Probing neutrino mass with multilepton production at the Tevatron in the simplest R-parity violation model

We analyse the production of multileptons in the simplest supergravity model with bilinear violation of R parity at the Fermilab Tevatron. Despite the small .R-parity violating couplings needed to generate the neutrino masses indicated by current atmospheric neutrino data, the lightest supersymmetric particle is unstable and can decay inside the detector. This leads to a phenomenology quite distinct from that of the R-parity conserving scenario. We quantify by how much the supersymmetric multilepton signals differ from the R-parity conserving expectations, displaying our results in the m0 ⊙ m1/2 plane. We show that the presence of bilinear R-parity violating interactions enhances the supersymmetric multilepton signals over most of the parameter space, specially at moderate and large m0. © SISSA/ISAS 2003.

Precision of yule-walker methods for the arma spectral model

In this work a new method is proposed of separated estimation for the ARMA spectral model based on the modified Yule-Walker equations and on the least squares method. The proposal of the new method consists of performing an AR filtering in the random process generated obtaining a new random estimate, which will reestimate the ARMA model parameters, given a better spectrum estimate. Some numerical examples will be presented in order to ilustrate the performance of the method proposed, which is evaluated by the relative error and the average variation coefficient.

Estimates of covariance functions for growth of Nelore cattle applying a parametric correlation structure to model within-animal correlations

A total of 20,065 weights recorded on 3016 Nelore animals were used to estimate covariance functions for growth from birth to 630 days of age, assuming a parametric correlation structure to model within-animal correlations. The model of analysis included fixed effects of contemporary groups and age of dam as quadratic covariable. Mean trends were taken into account by a cubic regression on orthogonal polynomials of animal age. Genetic effects of the animal and its dam and maternal permanent environmental effects were modelled by random regressions on Legendre polynomials of age at recording. Changes in direct permanent environmental effect variances were modelled by a polynomial variance function, together with a parametric correlation function to account for correlations between ages. Stationary and nonstationary models were used to model within-animal correlations between different ages. Residual variances were considered homogeneous or heterogeneous, with changes modelled by a step or polynomial function of age at recording. Based on Bayesian information criterion, a model with a cubic variance function combined with a nonstationary correlation function for permanent environmental effects, with 49 parameters to be estimated, fitted best. Modelling within-animal correlations through a parametric correlation structure can describe the variation pattern adequately. Moreover, the number of parameters to be estimated can be decreased substantially compared to a model fitting random regression on Legendre polynomial of age. © 2004 Elsevier B.V. All rights reserved.

Modeling energy utilization and growth parameter description for broiler chickens

Two experiments were conducted to develop and evaluate a model to estimate ME requirements and determine Gompertz growth parameters for broilers. The first experiment was conducted to determine maintenance energy requirements and the efficiencies of energy utilization for fat and protein deposition. Maintenance ME (ME m) requirements were estimated to be 157.8, 112.1, and 127.2 kcal of ME/kg 0.75 per day for broilers at 13, 23, and 32°C, respectively. Environmental temperature (T) had a quadratic effect on maintenance requirements (ME m = 307.87 - 15.63T + 0.3105T 2; r 2= 0.93). Energy requirements for fat and protein deposition were estimated to be 13.52 and 12.59 kcal of ME/g, respectively. Based on these coefficients, a model was developed to calculate daily ME requirements: ME = BW 0.75 (307.87 - 15.63T + 0.3105 T 2) + 13.52 G f + 12.59 G p. This model considers live BW, the effects of environmental temperature, and fractional fat (G f) and protein (G p) deposition. The second experiment was carried out to estimate the growth parameters of Ross broilers and to collect data to evaluate the ME requirement model proposed. Live BW, empty feather-free carcass, weight of the feathers, and carcass chemical compositions were analyzed until 16 wk of age. Parameters of Gompertz curves for each component were estimated. Males had higher growth potential and higher capacity to deposit nutrients than females, except for fat deposition. Data of BW and body composition collected in this experiment were fitted into the energy model proposed herein and the equations described by Emmans (1989) and Chwalibog (1991). The daily ME requirements estimated by the model determined in this study were closer to the ME intake observed in this trial compared with other models. ©2005 Poultry Science Association, Inc.

Dynamic behavior of cluster observables for the 2d Ising model

We present results of our numerical study of the critical dynamics of percolation observables for the two-dimensional Ising model. We consider the (Monte Carlo) short-time evolution of the system with small initial magnetization and heat-bath dynamics. We find qualitatively different dynamic behaviors for the magnetization M and for Ω, the so-called strength of the percolating cluster, which is the order parameter of the percolation transition. More precisely, we obtain a (leading) exponential form for Ω as a function of the Monte Carlo time t, to be compared with the power-law increase encountered for M at short times. Our results suggest that, although the descriptions in terms of magnetic or percolation order parameters may be equivalent in the equilibrium regime, greater care must be taken to interpret percolation observables at short times.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

Optimal Boussinesq model for shallow-water waves interacting with a microstructure

In this paper, we consider the propagation of water waves in a long-wave asymptotic regime, when the bottom topography is periodic on a short length scale. We perform a multiscale asymptotic analysis of the full potential theory model and of a family of reduced Boussinesq systems parametrized by a free parameter that is the depth at which the velocity is evaluated. We obtain explicit expressions for the coefficients of the resulting effective Korteweg-de Vries (KdV) equations. We show that it is possible to choose the free parameter of the reduced model so as to match the KdV limits of the full and reduced models. Hence the reduced model is optimal regarding the embedded linear weakly dispersive and weakly nonlinear characteristics of the underlying physical problem, which has a microstructure. We also discuss the impact of the rough bottom on the effective wave propagation. In particular, nonlinearity is enhanced and we can distinguish two regimes depending on the period of the bottom where the dispersion is either enhanced or reduced compared to the flat bottom case. © 2007 The American Physical Society.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.