Tabla de Contenidos: “…-- 13.3 Do Not Forget That Enable Pin -- 13.4 Check the DC Operating Point -- 13.5 The Gain Is Wrong -- 13.6 The Output Is Noisy -- 13.6.1 Conducted Emissions and Radiated Emissions -- 13.6.2 Radiated Susceptibility -- 13.6.3 Conducted Susceptibility -- 13.7 The Output Has an Offset -- 13.8 Conclusion -- 14 - Interfacing a Transducer to an Analog to Digital Converter -- 14.1 Introduction -- 14.2 System Information -- 14.3 Power Supply Information -- 14.4 Input Signal Characteristics -- 14.5 Analog to Digital Converter Characteristics -- 14.6 Interface Characteristics -- 14.7 Architectural Decisions -- 14.8 Conclusions -- 15 - Interfacing D/A Converters to Loads -- 15.1 Introduction -- 15.2 Load Characteristics -- 15.2.1 DC Loads -- 15.2.2 AC Loads -- 15.3 Understanding the D/A Converter and Its Specifications -- 15.3.1 Types of D/A Converters-Understanding the Trade-offs -- 15.3.2 The Resistor Ladder D/A Converter -- 15.3.3 The Weighted Resistor D/A Converter -- 15.3.4 The R/2R D/A Converter -- 15.3.5 The Sigma Delta D/A Converter -- 15.4 D/A Converter Error Budget -- 15.4.1 Accuracy Versus Resolution -- 15.4.2 DC Application Error Budget -- 15.4.3 AC Application Error Budget -- 15.4.3.1 Total Harmonic Distortion -- 15.4.3.2 Dynamic Range -- 15.4.4 RF Application Error Budget -- 15.5 D/A Converter Errors and Parameters -- 15.5.1 DC Errors and Parameters -- 15.5.1.1 Offset Error -- 15.5.1.2 Gain Error -- 15.5.1.3 Differential Nonlinearity Error -- 15.5.1.4 Integral Nonlinearity Error -- 15.5.1.5 Power Supply Rejection Ratio -- 15.5.2 AC Application Errors and Parameters…”

Tabla de Contenidos: “…3.5.6 Eine weitere Möglichkeit zur Handhabung der Klassenbezeichnungen und der Verlustfunktion -- 3.5.7 Hinreichend große zwischenliegende Layer sind wichtig -- 3.5.8 Weitere Experimente -- 3.5.9 Zusammenfassung -- 3.6 Ein Beispiel für eine Regression: Vorhersage der Kaufpreise von Häusern -- 3.6.1 Die Boston-Housing-Price-Datensammlung -- 3.6.2 Daten vorbereiten -- 3.6.3 Neuronales Netz erzeugen -- 3.6.4 K-fache Kreuzvalidierung des Ansatzes -- 3.6.5 Zusammenfassung -- 3.7 Zusammenfassung Kapitel 3 -- Kapitel 4: Grundlagen des Machine Learnings -- 4.1 Vier Teilgebiete des Machine Learnings -- 4.1.1 Überwachtes Lernen -- 4.1.2 Unüberwachtes Lernen -- 4.1.3 Selbstüberwachtes Lernen -- 4.1.4 Bestärkendes Lernen -- 4.2 Bewertung von Machine-Learning-Modellen -- 4.2.1 Trainings-, Validierungs- und Testmengen -- 4.2.2 Worauf zu achten ist -- 4.3 Datenvorverarbeitung, Merkmalserstellung und Erlernen von Merkmalen -- 4.3.1 Datenvorverarbeitung für neuronale Netze -- 4.3.2 Merkmalserstellung -- 4.4 Überanpassung und Unteranpassung -- 4.4.1 Das neuronale Netz verkleinern -- 4.4.2 Regularisierung der Gewichte -- 4.4.3 Dropout-Regularisierung -- 4.5 Ein allgemeiner Machine-Learning-Workflow -- 4.5.1 Definition der Aufgabe und Zusammenstellen einer Datenmenge -- 4.5.2 Auswahl eines Erfolgskriteriums -- 4.5.3 Auswahl einer Bewertungsmethode -- 4.5.4 Daten vorbereiten -- 4.5.5 Entwicklung eines Modells, das besser funktioniert als zufälliges Raten -- 4.5.6 Hochskalieren: Entwicklung eines Modells mit Überanpassung -- 4.5.7 Regularisierung des Modells und Abstimmung der Hyperparameter -- 4.6 Zusammenfassung Kapitel 4 -- Teil II: Deep Learning in der Praxis -- Kapitel 5: Deep Learning und maschinelles Sehen -- 5.1 Einführung in CNNs -- 5.1.1 Die Faltungsoperation -- 5.1.2 Die Max-Pooling-Operation -- 5.2 Ein CNN von Grund auf mit einer kleinen Datenmenge trainieren…”

