The Tradable Green Certificate (TGC) system scientifically guides renewable energy investment by internalising the positive externalities of renewable electricity. With the promotion of energy transition, the demand for TGC has increased significantly, and the scale of market players has gradually expanded. Market players will imitate other players’ trading strategies for reasons such as herd mentality, which is manifested as herd behaviour. If TGC market players ignore high-quality information and blindly imitate the behaviour of other players, it will limit the diffusion of effective information in the market and reduce the pricing efficiency of the market. Therefore, this paper explores the emergence law of herd behaviour in the TGC market based on a hybrid system dynamic model, with a view to providing theoretical and methodological support for the immediate identification of market risk. This paper portrays the emergence process of herd behaviour of TGC trading subjects, and analyses the emergence law through multi-scenario computational experiments. The results show that (1) herd behavior will emerge from all kinds of strategy subjects and there is a positive feedback relationship between the emergence speed and the return difference between subjects. (2) The emergence of herd behaviour of fundamental strategy subjects has scale and structural effects, and only when the initial imitation scale of such subjects reaches 40% or the market share is less than 50%, will the emergence of herd behaviour, and the depth of its emergence shows an ‘S’ type growth. (3) The herd mentality and the weakening of cognitive bias of TGC trading subjects will reduce the emergence speed of herd behaviour, but have almost no effect on the depth of emergence.

This paper studies integrated process planning and scheduling (IPPS), a typical workshop scheduling problem, and mainly investigates the uncertain problems in the actual industrial production process. Then, we introduce the theoretical knowledge of interval numbers and adopt the interval number comparison method. Specifically, interval numbers are used to replace the determined processing time, and uncertain IPPS problems are modeled based on the interval number theory. Based on this, a hybrid particle swarm algorithm is proposed to solve the uncertain IPPS. Meanwhile, the genetic operator is introduced to improve its ability to deal with combined optimization problems. The above theoretical results are applied to the process planning and scheduling of a mechanical workshop, thus verifying the effectiveness of the proposed method.

Rural digitalization and rural tourism are important tasks to achieve the goal of rural revitalization strategy, and researching whether there is a connection between them and the degree of association is helpful to accelerate the transformation of rural digitalization and promote the quality and upgrading of rural tourism. This paper constructs an evaluation system of rural digitalization and rural tourism, adopting 253 counties in China as samples to measure the development differences between regions of the two systems. A coupled coordination model is applied to explore the relationship between the two systems and reveals the distribution characteristics of the level of coupling and coordination in China. The findings show that the difference in the overall score of rural digitalization between counties is greater than that of rural tourism industry. There is a high degree of coupling between rural digitalization and rural tourism systems, and the two systems are currently at a barely coordinated stage in China. In addition, the degree of coordination varies significantly between counties, presenting a phenomenon of higher coupling coordination in the eastern coastal region, intermediate in the central and western inland regions, and lower in the northwest. This paper supports and validates some results of rural development projects in the research area to provide theoretical and decision support for coordinating rural digitalization and rural tourism services.

A fault diagnosis method for wind turbine gearbox based on adaptive probability random forest is proposed to address the issue of noise pollution in SCADA data of wind turbine gearbox. Firstly, SMOTE oversampling is used to balance sample categories, and then CART is trained and classified by constructing multiple balanced subsets. The sample error rate represents the weight of sample ambiguity, and the label uncertainty is determined. Monte Carlo simulation is used to calculate the mean distribution of features, which is fused with each sample instance to obtain the uncertainty of sample features. Utilizing adaptive labels and sample uncertainties as inputs to probabilistic random forest can enhance the ability to manage feature noise and label noise, thereby improving the robustness of fault diagnosis. Conduct an experimental evaluation using the SCADA dataset of wind turbine gearbox. The results show that this model outperforms other methods in terms of false alarm rate, false alarm rate, and F1 rating metrics when dealing with missing values, Gaussian noise, and label noise in the dataset, as compared to other methods. This method is of great significance for improving the accuracy and robustness of wind turbine gearbox fault diagnosis.

This paper focuses on the coupling and coordinated development of provincial sports industry and tourism industry. In view of the integration trend of the two as the pillar of the tertiary industry and driven by relevant policies, in view of the insufficient quantitative and regional comparison of existing studies, data from 31 provinces from 2014 to 2021 were selected for analysis. The connotation mechanism of coupling coordination is explained from the economic, social, ecological and cultural levels, and the system including industrial scale and structural indicators is constructed, and the coupling coordination degree model is used to calculate. The results show that the coupling coordination degree of the country is rising in a step, with the eastern starting point being high, the central part making great progress and the western part growing fast. The types of industrial development vary between regions and over time. The global Moreland index shows that there are significant autocorrelation and clustering in the space, the local “high-high” cluster in the east and part of the middle, and the “low-low” cluster in the west. Further, suggestions were put forward to strengthen policy guidance, optimize industrial structure, promote the development of talents and technology, and strengthen the protection and utilization of ecological culture, so as to provide decision-making reference for industrial upgrading and sustainable development of regional economy.

