Linear minimum mean square filter for discrete-time linear systems with Markov jumps and multiplicative noises

In this paper we obtain the linear minimum mean square estimator (LMMSE) for discrete-time linear systems subject to state and measurement multiplicative noises and Markov jumps on the parameters. It is assumed that the Markov chain is not available. By using geometric arguments we obtain a Kalman type filter conveniently implementable in a recurrence form. The stationary case is also studied and a proof for the convergence of the error covariance matrix of the LMMSE to a stationary value under the assumption of mean square stability of the system and ergodicity of the associated Markov chain is obtained. It is shown that there exists a unique positive semi-definite solution for the stationary Riccati-like filter equation and, moreover, this solution is the limit of the error covariance matrix of the LMMSE. The advantage of this scheme is that it is very easy to implement and all calculations can be performed offline. (c) 2011 Elsevier Ltd. All rights reserved.

Metal separation from mixed types of batteries using selective precipitation and liquid-liquid extraction techniques

The purpose of this paper is to study metal separation from a sample composed of a mixture of the main types of spent household batteries, using a hydrometallurgical route, comparing selective precipitation and liquid-liquid extraction separation techniques. The preparation of the solution consisted of: grinding the waste of mixed batteries, reduction and volatile metals elimination using electric furnace and acid leaching. From this solution two different routes were studied: selective precipitation with sodium hydroxide and liquid-liquid extraction using Cyanex 272 [bis(2,4,4-trimethylpentyl) phosphoric acid] as extracting agent. The best results were obtained from liquid-liquid extraction in which Zn had a 99% extraction rate at pH 2.5. More than 95% Fe was extracted at pH 7.0, the same pH at which more than 90% Ce was extracted. About 88% Mn, Cr and Co was extracted at this pH. At pH 3.0, more than 85% Ni was extracted, and at pH 3.5 more than 80% of Cd and La was extracted. (C) 2010 Elsevier Ltd. All rights reserved.

Different Approaches for Modeling Grouped Survival Data: A Mango Tree Study

Interval-censored survival data, in which the event of interest is not observed exactly but is only known to occur within some time interval, occur very frequently. In some situations, event times might be censored into different, possibly overlapping intervals of variable widths; however, in other situations, information is available for all units at the same observed visit time. In the latter cases, interval-censored data are termed grouped survival data. Here we present alternative approaches for analyzing interval-censored data. We illustrate these techniques using a survival data set involving mango tree lifetimes. This study is an example of grouped survival data.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

Optimized harvest scheduling in eucalyptus plantations under operational, spatial and climatic constraints

The representation of sustainability concerns in industrial forests management plans, in relation to environmental, social and economic aspects, involve a great amount of details when analyzing and understanding the interaction among these aspects to reduce possible future impacts. At the tactical and operational planning levels, methods based on generic assumptions usually provide non-realistic solutions, impairing the decision making process. This study is aimed at improving current operational harvesting planning techniques, through the development of a mixed integer goal programming model. This allows the evaluation of different scenarios, subject to environmental and supply constraints, increase of operational capacity, and the spatial consequences of dispatching harvest crews to certain distances over the evaluation period. As a result, a set of performance indicators was selected to evaluate all optimal solutions provided to different possible scenarios and combinations of these scenarios, and to compare these outcomes with the real results observed by the mill in the study case area. Results showed that it is possible to elaborate a linear programming model that adequately represents harvesting limitations, production aspects and environmental and supply constraints. The comparison involving the evaluated scenarios and the real observed results showed the advantage of using more holistic approaches and that it is possible to improve the quality of the planning recommendations using linear programming techniques.

ECONOMICAL OCCUPATION OF AGRICULTURAL AREA OF PIRACICABA (SAO PAULO STATE, BRAZIL), INCLUDING RISK THROUGH LINEAR PROGRAMMING

The economic occupation of an area of 500 ha for Piracicaba was studied with the irrigated cultures of maize, tomato, sugarcane and beans, having used models of deterministic linear programming and linear programming including risk for the Target-Motad model, where two situations had been analyzed. In the deterministic model the area was the restrictive factor and the water was not restrictive for none of the tested situations. For the first situation the gotten maximum income was of R$ 1,883,372.87 and for the second situation it was of R$ 1,821,772.40. In the model including risk a producer that accepts risk can in the first situation get the maximum income of R$ 1,883,372. 87 with a minimum risk of R$ 350 year(-1), and in the second situation R$ 1,821,772.40 with a minimum risk of R$ 40 year(-1). Already a producer averse to the risk can get in the first situation a maximum income of R$ 1,775,974.81 with null risk and for the second situation R$ 1.707.706, 26 with null risk, both without water restriction. These results stand out the importance of the inclusion of the risk in supplying alternative occupations to the producer, allowing to a producer taking of decision considered the risk aversion and the pretension of income.

