Tabla de Contenidos: “…Les dépenses de santé sont faibles mais comparables à celles des autres pays de la région -- Recommandations pour améliorer l'état de santé de tous -- Bibliographie -- Annexe 1. …”

Tabla de Contenidos: “…La productivité du secteur des services est faible comparée à celle du secteur manufacturier -- Le renforcement de la concurrence dans le secteur non manufacturier stimule la productivité dans les services et la compétitivité du secteur manufacturier -- Graphique 1.19. …”

Tabla de Contenidos: “…Taux de chômage et taux d'activité par tranche d'âge, pour les personnes nées en Australie et celles nées à l'étranger -- Graphique 47. Taux de chômage et taux d'activité par date d'entrée, région d'origine et sexe -- Graphique 48. …”

Tabla de Contenidos: “…Évolutions des politiques intérieures -- Des évolutions importantes ont eu lieu en 2002 -- ... mais les modifications annoncées ne sont pas encore toutes en vigueur -- Les évolutions des niveaux de soutien des prix du marché ont été variables... -- ... de même que celles du soutien budgétaire, en hausse dans certains cas... -- ... et en baisse dans d'autres -- Les réformes des programmes visant à réduire les coûts des intrants... -- Les pouvoirs publics de plusieurs pays ont apporté une aide aux agriculteurs... -- Les agriculteurs recevront des financements plus importants pour les améliorations apportées sur le plan de l'environnement... -- ... et pour améliorer la planification, la gestion et la diffusion d'informations au niveau de l'exploitation... -- ... et continuer d'encourager l'agriculture biologique -- Des mesures de promotion des biocarburants ont été prises. -- De nouvelles mesures destinées à améliorer la sécurité des aliments... -- ... et de nouvelles réglementations sur l'étiquetage ont été établies -- Les dépenses et la planification de la lutte contre les maladies se sont accrues... -- ... de même que les efforts en faveur du bien-être animal et du développement rural -- 2. …”

Tabla de Contenidos: “…Structure comparée des exportations du Royaume-Uni et de l'Allemagne par rapport à celle des importations des BRIC -- L'enseignement supérieur en tant qu'exportateur de services -- Encadré 2.1. …”

Tabla de Contenidos: “…Exemples de pays où l'expérience de la mise en place de stratégies souligne les limites de celles-ci -- Deuxième partie -- PASSAGE À LA RETRAITE ET VIEILLISSEMENT ACTIF -- Chapitre 8 -- LE PASSAGE À LA RETRAITE -- Résumé et principaux résultats -- Quand prend-on sa retraite et avec quelle progressivité ? …”

Tabla de Contenidos: “…Composition de l'estimation du soutien aux producteurs (ESP) 1986-2001 -- ... celles-ci varient considérablement selon les pays... -- Graphique I.3. …”

Tabla de Contenidos: “…Caractéristiques des migrants nés au Mexique par rapport à la population mexicaine et à celle des États-Unis -- Politiques migratoires -- Émigration du Mexique vers les États-Unis -- Immigration -- Aspects économiques des migrations -- Marché du travail et capital humain -- Graphique 34. …”

Tabla de Contenidos: “…Part des naissances étrangères dans le total des naissances, rapportée à celle ... -- Tableau I.3. Répartition de la population mondiale par principales régions en 1999 et projection... -- Tableau I.4. …”

