.4 Alkali Additives -- 4.5 Biological Additives -- 4.5.1 Microorganisms -- 4.5.2 Enzymes -- 4.6 Carbon-Based Additives -- 4.6.1 Graphene -- 4.6.2 Carbon Nanotubes -- 4.6.3 Activated Carbon -- 4.6.4 Biochar -- 4.7 Nanoparticles -- 4.7.1 Fe Nanoparticles -- 4.7.2 Nanoparticles of Ag and ZnO -- 4.7.3 Nanoparticles of Fe2O4 -- 4.8 Other Natural Additives -- 4.9 Conclusions -- Acknowledgment -- References -- Chapter 5 An Overview on Established and Emerging Biogas Upgradation Systems for Improving Biomethane Quality -- 5.1 Introduction -- 5.2 Available Biogas Upgradation Techniques -- 5.3 Microbial Methane Enrichment -- 5.4 Bioelectrochemical System -- 5.5 Photosynthetic Biogas Upgradation -- 5.6 Techno-Economics of Biological Biogas Upgradation Technologies -- 5.7 Conclusion -- Acknowledgement -- References -- Chapter 6 Renewable Feedstocks for Biofuels -- 6.1 Introduction -- 6.2 Sugar Containing Plant Crops -- 6.2.1 Sugar Cane (Saccharum officinarum) -- 6.2.2 Sugarbeet (Beta vulgaris L.) -- 6.2.3 Sweet Sorghum (Sorghum bicolor (L.) Moench) -- 6.3 Crops.
6.3.1 Corn (Zea mays) -- 6.3.2 CASSAVA (Manihot esculenta) -- 6.4 Oilseed -- 6.4.1 Soybean (Glycine max) -- 6.4.2 Palm (Elaeis guineensis) -- 6.4.3 Canola Oil -- 6.4.4 Sunflower Oil -- 6.4.5 Castor Oil -- 6.4.6 Cottonseed Oil -- 6.4.7 Jatropha Oil (Jatropha curcas) -- 6.4.8 Jojoba Oil -- 6.4.9 NEEM (Azadirachta indica) -- 6.5 Lignocellulosic Waste -- 6.5.1 Sugarcane Bagasse -- 6.5.2 Rice Husk -- 6.5.3 Corn Stover -- 6.5.4 Wheat Straw -- 6.6 Sea Waste -- 6.6.1 Algae Biomass and Oil -- 6.7 Liquid Waste -- 6.7.1 Vinasse -- 6.7.2 Glycerol -- 6.7.3 POME (Palm Oil Mill Effluent) -- 6.8 Conclusion -- References -- Chapter 7 Extraction Techniques of Gas.to.Liquids (GtL) Fuels -- 7.1 Introduction -- 7.2 History and Origin of Gas to Liquid Technology -- 7.3 What is Gas to Liquids (GtL) Fuel? -- 7.4 Need and Benefits from Gas to Liquid Technology -- 7.5 Extraction or Conversion Techniques of Gas to Liquid Fuels -- 7.5.1 Gas to Liquid by Direct Conversion -- 7.5.2 Gas to Liquid by Indirect Conversion -- 7.5.2.1 Natural Gas Reforming or Methane Reforming (Syngas) -- 7.5.2.2 Fischer-Tropsch (FT) Synthesis -- 7.5.2.3 Conversion -- 7.6 Advancements in Gas to Liquid Technology -- 7.7 Conclusions -- References -- Chapter 8 Second Generation Biofuels and Extraction Techniques -- List of Abbreviations -- 8.1 Introduction -- 8.2 Pre-Treatment of Lignocellulosic Biomasses -- 8.2.1 Physical Pre-Treatment Methods -- 8.2.2 Chemical Pre-Treatment Methods -- 8.2.3 Physico-Chemical Pre-Treatment Methods -- 8.2.4 Biological Pre-Treatment Methods -- 8.3 Extraction of Biofuel from Lignocellulosic Biomass -- 8.3.1 Pyrolysis -- 8.3.2 Hydrothermal Liquefaction -- 8.4 Bioethanol -- 8.4.1 Aromatic Lignocellulosic Biomass as Potential Candidate for Bioethanol -- 8.4.2 Enzymatic Saccharification -- 8.4.3 Ethanol Conversion Processes.
8.4.4 Process for the Production of Ethanol from Sugary Crops -- 8.4.5 Process for the Production of Ethanol from Starchy Crops -- 8.4.6 Process for the Production of Bioethanol from Cellulosic Biomass and Spent Aromatic Crops -- 8.4.7 Purification of Bioethanol -- 8.5 Biodiesel Production from Fatty Acids -- 8.5.1 Chemical Catalytic Process -- 8.5.1.1 Homogeneous Base-Catalysed Transesterification -- 8.5.1.2 Homogeneous Acid-Catalysed Transesterification -- 8.5.1.3 Heterogeneous Catalysts -- 8.5.1.4 Alkali Earth Metal Oxides -- 8.5.1.5 Acid/Base Zeolites -- 8.5.1.6 Heteropolyacids -- 8.5.1.7 Waste Biomass Derived Heterogeneous Catalysts -- 8.5.1.8 Heterogeneous Nanocatalysts -- 8.5.2 Biochemical Catalysts -- 8.6 Levulinic Acid (LA) -- 8.6.1 Extraction of Levulinic Acid (LA) from Waste and Lignocellulosic Biomass -- 8.7 Conclusions -- References -- Chapter 9 Bio-Alcohol: Production, Purification, and Analysis Using Analytical Techniques -- 9.1 Introduction -- 9.2 Biomethanol Extraction -- 9.2.1 Thermochemical Conversion Process -- 9.2.2 Biochemical Conversion Process -- 9.2.3 Anaerobic Digestion -- 9.3 Bioethanol Extraction -- 9.3.1 Extraction of Bioethanol from the Waste Flower (Starchy Material) -- 9.3.2 Analytical Methods for Determination of Bioethanol -- 9.3.3 Bioethanol Extraction from Sugarcane -- 9.4 Biopropanol Extraction -- 9.5 Bioglycerol Extraction -- 9.6 Bioethylene Glycol Extraction -- 9.7 Branched-Chain Bioalcohols Extraction -- 9.8 Purification of Bioalcohol -- 9.8.1 Distillation -- 9.8.2 Adsorption -- 9.8.3 Ozonation -- 9.8.4 Gas Striping -- 9.8.5 Pervaporation -- 9.8.6 Vaccum Fermentation -- 9.8.7 Solvent Extraction -- 9.9 Quantification of Bioalcohols -- 9.9.1 Gas Chromatography (GC) -- 9.9.2 High-Performance Liquid Chromatography (HPLC) -- 9.9.3 Infrared Spectroscopy (IR) -- 9.9.4 Olfactometry.
9.10 Recent Perspective of Bioalcohol Production -- 9.11 Conclusion and Future Trends of Bioalcohol -- References -- Chapter 10 Studies on Extraction Techniques of Bio-Hydrogen -- 10.1 Introduction -- 10.2 Bio-Hydrogen Production Process -- 10.2.1 Fermentation -- 10.2.1.1 Dark Fermentation -- 10.2.1.2 Photo Fermentation -- 10.2.1.3 Sequential Dark and Photo Fermentation -- 10.3 Bio-Photolysis -- 10.3.1 Direct Bio-Photolysis -- 10.3.2 Indirect Bio-Photolysis -- 10.4 Microbial Electrolysis Cell -- 10.5 Conclusion -- References -- Chapter 11 Valorization of By-Products Produced During the Extraction and Purification of Biofuels -- 11.1 Introduction -- 11.2 Biodiesel Production Process and Its Byproducts -- 11.2.1 Valorization of De-Oiled Seed Cakes -- 11.2.1.1 Valorization of De-Oiled Cake via Anaerobic Digestion Route -- 11.2.2 Valorization of Glycerol -- 11.2.2.1 Valorization of Glycerol via Anaerobic Digestion Route -- 11.2.2.2 Valorization of Glycerol via Biological Conversion Route -- 11.2.2.3 Valorization of Glycerol via Chemical Conversion Route -- 11.2.2.4 Valorization of Glycerol via Catalytic Conversion Route -- 11.2.2.5 Valorization of Glycerol via Thermochemical Conversion Route -- 11.3 Biorefinery Concept Based on Utilization of Whole Oilseed Plant -- 11.4 Valorization of Byproducts Obtained in the Bioethanol Fermentation Process -- 11.5 Valorization of Byproducts Obtained in Anaerobic Digestion Process -- 11.5.1 Valorization of CO2 Content in Biogas -- 11.5.2 Valorization of Digestate -- 11.6 Conclusion -- Acknowledgment -- References -- Chapter 12 Valorization of Byproducts Produced During Extraction and Purification of Biodiesel: A Promising Biofuel -- List of Abbreviations -- 12.1 Introduction -- 12.2 Glycerol -- 12.2.1 Properties of Glycerol -- 12.2.2 Classifications of Glycerol -- 12.2.3 Global Glycerol Market -- 12.2.4 Applications.
