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Yingli Li

As seen in: Nature, Wiley Online Library, PLOS ONE, Elsevier, Frontiers, MDPI, Dove Medical Press, Iranian Journal of Public Health, International Journal of Contemporary Hospitality Management, International Journal of Structural Integrity
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Articles

Interface Charge Induced Multifunctional Manipulations in Metals

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By Qiang Cao, Yingli Li, Senmiao Liu
| Wiley Online Library Verified
1 Introduction Metals are vital for modern information technology because they provide exceptional electrical conductivity and magnetic functionality. Achieving voltage control of both properties in metals holds transformative potential for next‑generation computing [1, 2], data storage [3, 4], and communication technologies [5, 6].
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Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate

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By Peng Zhou, Sitong Chen, Yingli Li
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Citation: Zhou P, Chen S, Li Y, Li Y (2026) Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate. PLoS One 21(4): e0348085. https://doi.org/10.1371/journal.pone.0348085 Editor: Shiva Ram Male, University of Hyderabad, INDIA Received: July 5, 2025; Accepted: April 11, 2026; Published: April 24, 2026 Copyright: © 2026 Zhou et al.
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LHSNet: large kernel hierarchical shrinkage network with deep adaptive denoising and modern wide-conv architecture for robust fault diagnosis

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By Yue Zhao, Yingli Li, Xi Chen, Hongqiao Yan, Jinhai Wang, Dechen Yao, Jianwei Yang, Fangwei Luo, Molin Su
| Frontiers
Abstract Traditional intelligent fault diagnosis techniques often struggle with signal distortion in environments characterized by strong multi-source noise, where fault signals are easily submerged and feature extraction becomes extremely unreliable. Furthermore, intelligent diagnosis is difficult to achieve using traditional denoising techniques that require extensive prior expert experience.
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