Yuyun Feng

Urban underground pipelines (UUPs) are critical infrastructure, and their safe operation has become a key concern in Chinese society. Currently, the tripartite social roles of the local people’s government, pipeline management departments, and the public are crucial in the informatization of urban underground pipelines. In this study, a survey was conducted among 126 professionals and technical personnel involved in underground pipeline informatization across eight cities in different regions of China. A quantitative weighted evaluation model was established using the Project Quantitative Index (PQI) and principal component analysis (PCA) to investigate the value of the tripartite aforementioned social groups in UUP informatization. The results indicate: (1) There is a significant positive correlation between the tripartite social roles and the promotion of UUP informatization. Moreover, the indicators with the highest PQI value are “Establishment of UUP informatization management departments” and “Support the work of the UUP informatization industry association” under the role of the local people’s government. (2) The informatization work of underground pipelines in different cities is affected differently by the tripartite social roles. This suggests that the local people’s government and professional management departments in different cities can proactively leverage their unique advantages in UUP informatization based on their specific circumstances. (3) PCA results showed that the indicators related to the public carried significant weight, indicating that the public also played an important role in UUP informatization. The degree of UUP informatization in the eight studied cities is ranked as follows: Guangzhou > Beijing > Qingdao > Kunming > Shanghai > Chengdu > Wuhan > Sian. This paper further discusses the unique roles and contributions of the tripartite social groups in UUP informatization, aiming to provide decision support for the future construction, management, and safe operation and maintenance of urban underground pipelines in China.

Powdery mildew is one of the most significant rubber tree diseases, with a substantial impact on the yield of natural rubber. This study aims to establish a detection approach that coupled continuous wavelet transform (CWT) and machine learning for the accurate assessment of powdery mildew severity in rubber trees. In this study, hyperspectral reflectance data (350–2500 nm) of healthy and powdery mildew-infected leaves were measured with a spectroradiometer in a laboratory. Subsequently, three types of wavelet features (WFs) were extracted using CWT. They were as follows: WFs dimensionally reduced by the principal component analysis (PCA) of significant wavelet energy coefficients (PCA-WFs); WFs extracted from the top 1% of the determination coefficient between wavelet energy coefficients and the powdery mildew disease class (1%R²-WFs); and all WFs at a single decomposition scale (SS-WFs). To assess the detection capability of the WFs, the three types of WFs were input into the random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN), respectively. As a control, 13 optimal traditional spectral features (SFs) were extracted and combined with the same classification methods. The results revealed that the WF-based models all performed well and outperformed those based on SFs. The models constructed based on PCA-WFs had a higher accuracy and more stable performance than other models. The model combined PCA-WFs with RF exhibited the optimal performance among all models, with an overall accuracy (OA) of 92.0% and a kappa coefficient of 0.90. This study demonstrates the feasibility of combining CWT with machine learning in rubber tree powdery mildew detection.

In recent years, there have been frequent disasters and accidents in the underground pipeline system of Chinese cities, posing a continuous threat to the safety of life and property of the public and the order of urban operations. This article uses statistical data on the spatiotemporal distribution of major underground pipeline disasters in various provinces of China in 2021. By using Pearson’s correlation coefficient and regression analysis, a causal analysis model for urban underground pipeline accidents is constructed, and the correlation between urban underground pipeline accident rate and economic and social indicators is analyzed. From the analysis, it has been found that the correlation coefficients of gross domestic product (GDP) per capita, the pipeline density in the built-up area, and the urban pipeline accident rate reach −0.4019 and −0.4275, respectively, showing a negative correlation. Further, the regression analysis results show that the underground pipeline accident rate shows a decreasing trend as the per capita GDP and the density of pipelines in the built-up areas increase. Among them, the results of the power function fitting model show that the GDP per capita accounts for 48.10% of the urban underground pipeline accidents, whereas the pipeline density of the built-up area accounts for 58.27% of these accidents. The construction of underground pipeline regulations is influential in reducing underground pipeline disaster accidents. In this study, the role and effectiveness of the factors, such as the construction of professional regulations in maintaining the safe operation of urban underground pipelines, has been discussed, and suggestions and methods to improve the problems that need to be solved, such as the construction of a municipal supporting emergency management system in the safe operation of underground pipelines in the future, have been proposed.