The Age of Longevity and the strength of the <i>Silver Economy</i>

The consumption of protein supplements and foods with added protein has grown significantly in recent years, with digital content related to health, aesthetics, and physical performance becoming a relevant part of the routine of Brazilians seeking a disciplined lifestyle. The present study aimed to analyze the consumption behavior of protein supplements and foods, as well as the influence of social media and digital influencers on consumers’ purchasing decisions. An exploratory research was conducted, using an online questionnaire applied to 62 participants via Google Forms, disseminated on Instagram, and a semi-structured interview with the CEO of a supplement brand. The data analysis was quali-quantitative. The results indicated that social media, especially Instagram, played a central role in consumers’ initial contact with brands. The majority of respondents associated the consumption of protein products with a healthy and fitness lifestyle, and considered the influence of digital content creators in stimulating experimentation and purchase. The significant impact of digital marketing and aspirational narratives on consumption behavior was identified, reinforcing the relationship between functional food, social identity, and belonging in the digital environment in the context of protein supplementation and food.

Keywords: Foods with added protein; Consumer behavior; Protein supplement consumption; Digital influencers; Digital marketing.

The application of data science in the operational management of electric power distribution was investigated with the objective of optimizing processes and strategic decision-making. The study analyzed how predictive algorithms and Business Intelligence dashboards can enhance operational management, aiming to optimize team dispatch, forecast demands, and monitor indicators in real-time for more agile and sustainable decisions. The research was applied in nature and considered historical, geographical, and operational data from EDP São Paulo in São José dos Campos, covering the period from January 2018 to December 2024. The data were processed in a Business Intelligence environment and subjected to machine learning algorithms, such as multiple linear regression and recurrent neural networks, for demand forecasting and fault pattern identification. Additionally, clustering was used to identify critical areas. The results indicated that the integration of forecasting models with real-time operational indicators reduced response time to incidents, increased efficiency in resource allocation, and promoted continuous operational improvement. Recurrent neural networks demonstrated higher accuracy in demand forecasting, and correlation analysis pointed to precipitation as a relevant factor for increased interruptions. The identification of critical areas by clustering allowed for the prioritization of maintenance actions. It was concluded that the adoption of data science methods modernizes the sector, reduces costs, increases reliability, and promotes greater sustainability, providing subsidies for more agile and sustainable decisions in energy management.

Keywords: Predictive algorithms; Business Intelligence; Data science; Energy distribution; Operational management.

The increase in renewable energies in Brazil, especially photovoltaic energy, highlighted the need to improve the prediction of power plant production, due to the high dependence on climatic factors and the consequent variability of electricity generation. Machine learning models were developed and evaluated for predicting the electricity generation of a photovoltaic power plant in Northeast Brazil, analyzing the impact of the progressive inclusion of meteorological, temporal, derived, and lagged variables. Hourly data on energy generation and meteorological variables from official and public databases were used. Three tree-based regression algorithms (Decision Tree, Random Forest, and XGBoost) were implemented in a modeling structured in three phases: meteorological, temporal and cyclical variables, and lags. The results indicated a strong correlation between solar radiation and energy generation. Moderate predictive performance was observed in the initial phases of modeling, with a significant improvement in the third phase, where XGBoost stood out with an R² of 0.898. It was concluded that the inclusion of temporal variables and feature engineering techniques was decisive for improving the predictive capacity of the three models, configuring itself as a strategy for practical applications in the photovoltaic sector.

Keywords: Tree-based algorithm; Meteorological data; Temporal dependence; Feature engineering; Solar farm.

An analytical architecture was built to understand and predict the purchasing behavior of customers of a free preparatory course provider. The objective was to integrate RFM segmentation, Markov Chain transition modeling, and propensity prediction by Logistic Regression, Random Forest, and Gradient Boosting, under the CRISP-DM framework. Transactional data from 7,123 customers of a free course provider, covering November 2022 to January 2026, were used. The CRISP-DM methodology guided RFM segmentation, Markov Chain transition modeling, and purchase propensity prediction by supervised models (Logistic Regression, Random Forest, and Gradient Boosting), with ‘out-of-time’ temporal validation. RFM segmentation revealed four profiles, with the Highly Recurrent segment (25% of the base) concentrating 60.5% of revenue. The Markov Chain indicated a trend of base degradation in the long term, with the Low Acquisition segment projected to grow from 25% to 31.3% at equilibrium. The supervised models achieved AUC between 0.811 and 0.826, demonstrating that predictive power resides in RFM variables, with recency emerging as the dominant predictor. It was concluded that the integration of these approaches provides concrete subsidies for strategic decisions on customer retention, segmentation, and prioritization in educational contexts, highlighting the need for structured actions to reverse base degradation and the importance of recency in repurchase prediction.

Keywords: Markov Chains; CRISP-DM; Propensity prediction; Random Forest; RFM segmentation.

The growth of Brazilian e-commerce has consolidated customer reviews as a rich source of unstructured data for business intelligence. However, traditional aggregated metrics, such as star ratings, fail to capture the complexity of mixed sentiments in a single comment. The study aimed to develop a multi-label Aspect-Based Sentiment Analysis (ABSA) model by Fine-Tuning the BERTimbau language model, seeking to classify sentiments across four business aspects and compare its performance with conventional baselines. For this purpose, the Brazilian E-Commerce Public Dataset by Olist was used. The results demonstrated superior performance of the fine-tuned model, which achieved an F1-Macro of 0.8149, representing a 23.7% improvement over conventional satisfaction baselines. It was concluded that the use of specialized LLMs allows for the transformation of subjective feedback into actionable diagnostic indicators, providing strategic subsidies for supply chain management and the optimization of customer experience in the digital environment.

