948 resultados para Integer variables
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Background: Nonalcoholic fatty liver disease (NAFLD) is a disorder associated with excessive fat accumulation, mainly in the intra-abdominal region. A simple technique to estimate abdominal fat in this region could be useful to assess the presence of NAFLD, in obese subjects who are more vulnerable to this disease. The aim of this cross-sectional study was to verify the reliability of waist circumference and body composition variables to identify the occurrence of NAFLD in obese children and adolescents.Methods: Sample was composed of 145 subjects, aged 11 to 17 years. Assessments of waist circumference (WC), trunk fat mass (TFM) and fat mass (FM) by dual-energy X-ray absorptiometry (DXA) and ultrasound for diagnosis of NAFLD and intra-abdominal adipose tissue (IAAT) were used. Correlation between variables was made by Spearman's coefficients; ROC curve parameters (sensitivity, specificity, area under curve) were used to assess the reliability of body composition variables to assess the presence of NAFLD. Statistical significance was set at 5%.Results: Significant correlations were observed between NAFLD and WC (p = 0.001), TFM (p = 0.002) and IAAT (p = 0.001). The higher values of area under the ROC curve were for WC (AUC = 0.720), TFM (AUC = 0.661) and IAAT (AUC = 0.741).Conclusions: Our findings indicated that TFM, IAAT and WC present high potential to identify NAFLD in obese children and adolescents.
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Purpose. - The purposes of this study were: i) to compare the physiological responses measured during a specific table tennis incremental test with the physiological responses measured during cycling, arm cranking, and treadmill running tests; and ii) to verify the accuracy of table tennis performance prediction based on the physiological responses from these tests.Methods. - Eleven national level male table tennis players participated in the study and undertook incremental tests using ergometers. Table tennis performance was defined as the ranking obtained during a simulated tournament between the participants.Results. - In general, peak values for physiological variables (e.g., (V) over dotO(2PEAK) and [La]PEAK) were significantly lower (P < 0.05) in the specific test (e.g., (V) over dotO(2PEAK) = 39.9 +/- 1.5 ml.kg(-1) per minute and [La]PEAK = 6.4 +/- 0.5 mmol.L-1) than during cycling (e.g., (V) over dotO(2PEAK) = 41.3 +/- 1.4 ml.kg(-1) per minute and [La]PEAK = 10.2 +/- 0.7 mmol.L-1) or running (e.g., (V) over dotO(2PEAK) = 43.9 +/- 1.5 ml.kg(-1) per minute and [La]PEAK = 10.0 +/- 0.7 mmol.L-1), but higher than during arm cranking (e.g., (V) over dotO(2PEAK) = 26.6 +/- 1.6 ml.kg(-1) per minute and [La]PEAK = 8.9 +/- 0.6 mmol.L-1). At respiratory compensation point intensity (RCP), only the variables measured on arm cranking were lower (P < 0.05) than on the other ergometers. Stepwise multiple regression analysis showed significant correlation between table tennis performance and lactate concentration ([La]) and also rate of perceived effort (RPE) at RCP during cycling (r = 0.89; P < 0.05).Conclusion. - In conclusion, the significant differences obtained between the specific and laboratory ergometers demonstrate the need to use a specific test to measure physiological parameters in table tennis and the physiological parameters measured, independent of the ergometer used, are unable to predict table tennis performance. (C) 2013 Elsevier Masson SAS. All rights reserved.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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The concept of Functional Urban Regions (FURs), also called Metropolitan Regions (MRs), is not simple. It is clear, though, that they are not simply a combination of adjacent municipalities or areas. Different methods can be used for their definition. However, especially in developing countries, the application of some methods is not possible, due to the unavailability of detailed data. Alternative approaches have been developed based on spatial analysis methods and using variables extracted from available data. The objective of this study is to compare the results of two spatial analysis methods exploring two variables: population density and an indicator of transport infrastructure supply. The first method regards Exploratory Spatial Data Analyses tools, which define uniform regions based on specific variables. The second method used the same variables and the spatial analysis technique available in the computer program SKATER - Spatial 'K'luster Analysis by Tree Edge Removal. Assuming that those classifications of regions with similar characteristics can be used for identifying potential FURS, the results of all analyses were compared with one another and with the 'official' MR. A combined approach was also considered for comparison, but none of the results match the existing MR boundaries, what challenges the official definitions. (C) 2014 Elsevier Ltd. All rights reserved.
