It is a trend to apply L320 and L360 high-strength angle members in ±800kV ultra high voltage(UHV) transmission lines. The current calculation method does not clarify the failure mode of tension member. Due to the quantities of bolts in the connection, each joint needs to be calculated multiple times, which is inconvenient in engineering. The training sets were established through the tests of multi alignment lines plate members and finite element analysis of large-size angles, and a random forest regression(RFR) model was constructed to perform feature analysis on the section reduction factor. The results show that the weight of adjacent alignment line(Δg₁) is too high and for the thickness (t) is low in the current calculation method, which may easily cause significant fluctuations in the results while geometric dimensions of models are changed. The calculation method for section reduction factor is simplified through the optimization of RFR model feature analysis, and the results are stable and more consistent with the FEA models. The predictive model has good generalization ability and could be used in angles with 2 alignment lines in one leg.

With the rapid development of the aviation industry, the service environment for aircraft has become increasingly harsh, and the demand for surface performance is higher than ever. Spraying is an important method to enhance properties such as corrosion resistance and oxidation resistance, and it is widely used in the aviation field. Compared to traditional manual spraying methods, automated spraying robots offer several advantages, including high precision, high efficiency, and consistent thickness, playing a more and more important role in the preparation of surface coatings for aircraft skins and engine components. The current application status and research progress of automated spraying technology in the aviation field were briefly described, and on this basis, the main technical challenges and future development trends were discussed.

To effectively address issues such as difficulties in material dispatching caused by emergencies, it is necessary to select the location for an emergency material reserve depot in H Province. Through literature analysis and expert interviews, a total of 14 secondary indicators was constructed across five categories, including natural factors, economic factors, and policy factors, among others. A hierarchical analysis method was used to determine the weights of these indicators, while a cloud model was employed to calculate the cloud parameters for different candidate locations. The results indicate that economic and natural factors carry greater weight within the evaluation system. Among the three candidate sites, Site Q has the highest evaluation rating and is thus considered the optimal location for the emergency material reserve depot. This conclusion offers decision-makers a solid scientific foundation for selecting an optimal location.

To support the emergency decision-making of highway emergency rescue area, the technical process of emergency response to secondary disasters in highway emergency rescue area was established from three aspects, including personnel composition, work flow and equipment. According to the workflow, the implementation steps of 12 h rapid identification of disaster damage stage, 24 h rapid assessment of risk stage and 36 h quantitative assessment of risk stage wae established. Taking the highway rescue after the strong earthquake in Luding on September 5 in 2022 as a typical case, the above process was used to evaluate the disaster situation. The results show that the length of road damage points A and B of landslide at a tunnel entrance in Luding Hailuogou is 181.31 m and 115.26 m respectively, and the width is 9.81 m, and the volume is 4.7 × 104 m³ and 2.6 × 104 m³ respectively. In the rescue of 2# landslide, the length error is 7%, the width error is 4%, and the volume error is 10%.

Based on the quasi-natural experimental conditions provided by three batches of smart city pilot policies, and on the basis of comprehensively measuring the resilience level of urban pharmaceutical manufacturing industry, the panel data of 54 prefecture-level cities from 2009 to 2019 was used to explore the impact of smart city pilot construction on the resilience of municipal pharmaceutical manufacturing industry by using multi-period DID (difference in differences) model. The results show that the pilot construction of smart city can significantly promote the resilience of urban pharmaceutical manufacturing industry. Mechanism analysis shows that the pilot construction of smart city promotes the resilience of pharmaceutical manufacturing industry through technological innovation, while the level of information infrastructure construction has a positive moderating effect.

