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This study integrates foundational theories of artificial intelligence to develop a classroom interaction feedback model tailored for environments leveraging artificial intelligence technologies. The model is structured into three primary phases: initiation, reaction, and feedback. To enhance the detection and analysis of interactive behaviors within English classrooms, this research introduces an advanced face detection algorithm derived from YOLOv5s, coupled with the GVT expression recognition model. These tools aim to assist educators in refining both the curriculum and pedagogical approaches based on dynamic classroom interactions. The practical applicability of this interaction model was evaluated through an empirical analysis involving English majors from College Q. The study revealed significant enhancements post-implementation of the intelligent English classroom interaction feedback model. Specifically, there was an increase in the average individual head-up rate by 0.2 and the overall head-up rate in the classroom by 0.178. Additionally, mood scores rose by 0.1 to 0.3, indicating a state of heightened engagement and active participation in classroom activities. Furthermore, the average academic performance of students exhibited a notable improvement of 13.47 points, with a standard deviation of approximately 5.86, suggesting a modest dispersion in achievement levels. The findings demonstrate that the intelligent English classroom interaction feedback model significantly boosts student engagement and concentration, thereby enhancing academic performance and supporting the development of core English literacy skills. This model provides a valuable framework for leveraging artificial intelligence to optimize educational outcomes in higher education settings.

In the postantibiotic era, the pathogenicity and resistance of pathogens have increased, leading to an increase in intestinal inflammatory disease. Bacterial infections remain the leading cause of animal mortality. With increasing resistance to antibiotics, there has been a significant decrease in resistance to both inflammation and disease in animals, thus decreasing production efficiency and increasing production costs. These side effects have serious consequences and have detracted from the development of China's pig industry. Microcin C7 (McC7) demonstrates potent antibacterial activity against a broad spectrum of pathogens, stable physicochemical properties, and low toxicity, reducing the likelihood of resistance development. Thus, McC7 has received increasing attention as a potential clinical antibacterial and immunomodulatory agent. McC7 has the potential to serve as a new generation of antibiotic substitutes; however, its commercial applications in the livestock and poultry industry have been limited. In this review, we summarize and discuss the biosynthesis, biochemical properties, structural characteristics, mechanism of action, and immune strategies of McC7. We also describe the ability of McC7 to improve intestinal health. Our aim in this study was to provide a theoretical basis for the application of McC7 as a new feed additive or new veterinary drug in the livestock and poultry breeding industry, thus providing a new strategy for alleviating resistance through feed and mitigating drug resistance. Furthermore, this review provides insight into the new functions and anti-infection mechanisms of bacteriocin peptides and proposes crucial ideas for the research, product development, and application of bacteriocin peptides in different fields, such as the food and medical industries. [ABSTRACT FROM AUTHOR]