934 resultados para Statistical Energy Analysis
Resumo:
Carbon formation on Ni/gamma-Al2O3 catalysts and its kinetics during methane reforming with carbon dioxide was studied in the temperature range of 500-700 degrees C using a thermogravimetric analysis technique. The activation energies of methane cracking, carbon gasification in CO2, as well as carbon deposition in CO2-CH4 reforming were obtained. The results show that the activation energy for carbon gasification is larger than that of carbon formation in methane cracking and that the activation energy of coking in CO2-CH4 reforming is also larger than that of methane decomposition to carbon. The dependencies of coking rate on partial pressures of CH4 and CO2 indicate that methane decomposition is the main route for carbon deposition. A mechanism and kinetic model for carbon deposition is proposed.
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Dual-energy X-ray absorptiometry (DXA) is a widely used method for measuring bone mineral in the growing skeleton. Because scan analysis in children offers a number of challenges, we compared DXA results using six analysis methods at the total proximal femur (PF) and five methods at the femoral neck (FN), In total we assessed 50 scans (25 boys, 25 girls) from two separate studies for cross-sectional differences in bone area, bone mineral content (BMC), and areal bone mineral density (aBMD) and for percentage change over the short term (8 months) and long term (7 years). At the proximal femur for the short-term longitudinal analysis, there was an approximate 3.5% greater change in bone area and BMC when the global region of interest (ROI) was allowed to increase in size between years as compared with when the global ROI was held constant. Trend analysis showed a significant (p < 0.05) difference between scan analysis methods for bone area and BMC across 7 years. At the femoral neck, cross-sectional analysis using a narrower (from default) ROI, without change in location, resulted in a 12.9 and 12.6% smaller bone area and BMC, respectively (both p < 0.001), Changes in FN area and BMC over 8 months were significantly greater (2.3 %, p < 0.05) using a narrower FN rather than the default ROI, Similarly, the 7-year longitudinal data revealed that differences between scan analysis methods were greatest when the narrower FN ROI was maintained across all years (p < 0.001), For aBMD there were no significant differences in group means between analysis methods at either the PF or FN, Our findings show the need to standardize the analysis of proximal femur DXA scans in growing children.
Resumo:
The primary purpose of this study was to estimate the magnitude and variability of peak calcium accretion rates in the skeletons of healthy white adolescents. Total-body bone mineral content (BMC) was measured annually on six occasions by dual-energy X-ray absorptiometry (DXA; Hologic 2000, array mode), a BMC velocity curve was generated for each child by a cubic spline fit, and peak accretion rates were determined. Anthropometric measures were collected every 6 months and a 24-h dietary recall was recorded two to three times per year. Of the 113 boys and 115 girls initially enrolled in the study, 60 boys and 53 girls who had peak height velocity (PHV) and peak BMC velocity values were used in this longitudinal analysis. When the individual BR IC velocity curves were aligned on the age of peak bone mineral velocity, the resulting mean peak bone mineral accrual rate was 407 g/year for boys (SD, 92 g/year; range, 226-651 g/year) and 322 g/year for girls (SD, 66 g/year; range, 194-520 g/year). Using 32.2% as the fraction of calcium in bone mineral, as determined by neutron activation analysis (Ellis et al., J Bone Miner Res 1996;11:843-848), these corresponded to peak calcium accretion rates of 359 mg/day for boys (81 mg/day; 199-574 mg/day) and 284 mg/day for girls (58 mg/day; 171-459 mg/day). These longitudinal results are 27-34% higher than our previous cross-sectional analysis in which we reported mean values of 282 mg/day for boys and 212 mg/day for girls (Martin et al., Am J Clin Nutr 1997;66:611-615). Mean age of peak calcium accretion was 14.0 years for the boys (1.0 years; 12.0-15.9 years), and 12.5 years for the girls (0.9 years; 10.5-14.6 years). Dietary calcium intake, determined as the mean of all assessments up to the age of peak accretion was 1140 mg/day (SD, 392 mg/day) for boys and 1113 mg/day (SD, 378 mg/day) for girls. We estimate that 26% of adult calcium is laid down during the 2 adolescent years of peak skeletal growth. This period of rapid growth requires high accretion rates of calcium, achieved in part by increased retention efficiency of dietary calcium.
