With the widespread application of artificial intelligence technology across various industries, our society has embarked on the journey towards the 4.0 intelligent era. To foster high-quality development in teaching reform at colleges and universities, which is empowered by information technologies such as artificial intelligence, in-depth interviews with 20 managers, implementers and educates involved in teaching reform across 11 colleges and universities were conducted. Qualitative analysis was performed using NVIVO software. Through analyzing the current application of artificial intelligence in teaching reform at colleges and universities, both achievements and challenges are identified. Finally, it is proposed to advance the intelligent application of teaching reform in colleges and universities from four perspectives such as people-oriented philosophy, talent support, financial support and institutional support.

Green development cannot be achieved without innovation in green technology. Only when green technology is innovated can a country achieve high-quality development. Regarding the construction of ecological civilization as a quasi natural experiment, green patent data was used to empirically analyze the impact of ecological civilization construction on green technology innovation through the double difference method. It is found that the construction of ecological civilization significantly promotes green technology innovation in the experimental zone. Ecological civilization construction can attract environmental investment and human capital, and is conducive to infrastructure improvement and industrial structure upgrading, thereby promoting the level of green technology innovation.

Digital resilience, as the ability of enterprises using digital technology to respond to major shocks, has received extensive attention from society. Through a review of domestic and foreign literature, it is found that the digital resilience of enterprises mainly consists of the ability to absorb shocks, the ability to adapt to shocks, and the ability to recover to a steady state. The multi-case study method was used to identify the typical practices adopted by chemical enterprises in the process of building digital resilience. On this basis, these scattered typical practices were further summarized and integrated to construct a universal model of the digital resilience building path for chemical enterprises. Through dynamic management and control based on digital innovation, organizational collaboration based on digital platforms, resource aggregation based on industrial Internet platforms, and agile response based on business model innovation, it helps enterprises effectively defuse major shocks and promotes their transition to a new steady state.

Through the study of the panel data of 17 listed city commercial banks in the five years from 2017 to 2022, the DEA-SBM-DDF model was used to measure their efficiency. The results show that the operational efficiency of urban commercial banks is generally high, but it shows a downward trend, and there are great differences in the management level among banks, and the operational efficiency of Bank of Beijing is the highest. Finally, the Tobit model was used to analyze the influencing factors affecting the operational efficiency of urban commercial banks, and the results show that the scale of Internet payment, asset scale and non-interest income non-performing loan ratio have a significant impact on the operational efficiency of urban commercial banks. The analysis results also have a certain reference role for other commercial banks’ goal setting, performance evaluation and job candidates’ selection of target commercial banks.

With the development of artificial intelligence technology, the unequal access to artificial intelligence, known as the “AI divide” phenomenon, is gradually becoming prominent. Using literature from the Web of Science database as corpus and identifies relevant topics under the topic of “Artificial Intelligence Divide” based on LDA model, text analysis was conducted through manual encoding. Finally, four core research themes such as intellectual barriers, digital education, healthcare and social effects are identified and summarized, the current situation and gaps in technological knowledge gaps, awareness gaps, digital education inequality and the unfair application of artificial intelligence in the healthcare field is revealed. Meanwhile, the social effects of the artificial intelligence divide, including ethical and cultural dilemmas, cultural convergence and social inequality, are also important factors affecting social structure and individual lives. This article aims to deepen the understanding of the core theme of the “artificial intelligence divide” and help artificial intelligence enhance human well-being and social progress.

In the financial business, effective risk avoidance and mitigation are perennial themes. In the face of the international economic and financial environment has become more complex and severe, extreme events caused by the tail risk brought about by the harm is very strong, China’s banking industry risk prevention and control work will face new challenges. Therefore, under the current globalization and complex and severe economic and financial environment, it is of great practical significance to improve the prediction ability of yield risks of commercial banks and take timely measures to prevent and resolve the risks. Selecting the monthly data of China’s A-share listed commercial banks from January 2013 to December 2022, a nonparametric Expectile Regression Forest(ERF) risk prediction model based on the Bagging algorithm to measure the tail risk of China’s commercial banks, and simultaneously incorporates the bank leverage ratio, asset size, economic policy uncertainty, financial market volatility and liquidity into the model at the same time were constructed, and then the mean absolute error and root mean square error of the training set and test set data under different models and risk levels respectively was calculateed. Finally, the results were compared with the traditional expected quantile regression(ER) model and Expectile Regression Tree(ERT) model to determine the respective predictive performance. The results show that the ERF model has outstanding performance in measuring the tail risk of commercial banks, and the estimation and prediction ability of the ERF model is significantly better than that of the ERT and ER models under different levels of risk. Further analysis reveals that the smallest and the largest errors in the prediction of tail risk of the four major types of commercial banks are those of the national large-scale commercial banks and the local rural commercial banks, respectively, and the error values of the joint-stock commercial banks and the local urban commercial banks are comparable. The error values of joint-stock commercial banks and local urban commercial banks are comparable.

