This integrated approach aims to prevent suboptimal prioritization, ensure effective resource allocation, and expedite the recovery of information systems. Methodology: the proposed methodology establishes a hierarchical, multi-factor prioritization approach. It integrates the quantitative technical severity assessment provided by CVSS with Ukraine’s national criticality levels. This dual-layer scoring is further supplemented by a structured tie-breaking mechanism using additional attributes to achieve precise prioritization. A structured dataset was constructed, covering nationwide impact, economic consequences, information-related impacts, functional consequences, recovery capabilities, and system/network specifics. A prioritization methodology was developed, involving five key phases: incident registration, data verification, correlation and aggregation, criticality assessment, and tie-breaking. Dedicated software was implemented to simulate the algorithm within the CERT-UA environment, enabling real-time registration, evaluation, and visualization of prioritized incidents. The simulation tested the algorithm’s effectiveness in handling incident inflows and its potential to streamline response efforts. Conclusions: this study presents a robust and novel multi-factor methodology that overcomes the insufficient granularity of existing national criticality levels. Introducing a hierarchical tie-breaking mechanism, the approach provides CERT-UA with a clear, decisive, and efficient tool for incident prioritization. Simulation and pilot implementations confirm the algorithm’s practical value and immediate applicability within the existing operational environments, significantly enhancing the ability of national-level response teams to mitigate the negative impacts of cyber threats. The system’s simplicity and adaptability ensure its applicability within existing operational environments, while its tie-breaking mechanism minimizes the risk of suboptimal prioritization. Future research directions include integrating artificial intelligence and machine learning to enhance prioritization accuracy and adapting this methodology for diverse organizational contexts. This work lays a strong foundation for advancing cyber incident management, addressing the evolving nature of cybersecurity challenges.

This study focuses on the development and evaluation of an explainable artificial intelligence (XAI) framework for multimodal sentiment analysis, specifically applied to territorial revitalization project management. The research addresses the critical problem of “black box” AI models, whose lack of transparency hinders their adoption by project managers who require trustworthy information for high-stakes decision-making in complex social environments. The goal of this study is to propose and rigorously validate a novel framework for multimodal sentiment analysis that is tailored to provide transparent, trustworthy, and actionable insights for decision-making in territorial revitalization project management. The tasks to be solved include developing a hybrid XAI technique that fuses insights from cross-modal attention and gradient-based attribution, designing a cohesive, user-centric explanation format combining highlighted text and image heatmaps, constructing a custom RevitalizeSent-MM dataset for this specific domain, and empirically evaluating the framework’s predictive accuracy and, crucially, the fidelity of its explanations. The methods used involve a transformer-based Multimodal Sentiment Analysis (MSA) model using BERT and ViT with cross-modal attention for information fusion. The explainability component is a hybrid XAI technique that integrates cross-modal attention analysis with Integrated Gradients to assign importance scores to input features. Evaluation was performed using standard classification metrics for performance and the “Accuracy Drop on Perturbation” metric for explanation fidelity. The results confirmed the efficacy of the framework. The multimodal model demonstrated superior accuracy over unimodal baselines, and the proposed XAI method achieved significantly higher fidelity than naive explanation approaches, demonstrating its ability to accurately reflect the model’s internal reasoning. The scientific novelty lies in three areas: the development of a fused, hybrid XAI technique specifically for transformer-based multimodal models, creation of a unique, domain-specific dataset for revitalization analysis, and validation of a methodology for adapting advanced XAI to solve critical trust and adoption barriers, thereby confirming its practical significance in project management.

