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How Machine Learning Is Innovating Today's World : A Concise Technical Guide
| How Machine Learning Is Innovating Today's World : A Concise Technical Guide |
| Autore | Dey Arindam |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (477 pages) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) |
NayakSukanta
KumarRanjan MohantySachi Nandan |
| Soggetto topico | Machine learning |
| ISBN |
1-394-21416-2
1-394-21415-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: Natural Language Processing (NLP) Applications -- Chapter 1 A Comprehensive Analysis of Various Tokenization Techniques and Sequence-to-Sequence Model in Natural Language Processing -- 1.1 Introduction -- 1.2 Literature Survey -- 1.3 Sequence-to-Sequence Models -- 1.3.1 Convolutional Seq2Seq Models -- 1.3.2 Pointer Generator Model -- 1.3.3 Attention-Based Model -- 1.4 Comparison Table -- 1.5 Comparison Graphs -- 1.6 Research Gap Identified -- 1.7 Conclusion -- References -- Chapter 2 A Review on Text Analysis Using NLP -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Comparison Table of Previous Techniques -- 2.4 Comparison Graphs -- 2.5 Research Gap -- 2.6 Conclusion -- References -- Chapter 3 Text Generation & -- Classification in NLP: A Review -- 3.1 Introduction -- 3.2 Literature Survey -- 3.3 Comparison Table of Previous Techniques -- 3.3.1 Sentiment Analysis -- 3.3.2 Translation -- 3.3.3 Tokenization Based on Noisy Texts -- 3.3.4 Question Answer Model -- 3.4 Research Gap -- 3.5 Conclusion -- References -- Chapter 4 Book Genre Prediction Using NLP: A Review -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Comparison Table -- 4.4 Research Gap Identified -- 4.5 Future Scope -- 4.6 Conclusion -- References -- Chapter 5 Mood Detection Using Tokenization: A Review -- 5.1 Introduction -- 5.2 Literature Survey -- 5.3 Comparison Table of Previous Techniques -- 5.4 Graphs -- 5.5 Research Gap -- 5.6 Conclusion -- References -- Chapter 6 Converting Pseudo Code to Code: A Review -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Comparison Table -- 6.4 Graphs of Comparison Done -- 6.5 Research Gap Identified -- 6.6 Conclusion -- References -- Part 2: Machine Learning Applications in Specific Domains.
Chapter 7 Evaluating the Readability of English Language Using Machine Learning Models -- 7.1 Introduction -- 7.2 Contribution in this Chapter -- 7.3 Research Gap -- 7.4 Literature Review -- 7.5 Proposed Model -- 7.6 Model Analysis with Result and Discussion -- 7.7 Conclusion -- References -- Chapter 8 Machine Learning in Maximizing Cotton Yield with Special Reference to Fertilizer Selection -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Materials and Methods -- 8.3.1 Problem Definition -- 8.3.2 Objectives -- 8.3.3 Data Collection -- 8.3.4 Data Preprocessing -- 8.3.5 Steps Involved in Combined Decision-Making Approach Using Machine Learning Algorithms -- 8.4 Application to the Fertilizer Selection Problem -- 8.5 Conclusion and Future Suggestions -- References -- Chapter 9 Machine Learning Approaches to Catalysis -- 9.1 Introduction -- 9.2 Chem-Workflow -- 9.3 ML Basic Concepts -- 9.4 ML Models in Catalysis -- 9.5 ML in Structure-Activity Prediction -- 9.6 Conclusion and Future Works -- References -- Chapter 10 Classification of Livestock Diseases Using Machine Learning Algorithms -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Materials and Methods -- 10.3.1 Definition of the Problem -- 10.3.2 Objectives -- 10.3.3 Data Collection -- 10.3.4 Data Preprocessing -- 10.3.5 Steps Involved in Supervised Learning Classifiers -- 10.4 Application of the Supervised Classifiers in Disease Classification -- 10.5 Results and Conclusion -- References -- Chapter 11 Image Enhancement Techniques to Modify an Image with Machine Learning Application -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Image Enhancement Techniques for Betterment of the Images -- 11.4 Proposed Image Enhancement Techniques -- 11.5 Conclusion -- References -- Chapter 12 Software Engineering in Machine Learning Applications: A Comprehensive Study -- 12.1 Introduction. 