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The quantification of the interplay between student behavior data and classroom teaching effectiveness using quantitative metrics has perennially posed a challenge in the evaluation of classroom instruction. Classroom activity serves as a reflection of student engagement, emotional ambiance, and other pertinent aspects during the pedagogical process. This article presents a methodology for quantifying student head posture during classroom instruction utilizing AI-driven video analysis technology, notably the Classroom Activity Index (CAI). A Classroom Activity Analysis System (CAAS) was designed and developed, integrating a multi-scale classification network based on ECA-ResNet50 and ECA-ResNet18. This network discerns and categorizes various head regions of students situated in both the frontal and real rows of a lecture-style classroom, irrespective of their dimensions. The classification network attains exceptional performance, boasting F1 score of 0.91 and 0.92 for student head-up and head-nodding. Drawing on the live classroom instruction at a higher vocational college in Wuhan, Hubei Province, China, a comparative experiment was executed. The findings revealed that three factors: teacher-student verbal interaction, teacher body language, and utilization of digital resource, all exert an influence on CAI. Simultaneously, the degree of classroom activity as gauged by FIAS and manual analysis fundamentally aligns with the CAI indicators quantified by CAAS, validating the efficacy of CAI in the quantification of classroom activity. Consequently, the incorporation of CAAS in teaching, research, and oversight scenarios can augment the precision and scientific rigor of classroom teaching assessment. [ABSTRACT FROM AUTHOR]

Background: Drug-resistant tuberculosis (DR-TB) is an increasingly serious global issue. DR-TB has a lower success rate and more severe interruption of treatment than ordinary tuberculosis. Incomplete treatment not only reduces recovery rate in DR-TB patients but also increases the spread of DR-TB. Optimizing medical security policies for DR-TB can reduce the economic burden of patients and can thereby improve treatment success rate. Methods: Patients with DR-TB who were registered in Wuhan Center for Tuberculosis Control and Prevention from January 2016 to December 2019 were selected as research subjects. General descriptive statistical analysis methods were used in analyzing patients' treatment outcomes and medical security compensation rate. The binary logistic regression was used in analyzing the impacts of medical security level on treatment outcomes of DR-TB. Results: A total of 409 DR-TB patients were included in the study, and the refusal rate was 12.47%. The treatment success rate was only 37.09% for patients who started treatment and had treatment outcomes. The total out-of-pocket expenses (OOPs) per capita for DR-TB patients were 13,005.61 Chinese yuan. The outpatient effective compensation ratio (ECR) of DR-TB patients was only 51.04%. The outpatient ECR of DR-TB with subsidies of public health projects (SPHPs) were nearly 80% higher than those without SPHP. high outpatient ECR helped optimize treatment outcomes (P < 0.001, OR = 1.038). The inpatient ECR had no effect on patients' treatment outcomes (P = 0.158, OR = 0.986). Conclusion: Many DR-TB patients did not receive complete treatment. The key breakthrough point in improving DR-TB treatment outcomes is to optimize the outpatient medical insurance compensation policy. Including the costs of DR-TB in expenses for severe diseases in outpatient care is recommended, and financial investment should be appropriately increased to ensure the high coverage ratio of subsidies for public health projects. [ABSTRACT FROM AUTHOR]