In order to make rational and efficient use of biomass resources, considering the operating cost and environmental cost of each microsource unit, an economic dispatching model of combined cooling heating and power microgrid based on improved biomass gasification was designed. In order to solve the problem that the sparrow search algorithm is easy to fall into the local optimum, an improved sparrow search algorithm(ISSA) was proposed to solve the proposed model. First, a sine chaos map was used to generate spatially evenly distributed early sparrow populations. Secondly, a mutually beneficial learning mechanism was added and a mutation strategy was introduced to enhance the information sharing and global search ability among individuals in this field. Finally, by comparing the iterative results of ISSA, SSA, gray wolf algorithm, whale algorithm and marine predator algorithm, it is proved that ISSA has good optimization effect and stability. Through the analysis of typical simulation cases, the effectiveness of the ISSA algorithm in solving the economic dispatching problem of combined cooling, heating and power microgrid is verified.

In order to improve the fire extinguishing efficiency of forest fires and reduce the damage to forest resources and ecological environment, a three-phase class A foam extinguishing agent consisting of class A foam extinguishing agent with nano-SiO₂ particles was prepared. The effects of nano-SiO₂ mass fraction on foam stability and fire extinguishing efficacy were investigated by using the double injector method and the wood stack fire extinguishing test platform. The results show that the nano-SiO₂ significantly enhanced the foam stability and delaye the foam precipitation and foam coarsening; the three-phase class A foam extinguishing agent extinguishe the flame more quickly and efficiently under the same driving pressure without rekindling and at a lower dosage than the traditional class A foam extinguishing agent; and the three-phase class A foam extinguishing agent with 1.5% SiO₂ show the best extinguishing efficacy. The three-phase A-type foam extinguishing agent significantly improves the fire extinguishing efficiency, reduces the consumption of extinguishing agent, and has low cost, which makes it suitable for large-scale forest fires. The study provides a new solution for the efficient extinguishing of forest fires and has a wide range of practical applications.

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.

Aiming at the limitations of current intelligent traceability and authenticity identification systems in extracting multiple surface texture features (such as continuous, non-continuous, etc.) of automotive components, a micro-visual and neural network-based automotive parts anti-counterfeiting feature extraction and automatic matching algorithm was proposed. This algorithm integrated artificial intelligence-based automatic matching technology with micro-visual image processing and a neural network hybrid algorithm for anti-counterfeiting feature extraction and identification of automotive parts. Initially, the micro-visual feature images of the automotive component surfaces were processed with frequency-domain transformation, filtering, and noise reduction. Subsequently, the texture types (including continuous, non-continuous, and contour types) were determined based on the two-dimensional frequency-domain features. For each texture type, an appropriate algorithm was selected from the algorithm library to extract and analyze key attribute feature points. Finally, a deep learning framework was constructed, and a micro-visual feature recognition model for automotive parts was built, which was then matched with a priori feature libraries to complete classification and authenticity determination. Experimental results demonstrate that the proposed algorithm effectively extracts and identifies anti-counterfeiting features on the surface of automotive components, achieving a significant improvement in accuracy compared to traditional methods. Through matching with the a priori feature library, the algorithm accurately distinguishes between genuine and counterfeit components, providing reliable anti-counterfeiting verification results. This method effectively addresses the complexity of extracting various surface texture features of automotive parts, enhancing the accuracy of anti-counterfeiting and traceability systems. The micro-visual and neural network-based automatic matching technology significantly improves the precision of authenticity identification, offering an innovative and efficient solution for automotive parts anti-counterfeiting.

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 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.

With respect to the surrounding rock collapse and water gushing in the water-rich fault, a high-speed railway tunnel in Yunnan was taken as the engineering background. The fluid-solid coupling numerical calculation of tunnel construction with the three-step method was carried out, and the deformation mechanism and groundwater seepage law of surrounding rock through water-rich fault were researched combined with the deformation field monitoring results. The results show that when the tunnel face is excavated to the water-rich fault, the rock and soil in the upper wall of the reverse fault will collapse downward, and the settlement of the arch roof will increase sharply. At the fault, the rock and soil mass of the middle and lower excavation parts cannot provide stable support for the surrounding rock, so the tunnel clearance increases first and then decreases. The groundwater mainly percolates along the step surface and the palm surface, and there is still a large pore pressure above the tunnel, so the drainage pipe can be added to lead the water into the side ditch.

The applicability of prefabricated subway station structures in inclined liquefiable sites was investigated. Based on the actual project of Shuangfeng subway station in Changchun City, the finite difference software FLAC3D was used to carry out the seismic response analysis of prefabricated subway station structures in liquefiable soil, for example, the pore water pressure of the foundation, the lateral motion of the liquefied soil, the dynamic response and uplift characteristics of the subway station structure, and the deformation characteristics of the prefabricated subway station structures were analyzed. The results show that the negative pore pressure phenomenon of pore water pressure on both sides of prefabricated subway station under inclined liquefiable site conditions is present, and the phenomenon becomes more obvious the closer the location of the station structure is to the station, and at the same time, the negative pore pressure of the soil on the left side of the structure (uphill) is significantly greater than that on the right side (downhill). The further away the foundation soil is from the station structure, the more pronounced the liquefaction is. The phenomenon of lateral slippage of the surrounding soil is significantly suppressed by prefabricated subway station structures. The principal stresses in the upslope sidewall (member C1) are greater than those in the downslope sidewall (member C2), and the principal stresses at the bottom of the upslope sidewall of the structure are greatest, so the upslope sidewall of the structure should be given priority in the seismic design.

In order to more accurately predict flight delays at different times of the year,flight delay prediction trends was investigated using operational and meteorological data from Atlanta Airport in the United States for the year 2023. A CA-PCA-Informer flight delay prediction model,incorporating correlation analysis (CA),principal component analysis (PCA),and the Informer model,was proposed. Mean absolute error (MAE) and root mean square error (RMSE) were utilized as evaluation metrics to assess the prediction error. The findings reveal that the CA-PCA-Informer model outperforms simpler combined models,demonstrating the lowest error compared to the CA-PCA-LSTM and CA-PCA-GRU models,with MAE and RMSE reductions of 20.2%~20.7% and 12.7%~14.1%,respectively. The CA-PCA-Informer model is particularly effective for one-hour ahead predictions,providing decision-makers with more accurate flight delay trends to enhance efficient flight operations.

The stiffly-expanded composite pile is a new type of composite pile formed by sinking rigid pile in cement soil mixing pile. In order to study its bearing characteristics in complex soft soil-sandy soil foundation, a numerical analysis model of rigid composite pile was established based on finite element software ABAQUS, with the effect of core pile length and diameter, soil-cement pile length, diameter and elastic modulus, as well as soil-cement and pre-stressed high-strength concrete(PHC) pile interface and soil-soil interface shear strength on the bearing performance was analyzed. The results show that the bearing capacity and economic advantages of the stiffened composite pile are obvious. The bearing capacity of composite pile increases with the increase of research parameters, but the improvement is limited by changing the research parameters beyond a certain range. With the increase of load, the slope of axial force distribution curve of composite pile increases, and the load proportion of PHC core pile is about 93.32%~95.40%. When the load exceeds 4 000 kN, the axial force of soil-cement pile increases sharply within the depth range of 10~16 m, and the axial stress ratio of core pile/soil-cement pile changes significantly near the depth of about 10 m. The research outcomes could provide references for the engineering design and application of the stiffly-expanded composite piles.