WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Now showing 1 - 10 of 6193
  • Article
    The Relation of Geogenic and Antrophogenic Factors with Blood and Hair Lead and Arsenic Levels in Women Living in Can and Bayramic Districts of Canakkale Province
    (Nobel Ilac, 2019) Baba, Alper; Gunduz, Orhan; Bakar, Coskun; Sulun, Serdar; Save, Dilsad
    Objective: Mining areas and associated industrial activities carry considerable risks for human health due to multi-pathway exposure of heavy metals such as arsenic and lead. The objective of this study was to compare arsenic and lead levels it human blood and hair samples in all industrial mining area in northwestern Turkey with that of non-exposed group demonstrating similar sociocultural characteristics. Material and Method: The population of the study consisted of 674 nonsmoker women over the age of 40 who were selected on random basis from mine region and control area. Venous blood samples were taken and analyzed fur blood lead and arsenic levels in all participants. Hair samples were later collected from 108 women with high levels in blood samples. Results: The results showed that the highest prevalence of occurrences was found in district centers whereas relatively lower values were observed in the villages. Hail arsenic and lead levels were comparably higher in the industrialized area (can Region) where low-quality coal combustion used in power generation and residential heating were dominant. Conclusion: Although high correlations were not found, blood and hair arsenic and lead levels in individuals living in industrial in agricultural areas were found to he high at levels influencing the human health On the other hand, these results should be further supported and verified with advanced and long duration monitoring activities.
  • Article
    Geogenic Determinants of Indoor Radon Exposure in Izmir (West Türkiye)
    (Pergamon-Elsevier Science Ltd, 2026) Alkan, Turkan; Simsek, Celalettin; Sac, Murat; Uzelli, Taygun; Taskin, Nurcihan
    Radon, a naturally occurring product of uranium decay, is the second leading cause of lung cancer. I(center dot)zmir Province in western T & uuml;rkiye, situated within the Aegean extensional regime, comprises complex fault-bounded basins that favor indoor radon accumulation. This study evaluates the spatial variability and geogenic controls of indoor radon to delineate radon-prone zones with public-health relevance. Indoor radon was measured in 79 dwellings distributed across major lithologies and structural settings; detectors were deployed in basements to capture soil-gas infiltration. Concentrations ranged from 12 to 366.5 Bq/m3 (mean 118 Bq/m3), exceeding the national average of 81 Bq/m3; 32 % of sites surpassed the EPA action level of 148 Bq/m3. Highest values cluster in Bornova, Buca, and Kemalpas, a, coincident with fault-controlled sedimentary basins and permeable units. Spatial mapping highlights the dominant influence of lithology and fault proximity on radon distribution and underscores the limitations of uniform, national-scale mitigation policies. We advocate targeted, geology-aware health policies and urban-planning measures for monitoring and mitigation in geogenically vulnerable districts. These findings contribute to medical geology by providing region-specific evidence of radon risk in one of T & uuml;rkiye's most seismically active metropolitan areas. These outputs provide decision-ready evidence for monitoring, mitigation, and building-code updates in seismically active metropolitan settings.
  • Article
    3D-Printed Soy Protein and Microalga Films: A Sustainable Approach with Antioxidant Functionality
    (Elsevier, 2026) Barekat, Sorour; Dogan, Buse; Uzuner, Sibel; Ubeyitogullari, Ali
    This study investigated the optimization and fabrication of soy protein isolate (SPI)-green microalga (MA) 3D-printed films. For optimizing 3D printing, the effects of MA concentration, nozzle size (0.52-0.81 mm), and speed (10-20 mm/s) were examined. The printed films were then dried, and color, mechanical properties, water vapor permeability, structure, and antioxidant activity were analyzed. All the formulations showed shear-thinning behavior and rapid recovery. The concentration of 3 % MA, nozzle size of 0.72 mm, and printing speed of 20 mm/s were selected as the optimized conditions for the best 3D printability. Compared with the control, their elongation at break decreased by more than 16 %, while puncture strength increased by over 12 %, and tensile strength rose by more than 40 %. Water vapor permeability decreased by more than 40 % with the addition of MA. The microstructure images and secondary structure confirmed the formation of a less porous and stronger gel network with an increase in MA concentration from 0 to 5 % (w/w). The antioxidant properties of SPI films also increased two-fold with the addition of MA. These findings highlight that the 3D-printed edible films with antioxidant properties could be used as an eco-friendly and nutritious alternative to petroleum-based films in food packaging.
