WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 38Citation - Scopus: 38pH- and Temperature-Responsive Amphiphilic Diblock Copolymers of 4-Vinylpyridine and Oligoethyleneglycol Methacrylate Synthesized by RAFT Polymerization(Elsevier Sci Ltd, 2014-01) Topuzogullari, Murat; Bulmus, Volga; Dalgakiran, Eray; Dincer, SevilDiblock copolymers of 4-vinylpyridine (4VP) and oligoethyleneglycol methyl ether methacrylate (OEGMA) were synthesized for the first time using RAFT polymerization technique as potential drug delivery systems. Effects of the number of ethylene glycol units in OEGMA, chain length of hydrophobic P4VP block, pH, concentration and temperature on the solution behavior of the copolymers were investigated comprehensively. Copolymer chains formed micelles at pH values higher than 5 whereas unimeric polymers were observed to exist below pH 5, owing to the repulsion between positively charged P4VP blocks. The size of the micelles was dependent on the relative length of blocks, P4VP and POEGMA. Thermo-responsive properties of copolymers were investigated depending on the pH and length of P4VP block. The increase in the length of P4VP block decreased the LCST substantially at pH 7. At pH 3, LCST of copolymers shifted to higher temperatures due to the increased interaction of copolymers with water through positively charged P4VP block. (C) 2013 Elsevier Ltd. All rights reserved.Article Citation - WoS: 24Citation - Scopus: 33Variable Structure Controllers for Unstable Processes(Elsevier Sci Ltd, 2015-08) Ablay, GunyazA variable structure control (VSC) method for unstable industrial processes is proposed. The proposed control method is able to provide a highly satisfactory system performance and to tackle with robustness issues of the processes in the presence of uncertainties. An ITAE-based numerical tuning algorithm for acquiring optimal control parameters, and a direct auto-tuning mechanism for the proposed controller are also provided. The performance of the proposed VSC method is illustrated on some unstable process models including a continuous stirred tank reactor (CSTR), in order to show its effectiveness, validity and feasibility. (C) 2015 Elsevier Ltd. All rights reserved.Article Citation - WoS: 56Citation - Scopus: 69Synchrosqueezing Transform Based Feature Extraction From EEG Signals for Emotional State Prediction(Elsevier Sci Ltd, 2019-07) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentThis paper presents a novel method for emotion recognition based on time-frequency analysis using multivariate synchrosqueezing transform (MSST) of multichannel electroencephalography (EEG) signals. With the advancements of the multichannel sensor applications, the need for multivariate algorithms has become obvious for extracting features that stem from multichannel dependency in addition to mono-channel features. In order to model the joint oscillatory structure of these multichannel signals, MSST has recently been proposed. It uses the concepts of joint instantaneous frequency and bandwidth. Electrophysiological data processing mostly requires joint time-frequency analysis in addition to both time and frequency analysis separately. The short-time Fourier transform (STFT) and wavelet transform (WT) are the main approaches utilized in time-frequency analysis. In this paper, the feasibility and performance of multivariate wavelet-based synchrosqueezing algorithm was demonstrated on EEG signals obtained from publically available DEAP database by comparing with its univariate version. Eight emotional states were considered by combining arousal-valence and dominance dimensions. Using linear support vector machines (SVM) as a classifier, MSST and its univariate version resulted in the highest prediction accuracy rates of (9) over tilde3% among all emotional states. (C) 2019 Elsevier Ltd. All rights reserved.Article Citation - WoS: 9Citation - Scopus: 13Student Performance Under Asynchronous and Synchronous Methods in Distance Education: A Quasi-Field Experiment(Elsevier Sci Ltd, 2022-11) Demirtas, Burak Kagan; Turk, UmutThis study examines student performance under asynchronous and synchronous methods in a microeconomics course during COVID-19 pandemic. We conduct a quasi-field experiment in a state university in Turkey. In the experiment, students were divided into synchronous and asynchronous groups and were taught the same weekly material of microeconomics by the methods respective to their group. At the end of the week, both groups took the same multiple question test. Our results showed that asynchronous group performed significantly better than the synchronous group. While showing the comparative advantage of the asynchronous method, our study also underlines the importance of interaction between instructors and students. We discuss our findings from a socioeconomic perspective, where we argue that the flexibility that the asynchronous method offers might have compensated for the accessibility issues (internet and/or computer) during the COVID-19 outbreak. As a policy recommendation, universities can offer lectures with a recorded option to allow students to interact with the course material multiple times.Article Citation - WoS: 18Citation - Scopus: 21Parameter Investigation of Topological Data Analysis for EEG Signals(Elsevier Sci Ltd, 2021-01) Altindis, Fatih; Yilmaz, Bulent; Borisenok, Sergey; Icoz, KutayTopological data analysis (TDA) methods have become appealing in EEG signal processing, because they may help the