RESUMO
The SARS-CoV-2 Coronavirus pandemic (COVID-19) forced educational institutions to move their programmes to the virtual world. Several tech-based solutions -including virtual training and tutoring, discussion forums, access to content and information, collaborative platforms, and Open Educational Resources (OER)- were implemented to address this shift and continue to be used in the post-pandemic era due to the advantages they offer, especially for hybrid and blended learning. However, the implementation of these tech-based solutions also revealed several accessibility issues that need to be addressed to fully leverage the technological benefits. This study aims to provide a framework to facilitate the adoption of good practices related to technological accessibility in virtual Higher Education. The implementation of the framework is divided into four basic actions, each of which should be tailored to the constraints and needs for improving accessibility in Higher Education Institutions (HEIs). The framework's instantiation in four HEIs serves as a proof-of-concept in real-world scenarios. The preliminary results suggest that the proposal is promising, as it was adaptable to the specific needs of each HEI fostering accessibility and inclusion through technological alternatives that align with their organisational structures and current levels of attention to accessibility.
RESUMO
Diabetes is one of the top 5 non-communicable diseases that occur worldwide according to the World Health Organization. Despite not being a fatal disease, a late diagnosis as well as poor control can cause a fatal outcome, because of that, several studies have been carried out with the aim of proposing additional techniques to the gold standard to assist in the diagnosis and control of this disease in a non-invasive way. Considering the above, and in order to provide a solid starting point for future researches, we share a primary research dataset with 1040 saliva samples obtained by Fourier Transform Infrared Spectroscopy considering the Attenuated Total Reflectance method. Database include: gender, age, individuals (patients) with/without diabetes, the glucose value, and the result to the A1C test for the diabetic population. We believe that sharing dataset as is could increase experimentation, research, and analysis of spectra through different strategies broaden its range of applicability by chemists, doctors, physicists, computer scientists, among others, to identify the effects that the virus causes in the body and to propose possible clinical treatments as well as to develop devices that allow us to assist in the characterization of possible carriers.
RESUMO
The World Health Organization has declared that diabetes is one of the four leading causes of death attributable to non-communicable diseases. Currently, many devices allow monitoring blood glucose levels for diabetes control based mainly on blood tests. In this paper, we propose a novel methodology based on the analysis of the Fourier Transform Infrared (FTIR) spectra of saliva using machine learning techniques to characterize controlled and uncontrolled diabetic patients, clustering patients in groups of a low, medium, and high glucose levels, and finally performing the point estimation of a glucose value. After analyzing the obtained results with Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Linear Regression (LR), we found that using ANN, it is possible to carry out the characterizations mentioned above efficiently since it allowed us to identify correctly the 540 spectra that make up our database studying the region 4000-2000 cm-1.