Monte Carlo Simulations Research Articles

Radiation protection of W–Al composite films/coatings for aviation using genetic algorithms

This study evaluated electron shielding capabilities of various materials using Geant4 Monte Carlo software. By analyzing materials with different atomic numbers, we designed and tested composite films and coatings for radiation protection. Under 1.2 MeV electron irradiation, these materials reduced the dose deposited in electronic components by over 70 %. The fabricated composite films (W–Al) reduced the actual total dose by 100 % with a 30.67 % mass increase, closely matching the simulation result of 75.36 %. The composite coatings (W–Al) reduced the dose by 89.2 % with a 33 % mass increase, matching the simulation result of 70 %. Cascade collision simulations revealed that higher PKA energies lead to longer times to reach the thermal peak, more peak defects, and more stable defect pairs. This is due to the displacement threshold energy of the atoms in aluminum and tungsten. These results demonstrate the effectiveness of our composite films and coatings in enhancing electron shielding performance and validate our design methods.

Simulation with Monte Carlo methods to focus quality improvement efforts on interventions with the greatest potential for reducing PACU length of stay: a cross-sectional observational study

BackgroundTime and money are limited resources to pursue quality improvement (QI) goals. Computer simulation using Monte Carlo methods may help focus resources towards the most efficacious interventions to pursue.MethodsThis observational,...

A propensity score-matched cost-effectiveness analysis of magnetic resonance-guided laser interstitial thermal therapy versus craniotomy for brain tumor radiation necrosis.

Radiation necrosis is becoming an increasingly prevalent complication in patients with brain tumors given the growing utility of stereotactic radiosurgery in modern treatment paradigms. Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) is a new minimally invasive modality that has exhibited an efficacy comparable to craniotomy in treating radiation necrosis. No studies to date have compared their cost-effectiveness despite the significant additional expenses associated with MRgLITT use. This study aimed to evaluate the cost-effectiveness of MRgLITT versus craniotomy in patients with comparable presentations of radiation necrosis. The National Inpatient Sample (NIS) was queried from 2011 to 2020 for patients with radiation necrosis and treated using craniotomy or MRgLITT. Admission charges and costs were inflation adjusted to 2020 $US. Surgical cohorts were propensity score-matched according to demographic, clinical, and admission characteristics. Multivariable linear and logistic regression analyses identified associations between type of intervention and outcomes. A semi-Markov model was created to simulate treatment with craniotomy versus MRgLITT. Cost, transition probabilities, and health state utilities were derived from the NIS, individual patient outcomes from multiple institutions, and prospectively collected quality-of-life data from a single institution and verified against other studies. Monte Carlo simulation and probabilistic sensitivity analysis were used to evaluate the cost-effectiveness between the two modalities. In the designated study period, 2869 patients had been admitted with brain tumor radiation necrosis and were managed with neurosurgical intervention. After propensity score matching, MRgLITT, relative to craniotomy, was independently associated with a shorter length of stay (LOS; β = -1.81, p = 0.002), lower odds of complications (OR 0.18, p = 0.033), and higher odds of home discharge (OR 3.05, p = 0.041), but there was no difference in total admission costs between the two modalities (β = $6229, p = 0.081). On Monte Carlo simulation, patients treated with MRgLITT had a lower probability of disease (radiation necrosis or tumor) recurrence (13.5% vs 22.0%, p < 0.001) but an equivalent mortality risk (22.8% vs 22.3%, p = 0.429) compared to the patients treated with craniotomy at the 1-year follow-up. Over a 4-year time horizon, MRgLITT had an incremental cost of -$25,685 and incremental effectiveness of 0.14 quality-adjusted life-year (QALY), resulting in an incremental cost-effectiveness ratio of -$183,464 per QALY relative to craniotomy. MRgLITT was a more cost-effective treatment strategy than craniotomy in the management of patients with brain tumor radiation necrosis. The cost-effectiveness of MRgLITT may be attributed to a shorter LOS, lower complication odds, and higher home discharge odds in the immediate postoperative period and a lower risk of disease recurrence over the long-term follow-up.

