It will be discussed the statistics of the extreme values in time series characterized by finite-term correlations
with non-exponential decay. Precisely, it will be considered the results of numerical analyses concerning the
return intervals of extreme values of the fluctuations of resistance and defect-fraction displayed by a resistor with
granular structure in a nonequilibrium stationary state. The resistance and defect-fraction are calculated as a
function of time by Monte Carlo simulations using a resistor network approach. It will be shown that when the
auto-correlation function of the fluctuations displays a non-exponential and non-power-law decay, the distribution
of the return intervals of extreme values is a stretched exponential, with exponent largely independent of the
threshold. Recently, a stretched exponential distribution of the return intervals of extreme values has been
identified in long-term correlated time series by Bunde et al. (2003) and Altmann and Kantz (2005). Thus, the
present results show that the stretched exponential distribution of the return intervals is not an exclusive feature
of long-term correlated time series.
We study the distribution of resistance fluctuations of conducting thin films with different levels of internal disorder. The film is modeled as a resistor network in a steady state determined by the competition between two biased processes, breaking and recovery of the elementary resistors. The fluctuations of the film resistance are calculated by Monte Carlo simulations which are performed under different bias conditions, from the linear regime up to the threshold for electrical breakdown. Depending on the value of the external current, on the level of disorder and on the size of the system, the distribution of the resistance fluctuations can exhibit significant deviations from Gaussianity. As a general trend, a size dependent, non universal distribution is found for systems with low and intermediate disorder. However, for strongly disordered systems, close to the critical point of the conductor-insulator transition, the non-Gaussianity persists when the size is increased and the distribution of resistance fluctuations is well described by the universal Bramwell-Holdsworth-Pinton distribution.
We present for the first time a complex network approach to the study of the electrical properties of single protein devices. In particular, we consider an electronic nanobiosensor based on a G-protein coupled receptor. By adopting a coarse grain description, the protein is modeled as a complex network of elementary impedances. The positions of the alpha-carbon atoms of each amino acid are taken as the nodes of the network. The amino acids are assumed to interact electrically among them. Consequently, a link is drawn between any
pair of nodes neighboring in space within a given distance and an elementary impedance is associated with each link. The value of this impedance can be related to the physical and chemical properties of the amino acid pair and to their relative distance. Accordingly, the conformational changes of the receptor induced by the capture of the ligand, are translated into a variation of its electrical properties. Stochastic fluctuations in the value of the elementary impedances of the network, which mimic different physical effects, have also been considered. Preliminary results concerning the impedance spectrum of the network and its fluctuations are presented and discussed for
different values of the model parameters.