Tabla de Contenidos: “…3.1.2.1 Covalent Functionalization of Carbon Allotropes: Synthesis and Characterization -- 3.1.2.2 Noncovalent Functionalization of Carbon Allotropes: Synthesis and Characterization -- 3.2 Applications of Functionalized Carbon Allotropes -- 3.2.1 Biomedical -- 3.2.2 Waste Treatment -- 3.2.3 Pollutants Decontamination -- 3.2.4 Anticorrosive -- 3.2.5 Tribological -- 3.2.6 Catalytic -- 3.2.7 Reinforced Materials -- 3.3 Conclusions and Future Directions -- References -- Chapter 4 Carbon Allotropes in Lead Removal -- 4.1 Introduction -- 4.2 Carbon Nanomaterials (CNMs) -- 4.3 Dimension-Based Types of Carbon Nanomaterials -- 4.4 Purification of Water Using Fullerenes -- 4.5 Application of Graphene and Its Derivatives in Water Purification -- 4.6 Application of Carbon Nanotubes (CNTs) in Water Purification -- 4.7 Conclusion -- References -- Chapter 5 Carbon Allotropes in Nickel Removal -- 5.1 Introduction -- 5.2 Carbon and Its Allotropes: As Remediation Technology for Ni -- 5.2.1 Nanotubes Based on Carbon -- 5.2.1.1 Overview -- 5.2.1.2 Features of CNTs -- 5.2.2 Fullerenes -- 5.2.3 Graphene -- 5.2.3.1 Overview -- 5.2.3.2 Properties -- 5.3 Removal of Ni in Wastewater by Use of Carbon Allotropes -- 5.3.1 Carbon Nanotubes for Ni Adsorption From Aqueous Solutions -- 5.3.2 Ni Adsorption From Aqueous Solutions on Composite Material of MWCNTs -- 5.3.3 GR and GO-Based Adsorbents for Removal of Ni -- 5.4 Conclusion -- References -- Chapter 6 Molybdenum-Modified Carbon Allotropes in Wastewater Treatment -- 6.1 Introduction -- 6.2 Carbon-Based Allotropes -- 6.2.1 Graphene -- 6.2.2 Graphite -- 6.2.3 Carbon Nanotubes -- 6.2.4 Glassy Carbon (GC) -- 6.3 Molybdenum Disulfide -- 6.3.1 Synthesis of MoS2 -- 6.3.2 Physical Methods -- 6.3.3 Chemical Methods -- 6.3.4 Properties -- 6.4 Application of MoS2 -- 6.4.1 Dye-Sensitized Solar Cells (DSSCs) -- 6.4.2 Catalyst…”

Tabla de Contenidos: “…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…”

Tabla de Contenidos: “…20.4 Frequency-Modulated Continuous-Wave Radar Link Budget -- 20.5 Frequency-Modulated Continuous-Wave Implementation Considerations -- 20.6 Frequency-Modulated Continuous-Wave Interference -- 20.7 Frequency-Modulated Continuous-Wave Beamforming -- 20.8 Frequency-Modulated Continuous-Wave Range-Doppler Processing -- 20.9 Frequency-Modulated Continuous-Wave Radar Front-End Processing -- 20.10 Frequency-Modulated Continuous-Wave Pulse-Doppler Processing -- 20.11 Frequency-Modulated Continuous-Wave Radar Back-End Processing -- 20.12 Noncoherent Antenna Magnitude Summation -- 20.13 Cell Averaging-Constant False Alarm Rate -- 20.14 Ordered Sort-Constant False Alarm Rate -- 20.15 Angle of Arrival Estimation -- 21 - Space Time Adaptive Processing (STAP) Radar -- 21.1 Space Time Adaptive Processing Radar Concept -- 21.2 Steering Vector -- 21.3 Interference Covariance Matrix -- 21.4 Space Time Adaptive Processing Optimal Filter -- 21.5 Space Time Adaptive Processing Radar Computational Requirements -- 22 - Synthetic Array Radar -- 22.1 Introduction -- 22.2 Synthetic Array Radar Resolution -- 22.3 Pulse Compression -- 22.4 Azimuth Resolution -- 22.5 Synthetic Array Radar Processing -- 22.6 Synthetic Array Radar Doppler Processing -- 22.7 Synthetic Array Radar Impairments -- 23 - Introduction to Video Processing -- 23.1 Color Spaces -- 23.2 Interlacing -- 23.3 Deinterlacing -- 23.4 Image Resolution and Bandwidth -- 23.5 Chroma Scaling -- 23.6 Image Scaling and Cropping -- 23.7 Alpha Blending and Compositing -- 23.8 Video Compression -- 23.9 Digital Video Interfaces -- 23.10 Legacy Analog Video Interfaces -- 24 - DCT, Entropy, Predictive Coding, and Quantization -- 24.1 Discrete Cosine Transform -- 24.2 Entropy -- 24.3 Huffman Coding -- 24.4 Markov Source -- 24.5 Predictive Coding -- 24.6 Differential Encoding -- 24.7 Lossless Compression -- 24.8 Quantization…”

Tabla de Contenidos: “…-- Le niveau d'administration en charge de la gestion des transitions varie d'un pays à l'autre -- D'autres acteurs, comme les services d'inspection, peuvent être impliqués dans la gestion des transitions -- Dans deux tiers des pays, les dépenses annuelles moyennes par enfant au titre de l'enseignement préprimaire sont inférieures à celles au titre de l'enseignement primaire -- Le suivi des transitions n'est pas courant -- Les transitions sont suivies à l'aide de plusieurs instruments -- Quels sont les principaux enjeux en matière d'organisation et de gestion et comment les surmonter ? …”

