10,438 research outputs found

    Upper critical dimension of the KPZ equation

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    Numerical results for the Directed Polymer model in 1+4 dimensions in various types of disorder are presented. The results are obtained for system size considerably larger than that considered previously. For the extreme strong disorder case (Min-Max system), associated with the Directed Percolation model, the expected value of the meandering exponent, zeta = 0.5 is clearly revealed, with very week finite size effects. For the week disorder case, associated with the KPZ equation, finite size effects are stronger, but the value of seta is clearly seen in the vicinity of 0.57. In systems with "strong disorder" it is expected that the system will cross over sharply from Min-Max behavior at short chains to weak disorder behavior at long chains. This is indeed what we find. These results indicate that 1+4 is not the Upper Critical Dimension (UCD) in the week disorder case, and thus 4+1 does not seem to be the upper critical dimension for the KPZ equation

    Eulerian spectral closures for isotropic turbulence using a time-ordered fluctuation-dissipation relation

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    Procedures for time-ordering the covariance function, as given in a previous paper (K. Kiyani and W.D. McComb Phys. Rev. E 70, 066303 (2004)), are extended and used to show that the response function associated at second order with the Kraichnan-Wyld perturbation series can be determined by a local (in wavenumber) energy balance. These time-ordering procedures also allow the two-time formulation to be reduced to time-independent form by means of exponential approximations and it is verified that the response equation does not have an infra-red divergence at infinite Reynolds number. Lastly, single-time Markovianised closure equations (stated in the previous paper above) are derived and shown to be compatible with the Kolmogorov distribution without the need to introduce an ad hoc constant.Comment: 12 page

    The Lagrangian frequency spectrum as a diagnostic for magnetohydrodynamic turbulence dynamics

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    For the phenomenological description of magnetohydrodynamic turbulence competing models exist, e.g. Boldyrev [Phys.Rev.Lett. \textbf{96}, 115002, 2006] and Gogoberidze [Phys.Plas. \textbf{14}, 022304, 2007], which predict the same Eulerian inertial-range scaling of the turbulent energy spectrum although they employ fundamentally different basic interaction mechanisms. {A relation is found that links} the Lagrangian frequency spectrum {with} the autocorrelation timescale of the turbulent fluctuations, τac\tau_\mathrm{ac}, and the associated cascade timescale, τcas\tau_{\mathrm{cas}}. Thus, the Lagrangian energy spectrum can serve to identify weak (τacτcas\tau_\mathrm{ac}\ll\tau_{\mathrm{cas}}) and strong (τacτcas\tau_\mathrm{ac}\sim\tau_{\mathrm{cas}}) interaction mechanisms providing insight into the turbulent energy cascade. The new approach is illustrated by results from direct numerical simulations of two- and three-dimensional incompressible MHD turbulence.Comment: accepted for publication in PR

    Spin squeezing of high-spin, spatially extended quantum fields

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    Investigations of spin squeezing in ensembles of quantum particles have been limited primarily to a subspace of spin fluctuations and a single spatial mode in high-spin and spatially extended ensembles. Here, we show that a wider range of spin-squeezing is attainable in ensembles of high-spin atoms, characterized by sub-quantum-limited fluctuations in several independent planes of spin-fluctuation observables. Further, considering the quantum dynamics of an f=1f=1 ferromagnetic spinor Bose-Einstein condensate, we demonstrate theoretically that a high degree of spin squeezing is attained in multiple spatial modes of a spatially extended quantum field, and that such squeezing can be extracted from spatially resolved measurements of magnetization and nematicity, i.e.\ the vector and quadrupole magnetic moments, of the quantum gas. Taking into account several experimental limitations, we predict that the variance of the atomic magnetization and nematicity may be reduced as far as 20 dB below the standard quantum limits.Comment: 18 pages, 5 figure

    Elevated atmospheric concentrations of carbon dioxide reduce monarch tolerance and increase parasite virulence by altering the medicinal properties of milkweeds

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    Hosts combat their parasites using mechanisms of resistance and tolerance, which together determine parasite virulence. Environmental factors, including diet, mediate the impact of parasites on hosts, with diet providing nutritional and medicinal properties. Here, we present the first evidence that ongoing environmental change decreases host tolerance and increases parasite virulence through a loss of dietary medicinal quality. Monarch butterflies use dietary toxins (cardenolides) to reduce the deleterious impacts of a protozoan parasite. We fed monarch larvae foliage from four milkweed species grown under either elevated or ambient CO2, and measured changes in resistance, tolerance, and virulence. The most high‐cardenolide milkweed species lost its medicinal properties under elevated CO2; monarch tolerance to infection decreased, and parasite virulence increased. Declines in medicinal quality were associated with declines in foliar concentrations of lipophilic cardenolides. Our results emphasize that global environmental change may influence parasite–host interactions through changes in the medicinal properties of plants.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/1/ele13101-sup-0003-TableS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/2/ele13101-sup-0007-TableS5.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/3/ele13101-sup-0006-TableS4.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/4/ele13101.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/5/ele13101_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/6/ele13101-sup-0004-TableS2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/7/ele13101-sup-0009-AppendixS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/8/ele13101-sup-0005-TableS3.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/9/ele13101-sup-0002-FigS2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/10/ele13101-sup-0008-TableS6.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145415/11/ele13101-sup-0001-FigS1.pd
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