Variation Of Model Parameters Research Articles

The impact of mass uncertainties on r-process nucleosynthesis in neutron star mergers

Theoretically predicted yields of elements created by the rapid neutron capture (r-) process carry potentially large uncertainties associated with incomplete knowledge of nuclear properties and approximative hydrodynamical modeling of the matter ejection processes. One of the dominant uncertainties in determining the ejecta composition and radioactive decay heat stems from the still unknown nuclear masses of exotic neutron-rich nuclei produced during the neutron irradiation. We investigate both the model (systematic) and parameter (statistical) uncertainties affecting nuclear mass predictions and explore their impact on r-process production, and subsequently on the composition of neutron star merger ejecta. The impact of correlated model uncertainties on masses is estimated by considering five different nuclear mass models that are known to provide an accurate description of known masses. In addition, the uncorrelated uncertainties associated with local variation of model parameters are estimated using a variant of the backward-forward Monte Carlo method, to constrain the parameter changes to experimentally known masses before propagating them consistently to the unknown masses of neutron-rich nuclei. The impact of nuclear mass uncertainties is propagated to r-process nucleosynthesis in a 1.38-1.38 M_⊙ neutron star merger model considering a large and representative number of trajectories. We find that the uncorrelated parameter uncertainties lead to ejected abundance uncertainties of 20% up to A ≃ 130 and 40% between A=150 and 200, with peaks around A ≃ 140 and A ≃ 203 giving rise to deviations around 100 to 300%. The correlated model uncertainties remain larger than the parameter ones for most nuclei. However, both model and parameter uncertainties have an important impact on heavy nuclei production. Improvements to nuclear models are still crucial in reducing uncertainties in predictions related to r-process nucleosynthesis. Both correlated model uncertainties and coherently determining parameter uncertainties are key in the sensitivity analysis.

A Mutual Information Based Multifeature Pattern Recognition Criterion for Identification and Optimization of Thermally Safe Operating Conditions for Single Diffusion‐Controlled Reactions Occurring in Isoperibolic Liquid–Liquid Semibatch Reactors

ABSTRACTPrevention of thermal runaway and establishment of safe‐economic operating conditions are crucial for the practical operation of single diffusion‐controlled liquid–liquid reactions occurring in isoperibolic semibatch reactors. Hence, in this study, a mutual information‐based multiple feature recognition criterion (MI‐MFR) was innovatively proposed, for which the maximum relevance minimum redundancy approach was introduced in feature screening; in combination with the support vector machine algorithm, various thermal behaviors can be effectively recognized. On this basis, a series of boundary diagrams were established accordingly, the impact of the variation in model parameters on which was systematically explored. Subsequently, an improved generalized thermal safety region was developed by introducing the upper thresholds in normal and cooling failure scenarios. Meanwhile, the robustness and progressiveness of the established MI‐MFR criterion were confirmed by comparison with other conventional criteria. Finally, MI‐MFR‐based procedures were constructed to identify thermal behaviors and optimize safe‐economic operating conditions in practices, based on which the optimum operating conditions of toluene mononitration were obtained.

Evaluating the Economic Efficiency of Fuel Reduction Treatments in Sagebrush Ecosystems That Vary in Ecological Resilience and Invasion Resistance

The concepts of resilience and resistance (R&R) have been used to improve wildland fuel treatment outcomes by identifying parts of the landscape that are more likely to respond well to treatment. This study examined how the economic benefits and costs of fuel treatments in sagebrush (Artemisia spp.) ecosystems varied with the resilience and resistance properties of the treatment site. Generalized ecological models were developed for the economic analysis of fuel treatments that integrated ecological succession, annual grass invasion, pinyon–juniper expansion, and wildfire to simulate ecosystem dynamics over time. The models incorporated resilience and resistance by varying model parameters related to each plant community’s ability to resist annual grass invasion and recover post-disturbance. Simulations produced estimates of the expected (ex ante) benefit–cost ratio for each treatment. The approach also considered the benefits associated with the system remaining in an ecologically favorable condition, allowing us to report a more holistic measure of the net economic benefits of fuel treatments. The results from the simulations indicated fuel treatment was economically efficient in late-successional sagebrush and early-successional juniper in mountain big sagebrush associations. For sagebrush associations where treatment was economically efficient, higher R&R status sites had higher benefit–cost ratios. The results suggested that treatment costs were more determinative of economic efficiency than treatment benefits.

