UNIFEI - Campus 1: Itajubáhttps://repositorio.unifei.edu.br/jspui/handle/123456789/552026-08-18T11:33:22Z2026-08-18T11:33:22ZAquisição e validação de sinal de ruído eletroquímicohttps://repositorio.unifei.edu.br/jspui/handle/123456789/31732024-02-05T10:36:02Z2022-02-17T00:00:00ZAquisição e validação de sinal de ruído eletroquímico
Corrosion is a global problem, which implies costs in industrialized countries of up to
4.5% of GDP, with either economic, but also social and environmental impacts. In the
case of Brazil, the waste of water supply networks due to leaks loss is quite significant
and much of it is caused by network degradation, indicating that corrosion control
should be promoted whenever possible. This study proposes a corrosion monitoring
system, in system subject to the use of inhibitor, with the approach of passive
technique for monitoring corrosion by electrochemical noise (EN), in which the
classification of events in a corrosion sensor by EN is part of methodological study for
structural integrity (or “health”) monitoring system (SHM). Due to very dynamic and
stochastic nature of the signal, this study and analysis of EN measurements (ENM)
considers numerical and graphic characteristics of two corrosion systems both in saline
aqueous solution: carbon steel and stainless steel. These experiments are repeated for
accumulating data, which allow the generation of several graphs in time and frequency
domains, from which at least one characteristic is extracted, which has a good
correlation with data from corrosion processes. Then, based on a supervised machine
learning system, the training data allows the model to be calibrated. From the test
data, the correctness rate of the model above 50% is verified.
Dissertação
2022-02-17T00:00:00ZA educação ambiental no processo de construção da profissionalidade docente de licenciandos em ciências biológicashttps://repositorio.unifei.edu.br/jspui/handle/123456789/44662026-08-11T17:22:42Z2026-04-02T00:00:00ZA educação ambiental no processo de construção da profissionalidade docente de licenciandos em ciências biológicas
In view of the urgent need to discuss socio-environmental issues in times of environmental collapse, this study investigates the process of constructing the professional identity of future Biology teachers enrolled in an Environmental Education course. From this context, the following research question emerged: which dimensions of teacher professionality are highlighted and/or developed by Biology undergraduate students when they are placed in situations in which they need to think about and develop Environmental Education projects? To answer this question, the general objective of this study is to identify and analyze the possibilities that a course exclusively focused on Environmental Education themes offers for the construction of teacher professionality. More specifically, the study aims to: understand the process of constructing teacher professionality among undergraduate students in a Biological Sciences teacher education program enrolled in a mandatory Environmental Education course; identify which dimensions of teacher professionality emerge from this educational process; and analyze which dimensions of teacher professionality are expressed by Biology undergraduate students in their discourse about future Environmental Education teaching practices. This research is qualitative in nature and characterized as a field study. The following procedures were used for data collection: participant observation in a mandatory Environmental Education course, questionnaires, and semi-structured interviews with students participating in the course. For data analysis, Content Analysis with a focus on thematic analysis was chosen due to its ability to organize and interpret qualitative data. The results highlight the contributions of Environmental Education to the initial teacher education process, making it possible to observe different relationships between Environmental Education and the construction of different aspects of teacher professionality, analyzed through four categories: moral obligation, which encompasses the teacher’s commitment to students’ education beyond the curricular dimension; commitment to the community, derived from the relationships established with the social community in which teachers carry out their practice and from the political aspects involving teaching work; professional competence, related to teachers’ practices, activities, and knowledge; and, finally, the construction of teacher professional identity.
Dissertação
2026-04-02T00:00:00ZClassificação morfológica de galáxias com Deep Learning nos Surveys SDSS e S-PLUShttps://repositorio.unifei.edu.br/jspui/handle/123456789/44652026-07-30T18:04:22Z2025-11-14T00:00:00ZClassificação morfológica de galáxias com Deep Learning nos Surveys SDSS e S-PLUS
Morphological classification of galaxies in large photometric surveys is central to galaxy evolution
studies, but it remains challenging when deep learning models are transferred across
different data domains. This dissertation addresses the problem by developing and evaluating
deep learning models for binary galaxy classification (regular vs. peculiar), with emphasis on
quantifying the domain shift between the Sloan Digital Sky Survey (SDSS) and the Southern
Photometric Local Universe Survey (S-PLUS).
