932 resultados para Convex combination


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In this paper I will investigate the conditions under which a convex capacity (or a non-additive probability which exhibts uncertainty aversion) can be represented as a squeeze of a(n) (additive) probability measure associate to an uncertainty aversion function. Then I will present two alternatives forrnulations of the Choquet integral (and I will extend these forrnulations to the Choquet expected utility) in a parametric approach that will enable me to do comparative static exercises over the uncertainty aversion function in an easy way.

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Industrial companies in developing countries are facing rapid growths, and this requires having in place the best organizational processes to cope with the market demand. Sales forecasting, as a tool aligned with the general strategy of the company, needs to be as much accurate as possible, in order to achieve the sales targets by making available the right information for purchasing, planning and control of production areas, and finally attending in time and form the demand generated. The present dissertation uses a single case study from the subsidiary of an international explosives company based in Brazil, Maxam, experiencing high growth in sales, and therefore facing the challenge to adequate its structure and processes properly for the rapid growth expected. Diverse sales forecast techniques have been analyzed to compare the actual monthly sales forecast, based on the sales force representatives’ market knowledge, with forecasts based on the analysis of historical sales data. The dissertation findings show how the combination of both qualitative and quantitative forecasts, by the creation of a combined forecast that considers both client´s demand knowledge from the sales workforce with time series analysis, leads to the improvement on the accuracy of the company´s sales forecast.

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We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the recourse functions. This formula can be used to obtain an efficient implementation of Stochastic Dual Dynamic Programming applied to convex nonlinear problems. We prove the almost sure convergence of these decomposition methods when the relatively complete recourse assumption holds. We also prove the almost sure convergence of these algorithms when applied to risk-averse multistage stochastic linear programs that do not satisfy the relatively complete recourse assumption. The analysis is first done assuming the underlying stochastic process is interstage independent and discrete, with a finite set of possible realizations at each stage. We then indicate two ways of extending the methods and convergence analysis to the case when the process is interstage dependent.

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We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the Stochastic Mirror Descent (SMD) algorithms. When the objective functions are uniformly convex, we also propose a multistep extension of the Stochastic Mirror Descent algorithm and obtain con dence intervals on both the optimal values and optimal solutions. Numerical simulations show that our con dence intervals are much less conservative and are quicker to compute than previously obtained con dence intervals for SMD and that the multistep Stochastic Mirror Descent algorithm can obtain a good approximate solution much quicker than its nonmultistep counterpart. Our con dence intervals are also more reliable than asymptotic con dence intervals when the sample size is not much larger than the problem size.

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In this dissertation, different ways of combining neural predictive models or neural-based forecasts are discussed. The proposed approaches consider mostly Gaussian radial basis function networks, which can be efficiently identified and estimated through recursive/adaptive methods. Two different ways of combining are explored to get a final estimate – model mixing and model synthesis –, with the aim of obtaining improvements both in terms of efficiency and effectiveness. In the context of model mixing, the usual framework for linearly combining estimates from different models is extended, to deal with the case where the forecast errors from those models are correlated. In the context of model synthesis, and to address the problems raised by heavily nonstationary time series, we propose hybrid dynamic models for more advanced time series forecasting, composed of a dynamic trend regressive model (or, even, a dynamic harmonic regressive model), and a Gaussian radial basis function network. Additionally, using the model mixing procedure, two approaches for decision-making from forecasting models are discussed and compared: either inferring decisions from combined predictive estimates, or combining prescriptive solutions derived from different forecasting models. Finally, the application of some of the models and methods proposed previously is illustrated with two case studies, based on time series from finance and from tourism.