Approximate P3 equation analysis in multi-layer slab media: steady-state and time-domain based on the diffusion model

This study aimed to establish a steady-state and time-domain solutions for the approximate P3 equation in arbitrary multi-layer slab media for light propagation, based on a diffusion model with an isotropic point source in the first layer and extrapolated boundary conditions. Spatially resolved diffuse reflectance and transmittance were calculated using the steady-state approximation of the P3 equation, whereas temporally resolved diffuse reflectance and transmittance were derived from the time-domain approximation. The validity of the approximate P3 solutions was confirmed by comparing their results with Monte Carlo simulations. In steady-state analysis, the approximate P3 equation demonstrated superior accuracy to the diffusion equation for reflectance, particularly at smaller thicknesses. Accuracy further improved as the absorption coefficient and detection distance increased. For transmittance, the approximate P3 equation closely matched the diffusion equation at low thickness, but divergence occurred with higher absorption. In time-domain analysis, the approximate P3 equation aligned closely with Monte Carlo simulations at peak values, while its numerical values were close approximations of the diffusion equation away from the peak. The potential application of the approximate P3 equation in multi-layer media offers significant advancements for optical non-invasive detection and treatment techniques, enabling the extraction of optical parameters from such media.

Numerical Methods and Computation in Financial Mathematics: Solving PDEs, Monte Carlo Simulations, and Machine Learning Applications

Abstract. This paper delves into three fundamental numerical methods and computational techniques in financial mathematics: Finite Difference Methods (FDM), Monte Carlo Simulations (MCS), and Machine Learning (ML) applications. Finite Difference Methods are widely utilized for solving partial differential equations (PDEs) in option pricing, with various schemes offering different stability and convergence properties. Monte Carlo Simulations provide a powerful approach for pricing complex derivatives and risk management, addressing the challenges of high-dimensionality and computational complexity. Machine Learning has revolutionized predictive modeling in finance, enabling sophisticated analysis of large datasets to uncover hidden patterns and enhance trading strategies. Through a detailed examination of these methods, including specific examples and data, this paper highlights their theoretical foundations, practical implementations, and the advancements they bring to computational finance. By bridging theoretical approaches with practical applications, we aim to offer insights into the future directions and challenges in financial mathematics.

Population pharmacokinetics of fluconazole for prevention or treatment of invasive candidiasis in Chinese young infants.

The dosing of fluconazole for young infants remains empirical because of the limited pharmacokinetic (PK) data. We aimed to establish a population PK model and assess the systematic exposure-response of commonly used regimens of fluconazole in Chinese infants.We included infants with a postnatal age of less than 120 days and received intravenous fluconazole. Both scheduled and scavenged plasma samples were collected, and fluconzaole concentration was determined by a validated ultra-performance liquid chromatography-tandem mass spectrometry assay. Population PK analysis was conducted using Phoenix NLME, and then Monte Carlo simulation was conducted to predict the probability of target attainment (PTA) of empirically used regimens of both prophylactic and therapeutic purposes.Based on 304 plasma samples from 183 young infants, fluconazole concentration data was best described by a one-compartment model with first-order elimination. Gestational Age (GA), postnatal age (PNA), and body weight (BW) were included in the final model as CL = 0.02*(GA/214)2.77*(PNA/13)0.24*exp(nCL); V = 1.56*(BW/1435)0.90*exp(nV). Model validation revealed the final model had qualified stability and acceptable predictive properties. Monte Carlo simulation indicated that under the same minimum inhibitory concentration (MIC) value and administration regimen, PTA decreased with GA and PNA. The commonly used prophylactic regimens can meet the clinical need, while higher doses might be needed for treatment of invasive candidiasis.This population PK model of fluconazole discriminated the impact of GA and PNA on CL and BW on V. Dosing adjustment was needed according to the GA and PNA of infants to achieve targeted exposures.