  • Article
    A Knowledge-Driven Computer Vision Framework for Automated Atomic Force Microscopy Surface Characterization
    (Elsevier Science Ltd, 2026) Deveci, D. Gemici; Barandir, T. Karakoyun; Unverdi, O.; Celebi, C.
    This study presents an innovative analytical framework developed to automate Atomic Force Microscopy (AFM)-based surface characterization. The proposed methodology integrates computer vision (CV) algorithms and machine learning (ML) techniques to overcome the limitations of conventional observer-dependent approaches and visual inspection methods. In the first stage of the two-step data processing pipeline, raw AFM signals were converted into structured datasets, correspondences between images acquired under different loading conditions were identified, and drift effects in both direction and magnitude were predicted using a LightGBM-based machine learning (ML) model to guide subsequent analytical processes. This process establishes a unified coordinate reference across varying force levels, enabling pixel-level comparability of surface maps. In the second stage, the aligned datasets are systematically analyzed through block-based local maxima detection, edge-based contour extraction, morphological filtering, and skeletonization algorithms. In this way, ridge-like surface features are reliably identified and quantitatively evaluated along their axes under varying force conditions. The framework ensures data integrity while enabling high-resolution and reproducible analyzes. Beyond its automation capability, it is distinguished by its integrated, modular architecture, where each component operates sequentially along a unified processing pipeline. The methodology was validated using epitaxial monolayer graphene grown on the C-face of SiC, successfully demonstrating its ability to resolve both geometric and force-dependent mechanical responses. In this regard, the proposed system extends beyond conventional cross-sectional analysis by providing a drift-aware, knowledge-guided compensation mechanism and directionally resolved evaluation, offering a robust, automation-ready infrastructure for nanoscale surface characterization.
  • Article
    Enhanced Oxidation and Thermal Shock Resistance of N-Type Mg2Si0.89(Sn0.1,Sb0.01) Thermoelectric Material Via Cr0.9Si0.1 Coating
    (Wiley-VCH Verlag GmbH, 2025) Gurtaran, Mikdat; Zhang, Zhenxue; Li, Xiaoying; Dong, Hanshan
    In this study, Cr0.9Si0.1 coatings are deposited onto Mg2Si0.89(Sn0.1Sb0.01) thermoelectric (TE) materials using a closed-field unbalanced magnetron sputtering system. The cyclic oxidation behavior of uncoated and Cr0.9Si0.1-coated TE materials is thoroughly investigated at 500 degrees C for 10 and 50 cycles, with each cycle lasting 1 h. Surface morphology, phase constitution, cross-sectional layer structure, and elemental distribution are analyzed using scanning electron microscopy, X-ray diffraction, and energy-dispersive X-ray spectroscopy. Oxidation kinetics are assessed by measuring the mass gain of samples after cyclic oxidation testing. The uncoated TE material exhibits significant surface degradation after cyclic oxidation, initially forming MgO particles, followed by the development of SiO2 and Mg2SiO4 phases in later stages. Encouragingly, the Cr0.9Si0.1 coating demonstrates excellent thermal stability on the n-type Mg2Si0.89(Sn0.1Sb0.01) substrate. Although some oxygen diffusion occurs along grain boundaries within the coating, it is effectively trapped, thereby preventing further penetration into the underlying substrate. The high oxygen affinity of Cr and/or Si atoms plays a critical role in blocking oxidation, offering robust protection. These findings strongly support the use of Cr0.9Si0.1 coatings as an effective antioxidant barrier for TE materials under harsh operational conditions, ensuring the long-term operation of TE modules at elevated temperatures.
