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Escola sem Partido: ideologia e discurso
(Universidade Federal de São Carlos, 2022-07-22) Toniol, Lucas Cardoso; Riscal, Sandra Aparecida; https://lattes.cnpq.br/0328618752218708; https://lattes.cnpq.br/6741163256395855
In the past few years, the self-styled No Party School Movement (MESP) and their discourses have risen throughout the Brazilian scene. It is understood that the MESP has the potential to cause drastic and at the same time retrograde changes in Brazilian education, especially over the school and in the way it relates itself with the students. Hence, to comprehend the historical construction of the MESP as a discourse, the concepts on which it is based and the types of subjects it seeks to produce and silence, configure an important topic of study in contemporary times. The research developed took the discourses supported by MESP as object of analysis. The analysis undertaken was supported by Michel Foucault's contributions regarding his studies on the production of truths and discourse. In addition, a study was developed on the appropriation and meaningfulness adopted by MESP regarding the concept of ideology, one of the pillars of the Movement, as well as its self-denomination as a social movement. The results provide a broad understanding of MESP as inserted in a neoconservative and neoliberal discursive field, defending an antidemocratic and excluding education project. Consequently, it points to the effects of MESP on teaching subjectivities in terms of control, repression and the construction of the other as a threat.
OxiTidy: motion artifact detection-reduction in photoplethysmographic signals using artificial neural networks
(Universidade Federal de São Carlos, 2022-07-22) Lima, Caíque Santos; Aroca, Rafael Vidal; https://lattes.cnpq.br/9262228584082064; Hernandes, André Carmona; https://lattes.cnpq.br/6806138514642732; https://lattes.cnpq.br/0894764660082882
Nowadays, technological evolution has allowed advances in several areas, especially in healthcare. Digital transformation in health has brought benefits to both professionals and patients. What was possible to do only with high-cost and unwieldy biomedical equipment, has been popularized with the emergence of wearable devices. This technology allows clinical monitoring beyond medical offices, being able to be incorporated into the daily life of patients and working as another tool for prevention and promotion of health and well-being. Among the various features present in wearables is pulse oximetry. Through this non-invasive technique, it is possible to measure physiological parameters, such as oxygen saturation (SpO2) and heart rate (HR). However, the way pulse oximeters are developed and used directly influences the quality of the information provided to the user. Photoplethysmographic (PPG) signals from pulse oximeters are susceptible to noise, which is largely caused by user movement during monitoring. These motion artifacts can cause measurement errors and false alarms. In order to mitigate these issues, this work proposes an algorithm based on artificial neural networks (ANNs) capable of detecting and reducing the undesirable effects produced by noise in PPG signals. The performance of this algorithm, called OxiTidy, was compared with three other approaches — raw, discrete Fourier transform (DFT) and simple moving average (SMA) —, using data from 17 healthy volunteers. OxiTidy identified the intervals where the measurements were incorrect and estimate new SpO2 values with a good approximation to the readings performed by a pulse oximeter certified by the Brazilian Health Regulatory Agency (Anvisa).