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335 results on '"Yang Weili"'

1. How to improve fishery eco-efficiency by building marine ranch in China

Author

Chen Yusheng, Ma Ye, Yang Weili, Sun Zhaofa, and Han Yang

Subjects
China’s marine ranch, fishery ecological efficiency, Super-SBM model, differences in differences model, food safety, Nutrition. Foods and food supply, TX341-641, Food processing and manufacture, TP368-456
Abstract

Based on the panel data of the province and municipality in 2010–2021, this paper measures the fishery eco-efficiency by using the SBM-DEA model and empirically analyses how China’s marine ranch construction improves the fishery eco-efficiency by using the double-difference fixed-effect model. The results show that, firstly, China’s marine ranch construction can improve the fishery eco-efficiency. Secondly, China’s marine ranch construction promotes fisheries eco-efficiency by improving the input efficiency of labor, fish species, fishing vessel, fishery technology extension, aquaculture area and by reducing pollutant outputs. Thirdly, the fishery eco-efficiency of the marine ranch construction region has increased annually since 2015, and technological changes, scale efficiency changes and technical efficiency changes can all contribute to the fishery eco-efficiency improvement, and increase in order. Based on the results of the empirical analyses, this paper proposes policy recommendations to improve the fishery eco-efficiency in the conjunction with the construction of marine ranch in China.

Published
2024
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8. CHD8 mutations increase gliogenesis to enlarge brain size in the nonhuman primate

Author

Li, Bang, Zhao, Hui, Tu, Zhuchi, Yang, Weili, Han, Rui, Wang, Lu, Luo, Xiaopeng, Pan, Mingtian, Chen, Xiusheng, Zhang, Jiawei, Xu, Huijuan, Guo, Xiangyu, Yan, Sen, Yin, Peng, Zhao, Zhiguang, Liu, Jianrong, Luo, Yafeng, Li, Yuefeng, Yang, Zhengyi, Zhang, Baogui, Tan, Zhiqiang, Xu, Hao, Jiang, Tianzi, Jiang, Yong-hui, Li, Shihua, Zhang, Yong Q., and Li, Xiao-Jiang

Published
2023
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27. Multi-view Finger Vein Recognition Using Attention-Based MVCNN

Author

Yang, Weili, Huang, Junduan, Chen, Zhuoming, Zhao, Junhong, Kang, Wenxiong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Deng, Weihong, editor, Feng, Jianjiang, editor, Huang, Di, editor, Kan, Meina, editor, Sun, Zhenan, editor, Zheng, Fang, editor, Wang, Wenfeng, editor, and He, Zhaofeng, editor

Published
2022
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35. Association between gut microbiota and Hirschsprung disease: a bidirectional two-sample Mendelian randomization study

Author

Liu, Wei, primary, Yan, Hanlei, additional, Jia, Wanying, additional, Huang, Jingjing, additional, Fu, Zihao, additional, Xu, Wenyao, additional, Yu, Hui, additional, Yang, Weili, additional, Pan, Weikang, additional, Zheng, Baijun, additional, Liu, Yong, additional, Chen, Xinlin, additional, Gao, Ya, additional, and Tian, Donghao, additional

Published
2024
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46. OPT-SAR-MS 2 Net: A Multi-Source Multi-Scale Siamese Network for Land Object Classification Using Remote Sensing Images.

Author

Hu, Wei, Wang, Xinhui, Zhan, Feng, Cao, Lu, Liu, Yong, Yang, Weili, Ji, Mingjiang, Meng, Ling, Guo, Pengyu, Yang, Zhi, and Liu, Yuhang

Subjects
ZONING, SYNTHETIC apertures, SYNTHETIC aperture radar, FEATURE extraction, CONTEXTUAL learning, REMOTE sensing
Abstract

The utilization of optical and synthetic aperture radar (SAR) multi-source data to obtain better land classification results has received increasing research attention. However, there is a large property and distributional difference between optical and SAR data, resulting in an enormous challenge to fuse the inherent correlation information to better characterize land features. Additionally, scale differences in various features in remote sensing images also influence the classification results. To this end, an optical and SAR Siamese semantic segmentation network, OPT-SAR-MS2Net, is proposed. This network can intelligently learn effective multi-source features and realize end-to-end interpretation of multi-source data. Firstly, the Siamese network is used to extract features from optical and SAR images in different channels. In order to fuse the complementary information, the multi-source feature fusion module fuses the cross-modal heterogeneous remote sensing information from both high and low levels. To adapt to the multi-scale features of the land object, the multi-scale feature-sensing module generates multiple information perception fields. This enhances the network's capability to learn contextual information. The experimental results obtained using WHU-OPT-SAR demonstrate that our method outperforms the state of the art, with an mIoU of 45.2% and an OA of 84.3%. These values are 2.3% and 2.6% better than those achieved by the most recent method, MCANet, respectively. [ABSTRACT FROM AUTHOR]

Published
2024
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