Results 101 to 110 of about 400 (136)
Brain Dynamics Underlying Reaching Movements Under Uncertainty
Human Brain Mapping, Volume 47, Issue 14, October 1, 2026.Combining computational modeling, fMRI, and dynamic causal modeling, we reveal two distinct functional subnetworks within the frontoparietal motor network during reaching: an uncertainty‐sensitive network comprising the intraparietal sulcus, caudal dorsal premotor cortex, and primary motor cortex, and a reprogramming network involving inferior parietal
Nan Liu +4 more
wiley +1 more source
Earth's Future, Volume 14, Issue 10, October 2026.Abstract By using a solar photovoltaic (PV) performance model and applying it to global data from 2000 to 2022, we demonstrated that although the net impact of aerosols on power generation was negative, this loss was almost entirely driven by soiling of panel surfaces and could be mitigated by developing an appropriate cleaning regime.
Xuesong Wang +3 more
wiley +1 more source
HydroDiffusion: Diffusion‐Based Probabilistic Streamflow Forecasting With a State Space Backbone
Water Resources Research, Volume 62, Issue 10, October 2026.Abstract Recent advances have introduced diffusion models for probabilistic streamflow forecasting, demonstrating strong early flood‐warning skill. However, current implementations rely on Long Short‐Term Memory (LSTM) backbones and discrete‐time diffusion formulations.
Yihan Wang +4 more
wiley +1 more source
Tropical Sources Dominate the Ocean Carbonyl Sulfide Budget
Global Biogeochemical Cycles, Volume 40, Issue 10, October 2026.Abstract Carbonyl sulfide (OCS) regulates stratospheric aerosol–climate interactions and helps to track terrestrial photosynthesis. Using OCS to infer drivers of stratospheric aerosol loading or biospheric productivity requires understanding its largest and most uncertain natural source—ocean emissions. Top‐down ocean OCS budget estimates inferred from
Wu Sun +4 more
wiley +1 more source
GeoHealth, Volume 10, Issue 10, October 2026.Abstract Atmospheric fine particulate matter (PM2.5) is one of the most important risk factors for various respiratory and cardiovascular diseases. We develop a two‐phase data‐driven framework that uses geostationary Aerosol Optical Depth (AOD) retrievals to predict hourly PM2.5 at 0.01° × 0.01° resolution and support exposure assessment across ...
K. Fan, A. Bloom, Z. Qu
wiley +1 more source
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.Abstract Physics‐based handcrafted features, such as P‐wave amplitude derived from early parts of active source ultrasonic waveforms, successfully predict shear stress evolution. This study investigates whether feature extraction can be automated by leveraging full active source ultrasonic waveforms recorded during a laboratory friction experiment.
Prabhav Borate +3 more
wiley +1 more source
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.Abstract Numerical wave models are widely used for wave forecasting and hindcast data set construction; however, their outputs frequently exhibit systematic biases stemming from uncertainties in wind forcing, physical parameterizations, and bathymetry. To address this issue, a deep learning–based bias correction approach is applied to significant wave ...
Jiageng Han +6 more
wiley +1 more source
Evaluation of Timing and Amplitude Biases in the Controls of Simulated Southern Ocean pCO2
Journal of Advances in Modeling Earth Systems, Volume 18, Issue 10, October 2026.Abstract The Southern Ocean is a major sink for atmospheric carbon dioxide and critical to the current and future carbon cycle. This net annual CO2 ${\text{CO}}_{2}$ flux reflects the balance of strong seasonal variability characterized by opposing periods of winter outgassing and summer uptake. Using a simple framework, we evaluate how model biases in
Seth M. Bushinsky +11 more
wiley +1 more source
Geographic Classification of Social Bees Using Automated Wing Pattern Analysis
Entomologia Experimentalis et Applicata, Volume 174, Issue 10, Page 1250-1267, October 2026.We present an end‐to‐end deep learning framework for automated geographic classification of honey bee populations based on wing morphology. A dataset of 1040 wing images from seven regions was analyzed using four CNN architectures with transfer learning.
Emine Dilara Çavuş, Cansu Özge Tozkar
wiley +1 more source
Journal of Oral Pathology &Medicine, Volume 55, Issue 9, Page 1073-1080, October 2026.ABSTRACT Background Actinic cheilitis (AC) is a potentially malignant oral disorder linked to lip squamous cell carcinoma (LSCC). Clinical diagnosis is hindered by lesion heterogeneity. While AI tools have shown promise in oral lesion detection and diagnosis, image segmentation remains underexplored. This study developed and externally validated a deep
Vitor Simbalista Teixeira Soares +16 more
wiley +1 more source