117 research outputs found
A sustainable pavement concrete using warm mix asphalt and hydrated lime treated recycled concrete aggregates
Recently, increasing material prices coupled with more acute environmental awareness and the implementation of regulation has driven a strong movement toward the adoption of sustainable construction technology. In the pavement industry, using low temperature asphalt mixes and recycled concrete aggregate are viewed as effective engineering solutions to address the challenges posed by climate change and sustainable development. However, to date, no research has investigated these two factors simultaneously for pavement material. This paper reports on initial work which attempts to address this shortcoming. At first, a novel treatment method is used to improve the quality of recycled concrete coarse aggregates. Thereafter, the treated recycled aggregates were used in warm mix asphalt at varied rates to replace virgin raw coarse aggregates. The asphalt concrete mixes produced were tested for modulus, tensile strength, permanent deformation, moisture susceptibility and fatigue life. The comparison of these properties with that of the mixes using the same rates of untreated course aggregates from the same source has demonstrated the effectiveness of the new technology. Lastly, the cost, material and energy saving implications are discussed
Ising model for distribution networks
An elementary Ising spin model is proposed for demonstrating cascading
failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in
realistic networks for distribution and delivery by suppliers to consumers. A
ferromagnetic Hamiltonian with quenched random fields results from policies
that maximize the gap between demand and delivery. Such policies can arise in a
competitive market where firms artificially create new demand, or in a solidary
environment where too high a demand cannot reasonably be met. Network failure
in the context of a policy of solidarity is possible when an initially active
state becomes metastable and decays to a stable inactive state. We explore the
characteristics of the demand and delivery, as well as the topological
properties, which make the distribution network susceptible of failure. An
effective temperature is defined, which governs the strength of the activity
fluctuations which can induce a collapse. Numerical results, obtained by Monte
Carlo simulations of the model on (mainly) scale-free networks, are
supplemented with analytic mean-field approximations to the geometrical random
field fluctuations and the thermal spin fluctuations. The role of hubs versus
poorly connected nodes in initiating the breakdown of network activity is
illustrated and related to model parameters
Message-Passing Methods for Complex Contagions
Message-passing methods provide a powerful approach for calculating the
expected size of cascades either on random networks (e.g., drawn from a
configuration-model ensemble or its generalizations) asymptotically as the
number of nodes becomes infinite or on specific finite-size networks. We
review the message-passing approach and show how to derive it for
configuration-model networks using the methods of (Dhar et al., 1997) and
(Gleeson, 2008). Using this approach, we explain for such networks how to
determine an analytical expression for a "cascade condition", which determines
whether a global cascade will occur. We extend this approach to the
message-passing methods for specific finite-size networks (Shrestha and Moore,
2014; Lokhov et al., 2015), and we derive a generalized cascade condition.
Throughout this chapter, we illustrate these ideas using the Watts threshold
model.Comment: 14 pages, 3 figure
Mechanical Metamaterials with Negative Compressibility Transitions
When tensioned, ordinary materials expand along the direction of the applied
force. Here, we explore network concepts to design metamaterials exhibiting
negative compressibility transitions, during which a material undergoes
contraction when tensioned (or expansion when pressured). Continuous
contraction of a material in the same direction of an applied tension, and in
response to this tension, is inherently unstable. The conceptually similar
effect we demonstrate can be achieved, however, through destabilisations of
(meta)stable equilibria of the constituents. These destabilisations give rise
to a stress-induced solid-solid phase transition associated with a twisted
hysteresis curve for the stress-strain relationship. The strain-driven
counterpart of negative compressibility transitions is a force amplification
phenomenon, where an increase in deformation induces a discontinuous increase
in response force. We suggest that the proposed materials could be useful for
the design of actuators, force amplifiers, micro-mechanical controls, and
protective devices.Comment: Supplementary information available at
http://www.nature.com/nmat/journal/v11/n7/abs/nmat3331.htm
Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling
This paper investigates the dependence of synchronization transitions of
bursting oscillations on the information transmission delay over scale-free
neuronal networks with attractive and repulsive coupling. It is shown that for
both types of coupling, the delay always plays a subtle role in either
promoting or impairing synchronization. In particular, depending on the
inherent oscillation period of individual neurons, regions of irregular and
regular propagating excitatory fronts appear intermittently as the delay
increases. These delay-induced synchronization transitions are manifested as
well-expressed minima in the measure for spatiotemporal synchrony. For
attractive coupling, the minima appear at every integer multiple of the average
