Results 61 to 70 of about 46,695 (300)

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

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

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

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

A 3-D Parallel Method of PSTD-Based Reverse Time Migration of GPR Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Ground penetrating radar (GPR) is a nondestructive technique with broad applications. Reverse time migration (RTM) of GPR data plays an important role in imaging complex structure.
Lingjia Huang, Bowen Ma, Qinghua Huang
doaj   +1 more source

GPR clutter amplitude processing to detect shallow geological targets [PDF]

, 2018
The analysis of clutter in A-scans produced by energy randomly scattered in some specific geological structures, provides information about changes in the shallow sedimentary geology.
Pérez Gracia, María de la Vega, Salinas Nadal, Victor, Santos Assunçao, Sonia   +2 more
core   +2 more sources

Decoding the Mechanism of Action of a Parasite TGFβ Antagonist Inspires the Creation of Cell‐Type‐Specific TGFβ Modulators

Advanced Science, EarlyView.
The mammalian TGFβ interacts with ubiquitously expressed TGFBR1 and TGFBR2, and current TGFβ‐targeting agents are non‐cell‐selective. The cooperative interaction of the modular parasite TGFβ antagonist with multiple host (co‐)receptors empowers the design of TGM chimeras and bispecific antibodies that activate or inhibit TGFβ signaling in a cell ...
Maarten van Dinther, Tristin Schwartze, Jiying Zhang, Kun Fan, Gerard van der Zon, Luke Power, Cynthia S. Hinck, Claire Ciancia, Ananya Mukundan, Roman Gonzalez‐Prieto, Peter van Veelen, Rick M. Maizels, Andrew P. Hinck, Peter ten Dijke   +13 more
wiley   +1 more source

3D-GPR-RM: A Method for Underground Pipeline Recognition Using 3-Dimensional GPR Images

Applied Sciences, 2023
Ground penetrating radar (GPR), as a non-destructive and rapid detection instrument, has been widely used for underground pipeline detection. However, as the interpretation of 3-dimensional GPR images is still manually performed, the process is inefficient.
Xu Bai, Yu Yang, Zhitao Wen, Shouming Wei, Jiayan Zhang, Jinlong Liu, Hongrui Li, Haoxiang Tian, Guanting Liu   +8 more
openaire   +2 more sources

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

Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time Warping

Remote Sensing
Ground-penetrating radar (GPR) is a rapid and non-destructive geophysical technique widely employed to detect and quantify subsurface structures and characteristics. Its capability for time lapse (TL) detection provides essential insights into subsurface
Jiahao Wen, Tianbao Huang, Xihong Cui, Yaling Zhang, Jinfeng Shi, Yanjia Jiang, Xiangjie Li, Li Guo   +7 more
doaj   +1 more source