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Jingyun Fang

Researcher at Peking University

Publications -  523
Citations -  53730

Jingyun Fang is an academic researcher from Peking University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 98, co-authored 435 publications receiving 40880 citations. Previous affiliations of Jingyun Fang include Beijing Forestry University & Harbin Institute of Technology.

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Fenton and fenton-like system hardening agent and usage thereof

TL;DR: In this article, Fenton and Fenton-like system enhancing agent and the usage thereof are provided and the method of use of enhancing agent comprises the steps of: adding Fenton or Fenton, an agent for enhancement and hydrogen peroxide into water subject to treatment; and mixing and allowing reaction.
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Climate and forest attributes influence above‐ground biomass of deciduous broadleaf forests in China

TL;DR: In this paper , a comprehensive forest inventory database from 772 plots distributed across temperate and subtropical deciduous broadleaf forests in China (23.51°−42.53°N and 104.24°−128.27°E) was used to explore how climate and forest attributes (species diversity, community-level functional traits and stand structures) affect AGB in different climatic forests (semi-arid forests, semi-humid forests and humid forests).
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ID:1329 ORIENT-11: Sintilimab + Pemetrexed + Platinum as First-Line Therapy for Locally Advanced or Metastatic Non-Squamous NSCLC

TL;DR: The Sun Yat-sen University Cancer Center, Guangzhou, Shandong Province Cancer Hospital, Jinan, Peking University cancer Hospital, Beijing, Shanghai chest hospital, shanghai/China, Henan Provincial Peoples Hospital, Zhengzhou, Harbin Medical University Cancer hospital, Harbi, Anhui Provincial Hospital, Hefei.
Posted ContentDOI

No significant changes in topsoil carbon in the grasslands of northern China between the 1980s and 2000s

TL;DR: Li et al. as discussed by the authors examined the changes in the soil organic carbon (SOCD) in the upper 30 cm of the grasslands of northern China between the 1980s and 2000s, using an improved approach that integrates field-based measurements into machine learning algorithms (artificial neural network and random forest).