Evaluating omadacycline dosing regimens against drug-resistant pathogens including Staphylococcus aureus, Streptococcus pneumoniae and Haemophilus influenzae in adults: a pharmacokinetic/pharmacodynamic analysis using Monte Carlo simulation.

The objective of this study was to evaluate the efficacy of various dosing regimens of omadacycline against main drug-resistant pathogens in the treatment of acute bacterial skin and skin structure infections (ABSSSI) and community-acquired bacterial pneumonia (CABP). Monte Carlo simulations were conducted using pharmacokinetic parameters and pharmacodynamic data to calculate cumulative fractions of response (CFRs) in terms of drug area under the concentration curve/minimum inhibition concentration targets.CFR ≥ 90% was considered optimal for a dosage regimen. CFR of any approved oral/intravenous regimen with loading-dose was ≥ 90% against methicillin-resistant Staphylococcus aureus (MRSA) for ABSSSI and penicillin-resistant Streptococcus pneumonia, tetracycline-resistant Streptococcus pneumonia, MRSA and β-lactamase positive Haemophilus influenzae for CABP. In conclusion, approved oral/intravenous loading and maintenance doses of omadacycline showed enough efficacy in the treatment of ABSSI and CABP caused by the main drug-resistant pathogens.

Evaluation of Dose Calculation Algorithms Accuracy for ISOgray Treatment Planning System in Motorized Wedged Treatment Fields

Abstract Background: Different dose calculation methods vary in accuracy and speed. While most methods sacrifice precision for efficiency Monte Carlo (MC) simulation offers high accuracy but slower calculation. ISOgray treatment planning system (TPS) uses Clarkson, collapsed cone convolution (CCC), and fast Fourier transform (FFT) algorithms for dose distribution. This study’s primary goal is to evaluate the dose calculation accuracy for ISOgray TPS algorithms in the presence of a wedge. Methods: This study evaluates the dose calculation algorithms using the ISOgray TPS in the context of radiation therapy. The authors compare ISOgray TPS algorithms on an Elekta Compact LINAC through MC simulations. The study compares MC simulations for open and wedge fields with ISOgray algorithms by using gamma index analysis for validation. Results: The percentage depth dose results for all open and wedge fields showed a more than 98% pass rate for points. However, there were differences in the dose profile gamma index results. Open fields passed the gamma index analysis in the in-plane direction, but not all points passed in the cross-plane direction. Wedge fields passed in the cross-plane direction, but not all in the in-plane direction, except for the Clarkson algorithms. Conclusion: In all investigated algorithms, error increases in the penumbra areas, outside the field, and at cross-plane of open fields and in-plane direction of wedged fields. By increasing the wedge angle, the discrepancy between the TPS algorithms and MC simulations becomes more pronounced. This discrepancy is attributed to the increased presence of scattered photons and the variation in the delivered dose within the wedge field, consequently impacts the beam quality. While the CCC and FFT algorithms had better accuracy, the Clarkson algorithm, particularly at larger effective wedge angles, exhibited greater effectiveness than the two mentioned algorithms.

The Detection of Possible Quasiperiodic Oscillations in the BL Lac 4FGL J2139.4−4235

We present periodicity search analyses on the long-term γ-ray light curve of the BL Lacertae object 4FGL J2139.4−4235 observed by the Fermi Large Area Telescope, over a period of more than 15 yr, from 2008 August 4 to 2023 December 10. To determine the quasiperiodic oscillation (QPO) behavior of 4FGL J2139.4−4235 in the 0.3–300 GeV energy range, we used four methods, namely the Lomb–Scargle periodogram, the weighted wavelet z-transform, the phase dispersion minimization, and the autoregressive integrated moving average model. A Monte Carlo simulation technique is used to evaluate the significance level of the QPO signal. Significant levels above 3.5σ were detected in the γ-ray light curve at about 650 days QPO, which is presented throughout the observation period. Interestingly, there was some correlation between the three bands in the discrete correlation function method calculations, which may be an indication that the variability trends between the three bands are similar. We explore the possible physical models and show that a supermassive binary black hole system or a jet helical motion model seem to be reasonable explanations for the potential QPO behavior.

