996 resultados para Quantified real constraint
Resumo:
Biomass burning is an important source of atmospheric Particulate Matter (PM) in Brazil: the burning of forests in the northwest and of sugar cane plantations in the southeast are important examples. The objective of this work is the measurement of the PM emission profile of burning of sugar cane and other characteristic vegetative burning in the region of Sao Carlos-SP/Brazil. Samples of PM(10) and PM(2.5) were collected in different conditions, including small laboratory controlled burnings and real ones. The samples were analysed by X-Ray Fluorescence (XRF) and 14 chemical elements quantified. t-Student tests were performed to compare the obtained profiles, using as a reference a vegetative burn profile taken from the USEPA data bank SPECIATE. All measured profiles presented significant amounts of Cl and K, which are confirmed as tracers of sugar cane foliage burning.
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In order to evaluate the effects of uncertainty about direction of mechanical perturbation and supra-postural task constraint on postural control young adults had their upright stance perturbed while holding a tray in a horizontal position Stance was perturbed by moving forward or backward a supporting platform contrasting situations of certainty versus uncertainty of direction of displacement Increased constraint on postural stability was Imposed by a supra-postural task of equilibrating a cylinder on the tray Performance was assessed through EMG of anterior leg muscles angular displacement of the main Joints involved in the postural reactions and displacement of the tray Results showed that both certainty on the direction of perturbation and Increased supra-postural task constraint led to decreased angular displacement of the knee and the hip Furthermore combination of certainty and high supra-postural task constraint produced shorter latency of muscular activation Such postural responses were paralleled by decreased displacement of the tray Thesi results suggest a functional integration between the tasks with central set priming reactive postural responses from contextual cues and Increased stability demand (C) 2010 Elsevier B V All rights reserved
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The power loss reduction in distribution systems (DSs) is a nonlinear and multiobjective problem. Service restoration in DSs is even computationally hard since it additionally requires a solution in real-time. Both DS problems are computationally complex. For large-scale networks, the usual problem formulation has thousands of constraint equations. The node-depth encoding (NDE) enables a modeling of DSs problems that eliminates several constraint equations from the usual formulation, making the problem solution simpler. On the other hand, a multiobjective evolutionary algorithm (EA) based on subpopulation tables adequately models several objectives and constraints, enabling a better exploration of the search space. The combination of the multiobjective EA with NDE (MEAN) results in the proposed approach for solving DSs problems for large-scale networks. Simulation results have shown the MEAN is able to find adequate restoration plans for a real DS with 3860 buses and 632 switches in a running time of 0.68 s. Moreover, the MEAN has shown a sublinear running time in function of the system size. Tests with networks ranging from 632 to 5166 switches indicate that the MEAN can find network configurations corresponding to a power loss reduction of 27.64% for very large networks requiring relatively low running time.
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Two different fuzzy approaches to voltage control in electric power distribution systems are introduced in this paper. The real-time controller in each case would act on power transformers equipped with under-load tap changers. Learning systems are employed to turn the voltage-control relays into adaptive devices. The scope of this study has been limited to the power distribution substation, and the voltage measurements and control actions are carried out on the secondary bus. The capacity of fuzzy systems to handle approximate data, together with their unique ability to interpret qualitative information, make it possible to design voltage-control strategies that satisfy the requirements of the Brazilian regulatory bodies and the real concerns of the electric power distribution companies. Fuzzy control systems based on these two strategies have been implemented and the test results were highly satisfactory.
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This paper analyses the presence of financial constraint in the investment decisions of 367 Brazilian firms from 1997 to 2004, using a Bayesian econometric model with group-varying parameters. The motivation for this paper is the use of clustering techniques to group firms in a totally endogenous form. In order to classify the firms we used a hybrid clustering method, that is, hierarchical and non-hierarchical clustering techniques jointly. To estimate the parameters a Bayesian approach was considered. Prior distributions were assumed for the parameters, classifying the model in random or fixed effects. Ordinate predictive density criterion was used to select the model providing a better prediction. We tested thirty models and the better prediction considers the presence of 2 groups in the sample, assuming the fixed effect model with a Student t distribution with 20 degrees of freedom for the error. The results indicate robustness in the identification of financial constraint when the firms are classified by the clustering techniques. (C) 2010 Elsevier B.V. All rights reserved.
