Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Tianfeng Shi"'
Publikováno v:
Frontiers in Psychology, Vol 12 (2021)
A preference for having a son has existed among Chinese parents for centuries due to, in part, sons having to provide financial support to elderly parents, while married daughters do not have this responsibility under Confucianism. Thus, this study e
Externí odkaz:
https://doaj.org/article/3927db22c07145f5ba8fd7a586e0e8e6
Publikováno v:
AMA Marketing & Public Policy Academic Conference Proceedings. 2023, Vol. 33, p152-155. 4p.
Autor:
Tianfeng Shi, Kunkun Liu, Yueyou Peng, Weibin Dai, Donglian Du, Xiaoqiong Li, Tingting Liu, Ningning Song, Yanfeng Meng
Publikováno v:
Cardiovascular Drugs and Therapy.
Autor:
Li Jiao, Tianfeng Shi
Publikováno v:
2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM).
Publikováno v:
2022 International Conference on Applied Physics and Computing (ICAPC).
Autor:
Kunkun Liu, Weibin Dai, Yueyou Peng, Tianfeng Shi, Tingting Liu, Ningning Song, Yueluan Jiang, Yunhui Kang, Yanfeng Meng
Background: Real-time MRI provides radiation-free alternative to X-ray guided interventions, enables superb tissue imaging without administration of contrast agents. In this study, we proposed an 3T MRI system, and evaluated the image quality of real
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d81a9014adf820ad6b967b74b6436b51
https://doi.org/10.21203/rs.3.rs-1956079/v1
https://doi.org/10.21203/rs.3.rs-1956079/v1
Publikováno v:
Journal of Cleaner Production. 377:134185
Publikováno v:
Journal of environmental management. 302
Extending product lifespan has recently been recognized as an important strategy to achieve sustainable development. A substantial corpus of literature explores product lifespan from the perspective of product design or manufacturing practices, but t
Autor:
Tianfeng Shi, Jing Peng
Publikováno v:
ICMLC
The genotype imputation is an important topic in the field of genomics. Many genome analyses require data without missing values, which requires to impute the missing data. In recent years, deep learning has become hot, and it is more suitable for te