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Article Citation - Scopus: 1Investigation of Harmonic Losses To Reduce Rotor Copper Loss in Induction Motors for Traction Applications(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Siddique, M.S.; Ertan, H.B.; Alam, M.S.; Khan, M.U.The focus of this paper is to seek means of increasing induction motor efficiency to a comparable level to a permanent magnet motor. Harmonic and high-frequency losses increase the rotor core and copper loss, often limiting IM efficiency. The research in this study focuses on reducing rotor core and copper losses for this purpose. An accurate finite element model of a prototype motor is developed. The accuracy of this model in predicting the performance and losses of the prototype motor is verified with experiments over a 32 Hz–125 Hz supply frequency range. The verified model of the motor is used to identify the causes of the rotor core and copper losses of the motor. It is found that the air gap flux density of the motor contains many harmonics, and the slot harmonics are dominant. The distribution of the core loss and the copper loss is investigated on the rotor side. It is discovered that up to 35% of the rotor copper losses and 90% rotor core losses occur in the regions up to 4 mm from the airgap where the harmonics penetrate. To reduce these losses, one solution is to reduce the magnitude of the air gap flux density harmonics. For this purpose, placing a sleeve to cover the slot openings is investigated. The FEA indicates that this measure reduces the harmonic magnitudes and reduces the core and bar losses. However, its effect on efficiency is observed to be limited. This is attributed to the penetration depth of flux density harmonics inside the rotor conductors. To remedy this problem, several FEA-based modifications to the rotor slot shape are investigated to place rotor bars deeper than the harmonic penetration. It is found that placing the bars further away from the rotor surface is very effective. Using a 1 mm sleeve across the stator’s open slots combined with a rotor tapered slot lip positions the bars slightly deeper than the major harmonic penetration depth, making it the optimal solution. This reduces the bar loss by 70% and increases the motor efficiency by 1%. Similar loss reduction is observed over the tested supply frequency range. © 2025 by the authors.Article Citation - WoS: 1Citation - Scopus: 1Mitigating Student Cynicism for Sustainable Academic Performance: University Identification and Academic Self-Efficacy(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Tosun, B.; Çetin, F.This study explores the complex relationships among student cynicism, student–university identification, academic self-efficacy, and academic performance within the context of Turkish higher education. Drawing on social identity and social cognitive theories, student cynicism is examined through four dimensions: academic, policy-related, institutional, and social. Survey data were collected from 630 university students in Ankara, Türkiye, using a cross-sectional design and self-reported measures. The results indicate that institutional cynicism is the strongest negative predictor of student–university identification, while academic cynicism shows a curvilinear (U-shaped) relationship with academic performance, suggesting that extreme cynicism may paradoxically be linked to modest performance rebounds. Contrary to expectations, student–university identification does not significantly predict academic performance, nor does it mediate the relationship between cynicism and performance. However, academic self-efficacy moderates the relationship between identification and performance, amplifying the benefits of identification for students with higher levels of self-efficacy. These findings offer culturally grounded insights into student disengagement and highlight the importance of fostering trust, transparency, and self-efficacy to support student well-being and academic resilience, key elements in advancing Sustainable Development Goals 4 (Quality Education) and 8 (Decent Work and Economic Growth). © 2025 by the authors.Article Citation - WoS: 3Citation - Scopus: 5Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Maiga,B.; Dalveren,Y.; Kara,A.; Derawi,M.Vehicle classification has an important role in the efficient implementation of Internet of Things (IoT)-based intelligent transportation system (ITS) applications. Nowadays, because of their higher performance, convolutional neural networks (CNNs) are mostly used for vehicle classification. However, the computational complexity of CNNs and high-resolution data provided by high-quality monitoring cameras can pose significant challenges due to limited IoT device resources. In order to address this issue, this study aims to propose a simple CNN-based model for vehicle classification in low-quality images collected by a standard security camera positioned far from a traffic scene under low lighting and different weather conditions. For this purpose, firstly, a new dataset that contains 4800 low-quality vehicle images with 100 × 100 pixels and a 96 dpi resolution was created. Then, the proposed model and several well-known CNN-based models were tested on the created dataset. The results demonstrate that the proposed model achieved 95.8% accuracy, outperforming Inception v3, Inception-ResNet v2, Xception, and VGG19. While DenseNet121 and ResNet50 achieved better accuracy, their complexity in terms of higher trainable parameters, layers, and training times might be a significant concern in practice. In this context, the results suggest that the proposed model could be a feasible option for IoT devices used in ITS applications due to its simple architecture. © 2023 by the authors.
