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    One-dimensional chemotaxis kinetic model

    , Article Nonlinear Differential Equations and Applications ; Volume 18, Issue 2 , 2011 , Pages 139-172 ; 10219722 (ISSN) Sharifi tabar, M ; Sharif University of Technology
    2011
    Abstract
    In this paper, we study a variation of the equations of a chemotaxis kinetic model and investigate it in one dimension. In fact, we use fractional diffusion for the chemoattractant in the Othmar-Dunbar-Alt system (Othmer in J Math Biol 26(3):263-298, 1988). This version was exhibited in Calvez in Amer Math Soc, pp 45-62, 2007 for the macroscopic well-known Keller-Segel model in all space dimensions. These two macroscopic and kinetic models are related as mentioned in Bournaveas, Ann Inst H Poincaré Anal Non Linéaire, 26(5):1871-1895, 2009, Chalub, Math Models Methods Appl Sci, 16(7 suppl):1173-1197, 2006, Chalub, Monatsh Math, 142(1-2):123-141, 2004, Chalub, Port Math (NS), 63(2):227-250,... 

    Blow-up For Chemotaxis Models

    , Ph.D. Dissertation Sharif University of Technology Sharifi Tabar, Mohsen (Author) ; Hesaaraki, Mahmoud (Supervisor)
    Abstract
    Moving of living organisms appears in many interesting problems, e.g. the growth of bacteria colonies, tumor growth, wound healing, color patterns of animals and etc. There are many ways to model such problems and PDE theory is widely used to investigate these problems. In this thesis, we study two well-known classic models. First, macroscopic “Keller–Segel” model and then kinetic “Othmer–Dunbar–Alt” System. Since these models have a nice behavior in two dimensions that they don’t have in other dimensions, we propose a way to alter them such that they behave in this way in all dimensions. Also none of the known models have the suitable dynamics in one dimension, so our model has the property... 

    Inverse Reinforcement Learning with Gaussian Processes

    , M.Sc. Thesis Sharif University of Technology Habibi, Beheshteh (Author) ; Sharifi Tabar, Mohsen (Supervisor)
    Abstract
    Inverse reinforcement learning (IRL) is one of the machine learning frameworks based on learning from humans; That is, instead of producing a decision process maximizing a predefined reward function, seeks to find the reward function based on the observed behavior of an agent. The biggest motivation of IRL is that, usually, determining a reward function for a problem is very difficult. We consider IRL in Markov decision processes; that is, the problem of extracting a reward function with the assumption of knowing the optimal behavior. IRL could be useful for apprenticeship learning to obtain skilled behavior, and for optimizing a reward function by a natural system. We first, determine a set... 

    Analysis and data-based reconstruction of complex nonlinear dynamical systems : using the methods of stochastic processes

    , Book Rahimi Tabar, M. Reza
    Springer International Publishing  2019
    Abstract
    This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni- or multi-variant sequentially measured set of experimental data (even noisy data), how should one analyse non-parametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation?
    Here, the term "non-parametrically" exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data.
    The book provides an overview of methods that have been developed for the analysis of... 

    An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus

    , Article Arthritis Research and Therapy ; Volume 22, Issue 1 , June , 2020 Maleknia, S ; Salehi, Z ; Rezaei Tabar, V ; Sharifi Zarchi, A ; Kavousi, K ; Sharif University of Technology
    BioMed Central  2020
    Abstract
    Background: A comprehensive intuition of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, is a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to underscore key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention. Methods: In the present study, the human peripheral blood mononuclear cell (PBMC) microarray datasets (n = 6), generated by three platforms, which included SLE patients (n = 220) and healthy control samples (n = 135) were collected. Across each platform, we integrated the datasets by... 

    Modeling, simulation, and optimal initiation planning for needle insertion into the liver

    , Article Journal of Biomechanical Engineering ; Volume 132, Issue 4 , 2010 ; 01480731 (ISSN) Sharifi Sedeh, R ; Ahmadian, M. T ; Janabi Sharifi, F ; Sharif University of Technology
    2010
    Abstract
    Needle insertion simulation and planning systems (SPSs) will play an important role in diminishing inappropriate insertions into soft tissues and resultant complications. Difficulties in SPS development are due in large part to the computational requirements of the extensive calculations in finite element (FE) models of tissue. For clinical feasibility, the computational speed of SPSs must be improved. At the same time, a realistic model of tissue properties that reflects large and velocity-dependent deformations must be employed. The purpose of this study is to address the aforementioned difficulties by presenting a cost-effective SPS platform for needle insertions into the liver. The study... 

