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95,173 results on '"Li, Jian"'

226. Interchain-expanded extra-large-pore zeolites

Author

Gao, Zihao Rei, Yu, Huajian, Chen, Fei-Jian, Mayoral, Alvaro, Niu, Zijian, Niu, Ziwen, Li, Xintong, Deng, Hua, Márquez-Álvarez, Carlos, He, Hong, Xu, Shutao, Zhou, Yida, Xu, Jun, Xu, Hao, Fan, Wei, Balestra, Salvador R. G., Ma, Chao, Hao, Jiazheng, Li, Jian, Wu, Peng, Yu, Jihong, and Camblor, Miguel A.

Published
2024
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227. A three-dimensional liquid diode for soft, integrated permeable electronics

Author

Zhang, Binbin, Li, Jiyu, Zhou, Jingkun, Chow, Lung, Zhao, Guangyao, Huang, Ya, Ma, Zhiqiang, Zhang, Qiang, Yang, Yawen, Yiu, Chun Ki, Li, Jian, Chun, Fengjun, Huang, Xingcan, Gao, Yuyu, Wu, Pengcheng, Jia, Shengxin, Li, Hu, Li, Dengfeng, Liu, Yiming, Yao, Kuanming, Shi, Rui, Chen, Zhenlin, Khoo, Bee Luan, Yang, Weiqing, Wang, Feng, Zheng, Zijian, Wang, Zuankai, and Yu, Xinge

Published
2024
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249. Finite-nuclear-size effect in hydrogen-like ions with relativistic nuclear structure

Author

Xie, Hui Hui, Li, Jian, Jiao, Li Guang, and Ho, Yew Kam

Subjects
Nuclear Theory, Physics - Atomic Physics
Abstract

The finite-nuclear-size (FNS) effect has a large contribution to the atomic spectral properties especially for heavy nuclei. By adopting the microscopic nuclear charge density distributions obtained from the relativistic continuum Hartree-Bogoliubov (RCHB) theory, we systematically investigate the FNS corrections to atomic energy levels and bound-electron $g$ factors of hydrogen-like ions with nuclear charge up to $118$. The comparison of the present numerical calculations with the predictions from empirical nuclear charge models, the non-relativistic Skyrme-Hartree-Fock calculations, and the results based on experimental charge densities indicate that both the nuclear charge radius and the detailed shape of charge density distribution play important roles in determining the FNS corrections. The variation of FNS corrections to energy levels and $g$ factors with respect to the nuclear charge are investigated for the lowest several bound states of hydrogen-like ions. It is shown that they both increase by orders of magnitude with increasing the nuclear charge, while the ratio between them has a relatively weak dependence on the nuclear charge. The FNS corrections to the $s_{1/2}$ and $p_{1/2}$ bound state energies from the RCHB calculations are generally in good agreement with the analytical estimations by Shabaev [J. Phys. B, 26, 1103 (1993)] based on the homogeneously charged sphere nuclear model, with the discrepancy indicating the distinct contribution of microscopic nuclear structure to the FNS effects., Comment: 13 pages, 4 figures

Published
2023
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250. Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights

Author

Xu, Hao, Chen, Yuntian, Zeng, Zhenzhong, Li, Nina, Li, Jian, and Zhang, Dongxiao

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning, Physics - Geophysics
Abstract

Despite the remarkable strides made by AI-driven models in modern precipitation forecasting, these black-box models cannot inherently deepen the comprehension of underlying mechanisms. To address this limitation, we propose an AI-driven knowledge discovery framework known as genetic algorithm-geographic weighted regression (GA-GWR). Our approach seeks to unveil the explicit equations that govern the intricate relationship between precipitation patterns and terrain characteristics in regions marked by complex terrain. Through this AI-driven knowledge discovery, we uncover previously undisclosed explicit equations that shed light on the connection between terrain features and precipitation patterns. These equations demonstrate remarkable accuracy when applied to precipitation data, outperforming conventional empirical models. Notably, our research reveals that the parameters within these equations are dynamic, adapting to evolving climate patterns. Ultimately, the unveiled equations have practical applications, particularly in fine-scale downscaling for precipitation predictions using low-resolution future climate data. This capability offers invaluable insights into the anticipated changes in precipitation patterns across diverse terrains under future climate scenarios, which enhances our ability to address the challenges posed by contemporary climate science.

Published
2023

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