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Aplicação do aprendizado de máquinas para classificação de cooperativas de crédito em risco de encerramento
(Universidade Federal de São Carlos, 2024-11-08) Oliveira, Ana Carolina Alcantara de; Carvalho, Flávio Leonel de; https://lattes.cnpq.br/9615144436796386; https://orcid.org/0000-0002-8488-9382; https://lattes.cnpq.br/9763606671341913
Introduction Credit cooperatives, entities formed by individuals with common economic interests, aim to achieve both the economic and social goals of their members (McKillop & Wilson, 2011). Given this, it is essential to monitor the risks associated with these entities, especially those related to credit granting, due to potential conflicts of interest. Determining the most relevant indicators for categorizing cooperatives with a potential for discontinuity can lead to more effective monitoring and support decision-making in the management process. Research Problem and Objective Due to the high risks involved in potential conflicts of interest, it is essential to monitor and assess capital adequacy, asset quality, management quality, profitability, and liquidity, which can be carried out using the CAMEL methodology. However, the large number of indicators can make it difficult to analyze the actual financial situation of these organizations. Thus, this study aimed to identify the most relevant accounting indicators for classifying Brazilian credit cooperatives at risk of future closure. Theoretical Framework Several studies (Silva, Santos, Ranciaro, 2023; Vieira, Bressan V., Bressan A., 2018) highlight the importance of performance analysis in credit cooperatives. Although their objective is not profit generation, cooperatives need to manage their resources efficiently to provide members with higher returns on their investments and better rates for financial operations. Understanding these results is essential to support their social and economic functions and ensure their survival in the financial system. Methodology To achieve this, the study employed the machine learning methodologies Random Forest and K-Nearest Neighbors (KNN). The Random Forest model enabled the identification of the most relevant indicators, while the KNN model was used to classify cooperatives based on their potential risk of closure. Using a sample of 8,552 observations from single credit cooperatives between 2018 and 2022, the dataset was divided into a training set (80% of the sample) and a test set (remaining 20%). Results Analysis The results from non-parametric mean difference tests showed that the analyzed CAMEL indicators exhibited statistically significant differences in the years preceding closure. However, the model achieved 92.9% accuracy in predicting the future operational status of the cooperatives, with a 93% success rate for active cooperatives but only 46% for those classified as closed. Thus, while the model was ineffective in identifying closed cooperatives, it was efficient in classifying the most impactful indicators. Conclusion Due to the low accuracy in identifying closed cooperatives, the study concludes that using the six most impactful indicators provides a similar level of information to that obtained with the full set of CAMEL indicators. Additionally, given the high error rate in classifying credit cooperatives at risk of closure, the findings suggest the need to explore alternative models or techniques for predicting credit cooperative closures. Contribution / Impact This study aimed to identify the most relevant CAMEL indicators for classifying cooperatives at higher risk of closure and to predict closure probabilities using machine learning. The main contribution lies in identifying key financial indicators for monitoring the performance of credit cooperatives. It is expected that this study will contribute to improving the oversight of these institutions and strengthening the cooperative system, enabling proactive interventions to reduce their vulnerabilities.
Avaliação em língua inglesa com crianças na Educação Infantil: a participação no processo avaliativo
(Universidade Federal de São Carlos, 2024-11-08) Lucena, Priscilla Pina de; Barbirato, Rita de Cassia; https://lattes.cnpq.br/6962819573963727; https://orcid.org/0000-0001-5896-9787; https://lattes.cnpq.br/1360118078214772; https://orcid.org/0009-0009-3190-3791
This qualitative interpretive collaborative ethnographic-based research investigated the participation of 5-year-old children during the writing of the English language assessment report in a private school in the city of São Paulo. We investigated 1) how children can participate in their English learning assessment process when the instrument is the class assessment report; 2) what challenges arise for the educator and for the children regarding this participation. Firstly, we analyzed official Brazilian educational documents to understand what is expected at this school stage. Next, we researched the conception of childhood and children to understand how their participation and rights were achieved. It was also important to conceptualize our understanding of participation. After this initial analysis of issues related to childhood, we researched theories about assessment in Early Childhood Education to understand the assessment scenario in Brazilian schools. Furthermore, we presented questions related to language teaching for children and English language assessment with children. The method used to study these issues with children was participant observation, and the data collection instruments included class observation, audio recording, field diary, interview, questionnaire, teacher planning, and school pedagogical project. Data triangulation led to results that showed us that children can participate in the assessment process by singing, gesturing, remembering situations, and adding information.