Tabla de Contenidos: “…4.2.2.1 Geometric Representation of Blocking Objects 95 -- 4.2.2.2 Attenuation Caused by Blocking Objects 96 -- 4.2.2.3 Channel Models 96 -- 4.2.2.4 Blockage States 96 -- 4.2.3 Accounting for mmWave Blockage 96 -- 4.2.4 Summary 97 -- 4.3 Modeling Consumer 5G-IoT Systems with Dynamic Blockage 97 -- 4.3.1 Spontaneous Public Event 97 -- 4.3.2 Moving Through the Crowd 98 -- 4.3.3 AR Sessions in Dense Moving Crowd 100 -- 4.3.4 Connected Vehicles 102 -- 4.3.5 Summary 103 -- 4.4 Dynamic Multi-Connectivity 103 -- 4.4.1 Multi-Connectivity at a Glance 103 -- 4.4.2 Optimizing the Degree of Multi-Connectivity 104 -- 4.4.3 Modeling 5G NR Systems with Multi-Connectivity 105 -- 4.4.4 Impact of Multi-Connectivity Policy 107 -- 4.4.5 Summary 108 -- 4.5 Bandwidth Reservation 109 -- 4.5.1 Session Continuity Mechanisms 109 -- 4.5.2 Concept of Bandwidth Reservation 109 -- 4.5.3 Summary 110 -- 4.6 Proactive Handover Mechanisms 111 -- 4.6.1 Dynamic Blockage Avoidance 111 -- 4.6.2 Deterministic AP Locations 112 -- 4.6.3 Deterministic UE Locations/Trajectories 113 -- 4.6.4 Summary 114 -- 4.7 Conclusions 114 -- References 115 -- 5 Radio Resource Management Techniques for 5G Verticals 119 /S.M. …”

Tabla de Contenidos: “…3.5.3 The barbershop problem: introducing fairness -- 3.5.4 Readers-writers -- 3.5.4.1 A solution favoring the readers -- 3.5.4.2 Giving priority to the writers -- 3.5.4.3 A fair solution -- 3.6 Monitors -- 3.6.1 Design approach 1: critical section inside the monitor -- 3.6.2 Design approach 2: monitor controls entry to critical section -- 3.7 Applying monitors in classical problems -- 3.7.1 Producers-consumers revisited -- 3.7.1.1 Producers-consumers: buffer manipulation within the monitor -- 3.7.1.2 Producers-consumers: buffer insertion/extraction exterior to the monitor -- 3.7.2 Readers-writers -- 3.7.2.1 A solution favoring the readers -- 3.7.2.2 Giving priority to the writers -- 3.7.2.3 A fair solution -- 3.8 Dynamic vs. static thread management -- 3.8.1 Qt's thread pool -- 3.8.2 Creating and managing a pool of threads -- 3.9 Debugging multithreaded applications -- 3.10 Higher-level constructs: multithreaded programming without threads -- 3.10.1 Concurrent map -- 3.10.2 Map-reduce -- 3.10.3 Concurrent filter -- 3.10.4 Filter-reduce -- 3.10.5 A case study: multithreaded sorting -- 3.10.6 A case study: multithreaded image matching -- Exercises -- Chapter 4: Shared-memory programming: OpenMP -- 4.1 Introduction -- 4.2 Your First OpenMP Program -- 4.3 Variable Scope -- 4.3.1 OpenMP Integration V.0: Manual Partitioning -- 4.3.2 OpenMP Integration V.1: Manual Partitioning Without a Race Condition -- 4.3.3 OpenMP Integration V.2: Implicit Partitioning with Locking -- 4.3.4 OpenMP Integration V.3: Implicit Partitioning with Reduction -- 4.3.5 Final Words on Variable Scope -- 4.4 Loop-Level Parallelism -- 4.4.1 Data Dependencies -- 4.4.1.1 Flow Dependencies -- 4.4.1.2 Antidependencies -- 4.4.1.3 Output Dependencies -- 4.4.2 Nested Loops -- 4.4.3 Scheduling -- 4.5 Task Parallelism -- 4.5.1 The sections Directive -- 4.5.1.1 Producers-Consumers in OpenMP…”

