In this paper, $$f(R,T,R_{\mu \nu } T^{\mu \nu }$$f ( R , T , R μ ν T μ ν ) gravity is considered. It is a modified theory of gravity that exhibits a strong coupling of gravitational and matter fields. Therefore, if gravity is governed by this model a number of issues must be re-examined. In this context, the question of causality and its violation is studied. Such analysis is carried out using the Gödel-type solutions. It is shown that this model allows both causal and non-causal solutions. These solutions depend directly on the content of matter present in the universe. For the non-causal solution, a critical radius is calculated, beyond which causality is violated. Taking different matter contents, an infinite critical radius emerges that leads to a causal solution. In this causal solution, a natural relationship emerges between the parameters that determine the matter considered.

The search for renewable energy sources has driven the desire for knowledge about the energy source of photovoltaic systems and the factors that can influence it. This study applies powerful machine learning techniques to identify the best model for predicting photovoltaic energy generation, using meteorological variables as key inputs. The energy generated data were collected in a photovoltaic plant installed in the city of Pontes e Lacerda, while the meteorological variables were collected from nearby INMET stations. Four different techniques were employed, including SVR (Support Vector Machine), Random Forest, LSTM Neural Network and SARIMAX. The results showed that the Random Forest technique presented the best performance, with calculated values for the coefficient of determination (R²) and Willmott index of 0.909 and 0.972, respectively, standing out for accuracy and efficiency in scenarios where data is available. On the other hand, it was revealed that the model generated by the SARIMAX technique had great potential for applications where there is little data availability, presenting satisfactory estimates. This study highlights the practical applications of machine learning in optimizing photovoltaic power generation plant design and management, including improving energy prediction accuracy, enabling better decision-making, and supporting the expansion of renewable energy sources, especially in areas with scarce data. The findings also reinforce the critical role of meteorological variables in influencing the performance of photovoltaic systems, offering valuable insights for future applications in energy systems planning and operation.

Microfluidic devices are increasingly valuable for their compact size and ability to handle tiny fluid volumes, making efficient mixing at this scale (micromixing) a critical focus. This research aims to optimize micromixer geometries to improve mixing efficiency while controlling pressure drop, providing a method that balances performance and computational cost. Building on previous work, we introduce a novel optimization framework in microfluidics combining computational fluid dynamics (CFD) and machine learning (ML) techniques, particularly Gaussian process (GP) modeling with Genetic Algorithm (GA) optimization. Inspired by a Y-type micromixer design with cylindrical grooves on the main channel’s surface and internal obstructions, our study examines the impact of circular obstructions on mixing percentage and pressure drop under varying obstruction diameter and offset. Simulations conducted using OpenFOAM software generate data for a reduced-order GP model, which provides model uncertainty. The geometry is then optimized using the GA algorithm on the reduced model. Results indicate that medium-sized obstructions (137 mm diameter, 10 mm offset) near the channel wall achieve optimal mixing and pressure performance, closely aligning with previous studies. The uncertainties, recorded as 3.9% and 21.5% for mixing percentage and pressure drop, respectively, further validate the robustness of our model. This study highlights an effective, uncertainty-quantified optimization process that leverages CFD and ML integration, setting a foundation for efficient microfluidic design strategies.

Atmospheric aerosols significantly influence climate systems and hydrological processes, but their impacts on evapotranspiration remain insufficiently understood, particularly in tropical savanna regions. This study investigates the direct and indirect effects of aerosol optical depth (AOD) on reference evapotranspiration ($E{T}_0$) in the Mato Grosso Cerrado, Brazil, a biome characterized by pronounced seasonal climatic variations. Using data collected from the AERONET network at Fazenda Miranda, AOD was analyzed alongside meteorological variables such as air temperature, global radiation, and $E{T}_0$, estimated using the FAO Penman–Monteith method. The results reveal distinct seasonal patterns, with aerosols having a more pronounced influence during the dry season. Positive correlations were observed between AOD and air temperature, while negative correlations were found between AOD and global radiation, especially during the dry season. The relationship between AOD and $E{T}_0$ varied between years and seasons, with significant reductions in $E{T}_0$ linked to high aerosol concentrations during the dry period. These findings demonstrate that aerosols play a critical role in modulating evapotranspiration and radiation balance, particularly in regions affected by biomass burning. This study provides valuable insights into the interplay between aerosols, climate variables, and hydrological processes, contributing to a better understanding of aerosols’ impacts on tropical ecosystems.