Results 71 to 80 of about 4,489 (189)

Application of artificial neural network and general machine learning modelling on CO2 adsorption in moisture equilibrated South African high and medium rank coals

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract This study investigates the effect of moisture on CO2 adsorption in South African coals using both experimental and machine learning approaches. Three coal samples (SL, TN, and EM) with varying ranks (RoVmr: 3.49%, 1.26%, and 0.64%, respectively) were collected from different regions of South Africa.
Kasturie Premlall, Lawrence Koech, Douw Faurie, Khanyisile Lepota   +3 more
wiley   +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

Efficient 5G MIMO antenna development using machine learning-driven parameter prediction

Franklin Open
This paper investigates machine learning algorithms (ML) to improve 5 g antenna. The proposed antenna consists of incorporates a combination of circular and semi-circular slot-loaded elements arranged in a 2 × 4 configuration and placed on a substrate ...
Bilal Aghoutane, Mohammed EL Ghzaoui, Hanan EL Faylali, Badre Bossoufi   +3 more
doaj   +1 more source

Barrier distribution extraction via Gaussian process regression [PDF]

EPJ Web of Conferences
This work presents a novel method for extracting potential barrier distributions from experimental fusion cross sections. We utilize a simple Gaussian process regression (GPR) framework to model the observed cross sections as a function of energy for ...
Godbey Kyle
doaj   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Machine Learning Unveils Isolated‐Surrounded Pt Motifs in High‐Entropy Alloys for Superior Low‐Temperature Ammonia Oxidation

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A physics‐informed machine learning approach successfully decodes the complex catalytic activity of high‐entropy alloys for ammonia oxidation. By revealing a synergistic mechanism involving lattice and electronic couplings, the study identifies a superior “isolated‐surrounded” platinum motif.
Shangfeng Jiang, Ting Tao, Kexiang Guo, Xinyu Fan, Xu Zhang, Zhen Zhou   +5 more
wiley   +1 more source

Pattern Recognition of Neurotransmitters: Complexity Reduction for Serotonin and Dopamine

Biosensors
In this work, we simultaneously detected and predicted the concentration levels of serotonin (SE) and dopamine (DA) neurotransmitters (NTs) for in vitro mixtures, with measurements obtained using conventional glassy carbon electrodes (CGCEs) and ...
Ibrahim Moubarak Nchouwat Ndumgouo, Emily Devoe, Silvana Andreescu, Stephanie Schuckers   +3 more
doaj   +1 more source

Surrogate‐Based Time‐Continuous Seismic Fragility Assessment of Corrosion‐Deteriorated Reinforced Concrete Bridges Under Climate Change

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Seismic fragility assessment of reinforced concrete (RC) bridges exposed to corrosive environments must reliably capture time‐dependent deterioration impacts. Climate change compounds this challenge through nonstationary temperature and humidity variations that accelerate bridge corrosion.
Yexiang Yan, Yazhou Xie
wiley   +1 more source

Model Calibration Method for Soft Sensors Using Adaptive Gaussian Process Regression

IEEE Access, 2019
The recursive Gaussian process regression (RGPR) is a popular calibrating method to make the developed soft sensor adapt to the new working condition. Most of existing RGPR models are on the assumption that hyperparameters in the covariance function are ...
Wei Guo, Tianhong Pan, Zhengming Li, Shan Chen   +3 more
doaj   +1 more source

Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

InfoScience, EarlyView.
Artificial intelligence can efferently accelerate the high‐throughput screening of battery materials, the analysis of multiphase mechanisms, and the precise prediction of capacity and cycle life. This review systematically summarizes the applications of machine learning (ML) in decoupling the complex structure‐activity relationships of lithium‐ion ...
Tao Wang, Yuhang Meng, Tianhao Chen, Xiangfen Jiang, Yoshio Bando, Jinjue Zeng, Xuebin Wang   +6 more
wiley   +1 more source