A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm.

Background: During the aging process, cognitive functions and performance of the muscular and neural system show signs of decline, thus making the elderly more susceptible to disease and death. These alterations, which occur with advanced age, affect functional performance in both the lower and upper members, and consequently human motor functions. Objective measurements are important tools to help understand and characterize the dysfunctions and limitations that occur due to neuromuscular changes related to advancing age.

Systematic error compensation for improving accuracy of the electrosurgical unit.

This paper presents an approach to systematic error correction for improving the accuracy of the electrosurgical unit (ESU), through a method that enables safer operation for both surgeons and patients. The equations for systematic errors encountered during the calibration of the eight ESUs were obtained using regression analysis. The ESU calibration was performed using a power analyzer and the reference power value was selected on the ESU panel; the power value indicated by the ESU was noted from the power analyzer and recorded.

[Use of an artificial neural network to predict the incidence of malaria in the city of Cantá, state of Roraima].

Introduction: Malaria is endemic in the Brazilian Amazon region, with different risks for each region. The City of Cantá, State of Roraima, presented one of the largest annual parasite indices in Brazil for the entire study period, with a value always greater than 50. The present study aimed to use an artificial neural network to predict the incidence of malaria in this city in order to assist health coordinators in planning and managing resources.

Fuzzy cluster stability analysis with missing values using resampling.

Exploratory data analysis is often necessary to evaluate potential hypotheses for subsequent studies such as grouping the data in clusters. In real data sets the occurrence of incompleteness is very common. We propose a method that tolerates missing values for fuzzy clustering using resampling (bootstrapping) and cluster stability analysis.