Coal mine surface subsidence detection determines the damage degree of coal mining, which is of great importance for the mitigation of hazards and property loss. Therefore, it is very important to detect deformation during coal mining. Currently, there are many methods used to detect deformations in coal mining areas. However, with most of them, the accuracy is difficult to guarantee in mountainous areas, especially for shallow seam mining, which has the characteristics of active, rapid, and high-intensity surface subsidence. In response to these problems, we made a digital subsidence model (DSuM) for deformation detection in coal mining areas based on airborne light detection and ranging (LiDAR). First, the entire point cloud of the study area was obtained by coarse to fine registration. Second, noise points were removed by multi-scale morphological filtering, and the progressive triangulation filtering classification (PTFC) algorithm was used to obtain the ground point cloud. Third, the DEM was generated from the clean ground point cloud, and an accurate DSuM was obtained through multiple periods of DEM difference calculations. Then, data mining was conducted based on the DSuM to obtain parameters such as the maximum surface subsidence value, a subsidence contour map, the subsidence area, and the subsidence boundary angle. Finally, the accuracy of the DSuM was analyzed through a comparison with ground checkpoints (GCPs). The results show that the proposed method can achieve centimeter-level accuracy, which makes the data a good reference for mining safety considerations and subsequent restoration of the ecological environment.

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This study aims to determine the function of topotecan (TPT) in acute lung injury (ALI) induced by sepsis. The mouse sepsis model was constructed through cecal ligation and puncture (CLP). The ALI score and lung wet/dry (W/D) weight ratio were applied to evaluate the level of lung injury. Hematoxylin-eosin staining was used to examine the role of TPT in lung tissue in a CLP-induced ALI mouse model. Enzyme-linked immunosorbent assay and quantitative real-time polymerase chain reaction were used to detect the concentrations of inflammatory factors, such as interleukin-6 (IL-6), IL-1β, and tumor necrosis factor-α. Western blot was used to detect relevant protein levels in the nuclear factor-κB (NF-κB) pathway. Moreover, 10-day survival was recorded by constructing

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Because of the difficulty in detecting final state taus, the mixing parameter |VτN | 2 for heavy neutrino N is not well studied at current experiments, compared with other mixing parameters |VeN | 2 and |VµN | 2 . In this paper, we focus on a challenging scenario where N mixes with active neutrino of tau flavour only, i.e. |VτN | 2 = 0 and |VeN | 2 = |VµN | 2 = 0. We derive current constraints on |VτN | 2 from the rare Z-boson decay and electroweak precision data (EWPD). To forecast the future limits, we also investigate the signal pp → τ ± τ ± jj via a Majorana heavy neutrino at future proton-proton colliders. To suppress the background, both taus are required to decay leptonically into muons, leading to the final state containing two same sign muons, at least two jets plus moderate missing energy. The signal and relevant background processes are simulated at the HL-LHC and SppC/FCC-hh with center-of-mass energy of 14 TeV and 100 TeV. The preselection and multivariate analyses based on machine-learning are performed to reduce background. Limits on |VτN | 2 are shown for heavy neutrino mass in the range 10-1000 GeV based on measurements from the rare Z-boson decay and EWPD, and searches at the HL-LHC and SppC/FCC-hh with integrated luminosities of 3 and 20 ab −1 .