Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Erratum Erratum to “Plasma Proteomic Markers of Interleukin-1β Pathway Associated With Incident Age-Related Macular Degeneration in Persons with AIDS” [Ophthalmol Sci. 2025;5:100794] (Ophthalmology Science (2025) 5(5), (S2666914525000922), (10.1016/J.xops.2025.100794))(Elsevier Inc., 2026) Hunt, P.W.; Olshen, A.B.; Murad, N.; Ambayec, G.C.; Sezgin, E.; Schneider, M.F.; Jabs, D.A.The publisher of this journal would like to note an error in the article “Plasma Proteomic Markers of Interleukin-1β Pathway Associated with Incident Age-Related Macular Degeneration in Persons with AIDS.” An earlier version of Figure 1 was inadvertently published instead of the final revised figure. The correct figure appears below.[Figure presented] © 2025 American Academy of OphthalmologyArticle 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 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 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.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, JoostThe 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 A Machine Learning Framework for Advanced Analytical Detection of CD36 Using Immunosensors Below Limit of Detection(Elsevier Ltd, 2026) Yeke, M.C.; Gelen, S.S.; Fil, H.; Yalcin, M.M.; Gumus, A.; Yazgan, I.; Odaci, D.We introduce a machine learning (ML)-based regression framework for quantitative electrochemical analysis, representing a paradigm shift from traditional univariate methods to a multivariate approach. Conventional analysis is constrained by reducing the entire signal to a single peak current feature to define a linear range and calculate a limit of detection (LOD). In contrast, our methodology treats the Differential Pulse Voltammetry (DPV) curve as time-series data, creating a high-dimensional fingerprint by systematically evaluating multiple data windows with varying widths around the main signal peak to identify the most informative segment. To validate this approach, a biosensor was developed by immobilizing Anti-CD36 antibodies on polydopamine-modified screen-printed carbon electrodes for the detection of CD36, a key protein in metabolism and immunity. Measurements were collected across 12 concentrations, including blank samples, spanning a range of 0 to 25 ng/mL. Following data augmentation, nine different regression models were evaluated, with the top-performing models achieving near-perfect prediction accuracy (R2>0.99) across this entire range. This high accuracy across the full concentration spectrum quantitatively demonstrates the method's ability to operate without relying on traditional concepts like linear range or LOD, enabling reliable detection at ultra-low levels. Furthermore, the immunosensor exhibited high selectivity against common interferents and excellent recovery in human serum. This methodology represents a significant advancement in analytical electrochemistry, providing a transferable approach for enhancing sensitivity in biomarker detection with potential applications in clinical diagnostics and biomedical research. The codes and dataset are made publicly available on GitHub to support further research: https://github.com/miralab-ai/biosensors-AI. © 2026 The Author(s)Article Energy Harvesting in High Altitude Platform Station Enabled Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2026) Tuylu, M.; Erdoǧan, E.High altitude platform station (HAPS) systems are becoming crucial facilitators for future wireless communication networks, enhancing connectivity across all vertical communication layers, including small Internet of Things (IoT) sensors and devices, terrestrial users, and aerial devices. In the context of the widely recognized vertical heterogeneous network (VHetNet) architecture, HAPS systems can provide service to both aerial and ground users. However, integrating HAPS systems as a core element in the VHetNet architecture presents a considerable energy challenge, marking a prominent constraint for their operation. Driven by this challenge, we introduce an energy harvesting (EH) strategy tailored for HAPS systems, enabling a HAPS system to gather energy from another HAPS system, which is not constrained by energy limitations. To assess the performance capabilities of the proposed model, we derive outage probability (OP), ergodic capacity (EC) and verify them by using Monte Carlo (MC) simulations. Moreover, we explore the system in terms of throughput. The findings reveal that harnessing full potential of EH stands as a viable approach to meet the energy demands of HAPS systems. © 2001-2012 IEEE.Book Part An Inquiry Into the Relationships Among Design Learning, Metacognitive Awareness, and Academic Goal Orientation(Design Research Society, 2024) Yazici, G.; Doǧan, F.This study examines the relationship between students' metacognitive awareness, academic goal orientations, and design course grades as a design learning criterion in design education and proposes improvements