Tabla de Contenidos: “…Chapter 5 Digital Twin System for Intelligent Construction of Large Span Assembly Type Steel Bridge -- 5.1 Introduction -- 5.1.1 Digital Twin Technology -- 5.1.2 Technologies Used -- 5.1.3 Why Digital Twin? …”

Tabla de Contenidos: “…9.3.3 Downloading a small file using the readAll() method -- 9.4 Distributed Program Call -- 9.5 Coding consideration for servlets and JSPs -- 9.5.1 Do not store large objects in HttpSessions -- 9.5.2 Releasing HttpSessions when finished -- 9.5.3 Do not create HttpSessions in JSPs by default -- 9.5.4 Do not use SingleThreadModel -- 9.5.5 Using the HttpServlet init() method -- 9.5.6 Using the HttpServlet destroy() method -- 9.5.7 Minimizing or eliminating the use of System.out.println() -- 9.5.8 Don't use Beans.instantiate() to create new bean instances -- 9.5.9 Using and reusing data sources for JDBC connections -- 9.5.10 Releasing JDBC resources when done -- 9.5.11 Page generation using strings -- 9.5.12 Consideration with static content -- 9.5.13 JSP include directive versus Action -- 9.5.14 Minimizing the useBean scope -- 9.6 Coding considerations for EJBs -- 9.6.1 Accessing entity beans from session beans -- 9.6.2 JNDI names -- 9.6.3 Reusing EJB home interfaces -- 9.6.4 Using info beans to reduce remote calls -- 9.6.5 Releasing resources in EJBs -- 9.6.6 Using local interfaces when possible -- 9.6.7 Removing stateful session beans when finished -- 9.6.8 Transactions -- 9.6.9 Using CMPs instead of BMPs -- 9.6.10 Message-driven beans -- 9.6.11 Using home methods -- 9.6.12 Using the setEntityContext() method -- 9.6.13 Don't use entity beans as simple data wrappers -- 9.6.14 Minimizing the use of stateful session beans -- 9.6.15 Using Data Class Access Beans -- 9.7 Profiling your application -- 9.7.1 Method speed -- 9.7.2 Finding memory leaks -- 9.8 Using a response time watcher -- Chapter 10. …”

Tabla de Contenidos: “…-- 4.8 Mesh Analysis of Circuits with Independent Current Sources -- 4.9 Mesh Analysis of Circuits Containing Dependent Sources -- 4.10 Summary -- 4.11 Problems -- Chapter 5: Circuit Theorems -- Introduction -- 5.1 Linearity of a Circuit and Superposition Theorem -- 5.1.1 Linearity of a Circuit -- 5.2 Star-Delta Transformation Theorem -- 5.3 Substitution Theorem -- 5.4 Compensation Theorem -- 5.5 Thevenin's Theorem and Norton's Theorem -- 5.6 Determination of Equivalents for Circuits with Dependent Sources -- 5.7 Reciprocity Theorem -- 5.8 Maximum Power Transfer Theorem -- 5.9 Millman's Theorem -- 5.10 Summary -- 5.11 Problems -- Chapter 6: The Operational Amplifier as a Circuit Element -- Introduction -- 6.1 Ideal Amplifiers and their Features -- 6.1.1 Ground in Electronic Amplifiers -- 6.2 The Role of DC Power Supply in Amplifiers -- 6.2.1 Linear Amplification in Electronic Amplifiers -- 6.2.2 Large-signal Operation of Amplifiers -- 6.2.3 Output Limits in Amplifiers -- 6.3 The Operational Amplifier -- 6.3.1 The Practical Operational Amplifier -- 6.4 Negative Feedback in Operational Amplifier Circuits -- 6.5 The Principles of 'Virtual Short' and 'Zero Input Current' -- 6.6 Analysis of Operational Amplifier Circuits using the IOA Model -- 6.6.1 The Non-Inverting Amplifier Circuit…”

