973 resultados para First order autoregressive model AR (1)
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The uptake of hexavalent chromium in free living floating aquatic macrophytes Eicchornia crassipes cultivated in non-toxic chromium-doped hydroponic solutions is presented. A Cr-uptake bioaccumulation experiment was carried out using healthy macrophytes grown in a temperature controlled greenhouse. Six samples of nutrient media and plants were collected during the 23 day experiment. Roots and leaves were acid digested with the addition of an internal Gallium standard, for thin film sample preparation and quantitative Cr analysis by PIXE method. The Cr(6+) mass uptake by the macrophytes reached up to 70% of the initial concentration, comparable to former results and literature data. The Cr-uptake data were described using a non-structural first order kinetic model. Due to low cost and high removal efficiency, living aquatic macrophytes E. crassipes are a viable biosorbent in an artificial wetland of a water effluent treatment plant. (c) 2009 Elsevier B.V. All rights reserved.
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The inactivation kinetics of enzymes polyphenol oxidase (PPO) and peroxidase (POD) was studied for the batch (discontinuous) microwave treatment of green coconut water. Inactivation of commercial PPO and POD added to sterile coconut water was also investigated. The complete time-temperature profiles of the experimental runs were used for determination of the kinetic parameters D-value and z-value: PPO (D(92.20 degrees C) = 52 s and z = 17.6 degrees C); POD (D(92.92 degrees C) = 16 s and z = 11.5 degrees C); PPO/sterile coconut water: (D(84.45 degrees C) = 43 s and z = 39.5 degrees C) and POD/sterile coconut water: (D(86.54 degrees C) = 20 s and z = 19.3 degrees C). All data were well fitted by a first order kinetic model. The enzymes naturally present in coconut water showed a higher resistance when compared to those added to the sterilized medium or other simulated solutions reported in the literature. The thermal inactivation of PPO and POD during microwave processing of green coconut water was significantly faster in comparison with conventional processes reported in the literature. (C) 2008 Elsevier Ltd. All rights reserved.
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O objetivo deste trabalho é caracterizar a Curva de Juros Mensal para o Brasil através de três fatores, comparando dois tipos de métodos de estimação: Através da Representação em Espaço de Estado é possível estimá-lo por dois Métodos: Filtro de Kalman e Mínimos Quadrados em Dois Passos. Os fatores têm sua dinâmica representada por um Modelo Autorregressivo Vetorial, VAR(1), e para o segundo método de estimação, atribui-se uma estrutura para a Variância Condicional. Para a comparação dos métodos empregados, propõe-se uma forma alternativa de compará-los: através de Processos de Markov que possam modelar conjuntamente o Fator de Inclinação da Curva de Juros, obtido pelos métodos empregados neste trabalho, e uma váriavel proxy para Desempenho Econômico, fornecendo alguma medida de previsão para os Ciclos Econômicos.
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A first order analytical model for optimal small amplitude attitude maneuvers of spacecraft with cylindrical symmetry in an elliptical orbits is presented. The optimization problem is formulated as a Mayer problem with the control torques provided by a power limited propulsion system. The state is defined by Seffet-Andoyer's variables and the control by the components of the propulsive torques. The Pontryagin Maximum Principle is applied to the problem and the optimal torques are given explicitly in Serret-Andoyer's variables and their adjoints. For small amplitude attitude maneuvers, the optimal Hamiltonian function is linearized around a reference attitude. A complete first order analytical solution is obtained by simple quadrature and is expressed through a linear algebraic system involving the initial values of the adjoint variables. A numerical solution is obtained by taking the Euler angles formulation of the problem, solving the two-point boundary problem through the shooting method, and, then, determining the Serret-Andoyer variables through Serret-Andoyer transformation. Numerical results show that the first order solution provides a good approximation to the optimal control law and also that is possible to establish an optimal control law for the artificial satellite's attitude. (C) 2003 COSPAR. Published by Elsevier B.V. Ltd. All rights reserved.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Neste trabalho são apresentados o desenvolvimento e a implementação de estratégias de controle digital para regulação automática de tensão e para o amortecimento de oscilações eletromecânicas em um sistema de potência em escala reduzida de 10kVA, localizado no Laboratório de Controle de Sistemas de Potência (LACSPOT), da Universidade Federal do Pará (UFPA). O projeto dos dois controladores é baseado na técnica de alocação polinomial de polos. Para o projeto do Regulador Automático de Tensão (RAT) foi adotado um modelo simplificado, de primeira ordem, da máquina síncrona, cujos parâmetros foram levantados experimentalmente. Para o controlador amortecedor, por sua vez, também chamado de Estabilizador de Sistemas de Potência (ESP), foi utilizado um modelo discreto, do tipo auto regressivo com entrada exógena (ARX). Este modelo foi estimado por meio de técnicas de identificação paramétrica, considerando para tal, o conjunto motor-gerador interligado a um sistema de maior porte (concessionária de energia elétrica). As leis de controle foram embarcadas em um microcontrolador de alto desempenho e, para a medição dos sinais utilizados nos controladores, foi desenvolvida uma instrumentação eletrônica baseada em amplificadores operacionais para o condicionamento dos sinais dos sensores. O sinal de controle é baseado na técnica de modulação por largura de pulso (PWM) e comanda o valor médio da tensão de um conversor CC-CC, o qual é utilizado como circuito de excitação que energiza o enrolamento de campo do gerador. Além disso, o acionamento elétrico das máquinas que compõem o grupo gerador de 10kVA foi projetado e automatizado somando segurança aos operadores e ao componentes deste sistema de geração. Os resultados experimentais demonstraram o bom desempenho obtido pela estratégia proposta.
