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Akademický článek

Microbial-enhanced phytoremediation for the management of endogenous contamination of substrates

Autor: GU Junjie, KANG Xingsheng, MENG Yingjie, FAN Hongkai, HUANG Lizhu, MENG Qianya, ZOU Xiaofeng

Publikováno v: Gongye shui chuli, Vol 44, Iss 9, Pp 98-103 (2024)

The effective control of endogenous contamination of black and odorous water has become an important work in China. Phytoremediation technology is more widely used, and microbial agent method is gradually accepted due to the absence of chemical agent

Externí odkaz: https://doaj.org/article/204ea921bfa1470790cfb9d97dc17703

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Akademický článek

Study on phytoremediation for heavy metal contaminated sediments by hydrophytes

Autor: Ma Tao, Zhang Wenhui, Fan Hongkai, Huang Lizhu, Xu Qing, Ma Guangxiang, Zhou Guofeng, Meng Yingjie, Sun Jianzhang, Qiu Jinwei, Gu Junjie, Kang Xingsheng

Publikováno v: E3S Web of Conferences, Vol 143, p 02020 (2020)

The remediation performances of heavy metals contaminaged sediment by hydrophytes including Alternanthera Philoxeroides, Canna indica L., Nymphaea tetragona, Typha orientalis, Phragmites australis, Phragmites australis, Hydrilla verticillata, Cyperus

Externí odkaz: https://doaj.org/article/e777ddc86ed24778a72feac6f7b2596d

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Akademický článek

Arsenic content in the Jie River and its release behaviour from river sediments

Autor: Ma Guangxiang, Huang Lizhu, Kang Xingsheng, Xu Qing, Zhou Guofeng, Fan Hongkai, Zhang Wenhui, Sun Jianzhang, Meng Yingjie, Qiu Jinwei, Gu Junjie, Ma Tao

Publikováno v: E3S Web of Conferences, Vol 143, p 02021 (2020)

Water and sediment samples of Jie River, China were collected and analyzed. The results showed that the average arsenic volume of water sample was 0.42 mg L-1 present in the Jie River. Among the riverway, the arsenic volume and pH kept rising with th

Externí odkaz: https://doaj.org/article/2629a6b9ed6d4762ad5b095fb7160797

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Memory-augmented Contrastive Learning for Talking Head Generation

Autor: Wang, Jianrong, Zhao, Yaxin, Liu, Li, Fan, Hongkai, Xu, Tianyi, Li, Qi, Li, Sen

Given one reference facial image and a piece of speech as input, talking head generation aims to synthesize a realistic-looking talking head video. However, generating a lip-synchronized video with natural head movements is challenging. The same spee

Externí odkaz: http://arxiv.org/abs/2302.13469