Aksaray Üniversitesi Kurumsal Akademik Arşivi
DSpace@Aksaray, Aksaray 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 telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Isolation, characterization, antimicrobial and cytotoxic properties of Cydalima perspectalis silk proteins
(Elsevier B.V., 2025) Kılcı, Leyla; Altun, Nurver; Bozdeveci, Arif; Karaduman Yeşildal, Tuğçe
Natural silk produced by silkworms and some arthropod spiders stands out as a traditional protein polymer. Fibroin and sericin, which are silk proteins obtained from cocoons, have been used in the medical field for centuries. This study is the first to evaluate the physicochemical properties and biological activities of these proteins, which were isolated from the boxwood moth (Cydalima perspectalis), as a biotechnological material. This followed this separation of the fibroin and sericin proteins obtained from the cocoons using a high-temperature and high-pressure method. The morphological, thermal, structural, and elemental properties of the obtained fibroin and sericin were analyzed using SEM, FTIR, TGA, XRD as well as elemental analysis. These studies were followed by biocompatibility and antimicrobial studies of the proteins. L929 (mouse fibroblast) cells were used in cytotoxicity assays, with cell viability evaluated using the MTT method. The study showed that the serine had no cytotoxic effect on NIH3T3 cells, whereas fibroin was cytotoxic for L929 cells. Fibroin surfaces exhibited antimicrobial activity by supporting the adhesion of, Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and the fungus Candida albicans. This study demonstrates that silk proteins derived from other organisms can be used as alternative biomedical materials.
Sustainable arsenic removal using iron-oxide-coated natural minerals: Integrating adsorption, machine learning, and process optimization
(Elsevier B.V., 2025) Dönmez Öztel, Merve; Alver, Alper; Akbal, Feryal; Altaş, Levent; Kuleyin, Ayşe
We investigated the sustainable removal of arsenite (As(III)) and arsenate (As(V)) from water using iron oxide-coated pumice (IOCP), sepiolite (IOCS), and zeolite (IOCZ) integrated with machine learning (ML) and optimization techniques. Adsorption kinetics followed a pseudo-second-order model, while equilibrium data were best represented by Langmuir and Sips isotherms, indicating chemisorption on heterogeneous surfaces. To predict and optimize performance, Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) were applied, with cross-validated results demonstrating the superior accuracy of ANN (R2 up to 0.96, RMSE 20–40 µg L-1). Coupling ANN with Genetic Algorithm and Bayesian Optimization identified global optima for pH, contact time, and initial concentration, yielding residual concentrations of ∼8.1 µg L-1 (IOCP-As(III)), ∼42 µg L-1 (IOCS-As(III)), and ∼1.7 µg L-1 (IOCZ-As(III)), and ∼1.3 µg L-1 (IOCP-As(V)), ∼28 µg L-1 (IOCS-As(V)), and ∼6.2 µg L-1 (IOCZ-As(V)). Compared with trial-and-error conditions (residuals of ∼112 µg L-1 for IOCS-As(III) and ∼27 µg L-1 for IOCP-As(V)), the optimized systems reduced chemical usage by up to 65 %, lowered treatment costs to ∼0.004–0.007 $ mg-1 As, and delivered positive environmental gains exceeding 80 % for IOCP-As(V) and IOCZ-As(III). These results demonstrate that natural mineral-based sorbents, when coupled with AI-driven optimization, can achieve near-complete removal of both As(III) and As(V) at low cost and with reduced environmental footprint, offering a technically robust and scalable framework for sustainable water treatment.
