Monte Carlo Simulations Research Articles

On the lattice ground state of densely packed hard ellipses.

Among lattice configurations of densely packed hard ellipses, Monte Carlo simulations are used to identify the so-called parallel and diagonal lattices as the two favorable states. The free energies of these two states are computed for several system sizes employing the Einstein crystal method. An accurate calculation of the free energy difference between the two states reveals the parallel lattice as the state with the lowest free energy. The origin of the entropic difference between the two states is further elucidated by assessing the roles of the translational and rotational degrees of freedom.

Partial-linear single-index transformation models with censored data.

In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients' mortality and a Veteran's Administration lung cancer trial.

Age of (152830) Dinkinesh I Selam constrained by secular tidal-BYORP theory

We constrained the age of the main belt binary asteroid system, (152830) Dinkinesh, through secular dynamics and assuming the secondary, Selam, is at equilibrium. We reproduced Selam's current semi-major axis and rotation period and Dinkinesh's current rotation period, starting from the initial conditions of the spin-up fission event. The method presented here includes the secular effects of YORP, BYORP, and tidal interactions, while also allowing for the strengths of these effects to be constrained. Based on this method, we performed a Monte Carlo simulation, which indicates that Selam's age is likely to be sim 1--10 Ma. Derivations for improved equations that describe tidal-BYORP equilibria in binary asteroid systems are also presented in this work. In particular, we derived: (1) a tidal-BYORP equilibrium equation that scales appropriately with the secondary mass and accounts for differences in the primary and secondary densities; and (2) an equation for YORP-driven primary spin stability, with the secondary being at equilibrium.

Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H3O2.

The singly hydrated hydroxide anion OH-(H2O) is of central importance to a detailed molecular understanding of water; therefore, there is strong motivation to develop a highly accurate potential to describe this anion. While this is a small molecule, it is necessary to have an extensive data set of energies and, if possible, forces to span several important stationary points. Here, we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs). These are successors to a PIP potential energy surface (PES) reported in 2004. We describe the details of both fitting methods and then compare the two PESs with respect to precision, properties, and speed of evaluation. While the precision of the potentials is similar, the PIP PES is much faster to evaluate for energies and energies plus gradient than the sGDML one. Diffusion Monte Carlo calculations of the ground vibrational state, using both potentials, produce similar large anharmonic downshift of the zero-point energy compared to the harmonic approximation of the PIP and sGDML potentials. The computational time for these calculations using the sGDML PES is roughly 300 times greater than using the PIP one.

PRODIGE - envelope to disk with NOEMA III. The origin of complex organic molecule emission in SVS13A

Complex organic molecules (COMs) have been found toward low-mass protostars, but the origins of the COM emission are still unclear. It can be associated with, for example, hot corinos, outflows, and/or accretion shock and disk atmospheres. We aim to disentangle the origin of the COM emission toward the chemically rich protobinary system SVS13A using six O-bearing COMs. We conducted NOrthern Extended Millimeter Array (NOEMA) observations toward SVS13A as part of the PROtostars DIsks: Global Evolution (PRODIGE) program. Our previous DCN observations reveal a possible infalling streamer, which may affect the chemistry of the central protobinary by inducing accretion outbursts and/or shocked gas. We further analyzed six O-bearing COMs: CH3OH aGg'- (CH2OH)2 C2H5OH CH2(OH)CHO CH3CHO and CH3OCHO . Although the COM emission is not spatially resolved, we constrained the source sizes to $ arcsec (90$-$120 au) by conducting uv-domain Gaussian fitting. Interestingly, the high-spectral-resolution data reveal complex line profiles with multiple peaks; although the line emission is likely dominated by the secondary, VLA4A, at LSR =7.36$ km s$^ $, the numbers of peaks (sim $2-5$), the velocities, and the linewidths of these six O-bearing COMs are different. The local thermodynamic equilibrium (LTE) fitting unveils differences in excitation temperatures and emitting areas among these COMs. We further conducted multiple-velocity-component LTE fitting to decompose the line emission into different kinematic components. As a result, the emission of these COMs is decomposed into up to six velocity components from the LTE modeling. The physical conditions (temperature, column density, and source size) of these components from each COM are obtained, and Markov chain Monte Carlo sampling was performed to test the fitting results. We find a variety in excitation temperatures ($100-500$ K) and source sizes (D$ au) from these kinematic components from different COMs. The emission of each COM can trace several components, and different COMs most likely trace different regions. Given this complex structure, we suggest that the central region is inhomogeneous and unlikely to be heated by only protostellar radiation. We conclude that accretion shocks induced by the large-scale infalling streamer likely exist and contribute to the complexity of the COM emission. This underlines the importance of high-spectral-resolution data when analyzing COM emission in protostars and deriving relative COM abundances.

