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Control of chaotic behavior in such systems can be achieved using a simple map-based control strategy.
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Many efforts have been made in attempt to characterize and understand the mechanisms involved in the ferromagnetic behavior observed in such systems.
Despite a few analytical models, as well as a small number of published numerical studies, there is currently little consensus regarding the large-scale flow behavior over time in such systems, particularly regarding the dominant flow regimes and whether or not reservoir properties or volumes can be estimated from well performance data.
Negative differential resistance behaviors are obtained in such systems and can be modulated to occur at much lower bias by the gate voltage.
The goal of this paper is to study equilibrium and disequilibrium behavior of artificial agents in such systems, and explore the efficient design of mechanisms to promote cooperation and coordination of self-interested artificial agents.
In such systems, the collective, global behavior emerges from the properties of individual elements and their interactions with each other.
An important challenge in such systems is context modeling, reasoning and adaptating services behavior.
We found that (1) the propagating behavior of electrons in such system exhibits a strong dependence on the direction of the incident electron wavevector and incident energy, giving the possibility to construct a wave vector and/or energy filter; (2) the spin orientation can be tuned by changing the magnetic barrier structure as well as the incident angles and energies.
Whether there is a series of bifurcations in such systems that could eventually lead to a transition to some form of quantum chaotic behavior is currently unknown.
In such systems descent defines bounded groups.
Nevertheless, long-term performance and durability issues regarding debonding behavior in such strengthening systems still remain largely uncertain and unanswered.
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