Monte Carlo Simulations Research Articles

Pharmacokinetic, Pharmacokinetic/Pharmacodynamic, and Safety Investigations of Cefiderocol in Chinese Healthy Subjects.

We aim to evaluate the safety and pharmacokinetic (PK) properties of cefiderocol in Chinese participants, following single and subsequent multiple administrations of 2g q8h with 3-h intravenous infusion, and to predict its efficacy for the treatment of Gram-negative bacilli (GNB) infection based on PK/pharmacodynamic (PD) analysis. This was an open-label, single-center, single- and multiple-dose phaseI study, conducted from September 2022 to October 2022, with 12 eligible healthy Chinese adults (6 men and 6 women). The PK profiles were described by noncompartmental analysis and a two-compartment model using WinNonlin (v.8.1). Monte Carlo simulations (MCS) were performed by R (v.4.3.1) to obtain the probability of target attainment (PTA) as well as the cumulative fraction of response (CFR), based on the previously published data of susceptibility studies for cefiderocol in China. Both single and multiple doses of 2g cefiderocol were well tolerated in healthy Chinese subjects, and no severe treatment-emergent adverse events occurred. The maximum plasma concentration of cefiderocol was observed approximately 3h after administration and the half-life was about 2.6h, with no accumulation after multiple dosing. It is worth noting that, the PK profiles, including CL, V1, Cmax, Ctrough, and AUC0-τ, were consistent with those of other populations, e.g., Caucasian. PK/PD analysis and MCS suggested that standard dosage regimen of cefiderocol would achieve satisfactory PTA and CFR (exceeding 90%) for Gram-negative pathogens with MICs up to 4μg/mL, using the proposed fT>MIC target of 75.0%. Consistently, more than 90% of PTA was reached for Enterobacterales, P.aeruginosa, and Acinetobacter spp. with MICs up to 4 μg/mL at their respective 73.3%, 72.2%, and 88.1% fT>MIC targets, with CFR exceeding 95%. Especially for S.maltophilia, both the PTA and CFR reached nearly 100% for those with MICs as high as 8 μg/mL. Cefiderocol is well tolerated by Chinese healthy participants at the dosage regimen of 2g cefiderocol q8h via 3-h infusion, which is expected to achieve satisfactory efficacy in treating GNB infections in China, although further data for model optimization might still be required. To our knowledge, this is the first study to describe the PK properties of cefiderocol in Chinese subjects, and to predict its microbiological efficacy for treating GNB infection in China. ChiCTR2300076607.

Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.

When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible aleatoric uncertainty due to noise in the data, epistemic uncertainty induced by insufficient data or inadequate parameterization and model-form uncertainty related to the use of misspecified model equations. In addition, recently proposed approaches provide flexible ways to combine information from data with full or partial satisfaction of equations that typically encode physical principles. Physics-based regularization interacts in non-trivial ways with aleatoric, epistemic and model-form uncertainty and their combination, and a better understanding of this interaction is needed to improve the predictive performance of physics-informed digital twins that operate under real conditions. To better understand this interaction, with a specific focus on biological and physiological models, this study investigates the decomposition of total uncertainty in the estimation of states and parameters of a differential system simulated with MC X-TFC, a new physics-informed approach for uncertainty quantification based on random projections and Monte Carlo sampling. After an introductory comparison between approaches for physics-informed estimation, MC X-TFC is applied to a six-compartment stiff ODE system, the CVSim-6 model, developed in the context of human physiology. The system is first analysed by progressively removing data while estimating an increasing number of parameters, and subsequently by investigating total uncertainty under model-form misspecification of nonlinear resistance in the pulmonary compartment. In particular, we focus on the interaction between the formulation of the discrepancy term and quantification of model-form uncertainty, and show how additional physics can help in the estimation process. The method demonstrates robustness and efficiency in estimating unknown states and parameters, even with limited, sparse and noisy data. It also offers great flexibility in integrating data with physics for improved estimation, even in cases of model misspecification.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.

