To analyze the main influencing factors and pathways of low altitude travel intention, a structural equation model was constructed based on travel intention model and value risk analysis. The interaction mechanism between travel preference, travel characteristics, perceived value, and perceived risk on low altitude aircraft travel intention was quantified. The path coefficients were solved using unweighted least squares method, and the mediating effects of perceived value, perceived risk, and other factors on travel preference were analyzed. Additionally, a multi group model invariance analysis of individual information such as gender and age on travelers was conducted.Finally, the fuzzy set qualitative comparative analysis (fsQCA) method was used to analyze the configuration of the antecedent variables of travel intention. The results showed that the chi square degree of freedom ratio, RMSEA (root mean square error of approximation)value, and CFI(comparative fit index) value of the structural model were 3.803, 0.063, and 0.938, respectively, which passed the model validation. Perceived value (0.38) is the most important factor affecting travel intention. Travel characteristics (0.08) have a positive direct impact on travel intention, while perceived risk (-0.22) has a negative direct impact. However, travel preferences have no significant impact on travel intention; Travel preferences have a negative effect on travel willingness, but travel characteristics and perceived value have a masking effect on travel preferences, while perceived risk has a mediating effect on them; The pre tax annual income in the individual information of travelers has a moderating effect on the model. As the travel distance increases, the high-income group is more willing to use low altitude aircraft than the low-income group. At the same time, the high-income group is more sensitive to the perceived risks of low altitude aircraft in terms of technological maturity and accident severity. fsQCA analysis shows that there are three configurations that can form travel intention, among which configuration 3 ( type of travel characteristics&perceived value) has the highest sample coverage, explaining 48.9% of the sample cases. When travelers are necessary to travel during peak hours and have a positive understanding of low altitude travel comfort, privacy, etc., they will develop a low altitude travel tendency. The research findings can provide data support for the promotion and policy formulation of low altitude aircraft.

To investigate the contents and pollution of heavy metals in calcium-containing biological minerals, 16 samples were collected from three primary categories: eggshells, shells, and animal bones. Inductively coupled plasma optical emission spectrometry (ICP-OES) was utilized to analyze the contents of seven metalloids and heavy metals (As, Cd, Pb, Cr, Cu, Zn, and Mn). The pollution degree and risk of heavy metals were evaluated using single-factor, Nemerow comprehensive pollution index method, Hakanson potential ecological risk index method, and health risk comprehensive assessment. The results show that As and Cd are not detected in all calcium-containing biological minerals, meanwhile Pb is not detected in both eggshells and shells. Five heavy metals (Pb, Cr, Cu, Zn, and Mn) are detected in the remaining samples, with their contents remaining below standard. Differences in heavy metal contents are observed among different categories and species. Cu, Mn, and Zn have the highest contents of 14.85, 21.47, and 201.99 mg/kg, respectively accumulated in eggshells, shells, and animal bones. The risk assessment results show that the single-factor pollution index of four heavy metals (Pb, Cr, Cu, Zn) in calcium-containing biological minerals is less than 1.0 and the comprehensive pollution index is less than 0.7. All samples are indicated as unpolluted. The potential ecological risk among three types of calcium-containing biological minerals is in descending order: animal bones > eggshells > shells. The non-carcinogenic total risk index of two exposure pathways for minors and adults is less than the safety threshold of 1.0. This finding indicates that five heavy metals in calcium-containing biological minerals are unlikely to threaten human health. Overall, calcium-containing biological minerals can be used as potential sources of fertilizers and soil conditioners in agricultural production. However, their usage should be controlled to prevent heavy metal accumulation and pollution.

In order to further analyze the visual characteristics and risk conditions of drivers at the confluence sections of entrance ramps at adjacent urban underground roadways, data collected from real-vehicle experiments were used. A linear fitting model was constructed and the pupil area growth rate was calculated to investigate the patterns of pupil area changes. The K-medoids clustering method was employed to classify the regions of interest in drivers' gaze patterns, and the characteristics of drivers' gaze behavior were analyzed. A game theory-extension cloud evaluation model was constructed to evaluate the driving risks at the confluence sections of entrance ramps at adjacent urban underground roadways. The results show that the pupil area increases linearly in the entrance section and decreases linearly in the exit section. The overall load follows the order of adjacent entrance confluence section > adjacent entrance split section > adjacent exit split section > adjacent exit confluence section. Additionally, drivers face certain driving risks due to various factors at the confluence sections of entrance ramps at adjacent urban underground roadways. Based on the evaluation and actual survey, the risk factors for each section were analyzed, and optimization and improvement suggestions were proposed.

