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
The critical role of resilience in the onset of major depressive disorder and its influence through eating behaviors and psychological needs
(SAGE Publications Inc., 2025) Kiraz Avcı, İlknur; Avcı, Mehmet
Major Depressive Disorder (MDD), a condition characterized by an undertreated trajectory and frequently marked by a chronic course, is broadly acknowledged to be a critical public health issue worldwide. While substantial evidence is available showing the significant role of resilience in depression, the current understanding of mediating factors influencing this relationship in the early stages of MDD remains limited. To fill this gap in the literature, in the present study, we explored the effect of eating behaviors and basic psychological needs in a cohort of individuals newly diagnosed with MDD. The model was tested using a sample of a total of 328 Turkish individuals newly diagnosed with MDD (87% women, Mage = 31.51 ± 11.03 years). The following four psychometrically sound instruments were employed to collect data immediately following diagnosis: Brief Resilience Scale (BRS), Depression-Anxiety-Stress Scale-21 (DASS-21), Basic Psychological Needs Scale (BPNS), and Three-Factor Eating Questionnaire (TFEQ). After controlling demographic variables, the results confirmed that resilience is significantly negatively associated with depression. Uncontrolled eating behavior and autonomy, competence, and relatedness needs were found to be four mediators and partially mediated the relation of resilience with depression. The results also revealed that cognitive restriction and emotional eating behaviors did not significantly mediate this relationship. These findings suggest that early interventions targeting eating behaviors, such as promoting healthy eating patterns and addressing unmet psychological needs could strengthen resilience and reduce the risk of chronic depression in individuals newly diagnosed with MDD.
Describing biological parameters of several endemic Salmo species from the Caspian Sea and Persian Gulf basins
(Sciendo, 2025) Bayçelebi, Esra; Kurtul, Irmak; Onay, Hatice; Kaya, Cüneyt; Haubrock, Phillip J.; Turan, Davut
Salmonids are a crucial component in aquatic ecosystems due to their trophic roles in nutrient cycling. They are also significant sources of food and salmonid fisheries make important contributions to local economies, while also being of cultural and recreational importance in many regions. To investigate the biological and population parameters of Salmonids in the Caspian Sea and Persian Gulf basins of Türkiye, we studied six endemic Salmo species. We sampled individuals from Salmo araxensis, S. ardahanensis, S. baliki, S. fahrettini, S. munzuricus, and S. murathani collected between 2006 and 2021 to analyse length-weight relationships and condition factors of these species. The total length of these Salmo specimens ranged from 3.60 cm to 27.40 cm, and their total weight varied from 38.00 g to 250.02 g. A high coefficient of determination (r² ≥ 0.97) was observed in the species' populations. The study found that the growth parameters a and b varied across all populations between 0.0076-0.0118 and 3.003-3.444. The results of this study provide valuable data on endemic Salmo species in Türkiye's ecosystems and beneficial baseline information for future conservation efforts concerning these species.
SwinFishNet: A Swin Transformer-based approach for automatic fish species classification using transfer learning
(Public Library of Science, 2025) Ergün, Ebru
The fish market is a crucial industry for both domestic economies and the global seafood trade. Accurate fish species classification (FSC) plays a significant role in ensuring sustainability, improving food safety, and optimizing market efficiency. This study introduces automatic FSC using Swin Transformer (ST) through transfer learning (SwinFishNet), which proposes an innovative approach to FSC by leveraging the ST model, a cutting-edge architecture known for its exceptional performance in computer vision tasks. The ST’s unique ability to capture both local and global features through its hierarchical structure enhances its effectiveness in complex image classification tasks. The model utilizes three distinct datasets: the 12-class BD-Freshwater-Fish dataset, the 10-class SmallFishBD dataset, and the 20-class FishSpecies dataset, focusing on image processing-based classification. Images were preprocessed by resizing to 224 224 pixels, normalizing, and converting to tensor format for compatibility with deep learning models. Transfer learning was applied using the ST, which was fine-tuned on these datasets and optimized with the AdamW algorithm. The model’s performance was evaluated using classification accuracy (CA), F1-score, recall, precision, Matthews correlation coefficient, Cohen’s kappa and confusion matrix metrics. The results yielded promising CAs: 0.9847 for BD-Freshwater-Fish, 0.9964 for SmallFishBD, and 0.9932 for the FishSpecies dataset. These results underscore the potential of the SwinFishNet in automating FSC and demonstrate its significant contributions to improving sustainability, market efficiency, and food safety in the seafood industry. This work offers a novel methodology with broad applications in both commercial and research settings, advancing the role of artificial intelligence in the fish market.
Assisting the diagnosis of cirrhosis in chronic hepatitis c patients based on machine learning algorithms: a novel non-invasive approach
(John Wiley and Sons Inc, 2025) Dirican, Emre; Bal, Tayibe; Onlen, Yusuf; Sarıgül, Figen; User, Ülkü; Sari, Nagehan Didem; Yıldız, İlknur Esen; Tabak, Ömer Fehmi
Aim: This study aimed to determine the important features and cut-off values after demonstrating the detectability of cirrhosis using routine laboratory test results of chronic hepatitis C (CHC) patients in machine learning (ML) algorithms. Methods: This retrospective multicenter (37 referral centers) study included the data obtained from the Hepatitis C Turkey registry of 1164 patients with biopsy-proven CHC. Three different ML algorithms were used to classify the presence/absence of cirrhosis with the determined features. Results: The highest performance in the prediction of cirrhosis (Accuracy = 0.89, AUC = 0.87) was obtained from the Random Forest (RF) method. The five most important features that contributed to the classification were platelet, αlpha-feto protein (AFP), age, gamma-glutamyl transferase (GGT), and prothrombin time (PT). The cut-off values of these features were obtained as platelet < 182.000/mm3, AFP > 5.49 ng/mL, age > 52 years, GGT > 39.9 U/L, and PT > 12.35 s. Using cut-off values, the risk coefficients were AOR = 4.82 for platelet, AOR = 3.49 for AFP, AOR = 4.32 for age, AOR = 3.04 for GGT, and AOR = 2.20 for PT. Conclusion: These findings indicated that the RF-based ML algorithm could classify cirrhosis with high accuracy. Thus, crucial features and cut-off values for physicians in the detection of cirrhosis were determined. In addition, although AFP is not included in non-invasive indexes, it had a remarkable contribution in predicting cirrhosis. Trial Registration: Clinicaltrials.gov identifier: NCT03145844.
Performance analysis of the impact of sloped absorber dimensions on the performance of solar chimney power plants
(Springer, 2025) Benettayeb, Youcef; Benbouali, Abderrahmen; Tahri, Toufik; Cüce, Erdem
Several geometric parameters have been investigated in solar chimney power with the aim of determining the optimal geometric parameters and enhancing power generation. The aim of this study is to determine the optimal dimensions of the sloped absorber surface and analyze its impact on the power output of the system at the Manzanares plant. The sloped absorber surface studied includes a singular triangular shape where the effect of the central high and absorber slope radius is studied. To conduct this study, A Computational Fluid Dynamics CFD model developed using COMSOL Multiphysics with the k–ε turbulence model was employed. The outcomes of this study demonstrate that the maximum air velocity in the reference Manzanares design, initially measured at 15 m s−1, increases by 26% with the optimized sloped absorber configuration. Specifically, at a central height of 1.10 m and an absorber slope radius of 122 m, the maximum velocity reaches 18.9 m s−1. Furthermore, the power output rises by 34%, reaching 67 kW compared to the reference case of 50 kW.
