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Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework.
Sci Rep
December 2024
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, 03680, Kyiv, Ukraine.
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.
View Article and Find Full Text PDFProfessionals' Perspectives of Smart Stationary Bikes in Rehabilitation: Qualitative Study.
JMIR Rehabil Assist Technol
December 2024
Centre de recherche interdisciplinaire en réadaptation du Montréal métropolitain (CRIR) - Institut universitaire sur la réadaptation en déficience physique de Montréal (IURDPM) du Centre intégré universitaire de santé et de services sociaux du Centre-Sud-de-l'Île-de-Montréal (CCSMTL), Université de Montréal, Institut de Réadaptation Gingras Lindsay de Montréal, 6300 avenue de Darlington, Montréal, QC, H3S 2J4, Canada, 1 514-343-6111.
Background: Stationary bikes are used in numerous rehabilitation settings, with most offering limited functionalities and types of training. Smart technologies, such as artificial intelligence and robotics, bring new possibilities to achieve rehabilitation goals. However, it is important that these technologies meet the needs of users in order to improve their adoption in current practice.
View Article and Find Full Text PDFSurvey evidence on public support for AI safety oversight.
Sci Rep
December 2024
Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway.
A number of AI safety concerns are being increasingly discussed by experts, including misinformation, invasion of privacy, job displacement, and criminal misuse. Two exploratory studies conducted in Germany and Spain (combined n = 2864) provide evidence that the general public largely supports strict oversight over safety of commercial artificial intelligence research. Among the factors that are associated with preferences for strict oversight are age, anticipated job displacement, innovativeness, and risk, time and altruistic preferences.
View Article and Find Full Text PDFApplication of artificial intelligence based on state grid ESG platform in clean energy scheduling optimization.
Sci Rep
December 2024
School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization.
View Article and Find Full Text PDFEarly prediction of functional impairment at hospital discharge in patients with osteoporotic vertebral fracture: a machine learning approach.
Sci Rep
December 2024
Department of Pharmacoepidemiology, Graduate School of Medicine and PublicHealth, Kyoto University, Kyoto, Japan.
Although conservative treatment is commonly used for osteoporotic vertebral fracture (OVF), some patients experience functional disability following OVF. This study aimed to develop prediction models for new-onset functional impairment following admission for OVF using machine learning approaches and compare their performance. Our study consisted of patients aged 65 years or older admitted for OVF using a large hospital-based database between April 2014 and December 2021.
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