Monte Carlo Simulations Research Articles

Constraining hybrid potential scalar field cosmological model in Lyra’s geometry with recent observational data

In this study, we investigate a scalar field cosmological model with Lyra’s geometry to explain the present cosmic expansion in a homogeneous and isotropic flat FRW universe. In Einstein’s field equations, we presupposed a variable displacement vector as an element of Lyra’s geometry. In the context of the conventional theory of gravity, we suggest a suitable parametrization of the scalar field’s dark energy density in the hybrid function of redshift [Formula: see text], confirming the essential transition behavior of the universe from a decelerating era to the present accelerated scenario. We present constraints on model parameters using the most recent observational datasets from OHD, BAO/CMB and Pantheon, taking Markov Chain Monte Carlo (MCMC) analysis into account. For the proposed model, the best estimated values of parameters for the combined dataset (OHD, BAO/CMB and Pantheon) are [Formula: see text][Formula: see text]km/s/Mpc, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]. The model exhibits a flipping nature, and the redshift transition occurs at [Formula: see text]. The current value of the decelerated parameter for the proposed model is calculated as [Formula: see text] for the combined dataset. Some dynamical properties of the model like energy density ([Formula: see text]), scalar field pressure ([Formula: see text]), EoS parameter of scalar field ([Formula: see text]), and effective EoS parameter ([Formula: see text]) are analyzed and presented. Further, we have also examined the statefinder diagnosis and jerk parameters of the derived model. The total density parameter for the derived model is found to be unity which is in nice agreement with recent standard findings.

A study on the small failure probabilities of cylindrical composite hydrogen storage tanks using subset simulation

Although composite hydrogen tanks are becoming increasingly intriguing, a detailed structural reliability analysis is still lacking. The current landscape of analysis for pressurised multilayer cylinders, while rich in research, lacks tools to deal with the low probabilities of failure that govern the design of hydrogen tanks. This study presents a computational framework integrating the Subset Simulation method (SS) for assessing the failure probability of composite tanks used for hydrogen storage. To explore how randomness impacts design parameters, this study utilises a limit state equation derived from the thin-walled circumferential model of composite pressure tanks. Five random variables, each with varying coefficients of variation (COVs), are incorporated into the analysis. For comparison and validation purposes, two methods Monte Carlo simulations and FORM have been used. The analysis revealed that SS excels at estimating the low failure probabilities of composite hydrogen tanks, and showed also the feasibility and accuracy of SS in the prediction of burst pressure since good agreement was obtained between the probabilistic approach and the experimental results. Furthermore, the uncertainties related to internal pressure and composite thickness affect significantly the reliability of the structure and can lead to the shrinkage of the safety margin and the failure of the vessel.

Implantation of CRT pacemaker during pregnancy: estimated fetal radiation dose.

The benefits of a cardiac resynchronization therapy (CRT) implantation are known in severe heart failure and its implantation may also be considered during pregnancy to ensure safe pregnancy and delivery for both the mother and the fetus. This study on a patient case aimed to estimate the absorbed dose (AD) to the fetus during the CRT implantation of 21st week of the pregnant woman. AD measurement was done using anthropomorphic phantom, radiophotoluminescence dosemeters and Monte Carlo simulation utilizing kerma area product (KAP) of the procedures. The measured AD for a phantom's uterus and heart was 0.116 and 14.7mGy with total KAP of 27.9 Gycm2. The estimated actual AD to the fetus and the mother's heart was 0.004 and 1.2mGy, with total KAP of 1.5 Gycm2. The dose to the fetus can be minimized with the optimized procedure.

Bootstrap inference on a factor model based average treatment effects estimator

We propose a novel bootstrap procedure for conducting inference for factor model-based average treatment effects estimators. Our method overcomes bias inherent to existing bootstrap procedures and substantially improves upon existing large sample normal inference theory in small sample settings. The finite sample improvements arising from the use of our proposed procedure are illustrated via a set of Monte Carlo simulations, and formal justification for the procedure is outlined.

