The profession of tourist guide has recently been subject to a significant loss of prestige in Hungary. There have been many career leavers who have been prevented from working due to an unregulated legal framework or a lack of government support during and in the post-COVID-19 period. The first problem - an ineffective and poorly regulated regulatory environment - has led to a significant increase in unauthorised tourism-related activities, undermining the reputation of the profession. As a result of the unregulated legal environment, the country - and Budapest in particular - is losing significant revenue and the situation is damaging the city’s image. Today, personal knowledge and experience are likely to be rendered worthless by the development of new technologies, tools and fast-paced lifestyles. Many people do not even know who exactly a tourist guide is, what their duties are and what regulations apply to their activities, despite the fact that tourist guides spend a lot of quality time with tourists visiting our country, providing them with information and acquainting them with our traditions. The transfer of value, which is the essence of their activity, is an important factor in shaping the image of the country and the perception of Hungary by visitors. Most people may not be aware of the remarkable difference between a qualified and licensed guide and an unqualified and unlicensed guide. The former presents a place authentically. This study aims to present the legal and professional background of this activity and the importance of this work in the light of current regulations, highlighting the important role of guides in the transmission of values today. It also focuses on the main changes and reactions brought about by the COVID-19 pandemic, as well as the uncertainties and concerns created by the legislative background. In order to illustrate the unique situation in Hungary, regulatory procedures and tourist management practices are also covered.

This paper aims to explore the issue of human actions in Islamic thought, focusing on the various stances regarding determinism, free will, and the intermediate position between them. This topic is linked to an ontological question: What are the limits of human responsibility for their actions? Our view is that the different positions on human actions reflect the presence of pluralism within Islamic thought, specifically through the discipline of Islamic theology (kalām). The difference in positions about the human actions within the science of theology expresses the vitality of Islamic thought and its appreciation of the right to differ between theological schools such as the Mu’tazila, Shi’a, and Sunnis, especially in an era dominated by the rationalism of Mu’tazila thought influenced by the methodology of Greek philosophical thought. This difference was recognized, especially in the third and fourth centuries AH/ninth and tenth centuries AD. We consider this difference in discussing the subject of the human actions as evidence of the principle of pluralism in Islam, which allows us to speak of the existence of a significant degree of intellectual tolerance, a subject that has not been studied to date. The prevailing view in studies today on this subject is that the theological groups accuse each other of unbelief, which is a mistaken position, because the saying of unbelief did not appear until after the fourth century AH/tenth century AD when transmission, reliability, and conservatism prevailed in Islamic thought. In addressing this issue, we examine three major stances on human actions as represented by three theological schools: The Mu’tazila (who advocated free will in human actions), the Jabriya (who advocated determinism in human actions), and the Ash’ariyya (who upheld the theory of acquisition). Once this is accomplished, we will explore the philosophy of pluralism in Islam through the lens of kalām. The most important conclusion we reached is that the debate on human actions opened, by the mid-4th century AH/10th century CE, an intellectual horizon that laid the foundations for pluralism in Islamic theological discussions. However, this horizon was soon closed due to various factors, which we have discussed throughout the paper.

The quality of indoor classroom conditions influences the well-being of its occupants, students and teachers. Especially the temperature, outside acceptable limits, can increase the risk of discomfort, illness, stress behaviors and cognitive processes. Assuming the importance of this, in this quantitative observational study, we investigated the relationship between two environmental variables, temperature and humidity, and students’ basic emotions. Data were collected over four weeks in a secondary school in Spain, with environmental variables recorded every 10 minutes using a monitoring kit installed in the classroom, and students’ emotions categorized using Emotion Recognition Technology (ERT). The results suggest that high recorded temperatures and humidity levels are associated with emotional responses among students. While linear regression models indicate that temperature and humidity may influence students’ emotional experiences in the classroom, the explanatory power of these models may be limited, suggesting that other factors could contribute to the observed variability in emotions. The implications and limitations of these findings for classroom conditions and student emotional well-being are discussed. Recognizing the influence of environmental conditions and monitoring them is a step toward establishing smart classrooms.

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.