Development and dosimetric evaluation of a modulated intraoperative radiotherapy (mIORT) system using the Zeiss intrabeam device.

Objective
Intraoperative radiotherapy (IORT) is a specialised radiotherapy technique that delivers a precise, single high-dose fraction to the tumour bed after surgical removal of the tumour, aiming to eliminate residual cancer cells. This study investigates the incorporation of novel applicators into an existing intraoperative radiotherapy (IORT) system to enable dose modulation, performing Monte Carlo simulations, 3D printing, and experimental validation. The Zeiss Intrabeam IORT device, a low-kV IORT system capable of delivering X-rays nearly isotropically, with energies up to 50 kV, was used in this study. 
Approach
Applicators were modified to alter dose distributions, incorporating features such as shielding or changes to an ellipsoid shape. The EGSnrc Monte Carlo (MC) code was employed to simulate the dose distributions of each applicator design, generating data such as dose maps, percentage depth dose (PDD) curves, percent difference maps between shielded and unshielded regions, and energy spectra to characterise each applicator. Gafchromic EBT3 film measurements were performed on select 3D printed applicators, to verify the MC simulations, with dose distribution data extracted for comparison. 
Main Results
Visual comparisons of dose and percentage different maps indicate a high correlation between the MC simulations and film measurements. Most PDD points for spherical applicators showed deviations within 4%, while ellipsoid applicators had deviations of 14% for the unshielded and 5% for the shielded applicators. All RMSEs were below 0.05 for spherical and 0.18 for ellipsoid designs. Based on film data, shielded ellipsoid applicators reduced the dose by ~99%, 48%, 22%, and 8% at 0.3, 1, 2, and 3 cm, respectively, while shielded spherical applicators achieved ~83%, 35%, 14%, and 7% reductions at the same distances. Energy spectra for photons exiting shielded regions were also generated. 
Significance
Results of this study may be used in the development of patient-specific IORT techniques, or the development of a treatment planning system involving mIORT. &#xD.

Bone and head kidney radiation dosimetry for roach from radioactively contaminated reservoirs: Method elaboration and application.

Bone and head kidney radiation dosimetry for roach from radioactively contaminated reservoirs: Method elaboration and application.

Positron stopping in multilayer materials.

Positron annihilation spectroscopy provides a sensitive toolset for defect characterization. In beam based studies of single-layer targets, the form of implantation profiles is well established, depending on the kinetic energy and angle of incident positrons relative to the target surface and the density and average atomic number of the target. For multilayer systems, the difference in density and across the layers makes derivation of an analytical form difficult. To date, the determination of positron stopping profiles in multilayer targets has primarily involved Monte Carlo simulations.
We present here an alternative approach that estimates the energy distribution dN/dE of those positrons transmitted past each layer boundary, by fitting the remaining tail of the stopping profile after each layer with a basis set comprised of calculated stopping profiles in the same material they are transmitted through. The stopping profile in the next layer is then found by summing a series of stopping profiles in the new medium in proportion to the determined distribution dN/dE. The results of our model are compared with simulation results in a system of alternating layers of Al and Au and find reasonable agreement in the predicted profile and excellent agreement in the predicted mean implantation depth. Lastly, we derived a simple formula-based approach for the calculation of the mean implantation depth in two-layer systems that provides results in excellent agreement with the full model.

Diffusion MRI with double diffusion encoding and variable mixing times disentangles water exchange from transient kurtosis

