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Catastrophic landslides are generally evolved from potential active landslides, and early identification of active landslides over an extensive region is vital to effective prevention and control of disastrous landslides in urban areas. Interferometric Synthetic Aperture Radar (InSAR) has immense potential in mapping active landslides. However, artificial interpretation of InSAR measurements and manual recognition of active landslides are very laborious and time-consuming, with a relatively high missing and false alarms. That hinders the application of InSAR technique and the identification of active landslides in wide areas. Automatic recognition of active landslides has always been a great challenge and has been relatively rarely investigated by previous studies. This work establishes comprehensive identification indices of geoenvironmental, disaster-triggering, and surface deformation features. Moreover, it suggests a novel deep learning algorithm of SDeepFM to conduct automatic identification of active landslides across a vast and landslide-serious area of Hualong County. Some new viewpoints are suggested as follows. (1) The identification indices consist of disaster-controlling, disaster-inducing, and active deformation characteristics and are constructed in terms of the cause characteristics of active landslides. Thus, it can effectively decrease the false alarms of active landslide identification. (2) The proposed SDeepFM algorithm features a spatial-perception ability and can adequately extract and fuse the low-level and high-level semantic features. It outperforms the classification and regression tree (CART), multi-layer perceptron (MLP), convolutional neural network (CNN), and deep neural network (DNN) algorithms. The test accuracy attains 0.91, 99.73%, 90.21%, 0.92, 0.96, and 0.91 in F1-score, Accuracy, Precision, Recall, AUC, and Kappa, respectively. (3) A total of 164 active landslides are exactly recognized, and 39 active landslides are newly identified in this work. (4) In Hualong County, the characteristics of slope deformation, spatial context, lithology, tectonic movement, human activity, and topography play important roles in active landslide identification. River distribution and rainfall also contribute to active landslide recognition. [ABSTRACT FROM AUTHOR]

In order to determine the damage law of narrow coal pillars in the roadway along the goaf, taking the 4-5(06) working face of a mine in Xinjiang as the research object, the 3D finite difference software FLAC3D is used to analyse the stress and displacement distribution characteristics of narrow coal pillars in the mining process. The results show that the internal stress of the coal pillar reaches the peak when the narrow coal pillar advances from 11m to 12m, and the coal pillar is subjected to the pre-supporting pressure, the range of the rupture zone is increased, and the bearing capacity is obviously reduced. When the working face advances 65m, the displacement in the coal column reaches a peak, and the deformation of the coal column is more serious. The field test results show that the damage law of the narrow coal pillars in the working face mining process is basically consistent with the simulation results. After the bolts, the cable-stayed anchor cable and the shotcrete, the deformation of the narrow coal pillar is in a controllable range, and the safety production requirements of the working face are realized.

Objective To observe the changes in blood glucose in patients with liver cirrhosis and investigate the relationship between glu-cose metabolism and hepatic encephalopathy (HE). Methods Sixty patients with liver cirrhosis (cirrhosis group) and 30 patients without liver cirhosis ( control group ), who were hospitalized in Department of Gastroenterology, Jiading District Central Hospital from August 2008 to August 2010, were included in the study. The general data were collected ; the consciousness of patients was observed ; the physical exam-ination was performed for detecting flapping tremors ; blood samples were collected for measuring blood glucose. The cirhosis group was fur-ther divided into flapping tremor subgroup and non - flapping tremor subgroup. The cirrhosis group and control group were compared in terms of the incidence rate of abnormal glucose metabolism. The patients were divided into normal glucose metabolism group and abnormal glucose metabolism group, and the two groups were compared in terms of the incidence rates of flapping tremors and HE. Results Of the 60 pa-tients in cirrhosis group, 28 had no flapping tremors, and 32 had flapping tremors. The incidence rate of abnormal glucose metabolism was significantly lower in the control group than in the cirrhosis group ( 13. 33% vs 26. 67% ; ?² =2. 058, ? < 0. 05 ). The incidence rates of flapping tremors and HE were 50. 00% and 34. 09% in the normal glucose metabolism group, versus 62. 50% and 37. 50% in the abnormal glucose metabolism group (?>0. 05). Conclusion The patients with liver cirrhosis have abnormal glucose metabolism, but which is not necessarily associated with HE. It suggests that the patients with HE may have impaired glucose utilization in the brain. [ABSTRACT FROM AUTHOR]