7,961 research outputs found
Physical approximations for the nonlinear evolution of perturbations in dark energy scenarios
The abundance and distribution of collapsed objects such as galaxy clusters
will become an important tool to investigate the nature of dark energy and dark
matter. Number counts of very massive objects are sensitive not only to the
equation of state of dark energy, which parametrizes the smooth component of
its pressure, but also to the sound speed of dark energy as well, which
determines the amount of pressure in inhomogeneous and collapsed structures.
Since the evolution of these structures must be followed well into the
nonlinear regime, and a fully relativistic framework for this regime does not
exist yet, we compare two approximate schemes: the widely used spherical
collapse model, and the pseudo-Newtonian approach. We show that both
approximation schemes convey identical equations for the density contrast, when
the pressure perturbation of dark energy is parametrized in terms of an
effective sound speed. We also make a comparison of these approximate
approaches to general relativity in the linearized regime, which lends some
support to the approximations.Comment: 15 pages, 2 figure
Knowledge Acquisition by Networks of Interacting Agents in the Presence of Observation Errors
In this work we investigate knowledge acquisition as performed by multiple
agents interacting as they infer, under the presence of observation errors,
respective models of a complex system. We focus the specific case in which, at
each time step, each agent takes into account its current observation as well
as the average of the models of its neighbors. The agents are connected by a
network of interaction of Erd\H{o}s-Renyi or Barabasi-Albert type. First we
investigate situations in which one of the agents has a different probability
of observation error (higher or lower). It is shown that the influence of this
special agent over the quality of the models inferred by the rest of the
network can be substantial, varying linearly with the respective degree of the
agent with different estimation error. In case the degree of this agent is
taken as a respective fitness parameter, the effect of the different estimation
error is even more pronounced, becoming superlinear. To complement our
analysis, we provide the analytical solution of the overall behavior of the
system. We also investigate the knowledge acquisition dynamic when the agents
are grouped into communities. We verify that the inclusion of edges between
agents (within a community) having higher probability of observation error
promotes the loss of quality in the estimation of the agents in the other
communities.Comment: 10 pages, 7 figures. A working manuscrip
Hora da colheita: hora de cuidar do seu produto e de você. Unidade móvel para sombreamento de hortaliças após a colheita.
bitstream/item/81824/1/cot-90.pd
Praga quarentenária A1* "Cacau swollen shoot", "Cacao swollen shoot virus".
bitstream/CENARGEN/27934/1/cot112.pd
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