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Hiroshi Komiyama's "Vision 2050" is a plan for paving a road to global sustainability. It lays out a path to a sustainable future for humanity that could realistically be achieved by 2050 through the application of science and technology. A prominent Japanese academic and leader in global sustainability, Komiyama draws upon realistic assumptions and solid scientific concepts to create a vision that makes the living standards enjoyed by developed countries today possible for all people by 2050. "Vision 2050" is built upon three fundamental principles – increased energy efficiency, recycling, and development of renewable energy sources – and the book argues for the technological potential of all three. Specifically, Komiyama envisions a three-fold increase in overall energy efficiency and a doubling of renewable energy resources by 2050. "Vision 2050: Roadmap for a Sustainable Earth" is written to address the concerned citizen as well as to inspire an exchange of ideas among experts, policy makers, industrial leaders, and the general public.
Contributed articles presented in the International Conference on Advances in the Theory of Ironmaking and Steelmaking; organized by the Dept. of Material Engineering, IISc., Bangalore.
The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science. The book Discusses basic as well as advance research in the field of prognostics Explores integration of data collection, fault detection, degradation modeling and reliability prediction in one volume Covers prognostics and health management (PHM) of engineering systems Discusses latest approaches in the field of prognostics based on machine learning The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science.