12.2 Related Works -- 12.3 Comparison Table -- 12.4 Graph of Comparison -- 12.5 Machine Learning in Software Engineering -- 12.6 Conclusion -- References -- Chapter 13 Machine Learning Applications in Battery Management System -- 13.1 Introduction -- 13.2 Battery Management System (BMS) -- 13.2.1 Key Parameters of Battery Management System -- 13.2.1.1 Voltage -- 13.2.1.2 Temperature -- 13.2.1.3 State of Charge -- 13.2.1.4 State of Health -- 13.2.1.5 State of Function -- 13.3 Estimation of Battery SOC and SOH -- 13.3.1 Methods of Estimating SOC -- 13.3.1.1 Coulomb Counting Method -- 13.3.1.2 Open Circuit Voltage (OCV) Method -- 13.3.1.3 Kalman Filtering Method -- 13.3.1.4 Artificial Neural Network (ANN) Method -- 13.3.1.5 Fuzzy # -- 13.3.1.6 Extended Kalman Filtering Method -- 13.3.1.7 Gray Box Modeling Method -- 13.3.1.8 Support Vector Machine (SVM) Method -- 13.3.1.9 Model Predictive Control Method -- 13.3.1.10 Adaptive Observer Method -- 13.3.1.11 Impedance-Based Method -- 13.3.1.12 Gray Prediction Method -- 13.3.2 Methods of Estimating SOH -- 13.3.2.1 Capacity Fade Model -- 13.3.2.2 Electrochemical Impedance Spectroscopy (EIS) Method -- 13.3.2.3 Voltage Relaxation Method -- 13.3.2.4 Fuzzy Logic Method -- 13.3.2.5 Particle Filter Method -- 13.3.2.6 Artificial Neural Network (ANN) Method -- 13.3.2.7 Support Vector Machine (SVM) Method -- 13.3.2.8 Gray Box Modeling Method -- 13.3.2.9 Kalman Filtering Method -- 13.3.2.10 Multi-Model Approach -- 13.4 Cell Balancing Mechanism for BMS -- 13.5 Industrial Applications -- 13.5.1 Industrial Applications of Machine Learning in Battery Management System -- 13.5.2 Machine Learning Algorithms That Are Used for Industrial Applications in Battery Management System -- 13.5.3 Steps Involved in Machine Learning Approach in BMS Applications -- 13.5.4 Applications of Different ML Algorithms in BMS. 13.5.4.1 Artificial Neural Networks (ANNs) -- 13.5.4.2 Decision Trees -- 13.5.4.3 Support Vector Machines (SVMs) -- 13.5.4.4 Random Forest -- 13.5.4.5 Gaussian Process -- 13.6 Case Studies of ML-Based BMS Applications in Industry -- 13.6.1 Machine Learning Approach to Predict SOH of Li-Ion Batteries -- 13.6.2 Anomaly Detection in Battery Management System Using Machine Learning -- 13.6.3 Optimization of Battery Life Cycle Using Machine Learning -- 13.6.4 Prediction of Remaining Useful Life Using Machine Learning -- 13.6.5 Fault Diagnosis of Battery Management System Using Machine Learning -- 13.6.6 Battery Parameter Estimation Using Machine Learning -- 13.6.7 Optimization of Battery Charging Using Machine Learning -- 13.6.8 ML Approach to Estimate State of Charge -- 13.6.9 Battery Capacity Estimation Using ML Approach -- 13.6.10 Anomaly Detection in Batteries Using Machine Learning -- 13.6.11 ML-Based BMS for Li-Ion Batteries -- 13.6.12 Battery Management System Based on Deep Learning for Electric Vehicles -- 13.6.13 A Review of ML Approaches for BMS -- 13.6.14 Battery Management Systems Using Machine Learning Techniques -- 13.6.15 Machine Learning for Lithium-Ion Battery Management: Challenges and Opportunities -- 13.6.16 An ML-Based BMS for Hybrid EVs -- 13.6.17 Battery Management System for EVs Using ML Techniques -- 13.6.18 A Hybrid BMS Using Machine Learning Techniques -- 13.7 Challenges -- 13.8 Conclusion -- References -- Chapter 14 ML Applications in Healthcare -- 14.1 Introduction -- 14.1.1 Supervised Learning -- 14.1.2 Unsupervised Learning -- 14.1.3 Semi-Supervised Learning -- 14.1.4 Reinforcement Learning -- 14.2 Applications of Machine Learning in Health Sciences -- 14.2.1 Diagnosis and Prediction of Disease -- 14.2.1.1 Predicting Thyroid Disease -- 14.2.1.2 Predicting Cardiovascular Disease -- 14.2.1.3 Predicting Cancer. 