Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Fuzzy Logic-Enhanced PMC Index for Assessing Policies for Decarbonization in Higher Education: Evidence from a Public University(MDPI, 2025-10-09) Fidan, Fatma Sener; Şener Fidan, FatmaHigher education institutions play a critical role in the transition to a low-carbon future due to their research capacity and societal influence. Accordingly, the calculation of greenhouse gas (GHG) emissions and the prioritization of mitigation strategies are of particular importance. In this study, a comprehensive campus-level GHG inventory was prepared for a public university in T & uuml;rkiye in alignment with the ISO 14064-1:2018 standard, and mitigation strategies were evaluated. To prioritize these strategies, both the classical Policy Modeling Consistency (PMC) index and, for the first time in the literature, a fuzzy extension of the PMC model was applied. The results reveal that the total GHG emissions for 2023 amounted to 4888.63 tCO2e (1.19 tCO2e per capita), with the largest shares originating from investments (31%) and purchased electricity (28.38%). While the classical PMC identified only two high-priority actions, the fuzzy PMC reduced score dispersion, resolved ranking ties, and expanded the number of high-priority actions to seven. The top strategies include awareness programs, energy-efficiency measures, virtual meeting practices, advanced electricity monitoring, and improved data management systems. By comparing the classical and fuzzy approaches, the study demonstrates that integrating fuzzy logic enhances the transparency, reproducibility, and robustness of strategy prioritization, thereby offering a practical roadmap for campus decarbonization and sustainability policy in higher education institutions.Article Citation - WoS: 1Citation - Scopus: 1Evaluating the Effects of Design and Manufacturing Parameters on Friction at the Surrogate Skin-3D Textile Interface(Sage Publications Ltd, 2025-10-30) Temel-Cicek, Mevra; Cicek, Umur I.; Lloyd, Alex B.; Johnson, Andrew A.Additive manufacturing (AM) is increasingly employed in the development of 3D-printed wearables, including medical wrist supports, textiles, and protective garments. While the general tribological behavior of 3D-printed components has been widely studied, limited research has focused on the friction behavior of 3D-printed wearables when in contact with human skin, which is a crucial factor for improving wearer comfort by minimizing local skin friction. This study, therefore, investigates the influence of material type, manufacturing technology, and print parameters of 3D-printed textiles on frictional behavior against skin. Specimens were fabricated using three AM technologies: material extrusion (MEX), vat photopolymerization (VATP), and powder bed fusion (PBF). Each technology employed various materials and print parameters, specifically layer thickness (ranging from 0.05 to 0.3 mm) and print orientations (horizontal and vertical). Friction was measured using a custom-built handheld device at the interface between 3D-printed specimens and two surrogate skin models: lorica (representing the dorsal forearm) and silicone (representing the chest). The results revealed that friction was significantly influenced by both layer thickness and print orientation. For MEX specimens, acrylonitrile butadiene styrene, acrylonitrile styrene acrylate, and polycarbonate showed the highest friction, while for VATP, durable resin resulted in the highest friction coefficient. In contrast, PBF specimens exhibited very similar frictional behavior. Regarding layer thickness, higher values consistently resulted in the highest friction coefficients, regardless of manufacturing method or material type. These findings provide valuable insights for designers and engineers seeking to optimize the comfort of 3D-printed wearables, guiding the selection of suitable AM processes and parameters for products intended for direct skin contact.Article Citation - WoS: 1Citation - Scopus: 2Green Synthesis and Characterization of Zinc Oxide Nanoparticles via Thyme for Biomedical Applications: Effect of Plant Extract Concentration and Drying Method(Springer, 2025-10-15) Karakaya, Humeyra; Kizilates, Burcu; Erdem, IlkerGreen synthesis of nano particles using plant extracts is sustainable, cost-effective, and eco-friendly. However, the synthesis parameters are still being investigated. In this study, zinc oxide nanoparticles (ZnO NPs) were