Zobrazeno 1 - 10
of 443
pro vyhledávání: '"Anping Chen"'
Publikováno v:
Communications Earth & Environment, Vol 5, Iss 1, Pp 1-6 (2024)
Abstract Contemporary fire-human-climate nexus has led to a surge in publication numbers across diverse research disciplines beyond the capability of experts from a single discipline. Here, we employed a generalized large language model to capture th
Externí odkaz:
https://doaj.org/article/2cc81e2686454c6297c48362a78468bd
Publikováno v:
Alexandria Engineering Journal, Vol 86, Iss , Pp 405-414 (2024)
Flexible sensors based on polymer materials have the potential to enable non-invasive health monitoring and exercise tracking. This review summarizes recent progress in developing flexible sensing platforms for healthcare and fitness applications. Ke
Externí odkaz:
https://doaj.org/article/ba8b4c8307284ec2882b8e0a5acccdef
Autor:
Zhihua Liu, Wen J. Wang, Ashley Ballantyne, Hong S. He, Xugao Wang, Shuguang Liu, Philippe Ciais, Michael C. Wimberly, Shilong Piao, Kailiang Yu, Qichao Yao, Yu Liang, Zhiwei Wu, Yunting Fang, Anping Chen, Wenru Xu, Jiaojun Zhu
Publikováno v:
Communications Earth & Environment, Vol 4, Iss 1, Pp 1-9 (2023)
The rate, frequency and extent of forest disturbances decreased in China over 1986–2020 probably due to forest protection policies, but while they intensified in the southeast, they weakened in the northeast, according to 30 m resolution Landsat sa
Externí odkaz:
https://doaj.org/article/47bafaf93fca48458a969145607247d8
Publikováno v:
Geophysical Research Letters, Vol 50, Iss 11, Pp n/a-n/a (2023)
Abstract This study develops a convolutional recurrent deep learning model to accurately predict fine‐resolution spatiotemporal changes in grass coverage in arid regions. Applying the model to the Gobi Desert reveals that ecological flow regulation
Externí odkaz:
https://doaj.org/article/8939555858f644e7b4570259ca165c87
Autor:
Tsechoe Dorji, Shilong Piao, Xuhui Wang, Chuang Zhao, Baohua Liu, Anping Chen, Shiping Wang, Tao Wang
Publikováno v:
Engineering, Vol 14, Iss , Pp 163-168 (2022)
Preserving Tibet’s unique history and cultural heritage relies on the sustainability of the Tibetan croplands, which are characterized by highland barley, the only cereal crop cultivated over 4000 m above sea level. Yet it is unknown how these crop
Externí odkaz:
https://doaj.org/article/a2699ca6768743e3b02d3bbf10e69991
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
Changing precipitation pattern has been suggested to expand tropical dry seasons. Here, the authors show that this lengthening can be even more severe when accounting for the simultaneous rise of ecosystem water demand in a warmer climate.
Externí odkaz:
https://doaj.org/article/3620fd74c8244fdbb0864a2c96c76ceb
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 4, Pp n/a-n/a (2023)
Abstract The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar‐induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the a
Externí odkaz:
https://doaj.org/article/25fb366c7d194611a936745c263984a0
Autor:
Baoai Wu, Chong Xu, Yiming Tian, Yu Zeng, Feng Yan, AnPing Chen, Jinfeng Zhao, Longchang Chen
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-14 (2022)
Abstract The role of aerobic exercise in preventing and improving non-alcoholic fatty liver has been widely established. SRA is a long non-coding RNA, which has received increasing attention due to its important role in lipid metabolism. However, it
Externí odkaz:
https://doaj.org/article/bab4b7cfdb6a4187a7650fa1d1ccff4b
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Abstract This study analyzed the effects of aerobic exercise on endogenous serum metabolites in response to chronic unpredictable mild stress (CUMS) using a rat model, aiming to identify the metabolic regulatory pathways involved in the antidepressan
Externí odkaz:
https://doaj.org/article/b97ab2fe61d5486291ba53f5f447c75a
Autor:
Yan Yu, Jiafu Mao, Stan D. Wullschleger, Anping Chen, Xiaoying Shi, Yaoping Wang, Forrest M. Hoffman, Yulong Zhang, Eric Pierce
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
A new study develops a machine learning framework to observationally constrain CMIP6-simulated fire carbon emissions, finding a weaker increase in 21st-century global fires but higher increase in their socioeconomic risks than previously thought.
Externí odkaz:
https://doaj.org/article/84c7cf8e7bb548648cd440040aee6ac3