Monte Carlo Simulation Model Research Articles

Application of Monte Carlo model in financial field

MC (Monte Carlo) model is also called statistical test method or statistical simulation method. According to the statistical sampling theory, it is a method of approximate solution through probability simulation and statistical test of random variable function. Compared with the traditional mathematical statistics method, MC method alleviates the difficulty of realization with the help of computer, and is easy to operate and understand. Other mathematical methods can find an appropriate way to deal with complex problems that are difficult or even impossible to deal with. This paper studies the application of MC model in the financial field: the application in the pricing of financial options and the application in the guaranteed price of multi-period income. Using MC simulation model, we can not only analyze the risk of investment projects, but also play a great role in management, financial analysis and marketing.

Development and Application of Particle Transport Monte Carlo Simulation Modeling Toolkit

In particle transport Monte Carlo simulation, the manual modeling method is not suitable for complex geometric models. A Monte Carlo Simulation Modeling Toolkit (pyMCMT) is developed based on Python language. The toolkit can generate input card in several classical format such as MCNP and GDML format automatically. In this paper, two typical applications are presented based on the pyMCMT toolkit: 1, constructing a multilayer circular geometric model in complex density distribution by pyMCMT toolkit in order to satisfy the requirement of building large-space fine model with the influence of blast wave. A two-dimensional adaptive model is built for non-uniform atmospheric induced by blast wave. The number of the cells in this model is reduced vastly with the same model resolution; 2, producing the optimized weight window parameter using the mesh-based weight window variance reduction combined with density iteration method which is realized to solve the high statistical fluctuation and low counting efficiency problem in deep penetration process. The successful application of pyMCMT shows that pyMCMT is a modularized, parameterized, customizable and easy-to-debug toolkit for particle transport Monte Carlo simulation modeling, which can improve the modeling efficiency tremendously.

Space neutron radiation shielding property of continuous fiber and functional filler reinforced polymer composite using Monte Carlo simulation

For radiation shielding and lightweight requirements, fiber-reinforced polymer (FRP) composite has advantages in serving as a structure-shielding multifunctional material due to low-Z element nature and excellent mechanical properties. This study established the Monte Carlo simulation model of continuous fiber and filler reinforced epoxy composite to research neutron radiation shielding, which contains the microstructure of the composite. For validation, the numerical results of the proposed model were compared with the experimental results and found in good agreement. Then, the effects of fiber and filler distribution were studied. The neutron linear and mass attenuation coefficients of various composites were simulated to evaluate the shielding performance and lightweight effect. It shows that the boron compound is critical for shielding thermal neutrons, and the shielding effectiveness for fast neutrons mainly depends on the type of fiber. The structural design and stacking sequence of fibers and fillers significantly affect neutron shielding performance. Compared with Al-alloy, obvious low-weight advantage is obtained for shielding neutrons. The shielding mechanism and energy deposition inside composites were also analyzed.

Kinetic modeling and interrelationship aspects of biogas production from waste activated sludge solubilized by enzymatic and thermal pre-treatment

Monte Carlo Simulation (MCS) and kinetic models were applied using data from the biogas production test of waste activated sludge (WAS) pre-treated samples. Indeed, eleven different pre-treatments were applied, combining enzymatic and thermals technologies of sludge solubilization. The main study was proposed to understand the kinetic dynamics of the anaerobic digestion (AD) process and its biogas production potential as a renewable energy source, optimized by distinct solubilization techniques that can be applied to enhance biogas production. As a result, most of the evaluated scenarios significantly improved the biogas production, when compared to the control scenario. Among those, the scenario with thermal solubilization and enzymatic solubilization associated in series, obtained the greatest biogas production potential, reaching 291 ± 3 NmL.gVSS-1, corresponding to a production increase of about 71%. The maximum rate of biogas production obtained was 33.5 ± 0.7 NmL.gVSS-1.day−1. The average lag phase duration (λ) was 0.56 days, and the hydrolysis coefficient (kh) varied between the minimum value of 0.08 ± 0.002 and the maximum of 0.15 ± 0.003 days−1. The two-way ANOVA indicated that there is an interrelationship between the solubilization techniques applied. The confidence interval obtained through MCS allowed us to conclude that the adjustments of the models and the obtention of its parameters to all the scenarios, with exception of the control one, do not have significant differences between them. Therefore, more than a single application of kinetic models to biogas production test results, the statistical approaches supported to elucidate and measure the influence of solubilization techniques in the bioconversion of WAS to biogas as renewable energy source.

