Monte Carlo Simulations Research Articles

Jet quenching for hadron-tagged jets in pA collisions

We calculate the medium modification factor IpA for 5.02 TeV p+Pb collisions. We use the Monte-Carlo Glauber model to determine the parameters of the quark-gluon plasma fireball in pA jet events. Our calculations show that the jet quenching effect for IpA turns out to be rather small. We have found that the theoretical IpA as a function of the underlying event charged multiplicity density, within errors, agrees with data from ALICE [18] for 5.02TeV p+Pb collisions. However, the experimental errors are too large to draw a firm conclusion on the possible presence of jet quenching.

Comprehensive Assessment of the Health Risk of Cadmium Exposure from Soil-wheat System in Wheat Fields in Northern China

Accurately assessing the health risks of cadmium （Cd） exposure in the soil-wheat system is of great importance for the formulation of regional health risk prevention measures. Based on regional large-scale survey and Monte Carlo simulation methods, a case study on the health risk of Cd exposure in a soil-wheat system in a wheat production area of North China Plain was conducted by constructing a comprehensive health risk assessment （CHRA） model. The results showed that 73% of the soil samples in the study area exceeded the screening value of Cd for farmland soil （GB 15618-2018） and 57.8% of the wheat samples exceeded the national standard （GB 2762-2017） for Cd. The hand-mouth ingestion pathway was the main route of soil Cd exposure for adults and children in the region, and under this pathway, 34.7% of adults and 57.4% of children had a cancer risk higher than the safe threshold recommended by USEPA （1E-06）. The comprehensive non-carcinogenic and carcinogenic risks of cadmium exposure from the soil-wheat system in the region were higher than the safe domain values for both adults and children, with the probabilities of 26.4% and 100% for adults and 62.5% and 100% for children, respectively. The overall trends of the comprehensive risks showed that children had higher risks than those of adults. The combined application of the comprehensive health risk assessment model and the uncertainty random simulation method helps to improve the accuracy of risk decision-making.

Integrated design of aluminum-enriched high-entropy refractory B2 alloys with synergy of high strength and ductility.

Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility. These Al-RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.7 gigapascals, fracture strains exceeding 50%, and notable high-temperature strength retention. They also demonstrate a tensile yield strength of 1.0 gigapascals with a ductility of 9%, albeit with B2 ordering. Furthermore, we identify valence electron count domains for alloy ductility and brittleness with the explanation from density functional theory and provide crucial insights into elemental influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles governing the mechanical properties of advanced structural alloys.

Abundance-mediated species interactions.

Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance-mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non-detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease- or predator-mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three-species network involving coyotes (Canis latrans), fishers (Pekania pennanti), and American marten (Martes americana) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance-mediated interactions rather than simple co-occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.

Effect of Cherenkov light on cell survival in x-ray irradiation of LINAC based on Monte Carlo simulation and cell survival measurements

Cherenkov radiation is emitted during x-ray irradiation in a linear accelerator (LINAC). Cherenkov light contains many short wavelength components, including ultraviolet (UV) light, which is well-known for its bactericidal effects. A similar phenomenon is probable for human cancer cells. To assess the effect of Cherenkov light on cell death in x-ray irradiation, we employed simulations and UV cell survival data. We measured the survival rates of HeLa cells exposed to 254 nm (UVC) and 310 nm (UVB) light to determine the 50% lethal dose (LD50) required to kill 50% of the cells. For other wavelengths, we utilized literature values to establish the relationship between wavelength and LD50. Due to the broad range of the Cherenkov light spectrum, we need LD50 as a function of wavelength to estimate cell survival solely from Cherenkov light. A Monte Carlo simulation was used to calculate the fluence distribution of Cherenkov light in a 300 mm3phantom comprised of soft tissue, both with and without absorption of visible light. The latter scenario is considered to be most influenced by Cherenkov light. By combining the fluence distribution and the wavelength-LD50 relationship, we determined the distribution of the survival rate. Our findings indicate that, in the absence of absorption, a radiation dose of approximately 90 Gy or greater is necessary for Cherenkov light to have any effect. As a result, the impact of Cherenkov light on cell survival can be considered negligible for typical dose of 2 Gy.

Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network.

