Executive Summary

A study investigated the determining factors for supermarket location in the state of São Paulo, with the objective of investigating the factors that explain the presence and expansion of these establishments, considering socioeconomic, demographic, and market dimensions. Data from the 2010 and 2022 Demographic Censuses of IBGE and information from the National Registry of Legal Entities of the Federal Revenue of Brazil were used to build a georeferenced database. A Random Forest classification model was applied, adjusted by grid search with cross-validation, prioritizing the recall-macro metric due to the imbalance of the dependent variable, which represented the presence or absence of supermarkets within a 50-meter buffer. The results indicated that supermarket location is strongly associated with demographic, income, and population characteristics in the surrounding area. The analysis of variable importance showed that sociodemographic factors, such as elderly literacy, household income, and the presence of other food establishments, exerted significant influence, especially in the immediate vicinity. The findings reinforced the hypothesis that the spatial distribution of supermarkets is not random, being conditioned by socioeconomic characteristics and the commercial structure of the territory, offering subsidies for business decisions and urban planning.

Keywords: Spatial Analysis; Machine learning; Expansion; Commercial location; Supermarkets.

Antiracist education, understood as a structuring axis of inclusive education and social development, was investigated in the Brazilian context. The study aimed to identify and analyze, based on legal documents and teachers’ perceptions, educational practices capable of promoting antiracism in school and society, and how the implementation of Laws nº 10.639/03 and nº 11.645/08 contributed to social justice. A qualitative and documentary approach was adopted, with analysis of educational legislation, curricular guidelines, institutional reports, and academic literature. Complementarily, a semi-structured questionnaire was applied to 295 Basic Education teachers. The data were evaluated quantitatively and qualitatively, through thematic content analysis, and validated with bibliographic studies. The results revealed a paradox: despite a robust legal framework, the implementation of antiracist policies proved fragile and sporadic, with a lack of teacher training, adequate teaching materials, and monitoring. Significant educational inequalities between white and black students were found to persist, and most teachers acknowledged the occurrence of racism in schools, but without clear institutional protocols. Neuroscientific analysis showed that racism negatively impacts students’ cognitive and emotional development. It was concluded that antiracist education is central to quality education, requiring political commitment, public investment, and intersectoral articulation. The integration of Neuroscience in teacher training and the production of qualified materials are crucial to strengthen the school’s role in building a more just and inclusive society.

Keywords: Social Development; Antiracist Education; Social Justice; Law 10.639/03; Inclusive Educational Practices.

Dual Exceptionality, characterized by the coexistence of High Abilities/Giftedness and neurodevelopmental disorders, represents a complex phenomenon that challenges traditional identification and schooling models. The study aimed to understand the perceptions of parents or guardians, teachers, and other education professionals regarding the schooling of students with Dual Exceptionality in the Brazilian context, investigating challenges, pedagogical strategies, and possibilities for inclusion based on equity. The research adopted a qualitative, exploratory, and descriptive approach, and collected data through an online, voluntary, and anonymous questionnaire answered by 25 participants. Discursive data were analyzed using thematic content analysis. The results indicated that knowledge about the topic is often built from personal and professional experiences, revealing gaps in systematic training. Difficulties were identified in identifying these students, in teacher training, and in implementing individualized educational plans, pedagogical flexibility, and curriculum enrichment. Socio-emotional repercussions, such as frustration and low self-esteem, were reported. However, some schools demonstrated inclusive practices based on equity, articulating specific needs and potentialities. Although the results do not allow for generalizations, they highlighted the need to strengthen professional training and the articulation between school, family, and specialized services. It was concluded that the inclusion of students with Dual Exceptionality requires practices that simultaneously recognize their difficulties and potentialities, ensuring equitable conditions for participation, learning, and development.

Keywords: Human development; Teacher training; School inclusion; Neurodivergence; Pedagogical practices.

The efficient management of logistics operations in distribution centers is fundamental for competitiveness, especially in labor-intensive activities, which impact operational costs and service level. The study aimed to develop and evaluate different demand forecasting models to optimize inventory and resource management in a food distribution center. For this purpose, historical movement data from a WMS system were used, applying linear regression models, Holt-Winters exponential smoothing, and the LightGBM algorithm. The results indicated that traditional models presented limitations in capturing demand dynamics, with inferior performance to LightGBM. The machine learning algorithm demonstrated superior performance, especially after incorporating greater data granularity and temporal variables, which allowed for better adherence to demand dynamics. The application of the model resulted in a 36.38% reduction in displacement for pallet picking and 31.98% for case picking, totaling an overall reduction of 33.64%. It was concluded that the use of predictive models contributed to the improvement of operational efficiency, allowing for better resource allocation and organization of items in the order picking process.

Keywords: Machine learning; ABC classification; Operational efficiency; Logistics; Demand forecasting.

