Abstract-The parameters set of the Jiles-Atherton hysteresis model is identified by using a real coded genetic algorithm. The parameters identification is performed by minimizing the mean squared error between experimental and simulated magnetic field curves. The procedure is validated by comparing experimental and simulated results.

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.

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Abstract-This paper presents the application of genetic algorithms in the optimization of an offset reflector antenna. The antenna shape is designed in order to obtain a uniform radiation pattern on the Brazilian territory. Modified genetic operators are proposed with the aim to increase the efficiency of the real coded genetic algorithms used here.

The alfalfa endosymbiont Ensifer meliloti strain1021 is known to be an incomplete denitrifier due to its inability to grow anoxically using nitrate as respiratory substrate to produce ATP and grow under anoxic conditions. Although this bacterium contains and expresses the complete set of denitrification genes napEFDABC, nirK, norECBQD and nosRZDFYLX encoding the periplasmic nitrate reductase (Nap), Cu-containing nitrite reductase (NirK), c-type nitric oxide (cNor) and nitrous oxide reductase (Nos), respectively, the reasons of its inability to grow under anoxic conditions are still very poorly understood. In the present study, we have constructed an E. meliloti strain overexpressing napEFDABC genes (Nap+) and demonstrated that this strain is able to grow through anaerobic nitrate respiration. Furthermore, Nap+ showed increased NapC levels as well as Nap, Nir and cNor activities and higher capacity to produce NO and N2O compared to wild-type cells. These results suggest that the inability of E. meliloti to grow under anaerobic conditions using nitrate as electron acceptor is attributable to a limitation in the expression of the periplasmic nitrate reductase.