Double diffusion encoding (DDE) makes diffusion MRI sensitive to a wide range of microstructural features, and the acquired data can be analysed using different approaches. Correlation tensor imaging (CTI) uses DDE to resolve three components of the diffusional kurtosis: isotropic, anisotropic, and microscopic kurtosis. The microscopic kurtosis is estimated from the contrast between single diffusion encoding (SDE) and parallel DDE signals at the same b-value. Another approach is multi-Gaussian exchange (MGE), which employs DDE to measure exchange. Sensitivity to exchange is obtained by contrasting SDE and DDE signals at the same b-value. CTI and MGE exploit the same signal contrast to quantify microscopic kurtosis and exchange, and this study investigates the interplay between these two quantities. We perform Monte Carlo simulations in different geometries with varying levels of exchange and study the behaviour of the parameters from CTI and MGE. We conclude that microscopic kurtosis from CTI is sensitive to the exchange rate and that intercompartmental exchange and the transient kurtosis of individual compartments are distinct sources of microscopic kurtosis. In an attempt to disentangle these two sources, we propose a heuristic signal representation referred to as tMGE (MGE incorporating transient kurtosis) that accounts for both effects by exploiting the distinct signatures of exchange and transient kurtosis with varying mixing time: exchange causes a slow dependence of the signal on mixing time while transient kurtosis arguably has a much faster dependence. We find that applying tMGE to data acquired with multiple mixing times for both parallel and orthogonal DDE may enable estimation of the exchange rate as well as isotropic, anisotropic, and transient kurtosis.

Hybrid Monte Carlo model for efficient tissue Cherenkov emission estimation to assess changes from beam size, energy and incidence.

Hybrid Monte Carlo model for efficient tissue Cherenkov emission estimation to assess changes from beam size, energy and incidence.

Exploring Multiple Magnetization Plateaus in Graphdiyne-Like Nanolattice Using Monte Carlo Simulations

In this study, we investigated multiple magnetization plateaus in a graphdiyne-type nanolattice using Monte Carlo simulations within the framework of the Blume–Capel model and the Metropolis algorithm. The analysis revealed distinct magnetization plateaus for specific parameter values, indicating a significant increase in the number of stable magnetic states. The total magnetization ([Formula: see text]) exhibits four plateaus corresponding to critical field levels ([Formula: see text], [Formula: see text], [Formula: see text]) and the saturation field ([Formula: see text]). The effects of the reduced exchange coupling parameter ([Formula: see text]), the crystal field parameter (d), and the temperature (t) on the magnetization plateaus were systematically examined. The magnetization plateau behavior observed in the graphdiyne-like nanolattice was comparable to that of other systems studied using Monte Carlo simulations. Future applications may include the development of advanced magnetic storage devices and quantum computing elements, where precise control of magnetic states is essential.

Optimization of sterilization efficiency for medical surgical blades in gamma irradiation using the Monte Carlo method

Sterilizing high-carbon steel medical surgical blades is crucial for public health and safety. To enhance the sterilization efficiency of medical surgical blades, we used the Monte Carlo simulation method to study the effects of gamma irradiation sterilization parameters on high-carbon steel samples. Results show that the time required for irradiation sterilization can be decreased to 23.14 min or even 6.20 min, significantly shorter than the several hours required for chemical sterilization, by optimizing the irradiation facility parameters. The gamma source is a 1 cm thick 60Co, while the reflector is 20 cm thick cylindrical graphite. The five layers of high-carbon steel samples, each 1 cm thick, have areas of 10 × 10 cm2, 40 × 40 cm2, 70 × 70 cm2, 100 × 100 cm2, and 130 × 130 cm2 from top to bottom. The high-carbon steel sample is positioned 30 cm from the radiation source, resulting in energy deposited in the middle layer II zone of 7.6493 × 10− 7 MeV/g, which is used for calculating the total sterilization time. This study provides a viable approach to improving the sterilization efficiency of disposable medical surgical blades and serves as a reference for further promoting irradiation sterilization technology in medical instruments.

Cosmic slowing down of acceleration with the Chaplygin–Jacobi gas as a dark fluid?