Tabla de Contenidos: “…. -- Figure 4.4 Plan of pit [3949] and wind break [4212] plan and section, located within Excavation Area B -- Figure 4.5 Comparison of the D-shaped enclosure within Excavation Area D with Kingsdown Camp -- The Romano-British period and the villa estate -- Figure 5.1 Plan showing pre-villa features in excavation areas A-D -- Figure 5.2 T-shaped corn-drying oven {6236} with foundation trench (4193) located in Excavation Area B -- Figure 5.3 Structure {4226} within Excavation Area B -- Figure 5.4 Excavation Area C, phase 4 rectangular enclosure ditch [2468] -- Figure 5.5 Plan and section of well {2330} within Excavation Area C -- Figure 5.6 Three-celled rectangular building {1650} within Excavation Area D -- Figure 5.7 Plan of Excavation Area B showing features relating to the use-life of the Roman villa -- Figure 5.8 Floor plan of the tripartite corridor villa found within Excavation Area B -- Figure 5.9 Plan of structure {4213} found within Excavation Area B -- Figure 5.10 Plan of structures {4196} and {4235} found within Excavation Area B -- Figure 5.11 Plan of structure {4219} and furnace 3615 found within Excavation Area B -- Figure 5.12 Plan of structure {3583} found within Excavation Area B -- Figure 5.13 Plan showing the location of the cremation burials found in areas B and D -- The Romano-British artefacts (mid-1st century AD to 5th century AD) -- Figure 6.1 Profile illustrations of the handmade ceramics produced in the Iron Age tradition -- Figure 6.2 Illustration of six cremation urns and one accessory vessel (no. 88) -- Figure 6.3 Profile illustrations of Early Roman pottery…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Chapter 1 Self-Powered Sensory Transducers: A Way Toward Green Internet of Things -- 1.1 Introduction -- 1.2 Need of the Work -- 1.3 Energy Scavenging Schemes in WSAN -- 1.3.1 Photovoltaic or Solar Cell -- 1.3.2 Temperature Gradient -- 1.3.3 Pressure Variations -- 1.3.4 Plant Microbial Fuel -- 1.3.5 Wind/Liquid Flow -- 1.3.6 Vibrations -- 1.3.7 Friction -- 1.4 Self Powered Systems and Green IoT (G-IoT) -- 1.5 Application Area and Scope of Self-Powered System in G-IoT -- 1.5.1 Terrestrial Applications -- 1.5.1.1 Agriculture -- 1.5.1.2 Smart Home and Cities -- 1.5.1.3 Industry -- 1.5.1.4 Medicines -- 1.5.1.5 Environment Monitoring -- 1.5.1.6 Structural Monitoring -- 1.5.1.7 Indoor Applications -- 1.5.1.8 Arial Vehicles -- 1.5.1.9 Military Applications -- 1.5.1.10 Underwater Applications -- 1.5.1.11 Submarine and Event Localization -- 1.5.1.12 Water Contamination -- 1.5.1.13 Intelligent Water Distribution and Smart Meter -- 1.5.1.14 Underground Applications -- 1.5.1.15 Coal and Petroleum Mining Application -- 1.5.1.16 Underground Structural Monitoring -- 1.6 Challenges and Future Scope of the Self-Powered G-IoT -- 1.6.1 Challenges Pertain to Energy Efficient Design and Protocols -- 1.6.2 Size and Cost of the Harvester -- 1.6.3 Energy-Efficient Routing and Scheduling Protocols -- 1.6.4 Design of Application-Specific Passive Wake-Up Receivers -- 1.6.5 Redefined Protocol with Application-Specific Goals -- 1.6.6 Embedded Operating Systems -- 1.6.7 AI and Cloud-Assisted Lifetime Prediction Techniques -- 1.6.8 Design of Energy-Efficient/Harvested Service-Oriented Architecture -- 1.6.9 Smart Web Interfaces for Monitoring -- 1.6.10 Cross Layer Exploitations with Energy Harvesting -- 1.6.11 Security Aspects and Need of Standardization…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Preface -- Acknowledgments -- Brief Contents -- Contents -- 1 Introduction -- 1.1 The Electrical/Electronics Industry -- 1.2 A Brief History -- 1.3 Units of Measurement -- 1.4 Systems of Units -- 1.5 Significant Figures, Accuracy, and Rounding Off -- 1.6 Powers of Ten -- 1.7 Fixed-Point, Floating-Point, Scientific, and Engineering Notation -- 1.8 Conversion Between Levels of Powers of Ten -- 1.9 Conversion Within and Between Systems of Units -- 1.10 Symbols -- 1.11 Conversion Tables -- 1.12 Calculators -- 1.13 Computer Analysis -- 2 Voltage and Current -- 2.1 Introduction -- 2.2 Atoms and Their Structure -- 2.3 Voltage -- 2.4 Current -- 2.5 Voltage Sources -- 2.6 Ampere-Hour Rating -- 2.7 Battery Life Factors -- 2.8 Conductors and Insulators -- 2.9 Semiconductors -- 2.10 Ammeters and Voltmeters -- 2.11 Applications -- 2.12 Computer Analysis -- 3 Resistance -- 3.1 Introduction -- 3.2 Resistance: Circular Wires -- 3.3 Wire Tables -- 3.4 Temperature Effects -- 3.5 Types of Resistors -- 3.6 Color Coding and Standard Resistor Values -- 3.7 Conductance -- 3.8 Ohmmeters -- 3.9 Resistance: Metric Units -- 3.10 The Fourth Element-The Memristor -- 3.11 Superconductors -- 3.12 Thermistors -- 3.13 Photoconductive Cell -- 3.14 Varistors -- 3.15 Applications -- 4 Ohm's Law, Power, and Energy -- 4.1 Introduction -- 4.2 Ohm's Law -- 4.3 Plotting Ohm's Law -- 4.4 Power -- 4.5 Energy -- 4.6 Efficiency -- 4.7 Circuit Breakers, GFCIs, and Fuses -- 4.8 Applications -- 4.9 Computer Analysis -- 5 Series dc Circuits -- 5.1 Introduction -- 5.2 Series Resistors -- 5.3 Series Circuits -- 5.4 Power Distribution in a Series Circuit -- 5.5 Voltage Sources in Series -- 5.6 Kirchhoff's Voltage Law -- 5.7 Voltage Division in a Series Circuit -- 5.8 Interchanging Series Elements -- 5.9 Notation -- 5.10 Ground Connection Awareness…”

