Zobrazeno 1 - 10
of 33 744
pro vyhledávání: '"Lihui An"'
Autor:
Yuyao Wang, Yuanrong Zhu, Guanghui Guo, Lihui An, Wen Fang, Yidan Tan, Juan Jiang, Xiaojie Bing, Qingshuai Song, Qihao Zhou, Zhongqi He
Publikováno v:
Ecotoxicology and Environmental Safety, Vol 285, Iss , Pp 117154- (2024)
Microplastics (MPs) are pervasive across ecosystems, likely posing significant environmental and health risks based on more and more evidence. In this study, we searched through the Web of Science Core Collection and obtained 1039 papers for visualiz
Externí odkaz:
https://doaj.org/article/43bfaa9f73a74ce6a7ab6828c5e114f5
Autor:
Xu Zhao, Zehua Ren, Zhubing Hu, Yinghua Li, Chaoyu Zhang, Qingbo Yang, Lihui An, Bo Zhu, Hongbo Wang, Jianli Liu
Publikováno v:
Ecotoxicology and Environmental Safety, Vol 269, Iss , Pp 115735- (2024)
In recent years, with the increasing global focus on environmental protection, the issue of microfiber release from denim during the washing process has gained attention. In this study, a programmable washing device simulating household drum washing
Externí odkaz:
https://doaj.org/article/925cdd12a75c493eb455040c6a043fa5
Publikováno v:
Electrochemistry Communications, Vol 157, Iss , Pp 107628- (2023)
At present, organic wastewater containing dyes has become one of the main threats to the environment, and wastewater pollution caused by dyes released from fabrics during home washing also accounts for a large proportion. In this study, Ti/IrO2 elect
Externí odkaz:
https://doaj.org/article/a98761e5ec5d49fa961da7d89b444c8b
Autor:
Jianli Liu, Zhubing Hu, Fangfang Du, Wei Tang, Siting Zheng, Shanzhou Lu, Lihui An, Jiannan Ding
Publikováno v:
Frontiers in Environmental Science, Vol 11 (2023)
Plastic pollution has been today’s most highly visible environmental problem in the world. How to responsibly manage plastic waste to control and eliminate plastic pollution has been a global challenge. We have begun to address these issues and dev
Externí odkaz:
https://doaj.org/article/f26f4bea10704aea9c157b7c40a6d084
Autor:
Zheng, Yilun, Zhang, Zhuofan, Wang, Ziming, Li, Xiang, Luan, Sitao, Peng, Xiaojiang, Chen, Lihui
To improve the performance of Graph Neural Networks (GNNs), Graph Structure Learning (GSL) has been extensively applied to reconstruct or refine original graph structures, effectively addressing issues like heterophily, over-squashing, and noisy stru
Externí odkaz:
http://arxiv.org/abs/2411.07672
Graph Neural Networks (GNNs) have demonstrated strong capabilities in processing structured data. While traditional GNNs typically treat each feature dimension equally during graph convolution, we raise an important question: Is the graph convolution
Externí odkaz:
http://arxiv.org/abs/2411.07663
Autor:
Wang, Wenxiao, Gu, Lihui, Zhang, Liye, Luo, Yunxiang, Dai, Yi, Shen, Chen, Xie, Liang, Lin, Binbin, He, Xiaofei, Ye, Jieping
The exponential growth of knowledge and the increasing complexity of interdisciplinary research pose significant challenges for researchers, including information overload and difficulties in exploring novel ideas. The advancements in large language
Externí odkaz:
http://arxiv.org/abs/2410.23166
GESH-Net: Graph-Enhanced Spherical Harmonic Convolutional Networks for Cortical Surface Registration
Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical methods is that for each pair of images to be registered, it is necessary to search for the optimal transform
Externí odkaz:
http://arxiv.org/abs/2410.14805
EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution
Single hyperspectral image super-resolution (single-HSI-SR) aims to improve the resolution of a single input low-resolution HSI. Due to the bottleneck of data scarcity, the development of single-HSI-SR lags far behind that of RGB natural images. In r
Externí odkaz:
http://arxiv.org/abs/2409.04050
Full waveform inversion (FWI) plays a crucial role in the field of geophysics. There has been lots of research about applying deep learning (DL) methods to FWI. The success of DL-FWI relies significantly on the quantity and diversity of the datasets.
Externí odkaz:
http://arxiv.org/abs/2408.08005