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The present paper proposes an approach to obtaining the activation energy distribution for chemisorption of oxygen onto carbon surfaces, while simultaneously allowing for the activation energy dependence of the pre-exponential factor of the rate constant. Prior studies in this area have considered this factor to be uniform, thereby biasing estimated distributions. The results show that the derived activation energy distribution is not sensitive to the chemisorption mechanism because of the step function like property of the coverage. The activation energy distribution is essentially uniform for some carbons, and has two or possibly more discrete stages, suggestive of at least two types of sites, each with its own uniform distribution. The pre-exponential factors of the reactions are determined directly from the experimental data, and are found not to be constant as assumed in earlier work, but correlated with the activation energy. The latter results empirically follow an exponential function, supporting some earlier statistical and experimental work. The activation energy distribution obtained in the present paper permits improved correlation of chemisorption data in comparison to earlier studies. (C) 2000 Elsevier Science Ltd. All rights reserved.
Resumo:
Protein kinases exhibit various degrees of substrate specificity. The large number of different protein kinases in the eukaryotic proteomes makes it impractical to determine the specificity of each enzyme experimentally. To test if it were possible to discriminate potential substrates from non-substrates by simple computational techniques, we analysed the binding enthalpies of modelled enzyme-substrate complexes and attempted to correlate it with experimental enzyme kinetics measurements. The crystal structures of phosphorylase kinase and cAMP-dependent protein kinase were used to generate models of the enzyme with a series of known peptide substrates and non-substrates, and the approximate enthalpy of binding assessed following energy minimization. We show that the computed enthalpies do not correlate closely with kinetic measurements, but the method can distinguish good substrates from weak substrates and non-substrates. Copyright (C) 2002 John Wiley Sons, Ltd.
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We conduct a theoretical analysis of steady-state heat transfer problems through mid-crustal vertical cracks with upward throughflow in hydrothermal systems. In particular, we derive analytical solutions for both the far field and near field of the system. In order to investigate the contribution of the forced advection to the total temperature of the system, two concepts, namely the critical Peclet number and the critical permeability of the system, have been presented and discussed in this paper. The analytical solution for the far field of the system indicates that if the pore-fluid pressure gradient in the crust is lithostatic, the critical permeability of the system can be used to determine whether or not the contribution of the forced advection to the total temperature of the system is negligible. Otherwise, the critical Peclet number should be used. For a crust of moderate thickness, the critical permeability is of the order of magnitude of 10(-20) m(2), under which heat conduction is the overwhelming mechanism to transfer heat energy, even though the pore-fluid pressure gradient in the crust is lithostatic. Furthermore, the lower bound analytical solution for the near field of the system demonstrates that the permeable vertical cracks in the middle crust can efficiently transfer heat energy from the lower crust to the upper crust of the Earth. Copyright (C) 2002 John Wiley Sons, Ltd.
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This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.
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This paper is part of a large study to assess the adequacy of the use of multivariate statistical techniques in theses and dissertations of some higher education institutions in the area of marketing with theme of consumer behavior from 1997 to 2006. The regression and conjoint analysis are focused on in this paper, two techniques with great potential of use in marketing studies. The objective of this study was to analyze whether the employement of these techniques suits the needs of the research problem presented in as well as to evaluate the level of success in meeting their premisses. Overall, the results suggest the need for more involvement of researchers in the verification of all the theoretical precepts of application of the techniques classified in the category of investigation of dependence among variables.
Resumo:
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.
