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
Genetic diversity and population structure of 72 Turkish ficus carica (l.) genotypes assessed using SCoT markers
(Springer, 2026) Sarıkaya, Meliha Feryal; Nadeem, Muhammad Azhar; Qureshi, Sarmad Ali; Bedir, Mehmet; Tatar, Muhammed; Altaf, Muhammad Tanveer; Baloch, Faheem Shehzad
Ficus carica L. is an economically important fruit crop widely cultivated in the Mediterranean region. In this study, genetic diversity and population structure were investigated in 72 F. carica genotypes collected from the Derecik and Çukurca regions of Hakkâri province, Türkiye, using 15 highly polymorphic Start Codon Targeted (SCoT) markers. A total of 481 amplification bands were obtained, of which 475 were polymorphic, resulting in a high average polymorphism rate of 98.60%. Genetic diversity indices indicated substantial variation among the genotypes, with a mean effective number of alleles of 1.53, gene diversity of 0.31, and Shannon information index of 0.47. The average genetic distance among genotypes was 0.37, with the highest pairwise distance (0.721) observed between genotypes HC4 and HD7. Analysis of molecular variance (AMOVA) revealed that most genetic variation was distributed within populations (93%), whereas only 7% was among populations. Bayesian STRUCTURE analysis identified two distinct genetic clusters corresponding largely to geographic origin, with 22 genotypes (30.56%) classified as admixed based on a membership coefficient threshold of < 0.70. Principal coordinate analysis (PcoA) clearly separated genotypes according to their sampling locations, where Axis 1 and Axis 2 explained 24.31% and 15.07% of the total genetic variation, respectively. Overall, these findings demonstrate the effectiveness of SCoT markers in assessing genetic diversity and population structure in F. carica germplasm from southeastern Türkiye. Future studies should use codominant markers (SSRs and SNPs) expand geographic sampling, and adopt open data repositories to enhance conservation and breeding strategies.
Aircraft takeoff speed prediction with deep learning: a comparative study of MLP, 1D-CNN, LSTM and attention-based architectures on Boeing 737-300 data
(Cambridge University Press, 2026) Konar, Mehmet; Ayaz, Hüseyin Alp; Özkat, Erkan Caner; Türkmen, Aydın
Modern aviation supports an ever-broader range of civil and military missions, and the airframes designed for these missions must satisfy stringent safety and performance requirements. The takeoff and landing phases are the most accident-prone portions of a flight despite representing only a short interval of the total block time, which makes the accurate prediction of takeoff speed a safety-relevant problem. A previous machine learning study addressed the takeoff-speed prediction problem of the Boeing 737-300 with classical regressors using pressure altitude, outside air temperature, gross weight and flap angle as the predictors. In the present work, the same regression problem is revisited under the deep learning paradigm. Four neural architectures are trained on an identical pre-processing pipeline and train-validation partition, namely a multilayer perceptron, a one-dimensional convolutional network, a long short-term memory network and a wide-and-deep architecture incorporating multi-head self-attention. Among the four candidates, the long short-term memory network attains the lowest root mean square error and mean square error on the unseen test file and is subsequently subjected to Bayesian hyperparameter optimisation through the Keras Tuner library. The predicted and the measured takeoff speeds are reported side by side for the first time in the deep learning literature for this airframe, and the simulation results indicate that the developed networks constitute an effective alternative tool for takeoff-speed prediction.
Monotone functional regression with hybrid depth weighting and block-conformal prediction bands for forward realized variance paths
(American Institute of Mathematical Sciences, 2026) Sözen, Çaglar; Şeyranlioglu, Onur; Çilek, Arif; Pilatin, Abdulmuttalip
We forecast forward realized variance (FRV) paths, defined as cumulative future daily variance proxy curves over a finite trading horizon, using a leakage-disciplined functional framework for multiday risk assessment. The framework combines multiresponse ridge regression, hybrid depth weighting, horizon-weighted blocked cross-validation, and isotonic post-projection to preserve the monotone structure of FRV paths. Uncertainty is summarized through upper one-sided block-calibrated conformal bands, interpreted as empirical risk envelopes under temporal dependence rather than exact distribution-free guarantees. In a fixed panel design for four liquid exchange-traded funds, GDX, GDXJ, XLE, and UUP, over the period 2010–2025, the proposed model reduces long-horizon mean squared error relative to rolling historical FRV by approximately 31.8%, 20.4%, 36.5%, and 28.0%, respectively, over h = 20:30. Comparisons with heterogeneous autoregressive (HAR) ridge and functional principal component autoregressive (FPCA-AR) benchmarks are asset-dependent. The proposed model is most favorable for GDX and remains close to HAR ridge for GDXJ, whereas HAR ridge and FPCA-AR remain competitive for XLE and UUP. Coverage is conservative or close to nominal at α = 0.05 but more heterogeneous at α = 0.10. Robustness checks support a cautious interpretation of the method as a shape-aware enhancement of rolling FRV forecasting.
