WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Quasi-Static Operation of 2-Axis Microscanners With AlN Piezoelectric Quad-Actuators
(Institute of Electrical and Electronics Engineers Inc., 2021) Hah, D.
Aluminum nitride (AlN) started to draw attentions as a material for piezoelectric actuation owing to its CMOS process compatibility and safeness for biomedical applications. Due to its relatively low piezoelectric coefficients, AlN-based piezoelectric actuators have been mostly operated in resonance modes, especially in optical scanning. This paper presents a novel design of a 2-axis-tilt microscanner with AlN piezoelectric quad-actuators and meander-shaped hinges for reasonable quasi-static operation. Through finite-element-method simulation, it is shown that the proposed device can have about 9 degree of optical scan angle in two dimensions with the voltage amplitude of 50 V. Lissajous scanning operation of the device is demonstrated as well via simulation. © 2021 Elsevier B.V., All rights reserved.
Effect of Yttrium/Lanthanum-Doped Ultrasonically Assisted Nano-Hydroxyapatite on Remineralization and Bracket Bond Strength in Artificial Enamel Lesions
(BMC, 2025) Ozturk, Taner; Mammadov, Elshan; Bulduk Karakaya, Humeyra; Yagci, Filiz; Dayan, Serkan; Yagci, Ahmet
Background This in vitro study aimed to evaluate the remineralization efficacy of ultrasonically assisted yttrium fluoride-doped (Ult-YF3-nHAP) and lanthanum fluoride-doped (Ult-LaF3-nHAP) nano-hydroxyapatite (nHAP) on artificially induced enamel lesions (aWSLs), and to compare their performance with acidulated phosphate fluoride (APF) gel, fluoride varnish, casein phosphopeptide-amorphous calcium phosphate (CPP-ACP), and resin infiltrant (ICON). Methods This in vitro study followed a four-phase design: enamel lesion creation, application of remineralization agents, a 14-day treatment protocol, and post-treatment analyses using QLF, Micro-CT, SEM-EDX, and SBS testing. This study included 168 extracted human premolars, divided into eight experimental groups (n = 21 per group): (1) Demineralized control (no remineralization treatment), (2) Acidulated phosphate fluoride (APF) gel, (3) Fluoride varnish, (4) Casein phosphopeptide-amorphous calcium phosphate (CPP-ACP), (5) Ultrasonically assisted nHAP (Control nHAP), (6) Ult-YF3-nHAP, (7) Ult-LaF3-nHAP, and (8) Resin infiltrant (ICON). The aWSLs were created under laboratory conditions. Brackets were bonded to the teeth with composite material, and aWSLs were created under laboratory conditions. After lesion formation and at the end of the experimental process, micro-computed tomography (Micro-CT) and laser-assisted quantitative light fluorescence (QLF) analysis were performed to assess lesion progression and remineralization. Additionally, scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) and shear bond strength (SBS) tests were conducted at the end of the study. Statistical analysis was performed using one-way ANOVA, Kruskal-Wallis, and Mann-Whitney U tests, with a significance level of p < 0.05. Results The bracket bond strength test data showed no significant differences between the groups (p = 0.156). Significant differences were found among groups for QLF fluorescence recovery (Delta F, p < 0.001), with the Ult-YF3-nHAP group showing the greatest increase (median: +0.5, IQR: -1.4 to + 0.7), while the control group showed the greatest decrease (median: -12.1, IQR: -12.4 to -10.2). Micro-CT analysis also revealed significant differences between groups (p = 0.008). The APF Gel group showed values comparable to those of all other experimental groups. The highest remineralization values were recorded in the Ult-YF3-nHAP group (6.87 +/- 3.03 mm(3)), whereas the lowest values were found in the Varnish group. The demineralized control group had significantly higher values than the Varnish group, but lower than the Ult-LaF3-nHAP group. SEM-EDX analysis revealed that fluoride weight was significantly lower in the Tooth Mousse and Varnish groups compared to the other experimental groups (p < 0.001). Ca/P ratio was significantly lower in the demineralized control, Varnish, and Ult-YF3-nHAP groups than in other experimental groups (p = 0.002). Conclusion Ult-YF3-nHAP showed higher efficacy in remineralization of aWSLs compared to fluoride-based treatments, CPP-ACP, and resin infiltrant. The highest remineralization was detected in the Ult-YF3-nHAP group by micro-CT and QLF analysis, while fluoride varnish gave the lowest result.
A Comprehensive Review on the Extraction and Recovery of Lithium from Primary and Secondary Sources: Advances Toward Battery-Grade Materials
(Wiley, 2025) Top, Soner; Kursunoglu, Sait; Altiner, Mahmut
Lithium-ion battery (LIB) technologies have become indispensable to modern energy systems, driving global demand for high-purity lithium compounds. This review focuses on lithium recovery and purification strategies for battery-grade lithium carbonate (Li2CO3) and lithium hydroxide (LiOH), addressing both primary sources (brines and minerals) and secondary sources (waste materials). Industrially established processes, such as evaporation-based brine treatment and conventional metallurgical methods, are discussed alongside emerging techniques, including membrane separation, solvent extraction, and CO2-assisted precipitation. Particular attention is given to lithium precipitation mechanisms, the behaviour of co-existing ions during extraction, and the specific quality requirements for cathode material synthesis. By evaluating process scalability, environmental impact, and product purity, this review provides a comprehensive understanding of current practices and future directions. Additionally, it highlights the growing importance of lithium in the context of accelerating electric vehicle (EV) adoption, underscoring the bright and expanding future of the lithium industry.
