WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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

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  • Article
    Identification of Turkish Extra Virgin Olive Oils Produced in Different Regions With Volatile Compounds
    (Innovhub SSI-Area SSOG, 2025) Sevim, Didar; Koseoglu, Oya; Ertan, Hasan; Ozdemir, Durmun; Ulan, Mehmet
    This study aims to characterize the composition of the volatile compounds in Turkish extra virgin olive oils (EVOOs) produced from three cultivars-Ayvalik, Gemlik, and Memecik-harvested in the South Marmara, South Aegean, and North Aegean regions during the 2014/15 and 2015/16 crop seasons. A total of 135 EVOO samples were obtained using industrial-scale 2-phase and 3-phase extraction systems. These samples were then analyzed using solid-phase microextraction (SPME) coupled with gas chromatography (GC). Among the twelve volatiles identified, trans-2-hexen-1-ol and cis-2-penten-1-ol exhibited the highest levels of abundance across all samples and seasons. Subsequently, 1-penten-3-one, hexanal, and cis-3-hexenyl acetate were identified, and it was determined that these contribute to the green and fruity sensory profile of high-quality olive oil. Two- and three-factor analyses of variance (ANOVA) revealed that volatile concentrations were significantly influenced by variety, harvest season, and extraction system. It is significant that 1-penten-3-one was found to be significantly influenced by both season and variety (p < 0.05), while 1-penten-3-ol exhibited a multifactorial dependency, with significant two-way interactions (season x variety, season x system, variety x system). Furthermore, PLS-DA-based classification successfully distinguished samples according to olive variety, indicating that volatile profiles could serve as reliable markers for authenticity and geographic origin. These findings underscore the potential of using volatile compounds as quality indicators and for geographic labelling in the olive oil industry.
  • Article
    Interpretable Structural Modeling of MR Images Using Q-Bezier Curves: A Geometry-Aware Paradigm Beyond Deep Learning
    (Elsevier Science Inc, 2026) Ozger, Faruk; Onan, Aytug; Turhan, Nezihe; Ozger, Zeynep Odemis
    Magnetic resonance (MR) imaging plays a critical role in diagnostic workflows, yet its reliability is frequently compromised by scanner-dependent bias, contrast variability, and intensity drift. Although deep learning methods achieve high performance, they generally require extensive supervision and demonstrate limited robustness across diverse clinical settings. To address these challenges, we propose a transparent, geometry-aware framework for annotation-free MR enhancement based on q-B & eacute;zier curves. This model incorporates an adaptive deformation parameter q(x) that modulates local curvature, facilitating flexible adaptation to complex anatomical boundaries. The framework comprises three principal mechanisms: (i) adaptive q(x) for local responsiveness, (ii) monotone q-Bezier tone curves for intensity standardization, and (iii) Tikhonov-regularized optimization for smooth mapping. As a result, the operator remains interpretable, operates in linear time, and provides explicit control over smoothness. The proposed approach was validated across five public cohorts (BraTS, ACDC, PROMISE12, fastMRI, IXI), demonstrating significant improvements in image fidelity (SSIM, CNR, NIQE) and downstream segmentation accuracy (Dice, HD95) relative to variational filters and state-of-theart foundation models. Additionally, cross-vendor experiments confirm its robustness without the need for retraining. Collectively, these findings establish q-Bezier modeling as a principled, lightweight, and clinically interpretable alternative that complements deep learning by providing a geometry-aware pathway to robust MR representation.
  • Article
    Ten Questions Concerning Circularity in the Built Environment
    (Pergamon-Elsevier Science Ltd, 2026) Kayacetin, N. Cihan; Aslanoglu, Rengin; Piccardo, Chiara; Afacan, Yasemin; Masera, Gabriele; Li, Qiuxian; Van Hoof, Joost
    The rapid urbanisation of our societies calls for an urban renewal movement, including developing new areas to accommodate housing facilities and services and regenerating existing urban areas. Yet, urban renewal projects pose trade-offs impacting both environmental and socio-economic aspects. The renovation and new construction of buildings can escalate the use of energy and material resources as well as increasing greenhouse gas emissions. The European Union plays a leading role in promoting the transition towards sustainable and inclusive cities, whereas other regions such as North America, Australia and Asia follow suit via Circular Economy Action Plans or Frameworks, highlighting the need to enhance resource efficiency in buildings through the use of durable and circular materials. Current research on resource efficiency in buildings follows the Circular Economy concept, which aims to reduce the use of raw materials and the waste of existing materials while retaining their value for as long as possible. However, the role of the circular economy in sustainable transition and the adoption of its principles in urban contexts remain unclear while its practical implementation still faces significant challenges, including the lack of analytical instruments and assessment methods as well as co-creative approaches. This 'Ten Questions contribution' provides an overview of the pressing issues concerning circularity in the built environment, the state-of-the-art and best practices, challenges and benefits, policies and regulations, as well as numerous strategies applied on the building and neighbourhood level, assessment methodologies and future trends.