Tabla de Contenidos: “…3.5 Functions with Default Arguments -- 3.6 inline Functions -- 3.7 Storage Classes -- 3.7.1 auto -- 3.7.2 Keyword register -- 3.7.3 Keyword static -- 3.7.4 Keyword extern -- 3.8 Recursion -- 3.9 Scope Rules -- 3.10 Arrays -- 3.10.1 Introduction -- 3.10.2 Array as function parameter -- 3.10.3 Arrays with multiple dimension -- 3.11 Structures -- 3.11.1 Assignment of structure variables -- 3.11.2 Using structure as function parameters -- 3.11.3 Pointer to structures -- 3.12 Union -- 3.12.1 Defining an union -- 3.12.2 Working with union -- 3.12.3 Anonymous union -- 3.13 Line Splicing -- 3.14 Command Line Arguments -- 3.15 Multi File Programs -- 3.16 Difference Between C and C++ -- 3.16.1 Major differences -- 3.16.2 Minor differences -- 3.17 End of Chapter Programs -- For Advanced Readers -- Summary -- Exercises -- Chapter 4: Object Orientation: An Introduction -- 4.1 Programming Paradigms -- 4.1.1 Spectrum of computer applications -- 4.1.2 Bird's-eye view of programming paradigms -- 4.2 Popular Recent Paradigms -- 4.2.1 Procedural programming -- 4.2.2 Object-oriented programming -- 4.2.3 Programming language C++ -- 4.2.4 Programming language JAVA -- 4.2.5 Procedural programming vs. object-oriented programming -- 4.3 An Introduction to Object Orientation -- 4.3.1 From structures to objects -- 4.4 Features of Object-Oriented Language -- 4.5 Benefits of Object-Oriented Programming -- 4.6 Writing Large Programs -- Summary -- Multiple Choice Questions and Answers -- Exercises -- Chapter 5: Classes and Objects -- 5.1 Introduction -- 5.2 Data Hiding and Encapsulation -- 5.2.1 Private and public data members -- 5.3 Member Functions -- 5.3.1 Scope resolution operator -- 5.4 Accessing Class Members -- 5.5 Some Short Questions and Answers I -- 5.6 Using Pointers for Objects -- 5.7 Objects as Function Parameters -- 5.8 Some Short Questions and Answers II…”

Tabla de Contenidos: “…Definition of Mass -- 4.3 Conservation of Linear Momentum -- 4.4 Invariance of Momentum Conservation Under Galilean Transformation -- 4.5 Illustrative Examples of Momentum Conservation -- 4.5.1 Example 1: Velocity of a Large Block After Being Hit by Bullets -- 4.5.2 Example 2: Recoil Velocity of a Cannon -- 4.6 Propulsion of a Rocket -- 4.7 Worked Out Examples. …”

Tabla de Contenidos: “…Intro -- Geleitwort von Per Kall -- Geleitwort von Martin Bucksteeg und Ralf Engler -- Vorwort -- Teil I - Grundlagen zum IT Service Management, ITIL und ITIL 4 -- 1 ITIL und IT Service Management -- 1.1 ITIL - ein erster Überblick -- 1.2 Grundlegende Begriffe im Service Management -- 1.2.1 Dienstleistungen (Services) -- 1.2.1.1 Wertschöpfungskonfiguration -- 1.2.1.2 Gestaltung von Dienstleistungsprozessen -- 1.2.2 Dienstleistungen in der IT (IT Services) -- 1.3 IT-Dienstleistungsmanagement mit ITIL -- 1.3.1 ITIL als Best Practice -- 1.3.2 Chancen und Vorteile -- 1.3.3 Risiken und Hemmnisse -- 1.4 Die Inhalte des ITIL 4 Frameworks -- 1.4.1 ITIL Service Value System -- 1.4.2 Vier-Dimensionen-Modell -- 1.5 Enterprise Service Management (ESM) -- 1.6 Zusammenfassung -- 1.7 Beispielfragen -- 2 ITIL und ITSM-Veröffentlichungen im Überblick -- 2.1 Die ITIL-Historie -- 2.1.1 ITIL V 1 -- 2.1.2 ITIL V 2 -- 2.1.3 ITIL V 3, ITIL Edition 2007 -- 2.1.4 ITIL V 3, ITIL Edition 2011 -- 2.1.5 ITIL Practitioner -- 2.1.6 ITIL 4 -- 2.2 Frameworks und Best Practices als ITIL-Inhalte -- 2.3 Weitere nicht proprietäre und proprietäre ITSM Frameworks -- 2.3.1 FitSM -- 2.3.2 YaSM -- 2.3.3 VeriSM -- 2.3.4 BSM -- 2.3.4.1 Services und Service Management -- 2.3.4.2 Menschen und Mitarbeiter -- 2.3.4.3 Practices -- 2.3.4.4 Tools und Technik der IT -- 2.3.5 CMMI-SVC -- 2.3.5.1 CMMI-Modelle -- 2.3.5.2 CMMI für Dienstleistungen -- 2.3.6 USMBOK -- 2.3.7 IT4IT -- 2.3.8 Agile Skalierungsframeworks -- 2.3.8.1 SAFe -- 2.3.8.2 Large Scale Scrum (LeSS) -- 2.4 Zusammenfassung -- Teil II - ITL 4 -- 3 Schlüsselkonzepte in ITIL 4 -- 3.1 Service Management und der Wertbeitrag für den Kunden -- 3.2 Stakeholder -- 3.3 Services und Produkte für die Wertschöpfung -- 3.4 Service-Beziehungen -- 3.5 Bestandteile des (Mehr-)Werts -- 3.5.1 Output und Ergebnis (Outcome) -- 3.5.2 Kosten -- 3.5.3 Risiken…”

