Bending-rigidity-driven transition and crumpling-point scaling of lattice vesicles

The crumpling transition of three-dimensional (3D) lattice vesicles subject to a bending fugacity \ensuremath{\rho}=exp(-\ensuremath{\kappa}/${\mathit{k}}_{\mathit{BT}}$) is investigated by Monte Carlo methods in a grand canonical framework. By also exploiting conjectures suggested by previous rigorous results, a critical regime with scaling behavior in the universality class of branched polymers is found to exist for \ensuremath{\rho}\ensuremath{\gtrsim}{\mathrm{\ensuremath{\rho}}}_{\mathit{c}}. For \ensuremath{\rho}{\mathrm{\ensuremath{\rho}}}_{\mathit{c}} the vesicles undergo a first-order transition that has remarkable similarities to the line of droplet singularities of inflated 2D vesicles. At the crumpling point (\ensuremath{\rho}={\mathrm{\ensuremath{\rho}}}_{\mathit{c}}), which has a tricritical character, the average radius and the canonical partition function of vesicles with n plaquettes scale as {\mathit{n}}^{{\ensuremath{\nu}}_{\mathit{c}}} and {\mathit{n}}^{\mathrm{\ensuremath{-}}{\mathrm{\ensuremath{\theta}}}_{\mathit{c}}}, respectively, with {\ensuremath{\nu}}_{\mathit{c}}=0.4825\ifmmode\pm\else\textpm\fi{}0.0015 and {\mathrm{\ensuremath{\theta}}}_{\mathit{c}}=1.78\ifmmode\pm\else\textpm\fi{}0.03. These exponents indicate a new class, distinct from that of branched polymers, for scaling at the crumpling point. \textcopyright{} 1996 The American Physical Society

A STRUCTURAL APPROACH TO THE TEMPORAL MODELING OF NETWORKS

Simulation of many dynamic real world systems such as the Internet and social networks requires developing dynamic models for the underlying networks in these systems. Currently, there is a large body of work devoted towards determining the underlying mechanisms that create these networks, but the resulting models have not realistically captured many of the important structural characteristics when compared with real world examples. Towards creating more realistic dynamic models, we propose a method of structurally constructing models of an evolving network. We then conduct a series of computational experiments in modeling the evolution of the autonomous system (AS) topology of the Internet to test the effectiveness of our approach

MEASURING THE EFFECTIVENESS OF THE S-METRIC TO PRODUCE BETTER NETWORK MODELS

Recent research has shown that while many complex networks follow a power-law distribution for their node degrees, it is not sufficient to model these networks based only on their degree distribution. In order to better distinguish between these networks, the metric s was introduced to measure how interconnected the hub nodes are in a network. We examine the effectiveness of creating network models based on this metric. Through a series of computational experiments, we compare how well a set of common structural network metrics are preserved between instances of the autonomous system Internet topology and a series of random models with identical degree sequences and similar s values. We demonstrate that creating models based on the s metric can produce moderate improvement in structural characteristics over strictly using degree distribution. Our results also indicate that some interesting relationships exist between the s metric and the various structural metrics

Former CiSE EICs Reflect on the Magazine\u27s 20th Anniversary

Editorial on the 20th Anniversary of Computing in Science and Engineering, an interdisciplinary publication focused on computational science--the application of computer science/software development to scientific and engineering problems