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[Objective] Excavation of foundation pit will cause deformation of surrounding strata and buildings , which affects construction progress and even causes engineering accidents in some degree. In order to ensure the stability and safety of deep large foundation pit in urban rail transit stations, it is necessary to study the stress and deformation characteristics of the soil layer and the enclosure structure during the foundation pit excavation. [Method] With the deep foundation pit excavation for a metro station in Qingdao as the project background and in view of the characteristics of soil rock composite stratum, numerical simulation method is used to analyze the deformation and internal force of the deep foundation pit. Based on the on-site data, the deformation law of ground settlement, retaining pile displacement and supporting axial force during excavation of deep foundation pit under soil rock composite stratum is studied. [Result & Conclusion] The numerical simulation shows that when biaxial high-pressure rotary jet grouting pile is not under construction, the lateral displacement occurs in the east, west and south walls of the end shaft, all towards the internal of the foundation pit, and the maximum lateral displacement of 2.19 mm occurs in the middle of the south wall. During the construction, the deformation of the side wall of the end shaft reaches peak in the process of reinforcement of the rotary jet grouting pile at the end shaft of the foundation pit, and the maximum deformation is 2.21 mm. When the biaxial high-pressure rotary jet grouting pile is not under construction, the peak value of the internal force of the diaphragm wall appears at its waist, and the maximum value is 101 kN. During construction, the internal force of the diaphragm wall structure at the stratum side strengthened by the end shaft jet grouting pile changes obviously, and the peak value of the internal force appears at the waist of the diaphragm wall, and the maximum value is 96 kN. It can be seen that the biaxial high-pressure rotary jet pile has certain effect on reducing the internal force of the diaphragm wall under different construction conditions, but has little effect on the deformation of foundation pit.

The randomly distributed fracture network in coal body is the main flow channel of coalbed methane, and the key to study the flow characteristics of coalbed methane is to accurately simulate the complex fracture network in coal body. Based on the pixel probability decomposition theory, a new discrete fracture network modeling method is introduced. Compared with the traditional method, which assumes that the fracture is a smooth fracture, this method can fully consider the rough characteristics of the fracture. Combined with numerical simulation, the influence of different fracture characteristic parameters on the permeability of the coal seam is analyzed. The results show that the porosity (0.1-0.25), fractal dimension (1.55-1.85), maximum fracture length (0.011-0.026 m) and proportion coefficient (0.006~0.08) have a good power-law growth relationship with the outlet velocity of the fracture network, while the tortuosity fractal dimension (1.0-1.02), fracture dip angle (10°-85°) and the outlet velocity of the fracture network have a power-law decrease relationship; among them, the proportion coefficient, fracture dip angle and maximum fracture length have the most significant effects on the outlet velocity of fracture network.

In order to comprehensively evaluate the temperature and humidity detection performance of the ground microwave radiometer (referred to as radiometer, the same below) and improve the accuracy of temperature and relative humidity profiles, by using the ground microwave radiometer data, radiosonde data, and ERA5 reanalysis data of Beijing from March to August 2021, this paper analyzes the temperature and humidity detection performance of the radiometer under sunny, cloudy and rainy days. In the meanwhile, this paper uses radiosonde data and corrected ERA5 data to verify temperature and humidity profiles of radiometer and correct temperature and humidity data of radiometer based on the linear deviation correction method. The results show that: (1) The temperature of microwave radiometer has good consistency with radiosonde temperature under sunny and cloudy days, but it is higher than the radiosonde temperature in rainy days. The relative humidity of microwave radiometer is higher than that of radiosonde in sunny days and lower in cloudy and rainy days. The linear deviation correction effect of radiosonde on ground-based microwave radiometers in rainy days is better than that in sunny and cloudy days. (2) Above 4 km, ERA5 relative humidity data has obvious deviation with radiosonde data, while the temperature correlation between ERA5 and radiosonde is higher than relative humidity correlation. (3) The ERA5 data after radiosonde correction can improve the temperature and humidity profile accuracy of the Microwave radiometer, and the effect is better in rainy days than in sunny days. In vertical direction, with the increase of height, the correction effect of radiometer temperature and humidity data is more obvious. (4) The two true values of radiosonde data and corrected ERA5 data were used to analyze the temperature and humidity data of the ground-based microwave radiometer corrected by ERA5, and the results showed that the vertical distribution characteristics of the error (mean error BIAS and root mean square error RMSE) profiles of radiometer and radiosonde, radiometer and corrected ERA5 data were similar under sunny and cloud conditions. Under rainy conditions, there were obvious differences between the two in the middle and upper height (7-10 km), among which the temperature RMSE differed by 4 K and the relative humidity RMSE differed by 20%.

