Prevalência de hipertensão arterial em militares jovens e fatores associados

OBJETIVO: Estimar a prevalência de hipertensão arterial entre militares jovens e fatores associados. MÉTODOS: Estudo transversal realizado com amostra de 380 militares do sexo masculino de 19 e 35 anos de idade em uma unidade da Força Aérea Brasileira em São Paulo, SP, entre 2000 e 2001. Os pontos de corte para hipertensão foram: >140mmHg para pressão sistólica e > 90mmHg para pressão diastólica. As variáveis estudadas incluíram fatores de risco e de proteção para hipertensão, como características comportamentais e nutricionais. Para análise das associações, utilizou-se regressão linear generalizada múltipla, com família binomial e ligação logarítmica, obtendo-se razões de prevalências com intervalo de 90% de confiança e seleção hierarquizada das variáveis. RESULTADOS: A prevalência de hipertensão arterial foi de 22% (IC 90%: 21;29). No modelo final da regressão múltipla verificou-se prevalência de hipertensão 68% maior entre os ex-fumantes em relação aos não fumantes (IC 90%: 1,13;2,50). Entre os indivíduos com sobrepeso (índice de massa corporal - IMC de 25 a 29kg/m2) e com obesidade (IMC>29kg/m2) as prevalências foram, respectivamente, 75% (IC 90%: 1,23;2,50) e 178% (IC 90%: 1,82;4,25) maiores do que entre os eutróficos. Entre os que praticavam atividade física regular, comparado aos que não praticavam, a prevalência foi 52% menor (IC 90%: 0,30;0,90). CONCLUSÕES: Ser ex-fumante e ter sobrepeso ou obesidade foram situações de risco para hipertensão, enquanto que a prática regular de atividade física foi fator de proteção em militares jovens.

Buckling and post-buckling of extensible rods revisited: A multiple-scale solution

An exact non-linear formulation of the equilibrium of elastic prismatic rods subjected to compression and planar bending is presented, electing as primary displacement variable the cross-section rotations and taking into account the axis extensibility. Such a formulation proves to be sufficiently general to encompass any boundary condition. The evaluation of critical loads for the five classical Euler buckling cases is pursued, allowing for the assessment of the axis extensibility effect. From the quantitative viewpoint, it is seen that such an influence is negligible for very slender bars, but it dramatically increases as the slenderness ratio decreases. From the qualitative viewpoint, its effect is that there are not infinite critical loads, as foreseen by the classical inextensible theory. The method of multiple (spatial) scales is used to survey the post-buckling regime for the five classical Euler buckling cases, with remarkable success, since very small deviations were observed with respect to results obtained via numerical integration of the exact equation of equilibrium, even when loads much higher than the critical ones were considered. Although known beforehand that such classical Euler buckling cases are imperfection insensitive, the effect of load offsets were also looked at, thus showing that the formulation is sufficiently general to accommodate this sort of analysis. (c) 2008 Elsevier Ltd. All rights reserved.

Predicting sprint running times from isokinetic and squat lift tests: A regression analysis

This study examined the relationship between isokinetic hip extensor/hip flexor strength, 1-RM squat strength, and sprint running performance for both a sprint-trained and non-sprint-trained group. Eleven male sprinters and 8 male controls volunteered for the study. On the same day subjects ran 20-m sprints from both a stationary start and with a 50-m acceleration distance, completed isokinetic hip extension/flexion exercises at 1.05, 4.74, and 8.42 rad.s(-1), and had their squat strength estimated. Stepwise multiple regression analysis showed that equations for predicting both 20-m maximum velocity nm time and 20-m acceleration time may be calculated with an error of less than 0.05 sec using only isokinetic and squat strength data. However, a single regression equation for predicting both 20-m acceleration and maximum velocity run times from isokinetic or squat tests was not found. The regression analysis indicated that hip flexor strength at all test velocities was a better predictor of sprint running performance than hip extensor strength.

A model to forecast financial failure, in non financial galician SMES

We are concerned with providing more empirical evidence on forecast failure, developing forecast models, and examining the impact of events such as audit reports. A joint consideration of classic financial ratios and relevant external indicators leads us to build a basic prediction model focused in non-financial Galician SMEs. Explanatory variables are relevant financial indicators from the viewpoint of the financial logic and financial failure theory. The paper explores three mathematical models: discriminant analysis, Logit, and linear multivariate regression. We conclude that, even though they both offer high explanatory and predictive abilities, Logit and MDA models should be used and interpreted jointly.