12.2.4.1 Conversion of Glycerol into Value-Added Product.
Record Nr. UNINA-9910830560803321
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biosensors Nanotechnology / / edited by Tariq Altalhi
Biosensors Nanotechnology / / edited by Tariq Altalhi
Edizione [Second edition.]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (510 pages)
Disciplina 610.284
Soggetto topico Nanotechnology
Biosensors
Health
ISBN 1-394-16713-X
1-394-16712-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910830641603321
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Chemistry and biological activities of ivermectin / / edited by Rashid Ali and Shahid ul-Islam
Chemistry and biological activities of ivermectin / / edited by Rashid Ali and Shahid ul-Islam
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (287 pages)
Disciplina 636.089
Soggetto topico Ivermectin
Pharmaceutical chemistry
Botanical chemistry
ISBN 1-394-16803-9
1-394-16802-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Ivermectin -- 1.1 Introduction -- 1.2 Sources and Synthesis -- 1.3 Pharmacological Potential of Ivermectin -- 1.3.1 Ivermectin in the Treatment of Cancer -- 1.3.1.1 Ovarian Cancer -- 1.3.1.2 Renal Cell Cancer -- 1.3.2 Ivermectin Against Viruses -- 1.3.3 Ivermectin in the Treatment of Bacterial Infections -- 1.4 Ivermectin's Beneficial Role in Cattle -- 1.5 Ivermectin in the Treatment of COVID-19 -- 1.5.1 Mode of Action -- 1.6 Toxicity of Ivermectin -- 1.6.1 Acute Toxicity -- 1.6.2 Developmental and Reproduction Toxicity -- 1.6.3 General and Safety Pharmacology -- 1.7 Conclusion -- References -- Chapter 2 Historical Background of and Synthetic Approaches to Ivermectin (IVM) and its Homologues -- 2.1 Introduction -- 2.1.1 Historical Background of Ivermectin -- 2.2 Synthetic Approaches Towards the Construction of IVM and Analogues -- 2.3 Biosynthesis -- 2.4 Chemical Synthetic Pathway -- 2.5 Crystal Structure -- 2.6 Conclusion and Outlook -- Acknowledgements -- References -- Chapter 3 Ivermectin as a Repurposed Drug for COVID-19 -- 3.1 Introduction -- 3.2 Symptoms of COVID-19 -- 3.3 Repurposing of the Drugs -- 3.4 Repurposed Drugs for COVID-19 -- 3.5 Repurposing of Ivermectin for COVID-19 -- 3.6 Proposed Possible Mechanism of Action -- 3.7 SARS COVID-19 Clinical Studies with Ivermectin -- 3.8 Conclusions -- 3.9 Acknowledgments -- References -- Chapter 4 Ivermectin as an Anti-Parasitic Agent -- Abbreviations -- 4.1 Introduction -- 4.2 Use of Ivermectin Against Various Human Parasitic Infections -- 4.2.1 Strongyloidiasis -- 4.2.2 Onchocerciasis -- 4.2.3 Lymphatic Filariasis -- 4.2.4 Loiasis -- 4.2.5 Scabies -- 4.2.6 Pediculosis -- 4.2.7 Mansonellosis -- 4.2.8 Ascariasis -- 4.2.9 Gnathostomiases -- 4.2.10 Leishmaniasis -- 4.2.11 Malaria.
4.3 Mode of Action Against Various Parasites -- 4.4 Conclusions -- 4.5 Acknowledgements -- References -- Chapter 5 Emerging Paradigm of Ivermectin and its Hybrids in Elimination of Malaria -- 5.1 Introduction -- 5.2 Malaria -- 5.2.1 World Malaria Report 2021 -- 5.2.2 Malaria in India: Statistics and Facts -- 5.2.3 Providing Malaria Treatment Despite All Odds -- 5.2.4 Meeting the Global Goal -- 5.2.5 Elimination of Malaria -- 5.2.6 Status of Anti-Malarial Drugs -- 5.3 Life Cycle of Malaria -- 5.4 Drug Against Hepatic Malarial Stage -- 5.5 About Ivermectin -- 5.5.1 Ivermectin Against Liver Cells of Malaria Parasite -- 5.6 Designing and Synthesis of Ivermectin Inhibitors -- 5.6.1 Hybrids of Ivermectin -- 5.7 Conclusions -- 5.8 Acknowledgments -- References -- Chapter 6 Ivermectin: A Potential Repurposed Anti-Cancer Therapeutic -- Abbreviations -- 6.1 Introduction -- 6.2 Mechanism of Anti-Carcinogenesis -- 6.3 Activation of Chloride Ion Channels -- 6.4 Anti-Mitotic Effect and Inhibition of Angiogenesis -- 6.5 Inhibition of Mitochondrial Respiration -- 6.6 Inhibitor of Cancer Stem Cells (CSCs) -- 6.7 Induction of Immunogenic Cell Death (ICD) -- 6.8 Epigenetic Modulator -- 6.9 Induction PAK1-Mediated Cytostatic Autophagy -- 6.10 Inhibition of P-glycoprotein (P-gp) -- 6.11 Inhibition of Yes-Associated Protein 1 (YAP1) -- 6.12 Inhibition of RNA Helicase -- 6.13 Caspase-Dependent Apoptosis -- 6.14 Activation of Transcription Factor E3 (TFE3) -- 6.15 Inhibition of Wnt-TCF Pathway Responses -- 6.16 Conclusions -- References -- Chapter 7 Ivermectin as an Anti-Inflammatory Agent -- 7.1 Introduction -- 7.2 Ant-Inflammatory Action of Ivermectin -- 7.3 Conclusions -- Acknowledgements -- References -- Chapter 8 Ivermectin: An Anthelminthic and Insecticide -- 8.1 Introduction -- 8.2 Ivermectin as an Anthelmintic -- 8.2.1 Mode of Action.
8.2.2 Ivermectin and Public Health -- 8.2.3 Challenges of Ivermectin Use as an Anthelminthic -- 8.3 Insecticidal Activity of Ivermectin -- 8.3.1 Mode of Action -- 8.3.2 Overview of Ivermectin as an Insecticide -- 8.3.3 Methods of Application to Animals and Plants -- 8.3.4 Disease Vector Control -- 8.3.5 Ivermectin Against Agricultural, Stored Grain Insect Pests and Other Insects -- 8.3.6 Ivermectin Usage in Livestock -- 8.3.7 Environmental Impact of Ivermectin -- Conclusions -- References -- Chapter 9 Potential Applications of Ivermectin (IVM) in Dermatology -- 9.1 Introduction -- 9.2 Mechanism of Action, Toxicity, and Side Effects of IVM -- 9.3 Motivational Approach of IVM in the Treatment of Skin -- 9.3.1 Anti-Bacterial Agent -- 9.3.2 Anti-Fungal Agent -- 9.3.3 Anti-Viral Agent -- 9.3.4 Anti-Protozoal Agent -- 9.4 Role of IVM with Good Anti-Parasitic Properties Against the Infection of Skin -- 9.4.1 Arthropods -- 9.4.2 Nematodes -- 9.5 Importance of IVM as an Anti-Cancer or Anti-Tumor Agent in Curing the Skin -- 9.6 Social Value of IVM in the Medical Care of Red Scrotum Syndrome (RSS) -- 9.7 Utility of IVM as an Anti-Inflammatory Drug in the Treatment of Skin-Related Issues -- 9.7.1 Allergy -- 9.7.2 Psoriasis or Crusted Scabies -- 9.7.3 Asthma -- 9.8 Conclusions -- Acknowledgements -- References -- Chapter 10 Antiviral Uses of Ivermectin -- 10.1 Introduction -- 10.2 Mechanism of Action of Ivermectin -- 10.3 Anti-Viral Effects Against Various DNA and RNA Viruses -- 10.3.1 RNA Viruses -- 10.3.1.1 COVID-19 -- 10.3.1.2 Zika Virus -- 10.3.1.3 Dengue Virus -- 10.3.1.4 Foot-and-Mouth Disease Virus -- 10.3.1.5 Hendra Virus -- 10.3.1.6 Newcastle Virus -- 10.3.1.7 Avian Influenza A Virus -- 10.3.1.8 Human Immunodeficiency Virus Type 1 -- 10.3.2 DNA Viruses -- 10.3.2.1 Equine Herpesvirus Type 1 (EHV-1) -- 10.3.2.2 Pseudorabies Virus (PRV).
10.3.2.3 BK Polyomavirus (BKPyV) -- 10.3.2.4 Porcine Circovirus 2 (PCV2) -- 10.4 Conclusion -- References -- Chapter 11 Toxicology, Safety, and Environmental Aspects of Ivermectin -- 11.1 Introduction -- 11.2 Ivermectin's Antiparasitic Activity -- 11.3 Pharmacology -- 11.4 Adverse Effects in Humans and Animals -- 11.5 Ivermectin and Ectoparasites -- 11.5.1 Scabies -- 11.5.2 Pediculosis -- 11.5.3 Strongyloidiasis -- 11.5.4 Onchocerciasis -- 11.5.5 Lymphatic Filariasis -- 11.5.6 Loasis -- 11.6 Environmental Impact and Biodegradation of Ivermectin -- 11.7 Conclusion -- References -- Index -- EULA.
