Monte Carlo Simulations Research Articles

Reliability framework for CLT floors in out-of-plane bending using Monte Carlo simulation

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index (β) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.

Population Pharmacokinetics of Adalimumab in Juvenile Idiopathic Arthritis Patients: A Retrospective Cohort Study Using Clinical Care Data.

Juvenile idiopathic arthritis (JIA) is a chronic autoimmune disorder that primarily affects the joints in children. Notably, it is known to co-occur with uveitis. Adalimumab, a monoclonal anti-TNF antibody, is effective in treating both conditions. A deeper understanding of the pharmacokinetics (PK) of adalimumab in JIA is crucial to advance in more personalized treatment approaches. The objective of this study is to evaluate the population PK profile of adalimumab in JIA and to explain causes for its variability. Adalimumab and antidrug antibody concentrations were retrospectively retrieved from the charts of patients with JIA. Initially, five literature-based population PK models of adalimumab were evaluated to assess their ability to describe the observed concentration-time profiles in the JIA cohort. These models included one specifically for the pediatric Crohn's disease population and four derived from studies in adult populations in healthy subjects and rheumatoid arthritis patients. Subsequently, a novel population PK model tailored to the JIA population was developed using NONMEM software. Monte Carlo simulations were then conducted utilizing the final PK model to visualize the concentration-time profile of adalimumab in patients with JIA and the impact of covariates. A cohort of 50 patients with JIA with 78 available adalimumab samples was assessed. The mean age was 11.8±3.9years, with a median body weight of 49kg (interquartile range 29.4-59.8kg). All literature models adequately described the concentration-time profiles in JIA. The best model, which was developed in patients with rheumatoid arthritis during the maintenance phase of treatment, served as a basis for estimating clearance in JIA, resulting in a value of 0.37L per day per 70kg. Patient body weight, antidrug antibodies, methotrexate use, CRP level, and comorbidity of uveitis were found to have a significant impact on adalimumab clearance, and these reduced the inter-patient variability from 58.6 to 28.0%. On steady state in the simulated patient population, the mean trough level was 7.4±5.5mg/L. The two dosing regimens of 20 and 40mg every other week, based on patients' body weight, resulted in comparable simulated overall drug exposure. Five literature models effectively described adalimumab PK in this pediatric cohort, highlighting the potential for extrapolating existing models to the pediatric population. The new JIA model confirmed the effect of several known covariates and found a novel association for drug clearance with methotrexate use (lower) and uveitis (higher), which might have clinical relevance for personalized dosing in JIA.

Investigations of different likelihood functions for the use in Bayesian model updating in structural applications

Model updating is a popular tool for damage localisation and quantification for structural applications like buildings, infrastructure and wind turbines. Taking into account the prevailing uncertainty inter alia due to measurements is very important for a more reliable result. A frequently used method to conduct model updating and simultaneously taking into account measurement uncertainty is the Transitional Markov Chain Monte Carlo (TMCMC) method. Model updating in general relies on a shift of damage sensitive features, mostly natural frequencies. This work shows, that the state-of-the-art method of TMCMC is leading to inaccurate results in structural application because of formulations in the likelihood function. In the state-of-the-art method, natural frequencies which are measured more certainly are more heavily weighted. Even though this approach is meaningful in general, for the application in structural application, this is leading to inaccurate results. This work will present the issues with the state-of-the-art method and presents an option to solve this by a new formulation, which is weighting every natural frequency equally. The two methods will be applied and compared on a laboratory steel beam with reversible damage mechanisms. This allows comparing the results on a variety of different damage scenarios. The first four natural frequencies related to pure vertical bending mode shapes are identified using the Bayesian operational model analysis (BAYOMA). BAYOMA is able to quantify the uncertainty in the natural frequencies.

DosePatch: physics-inspired cropping layout for patch-based Monte Carlo simulations to provide fast and accurate internal dosimetry

BackgroundDosimetry-based personalized therapy was shown to have clinical benefits e.g. in liver selective internal radiation therapy (SIRT). Yet, there is no consensus about its introduction into clinical practice, mainly as Monte Carlo simulations (gold standard for dosimetry) involve massive computation time. We addressed the problem of computation time and tested a patch-based approach for Monte Carlo simulations for internal dosimetry to improve parallelization. We introduce a physics-inspired cropping layout for patch-based MC dosimetry, and compare it to cropping layouts of the literature as well as dosimetry using organ-S-values, and dose kernels, taking whole-body Monte Carlo simulations as ground truth. This was evaluated in five patients receiving Yttrium-90 liver SIRT.ResultsThe patch-based Monte Carlo approach yielded the closest results to the ground truth, making it a valid alternative to the conventional approach. Our physics-inspired cropping layout and mosaicking scheme yielded a voxel-wise error of < 2% compared to whole-body Monte Carlo in soft tissue, while requiring only ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 10% of the time.ConclusionsThis work demonstrates the feasibility and accuracy of physics-inspired cropping layouts for patch-based Monte Carlo simulations.