  • Article
    Artificial Intelligence for Improving Thermal Comfort through Envelope Design in Residential Buildings: Recent Developments and Future Directions
    (Elsevier Science Sa, 2026) Bayraktar, Arda; Ekici, Berk
    Envelopes are vital components for improving thermal comfort in almost all building typologies. Yet, the design and analysis of envelopes are complex, as they involve multiple aspects and various parameters, ensuring comfort standards. Improving thermal comfort in residential buildings is within the scope of researchers to suggest sustainable design alternatives that consider multiple performance aspects and design parameters. Previous review articles have focused on improving thermal performance in residential buildings from the perspective of envelope technology, materials, and design strategies. However, none of them investigated current developments using artificial intelligence (AI), which inevitably supports decision-making in complex circumstances for a sustainable built environment. This review examines the contribution of AI methods, which consist of metaheuristic optimization and machine learning algorithms as sub-branches, to envelope parameters. The paper systematically reviews 95 relevant works on AI, including early approaches, to provide a comprehensive overview of current developments, following PRISMA guidelines. The results showed that early applications considered conventional approaches to improve thermal comfort and energy performance, which mostly limit the results to specified cases. On the other hand, studies utilizing AI methods dealt with numerous parameters, allowing them to cope with complex envelope systems in a reasonable amount of time. The study addresses relevant research questions related to the trends, research methods, system types, AI methods, data types, and their relation to performance and envelope parameters. The study also provides actionable insight, underlining gaps and future works for utilizing machine learning methods in the reviewed research domain.
  • Article
    Rice-Like, Hollow, and Rhombohedral Nano-Calcite Synthesis by Carbonization
    (Elsevier, 2026) Kilic, Sevgi; Toprak, Gorkem; Ozdemir, Ekrem
    Controlling the morphology and size of calcium carbonate (CaCO3) remains an essential challenge in the production of high-performance fillers and advanced functional materials. Here, we report a continuous carbonization strategy that enables the synthesis of monodisperse nano-calcite particles with tunable rice-like, hollow, and rhombohedral morphologies through precise control of CO2 dissolution into a flowing Ca(OH)2 solution under diffusion-limited conditions. A two-stage reactor was designed to decouple nucleation and growth by separating the gas-liquid interaction zone from a stabilization tank. pH and conductivity analyses revealed that crystallization is primarily governed by the CO2 dissolution kinetics rather than mixing intensity in the stabilization tank. SEM and XRD analyses demonstrate a distinct crystallization sequence such that initial formation of rice-like calcite, subsequent development of hollow nanoparticles through selective tip dissolution, and final transformation into rhombohedral calcite via dissolution-reprecipitation mechanism. The method provides a reproducible, template-free route for fabricating hollow CaCO3 nanoparticles, overcoming limitations of bubbletemplating and additive-mediated techniques. This scalable process provides a robust foundation for producing high-surface-area CaCO3 nanomaterials which have potential applications in thin films, ceramics, protective coatings, lightweight composites, thermal/acoustic insulation, adsorption, and catalysis, where tailored particle morphology and size can significantly enhance performance.
  • Book Part
    Mediated Narratives of Syrian Refugees: Mapping Victim-Threat Correlations in Turkish Newspapers
    (Routledge, 2025) Cox, Ayca Tunc
    Turkey has become the first and main transition hub for Syrian refugees. Furthermore, Turkey is spatially as well as culturally simultaneously referred to as European and Asian or Middle Eastern depending the point of view. Therefore, the representation of refugees in the Turkish press proves significant for the knowledge produced about refugees. Accordingly, this chapter strives to investigate the coverage of Syrian refugees in newspapers, which constitutes only one aspect of the overall reception of the issue in Turkey, and therefore does not claim to be exhaustive. Yet, because daily newspapers are still among the most important media sectors in Turkey, they constitute a special case of knowledge production worth investigating.
  • Article
    Univariate Deep Learning Models for Short-Term Electricity Load Forecasting from Renewables
    (Ankara University, Faculty of Science, 2025) Kabran, Fatma Basoglu; Unlu, Kamil Demirberk
    Renewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment. Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids. This study employs a univariate deep learning approach to predict daily renewable energy generation, evaluating Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) as candidate models. Five performance metrics-mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error and the coefficient of determination-are employed to assess the forecasting power of the algorithms. The empirical results show that CNN outperforms other models, achieving an R2 of almost 94%. This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.