Evaluation of endoscopic findings in gastrointestinal tract wall thickening detected on abdominal radiological imaging: a two-center retrospective descriptive study
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Ergin, Mustafa; Kıvrakoğlu, Fatih
Background and Objectives: The clinical significance of gastrointestinal (GI) tract wall thickening incidentally detected on abdominal imaging remains unclear. This study aimed to examine the relationship between GI tract wall thickening seen in imaging and what is found during endoscopy, as well as to explore how hemoglobin, C-reactive protein (CRP), and albumin levels can help predict the presence of malignancy. Materials and Methods: In this retrospectively designed study, 209 patients were included who were found to have GI tract wall thickening on radiological imaging and underwent endoscopy within 90 days. Endoscopic findings and laboratory data were recorded. Patients were compared based on the presence or absence of malignancy, and a receiver operating characteristic analysis was performed. Results: Malignancy was detected in 8.5% and 10.9% of the upper and lower GI tract cases, respectively. In patients with upper GI tract malignancy, hemoglobin levels were significantly lower and CRP levels were higher (p < 0.001 and p = 0.015, respectively). Similarly, in lower GI tract malignancy, hemoglobin levels were lower (p = 0.033), whereas CRP did not show a significant difference (p = 0.115). Cut-off values were determined as 11.8 g/dL for hemoglobin and 40.75 g/L for albumin, and both were found to have high negative predictive values. Conclusions: GI tract wall thickening detected radiologically is clinically significant and should be further investigated endoscopically. Certain biochemical parameters may aid in ruling out malignancy; however, endoscopy remains essential for definitive diagnosis.
Enhancing photovoltaic energy output predictions using ANN and DNN: a hyperparameter optimization approach
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Coşgun, Atıl Emre
This study investigates the use of artificial neural networks (ANNs) and deep neural networks (DNNs) for estimating photovoltaic (PV) energy output, with a particular focus on hyperparameter tuning. Supervised regression for photovoltaic (PV) direct current power prediction was conducted using only sensor-based inputs (PanelTemp, Irradiance, AmbientTemp, Humidity), together with physically motivated-derived features (ΔT, IrradianceEff, IrradianceSq, Irradiance × ΔT). Samples acquired under very low irradiance (<50 W m−2) were excluded. Predictors were standardized with training-set statistics (z-score), and the target variable was modeled in log space to stabilize variance. A shallow artificial neural network (ANN; single hidden layer, widths {4–32}) was compared with deeper multilayer perceptrons (DNN; stacks {16 8}, {32 16}, {64 32}, {128 64}, {128 64 32}). Hyperparameters were selected with a grid search using validation mean squared error in log space with early stopping; Bayesian optimization was additionally applied to the ANN. Final models were retrained and evaluated on a held-out test set after inverse transformation to watts. Test performance was obtained as MSE, RMSE, MAE, R2, and MAPE for the ANN and DNN. Hence, superiority in absolute/squared error and explained variance was exhibited by the ANN, whereas lower relative error was achieved by the DNN with a marginal MAE advantage. Ablation studies showed that moderate depth can be beneficial (e.g., two-layer variants), and a simple bootstrap ensemble improved robustness. In summary, the ANN demonstrated superior performance in terms of absolute-error accuracy, whereas the DNN exhibited better consistency with relative-error accuracy.
Effects of implementing interactive videos in an online flipped classroom on preservice special education teachers
(John Wiley and Sons Inc, 2025) Arslantaş, Tuğba Kamalı; Başer, Derya
This study investigates the impact of an online flipped classroom model, enriched with interactive videos, on preservice special education teachers' self-efficacy in using assistive technologies (AT) for visual impairment, their adoption of video-based learning, and their perceptions of interactive videos as effective learning tools. Addressing challenges such as limited access to AT tools and faculty expertise, the study explores how video-based instructional approaches can enhance teacher preparation for inclusive education. A mixed-methods intervention was conducted with 64 preservice teachers enrolled in an AT course at a university in Türkiye. Quantitative data were collected through pre- and post- intervention self-efficacy scales and a post-intervention adoption scale, while qualitative insights were gathered from weekly reflection reports and a final open-ended questionnaire. Results revealed a significant increase in preservice teachers' self-efficacies following the intervention. In addition, participants reported high levels of adoption of video-based learning, particularly in terms of perceived usefulness, enjoyment, ease of use, and intention to continue using interactive videos, suggesting a positive perception toward the integration of such tools in educational contexts. Qualitative findings reinforced the effectiveness of integrating interactive videos within an online flipped classroom to build AT-related competencies. The study offers practical implications for teacher education and suggests avenues for future research.