Quantifying changes in oxygen saturation of the internal jugular vein in vivo using deep neuron networks and subject-specific three-dimensional Monte Carlo models

Quantifying changes in oxygen saturation of the internal jugular vein in vivo using deep neuron networks and subject-specific three-dimensional Monte Carlo models

Identifying frequency de-correlated dust residuals in B-mode maps by exploiting the spectral capability of bolometric interferometry

Astrophysical polarized foregrounds represent the most critical challenge in cosmic microwave background (CMB) $B$-mode experiments, requiring multifrequency observations to constrain astrophysical foregrounds and isolate the CMB signal. However, recent observations indicate that foreground emission may be more complex than anticipated. Not properly accounting for these complexities during component separation can lead to a bias in the recovered tensor-to-scalar ratio. In this paper we investigate how the increased spectral resolution provided by band-splitting in bolometric interferometry (BI) through a technique called spectral imaging can help control the foreground contamination in the case of an unaccounted-for Galactic dust frequency de-correlation along the line of sight (LOS). We focused on the next-generation ground-based CMB experiment CMB-S4 and compared its anticipated sensitivity, frequency, and sky coverage with a hypothetical version of the same experiment based on BI (CMB-S4/BI). We performed a Monte Carlo analysis based on parametric component separation methods (FGBuster and Commander) and computed the likelihood of the recovered tensor-to-scalar ratio, $r$. The main result is that spectral imaging allows us to detect systematic uncertainties on $r$ from frequency de-correlation when this effect is not accounted for in the component separation. Conversely, an imager such as CMB-S4 would detect a biased value of $r$ and would be unable to spot the presence of a systematic effect. We find a similar result in the reconstruction of the dust spectral index, and we show that with BI we can more precisely measure the dust spectral index when frequency de-correlation is present and not accounted for in the component separation. The in-band frequency resolution provided by BI allows us to identify dust LOS frequency de-correlation residuals where an imager with a similar level of performance would fail. This creates the possibility of exploiting this potential in the context of future CMB polarization experiments that will be challenged by complex foregrounds in their quest for $B$-mode detection.

Time-dependent reliability assessment and optimal design of corroded reinforced concrete beams

It remains a significant challenge to quantitatively describe the corrosion of reinforced concrete (RC) structures under chloride penetration. Moreover, when considering uncertainties throughout the life cycle of corroded RC structures for assessing their safety, reliability, and optimal design, the complexity of the problem intensifies. To address these issues, this paper studies the time-dependent reliability analysis and optimal design of corroded RC beams. At first, the time-dependent reliability of beams is investigated by considering both the serviceability limit state (SLS), which corresponds to the corrosion initiation of the reinforced steel, and the ultimate limit state (ULS), associated with the bending failure of the beam. This analysis takes into account the time-dependent chloride diffusion coefficient and incorporates a stochastic process. The reliability is evaluated using the Monte Carlo Simulation (MCS) method and the cumulative distribution function (CDF) method. Subsequently, a time-dependent reliability-based design optimization (TRBDO) problem is formulated, and the PSO-MCS, a methodology incorporating a particle swarm optimization (PSO) algorithm and MCS is adopted to solve it. After optimization, the initial cost of the specific RC beam is reduced from 1351.879€ to 1247.075€, while the time-dependent reliability within [0, 100] years is improved from 0.6057 to 0.6508. The effectiveness of the CDF, MCS and PSO-MCS methods are demonstrated through reliability analysis and design examples of corroded RC beams.

