Monte Carlo-based Approach Research Articles

A Monte Carlo-based approach to reduce the cost of generating synthetic holograms for 3D computer graphics scenes

A Monte Carlo-based approach to reduce the cost of generating synthetic holograms for 3D computer graphics scenes

Dark Matter Distribution in Milky Way analog Galaxies

Our current understanding of how dark matter (DM) is distributed within the Milky Way (MW) halo, particularly in the solar neighborhood, is based on either careful studies of the local stellar orbits, model assumptions on the global shape of the MW halo, or from direct acceleration measurements. In this work, we undertake a study of external galaxies, with the intent of providing insight into the DM distribution in MW-analog galaxies. For this, we carefully select a sample of galaxies similar to the MW, based on maximum atomic hydrogen (H i) rotational velocity ( vmax,HI=200–280 km s−1) and morphological type (Sab–Sbc) criteria. With a need for deep, highly resolved H i, our resulting sample is composed of five galaxies from the VLA Imaging of Virgo in Atomic Gas (VIVA) survey and The H i Nearby Galaxy Survey (THINGS). To perform our baryonic analysis, we use deep mid-IR images at 3.6 and 4.5 μm from the Spitzer Survey of Stellar Structures in Galaxies (S4G). Based on the dynamical three-dimensional modeling software 3D-Based Analysis of Rotating Objects via Line Observations (3D-BAROLO), we construct rotation curves (RCs) and derive the gas and stellar contributions from the galaxy's gaseous and stellar disks' mass surface density profiles. Through a careful decomposition of their RCs into their baryonic (stars, gas) and DM components, we isolate the DM contribution by using a Markov Chain Monte Carlo-based approach. Based on the Sun's location and the MW’s R 25, we define the corresponding location of the solar neighborhood in these systems. We put forward a window for the DM density (ρ dm = 0.21–0.55 GeV cm−3) at these galactocentric distances in our MW analog sample, consistent with the values found for the MW’s local DM density, based on more traditional approaches found in the literature.

In-depth investigation of phase transition phenomena in network models derived from lattice models

Lattice models exhibit significant potential in investigating phase transitions, yet they encounter numerous computational challenges. To address these issues, this study introduces a Monte Carlo-based approach that transforms lattice models into a network model with intricate inter-node correlations. This framework enables a profound analysis of Ising, JQ, and XY models. By decomposing the network into a maximum entropy component and a conservative component, under the constraint of detailed balance, this work derives an estimation formula for the temperature-dependent magnetic induction in Ising models. Notably, the critical exponent β in the Ising model aligns well with the established results, and the predicted phase transition point in the three-dimensional Ising model exhibits a mere 0.7% deviation from numerical simulations.

On the long-term fatigue analysis of marine structures

This paper investigates the performance of several procedures available in literature used to evaluate the multi-dimensional integral associated with long-term fatigue assessment of marine structures. Direct integration methods, Monte Carlo-based approaches and Dimension-Reduction methods are investigated. The focus is on solving the four-dimensional integral for wave-induced fatigue cases when wind-sea and swell waves are simultaneously considered in the analysis. Although the procedures investigated can be used either for time-domain or frequency-domain fatigue approaches, all fatigue analyses performed in this work are restricted to the frequency-domain and are based on idealized stress transfer functions that attempt to mimic the complex behavior of some marine structures. To evaluate the performance of these methodologies, different transfer functions of unimodal and bimodal types are studied. The long-term fatigue damages are calculated using all the investigated methods, and their accuracies and efficiencies are compared. It is found that the efficiency of Monte Carlo Simulation-based methods is problem-dependent and may require many numerical simulations to achieve good accuracy for some cases. Additionally, the Dimension-Reduction methods can lead to highly suitable results with a very low number of integration points and, among all methods investigated, the BDRM (Bivariate Dimension-Reduction Method) stands out due to its excellent performance.

Optimizing the timing of an end-of-outbreak declaration: Ebola virus disease in the Democratic Republic of the Congo.

