https://repositorio.unifei.edu.br/jspui/handle/123456789/88 Mon, 07 Sep 2026 04:09:35 GMT 2026-09-07T04:09:35Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4467 Título: In-context learning versus trained models: generative AI and machine learning for surface roughness prediction in dry Ti-6Al-4V turning Abstract: Surface roughness Ra is a critical quality indicator in dry turning of Ti-6Al-4V titanium alloy, a material widely used in aerospace and biomedical applications. Empirical powerlaw models remain the industrial standard but cannot represent interaction effects or non-linear responses. Machine learning offers greater flexibility, yet its deployment is constrained by small experimental datasets and the need for rigorous validation protocols. Large language models represent an alternative predictive paradigm, potentially encoding implicit knowledge of machining physics that may support quantitative prediction through in-context learning alone. This work compares five predictive models, comprising two conventional mathematical approaches (Power Law and RSM) and three machine learning models (SVR, Gaussian Process Regression, and XGBoost), against three LLMs (Claude Sonnet 4.5, GPT-4o-mini, and Gemini 3 Flash Preview) for Ra prediction in Ti-6Al- 4V turning. A Central Composite Design with k = 3 factors and two carbide inserts of distinct nose radii yielded N = 38 observations. The conventional and ML models were evaluated under nested LOOCV with 5-fold grid search where applicable. LLMs were accessed via API at temperature zero under two prompting strategies, few-shot and chain-of-thought, across three independent runs, with results reported as mean ± standard deviation. Claude Sonnet 4.5 in few-shot mode achieved the best overall performance (RMSE = 0.288 ± 0.016 μm, R2 = 0.985), outperforming SVR, the best trained model (RMSE = 0.341 μm), by 16% without task-specific training or fine-tuning. The effect of prompting strategy was model dependent: few-shot prompting outperformed chain-ofthought for Claude and GPT, whereas Gemini CoT achieved a substantially lower mean RMSE than Gemini FewShot but exhibited markedly greater run-to-run variability. This variability was especially pronounced for Gemini CoT, whose RMSE ranged from 0.31 to 0.99 μm across identical executions. The results show that a capable LLM supplied with well-structured experimental demonstrations can surpass optimised trained models for Ra prediction without task-specific training, while also demonstrating that predictive performance and stability depend strongly on both model architecture and prompting strategy. Tipo: Tese Thu, 06 Aug 2026 00:00:00 GMT https://repositorio.unifei.edu.br/jspui/handle/123456789/4467 2026-08-06T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4464 Título: Teletrabalho no contexto brasileiro: fatores socioeconômicos, ambientais e de transporte a partir de uma abordagem de aprendizado de máquina Abstract: Telework emerges as a potential tool for reducing commuting, lowering external costs, and promoting quality of life. In this context, the main objective of this research is to investigate the adoption of telework in the Brazilian context from a machine learning perspective. Initially, a comprehensive conceptual model was developed to examine the relationships between telework and socioeconomic, transportation, and environmental variables, based on a systematic literature review of studies published up to 2025. The temporal analysis of the literature highlights the growing academic interest in the topic, emphasizing the profile of teleworkers, characterized by factors such as age, educational level, income, and occupation type. Additionally, interactions between telework and urban aspects are explored, including the adoption of sustainable transportation modes, such as walking and cycling, as well as its relationship with urban sprawl. The impacts on quality of life are also discussed, particularly regarding schedule flexibility and work–life balance. The thesis analyzes the evolution of telework in Brazil from 2022 to 2025 through the application of machine learning models to representative microdata from the Continuous National Household Sample Survey, covering approximately 210,000 households per period. A standardized processing workflow was implemented, including data preprocessing, handling of missing values, class balancing through random undersampling, variable encoding and normalization, as well as stratified data splitting with k-fold cross-validation. Nine classification algorithms were evaluated, including Multinomial Logistic Regression, Decision Trees, Random Forest, XGBoost, Support Vector Machines, MARS, and Neural Networks, with hyperparameter tuning performed using the ANOVA racing method. Model performance showed consistently high results (ROC AUC > 0.80) across all analyzed periods. The analysis of variable importance indicates that the main determinants of telework remain relatively stable over time, although their contributions vary, with a notable increase in the influence of gender in more recent periods. The results demonstrate that telework in Brazil is driven by a combination of sociodemographic and occupational factors, reinforcing its selective nature. Overall, this thesis contributes to a deeper understanding of the multiple dimensions of telework by integrating theoretical and empirical evidence. Furthermore, the findings provide relevant insights for policymakers, organizational managers, and urban planners, highlighting how telework shapes urban dynamics and can support efforts to address contemporary urban challenges. Tipo: Tese Wed, 20 May 2026 00:00:00 GMT https://repositorio.unifei.edu.br/jspui/handle/123456789/4464 2026-05-20T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4455 Título: Análise da vulnerabilidade energética dos aproveitamentos hidrelétricos do sistema interligado nacional frente às mudanças climáticas e aos padrões de usos consuntivos da água Abstract: Climate change (CC), resulting from natural processes, has been intensified by anthropogenic activities, mainly due to the increased