Tabla de Contenidos: “…Daling Investing in engineering, research and education in Africa to derive a roadmap for ensuring local digital mining success W. Assibey-Bonsu The application of correlation models for the analysis of market risk factors in KGHM capital group Ł. …”

Tabla de Contenidos: “…Wolhuter, Corene de Wet & Johannes (Hannes) L. van der Walt 199 -- Abstract 200 -- Introduction 200 -- Learner discipline in schools: Correlates or determinants 202 -- Learner discipline in South African schools and the need for a change in approach 204 -- Finland: The emergence of a noteworthy education system 207 -- The Programme for International Student Assessment studies 207 -- Finland: An unlikely achiever 207 -- The Finnish education system 209 -- Form-giving social-contextual powers 210 -- The education system: Historic development and current structure 211 -- Reasons for the success of the Finnish education system 216 -- What insights could be gleaned from the Finnish experience for the purposes of addressing shortcomings regarding discipline in the South African education system? …”

Tabla de Contenidos: “…-- 1.7.1 Additional reading -- 1.7.2 Exercises -- Summary -- 2 Planning for parallelization -- 2.1 Approaching a new project: The preparation -- 2.1.1 Version control: Creating a safety vault for your parallel code -- 2.1.2 Test suites: The first step to creating a robust, reliable application -- 2.1.3 Finding and fixing memory issues -- 2.1.4 Improving code portability -- 2.2 Profiling: Probing the gap between system capabilities and application performance -- 2.3 Planning: A foundation for success -- 2.3.1 Exploring with benchmarks and mini-apps -- 2.3.2 Design of the core data structures and code modularity -- 2.3.3 Algorithms: Redesign for parallel -- 2.4 Implementation: Where it all happens -- 2.5 Commit: Wrapping it up with quality -- 2.6 Further explorations…”

Tabla de Contenidos: “…10.4 Adapting IoT to New Needs: Challenges from Brazil -- 10.4.1 IoT RD&amp -- I Funding in Brazil -- 10.4.2 IoT Success Cases in Brazil -- 10.4.2.1 RFID/IoT Change of Paradigm -- 10.4.2.2 Smart Metering and Smart Grids -- 10.4.3 International Standardisation Related to IoT -- 10.4.4 EU-Brazil Collaboration on IoT -- 10.4.4.1 EU-Brazil Joint Call for IoT Pilots RIAs -- 10.4.4.2 EU-Brazil Mapping and Comparative Study -- 10.4.4.3 The EU-Brazil FUTEBOL Project -- 10.4.4.4 FurtherWork on EU-Brazil Cooperation -- 10.5 Do More with Less: Challenges for Africa. …”

Tabla de Contenidos: “…Réformes ayant assoupli les mesures de protection de l'emploi contre les licenciements -- Des réformes successives en France -- Le « Jobs Act » et la suppression de l'indemnité de mobilité en Italie -- Le nouveau Code du travail en Lituanie -- La nouvelle loi sur les relations d'emploi en Slovénie -- Réformes menées dans d'autres pays -- Mesures exceptionnelles en réponse à la crise du COVID-19 -- 3.4.2. …”

Tabla de Contenidos: “…Pas de relation évidente de la croissance des dépenses sur des périodes successives -- Incitations et passerelles entre régimes…”