A simheuristic for project portfolio optimization combining individual project risk, scheduling effects, interruptions, and project risk correlations

This paper introduces a simheuristic method to the Project Portfolio Selection Problem, designed to maximize the net present value of the portfolio while considering uncertain costs, schedules, interruptions, and inter-project risk correlations. The novel approach combines techniques from Monte Carlo simulation, critical path analysis, queuing theory, and optimization, integrating baseline schedules, project-level uncertainties, budgetary constraints, and risk correlations in a single model. A computational experiment is conducted on a realistic set of ten candidate projects and validated respect to the deterministic version of the problem, demonstrating its ability to select near optimal portfolio proposals with varying combinations of risk and net present value. The findings highlight the significant impact of factors such as contingency reserve allocation policies, operational interruptions, and project risk correlations on portfolio decisions, constituting a helpful framework for the decision-makers at portfolio level.

Prediction and reliability analysis of ultimate axial strength for outer circular CFRP-strengthened CFST columns with CTGAN and hybrid MFO-ET model

This study develops a novel hybrid machine learning model to estimate the ultimate axial strength and conduct a reliability analysis for outer circular carbon fiber-reinforced polymer (CFRP)-strengthened concrete-filled steel tube (CFST) columns. The experimental datasets are collected and enriched using the conditional tabular generative adversarial network (CTGAN). The column length, the steel properties (cross-section diameter, thickness, and yield strength), the CFRP properties (thickness, tensile strength, and elastic modulus), and concrete strength are selected as input variables to develop the Extra Trees (ET) model hybridized with Moth-Flame Optimization (MFO) algorithm for the ultimate axial strength estimation. The results reveal that the CTGAN can efficiently capture the actual data distribution of CFRP-strengthened CFST columns and the developed hybrid MFO-ET model can accurately predict the ultimate axial strength with a high accuracy (R2 of 0.985, A10 of 0.867, RMSE of 182.810 kN, and MAE of 124.534 kN) based on the synthetic database. In addition, compared with the best empirical model, the MFO-ET model increases the R2 by (6.78% and 13.48%) and A10 by (108.19% and 122.88%) and reduces the RMSE by (68.19% and 66.24%) and MAE by (71.33% and 68.48%) based on real and synthetic databases, respectively. Notably, a reliability analysis is performed to evaluate the safety of the developed MFO-ET model using Monte Carlo Simulation (MCS). Finally, a web application tool is created to make the developed MFO-ET model easier for users to design practical applications.

What will be the Euclidean dimension of an Ising ferromagnetic cubic shell?

The equilibrium and nonequilibrium properties of an Ising ferromagnetic cubic shell have been extensively studied by Monte Carlo simulation using Metropolis single spin flip algorithm. Although, geometrically the Euclidean dimension of the cubical shell is three, interestingly, the Ising ferromagnetic cubic shell undergoes ferromagnetic phase transition at a temperature which is very close to that for two-dimensional Ising ferromagnet. Surprisingly, the Ising ferromagnetic cubic shell shows a strange (neither exponential nor stretched exponential) kind of relaxation behaviour, instead of exponential relaxation as usually observed in the two dimensional Ising ferromagnet. The metastable lifetime of a ferromagnetic Ising cubical shell is studied as a function of the applied magnetic field. Here also, the cubic shell behaves more likely a two-dimensional object as found from statistical analysis and comparison with Becker-Döring prediction of classical nucleation theory.

An investigation of the adsorption of Congo red dye on two naturally occurring adsorbents Hydroxyapatite and Bentonite: An Experimental Analysis, DFT calculations, and Monte Carlo simulation

An investigation of the adsorption of Congo red dye on two naturally occurring adsorbents Hydroxyapatite and Bentonite: An Experimental Analysis, DFT calculations, and Monte Carlo simulation

Tunable disorder on the S-state majority-voter model.