    Effects of Dynamics and Structure on Population-level Oscillations in Homogeneous Neuronal Networks

    , M.Sc. Thesis Sharif University of Technology Vatandoost kamali, Maryam (Author) ; Razvan, Mohammad Reza (Supervisor) ; Sharifi Tabar, Mohsen (Co-Advisor)
    Abstract
    Networks of neurons produce diverse patterns of oscillations, arising from the net-work’s global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mech-anisms underlying emergent oscillations in neuronal networks whose individual com- ponents, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations atthepopulation level while individualneu-rons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case... 

    Corrosion behaviour of Ni-Co alloy coatings at Kish Island (marine) atmosphere

    , Article Bulletin of Materials Science ; Vol. 37, issue. 3 , May , 2014 , p. 713-719 Sharifi, K ; Ghorbani, M ; Sharif University of Technology
    2014
    Abstract
    In this study, the corrosion behaviour of Ni-Co alloys with low Co content, electroplated on steel substrate in sulphate bath, was investigated. The morphology of coatings was studied by optical and SEM microscopy. The corrosion products were analyzed using EDX. The results showed that Ni-1%Co coatings had a better corrosion resistance 0.30, 0.92 and 3.75 mpy for atmospheric, salt spray and polarization tests, respectively. These are 0.41, 1.20 and 5.40 mpy for pure nickel coatings that indicate the least corrosion resistance. Surface analysis revealed the presence of oxides, sulphides and chlorides in corrosion products  

    Numerical solution of stochastic differential equations: diffusion and jump-diffusion processes

    , Article Understanding Complex Systems ; 2019 , Pages 129-142 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    Stochastic differential equations (SDE) play an important role in a range of application areas, including biology, physics, chemistry, epidemiology, mechanics, microelectronics, economics, and finance [1]. However, most SDEs, especially nonlinear SDEs, do not have analytical solutions, so that one must resort to numerical approximation schemes in order to simulate trajectories of the solutions to the given equation. The simplest effective computational method for approximation of ordinary differential equations is the Euler’s method. The Euler–Maruyama method is the analogue of the Euler’s method for ordinary differential equations for numerical simulation of the SDEs [2]. Another numerical... 

    Stochastic processes with jumps and non-vanishing higher-order kramers–moyal coefficients

    , Article Understanding Complex Systems ; 2019 , Pages 99-110 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    In this chapter we study stochastic processes in the presence of jump discontinuity, and discuss the meaning of non-vanishing higher-order Kramers–Moyal coefficients. We describe in details the stochastic properties of Poisson jump processes. We derive the statistical moments of the Poisson process and the Kramers–Moyal coefficients for pure Poisson jump events. Growing evidence shows that continuous stochastic modeling (white noise-driven Langevin equation) of time series of complex systems should account for the presence of discontinuous jump components [1–6]. Such time series have some distinct important characteristics, such as heavy tails and occasionally sudden large jumps.... 

    Reconstruction of stochastic dynamical equations: exemplary diffusion, jump-diffusion processes and lévy noise-driven langevin dynamics

    , Article Understanding Complex Systems ; 2019 , Pages 227-241 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    In this chapter we reconstruct stochastic dynamical equations with known drift and diffusion coefficients, as well as known properties of jumps, jump amplitude and jump rate from synthetic time series, sampled with time interval τ. The examples have Langevin (white noise- and Lévy noise-driven) and jump-diffusion dynamical equations. We also study the estimation of the Kramers–Moyal coefficients for “phase” dynamics that enable us to investigate the phenomenon of synchronisation in systems with interaction. © 2019, Springer Nature Switzerland AG  

    Influence of finite time step in estimating of the kramers–moyal coefficients

    , Article Understanding Complex Systems ; 2019 , Pages 191-205 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    Data sampled at discrete times appear as successions of discontinuous jump events, even if the underlying trajectory is continuous. In this chapter we study finite sampling τ expansion of the Kramers-Moyal conditional moments for the Langevin and jump-diffusion dynamics. Using the expansion for the Langevin dynamics, we introduce a criterion to validate the method numerically, namely, the Pawula theorem, to judge whether the fourth-order KM moment tends to zero. The criterion is a relation between the fourth- and second-order KM conditional moments for small time lag τ [1]. © 2019, Springer Nature Switzerland AG  