Tabla de Contenidos: “…Environment variables -- 4.3.2. Ogrinfo -- 4.3.3. Importing with ogr2ogr -- 4.3.4. …”

Tabla de Contenidos: “…3.4 Reflection invariance -- 3.5 Discussion -- 3.5.1 Standardizations -- 3.5.2 Over-dimensioned case -- 3.5.3 Hierarchies -- 4 Shape space -- 4.1 Shape space distances -- 4.1.1 Procrustes distances -- 4.1.2 Procrustes -- 4.1.3 Differential geometry -- 4.1.4 Riemannian distance -- 4.1.5 Minimal geodesics in shape space -- 4.1.6 Planar shape -- 4.1.7 Curvature -- 4.2 Comparing shape distances -- 4.2.1 Relationships -- 4.2.2 Shape distances in R -- 4.2.3 Further discussion -- 4.3 Planar case -- 4.3.1 Complex arithmetic -- 4.3.2 Complex projective space -- 4.3.3 Kent's polar pre-shape coordinates -- 4.3.4 Triangle case -- 4.4 Tangent space coordinates -- 4.4.1 Tangent spaces -- 4.4.2 Procrustes tangent coordinates -- 4.4.3 Planar Procrustes tangent coordinates -- 4.4.4 Higher dimensional Procrustes tangent coordinates -- 4.4.5 Inverse exponential map tangent coordinates -- 4.4.6 Procrustes residuals -- 4.4.7 Other tangent coordinates -- 4.4.8 Tangent space coordinates in R -- 5 Size-and-shape space -- 5.1 Introduction -- 5.2 Root mean square deviation measures -- 5.3 Geometry -- 5.4 Tangent coordinates for size-and-shape space -- 5.5 Geodesics -- 5.6 Size-and-shape coordinates -- 5.6.1 Bookstein-type coordinates for size-and-shape analysis -- 5.6.2 Goodall-Mardia QR size-and-shape coordinates -- 5.7 Allometry -- 6 Manifold means -- 6.1 Intrinsic and extrinsic means -- 6.2 Population mean shapes -- 6.3 Sample mean shape -- 6.4 Comparing mean shapes -- 6.5 Calculation of mean shapes in R -- 6.6 Shape of the means -- 6.7 Means in size-and-shape space -- 6.7.1 Fréchet and Karcher means -- 6.7.2 Size-and-shape of the means -- 6.8 Principal geodesic mean -- 6.9 Riemannian barycentres -- 7 Procrustes analysis -- 7.1 Introduction -- 7.2 Ordinary Procrustes analysis -- 7.2.1 Full OPA -- 7.2.2 OPA in R -- 7.2.3 Ordinary partial Procrustes…”

Tabla de Contenidos: “…3.3.2.4 Document-level sentiment analysis -- 3.3.3 Steps to perform dimensional sentiment analysis -- 3.4 Open issues and challenges in dimensional sentiment analysis -- 3.5 Future directions -- 3.6 Conclusion -- References -- 4 A model of time in natural linguistic reasoning -- 4.1 Introduction -- 4.2 Human biology of time -- 4.2.1 Working hypotheses for time management -- 4.2.2 Criteria for validity -- 4.2.3 Materials and methods -- 4.2.4 Biological foundations of time in human mind -- 4.2.4.1 Animal encephalon structures involved -- 4.2.4.2 Nuclei functioning as a storage of information -- 4.2.4.3 Nuclei functioning as a time device -- 4.2.4.4 Time perception and its alterations -- 4.3 Evidence of timelines in the brain: time in linguistic reasoning -- 4.4 Some clues and tests -- 4.4.1 A device to model timelines -- 4.4.2 A use case to test timelines -- 4.4.3 Modeling the use case with NNR4 -- 4.4.3.1 First rule: timelessness out of C -- 4.4.3.2 Second rule: cycles of use case timelines -- 4.4.3.3 Third rule: poles in c according to timelines -- 4.4.3.4 Fourth rule: the physical expression of timelines -- 4.5 Conclusions and future work -- References -- 5 Hate speech detection using LSTM and explanation by LIME (local interpretable model-agnostic explanations) -- 5.1 Introduction -- 5.2 Bag of words -- 5.2.1 Limitations of the Bag of Words -- 5.2.2 Advantages of the Bag of Words -- 5.3 Term frequency-inverse document frequency -- 5.3.1 Why is TF-IDF? …”

