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The use of biofuels represents a promising means of achieving a sustainable future and offers considerable economic and environmental benefits. Since they are derived from organic sources, such as vegetable oils and animal fats, biofuels can mitigate the effects of greenhouse gas emissions, improve air quality, support local agriculture, create employment opportunities, and enhance energy security by reducing dependence on fossil fuels. However, introducing these alternative fuels to the aviation sector remains a significant challenge. Thus, it is vital to investigate the potential of sustainable aviation fuel (SAF) and discover how to overcome the technological obstacles to its integration into mainstream aviation to attain broader decarbonization objectives. This article seeks to contribute to a discussion about SAF by examining how it has evolved and its connections to related patents. This article is a comprehensive study of biofuel innovation, highlighting the complex relationships between academia, industry, and other stakeholders. It is hoped that the findings from this study will provide a clearer understanding of the catalysts involved in SAF innovation and provide valuable insights for policymakers, academics, and professionals in the field who are committed to shaping the trajectory of sustainable energy technologies in the future. [ABSTRACT FROM AUTHOR]

Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, improve forecasting methodologies, and achieve sustainable development goals. The evaluation encompasses the following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt–Winters, Trigonometric Seasonality Box–Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS), and draws attention to their respective strengths and limitations. Its findings reveal unique capabilities among the models, with SARIMA excelling in tracing seasonal patterns, FB Prophet demonstrating its potential applicability across various sectors, Holt–Winters adept at managing seasonal fluctuations, and TBATS offering flexibility albeit requiring significant data inputs. Additionally, the investigation explores the effect of external factors on energy consumption, by establishing connections through the Granger causality test and conducting correlation analyses. The accuracy of these models is assessed with and without exogenous variables, categorized as economical, industrial, and climatic. Ultimately, this investigation seeks to add to the body of knowledge on energy demand prediction, as well as to allow informed decision-making in sustainable energy planning and policymaking and, thus, make rapid progress toward SDG7 and its associated targets. This paper concludes that, although FB Prophet achieves the best accuracy, SARIMA is the most fit model, considering the residual autocorrelation, and it predicts that Brazil will demand approximately 70,000 GWh in 2033. [ABSTRACT FROM AUTHOR]

Burnout is most commonly defined as a syndrome characterized by emotional exhaustion, cynicism, and ineffectiveness, which occurs in response to chronic stressors at work. It can adversely affect health workers' physical and mental health, and the quality of care provided. The COVID-19 pandemic increased stressors and could impact burnout prevalence in this group. There is a lack of information regarding the prevalence of burnout among hospital health workers in Brazil. A newer definition of burnout has been proposed that considers three different clinical profiles: the frenetic, underchallenged and worn-out subtypes. This differentiation could lead to interventions tailored for each subtype. The present study aimed to estimate the prevalence of burnout, its subtypes, and associated factors in workers of a public hospital network in Brazil, during the pandemic. A total of 143 randomly selected participants answered an online form that included sociodemographic and occupational items, and the Burnout Clinical Subtypes Questionnaire, a summarized version. This questionnaire evaluates three burnout dimensions (overload, lack of development, neglect) that can be used to discriminate the three burnout subtypes (frenetic, underchallenged, worn-out, respectively); higher scores indicate higher burnout levels. The prevalence of burnout was high (53.85%), similar to other studies during the pandemic. The most common subtypes were 'frenetic' (34.97%), characterized by increased efforts to meet work demands, to the point of neglecting personal needs, and 'lack of development' (23.78%), characterized by a sense that work is uninteresting and does not contribute to personal development, and a perfunctory behavior towards tasks. Age was associated with burnout: workers with less than 51 years presented higher levels of burnout. These findings indicate the need for effective interventions to prevent and/or treat burnout. The assessment of burnout subtypes can allow managers to better understand the processes affecting employees, and inform actions to improve workforce health. [ABSTRACT FROM AUTHOR]

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