The integration of deep neural networks with the variational Monte Carlo (VMC) method has marked a substantial advancement in solving the Schrödinger equation. In this work we enforce spin symmetry in the neural-network-based VMC calculation using a modified optimization target. Our method is designed to solve for the ground state and multiple excited states with target spin symmetry at a low computational cost. It predicts accurate energies while maintaining the correct symmetry in strongly correlated systems, even in cases in which different spin states are nearly degenerate. Our approach also excels at spin-gap calculations, including the singlet-triplet gap in biradical systems, which is of high interest in photochemistry. Overall, this work establishes a robust framework for efficiently calculating various quantum states with specific spin symmetry in correlated systems.

Simultaneous Development of Antiferromagnetism and Local Symmetry Breaking in a Kagome Magnet (Co0.45Fe0.55)Sn.

CoSn and FeSn, two kagome-lattice metals, have recently attracted significant attention as hosts of electronic flat bands and emergent physical properties. However, current understandings of their physical properties are limited to knowledge of the average crystal structure. Here, we report the Fe-doping induced coemergence of the antiferromagentic (AFM) order and local symmetry breaking in (Co0.45Fe0.55)Sn. Rietveld analysis on the neutron and synchrotron X-ray diffraction data indicates A-type antiferromagnetic order with the moment pointing perpendicular to the kagome layers, associated with the anomaly in the MSn(1)2Sn(2)4 (M = Co/Fe) octahedral distortion and the lattice constant c. Reverse Monte Carlo (RMC) modeling of the synchrotron X-ray total scattering results captured the subtle local orthorhombic distortion involving off-axis displacements of Sn(2). Our results indicate that the stable hexagonal lattice above TN becomes unstable once the A-type AFM order is formed below TN. We argue that the local symmetry breaking has a magnetic origin, since the spatially varied M-Sn(2) bond lengths arise from out-of-plane magnetic exchange coupling Jc via the exchange pathway M-Sn(2)-M. Our study provides comprehensive information on the crystal structure in both long-range scale and local scale, unveiling unique coupling between AFM order, octahedral distortion, and hidden local symmetry breaking.

The 1,7-malaria reactive community-based testing and response (1,7-mRCTR) approach in Tanzania: a cost-effectiveness analysis

BackgroundReactive case detection (RACD) for malaria control has been found effective in low transmission settings, but its impact and cost-effectiveness in moderate-to-high transmission settings are unknown. We conducted an economic evaluation alongside an empirical trial of a modified RACD strategy (1,7-mRCTR) in three moderate-to-high malaria transmission districts in Tanzania.MethodsThe costs and cost savings associated with the intervention relative to passive case detection alone were estimated in the study sites of Kilwa, Kibiti, and Rufiji districts in Tanzania from 2019–2021. Empirical cost data were collected using household surveys. The incremental costs of the intervention were calculated from under a societal perspective. Costs are reported in 2022 US dollars.Trial data and malaria registers from health facilities were used to calculate the number of malaria cases detected. We simulated unobserved and distal health effects of the intervention to assess cost-effectiveness in terms of incremental cost-effectiveness ratios (ICERs).Propagated uncertainty was assessed via second-order Monte Carlo simulation, including bootstrapping of empirical data distributions. Incremental costs per disability-adjusted life year (DALY) averted were compared to a willingness-to-pay threshold based on estimated opportunity costs of healthcare spending in Tanzania.ResultsThe programmatic cost of the 1,7-mRCTR intervention was 5327 United States Dollars (USD) per 1000 population. The combination of reactive and passive case detection in the intervention arm resulted in an additional 445 malaria cases detected per 1000 compared to passive detection alone, yielding an incremental cost per additional case detected of 12.0 USD. Based on modelling results, for every percentage point decline in malaria prevalence, the intervention averted 95.2 cases and 0.04 deaths per 1000 population. On average, the 1,7-mRCTR intervention averted 19.1 DALYs per 1000 population. Compared to passive malaria detection, the ICERs for the 1,7-mRCTR intervention were 7.3 USD per case averted, 16,884 USD per death averted, and 163 USD per DALY averted.ConclusionsOur analysis demonstrates that the 1,7-mRCTR intervention appears to be cost-effective under a willingness-to-pay threshold of 417 USD per DALY averted, showing that modified RACD strategies can provide value for money in moderate-to-high transmission settings.Graphical