Predicting survival in non-small cell lung cancer treated with nivolumab: the role of spleen and bone marrow 18 F-FDG PET/CT parameters
(Springer, 2026) Bülbül, Ogün; Aydın, Esra; Nak, Demet; Bülbül, Hande Melike; Alyıldız, Neşe
Objective: Fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) may provide prognostic information in patients with non-small cell lung cancer (NSCLC) receiving immunotherapy. We investigated the predictive value of spleen and bone marrow metabolic parameters on 18F-FDG PET/CT for response to nivolumab, progression-free survival (PFS), and overall survival (OS) in stage IV NSCLC. Methods: Selected metabolic parameters of the spleen (such as baseline/post-treatment total spleen glycolysis [TSG]) and bone marrow (baseline and post-treatment standardized uptake values [SUVs]) were retrospectively evaluated on baseline and post-treatment PET/CT scans in 61 patients. The effects of all parameters on PFS and OS were examined. Results: Baseline spleen and bone marrow metabolic parameters did not differ significantly between responders and non-responders to nivolumab. The median OS was longer for patients with lower post-treatment TSG (27.0 vs. 19.6 months, p = 0.036) and baseline spleen volume (26.9 vs. 18.2 months, p = 0.022). The median OS was longer in patients whose post-treatment bone marrow SUVmean (25.3 vs. 15.7 months, p = 0.007) and spleen-to-liver SUVmax ratio (SLR_SUVmax) were lower than their baseline values (25.1 vs. 18.3 months, p = 0.043). Baseline spleen volume was the only independent predictor of PFS (hazard ratio [HR] = 1.02, p = 0.039). Age (≥ 70 years) (HR = 1.08, p = 0.019), baseline neutrophil-to-lymphocyte ratio (HR = 1.42, p = 0.012), and post-treatment decreases in bone marrow SUVmean (HR = 0.12, p = 0.001) and SLR_SUVmax (HR = 0.16, p = 0.042) from baseline were independent predictors of OS. Conclusions: Splenic glucose metabolism, baseline spleen volume, and bone marrow glucose metabolism were associated with survival after nivolumab in patients with metastatic NSCLC. Baseline spleen volume was the only independent predictor of PFS. Age, baseline neutrophil-to-lymphocyte ratio, and decreased bone marrow SUVmean and SLR_SUVmax after nivolumab were identified as independent predictors of OS.
Pollution status of microplastics in freshwater basins of Türkiye: evidence from all Barbus (Daudin, 1805) species
(Springer, 2026) Bayçelebi, Esra; Mutlu, Tanju; Karslı, Barış; Turan, Davut; Kaya, Cüneyt; Terzi, Yahya; Gedik, Kenan
Microplastic (MP) pollution in freshwater ecosystems is a growing global concern, yet long-term data tracking its historical progression remains scarce. This study provides a retrospective analysis of MP ingestion in 12 Barbus species collected from five major hydrological basins in Türkiye between 2004 and 2018. A total of 373 collection specimens were examined to quantify MP abundance, characterize polymer types using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, and evaluate spatio-temporal patterns. Microplastics were detected in 19.8% of the individuals, with an average abundance of 0.27 MP/individual. The Caspian Sea basin exhibited the highest observed MP concentrations (1.67 MP/individual), though no statistically significant differences were determined among basins. Fibers were the dominant morphology, and ethylene–vinyl acetate and polyamide were the most frequent polymer types. Notably, MP ingestion was persistent throughout the study period, indicating a chronic presence of plastic in Turkish freshwater systems rather than a significant temporal trend. The results suggest that benthic Barbus species serve as suitable indicators for monitoring sediment-associated MP pollution, although their capacity to differentiate localized pollution gradients requires further investigation. This study underscores the role of museum collections in bridging historical data gaps and provides a baseline for future freshwater management strategies.