Low-cost classroom and laboratory exercises for investigating both wave and event-related electroencephalogram potentials

Electroencephalography (EEG) has given rise to a myriad of new discoveries over the last 90 years. EEG is a non-invasive technique that has revealed insights into the spatial and temporal processing of brain activity over many neuroscience disciplines, including sensory, motor, sleep, and memory formation. However, most undergraduate students lack laboratory access to EEG recording equipment or the skills to perform an experiment independently. Here, we provide easy-to-follow instructions to measure both wave and event-related EEG potentials using a portable, low-cost amplifier (Backyard Brains, Ann Arbor, MI) that connects to smartphones and PCs, independent of their operating system. Using open-source software (SpikeRecorder) and analysis tools (Python, Google Colaboratory), we demonstrate tractable and robust laboratory exercises for students to gain insights into the scientific method and discover multidisciplinary neuroscience research. We developed 2 laboratory exercises and ran them on participants within our research lab (N = 17, development group). In our first protocol, we analyzed power differences in the alpha band (8-13 Hz) when participants alternated between eyes open and eyes closed states (n = 137 transitions). We could robustly see an increase of over 50% in 59 (43%) of our sessions, suggesting this would make a reliable introductory experiment. Next, we describe an exercise that uses a SpikerBox to evoke an event-related potential (ERP) during an auditory oddball task. This experiment measures the average EEG potential elicited during an auditory presentation of either a highly predictable (“standard”) or low-probability (“oddball”) tone. Across all sessions in the development group (n=81), we found that 64% (n=52) showed a significant peak in the standard response window for P300 with an average peak latency of 442ms. Finally, we tested the auditory oddball task in a university classroom setting. In 66% of the sessions (n=30), a clear P300 was shown, and these signals were significantly above chance when compared to a Monte Carlo simulation. These laboratory exercises cover the two methods of analysis (frequency power and ERP), which are routinely used in neurology diagnostics, brain-machine interfaces, and neurofeedback therapy. Arming students with these methods and analysis techniques will enable them to investigate this laboratory exercise's variants or test their own hypotheses.

A Theoretical Study on the Magnetic Features of Ru2MnX Nano-Heusler Alloys

This study focuses on examining magnetic property performances among three nano-Heusler Ru2MnX alloys (where X is replaced by the atoms V, Ta and Nb). The investigation was conducted through Monte Carlo simulations, exploring the magnetic properties of these alloys. The study established a clear transition from ferrimagnetic to superparamagnetic behavior. Furthermore, it exposed the effects of magnetization in each nano-Heusler alloy relative to physical factors.

Minimum Profile Hellinger Distance Estimation of General Covariate Models

Covariate models, such as polynomial regression models, generalized linear models, and heteroscedastic models, are widely used in statistical applications. The importance of such models in statistical analysis is abundantly clear by the ever-increasing rate at which articles on covariate models are appearing in the statistical literature. Because of their flexibility, covariate models are increasingly being exploited as a convenient way to model data that consist of both a response variable and one or more covariate variables that affect the outcome of the response variable. Efficient and robust estimates for broadly defined semiparametric covariate models are investigated, and for this purpose the minimum distance approach is employed. In general, minimum distance estimators are automatically robust with respect to the stability of the quantity being estimated. In particular, minimum Hellinger distance estimation for parametric models produces estimators that are asymptotically efficient at the model density and simultaneously possess excellent robustness properties. For semiparametric covariate models, the minimum Hellinger distance method is extended and a minimum profile Hellinger distance estimator is proposed. Its asymptotic properties such as consistency are studied, and its finite-sample performance and robustness are examined by using Monte Carlo simulations and three real data analyses. Additionally, a computing algorithm is developed to ease the computation of the estimator.

