In Iraq, solar radiation on building roofs can exceed 1100 W/m2, affecting indoor thermal comfort. This study aims to reduce the heat gain in buildings in hot climates. Thus, it introduces an experimental method to enhance roof thermal performance by integrating phase change material in aluminum pipes within a concrete slab, alongside water pipes linked to a geothermal source at a flow rate of 0.75 L/min. The phase change material used for the current experiments is paraffin with a melting point of 41 degrees C, which was encapsulated in aluminium pipes with dimensions of 2 cm & times; 4 cm and a length of 70 cm. Three roofs models were evaluated: a traditional roof (Model-1), one combining both PCM and geothermal elements (Model-2), and one with an embedded water pipe and geothermal source (Model-3). These models of roofs were placed on identical, dimensionally matched enclosures; each enclosure is 80 cm in length, 80 cm in width, and 100 cm in height. A comprehensive thermal, economic and environmental performance analysis was carried out. The results showed that the highest maximum inner surface temperature reduction was 57.7 % with the Model-2 compared to the measurements obtained from the traditional ceiling. Also, the effect of using phase change material pipes in a slab with a geothermal source was observed as offsetting the temperature by 9 % in the next cycle. Furthermore, reducing the heat gain in the Model-2 resulted in savings in electricity consumption costs and a reduction in CO2 emissions of 0.23 USD/day and 1.83 kg/day, respectively. These findings demonstrate that adding PCM to geothermal systems can improve ceiling performance considerably while providing both financial and environmental advantages.
The injection of carbon dioxide into depleted oil and gas reservoirs has emerged as a promising method for enhanced oil recovery (EOR) and safe carbon storage. This process significantly influences the density of CO2-hydrocarbon mixtures, a critical property that directly affects EOR efficiency and storage integrity. However, accurately predicting mixture density remains challenging due to the complex and nonlinear behavior of hydrocarbons, and many existing empirical models show limited reliability under varying conditions. This study primarily aims to develop robust tools leveraging advanced algorithms, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Bayesian Neural Network (BNN), Adaptive Boosting (AdaBoost), and Random Forest (RF) to accurately estimate the density of CO2-hydrocarbon mixtures, utilizing a rich and extensive databank (17,081). The model inputs included temperature (T), pressure (P), molecular weight (Mw), pseudo-critical temperature (Tpc), pseudo-critical pressure (Ppc), and the mole fraction of CO2. The results were evaluated against several equations of state (EoSs), including the Cubic-Plus-Association (CPA) model developed in this study, as well as Peng-Robinson (PR), Soave-Redlich-Kwong (SRK), and Redlich-Kwong (RK) models for representative CO2-hydrocarbon mixtures (n-Butane, Propane, and Hexadecane). For these specific mixtures, the CPA and PR models showed the best performance among the examined EoSs, while the XGBoost model achieved the highest overall accuracy, with an R2 of 0.9968 and an average absolute percent relative error (AAPRE) of 0.7647. High R2 values (0.97-0.99) indicate that LightGBM, BNN, AdaBoost, and RF all achieved strong predictive performance. Furthermore, trend analysis confirmed that the XGBoost model can accurately capture density variations in response to changes in pressure variable. Moreover sensitivity analysis indicated that pressure with relevancy factor of (0.2045) has the most significant effect on the output. The leverage technique showed that over 96% of the dataset is statistically reliable. In the final stage, Shapley additive explanations (SHAP) analysis demonstrated that both Tpc and Ppcpositively influence the output parameter. These results demonstrate that the XGBoost model provides a reliable and accurate alternative to experimental methods for predicting CO2-hydrocarbon density across a wide range of operating conditions.
In this paper, we investigate the cardinality of the distance set Δ(A,B) for sets A and B⊂Fqd where A or B is contained in a k-dimensional affine subspace over a finite field. Assuming that B lies in a k-coordinate plane up to translations and rotations, we prove that if |A||B|>2qd, then |Δ(A,B)|>q2, where |Δ(A,B)| denotes the number of distinct distances between elements of A and B. In particular, we show that our result recovers the sharp (d+1)/2 threshold for the Erdős–Falconer distance problem in odd dimensions, where distances are determined by a single set. As a further application, we also obtain an improved result on the Box distance problem posed by Borges, Iosevich, and Ou, in the case where 2 is a square in Fq.
Abstract This study tested accretion onto a charged scalar black hole (BH) model within a massive-gravity framework. In this context, the analysis emphasizes the detailed dynamics of infalling matter, the determination of sonic points, and the response of different test fluids under varying conditions. The background spacetime is described by a charged dilatonic BH solution, and the conservation laws for particle flux and energy-momentum are explicitly formulated to allow treatment as a dynamical system. By recasting the accretion equations into an autonomous system, the critical conditions corresponding to sonic transitions are systematically identified and analyzed. Also, many fluid models are considered, including isothermal, barotropic, and polytropic fluids, covering regimes from ultra-stiff to sub-relativistic. Each fluid model produces distinct modifications to the Hamiltonian trajectories and radial velocity profiles, thereby influencing the overall accretion pattern. The parameters of massive gravity, particularly $$c_1$$ c 1 and $$c_2$$ c 2 , shape the horizon structure, determine the positions of critical points, and potentially affect the formation and stability of accretion disks. The mass accretion rate, expressed in terms of metric function, fluid energy density, and radial inflow velocity, shows a decreasing trend with increasing $$c_1$$ c 1 and $$c_2$$ c 2 , which implies a reduction in accretion efficiency. Additionally, the radiative properties of thin disks, including emitted flux, disk temperature, radiative efficiency, and luminosity, are suppressed for higher values of these parameters. In this case, the results illustrate that massive gravity not only modifies the behavior of matter inflow but also substantially diminishes the radiative output, offering potentially observable differences that can distinguish charged scalar BHs in massive gravity from their counterparts in standard general relativity (GR). We also numerically model matter accretion via the Bondi–Hoyle–Lyttleton (BHL) mechanism in the framework of massive gravity, showing that the modified shock cone structure, mass accretion rate, and the resulting QPOs are consistent with theoretical expectations, and highlighting their observability and differences from GR.
When generative artificial intelligence (GenAI) technologies emerged a few years ago, they were welcomed as innovative tools with the potential to transform teaching and learning. Yet now this initial optimism has been replaced by scepticism and concern over their role in perpetuating digital neocolonialism through linguistic hierarchies embedded in these tools. This study addresses these issues by examining how language teachers in the Global South perceive and negotiate pedagogical, ethical, and policy implications of generative artificial intelligence in English language education. Drawing on qualitative survey data and semi-structured interviews with 12 multilingual teachers across the Global South, the study explores their perceptions of GenAI’s pedagogical impact and their awareness of biases that AI tools can promote. Findings reveal teachers operating as policy arbiters in institutional vacuums, demonstrating variable but emerging critical awareness of how AI tools privilege certain English varieties, erase local linguistic practices, and reflect Global North epistemologies. The study argues that equitable AI integration requires systemic change: participatory policy frameworks, critical AI literacy, and fundamental redesign of tools to recognise linguistic and cultural diversity beyond dominant Western norms.