14.2.1.4 Predicting Diabetes -- 14.2.1.5 Predicting Alzheimer's -- 14.2.2 Drug Development and Discovery -- 14.2.3 Clinical Decision Support (CDS) -- 14.2.4 Medical Image Examination -- 14.2.5 Monitoring of Health and Wearable Technology -- 14.2.6 Telemedicine and Remote Patient Monitoring -- 14.2.7 Chatbots and Virtual Medical Assistants -- 14.3 Why Machine Learning is Crucial in Healthcare -- 14.4 Challenges and Opportunities -- 14.5 Conclusion -- References -- Chapter 15 Enhancing Resource Management in Precision Farming through AI-Based Irrigation Optimization -- 15.1 Introduction to Precision Farming -- 15.1.1 Definition of Precision Farming -- 15.1.2 Importance of Precision Farming in Agriculture -- 15.2 Role of Artificial Intelligence (AI) in Precision Farming -- 15.2.1 Influence of AI in Precision Farming -- 15.2.2 Challenges and Limitations of AI in Precision Farming -- 15.3 Data Collection and Sensing for Precision Farming -- 15.3.1 Remote Sensing Techniques -- 15.3.2 Satellite Imagery Analysis -- 15.3.3 Unmanned Aerial Vehicles (UAVs) for Data Collection -- 15.3.4 Internet of Things (IoT) Sensors -- 15.3.5 Data Preprocessing and Integration -- 15.4 Crop Monitoring and Management -- 15.4.1 Crop Yield Prediction -- 15.4.2 Disease Detection and Diagnosis -- 15.4.3 Nutrient Management and Fertilizer Optimization -- 15.5 Precision Planting and Seeding -- 15.5.1 Variable Rate Planting -- 15.5.2 GPS and Auto-Steering Systems -- 15.5.3 Seed Singulation and Metering -- 15.5.4 Plant Health Monitoring and Care -- 15.6 Harvesting and Yield Estimation -- 15.6.1 Yield Estimation Models -- 15.6.2 Real-Time Crop Monitoring During Harvest -- 15.7 Data Analytics and Machine Learning -- 15.7.1 Predictive Analytics for Crop Yield -- 15.7.2 Machine Learning Algorithms for Precision Farming -- 15.7.3 Big Data Analytics in Precision Farming. 15.8 Integration of AI with Other Technologies. |
| Record Nr. | UNINA-9910877044903321 |
Dey Arindam
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mechanical Engineering in Biomedical Application : Bio-3D Printing, Biofluid Mechanics, Implant Design, Biomaterials, Computational Biomechanics, Tissue Mechanics
| Mechanical Engineering in Biomedical Application : Bio-3D Printing, Biofluid Mechanics, Implant Design, Biomaterials, Computational Biomechanics, Tissue Mechanics |
| Autore | Srivastava Jay Prakash |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (438 pages) |
| Altri autori (Persone) |
RanjanVinayak
KozakDrazan KumarRanjan KumarPankaj TayalShubham |
| ISBN |
1-394-17510-8
1-394-17509-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Part I: Additive Manufacturing -- Chapter 1 The Role of Additive Manufacturing Technologies for Rehabilitation in Healthcare and Medical Applications -- 1.1 Introduction -- 1.2 Classification of the Additive Manufacturing Process -- 1.2.1 Jetting-Based Bioprinting -- 1.2.2 Extrusion-Based Bioprinting -- 1.2.3 Laser-Assisted Bioprinting -- 1.2.4 Laser-Based Stereolithography -- 1.3 AM Materials for Medical Applications -- 1.4 Biomedical and Healthcare Applications of AM -- 1.5 Conclusion and Future Outlook -- References -- Chapter 2 Artificial Recreation of Human Organs by Additive Manufacturing -- 2.1 Introduction -- 2.2 Role of Additive Manufacturing for Human Organs -- 2.3 Role of Artificial Recreation -- 2.3.1 Decellularized Organ Regeneration -- 2.3.2 3D Bioprinting of Organs and Cells -- 2.3.3 Self-Healing and Shape Memory for Artificial Organs -- 2.4 Role of Additive Manufacturing in Orthopedics -- 2.5 Types of Bioadditive Manufacturing -- 2.5.1 Classification of Organoids Using AM -- 2.6 Conclusion -- References -- Chapter 3 Advances, Risks, and Challenges of 3D Bioprinting -- 3.1 Introduction -- 3.2 3D Bioprinting -- 3.2.1 Types of 3D Bioprinting -- 3.3 Biomaterials and Bioinks -- 3.4 Applications of 3D Bioprinting -- 3.5 A Case Study -- 3.6 Conclusions -- References -- Chapter 4 Laser-Induced Forward Transfer for Biosensor Application -- 4.1 Introduction -- 4.2 Biosensor -- 4.2.1 History/Background -- 4.2.2 Types of Biosensors -- 4.2.2.1 Potentiometric Biosensors -- 4.2.2.2 Amperometric Biosensors -- 4.2.2.3 Impedimetric Biosensors -- 4.2.2.4 Conductometric Biosensors -- 4.2.3 Biosensor Manufacturing Processes -- 4.3 Laser-Induced Forward Transfer (LIFT) -- 4.3.1 History and Process Description -- 4.3.2 Process Parameters -- 4.3.2.1 Fluence of Lasers.
