Rong Ma

Aiming at the existing pavement pothole detection algorithms that are susceptible to the interference of the background environment in multiple weather scenarios, and the poor image quality leading to the low detection accuracy of small targets and the lack of real-time performance, this paper proposes a multi-dimensional structural optimization to achieve a balance between accuracy and efficiency of the YOLOv8n model. First, the spatially separated and deformable convolutional layer is introduced into the backbone network to enhance the feature extraction capability of the model for small- and medium-scale potholes; second, a lightweight global feature pyramid network feature fusion architecture is designed to replace part of the C2f module, and a ‘light backbone-heavy neck’ network structure is constructed to improve the efficiency of multi-scale feature interaction; finally, the multi-scale dilated attention mechanism is integrated at the end of the backbone network to enhance spatial context modeling under multiple weather conditions. The experimental results show that the optimized models mAP50 and mAP50-95 reach 86.1% and 63.4%, respectively, which are 1.9 and 3.1 percentage points higher than the original YOLOv8n model, and the detection frame rate reaches 114.9 fps, which meets the real-time requirements of detection. This study can provide real-time and accurate pothole detection technical support for intelligent driving cars in multiple weather scenarios and light intensity.

Predictive modelling is very important for water pollution management. In this study, based on an electromagnetic field-enhanced vertical flow artificial wetland and using the actual measured data as inputs to the model, the ammonia nitrogen (NH₄⁺-N) effluent concentration of this wetland system was analyzed by Pearson’s correlation analysis to be related to six key factors, which were the NH⁺-N raw water concentration, the chemical oxygen demand (COD) raw water concentration, the treatment time, the magnetic field strength, the aeration time, and the electric field strength. Then, different artificial neural network models were constructed for comparison and the constructed models were evaluated based on statistical parameters. The results show that the PSO algorithm can improve the prediction effect of the BP neural network, but the prediction accuracy of the CNN model is better compared to the others. The prediction accuracy of the RF model is the highest compared to the others, and the evaluation parameters of R2, RMSE, and MAE of the test set are (0.9446, 2.4328, and 3.0943), respectively. The prediction error of this model is the smallest, and the model can predict the concentration of electric and magnetic fields in a wetland system with high accuracy compared to other models. This model can more accurately predict the NH₄⁺-N effluent concentration of the magnetic field-enhanced wetland system, which can provide a certain basis for the study of the management of water pollution.

The addition of physical field enhancement measures to improve the purification effect of vertical flow artificial wetlands has gradually become popular. In this study, a vertical flow artificial wetland system reinforced by electric and magnetic fields was constructed. These fields were first optimized using finite element 3D simulation software to obtain the optimal electric and magnetic field parameters. Then, the pollutant removal effects and changes in microbial community structure were comparatively analyzed. The optimal electromagnetic field parameters (applied voltage of 15 V and applied magnetic field of 20 mT) resulted in significantly enhanced removal rates of chemical oxygen demand (COD), nitrate nitrogen (NH₄⁺-N), total phosphorus (TP), and orthophosphorus (PO₄³⁻-P) in wastewater, with rates of 74.47%, 45.44%, 89.85%, and 90.04%, respectively. These rates were notably higher than those observed in the vertical flow artificial wetland system. The microbial community structure analysis revealed that the vertical flow constructed wetland with enhanced electric and magnetic fields exhibited (EM-VFCW) a more diverse and complex microbial community structure. Notably, the abundance of bacteria capable of removing NH₄⁺-N and COD, including Aspergillus, Fusarium, and Actinobacteria, was significantly elevated.

This paper starts from the current situation of rural sewage, analyzes the current common sewage treatment process at home and abroad and the existing mainstream sewage treatment process, compares and analyzes rural domestic sewage with urban domestic sewage, and proposes suitable treatment measures for the characteristics of rural domestic sewage - constructed wetland, because a single constructed wetland treatment measure will have problems such as low treatment efficiency and substandard effluent quality, so This paper presents its views on how to improve and optimize the constructed wetland using the existing reinforcement process, and improve the efficiency of the constructed wetland for rural sewage treatment by using some reinforcement measures. The article introduces and analyzes various existing reinforcement processes, compares the advantages and disadvantages of each reinforcement process, and especially discusses magnetic field reinforcement technology in detail, showing that magnetic field reinforcement technology has a certain role in sewage treatment, and compared with the traditional process without magnetic field reinforcement treatment, the reinforcement measures have significantly improved the treatment effect of sewage, and finally, the constructed wetland and reinforcement technology in rural Finally, the application of constructed wetland and intensification technology in rural sewage treatment is proposed.

For diesel engines, accurate prediction of NO_x (Nitrogen Oxides) emission plays an essential role in virtual NO_x sensor development and engine design under situations of actual road driving. However, due to the randomness and uncertainty in the driving process of diesel vehicles, it is difficult to make predictions about NO_x emissions. In order to solve this problem, this paper proposes differential models for noise reductions of NO_x emissions in time series. First, according to the internal fluctuation of time series, use SSA (Singular Spectrum Analysis) to reduce the noises of the original time series; second, use ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to decompose the noise-reducing data into several relatively stable subsequences; third, use the sample entropy to calculate the complexity of each subsequence, and divide the sequences into high-frequency ones and low-frequency ones; finally, use GRU (Gated Recurrent Unit) to complete the prediction of high-frequency sequences and SVR (Support Vector Regression) for the prediction of low-frequency sequences. To obtain the final models, integrate the prediction results of the subsequences. Make comparisons with five single models, SSA single-processing models, and ICEEMDAN single-processing models. The experimental results show that the proposed model can predict the instantaneous NO_x emissions of diesel engines better than the single model and the model processed by SSA, and the differentiated model can effectively improve the execution speed of the model.