WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
Browse
305 results
Search Results
Now showing 1 - 10 of 305
Article Deposition of (La,Sr)CoO₃-δ and (La,Sr)₂CoO₄-δ Cathode Layers on Gadolinia-Doped Ceria by Electrospray Deposition(Springer, 2025) Ergen, Emre; Akkurt, SedatLa-, Sr-, and Co-based oxides have proven their performances in the cathode layers of intermediate temperature levels of solid oxide fuel cells (SOFC), and hence have been frequently studied. They are deposited on the electrolyte layer by chemical vapor deposition (CVD), screen printing, pulsed laser deposition (PLD), etc. The electrospray deposition (ESD) proved itself as an effective and facile method for cathode deposition. Cathode layers deposited on gadolinia-doped ceria (GDC) with the compositions of (La0.5Sr0.5)CoO3, (La0.8Sr0.2)CoO3, (La0.5Sr0.5)2CoO4, and (La0.8Sr0.2)2CoO4 are known to provide low resistance values which are critical in cell performance. In this study, ESD is used for the first time as the coating method of these compositions. Area-specific resistance (ASR) measurements made by electrochemical impedance spectroscopy (EIS) showed promising results. Particularly, the sample coated in (La0.5Sr0.5)CoO3 composition showed an ASR value of 0.11 Omega.cm2 at 700 degrees C. ESD showed the ability to control the cathode coating microstructure by controlling the spraying parameters.Correction Citation - WoS: 11Measurement of Jet Multiplicity Distributions in T(t)over-Bar Production in pp Collisions at √s = 7 TeV (Vol 74, 3014, 2014)(Springer, 2015) Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Woods, N.Article Citation - WoS: 15Search for Supersymmetry in pp Collisions at √s=7 TeV in Events With a Single Lepton, Jets, and Missing Transverse Momentum(Springer, 2013) Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Aguilo, E.; Swanson, J.Results are reported from a search for new physics processes in events containing a single isolated high-transverse-momentum lepton (electron or muon), energetic jets, and large missing transverse momentum. The analysis is based on a 4.98 fb(-1) sample of proton-proton collisions at a center-of-mass energy of 7 TeV, obtained with the CMS detector at the LHC. Three separate background estimation methods, each relying primarily on control samples in the data, are applied to a range of signal regions, providing complementary approaches for estimating the background yields. The observed yields are consistent with the predicted standard model backgrounds. The results are interpreted in terms of limits on the parameter space for the constrained minimal supersymmetric extension of the standard model, as well as on cross sections for simplified models, which provide a generic description of the production and decay of new particles in specific, topology based final states.Article Citation - WoS: 7Citation - Scopus: 9A Holistic Overview of the Applications of Grace-Observed Terrestrial Water Storage in Hydrology and Climate Science(Springer, 2025) Khorrami, Behnam; Gunduz, OrhanTerrestrial Water Storage (TWS) represents a vital element of the hydrological cycle, with its fluctuations significantly impacting the climate of the Earth and its ecological balance. Since its launch in 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission has revolutionized the ability to observe and analyze large-scale mass changes within Earth's system components. This paper offers a comprehensive and current overview of GRACE satellite gravimetry, highlighting its relevance to hydrological and climate-related studies. It outlines the fundamental measurement principles of the GRACE mission, provides an in-depth explanation of GRACE data products (including spherical harmonic and mascon solutions), examines emerging trends in GRACE-based research, and reviews key applications in hydrology and climate science. Additionally, it addresses the major challenges in utilizing GRACE data and explores promising avenues for future research and applications.Article Vision Transformers-Based Deep Feature Generation Framework for Hydatid Cyst Classification in Computed Tomography Images(Springer, 2025) Sagik, Metin; Gumus, AbdurrahmanHydatid cysts, caused by Echinococcus granulosus, form progressively enlarging fluid-filled cysts in organs like the liver and lungs, posing significant public health risks through severe complications or death. This study presents a novel deep feature generation framework utilizing vision transformer models (ViT-DFG) to enhance the classification accuracy of hydatid cyst types. The proposed framework consists of four phases: image preprocessing, feature extraction using vision transformer models, feature selection through iterative neighborhood component analysis, and classification, where the performance of the ViT-DFG model was evaluated and compared across different classifiers such as k-nearest neighbor and multi-layer perceptron (MLP). Both methods were evaluated independently to assess classification performance from different approaches. The dataset, comprising five cyst types, was analyzed for both five-class and three-class classification by grouping the cyst types into active, transition, and inactive categories. Experimental results showed that the proposed VIT-DFG method achieves higher accuracy than existing methods. Specifically, the ViT-DFG framework attained an overall classification accuracy of 98.10% for the three-class and 95.12% for the five-class classifications using 5-fold cross-validation. Statistical analysis through one-way analysis of variance (ANOVA), conducted to evaluate significant differences between models, confirmed significant differences between the proposed framework and individual vision transformer models (p<0.05\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.05$$\end{document}). These results highlight the effectiveness of combining multiple vision transformer architectures with advanced feature selection techniques in improving classification performance. The findings underscore the ViT-DFG framework's potential to advance medical image analysis, particularly in hydatid cyst classification, while offering clinical promise through automated diagnostics and improved decision-making.Editorial Editorial: Advancing Biotechnology in Turkiye: a Dedication To All Women(Springer, 2025) Cadirci, Bilge Hilal; Buyukkileci, Ali Oguz; Binay, BarisArticle Citation - WoS: 6Citation - Scopus: 7Physics-Based Machine Learning for Modeling of Laminated Composite Plates Based on Refined Zigzag Theory(Springer, 2025) Ermis, Merve; Dorduncu, Mehmet; Aydogan, GokayPhysics-based machine learning techniques have recently gained prominence for their ability to model complex material and structural behavior, particularly in laminated composite structures. This study introduces an innovative approach, being the