Tabla de Contenidos: “…17.4 Do an Operation on Two BCD Numbers Based on the Value of X -- 17.5 Bubble Sort in Ascending/Descending Order as per Choice -- 17.6 Selection Sort in Ascending/Descending Order as per Choice -- 17.7 Add Contents of N Word Locations -- 17.8 Multiply Two 8-Bit Numbers (Shift and Add Method) -- 17.9 Multiply two 2-Digit BCD Numbers -- 17.10 Multiply two 16-Bit Binary Numbers -- Questions -- Part III: Programmable and Non-Programmable I/O Ports -- Chapter 18: Interrupts in 8085 -- 18.1 Data Transfer Schemes -- 18.2 General Discussion about 8085 Interrupts -- 18.3 EI and DI Instructions -- 18.4 INTR and INTA* Pins -- 18.5 RST5.5 and RST6.5 Pins -- 18.6 RST7.5 Pin -- 18.7 Trap Interrupt Pin -- 18.8 Execution of 'DAD rp' Instruction -- 18.9 SIM and RIM Instructions -- 18.10 HLT Instruction -- 18.11 Programs using Interrupts -- Questions -- Chapter 19: 8212 Non-Programmable8-Bit I/O Port -- 19.1 Working of 8212 -- 19.2 Applications of 8212 -- Questions -- Chapter 20: 8255 Programmable Peripheral Interface Chip -- 20.1 Description of 8255 PPI -- 20.2 Operational Modes of 8255 -- 20.3 Control Port of 8255 -- 20.4 Mode 1-Strobed I/O -- 20.5 Mode 2-Bi-Directional I/O -- Questions -- Chapter 21: Programs using Interface Modules -- 21.1 Description of Logic Controller Interface -- 21.2 Successive Approximation ADC Interface -- 21.3 Dual Slope ADC Interface -- 21.4 Digital to Analog Converter Interface -- 21.5 Stepper Motor Interface -- Questions -- Part IV: Support Chips -- Chapter 22: Interfacing of I/O Devices -- 22.1 Interfacing 7-Segment Display -- 22.2 Display Interface using Serial Transfer -- 22.3 Interfacing a Simple Keyboard -- 22.4 Interfacing a Matrix Keyboard -- 22.5 Description of Matrix Keyboard Interface -- 22.6 Intel 8279 Keyboard And Display Controller -- 22.7 Programs using 8279 -- Questions…”

Tabla de Contenidos: “…Response Times and Throughput -- 5.4.2. Degree of Success in Question Generation -- 5.4.3. User Feedback and Analysis of Question Usefulness -- Early Focus Groups -- In-Depth Evaluation -- 5.5. …”

Tabla de Contenidos: “…10.2.1 Traditional Task Allocation Methods -- 10.2.2 Need of Machine Learning in Task Allocation -- 10.3 Machine Learning-Based Task Allocation Model -- 10.4 Conclusion -- References -- Chapter 11 Software Quality Management by Agile Testing -- 11.1 Introduction -- 11.2 A Brief Introduction to JMeter -- 11.3 Review of Literature -- 11.4 Performance Testing Using JMeter -- 11.5 Proposed Work -- 11.6 Results and Discussions -- 11.7 Conclusion -- References -- Chapter 12 A Deep Drive into Software Development Agile Methodologies for Software Quality Assurance -- 12.1 Introduction -- 12.2 Background Work -- 12.2.1 Factors of Quality Assurance in Agility -- 12.3 Understanding Agile Software Methodologies -- 12.3.1 Need for Agile Software Methodology Framework -- 12.4 Agile Methodology Evaluation Framework -- 12.4.1 Extreme Programming (XP) -- 12.4.2 Scrum -- 12.4.3 Lean Development -- 12.4.4 Crystal Methodology -- 12.4.5 Kanban Methodology -- 12.4.6 Feature Driven Development (FDD) Methodology -- 12.4.7 Dynamic System Development Method (DSDM) -- 12.5 Agile Software Development - Issues and Challenges -- 12.6 Conclusion -- References -- Chapter 13 Factors and Techniques for Software Quality Assurance in Agile Software Development -- 13.1 Introduction -- 13.1.1 Values of the Agile Manifesto -- 13.1.2 The Twelve Agile Manifesto Principles -- 13.1.3 Agile for Software Quality Assurance -- 13.2 Literature Review -- 13.3 Agile Factors in Quality Assurance -- 13.3.1 Success Factors -- 13.3.2 Failure Factors -- 13.4 Quality Assurance Techniques -- 13.5 Challenges and Limitations of Agile Technology -- 13.6 Conclusion and Future Scope -- References -- Chapter 14 Classification of Risk Factors in Distributed Agile Software Development Based on User Story -- 14.1 Introduction -- 14.2 Software Risk Management -- 14.2.1 Risk Assessment -- 14.3 Literature Review…”

