The arrival of the “double carbon” era indicates that industrial carbon reduction with high energy consumption and high carbon emission is imperative. From the perspective of carbon emission driving factors, industrial carbon emission is decomposed into five influencing factors: energy intensity, energy structure, industrial structure, economic efficiency, and employee scale. Taking the data of 41 subindustries of industrial industry in Liaoning Province from 2010 to 2019 as the research sample, the carbon emission is calculated. The LMDI model is used to analyze and point out the difference in the driving contribution of carbon emissions of each subindustry. The results show that the total carbon emission of Liaoning province gradually decreases, decreases for the first time in 2014, and gradually turns from flat to upward. Economic efficiency is the only and most important reason to drive the increase of industrial carbon emissions in Liaoning Province, and energy efficiency is the primary factor to curb carbon emissions. High carbon industries play a significant role in promoting the increase of carbon emissions, while the medium and low carbon industries have a better effect on restraining carbon emissions. It provides reference and theoretical basis for the government to adjust the industrial structure, control industrial overcapacity, and realize the “double carbon” goal as soon as possible. It is of great significance for the country to optimize energy layout, ensure energy security, and implement the new energy strategy.

Due to the impact of COVID-19, universities are forced to suspend their classes, which begin to depend on the usage of online teaching. To investigate the relationship among e-learning self-efficacy, monitoring, willpower, attitude, motivation, strategy, and the e-learning effectiveness of college students in the context of online education during the outbreak of COVID-19. A 519 first- to fifth-year undergraduate students from a medical university were selected for the research in this study. Structural equation model (SEM) was used for a data analysis, which led to the results showing that: (1) e-learning self-efficacy and monitoring have significant positive influence on e-learning strategy, and indirectly influence e-learning effectiveness through e-learning strategy; (2) e-learning willpower and attitude have a significant positive influence on e-learning motivations, and indirectly influence e-learning effectiveness through e-learning motivation and strategy; (3) e-learning motivation is having significant influence on e-learning effectiveness, while e-learning strategy is playing a mediating role; (4) There is a significant positive correlation between e-learning strategy and e-learning effectiveness; and (5) The presence of e-learning experience has a moderating influence on e-learning effectiveness as well as its influential factors. Results from this study provide the necessary information as to how higher education institutions and students can enhance students’ effectiveness of the e-learning system in order to support the usage of online technologies in the learning and teaching process. These results offer important implications for online learning effectiveness.

Background: By analyzing 23 evaluation indicators included in 14 national basic public health service programs in a region of Hohhot City, Inner Mongolia Autonomous Region, China, the performance of basic public health services in the region in 2021 were analyzed to clarify the implementation and conduct of relevant programs. We also use this study as a basis to radiate the work of municipal basic public health services centered on the region and the outstanding problems reflected and to provide theoretical contents and suggestions that can be referred to for the same regions in central and western China as well as worldwide. Methods: Using the TOPSIS method as the basis for the data analysis method, the evaluation indexes are ranked in terms of their proximity to the idealized target, and combined with the entropy value method, Technique for order preference by similarity to an ideal solution (TOPSIS) and rank-sum ratio (RSR) were used to rank 14 basic health care providers by grade. A comprehensive evaluation of the performance of basic public health services in a region of Hohhot City, Inner Mongolia Autonomous Region in 2021 was conducted through a joint model of entropy -weighted TOPSIS and RSR, making full use of the characteristics and advantages of the fuzzy joint, and conducting a comprehensive analysis from the perspective of the ratio weight and the method of graded calculation, making the study more distinguishable and measureable. Results: In this study, for the regional basic public health services, a total of 23 evaluation indicators of basic public health service projects were included, among which the top three indicators with the weight of the entropy value method indicators were found to include the rate of Chinese medicine health management for the elderly, the rate of health management for the elderly, and the BCG vaccination rate after the analysis of the weight of the indicators; After the entropy-weighted TOPSIS evaluation showed that the Ci values of the regions were found to be between 0.378 and 0.715 through the calculation of the positive and negative ideal values of each indicator; RSR staging method evaluation showed that three community health centers (X2, X10, X12) had excellent evaluations of basic public health services; The number of evaluations as poor and moderate are 2 (X3, X9) and 9 (X1, X4, X5, X6, X7, X8, X11, X13, X14), respectively; Finally, the results of the entropy-weighted TOPSIS method and the fuzzy joint model of RSR staging method are basically consistent with the overall trend of the above two methods, and the reliability and credibility of the research results are high. Conclusion: The entropy-weighted TOPSIS and RSR joint model can evaluate the effectiveness of basic public health services in a more comprehensive and holistic way. The results of the RSR staging results and the related weight ratio analysis show that the basic public health service programs in Hohhot, Inner Mongolia Autonomous Region are relatively balanced, but there are some differences; The same genus of elderly Chinese medicine health management rate, health management rate of the elderly, BCG vaccination rate several indicators accounted for a higher weight, its correlation with the key population-related items is high, suggesting that the future key population health service items should be focused on, and future research should be suggested from two key research.