Analysis of the Operational Reliability of Different Types of Switching Substations Using the Monte Carlo Method

This study investigates the operational reliability of different types of switching substations within the context of power systems, employing the Monte Carlo method for analysis. The research focuses on evaluating the reliability of high-voltage substations, including single-busbar systems, double-busbar systems, and switchgears with a ring-type power supply. By conducting simulations and analyzing statistical data on device reliability, the study aims to identify the most reliable implementation of switching substations. The results are presented through graphical representations and comparative tables, highlighting the impact of factors such as the number of switching elements and their connection on operational reliability. The findings indicate that configurations with a greater number of busbars and a parallel connection of switching elements exhibit higher operational reliability. The study provides insights to inform decision-making in the construction of new switching substations, emphasizing the importance of stable operation within power systems.

Sensitivity analysis of commutation failure in multi-infeed HVDC systems: Exploring the impact of ac system representations and fault types

This paper investigates commutation failure (CF) in line commutated HVdc converters within multi-infeed HVDC systems and thereby provides valuable insights for the operation and design of HVdc systems. CF susceptibility across various operational scenarios, fault types, and ac system representations at different frequencies is explored. An Electromagnetic Transients (EMT) model is constructed and parameter changes are applied using Monte-Carlo Simulation. The process is fully automated with the use of a controlling program written in Python, which varies parameter values and facilitates result retrieval and post-simulation analysis. A typical analysis results in several hundred thousands of simulation runs, requiring the procedure to be implemented on a parallel computing platform. The results show that two-phase faults are the most critical CF triggers, an aspect often overlooked in previous literature focused on three-phase or single-phase-to-ground faults. Furthermore, the frequency dependent characteristics of the ac network can result in very different CF susceptibilities.

Data-driven discovery of novel metal organic frameworks with superior ammonia adsorption capacity

Ammonia (NH3) has been a subject of great interest due to its important roles in diverse technological applications. However, high toxicity and corrosiveness of NH3 has made it an important task to develop an efficient carrier to safely capture NH3 with high capacity. Here, we employ a machine learning (ML) model to discover high-performance metal organic frameworks (MOFs) that will work as efficient NH3 carriers. By constructing databases at two distinct conditions, adsorption and desorption, through Grand Canonical Monte Carlo (GCMC) simulations to train ML models, we identify eight novel MOFs as potentially efficient NH3 carriers through screening the large-scale MOF databases with the trained models and GCMC verification. The identified MOFs exhibit the average NH3 working capacity exceeding 1100 mg/g, and subsequent molecular dynamics simulations demonstrate mechanical stability of the predicted MOFs. Moreover, analyses of the diffusion mechanism within the proposed MOFs underscore the strong dependence of NH₃ gas diffusivity on the structural details of the materials.

Nanoscale architecture of synaptic vesicles and scaffolding complexes revealed by cryo-electron tomography

The spatial distribution of proteins and their arrangement within the cellular ultrastructure regulates the opening of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in response to glutamate release at the synapse. Fluorescence microscopy imaging revealed that the postsynaptic density (PSD) and scaffolding proteins in the presynaptic active zone (AZ) align across the synapse to form a trans-synaptic "nanocolumn," but the relation to synaptic vesicle release sites is uncertain. Here, we employ focused-ion beam (FIB) milling and cryoelectron tomography to image synapses under near-native conditions. Improved image contrast, enabled by FIB milling, allows simultaneous visualization of supramolecular nanoclusters within the AZ and PSD and synaptic vesicles. Surprisingly, membrane-proximal synaptic vesicles, which fuse to release glutamate, are not preferentially aligned with AZ or PSD nanoclusters. These synaptic vesicles are linked to the membrane by peripheral protein densities, often consistent in size and shape with Munc13, as well as globular densities bridging the synaptic vesicle and plasma membrane, consistent with prefusion complexes of SNAREs, synaptotagmins, and complexin. Monte Carlo simulations of synaptic transmission events using biorealistic models guided by our tomograms predict that clustering AMPARs within PSD nanoclusters increases the variability of the postsynaptic response but not its average amplitude. Together, our data support a model in which synaptic strength is tuned at the level of single vesicles by the spatial relationship between scaffolding nanoclusters and single synaptic vesicle fusion sites.

