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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 PDFElucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations.
Nat Commun
December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
View Article and Find Full Text PDFPiecing together the narrative of #longcovid: an unsupervised deep learning of 1,354,889 X (formerly Twitter) posts from 2020 to 2023.
Front Public Health
December 2024
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Objective: To characterize the public conversations around long COVID, as expressed through X (formerly Twitter) posts from May 2020 to April 2023.
Methods: Using X as the data source, we extracted tweets containing #long-covid, #long_covid, or "long covid," posted from May 2020 to April 2023. We then conducted an unsupervised deep learning analysis using Bidirectional Encoder Representations from Transformers (BERT).
Mitigating saturation effects in rice nitrogen estimation using Dualex measurements and machine learning.
Front Plant Sci
December 2024
Institute of Biotechnology, Jiaxing Academy of Agricultural Science, Jiaxing, China.
Nitrogen is essential for rice growth and yield formation, but traditional methods for assessing nitrogen status are often labor-intensive and unreliable at high nitrogen levels due to saturation effects. This study evaluates the effectiveness of flavonoid content (Flav) and the Nitrogen Balance Index (NBI), measured using a Dualex sensor and combined with machine learning models, for precise nitrogen status estimation in rice. Field experiments involving 15 rice varieties under varying nitrogen application levels collected Dualex measurements of chlorophyll (Chl), Flav, and NBI from the top five leaves at key growth stages.
View Article and Find Full Text PDFExploring perceptions of medical students about interprofessional education (IPE): a qualitative study.
BMC Med Educ
December 2024
Nursing Department, Shuguang Hospital Affiliated to Shanghai University of TCM, Shanghai, China.
Background: Integration of clinical medicine and other subjects has laid more emphasis on the cultivation of high-quality medical talents, with the increasing demand for interprofessional education (IPE). IPE has been promoted by a number of universities to create and sustain authentic IPE activities, with which students can engage. Although IPE has achieved certain results, there are still many problems.
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