High-resolution positronium lifetime tomography by the method of moments

Objective.Positronium lifetime tomography (PLT) is an emerging modality that aims to reconstruct 3D images of positronium lifetime in humans and animalsin vivo. The lifetime of ortho-positronium can be influenced by the microstructure and the concentration of bio-active molecules in tissue, providing valuable information for better understanding disease progression and treatment response. However, efficient high-resolution lifetime image reconstruction methods are currently lacking. Existing methods are either computationally intensive or have poor spatial resolution. This paper presents a fast, high-resolution lifetime image reconstruction method for PLT.Approach. The proposed method, called SIMPLE-Moment (Statistical IMage reconstruction of Positron annihilation LifetimE by Moment weighting), first reconstructs a set of moment images and then estimates the ortho-positronium lifetime image using the method of moments. The implementation of SIMPLE-Moment requires minimal modification to the conventional ordered subset expectation maximization algorithm.Main results.With reasonable assumptions, the proposed method can reconstruct an ortho-positronium lifetime image with a computational cost equivalent to three standard positron emission tomography (PET) image reconstructions. A Monte Carlo simulation study based on an existing time-of-flight PET scanner demonstrates that the ortho-positronium lifetime image reconstructed by SIMPLE-Moment is accurate and comparable to results obtained using the more computationally intensive Statistical Positronium Lifetime Image reconstruction via time-Thresholding (SPLIT) method.Significance. The proposed SIMPLE-Moment method provides an efficient approach to high-resolution reconstruction of ortho-positronium lifetime images. By reducing computational costs while enhancing spatial resolution, this method has the potential to make PLT more accessible and practical for clinical and research applications.

Estimating flexural strength of precast deck joints using Monte Carlo Model Averaging of non-fine-tuned machine learning models

Estimating flexural strength of precast deck joints using Monte Carlo Model Averaging of non-fine-tuned machine learning models

PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification.

Deep learning (DL) models are effective in leveraging latent representations from MR data, emerging as state-of-the-art solutions for accelerated MRI reconstruction. However, challenges arise due to the inherent uncertainties associated with undersampling in k-space, coupled with the over- or under-parameterized and opaque nature of DL models. Addressing uncertainty has thus become a critical issue in DL MRI reconstruction. Monte Carlo (MC) inference techniques are commonly employed to estimate uncertainty, involving multiple reconstructions of the same scan to compute variance as a measure of uncertainty. Nevertheless, these methods entail significant computational expenses, requiring multiple inferences through the DL model. In this context, we propose a novel approach to uncertainty estimation during MRI reconstruction using a pixel classification framework. Our method, PixCUE (Pixel Classification Uncertainty Estimation), generates both the reconstructed image and an uncertainty map in a single forward pass through the DL model. We validate the efficacy of this approach by demonstrating that PixCUE-generated uncertainty maps exhibit a strong correlation with reconstruction errors across various MR imaging sequences and under diverse adversarial conditions. We present an empirical relationship between uncertainty estimations using PixCUE and established reconstruction metrics such as NMSE, PSNR, and SSIM. Furthermore, we establish a correlation between the estimated uncertainties from PixCUE and the conventional MC method. Our findings affirm that PixCUE reliably estimates uncertainty in MRI reconstruction with minimal additional computational cost.

Macroevolutionary processes in turtles (Testudines): a view from biomic specialization and historical climatic changes

The resource-use hypothesis proposed by Elisabeth S. Vrba suggests that lineages display varying tendencies toward generalism or specialization in biome occupancy, with a tendency towards the accumulation of specialists due to their higher rate of speciation through vicariance. It also posits differences in biome occupancy patterns driven by the environmental characteristics of biomes, with a higher presence of biome specialist species in biomes that are placed in the extremes of the global climatic gradients. Here, we tested this hypothesis in turtles, a very ancient and morphologically stable lineage, representing a remarkable diversity with 357 species, many of which are threatened with extinction. We analyzed the resource-use hypothesis in a phylogenetic context within the Testudines lineage. For this purpose, a presence/absence matrix was compiled for all species across all 10 terrestrial biomes. Their distribution across biomes was contrasted with 10,000 Monte Carlo simulations. The relationship between diversification rates and both the biomic specialization index and the biomes occupied by specialists species was evaluated. The results demonstrate strong consistency with Vrba`s hypothesis, revealing a higher number of biome specialist species than expected by chance, with a significant accumulation of species in tropical ecosystems. These trends also were observed for ecological groups (terrestrial and freshwater species). In addition, higher diversification rates were observed for biome specialist species, although the particular biome occupied did not significantly influence their diversification rates.

Impact of nuclear fragmentation on the stopping power ratio of12C ion beams.