Tabla de Contenidos: “…3.3.3 Graceful terminations -- 3.3.4 Folder watcher -- 3.3.5 Action scripts -- 3.4 Running watchers -- 3.4.1 Testing watcher execution -- 3.4.2 Scheduling watchers -- Summary -- 4 Handling sensitive data -- 4.1 Principles of automation security -- 4.1.1 Do not store sensitive information in scripts -- 4.1.2 Principle of least privilege -- 4.1.3 Consider the context -- 4.1.4 Create role-based service accounts -- 4.1.5 Use logging and alerting -- 4.1.6 Do not rely on security through obscurity -- 4.1.7 Secure your scripts -- 4.2 Credentials and secure strings in PowerShell -- 4.2.1 Secure strings -- 4.2.2 Credential objects -- 4.3 Storing credentials and secure strings in PowerShell -- 4.3.1 The SecretManagement module -- 4.3.2 Set up the SecretStore vault -- 4.3.3 Set up a KeePass vault -- 4.3.4 Choosing the right vault -- 4.3.5 Adding secrets to a vault -- 4.4 Using credentials and secure strings in your automations -- 4.4.1 SecretManagement module -- 4.4.2 Using Jenkins credentials -- 4.5 Know your risks -- Summary -- 5 PowerShell remote execution -- 5.1 PowerShell remoting -- 5.1.1 Remote context -- 5.1.2 Remote protocols -- 5.1.3 Persistent sessions -- 5.2 Script considerations for remote execution -- 5.2.1 Remote execution scripts -- 5.2.2 Remote execution control scripts -- 5.3 PowerShell remoting over WSMan -- 5.3.1 Enable WSMan PowerShell remoting -- 5.3.2 Permissions for WSMan PowerShell remoting -- 5.3.3 Execute commands with WSMan PowerShell remoting -- 5.3.4 Connect to the desired version of PowerShell -- 5.4 PowerShell remoting over SSH -- 5.4.1 Enable SSH PowerShell remoting -- 5.4.2 Authenticating with PowerShell and SSH -- 5.4.3 SSH environment considerations -- 5.4.4 Execute commands with SSH PowerShell remoting -- 5.5 Hypervisor-based remoting -- 5.6 Agent-based remoting -- 5.7 Setting yourself up for success with PowerShell remoting…”

Tabla de Contenidos: “…6.6 Relationships of IoT and AI Strengthen the Agriculture Sector -- 6.7 Benefits of IoT in Agriculture -- 6.8 The Role of AI in Tomorrow's Farming -- 6.8.1 How Artificial Intelligence (AI) Can Help the Farming Industry -- 6.8.2 Selecting Seeds and Plants With the Help of AI -- 6.8.3 Use of Artificial Intelligence in Farming -- 6.8.4 A Predictive Model for Yield Using Artificial Intelligence -- 6.8.5 Implementing AI for Weed and Pest Management -- 6.8.6 AI-Powered Inventory Management and Sales Promotion -- 6.9 Robots in Agriculture-Perceptions and Pros, Cons -- 6.9.1 Farming Equipment With Automated Functions and the Positive Effects -- 6.10 Complications Associated With Robots Used in Agriculture -- 6.10.1 Difficulties and Limitations to be Conquered -- 6.10.2 Successful Applications and Other Case Studies -- 6.10.3 Societal and Ethical Implications -- 6.11 Conclusion -- References -- Chapter 7 Data Analytics in Agriculture: Predictive Models and Real-Time Decision-Making -- 7.1 Introduction -- 7.2 Data Collection and Management in Agriculture -- 7.2.1 Soil Sensors -- 7.2.2 Weather Stations -- 7.2.3 Drones -- 7.2.4 Satellite Imagery -- 7.2.5 Farm Machinery -- 7.2.6 Pest and Disease Monitoring Systems -- 7.2.7 Market Data -- 7.2.8 Crop Records -- 7.2.9 Livestock Records -- 7.3 Challenges in Collecting and Managing Agricultural Data -- 7.4 Strategies for Effective Data Collection and Management -- 7.5 Predictive Models in Agriculture -- 7.5.1 Types of Predictive Models -- 7.6 Applications of Predictive Models in Agriculture -- 7.7 Real-Time Decision-Making in Agriculture -- 7.7.1 Importance of Real-Time Decision-Making in Agriculture -- 7.7.2 Tools and Technologies for Real-Time Decision Making -- 7.8 Integration of Predictive Models and Real-Time Decision Making in Agriculture…”