Tabla de Contenidos: “…Application of the Transformation Method -- 6.9. Laws of Large Numbers -- 6.10. The Central Limit Theorem -- 6.11. …”

Tabla de Contenidos: “…7.4.2 Risk Communication -- 7.5 Key Findings -- 7.6 Key Information Needs -- 7.6.1 Understudied Health Effects -- 7.6.2 Health Benefits and Trade-Offs of Public Health Interventions -- 7.6.3 Economic Impacts -- 7.6.4 Central Repository of Standards and Actions -- 7.7 Conclusions -- References -- 8 Resource Manager Perspectives on the Need for Smoke Science -- 8.1 Introduction -- 8.2 Managing Wildland Fire to Improve Ecosystem Conditions While Minimizing Smoke Impacts -- 8.2.1 Smoke Concerns and Barriers to Prescribed Fire -- 8.2.2 Applying Prescribed Fire Across Large Landscapes -- 8.2.3 Utilizing Wildfires and Natural Ignitions -- 8.2.4 Implications of Wildfire Response Actions and Suppression for Air Quality -- 8.2.5 Alternatives to Burning-Evaluating Emissions Reduction -- 8.2.6 Effects of Fuel Moisture on Emissions and Dispersion -- 8.2.7 Fuel Type, Fuel Loading, and Fuel Consumption -- 8.2.8 Techniques for Minimizing Smoke Impacts -- 8.2.9 Components of Wildland Fire Smoke -- 8.2.10 Soils and Emissions -- 8.2.11 Remote Sensing and Data for Fuels, Fire, and Smoke -- 8.2.12 Prescribed Fire Tracking Data -- 8.2.13 Fire Emissions and the National Emissions Inventory -- 8.3 Wildland Fire and Smoke Decision Tools -- 8.3.1 Multiple Fires and Airshed Analysis -- 8.3.2 Fire Growth Models and Smoke Dispersion -- 8.3.3 Background Air Quality Conditions -- 8.3.4 Smoke Models for Fire Planning -- 8.3.5 Use of Air Quality Measurements -- 8.3.6 Air Quality Impacts of Prescribed Fire Versus Wildfire -- 8.3.7 Smoke Model Performance and Accuracy -- 8.3.8 Long-Range Forecasts and Projections for Planning and Early Warning -- 8.3.9 Tools and Data Needs for the Future -- 8.3.10 Identifying Areas at High Risk from Wildfire and Smoke -- 8.4 Health, Safety, and Societal Impacts of Smoke -- 8.4.1 What is a Smoke-Affected Day?…”

