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.

It is of great significance for accurate forecasting of multi-load to be carried out to improve the consumption of new energy, realize energy saving and emission reduction, and ensure the safe and reliable operation of the power grid. To enhance the accuracy of simultaneous multi-load forecasting,a model which singular spectrum analysis and bi-directional long short-term memory networks SSA-BiLSTM (singular spectrum analysis-bidirectional long short-term memory) was proposed. First, A approach Pearson correlation coefficients for coupled feature extraction was proposed to identify correlations and dependencies within multivariate load data. Then, SSA was employed for feature extraction to capture dynamic characteristics and reduced forecasting complexity. Finally, a multi-ask learning framework was introduced to leverage shared information among multiple forecasting tasks, improving prediction accuracy. Experimental using datasets from multi-area electricity, heat, cold multivariate loads, flexible and wind-solar power generation, the effectiveness of the model. The results show that the proposed model average improves in mean absolute percentage error (MAPE) for the prediction of electrical, heating, and cooling loads in multiple regions is 0.41%, with an average root mean square error (RMSE) increase of 0.02 MW.

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.

In order to explore the strengthening effect of reinforced concrete beams strengthened by carbon fiber reinforced polymer reinforced geopolymer matrix (FRGM), the mechanical properties of reinforced concrete beams reinforced with flexural reinforcement reinforced concrete in FRGM system were studied by numerical simulation. Three-dimensional finite element models were established to simulate the failure mode, characteristic load and load-mid-span deflection curve of reinforced concrete beams reinforced by FRGM system, and the influence of the thickness and length of the FRGM layer and the pre-damage degree of the original specimen on the reinforcement effect was discussed. The results show that the thickness of the FRGM layer has no obvious effect on the ultimate bearing capacity of reinforced concrete beam, and the increase range is 72.29%~79.38%, but the stiffness of the FRGM layer is improved to a certain extent, up to 38%. The ultimate bearing capacity of reinforced concrete beams can be increased by increasing the length of the FRGM layer, but with the increase of the length of the FRGM layer, the increase of the ultimate bearing capacity gradually weakens, indicating that there is no linear increase relationship between the length of the FRGM layer and the ultimate bearing capacity. Compared with the original components, the bearing capacity of reinforced concrete beam members is improved to a certain extent after pre-damage reinforcement(36.5%~73.66%), which shows the effectiveness of FRGM reinforcement method.

The structure and materials used in bridge deck pavement layers significantly impact their road performance. To design a pavement layer more suitable for cold regions, the layered modification of pavement materials was optimized based on the principle of layer function design. Firstly, materials for the surface functional layer and overall functional layer were selected using the layer function division, with lignocellulose and polyester fibers as modifiers, respectively. Secondly, the appropriate content of polyester fibers was determined through road performance tests. Finally, the choice of layered materials and appropriate modification methods were established. The results indicate that both the surface functional layer and the overall functional layer should use the same high-viscosity, high-elasticity modified asphalt, with lignocellulose and polyester fibers as modifiers, respectively. This approach avoids differences in thermal contraction coefficients of the layered materials, ensuring coordinated thermal deformation between the pavement layer and the bridge structure, thereby effectively improving the road performance of the asphalt bridge deck pavement. Furthermore, under engineering economic requirements, when the polyester fiber content is 1% of the total mixture mass, the prepared asphalt mixture's high-temperature performance increases by 7%, low-temperature performance by 23%, and water stability by 5%.

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.

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 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.

In order to solve the problems of inaccurate dense target recognition and difficult detection of small targets in bird recognition, a bird recognition algorithm based on improved YOLOv8 was proposed. Firstly, in order to solve the problem of difficult dense object recognition, the multi-scale linear attention mechanism EfficientViT was used to replace the backbone network to realize the global receptive field and multi-scale learning, improve the performance and efficiency of the model, and improve the dense object recognition effect. Then, in order to solve the problem that it is difficult to detect small target birds and is prone to missed detection, an efficient multi-scale attention EMA (efficient multi-scale attention) mechanism was introduced to realize cross-dimensional aggregation features through channel recombination, so as to better capture global information, realize multi-scale feature fusion, and reduce the probability of missed detection. The experimental results show that the mAP50 of the improved model on the benchmark dataset CUB-200-2011 and birds28 reaches 77.1% and 88.4%, respectively, which is 4.5 and 5.4 percentage points higher than the original YOLOv8 model, respectively, which verifies the effectiveness of the improved model.

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.