This paper examines the use of large language models (LLM) for creating educational materials. A practical methodology for generating high-quality educational content for the specific discipline of “Databases” is proposed and verified. A multi-stage methodology is presented, in which one LLM generates content, and a second, independent “reasoning” model verifies its quality and correctness. A comparison method with an authoritative source and a modified “verification chain” algorithm was used to check the generated materials for factual errors. The results confirm that this approach, when used with modern, high-performance LLMs (such as DeepSeek and Gemini), enables the creation of high-quality educational texts with a low probability of hallucinations. The methodology can significantly accelerate the development of reliable educational materials and can be optimized by reducing the number of iterations while maintaining a high-quality initial response.

This article examines the impact of modern globalization processes on intellectual property risk assessment in the format of transnational interactions. Globalization creates new challenges related to the interaction of various legal systems, cultural norms, and economic factors, significantly complicating the process of identifying, assessing, and managing intellectual property risks. Emphasis is placed on the need to consider key aspects, including legal paradigms, cultural differences, and technological innovations that shape risks. Diagrams of interactions between participants in the intellectual property risk assessment process, both with and without the use of blockchain technologies, are developed. Risk management strategies are considered, emphasizing the importance of corporate social responsibility and the need for flexibility and adaptability in business processes. The importance of an integrated approach to risk management in a rapidly changing external environment is demonstrated, and practical recommendations are offered for organizations seeking to effectively protect their intellectual property rights in the face of increasing international competition.

The introduction of digital technologies in the agro-industrial complex contributes to increased effi-ciency, manageability, and transparency of production processes at agricultural enterprises. However, in addition to positive opportunities, digitalization brings with it numerous risks that enterprises have not previously encountered. This article presents the development and testing of a mechanism for creating an optimal portfolio of risk-mitigation measures during the digital transformation of the agro-industrial complex, based on linear programming methods. This approach enables modeling various development scenarios, taking into account multiple factors, including economic, technological, and managerial ones, and developing the most effective risk response strategies within a limited budget.

A life table prototype was constructed based on fire fatality data in Belarus over the past 23 years. The need to transform traditional approaches to estimating the value of human life, taking into account the specific nature of deaths in emergency situations, was substantiated. An analysis of the age structure of the fatalities was conducted, allowing for a focus on the relationship between the probability of mortality and the age characteristics of the victims. Recommendations for possible uses of the obtained results are provided.

With the accelerated development of the global digital economy, digital commerce is becoming an increasingly important part of global trade. At the same time, widespread mobile internet penetration and technological advances are creating unprecedented marketing channels and transaction scenarios. Mobile marketing plays a significant role not only in traditional e-commerce but is also gradually becoming a key factor in the transformation of the digital commerce ecosystem. This article demonstrates how mobile marketing is reshaping user engagement paths, conversion mechanisms, transaction logic, and brand relationships, thereby profoundly influencing the development models and trends of digital commerce.

The article presents a diagram of an intelligent access control system, including a camera directed at entrance to the pemises, an operator, and an application on the operator՚s computer. Requirements for an access control system application for facial recognition from various video sources, an easy-to-use user interface, and modularity for easy system expansion are formulated. The developed intelligent access control system allows teachers to control attendance at classes, is capable of capturing videos from various video sources thanks to recognition and tracking algorithms, and is resistant to low video quality.

This paper examines the automation of medical image processing for diagnosing arterial hypertension using artificial intelligence and computer vision technologies. A software component has been developed that automatically extracts and structures information from visual representations of medical documents (including biochemical analysis results, complete blood counts, and 24-hour blood pressure monitoring data), minimizing errors and accelerating the process of entering and interpreting medical information. Algorithms for image preprocessing (increasing image resolution, noise removal, and tilt correction), segmentation, and text recognition were developed and tested using the Real-ESRGAN and EasyOCR neural network models. Particular attention was paid to improving text recognition quality in the presence of characteristic artifacts that arise when scanning or photographing documents. CER and WER metrics were used to evaluate quality, and the module's performance was assessed with and without superresolution. The results of the study confirmed the effectiveness of the proposed approach and demonstrated that the integration of Real-ESRGAN technology improves the accuracy of medical image processing in the presence of significant noise and low-resolution source data. The practical significance of the study lies in simplifying and accelerating the process of diagnosing hypertension and creating the basis for a personalized approach to patient treatment.

This paper describes an approach based on reducing the large traveling salesman problem to a set of simpler ones. A solution algorithm is presented with a detailed illustration and a description of the logic behind the steps. The algorithm allows for repeated visits to cities, which is acceptable for practical purposes, since the primary objective is to find a route of minimal overall length, visiting each city at least once. A novel feature is the consideration of intra- and inter-cluster connections by recalculating the matrix of pairwise shortest distances between settlements. The initial network of settlements is then divided into clusters. Within each cluster, an optimal route is found that passes through all its nodes without returning to the starting node. The shortest route linking the clusters is then found. Taking this factor into account more clearly “separates” pairs of settlements in the resulting cluster structure, which facilitates obtaining high-quality solutions.