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 aimed to examine the impact of working conditions and sociopsychological factors on job satisfaction among office workers. Using data from the 2017–2018 Working Conditions Survey, exploring how workplace conditions and sociopsychological elements could impact job satisfaction. This study examined data from 9801 workers to explore the effects of working conditions and psychosocial environments on job enthusiasm, which subsequently impacts job satisfaction. Analyzing 1416 office workers, it found that fewer working hours, better work-life balance, improved work conditions, and lower depression levels enhance job enthusiasm, significantly affecting job satisfaction. The work environment had the most substantial impact, encompassing relationships with colleagues, task completion time, and confidence. Work-life imbalance and depression were also significant, with work-life balance being crucial for modern society, especially the younger generation. Poor working conditions and unstable psychosocial environments negatively affect job enthusiasm and satisfaction, with findings supporting previous research on job stress and turnover intentions in various industries. This study highlights the need for organizational policies that support these aspects to improve overall employee well-being and productivity.

The promulgation of the Curriculum Standards for ordinary high School (2017 edition, 2020 revision) has effectively promoted the reform of high school mathematics classroom. In order to cope with the change of textbook content in the new curriculum reform, it has become one of the important tasks for high school mathematics teachers to implement teaching activities better and sort out and analyze the differences between the old and new textbooks. This paper analyzes the differences between old and new textbooks from the three dimensions of system structure, course content and example exercises, and gives some reasonable teaching suggestions. Among them, the new textbook uses 2019 "Ordinary High School Textbook" person-taught A version of Compulsory Mathematics 1, and the old textbook uses 2004 "Ordinary High School Mathematics Curriculum Standard Experimental Textbook" person-taught A version of compulsory Mathematics 4. In general, the adjustment of the new teaching materials is more in line with the cognitive characteristics of students, pay attention to the penetration of mathematical culture and pay attention to the development of students' mathematical core literacy.

The presented article focusses on the analysis of perception of the university social responsibility through the eyes of Slovak university students. The aim is to compare how the values, efficiency of the organisation (university), and the educational process influence the perception of social responsibility among university students themselves. The research is based on the application of quantitative methodology towards the evaluation of differences and similarities in perceptions using two types of tests for statistical analysis, comparative (Mann-Whitney U test) and correlational (bivariate correlation matrix of Spearman’s rho).The results of the research provide a deeper understanding of how universities can shape students’ approach to social responsibility through their values and educational processes, which has important implications for the development of university policies and practices.

Night tourism, increasingly recognized as integral to the travel experience, has gained attention for its impact on overall tourist satisfaction. This article offers a comprehensive analysis of night tourism development in Vietnam’s coastal cities, focusing on Nha Trang and Quang Ngai, as representative cases of mature and emerging destinations, respectively. Utilizing the Importance-Performance Analysis (IPA) tool, the study aims to provide practical insights for sustainable night tourism. Surveys with 524 domestic tourists were conducted to evaluate perceptions and satisfaction levels. Nha Trang emphasizes accessibility and vibrant nightlife, with a focus on the night market and outdoor shows. Conversely, Quang Ngai highlights its night landscape, dining options, and shopping areas. Recommendations for both destinations include enhancing entertainment offerings and reassessing priorities based on tourist preferences. The study underscores the need for tailored strategies to foster sustainable night tourism development that aligns with evolving tourist demands in coastal cities like Nha Trang and Quang Ngai.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.