65 research outputs found

    Topological Evolution of Dynamical Networks: Global Criticality from Local Dynamics

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    We evolve network topology of an asymmetrically connected threshold network by a simple local rewiring rule: quiet nodes grow links, active nodes lose links. This leads to convergence of the average connectivity of the network towards the critical value Kc=2K_c =2 in the limit of large system size NN. How this principle could generate self-organization in natural complex systems is discussed for two examples: neural networks and regulatory networks in the genome.Comment: 4 pages RevTeX, 4 figures PostScript, revised versio

    Surface spin-flop transition in a uniaxial antiferromagnetic Fe/Cr superlattice induced by a magnetic field of arbitrary direction

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    We studied the transition between the antiferromagnetic and the surface spin-flop phases of a uniaxial antiferromagnetic [Fe(14 \AA)/Cr(11 \AA]x20_{\rm x20} superlattice. For external fields applied parallel to the in-plane easy axis, the layer-by-layer configuration, calculated in the framework of a mean-field one-dimensional model, was benchmarked against published polarized neutron reflectivity data. For an in-plane field HH applied at an angle ψ≠0\psi \ne 0 with the easy axis, magnetometry shows that the magnetization MM vanishes at H=0, then increases slowly with increasing HH. At a critical value of HH, a finite jump in M(H)M(H) is observed for ψ<5o\psi<5^{\rm o}, while a smooth increase of MM vsvs HH is found for ψ>5o\psi>5^{\rm o}. A dramatic increase in the full width at half maximum of the magnetic susceptibility is observed for ψ≥5o\psi \ge 5^{\rm o}. The phase diagram obtained from micromagnetic calculations displays a first-order transition to a surface spin-flop phase for low ψ\psi values, while the transition becomes continuous for ψ\psi greater than a critical angle, ψmax≈4.75o\psi_{\rm max} \approx 4.75^{\rm o}. This is in fair agreement with the experimentally observed results.Comment: 24 pages, 7 figure

    Causes and importance of new particle formation in the present-day and pre-industrial atmospheres

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    New particle formation has been estimated to produce around half of cloud-forming particles in the present-day atmosphere, via gas-to-particle conversion. Here we assess the importance of new particle formation (NPF) for both the present-day and the pre-industrial atmospheres. We use a global aerosol model with parametrisations of NPF from previously published CLOUD chamber experiments involving sulphuric acid, ammonia, organic molecules and ions. We find that NPF produces around 67% of cloud condensation nuclei at 0.2% supersaturation (CCN0.2%) at the level of low clouds in the pre-industrial atmosphere (estimated uncertainty range 45-84%) and 54% in the present day (estimated uncertainty range 38-66%). Concerning causes, we find that the importance of biogenic volatile organic compounds (BVOCs) in NPF and CCN formation is greater than previously thought. Removing BVOCs and hence all secondary organic aerosol from our model reduces low-cloud-level CCN concentrations at 0.2% supersaturation by 26% in the present-day atmosphere and 41% in the pre-industrial. Around three-quarters of this reduction is due to the tiny fraction of the oxidation products of BVOCs that have sufficiently low volatility to be involved in NPF and early growth. Furthermore, we estimate that 40% of pre-industrial CCN0.2% are formed via ion-induced NPF, compared with 27% in the present-day, although we caution that the ion-induced fraction of NPF involving BVOCs is poorly measured at present. Our model suggests that the effect of changes in cosmic ray intensity on CCN is small and unlikely to be comparable to the effect of large variations in natural primary aerosol emissions

    Evaporation of sulfate aerosols at low relative humidity

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    Evaporation of sulfuric acid from particles can be important in the atmospheres of Earth and Venus. However, the equilibrium constant for the dissociation of H2SO4 to bisulfate ions, which is the one of the fundamental parameters controlling the evaporation of sulfur particles, is not well constrained. In this study we explore the volatility of sulfate particles at very low relative humidity. We measured the evaporation of sulfur particles versus temperature and relative humidity in the CLOUD chamber at CERN. We modelled the observed sulfur particle shrinkage with the ADCHAM model. Based on our model results, we conclude that the sulfur particle shrinkage is mainly governed by H2SO4 and potentially to some extent by SO3 evaporation. We found that the equilibrium constants for the dissociation of H2SO4 to HSO4-(KH2SO4) and the dehydration of H2SO4 to SO3 ((x) K-SO3) are K H2SO4 = 2-4 x 10(9) mol kg(-1) and (x) K SO3 >= 1.4 x 10(10) at 288.8 +/- 5 K.Peer reviewe

    Boolean Dynamics with Random Couplings

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    This paper reviews a class of generic dissipative dynamical systems called N-K models. In these models, the dynamics of N elements, defined as Boolean variables, develop step by step, clocked by a discrete time variable. Each of the N Boolean elements at a given time is given a value which depends upon K elements in the previous time step. We review the work of many authors on the behavior of the models, looking particularly at the structure and lengths of their cycles, the sizes of their basins of attraction, and the flow of information through the systems. In the limit of infinite N, there is a phase transition between a chaotic and an ordered phase, with a critical phase in between. We argue that the behavior of this system depends significantly on the topology of the network connections. If the elements are placed upon a lattice with dimension d, the system shows correlations related to the standard percolation or directed percolation phase transition on such a lattice. On the other hand, a very different behavior is seen in the Kauffman net in which all spins are equally likely to be coupled to a given spin. In this situation, coupling loops are mostly suppressed, and the behavior of the system is much more like that of a mean field theory. We also describe possible applications of the models to, for example, genetic networks, cell differentiation, evolution, democracy in social systems and neural networks.Comment: 69 pages, 16 figures, Submitted to Springer Applied Mathematical Sciences Serie

    Most Networks in Wagner's Model Are Cycling

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    In this paper we study a model of gene networks introduced by Andreas Wagner in the 1990s that has been used extensively to study the evolution of mutational robustness. We investigate a range of model features and parameters and evaluate the extent to which they influence the probability that a random gene network will produce a fixed point steady state expression pattern. There are many different types of models used in the literature, (discrete/continuous, sparse/dense, small/large network) and we attempt to put some order into this diversity, motivated by the fact that many properties are qualitatively the same in all the models. Our main result is that random networks in all models give rise to cyclic behavior more often than fixed points. And although periodic orbits seem to dominate network dynamics, they are usually considered unstable and not allowed to survive in previous evolutionary studies. Defining stability as the probability of fixed points, we show that the stability distribution of these networks is highly robust to changes in its parameters. We also find sparser networks to be more stable, which may help to explain why they seem to be favored by evolution. We have unified several disconnected previous studies of this class of models under the framework of stability, in a way that had not been systematically explored before
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