Estimating descending activation patterns from EMG in fast and slow movements using a model of the stretch reflex.

Due to spinal reflex loops, descending activation from the brain is not the only source of muscle activation that ultimately generates movement. This study directly estimates descending activation patterns from measured patterns of muscle activation (EMG) during human arm movements. A simple model of the spinal stretch reflex is calibrated in a postural unloading task and then used to estimate descending activation patterns from muscle EMG patterns and kinematics during voluntary arm motion performed at different speeds. We observed three key features of the estimated descending activation patterns: (1) Within about the first 15% of movement duration, descending and muscle activations are temporally aligned. Thereafter, they diverge and develop qualitatively different temporal profiles. (2) The time course of descending activation is monotonic for slow movements, non-monotonic for fast movements. (3) Varying model parameters like the spinal reflex gain or the level of co-contraction does not qualitatively change the temporal pattern of estimated descending activation. Our findings highlight the substantial contribution of spinal reflex loops to movement generation, while at the same time providing evidence that the brain must generate qualitatively different descending activation patterns for movements that vary in their mechanical dynamics.

A Backstepping‐Based Nonlinear Controller for Glucose‐Insulin System Dynamics in Type‐1 Diabetes Patients

ABSTRACTThis paper investigates the function of the artificial pancreas, which is devised based on a dynamical backstepping approach. The Bergman's minimal model, used to describe the glucose‐insulin system, has been extended to encompass the dynamics of the insulin pump and external disturbances to closely simulate real‐world scenarios. Three techniques, namely feedback linearization, conventional backstepping, and super‐twisting sliding‐mode control, are evaluated in comparison to dynamical backstepping in the context of regulating blood glucose levels in individuals with type‐1 diabetes. In order to enhance the comparison of the controllers, we have taken into account the measurement noise and faults in the insulin pump as well. Additionally, Monte‐Carlo analysis is utilized as a practical tool to experimentally evaluate the robustness of the nonlinear controllers against measurement errors and variations in model parameters for different individuals, as would be encountered in a clinical trial. The extensive numerical simulations confirm that the dynamical backstepping method closely emulates the functionality of the natural pancreas and surpasses the super‐twisting sliding‐mode control method, the feedback linearization method, and the conventional backstepping method when faced with measurement noise, insulin pump faults, and parameter variations.

Data-Driven Enhancements for MPC-Based Path Tracking Controller in Autonomous Vehicles.

The accuracy of the control model is essential for the effectiveness of model-based control methods. However, factors such as model simplification, parameter variations, and environmental noise can introduce inaccuracies in vehicle state descriptions, thereby compromising the precision of path tracking. This study introduces data-driven enhancements for an MPC-based path tracking controller in autonomous vehicles (DD-PTC). The approach consists of two parts: firstly, Kolmogorov-Arnold Networks (KANs) are utilized to estimate tire lateral forces and correct tire cornering stiffness, thereby establishing a dynamic predictive model. Secondly, Gaussian Process Regression (GPR) is deployed to accurately capture the unmodeled dynamics of the vehicle to form a comprehensive control model. This enhanced model allows for precise path tracking through steering control. The superiority of DD-PTC is confirmed through extensive testing on the Simulink-CarSim simulation platform, where it consistently surpasses normal MPC and Linear Quadratic Regulator (LQR) strategies, especially in minimizing lateral distance errors under challenging driving conditions.

Evaluating the Correlation Between Stimulus Frequency Otoacoustic Emission Group Delays and Tuning Sharpness in a Cochlear Model.

