A gestão do conhecimento na educação ambiental

Information and knowledge are resources of environmental education (EE) that can be performed through knowledge management (KM). The aim of this paper is to propose measures of knowledge creation (KC) to improve performance of EE. This study is based on the research literature without empirical findings; therefore, the results are limited by the methodological resources of the theoretical essay. However, this limitation is the greatest motivation for future research which could investigate the proximity of EE with KM and KC in empirical investigations. Some suggestions for developing the requirements of KC programs to EE are presented as the results: possibility of the SECI process to better perform various aspects of environmental education such as social learning, interaction activities, dialogue, experience exchanging, information and knowledge, and of different ideas and ways of acting, done by EE and, finally, the possibility of Ba to develop a proper space for creation of new environmental knowledge. This article contains academic contributions to KM by providing greater discussion and understanding of KC; to EE when it allows a different view based on the work of information and knowledge about the processes of teaching, when contributing to social programs for EE, improving their practices and, consequently, contributing to an environmentally sustainable economic development.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.