Results 51 to 60 of about 75,165 (315)
AI in chemical engineering: From promise to practice
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 +4 more
wiley +1 more source
Post-polio syndrome (PPS) in individuals with polio longer than 15 years is characterized by weakness and/or muscle fatigue, deficit of deglutition and breath and periodic limb movements (PLM) during sleep.
Maria Auxiliadora de Paiva Araujo +5 more
doaj +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
The simulation of the fatigue damage of laminated composites under multi-axial and variable amplitude loadings has to deal with several new challenges and several methods of damage modelling.
Michael Hack +7 more
doaj
Extending the wind profile beyond the surface layer by combining physical and machine learning approaches [PDF]
Accurate estimation of the wind profile, especially in the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector.
B. Liu +9 more
doaj +1 more source
RAM: A Relativistic Adaptive Mesh Refinement Hydrodynamics Code [PDF]
We have developed a new computer code, RAM, to solve the conservative equations of special relativistic hydrodynamics (SRHD) using adaptive mesh refinement (AMR) on parallel computers.
Andrew I. MacFadyen +8 more
core +1 more source
Revealing Protein–Protein Interactions Using a Graph Theory‐Augmented Deep Learning Approach
This study presents a fast, cost‐efficient approach for classifying protein–protein interactions by integrating graph‐theory parametrization with deep learning (DL). Multiscale features extracted from graph‐encoded polarized‐light microscopy (PLM) images enable accurate prediction of binding strengths.
Bahar Dadfar +5 more
wiley +1 more source
Glutathione-dependent depalmitoylation of phospholemman by peroxiredoxin 6
Summary: Phospholemman (PLM) regulates the cardiac sodium pump: PLM phosphorylation activates the pump whereas PLM palmitoylation inhibits its activity.
Jacqueline Howie +11 more
doaj +1 more source
The spectral characteristics of a fiber Bragg grating (FBG) with a transversely inhomogeneous refractive index profile, differs con- siderably from that of a transversely uniform one.
Andreas Tünnermann +30 more
core +1 more source
PLM and early stages collaboration in interactive design, a case study in the glass industry [PDF]
Product design activity is traditionally presented as a succession of four to six stages. In the early stages of design, during the search for concepts, multi-disciplinary teams are working together, sometimes on the fringe of the digital design chain ...
SEGONDS, Frédéric, VERON, Philippe
core +3 more sources