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Control charts are very important tools in statistical quality control of industrial processes and its use started last century. Since its development, the charts have always been attributed to independent processes, i.e. without any correlation between samples. But nowadays, with the high level of automation in the industrial environment, it is noticeable the autocorrelation factor between samples. The main Xcharts used in monitoring quality characteristics represented by continuous variables are the mean (X ), amplitude (R) and variance (S²). Therefore, this work aims to analyze the performance of X and R charts and in of X and S² charts with different sample sizes (4 and 5) for monitoring autocorrelated processes. Through computer simulations using the Fortran software and the use of mathematical expressions was possible to obtain data and performance analysis of the detection power charts for independent observations and for autocorrelated observations according to the model AR (1). The results show that the effect of autocorrelation reduces the ability of monitoring the control charts and that, the greater this effect, the slower the chart becomes in misfits signaling
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High-frequency respiratory impedance data measured noninvasively by the high-speed interrupter technique (HIT), particularly the first antiresonance frequency (f(ar,1)), is related to airway wall mechanics. The aim of this study was to evaluate the feasibility and repeatability of HIT in unsedated pre-term infants, and to compare values of f(ar,1) from 18 pre-term (post-conceptional age 32-37 weeks, weight 1,730-2,910 g) and 18 full-term infants (42-47 weeks, 3,920-5,340 g). Among the pre-term infants, there was good short-term repeatability of f(ar,1) within a single sleep epoch (mean (sd) coefficient of variance: 8 (1.7)%), but 95% limits of agreement for repeated measures of f(ar,1) after 3-8 h were relatively wide (-41 Hz; 37 Hz). f(ar,1) was significantly lower in pre-term infants (199 versus 257 Hz), indicating that wave propagation characteristics in pre-term airways are different from those of full-term infants. The present authors suggest that this is consistent with developmental differences in airway wall structure and compliance, including the influence of the surrounding tissue. Since flow limitation is determined by wave propagation velocity and airway cross-sectional area, it was hypothesised that the physical ability of the airways to carry large flows is fundamentally different in pre-term than in full-term infants.
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The usual Skolemization procedure, which removes strong quantifiers by introducing new function symbols, is in general unsound for first-order substructural logics defined based on classes of complete residuated lattices. However, it is shown here (following similar ideas of Baaz and Iemhoff for first-order intermediate logics in [1]) that first-order substructural logics with a semantics satisfying certain witnessing conditions admit a “parallel” Skolemization procedure where a strong quantifier is removed by introducing a finite disjunction or conjunction (as appropriate) of formulas with multiple new function symbols. These logics typically lack equivalent prenex forms. Also, semantic consequence does not in general reduce to satisfiability. The Skolemization theorems presented here therefore take various forms, applying to the left or right of the consequence relation, and to all formulas or only prenex formulas.
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This volume represents the proceedings of the Sixteenth Annual Biochemical Engineering Symposium held at Kansas State University on April 26, 1986. Some of the papers describe the progress of ongoing projects, and others contain the results of completed projects. Only brief summaries are given of many of the papers that will be published in full elsewhere. ContentsEnd-Product Inhibition of the Acetone-Butanol Fermentation—Bob Kuhn, Colorado State University Effect of Multiple Substrates in Ethanal Fermentations from Cheese Whey—C.J. Wang, University of Missouri Extraction and Fermentation of Ensiled Sweet Sorghum—Karl Noah, Colorado State University Removal of Nucleic Acids from Bakers' Yeast—Richard M. Cordes, Iowa State University Modeling the Effects of Plasmid Replication and Product Repression on the Growth Rate of Recombinant Bacteria—William E. Bentley, University of Colorado Indirect Estimates of Cell Concentrations in Mass Cultivation of Bacterial Cells—Andrew Fisher, University of Missouri A Mathematical Model for Liquid Recirculation in Airlift Columns—C.H.Lee, Kansas State University Characterization of Imperfect Mixing of Batch Reactors by Two Compartment Model—Peter Sohn, University of Missouri First Order Breakage Model for the Degradation of Pullalan in the Batch Fermentor—Stephen A. Milligan, Kansas State University Synthesis and Nuclear Magnetic Resonance of 13C-Labeled Amylopectin and Maltooligosaccharides—Bernard Y. Tao, Iowa State University Preparation of Fungal Starter Culture in Gas Fluidized Bed Reactor—Pal Mihaltz, Colorado State University Yeast Flocculation and Sedimentation—David Szlag, University of Colorado Protein Enrichment of Extrusion Cooked Corn by Solid Substrate Fermentation—Lucas Alvarez-Martinez, Colorado State University Optimum Design of a Hollow Fiber Mammalian Cell Reactor—Thomas Chresand, Colorado State University Gas Chromatography and Nuclear Magnetic Resonance of Trifluoroacetylated Carbohydrates—Steven T. Summerfelt, Iowa State University Kinetic and Bioenergetic Considerations for Modeling Photosynthetic Microbial P~ocesses in Producing Biomass and Treating Wastewater—H. Y. Lee, Kansas State University Mathematical Modeling and Simulation of Bicarbonate-Limited Photsynthetic Growth in Continuous Culture—Craig Curless, Kansas State University Data Acquisition and Control of a Rotary Drum Solid State Fermentor—Mnasria A. Habib, Colorado State University Biodegradation of 2,4-Dichlorophenoxyacetic Acid (2,4-D)—Greg Sinton, Kansas State University
