The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.

 This article provides an account of the tourism in Petra encompassing its development from the time of the Nabataean Kingdom until the early 20th century. It delves into the factors that sparked tourism travel routes taken, security measures implemented, and influential individuals who have shaped Petra’s tourism history. Located at a juncture in the Middle East, Petra has consistently fascinated people with its sense of adventure. The city’s historical importance as a trade hub and a melting pot for cultural exchanges during the Nabataean era laid a strong foundation for its enduring charm. The skillful navigation of trade routes and effective marketing strategies employed by the Nabataean Kingdom played a role in establishing Petra as an irresistible destination for travelers. Supported by findings and ancient records it becomes evident that extensive trade networks flourished during this period highlighting the city’s role in the region. Its allure transcended generations captivating observers from Greece to its rediscovery by Burckhardt (1818–1897).

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

This study aimed to assess the influence of awareness and health habituation techniques, student management activities, the role of stakeholders, and the character of healthy living on health independence. The method used in this study is quantitative with descriptive test analysis techniques, partial t statistics and F test. This research was conducted in elementary schools in East Java Province, consisting of 92 elementary schools in 5 regions at East Java. Samples were taken using purposive techniques, and the number of samples was 348 people, consisting of principals, teachers and students. The results found that awareness and health habituation techniques have a significant influence on the character of healthy life of students, student management activities have a significant influence on the character of healthy life, the role of stakeholders has a significant influence on the character of healthy life, awareness and health habituation technique have a significant influence on health independence, student management activities have a significant influence on health independence, the role of stakeholders has a significant influence on health independence, the character of healthy living has a significant effect on health independence, and student management activities and the role of stakeholders have a significant effect on the character of healthy life, and have a significant impact on health independence.

This study examines consumer attitudes toward cryptocurrencies in Slovakia, focusing on the perceived adequacy of their promotion and the influence of demographic factors such as education, gender, and age. The findings reveal that a significant majority of respondents view cryptocurrency promotion as insufficient, with 77.77% expressing dissatisfaction. Demographic factors were found to have minimal impact on attitudes, suggesting that universal barriers—such as trust, technological literacy, and perceived risks—play a more critical role. Social media emerged as a key platform for engaging consumers, particularly younger demographics, provided that campaigns are well-targeted and informative. These results highlight the need for innovative promotional strategies emphasizing transparency, education, and trust-building to bridge the gap between cryptocurrencies and broader consumer adoption. The study contributes to the growing literature on cryptocurrency marketing by providing actionable insights for addressing challenges in emerging markets like Slovakia.

The purpose of the work is to study the transformation processes of constructing professional identity under the influence of new information technologies and to consider the evolution of views on the processes of scientific and practical understanding of new media resources in the context of the development of convergent journalism as a phenomenon of the modern information society. It was established based on the conducted research that the values and beliefs of journalists, reflecting the process of professional self-identification, are forming in the process of choosing certain options among a variety of alternatives and transforming further under the current conditions of the information and communication environment. In the process of the study, the article identifies the features, content, and main trends in the transformational processes of professional identity and professional culture of journalists in the context of technological changes in the media industry. The dynamics of the development of media convergence are shown from the point of view of the mutual influence of traditional and new media and the tendency of improving their technological and dialogue features and capabilities in content creation and broadcasting. An assessment is made of the degree of adaptation of regional media to modern conditions of the information and communication environment in the context of organizational, professional, and communicative convergence.