scientists explore new features of complex and large amount of data by simplifying the process from a geometrical perspective. Time delay embedding is a common approach to embed EEG signals into the state space. Parameters of this embedding method are variable and the structure of the state space can be entirely different depending on their selection. Additionally, extracted persistent homologies of the state spaces depend on filtration level and the number of points used. In this study, we showed how to adapt false nearest neighbor (FNN) test to find out the suitable/optimal time embedding parameters (i.e., time delay and embedding dimension) for EEG signals, and compared their effects on different types of artefacts and motor intention waves that are commonly used in brain-computer interfaces. We extracted and compared persistent homologies of state spaces that were reconstructed with four different sets of parameters. Later, the effect of filtration level on extracted persistent homologies was compared, and statistical significance levels were computed between leftand right-hand movement imaginations. Finally, computational cost of the discussed methods was found, and the adaptability of this method to a real-time application was evaluated. We demonstrated that the discussed parameters of the TDA approach were highly crucial to extract true topological features of the EEG signals, and the adapted testing approaches depicted the applicability of this approach on real-time analysis of EEG signals.Article Citation - WoS: 10Citation - Scopus: 13Deep Learning Based Semantic Segmentation and Quantification for MRD Biochip Images(Elsevier Sci Ltd, 2022-08) Celebi, Fatma; Tasdemir, Kasim; Icoz, KutayMicrofluidic platforms offer prominent advantages for the early detection of cancer and monitoring the patient response to therapy. Numerous microfluidic platforms have been developed for capturing and quantifying the tumor cells integrating several readout methods. Earlier, we have developed a microfluidic platform (MRD Biochip) to capture and quantify leukemia cells. This is the first study which employs a deep learning-based segmentation to the MRD Biochip images consisting of leukemic cells, immunomagnetic beads and micropads. Implementing deep learning algorithms has two main contributions; firstly, the quantification performance of the readout method is improved for the unbalanced dataset. Secondly, unlike the previous classical computer vision -based method, it does not require any manual tuning of the parameters which resulted in a more generalized model against variations of objects in the image in terms of size, color, and noise. As a result of these benefits, the proposed system is promising for providing real time analysis for microfluidic systems. Moreover, we compare different deep learning based semantic segmentation algorithms on the image dataset which are acquired from the real patient samples using a bright-field microscopy. Without cell staining, hyper-parameter optimized, and modified U-Net semantic segmentation algorithm yields 98.7% global accuracy, 86.1% mean IoU, 92.2% mean precision, 92.2% mean recall and 92.2% mean F-1 score measure on the patient dataset. After segmentation, quantification result yields 89% average precision, 97% average recall on test images. By applying the deep learning algorithms, we are able to improve our previous results that employed conventional computer vision methods.Article Citation - WoS: 22Citation - Scopus: 29Automated Quantification of Immunomagnetic Beads and Leukemia Cells from Optical Microscope Images(Elsevier Sci Ltd, 2019-03) Uslu, Fatma; Icoz, Kutay; Tasdemir, Kasim; Yilmaz, BulentQuantification of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quantification of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identification and machine learning based methods were implemented to quantify targets in the images recorded by a bright field microscope. Images acquired by a 40x or a 20x objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40x objective and 79.7% for the 20x objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection. (C) 2019 Elsevier Ltd. All rights reserved.Article Citation - WoS: 14Citation - Scopus: 15An Efficient and Facile Method of Grafting Allyl Groups to Chemically Resistant Polyketone Membranes(Elsevier Sci Ltd, 2018-04) Jung, Youn Seo; Canlier, Ali; Hwang, Taek SungPolyketone is a thermoplastic polymer known for its strong mechanical properties and chemical resistance. Such superiorities make it difficult to process and chemically modify for further functionalizations and applications. In this work, we introduce a novel method for functionalizing the alpha carbon of polyketone. We succeeded to attach allyl groups to the backbone of polyketone by a heterogeneous reaction between polyketone enolate and allyl bromide. Allylated polyketone is not soluble in common solvents. Since we started with a membrane of polyketone, there is no need to cast again. Further functionalization is possible through pending allyl groups via alkene addition reactions and ionic or radicalic polymerization. FTIR, elemental analysis, solid NMR, FT-Raman, SEM and XPS methods were employed to confirm the elemental composition, molecular structure and morphology. In addition, X-ray diffractometer (XRD), UVeVisible spectroscopy and thermal analysis were used to investigate the crystal structure, physical and electronic properties. (c) 2018 Elsevier Ltd. All rights reserved.