  • Article
    Ensemble Machine Learning Algorithms for Thermal Comfort Prediction in HVAC Systems of Smart Buildings
    (Golden Light Publishing, 2025) Erdem, Merve Kuru; Gokalp, Osman; Calis, Gulben
    Predicting the thermal comfort of building occupants is of paramount importance in the operation of smart buildings, providing a data-driven approach to control Heating, Ventilation, and Air Conditioning (HVAC) systems for managing occupant thermal comfort and energy use, which aligns with modern sustainability and efficiency goals. Recently, ensemble machine learning (ML)-based thermal comfort prediction models have been proposed to provide more accurate estimation of thermal comfort; however, these efforts often lack a systematic and comprehensive evaluation across a wide range of ML models within a single study. To address this gap, this study presents a systematic comparative analysis of four ensemble ML frameworks (bagging, boosting, stacking, and voting) with six basic ML algorithms (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Multilayer Perceptron, and Multinomial Na & iuml;ve Bayes) and six advanced ensemble ML algorithms (Random Forest, Rotation Forest, Extra Trees, Gradient Boosting Classifier, Histogram Gradient Boosting Classifier, and Extreme Gradient Boosting). The analysis is conducted using the widely recognized ASHRAE Global Thermal Comfort Database II, providing both 3-point and 7-point Thermal Sensation Vote (TSV) predictions. Accuracy, precision, recall and F1 metrics are used for evaluation and 10-fold cross validation is applied for further comparison. The results demonstrate the Histogram Gradient Boosting (HGB) algorithm achieved the highest F1 score (0.638) for 7-point TSV prediction whereas the Random Forest (RF) algorithm provided the highest F1 score (0.549) for 7-point TSV prediction. In practice, these findings suggest that integrating RF and HGB models into Building Management Systems or IoT-based HVAC platforms can support real-time adaptive control, helping practitioners to reduce energy use while maintaining occupant comfort.
  • Article
    Optimizing Housing Floor Layout for Cool Terraces: A Comparative Analysis Using Constrained Problem Formulation
    (Yildiz Technical University, Faculty of Architecture, 2025) Durmusoglu, Betul; Ekici, Berk
    As urban densification increases, thermal stress in cities becomes a problem. The integration of climate-sensitive strategies into housing design has become a necessity. As a strategy, design of terraces, as thermally configured outdoor spaces can reduce solar radiation gain. Parametric modeling, one of the computational approaches, provides significant contributions to optimizing the integration of environmental analysis into the terrace design. Although some related studies have focused on optimizing urban mass organizations for thermal comfort and solar performance, none of them have addressed spatial organization of terraces in residential buildings. This study presents a computational housing model to investigate terrace allocation with respect to solar gain, including circulation and residential units. The interstitial spaces are considered "cool terraces", and the objective is to minimize the solar radiation on terraces by optimizing the location and size of the residential units using a genetic algorithm via the Galapagos plug-in, radial basis function optimization (RBFOpt), and covariance matrix adaptation with evolution strategy (CMA-ES) using Opossum plug-in. To provide feasible spatial organization, constraints are determined using the near feasibility threshold with the Optimus plug-in. Results showed that only CMA-ES discovered feasible spatial organization while improving the solar performance of cool terraces. When compared to the benchmark design scenarios, the optimized alternative performed 11-26% improvement in solar radiation minimization. The study discusses the challenges in identifying well-performing cool terrace solutions, the complexity of the problem, and the applicability of optimization algorithms.
  • Article
    Comparison of the Photoresponse Characteristics for 4H-SiC Schottky Barrier UV Photodetector with Graphene and Ni/Cr Electrode
    (Elsevier, 2026) Dulcel, Atilla Mert; Gozek, Melike; Unverdi, Ozhan; Celebi, Cem
    Gr/4H-SiC and Ni/Cr/4H-SiC Schottky junction UV photodetectors were fabricated and investigated to reveal the effect of electrode materials on the device performance such as spectral response and response speed. I-V characterization, spectral response, and response speed (on-off) measurements were conducted for the UV wavelength range between 200 and 400 nm. The maximum photo-responsivity was obtained as 0.081 A/W for Gr/4H-SiC and 0.041 A/W for Ni/Cr/4H-SiC at a wavelength of 260 nm. This result was attributed to the higher optical transmittance of the graphene electrode compared to the semitransparent Ni/Cr electrode. Zero bias response speed measurements were done under 280 nm wavelength UV light pulsed at different frequencies such as 100 Hz, 500 Hz, and 1000 Hz. The Gr/4H-SiC and Ni/Cr/4H-SiC photodetectors show distinctly different decay times of 5.04 ms and 305.1 mu s, respectively, while their rise times were found to be similar. This observation has been explained by the inclination of graphene to act as a trap site for photogenerated holes.