Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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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
    Self-Assembled Peptide Hydrogels with Cell Attachment Motifs for 3D Lung Cancer Model: Evaluation of Cell-Matrix Interactions and Drug Response
    (John Wiley and Sons Inc, 2026) Sırma Tarım, B.; Tamburaci, S.; Top, A.
    3D cancer models can mimic the tumor microenvironment, serving as a physiologically relevant platform to investigate the behavior of tumors and test anticancer therapeutics. Although bioactive peptide hydrogels have been widely evaluated for tissue engineering applications, their potential in 3D cancer models has been confirmed in only a few studies. In this study, self-assembling peptide hydrogels containing LDV (IBP1) and LDV and IKVAV cell attachment motifs (IBP2), and the control hydrogel without adhesion units (KLEI) were used for lung cancer modeling. The peptides self-assembled into hydrogels in a cell culture medium with storage moduli of ∼700–1500 Pa. The IBP1 and IBP2 hydrogels enhanced A549 cell proliferation and induced the formation of spheroids with average diameters between ∼70 and ∼150 µm. The cells in KLEI hydrogel with the highest stiffness exhibited mesenchymal-type migration, independent of the cell population, whereas transformation to mesenchymal migration necessitated a specific cell population in the IBP2 hydrogel. The cells in the IBP1 and IBP2 hydrogels with enhanced cell-cell interactions demonstrated higher resistance to docetaxel (DTX). Thus, our results indicate that these bioactive hydrogels can serve as a promising platform for in vitro assessment of cancer mechanisms and drug screening. © 2026 Wiley-VCH GmbH.
  • Article
    Epigallocatechin Gallate and Punicalagin Combination Reduces Aβ Aggregation and Promotes Neurogenesis in Adult Zebrafish Brain
    (John Wiley and Sons Inc, 2026) Nazli, D.; Ipekgil, D.; Poyraz, Y.K.; Can, K.; Okmen, I.; Turhanlar-Sahin, E.; Ozhan, G.
    Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and behavioral alterations. The pathogenesis of AD involves the accumulation of amyloid-beta (Aβ) plaques and the hyperphosphorylated tau proteins, which disrupt neuronal function and trigger neuroinflammation. This study explores the therapeutic potential of epigallocatechin gallate (EGCG) and punicalagin (PU) in mitigating Aβ-induced toxicity using an adult zebrafish model of AD. Our results demonstrate that the EGCG + PU combination significantly reduces Aβ accumulation, protects against cellular damage, suppresses acetylcholinesterase (AChE) activity, and normalizes the expression of amyloidogenic and AD-related genes. Additionally, EGCG + PU treatment alleviates neuroinflammation by suppressing glial activation, including reductions in L-plastin and proinflammatory cytokine expression, while promoting neuronal recovery through mechanisms of neurogenesis and neuroprotection. Notably, the combination treatment restored neuronal density and improved behavioral outcomes by alleviating anxiety- and aggression-like behaviors associated with Aβ toxicity. These results underscore the synergistic neuroprotective effects of EGCG + PU, highlighting their potential as a novel therapeutic approach for mitigating the pathological, behavioral, and inflammatory aspects of AD. © 2026 Wiley Periodicals LLC.
  • Article
    A Multidimensional Comparative Analysis of Human Expert vs. AI-Driven Feedback Approaches on Learner-Centered and Collaborative Groups
    (Routledge, 2026) Yıldız Durak, H.; Onan, A.
    The aim of this study is to examine the multidimensional effects of AI-based feedback in learner-centered and collaborative learning environments among university students. The study employed a five-group experimental design: two individual learning groups receiving either AI-based feedback(G1) or human expert feedback(G2), two collaborative learning groups receiving either AI(G3) or human expert feedback(G4), and control group(G5). According to the research results, G4 showed the highest level of development in the areas of creative problem solving, internal-external motivation, and critical thinking. G1 was the group with the highest performance, particularly in terms of system interaction, completed activities, and assignments. In contrast, G2 showed the lowest results in terms of both cognitive development and learning analytics. AI-based feedback in collaborative learning environments provided the highest development in cognitive skills, while AI-based in individual work was more effective in increasing system participation. Factorial MANCOVA indicated significant interactions between learning environment and feedback type on posttest outcomes, with strongest effects on self-efficacy, intrinsic motivation, and flexibility. These results show that AI-based feedback has different effects in both individual and collaborative learning contexts. Qualitative thematic analysis highlighted themes of cognitive facilitation, creativity enhancement, feedback quality perceptions, and environment preferences. © 2026 Informa UK Limited, trading as Taylor & Francis Group.