Risk Mitigation in Project Management Theoretical Issues and Case Study

The objective of this article is to evaluate how the risk management process is used in the software development industry and how practitioners are managing risks in early stages of a project. Further, a description of how risk assessment might help to mitigate risks associated to events that could occur to different phases of a project life cycle will be presented. The core of the research is the theoretical approach for designing a simulation model to estimate costs prior to a project. In the application part, this model is implemented using a case study and the results of the estimation costs which are analyzed and simulated using the theoretical framework of Monte Carlo simulation model are presented. In the conclusions section the final recommendations are drawn up.

Study of the matrix composition effect on gamma spectrometry analysis using Monte Carlo simulation: application for soil to crop transfer factor determination

Abstract Self-absorption effect has a significant impact on gamma spectrometry measurement for the highly precise determination of the radionuclides activity. Among the main factors affecting the self-absorption effect especially at low gamma energies is the matrix composition of the sample. For this purpose, we studied the matrix composition effect on the self-absorption correction for plant and soil matrices using a Monte Carlo simulation model, composed of High Purity Germanium (HPGe) detector and a cylindrical geometry source. The simulated configuration model was validated by comparison of simulated full energy peak efficiency (FEPE) values with the experimental ones using a standard radioactive solution, covering large interval gamma energies in the range of 59.54–1836.12 keV. The results show that the FEPE values of low gamma energies are affected by the matrix composition, where in the soil matrix the FEPEs decreased by 36% and only by 1% in the plant matrix. The Monte Carlo results were applied in order to accurately calculate the transfer factor of the environmental radionuclides 226Ra, 232Th and 40K from soils to several crops, mustard, artichoke and fennel through their specific activities.

Piezo-Transmissive Structure Using a Multi-layered Heterogeneous Film for Optical Transmittance Modulation.

As the damage caused by the recent climate crisis increases, efforts are being made to develop low-power and high-efficiency technologies to reduce pollution for energy production worldwide. Among them, research on the mechano-responsive optical transmittance modulation technology is being actively conducted as it can be applied to various application fields for reducing energy consumption: low-power sensors and smart windows. The piezo-transmittance structure, which is one of the optical transmittance modulation structures, has fewer constraints on the installation environment; therefore, many applications have been proposed. However, it is still challenging to fabricate a piezo-transmittance structure with a large-area production, high throughput, and good tunability because of complex curing and dissolution processes. Herein, we present an efficient fabrication method for a multi-layered piezo-transmittance structure using a large-area abrasive mold and thermal imprinting process. The piezo-transmittance performance (e.g., sensitivity and relative change of transmittance) shows temperature/humidity-independent characteristics and can be designed by tuning design parameters such as the number of layers, abrasive grade, and film material. Also, the surrogate model of the performance obtained from the Monte Carlo simulation and prediction model can offer tunability for various applications. Finally, we demonstrated two energy-efficient applications: the smart window integrated with a hydraulic pump showed high thermal efficiency in indoor environment control, and the telemetry system was demonstrated to measure pressure remotely.

