In order to solve the problems of weak internal motivation and insufficient resource capabilities in the ESG(environmental, social, and governance)development of Chinese enterprises, grounded theory was used to conduct a textual analysis of ESG reports from 102 A-share listed companies between 2020 and 2023. A dual-dimensional analysis model was constructed encompassing management attitude (“words”) and capital investment (“deeds”), with manual coding of the above ESG reports via Nvivo12 software, and statistical analysis of their patterns and trends. Key findings include from the perspective of management attitude, the sample companies currently focus on compliance ESG construction and institutional safeguards instead of strategic ESG development. From the viewpoint of capital investment, the sample firms’ inputs in the three ESG dimensions are significantly unbalanced, especially the environmental and governance dimensions that are under-invested. Through dual-dimensional cross-analysis, it is found that the “words” of enterprises do not match their “deeds” at this stage, but the current compliance management can be transformed into future strategic development. Industry comparative analysis shows significant differences between industries with high and low ESG ratings in both “Words” and “Deeds” dimensions, necessitating coordinated efforts to promote integrated collaboration between emerging and traditional industries. Accordingly, tripartite improvement pathways of corporate ESG construction are proposed for enterprises, government, and capital market.

In order to further explore the main research forces, knowledge base, evolution of knowledge structure and research front in the field of technology innovation management, the CiteSpace visual analysis software was used to analyze the paper data of the top 10 technology innovation management specialty journals in the Web of Science database during the year of 2000 and 2024. The results show that the main research force is concentrated in Europe, but are transferring to Asia.The knowledge base of this field includes two aspects, including theoretical perspective and research method. The evolution of knowledge structure in the field of technology innovation management is divided into four stages.The current research front in the field of technology innovation management include digital-intelligence technology innovation management and green technology innovation management.

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.

Addressing the issue of large rock block detachment during TBM (Tunnel Boring Machine) construction in unfavorable geological conditions at a metal mine in Jiangxi Province, China, the horizontal thrust force of the gripper shoes was systematically analyzed for the first time to reveal its mechanism of inducing tensile failure in surrounding rock, based on the PLAXIS 3D finite element simulation platform and field observations. An optimized support scheme combining “pre-grouting reinforcement and rock bolt-mesh-shotcrete support” was proposed. The study reveales that during TBM construction in Class V rock mass conditions, tensile failure zones developes on the tunnel roof and progressively expandes as the TBM advanced, leading to roof collapse phenomena observed in the project. The support optimization analysis compared three scenarios, such as no support, “rock bolt+mesh+shotcrete” (referred to as bolt-mesh-shotcrete support), and “pre-grouting reinforcement+bolt-mesh-shotcrete support.” Results show that the pre-grouting reinforcement scheme significantly enhances the stability of the surrounding rock, reducing the maximum roof deformation by 21.83% and the final deformation by 36.72%. While bolt-mesh-shotcrete support alone has limited effectiveness, it effectively restraines further deformation of the surrounding rock and prevents the detachment of loose rock masses. It is recommended that in similar TBM tunnel construction scenarios in metal mines encountering unfavorable geological conditions, the combined use of pre-grouting reinforcement and bolt-mesh-shotcrete support should be adopted to improve construction safety and efficiency.

The utilization of photovoltaic power generation represents a pivotal strategy for the transition of traditional energy systems towards decarbonization, and is of paramount importance in achieving global climate goals. A social network analysis was employed to examine the evolutionary characteristics of the global PV power generation trade network, based on the trade data of global photovoltaic devices from 2012 to 2021. The analysis aims to elucidate the basic situation and development trend of this network. It was found that during the period from 2012 to 2021, the global PV power generation trade network underwent substantial development and expansion, with trade links between countries becoming increasingly close. The trade network is distinguished by high aggregation and short path lengths, exhibiting pronounced “small-world” characteristics. The nodes of the global PV power generation trade network exhibit a stable “core-edge” structure. Concurrently, China’s central position is becoming increasingly evident, and its pivotal role in the global PV power generation trade network is intensifying. This has positioned China as a pivotal force in the advancement of trade in this field, with the center of gravity of trade activities demonstrating a tendency towards an eastward ascendance and a westward decline.

Regarding th special seismic response characteristics exhibited by super long-span bridge, a long-span continuous rigid-frame bridge was focused on as the research subject. A three-dimensional finite element model was established, and incremental dynamic analysis (IDA) was performed to systematically investigate the dynamic response characteristics under bi-directional and tri-directional seismic excitations. The results indicate that under bi-directional seismic excitation, the main pier exhibits a 43% probability of slight damage at PGA=0.4g. After incorporating vertical seismic excitation, the main pier shows a 22% probability of slight damage at PGA=0.2g, with the exceedance probability of complete damage state increasing by approximately 25%. Regarding the bearing system, the introduction of vertical seismic excitation significantly accelerates the damage progression rate, reaching a 100% probability of slight damage at PGA=0.4g.

In order to solve the problem of low accuracy and great lag in the inspection of hollow filter bar end face, a vision inspection system for hollow filter bar end face was studied based on machine vision technology. Using PLC to read the pulse signal on the encoder in real time, the industrial array camera was triggered to collect the image of the end face of the hollow filter bar. Image optimization technology was used to process the image of the filter bar end face to ensure the image quality. The dimension and roundness of the end face of the hollow filter rod were measured by using the least square method. Through the method of template matching difference, whether there are defects in the end face of the hollow filter rod is detected, and the alarm is displayed on the man-machine interface. The test results show that the system can detect the dimensions, roundness and defects of the end face of the hollow filter rod in real time, and the detection accuracy can reach 99% at different speeds, which lays the foundation for automatic visual inspection of the filter rod and improves the qualified rate of the product.

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.

Forecasting the stock market is a difficult and intricate task, as price series often display traits like significant noise, nonlinearity and non-stationarity. In order to improve the accuracy of predictions, a new method that combined the fuzzy C-means (FCM) clustering algorithm to identify and utilize local trend features in stock price prediction sequences was proposed. In the analysis, key market data of stocks, including opening price, highest price, lowest price, closing price, trading volume, and trading amount, was comprehensively considered as input features for the prediction model. Through experiments, an empirical analysis was conducted to compare the impact of different sliding window sizes (16, 32, 64) on the model’s predictive capability. It is found that the FCM-LSTM-Transformer method, which integrates FCM clustering with the LSTM-Transformer combination model, achieves higher prediction accuracy than both the standalone deep learning models and the LSTM-Transformer combination model. The evaluation metrics MAE, MAPE, MSE and RMSE reach their minimum errors, and the coefficient of determination R² improved by 2.75%, 2.4% and 2.19%, respectively. These results indicate that the proposed model has a significant advantage in handling the complexity of stock market data.

Railway transportation safety provides guarantees for ensuring the safety of passengers’ lives and property and the smooth transportation of goods. The current status and future directions of railway transportation safety were studied in China through two bibliometric analysis methods. The results show that railway transportation safety research can be divided into passive constraint stage, active constraint stage and active management improvement stage. The research focuses on the safety operation protection system, grassroots station safety management, prediction and evaluation of transportation safety accidents, and factors affecting transportation safety accidents. The future of railway transportation safety should focus on the integration of information technology for safety management, intelligent technology for train operation safety, and dynamic monitoring of the external environment of railways.