Following the apparent final case in an Ebola virus disease (EVD) outbreak, the decision to declare the outbreak over must balance societal benefits of relaxing interventions against the risk of resurgence. Estimates of the end-of-outbreak probability (the probability that no future cases will occur) provide quantitative evidence that can inform the timing of an end-of-outbreak declaration. An existing modeling approach for estimating the end-of-outbreak probability requires comprehensive contact tracing data describing who infected whom to be available, but such data are often unavailable or incomplete during outbreaks. Here, we develop a Markov chain Monte Carlo-based approach that extends the previous method and does not require contact tracing data. Considering data from two EVD outbreaks in the Democratic Republic of the Congo, we find that data describing who infected whom are not required to resolve uncertainty about when to declare an outbreak over.

Exploring the bias: how skin color influences oxygen saturation readings via Monte Carlo simulations.

Our goal is to understand the root cause of reported oxygen saturation ( ) overestimation in heavily pigmented skin types to devise solutions toward enabling equity in pulse oximeter designs. We aim to gain theoretical insights into the effect of skin tone on curves using a three-dimensional, four-layer tissue model representing a finger. A finger tissue model, comprising the epidermis, dermis, two arteries, and a bone, was developed using a Monte Carlo-based approach in the MCmatlab software. Two skin tones-light and dark-were simulated by adjusting the absorption and scattering properties within the epidermal layer. Following this, curves were generated in various tissue configurations, including transmission and reflection modes using red and infrared wavelengths. In addition, the influence of source-detector (SD) separation distances on both light and dark skin tissue models was studied. In transmission mode, curves did not deviate with changes in skin tones because both pulsatile and non-pulsatile terms experienced equal attenuation at red and infrared wavelengths. However, in reflection mode, measurable variations in curves were evident. This was due to differential attenuation of the red components, which resulted in a lower perfusion index at the red wavelength in darker skin. As the SD separation increased, the effect of skin tone on curves in reflection mode became less pronounced, with the largest SD separation exhibiting effects similar to those observed in transmission mode. Monte Carlo simulations have demonstrated that different light pathlengths within the tissue contribute to the overestimation of in people with darker skin in reflection mode pulse oximetry. Increasing the SD separation may mitigate the effect of skin tone on readings. These trends were not observed in transmission mode; however, further planned research using more complex models of the tissue is essential.

Estimating flashover occurrence in distribution lines: A novel approach focused on the current-peak-to-distance ratio

Lightning location systems provide an estimation of both lightning current peak amplitudes and stroke locations, which are useful information to correlate lightning strokes and faults in power distribution networks. The paper presents a novel method for assessing the correlation between a lightning-induced flashover and the lightning current-peak-to-distance ratio of indirect lightning events. This is motivated by the consideration that a minimum threshold/value of the ratio between current peak and stroke distance from the line must be reached for an insulation flashover to occur. The proposed procedure uses existing equations and models that relate induced voltage peak amplitudes, distance, current peak and other parameters. Specifically, the paper compares the results predicted by Rusck's and Darveniza's formulas with those provided by the more detailed model implemented in the LIOV code. It is found that a minimum lightning current-peak-to-distance ratio must be exceeded to trigger an insulation flashover and that Rusck's and Darveniza's formulas may be too conservative in estimating such a threshold value. The paper provides guidelines for properly estimating the minimum collecting area around the line including all hazardous events, as this information is essential for calculating the lightning performance of a power distribution line using a Monte Carlo-based approach.

Asymmetric imaging through engineered Janus particle obscurants using a Monte Carlo approach for highly asymmetric scattering media

The advancement of imaging systems has significantly ameliorated various technologies, including Intelligence Surveillance Reconnaissance Systems and Guidance Systems, by enhancing target detection, recognition, identification, positioning, and tracking capabilities. These systems can be countered by deploying obscurants like smoke, dust, or fog to hinder visibility and communication. However, these counter-systems affect the visibility of both sides of the cloud. In this sense, this manuscript introduces a new concept of a smoke cloud composed of engineered Janus particles to conceal the target image on one side while providing clear vision from the other. The proposed method exploits the unique scattering properties of Janus particles, which selectively interact with photons from different directions to open up the possibility of asymmetric imaging. This approach employs a model that combines a genetic algorithm with Discrete Dipole Approximation to optimize the Janus particles' geometrical parameters for the desired scattering properties. Moreover, we propose a Monte Carlo-based approach to calculate the image formed as photons pass through the cloud, considering highly asymmetric particles, such as Janus particles. The effectiveness of the cloud in disguising a target is evaluated by calculating the Probability of Detection (PD) and the Probability of Identification (PID) based on the constructed image. The optimized Janus particles can produce a cloud where it is possible to identify a target more than 50% of the time from one side (PID > 50%) while the target is not detected more than 50% of the time from the other side (PD < 50%). The results demonstrate that the Janus particle-engineered smoke enables asymmetric imaging with simultaneous concealment from one side and clear visualization from the other. This research opens intriguing possibilities for modern obscurant design and imaging systems through highly asymmetric and inhomogeneous particles besides target detection and identification capabilities in challenging environments.