Tabla de Contenidos: “…Deep learning framework for breast cancer diagnosis -- 3.1 MobileNet -- 3.1.1 Depthwise separable convolution -- 3.1.2 Pointwise convolution -- 3.1.3 ReLU activation -- 3.2 Long short-term memory -- 3.2.1 LSTM equation for cell state update -- 3.2.2 LSTM equation for hidden state update -- 3.2.3 LSTM equation for input gate -- 3.2.4 LSTM equation for output gate -- 3.2.5 LSTM equation for input transformation -- 4. …”

Tabla de Contenidos: “…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…”

Tabla de Contenidos: “…6.2 Related Work -- 6.3 Datasets -- 6.4 Experimental Setup -- 6.5 Results and Discussion -- 6.5.1 Evaluation Metrics -- 6.6 Conclusion -- 6.6.1 Significance of the Study -- References -- Chapter 7 Exploring the Potential of Dingo Optimizer: A Promising New Metaheuristic Approach -- 7.1 Introduction -- 7.2 Architecture of Dingo Optimizer -- 7.3 Initialization Process -- 7.3.1 Population Size -- 7.3.2 Dingo Population Initialization -- 7.3.3 Fitness Assessment -- 7.3.4 Best Dingo -- 7.3.5 Recordkeeping -- 7.4 Iteration Phase -- 7.6 Other Optimization Techniques -- 7.7 Conclusion -- References -- Chapter 8 Bioinspired Genetic Algorithm in Medical Applications -- 8.1 Introduction -- 8.2 The Genetic Algorithm -- 8.3 Radiology -- 8.4 Oncology -- 8.5 Endocrinology -- 8.6 Obstetrics and Gynecology -- 8.7 Pediatrics -- 8.8 Surgery -- 8.9 Infectious Diseases -- 8.10 Radiotherapy -- 8.11 Rehabilitation Medicine -- 8.12 Neurology -- 8.13 Health Care Management -- 8.14 Conclusion -- References -- Chapter 9 Artificial Immune System Algorithms for Optimizing Nanoparticle Design in Targeted Drug Delivery -- 9.1 Introduction -- 9.2 Artificial Immune Cells -- 9.3 The Artificial Immune System Architecture -- References -- Chapter 10 Diabetic Retinopathy Detection by Retinal Blood Vessel Segmentation and Classification Using Ensemble Model -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Proposed System -- 10.4 Conclusion and Future Scope -- References -- Chapter 11 Diabetes Prognosis Model Using Various Machine Learning Techniques -- 11.1 Introduction -- 11.1.1 Disease Identification -- 11.1.2 Data, Information, and Knowledge -- 11.1.3 Knowledge Discovery in Databases -- 11.1.4 Predictive Analytics -- 11.1.5 Supervised Learning and Machine Learning -- 11.1.6 Predictive Models -- 11.1.7 Data Validation and Cleaning -- 11.1.8 Discretization…”