Customer loyalty and retention are crucial for digital platforms, yet systematic studies integrating technological innovation and loyalty strategies remain scarce. This study addresses this gap by conducting a bibliometric analysis of key factors influencing customer retention in the digital environment. Our research employs a quantitative bibliometric approach using the Biblioshiny and Bibliometrix tools (RStudio 2022.02), analyzing 300 scientific articles from the Web of Science database (2021–2024). This study applies bibliometric techniques such as descriptive metrics, bibliographic coupling, co-citation, and scientific collaboration mapping to identify trends and thematic clusters. Our findings indicate that emerging technologies, including artificial intelligence and big data, significantly impact customer experience, trust, and engagement. Personalization and digital innovation emerge as fundamental drivers of customer retention, offering strategic insights for companies aiming to strengthen competitiveness in the global digital market.

The automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims to implement and analyze a predictive model based on machine learning within the automotive industry, validating its capability to reduce the impact of unplanned downtime. The implementation process involved identifying the central problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP), and selecting critical failure modes based on the Risk Priority Number (RPN) derived from the Process Failure Mode and Effects Analysis (PFMEA). Predictive algorithms were implemented to select the best-performing model based on error metrics. Data were collected, transformed, and cleaned for model preparation and training. Among the five machine learning models trained, Random Forest demonstrated the highest accuracy. This model was subsequently validated with real data, achieving an average accuracy of 80% in predicting failure cycles. The results indicate that the predictive model can effectively contribute to reducing the financial impact caused by unplanned downtime, enabling the anticipation of preventive actions based on the model’s predictions. This study highlights the importance of multidisciplinary approaches in Production Engineering, emphasizing the integration of machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the feasibility and effectiveness of predictive models in contributing to sustainability.

Poverty reduction is a complex global challenge where renewable energy emerges as a promising solution, particularly in resource-rich countries like Brazil. Renewable energy can enhance conditions in vulnerable communities without exacerbating climate change. This paper reviews the scientific literature on the role of renewable energy in alleviating energy poverty, focusing on Brazil and its global partnerships. Using the Web of Science database with the keywords poverty, renewable energy, and Brazil, this study analyzes 38 articles. The inclusion criteria were broad, focusing on the relationship between poverty in Brazil and renewable energy use. Keywords such as specific types of renewable energy (e.g., solar, wind) were excluded to avoid omitting important renewable sources. This review encompasses theoretical models, discourse analyses, case studies, and systematic reviews, addressing topics from rural electrification to socio-economic factors influencing sustainable energy adoption. Despite growing interest and international collaboration, research gaps remain, highlighting the need for increased academic engagement. The objective of this study is to address the existing gap in the literature concerning the impact of renewable energies on energy poverty in Brazil. Through a comprehensive review, this work maps current knowledge, identifies gaps, and highlights trends by integrating socio-economic analyses with technical assessments for a holistic perspective.

The automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims to apply a predictive model based on machine learning within the automotive industry, validating its capability to reduce the impact of unplanned downtime. The implementation process involved identifying the central problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP), and selecting critical failure modes based on the Risk Priority Number (RPN) derived from the Process Failure Mode and Effects Analysis (PFMEA). Predictive algorithms were implemented to select the best-performing model based on error metrics. Data were collected, transformed, and cleaned for model preparation and training. Among the five machine learning models trained, Random Forest demonstrated the highest accuracy. This model was subsequently validated with real data, achieving an average accuracy of 80% in predicting failure cycles. The results indicate that the predictive model can effectively contribute to reducing the financial impact caused by unplanned downtime, enabling the anticipation of preventive actions based on the model's predictions. This study highlights the importance of multidisciplinary approaches in Production Engineering, emphasizing the integration of machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the feasibility and effectiveness of predictive models in contributing to sustainability.

The main objective this paper is to assess the thermal model of the convective heat transfer from photovoltaic solar modules. Experiments were carried out in laboratory and in-situ conditions. Whereby the convective heat transfer in low wind speed was considered, while evaluating the relation between the classical and natural convective situations. The form as that system change heat with the environment causes impact in efficiency of the generation of energy. The energy balance is described by the incident irradiation in the photovoltaic modules and the forms that system converts that energy as irradiation, convection, conduction and generation of a potential difference. The classical problem of flat inclined plates was used to understand the behavior of the photovoltaics module. The existing correlations using dimensionless numbers, developed in experimental studies and numerical simulations, have limitations in their use and in many cases do not provide results that approximate a photovoltaic system in operation. In this work, an experimental bench was developed and placed in a natural convection chamber with a data acquisition system for temperature, voltage and current. The small-scale experiments allowed realistic estimation, for the assessment of the thermal performance in a real power plant configuration. The experimental data showed a suitable correlation, of more than 0.9, with the installed system, suggesting its capability to predict the behavior of the system.

The present work explores the assessment of lumped thermal modelling of solar PV modules and its convective heat transfer terms. The model of solar energy conversion in PV modules requires an accurate description of the electrical and thermal mechanisms involved in energy conversion. The variability of the heat flux over the surface is observed. It allows the uncertainty in the predicting of PV cell temperature and consequently to the conversion efficient is propagated to converted power, inducing an observed error in the efficiency of order of 0,1% for each °C (degree Celsius) and an error of 1W for each °C (degree Celsius). This error was estimated in the generation of a solar plant located in Pilar community in Rio de Janeiro and the possibility of a generation error of 0.6% was observed.