914 resultados para Random regression
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Topology optimization consists in finding the spatial distribution of a given total volume of material for the resulting structure to have some optimal property, for instance, maximization of structural stiffness or maximization of the fundamental eigenfrequency. In this paper a Genetic Algorithm (GA) employing a representation method based on trees is developed to generate initial feasible individuals that remain feasible upon crossover and mutation and as such do not require any repairing operator to ensure feasibility. Several application examples are studied involving the topology optimization of structures where the objective functions is the maximization of the stiffness and the maximization of the first and the second eigenfrequencies of a plate, all cases having a prescribed material volume constraint.
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Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.
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OBJECTIVE: To assess the effects of individual, household and healthcare system factors on poor children's use of vaccination after the reform of the Colombian health system. METHODS: A household survey was carried out in a random sample of insured poor population in Bogota, in 1999. The conceptual and analytical framework was based on the Andersen's Behavioral Model of Health Services Utilization. It considers two units of analysis for studying vaccination use and its determinants: the insured poor population, including the children and their families characteristics; and the health care system. Statistical analysis were carried out by chi-square test with 95% confidence intervals, multivariate regression models and Cronbach's alpha coefficient. RESULTS: The logistic regression analysis showed that vaccination use was related not only to population characteristics such as family size (OR=4.3), living area (OR=1.7), child's age (OR=0.7) and head-of-household's years of schooling (OR=0.5), but also strongly related to health care system features, such as having a regular health provider (OR=6.0) and information on providers' schedules and requirements for obtaining care services (OR=2.1). CONCLUSIONS: The low vaccination use and the relevant relationships to health care delivery systems characteristics show that there are barriers in the healthcare system, which should be assessed and eliminated. Non-availability of regular healthcare and deficient information to the population are factors that can limit service utilization.
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Dissertação de Mestrado, Gestão de Empresa (MBA), 16 de Julho de 2013, Universidade dos Açores.
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OBJECTIVE: A cross-sectional population-based study was conducted to assess, in active smokers, the relationship of number of cigarettes smoked and other characteristics to salivary cotinine concentrations. METHODS: A random sample of active smokers aged 15 years or older was selected using a stepwise cluster sample strategy, in the year 2000 in Rio de Janeiro, Brazil. The study included 401 subjects. Salivary cotinine concentration was determined using gas chromatography with nitrogen-phosphorus detection. A standard questionnaire was used to collect demographic and smoking behavioral data. The relation between the number of cigarettes smoked in the last 24h and cotinine level was examined by means of a nonparametric fitting technique of robust locally weighted regression. RESULTS: Significantly (p<0.05) higher adjusted mean cotinine levels were found in subjects smoking their first cigarette within five minutes after waking up, and in those smoking 1-20 cigarettes in the last 24h who reported inhaling more than ½ the time. In those smoking 1-20 cigarettes, the slope was significantly higher for those subjects waiting for more than five minutes before smoking their first cigarette after waking up, and those smoking "light" cigarettes when compared with their counterparts. These heterogeneities became negligible and non-significant when subjects with cotinine >40 ng/mL per cigarette were excluded. CONCLUSIONS: There was found a positive association between self-reporting smoking five minutes after waking up, and inhaling more than ½ the time are consistent and higher cotinine levels. These can be markers of dependence and higher nicotine intake. Salivary cotinine proved to be a useful biomarker of recent smoking and can be used in epidemiological studies and smoking cessation programs.
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OBJECTIVE: To assess risk factors for antepartum fetal deaths. METHODS: A population-based case-control study was carried out in the city of São Paulo from August 2000 to January 2001. Subjects were selected from a birth cohort from a linked birth and death certificate database. Cases were 164 antepartum fetal deaths and controls were drawn from a random sample of 313 births surviving at least 28 days. Information was collected from birth and death certificates, hospital records and home interviews. A hierarchical conceptual framework guided the logistic regression analysis. RESULTS: Statistically significant factors associated with antepartum fetal death were: mother without or recent marital union; mother's education under four years; mothers with previous low birth weight infant; mothers with hypertension, diabetes, bleeding during pregnancy; no or inadequate prenatal care; congenital malformation and intrauterine growth restriction. The highest population attributable fractions were for inadequacy of prenatal care (40%), hypertension (27%), intrauterine growth restriction (30%) and absence of a long-standing union (26%). CONCLUSIONS: Proximal biological risk factors are most important in antepartum fetal deaths. However, distal factors - mother's low education and marital status - are also significant. Improving access to and quality of prenatal care could have a large impact on fetal mortality.