Nuclear fragmentation generates a diverse dosimetric environment in the path of 12C ion beams. Concise parametrization of the beam's composition is paramount for determining key correction factors in clinical dosimetry. This study sets out to provide such a parametrization based on detailed Monte Carlo simulations of clinically relevant 12C beams. Special attention was paid to the products of nuclear fragmentations and their importance in determining the stopping power ratios. Using the Monte Carlo simulation package GATE, the spectral fluence of all primary and secondary particles in water were computed at different depths for selected clinically relevant incident energies. Collision-stopping power data was taken from the ICRU90, SRIM and MSTAR database, as well as from previous publications. The choice of stopping power data was shown to have a bigger impact on the resulting stopping power ratio than the choice of physics lists for the simulations. A comprehensive analysis of the relationship between fragmentation and dosimetric data has been provided. This study compared different methods for determining spectral fluence-based stopping power ratios, which is essential for accurate ion beam dosimetry.

Importance Sampling for Cost-Optimized Estimation of Burn Probability Maps in Wildfire Monte Carlo Simulations

Background: Wildfire modelers rely on Monte Carlo simulations of wildland fire to produce burn probability maps. These simulations are computationally expensive. Methods: We study the application of importance sampling to accelerate the estimation of burn probability maps, using L2 distance as the metric of deviation. Results: Assuming a large area of interest, we prove that the optimal proposal distribution reweights the probability of ignitions by the square root of the expected burned area divided by the expected computational cost and then generalize these results to the assets-weighted L2 distance. We also propose a practical approach to searching for a good proposal distribution. Conclusion: These findings contribute quantitative methods for optimizing the precision/computation ratio of wildfire Monte Carlo simulations without biasing the results, offer a principled conceptual framework for justifying and reasoning about other computational shortcuts, and can be readily generalized to a broader spectrum of simulation-based risk modeling.

Spatiotemporal patterns of youth isolation and loneliness in the US: a geospatial analysis of Crisis Text Line data (2016–2022)

In 2021, the US Surgeon General issued a national advisory citing an epidemic of isolation and loneliness. Even before the onset of the COVID-19 pandemic, approximately half of people in the US reported experiencing measurable levels of loneliness. Despite localized and select cross-sectional studies highlighting even higher increases in isolation/loneliness during the COVID-19 pandemic, additional research is needed, particularly for youth and young adults. This work examines patterns of isolation/loneliness across the US from 2016 to 2022 among individuals aged 24 and younger. Our study leverages a unique dataset, Crisis Text Line, which provides complete spatiotemporal coverage of crisis conversations in the US. We conducted a geospatial analysis using Kuldroff’s Space–Time SatScan to identify statistically significant clustering of elevated isolation/loneliness-related conversations. The statistical significance of spatiotemporal clusters was determined using Monte Carlo simulations (n = 9999). Results demonstrated local relative risk as high as 1.47 in high-risk populations in Southern, Midwest, and Atlantic states, indicating areas where the actual case count is 147% of the expected cases (p value < 0.01) from May to July 2020. Results also identified co-occurrence of isolation/loneliness and other crises concerns, including depression/sadness, anxiety, and multiple suicidality indicators, with higher rates among racial/ethnic minority, transgender and gender diverse, and younger individuals. This work makes a unique contribution to the literature by elucidating spatiotemporal disparities in isolation/loneliness among young people, providing much-needed knowledge as to where future public health interventions are immediately needed.

Mesoscopic Modelling of Li Deposition in Sulfide-Based Solid Electrolyte Using Two-Dimensional Artificial Polycrystal Structure

Abstract Understanding Li nucleation and growth mechanism during charging in solid electrolytes (SEs) is essential in development of all-solid-state batteries with metallic Li, because the Li dendrite could cause internal short-circuit by penetration of SE. However, it is still under debate how the factors including degradation of SE layer, the stacking pressure, and the microstructure affect the Li nucleation and growth in SE. In this study, the coupled current-deposition-stress models using the two-dimensional artificial SE structures have been developed by combination of finite element method and Monte Carlo simulations. The model assumed that Li flux on the SE grain induces an eigen strain in the deposited Li region, and Li grows into the SE layer by breaking grain boundary (GB). Degradation of SE was modelled as the decrease of fracture strength of GB using a coefficient. The effects of these microstructure and operation factors on GB fracture and Li deposition have been evaluated and discussed.

