Executive Summary

The Brazilian Tax Reform, instituted by Constitutional Amendment No. 132/2023 and regulated by Complementary Law No. 214/2025, altered consumption taxation, replacing taxes with the Tax on Goods and Services (IBS) and the Social Contribution on Goods and Services (CBS), under the principle of full non-cumulativeness. The study aimed to describe the main challenges perceived by controlling professionals in technology companies in adapting to the new model of non-cumulativeness and tax credit calculation. Applied research, descriptive, with a mixed approach (qualitative-quantitative), was conducted through a field survey. Data were collected from 15 controlling or fiscal management professionals, using a structured questionnaire. The responses were analyzed by frequency distribution and thematic content analysis. The results revealed that professionals perceive the Reform as an event with multifunctional impact, demanding revisions in Enterprise Resource Planning (ERP) systems, credit calculation criteria, team training, and interdepartmental integration. The limitation of credit utilization on labor costs emerged as a specific structural challenge for the sector. The strengthening of the controlling function’s strategic role in the tax transition was observed, especially in pricing, tax planning, and contracting structure, contributing to the debate on the managerial and operational implications of the Tax Reform.

Keywords: controlling; tax credits; technology companies; non-cumulativeness; Tax Reform.

In April 2024, Rio Grande do Sul faced the greatest climate tragedy in its history, highlighting the need to improve strategies for forecasting and preventing hydrometeorological disasters. The study aimed to develop a predictive model based on Long Short-Term Memory (LSTM) neural networks to classify high precipitation events in the state. Historical data from the National Institute of Meteorology (INMET) between 2013 and 2025, collected from 44 automatic weather stations, were used. The methodology included variable selection by the Random Forest algorithm, which identified total hourly precipitation and dew point temperature as the features with the highest predictive power, and modeling with a two-layer LSTM architecture, trained with SMOTE application for handling class imbalance. The model achieved an AUC-ROC of 0.9373 and a recall of 82.4% for extreme events, defined as precipitation above the 95th percentile of the historical distribution (8.60 mm/hour). The capacity to identify the vast majority of high precipitation occurrences in advance was demonstrated, confirming the viability of using LSTM neural networks for forecasting extreme hydrometeorological events. The work offers potential contributions to climate monitoring systems and public policies for natural disaster prevention in Brazil.

Keywords: Hydrometeorological disasters; LSTM; Extreme precipitation; Artificial Neural Networks; Rio Grande do Sul.

The housing densification in São Paulo, where 29.4% of the population resides in apartments, has established a highly competitive condominium management market that demands spatial data-based prospecting strategies. The study aimed to map the urban evolution of the São Paulo capital through spatial analysis algorithms and building age indicators for the development of competitive intelligence tools. The methodology comprised the extraction and integration of public CNPJ data via the KNIME platform, resulting in a database of 32,040 condominiums, 98% of which were geocoded with the Google Maps API. Macro-spatial analysis identified that the East and South Zones led recent real estate growth, while peaks in registrations in 1984 and 1985 were associated with changes in tax legislation and not with real construction cycles. In parallel, micro-spatial analysis used the BallTree algorithm to compare each condominium with its 20 closest neighbors, revealing that 29% of the units represented points of local renewal in consolidated areas. The results were consolidated in an interactive dashboard that allows strategic visualization of the market at different levels of granularity. It was concluded that the integration of data science to the territory allowed administrators to replace generic approaches with personalized prospecting tailored to the lifecycle and competitive reality of each condominium, optimizing the allocation of marketing resources and the effectiveness of commercial teams.

Keywords: BallTree Algorithm; Spatial Analysis; Urban Data Science; Condominium Management.

A study analyzed pedagogical documents that guide the choice of teaching materials in the National Program for Books and Teaching Materials (PNLD), considering their relevance for teaching practice and school management. The objective was to analyze the clarity, organization, and applicability of the PNLD 2023 Digital Guide – Early Years and the Support Guide for the Analysis of Teaching Materials from the Reúna Institute, identifying their limitations, potential, and implications for pedagogical practice. A qualitative, exploratory, and descriptive research was conducted, based on documentary analysis guided by the content analysis technique. Aspects such as structure, navigability, organization of criteria, textual density, and reading time estimation were examined. The analysis revealed tensions between state regulation, the publishing market, and school autonomy, and the results showed significant differences between the documents. The PNLD Guide proved to be extensive, technically dense, and predominantly normative. In contrast, the Reúna Institute’s Guide was organized progressively and didactically, with graphic resources that favored understanding and collective use by schools. It was concluded that the structural and discursive characteristics of the documents directly influence their usability and can impact the degree of autonomy and participation of school teams in the process of choosing teaching materials.

Keywords: teacher training; teaching materials; PNLD; curriculum policies; decision-making process.

The transparency and integrity of accounting information are fundamental requirements for the fiscal compliance of public entities, evaluated by the National Treasury Secretariat (STN) through the Ranking of the Brazilian Public Sector Accounting and Fiscal Information System (SICONFI). The study aimed to develop a proactive audit tool for a federation entity, seeking to mitigate fiscal inconsistencies in Dimension 1 (Information Management) before the official result. The methodology was based on Data Science, using the Python language for automated extraction of the Accounting Balances Matrix (MSC) via the Transparent Treasury Application Programming Interface (API), and the Power BI software for building a compliance dashboard. The project focused on data integration to ensure the symmetry of deadlines and deliveries between accounting records and fiscal statements. The results demonstrated that the automation of the data flow allowed for the early identification of discrepancies, promoting information reliability and the efficiency of governmental internal control, with a structured architecture for expansion to other dimensions of the ranking. The application of the tool to data from the 2025 fiscal year demonstrated its full functionality, with 100% compliance in the 38 control checks of Dimension 1. It was concluded that the solution acted as an innovative and scalable model for the modernization of public management, delivering a tool capable of mitigating fiscal inconsistencies prior to the consolidation of the SICONFI Ranking.