Parameter estimation of Chen distribution under improved adaptive type-II progressive censoring

Parameter estimation of Chen distribution under improved adaptive type-II progressive censoring

Modelling Cellular Response to Ionizing Radiation: Mechanistic, Semi-Mechanistic, and Phenomenological Approaches - A Historical Perspective.

Radiation research is a multidisciplinary field, and among its many branches, mathematical and computational modelers have played a significant role in advancing boundaries of knowledge. A fundamental contribution is modelling cellular response to ionizing radiation as that is the key to not only understanding how radiation can kill cancer cells, but also cause cancer and other health issues. The invention of microdosimetry in the 1950s by Harold Rossi paved the way for brilliant scientists to study the mechanism of radiation at cellular and sub-cellular scales. This paper reviews some snippets of ingenious mathematical and computational models published in microdosimetry symposium proceedings and publications of the radiation research community. Among these are simulations of radiation tracks at atomic and molecular levels using Monte Carlo methods, models of cell survival, quantification of the amount of energy required to create a single strand break, and models of DNA-damage-repair. These models can broadly be categorized into mechanistic, semi-mechanistic, and phenomenological approaches, and this review seeks to provide historical context of their development. We salute pioneers of the field and great teachers who supported and educated the younger members of the community and showed them how to build upon their work.

Simple proof that there is no sign problem in path integral Monte Carlo simulations of fermions in one dimension

Simple proof that there is no sign problem in path integral Monte Carlo simulations of fermions in one dimension

PV/PV-Battery hosting capacity estimation method based on hidden Markov model for effective stochastic computation

Monte Carlo is commonly applied to model uncertainties in the penetration of photovoltaic (PV) systems as random processes. However, Monte Carlo simulations require a large number of stochastic calculations to obtain the desired accuracy. This paper develops a hosting capacity estimation model using hidden Markov to provide an effective stochastic calculation of PV/PV-battery penetration. To improve the representative in modeling actual penetration scenarios, the proposed model considers the probabilities among candidates on the basis of the customer types, the customer with PV-only or the customer with PV-battery, and the size of the PV/PV-battery. To be used in the simulations, a technique is proposed to calculate the min–max load demand and PV generation curves. To assess the computational load of the proposed model, this work provides an accuracy evaluation with respect to the number of stochastic simulations. The findings indicate that the proposed solution can achieve a cost-effective calculation of the hosting capacity. In practice, this work can provide the distribution planner with useful direction to help make informed decisions about the distribution network reinforcement strategy to deal with high PV/PV-battery penetration.

Exhaustive review of acceleration strategies for Monte Carlo simulations in photon transit

Monte Carlo simulation techniques have become the quintessence and a pivotal nexus of inquiry in the realm of simulating photon movement within biological fabrics. Through the stochastic sampling of tissue archetypes delineated by explicit optical characteristics, Monte Carlo simulations possess the theoretical capacity to render unparalleled accuracy in the depiction of exceedingly intricate phenomena. Nonetheless, the quintessential challenge associated with Monte Carlo simulation methodologies resides in their extended computational duration, which significantly impedes the refinement of their precision. Consequently, this discourse is specifically dedicated to exploring innovations in strategies and technologies aimed at expediting Monte Carlo simulations. It delves into the foundational concepts of various acceleration tactics, evaluates these strategies concerning their speed, accuracy, and practicality, and amalgamates a comprehensive overview and critique of acceleration methodologies for Monte Carlo simulations. Ultimately, the discourse envisages prospective trajectories for the employment of Monte Carlo techniques within the domain of tissue optics.

Level set simulation of focused ion beam sputtering of a multilayer substrate.

The evolution of a multilayer sample surface during focused ion beam processing was simulated using the level set method and experimentally studied by milling a silicon dioxide layer covering a crystalline silicon substrate. The simulation took into account the redeposition of atoms simultaneously sputtered from both layers of the sample as well as the influence of backscattered ions on the milling process. Monte Carlo simulations were applied to produce tabulated data on the angular distributions of sputtered atoms and backscattered ions. Two sets of test structures including narrow trenches and rectangular boxes with different aspect ratios were experimentally prepared, and their cross sections were visualized in scanning transmission electron microscopy images. The superimposition of the calculated structure profiles onto the images showed a satisfactory agreement between simulation and experimental results. In the case of boxes that were prepared with an asymmetric cross section, the simulation can accurately predict the depth and shape of the structures, but there is some inaccuracy in reproducing the form of the left sidewall of the structure with a large amount of the redeposited material. To further validate the developed simulation approach and gain a better understanding of the sputtering process, the distribution of oxygen atoms in the redeposited layer derived from the numerical data was compared with the corresponding elemental map acquired by energy-dispersive X-ray microanalysis.