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An accurate estimate of machining time is very important for predicting delivery time, manufacturing costs, and also to help production process planning. Most commercial CAM software systems estimate the machining time in milling operations simply by dividing the entire tool path length by the programmed feed rate. This time estimate differs drastically from the real process time because the feed rate is not always constant due to machine and computer numerical controlled (CNC) limitations. This study presents a practical mechanistic method for milling time estimation when machining free-form geometries. The method considers a variable called machine response time (MRT) which characterizes the real CNC machine`s capacity to move in high feed rates in free-form geometries. MRT is a global performance feature which can be obtained for any type of CNC machine configuration by carrying out a simple test. For validating the methodology, a workpiece was used to generate NC programs for five different types of CNC machines. A practical industrial case study was also carried out to validate the method. The results indicated that MRT, and consequently, the real machining time, depends on the CNC machine`s potential: furthermore, the greater MRT, the larger the difference between predicted milling time and real milling time. The proposed method achieved an error range from 0.3% to 12% of the real machining time, whereas the CAM estimation achieved from 211% to 1244% error. The MRT-based process is also suggested as an instrument for helping in machine tool benchmarking.
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This article discusses the main aspects of the Brazilian real estate market in order to illustrate if it would be attractive for a typical American real estate investor to buy office-building portfolios in Brazil. The article emphasizes: [i] - the regulatory frontiers, comparing investment securitization, using a typical American REIT structure, with the Brazilian solution, using the Fundo de Investimento Imobiliario - FII; [ii] - the investment quality attributes in the Brazilian market, using an office building prototype, and [iii] - the comparison of [risk vs. yield] generated by an investment in the Brazilian market, using a FII, benchmarked against an existing REIT (OFFICE SUB-SECTOR) in the USA market. We conclude that investing dollars exchanged for Reais [the Brazilian currency] in a FII with a triple A office-building portfolio in the Sao Paulo marketplace will yield an annual income and a premium return above an American REIT investment. The highly aggressive scenario, along with the strong persistent exchange rate detachment to the IGP-M variations, plus instabilities affecting the generation of income, and even if we adopt a 300-point margin for the Brazil-Risk level, demonstrates that an investment opportunity in the Brazilian market, in the segment we have analyzed, outperforms an equivalent investment in the American market.
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We preserit a computational procedure to control art experimental chaotic system by applying the occasional proportional feedback (OPF) method. The method implementation uses the fuzzy theory to relate the variable correction to the necessary adjustment in the control parameter. As an application We control the chaotic attractors of the Chua circuit. We present file developed circuits and algorithms to implement this control in real time. To simplify the used procedure, we use it low resolution analog to digital converter compensated for a lowpass filter that facilitates similar applications to control other systems. (C) 2007 Elsevier Ltd. All rights reserved.
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Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.
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This paper studies a simplified methodology to integrate the real time optimization (RTO) of a continuous system into the model predictive controller in the one layer strategy. The gradient of the economic objective function is included in the cost function of the controller. Optimal conditions of the process at steady state are searched through the use of a rigorous non-linear process model, while the trajectory to be followed is predicted with the use of a linear dynamic model, obtained through a plant step test. The main advantage of the proposed strategy is that the resulting control/optimization problem can still be solved with a quadratic programming routine at each sampling step. Simulation results show that the approach proposed may be comparable to the strategy that solves the full economic optimization problem inside the MPC controller where the resulting control problem becomes a non-linear programming problem with a much higher computer load. (C) 2010 Elsevier Ltd. All rights reserved.
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In this paper, we consider a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries that occurs in a major Brazilian retail group. A single depot attends 519 stores of the group distributed in 11 Brazilian states. To find good solutions to this problem, we propose heuristics as initial solutions and a scatter search (SS) approach. Next, the produced solutions are compared with the routes actually covered by the company. Our results show that the total distribution cost can be reduced significantly when such methods are used. Experimental testing with benchmark instances is used to assess the merit of our proposed procedure. (C) 2008 Published by Elsevier B.V.
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Among several process variability sources, valve friction and inadequate controller tuning are supposed to be two of the most prevalent. Friction quantification methods can be applied to the development of model-based compensators or to diagnose valves that need repair, whereas accurate process models can be used in controller retuning. This paper extends existing methods that jointly estimate the friction and process parameters, so that a nonlinear structure is adopted to represent the process model. The developed estimation algorithm is tested with three different data sources: a simulated first order plus dead time process, a hybrid setup (composed of a real valve and a simulated pH neutralization process) and from three industrial datasets corresponding to real control loops. The results demonstrate that the friction is accurately quantified, as well as ""good"" process models are estimated in several situations. Furthermore, when a nonlinear process model is considered, the proposed extension presents significant advantages: (i) greater accuracy for friction quantification and (ii) reasonable estimates of the nonlinear steady-state characteristics of the process. (C) 2010 Elsevier Ltd. All rights reserved.
