Forum: social network for the surveillance and prevention of workplace accidents

In 2008, academic researchers and public service officials created a university extension studies platform based on online and on-site meetings denominated "Work-Related Accidents Forum: Analysis, Prevention, and Other Relevant Aspects. Its aim was to help public agents and social partners to propagate a systemic approach that would be helpful in the surveillance and prevention of work-related accidents. This article describes and analyses such a platform. Online access is free and structured to: support dissemination of updated concepts; support on-site meetings and capacity to build educational activities; and keep a permanent space for debate among the registered participants. The desired result is the propagation of a social-technical-systemic view of work-related accidents that replaces the current traditional view that emphasizes human error and results in blaming the victims. The Forum uses an educational approach known as permanent health education, which is based on the experience and needs of workers and encourages debate among participants. The forum adopts a problematizing pedagogy that starts from the requirements and experiences of the social actors and stimulates support and discussions among them in line with an ongoing health educational approach. The current challenge is to turn the platform into a social networking website in order to broaden its links with society.

Social cliques in male northern muriquis Brachyteles hypoxanthus

Analyses of spatial relationships and social interactions provide insights into the social structure of animal societies and the ways in which social preferences among and between dyads affect higher order social relationships. In this paper we describe the patterns of spatial associations and social interactions among adult male northern muriquis in order to evaluate the dynamics of their social networks above the dyadic levels. Systematic observations were made on the 17 adult males present in a multi-male/multi-female group from April 2004 through February 2005, and in July 2005. Analyses of their spatial relationships identified two distinct male cliques; some adult males (called "N" males) were more connected to the females and immatures than other adult males ("MU" males), which were more connected to one another. Affiliative interactions were significantly higher among dyads belonging to the same clique than to different cliques. Although frequencies of dyadic agonistic interactions were similarly low among individuals within and between cliques, MU males appeared to be subordinate to N males. Nonetheless, there were no significant differences in the copulation rates estimated for MU males and N males. Mutual benefits of cooperation between MU and N cliques in intergroup encounters might explain their ongoing associations in the same mixed-sex group [Current Zoology 58 (2): 342-352, 2012].

Hierarchical multi-label classification using local neural networks

Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.

Disentangling social networks from spatiotemporal dynamics: the temporal structure of a dolphin society

Social networks are static illustrations of dynamic societies, within which social interactions are constantly changing. Fundamental sources of variation include ranging behaviour and temporal demographic changes. Spatiotemporal dynamics can favour or limit opportunities for individuals to interact, and then a network may not essentially represent social processes. We examined whether a social network can embed such nonsocial effects in its topology, whereby emerging modules depict spatially or temporally segregated individuals. To this end, we applied a combination of spatial, temporal and demographic analyses to a long-term study of the association patterns of Guiana dolphins, Sotalia guianensis. We found that association patterns are organized into a modular social network. Space use was unlikely to reflect these modules, since dolphins' ranging behaviour clearly overlapped. However, a temporal demographic turnover, caused by the exit/entrance of individuals (most likely emigration/immigration), defined three modules of associations occurring at different times. Although this factor could mask real social processes, we identified the temporal scale that allowed us to account for these demographic effects. By looking within this turnover period (32 months), we assessed fission-fusion dynamics of the poorly known social organization of Guiana dolphins. We highlight that spatiotemporal dynamics can strongly influence the structure of social networks. Our findings show that hypothetical social units can emerge due to the temporal opportunities for individuals to interact. Therefore, a thorough search for a satisfactory spatiotemporal scale that removes such nonsocial noise is critical when analysing a social system. (C) 2012 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Multidimensional Projections for Visual Analysis of Social Networks

Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in attribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented.

MAXIMAL PSEUDOCOMPACT AND MAXIMAL R-CLOSED SPACES

In this paper a space X is pseudocompact if it is Tychonoff and every real-valued continuous function on X is bounded. We obtain conditions under which a Tychonoff space is maximal pseudocompact and study conditions under which a regular space is maximal R-closed.

Redes neurais artificiais na previsão da inflação: aplicação como ferramenta de apoio à análise de decisões financeiras em organizações de pequeno porte

As estimações das taxas de inflação são de fundamental importância para os gestores, pois as decisões de investimento estão intimamente ligadas a elas. Contudo, o comportamento inflacionário tende a ser não linear e até mesmo caótico, tornando difícil a sua correta estimação. Essa característica do fenômeno pode tornar imprecisos os modelos mais simples de previsão, acessíveis às pequenas organizações, uma vez que muitos deles necessitam de grandes manipulações de dados e/ou softwares especializados. O presente artigo tem por objetivo avaliar, por meio de análise formal estatística, a eficácia das redes neurais artificiais (RNA) na previsão da inflação, dentro da realidade de organizações de pequeno porte. As RNA são ferramentas adequadas para mensurar os fenômenos inflacionários, por se tratar de aproximações de funções polinomiais, capazes de lidar com fenômenos não lineares. Para esse processo, foram selecionados três modelos básicos de redes neurais artificiais Multi Layer Perceptron, passíveis de implementação a partir de planilhas eletrônicas de código aberto. Os três modelos foram testados a partir de um conjunto de variáveis independentes sugeridas por Bresser-Pereira e Nakano (1984), com defasagem de um, seis e doze meses. Para tal, foram utilizados testes de Wilcoxon, coeficiente de determinação R² e o percentual de erro médio dos modelos. O conjunto de dados foi dividido em dois, sendo um grupo usado para treinamento das redes neurais artificiais, enquanto outro grupo era utilizado para verificar a capacidade de predição dos modelos e sua capacidade de generalização. Com isso, o trabalho concluiu que determinados modelos de redes neurais artificiais têm uma razoável capacidade de predição da inflação no curto prazo e se constituem em uma alternativa razoável para esse tipo de mensuração.