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The vegetation net primary productivity (NPP) is a key indicator for evaluating vegetation carbon sequestration. Exploring its spatiotemporal changes and impact factors is essential for coal mining and ecological restoration in open‐pit mining areas. This study utilized the Carnegie‐Ames‐Stanford‐Approach (CASA) model to calculate monthly vegetation NPP in the Xiwan mine area, a typical open‐pit mine in northwestern China. The trend, stability, and persistence analysis were conducted, along with the development of a grading method to examine the vegetation NPP spatiotemporal variation across different land cover types. Statistical grading and correlation analysis were used to explore the relationships between the topographical factors, meteorological factors, human activities, and vegetation NPP. The following results were obtained: (1) The vegetation NPP in the study area exhibited a high stability and anti‐persistent decrease in trend. NPP reached a peak of 143.49 g C/(m2 year) in 2017, but declined to a low of 118.38 g C/(m2 year) in 2021. (2) The vegetation NPP decreases with increasing elevation and slope, and a relatively strong correlation with temperature and precipitation was also observed. (3) The impact intensity of human activities on vegetation NPP exhibited a rising and fluctuating volatile trend. In 2021, the inhibition of vegetation NPP by human activities reached its peak at 166.42 g C/(m2 year), with an impact effect share of 36.9%. This research provides a comprehensive framework for vegetation NPP analysis in open‐pit mining, offering valuable insights for ecological conservation in mining ecosystems. [ABSTRACT FROM AUTHOR]

Measuring trajectory similarity is a fundamental algorithm in trajectory data mining, playing a key role in trajectory clustering, pattern mining, and classification, for instance. However, existing trajectory similarity measures based on vector representation have challenges in achieving both fast and accurate similarity measurements. On one hand, most existing methods have a high computational complexity of O(n × m), resulting in low efficiency. On the other hand, many of them are sensitive to trajectory sampling rates and lack of accuracy. This article proposes QuadGridSIM, a quadrilateral grid‐based method for trajectory similarity analysis, which enables high‐performance trajectory similarity measure without the cost of low effectiveness. Specifically, we first realize the multiscale coding representation of trajectory data based on quadrilateral discrete grids. Then, a novel trajectory similarity measure is defined to reduce the computational complexity of O(n). Several effectiveness properties of QuadGridSIM are further optimized, including the spatial overlap, directionality, symmetry, and robustness to sampling rate variations. Experimental results based on real‐world and simulated taxi trajectory data indicate that QuadGridSIM outperforms most of the other tested algorithms developed previously in terms of effectiveness, particularly in its robustness regarding trajectory sampling rates. Furthermore, QuadGridSIM exhibits superior performance and is approximately one order of magnitude faster than previous methods in the literature. QuadGridSIM provides a solution to the low‐efficiency problem of massive trajectory similarity analysis and can be applied in many application scenarios, such as route recommendation and suspect detection. [ABSTRACT FROM AUTHOR]

Existing spatiotemporal similarity analysis methods for trajectories have the problems of spatiotemporal unsynchronization and low efficiency in processing large‐scale datasets, which cannot satisfy the increasingly urgent requirements of real‐time or quasi‐real‐time applications. To address these problems, this article proposes a grid‐based and synchronized spatiotemporal similarity analysis method based on the spatiotemporal grid model called gsstSIM. First, a low‐dimensional and multi‐scale trajectory coding representation is implemented based on the spatiotemporal grid model. Second, a synchronized spatiotemporal similarity measure is proposed based on trajectory codes. It transforms the similarity analysis from complex geometric calculations to simple algebraic operations of code sets, which reduces the computational complexity. In addition, the trajectory encoding representation with space‐time collinearity enables gsstSIM to measure the synchronized spatiotemporal similarity. Third, the efficient Multi‐scale grid index, called MSGrid, is established to realize fast query of top‐K similar trajectories for large‐scale datasets. Experimental results demonstrate that gsstSIM is more robust to noise positioning points and various sampling rates than the state‐of‐the‐art algorithms STLCSS, TWS and SWS. It can achieve a second‐level response of spatiotemporal similarity query in processing large‐scale datasets, which is much faster than existing algorithms. The proposed method has promising to support the applications with high time‐efficiency requirements such as epidemic tracking and traffic condition calculation. [ABSTRACT FROM AUTHOR]

Parcel‐level evaluation of urban land use efficiency is necessary for city administrations to understand land use status and problems, and to further optimize land use pattern and policy. However, existing research on urban land use efficiency is mainly carried out at city level or block level, and it is difficult to meet the demand for rapid access to fine‐grained land use efficiency across the entire city. In view of this problem, this article proposes a parcel‐level evaluation method of urban land use efficiency based on multisource spatiotemporal data, including an evaluation index system for parcel‐level land use efficiency, quantitative representation of evaluation indexes based on multisource spatiotemporal data, and a land use efficiency calculation model. The proposed method is then applied to China's Ningbo City, and the evaluation accuracy is 78% compared with the officially reported data and on‐site investigation. Experimental results also show that urban land use efficiency is strongly spatially autocorrelated in Ningbo City, and reveal the impact patterns of factors such as planning policy, facilities, traffic conditions and environmental conditions on the utilization efficiency of different types of land. This research has proved able to accurately evaluate the utilization efficiency of all land parcels across a city in a rapid manner, and to obtain their distribution characteristics and correlation factors. It can further provide data and technical support for formulating targeted urban planning policies and implementing scientific land development. [ABSTRACT FROM AUTHOR]

Leucine zipper‐EF‐hand–containing transmembrane protein 1 (LETM1) is a mitochondrial inner membrane protein that is highly expressed in various cancers. Although LETM1 is known to be associated with poor prognosis in colorectal cancer (CRC), its roles in autophagic cell death in CRC have not been explored. In this study, we examined the mechanisms through which LETM1 mediates autophagy in CRC. Our results showed that LETM1 was highly expressed in CRC tissues and that down‐regulation of LETM1 inhibited cell proliferation and induced S‐phase arrest. LETM1 silencing also suppressed cancer stem cell–like properties and induced autophagy in CRC cells. Additionally, the autophagy inhibitor 3‐methyladenine reversed the inhibitory effects of LETM1 silencing on proliferation and stemness, whereas the autophagy activator rapamycin had the opposite effects. Mechanistically, suppression of LETM1 increased the levels of reactive oxygen species (ROS) and mitochondrial ROS by regulation of SOD2, which in turn activated AMP‐activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR), initiated autophagy, and inhibited proliferation and stemness. Our findings suggest that silencing LETM1 induced autophagy in CRC cells by triggering ROS‐mediated AMPK/mTOR signalling, thus blocking CRC progression, which will enhance our understanding of the molecular mechanism of LETM1 in CRC. [ABSTRACT FROM AUTHOR]