Record Nr. UNINA-9910830998103321
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
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Lo trovi qui: Univ. Federico II
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Condition Monitoring, Troubleshooting and Reliability in Rotating Machinery / / edited by Robert X. Perez
Condition Monitoring, Troubleshooting and Reliability in Rotating Machinery / / edited by Robert X. Perez
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (439 pages)
Disciplina 621.816
Collana Rotating machinery fundamentals and advances
Soggetto topico Machinery - Maintenance and repair
Rotors - Maintenance and repair
ISBN 1-119-63162-9
1-119-63161-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Matter -- CONDITION MONITORING. An Introduction to Machinery Monitoring / Robert X Perez -- Centrifugal Pump Monitoring, Troubleshooting and Diagnosis Using Vibration Technologies / William D Marscher -- Proximity Probes are a Good Choice for Monitoring Critical Machinery with Fluid Film Bearings / Robert X Perez -- Optimizing Lubrication and Lubricant Analysis / Jim Fitch, Bennett Fitch -- Troubleshooting Temperature Problems / Robert X Perez -- Assessing Reciprocating Compressors and Engines / Robert X Perez -- Managing Critical Machinery Vibration Data / Robert X Perez -- TROUBLESHOOTING. Addressing Reciprocating Compressor Piping Vibration Problems / Robert X Perez -- Remember to Check the Rotational Speed When Encountering Process Machinery Flow Problems / Robert X Perez -- Troubleshooters Need to be Well Versed in the Equipment They are Evaluating / Robert X Perez -- Precise Coupling Properties are Required to Accurately Predict Torsional Natural Frequencies / Robert X Perez -- Is Vibration Beating on Machinery a Problem? / Robert X Perez, Andrew P Conkey -- RELIABILITY. Using Standby Machinery to Improve Process Reliability / Robert X Perez -- Gas Turbine Drivers / Robert X Perez -- Reliability Improvement Ideas for Integrally Geared Plant Air Compressors / Abdulrahman Alkhowaiter -- Failure Analysis & Design Evaluation of a 500 KW Regeneration Gas Blower / Abdulrahman Alkhowaiter -- Operating Centrifugal Pumps with Variable Frequency Drives in Static Head Applications / Robert X Perez -- Estimating Reciprocating Compressor Gas Flows / Robert X Perez -- Use Your Historical Records to Better Manage Time Dependent Machinery Failure Modes / Robert X Perez -- PROFESSIONAL DEVELOPMENT. Soft Skills and Habits that All Machinery Professionals Need to Develop / Robert X Perez -- Developing Rotating Machinery Competency / Robert X Perez -- About the Editor -- About the Contributors -- Index -- Also of Interest
Record Nr. UNINA-9910830572403321
Hoboken, New Jersey : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
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Factories of the future : technological advancements in the manufacturing industry / / edited by Chandan Deep Singh and Harleen Kaur
Factories of the future : technological advancements in the manufacturing industry / / edited by Chandan Deep Singh and Harleen Kaur
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (305 pages)
Disciplina 670
Soggetto topico Manufacturing industries - Technological innovations
Manufacturing industries - Management
ISBN 1-119-86521-2
1-119-86520-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Factories of the Future -- 1.0 Introduction -- 1.1 Factory of the Future -- 1.1.1 Plant Structure -- 1.1.2 Plant Digitization -- 1.1.3 Plant Processes -- 1.1.4 Industry of the Future: A Fully Integrated Industry -- 1.2 Current Manufacturing Environment -- 1.3 Driving Technologies and Market Readiness -- 1.4 Connected Factory, Smart Factory, and Smart Manufacturing -- 1.4.1 Potential Benefits of a Connected Factory -- 1.5 Digital and Virtual Factory -- 1.5.1 Digital Factory -- 1.5.2 Virtual Factory -- 1.6 Advanced Manufacturing Technologies -- 1.6.1 Advantages of Advanced Manufacturing Technologies -- 1.7 Role of Factories of the Future (FoF) in Manufacturing Performance -- 1.8 Socio-Econo-Techno Justification of Factories of the Future -- References -- Chapter 2 Industry 5.0 -- 2.1 Introduction -- 2.1.1 Industry 5.0 for Manufacturing -- 2.1.1.1 Industrial Revolutions -- 2.1.2 Real Personalization in Industry 5.0 -- 2.1.3 Industry 5.0 for Human Workers -- 2.2 Individualized Human-Machine-Interaction -- 2.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them -- 2.4 Concerns in Industry 5.0 -- 2.5 Humans Closer to the Design Process of Manufacturing -- 2.5.1 Enablers of Industry 5.0 -- 2.6 Challenges and Enablers (Socio-Econo-Techno Justification) -- 2.6.1 Social Dimension -- 2.6.2 Governmental and Political Dimension -- 2.6.3 Interdisciplinarity -- 2.6.4 Economic Dimension -- 2.6.5 Scalability -- 2.7 Concluding Remarks -- References -- Chapter 3 Machine Learning - A Survey -- 3.1 Introduction -- 3.2 Machine Learning -- 3.2.1 Unsupervised Machine Learning -- 3.2.2 Variety of Unsupervised Learning -- 3.2.3 Supervised Machine Learning -- 3.2.4 Categories of Supervised Learning -- 3.3 Reinforcement Machine Learning -- 3.3.1 Applications of Reinforcement Learning.
3.3.2 Dimensionality Reduction -- 3.4 Importance of Dimensionality Reduction in Machine Learning -- 3.4.1 Methods of Dimensionality Reduction -- 3.4.1.1 Principal Component Analysis (PCA) -- 3.4.1.2 Linear Discriminant Analysis (LDA) -- 3.4.1.3 Generalized Discriminant Analysis (GDA) -- 3.5 Distance Measures -- 3.6 Clustering -- 3.6.1 Algorithms in Clustering -- 3.6.2 Applications of Clustering -- 3.6.3 Iterative Distance-Based Clustering -- 3.7 Hierarchical Model -- 3.8 Density-Based Clustering -- 3.8.1 DBSCAN -- 3.8.2 OPTICS -- 3.9 Role of Machine Learning in Factories of the Future -- 3.10 Identification of the Probable Customers -- 3.11 Conclusion -- References -- Chapter 4 Understanding Neural Networks -- 4.1 Introduction -- 4.2 Components of Neural Networks -- 4.2.1 Neurons -- 4.2.2 Synapses and Weights -- 4.2.3 Bias -- 4.2.4 Architecture of Neural Networks -- 4.2.5 How Do Neural Networks Work? -- 4.2.6 Types of Neural Networks -- 4.2.6.1 Artificial Neural Network (ANN) -- 4.2.6.2 Recurrent Neural Network (RNN) -- 4.2.6.3 Convolutional Neural Network (CNN) -- 4.2.7 Learning Techniques in Neural Network -- 4.2.8 Applications of Neural Network -- 4.2.9 Advantages of Neural Networks -- 4.2.10 Disadvantages of Neural Network -- 4.2.11 Limitations of Neural Networks -- 4.3 Back-Propagation -- 4.3.1 Working of Back-Propagation -- 4.3.2 Types of Back-Propagation -- 4.3.2.1 Static Back-Propagation -- 4.3.2.2 Recurrent Back-Propagation -- 4.3.2.3 Advantages of Back-Propagation -- 4.3.2.4 Disadvantages of Back-Propagation -- 4.4 Activation Function (AF) -- 4.4.1 Sigmoid Active Function -- 4.4.1.1 Advantages -- 4.4.1.2 Disadvantages -- 4.4.2 RELU Activation Function -- 4.4.2.1 Advantages -- 4.4.2.2 Disadvantages -- 4.4.3 TANH Active Function -- 4.4.3.1 Advantages -- 4.4.3.2 Disadvantages -- 4.4.4 Linear Function -- 4.4.5 Advantages -- 4.4.6 Disadvantages.
4.4.7 Softmax Function -- 4.4.8 Advantages -- 4.5 Comparison of Activation Functions -- 4.6 Machine Learning -- 4.6.1 Applications of Machine Learning -- 4.7 Conclusion -- References -- Chapter 5 Intelligent Machining -- 5.1 Introduction -- 5.2 Requirements for the Developments of Intelligent Machining -- 5.3 Components of Intelligent Machining -- 5.3.1 Intelligent Sensors -- 5.3.1.1 Features of Intelligent Sensors -- 5.3.1.2 Functions of Intelligent Sensors -- 5.3.1.3 Data Acquisition and Management System to Process and Store Signals -- 5.3.2 Machine Learning and Knowledge Discovery Component -- 5.3.3 Database Knowledge Discovery -- 5.3.4 Programmable Logical Controller (PLC) -- 5.3.5 Role of Intelligent Machining for Implementation of Green Manufacturing -- 5.3.6 Information Integration via Knowledge Graphs -- 5.4 Conclusion -- References -- Chapter 6 Advanced Maintenance and Reliability -- 6.1 Introduction -- 6.2 Condition-Based Maintenance -- 6.3 Computerized Maintenance Management Systems (CMMS) -- 6.4 Preventive Maintenance (PM) -- 6.5 Predictive Maintenance (PdM) -- 6.6 Reliability Centered Maintenance (RCM) -- 6.6.1 RCM Principles -- 6.7 Condition Monitoring and Residual Life Prediction -- 6.8 Sustainability -- 6.8.1 Role of Sustainability in Manufacturing -- 6.9 Concluding Remarks -- References -- Chapter 7 Digital Manufacturing -- 7.1 Introduction -- 7.2 Product Life Cycle and Transition -- 7.3 Digital Thread -- 7.4 Digital Manufacturing Security -- 7.5 Role of Digital Manufacturing in Future Factories -- 7.6 Digital Manufacturing and CNC Machining -- 7.6.1 Introduction to CNC Machining -- 7.6.2 Equipment's Used in CNC Machining -- 7.6.3 Analyzing Digital Manufacturing Design Considerations -- 7.6.4 Finishing of Part After Machining -- 7.7 Additive Manufacturing -- 7.7.1 Objective of Additive Manufacturing -- 7.7.2 Design Consideration.
7.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries -- 7.9 Conclusion -- References -- Chapter 8 Artificial Intelligence in Machine Learning -- 8.1 Introduction -- 8.2 Case Studies -- 8.3 Advantages of A.I. in ML -- 8.4 Artificial Intelligence - Basics -- 8.4.1 History of A.I. -- 8.4.2 Limitations of Human Mind -- 8.4.3 Real Artificial Intelligence -- 8.4.4 Artificial Intelligence Subfields -- 8.4.5 The Positives of A.I. -- 8.4.6 Machine Learning -- 8.4.7 Machine Learning Models -- 8.4.8 Neural Networks -- 8.4.9 Constraints of Machine Learning -- 8.4.10 Different Kinds of Machine Learning -- 8.5 Application of Artificial Intelligence -- 8.5.1 Expert Systems -- 8.5.2 Natural Language Processing -- 8.5.3 Speech Recognition -- 8.5.4 Computer Vision -- 8.5.5 Robotics -- 8.6 Neural Networks (N.N.) Basics -- 8.6.1 Application of Neural Networks -- 8.6.2 Architecture of Neural Networks -- 8.6.3 Working of Artificial Neural Networks -- 8.7 Convolution Neural Networks -- 8.7.1 Working of Convolutional Neural Networks -- 8.7.2 Overview of CNN -- 8.7.3 Working of CNN -- 8.8 Image Classification -- 8.8.1 Concept of Image Classification -- 8.8.2 Type of Learning -- 8.8.3 Features of Image Classification -- 8.8.4 Examples of Image Classification -- 8.9 Text Classification -- 8.9.1 Text Classification Examples -- 8.9.2 Phases of Text Classification -- 8.9.3 Text Classification API -- 8.10 Recurrent Neural Network -- 8.10.1 Type of Recurrent Neural Network -- 8.11 Building Recurrent Neural Network -- 8.12 Long Short Term Memory Networks (LSTMs) -- References -- Chapter 9 Internet of Things -- 9.1 Introduction -- 9.2 M2M and Web of Things -- 9.3 Wireless Networks -- 9.4 Service Oriented Architecture -- 9.5 Complexity of Networks -- 9.6 Wireless Sensor Networks -- 9.7 Cloud Computing -- 9.8 Cloud Simulators.
