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Background Ruminal methane (CH4) emissions from ruminants not only pollute the environment and exacerbate the greenhouse effect, but also cause animal energy losses and low production efficiency. Consequently, it is necessary to find ways of reducing methane emissions in ruminants. Studies have reported that feed additives such as nitrogen-containing compounds, probiotics, prebiotics, and plant extracts significantly reduce ruminant methane; however, systematic reviews of such studies are lacking. The present article summarizes research over the past five years on the effects of nitrogen-containing compounds, probiotics, probiotics, and plant extracts on methane emissions in ruminants. The paper could provide theoretical support and guide future research in animal production and global warming mitigation. Methods This review uses the Web of Science database to search keywords related to ruminants and methane reduction in the past five years, and uses Sci-Hub, PubMed, etc. as auxiliary searchers. Read, filter, list, and summarize all the retrieved documents, and finally complete this article. Results Most of the extracts can not only significantly reduce CH4 greenhouse gas emissions, but they will not cause negative effects on animal and human health either. Therefore, this article reviews the mechanisms of CH4 production in ruminants and the application and effects of N-containing compounds, probiotics, prebiotics, and plant extracts on CH4 emission reduction in ruminants based on published studies over the past 5 years. Conclusion Our review provides a theoretical basis for future research and the application of feed additives in ruminant CH4 emission reduction activities.

To some extent, the information from social network can have an impact on the investment decisions of people. Based on behavioral finance theory, this thesis focuses on the influence of social networks on the stock market and forecast the stock price. Firstly, it collects data from the social network platform, and explores the relationship between social network information and stock trading volume and stock fluctuations. Secondly by means of SVM algorithm, then a stock price forecasting model is established to predict the stock price, which verifies that there is a positive correlation between stock market and social network information. Finally, the experimental results prove that the accuracy of SVM algorithm is high.