In this study, an on-road driving experiment was designed to investigate the visual attention fixation and transition characteristics of drivers when they are under different cognitive workloads. First, visual attention was macroscopically analyzed through the entropy method. Second, the Markov glance one- and two-step transition probability matrices were constructed, which can study the visual transition characteristics under different conditions from a microscopic perspective. Results indicate that the fixation entropy value of male drivers is 23.08% higher than that of female drivers. Under the normal driving state, drivers’ fixation on in-vehicle systems is not continuous and usually shifts to the front and left areas quickly after such fixation. When under cognitive workload, drivers’ vision transition is concentrated only in the front and right areas. In mild cognitive workload, drivers’ sight trajectory is mainly focused on the distant front area. As the workload level increases, the transition trajectory shifts to the junction near the front and far sides. The current study finds that the difference between an on-road test and a driving simulation is that during the on-road driving process, drivers are twice as attentive to the front area than to the driving simulator. The research provides practical guidance for the improvement of traffic safety.

Multi-sensor fault diagnosis, especially when using heterogeneous sensors, substantially enhances the accuracy of fault detection in asynchronous motors operating under high-interference conditions. A critical challenge in multi-sensor fault diagnosis lies in effectively fusing data from different sensors. Deep learning offers a promising solution by transforming multi-sensor data into a unified representation, thereby facilitating robust data fusion. However, existing approaches often fail to fully exploit inter-sensor correlations and inherent prior physical knowledge. To address this limitation, we propose a novel graph neural network-based model that emphasizes graph structure construction for heterogeneous multi-sensor information fusion. Our framework includes (1) a multi-task enhanced autoencoder for node feature extraction, enabling discriminative representation learning, particularly with heterogeneous sensor data; (2) an adjacency matrix builder integrated with physical prior constraints to improve the generalization and robustness of the model; and (3) a graph isomorphism network to derive graph-level representations for fault classification. Our experimental results demonstrate the model’s effectiveness in diagnosing faults, as it achieves superior performance compared to conventional methods on two heterogeneous asynchronous motor datasets.

Due to the high degree of weathering, the red clay slope has low anti-disintegration performance, and the clay easily becomes wet and disintegrates after soaking in water. It causes geological problems such as slope collapse caused by soil softening. To study the disintegration characteristics of modified red clay, the disintegration test of red clay modified by using lignin fiber, clay particles and lime was carried out, analyzing the disintegration characteristics of improved red clay from physical and chemical perspectives and analyzing the improvement mechanism of three modifiers with the scanning electron microscopy test. The analysis results show that the water-holding capacity and disintegration resistance of soil mixed with lignin fiber decrease; the disintegration time of reshaped red clay increases with the increase in clay content; and the average disintegration rate of the soil decreases with the increase in clay content. With the increase in lime content, the soil cement increases. The integrity of the soil is enhanced, and its anti-disintegration ability is improved; the saturated moisture content of reshaped red clay increases with the increase in lignin fiber and clay content, while the saturated moisture content of soil decreases with the increase in lime content. The damage analysis shows that the larger the damage factor of soil, the worse its anti-disintegration ability, and the easier the soil disintegrates. The purpose of this paper is to explore the essence of the soil disintegration phenomenon, and on this basis, using high-quality improved materials, to improve the soil, which easily disintegrates. This move aims to significantly enhance the anti-disintegration ability of the soil, thereby improving its resistance to softening and disintegration, thereby effectively improving and maintaining the ecological environment. At the same time, the improved soil will help to improve the utilization rate of the slope and foundation soil, thereby reducing the economic cost of maintenance engineering. Against the current background of sustainable economic, social, and ecological development, it is of great strategic significance to ensure the sustainable availability of land resources in specific areas and maintain their productivity and ecological stability for a long time. The research into this subject not only helps to deepen the understanding of soil disintegration, but also provides strong technical support for the rational utilization of land resources and the protection of the ecological environment.

With the rapid expansion of the aviation industry, traditional static scheduling methods have become inadequate to meet the increasingly complex demands of efficient airport operations. To enhance the operational efficiency of multi-runway airports, this paper introduced a two-stage dynamic departure scheduling method based on continuous Markov chains. The pushback rate control strategy was extended to multi-runway scenarios to identify the optimal taxiway queue threshold in stage I. In stage II, the pushback rate control strategy with a known queue threshold was introduced into a multi-objective optimization model, aiming to minimize flight delays and operational costs including pushback waiting times, taxi fuel consumption, and environmental impact. Then, continuous-time Markov chains (CTMC) were employed to track aircraft state transitions in the taxiway queue, and a nested whale optimization algorithm was proposed to optimize both the pushback sequence and runway resource allocation. Results indicate that the proposed method reduced the average taxiway queue time by 55.58%, with delay reductions of up to 73.06%, offering significant cost savings and environmental benefits while improving flight punctuality. This innovative approach highlights the potential for optimizing airport resource scheduling in complex and dynamic environments.

A low-cost, timely, and durable long-term approach to resident travel surveys is crucial for authorities to understand the city’s transportation systems and formulate transportation planning and management policies. This paper summarizes commonly used wireless positioning technologies and uses the STDBSCAN method to identify travel endpoints based on the characteristics of trajectory location information. It uses Shenzhen cellular signaling data to visually analyze the spatial and temporal distribution of urban traffic demand, traffic correlation, and asymmetry of traffic flow between different traffic zones. The results confirm that mobile internet information represented by cellular signaling information can effectively reflect the traffic status of urban areas, which, compared to traditional travel survey methods, has the advantages of lower cost, more timely feedback, and can be durably carried out in the long term.

The rigid pile composite foundation method is the most commonly used method for strengthening weak soil foundations. In this method, piles usually need to pass through weak soil layers, and the pile end falls on a bearing layer with good bearing capacity. Under existing technical conditions, the thicker the weak soil layer, the longer the pile body, and the more difficult it is to ensure the construction quality of the pile. In response to this issue, some scholars have adopted the rigid pile composite foundation method with a thick cushion layer for reinforcement treatment. This article uses PLAXIS 3D (V20.04.00.790) software to establish a finite element model of rigid pile composite foundation with a thick cushion layer and simulate the process of foundation reinforcement. The influence of parameters such as thickness, compression modulus, and shear strength index of the cushion layer on foundation settlement and pile–soil stress distribution is studied, and the reasonable range of these parameters is analyzed under the condition of considering reinforcement effect. Through comparative analysis, it can be concluded that for deep and weak soil areas, the thickness of the cushion layer can range from 0.5 to 2.6. The thickness and compressive modulus of the cushion layer have a significant impact on the settlement of the foundation, the pile–soil stress ratio, and the stress of the pile body, while the shear strength index of the cushion layer has a relatively small impact on these parameters. Reasonably selecting the geometric and mechanical parameters of the cushion layer can effectively reduce stress concentration at the pile top and better play the role of the soil between piles.