A particular generalization of the Chaplygin inflationary model, using the formalism of Hamilton–Jacobi and elliptic functions, results in a more general non-linear Chaplygin-type equation of state (Chaplygin–Jacobi model). We investigate the implementation of this model as a dark energy (DE) fluid to explain the recent acceleration of the universe. Unlike ΛCDM and other Chaplygin-like fluids, where the final fate of the universe is an eternal de Sitter (dS) phase, the dynamics of this model allows for the possibility of a decelerating phase in the future, following the current accelerating phase. In other words, a transient acceleration arises, accounting for the recently claimed slowing down phenomenon. This Chaplygin–Jacobi model shows important differences compared to the standard and generalized Chaplygin gas models. Additionally, we perform a Markov Chain Monte Carlo (MCMC) analysis using several datasets, including type Ia supernovae (SNIa), cosmic chronometers (CC), fast radio bursts (FRBs), and Baryon acoustic oscillations (BAO) to examine the observational viability of the model. Our results indicate that, although a transient phase of accelerated expansion is supported by current observations in the context of the Chaplygin–Jacobi model, this model is strongly disfavored in comparison with ΛCDM.

Ultra-high energy spectral prompt PET.

Utilizing prompt gammas in preclinical pinhole-collimated PET avoids image degradation due to positron range blurring and photon down scatter, enables multi-isotope PET and can improve counting statistics for low-abundance positron emitters. This was earlier reported for 124I, 89Zr and simultaneous 124I -18F PET using the VECTor scanner (MILabs, The Netherlands), demonstrating sub-mm resolution despite long positron ranges. The aim of the present study is to investigate if such sub-mm PET imaging is also feasible for a large variety of other isotopes including those with extremely high energy prompt gammas (>1 MeV) or with complex emission spectra of prompt gammas. We use Monte Carlo simulations to assess achievable image resolutions and uniformity across a broad range of spectrum types and emitted prompt gamma energies (603 keV - 2.2 MeV), using 52Mn, 94Tc, 89Zr, 44Sc, 86Y, 72As, 124I, 38K, and 66Ga. Our results indicate that sub-millimeter resolution imaging may be feasible for almost all isotopes investigated, with the currently used cluster pinhole collimators. At prompt gamma energies of 603 keV of 124I, an image resolution of ~0.65 mm was achieved, while for emissions at 703, 744, 834, and 909 keV of 94Tc, 52Mn, 72As, and 89Zr, respectively, ~0.7 mm resolution was obtained. Finally, at ultra-high energies of 1.2 (44Sc) and 1.4 MeV (52Mn) resolutions of ~0.75 mm and ~0.8 mm could still be achieved although ring artifacts were observed at the highest energies (1.4 MeV). For 38K (2.2 MeV), an image resolution of 1.2 mm was achieved utilizing its 2.2 MeV prompt emission. This work shows that current cluster pinhole collimators are suitable for sub-mm resolution prompt PET up till at least 1.4 MeV. This may open up new avenues to developing new tracer applications and therapies utilizing these PET isotopes.

Discretizing multiple continuous predictors with U-shaped relationships with lnOR: introducing the recursive gradient scanning method in clinical and epidemiological research

BackgroundAssuming a linear relationship between continuous predictors and outcomes in clinical prediction models is often inappropriate, as true linear relationships are rare, potentially resulting in biased estimates and inaccurate conclusions. Our research group addressed a single U-shaped independent variable before. Multiple U-shaped predictors can improve predictive accuracy by capturing nuanced relationships, but they also introduce challenges like increased complexity and potential overfitting. This study aims to extend the applicability of our previous research results to more common scenarios, thereby facilitating more comprehensive and practical investigations.MethodsIn this study, we proposed a novel approach called the Recursive Gradient Scanning Method (RGS) for discretizing multiple continuous variables that exhibit U-shaped relationships with the natural logarithm of the odds ratio (lnOR). The RGS method involves a two-step approach: first, it conducts fine screening from the 2.5th to 97.5th percentiles of the lnOR. Then, it utilizes an iterative process that compares AIC metrics to identify optimal categorical variables. We conducted a Monte Carlo simulation study to investigate the performance of the RGS method. Different correlation levels, sample sizes, missing rates, and symmetry levels of U-shaped relationships were considered in the simulation process. To compare the RGS method with other common approaches (such as median, Q1-Q3, minimum P-value method), we assessed both the predictive ability (e.g., AUC) and goodness of fit (e.g., AIC) of logistic regression models with variables discretized at different cut-points using a real dataset.ResultsBoth simulation and empirical studies have consistently demonstrated the effectiveness of the RGS method. In simulation studies, the RGS method showed superior performance compared to other common discretization methods in discrimination ability and overall performance for logistic regression models across various U-shaped scenarios (with varying correlation levels, sample sizes, missing rates, and symmetry levels of U-shaped relationships). Similarly, empirical study showed that the optimal cut-points identified by RGS have superior clinical predictive power, as measured by metrics such as AUC, compared to other traditional methods.ConclusionsThe simulation and empirical study demonstrated that the RGS method outperformed other common discretization methods in terms of goodness of fit and predictive ability. However, in the future, we will focus on addressing challenges related to separation or missing binary responses, and we will require more data to validate our method.

