Results 151 to 160 of about 95,729 (269)

Fabrication, Modification, and Applications of Functional Aerogels: A Review

Carbon and Hydrogen, EarlyView.
Advances in aerogel materials synthesis modification properties applications and future perspectives. ABSTRACT Aerogels, characterized by a highly porous three‐dimensional nanoscale structure, possess exceptional properties such as ultra‐low density, high specific surface area, and remarkably low thermal conductivity.
Jinshuo Zhang, Jianbing Wang, Yi Fang, Wangliang Li   +3 more
wiley   +1 more source

A Critical Review on Catalytic Regeneration of Amine Solutions for Energy‐Efficient CO2 Capture

Carbon and Hydrogen, EarlyView.
This review summarizes recent progress in acid‐catalyzed regeneration of CO2‐rich amine solutions. It highlights catalyst performance, mechanisms, and data‐driven design, aiming to bridge fundamental research with engineering practice for energy‐efficient carbon capture.
Qiyue Zhao, Chuanlei Liu, Peicheng Li, Yousheng Zhou, Benxian Shen, Hui Sun   +5 more
wiley   +1 more source

Using Machine Learning to Analyze the Predictors of Life Satisfaction: Focus on Lifestyle Attitudes and Psychological Factors. [PDF]

Int J Methods Psychiatr Res
Alptekin FB, Torlak E, Asik Ö, Karaaslan B, Turgal E, Burhan HS, Aytac HM, Guclu O.   +7 more
europepmc   +1 more source

Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions

Civil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.
Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley   +1 more source

28-day cement strength prediction via transformer-based feature extraction and XGBoost. [PDF]

PLoS One
Ju D, Ma X, Zhang R, Liu Z, Wang X, Huang B.   +5 more
europepmc   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Early Prediction of Hepatic Decompensation in Cirrhosis Using Optimised XGBoost Models at the Initial Outpatient Hepatology Visit. [PDF]

Liver Int
Grubert Van Iderstine M, Perez S, Jackson GS, Szun T, Stephanson C, Weisshaar M, Minuk GY, Faisal N.   +7 more
europepmc   +1 more source

Bridging Theory and Prediction: A Hybrid SEM and Machine Learning Approach to Optimize Lean Construction for Megaproject Sustainability in China

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Construction megaprojects, large‐scale, complex, and capital‐intensive, are particularly prone to inefficiencies, cost overruns, delays, and environmental degradation due to fragmented workflows, stakeholder misalignment, and resource intensity.
Abdelazim Ibrahim, Tarek Zayed, Zoubeir Lafhaj, Ahmed Maged, Ahmed Farouk Kineber, Jingchao Yang   +5 more
wiley   +1 more source

Forecasting Thailand's mobility trends using Feature Engineered XGBoost for pandemic crisis movement management. [PDF]

PLoS One
Siraphatwongkorn A, Methiyothin T, Onuean K, Chinnasarn K, Onuean A, Ahn I, Rasmequan S.   +6 more
europepmc   +1 more source

Advancing mine pillar design: Evaluating traditional methods and integrating AI for enhanced stability of pillars in the Great Dyke, Zimbabwe

Deep Underground Science and Engineering, EarlyView.
B1 is bord width 1, B2 is bord width 2, L is the pillar length, W is the pillar width, red color and letter A represent the pillars, and white color and number 1 represent excavated areas. Pstress is the average pillar stress; σv is the vertical component of the virgin stress, MPa; and e is the areal extraction ratio. e = B o B o + B P ${\rm{e}}=\frac{{
Tawanda Zvarivadza, Hendrik Grobler, Peter Apata Olubambi, Moshood Onifade, Manoj Khandelwal   +4 more
wiley   +1 more source