In order to study the reasonable sealing length of bedding gas drainage boreholes, based on the gas-air dual gas and the negative pressure attenuation effect of boreholes, a three-dimensional borehole drainage model was established by finite element simulation software to monitor and analyzed the gas pressure of coal seam in the sealing section. Through the field test of 20915 haulage roadway, the gas drainage effects of different sealing lengths were investigated. The results show that under the negative pressure attenuation effect, the negative pressure of borehole drainage is exponential function distribution, and the reduction of gas pressure in coal seams at different positions of borehole is different, and the closer to the sealing position, the smaller the reduction of gas pressure. The peak gas pressure in the sealing section is proportional to the sealing length. According to the field test, the gas drainage concentration decreases to about${10}\%$after 130 days when the sealing distance is${10}\mathrm{\;m}$. The sealing length of${20}\mathrm{\;m}$maintains at about${30}\%$, and the extraction concentration of the borehole with sealing length of${30}\mathrm{\;m}$maintains at above${60}\%$after 130 days.

In order to solve the problems of roof breakage, support difficulty, water and mud inrush encountered in underground space operations such as mining and tunnel excavation, polyester ammonia/water glass organic-inorganic hybrid grouting reinforcement material was taken as the research object. By establishing a similar simulation grouting model, the temperature and pressure of measuring points at different distances from the injection point during the model grouting process were studied, and the slurry diffusion law was analyzed. The mechanical properties of grouting reinforcement were analyzed to test the consolidation effect. The results show that the polyurethane(PU)/water glass double-liquid grouting material exhibits irregular diffusion in the broken rock test model, and the curing reaction is an exothermic reaction. It reaches a maximum of${83.2}\mathrm{C}$at 10 min after slurry injection, which is 6.6 times of room temperature. The maximum pressure is${5.7}\mathrm{{MPa}}$, which is 1.425 times of the test load. The temperature and pressure increase rapidly with time and then slowly decline and finally stabilize. The uniaxial compressive strength range of polyurethane/water glass double liquid grouting and solid is${15.32}\sim {32.57}\mathrm{{MPa}}$, the maximum bearing capacity of triaxial loading is${41.9}\mathrm{{MPa}}$, the residual strength is${25}\mathrm{{MPa}}$, the maximum load of creep failure is${25}\mathrm{{MPa}}$, and the strength of the injected material is greatly improved after consolidation. Through the microscopic analysis of grouting reinforcement, it can be seen that the rock and slurry are closely cemented at the junction, and the grouting reinforcement effect is good.

Design cycle is one of the key factors that influence the development of customized mechanical products (CMP). Secondary development technology (SDT) is an effective method to enhance the efficiency of CMP. To better promote high-quality development of SDT in CMP applications, first, key technological advancements in SDT were briefly introduced based on the analyzes of the evolution of customized product production and the parameterized design methods. Then, research and application status of SDT in CMP design was analyzed from different perspectives, and existing problems are summarized. Finally, the future trends of SDT were explored in conjunction with modern information technologies, such as artificial intelligence (AI), collaborative design (CD), cloud technology (CT), and digital twins (DT), and the importance of domestic development platforms was emphasized. The results provides a reference for the application and further development of SDT in CMP.

With the continuous development of modern network information technology, the traditional passive network security defences are static defences that can not effectively respond to new types of network threats and can no longer meet the needs of network security. As the main network defence mean, active defence overcomes the many defects of traditional defence, can effectively respond to unknown network activities, showing strong advantages. Starting from the development process of active defense, the main technologies currently existing in network security active defense were sorted out, and the advantages and disadvantages of the main technologies at four levels, namely, network security intrusion defence, network security intrusion detection, network security intrusion prediction, and network security intrusion response, were summarised and analyzed, as well as the analysis and outlook of its future development direction.