Health risk assessment of exposure to triclosan in pregnant women using Monte Carlo simulation techniques: based on biomonitoring data

This study aimed to assess the triclosan (TCS) health risk in an Iranian pregnant women sample by Monte Carlo simulation (MCS). The urinary TCS of 99 women after the 28th week of pregnancy was detected by gas chromatography/mass spectrometry detector (GC/MS), and the MCS model implemented a health risk assessment. The corresponding hazard quotient (HQ) and the sensitivity analysis were calculated. TCS was measured in 100% of the urine samples with a median concentration of 2.89 µg/L. The median of HQ was obtained at 1.93 × 10−4. The TCS exposure risk in the studied population was lower than the allowable limit. A comparison between HQ values in the two weight subgroups of pregnant women showed that the risk level is almost equal, and there was minimal health risk in pregnant women from exposure to TCS.

국내은행 스트레스테스트 모형개선에 관한 연구

Purpose - The main purpose of this study is to develop the stress test model for Korean banks by exploring the optimal Monte Carlo simulation and BIS forecasting model.
 Design/methodology/approach - This study selects 15 Korean banks as sample financial firms and collects relevant 76 quarterly data for the period between year 2000 and 2018 from KRX(Korea Excange), Bank of Korea, and FnGuide. The Regression analysis, Unit-root test, and Monte Carlo simulation are hired to analyze the data.
 Findings - First, most of the sample banks failed to keep 8% BIS ratio for the adverse and severely Adverse Scenarios, implying that Korean banks must make every effort to realize better BIS ratios under adverse market conditions. Second, we suggest the better Monte Carlo simulation model for the Korean banks by finding that the more appropriate volatility should be different depending on variables rather than simple two-sigma which has been used in the previous studies. Third, we find that the stepwise regression model is better fitted than simple regression model in forecasting macro-economic variables for the BIS variables. Fourth, we find that, for the more robust and significant statistical results in designing stress tests, Korean banks are required to construct more valid time-series and cross-sectional data-base.
 Research implications or Originality - The above results all together show that the optimal volatility in designing optimal Monte Carlo simulation varies depending on the country, and many Korean banks fail to pass sress test under the adverse and severely adverse scenarios, implying that Korean banks need to make improvement in the BIS ratio.

An improved method for evaluating LINAC isocenter.

The traditional approach to medical linear accelerator (LINAC) isocenter quality assurance is to compare the radius of the LINAC isocenter, determined via analysis of Winston-Lutz (WL) dataset, to a threshold in order to approve the LINAC for clinical use. This scalar metric provides little insight into beam-to-target accuracy with gantry and couchmotion. To develop a method for verifying isocenter with increased sensitivity over traditional WL techniques by accounting for geometric errors that could normally beoverlooked. From WL images, we construct radiation beam axis and marker shift locations in a 3D coordinate system. These axes and shift positions are used to construct a new isocenter performance metric, the -matrix, which predicts direction and magnitude of beam-to-target errors across combinations of couch and gantry position. We introduce clinical isocenter as the location optimizing a cost function derived from the -matrix, which serves as an optimal target for tumor positioning. We demonstrated these techniques on a clinical LINAC with an initial randomly positioned marker which was subsequently repositioned based on the optimized clinical isocenter location. The marker shifts, radiation isocenter, and -matrix were compared before and after repositioning. We compared the new technique against typically used WL techniques using a monte carlo simulation modeling variations in LINAC geometry, marker position, and measurementnoise. The technique was successfully demonstrated on a Varian LINAC. Marker repositioning to clinical isocenter yielding an error matrix with magnitudes all below 0.81 mm. As expected, marker position had little impact on the radiation isocenter location and radius, and also had little impact on clinical isocenter location. The verification results show the accuracy of the -matrix to predict beam to tumor geometric inaccuracies. The Monte Carlo simulations demonstrate that the -matrix is more sensitive and specific for detecting potential treatment errors compared to the traditional WLtechniques. We have developed and demonstrated the usefulness of a framework for verifying isocenter based on a 3D model of the radiation beam axes and tumor movement from couch rotations, derived from 2D WL transmission images. The -matrix replaces the scalar isocenter radius as a metric of isocenter quality, providing insight into contribution of couch and radiation beams to isocenter quality, and exposing treatment errors ignored by the traditional method. The clinical isocenter provides an alternate to physical isocenter as a target for tumor positioning in cases of suboptimal LINACgeometry.