The peak shear strength of the discontinuities can be estimated quickly by the roughness of the discontinuities. However, it is difficult to quantify the roughness of the structural surface using the single statistical parameter. In order to improve the prediction accuracy of the standard discontinuities roughness, eight statistical parameters in the aspects of undulating degree and trace length of 112 structural profile curves are collected, and the method of cross-validation of random forest regression model is used to evaluate the importance of statistical parameters. The evaluation results show that the importance of six statistical parameters, including the maximum undulation, undulation height standard deviation, mean undulating angle, undulating angle standard deviation, mean relative undulation rave and roughness profile index, accounts for 93.2%, and the regression fitting coefficient tends to be stable. Based on the importance assessment results, a random forest regression model is established. The model prediction results fitting excellence is up to 98.1%, showing the excellent prediction results. Compared with the traditional linear regression results, such as the results of the slope-based mean square root, structural function and roughness profile index, the random forest regression model has higher accuracy, smaller error and better fit. The random forest regression model is more suitable for structural roughness inversion.

The modelling processes and uncertainties of various machine learning models for landslide susceptibility prediction (LSP) are different, and effectively identifying the main conditioning factors of landslide susceptibility is of great significance. Aiming at these problems, this study aims to discuss the LSP processes and the uncertainties of landslide susceptibility based on machine learning models, namely, support vector machine (SVM) and random forest (RF), and then to innovatively propose the "weighted mean method" for calculating more accurate landslide main control factors. First, the landslide inventories and 10 basic environmental factors of Yanchang County in Shaanxi Province are obtained, and the frequency ratios (FRs) of the environmental factors are taken as the input variables of the SVM and RF models.Then, the landslide and randomly selected nonlandslide samples are divided into model training and testing datasets. Furthermore, the trained RF and SVM models are used to predict the landslide susceptibility and draw the landslide susceptibility prediction (LSP) map.Finally, the uncertainties of LSP modelling are evaluated by the receiver operating characteristic (ROC) curve, mean value and standard deviation, and the main landslide control factors are calculated.The results show that ① Machine learning models can effectively predict the susceptibility of regional landslides. The accuracy of RF in LSP is higher, and its uncertainties are lower than those of SVM. As a whole, the landslide susceptibility distribution rules of the two models are similar.②The main control factors of landslide susceptibility in Yanchang County calculated by the weighted mean method are slope, elevation and lithology.③Case studies and literature reviews show that the RF model is a more reliable susceptibility model than other types of machine learning models.

ObjectivesTransmitting extreme low frequency (ELF) signals with a high signal-noise ratio (SNR) to cover Chinese territory and major territorial sea areas can provide a radiating signal service for trial and exploratory research projects such as underground resource sounding, seismic prediction, etc. MethodsThis paper explains the primary principle of horizontal low-profile antennae in radiating ELF signals, introduces ELF transmission system composition, analyzes key technologies such as ELF power amplification, horizontal antennae tuning, detection and control of strong power electromagnetic fields, composite lightning-proof measures, etc., and designs and constructs the first-ever civilian ELF transmission station in the world, through which long-range field tests and trials with a coverage of thousands of kilometers are accomplished. ResultsThe test and trial results indicate that ELF signals can cover Chinese territory and major territorial sea area 10-20 dB higher than natural signals, with a sounding depth of up to 10 km. ConclusionsThe ELF transmission station can provide a brand-new open public platform for research on geological exploration and seismic prediction, thereby contributing to the development of primary disciplines in China including low-frequency radioactivity, space physics, geophysics, etc.

