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 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 Toward the Design of New Α-Carboline Derivatives Against Anaplastic Lymphoma Kinase (Alk): A Comprehensive in Silico Approach(Wiley-VCH Verlag GmbH, 2025-11) Sari, Ceyhun; Akcok, IsmailAfter the first description of anaplastic lymphoma kinase (ALK) in an anaplastic large cell lymphoma cell line as a nucleophosmin (NPM) fusion partner, ALK and its various fusion partners have been implicated in numerous cancers such as non-small cell lung cancer (NSCLC), anaplastic large cell lymphoma (ALCL), neuroblastoma, and rhabdomyosarcoma. In the last decade, several compounds targeting ALK have been developed and approved by the Food and Drug Administration (FDA). Despite the advances of generations of ALK inhibitors, a recent study highlighted that around half of the ALK-positive NSCLC patients will go through disease progression in response to first-line alectinib, which is a second-generation ALK inhibitor. In this study, we aimed to propose a novel alpha-carboline compound targeting the ALK tyrosine kinase domain to be used against various types of cancer in which ALK fusion proteins may be involved. In this regard, we designed more than 200 alpha-carboline derivatives and investigated their binding properties against ALK tyrosine kinase by using in silico protocols consisting of molecular docking studies, molecular dynamics simulations, MM/PBSA binding free energy calculation, and essential dynamics analysis. Considering the obtained results, we developed two promising candidates, compounds 208 & 209 with -9.05 and -9.80 binding energies, respectively, which demonstrated improved binding profiles over the course of a 300 ns simulation.Article An Extension of Lucas's Theorem(indian Nat Sci Acad, 2025-10-31) Cinkir, Zubeyir; Ozturkalan, AysegulWe 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.Article Forecasting the Consumer Price Index in Türkiye Using Machine Learning Models: A Comparative Analysis(Gazi Univ, 2025-09-01) Söylemez, İsmet; Ünlü, Ramazan; Nalici, Mehmet ErenThis study utilizes machine learning models to forecast Türkiye's Consumer Price Index (CPI), thereby addressing a critical gap in inflation prediction methodologies. The central research problem involves the forecasting of CPI in a volatile economic environment, which is essential for informed policymaking. The primary objective of this study is to evaluate the performance of three machine learning models, such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), in forecasting CPI over periods ranging from one to six months, utilizing data from 2012 to 2024. The study's unique contribution lies in the application of the \"SelectKBest\" method, which identifies the most relevant indices, thereby enhancing the efficiency of the models. An ensemble method, Averaging Voting, is also employed to combine the strengths of these models, producing more accurate and robust predictions. The findings indicate that while the RF model consistently generates the most accurate forecasts across all shifts, the SVM model demonstrates a particular strength in the domain of short-term predictions. The ensemble model demonstrates a substantial performance improvement, with a R2 value of 0.962 for one-month ahead of estimates and 0.956 for five-month forecasts. This combined approach has been shown to outperform individual models, offering a more reliable framework for CPI forecasting. The findings offer valuable insights for economic policymakers, enabling more precise and stable inflation predictions in Türkiye.Article Citation - WoS: 1Citation - Scopus: 1Interaction of Inula Viscosa (L.) Aiton with IBA1 via Rosmarinic Acid and Rutin: Insights from Computational Models and Biological Effects(Wiley-VCH verlag GmbH, 2025-10-29) Aktas Pepe, Nihan; Acar, Busra; Ceylan Ekiz, Yagmur; Senol, Ayse Merve; Semiz, Gurkan; Sen, Alaattin; Celik Turgut, GurbetInula viscosa (L.) Aiton is a traditional medicinal plant extensively utilized in Mediterranean nations for the treatment of rheumatic pain, inflammatory disorders, diabetes, anemia, and cancer. This study further explored its anti-inflammatory mechanisms through the highest components, chlorogenic acid, rosmarinic acid, and rutin, on the expression of the ionized calcium-binding adapter molecule 1 (Iba1) on monocyte-derived macrophage-like cells. Iba1 is known to contribute pathogenesis of diverse inflammatory diseases. HPLC analysis identified 13 major phenolic compounds, with rosmarinic acid, chlorogenic acid, and rutin as major components. The aqueous extract of the plant and its major components exhibited dose-dependent antiproliferative activity on pTHP-1, RAW264.7, and PCS-201-012 cells. Immunofluorescence