    Applications and Outlook

    , Article Understanding Complex Systems ; 2019 , Pages 243-260 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    The method outlined in the Chaps. 15 – 21 has been used for revealing nonlinear deterministic and stochastic behaviors in a variety of problems, ranging from physics, to neuroscience, biology and medicine. In most cases, alternative procedures with strong emphasis on deterministic features have been only partly successful, due to their inappropriate treatment of the dynamical fluctuations [1]. In this chapter, we provide a list of the investigated phenomena using the introduced reconstruction method. In the “outlook” possible research directions for future are discussed. © 2019, Springer Nature Switzerland AG  

    How to set up stochastic equations for real world processes: Markov–einstein time scale

    , Article Understanding Complex Systems ; 2019 , Pages 165-179 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    In Chaps. 16 – 21 we address a central question in the field of complex systems: Given a fluctuating (in time or space), sequentially uni- or multi-variant measured set of experimental data (even noisy data), how should one analyse the data non-parametrically, assess their underlying trends, discover the characteristics of the fluctuations, including diffusion and jump parts, and construct stochastic evolution equation for the data? © 2019, Springer Nature Switzerland AG  

    Equivalence of langevin and fokker–planck equations

    , Article Understanding Complex Systems ; 2019 , Pages 61-68 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    In this chapter we show the equivalence between the Langevin approach and the Fokker–Planck equation, and derive the equation for the statistical moments of the process whose dynamics is described by the Langevin equation. © 2019, Springer Nature Switzerland AG  

    The langevin equation and wiener process

    , Article Understanding Complex Systems ; 2019 , Pages 39-48 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    In this chapter we introduce the Langevin equation and Wiener process. All the statistical properties of Wiener process will be presented and discussed. © 2019, Springer Nature Switzerland AG  

    Introduction

    , Article Understanding Complex Systems ; 2019 , Pages 1-8 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    Complex systems are composed of a large number of subsystems that may interact with each other. The typically nonlinear and multiscale interactions often lead to large-scale behaviors, which are not easily predicted from the knowledge of only the behavior of individual subsystems. © 2019, Springer Nature Switzerland AG  

    Epileptic brain dynamics

    , Article Understanding Complex Systems ; 2019 , Pages 261-271 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    As an example of the analysis and reconstruction of nonlinear stochastic time series, we consider an important medical problem, namely, epileptic brain dynamics. © 2019, Springer Nature Switzerland AG  

    The kramers–moyal coefficients of non-stationary time series and in the presence of microstructure (measurement) noise

    , Article Understanding Complex Systems ; 2019 , Pages 181-189 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    Most real world time series have transient behaviours and are non-stationary. They exhibit different type of non-stationarities, such as trends, cycles, random-walking, and generally exhibit strong intermittency. Therefore local stochastic characteristics of time series, such as the drift and diffusion coefficients, as well as the jump rate and jump amplitude, will provide very important information for understanding and quantifying “real time” variability of time series. For diffusive processes the systems have a longer memory and a higher correlation time scale and, therefore, one expects the stochastic features of dynamics to change slowly. In contrast, a rapid change of dynamics with... 

    The friedrich–peinke approach to reconstruction of dynamical equation for time series: complexity in view of stochastic processes

    , Article Understanding Complex Systems ; 2019 , Pages 143-164 ; 18600832 (ISSN) Rahimi Tabar, M. R ; Sharif University of Technology
    Springer Verlag  2019
    Abstract
    In this chapter we study stochastic properties of spatially- and temporally-disordered structures, such as turbulence and rough surfaces, or temporal fluctuations of given time series, in scale. Experimental observables include the field increments, such as the difference in the velocity field between two points separated by a distance r, or difference of time series in a time lag r. Therefore, the lag r can be either spatial distance or a time interval. The change of the increments’ fluctuations as a function of the scale r can then be viewed as a stochastic process in a length or time scale and can, quite often, after pioneering work by Friedrich & Peinke, be mapped onto the mathematical...