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Ratoon rice production is an effective approach to improve overall rice productivity, while incurring the lowest operation- and/or production-related costs. Stubble management and the growth optimization of ratoon crops are examples of production systems that remain an issue that needs to be addressed properly to maximize the benefits. The present study was conducted considering the objective that stubble righting could substantially reduce the adverse effects of mechanical primary rice harvesting on RR production. Field experiments were conducted during the 2017 and 2018 rice seasons with three stubble righting treatments, i.e., CK, which included the use of stubbles without righting after rolling by the machine; T1, which included the use of stubbles righting by manual method after rolling by a machine; and T2, which included the use of stubbles without rolling by the machine, in Jiujiang, Jiangxi Province, China. The growth- and yield-related attributes of RR were investigated. The grain yield trends were ordered as T2 > T1 > CK. Compared with CK, T1 increased the grain yield by 17.17% and 58.87% in 2017 and 2018, respectively. The grain yield improvement in the T1 treatment resulted from the increased total dry weight, panicle number per m2, bud number, and regulation of the allocation bud number of RR compared with CK. Moreover, a significant correlation was noted between the grain yield and the total dry weight and panicle number per m2. Overall, stubble righting after machine rolling (during the harvest of the main crop) lowered the yield loss of RR due to its improved growth enhancement effects. Therefore, stubble righting is a better management approach to improve the growth and productivity of RR in rice production systems in China. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic., Objective: Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion., Methods: First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning linear regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model., Results: The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive., Conclusions: The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events., (©Feipeng Guo, Zixiang Liu, Qibei Lu, Shaobo Ji, Chen Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.01.2024.)