To study the influence of dense floor openings on existing main structures of subway stations, based on the reconstruction and expansion project of Dongsi Shitiao Station on Line 3 of Beijing Subway, finite element analysis method was used to simulate the dense openings on the bottom plate and the construction of vertical shafts. The stress and deformation characteristics of the station main structure were analyzed, and the impact of different phased opening schemes on the structural deformation of the station floor was discussed. Finally, verification was conducted in conjunction with on-site monitoring. The results show as follows. During the excavation and support process of shafts after floor opening, staggered excavation of adjacent shafts can effectively reduce the deformation of the station main structure, with the deformation being related to the depths of the two shafts and the distance between them. When the number of floor openings is large and dense, the opening order needs to be reasonably allocated. Through comparison and optimization of different phased opening schemes, it is found that compared to the three-phase opening scheme, the two-phase opening scheme increases the settlement by an average of 0.3 mm. However, the former shortens the construction period, improves construction efficiency, and is therefore recommended under the condition of meeting deformation control requirements.

To compare and analyze the differences in pore structure characteristics of coal from Huainan and Huaibei, this study focuses on No. 13 coal from Liuzhuang Mine in Huainan mining area and No. 7 coal from Qidong Mine in Huaibei mining area. Using mercury intrusion porosimetry and low-temperature nitrogen adsorption methods, the pore structures were analyzed, and fractal theory was applied to study the fractal characteristics of the pore structures. The differences in pore structures between Huainan and Huaibei coals were compared and analyzed. The mercury intrusion results show that the total pore volume and specific surface area of No.13 coal from Liuzhuang are 3.488 mL/g and 0.02 m²/g, respectively, while those of No.7 coal from Qidong was 4.926 mL/g and 0.027 m²/g, respectively, with Qidong coal having a larger pore volume. Both coals show the largest pore volume in macropores and the smallest in mesopores. Low-temperature nitrogen adsorption results indicate that the specific surface area ratio of micropores in No.13 coal from Liuzhuang is 76.25%, showing the most developed micropores. The specific surface area ratio of small pores in No.7 coal from Qidong is 79.79%, showing the most developed small pores. Fractal analysis demonstrate that both coals exhibited fractal characteristics. From mercury intrusion data, the fractal dimensions of pores larger than 100 nm for No.13 coal from Liuzhuang and No.7 coal from Qidong are 2.805 6 and 2.756 7, respectively. From low-temperature nitrogen adsorption data, the fractal dimensions of pores smaller than 100 nm for No.13 coal from Liuzhuang and No.7 coal from Qidong are 2.727 5 and 2.037 2, respectively, all within the range of 2 to 3. This indicates that the pore structure of No.13 coal from Liuzhuang is more complex and heterogeneous, especially in the range of small pores and micropores, where the fractal dimension differences are more significant. This may be related to differences in maceral composition and mineral content, where vitrinite is the main component for pore development in coal and shows a positive correlation with pore fractal dimensions. Exinite and inertinite are not primary components for pore development and show a negative correlation with pore fractal dimensions. Uneven mineral distribution may increase the heterogeneity and complexity of the coal pore structure, thus showing a positive correlation between mineral content and fractal dimensions in the two types of Huainan and Huaibei coal samples.

Aiming at the poor performance of existing algorithms in solving large-scale ship path planning problems and the lack of consideration of marine environmental factors such as eddies, a ship path planning method based on punishment pheromone ant colony optimization was proposed. Firstly, three evaluation functions were designed for the planned path: length, risk and heading. Secondly, ACO(ant colony optimization) algorithm inspired by reinforcement learning was designed to search the optimal path, which adds punishment pheromone to the traditional guidance pheromone, which can prevent ants from conducting ineffective searches. Finally, the simulation experiments of the improved algorithm under static environments demonstrate that the proposed algorithm is superior to traditional ACO, jump point search algorithm, and bi-directional search improved ACO in terms of path length, risk value and turn accumulation angle. Compared to the best metrics among these three algorithms, proposed algorithm still achieves a significant improvement in path length reduction of 6.1%, risk value reduction of 5.6%, heading accumulation angle reduction of 78.6%, and iteration number reduction of 53.3%. Especially when the mesoscale eddies and water flow are introduced, the proposed algorithm can still plan a more suitable path for ship navigation, which has positive application significance.

