Zobrazeno 1 - 10
of 482
pro vyhledávání: '"Xiaobo, Qu"'
Publikováno v:
Fundamental Research, Vol 4, Iss 6, Pp 1603-1612 (2024)
Buses are the most critical part of urban–rural transit systems. However, bus transit services in urban–rural areas face a trade-off between the need for better services and the low profitability resulting from low travel demand. In this study, w
Externí odkaz:
https://doaj.org/article/ec986cfd342948c58637d1775815c35c
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-18 (2024)
Abstract Automated Vehicles (AVs) promise significant advances in transportation. Critical to these improvements is understanding AVs’ longitudinal behavior, relying heavily on real-world trajectory data. Existing open-source trajectory datasets of
Externí odkaz:
https://doaj.org/article/d0b8faf7470b4157ac0be025c5b2e6af
Publikováno v:
Communications in Transportation Research, Vol 4, Iss , Pp 100144- (2024)
Externí odkaz:
https://doaj.org/article/6035b0db39df429ba30e9e6fb5e47b30
Publikováno v:
Communications in Transportation Research, Vol 4, Iss , Pp 100129- (2024)
Academic papers are the cornerstone of knowledge dissemination and crucial for researchers’ career development. This is particularly true for rapidly evolving research domains such as transportation, as evidenced by the surge of journals and papers
Externí odkaz:
https://doaj.org/article/8c056b375dfd42fcb5e8b256a0d12e9f
Publikováno v:
Communications in Transportation Research, Vol 4, Iss , Pp 100123- (2024)
Laboratory experiments are one of the important means used to investigate travel choice behavior under strategic uncertainty. Many experiment-based studies have shown that the Nash equilibrium can predict aggregated route choices, while the fluctuati
Externí odkaz:
https://doaj.org/article/7087e63c4c1d4a44a530ec06fc4b9046
Publikováno v:
Communications in Transportation Research, Vol 4, Iss , Pp 100117- (2024)
Externí odkaz:
https://doaj.org/article/c8adbd5f6f204474b1c9fe22ae78311a
Autor:
Chengyan Wang, Jun Lyu, Shuo Wang, Chen Qin, Kunyuan Guo, Xinyu Zhang, Xiaotong Yu, Yan Li, Fanwen Wang, Jianhua Jin, Zhang Shi, Ziqiang Xu, Yapeng Tian, Sha Hua, Zhensen Chen, Meng Liu, Mengting Sun, Xutong Kuang, Kang Wang, Haoran Wang, Hao Li, Yinghua Chu, Guang Yang, Wenjia Bai, Xiahai Zhuang, He Wang, Jing Qin, Xiaobo Qu
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-9 (2024)
Abstract Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnosti
Externí odkaz:
https://doaj.org/article/b9889864c8ba4b029aba56bd498b09d9
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image with kernels and successfully explore the local information. Nonetheless, the n
Externí odkaz:
https://doaj.org/article/2a03b6d5c6f146dabeaddfeee271a040
Autor:
Xiaobo Qu1 xiaobo@tsinghua.edu.cn, Hongzhang Shao2, Shuaian Wang3, Yunpeng Wang4 wangshuaian@gmail.com
Publikováno v:
Fundamental Research. Sep2024, Vol. 4 Issue 5, p1009-1016. 8p.
Autor:
Biao Qu, Hejuan Tan, Min Xiao, Dongbao Liu, Shijin Wang, Yiwen Zhang, Runhan Chen, Gaofeng Zheng, Yonggui Yang, Gen Yan, Xiaobo Qu
Publikováno v:
BMC Medical Imaging, Vol 23, Iss 1, Pp 1-9 (2023)
Abstract Background 1H magnetic resonance spectroscopy (1H-MRS) can be used to study neurological disorders because it can be utilized to examine the concentrations of related metabolites. However, the diagnostic utility of different field strengths
Externí odkaz:
https://doaj.org/article/76dba538bfe940368bbc1155acd90eb1