Results 141 to 150 of about 48,584 (226)

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

[Rare case of multifocal tuberculosis in Burkina Faso in a sickle cell SC patient with an atypical location: the sternoclavicular joint]. [PDF]

Med Trop Sante Int
Bayala YLT, Ayouba Tinni I, Kaboré F, Bonkoungou M, Zabsonré WJS, Ouedraogo DD.   +5 more
europepmc   +1 more source

Utilisation du sol et possibilités d'irrigation dans la région de N'Djamena [PDF]

, 1976
Tobias, Charles
core  

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Guide de pratique clinique pour la prise en charge de l’obésité chez l’enfant. [PDF]

CMAJ
Ball GDC, Merdad R, Birken CS, Cohen TR, Goodman B, Hadjiyannakis S, Hamilton J, Henderson M, Lammey J, Morrison KM, Moore SA, Mushquash AR, Patton I, Pearce N, Ramjist JK, Lebel TR, Timmons BW, Buchholz A, Cantwell J, Cooper J, Erdstein J, Fitzpatrick-Lewis D, Hatanaka D, Lindsay P, Sajwani T, Sebastianski M, Sherifali D, Pierre JS, Ali MU, Wijesundera J, Alberga AS, Ausman C, Baluyot TC, Burke E, Dadgostar K, Delacruz B, Dettmer E, Dymarski M, Esmaeilinezhad Z, Hale I, Harnois-Leblanc S, Ho J, Gehring ND, Kucera M, Langer JC, McPherson AC, Naji L, Oei K, O'Malley G, Rigsby AM, Wahi G, Zenlea IS, Johnston BC.   +52 more
europepmc   +1 more source

Utilisation par la canne à sucre des réserves hydriques d'un sol (premiers résultats) [PDF]

, 1970
Dancette, Claude, Ridders, J.R.
core  

Méthodes d'analyse et de caractérisation hydrodynamique des sols utilisées pour les études agropédologiques à Ouénarou, Maré et Pouembout, Nouvelle Calédonie [PDF]

, 1991
Brouwers, Marinus, Fortier, Michel
core   +1 more source

OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
OxSpred, an eXtreme‐Gradient‐Boosting‐‐based supervised learning model, accurately annotates oxidative stress in innate immune cells at the single‐cell level, providing interpretable embeddings with significant biological relevance. This innovative tool revolutionizes the understanding of innate immune cell functions during inflammation and enhances ...
Po‐Yuan Chen, Tai‐Ming Ko
wiley   +1 more source