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We propose a 3D-2D image registration method that relates image features of 2D projection images to the transformation parameters of the 3D image by nonlinear regression. The method is compared with a conventional registration method based on iterative optimization. For evaluation, simulated X-ray images (DRRs) were generated from coronary artery tree models derived from 3D CTA scans. Registration of nine vessel trees was performed, and the alignment quality was measured by the mean target registration error (mTRE). The regression approach was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach.
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The objective of the study was to develop regression models to describe the epidemiological profile of dental caries in 12-year-old children in an area of low prevalence of caries. Two distinct random probabilistic samples of schoolchildren (n=1,763) attending public and private schools in Piracicaba, Southeastern Brazil, were studied. Regression models were estimated as a function of the most affected teeth using data collected in 2005 and were validated using a 2001 database. The mean (SD) DMFT index was 1.7 (2.08) in 2001 and the regression equations estimated a DMFT index of 1.67 (1.98), which corresponds to 98.2% of the DMFT index in 2001. The study provided detailed data on the caries profile in 12-year-old children by using an updated analytical approach. Regression models can be an accurate and feasible method that can provide valuable information for the planning and evaluation of oral health services.
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OBJECTIVE: To examine the interaction between physical and psychosocial demands of work associated to low back pain. METHODS: Cross-sectional study carried out in a stratified proportional random sample of 577 plastic industry workers in the metropolitan area of the city of Salvador, Northeast Brazil in 2002. An anonymous standard questionnaire was administered in the workplace by trained interviewers. Physical demands at work were self-rated on a 6-point numeric scale, with anchors at each end of the scale. Factor analysis was carried out on 11 physical demand variables to identify underlying factors. Psychosocial work demands were measured by demand, control and social support questions. Multivariate analysis was performed using the likelihood ratio test. RESULTS: The factor analysis identified two physical work demand factors: material handling (factor 1) and repetitiveness (factor 2). The multiple logistic regression analysis showed that factor 1 was positively associated with low back pain (OR=2.35, 95% CI 1.50;3.66). No interaction was found between physical and psychosocial work demands but both were independently associated to low back pain. CONCLUSIONS: The study found independent effects of physical and psychosocial work demands on low back pain prevalence and emphasizes the importance of physical demands especially of material handling involving trunk bending forward and trunk rotation regardless of age, gender, and body fitness.
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Amulti-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.
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OBJECTIVE: To assess the impact of academic life on health status of university students. METHODS: Longitudinal study including 154 undergraduate students from the Universidade de Aveiro, Portugal, with at least two years of follow-up observations. Sociodemographic and behavioral characteristics were collected using questionnaires. Students' weight, height, blood pressure, serum glucose, serum lipids and serum homocysteine levels were measured. Regression analysis was performed using linear mixed-effect models, allowing for random effects at the participant level. RESULTS: A higher rate of dyslipidemia (44.0% vs. 28.6%), overweight (16.3% vs. 12.5%) and smoking (19.3% vs. 0.0%) was found among students exposed to the academic life when compared to freshmen. Physical inactivity was about 80%. Total cholesterol, high density lipoprotein-cholesterol (HDL-C), triglycerides, systolic blood pressure, and physical activity levels were significantly associated with gender (p<0.001). Academic exposure was associated with increased low density lipoprotein-cholesterol (LDL-C) levels (about 1.12 times), and marginally with total cholesterol levels (p=0.041). CONCLUSIONS: High education level does not seem to have a protective effect favoring a healthier lifestyle and being enrolled in health-related areas does not seem either to positively affect students' behaviors. Increased risk factors for non-transmissible diseases in university students raise concerns about their well-being. These results should support the implementation of health promotion and prevention programs at universities.
