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Jialin Li

As seen in: British Ecological Society (Wiley), Nature, Wiley Online Library, British Pharmacological Society, Cambridge University Press & Assessment, PLOS ONE, Eyes on Earth, Frontiers, Frontiers in Marine Science, Journal of International Money and Finance and

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Articles

Timing and dose of acupuncture as adjuncts to assisted reproduction: a meta-analysis and model-based network meta-analysis

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By Jiale Wei, YUE WANG, Kailin Zhang, Zheng Shen, Jialin Li, Jian Wang
| Frontiers
Abstract Objective: Acupuncture is widely used as an adjunct to assisted reproductive technology (ART), but its effects and the influence of treatment timing, modality, and dose remain uncertain. This study evaluated acupuncture-related interventions in ART and explored potential timing- and dose-response patterns.
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DeepH-pack: a general-purpose neural network package for deep-learning electronic structure calculations

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By Yanzhen Wang, Boheng Zhao, Xiaoxun Gong, Yuxiang Wang, Zechen Tang, Zixu Wang, Zilong Yuan, Jialin Li, Minghui Sun, Zezhou Chen, Baochun Wu, Yuhang Yu, Felipe H. da Jornada, Wenhui Duan, Yong Xu, Honggeng Tao
| Nature Verified
Abstract In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations.
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Improving decision reliability in transport safety engineering through a machine learning-based model

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By Jialin Li, ZIHAN ZHANG, Jian Le, Aaron Zhou, Xushuai Zhang, Jiachen Yao, Chang Yuan, Hanrui Feng, Yuchen Huang, Faan Chen, Ruikang Yan, Xingze Liu, Nanbo Aaron Zhang
| Nature Verified
Abstract Ensuring defensible policy inferences and robust decision reliability is fundamental for multi-criteria decision-making activities, particularly in transport safety engineering. This study developed a hybrid preference function-nested and machine learning-embedded decision model, i.e., EXPROM II–K-means with a linear discriminant analysis, to provide a robust support system for policy setting and decision making.
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