Tabla de Contenidos: “…3.2.1 Self-Mixed Anaerobic Digester (SMAD) for the Treatment Poultry Litter: Development and Demonstration -- 3.2.2 Anaerobic Gas Lift Reactor (AGR): Concept to Commissioning and Commercialization -- 3.2.2.1 Configuration and Working of AGR Technology -- 3.2.2.2 Demonstration and Performance Assessment of High-Rate Biomethanation Plant Based On AGR -- 3.2.3 Commercialization and Success Stories of AGR Technology -- 3.2.3.1 Model I: Waste to Energy From Kitchen to Kitchen, Biogas Plant at CSIR-IICT, Hyderabad -- 3.2.3.2 Model II: Biogas Plants Based On Organic Fraction of MSW to Power -- 3.2.3.3 Model III: Biogas Plants Based On Market and Vegetable Waste to Power -- 3.2.4 National Recognition of AGR Technology -- 3.2.5 Techno-Commercial Aspects of the Biogas Plants Against Their Capacities -- 3.2.6 Future Scope in the Biogas Industry -- 3.3 Conclusions -- Acknowledgements -- References -- 4 Cyanobacterial Degradation of Pesticides -- 4.1 Introduction -- 4.2 Pesticides - A Growing Concern -- 4.3 Impact of Pesticides On Human Health -- 4.4 Biodegradation of Pesticides -- 4.5 Cyanobacteria - A Precious Bioresource -- 4.6 Cyanobacteria as Bioremediating Agents -- 4.6.1 Cyanobacterial Degradation of Organophosphorus Pesticides -- 4.6.2 Cyanobacterial Degradation of Organochlorine Pesticides -- 4.7 Future Perspectives -- 4.8 Conclusion -- References -- 5 Upcycling of Plastic Waste -- 5.1 Introduction -- 5.2 Conventional Techniques of Plastic Waste Management -- 5.2.1 Mechanical Recycling -- 5.2.2 Waste to Energy -- 5.2.3 Landfilling -- 5.3 Plastic Waste Upcycling Techniques -- 5.3.1 Thermal Upcycling Technique -- 5.3.1.1 Carbonization -- 5.3.1.2 Pyrolysis -- 5.3.1.3 Gasification -- 5.3.2 Chemical Upcycling Techniques -- 5.3.2.1 Solvolysis -- 5.3.2.2 Hydrogenolysis -- 5.3.2.3 Photocatalysis -- 5.3.3 Chemo-Biotechnological Technique…”