With the rapid development of the Internet and social platforms, the problem of spammer detection has become a major technical challenge in building a harmonious Internet environment. However, user data collected from social platforms are often subject to issues such as missing information and data noise. Therefore, in graph-based learning models for bot army detection, methods that use point estimation as weights fail to express uncertainty in regions with sparse or missing data. A graph neural network model for bot army detection, VRGAT, integrating variational inference, was proposed. It introduces a probability distribution for the weights and derives a variational approximation of the true posterior. By applying different convolution operations to the mean and variance, the model more accurately captures the variability in the data. Simulations based on the Twibot-20 dataset show that, compared to the best existing benchmark for bot army detection (F₁ = 88.12), VRGAT achieved an improved performance with an F₁ score of 89.64.In robustness experiments, when random noise was added at varying levels, the accuracy drop for VRGAT is significantly slower than for other baseline models, demonstrating its superior noise resistance. The experimental results demonstrate that the introduction of variational inference can enhance the effectiveness of spammer detection and improve the model's robustness against noise.

In order to analyze the effect of maximum window area and multi-resolution DEM (digital elevation model) on the extraction of optimal area for relief amplitude, explore feasible solutions to quantitatively analyze the optimal area, and classify landforms based on the best results of various types data. Based on three DEM data obtained, including ALOS (advanced land observing satellite), ASTER GDEM.V2 (Version 2 of the advanced spaceborne thermal emission and reflection radiometer global digital elevation model) and SRTM3 (3 arc-seconds shuttle radar topography mission), the influence mechanism was statistically analyzed by calling the Arcpy module and using the mean variation point method, respectively, with the maximum window area and the resolution of the DEM as a single variable. In order to determine the appropriate maximum window area, the traversal method to correlate the relief amplitude classification results with the DEM data were proposed, to determine the optimal area for different data according to the maximum correlation coefficient, and to arbitrate the elevation classification data to obtain the geomorphic distribution map. The results show as follows. The optimal area increases in a stepwise manner with the increase of the maximum window area. There is a negative correlation between the DEM resolution and the optimal area in the same area range, that is, the corresponding area decreases sequentially with the increase of resolution. Based on the correlation analysis, the maximum correlation coefficient of ALOS DEM is 0.785 0, which corresponds to the optimal area of 0.21 km². ASTER GDEM.V2 DEM is 0.764, with an area of 0.32 km², and SRTM3 DEM is 0.782, with an area of 0.40 km². The geomorphological classification maps corresponding to the optimal areas of the three data were obtained, and by comparing them with the spatial distribution of China's 1∶1 million geomorphological types, it is concluded that the distribution of geomorphological features of the experimental data is more reasonable, and the boundaries are more clear. It is concluded that the maximum window area has a greater influence on the optimal area compared to the DEM resolution, and in order to solve the influence of the maximum window area, the correlation analysis can provide a theoretical basis for quantitatively determining the optimal area of relief amplitude.

Traffic accidents pose significant risks to public safety and represent a critical issue in transportation systems. The accurate prediction of accident severity is essential for implementing effective prevention and intervention measures. An ensemble learning approach, combining the advanced algorithms XGBoost and MLP, was proposed to enhance the accuracy of traffic accident severity predictions. A stacked classifier was established and its performance in traffic accident prediction was thoroughly evaluated. The experimental results demonstrate that the integrated model significantly improves prediction accuracy compared to the traditional XGBoost model, with a notable 20.41% increase in the macro-average F₁ score. The advantages and innovations of the model, including model integration and network transformation, were highlighted. Additionally, the key features affecting the prediction results were analyzed, and the model's potential value in practical applications was explored. This study provides more scientific and efficient decision support for traffic safety management and is expected to play a crucial role in fields such as traffic management and intelligent driving.

