Results 151 to 160 of about 429,595 (233)

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

The neurobiology of human sequential signal prediction: Insights from language, music, and mathematics.

Neurosci Res
Nakai T, Daikoku T, Oseki Y.
europepmc   +1 more source

Depression and self-esteem as key factors related to academic motivation in university students. [PDF]

BMC Public Health
Majauskiene D, Istomina N, Valanciene D, Dadeliene R, Sidlauskiene A, Aukstikalnis T, Jamontaite IE, Strazdaite E, Zilinskiene R, Gintiliene M, Sarkauskiene A, Skurvydas A.   +11 more
europepmc   +1 more source

Model‐based fault diagnosis and fault tolerant control in safety‐critical chemical reactors: An experimental study

AIChE Journal, EarlyView.
Abstract This study investigates a fault‐tolerant control (FTC) approach for continuous stirred‐tank reactors (CSTR), emphasizing the importance of timely interventions to ensure operational safety under fault conditions. A systematic methodology combining residual‐based fault estimation and Dynamic Safety Margin (DSM) monitoring is developed to guide ...
Pu Du, Ritu Ranjan, Benjamin Wilhite, Costas Kravaris   +3 more
wiley   +1 more source

On computation of information entropy measures for certain fractal molecular architectures. [PDF]

Front Chem
Roy S, Noah Antony Daniel Renai P, Jyothish K.   +2 more
europepmc   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

<i>N</i> =1 Super Virasoro Tensor Categories. [PDF]

Commun Math Phys
Creutzig T, McRae R, Orosz Hunziker F, Yang J.   +3 more
europepmc   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

Non-isomorphic abelian varieties with the same arithmetic. [PDF]

R Soc Open Sci
Bell J.
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source