Emil Andekin

Global university rankings differ in methodology, yet their published positions are frequently compared across systems and monitored over time. This study examines two dimensions of ranking stability: cross-sectional consistency across ranking systems and temporal persistence within QS, while assessing whether machine-learning models improve predictive accuracy beyond a persistence baseline. The analysis included 57 universities from 20 countries and seven regions. Cross-ranking consistency among QS-2026, THE-2026, and ARWU 2025 was evaluated using pairwise rank correlations, bootstrap confidence intervals, and normalized percentile positions. Temporal predictability was assessed on a balanced panel of 40 universities with QS positions for 2021–2024 and 2026 using an expanding-window design, a persistence baseline, linear regression, Random Forest, XGBoost, and LightGBM. Pairwise correlations were positive within observed overlaps, but the ranking systems were not interchangeable. The persistence baseline achieved the lowest development MAE. On the final two-year holdout, Random Forest (MAE 5.073) did not significantly outperform persistence (MAE 5.150; p = 0.902), and the latest QS position was the only predictor with consistently positive importance. Historical persistence errors bracketed the 2024–2026 result, indicating that predictability was driven mainly by ranking persistence, whereas additional machine-learning complexity provided limited benefits in this sample.

This study presents a methodology for cross-ranking analysis to evaluate the consistency of universities’ positions across the QS World University Rankings, Times Higher Education (THE) World University Rankings, and Academic Ranking of World Universities (ARWU). Data for 2022–2025 were harmonized by university names, countries, years, and ranking positions, resulting in a unified dataset of 316 observations. Ranking agreement was assessed using the overlap of ranked lists, a Spearman distance–based similarity measure, and a normalized inverse-rank metric. QS rankings were treated as the response variable, while THE and ARWU positions served as predictors. Model performance was evaluated using leave-one-year-out cross-validation for a median baseline, the average of THE and ARWU ranks, linear regression, Random Forest, and XGBoost. Random Forest achieved the lowest mean absolute error (MAE = 24.80 ± 3.26 ranking positions), whereas linear regression best preserved relative ordering (Spearman’s ρ = 0.794 ± 0.062). Additional analyses of the TOP-50, TOP-100, and TOP-200 subsets showed that prediction accuracy decreases as ranking depth increases. The findings demonstrate that QS, THE, and ARWU provide complementary rather than interchangeable assessments of university performance. The proposed framework is intended for comparative cross-ranking analysis rather than forecasting future university rankings.

This paper investigates the application of artificial intelligence and natural language processing techniques for the early detection of critical behavioral risks based on textual data analysis. A transformer-based BERT architecture is employed as the core model, enabling context-aware identification of latent linguistic indicators of psycho-emotional distress. Experimental validation demonstrates high sensitivity of the proposed system and confirms its effectiveness for early warning tasks related to critical behavioral risks. From an engineering perspective, particular attention is given to computational efficiency and the energy-related feasibility of deploying pre-trained models when scaling digital preventive platforms. It is shown that reducing repeated training costs and optimizing inference processes are key factors for integrating intelligent systems into distributed information environments of educational institutions and urban digital infrastructures. The obtained results confirm the potential of the proposed approach for developing energy-efficient and sustainable decision-support systems in the field of psycho-pedagogical support.

This study aims to improve the accuracy, speed, and safety of suicide risk assessment among adolescents in the digital ecosystems of smart cities. To achieve this goal, an integrated system architecture was developed that combines natural language processing methods, transformer models, and privacy-preserving computation. The methodological part includes large-scale textual data analysis, distributed processing in Apache Spark and Hadoop environments, and the use of federated learning, which allows models to be trained without transferring sensitive source information. The evaluation was conducted on open mental health datasets and supplemented by a series of experiments simulating the system's operation in real time, as well as surveys of specialists – psychologists, educators, and IT experts. The analysis showed that transformer models, particularly BERT, significantly outperform classical algorithms, achieving an AUC-ROC of 0.96 and an F1 score of 0.92 with an average response time of 2.4 seconds. Survey participants noted the importance of transparency and data protection, and the proposed architecture received high marks for reducing the risk of information leaks and providing robust audit mechanisms. The novelty of the work lies in the combination of predictive analytics, federated learning, differential privacy, and blockchain traceability in a single application-oriented system. The results show that ethically sound and rapid suicide risk detection can be implemented in schools, medical institutions, and municipal services, providing both practical benefits and contributing to methodological advancements.