Zobrazeno 1 - 10
of 472
pro vyhledávání: '"Zong Wang"'
Publikováno v:
Ecological Indicators, Vol 166, Iss , Pp 112476- (2024)
China is currently the world’s largest carbon emitter and has pledged to achieve carbon neutrality by 2060, which requires significant reductions in emissions and the removal of carbon dioxide removal. Precise and efficient forestation has become a
Externí odkaz:
https://doaj.org/article/1276ff1dbafc4d6c8802fdb10bb85764
Publikováno v:
Remote Sensing, Vol 16, Iss 16, p 3098 (2024)
Afforestation is an important way to effectively reduce carbon emissions from human activities and increase carbon sinks in forest ecosystems. It also plays an important role in climate change mitigation. Currently, few studies have examined the spat
Externí odkaz:
https://doaj.org/article/e3fffe7117684163ad0f44a13d68e2ef
Publikováno v:
Land, Vol 13, Iss 6, p 828 (2024)
Industrialization has increased global carbon emissions, necessitating effective climate change mitigation measures. China, the most populous developing nation, faces the challenge of strategizing emissions to meet national carbon neutrality objectiv
Externí odkaz:
https://doaj.org/article/ab230b8c437f49de9e0aa9e68c7413d2
Publikováno v:
Mathematics, Vol 12, Iss 8, p 1212 (2024)
This work focuses on the convergence of the numerical invariant measure for a stochastic age-dependent population–toxicant model with Markov switching. Considering that Euler–Maruyama (EM) has the advantage of fast computation and low cost, expli
Externí odkaz:
https://doaj.org/article/749b73decd704b0ab1c907e4bb881b05
Autor:
Boyi Liang, Jia Wang, Zheyuan Zhang, Jia Zhang, Junping Zhang, Elizabeth L. Cressey, Zong Wang
Publikováno v:
Fundamental Research, Vol 2, Iss 5, Pp 688-696 (2022)
Over the last several decades, China has taken multiple measures for afforestation and natural forest protection, including setting the goal of carbon neutrality by the middle of 21th century. In order to support the practice of relevant policies fro
Externí odkaz:
https://doaj.org/article/fe88d3f8fe2541729f5ee915d330be0a
Autor:
Boyi Liang, Hongyan Liu, Elizabeth L. Cressey, Chongyang Xu, Liang Shi, Lu Wang, Jingyu Dai, Zong Wang, Jia Wang
Publikováno v:
Remote Sensing, Vol 15, Iss 11, p 2920 (2023)
As more machine learning and deep learning models are applied in studying the quantitative relationship between the climate and terrestrial vegetation growth, the uncertainty of these advanced models requires clarification. Partial dependence plots (
Externí odkaz:
https://doaj.org/article/e70bb584895645e1aa328fa27e071658
Publikováno v:
APL Photonics, Vol 7, Iss 7, Pp 076103-076103-7 (2022)
A grating coupler with a high coupling efficiency and low back reflections is designed and demonstrated on the thin film lithium niobate platform, which facilitates an efficient interface between a lithium niobate ridge waveguide and a standard singl
Externí odkaz:
https://doaj.org/article/fc90270368974d0caac9d1685fa007cd
Autor:
Gengxin Chen, Kaixuan Chen, Ranfeng Gan, Ziliang Ruan, Zong Wang, Pucheng Huang, Chao Lu, Alan Pak Tao Lau, Daoxin Dai, Changjian Guo, Liu Liu
Publikováno v:
APL Photonics, Vol 7, Iss 2, Pp 026103-026103-8 (2022)
Thin-film lithium niobate (TFLN) based traveling-wave modulators maintain simultaneously excellent performances, including large modulation bandwidth, high extinction ratio, low optical loss, and high modulation efficiency. Nevertheless, there still
Externí odkaz:
https://doaj.org/article/0044f9fddd264484bb31f1a22ebfbbfd
Publikováno v:
Remote Sensing, Vol 15, Iss 3, p 587 (2023)
Vegetation changes and factors have a profound influence on the local ecology, the economy, and the long-term durability of human construction. This study focuses on the impacts of climate change and human activity on vegetation changes on the Qingha
Externí odkaz:
https://doaj.org/article/fcc73a7742ee4e118f0cad466e9d0a88
Publikováno v:
Ecological Indicators, Vol 129, Iss , Pp 107975- (2021)
Digital soil mapping approaches related to soil organic matter (SOM) are crucial to quantify the process of the carbon cycle in terrestrial ecosystems and thus, can better manage soil fertility. Recently, many studies have compared machine learning (
Externí odkaz:
https://doaj.org/article/56f5059ed42145c7b13d73dc757c01df