WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Rare Earth Elements in the Global Economy: Usage, Recovery, and the Quest for Supply Security – A Review(Springer Heidelberg, 2026) Top, Soner; Ayten, Asim Mustafa; Altiner, Mahmut; Demir, Idris; Kursunoglu, SaitOften described as the vitamins of modern industry, rare earth elements (REEs) are indispensable for the deployment of low-carbon and clean energy technologies. However, ensuring a secure and sustainable REE supply remains a major challenge due to the strong interdependence between application-driven demand, extraction and processing technologies, and the geopolitical concentration of resources. This review adopts an integrated analytical framework in which these three dimensions are treated as interconnected components shaping the resilience of global REE supply chains. First, the major application sectors of REEs are examined to clarify how emerging energy and advanced manufacturing technologies drive demand for specific elements and amplify their strategic importance. Second, extraction and processing technologies are reviewed in relation to both primary and secondary resources, highlighting how technological maturity, process selection, and material characteristics constrain or enable supply expansion. Finally, geopolitical and strategic aspects of the REE supply chain are analyzed to demonstrate how resource concentration, policy instruments, and international dependencies directly influence technological deployment and industrial competitiveness. By explicitly linking application-driven demand, technological pathways for extraction and processing, and geopolitical supply structures within a unified framework, this review provides a coherent understanding of the systemic challenges facing the REE sector and identifies key leverage points for improving the robustness and sustainability of REE supply chains in the context of the global clean energy transition.Article TEffectBayes: A Nextflow Pipeline for Exploring the Potential Effect of Transposable Elements in Gene Regulatory Network with Multi-Omic Bayesian Network Model(Springer Heidelberg, 2026-03-10) Karakülah, Gökhan; Güner, Hüseyin; Kutlu, Necati KaanTransposable elements (TEs) are critical contributors to gene regulatory networks, yet their repetitive and abundant nature complicates efforts to elucidate their precise regulatory roles. While existing computational tools facilitate systematic identification of associations between TEs and gene expression, these methods typically cannot account for confounding variables or capture causal and directional interactions. To address these limitations, we developed TEffectBayes, a Nextflow-based pipeline leveraging a multi-omic Bayesian network (BN) framework designed to systematically infer directional, probabilistic regulatory dependencies involving TEs. TEffectBayes integrates diverse omics datasets, including RNA-seq-derived gene and locus-specific TE expression, along with ChIP-seq-based histone modification data processed via custom R and Python scripts. Integrated multi-omic datasets are subsequently employed to build gene-centric Bayesian models, enabling robust inference of context-dependent, probabilistic relationships between TEs, chromatin modifications, and gene expression. TEffectBayes thus provides a reproducible and scalable computational framework for unraveling the complex regulatory landscape shaped by TEs. In summary, TEffectBayes supports systematic prioritization of TE-chromatin-gene regulatory candidates for downstream benchmarking and experimental validation, enabling hypothesis-driven follow-up studies in diverse biological contexts. The pipeline, along with comprehensive user tutorials and example datasets, is publicly accessible at https://github.com/nkaan-kutlu/TEffectBayes.Article Machine Learning for V2X-Enabled Microgrids: A Bibliometric and Thematic Review of Intelligent Energy Management Applications(Springer Heidelberg, 2026-03-09) Dogan, Yasemin; Unlu, RamazanModern power systems are evolving due to convergence of electric mobility, artificial intelligence, and renewable energy integration. Electric vehicles serve as dynamic, mobile energy storage units playing a vital role in ensuring resilient microgrid operations, via vehicle-to-everything (V2X) technology. However, despite the rise of machine learning (ML) in energy management, much of the existing literature remains fragmented lacking a holistic perspective across all facets of V2X-enabled microgrids. This study fills this gap by conducting a systematic bibliometric and thematic analysis of 310 articles obtained from Web of Science (2013-2024). By combining bibliometric mapping with thematic synthesis, the research identifies dominant and emerging ML techniques-ranging from reinforcement learning to federated learning-and evaluates their roles in microgrid management. The study highlights underexplored areas, including decentralized coordination, encouraging prosumer participation, understanding user behavior, safeguarding cybersecurity, improving real-time optimization, and the effective integration and adaptation of V2X technology within microgrid ecosystems. These gaps emphasize the need for interdisciplinary research and policy frameworks to address the social dimensions of future energy systems. Beyond a comprehensive overview, this paper proposes a research roadmap integrating technical, social, and policy dimensions. It offers actionable guidance for researchers, stakeholders aiming to unlock the potential of intelligent, human-centered, and socially inclusive energy ecosystems. Furthermore, the