  • Article
    Toward Reliable Annotation in Low-Resource NLP: A Mixture of Agents Framework and Multi-LLM Benchmarking
    (IEEE-Inst Electrical Electronics Engineers Inc, 2025) Onan, Aytug; Nasution, Arbi Haza; Celikten, Tugba
    This paper introduces the Mixture-of-Agents (MoA) framework, a structured approach for improving the reliability of large language model (LLM)-based text annotation in low-resource NLP contexts. MoA employs coordinated agent interactions to enhance agreement, interpretability, and robustness without manual supervision. Evaluations on Turkish classification benchmarks demonstrate that MoA achieves up to 10-point improvements in macro-F1 over single-model baselines and significantly increases inter-agent consistency. Additionally, three novel reliability metrics-Conflict Rate (CR), Ambiguity Resolution Success Rate (ARSR), and Refinement Correction Rate (RCR)-are proposed to quantify annotation stability and correction dynamics. The results indicate that multi-agent coordination can substantially improve label quality, offering a scalable pathway toward trustworthy annotation in low-resource and cross-domain applications. The framework is language-agnostic and adaptable to other low-resource contexts beyond Turkish, including morphologically rich or typologically diverse languages such as Indonesian, Urdu, and Swahili. These findings highlight the scalability of MoA as a generalizable solution for multilingual and cross-domain annotation.
  • Article
    Contrastive Retrieval Methodology for Turkish Metaphor Detection and Identification
    (Assoc Computing Machinery, 2025) Inan, Emrah
    Metaphorical expressions, as a form of figurative language, are individually limited in their use. However, whenboth literal and non-literal meanings are considered, they are frequently used in web content. Hence, producinga balanced dataset to learn superior representations is a challenging task, and metaphor detection suffers froma limited training dataset. To alleviate this problem, we present a retrieval-based contrastive learning approachwhich first identifies candidate metaphors in the input text and then detects metaphorical expressions as aclaim verification task in the inherently unbalanced setting of this study. Furthermore, we adapt contrastivelearning to make it easier to distinguish between the literal and figurative meanings of the same expression.For the experimental setup, we extract non-literal and literal expressions along with their meanings andsample sentences from a Turkish dictionary. In the metaphor detection subtask, performance evaluation shows that sparse and dense search variations using the Turkish-e5-Large model achieve a Recall@10 (R@10) scoreof 0.614. Moreover, the SimCSE-TR-Contr-Sample-Meaning model achieves the highest Recall@10 (R@10)of 0.9739 on the generated test dataset for the metaphor identification subtask. In the real-world scenario,it achieves a competitive R@10 score of 0.8684, and these results clearly demonstrate that our model cangeneralise to this real-world scenario
  • Article
    Hydrological Insights From SWOT: Comparative Analysis of Water Surface Elevation and Area Time Series From Hydrocron API
    (Elsevier, 2025) Karahan, Sait Mutlu; Gunduz, Orhan
    The Surface Water and Ocean Topography (SWOT) mission plays an essential role in enhancing the monitoring and management of inland water bodies by providing high-resolution global observations of surface water dynamics. A critical tool in leveraging SWOT data is the Hydrocron API (Application Programming Interface), which facilitates access to temporally consistent SWOT-derived hydrological datasets. In this study, SWOT's Lake data "L2_HR_LakeSP" time series data retrieved from Hydrocron was utilized to evaluate water surface elevation (WSE) and surface area dynamics across six distinct lake locations around the world. To quantify the accuracy of SWOT, error metrics including Symmetric Mean Absolute Percentage Error (SMAPE), Absolute Percentage Error (APE), and Normalized Root Mean Square Error as a percentage (NRMSE%) were computed for both WSE and surface area estimates. The results indicated that the highest WSE error, with a SMAPE of 3.83 %, was observed in the lake characterized by the smallest surface area, suggesting a sensitivity of SWOT measurements to spatial scale. Conversely, the greatest error in surface area estimation occurred in the shallowest lake with SMAPE and APE values of 19.56 % and 22.01 %, respectively, highlighting the influence of bathymetric complexity on SWOT's detection capabilities. Despite these localized variances, the overall performance of SWOT data was found to be highly promising, demonstrating strong potential for operational hydrological applications and long-term water resource monitoring. The integration of SWOT observations with hydrological models via platforms such as Hydrocron underscores the mission's potential in advancing the understanding of inland water dynamics at both regional and global scales.
