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Civil Engineering and Disaster Prevention focuses on the research of civil engineering, architecture and disaster prevention and control. These proceedings gather the most cutting-edge research and achievements, aiming to provide scholars and engineers with valuable research direction and engineering solutions. Subjects covered in the proceedings include: Civil Engineering Engineering Structure Architectural Materials Disaster Prevention and Control Building Electrical Engineering The works of these proceedings aim to promote the development of civil engineering and environment engineering. Thereby, fostering scientific information interchange between scholars from the top universities, research centers and high-tech enterprises working all around the world.
This book constitutes the refereed proceedings of the 19th International Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2022, held as part of the 23rd International Conference, HCI International 2022, which was held virtually in June/July 2022. The total of 1271 papers and 275 posters included in the HCII 2022 proceedings was carefully reviewed and selected from 5487 submissions. The EPCE 2022 proceedings covers subjects such as advances in applied cognitive psychology that underpin the theory, measurement and methodologies behind the development of human-machine systems. Cognitive Ergonomics describes advances in the design and development of user interfaces.
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Microbial ecology is pivotal in wastewater treatment, where microorganisms play a vital role in breaking down organic matter and ultimately reduce the levels of contaminants in treated water, making it safe for reuse in agriculture, industry, and other applications. The book, Microbial Nexus for Sustainable Wastewater Treatment: Resources, Efficiency, and Reuse, ventures into the dynamic world of microbial ecosystems, unveiling their pivotal role in reshaping wastewater treatment technologies. This book addresses novel microbial techniques related to sustainable, efficient technologies of wastewater treatment and wastewater reuse as well as obtaining high-quality effluents from treatment pla...
This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.
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