WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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

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  • 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
    FTIR Spectroscopy Coupled With Chemometrics for Evaluating Functional Food Efficacy in an in Vitro Model of Iron Deficiency Anemia
    (Elsevier Science Ltd, 2026) Dalyan, Eda; Cavdaroglu, Cagri; Ozen, Banu; Gulec, Sukru
    Vibrational spectroscopy offers a rapid, cost-effective approach for studying biological systems. This study employs Fourier Transform Infrared (FTIR) spectroscopy, combined with Soft Independent Modeling of Class Analogy (SIMCA), to evaluate treatment outcomes for iron deficiency anemia (IDA). The model was built using spectra from healthy and anemic cells, then validated with cells treated with commonly used iron supplements. In calibration, 9 of 10 control and all IDA samples were correctly classified; 14 of 15 validation samples were identified as healthy. The model was applied to cells treated with protein-iron complexes. All samples treated with a 60:1 protein-iron ratio matched the healthy group, while 3 of 4 treated with a 10:1 ratio matched the IDA group. These results were further supported by iron-regulated gene expression of transferrin receptor (TFR) and (Ankyrin Repeat Domain 37) ANKRD37. FTIR coupled with chemometrics enables rapid assessment of functional effects and shows potential for screening functional ingredients in anemia-targeted food products.
  • Article
    Disorder-Engineered Hybrid Plasmonic Cavities for Emission Control of Defects in hBN
    (American Chemical Society, 2026) Genc, Sinan; Yucel, Oguzhan; Aglarci, Furkan; Rodriguez-Fernandez, Carlos; Yilmaz, Alpay; Caglayan, Humeyra; Bek, Alpan
    Defect-based quantum emitters in hexagonal boron nitride (hBN) are promising building blocks for scalable quantum photonics due to their stable single-photon emission at room temperature. However, enhancing their emission intensity and controlling the decay dynamics remain significant challenges. This study demonstrates a low-cost, scalable fabrication approach to integrate plasmonic nanocavities with defect-based quantum emitters in hBN nanoflakes. Using the thermal dewetting process, we realize two distinct configurations: stochastic Ag nanoparticles (AgNPs) on hBN flakes and hybrid plasmonic nanocavities formed by AgNPs on top of hBN flakes supported on gold/silicon dioxide (Au/SiO2) substrates. While AgNPs on bare hBN yield up to a 2-fold photoluminescence (PL) enhancement with reduced emitter lifetimes, the hybrid nanocavity architecture provides a dramatic, up to 100-fold PL enhancement and improved uniformity across multiple emitters, all without requiring deterministic positioning. Finite-difference time-domain (FDTD) simulations and time-resolved PL measurements confirm size-dependent control over decay dynamics and cavity-emitter interactions. Our versatile solution overcomes key quantum photonic device development challenges, including material integration, emission intensity optimization, and spectral multiplexity.
  • 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
    AI-Supported Seismic Performance Evaluation of Structures: Challenges, Gaps, and Future Directions at Early Design Stages
    (Elsevier Sci Ltd, 2026) Ak, Fatma; Ekici, Berk; Demir, Ugur
    This study reviews 91 journal articles that intersect with earthquake-resistant building design and artificial intelligence (AI)- based modeling, utilizing machine learning, deep learning, and metaheuristic optimization algorithms. Previous reviews on AI applications have examined engineering problems without considering the impact of architectural design parameters and structural irregularities on seismic performance. This review discusses the role of AI in integrating architectural design variables and seismic performance objectives, highlighting challenges, gaps, and future directions in the early design phase. The reviewed articles demonstrate that AI is successful in addressing seismic performance objectives; however, a holistic framework for assessing architectural and structural variables has not been presented. The review highlights key findings, gaps, and future directions for those involved in earthquake-resistant building design utilizing AI.
  • 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
    Sustainable Recovery of Critical Raw Materials From Geothermal Igneous Systems: Geochemical, Mineralogical, and Techno-Economic Insights from the Dikili-Bergama Field (Western Anatolia, Turkiye)
    (Elsevier, 2026) Ayzit, Tolga; Baba, Alper
    The sustainable co-extraction of critical raw materials (CRMs) with renewable geothermal energy offers a dual pathway to support the circular economy and low-carbon transition. In this study, an integrated geochemical and mineralogical approach is used to comprehensively assess the recoverable lithium (Li) boron (B), strontium (Sr) and other critical raw materials in the geothermal reservoirs of the Dikili-Bergama region Turkiye. A geochemical analysis was carried out by systematic sampling and multi-element testing of geothermal water and reservoir rock. Hydrogeochemical studies of the geothermal fluids indicated the presence of remarkable concentrations of B (4.6 ppm), Sr (2.8 ppm) and Li (1.2 ppm), suggesting the possibility of active leaching processes in the deposit. Mineralogical studies using X-ray diffraction (XRD) have revealed a number of secondary mineral phases, such as quartz and labradorite, indicating the interaction between water and rock. These interactions affect not only the permeability and porosity of the deposit, but also the mobilization and precipitation of CRMs. A techno-economic analysis will be used to identify potential synergies that could improve the economic feasibility of geothermal projects in the region. The Monte Carlo simulation has shown that the Dikili-Bergama geothermal reservoirs have a potential of similar to 712 tons of Li. In this study, the CRM potential that emerged during the geothermal energy exploitation process in the region was calculated. The temporality and the process of obtaining are completely related to the extraction technology. This offers the dual benefit of renewable energy and strategic mineral extraction, contributing to sustainable resource management in volcanic environments.
