886 resultados para real option analysis


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Swallowing dynamics involves the coordination and interaction of several muscles and nerves which allow correct food transport from mouth to stomach without laryngotracheal penetration or aspiration. Clinical swallowing assessment depends on the evaluator`s knowledge of anatomic structures and of neurophysiological processes involved in swallowing. Any alteration in those steps is denominated oropharyngeal dysphagia, which may have many causes, such as neurological or mechanical disorders. Videofluoroscopy of swallowing is presently considered to be the best exam to objectively assess the dynamics of swallowing, but the exam needs to be conducted under certain restrictions, due to patient`s exposure to radiation, which limits periodical repetition for monitoring swallowing therapy. Another method, called cervical auscultation, is a promising new diagnostic tool for the assessment of swallowing disorders. The potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. Even so, the captured sound has an amount of noise, which can hamper the evaluator`s decision. In that way, the present paper proposes the use of a filter to improve the quality of audible sound and facilitate the perception of examination. The wavelet denoising approach is used to decompose the noisy signal. The signal to noise ratio was evaluated to demonstrate the quantitative results of the proposed methodology. (C) 2007 Elsevier Ltd. All rights reserved.

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Many works have shown the potential of the Brazilian sugarcane industry as an electricity supplier. However, few studies have studied how this potential could be achieved without jeopardizing the production of sugar and ethanol. Also, the impact of modifications in the cogeneration plant on the costs of production of sugar and ethanol has not been evaluated. This paper presents an approach to the problem of exergy optimization of cogeneration systems in sugarcane mills. A general model to the sugar and ethanol production processes is developed based on data supplied by a real plant, and an exergy analysis is performed. A discussion is made about the variables that most affect the performance of the processes. Then, a procedure is presented to evaluate modifications in the cogeneration system and in the process, and their impact on the production costs of sugar, ethanol and electricity. Furthermore, a discussion on the renewability of processes is made based on an exergy index of renewability. As a general conclusion, besides adding a new revenue to the mill, the generation of excess electricity improves the exergo-environmental performance of the mill as a whole. (C) 2010 Elsevier Ltd. All rights reserved.

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Cooling towers are widely used in many industrial and utility plants as a cooling medium, whose thermal performance is of vital importance. Despite the wide interest in cooling tower design, rating and its importance in energy conservation, there are few investigations concerning the integrated analysis of cooling systems. This work presents an approach for the systemic performance analysis of a cooling water system. The approach combines experimental design with mathematical modeling. An experimental investigation was carried out to characterize the mass transfer in the packing of the cooling tower as a function of the liquid and gas flow rates, whose results were within the range of the measurement accuracy. Then, an integrated model was developed that relies on the mass and heat transfer of the cooling tower, as well as on the hydraulic and thermal interactions with a heat exchanger network. The integrated model for the cooling water system was simulated and the temperature results agree with the experimental data of the real operation of the pilot plant. A case study illustrates the interaction in the system and the need for a systemic analysis of cooling water system. The proposed mathematical and experimental analysis should be useful for performance analysis of real-world cooling water systems. (C) 2009 Elsevier Ltd. All rights reserved.

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This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

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Cellular and humoral immune response, as well as cytokine gene expression, was assessed in Nelore cattle with different degrees of resistance to Cooperia punctata natural infection. One hundred cattle (male, weaned, 11-12 months old), kept together on pasture, were evaluated. Faecal and blood samples were collected for parasitological and immunological assays. Based on nematode faecal egg counts (FEC) and worm burden, the seven most resistant and the eight most susceptible animals were selected. Tissue samples of the small intestine were collected for histological quantification of inflammatory cells and analysis of cytokine gene expression (IL-2, IL-4, IL-8, IL-1 2p35, IL-13, TNF-alpha, IFN-gamma, MCP-1, MCP-2, and MUC- 1) using real-time RT-PCR. Mucus samples were also collected for IgA levels determination. Serum IgG1 mean levels against C. punctata antigens were higher in the resistant group, but significant differences between groups were only observed 14 days after the beginning of the experiment against infective larvae (1-3) and 14 and 84 days against adult antigens. The resistant group also presented higher IgA levels against C. punctata (L3 and adult) antigens with significant difference 14 days after the beginning of the trial (P < 0.05). In the small-intestine mucosa, levels of IgA anti-L3 and anti-adult C. punctata were higher in the resistant group, compared with the susceptible group (P < 0.05). Gene expression of both T(H)2 cytokines (IL-4 and IL-13) in the resistant group and T(H)1 cytokines (IL-2, IL-1 2p35, IFN-gamma and MCP-1) in the susceptible group was up-regulated. Such results suggested that immune response to C. punctata was probably mediated by TH2 cytokines in the resistant group and by T(H)1 cytokines in the susceptible group. (C) 2008 Elsevier B.V. All rights reserved.

