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
Investigation of radiation dose rates, health risks and radionuclide effects of wood and wood-based materials in indoor areas
(Springer Nature, 2026) Taşdemir, Taner; Çakıroğlu, Evren Osman; Dizman, Serdar; Kobya, Yaşar; Yeşilkanat, Cafer Mert
This study addresses the limited comparative data in the literature by systematically evaluating natural radioactivity and radiological hazard indices across 15 different solid wood species commonly used in indoor building applications. Gamma spectrometric analyses performed using a high-sensitivity HPGe detector revealed that the activity concentrations of the isotopes Ra-226, Th-232, and K-40 were in the ranges of 1.64–13.26, 0.76–9.46 and 6.08–163.66 Bq kg−1, respectively. In addition, some radiological risk parameters originating from natural radiation were also calculated for the samples examined. All obtained parameters were below the threshold values reported by international organizations, suggesting that wooden building materials are suitable for indoor use
Variability of 137Cs and natural radionuclides accumulation in mosses relative to soil activity in Iğdır, Türkiye
(Frontiers Media SA, 2026) Batan, Nevzat; Büyükuslu, Halim; Akçay, Nilay
Introduction: This study investigates the activity concentrations of 232Th, 226Ra, 40K, and 137Cs in soil and moss samples collected from locations in Iğdır Province, Türkiye to evaluate spatial patterns and radionuclide accumulation behavior. Methods: High-purity germanium (HPGe) gamma spectrometry was used to quantify radionuclide activities. Results: Statistical analyses included Shapiro–Wilk normality testing, descriptive comparisons between soil and moss, and correlation assessments. Concentration ratios (CR = Amoss/Asoil) were calculated to evaluate radionuclide accumulation patterns across species and sites. Spatial variability and multivariate structure were examined using PCA and k-means clustering to identify site- and nuclide-driven grouping patterns. Key radiological parameters calculated for the health risk analysis included absorbed gamma dose rate, internal and external hazard indices, radium equivalent activity, and annual effective dose equivalent. In moss samples, the mean activity concentrations of 226Ra, 232Th, 40K and 137Cs were measured as 13.74 ± 0.83 Bq kg-1, 13.79 ± 1.1 Bq kg-1, 244.72 ± 7.6 Bq kg-1, 129.47 ± 1.74 Bq kg-1, respectively, and in soil samples, 23.74 ± 0.82 Bq kg-1, 22.53 ± 1.11 Bq kg-1, 427.01 ± 8.95 Bq kg-1, 215.74 ± 1.83 Bq kg-1, respectively. Discussion: All calculated radiological hazard indices, derived from natural radionuclide concentrations, were within permissible recommended limits. Slightly elevated annual effective dose values and absorbed gamma dose rates are observed for the total activity concentrations of both anthropogenic and natural radionuclides, exceeding world population-weighted outdoor averages.
Robust eggplant disease recognition using a learnable weighted deep ensemble with test-time augmentation
(China Agricultural University, 2026) Ergün, Ebru; Okumus, Hatice
One of the most extensively cultivated vegetables worldwide, eggplant is significantly affected by a wide range of diseases that reduce both yield and quality. Ensuring sustainable crop production and food security therefore requires early and reliable disease diagnosis. The rapid evolution of deep learning architectures and computer vision techniques has established convolutional neural networks as powerful tools for plant disease recognition, often outperforming traditional diagnostic approaches. In this study, three benchmark eggplant image datasets with distinct class structures and imbalance characteristics (6-class Eggplant1, 5-class Eggplant2, and 7-class Eggplant3) were utilized to develop a robust disease classification framework. The proposed methodology introduces a learnable weighted ensemble that adaptively integrates ConvNeXt, DenseNet, and EfficientNet architectures within an end-to-end trainable fusion scheme. The framework is further reinforced by systematic test-time augmentation and cross-validation to enhance inference stability. Experimental evaluation demonstrates that the ensemble model achieves accuracies of 0.9970, 0.9375, and 0.9557 on Eggplant1, Eggplant2, and Eggplant3, respectively, while maintaining balanced performance across heterogeneous class distributions. These results confirm the effectiveness of the adaptive ensemble fusion in capturing complementary feature representations and mitigating inter-class variability across datasets with differing levels of complexity. Overall, this work contributes a methodologically robust and interpretable ensemble-based framework that advances more dependable image-based plant disease diagnosis by addressing practical reliability concerns in precision agriculture applications.
