Traditional first aid preparation methods often fail to reproduce realistic stress levels and to simulate visual difficulty in identifying lesions in critical situations. In emergencies, delays in recognising injuries or errors in protocols result in critical losses of human resources. The use of computer graphics and virtual reality technologies enables you to create a safe yet highly realistic environment for rescuers to test and improve their skills. The article presents an integrated methodological framework for assessing the effectiveness of VR first-aid training in conditions of damage to civilian infrastructure. The main focus is on developing mathematical models and algorithms to identify and evaluate the quality of rescuers' actions by analysing digital interaction signals in a virtual environment. A composite efficiency indicator is proposed that combines normalised parameters for reaction time, manipulation accuracy, stress level, and immersion. The work aims to formalise a mathematical model to assess the effectiveness of VR training in developing skills for lesion identification and first aid provision, using quantitative metrics. The study aims to identify statistically significant differences in learning speed and skill retention between groups using VR simulations and traditional methods. The project aims to validate innovative content creation methods, including mobile photogrammetry, to visualise damaged infrastructure and victim models. The study used a comprehensive approach that includes mobile photogrammetry and generative neural networks to create a library of 3D assets with varying degrees of detail. Performance score is based on composite indicator that integrates normalised data on reaction time, manipulation accuracy, error count, immersion rate. Linear mixed models, exponential approximations, and bootstrap estimation of effect stability were used to analyse hierarchical data and individual learning trajectories. The experimental part includes the use of mobile photogrammetry and generative neural networks to create realistic 3D models of affected environments and identify types of injuries (bleeding, burns, unconsciousness). To analyse the dynamics of learning and maintaining skills, models with mixed effects and exponential forgetting curves are used. The results confirm that the use of VR technologies provides a statistically significant acceleration in the development of automated skills for lesion identification and assistance compared to traditional methods. The proposed approach is a scalable tool for preparing civil and rescue services to act in critical situations. Experimental data showed that the integral performance score in the VR group increased from 0.42  0.10 to 0.76  0.08, while in the control group it increased from only 0.40  0.09 to 0.55  0.10 (p < 0.001). The largest effect was observed in the bleeding arrest scenario, where the effect size (Cohen's d) reached 2.3. The analysis of forgetting curves confirmed the superiority of VR: the skill loss rate in the VR group was 0.25, providing knowledge retention 1.8 times longer than in the control group (0.45). The study confirmed that VR simulations significantly accelerate the formation of automated behaviour patterns and reduce reaction time in extreme conditions. The proposed mathematical assessment model provides objective feedback and standardisation of the rescue training process. The results indicate the high practical value of introducing such tools into training programs for civilian and military structures to minimise losses in real emergencies.

In the context of the growth of information exchange in social networks, messengers, and chats, the problem of spreading disinformation and coordinated inauthentic behaviour by users is becoming increasingly relevant and poses a threat to the state's information security. Traditional manual monitoring methods are ineffective due to the scale and speed of information dissemination, necessitating the development of intelligent automated countermeasures. The paper proposes a hybrid information technology for the automatic detection of disinformation, its sources of spread, and inauthentic behaviour among chat users, combining methods of natural language processing (NLP), machine learning, stylistic and linguistic analysis of texts, and graph analysis of social interactions. Within the study, datasets of authentic and fake messages were compiled, and mathematical models and algorithms for identifying disinformation sources were developed using metrics of graph centrality, clustering, and bigram Laplace smoothing. 
Experimental studies using TF-IDF, BERT, MiniLM, ensemble methods, and transformers confirmed the effectiveness of the proposed approach. The achieved accuracy in disinformation classification is up to 89.5%, and integrating content, network, and behavioural analysis significantly improves the quality of detecting coordinated information attacks. The results obtained are both scientifically novel and of practical value. They can be used to create systems for monitoring information threats, supporting cybersecurity decision-making, fact-checking, and protecting Ukraine's information space.

In the context of globalisation of the scientific space and the growing role of scientometric indicators, the Hirsch index (h-index) remains one of the key tools for assessing scientific performance. At the same time, the influence of individual factors on the h-index varies significantly across the stages of a scientist's academic career, necessitating their comparative analysis. The purpose of this work is to conduct a comparative study of the Hirsch index and the factors that influence its formation, considering both novice and experienced scientits anaccounting for The study employed descriptive statistics, visual analysis, time-series smoothing (Kendall's method, Pollard's method, exponential and median smoothing), correlation analysis (Pearson's coefficients), and the k-means clustering method. The study was conducted on two large datasets representing novice and experienced scientists. It was found that the average h-index of experienced scientists is 37.78, approximately 2.6 times that of beginner scientists (14.59). Correlation analysis revealed a weak or negative relationship between the h-index and self-citation, with the strongest correlation observed between the h-index and co-authorship (r = 0.68–0.80). Medium identified 6 clusters, including one that unites scientific leaders with extremely high H-index values. The study's results confirm that, in the early stages of a scientific career, geographical and institutional factors play a significant role. In contrast, for experienced scientists, the Hirsch index becomes more predictable and is determined by the quality of scientific publications, the level of citation, and practical cooperation within scientific teams.