In order to study a non-destructive testing method for concrete beam stress, a ultrasonic tail stress identification algorithm coda wave-deep residual shrinkage network (C-DRSN) based on deep residual shrinkage network(DRSN) was proposed. According to the high-dimensional characteristics of the tail wave signal vector, the interference of signal noise to the measurement stress accuracy was reduced by introducing residual contraction block, using soft threshold function and attention mechanism, and the adaptive recognition and extraction of stress features in the signal were realized, and the recognition accuracy was improved. The characteristics were visually analyzed, and the mapping relationship between the tail wave sign and the stress was established. In order to verify the model's ability of stress recognition, ultrasonic tail wave signals of concrete I-beams under three-point bending and eccentric compression loads were collected respectively. The results show that the recognition rate can reach 99% under both loading modes, indicating that the proposed method is feasible in the stress recognition of concrete beams, and the accuracy of the proposed method is higher than that of the tail wave interference method.

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.

Aiming at the problems of poor accuracy, slow convergence rate and high jitter of traditional super-twisting sliding mode observer, a permanent magnet synchronous motor(PMSM) speed observation technique based on the improved super-twisting algorithm was proposed. Firstly, a segmented exponential function was used to replace the switching function to eliminate the phase delay problem due to the low-pass filter, and the adaptive sliding mode gain was designed to achieve stable tracking at different speeds. Then, in order to solve the problem that the traditional quadrature phase-locked loop failed when the motor steering was changed, an improved quadrature phase-locked loop was proposed, so that its output was independent of the rotation direction, so as to realized the correct tracking of the forward and reverse rotation. Finally, due to the wrong convergence point of the proposed improved quadrature phase locked loop, there was a 180° phase difference between the estimated position and the actual position, an adjustment function was designed to solve this problem. The simulation results show that compared with the traditional sliding mode observer, the proposed improvement method has faster response and better dynamic performance.

In deep foundation pit engineering, the support form of “bored pile + internal support” is often used for foundation pit support, but the support effect of different support arrangements on the foundation pit is different. Relying on a foundation pit project in a fast-track reconstruction and expansion project in Changzhi City, ABAQUS was used to numerically simulate the whole process of excavation of different steel support layout schemes, and the influence of different internal support layout schemes on the settlement of the surface soil around the excavation of the foundation pit, the horizontal displacement of the supporting structure and the uplift of the bottom of the foundation pit were analyzed, and then the reliability of the model was verified by combining the field monitoring results. The results show that the numerical simulation results can accurately reflect the deformation law and characteristics of the foundation pit in the process of excavation and support. When the depth of the internal support arrangement is small, it has a good control effect on the maximum horizontal displacement of the supporting pile and the surface settlement outside the pit. When the depth of the internal support arrangement is large, the maximum uplift at the bottom of the pit has a certain control effect. Increasing the number of internal supports has different effects on reducing the maximum horizontal displacement of the supporting pile and the maximum uplift at the bottom of the pit.

As a key link between carbon source and carbon sink in carbon capture, utilization and storage(CCUS) technology, CO₂ pipeline transportation will play an important role in the process of carbon neutralization in the future. For the pipeline water hammer condition, the pressure oscillation may exceed the pressure in the pipe and be lower than the inlet pressure of the pump. At present, the water hammer and control theory of supercritical CO₂ pipeline is not mature. A mathematical model based on the law of conservation of mass, momentum and energy was established to describe the one-dimensional gas flow in the pipeline. The characteristic line method was used to solve the model, and the MATLAB programming was used to calculate. The simulation results were compared with the simulation results of the gas transmission system model proposed by Kiuchi and the simulation results of the commercial software OLGA. The results show that the simulation results are generally consistent with the simulation results of the gas transmission system model. Compared with OLGA software, the maximum relative errors of pressure and flow are 0.02% and 2.32%, respectively, which meet the requirements of engineering calculation accuracy. For the fast transient process of pipeline parameter change caused by pipeline compressor start and stop, valve emergency switch and rapid change of flow in a short time, the rapid change value is set to simulate. The established model can calculate the parameter change of each node with high accuracy, which can provide theoretical support and technical support for the localization of supercritical CO₂ pipeline transportation process simulation software.

