Zobrazeno 1 - 10
of 987
pro vyhledávání: '"CAI Tian"'
Publikováno v:
BMC Women's Health, Vol 23, Iss 1, Pp 1-9 (2023)
Abstract Background The accuracy of ultrasound in distinguishing benign from malignant adnexal masses is highly correlated with the experience of ultrasound physicians. In China, most of ultrasound differentiation is done by junior physicians. Purpos
Externí odkaz:
https://doaj.org/article/762702c272fc41048294080f575ae548
Publikováno v:
Gong-kuang zidonghua, Vol 49, Iss 11, Pp 84-91 (2023)
Due to factors such as image acquisition equipment and geological environment, rock CT images have low resolution and unclear details. However, existing image super-resolution reconstruction methods are prone to losing details when characterizing hig
Externí odkaz:
https://doaj.org/article/bf60e0c3f8ff453bb2a7b522e62a429f
The observation of magnetic ordering in atomically thin CrI$_3$ and Cr$_2$Ge$_2$Te$_6$ monolayers has aroused intense interest in condensed matter physics and material science. Studies of van de Waals two-dimensional (2D) magnetic materials are of bo
Externí odkaz:
http://arxiv.org/abs/2301.13342
Autor:
Zhang, Wenhua, Zhou, Chunjie, Zhou, Fenglan, Zalán, Zsolt, Shi, Hui, Kan, Jianquan, Cai, Tian, Chen, Kewei
Publikováno v:
In Food Chemistry 15 September 2024 452
Publikováno v:
In Food Chemistry 1 September 2024 451
Autor:
Liang, Lixin, Cai, Tian, Li, Xiaojia, An, Jianhong, Yu, Sen, Zhang, Yang, Guo, Fengjie, Wei, Fang, He, Jie, Xie, Keping, Jiang, Tingting
Publikováno v:
In Genes & Diseases September 2024 11(5)
Autor:
Zhou, Fenglan, Zhou, Chunjie, Zhang, Wenhua, Zalán, Zsolt, Shi, Hui, Kan, Jianquan, Chen, Kewei, Cai, Tian
Publikováno v:
In Food Chemistry 1 February 2025 464 Part 3
Autor:
Cai, Tian1 (AUTHOR), Li, Gang1 (AUTHOR) ligang@lntu.edu.cn, Yang, Qinghe1 (AUTHOR), Zou, Junpeng1 (AUTHOR)
Publikováno v:
Scientific Reports. 7/19/2024, Vol. 14 Issue 1, p1-14. 14p.
Publikováno v:
In Environmental Research 15 December 2024 263 Part 3
Autor:
Cai, Tian, Xie, Li, Chen, Muge, Liu, Yang, He, Di, Zhang, Shuo, Mura, Cameron, Bourne, Philip E., Xie, Lei
Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from
Externí odkaz:
http://arxiv.org/abs/2111.14283