for future design education. Based on the view that metacognitive awareness and academic goal orientations are important in student's academic success, this study investigates whether there is a difference among students with different metacognitive awareness levels concerning their academic goal orientations and design course grades. The study was carried out with 84 undergraduate architecture students. Students were divided into two groups: students with high and low metacognitive awareness levels using the non-hierarchical cluster analysis method. Metacognitive Awareness Inventory and Academic Goal Orientation Questionnaire were used in the study. The results indicate that there is a statistically significant difference between the two groups, and it is a large effect size. Additionally, relationships between goal orientation, grades, and metacognitive awareness were determined. © 2024, Design Research Society. All rights reserved.Article Interaction of Hazelnut-Derived Polyphenols With Biodegradable Film Matrix: Structural, Barrier, and Functional Properties(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Hızır-Kadı, I.; Demircan, E.; Özçelik, B.The study presents a sustainable approach to valorizing hazelnut processing by-products, specifically skins and shells, through their conversion into bioactive polyphenol-rich extracts using pressurized hot water extraction (PHWE), an environmentally friendly green technology. PHWE yielded extracts with total phenolic contents of 25.4 mg GAE/g dw (shell) and 83.7 mg GAE/g dw (skin), which were incorporated into biodegradable poly(vinyl alcohol)/carboxymethyl cellulose (PVA/CMC) films at concentrations of 1–3% (w/v). The resulting composites were comprehensively characterized in terms of structural, mechanical, thermal, and barrier properties. FTIR, DSC, and XRD analyses demonstrated strong hydrogen bonding, increased thermal stability, and reduced crystallinity due to polyphenol–polymer interactions. Phenolic incorporation enhanced UV-blocking capability, increased antioxidant activity by up to five-fold, and reduced oxygen permeability from 0.048 to 0.015 (cm3·mm·m−2·day−1·atm−1) (69% reduction, p < 0.05), compared to neat PVA while maintaining desirable transparency (>70%). Optimal formulations (HSkE-II) exhibited a 39% increase in elongation at break and improved flexibility without compromising film integrity. Application tests using fresh-cut apples, watermelon, and chicken revealed significant reductions in microbial growth (up to ~1.2 log CFU/g), lipid oxidation, and weight loss during storage, confirming the films’ potential for active food packaging. This work highlights an efficient valorization strategy for nut industry by-products and demonstrates their functional integration into sustainable biodegradable packaging systems. © 2025 by the authors.Article A Study of the Environmental Challenges En Marche Towards Net-Zero: Case Study of Turkish Steel Industry(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Özdamar, A.B.; Kaya, M.; Bektas, A.; Bhattacharyya, S.; Şahindoğan, M.; Birat, Jean-Pierre; Dutta, A.The Turkish steel industry aims to reduce its sectoral carbon dioxide (CO<inf>2</inf>) emissions by 55% by 2030, in line with Türkiye’s Paris Agreement commitments and the European Green Deal (EGD), and consistent with the ambition of the European Union’s economy-wide ‘Fit for 55’ emissions-reduction target. Türkiye faces significant challenges in achieving net-zero greenhouse gas (GHG) emissions, particularly as a developing country confronting the impacts of climate change and in the market situation, such as the effects of the ongoing Russia-Ukraine conflict, limited access to affordable raw materials, and rising operational costs. This study serves as a guideline for the Turkish steel sector’s roadmap towards modernization and eventual compliance with net-zero targets. The consideration and integration of new technologies planned for the Turkish steel industry, in both electric arc furnace (EAF) and blast furnace-basic oxygen furnace (BF-BOF) facilities, have been outlined in conjunction with green hydrogen and with Carbon Capture and Storage (CCS) technologies. Four different scenarios were analysed to understand the reduction in CO<inf>2</inf> emissions: (1) In a Business-As-Usual (BAU) scenario without any reduction, (2) 39.9% CO<inf>2</inf> emission reduction with the Moderate scenario, (3) 59.6% reduction with the Advanced scenario, and (4) 82.9% reduction in CO<inf>2</inf> emissions from the Turkish steel sector with the Net-Zero scenario. To quantify the uncertainty in these long-term projections, a Monte Carlo simulation was conducted, generating probabilistic confidence intervals that reinforce the robustness and credibility of the net-zero pathway. The official roadmap for the sector is not available as of today; however, an in-depth discussion with a policy innovation leading to it is the objective of this study. © 2026 by the authors.