Zobrazeno 1 - 10
of 811
pro vyhledávání: '"Zeng Long"'
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
Lung cancer is a leading cause of cancer-related deaths globally, and traditional chemotherapy has limited efficacy in treating advanced non-small cell lung cancer (NSCLC). In recent years, the prognosis for patients with NSCLC has significantly impr
Externí odkaz:
https://doaj.org/article/1217565f5c804d43b0bd4434e46b1505
Publikováno v:
Shiyan dongwu yu bijiao yixue, Vol 44, Iss 2, Pp 220-226 (2024)
Intelligent control systems can effectively assist in the construction and management of laboratory animal facilities, improving operational efficiency, ensuring the reliability of animal experimental results, and significantly saving human resources
Externí odkaz:
https://doaj.org/article/e41eb805136f4752b7d5c09cc48eb7ff
Publikováno v:
PLoS ONE, Vol 19, Iss 2, p e0293341 (2024)
To construct an index system for evaluating the supply of Internet+ public health services, and to provide a practical tool for assessing the supply of Internet+ public health services in an objective and scientific manner. The research team drafted
Externí odkaz:
https://doaj.org/article/538892d20e814b70ab27338a3beb5b4f
Autor:
Zeng Long, Maohua Zhong
Publikováno v:
Journal of Intelligent Construction, Vol 1, Iss 4, Pp 9180030-9180030 (2023)
Fire accident seriously threatens the stable operation and personnel security in the subway station, and thermal stratification is an important factor used to determine smoke settlement. Therefore, this paper carried out a series of model experiments
Externí odkaz:
https://doaj.org/article/de7d75a9e16145a488739ce1edab1a1f
Publikováno v:
Case Studies in Thermal Engineering, Vol 41, Iss , Pp 102666- (2023)
Hot smoke test is a key method for fire studies in metro systems. But most hot smoke test scenarios are conducted only once, which ignores the experiment uncertainties and errors. To quantify the uncertainty and repeatability of hot smoke tests in su
Externí odkaz:
https://doaj.org/article/88475e5e6c164abebcb77601e286025e
Publikováno v:
SSM: Population Health, Vol 20, Iss , Pp 101265- (2022)
Background: There are more than 26 million elderly people in China, and due to the Health China strategy proposed in 2020, “Elderly Health” has become an important topic of concern for all sectors of society. Neighborhoods provide important socia
Externí odkaz:
https://doaj.org/article/90efe8dc90cb44608de551d01a3d79f0
Autor:
Cheng, Xi, Lei, Ruiqi, Huang, Di, Liao, Zhichao, Piao, Fengyuan, Chen, Yan, Feng, Pingfa, Zeng, Long
Parametric point clouds are sampled from CAD shapes, and have become increasingly prevalent in industrial manufacturing. However, most existing point cloud learning methods focus on the geometric features, such as developing efficient convolution ope
Externí odkaz:
http://arxiv.org/abs/2411.07747
Autor:
Liao, Zhichao, Huang, Di, Fang, Heming, Ma, Yue, Piao, Fengyuan, Li, Xinghui, Zeng, Long, Feng, Pingfa
Drawing freehand sketches of mechanical components on multimedia devices for AI-based engineering modeling has become a new trend. However, its development is being impeded because existing works cannot produce suitable sketches for data-driven resea
Externí odkaz:
http://arxiv.org/abs/2408.05966
Autor:
Yu, Jianxiang, Ding, Zichen, Tan, Jiaqi, Luo, Kangyang, Weng, Zhenmin, Gong, Chenghua, Zeng, Long, Cui, Renjing, Han, Chengcheng, Sun, Qiushi, Wu, Zhiyong, Lan, Yunshi, Li, Xiang
In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have explored the capabilities of Large Language Models (LLMs) for automat
Externí odkaz:
http://arxiv.org/abs/2407.12857
Autor:
Tang, Yifan, Tai, Cong, Chen, Fangxing, Zhang, Wanting, Zhang, Tao, Liu, Xueping, Liu, Yongjin, Zeng, Long
Publikováno v:
IEEE International Conference on Robotics & Automation,2024
Most existing robotic datasets capture static scene data and thus are limited in evaluating robots' dynamic performance. To address this, we present a mobile robot oriented large-scale indoor dataset, denoted as THUD (Tsinghua University Dynamic) rob
Externí odkaz:
http://arxiv.org/abs/2406.19791