oscillation period, while for the repulsive coupling, they appear at every odd
multiple of the half of the average oscillation period. The obtained results
are robust to the variations of the dynamics of individual neurons, the system
size, and the neuronal firing type. Hence, they can be used to characterize
attractively or repulsively coupled scale-free neuronal networks with delays.Comment: 15 pages, 9 figures; accepted for publication in PLoS ONE [related
work available at http://arxiv.org/abs/0907.4961 and
http://www.matjazperc.com/
Discovering universal statistical laws of complex networks
Different network models have been suggested for the topology underlying
complex interactions in natural systems. These models are aimed at replicating
specific statistical features encountered in real-world networks. However, it
is rarely considered to which degree the results obtained for one particular
network class can be extrapolated to real-world networks. We address this issue
by comparing different classical and more recently developed network models
with respect to their generalisation power, which we identify with large
structural variability and absence of constraints imposed by the construction
scheme. After having identified the most variable networks, we address the
issue of which constraints are common to all network classes and are thus
suitable candidates for being generic statistical laws of complex networks. In
fact, we find that generic, not model-related dependencies between different
network characteristics do exist. This allows, for instance, to infer global
features from local ones using regression models trained on networks with high
generalisation power. Our results confirm and extend previous findings
regarding the synchronisation properties of neural networks. Our method seems
especially relevant for large networks, which are difficult to map completely,
like the neural networks in the brain. The structure of such large networks
cannot be fully sampled with the present technology. Our approach provides a
method to estimate global properties of under-sampled networks with good
approximation. Finally, we demonstrate on three different data sets (C.
elegans' neuronal network, R. prowazekii's metabolic network, and a network of
synonyms extracted from Roget's Thesaurus) that real-world networks have
statistical relations compatible with those obtained using regression models
Node Vulnerability under Finite Perturbations in Complex Networks
A measure to quantify vulnerability under perturbations (attacks, failures, large fluctuations) in ensembles (networks) of coupled dynamical systems is proposed. Rather than addressing the issue of how the network properties change upon removal of elements of the graph (the strategy followed by most of the existing methods for studying the vulnerability of a network based on its topology), here a dynamical definition of vulnerability is introduced, referring to the robustness of a collective dynamical state to perturbing events occurring over a fixed topology. In particular, we study how the collective (synchronized) dynamics of a network of chaotic units is disrupted under the action of a finite size perturbation on one of its nodes. Illustrative examples are provided for three systems of identical chaotic oscillators coupled according to three distinct well-known network topologies. A quantitative comparison between the obtained vulnerability rankings and the classical connectivity/centrality rankings is made that yields conclusive results. Possible applications of the proposed strategy and conclusions are also discussed
Phase Locking Induces Scale-Free Topologies in Networks of Coupled Oscillators
An initial unsynchronized ensemble of networking phase oscillators is further subjected to a growing process where a set of forcing oscillators, each one of them following the dynamics of a frequency pacemaker, are added to the pristine graph. Linking rules based on dynamical criteria are followed in the attachment process to force phase locking of the network with the external pacemaker. We show that the eventual locking occurs in correspondence to the arousal of a scale-free degree distribution in the original graph
Clustering in large networks does not promote upstream reciprocity
Upstream reciprocity (also called generalized reciprocity) is a putative
mechanism for cooperation in social dilemma situations with which players help
others when they are helped by somebody else. It is a type of indirect
reciprocity. Although upstream reciprocity is often observed in experiments,
most theories suggest that it is operative only when players form short cycles
such as triangles, implying a small population size, or when it is combined
with other mechanisms that promote cooperation on their own. An expectation is
that real social networks, which are known to be full of triangles and other
short cycles, may accommodate upstream reciprocity. In this study, I extend the
upstream reciprocity game proposed for a directed cycle by Boyd and Richerson
to the case of general networks. The model is not evolutionary and concerns the
conditions under which the unanimity of cooperative players is a Nash
equilibrium. I show that an abundance of triangles or other short cycles in a
network does little to promote upstream reciprocity. Cooperation is less likely
for a larger population size even if triangles are abundant in the network. In
addition, in contrast to the results for evolutionary social dilemma games on
networks, scale-free networks lead to less cooperation than networks with a
homogeneous degree distribution.Comment: 5 figure
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