Keywords: Data science; Governmental compliance; Internal control; Public management; Business intelligence.

The growing pressure for economic-assistance sustainability in Brazilian supplementary healthcare has driven the adoption of management models for higher-risk populations. The study aimed to describe and analyze the structuring and operationalization of a high-assistance-risk portfolio management model, as well as to descriptively compare the aggregated indicators of hospitalizations and emergency room visits of the same cohort of 100 beneficiaries between 2023 and 2025. A single, descriptive, retrospective case study was conducted, integrating qualitative document analysis and descriptive quantitative analysis of administrative data. The model was implemented in a vertically integrated health plan, with 100 high-risk beneficiaries identified by an electronic marker. The process involved daily capture of assistance events, categorization by specialty, distribution of cases to the responsible team, and a multidisciplinary follow-up protocol for 28 days after emergency room visits or hospital discharges. Document analysis revealed greater standardization of workflows, definition of responsibilities, and traceability of follow-ups in the reformulated model. In the comparison between periods, hospitalizations decreased from 166 in 2023 to 52 in 2025, a reduction of 68.67%, and emergency room visits fell from 238 to 130, a variation of 45.38%. The reductions occurred during the period when the model was already incorporated into institutional routine, indicating a favorable temporal association. However, due to the descriptive design, the absence of a comparison group, and the impossibility of adjusting for clinical and contextual factors, the observed variations could not be exclusively attributed to the model’s implementation. The study contributed by systematizing an organizational experience of managing a high-risk portfolio in supplementary healthcare, offering elements for analysis and adaptation in similar institutional contexts.

Keywords: Care coordination; High-risk management; Population management; Post-event monitoring; Supplementary healthcare.

The monitoring and control of environmental variables is a critical factor in industry, commerce, and agriculture. The objective was to build an Internet of Things (IoT) based system for remote environmental automation and control, utilizing sensors and actuators effectively and flexibly. For this purpose, air humidity was selected as the variable to be monitored and controlled. Modules were developed for sensing and modifying environmental variables, along with a central server to process the collected information. The architecture included a DHT-11 sensor, ATMEGA328 microcontroller, ESP-8266 Wi-Fi module, and a Raspberry Pi as a web server. Functional tests were performed on a physical prototype with an isolated environment, where a micro-fan acted to control humidity. The system demonstrated the ability to collect data, respond to environmental variations, and control air humidity within the specified range, reducing it by exhaustion. However, humidity reduction by exhaustion proved limited by external humidity. It was concluded that the system fulfilled its objective of monitoring and controlling environmental variables in an automated manner, offering flexibility and modular architecture for various applications, such as laboratories and greenhouses, and promoting the democratization of access to technology in the field.

Keywords: Automation; Control; ESP-8266; Raspberry Pi; Wi-Fi.

Digital transformation has expanded the availability of Customer Relationship Management (CRM) systems, automation, and artificial intelligence in the sales process, but their strategic appropriation is not automatic. The study analyzed how organizational digital culture, sales automation, and CRM usage influence sales management and performance in a real estate market organization, investigating CRM usage practices, operational frictions, perceptions of brokers and leadership, and data-driven governance mechanisms. A mixed, applied, and descriptive approach was adopted, with structured questionnaires applied to 48 brokers and 4 leadership professionals, in addition to descriptive statistical analysis, construction of synthetic indices, and thematic analysis. The findings indicated that CRM is incorporated into the access routine, but not into the consistent production of data. Convergence was observed between brokers and leadership regarding registration weaknesses and fragmentation across parallel channels, although with distinct perceptions of the effects. Length of service was not linearly related to digital maturity, and the increase in the volume of opportunities was associated with greater difficulty in manual tracking. As a contribution, the GCOD (Governança Comercial Orientada a Dados – Data-Oriented Sales Governance) framework was proposed, which integrates CRM, analytical intelligence, sales orchestration, and continuous governance. It was concluded that sales efficiency depends on the organizational capacity to integrate technology, behavior, and governance to reduce frictions, preserve data quality, and orchestrate human and automated attention throughout the lead journey.

Keywords: Sales automation; CRM; Data culture; Sales governance; Digital transformation.

The study, of an exploratory nature, investigated how Artificial Intelligence and Computational Simulation techniques can be applied to pricing with a focus on corporate liquidity management. The objective was to propose and illustrate a method of Adaptive Dynamic Cash-Oriented Pricing, in which pricing depended on its expected effects on sales and, subsequently, on the cash flow trajectory. To this end, a hypothetical case was developed with synthetic data on prices and sales, a predictive sales model as a function of own price and average competitor price, a cash flow projection model capable of incorporating payment terms, defaults, transactional costs, inventory replenishment, and fixed costs, and a computational simulator for evaluating competitive scenarios. The predictive modeling process compared different machine learning algorithms, with a linear regression with quadratic and interaction terms being selected for presenting competitive predictive performance, good generalization capability, and greater interpretability. The simulation results indicated that the proposed approach outperformed a baseline fixed-price scenario from a liquidity perspective, producing superior cash trajectories in most of the analyzed horizon. It was concluded that pricing can be treated not only as a commercial problem but also as a cash management problem, opening space for practical applications and future studies with real data, multiple products, and more advanced sequential decision-making methods.

Keywords: Machine Learning; Cash flow; Pricing; Computational Simulation; Decision Making.

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