A high-accuracy system model and accuracy evaluation method for transfer alignment

Abstract With the increasing demand for high-accuracy navigation, distributed payloads put forward higher requirements for the initial alignment accuracy of their equipped slave inertial navigation systems (INSs). However, the existing transfer alignment models have poor compensation effect on flexible deformation, resulting in low alignment accuracy. Moreover, these models cannot accurately evaluate the accuracy of transfer alignment due to ignoring the errors of the master INS. To address these two problems, a new high-accuracy transfer alignment system model is established by comprehensively considering errors of the master and slave INSs. Further, the propagation characteristics of the master and slave inertial sensor errors in transfer alignment are analyzed, and the relationship expression between the master and slave inertial sensor accuracy and the transfer alignment accuracy is derived. Thus, the accuracy of transfer alignment can be pre-evaluated according to the master and slave inertial sensor accuracy. The experiment results show that the alignment accuracy of the proposed method is improved by more than 71.56% compared with the traditional methods, and the relative errors between the proposed accuracy evaluation method and the Monte Carlo simulations are less than 9.14%. Thus, the correctness, effectiveness and superiority of the proposed method are verified.

Health risk assessment of heavy metal pollutants in Iranian hen eggs: a systematic review and meta-analysis.

Eggs are a high-quality, nutrient-dense source of protein that is available at a relatively low price and the contamination of eggs by heavy metals is an important issue in public health. This review aimed to assess the risk of heavy metal pollutants in Iranian hen eggs. Original full-text available studies in Iran, detecting levels of Pb, Cd, As, and Hg in whole or part of the egg, and published between January 2000 and March 2023 were selected based on the inclusion criteria. The random-effect model was used to estimate the pooled concentrations of Pb, Cd, As, and Hg in Iranian eggs in meta-analysis. The incremental lifetime cancer risk (ILCR) and the target hazard quotient (THQ) were estimated by both calculation and Monte Carlo simulations to determine the carcinogenic and non-carcinogenic risk of egg consumption, respectively. The pooled concentrations of heavy metals in Iranian hen eggs from nine articles (11 datasets: 10 studies on Pb, 7 on Cd, and 5 on As and Hg concentrations) were Pb 0.29 (95% CI 0.20-0.39) mg kg-1, Cd 0.04 (95% CI 0.03-0.06) mg kg-1, As 0.05 (95% CI 0.03-0.07) mg kg-1, and Hg 0.03 (95% CI 0.02-0.04) mg kg-1. THQ did not show the non-carcinogenic risk; however, the ILCR for Pb concentration showed the threshold carcinogenic risk (mean ILCR = 8.94e - 4 and 9.0E - 4 by calculation and Monte Carlo simulations, respectively), with the greater risk for Cd (mean ILCR = 2.02e - 2). The carcinogenic risk of Pb and Cd concentration in Iranian hen eggs shows the urgent need for programs and policies to lower the risk for consumers by providing healthier feeding.

