We examine the coevolution of N cycles and force chains as part of a broader study which is designed to quantitatively characterize the role of the laterally supporting contact network to the evolution of force chains. Here, we elucidate the rheological function of these coexisting structures, especially in the lead up to failure. In analogy to force chains, we introduce the concept of force cycles: N cycles whose contacts each bear above average force. We examine their evolution around force chains in a discrete element simulation of a dense granular material under quasistatic biaxial loading. Three-force cycles are shown to be stabilizing structures that inhibit relative particle rotations and provide strong lateral support to force chains. These exhibit distinct behavior from other cycles. Their population decreases rapidly during the initial stages of the strain-hardening regime-a trend that is suddenly interrupted and reversed upon commencement of force chain buckling prior to peak shear stress. Results suggest that the three-force cycles are called upon for reinforcements to ward off failure via shear banding. Ultimately though, the resistance to buckling proves futile; buckling wins under the combined effects of dilatation and increasing compressive load. The sudden increase in three-force cycles may thus be viewed as an indicator of imminent failure via shear bands.

We examine epidemic thresholds for disease spread using susceptible-infected-susceptible models on scalefree networks with variable infectivity. Infectivity between nodes is modeled as a piecewise linear function of the node degree ͑rather than the less realistic linear transformation considered previously͒. With this nonlinear infectivity, we derive conditions for the epidemic threshold to be positive. The effects of various immunization schemes including ring and targeted vaccination are studied and compared. We find that both targeted and ring immunization strategies compare favorably to a proportional scheme in terms of effectiveness.

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a b s t r a c tOne of the great challenges in the science of complex materials -materials capable of emergent behavior such as self-organized pattern formation -is deciphering their ''inherent" structural design principles as they deform in response to external loads. We have been exploring the efficacy of techniques from complex networks to the study of dense granular materials as a means to: (i) uncover such design principles and (ii) identify suitable metrics that quantify the evolution of structure during deformation. Herein, we characterize the developing network structure and loss of connectivity in a quasistatically deforming granular medium from the perspective of complex networks. Attention is paid to the evolution of the contact and contact force networks at the local or mesoscopic level, i.e., a particle and its immediate neighbors, as well as the macroscopic level. We explore network motifs and other topological properties at these multiple length scales, in an attempt to find that which best correlates with the constitutive properties of nonaffine deformation and dissipation, spatially and with respect to strain. Key processes or rearrangement events that cause loss of connectivity within the material domain, e.g. microbanding and force chain buckling, are investigated. Network statistics of these processes, previously shown to be major sources of energy dissipation and nonaffine deformation, are then tied to corresponding trends observed in the evolving macroscopic network. It is shown that consideration of the unweighted contact network alone is insufficient to tie dissipation to loss of material connectivity.

We describe a stochastic small-world network model of transmission of the SARS virus. Unlike the standard Susceptible-Infected-Removed models of disease transmission, our model exhibits both geographically localised outbreaks and "super-spreaders". Moreover, the combination of localised and long range links allows for more accurate modelling of partial isolation and various public health policies. From this model, we derive an expression for the probability of a widespread outbreak and a condition to ensure that the epidemic is controlled. Moreover, multiple simulations are used to make predictions of the likelihood of various eventual scenarios for fixed initial conditions. The main conclusions of this study are: (i) "super-spreaders" may occur even if the infectiousness of all infected individuals is constant; (ii) consistent with previous reports, extended exposure time beyond 3-5 days (i.e. significant nosocomial transmission) was the key factor in the severity of the SARS outbreak in Hong Kong; and, (iii) the spread of SARS can be effectively controlled by either limiting long range links (imposing a partial quarantine) or enforcing rapid hospitalisation and isolation of symptomatic individuals. In addition, the epidemiological data currently available for Hong Kong is far superior to that of the Chinese mainland. 1 Two characteristic features were observed during the SARS outbreak in Hong Kong in 2003 (see Fig. 1 E-mail address: ensmall@polyu.edu.hk (M. Small). 1 During the epidemic, mainland authorities classified information on SARS infections as a state secret. Moreover, bureaucracy caused much of the available information to be concealed. Despite this, subsequent official investigation indicates that the infection rate for the Chinese mainland was significantly overreported. The reliability of data from China is therefore uncertain. a large number of cases; and persistent transmission within the community. Two widely cited SSEs were observed early in the epidemic and have been the subject of much attention: at the Amoy Gardens housing estate and at the Prince of Wales hospital. Epidemiological studies [5,1] have found that in Hong Kong:• the fatality rate was approximately 17% (compared to 11% globally); • the mean incubation period was 6.4 days (range 2-10 days) [6]; • the duration between onset of symptoms and hospitalisation was 3-5 days; and, • the mean number of individuals infected by each case during the initial phase of the epidemic (excluding SSEs) was 2.7 [4]. Standard deterministic SIR (susceptible-infected-removed) models of the spread of infectious diseases [7] make several important assumptions. An alternative approach [8], particularly popular for the study of sexually transmitted diseases [9][10][11], is to build an explicit network and model 0167-2789/$ -see front matter c

we construct a network of plausible transmission pathways for the spread of avian influenza among domestic and wild birds. The network structure we obtain is complex and exhibits scale-free (although not necessarily smallworld) properties. Communities within this network are connected with a distribution of links with infinite variance. Hence, the disease transmission model does not exhibit a threshold and so the infection will continue to propagate even with very low transmissibility. Consequentially, eradication with methods applicable to locally homogeneous populations is not possible. Any control measure needs to focus explicitly on the hubs within this network structure.

Complex systems techniques are used to analyse X-ray micro-CT measurements of grain kinematics in Hostun sand under triaxial compression. Network nodes with the least mean shortest path length to all other nodes, or highest relative closeness centrality, reside in the region where the persistent shear band ultimately develops. This trend, whereby a group of grains distinguishes themselves from the rest in the sample, remarkably manifests from the onset of loading. The shear band's boundaries and thickness, evident from the network communities' borders and essentially constant mean size, provide corroborating evidence of early detection of strain localization. Our findings raise the possibility that the formation and the location of the persistent shear band may be decided in the nascent stages of loading, well before peak shear stress. Grain-scale digital image correlation strain measurements and statistical tests confirm the results are robust. Moreover, the trends are unambiguously reproduced in a discrete element simulation of plane strain compression.

We construct complex networks from symbolic time series of particle properties within a dense quasistatically deforming granular assembly subjected to biaxial compression. The structure of the resulting networks embodies the evolving structural rearrangements in the granular material, in both contact forces and contact topologies. These rearrangements are usefully summarized through standard network statistics as well as building block motifs and community structures. Dense granular media respond to applied compression and shear by a process of self-organization to form two cooperatively evolving structures comprising the major load-bearing columnlike force chains, and the lateral trusslike three-cycle triangle topologies. We construct networks summarizing their individual evolution based on relationships between symbolic time series indicating a particle's chronological force chain and three-cycle membership histories. We test which particle membership histories are similar with respect to each other through the information theory-based measure of Hamming distance. The complex networks summarize the essential structural rearrangements, while the community structures within the networks partition the material into distinct zones of deformation, including interlacing subregions of failure inside the shear band. The taxonomy of granular rheology at the mesoscopic scale distills the inelastic structural rearrangements throughout loading history down to its core elements, and should lay bare an objective and physics-based formalism for thermodynamic internal variables and associated evolution laws.