202,240 research outputs found
Evaluating foam heterogeneity
New analytical tool is available to calculate the degree of foam heterogeneity based on the measurement of gas diffusivity values. Diffusion characteristics of plastic foam are described by a system of differential equations based on conventional diffusion theory. This approach saves research and computation time in studying mass or heat diffusion problems
Origin of synchronized traffic flow on highways and its dynamic phase transitions
We study the traffic flow on a highway with ramps through numerical
simulations of a hydrodynamic traffic flow model. It is found that the presence
of the external vehicle flux through ramps generates a new state of recurring
humps (RH). This novel dynamic state is characterized by temporal oscillations
of the vehicle density and velocity which are localized near ramps, and found
to be the origin of the synchronized traffic flow reported recently [PRL 79,
4030 (1997)]. We also argue that the dynamic phase transitions between the free
flow and the RH state can be interpreted as a subcritical Hopf bifurcation.Comment: 4 pages, source TeX file and 4 figures are tarred and compressed via
uufile
Maximizing Activity in Ising Networks via the TAP Approximation
A wide array of complex biological, social, and physical systems have
recently been shown to be quantitatively described by Ising models, which lie
at the intersection of statistical physics and machine learning. Here, we study
the fundamental question of how to optimize the state of a networked Ising
system given a budget of external influence. In the continuous setting where
one can tune the influence applied to each node, we propose a series of
approximate gradient ascent algorithms based on the Plefka expansion, which
generalizes the na\"{i}ve mean field and TAP approximations. In the discrete
setting where one chooses a small set of influential nodes, the problem is
equivalent to the famous influence maximization problem in social networks with
an additional stochastic noise term. In this case, we provide sufficient
conditions for when the objective is submodular, allowing a greedy algorithm to
achieve an approximation ratio of . Additionally, we compare the
Ising-based algorithms with traditional influence maximization algorithms,
demonstrating the practical importance of accurately modeling stochastic
fluctuations in the system
Extending sensor networks into the cloud using Amazon web services
Sensor networks provide a method of collecting environmental data for use in a variety of distributed applications. However, to date, limited support has been provided for the development of integrated environmental monitoring and modeling applications. Specifically, environmental dynamism makes it difficult to provide computational resources that are sufficient to deal with changing environmental conditions. This paper argues that the Cloud Computing model is a good fit with the dynamic computational requirements of environmental monitoring and modeling. We demonstrate that Amazon EC2 can meet the dynamic computational needs of environmental applications. We also demonstrate that EC2 can be integrated with existing sensor network technologies to offer an end-to-end environmental monitoring and modeling solution
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