Bridge expansion joints play a crucial role in bridges. In order to solve problems such as the relatively high maintenance costs of traditional expansion joints, an assembled and quickly installed multi-directional displacement bridge expansion joint (hereinafter referred to as the ZPF assembled expansion joint) was now proposed. According to the “General Technical Requirements for Highway Bridge Expansion Joints” (JT/T 327—2016), on-site fatigue tests were designed, and finite element analysis was also carried out at the same time. The test results show that the fatigue life of the ZPF assembled expansion joint decreases as the fatigue load increases. The fatigue life of the ZPF assembled expansion joint depends largely on the concrete under the embedded steel plate. During the loading of the fatigue performance test for 2 million cycles, the overall structure is safe and stable without any abnormal phenomena. The research results can provide a basis for the application of the ZPF assembled expansion joint.

The tourism industry is undergoing a profound transformation, with tourists’ demands and preferences showing a diversified trend. Accurately identifying tourists’ preferences and satisfaction levels is of crucial importance. Taking the Longji Terraced Fields Scenic Area in Guilin as a case study, deep learning technology and cluster analysis methods were employed to extract the themes of tourists’ service preferences from online reviews and identify groups of tourists with similar preferences. Combined with questionnaire survey data, the service satisfaction was quantitatively evaluated. The research finds that tourists’ service preferences cover accommodation, catering, transportation and information services. The improvement space for information services and accommodation services is the largest, while transportation services and catering services also have room for improvement. Based on this, corresponding suggestions are put forward.

To understand and grasp the impact of the planned project on the atmospheric environment of Yumen City and the surrounding counties and cities after its completion, the WRF-Chem model method was used to study the PM2.5 and O₃ concentrations in Yumen City after the completion of the project. Two scenarios were predicted. Scenario One, where the total amount of waste gas generated in the industrial park after the completion of the project is 3 127.75 t/a, and Scenario Two, where the total amount of waste gas generated is 5 112.74 t/a. The model simulation results indicate that during the planning period, the 8-hour average concentration of O₃ at each environmentally sensitive point, when added to the current concentration, meets the standards under the guaranteed rate. Under Scenario One, both the daily average and annual average concentrations of PM2.5, when added to the current concentration, meet the standards. Under Scenario Two, the daily average and annual average concentrations of PM2.5 at the boundaries of the Mahuangtan core area, Mahuangtan buffer zone, and the experimental area of the Nanshan Nature Reserve exceed the standards when added to the current concentration, while other sensitive points meet the standards. To meet the control targets of the planning area, it is recommended to implement stricter pollution source reduction measures to ensure continuous improvement of PM2.5 concentrations.

Under the backdrop of the Chinese government’s emphasis on the digital economy and data elements, relevant ministries and commissions further clarify the policy framework for the inclusion of data assets in corporate financial statements in 2024, thereby unlocking greater potential for data elements. Against this background, the panel data firstly used from 18 companies that had incorporated data assets into their financial statements, employing the Difference-in-Differences(DID) method to examine the impact of data asset inclusion on profitability metrics of listed companies. The findings reveal that, under the current data management framework, while the inclusion of data assets positively influences the profitability of listed companies, the impact remains relatively limited. Despite the undeniable positive significance of data asset inclusion for enterprises, the process has yet to significantly boost overall corporate profitability. This limitation arises from the fact that the value assessment system for data assets is not yet fully reflected in the existing accounting standards, and the maturity of the inclusion process requires further improvement. To better harness the potential of data assets and enable companies to reap the benefits of their inclusion, future policies should focus on optimizing the management and valuation systems for data assets, enhancing the market-driven application of these assets, and ensuring deeper integration of relevant accounting standards and regulations. These measures would help unlock the value of data assets, activate corporate data resources, and provide robust support for profit growth.