STOCHASTIC MODEL FOR SIMULATING MAIZE YIELD

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment.

Exact correlation functions in particle-reaction models with immobile particles

Exact results on particle densities as well as correlators in two models of immobile particles, containing either a single species or else two distinct species, are derived. The models evolve following a descent dynamics through pair annihilation where each particle interacts once at most throughout its entire history. The resulting large number of stationary states leads to a non-vanishing configurational entropy. Our results are established for arbitrary initial conditions and are derived via a generating function method. The single-species model is the dual of the 1D zero-temperature kinetic Ising model with Kimball-Deker-Haake dynamics. In this way, both in finite and semi-infinite chains and also the Bethe lattice can be analysed. The relationship with the random sequential adsorption of dimers and weakly tapped granular materials is discussed.

Metalloporphyrins as cytochrome P450 models for chlorhexidine metabolite prediction

The catalytic oxidation of chlorhexidine (CHX, a strong microbicidal agent) mediated by ironporphyrins has been investigated by using hydrogen peroxide, mCPBA, tBuOOH, or NaOCl as oxidant. All of these oxygen donors yielded p-chloroaniline (pCA) as the main product. The higher pCA yields amounted to 71% in the following conditions: catalyst/oxidant/substrate molar ratio of 1:150:50, aqueous medium, FeTMPyP as catalyst. The medium pH also had a strong effect on the pCA yields; in physiological pH, formation of this product was specially favored in the presence of the catalysts, with yields 58% higher than those achieved in control reactions. This provided strong evidence that CHX is metabolized to pCA upon ingestion. (c) 2012 Elsevier B.V. All rights reserved.