Results 121 to 130 of about 50,437 (269)

Artificial intelligence in enzyme catalysis: Emerging trends and applications in biocatalyst engineering

The Canadian Journal of Chemical Engineering, EarlyView.
Schematic representation of artificial intelligence approaches in enzyme catalysis, integrating bibliometric analysis, emerging research trends, and machine learning tools for enzyme design, prediction, and industrial biocatalytic applications. Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme ...
Misael Bessa Sales, Francisco Izaias da Silva Aires, Paulo Gonçalves de Sousa Junior, Calebe Da Rocha Silva, Emanuel Lino Rodrigues, Maria Cristiane Martins de Souza, José C. S. dos Santos   +6 more
wiley   +1 more source

DC bias optimization in intelligent DCO-OFDM Li-Fi systems using hybrid machine learning with hardware validation. [PDF]

Sci Rep
Abdelhakim E, Ragab DA, Abaza M, Moharam MH.   +3 more
europepmc   +1 more source

Hybrid machine learning and genetic algorithm approach for catalyst and process optimization in Fischer–Tropsch synthesis toward sustainable fuel production

The Canadian Journal of Chemical Engineering, EarlyView.
Graphical representation of a data‐driven framework for Fischer‐Tropsch synthesis (FTS) modelling and optimization. Abstract This study presents a data‐driven approach for predicting the relationships between catalyst design, process conditions, and product selectivity in Fischer–Tropsch synthesis (FTS).
Doaa M. Hassan, Ryan Gosselin, Nicolas Abatzoglou   +2 more
wiley   +1 more source

Analysis and comparison of two selected machine learning algorithms for enhancing wireless sensor network protocols. [PDF]

Sci Rep
Juwaied A, Jackowska-Strumillo L.
europepmc   +1 more source

Harnessing machine learning and optimization for informed chemical engineering decisions: A styrene reactor analysis

The Canadian Journal of Chemical Engineering, EarlyView.
This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin, Clémence Fauteux‐Lefebvre, Jules Thibault   +2 more
wiley   +1 more source

LHW-Net: An ensemble-based machine learning framework for brain tumor classification. [PDF]

PLoS One
Suryadevara T, Mahamkali N, Rafi M.
europepmc   +1 more source

Prediction of Acute Liver Injury Trajectory in Patients Following Acetaminophen Overdose: A Multibiomarker Machine Learning Proof‐of‐Concept Study

Clinical Pharmacology &Therapeutics, EarlyView.
Clinical translation of novel therapies can be hindered by heterogeneity‐driven sample size inflation in late‐stage trials. In acetaminophen‐induced liver injury (APAP DILI), many patients recover spontaneously, diluting investigational drug efficacy signals.
Chris Humphries, Alastair M. Kilpatrick, Kathleen M. Scullion, Rhona Aird, Lorraine Bruce, Maria Elena Candela, Tak Yung Man, Stuart J. Forbes, James W. Dear   +8 more
wiley   +1 more source

Degradable piezoelectric KNN/PLLA nanofibers for promoting osteogenesis and angiogenesis in bone regeneration. [PDF]

Biomed Eng Online
Zhu Q, Zhao Z, Liu A, Guan Y, Yang Y, Xia Y, Shu Y, Zhao Y.   +7 more
europepmc   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Intercomparison case study of data-driven reconstructions of a cloud-obscured Saharan dust plume in Europe. [PDF]

Sci Rep
Kanngießer F, Fiedler S.
europepmc   +1 more source