Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
  • Article
    Reconstructing Lost Heritage: Digital Presentation of 19th Century Rural Landscape of Gülbahçe (İzmir, Türkiye)
    (Elsevier Ltd, 2026) Tabur, Beylem Doğa; Kul, F.N.
    This study aims to provide an original methodological framework for the digital reconstruction of Gülbahçe, a historically layered settlement in western Anatolia, Türkiye, which has experienced significant transformations and heritage loss over time. Confronting the challenge of limited documentation regarding its original condition, the study employs hypothetical spatial assumption by integrating comparative typologies, oral history, architectural drawings, and environmental data to digitally reconstruct the village's 19th century spatial and cultural character. This character evolved dramatically following the 1922 population exchange and was further transformed in the 1970s through tourism-driven urban development, the establishment of a university campus, and counter-urbanisation triggered by pandemics and earthquakes. The novelty of this research lies in addressing a critical methodological gap within digital heritage studies by introducing a geometry-based reconstruction technique specifically created for data-scarce heritage contexts often excluded from approaches reliant on rich archival or photogrammetric datasets. The proposed method integrates limited data within a transparent, evidence-based process that presents both the reliability level and the interpretive assumptions behind each modelling decision. By producing a historically grounded and immersive digital environment, the approach responds to the technical and ethical challenges of representing lost heritage, reinforcing discussions on interpretive accountability, community memory, and intercultural dialogue. Ultimately, this interdisciplinary and ethically informed methodology positions digital reconstruction as both an analytical and communicative tool—an adaptable model for documenting, responsibly interpreting, and conveying heritage that has been physically lost but remembered for its cultural significance and is under threat from urbanisation or environmental change. © 2025 Elsevier Ltd.
  • 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
    Elasto-Plastic Phase-Field Modeling of Fracture in FDM-Printed ABS Components: Numerical Implementation and Experimental Validation
    (Taylor and Francis Ltd., 2025) Dengiz, C.G.; Yorulmazlar, B.; Dorduncu, M.; Taşdemirci, A.
    This study presents a computational framework for predicting fracture behavior in 3D-printed acrylonitrile butadiene styrene (ABS) components using an elasto-plastic phase-field approach (PFA) implemented within the ABAQUS finite element environment. A user-defined element (UEL) subroutine is employed to solve the coupled displacement and damage equations through a staggered scheme. The model captures crack initiation and propagation under various stress states and specimen configurations, including pure shear, oblique shear, and tensile loading, without requiring predefined crack paths or remeshing. Numerical predictions are validated against experimental results, showing strong agreement in both force–displacement response and failure morphology. Parametric studies are conducted to assess the influence of mesh size, time increment, length scale parameter, and critical energy release rate on fracture response. The results demonstrate that while the peak reaction force is largely insensitive to these parameters, displacement at fracture and damage localization are significantly affected. The calibrated model successfully captures elasto-plastic fracture evolution in printed ABS specimens, confirming its robustness and generalizability. The proposed framework offers a reliable tool for failure analysis of polymer-based additively manufactured components and establishes a foundation for future extensions involving anisotropy, fatigue, and microstructural heterogeneity. © 2025 Taylor & Francis Group, LLC.
  • Article
    AI-Powered Detection of Hate Speech Against Refugees in Turkish Social Media
    (Springer Science and Business Media B.V., 2025) Eğin, F.; Bulut, V.; Onan, A.
    Social media platforms can cause hate speech to spread rapidly, so it is important to address such content. The speed at which hate speech spreads on social media makes it impossible to obstruct such content manually. Artificial intelligence support can be a solution for this. Detecting hate speech with artificial intelligence requires determining which expressions are hate speech. In this research, a study was conducted specifically on hate speech against refugees. Considering that Turkiye is the country with the highest migration after the Syrian Civil War, and the hospitality to approximately 3.9 Syrian people, the study focused on the Turkish language. The research first aims to create a Turkiye dataset of social media posts. Posts containing hate speech were labeled using discourse analysis on this dataset. The next stage of the research is to detect Turkish hate speech against refugees with artificial intelligence. According to the research results, the BERTurk model trained with this dataset achieved an accuracy rate of 85% in the automatic detection of Turkish hate speech. In the current climate, hate speech can spread rapidly in society and can easily lead to violent acts. Therefore, taking the necessary measures against hate speech is crucial. This study is crucial for automatically detecting hate speech in Turkish. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.
  • Article
    An AI-Based Solution for Warehouse Safety: Video Surveillance System Based Anomaly Detection in Equipment-Human Interactions with Vanilla Autoencoder
    (Elsevier Ltd, 2025) Elçi, T.; Unlu, M.
    The significant growth of the logistics sector in recent years has resulted in the expansion of warehouse operations and an increased use of equipment, leading to a rise in workplace accidents. These incidents are predominantly attributed to factors such as carelessness, fatigue, high work intensity, individual behaviors, lack of experience, insufficient training, and employee negligence. To enhance warehouse safety, it is essential to implement a system capable of real-time prediction of human-equipment interactions. This study proposes a comprehensive video surveillance framework designed to improve occupational safety in warehouse environments. The system integrates key components, including object detection, object tracking, action recognition, and alarm classification, to effectively reduce risks and prevent accidents.The system employs YOLOv7, a deep learning model with the ability to quickly and accurately detect objects in a single network pass, as the object detection methodology, and DeepSORT, an algorithm for object tracking that assigns unique identifiers to each object and utilizes deep learning techniques to improve tracking performance. The action detection component of the system introduces a novel approach by analyzing and identifying actions and movements while detecting anomalies and potential risks. By leveraging features such as the speed, tags, movement direction, and coordinate data of individuals and equipment, the system estimates alarm levels and generates corresponding alarms, providing an innovative and dynamic solution for real-time risk assessment. The system, tested to demonstrate technological capabilities such as real-time responsiveness and high operational success rates, is designed to predict accidents in warehouse environments, generate alarms, and significantly reduce the risk of occupational accidents. © 2025 The Franklin Institute. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.