  • Article
    The Effect of Layered Cover Plate Material on the Ballistic Performance of Ceramic Armors: Experimental and Numerical Study
    (Pergamon-Elsevier Science Ltd, 2026) Cellek, Seven Burcin; Tasdemirci, Alper; Cimen, Gulden; Yildiztekin, Faki Murat; Toksoy, Ahmet Kaan; Guden, Mustafa
    This study investigates the ballistic performance of silicon carbide (SiC) ceramic armor systems reinforced with single and hybrid metallic cover plates composed of Ti-6Al-4V (Ti64) and copper. Controlled ballistic experiments combined with validated LS-DYNA simulations were conducted to examine how cover-plate material, thickness, and stacking sequence influence penetration resistance, energy dissipation, and failure mechanisms. The experimental results revealed that metallic cover plates significantly enhance protection by improving projectile erosion and extending dwell time. While both Ti64 and copper single layers increased the antipenetration capability (APC) compared with bare SiC, hybrid configurations achieved the highest performance. The optimal design, consisting of a 2 mm Ti64 plate placed in front of a 1 mm copper plate, produced the greatest reduction in penetration depth and the highest APC value. Numerical analyses closely replicated the experimental trends and provided insight into stress-wave interactions, pressure evolution, and damage progression within the ceramic. The findings demonstrate that hybrid Ti64-Cu systems not only improve initial impact resistance but also redistribute energy toward the front layers, reducing stress transmission to the backing and mitigating catastrophic ceramic failure. The combined experimental and numerical results establish a clear design framework for developing lightweight, high-efficiency ceramic armor through tailored hybrid layering strategies.
  • Article
    Hydrogeochemical Assessment and Health Risks of Groundwater in Sahand Volcanic Foreland (NW Iran): Arsenic Speciation and Heavy Metal Risk Indicators
    (Academic Press Inc Elsevier Science, 2026) Ghayurdoost, Farhad; Zarghami, Mahdi; Sadeghfam, Sina; Jabraili-Andaryan, Nasser; Nikmaram, Sara; Baba, Alper; Mosaferi, Mohammad
    Due to the toxic nature of arsenic (As) and its elevated concentrations in many water resources, numerous studies have focused on understanding its origin, distribution, and impacts. This study aimed to identify the dominant As species in groundwater of the Sahand Volcanic Foothills, assess water quality indices, and examine heavy metal (HM) concentrations to address rising concerns about groundwater contamination. A total of 21 groundwater samples were collected and analyzed in accordance with world health organization (WHO) guidelines. Although most samples fell within acceptable ranges, several (notably S10, S20, and S21) exhibited elevated levels of total dissolved solids (TDS), electrical conductivity (EC), and HMs, particularly iron (Fe) and As. Hydrochemical assessments using Piper, Gibbs, Stiff, and Schoeller diagrams indicated that geochemical processes resulting from rock dissolution were the main factors controlling groundwater chemistry, with limited influence from anthropogenic pollution. According to the groundwater quality index (GWQI), most samples were categorized as "good" to "excellent," though some areas ranged from "moderate" to "very poor." HM pollution indices revealed that As concentrations exceeded permissible limits. Health risk assessments further showed that both oral and dermal exposure posed significant carcinogenic and non-carcinogenic risks, especially for children. Speciation analysis indicated that arsenate (As V) was the dominant form of As, consistent with oxidizing aquifer conditions, and is less biologically hazardous than arsenite (As III). The study highlights the necessity of continuous groundwater monitoring, effective pollution source management, and implementation of protective regulations to mitigate environmental and health risks in the region.
  • Article
    Notum1a Inhibition Promotes Neurogenesis in the Adult Zebrafish Brain
    (Nature Portfolio, 2025) Kocagoz, Yigit; Erdogan, Nuray Sogunmez; Ozdinc, Sevval; Ipekgil, Dogac; Katkat, Esra; Ozhan, Gunes
    Notum is a carboxylesterase enzyme that modulates extracellular signaling by hydrolyzing palmitoleoyl residues from proteins, thereby influencing key pathways involved in cell differentiation, survival, and proliferation. While notum1 expression has been identified in the brain, its role in adult neurogenesis remains poorly understood. Using the adult zebrafish brain as a model system, we demonstrate that the notum1a homolog is broadly expressed across various brain cell types but is absent in undifferentiated radial glial cells. Pharmacological inhibition of Notum activity with the small molecule inhibitor ABC99 stimulates activation of radial glial cells, leading to increased neurogenesis. A BrdU pulse-chase assay confirms that ABC99-induced proliferation enhances the production of mature neurons. Despite Notum's established role in Wnt signaling, transcriptional analysis following ABC99 treatment reveals no sustained impact on Wnt pathway targets, suggesting that Notum may regulate neurogenesis through alternative mechanisms. Our findings highlight notum1a as a potential modulator of neural progenitor cell dynamics in the adult brain and suggest that targeting Notum could represent a novel therapeutic strategy for neurodegenerative conditions characterized by impaired neurogenesis.