Range-Based Wireless Sensor Network Localization by a Circumnavigating Mobile Anchor Without Position Information
(IEEE, 2024) Guler, Samet
Typical range-based wireless sensor network (WSN) localization approaches aim at estimating the sensor node positions by using a set of anchors with known positions. In some applications, assuming the knowledge of the anchors' positions may be impractical, and estimation of the sensors' positions in an arbitrary fixed frame may be sufficient. Considering such scenarios, we propose a WSN localization algorithm by single mobile anchor without self location information. The mobile anchor obtains distance measurements from the sensors while tracking a custom trajectory which is shown to improve the localization performance over time for high signal-to-noise ratio cases. By utilizing two stationary reference nodes within the WSN, the proposed framework generates sensor node position estimation up to translation and rotation with sufficient precision in the absence of global positioning aids. We foresee that the proposed framework can demonstrate benefits in several WSN applications ranging from internet-of-things to service robotics.
Integrated Quantitative Modelling for the Dimension Stone Quality Evaluation: Implications for Sustainable Resource Management
(Springer Heidelberg, 2025) Koken, Ekin; Strzalkowski, Pawel
The 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.
Microwave-Assisted Gelatin Methacryloyl Hydrogel (GelMA): A Reliable Extracellular Matrix for Brain Organoid Generation
(Wiley, 2025) Acar, B.; Kaya, M.; Pepe, N. Aktas; Demirtas, T. T.; Sen, A.
Citation - WoS: 26
Citation - Scopus: 31
miRcorrNet: Machine Learning-Based Integration of miRNA and mRNA Expression Profiles, Combined with Feature Grouping and Ranking
(PeerJ Inc., 2021) 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.
Effect of Different Pitch Ratios on the Flow Around Tandem Circular Cylinders with Spoilers
(Elsevier Ltd, 2025) İlkentapar, M.; Akşit, S.; Öner, A.A.; Genç, M.S.
This study experimentally investigates the unsteady aerodynamic characteristics of two tandem circular cylinders subjected to various pitch ratios and spoiler configurations in a controlled wind tunnel environment. The primary objective is to understand how the placement and presence of spoiler's influence flow separation, wake interference, surface pressure distributions, and overall aerodynamic performance. The experiments were conducted for three pitch ratios (2D, 4D, and 7D) and four spoiler configurations: NN (no spoilers on either cylinder), NS (spoiler on the downstream cylinder only), SN (spoiler on the upstream cylinder only), and SS (spoilers on both cylinders). Measurements included surface pressure, velocity distribution via hot-wire anemometry, and aerodynamic forces, while qualitative flow patterns were assessed using smoke-wire visualization. The results indicate that the usage of spoilers substantially alters the wake structure and pressure profiles, especially in closely spaced configurations. In the NN configuration, increasing the pitch ratio led to a progressive decoupling of the flow between the cylinders, transitioning from a merged wake to more isolated vortex shedding. In the SN and NS configurations, the asymmetrical placement of spoilers induced unsteady wake interactions and altered reattachment dynamics on the downstream body. The SS configuration exhibited the most disturbed flow regime at low pitch ratios, which gradually stabilized as the spacing increased. Violin plots derived from velocity measurements provided statistical insight into flow symmetry and turbulence intensity, while smoke visualizations captured coherent structures and transition zones across the configurations. The combined analysis demonstrates that both pitch ratio and spoiler configuration are critical parameters in controlling aerodynamic interference and unsteady wake behavior in tandem arrangements. These findings offer valuable implications for flow management and control strategies in offshore structures, cylindrical risers, and heat exchanger tube banks, where vortex-induced vibrations and flow separation play crucial roles. © 2025 Elsevier B.V., All rights reserved.
An Extension of Lucas's Theorem
(indian Nat Sci Acad, 2025) Cinkir, Zubeyir; Ozturkalan, Aysegul
We give elementary proofs of some congruence criteria to compute binomial coefficients modulo a prime number. These criteria are analogues to the symmetry property of binomial coefficients. We give extended version of Lucas's Theorem by using those criteria. We give applications of these criteria by describing a method to derive identities and congruences involving sums of binomial coefficients.
Therapeutic Potential of Pyrvinium Pamoate in Multiple Sclerosis Applying Brain Organoids
(Wiley, 2025) Demirkol, N. M.; Ayten, M. N.; Acar, B.; Pepe, N. Aktas; Sen, A.
Citation - WoS: 1
A Novel Bifunctional Organic Supported Nano-Titania Photocatalyst via the Sol-Gel Method Using Walnut-Shell
(Elsevier, 2026) Erdem, Ilker
Nano-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.
