Results 41 to 50 of about 11,470 (264)

Bagging-GPR method for ship RCS extrapolation in frequency domain

Zhongguo Jianchuan Yanjiu, 2023
Objective To solve the difficulty of obtaining a radar cross section (RCS) using traditional simulation and measurement methods under high frequency, this study proposes a hybrid method which combines bootstrap aggregation (Bagging) and spectral mixture ...
Muyu HOU, Shuhong GONG, Donghai XIAO, Yanchun ZUO, Yu LIU   +4 more
doaj   +1 more source

Machine Learning for Green Solvents: Assessment, Selection and Substitution

Advanced Science, EarlyView.
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta, Janhavi Nistane, Abhishek Sose, Harikrishna Sahu, Rampi Ramprasad   +4 more
wiley   +1 more source

Data interpretation method of coal-rock interface detection based on ground penetrating radar

Gong-kuang zidonghua, 2019
For problems that existing coal-rock interface identification methods lacked research on characteristics of coal-rock seam and were unable to interpret data well, a data interpretation method of coal-rock interface detection was proposed combining with ...
MIAO Shuguang, LIU Xiaowen, LI Huaijiang, WANG Xin   +3 more
doaj   +1 more source

Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

Applied Sciences, 2021
Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process.
Mahdi S. Alajmi, Abdullah M. Almeshal
doaj   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

TECTB Variants Reveal Tectorial Membrane Vulnerability in Dominant Non‐Syndromic Hearing Loss

Advanced Science, EarlyView.
TECTB is a non‐collagenous protein of the tectorial membrane – an extracellular matrix of the cochlea. This study identifies dominant missense variants in TECTB linked to human hereditary deafness in two unrelated families. Genetically engineered mice homozygous for one of the variants are profoundly deaf, whereas heterozygous mice have normal hearing ...
Evan B. Hale, Barbara Vona, Richard J. Goodyear, Richard T. Osgood, Jun Shen, Sami S. Amr, Karen Mojica, Michaela A. H. Hofrichter, Jörg Schröder, Sophie Flandin, Paul Gratias, Ricardo Vera‐Monroy, Katherine Callahan, Kerry L. Gudlewski, Rolen Quadros, Masato Ohtsuka, JoAnn McGee, Edward J. Walsh, Cynthia C. Morton, Thomas Haaf, Channabasavaiah Gurumurthy, James E. Saunders, Guy P. Richardson, Artur A. Indzhykulian   +23 more
wiley   +1 more source

Tunnel geomechanical parameters prediction using Gaussian process regression

Machine Learning with Applications, 2021
The purpose of this study is to apply a modern intelligent method of Gaussian process regression (GPR) to predict the geological parameter of Rock Quality Designation (RQD) along the tunnel route. This method can also be used for any geological parameter
Arsalan Mahmoodzadeh, Mokhtar Mohammadi, Hawkar Hashim Ibrahim, Tarik Ahmed Rashid, Adil Hussain Mohammed Aldalwie, Hunar Farid Hama Ali, Ako Daraei   +6 more
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

IL‐31 levels correlate with pruritus in patients with cholestatic and metabolic liver diseases and is farnesoid X receptor responsive in NASH

Hepatology, EarlyView., 2022
IL‐31 levels correlate with pruritus in patients with cholestatic and metabolic liver diseases Abstract Background and Aims Pruritus is associated with multiple liver diseases, particularly those with cholestasis, but the mechanism remains incompletely understood.
Jun Xu, Ya Wang, Mina Khoshdeli, Matt Peach, Jen‐Chieh Chuang, Julie Lin, Wen‐Wei Tsai, Sangeetha Mahadevan, Wesley Minto, Lauri Diehl, Ruchi Gupta, Michael Trauner, Keyur Patel, Mazen Noureddin, Kris V. Kowdley, Aliya Gulamhusein, Christopher L. Bowlus, Ryan S. Huss, Robert P. Myers, Chuhan Chung, Andrew N. Billin   +20 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source