In order to explore the impact of altitude on the emergency evacuation of aircraft passengers at airports, a simulation model for emergency evacuation of aircraft accidents at airports was built. By introducing panic factor, age factor, gender factor and altitude correction factor, passenger walking speed at different altitudes was quantitatively characterized, and passenger evacuation effects were analyzed under conditions such as different altitudes, with or without evacuation guidance, and cabin aisle spacing, dual aircraft evacuation. The results show that the impact of emergency evacuation time in a high plateau environment is mainly reflected in the middle and late stages of cabin evacuation and ground evacuation. When the altitude is 4 280 m, the cabin evacuation time is increased by 19.6 s and the overall evacuation time is increased by 44.1 s compared with the plain area. By setting cabin crew guidance, cabin evacuation time is reduced by 18.1 s and evacuation efficiency is increased by 16.5%. When the width of aisle spacing is increased to 60 cm, the evacuation time in the cabin is reduced by 20.8 s, and the evacuation efficiency is increased by 19%. Compared with single-aircraft evacuation, the evacuation time of dual-aircraft evacuation is significantly increased, so it is necessary to optimize the evacuation strategy. The research results can provide theoretical support for the optimization of aircraft emergency evacuation schemes at high plateau airport.

In order to reveal the influence of strain rates on the macroscopic failure characteristics and microscopic crack propagation laws of rock, sandstone was taken as the research object, and the uniaxial compression tests and real-time monitoring of acoustic emission information were carried out under different loading rates. The influence of loading rates on the macroscopic mechanical response and microscopic fracture morphology of sandstone specimens, such as strength and deformation characteristics, failure mode and fracture characteristics, was analyzed. Based on the evolution of acoustic emission b value with the loading process, the internal crack propagation laws of sandstone specimens under different loading rates were explored. The research results show that within the loading rate range of 1×10^-5~1×10^-2 s^-1, the uniaxial compressive strength and elastic modulus of sandstone samples were positively correlated with loading rates. For every 10 times increase in loading rate, the uniaxial compressive strength and elastic modulus increased by 2.66 MPa and 0.087 GPa, respectively, while the peak strain decreases by 0.213‰. As the loading rate increased, the failure characteristics showed a trend of gradually transitioning from a single inclined through fracture surface to a cross distribution of multiple fracture surfaces, and the average size of the fragments decreased, indicating an increase in the failure of the sandstone sample. At low loading rates, the microstructure of the fracture surface was mainly characterized by intergranular cracks, while as the loading rate increased, transgranular cracks and intergranular cracks alternated, and the fracture characteristics at the intersection of the cracks were obvious, resulting in large-scale grain peeling. As the loading rate increased, the ratio of stress to peak stress corresponding to the turning point where the acoustic emission b value changes from increasing to decreasing decreased. This indicated that the higher the loading rate, the more likely cracks were to propagate inside the specimen and form more obvious large cracks, leading to more severe damage characteristics and complex crack propagation patterns in the specimen. The research results have important guiding significance for understanding the failure characteristics of engineering surrounding rock under complex stress conditions, as well as predicting the damage deterioration law of the internal structure of surrounding rock based on acoustic emission monitoring information.

Complex equipment such as wind turbine blades faces both performance degradation and random shocks. There are two interdependent relationships between these two failure modes: the interdependence between internal factors in the degradation process and the interdependence between degradation and shock processes. These characteristics pose challenges to reliability analysis. To solve this problem, a new mutually dependent competing failure processes (MDCFPs) model was proposed based on two MDCFPs models. This new model integrated two interdependencies. Taking the wind turbine blade stiffness degradation model based on Gamma process and the extreme shock model based on homogeneous Poisson process as examples, the accuracy and differences of three models were analyzed using the control variate method, and the influence of key parameters was studied. The results show that, under the same conditions, the new model's reliability is closest to the observed empirical reliability, with an absolute error of no more than 0.12. At the same time, the new model's reliability is lower than that of the two base models, with maximum absolute errors of 0.26 and 0.40, respectively. After adjusting the parameters of the new model, the absolute errors in reliability compared to the base models are limited to 0.03 and 0.02. These findings suggest that the new model effectively accounts for interdependencies among factors, prevents overestimation of reliability, and can replace base models, demonstrating broader applicability.

