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.

The relevance of this study is driven by the urgent need to improve the effectiveness of prehospital first aid training under conditions of increased risk to civilian populations, particularly in emergency scenarios involving damage to civilian infrastructure. Traditional training approaches are limited in their ability to realistically simulate hazardous situations, objectively monitor participants’ actions, and quantitatively analyse learning dynamics. Virtual reality (VR) technologies enable the creation of fully controlled and repeatable simulation environments with automated logging of temporal, behavioural, and performance-related parameters, providing new opportunities for objective assessment of training effectiveness. This study aims to develop and experimentally validate an information technology for the quantitative evaluation of VR-based training effectiveness in developing prehospital first aid skills, compared with traditional training methods. An experimental study was conducted using a controlled design with VR and control groups, including pre-test, post-test, and delayed retention measurements. Training effectiveness was evaluated using a set of quantitative metrics, including reaction time RT, action accuracy, number of critical errors, Precision, Recall, F1-score, and a composite performance score S. Learning dynamics were analysed using exponential learning curve models, mixed-effects models for repeated measurements, parametric and non-parametric statistical tests, bootstrap confidence intervals, and effect size estimation (Cohen’s d). The results demonstrate a statistically confirmed advantage of VR-based training over traditional methods. The average reaction time for critical actions in the VR group was reduced by approximately 10–20% compared to the control group (e.g., 34 seconds vs. 40 seconds in bleeding control scenarios). Action accuracy increased from approximately 0.78 in the control group to 0.86 in the VR group, corresponding to an improvement of about 8–10%. The composite performance score S was higher in the VR group by 0.05–0.12 (on a 0–1 scale), depending on the scenario. F1-scores for automated action classification reached 0.90–0.92, and large effect sizes were observed, with Cohen’s d values up to approximately 2.3. Retention testing further indicated improved stability and long-term preservation of skills following VR-based training. The proposed information technology and experimental results support the use of VR as an effective, scalable, and data-driven approach for prehospital first aid training for civilians, emergency responders, and medical personnel in emergency and disaster-response contexts.

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.