Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Functional Characterization of Loss of RNF43 Reveals Neuronal Defects in a Caenorhabditis Elegans Model(MDPI, 2026) Kazan, Hasan Huseyin; Turkyilmaz, Zafer; Ekim, Burcu; Kaya, Cem; Ergun, Mehmet Ali; Sonmez, Kaan; Güzel, SinemRing finger protein 43 (RNF43) encodes a transmembrane E3 ubiquitin ligase that negatively regulates canonical Wnt signaling and is classically associated with serrated polyposis syndrome and colorectal cancer. In this study, regarding a homozygous truncating RNF43 variant (NM_001305545.1:c.1906C>T; p.Gln636Ter) in a patient segregating with a severe neurodevelopmental phenotype characterized by developmental delay, neonatal hypotonia, recurrent seizures, progressive microcephaly, and bilateral optic atrophy, the loss of polarity defective 1 (plr-1), an ortholog of RNF43, was modeled in Caenorhabditis elegans and the phenotype was primarily characterized. The results demonstrated that loss of the plr-1 disrupted gamma aminobutyric acid (GABA)ergic axon organization, reduced locomotor speed calculated from 60 s recordings, and altered developmental growth. These findings expand the phenotypic spectrum of RNF43 and support a dosage-dependent developmental role.Article A Steel-Reinforced Recycled Thermoplastic Composite for Wind Turbine Towers: Experimental and Full-Scale Validation(MDPI, 2026) Ciftci, Cihan; Altikaya, Hasan TolgaThe increasing demand for sustainable and lightweight structural systems has motivated the development of alternative materials for wind turbine tower applications, where conventional steel structures are associated with high material consumption and environmental impact. In this study, a novel steel-reinforced recycled thermoplastic composite system is proposed as an alternative structural solution. To enable the design and practical application of such composite systems, the mechanical properties of the recycled thermoplastic matrix were experimentally characterized. Compression and tensile tests revealed average yield strengths of approximately 32 MPa in compression and 7.8 MPa in tension. To account for the environmental conditions encountered in field applications, the temperature-dependent mechanical behavior of the material was investigated. Since the critical mechanical response of the thermoplastic matrix in the composite system is governed by compression rather than tension, the study was limited to compression tests under elevated temperatures. The results show that the compressive yield strength decreases to approximately 31 MPa at 55 degrees C. An analytical model based on the transformed-section approach was also developed to predict the flexural behavior of the composite section and was validated through three-point bending tests, with an analytically predicted yield load of approximately 31.5 kN showing good agreement with experimental results. To assess structural applicability at a larger scale, a full-scale composite wind turbine tower was designed and manufactured, and its dynamic performance was evaluated through field measurements under natural wind loading conditions. The results indicate that the composite tower exhibits comparable dynamic behavior to a conventional steel tower, with a first natural frequency of approximately 3.08 Hz compared to 2.89 Hz for the steel tower, along with enhanced damping characteristics. These findings demonstrate that steel-reinforced recycled thermoplastic composites offer a promising and sustainable alternative for wind turbine tower applications, with potential for broader use in structural systems.Article The Effect of Video Modeling on Gymnastics-Based Motor Skills in Children with Autism Spectrum Disorder(MDPI, 2026) Bozdag, Berkan; Sonmez, Huseyin Gazi; Turan, Ebru; Aldhahi, Monira I.; Kilinc, Omer; Ergin, Murat; Kocak, Calik VeliBackground and Objectives: While the effectiveness of video modeling (VM) in teaching academic, daily living, and social skills to individuals with Autism Spectrum Disorder (ASD) is frequently investigated, studies examining the use of VM in teaching gymnastics-based motor skills are limited. This study aimed to examine the effects of VM on the acquisition and maintenance of a gymnastics-based motor skills in preschool children with ASD. Methods: The study employed a multiple-probe method across participants in a single-subject research design. Three preschool children diagnosed with mild ASD participated in this study. Baseline, intervention, and follow-up data were systematically collected and analyzed. Social validity data were obtained through semi-structured interviews with parents and special education teachers. Results: The percentage of correct responses increased throughout the VM intervention sessions, and all participants reached the proficiency criterion. Follow-up data collected after the intervention showed that the acquired skill was maintained, and the percentages of correct responses ranged from 80% to 100%. Social validity findings revealed that both teachers and parents perceived VM as an effective and feasible teaching approach for teaching motor skills to children with ASD. Conclusions: The research findings