Archival Classification and Knowledge Organization: Theoretical Possibilities for the Archival Field

The main goal of this study is to outline a possible relation between archival classification and knowledge organization theory. In this sense, we seek to contribute to the conceptual classification in Archival Science, since there is a lack of systematization about archival classification; not just classification, but even the study of historical and conceptual aspects of the discipline. In the context of knowledge organization there is a considerable amount of research on how to build classification schemes and indexing systems that can help contribute to and expand archival classification theory. In order to comprehend this vast field of theories and methodologies we construct a parallel comparing the classification concepts in both areas and analyzing these concepts.

The plethysm technique applied to the classification of nuclear states

A short summary of the theory of symmetric group and symmetric functions needed to follow the theory of Schur functions and plethysms is presented. One then defines plethysm, gives its properties and presents a procedure for its calculation. Finally, some aplications in atomic physics and nuclear structure are given.

Mapping soil pollution by spatial analysis and fuzzy classification

Spatial analysis and fuzzy classification techniques were used to estimate the spatial distributions of heavy metals in soil. The work was applied to soils in a coastal region that is characterized by intense urban occupation and large numbers of different industries. Concentrations of heavy metals were determined using geostatistical techniques and classes of risk were defined using fuzzy classification. The resulting prediction mappings identify the locations of high concentrations of Pb, Zn, Ni, and Cu in topsoils of the study area. The maps show that areas of high pollution of Ni and Cu are located at the northeast, where there is a predominance of industrial and agricultural activities; Pb and Zn also occur in high concentrations in the northeast, but the maps also show significant concentrations of Pb and Zn in other areas, mainly in the central and southeastern parts, where there are urban leisure activities and trade centers. Maps were also prepared showing levels of pollution risk. These maps show that (1) Cu presents a large pollution risk in the north-northwest, midwest, and southeast sectors, (2) Pb represents a moderate risk in most areas, (3) Zn generally exhibits low risk, and (4) Ni represents either low risk or no risk in the studied area. This study shows that combining geostatistics with fuzzy theory can provide results that offer insight into risk assessment for environmental pollution.

Pattern recognition theory in nonlinear signal processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.

A Commentary on de Sousa′s towards an Integrative Theory of Consciousness

De Sousa′s comprehensive two-part review of a diversity of contemporary approaches to the study of consciousness is highly welcome. He makes us aware of a proliferation of theoretical and empirical approaches targeting a common theme, but diverging in many ways. He skilfully accomplishes a classification of kinds of approach, identification of the main representatives, their contributions, and respective limitations. However, he does not show how the desired integration could be accomplished. Besides summarising de Sousa′s efficient analytical work, I make critical comments and briefly report my contribution for the integration project.© MSM 2013.