A Multilevel Approach for Direct Core Calculation Schemes

A method of dynamic homogenization was recently proposed as an alternative technique for three-dimensional (3D) core calculations using a direct approach. This technique allows for producing homogenization parameters for a subdomain within the core at its actual state, and offers the advantage of avoiding off-line calculations, cross-section interpolations, and expensive 3D transport calculations. The methodology has shown a remarkable reduction in computational cost compared to 3D transport. However, the application of direct calculation schemes in multiphysics and multicycle problems still demands an extensive use of computational resources. Within the framework of traditional two-step calculations, several multilevel schemes have been developed to accelerate lattice calculations for the generation of the multiparameter cross-section libraries. With a similar objective, this work investigates how the multilevel approach can be applied within the framework of direct calculation schemes and discusses how depletion calculations may be performed. The objective is to enhance their performance and reduce memory requirements in favor of a high-fidelity model of the matter behavior in the reactor. We have then identified suitable flux solvers for each level and explored various homogenization options. We tested the methodology in a 3D pressurized water reactor core problem inspired by the TVA Watts Bar Unit 1 Multi-Physics Multi-Cycle OECD/NEA Benchmark, and performed a comparative analysis to assess the accuracy and computational efficiency of these new calculation schemes against direct single-level calculations and a two-step calculation based on pin-by-pin homogenization, as well as Monte Carlo simulations. Our analysis proves that the computational resources required to solve the full-core problem using a multilevel scheme are significantly reduced, and the best computational features are provided by multilevel dynamic homogenization. Furthermore, we observed that the multilevel approach not only allows for speedup, but can also be advantageously applied in order to improve the accuracy of the final solution of a calculation scheme when the core solver relies on an approximate transport operator.

Dynamic departure pushback control at airports: Part A—Linear penalty‐based algorithms and policies

AbstractAirport surface congestion can lead to significantly long taxi‐out times, thus resulting in increased fuel‐burn costs as well as excessive emissions of greenhouse gases. To curtail this undesirable syndrome, in this article, we propose a new penalty‐based dynamic departure pushback control (PDPC) strategy, which employs a linear penalty function dependent not only on the taxiway queue limit but also on the current queue length to ration the pushback frequency at airports, and trades taxiway queueing times with gate‐hold delays to minimize the total operational cost (fuel‐burn and gate‐hold costs). Using data from Beijing Capital International (PEK) airport, four different departure pushback control policies, namely: (i) no‐control (baseline case); (ii) traditional ‐control; (iii) PDPC with a constant taxiway limit; and (iv) PDPC with varying taxiway limits; are compared. Detailed Monte Carlo simulations, which showcase the sensitivity of the total cost function to various problem parameters are presented, and our results indicate that deploying the PDPC policy results in a 42% reduction in total operational costs and a 68% reduction in fuel‐burn (kg) as compared to the baseline case. To analytically reinforce these simulation results, an iterative Markov chain‐based optimization algorithm is also developed to estimate the optimal values of the pushback rate and taxiway queue limit that minimize the total cost function. Such an analytical framework is very useful in the absence of reliable airport data as it only requires estimates of the historical pushback request rates and service times at the taxiway, while yet retaining the capability to closely mirror the simulation results. Our Monte Carlo simulations as well as the Markov chain optimization model validate the strength and impact of the proposed PDPC policy, and demonstrate its practical efficacy in reducing airport surface congestion when applied using data from PEK airport.

Structural distortion and dynamical electron correlation driven enhanced ferromagnetism in Ni-doped two-dimensional Fe5GeTe2 beyond room temperature

Achieving beyond room-temperature ferromagnetism in two-dimensional (2D) magnets is immensely desirable for spintronic applications. Fe5GeTe2 is an exceptional van der Waals metallic ferromagnet due to its tunable physical properties and relatively higher Curie temperature ( TC ) than other 2D magnets. Using density functional theory combined with dynamical electron correlation and Monte Carlo simulations, we find the TC of (Fe 1−δ Ni δ)5 GeTe2 monolayer can increase up to ∼400 K at δ∼0.20 (δ: fractional occupation). Two specific Fe sublattices are identified to be the most energetically preferred sites to host Ni. Exchange interactions between particular Fe pairs play a dominating role in controlling TC , influenced by the dopant-induced structural distortions. Dynamical electron correlation induces site- and orbital-specific quasi-particle mass of Fe-d states with varying Ni concentrations. This work provides fundamental insights into 2D magnetism as an interplay of structural and electronic aspects and would guide to tailoring exciting magnetic phenomena in similar systems.

How to effectively achieve air pollutant reduction and carbon mitigation in China's industrial sector? A study based on decomposition analysis and scenario simulation.

