The object of this study is the municipal solid waste management system within a modern urban environment, where rapid urbanization and population growth pose significant challenges to ecological sustainability. The key problem addressed is the inefficiency of waste collection due to overflowing containers, poor route planning, and suboptimal resource allocation. To tackle these issues, an intelligent waste monitoring system has been developed that integrates Internet of Things (IoT) technologies, computer vision, data analytics, and a Regional Geographic Information System (RGIS). The system includes a computer vision model that analyzes images of waste containers to determine their fill level. Fine-tuning the model on locally collected image data, reflecting regional characteristics such as lighting, container types, and weather conditions, significantly improved detection accuracy and adaptability. Route optimization for waste collection is implemented using a mathematical formulation of the Traveling Salesman Problem (TSP), solved via Mixed Integer Linear Programming (MILP), which helped reduce fuel consumption, travel time, and staff workload. Integration with RGIS and GPS enables dynamic routing and real-time geospatial visualization of operational data. The proposed system forms a closed-loop control cycle that links automated detection, spatial analysis, and decision-making. Experimental results demonstrate high efficiency, adaptability to regional conditions, and scalability, confirming the system’s practical applicability to other urban areas. In the future, the system may be expanded to include environmental monitoring modules such as air quality, noise, and soil conditions and predictive modeling of waste generation, thereby supporting the sustainable development of smart city infrastructure

Modern challenges of the digital transformation of education require the development of new approaches to assessing academic success and monitoring students' educational trajectories. This study presents a functional model of the data analytics system and visualization of the digital profile of a graduate of Astana IT University (AITU), based on the Integrated IGPA (Integrated Grade Point Average) indicator, which combines the academic, research, and social achievements of students. The aim of the work is to create a system of analytics, visualization, and interpretation of data reflecting the comprehensive development of students and their readiness for professional activity. The theoretical part examines modern approaches to educational analytics in higher education. A critical analysis of scientific sources, including research on learning analytics, educational data mining, and the formation of digital profiles of students, was carried out. The emphasis on technical aspects and insufficient connection with educational practice reveals the main limitations of the existing models. The empirical part uses anonymized data from AITU students for 2022–2024, covering the indicators of Grade Point Average (GPA), Indicators of Research-Oriented Study (iROS), and Social Competition Indicators (SSCI). Dashboards built with the help of Power BI made it possible to visualize and interpret educational trajectories. The use of machine learning algorithms (K-means clustering, PCA analysis) ensured the typologization of student profiles. Using Python and the scikit-learn, seaborn, and pandas’ libraries allowed us to deeply explore the relationships between IGPA components. The results of the study demonstrate the possibilities of personalized academic support, strategic management of educational processes, and increased transparency of student achievement. The developed model can serve as a basis for making managerial decisions and improving the quality of educational programs in the context of digital transformation. The proposed approach can be scaled and adapted to other educational institutions, regardless of their size and specialization. Flexibility in integrating additional indicators reflecting the unique goals and values of a particular educational environment facilitates the model's versatility.

This study focuses on climate-dependent production processes, in particular grain crop yields, under conditions of climatic variability and uncertainty in Northern Kazakhstan. The problem addressed is the low effectiveness of deterministic risk monitoring approaches due to limited predictive power and the lack of formalized risk criteria, which leads to unreliable decision-making under uncertainty. The results include the development of a three-state hidden Markov model (S0–S2) and a TO-BE architecture for continuous risk monitoring and decision support. The model enabled the identification of latent climatic regimes and probabilistic assessment of risk states for 2025. The highest probability of an unfavorable regime was observed in Korgalzhyn (61.2%) and Ereymentau (58.8%), while Arshaly (42.9%) and Zhaksy (38.1%) showed moderate risk levels. The Brier score ranged from 0.106 to 0.199, confirming acceptable calibration of probabilistic estimates. The key feature of the approach is the representation of climate-dependent processes as transitions between latent probabilistic states, allowing the capture of temporal dependencies (climate memory) and the persistence of unfavorable conditions. Unlike deterministic models, the proposed framework enables dynamic risk tracking through continuously updated probability estimates integrated into a monitoring loop. The advantage of the approach lies in combining probabilistic modelling with an operational architecture, where risk probabilities serve as formalized decision-support signals. The results can be applied in early warning systems and digital monitoring platforms using remote sensing and IoT