Tabla de Contenidos: “…Installation and configuration - Java Edition -- 4.1 Installation overview -- 4.1.1 Machine topologies -- 4.2 Installation -- 4.2.1 Installation prerequisites -- 4.2.2 Installing Lotus Workplace Web Content Management -- 4.2.3 Pre-deployment steps -- 4.2.4 Deploying the Web module -- 4.2.5 Copy and setup -- 4.2.6 Final steps -- 4.2.7 Installation troubleshooting -- 4.2.8 E-mail configuration -- 4.3 Advanced configuration -- 4.3.1 Setting up IBM DB2 Universal DatabaseTM as the repository -- 4.3.2 Configuring resources -- 4.3.3 Setting up syndication -- 4.3.4 Pre-rendering configuration -- 4.4 LDAP integration and configuration -- 4.4.1 Overview of LDAP integration -- 4.4.2 Prerequisites for LDAP integration -- 4.4.3 LDAP integration process -- 4.4.4 Preparation and installation -- 4.4.5 Configuration -- 4.4.6 LDAP testing and validation -- 4.4.7 Groups and categories -- 4.5 Content portlet configuration in WebSphere Portal -- 4.5.1 WebSphere Portal access setup -- 4.5.2 Portlet application installation and setup -- 4.5.3 Portlet configuration -- 4.5.4 Troubleshooting -- 4.5.5 Portal search configuration -- 4.5.6 Content portlet configuration in Lotus Workplace…”

Tabla de Contenidos: “…3.3.1 Entropy and Mutual Information 65 -- 3.3.2 Encoding of an Information Source 66 -- 3.3.3 Variable Length Coding 68 -- 3.3.4 Quantization 71 -- 3.4 Encoder Architectures 73 -- 3.4.1 DPCM 73 -- 3.4.2 Hybrid Transform-DPCM Architecture 77 -- 3.4.3 A Typical Hybrid Transform DPCM-based Video Codec 79 -- 3.4.4 Motion Compensation 82 -- 3.4.5 DCT and Quantization 83 -- 3.4.6 Procedures Performed at the Decoder 84 -- 3.5 Wavelet-Based Video Compression 86 -- 3.5.1 Motion-Compensated Temporal Wavelet Transform Using Lifting 90 -- References 94 -- 4 4G Wireless Communications and Networking 97 -- 4.1 IMT-Advanced and 4G 97 -- 4.2 LTE 99 -- 4.2.1 Introduction 101 -- 4.2.2 Protocol Architecture 102 -- 4.2.3 LTE Layer 2 107 -- 4.2.4 The Evolution of Architecture 110 -- 4.2.5 LTE Standardization 110 -- 4.3 WIMAX-IEEE 802.16m 112 -- 4.3.1 Network Architecture 113 -- 4.3.2 System Reference Model 114 -- 4.3.3 Protocol Structure 114 -- 4.3.4 Other Functions Supported by IEEE 802.16m for Further Study 125 -- 4.4 3GPP2 UMB 125 -- 4.4.1 Architecture Reference Model 126 -- 4.4.2 Layering Architecture and Protocols 127 -- Acknowledgements 133 -- References 133 -- 5 Advanced Video Coding (AVC)/H.264 Standard 135 -- 5.1 Digital Video Compression Standards 135 -- 5.2 AVC/H.264 Coding Algorithm 138 -- 5.2.1 Temporal Prediction 139 -- 5.2.2 Spatial Prediction 147 -- 5.2.3 The Transform 148 -- 5.2.4 Quantization and Scaling 151 -- 5.2.5 Scanning 151 -- 5.2.6 Variable Length Lossless Codecs 152 -- 5.2.7 Deblocking Filter 155 -- 5.2.8 Hierarchy in the Coded Video 156 -- 5.2.9 Buffers 158 -- 5.2.10 Encapsulation/Packetization 159 -- 5.2.11 Profiles 160 -- 5.2.12 Levels 163 -- 5.2.13 Parameter Sets 167 -- 5.2.14 Supplemental Enhancement Information (SEI) 167 -- 5.2.15 Subjective Tests 168 -- References 168 -- 6 Content Analysis for Communications 171 -- 6.1 Introduction 171 -- 6.2 Content Analysis 173 -- 6.2.1 Low-Level Feature Extraction 174 -- 6.2.2 Image Segmentation 179 -- 6.2.3 Video Object Segmentation 185.…”