Keywords: Machine Learning; Customer Experience; Supply Chain Management; Marketplace; Natural Language Processing.

The demand for quantitative strategies in capital markets has driven the development of machine learning models. The present study implemented a machine learning pipeline for the ternary classification of Brazilian stock returns, aiming to diagnose overfitting, evaluate the use of meta-labeling and Hierarchical Risk Parity (HRP) to generate positive alpha, and document failure modes. 276 assets from B3 were gathered between 2000 and 2026, totaling 257,970 samples labeled by the triple-barrier scheme on CUSUM events. A grouped primary XGBoost classifier was trained with sample weights corrected for concurrency and uniqueness. Validation employed PurgedKFold and purged combinatorial cross-validation (CPCV), with overfitting diagnosis via PBO and DSR. Side decision followed the raw softmax of the primary model, and bet magnitude was delegated to a binary meta-labeler. Portfolio construction adopted Hierarchical Risk Parity and was evaluated by honest A/B/C comparison. Out-of-sample results indicated an accuracy of 0.52 and approval in the three statistical gates. However, at the portfolio level, the strategy’s Sharpe with timing was +0.23 against +0.87 for buy-and-hold HRP, resulting in an alpha of -0.63. It was concluded that the universe selection substantially explained the observed performance, not the classifier’s timing, despite approval in the operational-level statistical tests. The methodological contribution was a reproducible, auditable, and leakage-safe pipeline, with failure modes transparently documented, and the diagnosis of argmax collapse under isotonic calibration on imbalanced ternary labels.

Keywords: Deflated Sharpe Ratio; Fractional differentiation; Quantitative finance; Meta-labeling; Overfitting in backtests.

The influence of digital influencers on the trust and purchase intention of private label cosmetic brands was analyzed, contextualized by the expressive growth of social networks and the beauty sector in Brazil. The study aimed to analyze the relationship between consumer identification with digital influencers, trust, and purchase intention for private label cosmetic products. A quantitative, descriptive, and correlational approach was adopted, with a survey-type data collection applied to 108 valid respondents. The data were processed in R software, using exploratory factor analysis and multiple linear regression. The results indicated that trust consolidated as the main predictor of purchase intention, highlighting its relevance to consumer behavior. In contrast, identification obtained the lowest average and did not show statistical significance in the regression model, suggesting a limited impact on the purchase decision. It was concluded that the credibility and security transmitted by the brand are determinants for sales conversion, overcoming the need for personal similarity between follower and influencer in the cosmetics segment.

Keywords: Confidence; Cosmetics; Identification; Purchase intention; Influencer marketing.

The selection of suppliers in public procurement demanded instruments to enhance transparency and strengthen compliance in the face of legal and reputational risks. The objective was to apply the AHP-Gaussian method in supplier selection, based on quantitative criteria of integrity and operational capacity. The investigation, of an applied, documentary, and quantitative nature, used secondary data from the National Register of Unfit and Suspended Companies (CEIS), available on the Transparency Portal. Ten suppliers from the cleaning and maintenance segment were evaluated, according to six criteria: resources received, current sanctions, contracts signed, parliamentary amendments, services, and participation in tenders. The AHP-Gaussian method processed the data, calculated the weights of the criteria, aggregated the performances, and generated the final ranking of alternatives. The results indicated that the parliamentary amendments criterion showed the highest relative weight in the model, followed by participation in tenders and contracts signed. Alternative A4 was the best positioned in the final ranking, followed by A5 and A6. A sensitivity test confirmed the model’s coherence and robustness. It was concluded that AHP-Gaussian constituted an adequate tool to support supplier selection in the public sector, contributing to more consistent, traceable decisions aligned with the principles of integrity and governance.

Keywords: AHP-Gaussian; Multicriteria Decision Aid; Compliance; Public Procurement; Suppliers.

The expansion of global markets imposed on brands the challenge of overcoming digital standardization and avoiding disconnection with consumers from different cultural contexts. The study analyzed the strategic positioning and perception of the McDonald’s, Starbucks, and KFC brands in Brazil, France, and Japan, seeking to understand how tone of voice, visual aesthetics, and communication channels were shaped to local preferences. The methodology employed a multiple case study with a mixed approach, integrating content analysis of Instagram and TikTok profiles with the application of a structured questionnaire to 63 respondents. The results demonstrated significant contrasts in digital positioning: in Brazil, communication prioritized expressiveness and emotional proximity through influencers; in France, aesthetic individualism and ironic humor predominated as vectors of engagement; and in Japan, the strategy revealed high-context communication, guided by visual refinement, seasonality, and the use of regional channels such as the LINE app. The research ratified that 88.9% of consumers demanded adapted campaigns, evidencing cultural resonance as a determining variable for brand equity sustentation. It was concluded that the effectiveness of digital positioning depended on the balance between global identity and deep localization, making consumption culturally relevant and authentic.

Keywords: Branding; Consumer Behavior; Brand Localization; Intercultural Marketing; Social Media.

By bringing together skills and monitoring results, your contribution also reaches organizations that are not physically located in it. A technology park is a meeting place. In it, a company can find the knowledge needed to develop a solution, a researcher can discover an application for their work, and an entrepreneur can meet the missing partner […]

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