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In this paper, the optimal reactive power planning problem under risk is presented. The classical mixed-integer nonlinear model for reactive power planning is expanded into two stage stochastic model considering risk. This new model considers uncertainty on the demand load. The risk is quantified by a factor introduced into the objective function and is identified as the variance of the random variables. Finally numerical results illustrate the performance of the proposed model, that is applied to IEEE 30-bus test system to determine optimal amount and location for reactive power expansion.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Background: Investigation and discrimination of neuromuscular variables related to the complex aetiology of low back pain could contribute to clarifying the factors associated with symptoms. Objective: Analysing the discriminative power of neuromuscular variables in low back pain. Methods: This study compared muscle endurance, proprioception and isometric trunk assessments between women with low back pain (LBP, n=14) and a control group (CG, n=14). Multivariate analysis of variance and discriminant analysis of the data were performed. Results: The muscle endurance time (s) was shorter in the LBP group than in the CG (p=0.004) with values of 85.81 (37.79) and 134.25 (43.88), respectively. The peak torque (Nm/kg) for trunk extension was 2.48 (0.69) in the LBP group and 3.56 (0.88) in the GG (p=0.001); for trunk flexion, the mean torque was 1.49 (0.40) in the LBP group and 1.85 (0.39) in the CG (p=0.023). The repositioning error (degrees) before the endurance test was 2.66 (1.36) in the LBP group and 2.41 (1.46) in the CG (p=0.664), and after the endurance test, it was 2.95 (1.94) in the LBP group and 2.00 (1.16) in the CG (p=0.06). Furthermore, the variables showed discrimination between the groups (p=0.007), with 78.6% of the individuals with low back pain correctly classified in the LBP group. In turn, variables related to muscle activation showed no difference in discrimination between the groups (p=0.369). Conclusion: Based on these findings, the clinical management of low back pain should consist of both resistance and strength training, particularly in the extensor muscles.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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This paper presents a mixed-integer quadratically-constrained programming (MIQCP) model to solve the distribution system expansion planning (DSEP) problem. The DSEP model considers the construction/reinforcement of substations, the construction/reconductoring of circuits, the allocation of fixed capacitors banks and the radial topology modification. As the DSEP problem is a very complex mixed-integer non-linear programming problem, it is convenient to reformulate it like a MIQCP problem; it is demonstrated that the proposed formulation represents the steady-state operation of a radial distribution system. The proposed MIQCP model is a convex formulation, which allows to find the optimal solution using optimization solvers. Test systems of 23 and 54 nodes and one real distribution system of 136 nodes were used to show the efficiency of the proposed model in comparison with other DSEP models available in the specialized literature. (C) 2014 Elsevier Ltd. All rights reserved.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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The effects of body biometrics on cardiac measurements and description of cardiac anatomy were performed in red-tailed boas (Boa constrictor constrictor) (n = 29) using real-time B-mode ultrasonography. Statistical comparison of measured cardiac metrics according to sex and body measurements demonstrated no significant difference between sexes but a highly significant linear increase between body length and mass and all cardiac metrics.
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In this paper a novel Branch and Bound (B&B) algorithm to solve the transmission expansion planning which is a non-convex mixed integer nonlinear programming problem (MINLP) is presented. Based on defining the options of the separating variables and makes a search in breadth, we call this algorithm a B&BML algorithm. The proposed algorithm is implemented in AMPL and an open source Ipopt solver is used to solve the nonlinear programming (NLP) problems of all candidates in the B&B tree. Strategies have been developed to address the problem of non-linearity and non-convexity of the search region. The proposed algorithm is applied to the problem of long-term transmission expansion planning modeled as an MINLP problem. The proposed algorithm has carried out on five commonly used test systems such as Garver 6-Bus, IEEE 24-Bus, 46-Bus South Brazilian test systems, Bolivian 57-Bus, and Colombian 93-Bus. Results show that the proposed methodology not only can find the best known solution but it also yields a large reduction between 24% to 77.6% in the number of NLP problems regarding to the size of the systems.
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In this paper we show that the quaternion orders OZ[ √ 2] ≃ ( √ 2, −1)Z[ √ 2] and OZ[ √ 3] ≃ (3 + 2√ 3, −1)Z[ √ 3], appearing in problems related to the coding theory [4], [3], are not maximal orders in the quaternion algebras AQ( √ 2) ≃ ( √ 2, −1)Q( √ 2) and AQ( √ 3) ≃ (3 + 2√ 3, −1)Q( √ 3), respectively. Furthermore, we identify the maximal orders containing these orders.