Tabla de Contenidos:

Tabla de Contenidos: “…Things to keep in mind -- 4.3. Data preprocessing, feature engineering, and feature learning -- 4.3.1. …”

Tabla de Contenidos: “…4.1 Introduction -- 4.2 Light Absorption in MAPbI3 -- 4.2.1 Visible/UV Region -- 4.2.2 IR Region -- 4.2.3 THz Region -- 4.3 Band Gap of Hybrid Perovskites -- 4.3.1 Band Gap Analysis of MAPbI3 -- 4.3.2 Band Gap of Basic Perovskites -- 4.3.3 Band Gap Variation in Perovskite Alloys -- 4.4 True Absorption Coefficient of MAPbI3 -- 4.4.1 Principles of Optical Measurements -- 4.4.2 Interpretation of α Variation -- 4.5 Universal Rules for Hybrid Perovskite Optical Properties -- 4.5.1 Variation with Center Cation -- 4.5.2 Variation with Halide Anion -- 4.6 Subgap Absorption Characteristics -- 4.7 Temperature Effect on Absorption Properties -- 4.8 Excitonic Properties of Hybrid Perovskites -- References -- Chapter 5 Physical Properties Determined by Density Functional Theory -- 5.1 Introduction -- 5.2 What Is DFT? …”

Tabla de Contenidos: “…4.2.2 Artificial Intelligence -- 4.2.3 Deep Reinforcement Learning -- 4.2.4 Energy Efficiency in WSN -- 4.2.5 Reinforcement Learning -- 4.2.6 5G (5th Generation) -- 4.2.7 Smart Cities -- 4.3 Methodology -- 4.3.1 Clustering -- 4.3.2 Grouping of Nodes -- 4.3.3 Q-Learning -- 4.3.4 Reinforcement Learning -- 4.3.5 K-Means Clustering -- 4.4 Design Aspects -- 4.4.1 Design Considerations -- 4.4.1.1 Fault Tolerance -- 4.4.1.2 Lifetime -- 4.4.1.3 Scalability -- 4.4.1.4 Data Aggregation -- 4.4.1.5 Cost -- 4.4.1.6 Environment -- 4.4.1.7 Heterogeneity Support -- 4.4.1.8 Autonomous Operations -- 4.4.1.9 Limited Memory and Processing Capability -- 4.4.2 Node Creation -- 4.4.3 Distance Computation -- 4.4.4 Energy Parameters -- 4.4.5 WSN Environment -- 4.4.6 Q-Learning Agent -- 4.4.7 Classification of Different Nodes -- 4.4.8 Data Transfer Directly from Primary Node to Base Station -- 4.4.9 Selection of Base Nodes -- 4.5 Results -- 4.6 Conclusion -- References -- Chapter 5 The Role of 5G Networks in Healthcare Applications -- 5.1 Introduction -- 5.2 5G Networks -- 5.3 Applications of 5G Networks -- 5.3.1 Smart Healthcare Applications -- 5.3.2 Internet of Things (IoT) Devices in Healthcare Applications -- 5.3.3 5G Networks in Healthcare Applications -- 5.4 Challenges in the Deployment of 5G Networks in Healthcare Applications -- 5.4.1 Security and Data Privacy Issues -- 5.4.2 Ethical Issues -- 5.5 Recommendations for Leadership -- 5.6 Conclusion -- Reference list -- Chapter 6 Energy Consumption in Smart City Projects in the Era of 5G: An Analysis of User-Generated Content -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Research Development and Findings -- 6.4 Discussion and Implications -- 6.4.1 Discussion -- 6.4.2 Theoretical Implications -- 6.4.3 Managerial Implication -- 6.5 Conclusion -- References -- Chapter 7 The Role of 5G in Railway Applications…”

