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Published online: 26 November 2024 This study investigates the relationships between social and political trust and views on energy affordability, which are crucial for promoting sustainable energy practices. The findings indicate that social and especially political trust are negatively correlated with perceptions of energy affordability. The study also finds that the probability of being highly concerned about energy affordability is declining in the level of trust, especially in countries, as those in Southern European, that prioritize energy affordability. These findings confirm the critical role of trust in effective energy policies, fostering public support for transitions to sustainable energy systems. The study recommends that policymakers enhance transparency, accountability, and public engagement to build trust, thereby improving perceptions of energy affordability and supporting sustainable energy transitions.

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

Abstract This paper investigates the forced convection of alumina-water nanofluids within helical tubes, maintaining a constant wall temperature and assuming thermal equilibrium between the nanoparticles and the base fluid. The nanofluid model incorporates the effects of alumina (Al2O3) nanoparticle volume fraction, diameter, and temperature on thermophysical properties. The governing equations are solved using the Forward-Time Central-Space Finite Volume method in conjunction with the simple algorithm. Numerical results are validated against experimental data, demonstrating high accuracy. The study explores the effects of pitch size, curvature ratio, nanoparticle volume fraction, nanoparticle diameter, and Reynolds number on velocity contours, temperature profiles, secondary flow, thermophysical properties, friction coefficient, and heat transfer rate. Additionally, the figure of merit evaluates the impact of these parameters on the thermal performance of the system. The results indicate that an increase in Reynolds number and nanoparticle diameter negatively affects thermal performance, while higher nanoparticle volume fraction, curvature ratio, and pitch size enhance it. Furthermore, incorporating nanoparticles in straight tubes proves to be more advantageous compared to helical tubes. This study tested volumetric ratios of 1%, 2%, and 4%, which resulted in increases in heat transfer coefficients of 21%, 32%, and 43%, respectively, compared to pure water under similar conditions, such as Reynolds number and coil pitch.

The climate crisis, driven by greenhouse gas (GHG) emissions and environmental degradation, demands a transition to renewable energy for sustainable development. This paper analyzes the asymmetric effects of hydroelectric and biomass energy consumption on the ecological footprint (EFP) for 24 OECD countries from 1970 to 2022. By using a combination of advanced econometric approaches, including Method of Moments Quantile Regression (MMQR), Generalized Linear Models (GLM), and Robust Least Squares (RLS), with machine learning techniques such as Multivariate Adaptive Regression Splines (MARS) and Neural Networks (NN), this study will be able to identify complex nonlinearities that are not captured by traditional models. The results reveal that hydroelectric energy significantly reduces the EFP, particularly in high-pollution contexts, while biomass energy consumption worsens environmental degradation. These findings emphasize the urgent need for targeted policies to maximize the benefits of renewable energy sources and mitigate their risks. The study contributes to the literature by offering a comprehensive framework to analyze the environmental impacts of renewable energy, emphasizing the importance of methodological diversity and advanced modeling techniques as ways to achieve sustainability goals.

Over the past billion years, the fungal kingdom has diversified to more than two million species, with over 95% still undescribed. Beyond the well-known macroscopic mushrooms and microscopic yeast, fungi are heterotrophs that feed on almost any organic carbon, recycling nutrients through the decay of dead plants and animals and sequestering carbon into Earth's ecosystems. Human-directed applications of fungi extend from leavened bread, alcoholic beverages and biofuels to pharmaceuticals, including antibiotics and psychoactive compounds. Conversely, fungal infections pose risks to ecosystems ranging from crops to wildlife to humans; these risks are driven, in part, by human and animal movement, and might be accelerating with climate change. Genomic surveys are expanding our knowledge of the true biodiversity of the fungal kingdom, and genome-editing tools make it possible to imagine harnessing these organisms to fuel the bioeconomy. Here, we examine the fungal threats facing civilization and investigate opportunities to use fungi to combat these threats.