WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Comparative Analysis of Modulation Shapes on Laser Diode Performance with a High-Efficiency LLC Resonant Converter Driver(Wiley, 2026-02-06) Yigit, Hayri; Rifat boynuegri, Ali; Tekgun, Burak; Rifat Boynuegri, AliHigh-power laser diodes (LDs) are key components in laser-based wireless power transfer (WPT) systems, where end-to-end efficiency is one of the most critical performance metrics. This study investigates the driving performance of an LD powered by a high-efficiency LLC resonant converter under three distinct excitation waveforms-sinusoidal, triangular, and rectified-sine-using a MATLAB/SIMULINK model and an experimental setup designed to reproduce real-world operating conditions. Each waveform is synthesized through frequency modulation of a full-bridge inverter stage and filtered at the output. The analysis examines the impact of modulation shape on output current ripple, converter efficiency, and overall LD efficiency. Experimental validation confirms the simulation trends, underscoring the trade-offs between waveform smoothness, implementation complexity, and efficiency. Beyond confirming that constant-current operation yields the highest LD efficiency, this study explicitly quantifies how low-frequency current ripple induced by different modulation waveforms propagates through the LLC resonant converter, alters RMS current stress, and translates into measurable efficiency degradation at both the driver and LD levels. By experimentally correlating waveform symmetry, ripple magnitude, and loss mechanisms, the work establishes practical design boundaries for waveform-modulated laser drivers in WPT systems.Article Depositional Model, Cyclicity, and Hydrocarbon Potential of the Eocene Sakesar Carbonate Ramp, Salt Range, Pakistan(Springer, 2026-02-02) Shah, Syed Bilawal Ali; Shah, Syed Haider AliThe Sakesar Formation in the Salt Range, Pakistan, represents a well-developed Eocene carbonate ramp deposited along the southern Tethyan margin. This study integrates petrographic analysis, palynofacies evaluation, organic geochemical measurements and sequence stratigraphic interpretation to characterise the depositional environments, diagenetic evolution, and petroleum system potential of the formation. Six microfacies (MF1-MF6) were identified through thin-section petrography ranging from high-energy shoal grainstones to low-energy lagoonal marls. Quantitative palynofacies analysis shows energy dependent trends in organic matter composition, with shoal facies dominated by opaque phytoclasts and lagoonal facies enriched in amorphous organic matter (AOM). Organic geochemical measurements including Total Organic Carbon (TOC), Hydrogen Index (HI), Oxygen Index (OI), and Rock-Eval pyrolysis parameters, combined with vitrinite reflectance (Ro) data, indicate that lagoonal marl-micrite facies (MF6) contain Type II kerogen with the highest TOC values (2.80%), elevated HI (293 mg hydrocarbons per gram TOC), and peak oil-window maturity (0.72% Ro). These attributes identify MF6 as the primary oil-prone source rock. Mid-ramp wackestones and packstones (MF3-MF4) possess moderate generative potential and serve as internal seals or baffles, whereas high-energy shoal facies (MF1-MF2) show favourable reservoir characteristics but limited source potential. Sequence-stratigraphic analysis demonstrates that maximum flooding surfaces (MFS) frequently coincide with organic-rich MF6 intervals, producing predictable vertical stacking of source, seal, and reservoir units at parasequence scale. The integrated petrographic, palynofacies, and geochemical framework confirms the dual role of the Sakesar Formation as both a reservoir and a source-seal interval, with metre-scale cyclicity enhancing hydrocarbon charge and trapping efficiency. These findings refine the depositional and petroleum system model of the Sakesar carbonate ramp and provide valuable predictive analogues for Eocene carbonate exploration within the Himalayan foreland basin and related Tethyan settings.Article Spatial Dimension of the Local Phenomenon in Kayseri(Gazi University, Faculty of Engineering Architecture, 2025-12-31) Ozmen, Nihan Mus; Asiliskender, BurakKayseri is in the centre of Anatolia, at the intersection of trade and military routes, and possesses a rich cultural heritage. Throughout its history, the city has hosted various civilizations, developing around a central castle and continuing to expand, particularly after the 19th century. Kayseri has long served as a meeting point for diverse cultures. Within this diversity, families known as locals, whose origins date back to the oldest neighbourhoods within the city walls, have held significant mercantile power. These local families regard themselves as the actual owners of Kayseri and have influenced the city's developmental trajectory. Over time, they have moved outward from the centre to newly