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Xiaowei Yin on Muck Rack

Xiaowei Yin

As seen in: Nature, MDPI, Chemistry World, Mechanics & Industry
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Articles

Fungal infections: Classification performance and detectability with machine learning models

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By Xiaowei Yin, Shihui Jiang
| Nature Verified
Abstract This study explores the use of AI-driven machine learning algorithms for the early detection of fungal diseases in tomato plants, a method that enhances diagnostic accuracy and enables more effective crop protection. The research was conducted in 2023 in Shandong Province, China, using two tomato cultivars – Dongfeng-1 and Gypsy.
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PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency

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By Huaizhi Qu, Xiaowei Yin, He Mirabel Sun
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Experimental Study on the Effect of Shot Peening and Re-Shot Peening on the Residual Stress Distribution and Fatigue Life of 20CrMnTi

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By Wenxue Qian, Yi Wang, Kexin Liu, Xiaowei Yin
| MDPI
Abstract : As a surface peening technique, shot peening introduces residual compressive stresses to the surface of the part, which effectively increases the fatigue life of the structure and material. However, when structures are subjected to alternating loads, this can lead to stress relaxation on the material surface, weakening the effectiveness of the shot peening process. In addition, reasonable shot peening parameters are essential.
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