Perception of eating practices and stages of change among Brazilian adolescents

Objective. To evaluate the perception of eating practices and the stages of change among adolescents. Methods. Cross-sectional study involving a representative sample of 390 adolescents from 11 public schools in the city of Piracicaba, Brazil, in 2004. Food consumption was identified by a food frequency questionnaire and the perception of eating practices evaluation was conducted by comparing food consumption and individual classification of healthy aspects of the diet. The participants were classified within stages of change by means of a specific algorithm. A reclassification within new stages of change was proposed to identify adolescents with similar characteristics regarding food consumption and perception. Results. Low consumption of fruit and vegetables and high consumption of sweets and fats were identified. More than 44% of the adolescents had a mistaken perception of their diet. A significant relationship between the stages of change and food consumption was observed. The reclassification among stages of change, through including the pseudo-maintenance and non-reflective action stages was necessary, considering the high proportion of adolescents who erroneously classified their diets as healthy. Conclusion. Classification of the adolescents into stages of change, together with consumption and perception data, enabled identification of groups at risk, in accordance with their inadequate dietary habits and non-recognition of such habits. (C) 2009 Elsevier Inc. All rights reserved.

End-of-life practices in seven Brazilian pediatric intensive care units

Objective: To evaluate the incidence of life support limitation and medical practices in the last 48 hrs of life of children in seven Brazilian pediatric intensive care units (PICUs). Design. Cross-sectional multicenter retrospective study based on medical chart review. Setting: Seven PICUs belonging to university and tertiary hospitals located in three Brazilian regions: two in Porto Alegre (southern region), two in Sao Paulo (southeastern region), and three in Salvador (northeastern region). Patients. Medical records of all children who died in seven PICUs from January 2003 to December 2004. Deaths in the first 24 hrs of admission to the PICU and brain death were excluded. Interventions: Two pediatric intensive care residents from each PICU were trained to fill out a standard protocol (K = 0.9) to record demographic data and all medical management provided in the last 48 hrs of life (inotropes, sedatives, mechanical ventilation, full resuscitation maneuvers or not). Student`s t-test, analysis of variance, chi-square test, and relative risk were used for comparison of data. Measurements and Main Results. Five hundred and sixty-one deaths were identified; 97 records were excluded (61 because of brain death and 36 due to <24 hrs in the PICU). Thirty-six medical charts could not be found. Cardiopulmonary resuscitation was performed in 242 children (57%) with a significant difference between the southeastern and northeastern regions (p =.0003). Older age (p = .025) and longer PICU stay (p = .001) were associated with do-not-resuscitate orders. In just 52.5% of the patients with life support limitation, the decision was clearly recorded in the medical chart. No ventilatory support was provided in 14 cases. Inotropic drug infusions were maintained or increased in 66% of patients with do-not-resuscitate orders. Conclusions. The incidence of life support limitation has increased among Brazilian PICUs but with significant regional differences. Do-not-resuscitate orders are still the most common practice, with scarce initiatives for withdrawing or withholding life support measures.

Infeasibility handling in genetic algorithm using nested domains for production planning

In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers. (C) 2009 Elsevier Ltd. All rights reserved.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Formalization of an architectural model for exception handling coordination based on CA action concepts

Architectures based on Coordinated Atomic action (CA action) concepts have been used to build concurrent fault-tolerant systems. This conceptual model combines concurrent exception handling with action nesting to provide a general mechanism for both enclosing interactions among system components and coordinating forward error recovery measures. This article presents an architectural model to guide the formal specification of concurrent fault-tolerant systems. This architecture provides built-in Communicating Sequential Processes (CSPs) and predefined channels to coordinate exception handling of the user-defined components. Hence some safety properties concerning action scoping and concurrent exception handling can be proved by using the FDR (Failure Divergence Refinement) verification tool. As a result, a formal and general architecture supporting software fault tolerance is ready to be used and proved as users define components with normal and exceptional behaviors. (C) 2010 Elsevier B.V. All rights reserved.