Results 201 to 210 of about 148,669 (285)

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

A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery. [PDF]

Int J Mol Sci
Lin H, Vogel H, Zhang H.
europepmc   +1 more source

Gaborlet‐guided sparse filtering: A novel intelligent method for lithology identification by vibration signals while drilling

Deep Underground Science and Engineering, EarlyView.
The flowchart illustrates rock specimen testing, vibration signal acquisition, and feature extraction with Gaborlet and sparse filtering for classification. Abstract Traditional lithology identification methods mainly rely on core sampling and well‐logging data.
Jian Hao, Danqi Zhang, Zhenqi Song, Jinrui Wang, Qi Wang, Heqing Liu   +5 more
wiley   +1 more source

Representation of Molecules by Sequences of Instructions. [PDF]

J Chem Inf Model
Thurnhofer-Hemsi K, García-Aguilar I, Fernández-Rodriguez JD, López-Rubio E.   +3 more
europepmc   +1 more source

The role of cardiac acoustic biomarkers in monitoring patients with heart failure: A systematic literature review

ESC Heart Failure, Volume 12, Issue 2, Page 980-997, April 2025.
Abstract Heart failure (HF) creates a considerable clinical, humanistic and economic burden on patients and caregivers as well as on healthcare systems. To attenuate the significant burden of HF, there is a need for enhanced management of patients with HF.
Javed Butler, Malcolm Brown, Philippe Prokocimer, Ashley C. Humphries, Sophie Pope, Olivia Wright, Jun Su, Osama Elnawasany, Bogdan Muresan   +8 more
wiley   +1 more source

Enhancing deep chemical reaction prediction with advanced chirality and fragment representation. [PDF]

Chem Commun (Camb)
Mastrolorito F, Ciriaco F, Nicolotti O, Grisoni F.   +3 more
europepmc   +1 more source

AI‐driven circular economy optimization in waste management: A review of current evidence

Environmental Progress &Sustainable Energy, EarlyView.
Abstract The integration of artificial intelligence (AI) and machine learning (ML) in waste management has the potential to significantly advance circular economy objectives by enhancing efficiency, reducing waste, and optimizing resource recovery. However, realising these benefits depends on addressing significant technical, economic, and systemic ...
David Bamidele Olawade, Ram Narayan Yadav, James O Ijiwade, Ojima Z. Wada   +3 more
wiley   +1 more source

From Analysis to Assessment: Machine Learning for Non-Target Screening of Pollutants Using Chromatography Coupled with (Ion Mobility) Mass Spectrometry. [PDF]

Toxics
Lin D, Wang Z, Liao J, Li N, Li X.
europepmc   +1 more source

Appraising Policies Through Social Multi‐Criteria Evaluation: Lessons From a Systematic Review of Applications

European Policy Analysis, EarlyView.
ABSTRACT In response to renewed interest in analytical tools that address complex policy problems and move beyond linear‐rational approaches in policy appraisal, Social Multi‐Criteria Evaluation (SMCE) integrates participatory approaches with multi‐criteria analysis (MCA), thereby incorporating specialised knowledge and diverse social values and ...
Egle Basyte Ferrari
wiley   +1 more source

Predicting disease outcomes from remote monitoring using machine learning: a systematic review. [PDF]

BMC Med Inform Decis Mak
Wolber J, Schuppert A, Mücke M, Sellin J.   +3 more
europepmc   +1 more source