Tabla de Contenidos: “…6.3.2 Incorporation of Missingness -- 6.3.3 Settle on Careful Choices in defining Outcomes -- 6.3.4 Comprehend the Result in the Setting of a Social Insurance Framework -- 6.3.5 Be Careful with Mark Spillage -- 6.4 Inclined Opportunities in Healthcare -- 6.4.1 Automating Clinical Errands during Determination and Treatment -- 6.4.2 Computerizing Clinical Picture Assessment -- 6.4.3 Robotizing Routine Procedures -- 6.4.4 Streamlining Clinical Choice and Practice Support -- 6.4.5 Normalizing Clinical Procedures -- 6.4.6 Incorporating Divided Records -- 6.4.7 Growing Medicinal Capacities: New Skylines in Screening, Analysis and Treatment -- 6.4.8 Growing the Inclusion of Proof -- 6.4.9 Moving towards Constant Social Checking -- 6.5 Population Protection (Crowd Surveillance) -- 6.6 Marketing Strategy -- 6.7 Population Screening -- 6.8 Patient Advocacy -- 6.9 Role of Machine Learning in Society -- 6.10 Ayushman Bharat: A Step Forward -- 6.11 The National E-Health Authority (Neha) -- 6.12 Cancer Screening and Machine Learning -- 6.13 "Sick" Care to "Health" Care: Moving Forward -- 6.14 Machine Learning and Healthcare Opportunities -- 6.14.1 Computerizing Clinical Assignments during Determination and Treatment -- 6.14.2 Robotizing Clinical Picture Assessment -- 6.14.3 Robotizing Routine Procedures -- 6.14.4 Clinical Support and Augmentation -- 6.14.5 Expanding Clinical Capacities -- 6.14.6 Precision Medicine for Early Individualized Treatment -- 6.14.7 Open Doors for Innovative Research -- 6.14.8 Adding Communication to AI and Assessment -- 6.14.9 Distinguishing Representations in a Large and Multi-source Network -- 6.15 Common Machine Learning Applications in Healthcare -- 6.15.1 Machine Learning Application in Drug Discovery -- 6.15.2 Neuroscience and Image Computing -- 6.15.3 Cloud Computing Frameworks in building Machine Learning-based Healthcare…”

Tabla de Contenidos: “…-- 10.1.2 Downloading and playing with data -- 10.1.3 Too large vocabulary? N-grams to the rescue -- 10.1.4 Tokenizing text -- 10.1.5 Defining a tf.data pipeline -- 10.2 GRUs in Wonderland: Generating text with deep learning -- 10.3 Measuring the quality of the generated text -- 10.4 Training and evaluating the language model -- 10.5 Generating new text from the language model: Greedy decoding -- 10.6 Beam search: Enhancing the predictive power of sequential models -- Summary -- Answers to exercises -- Part 3 Advanced deep networks for complex problems -- 11 Sequence-to-sequence learning: Part 1 -- 11.1 Understanding the machine translation data -- 11.2 Writing an English-German seq2seq machine translator -- 11.2.1 The TextVectorization layer -- 11.2.2 Defining the TextVectorization layers for the seq2seq model -- 11.2.3 Defining the encoder -- 11.2.4 Defining the decoder and the final model -- 11.2.5 Compiling the model -- 11.3 Training and evaluating the model -- 11.4 From training to inference: Defining the inference model -- Summary -- Answers to exercises -- 12 Sequence-to-sequence learning: Part 2 -- 12.1 Eyeballing the past: Improving our model with attention -- 12.1.1 Implementing Bahdanau attention in TensorFlow -- 12.1.2 Defining the final model -- 12.1.3 Training the model -- 12.2 Visualizing the attention -- Summary -- Answers to exercises -- 13 Transformers -- 13.1 Transformers in more detail -- 13.1.1 Revisiting the basic components of the Transformer -- 13.1.2 Embeddings in the Transformer…”