Impact of reduced sampling frequency of illicit drug wastewater monitoring in the Netherlands

Drug consumption estimates are traditionally based on surveys or information from police seizures. Alternatively, residues of illicit drugs in untreated wastewater (influent) can be used to calculate mass loads and subsequently estimate drug consumption in the community throughout the week. For this purpose, wastewater is commonly sampled for seven consecutive days within the Sewage analysis CORe group Europe (SCORE), while other sampling schemes may be implemented in long-term studies outside this consortium. The current study demonstrates how sampling frequency of illicit drug residues in the influent of wastewater treatment plants (WWTPs) affects the derived weekly average. Thirty WWTPs were sampled over the course of 12 years and influents were analyzed for five drugs (metabolites): 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, amphetamine, benzoylecgonine (a metabolite of cocaine), and 11-nor-9-Carboxy tetrahydrocannabinol (THC-COOH). Subsequently, small and large WWTPs were grouped with a threshold of 100,000 inhabitants. After data curation, standardized loads were calculated (mg/d per 1000 inhabitants). Weekly averages of loads of the drug residues were calculated based on six scenarios (sampling one to six weekdays) and compared to the weekly average in the control situation (sampling seven weekdays) in a Monte Carlo simulation. Results indicate that drug residues with more dynamic loads over a week require more frequent sampling. The analysis illustrates that a decreased sampling frequency (4 or 5 days per week) still leads to a representative weekly average for all drugs tested when a deviation up to a factor of 1.25 is deemed acceptable. However, knowledge on typical levels is necessary to define outliers. We therefore recommend to study dynamics in drug residue loads for WWTPs before reducing sampling frequency in long term monitoring programs.

Simplified Treatment of Coating Layers in TRISO Fuel in Statistical Geometry Method in Monte Carlo Calculation

An improved sampling method for flight distance is proposed for Monte Carlo analysis of TRISO fuel particles using the statistical geometry (STG) method. The statistically uniform distribution of fuel particles, which is usually assumed as a default sampling method of flight distance of a neutron between fuel particles, shows considerable bias on k-infinity when coating layers of a TRISO fuel particle are homogenized with a graphite matrix. The proposed new sampling method almost resolves the difference between the no-coating-layer model (coating layers are homogenized with a graphite matrix) and the explicit-coating-layer model (explicitly considers coating layers, reference). By adopting the present method, the no-coating-layer model can be used without significant loss of accuracy in the STG method of a Monte Carlo analysis. The computation time with a continuous energy Monte Carlo code for a typical fuel compact cell of a high-temperature gas-cooled reactor is reduced to one-seventh of the explicit-coating-layer model when the no-coating-layer model is used.

A multi-objective coordinating model for distribution network with EVs, energy storage, and reactive power compensation devices

Promoting electric vehicles (EVs) is regarded as an effective solution for reducing carbon emissions and addressing growing environmental concerns. However, the increasing number of EVs has significantly heightened the demand for electric power charging, posing challenges to distribution systems. Studies have shown that a coordination strategy combining various compensation devices, such as energy storage systems and reactive power compensation devices, can enhance the integration capability of EVs. Furthermore, EVs can be managed as controllable loads that can be shed at low costs, contributing to the flexibility of the distribution network. This paper explores the uncertainties of EV load sizes and the coordination of active and reactive power. A multi-objective optimization model was developed to augment the flexibility of the distribution network and mitigate the risk of load shedding. The Monte Carlo method is utilized to simulate scenarios with EV load uncertainties. To simplify the power flow calculations in the proposed convex optimization model, the Distflow equation was adopted. Case study results demonstrate that managing EV loads in the distribution network can significantly reduce load shedding costs by 80 %, and the proposed EV charging strategy can effectively eliminate load shedding costs in most scenarios.

