7,060 research outputs found

    Lepton flavor violating Higgs boson decays in seesaw models: new discussions

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    The lepton flavor violating decay of the Standard Model-like Higgs boson (LFVHD), h->\mu\tau, is discussed in seesaw models at the one-loop level. Based on particular analytic expressions of Passarino-Veltman functions, the two unitary and 't Hooft Feynman gauges are used to compute the branching ratio of LFVHD and compare with results reported recently. In the minimal seesaw (MSS) model, the branching ratio was investigated in the whole valid range 10^{-9}-10^{15} GeV of new neutrino mass scale m_{n_6}. Using the Casas-Ibarra parameterization, this branching ratio enhances with large and increasing m_{n_6}. But the maximal value can reach only order of 10^{-11}. Interesting relations of LFVHD predicted by the MSS and inverse seesaw (ISS) model are discussed. The ratio between two LFVHD branching ratios predicted by the ISS and MSS is simply m^2_{n_6}\mu^{-2}_X, where \mu_X is the small neutrino mass scale in the ISS. The consistence between different calculations is shown precisely from analytical approach.Comment: 4 figures, 26 pages, some analytic formulas and statements are corrected. Main results are unchanged. New references added. Version published in NP

    An isogeometric analysis for elliptic homogenization problems

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    A novel and efficient approach which is based on the framework of isogeometric analysis for elliptic homogenization problems is proposed. These problems possess highly oscillating coefficients leading to extremely high computational expenses while using traditional finite element methods. The isogeometric analysis heterogeneous multiscale method (IGA-HMM) investigated in this paper is regarded as an alternative approach to the standard Finite Element Heterogeneous Multiscale Method (FE-HMM) which is currently an effective framework to solve these problems. The method utilizes non-uniform rational B-splines (NURBS) in both macro and micro levels instead of standard Lagrange basis. Beside the ability to describe exactly the geometry, it tremendously facilitates high-order macroscopic/microscopic discretizations thanks to the flexibility of refinement and degree elevation with an arbitrary continuity level provided by NURBS basis functions. A priori error estimates of the discretization error coming from macro and micro meshes and optimal micro refinement strategies for macro/micro NURBS basis functions of arbitrary orders are derived. Numerical results show the excellent performance of the proposed method

    Researchers who lead the trends

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    Xuan-Hung Doan, Phuong-Tram T. Nguyen, Viet-Phuong La, Hong-Kong T. Nguyen (2019). Chapter 5. Researchers who lead the trends. In Quan-Hoang Vuong, Trung Tran (Eds.), The Vietnamese Social Sciences at a Fork in the Road (pp. 98–120). Warsaw, Poland: De Gruyter. DOI:10.2478/9783110686081-010 Online ISBN: 9783110686081 © 2019 Sciend

    A novel approach to security enhancement of chaotic DSSS systems

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    In this paper, we propose a novel approach to the enhancement of physical layer security for chaotic direct-sequence spread-spectrum (DSSS) communication systems. The main idea behind our proposal is to vary the symbol period according to the behavior of the chaotic spreading sequence. As a result, the symbol period and the spreading sequence vary chaotically at the same time. This simultaneous variation aims at protecting DSSS-based communication systems from the blind estimation attacks in the detection of the symbol period. Discrete-time models for spreading and despreading schemes are presented and analyzed. Multiple access performance of the proposed technique in the presence of additional white Gaussian noise (AWGN) is determined by computer simulations. The increase in security at the physical layer is also evaluated by numerical results. Obtained results show that our proposed technique can protect the system against attacks based on the detection of the symbol period, even if the intruder has full information on the used chaotic sequence.Peer ReviewedPostprint (author's final draft
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