ObjectiveTo investigate the effect andmechanism among emotion regulation, relationship,happiness, learning motivation, target positioning, and resilience of university students.MethodA total of 904 university students in China were included in this cross-sectional survey from April to May this year. The self-administered questionnaires, including the adapted Mental Health Scale with a Healthy Personality Orientation for College Students, were used to construct structural equations to test the chain mediating effects of learning motivation and target positioning based on a multi-stage whole group sample of university students.ResultEmotion regulation indirectly affected happiness through the mediating effect of interpersonal relationship (Med = −0.387, p = 0.001). Learning motivation and target positioning play the chain mediating role in the effect of emotion regulation on happiness (Med = −0.307, p = 0.001) and resilience (Med = −0.275, p = 0.001).ConclusionEmotion regulation indirectly affected happiness and resilience through the chain mediating effect of learning motivation and target positioning.

Dosage is essential for studying the compatibility and effectiveness of traditional Chinese medicine. Danggui and Chuanxiong are widely used in traditional Chinese medicine for ailments and treatment of various disorders. 628 traditional Chinese medicine prescriptions containing Danggui and Chuanxiong were extracted from the self-built prescription database and screened for the three groups of prescriptions, i.e., irregular menstruation, sores, and stroke. We processed and tested the dosage of Danggui and Chuanxiong and selected the optimal copula function, Gumbel copula function, from the Archimedes function family and elliptical copula function family to establish the data model. To establish the presence of a correlation between the dose of Danggui and Chuanxiong, a graph of the joint distribution function of rank correlation coefficients, Kendall’s rank correlation coefficient and Spearman’s rank correlation coefficient, was used. Our results suggest that the model using the Gumbel copula function better reflects the correlation between the dose of Danggui and Chuanxiong. For irregular menstruation, sores, and strokes, Kendall’s rank correlation coefficients were 0.6724, 0.5930, and 0.7757, respectively, and Spearman’s correlation coefficients were 0.8536, 0.7812, and 0.9285, respectively. In all three prescription groups, the dose of Danggui and Chuanxiong was positively correlated, implying that, as the dosage of one drug increases, the dosage of the other increases as well. From the perspective of data mining and mathematical statistics, the use of the copula function model to evaluate the correlation between the prescribed dosage of the two drugs was innovative and provided a new model for the scientific interpretation of the compatibility of traditional drugs. This might also serve to guide the clinical use of traditional Chinese medicine.

Background: Influencing factors of community management of diabetes are complex and controversial, and how to select the most effective multiple influencing factors requires in-depth research. This study aims to analyse multiple influencing factors by adaptive-lasso logistic regression model for the effectiveness of diabetes management of the community to improve the efficiency and reduce the burden of diabetes. Methods: A cross-sectional survey (N=1,127) was adopted to establish the adaptive-lasso logistic regression model of influencing factors for community management of diabetic patients based on cluster sampling data of diabetic patients in Chengdu city, China. By comparing with the full-variable logistic model and the ridge logistic model to find the advantages of the adaptive lasso-logistic regression model in community diabetes management. Results: A total of 1,127 diabetic patients were included in the cross-sectional survey. The latest fasting blood glucose was included in the analysis. Among the included population, 90.6% of them had a fasting glucose level higher than 6.1mol/L, and 9.4% of them were below 6.1mol/L. By cross-validation, after folding eight times, the variables involved in the Adaptive lasso-logistic regression model include age, education level, main source of income, marital status, average monthly income, free medical service, basic medical insurance for residents, hospital history, number of follow-up evaluations by family doctor team, voluntary participation in community blood glucose measurement. The AIC and BIC criteria of adaptive lasso-logistic regression model were 2062 and 1981, which were lower than the full-variable logistic model (2349, 2023) and the ridge logistic model (2312, 2013). From the perspective of time cost, the adaptive-lasso logistic regression model was better than the other two models. Conclusions: The adaptive-lasso logistic regression model can be used to analyse the influencing factors of community management in patients with diabetes. Community intervention and intensive management measures can significantly improve the blood glucose status of patients with diabetes.

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