Theoretical investigation of mixed-metal metal-organic frameworks as H2 adsorbents: insights from GCMC and DFT simulations

ABSTRACT Molecular hydrogen ( H 2 ) is a renewable energy carrier, however, its practical applications are limited due to the challenges of developing safe and efficient H 2 storage devices. Metal Organic Frameworks (MOFs) containing at least two different metal ions in their structures are called as mixed-metal MOFs (MM-MOFs) and they could adsorb H 2 in higher amounts compared to structures containing single metal nodes. We theoretically examined the H 2 storage capacities of 26 MM-MOFs having various physical and chemical properties applying Grand Canonical Monte Carlo (GCMC) and Density Functional Theory (DFT) simulations. H 2 adsorption isotherms were calculated using a five-site anisotropic H 2 model. QIXSOG, YOMVIG, OSOYUR, Cu-Mg-BTC, Fe-Mg-BTC, and Cr-Mg-BTC were selected as top-performing MM-MOFs maximising H 2 adsorption gravimetrically and volumetrically at near-ambient conditions (233 K and 100 bar), approaching the DOE targets. YOMVIG has the largest H 2 adsorption enthalpy, calculated as − 9.93 kJ/mol at 233 K and 100 bar. DFT simulations have been conducted to analyse preferable H 2 adsorption sites as well as identify guest-host interactions. Electron density difference analysis showed that adsorbed H 2 molecules in the OSOYUR, Cr-Mg-BTC, Cu-Mg-BTC, and Fe-Mg-BTC are polarised. Our study challenges existing literature by identifying promising MM-MOFs as potential next-generation hydrogen storage adsorbents at near-ambient conditions.

Systematic Improvement of Quantum Monte Carlo Calculations in Transition Metal Oxides: sCI-Driven Wavefunction Optimization for Reliable Band Gap Prediction.

Accurate determination of the electronic properties of correlated oxides remains a significant challenge for computational theory. Traditional Hubbard-corrected density functional theory (DFT+U) frequently encounters limitations in precisely capturing electron correlation, particularly in predicting band gaps. We introduce a systematic methodology to enhance the accuracy of diffusion Monte Carlo (DMC) simulations for both ground and excited states, focusing on LiCoO2 as a case study. By employing a selected configuration interaction (sCI) approach, we demonstrate the capability to optimize wavefunctions beyond the constraints of single-reference DFT+U trial wavefunctions. We show that the sCI framework enables accurate prediction of band gaps in LiCoO2, closely aligning with experimental values and substantially improving traditional computational methods. The study uncovers a nuanced mixed state of t2g and eg orbitals at the band edges that is not captured by conventional single-reference methods, further elucidating the limitations of PBE+U in describing d-d excitations. Our findings advocate for the adoption of beyond-DFT methodologies, such as sCI, to capture the essential physics of excited-state wavefunctions in strongly correlated materials. The improved accuracy in band gap predictions and the ability to generate more reliable trial wavefunctions for DMC calculations underscore the potential of this approach for broader applications in the study of correlated oxides. This work not only provides a pathway for more accurate simulations of electronic structures in complex materials but also suggests a framework for future investigations of the excited states of other challenging systems.

Effect of surface roughness on methane adsorption in shale organic nanopores from the perspective of molecular simulations

Understanding the shale gas adsorption behavior within organic nanopores is crucial for its development. The pore surfaces commonly exhibit random inherent roughness, which can significantly impact gas adsorption, while it’s still in debate about whether the roughness could enhance the gas adsorption or not. To clarify the underlying mechanisms of the effect of surface roughness, in this study, the Grand Canonical Monte Carlo and Molecular Dynamic simulations are used to investigate the methane adsorption in wrinkled graphene nanochannels with various roughness. The heterogeneity of the adsorption layer along the rough topology is described in detail. The adsorption is enhanced near the concave region and weakened near the convex region. Additionally, increased surface roughness shifts the gas molecules’ aggregation modes in the concave region from a gradual gradient to oscillatory “adsorption points”. We observe that wall roughness due to the wrinkle can enhance the adsorption amount overall, attributed to the increased surface area and the increased unit adsorption capacity due to potential energy overlap. However, the contributions of both factors differ under various pressure and roughness conditions. Then a new mathematical method to estimate the gas adsorption amounts is provided and aligns well with molecular simulation results for a wide range of pressure and roughness conditions. And a potential answer the current debate about the positive or negative impact of adsorption due to roughness is promoted. The key may lie in the dominant role of the co-existed roughness caused by the fragment basal or edge surface. Our work could shed light on the underlying effect of surface roughness on the gas adsorption in shale, and have significant implications for the reserve estimation of shale gas formations.

