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The existence of fissures poses a serious threat to the safe production of underground mines, and this paper investigates a polymer grouting material for filling fissures in underground mines. To optimise the ratio of polymer grouting materials, this paper designed 16 test groups using the orthogonal test method to find the most reasonable slurry ratio. In order to study the gel diffusion process of polymer slurry in the fissure and to explore the changes of various parameters of the slurry after injection, simulated grouting tests were carried out, and the distribution laws of viscosity, pressure, and diffusion distance of the slurry were discussed. The findings indicate that when the proportion of ethylenediamine polypropylene oxide tetrol: glycerol polyether: catalyst: foam stabiliser is 10:8:0.5:0.4, the polymer grouting material has excellent compressive strength, and the maximum compressive strength can reach 12.31 MPa. Prior to reaching the gel time point, the viscosity of the polymer slurry was nearly constant, which is basically maintained at 0.772 Pa·s under normal temperature and pressure, but after reaching the gel time point, it abruptly rose. As the slurry mass increased, so did the penetration distance and pressure; in the simulated grouting test, when the slurry mass was 400 g, the maximum diffusion distance of the slurry reached 39 cm. Conversely, as the fracture pore size increased, the diffusion distance and pressure of the slurry decreased. Along the diffusion path, the slurry pressure progressively drops, but this change is not synchronised with the diffusion distance's change. This work can serve as a reference for the configuration of polymer slurry and aid in comprehending the diffusion law of the slurry within the fissure.
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In this study, organic polymer polyurethane grouting materials were prepared using isocyanate and polyether polyol as the main agents and various additives, the slurry coagulation process was investigated, and the mechanical properties of the polymer samples were tested to explore the influence of the density and soaking time of the polymer on the strength of the samples. The microstructure of the polymer was observed via electron microscopy, and relying on image analysis software, the structural parameters of the polymer cell were analyzed and calculated; the model equation between density and yield strength was established based on the strength model of porous materials developed by Gibson and Ashby. The results show that the initial viscosity and gel time of the polyurethane slurry decrease with the increase of the initial temperature, and the viscosity changes abruptly when the slurry reaches the gel point. The mechanical properties of the polymer increased with increasing density and decreased with increasing soaking time. The interior of the polymer is a porous structure and the pores are approximately spherical; the higher the density of the polymer material, the more uniform the stress distribution of the material, and the higher the percentage of the matrix, which in turn leads to better mechanical properties of the material. The diameter of the polymer cell is negatively correlated with the density, and the model established based on the microscopic parameters of the cell can better predict the yield strength of the polymer. This study helps to deepen the understanding of the microstructure and mechanical properties of polyurethane and provides a certain reference for the application of polyurethane in underground mine reinforcement engineering.
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Background: Depression rates among adolescents have risen dramatically over the past decade. Therefore, preventing depression among adolescents is particularly important. Differences in lifestyle habits may play a role in depression. Purpose: This study aimed to explore the influence of living habits on depression levels among rural middle school students in Northeast China and to provide a theoretical basis for developing interventions to reduce depression levels in middle school students. Methods: A total of 296 middle school students aged 13-15 years from Benxi City, Northeast China completed the anthropometric measurements, Physical Activity Scale-3 (PARS-3), and the Self-Rating Depression Scale (SDS). Their average screen time in the most recent week, parents' education level, and monthly family income were collected through a questionnaire. Results: Females had higher depression scores than males (41.0 ± 6.9 vs. 37.9 ± 8.0). Physical activity (ß = -0.38, t = -7.06, P < 0.01), family income (ß = -0.20, t = -4.07, P < 0.01), screen time (ß = 0.16, t = 3.34, P < 0.01), age (ß = 0.15, t = 3.16, P < 0.01), sex (ß = -0.13, t = -2.74, P < 0.01), and sleep quality (ß = -0.08, t = -1.87, P < 0.01) are important factors related to depression levels. Conclusion: The preliminary analysis results showed that among middle school students in rural Northeast China, the depression level of females was significantly higher than that of males. Poor quality sleep, low levels of physical activity, low household income, and long screen time were positively associated with depression. Therefore, strengthening physical activity, improving sleep quality, and reducing screen time are of clinical relevance in preventing and reducing depression.
Assuntos
Depressão , Estudantes , Masculino , Feminino , Adolescente , Humanos , Depressão/epidemiologia , Estilo de Vida , Inquéritos e Questionários , Exercício FísicoRESUMO
Due to the limitation of less information in a single image, it is very difficult to generate a high-precision 3D model based on the image. There are some problems in the generation of 3D voxel models, e.g., the information loss at the upper level of a network. To solve these problems, we design a 3D model generation network based on multi-modal data constraints and multi-level feature fusion, named as 3DMGNet. Moreover, 3DMGNet is trained by self-supervised method to achieve 3D voxel model generation from an image. The image feature extraction network (2DNet) and 3D feature extraction network (3D auxiliary network) are used to extract the features of the image and 3D voxel model. Then, feature fusion is used to integrate the low-level features into the high-level features in the 3D auxiliary network. To extract more effective features, each layer of the feature map in feature extraction network is processed by an attention network. Finally, the extracted features generate 3D models by a 3D deconvolution network. The feature extraction of 3D model and the generation of voxelization play an auxiliary role in the training of the whole network for the 3D model generation based on an image. Additionally, a multi-view contour constraint method is proposed, to enhance the effect of the 3D model generation. In the experiment, the ShapeNet dataset is adapted to prove the effect of the 3DMGNet, which verifies the robust performance of the proposed method.