Bootstrap-based confidence intervals for the standard two-sided power distribution

The two-parameter standard two-sided power family of distributions on (0, 1) is considered in this article. We propose bootstrap standard errors for the maximum likelihood estimators, as well as bootstrap confidence intervals for its parameters, once these important statistical measures of accuracy cannot be computed based on first-order asymptotic theory. We consider Monte Carlo simulation experiments to verify the performance of the bootstrap methods, and the numerical results are quite promising. Applications to real data are also considered to illustrate the proposed methodology in practice.

Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach

Urban energy poverty remains a critical challenge affecting millions worldwide, with significant implications for socio-economic development and sustainability. This study employs Monte Carlo simulations to analyze the variability in energy poverty, as measured by the Multidimensional Energy Poverty Index (MEPI). Through this approach, we explored how different socio-economic predictors affect MEPI scores across five scenarios: economic growth and diversification, energy transition and technological innovation, climate change impacts and vulnerability, policy intervention and social safety nets, and stagnation and rising inequality. The sensitivity analysis revealed that household income, size, and food security are paramount in influencing urban energy poverty levels. Scenarios involving economic growth and technological innovation demonstrated positive impacts on reducing energy poverty, while scenarios of climate change and economic stagnation highlighted the vulnerabilities and widening disparities within populations. The results suggest that to effectively alleviate urban energy poverty, policy interventions must be comprehensive, targeting the identified influential factors. The study emphasizes the need for targeted policy interventions on social safety nets to alleviate energy poverty.

Construction and evaluation of a practical model for measuring health-adjusted life expectancy (HALE) in China

BackgroundHALE is now a regular strategic planning indicator for all levels of the Chinese government. However, HALE measurements necessitate comprehensive data collection and intricate technology. Therefore, effectively converting numerous diseases into the years lived with disability (YLD) rate is a significant challenge for HALE measurements. Our study aimed to construct a simple YLD rate measurement model with high applicability based on the current situation of actual data resources within China to address challenges in measuring HALE target values during planning.MethodsFirst, based on the Chinese YLD rate in the Global Burden of Disease (GBD) 2019, Pearson correlation analysis, the global optimum method, etc., was utilized to screen the best predictor variables from the current Chinese data resources. Missing data for predictor variables were filled in via spline interpolation. Then, multiple linear regression models were fitted to construct the YLD rate measurement model. The Sullivan method was used to measure HALE. The Monte Carlo method was employed to generate 95% uncertainty intervals. Finally, model performances were assessed using the mean absolute error (MAE) and mean absolute percentage error (MAPE).ResultsA three-input-parameter model was constructed to measure the age-specific YLD rates by sex in China, directly using the incidence of infectious diseases, the incidence of chronic diseases among persons aged 15 and older, and the addition of an under-five mortality rate covariate. The total MAE and MAPE for the combined YLD rate were 0.0007 and 0.5949%, respectively. The MAE and MAPE of the combined HALE in the 0-year-old group were 0.0341 and 0.0526%, respectively. There were slightly fewer males (0.0197, 0.0311%) than females (0.0501, 0.0755%).ConclusionWe constructed a high-accuracy model to measure the YLD rate in China by using three monitoring indicators from the Chinese national routine as predictor variables. The model provides a realistic and feasible solution for measuring HALE at the national and especially regional levels, considering limited data.

Bioremediation experiments and dynamic model of petroleum hydrocarbon contaminated soil

