The Ordos Basin's marine carbonate reservoirs often experience downhole complexities such as gas invasion, lost circulation, and borehole collapse during drilling. It is particularly important to systematically evaluate the overpressure mechanisms and accurately predict formation pressure for the exploration and development of the target strata. The carbonate reservoir of the Ordovician Majiagou Formation in the eastern Ordos Basin was selected as the research target. Overpressure mechanisms were analyzed using the combination of well log curve analysis, effective stress-acoustic velocity method, acoustic velocity-density method, and comprehensive analysis method. A physical model of elastic modulus for carbonate rock matrix, framework, mixed pore fluid, and saturated fluid rock was established, and combined with the effective stress principle, a method for predicting formation pressure was developed, applicable to the Majiagou Formation in the eastern Ordos Basin. Finally, the model's accuracy was verified using field data. The results indicate that hydrocarbon generation is the primary cause of overpressure in the target reservoir, with undercompaction and structural compression as secondary factors. The new model achieves a relative error of less than 10%, demonstrating strong applicability and high predictive accuracy. The research results provide guidance for the study of hydrocarbon accumulation in the Majiagou Formation in the eastern Ordos Basin.

Aiming at the issue of how to realize the dynamic updating of the digital twin model of historic buildings, a “build-compare-update” updating idea was proposed. Firstly, the shape distribution method was used to draw the shape distribution curve of the digital twin model of the historical building at different time points, based on completing the construction of the digital twin model of the historical building. Secondly, the mean absolute error method was used to assess the similarity of the two models. Furthermore, according to the results of the similarity assessment, the updating strategy for the model of the components of the historic building was set up and the updating of the digital twin model was completed with the help of relevant software. Finally, to validate the design, the arch column component of the Jade Buddha Hall of Hongci Temple was used as a case study. The results show that the proposed idea of updating the historical building model and its implementation method not only lay the foundation for realizing the dynamic updating of the digital twin model of historical buildings, but also provide reference value for the intelligent protection of historical buildings.

The traditional steady-state visual evoked potential (SSVEP) brain computer interface system usually uses a small number of frequencies for encoding, resulting in a limited number of encodings to dozens, which cannot meet the demands of environmental tasks with a large number of instructions. To address this issue, a Hamming distance multi frequency code (HDMFC) paradigm and corresponding recognition algorithm based on Hamming distance were proposed. The Hamming distance was combined with stimulus paradigm encoding and signal recognition algorithms to encode 120 instructions using 8 frequency signals. Data collection and classification experiments were conducted on 7 subjects. The results show that the accuracy of the 120 encoding online experiment based on Hamming distance can reach 90.60%. The research results provide an effective method for increasing the number of SSVEP paradigm codes and improving classification performance, verifying the practicality and effectiveness of Hamming distance in this field.

In order to compare the clinical effect of different surgical technique of ligamentum flavum in percutaneous endoscopic interlaminar lumbar discectomy. From March 2015 to March 2019, 86 cases of L₅-S₁ lumbar disc herniation (LDH) were analyzed retrospectively. According to the different treatment of ligamentum flavum during operation, the patients were divided into control group (n=55) and modified group (n=31). The age, gender, course of disease, duration of surgery, intraoperative bleeding, hospital stay, and surgical related complications between the two groups of patients were analyzed and compared. One month, three months and six months after the operation, the routine outpatient reexamination and follow-up were carried out. Numerical rating scale(NRS) and modified macnab were used to evaluate the effect of operation. SPSS 19.0 was used to process the data. Mean±SD was used for measurement data. Comparison between groups using independent sample t-test, and analysis of variance of repeated measurement data was used for the NRS score comparison before and after operation. The counting data were expressed in percentage, Fisher exact test or χ² test were used for comparison between groups, and Mann Whitney rank sum test was used for comparison of follow-up time and grade grouping data between groups. The results show that the postoperative pain score of both groups decrease significantly(overall variance within the group F_within=197.477, P=0.001). There is no difference between the two groups (inter group overall variance value F_interg=1.701, P=0.203).The time factor has no effect on the results of the two groups(Interaction effect value F_inter=2.231, P=0.108). The NRS score of the modified group is significantly lower than that of the control group (t=2.086, P=0.046). According to modified Macnab criteria, there is no significant difference between the two groups (distribution of postoperative outcomes U=782.000, P=0.308). It is concluded that the modifiedsurgical technique of ligamentum flavum in the PEILD will not prolong the operation time or increase the amount of bleeding. While removing the herniated intervertebral disc, it can preserve the integrity of the relevant tissue structure, achieve satisfied clinical effect, and conforms to the core concept of minimally invasive surgery.

Modeling social networks using directed graphs and calculating the influence of nodes in settlement graphs using deep learning methods are important branches in the field of social research. In order to solve the problem that graph neural networks cannot well calculate the influence of nodes based on constructed node features, inspired by GraphGPS, a heat module was designed to propose a method of calculating the influence of nodes that integrated semantic, behavioral, and heat information in real networks. Firstly, the self-information obtained based on nodes' multiple centrality and orthogonal distribution sampling was used as the initial semantic features of the nodes. Secondly, the node features were fused by graph neural network. Once again, the node heat information was learned by the heat module. Finally, the fusion of the extracted semantic, behavioral, and heat features was implemented to calculate the node influence. Experiments were conducted on four real network datasets. The results show that the model with the addition of the heat module can effectively calculate node influence.

