Recep Tayyip Erdoğan Üniversitesi Kurumsal Akademik Arşivi
DSpace@RTEÜ, Recep Tayyip Erdoğan Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Logical qubit initialization and measurement in the heavy-hexagonal lattice
(Institute of Electrical and Electronics Engineers Inc., 2025) Özkan, Aziz Kerem; Kakiz, Muhammet Talha; Güler, Erkan; Cavdar, Tuğrul
Improvements in quality of life and technological advancement are often accompanied by increasingly complex societal challenges. When formulated as computational problems, these challenges can exceed the processing capacity of classical computing. Quantum algorithms can solve these problems more efficiently by leveraging the principles of superposition and entanglement. However, in practice, quantum computation is highly susceptible to qubit errors, which can compromise the results. Various quantum error correction methods were proposed to eliminate these errors. In this paper, the application of one such method-the surface code-to the heavy-hexagonal quantum processor layout is studied and the future research directions are highlighted.
Self-compassion as a spiritual shield: young adults in the shadow of social appearance anxiety
(2025) Karataş, Zeki; Karataş, Duygu
This study examined the predictive role of self-compassion on social appearance anxiety among university students, a concern heightened by social media pressures. Employing a correlational research design, the study included 402 university students recruited through convenience sampling. Data were collected using the Self-Compassion Scale-Short Form and the Social Appearance Anxiety Scale. Analyses revealed a moderate, negative, and significant relationship between self-compassion and social appearance anxiety (r = -.50, p <.001). A simple linear regression analysis indicated that self-compassion explained 25% of the variance in social appearance anxiety (R2 =.25) and was a significant negative predictor (β = -.50). Analyses also revealed that participants with higher body dissatisfaction and self-criticism reported significantly higher levels of social appearance anxiety. The findings strongly support that self-compassion serves as a key psychological resource and a ‘spiritual shield’ against social appearance anxiety for young adults. These results underscore the importance of implementing self-compassion-based interventions to support the mental health of young adults.
A vision transformer-based deep learning approach for lemon leaf disease detection
(Institute of Electrical and Electronics Engineers Inc., 2025) Ergün, Ebru; Okumuş, Hatice
Early detection and effective management of lemon leaf diseases play a critical role in modern agricultural practices. This study explores the potential of the Vision Transformer (ViT) model for classifying lemon leaf diseases, evaluating the success of deep learning-based approaches in this domain. A comprehensive performance analysis was conducted to assess the model's ability to accurately distinguish between various disease types. Experimental findings demonstrate that the ViT model outperforms other models with an accuracy rate of 99.32%. Furthermore, the results surpass previously reported accuracy rates in the literature by 0.76%, proving the proposed method to be more effective than existing approaches. The model's performance was evaluated in detail based on classification accuracy, confirming that the ViT model offers high precision in detecting lemon leaf diseases. The findings of this study highlight the critical importance of automation in agricultural disease detection and contribute significantly to disease management processes by reducing the need for manual observation. Early detection of diseases enables more targeted interventions, reduces unnecessary chemical usage, promotes environmental sustainability, and enhances crop productivity.
Sarcopenia is a bad harbinger of cancer-related survival in rectal cancer
(Taylor and Francis Lt, 2025) Rakıcı, Sema Yılmaz; Aksoy, Rahmi Atil; Burakgazi, Gülen; Terzi, Özlem; Aydın, Esra; Yazıcı, Zihni Açar; Özbek Okumuş, Nilgün
Background/Objectives: Sarcopenia, characterized by the progressive loss of skeletal muscle mass and function, has been linked to poor oncological outcomes. This study aimed to assess the relationship between sarcopenia—defined through combined radiological and biochemical assessments—and survival outcomes in patients with rectal cancer. Methods: Sarcopenia was evaluated using radiological measurements of skeletal muscle mass, visceral and subcutaneous fat tissue volumes, and biochemical parameters including albumin, protein, and Fib-4 index levels. Results: Deceased patients were older than survivors (mean 70 vs. 63years). Elevated Fib-4 scores (3.0–4.9) were mainly observed in non-operated patients with poor tumor regression. Post-treatment albumin levels were significantly higher in patients with complete response (42.0±3.5mg/dL) than in those with regression score-3 (37.3±8.7mg/dL) and non-operated patients (34.9±8.3mg/dL; p < 0.001). Pre-treatment skeletal muscle mass, subcutaneous fat, and visceral fat volumes were greater in survivors (22.3±7.0cm3 vs 19.2±7.0cm3, 27.7±20.3cm3 vs 18.7±14.0cm3 and 49.7±37.8cm3 vs 29.9±20.5cm3 respectively) than in deceased patients (p < 0.05). Larger tissue volumes—muscle≥16.95cm3, visceral fat≥39.35cm3, and subcutaneous fat≥17.65 cm3 were associated with longer overall survival. In univariate analysis, older age, low albumin, high Fib-4 index, and reduced tissue volumes predicted poorer survival, while multivariate analysis identified low post-treatment albumin as the only independent prognostic factor (HR 0.28, 95% CI:0.12–0.65, p = 0.003). Conclusions: Sarcopenia is associated with decreased overall survival in rectal cancer. In patients receiving neoadjuvant therapy, lower volumes of muscle mass, subcutaneous fat, and visceral fat, together with lower albumin and protein levels and higher Fib-4 scores, may serve as predictive markers of sarcopenia.
Robustness of SEViT and MedViTV2 models under MI-FGSM attacks and the effect of adversarial training
(Institute of Electrical and Electronics Engineers Inc, 2025) Akıncı Hazır, Rukiye; Ayas, Selen
Although deep learning-based classification models in the field of medical imaging often achieve high accuracy rates, they still pose significant security risks in clinical applications. This indicates that such models remain vulnerable to adversarial attacks. This study systematically investigates the performance of SEViT and MedViTV2 models under the Momentum Iterative Fast Gradient Sign Method (MI-FGSM) attack and examines the change in the robustness of these models following MI-FGSM-based adversarial training. The experiments show that the SEViT model achieved an accuracy rate of 90.00% on clean data, while the MedViTV2 model achieved an accuracy rate of 86.76%. However, when the MI-FGSM attack was applied, the accuracy rates of both models dropped sharply, even decreasing to 0%, rendering them almost non-functional. This clearly demonstrates how vulnerable deep learning models trained with conventional methods are to iterative adversarial attacks. After adversarial training with MI-FGSM, the defended models were again subjected to the MI-FGSM attack. In this case, the robustness of both models increased significantly. The accuracy rate increased noticeably for both SEViT and MedViTV2 models. However, although a decrease in accuracy was observed as the epsilon value increased, there was not a dramatic collapse as seen in the undefended models. In particular, the SEViT model demonstrated higher performance than the MedViTV2 model under the MI-FGSM attack after adversarial training, with an accuracy of 81.33%. The findings obtained indicate that adversarial training is an effective method for enhancing the security and robustness of models such as SEViT and MedViTV2 for clinical applications.