The subject of this study is the method of grouping image descriptors that are placed in the feature database of search systems. This study aims to develop a method for optimizing the clustering of descriptors in Big Data storage, represented by a Multidimensional Cube data model. Further use of the formed clusters for effective search in content-based image retrieval systems. The task is to: analyze modern approaches and solutions for forming groups of image descriptors in the feature database; formulate the problem of the clustering method in the Multidimensional Cube and the requirements for its optimization; develop an abstract optimization method; develop specific optimization algorithms for different types of model placement in memory; develop metrics; perform experiments and compare the results with analogs. The methodology includes analyzing the process of forming groups using different methods, determining their advantages and disadvantages, applying numerical methods to optimize the clustering process, conducting experiments with data sets available on the Internet, evaluating the effectiveness of the optimization method, and generating result tables for comparison with analogs. The following results were obtained: a method for optimizing the clustering of descriptors was developed for use in the Multidimensional Cube data model, and optimization algorithms were developed for model placement in random-access memory and databases. The results of clustering, determined by the metrics of time spent and cluster filling, were compared with those of descriptor clustering performed using the k-means algorithm and the Product Quantization approach with the implementation of Inverted Multi-Index. The results showed that the use of the model with the developed optimizations demonstrates that the quality of descriptor clustering is not worse than when using Inverted Multi-Index and better in terms of time spent than when using k-means or Inverted Multi-Index. Conclusions: The developed method for optimizing descriptor clustering significantly improves the distribution of descriptors within the Multidimensional Cube model and makes it a good alternative for use in content-based image retrieval systems.

The subject matter of this article is a HybridAttention mechanism integrated into a deep neural architecture for Network Intrusion Detection Systems (NIDS). This study aims to develop and study a HybridAttention mechanism based on a combination of global (self-attention) and local (dynamic local attention) models to improve the quality of traffic classification in real-time NIDS. The tasks to be solved are as follows: analyzing the applicability of existing attention mechanisms in network intrusion detection; integrating various attention types into a CNN-BiGRU architecture; developing a HybridAttention mechanism based on dynamic window alignment; optimizing the model using Optuna; and experimentally evaluating its performance on benchmark datasets using standard classification metrics. The methods used are: deep learning modeling with CNN-BiGRU architecture, integration of various attention mechanisms, including a novel HybridAttention, hyperparameter optimization using Optuna, and performance evaluation based on standard classification metrics. The results of this study show that the proposed HybridAttention mechanism demonstrates superiority over individual types of attention in all key metrics. The model achieved up to 99.85% accuracy on the NSL-KDD dataset training data and demonstrated strong generalization on the UNSW-NB15 dataset, achieving up to 98.06% accuracy in multi-class classification and up to 99.20% in binary classification. The proposed model also outperformed state-of-the-art approaches for processing unbalanced data and detecting various types of attacks. Conclusions. The scientific novelty of the results obtained is as follows: a HybridAttention mechanism combining self-attention and dynamic local attention was developed to enhance sequential pattern recognition in network traffic; the CNN-BiGRU architecture was improved by integrating multiple attention modules; systematic hyperparameter optimization using Optuna improved generalization on imbalanced data; and the proposed model outperformed existing approaches on benchmark datasets in detecting both known and novel cyberattacks.

The subject of the article is the study and comparison of two approaches to modelling the battery discharge of a CubeSat satellite: analytical using equivalent circuit and machine learning. The article aims to make a reasoned choice of the approach to modelling the battery discharge of a CubeSat satellite. Modelling the battery discharge of a satellite will enable the prediction of the consequences of disconnecting the autonomous power system and ensure the fault tolerance of equipment in orbit. Therefore, the selected study is relevant and promising. This study focuses on the analysis of CubeSat satellite data, based explicitly on orbital data samples of the power system, which include data available at the time of the article’s publication. The dataset contains data on the voltage (mV), current (mA), and temperature (Celsius) of the battery and solar panels attached to the five sides of the satellite. In this context, two approaches are considered: analytical modelling based on physical laws and machine learning, which uses empirical data to create a predictive model. Results: A comparative analysis of the modeling results reveals that the equivalent circuit approach has the advantage of transparency, as it identifies possible parameters that facilitate understanding of the relationships. However, the model is less flexible to environmental changes or non-standard satellite behavior. The machine learning model demonstrated more accurate results, as it can account for complex dependencies and adapt to actual conditions, even when they deviate from theoretical assumptions. However, the model requires prior training on a large amount of data and is less well understood in terms of physical laws. General conclusions. The equivalent circuit approach provides high accuracy and reliability under known conditions, but it is limited when external parameters change. The machine learning approach demonstrates better overall accuracy and stability, especially under variable or unpredictable conditions, but requires a large amount of high-quality data and more complex interpretation. Thus, the most effective approach may be a hybrid one, where the analytical model serves as the basis and machine learning is used as a tool for refining or compensating for inaccuracies.