Tabla de Contenidos: “…Intro -- Nanotechnology Applications for Solar Energy Systems -- Contents -- About the Editor -- List of Contributors -- Preface -- 1 Solar Energy Applications -- 1.1 Introduction and Recent Advances -- 1.2 Solar Energy Applications -- 1.2.1 Electricity Production Using Photovoltaics at Large Scale -- 1.2.2 Small-Scale Electricity Production for Houses and Commercial Buildings -- 1.2.3 Off-Grid Applications Using Photovoltaics -- 1.2.4 Concentrating Solar Thermal Electricity -- 1.2.5 Solar Thermochemical Processes -- 1.2.6 Solar Water Heating -- 1.2.7 Heating of Solar Architecture -- 1.2.8 Air Conditioning Through Water Evaporation -- 1.2.9 Artificial Photosynthesis -- 1.2.10 Decomposing Waste and Biofuels Production -- 1.3 Classification of Solar Energy Devices -- 1.3.1 Concentrating Solar Power -- 1.3.2 Building Integrated Solar Systems -- 1.3.3 Solar-Thermal Collectors -- 1.3.4 Solar Thermochemistry -- 1.3.5 Solar Thermal Energy Storage -- 1.3.6 Solar-Driven Water Distillation -- 1.4 Benefits and Opportunities -- 1.5 Challenges -- 1.6 Future Aspects -- 1.7 Conclusion -- References -- 2 Application of Nanofluid for Solar Stills -- 2.1 Introduction -- 2.2 Desalination Technology -- 2.2.1 What is a Solar Still? …”

Tabla de Contenidos: “…-- Fix a Cut-Off Area of My Photo? -- Add Something Large to My Image That Wasn't Really There? -- Add Smaller Things to My Image? …”

Tabla de Contenidos: “…-- 3.6.3 Nominal Interest Rates -- 3.7 Appendix: Force of Interest - An Analogy with Mortality Rates -- 3.8 Recommended Reading -- 4 Financial Mathematics (2): Miscellaneous Examples -- 4.1 Introduction -- 4.2 Writing Annuity Functions -- 4.2.1 Writing a function for an annuity certain -- 4.3 The 'presentValue' Function -- 4.4 Annuity Function -- 4.5 Bonds - Pricing and Yield Calculations -- 4.6 Bond Pricing: Non-Constant Interest Rates -- 4.7 The Effect of Future Yield Changes on Bond Prices Throughout the Term of the Bond -- 4.8 Loan Schedules -- 4.8.1 Introduction -- 4.8.2 Method 1 -- 4.8.3 Method 2 -- 4.9 Recommended Reading -- 5 Fundamental Statistics: A Selection of Key Topics -- 5.1 Introduction -- 5.2 Basic Distributions in Statistics -- 5.3 Some Useful Functions for Descriptive Statistics -- 5.3.1 Introduction -- 5.3.2 Bivariate or Higher Order Data Structure -- 5.4 Statistical Tests -- 5.4.1 Exploring for Normality or Any Other Distribution in the Data -- 5.4.2 Goodness-of-fit Testing for Fitted Distributions to Data -- 5.4.2.1 Continuous distributions -- 5.4.2.2 Discrete distributions -- 5.4.3 T-tests -- 5.4.3.1 One sample test for the mean -- 5.4.3.2 Two sample tests for the mean -- 5.4.4 F-test for Equal Variances -- 5.5 Main Principles of Maximum Likelihood Estimation -- 5.5.1 Introduction -- 5.5.2 MLE of the Exponential Distribution -- 5.5.2.1 Obtaining the MLE numerically using R -- 5.5.2.2 Obtaining the MLE analytically -- 5.5.3 Large Sample (Asymptotic) Properties of MLE…”