The injection head is the key equipment to drive the coiled tubing in the coiled tubing operation machine. A fault occurred during the operation of a LG450 injection head, and it was found that the chain drive system was seriously damaged after disassembly. The macro and micro morphology analysis of the chain drive system shows that there are arc-shaped scratches on the outer side of the gear teeth and inclined scratches on the tooth surface. The chemical composition test, hardness test and metallographic test were carried out on the sprocket teeth, and the results showed that the material met the process requirements. The finite element simulation of the meshing process of the sprocket and the chain is carried out. The results show that the contact pressure distribution is consistent with the friction marks on the tooth surface and side of the failed sprocket. It is revealed that the failure reason of the chain drive system of the injection head is the spatial intersection of the chain roller and the sprocket axis, and the abnormal meshing of the roller and the gear teeth. This study shows that the motion state of the sprocket chain will have an important impact on the safety of the injection head, which is of great significance to guide the design and processing of the injection head.

In order to ensure the safety and reliability of the structural connection of assembled bridges, the strength test design of different types of interface agents was carried out based on bridge engineering and structural mechanics, and the mechanical properties of different cement grades, water-cement ratio and ash-sand ratio were analyzed. The change law of the strength of cement mortar in the early stage is faster than that in the later stage. The change law of the compressive strength of different types of interface agents with different ages was obtained, the prediction model of the relationship between different ages and strength of cement mortar was constructed, and the optimal mechanical properties of SS-III were proposed from the perspective of the bending-compression ratio. The relationship between different interface agents and the tensile strength of adhesion splitting was tested and analyzed by developing the test device of adhesion splitting tensile strength. The bonding performance of SS-III was determined to be the best interface bonding agent for assembled bridges from the perspective of the bending ratio and bonding properties. cement mortar as the interface agent for assembled bridges is more reasonable, which provides a new research idea for the safety and reliability analysis of the interface connection of assembled bridges.

The quality of internal plunge grinding process is affected by the grinding performance of different grinding wheels. In order to online monitor the grinding performance of different grinding wheels under the same experimental parameters during the internal grinding process. A particle swarm optimization-back propagation(PSO-BP) neural network-based grinding performance monitoring method for different grinding wheels was proposed. Firstly, the feature parameters of acoustic emission signal, power signal, vibration signal, displacement signal and current signal were extracted. Then, according to the eigenvalue data samples of each sensor and the global optimization function of BP neural network by particle swarm optimization algorithm, the PSO-BP online monitoring model was established by using PSO algorithm to optimize the initial weights and thresholds of BP neural network to accurately monitor the grinding performance of different grinding wheels. Finally, the BP neural network model and the PSO-BP model were analyzed and compared with the experimental data. The results show that the PSO-BP monitoring model has higher monitoring accuracy than the BP neural network model, with an average correct rate as high as 97.6%, and the validity of PSO-BP is verified through a large number of experiments, which is able to effectively monitor the grinding performance status of different grinding wheels.

The tight oil reservoirs in the eastern Ordos Basin are characterized by shallow burial, low pressure, small principal geostress, and low fracture pressure, which are significantly different from the general mid-deep tight oil reservoirs. Previously, the development of horizontal wells in this area through hydraulic fracturing was mainly based on field experience, and the design of the fracturing construction lacked a theoretical foundation, making the impact pattern of construction parameters unclear and the enhancement of production effect uncertain. Hence, research on the optimization of key parameters in fracturing construction is urgently needed. To maximize production efficiency, an integrated research method involving fracturing simulation and numerical reservoir simulation has been adopted. FrSmart has been used for fracturing simulation, Petrel for building geological reservoir models, and tNavigator for numerical simulation. Through the comprehensive application of various numerical simulation software, optimal cluster spacing, displacement, and single-segment fluid volume suitable for horizontal well fracturing in the reservoir were determined. By adjusting the conventional volume fracturing process parameters of well YCN-1 in the study area to a cluster spacing of 20 m, a displacement of 12 m³/min, and increasing the single-segment fluid volume to 1 000 m³, significant improvements in fracturing and production enhancement effects were achieved. Field test results show that the production of well YCN-1 after optimizing fracturing parameters is 29.98% and 50.27% higher than that of the unoptimized wells N-2 and N-3, respectively. Therefore, a method of critical significance for guiding the fracturing construction of shallow tight oil reservoirs, enhancing fracturing efficiency, and improving production effects has been proposed.