A multi-source based Monte Carlo simulation model for spot scanning proton radiotherapy using GEANT4

Proton therapy is widely used for treating various tumor types due to its favorable dosimetric characteristics compared with conventional radiotherapy. However, a small error in dose calculation may lead to a substantial misadministration of planned radiation dose. This work aims to create a simplified and easy-to-use Monte Carlo (MC) simulation model, based on the multi-source model principle, for spot scanning proton radiotherapy. Our multi-source model contains a set of physical parameters acquired from a Varian ProBeam compact system at the South Florida Proton Therapy Institute (SFPTI). The source model input parameters are mean energy (σΕ) and beam spot (σs) standard deviations, which directly affect the integrated depth-dose (IDD) dosimetric characteristics such as beam width, proton range, and distal fall-off. Despite the simplicity of the presented model, all simulated results matched the corresponding experimental values within 2.5% and were found to be within the acceptable clinical limits, for a wide nominal proton energy range (i.e., 90–220 MeV). Additionally, the comparison of the simulated IDDs with the experimental IDDs was found to be highly conformal with 100% of the points passing the 2%/2 mm gamma index test. The model presented in this work can be efficiently used to model high energy (>80 MeV) in a central axis clinical scanning proton beam in a real clinical setting. The small number of input parameters selected allows for a more efficient and user-friendly MC modeling than previously developed models, while off-axis beam characteristics can be studied with the addition of a new sub-source parameter file.

A Survey on Quantitative Risk Estimation Approaches for Secure and Usable User Authentication on Smartphones.

Mobile user authentication acts as the first line of defense, establishing confidence in the claimed identity of a mobile user, which it typically does as a precondition to allowing access to resources in a mobile device. NIST states that password schemes and/or biometrics comprise the most conventional user authentication mechanisms for mobile devices. Nevertheless, recent studies point out that nowadays password-based user authentication is imposing several limitations in terms of security and usability; thus, it is no longer considered secure and convenient for the mobile users. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Alternatively, biometric-based user authentication has gained attention as a promising solution for enhancing mobile security without sacrificing usability. This category encompasses methods that utilize human physical traits (physiological biometrics) or unconscious behaviors (behavioral biometrics). In particular, risk-based continuous user authentication, relying on behavioral biometrics, appears to have the potential to increase the reliability of authentication without sacrificing usability. In this context, we firstly present fundamentals on risk-based continuous user authentication, relying on behavioral biometrics on mobile devices. Additionally, we present an extensive overview of existing quantitative risk estimation approaches (QREA) found in the literature. We do so not only for risk-based user authentication on mobile devices, but also for other security applications such as user authentication in web/cloud services, intrusion detection systems, etc., that could be possibly adopted in risk-based continuous user authentication solutions for smartphones. The target of this study is to provide a foundation for organizing research efforts toward the design and development of proper quantitative risk estimation approaches for the development of risk-based continuous user authentication solutions for smartphones. The reviewed quantitative risk estimation approaches have been divided into the following five main categories: (i) probabilistic approaches, (ii) machine learning-based approaches, (iii) fuzzy logic models, (iv) non-graph-based models, and (v) Monte Carlo simulation models. Our main findings are summarized in the table in the end of the manuscript.

Simulation of long-term impact of intravitreal anti-VEGF therapy on patients with severe non-proliferative diabetic retinopathy

ObjectiveA simulation model was constructed to assess long-term outcomes of proactively treating severe non-proliferative diabetic retinopathy (NPDR) with anti-vascular endothelial growth factor (anti-VEGF) therapy versus delaying treatment until PDR develops.Methods...

Selecting the best location of water quality sensors in water distribution networks by considering the importance of nodes and contaminations using NSGA-III (case study: Zahedan water distribution network, Iran).

