Results 131 to 140 of about 331,075 (300)

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

Genre: generative multi-turn question answering with contrastive learning for entity–relation extraction

Complex & Intelligent Systems
Extractive approaches have been the mainstream paradigm for identifying overlapping entity–relation extraction. However, limited by their inherently methodological flaws, which hardly deal with three issues: hierarchical dependent entity–relations ...
Lulu Wang, Kai Yu, Aishan Wumaier, Peng Zhang, Tuergen Yibulayin, Xi Wu, Jibing Gong, Maihemuti Maimaiti   +7 more
doaj   +1 more source

On Normal Relations

On Normal Relations
Article
openaire  

A new drag and lift correlation for spherocylinders from fully resolved Immersed Boundary Method

AIChE Journal, EarlyView.
Abstract Many industrial processes deal with non‐spherical particles, e.g., mineral mining and biomass conversion. It is crucial to understand the particles' hydrodynamics to control and optimize these processes. To extend the current state‐of‐the‐art from arrays of spherical particles to spherocylindrical particles, we performed extensive particle ...
A. H. Huijgen, Y. Sterkenburgh, Y. Tang, N. G. Deen, M. W. Baltussen   +4 more
wiley   +1 more source

Resolving passive heat transfer and phase‐change heat in cryogenic CO2 deposition (Desublimation) from CO2/N2 mixtures

AIChE Journal, EarlyView.
Abstract In cryogenic CO2 desublimation systems where phase change dominates both heat transfer and separation, conventional lumped thermal‐resistance treatments embed interfacial latent heat into an overall heat‐transfer coefficient, obscuring how phase‐change heat is partitioned between the gas phase and the coolant and limiting diagnostic insight ...
Shengwen Xiao, Hiroshi Machida, Koyo Norinaga   +2 more
wiley   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Automation of Surgical Workflow Recognition: Unveiling the Surgical Instrument Kinematics that Underly Robot‐Assisted Prostatectomy Procedures

Advanced Intelligent Discovery, EarlyView.
Automated procedural analysis is recognized as one of the major game changers for robotic surgery. Meaning digital analysis needs to replace the manual assessments that set todays standard. Mechanical robotic‐instrument tracking enables the derivation of quantitative kinematic metrics that support behavior‐based workflow segmentation into distinct ...
Kateryna Pirkovets, Matthias N. van Oosterom, Vera A. Ottens, Henk G. van der Poel, Fijs W. B. van Leeuwen   +4 more
wiley   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Model-Independent Determinations of B -> D l nu , D* l nu Form Factors

, 1995
We present nonperturbative, model-independent parametrizations of the individual QCD form factors relevant to B -> D* l nu and B -> D l nu decays. These results follow from dispersion relations and analyticity, without recourse to heavy quark symmetry ...
Boyd, C. Glenn, Grinstein, Benjamin, Lebed, Richard F.   +2 more
core  

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source