Using Machine Learning (ML) in educational management transforms higher education strategy. This study examines students’ views on machine learning (ML) technologies and how they might be used to plan, monitor, and predict student performance. The Faculty of Industrial Engineering and Robotics surveyed 118 third-year undergraduates. It featured closed- and open-ended questions to collect quantitative and qualitative data. Descriptive statistics showed broad patterns, inferential tests (Chi-square, t-test, ANOVA) showed group differences, regression models predicted school outcomes, and exploratory factor analysis (EFA) and clustering found hidden attitudes and student profiles. A multi-method quantitative approach combining descriptive statistics, inferential tests, regression modeling, and exploratory techniques (EFA and clustering) was employed. The findings show that most students realize that ML may help them be more productive, adapt their study pathways, and learn about the future. Concerns remain regarding its accuracy, overreliance, and morality. The findings indicate that ML can both support and challenge educational management, depending on how responsibly it is implemented. Results show that institutions may utilize ML as a strategic tool to boost academic progress and make better judgments, provided they incorporate it responsibly and follow ethical rules and training.

Detonation-based combustion has re-emerged as a promising pathway for enhancing the efficiency and compactness of future aerospace propulsion systems, motivated by the intrinsic pressure-gain characteristics of detonative heat release. This review provides a comprehensive synthesis of the physical foundations, technological progress, and practical limitations associated with pulse detonation engines, rotating detonation engines, and standing or oblique detonation wave concepts. By tracing the evolution from early theoretical models and laboratory-scale demonstrations to engine-relevant configurations, this article highlights how detonation physics, ignition mechanisms, wave stability, and flow–structure interactions collectively govern propulsion performance. Particular attention is paid to recent experimental and numerical studies, with the review focusing on their technological impact and on the feasibility of integrating detonation-based propulsion concepts into practical aerospace systems. The analysis evaluates these approaches’ potential to enhance system-level performance compared to conventional propulsion technologies, while highlighting key challenges associated with scalability, operability, and compatibility with existing aerospace architectures. The review further identifies emerging design strategies, including geometry tailoring, adaptive flow control, and hybrid architectures, as key enablers for extending operability and system integration. Overall, the findings indicate that future progress in detonation-based propulsion will depend less on demonstrating detonation itself and more on achieving robust, controllable, and scalable implementations suitable for realistic aerospace applications.

A Type 22 Broadsword class of frigate uses a combined gas or gas (COGOG) propulsion system that utilizes four different gas turbines, depending on the mode of operation. Its twin ST40M cruise gas turbines are operational most frequently, and an increase in their efficiency would significantly impact fuel usage and ship range. This study evaluates the option to upgrade the cruise gas turbines to a combined gas turbine–electric and steam (COGES) system, which utilizes a steam Rankine cycle to recover energy from the exhaust gases of the gas turbine. An alternative system using hot air is also analyzed for performance comparison. The analysis contains calculations for the energy extraction and dimensions of heat exchangers, for the power extracted from the additional steam or air turbine and for pressure losses of the exhaust gas. Different configurations for the heat exchangers were investigated, as well as various parameters for the steam and hot air. The size and mass of the system were an important aspect of the analysis. It was concluded that an auxiliary system operating at a maximum steam pressure of 20 bar could generate an additional 771 kW of power while adding a mass of 7.4 tons to the frigate. These findings suggest that upgrading to a COGES system could cover the electricity needs of the entire ship and thus reduce the overall fuel consumption, resulting in lower operational costs and less emissions.

This paper presents a Python-based v3.12 framework for robotic object sorting in a virtual workcell, combining learning-from-demonstration with a comparative evaluation of classical machine learning classifiers. A user provides a minimal demonstration (e.g., one cube and one cylinder placed into two bins) from which a dynamic type-to-bin rule is inferred. In this study, learning-from-demonstration is implemented at the level of rule acquisition from minimal task examples rather than at the level of trajectory imitation or low-level motion teaching. This rule is used to relabel a larger dataset of pre-generated object positions, enabling training with a selectable number of file-based samples (2–1600) optionally augmented with manual samples. Five classifiers—decision tree, k-nearest neighbors, logistic regression, naive Bayes, and linear SVM—were trained and then used to drive autonomous pick-and-place execution while logging replication time and correctness (correct/incorrect moves and accuracy). Because the task reaches accuracy saturation under a deterministic rule, an additional offline inference benchmark was included to compare prediction throughput using 10,000 probes with repeated timing (median over 50 runs or mean ± standard deviation over 30 runs). To complement this nominal evaluation, the framework also included a perturbation-aware robustness protocol based on controlled positional perturbation, systematic bias, controlled shape corruption, repeated perturbation voting, and stability-aware scoring. This additional layer makes it possible to examine classifier behavior under controlled uncertainty, especially in reduced-data settings, without changing the compact simulator-based nature of the workflow. Results indicate identical sorting accuracy across models, while inference-time differences remain measurable, highlighting deployment-oriented trade-offs and confirming that end-to-end cycle time is dominated by robot motion rather than model computation.

Building on the previous theoretical parametrisation of oblique detonation wave (ODW) stability limits for hydrogen–oxygen mixtures, the present work advances the investigation by combining CFD analysis with the complete mechanical design of a planar ODW test bench. While the earlier study established feasible inflow conditions and wedge-angle ranges through detonation theory and oblique shock relations, the current work focuses on the numerical characterisation of ODW-relevant flow fields and the constructive development of the experimental facility. CFD simulations were performed for seven wedge deflection angles to assess shock formation. A key finding is the identification and systematic characterisation of a recirculation-induced secondary deflection mechanism, which emerges for wedge angles above ~20°. The recirculation zone forms an effective secondary wedge whose boundary angles remain nearly constant, thereby altering the resulting shock angle and explaining the deviation from classical oblique-shock predictions. This behaviour was quantified through velocity-component sampling and shown to be consistent across multiple geometries. Based on the numerically optimised configuration, the full 3D design of the test bench is developed, including diagnostics integration, sealing solutions, and a modular wedge system enabling rapid geometry replacement. By linking theoretical analysis, numerical validation, and experimental design, the study establishes a robust foundation for forthcoming ODW experiments and contributes to the development of pressure-gain combustion concepts for rocket-type propulsion.