Results 161 to 170 of about 122,876 (196)

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source

An Uncertainty-Aware Bayesian Deep Learning Method for Automatic Identification and Capacitance Estimation of Compensation Capacitors. [PDF]

Sensors (Basel)
Wang T, Wang P.
europepmc   +1 more source

Random finite element analysis on ground subsidence caused by tunnel excavation in karst regions with spatial variable soil

Deep Underground Science and Engineering, EarlyView.
This study investigates ground subsidence during tunnel excavation in karst areas, highlighting the combined effects of karst cave proximity, cave size, and soil spatial variability. Findings suggest that shorter cave distances and larger cave sizes increase subsidence variability, and a modified Peck formula is proposed for more accurate subsidence ...
Zhenghong Su, Yong Fu, Miaomiao Sun, Kailin Ding, Ying Zhao   +4 more
wiley   +1 more source

Frontoparietal Hubs Leverage Probabilistic Representations and Integrated Uncertainty to Guide Cognitive Flexibility. [PDF]

J Neurosci
Leach SC, Hollow H, Jiang J, Hwang K.
europepmc   +1 more source

Real‐time lithology identification while drilling based on drill cuttings image analysis with ensemble learning

Deep Underground Science and Engineering, EarlyView.
A lithology identification while drilling method was developed, integrating an automated cuttings sampling system, a smart drilling rig, and an ensemble learning model. Underground trials achieved 97.42% accuracy in real‐time identification of cuttings lithology and composition, enhancing hazard management and supporting unmanned drilling technology in
Kun Li, Ting Ren, Ningping Yao, Jon Roberts, Haitao Song, Zhongbei Li, Xueqiu He, Liang Zhao   +7 more
wiley   +1 more source

Tonal Surprisal and Contextual Shifts Evoke Distinct Pupil Dilation During Dynamic Sound Sequences. [PDF]

Eur J Neurosci
van Haren JJG, Schröder JL, de Lange FP, Kotz SA, De Martino F.   +4 more
europepmc   +1 more source

Powerful yet challenging: mechanistic niche models for predicting invasive species potential distribution under climate change

Ecography, EarlyView.
Risk assessments of invasive species present one of the most challenging applications of species distribution models (SDMs) due to the fundamental issues of distributional disequilibrium, niche changes, and truncation. Invasive species often occupy only a fraction of their potential environmental and geographic ranges, as their spatiotemporal dynamics ...
Erola Fenollosa, Sean E. H. Pang, Natalie J. Briscoe, Antoine Guisan, Roberto Salguero-Gómez   +4 more
wiley   +1 more source

A network of Bayesian agents for reward prediction and noise tolerance. [PDF]

iScience
Gandolfi D, Tincani M, Boiani GM, Benatti L, Zanotti T, Pezzulo G, Pagnoni G, Puglisi FM, Mapelli J.   +8 more
europepmc   +1 more source

Timing is everything: Expert opinion on researching epilepsy rhythms by the ILAE Task Force on Chronobiology

Epilepsia, EarlyView.
Abstract Recurrent seizures, the hallmark of epilepsy, are influenced by rhythms operating over multiple timescales. Chronobiology is the study of biological timing that aims to explain temporal patterns of events like seizures. Fueled by recent advances in genetics, computational modeling, and device engineering, the chronobiology of epilepsy is now a
Maxime O. Baud, Christophe Bernard, Birgit Frauscher, Philippa J. Karoly, Vikram R. Rao   +4 more
wiley   +1 more source

Deep Mixture of Linear Mixed Models for Complex Longitudinal Data. [PDF]

Stat Med
Kock L, Klein N, Nott DJ.
europepmc   +1 more source