Results 191 to 200 of about 8,272,458 (291)

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

Development of a prediction model for postoperative urinary tract infection in ureteral stone patients based on automated machine learning models. [PDF]

Front Physiol
Li D, Fang T, Li S.
europepmc   +1 more source

Automated monitoring in vitro diagnostics: enhancing precision with machine learning and computer vision

In Vitro Diagnostics (IVD) is an application belonging to the fields of science and technology, able to extract from human biological sample reliable information which are related to the diagnosis of the state of health of an individual.
Giulia Tufo
core  

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Development of an automated machine learning-based risk prediction and decision support system for postoperative cubitus varus complicating pediatric lateral humeral condyle fracture. [PDF]

Front Surg
Wan Y, Deng K, Yuan Y, Zhang L, Li C, Cai H, Wang Y.   +6 more
europepmc   +1 more source

A Machine Vision‐Guided Microphysiological Platform With Automated Microfluidics Enables Longitudinal Biomarker Monitoring and Emulation of Translationally Relevant Exposure Scenarios

Advanced Science, EarlyView.
A pneumatically actuated multi‐tissue microphysiological system is integrated with AI‐based machine vision and automatic sampling and replenishment systems. The platform allows for the emulation of translationally relevant long‐term pharmacokinetic exposure scenarios for multiple weeks while enabling longitudinal monitoring of response biomarkers ...
Jibbe Keulen, Yi Zhong, Laura Dangel, Sonia Youhanna, Reza Zandi Shafagh, Jinhye Ryu, Domnica‐Gabriela Gurau, Mathias Haag, Thomas E. Mürdter, Yingxin Liang, Iro Beligiani, Nicole Ziegler, Sabine Willems, Nayere Taebnia, Wouter van der Wijngaart, Volker M. Lauschke   +15 more
wiley   +1 more source

Comparing manual vs. automated machine learning and deep learning models for predicting one-year mortality in elderly hip fracture patients. [PDF]

Front Med (Lausanne)
Shuchami A, Glebov M, Katsin M, Portnoy Y, Berkenstadt H, Orkin D, Lazebnik T.   +6 more
europepmc   +1 more source

Automating Chemical Reasoning in High‐Throughput Phase Identification With a Probabilistic, LLM‐Guided Framework

Advanced Science, EarlyView.
Autonomous laboratories can now synthesize materials faster than experts can interpret the resulting diffraction data. A probabilistic framework combines refinement‐fit metrics with large language model‐derived chemical reasoning to rank competing phase interpretations and flag those unsuitable for autonomous use.
Olympia Dartsi, Lauren N. Walters, Amalie E. Trewartha, Steven B. Torrisi, Amanda Volk, Liam Joyce, Gerbrand Ceder, Anubhav Jain   +7 more
wiley   +1 more source

Automated machine learning achieves accurate water quality prediction with reduced parameter requirements. [PDF]

Sci Rep
Campos D, Galvão V, de Rezende ML, Braga A, Bodini M, Aires URV, Yonaba R, Goliatt L.   +7 more
europepmc   +1 more source

Heterophily-informed Message Passing

Publisher Copyright: © 2025, Transactions on Machine Learning Research. All rights reserved.Graph neural networks (GNNs) are known to be vulnerable to oversmoothing due to their implicit homophily assumption.
Solin, Arno, Garg, Vikas, Wang, Haishan
core