9.9 Fog Computing -- 9.10 Applications of IoT -- 9.11 Research Gaps and Challenges in IoT -- 9.12 Concluding Remarks -- References -- Chapter 10 Product Life Cycle -- 10.1 Introduction -- 10.2 Product Lifecycle Management (PLM) -- 10.2.1 Why Product Lifecycle Management? -- 10.2.2 Biological Product Lifecycle Stages -- 10.2.3 An Example Related to Stages in Product Lifecycle Management -- 10.2.4 Advanced Stages in Product Lifecycle Management -- 10.2.5 Strategies of Product Lifecycle Management -- 10.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies -- 10.3.1 Considerations in High and Low-Level Pricing -- 10.3.2 Penetration Pricing Strategy -- 10.3.3 Example for Penetration Pricing Strategy -- 10.3.4 Considerations in Penetration Pricing -- 10.4 How Do Product Lifecycle Management Work? -- 10.5 Application Process of Product Lifecycle Management (PLM) -- 10.6 Role of Unified Modelling Language (UML) -- 10.6.1 UML Activity Diagrams -- 10.7 Management of Product Information Throughout the Entire Product Lifecycle -- 10.8 PDM System in an Organization -- 10.8.1 Benefits of PDM -- 10.8.2 How Does the PDM Work? -- 10.8.3 The Services of Product Data Management -- 10.9 System Architecture -- 10.9.1 Process of System Architecture -- 10.10 Concepts of Model-Based System Engineering (MBSE) -- 10.10.1 Benefits of Model-Based System Engineering (MBSE) -- 10.11 Challenges of Post-COVID 19 in Manufacturing Sector -- 10.12 Recent Updates in Product Life Cycle -- 10.13 Conclusion -- References -- Chapter 11 Case Studies -- 11.1 Case Study in a Two-Wheeler Manufacturing Industry -- 11.1.1 Company Strategy -- 11.1.2 Initiatives Towards Technological Advancement -- 11.1.3 Management Initiatives -- 11.1.4 Sustainable Development Goals -- 11.1.5 Growth Framework with Customer Needs -- 11.1.6 Vision for the Future.
11.2 Case Study in a Four-Wheeler Manufacturing Unit.
Record Nr. UNINA-9910830556703321
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
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Lo trovi qui: Univ. Federico II
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Human-Machine Interface : Making Healthcare Digital / / edited by Rishabha Malviya [and three others]
Human-Machine Interface : Making Healthcare Digital / / edited by Rishabha Malviya [and three others]
Edizione [First edition.]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2024]
Descrizione fisica 1 online resource (519 pages)
Disciplina 621.39
Soggetto topico Computer vision
Human-computer interaction
ISBN 1-394-20034-X
1-394-20033-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Foreword -- Preface -- Acknowledgement -- Part I: Advanced Patient Care with HMI -- Chapter 1 Introduction to Human-Machine Interface -- 1.1 Introduction -- 1.2 Types of HMI -- 1.2.1 The Pushbutton Replacer -- 1.2.2 The Data Handler -- 1.2.3 The Overseer -- 1.3 Transformation of HMI -- 1.4 Importance and COVID Relevance With HMI -- 1.5 Applications -- 1.5.1 Biological Applications -- 1.5.1.1 HMI Signal Detection and Procurement Method -- 1.5.1.2 Healthcare and Rehabilitation -- 1.5.1.3 Magnetoencephalography -- 1.5.1.4 Flexible Hybrid Electronics (FHE) -- 1.5.1.5 Robotic-Assisted Surgeries -- 1.5.1.6 Flexible Microstructural Pressure Sensors -- 1.5.1.7 Biomedical Applications -- 1.5.1.8 CB-HMI -- 1.5.1.9 HMI in Medical Devices -- 1.5.2 Industrial Applications -- 1.5.2.1 Metal Industries -- 1.5.2.2 Video Game Industry -- 1.5.2.3 Aerospace and Defense -- 1.5.2.4 Water Purification Plant HMI Based on Multi-Agent Systems (MAS) -- 1.5.2.5 Virtual and Haptic Interfaces -- 1.5.2.6 Space Crafts -- 1.5.2.7 Car Wash System -- 1.5.2.8 Pharmaceutical Processing and Industries -- 1.6 Challenges -- 1.7 Conclusion and Future Prospects -- References -- Chapter 2 Improving Healthcare Practice by Using HMI Interface -- 2.1 Background of Human-Machine Interaction -- 2.2 Introduction -- 2.2.1 Healthcare Practice -- 2.2.2 Human-Machine Interface System in Healthcare -- 2.3 Evolution of HMI Design -- 2.3.1 HMI Design 1.0 -- 2.3.2 HMI Design 2.0 -- 2.3.3 HMI Design 3.0 -- 2.3.4 HMI Design 4.0 -- 2.4 Anatomy of Human Brain -- 2.5 Signal Associated With Brain -- 2.5.1 Evoked Signals -- 2.5.2 Spontaneous Signals -- 2.5.3 Hybrid Signals -- 2.6 HMI Signal Processing and Acquisition Methods -- 2.7 Human-Machine Interface-Based Healthcare System -- 2.7.1 Healthcare Practice System.
2.7.1.1 Healthcare Practice -- 2.7.1.2 Current State of Healthcare Provision -- 2.7.1.3 Concerns With Domestic Healthcare -- 2.7.2 Medical Education System -- 2.7.2.1 Traditional and Modern Way of Providing Medical Education -- 2.8 Working Model of HMI -- 2.9 Challenges and Limitations of HMI Design -- 2.10 Role of HMI in Healthcare Practice -- 2.10.1 Simple to Clean -- 2.10.2 High Chemical Tolerance -- 2.10.3 Transportable and Light -- 2.10.4 Enhancing Communication -- 2.11 Application of HMI Technology in Medical Fields -- 2.11.1 Medical and Rehabilitative Engineering Using HMI -- 2.11.2 Controls for Robotic Surgery and Human Prosthetics -- 2.11.3 Sensory Replacement Mechanism -- 2.11.4 Wheelchairs and Moving Robots Along With Neurological Interface -- 2.11.5 Cognitive Improvement -- 2.12 Conclusion and Future Perspective -- References -- Chapter 3 Human-Machine Interface and Patient Safety -- 3.1 Introduction -- 3.2 Detecting Anesthesia-Related Drug Administration Errors and Predicting Their Impact -- 3.2.1 Methodological Difficulties in Studying Rare, Dangerous Phenomena -- 3.2.2 Consequences of Errors -- 3.2.3 Lessons From Other Industries -- 3.2.4 The Double-Human Interface -- 3.2.5 The Culture of Denial and Effort -- 3.2.6 Poor Labeling -- 3.3 Systematic Approaches to Improve Patient Safety During Anesthesia -- 3.3.1 Design Principles -- 3.3.2 Evidence of Safety Gains -- 3.3.3 Consistent Color-Coding -- 3.3.4 The Codonics Label System -- 3.4 The Triumph of Software -- 3.4.1 Software in Hospitals -- 3.4.2 Software in Anesthesia -- 3.4.3 The Alarm Problem -- 3.5 Environments that Audit Themselves -- 3.6 New Risks and Dangers -- 3.7 Conclusion -- References -- Chapter 4 Human-Machine Interface Improving Quality of Patient Care -- 4.1 Introduction -- 4.2 An Advanced Framework for Human-Machine Interaction.
4.2.1 A Simulated Workplace Safety and Health Program -- 4.3 Human-Computer Interaction (HCI) -- 4.4 Multimodal Processing -- 4.5 Integrated Multimodality at a Lower Order (Stimulus Orientation) -- 4.6 Higher-Order Multimodal Integration (Perceptual Binding) -- 4.7 Gains in Performance From Multisensory Stimulation -- 4.8 Amplitude Envelope and Alarm Design -- 4.9 Recent Trends in Alarm Tone Design for Medical Devices -- 4.10 Percussive Tone Integration in Multimodal User Interfaces -- 4.11 Software in Hospitals -- 4.12 Brain-Machine Interface (BCI) Outfit -- 4.13 BCI Sensors and Techniques -- 4.13.1 EEG -- 4.13.2 ECoG -- 4.13.3 ECG -- 4.13.4 EMG -- 4.13.5 MEG -- 4.13.6 FMRI -- 4.14 New Generation Advanced Human-Machine Interface -- 4.15 Conclusion -- References -- Chapter 5 Smart Patient Engagement through Robotics -- 5.1 Introduction -- 5.1.1 Robotics in Healthcare -- 5.1.2 Patient Engagement Tasks (Front End) -- 5.1.2.1 Robotics in Nursing, Patient Handling, and Support -- 5.1.2.2 Robotics in Patient Reception -- 5.1.2.3 Robotics in Ambulance Services -- 5.1.2.4 Robotics in Serving (Food and Medicine) -- 5.1.2.5 Robotics in Surgery and Surgical Assistance -- 5.1.2.6 Robotics in Cleaning, Moping, Spraying and Disinfecting -- 5.1.2.7 Robotics in Physiotherapy, Radiology, Lab Diagnostics and Rehabilitation (Exoskeletons) -- 5.1.2.8 Robotics in Tele-Presence -- 5.1.2.9 Robotics in Hospital Kitchen and Pantry Management -- 5.1.2.10 Robotics in Outdoor Medicine Delivery -- 5.1.2.11 Robotics in Home Healthcare -- 5.1.3 Documentation and Other Hospital Management Tasks (Back End) -- 5.1.3.1 Robotics in Patient Data Feeding and Storing -- 5.1.3.2 Robotics in Data Mining -- 5.1.3.3 Robotics in Job Allocation to Hospital Staffs -- 5.1.3.4 Robotics in Payroll Management -- 5.1.3.5 Robotics in Medicine and Medical Equipment Logistics.