Localization of Pantoea ananatis inside lesions of maize White Spot Disease using transmission electron microscopy and molecular techniques

The etiological agent of maize white spot (MWS) disease has been a subject of controversy and discussion. Initially the disease was described as Phaeosphaeria leaf spot caused by Phaeosphaeria maydis. Other authors have Suggested the existence of different fungal species causing similar symptoms. Recently, a bacterium, Pantoea ananatis, was described as the causal agent of this disease. The purpose of this Study was to offer additional information on the correct etiology of this disease by providing visual evidence of the presence of the bacterium in the interior of the MWS lesions by using transmission electron microscopy (TEM) and molecular techniques. The TEM allowed Visualization of a large amount of bacteria in the intercellular spaces of lesions collected from both artificially and naturally infected plants. Fungal structures were not visualized in young lesions. Bacterial primers for the 16S rRNA and rpoB genes were used in PCR reactions to amplify DNA extracted from water-soaked (young) and necrotic lesions. The universal fungal oligonucleotide ITS4 was also included to identity the possible presence of fungal structures inside lesions. Positive PCR products from water-soaked lesions, both from naturally and artificially inoculated plants, were produced with bacterial primers, whereas no amplification was observed when ITS4 oligonucleotide was used. On the other hand, DNA amplification with ITS4 primer was observed when DNA was isolated from necrotic (old) lesions. These results reinforced previous report of P. ananatis as the primary pathogen and the hypothesis that fungal species may colonize lesions pre-established by P. ananatis.

Soil CO(2) emission estimated by different interpolation techniques

Soil CO(2) emissions are highly variable, both spatially and across time, with significant changes even during a one-day period. The objective of this study was to compare predictions of the diurnal soil CO(2) emissions in an agricultural field when estimated by ordinary kriging and sequential Gaussian simulation. The dataset consisted of 64 measurements taken in the morning and in the afternoon on bare soil in southern Brazil. The mean soil CO(2) emissions were significantly different between the morning (4.54 mu mol m(-2) s(-1)) and afternoon (6.24 mu mol m(-2) s(-1)) measurements. However, the spatial variability structures were similar, as the models were spherical and had close range values of 40.1 and 40.0 m for the morning and afternoon semivariograms. In both periods, the sequential Gaussian simulation maps were more efficient for the estimations of emission than ordinary kriging. We believe that sequential Gaussian simulation can improve estimations of soil CO(2) emissions in the field, as this property is usually highly non-Gaussian distributed.

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and interannually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R(2) = 0.80 and R(2) = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages. (C) 2010 Elsevier Inc. All rights reserved.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

Biophysical techniques used to evaluate the stratum corneum

Assortments of biophysical methods are used to the study the stratum corneum morphology and dynamic with the objective to elucidate the correlation between its structure and functions. Among these methods, there are: X-ray diffraction, electron paramagnetic resonance, differential scanning calorimetry, Raman spectroscopy with Fourrier transform, infrared spectroscopy and photoacustic spectroscopy. In this manuscript, methods are presented and discussed in relation to the use indication, interpretation of results and advantages and limitations to the stratum corneum analysis.

From Midden to Sieve: The Impact of Differential Recovery and Quantification Techniques on Interpretations of Shellfish Remains in Australian Coastal Archaeology

Experimental mechanical sieving methods are applied to samples of shellfish remains from three sites in southeast Queensland, Seven Mile Creek Mound, Sandstone Point and One-Tree, to test the efficacy of various recovery and quantification procedures commonly applied to shellfish assemblages in Australia. There has been considerable debate regarding the most appropriate sieve sizes and quantification methods that should be applied in the recovery of vertebrate faunal remains. Few studies, however, have addressed the impact of recovery and quantification methods on the interpretation of invertebrates, specifically shellfish remains. In this study, five shellfish taxa representing four bivalves (Anadara trapezia, Trichomya hirsutus, Saccostrea glomerata, Donax deltoides) and one gastropod (Pyrazus ebeninus) common in eastern Australian midden assemblages are sieved through 10mm, 6.3mm and 3.15mm mesh. Results are quantified using MNI, NISP and weight. Analyses indicate that different structural properties and pre- and postdepositional factors affect recovery rates. Fragile taxa (T. hirsutus) or those with foliated structure (S. glomerata) tend to be overrepresented by NISP measures in smaller sieve fractions, while more robust taxa (A. trapezia and P. ebeninus) tend to be overrepresented by weight measures. Results demonstrate that for all quantification methods tested a 3mm sieve should be used on all sites to allow for regional comparability and to effectively collect all available information about the shellfish remains.

Quantum noise in optical fibers. I. Stochastic equations

We analyze the quantum dynamics of radiation propagating in a single-mode optical fiber with dispersion, nonlinearity, and Raman coupling to thermal phonons. We start from a fundamental Hamiltonian that includes the principal known nonlinear effects and quantum-noise sources, including linear gain and loss. Both Markovian and frequency-dependent, non-Markovian reservoirs are treated. This treatment allows quantum Langevin equations, which have a classical form except for additional quantum-noise terms, to be calculated. In practical calculations, it is more useful to transform to Wigner or 1P quasi-probability operator representations. These transformations result in stochastic equations that can be analyzed by use of perturbation theory or exact numerical techniques. The results have applications to fiber-optics communications, networking, and sensor technology.

A scheme for efficient quantum computation wtih linear optics

Quantum computers promise to increase greatly the efficiency of solving problems such as factoring large integers, combinatorial optimization and quantum physics simulation. One of the greatest challenges now is to implement the basic quantum-computational elements in a physical system and to demonstrate that they can be reliably and scalably controlled. One of the earliest proposals for quantum computation is based on implementing a quantum bit with two optical modes containing one photon. The proposal is appealing because of the ease with which photon interference can be observed. Until now, it suffered from the requirement for non-linear couplings between optical modes containing few photons. Here we show that efficient quantum computation is possible using only beam splitters, phase shifters, single photon sources and photo-detectors. Our methods exploit feedback from photo-detectors and are robust against errors from photon loss and detector inefficiency. The basic elements are accessible to experimental investigation with current technology.