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This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

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This study describes the pedagogical impact of real-world experimental projects undertaken as part of an advanced undergraduate Fluid Mechanics subject at an Australian university. The projects have been organised to complement traditional lectures and introduce students to the challenges of professional design, physical modelling, data collection and analysis. The physical model studies combine experimental, analytical and numerical work in order to develop students’ abilities to tackle real-world problems. A first study illustrates the differences between ideal and real fluid flow force predictions based upon model tests of buildings in a large size wind tunnel used for research and professional testing. A second study introduces the complexity arising from unsteady non-uniform wave loading on a sheltered pile. The teaching initiative is supported by feedback from undergraduate students. The pedagogy of the course and projects is discussed with reference to experiential, project-based and collaborative learning. The practical work complements traditional lectures and tutorials, and provides opportunities which cannot be learnt in the classroom, real or virtual. Student feedback demonstrates a strong interest for the project phases of the course. This was associated with greater motivation for the course, leading in turn to lower failure rates. In terms of learning outcomes, the primary aim is to enable students to deliver a professional report as the final product, where physical model data are compared to ideal-fluid flow calculations and real-fluid flow analyses. Thus the students are exposed to a professional design approach involving a high level of expertise in fluid mechanics, with sufficient academic guidance to achieve carefully defined learning goals, while retaining sufficient flexibility for students to construct there own learning goals. The overall pedagogy is a blend of problem-based and project-based learning, which reflects academic research and professional practice. The assessment is a mix of peer-assessed oral presentations and written reports that aims to maximise student reflection and development. Student feedback indicated a strong motivation for courses that include a well-designed project component.

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The second edition of An Introduction to Efficiency and Productivity Analysis is designed to be a general introduction for those who wish to study efficiency and productivity analysis. The book provides an accessible, well-written introduction to the four principal methods involved: econometric estimation of average response models; index numbers, data envelopment analysis (DEA); and stochastic frontier analysis (SFA). For each method, a detailed introduction to the basic concepts is presented, numerical examples are provided, and some of the more important extensions to the basic methods are discussed. Of special interest is the systematic use of detailed empirical applications using real-world data throughout the book. In recent years, there have been a number of excellent advance-level books published on performance measurement. This book, however, is the first systematic survey of performance measurement with the express purpose of introducing the field to a wide audience of students, researchers, and practitioners. Indeed, the 2nd Edition maintains its uniqueness: (1) It is a well-written introduction to the field. (2) It outlines, discusses and compares the four principal methods for efficiency and productivity analysis in a well-motivated presentation. (3) It provides detailed advice on computer programs that can be used to implement these performance measurement methods. The book contains computer instructions and output listings for the SHAZAM, LIMDEP, TFPIP, DEAP and FRONTIER computer programs. More extensive listings of data and computer instruction files are available on the book's website: (www.uq.edu.au/economics/cepa/crob2005).

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The basic framework for the JAK/STAT pathway is well documented. Recruitment of latent cytoplasmic STAT transcription factors to tyrosine phosphorylated docking sites on cytokine receptors and their JAK-mediated phosphorylation instigates their translocation to the nucleus and their ability to bind DNA, The biochemical processes underlying recruitment and activation of this pathway have commonly been studied in reconstituted in vitro systems using previously defined recombinant signaling components. We have dissected the Interferon gamma (IFN gamma) signal transduction pathway in crude extracts from wild-type and STAT1-negative mutant cell Lines by real-time BIAcore analysis, size-exclusion (SE) chromatography and immune-detection. The data indicate that in detergent-free cell extracts: (1) the phospho-tyrosine (Y440P)-containing peptide motif of the IFN gamma-receptor ct-chain interacts directly with STAT1, or STAT1 complexes, and no other protein; (2) nonactivated STAT 1 is present in a higher molecular weight complex(es) and, at least for IFN gamma-primed cells, is available for recruitment to the activated IFN gamma-receptor from only a subset of such complexes; (3) activated STAT1 is released from the receptor as a monomer.

