We analyze the effects of thermal fluctuations on a regular black hole (RBH) of the non-minimal Einstein–Yang–Mill theory with gauge field of magnetic Wu–Yang type and a cosmological constant. We consider the logarithmic corrected entropy in order to analyze the thermal fluctuations corresponding to non-minimal RBH thermodynamics. In this scenario, we develop various important thermodynamical quantities, such as entropy, pressure, specific heats, Gibb’s free energy and Helmholtz free energy. We investigate the first law of thermodynamics in the presence of logarithmic corrected entropy and non-minimal RBH. We also discuss the stability of this RBH using various frameworks such as the \(\gamma \) factor (the ratio of heat capacities), phase transition, grand canonical ensemble and canonical ensemble. It is observed that the non-minimal RBH becomes globally and locally more stable if we increase the value of the cosmological constant.

The aim of this paper is to examine the tidal forces occurring in a Kiselev black hole surrounded by radiation and dust fluids. It is noted that the radial and angular components of the tidal force change the sign between event and Cauchy horizons. We solve the geodesic deviation equation for radially free-falling bodies toward Kiselev black holes. We explain the geodesic deviation vector graphically and point out the location of the event and Cauchy horizons for specific values of the radiation and dust parameters.

Climate change has emerged as one of the most significant challenges in modern agriculture, with potential implications for global food security. The impact of changing climatic conditions on crop yield, particularly for staple crops like wheat, has raised concerns about future food production. By integrating historical climate data, GCM (CMIP3) projections, and wheat-yield records, our analysis aims to provide significant insights into how climate change may affect wheat output. This research uses advanced machine learning models to explore the intricate relationship between climate change and wheat-yield prediction. Machine learning models used include multiple linear regression (MLR), boosted tree, random forest, ensemble models, and several types of ANNs: ANN (multi-layer perceptron), ANN (probabilistic neural network), ANN (generalized feed-forward), and ANN (linear regression). The model was evaluated and validated against yield and weather data from three Punjab, Pakistan, regions (1991–2021). The calibrated yield response model used downscaled global climate model (GCM) outputs for the SRA2, B1, and A1B average collective CO₂ emissions scenarios to anticipate yield changes through 2052. Results showed that maximum temperature (R = 0.116) was the primary climate factor affecting wheat yield in Punjab, preceding the $T_{min}$ (R = 0.114), while rainfall had a negligible impact (R = 0.000). The ensemble model (R = 0.988, nRMSE= 8.0%, MAE = 0.090) demonstrated outstanding yield performance, outperforming Random Forest Regression (R = 0.909, nRMSE = 18%, MAE = 0.182), ANN(MLP) (R = 0.902, MAE = 0.238, nRMSE = 17.0%), and boosting tree (R = 0.902, nRMSE = 20%, MAE = 0.198). ANN(PNN) performed inadequately. The ensemble model and RF showed better yield results with $R^2$ = 0.953, 0.791. The expected yield is 5.5% lower than the greatest average yield reported at the site in 2052. The study predicts that site-specific wheat output will experience a significant loss due to climate change. This decrease, which is anticipated to be 5.5% lower than the highest yield ever recorded, points to a potential future loss in wheat output that might worsen food insecurity. Additionally, our findings highlighted that ensemble approaches leveraging multiple model strengths could offer more accurate and reliable predictions under varying climate scenarios. This suggests a significant potential for integrating machine learning in developing climate-resilient agricultural practices, paving the way for future sustainable food security solutions.

We investigate the accretion of test fluids onto regular black holes such as Kehagias–Sfetsos black holes and regular black holes with Dagum distribution function. We analyze the accretion process when different test fluids are falling onto these regular black holes. The accreting fluid is being classified through the equation of state according to the features of regular black holes. The behavior of fluid flow and the existence of sonic points is being checked for these regular black holes. It is noted that the three-velocity depends on critical points and the equation of state parameter on phase space.