A theoretical framework based on coherent reflection and filter theory predicts that the phase-gradient delays of stimulus frequency otoacoustic emissions (SFOAEs) are correlated with tuning sharpness in the mammalian cochlea. In this paper, we use a computational model of the cochlea to test this theory and to evaluate how SFOAE phase-gradient delays may be used to estimate the sharpness of cochlear tuning. This study is based on a physiologically motivated model which has been previously shown to predict key aspects of cochlear micromechanics. Cochlear roughness is introduced to model the reflection mechanism which underlies SFOAE generation. We then examine how varying the values of key model parameters or of the sound pressure level of the stimulus affects the relation between cochlear tuning and SFOAE delays. Finally, we quantify the ability of model simulations of SFOAE phase-gradient delays to provide reliable estimates of the tuning sharpness of the model. We find that variations of model parameters that cause significant broadening of basilar membrane (BM) tuning typically give rise to a sizeable reduction in SFOAE phase-gradient delays. However, some changes in model parameters may cause a significant broadening of BM tuning with only a moderate decrease in SFOAE delays. SFOAE delays can be used to estimate the tuning sharpness of the model with reasonable accuracy only in cases where broadening of cochlear tuning is associated with a significant reduction in SFOAE delays. The numerical results provide key insights about the correlations between cochlear tuning and SFOAE delays.

A comparison of stochastic and deterministic dynamics of tuberculosis model

. Tuberculosis is a deadly infectious disease leading to a major health concern in Southern India while there are major obstacles to controlling its spread. Here, we present a mathematical model with five compartments. We categorize the infected compartment into two subcategories: latent TB-infected individuals and active TB-infected individuals. By introducing white noise in a deterministic system, we formulate a stochastic system. We investigate the model in deterministic and stochastic framework, followed by data calibration using TB infection data from Kanyakumari District in South India from 2019 to 2023. In the deterministic model, we derive the disease-free and endemic equilibrium points, compute the basic reproduction number, and examine their stability. Moreover, we perform sensitivity analysis to evaluate how variations in model parameters affect TB prevalence. In addition, we study the uniqueness of solutions of stochastic model. After that we derive the conditions for disease extinction and stochastic permanence, and execute extensive simulations to capture the variability and randomness in TB transmission dynamics. This study signifies that stochastic dynamics is richer than deterministic one in the way of mitigation TB transmission.

Morphine and hydromorphone pharmacokinetics in human volunteers: population-based modelling of interindividual and opioid-related variability

BackgroundMorphine and hydromorphone have differing onsets, magnitudes, and durations of effects and side-effects. Differences between opioids in their interindividual variabilities in pharmacokinetics and pharmacodynamics might influence rational drug selection. Crossover drug studies can provide more informative interindividual variability data than parallel group studies. Using data from a crossover study of i.v. morphine and hydromorphone in healthy volunteers, we tested the hypothesis that morphine and hydromorphone differ in their interindividual pharmacokinetic variability. MethodsArterial opioid and metabolite concentrations from a randomised crossover study in 51 volunteers receiving a 2-h infusion of hydromorphone (0.05 or 0.1 mg kg−1 i.v.) or morphine (total 0.1 or 0.2 mg kg−1 i.v.) 1–2 weeks apart were evaluated with a three-compartmental model for parent opioid and incorporating glucuronides using population modelling (NONMEM). The primary outcome was interindividual variability in pharmacokinetics, based on the coefficient of variation (%CV) of individual model parameters, calculated as √[exp(ω2)–1]×100 where ω2 is the interindividual variability. ResultsData were analysed per drug and in a combined morphine–hydromorphone model. Both analyses indicate that interindividual variabilities for hydromorphone and morphine were comparable with %CV ranging from 9% to 31% for structural model parameters (combined analysis). Similarly, additive and relative residual errors had comparable variabilities, 20–40% and 72–87%, respectively, for morphine and hydromorphone (combined analysis). ConclusionsMorphine and hydromorphone did not differ in a statistically significant or clinically meaningful manner in their interindividual pharmacokinetic variability. Interindividual pharmacokinetic variability does not appear a meaningful consideration in the choice between these two opioids.

Phenotypic homogenization and potential fitness constraints following non-native introgression in an endemic sportfish.

