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ObjectiveTo analyze the temporal distribution characteristics of crisis calls and eight major counseling issues to the Shanghai mental health hotline, and to provide recommendations for improving hotline management.MethodsDescriptive statistics were used to analyze 1 106 crisis calls and the calls of eight major counseling issues to the Shanghai mental health hotline across months, time periods and weeks from October 2021 to September 2023, and the chi-square test was used to analyze whether there was any difference in the distribution of 1 106 crisis calls and the calls of eight major counseling issues across months, time periods and weeks.ResultsThere were significant differences in the number of crises calls across different months (χ2=87.816, P

This study addresses the critical challenge of integrating sustainable financial management within the framework of green economic growth in China from 1990 to 2022, underscored by rapid industrialization and environmental concerns. The primary aim is to analyze how big data technology innovations have facilitated sustainable financial practices that align with green economic objectives. The objectives include assessing the impact of these technologies on environmental sustainability, financial inclusion, and economic resilience. Using China as a case study, the research employs a longitudinal analysis to trace the evolution and integration of sustainable financial management and green economic growth, underpinned by technological advancements. Results indicate that big data technology has significantly enhanced the capacity for real-time analysis, risk assessment, and decision-making in financial institutions, leading to improved environmental and economic outcomes. Policy implications suggest that the government should foster an innovation-friendly environment that encourages the development and adoption of big data technologies, to further integrate sustainable financial management within the green growth agenda.

With the rapid development of education informatization and intelligence, personalized teaching and course recommendations have gradually become an important research direction in the field of education technology. A knowledge graph, as a structured knowledge representation and organization, provides rich semantic information and a relationship network for teaching course recommendations. Based on this research background, this paper focuses on the research of knowledge graph multivariate environment-aware recommendation algorithms for teaching courses. The article firstly constructs the ontology of course resources, researches and analyzes the methods and steps of acquiring and processing data in order to build the knowledge graph, and then constructs the framework of the recommendation model combining multivariate perception in order to further analyze and explore the user’s course-aware representation learning and the user’s course-aware embedded learning. Embedded Learning, and finally, the teaching course of a school as an empirical study, it is shown that the performance of Ripple_mlp+ and Ripple_mlp in this article are higher than the benchmark algorithms through the experimental class and the control class of the pre and post-test scores have been sorted out and counted. The experimental students’ scores are steadily increasing, and the overall scores are better than the control class. In the experimental class, the significance value is 0.013, indicating that there is a significant difference between the midterm and final grades of the experimental class. Through the paired samples t-test on the midterm and final grades of the experimental class, after analyzing and comparing the midterm and final grades of students at different levels, it can be concluded that the t-value of the superior students is −3.326. The value of significance is 0.049, indicating that there is a significant difference between the midterm and final grades of the exceptional students. The T-value for intermediate students is −0.134, and the value of significance is 0.045, which indicates that there is a significant difference in the midterm final grades of intermediate students. The T-value of −1.623 and the value of significance is 0.534 for advanced students indicates that the difference in midterm final grades of advanced students is not significant.

Abstract Background The pathogenesis of severe acute respiratory disease syndrome (SARS) is not fully understood. One case-control study has reported an association between susceptibility to SARS and mannan-binding lectin (MBL) in China. As the downstream protein of MBL, variants of the MBL-associated serine protease-2 (MASP2) gene may be associated with SARS coronavirus (SARS-CoV) infection in the same population. Methods Thirty individuals with SARS were chosen for analysis of MASP2 polymorphisms by means of PCR direct sequencing. Tag single nucleotide polymorphisms (tagSNPs) were chosen using pairwise tagging algorithms. The frequencies of four tag SNPs (rs12711521, rs2261695, rs2273346 and rs7548659) were ascertained in 376 SARS patients and 523 control subjects, using the Beckman SNPstream Ultra High Throughput genotyping platform. Results There is no significant association between alleles or genotypes of the MASP2 tagSNP and susceptibility to SARS-CoV in both Beijing and Guangzhou populations. Diplotype (rs2273346 and rs12711521)were analyzed for association with susceptibility to SARS, no statistically significant evidence of association was observed. The Beijing and Guangzhou sample groups were homogeneous regarding demographic and genetic parameters, a joined analysis also showed no statistically significant evidence of association. Conclusion Our data do not suggest a role for MASP2 polymorphisms in SARS susceptibility in northern and southern China.