4.3.2.2 Film-Acceptor Substrate Distance -- 4.3.2.3 Material Selection -- 4.3.2.4 Pulse Characteristics of Lasers -- 4.3.2.5 Laser Spot Size -- 4.4 Laser-Induced Forward Transfer for Biosensor Manufacturing -- 4.5 Outlook and Conclusion -- References -- Part II: Biomaterials -- Chapter 5 The Effect of the Nanostructured Surface Modification on the Morphology and Biocompatibility of Ultrafine-Grained Titanium Alloy for Medical Application -- 5.1 Introduction -- 5.1.1 Titanium-Based Materials for Biomedical Application -- 5.1.2 Ultrafine-Grained Titanium-Based Materials Obtained by Severe Plastic Deformation (SPD) -- 5.1.3 Electrochemical Anodization of Titanium-Based Materials -- 5.2 Materials and Methods -- 5.2.1 High-Pressure Torsion Process -- 5.2.2 Electrochemical Anodization -- 5.2.3 Characterization of the Surface Topography by Atomic Force Microscopy (AFM) -- 5.2.4 Biocompatibility Examination -- 5.3 Results and Discussion -- 5.3.1 The Microstructure of the Ultrafine-Grained Two-Phase Ti-13Nb-13Zr Alloy -- 5.3.2 Morphology of Nanostructured Surfaces of the Materials -- 5.3.3 Characterization of the Surface Topography -- 5.3.4 Biocompatibility Examination -- Conclusions -- Acknowledgments -- References -- Chapter 6 Powder Metallurgy-Prepared Ti-Based Biomaterials with Enhanced Biocompatibility -- 6.1 Introduction -- 6.2 Powder Metallurgy of Ti-Based Materials -- 6.2.1 Powder Metallurgy of Ti and Ti Alloys -- 6.2.2 Powder Metallurgy of Ti-Based Composites -- 6.2.2.1 Porosity of PM Ti-Based Materials -- 6.2.2.2 Effect of Reinforcing Particles on the Biological Behavior of Ti-Based Composites -- 6.3 Laser Surface Treatment of Materials for Enhanced Human Cell Osteodifferentiation -- 6.3.1 Laser-Treated Surfaces of PM Ti-Based Materials -- Conclusion -- Acknowledgments -- References. Chapter 7 Total Hip Replacement Response to a Variation of the Radial Clearance Through In Silico Models -- 7.1 Introduction -- 7.2 The Musculoskeletal Multibody Model -- 7.2.1 Kinematical Analysis -- 7.2.2 Dynamical Analysis -- 7.2.3 The Muscle Actuator -- 7.2.4 The Geodesic Muscle Wrapping -- 7.2.5 The Hill Muscle-Tendon Model -- 7.2.6 The Static Optimization -- 7.3 The Lubrication/Contact Model -- 7.3.1 The Hip Joint -- 7.3.2 The Reynolds Equation -- 7.3.3 Numerical Resolution -- 7.3.4 Coupling Models -- 7.4 Simulations -- 7.4.1 Gait Cycle Results -- 7.4.2 Tribological Results -- 7.4.3 Radial Clearance Sensitivity Analysis -- 7.5 Conclusions -- References -- Chapter 8 Techniques of Biopolymer and Bioceramic Coatings on Prosthetic Implants -- 8.1 Introduction -- 8.2 Driving Factors for the Application of Coatings -- 8.2.1 Corrosion of Metal Implants and Its Categories -- 8.2.1.1 Uniform Attack -- 8.2.1.2 Fretting Corrosion -- 8.2.1.3 Galvanic Corrosion -- 8.2.1.4 Pitting Corrosion -- 8.2.1.5 Crevice Corrosion -- 8.2.1.6 Leaching -- 8.2.1.7 Stress Corrosion Cracking (SCC) -- 8.2.2 Bioactivity of the Surface -- 8.2.2.1 Immune Rejection, Osteoinduction, Osteoconduction, and Osseointegration -- 8.2.2.2 Toxicity and Bacterial Biofilm Formation -- 8.3 The Development of Implant Coatings -- 8.3.1 Strategies for Coating the Implants -- 8.4 Conclusions -- References -- Chapter 9 Mechanical Behavior of Bioglass Materials for Bone Implantation -- 9.1 Introduction on Bio Materials -- 9.2 Aim and Objective of the Work -- 9.3 Role of REEs (CeO2, La2O3, and Sm2O3) -- 9.4 Uses of Rare Earth Elements -- 9.5 Biomaterials -- 9.6 Simulated Body Fluid -- 9.7 Bioactive Glasses -- 9.8 Bioactive Composites -- 9.9 Area of Biomaterials -- References -- Chapter 10 Biomedical Applications of Composite Materials -- 10.1 Introduction. 