This article analyzes the current state of railway automation and telemechanics systems operating on the railway network of Kazakhstan. It identifies their critical indicators in terms of operational lifespan and reveals key issues within the mainline network. The division of station systems into zones of criticality based on operational lifespan is conducted considering the class of the station and the cargo intensity of maneuvering areas. The article proposes the development of a national microprocessor-based system for centralizing switches and signals as a solution to the identified existing problems. For this purpose, the authors have developed and proposed an operational algorithm for the control controller of the microprocessor-based centralization system using HIMA CENELEC SIL4 (controllers compliant with Safety Integrity Level 4 (SIL 4) according to CENELEC standards by HIMA) controllers to manage switches and signals at stations. The authors also present the architecture and key features of the developing microprocessor-based system. Comprehensive indicators of the functional safety of the national microprocessor centralization system with modular architecture (hereinafter - KZ-MPC-MA) are defined to demonstrate compliance with the SIL4 according to CENELEC (European Committee for Electrotechnical Standardization) and TR/CU (Technical regulations in force within the Customs Union) requirements.

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.

The article addresses the challenges of modernizing Kazakhstan’s railway infrastructure under conditions of technological dependence on foreign automation systems and obsolete relay-based equipment. These factors pose significant risks to economic and information security and limit the throughput capacity of level crossings. A digital system, KZ-DALCS-AI, is proposed, based on a multi-level safety architecture and the integration of artificial intelligence into monitoring and control processes. A key component is an obstacle detection and classification algorithm that considers object types (vehicles, humans and animals, foreign objects, and environmental factors) and enables intelligent real-time decision making using the KZ-ODC-AI controller with data from video surveillance, microwave sensors, and inductive loops. The system architecture, operational logic, and level crossing control algorithm are developed, including optimization of closing time by minimizing the deviation between calculated and actual values. The results of the performed calculations confirm the effectiveness of the proposed notification algorithm, ensuring the required level of safety while reducing unnecessary delays for road traffic. The implementation of the system improves throughput, reduces operational costs, enhances reliability, and minimizes the impact of the human factor.

В статье рассмотрены вопросы обеспечения надежной передачи пакетов данных в системе интервального регулирования движения поездов на базе радиоканала TETRA. Изучены наиболее часто возникающие ошибки в канале передачи, в частности потеря связи между бортовой системой безопасности и радиоблок центром. С помощью программы MATLAB смоделированы канали передачи пакетов и влияние BER (Bit Error Rate) на качество передаваемого пакета данных. Проанализированы возможные причины сбоя хандовера в TETRA и предложены возможные пути решения проблем с хандовером в сети TETRA. Так как решение таких проблем может быть сложным процессом, зависящие от многих факторов, включая конфигурацию сети, настройки оборудования, окружающую среду и многое другое простроение модели и проведение анализа и оценивания радиоканала на этой модели позволяет исследователям сэкономить материальные (расходы на оборудование, организация экспериментов) и нематериальные (время, человеческий труд) ресурсы.

This study investigates the realization and verification of safety-related functions of the KZ-MPC-MA national modular microprocessor-based railway interlocking system for Kazakhstan. The work focuses on the realization stage of the IEC 61508 safety lifecycle and establishes traceability between station-specific safety requirements, interlocking algorithms, software implementation, hardware–software integration, and functional-safety assessment. The implemented interlocking logic incorporates 29 traffic safety conditions defined for the considered station configuration. A fail-safe control architecture based on central and distributed controller modules was implemented using certified safety-related industrial controllers and the SILworX development environment. A laboratory prototype was developed to verify route-setting functions, switch and signal control, and fail-safe system response under specified operational and failure conditions. Quantitative functional-safety assessment was performed using the probability of failure on demand ($PF{D}_{\mathrm{avg}}$) as a supplementary measure and the probability of dangerous failure per hour ($PFH$) as the governing criterion for continuous/high-demand operation within the defined assessment boundary and assumptions. The obtained $PFH$ demonstrates that the investigated safety-related controller subsystem satisfies the specified SIL4 quantitative criterion. The study demonstrates the feasibility of realizing safety-related interlocking functions on the selected hardware–software platform and provides a basis for further system-level verification and validation.

The growing use of artificial intelligence (AI) in education highlights the need to distinguish teachers’ AI adoption from their professional development needs. This study examined these outcomes as separate predictive tasks using OECD TALIS 2024 data for 7360 teachers in Kazakhstan at ISCED level 2. After excluding invalid outcome records, the analytical samples comprised 2430 teachers for AI adoption and 4868 for professional development needs. Population estimates and confidence intervals were calculated using teacher weights and 100 Fay-BRR replicate weights, while predictive performance was assessed using out-of-fold predictions and school-grouped validation. The weighted prevalence of AI adoption was 59.0% (95% CI: 56.1–61.9). Logistic Regression and CatBoost demonstrated comparable discrimination (ROC-AUC = 0.754 and 0.753, respectively), with no clear performance advantage for CatBoost. SHAP analysis identified prior AI-related professional learning, uncertainty regarding AI attitudes, and perceived AI benefits as prominent predictive factors. Moderate or high professional development need was estimated at 80.3% (95% CI: 78.7–81.9). However, individual prediction was weak, with balanced accuracy of 0.268 and a quadratic weighted kappa of 0.082. These findings indicate that TALIS variables provide a moderate predictive signal for AI adoption but are insufficient for reliable, individualized professional development decisions.