In order to improve the accuracy of in-camera parameter calibration and reduce the error caused by inaccurate estimation of corner coordinates, a subpixel iterative corner optimization algorithm was proposed. By modeling the camera image, integrating the distortion mathematical model, and using the subpixel iterative algorithm, the gradient value changes of points in the search window were calculated and the corner coordinates were iteratively optimized to provide more accurate initial values for calibration. Combining with the optimized corner coordinates, Zhang's calibration method was used to solve the internal parameters, and the influence of corner points on the acquisition of diagonal points and camera calibration was discussed through experiments. The calibration accuracy was characterized by reprojection error, and the effectiveness of the iterative algorithm was tested. The experimental results show that the algorithm can effectively improve the accuracy of corner acquisition and the calibration accuracy of camera parameters by using fewer iterations.

An improved DGA-YOLOv8 offshore ship target detection algorithm was proposed to tackle the issues of low accuracy and single ship detection categories that are present in traditional ship target detection algorithms. Firstly, the network was adapted to include deformable convolution, which expanded the model's receptive field. Learnable offsets were introduced, allowing the model to adaptively adjust the size and shape of the receptive field in response to the actual shape of the object, ensuring that the convolution area can precisely cover the contour of the ship object. Secondly, the incorporation of a GAM(global attention mechanism) attention mechanism enabled the network to effectively emphasize the key features of ship targets, thereby enhancing the target recognition capability. The experimental results demonstrate that the improved algorithm achieves accuracy and average accuracy mean (mAP) of 96.4% and 92.2%, respectively. An frames per second(FPS) of 43.55 is recorded, indicating not only an enhancement in accuracy but also the maintenance of a certain detection speed, thus fulfilling the requirements for real-time detection. When compared with other mainstream algorithms, such as faster region-based convolutional neural network(Faster R-CNN) and YOLOv5s, YOLOv10. The results show that the proposed algorithm exhibits higher average accuracy and significant superior classification performance.

The issue of gate assignment at modern airports involves coordinating multiple interests, including passenger satisfaction, efficient allocation of airport resources, and control of carbon emissions. A multi-objective nonlinear integer programming model was developed to solve this complex problem, which considering constraints such as flight type, aircraft model, and gate availability. The optimization objectives of model include minimizing passengers' walking distance, maximizing aircraft-gate matching, and minimizing carbon emissions. An improved adaptive genetic algorithm was proposed to solve the gate assignment problem. In the population initialization phase, a combination of random and greedy-perturbation strategies was employed to generate a more diverse initial population. The probabilities of crossover and mutation were adaptively adjusted during the algorithm's iterations. Both crossover-first and mutation-first evolutionary strategies were applied to enhance solution efficiency and global search ability. To validate the effectiveness of the algorithm, some simulation experiments were conducted using operational data from the domestic airport. The improved adaptive genetic algorithm was compared with traditional genetic algorithms and particle swarm optimization algorithms. Results show that the improved algorithm significantly outperforms the others in terms of gate utilization efficiency, passenger satisfaction, and carbon emission control. Furthermore, the effectiveness of the proposed improvement strategies was confirmed through experimental analysis, demonstrating the stability and performance of the algorithm. The proposed model and algorithm provide robust decision support for gate management, contributing to enhanced passenger satisfaction, efficient resource utilization, and sustainable environmental development.

The mining method of ion-type rare earth ores has been optimized by the in-situ leaching process. However, unorganized leakage may cause resource loss and impacts on the ecological environment. Therefore, anti-seepage work during the mining process is of particular importance. Based on three grouting anti-seepage materials, namely cement composite material (C-CM), liquid silicon-based composite material (Si-CM), and acrylate composite material (A-CM), and compounded with kaolin tailings, various indicators of each curtain grouting material were analyzed and compared through tests including the pH, density, viscosity, setting time, solid sand body strength test, slurry diffusion simulation, permeability, and durability test of the curtain grouting materials. The results indicate that the solid sand body of C-CM has relatively high strength, while A-CM and Si-CM have lower viscosity, stronger groutability, and the solid sand bodies formed have lower permeability coefficients, making them more suitable for anti-seepage in areas with smaller fractures. Si-CM degrades relatively quickly in dry-wet cycles and freeze-thaw cycles, and the degradation product is mainly SiO₂. In addition, the strength and anti-seepage performance of the solid sand body of the anti-seepage material can be improved to a certain extent by adding kaolin tailings, and the early durability of the material is also increased.

The water absorption and expansion characteristics of the swelling surrounding rock can have adverse effects on the tunnel lining structure, which can easily lead to the induction of various engineering disasters, and impact the progress of the project, as well as the normal operation of the train in subsequent stages. Therefore, the mechanical characteristics and support method selection of such tunnels have always been of great concern. Taking a tunnel passing through expansive mudstone in Inner Mongolia as an example, based on field monitoring and numerical calculation methods, the deformation and failure characteristics of the tunnel lining structure in mudstone tunnel under the original lining conditions were analyzed. Subsequently, the applicability of double-layer preliminary lining in such tunnels was studied from the aspects of support deformation and mechanical properties, and corresponding lining parameter suggestions were proposed. The results indicate that the expansion of mudstone causes the deformation of the original lining structure (single-layer preliminary lining) to reach a magnitude of 309 mm. Such deformation leads to significant encroachment of the lining and necessitated forced arch replacement during the tunnel construction process. In comparison to the single-layer preliminary lining, the double-layer preliminary lining construction method has been found to significantly improve the deformation and stress state of the tunnel in expansive mudstone, achieving a deformation reduction rate that exceeds 63.88%. As the thickness of the secondary preliminary lining increases, the deformation and stress of the preliminary lining gradually decrease, achieving a maximum deformation reduction of 234.5 mm and a compressive stress reduction rate of up to 39% under various conditions. Consequently, the double-layer lining construction method can be considered for tunnels in expansive mudstone, with the thickness of the secondary preliminary lining being reasonably selected based on deformation requirements. The research findings can serve as a valuable reference for similar projects in the future.