The local-world (LW) evolving network model shows a transition for the degree distribution between the exponential and power-law distributions, depending on the LW size. Cascading failures under intentional attacks in LW network models with different LW sizes were investigated using the cascading failures load model. We found that the LW size has a significant impact on the network's robustness against deliberate attacks. It is much easier to trigger cascading failures in LW evolving networks with a larger LW size. Therefore, to avoid cascading failures in real networks with local preferential attachment such as the Internet, the World Trade Web and the multi-agent system, the LW size should be as small as possible.
Obtaining an electrocorticograms(ECoG)signal requires an invasive procedure in which brain activity is recorded from the cortical surface.In contrast,obtaining electroencephalograms(EEG)recordings requires the non-invasive procedure of recording the brain activity from the scalp surface,which allows EEG recordings to be performed more easily on healthy humans.In this work,a technique previously used to study spatial-temporal patterns of brain activity on animal ECoG was adapted for use on EEG.The main issues are centered on solving the problems introduced by the increment on the interelectrode distance and the procedure to detect stable frames.The results showed that spatial patterns of beta and gamma activity can also be extracted from the EEG signal by using stable frames as time markers for feature extraction.This adapted technique makes it possible to take advantage of the cognitive and phenomenological awareness of a normal healthy subject.
