Zobrazeno 1 - 10
of 720
pro vyhledávání: '"Dan, Xue"'
Autor:
Zhi-Lin Yuan, Jing Ren, Meng-Lin Huang, Ya-Fei Qi, Xin Gao, Yi-Ying Sun, Yong-Lan He, Lan Zhu, Hua-Dan Xue
Publikováno v:
Insights into Imaging, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract Objectives To develop an innovative magnetic resonance imaging (MRI)-based PUMCH (Peking Union Medical College Hospital) classification system aimed at standardising the diagnosis of congenital cervical malformations (CCMs) by identifying th
Externí odkaz:
https://doaj.org/article/c4e05791b2974fd295ccfd2ce154621c
Autor:
Ting Zhou, Lan lin, Yawen Zhan, Ziyao Zhang, Ying Jiang, Mi Wu, Dan Xue, Limin Chen, Xiufang Weng, Zhenghui Huang
Publikováno v:
Molecular Medicine, Vol 30, Iss 1, Pp 1-13 (2024)
Abstract Background The development of pulmonary fibrosis involves a cascade of events, in which inflammation mediated by immune cells plays a pivotal role. Chemotherapeutic drugs have been shown to have dual effects on fibrosis, with bleomycin exace
Externí odkaz:
https://doaj.org/article/6c62c71c6bcf457c9f21e07e3fa4622c
Publikováno v:
European Journal of Remote Sensing, Vol 57, Iss 1 (2024)
ABSTRACTAiming the problems that the classification performance of hyperspectral images in existing classification algorithms is highly dependent on spatial-spectral information and that detailed features are ignored in single convolutional channel f
Externí odkaz:
https://doaj.org/article/375a6a388c3c42a9bc1c3c4b46c0cb38
Publikováno v:
IEEE Access, Vol 12, Pp 38542-38550 (2024)
Multiscale Dense Network (MSDN) is a deep convolutional neural network method proposed to solve the loss of fine features in hyperspectral remote sensing images. Multiscale Dense Network (MSDN), which makes full use of the different scale information
Externí odkaz:
https://doaj.org/article/13abe45bb2d945749579e369797006b6
Autor:
Dan-Xue Zheng, Yi-Xing Chen, Jing Sun, Yong Hu, Ping Yang, Yang Zhang, Xue-Zhang Duan, Zhao-Chong Zeng
Publikováno v:
Clinical and Translational Radiation Oncology, Vol 46, Iss , Pp 100767- (2024)
Centrally located hepatocellular carcinoma (HCC) is difficult to be radically resected due to its special location close to major hepatic vessels. Thus, we aimed to assess whether stereotactic body radiation therapy (SBRT) can be an effective and saf
Externí odkaz:
https://doaj.org/article/6bb2ed03f51146d19e6f855c727952d9
Autor:
Dan Xue, Liangliang Jiang, Zixiang Wei, Maojie Chai, Jiang Liu, Peng Deng, Fuhe Lin, Jian Li, Jiansheng Zhang, Zhangxin Chen
Publikováno v:
Energy Reviews, Vol 3, Iss 1, Pp 100056- (2024)
On the backdrop of dwindling conventional reserves, unconventional reservoirs have emerged as a pivotal chapter in resource extraction. Despite their challenges, such as low permeability, complex fluid storage, and flow mechanisms, hydraulic fracturi
Externí odkaz:
https://doaj.org/article/b6440ab408d147eebc6c4d34d6bbc923
Autor:
Dan Xue, Tengteng Zhu, Hongguang Lin, Peilin Guo, Mengling Li, Mei'e Yu, Fan Yang, Sheng Yang, Xiangqi Chen, Peifang Wei
Publikováno v:
Chinese Medical Journal, Vol 136, Iss 24, Pp 3019-3021 (2023)
Externí odkaz:
https://doaj.org/article/d1408d4a4250432baf7509be5788d11d
Autor:
Zheng, Dan-Xue, Chen, Yi-Xing, Sun, Jing, Hu, Yong, Yang, Ping, Zhang, Yang, Duan, Xue-Zhang, Zeng, Zhao-Chong
Publikováno v:
In Clinical and Translational Radiation Oncology May 2024 46
Publikováno v:
Mathematics, Vol 12, Iss 15, p 2393 (2024)
In this paper, we propose a stochastic primal-dual adaptive method based on an inexact augmented Lagrangian function to solve non-convex programs, referred to as the SPDAM. Different from existing methods, SPDAM incorporates adaptive step size and mo
Externí odkaz:
https://doaj.org/article/42bf694a065c4a4eb4c4e2c62fd2f43d
Publikováno v:
Sensors, Vol 24, Iss 15, p 4771 (2024)
In order to achieve the non-destructive testing and quality evaluation of stainless-steel resistance spot welding (RSW) joints, a portable ultrasonic spiral C-scan testing instrument was developed based on the principle of ultrasonic pulse reflection
Externí odkaz:
https://doaj.org/article/f39c3bb0d42c4381b27bd063ac5b5e1f