The secondary packaging and secondary transportation caused by products’ online return lead to a large amount of resource waste and environmental damage, which are not conducive to the green and sustainable development of enterprises. As consumers become more aware of environmental protection, their purchase and return behaviors will also change, prompting e-commerce platforms to adjust their return strategies. In this context, this paper aims to study the optimal return strategy that balances enterprises’ social benefits and environmental impact. The Stackelberg game models are constructed based on two behaviors: environmental protection publicity of e-commerce platforms and consumer return. The impacts of return strategies on the environment and the benefits of supply chain members are investigated. Results show that environmental protection publicity and return compensation can stimulate the expected sales volume. The optimal environmental protection publicity depends on the return rate. When the return rate is high, and the repurchase price is low, the optimal decision of the e-commerce platform is not to introduce return freight insurance so as to maintain its own benefits and reduce the environmental impact.

Objective To investigate the correlation associated with long-term glycemic variability on cognitive function in middle-aged and elderly patients with T2DM. Methods This study was a single-institution, retrospective analysis of data. A total of 138 patients who were hospitalized in the Department of Endocrinology, the Second Affiliated Hospital of Anhui Medical University from June 2021 to November 2022 were recruited. The Montreal Cognitive Assessment (MoCA) was applied to assess the cognitive function of the patients, which were divided into MCI and non-MCI. Glycated hemoglobin A1c standard deviation (HbAlc-SD) and fasting plasma glucose standard deviation (FPG-SD) were used to measure long-term blood glucose fluctuations. General clinical data, blood biochemical indicators, and glycemic variability indicators were compared between the two groups of patients. The differences between the groups were compared using t-test, x2 test, ornonparametric test. Correlation and diagnostic power were further analyzed using multiple logistic regression analysis and ROC curve analysis. Results The differences in age, BMI, HbA1c-M, HbA1c-SD, FPG-M, FPG-SD, GFR, 24h urinary protein, and UACR were statistically significant between the two groups (P<0.05). In a multiple logistic regression analysis, HbA1c-SD and FPG-SD were found to be risk factors for cognitive dysfunction and eGFR to be a protective factor. The area under the curve (AUC) of HbA1c-SD for predicting MCI prevalence was 0.828 (95% CI 0.754～0.887, P<0.001), with a sensitivity of 62.69%, a specificity of 94.29%, and an optimal diagnostic value 1.01. The area under the curve (AUC) of FPG-SD for predicting MCI prevalence was 0.737 (95% CI 0.655～0.808, P<0.001), with a sensitivity of 76.12%, a specificity of 61.43%, and an best diagnostic value 0.94. The area under the curve (AUC) of eGFR for prediction of MCI prevalence was 0.712 (95% CI 0.628～0.786, P<0.01), with a sensitivity of 70.15 %, a specificity of 64.29 %, and an optimal diagnostic value 79.82 ml/min/1.73m2. Conclusions Long-term blood glucose variability affects cognitive function in middle-aged and elderly T2DM patients, and cognitive function is poorer in those with high blood glucose variability, for whom renal function is a protective factor.