https://repositorio.uema.br/jspui/handle/123456789/1907 2026-09-21T20:58:16Z https://repositorio.uema.br/jspui/handle/123456789/6765 Título: Polimorfismo em Óxidos: Um Estudo de Primeiros Princípios das Fases Trigonal e Monoclínica do Al2O3 Abstact: Among ceramic oxides, aluminum oxide (Al2O3) stands out as one of the most important materials for advanced technological applications, especially in the aerospace sector, due to its high thermal stability, chemical resistance, dielectric properties, and favorable optical behavior. This material exhibits a very rich polymorphism, with different crystalline phases coexisting or transforming depending on thermodynamic and kinetic conditions, which leads to distinct physical properties. The trigonal phase (α-Al2O3, corundum) is well characterized and widely used in severe applications; however, less-studied phases, such as the monoclinic one, still lack systematic investigations that relate crystal structure to fundamental properties. In this work, the trigonal and monoclinic phases of Al2O3 are investigated using first-principles methods based on Density Functional Theory (DFT), employing different exchange–correlation functional approximations. Structural, electronic, optical, and thermodynamic properties are analyzed, including lattice parameters, electronic density of states, dielectric functions, optical absorption spectra, enthalpy, entropy, and free energy, in order to understand how polymorphism influences the physical behavior of the material. The results show that the trigonal phase is thermodynamically the most stable over a wide temperature range, exhibits high structural rigidity, and presents optical behavior consistent with its established use in thermal protection systems and structural components. In contrast, the monoclinic phase displays distinct optical and dielectric properties due to its lower crystal symmetry and greater vibrational complexity, which suggests emerging potential for applications in aerospace environments, such as selective coatings, dielectric components, optical filters, and devices operating under intense radiation and extreme temperature conditions. In addition, a scientometric analysis of publications on Al2O3 is carried out based on the Scopus and Web of Science databases to map the evolution of studies, research trends, leading research centers, and expanding application areas. The growth of scientific interest in the functional properties of alumina, evidenced by this analysis, reinforces the current relevance of studies devoted to polymorphism and to less-explored phases. Thus, this work advances the understanding of the interrelationship between crystal structure and the physical properties of Al2O3, providing a robust theoretical foundation that can be exploited in the search for new aerospace applications, particularly with regard to the monoclinic phase, while also indicating promising directions for future investigations involving mechanical and vibrational properties and experimental validation. 2026-06-24T00:00:00Z https://repositorio.uema.br/jspui/handle/123456789/6753 Título: Comparação de algoritmos de aprendizado de máquinas para desenvolvimento de um sistema para triagem de adolescentes obesos utilizando variáveis clínicas Abstact: In recent decades, many countries have entered into a development process in general, due to the globalized world that requires high dynamics, there has been a specific increase in the rate of poor human nutrition, causing a rapid nutritional and epidemiological transition, resulting in in considerable individuals with excess body fat even in adolescence. The high prevalence of excess weight during adolescence has become a major problem in human health in general. Adolescence is characterized as a phase in which the human body develops the most and is directly associated with a range of diseases that can put the health of an overweight individual at risk, so the present study aims to estimate the percentage of body fat in adolescents. In order to achieve this objective, machine learning algorithms were selected that were compared, to Even if you could select the one that performs best and can deliver an overwhelming result in general, the database used to apply the algorithms is obtained from data collected from public school students in the city of São Luís do Maranhão. In 2018, the database contains 772 entries of both genders aged 10 to 19 years. With this data it was possible to evaluate indicators such as: age, gender, body mass, height, waist characteristics, hip characteristics, body characteristics calf and arm functionality, as well as the percentage of body fat acquired through bioimpedance. After applying the algorithms, K-SVM had the best performance, showing potential to be used in a future application 2023-11-10T00:00:00Z https://repositorio.uema.br/jspui/handle/123456789/6750 Título: Um olhar não supervisionado sobre o desempenho dos participantes maranhenses no Enem 2023 Abstact: Data Science is an interdisciplinary field that can contribute to addressing humanity's challenges