Heterogeneous photocatalytic degradation of acid dyes in aqueous TiO2 suspensions: Kinetics and toxicity

This work assesses the photocatalytic (TiO2/UV) degradation of a simulated acid dye bath (Yellow 3, Red 51, Blue 74, and auxiliary chemicals). Color and phytotoxicity removal were monitored by spectrophotometry and lettuce (Lactuca sativa) seeds as the test organism, respectively. Mineralization was determined by DOC analyses. Photocatalytic, photolytic, and adsorption experiments were performed, showing that adsorption was negligible. After 240 minutes of irradiation, it was achieved 96% and 78% of color removal with photocatalysis and photolysis, respectively. 37% of mineralization occurred with photocatalysis only. The dye bath was rendered completely non-toxic after 60 minutes of photocatalytic treatment; the same result was only achieved with photolysis after 90 minutes. A kinetic model composed of two first-order in series reactions was used. The first photocatalytic decolorization rate constant was k(1) = 0.062 min(-1) and the second k(2) = 0.0043 min(-1), approximately two times greater than the photolytic ones.

DC traction load flow including AC distribution network

In the first part some information and characterisation about an AC distribution network that feeds traction substations and their possible influences on the DC traction load flow are presented. Those influences are investigated and mathematically modelled. To corroborate the mathematical model, an example is presented and their results are confronted with real measurements.

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.