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To investigate the spatial and temporal distribution, sources, and ecological risk of soil polycyclic aromatic hydrocarbons (PAHs) in China’s urban agglomerations from 2000 to 2020, a comprehensive search strategy was employed using the keywords “soil”, “PAHs”, and “city”. A total of 122 relevant studies that provided information on individual PAH content during the specified time period were collected. These studies encompassed 20 urban agglomerations in China, which were further categorized into two distinct periods: 2000 to 2010 and 2011 to 2020. The diagnostic ratio method and principal component analysis were employed to identify the sources of PAHs, and a risk quotient model was used to evaluate the soil ecological risk. The results revealed the average PAH content in the 20 urban agglomerations in China from 2011 to 2020 was 2,439 ± 4,633 ng·g-1, which exceeded the severe pollution level cut-off (> 1,000 ng·g-1). The soil PAH content in the period from 2011 to 2020 decreased by 28% compared to the soil PAH content in the period from 2000 to 2010. Soil PAH pollution was more severe in the northern urban agglomerations than in the southern urban agglomerations. Diagnostic ratios and principal component analysis demonstrated that the principal sources in most urban agglomerations in China were traffic and coal combustion. GeoDetector found that coal and fuel oil consumption were the main factors affecting the spatial differentiation of PAHs. The ecological risk quotient showed that approximately 80% of the urban agglomerations were at a medium-high ecological risk from 2000 to 2010, compared with 72% from 2011 to 2020. Thus, it is necessary to deepen energy structure reform to alleviate the threat of serious pollution caused by coal and fuel oil in urban agglomerations.

Wind turbine blades are prone to failure due to high tip speed, rain, dust and so on. A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed. On the experimental measurement data, variational mode decomposition filtering and Mel spectrogram drawing are conducted first. The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network. Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients, considering the complexity of the real environment. The surfaces of Wind turbine blades are classified into four types: standard, attachments, polishing, and serrated trailing edge. The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%. In addition to support the differentiation of trained models, utilizing proper score coefficients also permit the screening of unknown types.

With the development of network science and graph theory, brain network research has unique advantages in explaining those mental diseases, the neural mechanism of which is unclear. Additionally, it can provide a new perspective in revealing the pathophysiological mechanism of brain diseases from the system level. The selection of threshold plays an important role in brain networks construction. There are no generally accepted criteria for determining the proper threshold. Therefore, based on the topological data analysis of persistent homology theory, this study developed a multi-scale brain network modeling analysis method, which enables us to quantify various persistent topological features at different scales in a coherent manner. In this method, the Vietoris–Rips filtering algorithm is used to extract dynamic persistent topological features by gradually increasing the threshold in the range of full-scale distances. Subsequently, the persistent topological features are visualized using barcodes and persistence diagrams. Finally, the stability of persistent topological features is analyzed by calculating the Bottleneck distances and Wasserstein distances between the persistence diagrams. Experimental results show that compared with the existing methods, this method can extract the topological features of brain networks more accurately and improves the accuracy of diagnostic and classification. This work not only lays a foundation for exploring the higher-order topology of brain functional networks in schizophrenia patients, but also enhances the modeling ability of complex brain systems to better understand, analyze, and predict their dynamic behaviors.

Ambient PM2.5 (fine particulate matter with aerodynamic diameters ≤2.5 μm) is a major threat to human health. Environmental fates and human exposure to PM2.5 can be affected by various factors, and environmental greenness have been documented to be significantly associated with the exposure disparities; however, the relationship between the greenness and ambient PM2.5 on the region and city levels, and variations across different land cover types remain unclear. In this study, PM2.5 changes from 2001 to 2020 varying over different land cover types and cities were analyzed, and discussed for the relationships with environmental greenness, by taking Shanxi province as an example. The results showed in the past 2 decades, the mean annual NDVI (normalized difference vegetation index) of the study area showed a significant increasing trend (p < 0.01), and the PM2.5 concentration decreased as environmental greenness get better. The same trends were observed across different land cover types and cities. The negative correlation was stronger in the construction land with more frequent human activities, especially in the built-up areas with low vegetation coverage; but limited in the high green space coverage areas. These results provide quantitative decision-making references for the rational development, utilization and management of land resources, but also achieving regional coordinated controls of PM2.5 pollution by optimizing land use.

Breast cancer is the most common form of cancer in women. The importance of diagnosing breast cancer is one of the important issues in medical science. Diagnosis of benign or malignant cancer is of great importance in addition to reducing costs in the direction of treatment. A non-destructive test method for early detection of breast cancer is image processing. Image processing has various uncertainties which are generated by different reasons such as sampling to noise, initial digitalization, intensity, and special domain. In this study, a strong image segmentation method based on interval uncertainties is proposed for the breast cancer images diagnosis. The main purpose of this paper is to improve the ordinary Sobel filter based on interval analysis by considering the intensity uncertainties. Simulation results have been implemented on MIAS which is an applicable database for breast cancer detection. The results of the proposed method have been compared with some state of the art methods such as LoG, Prewitt and canny filters. Final results showed that using the proposed method gives better achievement than the others by considering some kinds of uncertainties like Gaussian noise and salt and pepper noise.