In order to explore the concentration level and health risks of polycyclic aromatic hydrocarbons (PAHs) in cabin of new cars, air samples of 15 newly produced passenger cars in the gaseous and particulate phases under normal temperature and high temperature conditions were collected by using particulate samplers in series with polyurethane foam (PUF) sleeves. The contents of 16 priority PAHs in the samples were determined by GC-MS, and the health risk assessment of drivers and passengers was carried out. The results show that the average detection rates of 16 PAHs in the gaseous and particulate phases is 2.17~50.00.Among them, naphthalene (Nap), phenanthrene (Ace), and phenanthrene (Phe) are detected in some sample cars under high and normal temperature conditions, while phenanthrene (Acy) is detected in all sample cars under high and normal temperature conditions, and the rest of the substances are only detected under high temperature conditions. The concentration of PAHs in cabin of cars under high temperature conditions is higher than that under normal temperature conditions, and the concentration in the gas phase is higher than that in the particulate phase. Overall, Nap exhibites the highest concentration. The carcinogenic health risks of Nap, Ace, Phe, and Acy under high temperature conditions range from 3.53×10^-11 to 4.54×10^-9, while under normal temperature conditions, they range from 1.70×10^-11 to 5.16×10^-9. It can be seen that PAHs in the gas phase and particulate phase in cabin of new cars, can be effectively collected by using particulate matter samplers in series with a polyurethane foam (PUF) sleeves for sampling. The detected concentrations of PAHs in cabin of cars, is low, the overall carcinogenic health risk is less than 10^-6, and the carcinogenic risk is low.

The concept of “resilience” is introduced as a new research direction for cities to withstand uncertain risks in the face of complex challenges such as global environmental changes, accelerated urbanization, and frequent epidemics. To explore the spatiotemporal evolution and obstacle factors of urban natural disaster resilience in Shaanxi Province to enhance resilience against natural disasters. The entropy weight-technique for order preference by similarity to ideal solution(TOPSIS) method was used to assess resilience levels across four dimensions: economy, society, infrastructure, and ecological environment. The spatiotemporal evolution characteristics of resilience in Shaanxi Province from 2018 to 2022 were examined using a combination of GIS, Theil index, and center-standard deviation ellipse. An obstacle degree diagnosis model was employed to analyze influencing factors. The results indicate that over time, the overall resilience level against natural disasters in cities, except for Xianyang, shows an upward trend. Overall resilience, social resilience, and infrastructure resilience increase, while ecological environment resilience slightly declines. Spatially, the resilience in Shaanxi Province follows a pattern of “Guanzhong region > Northern Shaanxi region > Southern Shaanxi region.” The major influencing factors are infrastructure resilience and ecological environment resilience, with the length of urban drainage pipelines and greening coverage area identified as the top obstacles restricting urban resilience. The research findings are expected to provide theoretical references for regional natural disaster management and resilient urban planning in Shaanxi Province.

Oil and gas drilling and production wellsites are complex and have many types of potential safety hazards, in order to improve the accuracy of the identification of potential safety hazards in wellsites, an oil and gas drilling and production wellsite potential safety hazard identification method based on improved YOLOv5 was proposed. Firstly, in order to solve the problem that the background of the picture was complex and the recognition difficulty increased, the SimAM attention mechanism was introduced in the backbone network; secondly, in order to solve the problem that the scales of the types of hidden hazards were different and there were multiple scales in one picture, the original feature fusion was replaced by adaptive spatial fusion of features (ASFF). Lastly, the hidden hazard recognition effect of the improved model was validated by comparing the model with other models. The results show that the improved YOLOv5 model improves the average accuracy value of recognition by 10.4%, and has a better recognition effect on the safety hazards of oil and gas drilling and production well sites. In order to solve the limitation of video monitoring and identification of oil and gas drilling and production wellsite safety hazards, a set of intelligent wearable device was developed, which effectively improved the portability of the identification of wellsite safety hazards.

