With the implementation of China’s national strategy of “Culture Going-out”, it is important to translate Qingdao folk culture well in order to promote the effective communication of Qingdao folk culture worldwide. However, the uniqueness of folk culture poses certain difficulties for translation. Skopos theory can effectively guide the translation of Qingdao folk culture. From the perspective of this theory, the author analyzes the principles and strategies of English translation of Qingdao folk culture and proposes four translation methods, such as literal translation with notes, equivalent translation, phonetic translation with notes and free translation. Translators can choose the appropriate translation methods according to specific translation purposes.

The advent of Artificial Intelligence (AI) has transformed Learning Management Systems (LMSs), enabled personalized adaptation and facilitated distance education. This study employs a bibliometric analysis based on PRISMA-2020 to examine the integration of AI in LMSs from an educational perspective. Despite the rapid progress observed in this field, the literature reveals gaps in the effectiveness and acceptance of virtual assistants in educational contexts. Therefore, the objective of this study is to examine research trends on the use of AI in LMSs. The results indicate a quadratic polynomial growth of 99.42%, with the years 2021 and 2015 representing the most significant growth. Thematic references include authors such as Li J and Cavus N, the journal Lecture Notes in Computer Science, and countries such as China and India. The thematic evolution can be observed from topics such as regression analysis to LMS and e-learning. The terms e-learning, ontology, and ant colony optimization are highlighted in the thematic clusters. A temporal analysis reveals that suggestions such as a Cartesian plane and a league table offer a detailed view of the evolution of key terms. This analysis reveals that emerging and growing words such as Learning Style and Learning Management Systems are worthy of further investigation. The development of a future research agenda emerges as a key need to address gaps.

At the beginning of the 21st century, sustainability is today’s most important issue, but it is achieved only in those areas where there is environmental awareness. Natural heritage is a part of heritage tourism in terms of the grouping of attraction types. The conceptualization of heritage and cultural heritage itself is not uniform in the national and domestic literature, with some considering heritage tourism to be synonymous with cultural tourism and others interpreting it as a connotation. This study aims to present the natural heritage of Győr-Moson-Sopron County (Hungary). Quantitative research was used to analyze the topic (N = 666), the sample is not representative and the selection of respondents was random. Data were collected between 1 September 2023 and 31 October 2023 using electronic questionnaires shared on Google Drive. Data were processed using SPSS 25.0 and MS Office Excel in addition to the descriptive statistical data (modus, median, standard deviation), correlation, and cross-tabulation analyses. In the framework of quantitative research, respondents’ travel willingness to visit tourist attractions, their specific expenditures, and their intention to participate in various events were conducted. The following questions are addressed in the study, whether all three national parks (Fertő-Hanság, Pannontáj-Sokoró and Szigetköz) are equally popular among tourists, whether the educational level of tourists influences the visitation of Lake Fertő, whether the respondents’ place of residence and the Danube floodplain influence the visitation of the lake and whether the age of the respondents influences the visitation of the 700-year-old oak in Hédervár. The significant finding of the study is that the mean of non-young people’s visitation is higher than that of young people in all three national parks.

Nowadays, urban ecosystems require major transformations aimed at addressing the current challenges of urbanization. In recent decades, policy makers have increasingly turned their attention to the smart city paradigm, recognizing its potential to promote positive changes. The smart city, through the conscious use of technologies and sustainability principles, allows for urban development. The scientific literature on smart cities as catalysts of public value continues to develop rapidly and there is a need to systematize its knowledge structure. Through a three-phase methodological approach, combining bibliometric, network and content analyses, this study provides a systematic review of the scientific literature in this field. The bibliometric results showed that public value is experiencing an evolutionary trend in smart cities, representing a challenging research topic for scholars. Network analysis of keyword co-occurrences identified five different clusters of related topics in the analyzed field. Content analysis revealed a strong focus on stakeholder engagement as a lever to co-create public value and a greater emphasis on social equity over technological innovation and environmental protection. Furthermore, it was observed that although environmental concerns were prioritized during the policy planning phase, their importance steadily decreased as the operational phases progressed.

The problem of stunting is not only related to children’s short height, but also has an impact on high morbidity rates, due to long-term nutritional deficiencies. which hinders motor and mental development in children. The objectives of this research are: 1) to understand household food security, 2) to understand the eating habits of pregnant women and toddlers regarding existing belief systems and traditions, and 3) to understand resilience mechanisms in overcoming food emergencies to prevent stunting. The data collection process uses a mixed methods approach by combining qualitative and quantitative research. The research results show that the determining factor for the incidence of stunting in coastal areas of Indonesia is the lack of household food availability due to subsistence economic life which then has an impact on eating behavior in the household, namely the lack of quality and quantity of the types of food consumed. daily. Apart from that, there is still a lack of understanding by pregnant women regarding the importance of providing complementary breast milk food to toddlers, low literacy of food diversity among toddlers, and low public trust in the importance of immunization. Furthermore, the high rate of early marriage in society and the limited awareness of using clean water is caused by a philosophy that still considers rivers as a source of life, so the water is used for consumption. Apart from that, socio-cultural mechanisms as a strategy to resolve the problem of food shortages have not yet been implemented.

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.