10.2 Different Types of Composites Used in Biomedical Applications -- 10.3 Application of Composites in Tissues -- 10.4 Application of Composites in Dentistry -- 10.5 Application of Composites in Total Joint Replacements -- 10.6 Application of Composites in Hip Joint Replacement -- Conclusions -- References -- Part III: Biofluid Mechanics -- Chapter 11 Materials Advancement, Biomaterials, and Biosensors -- 11.1 Introduction -- 11.2 Design of Biomaterials -- 11.3 Polymers -- 11.4 Metals as Biomaterials -- 11.5 Bioactive Material and Concept of Bioactivity -- 11.6 Biocompatibility of the Titanium Binder Element -- 11.7 Classification -- 11.8 Interaction Between Biomaterials and Biological Systems -- 11.9 Biomaterials: Protein Surface Interactions -- 11.10 Dental Material Cavity Fillers -- 11.11 Bridges, Crowns, and Dentures -- 11.12 Bone Fractures -- 11.13 Biosensors -- 11.14 Biosensor Classification -- 11.14.1 Resonant Biosensor -- 11.14.2 Optical Biosensors -- 11.14.3 Surface Plasmon Resonance (SPR) Biosensor -- 11.14.4 Piezoelectric Biosensors -- 11.14.5 Thermal Biosensors -- 11.14.6 Electrochemical Biosensors -- 11.14.7 Bioluminescence Sensors -- 11.14.8 Nucleic Acid-Based Biosensors -- 11.14.9 Nanobiosensors -- 11.14.10 Microbial Biosensors -- 11.14.11 Bioreceptor-Based Category -- 11.14.12 Transducer-Based Category -- 11.15 Biosensors: Precursors of Contemporary Biomaterial Succession -- 11.15.1 Carbon-Based Nanomaterials -- 11.15.2 Carbon Nanotubes -- 11.15.3 Electrochemical Biosensors Based on Carbon Nanotubes -- 11.15.4 Carbon Nanotube-Based Immunosensors -- 11.15.5 Optical Sensors Composed of Carbon Nanotubes -- 11.15.6 Graphene-Based Biosensors -- 11.15.7 Electrochemical Biosensors Based on Graphene -- 11.15.8 Graphene-Based Immunosensors -- 11.15.9 Graphene-Modulated Gene Biosensors -- 11.15.10 Conductive Polymers -- 11.15.11 Polypyrrole. 11.15.12 Polythiophene -- 11.15.13 Polyaniline and Its Byproducts -- 11.15.14 Polyacetylene -- References -- Chapter 12 Blockage Study in Carotid Arteries -- 12.1 Introduction -- 12.2 Numerical Model and Its Implementation -- 12.2.1 Geometry -- 12.2.2 Meshing and GIT -- 12.2.3 Governing Equations -- 12.2.4 Boundary Conditions -- 12.3 Results and Discussion -- 12.3.1 Effect of Blockage on Blood Flow Velocity -- 12.3.2 Effect of Blood Flow Velocities on Wall Stress -- 12.3.3 Effect of Stenosis on Dynamic Pressure Distribution -- 12.3.4 Effect of Stenosis on Viscosity and Mass Imbalance -- 12.4 Conclusion -- References -- Chapter 13 Mechanical Properties of Human Synovial Fluid: An Approach for Osteoarthritis Treatment -- 13.1 Introduction -- 13.1.1 Synovial Fluid -- 13.1.2 Structure and Composition of Synovial Fluid -- 13.2 Osteoarthritis and Its Treatments -- 13.3 Viscosupplements -- 13.3.1 Hylan G-F 20 -- 13.3.2 Sodium Hyaluronate -- 13.3.3 Hyaluronan -- 13.4 Synovial Mimic Fluid/PVP -- 13.5 Conclusion -- References -- Chapter 14 Artificial Human Heart Biofluid Simulation as a Boon to Humankind: A Review