100,851 research outputs found
Friction law for dense granular flows: application to the motion of a mass down a rough inclined plane
The problem of the spreading of a granular mass released at the top of a
rough inclined plane was investigated. We experimentally measure the evolution
of the avalanche from the initiation up to the deposit using a Moir\'e image
processing technique. The results are quantitatively compared with the
prediction of an hydrodynamic model based on depth averaged equations. In the
model, the interaction between the flowing layer and the rough bottom is
described by a non trivial friction force whose expression is derived from
measurements on steady uniform flows. We show that the spreading of the mass is
quantitatively predicted by the model when the mass is released on a plane free
of particles. When an avalanche is triggered on an initially static layer, the
model fails in quantitatively predicting the propagation but qualitatively
captures the evolution.Comment: 19 pages, 10 figures, to be published in J. Fluid Mec
Estimating the historical and future probabilities of large terrorist events
Quantities with right-skewed distributions are ubiquitous in complex social
systems, including political conflict, economics and social networks, and these
systems sometimes produce extremely large events. For instance, the 9/11
terrorist events produced nearly 3000 fatalities, nearly six times more than
the next largest event. But, was this enormous loss of life statistically
unlikely given modern terrorism's historical record? Accurately estimating the
probability of such an event is complicated by the large fluctuations in the
empirical distribution's upper tail. We present a generic statistical algorithm
for making such estimates, which combines semi-parametric models of tail
behavior and a nonparametric bootstrap. Applied to a global database of
terrorist events, we estimate the worldwide historical probability of observing
at least one 9/11-sized or larger event since 1968 to be 11-35%. These results
are robust to conditioning on global variations in economic development,
domestic versus international events, the type of weapon used and a truncated
history that stops at 1998. We then use this procedure to make a data-driven
statistical forecast of at least one similar event over the next decade.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS614 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
We present several methods towards construction of precursors, which show
great promise towards early predictions, of solar flare events in this paper. A
data pre-processing pipeline is built to extract useful data from multiple
sources, Geostationary Operational Environmental Satellites (GOES) and Solar
Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare
inputs for machine learning algorithms. Two classification models are
presented: classification of flares from quiet times for active regions and
classification of strong versus weak flare events. We adopt deep learning
algorithms to capture both the spatial and temporal information from HMI
magnetogram data. Effective feature extraction and feature selection with raw
magnetogram data using deep learning and statistical algorithms enable us to
train classification models to achieve almost as good performance as using
active region parameters provided in HMI/Space-Weather HMI-Active Region Patch
(SHARP) data files. Case studies show a significant increase in the prediction
score around 20 hours before strong solar flare events
A delay differential model of ENSO variability: Parametric instability and the distribution of extremes
We consider a delay differential equation (DDE) model for El-Nino Southern
Oscillation (ENSO) variability. The model combines two key mechanisms that
participate in ENSO dynamics: delayed negative feedback and seasonal forcing.
We perform stability analyses of the model in the three-dimensional space of
its physically relevant parameters. Our results illustrate the role of these
three parameters: strength of seasonal forcing , atmosphere-ocean coupling
, and propagation period of oceanic waves across the Tropical
Pacific. Two regimes of variability, stable and unstable, are separated by a
sharp neutral curve in the plane at constant . The detailed
structure of the neutral curve becomes very irregular and possibly fractal,
while individual trajectories within the unstable region become highly complex
and possibly chaotic, as the atmosphere-ocean coupling increases. In
the unstable regime, spontaneous transitions occur in the mean ``temperature''
({\it i.e.}, thermocline depth), period, and extreme annual values, for purely
periodic, seasonal forcing. The model reproduces the Devil's bleachers
characterizing other ENSO models, such as nonlinear, coupled systems of partial
differential equations; some of the features of this behavior have been
documented in general circulation models, as well as in observations. We
expect, therefore, similar behavior in much more detailed and realistic models,
where it is harder to describe its causes as completely.Comment: 22 pages, 9 figure
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