A linear optimization approach to the combined production planning model

Two fundamental processes usually arise in the production planning of many industries. The first one consists of deciding how many final products of each type have to be produced in each period of a planning horizon, the well-known lot sizing problem. The other process consists of cutting raw materials in stock in order to produce smaller parts used in the assembly of final products, the well-studied cutting stock problem. In this paper the decision variables of these two problems are dependent of each other in order to obtain a global optimum solution. Setups that are typically present in lot sizing problems are relaxed together with integer frequencies of cutting patterns in the cutting problem. Therefore, a large scale linear optimizations problem arises, which is exactly solved by a column generated technique. It is worth noting that this new combined problem still takes the trade-off between storage costs (for final products and the parts) and trim losses (in the cutting process). We present some sets of computational tests, analyzed over three different scenarios. These results show that, by combining the problems and using an exact method, it is possible to obtain significant gains when compared to the usual industrial practice, which solve them in sequence. (C) 2010 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

An asymmetric sandpile model with height restriction

We analyze by numerical simulations and mean-field approximations an asymmetric version of the stochastic sandpile model with height restriction in one dimension. Each site can have at most two particles. Single particles are inactive and do not move. Two particles occupying the same site are active and may hop to neighboring sites following an asymmetric rule. Jumps to the right or to the left occur with distinct probabilities. In the active state, there will be a net current of particles to the right or to the left. We have found that the critical behavior related to the transition from the active to the absorbing state is distinct from the symmetrical case, making the asymmetry a relevant field.

Density profiles in the raise and peel model with and without a wall; physics and combinatorics

We consider the raise and peel model of a one-dimensional fluctuating interface in the presence of an attractive wall. The model can also describe a pair annihilation process in disordered unquenched media with a source at one end of the system. For the stationary states, several density profiles are studied using Monte Carlo simulations. We point out a deep connection between some profiles seen in the presence of the wall and in its absence. Our results are discussed in the context of conformal invariance ( c = 0 theory). We discover some unexpected values for the critical exponents, which are obtained using combinatorial methods. We have solved known ( Pascal`s hexagon) and new (split-hexagon) bilinear recurrence relations. The solutions of these equations are interesting in their own right since they give information on certain classes of alternating sign matrices.

Axially symmetric soliton solutions in a Skyrme-Faddeev-type model with Gies`s extension

We construct static soliton solutions with non-zero Hopf topological charges to a theory which is the extended Skyrme-Faddeev model with a further quartic term in derivatives. We use an axially symmetric ansatz based on toroidal coordinates, and solve the resulting two coupled nonlinear partial differential equations in two variables by a successive over-relaxation method. We construct numerical solutions with the Hopf charge up to 4. The solutions present an interesting behavior under the changes of a special combination of the coupling constants of the quartic terms.

A multivariate ultrastructural errors-in-variables model with equation error

This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented Asymptotic distributions for the line regression estimators are derived Applications to the elliptical class of distributions with two error assumptions are presented The model generalizes previous results aimed at univariate scenarios (C) 2010 Elsevier Inc All rights reserved

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.

Bias correction in a multivariate normal regression model with general parameterization

This paper derives the second-order biases Of maximum likelihood estimates from a multivariate normal model where the mean vector and the covariance matrix have parameters in common. We show that the second order bias can always be obtained by means of ordinary weighted least-squares regressions. We conduct simulation studies which indicate that the bias correction scheme yields nearly unbiased estimators. (C) 2009 Elsevier B.V. All rights reserved.

A robust Bayesian approach to null intercept measurement error model with application to dental data

Measurement error models often arise in epidemiological and clinical research. Usually, in this set up it is assumed that the latent variable has a normal distribution. However, the normality assumption may not be always correct. Skew-normal/independent distribution is a class of asymmetric thick-tailed distributions which includes the Skew-normal distribution as a special case. In this paper, we explore the use of skew-normal/independent distribution as a robust alternative to null intercept measurement error model under a Bayesian paradigm. We assume that the random errors and the unobserved value of the covariate (latent variable) follows jointly a skew-normal/independent distribution, providing an appealing robust alternative to the routine use of symmetric normal distribution in this type of model. Specific distributions examined include univariate and multivariate versions of the skew-normal distribution, the skew-t distributions, the skew-slash distributions and the skew contaminated normal distributions. The methods developed is illustrated using a real data set from a dental clinical trial. (C) 2008 Elsevier B.V. All rights reserved.

Kinetics of autocatalytic acid hydrolysis of cellulose with crystalline and amorphous fractions

The acid hydrolysis of cellulose with crystalline and amorphous fractions is analyzed on the basis of autocatalytic model with a positive feedback of acid production from the degraded biopolymer. In the condition of low acid rate production compared with hydrolysis rate, both fraction of cellulose decrease exponentially with linear and cubic time dependence, and the normalized number of scissions per cellulose chain follows a sigmoid behavior with reaction time. The model predicts that self generated acidic compounds from cellulose accelerate the degradation of the biopolymer. However, if the acidic compounds produced are volatile species, then their release under low pressure will reduce the global rate of degradation of cellulose toward its intrinsic rate value determined by the residual acid catalyst present in the starting material.

