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This study is situated within corpus-based discourse analysis and provides a critical discussion on China in the Russian mainstream media RIA Novosti during the COVID-19 epidemic. The paper analyzes RIA Novosti’s reports on China during the pandemic COVID-19. The authors use Fairclough’s Three-Dimensional Model to explore the discourse representations of RIA Novosti’s reports on China during the epidemic and thus uncover the attitudes and stances of the Russian media and social cognition. The authors come to conclusion that RIA Novosti shows great concern about China during the pandemic by focusing on the epidemic itself and its impact. Additionally, Russian reports reflect the stages of China’s fight against the pandemic objectively, truthfully, and comprehensively. RIA Novosti holds a positive attitude towards China’s efforts to fight the epidemic. The study broadens the perspective of academic study of COVID-19 pandemic coverage in China from foreign media and enriches empirical research in Russian.

In engineering practice, the uncertainty and correlation may coexist in the input parameters, as well as in the output responses. To address such cases, several methods are developed for the non-probabilistic uncertainty and correlation propagation analysis in this study. In the proposed methods, the multidimensional parallelepiped model (MPM) is introduced to quantify the uncertainty and correlation of input parameters. In the uncertainty propagation analysis, three methods are presented to calculate the interval bounds of output responses. Among the methods, the Monte Carlo uncertainty analysis method (MCUAM) is firstly presented as a reference method, and then the first-order perturbation method (FOPM) is employed to promote the computational efficiency, and the sub-parallelepiped perturbation method (SPPM) is further developed to handle the correlated parameters with large uncertainty. In the correlation propagation analysis, the Monte Carlo correlation analysis method (MCCAM) is proposed based on the MPM and Monte Carlo simulation, which aims to compute the correlation among different output responses. The uncertainty domains between any two responses can also be constructed by the MCCAM. The effectiveness of the proposed methods on dealing with the uncertainty and correlation propagation problems is demonstrated by three numerical examples. [ABSTRACT FROM AUTHOR]

Animal gut is home to a large number and rich variety of microbial flora. Intestinal microbes and their metabolites are believed to play an important role in regulating the metabolism of animal nutrients and maintaining intestinal immunity. The balance of intestinal microbiota is affected by diet, environment and metabolites (such as short-chain fatty acids, tryptophan metabolites and vitamins, etc.), and have certain effect on body metabolism and immune response of animals through metabolites. Studying the effect of intestinal microbial metabolites on immune function helps to understand the interaction mechanism between intestinal microbes and animals. The article reviews the synthesis of intestinal microbial metabolites and the mechanisms mediating intestinal immunity, aiming to further explore the role of intestinal microbial metabolites in maintaining animal intestinal health, in order to provide reference for functional study of intestinal microbial metabolites and their application in animal production. [ABSTRACT FROM AUTHOR]

Uncertain structures may exhibit fuzzy uncertainty involving imprecise membership function (FuIMF). In this study, the uncertain parameters in FuIMF case are characterized as fuzzy variables, whereas the key parameters of their membership functions are treated as interval variables rather than exact values. Two ideas are put forward to handle FuIMF variables. First, the interval-boundary interval method (IBIM) is derived to conduct uncertainty propagation analysis, in which the λ -cut of FuIMF variables are considered as interval-boundary intervals. Second, the λ -cut of FuIMF variables are presented by the conservative and radical approximations, and the conservative and radical approximations method I (CRAM I) is proposed to conduct uncertainty propagation analysis. To further promote the computational efficiency, the conservative and radical approximations method II (CRAM II) is developed. Afterwards, a reference method based on Monte Carlo simulation is presented to verify the proposed methods. Finally, the effectiveness of proposed methods is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]