Azerbaijan Technical University (AzTU; Azerbaijani: Azərbaycan Texniki Universiteti) is a public university, specialized in engineering, located in Baku, Azerbaijan. The University has 9 schools and 54 departments, 884 faculty members and approximately 6500 students.
For a pipe nipple in a hydraulic system, the formation and characteristics of special dimensional chains are considered. Various design problems are solved.
The paper considers the efficiency of waterjet cutting of the die cutters used in the manufacture of parts made of Kh12 and Kh12M steels. Investigations are carried out of the waterjet cutting methods of chromium-nickel and chromium-molybdenum alloys. It is found that waterjet cutting of workpieces made of Kh12 and Kh12M steels provides up to 5.65 kg of metal savings compared with other processing methods.
Several successive crises during the first three decades of the third millennium created the premises for a world that, after expanding international relations, entered a new reality of slowbalization or deglobalization, shaping new development paradigms for national economies. In this context, where economic activity remains highly sensitive to energy market disruptions and strategic resource constraints, nations seek new opportunities to reduce their foreign dependencies through energy diversification and a green transition. Nations are seeking strategies to leverage their advantages and moderate their weaknesses. This research evaluates the relationship between energy-related features and economic growth in a complex context, describing dependency on foreign markets. Furthermore, the study discusses the effects of a selection of variables describing the green transition (energy import dependency, energy diversification, and the share of renewable energy) on economic growth. The data covers the period between 1995 and 2024 for 25 European countries. The study uses cross-sectionally ARD (CS-ARDL) for the main empirical analysis and augmented mean group (AMG) to check the robustness of the main results. Furthermore, the method of moments quantile regression (MMQR) is employed to capture the impact more precisely across various stages of countries’ development. The findings suggest a direct relationship between employment and renewable energy adoption across all quantiles. Moreover, the negative coefficient for the energy dependency in the first quantile documents an increased sensitivity of less developed economies to energy market uncertainties.
Context As generative AI (GenAI) tools such as ChatGPT and GitHub Copilot become pervasive in education, concerns are rising about students using them to complete rather than learn from coursework—risking overreliance, reduced critical thinking, and long-term skill deficits. Objective This paper proposes a design-oriented conceptual model (named Guide-AI-Ed) to support instructors in reasoning about how course and curriculum design choices may encourage responsible GenAI use in software engineering education. Method Using a design-based research approach, we applied the Guide-AI-Ed model in two contexts: (1) revising four extensive lab assignments of a final-year Software Testing course at Queen’s University Belfast (QUB), and (2) embedding GenAI-related competencies into the curriculum of a newly developed SE BSc program at Azerbaijan Technical University (AzTU). Interventions included GenAI usage declarations, output validation tasks, peer-review of AI artifacts, and career-relevant messaging. Results In the course-level case, instructor observations and student artifacts indicated increased critical engagement with GenAI, reduced passive reliance, and improved awareness of validation practices. In the curriculum-level case, the model guided integration of GenAI learning outcomes across multiple modules and levels, enabling longitudinal scaffolding of AI literacy. Conclusion The Guide-AI-Ed model has served as both a design scaffold and a reflection tool. It has helped us align GenAI-related pedagogy with SE education goals. It can offer a transferable approach to align GenAI integration with SEEd goals and can support broader curriculum innovation in response to rapidly evolving GenAI capabilities.
This paper introduces a unified control framework for coordinated operation of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs), where Model Predictive Control (MPC) is integrated with adaptive mechanisms based on artificial intelligence. Within this architecture, MPC functions as the core decisionmaking and trajectory planning component, whereas a neural network module is employed to compensate for modeling uncertainties and external disturbances. The nonlinear continuous-time dynamics of the system are examined using a Lyapunov-based analysis to ensure practical stability of the closed-loop behavior. The analysis confirms that the system trajectories remain uniformly ultimately bounded even in the presence of unmodeled nonlinear effects. Simulation results indicate that, compared to conventional MPC approaches under identical conditions, the proposed method achieves faster convergence, reduced control energy consumption, and more coordinated and smoother trajectories. In general, the incorporation of AI-driven adaptation into the MPC framework significantly improves robustness, operational efficiency, and cooperative capabilities in heterogeneous autonomous systems.