The growing demand in ecotourism in Minas Gerais has driven the search for business intelligence to transform customer data into strategic information. The study aimed to structure a data science pipeline to collect, segment, and classify the customer base of an ecotourism operation, in order to optimize marketing actions and anticipate market movements. An exploratory, quali-quantitative research was conducted through a case study. 2,777 transactional records from an ecotourism company, referring to January 2024 to December 2025, were used. The methodological process involved automated data collection (Google Sheets API), processing and enrichment (ETL), validation, and creation of RFM (Recency, Frequency, and Monetary Value) attributes. Dimensionality reduction via PCA and K-Means clustering was applied, with the number of clusters defined by the Elbow method and Silhouette Score. The results were validated with DBSCAN and K-Medoids. The results revealed the identification of three behavioral customer segments: “Loyal”, “Low Value”, and “Potential”. The “Loyal” segment represented the highest accumulated economic value, while the “Potential” segment stood out for its high average ticket and potential for conversion into recurrence. The integration of data analysis techniques proved to be a robust and replicable method for generating intelligence in ecotourism. It was concluded that the structured data science pipeline enabled the behavioral segmentation of the customer base, the statistical validation of the groups, and the creation of a predictive system for new buyers, providing subsidies for data-driven strategic decisions and future analyses.

Keywords: Clustering; Business intelligence; Machine Learning; Customer segmentation; Decision making.

The risk of default in credit operations demanded analytical approaches to anticipate losses. This study comparatively evaluated the performance of supervised machine learning models in classifying defaulting customers in credit card operations. The public dataset “Default of Credit Card Clients” from the University of California Irvine was used, with 30,000 observations and class imbalance. The algorithms Logistic Regression, Random Forest, and Extreme Gradient Boosting were employed. The imbalance was addressed by assigning weights to the classes, and model optimization occurred with the RandomizedSearchCV method, prioritizing sensitivity. Cross-validation results indicated that the Extreme Gradient Boosting model showed a higher capacity for identifying the defaulting class and better discriminatory performance, followed by Random Forest and Logistic Regression, with a sensitivity of 0.8250 and an AUC-ROC of 0.7844 for XGBoost. Interpretability analysis, conducted by the Shapley Additive Explanations (SHAP) technique, highlighted the predominance of variables associated with payment behavior, especially the history of delays. It was concluded that tree-based models, particularly boosting techniques, proved to be more suitable for capturing complex patterns in the data, configuring themselves as consistent alternatives for credit risk management.

Keywords: Machine Learning; Credit Card; Classification; Extreme Gradient Boosting; Credit Risk.

A study analyzed public perception of Brazilian financial institutions on the Twitter/X platform, highlighting the importance of sentiment monitoring on social networks for understanding reputation and customer experience in the banking sector. The objective was to compare user perception of the image and reputation of traditional and digital banks, based on the sentiment patterns identified in the analyzed manifestations, seeking to identify structural differences between these groups. The methodology was based on the analysis of 1,096 tweets collected between November 2022 and June 2023. Two complementary sentiment analysis approaches were used, the sum and the average of labels, to capture the majority sentiment and nuances of perception. Additionally, the Market Profile Model, with indicators of emotional reputation, reputational risk, neutrality, and polarization, and the Banking Clustering Model, which allowed grouping institutions according to perception patterns, were developed. The results indicated a predominance of neutral and negative sentiments, a higher volume of interactions in digital banks, and structural differences in the emotional intensity of perceptions, with greater stability in digital banks and greater polarization in traditional ones. It was concluded that the combination of analytical and statistical techniques contributed to an in-depth understanding of institutional image in the digital environment, demonstrating the importance of data-driven reputation management strategies.

Keywords: Digital banks; Traditional banks; Data modeling; Opinion mining; Social Networks.

The Annual Budget Planning (POA) is a crucial process for translating organizational strategy into operational and financial goals, but it frequently faces deadline pressures, interdepartmental dependencies, and the repetition of habitual expenses. The study aimed to analyze how the combined application of project management practices and Zero-Based Budgeting (OBZ) can optimize the POA. To this end, a case study was developed in the Brazilian operation of a publicly traded company in the beverage sector, using documentary research of its 2023 results report and an anonymous questionnaire applied to 47 respondents. Documentary analysis indicated growth in net revenue, expansion of gross profit and adjusted EBITDA, and contained advancement of selling, general, and administrative expenses, suggesting cost discipline and operational leverage. The complementary survey revealed a high perception of cascading effect on the schedule, strong support for defining cost package owners, and a preference for technical justification of expenses, in addition to demand for controlled flexibility after the baseline definition. It was concluded that structuring the POA as a project, associated with the rigor of OBZ, increased the process predictability, reinforced accountability for expenses, and broadened the coherence between budgetary execution and economic-financial performance.

Keywords: Cost Control; Operational Efficiency; Zero-Based Budgeting; PMBOK; Beverage Sector.

The study analyzed how innovation applied in traditional business models impacts the work environment. With this objective, a field survey was conducted involving 225 professionals from different sectors and hierarchical levels. Data collection occurred through a structured questionnaire, which investigated the profile, perceptions, and behaviors of the participants in relation to innovation. The results obtained indicated that employees of companies considered innovative perceived high levels of innovation, demonstrated greater engagement, motivation to propose improvements, and a better understanding of the implemented changes. In contrast, professionals from traditional companies presented a lower perception of innovation, greater neutrality towards transformations, and higher levels of stress and insecurity in the work environment. It was also observed that leadership, clear communication, support, and adequate training were identified as essential factors for facilitating adaptation to changes and increasing employee receptivity. It was concluded that organizational typology, associated with culture and management practices, directly influences how innovation is experienced internally and its effects on the work environment.

Keywords: Engagement; Traditional companies; Change management.

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