The article analyzes known non-contact one-parameter and two-parameter eddy current methods and devices for controlling the geometric magnetic and electrical parameters of metal cylindrical products. The subject of the study is the development of a non-contact method and device, based on a transformer eddy current transducer, for the joint determination of three parameters of a metal cylindrical product, namely: diameter, magnetic permeability, and electrical conductivity. The aim of this work is to increase the sensitivity of three-parameter eddy current quality control of metal cylindrical products and develop a device scheme for their rejection. To achieve the aim, the following tasks were solved: to develop the theoretical foundations of a control method based on finding the frequency of the electromagnetic field of a transformer transducer with a rod under study, which corresponds to the extremum of the transformation function; to develop a device scheme and an algorithm for implementing the developed method; to conduct experimental studies in order to compare the results obtained by the developed method with control methods. Results obtained: the main mathematical expressions were found that link the measured parameters of the primary transducer signal with the information geometric, magnetic and electrical parameters of the controlled metal product; a device and an algorithm for implementing the proposed method were developed, based on finding the frequency of the field of the electromagnetic transducer with a rod, which corresponds to the extremum of its conversion function in the case of compensating for the influence of the air gap between the transducer and the rod; the use of extreme points of the sensor conversion functions with the product when implementing measurements allows achieving the highest sensitivity to the parameters being measured; the developed method and device allow increasing the resolution of the transducer to geometric magnetic and electrical parameters, which are 2%, 5% and 8%, respectively. Conclusion: the developed method and device are of great importance for the further development of multiparameter methods for quality control of metal products, in particular when conducting express analysis of material blanks, quality control of the technological process of manufacturing products, as well as selective control of reliability of operation during their operation.

The subject matter of this article is the design and analysis of charging pads based on capacitive coupling for wireless power transfer (WPT) in electric vehicle (EV) charging systems. The main goal of this study is to improve the efficiency and overall performance of CPT-based EV charging systems by enhancing the self-capacitance, increasing the coupling efficiency, and minimizing the reliance on oversized or auxiliary passive components while maintaining the compact pad geometry. To achieve this task, two novel four-sheet pad configurations are introduced and evaluated: The M2-four-sheet parallel formation, in which four aluminum sheets are arranged horizontally, and the M2-four-sheet stacked formation, in which the sheets are aligned vertically. Their performance was systematically compared with that of conventional four-sheet layouts to validate the proposed improvements. The proposed formations maintain equal pad dimensions (610 mm × 610 mm) on the primary and secondary sides to ensure effective coupling and high self-capacitance. The methods used include LCLC and LCL compensation topologies resonate with the capacitive coupler and deliver high voltage to the charging pads. Finite-element analysis (FEA) was employed to simulate the coupling capacitance and optimize the coupler’s geometry, while MATLAB software was used to simulate the complete WPT system. The results show that the M2-four-sheet parallel formation achieved significantly higher self-capacitance compared to the traditional layout, with little variation in the coupling capacitance. The M2-four-sheet stacked formation provides even higher self-capacitance than the parallel one and enhances the overall system efficiency without increasing the pad size. Based on these findings, a 1.5-kW CPT system was designed using four aluminum pads with a 150-mm air gap, achieving a DC-DC efficiency greater than 90.5%. Conclusions. The M2-four-sheet parallel formation demonstrates a 175% increase in self-capacitance compared to the traditional layout. Meanwhile, the M2-four-sheet stacked formation improved the coupling coefficient by 21.4%, although it nearly half the self-capacitance relative to the traditional layout. Both designs contribute to significant improvements in system performance and efficiency. Therefore, this study presents a novel symmetric four-sheet pad configuration that significantly improves the self-capacitance and coupling performance, thereby reducing the reliance on external capacitors while maintaining compact geometry and high transfer efficiency.