Investigating the Relationship Between the Dynamics of the Structure of Intermediate Consumption and Output of Industries: Cross-Country Analysis

This paper examines one of the main prerequisites of the inter-industry balance methodology on the proportional dependence of the output of industries and their intermediate consumption. The statistical base of the study is the world input-output tables collected for the period from 2000 to 2014. According to the results of the work, it was revealed that the prerequisite of a proportional change in the vector of intermediate consumption with a change in output is not consistent. However, significant positive relationships were found between intermediate consumption and the output of industries. On the basis of the identified relationships, we conducted a clustering procedure on industries, where, in particular, clusters with strong, medium, and weak relationships between intermediate consumption and output are distinguished. Thus, the paper proposes to expand the input-output methodology by introducing proportionality coefficients for changes in the intermediate consumption of an industry with a change in its output. Proportionality coefficients are proposed to be obtained on the basis of regression equations built on statistical data from the input-output tables. It should be especially noted that in addition to obtaining point forecasts of intermediate consumption for each industry when its output changes, it is also possible to obtain probability distributions for the gross output of each industry, their value added, wages, taxes, and profits through Monte Carlo simulations based on the constructed regression equations, which will facilitate a scenario analysis of the development of the considered economic system. The results of this work can be useful to executive authorities in planning large state economic projects as well as in assessing the impact of external shocks on the countryʼs economy.

Non-stationary response determination of nonlinear systems endowed with fractional derivative elements subjected to evolutionary stochastic excitation

This study proposes a statistical linearization-based approach for the analysis of the stochastic response of nonlinear systems endowed with fractional derivative elements under non-stationary stochastic excitation characterized by non-separable power spectral density. The method involves iteratively solving a time-varying fractional-order equivalent linear equation obtained by statistical linearization. Specifically, an analytical solution for the response power spectrum of the fractional-order linear system is developed within the frequency-domain random vibration context where the analytical time-varying frequency response function (FRF) is formulated as a convolution integral involving a modulated Fourier kernel and the impulse response function (IRF). The analytical solution for the IRF is obtained through eigen-analysis of the associated fractional linear system. To demonstrate the method’s efficacy, we examine fractional time-invariant/time-variant linear systems, along with a nonlinear system exhibiting cubic stiffness (Duffing oscillator) subject to a stochastic excitation with non-separable power spectral density of the Spanos-Solomos and Conte-Peng kinds. Pertinent Monte Carlo simulations demonstrate the accuracy and applicability of the proposed method.

Semi-analytical modeling of prompt redeposition in a steady-state plasma

Abstract A steady-state, 1D semi-analytical model for prompt redeposition based on the separation between redeposition caused by the electric field in the sheath and redeposition related to gyromotion is here described. The model allows for the estimation of not only the fraction of promptly redeposited flux but also the energy and angular distribution of the non-promptly redeposited population, along with their average charge state. Thus, the temperature and mean parallel-to-B velocity of the non-promptly redeposited flux are also available. The semi-analytical model was validated against equivalent Monte Carlo simulations across a broad range of input parameters. In this paper the eroded material under exam was tungsten (W) for which the code demonstrated consistent agreement with respect to numerical results, within its defined validity limits. The model can theoretically provide a solution for any material, temperature and electron density profile in the sheath, monotonic potential drop profile, and sputtered particles energy and angular distribution at the wall. As such, this code emerges as a potential tool for addressing the boundary redeposition phenomenon in fluid impurity transport simulations.