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Purposes In this paper, research on various stages of the data life cycle and various security governance technologies has been conducted to achieve data security governance progress by searching and analyzing related technical literatures. Motheds First, the importance of strengthening research on data security governance is analyzed by introducing the status quo of data security governance. Second, the theory of the whole data life cycle is expounded in detail, and the different stages of the data life cycle are given. Third, the data security governance technologies and their applications in different life cycle stages such as data preparation, use, storage, and destruction are systematicalhy summarized. At last, the challenges and future research directions of data security governance are analyzed and prospected. Conclusions Strengthening data security governance is of great significance, whether for the privacy protection of personal information, or for the development of the data industry itself, or even for the information security of the country.

Objective: To investigate the protective effect and potential mechanism of Rabdosia serra on alcoholic liver injury. Method: Forty female Kunming mice were randomly divided into blank group, model group, Rabdosia serra group and positive control group. All mice received 0.1 mL/10 g white spirit to established alcoholic liver injury model, and continuous administration for 10 days. After the last administration, HE staining was used to observe the pathological changes of liver tissue. Serum indexes were determined by kit method. Network pharmacology method was used to analyze the potential effective components, signal pathways and molecular targets of Rabdosia serra in the treatment of alcoholic liver injury, and Western blot was used to verify the related molecular targets. Results: The protein expression levels of AST, m-AST and GDH in Rabdosia serra group were significantly higher than those in model group (P

Background The incidence of cervical and upper thoracic esophageal cancer is relatively low, with difficulties and controversies of mode in treatment, lack of convenient and accurate prognostic biomarkers, and poor overall prognosis. Objective To investigate the long-term prognosis of patients with cervical and upper thoracic esophageal squamous cell carcinoma under concurrent chemotherapy and radio-therapy (CCRT) . Methods Patients with cervical and thoracic upper esophageal cancer who received CCRT in the Department of Radiotherapy of the Fourth Hospital of Hebei Medical University from January 2013 to December 2017 were selected as the research subjects. General data, Eastern Cooperative Oncology Group (ECOG) score, tumor site, tumor length, TNM stage, radiotherapy dose, irradiation mode, chemotherapy regimen, toxic and side effects were collected by electronic medical record system, and neutrophil to lymphocyte ratio (dNLR) was calculated. The patients were divided into the dNLR

Due to the poor underground environment, which makes the images of underground video in coal mines seriously degraded. However, the existing semantic segmentation model based on deep learning has the problem of fuzzy edge segmentation after image sharpness. A new method is proposed, which uses the fusion edge optimization module to process the boundary information and uses the gated convolution layer to connect the traditional feature extraction module to process the information in parallel. In order to supervise the learning of contour information, the binary cross entropy loss function was used to improve the learning effect, and the segmentation effect was optimized together with the loss function of conventional branches. The experimental results show that compared with other methods, the method based on the fusion edge optimization module improves the overall semantic segmentation accuracy and edge segmentation accuracy when the semantic segmentation task of the clarified coal mine image is performed.

Aiming at the problems of roadway deformation and support difficulty under the influence of lateral mining, taking 22304 return air roadway of Bulianta Coal Mine as the research background, through theoretical analysis and numerical simulation method, the distribution law of surrounding rock stress is obtained, and the relationship among different positions of surrounding rock, main stress difference and plastic area is analyzed from multi-dimensional perspective, and the failure mechanism of surrounding rock is obtained, and the control effect of surrounding rock reinforcement is further analyzed. The results show that the stress distribution of the surrounding rock in the side entry of the goaf shows a certain regularity in space, the main stress changes a lot in the rear of the working face, and the main stress difference is stable about 300 m behind the coal wall; the main stress difference has a positive correlation with the size of the plastic area; the main stress size and direction change of the surrounding rock behind the working face lead to the asymmetric distribution characteristics of the plastic area; then, the key parts of the reinforcement support are determined. Through analysis, the reinforcement scheme is highly targeted and can achieve the purpose of synergistic control of the two sides and the roof to ensure the stability of surrounding rocks.