5.1.3.6 Robotics in Medical Waste Residual Management -- 5.2 Theoretical Framework -- 5.3 Objectives -- 5.4 Research Methodology -- 5.5 Primary and Secondary Data -- 5.6 Factors for Consideration -- 5.6.1 Patient Demographics -- 5.6.2 Hospital/Health Institutes Demographics -- 5.6.3 Patient Perception Factors -- 5.6.4 Hospital's Feasibility Factors and Hospital's Economic Factors for Implementation -- 5.7 Robotics Implementation -- 5.8 Tools for Analysis -- 5.9 Analysis of Patient's Perception -- 5.10 Review of Literature -- 5.11 Hospitals Considered for the Study (Through Indirect Sources) -- 5.12 Analysis and Interpretation -- 5.12.1 Crosstabulation -- 5.12.2 Regression and Model Fit -- 5.12.3 Factor Analysis -- 5.12.4 Regression Analysis -- 5.12.5 Descriptive Statistics -- 5.13 Conclusion -- References -- Annexure -- Chapter 6 Accelerating Development of Medical Devices Using Human-Machine Interface -- 6.1 Introduction -- 6.2 HMI Machineries -- 6.3 Brain-Computer Interface and HMI -- 6.4 HMI for a Mobile Medical Exoskeleton -- 6.5 Human Artificial Limb and Robotic Surgical Treatment by HMI -- 6.6 Cognitive Enhancement by HMI -- 6.7 Soft Electronics for the Skin Using HMI -- 6.8 Safety Considerations -- 6.9 Conclusion -- References -- Chapter 7 The Role of a Human-Machine Interaction (HMI) System on the Medical Devices -- 7.1 Introduction -- 7.2 Machine Learning for HCI Systems -- 7.3 Patient Experience -- 7.4 Cognitive Science -- 7.5 HCI System Based on Image Processing -- 7.5.1 Patient's Facial Expression -- 7.5.2 Gender and Age -- 7.5.3 Emotional Intelligence -- 7.6 Blockchain -- 7.7 Virtual Reality -- 7.8 The Challenges in Designing HCI Systems for Medical Devices -- 7.9 Conclusion -- References -- Chapter 8 Human-Machine Interaction in Leveraging the Concept of Telemedicine -- 8.1 Introduction.
8.2 Innovative Development in HMI Technologies and Its Use in Telemedicine -- 8.2.1 Nanotechnology -- 8.2.2 The Internet of Things (IoT) -- 8.2.3 Internet of Medical Things (IoMT) -- 8.2.3.1 Motion Detection Sensors -- 8.2.3.2 Pressure Sensors -- 8.2.3.3 Temperature Sensors -- 8.2.3.4 Monitoring Cardiovascular Disease -- 8.2.3.5 Glucose Level Monitoring -- 8.2.3.6 Asthma Monitoring -- 8.2.3.7 GPS Smart Soles and Motion Detection Sensors -- 8.2.3.8 Wireless Fetal Monitoring -- 8.2.3.9 Smart Clothing -- 8.2.4 AI -- 8.2.5 Machine Learning Techniques -- 8.2.6 Deep Learning -- 8.2.7 Home Monitoring Devices, Augmented and Virtual -- 8.2.8 Drone Technology -- 8.2.9 Robotics -- 8.2.9.1 Robotics in Healthcare -- 8.2.9.2 History of Robotics -- 8.2.9.3 Tele-Surgery/Remote Surgery -- 8.2.10 5G Technology -- 8.2.11 6G -- 8.2.12 Big Data -- 8.2.13 Cloud Computing -- 8.2.14 Blockchain -- 8.2.14.1 Clinical Trials -- 8.2.14.2 Patient Records -- 8.2.14.3 Drug Tracking -- 8.2.14.4 Device Tracking -- 8.3 Advantages of Utilizing HMI in Healthcare for Telemedicine -- 8.3.1 Emotive Telemedicine -- 8.3.2 Ambient Assisted Living -- 8.3.2.1 Wearable Sensors for AAL -- 8.3.3 Monitoring and Controlling Intelligent Self-Management and Wellbeing -- 8.3.4 Intelligent Reminders for Treatment, Compliance, and Adherence -- 8.3.5 Personalized and Connected Healthcare -- 8.4 Obstacles to the Utilize, Accept, and Implement HMI in Telemedicine -- 8.4.1 Data Inconsistency and Disintegration -- 8.4.2 Standards and Interoperability are Lacking -- 8.4.3 Intermittent or Non-Existent Network Connectivity -- 8.4.4 Sensor Data Unreliability and Invalidity -- 8.4.5 Privacy, Confidentiality, and Data Consistency -- 8.4.6 Scalability Issues -- 8.4.7 Health Consequences -- 8.4.8 Clinical Challenges -- 8.4.9 Nanosensors and Biosensors Offer Health Risks.
8.4.10 Limited Computing Capability and Inefficient Energy Use.
Record Nr. UNINA-9910747099103321
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2024]
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Intelligent manufacturing management systems : operational applications of evolutionary digital technologies in mechanical and industrial engineering / / edited by Kamalakanta Muduli [and four others]
Intelligent manufacturing management systems : operational applications of evolutionary digital technologies in mechanical and industrial engineering / / edited by Kamalakanta Muduli [and four others]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (402 pages)
Disciplina 006.3
Soggetto topico Production control
ISBN 1-119-83678-6
1-119-83677-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Smart Technologies in Manufacturing -- Chapter 1 Smart Manufacturing Systems for Industry 4.0 -- Abbreviations -- 1.1 Introduction -- 1.2 Research Methodology -- 1.3 Pillars of Smart Manufacturing -- 1.3.1 Manufacturing Technology and Processes -- 1.3.2 Materials -- 1.3.3 Data -- 1.3.4 Sustainability -- 1.3.5 Resource Sharing and Networking -- 1.3.6 Predictive Engineering -- 1.3.7 Stakeholders -- 1.3.8 Standardization -- 1.4 Enablers and Their Applications -- 1.4.1 Smart Design -- 1.4.2 Smart Machining -- 1.4.3 Smart Monitoring -- 1.4.4 Smart Control -- 1.4.5 Smart Scheduling -- 1.5 Assessment of Smart Manufacturing Systems -- 1.6 Challenges in Implementation of Smart Manufacturing Systems -- 1.6.1 Technological Issue -- 1.6.2 Methodological Issue -- 1.7 Implications of the Study for Academicians and Practitioners -- 1.8 Conclusion -- References -- Chapter 2 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities -- Abbreviations -- 2.1 Introduction to Smart Manufacturing -- 2.1.1 Background of SM -- 2.1.2 Traditional Manufacturing versus Smart Manufacturing -- 2.1.3 Concept and Evolution of Industry 4.0 -- 2.1.4 Motivations for Research in Smart Manufacturing -- 2.1.5 Objectives and Need of Industry 4.0 -- 2.1.6 Research Methodology -- 2.1.7 Principles of I4.0 -- 2.1.8 Benefits/Advantages of Industry 4.0 -- 2.2 Technology Pillars of Industry 4.0 -- 2.2.1 Automation in Industry 4.0 -- 2.2.1.1 Need of Automation -- 2.2.1.2 Components of Automation -- 2.2.1.3 Applications of Automation -- 2.2.2 Robots in Industry 4.0 -- 2.2.2.1 Need of Robots -- 2.2.2.2 Advantages of Robots -- 2.2.2.3 Applications of Robots -- 2.2.2.4 Advances Robotics -- 2.2.3 Additive Manufacturing (AM) -- 2.2.3.1 Additive Manufacturing's Potential Applications.
2.2.4 Big Data Analytics -- 2.2.5 Cloud Computing -- 2.2.6 Cyber Security -- 2.2.6.1 Cyber-Security Challenges in Industry 4.0 -- 2.2.7 Augmented Reality and Virtual Reality -- 2.2.8 Simulation -- 2.2.8.1 Need of Simulation in Smart Manufacturing -- 2.2.8.2 Advantages of Simulation -- 2.2.8.3 Simulation and Digital Twin -- 2.2.9 Digital Twins -- 2.2.9.1 Integration of Horizontal and Vertical Systems -- 2.2.10 IoT and IIoT in Industry 4.0 -- 2.2.11 Artificial Intelligence in Industry 4.0 -- 2.2.12 Implications of the Study for Academicians and Practitioners -- 2.3 Summary and Conclusions -- 2.3.1 Benefits of Industry 4.0 -- 2.3.2 Challenges in Industry 4.0 -- 2.3.3 Future Directions -- Acknowledgement -- References -- Chapter 3 IoT-Based Intelligent Manufacturing System: A Review -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Research Procedure -- 3.3.1 The Beginning and Advancement of SM/IM -- 3.3.2 Beginning of SM/IM -- 3.3.3 Defining SM/IM -- 3.3.4 Potential of SM/IM -- 3.3.5 Statistical Analysis of SM/IM -- 3.3.6 Future Endeavour of SM/IM -- 3.3.7 Necessary Components of IoT Framework -- 3.3.8 Proposed System Based on IoT -- 3.3.9 Development of IoT in Industry 4.0 -- 3.4 Smart Manufacturing -- 3.4.1 Re-Configurability Manufacturing System -- 3.4.2 RMS Framework Based Upon IoT -- 3.4.3 Machine Control -- 3.4.4 Machine Intelligence -- 3.4.5 Innovation and the IIoT -- 3.4.6 Wireless Technology -- 3.4.7 IP Mobility -- 3.4.8 Network Functionality Virtualization (NFV) -- 3.5 Academia Industry Collaboration -- 3.6 Conclusions -- References -- Chapter 4 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process -- Abbreviations -- 4.1 Introduction and Literature Reviews -- 4.1.1 Motivation Behind the Study -- 4.1.2 Objective of the Chapter -- 4.2 Network in Smart Manufacturing System.