Proof of concept is an important link in the long chain of transformation of scientific and technological achievements. At present, the proof-of-concept center is being supported by more and more regions in China to open up the “first mile” of the transformation of scientific and technological achievements into industrialization. However, as a new innovation carrier, the construction of the PoC Center is facing many problems and challenges. On the basis of investigating multiple proof-of-concept center across the country, the exploration practices of different regions were sorted, and the current development characteristics, beneficial experiences and existing problems were summarized. It aims to provide reference and guidance for the country and regions to support and promote the proof-of-concept center better, in order to accelerate the transformation of scientific and technological achievements, and cultivate and develop new quality productivity.

Driven by digital economy, Guizhou rural sports tourism industry is ushering in a critical period of its digital transformation and upgrading. As the engine of the new era, the digital economy provides a powerful impetus for the digital transformation and upgrading of Guizhou rural sports tourism industry through cutting-edge technologies such as big data, cloud computing, Internet of Things and artificial intelligence. Research methods such as literature method, expert interview method and fieldwork method were used to demonstrate the background of digital economy driving digital transformation and upgrading of Guizhou rural sports tourism industry, the connotation of digital economy driving digital transformation and upgrading of Guizhou rural sports tourism industry were analyzed, the power mechanism of digital economy driving digital transformation and upgrading of Guizhou rural sports tourism industry were constructed. The strategy of digital economy driving digital transformation are put forward for upgrading of Guizhou rural sports tourism digital transformation and upgrading of the industry, aiming to provide theoretical references and countermeasure suggestions for the promotion of Guizhou rural sports tourism industry to achieve high-quality development.

Through different research perspectives, the current situation of domestic and international literature publication on environmental regulation and carbon emission reduction was sorted out, and targeted improvement measures and suggestions are put forward in combination with domestic and international research trends and cutting-edge dynamics, so as to promote the enhancement and advancement of the research level of the field of environmental regulation and carbon emission reduction in China. Based on the bibliometric method, 822 English documents included in the core set of Web of Science (WoS) and 4 768 Chinese documents in the core journals of CSSCI were compared and analyzed by CiteSpace software for the theme of environmental regulation and carbon emission reduction. Domestic research on environmental regulation and carbon emission reduction started late, but the international recognition is increasing year by year. Compared with foreign studies, there are fewer domestic studies on energy consumption, FDI and performance. In terms of modeling methodology, fewer domestic studies utilize the data envelopment analysis (DEA) method. The conclusions drawn from bibliometric methods favor probability laws, which can reduce the problem of subjective selection by authors. WoS databases have less relevant literature due to precise limitations, and the analysis is not comprehensive. The data exported from domestic CSSCI cannot be analyzed for citations and journal sources, and the breadth of analysis is limited.

With the rapid development of information technology, cultural digitization has become an important means of cultural protection and inheritance. Based on “CNKI Database” and “WoS database” from 2014 to 2023, CiteSpace software was used to carry out cultural digital visualization analysis. The results show that in the past decade, the number of publications on cultural digitalization research has shown an overall increasing trend. Digitalization, cultural heritage, protection, inheritance, cultural industry and three-dimensional scanning are high-frequency co-occurrence keywords in cultural digitalization research. Digital technology and cultural digitalization, cultural heritage digitalization, cultural participation and accessibility of special groups are hot topics in foreign research. Cultural undertakings, industrial development and cultural self-confidence are hot topics in domestic research. In the future, more attention should be paid to technological integration and innovation, addressing the challenges of copyright and intellectual property rights, interdisciplinary cooperation and cultural sustainable development, and enriching the content of cultural digitization research.

With the advancement of technology and digital transformation, understanding and enhancing students’ emotional experiences are key concerns for educators and policymakers. In this study, 2 000 manually labeled review data were first used to train three machine learning models. The trained SVM model was then used to automatically label 76 000 review data. These labeled datasets were subsequently used to train a Convolutional Neural Network (CNN) model. The CNN model converged successfully after 10 iterations and achieved an accuracy of 94.96% on the validation set, significantly outperforming the SVM model. The results show that combining traditional machine learning with deep learning methods can effectively improve the accuracy of sentiment analysis. This provides data support for optimizing educational strategies.