J. Selye University

Multilevel power inverters have a complex semiconductor structure that elevates the risk of switch faults. Furthermore, voltage drops across floating capacitors, which are integral components of multilevel power converter structures, can disrupt accurate system status assessment and lead to incorrect or delayed fault detection. This article proposes a novel approach for short-circuit fault detection and location in multilevel power converters using artificial intelligence with a focus on reliability prioritization. Five reference voltage prediction methods were analyzed including a switching algorithm and four deep learning-based techniques i.e., convolutional neural networks, gated recurrent units, long short-term memory networks, and a hybrid model combining convolutional neural networks with long short-term memory networks. Fault location was performed through a reliability-based strategy prioritizing components with higher failure probabilities, significantly improving the fault identification speed. Our method reduced the duration of fault detection compared to similar methods and included a novel fault location method based on prioritizing fault detection according to the lifetime of fundamental components. We predicted and verified online voltage references using four different deep learning methods and compare the outcomes in an experimental setup. Simulation and experimental results demonstrated the effectiveness and practicability of the proposed method in detecting and locating faults in various types of multilevel power inverters.

The primary aim of this study is to identify how residents on the Hungarian and Slovak sides of the Lower Ipoly Valley perceive the most considerable material and immaterial local values and unique resources of their settlements. The research was designed to explore and compare the territorial capital of the two sides of the study area based on three major capital types: natural, social, and economic capitals. Data collection included 254 semi-structured interviews, conducted with 136 residents on the Slovak side (14 settlements) and 118 residents on the Hungarian side (12 settlements). Detailed interview summaries were analysed with qualitative content analysis using emergent coding. This resulted in an analytical framework comprising nine value dimensions. Across both sides of the border, we examined the same nine value dimensions: local workforce, local enterprises, civil organisations and cultural groups, local and community events, local gastronomy, natural values, built heritage, holders of local knowledge, and local products. Items mentioned that related to each value dimension were counted for each dimension and normalised to a 0–10 scale. The quantified values were aggregated at the settlement level (90 points being the maximum score). The average score of each dimension was also calculated for both sides of the study area. Our findings show that the Hungarian side achieved a total score of 40.4, while the Slovak side reached 36.6, both reflecting a similarly weak–moderate state of the local capitals with minimal differences. This indicates that the two sides of the study area share comparable developmental challenges but also considerable potential for improvement. The Hungarian side performed slightly better in nearly all dimensions except local gastronomy, where the Slovak side proved stronger. The Hungarian side’s relative advantages include a higher presence of local enterprises, a richer network of civil and cultural groups, and more diverse built heritage; in other dimensions, differences are marginal. One of the most pressing regional challenges is improving employment opportunities and stimulating entrepreneurial activity, which are essential for enhancing population retention. Overall, the results indicate that the settlements possess substantial—yet largely underutilised—value assets, whose conscious and consensus-based development could form a strong foundation for creative and innovative local development. Each settlement’s value matrix includes elements that define its uniqueness, enabling the identification of numerous potential development pathways, whether through nature-based educational and recreational programmes, the utilisation of culturally considerable built heritage, the revitalisation of living traditions, or the promotion of local traditional gastronomy on both sides of the study area.

Climate-related emotions, attitudes, and self-efficacy are increasingly recognized as interlinked drivers of adolescents' pro-environmental behavior, yet evidence on their joint role across cultural contexts remains limited. This study explores the interplay between climate-related emotions, attitudes, and pro-environmental behaviors among secondary school students from the Czech Republic, Slovakia, and Hungary. It is based on previous studies focused on eco-anxiety, eco-guilt, and environmental hope of students in various countries, as well as research investigating the factors influencing human pro-environmental behavior. Collecting data from 17-year-old students in October 2022 (N = 6,477), it analyzed the role of climate-related emotions in shaping students' behaviors and actions. Based on the findings, while eco-anxiety and climate change attitudes were linked to habitual pro-environmental behaviors (e.g. recycling, energy conservation), their impact on deliberate pro-environmental actions was indirect, mediated by environmental self-efficacy and eco-guilt. Environmental self-efficacy emerged as the strongest predictor of pro-environmental behavior. While girls exhibited a higher level of almost all emotions than boys, country differences had no practical significance. Based on these findings, the study emphasizes the importance of fostering students' environmental self-efficacy and equipping them with coping strategies with climate-related emotions.

Teachers’ readiness in bilingual early childhood education is increasingly recognized as a multidimensional construct shaped by both professional and language-related factors. However, existing research has typically examined these factors separately, with limited evidence on how they combine across teacher groups, particularly in minority-language contexts. This study examined teachers’ readiness to deliver state-language instruction to dual language learners (DLLs) in Hungarian-medium kindergartens in Slovakia. A total of 313 kindergarten teachers participated in the study. Data were collected through a survey assessing multiple dimensions of readiness. Principal component analysis and confirmatory factor analysis supported a six-factor model comprising professional preparation, teacher competencies, challenge management, instructional aids use, professional needs, and Slovak language use outside kindergarten. Latent profile analysis identified three readiness profiles (low, moderate, and high), reflecting differences in overall preparedness. Background characteristics, particularly age, teaching experience, and language-related factors, were significantly associated with higher readiness. Teachers who used Slovak more frequently in everyday contexts showed higher readiness. Mediation analysis indicated that language proficiency and preferred language use did not mediate the relationship between teaching experience and teachers’ readiness, but functioned as independent predictors. These findings highlight the joint importance of professional and language-related factors in shaping teachers’ readiness and offer implications for teacher education and policy in bilingual early childhood settings.

In this paper, we study the subsequences of homogeneous linear recurrences of order r >= 2 such that a subsequence consists of every qth (q >= 2) term of the original sequence. There exist general and sporadic results on this area, the main advantage of our method is that we use only the coefficients of the input sequence to find the coefficients of the claimed subsequence explicitly. We do not use any intermediate object to formalize the final results. The two approaches we suggest work, at least in theory, for any given r and q, but the computation becomes more and more technical and difficult for larger values. In this work, we handle explicitly the cases r = 2, 3, 4 for arbitrary q, and q = 2, 3 with arbitrary r. Moreover, we give some information to deal with the problem of q = 4. The computational results are displayed in clear tables. The main tool we apply is the theory of symmetric polynomials.