146,155 research outputs found
The Lyman <span class='mathrm'>α</span> and Lyman <span class='mathrm'>β</span> lines in solar coronal streamers
No abstract available
Inflation from Geometrical Tachyons
We propose an alternative formulation of tachyon inflation using the
geometrical tachyon arising from the time dependent motion of a BPS -brane
in the background geometry due to parallel 5-branes arranged around a
ring of radius . Due to the fact that the mass of this geometrical tachyon
field is times smaller than the corresponding open-string tachyon
mass, we find that the slow roll conditions for inflation and the number of
e-foldings can be satisfied in a manner that is consistent with an effective
4-dimensional model and with a perturbative string coupling. We also show that
the metric perturbations produced at the end of inflation can be sufficiently
small and do not lead to the inconsistencies that plague the open string
tachyon models. Finally we argue for the existence of a minimum of the
geometrical tachyon potential which could give rise to a traditional reheating
mechanism.Comment: Latex, 20 pages, 4 figures; correction of algebraic errors in section
5 concerning the tachyon potential near its minimum. Conclusions unchange
Nodeless superconductivity in Ca3Ir4Sn13: evidence from quasiparticle heat transport
We report resistivity and thermal conductivity measurements
on CaIrSn single crystals, in which superconductivity with K was claimed to coexist with ferromagnetic spin-fluctuations. Among
three crystals, only one crystal shows a small hump in resistivity near 20 K,
which was previously attributed to the ferromagnetic spin-fluctuations. Other
two crystals show the Fermi-liquid behavior at low temperature.
For both single crystals with and without the resistivity anomaly, the residual
linear term is negligible in zero magnetic field. In low fields,
shows a slow field dependence. These results demonstrate that
the superconducting gap of CaIrSn is nodeless, thus rule out
nodal gap caused by ferromagnetic spin-fluctuations.Comment: 5 pages, 4 figure
Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach
This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the state-space model for the lateral flow immunoassay through available but short time-series measurement. The lateral flow immunoassay model is viewed as a nonlinear dynamic stochastic model consisting of the equations for the biochemical reaction system as well as the measurement output. The renowned extended Kalman filter is chosen as the importance density of the particle filter for the purpose of modeling the nonlinear lateral flow immunoassay. By using the developed particle filter, both the states and parameters of the nonlinear state-space model can be identified simultaneously. The identified model is of fundamental significance for the development of lateral flow immunoassay quantification. It is shown that the proposed particle filtering approach works well for modeling the lateral flow immunoassay.This work was supported in part by the International Science and Technology
Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant
2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
Optimal Market Timing
We use a fully-specified neoclassical model augmented with costly external equity as a laboratory to study the relations between stock returns and equity financing decisions. Simulations show that the model can simultaneously and in many cases quantitatively reproduce: procyclical equity issuance; the negative relation between aggregate equity share and future stock market returns; long-term underperformance following equity issuance and the positive relation of its magnitude with the volume of issuance; the mean-reverting behavior in the operating performance of issuing firms; and the positive long-term stock price drift of firms distributing cash and its positive relation with book-to-market. We conclude that systematic mispricing seems unnecessary to generate the return-related evidence often interpreted as behavioral underreaction to market timing.
Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering
Copyright [2011] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected].
By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, a mathematical model for sandwichtype lateral flow immunoassay is developed via short available time series. A nonlinear dynamic stochastic model is considered that consists of the biochemical reaction system equations and the observation equation. After specifying the model structure, we apply the extend Kalman filter (EKF) algorithm for identifying both the states and parameters of the nonlinear state-space model. It is shown that the EKF algorithm can accurately identify the parameters and also predict the system states in the nonlinear dynamic stochastic model through an iterative procedure by using a small number of observations. The identified mathematical model provides a powerful tool for testing the system hypotheses and also inspecting the effects from various design parameters in a both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes of the concentration of antigens and antibodies can be predicted, thereby making it possible for us to analyze, optimize and design the properties of lateral flow immunoassay devices.This work was supported in part by the International Science and Technology
Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of Fujian Province of China under Grants 2009J01280 and 2009J01281
Disk Accretion onto Magnetized Neutron Stars: The Inner Disk Radius and Fastness Parameter
It is well known that the accretion disk around a magnetized compact star can
penetrate inside the magnetospheric boundary, so the magnetospheric radius
\ro does not represent the true inner edge \rin of the disk; but
controversies exist in the literature concerning the relation between \ro and
\rin. In the model of Ghosh & Lamb, the width of the boundary layer is given
by \delta=\ro-\rin\ll\ro, or \rin\simeq\ro, while Li & Wickramasinghe
recently argued that \rin could be significantly smaller than \ro in the
case of a slow rotator. Here we show that if the star is able to absorb the
angular momentum of disk plasma at \ro, appropriate for binary X-ray pulsars,
the inner disk radius can be constrained by 0.8\lsim \rin/\ro\lsim 1, and the
star reaches spin equilibrium with a relatively large value of the fastness
parameter (). For accreting neutron stars in low-mass X-ray
binaries (LMXBs), \ro is generally close to the stellar radius \rs so that
the toroidal field cannot transfer the spin-up torque efficiently to the star.
In this case the critical fastness parameter becomes smaller, but \rin is
still near \ro.Comment: 7 pages, 2 figures, to appear in Ap
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