Background: With the popularization of higher education, the problems of academic adaptability and learning burnout among college students have become increasingly prominent. The purpose of this study was to investigate the relationship between academic adaptability, learning burnout, self-esteem and self-efficacy of college students and their underlying mechanisms. Methods: The study was conducted on 2110 college students using the College Student Learning Adjustment Scale, the Learning Burnout Undergraduates Scale, the Self-Esteem Scale, and the Self-Efficacy Scale to establish a mediating model of adjustment. SPSS 26.0 was used for descriptive statistics and Pearson correlation analysis. Model 4 and Model 14 in the process plug-in prepared by Hayes (2017) were used for mediating effects analysis and moderating mediator analysis respectively, and the significance of the mediating effects was tested using the bias-corrected percentile Bootstrap method. Results: (1) academic adaptability significantly and positively predicted self-esteem; (2) self-esteem significantly and negatively predicted learning burnout; (3) academic adaptability significantly and negatively predicted learning burnout; (4) self-esteem partially mediated the effect of academic adaptability on learning burnout; and (5) self-efficacy moderated the latter half of the mediation process of academic adaptability-self-esteem-learning burnout. Conclusion: These findings are useful for college educators and related researchers to better understand the mechanisms underlying the relationship between academic adaptability and learning burnout, thus providing practical and effective operational suggestions on the prevention and intervention of learning burnout in college students.

Introduction: Smartphone dependence is closely related to the physical and mental health development of undergraduates and their learning. The purpose of this study was to explore the relationship between smartphone dependence, academic adaptability, self-efficacy and learning burnout among undergraduates and its underlying mechanisms.Methods: The study was conducted on 2,110 undergraduates using the Smartphone Dependence Scale, the Undergraduates Learning Adjustment Scale, the Learning Burnout Undergraduates Scale and the Self-Efficacy Scale to develop a mediation model and a moderation model.Results: The findings of this study revealed that (1) smartphone dependence significantly negatively predicted academic adaptability; (2) academic adaptability significantly negatively predicted learning burnout; (3) smartphone dependence significantly positively predicted learning burnout; (4) academic adaptability partially mediated the effect of smartphone dependence on learning burnout; (5) self-efficacy played a moderating role in the effect of academic adaptability on learning burnout.Conclusion: These findings can help researchers and educators better understand the underlying mechanisms between smartphone dependence and learning burnout in undergraduates.

Background: The phenomenon of university students’ learning burnout has attracted the research of many scholars because of its typicality. This study aims to explore the relationship between life satisfaction, academic motivation, social support and learning burnout among university students and its underlying mechanisms. Methods: A total of 1917 university students participated in this cross-sectional study. Research instruments included the Adolescent Student Life Satisfaction Scale, University Students’ Academic Motivation Questionnaire, Adolescent Learning Burnout Scale and Adolescent Social Support Scale. The data analysis comprised descriptive statistics, correlation analyses, and assessment of multicollinearity through Variance Inflation Factor (VIF). Advanced analyses were conducted using Model 4 for mediation and Model 1 for moderation from the PROCESS macro. Results: (1) life satisfaction significantly and positively predicts academic motivation; (2) academic motivation significantly and negatively predicts learning burnout; and (3) life satisfaction significantly and negatively predicts learning burnout; (4) academic motivation partially mediates the effect of life satisfaction on learning burnout; and (5) social support plays a moderating role in the effect of academic motivation on learning burnout. Discussions: These results illuminate the complex web of relationships among life satisfaction, academic motivation, social support, and learning burnout. The partial mediating role of academic motivation underscores its significance in the link between life satisfaction and learning burnout. Additionally, the moderating impact of social support emphasizes its role in ameliorating or exacerbating the effects of academic motivation on learning burnout. Conclusion: These findings can help researchers and educators better understand the underlying mechanisms between life satisfaction and learning burnout. Meanwhile, the results of the study can provide practical and effective operational suggestions for preventing and intervening in university students’ learning burnout and improving their academic motivation.

Background: The critical role of social support in college students’ academic development cannot be ignored. This study aims to analyze the interrelationships and potential mechanisms between social support, sense of security, family cultural capital and academic self-efficacy.Methods: A multivariate moderated mediation model was constructed by surveying 1,119 college students using the Social Support Scale, the Sense of Security Scale, the Academic Self-efficacy Scale, and the Family Cultural Capital Scale.Results: (1) social support significantly increases sense of security; (2) sense of security significantly enhances academic self-efficacy; (3) social support directly contributes to academic self-efficacy; and (4) sense of security partially mediates the relationship between social support and academic self-efficacy; (5) family cultural capital plays a moderating role in the effects of social support on academic self-efficacy, especially in the first half of the direct and indirect effects.Conclusion: These findings could provide an important theoretical basis and practical guidance for further understanding and enhancing academic self-efficacy among college students.

Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a “Two-stage” data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved.

Synergistic development is a pivotal force in advancing regional modernization. China has effectively promoted rural revitalization through the collaborative integration of various digital factors, thereby narrowing the urban–rural development gap and laying a solid foundation for achieving sustainable development. This study examines factors driving rural revitalization through the analysis of 193 cases in less urbanized Chinese regions from 2018 to 2022 using panel-data qualitative comparative analysis. This study identifies digital infrastructure as the key to rural development, while the importance of industrial digitalization is increasingly recognized. We found two combinations of the digital economy that can promote rural revitalization: one that links digital infrastructure with digital industry, and the other that combines digital infrastructure with data elementization. Moreover, we also analyzed how regions with different innovation capabilities and economic foundations should develop differentiated strategies to achieve rural revitalization. This research provides a scientific basis for various regions to formulate rural revitalization strategies that are in line with their own characteristics, which will help achieve the goal of regional sustainable development.