findings align with UN Sustainable Development Goals (SDG 7, 11, and 13), while also creating a positive impact on humanity by supporting the well-being of both society and the planet. Ultimately, this reinforces the indispensable role of ML in advancing the zero-carbon transition.Article G-C3N4@Fe3O4 Nanomaterial Synthesis for Magnetic Solid-Phase Extraction and Photocatalytic Removal of Basic Blue 3(Springer Heidelberg, 2025-12-16) Kizil, Nebiye; Kayaci, Nilgun; Erbilgin, Duygu Erkmen; Yola, Mehmet Lutfi; Yilmaz, Erkan; Soylak, MustafaThe present research synthesized a g-C3N4@Fe3O4 hybrid material for efficient magnetic solid-phase extraction (MSPE) and photocatalytic degradation of Basic Blue 3 (BB3) dye from wastewater. Characterization of the synthesized g-C3N4@Fe3O4 was conducted through Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX). The optimization of the method was carried out by examining parameters such as pH, g-C3N4@Fe3O4 amount, sample volume, and adsorption/desorption duration. In addition, analytical performance criteria such as limit of detection (LOD), limit of quantification (LOQ), and relative standard deviation (RSD) of the MSPE method were calculated as 1.29 mu g L-1, 4.28 mu g L-1, and 1.9%, respectively. The method was applied to real samples, including wastewater and textiles, and validated through addition/recovery studies for the magnetic solid-phase extraction procedure. The recoveries were gained between 91 and 100%. The reusability synthesized g-C3N4@Fe3O4 was also evaluated. The recoveries for Basic Blue 3 dye decreased to 81% after the fourth experiment. Furthermore, the photocatalytic performance of the g-C3N4@Fe3O4 hybrid material was evaluated due to its good surface area and strong interaction with Basic Blue 3 dye. The photocatalytic activity of g-C3N4@Fe3O4 hybrid material was calculated as 96.8% for 100 mg in 300 min.Article Crashworthiness Evaluation of 3D-Printed Hybrid-Design Multi-Cell Energy Absorbers Under Lateral Compression for Unmanned Aerial Vehicles(Springer Heidelberg, 2025-11-25) Atahan, M. Gokhan; Zeybek, Halil; Ozturk, SezginEnergy absorbers can be strategically integrated into critical areas of unmanned aerial vehicles to protect their structural integrity and electronic components in the event of an accident. In this study, hybrid-design multi-cell energy absorber configurations were proposed, and their crashworthiness performance and collapse mechanisms were comparatively analyzed. Hybrid energy absorbers were designed considering circular, square, hexagonal, and re-entrant unit cell geometries. The energy absorber configurations were produced via additive manufacturing. Compared to the single-cell circular energy absorber, the hybrid-design multi-cell approach resulted in a higher peak crushing force value, while offering considerable enhancements in other crashworthiness parameters. Configuration 3 is recommended for use in energy absorber applications in unmanned aerial vehicles due to its superior crashworthiness performance. Moreover, in hybrid-design multi-cell energy absorbers, the selection of layer geometries significantly influences deformation capability. Compared to the single-cell circular configuration (Configuration 1), Configuration 3 demonstrated superior crashworthiness performance by increasing the MCF, EA, and SEA values by 7.47, 4.47, and 1.41 times, respectively.Article Integrated Quantitative Modelling for the Dimension Stone Quality Evaluation: Implications for Sustainable Resource Management(Springer Heidelberg, 2025-09-30) Koken, Ekin; Strzalkowski, Pawel; Strzałkowski, PawełThe growing demand for dimensional stones in construction and monument conservation requires fast, repeatable and scientifically valid quality assessment procedures. The present study, in this context, established a solid foundation for quantifying the quality of dimension stones by adopting two quantitative methods: the Suitability Index (SI) and Dimension Stone Field Performance Coefficient (DSFPC). Both methods were coded in the MATLAB environment and implemented for 20 different rock types used in various dimension stone applications in Turkey. Evaluations based on the above-mentioned methods demonstrate that the DSFPC provides a more conservative assessment than the SI method. Additionally, engineering interpretations derived from the SI and DSFPC approaches are compared with recently published classification systems developed for the dimension stone industry. Focusing on this comparison, it is concluded that the adopted methods offer a more holistic evaluation framework compared to the approaches based solely on a single input parameter, such as effective porosity (ne), uniaxial compressive strength (UCS), or B & ouml;hme abrasion value (BAV) of rocks. Furthermore, it is concluded that the adopted methods complement each other by yielding supportive outcomes. The coded methods can be adapted to other lithological series and integrated with spatial information systems to support decision-making in mining and construction sectors. From this point of view, the present study may be considered a case study supporting holistic approaches to sustainable resource management in the dimension stone industry.Article Citation - WoS: 29Citation - Scopus: 32Wind Farm Site Selection Using GIS-Based Multicriteria Analysis With Life Cycle Assessment Integration(Springer Heidelberg, 2024-01-19) Demir, Abdullah; Dincer, Ali Ersin; Ciftci, Cihan; Gulcimen, Sedat; Uzal, Nigmet; Yilmaz, KutayThe sustainability of wind power plants depends on the selection of suitable installation