demonstrate that VM is an effective and socially valid teaching method for teaching and maintaining gymnastics-based motor skills in preschool children with ASD. These results contribute to the existing literature by demonstrating the applicability of video modeling in the context of gymnastics-based training.Article Strategic Modeling of Hybrid Smart Micro Energy Communities: A Decision-Oriented Approach(MDPI, 2026-02-10) Perez-Sanchez, Modesto; Coronado-Hernandez, Oscar E.; McNabola, Aonghus; Erdfarb, Alex; Ramos, Helena M.; Demircan, Isil; Koca, KemalHybrid renewable energy systems are increasingly important for enabling sustainable and resilient energy supply in rural smart communities, yet existing tools often lack the ability to integrate environmental variability, multi-technology interactions, and economic-environmental assessment in a unified framework. This study presents Hybrid Smart Micro Energy Community (HySMEC), a novel modeling approach that combines high-resolution meteorological data, technology-specific generation models, detailed demand characterization, and financial analysis to evaluate hybrid configurations of hydropower, solar PV, wind, battery storage, and grid interaction. Hourly simulations capture seasonal dynamics and system behavior under realistic technical efficiencies, investment costs, and emission factors, enabling a transparent assessment of energy flows, self-consumption, and grid dependence. The results show that hybrid systems can achieve competitive economic performance, low Levelized Costs of Energy, and significant CO2 emission reductions across diverse rural community profiles, even when space or demand constraints are present. The analysis confirms the technical feasibility and environmental benefits of integrating multiple renewable sources with storage, highlighting the importance of self-consumption ratios in improving system profitability. Overall, HySMEC provides a robust and scalable tool to support data-driven design and optimization of distributed energy systems, offering valuable insights for researchers, planners, and decision-makers involved in sustainable rural energy development.Article Frequency-Based Deep Occlusion Awareness Instance Segmentation(MDPI, 2026-02-26) Guzel, Yasin; Aydin, Zafer; Talu, Muhammed FatihOne major challenge faced by deep learning-based methods that detect target objects in the form of bounding boxes is object occlusion. High degrees of occlusion significantly diminish the accuracy of instance segmentation. Nonetheless, complex-valued Fourier descriptors can robustly represent object boundaries using minimal information. In this study, the impact of integrating Fourier descriptors-renowned for their strong representational capacity-with deep network models (UNet) that exhibit high generalization performance on instance segmentation accuracy was investigated. Within the scope of the research, nine network models were designed based on different strategies for utilizing frequency components. These variants fall into four strategy families: (i) UNet-style spectrum regression on fixed low-frequency windows (FUNet), (ii) magnitude-guided frequency selection/ROI construction (FUNet-Thr, FUNet-BBox), (iii) sequence models over tokenized FFT coefficients (BiLSTM Patch/Sorted), and (iv) encoder-only spectrum predictors with different depth/capacity (EncoderFFT1/2). To fairly evaluate the models' performance in segmenting objects subjected to disruptive factors (e.g., occlusion, blurring, noise), a specialized synthetic dataset was prepared. The task is formulated as single-target (single-instance), single-class segmentation. This dataset, automatically generated according to initial parameter values, contains images of objects moving at various speeds within a single frame. Among these models, the one termed FUNet, which relies on partial matching of central frequency components, achieved the highest segmentation accuracy despite the disruptive effects. Under the challenging Dataset 8 setting, the proposed FUNet achieved the highest overlap-based performance (Dice = 0.9329, IoU = 0.8842) among Attention U-Net, U-Net, and FourierNet, with statistically significant gains confirmed by paired per-image tests.Article Citation - WoS: 3Citation - Scopus: 3A Novel Biomass-Derived Reductant for Nitric Acid Dissolution of Manganiferous Iron Ore: Comparative Assessment of Organic Reductants(MDPI, 2025-12-31) Top, Soner; Altiner, Mahmut; Vapur, Huseyin; Kursunoglu, Sait; Stopic, SreckoThis study investigates the selective dissolution of manganese from a manganiferous iron ore using nitric acid (HNO3) in the presence of various organic reductants. A series of leaching experiments was performed to evaluate the effects of temperature, reductant type, and leaching time on Mn recovery, with particular emphasis on biomass (horse dung) and tartaric acid as novel reducing agents. The dissolution behaviour of Fe, Mn, Mg, Ca, and Al was systematically examined, revealing that Mn extraction was strongly enhanced in the presence of reductants, while Fe dissolution remained below 10% under all conditions. The maximum Mn dissolution exceeded 90% at 90 degrees C using