Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Shiquan Shen"'
Autor:
Ji Wang, Shiquan Shen, Jian You, Zhaotao Wang, Yan Li, Yanming Chen, Yonghua Tuo, Danmin Chen, Haoming Yu, Jingbo Zhang, Fangran Wang, Xiao Pang, Zongyu Xiao, Qing Lan, Yezhong Wang
Publikováno v:
Cell Death and Disease, Vol 15, Iss 7, Pp 1-13 (2024)
Abstract Invasion and migration are the key hallmarks of cancer, and aggressive growth is a major factor contributing to treatment failure and poor prognosis in glioblastoma. Protein arginine methyltransferase 6 (PRMT6), as an epigenetic regulator, h
Externí odkaz:
https://doaj.org/article/94d993e7d01f479fb3405fccedb7cb20
Autor:
Shiquan Shen, Honglong Zhou, Zongyu Xiao, Shaofen Zhan, Yonghua Tuo, Danmin Chen, Xiao Pang, Yezhong Wang, Ji Wang
Publikováno v:
Cell Communication and Signaling, Vol 22, Iss 1, Pp 1-24 (2024)
Abstract Protein arginine methyltransferase 1 (PRMT1), the predominant type I protein arginine methyltransferase, plays a crucial role in normal biological functions by catalyzing the methylation of arginine side chains, specifically monomethylargini
Externí odkaz:
https://doaj.org/article/a33297391e1340fe9a1f49e5622256aa
Autor:
Simin Wu, Zheng Chen, Shiquan Shen, Jiangwei Shen, Fengxiang Guo, Yonggang Liu, Yuanjian Zhang
Publikováno v:
IET Intelligent Transport Systems, Vol 17, Iss 8, Pp 1560-1574 (2023)
Abstract Vehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco‐driving studies mainly focus on the optimal control of a
Externí odkaz:
https://doaj.org/article/a7ff35b3036d4757bbb3027e09773b6e
Autor:
Shiquan Shen, Shun Gao, Yonggang Liu, Yuanjian Zhang, Jiangwei Shen, Zheng Chen, Zhenzhen Lei
Publikováno v:
IEEE Access, Vol 10, Pp 131076-131089 (2022)
Plug-in hybrid electric vehicles (PHEVs) have been validated as a preferable solution to transportation due to its great advantages in fuel economy promotion, harmful emission reduction and mileage anxiety mitigation. While, designing an effective en
Externí odkaz:
https://doaj.org/article/dbca72be829642c885394ec73577e75a
Publikováno v:
STAR Protocols, Vol 3, Iss 2, Pp 101272- (2022)
Summary: Accurate estimates of State of Health (SoH) are critical for characterizing the aging of lithium-ion batteries. This protocol combines feature extraction and a representative machine learning algorithm (i.e., least-squares support vector mac
Externí odkaz:
https://doaj.org/article/a91bd83cd72b44c9a1e5c8a132fad7d1
Publikováno v:
IEEE Access, Vol 9, Pp 777-788 (2021)
In this paper, an improved online particle swarm optimization (PSO) is proposed to optimize the traditional search controller for improving the operating efficiency of the permanent magnet synchronous motor (PMSM). This algorithm combines the advanta
Externí odkaz:
https://doaj.org/article/2c76de2b8f7040e1aa0084f53dd9ce27
Publikováno v:
IEEE Access, Vol 8, Pp 172783-172798 (2020)
Capacity prediction of lithium-ion batteries represents an important function of battery management systems. Conventional machine learning-based methods for capacity prediction are inefficient to learn long-term dependencies during capacity degradati
Externí odkaz:
https://doaj.org/article/6b48ba759b2a4ad5aec19efb08512bcf
Publikováno v:
Remote Sensing, Vol 15, Iss 5, p 1212 (2023)
With the rapid development of urban ground transportation, lane line detection is gradually becoming a major technological direction to help to realize safe vehicle navigation. However, lane line detection results may have incompleteness issues, such
Externí odkaz:
https://doaj.org/article/b47676e2c244416e82f284dbc899ea8c
Publikováno v:
iScience, Vol 24, Iss 11, Pp 103265- (2021)
Summary: Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches h
Externí odkaz:
https://doaj.org/article/d9e93810ba434890864e31059f126034
Autor:
Zhanying Zhang, Jinjie Li, Yinghua Pan, Jilong Li, Lei zhou, Hongli Shi, Yawen Zeng, Haifeng Guo, Shuming Yang, Weiwei Zheng, Jianping Yu, Xingming Sun, Gangling Li, Yanglin Ding, Liang Ma, Shiquan Shen, Luyuan Dai, Hongliang Zhang, Shuhua Yang, Yan Guo, Zichao Li
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-13 (2017)
Low temperature is a major factor limiting productivity in rice. Here the authors show that theCTB4a gene confers cold tolerance to japonicavarieties adapted to cold habitats at the booting stage of development, and propose that CTB4a acts via an int
Externí odkaz:
https://doaj.org/article/904f0dcea42841b08ba234d14174db9c