Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Structural Optimization for Robotic Concrete Construction: A Systematic Review(MDPI, 2026) Gürer, Ethem; Pektaş, Ömer Korkut; Alaçam, Sema; Güzelci, Orkan Zeynel; Çevik, Ayşe Nesligül; Türel, Ahmet; Özdemir, SalihConcrete construction is associated with high environmental impact and geometric limitations imposed by conventional formwork, which has led to growing interest in combining structural optimization with robotic fabrication. In this study, structural optimization refers to computational methods such as topology optimization, shape optimization, and form finding that aim to improve material efficiency and load-bearing performance by modifying the geometry of structural elements. This systematic review investigates how these optimization approaches are translated into fabrication-aware design workflows for robotic concrete construction. Following a PRISMA-based methodology, 90 peer-reviewed studies published between 2015 and 2025 were analyzed. The review focuses on fabrication routes including (i) 3D concrete printing, (ii) 3D-printed formwork, (iii) shotcrete-based additive manufacturing, and (iv) controlled casting systems, and examines how each route constrains geometry representation, design decisions, toolpath generation, and robotic execution. The review analyzes design-to-fabrication workflows that link optimized structural geometry to production logic and process control. Key findings indicate that incorporating fabrication constraints at early design stages can support buildability and potential material efficiency, while reinforcement integration and quality control remain critical challenges for structural reliability. The review also highlights the increasing role of in situ sensing and feedback-driven automation in improving process stability. Overall, the study clarifies current practices, limitations, and emerging directions for integrating structural optimization with robotic concrete fabrication.Article Radar Resolution Enhancement Based on Burg-Aided MIMO-DBS and Burg-Aided MIMO-SAR †(MDPI, 2026) Bekar, Muge; Bekar, Ali; Baker, Christopher John; Gashinova, Marina; Pirkani, AnumAutonomous systems require sensors that provide high-resolution imagery in adverse lighting and weather conditions for advanced situational awareness. In this regard, radars are a mandatory component of autonomous systems. Although Multiple-Input Multiple-Output (MIMO) radars provide high angular resolution beyond that of their actual physical dimension, much higher cross-range resolutions are required, especially in traffic congested areas, to differentiate and recognize closely positioned targets. The motion of the MIMO radar platform can be exploited to obtain higher cross-range resolution in the off-boresight direction, using Synthetic Aperture Radar (SAR) and Doppler Beam Sharpening (DBS) techniques, but improvements in the boresight direction, the most crucial direction for path planning, require the use of super-resolution techniques. This paper proposes a technique that combines the Burg algorithm with MIMO-SAR and MIMO-DBS radar data to enhance the cross-range resolution in the boresight direction and to achieve further enhanced cross-range resolution in off-boresight directions. The proposed technique is applied to both frequency domain and time domain data in back-projection (BP) and DBS image formation processing. A comprehensive comparison is made, with evaluation of corresponding performance and operational complexity. The performance of the technique is validated through simulation, lab-based and real-world experiments at a frequency of 77 GHz.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 Performance Evaluation of Multi-Modal Radar Signal Processing in Dense Co-Existent Environments(MDPI, 2026) Norouzian, Fatemeh; Bekar, Muge; Bekar, Ali; Gashinova, Marina; Pirkani, AnumThe wide-scale deployment of radars, distributed across a platform and across multiple platforms for reliable 360 degrees situational awareness (SA), introduces the challenge of radar interference. Interference can broadly be categorised as self-interference (between radars mounted on the same platform) and mutual interference (signals received from radars on other platforms). Both types of interference impede the reliability of SA delivered by such systems, particularly in dense environments where numerous radars operate simultaneously within the same frequency band. This work presents a comprehensive evaluation of a multi-modal beamforming approach that combines unfocused synthetic aperture radar with the traditional Multiple-Input, Multiple-Output beamformer to enhance radar resolution and suppress interference. Additionally, various aspects of sensor configurations defining hardware and software capabilities of state-of-the-art radars are discussed, and a systematic analysis of signal-to-interference-plus-noise ratio at each step of the processing is presented. Extensive simulations and experimental results in both automotive and maritime environments are shown to validate the effectiveness of the proposed approach.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.