Executive Summary

The current fashion landscape, marked by excessive “fast fashion” consumption and the exclusion of dissident bodies, has highlighted the need for brands focused on awareness, “upcycling”, and textile transmutation. In this context, the study aimed to analyze the economic feasibility of a sustainable and inclusive fashion brand, targeted at the LGBTQIA+ audience in the city of São Paulo. The research was characterized as a quantitative case study, based on the application of a questionnaire to measure consumption behavior and the projection of financial scenarios for a five-year period. The results revealed a viable average ticket of R$200.00 and confirmed the operation’s profitability with a Net Present Value of R$47,699.49, an Internal Rate of Return of 343.70%, and a Modified Internal Rate of Return of 115.80%. Sensitivity analysis proved the project’s robustness, demonstrating positive returns even in pessimistic simulations. The venture proved viable, indicating that the union of sustainability values, social economy, and an efficient financial structure results in a low-risk business.

Keywords: Investment analysis; Circular economy; Entrepreneurship; Socio-environmental impact; Apparel.

Infection by the influenza A(H1N1)pdm09 virus can progress to Severe Acute Respiratory Syndrome (SARS), with a higher risk of death in the elderly. The objective was to analyze predictive factors of death in cases of SARS confirmed for influenza A(H1N1)pdm09, among the elderly aged 60 years or older, considering individual and contextual factors of the Administrative Regions (RA) of the State of São Paulo (ESP), and to compare the performance of regression models. A descriptive and analytical study was conducted in ESP, from 2023 to 2024, using data from SIVEP-Gripe and the SEADE Foundation. 714 cases confirmed by RT-PCR for influenza A(H1N1)pdm09 in residents of ESP aged 60 years or older were included. Sociodemographic, clinical, hospitalization, treatment, vaccination, and residence location variables were analyzed. Binary logistic regression and hierarchical logistic regression were used, with death as the outcome. Of the 714 cases, 202 resulted in death. The final binary logistic regression model associated a higher chance of death with the male sex, advanced age, oxygen saturation below 95%, ICU admission, and use of invasive ventilatory support. The presence of cough and the use of antiviral were associated with a lower chance of death. The hierarchical logistic model showed similar performance, with a small residual contextual contribution from the RAs. It was concluded that the binary logistic regression model was sufficient and simpler to represent the data, being adopted as the final model.

Keywords: Influenza A(H1N1)pdm09; Hierarchical logistic modeling; Death from Influenza A(H1N1)pdm09; Logistic Regression; Severe Acute Respiratory Syndrome.

The female interest in aesthetics manifests in various societies, being amplified by the advancement of the internet and social media, which have driven the emergence of digital influencers. The study analyzed how digital influencer marketing impacts the consumption behavior and aesthetic perception of Generation Alpha girls (3 to 15 years old) in the state of São Paulo, from the perspective of their parents and/or guardians, focusing on the early consumption of makeup and skincare and the construction of aesthetic standards. To this end, a bibliographic research was conducted, a quantitative online and in-person survey was applied, and an interview was held with a dermatologist. The results indicated that interest in beauty arises early, with encouragement from guardians. The young girls showed a desire for belonging, and makeup items presented a slight predominance over skincare items. The majority of participants believed that influencer marketing impacts purchasing decisions. It was concluded that interest in beauty and encouragement of self-care are naturalized from an early age, and that influencer marketing contributes to the formation of consumption desires, although the non-probabilistic sample limits generalizations.

Keywords: Adolescents; Children; Aesthetics; Influencers; Social Media.

Road transport contributes to approximately 20% of global CO2 emissions, driving the search for cleaner mobility solutions, such as electric vehicles. The objective of this study was to compare the electric vehicle markets in Brazil and the European Union, contrasting their policies, consumer adoption, and infrastructure development. A comparative descriptive research design was employed, analyzing quantitative data from the Brazilian Electric Vehicle Association, National Association of Automotive Vehicle Manufacturers, European Alternative Fuels Observatory, and European Environment Agency for the period from 2015 to 2024, complemented by qualitative analysis of policy documents. The results revealed a fundamental structural divergence: Brazil showed exponential growth of 80.7% per year (2022-2025), with hybrids comprising approximately 66% of sales and driven by tax incentives that leverage the ethanol bioeconomy. The European Union, in turn, showed expansion in a consolidation phase at an annual rate of 8.35% (2008-2025), with battery electric vehicles exceeding 40% of new registrations, driven by binding regulatory mandates. Infrastructure density analysis indicated that the European Union’s public charging network (55.3 points per thousand alternative fuel vehicles) was double that of Brazil’s (27.9 points per thousand electrified vehicles). Environmental quantification confirmed that Brazil’s predominantly renewable energy matrix (64 gCO2/kWh) resulted in substantially lower well-to-wheel emissions compared to the European Union’s matrix (246 gCO2/kWh). It was concluded that the Brazilian model, with technology-neutral tax incentives and leveraging biofuel infrastructure, can accelerate hybrid adoption in emerging economies with low public spending. For mature markets, the European Union’s model, with regulatory mandates, has established a dominant battery electric vehicle market, albeit with a higher fiscal cost.

Keywords: Renewable energy; Market; Qualitative; Quantitative.