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P>The aim of the work was to shed light into histological, physiological and molecular changes of Fagus sylvatica seedlings infected with the root pathogen Phytophthora citricola with the final goal to distinguish between local and systemic responses. Real-time quantitative PCR analysis proved that P. citricola was able to grow from infected roots into hypocotyl and epicotyl tissue of F. sylvatica seedlings. Light microscopy showed many collapsed parenchyma cells of the cortex without being penetrated by the pathogen. Hyphae were mainly growing intracellular in parenchyma and xylem tissue. Transmission electron microscopy displayed disintegration of xylem vessels and of parenchyma cells. Inhibition of water uptake of infected beech seedlings was positively correlated with the concentration of zoospores used in the experiment. In addition, a split root experiment indicated that invertases were possibly involved locally and systemically in the conversion of sucrose of P. citricola infected roots. During the growth of the pathogen in roots, a transient expression of the 1-aminocyclopropane-1-carboxylic acid (ACC)-oxidase gene was quantified in leaves which was detected in parallel with the first peak of a biphasic ethylene outburst. Additionally a systemic upregulation of aquaporin transcripts was mainly detected in leaves of beech seedlings infected with P. citricola.
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Introduction: Porphyromonas gingivalis and Tannerella forsythia are anaerobic bacteria commonly involved in root canal infections. Although previous investigations have assessed these species by strictly qualitative approaches, accurate determination of their cell levels by a sensitive quantitative technique may contribute with additional information regarding relevance in pain of endodontic origin. Method: The root canal levels of P gingivalis, T forsythia, and total bacteria were investigated by a quantitative polymerase chain reaction (PCR) assay based on unique copy molecular markers. A total of 32 symptomatic (n = 14) and asymptomatic (n = 18) cases of endodontic infections were analyzed. Root canal samples were collected; genomic DNA was extracted and submitted to SYBR Green I real-time PCR targeting the rgpB (P gingivalis), bspA (T forsythia), and rpoB (total bacteria) single copy genes. Results: Overall, R gingivalis, T forsythia, and the coexistence of both species were encountered in 28%, 66%, and 22% of the subjects, respectively. P gingivalis and T forsythia levels ranged from 5.65 x 10(-6) to 1.20 x 10(-2) and from 5.76 x 10(-6) to 1.35 x 10(-1). T forsythia was highly prevalent and numerous in the study groups, whereas P gingivalis was moderately frequent and less abundant, displaying 19-fold lower average levels than the former. Conclusions: The endodontic levels of P gingivalis and T forsythia, individually or in conjunction, did not display significant associations with the manifestation of pain of endodontic origin. (J Endod 2009,35:1518-1524)
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Background: The transcription factors SREBP1 and SCAP are involved in intracellular cholesterol homeostasis. Polymorphisms of these genes have been associated with variations on serum lipid levels and response to statins that are potent cholesterol-lowering drugs. We evaluated the effects of atorvastatin on SREBF1a and SCAP mRNA expression in peripheral blood mononuclear cells (PBMC) and a possible association with gene polymorphisms and lowering-cholesterol response. Methods: Fifty-nine hypercholesterolemic patients were treated with atorvastatin (10 mg/day for 4 weeks). Serum lipid profile and mRNA expression in PBMC were assessed before and after the treatment. Gene expression was quantified by real-time PCR using GAPD as endogenous reference and mRNA expression in HepG2 cells as calibrator. SREBF1 -36delG and SCAP A2386G polymorphisms were detected by PCR-RFLP. Results: Our results showed that transcription of SREBF1a and SCAP was coordinately regulated by atorvastatin (r=0.595, p<0.001), and that reduction in SCAP transcription was associated with the 2386AA genotype (p=0.019). Individuals who responded to atorvastatin with a downregulation of SCAP had also a lower triglyceride compared to those who responded to atorvastatin with an upregulation of SCAP. Conclusion: Atorvastatin has differential effects on SREBF1a and SCAP mRNA expression in PBMC that are associated with baseline transcription levels, triglycerides response to atorvastatin and SCAP A2386G polymorphism. (c) 2008 Elsevier B.V. All rights reserved.