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BACKGROUND: Non-invasive diagnostic strategies aimed at identifying biomarkers of cancer are of great interest for early cancer detection. Urine is potentially a rich source of volatile organic metabolites (VOMs) that can be used as potential cancer biomarkers. Our aim was to develop a generally reliable, rapid, sensitive, and robust analytical method for screening large numbers of urine samples, resulting in a broad spectrum of native VOMs, as a tool to evaluate the potential of these metabolites in the early diagnosis of cancer. METHODS: To investigate urinary volatile metabolites as potential cancer biomarkers, urine samples from 33 cancer patients (oncological group: 14 leukaemia, 12 colorectal and 7 lymphoma) and 21 healthy (control group, cancer-free) individuals were qualitatively and quantitatively analysed. Dynamic solid-phase microextraction in headspace mode (dHS-SPME) using a carboxenpolydimethylsiloxane (CAR/PDMS) sorbent in combination with GC-qMS-based metabolomics was applied to isolate and identify the volatile metabolites. This method provides a potential non-invasive method for early cancer diagnosis as a first approach. To fulfil this objective, three important dHS-SPME experimental parameters that influence extraction efficiency (fibre coating, extraction time and temperature of sampling) were optimised using a univariate optimisation design. The highest extraction efficiency was obtained when sampling was performed at 501C for 60min using samples with high ionic strengths (17% sodium chloride, wv 1) and under agitation. RESULTS: A total of 82 volatile metabolites belonging to distinct chemical classes were identified in the control and oncological groups. Benzene derivatives, terpenoids and phenols were the most common classes for the oncological group, whereas ketones and sulphur compounds were the main classes that were isolated from the urine headspace of healthy subjects. The results demonstrate that compound concentrations were dramatically different between cancer patients and healthy volunteers. The positive rates of 16 patients among the 82 identified were found to be statistically different (Po0.05). A significant increase in the peak area of 2-methyl3-phenyl-2-propenal, p-cymene, anisole, 4-methyl-phenol and 1,2-dihydro-1,1,6-trimethyl-naphthalene in cancer patients was observed. On average, statistically significant lower abundances of dimethyl disulphide were found in cancer patients. CONCLUSIONS: Gas chromatographic peak areas were submitted to multivariate analysis (principal component analysis and supervised linear discriminant analysis) to visualise clusters within cases and to detect the volatile metabolites that are able to differentiate cancer patients from healthy individuals. Very good discrimination within cancer groups and between cancer and control groups was achieved.

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This manuscript describes the development and validation of an ultra-fast, efficient, and high throughput analytical method based on ultra-high performance liquid chromatography (UHPLC) equipped with a photodiode array (PDA) detection system, for the simultaneous analysis of fifteen bioactive metabolites: gallic acid, protocatechuic acid, (−)-catechin, gentisic acid, (−)-epicatechin, syringic acid, p-coumaric acid, ferulic acid, m-coumaric acid, rutin, trans-resveratrol, myricetin, quercetin, cinnamic acid and kaempferol, in wines. A 50-mm column packed with 1.7-μm particles operating at elevated pressure (UHPLC strategy) was selected to attain ultra-fast analysis and highly efficient separations. In order to reduce the complexity of wine extract and improve the recovery efficiency, a reverse-phase solid-phase extraction (SPE) procedure using as sorbent a new macroporous copolymer made from a balanced ratio of two monomers, the lipophilic divinylbenzene and the hydrophilic N-vinylpyrrolidone (Oasis™ HLB), was performed prior to UHPLC–PDA analysis. The calibration curves of bioactive metabolites showed good linearity within the established range. Limits of detection (LOD) and quantification (LOQ) ranged from 0.006 μg mL−1 to 0.58 μg mL−1, and from 0.019 μg mL−1 to 1.94 μg mL−1, for gallic and gentisic acids, respectively. The average recoveries ± SD for the three levels of concentration tested (n = 9) in red and white wines were, respectively, 89 ± 3% and 90 ± 2%. The repeatability expressed as relative standard deviation (RSD) was below 10% for all the metabolites assayed. The validated method was then applied to red and white wines from different geographical origins (Azores, Canary and Madeira Islands). The most abundant component in the analysed red wines was (−)-epicatechin followed by (−)-catechin and rutin, whereas in white wines syringic and p-coumaric acids were found the major phenolic metabolites. The method was completely validated, providing a sensitive analysis for bioactive phenolic metabolites detection and showing satisfactory data for all the parameters tested. Moreover, was revealed as an ultra-fast approach allowing the separation of the fifteen bioactive metabolites investigated with high resolution power within 5 min.