In order to reduce the risk of logistics drones to the ground after failure or collision in the air, a model for logistics drones to the ground risk assessment was constructed, focusing on the risk analysis of drone failure and crash. Firstly, in the drone failure and crash risk model, the possible failure of drones in different flight stages was discussed in detail, including vertical fall and horizontal fall, as well as the casualties and economic losses that these falls may cause to ground personnel. Secondly, the collision and crash risk model was analyzed the dynamic behavior of drones after a collision in the air and its impact on ground safety, including collision momentum-energy conservation and collision consequence assessment. Finally, a comprehensive three-dimensional risk assessment matrix was constructed, combining the possibility of accidents, the number of deaths on the ground and the economic losses to evaluate the risk level in different scenarios. The case analysis shows that the number of deaths on the ground is within the order of 10^-6, and the risk level in different regions is obtained. It can be seen that this method provides important risk assessment tools and data support for logistics drone operators and relevant policy makers.

The efficiency, precision, and automation of fruit and vegetable picking are realized through the integration of multiple mechanical arms in multi-mechanical arm cooperative picking technology, effectively addressing the high costs and low efficiency associated with traditional manual picking methods. The research progress in multi-mechanical arm cooperative picking technology was summarized, and the framework of the multi-mechanical arm cooperative picking system was comprehended. In light of the decision-making challenges in cooperative picking, the cooperative methods and task allocation within cooperative picking task planning was analyzed, and the collision detection, obstacle avoidance strategies, and path planning techniques utilized in cooperative picking with multiple robotic arms was reviewed. The future development direction of multi-mechanical arm cooperative picking technology is outlined, with a proposed development trend that envisions the combination of machine and agronomy, human-machine collaboration, decision-making big models, and multi-algorithm fusion.

Strengthening green and low-carbon environmental control technologies is critical because the industrial, agricultural, and construction sectors face three major challenges: high energy consumption, high emissions, and low energy efficiency in controlling temperature and humidity in specific areas. Heat pump-driven desiccant wheel air conditioning provides high evaporative temperature cooling, effective humidity management, and easy integration with renewable energy equipment. The typical heat pump-driven desiccant wheel air conditioning system was analyzed, the research progress of heat pump independently driven desiccant wheel air conditioning system under various coupling methods was reviewed, and the performance, regeneration temperature, and application occasions of heat pump independently driven desiccant wheel air conditioning system under various working conditions were summarized. Furthermore, the solar-assisted heat pump-driven desiccant wheel air conditioning system was introduced. Finally, existing challenges and future developments of heat pump-driven desiccant wheel air conditioning system were analyzed and projected.

The interporosity flow between matrix pores and fractures is the main cause of strong attenuation and dispersion of elastic wave in the seismic frequency band. A fractional equation for interporosity flow is developed. The constitutive relationship of double-porosity medium was improved in the frequency domain, in which three factors representing the effects of fractional interporosity flow were introduced. A wave equation considering fractional interporosity flow for a double-porosity medium was developed by substituting the improved constitutive relationship into the Lagrangian equation. The wave velocity and inverse quality factor was obtained and analyzed by plotting. Comparing to the traditional interporosity flow, the fractional interporosity flow provides a more flexible and accurate description for the dispersion and attenuation of elastic waves in the seismic frequency band.