first to employ physics-informed neural networks (PINNs) in conjunction with refined zigzag theory (RZT) for the stress analysis of laminated composite plates. A multi-objective loss function integrates governing partial differential equations (PDEs) and boundary conditions, embedding physical principles into the analysis. Using multiple fully connected artificial neural networks, called feedforward deep neural networks, tailored to handle PDEs, PINNs are trained using automatic differentiation. This training process minimizes a loss function that incorporates the PDEs governing the underlying physical laws. RZT, particularly suitable for the stress analysis of thick and moderately thick plates, simplifies the formulation by using only seven kinematic variables, eliminating the need for shear correction factors. The capability of the proposed method is validated through several benchmark cases in stress analysis, including 3D elasticity solutions, analytical solutions, and experimental results from a three-point bending test based on displacement measurements reported in the literature. These results show consistent agreement with the referenced solutions, confirming the accuracy and reliability of the proposed method. Comprehensive evaluations are conducted to examine the effects of softcore presence, elastic foundation, various lamination schemes, and differing loading and boundary conditions on the stress distribution in laminated plates.Article Citation - WoS: 4Citation - Scopus: 4Optimization of Resource-Aware Parallel and Distributed Computing: a Review(Springer, 2025) Czarnul, Pawel; Antal, Marcel; Baniata, Hamza; Griebler, Dalvan; Kertesz, Attila; Kessler, Christoph W.; Rakic, GordanaThis paper presents a review of state-of-the-art solutions concerning the optimization of computing in the field of parallel and distributed systems. Firstly, we contribute by identifying resources and quality metrics in this context including servers, network interconnects, storage systems, computational devices as well as execution time/performance, energy, security, and error vulnerability, respectively. We subsequently identify commonly used problem formulations and algorithms for integer linear programming, greedy algorithms, dynamic programming, genetic algorithms, particle swarm optimization, ant colony optimization, game theory, and reinforcement learning. Afterward, we characterize frequently considered optimization problems by stating these terms in domains such as data centers, cloud, fog, blockchain, high performance, and volunteer computing. Based on the extensive analysis, we identify how particular resources and corresponding quality metrics are considered in these domains and which problem formulations are used for which system types, either parallel or distributed environments. This allows us to formulate open research problems and challenges in this field and analyze research interest in problem formulations/domains in recent years.Article Citation - WoS: 1Citation - Scopus: 1Efficiency Evaluation of Optimal Tlcd and Tmd for the Seismic Response Reduction of Buildings Considering Soil-Structure Interaction Effect(Springer, 2025) Roozbahan, Mostafa; Masnata, Chiara; Turan, Guersoy; Pirrotta, AntoninaTuned Mass Dampers (TMDs) and Tuned Liquid Column Dampers (TLCDs) are widely recognized passive vibration control devices used to reduce structural vibrations. While TMDs have been extensively studied for mitigating the seismic responses of multi-story buildings considering Soil-Structure Interaction (SSI), the efficiency of TLCDs in these conditions remains largely unexplored. Furthermore, a direct comparison of these devices under similar conditions has not been conducted. Then, to address these gaps, this study investigates the efficiency of TLCDs and compares them to TMDs in reducing seismic-induced vibrations, focusing on the influence of SSI. The control performance of both devices depends on various parameters, primarily the frequency and damping ratios. Therefore, the Mouth Brooding Fish (MBF) metaheuristic algorithm is applied to optimize these parameters, accounting for SSI effects. To evaluate the different efficiency between TMDs and TLCDs under SSI conditions, three types of shear buildings are considered: an eight-story, a sixteen-story and a forty-story structure. The seismic responses of the uncontrolled, TMD-controlled, and TLCD-controlled buildings are examined under twenty-two far-field and fourteen near-field earthquakes, considering both fixed-base and flexible-base scenarios. Results indicate that while both devices significantly reduce seismic responses, TMDs generally outperform TLCDs, particularly in taller buildings where the impact of SSI is more significant. Further, this study highlights that neglecting SSI in the design of these devices may lead to an overestimation of their effectiveness, especially in softer soils, emphasizing the importance of considering SSI in the optimization process for accurate and reliable outcomes.Article Ggnn: Group-Guided Nearest Neighbors for Efficient Image Matching(Springer, 2025) Cine, Ersin; Bastanlar, Yalin; Ozuysal, MustafaThe widely adopted image matching approach remains dependent on exhaustive matching of local features across images. Existing methods aiming to improve efficiency either approximate nearest neighbor (NN) search, compromising accuracy, or apply filtering only after establishing tentative matches, which restricts potential efficiency gains. We challenge the assumption that exhaustive NN search is necessary by proposing a more efficient hierarchical approach that maintains matching accuracy without relying on full-scale NN search. Our key insight is that efficiently identifying sufficiently similar, geometrically meaningful feature matches-rather than the most similar but geometrically random ones-can improve or maintain performance at a lower computational cost. We propose a novel method, Group-Guided Nearest Neighbors (GGNN), which matches groups of features first and then matches individual features only within these matched groups. This hierarchical pipeline reduces the computational complexity of feature matching from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta (n<^>2)$$\end{document} to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta (n \sqrt{n})$$\end{document}, significantly improving efficiency. Experimental results on homography estimation demonstrate that GGNN outperforms standard NN search while achieving performance comparable to state-of-the-art methods. Additionally, we formulate GGNN as a general framework, where conventional NN search is a special case with a single global feature group. This formulation provides a continuum of feature matching methods with varying computational costs, enabling automatic selection based on a given time budget.