Background: The rapid growth of artificial intelligence (AI) technologies has been driven by the latest advances in computing power. Although, there exists a dearth of research on the application of AI in medical education.Methods: this study is based on the TAM-ISSM-UTAUT model and introduces STARA awareness and chilling effect as moderating variables. A total of 657 valid questionnaires were collected from students of a medical university in Dalian, China, and data were statistically described using SPSS version 26, Amos 3.0 software was used to validate the research model, as well as moderated effects analysis using Process (3.3.1) software, and Origin (2021) software.Results: The findings reveal that both information quality and perceived usefulness are pivotal factors that positively influence the willingness to use AI products. It also uncovers the moderating influence of the chilling effect and STARA awareness.Conclusions: This suggests that enhancing information quality can be a key strategy to encourage the widespread use of AI products. Furthermore, this investigation offers valuable insights into the intersection of medical education and AI use from the standpoint of medical students. This research may prove to be pertinent in shaping the promotion of Medical Education Intelligence in the future.

Considering the importance of energy in our lives and its impact on other critical infrastructures, this paper starts from the whole life cycle of big data and divides the security and privacy risk factors of energy big data into five stages: data collection, data transmission, data storage, data use, and data destruction. Integrating into the consideration of cloud environment, this paper fully analyzes the risk factors of each stage and establishes a risk assessment index system for the security and privacy of energy big data. According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it. Then, the trained model is used to evaluate a case to verify the applicability of the model.

Artificial intelligence-assisted teaching, as an innovative model that combines intelligent technology and personalized education, is increasingly being emphasized in higher medical education. This study included 523 participants, with a valid response rate of 87.2%. An integrated model based on the ARCS motivation model and constructivist theory was developed to explore the factors influencing medical students’ learning outcomes in the context of AI-assisted instruction. Descriptive statistics were conducted using SPSS 23.0, and a structural equation model was constructed and validated using Amos 23.0. Mediation analysis was performed with Process (version 3.3.1). The study confirmed that teaching quality had a positive effect on learning motivation (β = 0.645, P < 0.001) and learning outcomes (β = 0.128, P = 0.032). Learning motivation positively influenced learning attitude (β = 0.822, P < 0.001) and learning satisfaction (β = 0.350, P < 0.001). Learning attitude had a positive impact on both learning satisfaction (β = 0.530, P < 0.001) and learning outcomes (β = 0.232, P = 0.020). Learning satisfaction was also positively associated with learning outcomes (β = 0.415, P < 0.001). The external environment had a positive effect on learning motivation (β = 0.449, P < 0.001) and learning outcomes (β = 0.101, P = 0.033). Moreover, learning motivation played a significant mediating role in the relationships between teaching quality and learning outcomes (β_inmedia = 0.343, 95% CI [0.273, 0.414]), as well as between the external environment and learning outcomes (β_inmedia = 0.287, 95% CI [0.218, 0.355]). Teaching quality and external environment indirectly enhance medical learning outcomes by strengthening learning motivation. Learning motivation plays a key role in shaping learning attitude, satisfaction, and outcomes, confirming the positive value of AI-assisted teaching in optimizing learning mechanisms. This study contributes to the application of AI-assisted teaching in medical education and provides empirical support for improving medical students’ learning performance.