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El número de asociaciones en los centros está creciendo. El objetivo del trabajo es ofrecer un marco de análisis para entender sus prácticas y forma de interacción, deforma que se consiga mayor calidad en todos los proceso. Si revelamos las características de sus motivaciones, podemos entender sus contradicciones y los factores que inhiben o facilitan las interacciones con el resto de estamentos que actúan en la Universidad (profesorado, investigadores, administrativos, personal externo y de servicios). ----------ABSTRACT---------- The number of associations in our centers is growing. The objective of this paper is to offer a framework for the analysis of students assotiation 'practices and form of interaction, in order to achieve greater quality in all university' processes. If we reveal the characteristics of their motivations, we can understand their contradictions and the factors that inhibit or facilitate interactions with the rest of univeristy stakeholder (professors, researchers, administrative, service staff and external personnel).

Knowledge on soil pore geometry is important for understanding soil processes as it controls the movement and storage of fluids on various scales. With the advent of modern non-destructive tomography techniques there have been many attempts made to analyze pore space features mainly concentrating on the visualization Of Soil Structure. Multifractal formalism or the wavelet transform has been revealed as a useful tool in these cases where highly complex and heterogeneous media are studied. The field of 3D pore space analysis opens a challenging opportunity to develop techniques for quantifying and describing pore space properties. One of these quantifications can be the maximum depth pore network (MD), analogous as the quantification of the preferential flow paths. In this paper, a variation of the wavelet transform modulo maxima (WTMM) method used to compute multifractal behavior is presented. As a wavelet transform analysis (WTA), it allows us to focus on every scale which can be useful to select the range of scales where multifractal analysis (MFA) can be applied, revealing the MD global scaling patterns. In addition, the proposed method does not make any global estimate, so it can also be used to focus on local distribution of singularities. So, in the context of multiscaling structure analysis, the proposed wavelet-based method can complement box-counting analysis in order to statistically describe preferential flow path geometry and flow processes. The methodology is applied to determine the multifiractal behaviour of 3D images of soil samples with 45.1 mu m resolution (256 x 256 x 256 voxels) with closer porosities (ranging from 12% to 14%) and different spatial arrangements. (C) 2009 Elsevier B.V. All rights reserved. 2.178 JCR (2010) Q1, 5/32 Soil science UEM

Most soil parameters as the spatial vaziability of preferential pathways for water and chemical transport in field soils show complex variations at different scales that cannot accurately described with stationary assumptions. This is why multifractal formalism or the wavelet transform reveals as useful tools for classifying and quantifying the spatial variability of these preferential pathways, as visualized through dye infiltration experiments. The high-resolution images resulting from these experiments are analyzed using both box-counting methods and wavelet transform analysis (WTA). The box-counting methods reveals global scaling patterns while the WTA focuses on local distribution of singulazities. So, in the context of multiscaling structure analysis the wavelet methods can complement box-counting analysis which could be useful for statistically describing preferential flow path geometry and flow processes. The methodology is illustrated using well-known fractal structures as multifractal Sierpinsky carpets and results are illustrated with images of horizontal planes of the subsoil, acquired after dye infiltration into a 4m2 plot located on a Vertisol soil near College Station, Texas. Sin financiación 0.349 JCR (2009) Q4, 51/59 Automation & control systems, 96/103 Computer science, artificial intelligences UEM