We investigate the nonequilibrium phase transition in the S-state majority-vote model for S=2,3, and 4. Each site, k, is characterized by a distinct noise threshold, qk, which indicates its resistance to adopting the majority state of its Nv nearest neighbors. Precisely, this noise threshold is governed by a hyperbolic distribution, P(k)∼1/k, bounded within the limits e-α/2<qk<1/2. Here, the parameter α plays a pivotal role as it determines the extent of disorder in the system through the spread of the threshold distribution. Through Monte Carlo simulations and finite-size scaling analyses on regular square lattices, we deduced that the model undergoes a continuous order-disorder phase transition at a specific α=αc. Interestingly, the critical threshold exhibits a power-law decay, αc∼Nv-δ, as the number Nv of neighboring sites increases. From the least square fits to the data sets results in δ=0.65±0.01 for S=2, δ=0.92±0.01 for S=3, and δ=0.93±0.01 for S=4. Furthermore, the critical exponents β/ν and γ/ν are consistent with those found in the S-state Potts model.

Dosing strategy of tacrolimus when co-administered with Wuzhi tablet in renal transplant recipients.

Tacrolimus (TAC) is a first-line immunosuppressant to prevent allograft rejection. Wuzhi tablet is widely used as a TAC-sparing agent in China that could significantly elevate TAC exposure. However, insufficient data support the dose recommendation of TAC when co-administered with Wuzhi. A total of 305 adult renal transplant patients with 2,541 TAC trough concentrations (C0) were enrolled for population pharmacokinetic (PPK) modeling. CYP3A5 polymorphism was genotyped, and corresponding clinical factors were recorded. Nonlinear mixed-effects modeling and Monte Carlo simulation were used for dose recommendation. PK parameters were calculated based on one-compartment model with first-order absorption and elimination. The estimated total clearance (CL/F) and volume of distribution (Vd/F) of TAC were 23.84 L/h and 1,075.96 L, respectively. Wuzhi, CYP3A5 genotype, hematocrit (HCT), and weight were found to have a significant influence on CL/F. CL/F was significantly lower in the individuals who were CYP3A5 non-expressers and received TAC together with Wuzhi. CYP3A5 genotype (expressers or non-expressers), body weight (40-80 kg), and hematocrit (20 - 40%) were selected as the specific clinical scenarios, and the starting dose of TAC ranged from 1.5 to 4.5 mg when co-administered with Wuzhi. We establish a TAC PPK model comprising Wuzhi as a covariate in renal transplant recipients and recommend an initial dose of TAC when co-administered with Wuzhi, which could provide reference for the individualized regimens of TAC.

Reweighted Nadaraya–Watson estimation of stochastic volatility jump-diffusion models

In this paper, we construct the reweighted Nadaraya–Watson estimators of the infinitesimal moments for the volatility process of the stochastic volatility models, with the application of the threshold estimator of the unobserved volatility process. Our model includes jumps in both the underlying asset price and its volatility process. We derive the asymptotic properties of the estimators under the infill and long span assumptions. The results are useful for identification of the process. The finite-sample performance of the estimators is studied through Monte Carlo simulation.

DFT and Monte Carlo simulation studies of potential corrosion inhibition properties of some basic heterocyclic compounds

ABSTRACT This study utilises a combination of density functional theory (DFT) approach, 6-31G (d, p) basis set, and Monte Carlo (MC) simulations. The goal is to understand the relationship between the structure and properties of heterocyclic inhibitors and how they interact with Fe (110) surfaces. Furan, pyrrole, thiophene, pyridine, pyridazine, pyrimidine, indole, benzofuran, carbazole, quinolone, isoquinoline, and imidazole are the chemicals that were looked at in this study. The DFT calculations provide important insights into various properties of the inhibitors, such as molecular structure, dipole moments, electrostatic potential maps, Reduced Density Gradient (RDG), UV spectroscopy, and thermal characteristics. Carbazole has the smallest energy difference (4.789 eV) and the highest softness (0.209 eV−1), It also has the most chemical activity, Furthermore, the Monte Carlo models demonstrate that these derivatives have favourable adsorption energies on the Fe (110) surface. Among them, carbazole shows significant inhibitory potential. The study confirms that the carbazole compound with the biggest negative adsorption energy (−95.495) actually has the smallest energy difference and fits with the MC simulation for adsorption on Fe (110) when it is not charged. Carbazole has much stronger adsorption than oxazole, showing how the structure of a molecule affects how well an inhibitor works.