4.2.1 Challenges for Smart Manufacturing Industries -- 4.2.2 Smart Manufacturing Current Market Scenario -- 4.3 Data Drives in Smart Manufacturing -- 4.3.1 Benefits of Data-Driven Manufacturing -- 4.4 Manufacturing of Product Through 3D Printing Process -- 4.4.1 3D Printing Technology -- 4.4.2 3D Printing Technologies Classification -- 4.4.3 3D Printer Parameters -- 4.4.4 Significance of Honeycomb Structure -- 4.4.5 Acrylonitrile Butadiene Styrene (ABS) Thermoplastic Polymer Used for Honeycomb Structures Model -- 4.4.6 3D Printing Parameters and Their Descriptions -- 4.5 Conclusion -- References -- Chapter 5 Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management -- 5.1 Introduction -- 5.2 Objectives -- 5.3 Research Methodology -- 5.4 Literature Review -- 5.5 Components of SIM -- 5.5.1 Supply Chain Management (SCM) -- 5.5.2 Inventory Management System (IMS) -- 5.5.3 Internet of Things (IoT) -- 5.5.4 RFID System -- 5.5.5 Maintenance, Repair, and Operations -- 5.5.6 Deep Reinforcement Learning -- 5.6 Framework -- 5.7 Optimization -- 5.7.1 Inventory Optimization -- 5.8 Results and Discussion -- 5.9 A Mirror to Researchers and Managers -- 5.10 Conclusions -- 5.11 Future Scope -- References -- Chapter 6 Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0 -- 6.1 Introduction -- 6.2 Machine Learning -- 6.3 Smart Factory -- 6.4 Intelligent Machining -- 6.5 Machine Learning Processes Used in Machining Process -- 6.6 Performance Improvement of Machine Structure Using Machine Learning -- 6.7 Conclusions -- References -- Chapter 7 Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies -- Abbreviations -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Methodology -- 7.3.1 Dataset Preparation -- 7.3.2 CWRU Dataset.
7.3.3 Methodology Flow Chart -- 7.3.4 Data Pre-Processing -- 7.3.5 Models Deployed -- 7.3.6 Training and Testing -- 7.4 Analysis -- 7.4.1 Datasets -- 7.4.2 Feature Extraction -- 7.4.3 Splitting of Data into Samples -- 7.4.4 Algorithms Used -- 7.4.4.1 Multinomial Logistic Regression -- 7.4.4.2 K-Nearest Neighbors -- 7.4.4.3 Decision Tree -- 7.4.4.4 Support Vector Machine (SVM) -- 7.4.4.5 Random Forest -- 7.5 Results and Discussion -- 7.5.1 Importance of Classification Reports -- 7.5.2 Importance of Confusion Matrices -- 7.5.3 Decision Tree -- 7.5.4 Random Forest -- 7.5.5 K-Nearest Neighbors -- 7.5.6 Logistic Regression -- 7.5.7 Support Vector Machine -- 7.5.8 Comparison of the Algorithms -- 7.5.8.1 Accuracies -- 7.5.8.2 Precision and Recall -- 7.6 Conclusions -- 7.7 Scope of Future Work -- References -- Chapter 8 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment -- 8.1 Introduction -- 8.1.1 Color Image Processing -- 8.1.2 Motivation -- 8.1.3 Objectives -- 8.2 Literature Review -- 8.2.1 Gas Turbine Power Plants -- 8.2.2 Artificial Intelligent Methods -- 8.3 Materials and Methods -- 8.3.1 Feature Extraction -- 8.3.2 Classification -- 8.4 Results and Discussion -- 8.4.1 Fisher's Linear Discriminant Function (FLDA) and Curvelet -- 8.5 Conclusion -- 8.5.1 Future Scope of Work -- References -- Chapter 9 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate -- Abbreviations -- 9.1 Introduction -- 9.2 Numerical Experimentation Program -- 9.3 Discussion of the Results -- 9.4 Conclusion -- Acknowledgements -- References -- Part II: Integration of Digital Technologies to Operations -- Chapter 10 Edge Computing-Based Conditional Monitoring -- 10.1 Introduction -- 10.1.1 Problem Statement -- 10.2 Literature Review -- 10.3 Edge Computing -- 10.4 Methodology.
10.5 Discussion -- 10.5.1 Predictive Maintenance -- 10.5.2 Energy Efficiency Management -- 10.5.3 Smart Manufacturing -- 10.5.4 Conditional Monitoring via Edge Computing Locally -- 10.5.5 Lesson Learned -- 10.6 Conclusion -- References -- Chapter 11 Optimization Methodologies in Intelligent Manufacturing Systems: Application and Challenges -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Intelligent Manufacturing System Framework -- 11.3.1 Principles of Developing Industry 4.0 Solutions -- 11.3.2 Quantitative Analysis -- 11.3.2.1 Optimization Characteristics and Requirements of Industry 4.0 -- 11.3.3 Optimization Methodologies and Algorithms -- 11.4 Bayesian Networks (BNs) -- 11.4.1 Instance-Based Learning (IBL) -- 11.4.2 The IB1 Algorithm -- 11.4.3 Artificial Neural Networks -- 11.4.4 A Comparison Between Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) -- 11.5 Problems of Implementing Machine Learning in Manufacturing -- 11.6 Conclusions -- References -- Chapter 12 Challenges of Warehouse Management Towards Smart Manufacturing: A Case of an Indian Consumer Electrical Company -- 12.1 Introduction -- 12.2 Literature Review -- 12.2.1 Shortage of Space -- 12.2.2 Non-Moving Materials -- 12.2.3 Lack of Action on Liquidation -- 12.2.4 Defective Material from Both Ends -- 12.2.5 Gap Between the Demand and the Supply -- 12.2.6 Multiple Price Revision -- 12.2.7 More Manual Timing for Loading and Unloading -- 12.2.8 Operational Challenges for Seasonal Products -- 12.2.9 Lack of Automation -- 12.2.10 Manpower Balancing Between Peak and Off -- 12.3 The Proposed ISM Methodology -- 12.3.1 Establishment of the Structural Self-Interaction Matrix (SSIM) -- 12.3.2 Creation of the Reachability Matrix -- 12.3.3 Implementation of the Level Partitions -- 12.3.4 Classification of the Selected Challenges.
12.3.5 Development of the Final ISM Model.
Record Nr. UNINA-9910830450703321
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
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The Mathematical Biology of Diatoms / / edited by Janice L. Pappas
The Mathematical Biology of Diatoms / / edited by Janice L. Pappas
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (473 pages)
Disciplina 579.85
Collana Diatoms
Soggetto topico Diatoms
ISBN 1-119-75193-4
1-119-75192-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- List of Figures -- List of Tables -- Preface -- Part I: Diatom Form and Size Dynamics -- Chapter 1 Modeling the Stiffness of Diploneis Species Based on Geometry of the Frustule Cut with Focused Ion Beam Technology -- 1.1 Introduction -- 1.2 Material and Methods -- 1.2.1 Focused Ion Beam (FIB) Milling -- 1.2.2 Modeling -- 1.3 Results -- 1.3.1 FIB Processing -- 1.3.2 Modeling -- 1.4 Discussion -- 1.4.1 Practical Meaning of the Frustule Geometric Characters -- 1.4.2 Documenting the Mechanical Strength of the Diatom Frustule -- Acknowledgments -- References -- Chapter 2 Size-Resolved Modeling of Diatom Populations: Old Findings and New Insights -- 2.1 Introduction -- 2.2 The MacDonald-Pfitzer Rule and the Need for Matrix Descriptions -- 2.3 Cardinal Points and Cycle Lengths -- 2.3.1 Considered Cardinal Parameters -- 2.3.2 Factors Determining Cardinal Points -- 2.3.3 Experimental Data -- 2.4 Asymmetry, Delay and Fibonacci Growth -- 2.4.1 The Müller Model -- 2.4.2 The Laney Model -- 2.5 Continuous vs. Discrete Modeling -- 2.5.1 Discrete Dynamical Systems -- 2.5.2 The Perron-Frobenius Theorem -- 2.5.3 Continuous Dynamical Systems -- 2.5.4 Extensions and Generalizations -- 2.5.5 Individual-Based Models -- 2.6 Simulation Models -- 2.6.1 The Schwarz et al. Model -- 2.6.2 The D'Alelio et al. Model -- 2.6.3 The Hense-Beckmann Model -- 2.6.4 The Terzieva-Terziev Model -- 2.6.5 The Fuhrmann-Lieker et al. Model -- 2.7 Oscillatory Behavior -- 2.7.1 Reproduction of Experimental Data -- 2.7.2 Coupling to External Rhythms -- 2.8 Conclusion -- Acknowledgment -- References -- Chapter 3 On the Mathematical Description of Diatom Algae: From Siliceous Exoskeleton Structure and Properties to Colony Growth Kinetics, and Prospective Nanoengineering Applications -- 3.1 Introduction.