Tabla de Contenidos: “…Chapter 7 Renewable Energy Smart Grids for Electric Vehicles -- 7.1 Introduction -- 7.2 Integration of Electric Vehicles (EVs) into the Power Grid -- 7.3 EV Charging and Electric Grid Interaction -- 7.4 EVs with V2G System Architecture -- 7.5 EVs and Smart Grid Infrastructure -- 7.6 Renewable Energy Sources Integration With EVs -- 7.6.1 PV Solar Energy With EVs -- 7.6.2 Wind Energy With EVs -- 7.7 Application in Transport Sector -- 7.8 Application in Micro-Grid -- 7.9 State-of-the-Art Review -- 7.10 Future Trends -- References -- Chapter 8 Recent Advances in Integrating Renewable Energy Micro-Grid Systems With Electric Vehicles -- 8.1 Introduction -- 8.2 Electric Vehicles and Renewable Energy Sources: A General Overview -- 8.2.1 Electric Vehicles -- 8.2.2 Battery Electric Vehicles -- 8.2.3 Parallel Hybrid Electric Vehicles -- 8.2.4 Battery Chargers for EVs -- 8.2.5 Renewable Energy Sources -- 8.2.5.1 Wind Energy -- 8.2.5.2 Solar Energy -- 8.3 Microgrid -- 8.3.1 Domestic Use -- 8.3.2 Industrial Use -- 8.3.3 Benefits of Microgrids -- 8.3.4 Locations of Microgrid -- 8.4 Interactions Between Cost-Conscious EVs and RESs -- 8.4.1 Operational Cost Reduction -- 8.4.2 Lowering the Electricity Generation Cost -- 8.4.3 Growth in Profit or Benefit -- 8.4.4 Reduction in Charging Cost for EVs Owners -- 8.4.5 Other Cost-Conscious Efforts -- 8.5 Interaction Between Efficiency-Conscious EVs and RESs -- 8.5.1 Microgrid Implementation -- 8.5.2 Increasing the Use of RESs -- 8.5.3 Other Works With a Focus on Efficiency -- 8.6 Open Problems -- 8.6.1 Grid Integration of RESs on a Large Scale -- 8.6.2 The Use of EV Batteries in Conjunction With RESs -- 8.6.3 V2G's Ability to Allow the Interaction of RESs -- 8.7 Conclusion -- References -- Chapter 9 Overview of Fast Charging Technologies of Electric Vehicles -- 9.1 Introduction…”

Tabla de Contenidos: “…-- 13.3 Corporations' Use Of Machine Learning To Strengthen Their Cyber Security Systems -- 13.4 Cyber Attack/Cyber Security Threats And Attacks -- 13.4.1 Malware -- 13.4.2 Data Breach -- 13.4.3 Structured Query Language Injection (SQL-I) -- 13.4.4 Cross-Site Scripting (XSS) -- 13.4.5 Denial-Of-Service (DOS) Attack -- 13.4.6 Insider Threats -- 13.4.7 Birthday Attack -- 13.4.8 Network Intrusions -- 13.4.9 Impersonation Attacks -- 13.4.10 DDoS Attacks Detection On Online Systems -- 13.5 Different Machine Learning Techniques In Cyber Security -- 13.5.1 Support Vector Machine (SVM) -- 13.5.2 K-Nearest Neighbor (KNN) -- 13.5.3 Naïve Bayes -- 13.5.4 Decision Tree -- 13.5.5 Random Forest (RF) -- 13.5.6 Multilayer Perceptron (MLP) -- 13.6 Application Of Machine Learning -- 13.6.1 ML in Aviation Industry -- 13.6.2 Cyber ML Under Cyber Security Monitoring -- 13.6.3 Battery Energy Storage System (BESS) Cyber Attack Mitigation -- 13.6.4 Energy-Based Cyber Attack Detection in Large-Scale Smart Grids -- 13.6.5 IDS for Internet of Vehicles (IoV) -- 13.7 Deep Learning Techniques In Cyber Security -- 13.7.1 Deep Auto-Encoder -- 13.7.2 Convolutional Neural Networks (CNN) -- 13.7.3 Recurrent Neural Networks (RNNs) -- 13.7.4 Deep Neural Networks (DNNs) -- 13.7.5 Generative Adversarial Networks (GANs) -- 13.7.6 Restricted Boltzmann Machine (RBM) -- 13.7.7 Deep Belief Network (DBN)…”