Deep learning aided underwater acoustic OFDM receivers: Model-driven or data-driven?

The Underwater Acoustic (UWA) channel is bandwidth-constrained and experiences doubly selective fading. It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing (OFDM) communications using a finite number of pilots. On the other hand, Deep Learning (DL) approaches have been very successful in wireless OFDM communications. However, whether they will work underwater is still a mystery. For the first time, this paper compares two categories of DL-based UWA OFDM receivers: the Data-Driven (DD) method, which performs as an end-to-end black box, and the Model-Driven (MD) method, also known as the model-based data-driven method, which combines DL and expert OFDM receiver knowledge. The encoder-decoder framework and Convolutional Neural Network (CNN) structure are employed to establish the DD receiver. On the other hand, an unfolding-based Minimum Mean Square Error (MMSE) structure is adopted for the MD receiver. We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios. Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers. It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.

Understanding the Uncertainty in the Technical Performance Level Assessment for Wave Energy

The design of wave energy converters (WECs) has been explored with interest, with varying design concepts emerging across both research and industry. One critical element that governs the speed of adoption is the performance of a WEC concept. WEC performance has been assessed using the Technology Performance Level (TPL) assessment, which provides designers with a quantitative score, situating a grid-scale WEC concept on a scale from 1-9. The TPL assessment is designed to be used during design iteration, when a WEC concept is fully ideated, to enable designers to consider potential means of improving the downstream performance of the concept. One concern that may be slowing developers’ adoption of TPL is the inherent uncertainty in the assessment, and how uncertainty in the individual questions may contribute to the final score. In this work, we quantify the uncertainty present in the assessment using both traditional mathematical operations and a Monte Carlo simulation. Results show areas of improvement of the TPL assessment, enabling TPL practitioners and users to understand with more accuracy those design elements that can be improved to impact device performance.

Stochastic dynamics analysis for unilateral vibro-impact systems under combined excitation

Vibro-impact system, as an important type of non-smooth system, exhibits intricately nonlinear characteristics. Inevitably, the vibro-impact system will encounter random excitations, but the conventional methods ar7e not eligible for simultaneous determination of its transient responses and reliabilities. Commonly, existing methods of applying non-smooth transformation tend to ignore the essential non-smooth characteristics of vibro-impact system. To this end, this paper proposes a unified framework based on direct probability integral method (DPIM) to simultaneously determine stochastic dynamic responses and reliabilities of unilateral vibro-impact systems under combined harmonic and random excitation without non-smooth transformation, and captures their complicated dynamical behaviors. Firstly, the impact velocity dependent coefficient of restitution is introduced to establish the motion equation of vibro-impact system. Secondly, the probability density integral equation (PDIE) for the unilateral vibro-impact system is derived from the perspective of probability conservation. Then, the PDIE and governing differential equation of the system is solved in a decoupled and efficient way. Moreover, the first-passage reliability is assessed by introducing extreme value mapping of the stochastic dynamic response. Numerical results of three typical examples using the proposed framework are compared with those using Monte Carlo simulation (MCS), quasi-MCS and from the reference, which highlights the advantages of DPIM in computing the stochastic responses and reliabilities of vibro-impact system under random excitations and random parameters. The stationary probability density functions exhibit periodic fluctuations under combined harmonic and stochastic excitation. Specially, the noise intensity and frequency of harmonic excitation pose the great influence on the reliabilities of systems.