Reduction capability of soil humic substances from the Rio Negro basin, Brazil, towards Hg(II) studied by a multimethod approach and principal component analysis (PCA)

This paper characterizes humic substances (HS) extracted from soil samples collected in the Rio Negro basin in the state of Amazonas, Brazil, particularly investigating their reduction capabilities towards Hg(II) in order to elucidate potential mercury cycling/volatilization in this environment. For this reason, a multimethod approach was used, consisting of both instrumental methods (elemental analysis, EPR, solid-state NMR, FIA combined with cold-vapor AAS of Hg(0)) and statistical methods such as principal component analysis (PCA) and a central composite factorial planning method. The HS under study were divided into groups, complexing and reducing ones, owing to different distribution of their functionalities. The main functionalities (cor)related with reduction of Hg(II) were phenolic, carboxylic and amide groups, while the groups related with complexation of Hg(II) were ethers, hydroxyls, aldehydes and ketones. The HS extracted from floodable regions of the Rio Negro basin presented a greater capacity to retain (to complex, to adsorb physically and/or chemically) Hg(II), while nonfloodable regions showed a greater capacity to reduce Hg(II), indicating that HS extracted from different types of regions contribute in different ways to the biogeochemical mercury cycle in the basin of the mid-Rio Negro, AM, Brazil. (c) 2007 Published by Elsevier B.V.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

Redução no consumo de efluente gerado em abatedouro de tilápia do nilo através da implantação de conceitos de produção mais limpa (p+l)

the nourishing industry is a segment that makes use of high water consumption due to necessity of the hygienic cleaning of the establishments and the maintenance of the good quality of the food. It enters the nourishing industries with bigger water consumption we have the fishery industry which generates effluent around 5.4 m(3). t(-1) of processed fish. This work had as objective the reduction in the water consumption of the processing of Nile tilapia through the implantation of P+L techniques, for had been in such a way carried through hydraulical alterations in the filleting tables aiming at the minimum possible water consumption for two methods of filleting, eviscerated and not eviscerated, as well as the comment of the alterations in the generated quality of the effluent one. In the present study, the reduction in the water consumption in the filleting process corresponded 98.16% for method EV considering an average time of processing of 3 hours for 32.99 kg of fish, and for method NEV the reduction was of 97.97% with average time of processing of 2.1 hours for 34.96 kg of fish, thus demonstrating that the P+L techniques are efficient for the reduction of the water consumption.