emission of greenhouse gases (GHGs). These changes have affected several sectors of society, including the environment, water resources, and electricity generation, among others. The latest climate assessment reports published by the Intergovernmental Panel on Climate Change (IPCC) indicate future reductions in precipitation in some regions of Brazil, an increase in the frequency and intensity of extreme events such as droughts and floods—particularly in the North and Northeast regions—and a rise in global average temperatures. These factors, combined with changes in water consumption patterns, which are also influenced by climate conditions, may further impact the Brazilian Electric Sector (BES) due to its strong dependence on hydropower generation. This study assesses the impacts of climate change and water demands on streamflows in the main river basins and hydropower plants in Brazil, as well as their effects on electricity generation. For this purpose, the MGB-IPH hydrological model was applied to simulate the historical period (1970–2014) and future scenarios (2015–2100) under climate change conditions. The simulations were driven by projections from five climate models from the NASA Earth Exchange Global Daily Downscaled Projections/Coupled Model Intercomparison Project Phase 6 (NEX-GDDP/CMIP6) dataset, considering two greenhouse gas emission scenarios, namely the Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), as well as projections of consumptive water uses provided by the Brazilian National Water and Basic Sanitation Agency (ANA). The energy assessment proposed in this study is based on Natural Inflow Energy (NIE), since this variable is directly related to streamflows at hydropower plants. The results show that all five climate models consistently project reductions in streamflows for the future scenarios at the major hydropower plants evaluated. However, the models differ regarding the magnitude of these reductions. The NIE projections obtained with the CanESM5 model indicate the largest decreases, reaching reductions of up to 100% in the Paraguaçu River Basin. In contrast, the projections generated by the INM model indicate positive variations, with increases approaching 10%. For the remaining climate models, reductions in NIE average around 20%. The most significant findings of this study highlight how climate change and increasing water consumption may affect hydropower generation and further increase the vulnerability of the sector. Therefore, the results presented here can provide valuable information to support planning and decision-making by responsible agencies, enabling the implementation of measures to mitigate future impacts on hydropower generation and enhance the resilience of the Brazilian electricity sector. Tipo: Tese Fri, 13 Mar 2026 00:00:00 GMT https://repositorio.unifei.edu.br/jspui/handle/123456789/4455 2026-03-13T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4448 Título: Projeto e análise de viabilidade econômica do pré-tratamento de casca de café para produção de briquetes e aplicação em bioenergia Abstract: Brazil, the world's largest coffee producer and exporter, generates substantial amounts of coffee husks, the main solid residue from wet processing. Recent research has demonstrated its energy potential. However, the high moisture, low density and irregular particle size require pre-treatment for efficient thermochemical conversion. This study aimed to design and perform the economic analysis of a coffee husk pre-treatment plant, including drying, comminution and densification steps, for the production of briquettes. The economic feasibility of the bioenergy plant, i.e., the pre-treatment associated with the combustion of briquettes for electricity generation, was also analyzed. Sensitivity analyses of the design and economic variables were performed, focusing on the influence of the drying operating conditions. The Net Present Value (𝑁𝑃𝑉), Internal Rate of Return (𝐼𝑅𝑅), simple payback (𝑃) and discounted payback (𝑃𝑑) were evaluated. The Levelized Cost of Production per Briquette Mass (𝐿𝐶𝑀) for the pre-treatment plant and the Levelized Cost of Electricity (𝐿𝐶𝑂𝐸) for the bioenergy plant were calculated. The results demonstrated that the commercialization of the briquettes was viable, with 𝑁𝑃𝑉 in the range of US$ 38,635 to US$ 192,046, 𝐼𝑅𝑅 of 11.62% to 11.95%, simple payback of 5.66 to 5.74 years and discounted payback of 9.27 to 9.48 years. The lowest 𝐿𝐶𝑀 of R$ 602 t−1 (US$ 0.115 kg−1) occurred from higher values of drying air temperature (120 ºC), bed height (4 cm) and biomass flow rate (1500 kg h−1). Under these conditions, the minimum price (𝑃𝑏𝑟𝑖𝑞) and sales value (𝑉𝑏𝑟𝑖𝑞) of the briquette were R$ 609 t−1 (US$ 0.116 kg−1) and R$ 811 t−1 (US$ 0.155 kg−1), respectively, within the ranges reported in the literature and some advertisements in the Brazilian market. However, 𝑉𝑏𝑟𝑖𝑞 was not attractive when compared to firewood, which may make it difficult to accept coffee husk briquettes in practice, if there are no tax incentives. The bioenergy plant was unfeasible, with an average 𝑁𝑃𝑉 of − US$ 7.92 million, mainly due to the high investment in combustion technology (63.7% of the total investment) and high drying costs (56.8% of the operating costs). The lowest 𝐿𝐶𝑂𝐸 (US$ 198.58 MWh−1) was achieved at the highest air temperature in the optimistic scenario (𝐶𝑢𝑛 = US$ 2,000 kW−1), but above the base tariff (US$ 67.32 MWh−1). Air temperature, followed by bed height, were the drying variables with the greatest influence on the bioenergy plant economics, while air velocity was inelastic. The unitary cost of conversion technology (𝐶𝑢𝑛) was the economic variable with the greatest impact. Therefore, in addition to considering economic improvements in drying, it is also necessary to reduce the capital costs of combustion or investigate more affordable conversion technologies. The pre-treatment plant thus emerges as the most promising alternative for the utilization of wet-processed coffee husks. Tipo: Tese Sat, 13 Jun 2026 00:00:00 GMT https://repositorio.unifei.edu.br/jspui/handle/123456789/4448 2026-06-13T00:00:00Z