Introgressive hybridization may lead to contrasting evolutionary outcomes that are difficult to predict, since they depend on the fitness effects of endogenous genomic interactions and environmental factors. Conservation of endemic biodiversity may be more effective with require direct measurement of introgressed ancestry and fitness in wild populations, especially for keystone taxa at risk of hybridization following species introductions. We assessed the relationship of non-native ancestry with growth and body condition in the basin-restricted Neosho Bass (Micropterus velox; NB), focusing on two streams in the NB native range that are admixed extensively with non-native Smallmouth Bass (M. dolomieu; SMB). We quantified genetic composition of 116 fish from Big Sugar Creek (N=46) and Elk River (N=70) at 14 microsatellite loci. Using back-calculated total length-at-age estimated from sagittal otoliths, we assessed whether genetic ancestry explained variation in von Bertalanffy growth model parameters, accounting for sex and stream effects. We then assessed the relationship of ancestry and body condition. We found no differences in growth parameters by sex, stream, or ancestry, suggesting phenotypic homogenization which could be mediated by selection on body size. We found a negative correlation between SMB ancestry and condition, including lower condition in Big Sugar Creek, possibly reflecting a trade-off between maximum length and condition with respect to overall fitness. We show that ongoing non-native introgression, which may be augmented by anthropogenic SMB introductions, may attenuate evolutionary differentiation between species and directly influence fitness, possibly having critical implications for long-term persistence and management of adaptive potential in a popular and ecologically important endemic sportfish.

The Variation of Several Parameters in the Premature Ovarian Failure Model by D-Galactose and the Effect of Some Drugs on the Model: Fundamental Study

The Variation of Several Parameters in the Premature Ovarian Failure Model by D-Galactose and the Effect of Some Drugs on the Model: Fundamental Study

Thin accretion disk around black hole in Einstein–Maxwell-scalar theory

We examine the accretion process in a thin disk surrounding a supermassive black hole within the framework of Einstein–Maxwell-scalar (EMS) gravity. Our investigation aims to elucidate how variations in model parameters affect different physical properties of the disk. When keeping EMS parameters β and q constant, we observe a reduction in radiation flux and temperature as α increases. However, the luminosity and radiative efficiency exhibit relatively minor variation. Conversely, under fixed α and q, an escalation in β leads to heightened levels of radiation flux, temperature, luminosity, and radiative efficiency. These results underscore the diverse influences of model parameters on observable metrics, providing valuable insights for the astronomical study of distinct black holes.

Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study.

Most patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities, with only a limited number regaining full arm use. Robotic and technological rehabilitation has been demonstrated to be a feasible solution to guarantee an effective rehabilitation to recover upper limb performance or to prevent complications of upper limb immobility. However, there is currently a lack of studies which analyze the sustainability of robotic and technological rehabilitation by comparing its costs to conventional rehabilitation pathways. Since technology-based and conventional rehabilitation of the upper limb have been demonstrated to have comparable efficacy when the rehabilitation dose is matched, our study concentrates on a cost minimization analysis. The aim of the study is to compare the costs of a "mixed" rehabilitation cycle, which combines conventional and technology-based treatments (the latter delivered with a single therapist supervising several patients), with a cycle of purely conventional treatments. This has been done by developing a cost model and retrospectively analyzing the costs sustained by an Italian hospital which has adopted such a mixed model. A sensitivity analysis has been done to identify the parameters of the model that have the greatest influence on cost difference and to evaluate their optimal values in terms of efficiency of mixed rehabilitation. Finally, probabilistic simulations have been applied to consider the variability of model parameters around such optimized values and evaluate the probability of achieving a given level of savings. We found a cost difference of 49.60 € per cycle in favor of mixed rehabilitation. The sensitivity analysis demonstrated that, in the situation of the hospital under investigation, the parameter having the largest influence on the cost difference is the number of robotic treatments in a mixed rehab cycle. Probabilistic simulations indicate a probability higher than 98% of an optimized mixed rehabilitation cycle being less expensive than a pure conventional one. Through a retrospective cost analysis, we found that the technology-based mixed rehabilitation approach, within a specific organizational model allowing a single physiotherapist to supervise up to four patients concurrently, allowed cost savings compared to the conventional rehabilitation model.

A radar backscatter simulation of a forest canopy using 3D physical structures derived from LiDAR scanning

