The external human-machine interface is used to enhance communication between autonomous vehicles and road users like pedestrians and cyclists, thus traffic safety and user experience are improved. The recognizability of the external interface is considered the foundation for ensuring effective and understandable signal functions, and it is explored to ensure pedestrians crossing safety. The form of information expression, interface location, and the speed of autonomous vehicles were considered as independent variables, and eye-tracking technology was used to collect eye movement and behavioral data. The identifiability of the interface was evaluated through repeated measures analysis of variance and logistic regression. The results show that the form of interface information, location, and vehicle speed significantly affect identifiability. The light band has the best identifiability; higher recognition efficiency is observed when the vehicle is traveling at a low speed; and the highest recognition efficiency is found when the interface is at P3, while the lowest is noted at P1 and P4.From the perspective of enhancing pedestrian traffic safety, this study provides a reference for the design of the external interface of autonomous vehicle while driving, and helps to improve pedestrian attention and recognition accuracy of the external interface.

As a key equipment and a major source of energy consumption in a building, chiller plant, if it fails, it will not only affect the normal operation of the system, but also cause serious energy waste. In order to improve the reliability of chiller system operation. A multi-strategy IDBO(improved dung beetle optimization algorithm) combined with a HKELM(hybrid kernel extreme learning machine) fusion fault diagnosis model was constructed to achieve accurate diagnosis of early faults in chiller systems. The model firstly employs hybrid kernel functions to improve the learning ability and generalization of KELM(kernel-extreme learning machine). Secondly, Bernoulli mapping, adaptive inertia factor, and Levy flight fusion dynamic weight coefficients strategies were used to improve the DBO(dung beetle optimization) algorithm in order to balance the global exploration performance of the DBO algorithm. Finally, the effectiveness of the IDBO algorithm was verified by benchmark functions, and the HKELM hyperparameters are optimized using the IDBO algorithm to construct a data-driven model for early fault diagnosis of chiller units. Through relevant training simulations and experimental validation, the accuracy of the proposed IDBO-HKELM model for early fault diagnosis of chillers is improved to 99.71%, which is an obvious advantage over other algorithms.

In the ASIST system, data on 86 917 abnormal events from 2017 to 2023 are collected as research objects, and an indicator system for abnormal events was established. To ensure the safety of aviation operations, accurate and reliable risk assessment models were developed to analyze abnormal events in depth, thereby achieving effective risk management. Firstly, the principle of catastrophe theory was introduced into the fuzzy inference system, which enables it to better handle complex issues and enhance the accuracy of evaluations. Then, a risk assessment model based on catastrophe theory and fuzzy inference system was developed to assess the risks of abnormal aviation events. Additionally, 56 cases with detailed background information records were selected for instance analysis, and compared with the cloud model, to verify the feasibility and accuracy of the model. Finally, relevant indicators were controlled using fuzzy methods, providing guidance for the safety management work of aviation operations.

A critical human factors analysis method for aircraft runway overrun and veer-off event was proposed to address the complex causal relationship of human factors that have not been fully revealed in the investigation of the event. Firstly, improve the human factors analysis and classification system model to identify the root cause of overrun and excursion event, and use the fault tree analysis method to identify the direct cause of runway overrun and veer-off event. Secondly, a human factors investigation decision support model was constructed, and obtained the causal chain of aircraft runway overrun and veer-off event. Then, a Bayesian network was constructed to quantify the importance of the causal factors for the runway overrun and veer-off event caused by human factors. Through predictive reasoning, diagnostic reasoning, and sensitivity analysis, the key causal chain of the runway overrun and veer-off event was obtained. The results indicate that 32 causal chains of overrun and veer-off events is obtained based on the proposed human factors investigation decision support model, comprehensively revealing the human factors and their coupling relationships of the event. Bayesian inference can obtain the key causal chain of “improper action execution/improper information preprocessing → unsafe behavior → runway overrun and veer-off”. The above conclusions are basically consistent with the findings of investigations into aircraft runway overrun and veer-off events, and they hold positive significance for improving the early warning and prevention capabilities of such incidents.

Pipeline leakage is a major cause of urban road collapse accidents. Understanding the evolution process and catastrophic mechanisms of road subsidence is crucial for preventing such safety incidents. Focusing on sewage pipelines in Beijing municipal roads, this study employs DEM-CFD(discrete element method-computational fluid dynamics) coupled flow-solid approach. Microscopic model parameters were calibrated based on laboratory experiments to simulate deformation and cavity evolution in sandy soil layers under various pipeline leakage locations and burial depths. Key parameters, including particle displacement, soil compactness, and medium flow, were analyzed during cavity formation. The results indicate that leakage at the top and middle of the pipeline leads to the formation of a funnel-shaped cavity as water and soil are lost. Without traffic load, the road surface exhibits negligible settlement. By analyzing particle displacement and compactness variations, the soil deformation was divided into stable, loose, and cavity zones, and an elliptical partition model was established for the loose zone. Based on the particle loss rate, the progressive failure process of the soil was classified into three stages: particle migration, rapid loss, and gradual convergence. In terms of cavity formation time, subsidence extent, particle loss rate, and total particle loss, leakage at the pipeline’s middle section yielded the highest values, followed by the top section, with the lowest at the bottom section. However, bottom leakage resulted in the largest loose zone. These findings provide theoretical support for detecting and identifying underground risks associated with urban road collapse disasters.

