41 research outputs found

    Subdiffusive axial transport of granular materials in a long drum mixer

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    Granular mixtures rapidly segregate radially by size when tumbled in a partially filled horizontal drum. The smaller component moves toward the axis of rotation and forms a buried core, which then splits into axial bands. Models have generally assumed that the axial segregation is opposed by diffusion. Using narrow pulses of the smaller component as initial conditions, we have characterized axial transport in the core. We find that the axial advance of the segregated core is well described by a self-similar concentration profile whose width scales as tαt^\alpha, with α∼0.3<1/2\alpha \sim 0.3 < 1/2. Thus, the process is subdiffusive rather than diffusive as previously assumed. We find that α\alpha is nearly independent of the grain type and drum rotation rate within the smoothly streaming regime. We compare our results to two one-dimensional PDE models which contain self-similarity and subdiffusion; a linear fractional diffusion model and the nonlinear porous medium equation.Comment: 4 pages, 4 figures, 1 table. Submitted to Phys Rev Lett. For more info, see http://www.physics.utoronto.ca/nonlinear

    The United States COVID-19 Forecast Hub dataset

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    Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

    Body postures of <i>C. elegans</i> swimming in water.

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    <p>(A) -shaped and (B) sine-shaped nematode postures. Left column: experimental images. Right column: the corresponding skeletonized data and single-mode harmonic-curvature fits.</p

    Loop turn with two distinct piecewise curvature modes.

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    <p>(A) Experimental image. (B) The two-mode harmonic-curvature representation (solid line); the skeleton data (open circles); the location of the piecewise-mode change (filled circle).</p
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