3.2 Hierarchical Structuring of Matter: Diatom Algae and the Bio-Assisted Nanostructured Additive Manufacturing Paradigm -- 3.3 Structural Design of Diatom Frustules -- 3.4 Mechanical Performance of Diatom Frustules - Experimental Characterization -- 3.4.1 Nanoindentation Testing of Diatom Frustules -- 3.4.2 AFM Studies of Diatom Frustules -- 3.5 Engineering Applications of Diatomaceous Earth -- 3.6 NEMS/MEMS Perspective -- 3.7 On the Mathematical Description of Self-Organized Diatom Frustule Growth -- 3.8 On the Kinetics of Diatom Colony Growth -- 3.9 Advanced Pattern Analysis of the Hierarchical Structure of Diatom Frustules -- 3.10 Concluding Remarks -- Acknowledgement -- References -- Part II: Diatom Development, Growth and Metabolism -- Chapter 4 Ring to the Linear: Valve Ontogeny Indicates Two Potential Evolutionary Pathways of Core Araphid Diatoms -- 4.1 Introduction -- 4.2 Material and Methods -- 4.2.1 Fragilaria mesolepta -- 4.2.2 Staurosira binodis -- 4.2.3 Induction of Synchronous Division -- 4.2.4 Electron Microscopy -- 4.3 Results -- 4.3.1 Fragilaria mesolepta -- 4.3.2 Staurosira binodis -- 4.4 Discussion -- 4.5 Conclusion -- References -- Chapter 5 Mathematical Basis for Diatom Growth Modeling -- 5.1 Introduction -- 5.2 General Physiology of Diatoms -- 5.3 Mathematical View of Diatom Growth -- 5.4 Physical Basis for Diatom Modeling -- 5.4.1 Diatom Dimensions -- 5.4.2 Ambient Temperature -- 5.4.3 Light Intensity and Duration -- 5.5 Review of Existing Mathematical Models -- 5.5.1 Gompertz Model -- 5.5.2 Monod Model -- 5.5.3 Michaelis-Menten Model -- 5.5.4 Droop Model -- 5.5.5 Aquaphy Model -- 5.5.6 Mechanistic Model -- 5.6 Results -- 5.7 Conclusion -- 5.8 Prospects -- References -- Chapter 6 Diatom Growth: How to Improve the Analysis of Noisy Data -- 6.1 Introduction -- 6.1.1 What is a Growth Curve? -- 6.1.2 Why Measure Growth?.
6.1.3 Diatoms and Their Growth -- 6.1.4 Growth Data Analysis and Growth Parameter Estimation -- 6.2 Simulation Trials -- 6.2.1 Methodology for the Simulation Trials -- 6.2.2 Candidate Methods for Estimating the Specific Growth Rate -- 6.2.3 Simulation Trials Results -- 6.2.3.1 Results with Only the Noise Challenge -- 6.2.3.2 Results when Crashing Occurs -- 6.2.3.3 Results when Censoring Occurs -- 6.2.3.4 Overall Results and Ranking of the Methods -- 6.3 Empirical Example -- 6.4 Conclusions and Recommendations -- References -- Chapter 7 Integrating Metabolic Modeling and High-Throughput Data to Characterize Diatoms Metabolism -- 7.1 Introduction -- 7.2 Characterization of Diatom Genomes -- 7.2.1 Available Genomics Data -- 7.2.2 Computational Tools to Allocate Gene Functions by Subcellular Localization -- 7.3 Metabolic Modeling of Diatoms: Data and Outcomes -- 7.3.1 Using Genomic Information to Build Genome-Scale Metabolic Models -- 7.3.2 Comprehensive Diatom Omic Datasets Are Useful to Constrain Metabolic Models -- 7.3.3 Unraveling New Knowledge About Central Carbon Metabolism of Diatoms -- 7.3.4 Light-Driven Metabolism that Enables Acclimation to High Light Intensities -- 7.4 Modeling Applications to Study Bioproduction and Genome Changes in Diatoms -- 7.4.1 Predicting Diatom-Heterotroph Interactions and Horizontal Gene Transfer Using Community Metabolic Models -- 7.4.2 Optimization and Scale-Up of the Production of Valuable Metabolites -- 7.4.3 Potential for the Study of Proteome Allocation in Diatoms -- 7.5 Conclusions -- References -- Part III: Diatom Motility -- Chapter 8 Modeling the Synchronization of the Movement of Bacillaria paxillifer by a Kuramoto Model with Time Delay -- 8.1 Introduction -- 8.2 Materials and Methods -- 8.3 Time Dependence of the Relative Motion of Adjacent Diatoms.
8.4 Modeling Interacting Oscillators of a Bacillaria Colony -- 8.4.1 Observation of the Movement Activity at Uncovered Rhaphes -- 8.4.2 Interaction of Neighboring Diatoms -- 8.4.3 Coupled Oscillators -- 8.5 Kuramoto Model -- 8.5.1 Adaptation of the Kuramoto Model for a Bacillaria Colony -- 8.5.2 Analyses and Approximations -- 8.5.3 Critical Coupling -- 8.5.3.1 Uncoupled Oscillators -- 8.5.3.2 Two Oscillators -- 8.5.3.3 Chains without Retardation -- 8.5.3.4 Identical Oscillator Frequencies and Sufficiently Small Delay -- 8.5.3.5 Remarks on the General Case -- 8.5.4 Statistical Considerations and Monte Carlo Simulations -- 8.5.4.1 Expected Value and Standard Deviation -- 8.5.4.2 Distribution of Critical Coupling -- 8.5.5 Simulation of Non-Synchronous States -- 8.5.5.1 Numerical Integration -- 8.5.5.2 Visualization of the Transient -- 8.5.5.3 Discrete Fourier Transform -- 8.5.6 Coupling to a Periodic Light Source -- 8.6 Discussion -- Acknowledgment -- References -- Chapter 9 The Psychophysical World of the Motile Diatom Bacillaria paradoxa -- Abbreviations -- 9.1 Introduction -- 9.1.1 Aneural Architecture of Bacillaria -- 9.1.2 Aneural Cognition in a Broader Context -- 9.1.3 Psychophysics as Diatom Information Processing -- 9.1.4 Information Processing and Aneural Cognition -- 9.1.5 Hebbian Intelligence and Predictive Processing -- 9.2 Measurement Techniques -- 9.2.1 Weber-Fechner Law -- 9.2.2 Connectionist Network -- 9.2.3 Algorithmic Information -- 9.2.4 Collective Pattern Generator -- 9.2.5 Dynamical States of the CoPG -- 9.3 CPGs vs. CoPGs -- 9.3.1 Potential of Predictive Processing -- 9.3.2 Phase Transitions in Bacillaria Movement -- 9.4 Aneural Regulation -- 9.5 Broader Picture of Intelligence and Emergence -- 9.5.1 Pseudo-Intelligence -- 9.6 Discussion -- Acknowledgments -- References.
Chapter 10 Pattern Formation in Diatoma vulgaris Colonies: Observations and Description by a Lindenmayer-System -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Cultivation and Recording of Images -- 10.2.2 Formal Notation of Types of Concatenation and Splitting Processes -- 10.2.3 Methods to Observe the Processes -- 10.2.3.1 Basic Options -- 10.2.3.2 Long-Term Observations -- 10.2.3.3 Analysis of Single Images -- 10.3 Results -- 10.3.1 Observation of Elementary Splitting Processes -- 10.3.2 Observation of Synchronism -- 10.3.3 Observation of the Processes and Appearance of Colonies -- 10.3.3.1 Splitting of Elements of Types c and d -- 10.3.3.2 Splitting of Elements of Types a and b - Dynamic Analysis -- 10.3.3.3 Separation of Elements of Types a and b - Static Analysis -- 10.3.3.4 Dependence on Environmental Parameters -- 10.3.4 Theory Formation -- 10.3.4.1 Description of the Asymmetry -- 10.3.4.2 Lindenmayer System -- 10.3.5 Outer Shape of the Colonies -- 10.4 Discussion -- Acknowledgment -- Appendix 10A: Calculation Scheme -- Appendix 10B: Accordance with the D0L-System -- References -- Chapter 11 RAPHE: Simulation of the Dynamics of Diatom Motility at the Molecular Level - The Domino Effect Hydration Model with Concerted Diffusion -- 11.1 Introduction -- 11.2 Parameters -- 11.3 Ising Lattice Modeling -- 11.4 Allowing Bias -- 11.5 Computer Representation -- 11.6 The Roles of the Cell Membrane, Canal Raphes, and the Diatotepum -- 11.7 Raphan and the Raphe -- 11.8 The Jerky Motion of Diatoms -- 11.9 Diffusion and Concerted Diffusion of Raphan -- 11.10 Shear and Janus-Faced Causation: Motility and Raphan Tilting -- 11.11 The Domino Effect Causes Size Independence of Diatom Speed -- 11.12 Quantitating the Swelling of Raphan in the Diatom Trail -- 11.13 A Schematic of Raphan Discharge -- 11.14 Transitions of Raphan.
11.15 The Roles of the Diatom Trail.
Record Nr. UNINA-9910713829503321
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
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Medical imaging / / H. S. Sanjay and M. Niranjanamurthy
Medical imaging / / H. S. Sanjay and M. Niranjanamurthy
Autore Sanjay H. S.
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (260 pages)
Disciplina 616.0754
Soggetto topico Diagnostic imaging
ISBN 1-119-78559-6
1-119-78558-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Chapter 1 Introduction to Medical Imaging -- 1.1 Medical Imaging - An Insight -- 1.2 Types of Diagnostic Imaging Modalities -- 1.2.1 Radiography -- 1.2.2 Tomography -- 1.2.3 Ultrasound -- 1.2.4 Nuclear Medicine -- 1.2.5 Magnetic Resonance Imaging -- 1.2.6 Functional Magnetic Resonance Imaging (fMRI) -- 1.2.7 Functional Near Infrared Imaging -- 1.2.8 Elastography -- 1.2.9 Photoacoustic Imaging -- 1.2.10 Magnetic Particle Imaging -- 1.3 3D Rendering -- 1.4 Diagnostic Images -- 1.5 Medical Imaging in Pharmaceutical Applications -- Glossary-Appendix -- Chapter 2 Fundamentals of X-Rays -- 2.1 Electromagnetic Radiations -- 2.2 Wave Nature -- 2.2.1 Particle Nature -- 2.2.2 Intensity of an X-Ray Beam -- 2.2.3 Roentgen (R) -- 2.2.4 Radiation Absorbed Dose (rad) -- 2.2.5 X-Ray Interactions -- 2.2.6 Interaction Between X-Ray and Matter -- 2.2.7 Coherent Scattering -- 2.2.8 Compton Effect -- 2.3 Photoelectric Effect -- 2.3.1 Pair Production -- 2.3.2 Photodisintegration -- 2.4 Interaction Between X-Ray and Tissues -- 2.5 Factors Affecting Attenuation Coefficients -- 2.6 Attenuation Due to Coherent Scattering (βcoh) -- 2.7 Attenuation Due to Compton Scattering (βcom) and Photoelectric Effect (βpho) -- 2.8 Generation and Detection of X-Rays -- 2.8.1 Generation of X-Rays -- 2.8.2 White Radiation -- 2.8.3 Characteristic Radiation -- 2.9 X-Ray Generators -- 2.9.1 Line Focus Principle -- 2.9.2 X-Ray Tube Ratings -- 2.9.3 Target Material -- 2.9.4 Tube Voltage -- 2.9.5 Tube Current -- 2.9.6 Filament Current -- 2.10 Filters -- 2.10.1 Beam Restrictors -- 2.10.2 Aperture Diaphragms -- 2.10.3 Cones and Cylinders -- 2.10.4 Collimators -- 2.10.5 Grids -- 2.11 X-Ray Visualization -- 2.11.1 Intensifying Screens -- 2.11.2 Image Intensifiers -- 2.12 Detection of X-Rays -- 2.12.1 X-Ray Film.