3Dπ: three-dimensional positron imaging, a novel total-body PET scanner using xenon-doped liquid argon scintillator

Objective.This paper introduces a novel PET imaging methodology called 3-dimensional positron imaging (3Dπ), which integrates total-body coverage, time-of-flight (TOF) technology, ultra-low dose imaging capabilities, and ultra-fast readout electronics inspired by emerging technology from the DarkSide collaboration.Approach.The study evaluates the performance of 3Dπusing Monte Carlo simulations based on NEMA NU 2-2018 protocols. The methodology employs a homogenous, monolithic scintillator composed of liquid argon (LAr) doped with xenon (Xe) with silicon photomultipliers (SiPMs) operating at cryogenic temperatures.Main results.Substantial improvements in system performance are observed, with the 3Dπsystem achieving a noise equivalent count rate of 3.2 Mcps at 17.3 kBq ml-1, continuing to increase up to 4.3 Mcps at 40 kBq ml-1. Spatial resolution measurements show an average FWHM of 2.7 mm across both axial positions. The system exhibits superior sensitivity, with values reaching 373 kcps MBq-1with a line source at the center of the field of view. Additionally, 3Dπachieves a TOF resolution of 151 ps at 5.3 kBq ml-1, highlighting its potential to produce high-quality images with reduced noise levels.Significance.The study underscores the potential of 3Dπin improving PET imaging performance, offering the potential for shorter scan times and reduced radiation exposure for patients. The Xe-doped LAr offers advantages such as fast scintillation, enhanced light yield, and cost-effectiveness. Future research will focus on optimizing system geometry and further refining reconstruction algorithms to exploit the strengths of 3Dπfor clinical applications.

Designing single-polymer-chain nanoparticles to mimic biomolecular hydration frustration.

Native folded proteins rely on sculpting the local chemical environment of their active or binding sites, as well as their shapes, to achieve functionality. In particular, proteins use hydration frustration-control over the dehydration of hydrophilic residues and the hydration of hydrophobic residues-to amplify their chemical or binding activity. Here we uncover that single-polymer-chain nanoparticles formed by random heteropolymers comprising four or more components can display similar levels of hydration frustration. We categorize these nanoparticles into three types based on whether either hydrophobic or hydrophilic residues, or both types, display frustrated states. We propose a series of physicochemical rules that determine the state of these nanoparticles. We demonstrate the generality of these rules in atomistic and simplified Monte Carlo models of single-polymer-chain nanoparticles with different backbones and residues. Our work provides insights into the design of single-chain nanoparticles, an emerging polymer modality that achieves the ease and cost of fabrication of polymeric material with the functionality of biological proteins.

Regularized origin ensemble with a beam prior for range verification in particle therapy with Compton-camera data.

