Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-28 Cooperative journals: 《中国科学院院刊》
Abstract： The carbon neutrality strategy will be the largest orderly human activity in this century, which requires strong scientific supports. This study introduces some key scientific questions and remaining knowledge gaps in earth system science (i.e. atmosphere, land, ocean science), and also discusses some key techniques and the associated challenges, including Earth System Model, climate metrics, greenhouse gasses monitoring techniques, carbon inventories from regional to global scales. On this basis, this study recommends to construct climate monitoring system and platform in China, better describe and understand the earth system coupling processes and mechanisms. To consolidate the techniques for carbon neutrality, this study also suggests to build carbon monitoring and inventory assessment platform and to improve the Earth System Model.
Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-28 Cooperative journals: 《中国科学院院刊》
Abstract： The Luancheng Agro-Ecosystem Experimental Station, the Chinese Academy of Sciences, is located in the piedmont plain area of North China Plain and the middle section of the Taihang Mountain alluvial plain. It is a typical representative of the high-yield agricultural eco-system in the groundwater irrigation area. Due to the large-scale and high-intensity agricultural production in recent decades, the regional groundwater has been seriously over exploited, and the risk of groundwater pollution has increased. However, the surface agro-ecosystem and aquifer are separated by thick vadose zones, hence the mechanism of the impact of surface agricultural activities on the quantity and quality of groundwater is still unclear. The in-situ monitoring throughout the entire vadose zone is the only method to reveal the mechanism of this process. Therefore, the establishment of a large-scale critical zone observatory is the most important requisite for conducting research on the relationship between agriculture and groundwater. With the support of the corner#2;stone research infrastructure project for the field station network of the Chinese Academy of Sciences, Luancheng Station began to construct the “Water and Solute Cycle Observation Platform in Groundwater-Soil-Crop System” in 2016, and the construction was completed in November 2020. An observatory caisson with a depth of 48 m and an inner diameter of 2.8 m has been built. The platform was officially named ‘Luancheng Critical Zone Observatory’ (LC-CZO). LC-CZO can measure the key variables such as soil water content, matric potential, soil temperature, and solute concentration in the root zone-deep vadose zone-saturated zone. The monitoring results enable the study on the patterns of the transport and transformation of water,solutes and pollutants in the thick vadose zone reveal the hydrological process of the saturated-unsaturated transition zone and its relationship with groundwater replenishment, explain the migration and transformation of nitrogen applied in farmland and its impact on groundwater quality, and provide experimental platform support for exploring the impact mechanism of land surface process on the groundwater quantity and quality.
Subjects: Biology >> Botany >> Applied botany submitted time 2020-08-02 Cooperative journals: 《广西植物》
Abstract：鄂西南地区密集分布有后河、木林子、七姊妹山和星斗山四大国家级自然保护区, 共同形成了一个珍稀动植物大体相近，互相补充的保护区群。同时，该区域丰富的地貌类型和水气资源为苔藓植物营造了良好的居住环境。本文采用野外调查和文献资料整理相结合的方法，对鄂西南国家级自然保护区群内的苔藓植物丰富度和组成特征进行分析，并与渝东南、湘西北的苔藓植物多样性进行比较。结果表明：鄂西南国家级自然保护区群共有苔藓植物77 科 197 属 601 种，物种数分别占中国和湖北苔藓总数的 19.89%和 71.46%。其中，中国特有种 27 种，仅含一种的科有 15 科，仅含一种的属有 91 属。另外，鄂西南国家级保护区群内的各保护区之间的苔藓物种存在一定的相似性与互补性，符合我国生物多样性保护原则。同时，鄂西南国家级自然保护区群苔藓植物区系类型全面，物种数量也显著高于同处武陵山区的渝东南、湘西北等地区。因此，鄂西南国家级自然保护区群不但有效地保护了大型珍稀濒危动植物，也孕育了丰富多彩的苔藓植物类群，是苔藓植物多样性保护的重要场所。
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-08-13 Cooperative journals: 《计算机应用研究》
Abstract： As the traditional spectral clustering algorithms 1) only considered the global structures of the samples while ignoring their local structures for the construction of the correlation matrix; 2) conducted clustering with a predefined cluster number; 3) could not divide the intersections correctly. This paper proposes a new method based on the local principal component analysis and the decomposition method of the connected graph. Specifically, the proposed method automatically learns the centroids of the selected subset of the samples, obtains the correlation matrix of the samples based on the local principal component analysis, and uses the decomposition method of the connected graph to partition the resulting correlation matrix. Experimental results show that the proposed algorithm performs better than the existing algorithms.
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