The extended unsymmetric frontal solution for multiple-point constraints

The purpose of this paper is to discuss the linear solution of equality constrained problems by using the Frontal solution method without explicit assembling. Design/methodology/approach - Re-written frontal solution method with a priori pivot and front sequence. OpenMP parallelization, nearly linear (in elimination and substitution) up to 40 threads. Constraints enforced at the local assembling stage. Findings - When compared with both standard sparse solvers and classical frontal implementations, memory requirements and code size are significantly reduced. Research limitations/implications - Large, non-linear problems with constraints typically make use of the Newton method with Lagrange multipliers. In the context of the solution of problems with large number of constraints, the matrix transformation methods (MTM) are often more cost-effective. The paper presents a complete solution, with topological ordering, for this problem. Practical implications - A complete software package in Fortran 2003 is described. Examples of clique-based problems are shown with large systems solved in core. Social implications - More realistic non-linear problems can be solved with this Frontal code at the core of the Newton method. Originality/value - Use of topological ordering of constraints. A-priori pivot and front sequences. No need for symbolic assembling. Constraints treated at the core of the Frontal solver. Use of OpenMP in the main Frontal loop, now quantified. Availability of Software.

Deterministic approach for multiple-source tsunami hazard assessment for Sines, Portugal

In this paper, we present a deterministic approach to tsunami hazard assessment for the city and harbour of Sines, Portugal, one of the test sites of project ASTARTE (Assessment, STrategy And Risk Reduction for Tsunamis in Europe). Sines has one of the most important deep-water ports, which has oil-bearing, petrochemical, liquid-bulk, coal, and container terminals. The port and its industrial infrastructures face the ocean southwest towards the main seismogenic sources. This work considers two different seismic zones: the Southwest Iberian Margin and the Gloria Fault. Within these two regions, we selected a total of six scenarios to assess the tsunami impact at the test site. The tsunami simulations are computed using NSWING, a Non-linear Shallow Water model wIth Nested Grids. In this study, the static effect of tides is analysed for three different tidal stages: MLLW (mean lower low water), MSL (mean sea level), and MHHW (mean higher high water). For each scenario, the tsunami hazard is described by maximum values of wave height, flow depth, drawback, maximum inundation area and run-up. Synthetic waveforms are computed at virtual tide gauges at specific locations outside and inside the harbour. The final results describe the impact at the Sines test site considering the single scenarios at mean sea level, the aggregate scenario, and the influence of the tide on the aggregate scenario. The results confirm the composite source of Horseshoe and Marques de Pombal faults as the worst-case scenario, with wave heights of over 10 m, which reach the coast approximately 22 min after the rupture. It dominates the aggregate scenario by about 60 % of the impact area at the test site, considering maximum wave height and maximum flow depth. The HSMPF scenario inundates a total area of 3.5 km2. © Author(s) 2015.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Multi-level modeling of aspects associated with poor mental health in a sample of prehospital emergency professionals.

BACKGROUND: The goal of this paper is to investigate the respective influence of work characteristics, the effort-reward ratio, and overcommitment on the poor mental health of out-of-hospital care providers. METHODS: 333 out-of-hospital care providers answered a questionnaire that included queries on mental health (GHQ-12), demographics, health-related information and work characteristics, questions from the Effort-Reward Imbalance Questionnaire, and items about overcommitment. A two-level multiple regression was performed between mental health (the dependent variable) and the effort-reward ratio, the overcommitment score, weekly number of interventions, percentage of non-prehospital transport of patients out of total missions, gender, and age. Participants were first-level units, and ambulance services were second-level units. We also shadowed ambulance personnel for a total of 416 hr. RESULTS: With cutoff points of 2/3 and 3/4 positive answers on the GHQ-12, the percentages of potential cases with poor mental health were 20% and 15%, respectively. The effort-reward ratio was associated with poor mental health (P < 0.001), irrespective of age or gender. Overcommitment was associated with poor mental health; this association was stronger in women (β = 0.054) than in men (β = 0.020). The percentage of prehospital missions out of total missions was only associated with poor mental health at the individual level. CONCLUSIONS: Emergency medical services should pay attention to the way employees perceive their efforts and the rewarding aspects of their work: an imbalance of those aspects is associated with poor mental health. Low perceived esteem appeared particularly associated with poor mental health. This suggests that supervisors of emergency medical services should enhance the value of their employees' work. Employees with overcommitment should also receive appropriate consideration. Preventive measures should target individual perceptions of effort and reward in order to improve mental health in prehospital care providers.

Influence of weather, rank, and home advantage on football outcomes in the gulf region.

PURPOSE: The objective of this study was to investigate the effects of weather, rank, and home advantage on international football match results and scores in the Gulf Cooperation Council (GCC) region. METHODS: Football matches (n = 2008) in six GCC countries were analyzed. To determine the weather influence on the likelihood of favorable outcome and goal difference, generalized linear model with a logit link function and multiple regression analysis were performed. RESULTS: In the GCC region, home teams tend to have greater likelihood of a favorable outcome (P < 0.001) and higher goal difference (P < 0.001). Temperature difference was identified as a significant explanatory variable when used independently (P < 0.001) or after adjustment for home advantage and team ranking (P < 0.001). The likelihood of favorable outcome for GCC teams increases by 3% for every 1-unit increase in temperature difference. After inclusion of interaction with opposition, this advantage remains significant only when playing against non-GCC opponents. While home advantage increased the odds of favorable outcome (P < 0.001) and goal difference (P < 0.001) after inclusion of interaction term, the likelihood of favorable outcome for a GCC team decreased (P < 0.001) when playing against a stronger opponent. Finally, the temperature and wet bulb globe temperature approximation were found as better indicators of the effect of environmental conditions than absolute and relative humidity or heat index on match outcomes. CONCLUSIONS: In GCC region, higher temperature increased the likelihood of a favorable outcome when playing against non-GCC teams. However, international ranking should be considered because an opponent with a higher rank reduced, but did not eliminate, the likelihood of a favorable outcome.

