984 research outputs found
An exactly solvable model for driven dissipative systems
We introduce a solvable stochastic model inspired by granular gases for
driven dissipative systems. We characterize far from equilibrium steady states
of such systems through the non-Boltzmann energy distribution and compare
different measures of effective temperatures. As an example we demonstrate that
fluctuation-dissipation relations hold, however with an effective temperature
differing from the effective temperature defined from the average energy.Comment: Some further clarifications. No changes in results or conclusion
The spatio-temporal distribution of lightning over Israel and the neighboring area and its relation to regional synoptic systems
The spatio-temporal distribution of lightning flashes over Israel and the neighboring area and its relation to the regional synoptic systems has been studied, based on data obtained from the Israel Lightning Location System (ILLS) operated by the Israel Electric Corporation (IEC). The system detects cloud-to-ground lightning discharges in a range of ~500 km around central Israel (32.5° N, 35° E). The study period was defined for annual activity from August through July, for 5 seasons in the period 2004–2010. <br><br> The spatial distribution of lightning flash density indicates the highest concentration over the Mediterranean Sea, attributed to the contribution of moisture as well as sensible and latent heat fluxes from the sea surface. Other centers of high density appear along the coastal plain, orographic barriers, especially in northern Israel, and downwind from the metropolitan area of Tel Aviv, Israel. The intra-annual distribution shows an absence of lightning during the summer months (JJA) due to the persistent subsidence over the region. The vast majority of lightning activity occurs during 7 months, October to April. Although over 65 % of the rainfall in Israel is obtained during the winter months (DJF), only 35 % of lightning flashes occur in these months. October is the richest month, with 40 % of total annual flashes. This is attributed both to tropical intrusions, i.e., Red Sea Troughs (RST), which are characterized by intense static instability and convection, and to Cyprus Lows (CLs) arriving from the west. <br><br> Based on daily study of the spatial distribution of lightning, three patterns have been defined; "land", "maritime" and "hybrid". CLs cause high flash density over the Mediterranean Sea, whereas some of the RST days are typified by flashes over land. The pattern defined "hybrid" is a combination of the other 2 patterns. On CL days, only the maritime pattern was noted, whereas in RST days all 3 patterns were found, including the maritime pattern. It is suggested that atmospheric processes associated with RST produce the land pattern. Hence, the occurrence of a maritime pattern in days identified as RST reflects an "apparent RST". The hybrid pattern was associated with an RST located east of Israel. This synoptic type produced the typical flash maximum over the land, but the upper-level trough together with the onshore winds it induced over the eastern coast of the Mediterranean resulted in lightning activity over the sea as well, similar to that of CLs. <br><br> It is suggested that the spatial distribution patterns of lightning may better identify the synoptic system responsible, a CL, an "active RST" or an "apparent RST". The electrical activity thus serves as a "fingerprint" for the synoptic situation responsible for its generation
Measuring the Nonlinear Biasing Function from a Galaxy Redshift Survey
We present a simple method for evaluating the nonlinear biasing function of
galaxies from a redshift survey. The nonlinear biasing is characterized by the
conditional mean of the galaxy density fluctuation given the underlying mass
density fluctuation, or by the associated parameters of mean biasing and
nonlinearity (following Dekel & Lahav 1999). Using the distribution of galaxies
in cosmological simulations, at smoothing of a few Mpc, we find that the mean
biasing can be recovered to a good accuracy from the cumulative distribution
functions (CDFs) of galaxies and mass, despite the biasing scatter. Then, using
a suite of simulations of different cosmological models, we demonstrate that
the matter CDF is robust compared to the difference between it and the galaxy
CDF, and can be approximated for our purpose by a cumulative log-normal
distribution of 1+\delta with a single parameter \sigma. Finally, we show how
the nonlinear biasing function can be obtained with adequate accuracy directly
from the observed galaxy CDF in redshift space. Thus, the biasing function can
be obtained from counts in cells once the rms mass fluctuation at the
appropriate scale is assumed a priori. The relative biasing function between
different galaxy types is measurable in a similar way. The main source of error
is sparse sampling, which requires that the mean galaxy separation be smaller
than the smoothing scale. Once applied to redshift surveys such as PSCz, 2dF,
SDSS, or DEEP, the biasing function can provide valuable constraints on galaxy
formation and structure evolution.Comment: 23 pages, 7 figures, revised version, accepted for publication in Ap
PHA*: Finding the Shortest Path with A* in An Unknown Physical Environment
We address the problem of finding the shortest path between two points in an
unknown real physical environment, where a traveling agent must move around in
the environment to explore unknown territory. We introduce the Physical-A*
algorithm (PHA*) for solving this problem. PHA* expands all the mandatory nodes
that A* would expand and returns the shortest path between the two points.
However, due to the physical nature of the problem, the complexity of the
algorithm is measured by the traveling effort of the moving agent and not by
the number of generated nodes, as in standard A*. PHA* is presented as a
two-level algorithm, such that its high level, A*, chooses the next node to be
expanded and its low level directs the agent to that node in order to explore
it. We present a number of variations for both the high-level and low-level
procedures and evaluate their performance theoretically and experimentally. We
show that the travel cost of our best variation is fairly close to the optimal
travel cost, assuming that the mandatory nodes of A* are known in advance. We
then generalize our algorithm to the multi-agent case, where a number of
cooperative agents are designed to solve the problem. Specifically, we provide
an experimental implementation for such a system. It should be noted that the
problem addressed here is not a navigation problem, but rather a problem of
finding the shortest path between two points for future usage
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