Optimal configuration of energy storage for alleviating transmission congestion in renewable energy enrichment region

High-penetration renewable energy development causes transmission congestion in power system operation. Such transmission congestion in short period can be alleviated by energy storage configuration, instead of investing and expanding new transmission lines. This paper presents an optimal configuration method of energy storage for alleviating transmission congestion in renewable energy enrichment region. In order to obtain the characteristics of renewable energy outputs, a scenario generation technique for renewable energy outputs considering spatio-temporal correlation is first proposed. A Monte Carlo-based approach for evaluating the transmission congestion is proposed for identifying the potential locations of energy storage installation. Finally, an optimal configuration method of energy storage based on stochastic programming is proposed for alleviating transmission congestion. Numerical experiments are carried out on a modified IEEE-RTS 24-bus system and a practical 129-bus system. Numerical results show that energy storage can improve the flexibility of power system operation and the utilization of renewable energy generation. Especially, in the practical 129-bus system, the proposed method reduces renewable energy curtailment by 625.26 MWh and 444.43 MWh under fall and winter typical scenarios, respectively. And, investing some energy storage devices is more economical when the duration of transmission congestion is relatively short.

Risk assessment in cryptocurrency portfolios: a composite hidden Markov factor analysis framework

In this paper, we deal with the estimation of two widely used risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES) in a cryptocurrency context. To face the presence of regime switching in the cryptocurrency volatilities and the dynamic interconnection between them, we propose a Monte Carlo-based approach using heteroskedastic factor analysis and hidden Markov models (HMM) combined with a structured variational Expectation-Maximization (EM) learning approach. This composite approach allows the construction of a diversified portfolio and determines an optimal allocation strategy making it possible to minimize the conditional risk of the portfolio and maximize the return. The out-of-sample prediction experiments show that the composite factorial HMM approach performs better, in terms of prediction accuracy, than some other baseline methods presented in the literature. Moreover, our results show that the proposed methodology provides the best performing crypto-asset allocation strategies and it is also clearly superior to the existing methods in VaR and ES predictions.

Mechanical modeling, seismic fragility, and correlation issues for groups of spherical pressure vessels

A roadmap is outlined for determining comprehensive seismic fragility curves for (single or groups of) spherical pressure vessel structures commonly found in oil refineries. The developed modeling techniques aim to strike a balance between accuracy and computational efficiency, with a focus on capturing the most pertinent failure modes relevant to these structural types. First, a set of “partial” fragilities is determined based on each vessel’s fill ratio, as the response varies substantially depending on the amount of liquid content. Considering that a seismic assessment process invariably involves simultaneous consideration of such partial fragilities, a Monte Carlo-based approach is employed for their combination. The results naturally depend on the level of correlation employed, but can be almost perfectly matched by simpler analytical methods in the edge cases of full and zero correlation.

Full-Wave Simulation and Scattering Modeling for Terahertz Communications

This paper presents a comprehensive study for analyzing and modeling the scattering phenomenon of terahertz (THz) waves bouncing on rough surfaces. More specifically, a generic parametric methodology to accurately model the rough characteristics focusing on the root-mean-square (RMS) height and correlation length of a surface is proposed. Firstly, a Monte Carlo-based approach is developed for the efficient and accurate software realization of such rough surfaces. Then, the full-wave simulator FEKO is used to simulate at 300 GHz the far-field behavior of scattering waves on 30 distinct surfaces having 6 RMS heights and 5 correlation lengths. By employing the directive scattering (DS) model, the variation of the scattering coefficient, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> , and its equivalent roughness, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha _{R}$</tex-math></inline-formula> , are studied for the scattering amplitude aspect. Furthermore, the statistical characteristics of the phase and polarization of the scattered signals are obtained and compared with those available in existing 3GPP channel model standards which are valid for lower frequency bands. These comparisons show that although the phases follow the normal distribution, as is the case with equivalent 3GPP channel models, the cross-polarization ratios (XPRs) follow the logistic distribution. This is an important difference which could have a great impact on the standardization activities of the current 3GPP-related working groups. Finally, the proposed scattering model at the THz band is realized through an algorithmic implementation. Compared with the classical two-dimensional DS model which characterizes only the amplitude of the scattered signal, the proposed three-dimensional scattering model can be efficiently used to accurately characterize all the important parameters of the scattered signals, namely, amplitude, phase, and polarization at the THz band. Additionally, the proposed scattering model can effectively work in conjunction with ray-tracing (RT) schemes leading to precise scattering and channel modeling for new applications, such as holographic radios and multi-user multiple-input multiple-output (MU-MIMO) systems.

