The relevance of this study stems from the need for a comprehensive assessment of the digital transformation of foreign economic activity. Existing approaches lack a comparison of resource potential and the effectiveness of its use. To achieve this, this article examines the combined application of two metrics: the Network Readiness Index and the UN Global Trade Facilitation Study. Their integrated use, based on comparative analysis, allows us to view the digital transformation of foreign economic activity as the result of the interaction between technological development and specialized institutional reforms. The study's results allow for a classification of countries and demonstrate that a high network readiness index is a necessary but not sufficient condition for the digitalization of foreign economic relations. An optimal model for the digitalization of foreign economic activity presupposes a balanced development of both areas: the rate of modernization of the regulatory framework should correlate with the pace of technological progress, ensuring the legitimacy and security of international trade interactions. This dictates a transition from a policy of digital saturation to a policy of regulatory openness and standardization.

This article presents a feasibility study for the use of an electronic competency genome as an innovative solution for in-depth analysis of employee professional profiles, integrating artificial intelligence. The relevance of the study stems from the need for an objective assessment of not only formal qualifications but also evidence of their practical application in the selection of specialists for key positions in the context of the digitalization of the Belarusian economy. The research methodology included a comparative feasibility study of traditional and innovative candidate assessment solutions, an expert assessment method for determining time and cost parameters, the calculation of economic performance indicators taking into account direct and indirect effects, and business process modeling for in-depth candidate assessment.

This paper examines digital technologies used in logistics infrastructure facilities. It emphasizes the specific nature of logistics activities, whose digitalization creates a new class of threat – cyber-physical risks. These risks, when realized, not only disrupt material flows, but also cause multiplicative economic losses throughout the supply chain. Economic and mathematical models are examined, and their effectiveness for logistics infrastructure facilities is substantiated. The importance of an integrated approach to information security management in a rapidly changing external environment is demonstrated.

This paper examines the integration of biotechnical devices and systems into a medical information network. The use of cryptographic protection methods for medical data transferred between various systems, applications, and healthcare organizations is substantiated to ensure their confidentiality and integrity, taking into account requirements for handling personal data and medical confidentiality. A software tool has been developed that implements the processing of information in the Health Level Seven message format, as well as their encryption and decryption. The dependence of encryption speed and output data size on the cryptographic algorithm used is analyzed, allowing for the selection of an algorithm offering high performance and minimal impact on the functioning of biotechnical systems. Based on the results of the study on the protection of information transferred using Health Level Seven, the feasibility of hybrid encryption is demonstrated.

This article examines the development of an algorithm for creating a computer simulation model (digital twin) for a comprehensive assessment of the energy and economic efficiency of design solutions and utility systems in residential buildings. A manageable and reproducible tool is presented that enables virtual experiments, comparison of alternatives, and justification of optimal solutions during the design stage. The proposed algorithm systematizes the collection and integration of various data – from architectural drawings and thermal performance data to utility system parameters and economic indicators – into a single model. The resulting tool minimizes risks and reduces operating costs. The algorithm will enable housing and utility companies and other management organizations to conduct online monitoring, remotely manage, and control the condition of buildings.

Machine learning and artificial intelligence (AI) are currently actively researching methods for optimizing and tuning model hyperparameters. One key area of research is analyzing the impact of varying hyperparameters, such as the number of two-dimensional convolution (Conv2D) layers and their parameters (number of filters, kernel size), the size and stride of maximum pooling (MaxPooling2D) layers, the number of neurons in fully connected layers, activation functions, batch size (batch_size), and the number of training epochs, on the prediction accuracy of machine learning models using a convolutional neural network architecture on the Fashion-MNIST