To investigate the influence of Allee effect on population dynamics, the center theorem and bifurcation theory were used to study the bifurcation of a discrete predator-prey system with Allee effect on prey. The results indicate that an appropriate increase in the growth rate of the prey population will lead to the emergence of predators, the natural enemies of the prey by the transcritical bifurcation; Under the appropriate Allee effect, the system is stable, but if the Allee effect exceeds the critical value the system will undergo a double period flip bifurcation. The bifurcation parameter diagram also shows that as the Allee effect further strengthens, this flip bifurcation will lead to chaos in the system. From a biological perspective, in a certain ecological environment, a species that reproduces to a certain extent will encounter its natural enemies through “natural selection”. An appropriate Allee effect is beneficial for the stability of the predator-prey system, but if the Allee effect of the prey is too strong, it will cause a sharp decrease in predators and an effective restriction of no natural enemies. The prey will undergo a brief surge, exceed the environmental capacity, and then die out. To reduce the adverse effects of the bifurcations on the system, the state feedback is used to control the transcritical bifurcation and facilitate hybrid control to control the flip bifurcation. The numerical simulation results show complete consistency with the theoretical analyses.

Research on the factors affecting soil organic carbon density is of great significance for regulating climate change and sustainable agricultural development. Previous studies have mainly explored the relationship between various factors (e.g., climate, altitude, soil physicochemical properties, etc.) and the influence of soil organic carbon density, but less involved in the interaction relationship between factors. Typical soil profiles were collected in Anhui Province to estimate the soil organic carbon density (SOCD) in the 0~10 cm, 10~20 cm, 20~30 cm and 30~100 cm soil horizons. The structural equation model was used to analyze the effects of climate, elevation, vegetation, soil water content, human activities and other environmental factors on SOCD. The results are as follows. In the 0~30 cm soil layer, SOCD show a gradually decreasing trend, and the average SOCD in the 0~10 cm, 10~20 cm and 20~30 cm soil layers were 2.09, 1.63 and 1.10 kg/m², respectively. The average SOCD of 30~100 cm soil layer is 4.46 kg/m². The spatial distribution of SOCD in the province gradually increased from north to south. The SOCD of 0~10 cm and 10~20 cm soil layer is higher than 5.00 kg/m^-2, mainly distributed in the Jianghuai hilly downland and the Riverine Plain. The areas with SOCD higher than 3.00 kg/m² in the 20~30 cm soil layer were distributed in the South Anhui hilly region. The high SOCD values of 30~100 cm are mainly distributed in the South Anhui hilly region. In the structural equation model of 0~10 cm, 10~20 cm and 20~30 cm soil layer, land use has the largest positive influence on SOCD, and the influence coefficients are 0.22, 0.20 and 0.22, respectively. The average annual temperature has the largest negative influence on SOCD, and the influence coefficients are -0.04 and -0.03. Annual rainfall was the most significant in 30~100 cm soil layer, but land use and NDVI were not significantly affected (p>0.05). Topography affects SOCD through four paths: land use, NDVI, annual precipitation and average annual temperature. Human footprint affected SOCD through NDVI, and the effect on NDVI reached a very significant level (p<0.001). The structural equation model established in this study initially explained the relationship between different environmental factors, and provid a theoretical basis for SOCD regulation and agricultural sustainable development.

Controlled-source audio-frequency magnetotellurics (CSAMT) uses artificial sources, providing strong anti-interference capabilities. It is widely used in oil exploration, mineral surveys and other areas. Traditional 2D inversion technology is mature, and deep learning has recently made some research advancements in geophysical exploration. There is still a research gap in applying deep learning to CSAMT inversion. Therefore, developing a 2D inversion algorithm for CSAMT based on deep learning is highly significant for advancing the use of deep learning in electromagnetic exploration. The characteristics of deep learning components such as convolutional layers, pooling layers, fully connected layers, and the UNet network were introduced. An explanation was provided on how to construct the training dataset, the UNet network used in this study, and how to set various training parameters. The network was saved after training. When the inversion was needed, the net was loaded and the algorithm could predict the result. Several theoretical models were designed for inversion, and the experiment results verified the reliability and effectiveness of the algorithm. The time of the deep learning inversion and the tranditional inversion was recorded. Building training set needed much time, but the time of deep learning inverison was much less than the tranditional inversion. The deep learning inversion is more efficient than the traditional inversion.