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Functional magnetic resonance imaging (FMRI) analysis methods can be quite generally divided into hypothesis-driven and data-driven approaches. The former are utilised in the majority of FMRI studies, where a specific haemodynamic response is modelled utilising knowledge of event timing during the scan, and is tested against the data using a t test or a correlation analysis. These approaches often lack the flexibility to account for variability in haemodynamic response across subjects and brain regions which is of specific interest in high-temporal resolution event-related studies. Current data-driven approaches attempt to identify components of interest in the data, but currently do not utilise any physiological information for the discrimination of these components. Here we present a hypothesis-driven approach that is an extension of Friman's maximum correlation modelling method (Neurolmage 16, 454-464, 2002) specifically focused on discriminating the temporal characteristics of event-related haemodynamic activity. Test analyses, on both simulated and real event-related FMRI data, will be presented.

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This paper aims to study the relationship between the debt level and the asset structure of Brazilian companies of the agribusiness sector, since it is considered a current and relevant discussion: to evaluate the mechanisms for fund-raising and guarantees. The methodology of Granger`s Causality test and Autoregressive Vectors was used to conduct a comparative analysis, applied to a financial database of companies with open capital of Brazilian agribusiness, in particular the agricultural sector and Fisheries and Food and Beverages in a period of 10 years (1997-2007) from quarterly series available in the database of Economatica(R). The results demonstrated that changes in leverage generate variations in the tangibility of the companies, a fact that can be explained by the large search of funding secured by fiduciary transfer of fixed assets, which facilitates access to credit by business of the Agribusiness sector, increasing the payment time and lowering interest rates.

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This paper examines the hysteresis hypothesis in the Brazilian industrialized exports using a time series analysis. This hypothesis finds an empirical representation into the nonlinear adjustments of the exported quantity to relative price changes. Thus, the threshold cointegration analysis proposed by Balke and Fomby [Balke, N.S. and Fomby, T.B. Threshold Cointegration. International Economic Review, 1997; 38; 627-645.] was used for estimating models with asymmetric adjustment of the error correction term. Amongst sixteen industrial sectors selected, there was evidence of nonlinearities in the residuals of long-run relationships of supply or demand for exports in nine of them. These nonlinearities represent asymmetric and/or discontinuous responses of exports to different representative measures of real exchange rates, in addition to other components of long-run demand or supply equations. (C) 2007 Elsevier B.V. All rights reserved.

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This paper develops a multi-regional general equilibrium model for climate policy analysis based on the latest version of the MIT Emissions Prediction and Policy Analysis (EPPA) model. We develop two versions so that we can solve the model either as a fully inter-temporal optimization problem (forward-looking, perfect foresight) or recursively. The standard EPPA model on which these models are based is solved recursively, and it is necessary to simplify some aspects of it to make inter-temporal solution possible. The forward-looking capability allows one to better address economic and policy issues such as borrowing and banking of GHG allowances, efficiency implications of environmental tax recycling, endogenous depletion of fossil resources, international capital flows, and optimal emissions abatement paths among others. To evaluate the solution approaches, we benchmark each version to the same macroeconomic path, and then compare the behavior of the two versions under a climate policy that restricts greenhouse gas emissions. We find that the energy sector and CO(2) price behavior are similar in both versions (in the recursive version of the model we force the inter-temporal theoretical efficiency result that abatement through time should be allocated such that the CO(2) price rises at the interest rate.) The main difference that arises is that the macroeconomic costs are substantially lower in the forward-looking version of the model, since it allows consumption shifting as an additional avenue of adjustment to the policy. On the other hand, the simplifications required for solving the model as an optimization problem, such as dropping the full vintaging of the capital stock and fewer explicit technological options, likely have effects on the results. Moreover, inter-temporal optimization with perfect foresight poorly represents the real economy where agents face high levels of uncertainty that likely lead to higher costs than if they knew the future with certainty. We conclude that while the forward-looking model has value for some problems, the recursive model produces similar behavior in the energy sector and provides greater flexibility in the details of the system that can be represented. (C) 2009 Elsevier B.V. All rights reserved.

