Reyes Juárez Ramírez – författare

Blockchain Technology in Water Management and New Energy Economy explores the transformative role of blockchain in addressing two of the most pressing global challenges: efficient water resource management and the transition to a sustainable energy future. As climate change, population growth, and urbanization exert increasing pressure on natural resources, traditional systems for managing water and energy are proving inadequate. This book presents blockchain as a powerful enabler of transparency, decentralization, and trust in resource governance, offering innovative solutions that can reshape these critical sectors. The book is divided into two major sections. The first focuses on water management, covering how blockchain can enhance monitoring, prevent water theft, support real-time quality assessments, and enable equitable distribution through smart contracts. It examines case studies where decentralized ledger technology has improved water conservation, optimized irrigation systems in agriculture, and facilitated peer-to-peer water trading.The second section delves into the new energy economy, highlighting blockchain’s potential in supporting renewable energy integration, smart grids, decentralized energy markets, and carbon credit tracking. It discusses how blockchain can empower consumers to become "prosumers" — both producing and consuming energy — while maintaining secure and traceable transactions. Additionally, the book explores cross-cutting themes such as data privacy, regulatory challenges, scalability, and the socio-economic impact of blockchain adoption. Through real-world examples, technical insights, and forward-looking perspectives, this book provides a comprehensive guide for researchers, policy makers, engineers, and sustainability advocates seeking to understand and apply blockchain technology for a greener and more resource-efficient future.

In today’s data-driven era, the convergence of mathematics, computing, artificial intelligence, and blockchain is emerging as a significant area at the intersection of applied mathematics and computer science, particularly in decision-making. This book explores the applications of advanced mathematical models and computational algorithms to AI-driven strategies and blockchain technologies.It covers advanced linear algebra techniques, probability theory, optimization methods, game theory, cryptography, and statistical learning, providing deep mathematical insights into AI, blockchain, and data-driven decision-making. The book delves into matrix computations and eigenvalue problems relevant to deep learning, Bayesian inference for predictive modeling, and reinforcement learning for dynamic decision-making.Additionally, optimization methods such as convex programming and Lagrangian multipliers enhance resource allocation, while cryptographic protocols ensure the security of blockchain systems. By integrating these mathematical frameworks, this book provides researchers, professionals, and students with practical tools for addressing complex business challenges ranging from fraud detection to automated contract execution.

In today’s data-driven era, the convergence of mathematics, computing, artificial intelligence, and blockchain is emerging as a significant area at the intersection of applied mathematics and computer science, particularly in decision-making. This book explores the applications of advanced mathematical models and computational algorithms to AI-driven strategies and blockchain technologies.It covers advanced linear algebra techniques, probability theory, optimization methods, game theory, cryptography, and statistical learning, providing deep mathematical insights into AI, blockchain, and data-driven decision-making. The book delves into matrix computations and eigenvalue problems relevant to deep learning, Bayesian inference for predictive modeling, and reinforcement learning for dynamic decision-making.Additionally, optimization methods such as convex programming and Lagrangian multipliers enhance resource allocation, while cryptographic protocols ensure the security of blockchain systems. By integrating these mathematical frameworks, this book provides researchers, professionals, and students with practical tools for addressing complex business challenges ranging from fraud detection to automated contract execution.

Affective components are as important as cognitive components in tutoring assisted learning process. Feedback from tutors is essential in keeping students motivated. Affectivity and motivation are also significant in computer-based tutoring systems. However, several educational frameworks do not include this kind of interaction between students and tutoring systems. In those cases, the students learning interest and motivation to learn could be negatively affected, and student profits from the system could be impoverished. This is why tutoring systems need to provide direct and affective interaction with students; it can encourage them and increase the motivation to learn. This book introduces a broad range of topics in affective learning in computer-based systems. The text offers a deep conceptual background, covering relevant concepts of affectivity, feedback and motivational components in learning environments. It describes the design of a proposed model for providing affective feedback, the mathematical validation of the conceptual model and its implementation. Moreover, it presents an analysis of the impact of the affective feedback on student motivation to learn. Finally, the book offers research perspectives of the impact and applicability of the affective feedback in computer-based tutoring environments. Affective Feedback in Intelligent Tutoring Systems can be used by human tutors who want to include motivational and affective elements in the learning process, researchers in Human-Computer Interaction and Education and by software developers who want to develop learning systems using these elements.

This book looks at how quantum computing, neural networks, and next-generation communication systems can work together in new and exciting ways. The book presents details about cutting edge techniques, such as Tensor Networks and the n-Sci framework, that make 6G and 7G networks faster, safer, and able to grow. It has in-depth discussion on quantum-assisted channel estimates, energy-efficient designs, and ultra-reliable low-latency communication (URLLC), which makes it an important tool for wireless communication scholars and engineers. Some of the unique features are- in-depth case studies, real-life examples in smart cities and autonomous systems, and the use of graphs and tables to make complicated ideas easy to understand. Readers will learn a lot about how future communication will work and how quantum neural networks can be used to change the development of global wireless standards.