COVID-19 pandemic impact on follow-up of child growth and development in Brazil.

Objectives: This study investigated the impact of the COVID-19 pandemic on the primary health care (PHC) services to follow-up the child growth and development (CGD) in Brazil.

Methods: A cross-sectional study was conducted using secondary data related visits to assess the growth and development of children up to five years between Apr-2017 to Mar-2021. Differences between monthly rate of visits (per thousand inhabitants up to five) during the pandemic (Apr-2020 to Mar-2021) and before (Apr-2017 to Mar-2020) were analyzed using paired t test and control diagrams (averages ± 1.

A combined method for segmentation and registration for an advanced and progressive evaluation of thermal images.

In this paper, a method that combines image analysis techniques, such as segmentation and registration, is proposed for an advanced and progressive evaluation of thermograms. The method is applied for the prevention of muscle injury in high-performance athletes, in collaboration with a Brazilian professional soccer club. The goal is to produce information on spatio-temporal variations of thermograms favoring the investigation of the athletes' conditions along the competition.

The Q-norm complexity measure and the minimum gradient method: a novel approach to the machine learning structural risk minimization problem.

This paper presents a novel approach for dealing with the structural risk minimization (SRM) applied to a general setting of the machine learning problem. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem in which two conflicting objectives should be minimized. The objectives are related to the empirical training error and the machine complexity.

Identification of SPAM messages using an approach inspired on the immune system.

In this paper, an immune-inspired model, named innate and adaptive artificial immune system (IA-AIS) is proposed and applied to the problem of identification of unsolicited bulk e-mail messages (SPAM). It integrates entities analogous to macrophages, B and T lymphocytes, modeling both the innate and the adaptive immune systems. An implementation of the algorithm was capable of identifying more than 99% of legitimate or SPAM messages in particular parameter configurations.

Nonlinear system identification using a neo fuzzy neuron algorithm: electrical drive application.

This work presents a Neo-Fuzzy-Neuron algorithm for the identification of nonlinear dynamic systems at the point of view of a rotor flux observer. The algorithm training is on-line, has low computational cost, does not require previous training and its convergence in one step is proved. The gradient descent method is used for its weights adjustment.