Digital Technologies in Modeling and Management: Insights in Education and Industry explores the use of digital technologies in the modeling and control of complex systems in various fields, such as social networks, education, technical systems, and their protection and security. The book consists of two parts, with the first part focusing on modeling complex systems using digital technologies, while the second part deals with the digitalization of economic processes and their management. The book results from research conducted by leading universities' teaching staff and contains the results of many years of scientific experiments and theoretical conclusions. The book is for a wide range of readers, including the teaching staff of higher educational institutions, graduate students, students in computer science and modeling, and management technologies, including economics. It is also a valuable resource for IT professionals and business analysts interested in using digital technologies to model and control complex systems.

The complexity of managing and modeling various systems, from social networks to educational institutions and technical systems, has reached unprecedented levels. Traditional approaches struggle to keep pace with the dynamic nature of these challenges, leaving scholars, educators, and professionals searching for innovative solutions. This growing problem calls for a comprehensive guide to harnessing the potential of digital technologies for effective modeling and management. Educational Perspectives on Digital Technologies in Modeling and Management is a groundbreaking book that addresses the pressing need for modern solutions to keep up with the evolving challenges of modeling systems. This research book emerges from the collaborative efforts of teaching staff from leading universities, and years of dedicated scientific research and experimentation. It focuses on modeling complex systems using digital technologies, presenting fresh insights and innovative methodologies for academics, scholars, and aspiring professionals who are delving into the world of digitalization of economic processes and their management. The text caters to the specific needs of educators, IT professionals, and business analysts. Covering a wide array of topics, from information security to speaker verification and turbulent gas flow, this book serves as a valuable resource for a diverse audience seeking to excel in their respective fields.

Digital Technologies in Modeling and Management: Insights in Education and Industry explores the use of digital technologies in the modeling and control of complex systems in various fields, such as social networks, education, technical systems, and their protection and security. The book consists of two parts, with the first part focusing on modeling complex systems using digital technologies, while the second part deals with the digitalization of economic processes and their management. The book results from research conducted by leading universities' teaching staff and contains the results of many years of scientific experiments and theoretical conclusions. The book is for a wide range of readers, including the teaching staff of higher educational institutions, graduate students, students in computer science and modeling, and management technologies, including economics. It is also a valuable resource for IT professionals and business analysts interested in using digital technologies to model and control complex systems.

There are many reasons to be curious about the way people learn, and the past several decades have seen an explosion of research that has important implications for individual learning, schooling, workforce training, and policy. In 2000, How People Learn: Brain, Mind, Experience, and School: Expanded Edition was published and its influence has been wide and deep. The report summarized insights on the nature of learning in school-aged children; described principles for the design of effective learning environments; and provided examples of how that could be implemented in the classroom. Since then, researchers have continued to investigate the nature of learning and have generated new findings related to the neurological processes involved in learning, individual and cultural variability related to learning, and educational technologies. In addition to expanding scientific understanding of the mechanisms of learning and how the brain adapts throughout the lifespan, there have been important discoveries about influences on learning, particularly sociocultural factors and the structure of learning environments. How People Learn II: Learners, Contexts, and Cultures provides a much-needed update incorporating insights gained from this research over the past decade. The book expands on the foundation laid out in the 2000 report and takes an in-depth look at the constellation of influences that affect individual learning. How People Learn II will become an indispensable resource to understand learning throughout the lifespan for educators of students and adults.

The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics.

With widespread testing and standards-driven curriculum and accountability pressure in public schools, teachers are expected to be highly skilled practitioners. There is a pressing need for college faculty to prepare current and future teachers for the demands of modern classrooms and to address the academic readiness skills of their students to succeed in their programs. The Handbook of Research on Literacy and Digital Technology Integration in Teacher Education is an essential academic publication that provides comprehensive research on the influence of standards-driven education on educators and educator preparation as well as the applications of technology for the preparation of teachers. Featuring a wide range of topics such as academic success, professional development, and teacher education, this book is essential for academicians, educators, administrators, educational software developers, IT consultants, researchers, professionals, students, and curriculum designers.

In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban planning and beyond, is intrinsically linked to time and space. This synergy has given rise to an avalanche of spatiotemporal data, and the pressing question is how to manage, model, and query this voluminous information effectively. The existing approaches often fall short in addressing the intricacies and uncertainties that come with spatiotemporal data, leaving scholars struggling to unlock its full potential. Uncertain Spatiotemporal Data Management for the Semantic Web is the definitive solution to the challenges faced by academic scholars in the realm of spatiotemporal data. This book offers a visionary approach to an all-encompassing guide in modeling and querying spatiotemporal data using innovative technologies like XML and RDF. Through a meticulously crafted set of chapters, this book sheds light on the nuances of spatiotemporal data and also provides practical solutions that empower scholars to navigate the complexities of this domain effectively.