To investigate the spontaneous imbibition mechanism of shale reservoirs to fracturing fluid, the marine shale of Longmaxi Formation in Sichuan Basin and the continental shale of Yanchang Formation in Ordos Basin were taken as the research object, and the actual fracturing fluid was taken as the imbibition fluid. The spontaneous imbibition characteristics and control mechanism of marine and continental shales on fracturing fluid were studied by the spontaneous imbibition test system and the characterization methods of mineral composition and microscopic pore. The results show that the spontaneous imbibition curves of the two groups of shale increase rapidly and then decrease slowly with the increase of imbibition time, and the decrease of imbibition is mainly due to the decrease of shale weight caused by the dissolution of soluble minerals. TOC (total organic carbon) and quartz content of the two groups of shale exhibit a negative correlation with the maximum imbibition, while clay minerals, carbonate and pore volume exhibit a positive correlation with the maximum imbibition. Although the clay mineral content of Yanchang Formation shale is higher than that of Longmaxi Formation shale, its maximum imbibition is relatively low, which may be due to the more developed pore structure of Longmaxi Formation shale. The adsorption of hydrophilic minerals in shale and the capillary action of microscopic pores are the main driving forces for the spontaneous imbibition of fracturing fluid, and the influence of microscopic pores is stronger than that of hydrophilic minerals. For the flowback problem of fracturing fluid in shale gas wells, a better fracturing fluid ratio scheme can be proposed by adjusting the chemical composition of fracturing fluid and combining the spontaneous imbibition test results of fracturing fluid to meet the actual engineering needs. The research results can provide theoretical references for efficient development of shale gas.

The issue of distributed PV(photovoltaic) integration capacity allocation in distribution networks was addressed, which focuses on the photovoltaic integration capacity configuration based on load-storage coordination optimization. A coordinated regulation model for load-storage systems, incorporating energy storage, dispatchable load, and interruptible load, was first established. Based on this model, the constraints of load-storage regulation capabilities were considered. The optimization model for the configuration of distributed PV integration in the distribution network was developed with the objective of maximizing the capacity of distributed PV and the net investment and operational profit of the distribution network. Simulation results show that through the coordinated regulation of distributed PV with energy storage, dispatchable loads, and interruptible loads, significant improvements in the integration of distributed PV into the grid can be achieved.

Aiming at the thermoacoustic oscillation in the combustion process of a new generation of gas turbine, several time-domain analysis methods of high frequency data signals were retested and compared by constructing a complex network model. The results show that the two complex network models, node strength and network diameter, can give an earlier warning of thermoacoustic oscillations than the traditional time-domain analysis methods(root mean square and time kurtosis). Coupling prediction effect and data processing time, the node strength is preferred to construct the complex network model. Finally, the method was applied to the experimental data analysis of multi-nozzle micro-mixing burners, and it was found that the characteristic turning point of the method is about 2.3 s ahead of limit cycle analysis and the characteristic point of statistical analysis.

Recently, SSVEP(steady-state visual evoked potential-based BCI(brain-computer interface) researches have achieved significant development. However, the practical application of BCIs are still limited by several factors, one of which is the visual stimulus source. Most SSVEP-BCI systems rely on monitors, which are not portable and thus restrict the practical use in daily life. VR glasses, as wearable and portable devices, can provide realistic and immersive stimulus sources, which do not rely on monitors. Thus, they offer significant potentials for BCI applications. The VR(virtual reality) technology was introduced to display VR-SSVEP visual stimuli in 3D environment and enables subjects to immersively engage in BCI. The performance of 3D and 2D visual stimuli based on VR-SSVEP were compared in this study. The experimental results demonstrate that the performance of 3D visual stimuli is better than that of 2D visual stimuli. The average classification accuracy of 3D stimuli reaches 90.10%, which is 7.08% higher than 2D stimuli. Additionally, a 2-second stimulation duration achieves an optimal information transfer rate. This study confirms that 3D visual stimuli can effectively enhance SSVEP recognition performance, which indicates a practical use of the system and provides a novel approach for applying VR devices to the SSVEP paradigm.

The effects of different diversion schemes on air flow and heat transfer in a countercurrent drying tower were studied by numerical simulation of maize drying process based on porous medium model. The influence of the angle box arrangement on the temperature field and velocity field in the tower was studied by numerical simulation and experiment. The results show that the cross arrangement of corner boxes can improve heat and mass transfer efficiency, reduce heat loss and solve the problem of uneven drying. At the same time, the increase of the inlet speed can also improve the uneven temperature distribution and improve the drying effect. It can be seen that the two key factors, the arrangement of the corner box and the inlet speed, should be fully considered in the design of the counter-current drying tower for corn drying. The optimization of drying tower structure can improve the overall drying efficiency and corn quality. The conclusion of this paper provides a useful reference for corn drying industry to reduce the cost and improve the quality of corn.