Tabla de Contenidos: “…3.7.3.3 Data transmission through 5G terminal by ZigBee network -- 3.8 Conclusions -- References -- 4 An overview of low power hardware architecture for edge computing devices -- 4.1 Introduction -- 4.2 Basic concepts of cloud, fog and edge computing infrastructure -- 4.2.1 Role of edge computing in Internet of Things -- 4.2.2 Edge intelligence and 5G in Internet of Things based smart healthcare system -- 4.3 Low power hardware architecture for edge computing devices -- 4.3.1 Objectives of hardware development in edge computing -- 4.3.2 System architecture -- 4.3.3 Central processing unit architecture -- 4.3.4 Input-output architecture -- 4.3.5 Power consumption -- 4.3.6 Data processing and algorithmic optimization -- 4.4 Examples of edge computing devices -- 4.5 Edge computing for intelligent healthcare applications -- 4.5.1 Edge computing for healthcare applications -- 4.5.2 Advantages of edge computing for healthcare applications -- 4.5.3 Implementation challenges of edge computing in healthcare systems -- 4.5.4 Applications of edge computing based healthcare system -- 4.5.5 Patient data security in edge computing -- 4.6 Impact of edge computing, Internet of Things and 5G on smart healthcare systems -- 4.7 Conclusion and future scope of research -- References -- 5 Convergent network architecture of 5G and MEC -- 5.1 Introduction -- 5.2 Technical overview on 5G network with MEC -- 5.2.1 5G with multi-access edge computing (MEC): a technology enabler -- 5.2.2 Application splitting in MEC -- 5.2.3 Layered service oriented architecture for 5G MEC -- 5.3 Convergent network architecture for 5G with MEC -- 5.4 Current research in 5G with MEC -- 5.5 Challenges and issues in implementation of MEC -- 5.5.1 Communication and computation perspective -- 5.5.1.1 MEC service orchestration and programmability…”

Tabla de Contenidos:

Tabla de Contenidos: “…Fast Diffusing Elements -- 6.6 Summary -- References -- 7 Precipitation Hardening -- 7.1 General -- 7.2 Previous Models for the Influence of Particles on the Creep Strength -- 7.2.1 Threshold Stress -- 7.2.2 Orowan Model -- 7.2.3 The Role of the Energy Barrier -- 7.3 Precipitation Hardening Based on Time Control -- 7.4 Application of the Precipitation Hardening Model -- 7.4.1 Analyzed Materials -- 7.4.2 Pure Copper -- 7.4.3 Cu-Co Alloys -- 7.5 Summary -- References -- 8 Cells and Subgrains. …”

