This study presents an optimal design process for the muffler geometry to reduce the pressure drop of a motorcycle thermoelectric generator system by applying elliptical and contraction profiles. The elliptical profile is used at the junction between the front wall of the muffler and the inlet pipe as well as between the outlet pipe and the rear wall, with optimized semi-major axes, b and e, respectively. Two profiles are applied and compared at the junction between the front wall and muffler body, containing an elliptical profile with optimal semi-major axis, c, and contraction with optimal polynomial order, P-i. Finally, the contraction profiles are selected to be used at the junction between the rear wall and the muffler body, P-o. The results reveal that these profiles reduce the impact of sudden cross-sectional changes and diminish vortex formation, thus decreasing local pressure drop at the applied locations and contributing significantly to the overall pressure drop reduction of the thermoelectric generator system. Besides, the effects of optimal elliptical profiles at the inlet and outlet are more substantial than those of the optimal contraction profiles at the muffler body. The optimal parameters, b = 20mm, third-order polynomial P-i, e = 20mm, fifth-order polynomial P-o, reduce pressure drop by 43% compared to the original and nearly 8% compared to the previous. This study contributes an optimal design process to reduce pressure drop for motorcycle thermoelectric generator systems by practically applying high-efficiency aerodynamic profiles, thereby providing a foundation for designing, manufacturing, and commercializing the system.
Correlation analysis is one of the statistical methods extensively used in the processes of data analysis. Since this method is focused on the identification of the prediction potential of an attribute pair in a dataset, it creates connections between multiple types of data analysis -from descriptive, through explorative to predictive. Yet, despite its importance, the utilization of visual models in conjunction with correlation analysis remains underexplored. Therefore, the main objective of this work is the design and implementation of graphical models for correlation analysis purposes called correlation trees and correlation forests. These models are focused on the visual presentation of attribute sequences of a dataset that bear strong potential for predictive analysis purposes. After the implementation of the proposed models, correlation trees and forests are constructed over three benchmarking datasets, and the approach is evaluated from three points of view. Firstly, visualization of the correlation trees and forests is evaluated. Then the utilization of correlation trees in regression analysis is verified with the use of LOESS and SVR regressors. And lastly, the main advantages and disadvantages of the proposed approach are identified. Experiments conducted on the considered datasets show that the use of correlation trees and forests is beneficial in the interpretation of correlation analysis results and in lowering regression model error.
A sliding mode controller (SMC) fora photovoltaic (PV)-powered DC to DC buck-boost converter to supply a constant load voltage is investigated in this paper. The indirect method is used to adjust the PV source to its maximum power point by the converter and SMC. This study presents SMC without using system dynamics, models, and transfer functions as an effective and easily adjusted controller. The PV source and battery loads behave differently from linear sources and require a robust controller to accommodate various loads and PV conditions. Compared with classical PI/PID and non-sliding nonlinear controllers, SMC provides better disturbance rejection, finite-time convergence, and independence from exact model parameters. These advantages are essential for PV-powered converters, where voltage levels vary continuously. The PV-powered converter and SMC system were simulated for these conditions. Also, the setup was implemented using the digital signal processor (DSP) TMS320F28335 to verify the proposed idea and simulation results. The inductance current variation and the load voltage of the converter were used as control variables for the SMC. The system's performance was investigated for different voltage variations of the converter, and simulated using different reference voltage values with the PV module as a voltage source. Total harmonic distortions (THD) for the converter were also investigated. The effects of voltage variations were observed, and the results were compared for the stability analyses. For the stability of the whole system, the Lyapunov stability analysis was used in this study. The mathematical equations to supply these criteria are given with the small signal analysis method. The simulation and experimental results overlapped with the theoretical expectation. The performance of the DSP in processing the SMC algorithm using the current and voltage feedback signals was observed experimentally.
This study examines the use of large language models for grading assignments in computer science education. A dataset of authentic student submissions, including source code, written documents, and image-based content, was evaluated using a controlled prompt strategy designed to enforce uniform numeric scoring. The models were assessed based on their ability to produce grades aligned with human evaluations while adhering to strict output constraints. The analysis focuses on grading accuracy, consistency, and sensitivity to task structure and prompt formulation. The results indicate that language models can support grading in structured assignments with clearly defined expectations, while their reliability decreases in open-ended or loosely specified tasks. Prompt formulation influenced output stability, particularly for incomplete or ambiguous submissions. Overall, the findings suggest that large language models can assist instructors in scaling assessment, provided they are deployed with clear procedures and human oversight to maintain fairness and alignment with educational objectives.
Power networks substation control systems (substation IACS) are special, from cyber security point of view. As part of the critical infrastructure, the availability requirements of substation IACS are high. Business interruption, caused by security incidents, will have severe consequences nationwide. Protecting substation IACS is becoming more than just the financial interest of the power distribution company, it is under present political situation also a matter of state security. The research uses the terminology and follows the recommendations of ISA 62443 , in particular ISA 62443-3-3. It expresses security maturity, both present and targeted, in Security Levels (see Fig 1, definition of security levels of ISA 62443). This research analyses particularly the backup system of substation IACS. It gives a view of present security maturity of those backup systems, recommends a new level of security based on recent and expected security incidents, and offers a solution for introducing enhancements through application of a Version Control System as an automated solution for backups. In comparison to other similar research on substation automation system security, it elaborates the use of Version Control Systems from the point of view of increasing Security Level of Substation IACS backups, considering the relevant Security Requirement according to ISA/ENSI 62443. The NIS2 EU directive on critical infrastructure security also urges improvement of backup systems of substation IACS. As power distribution organizations will be declared, as essential entities according to NIS 2, the OT security controls will need enhancement.
