Diagnóstico de enfermagem: estratégia educativa fundamentada na aprendizagem baseada em problemas

Este estudo teve por objetivo avaliar a eficácia de uma estratégia de ensino sobre diagnósticos de enfermagem, fundamentada na aprendizagem, baseada em problemas no desempenho do raciocínio clínico e julgamento diagnóstico dos discentes de graduação. É estudo experimental, realizado em duas fases: validação de conteúdo dos problemas e aplicação da estratégia educativa. Os resultados mostraram melhora na capacidade de agrupamento dos dados dos discentes do grupo experimental. Conclui-se que houve influência positiva da estratégia implementada

Relação entre ambiente familiar e capacidade funcional de idosos residentes no município de Jequié-BA

(Objective) Assess the functional capacity and determine the difference between the means of functional capacity (basic and instrumental activities of daily living) and the age groups of elderly residents in an outlying area in the hinterland of Bahia/Northeast of Brazil. (Methods) Analytical study with cross-sectional design and a sample of 150 elderly individuals enrolled in four Health Units in the municipality of Jequié, Bahia, Brazil. The instrument consisted of sociodemographic and health data, the Barthel Index and the Lawton scale. (Results) In all, 78.00% of the elderly were classified as dependent in the basic activities and 65.33% in the instrumental activities of daily living. Using the Kruskal- Wallis test, we found a statistically significant difference between the means of instrumental activities and the age groups (p= 0.011). (Conclusion) An elevated number of elderly were classified as dependent in terms of functional capacity and increased age is related to greater impairment in the execution of instrumental activities of daily living

Uso de renderização volumétrica e realidade virtual para problemas de percolação na engenharia

In the recovering process of oil, rock heterogeneity has a huge impact on how fluids move in the field, defining how much oil can be recovered. In order to study this variability, percolation theory, which describes phenomena involving geometry and connectivity are the bases, is a very useful model. Result of percolation is tridimensional data and have no physical meaning until visualized in form of images or animations. Although a lot of powerful and sophisticated visualization tools have been developed, they focus on generation of planar 2D images. In order to interpret data as they would be in the real world, virtual reality techniques using stereo images could be used. In this work we propose an interactive and helpful tool, named ZSweepVR, based on virtual reality techniques that allows a better comprehension of volumetric data generated by simulation of dynamic percolation. The developed system has the ability to render images using two different techniques: surface rendering and volume rendering. Surface rendering is accomplished by OpenGL directives and volume rendering is accomplished by the Zsweep direct volume rendering engine. In the case of volumetric rendering, we implemented an algorithm to generate stereo images. We also propose enhancements in the original percolation algorithm in order to get a better performance. We applied our developed tools to a mature field database, obtaining satisfactory results. The use of stereoscopic and volumetric images brought valuable contributions for the interpretation and clustering formation analysis in percolation, what certainly could lead to better decisions about the exploration and recovery process in oil fields

Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

Diagnóstico de enfermagem: estratégia educativa fundamentada na aprendizagem baseada em problemas

Este estudo teve por objetivo avaliar a eficácia de uma estratégia de ensino sobre diagnósticos de enfermagem, fundamentada na aprendizagem, baseada em problemas no desempenho do raciocínio clínico e julgamento diagnóstico dos discentes de graduação. É estudo experimental, realizado em duas fases: validação de conteúdo dos problemas e aplicação da estratégia educativa. Os resultados mostraram melhora na capacidade de agrupamento dos dados dos discentes do grupo experimental. Conclui-se que houve influência positiva da estratégia implementada