To grasp the current status of network public opinion (NPO) research and explore the knowledge base and hot trends from a quantitative perspective, we retrieved 1385 related papers and conducted a bibliometric mapping analysis on them. Co-occurrence analysis, cluster analysis, co-citation analysis and keyword burst analysis were performed using VOSviewer and CiteSpace software. The results show that the NPO is mainly distributed in the disciplinary fields associated with journalism and communication and public management. There are four main hotspots: analysis of public opinion, analysis of communication channels, technical means and challenges faced. The knowledge base in the field of NPO research includes social media, user influence, and user influence related to opinion dynamic modeling and sentiment analysis. With the advent of the era of big data, big data technology has been widely used in various fields and to some extent can be said to be the research frontier in the field. Transforming big data public opinion into early warning, realizing in-depth analysis and accurate prediction of public opinion as well as improving decision-making ability of public opinion are the future research directions of NPO.

Risk assessment is of great significance in industrial production and sustainable development. Great potential is attributed to machine learning in industrial risk assessment as a promising technology in the fields of computer science and the internet. To better understand the role of machine learning in this field and to investigate the current research status, we selected 3116 papers from the SCIE and SSCI databases of the WOS retrieval platform between 1991 and 2022 as our data sample. The VOSviewer, Bibliometrix R, and CiteSpace software were used to perform co-occurrence analysis, clustering analysis, and dual-map overlay analysis of keywords. The results indicate that the development trend of machine learning in industrial risk assessment can be divided into three stages: initial exploration, stable development, and high-speed development. Machine learning algorithm design, applications in biomedicine, risk monitoring in construction and machinery, and environmental protection are the knowledge base of this study. There are three research hotspots in the application of machine learning to industrial risk assessment: the study of machine learning algorithms, the risk assessment of machine learning in the Industry 4.0 system, and the application of machine learning in autonomous driving. At present, the basic theories and structural systems related to this research have been established, and there are numerous research directions and extensive frontier branches. “Random Forest”, “Industry 4.0”, “supply chain risk assessment”, and “Internet of Things” are at the forefront of the research.

Introduction: Internal erosion triggered by water pipeline leaks seriously threatens the stability of the urban ground. Hangzhou, a city in Zhejiang Province, China, is facing critical challenges due to urban ground collapse (UGC) caused by internal erosion. However, there is a lack of research on the prevention of UGC by improving the internal erodibility of underground soil. Addressing this issue is of utmost importance to ensure the city’s stability and safety. This paper proposes to improve the internal erodibility of typical sandy silt soils with chemical stabilisers.Methods: The effects of three chemical stabilisers, lignosulphonate (LS), lime (LI), and lignin fibre (LF), on the critical shear stress (τ_c) and erosion coefficient (k_d) of sandy silt soils were investigated, which from Hangzhou, Zhejiang, China, by the hole erosion test (HET) at different mixing amounts and at different conservation times.Results: The findings indicate that LF mainly improves the erosion resistance of sandy silt by increasing τ_c, and the maximum increase is 2.38 times; LI mainly improves the erosion resistance by decreasing k_d, and the maximum decrease is 2.18 times. After adding LS, τ_c and k_d did not change significantly. The scanning electron microscope (SEM) test revealed that the inclusion of LF led to the formation of larger agglomerates in the sandy silt soil. The microstructure of sandy silt soil remained dispersed even after adding LS. Various chemical stabilisers used to improve sandy silt soils exhibited distinct erosion mechanisms. Sandy silt soils improved with LF exfoliated into agglomerates, displaying high resistance to erosion. On the other hand, the sandy silt treated with LF still lacks a protective layer and shows minimal improvements in its ability to withstand erosion. In contrast, the LS-amended sandy silt remains stripped with individual soil particles with insignificant changes in erosion resistance.Discussion: This study can provide a conceptual framework for choosing foundation treatment techniques in future urban development projects.