В данной работе представлен разработанный и сформированный набор данных, предназначенный для моделирования и прогнозирования исходов судебных дел в Республике Казахстан с применением методов машинного обучения. Основу данных составили статьи Кодекса Республики Казахстан, охватывающие широкий спектр уголовных и административных правонарушений. Сформированный корпус включает 100 000 судебных дел с различными атрибутами, такими как возраст обвиняемого, степень вины, а также наличие смягчающих и отягчающих обстоятельств. Указанный набор данных послужил основой для построения прогностических моделей, ориентированных на анализ и предсказание юридических исходов. В статье подробно рассмотрены методы формирования датасета и представлены результаты применения различных алгоритмов машинного обучения для решения задачи прогнозирования судебных решений. Актуальность исследования определяется необходимостью автоматизации правоприменительных процессов в Казахстане и повышением точности прогнозирования судебных исходов. Традиционные аналитические подходы требуют значительных временных и ресурсных затрат, а также ограничены конфиденциальностью реальных данных. Использование синтетических данных, сгенерированных на основе действующего законодательства, позволяет выявлять закономерности и разрабатывать эффективные прогнозные модели, способствующие совершенствованию правоприменительной практики. Целью настоящего исследования является создание комплексного и репрезентативного набора данных для анализа судебных дел с использованием современных методов интеллектуального анализа данных.

Linear integrators are traditionally used in motion control systems to compensate for static effects and suppress low-frequency disturbances. However, their use is inevitably accompanied by phase delays that limit the performance and robustness of control systems, especially in conditions of parametric uncertainty. In this regard, nonlinear integrators have been considered for several decades as a promising alternative that can weaken phase constraints and improve the quality of transients. In this paper, the concept of nonlinear integrators is reinterpreted in the context of self-organizing motion control of precision stages. In contrast to traditional approaches focused primarily on frequency analysis and the method of describing the function, a method is proposed for the synthesis of a self-organizing control system for nonlinear SISO objects based on catastrophe theory, namely in the class of elliptical dynamics with the property of structural stability. The control action is formed in such a way that transitions between stable modes occur due to bifurcation-conditioned self-organization, without using external switching logic. To ensure strict analytical guarantees of stability, the Lyapunov gradient-velocity vector function method is used, which guarantees aperiodic robust stability, suppression of oscillatory and chaotic modes, as well as monotonic convergence of trajectories under conditions of parameter uncertainty. The parameters of the nonlinear integrator are adapted using Self-Organizing Maps (SOM), while any parameter changes are allowed only within the regions that meet the conditions of Lyapunov stability. This approach ensures the alignment of analytical and data-oriented methods without violating the structural stability of the system. The results of numerical experiments demonstrate the superiority of the proposed method in comparison with classical linear and adaptive regulators in problems of controlling the movement of stages, especially near bifurcation boundaries and with significant parametric uncertainty. The results obtained confirm that the integration of nonlinear integrators with catastrophe theory and self-organization mechanisms forms a promising basis for the creation of robust and high-precision motion control systems of a new generation.

In the context of the digitalization of the oil refining industry, the need for intelligent methods for ana-lyzing technological processes characterized by incomplete and fuzzy information is increasing. This study addresses the problem of expert evaluation for a benzene production unit, where experts can provide only linguistic rather than quantitative assessments of the influence of input parameters on output indicators. Based on fuzzy set theory and artificial intelligence, an intelligent method of expert evaluation in a fuzzy environment (I-EE-FE) is proposed, integrating fuzzy logical inference, machine learning, and elements of the Delphi method. The proposed method enables the automatic formation of term sets and membership functions, verification of expert opinion consistency, and the construction of linguistic and fuzzy models. Using the benzene production unit as an example, it is demonstrated that the application of I-EE-FE makes it possible to develop hybrid models of the “expert knowledge + AI” type, suitable for decision-support systems and digital twins, thereby enhancing the accuracy and justification of technological process control.

The growing problems of distraction, unwillingness to learn and lack of motivation of students have become major issues in educational research in recent years. There is an increasingly common opinion in the literature that early fascination with mobile games and social networks is a key factor in reducing students’ interest in educational content. Mobile games are designed to capture attention and maintain long-term engagement through carefully crafted motivational stimuli, leading to a cycle resembling ad- diction caused by neurochemical reactions, in particular spikes in dopamine. Such hyperstimulation often leads to a chronic decrease in attention, which makes traditional educational materials less able to induce similar cognitive engagement. This study examines the application of adapted gamification in the 8th grade physics course using en- gagement strategies used in popular mobile games. Four basic principles were implemented: (1) constant engagement, when students had to solve daily tasks for 24 hours to earn points and maintain the continuity of the “streak” (continuous sequence of days of engagement), since students with the longest “streak” at the end of the quarter received rewards; (2) short cycles achievements, when successful completion of tasks gave points in the form of “stars”, while additional stars could be obtained for solving more complex tasks. The accumulation of stars opened access to achievements visible to other students, which contri- buted to the development of competition and motivation; (3) joint problem solving, when weekly difficult tasks had several possible solutions, and the overall performance of the class determined the distribution of points, encouraging teamwork and interaction with peers; (4) Self-study, where students could access any educational content at their own pace, rather than following a fixed schedule set by the teacher. The experimental study was conducted for one quarter and compared two 8th grade physics classes. The results showed that the experimental group learned the material 14% faster, completed 47% more elective and advanced tasks, and overall learned 58% more educational content compared to the control group. The results suggest that gamification strategies inspired by mobile games can significantly increase motivation, engagement and learning effectiveness in educational institutions. The study suggests that further research into integrating gamification into curricula may help address the growing problems of lack of interest, engagement, and motivation among students.

In the face of escalating environmental challenges, the role of physics education in fostering ecological responsibility demands closer scrutiny. Physics graduates often contribute to groundbreaking developments, such as renewable energy technologies or resource-intensive industrial processes, underscoring the need for curricular models that integrate both scientific rigor and environmental consciousness. This article examines how targeted instructional interventions can cultivate environmental competence and identity among physics students, thereby equipping them to anticipate and mitigate ecological impacts in their future work. Drawing upon insights from a semester-long study that engaged 153 participants—comprising students and educators—this research outlines how embedding ecological themes into standard physics coursework contributes to shifts in knowledge, attitudes, and self-perceived responsibility. Central to this inquiry is the presentation of comprehensive quantitative data, including comparative analyses of pre- and post-intervention performance, as well as participants’ reflections on ecological decision-making. Findings demonstrate statistically significant improvements in both environmental competence and identity. Students not only displayed stronger factual knowledge about energy efficiency, pollution, and climate systems but also reported deeper personal alignment with ecological values. Concurrently, educators observed improved classroom engagement and reported positive shifts in pedagogical approaches. These results suggest that physics education can serve as a powerful platform for instilling ecological responsibility, provided the curriculum intentionally aligns scientific content with sustainability priorities. Overall, this study provides new empirical evidence that harmonizing physics education with environmental imperatives yields tangible benefits for learners and educators alike. By foregrounding robust data and real-world implications, the article offers a blueprint for institutions seeking to expand the mandate of physics instruction to include ecological stewardship—a necessary step for twenty-first-century scientific and societal progress.