Study -- 14.1 Introduction -- 14.2 Biofluid Simulation -- 14.3 Heart Valve Fluid Flow -- 14.4 Artificial Heart as a Boon to Humankind -- 14.5 Conclusion -- References -- Part IV: Robotics -- Chapter 15 Robotics in Medical Science -- 15.1 Introduction -- 15.2 Minimally Invasive Surgery (MIS) -- 15.3 Human-Robot Interaction -- 15.4 Robotic Manipulation -- 15.5 The Role of Human-Computer Interaction (HCI) -- 15.6 Soft Robotics in Medicine -- 15.7 Haptics in Medicine -- 15.8 Automation and Control -- 15.9 Dental -- 15.10 CAD/CAM -- 15.11 Conclusion -- References -- Chapter 16 A Research Perspective on Ankle-Foot Prosthetics Designs for Transtibial Amputees -- 16.1 Introduction -- 16.2 Biomechanics of Biological Ankle and Foot -- 16.3 Prosthetic Foot. 16.3.1 Design of Passive Prosthetic Ankle-Foot. |
| Record Nr. | UNINA-9910829903303321 |
|
Srivastava Jay Prakash
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mechanical Engineering in Biomedical Application : Bio-3D Printing, Biofluid Mechanics, Implant Design, Biomaterials, Computational Biomechanics, Tissue Mechanics
| Mechanical Engineering in Biomedical Application : Bio-3D Printing, Biofluid Mechanics, Implant Design, Biomaterials, Computational Biomechanics, Tissue Mechanics |
| Autore | Srivastava Jay Prakash |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (438 pages) |
| Altri autori (Persone) |
RanjanVinayak
KozakDrazan KumarRanjan KumarPankaj TayalShubham |
| ISBN |
1-394-17510-8
1-394-17509-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Part I: Additive Manufacturing -- Chapter 1 The Role of Additive Manufacturing Technologies for Rehabilitation in Healthcare and Medical Applications -- 1.1 Introduction -- 1.2 Classification of the Additive Manufacturing Process -- 1.2.1 Jetting-Based Bioprinting -- 1.2.2 Extrusion-Based Bioprinting -- 1.2.3 Laser-Assisted Bioprinting -- 1.2.4 Laser-Based Stereolithography -- 1.3 AM Materials for Medical Applications -- 1.4 Biomedical and Healthcare Applications of AM -- 1.5 Conclusion and Future Outlook -- References -- Chapter 2 Artificial Recreation of Human Organs by Additive Manufacturing -- 2.1 Introduction -- 2.2 Role of Additive Manufacturing for Human Organs -- 2.3 Role of Artificial Recreation -- 2.3.1 Decellularized Organ Regeneration -- 2.3.2 3D Bioprinting of Organs and Cells -- 2.3.3 Self-Healing and Shape Memory for Artificial Organs -- 2.4 Role of Additive Manufacturing in Orthopedics -- 2.5 Types of Bioadditive Manufacturing -- 2.5.1 Classification of Organoids Using AM -- 2.6 Conclusion -- References -- Chapter 3 Advances, Risks, and Challenges of 3D Bioprinting -- 3.1 Introduction -- 3.2 3D Bioprinting -- 3.2.1 Types of 3D Bioprinting -- 3.3 Biomaterials and Bioinks -- 3.4 Applications of 3D Bioprinting -- 3.5 A Case Study -- 3.6 Conclusions -- References -- Chapter 4 Laser-Induced Forward Transfer for Biosensor Application -- 4.1 Introduction -- 4.2 Biosensor -- 4.2.1 History/Background -- 4.2.2 Types of Biosensors -- 4.2.2.1 Potentiometric Biosensors -- 4.2.2.2 Amperometric Biosensors -- 4.2.2.3 Impedimetric Biosensors -- 4.2.2.4 Conductometric Biosensors -- 4.2.3 Biosensor Manufacturing Processes -- 4.3 Laser-Induced Forward Transfer (LIFT) -- 4.3.1 History and Process Description -- 4.3.2 Process Parameters -- 4.3.2.1 Fluence of Lasers.