Tabla de Contenidos: “…1.15.5 Superconductors -- 1.15.6 3D printing -- 1.15.7 Autonomous vehicle -- 1.16 Conclusion -- References -- Chapter 2: Advances of deep learning and related applications -- 2.1 Introduction -- 2.2 Deep learning techniques -- 2.3 Multilayer perceptron -- 2.4 Convolutional neural network -- 2.5 Recurrent neural network -- 2.6 Long-term short-term memory -- 2.7 GRU -- 2.8 Autoencoders -- 2.9 Attention mechanism -- 2.10 Deep generative models -- 2.11 Restricted Boltzmann machine -- 2.12 Deep belief network -- 2.13 Modern deep learning platforms -- 2.13.1 PyTorch -- 2.13.2 TensorFlow -- 2.13.3 Keras -- 2.13.4 Caffe (Convolutional architecture for fast feature embedding) and Caffe2 -- 2.13.5 Deeplearning4j -- 2.13.6 Theano -- 2.13.7 Microsoft cognitive toolkit (CNTK) -- 2.14 Challenges of deep learning -- 2.15 Applications of deep learning -- 2.16 Conclusion -- References -- Chapter 3: Machine learning for big data and neural networks -- 3.1 Introduction -- 3.2 Machine learning and fundamentals -- 3.2.1 Supervised learning -- 3.2.2 Decision trees -- 3.2.3 Linear regression -- 3.2.4 Naive Bayes -- 3.2.5 Logistic regression -- 3.3 Unsupervised learning -- 3.3.1 K-Means algorithm -- 3.3.2 Principal component analysis -- 3.4 Reinforcement learning -- 3.5 Machine learning in large-scale data -- 3.6 Data analysis in big data -- 3.7 Predictive modelling -- 3.7.1 Understanding customer behavior and preferences -- 3.7.2 The role of supply chain and performance management in organizational success -- 3.7.3 Management of quality and enhancement -- 3.7.4 Risk mitigation and detection of fraud -- 3.8 Neural networks -- 3.8.1 Artificial neural network -- 3.8.2 RNN -- 3.8.3 CNN -- 3.8.4 Deep learning using convolutional neural networks to find building defects -- 3.9 Data generation and manipulation -- 3.9.1 Generative Adversarial Networks…”

Tabla de Contenidos: “…Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Table of Contents -- Aim and Scope -- Preface -- About the Editors -- Contributors -- Acknowledgment -- Chapter 1 Digital Manufacturing: Artificial Intelligence in Industry 5.0 -- 1.1 Introduction -- 1.2 Transformative Journey: Industry 1.0 to 5.0 -- 1.3 Artificial Intelligence in Industry 5.0 -- 1.3.1 AI-Powered Digital Manufacturing Processes -- 1.3.2 AI-Powered Smart Automation and Robotics -- 1.3.3 AI-Powered Supply Chain Optimization -- 1.3.4 AI-Powered Product Quality and Inspection -- 1.3.5 Human-Machine Collaboration in Industry 5.0 -- 1.3.6 AI-Powered Digital Twins -- 1.3.6.1 Digital Twins Applications -- 1.4 Success Stories in AI-Powered Manufacturing -- 1.4.1 Case 1: Ford Motors-Predictive Maintenance With AI -- 1.4.2 Case 2: Boeing-AI-Powered Defect Detection in Aircraft Manufacturing -- 1.4.3 Case 3: Adidas-AI-Driven Demand Forecasting and Customization -- 1.4.4 Case 4: Siemens-AI-Powered Optimization of Production Lines -- 1.4.5 Case 5: Schneider Electric-AI-Powered Supply Chain Management -- 1.5 Challenges and Opportunities -- 1.6 Conclusion -- References -- Chapter 2 The Rise of Industry 5.0: How Artificial Intelligence Is Shaping the Future of Manufacturing -- 2.1 Introduction -- 2.1.1 First Industrial Revolution (Eighteenth to Nineteenth Century) -- 2.1.2 Second Industrial Revolution (After Nineteenth to Before Twentieth Century) -- 2.1.3 Third Industrial Revolution (Late Twentieth Century) -- 2.1.4 Fourth Industrial Revolution (Twenty-First Century) -- 2.2 Characteristics of Industry 5.0 -- 2.3 Importance of AI in Industry 5.0 -- 2.3.1 Techniques and Approaches -- 2.3.2 Challenges and Considerations -- 2.4 Importance of AI in Industrial Transformation -- 2.5 Key Drivers for AI Adoption in Industry 5.0 -- 2.6 Applications of AI in Industry 5.0.…”

