The Spread of Opinions and Proportional Voting

Election results are determined by numerous social factors that affect the formation of opinion of the voters, including the network of interactions between them and the dynamics of opinion influence. In this work we study the result of proportional elections using an opinion dynamics model similar to simple opinion spreading over a complex network. Erdos-Renyi, Barabasi-Albert, regular lattices and randomly augmented lattices are considered as models of the underlying social networks. The model reproduces the power law behavior of number of candidates with a given number of votes found in real elections with the correct slope, a cutoff for larger number of votes and a plateau for small number of votes. It is found that the small world property of the underlying network is fundamental for the emergence of the power law regime.Comment: 10 pages, 7 figure

Strategic CSR shifts towards adaptive food governance under environmental change: A comparison between South African and Brazilian retailers

Governance in the food system has become a key topic of discussion in light of the 2007-08 food price crisis. Of special importance has been the shift to include the role that non-state actors are likely to play in achieving food security under global environmental change (GEC). This paper aims to compare private sector food system governance trends in two emerging economies, Brazil and South Africa. It focuses on practices around adaptation, an area largely neglected in climate change discussion, yet a critical factor in coping with the societal consequences of GEC. This study identifies several processes, particularly within the retail sector, that could indicate normative mechanisms through which 'good governance' can be translated into practice

Statistical Mechanics Characterization of Neuronal Mosaics

The spatial distribution of neuronal cells is an important requirement for achieving proper neuronal function in several parts of the nervous system of most animals. For instance, specific distribution of photoreceptors and related neuronal cells, particularly the ganglion cells, in mammal's retina is required in order to properly sample the projected scene. This work presents how two concepts from the areas of statistical mechanics and complex systems, namely the \emph{lacunarity} and the \emph{multiscale entropy} (i.e. the entropy calculated over progressively diffused representations of the cell mosaic), have allowed effective characterization of the spatial distribution of retinal cells.Comment: 3 pages, 1 figure, The following article has been submitted to Applied Physics Letters. If it is published, it will be found online at http://apl.aip.org

The complex channel networks of bone structure

Bone structure in mammals involves a complex network of channels (Havers and Volkmann channels) required to nourish the bone marrow cells. This work describes how three-dimensional reconstructions of such systems can be obtained and represented in terms of complex networks. Three important findings are reported: (i) the fact that the channel branching density resembles a power law implies the existence of distribution hubs; (ii) the conditional node degree density indicates a clear tendency of connection between nodes with degrees 2 and 4; and (iii) the application of the recently introduced concept of hierarchical clustering coefficient allows the identification of typical scales of channel redistribution. A series of important biological insights is drawn and discussedComment: 3 pages, 1 figure, The following article has been submitted to Applied Physics Letters. If it is published, it will be found online at http://apl.aip.org

A Complex Network Approach to Topographical Connections

The neuronal networks in the mammals cortex are characterized by the coexistence of hierarchy, modularity, short and long range interactions, spatial correlations, and topographical connections. Particularly interesting, the latter type of organization implies special demands on the evolutionary and ontogenetic systems in order to achieve precise maps preserving spatial adjacencies, even at the expense of isometry. Although object of intensive biological research, the elucidation of the main anatomic-functional purposes of the ubiquitous topographical connections in the mammals brain remains an elusive issue. The present work reports on how recent results from complex network formalism can be used to quantify and model the effect of topographical connections between neuronal cells over a number of relevant network properties such as connectivity, adjacency, and information broadcasting. While the topographical mapping between two cortical modules are achieved by connecting nearest cells from each module, three kinds of network models are adopted for implementing intracortical connections (ICC), including random, preferential-attachment, and short-range networks. It is shown that, though spatially uniform and simple, topographical connections between modules can lead to major changes in the network properties, fostering more effective intercommunication between the involved neuronal cells and modules. The possible implications of such effects on cortical operation are discussed.Comment: 5 pages, 5 figure

Desenvolvimento profissional para a ação docente em ciência-tecnologia-sociedade : a formação continuada de professores de física do ensino médio

Esta pesquisa ocorreu ao longo de um curso de formação continuada, envolvendo quatro professores de Física do ensino médio de escolas públicas do Rio de Janeiro, no Brasil. A ação de formação foi estruturada a partir de um trabalho colaborativo de construção de estratégias didáticas para abordagem do tema produção e consumo da energia elétrica, considerando-se a perspectiva do enfoque Ciência-Tecnologia-Sociedade (CTS) para o ensino de Física. Dentre os aspectos estudados, destacamos a relação entre a precariedade da situação profissional dos participantes e os desafios postos pelo enfoque CTS. Os resultados apontam para a necessidade de desenvolvimento de uma ‘autonomia em CTS’ e, o espaço de formação mostrou-se adequado para um desenvolvimento profissional na direção de práticas mais autônomas em relação papel do ‘educador CTS’

Current pathophysiological concepts and management of pulmonary hypertension

Pulmonary hypertension (PH), increasingly recognized as a major health burden, remains underdiagnosed due mainly to the unspecific symptoms. Pulmonary arterial hypertension (PAH) has been extensively investigated. Pathophysiological knowledge derives mostly from experimental models. Paradoxically, common non-PAH PH forms remain largely unexplored. Drugs targeting lung vascular tonus became available during the last two decades, notwithstanding the disease progresses in many patients. The aim of this review is to summarize recent advances in epidemiology, pathophysiology and management with particular focus on associated myocardial and systemic compromise and experimental therapeutic possibilities. PAH, currently viewed as a panvasculopathy, is due to a crosstalk between endothelial and smooth muscle cells, inflammatory activation and altered subcellular pathways. Cardiac cachexia and right ventricular compromise are fundamental determinants of PH prognosis. Combined vasodilator therapy is already mainstay for refractory cases, but drugs directed at these new pathophysiological pathways may constitute a significant advance

Performance of networks of artificial neurons: The role of clustering

The performance of the Hopfield neural network model is numerically studied on various complex networks, such as the Watts-Strogatz network, the Barab{\'a}si-Albert network, and the neuronal network of the C. elegans. Through the use of a systematic way of controlling the clustering coefficient, with the degree of each neuron kept unchanged, we find that the networks with the lower clustering exhibit much better performance. The results are discussed in the practical viewpoint of application, and the biological implications are also suggested.Comment: 4 pages, to appear in PRE as Rapid Com

Transient dynamics for sequence processing neural networks: effect of degree distributions

We derive a analytic evolution equation for overlap parameters including the effect of degree distribution on the transient dynamics of sequence processing neural networks. In the special case of globally coupled networks, the precisely retrieved critical loading ratio $\alpha_c = N ^{-1/2}$ is obtained, where $N$ is the network size. In the presence of random networks, our theoretical predictions agree quantitatively with the numerical experiments for delta, binomial, and power-law degree distributions.Comment: 11 pages, 6 figure

Intermittent exploration on a scale-free network

We study an intermittent random walk on a random network of scale-free degree distribution. The walk is a combination of simple random walks of duration $t_w$ and random long-range jumps. While the time the walker needs to cover all the nodes increases with $t_w$, the corresponding time for the edges displays a non monotonic behavior with a minimum for some nontrivial value of $t_w$. This is a heterogeneity-induced effect that is not observed in homogeneous small-world networks. The optimal $t_w$ increases with the degree of assortativity in the network. Depending on the nature of degree correlations and the elapsed time the walker finds an over/under-estimate of the degree distribution exponent.Comment: 12 pages, 3 figures, 1 table, published versio