Traditional construction project cost estimation methods rely on expert experience and statistical models, which are difficult to handle complex data and multimodal features effectively and have low prediction precision. This paper constructs an intelligent building engineering cost estimation model that combines subtractive clustering, a self-learning mechanism, and convolutional neural networks (CNN) to address this problem. In the data preprocessing stage, subtractive clustering is applied to optimize multimodal data, screen key features, and eliminate redundant information. Subsequently, the model parameters are dynamically adjusted according to the error feedback through a self-learning mechanism to improve its adaptability to diverse construction projects. In the feature extraction and estimation stage, the CNN module is combined to extract deep features from images, texts, and numerical data to achieve high-precision estimation. The experimental results show that the model in this paper outperforms traditional methods in terms of MSE (mean-square error), MAE (mean absolute error), R² (coefficient of determination), MAPE (mean absolute percentage error), with the mean values being 73.18, 8.33, 0.9477, and 5.33%, respectively. In summary, the model in this paper demonstrates superior precision, adaptability, and robustness in construction project cost estimation.

Foreign direct investment plays a more important role in China’s economic development. This paper examines the impact of FDI on China’s GDP and analyzes regional variability through OLS and quantile regression models. Then the spatial correlation-Moran, I scatter plot is used to visualize the clustering pattern of regional units. The analysis shows that FDI has a significant positive effect on China’s high economic growth at the 25% quantile. However, the higher the economic growth rate, the margin of positive effect of FDI on economic growth gradually decreases. China’s regional economic development is characterized by a dualistic structure. The elasticity coefficient of FDI in the eastern region is 0.099, and that in the western region is 0.05. Therefore, FDI has a greater impact on the eastern region than on the western region. With the development of China, foreign investment began to discrete, gradually spreading from coastal areas to inland areas.

In the current context of China’s economic transition, focusing on the issue of corporate innovation performance can lay a solid foundation for the acceleration of the digital transformation process as well as the improvement of corporate innovation performance. This paper selects the relevant data of a listed enterprise from 2018 to 2023 as a research sample for empirical analysis. Combined with the DIT model to test the role of digital transformation on innovation performance, and on the two perspectives of financing constraints and intellectual property protection, it specifically studies the mediating effect and adjustment mechanism between digital transformation and enterprise innovation performance. Finally, from the perspective of enterprise heterogeneity (whether stateowned or not, enterprise size, geographical policy), the actual impact of digital transformation on performance under different enterprises is specifically analyzed. The results show that digital transformation has a positive effect on enterprise innovation performance, and digital transformation can reduce financing constraints to a certain extent, ensure sufficient financial support for enterprise operations, and contribute to the improvement of enterprise innovation performance. Research on the moderating mechanism shows that intellectual property rights have a positive impact on digital transformation to promote the enhancement of enterprise innovation performance. Further heterogeneity analysis shows that digital transformation has a more prominent effect on innovation performance in large-scale enterprises.

Along with the fast developing of IT, it is more and more popular to apply the modem interaction technique to the educational domain, particularly in the college musical educational potentiality. Based on the perspectives of psychology and interactive technology, the author analyzes the latest progress of interactive technology in human-computer interaction, emotional computing, and design psychology, as well as its impact on music education in universities. It is found that the educational effectiveness of MCAI has been maintained at 92 percent and that of the others has been rising. However, there are some differences between them and the new system. Interactive technology can not only optimize the learning experience and enhance teacher-student interaction, but also provide personalized and intelligent learning support for students through emotional computing and ubiquitous computing technology, thereby enhancing learning effectiveness and artistic creativity. By building a student-centered teaching ecosystem, the deep integration of technology and art education will help promote innovation and improvement in music education in universities in the information age.

The aggravation of population aging makes the demand for elderly care expanding. In this paper, we propose an integrated care model based on deep learning to build an intelligent service robot system for elder care organizations by integrating sentiment analysis and knowledge reasoning techniques. The model is driven by the dynamic needs in long-term care scenarios, and two modules are innovatively designed. In the sentiment analysis module, multimodal sensors (facial expression, audio state, textual content) and graph attention networks are integrated, and global contextual information is modeled on these features to identify long-distance emotional dependencies of the elderly. In the knowledge inference module, graph representation learning is combined with knowledge graph temporal inference to construct an inference model to speculate the care needs of the elderly. The experiment shows that after the system performs long-term service, the depression condition of the elderly is significantly improved, and the nursing care safety risk perception shows a significant difference from that before the system is used (P<0.001). The integrated care model studied in this paper provides a practical technical solution to the problem of aging care resource shortage.