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According to studies by relevant scholars, the ground subsidence rate in Sanhe City, Hebei, increased gradually from 2003 to 2016, with the most severe subsidence observed in the Yanjiao area, which has become contiguous with the subsidence area in Tongzhou, Beijing. However, the development trend of ground subsidence disasters in Sanhe City after 2016 remains unclear. With the implementation of national policies such as the integration of Beijing-Tianjin-Hebei region and the construction of Beijing's sub-center, it is crucial to identify the development and evolution characteristics of land subsidence disasters in Sanhe City and analyze their causes to ensure the urban safety and sustainable development of Sanhe City. In this context, the authors used SBAS-InSAR technology to interpret the evolution characteristics of ground subsidence in Sanhe City from 2018 to 2020, and analyzed several inducing factors that led to land subsidence in Sanhe City, summarizing the main causes. Through this study, the spatial distribution and evolutionary characteristics of land subsidence disasters in Sanhe City were grasped: the western part of the city exhibits severe subsidence, while the eastern part is less affected, with a focus on the overall characteristics of key subsidence areas. Two main subsidence areas were identified, namely, the Yanjiao Town subsidence area with three subsidence funnels and the DuanJialing town subsidence area with one subsidence funnel, with the former being the most severely affected. From 2018 to 2020, the overall trend of ground subsidence disasters in Sanhe City showed a slowdown. Through comparative analysis, it was determined that severe overexploitation of groundwater, soil properties, urbanization development, and population growth are the main factors contributing to the occurrence and development of ground subsidence disasters in Sanhe City. The research results of this paper will provide reference for the prevention and control of ground subsidence disasters in the region. [ABSTRACT FROM AUTHOR]

Rare earth elements (REEs) are vital in high‐tech industries and defense due to their strategic significance. Crafting an efficient membranes channel for REE separation poses a significant challenge. Employing 2‐methylimidazole‐hydrolyzed OH−, dopamine polymerization is initiated and then Zn2+ coordinates with 2‐methylimidazole on PDA surfaces. The confined symbiotic reaction yields 2D vertical heterojunctions of GO/ZIF‐8/PDA (G/Z/P). During separation, partially dehydrated smaller hydrated lanthanide ions preferentially access interlayers, expanding and stabilizing the interlayer space by coordinating with PDA, thus excluding larger hydrated scandium ions from the membranes. Some scandium ions entering interlayer channels are sequestered by N in size‐matched ZIF‐8 pores. This distinctive mechanism facilitates selective scandium separation from other REEs (other REEs/Sc selectivity ≈68.73), achieving nearly complete Sc3+ rejection in a single step. The methodology offers unprecedented insights into precise nano‐space material synthesis, indicating promising strides in advancing scandium production. [ABSTRACT FROM AUTHOR]

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes by using time-of-flight photon information after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly likely to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.