Results 131 to 140 of about 50,437 (269)

Uniaxial compressive strength prediction and ratio parameter optimization of titanium tailings composite backfill materials using intelligent hybrid models

Deep Underground Science and Engineering, EarlyView.
Based on the 90 datasets, ERT and four optimization algorithms were used to build four hybrid models to predict the UCS of the backfill body. The SMA‐ERT model was the most effective model, and it can reliably guide the design of the backfill ratio parameters. Abstract This study analyzed the feasibility of using titanium (Ti) tailings as a backfilling
Weijun Liu, Zida Liu, Zhixiang Liu
wiley   +1 more source

Leveraging Clinical Data for Early Heart Disease Prediction: A Machine Learning Approach With Interpretability Analysis. [PDF]

Biomed Eng Comput Biol
Qumsiyeh E, Al-Wirdian Q, Ersoz NS.
europepmc   +1 more source

Explainable hybrid stacking ensemble method for hard rock pillar stability prediction and engineering applications

Deep Underground Science and Engineering, EarlyView.
This research proposes an interpretable hybrid stacking ensemble framework, optimized by the Sparrow Search Algorithm, to enhance hard rock pillar stability prediction. By integrating six machine learning models—k‐nearest neighbors, support vector machines, random forests, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and Light Gradient ...
Ning Wang, Guoyan Zhao, Meng Wang, Taraka Pattela   +3 more
wiley   +1 more source

Piezo-herbal microneedle patches enable wireless endometrial regeneration and fertility recovery. [PDF]

J Nanobiotechnology
Zhao R, Ni S, Yang M, Gu Z, Zhu Y.
europepmc   +1 more source

Toward a Paradigm Shift in Low‐Temperature SCR Catalyst Design: Defect Engineering and Data‐Driven Integration

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This work systematically reviews the key factors influencing the performance of low‐temperature NH3‐SCR. The mechanism and challenges of defect engineering strategies, such as oxygen vacancies, heteroatom doping, crystal facet exposure, and surface reconstruction, in controlling both activity and selectivity were analyzed.
Rongrong Kan, Pinjing He, Fan Lü, Hua Zhang   +3 more
wiley   +1 more source

Observing the in-situ formation of the lead-free piezoceramic potassium sodium niobate (KNN) with SAED [PDF]

BIO Web of Conferences
Rambaran Mark, Jacobsson Daniel, Lehmann Sebastian, Dick Kimberly   +3 more
doaj   +1 more source

Hidden in the Pangenome? Machine Learning-Driven Discovery of Antimicrobial Potential in <i>Corynebacterium glutamicum</i>. [PDF]

Bioinform Biol Insights
Islam SI, Shahed K, Parvin MM, Sanjida S.   +3 more
europepmc   +1 more source

Nanophotonic Biosensors and the Paradigm of Trans‐Scale Autonomous Bio‐Integrated Diagnostics for Addressing Health Complexities

Electron, EarlyView.
Physics‐driven advances in optical nanobiosensors for rapid, miniaturized, and point‐of‐care diagnostics for next‐generation decentralized and personalized healthcare based on sensor intelligence. ABSTRACT Public health emergencies and the escalating burden of chronic diseases necessitate a paradigm shift from centralized laboratory testing to rapid ...
Vishal Chaudhary, Harsh Sable, Sonu Sonu, Lakshita Phor, Reddicherla Umapathi, Pradeep Bhadola   +5 more
wiley   +1 more source

Can epilepsy be predicted after the first febrile seizure? Insights from machine learning of postictal EEG

Epileptic Disorders, EarlyView.
Abstract Objective Febrile seizures (FS) are the most common seizures in childhood, yet identifying children at risk of developing epilepsy after the first FS remains challenging. We aimed to evaluate the prognostic potential of machine learning (ML) algorithms applied to post‐febrile seizure electroencephalography (EEG) recordings.
Boran Şekeroğlu, Hüseyin Öztoprak, Özlem Yayıcı Köken, Hatice Demir, Mehpare Sarı Yanartaş, Deniz Yılmaz, Burçin Şanlıdağ, Ayşegül Neşe Çıtak Kurt   +7 more
wiley   +1 more source

Identifying factors and predicting mental health issues in polypharmacy elderly using machine learning: a study based on the English longitudinal study of aging. [PDF]

Front Psychol
Wang H, Xie F, Wu Y, Zhou Y, Guo X, Yan S, Wei H, Lin S, Yang F, Liu C.   +9 more
europepmc   +1 more source