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Science LTD examines the evolving relationship between scientific knowledge, technology, and society in an era where deep tech companies are increasingly driven by discoveries rather than market demand.
Furthermore, the book provides real-world examples of successful project implementations and offers expanded coverage on tokenization, highlighting its relevance across diverse industries and governmental sectors.
Data Science in Engineering, Volume 11: Proceedings of the 43rd IMAC, A Conference and Exposition on Structural Dynamics, 2025, the eleventh volume of twelve from the Conference brings together contributions to this important area of research and engineering.
A case study of the research, design, implementation and commercial distribution of PACE, a decision support system, is provided by this work. The... Læs mere
Covers the field of computing, from its technological status through its social, economic and political significance. This book contains more than 30... Læs mere
This book treats, in five sections, the existing and future surveys, their digitization and their impact on astronomy. It is designed to serve as a reference for people in the field and for those who wish to engage in using or producing sky surveys.
Storing, managing and giving access to data collected by astronomical observatories is a challenge of modern astronomy. This book presents a selection of... Læs mere
This volume aims to present recent advances in database technology from the viewpoint of the novel database paradigms proposed in the last decade. It focuses on the... Læs mere
Discussing some key scientific and technological developments in high performance computing, identifying significant trends, and defining desirable research objectives, this text covers general concepts and emerging systems, software technology, algorithms and applications.
Test-driven data analysis is the synthesis of ideas from test-driven development of software to data-intensive work including data science, data analysis, and data engineering. It is a methodology for improving the quality of data and of analytical pipelines and processes.
This book offers a comprehensive discussion of the growing importance of ethical considerations in algorithmic systems to introduce practical frameworks and tools for identifying ethical risks, ensuring fair outcomes, and maintaining transparency in algorithmic processes.