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Yingxin Zhao on Muck Rack

Yingxin Zhao

As seen in: Wiley Online Library, ScienceDirect, Frontiers, MDPI, Taylor & Francis Online, ACS Publications, Cell Press, Canadian Journal of Infectious Diseases & Medical Microbiology, The Journal of Immunology, Physician's Weekly and
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Articles

Climate-Driven Challenges for Production of Clean and Safe Potable Water: Concerns for Dissolved Organic Matter and Anthropogenic Pollution Click to copy article link Article link copied!

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By Hongyan Zhai, Peizhe Sun, Yingxin Zhao
| ACS Publications
Cite Jump to ExpandCollapse HighlightMarch 18, 2026 Climate-Driven Challenges for Production of Clean and Safe Potable Water: Concerns for Dissolved Organic Matter and Anthropogenic Pollution Click to copy article linkArticle link copied! Hongyan Zhai Hongyan Zhai School of Environmental Science and Engineering, Tianjin University, Weijin Road 92, Tianjin, 300072, China Peizhe Sun Peizhe Sun School of Environmental Science and Engineering, Tianjin University, Weijin Road 92, Tianjin, 300072,...
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Predictors of neurological recovery in spinal epidural abscess: the impact of radiographic compression, ASIA scores, and surgical delay

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By Wentao Zhao, Yongrui Yang, Wenkai Ruan, Jianlong Li, Rongpan Dang, Liang Xu, Lu Xing, Yingxin Zhao, Hongdong Tan, Chenggui Zhang, Huigang An
| Frontiers
Abstract Background: Controversy remains whether spinal epidural abscess (SEA) should be managed conservatively or by early surgical decompression. It is essential to understand how clinical and radiological parameters correlate with neurological deficits and how they predict recovery trajectories.
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Data-driven wear prediction method for complex engineering structures

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By Haibo Zhang, Qingyuan Zhao, Yingxin Zhao, Meng Zhao, Chuang Liu, Baiyang Zhao
| Frontiers
Abstract Predicting the evolution of wear in metallic structural components is vital for accurately estimating the lifetime of engineering equipment. However, this remains a significant challenge due to the prohibitively large number of cycles required for traditional experiments or simulations. To address this, we established a data-driven approach to predict metal wear evolution during dynamic mechanical interactions.
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