187,203 research outputs found
Scaling of nuclear modification factors for hadrons and light nuclei
The number of constituent quarks (NCQ-) scaling of hadrons and the number of
constituent nucleons (NCN-) scaling of light nuclei are proposed for nuclear
modification factors () of hadrons and light nuclei, respectively,
according to the experimental investigations in relativistic heavy-ion
collisions. Based on coalescence mechanism the scalings are performed for pions
and protons in quark level, and light nuclei and He for
nucleonic level, respectively, formed in Au + Au and Pb + Pb collisions and
nice scaling behaviour emerges. NCQ or NCN scaling law of can be
respectively taken as a probe for quark or nucleon coalescence mechanism for
the formation of hadron or light nuclei in relativistic heavy-ion collisions.Comment: 6 pages, 6 figure
First-Principles Study of Integer Quantum Hall Transitions in Mesoscopic Samples
We perform first principles numerical simulations to investigate resistance
fluctuations in mesoscopic samples, near the transition between consecutive
Quantum Hall plateaus. We use six-terminal geometry and sample sizes similar to
those of real devices. The Hall and longitudinal resistances extracted from the
generalized Landauer formula reproduce all the experimental features uncovered
recently. We then use a simple generalization of the Landauer-B\"uttiker model,
based on the interplay between tunneling and chiral currents -- the co-existing
mechanisms for transport -- to explain the three distinct types of fluctuations
observed, and identify the central region as the critical region.Comment: changes to acknowledgements onl
Random Networks with given Rich-club Coefficient
In complex networks it is common to model a network or generate a surrogate
network based on the conservation of the network's degree distribution. We
provide an alternative network model based on the conservation of connection
density within a set of nodes. This density is measure by the rich-club
coefficient. We present a method to generate surrogates networks with a given
rich-club coefficient. We show that by choosing a suitable local linking term,
the generated random networks can reproduce the degree distribution and the
mixing pattern of real networks. The method is easy to implement and produces
good models of real networks.Comment: revised version, new figure
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