  • Article
    3D Magnetic Nanocomposite Aerogel (3D-MANCA) for Humidity Sensing and Dye Adsorption Applications
    (Institute of Physics, 2026) Shah, N.; Tetik, H.; Lin, D.
    Introducing magnetic properties to aerogels not only opens new application areas but also enhances their performance in various applications. Herein, we report a novel 3D magnetic agar nanocomposite aerogel (3D-MANCA) with outstanding characteristics such as high porosity, magnetic property, rapid swelling behavior, and a unique stimuli-driven electrical conductivity. Agar and nanocellulose mixture were selected as the matrix material, while magnetic Fe<inf>3</inf>O<inf>4</inf> nanoparticles, CuO nanoparticles, and graphene nanopowder were incorporated as functional additives. 3D-MANCA obtained after a uni-directional freeze casting process exhibited a highly-ordered microporosity. It showed excellent magnetic properties and methylene-blue adsorption capability and a great performance as humidity sensor. © 2026 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.
  • Article
    Robust CVD Polymer Encapsulation for Thermally and Chemically Resistant Fluorescent Sensor Nanoprobes
    (Elsevier Ltd, 2026) Karabıyık, M.; Cihanoğlu, G.; Ebil, Ö.
    Semiconductor quantum dots (QDs) are attractive fluorophores for sensor applications due to their narrow emission bandwidths and high photostability; however, their performance is often limited by insufficient chemical and thermal durability under operating conditions. In this study, a solvent-free encapsulation strategy based on initiated chemical vapor deposition (iCVD) is proposed to enhance the stability of QD-based sensor nanoprobes. Cross-linked poly (glycidyl methacrylate-co-ethylene glycol dimethacrylate) (ECOP) thin films were conformally deposited as encapsulation layers onto CdTe QD-functionalized poly(GMA) sensor surfaces. The encapsulated nanoprobes were evaluated under chemically aggressive environments (water, salt water, toluene, and sulfuric acid) and elevated temperatures. Following exposure to aggressive solvents, both the polymer film thickness variation and QD fluorescence intensity change remained below 10 %, confirming the robustness of the cross-linked network. Also, thermal durability tests showed stable fluorescence performance after annealing at 250 °C, with structural and optical changes remaining within the accepted 10 % threshold. The results demonstrate that coatings deposited using iCVD exhibit conformal coverage and enhanced stability. This enables reliable protection of QD-based sensor nanoprobes without compromising optical performance. This study presents a promising method to extend the operational lifetime and environmental durability of QD-integrated sensor platforms by using chemically and thermally stable polymer encapsulation. © 2026 Elsevier Ltd
  • Article
    Valorization of Recycled Waste in Green/White Purification and LC-QTOF/MS Analysis of Beverages Adulterated with Incapacitating Drugs
    (Elsevier B.V., 2026) Anilanmert, Beril; Yonar, Fatma Cavus; Er, Elif Ozturk; Pekcaliskan, Elif Yılmaz; Cengiz, Salih
    Incapacitating drugs constitute a growing threat for the community, since victims may drink adulterated beverages without noticing. A validated eco-friendly/economical purification/analysis kit prototype, along with an LC-QToF/MS method has been developed in coke and mixed fruit-juice, for simultaneous determination of 10 drugs used for incapacitating victims (zaleplone, zolpidem, zopiclone, mephedrone, fentanyl, phenytoin, thiopental, sertraline, ketamine and GHB). A combination of two different waste nut-shells which yielded the highest recovery for these drugs were directly used as adsorbent after grinding and modification and a reusable separation apparatus recycled from waste were utilized for the first time in a toxicological analysis. In the method, after adding the adsorbent on to the sample, pH was adjusted. Following 25-min (min) automatic vortexing for adsorption, matrix was removed easily, using the separation apparatus. After 25-min desorption via cold ultrasonication using 500 μL methanol, a 9.5-min LC-QToF/MS analysis was performed. The validated method in fruit-juice and coke, extraordinarily gave successful results also in urine and saliva. Assessment tools for greenness/whiteness and pictograms confirmed the environmental friendliness of the method kit. © 2025 Elsevier B.V.
