Results 191 to 200 of about 264,274 (268)

Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Deep Underground Science and Engineering, EarlyView.
How artificial intelligence (AI) and digital twin (DT) technologies are revolutionizing tunnel surveillance, offering proactive maintenance strategies and enhanced safety protocols. It explores AI's analytical power and DT's virtual replicas of infrastructure, emphasizing their role in optimizing maintenance and safety in tunnel management.
Mohammad Afrazi, Danial Jahed Armaghani, Hossein Afrazi, Hadi Fattahi, Pijush Samui   +4 more
wiley   +1 more source

Research progress and current status of dynamic wave propagation characteristics in rock mass: A review

Deep Underground Science and Engineering, EarlyView.
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu, Tao Li, Ruiqi Yuan, Yue Li, Bingni Wu, Bo Liu, Zhengding Deng, Galindo Rubén, Fausto Molina‐Gómez   +8 more
wiley   +1 more source

From surface to depth: Using deep learning to predict striatal fMRI reward signaling from EEG. [PDF]

Imaging Neurosci (Camb)
Herzog N, Vähäsarja L, Reinfeld P, Scherf N, Hofmann SM, Andreou C, Villringer A, Mulert C.   +7 more
europepmc   +1 more source

Whole tissue imaging of cellular boundaries at sub‐micron resolutions for deep learning cell segmentation: Applications in the analysis of epithelial bending of ectoderm

Developmental Dynamics, EarlyView.
Abstract Background To understand cellular morphology, biologists have relied on traditional optical microscopy of tissues combined with tissue clearing protocols to image structures deep within tissues. Unfortunately, these protocols often struggle to retain cell boundary markers, especially at high enough resolutions necessary for precise cell ...
Sam C. P. Norris, Jimmy K. Hu, Neil H. Shubin   +2 more
wiley   +1 more source

Deep Learning-Enabled Diabetic Retinopathy Screening: A Techno-Clinical Revolution or Just More Artificial Intelligence Hype? [PDF]

Diabetes Care
Dave D, Steinhubl SR, McQueen RB.
europepmc   +1 more source

Intensive Multidisciplinary Intervention for Young Children With ARFID: Clinical Outcomes and Parental Experiences From a Prospective Cohort Study

International Journal of Eating Disorders, EarlyView.
ABSTRACT Objective To assess clinical outcomes and parental experiences following an intensive multidisciplinary intervention (IMI) for children with Avoidant Restrictive Food Intake Disorder (ARFID). Method A prospective cohort of 28 children (aged 2–8 years) with severe eating difficulties, all meeting ARFID diagnostic criteria at baseline ...
Helena Holmäng, Katarzyna Brimo, Anna Ås, Lisa Dinkler, Rachel Bryant‐Waugh, Christopher Gillberg, Maria Råstam, Maj‐Britt Posserud   +7 more
wiley   +1 more source

Performance evaluation of deep learning models for overbite classification on cephalometric radiographs. [PDF]

Eur Oral Res
Kartbak SBA, Özel MB, Çakmak M.
europepmc   +1 more source

Machine learning‐based prediction of elevated N terminal pro brain natriuretic peptide among US general population

ESC Heart Failure, Volume 12, Issue 2, Page 859-868, April 2025.
Abstract Aims Natriuretic peptide‐based pre‐heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre‐heart failure, has not been well established.
Yuichiro Mori, Shingo Fukuma, Kyohei Yamaji, Atsushi Mizuno, Naoki Kondo, Kosuke Inoue   +5 more
wiley   +1 more source

DL-GapFilling: a novel deep learning framework for improved plant genome gap filling. [PDF]

Brief Bioinform
Chen Y, Wang Z, Wang G, Wang G.
europepmc   +1 more source