in the 21st century, including the search for inclusive, equitable and quality education, particularly in the Brazilian context, where illiteracy still affects millions of people, more severely impacting black individuals in the Northeast Region. The National High School Exam (Enem) has played a significant role in evaluating brazilian basic education and has become the primary gateway to higher education. However, the results of brazilian students, especially in the state of Maranhão, serve as a warning and highlight the need for improvements in basic education. This study is justified by Maranhão's performance in the Ideb and the Enem, placing it among the lowest average results in the country in the 2023 edition. The research problem lies in understanding the variables that play a predominant role in the performance of Maranhão's students in the most important assessment of basic education, from the perspective of educational data mining. Therefore, the general objective is to investigate the factors influencing the performance of Maranhão's students in the 2023 Enem, using exploratory statistical analysis tools and extracting knowledge through unsupervised machine learning, specifically association rule mining with the FP-Growth algorithm. The research approach is quantitative in nature and the methodology employed the CRISP-DM process for knowledge discovery in databases. Computational experiments included the selection of ten attributes through the application of RFE combined with the Random Forest algorithm, as well as the subsequent application of the FP-Growth algorithm in ten trials/configurations on the subset of microdata from Maranhão's participants in the 2023 Enem. The study identified another relevant variable for participants' performance, which enhances the understanding and socioeconomic characterization of Maranhão's population, namely the nature of the occupation of the father or male guardian. Additionally, it revealed underlying patterns in the data not observed in the exploratory analysis, such as the discovery of groups cumulatively affected by different attributes. For example, low performance conditioned by the father's education level and occupation, or the socioeconomic profile characterization of Maranhão's participants based on the selected variables, with a high confidence index. Despite inherent limitations in the quality of the database, the study proved to be socially relevant and has the potential for replication in other regions 2025-02-26T00:00:00Z https://repositorio.uema.br/jspui/handle/123456789/6729 Título: Estudo comparativo de frameworks de gamificação em disciplinas de técnicas de programação e algoritmos no ensino médio técnico profissionalizante Abstact: This dissertation investigates how the comparative application of three gamification frameworks Octalysis, MDA, and the Gamified Design Framework (GDF) impacts student engagement and performance in Techniques of Programming and Algorithms courses within technical and vocational upper-secondary education. To this end, the Engajando Saberes platform was developed with a modular architecture that allows configuring and switching gamified elements according to each model (points, missions, leaderboards, feedback, narrative), enabling the monitoring of each approach’s effect on the student experience. The study, descriptive–comparative with a mixed-methods design, was conducted with 28 students in Object-Oriented Programming; data were collected through a diagnostic questionnaire, endof- cycle forms (Octalysis, MDA, and GDF), classroom observations, an interview with the instructor, and the SUS usability scale, in addition to an analysis of grades before and after the intervention. Results indicate excellent usability of the platform (SUS mean = 87.50), with high scores for ease of use, consistency, and integration; a priority improvement identified was the implementation of notifications for deadlines and content. Regarding academic performance, the class average on the second assessment (AV2) increased from 5.96 (1st semester) to 8.43 (2nd semester) after the gamified intervention. In the comparison among frameworks, Octalysis showed strong acceptance, with high ratings for narrative, missions, and avatar-driven immersion, as well as interest in adoption in other subjects; MDA combined level progression, pair work, quizzes, and a forum, raising engagement, although with reports of unbalanced participation within pairs and a need to adjust quiz pacing; and GDF sustained learning rhythm through daily quizzes and individualized feedback, fostering reflection while showing signs of overload when cadence is high. It is concluded that integrating motivational elements (narrative, progression, recognition) and collaborative elements (pair work and forum), orchestrated by a modular platform, is a promising way to enhance engagement and performance in technical secondary education. It is recommended to incorporate a notification module and calibrate activity cadence to maximize positive effects and mitigate fatigue 2025-09-05T00:00:00Z