Browsing by Author "Baysazan, Emir"

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    A Coordinate-Free Approach to Obtaining Exact Solutions in General Relativity: The Newman-Unti-Tamburino Solution Revisited
    (Springer/Plenum Publishers, 2026) Bilge, Ayse Humeyra; Dereli, Tekin; Baysazan, Emir; Birkandan, Tolga
    The Newman-Unti-Tamburino (NUT) solution is characterized as the unique Petrov Type D vacuum metric such that the two double principal null directions form an integrable distribution. The uniqueness of the NUT is established by evaluating the integrability conditions of the Newman-Penrose equations up to SL(2,C)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$SL(2,\mathbb {C})$$\end{document} transformations, resulting in a coordinate-free characterization of the solution.
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    Citation - WoS: 1
    Citation - Scopus: 1
    Covid-19 Modeling Based on Real Geographic and Population Data
    (Tubitak Scientific & Technological Research Council Turkey, 2023) Baysazan, Emir; Berker, A. Nihat; Mandal, Hasan; Kaygusuz, Hakan
    Background/aim: Intercity travel is one of the most important parameters for combating a pandemic. The ongoing COVID-19 pandemic has resulted in different computational studies involving intercity connections. In this study, the effects of intercity connections during an epidemic such as COVID-19 are evaluated using a new network model. Materials and methods: This model considers the actual geographic neighborhood and population density data. This new model is applied to actual Turkish data by means of provincial connections and populations. A Monte Carlo algorithm with a hybrid lattice model is applied to a lattice with 8802 data points. Results: Around Monte Carlo step 70, the number of active cases in Turkiye reaches up to 8.0% of the total population, which is followed by a second wave at around Monte Carlo step 100. The number of active cases vanishes around Monte Carlo step 160. Starting with Istanbul, the epidemic quickly expands between steps 60 and 100. Simulation results fit the actual mortality data in Turkiye. Conclusion: This model is quantitatively very efficient in modeling real-world COVID-19 epidemic data based on populations and geographical intercity connections, by means of estimating the number of deaths, disease spread, and epidemic termination.