Predicting perceived tranquillity in urban parks and open spaces

A method is described that enables the potential tranquillity of an amenity area, such as a park, green, or square to be assessed. The method involves the assessment of traffic noise levels and the measurement of the percentage of natural and contextual features using photographs of the scenes. Examples are taken from three amenity areas in Bradford, West Yorkshire, UK. Using published noise maps, sampling was taken at points in the three parks where visitors were likely to be found and where noise levels were likely to be highest and lowest. At these locations, predictions of the traffic noise levels were made and then the tranquillity rating and the mean value and distribution of ratings were compared. Recommendations for improving the perceived tranquillity are discussed

Random Networks with given Rich-club Coefficient

In complex networks it is common to model a network or generate a surrogate network based on the conservation of the network's degree distribution. We provide an alternative network model based on the conservation of connection density within a set of nodes. This density is measure by the rich-club coefficient. We present a method to generate surrogates networks with a given rich-club coefficient. We show that by choosing a suitable local linking term, the generated random networks can reproduce the degree distribution and the mixing pattern of real networks. The method is easy to implement and produces good models of real networks.Comment: revised version, new figure

Growing Scale-Free Networks with Small World Behavior

In the context of growing networks, we introduce a simple dynamical model that unifies the generic features of real networks: scale-free distribution of degree and the small world effect. While the average shortest path length increases logartihmically as in random networks, the clustering coefficient assumes a large value independent of system size. We derive expressions for the clustering coefficient in two limiting cases: random (C ~ (ln N)^2 / N) and highly clustered (C = 5/6) scale-free networks.Comment: 4 pages, 4 figure

Properties of highly clustered networks

We propose and solve exactly a model of a network that has both a tunable degree distribution and a tunable clustering coefficient. Among other things, our results indicate that increased clustering leads to a decrease in the size of the giant component of the network. We also study SIR-type epidemic processes within the model and find that clustering decreases the size of epidemics, but also decreases the epidemic threshold, making it easier for diseases to spread. In addition, clustering causes epidemics to saturate sooner, meaning that they infect a near-maximal fraction of the network for quite low transmission rates.Comment: 7 pages, 2 figures, 1 tabl

The state of tranquility: Subjective perception is shaped by contextual modulation of auditory connectivity

In this study, we investigated brain mechanisms for the generation of subjective experience from objective sensory inputs. Our experimental construct was subjective tranquility. Tranquility is a mental state more likely to occur in the presence of objective sensory inputs that arise from natural features in the environment. We used functional magnetic resonance imaging to examine the neural response to scenes that were visually distinct (beach images vs. freeway images) and experienced as tranquil (beach) or non-tranquil (freeway). Both sets of scenes had the same auditory component because waves breaking on a beach and vehicles moving on a freeway can produce similar auditory spectral and temporal characteristics, perceived as a constant roar. Compared with scenes experienced as non-tranquil, we found that subjectively tranquil scenes were associated with significantly greater effective connectivity between the auditory cortex and medial prefrontal cortex, a region implicated in the evaluation of mental states. Similarly enhanced connectivity was also observed between the auditory cortex and posterior cingulate gyrus, temporoparietal cortex and thalamus. These findings demonstrate that visual context can modulate connectivity of the auditory cortex with regions implicated in the generation of subjective states. Importantly, this effect arises under conditions of identical auditory input. Hence, the same sound may be associated with different percepts reflecting varying connectivity between the auditory cortex and other brain regions. This suggests that subjective experience is more closely linked to the connectivity state of the auditory cortex than to its basic sensory inputs

Onset of Vortices in Thin Superconducting Strips and Wires

Spontaneous nucleation and the consequent penetration of vortices into thin superconducting films and wires, subjected to a magnetic field, can be considered as a nonlinear stage of primary instability of the current-carrying superconducting state. The development of the instability leads to the formation of a chain of vortices in strips and helicoidal vortex lines in wires. The boundary of instability was obtained analytically. The nonlinear stage was investigated by simulations of the time-dependent generalized Ginzburg-Landau equation.Comment: REVTeX 3.0, 12 pages, 5Postscript figures (uuencoded). Accepted for Phys. Rev.

An algorithm to discover the k-clique cover in networks

In social network analysis, a k-clique is a relaxed clique, i.e., a k-clique is a quasi-complete sub-graph. A k-clique in a graph is a sub-graph where the distance between any two vertices is no greater than k. The visualization of a small number of vertices can be easily performed in a graph. However, when the number of vertices and edges increases the visualization becomes incomprehensible. In this paper, we propose a new graph mining approach based on k-cliques. The concept of relaxed clique is extended to the whole graph, to achieve a general view, by covering the network with k-cliques. The sequence of k-clique covers is presented, combining small world concepts with community structure components. Computational results and examples are presented

Dynamics of 2D pancake vortices in layered superconductors

The dynamics of 2D pancake vortices in Josephson-coupled superconducting/normal - metal multilayers is considered within the time-dependent Ginzburg-Landau theory. For temperatures close to $T_{c}$ a viscous drag force acting on a moving 2D vortex is shown to depend strongly on the conductivity of normal metal layers. For a tilted vortex line consisting of 2D vortices the equation of viscous motion in the presence of a transport current parallel to the layers is obtained. The specific structure of the vortex line core leads to a new dynamic behavior and to substantial deviations from the Bardeen-Stephen theory. The viscosity coefficient is found to depend essentially on the angle $\gamma$ between the magnetic field ${\bf B}$ and the ${\bf c}$ axis normal to the layers. For field orientations close to the layers the nonlinear effects in the vortex motion appear even for slowly moving vortex lines (when the in-plane transport current is much smaller than the Ginzburg-Landau critical current). In this nonlinear regime the viscosity coefficient depends logarithmically on the vortex velocity $V$.Comment: 15 pages, revtex, no figure

Introducing Small-World Network Effect to Critical Dynamics

We analytically investigate the kinetic Gaussian model and the one-dimensional kinetic Ising model on two typical small-world networks (SWN), the adding-type and the rewiring-type. The general approaches and some basic equations are systematically formulated. The rigorous investigation of the Glauber-type kinetic Gaussian model shows the mean-field-like global influence on the dynamic evolution of the individual spins. Accordingly a simplified method is presented and tested, and believed to be a good choice for the mean-field transition widely (in fact, without exception so far) observed on SWN. It yields the evolving equation of the Kawasaki-type Gaussian model. In the one-dimensional Ising model, the p-dependence of the critical point is analytically obtained and the inexistence of such a threshold p_c, for a finite temperature transition, is confirmed. The static critical exponents, gamma and beta are in accordance with the results of the recent Monte Carlo simulations, and also with the mean-field critical behavior of the system. We also prove that the SWN effect does not change the dynamic critical exponent, z=2, for this model. The observed influence of the long-range randomness on the critical point indicates two obviously different hidden mechanisms.Comment: 30 pages, 1 ps figures, REVTEX, accepted for publication in Phys. Rev.