The use of firearms, their frequency, and legitimacy through self-defence and extreme necessity are socially relevant in Czechia and Slovakia. Legal firearm ownership for defence purposes impacts overall social security, influenced by factors like firearm legislation, cultural traditions, legal awareness, and violent crime rates. Understanding this issue requires considering subjective interpretations, even among security experts. This paper explores the theoretical foundations of self-defence and extreme necessity from criminal law, alongside practical implications supported by police statistics on violent crimes involving firearms in Czechia and Slovakia. It also includes a comparison with selected EU countries. The authors’ research uses a questionnaire to assess attitudes towards choosing defensive firearms, preparation for firearms licensure, and potential support for state security forces. The findings provide insights into legal firearm owners’ behaviours and attitudes toward defence and security. The study aims to contribute to a deeper understanding of firearm use for self-defence, correlating training, weapon preferences, and willingness to enhance state security.

As one of the key initiatives promoted by the Chinese government, precision poverty alleviation aims to lift information-blocked areas out of poverty and ensure their sustainable economic development. Yunnan Province, characterized by its combination of old, young, border, and poor areas, is the province with the most diverse types of poverty, the widest poverty coverage, and the deepest poverty levels in the country. Yunnan has carried out anti-poverty work in tandem with the national efforts for 42 years in a planned and organized manner, ultimately achieving the goal of zero absolute poverty. In this process, digital rural development has played a very important role. Based on the current experience of digital construction in developed regions, completing regional digitalization requires meeting the needs of information resources, information environment, and information supply and demand. Through keyword search, text analysis, and field visits, we summarized the factors considered by local governments in policy formulation. We also attempted to map out the path for rural governments to build digital villages. With the ultimate goal of bridging the urban-rural gap, the study of digital rural development in Yunnan will provide an effective case.

The article examines the role of electronic arbitration in settling commercial disputes. The article relies on the analytical approach to study legal texts and the comparative approach to examine the rules of international law and national laws in the field of electronic arbitration. In addition, the article discusses the concept of electronic arbitration and its distinction from traditional forms of arbitration. The article also explains the legal provisions related to it, especially those related to electronic arbitration agreements. Finally, the article explains the challenges related to its implementation how to take advantage of its benefits.

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.