Results 181 to 190 of about 24,126 (306)

Feasibility of Wind‐Powered Green Hydrogen Production via a Hybrid Graph Neural Network‐Transformer Forecasting Model

Energy Science &Engineering, EarlyView.
ABSTRACT Accurate long‐term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno‐economic feasibility of wind‐powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes
Iman Baghaei, Mojtaba Mirhosseini, Alireza Zahedi   +2 more
wiley   +1 more source

Decentralized Federated Learning for Wind Turbine Bearing Prognostics Under Data Scarcity and Statistical Heterogeneity

Energy Science &Engineering, EarlyView.
This paper proposes a decentralized peer‐to‐peer federated learning framework for wind turbine bearing remaining useful life prediction, introducing a virtual client paradigm in which statistical health indicators serve as independent feature‐level clients—enabling privacy‐preserving collaborative prognostics from a single physical asset under ...
Jihene Sidhom, Lotfi Saidi, Eric Bechhoefer   +2 more
wiley   +1 more source

A Comprehensive Review of AI‐Powered Energy Systems

Energy Science &Engineering, EarlyView.
The role of Artificial Intelligence (AI) in developing next‐generation energy systems is getting more day by day. Therefore, incorporating AI enables real‐time decision‐making and advanced grid management, which are essential for optimizing the use of intermittent renewable sources like wind and solar power.
Armin Razmjoo, Pedram Asef, Masoumeh Javadi, Mousa Marzband, Shady S. Refaat, Muhyaddin Rawa   +5 more
wiley   +1 more source

Clustering single-cell multi-omics data via weighted distance penalty and adaptive consistent graph regularization. [PDF]

PLoS Comput Biol
Zhang W, Yu Y, Zheng X, Shen J, Li Y.
europepmc   +1 more source

Inter‐Material Transfer Learning for Accelerated Nanofluid Heat Transfer Prediction: A Machine Learning Approach for Energy Systems

Energy Science &Engineering, EarlyView.
This study presents an inter‐material transfer learning framework for nanofluid heat transfer prediction in energy systems. By leveraging knowledge from Al2O3‐water data, the model accurately predicts hybrid Al2O3‐TiO2 nanofluid performance with only 20 simulations, achieving R2 = 0.985 and reducing computational requirements by 78. ABSTRACT This paper
Soumaya Hadj Salah, Aymen Ochi, Sonia Bezi   +2 more
wiley   +1 more source

Identifying retinopathy in optical coherence tomography images with less labeled data via contrastive graph regularization. [PDF]

Biomed Opt Express
Hu S, Tang H, Luo Y.
europepmc   +1 more source

Brain–Computer Interfaces: The Dawn of a New Era in Disease Treatment

Exploration, EarlyView.
This study investigates the potential of brain–computer interface (BCI) technology in treating neuropsychiatric disorders, such as movement and communication barriers. Our review examines the history, signal paradigms, and diverse applications of BCI while also discussing ongoing research into novel materials and emerging technologies that offer ...
Yuqi Feng, Wangzheqi Zhang, Jun Chen, Huang Wu, Lei Wu, Yanhao Qiu, Xiaoming Deng, Chenglong Zhu, Yisheng Chen, Zhijie Zhao, Changli Wang, Xiaomin Zhang   +11 more
wiley   +1 more source

Intraday Functional PCA Forecasting of Cryptocurrency Returns

Journal of Forecasting, EarlyView.
ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley   +1 more source

Progress of metabolomics‐centric multi‐omics research in medicine

iMetaOmics, EarlyView.
The graphical abstract illustrates a holistic roadmap for metabolomics‐centric multi‐omics integration in medical research. The upper panel depicts the technological transition from traditional bulk analysis to high‐resolution single‐cell and spatial methodologies, specifically addressing inherent challenges such as molecular complexity and dynamic ...
Ziyi Wang, Shiwen Deng, Xiaoxu Wang, Xiao Chang, Na Guo, Peng Chen, Hongjun Yang   +6 more
wiley   +1 more source

On Regularization for Explaining Graph Neural Networks: An Information Theory Perspective

This work studies the explainability of graph neural networks (GNNs), which is important for the credibility of GNNs in practical usage. Existing mask-based explanation methods mostly follow the two-phase paradigm to interpret a prediction: <italic>
Chu, Xiaowen, Liang, Yuxuan, Zhang, Guibin, Duan, Yifan, Zimmermann, Roger, Wu, Yuankai, Du, Wenjie, Fang, Junfeng, Wang, Kun   +8 more
core   +1 more source