4.3.2.2 Film-Acceptor Substrate Distance -- 4.3.2.3 Material Selection -- 4.3.2.4 Pulse Characteristics of Lasers -- 4.3.2.5 Laser Spot Size -- 4.4 Laser-Induced Forward Transfer for Biosensor Manufacturing -- 4.5 Outlook and Conclusion -- References -- Part II: Biomaterials -- Chapter 5 The Effect of the Nanostructured Surface Modification on the Morphology and Biocompatibility of Ultrafine-Grained Titanium Alloy for Medical Application -- 5.1 Introduction -- 5.1.1 Titanium-Based Materials for Biomedical Application -- 5.1.2 Ultrafine-Grained Titanium-Based Materials Obtained by Severe Plastic Deformation (SPD) -- 5.1.3 Electrochemical Anodization of Titanium-Based Materials -- 5.2 Materials and Methods -- 5.2.1 High-Pressure Torsion Process -- 5.2.2 Electrochemical Anodization -- 5.2.3 Characterization of the Surface Topography by Atomic Force Microscopy (AFM) -- 5.2.4 Biocompatibility Examination -- 5.3 Results and Discussion -- 5.3.1 The Microstructure of the Ultrafine-Grained Two-Phase Ti-13Nb-13Zr Alloy -- 5.3.2 Morphology of Nanostructured Surfaces of the Materials -- 5.3.3 Characterization of the Surface Topography -- 5.3.4 Biocompatibility Examination -- Conclusions -- Acknowledgments -- References -- Chapter 6 Powder Metallurgy-Prepared Ti-Based Biomaterials with Enhanced Biocompatibility -- 6.1 Introduction -- 6.2 Powder Metallurgy of Ti-Based Materials -- 6.2.1 Powder Metallurgy of Ti and Ti Alloys -- 6.2.2 Powder Metallurgy of Ti-Based Composites -- 6.2.2.1 Porosity of PM Ti-Based Materials -- 6.2.2.2 Effect of Reinforcing Particles on the Biological Behavior of Ti-Based Composites -- 6.3 Laser Surface Treatment of Materials for Enhanced Human Cell Osteodifferentiation -- 6.3.1 Laser-Treated Surfaces of PM Ti-Based Materials -- Conclusion -- Acknowledgments -- References. Chapter 7 Total Hip Replacement Response to a Variation of the Radial Clearance Through In Silico Models -- 7.1 Introduction -- 7.2 The Musculoskeletal Multibody Model -- 7.2.1 Kinematical Analysis -- 7.2.2 Dynamical Analysis -- 7.2.3 The Muscle Actuator -- 7.2.4 The Geodesic Muscle Wrapping -- 7.2.5 The Hill Muscle-Tendon Model -- 7.2.6 The Static Optimization -- 7.3 The Lubrication/Contact Model -- 7.3.1 The Hip Joint -- 7.3.2 The Reynolds Equation -- 7.3.3 Numerical Resolution -- 7.3.4 Coupling Models -- 7.4 Simulations -- 7.4.1 Gait Cycle Results -- 7.4.2 Tribological Results -- 7.4.3 Radial Clearance Sensitivity Analysis -- 7.5 Conclusions -- References -- Chapter 8 Techniques of Biopolymer and Bioceramic Coatings on Prosthetic Implants -- 8.1 Introduction -- 8.2 Driving Factors for the Application of Coatings -- 8.2.1 Corrosion of Metal Implants and Its Categories -- 8.2.1.1 Uniform Attack -- 8.2.1.2 Fretting Corrosion -- 8.2.1.3 Galvanic Corrosion -- 8.2.1.4 Pitting Corrosion -- 8.2.1.5 Crevice Corrosion -- 8.2.1.6 Leaching -- 8.2.1.7 Stress Corrosion Cracking (SCC) -- 8.2.2 Bioactivity of the Surface -- 8.2.2.1 Immune Rejection, Osteoinduction, Osteoconduction, and Osseointegration -- 8.2.2.2 Toxicity and Bacterial Biofilm Formation -- 8.3 The Development of Implant Coatings -- 8.3.1 Strategies for Coating the Implants -- 8.4 Conclusions -- References -- Chapter 9 Mechanical Behavior of Bioglass Materials for Bone Implantation -- 9.1 Introduction on Bio Materials -- 9.2 Aim and Objective of the Work -- 9.3 Role of REEs (CeO2, La2O3, and Sm2O3) -- 9.4 Uses of Rare Earth Elements -- 9.5 Biomaterials -- 9.6 Simulated Body Fluid -- 9.7 Bioactive Glasses -- 9.8 Bioactive Composites -- 9.9 Area of Biomaterials -- References -- Chapter 10 Biomedical Applications of Composite Materials -- 10.1 Introduction. 