  • Article
    Determining Area Affected by Corona in Lung Computed Tomography Images by Three-Phase Level Set and Shearlet Transform
    (Wolters Kluwer Medknow Publications, 2025) Aghazadeh, Nasser; Noras, Parisa; Moghaddasighamchi, Sevda
    Background:The COVID-19 pandemic has created a critical global situation, causing widespread challenges and numerous fatalities due to severe respiratory complications. Since lung involvement is a key factor in COVID-19 diagnosis and treatment, accurate identification of infected regions in lung images is essential.Methods:We propose a multiphase segmentation method based on the level set framework to determine lunginvolved areas. The shearlet transform, a high-precision directional multiresolution transform, is employed to guide the gradient flow in the level set formulation. Additionally, the phase stretch transform (PST) is applied to enhance the contrast between infected and healthy regions, improving convergence speed during segmentation.Results:The proposed algorithm was tested on 500 lung images. The method accurately identified infected areas, enabling precise calculation of the percentage of lung involvement. The use of the shearlet transform also allowed clear delineation of ground-glass opacity boundaries.Conclusion:The proposed multiphase level set method, enhanced with shearlet and phase stretch transforms, effectively segments COVID-19-infected lung regions. This approach improves segmentation accuracy and computational efficiency, offering a reliable tool for quantitative lung involvement assessment.
  • Article
    Lipoxygenase Inhibitory Activity Evaluation of Achillea Biebersteinii Afan. by Activity-Guided Fractionation
    (Elsevier Ireland Ltd, 2026) Subasi, Bilgen; Gunbatan, Tugba; Gurbuz, Ihan; Dilmac, Elif; Bedir, Erdal; Demirci, Fatih
    Ethnopharmacological relevance: Achillea biebersteinii Afan. is traditionally utilized as folk medicine for a broad range of therapeutic applications, especially for promoting the maturation of abscesses, wound healing; against inflammation, and rheumatism in T & uuml;rkiye. Aim of the study: The anti-inflammatory potential of A. biebersteinii was evaluated through activity-guided fractionation (AGF) targeting lipoxygenase (15-LOX) inhibition. Materials and methods: Different chromatographic techniques were used for the AGF of the ethyl acetate extract of A. biebersteinii aerial parts. The in vitro 15-LOX inhibitory activity evaluation was performed to address the antiinflammatory activity. The structures of purified compounds from the fractions were confirmed by LC-HRMS, 1H NMR, and 13C NMR analyses, respectively. Results: The fractionation and isolation process culminated in the identification of three key flavonoids namely; patulitrin, axillarin, quercetagetin-7-O-beta-glucopyranoside, and 4,5-dicaffeoylquinic acid, which showed statistically remarkable 15-LOX inhibitory activity with inhibition rates of 82.27%, 96.81 %, 84.65% and 77.47 %, respectively. Two flavonoids were isolated by using the AGF method, where quinic acid was spotted to have significant 15-LOX inhibitory activity. Conclusion: These findings support the future therapeutic potential of A. biebersteinii as a natural antiinflammatory source.