Resumo:
Background-Randomized trials that studied clinical outcomes after percutaneous coronary intervention (PCI) with bare metal stenting versus coronary artery bypass grafting (CABG) are underpowered to properly assess safety end points like death, stroke, and myocardial infarction. Pooling data from randomized controlled trials increases the statistical power and allows better assessment of the treatment effect in high-risk subgroups. Methods and Results-We performed a pooled analysis of 3051 patients in 4 randomized trials evaluating the relative safety and efficacy of PCI with stenting and CABG at 5 years for the treatment of multivessel coronary artery disease. The primary end point was the composite end point of death, stroke, or myocardial infarction. The secondary end point was the occurrence of major adverse cardiac and cerebrovascular accidents, death, stroke, myocardial infarction, and repeat revascularization. We tested for heterogeneities in treatment effect in patient subgroups. At 5 years, the cumulative incidence of death, myocardial infarction, and stroke was similar in patients randomized to PCI with stenting versus CABG (16.7% versus 16.9%, respectively; hazard ratio, 1.04, 95% confidence interval, 0.86 to 1.27; P = 0.69). Repeat revascularization, however, occurred significantly more frequently after PCI than CABG (29.0% versus 7.9%, respectively; hazard ratio, 0.23; 95% confidence interval, 0.18 to 0.29; P<0.001). Major adverse cardiac and cerebrovascular events were significantly higher in the PCI than the CABG group (39.2% versus 23.0%, respectively; hazard ratio, 0.53; 95% confidence interval, 0.45 to 0.61; P<0.001). No heterogeneity of treatment effect was found in the subgroups, including diabetic patients and those presenting with 3-vessel disease. Conclusions-In this pooled analysis of 4 randomized trials, PCI with stenting was associated with a long-term safety profile similar to that of CABG. However, as a result of persistently lower repeat revascularization rates in the CABG patients, overall major adverse cardiac and cerebrovascular event rates were significantly lower in the CABG group at 5 years.
Resumo:
Aims: This study has compared the tissue expression of the p53 tumour suppressor protein and DNA repair proteins APE1, hMSH2 and ERCC1 in normal, dysplastic and malignant lip epithelium. Methods and results: Morphological analysis and immunohistochemistry were performed on archived specimens of normal lip mucosa (n = 15), actinic cheilitis (AC) (n = 30), and lip squamous cell carcinoma (LSCC) (n = 27). AC samples were classified morphologically according to the severity of epithelial dysplasia and risk of malignant transformation. LSCC samples were morphologically staged according to WHO and invasive front grading (IFG) criteria. Differences between groups and morphological stages were determined by bivariate statistical analysis. Progressive increases in the percentage of epithelial cells expressing p53 and APE1 were associated with increases in morphological malignancy from normal lip mucosa to LSCC. There was also a significant reduction in epithelial cells expressing hMSH2 and ERCC1 proteins in the AC and LSCC groups. A higher percentage of malignant cells expressing APE1 was found in samples with an aggressive morphological IFG grade. Conclusions: Our data showed that epithelial cells from premalignant to malignant lip disease exhibited changes in the expression of p53, APE1, hMSH2 and ERCC1 proteins; these molecular change might contribute to lip carcinogenesis.
Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI
Resumo:
Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis. (C) 2010 Elsevier B.V. All rights reserved.
Resumo:
The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.
Resumo:
Smoothing the potential energy surface for structure optimization is a general and commonly applied strategy. We propose a combination of soft-core potential energy functions and a variation of the diffusion equation method to smooth potential energy surfaces, which is applicable to complex systems such as protein structures; The performance of the method was demonstrated by comparison with simulated annealing using the refinement of the undecapeptide Cyclosporin A as a test case. Simulations were repeated many times using different initial conditions and structures since the methods are heuristic and results are only meaningful in a statistical sense.
Resumo:
This study aimed to compare the resting energy expenditure (REE) of white and non-white severely obese Brazilian women. REE was examined in 83 severely obese Brazilian women (n = 58 white and 25 non-white) with mean (+/- SD) age 42.99 +/- 11.35 and body mass index 46.88 +/- 6.22 kg/m(2) who were candidates for gastric bypass surgery. Body composition was assessed by air displacement plethysmography (ADP) BOD PODO body composition system (Life Measurement Instruments, Concord, CA) and REE was measured, under established protocol, with an open-circuit calorimeter (Deltatrac II MBM-200, Datex-Ohmeda, Madison, WI, USA). There was no significant difference between the REE of white and non-white severely obese women (1,953 +/- 273 and 1,906 +/- 271 kcal/d, respectively; p = 0.48). However, when adjusted for fat free mass (MLG), REE was significantly higher in non-white severely obese women (difference between groups of 158.4 kcal, p < 0.01). REE in white women was positively and significantly correlated to C-reactive protein (PCR) (r = 0.41.8; P < 0.001) and MLG (r = 0.771; P < 0.001). In the non-white women, REE was only significantly correlated to MLG (r = 0.753; P < 0.001). The multiple linear regression analysis showed that skin color, MLG and PCR were the significant determinants of REE (R(2) = 0.55). This study showed that, after adjustment for MLG, non-white severely obese women have a higher REE than the white ones. The association of body composition inflammation factors and REE in severely obese Brazilian women remains to be further investigated.