This project aims at the requalification of the floor of the school’s playground. Students organized class assemblies and students’ representative assemblies, and during these meetings they identified as a problem the floor full of irregular pebbles in the playground, very popular equipment in school. The pebbles have caused some accidents and, when it rains, they take a long time to dry, preventing the use of the park. During the assemblies, students tried to findways to solve the problem: they collected information, talked to parents and contacted different entities. The project is still ongoing but has already allowed students to learn much from it.
In this article, an unsupervised learning method is presented with the objective of modeling, in real-time, the main operating modes (OM) of distribution transformers. This model is then used to assess the operational condition through use of two tools: the operation map and the health index. This approach allows, mainly, for a reduction in the need for the interpretation of results by specialists. The method used the concepts of k-nearest neighbors (k-NN) and Gaussian mixture model (GMM) clustering to identify and update the main OMs and characterize these through operating mode clusters (OMC). The evaluation of the method was performed using data from a case study of almost one year in duration, along with five in-service distribution transformers. The model was able to synthesize 11 magnitudes measured directly in the transformer into two latent variables using the principal component analysis technique, while preserving on average more than 86% of the information present. The operation map was able to categorize the transformer operation into previously parameterized levels (appropriate, precarious, critical) with errors below 0.26 of standard deviation. In addition, the health index opened the possibility of identifying and quantifying the main abnormal variations in the operating pattern of the transformers.
The Quota Law in Brazil has been of great importance for entry of Brazilians (who meet the requirements) to the Public University, proving to be necessary to guarantee them the right to education. The objective of the study was to analyze the perception of quota students on the factors that influence the academic performance of quota students in the Undergraduate Course in Biological Sciences -Campus Umuarama -at UFU after the implementation of the Quota Law. The study methodology was documentary research (secondary data) and field research carried out with Quota-holders and Widely Competitiveness students, corresponding to a questionnaire applied to 39 quota-and non-quota students (low and high-performance) of the Biological Sciences course, prioritizing evaluation the factors that influence academic performance. The analysis of secondary data was carried out using a quantitative method in order to assess the academic performance of students from different types of admission. For the field research, qualitative analysis was used with semi-structured interviews whose data were categorized and tabulated for interpretation. The results showed that among the types of entrances, the ones with the highest average (72) of performance was modality 4 (shareholders from Public School/independent of income. The study revealed that among the quotaholders, the motivation to study and dedication were determinant factors for their good performance. The research carried out indicates that there were no significant differences in income between quota holders and non-quota students from the point of view of socioeconomic factors, contrary to studies that place these as indicative of a drop in the university's academic standard. As a technological product, an executive summary was constructed to be presented to the course, containing the findings and some possible actions.
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