Reducing air pollutant and carbon emissions in the industrial sector is crucial for the ecological civilization construction in China. In this study, we first employ the generalized Divisia index method to analyze the driving factors of industrial CO2 and SO2 emissions, incorporating fixed asset investment and R&D input. The key sub-sectors that exert significant impact on emissions of the whole industrial sector are identified. And then, scenario analysis and Monte Carlo simulation are utilized to predict future trends and potential for reducing CO2 and SO2 emissions. Furthermore, the carbon peaking time of the industrial sub-sectors is investigated. The results indicate that fixed asset investment and R&D input both have played positive roles in CO2 and SO2 emissions. Emission reduction is mainly driven by investment emission intensity, output emission intensity, and R&D emission intensity. Sub-sectors S09, S10, S11, S12, and S18 present substantial potential for reducing air pollutant and carbon emissions. Although SO2 emissions from the industrial sector are projected to decrease in the future, the peak of CO2 emissions have not been reached. The carbon peak time for the whole industrial sector is predicted in 2025, with the peak of 7892.33 Mt. The five key sub-sectors are anticipated to reach the respective carbon emission peaks at different times. Therefore, to effectively implement industrial air pollutant and carbon reduction, the role of fixed asset investment and R&D input should be fully utilized, and high-energy consumption and high-emission sub-sectors should be prioritized for action.

Reliability Estimation for Burr XII Distribution under the Weighted Q-Symmetric Entropy Loss Function

Considering that the choice of loss function plays a significant role in the derivation of Bayesian estimators, we propose a novel asymmetric loss function named the weighted Q-symmetric entropy loss for computing the estimates of the parameter and reliability function of the Burr XII distribution. This paper covers the classical maximum-likelihood, uniformly minimum-variance unbiased, and Bayesian estimation methods under the squared error loss, general entropy loss, Q-symmetric entropy loss, and new loss functions. Through Monte Carlo simulation, the respective performances of the considered estimators for the reliability function are evaluated, indicating that the Bayesian estimator under the new loss function is more efficient than those under other loss functions. Finally, a real data set is used to demonstrate the practicality of the presented estimators.

Evaluation and correction of laser scattering-based particle size measurements at high obscurations using the Monte Carlo method

Evaluation and correction of laser scattering-based particle size measurements at high obscurations using the Monte Carlo method

Novel combined Shewhart-CUmulative EWMA-SUM mean charts without- and with measurement error

The precision of process monitoring often encounters challenges in determining the exact shift size. Therefore, combined control charts have gained considerable attention because of their excellent speed to detect simultaneously small-to-moderate and large-size shifts. The effectiveness of the applied quality control methods strongly depends on the performance of the measurement system. Measurement error presence contributes significantly negatively toward the performance of the usual control charting schemes. This article proposes novel two-sided combined Shewhart-Cumulative EWMA-sum (Shewhart-CUESUM) control charts designed to efficiently monitor the mean of normally distributed processes. In addition, to address measurement errors, the M-Shewhart-CUESUM chart is proposed, incorporating an additive measurement error model. Evaluation of the charts through Monte-Carlo simulations, considering metrics such as average run length (ARL), extra quadratic loss, relative ARL, and performance comparison index. It is found that the combined Shewhart-CUESUM outperforms than CUESUM chart. The results show that the presence of measurement errors can significantly diminish the charts’ performance, which can be mitigated by utilizing a multiple measurements scheme. Among the different well-established combined charts examined, the M-Shewhart-CUESUM chart shows considerably more sensitive to detecting simultaneously detect small and large size shifts. To employ simulated datasets to illustrate the impact of measurement errors and demonstrate the implications of the proposed charts on process mean shifts.

Age and growth of the blue shark Prionace glauca (Linnaeus, 1758) in the Ecuadorian Pacific: Bayesian multi-models.

