Results 111 to 120 of about 47,392 (266)
Earthquake Engineering &Structural Dynamics, EarlyView.ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen +1 more
wiley +1 more source
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 +2 more
wiley +1 more source
Spatio‐Temporal Dual‐Encoder Transformer for Short‐Term Regional Wind Power Forecasting
Energy Science &Engineering, EarlyView.ST‐DualFormer separates temporal and spatial encoding to model complex dependencies in regional wind power forecasting. The fused dual‐stream representation enables accurate short‐term regional forecasts from multi‐farm meteorological and historical power data. The method achieved 5.25% nMAE and 7.53% nRMSE for three‐day‐ahead forecasting on real‐world
Jianfeng Che +4 more
wiley +1 more source
Energy Science &Engineering, EarlyView.This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem +3 more
wiley +1 more source
Providing affective and supportive video feedback in a multidisciplinary unit during the pandemic
Journal of Learning Development in Higher Education, 2021 Abdul Razeed, Pat Norman, Kristna Gurney
doaj +1 more source
Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models
Journal of Forecasting, EarlyView.ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly +2 more
wiley +1 more source
Head &Neck, EarlyView.ABSTRACT Background Head and neck cancer (HNC) is a devastating diagnosis, with advanced‐stage disease leading to poorer outcomes. This qualitative study aimed to identify health system factors associated with stage of HNC diagnosis. Methods Qualitative semistructured interviews with HNC patients and clinicians were undertaken in two purposively ...
Grant Creaney +196 more
wiley +1 more source
Effects of sensorimotor delays and muscle force capacity limits on the performance of feedforward and feedback control in animals of different sizes. [PDF]
PLoS Comput BiolMohamed Thangal SN +3 more
europepmc +1 more source
A caveat regarding the unfolding argument: implications of plasticity. [PDF]
Neurosci ConsciousO'Reilly-Shah VN +2 more
europepmc +1 more source
How the visual brain can learn to parse images using a multiscale, incremental grouping process. [PDF]
PLoS Comput BiolMollard S, Bohte SM, Roelfsema PR.
europepmc +1 more source