The author of this work provides a statistical model of the interconnection of scientists linked to each other via joint publications.
To this end, we present an analytical performance model for the interconnection networks of heterogeneous multi-cluster systems.
As a divide et impera approach is used to overcome the scalability issues of a centralized implementation, the large-scale system being monitored is modelled as the interconnection of several subsystems.
An advantage of this model is the interconnection between Ge nanocrystals in porous Ge and then, all the phonon states are delocalized.
For p-Ge, contrary to the crystallite approach, the supercell model emphasizes the interconnection of the system, which could be relevant for long-range correlated phenomena, such as the Raman scattering.
The monitored system is modeled as the interconnection of several subsystems and a divide et impera approach using an overlapping decomposition is adopted.
The proposed modeling framework identifies the implementation (or not) of the interconnection of the Crete Island with the mainland electric system, as well as the optimum interconnection capacity.
The local diagnostic decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems.
This general model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, and recurrent multilayer perceptrons (RMLPs) with or without delays.
In this paper, we present a mathematical background for a new approach for performances modeling of interconnection networks, based on analyzing the packet blocking and waiting time spent in each channel passing through all possible paths in the channel dependency graph.