In particle therapy (PT), several methods are being investigated to help reduce range margins and identify deviations from the original treatment plan, such as prompt-gamma (PG) imaging with Compton cameras (CC). To reconstruct the images, the Origin Ensemble (OE) algorithm is commonly used. In the context of PT, artifacts and strong noise often affect CC images. To improve the ability of OE to identify range shifts, and also to enhance image quality, we propose to regularize OE using beam a-priori knowledge (beam prior).
Approach: We implemented the beam prior to OE using the class of Gibbs' distribution functions. For evaluation, Monte-Carlo simulations of centered and off-center beams with therapeutic energies impinging on a PMMA target were conducted in GATE. To introduce range shifts, air layers were introduced into the target. In addition, the effect of a bone layer, closer to a realistic scenario, was investigated. OE with the beam prior (BP-OE) and conventional OE (reference) were compared using the spill-over-ratio (SOR) as well as shifts in the distal falloff in projections using cubic splines with Chebyshev nodes.
Main results: BP-OE improved the shift estimates by up to 11% compared to conventional OE for centered and up to 250% with off-centered beams. BP-OE
decreased the image noise level, improving the SOR significantly by up to 96%.
Significance: BP-OE applied to CC data can improve shift estimations compared to conventional OE. The developed Gibbs-based regularization framework also allows
further prior functions to be included into OE, for instance, smoothing or edge-preserving priors. BP-OE could be extended to PET-based range verification or multiple-beam scenarios.

Quantum magic and multipartite entanglement in the structure of nuclei

Motivated by the Gottesman-Knill theorem, we present a detailed study of the quantum complexity of p-shell and sd-shell nuclei. Valence-space nuclear shell-model wave functions generated by the code are mapped to qubit registers using the Jordan-Wigner mapping (12 qubits for the p shell and 24 qubits for the sd shell), from which measures of the many-body entanglement (n-tangles) and magic (nonstabilizerness) are determined. While exact evaluations of these measures are possible for nuclei with a modest number of active nucleons, Monte Carlo simulations are required for the more complex nuclei. The broadly applicable Pauli-string ÎẐ exact (PSIZe) Markov chain Monte Carlo (MCMC) technique is introduced to accelerate the evaluation of measures of magic in deformed nuclei (with hierarchical wave functions), by factors of ≈8 for some nuclei. Significant multinucleon entanglement is found in the sd shell, dominated by proton-neutron configurations, along with significant measures of magic. This is evident not only for the deformed states, but also for nuclei on the path to instability via regions of shape coexistence and level inversion. These results indicate that quantum-computing resources will accelerate precision simulations of such nuclei and beyond. Published by the American Physical Society 2025

Direct and indirect impacts of the COVID-19 pandemic on life expectancy and person-years of life lost with and without disability: A systematic analysis for 18 European countries, 2020-2022.

The direct and indirect impacts of the COVID-19 pandemic on life expectancy (LE) and years of life lost with and without disability remain unclear. Accounting for pre-pandemic trends in morbidity and mortality, we assessed these impacts in 18 European countries, for the years2020-2022. We used multi-state Markov modeling based on several data sources to track transitions of the population aged 35 or older between eight health states from disease-free, combinations of cardiovascular disease, cognitive impairment, dementia, and disability, through to death. We quantified separately numbers and rates of deaths attributable to COVID-19 from those related to mortality from other causes during 2020-2022, and estimated the proportion of loss of life expectancy and years of life with and without disability that could have been avoided if the pandemic had not occurred. Estimates were disaggregated by COVID-19 versus non-COVID causes of deaths, calendar year, age, sex, disability status, and country. We generated the 95% uncertainty intervals (UIs) using Monte Carlo simulations with 500 iterations. Among the 289 million adult population in the 18 countries, person-years of life lost (PYLL) in millions were 4.7 (95% UI 3.4-6.0) in 2020, 7.1 (95% UI 6.6-7.9) in 2021, and 5.0 (95% UI 4.1-6.2) in 2022, totaling 16.8 (95% UI 12.0-21.8) million. PYLL per capita varied considerably between the 18 countries ranging between 20 and 109 per 1,000 population. About 60% of the total PYLL occurred among persons aged over 80, and 30% in those aged 65-80. If the pandemic were avoided, over half (9.8 million (95% UI 4.7-15.1)) of the 16.8 million PYLL were estimated to have been lived without disability. Of the total PYLL, 11.6-13.2 million were due to registered COVID-19 deaths and 3.6-5.3 million due to non-COVID mortality. Despite a decrease in PYLL attributable to COVID-19 after 2021, PYLL associated with other causes of death continued to increase from 2020 to 2022 in most countries. Lower income countries had higher PYLL per capita as well as a greater proportion of disability-free PYLL during 2020-2022. Similar patterns were observed for life expectancy. In 2021, LE at age 35 (LE-35) declined by up to 2.8 (95% UI 2.3-3.3) years, with over two-thirds being disability-free. With the exception of Sweden, LE-35 in the studied countries did not recover to 2019 levels by 2022. The considerable loss of life without disability and the rise in premature mortality not directly linked to COVID-19 deaths during 2020-2022 suggest a potential broader, longer-term and partially indirect impact of the pandemic, possibly resulting from disruptions in healthcare delivery and services for non-COVID conditions and unintended consequences of COVID-19 containment measures. These findings highlight a need for better pandemic preparedness in Europe, ideally, as part of a more comprehensive global public health agenda.

