Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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, NurcihanRadon, 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 Knowledge-Based Training of Learning Architectures Under Input Sensitivity Constraints for Improved Explainability(Pergamon-Elsevier Science Ltd, 2026) Sildir, Hasan; Erturk, Emrullah; Edizer, Deniz Tuna; Deliismail, Ozgun; Durna, Yusuf Muhammed; Hamit, BahtiyarThe traditional machine learning (ML) training problem is unconstrained and lacks an explicit formulation of the underlying driving phenomena. Such a formulation, based solely on experimental data, does not ensure the delivery of qualitative knowledge among variables due to many theoretical issues in the optimization task. This study further tightens Artificial Neural Networks (ANNs) training by including input sensitivities as additional constraints and applies to regression and classification tasks based on literature data. In theory, such sensitivity represents the change direction of the target variable per change in measurements from indicators. The resulting nonlinear optimization problem is solved th rough a rigorous solver and includes the sensitivity expressions through algorithmic differentiation. Compared to traditional methods, with an acceptable decrease in the prediction capability, the proposed model delivers more intuitive, explainable, and experimentally verifiable predictions under input variable variations, under robustness to overfitting, while serving robust identification tasks. A classification case study includes a patient-oriented clinical decision support system development based on the impact of cancer-indicating variables. A competitive test prediction accuracy is obtained compared to commonly used algorithms despite 10 % decrease in the training. The regression case is built upon the energy load estimation to account for prominent considerations to obtain desired sensitivity patterns and proposed methodology delivers significant accuracy drop compared to some formulations to address knowledge patterns. The approach delivers a compatible pattern with practitioner expertise and is compared to widely used machine learning algorithms, whose performances are evaluated through common statistics in addition to multi-variable response graphs.Article Citation - WoS: 33Citation - Scopus: 39High Transparent, Low Surface Resistance Zto/Ag Multilayer Thin Film Electrodes on Glass and Polymer Substrates(Pergamon-Elsevier Science Ltd, 2021) Ekmekçioğlu, Merve; Erdoğan, Nursev; Astarlıoğlu, Aziz Taner; Yiğen, Serap; Aygün, Gülnur; Özyüzer, Lütfi; Özdemir, MehtapZinc tin oxide (ZTO)/Ag/ZTO multilayer thin films were grown by direct current (DC) magnetron sputtering technique at room temperature on soda lime glass (SLG) and different polymer substrates such as polycarbonate (PC) and polyethylene terephthalate (PET) for transparent conductive electrode (TCE) applications. The effect of substrate on the structural, optical and electrical characteristics of ZTO/Ag/ZTO multilayers was investigated. All prepared ZTO/Ag/ZTO films presented amorphous structure as expected from room temperature deposition process and smooth surface quality with very low surface roughness. We found that ZTO/Ag/ZTO multilayer films grown on SLG, PET and PC substrates have very high optical transmission and low surface resistance. Moreover, after ZTO/Ag/ZTO multilayer thin film deposition on polymer substrates, the optical transmission was found to be enhanced because the higher absorption due to Ag layer is compensated by lower reflectance. Our results suggest that ZTO/Ag/ZTO multilayer thin films on any substrate can be a promising alternative to indium tin oxide (ITO) films as a cost-effective, indium-free, flexible and transparent electrode for various applications.