One of the most effective ways to minimize polluted water consumption is to arrange quality sensors properly in the water distribution networks (WDNs). In this study, the NSGA-III algorithm is developed to improve the optimal locations of sensors by balancing four conflicting objectives: (1) detection likelihood, (2) expected detection time, (3) detection redundancy, and (4) the affected nodes before detection. The research procedure proposed the dynamic variations of chlorine between defined upper and lower bounds, which were determined utilizing the Monte Carlo simulation model. For selecting a contamination matrix with the same characteristics and effects of all possible events, a heuristic method was applied. The coefficients of importance are introduced in this study for the assessment of contamination events and network nodes. The Pareto fronts for each of the two sets of conflicting objectives were computed for benchmark and real water distribution networks using the proposed simulation-optimization approach. Results indicated that sensors should be installed downstream of the network to maximize sensor detection likelihood; however, this increases detection time. For the benchmark network, maximum and minimum detection likelihoods were calculated as 92.8% and 61.1%, respectively, which corresponded to the worst detection time of 11.58min and the best detection time of 5.06min. So, the position of sensors regarding the two objective functions conflicts with each other. Also, the sensitivity analysis related to the number of sensors illustrated that the Pareto fronts became a more efficient tool when the number of sensors increased. The best pollution detection likelihood in the real water network increased by 18.93% and 24.66% by incrementing the number of sensors from 5 to 10 and 5 to 15, respectively. Moreover, adding more than 10 sensors to the benchmark network and more than 15 to the real system will provide little additional detection likelihood.

Thickness measurement of material plates using low-activity sources with various energies in gamma-ray transmission technique

This study proposes an approach based on the gamma-ray transmission technique to accurately determine the thickness of material plates using a low-activity source. For this purpose, we have set up an experimental configuration without collimators for both source and detector. Besides, a Monte Carlo simulation model using MCNP6 code has been created with the same geometric parameters as the empirical one. The calibration curves of thickness measurement were constructed for various energies of the incident gamma rays in the range of 123–661.7 keV and two materials of aluminum and copper using Monte Carlo simulation data. The thickness of the material plate was determined by applying experimental data to a known calibration curve. For a given material and gamma energy, the measurable thickness range (MTR) was estimated by investigating the dependence of the expected relative error on the thickness of the material plate. The obtained results show that the approach is feasible with the relative deviation between the measured and reference thicknesses of mostly less than 2% and the relative uncertainty of less than 3%. Such an approach could suggest a practical and cost-effective evaluation tool for optimizing the configuration to achieve a given accuracy corresponding to each type of material.

Monte Carlo simulation of the nuclear spin decoherence process in Eu3+ : Y2SiO5 crystals

${\mathrm{Eu}}^{3+}$:${\mathrm{Y}}_{2}{\mathrm{SiO}}_{5}$ crystals are promising candidates for quantum memories because their nuclear spin coherence lifetime can be extended to 47 seconds at a particular magnetic field. Although an extended coherence lifetime of 6 hours can be achieved with a large number of dynamic decoupling sequences, these pulses will also introduce unwanted noise. To realize quantum storage with a lifetime of hours, it is necessary to quantitatively characterize the spin decoherence process of ${\mathrm{Eu}}^{3+}$ ions to guide the experimental efforts. In practice, a number of limiting factors could impose a limit on the achievable spin coherence lifetime, which includes the spin inhomogeneous broadening of the ${\mathrm{Eu}}^{3+}$ ensemble and the homogeneity and stability of the external magnetic field. Here, we provide a Monte Carlo simulation model of the nuclear spin decoherence process of ${\mathrm{Eu}}^{3+}$ ions in ${\mathrm{Y}}_{2}{\mathrm{SiO}}_{5}$ crystals, and these limiting factors are considered to provide an accurate prediction of the spin coherence lifetimes. This method is universal and can be applied to other rare-earth-ion-doped crystals.

