Results 161 to 170 of about 95,777 (258)

Achieving Explainable ENSO Prediction Using Small Data Training

Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.
Abstract Despite substantial progress over the past four decades, accurately predicting the spatiotemporal structure of the El Niño–Southern Oscillation (ENSO) remains a persistent challenge for dynamical models. While deep learning models have demonstrated improved prediction skills, their performances are constrained by biases in climate models used ...
Jie Feng, Tao Lian, Ting Liu, Dake Chen, Xichen Li, Youmin Tang, Yanqiu Gao, Xunshu Song   +7 more
wiley   +1 more source

Nine quick tips for trustworthy machine learning in the biomedical sciences. [PDF]

PLoS Comput Biol
Oneto L, Chicco D.
europepmc   +1 more source

Predicting Atmospheric Effective Sound Speed Using Synthetic Infrasound and Machine Learning

Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.
Abstract We introduce a machine learning (ML) model that reconstructs atmospheric conditions at the location of an explosive source using regionally recorded synthetic infrasound. A convolutional neural network (CNN) processes full waveforms and source‐receiver geometries to estimate the vertical profile of effective sound speed.
Alex Witsil, Stefan Awender, David Fee, Roger Waxler, Mark Leidig   +4 more
wiley   +1 more source

An AIoT-Based Home Monitoring System for Arteriovenous Fistula Surveillance in Hemodialysis Patients: Development, Evaluation, and Clinical Potential. [PDF]

J Multidiscip Healthc
Pan T.
europepmc   +1 more source

“Hearing” Wind Speed: Ground Wind Measurement Using Deep Learning From Surveillance Audio

Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.
Abstract This study presents a novel method for measuring ground wind speed (WS) using audio data collected from surveillance cameras. The continuous wavelet transform is employed to model wind sounds and capture the dynamic variations over time. A deep‐learning model integrating attention‐enhanced Convolutional Neural Network and Bidirectional Gated ...
Xing Wang, Kun Zhao, Ang Zhou, Hao Huang, Haiqin Chen   +4 more
wiley   +1 more source

Revolutionizing the way students learn photographic arts through experiential education using AI and AR systems. [PDF]

Sci Rep
Gupta SK, Alemran A, Basha US, Zakari AI, Kim S, Boddu RSK, Vohra SK.   +6 more
europepmc   +1 more source

A Neural Network Model of Equatorial Electric Field Structures in the Inner Magnetosphere

Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.
Abstract Enabled by state‐of‐the‐art electric field measurements from the Van Allen Probes and careful calibration of the high‐quality data, we developed the first machine‐learning based inner‐magnetosphere electric field model, which covers L = 2.5–6.0 within 20° ${}^{\circ}$ around the magnetic equator.
M. Hua, S. Tian, J. Bortnik, D. Ma, Y. Wang, L. R. Lyons, J. R. Wygant   +6 more
wiley   +1 more source

Real-Time Heartbeat Classification on Distributed Edge Devices: A Performance and Resource Utilization Study. [PDF]

Sensors (Basel)
Pramukantoro ES, Amron K, Kamila PA, Wardhani V.   +3 more
europepmc   +1 more source

Unveiling Phonon Contributions to Thermal Conductivity and the Applicability of the Wiedemann—Franz Law in Ruthenium and Tungsten Thin Films

Advanced Functional Materials, Volume 36, Issue 12, 9 February 2026.
Thermal transport in Ru and W thin films is studied using steady‐state thermoreflectance, ultrafast pump–probe spectroscopy, infrared‐visible spectroscopy, and computations. Significant Lorenz number deviations reveal strong phonon contributions, reaching 45% in Ru and 62% in W.
Md. Rafiqul Islam, Pravin Karna, Niraj Bhatt, Sandip Thakur, Helge Heinrich, Daniel M. Hirt, Saman Zare, Christopher Jezewski, Rinus T.P. Lee, Kandabara Tapily, John T. Gaskins, Colin D. Landon, Sean W. King, Ashutosh Giri, Patrick E. Hopkins   +14 more
wiley   +1 more source

Self-Sensing of Piezoelectric Micropumps: Gas Bubble Detection by Artificial Intelligence Methods on Limited Embedded Systems. [PDF]

Sensors (Basel)
Axelsson K, Sheikhsarraf M, Kutter C, Richter M.   +3 more
europepmc   +1 more source