locations, which should consider not only economic and technical factors including manufacturing and raw materials, but also issues pertaining to the environment. In the present study, a novel methodology is proposed to determine the suitable locations for wind turbine farms by analyzing from the environmental perspective. In the methodology, the life cycle assessment (LCA) of wind turbines is incorporated into the decision process. The criteria are ranked using analytical hierarchy process (AHP). The study area is chosen as the western region of Turkiye. The obtained suitability map reveals that wind speed is not the sole criterion for selecting a site for wind turbine farms; other factors, such as bird migration paths, distance from urban areas and land use, are also crucial. The results also reveal that constructing wind power plants in the vicinity of Izmir, canakkale, Istanbul, and Balikesir in Turkiye can lead to a reduction in emissions. Izmir and its surrounding area show the best environmental performance with the lowest CO2 per kilowatt-hour (7.14 g CO2 eq/kWh), to install a wind turbine due to its proximity to the harbor and steel factory across the study area. canakkale and the northwest region of Turkiye, despite having high wind speeds, are less environmentally favorable than Izmir, Balikesir, and Istanbul. The findings of LCA reveal that the nacelle and rotor components of the wind turbine contribute significantly (43-97%) to the environmental impact categories studied, while the tower component (0-36%) also has an impact.Article Citation - WoS: 9Citation - Scopus: 13The Impact and Future of Artificial Intelligence in Medical Genetics and Molecular Medicine: An Ongoing Revolution(Springer Heidelberg, 2024-08) Ozcelik, Firat; Dundar, Mehmet Sait; Yildirim, A. Baki; Henehan, Gary; Vicente, Oscar; Sanchez-Alcazar, Jose A.; Dundar, MunisArtificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.Article Citation - WoS: 1Citation - Scopus: 1Spec17Tre: A New Dataset in Hardware Security and Using Deep Learning for Detecting Spectre Attacks(Springer Heidelberg, 2025-05-21) Aktas-Aydin, Hatice; Yalcin, GulayComputer performance has become a significant subject of study due to the processing of big data, the complexity of calculations and the importance of time efficiency. Many companies are improving processor operating principles to increase performance. The most common methods for this purpose are speculative execution and cache usage. While these techniques improve performance, they also introduce certain security vulnerabilities. Spectre is an attack that exploits vulnerabilities created by speculative execution, affecting all modern processor architectures. Research has shown that using machine learning to detect these attacks can be quite effective, although the features are typically gathered at the software level, which may limit detection since some performance parameters are not conveyed to the software. This study presents an analysis of Spectre attacks and their detection using machine learning and deep learning methods at the hardware level. Experiments are conducted using GEM5, a full-system hardware simulator, to ensure that only hardware-visible performance parameters are also collected. Attack detection is performed using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) methods. The LSTM method is used in conjunction with SVM and Convolutional Neural Network (CNN) techniques, and all models were tested on a new dataset, Spec17Tre, created using "519.lbm" from the SPEC CPU2017 benchmarks. The study achieved a 95% accuracy rate in attack detection using the LSTM + CNN hybrid model, which also yielded an F1 score of 0.999 for detecting applied Spectre attack scenarios.Article Citation - WoS: 3Citation - Scopus: 3RF MEMS Variable Attenuators With Improved dB-Linearity(Springer Heidelberg, 2023-02-22) Hah, DooyoungA variable attenuator is one of the essential components in radio frequency (RF) systems, such as automatic gain control amplifiers and full-duplex systems. Variable attenuators based on microelectromechanical systems (MEMS) technology have several advantages over the semiconductor counterparts, including low power consumption and suppressed harmonics. Attenuation can be realized by disruption of signal propagation, which is induced by moving electrodes placed next to a signal line. In this work, the effect of the moving electrodes on the RF characteristics of the variable attenuators is studied via numerical simulation. It is observed that 10 lm of moving electrode displacement can result in 18 dB of attenuation dynamic range at 20 GHz. The similar type of RF MEMS variable attenuators reported previously showed substantial nonlinearity in attenuation-voltage characteristics, which becomes a serious drawback for applications where high-precision attenuation management is required. The main objective of the current study is, therefore, to achieve high dB-linearity, by employing shaped-finger comb-drive actuators in the moving electrode displacement control. In addition, a nonlinear relationship between force and displacement in a clamped-clamped beam spring is taken into account for more accurate device modelling. Through finite element analysis, it is shown that an improvement by a factor of twelve can be obtained in dB-linearity by using a single-comb shaped-finger actuator, compared to standard straight-finger comb-drives. The study also shows that the dB-linearity can be further (2.2 times additionally) improved by utilizing dual-comb shaped finger actuators.
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