developed neighbourhoods, first to the north and then to the east. This study examines the urban development of Kayseri in the 20th century and the spatial mobility of these local families. It employs qualitative methods such as ethnographic observation, oral history interviews, and GIS-based thematic mapping to analyse these movements in a multi-layered way. The study also aims to understand Kayseri's socio-cultural dynamics and historical texture by investigating the role of local families in the city's physical and functional transformations. In this context, it addresses the physical and functional changes in neighbourhoods vacated by these relocations.Article Citation - Scopus: 1eTNT: Enhanced Textnettopics With Filtered LDA Topics and Sequential Forward / Backward Topic Scoring Approaches(Science and Information Organization, 2024) Voskergian, Daniel; Jayousi, Rashid; Bakir-Güngör, BurcuTextNetTopics is a novel text classification-based topic modelling approach that focuses on topic selection rather than individual word selection to train a machine learning algorithm. However, one key limitation of TextNetTopics is its scoring component, which evaluates each topic in isolation and ranks them accordingly, ignoring the potential relationships between topics. In addition, the chosen topics may contain redundant or irrelevant features, potentially increasing the feature set size and introducing noise that can degrade the overall model performance. To address these limitations and improve the classification performance, this study introduces an enhancement to TextNetTopics. eTNT integrates two novel scoring approaches: Sequential Forward Topic Scoring (SFTS) and Sequential Backward Topic Scoring (SBTS), which consider topic interactions by assessing sets of topics simultaneously. Moreover, it incorporates a filtering component that aims to enhance topics' quality and discriminative power by removing non-informative features from each topic using Random Forest feature importance values. These integrations aim to streamline the topic selection process and enhance classifier efficiency for text classification. The results obtained from the WOS-5736, LitCovid, and MultiLabel datasets provide valuable insights into the superior effectiveness of eTNT compared to its counterpart, TextNetTopics. © 2024 Elsevier B.V., All rights reserved.Article Citation - WoS: 17Citation - Scopus: 23Women's Tertiary Education Masks the Gender Wage Gap in Turkey(Springer, 2017-03-10) Tekguc, Hasan; Eryar, Deger; Cindoglu, DilekThis paper investigates the gender wage gap for full-time formal sector employees, disaggregated by education level. The gap between the labor force participation rate of women with tertiary education and those with lower levels of education is substantial. There is no such gap for men. Hence, existing gender wage gap studies for Turkey, where we observe lopsided labor force participation rates by education levels, compare two very different populations. We disaggregate the whole sample by education level to create more homogenous sub-groups. For Turkey, without disaggregation, the gender wage gap was 13% in 2011, and women are significantly over-qualified relative to men on observed characteristics. Once we disaggregate the sample by education level, we show that the gender wage gap is 24% for less educated women and 9% for women with tertiary education in full-time formal employment. Observed characteristics only explain 1 % of this gap in absolute terms. We further disaggregate the data by public and private employment. The gender gap is higher in the private sector. However, women with tertiary education in the public sector are significantly better qualified compared to men, and consequently the adjusted gender wage gap is higher for women with tertiary education in the public sector. Our estimates also indicate a rise in the gender wage gap between 2004 and 2011.Article Citation - WoS: 1When the Railway Reached Istanbul: The Making of Sirkeci Terminus, 1870-1888(Routledge Journals, Taylor & Francis Ltd, 2017-07-16) Tozoglu, AhmetSince its establishment as a capital city, the historical topography of Istanbul has witnessed significant changes, created not only by devastating earthquakes and fires, but also by the implementation of large-scale imperial projects. In the existing literature, the transformation of Istanbul's urban area in the nineteenth century has largely explored the topics of new urban regulations, institutions and their implication after the Tanzimat (reform) decree of 1839. This article aims to explore a lesser-known dimension of nineteenth-century developments of the city: the extension of the railway into the heart of Istanbul's historical peninsula, and the spatial change around the Sirkeci district due to the physical expansion of the terminus area. The construction of a larger terminus (inaugurated in 1890) is relatively well