Theoretical limits of passive daytime radiative cooling in extraterrestrial environments: implications for space exploration

Passive daytime radiative cooling (PDRC) enables sub-ambient cooling without external energy input, a feature that has attracted significant research attention. While most studies have focused on PDRC performance in terrestrial environments, extraterrestrial settings offer the potential for superior cooling due to the absence of convective heat transfer and atmospheric thermal radiation. To address this gap, we investigate the extreme optical properties and radiative cooling performance of PDRC materials in extraterrestrial settings, specifically paints and ceramics, known for their strong solar reflectance, high thermal emissivity, and ease of fabrication. This investigation is grounded in a comprehensive theoretical framework that incorporates Mie scattering, Monte Carlo simulations, effective medium theory, and the transfer matrix method for ultra-broadband spectral simulations from vacuum ultraviolet to far-infrared wavelengths, followed by energy balance analysis in extraterrestrial environments. We explore the influence of particle size, volume fraction, and thickness in paints and ceramics on cooling performance to elucidate the theoretical limit of extraterrestrial PDRC. This study broadens the scope of PDRC research and provides valuable insights into the application of PDRC materials for future deep-space exploration.

Propagation of Systematic Sensor Uncertainty Into the Frequency Domain

Abstract When it comes to the identification of dynamic system parameters, like stiffness and damping, the systematic measurement uncertainty is mostly ignored in the frequency domain because of its complicated and elaborate uncertainty propagation. Nevertheless, the uncertainty is far from being negligible. In order to examine the importance of the systematic measurement uncertainty in the frequency domain, the propagation of systematic uncertainty caused by four different sensor error types is investigated by using Monte Carlo simulations. It is shown that an uncertainty in the bias only affects static measurements, i.e., with an excitation frequency of the system being Ω=0. Furthermore, the uncertainty of the linearity and sensitivity only impacts the magnitude of the Fourier coefficient, whereas the uncertainty in the hysteresis results only in an uncertainty in the phase. Subsequently, it is shown that the linearity and hysteresis uncertainty are completely independent in the frequency domain. The results lead to simple propagation relations applicable to various engineering, science, and industry challenges without the need for the conduction of complex Monte Carlo simulations.

Three‐dimensional gravity inversion based on the Hamiltonian Monte Carlo method

AbstractIn geophysics, Bayesian inversion methods are of significant prominence. Here, we present a novel approach utilizing the Hamiltonian Monte Carlo (HMC) method in gravity inversion for elucidating three‐dimensional (3D) density structures. HMC provides a multi‐dimensional sampling method that demonstrates enhanced optimization efficiency, facilitating the attainment of distant proposals with elevated acceptance probabilities. Its applicability also extends to resolving linear inverse problems. Three synthetic models of cubic bodies, dipping dykes and a combined model were designed for tests. The testes underscore the promising potential of HMC in recovering subsurface density source bodies and giving the uncertainty of the inversion model. Furthermore, an inversion test conducted on the Vinton salt dome yields a reasonable 3D distribution of cap rock, consistent with prior studies in this area. The modelling and field experiments showed that the proposed HMC gravity inversion method had higher accuracy and application potential.