Lattice-gas model of methane and carbon dioxide sI clathrate hydrates: A comprehensive study using analytical cluster approximation and Monte Carlo simulations

Lattice-gas model of methane and carbon dioxide sI clathrate hydrates: A comprehensive study using analytical cluster approximation and Monte Carlo simulations

Modified Characteristics of Hadronic Interactions in Ultra-High-Energy Cosmic-Ray Showers

Data from multiple experiments suggest that the current interaction models used in Monte Carlo simulations do not correctly reproduce the hadronic interactions in air showers produced by ultra-high-energy cosmic rays (UHECR), in particular – but not limited to – the production of muons during the showers. We have created a large library of UHECR simulations where the interactions at the highest energies are slightly modified in various ways – but always within the constraints of the accelerator data, without any abrupt changes with energy and without assuming any specific mechanism or dramatically new physics at the ultra-high energies. We will find that even when very different properties – cross section, elasticity, and multiplicity – of the interactions are modified, the resulting changes in some air-shower observables are still mutually correlated. Thus, not all possible combinations of changes of observables are easily reproduced by some combination of the modifications. Most prominently, the recent results of the Pierre Auger Observatory, which call for a change in the prediction of both the muon content at ground and the depth of the maximum of longitudinal development of the showers, are rather difficult to reproduce with such modifications, in particular when accounting for other cosmic-ray data. While some of these results are related to the assumptions we place on the modifications, the overall lessons are general and provide valuable insight into how the UHECR data can be interpreted from the point of view of hadronic physics.

Effect of the oblique incidence of radiation beams on emerging radiation behind lead and concrete shields: a multilayer method for dose transmission calculations.

For calculating shielding in X-ray rooms, it is often assumed that the beams impinge perpendicularly on the protective barriers. This is not always true, but this premise simplifies the calculations and enhances protection by being a conservative calculation. In this work, a method for calculating radiation transmission through planar shielding that considers the obliquity of the incident beam is presented.&#xD;Approach: The output of the method produces energy spectra according to the direction of radiation impinging on the shielding. Four angles of incidence on the barrier are considered, along with monoenergetic pencil beams with energies ranging from 10 to 150 keV and two materials: lead and concrete. The direction of emerging photons is discretized into 49 different direction vectors. Monte Carlo calculations are performed for thicknesses of 0.1, 0.5, and 1.0 mm of lead, and 1, 5, 10, and 15 cm of concrete. Additionally, a multilayer iterative method is implemented for calculating attenuation of other thicknesses.&#xD;Main results: The distribution of radiant energy according to the coordinates of its directional vector illustrates the effect of the obliquity of the incidence and the significance of the shielding material employed. In the case of concrete, the dispersion of radiation away from the original direction of incidence is much more pronounced than in the case of lead at energies below its K-edge. The multilayer iterative method provides highly accurate values of transmitted radiant energy in both monoenergetic and polyenergetic beams, for both lead and concrete, across the various studied incidence directions.&#xD;Significance: Considering the direction of the photons reaching a shield and the direction of the photons passing through it allows multilayer composite shielding calculations to closely approximate the calculation made for the composite shielding.&#xD.

Comprehensive propagation of errors for the prediction of woody biomass

Abstract Managing vegetation to sequester carbon in biomass requires estimates to meet standards for accuracy, with methods that are transparent, verifiable and cost‐effective. Allometric models are commonly used to predict biomass from non‐destructive field inventory data. Although a number of studies have addressed biomass error propagation, none have provided a general set of methods for linking errors all the way from initial allometric model development through to the final site‐based biomass prediction, for both above‐ and below‐ground biomass. Error sources in total biomass (above‐ + below‐ground) were quantified using a combination of analytical and Monte Carlo methods, illustrated with four contrasting case studies using either site‐ and‐species‐specific, species‐specific or generalised allometric models. Sampling error was found to be the most important contributor to site‐level biomass uncertainty, arising from the interaction between spatial variability and the field sampling design. The contribution of allometric model covariance to total error was also quantified, with errors in the determination of moisture content during allometric model development identified as a potentially important yet often overlooked error source. Application of different allometric models to the same inventory data suggested the error from generalised models was no greater than that from site‐ or species‐specific models, with increases in the generalised model prediction error balanced by decreases in other error sources associated with the increased sample size on which generalised models are based. Recommendations for reducing errors in predicted biomass include increasing field survey sample size, adopting field survey designs that ensure spatial representativeness and improving moisture content measurement protocols and increasing the moisture content sample size during allometric model development. To reduce costs while maintaining acceptable accuracy, the use of generalised allometric models is recommended, with the caveat that additional biomass sampling for model validation may be required to limit the potential for biased predictions.