Spectrophotometric flow-injection determination of sulphite in white wines involving gas diffusion through a concentric tubular membrane

A flow-injection system is proposed for the spectrophotometric determination of sulphite in white wines. The method involves analyte conversion to SO2, gas diffusion through a Teflon® semi-permeable membrane, collection into an alkaline stream (pH 8), reaction with Malachite green (MG) and monitoring at 620 nm. With a concentric tubular membrane, the system design was simplified. Influence of reagent concentrations, pH of donor and acceptor streams, temperature, timing, surfactant addition and presence of potential interfering species of the wine matrix were investigated. A pronounced (ca. 100%) enhancement in sensitivity was noted by adding cetylpyridinium chloride (CPC). The proposed system is robust and baseline drift is not observed during 4 h operating periods. Only 400 μL of sample and 0.32 mg MG are required per determination. The system handles 30 samples per hour, yielding precise results (r.s.d. < 0.015 for 1.0 - 20.0 mg L-1 SO2) in agreement with those obtained by an alternative procedure.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

Non-Markovianity through flow of information between a system and an environment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

On the optical flow model selection through metaheuristics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)