Air-coupled ground penetrating radar is widely used in areas with poor terrain conditions and many surface obstructions. In order to precisely evaluate the influence of surface obstructions on the air-coupled ground penetrating radar detection, the horizontal distance between surface obstructions and the detection object was quantitatively studied. The air-coupled ground penetrating radar principle was first used to design an indoor experiment. The influence of the distance between the obstacle and the detection object on the depth of detection and the amplitude changes of the reflection interface of the detection object was analyzed. The relevant laws were then verified by forward simulation. Finally, the relationship between the distance between the obstacle and the detection object and the parameters of the air-coupled ground penetrating radar when it was working was derived based on the analysis of the electromagnetic wave propagation laws of the air-coupled ground penetrating radar. The research results show that the physical experiment obstacles used for air-coupled ground-penetrating radar detection of underground objects have no significant impact on the detection depth. As the obstacle gradually moves away from the detection object, the amplitude of the reflection interface of the detection object will first increase and then stabilize. The amplitude increase stage follows an exponential function distribution law, and the amplitude stability stage has the same amplitude value as when there are no obstacles. In this paper, the distance between the obstacle and the target object at which the amplitude is just approaching stability is defined as the critical distance at which the obstacle affects the amplitude of the radar signal. The height of the antenna, the depth of the target object, the dielectric constant of the detection medium, and the angle of incidence of the electromagnetic wave are all related to the critical distance. The result quantifies the influence of surface obstacles on the physical characteristics of the air-coupled ground penetrating radar and provides guidance for the operation of air-coupled ground penetrating radar.

The loess hilly area is one of the areas with a high incidence of geological disasters, and it is urgent to use appropriate evaluation factors and training models to conduct research on the susceptibility assessment of geological disasters. Kangdian Town, Gongyi City, the township hardest hit during the “7·20” extremely heavy rainstorm in Zhengzhou, was taken as the study area. Based on satellite remote sensing interpretation, field survey, UAV aerial photography and relevant data collection, an evaluation system covering 13 influencing factors of three main control factors, namely loess interface, human engineering activities and hydrodynamic effects, was constructed. CatBoost model, XGBoost model and LightGBM model were used to carry out the evaluation study of geological disaster vulnerability. Based on the machine learning model with the best performance, SHAP(shapley additive explanations) algorithm was used to complete the global interpretation of characteristics and dependency analysis. The results show that the CatBoost model has higher accuracy than other models (XGBoost and LightGBM), and performs the best in AUC(area under curve) value, accuracy, precision, recall, F₁ score, and field validation. The proportion of areas with extremely high, high, medium, low, and extremely low susceptibility is 3.19%, 1.40%, 2.04%, 5.93%, and 87.44%, respectively. The extremely high and high susceptibility areas are mainly distributed on both sides of gullies with strong human activities, and slope cutting and building are important causes of geological disasters. The aim of this study is to optimize the modeling approach, investigate the uncertainty and interpretability of the modeling process, explain and analyze the decision-making mechanism of machine learning susceptibility, and provide scientific basis for geological disaster prevention and control in the loess hilly area of western Henan.

In order to improve the accuracy of geological hazard susceptibility assessment, Fuyang District in Hangzhou, Zhejiang Province was taken as the research area and a random forest method was proposed for evaluating geological hazard susceptibility, considering buffer zone optimization strategies. Firstly, nine evaluation factors were selected: normalized difference vegetation index, distance to roads, distance to faults, rainfall during the flood season, slope, aspect, ruggedness, distance to water systems, and lithology. Multicollinearity analysis was conducted to ensure the independence of the factors. Secondly, buffer zones of 0.5 km, 1 km, 1.5 km, and 2 km were constructed. Negative sample points were generated using random sampling to avoid cross-contamination between positive and negative samples, enhance sample representativeness, and improve the model's discrimination capability. An additional set of random sampling points without buffer zones was also established for comparison. The random forest algorithm was then used to train and test the geological hazard susceptibility model. Results indicated that the buffer zone optimization strategy significantly improved the model's predictive accuracy and that there was an optimal boundary for the buffer zone. The model's AUC(area under curve) value was highest at 0.815 for the 1 km buffer zone, indicating that negative samples collected within this buffer zone could more accurately distinguish geological hazard characteristics. Finally, based on the susceptibility evaluation results of the optimal buffer zone and the random forest model, high susceptibility areas were mainly concentrated in the mountainous regions in the northwest and southeast. The frequency ratio increased with the susceptibility level, validating the scientific validity of this method. This approach can provide a basis for geological hazard prevention and control in Fuyang District.