Results 191 to 200 of about 82,540 (233)

Advances in vital‐sign prediction and early‐warning models for underground coal mine workers integrating environmental factors

Deep Underground Science and Engineering, EarlyView.
This review synthesizes advances in predicting miners' vital signs by integrating environmental monitoring (dust, temperature, and gas) with physiological data. It highlights multi‐source data fusion techniques and early‐warning models for enhanced occupational safety in underground coal mines.
Junji Zhu, Dawei Cui, Yingjie Liu, Yuntao Liang, Yue Zhang   +4 more
wiley   +1 more source

Model-independent searches of new physics in DARWIN with deep learning. [PDF]

Eur Phys J C Part Fields
XLZD Collaboration.
europepmc   +1 more source

Synergetic Effect of Silicate Addition on Modifying Zincate Solvation and Regulating ZnO Formation Revealed by In Situ Raman Observation during Zn Discharging

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A practical and synergetic interfacial engineering strategy via silicate addition to modulate the Zn passivation issue for developing rechargeable Zn batteries is introduced. Zn offers higher safety and lower material cost over Li, making it promising alternative anode material for secondary batteries and grid‐scale sustainable energy storage ...
Tanyanyu Wang, Masahiro Kunimoto, Kyoichiro Yokota, Takayuki Homma   +3 more
wiley   +1 more source

Temporary Seismic Array Installation in the Contursi Terme Hydrothermal System: A Step Toward Geothermal Assessment. [PDF]

Sensors (Basel)
Serlenga V, Napolitano F, Panebianco S, Mungiello G, Stabile TA, Giampaolo V, Blasone M, Balasco M, Perrone A, De Martino G, Lucente S, Martino L, Capuano P, Amoroso O.   +13 more
europepmc   +1 more source

Development and validation of a deep survival model to predict time to seizure from routine electroencephalography

Epilepsia, EarlyView.
Abstract Objective This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that analyzes routine electroencephalography (EEG) to predict individual seizure risk over time, comparing its performance to traditional clinical predictors such as interictal epileptiform discharges (IEDs).
Émile Lemoine, An Qi Xu, Mezen Jemel, Frédéric Lesage, Dang K. Nguyen, Elie Bou Assi   +5 more
wiley   +1 more source

TSA-Net: Multivariate Time Series Anomaly Detection Based on Two-Stage Temporal Attention. [PDF]

Sensors (Basel)
Wu H, Le W, Jia ZH, Zhao H, Zhang S, Zhang ZS.   +5 more
europepmc   +1 more source

Decoding epilepsy's molecular blueprint: Machine learning unravels transcriptomic subtypes and regulatory networks

Epilepsia, EarlyView.
Abstract Objective Drug‐resistant epilepsy (DRE) affects approximately one‐third of patients with epilepsy. The molecular heterogeneity underlying DRE remains poorly defined, largely due to limited access to resected brain tissue and substantial genetic diversity.
Yanping Weng, Yu Ma, Wanwan Hou, Haibo Li, Yuanfeng Zhou, Rui Zhao, Hao Li, Lian Chen, Yangyang Ma, Li Jin, Yi Wang, Yu An   +11 more
wiley   +1 more source

Robust crowd anomaly detection via hybrid ensemble learning for real-world surveillance. [PDF]

Sci Rep
Mabrouk D, Abdel-Fattah MA, Taha A.
europepmc   +1 more source

Epilepsy syndromes classification

Epilepsia Open, EarlyView.
Abstract Epilepsy syndromes are distinct electroclinical entities which have been recently defined by the International League Against Epilepsy Nosology and Definitions Task Force. Each syndrome is associated with “a characteristic cluster of clinical and EEG features, often supported by specific etiologic findings”.
Elaine C. Wirrell, Nicola Specchio, Rima Nabbout, Phillip L. Pearl, Kate Riney   +4 more
wiley   +1 more source