documented in architectural history, yet developments prior to this monumental construction have been less explored so far. Thus, this article also aims to investigate the project's development and implementation phases in the second half of the nineteenth century, when the city witnessed continuous urban reformation processes by focusing on the intertwined relations of different agents in the urban space.Conference Object Citation - WoS: 7Citation - Scopus: 12Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces(European Signal Processing Conference, EUSIPCO, 2018-09) Altindis, Fatih; Yilmaz, Bulent; İçöz, Kutay; Borisenok, S.This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on brain-computer interface (BCI) applications. Our study focused on extracting topological features of EEG signals obtained from the motor cortex area of the brain. EEG signals from 8 subjects were used for forming data point clouds with a real-time simulation scenario and then each cloud was processed with JPlex toolbox in order to find out corresponding Betti numbers. These numbers represent the topological structure of the point data cloud related to the persistent homologies, which differ for different motor activity tasks. The estimated Betti numbers has been used as features in k-NN classifier to discriminate left or right hand motor intentions. © 2019 Elsevier B.V., All rights reserved.Article Citation - WoS: 2Citation - Scopus: 7Triple Positive Solutions for M-Point Boundary-Value Problems of Dynamic Equations on Time Scales With P-Laplacian(Texas State Univ, 2015) Dogan, AbdulkadirIn this article we study the existence of positive solutions for m-point dynamic equation on time scales with p-Laplacian. We prove that the boundary-value problem has at least three positive solutions by applying the five functionals fixed-point theorem. An example demonstrates the main results.Article Citation - WoS: 2Citation - Scopus: 2Transparent Colloidal Crystals With Structural Colours(Frontiers Media S.A., 2022-03-07) Erdem, Talha; O'Neill, Thomas; Zupkauskas, Mykolas; Caciagli, Alessio; Xu, Peicheng; Lan, Yang; Eiser, Erika; O’Neill, ThomasSpatially ordered arrangements of spherical colloids are known to exhibit structural colours. The intensity and brilliance of these structural colours typically improve with colloidal monodispersity, low concentrations of point and line defects and with increasing refractive index contrast between the colloids and the embedding medium. Here we show that suspensions of charge stabilised, fluorinated latex particles with low refractive-index contrast to their aqueous background form Wigner crystals with FCC symmetry for volume fractions between 13 and 40%. In reflection they exhibit both strong, almost angle-independent structural colours and sharp, more brilliant Bragg peaks despite the particle polydispersity and bimodal distribution. Simultaneously, these suspensions appear transparent in transmission. Furthermore, binary AB, A(2)B and A(13)B type mixtures of these fluorinated and similarly sized polystyrene particles appeared predominantly white but with clear Bragg peaks indicating a CsCl-like BCC structure and more complex crystals. We characterised the suspensions using a combination of reflectivity measurements and small-angle x-ray scattering, complemented by reflectivity modelling.Article Topological Feature Generation for Link Prediction in Biological Networks(PeerJ Inc, 2023-05-09) Temiz, Mustafa; Bakir-Gungor, Burcu; Sahan, Pinar Guner; Coskun, Mustafa; Güner Şahan, PınarGraph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks. Graph embedding methods learn representations of nodes and interactions in a graph with low-dimensional vectors, which facilitates research to predict potential interactions in networks. However, most graph embedding methods suffer from high computational costs in the form of high computational complexity of the embedding methods and learning times of the classifier, as well as the high dimensionality of complex biological networks. To address these challenges, in this study, we use the Chopper algorithm as an alternative approach to graph embedding, which accelerates the iterative processes and thus reduces the running time of the iterative algorithms for three different (nervous system, blood, heart) undirected protein-protein interaction (PPI) networks. Due to the high dimensionality of the matrix obtained after the embedding process, the data are transformed into a smaller representation by applying feature regularization techniques. We evaluated the performance of the proposed method by comparing it with state-of-the-art methods. Extensive experiments demonstrate that the proposed approach reduces the learning time of the classifier and performs better in link prediction. We have also shown that the proposed embedding method is faster than state-of-the-art methods on three different PPI datasets.