The probiotic
Lactobacillus acidophilus
LA14 is widely used worldwide, but its effect on liver diseases has not been elucidated. We explored the protective effect of
L. acidophilus
LA14 on the liver using rats with
d
-galactosamine-induced liver injury.
Connectivity is one of the most important parameters in network monitoring.
The connectivity model of Opportunistic Sensor Networks (OSN) can hardly be
established by traditional graph models due to the fact that its
connectivity is timing correlative and evolutionary, which makes it
extremely difficult to monitor an OSN. In order to solve the monitoring
problem, this paper builds an evolving graph model based on the theory of
evolving graph as a description of an OSN. It defines a series of parameters
to measure the connectivity of the OSN and establishes an monitoring model.
Meanwhile, this paper gives the key algorithms in building the model, the
Evolving-Graph-Modeling (EGM) algorithm and the Connected-Journey (CJ)
algorithm. The rationality of the monitoring model has been proven by a
prototype system and the simulation results. Extensive simulation results
show that the proposed connectivity monitoring model can indicate real
circumstances of OSN? connectivity, and it is applicable to monitoring an
opportunistic sensor network.
Connectivity is an important indicator of network performance. But the opportunistic sensor networks (OSNs) have temporal evolution characteristics, which are hard to modelled with traditional graphs. After analyzing the characteristics of OSNs, this paper constructs OSNs connectivity model based on time graph theory. The overall connectivity degree of the network is defined, and is used to estimate actual network connectivity. We also propose a computing method that uses the adjacency matrix of each snapshot. The simulation results show that network connectivity degree can reflect the overall connectivity of OSNs, which provide a basis for improving the OSNs performance.
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