10.2 Different Types of Composites Used in Biomedical Applications -- 10.3 Application of Composites in Tissues -- 10.4 Application of Composites in Dentistry -- 10.5 Application of Composites in Total Joint Replacements -- 10.6 Application of Composites in Hip Joint Replacement -- Conclusions -- References -- Part III: Biofluid Mechanics -- Chapter 11 Materials Advancement, Biomaterials, and Biosensors -- 11.1 Introduction -- 11.2 Design of Biomaterials -- 11.3 Polymers -- 11.4 Metals as Biomaterials -- 11.5 Bioactive Material and Concept of Bioactivity -- 11.6 Biocompatibility of the Titanium Binder Element -- 11.7 Classification -- 11.8 Interaction Between Biomaterials and Biological Systems -- 11.9 Biomaterials: Protein Surface Interactions -- 11.10 Dental Material Cavity Fillers -- 11.11 Bridges, Crowns, and Dentures -- 11.12 Bone Fractures -- 11.13 Biosensors -- 11.14 Biosensor Classification -- 11.14.1 Resonant Biosensor -- 11.14.2 Optical Biosensors -- 11.14.3 Surface Plasmon Resonance (SPR) Biosensor -- 11.14.4 Piezoelectric Biosensors -- 11.14.5 Thermal Biosensors -- 11.14.6 Electrochemical Biosensors -- 11.14.7 Bioluminescence Sensors -- 11.14.8 Nucleic Acid-Based Biosensors -- 11.14.9 Nanobiosensors -- 11.14.10 Microbial Biosensors -- 11.14.11 Bioreceptor-Based Category -- 11.14.12 Transducer-Based Category -- 11.15 Biosensors: Precursors of Contemporary Biomaterial Succession -- 11.15.1 Carbon-Based Nanomaterials -- 11.15.2 Carbon Nanotubes -- 11.15.3 Electrochemical Biosensors Based on Carbon Nanotubes -- 11.15.4 Carbon Nanotube-Based Immunosensors -- 11.15.5 Optical Sensors Composed of Carbon Nanotubes -- 11.15.6 Graphene-Based Biosensors -- 11.15.7 Electrochemical Biosensors Based on Graphene -- 11.15.8 Graphene-Based Immunosensors -- 11.15.9 Graphene-Modulated Gene Biosensors -- 11.15.10 Conductive Polymers -- 11.15.11 Polypyrrole. 11.15.12 Polythiophene -- 11.15.13 Polyaniline and Its Byproducts -- 11.15.14 Polyacetylene -- References -- Chapter 12 Blockage Study in Carotid Arteries -- 12.1 Introduction -- 12.2 Numerical Model and Its Implementation -- 12.2.1 Geometry -- 12.2.2 Meshing and GIT -- 12.2.3 Governing Equations -- 12.2.4 Boundary Conditions -- 12.3 Results and Discussion -- 12.3.1 Effect of Blockage on Blood Flow Velocity -- 12.3.2 Effect of Blood Flow Velocities on Wall Stress -- 12.3.3 Effect of Stenosis on Dynamic Pressure Distribution -- 12.3.4 Effect of Stenosis on Viscosity and Mass Imbalance -- 12.4 Conclusion -- References -- Chapter 13 Mechanical Properties of Human Synovial Fluid: An Approach for Osteoarthritis Treatment -- 13.1 Introduction -- 13.1.1 Synovial Fluid -- 13.1.2 Structure and Composition of Synovial Fluid -- 13.2 Osteoarthritis and Its Treatments -- 13.3 Viscosupplements -- 13.3.1 Hylan G-F 20 -- 13.3.2 Sodium Hyaluronate -- 13.3.3 Hyaluronan -- 13.4 Synovial Mimic Fluid/PVP -- 13.5 Conclusion -- References -- Chapter 14 Artificial Human Heart Biofluid Simulation as a Boon to Humankind: A Review Study -- 14.1 Introduction -- 14.2 Biofluid Simulation -- 14.3 Heart Valve Fluid Flow -- 14.4 Artificial Heart as a Boon to Humankind -- 14.5 Conclusion -- References -- Part IV: Robotics -- Chapter 15 Robotics in Medical Science -- 15.1 Introduction -- 15.2 Minimally Invasive Surgery (MIS) -- 15.3 Human-Robot Interaction -- 15.4 Robotic Manipulation -- 15.5 The Role of Human-Computer Interaction (HCI) -- 15.6 Soft Robotics in Medicine -- 15.7 Haptics in Medicine -- 15.8 Automation and Control -- 15.9 Dental -- 15.10 CAD/CAM -- 15.11 Conclusion -- References -- Chapter 16 A Research Perspective on Ankle-Foot Prosthetics Designs for Transtibial Amputees -- 16.1 Introduction -- 16.2 Biomechanics of Biological Ankle and Foot -- 16.3 Prosthetic Foot. 16.3.1 Design of Passive Prosthetic Ankle-Foot. |
| Record Nr. | UNINA-9910876505903321 |
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Srivastava Jay Prakash
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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