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Ce texte propose des méthodes d’inférence exactes (tests et régions de confiance) sur des modèles de régression linéaires avec erreurs autocorrélées suivant un processus autorégressif d’ordre deux [AR(2)], qui peut être non stationnaire. L’approche proposée est une généralisation de celle décrite dans Dufour (1990) pour un modèle de régression avec erreurs AR(1) et comporte trois étapes. Premièrement, on construit une région de confiance exacte pour le vecteur des coefficients du processus autorégressif (φ). Cette région est obtenue par inversion de tests d’indépendance des erreurs sur une forme transformée du modèle contre des alternatives de dépendance aux délais un et deux. Deuxièmement, en exploitant la dualité entre tests et régions de confiance (inversion de tests), on détermine une région de confiance conjointe pour le vecteur φ et un vecteur d’intérêt M de combinaisons linéaires des coefficients de régression du modèle. Troisièmement, par une méthode de projection, on obtient des intervalles de confiance «marginaux» ainsi que des tests à bornes exacts pour les composantes de M. Ces méthodes sont appliquées à des modèles du stock de monnaie (M2) et du niveau des prix (indice implicite du PNB) américains
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In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.
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High ³⁷Ar activity concentration in soil gas is proposed as a key evidence for the detection of underground nuclear explosion by the Comprehensive Nuclear Test-Ban Treaty. However, such a detection is challenged by the natural background of ³⁷Ar in the subsurface, mainly due to Ca activation by cosmic rays. A better understanding and improved capability to predict ³⁷Ar activity concentration in the subsurface and its spatial and temporal variability is thus required. A numerical model integrating ³⁷Ar production and transport in the subsurface is developed, including variable soil water content and water infiltration at the surface. A parameterized equation for ³⁷Ar production in the first 15 m below the surface is studied, taking into account the major production reactions and the moderation effect of soil water content. Using sensitivity analysis and uncertainty quantification, a realistic and comprehensive probability distribution of natural ³⁷Ar activity concentrations in soil gas is proposed, including the effects of water infiltration. Site location and soil composition are identified as the parameters allowing for a most effective reduction of the possible range of ³⁷Ar activity concentrations. The influence of soil water content on ³⁷Ar production is shown to be negligible to first order, while ³⁷Ar activity concentration in soil gas and its temporal variability appear to be strongly influenced by transient water infiltration events. These results will be used as a basis for practical CTBTO concepts of operation during an OSI.
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No estudo de séries temporais, os processos estocásticos usuais assumem que as distribuições marginais são contínuas e, em geral, não são adequados para modelar séries de contagem, pois as suas características não lineares colocam alguns problemas estatísticos, principalmente na estimação dos parâmetros. Assim, investigou-se metodologias apropriadas de análise e modelação de séries com distribuições marginais discretas. Neste contexto, Al-Osh and Alzaid (1987) e McKenzie (1988) introduziram na literatura a classe dos modelos autorregressivos com valores inteiros não negativos, os processos INAR. Estes modelos têm sido frequentemente tratados em artigos científicos ao longo das últimas décadas, pois a sua importância nas aplicações em diversas áreas do conhecimento tem despertado um grande interesse no seu estudo. Neste trabalho, após uma breve revisão sobre séries temporais e os métodos clássicos para a sua análise, apresentamos os modelos autorregressivos de valores inteiros não negativos de primeira ordem INAR (1) e a sua extensão para uma ordem p, as suas propriedades e alguns métodos de estimação dos parâmetros nomeadamente, o método de Yule-Walker, o método de Mínimos Quadrados Condicionais (MQC), o método de Máxima Verosimilhança Condicional (MVC) e o método de Quase Máxima Verosimilhança (QMV). Apresentamos também um critério automático de seleção de ordem para modelos INAR, baseado no Critério de Informação de Akaike Corrigido, AICC, um dos critérios usados para determinar a ordem em modelos autorregressivos, AR. Finalmente, apresenta-se uma aplicação da metodologia dos modelos INAR em dados reais de contagem relativos aos setores dos transportes marítimos e atividades de seguros de Cabo Verde.