Tabla de Contenidos: “…Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- About the Editors -- Contributors -- Preface -- 1 Increasing the Order of Convergence of Three-step-Modified Potra-Pták-Chebyshev Methods for Systems and Equations -- 1.1 Introduction -- 1.2 Development of the Method -- 1.3 Multi-step Version Method -- 1.4 Local Convergence Analysis -- 1.5 Numerical Examples -- References -- 2 Mathematical Model to Distinguish the Symptomatic Patient of COVID-19 -- 2.1 Introduction -- 2.2 Preliminaries -- 2.2.1 Basic Definition -- 2.2.2 Arithmetic Operations -- 2.2.3 Max-min Principle -- 2.3 Approach -- 2.4 Model Representation -- 2.5 Case Study -- 2.6 Results and Discussion -- 2.7 Conclusion and Future Scope -- References -- 3 Maximum Cost cell Method for IBFS of Transportation Problems -- 3.1 Introduction -- 3.2 Mathematical Representation -- 3.3 Proposed Algorithm (PA) -- 3.4 Illustrative Example -- 3.5 Comparative Study -- 3.6 Conclusion -- References -- 4 Optimization Techniques and Applications to Solve Real-world Industrial Optimization Problems -- 4.1 Introduction -- 4.1.1 Background -- 4.2 Recent Works -- 4.2.1 Optimizing Maintenance Schedule -- 4.2.2 Search-based Software Testing (SBST) -- 4.2.3 Cost Optimization of Turning Process -- 4.3 Problem Statement -- 4.3.1 Research Questions -- 4.3.2 Research Objectives -- 4.4 Using Industrial data for Optimization Practices -- 4.4.1 Centralization and Collection of Data -- 4.4.2 Automation of Monitoring -- 4.4.3 Real-time Data Visualization -- 4.4.4 Predictive Analysis -- 4.5 Optimization Techniques and Applications to Solve Real-world Problems -- 4.5.1 Optimization Models -- 4.5.2 Optimization Methods -- 4.5.3 The Complex Nature of Production Optimization -- 4.5.4 Solving Optimization Problem with Machine Learning -- 4.5.5 Components of ML-based Production Optimization…”

Tabla de Contenidos: “…Rekursion -- 3.1.2 Referentielle Transparenz und Ersetzungsprinzip -- 3.1.3 Funktionen mit Gedächtnis -- 3.2 Funktionale Ausnahmebehandlung mit Optional -- 3.3 Funktionale Listen -- 3.3.1 Beispielanwendung -- 3.4 Paare und Tupel -- 3.5 Zusammenfassung -- 4 Programmieren mit Funktionsparametern -- 4.1 Listenverarbeitung mit Funktionen höherer Ordnung -- 4.2 Flexible Programmschnittstellen -- 4.3 Algorithmen -- 4.3.1 Tiefensuche -- 4.3.2 Verallgemeinerung der Suche -- 4.4 Entwurfsmuster -- 4.4.1 Strategie -- 4.4.2 Kommando -- 4.4.3 Besucher -- 4.5 Eingebettete und bedingte Ausführung -- 4.5.1 Eingebetteter Code -- 4.5.2 Bedingte Ausführung -- 4.5.3 Fallunterscheidungen -- 4.5.4 Typtests -- 4.6 Auswertung nach Bedarf -- 4.6.1 Faule Iteratoren -- 4.6.2 Unendliche Folgen -- 4.6.3 Faule Iteration über die Knoten eines Graphen -- 4.7 Zusammenfassung -- 5 Kombination von Funktionen -- 5.1 Flüssige Schnittstellen -- 5.2 Funktionskomposition -- 5.2.1 Aufrufketten beim funktionalen Interface Function -- 5.2.2 Logische Verknüpfungen bei Predicate -- 5.2.3 Bilden von Vergleichsketten mit Comparator -- 5.2.4 Beispiel-Workflows -- 5.3 Kombinator-Parser…”

Tabla de Contenidos: “…Importance of food quality analysis in relation to food safety and human health and COVID-19 in particular -- 1.1 Introduction -- 1.2 Importance of food quality analysis -- 1.3 Compositional analysis of foods -- 1.3.1 Moisture analysis -- 1.3.2 Fat analysis -- 1.3.3 Protein analysis -- 1.3.4 Carbohydrate analysis -- 1.3.5 Vitamin analysis -- 1.3.6 Mineral analysis -- 1.4 Chemical analysis of foods -- 1.4.1 pH analysis -- 1.4.2 Enzyme analysis -- 1.4.3 Food contaminants analysis -- 1.4.3.1 Pesticide analysis -- 1.4.3.2 Mycotoxin analysis -- 1.5 Importance of food analysis in relation to COVID-19 -- 1.5.1 Food safety management in COVID-19 era -- 1.5.2 SARS-CoV-2 virus analysis -- 1.6 Future prospects -- 1.7 Conclusions -- References -- 2. …”

Tabla de Contenidos: “…CLASIFICACIÓN DE LA INFORMACIÓN -- 4.3. EVALUACIÓN DE LA INFORMACIÓN -- 4.4. ANÁLISIS DE LA INFORMACIÓN -- CAPÍTULO V - ETAPAS DEL PROCESO DE LA INFORMACIÓN -- 5.1. …”
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