Efeitos do clima e da paisagem nos padrões de distribuição e de riqueza de felinos da Mata Atlântica
(Universidade Federal de São Carlos, 2024-11-08) Ribeiro-Souza, Paula Danyelle; Graipel, Maurício Eduardo; https://lattes.cnpq.br/3039321868126712; Pires, José Salatiel Rodrigues; https://lattes.cnpq.br/3412204514901170; https://orcid.org/0000-0001-5059-1919; https://lattes.cnpq.br/5352429844377751; https://orcid.org/0000-0002-2629-3417
Two of the most significant forces affecting biodiversity worldwide are Global Climate Change (GCC) and Land Use and Land Cover Change (LULC). To better understand the impacts of GCC and LULC on biodiversity, it is crucial to analyze how these factors interact with terrestrial ecosystems, including the Atlantic Forest biome. In this context, the main objectives of this thesis were: (1) to evaluate how climate and landscape currently influence the distribution and richness patterns of the species Leopardus emiliae, L. guttulus, L. wiedii, Herpailurus yagouaroundi, L. pardalis, Puma concolor, and Panthera onca in the Atlantic Forest; (2) to investigate the extent to which this distribution is protected by Strict Protection Conservation Units (SPCUs); and (3) to understand how the taxonomic richness patterns of wild felines will respond to GCC and LULC in the Atlantic Forest by 2050. To achieve the first and second objectives, species distribution modeling were used to estimate suitable areas for both the current scenario and the year 2050, for the distribution of each species and for the taxonomic richness of felines. The models were created using climatic and landscape variables, employing different algorithms. All species occurrence data were obtained from data papers, other scientific articles, scientific collections, and databases. The results indicated that only 30% of the Atlantic Forest is currently suitable for feline richness, with areas of low species richness located in northeastern Brazil and Argentina. Only 9% of these suitable areas are covered by FPAs, highlighting a significant gap in feline conservation. Species such as L. emiliae (1.37%) and P. onca (1.97%) are the least protected. Projections for 2050 indicated that the loss of suitable habitat will be more pronounced under the pessimistic scenario (~87%) due to the combination of GCC and LULC. Additionally, about 61% of the analyzed areas in the Atlantic Forest have low permeability for feline richness. The results of this thesis underscore the urgent need for conservation actions in the Atlantic Forest, including the implementation, maintenance, and expansion of FPAs, landscape restoration, and increased connectivity between suitable areas. These measures could mitigate the negative impacts of GCC and LULC on feline richness in the Atlantic Forest.
O ensino de ortopedia e traumatologia na graduação em medicina: como a faculdade nos prepara para a vida profissional?
(Universidade Federal de São Carlos, 2024-11-08) Penido, Vitor Brasil Pereira; Neves, Fábio Fernandes; https://lattes.cnpq.br/2652568399519714; https://lattes.cnpq.br/7470108285867678
This Course Conclusion Work evaluates the teaching of Orthopedics and Traumatology during the undergraduate course in Medicine at UFSCar, based on the author's educational background, and proposes a critical-reflective analysis of the gaps in the average training of general medical in comparison with best practices recommended for the development of theoretical-practical skills and application in real scenarios, occupied by newly graduated doctors. In this way, the text reflects on orthopedic education and the enhancement of newly trained doctors' ability to manage the most prevalent orthopedic complaints, especially within primary care and emergency care settings. The methodology includes research in databases such as SciELO and PubMed and explores theoretical teaching and practical experiences throughout the course.