WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Modeling Commuter Mobility in Stockholm: A Spatial Panel Approach Using Mobile Phone Data(Springer Heidelberg, 2026) Fischer, Manfred M.; Osth, John; Toger, Marina; Turk, UmutThis paper examines the sociodemographic and socioeconomic determinants of regional commuter mobility in the Greater Stockholm Area using a heteroscedastic spatial Durbin panel data model estimated via Bayesian Markov Chain Monte Carlo methods. Drawing on mobile phone-derived origin-destination flows from the MIND database, the analysis exploits unusually fine spatial and temporal granularity across a balanced panel of 675 regions over the period 2018-2023. A k-nearest neighbor spatial weight matrix (k = 18), selected via Bayesian model comparison, captures the topological structure of interregional connectivity. By modeling spatial lags in both the dependent and independent variables, the framework enables explicit recovery of direct (own-region) and indirect (spillover) effects from scalar summary measures of the matrix of partial derivatives - providing robust posterior inference on how sociodemographic and socioeconomic conditions propagate through space. This approach addresses a key limitation of conventional non-spatial methods, which risk producing biased estimates by ignoring spatial interdependence. Empirical results confirm that spatial spillovers predominate over direct effects, with educational attainment and car ownership emerging as the principal determinants of commuter mobility, while age composition plays a comparatively modest role. These findings underscore that evaluating direct effects in isolation systematically underestimates the broader societal returns to mobility-enhancing regional policies.Article Cellulose-Based Hydrogel Matrix Enhances Antimicrobial and Biofilm-Inhibitory Responses of Palatal Mesenchymal Stem Cells(Springer Heidelberg, 2026) Fidan, Özkan; Bicer, Mesude; Öztürk, Esengül; Sener, FatmaMesenchymal stem cells (MSCs) have emerged as promising alternatives to fight drug-resistant bacterial infections. This study investigates the antibacterial activity of palatal adipose tissue-derived MSCs (PMSCs), particularly when cultured within a 3D nanofibrillar cellulose hydrogel, against four clinically relevant pathogens: Pseudomonas aeruginosa K6, Staphylococcus aureus ATCC 25,923, Bacillus cereus K9 and Escherichia coli O157:H7. This study showed that both PMSCs alone and PMSCs in 3D cellulose-based hydrogel effectively inhibited the growth of bacterial burden. Notably, PMSCs cultured in the 3D system demonstrated an excellent effect, reducing bacterial burden by up to 14 log in E. coli and 12 log in P. aeruginosa K6 at a 120 & micro;L inoculum after 2 h of incubation. RT-PCR and immunocytochemical analyses found out a remarkable upregulation of the Cathelicidin (LL-37) in PMSCs 3D cultures compared to PMSCs. Furthermore, 3D cellulose-based hydrogel exhibited a significant biofilm-inhibitory effect, reaching a 57.65% reduction. The results demonstrated the importance of 3D cellulose-based hydrogel for treating antibiotic-resistant infections. PMSC therapy based on 3D hydrogel may therefore be offered as more effective antimicrobial agent to overcome drug-resistant bacterial infections.Article Citation - WoS: 3Citation - Scopus: 4Rare 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 Citation - WoS: 1Citation - Scopus: 1Machine 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 Citation - WoS: 33Citation - Scopus: 37Wind 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.Correction Citation - WoS: 2Citation - Scopus: 2The Influence of Cement Kiln Dust on Strength and Durability Properties of Cement-Based Systems(Springer Heidelberg, 2022-06-15) Hakkomaz, Hadiye; Yorulmaz, Hediye; Durak, Ugur; Ilkentapar, Serhan; Karahan, Okan; Atis, Cengiz DuranArticle 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: 2Spec17Tre: 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: 42Citation - Scopus: 39Microstructural Analysis of Low-Calcium Fly Ash-Based Geopolymer Concrete With Different Ratios of Activator and Binder Under High Temperatures(Springer Heidelberg, 2024-06-25) Kucukgoncu, Hurmet; Ozbayrak, AhmetGeopolymer concretes have emerged as an alternative to traditional Portland cement concretes with high strength, good durability, well corrosion performance and high-temperature resistance, and being a sustainable and environmentally friendly material. In this study, a comprehensive microstructural analysis of low-calcium fly ash-based geopolymer concrete samples with different alkali activator to binder ratios was conducted after exposure to temperatures ranging from 400 to 800 degrees C. The experimental results of the geopolymer concrete specimens found out significant findings, including a notable loss of mass and an approximate 80% decrease in compressive strength after exposure to 800 degrees C. The microstructural analysis underlined crack formation, voids and porosities in the geopolymer matrix at elevated temperatures, affecting the physical and mechanical properties of the material. The study presents significant insights into the behaviour of low-calcium fly ash-based geopolymer concrete with different binder and alkali activator ratios under high temperatures, revealing the performance of geopolymer concretes in extreme environments and the effect of incompatibility between geopolymer concrete and aggregate due to thermal temperature effects on this performance.Article Citation - WoS: 1Citation - Scopus: 1Measurements of Flow Characterization Revealing Transition to Turbulence Associated With the Partial Flexibility-Based Flow Control at Low Reynolds Number(Springer Heidelberg, 2024-07-26) Koca, Kemal; Keskin, Sinem; Sahin, Rumeysa; Veerasamy, Dhamotharan; Genc, Mustafa SerdarIn order to comprehend the flow characteristics of both controlled and uncontrolled SD7062 wind turbine airfoils with local flexible membrane material throughout a variety of angles of attack at a Reynolds number of 1.05 x 105, an experimental investigation was conducted. The time-dependent force measurement, the hot-wire experiment with a boundary layer and glue-on probes, and the oil-flow visualization technique were all utilized in the present study to measure the flow over the airfoil and examine the laminar-turbulent transition, laminar separation bubble, and the impact of a special flow control method that uses flexibility. A comprehensive intermittency analysis by utilizing hot-wire results was employed to obtain the flow physics effects of the local flexibility the first in the literature. The key results of the experiment demonstrated that the stall was delayed from alpha = 10 degrees to 12 degrees by the local flexibility. The hot-wire results are dedicated to laminar, transitional and turbulent regions and the transition phenomena at different locations over the suction surface of the airfoil in the analysis graphs. As demonstrated by the results of the oil-flow visualization experiment, in the uncontrolled case, the laminar separation bubble formed over the airfoil at alpha = 8 degrees between x/c = 0.16 and x/c = 0.42. The use of flexible membrane material over the airfoil provided that the oscillation of this material triggered the transition to turbulence and a bypass transition, which resulted in the reattached flow.