Combined experimental and grand canonical Monte Carlo simulation study of CO2 capture in nitrogen and sulfur co-doped biochar derived from biowaste: cost analysis, kinetics, and equilibrium

Nitrogen (N) and sulfur (S) co-doped biochar was produced from an agricultural waste. Heat treatment was combined with the use of dopants including thiourea, CO2, and air. The results show that the modified N and S co-doped biochar with the use of CO2 gas agent at 1123K for 2hr in the modification process, had the highest CO2 adsorption capacity of 4.36 and 3.05mmol/g at 273 and 298K at 1bar among others in literature even it had lower surface area or pore volume. This is due to the containing of N and S on the solid surface and superior pore size of 1.0nm. Moreover, the modified biochar had superior kinetics and CO2/N2 selectivity and was more economical than its unmodified biochar. To support experimental observation and better understand individual characteristics of functionalized carbons, a grand canonical Monte Carlo simulation was carried out to systematically illustrate the effect of N and S functional groups, pore size and pore volume on CO2 adsorption. The surface chemistry was the minor factor and played the key role in CO2 adsorption capacity at low pressures until it became saturated by adsorbate molecules at 0.3bar. S was stronger than N for enhanced CO2 adsorption capacity. Whereas the pore size was the major factor in both low and moderate pressures (< 23bar). The optimum pore size was found in a range of 0.66⎼4.0nm which was pressure-dependent. The pore volume became the main factor at higher pressures (> 23bar).

The Essential Role of Monte Carlo Simulations for Lung Dosimetry in Liver Radioembolization with 90Y Microspheres

The Essential Role of Monte Carlo Simulations for Lung Dosimetry in Liver Radioembolization with 90Y Microspheres

From Lévy walks to fractional material derivative: Pointwise representation and a numerical scheme

The fractional material derivative appears as the fractional operator that governs the dynamics of the scaling limits of Lévy walks - a stochastic process that originates from the famous continuous-time random walks. It is usually defined as the Fourier–Laplace multiplier, therefore, it can be thought of as a pseudo-differential operator. In this paper, we show that there exists a local representation in time and space, pointwise, of the fractional material derivative. This allows us to define it on a space of locally integrable functions which is larger than the original one in which Fourier and Laplace transform exist as functions.We consider several typical differential equations involving the fractional material derivative and provide conditions for their solutions to exist. In some cases, the analytical solution can be found. For the general initial value problem, we devise a finite volume method and prove its stability, convergence, and conservation of probability. Numerical illustrations verify our analytical findings. Moreover, our numerical experiments show superiority in the computation time of the proposed numerical scheme over a Monte Carlo method applied to the problem of probability density function’s derivation.

A stochastic averaging mathematical framework for design and optimization of nonlinear energy harvesters with several electrical DOFs

Energy harvesters for mechanical vibrations are electro-mechanical systems designed to capture ambient dispersed kinetic energy, and to convert it into usable electrical power. The random nature of mechanical vibrations, combined with the intrinsic non-linearity of the harvester, implies that long, time domain Monte-Carlo simulations are required to assess the device performance, making the analysis burdensome when a large parameter space must be explored. Therefore a simplified, albeit approximate, semi-analytical analysis technique is of paramount importance. In this work we present a methodology for the analysis and design of nonlinear piezoelectric energy harvesters for random mechanical vibrations. The methodology is based on the combined application of model order reduction, to project the dynamics onto a lower dimensional space, and of stochastic averaging, to calculate the stationary probability density function of the reduced variables. The probability distribution is used to calculate expectations of the most relevant quantities, like output voltage, harvested power and power efficiency. Based on our previous works, we consider an energy harvester with a matching network, interposed between the harvester and the load, that reduces the impedance mismatch between the two stages. The methodology is applied to the optimization of the matching network, allowing to maximize the global harvested power and the conversion efficiency. We show that the proposed methodology gives accurate predictions of the harvester’s performance, and that it can be used to significantly simplify the analysis, design and optimization of the device.