Large language models for cochlear implant education: A comparison of ChatGPT, Gemini, Claude, and DeepSeek
(Wiley, 2026) Birinci, Mehmet; Kilictas, Ahmet Ufuk; Kilictas, Ahmet Ufuk; Yemiş, Tuğba; Erdivanlı, Başar; Çeliker, Metin; Çelebi Erdivanlı, Özlem; Dursun, Engin
Objective: Artificial intelligence-supported large language models (LLMs) have become increasingly widespread in recent years in the health communication and patient education. Models such as ChatGPT, Claude, Gemini, and DeepSeek are used to provide information on complex medical topics, thanks to their natural language processing capabilities. This study compares the responses of models to 5 frequently asked questions about cochlear implants in terms of content and communication quality. Study Design: Comparative analysis of 4 LLMs using expert-evaluated responses to cochlear implant queries. Setting: Virtual simulation with blinded specialist assessments. Methods: Five of the most frequently searched cochlear implant questions on Google were selected. Each question was individually posed to ChatGPT-4, Gemini 2.0, Claude 3.7, and DeepSeek v3. The responses from each model were evaluated by 5 otolaryngology specialists using a 5-point scale based on content accuracy and communication appropriateness. One-way ANOVA and post hoc tests were used for statistical analysis. Results: Statistically significant differences were identified among the models in both content and communication quality (P <.05). The DeepSeek model achieved the highest average scores in both areas, while the Claude model generally received the lowest scores. ChatGPT-4 demonstrated a balanced performance, while Gemini stood out in certain communication criteria. Conclusion: This study is one of the first comparative analyses evaluating the performance of 4 different large language models in the context of patient education about cochlear implants. Although some models appear more suitable for patient education, the findings indicate that these systems still have limitations when used without expert oversight.
An evaluation of immature granulocytes as predictors of malignancy in patients with atypia of undetermined significance thyroid nodules
(Georg Thieme Verlag, 2026) Tüfekçi, Damla; Güçer, Hasan
This retrospective study aimed to evaluate hematological and inflammatory markers as predictors of thyroid cancer in patients with atypia of undetermined significance thyroid nodules. A total of 174 patients with atypia of undetermined significance who underwent thyroidectomy were included. Pre- and postoperative immature granulocyte counts, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio were analyzed after achieving euthyroid status. Propensity score matching for age and gender resulted in a final cohort of 128 patients (64 benign and 64 malignant). Static preoperative and postoperative immature granulocyte values did not differ significantly between the benign and malignant groups; however, the delta immature granulocyte value, defined as the change between pre- and postoperative measurements, was significantly lower in malignant cases (p =0.007). Receiver operating characteristic analysis demonstrated an area under the curve of 0.651 at a cut-off value of≤- 0.01, with a sensitivity of 46.2% and a specificity of 79.2%. Univariate logistic regression revealed that delta immature granulocytes independently predicted malignancy in the overall cohort (odds ratio=3.273 and p =0.007) and in patients younger than 55 years (odds ratio=5.082 and p =0.007), whereas this association was not observed in patients aged 55 years and older. The neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios were not significant predictors. These findings suggest that dynamic changes in immature granulocyte levels between the pre- and postoperative periods, rather than single-time-point measurements, may serve as a cost-effective and accessible complementary tool for malignancy prediction in atypia of undetermined significance thyroid nodules.
