Results 61 to 70 of about 18,020 (267)

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Method for Ground Penetrating Radar Identification of Intercalated Gangue Layers during Complex Structure Coal Seam Tunneling [PDF]

Journal of Electromagnetic Engineering and Science
During complex coal seam tunneling, the interlayer gangue impacts mining equipment cutting operations. Identifying interlayer gangue positions is crucial for guiding the advanced control of the cutting arm swing speed.
Ying Tian, Chunzhi Li, Zihao Wang, Shuo Chen, Fuyan Lyu, Qiang Zhang, Denggao Hu   +6 more
doaj   +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Accuracy analysis in determining the location of underground objects using GPR involving lidar data [PDF]

Archives of Civil Engineering
Ground penetrating radar (GPR) is one of the most useful non-destructive techniques for locating underground objects. Advancements in this technology have facilitated the development of new sensors over the past decade.
Anna Lejzerowicz, Paweł Czernic, Magdalena Pilarska-Mazurek, Kamil Załęgowski, Jakub Górka, Krzysztof Bakuła   +5 more
doaj   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 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

GPR data noise attenuation based on the Shearlet transform

矿业科学学报, 2017
When using ground penetrating radar(GPR) to detect the underground target distribution, valid signal in receiving data tend to be susceptible to noise and the interference of air-ground waves, which will affect the accuracy of target recognition and ...
Zheng Jing, Yu Ke, Wang Pengyue, Jiang Shuqi, Teng Xingzhi   +4 more
doaj  

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Adeno‐associated virus serotype 2 capsid variants for improved liver‐directed gene therapy

Hepatology, EarlyView., 2022
Abstract Background and Aims Current liver‐directed gene therapies look for adeno‐associated virus (AAV) vectors with improved efficacy. With this background, capsid engineering is explored. Whereas shuffled capsid library screenings have resulted in potent liver targeting variants with one first vector in human clinical trials, modifying natural ...
Nadja Meumann, Marti Cabanes‐Creus, Moritz Ertelt, Renina Gale Navarro, Julie Lucifora, Qinggong Yuan, Karin Nien‐Huber, Ahmed Abdelrahman, Xuan‐Khang Vu, Liang Zhang, Ann‐Christin Franke, Christian Schmithals, Albrecht Piiper, Annabelle Vogt, Maria Gonzalez‐Carmona, Jochen T. Frueh, Evelyn Ullrich, Philip Meuleman, Steven R. Talbot, Margarete Odenthal, Michael Ott, Erhard Seifried, Clara T. Schoeder, Joachim Schwäble, Leszek Lisowski, Hildegard Büning   +25 more
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source