Polycyclic aromatic hydrocarbons (PAHs) in a large South American industrial coastal area (Santos Estuary, Southeastern Brazil): Sources and depositional history

Located in southeastern Brazil, the Santos Estuary has the most important industrial and urban population area of South America. Since the 1950`s, increased urbanization and industrialization near the estuary margins has caused the degradation of mangroves and has increased the discharge of sewage and industrial effluents. The main objectives of this work were to determine the concentrations and sources of polycyclic aromatic hydrocarbons (PAHs) in sediment cores in order to investigate the input of these substances in the last 50 years. The PAHs analyses indicated multiple sources of these compounds (oil and pyrolitic origin), basically anthropogenic contributions from biomass, coal and fossil fuels combustion. The distribution of PAHs in the cores was associated with the formation and development of Cubatao industrial complex and the Santos harbour, waste disposal, world oil crisis and the pollution control program, which results in the decrease of organic pollutants input in this area. (C) 2011 Elsevier Ltd. All rights reserved.

A system for classification of time-series data from industrial non-destructive device

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.