Tabla de Contenidos: “…8.2.4.2 ML-based classification comparison -- 8.2.4.3 Benefits of using a single OSA -- 8.2.4.4 Benefits of feature transformation for classification -- 8.2.4.5 Failure localization -- 8.2.5 Conclusions -- 8.3 Residual-based spectrum monitoring -- 8.3.1 Residual-based approach for optical spectrum analysis -- 8.3.2 Facilitating ML algorithm deployment using residual signals -- 8.3.3 Illustrative results -- 8.3.3.1 Comparison of residual-based and feature-based approaches -- 8.3.3.2 The efficiency of residual adaptation mechanism -- 8.3.4 Conclusions -- 8.4 Monitoring of filterless optical networks -- 8.4.1 Motivation of optical monitoring in FONs -- 8.4.2 Signal identification and classification -- 8.4.3 Optical signal tracking -- 8.4.3.1 Feature-based tracking -- Individual feature -- Super features -- 8.4.3.2 Residual-based tracking -- 8.4.4 Illustrative results -- 8.4.4.1 PAM4 scenario -- 8.4.4.2 QPSK scenario -- 8.4.5 Conclusions -- 8.5 Concluding remarks and future work -- List of acronyms -- References -- 9 Machine learning and data science for low-margin optical networks -- 9.1 The shape of networks to come -- 9.2 Current QoT margin taxonomy and design -- 9.3 Generalization of optical network margins -- 9.3.1 Optimal spectral efficiency -- 9.3.2 Field margins -- 9.3.3 Uncertainty margins -- 9.3.4 Unallocated and implementation margins -- 9.3.5 Protection margins -- 9.3.6 Total spectral efficiency margin and QoT margin equivalency -- 9.4 Large scale assessment of margins and their time variations in a deployed network -- 9.4.1 Assessing the quality of transmission -- 9.4.2 Description of the dataset -- 9.4.3 Example of SNR variations in time -- 9.4.4 Distributions of the minimal, maximal and median margins for all connections in the dataset -- 9.4.5 System margins and long term performance variations…”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Bioinspired Algorithms: Opportunities and Challenges -- 1.1 Introduction -- 1.1.1 Definition and Significance of Bioinspired Algorithms -- 1.1.2 Overview of the Chapter -- 1.2 Bioinspired Principles and Algorithms -- 1.2.1 Evolutionary Algorithms -- 1.2.2 Swarm Intelligence Algorithms -- 1.2.3 Artificial Neural Networks -- 1.2.4 Other Bioinspired Algorithms -- 1.3 Opportunities of Bioinspired Algorithms -- 1.3.1 Solving Complex Optimization Problems -- 1.3.2 Robustness in Dealing With Uncertainty and Noise -- 1.3.3 Parallel and Distributed Computing -- 1.3.4 Application Areas and Success Stories -- 1.4 Challenges of Bioinspired Algorithms -- 1.4.1 Parameter Tuning and Algorithm Configuration -- 1.4.2 Lack of Theoretical Analysis and Understanding -- 1.4.3 Risk of Premature Convergence -- 1.4.4 Computational Cost for Large-Scale Problems -- 1.4.5 Ethical Considerations and Limitations -- 1.5 Prominent Bioinspired Algorithms -- 1.5.1 Genetic Algorithms -- 1.5.2 Particle Swarm Optimization -- 1.5.3 Ant Colony Optimization -- 1.5.4 Artificial Neural Networks -- 1.6 Applications of Bioinspired Algorithms -- 1.6.1 Optimization Problems -- 1.6.2 Pattern Recognition and Machine Learning -- 1.6.3 Swarm Robotics -- 1.6.4 Other Domains -- 1.7 Future Research Directions -- 1.7.1 Improving Efficiency and Scalability -- 1.7.2 Enhancing Interpretability and Explainability -- 1.7.3 Integration With Other Computational Techniques -- 1.7.4 Addressing Ethical Concerns -- 1.8 Conclusion -- 1.8.1 Summary of Key Points -- 1.8.2 Implications and Future Prospects of Bioinspired Algorithms -- References -- Chapter 2 Evaluation of Phytochemical Screening and In Vitro Antiurolithiatic Activity of Myristica fragrans by Titrimetry Method Using Machine Learning -- 2.1 Introduction…”

“…This report analyses the impact of "Shifting wealth" on social cohesion, largely focusing on high-growth converging countries. …”