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Running metro trains would inevitably influence the vibration deformation in the lower part of the roadbed structure. In order to study the effects of vibration, Qingdao metro line 3 tunnel was taken as the research background. The track coupling model was established using Abaqus-Simpack to analyze the train velocity, train axle load, and trackbed vibration forms for the vertical deformation of roadbed calendar effect to provide the reference in practical engineering design and deformation control. According to the analysis, influenced by the formation of Qingdao Metro Line 3, the time-history curve of the displacement excitation of the trackbed presents the distribution law of “large in the middle and small at both ends.” According to the sensitivity analysis, the deformation of the trackbed is most obviously affected by the form of trackbed; the second was the influence of the axle load of the train. The speed change of the subway train presented relatively little influence on the displacement of the trackbed. Although the deformation control ability of the integral trackbed was better, its vibration reduction performance was poor. The vibration reduction performance of the rubber cushion floating trackbed was the best, followed by the steel spring floating trackbed.

Complex geological conditions such as fault fracture zone will have a significant adverse impact on tunnel engineering, and collapse, large deformation, and other problems are prominent. The research on the large deformation mechanism and control of tunnel crossing fault fracture zone can provide guidance for tunnel safety construction. Based on the Jingzhai tunnel, combined with geological analysis, theoretical research, numerical simulation, and other means, this paper studies and analyzes the large deformation mechanism of the tunnel. The control effect of different advanced reinforcement measures is studied.

Carbonaceous slate is heterogeneous and anisotropic, which has a great influence on the stability of tunnel. In this paper, by means of laboratory test, field measurement, and numerical simulation, the surrounding rock stability and plastic zone distribution characteristics of the carbonaceous slate tunnel at different intersection angles are analyzed. First, combined with the Haibaluo tunnel project, Brazilian splitting and uniaxial compression tests of jointed carbonaceous slate are performed. The test results show that the tensile strength of carbonaceous slate is related to joint dip angle. When the joint angle is 0°, the tensile strength is the largest and decreases with the increase of the joint angle. The uniaxial strength of rock decreases first and then increases. Based on the discrete fracture network (DFN) technology, a calculation model is established. The calculation results show that the maximum displacement is 0.45 m, when the dip angle of the surrounding rock joint is 45°. The field measurement also shows that the dip angle of the surrounding rock joint has an important influence on the distribution of the plastic zone. When the joint dip angle is 45°, the plastic zone develops most strongly.

The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel.

Abstract Background Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in China in 2009. The purpose of this study was to describe the spatiotemporal distribution of SFTS and to identify its environmental influencing factors and potential high-risk areas in Shandong Province, China. Methods Data on the SFTS incidence from 2010 to 2021 were collected. Spatiotemporal scan statistics were used to identify the time and area of SFTS clustering. The maximum entropy (MaxEnt) model was used to analyse environmental influences and predict high-risk areas. Results From 2010 to 2021, a total of 5705 cases of SFTS were reported in Shandong. The number of SFTS cases increased yearly, with a peak incidence from April to October each year. Spatiotemporal scan statistics showed the existence of one most likely cluster and two secondary likely clusters in Shandong. The most likely cluster was in the eastern region, from May to October 2021. The first secondary cluster was in the central region, from May to October 2021. The second secondary cluster was in the southeastern region, from May to September 2020. The MaxEnt model showed that the mean annual wind speed, NDVI, cattle density and annual cumulative precipitation were the key factors influencing the occurrence of SFTS. The predicted risk map showed that the area of high prevalence was 28,120 km2, accounting for 18.05% of the total area of the province. Conclusions The spatiotemporal distribution of SFTS was heterogeneous and influenced by multidimensional environmental factors. This should be considered as a basis for delineating SFTS risk areas and developing SFTS prevention and control measures.

ObjectivePolycystic ovary syndrome (PCOS) is a heterogeneous endocrine and metabolic disease. The gut microbiota is highly correlated with androgen secretion and insulin resistance (IR), which are two potential major pathogenic mechanisms of PCOS. Currently, an antibiotic cocktail (ABX) is often used to construct pseudo germ-free mouse models for studies on the gut microbiota and PCOS. Our work aimed to study the effects of dehydroepiandrosterone (DHEA), a high-fat diet (HFD) and ABX on the heterogeneous phenotypes of PCOS mouse models by regulating the gut microbiota.MethodsPCOS mouse models were established by subcutaneous injection of DHEA alone or in combination with a HFD in wild-type and pseudo germ-free mice. The changes in ovary morphology and sex hormonal and glycolipid metabolic parameters were evaluated.ResultsWild-type mice treated with DHEA or DHEA+HFD showed a PCOS-like phenotype of hyperandrogenism, anovulation and polycystic ovaries. The former was combined with hyperinsulinemia and IR, while the latter was combined with glucolipid metabolic disorders, extremely heterogeneous hyperinsulinemia and IR. The phenotype of PCOS mice, especially the metabolic parameters, was correlated with the gut microbiota. The pseudo germ-free mice treated with DHEA or DHEA+HFD also showed a PCOS-like phenotype. However, DHEA could not induce hyperinsulinemia or IR in pseudo germ-free mice. Pseudo germ-free mice treated with DHEA+HFD exhibited decreased serum AMH level, glucolipid metabolic disorders and IR. Compared with the wild-type mice, the pseudo germ-free mice treated with DHEA showed significantly higher testosterone and lipid levels and lower blood glucose levels, and they did not present with hyperinsulinemia or IR.ConclusionA better and stabilized mouse model simulating the pathophysiological defects of PCOS was induced by DHEA alone rather than by DHEA+HFD. The ABX intervention improved glucose metabolic disorders and hyperinsulinemia but aggravated the hyperandrogenism and lipid metabolic disorders of the PCOS mice. This study suggests that the gut microbiota plays an important role in the heterogeneous phenotypes of PCOS mouse models.