Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Xiaofeng Yue"'
Publikováno v:
Food Frontiers, Vol 4, Iss 4, Pp 2013-2023 (2023)
Abstract Microbial toxins are important factors that contribute to human diseases and food losses. Predicting the production of these toxins before they occur is a significant challenge. In this study, we innovatively developed a strategy called targ
Externí odkaz:
https://doaj.org/article/26fff22334dc4a27be87353b4d8ef40b
Autor:
Zelin Chen, Xu Tan, Taotao Jin, Yu Wang, Linyong Dai, Gufang Shen, Can Zhang, Langfan Qu, Lei Long, Chongxing Shen, Xiaohui Cao, Jianwu Wang, Huijuan Li, Xiaofeng Yue, Chunmeng Shi
Publikováno v:
Advanced Science, Vol 11, Iss 9, Pp n/a-n/a (2024)
Abstract To rescue ischemic myocardium from progressing to myocardial infarction, timely identification of the infarct size and reperfusion is crucial. However, fast and accurate identification, as well as the targeted protection of injured cardiomyo
Externí odkaz:
https://doaj.org/article/7125586403bc4f3cace5d414512a6411
Publikováno v:
npj Science of Food, Vol 7, Iss 1, Pp 1-8 (2023)
Abstract Aflatoxin is a group of strongly toxic and carcinogenic mycotoxins produced by Aspergillus flavus and other Aspergillus species, which caused food contamination and food loss problems widely across the world especially in developing countrie
Externí odkaz:
https://doaj.org/article/ff01d0d59421468694de8de678c4996f
Publikováno v:
Oil Crop Science, Vol 8, Iss 3, Pp 143-148 (2023)
The microbial agent ARC-BBBE demonstration trials were conducted in four provinces in the main peanut-producing areas of the Huang-huai-hai plain of China. The results revealed that the application of ARC-BBBE led to a 1.09–1.70 fold increase in th
Externí odkaz:
https://doaj.org/article/44298b42af3144cfac51a565382c0e1a
Autor:
Xiaohan Liu, Jiayun Fu, Mingbo Wen, Haohua Gu, Pingping Ji, Xiaofeng Yue, Xiaoqian Tang, Meijuan Liang, Yang Zhou, Qi Zhang, Peiwu Li
Publikováno v:
Oil Crop Science, Vol 8, Iss 2, Pp 127-132 (2023)
In order to grasp the distribution of Aspergillus flavus in the soil of peanut production areas in China, A. flavus biomarkers were tested on 555 soil samples from 37 sampling points in 17 provinces, peanut fields in four agroecological zones (Southe
Externí odkaz:
https://doaj.org/article/858c9360c1364f2e8e355aa366a36292
Publikováno v:
Chinese Journal of Population, Resources and Environment, Vol 21, Iss 1, Pp 13-25 (2023)
The digital economy has become an important driver for stimulating economic growth. The digital economy has now widely penetrated the fields of economy and society, providing new opportunities for the development of urban-rural integration. Based on
Externí odkaz:
https://doaj.org/article/6a111d5a6d4f47e099cf2b87f4fcb188
Autor:
Ximei Xu, Xiaofeng Yue, Du Wang, Mengxue Fang, Li Yu, Fei Ma, Nanri Yin, Xuefang Wang, Baocheng Xu, Liangxiao Zhang, Peiwu Li
Publikováno v:
Agriculture, Vol 14, Iss 6, p 881 (2024)
Cadmium is the main heavy metal contaminant of food in the world. The extent of cadmium pollution in peanut in China remains unclear. To determine the cadmium pollution level in peanut, samples from the main producing regions in China were assessed.
Externí odkaz:
https://doaj.org/article/1770d1cc81f54087aa2afe9751572bcf
Publikováno v:
Food Chemistry: X, Vol 18, Iss , Pp 100676- (2023)
Green leaf volatiles (GLVs), play important roles in the green and fresh aroma characteristics of grape berries. The evolution of GLV profiles regarding the varietal difference during grapevine phenological ripening is not well understood. This study
Externí odkaz:
https://doaj.org/article/6f049785beb44e8c9d24b619fb6f2f80
Publikováno v:
Symmetry, Vol 15, Iss 12, p 2128 (2023)
In this paper, a point cloud coarse–fine registration method based on a new improved version of the whale optimization algorithm (NIWOA) and iterative closest point (ICP) algorithm is proposed; we use three strategies to improve the whale optimizat
Externí odkaz:
https://doaj.org/article/f2ddf5e53a7a47d8aa59e0270770968e
Publikováno v:
Mathematics, Vol 11, Iss 22, p 4634 (2023)
In recent years, deep learning has been increasingly used in fault diagnosis of rotating machinery. However, the actual acquisition of rolling bearing fault signals often contains ambient noise, making it difficult to determine the optimal values of
Externí odkaz:
https://doaj.org/article/bf7a3d47197841ea95169acd628b695a