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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.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.