A systematic study was conducted on the issue of gate assignment, with the goal of minimizing the number of remote gate assignments and the idle time of near gates. A multi-objective mathematical model was proposed to address the multi-objective and multi-constraint characteristics of the problem. The model was designed to minimize the number of remote gate assignments and the idle time of near gates while taking into account parameters such as actual flight arrival and departure times, aircraft types, and the interrelationships among gates. The gate assignment process was optimized using the deep reinforcement learning method, specifically the deep deterministic policy gradient(DDPG) algorithm. To enhance the optimization ability and performance of the algorithm, an improved DDPG algorithm was developed by incorporating prioritized experience replay and multi-strategy exploration mechanisms. Comparative experiments were conducted, and the results show that the improved algorithm significantly reduces the number of remote gate assignments and optimized time utilization. The algorithm also achieves faster convergence and stronger global optimization capabilities, confirming its effectiveness.

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.

The smart skin of an aircraft is realized by integrating distributed sensors, actuators, and controllers into the composite skin, thereby enabling it to monitor its own state and detect damages. The physical field inversion algorithm plays a key role in the signal processing of the smart skin. However, due to factors such as the low sensor density, traditional inversion algorithms exhibit limited accuracy. In order to enhance the monitoring precision of the smart skin, a back propagation(BP) neural network-improved grey wolf optimizer(IGWO) inversion algorithm, which combined a BP neural network with an IGWO-optimized Kriging model, was proposed. A prototype of the smart skin was subsequently fabricated, and wind tunnel tests were conducted to validate the proposed algorithm. The results demonstrate that the BP-IGWO inversion algorithm achieves higher accuracy and superior detail representation compared to traditional inversion algorithms, and can better monitor the state of smart skin.

In order to better address incidents of online violence resulting from uncontrolled public opinion in the era of self-media, network platforms are involved in decision-making during the early stages of online violence opinion formation. This can effectively prevent the formation of online violence opinions. Firstly, based on the inducement behaviors of online violence and considering the internal self-purification effects among platforms, self-media, and netizens, the costs and benefits of their autonomous behaviors during the initial stages of online violence opinion formation were defined. Next, a “platform-self-media-netizen” three-party evolutionary game model was constructed, and the behaviors of each subject and their evolutionary stable strategies were analyzed. Finally, numerical simulation experiments were conducted using MATLAB to verify the accuracy of the model and evolutionary results. To further reveal the factors influencing the cooperation among these parties, the impact of their initial cooperation willingness and related parameters on the system was explored. Simulation results show that a strong regulatory strategy adopted by network platforms, with clear guidance, can effectively enhance the cooperation willingness of self-media and netizens, effectively curbing the formation of online violence opinions.

Dust deposition can affect the normal operation of equipment. To accurately and efficiently detect dust on equipment and formulate a scientific cleaning strategy, a lightweight dust deposition detection method based on Fast-UNet was proposed. By effectively pruning UNet and adopting max pooling and bilinear interpolation for down-sampling and up-sampling operations, the parameter redundancy was reduced, and a compact basic network was obtained. The lightweight Ghost Module was used to replace the ordinary convolution in the basic network, further reducing the complexity of the network. An convolutional block attention module(CBAM) that integrated channel and spatial attention was embedded in the encoding process, which made the network pay more attention to the target area while introducing minimal parameters. Experiments on a simulated dust deposition dataset show that, compared with the original model, Fast-UNet reduces the number of parameters by 99.6%, decreases computational complexity by 98.7%, achieves an inference speed of 94.18 frames per second, and maintains a recognition accuracy of 91.17%. Compared with five other mainstream segmentation models, Fast-UNet also demonstrates advantages in both accuracy and speed. This method meets the needs of dust detection for both accuracy and efficiency, providing a technical reference for dust quantitative analysis.