ABSTRACT This study aims to explore the forest aboveground biomass relationship to C-band backscatter. A one-hectare area in Wytham Woods, located west of Oxford, was selected for this research. The area has a total of 525 trees of seven different tree species. The Michigan Microwave Canopy Scattering Model (MIMICS), a two-layer radiative transfer model, was applied to simulate canopy backscatter responses in this deciduous UK forest. Model parameters related to forest structure were derived from previously published terrestrial laser (LiDAR) data. Simulated backscatter was performed for co-polarized and cross-polarized modes at a C-band frequency range and incidence angles (20° to 45° at 5° increments). The research includes five objectives: i) backscatter sensitivity analysis from the variation of different model parameters; ii) backscatter seasonal effect under spring–summer (leaf-on) and autumn–winter (leaf-off) periods; iii) backscatter comparison between species as well as between simulated and satellite observations in a spatial pattern; iv) relationship between simulated backscatter and aboveground biomass; and v) relationship between MIMICS forest structure parameters and simulated backscatter. Sensitivity analysis results showed significant differences in backscatter from changes in leaf distribution, leaf thickness and water content (leaf, trunk and soil) for both polarization modes in leaf-on scenarios. Leaf-off scenarios presented significant differences from changes in branch distribution but only for the co-polarized mode. Seasonal variations presented significant backscatter differences between spring–summer and autumn–winter scenarios; additionally, backscatter differences among species for each seasonality in the co-polarized mode were observed. The computation of the grid resolution (20 m × 20 m) showed a range of backscatter values depending on the grid and incidence angle. Higher backscatter values were observed for the satellite data than for the simulated data. Finally, the aboveground biomass and the MIMICS input parameters presented high random variability and little systematic co-variation with the total backscatter on both simulated seasons, suggesting that more robust allometric equations for biomass estimation are required.

Induction of subject-ventilator asynchrony by variation of respiratory parameters in a lung injury model in pigs

BackgroundSubject-ventilator asynchrony (SVA) was shown to be associated with negative clinical outcomes. To elucidate pathophysiology pathways and effects of SVA on lung tissue histology a reproducible animal model of artificially induced asynchrony was developed and evaluated.MethodsAlterations in ventilator parameters were used to induce the three main types of asynchrony: ineffective efforts (IE), auto-triggering (AT), and double-triggering (DT). Airway flow and pressure, as well as oesophageal pressure waveforms, were recorded, asynchrony cycles were manually classified and the asynchrony index (AIX) was calculated. Bench tests were conducted on an active lung simulator with ventilator settings altered cycle by cycle. The developed algorithm was evaluated in three pilot experiments and a study in pigs ventilated for twelve hours with AIX = 25%.ResultsIE and AT were induced reliably and fail-safe by end-expiratory hold and adjustment of respiratory rate, respectively. DT was provoked using airway pressure ramp prolongation, however not controlled specifically in the pilots. In the subsequent study, an AIX = 28.8% [24.0%-34.4%] was induced and maintained over twelve hours.ConclusionsThe method allows to reproducibly induce and maintain three clinically relevant types of SVA observed in ventilated patients and may thus serve as a useful tool for future investigations on cellular and inflammatory effects of asynchrony.

Quasinormal modes, thermodynamics and shadow of black holes in Hu–Sawicki f(R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{f(R)}$$\\end{document} gravity theory

We derive novel black hole solutions in a modified gravity theory, namely the Hu–Sawicki model of f(R) gravity. After obtaining the black hole solution, we study the horizon radius of the black hole from the metric and then analyse the dependence of the model parameters on the horizon. We then use the 6th order WKB method to study the quasinormal modes of oscillations (QNMs) of the black hole perturbed by a scalar field. The dependence of the amplitude and damping part of the QNMs are analysed with respect to variations in model parameters and the error associated with the QNMs are also computed. After that we study some thermodynamic properties associated with the black hole such as its thermodynamic temperature as well as greybody factors. It is found that the black hole has the possibility of showcasing negative temperatures and is thermodynamically unstable for feasible values of model parameters. Then we analyse the geodesics and derive the photon sphere radius as well as the shadow radius of the black hole. The photon radius is independent of the model parameters while shadow radius showed fair amount of dependence on the model parameters. We tried to constrain the parameters with the help of Keck and VLTI observational data and obtained some bounds on m and c2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$c_{2}$$\\end{document} parameters.