Influence of Eccentricity Faults on IPM Motor Equivalent Circuit Characteristics
(IEEE, 2025) Tekgun, Didem
Interior Permanent Magnet (IPM) machines are preferred in various modern applications due to their high efficiency, compact design, and reliability. They are especially favored in electric vehicle (EV) powertrains but also play a key role in hybrid vehicles, electric motorcycles, industrial automation systems, robotics, and home appliances such as air conditioners and washing machines. Eccentricity is a critical and challenging issue since it causes an unbalanced airgap magnetic flux and forces, eventually resulting in vibration, noise, and a higher likelihood of motor malfunction over time. This study investigates the effects of eccentricity faults on the motor's magnetic flux density and corresponding equivalent circuit parameters through Finite Element Analysis (FEA). The results show that the two types of eccentricity, static and dynamic, produce noticeable variations in the airgap magnetic flux as well as in key equivalent circuit parameters. Specific equivalent circuit parameters are particularly sensitive to different eccentricity faults, making them key indicators for early fault detection.
Novel Biomarker for Measuring NADPH Oxidase Activity: A Preliminary Study
(Wiley, 2025) Saraymen, B.; Saraymen, E.; Cetin, A.; Saraymen, R.
Citation - WoS: 2
Citation - Scopus: 1
94.8 Km-Range Direct Detection Fiber Optic Distributed Acoustic Sensor
(Optica Publishing Group (Formerly OSA), 2019) Uyar, F.; Onat, T.; Unal, C.; Kartaloǧlu, T.; Ozdur, I.; Özbay, E.
This work demonstrates an ultra-long range direct detection fiber optic distributed acoustic sensor which can detect vibrations at a distance of 94.8 km with 10 m resolution along the sensing fiber. © 2023 Elsevier B.V., All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Cascade Control of Magnetic Levitation with Sliding Modes
(EDP Sciences edps@edpsciences.com, 2016) Eroǧlu, Y.; Ablay, G.
The effectiveness and applicability of magnetic levitation systems need precise feedback control designs. A cascade control approach consisting of sliding mode control plus sliding mode control (SMC plus SMC) is designed to solve position control problem and to provide a high control performance and robustness to the magnetic levitation plant. It is shown that the SMC plus SMC cascade controller is able to eliminate the effects of the inductance related uncertainties of the electromagnetic coil of the plant and achieve a robust and precise position control. Experimental and numerical results are provided to validate the effectiveness and feasibility of the method. © 2016 Elsevier B.V., All rights reserved.
Identification of Key Biological Pathways and Genes in Multiple Sclerosis via Integrating Domain Knowledge into the Machine Learning Model
(Wiley, 2025) Ersoz, N. S.; Yousef, M.; Guner, S. Ayaz; Gungor, B.; Sen, A.
Citation - WoS: 1
Green Synthesis and Characterization of Zinc Oxide Nanoparticles via Thyme for Biomedical Applications: Effect of Plant Extract Concentration and Drying Method
(Springer, 2025) Karakaya, Humeyra; Kizilates, Burcu; Erdem, Ilker
Green 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.
A Comprehensive Investigation into Strip Steel Defect Detection Using Traditional Machine Learning and Deep Learning Models
(IEEE, 2025) Erkantarci, Betul; Kurban, Rifat; Bakal, Mehmet Gokhan; Kose, Abdulkadir
The steel manufacturing sector places great importance on guaranteeing the quality of strip steel products, which has led to a thorough investigation of defect detection approaches. This work conducts a comparative analysis of traditional machine learning and deep learning models to determine their efficacy in detecting defects in strip steel. Our analysis is based on a dataset that includes a variety of images of strip steel surfaces showing different types of defects. In this work, we adopt image preprocessing techniques to improve the quality of input images prior to the application of classification methods. We employ traditional ML algorithms including Support Vector Machine and Random Forest, and deep learning model AlexNet Convolutional Neural Networks for effective defect classification. Consequently, we present comparative evaluations that highlight the strengths and weaknesses of each approach, considering accuracy scores.
Fuzzy Logic-Enhanced PMC Index for Assessing Policies for Decarbonization in Higher Education: Evidence from a Public University
(MDPI, 2025) Fidan, Fatma Sener
Higher 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.
NLP-Driven Fake News Detection: A Machine Learning Perspective
(IEEE, 2025) Coban, Mert Korkut; Bakal, Gokhan
The rapid spread of fake news poses a significant challenge, impacting public opinion, decision-making, and societal trust. This study explores the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques for robust fake news detection. Using datasets such as ISOT Fake News, WELFake, and Football Fake News, the project employs advanced preprocessing methods and feature extraction techniques, including TF-IDF, Word2Vec, and GloVe. A comprehensive evaluation of machine learning models-Random Forest, Support Vector Machines (SVM), and Neural Networks-was conducted to identify the optimal configuration. Results demonstrate that Random Forest with TF-IDF excels in in-domain detection, achieving an F1-score of 99.70%, while Neural Networks paired with Word2Vec and GloVe embeddings outperform in cross-dataset scenarios. The study highlights the importance of dataset size, domain relevance, and feature representation in achieving high generalizability. These findings provide a scalable framework for combating misinformation on digital platforms.

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