Compound flood events: analysing the joint occurrence of extreme river discharge events and storm surges in northern and central Europe

Abstract. The simultaneous occurrence of extreme events gained more and more attention from scientific research in the last couple of years. Compared to the occurrence of single extreme events, co-occurring or compound extremes may substantially increase risks. To adequately address such risks, improving our understanding of compound flood events in Europe is necessary and requires reliable estimates of their probability of occurrence together with potential future changes. In this study compound flood events in northern and central Europe were studied using a Monte Carlo-based approach that avoids the use of copulas. Second, we investigate if the number of observed compound extreme events is within the expected range of 2 standard deviations of randomly occurring compound events. This includes variations of several parameters to test the stability of the identified patterns. Finally, we analyse if the observed compound extreme events had a common large-scale meteorological driver. The results of our investigation show that rivers along the west-facing coasts of Europe experienced a higher amount of compound flood events than expected by pure chance. In these regions, the vast majority of the observed compound flood events seem to be related to the cyclonic westerly general weather pattern (Großwetterlage).

Sample size estimation for comparing dynamic treatment regimens in a SMART: A Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes.

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual's status and progress should be used in practice to decide which treatment (e.g. type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Furthermore, in many health domains, count data are overdispersed-having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation.

A Monte Carlo-Based Approach to Assess the Reinforcement Depassivation Probability of RC Structures: Simulation and Analysis

In this work, an approach is presented to assess the reinforcement depassivation probability of reinforced concrete structures under corrosion induced by carbonation or chloride diffusion. The model consists of coupling mathematical formulations of CO2 and Cl− diffusion with Monte Carlo simulation (MCS). Random events were generated using MCS to create several design life and environmental scenarios. A case study was performed by simulating five Brazilian environmental conditions and distinct mixes of concrete. The effect of input parameters on the reinforcement concrete depassivation probability was evaluated. The results point out that the depassivation probability due to carbonation is more significant in urban centers, and the compressive strength of concrete has the main influence on the depassivation probability. Results also showed that the depassivation probability due to chloride ingress is influenced by, in order of importance, the chloride content on the surface (61.4%), concrete cover (20.3%), compressive strength (7.1%), relative humidity (6.1%), and temperature (5.1%). In addition, an increase in the compressive strength of concrete, from 30 to 50 MPa, can reduce depassivation probability by up to 70%, resulting in a concrete structure that attends the durability limit state. Thus, by incorporating probabilistic approaches, this model can be a valuable tool in the civil construction industry for studying the improvement of durability, reliability, and safety of reinforced concrete structures.

Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data.

Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data structures are obtained when high-throughput transcriptome sequencing data are collected for n genes across p conditions at r occasions. Matrix variate distributions offer a natural way to model three-way data and mixtures of matrix variate distributions can be used to cluster three-way data. Clustering of gene expression data is carried out as means of discovering gene co-expression networks. In this work, a mixture of matrix variate Poisson-log normal distributions is proposed for clustering read counts from RNA sequencing. By considering the matrix variate structure, full information on the conditions and occasions of the RNA sequencing dataset is simultaneously considered, and the number of covariance parameters to be estimated is reduced. We propose three different frameworks for parameter estimation: a Markov chain Monte Carlo-based approach, a variational Gaussian approximation-based approach, and a hybrid approach. Various information criteria are used for model selection. The models are applied to both real and simulated data, and we demonstrate that the proposed approaches can recover the underlying cluster structure in both cases. In simulation studies where the true model parameters are known, our proposed approach shows good parameter recovery. The GitHub R package for this work is available at https://github.com/anjalisilva/mixMVPLN and is released under the open source MIT license.