This paper investigates the generation of deterministic chaos in nonlinear spacecraft control systems using the gradient–velocity method of Lyapunov vector functions. Unlike traditional Lyapunov-based approaches, which lack a universal method for constructing functions, the proposed approach provides explicit algebraic inequalities that determine the conditions for chaotic behaviour. A nonlinear spacecraft model with linear control laws is analysed, incorporating actuator dynamics and measurement uncertainties. Numerical simulations demonstrate the transition between robustly stable and chaotic regimes under variations of system parameters. The results are validated through phase portraits and transient responses, and further supported by quantitative chaos indicators such as Lyapunov exponents and bifurcation analysis. The study shows that when robust stability boundaries are preserved, the system remains periodically stable, whereas violation of these conditions leads to deterministic chaos with the formation of strange attractors. Compared with existing methods, the gradient–velocity approach offers a systematic framework applicable to high-dimensional nonlinear systems. The proposed methodology can be extended to the synthesis of spacecraft attitude control systems, where maintaining stability under uncertainty is critical. This work contributes to the theoretical understanding and practical mitigation of chaos in aerospace applications.

Evaluating Key Performance Indicators (KPIs) of faculty and staff is critical to ensuring accountability and promoting institutional effectiveness in higher education. However, the management of these processes often relies on manual, error-prone systems, creating significant administrative burdens. This study addresses these challenges by presenting a novel, replicable framework for translating complex institutional regulations into an automated, multi-stakeholder KPI management system. We detail the design and implementation of a web-based platform at Astana IT University, which was developed by programmatically encoding the institution's official KPI calculation and validation rules. The system features a multi-perspective analytical ecosystem, providing role-specific dashboards for faculty, review committees, department heads, and central administration to support synchronized decision-making. The core scientific contribution is a holistic methodology that combines stakeholder-driven requirements analysis with a "Policy-as-Code" approach to create a transparent, auditable, and scalable solution. Preliminary results indicate significant improvements in efficiency and data accuracy, demonstrating the framework's effectiveness. This study contributes not only a practical solution for KPI management but also a validated methodological blueprint for digital transformation applicable to other higher education institutions facing similar regulatory and administrative complexities. Future work will explore the integration of predictive analytics to enable early intervention in cases of underperformance. Additional modules such as goal-setting tools, peer comparison features, and customizable reporting templates are also planned to enhance usability and strategic planning capabilities. By fostering a data-driven culture and ensuring alignment with institutional goals, such systems can play a key role in long-term academic quality assurance and workforce development.

The object of the research is a monitoring and analysis system for software quality based on user feedback collected from open-source projects on GitHub. The problem addressed is the lack of effective automated tools that can process large volumes of unstructured user feedback to identify quality issues, prioritize tasks, and detect negative trends in real time. Traditional quality assurance methods, while important, fail to capture the nuance of user sentiment and the contextual details present in natural language feedback, leading to delays in problem detection and resolution. The developed system integrates three key modules: sentiment analysis for assessing user satisfaction, issue categorization for structuring feedback into actionable types, and anomaly detection for identifying sudden changes in sentiment or feedback dynamics. The results show that transformer-based models, particularly fine-tuned BERT, outperform rule-based and traditional machine learning approaches in both accuracy and robustness. This advantage is explained by their ability to capture domain-specific language, sarcasm, and contextual dependencies, enabling more precise interpretation of complex feedback. The anomaly detection component, using LSTM autoencoders and Isolation Forest, demonstrated the ability to identify critical quality regressions up to two days before official issue reporting. These results can be applied in practice for continuous software quality monitoring in agile, open-source, or user-centric development environments where timely, data-driven decision-making is essential. The approach supports real-time insight generation, helping development teams respond proactively to quality risks and improve overall user satisfaction