Absence of Melatonin Induces Night-Time Hepatic Insulin Resistance and Increased Gluconeogenesis Due to Stimulation of Nocturnal Unfolded Protein Response

It is known that the circadian rhythm in hepatic phosphoenolpyruvate carboxykinase expression (a limiting catalytic step of gluconeogenesis) and hepatic glucose production is maintained by both daily oscillation in autonomic inputs to the liver and night feeding behavior. However, increased glycemia and reduced melatonin (Mel) levels have been recently shown to coexist in diabetic patients at the end of the night period. In parallel, pinealectomy (PINX) is known to cause glucose intolerance with increased basal glycemia exclusively at the end of the night. The mechanisms that underlie this metabolic feature are not completely understood. Here, we demonstrate that PINX rats show night-time hepatic insulin resistance characterized by reduced insulin-stimulated RAC-alpha serine/threonine-protein kinase phosphorylation and increased phosphoenolpyruvate carboxykinase expression. In addition, PINX rats display increased conversion of pyruvate into glucose at the end of the night. The regulatory mechanism suggests the participation of unfolded protein response (UPR), because PINX induces night-time increase in activating transcription factor 6 expression and prompts a circadian fashion of immunoglobulin heavy chain-binding protein, activating transcription factor 4, and CCAAT/enhancer-binding protein-homologous protein expression with Zenith values at the dark period. PINX also caused a night-time increase in Tribble 3 and regulatory-associated protein of mammalian target of rapamycin; both were reduced in liver of PINX rats treated with Mel. Treatment of PINX rats with 4-phenyl butyric acid, an inhibitor of UPR, restored night-time hepatic insulin sensitivity and abrogated gluconeogenesis in PINX rats. Altogether, the present data show that a circadian oscillation of UPR occurs in the liver due to the absence of Mel. The nocturnal UPR activation is related with night-time hepatic insulin resistance and increased gluconeogenesis in PINX rats. (Endocrinology 152: 1253-1263, 2011)

Heme Impairs Prostaglandin E(2) and TGF-beta Production by Human Mononuclear Cells via Cu/Zn Superoxide Dismutase: Insight into the Pathogenesis of Severe Malaria

In many hemolytic disorders, such as malaria, the release of free heme has been involved in the triggering of oxidative stress and tissue damage. Patients presenting with severe forms of malaria commonly have impaired regulatory responses. Although intriguing, there is scarce data about the involvement of heme on the regulation of immune responses. In this study, we investigated the relation of free heme and the suppression of anti-inflammatory mediators such as PGE(2) and TGF-beta in human vivax malaria. Patients with severe disease presented higher hemolysis and higher plasma concentrations of Cu/Zn superoxide dismutase (SOD-1) and lower concentrations of PGE(2) and TGF-beta than those with mild disease. In addition, there was a positive correlation between SOD-1 concentrations and plasma levels of TNF-alpha. During antimalaria treatment, the concentrations of plasma SOD-1 reduced whereas PGE(2) and TGF-beta increased in the individuals severely ill. Using an in vitro model with human mononuclear cells, we demonstrated that the heme effect on the impairment of the production of PGE(2) and TGF-beta partially involves heme binding to CD14 and depends on the production of SOD-1. Aside from furthering the current knowledge about the pathogenesis of vivax malaria, the present results may represent a general mechanism for hemolytic diseases and could be useful for future studies of therapeutic approaches. The Journal of Immunology, 2010, 185: 1196-1204.

Holocene fires in East Amazonia (Caraja`s), new evidences, chronology and relation with paleoclimate

Past studies have evidenced the presence of charcoal in soils and lacustrine sediments of Amazonia region and suggested occurrences of widespread fires during the Middle Holocene. However, the available records do not indicate the changes in fire regime with enough time resolution. We quantified charcoal fragments in lacustrine sediments in a lake of North Carajas plateau in East Amazonia (5 degrees 50`-6 degrees 35`S and 49 degrees 30`-52 degrees 00`W). The charcoal quantification was compared to other sediment proxies, allowing a connection between paleofires and climate changes. Large variations in sediment characteristics led to distinct stages of sedimentation. From 11,800 (base of CSN 93/4) to 4750 cal yr B.P., low accumulation rates of organic matter are observed. Between 7600 cal yr B.P. (base of CSN 93/3 core) and 4750 cal yr B.P., this initial phase of sedimentation is characterized by low chlorophyll derivate accumulation rates and high accumulation rates of Botryococcus braunii, an alga resistant to episodic drought. The first phase of sedimentation would therefore correspond to, a low take level and a drier climate than today. Large biomass burning events occurred between 7450 cal yr B.P. and 4750 cal yr B.P., as indicated by the high charcoal particle concentration. From 4750 cal yr B.P. to 2800 cal yr B.P., accumulation rates of charcoal particles decreased, and the accumulation rate of chlorophyll derivate was low. From 2800 cal yr B.P. to 1300 cal yr B.P., the charcoal accumulation rates reached their lowest values in the core and a rapid increase in lacustrine production is evidenced by the increase in chlorophyll derivates and carbon accumulation rate. From 1300 cal yr B.P. to the last century, the charcoal accumulation rates increased. During the most recent period, the record is characterized by high accumulation rates of chlorophyll derivates while the charcoal particle accumulation rate decreased. This region is still unaffected by the current increase of anthropogenic fires. (c) 2007 Elsevier B.V. All rights reserved.

Approximate solution of the autocatalytic hydrolysis of cellulose

Depolymerization of cellulose in homogeneous acidic medium is analyzed on the basis of autocatalytic model of hydrolysis with a positive feedback of acid production from the degraded biopolymer. The normalized number of scissions per cellulose chain, S(t)/nA degrees A = 1 - C(t)/C(0), follows a sigmoid behavior with reaction time t, and the cellulose concentration C(t) decreases exponentially with a linear and cubic time dependence, C(t) = C(0)exp[-at - bt (3)], where a and b are model parameters easier determined from data analysis.