Population aging and low fertility rates present complex demographic challenges in Japan, accompanied by the expansion of foreign immigration as a response to labor supply constraints. The study aimed to analyze Japanese demographic trends and project their evolution until 2044, with an emphasis on the relationships between population decline, fertility, and immigration. The ARIMA and Prophet time series models were applied to historical data from the World Bank, the National Institute of Population and Social Security Research of Japan, and the Immigration Services Agency of Japan, covering the period from 2003 to 2023 for the population series and from 2014 to 2024 for the immigration series. The analysis included descriptive statistics, Pearson correlation, augmented Dickey-Fuller stationarity test, and Granger causality tests, in addition to predictive evaluation using MAE and MAPE metrics. The results showed the continuation of population reduction and the growth of the immigrant contingent in projections up to 2044. Prophet presented lower MAE and MAPE values on the test sets, although the short duration of the immigration series limited generalization capacity. Temporal causality tests did not identify statistically significant evidence of precedence between population and immigration. It was concluded that immigration can contribute to mitigating labor supply constraints, but it does not, in isolation, constitute a sufficient mechanism to reverse Japan’s population decline.

Keywords: Population decline; Demography; Fertility; Migration flows; Time series.

The phenomenon of true crime has consolidated itself as a growth niche on Brazilian YouTube, driven by the expansion of digital media and public fascination. However, many creators have faced difficulties in achieving sustainable growth. This study analyzed how digital marketing strategies influenced the growth, engagement, and monetization of Brazilian true crime channels on the YouTube platform. A mixed-methods research approach was adopted, combining qualitative and quantitative methods, with an exploratory and descriptive nature. Four Brazilian channels in the niche were analyzed through documentary analysis of content, collection of public metrics via Social Blade and Noxinfluencer, and the application of questionnaires to four creators and 104 viewers. The results showed that the highest-performing channels adopted distinct strategies, such as a linear and contextualized narrative approach, personalized emotional communication, a technical-informative stance, or provocative opinion-based content. Convergence was observed between creators and the audience regarding the centrality of storytelling for retention, with most viewers stating that the way the story was told determined continued viewing. Creators indicated average retention time as the most predictive metric for sustainable growth, although 100% depended exclusively on AdSense for monetization, revealing financial vulnerability. It was concluded that success in the niche depends on the integration of digital marketing, consistent narrative identity, and ethical responsibility.

Keywords: creator economy; digital engagement; content marketing; storytelling; true crime.

The sizing of personnel and the management of retirement flows in federal public bodies constituted complex strategic challenges. The study aimed to apply unsupervised Machine Learning techniques, specifically Clustering (K-means) and Multiple Correspondence Analysis (MCA), to improve the planning of functional reviews in a Brazilian federal autarchy. The methodology consisted of processing a validated database of 487 active servers. Initially, quantitative variables, such as age, contribution time, and remuneration, were subjected to the K-means algorithm, resulting in the formation of five clusters, supported by the Elbow Method and Silhouette Coefficient. Subsequently, the groups were integrated with qualitative variables via MCA, projected onto a perceptual map that explained 74.81% of the data’s inertia. The results revealed that clusters 0 and 3 represented profiles of greatest pension criticality, concentrating all beneficiaries of tenure bonus and the majority of extinct positions. The research generated a phased Action Plan, prioritizing the 75 servers in these clusters who met the retirement requirements by 2025, with a similar guideline for 2026. In contrast, cluster 2 presented lower risk, with inactivity predicted only from 2031 onwards. The model proved to be a widely favorable tool for auditing and remediation, although its full effectiveness depends on more rigorous data governance in public service.

Keywords: Clustering; Multiple Correspondence Analysis (MCA); Retirement; Clusterization.

Sales management plays a strategic role in organizational performance, especially in real estate development sectors, characterized by commercial complexity. This study aimed to analyze the contribution of sales management practices to the commercial and organizational performance of a company in the real estate sector, considering its operating model and commercial structure. The research was characterized as exploratory, with a qualitative and quantitative approach, adopting a single case study. Data were collected through triangulation, using documentary research and a structured questionnaire applied to the outsourced sales team. The results indicated that the company structured its commercial management practices with monthly and annual goals, marketing campaigns, and incentive systems. Performance was monitored by quantitative indicators, such as sales volume and goal achievement percentage. In the analyzed period, 179 sales were made, corresponding to 74.6% of the established annual goal. Opportunities for improvement were observed in the alignment between commercial planning, performance monitoring, and strategy effectiveness. The brokers’ perception pointed to limitations in the incentive systems, such as the absence of progressive commissions and the duration of bonuses. It was concluded that, although sales management influences organizational performance, there are weaknesses in the alignment between effort, reward, and sales force motivation. Strengthening systematic monitoring, improving communication, defining more realistic goals, and implementing progressive incentives were recommended to enhance commercial results.

Keywords: Sales strategies; Sales force; Commercial management; Performance indicators; Real estate sector.

The evolution of artificial intelligence and deep neural networks has revolutionized human-machine interactions. Generative models, while promising for image synthesis, face challenges in prototyping user interfaces (UI) by failing to respect spatial structure. To address this divergence, a conditioned latent diffusion approach was proposed, integrating a control architecture with a large-scale model fine-tuned for mobile interfaces, which supported UI sketches to guide image inference. The training dataset comprised approximately 12,400 UI images and sketches from public sources, pre-processed and enriched with synthetic data and textual instructions. The approach utilized a Stable Diffusion 3.5 Large model as a base, adapted with ControlNet canny edges and a LoRA adapter. The approach’s feasibility was validated by quantitative evaluation, employing a normalized semantic similarity (NSS) metric based on Sentence-BERT, and by qualitative analysis with 20 industry experts. The results confirmed that the method surpassed the original model’s capabilities. However, the evaluation highlighted that the effectiveness in logical interface structuring still faced aesthetic degradation and loss of functional elements. It was concluded that, although the technology proves its technical feasibility for optimizing the transition between requirements and code implementation, the automation of creative processes demands architectural improvements to achieve the visual rigor required by the software development industry.

Keywords: Low-rank adaptation; Machine learning; Conditioned latent diffusion; Deep neural networks.

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