Zobrazeno 1 - 10
of 1 778
pro vyhledávání: '"Liu Hongsheng"'
Publikováno v:
Xiehe Yixue Zazhi, Vol 13, Iss 5, Pp 845-851 (2022)
Objective To evaluate the safety and clinical value of laparoscopic jejunostomy in minimal invasive McKeown esophagectomy. Methods The clinical data of the patients undergoing minimally invasive McKeown esophagectomy in the Department of Thoracic Sur
Externí odkaz:
https://doaj.org/article/8e6b0fc5359440ab80d91b29ab632cb3
Autor:
LIU Zijia, ZHANG Lu, LIU Hongsheng, CANG Jing, WANG Tianlong, MIN Su, CHEN Lixia, CHEN Wei, LI Shanqing, HUANG Yuguang, Working Group of "Expert Consensus on Prehabilitation Management of Thoracic Surgery", Chinese Society of Anesthesiology
Publikováno v:
Xiehe Yixue Zazhi, Vol 13, Iss 3, Pp 387-401 (2022)
Prehabilitation is the important starting part of enhanced recovery after surgery (ERAS). Multimodal prehabilitation management before thoracic surgery can increase the perioperative functional capacity of patients and help to improve the prognosis,
Externí odkaz:
https://doaj.org/article/4806e43c6dff44d1bc1f4524ee1bcbad
Autor:
Wang, Qi, Ren, Pu, Zhou, Hao, Liu, Xin-Yang, Deng, Zhiwen, Zhang, Yi, Chengze, Ruizhi, Liu, Hongsheng, Wang, Zidong, Wang, Jian-Xun, Ji-Rong_Wen, Sun, Hao, Liu, Yang
When solving partial differential equations (PDEs), classical numerical methods often require fine mesh grids and small time stepping to meet stability, consistency, and convergence conditions, leading to high computational cost. Recently, machine le
Externí odkaz:
http://arxiv.org/abs/2411.00040
Autor:
Zeng, Bocheng, Wang, Qi, Yan, Mengtao, Liu, Yang, Chengze, Ruizhi, Zhang, Yi, Liu, Hongsheng, Wang, Zidong, Sun, Hao
Solving partial differential equations (PDEs) serves as a cornerstone for modeling complex dynamical systems. Recent progresses have demonstrated grand benefits of data-driven neural-based models for predicting spatiotemporal dynamics (e.g., tremendo
Externí odkaz:
http://arxiv.org/abs/2410.01337
Autor:
Ye, Zhanhong, Huang, Xiang, Chen, Leheng, Liu, Zining, Wu, Bingyang, Liu, Hongsheng, Wang, Zidong, Dong, Bin
This paper introduces PDEformer-1, a versatile neural solver capable of simultaneously addressing various partial differential equations (PDEs). With the PDE represented as a computational graph, we facilitate the seamless integration of symbolic and
Externí odkaz:
http://arxiv.org/abs/2407.06664
Publikováno v:
IEEE Access, Vol 8, Pp 64646-64652 (2020)
In this work, we present and evaluate a three dimensional Convolutional Neural Network algorithm to accurately detect EEG abnormalities from multi-channel EEG signals. This research synthesizes several heterogeneous datasets, constructs a dataset 10
Externí odkaz:
https://doaj.org/article/ba5c14275755433daf9355231f32cbc0
Autor:
Zhao, Luneng, Liu, Hongsheng, Chang, Yuan, Shi, Xiaoran, Gao, Junfeng, Zhao, Jijun, Ding, Feng
The primary restrictions on 2D transition metal dichalcogenides (TMD) vdW heterostructures (vdWHs) are size limitation and alloying. Recently, a two-step vapor deposition method was reported to grow wafer-scale TMD vdWHs with little contamination [Na
Externí odkaz:
http://arxiv.org/abs/2405.04939
This paper introduces PDEformer, a neural solver for partial differential equations (PDEs) capable of simultaneously addressing various types of PDEs. We propose to represent the PDE in the form of a computational graph, facilitating the seamless int
Externí odkaz:
http://arxiv.org/abs/2402.12652
Autor:
Shi, Xiaoran, Gao, Weiwei, Liu, Hongsheng, Fu, Zhen-Guo, Zhang, Gang, Zhang, Yong-Wei, Gao, Junfeng, Zhao, Jijun
Design and synthesis of novel two-dimensional (2D) materials that possess robust structural stability and unusual physical properties may open up enormous opportunities for device and engineering applications. Herein we propose a 2D sumanene lattice
Externí odkaz:
http://arxiv.org/abs/2311.07273