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Práticas corporais de aventura na natureza e educação ambiental: uma unidade didática para o ensino médio
(Universidade Federal de São Carlos, 2025-07-01) Passos, Yunã Lurie Araújo; Lemos, Fábio Ricardo Mizuno; https://lattes.cnpq.br/9720009502941255; https://orcid.org/0000-0001-6512-5056; https://lattes.cnpq.br/6082433018691674; https://orcid.org/0000-0002-7139-0128
Recognizing the potential of Outdoor Adventure Physical Practices (PCAN) to foster environmental awareness and contribute to the development of a critical and sustainable mindset among students, this dissertation aims to describe and analyze the planning and implementation of a didactic unit focused on PCAN. The unit was carried out with a first-year high school class at a federal public school in Caracaraí, Roraima, Brazil. Adopting a qualitative, applied research approach, the study was structured around a 14-lesson pedagogical intervention, with each session lasting 60 minutes and conducted in both school and natural environments. Data were collected through field notes recorded by the teacher-researcher and analyzed using thematic categories, allowing for reflective interpretation of the experiences. The implementation of the unit revealed stronger student bonds with the environment, growing interest in outdoor physical activities, and the emergence of cooperative and ethical behaviors in peer and territorial interactions. The activities also promoted the integration of interdisciplinary and community knowledge by connecting Physical Education with subjects such as Biology, Philosophy, and Animal Science, resulting in contextualized and meaningful learning. The experience redefined how students perceived Physical Education classes, transforming them into spaces of care, belonging, agency, and environmental reflection. It also led to improvements in the teacher-researcher’s pedagogical practices, particularly in the use of active methodologies, attentive student engagement, and the integration of body, nature, and education. The study concludes that integrating PCAN with Environmental Education in schools is an effective strategy to foster meaningful learning, engage youth, and support the development of critical and responsible attitudes toward today’s socio-environmental challenges.
Introdução à gravitação quântica de laços
(Universidade Federal de São Carlos, 2025-07-01) Sousa, Luigi Teixeira de; Schmidtt, Fernando David Marmolejo; https://lattes.cnpq.br/7431492489687677; https://orcid.org/0000-0001-9797-8483; https://lattes.cnpq.br/1069388995865317
In the present work, the author will introduce Loop Quantum Gravity (LQG) through the quantization of General Relativity (GR) discretized on a 2-complex via the canonical (introducing operators satisfying the classical Poisson algebra) and covariant (via transition amplitudes given by path integrals) approaches resulting on the Spin Netowork and Spin Foam formalisms respectively, showing the discreteness of the spectra of area and volume and using those results to derive the Bekenstein-Hawking entropy and analyse Big Bang cosmology.
Classificação de caracteres japoneses cursivos utilizando modelos de aprendizado profundo
(Universidade Federal de São Carlos, 2025-07-01) Borges, Fernando; Ramos, Thiago Rodrigo; https://lattes.cnpq.br/1634196230063965; https://lattes.cnpq.br/3130182835247681
With recent advances in machine learning, image classification has become essential in various fields of science. In linguistics, tasks such as deciphering, transcribing, and preserving ancient documents are benefited from the application of deep learning models. The study of cursive Japanese characters, for example, involves a type of writing that differs significantly from its modern version, to the extent that only a small portion of Japanese speakers can understand it fluently. Furthermore, due to the way the Japanese syllabary has evolved throughout history, the application of deep learning methods is appropriate for the task of classifying these ancient Japanese writings, given that there are dozens of variants for each character. Thus, this work aimed to develop a classification model capable of predicting cursive Japanese characters in ancient scripts. To achieve this, four traditional classification methods - logistic regression, k-nearest neighbors, support vector machines and boosting - were adjusted, as well as three architectures of a deep learning method - convolutional neural networks - using datasets related to handwritten cursive characters. As a final result, the model that achieved the best performance was a convolutional neural network, with an accuracy of 92.9% in classifying the 49 classes that make up the hiragana syllabary. In addition, a U-Net model was developed to extract characters from new pages of handwritten texts. However, when applying the trained classifier to real data, the accuracy dropped to 69.1%, highlighting the challenges associated with the variability of writing styles and the presence of noise in scanned images.