A new family of graphs, entangled networks, with optimal properties in many respects, is introduced. By definition, their topology is such that it optimizes synchronizability for many dynamical processes. These networks are shown to have an extremely homogeneous structure: degree, node distance, betweenness, and loop distributions are all very narrow. Also, they are characterized by a very interwoven (entangled) structure with short average distances, large loops, and no well-defined community structure. This family of nets exhibits an excellent performance with respect to other flow properties such as robustness against errors and attacks, minimal first-passage time of random walks, efficient communication, etc. These remarkable features convert entangled networks in a useful concept, optimal or almost optimal in many senses, and with plenty of potential applications in computer science or neuroscience.

Significance: The fact that large, complex ecosystems are particularly robust is mysterious in the light of mathematical arguments that suggest they should be unstable; i.e., susceptible to runaway fluctuations in species’ abundances. Here we show that food webs (networks describing who eats whom in an ecosystem) exhibit a property we call trophic coherence, a measure of how neatly the species fall into distinct levels. We find that this property makes networks far more linearly stable than if the links (predator–prey interactions) were placed randomly between species, or according to existing structural models. A simple model we propose to capture this feature shows that networks can, in fact, become more stable with size and complexity, suggesting a possible solution to the paradox.

We demonstrate the confinement of acoustic phonons in ultrathin silicon layers and study its effect on electron mobility. We develop a model for confined acoustic phonons in an ideal single-layer structure and in a more realistic three-layer structure. Phonon quantization is recovered, and the dispersion relations for distinct phonon modes are computed. This allows us to obtain the confined phonon scattering rates and, using Monte Carlo simulations, to compute the electron mobility in ultrathin silicon on insulator inversion layers. Thus, comparing the results with those obtained using the bulk phonon model, we are able to conclude that it is very important to include confined acoustic phonon models in the electron transport simulations of ultrathin devices, if we want to reproduce the actual behavior of electron transport in silicon layers of nanometric thickness.

In this paper, simulation-based research on the electrostatics of Pi-gate silicon-on-insulator (SOI) MOSFETs is carried out. To do so, a 2-D self-consistent Schrodinger-Poisson solver has been implemented. The inclusion of the quantum effects has been demonstrated to be necessary for the accurate simulation of these devices in the nanometer range. Specifically, this paper is focused on the corner effects in multiple-gate SOI MOSFETs, defined as the formation of independent channels with different threshold voltages. Corner effects are studied as a function of different parameters, such as the doping density, silicon-fin dimensions, corner rounding, and gate oxide thickness. Finally, the relation between corner effects and the transition from a fully to a partially depleted body is analyzed.

We review and improve a recently introduced method for the detection of communities in complex networks. This method combines spectral properties of some matrices encoding the network topology, with well known hierarchical clustering techniques, and the use of the modularity parameter to quantify the goodness of any possible community subdivision. This provides one of the best available methods for the detection of community structures in complex systems.