To enhance the safety monitoring of power transmission lines, the distributed fiber Bragg grating array sensing technology was employed to measure the dynamic motion characteristics of optical power ground wire (OPGW) cables in laboratory conditions. The results show that this technology can effectively monitor the dynamic behavior of OPGW under simulated aeolian vibrations and galloping states, clearly recording various vibration patterns. The experimental data reveal that by increasing the spatial density of the grating array sensors and reducing the system's low-frequency phase drift, the monitoring performance can be further enhanced. It is evident that the distributed vibration sensing technology based on fiber Bragg grating arrays provides a novel technical approach for the distributed dynamic structural health monitoring of OPGW cables.

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 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.

In order to improve the prediction accuracy of pedestrian crossing patterns by conventional vehicles in unsignalized crosswalk road sections, a pedestrian crossing pattern prediction model integrating extreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms was proposed. First, the pedestrian-vehicle interaction data in the unsignalized crosswalk section were collected based on the cameras and LiDAR installed on the roadside, and the behavioral characteristics of pedestrians and vehicles were analyzed, and then the factors affecting the pedestrian crossing patterns were screened. Next, the predictive effects of different combinations when used as model inputs were explored. Finally, vehicle speed, vehicle-to-zebra crossing distance, time to collision(TTC) and pedestrian step speed were used as model inputs, and pedestrian crossing patterns were categorized into direct crossing and waiting crossing and used as model outputs, and the XGBoost-MLP model for pedestrian crossing pattern prediction was established. The prediction accuracy of this model for pedestrian crossing patterns reaches 88.65%, which compares with the single XGBoost model and the MLP model, and its accuracy is improved by 3.85% and 2.61% compared to the single XGBoost model and MLP model, respectively.

The armature structure and characteristic parameters in electromagnetic launch systems have a significant impact on the launch performance. In order to explore the dynamic characteristics and influencing factors of composite armatures, based on Maxwell's equations and electromagnetic field theory, a mathematical model of dynamic emission coupling between embedded and semi embedded composite armatures was derived. The influence of factors such as the capacity and initial working voltage of energy storage capacitors, the number of turns of driving coils, and the structural parameters of composite armatures on acceleration performance were analyzed and studied. The influence of structural parameters a, c, and d of composite armatures on emission performance was calculated. The results show that the higher the initial working voltage and capacitance of the energy storage capacitor, the higher the emission efficiency of the composite armature. The number of turns of the driving coil in the synchronous induction coil gun also affects the firing performance of the composite armature. As the number of turns increases, the firing efficiency does not always increase. The parameters a and c have little impact on the emission performance, while parameter d has a significant impact on the emission performance. The proposed mathematical model and analysis results can provide a theoretical basis and data reference for the design of composite armatures.

In order to resolve the problem that the volume of APP-based car-hailing industry increases and the trip counting results are susceptible to errors due to environmental influences, a novel method of APP-based car-hailing trip counting detection was proposed. The conversion mechanism between global navigation satellite system(GNSS) coordinate system and other coordinate systems was analyzed, and a network car counting device was designed by using loose coupling model and Kalman filter processing for combined navigation. The test results show that the distance counting device has a distance counting error of 0.42% in the straight road section and a distance counting error of 0.56% in the circular road section, and the distance counting accuracy meets the maximum error range required by the Verification Regulation of Taximeters(JJG 517—2016), which provides a new method for solving the distance counting error problem of APP-based car-hailing.

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.

Lithium slag holds significant potential for recycling and reuse. The barriers in the recycling process were addressed, such as surface protectants and surface by-products (diffusion pump oil and lithium hydroxide), that impeded efficiency, and the risk of uncontrolled reactions leading to explosions. A method of using water jet impact to desorb surface protectants and by-products from lithium slag and prevent reactive explosions was proposed. Based on the binding relationship between the lithium slag, surface protectants, and lithium hydroxide by-products, a bridging model for oil-lithium-hydroxide lithium particles was proposed. Subsequently, a water jet computational fluid dynamics-discrete element method (CFD-DEM) triple-component coupled depolymerization action model was proposed to explore the depolymerization characteristics of the oil-lithium-hydroxide lithium bridging model under different water jet pressures. The results show that the destruction time of particle adhesive bonds in the oil-lithium-hydroxide lithium model is inversely proportional to the jet pressure. At a jet pressure of 0.1 MPa, a particle adhesive bond destruction rate of over 95% can be achieved within 0.05 s. When the jet pressure exceeds 0.5 MPa, the time to reach a 95% bond destruction rate is just 0.015 s. This method effectively removes the surface protectant of the lithium slag and promptly eliminates lithium hydroxide and the foam it forms while ensuring safety, efficiency, and continuous digestion operations. These findings provide significant guidance for the application of water jet technology in the recovery of reactive metals and can be specifically applied to the high-efficiency, controlled, and safe recycling field of lithium slag.

Traumatic brain hemorrhage(TBH) often results in persistent cognitive and behavioral dysfunctions, with traditional rehabilitation models failing to adequately address self-efficacy and environmental interactions. Stage-based nursing interventions grounded in social cognitive theory(SCT) synergize cognitive restructuring, behavioral reinforcement, and environmental support. However, their application in TBH remains understudied. Aiming to examine the impact of SCT-based stage nursing interventions on cognitive function, self-management, self-efficacy, and daily living ability in TBH patients, a total of 105 TBH patients were randomly assigned to an intervention group(n=53) and a control group(n=52) by using a random number method. The control group received conventional rehabilitation therapy, while the intervention group received stage-based nursing interventions based on SCT in addition to conventional therapy. Changes in cognitive function, self-management ability, self-efficacy, and activities of daily living were compared between the two groups before and after the intervention. Cognitive function was assessed by using the Montreal cognitive assessment-basic(MoCA-Basic), self-management ability was evaluated by using the appraisal of self-care agency scale-revised(ASAS-R-C), self-efficacy was measured by using the general self-efficacy scale(GSES), and activities of daily living were assessed by using the Barthel index. The results reveal that after the SCT-based stage nursing intervention, the intervention group show significantly higher scores on MoCA-Basic, ASAS-R-C, GSES, and the Barthel index compared to the control group(P<0.05), with substantial improvements observed across all indicators. Stage-based nursing interventions grounded in SCT can significantly enhance cognitive function, self-management, self-efficacy, and activities of daily living in TBH patients, demonstrating considerable clinical value.