The blue shark Prionace glauca plays a critical role as a predator in marine ecosystems but is threatened by by-catch. To obtain more precise biological data, a Bayesian approach was used, and 536 vertebrae samples collected during 1 year at the landing stage called "Playita Mía" Manta, Ecuador, were analysed. The objective was to estimate the age and growth parameters of the species. The size of the specimens varied between 116 and 310 cm in total length (TL). Using a Bayesian approach based on the Markov Chain Monte Carlo (MCMC) method, growth parameters were evaluated. The von Bertalanffy model was the one that best fitted the data and provided more adequate estimates (females: = 325.50 cm, = 53.23 cm, and k = 0.12 years; males: = 331.47 cm, = 51.59 cm, k = 0.12 years -1; combined sexes: = 329.65 cm, = 53.64 cm, k = 0.11 year-1). The results indicated that females and males have a similar growth, and that the species has a slow growth. Further studies using multi-model Bayesian approaches and covering a broader range of sizes in the Pacific Ocean are suggested. These studies will provide crucial information for the management and conservation of this species.

2D probabilistic inversion of MT data and uncertainty quantification using the Hamiltonian Monte Carlo method

Summary Bayesian methods provide a valuable framework for rigorously quantifying the model uncertainty arising from the inherent non-uniqueness in the magnetotelluric (MT) inversion. However, widely-used Markov chain Monte Carlo (MCMC) sampling approaches usually require a significant number of model samples for accurate uncertainty estimates, making their applications computationally challenging for 2D or 3D MT problems. In this study, we explore the applicability of the Hamiltonian Monte Carlo (HMC) method for 2D probabilistic MT inversion. The HMC provides a mechanism for efficient exploration in high-dimensional model space by making use of gradient information of the posterior probability distribution, resulting in a substantial reduction in the number of samples needed for reliable uncertainty quantification compared to the conventional MCMC methods. Numerical examples with synthetic data demonstrate that the HMC method achieves rapid convergence to the posterior probability distribution of model parameters with a limited number of model samples, indicating the computational advantages of the HMC in high-dimensional model space. Finally, we applied the developed approach to the COPROD2 field dataset. The statistical models derived from the HMC approach agree well with previous results obtained by 2D deterministic inversions. Most importantly, the probabilistic inversion provides valuable quantitative model uncertainty information associated with the resistivity structures derived from the observed data, which facilitates model interpretation.

Masses and magnetic moments of doubly heavy tetraquarks via diffusion Monte Carlo method

AbstractWe present mass spectrum and magnetic moments of the $$\bar{n}\bar{n}QQ$$ n ¯ n ¯ Q Q states, where $$n=u,d,s$$ n = u , d , s and $$Q=c,b.$$ Q = c , b . We solve four-body Schrödinger equation with a quark potential model by using diffusion Monte Carlo (DMC) method. The quark potential is based on the Coulomb, confinement and spin–spin interaction terms. We find mass and magnetic moment of the $$T_{cc}^+$$ T cc + state as $$M_{T_{cc}^+}=3892 ~\text {MeV}$$ M T cc + = 3892 MeV and $$\mu =0.28 \mu _N,$$ μ = 0.28 μ N , respectively. We also find the mass and magnetic moment of $$T_{bb}^-$$ T bb - as $$M_{T_{bb}^-}=10338 ~\text {MeV}$$ M T bb - = 10338 MeV and $$\mu =-0.32 \mu _N,$$ μ = - 0.32 μ N , respectively. We find some bound state candidates of doubly heavy tetraquark systems with $$I(J^P)=0(1)^+$$ I ( J P ) = 0 ( 1 ) + $$nn {\bar{b}} {\bar{b}},$$ n n b ¯ b ¯ , $$I(J^P)=0(0)^+$$ I ( J P ) = 0 ( 0 ) + $$nn {\bar{c}} {\bar{b}},$$ n n c ¯ b ¯ , $$I(J^P)=0(1)^+$$ I ( J P ) = 0 ( 1 ) + $$nn {\bar{c}} \bar{b},$$ n n c ¯ b ¯ , and $$I(J^P)=1/2(1)^+$$ I ( J P ) = 1 / 2 ( 1 ) + $$ns {\bar{b}} {\bar{b}}.$$ n s b ¯ b ¯ . We compare our results with other approaches in the literature.