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Supervised and unsupervised machine learning for elemental changes evaluation of torrefied biochars
C Zhang, CB Felix, WH Chen, Y Zhang - Energy, 2024 - Elsevier
This study implements a comprehensive analysis of supervised and unsupervised learning
to evaluate elemental changes and investigate the merits of the machine learning method …
to evaluate elemental changes and investigate the merits of the machine learning method …
[HTML][HTML] A Novel Proposal in Wind Turbine Blade Failure Detection: An Integrated Approach to Energy Efficiency and Sustainability
J Abarca-Albores, DC Gutiérrez Cabrera… - Applied Sciences, 2024 - mdpi.com
This paper presents a novel methodology for detecting faults in wind turbine blades using
computational learning techniques. The study evaluates two models: the first employs …
computational learning techniques. The study evaluates two models: the first employs …
Structural evolution of particle configurations: Zero-temperature phases under increasing confinement
SWS Apolinario - The Journal of Chemical Physics, 2025 - pubs.aip.org
In this study, we investigate the phase behavior and structural organization of colloidal
particles in a two-dimensional (2D) system under isotropic harmonic confinement using …
particles in a two-dimensional (2D) system under isotropic harmonic confinement using …