An improved PSPNet(pyramid scene parseing network) network was proposed to automatically identify fractures in electrical imaging logging images, which was difficult to extract fracture features and led to low segmentation accuracy and large calculation of network parameters. Firstly, the backbone network in PSPNet was replaced with the optimized MobileNetV3 network, which could significantly reduce the number of network parameters and the amount of computation. Secondly, the asymptotic feature pyramid network(AFPN) was introduced to increase the interaction of multi-scale information and enhance the recognition ability of small cracks. Then, multi-depthwise Conv head transposed attention(MDTA) was introduced to extract global features and improve the extraction ability of key information. Finally, the combination of Focal Loss and Dice Loss were used as a loss function to solve the problem of unbalanced proportion of data sets. The experimental results show that the improved PSPNet network has a good segmentation effect on the fracture in the electrical imaging logging. Compared with the PSPNet network, mIoU(mean intersection over union) improved by 3.17% and mPA(mean pixel accuracy) improved by 6.38%. In addition, the number of parameters, calculation amount and weight of the proposed algorithm are reduced by 94.3%, 95.7% and 93.8% respectively compared with the original model. At the same time, the crack identification system based on CIFLog is developed, which can meet the practical needs of the electrical imaging logging.

A large number of loess landfills have been generated by the Pingshan land formation project carried out in Lanzhou area, which has a large safety hazard due to its low compaction and lack of necessary protection, resulting in the extensive development of geologic hazards such as loess caves, landslides, and so on. The pore microstructure of in-situ loess landfill with different water content was quantitatively studied through scanning electron microscope and Image J software, combined with fractal theory, the pore type, number, area, and change rule of the number of dimensions of the pore of the landfill loess with different water content was obtained, and the dynamic relationship between the pore structure and the wetting of loess and development of the caves were preliminarily analyzed. The results show that: with the increase of water content, the number and area of large and medium-sized hollow pores gradually decrease, while the number and area of small hollow pores decrease, but the area of small hollow pores increase, and the collapse of large and medium-sized hollow pores is the main reason for wet subsidence and deformation of loess; the pore dimension number of landfill topsoil has a linear negative correlation with water content, and a positive correlation with wet subsidence; the average pore dimension number of in-situ topsoil is 1.251, and the average pore structure is 1.251. The average pore dimension is 1.251; based on the characteristics of cave development in loess landfill, it is proposed that the protection treatment should be carried out in three aspects, such as the construction of drainage ditches, the reinforcement of caves, and the protection of slopes. The research results can provide theoretical support for the engineering construction and geologic disaster prevention and control research in Lanzhou landfill loess area.

Machine learning methods have been employed in the study area of Changyang Tujia Autonomous County for landslide hazard assessment, it could provide a scientific basis for geological disaster prevention and control efforts. Through the correlation analysis of 12 evaluation indicators (planar curvature, terrain undulation, surface roughness, slope, vegetation coverage, engineering lithology, distance to fault zone, distance to water system, rainfall, land use type, distance to buildings, and distance to roads) in the study area selected by historical landslide points, they were selected. And the evaluation model of the study area was constructed by calculating the information content of factors and integrate support vector machine (SVM) and gradient boosting decision tree (GBDT) models. The hazard of the study area was classified into four levels: extreme high, high, medium, and low, to generate hazard zoning. Subsequently, an assessment of the evaluation model was conducted. The results indicated that the very high hazard zone was mainly distributed in the southwest, central, and eastern parts of the research area. The distribution percentages of very high, high, medium, and low hazard zones predicted by the I-SVM and I-GBDT models were 15.86%, 21.29%, 33.51%, 28.68%, and 30.08%, 7.41%, 13.28%, 49.22%, respectively. The prediction of hazard zones by the I-SVM model aligned more closely with reality. The AUC values for the I-SVM and I-GBDT models were 0.859 and 0.829, respectively. The prediction of risk zones by the I-SVM model is deemed more reasonable and reliable.