Accelerated Microstructure Imaging via Convex Optimisation for regions with multiple fibres (AMICOx)

This paper reviews and extends our previous work to enable fast axonal diameter mapping from diffusion MRI data in the presence of multiple fibre populations within a voxel. Most of the existing mi-crostructure imaging techniques use non-linear algorithms to fit their data models and consequently, they are computationally expensive and usually slow. Moreover, most of them assume a single axon orientation while numerous regions of the brain actually present more complex configurations, e.g. fiber crossing. We present a flexible framework, based on convex optimisation, that enables fast and accurate reconstructions of the microstructure organisation, not limited to areas where the white matter is coherently oriented. We show through numerical simulations the ability of our method to correctly estimate the microstructure features (mean axon diameter and intra-cellular volume fraction) in crossing regions.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Reference values for lung function tests: I. Static volumes

Static lung volume (LV) measurements have a number of clinical and research applications; however, no previous studies have provided reference values for such tests using a healthy sample of the adult Brazilian population. With this as our main purpose, we prospectively evaluated 100 non-smoking subjects (50 males and 50 females), 20 to 80 years old, randomly selected from more than 8,000 individuals. Gender-specific linear prediction equations were developed by multiple regression analysis with total lung capacity (TLC), functional residual capacity (FRC), residual volume (RV), RV/TLC ratio and inspiratory capacity (IC) as dependent variables, and with age, height, weight, lean body mass and indexes of physical fitness as independent ones. Simpler demographic and anthropometric variables were as useful as more complex measurements in predicting LV values, independent of gender and age (R2 values ranging from 0.49 to 0.78, P<0.001). Interestingly, prediction equations from North American and European studies overestimated the LV at low volumes and underestimated them at high volumes (P<0.05). Our results, therefore, provide a more appropriate frame of reference to evaluate the normalcy of static lung volume values in Brazilian males and females aged 20 to 80 years.

Reference values for lung function tests: II. Maximal respiratory pressures and voluntary ventilation

The strength of the respiratory muscles can be evaluated from static measurements (maximal inspiratory and expiratory pressures, MIP and MEP) or inferred from dynamic maneuvers (maximal voluntary ventilation, MVV). Although these data could be suitable for a number of clinical and research applications, no previous studies have provided reference values for such tests using a healthy, randomly selected sample of the adult Brazilian population. With this main purpose, we prospectively evaluated 100 non-smoking subjects (50 males and 50 females), 20 to 80 years old, selected from more than 8,000 individuals. Gender-specific linear prediction equations for MIP, MEP and MVV were developed by multiple regression analysis: age and, secondarily, anthropometric measurements explained up to 56% of the variability of the dependent variables. The most cited previous studies using either Caucasian or non-Caucasian samples systematically underestimated the observed values of MIP (P<0.05). Interestingly, the self-reported level of regular physical activity and maximum aerobic power correlates strongly with both respiratory and peripheral muscular strength (knee extensor peak torque) (P<0.01). Our results, therefore, provide a new frame of reference to evaluate the normalcy of some useful indexes of respiratory muscle strength in Brazilian males and females aged 20 to 80.

Reference values for lung function tests: III. Carbon monoxide diffusing capacity (transfer factor)

Carbon monoxide diffusing capacity (DLCO) or transfer factor (TLCO) is a particularly useful test of the appropriateness of gas exchange across the lung alveolocapillary membrane. With the purpose of establishing predictive equations for DLCO using a non-smoking sample of the adult Brazilian population, we prospectively evaluated 100 subjects (50 males and 50 females aged 20 to 80 years), randomly selected from more than 8,000 individuals. Gender-specific linear prediction equations were developed by multiple regression analysis with single breath (SB) absolute and volume-corrected (VA) DLCO values as dependent variables. In the prediction equations, age (years) and height (cm) had opposite effects on DLCOSB (ml min-1 mmHg-1), independent of gender (-0.13 (age) + 0.32 (height) - 13.07 in males and -0.075 (age) + 0.18 (height) + 0.20 in females). On the other hand, height had a positive effect on DLCOSB but a negative one on DLCOSB/VA (P<0.01). We found that the predictive values from the most cited studies using predominantly Caucasian samples were significantly different from the actually measured values (P<0.05). Furthermore, oxygen uptake at maximal exercise (VO2max) correlated highly to DLCOSB (R = 0.71, P<0.001); this variable, however, did not maintain an independent role to explain the VO2max variability in the multiple regression analysis (P>0.05). Our results therefore provide an original frame of reference for either DLCOSB or DLCOSB/VA in Brazilian males and females aged 20 to 80 years, obtained from the standardized single-breath technique.