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Objective-To evaluate analgesic effects of epidurally administered neostigmine alone or in combination with morphine in dogs after ovariohysterectomy.Animals-40 healthy bitches.Procedures-After acepromazine premedication, anesthesia was induced. Dogs randomly received 1 of the following 4 epidural treatments 30 minutes before ovariohysterectomy (n = 10/group): saline (0.9% NaCl) solution (control), morphine (0.1 mg/kg), neostigmine (10 pg/kg), or morphine-neostigmine (0.1 mg/kg and 10 pg/kg, respectively). Analgesia was assessed for 24 hours after surgery by use of a visual analogue.scale (VAS; scale of 0 to 10) or numeric descriptive scale (NDS; scale of 0 to 24) and by the need for supplemental analgesia (morphine [0.5 mg/kg, IM] administered when VAS was >= 4 or NDS was >= 8).Results-Significantly more control dogs (n = 8) received supplemental analgesia, compared with the number of neostigmine-treated dogs (1); no dogs in the remaining groups received supplemental analgesia. Compared with values for the control dogs, the NDS scores were lower for morphine-neostigmine-treated dogs (from 2 to 6 hours and at 12 hours) and for morphine-treated dogs (all time points). The NDS scores were lower for morphine-treated dogs at 3, 12, and 24 hours, compared with values for neostigmine-treated dogs. The VAS was less sensitive than the NDS for detecting differences among groups.Conclusions and Clinical Relevance-Epidurally administered neostigmine reduced the use of supplemental analgesia after ovariohysterectorny in dogs. However, analgesic effects were less pronounced than for epidurally administered morphine or morphine-neostigmine. Adding neostigmine to epidurally administered morphine did not potentiate opioid-induced analgesia.

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Objective To examine the anesthetic effects of a xylazine-diazepam-ketamine (XDK) combination in roosters.Study design Prospective experimental trial.Animals Six healthy white Leghorn roosters weighing 2.03 +/- 0.08 kg.Methods Each rooster was pre-medicated with xylazine (3 mg kg(-1), IM) and after 15 minutes anesthesia was induced with a diazepam (4 mg kg(-1)) and ketamine (25 mg kg(-1)) combination injected into the pectoral muscles. Heart and respiratory rates were recorded before anesthesia and every 15 minutes after induction for 165 minutes. Cloacal temperature was measured before and 15 minutes after pre-medication and every 75 minutes thereafter during anesthesia. Quality of induction and recovery were scored subjectively; duration of loss of righting reflex, abolition of response to a painful stimulus and palpebral reflex were also recorded.Results Intramuscular injection of xylazine smoothly induced loss of the righting reflex within 3-4 minutes. Loss of response to a painful stimulus occurred at 13.1 +/- 2.9 minutes (mean +/- SD) after the administration of the D-K combination, and lasted for 63.0 +/- 5.3 minutes. Roosters anesthetized with this combination had a significant decrease in heart and respiratory rates and cloacal temperature. The recovery period lasted for up to 4 hours (227.5 +/- 15.4 minutes). Quality of recovery was satisfactory for four roosters but excitation was noted in two birds.Conclusions and clinical relevance The XDK combination was a useful anesthetic technique for typhlectomy in roosters. Nevertheless this drug combination should be used with caution and cardiopulmonary parameters monitored carefully. Under the conditions of this experiment it was associated with a decreased cloacal temperature and prolonged recoveries.

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A new device was developed to hold linear transducers for transvaginal follicle aspiration. Efficacy of follicle aspiration was compared using a linear 6 MHz and a convex 5 MHz transducer. Fifty-five cows were submitted to follicle aspiration at random days of the estrous cycle. Aspirations were conducted with linear (n = 28) and convex (n = 38) transducers with 18 G needles at a negative pressure corresponding to 13 ml H2O/min. A greater number of follicles were aspirated using convex than to linear probe (12.4 versus 7.8, respectively, P < 0.05). Mean number of oocytes and recovery rates were similar for convex (5.4 and 48.6%) and linear (4.6 and 59.3%) transducers. Limited space between the linear transducer and needle guide restricted access to some portions of the ovary, reducing the number of follicles aspirated using a linear transducer. The newly developed adaptor allowed greater stability, holding the ovaries firmly against the linear transducer. This diminished mobility permitted a similar number of oocytes to be recovered with both transducers. In conclusion, this new adaptor provided a low cost alternative for routine follicle aspiration and oocyte recovery in cattle. (C) 2002 Elsevier B.V. All rights reserved.

