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Surfacic networks.
PNAS Nexus
January 2025
Faculty of Architecture, and Urban Systems Institute, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
Surfacic networks are structures built upon a 2D manifold. Many systems, including transportation networks and various urban networks, fall into this category. The fluctuations of node elevations imply significant deviations from typical plane networks and require specific tools to understand their impact.
View Article and Find Full Text PDFSlope changing osteotomies in the knee: Time to go Infra.
Knee Surg Sports Traumatol Arthrosc
January 2025
Department of Orthopedic Surgery and Traumatology, Kantonsspital Baselland, Bruderholz, Switzerland.
Pervasive aquatic vegetation organic carbon losses in China's lakes.
Sci Total Environ
January 2025
Soil and Fertilizer Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
One of the major global concerns is to mitigate carbon dioxide emissions to addressing the detrimental impacts of climate change. Aquatic vegetation, as a natural carbon pool, offers a potential solution to such problems. However, a crucial impediment is the absence of comprehensive estimates of its organic carbon storage.
View Article and Find Full Text PDFSpatiotemporal evolution of landscape ecological risk and its driving factors of the Beijing-Tianjin-Hebei major mineral belt, 1985-2022.
Sci Rep
January 2025
College of Mining Engineering, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian District, Tangshan, 063210, Hebei, China.
The Beijing-Tianjin-Hebei major mineral belt represents a significant economic development area in China. Effective monitoring and assessment of the regional landscape ecological risk can provide a scientific basis for an ecological protection strategy for the environmental protection of the Beijing-Tianjin-Hebei major mineral belt. In this study, a landscape ecological risk index was constructed based on land use/land cover, and the spatial and temporal variations of landscape ecological risk were subsequently analyzed.
View Article and Find Full Text PDFExploring the impact of natural and human activities on vegetation changes: An integrated analysis framework based on trend analysis and machine learning.
J Environ Manage
January 2025
School of Urban Planning and Design, Peking University, Shenzhen, 518055, China. Electronic address:
Climate, human activities and terrain are crucial factors influencing vegetation changes. Despite their crucial role, there is a notable lack of research exploring the nonlinear relationships between them and vegetation changes, especially over extended time series. This research integrates trend analysis with machine learning and SHAP technology, proposing a methodological analysis framework named Theil-Sen - Mann-Kendall - XGBoost - SHAP (TMXS), aiming to explore the nonlinear relationships between vegetation changes and their influencing factors.
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