biomass and reached nearly 85%-90% with tartaric acid at elevated temperatures. Kinetic studies were conducted by applying reaction order models and the shrinking core model. The results indicated that Mn dissolution in HNO3 medium is predominantly controlled by surface chemical reaction, with Arrhenius analysis yielding activation energies of 27.74 kJ/mol for biomass and 21.26 kJ/mol for tartaric acid. These relatively low values confirm the efficiency of organic reductants in facilitating Mn reduction and dissolution. To sum up, comparison of reductant efficiency revealed that, at the lowest concentrations, the dissolution of Mn followed the sequence glucose > sucrose > oxalic acid > tartaric acid > maleic acid > biomass > citric acid > acetic acid. At the highest concentrations, the trend shifted, with citric acid emerging as the most effective, followed by tartaric acid > oxalic acid > glucose > sucrose > maleic acid > biomass > acetic acid.Article Citation - WoS: 1Citation - Scopus: 1Unit Sizing and Feasibility Analysis of Green Hydrogen Storage Utilizing Excess Energy for Energy Islands(MDPI, 2026-01-14) Koca, Kemal; Dursun, Erkan; Bekci, Eyup; Ucar, Suat; Akpolat, Alper Nabi; Tsami, Maria; Borg, Ruben PaulThis study examines whether green hydrogen production using combined wind and solar energy on Marmara Island can meet the island's electricity demand and fuel the fuel needs of a hydrogen-powered ferry. A hybrid system consisting of a 10 MW wind farm, a 3 MW solar PV system, and a PEM electrolyzer sized to meet the island's hydrogen demand was modeled for the island, located in the southwestern Sea of Marmara. The hydrogen production potential, energy flows, and techno-economic performance were evaluated using HOMER-Pro 3.18.4 version. According to the simulation results, the hybrid system generates approximately 62.6 GWh of electricity annually, achieving an 82.8% renewable energy share. A significant portion of the produced energy is transferred to the electrolyzer, producing approximately 729 tons of green hydrogen annually. The economic analysis demonstrates that the system is financially viable, with a net present cost of USD 61.53 million and a levelized energy cost of USD 0.175/kWh. Additionally, the design has the potential to reduce approximately 2637 tons of CO2 emissions over a 25-year period. The results demonstrate that integrating renewable energy sources with hydrogen production can provide a cost-effective and low-carbon solution for isolated communities such as islands, strengthening energy independence and supporting sustainable transportation options. It has been demonstrated that hydrogen produced by PEM electrolyzers powered by excess energy from the hybrid system could provide a reliable fuel source for hydrogen-fueled ferries operating between Marmara Island and the mainland. Overall, the findings indicate that pairing renewable energy generation with hydrogen production offers a realistic pathway for islands seeking cleaner transportation options and greater energy independence.Editorial Advances in Natural Building and Construction Materials(MDPI, 2025-12-16) Strzalkowski, Pawel; Sousa, Luis; Koken, Ekin; Strzałkowski, PawełArticle G-S a Prior Biological Knowledge-Based Pattern Detection and Enrichment Framework for Multi-Omics Data Integration(MDPI, 2025-11-29) Unlu Yazici, Miray; Bakir-Gungor, Burcu; Yousef, MalikThe rapid advancements in high-throughput technologies have led to a dramatic increase in diverse -omics data types, enabling comprehensive analyses, especially for complex diseases like cancer. Despite the development of multi-omics approaches, the challenges of scaling integration to massive, heterogeneous -omics datasets suggest that novel computational tools need to be designed. In this study, we propose an approach for integrating microRNA (miRNA) and messenger RNA (mRNA) expression data, incorporating prior biological knowledge (PBK). This approach scores and ranks groups of miRNAs and their associated genes using cross-validation iterations. The proposed method incorporates a Pattern detection (P) component to identify molecular motifs unique to each biological group. The analysis also facilitates the visualization of the groups, facilitating the identification of co-occurring groups and their characteristic features across iterations. Furthermore, the groups are scored using an over-representation analysis through a new Enrichment (E) component in each iteration. The clusters of the groups based on the Enrichment Scores (ESs) are visualized in a heatmap to obtain novel insights into the collective behavior and dependencies of the groups, aiming to understand the molecular mechanisms of complex diseases. The developed G-S-M-E tool not only provides performance metrics and biological scores at the group level but also offers comprehensive insights into intricate multi-omics interactions. In summary, our study emphasizes the importance of mathematical and data science methodologies in elucidating intricate multi-omics integration, yielding a formalized approach that deepens our comprehension of complex diseases.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.