This study aims to explore the research on Chinese higher education policy from 2005 to 2024 through a bibliometric analysis. It is revealed that a continuous growth trend and sustained academic interest in this field. Mainland China leads in publication quantity, showcasing the active involvement of Chinese scholars in higher education policy research. Institutions like Peking University, the University of Hong Kong, and Beijing Normal University play significant roles in this research domain. The focus of research has shifted from student attitudes to international students, teachers, innovation models, changing demands, and urban education development, reflecting a growing emphasis on sustainability and internationalization. The study highlights the positive development trajectory of Chinese higher education policy research, with expanding research focuses and deepening concerns for sustainability and internationalization.

This study investigates pedagogical content knowledge (PCK) among teachers teaching mathematics at the preschool level in Colombia, highlighting the importance of integrating mathematical knowledge with innovative and effective pedagogical strategies. Using a mixed exploratory and transactional methodology, the perceptions and practices of 82 teachers were examined, focusing on their understanding of mathematical content, pedagogical skills, and knowledge of children's cognitive development. The findings reveal a significant gap in teachers' understanding of these concepts, indicating a critical need to strengthen PCK among teachers. To this end, training should be provided to enable teachers to foster meaningful and contextualized mathematical learning in preschool students. The study suggests reviewing teacher training curricula and fostering the development of pedagogical strategies that prioritize conceptual understanding and mathematical reasoning. Additionally, it identifies critical areas for improvement and offers concrete recommendations for transforming mathematics teaching in preschool education. To enhance the quality of mathematics education, several measures are proposed: ensuring continued availability of training programs for teachers, encouraging collaboration between educators, adopting constructivist approaches, and helping teachers understand the value of mathematics learning outside the school.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

This study aims to investigate what influences local workers over the age of 40 to work and stay employed in oil palm plantations. 414 individuals participated in a face-to-face interview that provided the study’s primary source of data. Exploratory Factor Analysis was used to analyse the given data. The study revealed that factors influencing local workers over the age of 40 years to leave or continue working in oil palm plantations can be classified as income factors, internal factors and external factors. The income factor was the most significant factor as the percentage variance explained by the factor was 26.792% and Cronbach Alpha was high at 0.870. Therefore, the study suggested that the oil palm plantation managements pay more attention to income elements such as basic salary, wage rate paid to the workers and allowance given to the workers since these elements contribute to the monthly total income received by the workers and in turn be able to attract more local workers to work and remain in the plantations.

Despite the current craze for e-commerce live streaming, its specific impact on consumer repurchase intentions and the underlying mechanisms remain insufficiently explored, creating a notable gap in existing research. The purpose of this study is to investigate the precise impact of e-commerce live streaming on consumers’ repurchase intentions and to uncover the path through which this influence occurs. Drawing on behavioral cognitive theory, this paper employs a contextual experimental method to examine how e-commerce live streaming affects consumer repurchase behavior. The experimental results show that e-commerce live can significantly improve consumer repurchase intention, consumer loyalty and market order can positively regulate the effect of e-commerce live. This paper not only verifies the effectiveness of e-commerce live broadcasting, but also provides new ideas for brands and governments to strengthen the ability of e-commerce live broadcasting to “bring goods”.

Background: Various studies have demonstrated the usefulness of Google search data for public health-monitoring systems. The aim of this study is to be estimated interest of public in infectious diseases in infectious diseases in South Korea, the five other countries. Methods: We conducted cross-country comparisons for queries related to the H1N1 virus and Middle East respiratory syndrome coronavirus (MERS-CoV). We analyzed queries related to the novel coronavirus disease (COVID-19) from 20 January to 13 April 2020, and performed time-descriptive and correlation analyses on trend patterns. Results: Trends in H1N1, MERS-CoV, and COVID-19 queries in South Korea matched those in the five other countries and worldwide. The relative search volume (RSV) for the MERS-CoV virus increased as the cumulative number of confirmed cases in South Korea increased and decreased significantly as the number of confirmed cases decreased. The volume of COVID-19 queries dramatically increased as South Korea’s confirmed COVID-19 cases grew significantly at the community level. However, RSV remained stable over time. Conclusions: Google Trends provides real-time data based on search patterns related to infectious diseases, allowing for continuous monitoring of public reactions, disease spread, and changes in perceptions or concerns. We can use this information to adjust their strategies of the prevention of epidemics or provide timely updates to the public.