In this study, we present solutions for the investigation of anisotropic bosonic dark-matter stars (DMS) within the framework of the regularized four-dimensional Einstein–Gauss–Bonnet (4D EGB) theory of gravity. Using dimensional regularization, we solve modified Tolman–Oppenheimer–Volkoff equations for a self-interacting complex scalar field in the dilute polytropic regime, p_r = K ρ ^2 , with anisotropy parameterized as σ = β p_r ( 1 - e^-2λ) . We perform a comprehensive numerical analysis across the (α ,β ) parameter domain, where α =(0,2,4,6,8) textkm^2 and β =(-2,-1.5,-1,0) , to examine Mass–Radius relations and evaluate multiple stability indicators including static equilibrium dM/dp_c , sound-speed causality, the radial adiabatic index, and energy conditions. The positive Gauss–Bonnet coupling increases both the maximum mass and compactness (e.g., M_max≈ 1.62 M_⊙ at α =0 rising to ≈ 2.09 M_⊙ at α = 8 km^2 ). Conversely, negative anisotropy leads to a reduction in the maximum mass and compactness (e.g., from ≈ 2.21 M_⊙ at β =0 to ≈ 1.73 M_⊙ at β =-2 ). These results highlight how modified-gravity parameters can significantly affect the observable properties of compact dark-matter configurations. Finally, this study extends previous analyses of anisotropic bosonic dark-matter stars in Einstein gravity and gravity’s rainbow and systematically analyzes these stars within the framework of regularized 4D EGB gravity.
This paper introduces a four-parameter extended odd log-logistic-Lindley distribution from which moments, hazard, and quantile functions are then obtained. The statistical properties of this distribution show the high flexibility of the proposed distribution. The maximum likelihood and least-squares estimators of the extended odd log-logistic-Lindley parameters are studied. Moreover, a simulation study is carried out for evaluating the performance of the estimation methods, and the usefulness of the new distribution is illustrated using two real data sets. Finally, Bayesian analysis and efficiency of Gibbs sampling are provided on the basis of two real data sets.
This study numerically investigates the compressive buckling stability of Aluminium stiffened plates, which are critical components in marine structures and heavy machinery used in the textile industry, with a particular focus on the effects of crack orientation and eccentricity. Using linear finite element analysis (FEA), the plates were modelled with central and off-centre cracks under both clamped-free and simply supported-free boundary conditions. The analysis systematically varied the crack angle and its distance from the plate's centre to understand their influence on buckling modes and critical load coefficients. The results demonstrate a significant reduction in buckling stability due to the presence of cracks. This reduction is highly dependent on the crack's characteristics, as the most critical case for buckling occurs when the crack is oriented parallel to the stiffeners (at a 90 degrees angle), significantly lowering the buckling load for both boundary conditions. Furthermore, the research findings of this article reveal that crack eccentricity plays a crucial role. For simply supported-free plates, the buckling load is most critically reduced when the crack is closer to the loaded edge (ey=80mm), while for clamped-free plates, the opposite is true. Quantitatively, a crack can decrease the buckling coefficient by approximately 11% in clamped-free plates and 17% in simply supported-free plates in the most critical configurations. This research provides essential insights into the failure mechanisms of stiffened plates, emphasising the importance of considering crack parameters in structural design and maintenance to ensure the integrity of marine structures.
Polymer flooding is widely established as an effective mobility-control technology in enhanced oil recovery (EOR), yet its deployment in high-salinity and high-temperature (HSHT) reservoirs remains inconsistent and difficult to predict. Existing reviews have primarily catalogued polymer chemistries, degradation mechanisms, field cases, and simulation practices as parallel themes. While valuable, this compartmentalized approach implicitly assumes that polymer selection, degradation modeling, water quality management, and operational design are sequential and largely independent decisions. Field evidence from HSHT reservoirs contradicts this assumption.This review advances a new conceptual framework: HSHT polymer flooding is a dynamically coupled, feedback-controlled system in which chemical degradation, adsorption-desorption hysteresis, mechanical retention, injectivity evolution, and operational variables co-evolve over time. We formulate a testable hypothesis that viscosity loss and injectivity impairment in HSHT reservoirs are governed primarily by oxidative-ionic degradation and retention feedback mechanisms, rather than by intrinsic thermal stability alone. In this view, temperature acts as an accelerator within a multi-mechanism network, not as the dominant failure variable.Through systematic synthesis of laboratory experiments, pilot studies, and field implementations, we demonstrate that polymer performance trajectories in HSHT conditions are nonlinear and path-dependent. Degradation alters molecular weight distribution and charge density; these changes modify adsorption behavior and mechanical entrapment; retention shifts injectivity and shear exposure; shear accelerates further degradation. Conventional screening protocols and commercial simulators fail to capture these coupled dynamics, leading to structural optimism in recovery forecasts.Building on this synthesis, we propose a closed-loop deployment workflow that integrates polymer chemistry selection, water-treatment design, mechanistic laboratory testing, calibrated numerical modeling, and real-time field surveillance within an adaptive decision architecture. From this framework, we derive an actionable screening matrix that enables reservoir-specific risk classification based on coupled chemical-operational sensitivities.By reframing HSHT polymer flooding as a co-designed chemical-operational-modeling system governed by feedback interactions, this review moves beyond critique to provide a testable paradigm and a practical decision methodology. The resulting framework offers a pathway toward predictable, scalable polymer EOR deployment in harsh reservoir environments.
The level of pollutant emissions from electricity generation, particularly carbon dioxide and other greenhouse gases, is heavily dependent on the energy source—namely, whether it is from renewable or non-renewable resources such as coal, oil, and gas. The primary aim of this paper was to study the effect of electricity generation from renewable and non-renewable resources as well as the effect of technological innovations on the carbon dioxide emissions using the environmental Kuznets curve (EKC). This study employs the Autoregressive Distributed Lag (ARDL) approach and the Error Correction Model (ECM) for the 1990–2020 period. The results of this research confirm the EKC for Iran and show that the country is close to its turning point. These results also show that, of all factors affecting environmental pollution, economic growth had the highest positive impact on CO2 emissions in Iran, and technological innovation had a positive effect on CO2 emissions in the long run. Production of electricity using renewable resources had a negative but insignificant effect on CO2 emissions, although this variable showed a significant negative impact on the emissions in the short run. This study concludes that achieving ambitious renewable energy development goals requires a comprehensive approach with policy reforms, attracting sustainable investments, and facilitating implementation processes to effectively leverage the country's immense potential.
