Prediction of metabolic syndrome and its associated risk factors in patients with chronic kidney disease using machine learning techniques.

Introduction: Chronic kidney disease (CKD) and metabolic syndrome (MS) are recognized as public health problems which are related to overweight and cardiometabolic factors. The aim of this study was to develop a model to predict MS in people with CKD.

Methods: This was a prospective cross-sectional study of patients from a reference center in São Luís, MA, Brazil.

Monopole directional antenna bioinspired in elliptical leaf with golden ratio for WLAN and 4G applications.

In this work, it is proposed the development a new monopole directional antenna, bioinspired in elliptical leaf, with cut by golden ratio, for 4G band application, by the use of the technique of the cut of the radiating element for the increasing of the antenna perimeter, being the first work to use this technique in a bioinspired antenna, promotes resonance frequency turned, and reconfiguring of the antenna parameters as bandwidth, radiation pattern and gain, with the use of the reflector near to the group plane, without the insertion of active devices as the pin diode or change in radiating element. The shape antenna is generated by Gielis formula, built in FR4 substrate, with cuts calculated by golden ratio. To compare the results of the bioinspired monopole on the elliptical sheet, a square-shaped monopole antenna was designed, simulated and measured, the structures were designed in the MATLAB software version 2015(b) and the simulations were performed in the Ansys software version 2016.