Zobrazeno 1 - 10
of 210
pro vyhledávání: '"Yuanchun ZHOU"'
Autor:
Guomei Fan, Chongye Guo, Qian Zhang, Dongmei Liu, Qinglan Sun, Zhigang Cui, Haijian Zhou, Yuanchun Zhou, Zhibin Guo, Juncai Ma, Linhuan Wu
Publikováno v:
Biosafety and Health, Vol 6, Iss 4, Pp 235-243 (2024)
Investigating the genetic and developmental characteristics, infection transmission attributes, and epidemiological trends of pathogens using genomic data represents the foundation for pathogen surveillance and is a crucial prerequisite for guarantee
Externí odkaz:
https://doaj.org/article/4474183fdb504813ba85533599b1478f
Autor:
Ludi Wang, Yang Gao, Xueqing Chen, Wenjuan Cui, Yuanchun Zhou, Xinying Luo, Shuaishuai Xu, Yi Du, Bin Wang
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract The electrocatalytic CO2 reduction process has gained enormous attention for both environmental protection and chemicals production. Thereinto, the design of new electrocatalysts with high activity and selectivity can draw inspiration from t
Externí odkaz:
https://doaj.org/article/3e53cb771ea04cb292b37de7c15979ae
Autor:
Chengrui Wang, Pengjiang Li, Qingqing Long, Haotian Chen, Pengfei Wang, Zhen Meng, Xuezhi Wang, Yuanchun Zhou
Publikováno v:
Minerals, Vol 14, Iss 3, p 275 (2024)
Refined lithology identification is an essential task, often constrained by the subjectivity and low efficiency of classical methods. Computer-aided automatic identification, while useful, has seldom been specifically geared toward refined lithology
Externí odkaz:
https://doaj.org/article/ac3c9db7e95549d5b256d99bfc6c2106
Publikováno v:
Frontiers in Environmental Science, Vol 11 (2023)
The source classification of domestic waste is important for protecting China’s rural environment but this is more difficult in rural areas than in urban areas due to the characteristics of farmers in China. This study discussed influencing factors
Externí odkaz:
https://doaj.org/article/bbb434645a19408f9d7f1f9d8326cc49
Autor:
Wenyu Shi, Guomei Fan, Zhihong Shen, Chuan Hu, Juncai Ma, Yuanchun Zhou, Zhen Meng, Songnian Hu, Yuhai Bi, Liang Wang, Haiying Yu, Siru Lin, Xiuqiang Sun, Xinjiao Zhang, Dongmei Liu, Qinlan Sun, Linhuan Wu
Publikováno v:
mLife, Vol 1, Iss 1, Pp 92-95 (2022)
Externí odkaz:
https://doaj.org/article/197da5ae7fa0430183b2a790fa17763e
Publikováno v:
Journal of Water and Climate Change, Vol 13, Iss 2, Pp 463-481 (2022)
The relationship between changing climate and the three sectors of water, energy and food is increasingly drawing attention today while all of them are vital for sustainable development. This paper undertakes a bibliometric analysis of 1,959 publishe
Externí odkaz:
https://doaj.org/article/7dea4d08b8b542b8ab5badb12aede6d9
Autor:
Farah Deeba, Yuanchun Zhou, Fayaz Ali Dharejo, Muhammad Ashfaq Khan, Bhagwan Das, Xuezhi Wang, Yi Du
Publikováno v:
IET Image Processing, Vol 15, Iss 8, Pp 1679-1687 (2021)
Abstract Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super‐resolution image, etc. Due to the orbital location, revision time
Externí odkaz:
https://doaj.org/article/7b04160349a643d78066041606a9868a
Publikováno v:
IET Image Processing, Vol 15, Iss 1, Pp 47-56 (2021)
Abstract The object identification within an image captured during rough weather conditions (such as haze, fog) poses difficulty due to the reduction of an image. The rough weather conditions lead not only to the variation of the image's visual effec
Externí odkaz:
https://doaj.org/article/4beb0611ad43402d9fb6e032c8e9f143
Publikováno v:
IET Image Processing, Vol 14, Iss 11, Pp 2365-2375 (2020)
It is very interesting to reconstruct high‐resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super‐resolution method for CT medical images in the sparse
Externí odkaz:
https://doaj.org/article/5b17e6c2afa941d2afef848dabb067dd
Publikováno v:
IEEE Access, Vol 8, Pp 47209-47219 (2020)
Machine learning is becoming prevalent increasingly for reservoir characteristics analysis in the petroleum industry. This investigation proposes an alternative way for evaluating interwell connectivity in oil fields utilizing machine learning. In th
Externí odkaz:
https://doaj.org/article/14f7e4f3b0f84077bd8960949c0e2016