The subject matter of this article is cyber hygiene of the digital twin of the occupational safety management system of engineering and technical personnel of civil aviation. The goal is to develop a methodology for assessing the cyber hygiene of the digital twin of the occupational safety management system of engineering and technical personnel of civil aviation in the context of transformation into the quantum future. The tasks: to develop a methodology for assessing the state of the digital twin of the occupational safety management system of engineering and technical personnel of civil aviation using an integral cyber hygiene index; to develop a model of transitions of the functional state of the digital twin, allowing to predict its cybersecurity, cyber vulnerability and recovery capabilities in case of cyber-attacks; to determine cyber hygiene measures for the digital twin in the context of quantum transformation. Results: a methodology for assessing the cyber hygiene of the digital twin of the occupational safety management system of engineering and technical personnel of civil aviation in the context of quantum transformation has been developed; it has been proven that the process of transition of the cyber hygiene system from one state to another, in response to the measures taken, is an iterative process, which allows dynamically monitoring the effectiveness of the taken measures on DT cyber hygiene and predicting its further state; algorithm for the sequence of stages of assessing the state of DT cyber hygiene and their relationship within a cyclic process has been developed. Conclusions: a method for assessing the state of DT cyber hygiene has been developed, which, due to its iterative nature and the use of Markov chains, allows determining the probability of the transition of the cyber hygiene system from one state to another, as a response to the measures taken. The application of this method in practice will allow dynamic monitoring of the cyber hygiene of the digital twin and the effectiveness of the measures taken, as well as predicting its future state.

The subject matter is that the characteristics of tuberculosis are difficult to study visually. Therefore, a computer-aided system based on deep learning can be applied to X-ray image recognition. Many studies have been conducted in this area but have yet to achieve a high accuracy rate. The goal of this study is to determine the effect of using various datasets in developing deep learning models. The tasks to be solved include exploring various deep learning architectures and deep fine-tuning hyperparameters, as well as using various dataset sources. The method used is the development of a deep learning model of convolutional neural network (CNN) using transfer learning to classify X-ray images into binary classes of normal and tuberculosis (TB). The CNN architectures used are the pretrained networks of ResNet and EfficientNet, along with their variants. The pre-trained network was trained on a dataset obtained from four sources: Shenzhen, Montgomery, RSNA CXR, and Belarus. The dataset is divided into three schemes: Scheme one consists of the Shenzhen dataset with low-quality X-ray images; Scheme two is the Montgomery, RSNA, and Belarus datasets that show good contrast in the indicated TB area; and Scheme three contains datasets from all sources to allow for more datasets to be learned. The augmentation, dropout, and L2 regularization methods were also applied to enhance learning performance. The following results were obtained: the models performed better with the high-quality X-ray images in Scheme Two but not with the large dataset in Scheme Three. Regarding network performance, the models resulting from ResNet-101 and EfficientNetB0 outperformed the others with good fit learning and capability in recognizing X-ray images with an accuracy rate of 99.2%. In conclusion, the best approach to enhance learning performance is to use high-quality input and apply regularizations.

Subject of the research – visual object tracking using various image hashing algorithms for real-time tracking tasks. The goal of this study is to evaluate the tracking success and processing speed of existing and new hashing algorithms for object tracking and to identify the most suitable algorithms to be used under limited computational resources. The objectives of the research include: developing and implementing object tracking based on the aHash, dHash, pHash, mHash, LHash, and LDHash algorithms; comparing the processing speed and accuracy of these methods on the video sequences "OccludedFace2," "David," and "Sylvester"; determining the tracking success rate (TSR) and frames per second (FPS) metrics for each algorithm; analyzing the impact of the search window size, search strategy, and type of hashing on tracking quality, and providing recommendations for their use. The study also explores the trade-off between accuracy and processing speed for each algorithm considering the constraints of limited computational resources. The methods of this study involve testing and evaluating the accuracy and speed of image hashing algorithms on different test video sequences, as well as the use of metrics to determine object similarity using the Hamming distance. The results demonstrate that the aHash and mHash algorithms demonstrate the best accuracy indicators for all hash window sizes, aHash has a higher processing speed, and mHash offers better robustness to changes in lighting and object position. The dHash and pHash algorithms were less effective than the aHash and mHash algorithms due to their sensitivity to changes in scale and rotation. However, perceptual hashing-based methods, such as pHash, are more robust to contrast and blurring. Conclusions. The best hashing algorithms for real-time object-tracking tasks are aHash and mHash. This study underscores the significance of selecting suitable hashing algorithms and search strategies tailored to specific application scenarios and offers possibilities for further optimization.