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Article Citation - WoS: 3Citation - Scopus: 3Mitigating 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: 5Citation - Scopus: 8Convolutional 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.Article Citation - WoS: 1Citation - Scopus: 3Investigation 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 Ann-Based Maximum Power Tracking for a Grid-Synchronized Wind Turbine-Driven Doubly Fed Induction Generator Fed by Matrix Converter(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Alarabi, M.A.; Sünter, S.The integration of renewable energy sources, such as wind power, into the electrical grid is essential for the development of sustainable energy systems. Doubly fed induction generators (DFIGs) have been significantly utilized in wind energy conversion systems (WECSs) because of their efficient power generation and variable speed operation. However, optimizing wind power extraction at variable wind speeds remains a major challenge. To address this, an artificial neural network (ANN) is adopted to predict the optimal shaft speed, ensuring maximum power point tracking (MPPT) for a wind energy-driven DFIG connected to a matrix converter (MC). The DFIG is controlled via field-oriented control (FOC), which allows independent power output regulation and separately controls the stator active and reactive power components. Through its compact design, bidirectional power flow, and enhanced harmonic performance, the MC, which is controlled by the simplified Venturini modulation technique, improves the efficiency and dependability of the system. Simulation outcomes confirm that the ANN-based MPPT enhances the power extraction efficiency and improves the system performance. This study shows how wind energy systems can be optimized for smart grids by integrating advanced control techniques like FOC and simplified Venturini modulation with intelligent algorithms like ANN. © 2025 by the authors.Article Comparative Analysis of Vibration Axis Effects on Ultrasonic Vibration-Assisted Machining of Inconel 718(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Namlu, R.H.; Kilic, Z.M.Inconel 718 is widely utilized in critical engineering sectors, particularly aerospace, owing to its exceptional creep resistance, corrosion resistance, and retention of mechanical strength at elevated temperatures. However, its high hardness, low thermal conductivity, and strong work-hardening tendency make it extremely difficult to machine using conventional techniques. Ultrasonic Vibration-Assisted Machining (UVAM) has emerged as an effective strategy to overcome these limitations by superimposing high-frequency, low-amplitude vibrations onto the cutting process. Depending on the vibration direction, UVAM can significantly change chip formation, tool–workpiece interaction, and surface integrity. In this study, the influence of three UVAM modes—longitudinal (Z-UVAM), feed-directional (X-UVAM), and multi-axial (XZ-UVAM)—on the machining behavior of Inconel 718 was systematically investigated. The findings reveal that XZ-UVAM provides the most advantageous outcomes, primarily due to its intermittent cutting mechanism. Compared with Conventional Machining (CM), XZ-UVAM reduced cutting forces by up to 43% and areal surface roughness by 37%, while generating surfaces with more uniform topographies and smaller peak-to-valley variations. Furthermore, UVAM enhanced subsurface microhardness as a result of the surface hammering effect, which may improve fatigue performance. XZ-UVAM also effectively minimized burr formation, demonstrating its potential for high-quality, sustainable, and efficient machining of Inconel 718. © 2026 by the authors.