2.12.2 Optical Density -- 2.12.3 Characteristic Curve -- 2.12.4 Film Gamma -- 2.12.5 Speed -- 2.12.6 Film Latitude -- 2.12.7 Double-Emulsion Film -- 2.13 Radiation Detectors -- 2.13.1 Scintillation Detector -- 2.13.2 Ionization Chamber -- 2.14 X-Ray Diagnostic Approaches -- 2.14.1 Conventional X-Ray Radiography -- 2.14.2 Penumbra -- 2.14.3 Field Size -- 2.14.4 Film Magnification -- 2.15 Fluoroscopy -- 2.16 Angiography -- 2.17 Mammography -- 2.18 Xeroradiography -- 2.19 Image Subtraction -- 2.19.1 Digital Subtraction Angiography (DSA) -- 2.19.2 Dual Energy Subtraction -- 2.19.3 K-Edge Subtraction -- 2.20 Conventional Tomography -- 2.20.1 X-Ray Image Attributes -- 2.20.2 Spatial Resolution -- 2.21 Point Spread Function (PSF) -- 2.21.1 Line Spread Function (LSF) -- 2.21.2 Edge Spread Function (ESF) -- 2.21.3 System Transfer Function (STF) -- 2.22 Image Noise -- 2.23 Image Contrast -- 2.24 Receiver Operating Curve (ROC) -- 2.25 Biological Effects of X-Ray Radiations -- 2.25.1 Determinants of Biological Effects -- Glossary-Appendix -- Chapter 3 X-Ray Computed Tomography -- 3.1 Introduction to X-Ray Computed Tomography -- 3.2 CT Number -- 3.3 X-Ray Detectors in CT Machines -- 3.3.1 Energy Integrating Detectors -- 3.3.2 Photon Counting Detectors -- 3.4 CT Imaging -- 3.4.1 Radon Transform -- 3.4.2 Sampling -- 3.4.3 2D Image Reconstruction -- 3.4.4 Direct Fourier Transform -- 3.4.5 Filtered Back Projection (FBP)/Convolution Back Projection (CBP) -- 3.4.6 Fan Beam Projections -- 3.5 Computer Tomography-Based Diagnostics -- 3.5.1 Single Slice Computed Tomography -- 3.5.2 Multislice Computed Tomography -- 3.5.3 Cardiac CT -- 3.5.4 Dual Energy Computer Tomography -- 3.6 Image Quality -- 3.6.1 Resolution -- 3.6.2 Noise -- 3.6.3 Contrast -- 3.6.4 Image Artifacts -- 3.7 CT Machine - The Hardware Aspects -- 3.8 Generations of CT Machines.
3.9 Biological Effects and Safety-Based Aspects -- Glossary-Appendix -- Chapter 4 Ultrasound Imaging -- 4.0 Ultrasound -- 4.1 Basics of Acoustic Waves -- 4.2 Propagation of Waves in Homogeneous Media -- 4.3 Linear Wave Equation -- 4.4 Loudness and Intensity -- 4.5 Interference -- 4.6 Attenuation -- 4.7 Nonlinearity -- 4.8 Propagation of Waves in Non-Homogeneous Media -- 4.9 Reflection and Refraction -- 4.10 Scattering -- 4.11 Doppler Effect in the Propagation of the Acoustic Wave -- 4.12 Generation and Detection of Ultrasound -- 4.13 Ultrasonic Transducer -- 4.14 Mechanical Matching -- 4.15 Electrical Matching -- 4.16 Ultrasound Imaging -- 4.16.1 Gray Scale Imaging -- 4.16.1.1 Data Acquisition -- 4.16.1.2 Amplitude Mode (A-Mode) -- 4.16.1.3 Brightness Mode (B-Mode) -- 4.16.1.4 Motion Mode (M-Mode) -- 4.17 Image Reconstruction -- 4.18 Schlieren System -- 4.19 Doppler Imaging Approaches -- 4.19.1 Continuous Wave Doppler System -- 4.19.2 Pulse Wave Doppler System -- 4.19.3 Color Doppler Flow Imaging -- 4.20 Tissue Characterization -- 4.20.1 Velocity -- 4.20.2 Absorption -- 4.20.3 Scattering -- 4.21 Ultrasound Image Characteristics -- 4.21.1 Spatial Resolution -- 4.21.2 Image Contrast -- 4.21.3 Ultrasonic Texture -- 4.22 Biological Effects of Ultrasound -- 4.22.1 Acoustic Aspects at High Intensity Levels -- 4.22.2 Cavitation -- 4.22.3 Transient Cavitation -- 4.22.4 Stable Cavitation -- 4.22.5 Wave Distortion -- 4.22.6 Bioeffects (Thermal and Non-Thermal Effects) -- Glossary-Appendix -- Chapter 5 Radionuclide Imaging -- 5.1 Radionuclide Imaging - A Brief History -- 5.2 An Insight Into Radioactivity -- 5.2.1 Nuclear Particles -- 5.2.2 Radioactive Decay -- 5.2.3 Specific Activity -- 5.2.4 Interactions Between Nuclear Particles and Matter -- 5.2.4.1 Alpha Particles -- 5.2.4.2 Beta Particles -- 5.2.4.3 Gamma Particles -- 5.2.5 Properties of Radionuclides.
5.2.5.1 Physical Properties -- 5.2.5.2 Biological Properties -- 5.3 Generation of Nuclear Emission -- 5.3.1 Nuclear Sources -- 5.3.2 99mTc Radionuclide Generator -- 5.3.3 Detection of Nuclear Emissions -- 5.3.3.1 Ion Collection Detectors -- 5.3.3.2 Scintillation Fetectors -- 5.3.3.3 Solid State Detectors -- 5.3.3.4 Collimator -- 5.4 Radionuclide Detection -- 5.4.1 Rectilinear Scanning Machines -- 5.4.2 Scintillation Camera (Gamma Camera) -- 5.4.2.1 Collimator -- 5.4.2.2 Scintillation Crystal -- 5.4.2.3 Photomultiplier Tube -- 5.4.3 Longitudinal Section Tomography (LST) -- 5.4.4 Single Photon Emission Computer Tomography (SPECT) -- 5.4.5 Positron Emission Tomography (PET) -- 5.5 Diagnostic Approaches Using Radiation Detector Probes -- 5.5.1 Thyroid Function Assessment -- 5.5.2 Renal Function Test -- 5.5.3 Blood Volume Assessment -- 5.6 Radionuclide Image Characteristics -- 5.6.1 Spatial Resolution -- 5.6.2 Image Contrast -- 5.6.3 Image Noise -- 5.7 Biological Effects of Radionuclides -- Glossary-Appendix -- Chapter 6 Magnetic Resonance Imaging -- 6.1 Basics of Nuclear Magnetic Resonance -- 6.2 Larmor Frequency -- 6.3 Relaxation -- 6.3.1 T1 (Longitudinal Relaxation) -- 6.3.2 T2 (Transverse Relaxation) -- 6.4 Image Contrast -- 6.5 Repetition Time (TR) and T1 Weighting -- 6.6 Echo Time (TE) and T2 Weighting -- 6.7 Saturation at Short Repetition Times -- 6.8 Flip Angle/Tip Angle -- 6.9 Presaturation -- 6.10 Magnetization Transfer -- 6.11 Slice Selection -- 6.12 Spatial Encoding -- 6.13 Phase Encoding -- 6.14 Frequency Encoding -- 6.15 K-Space -- 6.16 Image Noise -- 6.17 The MR Scanning Machine -- 6.17.1 The Magnet -- 6.17.2 Permanent Magnet -- 6.17.3 Resistive Magnets -- 6.17.4 Superconducting Magnets -- 6.17.5 Quenching -- 6.17.6 Shimming -- 6.17.7 Shielding -- 6.17.8 The Gradient System -- 6.17.9 The Radiofrequency System -- 6.17.10 The Computer System.
6.18 Pulse Sequences -- 6.18.1 Spin Echo Sequence -- 6.18.1.1 Black Blood Effect -- 6.18.2 Inversion Recovery Sequence -- 6.18.3 Short TI Inversion Recovery (STIR) Sequences -- 6.18.4 Fluid Attenuated Recovery (FLAIR) Sequences -- 6.18.5 Gradient Echo Sequence -- 6.19 Parallel Imaging -- 6.20 MR Artifacts -- 6.21 Motion Artifacts -- 6.22 Flow Artifacts -- 6.23 Phase Wrapping -- 6.24 Chemical Shift -- 6.25 Magnetic Susceptibility -- 6.26 Truncation Artifact -- 6.27 Magic Angle -- 6.28 Eddy Currents -- 6.29 Partial Volume Artifact -- 6.30 Inhomogeneous Fat Suppression -- 6.31 Zipper Artifacts -- 6.32 Crisscross Artifact -- 6.33 Bioeffects and Safety -- Glossary-Appendix -- About the Authors -- Index -- EULA.
Record Nr. UNINA-9910830561103321
Sanjay H. S.  
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
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