Using transfer learning, a benchmark of 11 pre-trained architectures was conducted, including
EfficientNet (B0-B4), ResNet/ResNeXt, and Vision Transformer (ViT-B/16 and ViT-B/32), plus
a simple CNN trained from scratch. The models were trained and validated on a curated dataset
of 11,662 galaxies with images from SDSS and S-PLUS, labeled from the RC3 catalog.
In the SDSS domain, the best model was ViT-B/32, with an F1-score of 0.923 and an AUC
of 0.963, followed by EfficientNet-B4 (F1-score 0.910) and ViT-B/16 (F1-score 0.899). The
superiority of attention-based models suggests that long-range spatial modeling is advantageous
for this task. In contrast, the simple CNN showed the worst performance (F1-score 0.655),
reinforcing the importance of transfer learning.
Evaluation of the best model on S-PLUS quantifies the performance degradation, with a drop
of 14.8 percentage points in F1-score (from 0.923 to 0.775), confirming a significant domain
shift effect. The Peculiar class is the most affected, with a 29.8% drop in recall, while average
predictive uncertainty increases by 65.4% and calibration worsens by more than 50%.
This work contributes a comparative architecture benchmark, a curated dataset, and a quantitative
analysis of cross-survey generalization, showing that domain adaptation is an indispensable
methodological step for the practical use of these models in different observational contexts.
Dissertação
2025-11-14T00:00:00ZTeletrabalho no contexto brasileiro: fatores socioeconômicos, ambientais e de transporte a partir de uma abordagem de aprendizado de máquinahttps://repositorio.unifei.edu.br/jspui/handle/123456789/44642026-07-28T17:37:22Z2026-05-20T00:00:00ZTeletrabalho no contexto brasileiro: fatores socioeconômicos, ambientais e de transporte a partir de uma abordagem de aprendizado de máquina
Telework emerges as a potential tool for reducing commuting, lowering external costs, and
promoting quality of life. In this context, the main objective of this research is to investigate
the adoption of telework in the Brazilian context from a machine learning perspective.
Initially, a comprehensive conceptual model was developed to examine the relationships
between telework and socioeconomic, transportation, and environmental variables, based
on a systematic literature review of studies published up to 2025. The temporal analysis
of the literature highlights the growing academic interest in the topic, emphasizing
the profile of teleworkers, characterized by factors such as age, educational level, income,
and occupation type. Additionally, interactions between telework and urban aspects are
explored, including the adoption of sustainable transportation modes, such as walking
and cycling, as well as its relationship with urban sprawl. The impacts on quality of
life are also discussed, particularly regarding schedule flexibility and work–life balance.
The thesis analyzes the evolution of telework in Brazil from 2022 to 2025 through the
application of machine learning models to representative microdata from the Continuous
National Household Sample Survey, covering approximately 210,000 households per period.
A standardized processing workflow was implemented, including data preprocessing,
handling of missing values, class balancing through random undersampling, variable encoding
and normalization, as well as stratified data splitting with k-fold cross-validation.
Nine classification algorithms were evaluated, including Multinomial Logistic Regression,
Decision Trees, Random Forest, XGBoost, Support Vector Machines, MARS, and Neural
Networks, with hyperparameter tuning performed using the ANOVA racing method.
Model performance showed consistently high results (ROC AUC > 0.80) across all analyzed
periods. The analysis of variable importance indicates that the main determinants
of telework remain relatively stable over time, although their contributions vary, with a
notable increase in the influence of gender in more recent periods. The results demonstrate
that telework in Brazil is driven by a combination of sociodemographic and occupational
factors, reinforcing its selective nature. Overall, this thesis contributes to a deeper understanding
of the multiple dimensions of telework by integrating theoretical and empirical
evidence. Furthermore, the findings provide relevant insights for policymakers, organizational
managers, and urban planners, highlighting how telework shapes urban dynamics
and can support efforts to address contemporary urban challenges.
Tese
2026-05-20T00:00:00Z