Impacts of Measurement Errors on Real-Time Thermal Rating Estimation for Overhead Lines

To exploit additional capacity on legacy transmission assets, network owners have developed a variety of techniques to support real-time thermal rating (RTTR) of overhead lines so as to get the maximum possible capacity within their operating range. Most RTTR techniques are inferred from effective weather conditions (EWCs) that are used to assess the thermal equilibrium of conductors indicated by the measured temperature <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${T}_c$</tex-math></inline-formula> , thus making RTTR estimates sensitive to measurement errors (MEs). To highlight the impacts that MEs have on RTTRs, this paper describes two EWC-based approaches to RTTR estimation and assesses the accuracy of their resulting estimates under steady state conditions given different levels of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${T}_c$</tex-math></inline-formula> and weather variables. Furthermore, the paper develops a Monte Carlo-based approach to model the propagation of measurement uncertainties through to RTTR estimates. Numerous rating scenarios for a particular instance are generated through combining a Monte Carlo method, where possible MEs are sampled within the sensor specification based limits, with an enhanced EWC-based approach that models transient changes of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${T}_c$</tex-math></inline-formula> in each scenario. The lower RTTR percentiles extracted from the rating samples can not only mitigate the RTTR overestimation due to MEs, but also inform system operators of the risk associated with RTTR decisions.

Partitioning the contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding

Abstract. Getting a deep insight into the role of coastal flooding drivers is of great interest for the planning of adaptation strategies for future climate conditions. Using global sensitivity analysis, we aim to measure the contributions of the offshore forcing conditions (wave–wind characteristics, still water level and sea level rise (SLR) projected up to 2200) to the occurrence of a flooding event at Gâvres town on the French Atlantic coast in a macrotidal environment. This procedure faces, however, two major difficulties, namely (1) the high computational time costs of the hydrodynamic numerical simulations and (2) the statistical dependence between the forcing conditions. By applying a Monte Carlo-based approach combined with multivariate extreme value analysis, our study proposes a procedure to overcome both difficulties by calculating sensitivity measures dedicated to dependent input variables (named Shapley effects) using Gaussian process (GP) metamodels. On this basis, our results show the increasing influence of SLR over time and a small-to-moderate contribution of wave–wind characteristics or even negligible importance in the very long term (beyond 2100). These results were discussed in relation to our modelling choices, in particular the climate change scenario, as well as the uncertainties of the estimation procedure (Monte Carlo sampling and GP error).

Primary standardization of 212Pb activity by liquid scintillation counting

An activity standard for 212Pb in equilibrium with its progeny was realized, based on triple-to-double coincidence ratio (TDCR) liquid scintillation (LS) counting. A Monte Carlo-based approach to estimating uncertainties due to nuclear decay data (branching ratios, beta endpoint energies, γ-ray energies, and conversion coefficients for 212Pb and 208Tl) led to combined standard uncertainties ≤ 0.20 %. Confirmatory primary measurements were made by LS efficiency tracing with tritium and 4παβ(LS)-γ(NaI(Tl)) anticoincidence counting. The standard is discussed in relation to current approaches to 212Pb activity calibration. In particular, potential biases encountered when using inappropriate radionuclide calibrator settings are discussed.

Switching latent factor value-at-risk models for conditionally heteroskedastic portfolios: A comparative approach

In this paper, a computationally efficient Monte Carlo-based latent factor modeling approach for portfolio Value-at-Risk (VaR) estimation is introduced. We examine whether the inclusion of conditional heteroskedasticity in financial returns, and taking into account for possible hidden (Markovian) “regime changes” in the latent correlation structure of the portfolio can enhance the accuracy of VaR forecasts. Practical details of training such models with the expectation-maximization algorithm are also discussed. In conjunction with an approximated version of the Kalman filter, we show how to calculate maximum likelihood estimates of the model parameters, and to yield inferences about the unobservable path of the common factors, their volatilities and the hidden state sequence of the Markov process. The methodology is illustrated by an example using data from the Tunisian foreign exchange market, over the period of the Tunisian revolution from January 02, 2010 to December 30, 2012. We found that this new specification exhibits a good fit to the data, improves the accuracy of VaR predictions of the Tunisian foreign public debt portfolio and reduces the number and average size of back-testing breaches when a financial crisis occurs.