To address the issues of low path planning efficiency, poor obstacle avoidance capability, and low path quality of the RRT-Connect algorithm in complex environments, an improved RRT-Connect algorithm was proposed. Firstly, a bidirectional goal bias strategy was introduced to enhance the goal-directedness and path planning efficiency of the algorithm. Secondly, an obstacle avoidance optimization strategy was proposed to increase the algorithm's active obstacle avoidance capability and passage ability in complex environments. Finally, a path recombination strategy and a smoothing strategy were added to optimize the generated initial path, reducing path length and the number of turns, and improving path quality. The improved algorithm was compared with other algorithms in three complex environments using MATLAB. Simulation results show that the improved algorithm has less planning time, shorter path length, fewer sampling times, and a higher success rate of path planning, demonstrating the effectiveness of the improved algorithm in complex environments.

In order to explore the connection between brain and vision and improve the clarity and accuracy of brain activity reconstruction video, a new method called high quality electroencephalogram video reconstruction (HQEEGVR) was proposed to reconstruct video from EEG (electroencephalogram) signals. Firstly, the masking spatio-temporal frequency fusion network (MSTFFNet), a three-branch EEG feature extraction network, was proposed to extract brain activity information from EEG signals and dig deeper into the semantics behind brain activity changes, spatio-temporal frequency information was extracted at the same time. Secondly, cross-modal contrast learning was introduced to align EEG, text and image features for use in the generation stage. Then, a cascade video diffusion model was proposed, specifically, the stable diffusion model was used to generate reference video frames based on EEG features, and then the video frames were used as references, motion vectors were integrated, and the video diffusion model was introduced to capture the video time features. High quality videos were ultimately generated. The results show that the model performs well in the reconstruction of the subject, motion, color and semantics of the video. It can be seen that the EEG signal can be used to capture the visual and semantic information of the brain activity, so as to reconstruct the video with high fidelity and visual authenticity.

The accurate prediction of soil compaction parameters has practical significance for improving soil bearing capacity and reducing compressibility in geotechnical engineering. The existing models have certain limitations in prediction progress and engineering applicability, and ignore the quantification of model prediction uncertainty. Genetic programming (GP) was used to model and predict two important soil compaction parameters (optimal water content and maximum dry density) for 226 groups of soil compaction test data with extensive and representativeness. The optimal display models of optimal water content and maximum dry density were obtained respectively, and the prediction results were compared with the results of existing prediction models. The GP model was quantified by combining quantile regression method and uncertainty statistics. The results show that the compaction parameters are most affected by fine grain content and plastic limit, while the gravel content and liquid limit have the least influence on them. Therefore, in practical engineering, the optimal compaction effect can be achieved by preferentially adjusting the fine grain content and plastic limit, while the gravel content (CG) and the liquid limit have the least influence on them. Therefore, in practical engineering, the optimal compaction effect can be achieved by preferentially adjusting the fine grain content (CF) and the plastic limit in the soil. In addition, the quantile regression (QR) method provides 90 % confidence and the mean prediction interval (MPI) is less than 0.3.At the same time, most of the data fall within the range of uncertain bands, indicating that the GP algorithm has strong prediction ability and high prediction accuracy. This interpretable display model is more convenient for engineering applications.

The slope geological structure characteristics is detected with the high density resistivity method by the inversion of the soil resistivity, and it could provide a geological model for slope stability analysis. However, the indirect evaluation of the resistivity for the soil shear strength is still limited. Taking the laterite on the slope as an example, the resistivity and shear strength of laterite samples with different dry density and water contents were tested to discuss the relationship between the resistivity and undrained shear strength of the laterite, and finally the corresponding quantitative model was established. The results show that the resistivity of laterite decreases with the increasing water content and increases with the increasing porosity. The undrained shear strength of laterite increases first and then decreases with the increasing water content (the peak of shear strength near the optimal water content ) and decreases with the increasing porosity. The evaluation model of undrained shear strength resistivity of unsaturated laterite considering critical saturation is derived, which is based on the three-phase conductivity theory of unsaturated soil and the shear strength theory of soil. The accuracy of the model is verified to be high, and there is a corresponding critical resistivity value for the peak change of undrained shear strength of laterite. As a physical parameter of soil, resistivity can be quickly detected and obtained. This model can provide new ideas for shear strength calibration, slope stability analysis and monitoring and early warning of laterite slope.