Rapid Inverse Parameter Inference Using Physics-Informed Neural Networks

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration.However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model.Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs. Figure~\\ref{fig:spm_2d} illustrates the absolute relative errors of trained PINN networks using several different training losses and neural network architectures.After determining a consistent training strategy for both the SPM and P2D PINN surrogate models, the PINNs are extended to determine additional internal state-of-health parameters. As more and more parameters were introduced, the PINN training suffered from ``the curse of dimensionality", which was mitigated by using a hierarchical training approach (where a PINN trained with fewer variable model parameters was used to train a PINN with more variable model parameters).Next, the high-dimensionality PINN surrogates are then integrated into Bayesian calibration schemes to identify internal Li-ion battery properties from experimentally measured voltages. Interpreting the high-dimensional parameter posteriors is discussed with respect to model error, parameter prior choices, and experimental errors. Figure 1

Reconstructing the Pseudo-2-Dimensional (p2D) Model in Comsol Multiphysics Using Mathematical Interfaces

The classic ‘pseudo-2-dimensional’ (p2D) model of Newman and co-workers (also known as the DFN model), with its different efficient implementations, is the state-of-the-art physics-based electrochemical model for lithium-ion batteries. COMSOL Multiphysics (COMSOL) is a popular modeling software for multiphysics simulations, and its Battery Design Module for Li-ion batteries is widely used in the battery modeling community. This work presents a step-wise model development for reconstructing the detailed p2D model equations. This includes variable model parameters and weak-form implementation for the solid-phase diffusion, eliminating the need for non-local couplings and approximations for modeling the pseudo radial-dimension in the solid-phase.To model and understand the effect of capacity degradation on lithium-ion cells, the battery models in COMSOL need to incorporate additional degradation mechanisms. In this work, we have followed the approach of equation-based modeling in COMSOL, which enables us to formulate the p2D model using the mathematical PDE interfaces. This approach provides us with a lot of flexibility in terms of battery degradation model formulation and helps develop a clearer understanding of the p2D model equation in light of the assumptions involved.The steps in the model development involved writing the conservation equations in the generic mathematics interfaces coupled with the additional equations for the degradation mechanisms. The efforts were centered on developing cycling simulations based on Events for practically relevant operating conditions. This custom model was validated against the results from COMSOL’s Battery Interface, in-house codes, and baseline cycle data from the experiments and further developed as a physicochemical degradation model for automotive, large-format Lithium-ion batteries.This equation-based COMSOL model with cycling works without the requirement for Battery Design Module, LiveLink and Application Builder and can be adopted for a variety of electrochemical modeling applications by the electrochemical modeling community.

Interacting quark star with pressure anisotropy and recent astrophysical observations

This paper investigates the internal structure and equation of state (EoS) of quark stars (QS) with pressure anisotropy, incorporating recent astrophysical observations. Utilizing perturbative QCD corrections and color superconductivity, the EoS is expressed as a dimensionless function dependent on a single parameter, providing a comprehensive exploration of strong interaction effects. By rescaling the EoS and introducing dimensionless variables, the study encompasses scenarios ranging from noninteracting quark matter to extreme stiffness characterized by a parameter, λ̄. Anisotropic QS configurations are constructed using a spherically symmetric metric, with locally anisotropic matter described by a generalized EoS for tangential pressure. The Tolman–Oppenheimer–Volkoff equations governing star structure are presented in dimensionless form, and numerical solutions explore the parameter space defined by effective bag constants and λ̄. Mass–radius relations and the compactness of anisotropic QSs are analyzed under various model parameter variations, considering constraints from pulsar masses and gravitational wave phenomena. The study highlights the influence of pressure anisotropy on the maximum mass of QSs and discusses the impact of model parameters on internal properties. Additionally, the paper considers static stability, adiabatic index, and sound velocity profiles, providing a comprehensive understanding of QS behavior. Overall, this investigation contributes valuable insights into the properties of QSs and their compatibility with observational constraints, offering a detailed exploration of their EoS and internal structure.

Modeling the dependence of electrolyte design on lithium-sulfur battery performance

Herein, we offer two electrochemical models, a zero-dimensional (0-D) and a one-dimensional (1-D) model, to mechanistically explore the impact of the electrolyte-to-sulfur (E/S) ratio and electrolyte properties on the discharge behavior of a lithium-sulfur (Li-S) cell. The electrochemically active area in both models is defined based on the carbon weight fraction and reference porosity, so the influence of the carbon loading and E/S ratio on the reaction kinetics is included. Mass transfer of the species must be considered to model the reliance of the discharge capacity on the current density. Instead, the 0-D model is more responsive to the influence of the E/S ratio on the discharge capacity. The effect of electrolyte properties can be modeled implicitly through model parameters. The 0-D model is less sensitive to the variance in model parameters than the 1-D model in terms of the predicted capacity.