Clarifying the occurrence and morphological characteristics of petroleum hydrocarbons (PHs) in soil can facilitate a comprehensive understanding of their migration and transformation patterns in soil/sediment. Additionally, by establishing the dynamic transformation process of each occurrence state, the ecological impact and environmental risk associated with PHs in soil/sediment can be assessed more precisely. The adsorption experiments and closed static incubation experiments was carried out to explore the PHs degradation and fraction distribution in aged contaminated soil under two remediation scenarios of natural attenuation (NA) and bioaugmentation (BA) by exogenous bacteria through a new sequential extraction method based on Tenax-TA, Hydroxypropyl-β-cyclodextrin and Rhamnolipid (HPCD/RL), accelerated solvent extractor (ASE) unit and alkaline hydrolysis extraction. The adsorption experiment results illustrated that bioaugmentation could promote the desorption of PHs in the adsorption phase, and the soil-water partition coefficient Kd decreased from 0.153 L/g to 0.092 L/g. The incubation experiment results showed that compared with natural attenuation, bioaugmentation could improve the utilization of PHs in aged soil and promote the generation of non-extractable hydrocarbons. On the 90th day of the experiment, the concentrations of weakly adsorbed hydrocarbons in the natural attenuation and bioaugmentation experimental groups decreased by 46.44% and 87.07%, respectively, while the concentrations of strongly adsorbed hydrocarbons and non-extractable hydrocarbons increased by 77.93%, 182.14%, and 80.91%, and 501.19%, respectively, compared their initial values. We developed a novel dynamic model and inverted the kinetic parameters of the model by the parameter scanning function and the Markov Chain Monte Carlo (MCMC) method based on the Bayesian approach in COMSOL Multiphysics® finite element software combined with experimental data. There was a good linear relationship between experimental interpolation data and model prediction data. The R2 for the concentrations of weakly adsorbed hydrocarbons ranged from 0.9953 to 0.9974, for strongly adsorbed hydrocarbons from 0.9063 to 0.9756, and for non-extractable hydrocarbons from 0.9931 to 0.9982. These extremely high correlation coefficients demonstrate the high accuracy of the parameters calculated using the Bayesian inversion method.

Adsorption properties of n-octane/1-octene binary mixtures in the zeolitic imidazolate frameworks, ZIF-8 and ZIF-67: Combined experimental and molecular simulation study

1-Octene, a linear α-olefin, can be used as a comonomer for producing plastics, oils, waxes, and surfactants. High-purity 1-octene can be obtained by removing paraffin byproducts using energy-intensive distillation. Adsorptive separation using porous material–based adsorbents is a promising alternative to distillation. Most reported adsorbents are zeolites, which preferentially adsorb 1-octene rather than n-octane owing to their polar framework. However, the existing studies are insufficient for investigating apolar adsorbents that adsorb more amounts of n-octane than 1-octene. Herein, the adsorption properties of the apolar adsorbents, zeolitic imidazolate frameworks-8 (ZIF-8) and ZIF-67, obtained from a n-octane/1-octene liquid binary mixture were systematically investigated by conducting batch adsorption experiments for the n-octane volume fraction of 10 %–90 %. Results show that ZIF-8 and ZIF-67 are promising adsorbents for the selective adsorption of n-octane owing to the preferential adsorption of n-octane in the micropores of the ZIFs at the high concentration of n-octane. Configurational-bias grand canonical Monte Carlo simulations confirm that the host–adsorbate energy between ZIF-8 and n-octane is higher than that between ZIF-8 and 1-octene, whereas the host–adsorbate energy between ZIF-67 and n-octane is similar to that between ZIF-67 and 1-octene.

Assessment of fluence- and dose-averaged linear energy transfer with passive luminescence detectors in clinical proton beams

Objective. This work investigates the use of passive luminescence detectors to determine different types of averaged linear energy transfer ( LET― ) for the energies relevant to proton therapy. The experimental results are compared to reference values obtained from Monte Carlo simulations. Approach. Optically stimulated luminescence detectors (OSLDs), fluorescent nuclear track detectors (FNTDs), and two different groups of thermoluminescence detectors (TLDs) were irradiated at four different radiation qualities. For each irradiation, the fluence- ( LET―f ) and dose-averaged LET ( LET―d ) were determined. For both quantities, two sub-types of averages were calculated, either considering the contributions from primary and secondary protons or from all protons and heavier, charged particles. Both simulated and experimental data were used in combination with a phenomenological model to estimate the relative biological effectiveness (RBE). Main results. All types of LET― could be assessed with the luminescence detectors. The experimental determination of LET―f is in agreement with reference data obtained from simulations across all measurement techniques and types of averaging. On the other hand, LET―d can present challenges as a radiation quality metric to describe the detector response in mixed particle fields. However, excluding secondaries heavier than protons from the LET―d calculation, as their contribution to the luminescence is suppressed by ionization quenching, leads to equal accuracy between LET―f and LET―d . Assessment of RBE through the experimentally determined LET―d values agrees with independently acquired reference values, indicating that the investigated detectors can determine LET― with sufficient accuracy for proton therapy. Significance. OSLDs, TLDs, and FNTDs can be used to determine LET― and RBE in proton therapy. With the capability to determine dose through ionization quenching corrections derived from LET― , OSLDs and TLDs can simultaneously ascertain dose, LET― , and RBE. This makes passive detectors appealing for measurements in phantoms to facilitate validation of clinical treatment plans or experiments related to proton therapy.