Kinetics and mathematical model of killer/sensitive interaction under different physicochemical conditions of must/wine: a study from a biological point of view.

Fermentation of grape must to wine is carried out by a complex microbial mixture, which also involves spoilage yeasts of wine. The latter yeasts produce organoleptic changes that cause significant economic losses to the wine industry. SO2 is traditionally used to control this spoilage populations, but because of its harmful effects on human health, biocontrol has emerged as an alternative treatment. Although studies have been carried out to select biocontroller yeasts and examine their underlying mechanisms of action, reports on their application have not been published yet. To better understand the interaction and the successful application of biocontrol, the use of mathematical models, among other methods, is important, as they facilitate the prediction of success or failure of the antagonist. The objective of the present study was to use an existing mathematical model to obtain information about the yeast's interaction assayed and to validate its predictive use under different physicochemical conditions during the wine fermentation, and eventually predict biocontrol kinetics. The mathematical model was applied to the fermentation conditions and provided information on the kinetic parameters of the biocontrol interaction and allowed interpretations about other parameters. The model was applied in the different physicochemical conditions for the biocontrol and did not fit correctly to experimental data, and therefore an improvement was proposed which was successful and presented new hypotheses.

Neural networks for advanced control of robot manipulators.

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.