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Real time three-dimensional echocardiography (RT3DE) has been demonstrated to be an accurate technique to quantify left ventricular (LV) volumes and function in different patient populations. We sought to determine the value of RT3DE for evaluating patients with hypertrophic cardiomyopathy (HCM), in comparison with cardiac magnetic resonance imaging (MRI). Methods: We studied 20 consecutive patients with HCM who underwent two-dimensional echocardiography (2DE), RT3DE, and MRI. Parameters analyzed by echocardiography and MRI included: wall thickness, LV volumes, ejection fraction (LVEF), mass, geometric index, and dyssynchrony index. Statistical analysis was performed by Lin agreement coefficient, Pearson linear correlation and Bland-Altman model. Results: There was excellent agreement between 2DE and RT3DE (Rc = 0.92), 2DE and MRI (Rc = 0.85), and RT3DE and MRI (Rc = 0.90) for linear measurements. Agreement indexes for LV end-diastolic and end-systolic volumes were Rc = 0.91 and Rc = 0.91 between 2DE and RT3DE, Rc = 0.94 and Rc = 0.95 between RT3DE and MRI, and Rc = 0.89 and Rc = 0.88 between 2DE and MRI, respectively. Satisfactory agreement was observed between 2DE and RT3DE (Rc = 0.75), RT3DE and MRI (Rc = 0.83), and 2DE and MRI (Rc = 0.73) for determining LVEF, with a mild underestimation of LVEF by 2DE, and smaller variability between RT3DE and MRI. Regarding LV mass, excellent agreement was observed between RT3DE and MRI (Rc = 0.96), with bias of -6.3 g (limits of concordance = 42.22 to -54.73 g). Conclusion: In patients with HCM, RT3DE demonstrated superior performance than 2DE for the evaluation of myocardial hypertrophy, LV volumes, LVEF, and LV mass.

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Objectives: Pneumothorax is a frequent complication during mechanical ventilation. Electrical impedance tomography (EIT) is a noninvasive tool that allows real-time imaging of regional ventilation. The purpose of this study was to 1) identify characteristic changes in the EIT signals associated with pneumothoraces; 2) develop and fine-tune an algorithm for their automatic detection; and 3) prospectively evaluate this algorithm for its sensitivity and specificity in detecting pneumothoraces in real time. Design: Prospective controlled laboratory animal investigation. Setting: Experimental Pulmonology Laboratory of the University of Sao Paulo. Subjects: Thirty-nine anesthetized mechanically ventilated supine pigs (31.0 +/- 3.2 kg, mean +/- SD). Interventions. In a first group of 18 animals monitored by EIT, we either injected progressive amounts of air (from 20 to 500 mL) through chest tubes or applied large positive end-expiratory pressure (PEEP) increments to simulate extreme lung overdistension. This first data set was used to calibrate an EIT-based pneumothorax detection algorithm. Subsequently, we evaluated the real-time performance of the detection algorithm in 21 additional animals (with normal or preinjured lungs), submitted to multiple ventilatory interventions or traumatic punctures of the lung. Measurements and Main Results: Primary EIT relative images were acquired online (50 images/sec) and processed according to a few imaging-analysis routines running automatically and in parallel. Pneumothoraces as small as 20 mL could be detected with a sensitivity of 100% and specificity 95% and could be easily distinguished from parenchymal overdistension induced by PEEP or recruiting maneuvers, Their location was correctly identified in all cases, with a total delay of only three respiratory cycles. Conclusions. We created an EIT-based algorithm capable of detecting early signs of pneumothoraces in high-risk situations, which also identifies its location. It requires that the pneumothorax occurs or enlarges at least minimally during the monitoring period. Such detection was operator-free and in quasi real-time, opening opportunities for improving patient safety during mechanical ventilation.