staining revealed a significant reduction in Iba1 protein expression, which is associated with inflammation, at the high dose of I. viscosa and rutin. Molecular docking studies indicated that rosmarinic acid and rutin had the strongest predicted interactions with Iba1, with docking scores of -12.403 and -12.301 kcal/mol and MM/GBSA binding energies of -64.47 and -84.20 kcal/mol, respectively. I. visoca and its major components were observed to significantly suppress iNOS activity in LPS-stimulated cells; these findings were also supported by RT-PCR results. Treatment with the high dose of I. viscosa resulted in 9.45% necrotic cells and caused cell cycle arrest in the S phase (59.2 +/- 5.23%). This suggests that it may potentially reduce the proliferation of activated macrophages. In the fibroblast migration assays, the relative wound closure rate was found to be significant 27.06 +/- 18.09% at the low dose of I. viscosa and 31.59 +/- 22.42% at the high dose of I. viscosa. Although the relatively low wound closure rate limits tissue repair, it may benefit chronic wounds and fibrosis by suppressing excessive cell proliferation and inflammation. These results suggest that I. viscosa is a promising natural source of bioactive compounds with potential applications in anti-inflammatory drug development.Article Tuning Mechanical Performance of PCL Scaffolds: Influence of 3D Bioprinting Parameters, Polymer Concentration, and Solvent Selection(IOP Publishing Ltd, 2025-09-01) Ceylan, Saniye Aylin; Baltacioglu, Mehmet Furkan; Bal, Burak; Bayram, Ferdi Caner; Isoglu, Ismail AlperThe mechanical performance of three-dimensional (3D) bioprinted scaffolds is susceptible to printing parameters and material formulation. In this study, poly (epsilon-caprolactone) (PCL) scaffolds were fabricated using four different polymer concentrations (10%, 25%, 50%, and 75% w/v) to investigate how these variations, along with process parameters, influence mechanical behavior. Maintaining the structural integrity of bioprinted constructs requires careful optimization of polymer concentration and precise control over parameters such as printing speed, pressure, and infill density. Tensile tests were conducted to evaluate the effects of these variables. Among the tested conditions, a 50% (w/v) concentration allowed for a broader operational window, enabling fabrication across a range of printing speeds and pressures. At a printing speed of 5 mm s-1, PCL-DCM exhibited a Young's modulus of 39.0 MPa, while PCL-CF samples printed at 10 mm s-1 achieved the highest modulus of 32.0 MPa. Notably, when the printing speed was kept constant, applying higher pressures led to an increase in Young's modulus, suggesting that pressure plays a key role in enhancing scaffold stiffness. When comparing the 50% and 75% (w/v) polymer concentrations, the 50% (w/v) formulation stood out by offering both higher elongation and greater stiffness, which makes it particularly suitable for load-bearing applications. These findings provide a quantitative framework for optimizing extrusion-based bioprinting of PCL scaffolds, with implications for customized biomedical implants and regenerative medicine.Article Citation - WoS: 1Citation - Scopus: 1Prediction of the Diffusible Hydrogen Concentration After Electrochemical Charging Utilizing Artificial Intelligence(IOP Publishing Ltd, 2025-09-01) Sivesoglu, Abdurrahman; Li, Yang; Bal, BurakThe concentration of diffusible hydrogen in a material is of high importance as it helps to predict the hydrogen embrittlement effect in the material, and the amount of mechanical properties' degradation after reaching a critical concentration. Despite that, a simple experimental setup is not available to measure hydrogen concentration at service. In this paper, a multi-layer perceptron (MLP) model is developed using weight initialization, which can estimate the diffusible hydrogen concentration of Face-Centred-Cubic (FCC) metals after electrochemical charging. The input properties of the model include the electrochemical charging parameters of current density, temperature, and charging time as well as the grain size of the specimen. The MLP model with and without the weight initialization was validated and tested with unseen test dataset. The model in both cases showed an excellent predictive performance with a higher accuracy and faster convergence when using weight initialization. A linear correlation of 89% between the experimental and predicted hydrogen concentration was observed. This demonstrates that for the family of FCC metals under electrochemical charging, the estimation of diffusible hydrogen concentration is a feasible path for material safety design analysis.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.