  • Article
    Reduced Phase Space Quantization and Quantum Corrected Entropy of Schwarzschild-De Sitter Horizons
    (Elsevier B.V., 2026) Jalalzadeh, S.; Moradpour, H.
    This paper investigates the quantization of the Schwarzschild–de Sitter (SdS) black hole (BH) using the Misner–Sharp–Hernandez (MSH) mass as the internal energy in a reduced phase space framework. After introducing the canonical variables of the reduced phase space, we derive a discrete spectrum for the surface areas of the BH event horizon (EH) as well as MSH masses. We utilized the MSH mass spectrum to obtain the entropy of the BH. The entropy of the BH and cosmic EHs reveals a logarithmic correction to the Bekenstein–Hawking term. Our results support the robustness of the logarithmic form of quantum corrections in SdS thermodynamics. © 2026 The Authors.
  • Article
    On Group Connected Transmissive Beyond Diagonal RIS for MIMO Systems
    (Institute of Electrical and Electronics Engineers Inc., 2026) Ilguy, M.; Özbek, B.; Le Ruyet, D.
    Reconfigurable intelligent surfaces (RIS) have emerged as an important technology for next-generation wireless networks by intelligently manipulating the wireless propagation environment. Beyond Diagonal RIS (BD-RIS) extends the traditional RIS architecture by allowing non-diagonal reflection matrices, enabling more flexible signal manipulation. Transmissive RIS (T-RIS), on the other hand, facilitates the transmission of signals through the metasurfaces. In this paper, we propose a novel design called transmissive BD-RIS (T-BD-RIS), which integrates the functionalities of BD-RIS and T-RIS to enhance the user data rate. We design an algorithm for the group connected (GC) configuration, which jointly optimizes the beamforming at the base station, the T-BD-RIS transmission matrix, and the receive combiner at the user side. The fully connected (FC) and single connected (SC) cases are special instances of the proposed generic GC design, obtained by an appropriate choice of the number of groups. We evaluate the performance of various schemes, demonstrating the potential of the proposed approach. © 1997-2012 IEEE.
  • Article
    Improved Colorectal Gland Segmentation in Histopathology Images with Adaptive Resizer-Enhanced U-Net Models
    (Springer Science and Business Media Deutschland GmbH, 2026) Fidan, E.; Gumus, A.
    Utilizing low-resolution images for computer vision tasks such as classification and segmentation can sometimes hinder the model’s ability to accurately learn essential features. While using high-resolution images and designing compatible models might seem like viable solutions, they are not always feasible due to energy efficiency and graphical computation constraints. Downsizing images for model training and application is an effective approach for improving computational efficiency and optimizing model performance.The bilinear resizing method, commonly employed for this purpose, inherently causes information loss due to its numerical approach, which relies solely on the four nearest pixel values to compute each target pixel. This limitation becomes more pronounced with high-resolution images, where the down sampling process intensifies the loss of critical information. However, recent advancements have introduced adaptive resizer modules, which dynamically adjust image dimensions to better preserve essential features before processing by deep learning models. In this study, an adaptive resizer-based segmentation framework is proposed for the gland segmentation task, which is crucial for accurate disease diagnosis, particularly in cancer analysis. Three distinct encoder-decoder architecture segmentation models are assessed for image segmentation using the Colorectal Adenocarcinoma Gland (CRAG) gland segmentation database. Each architecture was tested separately, employing six different backbone encoders that were pretrained on the ImageNet dataset. The comparative analysis showed that the adaptive resizer improved segmentation performance, increasing the Intersection over Union (IoU) metric by an average of 5.6%. This enhancement raised the lowest IoU from 62% to 70% and the highest to 78%. The code is available on GitHub at https://github.com/miralab-ai/adaptive-resizer-segmentation. © The Author(s) 2026.