Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 1Modeling and Simulation of Advanced Nuclear Reactors(IEEE Computer Society help@computer.org, 2013-11) Ablay, GünyazAdvanced nuclear power plants are currently being proposed with a number of various designs. However, there is a lack of modeling and control strategies to deal with load following operations. This research investigates a possible modeling approach for advanced nuclear power plants in order to provide an assessment way to the concept designs. The modeling approach covers linear and nonlinear reactor modeling and linear modeling of heat exchanger-turbine- generator systems. Numerical results are presented on an example molten-salt type nuclear power plant system to demonstrate the validity and effectiveness of the modeling approach. © 2013 IEEE. © 2014 Elsevier B.V., All rights reserved.Conference Object Metabolomics Data Analysis to Discover Chronic Granulomatous Disease-Associated Biomarkers Utilizing G-S-M Machine Learning Model via Grouping Metabolites According to Ion Type(Institute of Electrical and Electronics Engineers Inc., 2024-10-16) Ersöz, Nur Sebnem; Bakir-Güngör, Burcu; Yousef, MalikChronic Granulomatous Disease (CGD) is a rare, inherited immunodeficiency disorder characterized by white blood cells unable to effectively kill certain bacteria and fungi. This defect results in the formation of clusters of immune cells called granulomas that form at sites of infection or inflammation. Therefore, identification of disease-related biomarkers is a critical step in advancing precision medicine and improving diagnostic accuracy. In this study, we applied a G-S-M machine learning approach to metabolomics data to uncover CGD-Associated biomarkers. We obtained a metabolomics dataset from Gene Expression Omnibus with GSE220260 accession number. Data includes 85 samples (16 healthy controls and 69 CGD samples) with comprehensive metabolic profiles obtained using liquid chromatography-mass spectrometry analysis. Dataset includes metabolite names with their ion type and formula. In order to identify CGD related metabolites and their ion types, G-S-M was used as a grouping function when performing machine learning oriented metabolomics data analysis. We have performed the G-S-M approach by grouping metabolites according to their ion type. In the training part of the G-S-M approach, metabolites annotated with selected ion types have been utilized to perform a two-class classification task which generates an important set of ion type output. We also compared the performance results of the G-S-M machine learning model with traditional feature selection methods; XGB, SKB, IG, FCBF, MRMR, CMIM with random forest classifier. 100 times Monte-Carlo Cross Validation was used in our experiments. It was observed that G-S-M, XGB, SKB and FCBF methods similarly provided the best performances. In this study, besides its performance, G-S-M method used groups based on ion types unlike TFS, and then identified relevant Chronic Granulomatous Disease-associated metabolites. © 2024 Elsevier B.V., All rights reserved.Conference Object A Horizontal Magnetic Tweezer for Single Molecule Micromanipulations(Institute of Electrical and Electronics Engineers Inc., 2018-10) Ablay, Günyaz; Böyük, Mustafa; Eroǧlu, Yakup; İçöz, KutayMagnetic tweezers are able to manipulate cells or biomolecules for various applications and measurements. In this work, a horizontal magnetic tweezer is designed, modeled and controlled for single molecule manipulations. A method is presented for dynamic modeling of horizontal magnetic tweezers. A linear control method is designed to ensure a wide range of operation conditions with zero steady-state error. The horizontal magnetic tweezer is able to generate a wide range of piconewton scale forces on a superparamagnetic microparticle for single molecule separation, and biosensor developments. © 2019 Elsevier B.V., All rights reserved.