prepared via thyme extract (green synthesis) and the effect of synthesis parameters were investigated. Samples with different concentrations of thyme plant extract (PE) (10, 16 & 24% (v/v) PE / Zn salt solution) were prepared and two different drying methods (freeze-drying (FD) and oven-drying (OD)) were performed. XRD results showed the hexagonal crystalline ZnO were formed with considerable crystallinity (70.8-75.1%) without further heat treatment (calcination). The crystallite sizes of ZnO NPs were determined to be in the range of 11.9-14.8 nm. The ZnO NPs prepared via PE concentration of 16% (v/v) and freeze-drying was with the finest crystallite size (11.9 nm) and considerable crystallinity (72.9%). ZnO NPs prepared via FD method were found to have smaller particle sizes, thus providing a higher surface-to-volume ratio. DLS (dynamic light scattering) analysis was used for determining the particle size distribution (PSD) and surface charge of ZnO NPs at acidic, neutral and basic pH values. The antibacterial characteristics of ZnO NPs were determined against Gram (+) and (-) bacteria. The ZnO NPs with the finest microstructure (16% PE (v/v), FD) had the highest antibacterial activity. The green synthesized ZnO NPs prepared in this study may be promising candidates for various applications including biomaterials and biomedical applications with their fine microstructure and considerable antibacterial activity.Article Enhanced Photoluminescence via Plasmonic Gold Nanoparticles and Improved Stability of Perovskite Nanocrystals in Macroporous (Polydimethylsiloxane) PDMS Matrices(Springer, 2025-10-09) Ocal, Sema Karabel; Tiras, Kevser Sahin; Onses, M. Serdar; Mutlugun, EvrenIn this work, we report a simple and cost-effective method for improving both the environmental stability and photoluminescence quantum efficiency (PLQY) of perovskite nanocrystals (PNCs). Through their embedding in a specially designed macroporous polydimethylsiloxane (MPDMS) matrix and incorporation of plasmonic gold nanoparticles (Au NPs), remarkable improvements are achieved. The resulting MPDMS@PNC composites are seen to retain near-unity quantum efficiency even after 24-h immersion in water and are observed to retain over 85% of the original efficiency even at 75 degrees C, displaying excellent thermal stability. More interestingly, by incorporating Au NPs and subjecting the material to mechanical pressure, the lifetime of the PNCs gets further increased. This is due to the more intimate spatial arrangement of Au NPs in the porous matrix, enhancing localized surface plasmon resonance (LSPR) coupling and thereby enhancing the photoluminescence (PL) of the PNCs. In general, this approach offers a scalable and robust route to designing stable, high-performance perovskite-based materials for next-generation optoelectronic applications.Article Burg-Aided 2D MIMO Array Extrapolation for Improved Spatial Resolution(MDPI, 2025-10-12) Bekar, Muge; Bekar, Ali; Pirkani, Anum; Baker, Christopher John; Gashinova, MarinaIn this paper, the extrapolation of a 2D multiple-input multiple-output (MIMO) array is proposed using the Burg algorithm to achieve higher angular resolution beyond that of the corresponding 2D MIMO virtual array. The main advantage of such an approach is that it allows us to dramatically decrease both the physical size and the number of antenna elements of the MIMO array. The performance and limitations of the Burg algorithm are examined through both simulation and experimentation at 77 GHz. The experimental methodology used to acquire 3D data of range, azimuth and elevation information with the 1D MIMO off-the-shelf radar is described. Using this method, the performance of the proposed array can be tested experimentally, especially at frequencies where it is desired to assess the antenna response prior to fabricating the antenna.Article Contributions Toward Net-Zero Carbon in the Water Sector: Application to a Case Study(IWA Publishing, 2025-09-01) Ramos, Helena M.; Perez-Sanchez, Modesto; Correia, Tiago; Bekci, E.; Besharat, M.; Kuriqi, Alban; Coronado-Hernandez, Oscar E.This study presents an integrated smart water-energy nexus framework combining IoT-based water monitoring, hybrid renewables (hydropower/solar/wind), and AI-driven optimization. Real-time sensor data enables automated grid management, while AI analytics optimize operations and predict maintenance needs through a closed-loop system. The solution achieves bidirectional energy