Tabla de Contenidos: “…5.11.3 Smart Micro-Grid -- 5.11.4 Demand Side -- 5.11.5 Virtualization -- 5.12 Conclusion -- References -- Chapter 6 IOT-Based Advanced Energy Management in Smart Factories -- 6.1 Introduction -- 6.2 Smart Factory Benefits of IOT-Based Advanced Energy Management -- 6.3 Role of IOT Technology in Energy Management -- 6.4 Developing an IOT Information Model for Energy Efficiency -- 6.5 Integrating Intelligent Energy Systems (IES) and Demand Response (DR) -- 6.6 How to Accurately Measure and Manage Your Energy Usage -- 6.7 Introduction to Energy Efficiency Measures -- 6.8 Identifying Opportunities to Reduce Energy Use -- 6.9 Monitoring and Measuring Energy Usage -- 6.10 Establishing Accounting and Incentives -- 6.11 Sustaining the Long-Term Benefits of Optimized Energy Usage -- 6.12 Role of Cyber Security When Implementing IoT-Based Advanced Energy Solutions -- 6.13 Materials Required in Smart Factories -- 6.14 Methods in IoT-Based Smart Factory Implementation -- 6.15 Steps for Developing an IoT-Based Energy Management System -- 6.15.1 Assess Current Energy Usage -- 6.15.2 Develop an Energy Conservation Plan -- 6.15.3 Implement IoT Technology -- 6.15.4 Monitor Results -- 6.16 Challenges For Adopting IoT-Based Energy Management Systems -- 6.16.1 Big Data and Analytics -- 6.16.2 Connectivity Constraints -- 6.16.3 Data Security and Privacy Issues -- 6.16.4 Device Troubleshooting -- 6.17 Recommendations for Overcoming the Challenges With Implementing IoT-Based Advanced Energy Solution -- 6.17.1 IoT-Enabled Automation -- 6.17.2 Smart Sensors -- 6.17.3 Predictive Analytics -- 6.18 Case Studies -- 6.18.1 Automated Demand Response (ADR) -- 6.18.2 Automated Maintenance -- 6.18.3 Predictive Analytics -- 6.19 Case Studies for Successful Implementation -- 6.20 Applications -- 6.21 Different Techniques for Monitoring and Control of IoT Devices…”

Tabla de Contenidos: “…International reserves in months of import -- International reserve and short-term debt -- International reserve and broad money -- 8.4 Data Description -- 8.5 Empirical Models and Results -- 8.6 Conclusion -- Acknowledgment -- References -- Part Tow: Market Developments and Governance Issues -- 9 Singapore's Financial Market: Challenges and Future Prospects -- 9.1 Introduction -- 9.2 Growth of the Financial Sector -- 9.2.1 Singapore's Financial Sector Success Factors -- 9.2.2 The Development, Regulatory, and Supervisory Phases -- The development phase -- The regulatory phase -- The supervisory phase -- 9.3 The Prospects -- 9.4 The Challenges -- 9.4.1 The Flood, the Reservoir, and Finance -- 9.4.2 High-Frequency Trading and H -- 9.4.3 Asian Crisis: Lesson Learned -- 9.4.4 Spain's Banking Crisis -- 9.5 The Instability Paradox -- 9.6 Micro- and Macroprudential Policies -- 9.7 Conclusion -- References -- 10 Wealth Management: A Comparison of Switzerland, Singapore, and Hong Kong -- 10.1 Introduction -- 10.2 Switzerland -- 10.3 Singapore -- 10.4 Hong Kong -- 10.5 A Comparison of Switzerland, Singapore, and Hong Kong -- 10.6 Voices of Practitioners in Singapore -- 10.7 Conclusion -- References -- 11 Asian Market Reactions to US Macroeconomic News Surprises -- 11.1 Introduction -- 11.2 Data -- 11.2.1 Stock Market Data -- 11.2.2 Announcement Data -- 11.3 Methodology -- 11.4 Empirical Results -- 11.5 Conclusion -- Acknowledgment -- References -- 12 Monetary Policy in Taiwan: The Implications of Liquidity -- 12.1 Introduction -- 12.2 Financial Innovation and the Divisia Monetary Aggregate in Taiwan -- 12.3 Data and Methodology -- 12.3.1 Divisia Aggregation -- 12.3.2 Divisia Money and the Official Aggregates -- 12.3.3 Data -- 12.4 Results and Discussions -- 12.4.1 Data Selection -- 12.4.2 VAR Identification -- 12.5 Conclusion -- References…”