Monitoring of dead space was useful for detecting lung collapse and for establishing open-lung PEEP after a recruitment maneuver.

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ResumenEste trabajo plantea la optimización de la configuración topológica de redes eléctricas de distribución secundarias tendiente a minimizar las pérdidas técnicas por efecto Joule, utilizando Algoritmos Genéticos. Mediante la aplicación sobre dos sistemas de distribución, se encontró que el método de optimización utilizado es capaz de hallar la solución óptima entre todas las posibles combinaciones que ofrecen las maniobras de los interruptores, comprobándose su flexibilidad para adaptarse a las restricciones de radialidad y nivel de tensión, involucrando un tiempo menor que el necesario para una búsqueda exhaustiva. Durante el desarrollo de la aplicación se validaron los operadores genéticos, determinándose cuáles eran aquéllos que proporcionaron el mejor desempeño en la búsqueda de la solución. Los resultados indican la factibilidad y viabilidad de la aplicación en la configuración óptima de sistemas de distribución eléctrica. Palabras clave: algoritmos genéticos, optimización, redes de distribución eléctrica, flujo de carga Optimization of Electrical Networks using Genetic Algorithms AbstractThis work proposes the optimization of the topological configuration of secondary electrical distribution networks to minimize technical losses produced during their exploitation due to Joule effect, using for this Genetic Algorithms. Through the application of the method to two systems, it was found that the optimization technique is able to find the optimum solution among all the possible combinations that switch operations offer. Its flexibility to adapt to the restrictions of radiality and voltage level in less time than that of exhaustive search is also proved. During the development of the application the genetic operators were validated, determining those that gave the best performance in the search of the solution. The results show the feasibility and viability of the application for the optimum configuration of electrical distribution systems.

Background:The Watershed Transform consists of an image partitioning into its constitutive regions. This transform is easily adapted to be used in different types of images and it allows distinguishing complex objects. However, the implementation of the Watershed Transform for very complex images actually produces over-segmentation. In this paper we propose two algorithms to solve this over-segmentation problem. Methods:We define internal markers, by algorithms based on clustering and fuzzy logic in order to join the oversegmented regions with statistical features. To define the algorithm parameters and evaluate their performance, errors against images segmented manually were measured and ROC curves were determined. Results:The results show that the proposed methods self-adapt to the different image objects characteristics. An improvement of the accuracy is obtained.Conclusions: This analysis will contribute in images segmentation where complexity of the objects is high.

We propose the use of a learning procedure to identify regions of similar dynamics in speckle image sequences that includes more than one descriptor. This procedure is based on the application of a naïve Bayes statistical classifier comprising the use of several descriptors. The class frontiers can be depicted so that the proportion of identified regions may be measured. To demonstrate the results, assembly of an RGB image, where each plane (R, G, and B) is associated with a particular region (class), was labeled according to its biospeckle dynamics. A high brightness in one color means a high probability of the pixel belonging to the corresponding class, and vice versa.