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.
Millets, classified as climate-smart Nutri-cereals, are characterized by considerable postharvest losses at a time when the global production of these cereals has been gradually escalating. This review briefs smart sensor technologies ranging from physical, chemical, and optical to biosensors interfaced effectively with machine learning algorithms, DL algorithms for automatic assessment, identification of contamination, as well as predictive forecasts, respectively. Solutions based on operational issues of millet-scaled sensor calibration, along with reduced on-farm applicability of existing lab-based models, will be described. Additionally, a step-wise growth plan for a low-cost, handheld, farmer-friendly sensor will be discussed.
