Fe3+-doped nano-TiO2 powders were prepared by sol-gel method. The photocatalytic activity of Fe3+-doped TiO2 nanoparticles was studied by using UV lamp as light source and methylene blue as degradation target. The photocatalytic activity of Fe3+-doped TiO2 was studied by degradation of 4L methylene blue solution with initial concentration of 10mg · L - 1. The results show that the photocatalytic activity of TiO2 can be improved by the addition of Fe3+. When the molar ratio of Fe3+ is 0.5-1%, the calcination temperature is 500 ℃. The photocatalytic degradation of methylene blue is the best.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

Young people are a traditional risk group for radicalization and involvement in protest and extremist activities. The relevance of this topic is due to the growing threat of youth radicalization, the expansion of the activities of extremist organizations, and the need to organize high-quality preventive work in educational organizations at various levels. The article provides an overview of research on the topic under consideration and also presents the results of a series of surveys in general educational institutions and organizations of secondary vocational education (n = 11,052), universities (n = 3966) located in the Arctic zone of the Russian Federation. The results of the study on aspects of students’ ideas about extremism are presented in terms of assessing their own knowledge about extremism, the presence/absence of radically minded people around them, determining the degree of threat from the activities of extremist groups for themselves and their social environment, and identifying approaches to preventing the growth of extremism in society. Conclusions are drawn about the need to improve preventive work models in educational organizations towards a targeted (group) approach.

In view of the fact that the convolution neural network segmentation method lacks to capture the global dependency of infected areas in COVID-19 images, which is not conducive to the complete segmentation of scattered lesion areas, this paper proposes a COVID-19 lesion segmentation method UniUNet based on UniFormer with its strong ability to capture global dependency. Firstly, a U-shaped encoder-decoder structure based on UniFormer is designed, which can enhance the cooperation ability of local and global relations. Secondly, Swin spatial pyramid pooling module is introduced to compensate the influence of spatial resolution reduction in the encoder process and generate multi-scale representation. Multi-scale attention gate is introduced at the skip connection to suppress redundant features and enhance important features. Experiment results show that, compared with the other four methods, the proposed model achieves better results in Dice, loU and Recall on COVID-19-CT-Seg and CC-CCIII dataset, and achieves a more complete segmentation of the lesion area.

Objectives: This study aims to examine the impact of Sun Tzu’s Art of War Five Virtues Leadership on innovation and the efficiency of the Chinese brand passenger vehicle industry, explore the role of innovation in enhancing industry efficiency, and propose strategies for leveraging the Five Virtues Leadership to improve operational performance and competitiveness in the sector. Methodology: A mixed research method using quantitative research (questionnaire survey) as the main method and qualitative research (in-depth interview) as the auxiliary method. Result: Quantitative and qualitative research results confirm the positive correlation between the Five Virtues Leadership, innovation, and the efficiency of Chinese brand passenger vehicle companies. And through effective data analysis, it explains the importance of the five virtues of leadership in traditional Chinese culture. Further understanding of the effectiveness and competitiveness of China’s passenger car brands, with leadership references. Conclusion: Five Virtues Leadership can foster a favorable environment for innovation, enhance time utilization, optimize resource allocation, and strengthen brand image. By developing and validating a measurement for Five Virtues Leadership, this study enhances the understanding of its role and significance in modern management, paving the way for future research.

In the context of globalization and integration of world markets, import operations occupy an important place in the activities of enterprises, forming a significant part of their economic processes. Effective management of these operations requires accurate and timely accounting and high-quality auditing, which becomes especially relevant in modern conditions. The study of methodological features of accounting and auditing of import operations is a relevant and timely area that helps improve the quality of financial reporting and management decisions. The purpose of the study is to analyze the problems and prospects of methodological features of accounting and audit of import operations, as well as to develop recommendations for their improvement. The study examined the main methodological approaches, existing problems and challenges, and proposed solutions aimed at increasing the efficiency and reliability of accounting and auditing in a global economy. The improvement of methodological approaches to the accounting and auditing of import operations will improve the accuracy and reliability of financial reporting, reduce the risks of non-compliance with regulatory requirements, as well as improve management decision-making and the overall financial stability of companies. The development and implementation of effective accounting and auditing methods that comply with international standards and best practices will minimize financial risks and increase the competitiveness of enterprises in the global market. A study of the problems and prospects of methodological features of accounting and auditing of import operations has revealed a number of key issues that require attention and solutions. The main challenges are the complexity and diversity of regulatory requirements, currency fluctuations, the diversity of imported goods and services, difficulties in assessing and recognizing imported goods, and the lack of qualified specialists.