exchange, with the full hybrid system (G + H + PV + W) reducing costs by 41.5% (<euro>831K) and LCOE by 57.2% (<euro>0.0475/kWh). Financial analysis confirms viability with 26.4% IRR and 3.8-year payback, while achieving negative CO2 emissions (-160,476 kg/year). Progressive renewable integration enhances all key performance indicators (KPIs), cutting OPEX by 89.9% (<euro>7,156/year) through optimized operations. Dual water-energy performance metrics (leakage, pressure, % renewable share) ensure balanced and sustainable grid management. Key innovations include IoT-energy synergy, AI-driven predictive maintenance, and circular resource efficiency. The framework demonstrates how smart water grids can achieve both economic and environmental benefits through renewable energy integration and advanced digital solutions.Correction Correction: Engineering Novel Features for Diabetes Complication Prediction Using Synthetic Electronic Health Records(Frontiers Media S.A., 2025-08-29) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikArticle Citation - WoS: 26Citation - Scopus: 31miRcorrNet: Machine Learning-Based Integration of miRNA and mRNA Expression Profiles, Combined with Feature Grouping and Ranking(PeerJ Inc., 2021-05-19) Yousef, M.; Göy, G.; Mitra, R.; Eischen, C.M.; Jabeer, A.; Bakir-Güngör, B.A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/ malikyousef/miRcorrNet. © 2021 Elsevier B.V., All rights reserved.Article Citation - WoS: 1Citation - Scopus: 1Synthesis, Characterization, and Comprehensive in Vitro and in Silico Evaluation of the Anti-Inflammatory Potential of Novel 1,2,3-Triazole–Arylidenehydrazide/Thiazolidinone Hybrids(Wiley-VCH verlag GmbH, 2025-09) Pepe, Nihan Aktas; Cakir, Furkan; Atalay, Tugba; Acar, Busra; Turgut, Gurbet Celik; Sen, Alaattin; Senol, HalilFive novel 1,2,3-triazole/arylidenehydrazide/thiazolidinone hybrid compounds (7-11) were synthesized and characterized using NMR, HRMS, IR, and HPLC purity analysis. The cytotoxicity of these compounds was evaluated on fibroblasts and THP-1 cells, showing that all compounds were nontoxic at the tested concentrations. The wound healing assay revealed that compounds 7, 9, and 10 significantly enhanced wound closure, with a 7.74%-32.69% improvement in treated cells. Compounds 8 and 11 showed moderate effects. Anti-inflammatory activity was assessed through qRT-PCR, demonstrating that compound 10 led to the most significant reduction in proinflammatory cytokines TNF-alpha, IL-1 beta, and NF-kappa B1. In addition, the expression of Iba1 protein in THP-1 cells confirmed that compound 8 showed the strongest anti-inflammatory effect, surpassing that of aspirin. Compound 10 showed the highest inhibition of NF-kappa B signaling and iNOS activity. Molecular docking studies revealed that compounds 10 and 11 had strong binding affinities to TNF-alpha and iNOS, with compound 11 showing the most stable interactions. Molecular dynamics simulations supported these findings, indicating that compound 11 demonstrated more stable binding to both targets. Overall, the results suggest that compounds 10 and 11 are promising anti-inflammatory candidates with potential for further development in therapeutic applications for inflammatory diseases.Article Citation - WoS: 1Citation - Scopus: 1A Novel Bifunctional Organic Supported Nano-Titania Photocatalyst via the Sol-Gel Method Using Walnut-Shell(Elsevier, 2026-01) Erdem, IlkerNano-structured photocatalytic titania was prepared via the sol-gel method on the surface of carbon-rich organic support in situ to be used as a supported photocatalyst. The preparation process was lean, including sol preparation, mixing and calcination (450 degrees C). The microstructure and crystallinity were characterized by using SEM and XRD analysis. The prepared photocatalytic material shows better water clarification (dye removal) efficiencies than commercial nano titania, either excited by UV or visible light, or kept in the darkness. A bifunctional composite having both photocatalysis and adsorption capabilities simultaneously was prepared using walnut shell (WNS) as organic support for the first time. It has considerably higher dye removal rates (kapp values (min(-1))) when compared with commercial nano titania: 0.1827 (2.83 times higher), 0.1188 (9.35 times higher) and 0.1066 (12.25 times higher) under UV light, under visible light, and in the darkness, respectively, making it a promising candidate for water clarification processes.