The Modern Cities Program is the largest-scale urban development effort in the history of the country, with which the Government of Hungary aims to promote the simultaneous development of municipalities at the same hierarchical level. Its projects focus on the preservation of intangible and tangible cultural heritage, the transformation of urban public spaces and green areas into community spaces, the creation of institutions for sports and recreational activities, research and development, digitalization, projects for innovative and creative professionals, and public educational and cultural institutions. The study aims to analyze the funding granted for developing the cultural and creative sector of cities with county rights through the Modern Cities Program in the period 2016–2025, by comparing the size of their population, their strategic importance in regional economic policy and the relationship between the value of the cultural heritage with the amount of funding received. The paper unveils the distribution of grants over time and space, the modalities and proportion of grants, and the way the cities that has received grants align with the national strategy. This will also reveal a shift in the regional importance of the cities and their relationship. Until February 2024, the Government of Hungary has contributed more than HUF 322.6 billion (809.5 million EUR) to the implementation of 98 cultural and creative projects in 22 cities with county rights through its urban development support program that has been established for the development and regeneration of cities with county rights and to counter the dominance of the capital.

Objective: To describe magnetic resonance imaging (MRI) findings of the brain in patients younger than 65 years who were studied by transcranial Doppler (TCD) with microbubble contrast, with a history of cryptogenic cerebrovascular accident (CVA) and suspected patent foramen ovale (PFO).

Materials and methods: This retrospective cross-sectional study included patients of both sexes, younger than 65 years of age.

Results: Our sample (n = 47.47% male and 53% female, mean age is 42 years) presented high-intensity transient signals (HITS) positive in 61.7% and HITS-negative in 38.3%. In HITS-positive patients, lesions at the level of the subcortical U-brains, single or multiple with bilaterally symmetrical distribution, predominated. In patients with moderate HITS, lesions in the vascular territory of the posterior circulation predominated.

Conclusion: In patients younger than 65 years with cryptogenic stroke and subcortical, single or multiple U-shaped lesions with bilateral and symmetrical distribution, a PFO should be considered as a possible cause of these lesions.

This paper aims to explore the practice and effect of integrating ideological and politics in higher vocational mathematics education. Through the review of relevant literature and the analysis of practical cases, this study analyzes the necessity and feasibility of integrating ideological and political education into higher vocational mathematics teaching, as well as the promoting effect of students' ideological and political education. At the same time, it also discusses how to effectively combine the curriculum thinking and politics with higher vocational mathematics teaching, as well as the strategies and methods to achieve positive results, in order to provide some reference for the majority of higher vocational mathematics teachers.

For a long time, kindergarten literature reading course is often a mere formality, preschool children's reading invalid, random phenomenon. In order to improve preschool children's reading interest and reading comprehension ability, teachers should start from the core quality and deconstruct the characteristics of children's literature. Make use of multiple resources to optimize literary reading materials; Integrate contents in various fields and implement rich curriculum activities; Construct performance evaluation system and form reading evaluation model.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.

This paper aims to explore the impact of V-Girls APP on the improvement of female college students' Health literacy and its mechanism. Using a questionnaire survey method, the survey subjects were female students from a certain university. The results showed that using the V-Girls app can significantly improve the health knowledge level, health behavior habits, and mental health status of female college students. Further analysis reveals that the impact mechanisms of V-Girls APP mainly include cognitive mechanisms, social support mechanisms, and behavioral guidance mechanisms. The results of this study provide new ways and ideas for improving female college students' Health literacy.