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OBJECTIVE: To examine the relationship between social contextual factors and child and adolescent labor. METHODS: Population-based cohort study carried out with 2,512 families living in 23 subareas of a large urban city in Brazil from 2000 to 2002. A random one-stage cluster sampling was used to select families. Data were obtained through individual household interviews using questionnaires. The annual cumulative incidence of child and adolescent labor was estimated for each district. New child and adolescent labor cases were those who had their first job over the two-year follow-up. The annual cumulative incidence of child and adolescent labor was the response variable and predictors were contextual factors such as lack of social support, social deprivation, unstructured family, perceived violence, poor school quality, poor environment conditions, and poor public services. Pearson's correlation and multiple linear regression were used to assess the associations. RESULTS: There were selected 943 families corresponding to 1,326 non-working children and adolescents aged 8 to 17 years. Lack of social support, social deprivation, perceived violence were all positively and individually associated with the annual cumulative incidence of child and adolescent labor. In the multiple linear regression model, however, only lack of social support and perceived violence in the neighborhood were positively associated to child and adolescent labor. No effect was found for poor school quality, poor environment conditions, poor public services or unstructured family. CONCLUSIONS: Poverty reduction programs can reduce the contextual factors associated with child and adolescent labor. Violence reduction programs and strengthening social support at the community level may contribute to reduce CAL.
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OBJECTIVE To analyze the prevalence of depression in older adults and associated factors. METHODS Cross-sectional study using a stratified random sample of 621 individuals aged ≥ 60 from 27 family health teams in Porto Alegre, RS, Southern Brazil, between 2010 and 2012. Community health agents measured depression using the 15-item Geriatric Depression Scale. Scores of ≥ 6 were considered as depression and between 11 and 15 as severe depression. Poisson regression was used to search for independent associations of sociodemographic and self-perceived health with both depression and its severity. RESULTS The prevalence of depression was 30.6% and was significantly higher in women (35.9% women versus 20.9% men, p < 0.001). The variables independently associated with depression were: female gender (PR = 1.4, 95%CI 1.1;1.8); low education, especially illiteracy (PR = 1.8, 95%CI 1.2;2 6); regular self-rated health (OR = 2.2, 95%CI 1.6;3.0); and poor/very poor self-rated health (PR = 4.0, 95%CI 2.9;5.5). Except for education, the strength of association of these factors increases significantly in severe depression. CONCLUSIONS A high prevalence of depression was observed in the evaluations conducted by community health agents, professionals who are not highly specialized. The findings identified using the 15-item Geriatric Depression Scale in this way are similar to those in the literature, with depression more associated with low education, female gender and worse self-rated health. From a primary health care strategic point of view, the findings become still more relevant, indicating that community health agents could play an important role in identifying depression in older adults.
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Epidemiological studies have shown the effect of diet on the incidence of chronic diseases; however, proper planning, designing, and statistical modeling are necessary to obtain precise and accurate food consumption data. Evaluation methods used for short-term assessment of food consumption of a population, such as tracking of food intake over 24h or food diaries, can be affected by random errors or biases inherent to the method. Statistical modeling is used to handle random errors, whereas proper designing and sampling are essential for controlling biases. The present study aimed to analyze potential biases and random errors and determine how they affect the results. We also aimed to identify ways to prevent them and/or to use statistical approaches in epidemiological studies involving dietary assessments.
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OBJECTIVE To analyze the prevalence of individuals at risk of dependence and its associated factors.METHODS The study was based on data from the Catalan Health Survey, Spain conducted in 2010 and 2011. Logistic regression models from a random sample of 3,842 individuals aged ≥ 15 years were used to classify individuals according to the state of their personal autonomy. Predictive models were proposed to identify indicators that helped distinguish dependent individuals from those at risk of dependence. Variables on health status, social support, and lifestyles were considered.RESULTS We found that 18.6% of the population presented a risk of dependence, especially after age 65. Compared with this group, individuals who reported dependence (11.0%) had difficulties performing activities of daily living and had to receive support to perform them. Habits such as smoking, excessive alcohol consumption, and being sedentary were associated with a higher probability of dependence, particularly for women.CONCLUSIONS Difficulties in carrying out activities of daily living precede the onset of dependence. Preserving personal autonomy and function without receiving support appear to be a preventive factor. Adopting an active and healthy lifestyle helps reduce the risk of dependence.