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Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (A) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of A can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PS0)-POCS image restoration algorithm, in which the A value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms. (C) 2010 Elsevier B.V. All rights reserved.

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This paper is concerned with the numerical solutions of time dependent two-dimensional incompressible flows. By using the primitive variables of velocity and pressure, the Navier-Stokes and mass conservation equations are solved by a semi-implicit finite difference projection method. A new bounded higher order upwind convection scheme is employed to deal with the non-linear (advective) terms. The procedure is an adaptation of the GENSMAC (J. Comput. Phys. 1994; 110: 171-186) methodology for calculating confined and free surface fluid flows at both low and high Reynolds numbers. The calculations were performed by using the 2D version of the Freeflow simulation system (J. Comp. Visual. Science 2000; 2:199-210). In order to demonstrate the capabilities of the numerical method, various test cases are presented. These are the fully developed flow in a channel, the flow over a backward facing step, the die-swell problem, the broken dam flow, and an impinging jet onto a flat plate. The numerical results compare favourably with the experimental data and the analytical solutions. Copyright (c) 2006 John Wiley & Sons, Ltd.

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This paper presents a consistent and concise analysis of the free and forced vibration of a mass supported by a parallel combination of a spring and an elastically supported damper (a Zener model). The results are presented in a compact form and the physical behaviour of the system is emphasised. This system is very similar to the conventional single-degree-of freedom system (sdof)-(Voigt model), but the dynamics can be quite different depending on the system parameters. The usefulness of the additional spring in series with the damper is investigated, and optimum damping values for the system subject to different types of excitation are determined and compared.There are three roots to the characteristic equation for the Zener model; two are complex conjugates and the third is purely real. It is shown that it is not possible to achieve critical damping of the complex roots unless the additional stiffness is at least eight times that of the main spring. For a harmonically excited system, there are some possible advantages in using the additional spring when the transmitted force to the base is of interest, but when the displacement response of the system is of interest then the benefits are marginal. It is shown that the additional spring affords no advantages when the system is excited by white noise. (c) 2007 Elsevier Ltd. All rights reserved.

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O método de combinação de Nelson-Oppen permite que vários procedimentos de decisão, cada um projetado para uma teoria específica, possam ser combinados para inferir sobre teorias mais abrangentes, através do princípio de propagação de igualdades. Provadores de teorema baseados neste modelo são beneficiados por sua característica modular e podem evoluir mais facilmente, incrementalmente. Difference logic é uma subteoria da aritmética linear. Ela é formada por constraints do tipo x − y ≤ c, onde x e y são variáveis e c é uma constante. Difference logic é muito comum em vários problemas, como circuitos digitais, agendamento, sistemas temporais, etc. e se apresenta predominante em vários outros casos. Difference logic ainda se caracteriza por ser modelada usando teoria dos grafos. Isto permite que vários algoritmos eficientes e conhecidos da teoria de grafos possam ser utilizados. Um procedimento de decisão para difference logic é capaz de induzir sobre milhares de constraints. Um procedimento de decisão para a teoria de difference logic tem como objetivo principal informar se um conjunto de constraints de difference logic é satisfatível (as variáveis podem assumir valores que tornam o conjunto consistente) ou não. Além disso, para funcionar em um modelo de combinação baseado em Nelson-Oppen, o procedimento de decisão precisa ter outras funcionalidades, como geração de igualdade de variáveis, prova de inconsistência, premissas, etc. Este trabalho apresenta um procedimento de decisão para a teoria de difference logic dentro de uma arquitetura baseada no método de combinação de Nelson-Oppen. O trabalho foi realizado integrando-se ao provador haRVey, de onde foi possível observar o seu funcionamento. Detalhes de implementação e testes experimentais são relatados

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)