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Now showing 1 - 4 of 4
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    A model of non-minimally coupled gravitation and electromagnetism in (1+2) dimensions
    (IOP Publishing Ltd, 2022) Ünlütürk, Kıvanç İbrahim; Yetişmişoğlu, Cem; PhD Student; Phd Student; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; N/A; N/A
    Following earlier works of Dereli and collaborators, we study a three dimensional toy model where we extend the topologically massive gravity with electrodynamics by the most general RF 2-type non-minimal coupling terms. Here R denotes the possible curvature terms and F denotes the electromagnetic 2-form. We derive the variational feld equations and look for exact solutions on constant negative curvature space-times with a constant, self-dual electromagnetic feld. The notion of self-dual electromagnetic felds in three dimensions is introduced by Dereli and collaborators in the study of exact solutions of models with gravity-electromagnetism couplings. We note the conditions that the parameters of the model have to satisfy for these self-dual solutions to exist.
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    Machine learning assisted design of biomedicel high entropy alloys with low elastic modulus for orthopedic applications
    (Koç University, 2024) Özdemir, Hüseyin Can; Canadinç, Demircan; 0000-0001-9961-7702; Koç University Graduate School of Sciences and Engineering; Mechanical Engineering; 23433
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    Optimizing mechanical properties and Ag ion release rate of silver coatings deposited on Ti-based high entropy alloys
    (Elsevier Ltd, 2023) Yilmaz R.; N/A; N/A; N/A; Department of Mechanical Engineering; Özdemir, Hüseyin Can; Yağcı, Mustafa Barış; Kılıç, Elif Bedir; Canadinç, Demircan; PhD Student; Researcher; PhD Student; Faculty Member; Department of Mechanical Engineering; Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); Graduate School of Sciences and Engineering; N/A; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 23433
    This paper details the characterization of microstructure, texture, mechanical properties, and ion release behavior of antibacterial Ag thin films sputtered on two novel biomedical high entropy alloys (HEAs), namely the Ti23Ta10Hf27Nb12Zr28 (HEA–Ti23) and Ti28Ta10Hf30Nb14Zr18 (HEA–Ti28) alloys. Specifically, the influences of varying deposition time and Ar flow rate were investigated to reveal the mechanisms dictating the microstructure, texture, and mechanical properties of the coatings. In addition, static immersion experiments were carried out in simulated body fluid (SBF) for 28 days to establish the relationship between ion release from the coatings and the deposition parameters, microstructure, and surface texture. It was shown that texture evolution in Ag thin films depends on both film thickness and Ar flow rate, such that there exists a critical thickness at which the energy minimization mechanism is altered. A very good correlation was also observed between an increase in (111) peak intensity and a decrease in released Ag ion fraction. Overall, the findings of the work presented herein suggest that the alterations in Ag deposition parameters could be optimized to obtain the desired mechanical properties while enhancing the biocompatibility of the HEA substrates by coating them with antibacterial Ag films. 2023 Elsevier B.V.
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    PySio: a new python toolbox for physiological signal visualization and feature analysis
    (Verasonics, 2022) Department of Electrical and Electronics Engineering; Department of Computer Engineering; Gürsoy, Beren Semiz; Nacitarhan, Özgün Ozan; Faculty Member; Undergraduate Student; Department of Electrical and Electronics Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; 332403; N/A
    In physiological signal analysis, identifying meaningful relationships and inherent patterns in signals can provide valuable information regarding subjects' physiological state and changes. Although MATLAB has been widely used in signal processing and feature analysis, Python has recently dethroned MATLAB with the rise of data science, machine learning and artificial intelligence. Hence, there is a compelling need for a Python package for physiological feature analysis and extraction to achieve compatibility with downstream models often trained in Python. Thus, we present a novel visualization and feature analysis Python toolbox, PySio, to enable rapid, efficient and user-friendly analysis of physiological signals. First, the user should import the signal-of-interest with the corresponding sampling rate. After importing, the user can either analyze the signal as it is, or can choose a specific region for more detailed analysis. PySio enables the user to (i) visualize and analyze the physiological signals (or user-selected segments of the signals) in time domain, (ii) study the signals (or user-selected segments of the signals) in frequency domain through discrete Fourier transform and spectrogram representations, and (iii) investigate and extract the most common time (energy, entropy, zero crossing rate and peaks) and frequency (spectral entropy, rolloff, centroid, spread, peaks and bandpower) domain features, all with one click. Clinical relevance - As the physiological signals originate directly from the underlying physiological events, proper analysis of the signal patterns can provide valuable information in personalized treatment and wearable technology applications.