Efecto del sistema lactoperoxidasa en la conservación de la leche cruda en dos fincas de la comunidad Monte Rosa, El Rama - RAAS, Nicaragua

RESUMEN Con el objetivo de conocer el efecto del sistema lactoperoxidasa, mejor conocido como stabilak, para la conservación de la leche, se desarrollo un estudio en El Rama ubicado en la Región Autónoma Sur de Nicaragua, en las fincas El paraíso y Las Lomas, del 17 de Agosto al 24 de Septiembre – 2011, se determinaron dos tratamientos T1 con stabilak y T2 sin stabilak o testigo, se tomaron un total de 480 muestras y se analizaron, para valorar su calidad. En las fincas seleccionadas se realizó el muestreo, tres veces por semana, en cada día se tomaba tres muestras de leche, para cada una de ellas se les determinó la prueba de acidez (204 muestras) y la prueba de alcohol (204 muestras), excepto para la prueba de reductasa que se realizó una por día (72 muestras). Los datos, se pr ocesaron en los programas estadísticos SAS y SPSS. Se encontró que los mejores comportamientos de preservación de la leche fue en la finca Las Lomas y cuando se utilizó el stabilak, donde la acidez reportó veinte y siete muestras con acidez aceptable, de 1 5 a 16 ml NaOH 0.1N/100 ml, con la prueba de alcohol, veinte y siete muestras negativas, en cambio en El Paraíso, veinte y cuatro muestras con acidez aceptable y con la prueba de alcohol reportaron 27 muestras negativas. La clasificación de la leche, A, B y C, se realizó, con la prueba de Reductasa en: A Las Lomas con 13 muestras y El Paraíso con 8, en B: 10 muestras en El Paraíso, 5 en Las Lomas, en C no se reportó ninguna. En conclusión, el efecto del stabilak, mantiene los parámetros de calidad, según normativas nacional, Siendo el sistema lactoperoxidasa más eficaz, cuando se aplican las buenas prácticas de higiene en el ordeño.

Monte Carlo filtering and smoothing with application to time-varying spectral estimation

We develop methods for performing filtering and smoothing in non-linear non-Gaussian dynamical models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. In particular, novel techniques are presented for generation of random realisations from the joint smoothing distribution and for MAP estimation of the state sequence. Realisations of the smoothing distribution are generated in a forward-backward procedure, while the MAP estimation procedure can be performed in a single forward pass of the Viterbi algorithm applied to a discretised version of the state space. An application to spectral estimation for time-varying autoregressions is described.

Permeability of Fractal Porous Media by Monte Carlo Simulations

The permeability of the fractal porous media is simulated by Monte Carlo technique in this work. Based oil the fractal character of pore size distribution in porous media, the probability models for pore diameter and for permeability are derived. Taking the bi-dispersed fractal porous media as examples, the permeability calculations are performed by the present Monte Carlo method. The results show that the present simulations present a good agreement compared with the existing fractal analytical solution in the general interested porosity range. The proposed simulation method may have the potential in prediction of other transport properties (such as thermal conductivity, dispersion conductivity and electrical conductivity) in fractal porous media, both saturated and unsaturated.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.

Pressure and thermal effects on elastic properties of single crystal alpha-Al2O3 from Monte-Carlo simulation

A rectangular structural unit cell of a-Al2O3 is generated from its hexagonal one. For the rectangular structural crystal with a simple interatomic potential [Matsui, Mineral Mag. 58A, 571 (1994)], the relations of lattice constants to homogeneous pressure and temperature are calculated by using Monte-Carlo method at temperature 298K and 0 GPa, respectively. Both numerical results agree with experimental ones fairly well. By comparing pair distribution function, the crystal structure of a-Al2O3 has no phase transition in the range of systematic parameters. Based on the potential model, pressure dependence of isothermal bulk moduli is predicted. Under variation of general strains, which include of external and internal strains, elastic constants of a-Al2O3 in the different homogeneous load are determined. Along with increase of pressure, axial elastic constants increase appreciably, but nonaxial elastic constants are slowly changed.

Reproduccion de piña (Ananas comosus L. Merrill) a partir de tallos e hijos en los cultivares Cayena lisa y monte lirio

Se determinó el efecto del tipo de material vegetativo y del regulador de crecimiento (Hoja verde 48 SL) sobre la brotación de yemas en los cultivares de piña (Ananas comosus L. Merrill) Cayena Lisa (CL) y Monte Lirio (ML). Se establecieron tres ensayos en arreglo de diseño de bloques completos al azar en canteros de arena. En el primer ensayo se evaluó durante tres cortes (cada 30 días) el empleo detallo entero de planta adulta(TE), tallo de planta adulta cortado longitudinalmente (TCL), tallo de planta adulta cortado transversalmente (TCT) y tallo entero de hijo (HE) en el cultivar ML; en el segundo ensayo se evaluaron TE y TCT en el cultivar CL. En el tercer ensayo se estudió la aplicación de 0.5, 1.0 y 2.0 ml l-1 agua de Hoja verde 48 SL y el testigo en el cultivar ML, durante doscortes. Se comparó la brotación de los cultivares CL y ML usando TE y TCT. A los datos de longitud (cm), grosor (cm), peso (g), número de hojas e hijos por tratamiento se les realizó análisis de varianza y separación de media (Waller-Duncan,α = 5%). En el primer ensayo no hubo diferencia significativa en número de brotes, sin embargo los provenientes TE presentaron mayor peso (8.0 y 9.3 g), grosor (1.35 cm) y longitud (8.02 cm). En el segundo ensayo hubo diferencias significativas solamente en número de brotes en el corte 1, TE produjo 2.1 brotes. TCL usando TE registró los mejores promedios en longitud, grosor, peso, número de hojas e hijos. En el tercer ensayo no hubo diferencias significativas entre los tratamientos, a excepción del corte 1 donde presentó la mayor longitud el testigo (11.60 cm). Se produjeron 800 plantas en total, la mayoría establecidas en el campo.

Forward Smoothing Using Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a new SMC algorithm to compute the expectation of additive functionals recursively. Essentially, it is an on-line or "forward only" implementation of a forward filtering backward smoothing SMC algorithm proposed by Doucet, Godsill and Andrieu (2000). Compared to the standard \emph{path space} SMC estimator whose asymptotic variance increases quadratically with time even under favorable mixing assumptions, the non asymptotic variance of the proposed SMC estimator only increases linearly with time. We show how this allows us to perform recursive parameter estimation using an SMC implementation of an on-line version of the Expectation-Maximization algorithm which does not suffer from the particle path degeneracy problem.

Quantum Monte Carlo study of the CO interaction with a dimer model surface for Cr(110)

The chemisorption of CO on a Cr( 110) surface is investigated using the quantum Monte Carlo method in the diffusion Monte Carlo (DMC) variant and a model Cr2CO cluster. The present results are consistent with the earlier ab initio HF study with this model that showed the tilted/ near-parallel orientation as energetically favoured over the perpendicular arrangement. The DMC energy difference between the two orientations is larger (1.9 eV) than that computed in the previous study. The distribution and reorganization of electrons during CO adsorption on the model surface are analysed using the topological electron localization function method that yields electron populations, charge transfer and clear insight on the chemical bonding that occurs with CO adsorption and dissociation on the model surface.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.