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It is well known that the proper allocation of learning time is particularly important for promoting students' academic performance. Based on the data from PISA 2018, this research used the method of threshold regression and quantile regression to explore the optimal length of learning time to promote the students' academic performance. At the same time, this research also explored the heterogeneity of the effect of learning time on different academic levels of students. The results show that for four Chinese provinces and cities, including Beijing, Shanghai, Jiangsu province and Zhejiang province, students who study in rural areas and private schools usually have longer learning time than students in cities and public schools. Moreover, it is suggested that there is no significant association between school quality and students' learning time. The average learning time of students from the four Chinese cities and provinces is obviously longer than that in OECD countries. Moreover, it is found that the impact of learning time on academic performance across subjects is inverted U-shaped, and the optimal study time can be found in the learning of mathematics, science, and reading related subjects. As for the effect of learning time, the results showed that learning time commitment is more effective for students who are academically disadvantaged. At the same time, this study found that there is a relationship between students' excessive learning time and students' subjective well-being and attitudes toward learning activities. The non-cognitive factors can influence students' academic performance gradually. According to the results of this research, it is suggested that students need to balance their learning time allocation against the appropriate learning time standards. Moreover, schools should adopt different learning time allocation schemes for students at different academic achievement levels. The teachers also should uphold a more scientific design of students' after-school homework, and teachers and parents should also focus on improving students' learning efficiency.

Antibiotics usage in animal production is considered a primary driver of the occurrence, supply and spread of antibiotic resistance genes (ARGs) in the environment. Pig farms and fish ponds are important breeding systems in food animal production. In this study, we compared and analyzed broad ARGs profiles, mobile genetic elements (MGEs) and bacterial communities in a representative pig farm and neighboring fish ponds around Poyang Lake, the largest freshwater lake in China. The factors influencing the distribution of ARGs were also explored. The results showed widespread detection of ARGs (from 57 to 110) among 283 targeted ARGs in the collected water samples. The differences in the number and relative abundance of ARGs observed from the pig farm and neighboring fish ponds revealed that ARG contamination was more serious on the pig farm than in the fish ponds and that the water treatment plant on the pig farm was not very effective. Based on the variance partition analysis (VPA), MGEs, bacterial communities and water quality indicators (WIs) codrive the relative abundance of ARGs. Based on network analysis, we found that total phosphorus and Tp614 were the most important WIs and MGEs affecting ARG abundance, respectively. Our findings provide fundamental data on farms in lakeside districts and provide insights into establishing standards for the discharge of aquaculture wastewater., (Copyright © 2022 Elsevier Ltd. All rights reserved.)