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Abstract: This chapter describes the disaster of the Corrego do Feijao mine and deals with the construction and consolidation of conflicts, risks and environmental impacts associated with dams (CRIAB) and his challenges and further developments. Interaction with the research groups and those affected populations will certainly affect the dynamics of the CRIAB group, and perhaps change its members or partners significantly. The workgroups that makeup CRIAB intend to act not as a bureaucratic NGO or a cold ‘scientific’ academic research team with no relation with the social actors present in these conflicts. Several problems and issues involving sustainability issues must necessarily arise from this change. The intention is that the deepening of these issues will strengthen the human rights of the populations living in mining areas with respect for the rights for the well-being of these populations, respect for the environment and its future perspectives and for an economic arrangement that does not sacrifice the necessary resources for future generations

Abstract: Mathematical morphology is a theory with applications in image and signal processing and analysis. This paper presents a quantale-based approach to color morphology based on the CIELab color space with spherical coordinates. The novel morphological operations take into account the perceptual difference between color elements by using a distance-based ordering scheme. Furthermore, the novel approach allows the use of non-flat structuring elements. Although the paper focuses on dilations and erosions, many other morphological operations can be obtained by combining these two elementary operations. An illustrative example reveals that non-flat dilations and erosions may preserve more features of a natural color image than their corresponding flat operations

Abstract: The Molecular Distance Geometry Problem (MDGP) is the problem of finding the possible conformations of a molecule by exploiting available information about distances between some atom pairs. When particular assumptions are satisfied, the MDGP can be discretized, so that the search domain of the problem becomes a tree. This tree can be explored by using an interval Branch & Prune (iBP) algorithm. In this context, the order given to the atoms of the molecules plays an important role. In fact, the discretization assumptions are strongly dependent on the atomic ordering, which can also impact the computational cost of the iBP algorithm. In this work, we propose a new partial discretization order for protein backbones. This new atomic order optimizes a set of objectives, that aim at improving the iBP performances. The optimization of the objectives is performed by Answer Set Programming (ASP), a declarative programming language that allows to express our problem by a set of logical constraints. The comparison with previously proposed orders for protein backbones shows that this new discretization order makes iBP perform more efficiently

Abstract: The importance of H(div)-conforming approximations is well recognized for conservative mixed formulations of multiphysics systems. There exists in the literature a variety of such approximation spaces, which are usually restricted to standard element geometry: triangular, quadrilateral, hexahahedral, tetrahedral, or prismatic meshes. We describe the principles in the construction of more general H(div)-conforming contexts, the meshes allowing non-convex polygonal (polyhedral) local subdomains. Given a finite dimensional normal flux space ?c, piecewise defined over a partition of the mesh skeleton, H(div)-conforming approximation spaces Vc keep fixed the face flux components constrained by ?c, but the internal flux components and the potential approximations inside the subdomains may be enriched in different extents: with respect to internal mesh size, internal polynomial degree, or both. The definition and implementation of such constrained space configurations are possible due to an available hierarchy of vector shape functions for classic polynomial spaces of arbitrary degree defined on usual master elements

Abstract: Preprocessing microarray data consists of a number of statistical procedures that convert the observed intensities into quantities that represent biological events of interest, like gene expression and allele-specific abundances. Here, we present a summary of the theory behind microarray data preprocessing for expression, whole transcriptome and SNP designs and focus on the computational protocol used to obtain processed data that will be used on downstream analyses. We describe the main features of the oligo Bioconductor package, an application designed to support oligonucleotide microarrays using the R statistical environment and the infrastructure provided by Bioconductor, allowing the researcher to handle probe-level data and interface with advanced statistical tools under a simplified framework. We demonstrate the use of the package by preprocessing data originated from three different designs