Determination of lead and cadmium in fruit juice: occurrence and risk characterisation in Iranian population

ABSTRACT In the present study, cadmium (Cd) and lead (Pb) were monitored in different juice samples including orange, grape, apple and multi-fruit juices collected from Zanjan, Iran. The collected fruit juice samples were digested by using a microwave digestion system and their metals content was determined under graphite furnace atomic absorption spectrometry (GFAAS). In addition, carcinogenic and non-carcinogenic risk resulting from Cd and Pb entered through fruit juice consumption were evaluated. Cd and Pb were detected in more than 41% and 80% of analysed fruit juices. The highest concentration of Pb and Cd was observed in orange juice (99 and 9.4 µg/l). Furthermore, incidence of Pb and Cd in 58.9% and 4.9% of fruit juices were higher than the established Codex maximum permissible level. The data showed that total hazard quotients (THQ) for Pb and Cd metals in fruit juices were less than 1 in adults and children, meaning that no major non-carcinogenic risk is resulted from consumption of fruit juice samples. In Cd, the highest calculated CR at the 95th percentile for carcinogenic risk was 1.47 × 10−5 (multi-fruits for children) and 1.43 × 10−5 (orange juice for children) as determined by Monte Carlo simulation model. In addition, for the carcinogenic risk of Pb in adults and children, the safest risk value (≤10−6) was indicated. The calculated CR of Cd and Pb in juices for adults and children was lower than the permissible carcinogenic risk value set by USEPA (1 × 10−5). Based on the results, fruit juices are not the major source of Cd and Pb intake by Iranian consumers.

Simulation uncertainty determination of single flank rolling tests using monte carlo simulation and skin model shapes for zero defect manufacturing of micro gears

Simulative gear rolling enables the integration of function-oriented quality assurance into production processes for Zero Defect Manufacturing (ZDM). A simulated single flank rolling test can be carried out using inline measurements. With the aim of integration for the control of production parameters and a functional forecast, this paper aims at an uncertainty evaluation of an analytic tooth contact analysis software. Based on optical measurements of focus variation technology and actual micro gear test facility procedures, task-specific simulation uncertainties for the four parameters of the single flank rolling inspection are evaluated. This approach uses an optimized Latin Hypercube Sampling (LHS) approach to generate artificial gear point clouds with specified deviations for a Monte Carlo simulation (MCS) based on an adaptive scheme of the Guide to the Expression of Uncertainty in Measurement (GUM) for determining model uncertainties. It was shown that the fast simulative results have a relatively low expanded uncertainty of less than 6 µm. The use case is a high-volume micro-production.

Early detection of local SARS-CoV-2 outbreaks by wastewater surveillance: a feasibility study.

Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2.

Advancing hospital-based health technology assessment: evaluating genomic panel contracting strategies for blood tumors through a multimethodology.

The adoption of genomic technologies in the context of hospital-based health technology assessment presents multiple practical and organizational challenges. This study aimed to assist the Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa) decision makers in analyzing which acute myeloid leukemia (AML) genomic panel contracting strategies had the highest value-for-money. A tailored, three-step approach was developed, which included: mapping clinical pathways of AML patients, building a multicriteria value model using the MACBETH approach to evaluate each genomic testing contracting strategy, and estimating the cost of each strategy through Monte Carlo simulation modeling. The value-for-money of three contracting strategies - "Standard of care (S1)," "FoundationOne Heme test (S2)," and "New diagnostic test infrastructure (S3)" - was then analyzed through strategy landscape and value-for-money graphs. Implementing a larger gene panel (S2) and investing in a new diagnostic test infrastructure (S3) were shown to generate extra value, but also to entail extra costs in comparison with the standard of care, with the extra value being explained by making available additional genetic information that enables more personalized treatment and patient monitoring (S2 and S3), access to a broader range of clinical trials (S2), and more complete databases to potentiate research (S3). The proposed multimethodology provided IPO Lisboa decision makers with comprehensive and insightful information regarding each strategy's value-for-money, enabling an informed discussion on whether to move from the current Strategy S1 to other competing strategies.