https://ri.ufs.br/jspui/handle/riufs/2557 2026-04-08T06:59:04Z 2026-04-08T06:59:04Z Santos, Diego Silva Barbosa dos https://ri.ufs.br/jspui/handle/riufs/24798 2026-03-23T12:07:40Z 2026-03-03T00:00:00Z

Título: Perfil da violência doméstica e familiar contra a mulher em Estância/SE : uma abordagem baseada em Business Intelligence Autor(es): Santos, Diego Silva Barbosa dos Abstract: This study analyzes the profile of domestic and family violence against women in the municipality of Estância, Sergipe, Brazil, aiming to demonstrate the effectiveness of applying Business Intelligence (BI) tools in public security data management. The research is characterized as quantitative and descriptive, based on documentary data collected from the Center for Analysis and Research in Security and Citizenship (NAPSEC) of the Sergipe State Department of Public Security (SSP/SE). The temporal scope covers the historical series from 2019 to 2024 and includes victims who agreed to receive follow-up assistance from the Maria da Penha Patrol. Data processing and visualization were carried out using Microsoft Power BI, enabling the development of interactive dashboards. The results revealed a spatial concentration of incidents in the Cidade Nova and Centro neighborhoods, as well as a predominant profile of victims who self-identify as mixed-race, experience economic vulnerability, and have children with the aggressor. Regarding perpetrators, most were identified as former partners, many with a history of recidivism. A significant increase in the number of cases was also observed in 2024. The study concludes that data structuring through BI provides an accurate diagnostic framework to support the Maria da Penha Patrol and public managers in strategic decision-making and in breaking the cycle of violence.

2026-03-03T00:00:00Z Cunha, Jacinto Michael Menezes https://ri.ufs.br/jspui/handle/riufs/24796 2026-03-23T12:03:40Z 2026-03-04T00:00:00Z

Título: Análise e previsão da taxa de ocupação hospitalar em um hospital privado de Sergipe Autor(es): Cunha, Jacinto Michael Menezes Abstract: This study analyzes and forecasts the hospital occupancy rate in a private hospital in Sergipe, Brazil, using patient-day and bed-day data from 2022 to 2025. The monthly series are segmented by accommodation type (Private Room, Ward, and ICU). The research adopts a quantitative approach, is applied in nature, and follows an observational, retrospective, and documentary design. Data were extracted from the TASY electronic health record (EHR) system, followed by ETL (Extract, Transform, Load) stages and exploratory analysis to characterize the patient occupancy profile by accommodation type. Subsequently, monthly time series of the occupancy rate were constructed for each category. The study performed stationarity tests, decomposition, and autocorrelation analysis, fitting classical time series models (ARIMA and additive ETS/Holt-Winters) alongside an XGBoost machine learning model. The models were evaluated using MAE, RMSE, and MAPE metrics on training and test sets, and compared in consolidated tables by accommodation type for the 2022–2025 period. The results indicate that XGBoost generally yields a lower MAPE for Private Rooms and the ICU, while the ETS(A,N,A) model proved more suitable for the Ward series regarding projection behavior. Forecasts for 2026 suggest that occupancy levels will remain similar to those observed at the end of 2025, with moderate seasonal variation. The study concludes that the combination of classical time series and XGBoost is useful for supporting tactical bed and resource planning, allowing for the anticipation of periods of high clinical demand, reducing the risks of overcrowding or idle capacity, and providing a basis for management decisions based on the local reality of the analyzed hospital.

2026-03-04T00:00:00Z Santos, Evany Dandara Souza dos https://ri.ufs.br/jspui/handle/riufs/23878 2025-11-19T11:32:36Z 2025-11-03T00:00:00Z

Título: Identificação de subdeclarações de renda no cadastro único para programas sociais utilizando regressão logística Autor(es): Santos, Evany Dandara Souza dos Abstract: Income transfer programs are very important to guarantee the basic human rights of housing and food. They are government initiatives through the Ministry of Development and Social Assistance, Family and Fight against Hunger, with the Bolsa Família Program being the most important and covering the largest number of families. One of the requirements that a family must meet to become a beneficiary of the program is not to have a per capita family income higher than the level established by the Federal Government. To verify the eligibility of families regarding this income criterion, the Federal Government uses the per capita family income, calculated based on the individual income of each family member, reported in the Single Registry. One problem with this approach is that, in order to benefit from the program, a person may declare having a lower individual income than they actually have or even omit their individual income, reducing their per capita family income. One of the mechanisms adopted by the Federal Government to try to identify this type of fraud is to cross-reference CadÚnico data with other databases, such as RAIS and CAGED. In addition to this mechanism, the use of mathematical/statistical models to identify families with under-declared income in CadÚnico has been proposed. This paper proposes a procedure, based on logistic regression, to identify families suspected of having fraudulently declared income below the level established by the Federal Government. Families are considered suspicious when they declare having an income below the level established by the Federal Government, but according to the adjusted logistic regression model, they have a “small” probability of meeting this requirement. The procedure was applied to CadÚnico data for the year 2018 and identified 33 suspicious families. Of these, 14 were beneficiaries of the program.

2025-11-03T00:00:00Z Lopes, Gláucia Araújo Santos https://ri.ufs.br/jspui/handle/riufs/23877 2025-11-19T11:24:44Z 2025-10-29T00:00:00Z

Título: Um estudo sobre os resultados acadêmicos dos alunos da UFS beneficiados pelo Programa Nacional de Assistência Estudantil (PNAES) nos anos de 2010, 2015 e 2017 Autor(es): Lopes, Gláucia Araújo Santos Abstract: This work aims to analyze evidence that the academic performance of students benefited by the National Student Assistance Program (PNAES) at the Federal University of Sergipe is superior to students who do not participate in these programs. During the research, it was found that students who receive assistance from government programs had superior performances in the Average Conclusion (MC), General Weighted Average (MGP), Efficiency Index in Hours Load (IECH) and in the Efficiency Index in Teaching Periods (IEPL) when compared to students who are not included in the programs. Descriptive analysis was the statistical method used in the elaboration of the panorama of the Cohorts of freshmen in 2010, 2015 and 2017, and in the investigation of the possible causes of the dropout of UFS students, as well as in the profiling of the student assisted by the program. The regression model was the methodological process applied to compare the performance of students benefiting from the PNAES with other non-assisted students.

2025-10-29T00:00:00Z