Vol 32, No 5 (2026)

The results of the combined application of the empirical mode decomposition methods, internal decomposition over the time scale and the Hilbert transform for individual modes in order to identify the diagnostic feature of the main event precursor are presented. А computational experiment aimed at a comparative study of the reliability and stability of the obtained forecast for detecting earthquake precursors on a specific sample of real observations against the neural network algorithm was conducted.

This paper discusses Named Data Networking (NDN), where addressing occurs by packet name. For these networks, there is a problem of finding software for developing training stands, primarily with the ability to study their features — aggregation and caching. It is shown that in this issue it is preferable not to rely on ready-made software, but to learn on test examples, and then deploy your training stand based on a virtual laboratory or single-board computers. The article provides a comparative table of methods for deploying experiments in NDN, and also provides basic commands for mastering work with NDN networks and utilities for them. The capabilities of working with a network based on PNetLab or Eve-NG software have proven themselves from the best side, since it is convenient to study large topologies in virtual laboratories, but there are also alternatives to take into account the requirements and capabilities of the developer. Thus, the most lightweight in terms of CPU and RAM resources is to build a stand using Mini-NDN software, and in terms of the clarity of the experimental results, the capabilities of single-board computers or virtual machines in the hypervisor network are well suited. With this option, NDN traffic is easier to examine with standard packet analyzers. This work can be useful for network engineers who already widely use digital twins of real computer networks, as well as for novice researchers in this field.

The article proposes the method of trust management in decentralized UAV networks, which leverages the context of interactions between drones to more accurately identify reliable and suspicious nodes. This solution reduces false accusations, improves adaptability, and enhances energy efficiency compared to existing approaches, performing effectively under complex and dynamic network conditions.

The study addresses the issues related to the evaluation of trustworthiness of assessments used in decision-making tasks. The relevance of evaluating the trustworthiness of assessment results within automated systems is demonstrated. А method for assessing the trustworthiness of automated assessments is proposed, based on the principle of trust transitivity. The validation of this method is described in the context of an automated system for assessing the functional state of software users. The applicability of the designed method for analysing the trustworthiness of assessments obtained in software systems that automate various aspects of human activity, requiring the assessment of objects and/or processes, also demonstrated.

The purpose of this study is to develop a method for determining the epicenter of a traveling wave of electrical activity in the human cortex, taking into account the heterogeneous distribution of inhibitory effects. For this purpose, a mathematical model of an Amari-type neural field is used, which allows taking into account the directional variability of the travelling wave velocity. Within the framework of the hypothesis of radially asymmetric wave propagation, an algorithm has been developed that includes numerical simulation of the wave process, approximation of experimental data, and minimization of the error functional. The main result of the work is to clarify the position of the epicenter of the wave based on a comparison of calculated and experimental MEG data.