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      Evaluation and optimization of ecological spatial resilience of Yanhe River Basin based on complex network theory

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          Abstract

          The loess hilly and gully areas have broken terrain, vertical and horizontal ravines and fragile ecological environments. Improving the resilience of the regional ecological space is conducive to improving the quality of the local ecological environment. With the ecological space of the Yanhe River Basin selected as the research object, this paper constructs a research framework of "network identification topology-resilience evaluation-spatial optimization" and uses morphological spatial pattern analysis (MSPA) and the minimum cumulative resistance model (MCR) to identify ecological spatial networks. Based on circuit theory, the ecological pinch point is identified, the ecological spatial network is optimized, and scenario simulation is performed. Through complex network theory and related indicators, the ecological spatial resilience of the basin is evaluated, and the hierarchical optimization strategy of the ecological space is confirmed. According to the ecological function of the source area and the results of the resilience evaluation, the boundaries of the protected control area, guidance development area, remediation area, and maintenance and improvement area of the basin are delineated. The importance of ecological source and corridor protection is classified, and corresponding protection strategies are proposed. The research results can provide theoretical support and practical guidance for the territorial spatial planning and ecological space construction of the Yanhe River Basin and provide a reference for the ecological restoration, resource development and environmental governance of the Yanhe River Basin.

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          Efficient Behavior of Small-World Networks

          We introduce the concept of efficiency of a network as a measure of how efficiently it exchanges information. By using this simple measure, small-world networks are seen as systems that are both globally and locally efficient. This gives a clear physical meaning to the concept of "small world," and also a precise quantitative analysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency.
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                Author and article information

                Contributors
                jizhezhou@chd.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 January 2024
                16 January 2024
                2024
                : 14
                : 1361
                Affiliations
                [1 ]School of Architecture, Chang’an University, ( https://ror.org/05mxya461) Xi’an, 710061 China
                [2 ]Engineering Research Center of Collaborative Planning of Low-Carbon Urban Space and Transportation, Universities of Shaanxi Province, Xi’an, 710061 China
                [3 ]West Airport Group, Xi’an, 710049 China
                [4 ]School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, ( https://ror.org/017zhmm22) Xi’an, 710049 China
                Article
                51966
                10.1038/s41598-024-51966-z
                10791970
                38228700
                4d2d05fc-ad0b-4419-adfb-355ef668d228
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 July 2023
                : 11 January 2024
                Funding
                Funded by: National Key Research and Development Program
                Award ID: 2022YFC3802803
                Award Recipient :
                Funded by: National Natural Science Foundation of China
                Award ID: 52178030
                Award Recipient :
                Funded by: Philosophy and Social Science Research Project in Shaanxi
                Award ID: 2022HZ1870
                Award ID: 2022HZ1860
                Award Recipient :
                Funded by: Special Science and Technology Project of Gansu
                Award ID: 22JR4ZA103
                Award Recipient :
                Categories
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                © Springer Nature Limited 2024

                Uncategorized
                ecology,environmental sciences
                Uncategorized
                ecology, environmental sciences

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